From 74956888d1d9eafded7140ad7821b4b39c863756 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Bartek=20G=C4=85siorzewski?= Date: Mon, 7 May 2018 21:50:58 +0200 Subject: [PATCH 0001/1365] Fix yolov3-tiny mask --- cfg/yolov3-tiny.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cfg/yolov3-tiny.cfg b/cfg/yolov3-tiny.cfg index 42c0fcf9195..cfca3cfa641 100644 --- a/cfg/yolov3-tiny.cfg +++ b/cfg/yolov3-tiny.cfg @@ -172,7 +172,7 @@ filters=255 activation=linear [yolo] -mask = 1,2,3 +mask = 0,1,2 anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 classes=80 num=6 From 573d7e80814a4cc3c08897f6c0f67ea189339856 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 16 May 2018 03:03:23 +0300 Subject: [PATCH 0002/1365] Added def array_to_image(arr): to the darknet.py Usage: import scipy.misc sci_image = scipy.misc.imread(image) im, arr = array_to_image(sci_image) --- build/darknet/x64/darknet.py | 15 +++++++++++++++ darknet.py | 15 +++++++++++++++ 2 files changed, 30 insertions(+) diff --git a/build/darknet/x64/darknet.py b/build/darknet/x64/darknet.py index 873dab74323..700ed0c8261 100644 --- a/build/darknet/x64/darknet.py +++ b/build/darknet/x64/darknet.py @@ -188,6 +188,18 @@ class METADATA(Structure): predict_image.argtypes = [c_void_p, IMAGE] predict_image.restype = POINTER(c_float) +def array_to_image(arr): + import numpy as np + # need to return old values to avoid python freeing memory + arr = arr.transpose(2,0,1) + c = arr.shape[0] + h = arr.shape[1] + w = arr.shape[2] + arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0 + data = arr.ctypes.data_as(POINTER(c_float)) + im = IMAGE(w,h,c,data) + return im, arr + def classify(net, meta, im): out = predict_image(net, im) res = [] @@ -206,6 +218,9 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False): """ #pylint: disable= C0321 im = load_image(image, 0, 0) + #import scipy.misc + #sci_image = scipy.misc.imread(image) + #im, arr = array_to_image(sci_image) # you should comment line below: free_image(im) if debug: print("Loaded image") num = c_int(0) if debug: print("Assigned num") diff --git a/darknet.py b/darknet.py index 873dab74323..700ed0c8261 100644 --- a/darknet.py +++ b/darknet.py @@ -188,6 +188,18 @@ class METADATA(Structure): predict_image.argtypes = [c_void_p, IMAGE] predict_image.restype = POINTER(c_float) +def array_to_image(arr): + import numpy as np + # need to return old values to avoid python freeing memory + arr = arr.transpose(2,0,1) + c = arr.shape[0] + h = arr.shape[1] + w = arr.shape[2] + arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0 + data = arr.ctypes.data_as(POINTER(c_float)) + im = IMAGE(w,h,c,data) + return im, arr + def classify(net, meta, im): out = predict_image(net, im) res = [] @@ -206,6 +218,9 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False): """ #pylint: disable= C0321 im = load_image(image, 0, 0) + #import scipy.misc + #sci_image = scipy.misc.imread(image) + #im, arr = array_to_image(sci_image) # you should comment line below: free_image(im) if debug: print("Loaded image") num = c_int(0) if debug: print("Assigned num") From 89c11f83ed49c58c3031200d8ed053b4f671db09 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 16 May 2018 15:05:46 +0300 Subject: [PATCH 0003/1365] Fixed darknet.py - uses batch=1 by default --- build/darknet/x64/darknet.py | 6 +++++- build/darknet/x64/darknet_python.cmd | 8 ++++++++ darknet.py | 6 +++++- src/data.c | 5 ++++- src/network.c | 9 +++++++-- src/network.h | 1 + 6 files changed, 30 insertions(+), 5 deletions(-) diff --git a/build/darknet/x64/darknet.py b/build/darknet/x64/darknet.py index 700ed0c8261..2deff4fbdb8 100644 --- a/build/darknet/x64/darknet.py +++ b/build/darknet/x64/darknet.py @@ -160,6 +160,10 @@ class METADATA(Structure): load_net.argtypes = [c_char_p, c_char_p, c_int] load_net.restype = c_void_p +load_net_custom = lib.load_network_custom +load_net_custom.argtypes = [c_char_p, c_char_p, c_int, c_int] +load_net_custom.restype = c_void_p + do_nms_obj = lib.do_nms_obj do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float] @@ -325,7 +329,7 @@ def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yo if not os.path.exists(metaPath): raise ValueError("Invalid data file path `"+os.path.abspath(metaPath)+"`") if netMain is None: - netMain = load_net(configPath.encode("ascii"), weightPath.encode("ascii"), 0) + netMain = load_net_custom(configPath.encode("ascii"), weightPath.encode("ascii"), 0, 1) # batch size = 1 if metaMain is None: metaMain = load_meta(metaPath.encode("ascii")) if altNames is None: diff --git a/build/darknet/x64/darknet_python.cmd b/build/darknet/x64/darknet_python.cmd index b45f6798bfa..2c9243c68ec 100644 --- a/build/darknet/x64/darknet_python.cmd +++ b/build/darknet/x64/darknet_python.cmd @@ -6,4 +6,12 @@ rem C:\Python27\Scripts\pip install scipy C:\Python27\python.exe darknet.py + +rem Python 3.6 +rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\Scripts\pip install numpy +rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\Scripts\pip install scikit-image +rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\Scripts\pip install scipy + +rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\python.exe darknet.py + pause \ No newline at end of file diff --git a/darknet.py b/darknet.py index 700ed0c8261..2deff4fbdb8 100644 --- a/darknet.py +++ b/darknet.py @@ -160,6 +160,10 @@ class METADATA(Structure): load_net.argtypes = [c_char_p, c_char_p, c_int] load_net.restype = c_void_p +load_net_custom = lib.load_network_custom +load_net_custom.argtypes = [c_char_p, c_char_p, c_int, c_int] +load_net_custom.restype = c_void_p + do_nms_obj = lib.do_nms_obj do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float] @@ -325,7 +329,7 @@ def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yo if not os.path.exists(metaPath): raise ValueError("Invalid data file path `"+os.path.abspath(metaPath)+"`") if netMain is None: - netMain = load_net(configPath.encode("ascii"), weightPath.encode("ascii"), 0) + netMain = load_net_custom(configPath.encode("ascii"), weightPath.encode("ascii"), 0, 1) # batch size = 1 if metaMain is None: metaMain = load_meta(metaPath.encode("ascii")) if altNames is None: diff --git a/src/data.c b/src/data.c index 3b014b470f0..4bb1b0f97d2 100644 --- a/src/data.c +++ b/src/data.c @@ -137,7 +137,10 @@ box_label *read_boxes(char *filename, int *n) { box_label *boxes = calloc(1, sizeof(box_label)); FILE *file = fopen(filename, "r"); - if(!file) file_error(filename); + if (!file) { + printf("Can't open label file. \n"); + file_error(filename); + } float x, y, h, w; int id; int count = 0; diff --git a/src/network.c b/src/network.c index 81b53f39d5b..f992fef96ac 100644 --- a/src/network.c +++ b/src/network.c @@ -30,11 +30,11 @@ #include "yolo_layer.h" #include "parser.h" -network *load_network(char *cfg, char *weights, int clear) +network *load_network_custom(char *cfg, char *weights, int clear, int batch) { printf(" Try to load cfg: %s, weights: %s, clear = %d \n", cfg, weights, clear); network *net = calloc(1, sizeof(network)); - *net = parse_network_cfg(cfg); + *net = parse_network_cfg_custom(cfg, batch); if (weights && weights[0] != 0) { load_weights(net, weights); } @@ -42,6 +42,11 @@ network *load_network(char *cfg, char *weights, int clear) return net; } +network *load_network(char *cfg, char *weights, int clear) +{ + return load_network_custom(cfg, weights, clear, 0); +} + int get_current_batch(network net) { int batch_num = (*net.seen)/(net.batch*net.subdivisions); diff --git a/src/network.h b/src/network.h index fe160a9ba1d..01a6ab9051e 100644 --- a/src/network.h +++ b/src/network.h @@ -138,6 +138,7 @@ YOLODLL_API detection *get_network_boxes(network *net, int w, int h, float thres YOLODLL_API detection *make_network_boxes(network *net, float thresh, int *num); YOLODLL_API void free_detections(detection *dets, int n); YOLODLL_API void reset_rnn(network *net); +YOLODLL_API network *load_network_custom(char *cfg, char *weights, int clear, int batch); YOLODLL_API network *load_network(char *cfg, char *weights, int clear); YOLODLL_API float *network_predict_image(network *net, image im); YOLODLL_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show); From b34c913d2eb396b6eb845c8573edbd4002777e74 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 16 May 2018 15:16:41 +0300 Subject: [PATCH 0004/1365] darknet.py change libdarknet.so to darknet.so --- build/darknet/x64/darknet.py | 2 +- darknet.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/build/darknet/x64/darknet.py b/build/darknet/x64/darknet.py index 2deff4fbdb8..9888c229f2d 100644 --- a/build/darknet/x64/darknet.py +++ b/build/darknet/x64/darknet.py @@ -118,7 +118,7 @@ class METADATA(Structure): lib = CDLL(winGPUdll, RTLD_GLOBAL) print("Environment variables indicated a CPU run, but we didn't find `"+winNoGPUdll+"`. Trying a GPU run anyway.") else: - lib = CDLL("./libdarknet.so", RTLD_GLOBAL) + lib = CDLL("./darknet.so", RTLD_GLOBAL) lib.network_width.argtypes = [c_void_p] lib.network_width.restype = c_int lib.network_height.argtypes = [c_void_p] diff --git a/darknet.py b/darknet.py index 2deff4fbdb8..9888c229f2d 100644 --- a/darknet.py +++ b/darknet.py @@ -118,7 +118,7 @@ class METADATA(Structure): lib = CDLL(winGPUdll, RTLD_GLOBAL) print("Environment variables indicated a CPU run, but we didn't find `"+winNoGPUdll+"`. Trying a GPU run anyway.") else: - lib = CDLL("./libdarknet.so", RTLD_GLOBAL) + lib = CDLL("./darknet.so", RTLD_GLOBAL) lib.network_width.argtypes = [c_void_p] lib.network_width.restype = c_int lib.network_height.argtypes = [c_void_p] From 8587c0a161965ab8fa941db1b990bd79fb0164f9 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 16 May 2018 15:19:01 +0300 Subject: [PATCH 0005/1365] Fixed OPENCV_VERSION --- src/detector.c | 4 ++-- src/yolo_console_dll.cpp | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/src/detector.c b/src/detector.c index 2b54a4af4e7..de4bc90c444 100644 --- a/src/detector.c +++ b/src/detector.c @@ -21,10 +21,10 @@ #ifndef CV_VERSION_EPOCH #include "opencv2/videoio/videoio_c.h" -#define OPENCV_VERSION CVAUX_STR(CV_VERSION_MAJOR)""CVAUX_STR(CV_VERSION_MINOR)""CVAUX_STR(CV_VERSION_REVISION) +#define OPENCV_VERSION CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR)"" CVAUX_STR(CV_VERSION_REVISION) #pragma comment(lib, "opencv_world" OPENCV_VERSION ".lib") #else -#define OPENCV_VERSION CVAUX_STR(CV_VERSION_EPOCH)""CVAUX_STR(CV_VERSION_MAJOR)""CVAUX_STR(CV_VERSION_MINOR) +#define OPENCV_VERSION CVAUX_STR(CV_VERSION_EPOCH)"" CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR) #pragma comment(lib, "opencv_core" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib") diff --git a/src/yolo_console_dll.cpp b/src/yolo_console_dll.cpp index ea4330e3527..2eca267890d 100644 --- a/src/yolo_console_dll.cpp +++ b/src/yolo_console_dll.cpp @@ -28,7 +28,7 @@ #include "opencv2/core/version.hpp" #ifndef CV_VERSION_EPOCH #include "opencv2/videoio/videoio.hpp" -#define OPENCV_VERSION CVAUX_STR(CV_VERSION_MAJOR)""CVAUX_STR(CV_VERSION_MINOR)""CVAUX_STR(CV_VERSION_REVISION) +#define OPENCV_VERSION CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR)"" CVAUX_STR(CV_VERSION_REVISION) #pragma comment(lib, "opencv_world" OPENCV_VERSION ".lib") #ifdef TRACK_OPTFLOW #pragma comment(lib, "opencv_cudaoptflow" OPENCV_VERSION ".lib") From e29fcb703f8d936e17507bf78043a8b8bc6279b0 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 16 May 2018 15:23:34 +0300 Subject: [PATCH 0006/1365] Fixed time for ./darknet detector valid --- src/detector.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/detector.c b/src/detector.c index de4bc90c444..bed7467b8c3 100644 --- a/src/detector.c +++ b/src/detector.c @@ -425,7 +425,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out fprintf(fp, "\n]\n"); fclose(fp); } - fprintf(stderr, "Total Detection Time: %f Seconds\n", time(0) - start); + fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)time(0) - start); } void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile) From fb9fcfb3ba0057b1196d1b95fd53cdb0b874b1c3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 17 May 2018 03:04:42 +0300 Subject: [PATCH 0007/1365] Support of .ppm files is realised --- src/coco.c | 1 + src/data.c | 3 +++ src/detector.c | 3 +++ src/yolo.c | 1 + 4 files changed, 8 insertions(+) diff --git a/src/coco.c b/src/coco.c index c95e30d6c56..b8fc346463b 100644 --- a/src/coco.c +++ b/src/coco.c @@ -285,6 +285,7 @@ void validate_coco_recall(char *cfgfile, char *weightfile) find_replace(labelpath, "JPEGImages", "labels", labelpath); find_replace(labelpath, ".jpg", ".txt", labelpath); find_replace(labelpath, ".JPEG", ".txt", labelpath); + find_replace(labelpath, ".ppm", ".txt", labelpath); int num_labels = 0; box_label *truth = read_boxes(labelpath, &num_labels); diff --git a/src/data.c b/src/data.c index 4bb1b0f97d2..8da1234110d 100644 --- a/src/data.c +++ b/src/data.c @@ -218,6 +218,7 @@ void fill_truth_swag(char *path, float *truth, int classes, int flip, float dx, find_replace(labelpath, ".jpg", ".txt", labelpath); find_replace(labelpath, ".JPG", ".txt", labelpath); find_replace(labelpath, ".JPEG", ".txt", labelpath); + find_replace(labelpath, ".ppm", ".txt", labelpath); int count = 0; box_label *boxes = read_boxes(labelpath, &count); @@ -258,6 +259,7 @@ void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int find_replace(labelpath, ".png", ".txt", labelpath); find_replace(labelpath, ".JPG", ".txt", labelpath); find_replace(labelpath, ".JPEG", ".txt", labelpath); + find_replace(labelpath, ".ppm", ".txt", labelpath); int count = 0; box_label *boxes = read_boxes(labelpath, &count); randomize_boxes(boxes, count); @@ -309,6 +311,7 @@ void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, find_replace(labelpath, ".bmp", ".txt", labelpath); find_replace(labelpath, ".JPG", ".txt", labelpath); find_replace(labelpath, ".JPEG", ".txt", labelpath); + find_replace(labelpath, ".ppm", ".txt", labelpath); int count = 0; int i; box_label *boxes = read_boxes(labelpath, &count); diff --git a/src/detector.c b/src/detector.c index bed7467b8c3..5ccb6c53d1b 100644 --- a/src/detector.c +++ b/src/detector.c @@ -479,6 +479,7 @@ void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile) find_replace(labelpath, ".bmp", ".txt", labelpath); find_replace(labelpath, ".JPG", ".txt", labelpath); find_replace(labelpath, ".JPEG", ".txt", labelpath); + find_replace(labelpath, ".ppm", ".txt", labelpath); int num_labels = 0; box_label *truth = read_boxes(labelpath, &num_labels); @@ -636,6 +637,7 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float find_replace(labelpath, ".bmp", ".txt", labelpath); find_replace(labelpath, ".JPG", ".txt", labelpath); find_replace(labelpath, ".JPEG", ".txt", labelpath); + find_replace(labelpath, ".ppm", ".txt", labelpath); int num_labels = 0; box_label *truth = read_boxes(labelpath, &num_labels); int i, j; @@ -899,6 +901,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int find_replace(labelpath, ".bmp", ".txt", labelpath); find_replace(labelpath, ".JPG", ".txt", labelpath); find_replace(labelpath, ".JPEG", ".txt", labelpath); + find_replace(labelpath, ".ppm", ".txt", labelpath); int num_labels = 0; box_label *truth = read_boxes(labelpath, &num_labels); //printf(" new path: %s \n", labelpath); diff --git a/src/yolo.c b/src/yolo.c index 238454e67b3..4481165821e 100644 --- a/src/yolo.c +++ b/src/yolo.c @@ -256,6 +256,7 @@ void validate_yolo_recall(char *cfgfile, char *weightfile) find_replace(labelpath, "JPEGImages", "labels", labelpath); find_replace(labelpath, ".jpg", ".txt", labelpath); find_replace(labelpath, ".JPEG", ".txt", labelpath); + find_replace(labelpath, ".ppm", ".txt", labelpath); int num_labels = 0; box_label *truth = read_boxes(labelpath, &num_labels); From 0ecbeeff42b272b2a8973a8c3dde95703f227915 Mon Sep 17 00:00:00 2001 From: salbatron <29922029+salbatron@users.noreply.github.com> Date: Thu, 17 May 2018 15:22:49 +0200 Subject: [PATCH 0008/1365] Create get_openimages_dataset.py Script that downloads images with selected classes from OpenImages dataset and create labels in "yolo format" --- scripts/get_openimages_dataset.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) create mode 100644 scripts/get_openimages_dataset.py diff --git a/scripts/get_openimages_dataset.py b/scripts/get_openimages_dataset.py new file mode 100644 index 00000000000..3508b3ec650 --- /dev/null +++ b/scripts/get_openimages_dataset.py @@ -0,0 +1,19 @@ +import csv +import os + +#select classes you want to download at https://github.com/openimages/dataset/blob/master/dict.csv +CLASS_LIST = ('/m/01g317','/m/04yx4') +img_name = "111111111111" + +#download csv from https://storage.googleapis.com/openimages/web/download.html +with open('path\\train-annotations-bbox.csv', newline='') as csvfile: + bboxs = csv.reader(csvfile, delimiter=',', quotechar='|') + for bbox in bboxs: + if bbox[2] in CLASS_LIST: + if img_name != bbox[0]: + if not os.path.isfile("destination_path\\%s.jpg"%bbox[0]): + os.system("gsutil cp gs://open-images-dataset/train/%s.jpg destination_path"%bbox[0]) + out_file = open("destination_path\\%s.txt"%bbox[0], 'w') + img_name = bbox[0] + if img_name == bbox[0]: + out_file.write(str(CLASS_LIST.index(bbox[2])) + " " + str(float(bbox[4])+(float(bbox[5])-float(bbox[4]))/2) + " " + str(float(bbox[6])+(float(bbox[7])-float(bbox[6]))/2)+ " " + str(float(bbox[5])-float(bbox[4])) + " " + str(float(bbox[7])-float(bbox[6])) + '\n') From 9ba0beb413d8b443376c36c1701a0dbce1e8d566 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 17 May 2018 17:02:42 +0300 Subject: [PATCH 0009/1365] Fixed resize of network for low resolution network like 128x128 --- src/detector.c | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/detector.c b/src/detector.c index 5ccb6c53d1b..b7fbfbd9243 100644 --- a/src/detector.c +++ b/src/detector.c @@ -132,6 +132,8 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i int random_val = rand() % 12; int dim_w = (random_val + (init_w / 32 - 5)) * 32; // +-160 int dim_h = (random_val + (init_h / 32 - 5)) * 32; // +-160 + if (dim_w < 32) dim_w = 32; + if (dim_h < 32) dim_h = 32; printf("%d x %d \n", dim_w, dim_h); args.w = dim_w; From 71437a936d398ee40342cd140a14c2e4f00bea9f Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 17 May 2018 22:50:27 +0300 Subject: [PATCH 0010/1365] Update Readme.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 0dc62301ba7..3ca3412fe4f 100644 --- a/README.md +++ b/README.md @@ -99,7 +99,7 @@ On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector * 186 MB Yolo9000 - image: `darknet.exe detector test cfg/combine9k.data yolo9000.cfg yolo9000.weights` * Remeber to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app * To process a list of images `data/train.txt` and save results of detection to `result.txt` use: - `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -dont_show < data/train.txt > result.txt` + `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -dont_show -ext_output < data/train.txt > result.txt` You can comment this line so that each image does not require pressing the button ESC: https://github.com/AlexeyAB/darknet/blob/6ccb41808caf753feea58ca9df79d6367dedc434/src/detector.c#L509 ##### For using network video-camera mjpeg-stream with any Android smartphone: From 24f563ce7165c2c44486fe8d0a2caea8d7c5a887 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 17 May 2018 23:26:55 +0300 Subject: [PATCH 0011/1365] Example of usage cv2.imread() in the darknet.py --- build/darknet/x64/darknet.py | 6 ++++-- build/darknet/x64/darknet_python.cmd | 2 ++ darknet.py | 6 ++++-- 3 files changed, 10 insertions(+), 4 deletions(-) diff --git a/build/darknet/x64/darknet.py b/build/darknet/x64/darknet.py index 9888c229f2d..b290a358b23 100644 --- a/build/darknet/x64/darknet.py +++ b/build/darknet/x64/darknet.py @@ -222,9 +222,11 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False): """ #pylint: disable= C0321 im = load_image(image, 0, 0) + #import cv2 + #custom_image = cv2.imread(image) # use: detect(,,imagePath,) #import scipy.misc - #sci_image = scipy.misc.imread(image) - #im, arr = array_to_image(sci_image) # you should comment line below: free_image(im) + #custom_image = scipy.misc.imread(image) + #im, arr = array_to_image(custom_image) # you should comment line below: free_image(im) if debug: print("Loaded image") num = c_int(0) if debug: print("Assigned num") diff --git a/build/darknet/x64/darknet_python.cmd b/build/darknet/x64/darknet_python.cmd index 2c9243c68ec..d64fe3649a6 100644 --- a/build/darknet/x64/darknet_python.cmd +++ b/build/darknet/x64/darknet_python.cmd @@ -2,6 +2,7 @@ rem download Python 2.7.14 from: https://www.python.org/downloads/release/python rem C:\Python27\Scripts\pip install numpy rem C:\Python27\Scripts\pip install scikit-image rem C:\Python27\Scripts\pip install scipy +rem C:\Python27\Scripts\pip install opencv-python C:\Python27\python.exe darknet.py @@ -11,6 +12,7 @@ rem Python 3.6 rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\Scripts\pip install numpy rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\Scripts\pip install scikit-image rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\Scripts\pip install scipy +rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\Scripts\pip install opencv-python rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\python.exe darknet.py diff --git a/darknet.py b/darknet.py index 9888c229f2d..b290a358b23 100644 --- a/darknet.py +++ b/darknet.py @@ -222,9 +222,11 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False): """ #pylint: disable= C0321 im = load_image(image, 0, 0) + #import cv2 + #custom_image = cv2.imread(image) # use: detect(,,imagePath,) #import scipy.misc - #sci_image = scipy.misc.imread(image) - #im, arr = array_to_image(sci_image) # you should comment line below: free_image(im) + #custom_image = scipy.misc.imread(image) + #im, arr = array_to_image(custom_image) # you should comment line below: free_image(im) if debug: print("Loaded image") num = c_int(0) if debug: print("Assigned num") From ae9fb2abbd8e2b7e079b56bf1cf39754d7c091ac Mon Sep 17 00:00:00 2001 From: Nadeen Udantha Date: Fri, 18 May 2018 14:19:19 +0530 Subject: [PATCH 0012/1365] ojbects -> objects --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 3ca3412fe4f..352edf52c2e 100644 --- a/README.md +++ b/README.md @@ -349,7 +349,7 @@ Usually sufficient 2000 iterations for each class(object). But for a more precis 2. Once training is stopped, you should take some of last `.weights`-files from `darknet\build\darknet\x64\backup` and choose the best of them: -For example, you stopped training after 9000 iterations, but the best result can give one of previous weights (7000, 8000, 9000). It can happen due to overfitting. **Overfitting** - is case when you can detect objects on images from training-dataset, but can't detect ojbects on any others images. You should get weights from **Early Stopping Point**: +For example, you stopped training after 9000 iterations, but the best result can give one of previous weights (7000, 8000, 9000). It can happen due to overfitting. **Overfitting** - is case when you can detect objects on images from training-dataset, but can't detect objects on any others images. You should get weights from **Early Stopping Point**: ![Overfitting](https://hsto.org/files/5dc/7ae/7fa/5dc7ae7fad9d4e3eb3a484c58bfc1ff5.png) From 985ba2720f72ab8ad54000be85c640836c485670 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 18 May 2018 23:27:27 +0300 Subject: [PATCH 0013/1365] yolov3-tiny_obj.cfg - is example for training custom objects --- build/darknet/x64/cfg/yolov3-tiny_obj.cfg | 182 ++++++++++++++++++++++ cfg/yolov3-tiny_obj.cfg | 182 ++++++++++++++++++++++ 2 files changed, 364 insertions(+) create mode 100644 build/darknet/x64/cfg/yolov3-tiny_obj.cfg create mode 100644 cfg/yolov3-tiny_obj.cfg diff --git a/build/darknet/x64/cfg/yolov3-tiny_obj.cfg b/build/darknet/x64/cfg/yolov3-tiny_obj.cfg new file mode 100644 index 00000000000..8308e442517 --- /dev/null +++ b/build/darknet/x64/cfg/yolov3-tiny_obj.cfg @@ -0,0 +1,182 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=2 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=16 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=1 + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +########### + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + + +[yolo] +mask = 3,4,5 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 8 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + +[yolo] +mask = 0,1,2 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 diff --git a/cfg/yolov3-tiny_obj.cfg b/cfg/yolov3-tiny_obj.cfg new file mode 100644 index 00000000000..8308e442517 --- /dev/null +++ b/cfg/yolov3-tiny_obj.cfg @@ -0,0 +1,182 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=2 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=16 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=1 + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +########### + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + + +[yolo] +mask = 3,4,5 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 8 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + +[yolo] +mask = 0,1,2 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 From 94c84da85015f0dcc6deaff53b24ff46a730711c Mon Sep 17 00:00:00 2001 From: Alexey Date: Fri, 18 May 2018 23:31:20 +0300 Subject: [PATCH 0014/1365] Update Readme.md --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 352edf52c2e..b56299e6a58 100644 --- a/README.md +++ b/README.md @@ -322,10 +322,10 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an ### How to train tiny-yolo (to detect your custom objects): Do all the same steps as for the full yolo model as described above. With the exception of: -* Download default weights file for yolov2-tiny-voc: http://pjreddie.com/media/files/yolov2-tiny-voc.weights -* Get pre-trained weights yolov2-tiny-voc.conv.13 using command: `darknet.exe partial cfg/yolov2-tiny-voc.cfg yolov2-tiny-voc.weights yolov2-tiny-voc.conv.13 13` -* Make your custom model `yolov2-tiny-obj.cfg` based on `cfg/yolov2-tiny-voc.cfg` instead of `yolov3.cfg` -* Start training: `darknet.exe detector train data/obj.data yolov2-tiny-obj.cfg yolov2-tiny-voc.conv.13` +* Download default weights file for yolov3-tiny: https://pjreddie.com/media/files/yolov3-tiny.weights +* Get pre-trained weights `yolov3-tiny.conv.15` using command: `darknet.exe partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15` +* Make your custom model `yolov3-tiny-obj.cfg` based on `cfg/yolov3-tiny_obj.cfg` instead of `yolov3.cfg` +* Start training: `darknet.exe detector train data/obj.data yolov3-tiny-obj.cfg yolov3-tiny.conv.15` For training Yolo based on other models ([DenseNet201-Yolo](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/densenet201_yolo.cfg) or [ResNet50-Yolo](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/resnet50_yolo.cfg)), you can download and get pre-trained weights as showed in this file: https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/partial.cmd If you made you custom model that isn't based on other models, then you can train it without pre-trained weights, then will be used random initial weights. From c1bb8c129d7d4d8fc85348d5e7ac5441fa36ead5 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 19 May 2018 16:52:05 +0300 Subject: [PATCH 0015/1365] Fixed xnor for random=1 --- src/convolutional_layer.c | 9 +++++++++ src/detector.c | 32 +++++++++++++++++++++++++++++++- 2 files changed, 40 insertions(+), 1 deletion(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index a34945411c1..f4a5d89977e 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -465,6 +465,10 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) l->x_norm = realloc(l->x_norm, l->batch*l->outputs*sizeof(float)); } + if (l->xnor) { + //l->binary_input = realloc(l->inputs*l->batch, sizeof(float)); + } + #ifdef GPU if (old_w < w || old_h < h) { cuda_free(l->delta_gpu); @@ -480,6 +484,11 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) l->x_gpu = cuda_make_array(l->output, l->batch*l->outputs); l->x_norm_gpu = cuda_make_array(l->output, l->batch*l->outputs); } + + if (l->xnor) { + cuda_free(l->binary_input_gpu); + l->binary_input_gpu = cuda_make_array(0, l->inputs*l->batch); + } } #ifdef CUDNN cudnn_convolutional_setup(l, cudnn_fastest); diff --git a/src/detector.c b/src/detector.c index b7fbfbd9243..3b486fbb8e8 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1109,12 +1109,42 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); if (nms) do_nms_sort(dets, nboxes, l.classes, nms); draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, ext_output); - free_detections(dets, nboxes); save_image(im, "predictions"); if (!dont_show) { show_image(im, "predictions"); } +/* + // pseudo labeling concept - fast.ai + { + char labelpath[4096]; + find_replace(input, ".jpg", ".txt", labelpath); + find_replace(labelpath, ".png", ".txt", labelpath); + find_replace(labelpath, ".bmp", ".txt", labelpath); + find_replace(labelpath, ".JPG", ".txt", labelpath); + find_replace(labelpath, ".JPEG", ".txt", labelpath); + find_replace(labelpath, ".ppm", ".txt", labelpath); + FILE* fw = fopen(labelpath, "wb"); + int i; + for (i = 0; i < nboxes; ++i) { + char buff[1024]; + int class_id = -1; + float prob = 0; + for (j = 0; j < l.classes; ++j) { + if (dets[i].prob[j] > thresh && dets[i].prob[j] > prob) { + prob = dets[i].prob[j]; + class_id = j; + } + } + if (class_id >= 0) { + sprintf(buff, "%d %2.4f %2.4f %2.4f %2.4f\n", class_id, dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h); + fwrite(buff, sizeof(char), strlen(buff), fw); + } + } + fclose(fw); + } +*/ + free_detections(dets, nboxes); free_image(im); free_image(sized); //free(boxes); From 7b7c669f4952479ac7667cf7def4d09109cc6789 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 20 May 2018 00:39:03 +0300 Subject: [PATCH 0016/1365] Added flag -save_labels to save detections as training labels txt-files (pseudo labeling concept) --- src/detector.c | 85 ++++++++++++++++++++++++++++++-------------------- src/image.c | 12 +++++++ 2 files changed, 63 insertions(+), 34 deletions(-) diff --git a/src/detector.c b/src/detector.c index 3b486fbb8e8..bbe60cfd200 100644 --- a/src/detector.c +++ b/src/detector.c @@ -36,6 +36,17 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_ static int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90}; +void replace_image_to_label(char *input_path, char *output_path) { + find_replace(input_path, "images", "labels", output_path); + find_replace(output_path, "JPEGImages", "labels", output_path); + find_replace(output_path, ".jpg", ".txt", output_path); + find_replace(output_path, ".png", ".txt", output_path); + find_replace(output_path, ".bmp", ".txt", output_path); + find_replace(output_path, ".JPG", ".txt", output_path); + find_replace(output_path, ".JPEG", ".txt", output_path); + find_replace(output_path, ".ppm", ".txt", output_path); +} + void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show) { list *options = read_data_cfg(datacfg); @@ -474,14 +485,15 @@ void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile) if (nms) do_nms_obj(dets, nboxes, 1, nms); char labelpath[4096]; - find_replace(path, "images", "labels", labelpath); - find_replace(labelpath, "JPEGImages", "labels", labelpath); - find_replace(labelpath, ".jpg", ".txt", labelpath); - find_replace(labelpath, ".png", ".txt", labelpath); - find_replace(labelpath, ".bmp", ".txt", labelpath); - find_replace(labelpath, ".JPG", ".txt", labelpath); - find_replace(labelpath, ".JPEG", ".txt", labelpath); - find_replace(labelpath, ".ppm", ".txt", labelpath); + replace_image_to_label(path, labelpath); + //find_replace(path, "images", "labels", labelpath); + //find_replace(labelpath, "JPEGImages", "labels", labelpath); + //find_replace(labelpath, ".jpg", ".txt", labelpath); + //find_replace(labelpath, ".png", ".txt", labelpath); + //find_replace(labelpath, ".bmp", ".txt", labelpath); + //find_replace(labelpath, ".JPG", ".txt", labelpath); + //find_replace(labelpath, ".JPEG", ".txt", labelpath); + //find_replace(labelpath, ".ppm", ".txt", labelpath); int num_labels = 0; box_label *truth = read_boxes(labelpath, &num_labels); @@ -632,14 +644,15 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float if (nms) do_nms_sort(dets, nboxes, l.classes, nms); char labelpath[4096]; - find_replace(path, "images", "labels", labelpath); - find_replace(labelpath, "JPEGImages", "labels", labelpath); - find_replace(labelpath, ".jpg", ".txt", labelpath); - find_replace(labelpath, ".png", ".txt", labelpath); - find_replace(labelpath, ".bmp", ".txt", labelpath); - find_replace(labelpath, ".JPG", ".txt", labelpath); - find_replace(labelpath, ".JPEG", ".txt", labelpath); - find_replace(labelpath, ".ppm", ".txt", labelpath); + replace_image_to_label(path, labelpath); + //find_replace(path, "images", "labels", labelpath); + //find_replace(labelpath, "JPEGImages", "labels", labelpath); + //find_replace(labelpath, ".jpg", ".txt", labelpath); + //find_replace(labelpath, ".png", ".txt", labelpath); + //find_replace(labelpath, ".bmp", ".txt", labelpath); + //find_replace(labelpath, ".JPG", ".txt", labelpath); + //find_replace(labelpath, ".JPEG", ".txt", labelpath); + //find_replace(labelpath, ".ppm", ".txt", labelpath); int num_labels = 0; box_label *truth = read_boxes(labelpath, &num_labels); int i, j; @@ -896,14 +909,15 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int for (i = 0; i < number_of_images; ++i) { char *path = paths[i]; char labelpath[4096]; - find_replace(path, "images", "labels", labelpath); - find_replace(labelpath, "JPEGImages", "labels", labelpath); - find_replace(labelpath, ".jpg", ".txt", labelpath); - find_replace(labelpath, ".png", ".txt", labelpath); - find_replace(labelpath, ".bmp", ".txt", labelpath); - find_replace(labelpath, ".JPG", ".txt", labelpath); - find_replace(labelpath, ".JPEG", ".txt", labelpath); - find_replace(labelpath, ".ppm", ".txt", labelpath); + replace_image_to_label(path, labelpath); + //find_replace(path, "images", "labels", labelpath); + //find_replace(labelpath, "JPEGImages", "labels", labelpath); + //find_replace(labelpath, ".jpg", ".txt", labelpath); + //find_replace(labelpath, ".png", ".txt", labelpath); + //find_replace(labelpath, ".bmp", ".txt", labelpath); + //find_replace(labelpath, ".JPG", ".txt", labelpath); + //find_replace(labelpath, ".JPEG", ".txt", labelpath); + //find_replace(labelpath, ".ppm", ".txt", labelpath); int num_labels = 0; box_label *truth = read_boxes(labelpath, &num_labels); //printf(" new path: %s \n", labelpath); @@ -1056,7 +1070,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int #endif // OPENCV void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, - float hier_thresh, int dont_show, int ext_output) + float hier_thresh, int dont_show, int ext_output, int save_labels) { list *options = read_data_cfg(datacfg); char *name_list = option_find_str(options, "names", "data/names.list"); @@ -1113,16 +1127,18 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam if (!dont_show) { show_image(im, "predictions"); } -/* + // pseudo labeling concept - fast.ai + if(save_labels) { char labelpath[4096]; - find_replace(input, ".jpg", ".txt", labelpath); - find_replace(labelpath, ".png", ".txt", labelpath); - find_replace(labelpath, ".bmp", ".txt", labelpath); - find_replace(labelpath, ".JPG", ".txt", labelpath); - find_replace(labelpath, ".JPEG", ".txt", labelpath); - find_replace(labelpath, ".ppm", ".txt", labelpath); + replace_image_to_label(input, labelpath); + //find_replace(input, ".jpg", ".txt", labelpath); + //find_replace(labelpath, ".png", ".txt", labelpath); + //find_replace(labelpath, ".bmp", ".txt", labelpath); + //find_replace(labelpath, ".JPG", ".txt", labelpath); + //find_replace(labelpath, ".JPEG", ".txt", labelpath); + //find_replace(labelpath, ".ppm", ".txt", labelpath); FILE* fw = fopen(labelpath, "wb"); int i; @@ -1143,7 +1159,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam } fclose(fw); } -*/ + free_detections(dets, nboxes); free_image(im); free_image(sized); @@ -1193,6 +1209,7 @@ void run_detector(int argc, char **argv) // extended output in test mode (output of rect bound coords) // and for recall mode (extended output table-like format with results for best_class fit) int ext_output = find_arg(argc, argv, "-ext_output"); + int save_labels = find_arg(argc, argv, "-save_labels"); if(argc < 4){ fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); return; @@ -1229,7 +1246,7 @@ void run_detector(int argc, char **argv) if(strlen(weights) > 0) if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0; char *filename = (argc > 6) ? argv[6]: 0; - if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output); + if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels); else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show); else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile); else if(0==strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights); diff --git a/src/image.c b/src/image.c index 317b2466174..a1e4c77e673 100644 --- a/src/image.c +++ b/src/image.c @@ -441,6 +441,8 @@ void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float t { int i, j; if (!show_img) return; + static int frame_id = 0; + frame_id++; for (i = 0; i < num; ++i) { char labelstr[4096] = { 0 }; @@ -516,6 +518,16 @@ void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float t color.val[1] = green * 256; color.val[2] = blue * 256; + // you should create directory: result_img + //static int img_id = 0; + //img_id++; + //char image_name[1024]; + //sprintf(image_name, "result_img/img_%d_%d_%d.jpg", frame_id, img_id, class_id); + //CvRect rect = cvRect(pt1.x, pt1.y, pt2.x - pt1.x, pt2.y - pt1.y); + //cvSetImageROI(show_img, rect); + //cvSaveImage(image_name, show_img, 0); + //cvResetImageROI(show_img); + cvRectangle(show_img, pt1, pt2, color, width, 8, 0); //printf("left=%d, right=%d, top=%d, bottom=%d, obj_id=%d, obj=%s \n", left, right, top, bot, class_id, names[class_id]); cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0); From efdeb01a3a7994fc2bfbbcd98682f4c356dc229e Mon Sep 17 00:00:00 2001 From: Vinjn Zhang Date: Sun, 20 May 2018 13:57:21 +0800 Subject: [PATCH 0017/1365] darknet.py - remove unnecessary check of CUDA_HOME --- darknet.py | 1 - 1 file changed, 1 deletion(-) diff --git a/darknet.py b/darknet.py index b290a358b23..4e56d98f4a0 100644 --- a/darknet.py +++ b/darknet.py @@ -84,7 +84,6 @@ class METADATA(Structure): for k, v in os.environ.items(): envKeys.append(k) try: - tmp = os.environ["CUDA_HOME"] try: tmp = os.environ["FORCE_CPU"].lower() if tmp in ["1", "true", "yes", "on"]: From dbcdac88a571d638e55adc512148dc7b609cbfee Mon Sep 17 00:00:00 2001 From: Vinjn Zhang Date: Sun, 20 May 2018 14:20:55 +0800 Subject: [PATCH 0018/1365] darknet.py - loading dll files by absolute name --- darknet.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/darknet.py b/darknet.py index 4e56d98f4a0..75ef04b5033 100644 --- a/darknet.py +++ b/darknet.py @@ -78,8 +78,10 @@ class METADATA(Structure): #lib = CDLL("darknet.so", RTLD_GLOBAL) hasGPU = True if os.name == "nt": - winGPUdll = "yolo_cpp_dll.dll" - winNoGPUdll = "yolo_cpp_dll_nogpu.dll" + cwd = os.path.dirname(__file__) + os.environ['PATH'] = cwd + ';' + os.environ['PATH'] + winGPUdll = os.path.join(cwd, "yolo_cpp_dll.dll") + winNoGPUdll = os.path.join(cwd, "yolo_cpp_dll_nogpu.dll") envKeys = list() for k, v in os.environ.items(): envKeys.append(k) From c6ca54b411478a8bcf3ec8bc7615e80d63bdaba7 Mon Sep 17 00:00:00 2001 From: Vinjn Zhang Date: Sun, 20 May 2018 21:12:35 +0800 Subject: [PATCH 0019/1365] darknet.py - apply the same changes to build/darknet/x64/ --- build/darknet/x64/darknet.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/build/darknet/x64/darknet.py b/build/darknet/x64/darknet.py index b290a358b23..75ef04b5033 100644 --- a/build/darknet/x64/darknet.py +++ b/build/darknet/x64/darknet.py @@ -78,13 +78,14 @@ class METADATA(Structure): #lib = CDLL("darknet.so", RTLD_GLOBAL) hasGPU = True if os.name == "nt": - winGPUdll = "yolo_cpp_dll.dll" - winNoGPUdll = "yolo_cpp_dll_nogpu.dll" + cwd = os.path.dirname(__file__) + os.environ['PATH'] = cwd + ';' + os.environ['PATH'] + winGPUdll = os.path.join(cwd, "yolo_cpp_dll.dll") + winNoGPUdll = os.path.join(cwd, "yolo_cpp_dll_nogpu.dll") envKeys = list() for k, v in os.environ.items(): envKeys.append(k) try: - tmp = os.environ["CUDA_HOME"] try: tmp = os.environ["FORCE_CPU"].lower() if tmp in ["1", "true", "yes", "on"]: From 4403e71b330b42d3cda1e0721fb645cf41bac14f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 20 May 2018 18:43:15 +0300 Subject: [PATCH 0020/1365] Supported files for training: jpg, jpeg, bmp, png, ppm - and UPPER CASE extensions --- src/coco.c | 6 +----- src/data.c | 31 ++++++------------------------- src/detector.c | 49 +++---------------------------------------------- src/utils.c | 15 +++++++++++++++ src/utils.h | 1 + src/yolo.c | 6 +----- 6 files changed, 27 insertions(+), 81 deletions(-) diff --git a/src/coco.c b/src/coco.c index b8fc346463b..86da5b50557 100644 --- a/src/coco.c +++ b/src/coco.c @@ -281,11 +281,7 @@ void validate_coco_recall(char *cfgfile, char *weightfile) if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms_thresh); char labelpath[4096]; - find_replace(path, "images", "labels", labelpath); - find_replace(labelpath, "JPEGImages", "labels", labelpath); - find_replace(labelpath, ".jpg", ".txt", labelpath); - find_replace(labelpath, ".JPEG", ".txt", labelpath); - find_replace(labelpath, ".ppm", ".txt", labelpath); + replace_image_to_label(path, labelpath); int num_labels = 0; box_label *truth = read_boxes(labelpath, &num_labels); diff --git a/src/data.c b/src/data.c index 8da1234110d..a4298628e64 100644 --- a/src/data.c +++ b/src/data.c @@ -213,12 +213,7 @@ void correct_boxes(box_label *boxes, int n, float dx, float dy, float sx, float void fill_truth_swag(char *path, float *truth, int classes, int flip, float dx, float dy, float sx, float sy) { char labelpath[4096]; - find_replace(path, "images", "labels", labelpath); - find_replace(labelpath, "JPEGImages", "labels", labelpath); - find_replace(labelpath, ".jpg", ".txt", labelpath); - find_replace(labelpath, ".JPG", ".txt", labelpath); - find_replace(labelpath, ".JPEG", ".txt", labelpath); - find_replace(labelpath, ".ppm", ".txt", labelpath); + replace_image_to_label(path, labelpath); int count = 0; box_label *boxes = read_boxes(labelpath, &count); @@ -252,15 +247,9 @@ void fill_truth_swag(char *path, float *truth, int classes, int flip, float dx, void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int flip, float dx, float dy, float sx, float sy) { char labelpath[4096]; - find_replace(path, "images", "labels", labelpath); - find_replace(labelpath, "JPEGImages", "labels", labelpath); - - find_replace(labelpath, ".jpg", ".txt", labelpath); - find_replace(labelpath, ".png", ".txt", labelpath); - find_replace(labelpath, ".JPG", ".txt", labelpath); - find_replace(labelpath, ".JPEG", ".txt", labelpath); - find_replace(labelpath, ".ppm", ".txt", labelpath); - int count = 0; + replace_image_to_label(path, labelpath); + + int count = 0; box_label *boxes = read_boxes(labelpath, &count); randomize_boxes(boxes, count); correct_boxes(boxes, count, dx, dy, sx, sy, flip); @@ -302,16 +291,8 @@ void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, int small_object, int net_w, int net_h) { char labelpath[4096]; - find_replace(path, "images", "labels", labelpath); - find_replace(labelpath, "JPEGImages", "labels", labelpath); - - find_replace(labelpath, "raw", "labels", labelpath); - find_replace(labelpath, ".jpg", ".txt", labelpath); - find_replace(labelpath, ".png", ".txt", labelpath); - find_replace(labelpath, ".bmp", ".txt", labelpath); - find_replace(labelpath, ".JPG", ".txt", labelpath); - find_replace(labelpath, ".JPEG", ".txt", labelpath); - find_replace(labelpath, ".ppm", ".txt", labelpath); + replace_image_to_label(path, labelpath); + int count = 0; int i; box_label *boxes = read_boxes(labelpath, &count); diff --git a/src/detector.c b/src/detector.c index bbe60cfd200..f9cfdae5ac5 100644 --- a/src/detector.c +++ b/src/detector.c @@ -36,17 +36,6 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_ static int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90}; -void replace_image_to_label(char *input_path, char *output_path) { - find_replace(input_path, "images", "labels", output_path); - find_replace(output_path, "JPEGImages", "labels", output_path); - find_replace(output_path, ".jpg", ".txt", output_path); - find_replace(output_path, ".png", ".txt", output_path); - find_replace(output_path, ".bmp", ".txt", output_path); - find_replace(output_path, ".JPG", ".txt", output_path); - find_replace(output_path, ".JPEG", ".txt", output_path); - find_replace(output_path, ".ppm", ".txt", output_path); -} - void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show) { list *options = read_data_cfg(datacfg); @@ -486,14 +475,6 @@ void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile) char labelpath[4096]; replace_image_to_label(path, labelpath); - //find_replace(path, "images", "labels", labelpath); - //find_replace(labelpath, "JPEGImages", "labels", labelpath); - //find_replace(labelpath, ".jpg", ".txt", labelpath); - //find_replace(labelpath, ".png", ".txt", labelpath); - //find_replace(labelpath, ".bmp", ".txt", labelpath); - //find_replace(labelpath, ".JPG", ".txt", labelpath); - //find_replace(labelpath, ".JPEG", ".txt", labelpath); - //find_replace(labelpath, ".ppm", ".txt", labelpath); int num_labels = 0; box_label *truth = read_boxes(labelpath, &num_labels); @@ -645,14 +626,6 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float char labelpath[4096]; replace_image_to_label(path, labelpath); - //find_replace(path, "images", "labels", labelpath); - //find_replace(labelpath, "JPEGImages", "labels", labelpath); - //find_replace(labelpath, ".jpg", ".txt", labelpath); - //find_replace(labelpath, ".png", ".txt", labelpath); - //find_replace(labelpath, ".bmp", ".txt", labelpath); - //find_replace(labelpath, ".JPG", ".txt", labelpath); - //find_replace(labelpath, ".JPEG", ".txt", labelpath); - //find_replace(labelpath, ".ppm", ".txt", labelpath); int num_labels = 0; box_label *truth = read_boxes(labelpath, &num_labels); int i, j; @@ -668,11 +641,8 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float char *path_dif = paths_dif[image_index]; char labelpath_dif[4096]; - find_replace(path_dif, "images", "labels", labelpath_dif); - find_replace(labelpath_dif, "JPEGImages", "labels", labelpath_dif); - find_replace(labelpath_dif, ".jpg", ".txt", labelpath_dif); - find_replace(labelpath_dif, ".JPEG", ".txt", labelpath_dif); - find_replace(labelpath_dif, ".png", ".txt", labelpath_dif); + replace_image_to_label(path_dif, labelpath_dif); + truth_dif = read_boxes(labelpath_dif, &num_labels_dif); } @@ -910,14 +880,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int char *path = paths[i]; char labelpath[4096]; replace_image_to_label(path, labelpath); - //find_replace(path, "images", "labels", labelpath); - //find_replace(labelpath, "JPEGImages", "labels", labelpath); - //find_replace(labelpath, ".jpg", ".txt", labelpath); - //find_replace(labelpath, ".png", ".txt", labelpath); - //find_replace(labelpath, ".bmp", ".txt", labelpath); - //find_replace(labelpath, ".JPG", ".txt", labelpath); - //find_replace(labelpath, ".JPEG", ".txt", labelpath); - //find_replace(labelpath, ".ppm", ".txt", labelpath); + int num_labels = 0; box_label *truth = read_boxes(labelpath, &num_labels); //printf(" new path: %s \n", labelpath); @@ -1133,12 +1096,6 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam { char labelpath[4096]; replace_image_to_label(input, labelpath); - //find_replace(input, ".jpg", ".txt", labelpath); - //find_replace(labelpath, ".png", ".txt", labelpath); - //find_replace(labelpath, ".bmp", ".txt", labelpath); - //find_replace(labelpath, ".JPG", ".txt", labelpath); - //find_replace(labelpath, ".JPEG", ".txt", labelpath); - //find_replace(labelpath, ".ppm", ".txt", labelpath); FILE* fw = fopen(labelpath, "wb"); int i; diff --git a/src/utils.c b/src/utils.c index 8b1dcf526bc..d9d796e1829 100644 --- a/src/utils.c +++ b/src/utils.c @@ -183,6 +183,21 @@ void find_replace(char *str, char *orig, char *rep, char *output) sprintf(output, "%s%s%s", buffer, rep, p+strlen(orig)); } +void replace_image_to_label(char *input_path, char *output_path) { + find_replace(input_path, "images", "labels", output_path); + find_replace(output_path, "JPEGImages", "labels", output_path); + find_replace(output_path, ".jpg", ".txt", output_path); + find_replace(output_path, ".JPG", ".txt", output_path); + find_replace(output_path, ".jpeg", ".txt", output_path); + find_replace(output_path, ".JPEG", ".txt", output_path); + find_replace(output_path, ".png", ".txt", output_path); + find_replace(output_path, ".PNG", ".txt", output_path); + find_replace(output_path, ".bmp", ".txt", output_path); + find_replace(output_path, ".BMP", ".txt", output_path); + find_replace(output_path, ".ppm", ".txt", output_path); + find_replace(output_path, ".PPM", ".txt", output_path); +} + float sec(clock_t clocks) { return (float)clocks/CLOCKS_PER_SEC; diff --git a/src/utils.h b/src/utils.h index 8e8e1c753ff..e2afba4579e 100644 --- a/src/utils.h +++ b/src/utils.h @@ -40,6 +40,7 @@ void write_all(int fd, char *buffer, size_t bytes); int read_all_fail(int fd, char *buffer, size_t bytes); int write_all_fail(int fd, char *buffer, size_t bytes); void find_replace(char *str, char *orig, char *rep, char *output); +void replace_image_to_label(char *input_path, char *output_path); void error(const char *s); void malloc_error(); void file_error(char *s); diff --git a/src/yolo.c b/src/yolo.c index 4481165821e..28f905f08d5 100644 --- a/src/yolo.c +++ b/src/yolo.c @@ -252,11 +252,7 @@ void validate_yolo_recall(char *cfgfile, char *weightfile) if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms); char labelpath[4096]; - find_replace(path, "images", "labels", labelpath); - find_replace(labelpath, "JPEGImages", "labels", labelpath); - find_replace(labelpath, ".jpg", ".txt", labelpath); - find_replace(labelpath, ".JPEG", ".txt", labelpath); - find_replace(labelpath, ".ppm", ".txt", labelpath); + replace_image_to_label(path, labelpath); int num_labels = 0; box_label *truth = read_boxes(labelpath, &num_labels); From b88b7b645bc71ddcd83f81744c78419e0ed15078 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 21 May 2018 15:04:10 +0300 Subject: [PATCH 0021/1365] Save jpg-images for detector demo if used flag: -prefix --- src/demo.c | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/demo.c b/src/demo.c index 9041c73ba5d..ae2150ba147 100644 --- a/src/demo.c +++ b/src/demo.c @@ -243,7 +243,8 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int } }else{ char buff[256]; - sprintf(buff, "%s_%08d", prefix, count); + sprintf(buff, "%s_%08d.jpg", prefix, count); + cvSaveImage(buff, show_img, 0); //save_image(disp, buff); } From 46028c6621f1a75874054db23dfa103a7759a483 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 21 May 2018 19:12:55 +0300 Subject: [PATCH 0022/1365] Fixed demo for video files: is used do_nms_sort() instead of do_nms_obj(). --- src/demo.c | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/src/demo.c b/src/demo.c index ae2150ba147..2d38269ae26 100644 --- a/src/demo.c +++ b/src/demo.c @@ -98,7 +98,9 @@ void *detect_in_thread(void *ptr) int letter = 0; int nboxes = 0; detection *dets = get_network_boxes(&net, det_s.w, det_s.h, demo_thresh, demo_thresh, 0, 1, &nboxes, letter); - if (nms) do_nms_obj(dets, nboxes, l.classes, nms); + //if (nms) do_nms_obj(dets, nboxes, l.classes, nms); // bad results + if (nms) do_nms_sort(dets, nboxes, l.classes, nms); + printf("\033[2J"); printf("\033[1;1H"); @@ -165,7 +167,12 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int //#endif } - if(!cap) error("Couldn't connect to webcam.\n"); + if (!cap) { +#ifdef WIN32 + printf("Check that you have copied file opencv_ffmpeg340_64.dll to the same directory where is darknet.exe \n"); +#endif + error("Couldn't connect to webcam.\n"); + } layer l = net.layers[net.n-1]; int j; From a720bf74e233c956e5281871ede722a2c7cf54db Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 22 May 2018 00:15:04 +0300 Subject: [PATCH 0023/1365] Added flag -ext_output for detector demo. Fixed proportional resize of network for random=1 --- src/demo.c | 10 ++++++---- src/detector.c | 14 ++++++++++---- src/image.c | 35 ++++++++++++++++++++++------------- 3 files changed, 38 insertions(+), 21 deletions(-) diff --git a/src/demo.c b/src/demo.c index 2d38269ae26..3452706aa00 100644 --- a/src/demo.c +++ b/src/demo.c @@ -40,6 +40,7 @@ static CvCapture * cap; static int cpp_video_capture = 0; static float fps = 0; static float demo_thresh = 0; +static int demo_ext_output = 0; static float *predictions[FRAMES]; static int demo_index = 0; @@ -48,7 +49,7 @@ static IplImage* ipl_images[FRAMES]; static float *avg; void draw_detections_cv(IplImage* show_img, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes); -void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes); +void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output); void show_image_cv_ipl(IplImage *disp, const char *name); image get_image_from_stream_resize(CvCapture *cap, int w, int h, IplImage** in_img, int cpp_video_capture); IplImage* in_img; @@ -114,7 +115,7 @@ void *detect_in_thread(void *ptr) demo_index = (demo_index + 1)%FRAMES; //draw_detections(det, l.w*l.h*l.n, demo_thresh, boxes, probs, demo_names, demo_alphabet, demo_classes); - draw_detections_cv_v3(det_img, dets, nboxes, demo_thresh, demo_names, demo_alphabet, demo_classes); + draw_detections_cv_v3(det_img, dets, nboxes, demo_thresh, demo_names, demo_alphabet, demo_classes, demo_ext_output); //draw_detections_cv(det_img, l.w*l.h*l.n, demo_thresh, boxes, probs, demo_names, demo_alphabet, demo_classes); free_detections(dets, nboxes); @@ -131,7 +132,7 @@ double get_wall_time() } void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, - int frame_skip, char *prefix, char *out_filename, int http_stream_port, int dont_show) + int frame_skip, char *prefix, char *out_filename, int http_stream_port, int dont_show, int ext_output) { //skip = frame_skip; image **alphabet = load_alphabet(); @@ -140,6 +141,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int demo_alphabet = alphabet; demo_classes = classes; demo_thresh = thresh; + demo_ext_output = ext_output; printf("Demo\n"); net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 if(weightfile){ @@ -313,7 +315,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int } #else void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, - int frame_skip, char *prefix, char *out_filename, int http_stream_port, int dont_show) + int frame_skip, char *prefix, char *out_filename, int http_stream_port, int dont_show, int ext_output) { fprintf(stderr, "Demo needs OpenCV for webcam images.\n"); } diff --git a/src/detector.c b/src/detector.c index f9cfdae5ac5..6150df407b8 100644 --- a/src/detector.c +++ b/src/detector.c @@ -129,9 +129,15 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i //int dim = (rand() % 12 + (init_w/32 - 5)) * 32; // +-160 //int dim = (rand() % 4 + 16) * 32; //if (get_current_batch(net)+100 > net.max_batches) dim = 544; - int random_val = rand() % 12; - int dim_w = (random_val + (init_w / 32 - 5)) * 32; // +-160 - int dim_h = (random_val + (init_h / 32 - 5)) * 32; // +-160 + + //int random_val = rand() % 12; + //int dim_w = (random_val + (init_w / 32 - 5)) * 32; // +-160 + //int dim_h = (random_val + (init_h / 32 - 5)) * 32; // +-160 + + float random_val = rand_scale(1.4); // *x or /x + int dim_w = roundl(random_val*init_w / 32) * 32; + int dim_h = roundl(random_val*init_h / 32) * 32; + if (dim_w < 32) dim_w = 32; if (dim_h < 32) dim_h = 32; @@ -1218,7 +1224,7 @@ void run_detector(int argc, char **argv) if(strlen(filename) > 0) if (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0; demo(cfg, weights, thresh, hier_thresh, cam_index, filename, names, classes, frame_skip, prefix, out_filename, - http_stream_port, dont_show); + http_stream_port, dont_show, ext_output); } else printf(" There isn't such command: %s", argv[2]); } diff --git a/src/image.c b/src/image.c index a1e4c77e673..fc4dc50841f 100644 --- a/src/image.c +++ b/src/image.c @@ -287,7 +287,7 @@ void draw_detections_v3(image im, detection *dets, int num, float thresh, char * const int best_class = selected_detections[i].best_class; printf("%s: %.0f%%", names[best_class], selected_detections[i].det.prob[best_class] * 100); if (ext_output) - printf("\t(left: %.0f \ttop: %.0f \tw: %0.f \th: %0.f)\n", + printf("\t(left: %4.0f top: %4.0f w: %4.0f h: %4.0f)\n", (selected_detections[i].det.bbox.x - selected_detections[i].det.bbox.w / 2)*im.w, (selected_detections[i].det.bbox.y - selected_detections[i].det.bbox.h / 2)*im.h, selected_detections[i].det.bbox.w*im.w, selected_detections[i].det.bbox.h*im.h); @@ -437,7 +437,7 @@ void draw_detections(image im, int num, float thresh, box *boxes, float **probs, #ifdef OPENCV -void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes) +void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output) { int i, j; if (!show_img) return; @@ -457,18 +457,16 @@ void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float t strcat(labelstr, ", "); strcat(labelstr, names[j]); } - printf("%s: %.0f%%\n", names[j], dets[i].prob[j] * 100); + printf("%s: %.0f%% ", names[j], dets[i].prob[j] * 100); } } if (class_id >= 0) { int width = show_img->height * .006; - /* - if(0){ - width = pow(prob, 1./2.)*10+1; - alphabet = 0; - } - */ + //if(0){ + //width = pow(prob, 1./2.)*10+1; + //alphabet = 0; + //} //printf("%d %s: %.0f%%\n", i, names[class_id], prob*100); int offset = class_id * 123457 % classes; @@ -519,17 +517,28 @@ void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float t color.val[2] = blue * 256; // you should create directory: result_img + //static int copied_frame_id = -1; + //static IplImage* copy_img = NULL; + //if (copied_frame_id != frame_id) { + // copied_frame_id = frame_id; + // if(copy_img == NULL) copy_img = cvCreateImage(cvSize(show_img->width, show_img->height), show_img->depth, show_img->nChannels); + // cvCopy(show_img, copy_img, 0); + //} //static int img_id = 0; //img_id++; //char image_name[1024]; //sprintf(image_name, "result_img/img_%d_%d_%d.jpg", frame_id, img_id, class_id); //CvRect rect = cvRect(pt1.x, pt1.y, pt2.x - pt1.x, pt2.y - pt1.y); - //cvSetImageROI(show_img, rect); - //cvSaveImage(image_name, show_img, 0); - //cvResetImageROI(show_img); + //cvSetImageROI(copy_img, rect); + //cvSaveImage(image_name, copy_img, 0); + //cvResetImageROI(copy_img); cvRectangle(show_img, pt1, pt2, color, width, 8, 0); - //printf("left=%d, right=%d, top=%d, bottom=%d, obj_id=%d, obj=%s \n", left, right, top, bot, class_id, names[class_id]); + if (ext_output) + printf(" (left: %4.0f top: %4.0f w: %4.0f h: %4.0f)\n", + (float)left, (float)right, b.w*show_img->width, b.h*show_img->height); + else + printf("\n"); cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0); cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0); // filled CvScalar black_color; From d01a686246550b524140d4c9c3580bf066d8d71e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 22 May 2018 00:33:24 +0300 Subject: [PATCH 0024/1365] Fixed compile --- src/coco.c | 3 ++- src/demo.h | 2 +- src/yolo.c | 3 ++- 3 files changed, 5 insertions(+), 3 deletions(-) diff --git a/src/coco.c b/src/coco.c index 86da5b50557..1913a47836a 100644 --- a/src/coco.c +++ b/src/coco.c @@ -372,6 +372,7 @@ void run_coco(int argc, char **argv) float hier_thresh = find_float_arg(argc, argv, "-hier", .5); int cam_index = find_int_arg(argc, argv, "-c", 0); int frame_skip = find_int_arg(argc, argv, "-s", 0); + int ext_output = find_arg(argc, argv, "-ext_output"); if(argc < 4){ fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); @@ -386,5 +387,5 @@ void run_coco(int argc, char **argv) else if(0==strcmp(argv[2], "valid")) validate_coco(cfg, weights); else if(0==strcmp(argv[2], "recall")) validate_coco_recall(cfg, weights); else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, hier_thresh, cam_index, filename, coco_classes, 80, frame_skip, - prefix, out_filename, http_stream_port, dont_show); + prefix, out_filename, http_stream_port, dont_show, ext_output); } diff --git a/src/demo.h b/src/demo.h index 5340db6c2c9..7ad7addff0f 100644 --- a/src/demo.h +++ b/src/demo.h @@ -2,6 +2,6 @@ #define DEMO #include "image.h" -void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, int frame_skip, char *prefix, char *out_filename, int http_stream_port, int dont_show); +void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, int frame_skip, char *prefix, char *out_filename, int http_stream_port, int dont_show, int ext_output); #endif diff --git a/src/yolo.c b/src/yolo.c index 28f905f08d5..4d344f0daf6 100644 --- a/src/yolo.c +++ b/src/yolo.c @@ -345,6 +345,7 @@ void run_yolo(int argc, char **argv) float hier_thresh = find_float_arg(argc, argv, "-hier", .5); int cam_index = find_int_arg(argc, argv, "-c", 0); int frame_skip = find_int_arg(argc, argv, "-s", 0); + int ext_output = find_arg(argc, argv, "-ext_output"); if(argc < 4){ fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); return; @@ -358,5 +359,5 @@ void run_yolo(int argc, char **argv) else if(0==strcmp(argv[2], "valid")) validate_yolo(cfg, weights); else if(0==strcmp(argv[2], "recall")) validate_yolo_recall(cfg, weights); else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, hier_thresh, cam_index, filename, voc_names, 20, frame_skip, - prefix, out_filename, http_stream_port, dont_show); + prefix, out_filename, http_stream_port, dont_show, ext_output); } From f9ecf6fd3f0df305d7103ecc3f15b23bba260baf Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 22 May 2018 02:37:52 +0300 Subject: [PATCH 0025/1365] For Release build - equality check: output of [convolutional] == input of [yolo]. --- src/parser.c | 14 ++++++++++++-- src/yolo_layer.c | 2 +- 2 files changed, 13 insertions(+), 3 deletions(-) diff --git a/src/parser.c b/src/parser.c index 2b013de96a1..188ba54cf16 100644 --- a/src/parser.c +++ b/src/parser.c @@ -270,7 +270,12 @@ layer parse_yolo(list *options, size_params params) int *mask = parse_yolo_mask(a, &num); int max_boxes = option_find_int_quiet(options, "max", 30); layer l = make_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes, max_boxes); - assert(l.outputs == params.inputs); + if (l.outputs != params.inputs) { + printf("Error: l.outputs == params.inputs \n"); + printf("filters= in the [convolutional]-layer doesn't correspond to classes= or mask= in [yolo]-layer \n"); + exit(EXIT_FAILURE); + } + //assert(l.outputs == params.inputs); //l.max_boxes = option_find_int_quiet(options, "max", 90); l.jitter = option_find_float(options, "jitter", .2); @@ -308,7 +313,12 @@ layer parse_region(list *options, size_params params) int max_boxes = option_find_int_quiet(options, "max", 30); layer l = make_region_layer(params.batch, params.w, params.h, num, classes, coords, max_boxes); - assert(l.outputs == params.inputs); + if (l.outputs != params.inputs) { + printf("Error: l.outputs == params.inputs \n"); + printf("filters= in the [convolutional]-layer doesn't correspond to classes= or num= in [region]-layer \n"); + exit(EXIT_FAILURE); + } + //assert(l.outputs == params.inputs); l.log = option_find_int_quiet(options, "log", 0); l.sqrt = option_find_int_quiet(options, "sqrt", 0); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index e37c175bbf4..a03c18be6dc 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -112,7 +112,7 @@ float delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i void delta_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, int focal_loss) { int n; - if (delta[index]){ + if (delta[index + stride*class_id]){ delta[index + stride*class_id] = 1 - output[index + stride*class_id]; if(avg_cat) *avg_cat += output[index + stride*class_id]; return; From 032acd408acd119a5bd5c132765fcdd6caedea6b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 22 May 2018 16:38:04 +0300 Subject: [PATCH 0026/1365] Experimental reinforcement learning. --- src/detector.c | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/src/detector.c b/src/detector.c index 6150df407b8..8b5390763e0 100644 --- a/src/detector.c +++ b/src/detector.c @@ -542,6 +542,7 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float char *mapf = option_find_str(options, "map", 0); int *map = 0; if (mapf) map = read_map(mapf); + FILE* reinforcement_fd = NULL; network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 if (weightfile) { @@ -720,9 +721,18 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float } } } - + unique_truth_count += num_labels; + //static int previous_errors = 0; + //int total_errors = fp_for_thresh + (unique_truth_count - tp_for_thresh); + //int errors_in_this_image = total_errors - previous_errors; + //previous_errors = total_errors; + //if(reinforcement_fd == NULL) reinforcement_fd = fopen("reinforcement.txt", "wb"); + //char buff[1000]; + //sprintf(buff, "%s\n", path); + //if(errors_in_this_image > 0) fwrite(buff, sizeof(char), strlen(buff), reinforcement_fd); + free_detections(dets, nboxes); free(id); free_image(val[t]); @@ -843,6 +853,7 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float free(truth_classes_count); fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); + if (reinforcement_fd != NULL) fclose(reinforcement_fd); } #ifdef OPENCV From a2666d5b3cfdcbab1e2eb66ca41f8b6835d4ffc8 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 22 May 2018 18:23:48 +0300 Subject: [PATCH 0027/1365] Fixed replace_image_to_label(): replaces directories only for Pascal VOC and COCO, replaces only extensions for other. --- src/image.c | 1 + src/utils.c | 51 ++++++++++++++++++++++++++++++++++++++------------- 2 files changed, 39 insertions(+), 13 deletions(-) diff --git a/src/image.c b/src/image.c index fc4dc50841f..003d5da376f 100644 --- a/src/image.c +++ b/src/image.c @@ -999,6 +999,7 @@ image get_image_from_stream_resize(CvCapture *cap, int w, int h, IplImage** in_i src = get_webcam_frame(cap); if (!src) return make_empty_image(0, 0, 0); } while (src->width < 1 || src->height < 1 || src->nChannels < 1); + printf("Video stream: %d x %d \n", src->width, src->height); } else src = get_webcam_frame(cap); } diff --git a/src/utils.c b/src/utils.c index d9d796e1829..fe89eb0d32b 100644 --- a/src/utils.c +++ b/src/utils.c @@ -169,7 +169,7 @@ void pm(int M, int N, float *A) void find_replace(char *str, char *orig, char *rep, char *output) { - char buffer[4096] = {0}; + char *buffer = calloc(8192, sizeof(char)); char *p; sprintf(buffer, "%s", str); @@ -181,21 +181,46 @@ void find_replace(char *str, char *orig, char *rep, char *output) *p = '\0'; sprintf(output, "%s%s%s", buffer, rep, p+strlen(orig)); + free(buffer); +} + +void find_replace_extension(char *str, char *orig, char *rep, char *output) +{ + char *buffer = calloc(8192, sizeof(char)); + + sprintf(buffer, "%s", str); + char *p = strstr(buffer, orig); + int offset = (p - buffer); + int chars_from_end = strlen(buffer) - offset; + if (!p || chars_from_end != strlen(orig)) { // Is 'orig' even in 'str' AND is 'orig' found at the end of 'str'? + sprintf(output, "%s", str); + return; + } + + *p = '\0'; + + sprintf(output, "%s%s%s", buffer, rep, p + strlen(orig)); + free(buffer); } void replace_image_to_label(char *input_path, char *output_path) { - find_replace(input_path, "images", "labels", output_path); - find_replace(output_path, "JPEGImages", "labels", output_path); - find_replace(output_path, ".jpg", ".txt", output_path); - find_replace(output_path, ".JPG", ".txt", output_path); - find_replace(output_path, ".jpeg", ".txt", output_path); - find_replace(output_path, ".JPEG", ".txt", output_path); - find_replace(output_path, ".png", ".txt", output_path); - find_replace(output_path, ".PNG", ".txt", output_path); - find_replace(output_path, ".bmp", ".txt", output_path); - find_replace(output_path, ".BMP", ".txt", output_path); - find_replace(output_path, ".ppm", ".txt", output_path); - find_replace(output_path, ".PPM", ".txt", output_path); + //find_replace(input_path, "images", "labels", output_path); // COCO + find_replace(input_path, "images/train2014/", "labels/train2014/", output_path); // COCO + find_replace(input_path, "images/val2014/", "labels/val2014/", output_path); // COCO + //find_replace(output_path, "JPEGImages", "labels", output_path); // PascalVOC + find_replace(output_path, "VOC2007/JPEGImages", "VOC2007/labels", output_path); // PascalVOC + find_replace(output_path, "VOC2012/JPEGImages", "VOC2012/labels", output_path); // PascalVOC + // replace only ext of files + find_replace_extension(output_path, ".jpg", ".txt", output_path); + find_replace_extension(output_path, ".JPG", ".txt", output_path); + find_replace_extension(output_path, ".jpeg", ".txt", output_path); + find_replace_extension(output_path, ".JPEG", ".txt", output_path); + find_replace_extension(output_path, ".png", ".txt", output_path); + find_replace_extension(output_path, ".PNG", ".txt", output_path); + find_replace_extension(output_path, ".bmp", ".txt", output_path); + find_replace_extension(output_path, ".BMP", ".txt", output_path); + find_replace_extension(output_path, ".ppm", ".txt", output_path); + find_replace_extension(output_path, ".PPM", ".txt", output_path); } float sec(clock_t clocks) From 60cd28bdfb410539ee582963e8f22bec4f0b7182 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 22 May 2018 20:10:04 +0300 Subject: [PATCH 0028/1365] Fixed seg-fault that occurred if in labels more than classes= in the cfg-file. --- src/region_layer.c | 6 ++++-- src/yolo_layer.c | 16 ++++++++++------ 2 files changed, 14 insertions(+), 8 deletions(-) diff --git a/src/region_layer.c b/src/region_layer.c index 62c8b347333..9d5fd720fd5 100644 --- a/src/region_layer.c +++ b/src/region_layer.c @@ -256,6 +256,8 @@ void forward_region_layer(const region_layer l, network_state state) int best_class_id = -1; for(t = 0; t < l.max_boxes; ++t){ box truth = float_to_box(state.truth + t*5 + b*l.truths); + int class_id = state.truth[t * 5 + b*l.truths + 4]; + if (class_id >= l.classes) continue; // if label contains class_id more than number of classes in the cfg-file if(!truth.x) break; float iou = box_iou(pred, truth); if (iou > best_iou) { @@ -293,6 +295,8 @@ void forward_region_layer(const region_layer l, network_state state) } for(t = 0; t < l.max_boxes; ++t){ box truth = float_to_box(state.truth + t*5 + b*l.truths); + int class_id = state.truth[t * 5 + b*l.truths + 4]; + if (class_id >= l.classes) continue; // if label contains class_id more than number of classes in the cfg-file if(!truth.x) break; float best_iou = 0; @@ -339,8 +343,6 @@ void forward_region_layer(const region_layer l, network_state state) l.delta[best_index + 4] = l.object_scale * (iou - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]); } - - int class_id = state.truth[t*5 + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; delta_region_class(l.output, l.delta, best_index + 5, class_id, l.classes, l.softmax_tree, l.class_scale, &avg_cat, l.focal_loss); ++count; diff --git a/src/yolo_layer.c b/src/yolo_layer.c index a03c18be6dc..87dedaec216 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -200,6 +200,8 @@ void forward_yolo_layer(const layer l, network_state state) int best_t = 0; for(t = 0; t < l.max_boxes; ++t){ box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); + int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; + if (class_id >= l.classes) continue; // if label contains class_id more than number of classes in the cfg-file if(!truth.x) break; float iou = box_iou(pred, truth); if (iou > best_iou) { @@ -216,10 +218,10 @@ void forward_yolo_layer(const layer l, network_state state) if (best_iou > l.truth_thresh) { l.delta[obj_index] = 1 - l.output[obj_index]; - int class = state.truth[best_t*(4 + 1) + b*l.truths + 4]; - if (l.map) class = l.map[class]; + int class_id = state.truth[best_t*(4 + 1) + b*l.truths + 4]; + if (l.map) class_id = l.map[class_id]; int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); - delta_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, 0, l.focal_loss); + delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss); box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h); } @@ -228,6 +230,8 @@ void forward_yolo_layer(const layer l, network_state state) } for(t = 0; t < l.max_boxes; ++t){ box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); + int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; + if (class_id >= l.classes) continue; // if label contains class_id more than number of classes in the cfg-file if(!truth.x) break; float best_iou = 0; @@ -256,10 +260,10 @@ void forward_yolo_layer(const layer l, network_state state) avg_obj += l.output[obj_index]; l.delta[obj_index] = 1 - l.output[obj_index]; - int class = state.truth[t*(4 + 1) + b*l.truths + 4]; - if (l.map) class = l.map[class]; + int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; + if (l.map) class_id = l.map[class_id]; int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); - delta_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, &avg_cat, l.focal_loss); + delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss); ++count; ++class_count; From 768b190b10e61932d78547e44aa9475ae950e409 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 22 May 2018 23:00:22 +0300 Subject: [PATCH 0029/1365] Minor fix: warning-message if in labels class_id >= classes in cfg-file --- src/region_layer.c | 5 ++++- src/yolo_layer.c | 5 ++++- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/src/region_layer.c b/src/region_layer.c index 9d5fd720fd5..a2ca440c5a1 100644 --- a/src/region_layer.c +++ b/src/region_layer.c @@ -296,7 +296,10 @@ void forward_region_layer(const region_layer l, network_state state) for(t = 0; t < l.max_boxes; ++t){ box truth = float_to_box(state.truth + t*5 + b*l.truths); int class_id = state.truth[t * 5 + b*l.truths + 4]; - if (class_id >= l.classes) continue; // if label contains class_id more than number of classes in the cfg-file + if (class_id >= l.classes) { + printf("Warning: in txt-labels class_id=%d >= classes=%d in cfg-file\n", class_id, l.classes); + continue; // if label contains class_id more than number of classes in the cfg-file + } if(!truth.x) break; float best_iou = 0; diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 87dedaec216..c1309c8ffbd 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -201,7 +201,10 @@ void forward_yolo_layer(const layer l, network_state state) for(t = 0; t < l.max_boxes; ++t){ box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; - if (class_id >= l.classes) continue; // if label contains class_id more than number of classes in the cfg-file + if (class_id >= l.classes) { + printf("Warning: in txt-labels class_id=%d >= classes=%d in cfg-file\n", class_id, l.classes); + continue; // if label contains class_id more than number of classes in the cfg-file + } if(!truth.x) break; float iou = box_iou(pred, truth); if (iou > best_iou) { From ec68838342b42541776607b0c14e40fb89f7e3d8 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 23 May 2018 18:27:18 +0300 Subject: [PATCH 0030/1365] Fixed memory leaks for Yolo: train, test --- build/darknet/darknet.vcxproj | 5 +++-- src/convolutional_layer.c | 2 +- src/demo.c | 27 +++++++++++++++++++++++++++ src/detector.c | 30 +++++++++++++++++++++++------- src/layer.c | 1 + src/list.c | 12 ++++++++++++ src/list.h | 1 + src/utils.c | 8 +++++--- 8 files changed, 73 insertions(+), 13 deletions(-) diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj index bc4b82d2822..8fbf93c3c0d 100644 --- a/build/darknet/darknet.vcxproj +++ b/build/darknet/darknet.vcxproj @@ -89,9 +89,10 @@ Disabled true C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include - _CRTDBG_MAP_ALLOC;_MBCS;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) - CUDNN;OPENCV; + CUDNN;_CRTDBG_MAP_ALLOC;_MBCS;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + OPENCV; true + stdlib.h;crtdbg.h;%(ForcedIncludeFiles) true diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index f4a5d89977e..90f9551b048 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -502,7 +502,7 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) size_t total_byte; check_error(cudaMemGetInfo(&free_byte, &total_byte)); if (l->workspace_size > free_byte || l->workspace_size >= total_byte / 2) { - printf(" used slow CUDNN algo without Workspace! Need memory: %d, available: %d\n", l->workspace_size, (free_byte < total_byte/2) ? free_byte : total_byte/2); + printf(" used slow CUDNN algo without Workspace! Need memory: %zu, available: %zu\n", l->workspace_size, (free_byte < total_byte/2) ? free_byte : total_byte/2); cudnn_convolutional_setup(l, cudnn_smallest); l->workspace_size = get_workspace_size(*l); } diff --git a/src/demo.c b/src/demo.c index 3452706aa00..81eddb297ae 100644 --- a/src/demo.c +++ b/src/demo.c @@ -312,6 +312,33 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cvReleaseVideoWriter(&output_video_writer); printf("output_video_writer closed. \n"); } + + // free memory + cvReleaseImage(&show_img); + cvReleaseImage(&in_img); + free_image(in_s); + + free(avg); + for (j = 0; j < FRAMES; ++j) free(predictions[j]); + for (j = 0; j < FRAMES; ++j) free_image(images[j]); + + for (j = 0; j < l.w*l.h*l.n; ++j) free(probs[j]); + free(boxes); + free(probs); + + free_ptrs(names, net.layers[net.n - 1].classes); + + int i; + const int nsize = 8; + for (j = 0; j < nsize; ++j) { + for (i = 32; i < 127; ++i) { + free_image(alphabet[j][i]); + } + free(alphabet[j]); + } + free(alphabet); + + free_network(net); } #else void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, diff --git a/src/detector.c b/src/detector.c index 8b5390763e0..536b9d7b63e 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1,8 +1,3 @@ -#ifdef _DEBUG -#include -#include -#endif - #include "network.h" #include "region_layer.h" #include "cost_layer.h" @@ -221,8 +216,25 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i sprintf(buff, "%s/%s_final.weights", backup_directory, base); save_weights(net, buff); - //cvReleaseImage(&img); - //cvDestroyAllWindows(); +#ifdef OPENCV + cvReleaseImage(&img); + cvDestroyAllWindows(); +#endif + + // free memory + pthread_join(load_thread, 0); + free_data(buffer); + + free(base); + free(paths); + free_list_contents(plist); + free_list(plist); + + free_list_contents_kvp(options); + free_list(options); + + free(nets); + free_network(net); } @@ -1150,6 +1162,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam // free memory free_ptrs(names, net.layers[net.n - 1].classes); + free_list_contents_kvp(options); free_list(options); int i; @@ -1236,6 +1249,9 @@ void run_detector(int argc, char **argv) if (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0; demo(cfg, weights, thresh, hier_thresh, cam_index, filename, names, classes, frame_skip, prefix, out_filename, http_stream_port, dont_show, ext_output); + + free_list_contents_kvp(options); + free_list(options); } else printf(" There isn't such command: %s", argv[2]); } diff --git a/src/layer.c b/src/layer.c index 582cbb39a99..3b479172590 100644 --- a/src/layer.c +++ b/src/layer.c @@ -11,6 +11,7 @@ void free_layer(layer l) #endif return; } + if (l.mask) free(l.mask); if (l.cweights) free(l.cweights); if (l.indexes) free(l.indexes); if (l.input_layers) free(l.input_layers); diff --git a/src/list.c b/src/list.c index 0e4165d3780..e83f63ef9ec 100644 --- a/src/list.c +++ b/src/list.c @@ -1,6 +1,7 @@ #include #include #include "list.h" +#include "option_list.h" list *make_list() { @@ -79,6 +80,17 @@ void free_list_contents(list *l) } } +void free_list_contents_kvp(list *l) +{ + node *n = l->front; + while (n) { + kvp *p = n->val; + free(p->key); + free(n->val); + n = n->next; + } +} + void **list_to_array(list *l) { void **a = calloc(l->size, sizeof(void*)); diff --git a/src/list.h b/src/list.h index fb818c2a0c9..c06d1429270 100644 --- a/src/list.h +++ b/src/list.h @@ -22,5 +22,6 @@ void **list_to_array(list *l); void free_list(list *l); void free_list_contents(list *l); +void free_list_contents_kvp(list *l); #endif diff --git a/src/utils.c b/src/utils.c index fe89eb0d32b..7b25e9c3ccf 100644 --- a/src/utils.c +++ b/src/utils.c @@ -175,6 +175,7 @@ void find_replace(char *str, char *orig, char *rep, char *output) sprintf(buffer, "%s", str); if(!(p = strstr(buffer, orig))){ // Is 'orig' even in 'str'? sprintf(output, "%s", str); + free(buffer); return; } @@ -194,6 +195,7 @@ void find_replace_extension(char *str, char *orig, char *rep, char *output) int chars_from_end = strlen(buffer) - offset; if (!p || chars_from_end != strlen(orig)) { // Is 'orig' even in 'str' AND is 'orig' found at the end of 'str'? sprintf(output, "%s", str); + free(buffer); return; } @@ -206,13 +208,13 @@ void find_replace_extension(char *str, char *orig, char *rep, char *output) void replace_image_to_label(char *input_path, char *output_path) { //find_replace(input_path, "images", "labels", output_path); // COCO find_replace(input_path, "images/train2014/", "labels/train2014/", output_path); // COCO - find_replace(input_path, "images/val2014/", "labels/val2014/", output_path); // COCO + find_replace(output_path, "images/val2014/", "labels/val2014/", output_path); // COCO //find_replace(output_path, "JPEGImages", "labels", output_path); // PascalVOC find_replace(output_path, "VOC2007/JPEGImages", "VOC2007/labels", output_path); // PascalVOC find_replace(output_path, "VOC2012/JPEGImages", "VOC2012/labels", output_path); // PascalVOC // replace only ext of files find_replace_extension(output_path, ".jpg", ".txt", output_path); - find_replace_extension(output_path, ".JPG", ".txt", output_path); + find_replace_extension(output_path, ".JPG", ".txt", output_path); // error find_replace_extension(output_path, ".jpeg", ".txt", output_path); find_replace_extension(output_path, ".JPEG", ".txt", output_path); find_replace_extension(output_path, ".png", ".txt", output_path); @@ -285,7 +287,7 @@ void strip(char *s) size_t offset = 0; for(i = 0; i < len; ++i){ char c = s[i]; - if(c==' '||c=='\t'||c=='\n'||c =='\r') ++offset; + if(c==' '||c=='\t'||c=='\n'||c =='\r'||c==0x0d||c==0x0a) ++offset; else s[i-offset] = c; } s[len-offset] = '\0'; From eef9f8e5bb9fdbe22c6301a56f12e315f9ac64d9 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 23 May 2018 18:35:08 +0300 Subject: [PATCH 0031/1365] Minor fixes --- src/box.h | 5 ----- src/darknet.c | 5 ----- src/http_stream.cpp | 2 +- src/network.c | 4 ++-- src/option_list.c | 2 +- 5 files changed, 4 insertions(+), 14 deletions(-) diff --git a/src/box.h b/src/box.h index 9553b123a7b..6063a480e54 100644 --- a/src/box.h +++ b/src/box.h @@ -1,11 +1,6 @@ #ifndef BOX_H #define BOX_H -#ifdef _DEBUG -#include -#include -#endif - #ifdef YOLODLL_EXPORTS #if defined(_MSC_VER) #define YOLODLL_API __declspec(dllexport) diff --git a/src/darknet.c b/src/darknet.c index 540726952f3..ece518c0225 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -1,8 +1,3 @@ -#ifdef _DEBUG -#include -#include -#endif - #include #include #include diff --git a/src/http_stream.cpp b/src/http_stream.cpp index a2ffc0c1b8c..9192f75a6f7 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -208,7 +208,7 @@ CvCapture* get_capture_video_stream(char *path) { cap = (CvCapture*)new cv::VideoCapture(path); } catch (...) { - std::cout << " Error: Web-camera " << path << " can't be opened! \n"; + std::cout << " Error: video-stream " << path << " can't be opened! \n"; } return cap; } diff --git a/src/network.c b/src/network.c index f992fef96ac..c0676bf71aa 100644 --- a/src/network.c +++ b/src/network.c @@ -177,7 +177,7 @@ network make_network(int n) net.n = n; net.layers = calloc(net.n, sizeof(layer)); net.seen = calloc(1, sizeof(int)); - #ifdef GPU +#ifdef GPU net.input_gpu = calloc(1, sizeof(float *)); net.truth_gpu = calloc(1, sizeof(float *)); @@ -185,7 +185,7 @@ network make_network(int n) net.output16_gpu = calloc(1, sizeof(float *)); net.max_input16_size = calloc(1, sizeof(size_t)); net.max_output16_size = calloc(1, sizeof(size_t)); - #endif +#endif return net; } diff --git a/src/option_list.c b/src/option_list.c index 9411be302f3..3e835de265d 100644 --- a/src/option_list.c +++ b/src/option_list.c @@ -12,7 +12,7 @@ list *read_data_cfg(char *filename) int nu = 0; list *options = make_list(); while((line=fgetl(file)) != 0){ - ++ nu; + ++nu; strip(line); switch(line[0]){ case '\0': From f52040f0bf2cacc736ae666a3969cd99c73d828a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 23 May 2018 18:43:51 +0300 Subject: [PATCH 0032/1365] Classifier uses C++ OpenCV cv::VideoCapture that hasn't bugs. --- src/classifier.c | 43 ++++++++++++++++++++++++++++--------------- src/image.c | 22 ++++++++++++++++++++++ 2 files changed, 50 insertions(+), 15 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index e45f5a1dd23..c84dcb20c20 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -21,6 +21,8 @@ #include "opencv2/videoio/videoio_c.h" #endif image get_image_from_stream(CvCapture *cap); +image get_image_from_stream_cpp(CvCapture *cap); +#include "http_stream.h" #endif float *get_regression_values(char **labels, int n) @@ -860,11 +862,14 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i srand(2222222); CvCapture * cap; - if(filename){ - cap = cvCaptureFromFile(filename); - }else{ - cap = cvCaptureFromCAM(cam_index); - } + if (filename) { + //cap = cvCaptureFromFile(filename); + cap = get_capture_video_stream(filename); + } + else { + //cap = cvCaptureFromCAM(cam_index); + cap = get_capture_webcam(filename); + } int top = option_find_int(options, "top", 1); @@ -886,7 +891,8 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i struct timeval tval_before, tval_after, tval_result; gettimeofday(&tval_before, NULL); - image in = get_image_from_stream(cap); + //image in = get_image_from_stream(cap); + image in = get_image_from_stream_cpp(cap); if(!in.data) break; image in_s = resize_image(in, net.w, net.h); @@ -992,11 +998,14 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde srand(2222222); CvCapture * cap; - if(filename){ - cap = cvCaptureFromFile(filename); - }else{ - cap = cvCaptureFromCAM(cam_index); - } + if (filename) { + //cap = cvCaptureFromFile(filename); + cap = get_capture_video_stream(filename); + } + else { + //cap = cvCaptureFromCAM(cam_index); + cap = get_capture_webcam(filename); + } int top = option_find_int(options, "top", 1); @@ -1015,7 +1024,8 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde struct timeval tval_before, tval_after, tval_result; gettimeofday(&tval_before, NULL); - image in = get_image_from_stream(cap); + //image in = get_image_from_stream(cap); + image in = get_image_from_stream_cpp(cap); image in_s = resize_image(in, net.w, net.h); show_image(in, "Threat Detection"); @@ -1070,9 +1080,11 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind CvCapture * cap; if(filename){ - cap = cvCaptureFromFile(filename); + //cap = cvCaptureFromFile(filename); + cap = get_capture_video_stream(filename); }else{ - cap = cvCaptureFromCAM(cam_index); + //cap = cvCaptureFromCAM(cam_index); + cap = get_capture_webcam(filename); } int top = option_find_int(options, "top", 1); @@ -1092,7 +1104,8 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind struct timeval tval_before, tval_after, tval_result; gettimeofday(&tval_before, NULL); - image in = get_image_from_stream(cap); + //image in = get_image_from_stream(cap); + image in = get_image_from_stream_cpp(cap); image in_s = resize_image(in, net.w, net.h); show_image(in, "Classifier"); diff --git a/src/image.c b/src/image.c index 003d5da376f..9c297c40fb6 100644 --- a/src/image.c +++ b/src/image.c @@ -988,6 +988,28 @@ image get_image_from_stream(CvCapture *cap) return im; } +image get_image_from_stream_cpp(CvCapture *cap) +{ + //IplImage* src = cvQueryFrame(cap); + IplImage* src; + static int once = 1; + if (once) { + once = 0; + do { + src = get_webcam_frame(cap); + if (!src) return make_empty_image(0, 0, 0); + } while (src->width < 1 || src->height < 1 || src->nChannels < 1); + printf("Video stream: %d x %d \n", src->width, src->height); + } + else + src = get_webcam_frame(cap); + + if (!src) return make_empty_image(0, 0, 0); + image im = ipl_to_image(src); + rgbgr_image(im); + return im; +} + image get_image_from_stream_resize(CvCapture *cap, int w, int h, IplImage** in_img, int cpp_video_capture) { IplImage* src; From 6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7 Mon Sep 17 00:00:00 2001 From: Alexey Date: Wed, 23 May 2018 19:11:10 +0300 Subject: [PATCH 0033/1365] Update Readme.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index b56299e6a58..e56efa8ce30 100644 --- a/README.md +++ b/README.md @@ -218,7 +218,7 @@ If required change pathes in the file `build\darknet\x64\data\voc.data` More information about training by the link: http://pjreddie.com/darknet/yolo/#train-voc - **Note:** If during training you see `nan` values in some lines then training goes well, but if `nan` are in all lines then training goes wrong. + **Note:** If during training you see `nan` values for `avg` (loss) field - then training goes wrong, but if `nan` is in some other lines - then training goes well. ## How to train with multi-GPU: @@ -317,7 +317,7 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an * Also you can get result earlier than all 45000 iterations. - **Note:** If during training you see `nan` values in some lines then training goes well, but if `nan` are in all lines then training goes wrong. + **Note:** If during training you see `nan` values for `avg` (loss) field - then training goes wrong, but if `nan` is in some other lines - then training goes well. ### How to train tiny-yolo (to detect your custom objects): From 6b6f0f0ca3193ff6707dd0acac8ccd3e3e3b3873 Mon Sep 17 00:00:00 2001 From: vinjn Date: Sat, 26 May 2018 11:49:59 +0800 Subject: [PATCH 0034/1365] classifier.c - set batch=1 in "predict", "demo" and "try" mode. --- src/classifier.c | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index c84dcb20c20..98dd4c63fa1 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -598,7 +598,7 @@ void validate_classifier_multi(char *datacfg, char *filename, char *weightfile) void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int layer_num) { - network net = parse_network_cfg(cfgfile); + network net = parse_network_cfg_custom(cfgfile, 1); if(weightfile){ load_weights(&net, weightfile); } @@ -679,7 +679,7 @@ void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filena void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top) { - network net = parse_network_cfg(cfgfile); + network net = parse_network_cfg_custom(cfgfile, 1); if(weightfile){ load_weights(&net, weightfile); } @@ -868,7 +868,7 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i } else { //cap = cvCaptureFromCAM(cam_index); - cap = get_capture_webcam(filename); + cap = get_capture_webcam(cam_index); } int top = option_find_int(options, "top", 1); @@ -1004,7 +1004,7 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde } else { //cap = cvCaptureFromCAM(cam_index); - cap = get_capture_webcam(filename); + cap = get_capture_webcam(cam_index); } int top = option_find_int(options, "top", 1); @@ -1069,7 +1069,7 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind { #ifdef OPENCV printf("Classifier Demo\n"); - network net = parse_network_cfg(cfgfile); + network net = parse_network_cfg_custom(cfgfile, 1); if(weightfile){ load_weights(&net, weightfile); } @@ -1084,7 +1084,7 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind cap = get_capture_video_stream(filename); }else{ //cap = cvCaptureFromCAM(cam_index); - cap = get_capture_webcam(filename); + cap = get_capture_webcam(cam_index); } int top = option_find_int(options, "top", 1); From b36512ea2e7e6d2ec36a3241c1bf751e4e074fe1 Mon Sep 17 00:00:00 2001 From: vinjn Date: Sat, 26 May 2018 21:42:53 +0800 Subject: [PATCH 0035/1365] classifier.c - add the awesome training chart and make sure "top" is not bigger than "classes" in datacfg file. --- src/classifier.c | 116 ++++++++++++++++++++++++++++++++--------------- 1 file changed, 80 insertions(+), 36 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index 98dd4c63fa1..b38f2fe23c2 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -23,6 +23,10 @@ image get_image_from_stream(CvCapture *cap); image get_image_from_stream_cpp(CvCapture *cap); #include "http_stream.h" + +IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size); +void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches); + #endif float *get_regression_values(char **labels, int n) @@ -37,7 +41,7 @@ float *get_regression_values(char **labels, int n) return v; } -void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear) +void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show) { int i; @@ -104,13 +108,23 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, args.labels = labels; args.type = CLASSIFICATION_DATA; +#ifdef OPENCV + args.threads = 3; + IplImage* img = NULL; + float max_img_loss = 5; + int number_of_lines = 100; + int img_size = 1000; + if (!dont_show) + img = draw_train_chart(max_img_loss, net.max_batches, number_of_lines, img_size); +#endif //OPENCV + data train; data buffer; pthread_t load_thread; args.d = &buffer; load_thread = load_data(args); - int epoch = (*net.seen)/N; + int iter_save = get_current_batch(net); while(get_current_batch(net) < net.max_batches || net.max_batches == 0){ time=clock(); @@ -133,24 +147,38 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, #endif if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; + + i = get_current_batch(net); + printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); - free_data(train); - if(*net.seen/N > epoch){ - epoch = *net.seen/N; - char buff[256]; - sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch); - save_weights(net, buff); - } - if(get_current_batch(net)%100 == 0){ +#ifdef OPENCV + if(!dont_show) + draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches); +#endif // OPENCV + + if (i >= (iter_save + 100)) { + iter_save = i; +#ifdef GPU + if (ngpus != 1) sync_nets(nets, ngpus, 0); +#endif char buff[256]; - sprintf(buff, "%s/%s.backup",backup_directory,base); + sprintf(buff, "%s/%s_%d.weights",backup_directory,base, i); save_weights(net, buff); } + free_data(train); } +#ifdef GPU + if (ngpus != 1) sync_nets(nets, ngpus, 0); +#endif char buff[256]; - sprintf(buff, "%s/%s.weights", backup_directory, base); + sprintf(buff, "%s/%s_final.weights", backup_directory, base); save_weights(net, buff); +#ifdef OPENCV + cvReleaseImage(&img); + cvDestroyAllWindows(); +#endif + free_network(net); free_ptrs((void**)labels, classes); free_ptrs((void**)paths, plist->size); @@ -285,6 +313,7 @@ void validate_classifier_crop(char *datacfg, char *filename, char *weightfile) char *valid_list = option_find_str(options, "valid", "data/train.list"); int classes = option_find_int(options, "classes", 2); int topk = option_find_int(options, "top", 1); + if (topk > classes) topk = classes; char **labels = get_labels(label_list); list *plist = get_paths(valid_list); @@ -353,6 +382,7 @@ void validate_classifier_10(char *datacfg, char *filename, char *weightfile) char *valid_list = option_find_str(options, "valid", "data/train.list"); int classes = option_find_int(options, "classes", 2); int topk = option_find_int(options, "top", 1); + if (topk > classes) topk = classes; char **labels = get_labels(label_list); list *plist = get_paths(valid_list); @@ -425,6 +455,7 @@ void validate_classifier_full(char *datacfg, char *filename, char *weightfile) char *valid_list = option_find_str(options, "valid", "data/train.list"); int classes = option_find_int(options, "classes", 2); int topk = option_find_int(options, "top", 1); + if (topk > classes) topk = classes; char **labels = get_labels(label_list); list *plist = get_paths(valid_list); @@ -488,6 +519,7 @@ void validate_classifier_single(char *datacfg, char *filename, char *weightfile) char *valid_list = option_find_str(options, "valid", "data/train.list"); int classes = option_find_int(options, "classes", 2); int topk = option_find_int(options, "top", 1); + if (topk > classes) topk = classes; char **labels = get_labels(label_list); list *plist = get_paths(valid_list); @@ -548,6 +580,7 @@ void validate_classifier_multi(char *datacfg, char *filename, char *weightfile) char *valid_list = option_find_str(options, "valid", "data/train.list"); int classes = option_find_int(options, "classes", 2); int topk = option_find_int(options, "top", 1); + if (topk > classes) topk = classes; char **labels = get_labels(label_list); list *plist = get_paths(valid_list); @@ -609,7 +642,9 @@ void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filena char *name_list = option_find_str(options, "names", 0); if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list"); + int classes = option_find_int(options, "classes", 2); int top = option_find_int(options, "top", 1); + if (top > classes) top = classes; int i = 0; char **names = get_labels(name_list); @@ -690,7 +725,9 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi char *name_list = option_find_str(options, "names", 0); if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list"); - if(top == 0) top = option_find_int(options, "top", 1); + int classes = option_find_int(options, "classes", 2); + if (top == 0) top = option_find_int(options, "top", 1); + if (top > classes) top = classes; int i = 0; char **names = get_labels(name_list); @@ -710,7 +747,7 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi strtok(input, "\n"); } image im = load_image_color(input, 0, 0); - image r = letterbox_image(im, net.w, net.h); + image r = letterbox_image(im, net.w, net.h); //image r = resize_min(im, size); //resize_network(&net, r.w, r.h); printf("%d %d\n", r.w, r.h); @@ -862,16 +899,18 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i srand(2222222); CvCapture * cap; - if (filename) { - //cap = cvCaptureFromFile(filename); - cap = get_capture_video_stream(filename); - } - else { - //cap = cvCaptureFromCAM(cam_index); - cap = get_capture_webcam(cam_index); - } + if (filename) { + //cap = cvCaptureFromFile(filename); + cap = get_capture_video_stream(filename); + } + else { + //cap = cvCaptureFromCAM(cam_index); + cap = get_capture_webcam(cam_index); + } + int classes = option_find_int(options, "classes", 2); int top = option_find_int(options, "top", 1); + if (top > classes) top = classes; char *name_list = option_find_str(options, "names", 0); char **names = get_labels(name_list); @@ -891,7 +930,7 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i struct timeval tval_before, tval_after, tval_result; gettimeofday(&tval_before, NULL); - //image in = get_image_from_stream(cap); + //image in = get_image_from_stream(cap); image in = get_image_from_stream_cpp(cap); if(!in.data) break; image in_s = resize_image(in, net.w, net.h); @@ -998,16 +1037,18 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde srand(2222222); CvCapture * cap; - if (filename) { - //cap = cvCaptureFromFile(filename); - cap = get_capture_video_stream(filename); - } - else { - //cap = cvCaptureFromCAM(cam_index); - cap = get_capture_webcam(cam_index); - } + if (filename) { + //cap = cvCaptureFromFile(filename); + cap = get_capture_video_stream(filename); + } + else { + //cap = cvCaptureFromCAM(cam_index); + cap = get_capture_webcam(cam_index); + } + int classes = option_find_int(options, "classes", 2); int top = option_find_int(options, "top", 1); + if (top > classes) top = classes; char *name_list = option_find_str(options, "names", 0); char **names = get_labels(name_list); @@ -1024,7 +1065,7 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde struct timeval tval_before, tval_after, tval_result; gettimeofday(&tval_before, NULL); - //image in = get_image_from_stream(cap); + //image in = get_image_from_stream(cap); image in = get_image_from_stream_cpp(cap); image in_s = resize_image(in, net.w, net.h); show_image(in, "Threat Detection"); @@ -1081,13 +1122,15 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind if(filename){ //cap = cvCaptureFromFile(filename); - cap = get_capture_video_stream(filename); + cap = get_capture_video_stream(filename); }else{ //cap = cvCaptureFromCAM(cam_index); - cap = get_capture_webcam(cam_index); + cap = get_capture_webcam(cam_index); } + int classes = option_find_int(options, "classes", 2); int top = option_find_int(options, "top", 1); + if (top > classes) top = classes; char *name_list = option_find_str(options, "names", 0); char **names = get_labels(name_list); @@ -1104,7 +1147,7 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind struct timeval tval_before, tval_after, tval_result; gettimeofday(&tval_before, NULL); - //image in = get_image_from_stream(cap); + //image in = get_image_from_stream(cap); image in = get_image_from_stream_cpp(cap); image in_s = resize_image(in, net.w, net.h); show_image(in, "Classifier"); @@ -1166,6 +1209,7 @@ void run_classifier(int argc, char **argv) ngpus = 1; } + int dont_show = find_arg(argc, argv, "-dont_show"); int cam_index = find_int_arg(argc, argv, "-c", 0); int top = find_int_arg(argc, argv, "-t", 0); int clear = find_arg(argc, argv, "-clear"); @@ -1177,7 +1221,7 @@ void run_classifier(int argc, char **argv) int layer = layer_s ? atoi(layer_s) : -1; if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename, top); else if(0==strcmp(argv[2], "try")) try_classifier(data, cfg, weights, filename, atoi(layer_s)); - else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear); + else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear, dont_show); else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename); else if(0==strcmp(argv[2], "gun")) gun_classifier(data, cfg, weights, cam_index, filename); else if(0==strcmp(argv[2], "threat")) threat_classifier(data, cfg, weights, cam_index, filename); From 160eddddc4e265d5ee59a38797c30720bf46cd7c Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 27 May 2018 16:53:42 +0300 Subject: [PATCH 0036/1365] Minor fix --- build/darknet/x64/darknet_yolo_v3_video.cmd | 2 +- src/detector.c | 2 +- src/image.c | 4 ++-- src/network.c | 1 + 4 files changed, 5 insertions(+), 4 deletions(-) diff --git a/build/darknet/x64/darknet_yolo_v3_video.cmd b/build/darknet/x64/darknet_yolo_v3_video.cmd index 729ec08fe98..26efcfdc048 100644 --- a/build/darknet/x64/darknet_yolo_v3_video.cmd +++ b/build/darknet/x64/darknet_yolo_v3_video.cmd @@ -1,5 +1,5 @@ -darknet.exe detector demo data/coco.data yolov3.cfg yolov3.weights -i 0 -thresh 0.25 test.mp4 +darknet.exe detector demo data/coco.data yolov3.cfg yolov3.weights -i 0 -thresh 0.25 -ext_output test.mp4 pause \ No newline at end of file diff --git a/src/detector.c b/src/detector.c index 536b9d7b63e..6fc6b677c12 100644 --- a/src/detector.c +++ b/src/detector.c @@ -189,7 +189,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i avg_loss = avg_loss*.9 + loss*.1; i = get_current_batch(net); - printf("\n %d: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now()-time), i*imgs); + printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now()-time), i*imgs); #ifdef OPENCV if(!dont_show) diff --git a/src/image.c b/src/image.c index 9c297c40fb6..eb8c6d56bf4 100644 --- a/src/image.c +++ b/src/image.c @@ -287,7 +287,7 @@ void draw_detections_v3(image im, detection *dets, int num, float thresh, char * const int best_class = selected_detections[i].best_class; printf("%s: %.0f%%", names[best_class], selected_detections[i].det.prob[best_class] * 100); if (ext_output) - printf("\t(left: %4.0f top: %4.0f w: %4.0f h: %4.0f)\n", + printf("\t(left_x: %4.0f top_y: %4.0f width: %4.0f height: %4.0f)\n", (selected_detections[i].det.bbox.x - selected_detections[i].det.bbox.w / 2)*im.w, (selected_detections[i].det.bbox.y - selected_detections[i].det.bbox.h / 2)*im.h, selected_detections[i].det.bbox.w*im.w, selected_detections[i].det.bbox.h*im.h); @@ -535,7 +535,7 @@ void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float t cvRectangle(show_img, pt1, pt2, color, width, 8, 0); if (ext_output) - printf(" (left: %4.0f top: %4.0f w: %4.0f h: %4.0f)\n", + printf("\t(left_x: %4.0f top_y: %4.0f width: %4.0f height: %4.0f)\n", (float)left, (float)right, b.w*show_img->width, b.h*show_img->height); else printf("\n"); diff --git a/src/network.c b/src/network.c index c0676bf71aa..6e50d54a49e 100644 --- a/src/network.c +++ b/src/network.c @@ -28,6 +28,7 @@ #include "route_layer.h" #include "shortcut_layer.h" #include "yolo_layer.h" +#include "upsample_layer.h" #include "parser.h" network *load_network_custom(char *cfg, char *weights, int clear, int batch) From 1b7a1a0f33538c9544ad4bf693b4a4247a5548b8 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 28 May 2018 19:02:05 +0300 Subject: [PATCH 0037/1365] Fixed top-coordinate output when -ext_output is used for video --- src/image.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/image.c b/src/image.c index eb8c6d56bf4..7545e7dbbb2 100644 --- a/src/image.c +++ b/src/image.c @@ -536,7 +536,7 @@ void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float t cvRectangle(show_img, pt1, pt2, color, width, 8, 0); if (ext_output) printf("\t(left_x: %4.0f top_y: %4.0f width: %4.0f height: %4.0f)\n", - (float)left, (float)right, b.w*show_img->width, b.h*show_img->height); + (float)left, (float)top, b.w*show_img->width, b.h*show_img->height); else printf("\n"); cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0); From 6fd90762c5d1c322eafefc8fd21fc6d750853d21 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 28 May 2018 22:57:53 +0300 Subject: [PATCH 0038/1365] Fix: Ignore incorrect dataset labels instead of causing seg-fault error. --- src/data.c | 44 ++++++++++++++++++++++++++++---------------- 1 file changed, 28 insertions(+), 16 deletions(-) diff --git a/src/data.c b/src/data.c index a4298628e64..e8bfa927484 100644 --- a/src/data.c +++ b/src/data.c @@ -287,15 +287,15 @@ void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int free(boxes); } -void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, int flip, float dx, float dy, float sx, float sy, +void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, int flip, float dx, float dy, float sx, float sy, int small_object, int net_w, int net_h) { - char labelpath[4096]; + char labelpath[4096]; replace_image_to_label(path, labelpath); - int count = 0; + int count = 0; int i; - box_label *boxes = read_boxes(labelpath, &count); + box_label *boxes = read_boxes(labelpath, &count); float lowest_w = 1.F / net_w; float lowest_h = 1.F / net_h; if (small_object == 1) { @@ -304,23 +304,35 @@ void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, if (boxes[i].h < lowest_h) boxes[i].h = lowest_h; } } - randomize_boxes(boxes, count); - correct_boxes(boxes, count, dx, dy, sx, sy, flip); - if(count > num_boxes) count = num_boxes; - float x,y,w,h; - int id; - - for (i = 0; i < count; ++i) { - x = boxes[i].x; - y = boxes[i].y; - w = boxes[i].w; - h = boxes[i].h; - id = boxes[i].id; + randomize_boxes(boxes, count); + correct_boxes(boxes, count, dx, dy, sx, sy, flip); + if (count > num_boxes) count = num_boxes; + float x, y, w, h; + int id; + + for (i = 0; i < count; ++i) { + x = boxes[i].x; + y = boxes[i].y; + w = boxes[i].w; + h = boxes[i].h; + id = boxes[i].id; // not detect small objects //if ((w < 0.001F || h < 0.001F)) continue; // if truth (box for object) is smaller than 1x1 pix if ((w < lowest_w || h < lowest_h)) continue; + if (x == 999999 || y == 999999) { + printf("\n Wrong annotation: x = 0, y = 0 \n"); + continue; + } + if (x < 0 || x > 1 || y < 0 || y > 1) { + printf("\n Wrong annotation: x = %f, y = %f \n", x, y); + continue; + } + if (w > 1) printf("\n Wrong annotation: w = %f \n", w), w = 1; + if (h > 1) printf("\n Wrong annotation: h = %f \n", h), h = 1; + if (x == 0) x += lowest_w; + if (y == 0) y += lowest_h; truth[i*5+0] = x; truth[i*5+1] = y; From ec766fc3f114dd4faaa3add500776a7bae957ba5 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 29 May 2018 14:35:32 +0300 Subject: [PATCH 0039/1365] Ignore empty extra lines in train.txt --- src/data.c | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/src/data.c b/src/data.c index e8bfa927484..a15bc1d735f 100644 --- a/src/data.c +++ b/src/data.c @@ -45,11 +45,13 @@ char **get_random_paths(char **paths, int n, int m) int i; pthread_mutex_lock(&mutex); //printf("n = %d \n", n); - for(i = 0; i < n; ++i){ - int index = random_gen() % m; - random_paths[i] = paths[index]; - //if(i == 0) printf("%s\n", paths[index]); - //printf("grp: %s\n", paths[index]); + for(i = 0; i < n; ++i){ + do { + int index = random_gen() % m; + random_paths[i] = paths[index]; + //if(i == 0) printf("%s\n", paths[index]); + //printf("grp: %s\n", paths[index]); + } while (strlen(random_paths[i]) == 0); } pthread_mutex_unlock(&mutex); return random_paths; From 01bec657080d123a5b44a10ec94ebe132e20b1f3 Mon Sep 17 00:00:00 2001 From: IlyaOvodov Date: Wed, 30 May 2018 19:34:38 +0300 Subject: [PATCH 0040/1365] "channel" parameter of [net] is used in detector when preparing images for net. --- src/data.c | 20 +++++++++++--------- src/data.h | 5 +++-- src/demo.c | 4 ++-- src/detector.c | 9 ++++++--- src/http_stream.cpp | 25 ++++++++++++++++--------- src/image.c | 42 ++++++++++++++++++++++++++---------------- 6 files changed, 64 insertions(+), 41 deletions(-) diff --git a/src/data.c b/src/data.c index a15bc1d735f..dc803f0ad5f 100644 --- a/src/data.c +++ b/src/data.c @@ -687,8 +687,9 @@ data load_data_swag(char **paths, int n, int classes, float jitter) #include "http_stream.h" -data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, int classes, int use_flip, float jitter, float hue, float saturation, float exposure, int small_object) +data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, float jitter, float hue, float saturation, float exposure, int small_object) { + c = c ? c : 3; char **random_paths = get_random_paths(paths, n, m); int i; data d = {0}; @@ -696,13 +697,13 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, in d.X.rows = n; d.X.vals = calloc(d.X.rows, sizeof(float*)); - d.X.cols = h*w*3; + d.X.cols = h*w*c; d.y = make_matrix(n, 5*boxes); for(i = 0; i < n; ++i){ const char *filename = random_paths[i]; - int flag = 1; + int flag = (c >= 3); IplImage *src; if ((src = cvLoadImage(filename, flag)) == 0) { @@ -754,8 +755,9 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, in return d; } #else // OPENCV -data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, int classes, int use_flip, float jitter, float hue, float saturation, float exposure, int small_object) +data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, float jitter, float hue, float saturation, float exposure, int small_object) { + c = c ? c : 3; char **random_paths = get_random_paths(paths, n, m); int i; data d = { 0 }; @@ -763,11 +765,11 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, in d.X.rows = n; d.X.vals = calloc(d.X.rows, sizeof(float*)); - d.X.cols = h*w * 3; + d.X.cols = h*w*c; d.y = make_matrix(n, 5 * boxes); for (i = 0; i < n; ++i) { - image orig = load_image_color(random_paths[i], 0, 0); + image orig = load_image(random_paths[i], 0, 0, c); int oh = orig.h; int ow = orig.w; @@ -827,16 +829,16 @@ void *load_thread(void *ptr) } else if (a.type == REGION_DATA){ *a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure); } else if (a.type == DETECTION_DATA){ - *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.flip, a.jitter, a.hue, a.saturation, a.exposure, a.small_object); + *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.c, a.num_boxes, a.classes, a.flip, a.jitter, a.hue, a.saturation, a.exposure, a.small_object); } else if (a.type == SWAG_DATA){ *a.d = load_data_swag(a.paths, a.n, a.classes, a.jitter); } else if (a.type == COMPARE_DATA){ *a.d = load_data_compare(a.n, a.paths, a.m, a.classes, a.w, a.h); } else if (a.type == IMAGE_DATA){ - *(a.im) = load_image_color(a.path, 0, 0); + *(a.im) = load_image(a.path, 0, 0, a.c); *(a.resized) = resize_image(*(a.im), a.w, a.h); }else if (a.type == LETTERBOX_DATA) { - *(a.im) = load_image_color(a.path, 0, 0); + *(a.im) = load_image(a.path, 0, 0, a.c); *(a.resized) = letterbox_image(*(a.im), a.w, a.h); } else if (a.type == TAG_DATA){ *a.d = load_data_tag(a.paths, a.n, a.m, a.classes, a.flip, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure); diff --git a/src/data.h b/src/data.h index 57f4702e61c..b46143fb9d7 100644 --- a/src/data.h +++ b/src/data.h @@ -44,7 +44,8 @@ typedef struct load_args{ char **labels; int h; int w; - int out_w; + int c; // color depth + int out_w; int out_h; int nh; int nw; @@ -84,7 +85,7 @@ void print_letters(float *pred, int n); data load_data_captcha(char **paths, int n, int m, int k, int w, int h); data load_data_captcha_encode(char **paths, int n, int m, int w, int h); data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h); -data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, int classes, int use_flip, float jitter, float hue, float saturation, float exposure, int small_object); +data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, float jitter, float hue, float saturation, float exposure, int small_object); data load_data_tag(char **paths, int n, int m, int k, int use_flip, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); data load_data_super(char **paths, int n, int m, int w, int h, int scale); diff --git a/src/demo.c b/src/demo.c index 81eddb297ae..72f9c03e574 100644 --- a/src/demo.c +++ b/src/demo.c @@ -51,7 +51,7 @@ static float *avg; void draw_detections_cv(IplImage* show_img, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes); void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output); void show_image_cv_ipl(IplImage *disp, const char *name); -image get_image_from_stream_resize(CvCapture *cap, int w, int h, IplImage** in_img, int cpp_video_capture); +image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture); IplImage* in_img; IplImage* det_img; IplImage* show_img; @@ -61,7 +61,7 @@ static int flag_exit; void *fetch_in_thread(void *ptr) { //in = get_image_from_stream(cap); - in_s = get_image_from_stream_resize(cap, net.w, net.h, &in_img, cpp_video_capture); + in_s = get_image_from_stream_resize(cap, net.w, net.h, net.c, &in_img, cpp_video_capture); if(!in_s.data){ //error("Stream closed."); printf("Stream closed.\n"); diff --git a/src/detector.c b/src/detector.c index 6fc6b677c12..202fbf9b566 100644 --- a/src/detector.c +++ b/src/detector.c @@ -87,7 +87,8 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i load_args args = {0}; args.w = net.w; args.h = net.h; - args.paths = paths; + args.c = net.c; + args.paths = paths; args.n = imgs; args.m = plist->size; args.classes = classes; @@ -388,6 +389,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out load_args args = { 0 }; args.w = net.w; args.h = net.h; + args.c = net.c; args.type = IMAGE_DATA; //args.type = LETTERBOX_DATA; @@ -482,7 +484,7 @@ void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile) for (i = 0; i < m; ++i) { char *path = paths[i]; - image orig = load_image_color(path, 0, 0); + image orig = load_image(path, 0, 0, net.c); image sized = resize_image(orig, net.w, net.h); char *id = basecfg(path); network_predict(net, sized.data); @@ -595,6 +597,7 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float load_args args = { 0 }; args.w = net.w; args.h = net.h; + args.c = net.c; args.type = IMAGE_DATA; //args.type = LETTERBOX_DATA; @@ -1093,7 +1096,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam if(!input) return; strtok(input, "\n"); } - image im = load_image_color(input,0,0); + image im = load_image(input,0,0,net.c); int letterbox = 0; //image sized = resize_image(im, net.w, net.h); image sized = letterbox_image(im, net.w, net.h); letterbox = 1; diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 9192f75a6f7..acb6c8ef624 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -283,19 +283,26 @@ image image_data_augmentation(IplImage* ipl, int w, int h, // HSV augmentation // CV_BGR2HSV, CV_RGB2HSV, CV_HSV2BGR, CV_HSV2RGB - cv::Mat hsv_src; - cvtColor(sized, hsv_src, CV_BGR2HSV); // also BGR -> RGB + if (ipl->nChannels >= 3) + { + cv::Mat hsv_src; + cvtColor(sized, hsv_src, CV_BGR2HSV); // also BGR -> RGB - std::vector hsv; - cv::split(hsv_src, hsv); + std::vector hsv; + cv::split(hsv_src, hsv); - hsv[1] *= dsat; - hsv[2] *= dexp; - hsv[0] += 179 * dhue; + hsv[1] *= dsat; + hsv[2] *= dexp; + hsv[0] += 179 * dhue; - cv::merge(hsv, hsv_src); + cv::merge(hsv, hsv_src); - cvtColor(hsv_src, sized, CV_HSV2RGB); // now RGB instead of BGR + cvtColor(hsv_src, sized, CV_HSV2RGB); // now RGB instead of BGR + } + else + { + sized *= dexp; + } // Mat -> IplImage -> image IplImage src = sized; diff --git a/src/image.c b/src/image.c index 7545e7dbbb2..3ffd552ec15 100644 --- a/src/image.c +++ b/src/image.c @@ -957,7 +957,7 @@ image load_image_cv(char *filename, int channels) { IplImage* src = 0; int flag = -1; - if (channels == 0) flag = -1; + if (channels == 0) flag = 1; else if (channels == 1) flag = 0; else if (channels == 3) flag = 1; else { @@ -975,7 +975,8 @@ image load_image_cv(char *filename, int channels) } image out = ipl_to_image(src); cvReleaseImage(&src); - rgbgr_image(out); + if (out.c > 1) + rgbgr_image(out); return out; } @@ -1010,8 +1011,9 @@ image get_image_from_stream_cpp(CvCapture *cap) return im; } -image get_image_from_stream_resize(CvCapture *cap, int w, int h, IplImage** in_img, int cpp_video_capture) +image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture) { + c = c ? c : 3; IplImage* src; if (cpp_video_capture) { static int once = 1; @@ -1029,14 +1031,15 @@ image get_image_from_stream_resize(CvCapture *cap, int w, int h, IplImage** in_i if (!src) return make_empty_image(0, 0, 0); if (src->width < 1 || src->height < 1 || src->nChannels < 1) return make_empty_image(0, 0, 0); - IplImage* new_img = cvCreateImage(cvSize(w, h), IPL_DEPTH_8U, 3); - *in_img = cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, 3); + IplImage* new_img = cvCreateImage(cvSize(w, h), IPL_DEPTH_8U, c); + *in_img = cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c); cvResize(src, *in_img, CV_INTER_LINEAR); cvResize(src, new_img, CV_INTER_LINEAR); image im = ipl_to_image(new_img); cvReleaseImage(&new_img); if (cpp_video_capture) cvReleaseImage(&src); - rgbgr_image(im); + if (c>1) + rgbgr_image(im); return im; } @@ -1589,16 +1592,23 @@ void exposure_image(image im, float sat) void distort_image(image im, float hue, float sat, float val) { - rgb_to_hsv(im); - scale_image_channel(im, 1, sat); - scale_image_channel(im, 2, val); - int i; - for(i = 0; i < im.w*im.h; ++i){ - im.data[i] = im.data[i] + hue; - if (im.data[i] > 1) im.data[i] -= 1; - if (im.data[i] < 0) im.data[i] += 1; - } - hsv_to_rgb(im); + if (im.c >= 3) + { + rgb_to_hsv(im); + scale_image_channel(im, 1, sat); + scale_image_channel(im, 2, val); + int i; + for(i = 0; i < im.w*im.h; ++i){ + im.data[i] = im.data[i] + hue; + if (im.data[i] > 1) im.data[i] -= 1; + if (im.data[i] < 0) im.data[i] += 1; + } + hsv_to_rgb(im); + } + else + { + scale_image_channel(im, 0, val); + } constrain_image(im); } From 6c675a09278aeb7f579ff9ee7c20e990119be8ff Mon Sep 17 00:00:00 2001 From: Yu Hao Date: Thu, 31 May 2018 14:04:16 +0800 Subject: [PATCH 0041/1365] Fix openCV 3.4.1 highgui --- src/http_stream.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 9192f75a6f7..d9915b263e3 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -44,7 +44,7 @@ using std::cerr; using std::endl; #include "opencv2/opencv.hpp" -#include "opencv2/highgui.hpp" +#include "opencv2/highgui/highgui.hpp" #include "opencv2/highgui/highgui_c.h" #include "opencv2/imgproc/imgproc_c.h" #ifndef CV_VERSION_EPOCH @@ -305,4 +305,4 @@ image image_data_augmentation(IplImage* ipl, int w, int h, } -#endif // OPENCV \ No newline at end of file +#endif // OPENCV From 51293290fad1465bf9fdb9b9b4eda0a661d67444 Mon Sep 17 00:00:00 2001 From: IlyaOvodov Date: Thu, 31 May 2018 13:11:19 +0300 Subject: [PATCH 0042/1365] Fix error in allocation size --- src/network.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/network.c b/src/network.c index 6e50d54a49e..050d3347403 100644 --- a/src/network.c +++ b/src/network.c @@ -582,7 +582,7 @@ void custom_get_region_detections(layer l, int w, int h, int net_w, int net_h, f box *boxes = calloc(l.w*l.h*l.n, sizeof(box)); float **probs = calloc(l.w*l.h*l.n, sizeof(float *)); int i, j; - for (j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *)); + for (j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float)); get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, map); for (j = 0; j < l.w*l.h*l.n; ++j) { dets[j].classes = l.classes; From af4cc09a38ee33e69885c929aae0cf25dba29bfa Mon Sep 17 00:00:00 2001 From: Chih-Hsu Lin Date: Fri, 1 Jun 2018 00:07:04 -0500 Subject: [PATCH 0043/1365] Fix the path to voc.data --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index e56efa8ce30..35bb7549a03 100644 --- a/README.md +++ b/README.md @@ -210,11 +210,11 @@ Then add to your created project: 7. Start training by using `train_voc.cmd` or by using the command line: - `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg darknet53.conv.74` + `darknet.exe detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74` (**Note:** To disable Loss-Window use flag `-dont_show`. If you are using CPU, try `darknet_no_gpu.exe` instead of `darknet.exe`.) -If required change pathes in the file `build\darknet\x64\data\voc.data` +If required change pathes in the file `build\darknet\cfg\voc.data` More information about training by the link: http://pjreddie.com/darknet/yolo/#train-voc From ba70801e982ada241ced55bedad2a411da1896c4 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 1 Jun 2018 14:58:30 +0300 Subject: [PATCH 0044/1365] Minor fixes --- build/darknet/x64/partial.cmd | 2 +- src/region_layer.c | 3 ++- src/utils.c | 3 +++ src/yolo_layer.c | 3 ++- 4 files changed, 8 insertions(+), 3 deletions(-) diff --git a/build/darknet/x64/partial.cmd b/build/darknet/x64/partial.cmd index a6ac26257bb..acbe3fa952b 100644 --- a/build/darknet/x64/partial.cmd +++ b/build/darknet/x64/partial.cmd @@ -18,7 +18,7 @@ darknet.exe partial cfg/yolo-voc.cfg yolo-voc.weights yolo-voc.conv.23 23 darknet.exe partial cfg/yolov2.cfg yolov2.weights yolov2.conv.23 23 -darknet.exe partial cfg/yolov3.cfg yolov3.weights yolov3.conv.105 105 +darknet.exe partial cfg/yolov3.cfg yolov3.weights yolov3.conv.81 81 darknet.exe partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15 diff --git a/src/region_layer.c b/src/region_layer.c index a2ca440c5a1..4e1e03a59cc 100644 --- a/src/region_layer.c +++ b/src/region_layer.c @@ -297,7 +297,8 @@ void forward_region_layer(const region_layer l, network_state state) box truth = float_to_box(state.truth + t*5 + b*l.truths); int class_id = state.truth[t * 5 + b*l.truths + 4]; if (class_id >= l.classes) { - printf("Warning: in txt-labels class_id=%d >= classes=%d in cfg-file\n", class_id, l.classes); + printf(" Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes-1); + getchar(); continue; // if label contains class_id more than number of classes in the cfg-file } diff --git a/src/utils.c b/src/utils.c index 7b25e9c3ccf..f1cc21aa6a4 100644 --- a/src/utils.c +++ b/src/utils.c @@ -212,6 +212,9 @@ void replace_image_to_label(char *input_path, char *output_path) { //find_replace(output_path, "JPEGImages", "labels", output_path); // PascalVOC find_replace(output_path, "VOC2007/JPEGImages", "VOC2007/labels", output_path); // PascalVOC find_replace(output_path, "VOC2012/JPEGImages", "VOC2012/labels", output_path); // PascalVOC + + //find_replace(output_path, "/raw/", "/labels/", output_path); + // replace only ext of files find_replace_extension(output_path, ".jpg", ".txt", output_path); find_replace_extension(output_path, ".JPG", ".txt", output_path); // error diff --git a/src/yolo_layer.c b/src/yolo_layer.c index c1309c8ffbd..f79bc418943 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -202,7 +202,8 @@ void forward_yolo_layer(const layer l, network_state state) box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; if (class_id >= l.classes) { - printf("Warning: in txt-labels class_id=%d >= classes=%d in cfg-file\n", class_id, l.classes); + printf(" Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1); + getchar(); continue; // if label contains class_id more than number of classes in the cfg-file } if(!truth.x) break; From 4cf4a89f05c671041c217a724fc9b4b026eb9de9 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 2 Jun 2018 00:33:17 +0300 Subject: [PATCH 0045/1365] Minor fix --- src/parser.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/parser.c b/src/parser.c index 188ba54cf16..c1ee98c3a7a 100644 --- a/src/parser.c +++ b/src/parser.c @@ -268,7 +268,7 @@ layer parse_yolo(list *options, size_params params) char *a = option_find_str(options, "mask", 0); int *mask = parse_yolo_mask(a, &num); - int max_boxes = option_find_int_quiet(options, "max", 30); + int max_boxes = option_find_int_quiet(options, "max", 90); layer l = make_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes, max_boxes); if (l.outputs != params.inputs) { printf("Error: l.outputs == params.inputs \n"); @@ -310,7 +310,7 @@ layer parse_region(list *options, size_params params) int coords = option_find_int(options, "coords", 4); int classes = option_find_int(options, "classes", 20); int num = option_find_int(options, "num", 1); - int max_boxes = option_find_int_quiet(options, "max", 30); + int max_boxes = option_find_int_quiet(options, "max", 90); layer l = make_region_layer(params.batch, params.w, params.h, num, classes, coords, max_boxes); if (l.outputs != params.inputs) { From 483fdfa951feeb20903da1598cb9d611b93de6ca Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 3 Jun 2018 14:21:15 +0300 Subject: [PATCH 0046/1365] Fix for multi-GPU and DGX --- src/detector.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/detector.c b/src/detector.c index 202fbf9b566..fc781024696 100644 --- a/src/detector.c +++ b/src/detector.c @@ -106,7 +106,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i args.hue = net.hue; #ifdef OPENCV - args.threads = 3; + args.threads = 3 * ngpus; IplImage* img = NULL; float max_img_loss = 5; int number_of_lines = 100; From 5d8e3526edb9c7df26a4430b5b294b221bab3cb2 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 3 Jun 2018 15:21:48 +0300 Subject: [PATCH 0047/1365] Fix for unreliable IP-cameras --- src/demo.c | 5 +++-- src/image.c | 13 ++++++++++--- 2 files changed, 13 insertions(+), 5 deletions(-) diff --git a/src/demo.c b/src/demo.c index 72f9c03e574..0a6a4cb8897 100644 --- a/src/demo.c +++ b/src/demo.c @@ -51,7 +51,7 @@ static float *avg; void draw_detections_cv(IplImage* show_img, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes); void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output); void show_image_cv_ipl(IplImage *disp, const char *name); -image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture); +image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close); IplImage* in_img; IplImage* det_img; IplImage* show_img; @@ -61,7 +61,8 @@ static int flag_exit; void *fetch_in_thread(void *ptr) { //in = get_image_from_stream(cap); - in_s = get_image_from_stream_resize(cap, net.w, net.h, net.c, &in_img, cpp_video_capture); + int dont_close_stream = 0; // set 1 if your IP-camera periodically turns off and turns on video-stream + in_s = get_image_from_stream_resize(cap, net.w, net.h, net.c, &in_img, cpp_video_capture, dont_close_stream); if(!in_s.data){ //error("Stream closed."); printf("Stream closed.\n"); diff --git a/src/image.c b/src/image.c index 3ffd552ec15..35e8551ecf8 100644 --- a/src/image.c +++ b/src/image.c @@ -1011,7 +1011,7 @@ image get_image_from_stream_cpp(CvCapture *cap) return im; } -image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture) +image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close) { c = c ? c : 3; IplImage* src; @@ -1029,8 +1029,15 @@ image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage } else src = cvQueryFrame(cap); - if (!src) return make_empty_image(0, 0, 0); - if (src->width < 1 || src->height < 1 || src->nChannels < 1) return make_empty_image(0, 0, 0); + if (!src) { + if (dont_close) src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, c); + else return make_empty_image(0, 0, 0); + } + if (src->width < 1 || src->height < 1 || src->nChannels < 1) { + if (cpp_video_capture) cvReleaseImage(&src); + if (dont_close) src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, c); + else return make_empty_image(0, 0, 0); + } IplImage* new_img = cvCreateImage(cvSize(w, h), IPL_DEPTH_8U, c); *in_img = cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c); cvResize(src, *in_img, CV_INTER_LINEAR); From 7359843a4b0ed053aaa4cb5cc4337e821e83a1a1 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 3 Jun 2018 23:03:16 +0300 Subject: [PATCH 0048/1365] Store wrong annotations to the file: bad_labels.list --- src/data.c | 32 +++++++++++++++++++++++++++++--- 1 file changed, 29 insertions(+), 3 deletions(-) diff --git a/src/data.c b/src/data.c index dc803f0ad5f..c98ede9be6f 100644 --- a/src/data.c +++ b/src/data.c @@ -322,17 +322,43 @@ void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, // not detect small objects //if ((w < 0.001F || h < 0.001F)) continue; // if truth (box for object) is smaller than 1x1 pix - if ((w < lowest_w || h < lowest_h)) continue; + char buff[256]; + if (id >= classes) { + printf("\n Wrong annotation: class_id = %d. But class_id should be [from 0 to %d] \n", id, classes); + sprintf(buff, "echo %s \"Wrong annotation: class_id = %d. But class_id should be [from 0 to %d]\" >> bad_label.list", labelpath, id, classes); + system(buff); + getchar(); + continue; + } + if ((w < lowest_w || h < lowest_h)) { + //sprintf(buff, "echo %s \"Very small object: w < lowest_w OR h < lowest_h\" >> bad_label.list", labelpath); + //system(buff); + continue; + } if (x == 999999 || y == 999999) { printf("\n Wrong annotation: x = 0, y = 0 \n"); + sprintf(buff, "echo %s \"Wrong annotation: x = 0 or y = 0\" >> bad_label.list", labelpath); + system(buff); continue; } if (x < 0 || x > 1 || y < 0 || y > 1) { printf("\n Wrong annotation: x = %f, y = %f \n", x, y); + sprintf(buff, "echo %s \"Wrong annotation: x = %f, y = %f\" >> bad_label.list", labelpath, x, y); + system(buff); continue; } - if (w > 1) printf("\n Wrong annotation: w = %f \n", w), w = 1; - if (h > 1) printf("\n Wrong annotation: h = %f \n", h), h = 1; + if (w > 1) { + printf("\n Wrong annotation: w = %f \n", w); + sprintf(buff, "echo %s \"Wrong annotation: w = %f\" >> bad_label.list", labelpath, w); + system(buff); + w = 1; + } + if (h > 1) { + printf("\n Wrong annotation: h = %f \n", h); + sprintf(buff, "echo %s \"Wrong annotation: h = %f\" >> bad_label.list", labelpath, h); + system(buff); + h = 1; + } if (x == 0) x += lowest_w; if (y == 0) y += lowest_h; From 132251d72325e1005ef6c47f83d6a4e9b9355d12 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 4 Jun 2018 00:37:08 +0300 Subject: [PATCH 0049/1365] You can do: fuse_conv_batchnorm(network net) from DLL/SO-library --- src/network.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/network.h b/src/network.h index 01a6ab9051e..4198c4bcf23 100644 --- a/src/network.h +++ b/src/network.h @@ -147,7 +147,7 @@ YOLODLL_API int network_height(network *net); int get_network_nuisance(network net); int get_network_background(network net); -void fuse_conv_batchnorm(network net); +YOLODLL_API void fuse_conv_batchnorm(network net); #ifdef __cplusplus } From d502dea9a451c290f602ba18bc61f4f79c51be0c Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 4 Jun 2018 13:55:27 +0300 Subject: [PATCH 0050/1365] Update Readme.md --- README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/README.md b/README.md index e56efa8ce30..40289e9e2d0 100644 --- a/README.md +++ b/README.md @@ -421,6 +421,11 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last layer [region] in your cfg-file + * General rule - you should keep relative size of objects in the Training and Testing datasets the same: + + * `train_network_width * train_obj_width / train_image_width ~= detection_network_width * detection_obj_width / detection_image_width` + * `train_network_height * train_obj_height / train_image_height ~= detection_network_height * detection_obj_height / detection_image_height` + * to speedup training (with decreasing detection accuracy) do Fine-Tuning instead of Transfer-Learning, set param `stopbackward=1` in one of the penultimate convolutional layers before the 1-st `[yolo]`-layer, for example here: https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L598 2. After training - for detection: From a7ddb205067bd0b1e83e4f5ce6f40bfd1d18afae Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 4 Jun 2018 14:23:13 +0300 Subject: [PATCH 0051/1365] Update Readme.md --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index 40289e9e2d0..8e93f9b5a27 100644 --- a/README.md +++ b/README.md @@ -421,6 +421,9 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last layer [region] in your cfg-file + * for training for small objects - set `layers = -1, 11` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L720 + and set `stride=4` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L717 + * General rule - you should keep relative size of objects in the Training and Testing datasets the same: * `train_network_width * train_obj_width / train_image_width ~= detection_network_width * detection_obj_width / detection_image_width` From efaf684cb20b996db0cd6d99d20fabb4bc26859a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 4 Jun 2018 15:38:18 +0300 Subject: [PATCH 0052/1365] By default letter_box is disabled --- src/detector.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/detector.c b/src/detector.c index fc781024696..e099e91a04d 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1098,8 +1098,8 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam } image im = load_image(input,0,0,net.c); int letterbox = 0; - //image sized = resize_image(im, net.w, net.h); - image sized = letterbox_image(im, net.w, net.h); letterbox = 1; + image sized = resize_image(im, net.w, net.h); + //image sized = letterbox_image(im, net.w, net.h); letterbox = 1; layer l = net.layers[net.n-1]; //box *boxes = calloc(l.w*l.h*l.n, sizeof(box)); From c8ad33ceba05cbb60c5197e4db4d3bdef28edd6d Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 4 Jun 2018 15:57:13 +0300 Subject: [PATCH 0053/1365] Update Readme.md --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 8e93f9b5a27..7cf0b7702c0 100644 --- a/README.md +++ b/README.md @@ -415,6 +415,8 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416` then set the same 9 `anchors` in each of 3 `[yolo]`-layers in your cfg-file + * check that each object are mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: https://github.com/AlexeyAB/Yolo_mark + * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds * desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty `.txt` files) @@ -424,7 +426,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * for training for small objects - set `layers = -1, 11` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L720 and set `stride=4` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L717 - * General rule - you should keep relative size of objects in the Training and Testing datasets the same: + * General rule - you should keep relative size of objects in the Training and Testing datasets roughly the same: * `train_network_width * train_obj_width / train_image_width ~= detection_network_width * detection_obj_width / detection_image_width` * `train_network_height * train_obj_height / train_image_height ~= detection_network_height * detection_obj_height / detection_image_height` From 5291935b70c21ddbbc7bbd592f87921cb1f8a2ff Mon Sep 17 00:00:00 2001 From: IlyaOvodov Date: Mon, 4 Jun 2018 18:07:39 +0300 Subject: [PATCH 0054/1365] Net color depth is exposed from Detector class --- src/yolo_v2_class.cpp | 4 ++++ src/yolo_v2_class.hpp | 1 + 2 files changed, 5 insertions(+) diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index 03d581ad347..faaf8d11f26 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -119,6 +119,10 @@ YOLODLL_API int Detector::get_net_height() const { detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); return detector_gpu.net.h; } +YOLODLL_API int Detector::get_net_color_depth() const { + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + return detector_gpu.net.c; +} YOLODLL_API std::vector Detector::detect(std::string image_filename, float thresh, bool use_mean) diff --git a/src/yolo_v2_class.hpp b/src/yolo_v2_class.hpp index 00c2a5b766f..fd00dc0695a 100644 --- a/src/yolo_v2_class.hpp +++ b/src/yolo_v2_class.hpp @@ -57,6 +57,7 @@ class Detector { static YOLODLL_API void free_image(image_t m); YOLODLL_API int get_net_width() const; YOLODLL_API int get_net_height() const; + YOLODLL_API int get_net_color_depth() const; YOLODLL_API std::vector tracking_id(std::vector cur_bbox_vec, bool const change_history = true, int const frames_story = 10, int const max_dist = 150); From 1e512b23089b23e91fa5ed6a617641d9286d1417 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 4 Jun 2018 22:11:05 +0300 Subject: [PATCH 0055/1365] Letter box is disabled in the darknet.py by defualt --- build/darknet/x64/darknet.py | 2 +- darknet.py | 2 +- src/image.h | 2 +- src/network.c | 3 ++- 4 files changed, 5 insertions(+), 4 deletions(-) diff --git a/build/darknet/x64/darknet.py b/build/darknet/x64/darknet.py index 75ef04b5033..dff0a314d7d 100644 --- a/build/darknet/x64/darknet.py +++ b/build/darknet/x64/darknet.py @@ -235,7 +235,7 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False): if debug: print("Assigned pnum") predict_image(net, im) if debug: print("did prediction") - dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum, 1) + dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum, 0) if debug: print("Got dets") num = pnum[0] if debug: print("got zeroth index of pnum") diff --git a/darknet.py b/darknet.py index 75ef04b5033..dff0a314d7d 100644 --- a/darknet.py +++ b/darknet.py @@ -235,7 +235,7 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False): if debug: print("Assigned pnum") predict_image(net, im) if debug: print("did prediction") - dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum, 1) + dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum, 0) if debug: print("Got dets") num = pnum[0] if debug: print("got zeroth index of pnum") diff --git a/src/image.h b/src/image.h index d047f621dc6..84b122496f0 100644 --- a/src/image.h +++ b/src/image.h @@ -30,7 +30,7 @@ image crop_image(image im, int dx, int dy, int w, int h); image random_crop_image(image im, int w, int h); image random_augment_image(image im, float angle, float aspect, int low, int high, int size); void random_distort_image(image im, float hue, float saturation, float exposure); -image resize_image(image im, int w, int h); +YOLODLL_API image resize_image(image im, int w, int h); void fill_image(image m, float s); void letterbox_image_into(image im, int w, int h, image boxed); YOLODLL_API image letterbox_image(image im, int w, int h); diff --git a/src/network.c b/src/network.c index 050d3347403..1498a2ad4fd 100644 --- a/src/network.c +++ b/src/network.c @@ -640,7 +640,8 @@ void free_detections(detection *dets, int n) float *network_predict_image(network *net, image im) { - image imr = letterbox_image(im, net->w, net->h); + //image imr = letterbox_image(im, net->w, net->h); + image imr = resize_image(im, net->w, net->h); set_batch_network(net, 1); float *p = network_predict(*net, imr.data); free_image(imr); From 6c54f5ffb240f0c97b7d33a55ce852716d422919 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 7 Jun 2018 02:20:44 +0300 Subject: [PATCH 0056/1365] Show inconsistent information if it is present in .cfg and .names files --- src/data.c | 8 +++++++- src/data.h | 1 + src/detector.c | 8 +++++++- src/network.c | 6 ++++++ 4 files changed, 21 insertions(+), 2 deletions(-) diff --git a/src/data.c b/src/data.c index c98ede9be6f..aae866704a7 100644 --- a/src/data.c +++ b/src/data.c @@ -512,14 +512,20 @@ matrix load_tags_paths(char **paths, int n, int k) return y; } -char **get_labels(char *filename) +char **get_labels_custom(char *filename, int *size) { list *plist = get_paths(filename); + if(size) *size = plist->size; char **labels = (char **)list_to_array(plist); free_list(plist); return labels; } +char **get_labels(char *filename) +{ + return get_labels_custom(filename, NULL); +} + void free_data(data d) { if(!d.shallow){ diff --git a/src/data.h b/src/data.h index b46143fb9d7..f7ab585ce95 100644 --- a/src/data.h +++ b/src/data.h @@ -100,6 +100,7 @@ data load_data_writing(char **paths, int n, int m, int w, int h, int out_w, int list *get_paths(char *filename); char **get_labels(char *filename); +char **get_labels_custom(char *filename, int *size); void get_random_batch(data d, int n, float *X, float *y); data get_data_part(data d, int part, int total); data get_random_data(data d, int num); diff --git a/src/detector.c b/src/detector.c index e099e91a04d..0c0b14d8450 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1069,7 +1069,8 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam { list *options = read_data_cfg(datacfg); char *name_list = option_find_str(options, "names", "data/names.list"); - char **names = get_labels(name_list); + int names_size = 0; + char **names = get_labels_custom(name_list, &names_size); //get_labels(name_list); image **alphabet = load_alphabet(); network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 @@ -1078,6 +1079,11 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam } //set_batch_network(&net, 1); fuse_conv_batchnorm(net); + if (net.layers[net.n - 1].classes != names_size) { + printf(" Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", + name_list, names_size, net.layers[net.n - 1].classes, datacfg); + if(net.layers[net.n - 1].classes > names_size) getchar(); + } srand(2222222); double time; char buff[256]; diff --git a/src/network.c b/src/network.c index 1498a2ad4fd..b32dfc00582 100644 --- a/src/network.c +++ b/src/network.c @@ -602,12 +602,18 @@ void custom_get_region_detections(layer l, int w, int h, int net_w, int net_h, f void fill_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, detection *dets, int letter) { + int prev_classes = -1; int j; for (j = 0; j < net->n; ++j) { layer l = net->layers[j]; if (l.type == YOLO) { int count = get_yolo_detections(l, w, h, net->w, net->h, thresh, map, relative, dets, letter); dets += count; + if (prev_classes < 0) prev_classes = l.classes; + else if (prev_classes != l.classes) { + printf(" Error: Different [yolo] layers have different number of classes = %d and %d - check your cfg-file! \n", + prev_classes, l.classes); + } } if (l.type == REGION) { custom_get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets, letter); From 3010e1f755c67eeba03019727c40848ef9783799 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 7 Jun 2018 03:02:35 +0300 Subject: [PATCH 0057/1365] Minor fix --- src/data.c | 1 + 1 file changed, 1 insertion(+) diff --git a/src/data.c b/src/data.c index aae866704a7..a685fb4dec2 100644 --- a/src/data.c +++ b/src/data.c @@ -51,6 +51,7 @@ char **get_random_paths(char **paths, int n, int m) random_paths[i] = paths[index]; //if(i == 0) printf("%s\n", paths[index]); //printf("grp: %s\n", paths[index]); + if (strlen(random_paths[i]) <= 4) printf(" Very small path to the image: %s \n", random_paths[i]); } while (strlen(random_paths[i]) == 0); } pthread_mutex_unlock(&mutex); From 1c05ebf522f0bb5776ba51a46d94aa101220fea1 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 7 Jun 2018 03:39:30 +0300 Subject: [PATCH 0058/1365] Minor fix --- src/image.c | 9 ++++++++- src/network.c | 4 ++-- src/parser.c | 2 +- 3 files changed, 11 insertions(+), 4 deletions(-) diff --git a/src/image.c b/src/image.c index 35e8551ecf8..9084e7e9cc3 100644 --- a/src/image.c +++ b/src/image.c @@ -1034,7 +1034,14 @@ image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage else return make_empty_image(0, 0, 0); } if (src->width < 1 || src->height < 1 || src->nChannels < 1) { - if (cpp_video_capture) cvReleaseImage(&src); + if (cpp_video_capture) { + cvReleaseImage(&src); + int z = 0; + for (z = 0; z < 10; ++z) { + get_webcam_frame(cap); + cvReleaseImage(&src); + } + } if (dont_close) src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, c); else return make_empty_image(0, 0, 0); } diff --git a/src/network.c b/src/network.c index b32dfc00582..499bf0f1400 100644 --- a/src/network.c +++ b/src/network.c @@ -443,8 +443,8 @@ int resize_network(network *net, int w, int h) } #ifdef GPU if(gpu_index >= 0){ - printf(" try to allocate workspace = %zu * sizeof(float), ", (workspace_size - 1) / sizeof(float) + 1); - net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1); + printf(" try to allocate workspace = %zu * sizeof(float), ", workspace_size / sizeof(float) + 1); + net->workspace = cuda_make_array(0, workspace_size/sizeof(float) + 1); printf(" CUDA allocate done! \n"); }else { free(net->workspace); diff --git a/src/parser.c b/src/parser.c index c1ee98c3a7a..184d1de3e0e 100644 --- a/src/parser.c +++ b/src/parser.c @@ -817,7 +817,7 @@ network parse_network_cfg_custom(char *filename, int batch) //printf("%ld\n", workspace_size); #ifdef GPU if(gpu_index >= 0){ - net.workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1); + net.workspace = cuda_make_array(0, workspace_size/sizeof(float) + 1); }else { net.workspace = calloc(1, workspace_size); } From 35b9c3d0e761afa4ae2d900c818e399c0d4e73d4 Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 7 Jun 2018 14:15:10 +0300 Subject: [PATCH 0059/1365] Update Readme.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 7cf0b7702c0..541f7998003 100644 --- a/README.md +++ b/README.md @@ -159,6 +159,8 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c 5. If you have GPU with Tensor Cores (nVidia Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x: `\darknet.sln` -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add here: `CUDNN_HALF;` + + **Note:** CUDA must be installed only after that MSVS2015 had been installed. ### How to compile (custom): From 9e79236996efae9336c63539ef03786631c97000 Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 7 Jun 2018 14:20:45 +0300 Subject: [PATCH 0060/1365] Update Readme.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 541f7998003..f9e7dfbd7fd 100644 --- a/README.md +++ b/README.md @@ -419,7 +419,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * check that each object are mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: https://github.com/AlexeyAB/Yolo_mark - * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds + * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 images for each class or more * desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty `.txt` files) From 63aeb63dee51aff8d0b7c862f9c7966e055eb061 Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 7 Jun 2018 16:46:23 +0300 Subject: [PATCH 0061/1365] Update Readme.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f9e7dfbd7fd..23688af4eea 100644 --- a/README.md +++ b/README.md @@ -182,7 +182,7 @@ Then add to your created project: `OPENCV;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)` -- compile to .exe (X64 & Release) and put .dll-s near with .exe: +- compile to .exe (X64 & Release) and put .dll-s near with .exe: https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg * `pthreadVC2.dll, pthreadGC2.dll` from \3rdparty\dll\x64 From 17520296c730c7d7e2683452b11bf50fc8959688 Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 7 Jun 2018 16:47:52 +0300 Subject: [PATCH 0062/1365] Update Readme.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 23688af4eea..27c622d0a34 100644 --- a/README.md +++ b/README.md @@ -133,7 +133,7 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c ### How to compile on Windows: -1. If you have **MSVS 2015, CUDA 9.1, cuDNN 7.0 and OpenCV 3.x** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release**, and do the: Build -> Build darknet. **NOTE:** If installing OpenCV, use OpenCV 3.4.0 or earlier. This is a bug in OpenCV 3.4.1 in the C API (see [#500](https://github.com/AlexeyAB/darknet/issues/500)). +1. If you have **MSVS 2015, CUDA 9.1, cuDNN 7.0 and OpenCV 3.x** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release** https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg and do the: Build -> Build darknet. **NOTE:** If installing OpenCV, use OpenCV 3.4.0 or earlier. This is a bug in OpenCV 3.4.1 in the C API (see [#500](https://github.com/AlexeyAB/darknet/issues/500)). 1.1. Find files `opencv_world320.dll` and `opencv_ffmpeg320_64.dll` (or `opencv_world340.dll` and `opencv_ffmpeg340_64.dll`) in `C:\opencv_3.0\opencv\build\x64\vc14\bin` and put it near with `darknet.exe` From 83b2ae66e13ef2b83153699361127c73936a8763 Mon Sep 17 00:00:00 2001 From: vinjn Date: Sun, 10 Jun 2018 09:45:39 +0800 Subject: [PATCH 0063/1365] Expose YOLODLL_API layer* get_network_layer(network* net, int i); --- src/network.c | 5 +++++ src/network.h | 1 + 2 files changed, 6 insertions(+) diff --git a/src/network.c b/src/network.c index 499bf0f1400..d135a29fc67 100644 --- a/src/network.c +++ b/src/network.c @@ -493,6 +493,11 @@ image get_network_image_layer(network net, int i) return def; } +layer* get_network_layer(network* net, int i) +{ + return net->layers + i; +} + image get_network_image(network net) { int i; diff --git a/src/network.h b/src/network.h index 4198c4bcf23..66df6ffe32e 100644 --- a/src/network.h +++ b/src/network.h @@ -134,6 +134,7 @@ int resize_network(network *net, int w, int h); void set_batch_network(network *net, int b); int get_network_input_size(network net); float get_network_cost(network net); +YOLODLL_API layer* get_network_layer(network* net, int i); YOLODLL_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter); YOLODLL_API detection *make_network_boxes(network *net, float thresh, int *num); YOLODLL_API void free_detections(detection *dets, int n); From 22796c5a496a89e47dfafa68342e090d576c4866 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 10 Jun 2018 13:58:37 +0300 Subject: [PATCH 0064/1365] Minor fix --- src/darknet.c | 6 ++++++ src/demo.c | 15 +-------------- src/detector.c | 3 ++- src/image.c | 38 ++++++++++++++++++++++---------------- 4 files changed, 31 insertions(+), 31 deletions(-) diff --git a/src/darknet.c b/src/darknet.c index ece518c0225..39f9522ac4d 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -354,6 +354,12 @@ int main(int argc, char **argv) _CrtSetDbgFlag(_CRTDBG_ALLOC_MEM_DF | _CRTDBG_LEAK_CHECK_DF); #endif + int i; + for (i = 0; i < argc; ++i) { + if (!argv[i]) continue; + strip(argv[i]); + } + //test_resize("data/bad.jpg"); //test_box(); //test_convolutional_layer(); diff --git a/src/demo.c b/src/demo.c index 0a6a4cb8897..2ff2343df11 100644 --- a/src/demo.c +++ b/src/demo.c @@ -87,16 +87,7 @@ void *detect_in_thread(void *ptr) l.output = avg; free_image(det_s); - /* - if(l.type == DETECTION){ - get_detection_boxes(l, 1, 1, demo_thresh, probs, boxes, 0); - } else if (l.type == REGION){ - get_region_boxes(l, 1, 1, demo_thresh, probs, boxes, 0, 0); - } else { - error("Last layer must produce detections\n"); - } - if (nms > 0) do_nms(boxes, probs, l.w*l.h*l.n, l.classes, nms); - */ + int letter = 0; int nboxes = 0; detection *dets = get_network_boxes(&net, det_s.w, det_s.h, demo_thresh, demo_thresh, 0, 1, &nboxes, letter); @@ -109,15 +100,11 @@ void *detect_in_thread(void *ptr) printf("\nFPS:%.1f\n",fps); printf("Objects:\n\n"); - //images[demo_index] = det; - //det = images[(demo_index + FRAMES/2 + 1)%FRAMES]; ipl_images[demo_index] = det_img; det_img = ipl_images[(demo_index + FRAMES / 2 + 1) % FRAMES]; demo_index = (demo_index + 1)%FRAMES; - //draw_detections(det, l.w*l.h*l.n, demo_thresh, boxes, probs, demo_names, demo_alphabet, demo_classes); draw_detections_cv_v3(det_img, dets, nboxes, demo_thresh, demo_names, demo_alphabet, demo_classes, demo_ext_output); - //draw_detections_cv(det_img, l.w*l.h*l.n, demo_thresh, boxes, probs, demo_names, demo_alphabet, demo_classes); free_detections(dets, nboxes); return 0; diff --git a/src/detector.c b/src/detector.c index 0c0b14d8450..228647a475a 100644 --- a/src/detector.c +++ b/src/detector.c @@ -976,6 +976,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int //for (i = 0; i < number_of_boxes; ++i) // printf("%2.2f,%2.2f, ", points->data.fl[i * 2], points->data.fl[i * 2 + 1]); + printf("\n"); float avg_iou = 0; for (i = 0; i < number_of_boxes; ++i) { float box_w = points->data.fl[i * 2]; @@ -999,7 +1000,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int float box_intersect = min_w*min_h; float box_union = box_w*box_h + anchor_w*anchor_h - box_intersect; float iou = box_intersect / box_union; - if (iou > 1 || iou < 0) { + if (iou > 1 || iou < 0 || box_w > width || box_h > height) { printf(" i = %d, box_w = %d, box_h = %d, anchor_w = %d, anchor_h = %d, iou = %f \n", i, box_w, box_h, anchor_w, anchor_h, iou); } diff --git a/src/image.c b/src/image.c index 9084e7e9cc3..625e7df6de9 100644 --- a/src/image.c +++ b/src/image.c @@ -1011,6 +1011,26 @@ image get_image_from_stream_cpp(CvCapture *cap) return im; } +int wait_for_stream(CvCapture *cap, IplImage* src, int dont_close) { + if (!src) { + if (dont_close) src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3); + else return 0; + } + if (src->width < 1 || src->height < 1 || src->nChannels < 1) { + if (dont_close) { + cvReleaseImage(&src); + int z = 0; + for (z = 0; z < 20; ++z) { + get_webcam_frame(cap); + cvReleaseImage(&src); + } + src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3); + } + else return 0; + } + return 1; +} + image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close) { c = c ? c : 3; @@ -1029,22 +1049,8 @@ image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage } else src = cvQueryFrame(cap); - if (!src) { - if (dont_close) src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, c); - else return make_empty_image(0, 0, 0); - } - if (src->width < 1 || src->height < 1 || src->nChannels < 1) { - if (cpp_video_capture) { - cvReleaseImage(&src); - int z = 0; - for (z = 0; z < 10; ++z) { - get_webcam_frame(cap); - cvReleaseImage(&src); - } - } - if (dont_close) src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, c); - else return make_empty_image(0, 0, 0); - } + if (cpp_video_capture) + if(!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0); IplImage* new_img = cvCreateImage(cvSize(w, h), IPL_DEPTH_8U, c); *in_img = cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c); cvResize(src, *in_img, CV_INTER_LINEAR); From eff487ba3626a39e135d13929117e04bc4cf5823 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 10 Jun 2018 16:36:33 +0300 Subject: [PATCH 0065/1365] Minor fix --- src/data.c | 2 +- src/detector.c | 14 ++++++++++++-- 2 files changed, 13 insertions(+), 3 deletions(-) diff --git a/src/data.c b/src/data.c index a685fb4dec2..d92e307889a 100644 --- a/src/data.c +++ b/src/data.c @@ -342,7 +342,7 @@ void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, system(buff); continue; } - if (x < 0 || x > 1 || y < 0 || y > 1) { + if (x <= 0 || x > 1 || y <= 0 || y > 1) { printf("\n Wrong annotation: x = %f, y = %f \n", x, y); sprintf(buff, "echo %s \"Wrong annotation: x = %f, y = %f\" >> bad_label.list", labelpath, x, y); system(buff); diff --git a/src/detector.c b/src/detector.c index 228647a475a..2a4c4a2264a 100644 --- a/src/detector.c +++ b/src/detector.c @@ -916,8 +916,18 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int int num_labels = 0; box_label *truth = read_boxes(labelpath, &num_labels); //printf(" new path: %s \n", labelpath); + char buff[1024]; for (j = 0; j < num_labels; ++j) { + if (truth[j].x > 1 || truth[j].x <= 0 || truth[j].y > 1 || truth[j].y <= 0 || + truth[j].w > 1 || truth[j].w <= 0 || truth[j].h > 1 || truth[j].h <= 0) + { + printf("\n\nWrong label: %s - j = %d, x = %f, y = %f, width = %f, height = %f \n", + labelpath, j, truth[j].x, truth[j].y, truth[j].w, truth[j].h); + sprintf(buff, "echo \"Wrong label: %s - j = %d, x = %f, y = %f, width = %f, height = %f\" >> bad_label.list", + labelpath, j, truth[j].x, truth[j].y, truth[j].w, truth[j].h); + system(buff); + } number_of_boxes++; rel_width_height_array = realloc(rel_width_height_array, 2 * number_of_boxes * sizeof(float)); rel_width_height_array[number_of_boxes * 2 - 2] = truth[j].w * width; @@ -1000,8 +1010,8 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int float box_intersect = min_w*min_h; float box_union = box_w*box_h + anchor_w*anchor_h - box_intersect; float iou = box_intersect / box_union; - if (iou > 1 || iou < 0 || box_w > width || box_h > height) { - printf(" i = %d, box_w = %d, box_h = %d, anchor_w = %d, anchor_h = %d, iou = %f \n", + if (iou > 1 || iou < 0) { // || box_w > width || box_h > height) { + printf(" Wrong label: i = %d, box_w = %d, box_h = %d, anchor_w = %d, anchor_h = %d, iou = %f \n", i, box_w, box_h, anchor_w, anchor_h, iou); } else avg_iou += iou; From 6d44529cf93211c319813c90e0c1adb34426abe5 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 13 Jun 2018 13:50:02 +0300 Subject: [PATCH 0066/1365] Minor fix --- README.md | 3 +-- src/detector.c | 3 +++ src/utils.c | 12 ++++++------ 3 files changed, 10 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 27c622d0a34..5f81cd3c6a4 100644 --- a/README.md +++ b/README.md @@ -100,7 +100,6 @@ On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector * Remeber to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app * To process a list of images `data/train.txt` and save results of detection to `result.txt` use: `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -dont_show -ext_output < data/train.txt > result.txt` - You can comment this line so that each image does not require pressing the button ESC: https://github.com/AlexeyAB/darknet/blob/6ccb41808caf753feea58ca9df79d6367dedc434/src/detector.c#L509 ##### For using network video-camera mjpeg-stream with any Android smartphone: @@ -428,7 +427,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * for training for small objects - set `layers = -1, 11` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L720 and set `stride=4` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L717 - * General rule - you should keep relative size of objects in the Training and Testing datasets roughly the same: + * General rule - your training dataset should include such a set of relative sizes of objects that you want to detect - differing by no more than 2 times: * `train_network_width * train_obj_width / train_image_width ~= detection_network_width * detection_obj_width / detection_image_width` * `train_network_height * train_obj_height / train_image_height ~= detection_network_height * detection_obj_height / detection_image_height` diff --git a/src/detector.c b/src/detector.c index 2a4c4a2264a..afb91b22077 100644 --- a/src/detector.c +++ b/src/detector.c @@ -65,6 +65,9 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i printf("\n Error: You set incorrect value batch=1 for Training! You should set batch=64 subdivision=64 \n"); getchar(); } + else if ((net.batch * net.subdivisions) < 64) { + printf("\n Warning: You set batch= lower than 64! It is recommended to set batch=64 subdivision=64 \n"); + } int imgs = net.batch * net.subdivisions * ngpus; printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); diff --git a/src/utils.c b/src/utils.c index f1cc21aa6a4..4b8ca7daaef 100644 --- a/src/utils.c +++ b/src/utils.c @@ -206,12 +206,12 @@ void find_replace_extension(char *str, char *orig, char *rep, char *output) } void replace_image_to_label(char *input_path, char *output_path) { - //find_replace(input_path, "images", "labels", output_path); // COCO - find_replace(input_path, "images/train2014/", "labels/train2014/", output_path); // COCO - find_replace(output_path, "images/val2014/", "labels/val2014/", output_path); // COCO - //find_replace(output_path, "JPEGImages", "labels", output_path); // PascalVOC - find_replace(output_path, "VOC2007/JPEGImages", "VOC2007/labels", output_path); // PascalVOC - find_replace(output_path, "VOC2012/JPEGImages", "VOC2012/labels", output_path); // PascalVOC + //find_replace(input_path, "/images/", "/labels/", output_path); // COCO + find_replace(input_path, "/images/train2014/", "/labels/train2014/", output_path); // COCO + find_replace(output_path, "/images/val2014/", "/labels/val2014/", output_path); // COCO + //find_replace(output_path, "/JPEGImages/", "/labels/", output_path); // PascalVOC + find_replace(output_path, "/VOC2007/JPEGImages/", "/VOC2007/labels/", output_path); // PascalVOC + find_replace(output_path, "/VOC2012/JPEGImages/", "/VOC2012/labels/", output_path); // PascalVOC //find_replace(output_path, "/raw/", "/labels/", output_path); From 6ed2474e14d31768b789611fa31e012e8ed6974c Mon Sep 17 00:00:00 2001 From: Alexey Date: Fri, 15 Jun 2018 00:42:35 +0300 Subject: [PATCH 0067/1365] Update Readme.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 5f81cd3c6a4..db0f9459bec 100644 --- a/README.md +++ b/README.md @@ -432,7 +432,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * `train_network_width * train_obj_width / train_image_width ~= detection_network_width * detection_obj_width / detection_image_width` * `train_network_height * train_obj_height / train_image_height ~= detection_network_height * detection_obj_height / detection_image_height` - * to speedup training (with decreasing detection accuracy) do Fine-Tuning instead of Transfer-Learning, set param `stopbackward=1` in one of the penultimate convolutional layers before the 1-st `[yolo]`-layer, for example here: https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L598 + * to speedup training (with decreasing detection accuracy) do Fine-Tuning instead of Transfer-Learning, set param `stopbackward=1` here: https://github.com/AlexeyAB/darknet/blob/6d44529cf93211c319813c90e0c1adb34426abe5/cfg/yolov3.cfg#L548 2. After training - for detection: From e066cad1ec730cea0fa76348e34c15ff637c6975 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 15 Jun 2018 17:25:58 +0300 Subject: [PATCH 0068/1365] Added warning if width or height isn't divisible by 32 for default Yolo-networks --- src/parser.c | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/src/parser.c b/src/parser.c index 184d1de3e0e..1a324078abe 100644 --- a/src/parser.c +++ b/src/parser.c @@ -825,6 +825,11 @@ network parse_network_cfg_custom(char *filename, int batch) net.workspace = calloc(1, workspace_size); #endif } + LAYER_TYPE lt = net.layers[net.n - 1].type; + if ((net.w % 32 != 0 || net.h % 32 != 0) && (lt == YOLO || lt == REGION || lt == DETECTION)) { + printf("\n Warning: width=%d and height=%d in cfg-file must be divisible by 32 for default networks Yolo v1/v2/v3!!! \n\n", + net.w, net.h); + } return net; } From 2b964134b42617ff34448aaa03b0335481a1af1a Mon Sep 17 00:00:00 2001 From: Alexey Date: Fri, 15 Jun 2018 17:43:00 +0300 Subject: [PATCH 0069/1365] Update Readme.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index db0f9459bec..b40d531d8e2 100644 --- a/README.md +++ b/README.md @@ -320,6 +320,8 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an **Note:** If during training you see `nan` values for `avg` (loss) field - then training goes wrong, but if `nan` is in some other lines - then training goes well. + **Note:** If you changed width= or height= in your cfg-file, then new width and height must be divisible by 32. + ### How to train tiny-yolo (to detect your custom objects): Do all the same steps as for the full yolo model as described above. With the exception of: From e1e5abe19197f2bb56a9d320b31275db4706aa4d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 16 Jun 2018 01:27:11 +0300 Subject: [PATCH 0070/1365] Fixed some security issues --- src/image.c | 26 ++++++++++++++++++-------- 1 file changed, 18 insertions(+), 8 deletions(-) diff --git a/src/image.c b/src/image.c index 625e7df6de9..53139642c2e 100644 --- a/src/image.c +++ b/src/image.c @@ -966,10 +966,15 @@ image load_image_cv(char *filename, int channels) if( (src = cvLoadImage(filename, flag)) == 0 ) { - fprintf(stderr, "Cannot load image \"%s\"\n", filename); - char buff[256]; - sprintf(buff, "echo %s >> bad.list", filename); - system(buff); + char shrinked_filename[1024]; + if (strlen(filename) >= 1024) sprintf(shrinked_filename, "name is too long"); + else sprintf(shrinked_filename, "%s", filename); + fprintf(stderr, "Cannot load image \"%s\"\n", shrinked_filename); + FILE* fw = fopen("bad.list", "a"); + fwrite(shrinked_filename, sizeof(char), strlen(shrinked_filename), fw); + char *new_line = "\n"; + fwrite(new_line, sizeof(char), strlen(new_line), fw); + fclose(fw); return make_image(10,10,3); //exit(EXIT_FAILURE); } @@ -1764,10 +1769,15 @@ image load_image_stb(char *filename, int channels) int w, h, c; unsigned char *data = stbi_load(filename, &w, &h, &c, channels); if (!data) { - fprintf(stderr, "Cannot load image \"%s\"\nSTB Reason: %s\n", filename, stbi_failure_reason()); - char buff[256]; - sprintf(buff, "echo %s >> bad.list", filename); - system(buff); + char shrinked_filename[1024]; + if (strlen(filename) >= 1024) sprintf(shrinked_filename, "name is too long"); + else sprintf(shrinked_filename, "%s", filename); + fprintf(stderr, "Cannot load image \"%s\"\nSTB Reason: %s\n", shrinked_filename, stbi_failure_reason()); + FILE* fw = fopen("bad.list", "a"); + fwrite(shrinked_filename, sizeof(char), strlen(shrinked_filename), fw); + char *new_line = "\n"; + fwrite(new_line, sizeof(char), strlen(new_line), fw); + fclose(fw); return make_image(10, 10, 3); //exit(EXIT_FAILURE); } From 7f0079dec5da19072cfe64aee233ecacd8ef2ffd Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 19 Jun 2018 15:00:36 +0300 Subject: [PATCH 0071/1365] Output video-file has the same FPS as source video-stream. Exit from demo by ESC. --- src/demo.c | 21 ++++++++++++++------- src/http_stream.cpp | 16 ++++++++++++++++ src/http_stream.h | 1 + src/image.c | 12 ++++++++++++ 4 files changed, 43 insertions(+), 7 deletions(-) diff --git a/src/demo.c b/src/demo.c index 2ff2343df11..6929e0870e1 100644 --- a/src/demo.c +++ b/src/demo.c @@ -52,6 +52,7 @@ void draw_detections_cv(IplImage* show_img, int num, float thresh, box *boxes, f void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output); void show_image_cv_ipl(IplImage *disp, const char *name); image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close); +int get_stream_fps(CvCapture *cap, int cpp_video_capture); IplImage* in_img; IplImage* det_img; IplImage* show_img; @@ -208,15 +209,17 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int { CvSize size; size.width = det_img->width, size.height = det_img->height; + int src_fps = 25; + src_fps = get_stream_fps(cap, cpp_video_capture); //const char* output_name = "test_dnn_out.avi"; - //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('H', '2', '6', '4'), 25, size, 1); - output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('D', 'I', 'V', 'X'), 25, size, 1); - //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'J', 'P', 'G'), 25, size, 1); - //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'P', '4', 'V'), 25, size, 1); - //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'P', '4', '2'), 25, size, 1); - //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('X', 'V', 'I', 'D'), 25, size, 1); - //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('W', 'M', 'V', '2'), 25, size, 1); + //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('H', '2', '6', '4'), src_fps, size, 1); + output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('D', 'I', 'V', 'X'), src_fps, size, 1); + //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'J', 'P', 'G'), src_fps, size, 1); + //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'P', '4', 'V'), src_fps, size, 1); + //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'P', '4', '2'), src_fps, size, 1); + //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('X', 'V', 'I', 'D'), src_fps, size, 1); + //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('W', 'M', 'V', '2'), src_fps, size, 1); } double before = get_wall_time(); @@ -237,6 +240,10 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int else if (frame_skip == 60) frame_skip = 4; else frame_skip = 0; } + else if (c == 27 || c == 1048603) // ESC - exit (OpenCV 2.x / 3.x) + { + flag_exit = 1; + } } }else{ char buff[256]; diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 1b651730076..b122f5b78ce 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -248,6 +248,22 @@ IplImage* get_webcam_frame(CvCapture *cap) { } return src; } + +int get_stream_fps_cpp(CvCapture *cap) { + int fps = 25; + try { + cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; +#ifndef CV_VERSION_EPOCH // OpenCV 3.x + fps = cpp_cap.get(CAP_PROP_FPS); +#else // OpenCV 2.x + fps = cpp_cap.get(CV_CAP_PROP_FPS); +#endif + } + catch (...) { + std::cout << " Can't get FPS of source videofile. For output video FPS = 25 by default. \n"; + } + return fps; +} // ---------------------------------------- extern "C" { image ipl_to_image(IplImage* src); // image.c diff --git a/src/http_stream.h b/src/http_stream.h index 78597a09f7a..bff18fb79f2 100644 --- a/src/http_stream.h +++ b/src/http_stream.h @@ -11,6 +11,7 @@ void send_mjpeg(IplImage* ipl, int port, int timeout, int quality); CvCapture* get_capture_webcam(int index); CvCapture* get_capture_video_stream(char *path); IplImage* get_webcam_frame(CvCapture *cap); +int get_stream_fps_cpp(CvCapture *cap); image image_data_augmentation(IplImage* ipl, int w, int h, int pleft, int ptop, int swidth, int sheight, int flip, diff --git a/src/image.c b/src/image.c index 53139642c2e..dd0098fbeae 100644 --- a/src/image.c +++ b/src/image.c @@ -1068,6 +1068,18 @@ image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage return im; } +int get_stream_fps(CvCapture *cap, int cpp_video_capture) +{ + int fps = 25; + if (cpp_video_capture) { + fps = get_stream_fps_cpp(cap); + } + else { + fps = cvGetCaptureProperty(cap, CV_CAP_PROP_FPS); + } + return fps; +} + void save_image_jpg(image p, const char *name) { image copy = copy_image(p); From 56bff1dc8cbafd718b5733f22bdb63ab4767703b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 20 Jun 2018 01:06:39 +0300 Subject: [PATCH 0072/1365] Fixed darknet.py when OpenCV is used (added BGR to RGB conversion) --- build/darknet/x64/darknet.py | 4 +++- darknet.py | 4 +++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/build/darknet/x64/darknet.py b/build/darknet/x64/darknet.py index dff0a314d7d..a8e1d12f466 100644 --- a/build/darknet/x64/darknet.py +++ b/build/darknet/x64/darknet.py @@ -224,7 +224,8 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False): #pylint: disable= C0321 im = load_image(image, 0, 0) #import cv2 - #custom_image = cv2.imread(image) # use: detect(,,imagePath,) + #custom_image_bgr = cv2.imread(image) # use: detect(,,imagePath,) + #custom_image = cv2.cvtColor(custom_image_bgr, cv2.COLOR_BGR2RGB) #import scipy.misc #custom_image = scipy.misc.imread(image) #im, arr = array_to_image(custom_image) # you should comment line below: free_image(im) @@ -362,6 +363,7 @@ def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yo if not os.path.exists(imagePath): raise ValueError("Invalid image path `"+os.path.abspath(imagePath)+"`") # Do the detection + #detections = detect(netMain, metaMain, imagePath, thresh) # if is used cv2.imread(image) detections = detect(netMain, metaMain, imagePath.encode("ascii"), thresh) if showImage: try: diff --git a/darknet.py b/darknet.py index dff0a314d7d..a8e1d12f466 100644 --- a/darknet.py +++ b/darknet.py @@ -224,7 +224,8 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False): #pylint: disable= C0321 im = load_image(image, 0, 0) #import cv2 - #custom_image = cv2.imread(image) # use: detect(,,imagePath,) + #custom_image_bgr = cv2.imread(image) # use: detect(,,imagePath,) + #custom_image = cv2.cvtColor(custom_image_bgr, cv2.COLOR_BGR2RGB) #import scipy.misc #custom_image = scipy.misc.imread(image) #im, arr = array_to_image(custom_image) # you should comment line below: free_image(im) @@ -362,6 +363,7 @@ def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yo if not os.path.exists(imagePath): raise ValueError("Invalid image path `"+os.path.abspath(imagePath)+"`") # Do the detection + #detections = detect(netMain, metaMain, imagePath, thresh) # if is used cv2.imread(image) detections = detect(netMain, metaMain, imagePath.encode("ascii"), thresh) if showImage: try: From 3a33d00d22ef55247fe379b8e6c53850f43a32a8 Mon Sep 17 00:00:00 2001 From: Alexey Date: Wed, 20 Jun 2018 01:29:59 +0300 Subject: [PATCH 0073/1365] Update Readme.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index b40d531d8e2..d43d7974316 100644 --- a/README.md +++ b/README.md @@ -422,14 +422,14 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 images for each class or more - * desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty `.txt` files) + * desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty `.txt` files) - use as many images of negative samples as there are images with objects * for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last layer [region] in your cfg-file * for training for small objects - set `layers = -1, 11` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L720 and set `stride=4` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L717 - * General rule - your training dataset should include such a set of relative sizes of objects that you want to detect - differing by no more than 2 times: + * General rule - your training dataset should include such a set of relative sizes of objects that you want to detect: * `train_network_width * train_obj_width / train_image_width ~= detection_network_width * detection_obj_width / detection_image_width` * `train_network_height * train_obj_height / train_image_height ~= detection_network_height * detection_obj_height / detection_image_height` From 76e258520edb50e8bb897ba15aa9467579e70a6a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 20 Jun 2018 13:28:25 +0300 Subject: [PATCH 0074/1365] Minor fix --- src/utils.c | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/utils.c b/src/utils.c index 4b8ca7daaef..cafbeb5ad08 100644 --- a/src/utils.c +++ b/src/utils.c @@ -209,9 +209,9 @@ void replace_image_to_label(char *input_path, char *output_path) { //find_replace(input_path, "/images/", "/labels/", output_path); // COCO find_replace(input_path, "/images/train2014/", "/labels/train2014/", output_path); // COCO find_replace(output_path, "/images/val2014/", "/labels/val2014/", output_path); // COCO - //find_replace(output_path, "/JPEGImages/", "/labels/", output_path); // PascalVOC - find_replace(output_path, "/VOC2007/JPEGImages/", "/VOC2007/labels/", output_path); // PascalVOC - find_replace(output_path, "/VOC2012/JPEGImages/", "/VOC2012/labels/", output_path); // PascalVOC + find_replace(output_path, "/JPEGImages/", "/labels/", output_path); // PascalVOC + //find_replace(output_path, "/VOC2007/JPEGImages/", "/VOC2007/labels/", output_path); // PascalVOC + //find_replace(output_path, "/VOC2012/JPEGImages/", "/VOC2012/labels/", output_path); // PascalVOC //find_replace(output_path, "/raw/", "/labels/", output_path); From 4a2a2250f9470e28e83c9e5db900e8ff71dfc416 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 21 Jun 2018 02:42:39 +0300 Subject: [PATCH 0075/1365] Fixed removing escape symbols for command line arguments --- src/darknet.c | 2 +- src/utils.c | 14 ++++++++++++++ src/utils.h | 1 + 3 files changed, 16 insertions(+), 1 deletion(-) diff --git a/src/darknet.c b/src/darknet.c index 39f9522ac4d..f6d61c09777 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -357,7 +357,7 @@ int main(int argc, char **argv) int i; for (i = 0; i < argc; ++i) { if (!argv[i]) continue; - strip(argv[i]); + strip_args(argv[i]); } //test_resize("data/bad.jpg"); diff --git a/src/utils.c b/src/utils.c index cafbeb5ad08..5dca21236bb 100644 --- a/src/utils.c +++ b/src/utils.c @@ -296,6 +296,20 @@ void strip(char *s) s[len-offset] = '\0'; } + +void strip_args(char *s) +{ + size_t i; + size_t len = strlen(s); + size_t offset = 0; + for (i = 0; i < len; ++i) { + char c = s[i]; + if (c == '\t' || c == '\n' || c == '\r' || c == 0x0d || c == 0x0a) ++offset; + else s[i - offset] = c; + } + s[len - offset] = '\0'; +} + void strip_char(char *s, char bad) { size_t i; diff --git a/src/utils.h b/src/utils.h index e2afba4579e..58cea936df8 100644 --- a/src/utils.h +++ b/src/utils.h @@ -45,6 +45,7 @@ void error(const char *s); void malloc_error(); void file_error(char *s); void strip(char *s); +void strip_args(char *s); void strip_char(char *s, char bad); void top_k(float *a, int n, int k, int *index); list *split_str(char *s, char delim); From 455b2fc06ff590914f3a1fb13261510d2b95de2e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 21 Jun 2018 14:45:43 +0300 Subject: [PATCH 0076/1365] Able letterbox for video --- src/demo.c | 14 +++++++++++--- src/image.c | 31 +++++++++++++++++++++++++++++++ 2 files changed, 42 insertions(+), 3 deletions(-) diff --git a/src/demo.c b/src/demo.c index 6929e0870e1..34627ce1fa9 100644 --- a/src/demo.c +++ b/src/demo.c @@ -52,18 +52,23 @@ void draw_detections_cv(IplImage* show_img, int num, float thresh, box *boxes, f void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output); void show_image_cv_ipl(IplImage *disp, const char *name); image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close); +image get_image_from_stream_letterbox(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close); int get_stream_fps(CvCapture *cap, int cpp_video_capture); IplImage* in_img; IplImage* det_img; IplImage* show_img; static int flag_exit; +static int letter_box = 0; void *fetch_in_thread(void *ptr) { //in = get_image_from_stream(cap); int dont_close_stream = 0; // set 1 if your IP-camera periodically turns off and turns on video-stream - in_s = get_image_from_stream_resize(cap, net.w, net.h, net.c, &in_img, cpp_video_capture, dont_close_stream); + if(letter_box) + in_s = get_image_from_stream_letterbox(cap, net.w, net.h, net.c, &in_img, cpp_video_capture, dont_close_stream); + else + in_s = get_image_from_stream_resize(cap, net.w, net.h, net.c, &in_img, cpp_video_capture, dont_close_stream); if(!in_s.data){ //error("Stream closed."); printf("Stream closed.\n"); @@ -89,9 +94,12 @@ void *detect_in_thread(void *ptr) free_image(det_s); - int letter = 0; int nboxes = 0; - detection *dets = get_network_boxes(&net, det_s.w, det_s.h, demo_thresh, demo_thresh, 0, 1, &nboxes, letter); + detection *dets = NULL; + if (letter_box) + dets = get_network_boxes(&net, in_img->width, in_img->height, demo_thresh, demo_thresh, 0, 1, &nboxes, 1); // letter box + else + dets = get_network_boxes(&net, det_s.w, det_s.h, demo_thresh, demo_thresh, 0, 1, &nboxes, 0); // resized //if (nms) do_nms_obj(dets, nboxes, l.classes, nms); // bad results if (nms) do_nms_sort(dets, nboxes, l.classes, nms); diff --git a/src/image.c b/src/image.c index dd0098fbeae..a2dc3cacd80 100644 --- a/src/image.c +++ b/src/image.c @@ -1068,6 +1068,37 @@ image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage return im; } +image get_image_from_stream_letterbox(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close) +{ + c = c ? c : 3; + IplImage* src; + if (cpp_video_capture) { + static int once = 1; + if (once) { + once = 0; + do { + src = get_webcam_frame(cap); + if (!src) return make_empty_image(0, 0, 0); + } while (src->width < 1 || src->height < 1 || src->nChannels < 1); + printf("Video stream: %d x %d \n", src->width, src->height); + } + else + src = get_webcam_frame(cap); + } + else src = cvQueryFrame(cap); + + if (cpp_video_capture) + if (!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0); + *in_img = cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c); + cvResize(src, *in_img, CV_INTER_LINEAR); + image tmp = ipl_to_image(src); + image im = letterbox_image(tmp, w, h); + free_image(tmp); + if (cpp_video_capture) cvReleaseImage(&src); + if (c>1) rgbgr_image(im); + return im; +} + int get_stream_fps(CvCapture *cap, int cpp_video_capture) { int fps = 25; From 026a679dedbc45486a11ecabf56c898372d8cac5 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 21 Jun 2018 19:23:30 +0300 Subject: [PATCH 0077/1365] Minor fix: darknet.py for OpenCV --- build/darknet/x64/darknet.py | 2 ++ darknet.py | 2 ++ 2 files changed, 4 insertions(+) diff --git a/build/darknet/x64/darknet.py b/build/darknet/x64/darknet.py index a8e1d12f466..4bca5f5a3b9 100644 --- a/build/darknet/x64/darknet.py +++ b/build/darknet/x64/darknet.py @@ -226,6 +226,7 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False): #import cv2 #custom_image_bgr = cv2.imread(image) # use: detect(,,imagePath,) #custom_image = cv2.cvtColor(custom_image_bgr, cv2.COLOR_BGR2RGB) + #custom_image = cv2.resize(custom_image,(lib.network_width(net), lib.network_height(net)), interpolation = cv2.INTER_LINEAR) #import scipy.misc #custom_image = scipy.misc.imread(image) #im, arr = array_to_image(custom_image) # you should comment line below: free_image(im) @@ -236,6 +237,7 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False): if debug: print("Assigned pnum") predict_image(net, im) if debug: print("did prediction") + #dets = get_network_boxes(net, custom_image_bgr.shape[1], custom_image_bgr.shape[0], thresh, hier_thresh, None, 0, pnum, 0) # OpenCV dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum, 0) if debug: print("Got dets") num = pnum[0] diff --git a/darknet.py b/darknet.py index a8e1d12f466..4bca5f5a3b9 100644 --- a/darknet.py +++ b/darknet.py @@ -226,6 +226,7 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False): #import cv2 #custom_image_bgr = cv2.imread(image) # use: detect(,,imagePath,) #custom_image = cv2.cvtColor(custom_image_bgr, cv2.COLOR_BGR2RGB) + #custom_image = cv2.resize(custom_image,(lib.network_width(net), lib.network_height(net)), interpolation = cv2.INTER_LINEAR) #import scipy.misc #custom_image = scipy.misc.imread(image) #im, arr = array_to_image(custom_image) # you should comment line below: free_image(im) @@ -236,6 +237,7 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False): if debug: print("Assigned pnum") predict_image(net, im) if debug: print("did prediction") + #dets = get_network_boxes(net, custom_image_bgr.shape[1], custom_image_bgr.shape[0], thresh, hier_thresh, None, 0, pnum, 0) # OpenCV dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum, 0) if debug: print("Got dets") num = pnum[0] From 5de3a00496dd6e5c0e4c96694ae6b73f1373c6d0 Mon Sep 17 00:00:00 2001 From: Alexey Date: Sat, 23 Jun 2018 00:23:59 +0300 Subject: [PATCH 0078/1365] Update Readme.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index d43d7974316..690cd74e02f 100644 --- a/README.md +++ b/README.md @@ -322,6 +322,8 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an **Note:** If you changed width= or height= in your cfg-file, then new width and height must be divisible by 32. + **Note:** After training use such command for detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights` + ### How to train tiny-yolo (to detect your custom objects): Do all the same steps as for the full yolo model as described above. With the exception of: From 00903aebd3d4979ff5128c981d2f13e5595454c6 Mon Sep 17 00:00:00 2001 From: Tino Hager Date: Sat, 23 Jun 2018 11:02:37 +0200 Subject: [PATCH 0079/1365] .NET/C# support integration Add logic for .NET/C# integration (DllImport) --- build/darknet/darknet.sln | 11 ++++-- build/darknet/darknet.vcxproj | 29 ++++++++------- build/darknet/yolo_cpp_dll.vcxproj | 16 ++++---- build/darknet/yolo_cpp_dll_no_gpu.vcxproj | 24 ++++++------ src/yolo_v2_class.cpp | 45 +++++++++++++++++++++++ src/yolo_v2_class.hpp | 11 ++++++ 6 files changed, 99 insertions(+), 37 deletions(-) diff --git a/build/darknet/darknet.sln b/build/darknet/darknet.sln index c49e10efa09..3d7efef25e0 100644 --- a/build/darknet/darknet.sln +++ b/build/darknet/darknet.sln @@ -1,7 +1,7 @@  Microsoft Visual Studio Solution File, Format Version 12.00 -# Visual Studio 14 -VisualStudioVersion = 14.0.25420.1 +# Visual Studio 15 +VisualStudioVersion = 15.0.27703.2018 MinimumVisualStudioVersion = 10.0.40219.1 Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "darknet", "darknet.vcxproj", "{4CF5694F-12A5-4012-8D94-9A0915E9FEB5}" EndProject @@ -13,8 +13,8 @@ Global Release|x64 = Release|x64 EndGlobalSection GlobalSection(ProjectConfigurationPlatforms) = postSolution - {4CF5694F-12A5-4012-8D94-9A0915E9FEB5}.Debug|Win32.ActiveCfg = Debug|Win32 - {4CF5694F-12A5-4012-8D94-9A0915E9FEB5}.Debug|Win32.Build.0 = Debug|Win32 + {4CF5694F-12A5-4012-8D94-9A0915E9FEB5}.Debug|Win32.ActiveCfg = Debug|x64 + {4CF5694F-12A5-4012-8D94-9A0915E9FEB5}.Debug|Win32.Build.0 = Debug|x64 {4CF5694F-12A5-4012-8D94-9A0915E9FEB5}.Debug|x64.ActiveCfg = Debug|x64 {4CF5694F-12A5-4012-8D94-9A0915E9FEB5}.Debug|x64.Build.0 = Debug|x64 {4CF5694F-12A5-4012-8D94-9A0915E9FEB5}.Release|Win32.ActiveCfg = Release|Win32 @@ -25,4 +25,7 @@ Global GlobalSection(SolutionProperties) = preSolution HideSolutionNode = FALSE EndGlobalSection + GlobalSection(ExtensibilityGlobals) = postSolution + SolutionGuid = {C904B7F0-81ED-4CBE-82EC-94566A77D9AF} + EndGlobalSection EndGlobal diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj index 8fbf93c3c0d..9286164247a 100644 --- a/build/darknet/darknet.vcxproj +++ b/build/darknet/darknet.vcxproj @@ -1,5 +1,5 @@  - + Debug @@ -21,38 +21,38 @@ {4CF5694F-12A5-4012-8D94-9A0915E9FEB5} darknet - 8.1 + 10.0.17134.0 Application true - v140 + v141 MultiByte Application true - v140 + v141 MultiByte Application false - v140 + v141 true MultiByte Application false - v140 + v141 true MultiByte - + @@ -88,15 +88,16 @@ Level3 Disabled true - C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include - CUDNN;_CRTDBG_MAP_ALLOC;_MBCS;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) - OPENCV; + C:\opencv\opencv340\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include + OPENCV;CUDNN;_CRTDBG_MAP_ALLOC;_MBCS;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + + true stdlib.h;crtdbg.h;%(ForcedIncludeFiles) true - C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;C:\opencv_2.4.13\opencv\build\x64\vc14\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) + C:\opencv\opencv340\build\x64\vc15\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) $(OutDir)\$(TargetName)$(TargetExt) ..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;%(AdditionalDependencies) true @@ -132,7 +133,7 @@ true true true - C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include + C:\opencv\opencv340\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include OPENCV;CUDNN;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y @@ -146,7 +147,7 @@ true true true - C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) + C:\opencv\opencv340\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) ..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;%(AdditionalDependencies) $(OutDir)\$(TargetName)$(TargetExt) @@ -288,6 +289,6 @@ - + \ No newline at end of file diff --git a/build/darknet/yolo_cpp_dll.vcxproj b/build/darknet/yolo_cpp_dll.vcxproj index c33be37f244..5deb9d41850 100644 --- a/build/darknet/yolo_cpp_dll.vcxproj +++ b/build/darknet/yolo_cpp_dll.vcxproj @@ -1,5 +1,5 @@  - + Debug @@ -21,38 +21,38 @@ {4CF5694F-12A5-4012-8D94-9A0915E9FEB5} darknet - 8.1 + 10.0.17134.0 Application true - v140 + v141 MultiByte DynamicLibrary true - v140 + v141 MultiByte Application false - v140 + v141 true MultiByte DynamicLibrary false - v140 + v141 true MultiByte - + @@ -291,6 +291,6 @@ - + \ No newline at end of file diff --git a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj index 56753c45480..123b96e79e2 100644 --- a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj +++ b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj @@ -1,5 +1,5 @@  - + Debug @@ -21,32 +21,32 @@ {4CF5694F-12A5-4012-8D94-9A0915E9FEB5} darknet - 8.1 + 10.0.17134.0 Application true - v140 + v141 MultiByte DynamicLibrary true - v140 + v141 MultiByte Application false - v140 + v141 true MultiByte DynamicLibrary false - v140 + v141 true MultiByte @@ -111,7 +111,7 @@ true true true - C:\opencv_2.4.9\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include + C:\opencv\opencv340\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include YOLODLL_EXPORTS;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) true @@ -119,7 +119,7 @@ true true true - C:\opencv_2.4.9\opencv\build\x86\vc14\lib;C:\opencv_2.4.9\opencv\build\x86\vc12\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) + C:\opencv\opencv340\build\x64\vc15\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) ..\..\3rdparty\lib\x86\pthreadVC2.lib;cudart.lib;cublas.lib;curand.lib;%(AdditionalDependencies) @@ -130,8 +130,8 @@ true true true - ..\..\3rdparty\include;%(AdditionalIncludeDirectories) - YOLODLL_EXPORTS;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + ..\..\3rdparty\include;C:\opencv\opencv340\build\include;%(AdditionalIncludeDirectories) + YOLODLL_EXPORTS;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;OPENCV;%(PreprocessorDefinitions) c11 c++1y CompileAsCpp @@ -140,12 +140,14 @@ true true + + true true true - %(AdditionalLibraryDirectories) + C:\opencv\opencv340\build\x64\vc15\lib;%(AdditionalLibraryDirectories) ..\..\3rdparty\lib\x64\pthreadVC2.lib;%(AdditionalDependencies) $(OutDir)\$(TargetName)$(TargetExt) diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index faaf8d11f26..6147e3c4e1f 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -22,6 +22,51 @@ extern "C" { #define FRAMES 3 +int max_objects() { return C_SHARP_MAX_OBJECTS; } + +static Detector* detector; +//static std::unique_ptr detector; + +int init(const char *configurationFilename, const char *weightsFilename, int gpu) { + std::string configurationFilenameString; + configurationFilenameString = configurationFilename; + std::string weightsFilenameString; + weightsFilenameString = weightsFilename; + + detector = new Detector(configurationFilenameString, weightsFilenameString, gpu); + return 1; +} + +int detect_image(const char *filename, bbox_t_container &container) { + std::string filenameString; + filenameString = filename; + + std::vector detection = detector->detect(filenameString); + for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i) + container.candidates[i] = detection[i]; + return detection.size(); +} + +int detect_image2(const uint8_t* data, const size_t data_length, bbox_t_container &container) { +#ifdef OPENCV + std::vector vdata(data, data + data_length); + cv::Mat image = imdecode(cv::Mat(vdata), 1); + + std::vector detection = detector->detect(image); + for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i) + container.candidates[i] = detection[i]; + return detection.size(); +#else + return -1; +#endif // OPENCV +} + +int dispose() { + detector->~Detector(); + //detector.reset(); + return 1; +} + #ifdef GPU void check_cuda(cudaError_t status) { if (status != cudaSuccess) { diff --git a/src/yolo_v2_class.hpp b/src/yolo_v2_class.hpp index fd00dc0695a..2416050b071 100644 --- a/src/yolo_v2_class.hpp +++ b/src/yolo_v2_class.hpp @@ -28,6 +28,11 @@ struct image_t { float *data; // pointer to the image data }; +#define C_SHARP_MAX_OBJECTS 1000 +struct bbox_t_container { + bbox_t candidates[C_SHARP_MAX_OBJECTS]; +}; + #ifdef __cplusplus #include #include @@ -40,6 +45,12 @@ struct image_t { #include "opencv2/imgproc/imgproc_c.h" // C #endif // OPENCV +extern "C" __declspec(dllexport) int max_objects(); +extern "C" __declspec(dllexport) int init(const char *configurationFilename, const char *weightsFilename, int gpu); +extern "C" __declspec(dllexport) int detect_image(const char *filename, bbox_t_container &container); +extern "C" __declspec(dllexport) int detect_image2(const uint8_t* data, const size_t data_length, bbox_t_container &container); +extern "C" __declspec(dllexport) int dispose(); + class Detector { std::shared_ptr detector_gpu_ptr; std::deque> prev_bbox_vec_deque; From 79caf3837a955c1a80186e43e3c0a9eaf1130332 Mon Sep 17 00:00:00 2001 From: Tino Hager Date: Sat, 23 Jun 2018 11:05:26 +0200 Subject: [PATCH 0080/1365] Revert ".NET/C# support integration" This reverts commit 00903aebd3d4979ff5128c981d2f13e5595454c6. --- build/darknet/darknet.sln | 11 ++---- build/darknet/darknet.vcxproj | 29 +++++++-------- build/darknet/yolo_cpp_dll.vcxproj | 16 ++++---- build/darknet/yolo_cpp_dll_no_gpu.vcxproj | 24 ++++++------ src/yolo_v2_class.cpp | 45 ----------------------- src/yolo_v2_class.hpp | 11 ------ 6 files changed, 37 insertions(+), 99 deletions(-) diff --git a/build/darknet/darknet.sln b/build/darknet/darknet.sln index 3d7efef25e0..c49e10efa09 100644 --- a/build/darknet/darknet.sln +++ b/build/darknet/darknet.sln @@ -1,7 +1,7 @@  Microsoft Visual Studio Solution File, Format Version 12.00 -# Visual Studio 15 -VisualStudioVersion = 15.0.27703.2018 +# Visual Studio 14 +VisualStudioVersion = 14.0.25420.1 MinimumVisualStudioVersion = 10.0.40219.1 Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "darknet", "darknet.vcxproj", "{4CF5694F-12A5-4012-8D94-9A0915E9FEB5}" EndProject @@ -13,8 +13,8 @@ Global Release|x64 = Release|x64 EndGlobalSection GlobalSection(ProjectConfigurationPlatforms) = postSolution - {4CF5694F-12A5-4012-8D94-9A0915E9FEB5}.Debug|Win32.ActiveCfg = Debug|x64 - {4CF5694F-12A5-4012-8D94-9A0915E9FEB5}.Debug|Win32.Build.0 = Debug|x64 + {4CF5694F-12A5-4012-8D94-9A0915E9FEB5}.Debug|Win32.ActiveCfg = Debug|Win32 + {4CF5694F-12A5-4012-8D94-9A0915E9FEB5}.Debug|Win32.Build.0 = Debug|Win32 {4CF5694F-12A5-4012-8D94-9A0915E9FEB5}.Debug|x64.ActiveCfg = Debug|x64 {4CF5694F-12A5-4012-8D94-9A0915E9FEB5}.Debug|x64.Build.0 = Debug|x64 {4CF5694F-12A5-4012-8D94-9A0915E9FEB5}.Release|Win32.ActiveCfg = Release|Win32 @@ -25,7 +25,4 @@ Global GlobalSection(SolutionProperties) = preSolution HideSolutionNode = FALSE EndGlobalSection - GlobalSection(ExtensibilityGlobals) = postSolution - SolutionGuid = {C904B7F0-81ED-4CBE-82EC-94566A77D9AF} - EndGlobalSection EndGlobal diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj index 9286164247a..8fbf93c3c0d 100644 --- a/build/darknet/darknet.vcxproj +++ b/build/darknet/darknet.vcxproj @@ -1,5 +1,5 @@  - + Debug @@ -21,38 +21,38 @@ {4CF5694F-12A5-4012-8D94-9A0915E9FEB5} darknet - 10.0.17134.0 + 8.1 Application true - v141 + v140 MultiByte Application true - v141 + v140 MultiByte Application false - v141 + v140 true MultiByte Application false - v141 + v140 true MultiByte - + @@ -88,16 +88,15 @@ Level3 Disabled true - C:\opencv\opencv340\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include - OPENCV;CUDNN;_CRTDBG_MAP_ALLOC;_MBCS;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) - - + C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include + CUDNN;_CRTDBG_MAP_ALLOC;_MBCS;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + OPENCV; true stdlib.h;crtdbg.h;%(ForcedIncludeFiles) true - C:\opencv\opencv340\build\x64\vc15\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) + C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;C:\opencv_2.4.13\opencv\build\x64\vc14\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) $(OutDir)\$(TargetName)$(TargetExt) ..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;%(AdditionalDependencies) true @@ -133,7 +132,7 @@ true true true - C:\opencv\opencv340\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include + C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include OPENCV;CUDNN;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y @@ -147,7 +146,7 @@ true true true - C:\opencv\opencv340\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) + C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) ..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;%(AdditionalDependencies) $(OutDir)\$(TargetName)$(TargetExt) @@ -289,6 +288,6 @@ - + \ No newline at end of file diff --git a/build/darknet/yolo_cpp_dll.vcxproj b/build/darknet/yolo_cpp_dll.vcxproj index 5deb9d41850..c33be37f244 100644 --- a/build/darknet/yolo_cpp_dll.vcxproj +++ b/build/darknet/yolo_cpp_dll.vcxproj @@ -1,5 +1,5 @@  - + Debug @@ -21,38 +21,38 @@ {4CF5694F-12A5-4012-8D94-9A0915E9FEB5} darknet - 10.0.17134.0 + 8.1 Application true - v141 + v140 MultiByte DynamicLibrary true - v141 + v140 MultiByte Application false - v141 + v140 true MultiByte DynamicLibrary false - v141 + v140 true MultiByte - + @@ -291,6 +291,6 @@ - + \ No newline at end of file diff --git a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj index 123b96e79e2..56753c45480 100644 --- a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj +++ b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj @@ -1,5 +1,5 @@  - + Debug @@ -21,32 +21,32 @@ {4CF5694F-12A5-4012-8D94-9A0915E9FEB5} darknet - 10.0.17134.0 + 8.1 Application true - v141 + v140 MultiByte DynamicLibrary true - v141 + v140 MultiByte Application false - v141 + v140 true MultiByte DynamicLibrary false - v141 + v140 true MultiByte @@ -111,7 +111,7 @@ true true true - C:\opencv\opencv340\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include + C:\opencv_2.4.9\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include YOLODLL_EXPORTS;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) true @@ -119,7 +119,7 @@ true true true - C:\opencv\opencv340\build\x64\vc15\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) + C:\opencv_2.4.9\opencv\build\x86\vc14\lib;C:\opencv_2.4.9\opencv\build\x86\vc12\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) ..\..\3rdparty\lib\x86\pthreadVC2.lib;cudart.lib;cublas.lib;curand.lib;%(AdditionalDependencies) @@ -130,8 +130,8 @@ true true true - ..\..\3rdparty\include;C:\opencv\opencv340\build\include;%(AdditionalIncludeDirectories) - YOLODLL_EXPORTS;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;OPENCV;%(PreprocessorDefinitions) + ..\..\3rdparty\include;%(AdditionalIncludeDirectories) + YOLODLL_EXPORTS;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y CompileAsCpp @@ -140,14 +140,12 @@ true true - - true true true - C:\opencv\opencv340\build\x64\vc15\lib;%(AdditionalLibraryDirectories) + %(AdditionalLibraryDirectories) ..\..\3rdparty\lib\x64\pthreadVC2.lib;%(AdditionalDependencies) $(OutDir)\$(TargetName)$(TargetExt) diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index 6147e3c4e1f..faaf8d11f26 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -22,51 +22,6 @@ extern "C" { #define FRAMES 3 -int max_objects() { return C_SHARP_MAX_OBJECTS; } - -static Detector* detector; -//static std::unique_ptr detector; - -int init(const char *configurationFilename, const char *weightsFilename, int gpu) { - std::string configurationFilenameString; - configurationFilenameString = configurationFilename; - std::string weightsFilenameString; - weightsFilenameString = weightsFilename; - - detector = new Detector(configurationFilenameString, weightsFilenameString, gpu); - return 1; -} - -int detect_image(const char *filename, bbox_t_container &container) { - std::string filenameString; - filenameString = filename; - - std::vector detection = detector->detect(filenameString); - for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i) - container.candidates[i] = detection[i]; - return detection.size(); -} - -int detect_image2(const uint8_t* data, const size_t data_length, bbox_t_container &container) { -#ifdef OPENCV - std::vector vdata(data, data + data_length); - cv::Mat image = imdecode(cv::Mat(vdata), 1); - - std::vector detection = detector->detect(image); - for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i) - container.candidates[i] = detection[i]; - return detection.size(); -#else - return -1; -#endif // OPENCV -} - -int dispose() { - detector->~Detector(); - //detector.reset(); - return 1; -} - #ifdef GPU void check_cuda(cudaError_t status) { if (status != cudaSuccess) { diff --git a/src/yolo_v2_class.hpp b/src/yolo_v2_class.hpp index 2416050b071..fd00dc0695a 100644 --- a/src/yolo_v2_class.hpp +++ b/src/yolo_v2_class.hpp @@ -28,11 +28,6 @@ struct image_t { float *data; // pointer to the image data }; -#define C_SHARP_MAX_OBJECTS 1000 -struct bbox_t_container { - bbox_t candidates[C_SHARP_MAX_OBJECTS]; -}; - #ifdef __cplusplus #include #include @@ -45,12 +40,6 @@ struct bbox_t_container { #include "opencv2/imgproc/imgproc_c.h" // C #endif // OPENCV -extern "C" __declspec(dllexport) int max_objects(); -extern "C" __declspec(dllexport) int init(const char *configurationFilename, const char *weightsFilename, int gpu); -extern "C" __declspec(dllexport) int detect_image(const char *filename, bbox_t_container &container); -extern "C" __declspec(dllexport) int detect_image2(const uint8_t* data, const size_t data_length, bbox_t_container &container); -extern "C" __declspec(dllexport) int dispose(); - class Detector { std::shared_ptr detector_gpu_ptr; std::deque> prev_bbox_vec_deque; From 8d2f2653c8a9f7101dbd4c0727beccaa48c98634 Mon Sep 17 00:00:00 2001 From: Tino Hager Date: Sat, 23 Jun 2018 11:06:43 +0200 Subject: [PATCH 0081/1365] .NET/C# support integration Add logic for .NET/C# integration (DllImport) --- src/yolo_v2_class.cpp | 45 +++++++++++++++++++++++++++++++++++++++++++ src/yolo_v2_class.hpp | 11 +++++++++++ 2 files changed, 56 insertions(+) diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index faaf8d11f26..6147e3c4e1f 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -22,6 +22,51 @@ extern "C" { #define FRAMES 3 +int max_objects() { return C_SHARP_MAX_OBJECTS; } + +static Detector* detector; +//static std::unique_ptr detector; + +int init(const char *configurationFilename, const char *weightsFilename, int gpu) { + std::string configurationFilenameString; + configurationFilenameString = configurationFilename; + std::string weightsFilenameString; + weightsFilenameString = weightsFilename; + + detector = new Detector(configurationFilenameString, weightsFilenameString, gpu); + return 1; +} + +int detect_image(const char *filename, bbox_t_container &container) { + std::string filenameString; + filenameString = filename; + + std::vector detection = detector->detect(filenameString); + for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i) + container.candidates[i] = detection[i]; + return detection.size(); +} + +int detect_image2(const uint8_t* data, const size_t data_length, bbox_t_container &container) { +#ifdef OPENCV + std::vector vdata(data, data + data_length); + cv::Mat image = imdecode(cv::Mat(vdata), 1); + + std::vector detection = detector->detect(image); + for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i) + container.candidates[i] = detection[i]; + return detection.size(); +#else + return -1; +#endif // OPENCV +} + +int dispose() { + detector->~Detector(); + //detector.reset(); + return 1; +} + #ifdef GPU void check_cuda(cudaError_t status) { if (status != cudaSuccess) { diff --git a/src/yolo_v2_class.hpp b/src/yolo_v2_class.hpp index fd00dc0695a..2416050b071 100644 --- a/src/yolo_v2_class.hpp +++ b/src/yolo_v2_class.hpp @@ -28,6 +28,11 @@ struct image_t { float *data; // pointer to the image data }; +#define C_SHARP_MAX_OBJECTS 1000 +struct bbox_t_container { + bbox_t candidates[C_SHARP_MAX_OBJECTS]; +}; + #ifdef __cplusplus #include #include @@ -40,6 +45,12 @@ struct image_t { #include "opencv2/imgproc/imgproc_c.h" // C #endif // OPENCV +extern "C" __declspec(dllexport) int max_objects(); +extern "C" __declspec(dllexport) int init(const char *configurationFilename, const char *weightsFilename, int gpu); +extern "C" __declspec(dllexport) int detect_image(const char *filename, bbox_t_container &container); +extern "C" __declspec(dllexport) int detect_image2(const uint8_t* data, const size_t data_length, bbox_t_container &container); +extern "C" __declspec(dllexport) int dispose(); + class Detector { std::shared_ptr detector_gpu_ptr; std::deque> prev_bbox_vec_deque; From 99c92f98e08c007b23b21d2e0887a59f14045efb Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 23 Jun 2018 15:24:48 +0300 Subject: [PATCH 0082/1365] Fixed ability for support OpenCV for yolo_cpp_dll_no_gpu.sln --- build/darknet/yolo_cpp_dll.vcxproj | 4 ++-- build/darknet/yolo_cpp_dll_no_gpu.vcxproj | 6 ++++-- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/build/darknet/yolo_cpp_dll.vcxproj b/build/darknet/yolo_cpp_dll.vcxproj index c33be37f244..4361f18bdaa 100644 --- a/build/darknet/yolo_cpp_dll.vcxproj +++ b/build/darknet/yolo_cpp_dll.vcxproj @@ -133,7 +133,7 @@ true true true - ..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include + C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include CUDNN;GPU;YOLODLL_EXPORTS;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y @@ -147,7 +147,7 @@ true true true - $(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) + C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) ..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;%(AdditionalDependencies) $(OutDir)\$(TargetName)$(TargetExt) diff --git a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj index 56753c45480..e9e582be47c 100644 --- a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj +++ b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj @@ -130,7 +130,7 @@ true true true - ..\..\3rdparty\include;%(AdditionalIncludeDirectories) + C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories) YOLODLL_EXPORTS;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y @@ -145,7 +145,7 @@ true true true - %(AdditionalLibraryDirectories) + C:\opencv_3.0\opencv\build\x64\vc14\lib;%(AdditionalLibraryDirectories) ..\..\3rdparty\lib\x64\pthreadVC2.lib;%(AdditionalDependencies) $(OutDir)\$(TargetName)$(TargetExt) @@ -188,6 +188,7 @@ + @@ -246,6 +247,7 @@ + From 9ed3e2df4a2dc765245fa2e75ac4a9f4e6187b83 Mon Sep 17 00:00:00 2001 From: Tino Hager Date: Sun, 24 Jun 2018 21:22:50 +0200 Subject: [PATCH 0083/1365] rename c++ method detect_image2 -> detect_mat --- src/yolo_v2_class.cpp | 2 +- src/yolo_v2_class.hpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index 6147e3c4e1f..adba7324098 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -47,7 +47,7 @@ int detect_image(const char *filename, bbox_t_container &container) { return detection.size(); } -int detect_image2(const uint8_t* data, const size_t data_length, bbox_t_container &container) { +int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container &container) { #ifdef OPENCV std::vector vdata(data, data + data_length); cv::Mat image = imdecode(cv::Mat(vdata), 1); diff --git a/src/yolo_v2_class.hpp b/src/yolo_v2_class.hpp index 2416050b071..2715ce3910d 100644 --- a/src/yolo_v2_class.hpp +++ b/src/yolo_v2_class.hpp @@ -48,7 +48,7 @@ struct bbox_t_container { extern "C" __declspec(dllexport) int max_objects(); extern "C" __declspec(dllexport) int init(const char *configurationFilename, const char *weightsFilename, int gpu); extern "C" __declspec(dllexport) int detect_image(const char *filename, bbox_t_container &container); -extern "C" __declspec(dllexport) int detect_image2(const uint8_t* data, const size_t data_length, bbox_t_container &container); +extern "C" __declspec(dllexport) int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container &container); extern "C" __declspec(dllexport) int dispose(); class Detector { From b2c03b1615535dc717e196c560901e9e420c96d6 Mon Sep 17 00:00:00 2001 From: Tino Hager Date: Sun, 24 Jun 2018 22:45:07 +0200 Subject: [PATCH 0084/1365] fix build error, add c# wrapper class --- .gitignore | 3 +- build/darknet/YoloWrapper.cs | 83 ++++++++++++++++++++++++++++++++++++ src/yolo_v2_class.hpp | 10 ++--- 3 files changed, 90 insertions(+), 6 deletions(-) create mode 100644 build/darknet/YoloWrapper.cs diff --git a/.gitignore b/.gitignore index bbfed659be5..1bd67d08836 100644 --- a/.gitignore +++ b/.gitignore @@ -14,7 +14,8 @@ convnet/ decaf/ submission/ cfg/ -darknet +build/darknet/* +!build/darknet/YoloWrapper.cs .fuse* # OS Generated # diff --git a/build/darknet/YoloWrapper.cs b/build/darknet/YoloWrapper.cs new file mode 100644 index 00000000000..ade00597db8 --- /dev/null +++ b/build/darknet/YoloWrapper.cs @@ -0,0 +1,83 @@ +using System; +using System.Runtime.InteropServices; + +namespace Darknet +{ + public class YoloWrapper : IDisposable + { + private const string YoloLibraryName = "yolo_cpp_dll.dll"; + + [DllImport(YoloLibraryName, EntryPoint = "init")] + public static extern int InitializeYolo(string configurationFilename, string weightsFilename, int gpu); + + [DllImport(YoloLibraryName, EntryPoint = "detect_image")] + public static extern int DetectImage(string filename, ref BboxContainer container); + + [DllImport(YoloLibraryName, EntryPoint = "detect_mat")] + public static extern int DetectImage(IntPtr pArray, int nSize, ref BboxContainer container); + + [DllImport(YoloLibraryName, EntryPoint = "dispose")] + public static extern int DisposeYolo(); + + [StructLayout(LayoutKind.Sequential)] + public struct bbox_t + { + public UInt32 x, y, w, h; // (x,y) - top-left corner, (w, h) - width & height of bounded box + public float prob; // confidence - probability that the object was found correctly + public UInt32 obj_id; // class of object - from range [0, classes-1] + public UInt32 track_id; // tracking id for video (0 - untracked, 1 - inf - tracked object) + public UInt32 frames_counter; + }; + + [StructLayout(LayoutKind.Sequential, Size = 10)] + public struct BboxContainer + { + [MarshalAs(UnmanagedType.ByValArray, SizeConst = 10)] + public bbox_t[] candidates; + } + + public YoloWrapper(string configurationFilename, string weightsFilename, int gpu) + { + InitializeYolo(configurationFilename, weightsFilename, gpu); + } + + public void Dispose() + { + DisposeYolo(); + } + + public bbox_t[] Detect(string filename) + { + var container = new BboxContainer(); + var count = DetectImage(filename, ref container); + + return container.candidates; + } + + public bbox_t[] Detect(byte[] imageData) + { + var container = new BboxContainer(); + + var size = Marshal.SizeOf(imageData[0]) * imageData.Length; + var pnt = Marshal.AllocHGlobal(size); + + try + { + // Copy the array to unmanaged memory. + Marshal.Copy(imageData, 0, pnt, imageData.Length); + DetectImage(pnt, imageData.Length, ref container); + } + catch (Exception exception) + { + return null; + } + finally + { + // Free the unmanaged memory. + Marshal.FreeHGlobal(pnt); + } + + return container.candidates; + } + } +} diff --git a/src/yolo_v2_class.hpp b/src/yolo_v2_class.hpp index 2715ce3910d..9875878352b 100644 --- a/src/yolo_v2_class.hpp +++ b/src/yolo_v2_class.hpp @@ -45,11 +45,11 @@ struct bbox_t_container { #include "opencv2/imgproc/imgproc_c.h" // C #endif // OPENCV -extern "C" __declspec(dllexport) int max_objects(); -extern "C" __declspec(dllexport) int init(const char *configurationFilename, const char *weightsFilename, int gpu); -extern "C" __declspec(dllexport) int detect_image(const char *filename, bbox_t_container &container); -extern "C" __declspec(dllexport) int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container &container); -extern "C" __declspec(dllexport) int dispose(); +extern "C" YOLODLL_API int max_objects(); +extern "C" YOLODLL_API int init(const char *configurationFilename, const char *weightsFilename, int gpu); +extern "C" YOLODLL_API int detect_image(const char *filename, bbox_t_container &container); +extern "C" YOLODLL_API int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container &container); +extern "C" YOLODLL_API int dispose(); class Detector { std::shared_ptr detector_gpu_ptr; From f88d23ad34fc0a33c32caa98830a56c53f1cd844 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 27 Jun 2018 01:54:09 +0300 Subject: [PATCH 0085/1365] Minor fix --- src/detector.c | 7 ++++--- src/yolo_v2_class.cpp | 27 +++++++++++---------------- 2 files changed, 15 insertions(+), 19 deletions(-) diff --git a/src/detector.c b/src/detector.c index afb91b22077..15729e22aa5 100644 --- a/src/detector.c +++ b/src/detector.c @@ -61,12 +61,13 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i srand(time(0)); network net = nets[0]; - if ((net.batch * net.subdivisions) == 1) { + const int actual_batch_size = net.batch * net.subdivisions; + if (actual_batch_size == 1) { printf("\n Error: You set incorrect value batch=1 for Training! You should set batch=64 subdivision=64 \n"); getchar(); } - else if ((net.batch * net.subdivisions) < 64) { - printf("\n Warning: You set batch= lower than 64! It is recommended to set batch=64 subdivision=64 \n"); + else if (actual_batch_size < 64) { + printf("\n Warning: You set batch=%d lower than 64! It is recommended to set batch=64 subdivision=64 \n", actual_batch_size); } int imgs = net.batch * net.subdivisions * ngpus; diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index adba7324098..c518fd77d3a 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -24,24 +24,18 @@ extern "C" { int max_objects() { return C_SHARP_MAX_OBJECTS; } -static Detector* detector; -//static std::unique_ptr detector; +//static Detector* detector = NULL; +static std::unique_ptr detector; -int init(const char *configurationFilename, const char *weightsFilename, int gpu) { - std::string configurationFilenameString; - configurationFilenameString = configurationFilename; - std::string weightsFilenameString; - weightsFilenameString = weightsFilename; - - detector = new Detector(configurationFilenameString, weightsFilenameString, gpu); +int init(const char *configurationFilename, const char *weightsFilename, int gpu) +{ + detector.reset(new Detector(configurationFilename, weightsFilename, gpu)); return 1; } -int detect_image(const char *filename, bbox_t_container &container) { - std::string filenameString; - filenameString = filename; - - std::vector detection = detector->detect(filenameString); +int detect_image(const char *filename, bbox_t_container &container) +{ + std::vector detection = detector->detect(filename); for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i) container.candidates[i] = detection[i]; return detection.size(); @@ -62,8 +56,9 @@ int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container & } int dispose() { - detector->~Detector(); - //detector.reset(); + //if (detector != NULL) delete detector; + //detector = NULL; + detector.reset(); return 1; } From 51ae7b0b5047b7ada0afde7bd8440f860397f6d2 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 27 Jun 2018 03:38:06 +0300 Subject: [PATCH 0086/1365] Minor fix --- src/detector.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/detector.c b/src/detector.c index 15729e22aa5..0a0ecdad048 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1096,7 +1096,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam fuse_conv_batchnorm(net); if (net.layers[net.n - 1].classes != names_size) { printf(" Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", - name_list, names_size, net.layers[net.n - 1].classes, datacfg); + name_list, names_size, net.layers[net.n - 1].classes, cfgfile); if(net.layers[net.n - 1].classes > names_size) getchar(); } srand(2222222); From db550bd8044ec497845addb6d18a482d1f5aab91 Mon Sep 17 00:00:00 2001 From: vinjn Date: Wed, 27 Jun 2018 11:56:21 +0800 Subject: [PATCH 0087/1365] Expose optimize_picture() in DLL --- src/network.h | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/network.h b/src/network.h index 66df6ffe32e..f80f58d9ded 100644 --- a/src/network.h +++ b/src/network.h @@ -146,6 +146,8 @@ YOLODLL_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, YOLODLL_API int network_width(network *net); YOLODLL_API int network_height(network *net); +YOLODLL_API void optimize_picture(network *net, image orig, int max_layer, float scale, float rate, float thresh, int norm); + int get_network_nuisance(network net); int get_network_background(network net); YOLODLL_API void fuse_conv_batchnorm(network net); From d6cb0fcabcece079c6a5b511159127f585897dba Mon Sep 17 00:00:00 2001 From: Tino Hager Date: Wed, 27 Jun 2018 23:11:33 +0200 Subject: [PATCH 0088/1365] optimize max object definition it is not possible to set the size on runtime --- build/darknet/YoloWrapper.cs | 5 +++-- src/yolo_v2_class.cpp | 2 -- src/yolo_v2_class.hpp | 1 - 3 files changed, 3 insertions(+), 5 deletions(-) diff --git a/build/darknet/YoloWrapper.cs b/build/darknet/YoloWrapper.cs index ade00597db8..14bf56d48b8 100644 --- a/build/darknet/YoloWrapper.cs +++ b/build/darknet/YoloWrapper.cs @@ -6,6 +6,7 @@ namespace Darknet public class YoloWrapper : IDisposable { private const string YoloLibraryName = "yolo_cpp_dll.dll"; + private const int MaxObjects = 1000; [DllImport(YoloLibraryName, EntryPoint = "init")] public static extern int InitializeYolo(string configurationFilename, string weightsFilename, int gpu); @@ -29,10 +30,10 @@ public struct bbox_t public UInt32 frames_counter; }; - [StructLayout(LayoutKind.Sequential, Size = 10)] + [StructLayout(LayoutKind.Sequential)] public struct BboxContainer { - [MarshalAs(UnmanagedType.ByValArray, SizeConst = 10)] + [MarshalAs(UnmanagedType.ByValArray, SizeConst = MaxObjects)] public bbox_t[] candidates; } diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index adba7324098..aad5876068c 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -22,8 +22,6 @@ extern "C" { #define FRAMES 3 -int max_objects() { return C_SHARP_MAX_OBJECTS; } - static Detector* detector; //static std::unique_ptr detector; diff --git a/src/yolo_v2_class.hpp b/src/yolo_v2_class.hpp index 9875878352b..8f7f6cab5c6 100644 --- a/src/yolo_v2_class.hpp +++ b/src/yolo_v2_class.hpp @@ -45,7 +45,6 @@ struct bbox_t_container { #include "opencv2/imgproc/imgproc_c.h" // C #endif // OPENCV -extern "C" YOLODLL_API int max_objects(); extern "C" YOLODLL_API int init(const char *configurationFilename, const char *weightsFilename, int gpu); extern "C" YOLODLL_API int detect_image(const char *filename, bbox_t_container &container); extern "C" YOLODLL_API int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container &container); From ab5368e2795c3340aa8d7a12f973773572ceb28a Mon Sep 17 00:00:00 2001 From: Tino Hager Date: Wed, 27 Jun 2018 23:49:41 +0200 Subject: [PATCH 0089/1365] merge changes --- src/yolo_v2_class.cpp | 473 +++++++++++++++++++++--------------------- 1 file changed, 234 insertions(+), 239 deletions(-) diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index aad5876068c..7fdb4fda526 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -22,24 +22,18 @@ extern "C" { #define FRAMES 3 -static Detector* detector; -//static std::unique_ptr detector; +//static Detector* detector = NULL; +static std::unique_ptr detector; -int init(const char *configurationFilename, const char *weightsFilename, int gpu) { - std::string configurationFilenameString; - configurationFilenameString = configurationFilename; - std::string weightsFilenameString; - weightsFilenameString = weightsFilename; - - detector = new Detector(configurationFilenameString, weightsFilenameString, gpu); +int init(const char *configurationFilename, const char *weightsFilename, int gpu) +{ + detector.reset(new Detector(configurationFilename, weightsFilename, gpu)); return 1; } -int detect_image(const char *filename, bbox_t_container &container) { - std::string filenameString; - filenameString = filename; - - std::vector detection = detector->detect(filenameString); +int detect_image(const char *filename, bbox_t_container &container) +{ + std::vector detection = detector->detect(filename); for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i) container.candidates[i] = detection[i]; return detection.size(); @@ -60,305 +54,306 @@ int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container & } int dispose() { - detector->~Detector(); - //detector.reset(); + //if (detector != NULL) delete detector; + //detector = NULL; + detector.reset(); return 1; } #ifdef GPU void check_cuda(cudaError_t status) { - if (status != cudaSuccess) { - const char *s = cudaGetErrorString(status); - printf("CUDA Error Prev: %s\n", s); - } + if (status != cudaSuccess) { + const char *s = cudaGetErrorString(status); + printf("CUDA Error Prev: %s\n", s); + } } #endif struct detector_gpu_t { - network net; - image images[FRAMES]; - float *avg; - float *predictions[FRAMES]; - int demo_index; - unsigned int *track_id; + network net; + image images[FRAMES]; + float *avg; + float *predictions[FRAMES]; + int demo_index; + unsigned int *track_id; }; YOLODLL_API Detector::Detector(std::string cfg_filename, std::string weight_filename, int gpu_id) : cur_gpu_id(gpu_id) { - wait_stream = 0; - int old_gpu_index; + wait_stream = 0; + int old_gpu_index; #ifdef GPU - check_cuda( cudaGetDevice(&old_gpu_index) ); + check_cuda(cudaGetDevice(&old_gpu_index)); #endif - detector_gpu_ptr = std::make_shared(); - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + detector_gpu_ptr = std::make_shared(); + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); #ifdef GPU - //check_cuda( cudaSetDevice(cur_gpu_id) ); - cuda_set_device(cur_gpu_id); - printf(" Used GPU %d \n", cur_gpu_id); + //check_cuda( cudaSetDevice(cur_gpu_id) ); + cuda_set_device(cur_gpu_id); + printf(" Used GPU %d \n", cur_gpu_id); #endif - network &net = detector_gpu.net; - net.gpu_index = cur_gpu_id; - //gpu_index = i; - - char *cfgfile = const_cast(cfg_filename.data()); - char *weightfile = const_cast(weight_filename.data()); - - net = parse_network_cfg_custom(cfgfile, 1); - if (weightfile) { - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - net.gpu_index = cur_gpu_id; - fuse_conv_batchnorm(net); - - layer l = net.layers[net.n - 1]; - int j; - - detector_gpu.avg = (float *)calloc(l.outputs, sizeof(float)); - for (j = 0; j < FRAMES; ++j) detector_gpu.predictions[j] = (float *)calloc(l.outputs, sizeof(float)); - for (j = 0; j < FRAMES; ++j) detector_gpu.images[j] = make_image(1, 1, 3); - - detector_gpu.track_id = (unsigned int *)calloc(l.classes, sizeof(unsigned int)); - for (j = 0; j < l.classes; ++j) detector_gpu.track_id[j] = 1; + network &net = detector_gpu.net; + net.gpu_index = cur_gpu_id; + //gpu_index = i; + + char *cfgfile = const_cast(cfg_filename.data()); + char *weightfile = const_cast(weight_filename.data()); + + net = parse_network_cfg_custom(cfgfile, 1); + if (weightfile) { + load_weights(&net, weightfile); + } + set_batch_network(&net, 1); + net.gpu_index = cur_gpu_id; + fuse_conv_batchnorm(net); + + layer l = net.layers[net.n - 1]; + int j; + + detector_gpu.avg = (float *)calloc(l.outputs, sizeof(float)); + for (j = 0; j < FRAMES; ++j) detector_gpu.predictions[j] = (float *)calloc(l.outputs, sizeof(float)); + for (j = 0; j < FRAMES; ++j) detector_gpu.images[j] = make_image(1, 1, 3); + + detector_gpu.track_id = (unsigned int *)calloc(l.classes, sizeof(unsigned int)); + for (j = 0; j < l.classes; ++j) detector_gpu.track_id[j] = 1; #ifdef GPU - check_cuda( cudaSetDevice(old_gpu_index) ); + check_cuda(cudaSetDevice(old_gpu_index)); #endif } -YOLODLL_API Detector::~Detector() +YOLODLL_API Detector::~Detector() { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - layer l = detector_gpu.net.layers[detector_gpu.net.n - 1]; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + layer l = detector_gpu.net.layers[detector_gpu.net.n - 1]; - free(detector_gpu.track_id); + free(detector_gpu.track_id); - free(detector_gpu.avg); - for (int j = 0; j < FRAMES; ++j) free(detector_gpu.predictions[j]); - for (int j = 0; j < FRAMES; ++j) if(detector_gpu.images[j].data) free(detector_gpu.images[j].data); + free(detector_gpu.avg); + for (int j = 0; j < FRAMES; ++j) free(detector_gpu.predictions[j]); + for (int j = 0; j < FRAMES; ++j) if (detector_gpu.images[j].data) free(detector_gpu.images[j].data); - int old_gpu_index; + int old_gpu_index; #ifdef GPU - cudaGetDevice(&old_gpu_index); - cuda_set_device(detector_gpu.net.gpu_index); + cudaGetDevice(&old_gpu_index); + cuda_set_device(detector_gpu.net.gpu_index); #endif - free_network(detector_gpu.net); + free_network(detector_gpu.net); #ifdef GPU - cudaSetDevice(old_gpu_index); + cudaSetDevice(old_gpu_index); #endif } YOLODLL_API int Detector::get_net_width() const { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - return detector_gpu.net.w; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + return detector_gpu.net.w; } YOLODLL_API int Detector::get_net_height() const { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - return detector_gpu.net.h; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + return detector_gpu.net.h; } YOLODLL_API int Detector::get_net_color_depth() const { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - return detector_gpu.net.c; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + return detector_gpu.net.c; } YOLODLL_API std::vector Detector::detect(std::string image_filename, float thresh, bool use_mean) { - std::shared_ptr image_ptr(new image_t, [](image_t *img) { if (img->data) free(img->data); delete img; }); - *image_ptr = load_image(image_filename); - return detect(*image_ptr, thresh, use_mean); + std::shared_ptr image_ptr(new image_t, [](image_t *img) { if (img->data) free(img->data); delete img; }); + *image_ptr = load_image(image_filename); + return detect(*image_ptr, thresh, use_mean); } static image load_image_stb(char *filename, int channels) { - int w, h, c; - unsigned char *data = stbi_load(filename, &w, &h, &c, channels); - if (!data) - throw std::runtime_error("file not found"); - if (channels) c = channels; - int i, j, k; - image im = make_image(w, h, c); - for (k = 0; k < c; ++k) { - for (j = 0; j < h; ++j) { - for (i = 0; i < w; ++i) { - int dst_index = i + w*j + w*h*k; - int src_index = k + c*i + c*w*j; - im.data[dst_index] = (float)data[src_index] / 255.; - } - } - } - free(data); - return im; + int w, h, c; + unsigned char *data = stbi_load(filename, &w, &h, &c, channels); + if (!data) + throw std::runtime_error("file not found"); + if (channels) c = channels; + int i, j, k; + image im = make_image(w, h, c); + for (k = 0; k < c; ++k) { + for (j = 0; j < h; ++j) { + for (i = 0; i < w; ++i) { + int dst_index = i + w * j + w * h*k; + int src_index = k + c * i + c * w*j; + im.data[dst_index] = (float)data[src_index] / 255.; + } + } + } + free(data); + return im; } YOLODLL_API image_t Detector::load_image(std::string image_filename) { - char *input = const_cast(image_filename.data()); - image im = load_image_stb(input, 3); + char *input = const_cast(image_filename.data()); + image im = load_image_stb(input, 3); - image_t img; - img.c = im.c; - img.data = im.data; - img.h = im.h; - img.w = im.w; + image_t img; + img.c = im.c; + img.data = im.data; + img.h = im.h; + img.w = im.w; - return img; + return img; } YOLODLL_API void Detector::free_image(image_t m) { - if (m.data) { - free(m.data); - } + if (m.data) { + free(m.data); + } } YOLODLL_API std::vector Detector::detect(image_t img, float thresh, bool use_mean) { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - network &net = detector_gpu.net; - int old_gpu_index; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + network &net = detector_gpu.net; + int old_gpu_index; #ifdef GPU - cudaGetDevice(&old_gpu_index); - if(cur_gpu_id != old_gpu_index) - cudaSetDevice(net.gpu_index); + cudaGetDevice(&old_gpu_index); + if (cur_gpu_id != old_gpu_index) + cudaSetDevice(net.gpu_index); - net.wait_stream = wait_stream; // 1 - wait CUDA-stream, 0 - not to wait + net.wait_stream = wait_stream; // 1 - wait CUDA-stream, 0 - not to wait #endif - //std::cout << "net.gpu_index = " << net.gpu_index << std::endl; - - //float nms = .4; - - image im; - im.c = img.c; - im.data = img.data; - im.h = img.h; - im.w = img.w; - - image sized; - - if (net.w == im.w && net.h == im.h) { - sized = make_image(im.w, im.h, im.c); - memcpy(sized.data, im.data, im.w*im.h*im.c * sizeof(float)); - } - else - sized = resize_image(im, net.w, net.h); - - layer l = net.layers[net.n - 1]; - - float *X = sized.data; - - float *prediction = network_predict(net, X); - - if (use_mean) { - memcpy(detector_gpu.predictions[detector_gpu.demo_index], prediction, l.outputs * sizeof(float)); - mean_arrays(detector_gpu.predictions, FRAMES, l.outputs, detector_gpu.avg); - l.output = detector_gpu.avg; - detector_gpu.demo_index = (detector_gpu.demo_index + 1) % FRAMES; - } - //get_region_boxes(l, 1, 1, thresh, detector_gpu.probs, detector_gpu.boxes, 0, 0); - //if (nms) do_nms_sort(detector_gpu.boxes, detector_gpu.probs, l.w*l.h*l.n, l.classes, nms); - - int nboxes = 0; - int letterbox = 0; - float hier_thresh = 0.5; - detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); - if (nms) do_nms_sort(dets, nboxes, l.classes, nms); - - std::vector bbox_vec; - - for (size_t i = 0; i < nboxes; ++i) { - box b = dets[i].bbox; - int const obj_id = max_index(dets[i].prob, l.classes); - float const prob = dets[i].prob[obj_id]; - - if (prob > thresh) - { - bbox_t bbox; - bbox.x = std::max((double)0, (b.x - b.w / 2.)*im.w); - bbox.y = std::max((double)0, (b.y - b.h / 2.)*im.h); - bbox.w = b.w*im.w; - bbox.h = b.h*im.h; - bbox.obj_id = obj_id; - bbox.prob = prob; - bbox.track_id = 0; - - bbox_vec.push_back(bbox); - } - } - - free_detections(dets, nboxes); - if(sized.data) - free(sized.data); + //std::cout << "net.gpu_index = " << net.gpu_index << std::endl; + + //float nms = .4; + + image im; + im.c = img.c; + im.data = img.data; + im.h = img.h; + im.w = img.w; + + image sized; + + if (net.w == im.w && net.h == im.h) { + sized = make_image(im.w, im.h, im.c); + memcpy(sized.data, im.data, im.w*im.h*im.c * sizeof(float)); + } + else + sized = resize_image(im, net.w, net.h); + + layer l = net.layers[net.n - 1]; + + float *X = sized.data; + + float *prediction = network_predict(net, X); + + if (use_mean) { + memcpy(detector_gpu.predictions[detector_gpu.demo_index], prediction, l.outputs * sizeof(float)); + mean_arrays(detector_gpu.predictions, FRAMES, l.outputs, detector_gpu.avg); + l.output = detector_gpu.avg; + detector_gpu.demo_index = (detector_gpu.demo_index + 1) % FRAMES; + } + //get_region_boxes(l, 1, 1, thresh, detector_gpu.probs, detector_gpu.boxes, 0, 0); + //if (nms) do_nms_sort(detector_gpu.boxes, detector_gpu.probs, l.w*l.h*l.n, l.classes, nms); + + int nboxes = 0; + int letterbox = 0; + float hier_thresh = 0.5; + detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); + if (nms) do_nms_sort(dets, nboxes, l.classes, nms); + + std::vector bbox_vec; + + for (size_t i = 0; i < nboxes; ++i) { + box b = dets[i].bbox; + int const obj_id = max_index(dets[i].prob, l.classes); + float const prob = dets[i].prob[obj_id]; + + if (prob > thresh) + { + bbox_t bbox; + bbox.x = std::max((double)0, (b.x - b.w / 2.)*im.w); + bbox.y = std::max((double)0, (b.y - b.h / 2.)*im.h); + bbox.w = b.w*im.w; + bbox.h = b.h*im.h; + bbox.obj_id = obj_id; + bbox.prob = prob; + bbox.track_id = 0; + + bbox_vec.push_back(bbox); + } + } + + free_detections(dets, nboxes); + if (sized.data) + free(sized.data); #ifdef GPU - if (cur_gpu_id != old_gpu_index) - cudaSetDevice(old_gpu_index); + if (cur_gpu_id != old_gpu_index) + cudaSetDevice(old_gpu_index); #endif - return bbox_vec; + return bbox_vec; } -YOLODLL_API std::vector Detector::tracking_id(std::vector cur_bbox_vec, bool const change_history, - int const frames_story, int const max_dist) +YOLODLL_API std::vector Detector::tracking_id(std::vector cur_bbox_vec, bool const change_history, + int const frames_story, int const max_dist) { - detector_gpu_t &det_gpu = *static_cast(detector_gpu_ptr.get()); - - bool prev_track_id_present = false; - for (auto &i : prev_bbox_vec_deque) - if (i.size() > 0) prev_track_id_present = true; - - if (!prev_track_id_present) { - for (size_t i = 0; i < cur_bbox_vec.size(); ++i) - cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++; - prev_bbox_vec_deque.push_front(cur_bbox_vec); - if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back(); - return cur_bbox_vec; - } - - std::vector dist_vec(cur_bbox_vec.size(), std::numeric_limits::max()); - - for (auto &prev_bbox_vec : prev_bbox_vec_deque) { - for (auto &i : prev_bbox_vec) { - int cur_index = -1; - for (size_t m = 0; m < cur_bbox_vec.size(); ++m) { - bbox_t const& k = cur_bbox_vec[m]; - if (i.obj_id == k.obj_id) { - float center_x_diff = (float)(i.x + i.w/2) - (float)(k.x + k.w/2); - float center_y_diff = (float)(i.y + i.h/2) - (float)(k.y + k.h/2); - unsigned int cur_dist = sqrt(center_x_diff*center_x_diff + center_y_diff*center_y_diff); - if (cur_dist < max_dist && (k.track_id == 0 || dist_vec[m] > cur_dist)) { - dist_vec[m] = cur_dist; - cur_index = m; - } - } - } - - bool track_id_absent = !std::any_of(cur_bbox_vec.begin(), cur_bbox_vec.end(), - [&i](bbox_t const& b) { return b.track_id == i.track_id && b.obj_id == i.obj_id; }); - - if (cur_index >= 0 && track_id_absent){ - cur_bbox_vec[cur_index].track_id = i.track_id; - cur_bbox_vec[cur_index].w = (cur_bbox_vec[cur_index].w + i.w) / 2; - cur_bbox_vec[cur_index].h = (cur_bbox_vec[cur_index].h + i.h) / 2; - } - } - } - - for (size_t i = 0; i < cur_bbox_vec.size(); ++i) - if (cur_bbox_vec[i].track_id == 0) - cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++; - - if (change_history) { - prev_bbox_vec_deque.push_front(cur_bbox_vec); - if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back(); - } - - return cur_bbox_vec; + detector_gpu_t &det_gpu = *static_cast(detector_gpu_ptr.get()); + + bool prev_track_id_present = false; + for (auto &i : prev_bbox_vec_deque) + if (i.size() > 0) prev_track_id_present = true; + + if (!prev_track_id_present) { + for (size_t i = 0; i < cur_bbox_vec.size(); ++i) + cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++; + prev_bbox_vec_deque.push_front(cur_bbox_vec); + if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back(); + return cur_bbox_vec; + } + + std::vector dist_vec(cur_bbox_vec.size(), std::numeric_limits::max()); + + for (auto &prev_bbox_vec : prev_bbox_vec_deque) { + for (auto &i : prev_bbox_vec) { + int cur_index = -1; + for (size_t m = 0; m < cur_bbox_vec.size(); ++m) { + bbox_t const& k = cur_bbox_vec[m]; + if (i.obj_id == k.obj_id) { + float center_x_diff = (float)(i.x + i.w / 2) - (float)(k.x + k.w / 2); + float center_y_diff = (float)(i.y + i.h / 2) - (float)(k.y + k.h / 2); + unsigned int cur_dist = sqrt(center_x_diff*center_x_diff + center_y_diff * center_y_diff); + if (cur_dist < max_dist && (k.track_id == 0 || dist_vec[m] > cur_dist)) { + dist_vec[m] = cur_dist; + cur_index = m; + } + } + } + + bool track_id_absent = !std::any_of(cur_bbox_vec.begin(), cur_bbox_vec.end(), + [&i](bbox_t const& b) { return b.track_id == i.track_id && b.obj_id == i.obj_id; }); + + if (cur_index >= 0 && track_id_absent) { + cur_bbox_vec[cur_index].track_id = i.track_id; + cur_bbox_vec[cur_index].w = (cur_bbox_vec[cur_index].w + i.w) / 2; + cur_bbox_vec[cur_index].h = (cur_bbox_vec[cur_index].h + i.h) / 2; + } + } + } + + for (size_t i = 0; i < cur_bbox_vec.size(); ++i) + if (cur_bbox_vec[i].track_id == 0) + cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++; + + if (change_history) { + prev_bbox_vec_deque.push_front(cur_bbox_vec); + if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back(); + } + + return cur_bbox_vec; } \ No newline at end of file From d765ed84952668bf4256549b129c4a192793a4ed Mon Sep 17 00:00:00 2001 From: Tino Hager Date: Wed, 27 Jun 2018 23:54:21 +0200 Subject: [PATCH 0090/1365] repair tabs spaces --- src/yolo_v2_class.cpp | 466 +++++++++++++++++++++--------------------- 1 file changed, 233 insertions(+), 233 deletions(-) diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index 7fdb4fda526..a93f3b93247 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -27,333 +27,333 @@ static std::unique_ptr detector; int init(const char *configurationFilename, const char *weightsFilename, int gpu) { - detector.reset(new Detector(configurationFilename, weightsFilename, gpu)); - return 1; + detector.reset(new Detector(configurationFilename, weightsFilename, gpu)); + return 1; } int detect_image(const char *filename, bbox_t_container &container) { - std::vector detection = detector->detect(filename); - for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i) - container.candidates[i] = detection[i]; - return detection.size(); + std::vector detection = detector->detect(filename); + for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i) + container.candidates[i] = detection[i]; + return detection.size(); } int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container &container) { #ifdef OPENCV - std::vector vdata(data, data + data_length); - cv::Mat image = imdecode(cv::Mat(vdata), 1); + std::vector vdata(data, data + data_length); + cv::Mat image = imdecode(cv::Mat(vdata), 1); - std::vector detection = detector->detect(image); - for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i) - container.candidates[i] = detection[i]; - return detection.size(); + std::vector detection = detector->detect(image); + for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i) + container.candidates[i] = detection[i]; + return detection.size(); #else - return -1; + return -1; #endif // OPENCV } int dispose() { - //if (detector != NULL) delete detector; - //detector = NULL; - detector.reset(); - return 1; + //if (detector != NULL) delete detector; + //detector = NULL; + detector.reset(); + return 1; } #ifdef GPU void check_cuda(cudaError_t status) { - if (status != cudaSuccess) { - const char *s = cudaGetErrorString(status); - printf("CUDA Error Prev: %s\n", s); - } + if (status != cudaSuccess) { + const char *s = cudaGetErrorString(status); + printf("CUDA Error Prev: %s\n", s); + } } #endif struct detector_gpu_t { - network net; - image images[FRAMES]; - float *avg; - float *predictions[FRAMES]; - int demo_index; - unsigned int *track_id; + network net; + image images[FRAMES]; + float *avg; + float *predictions[FRAMES]; + int demo_index; + unsigned int *track_id; }; YOLODLL_API Detector::Detector(std::string cfg_filename, std::string weight_filename, int gpu_id) : cur_gpu_id(gpu_id) { - wait_stream = 0; - int old_gpu_index; + wait_stream = 0; + int old_gpu_index; #ifdef GPU - check_cuda(cudaGetDevice(&old_gpu_index)); + check_cuda(cudaGetDevice(&old_gpu_index)); #endif - detector_gpu_ptr = std::make_shared(); - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + detector_gpu_ptr = std::make_shared(); + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); #ifdef GPU - //check_cuda( cudaSetDevice(cur_gpu_id) ); - cuda_set_device(cur_gpu_id); - printf(" Used GPU %d \n", cur_gpu_id); + //check_cuda( cudaSetDevice(cur_gpu_id) ); + cuda_set_device(cur_gpu_id); + printf(" Used GPU %d \n", cur_gpu_id); #endif - network &net = detector_gpu.net; - net.gpu_index = cur_gpu_id; - //gpu_index = i; + network &net = detector_gpu.net; + net.gpu_index = cur_gpu_id; + //gpu_index = i; - char *cfgfile = const_cast(cfg_filename.data()); - char *weightfile = const_cast(weight_filename.data()); + char *cfgfile = const_cast(cfg_filename.data()); + char *weightfile = const_cast(weight_filename.data()); - net = parse_network_cfg_custom(cfgfile, 1); - if (weightfile) { - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - net.gpu_index = cur_gpu_id; - fuse_conv_batchnorm(net); + net = parse_network_cfg_custom(cfgfile, 1); + if (weightfile) { + load_weights(&net, weightfile); + } + set_batch_network(&net, 1); + net.gpu_index = cur_gpu_id; + fuse_conv_batchnorm(net); - layer l = net.layers[net.n - 1]; - int j; + layer l = net.layers[net.n - 1]; + int j; - detector_gpu.avg = (float *)calloc(l.outputs, sizeof(float)); - for (j = 0; j < FRAMES; ++j) detector_gpu.predictions[j] = (float *)calloc(l.outputs, sizeof(float)); - for (j = 0; j < FRAMES; ++j) detector_gpu.images[j] = make_image(1, 1, 3); + detector_gpu.avg = (float *)calloc(l.outputs, sizeof(float)); + for (j = 0; j < FRAMES; ++j) detector_gpu.predictions[j] = (float *)calloc(l.outputs, sizeof(float)); + for (j = 0; j < FRAMES; ++j) detector_gpu.images[j] = make_image(1, 1, 3); - detector_gpu.track_id = (unsigned int *)calloc(l.classes, sizeof(unsigned int)); - for (j = 0; j < l.classes; ++j) detector_gpu.track_id[j] = 1; + detector_gpu.track_id = (unsigned int *)calloc(l.classes, sizeof(unsigned int)); + for (j = 0; j < l.classes; ++j) detector_gpu.track_id[j] = 1; #ifdef GPU - check_cuda(cudaSetDevice(old_gpu_index)); + check_cuda(cudaSetDevice(old_gpu_index)); #endif } YOLODLL_API Detector::~Detector() { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - layer l = detector_gpu.net.layers[detector_gpu.net.n - 1]; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + layer l = detector_gpu.net.layers[detector_gpu.net.n - 1]; - free(detector_gpu.track_id); + free(detector_gpu.track_id); - free(detector_gpu.avg); - for (int j = 0; j < FRAMES; ++j) free(detector_gpu.predictions[j]); - for (int j = 0; j < FRAMES; ++j) if (detector_gpu.images[j].data) free(detector_gpu.images[j].data); + free(detector_gpu.avg); + for (int j = 0; j < FRAMES; ++j) free(detector_gpu.predictions[j]); + for (int j = 0; j < FRAMES; ++j) if (detector_gpu.images[j].data) free(detector_gpu.images[j].data); - int old_gpu_index; + int old_gpu_index; #ifdef GPU - cudaGetDevice(&old_gpu_index); - cuda_set_device(detector_gpu.net.gpu_index); + cudaGetDevice(&old_gpu_index); + cuda_set_device(detector_gpu.net.gpu_index); #endif - free_network(detector_gpu.net); + free_network(detector_gpu.net); #ifdef GPU - cudaSetDevice(old_gpu_index); + cudaSetDevice(old_gpu_index); #endif } YOLODLL_API int Detector::get_net_width() const { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - return detector_gpu.net.w; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + return detector_gpu.net.w; } YOLODLL_API int Detector::get_net_height() const { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - return detector_gpu.net.h; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + return detector_gpu.net.h; } YOLODLL_API int Detector::get_net_color_depth() const { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - return detector_gpu.net.c; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + return detector_gpu.net.c; } YOLODLL_API std::vector Detector::detect(std::string image_filename, float thresh, bool use_mean) { - std::shared_ptr image_ptr(new image_t, [](image_t *img) { if (img->data) free(img->data); delete img; }); - *image_ptr = load_image(image_filename); - return detect(*image_ptr, thresh, use_mean); + std::shared_ptr image_ptr(new image_t, [](image_t *img) { if (img->data) free(img->data); delete img; }); + *image_ptr = load_image(image_filename); + return detect(*image_ptr, thresh, use_mean); } static image load_image_stb(char *filename, int channels) { - int w, h, c; - unsigned char *data = stbi_load(filename, &w, &h, &c, channels); - if (!data) - throw std::runtime_error("file not found"); - if (channels) c = channels; - int i, j, k; - image im = make_image(w, h, c); - for (k = 0; k < c; ++k) { - for (j = 0; j < h; ++j) { - for (i = 0; i < w; ++i) { - int dst_index = i + w * j + w * h*k; - int src_index = k + c * i + c * w*j; - im.data[dst_index] = (float)data[src_index] / 255.; - } - } - } - free(data); - return im; + int w, h, c; + unsigned char *data = stbi_load(filename, &w, &h, &c, channels); + if (!data) + throw std::runtime_error("file not found"); + if (channels) c = channels; + int i, j, k; + image im = make_image(w, h, c); + for (k = 0; k < c; ++k) { + for (j = 0; j < h; ++j) { + for (i = 0; i < w; ++i) { + int dst_index = i + w * j + w * h*k; + int src_index = k + c * i + c * w*j; + im.data[dst_index] = (float)data[src_index] / 255.; + } + } + } + free(data); + return im; } YOLODLL_API image_t Detector::load_image(std::string image_filename) { - char *input = const_cast(image_filename.data()); - image im = load_image_stb(input, 3); + char *input = const_cast(image_filename.data()); + image im = load_image_stb(input, 3); - image_t img; - img.c = im.c; - img.data = im.data; - img.h = im.h; - img.w = im.w; + image_t img; + img.c = im.c; + img.data = im.data; + img.h = im.h; + img.w = im.w; - return img; + return img; } YOLODLL_API void Detector::free_image(image_t m) { - if (m.data) { - free(m.data); - } + if (m.data) { + free(m.data); + } } YOLODLL_API std::vector Detector::detect(image_t img, float thresh, bool use_mean) { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - network &net = detector_gpu.net; - int old_gpu_index; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + network &net = detector_gpu.net; + int old_gpu_index; #ifdef GPU - cudaGetDevice(&old_gpu_index); - if (cur_gpu_id != old_gpu_index) - cudaSetDevice(net.gpu_index); + cudaGetDevice(&old_gpu_index); + if (cur_gpu_id != old_gpu_index) + cudaSetDevice(net.gpu_index); - net.wait_stream = wait_stream; // 1 - wait CUDA-stream, 0 - not to wait + net.wait_stream = wait_stream; // 1 - wait CUDA-stream, 0 - not to wait #endif - //std::cout << "net.gpu_index = " << net.gpu_index << std::endl; - - //float nms = .4; - - image im; - im.c = img.c; - im.data = img.data; - im.h = img.h; - im.w = img.w; - - image sized; - - if (net.w == im.w && net.h == im.h) { - sized = make_image(im.w, im.h, im.c); - memcpy(sized.data, im.data, im.w*im.h*im.c * sizeof(float)); - } - else - sized = resize_image(im, net.w, net.h); - - layer l = net.layers[net.n - 1]; - - float *X = sized.data; - - float *prediction = network_predict(net, X); - - if (use_mean) { - memcpy(detector_gpu.predictions[detector_gpu.demo_index], prediction, l.outputs * sizeof(float)); - mean_arrays(detector_gpu.predictions, FRAMES, l.outputs, detector_gpu.avg); - l.output = detector_gpu.avg; - detector_gpu.demo_index = (detector_gpu.demo_index + 1) % FRAMES; - } - //get_region_boxes(l, 1, 1, thresh, detector_gpu.probs, detector_gpu.boxes, 0, 0); - //if (nms) do_nms_sort(detector_gpu.boxes, detector_gpu.probs, l.w*l.h*l.n, l.classes, nms); - - int nboxes = 0; - int letterbox = 0; - float hier_thresh = 0.5; - detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); - if (nms) do_nms_sort(dets, nboxes, l.classes, nms); - - std::vector bbox_vec; - - for (size_t i = 0; i < nboxes; ++i) { - box b = dets[i].bbox; - int const obj_id = max_index(dets[i].prob, l.classes); - float const prob = dets[i].prob[obj_id]; - - if (prob > thresh) - { - bbox_t bbox; - bbox.x = std::max((double)0, (b.x - b.w / 2.)*im.w); - bbox.y = std::max((double)0, (b.y - b.h / 2.)*im.h); - bbox.w = b.w*im.w; - bbox.h = b.h*im.h; - bbox.obj_id = obj_id; - bbox.prob = prob; - bbox.track_id = 0; - - bbox_vec.push_back(bbox); - } - } - - free_detections(dets, nboxes); - if (sized.data) - free(sized.data); + //std::cout << "net.gpu_index = " << net.gpu_index << std::endl; + + //float nms = .4; + + image im; + im.c = img.c; + im.data = img.data; + im.h = img.h; + im.w = img.w; + + image sized; + + if (net.w == im.w && net.h == im.h) { + sized = make_image(im.w, im.h, im.c); + memcpy(sized.data, im.data, im.w*im.h*im.c * sizeof(float)); + } + else + sized = resize_image(im, net.w, net.h); + + layer l = net.layers[net.n - 1]; + + float *X = sized.data; + + float *prediction = network_predict(net, X); + + if (use_mean) { + memcpy(detector_gpu.predictions[detector_gpu.demo_index], prediction, l.outputs * sizeof(float)); + mean_arrays(detector_gpu.predictions, FRAMES, l.outputs, detector_gpu.avg); + l.output = detector_gpu.avg; + detector_gpu.demo_index = (detector_gpu.demo_index + 1) % FRAMES; + } + //get_region_boxes(l, 1, 1, thresh, detector_gpu.probs, detector_gpu.boxes, 0, 0); + //if (nms) do_nms_sort(detector_gpu.boxes, detector_gpu.probs, l.w*l.h*l.n, l.classes, nms); + + int nboxes = 0; + int letterbox = 0; + float hier_thresh = 0.5; + detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); + if (nms) do_nms_sort(dets, nboxes, l.classes, nms); + + std::vector bbox_vec; + + for (size_t i = 0; i < nboxes; ++i) { + box b = dets[i].bbox; + int const obj_id = max_index(dets[i].prob, l.classes); + float const prob = dets[i].prob[obj_id]; + + if (prob > thresh) + { + bbox_t bbox; + bbox.x = std::max((double)0, (b.x - b.w / 2.)*im.w); + bbox.y = std::max((double)0, (b.y - b.h / 2.)*im.h); + bbox.w = b.w*im.w; + bbox.h = b.h*im.h; + bbox.obj_id = obj_id; + bbox.prob = prob; + bbox.track_id = 0; + + bbox_vec.push_back(bbox); + } + } + + free_detections(dets, nboxes); + if (sized.data) + free(sized.data); #ifdef GPU - if (cur_gpu_id != old_gpu_index) - cudaSetDevice(old_gpu_index); + if (cur_gpu_id != old_gpu_index) + cudaSetDevice(old_gpu_index); #endif - return bbox_vec; + return bbox_vec; } YOLODLL_API std::vector Detector::tracking_id(std::vector cur_bbox_vec, bool const change_history, - int const frames_story, int const max_dist) + int const frames_story, int const max_dist) { - detector_gpu_t &det_gpu = *static_cast(detector_gpu_ptr.get()); - - bool prev_track_id_present = false; - for (auto &i : prev_bbox_vec_deque) - if (i.size() > 0) prev_track_id_present = true; - - if (!prev_track_id_present) { - for (size_t i = 0; i < cur_bbox_vec.size(); ++i) - cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++; - prev_bbox_vec_deque.push_front(cur_bbox_vec); - if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back(); - return cur_bbox_vec; - } - - std::vector dist_vec(cur_bbox_vec.size(), std::numeric_limits::max()); - - for (auto &prev_bbox_vec : prev_bbox_vec_deque) { - for (auto &i : prev_bbox_vec) { - int cur_index = -1; - for (size_t m = 0; m < cur_bbox_vec.size(); ++m) { - bbox_t const& k = cur_bbox_vec[m]; - if (i.obj_id == k.obj_id) { - float center_x_diff = (float)(i.x + i.w / 2) - (float)(k.x + k.w / 2); - float center_y_diff = (float)(i.y + i.h / 2) - (float)(k.y + k.h / 2); - unsigned int cur_dist = sqrt(center_x_diff*center_x_diff + center_y_diff * center_y_diff); - if (cur_dist < max_dist && (k.track_id == 0 || dist_vec[m] > cur_dist)) { - dist_vec[m] = cur_dist; - cur_index = m; - } - } - } - - bool track_id_absent = !std::any_of(cur_bbox_vec.begin(), cur_bbox_vec.end(), - [&i](bbox_t const& b) { return b.track_id == i.track_id && b.obj_id == i.obj_id; }); - - if (cur_index >= 0 && track_id_absent) { - cur_bbox_vec[cur_index].track_id = i.track_id; - cur_bbox_vec[cur_index].w = (cur_bbox_vec[cur_index].w + i.w) / 2; - cur_bbox_vec[cur_index].h = (cur_bbox_vec[cur_index].h + i.h) / 2; - } - } - } - - for (size_t i = 0; i < cur_bbox_vec.size(); ++i) - if (cur_bbox_vec[i].track_id == 0) - cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++; - - if (change_history) { - prev_bbox_vec_deque.push_front(cur_bbox_vec); - if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back(); - } - - return cur_bbox_vec; + detector_gpu_t &det_gpu = *static_cast(detector_gpu_ptr.get()); + + bool prev_track_id_present = false; + for (auto &i : prev_bbox_vec_deque) + if (i.size() > 0) prev_track_id_present = true; + + if (!prev_track_id_present) { + for (size_t i = 0; i < cur_bbox_vec.size(); ++i) + cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++; + prev_bbox_vec_deque.push_front(cur_bbox_vec); + if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back(); + return cur_bbox_vec; + } + + std::vector dist_vec(cur_bbox_vec.size(), std::numeric_limits::max()); + + for (auto &prev_bbox_vec : prev_bbox_vec_deque) { + for (auto &i : prev_bbox_vec) { + int cur_index = -1; + for (size_t m = 0; m < cur_bbox_vec.size(); ++m) { + bbox_t const& k = cur_bbox_vec[m]; + if (i.obj_id == k.obj_id) { + float center_x_diff = (float)(i.x + i.w / 2) - (float)(k.x + k.w / 2); + float center_y_diff = (float)(i.y + i.h / 2) - (float)(k.y + k.h / 2); + unsigned int cur_dist = sqrt(center_x_diff*center_x_diff + center_y_diff * center_y_diff); + if (cur_dist < max_dist && (k.track_id == 0 || dist_vec[m] > cur_dist)) { + dist_vec[m] = cur_dist; + cur_index = m; + } + } + } + + bool track_id_absent = !std::any_of(cur_bbox_vec.begin(), cur_bbox_vec.end(), + [&i](bbox_t const& b) { return b.track_id == i.track_id && b.obj_id == i.obj_id; }); + + if (cur_index >= 0 && track_id_absent) { + cur_bbox_vec[cur_index].track_id = i.track_id; + cur_bbox_vec[cur_index].w = (cur_bbox_vec[cur_index].w + i.w) / 2; + cur_bbox_vec[cur_index].h = (cur_bbox_vec[cur_index].h + i.h) / 2; + } + } + } + + for (size_t i = 0; i < cur_bbox_vec.size(); ++i) + if (cur_bbox_vec[i].track_id == 0) + cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++; + + if (change_history) { + prev_bbox_vec_deque.push_front(cur_bbox_vec); + if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back(); + } + + return cur_bbox_vec; } \ No newline at end of file From 8a504c737d2bc3b9b37cb79cb50fbf7eecda07df Mon Sep 17 00:00:00 2001 From: Tino Hager Date: Wed, 27 Jun 2018 23:56:47 +0200 Subject: [PATCH 0091/1365] repair tabs spaces --- src/yolo_v2_class.cpp | 468 +++++++++++++++++++++--------------------- 1 file changed, 235 insertions(+), 233 deletions(-) diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index a93f3b93247..14e19fb5a41 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -22,338 +22,340 @@ extern "C" { #define FRAMES 3 +int max_objects() { return C_SHARP_MAX_OBJECTS; } + //static Detector* detector = NULL; static std::unique_ptr detector; int init(const char *configurationFilename, const char *weightsFilename, int gpu) { - detector.reset(new Detector(configurationFilename, weightsFilename, gpu)); - return 1; + detector.reset(new Detector(configurationFilename, weightsFilename, gpu)); + return 1; } int detect_image(const char *filename, bbox_t_container &container) { - std::vector detection = detector->detect(filename); - for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i) - container.candidates[i] = detection[i]; - return detection.size(); + std::vector detection = detector->detect(filename); + for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i) + container.candidates[i] = detection[i]; + return detection.size(); } int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container &container) { #ifdef OPENCV - std::vector vdata(data, data + data_length); - cv::Mat image = imdecode(cv::Mat(vdata), 1); + std::vector vdata(data, data + data_length); + cv::Mat image = imdecode(cv::Mat(vdata), 1); - std::vector detection = detector->detect(image); - for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i) - container.candidates[i] = detection[i]; - return detection.size(); + std::vector detection = detector->detect(image); + for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i) + container.candidates[i] = detection[i]; + return detection.size(); #else - return -1; + return -1; #endif // OPENCV } int dispose() { - //if (detector != NULL) delete detector; - //detector = NULL; - detector.reset(); - return 1; + //if (detector != NULL) delete detector; + //detector = NULL; + detector.reset(); + return 1; } #ifdef GPU void check_cuda(cudaError_t status) { - if (status != cudaSuccess) { - const char *s = cudaGetErrorString(status); - printf("CUDA Error Prev: %s\n", s); - } + if (status != cudaSuccess) { + const char *s = cudaGetErrorString(status); + printf("CUDA Error Prev: %s\n", s); + } } #endif struct detector_gpu_t { - network net; - image images[FRAMES]; - float *avg; - float *predictions[FRAMES]; - int demo_index; - unsigned int *track_id; + network net; + image images[FRAMES]; + float *avg; + float *predictions[FRAMES]; + int demo_index; + unsigned int *track_id; }; YOLODLL_API Detector::Detector(std::string cfg_filename, std::string weight_filename, int gpu_id) : cur_gpu_id(gpu_id) { - wait_stream = 0; - int old_gpu_index; + wait_stream = 0; + int old_gpu_index; #ifdef GPU - check_cuda(cudaGetDevice(&old_gpu_index)); + check_cuda(cudaGetDevice(&old_gpu_index)); #endif - detector_gpu_ptr = std::make_shared(); - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + detector_gpu_ptr = std::make_shared(); + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); #ifdef GPU - //check_cuda( cudaSetDevice(cur_gpu_id) ); - cuda_set_device(cur_gpu_id); - printf(" Used GPU %d \n", cur_gpu_id); + //check_cuda( cudaSetDevice(cur_gpu_id) ); + cuda_set_device(cur_gpu_id); + printf(" Used GPU %d \n", cur_gpu_id); #endif - network &net = detector_gpu.net; - net.gpu_index = cur_gpu_id; - //gpu_index = i; + network &net = detector_gpu.net; + net.gpu_index = cur_gpu_id; + //gpu_index = i; - char *cfgfile = const_cast(cfg_filename.data()); - char *weightfile = const_cast(weight_filename.data()); + char *cfgfile = const_cast(cfg_filename.data()); + char *weightfile = const_cast(weight_filename.data()); - net = parse_network_cfg_custom(cfgfile, 1); - if (weightfile) { - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - net.gpu_index = cur_gpu_id; - fuse_conv_batchnorm(net); + net = parse_network_cfg_custom(cfgfile, 1); + if (weightfile) { + load_weights(&net, weightfile); + } + set_batch_network(&net, 1); + net.gpu_index = cur_gpu_id; + fuse_conv_batchnorm(net); - layer l = net.layers[net.n - 1]; - int j; + layer l = net.layers[net.n - 1]; + int j; - detector_gpu.avg = (float *)calloc(l.outputs, sizeof(float)); - for (j = 0; j < FRAMES; ++j) detector_gpu.predictions[j] = (float *)calloc(l.outputs, sizeof(float)); - for (j = 0; j < FRAMES; ++j) detector_gpu.images[j] = make_image(1, 1, 3); + detector_gpu.avg = (float *)calloc(l.outputs, sizeof(float)); + for (j = 0; j < FRAMES; ++j) detector_gpu.predictions[j] = (float *)calloc(l.outputs, sizeof(float)); + for (j = 0; j < FRAMES; ++j) detector_gpu.images[j] = make_image(1, 1, 3); - detector_gpu.track_id = (unsigned int *)calloc(l.classes, sizeof(unsigned int)); - for (j = 0; j < l.classes; ++j) detector_gpu.track_id[j] = 1; + detector_gpu.track_id = (unsigned int *)calloc(l.classes, sizeof(unsigned int)); + for (j = 0; j < l.classes; ++j) detector_gpu.track_id[j] = 1; #ifdef GPU - check_cuda(cudaSetDevice(old_gpu_index)); + check_cuda(cudaSetDevice(old_gpu_index)); #endif } YOLODLL_API Detector::~Detector() { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - layer l = detector_gpu.net.layers[detector_gpu.net.n - 1]; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + layer l = detector_gpu.net.layers[detector_gpu.net.n - 1]; - free(detector_gpu.track_id); + free(detector_gpu.track_id); - free(detector_gpu.avg); - for (int j = 0; j < FRAMES; ++j) free(detector_gpu.predictions[j]); - for (int j = 0; j < FRAMES; ++j) if (detector_gpu.images[j].data) free(detector_gpu.images[j].data); + free(detector_gpu.avg); + for (int j = 0; j < FRAMES; ++j) free(detector_gpu.predictions[j]); + for (int j = 0; j < FRAMES; ++j) if (detector_gpu.images[j].data) free(detector_gpu.images[j].data); - int old_gpu_index; + int old_gpu_index; #ifdef GPU - cudaGetDevice(&old_gpu_index); - cuda_set_device(detector_gpu.net.gpu_index); + cudaGetDevice(&old_gpu_index); + cuda_set_device(detector_gpu.net.gpu_index); #endif - free_network(detector_gpu.net); + free_network(detector_gpu.net); #ifdef GPU - cudaSetDevice(old_gpu_index); + cudaSetDevice(old_gpu_index); #endif } YOLODLL_API int Detector::get_net_width() const { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - return detector_gpu.net.w; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + return detector_gpu.net.w; } YOLODLL_API int Detector::get_net_height() const { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - return detector_gpu.net.h; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + return detector_gpu.net.h; } YOLODLL_API int Detector::get_net_color_depth() const { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - return detector_gpu.net.c; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + return detector_gpu.net.c; } YOLODLL_API std::vector Detector::detect(std::string image_filename, float thresh, bool use_mean) { - std::shared_ptr image_ptr(new image_t, [](image_t *img) { if (img->data) free(img->data); delete img; }); - *image_ptr = load_image(image_filename); - return detect(*image_ptr, thresh, use_mean); + std::shared_ptr image_ptr(new image_t, [](image_t *img) { if (img->data) free(img->data); delete img; }); + *image_ptr = load_image(image_filename); + return detect(*image_ptr, thresh, use_mean); } static image load_image_stb(char *filename, int channels) { - int w, h, c; - unsigned char *data = stbi_load(filename, &w, &h, &c, channels); - if (!data) - throw std::runtime_error("file not found"); - if (channels) c = channels; - int i, j, k; - image im = make_image(w, h, c); - for (k = 0; k < c; ++k) { - for (j = 0; j < h; ++j) { - for (i = 0; i < w; ++i) { - int dst_index = i + w * j + w * h*k; - int src_index = k + c * i + c * w*j; - im.data[dst_index] = (float)data[src_index] / 255.; - } - } - } - free(data); - return im; + int w, h, c; + unsigned char *data = stbi_load(filename, &w, &h, &c, channels); + if (!data) + throw std::runtime_error("file not found"); + if (channels) c = channels; + int i, j, k; + image im = make_image(w, h, c); + for (k = 0; k < c; ++k) { + for (j = 0; j < h; ++j) { + for (i = 0; i < w; ++i) { + int dst_index = i + w * j + w * h*k; + int src_index = k + c * i + c * w*j; + im.data[dst_index] = (float)data[src_index] / 255.; + } + } + } + free(data); + return im; } YOLODLL_API image_t Detector::load_image(std::string image_filename) { - char *input = const_cast(image_filename.data()); - image im = load_image_stb(input, 3); + char *input = const_cast(image_filename.data()); + image im = load_image_stb(input, 3); - image_t img; - img.c = im.c; - img.data = im.data; - img.h = im.h; - img.w = im.w; + image_t img; + img.c = im.c; + img.data = im.data; + img.h = im.h; + img.w = im.w; - return img; + return img; } YOLODLL_API void Detector::free_image(image_t m) { - if (m.data) { - free(m.data); - } + if (m.data) { + free(m.data); + } } YOLODLL_API std::vector Detector::detect(image_t img, float thresh, bool use_mean) { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - network &net = detector_gpu.net; - int old_gpu_index; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + network &net = detector_gpu.net; + int old_gpu_index; #ifdef GPU - cudaGetDevice(&old_gpu_index); - if (cur_gpu_id != old_gpu_index) - cudaSetDevice(net.gpu_index); + cudaGetDevice(&old_gpu_index); + if (cur_gpu_id != old_gpu_index) + cudaSetDevice(net.gpu_index); - net.wait_stream = wait_stream; // 1 - wait CUDA-stream, 0 - not to wait + net.wait_stream = wait_stream; // 1 - wait CUDA-stream, 0 - not to wait #endif - //std::cout << "net.gpu_index = " << net.gpu_index << std::endl; - - //float nms = .4; - - image im; - im.c = img.c; - im.data = img.data; - im.h = img.h; - im.w = img.w; - - image sized; - - if (net.w == im.w && net.h == im.h) { - sized = make_image(im.w, im.h, im.c); - memcpy(sized.data, im.data, im.w*im.h*im.c * sizeof(float)); - } - else - sized = resize_image(im, net.w, net.h); - - layer l = net.layers[net.n - 1]; - - float *X = sized.data; - - float *prediction = network_predict(net, X); - - if (use_mean) { - memcpy(detector_gpu.predictions[detector_gpu.demo_index], prediction, l.outputs * sizeof(float)); - mean_arrays(detector_gpu.predictions, FRAMES, l.outputs, detector_gpu.avg); - l.output = detector_gpu.avg; - detector_gpu.demo_index = (detector_gpu.demo_index + 1) % FRAMES; - } - //get_region_boxes(l, 1, 1, thresh, detector_gpu.probs, detector_gpu.boxes, 0, 0); - //if (nms) do_nms_sort(detector_gpu.boxes, detector_gpu.probs, l.w*l.h*l.n, l.classes, nms); - - int nboxes = 0; - int letterbox = 0; - float hier_thresh = 0.5; - detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); - if (nms) do_nms_sort(dets, nboxes, l.classes, nms); - - std::vector bbox_vec; - - for (size_t i = 0; i < nboxes; ++i) { - box b = dets[i].bbox; - int const obj_id = max_index(dets[i].prob, l.classes); - float const prob = dets[i].prob[obj_id]; - - if (prob > thresh) - { - bbox_t bbox; - bbox.x = std::max((double)0, (b.x - b.w / 2.)*im.w); - bbox.y = std::max((double)0, (b.y - b.h / 2.)*im.h); - bbox.w = b.w*im.w; - bbox.h = b.h*im.h; - bbox.obj_id = obj_id; - bbox.prob = prob; - bbox.track_id = 0; - - bbox_vec.push_back(bbox); - } - } - - free_detections(dets, nboxes); - if (sized.data) - free(sized.data); + //std::cout << "net.gpu_index = " << net.gpu_index << std::endl; + + //float nms = .4; + + image im; + im.c = img.c; + im.data = img.data; + im.h = img.h; + im.w = img.w; + + image sized; + + if (net.w == im.w && net.h == im.h) { + sized = make_image(im.w, im.h, im.c); + memcpy(sized.data, im.data, im.w*im.h*im.c * sizeof(float)); + } + else + sized = resize_image(im, net.w, net.h); + + layer l = net.layers[net.n - 1]; + + float *X = sized.data; + + float *prediction = network_predict(net, X); + + if (use_mean) { + memcpy(detector_gpu.predictions[detector_gpu.demo_index], prediction, l.outputs * sizeof(float)); + mean_arrays(detector_gpu.predictions, FRAMES, l.outputs, detector_gpu.avg); + l.output = detector_gpu.avg; + detector_gpu.demo_index = (detector_gpu.demo_index + 1) % FRAMES; + } + //get_region_boxes(l, 1, 1, thresh, detector_gpu.probs, detector_gpu.boxes, 0, 0); + //if (nms) do_nms_sort(detector_gpu.boxes, detector_gpu.probs, l.w*l.h*l.n, l.classes, nms); + + int nboxes = 0; + int letterbox = 0; + float hier_thresh = 0.5; + detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); + if (nms) do_nms_sort(dets, nboxes, l.classes, nms); + + std::vector bbox_vec; + + for (size_t i = 0; i < nboxes; ++i) { + box b = dets[i].bbox; + int const obj_id = max_index(dets[i].prob, l.classes); + float const prob = dets[i].prob[obj_id]; + + if (prob > thresh) + { + bbox_t bbox; + bbox.x = std::max((double)0, (b.x - b.w / 2.)*im.w); + bbox.y = std::max((double)0, (b.y - b.h / 2.)*im.h); + bbox.w = b.w*im.w; + bbox.h = b.h*im.h; + bbox.obj_id = obj_id; + bbox.prob = prob; + bbox.track_id = 0; + + bbox_vec.push_back(bbox); + } + } + + free_detections(dets, nboxes); + if (sized.data) + free(sized.data); #ifdef GPU - if (cur_gpu_id != old_gpu_index) - cudaSetDevice(old_gpu_index); + if (cur_gpu_id != old_gpu_index) + cudaSetDevice(old_gpu_index); #endif - return bbox_vec; + return bbox_vec; } YOLODLL_API std::vector Detector::tracking_id(std::vector cur_bbox_vec, bool const change_history, - int const frames_story, int const max_dist) + int const frames_story, int const max_dist) { - detector_gpu_t &det_gpu = *static_cast(detector_gpu_ptr.get()); - - bool prev_track_id_present = false; - for (auto &i : prev_bbox_vec_deque) - if (i.size() > 0) prev_track_id_present = true; - - if (!prev_track_id_present) { - for (size_t i = 0; i < cur_bbox_vec.size(); ++i) - cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++; - prev_bbox_vec_deque.push_front(cur_bbox_vec); - if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back(); - return cur_bbox_vec; - } - - std::vector dist_vec(cur_bbox_vec.size(), std::numeric_limits::max()); - - for (auto &prev_bbox_vec : prev_bbox_vec_deque) { - for (auto &i : prev_bbox_vec) { - int cur_index = -1; - for (size_t m = 0; m < cur_bbox_vec.size(); ++m) { - bbox_t const& k = cur_bbox_vec[m]; - if (i.obj_id == k.obj_id) { - float center_x_diff = (float)(i.x + i.w / 2) - (float)(k.x + k.w / 2); - float center_y_diff = (float)(i.y + i.h / 2) - (float)(k.y + k.h / 2); - unsigned int cur_dist = sqrt(center_x_diff*center_x_diff + center_y_diff * center_y_diff); - if (cur_dist < max_dist && (k.track_id == 0 || dist_vec[m] > cur_dist)) { - dist_vec[m] = cur_dist; - cur_index = m; - } - } - } - - bool track_id_absent = !std::any_of(cur_bbox_vec.begin(), cur_bbox_vec.end(), - [&i](bbox_t const& b) { return b.track_id == i.track_id && b.obj_id == i.obj_id; }); - - if (cur_index >= 0 && track_id_absent) { - cur_bbox_vec[cur_index].track_id = i.track_id; - cur_bbox_vec[cur_index].w = (cur_bbox_vec[cur_index].w + i.w) / 2; - cur_bbox_vec[cur_index].h = (cur_bbox_vec[cur_index].h + i.h) / 2; - } - } - } - - for (size_t i = 0; i < cur_bbox_vec.size(); ++i) - if (cur_bbox_vec[i].track_id == 0) - cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++; - - if (change_history) { - prev_bbox_vec_deque.push_front(cur_bbox_vec); - if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back(); - } - - return cur_bbox_vec; + detector_gpu_t &det_gpu = *static_cast(detector_gpu_ptr.get()); + + bool prev_track_id_present = false; + for (auto &i : prev_bbox_vec_deque) + if (i.size() > 0) prev_track_id_present = true; + + if (!prev_track_id_present) { + for (size_t i = 0; i < cur_bbox_vec.size(); ++i) + cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++; + prev_bbox_vec_deque.push_front(cur_bbox_vec); + if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back(); + return cur_bbox_vec; + } + + std::vector dist_vec(cur_bbox_vec.size(), std::numeric_limits::max()); + + for (auto &prev_bbox_vec : prev_bbox_vec_deque) { + for (auto &i : prev_bbox_vec) { + int cur_index = -1; + for (size_t m = 0; m < cur_bbox_vec.size(); ++m) { + bbox_t const& k = cur_bbox_vec[m]; + if (i.obj_id == k.obj_id) { + float center_x_diff = (float)(i.x + i.w / 2) - (float)(k.x + k.w / 2); + float center_y_diff = (float)(i.y + i.h / 2) - (float)(k.y + k.h / 2); + unsigned int cur_dist = sqrt(center_x_diff*center_x_diff + center_y_diff * center_y_diff); + if (cur_dist < max_dist && (k.track_id == 0 || dist_vec[m] > cur_dist)) { + dist_vec[m] = cur_dist; + cur_index = m; + } + } + } + + bool track_id_absent = !std::any_of(cur_bbox_vec.begin(), cur_bbox_vec.end(), + [&i](bbox_t const& b) { return b.track_id == i.track_id && b.obj_id == i.obj_id; }); + + if (cur_index >= 0 && track_id_absent) { + cur_bbox_vec[cur_index].track_id = i.track_id; + cur_bbox_vec[cur_index].w = (cur_bbox_vec[cur_index].w + i.w) / 2; + cur_bbox_vec[cur_index].h = (cur_bbox_vec[cur_index].h + i.h) / 2; + } + } + } + + for (size_t i = 0; i < cur_bbox_vec.size(); ++i) + if (cur_bbox_vec[i].track_id == 0) + cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++; + + if (change_history) { + prev_bbox_vec_deque.push_front(cur_bbox_vec); + if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back(); + } + + return cur_bbox_vec; } \ No newline at end of file From 170cebf8af215f8d7c20cbbe4e19a9356f235b60 Mon Sep 17 00:00:00 2001 From: Tino Hager Date: Wed, 27 Jun 2018 23:57:54 +0200 Subject: [PATCH 0092/1365] repair tabs spaces --- src/yolo_v2_class.cpp | 454 +++++++++++++++++++++--------------------- 1 file changed, 227 insertions(+), 227 deletions(-) diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index 14e19fb5a41..c518fd77d3a 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -27,13 +27,13 @@ int max_objects() { return C_SHARP_MAX_OBJECTS; } //static Detector* detector = NULL; static std::unique_ptr detector; -int init(const char *configurationFilename, const char *weightsFilename, int gpu) +int init(const char *configurationFilename, const char *weightsFilename, int gpu) { detector.reset(new Detector(configurationFilename, weightsFilename, gpu)); return 1; } -int detect_image(const char *filename, bbox_t_container &container) +int detect_image(const char *filename, bbox_t_container &container) { std::vector detection = detector->detect(filename); for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i) @@ -56,306 +56,306 @@ int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container & } int dispose() { - //if (detector != NULL) delete detector; - //detector = NULL; + //if (detector != NULL) delete detector; + //detector = NULL; detector.reset(); return 1; } #ifdef GPU void check_cuda(cudaError_t status) { - if (status != cudaSuccess) { - const char *s = cudaGetErrorString(status); - printf("CUDA Error Prev: %s\n", s); - } + if (status != cudaSuccess) { + const char *s = cudaGetErrorString(status); + printf("CUDA Error Prev: %s\n", s); + } } #endif struct detector_gpu_t { - network net; - image images[FRAMES]; - float *avg; - float *predictions[FRAMES]; - int demo_index; - unsigned int *track_id; + network net; + image images[FRAMES]; + float *avg; + float *predictions[FRAMES]; + int demo_index; + unsigned int *track_id; }; YOLODLL_API Detector::Detector(std::string cfg_filename, std::string weight_filename, int gpu_id) : cur_gpu_id(gpu_id) { - wait_stream = 0; - int old_gpu_index; + wait_stream = 0; + int old_gpu_index; #ifdef GPU - check_cuda(cudaGetDevice(&old_gpu_index)); + check_cuda( cudaGetDevice(&old_gpu_index) ); #endif - detector_gpu_ptr = std::make_shared(); - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + detector_gpu_ptr = std::make_shared(); + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); #ifdef GPU - //check_cuda( cudaSetDevice(cur_gpu_id) ); - cuda_set_device(cur_gpu_id); - printf(" Used GPU %d \n", cur_gpu_id); + //check_cuda( cudaSetDevice(cur_gpu_id) ); + cuda_set_device(cur_gpu_id); + printf(" Used GPU %d \n", cur_gpu_id); #endif - network &net = detector_gpu.net; - net.gpu_index = cur_gpu_id; - //gpu_index = i; - - char *cfgfile = const_cast(cfg_filename.data()); - char *weightfile = const_cast(weight_filename.data()); - - net = parse_network_cfg_custom(cfgfile, 1); - if (weightfile) { - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - net.gpu_index = cur_gpu_id; - fuse_conv_batchnorm(net); - - layer l = net.layers[net.n - 1]; - int j; - - detector_gpu.avg = (float *)calloc(l.outputs, sizeof(float)); - for (j = 0; j < FRAMES; ++j) detector_gpu.predictions[j] = (float *)calloc(l.outputs, sizeof(float)); - for (j = 0; j < FRAMES; ++j) detector_gpu.images[j] = make_image(1, 1, 3); - - detector_gpu.track_id = (unsigned int *)calloc(l.classes, sizeof(unsigned int)); - for (j = 0; j < l.classes; ++j) detector_gpu.track_id[j] = 1; + network &net = detector_gpu.net; + net.gpu_index = cur_gpu_id; + //gpu_index = i; + + char *cfgfile = const_cast(cfg_filename.data()); + char *weightfile = const_cast(weight_filename.data()); + + net = parse_network_cfg_custom(cfgfile, 1); + if (weightfile) { + load_weights(&net, weightfile); + } + set_batch_network(&net, 1); + net.gpu_index = cur_gpu_id; + fuse_conv_batchnorm(net); + + layer l = net.layers[net.n - 1]; + int j; + + detector_gpu.avg = (float *)calloc(l.outputs, sizeof(float)); + for (j = 0; j < FRAMES; ++j) detector_gpu.predictions[j] = (float *)calloc(l.outputs, sizeof(float)); + for (j = 0; j < FRAMES; ++j) detector_gpu.images[j] = make_image(1, 1, 3); + + detector_gpu.track_id = (unsigned int *)calloc(l.classes, sizeof(unsigned int)); + for (j = 0; j < l.classes; ++j) detector_gpu.track_id[j] = 1; #ifdef GPU - check_cuda(cudaSetDevice(old_gpu_index)); + check_cuda( cudaSetDevice(old_gpu_index) ); #endif } -YOLODLL_API Detector::~Detector() +YOLODLL_API Detector::~Detector() { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - layer l = detector_gpu.net.layers[detector_gpu.net.n - 1]; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + layer l = detector_gpu.net.layers[detector_gpu.net.n - 1]; - free(detector_gpu.track_id); + free(detector_gpu.track_id); - free(detector_gpu.avg); - for (int j = 0; j < FRAMES; ++j) free(detector_gpu.predictions[j]); - for (int j = 0; j < FRAMES; ++j) if (detector_gpu.images[j].data) free(detector_gpu.images[j].data); + free(detector_gpu.avg); + for (int j = 0; j < FRAMES; ++j) free(detector_gpu.predictions[j]); + for (int j = 0; j < FRAMES; ++j) if(detector_gpu.images[j].data) free(detector_gpu.images[j].data); - int old_gpu_index; + int old_gpu_index; #ifdef GPU - cudaGetDevice(&old_gpu_index); - cuda_set_device(detector_gpu.net.gpu_index); + cudaGetDevice(&old_gpu_index); + cuda_set_device(detector_gpu.net.gpu_index); #endif - free_network(detector_gpu.net); + free_network(detector_gpu.net); #ifdef GPU - cudaSetDevice(old_gpu_index); + cudaSetDevice(old_gpu_index); #endif } YOLODLL_API int Detector::get_net_width() const { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - return detector_gpu.net.w; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + return detector_gpu.net.w; } YOLODLL_API int Detector::get_net_height() const { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - return detector_gpu.net.h; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + return detector_gpu.net.h; } YOLODLL_API int Detector::get_net_color_depth() const { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - return detector_gpu.net.c; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + return detector_gpu.net.c; } YOLODLL_API std::vector Detector::detect(std::string image_filename, float thresh, bool use_mean) { - std::shared_ptr image_ptr(new image_t, [](image_t *img) { if (img->data) free(img->data); delete img; }); - *image_ptr = load_image(image_filename); - return detect(*image_ptr, thresh, use_mean); + std::shared_ptr image_ptr(new image_t, [](image_t *img) { if (img->data) free(img->data); delete img; }); + *image_ptr = load_image(image_filename); + return detect(*image_ptr, thresh, use_mean); } static image load_image_stb(char *filename, int channels) { - int w, h, c; - unsigned char *data = stbi_load(filename, &w, &h, &c, channels); - if (!data) - throw std::runtime_error("file not found"); - if (channels) c = channels; - int i, j, k; - image im = make_image(w, h, c); - for (k = 0; k < c; ++k) { - for (j = 0; j < h; ++j) { - for (i = 0; i < w; ++i) { - int dst_index = i + w * j + w * h*k; - int src_index = k + c * i + c * w*j; - im.data[dst_index] = (float)data[src_index] / 255.; - } - } - } - free(data); - return im; + int w, h, c; + unsigned char *data = stbi_load(filename, &w, &h, &c, channels); + if (!data) + throw std::runtime_error("file not found"); + if (channels) c = channels; + int i, j, k; + image im = make_image(w, h, c); + for (k = 0; k < c; ++k) { + for (j = 0; j < h; ++j) { + for (i = 0; i < w; ++i) { + int dst_index = i + w*j + w*h*k; + int src_index = k + c*i + c*w*j; + im.data[dst_index] = (float)data[src_index] / 255.; + } + } + } + free(data); + return im; } YOLODLL_API image_t Detector::load_image(std::string image_filename) { - char *input = const_cast(image_filename.data()); - image im = load_image_stb(input, 3); + char *input = const_cast(image_filename.data()); + image im = load_image_stb(input, 3); - image_t img; - img.c = im.c; - img.data = im.data; - img.h = im.h; - img.w = im.w; + image_t img; + img.c = im.c; + img.data = im.data; + img.h = im.h; + img.w = im.w; - return img; + return img; } YOLODLL_API void Detector::free_image(image_t m) { - if (m.data) { - free(m.data); - } + if (m.data) { + free(m.data); + } } YOLODLL_API std::vector Detector::detect(image_t img, float thresh, bool use_mean) { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - network &net = detector_gpu.net; - int old_gpu_index; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + network &net = detector_gpu.net; + int old_gpu_index; #ifdef GPU - cudaGetDevice(&old_gpu_index); - if (cur_gpu_id != old_gpu_index) - cudaSetDevice(net.gpu_index); + cudaGetDevice(&old_gpu_index); + if(cur_gpu_id != old_gpu_index) + cudaSetDevice(net.gpu_index); - net.wait_stream = wait_stream; // 1 - wait CUDA-stream, 0 - not to wait + net.wait_stream = wait_stream; // 1 - wait CUDA-stream, 0 - not to wait #endif - //std::cout << "net.gpu_index = " << net.gpu_index << std::endl; - - //float nms = .4; - - image im; - im.c = img.c; - im.data = img.data; - im.h = img.h; - im.w = img.w; - - image sized; - - if (net.w == im.w && net.h == im.h) { - sized = make_image(im.w, im.h, im.c); - memcpy(sized.data, im.data, im.w*im.h*im.c * sizeof(float)); - } - else - sized = resize_image(im, net.w, net.h); - - layer l = net.layers[net.n - 1]; - - float *X = sized.data; - - float *prediction = network_predict(net, X); - - if (use_mean) { - memcpy(detector_gpu.predictions[detector_gpu.demo_index], prediction, l.outputs * sizeof(float)); - mean_arrays(detector_gpu.predictions, FRAMES, l.outputs, detector_gpu.avg); - l.output = detector_gpu.avg; - detector_gpu.demo_index = (detector_gpu.demo_index + 1) % FRAMES; - } - //get_region_boxes(l, 1, 1, thresh, detector_gpu.probs, detector_gpu.boxes, 0, 0); - //if (nms) do_nms_sort(detector_gpu.boxes, detector_gpu.probs, l.w*l.h*l.n, l.classes, nms); - - int nboxes = 0; - int letterbox = 0; - float hier_thresh = 0.5; - detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); - if (nms) do_nms_sort(dets, nboxes, l.classes, nms); - - std::vector bbox_vec; - - for (size_t i = 0; i < nboxes; ++i) { - box b = dets[i].bbox; - int const obj_id = max_index(dets[i].prob, l.classes); - float const prob = dets[i].prob[obj_id]; - - if (prob > thresh) - { - bbox_t bbox; - bbox.x = std::max((double)0, (b.x - b.w / 2.)*im.w); - bbox.y = std::max((double)0, (b.y - b.h / 2.)*im.h); - bbox.w = b.w*im.w; - bbox.h = b.h*im.h; - bbox.obj_id = obj_id; - bbox.prob = prob; - bbox.track_id = 0; - - bbox_vec.push_back(bbox); - } - } - - free_detections(dets, nboxes); - if (sized.data) - free(sized.data); + //std::cout << "net.gpu_index = " << net.gpu_index << std::endl; + + //float nms = .4; + + image im; + im.c = img.c; + im.data = img.data; + im.h = img.h; + im.w = img.w; + + image sized; + + if (net.w == im.w && net.h == im.h) { + sized = make_image(im.w, im.h, im.c); + memcpy(sized.data, im.data, im.w*im.h*im.c * sizeof(float)); + } + else + sized = resize_image(im, net.w, net.h); + + layer l = net.layers[net.n - 1]; + + float *X = sized.data; + + float *prediction = network_predict(net, X); + + if (use_mean) { + memcpy(detector_gpu.predictions[detector_gpu.demo_index], prediction, l.outputs * sizeof(float)); + mean_arrays(detector_gpu.predictions, FRAMES, l.outputs, detector_gpu.avg); + l.output = detector_gpu.avg; + detector_gpu.demo_index = (detector_gpu.demo_index + 1) % FRAMES; + } + //get_region_boxes(l, 1, 1, thresh, detector_gpu.probs, detector_gpu.boxes, 0, 0); + //if (nms) do_nms_sort(detector_gpu.boxes, detector_gpu.probs, l.w*l.h*l.n, l.classes, nms); + + int nboxes = 0; + int letterbox = 0; + float hier_thresh = 0.5; + detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); + if (nms) do_nms_sort(dets, nboxes, l.classes, nms); + + std::vector bbox_vec; + + for (size_t i = 0; i < nboxes; ++i) { + box b = dets[i].bbox; + int const obj_id = max_index(dets[i].prob, l.classes); + float const prob = dets[i].prob[obj_id]; + + if (prob > thresh) + { + bbox_t bbox; + bbox.x = std::max((double)0, (b.x - b.w / 2.)*im.w); + bbox.y = std::max((double)0, (b.y - b.h / 2.)*im.h); + bbox.w = b.w*im.w; + bbox.h = b.h*im.h; + bbox.obj_id = obj_id; + bbox.prob = prob; + bbox.track_id = 0; + + bbox_vec.push_back(bbox); + } + } + + free_detections(dets, nboxes); + if(sized.data) + free(sized.data); #ifdef GPU - if (cur_gpu_id != old_gpu_index) - cudaSetDevice(old_gpu_index); + if (cur_gpu_id != old_gpu_index) + cudaSetDevice(old_gpu_index); #endif - return bbox_vec; + return bbox_vec; } -YOLODLL_API std::vector Detector::tracking_id(std::vector cur_bbox_vec, bool const change_history, - int const frames_story, int const max_dist) +YOLODLL_API std::vector Detector::tracking_id(std::vector cur_bbox_vec, bool const change_history, + int const frames_story, int const max_dist) { - detector_gpu_t &det_gpu = *static_cast(detector_gpu_ptr.get()); - - bool prev_track_id_present = false; - for (auto &i : prev_bbox_vec_deque) - if (i.size() > 0) prev_track_id_present = true; - - if (!prev_track_id_present) { - for (size_t i = 0; i < cur_bbox_vec.size(); ++i) - cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++; - prev_bbox_vec_deque.push_front(cur_bbox_vec); - if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back(); - return cur_bbox_vec; - } - - std::vector dist_vec(cur_bbox_vec.size(), std::numeric_limits::max()); - - for (auto &prev_bbox_vec : prev_bbox_vec_deque) { - for (auto &i : prev_bbox_vec) { - int cur_index = -1; - for (size_t m = 0; m < cur_bbox_vec.size(); ++m) { - bbox_t const& k = cur_bbox_vec[m]; - if (i.obj_id == k.obj_id) { - float center_x_diff = (float)(i.x + i.w / 2) - (float)(k.x + k.w / 2); - float center_y_diff = (float)(i.y + i.h / 2) - (float)(k.y + k.h / 2); - unsigned int cur_dist = sqrt(center_x_diff*center_x_diff + center_y_diff * center_y_diff); - if (cur_dist < max_dist && (k.track_id == 0 || dist_vec[m] > cur_dist)) { - dist_vec[m] = cur_dist; - cur_index = m; - } - } - } - - bool track_id_absent = !std::any_of(cur_bbox_vec.begin(), cur_bbox_vec.end(), - [&i](bbox_t const& b) { return b.track_id == i.track_id && b.obj_id == i.obj_id; }); - - if (cur_index >= 0 && track_id_absent) { - cur_bbox_vec[cur_index].track_id = i.track_id; - cur_bbox_vec[cur_index].w = (cur_bbox_vec[cur_index].w + i.w) / 2; - cur_bbox_vec[cur_index].h = (cur_bbox_vec[cur_index].h + i.h) / 2; - } - } - } - - for (size_t i = 0; i < cur_bbox_vec.size(); ++i) - if (cur_bbox_vec[i].track_id == 0) - cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++; - - if (change_history) { - prev_bbox_vec_deque.push_front(cur_bbox_vec); - if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back(); - } - - return cur_bbox_vec; + detector_gpu_t &det_gpu = *static_cast(detector_gpu_ptr.get()); + + bool prev_track_id_present = false; + for (auto &i : prev_bbox_vec_deque) + if (i.size() > 0) prev_track_id_present = true; + + if (!prev_track_id_present) { + for (size_t i = 0; i < cur_bbox_vec.size(); ++i) + cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++; + prev_bbox_vec_deque.push_front(cur_bbox_vec); + if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back(); + return cur_bbox_vec; + } + + std::vector dist_vec(cur_bbox_vec.size(), std::numeric_limits::max()); + + for (auto &prev_bbox_vec : prev_bbox_vec_deque) { + for (auto &i : prev_bbox_vec) { + int cur_index = -1; + for (size_t m = 0; m < cur_bbox_vec.size(); ++m) { + bbox_t const& k = cur_bbox_vec[m]; + if (i.obj_id == k.obj_id) { + float center_x_diff = (float)(i.x + i.w/2) - (float)(k.x + k.w/2); + float center_y_diff = (float)(i.y + i.h/2) - (float)(k.y + k.h/2); + unsigned int cur_dist = sqrt(center_x_diff*center_x_diff + center_y_diff*center_y_diff); + if (cur_dist < max_dist && (k.track_id == 0 || dist_vec[m] > cur_dist)) { + dist_vec[m] = cur_dist; + cur_index = m; + } + } + } + + bool track_id_absent = !std::any_of(cur_bbox_vec.begin(), cur_bbox_vec.end(), + [&i](bbox_t const& b) { return b.track_id == i.track_id && b.obj_id == i.obj_id; }); + + if (cur_index >= 0 && track_id_absent){ + cur_bbox_vec[cur_index].track_id = i.track_id; + cur_bbox_vec[cur_index].w = (cur_bbox_vec[cur_index].w + i.w) / 2; + cur_bbox_vec[cur_index].h = (cur_bbox_vec[cur_index].h + i.h) / 2; + } + } + } + + for (size_t i = 0; i < cur_bbox_vec.size(); ++i) + if (cur_bbox_vec[i].track_id == 0) + cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++; + + if (change_history) { + prev_bbox_vec_deque.push_front(cur_bbox_vec); + if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back(); + } + + return cur_bbox_vec; } \ No newline at end of file From bff7644f31501fb8dd547e032e5ef6de67cf673e Mon Sep 17 00:00:00 2001 From: Tino Hager Date: Wed, 27 Jun 2018 23:59:10 +0200 Subject: [PATCH 0093/1365] remove max_objects --- src/yolo_v2_class.cpp | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index c518fd77d3a..4df9be5d44f 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -22,8 +22,6 @@ extern "C" { #define FRAMES 3 -int max_objects() { return C_SHARP_MAX_OBJECTS; } - //static Detector* detector = NULL; static std::unique_ptr detector; From eae7b34cc0906268ddef2e64551ea5ca0951c0c8 Mon Sep 17 00:00:00 2001 From: Ruslan_Lenin <9992012@gmail.com> Date: Fri, 29 Jun 2018 11:39:03 +0300 Subject: [PATCH 0094/1365] Update gemm.c optimization calculations --- src/gemm.c | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/src/gemm.c b/src/gemm.c index 317fd650354..506687be4ab 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -273,10 +273,12 @@ void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA, float *C, int ldc) { //printf("cpu: %d %d %d %d %d %f %d %d %f %d\n",TA, TB, M, N, K, ALPHA, lda, ldb, BETA, ldc); - int i, j; - for(i = 0; i < M; ++i){ - for(j = 0; j < N; ++j){ - C[i*ldc + j] *= BETA; + if (BETA != 1){ + int i, j; + for(i = 0; i < M; ++i){ + for(j = 0; j < N; ++j){ + C[i*ldc + j] *= BETA; + } } } From 1e9d1fcedf1a361bcdb384f15b5b14bdb526576d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 30 Jun 2018 23:12:25 +0300 Subject: [PATCH 0095/1365] Fixed arch=compute_53,code=[sm_53,compute_53] for Jetson TX1 --- Makefile | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Makefile b/Makefile index 5c39df1f554..ba610865ec2 100644 --- a/Makefile +++ b/Makefile @@ -29,8 +29,8 @@ OS := $(shell uname) # GP100/Tesla P100 – DGX-1 # ARCH= -gencode arch=compute_60,code=sm_60 -# For Jetson Tx1 uncomment: -# ARCH= -gencode arch=compute_51,code=[sm_51,compute_51] +# For Jetson TX1, Tegra X1, DRIVE CX, DRIVE PX - uncomment: +# ARCH= -gencode arch=compute_53,code=[sm_53,compute_53] # For Jetson Tx2 or Drive-PX2 uncomment: # ARCH= -gencode arch=compute_62,code=[sm_62,compute_62] From 2bac3681fcecb225c5e5a6376d7f95835cd8f89e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 2 Jul 2018 13:52:11 +0300 Subject: [PATCH 0096/1365] Minor fixes --- build/darknet/x64/darknet_yolov3_pseuso_labeling.cmd | 10 ++++++++++ build/darknet/x64/yolov3.cfg | 6 +++--- 2 files changed, 13 insertions(+), 3 deletions(-) create mode 100644 build/darknet/x64/darknet_yolov3_pseuso_labeling.cmd diff --git a/build/darknet/x64/darknet_yolov3_pseuso_labeling.cmd b/build/darknet/x64/darknet_yolov3_pseuso_labeling.cmd new file mode 100644 index 00000000000..160d91048e8 --- /dev/null +++ b/build/darknet/x64/darknet_yolov3_pseuso_labeling.cmd @@ -0,0 +1,10 @@ +darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights -thresh 0.25 -dont_show -save_labels < images.txt + + + +rem -thresh 0.1 + + + + +pause \ No newline at end of file diff --git a/build/darknet/x64/yolov3.cfg b/build/darknet/x64/yolov3.cfg index 33d94217175..b2dd2b84e3e 100644 --- a/build/darknet/x64/yolov3.cfg +++ b/build/darknet/x64/yolov3.cfg @@ -610,7 +610,7 @@ anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 37 classes=80 num=9 jitter=.3 -ignore_thresh = .5 +ignore_thresh = .7 truth_thresh = 1 random=1 @@ -696,7 +696,7 @@ anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 37 classes=80 num=9 jitter=.3 -ignore_thresh = .5 +ignore_thresh = .7 truth_thresh = 1 random=1 @@ -783,7 +783,7 @@ anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 37 classes=80 num=9 jitter=.3 -ignore_thresh = .5 +ignore_thresh = .7 truth_thresh = 1 random=1 From 6d2e31bf2026301a25a3e8dabeae0680c159328b Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 2 Jul 2018 19:29:10 +0300 Subject: [PATCH 0097/1365] Update Readme.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 690cd74e02f..a52aafa33ff 100644 --- a/README.md +++ b/README.md @@ -422,7 +422,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * check that each object are mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: https://github.com/AlexeyAB/Yolo_mark - * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 images for each class or more + * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 different images for each class or more, and you should train `2000*classes` iterations or more * desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty `.txt` files) - use as many images of negative samples as there are images with objects From f4d4099104c0a746e2ea2349d724e6fbca0a8757 Mon Sep 17 00:00:00 2001 From: Tino Hager Date: Tue, 3 Jul 2018 22:29:10 +0200 Subject: [PATCH 0098/1365] Optimize YoloWrapper protect internal methods, add exception if openCV support is not available --- build/darknet/YoloWrapper.cs | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/build/darknet/YoloWrapper.cs b/build/darknet/YoloWrapper.cs index 14bf56d48b8..f0c1b0c270f 100644 --- a/build/darknet/YoloWrapper.cs +++ b/build/darknet/YoloWrapper.cs @@ -9,16 +9,16 @@ public class YoloWrapper : IDisposable private const int MaxObjects = 1000; [DllImport(YoloLibraryName, EntryPoint = "init")] - public static extern int InitializeYolo(string configurationFilename, string weightsFilename, int gpu); + private static extern int InitializeYolo(string configurationFilename, string weightsFilename, int gpu); [DllImport(YoloLibraryName, EntryPoint = "detect_image")] - public static extern int DetectImage(string filename, ref BboxContainer container); + private static extern int DetectImage(string filename, ref BboxContainer container); [DllImport(YoloLibraryName, EntryPoint = "detect_mat")] - public static extern int DetectImage(IntPtr pArray, int nSize, ref BboxContainer container); + private static extern int DetectImage(IntPtr pArray, int nSize, ref BboxContainer container); [DllImport(YoloLibraryName, EntryPoint = "dispose")] - public static extern int DisposeYolo(); + private static extern int DisposeYolo(); [StructLayout(LayoutKind.Sequential)] public struct bbox_t @@ -66,7 +66,11 @@ public bbox_t[] Detect(byte[] imageData) { // Copy the array to unmanaged memory. Marshal.Copy(imageData, 0, pnt, imageData.Length); - DetectImage(pnt, imageData.Length, ref container); + var count = DetectImage(pnt, imageData.Length, ref container); + if (count == -1) + { + throw new NotSupportedException($"{YoloLibraryName} has no OpenCV support"); + } } catch (Exception exception) { From 4b9120105199c1803b13fb659935ff78378448a8 Mon Sep 17 00:00:00 2001 From: vinjn Date: Wed, 4 Jul 2018 23:16:42 +0800 Subject: [PATCH 0099/1365] Expose YOLODLL_API void cuda_pull_array() --- src/cuda.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/cuda.h b/src/cuda.h index e167d367db8..92a3ed254fa 100644 --- a/src/cuda.h +++ b/src/cuda.h @@ -41,7 +41,7 @@ extern "C" { float *cuda_make_array(float *x, size_t n); int *cuda_make_int_array(size_t n); void cuda_push_array(float *x_gpu, float *x, size_t n); - void cuda_pull_array(float *x_gpu, float *x, size_t n); + YOLODLL_API void cuda_pull_array(float *x_gpu, float *x, size_t n); YOLODLL_API void cuda_set_device(int n); int cuda_get_device(); void cuda_free(float *x_gpu); From f7961c3ca883f5ab63216e869d45e8925fc1cee7 Mon Sep 17 00:00:00 2001 From: Alexey Date: Wed, 4 Jul 2018 19:00:42 +0300 Subject: [PATCH 0100/1365] Update Readme.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a52aafa33ff..89b053fd37b 100644 --- a/README.md +++ b/README.md @@ -128,7 +128,7 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c * `DEBUG=1` to bould debug version of Yolo * `OPENMP=1` to build with OpenMP support to accelerate Yolo by using multi-core CPU * `LIBSO=1` to build a library `darknet.so` and binary runable file `uselib` that uses this library. Or you can try to run so `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib test.mp4` How to use this SO-library from your own code - you can look at C++ example: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp - + or use in such a way: `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov3.cfg yolov3.weights test.mp4` ### How to compile on Windows: From b7a6dffd3e213541ac9ac654dfa0b6395f326a47 Mon Sep 17 00:00:00 2001 From: Alexey Date: Fri, 6 Jul 2018 21:58:35 +0300 Subject: [PATCH 0101/1365] Update Readme.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 89b053fd37b..f980b9d8ef6 100644 --- a/README.md +++ b/README.md @@ -324,6 +324,8 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an **Note:** After training use such command for detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights` + **Note:** if error `Out of memory` occurs then in `.cfg`-file you should increase `subdivisions=16`, 32 or 64: [link](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4) + ### How to train tiny-yolo (to detect your custom objects): Do all the same steps as for the full yolo model as described above. With the exception of: From 564877ad6a3f53d3d866b0015237d07f4af2eaa2 Mon Sep 17 00:00:00 2001 From: vinjn Date: Sat, 7 Jul 2018 12:30:45 +0800 Subject: [PATCH 0102/1365] cuda.h - converts tab to space --- src/cuda.h | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/src/cuda.h b/src/cuda.h index 92a3ed254fa..50bb82b402a 100644 --- a/src/cuda.h +++ b/src/cuda.h @@ -36,19 +36,19 @@ extern int gpu_index; #ifdef __cplusplus extern "C" { #endif // __cplusplus - void check_error(cudaError_t status); - cublasHandle_t blas_handle(); - float *cuda_make_array(float *x, size_t n); - int *cuda_make_int_array(size_t n); - void cuda_push_array(float *x_gpu, float *x, size_t n); + void check_error(cudaError_t status); + cublasHandle_t blas_handle(); + float *cuda_make_array(float *x, size_t n); + int *cuda_make_int_array(size_t n); + void cuda_push_array(float *x_gpu, float *x, size_t n); YOLODLL_API void cuda_pull_array(float *x_gpu, float *x, size_t n); - YOLODLL_API void cuda_set_device(int n); - int cuda_get_device(); - void cuda_free(float *x_gpu); - void cuda_random(float *x_gpu, size_t n); - float cuda_compare(float *x_gpu, float *x, size_t n, char *s); - dim3 cuda_gridsize(size_t n); - cudaStream_t get_cuda_stream(); + YOLODLL_API void cuda_set_device(int n); + int cuda_get_device(); + void cuda_free(float *x_gpu); + void cuda_random(float *x_gpu, size_t n); + float cuda_compare(float *x_gpu, float *x, size_t n, char *s); + dim3 cuda_gridsize(size_t n); + cudaStream_t get_cuda_stream(); #ifdef __cplusplus } #endif // __cplusplus From e7f64b15066b58879b086fb9d89d28e8d16519ec Mon Sep 17 00:00:00 2001 From: Alexey Date: Sat, 7 Jul 2018 13:15:08 +0300 Subject: [PATCH 0103/1365] Update Readme.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f980b9d8ef6..b02da0164d7 100644 --- a/README.md +++ b/README.md @@ -282,7 +282,7 @@ Training Yolo v3: It will create `.txt`-file for each `.jpg`-image-file - in the same directory and with the same name, but with `.txt`-extension, and put to file: object number and object coordinates on this image, for each object in new line: ` ` Where: - * `` - integer number of object from `0` to `(classes-1)` + * `` - integer object number from `0` to `(classes-1)` * ` ` - float values relative to width and height of image, it can be equal from (0.0 to 1.0] * for example: ` = / ` or ` = / ` * atention: ` ` - are center of rectangle (are not top-left corner) From 81d4688b12702ecede13bbc50993eece57469db3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 10 Jul 2018 19:12:39 +0300 Subject: [PATCH 0104/1365] Fix for MS COCO validation --- src/data.c | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/src/data.c b/src/data.c index d92e307889a..ec7d36f3590 100644 --- a/src/data.c +++ b/src/data.c @@ -141,8 +141,16 @@ box_label *read_boxes(char *filename, int *n) box_label *boxes = calloc(1, sizeof(box_label)); FILE *file = fopen(filename, "r"); if (!file) { - printf("Can't open label file. \n"); - file_error(filename); + printf("Can't open label file. (This can be normal only if you use MSCOCO) \n"); + //file_error(filename); + FILE* fw = fopen("bad.list", "a"); + fwrite(filename, sizeof(char), strlen(filename), fw); + char *new_line = "\n"; + fwrite(new_line, sizeof(char), strlen(new_line), fw); + fclose(fw); + + *n = 0; + return boxes; } float x, y, h, w; int id; From c021841a52a7b71224b755bfbf779b05012b59d5 Mon Sep 17 00:00:00 2001 From: Tino Hager Date: Tue, 10 Jul 2018 21:46:14 +0200 Subject: [PATCH 0105/1365] Get gpu count and name Add two new methods to get informations about graphic device --- src/yolo_v2_class.cpp | 22 ++++++++++++++++++++++ src/yolo_v2_class.hpp | 2 ++ 2 files changed, 24 insertions(+) diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index 4df9be5d44f..64f2afc0aed 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -60,6 +60,28 @@ int dispose() { return 1; } +int get_device_count() { +#ifdef GPU + int count = 0; + cudaGetDeviceCount(&count); + return count; +#else + return -1; +#endif // GPU +} + +int get_device_name(int gpu, char* deviceName) { +#ifdef GPU + cudaDeviceProp prop; + cudaGetDeviceProperties(&prop, gpu); + std::string result = prop.name; + std::copy(result.begin(), result.end(), deviceName); + return 1; +#else + return -1; +#endif // GPU +} + #ifdef GPU void check_cuda(cudaError_t status) { if (status != cudaSuccess) { diff --git a/src/yolo_v2_class.hpp b/src/yolo_v2_class.hpp index 8f7f6cab5c6..9ed7dd4ecad 100644 --- a/src/yolo_v2_class.hpp +++ b/src/yolo_v2_class.hpp @@ -49,6 +49,8 @@ extern "C" YOLODLL_API int init(const char *configurationFilename, const char *w extern "C" YOLODLL_API int detect_image(const char *filename, bbox_t_container &container); extern "C" YOLODLL_API int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container &container); extern "C" YOLODLL_API int dispose(); +extern "C" YOLODLL_API int get_device_count(); +extern "C" YOLODLL_API int get_device_name(int gpu, char* deviceName); class Detector { std::shared_ptr detector_gpu_ptr; From cfc5fedbb6df2471493b1ec162d0024485618211 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 10 Jul 2018 23:29:15 +0300 Subject: [PATCH 0106/1365] Just used spaces for indents instead of Tabs --- src/.editorconfig | 8 + src/batchnorm_layer.c | 152 +-- src/blas.c | 32 +- src/blas_kernels.cu | 62 +- src/box.c | 148 +-- src/convolutional_kernels.cu | 470 ++++----- src/convolutional_layer.c | 226 ++-- src/cuda.c | 46 +- src/data.c | 326 +++--- src/demo.c | 274 ++--- src/detector.c | 1900 +++++++++++++++++----------------- src/gemm.c | 216 ++-- src/gettimeofday.c | 2 +- src/http_stream.cpp | 484 ++++----- src/http_stream.h | 4 +- src/image.c | 1174 ++++++++++----------- src/list.c | 74 +- src/network.c | 366 +++---- src/network_kernels.cu | 46 +- src/option_list.c | 28 +- src/parser.c | 234 ++--- src/region_layer.c | 296 +++--- src/reorg_layer.c | 48 +- src/reorg_old_layer.c | 48 +- src/shortcut_layer.c | 24 +- src/tree.c | 54 +- src/utils.c | 148 +-- src/yolo_console_dll.cpp | 806 +++++++------- src/yolo_layer.c | 148 +-- src/yolo_v2_class.cpp | 448 ++++---- src/yolo_v2_class.hpp | 1012 +++++++++--------- 31 files changed, 4656 insertions(+), 4648 deletions(-) create mode 100644 src/.editorconfig diff --git a/src/.editorconfig b/src/.editorconfig new file mode 100644 index 00000000000..2eb162b28f6 --- /dev/null +++ b/src/.editorconfig @@ -0,0 +1,8 @@ +root=true + +[*] +trim_trailing_whitespace = true +indent_style = space +indent_size = 4 + + diff --git a/src/batchnorm_layer.c b/src/batchnorm_layer.c index d35d9d2ec26..3fa129db1f5 100644 --- a/src/batchnorm_layer.c +++ b/src/batchnorm_layer.c @@ -53,10 +53,10 @@ layer make_batchnorm_layer(int batch, int w, int h, int c) layer.x_gpu = cuda_make_array(layer.output, layer.batch*layer.outputs); layer.x_norm_gpu = cuda_make_array(layer.output, layer.batch*layer.outputs); #ifdef CUDNN - cudnnCreateTensorDescriptor(&layer.normTensorDesc); - cudnnCreateTensorDescriptor(&layer.normDstTensorDesc); - cudnnSetTensor4dDescriptor(layer.normDstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, layer.batch, layer.out_c, layer.out_h, layer.out_w); - cudnnSetTensor4dDescriptor(layer.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, layer.out_c, 1, 1); + cudnnCreateTensorDescriptor(&layer.normTensorDesc); + cudnnCreateTensorDescriptor(&layer.normDstTensorDesc); + cudnnSetTensor4dDescriptor(layer.normDstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, layer.batch, layer.out_c, layer.out_h, layer.out_w); + cudnnSetTensor4dDescriptor(layer.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, layer.out_c, 1, 1); #endif #endif return layer; @@ -179,93 +179,93 @@ void push_batchnorm_layer(layer l) void forward_batchnorm_layer_gpu(layer l, network_state state) { - if (l.type == BATCHNORM) copy_ongpu(l.outputs*l.batch, state.input, 1, l.output_gpu, 1); - copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1); - if (state.train) { + if (l.type == BATCHNORM) copy_ongpu(l.outputs*l.batch, state.input, 1, l.output_gpu, 1); + copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1); + if (state.train) { #ifdef CUDNN - float one = 1; - float zero = 0; - cudnnBatchNormalizationForwardTraining(cudnn_handle(), - CUDNN_BATCHNORM_SPATIAL, - &one, - &zero, - l.normDstTensorDesc, - l.x_gpu, // input - l.normDstTensorDesc, - l.output_gpu, // output - l.normTensorDesc, - l.scales_gpu, - l.biases_gpu, - .01, - l.rolling_mean_gpu, // output (should be FP32) - l.rolling_variance_gpu, // output (should be FP32) - .00001, - l.mean_gpu, // output (should be FP32) - l.variance_gpu); // output (should be FP32) + float one = 1; + float zero = 0; + cudnnBatchNormalizationForwardTraining(cudnn_handle(), + CUDNN_BATCHNORM_SPATIAL, + &one, + &zero, + l.normDstTensorDesc, + l.x_gpu, // input + l.normDstTensorDesc, + l.output_gpu, // output + l.normTensorDesc, + l.scales_gpu, + l.biases_gpu, + .01, + l.rolling_mean_gpu, // output (should be FP32) + l.rolling_variance_gpu, // output (should be FP32) + .00001, + l.mean_gpu, // output (should be FP32) + l.variance_gpu); // output (should be FP32) #else - fast_mean_gpu(l.output_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.mean_gpu); - fast_variance_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.variance_gpu); + fast_mean_gpu(l.output_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.mean_gpu); + fast_variance_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.variance_gpu); - scal_ongpu(l.out_c, .99, l.rolling_mean_gpu, 1); - axpy_ongpu(l.out_c, .01, l.mean_gpu, 1, l.rolling_mean_gpu, 1); - scal_ongpu(l.out_c, .99, l.rolling_variance_gpu, 1); - axpy_ongpu(l.out_c, .01, l.variance_gpu, 1, l.rolling_variance_gpu, 1); + scal_ongpu(l.out_c, .99, l.rolling_mean_gpu, 1); + axpy_ongpu(l.out_c, .01, l.mean_gpu, 1, l.rolling_mean_gpu, 1); + scal_ongpu(l.out_c, .99, l.rolling_variance_gpu, 1); + axpy_ongpu(l.out_c, .01, l.variance_gpu, 1, l.rolling_variance_gpu, 1); - copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1); - normalize_gpu(l.output_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); - copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_norm_gpu, 1); + copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1); + normalize_gpu(l.output_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); + copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_norm_gpu, 1); - scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); - add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h); + scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h); #endif - } - else { - normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); - scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); - add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h); - } + } + else { + normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); + scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h); + } } void backward_batchnorm_layer_gpu(layer l, network_state state) { - if (!state.train) { - l.mean_gpu = l.rolling_mean_gpu; - l.variance_gpu = l.rolling_variance_gpu; - } + if (!state.train) { + l.mean_gpu = l.rolling_mean_gpu; + l.variance_gpu = l.rolling_variance_gpu; + } #ifdef CUDNN - float one = 1; - float zero = 0; - cudnnBatchNormalizationBackward(cudnn_handle(), - CUDNN_BATCHNORM_SPATIAL, - &one, - &zero, - &one, - &one, - l.normDstTensorDesc, - l.x_gpu, // input - l.normDstTensorDesc, - l.delta_gpu, // input - l.normDstTensorDesc, - l.x_norm_gpu, // output - l.normTensorDesc, - l.scales_gpu, // output (should be FP32) - l.scale_updates_gpu, // output (should be FP32) - l.bias_updates_gpu, // output (should be FP32) - .00001, - l.mean_gpu, // input (should be FP32) - l.variance_gpu); // input (should be FP32) - copy_ongpu(l.outputs*l.batch, l.x_norm_gpu, 1, l.delta_gpu, 1); + float one = 1; + float zero = 0; + cudnnBatchNormalizationBackward(cudnn_handle(), + CUDNN_BATCHNORM_SPATIAL, + &one, + &zero, + &one, + &one, + l.normDstTensorDesc, + l.x_gpu, // input + l.normDstTensorDesc, + l.delta_gpu, // input + l.normDstTensorDesc, + l.x_norm_gpu, // output + l.normTensorDesc, + l.scales_gpu, // output (should be FP32) + l.scale_updates_gpu, // output (should be FP32) + l.bias_updates_gpu, // output (should be FP32) + .00001, + l.mean_gpu, // input (should be FP32) + l.variance_gpu); // input (should be FP32) + copy_ongpu(l.outputs*l.batch, l.x_norm_gpu, 1, l.delta_gpu, 1); #else - backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h); - backward_scale_gpu(l.x_norm_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.scale_updates_gpu); + backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h); + backward_scale_gpu(l.x_norm_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.scale_updates_gpu); - scale_bias_gpu(l.delta_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); + scale_bias_gpu(l.delta_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); - fast_mean_delta_gpu(l.delta_gpu, l.variance_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.mean_delta_gpu); - fast_variance_delta_gpu(l.x_gpu, l.delta_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.variance_delta_gpu); - normalize_delta_gpu(l.x_gpu, l.mean_gpu, l.variance_gpu, l.mean_delta_gpu, l.variance_delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu); + fast_mean_delta_gpu(l.delta_gpu, l.variance_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.mean_delta_gpu); + fast_variance_delta_gpu(l.x_gpu, l.delta_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.variance_delta_gpu); + normalize_delta_gpu(l.x_gpu, l.mean_gpu, l.variance_gpu, l.mean_delta_gpu, l.variance_delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu); #endif - if (l.type == BATCHNORM) copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, state.delta, 1); + if (l.type == BATCHNORM) copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, state.delta, 1); } #endif \ No newline at end of file diff --git a/src/blas.c b/src/blas.c index 4ff0b8343ab..ccf05223dab 100644 --- a/src/blas.c +++ b/src/blas.c @@ -11,8 +11,8 @@ void reorg_cpu(float *x, int out_w, int out_h, int out_c, int batch, int stride, int b,i,j,k; int in_c = out_c/(stride*stride); - //printf("\n out_c = %d, out_w = %d, out_h = %d, stride = %d, forward = %d \n", out_c, out_w, out_h, stride, forward); - //printf(" in_c = %d, in_w = %d, in_h = %d \n", in_c, out_w*stride, out_h*stride); + //printf("\n out_c = %d, out_w = %d, out_h = %d, stride = %d, forward = %d \n", out_c, out_w, out_h, stride, forward); + //printf(" in_c = %d, in_w = %d, in_h = %d \n", in_c, out_w*stride, out_h*stride); for(b = 0; b < batch; ++b){ for(k = 0; k < out_c; ++k){ @@ -24,7 +24,7 @@ void reorg_cpu(float *x, int out_w, int out_h, int out_c, int batch, int stride, int w2 = i*stride + offset % stride; int h2 = j*stride + offset / stride; int out_index = w2 + out_w*stride*(h2 + out_h*stride*(c2 + in_c*b)); - if(forward) out[out_index] = x[in_index]; // used by default for forward (i.e. forward = 0) + if(forward) out[out_index] = x[in_index]; // used by default for forward (i.e. forward = 0) else out[in_index] = x[out_index]; } } @@ -293,17 +293,17 @@ void softmax_cpu(float *input, int n, int batch, int batch_offset, int groups, i void upsample_cpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out) { - int i, j, k, b; - for (b = 0; b < batch; ++b) { - for (k = 0; k < c; ++k) { - for (j = 0; j < h*stride; ++j) { - for (i = 0; i < w*stride; ++i) { - int in_index = b*w*h*c + k*w*h + (j / stride)*w + i / stride; - int out_index = b*w*h*c*stride*stride + k*w*h*stride*stride + j*w*stride + i; - if (forward) out[out_index] = scale*in[in_index]; - else in[in_index] += scale*out[out_index]; - } - } - } - } + int i, j, k, b; + for (b = 0; b < batch; ++b) { + for (k = 0; k < c; ++k) { + for (j = 0; j < h*stride; ++j) { + for (i = 0; i < w*stride; ++i) { + int in_index = b*w*h*c + k*w*h + (j / stride)*w + i / stride; + int out_index = b*w*h*c*stride*stride + k*w*h*stride*stride + j*w*stride + i; + if (forward) out[out_index] = scale*in[in_index]; + else in[in_index] += scale*out[out_index]; + } + } + } + } } \ No newline at end of file diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 1edbbbde950..34c0008b531 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -157,16 +157,16 @@ extern "C" void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2 extern "C" void adam_update_gpu(float *w, float *d, float *m, float *v, float B1, float B2, float eps, float decay, float rate, int n, int batch, int t) { - scal_ongpu(n, B1, m, 1); - scal_ongpu(n, B2, v, 1); - axpy_ongpu(n, -decay*batch, w, 1, d, 1); + scal_ongpu(n, B1, m, 1); + scal_ongpu(n, B2, v, 1); + axpy_ongpu(n, -decay*batch, w, 1, d, 1); - axpy_ongpu(n, (1 - B1), d, 1, m, 1); - mul_ongpu(n, d, 1, d, 1); - axpy_ongpu(n, (1 - B2), d, 1, v, 1); + axpy_ongpu(n, (1 - B1), d, 1, m, 1); + mul_ongpu(n, d, 1, d, 1); + axpy_ongpu(n, (1 - B2), d, 1, v, 1); - adam_gpu(n, w, m, v, B1, B2, rate, eps, t); - fill_ongpu(n, 0, d, 1); + adam_gpu(n, w, m, v, B1, B2, rate, eps, t); + fill_ongpu(n, 0, d, 1); } __global__ void normalize_kernel(int N, float *x, float *mean, float *variance, int batch, int filters, int spatial) @@ -237,7 +237,7 @@ __global__ void fast_mean_delta_kernel(float *delta, float *variance, int batch, local[id] += (i+id < spatial) ? delta[index] : 0; } } - __syncthreads(); + __syncthreads(); if(id == 0){ mean_delta[filter] = 0; @@ -266,7 +266,7 @@ __global__ void fast_variance_delta_kernel(float *x, float *delta, float *mean, local[id] += (i+id < spatial) ? delta[index]*(x[index] - mean[filter]) : 0; } } - __syncthreads(); + __syncthreads(); if(id == 0){ variance_delta[filter] = 0; @@ -462,7 +462,7 @@ __global__ void fast_mean_kernel(float *x, int batch, int filters, int spatial, local[id] += (i+id < spatial) ? x[index] : 0; } } - __syncthreads(); + __syncthreads(); if(id == 0){ mean[filter] = 0; @@ -491,7 +491,7 @@ __global__ void fast_variance_kernel(float *x, float *mean, int batch, int filt local[id] += (i+id < spatial) ? powf((x[index] - mean[filter]), 2) : 0; } } - __syncthreads(); + __syncthreads(); if(id == 0){ variance[filter] = 0; @@ -787,31 +787,31 @@ extern "C" void softmax_gpu(float *input, int n, int offset, int groups, float t __global__ void upsample_kernel(size_t N, float *x, int w, int h, int c, int batch, int stride, int forward, float scale, float *out) { - size_t i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; - if (i >= N) return; - int out_index = i; - int out_w = i % (w*stride); - i = i / (w*stride); - int out_h = i % (h*stride); - i = i / (h*stride); - int out_c = i%c; - i = i / c; - int b = i%batch; + size_t i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (i >= N) return; + int out_index = i; + int out_w = i % (w*stride); + i = i / (w*stride); + int out_h = i % (h*stride); + i = i / (h*stride); + int out_c = i%c; + i = i / c; + int b = i%batch; - int in_w = out_w / stride; - int in_h = out_h / stride; - int in_c = out_c; + int in_w = out_w / stride; + int in_h = out_h / stride; + int in_c = out_c; - int in_index = b*w*h*c + in_c*w*h + in_h*w + in_w; + int in_index = b*w*h*c + in_c*w*h + in_h*w + in_w; - if (forward) out[out_index] += scale * x[in_index]; - else atomicAdd(x + in_index, scale * out[out_index]); + if (forward) out[out_index] += scale * x[in_index]; + else atomicAdd(x + in_index, scale * out[out_index]); } extern "C" void upsample_gpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out) { - size_t size = w*h*c*batch*stride*stride; - upsample_kernel << > >(size, in, w, h, c, batch, stride, forward, scale, out); - check_error(cudaPeekAtLastError()); + size_t size = w*h*c*batch*stride*stride; + upsample_kernel << > >(size, in, w, h, c, batch, stride, forward, scale, out); + check_error(cudaPeekAtLastError()); } \ No newline at end of file diff --git a/src/box.c b/src/box.c index a2c676f23af..718215fe2b0 100644 --- a/src/box.c +++ b/src/box.c @@ -278,88 +278,88 @@ void do_nms_sort_v2(box *boxes, float **probs, int total, int classes, float thr int nms_comparator_v3(const void *pa, const void *pb) { - detection a = *(detection *)pa; - detection b = *(detection *)pb; - float diff = 0; - if (b.sort_class >= 0) { - diff = a.prob[b.sort_class] - b.prob[b.sort_class]; - } - else { - diff = a.objectness - b.objectness; - } - if (diff < 0) return 1; - else if (diff > 0) return -1; - return 0; + detection a = *(detection *)pa; + detection b = *(detection *)pb; + float diff = 0; + if (b.sort_class >= 0) { + diff = a.prob[b.sort_class] - b.prob[b.sort_class]; + } + else { + diff = a.objectness - b.objectness; + } + if (diff < 0) return 1; + else if (diff > 0) return -1; + return 0; } void do_nms_obj(detection *dets, int total, int classes, float thresh) { - int i, j, k; - k = total - 1; - for (i = 0; i <= k; ++i) { - if (dets[i].objectness == 0) { - detection swap = dets[i]; - dets[i] = dets[k]; - dets[k] = swap; - --k; - --i; - } - } - total = k + 1; - - for (i = 0; i < total; ++i) { - dets[i].sort_class = -1; - } - - qsort(dets, total, sizeof(detection), nms_comparator_v3); - for (i = 0; i < total; ++i) { - if (dets[i].objectness == 0) continue; - box a = dets[i].bbox; - for (j = i + 1; j < total; ++j) { - if (dets[j].objectness == 0) continue; - box b = dets[j].bbox; - if (box_iou(a, b) > thresh) { - dets[j].objectness = 0; - for (k = 0; k < classes; ++k) { - dets[j].prob[k] = 0; - } - } - } - } + int i, j, k; + k = total - 1; + for (i = 0; i <= k; ++i) { + if (dets[i].objectness == 0) { + detection swap = dets[i]; + dets[i] = dets[k]; + dets[k] = swap; + --k; + --i; + } + } + total = k + 1; + + for (i = 0; i < total; ++i) { + dets[i].sort_class = -1; + } + + qsort(dets, total, sizeof(detection), nms_comparator_v3); + for (i = 0; i < total; ++i) { + if (dets[i].objectness == 0) continue; + box a = dets[i].bbox; + for (j = i + 1; j < total; ++j) { + if (dets[j].objectness == 0) continue; + box b = dets[j].bbox; + if (box_iou(a, b) > thresh) { + dets[j].objectness = 0; + for (k = 0; k < classes; ++k) { + dets[j].prob[k] = 0; + } + } + } + } } void do_nms_sort(detection *dets, int total, int classes, float thresh) { - int i, j, k; - k = total - 1; - for (i = 0; i <= k; ++i) { - if (dets[i].objectness == 0) { - detection swap = dets[i]; - dets[i] = dets[k]; - dets[k] = swap; - --k; - --i; - } - } - total = k + 1; - - for (k = 0; k < classes; ++k) { - for (i = 0; i < total; ++i) { - dets[i].sort_class = k; - } - qsort(dets, total, sizeof(detection), nms_comparator_v3); - for (i = 0; i < total; ++i) { - //printf(" k = %d, \t i = %d \n", k, i); - if (dets[i].prob[k] == 0) continue; - box a = dets[i].bbox; - for (j = i + 1; j < total; ++j) { - box b = dets[j].bbox; - if (box_iou(a, b) > thresh) { - dets[j].prob[k] = 0; - } - } - } - } + int i, j, k; + k = total - 1; + for (i = 0; i <= k; ++i) { + if (dets[i].objectness == 0) { + detection swap = dets[i]; + dets[i] = dets[k]; + dets[k] = swap; + --k; + --i; + } + } + total = k + 1; + + for (k = 0; k < classes; ++k) { + for (i = 0; i < total; ++i) { + dets[i].sort_class = k; + } + qsort(dets, total, sizeof(detection), nms_comparator_v3); + for (i = 0; i < total; ++i) { + //printf(" k = %d, \t i = %d \n", k, i); + if (dets[i].prob[k] == 0) continue; + box a = dets[i].bbox; + for (j = i + 1; j < total; ++j) { + box b = dets[j].bbox; + if (box_iou(a, b) > thresh) { + dets[j].prob[k] = 0; + } + } + } + } } void do_nms(box *boxes, float **probs, int total, int classes, float thresh) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 324fc508891..5389828e720 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -76,36 +76,36 @@ void binarize_weights_gpu(float *weights, int n, int size, float *binary) __global__ void cuda_f32_to_f16(float* input_f32, size_t size, half *output_f16) { - int idx = blockIdx.x * blockDim.x + threadIdx.x; - if (idx < size) output_f16[idx] = __float2half(input_f32[idx]); - //if (idx < size) *((unsigned short *)output_f16 + idx) = __float2half(input_f32[idx]); + int idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx < size) output_f16[idx] = __float2half(input_f32[idx]); + //if (idx < size) *((unsigned short *)output_f16 + idx) = __float2half(input_f32[idx]); } void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16) { - cuda_f32_to_f16 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> (input_f32, size, (half *)output_f16); + cuda_f32_to_f16 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> (input_f32, size, (half *)output_f16); } __global__ void cuda_f16_to_f32(half* input_f16, size_t size, float *output_f32) { - int idx = blockIdx.x * blockDim.x + threadIdx.x; - if (idx < size) output_f32[idx] = __half2float(input_f16[idx]); - //if (idx < size) output_f32[idx] = __half2float(*((unsigned short *)input_f16 + idx)); + int idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx < size) output_f32[idx] = __half2float(input_f16[idx]); + //if (idx < size) output_f32[idx] = __half2float(*((unsigned short *)input_f16 + idx)); } void cuda_convert_f16_to_f32(float* input_f16, size_t size, float *output_f32) { - cuda_f16_to_f32 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> ((half *)input_f16, size, output_f32); + cuda_f16_to_f32 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> ((half *)input_f16, size, output_f32); } half *cuda_make_f16_from_f32_array(float *src, size_t n) { - half *dst16; - size_t size = sizeof(half)*n; - check_error(cudaMalloc((void **)&dst16, size)); - if (src) { - cuda_convert_f32_to_f16(src, n, (float *)dst16); - } - if (!dst16) error("Cuda malloc failed\n"); - return dst16; + half *dst16; + size_t size = sizeof(half)*n; + check_error(cudaMalloc((void **)&dst16, size)); + if (src) { + cuda_convert_f32_to_f16(src, n, (float *)dst16); + } + if (!dst16) error("Cuda malloc failed\n"); + return dst16; } void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) @@ -124,96 +124,96 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) } #ifdef CUDNN - float one = 1; // alpha[0], beta[0] is float for HALF and FLOAT - float alpha = 1, beta = 0; + float one = 1; // alpha[0], beta[0] is float for HALF and FLOAT + float alpha = 1, beta = 0; #ifdef CUDNN_HALF - // Note: For improved performance it is advised to use beta[0] = 0.0. - // For Tensor Core: cudnnSetConvolutionMathType() where cudnnMathType_t mathType = CUDNN_TENSOR_OP_MATH; - // 1. or CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM and use CUDNN_DATA_HALF - // 2. or CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED - // More: http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#tensor_ops - - const size_t input16_size = l.batch*l.c*l.w*l.h; - const size_t output16_size = l.batch*l.out_c*l.out_h*l.out_w; - - if (*state.net.max_input16_size < input16_size) { - //printf("\n input16_size: cur = %zu \t max = %zu \n", input16_size, *state.net.max_input16_size); - *state.net.max_input16_size = input16_size; - if (*state.net.input16_gpu) cuda_free(*state.net.input16_gpu); - *state.net.input16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_input16_size); - } - float *input16 = *state.net.input16_gpu; - - if (*state.net.max_output16_size < output16_size) { - *state.net.max_output16_size = output16_size; - if (*state.net.output16_gpu) cuda_free(*state.net.output16_gpu); - *state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size); - } - float *output16 = *state.net.output16_gpu; - - cuda_convert_f32_to_f16(state.input, input16_size, input16); - - //fill_ongpu(output16_size / 2, 0, (float *)output16, 1); - cudnnConvolutionForward(cudnn_handle(), - &alpha, - l.srcTensorDesc, - input16, - l.weightDesc, - l.weights_gpu16, - l.convDesc, - l.fw_algo, - state.workspace, - l.workspace_size, - &beta, - l.dstTensorDesc, - output16); - - - if (l.batch_normalize) - { - if (state.train) // Training - { - copy_ongpu(l.outputs*l.batch / 2, output16, 1, l.x_gpu, 1); - //cudaMemcpyAsync(l.x_gpu, output16, l.outputs*l.batch*sizeof(half), cudaMemcpyDefault, get_cuda_stream()); - float one = 1; - float zero = 0; - // Batch-normalization can still take FP16 inputs and outputs, saving half the bandwidth - // compared to FP32, it’s just that the statistics and value adjustment should be done in FP32. - cudnnBatchNormalizationForwardTraining(cudnn_handle(), - CUDNN_BATCHNORM_SPATIAL, - &one, - &zero, - l.normDstTensorDescF16, - l.x_gpu, // input - l.normDstTensorDescF16, - output16, // output - l.normTensorDesc, - l.scales_gpu, - l.biases_gpu, - .01, - l.rolling_mean_gpu, // output (should be FP32) - l.rolling_variance_gpu, // output (should be FP32) - .00001, - l.mean_gpu, // output (should be FP32) - l.variance_gpu); // output (should be FP32) - - cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); - //forward_batchnorm_layer_gpu(l, state); - } - else // Detection - { - cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); - normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); - scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); - add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h); - } - } - else // BIAS only - { - cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); - add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); - } + // Note: For improved performance it is advised to use beta[0] = 0.0. + // For Tensor Core: cudnnSetConvolutionMathType() where cudnnMathType_t mathType = CUDNN_TENSOR_OP_MATH; + // 1. or CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM and use CUDNN_DATA_HALF + // 2. or CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED + // More: http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#tensor_ops + + const size_t input16_size = l.batch*l.c*l.w*l.h; + const size_t output16_size = l.batch*l.out_c*l.out_h*l.out_w; + + if (*state.net.max_input16_size < input16_size) { + //printf("\n input16_size: cur = %zu \t max = %zu \n", input16_size, *state.net.max_input16_size); + *state.net.max_input16_size = input16_size; + if (*state.net.input16_gpu) cuda_free(*state.net.input16_gpu); + *state.net.input16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_input16_size); + } + float *input16 = *state.net.input16_gpu; + + if (*state.net.max_output16_size < output16_size) { + *state.net.max_output16_size = output16_size; + if (*state.net.output16_gpu) cuda_free(*state.net.output16_gpu); + *state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size); + } + float *output16 = *state.net.output16_gpu; + + cuda_convert_f32_to_f16(state.input, input16_size, input16); + + //fill_ongpu(output16_size / 2, 0, (float *)output16, 1); + cudnnConvolutionForward(cudnn_handle(), + &alpha, + l.srcTensorDesc, + input16, + l.weightDesc, + l.weights_gpu16, + l.convDesc, + l.fw_algo, + state.workspace, + l.workspace_size, + &beta, + l.dstTensorDesc, + output16); + + + if (l.batch_normalize) + { + if (state.train) // Training + { + copy_ongpu(l.outputs*l.batch / 2, output16, 1, l.x_gpu, 1); + //cudaMemcpyAsync(l.x_gpu, output16, l.outputs*l.batch*sizeof(half), cudaMemcpyDefault, get_cuda_stream()); + float one = 1; + float zero = 0; + // Batch-normalization can still take FP16 inputs and outputs, saving half the bandwidth + // compared to FP32, it’s just that the statistics and value adjustment should be done in FP32. + cudnnBatchNormalizationForwardTraining(cudnn_handle(), + CUDNN_BATCHNORM_SPATIAL, + &one, + &zero, + l.normDstTensorDescF16, + l.x_gpu, // input + l.normDstTensorDescF16, + output16, // output + l.normTensorDesc, + l.scales_gpu, + l.biases_gpu, + .01, + l.rolling_mean_gpu, // output (should be FP32) + l.rolling_variance_gpu, // output (should be FP32) + .00001, + l.mean_gpu, // output (should be FP32) + l.variance_gpu); // output (should be FP32) + + cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); + //forward_batchnorm_layer_gpu(l, state); + } + else // Detection + { + cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); + normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); + scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h); + } + } + else // BIAS only + { + cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); + } #else @@ -230,7 +230,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) &one, l.dstTensorDesc, l.output_gpu); -#endif // CUDNN_HALF +#endif // CUDNN_HALF #else @@ -250,16 +250,16 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) #ifndef CUDNN_HALF if (l.batch_normalize) { forward_batchnorm_layer_gpu(l, state); - } - else { - add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); - } + } + else { + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); + } #endif // no CUDNN_HALF activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if(l.dot > 0) dot_error_gpu(l); if(l.binary || l.xnor) swap_binary(&l); - //cudaDeviceSynchronize(); // for correct profiling of performance + //cudaDeviceSynchronize(); // for correct profiling of performance } void backward_convolutional_layer_gpu(convolutional_layer l, network_state state) @@ -272,126 +272,126 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state if(l.batch_normalize){ backward_batchnorm_layer_gpu(l, state); } else { - //backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); + //backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); } #endif // no CUDNN_HALF float *original_input = state.input; if(l.xnor) state.input = l.binary_input_gpu; #ifdef CUDNN - float one = 1; - float alpha = 1, beta = 0; + float one = 1; + float alpha = 1, beta = 0; #ifdef CUDNN_HALF - - const size_t input16_size = l.batch*l.c*l.w*l.h; - const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h; - - if (*state.net.max_input16_size < input16_size) { - *state.net.max_input16_size = input16_size; - if(*state.net.input16_gpu) cuda_free(*state.net.input16_gpu); - *state.net.input16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_input16_size); - } - float *input16 = *state.net.input16_gpu; - - if (*state.net.max_output16_size < delta16_size) { - *state.net.max_output16_size = delta16_size; - if(*state.net.output16_gpu) cuda_free(*state.net.output16_gpu); - *state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size); - } - float *delta16 = *state.net.output16_gpu; - - cuda_convert_f32_to_f16(state.input, input16_size, input16); - cuda_convert_f32_to_f16(l.delta_gpu, delta16_size, delta16); - - if (l.batch_normalize) { - //if (!state.train) { - // l.mean_gpu = l.rolling_mean_gpu; - // l.variance_gpu = l.rolling_variance_gpu; - //} - float one = 1; - float zero = 0; - cudnnBatchNormalizationBackward(cudnn_handle(), - CUDNN_BATCHNORM_SPATIAL, - &one, - &zero, - &one, - &one, - l.normDstTensorDescF16, - l.x_gpu, // input - l.normDstTensorDescF16, - delta16, // input - l.normDstTensorDescF16, - l.x_norm_gpu, // output - l.normTensorDesc, - l.scales_gpu, // output (should be FP32) - l.scale_updates_gpu, // output (should be FP32) - l.bias_updates_gpu, // output (should be FP32) - .00001, - l.mean_gpu, // input (should be FP32) - l.variance_gpu); // input (should be FP32) - copy_ongpu(l.outputs*l.batch / 2, l.x_norm_gpu, 1, delta16, 1); - //cudaMemcpyAsync(delta16, l.x_norm_gpu, l.outputs*l.batch * sizeof(half), cudaMemcpyDefault, get_cuda_stream()); - } - else - { - //backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); - } - - // convert input: state.input (x), l.delta_gpu (y) from fp32 to fp16 - // get output: l.weight_updates_gpu (dw) and convert it to fp32 (ONLY if it is fp16) - - // calculate conv weight updates - // Already: l.weight_updates_gpu = (l.weight_updates_gpu - l.weight*decay*batch*subdivision)*momentum - // so we should copy f32 to f16, or compute: f16=(w_up - w*d*b*s)*m - cuda_convert_f32_to_f16(l.weight_updates_gpu, l.c*l.n*l.size*l.size, l.weight_updates_gpu16); - - cudnnConvolutionBackwardFilter(cudnn_handle(), - &one, - l.srcTensorDesc, - input16, //state.input, - l.ddstTensorDesc, - delta16, //l.delta_gpu, - l.convDesc, - l.bf_algo, - state.workspace, - l.workspace_size, - &one, - l.dweightDesc, - l.weight_updates_gpu16); // l.weight_updates_gpu); - - cuda_convert_f16_to_f32(l.weight_updates_gpu16, l.c*l.n*l.size*l.size, l.weight_updates_gpu); - - if (state.delta) { - if (l.binary || l.xnor) swap_binary(&l); - - // http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData - // calculate delta for the next layer - // convert input: l.weights_gpu (w), l.delta_gpu (dy) from fp32 to fp16 - // get output: state.delta (dx) and convert it to fp32 (ONLY if it is fp16) - cudnnConvolutionBackwardData(cudnn_handle(), - &alpha, - l.weightDesc, - l.weights_gpu16, //l.weights_gpu, - l.ddstTensorDesc, - delta16, //l.delta_gpu, - l.convDesc, - l.bd_algo, - state.workspace, - l.workspace_size, - &beta, - l.dsrcTensorDesc, - input16); // state.delta); - - cuda_convert_f16_to_f32(input16, input16_size, state.delta); - - if (l.binary || l.xnor) swap_binary(&l); - if (l.xnor) gradient_array_ongpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, state.delta); - } -#else // CUDNN_HALF - - // calculate conv weight updates - // if used: beta=1 then loss decreases faster + + const size_t input16_size = l.batch*l.c*l.w*l.h; + const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h; + + if (*state.net.max_input16_size < input16_size) { + *state.net.max_input16_size = input16_size; + if(*state.net.input16_gpu) cuda_free(*state.net.input16_gpu); + *state.net.input16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_input16_size); + } + float *input16 = *state.net.input16_gpu; + + if (*state.net.max_output16_size < delta16_size) { + *state.net.max_output16_size = delta16_size; + if(*state.net.output16_gpu) cuda_free(*state.net.output16_gpu); + *state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size); + } + float *delta16 = *state.net.output16_gpu; + + cuda_convert_f32_to_f16(state.input, input16_size, input16); + cuda_convert_f32_to_f16(l.delta_gpu, delta16_size, delta16); + + if (l.batch_normalize) { + //if (!state.train) { + // l.mean_gpu = l.rolling_mean_gpu; + // l.variance_gpu = l.rolling_variance_gpu; + //} + float one = 1; + float zero = 0; + cudnnBatchNormalizationBackward(cudnn_handle(), + CUDNN_BATCHNORM_SPATIAL, + &one, + &zero, + &one, + &one, + l.normDstTensorDescF16, + l.x_gpu, // input + l.normDstTensorDescF16, + delta16, // input + l.normDstTensorDescF16, + l.x_norm_gpu, // output + l.normTensorDesc, + l.scales_gpu, // output (should be FP32) + l.scale_updates_gpu, // output (should be FP32) + l.bias_updates_gpu, // output (should be FP32) + .00001, + l.mean_gpu, // input (should be FP32) + l.variance_gpu); // input (should be FP32) + copy_ongpu(l.outputs*l.batch / 2, l.x_norm_gpu, 1, delta16, 1); + //cudaMemcpyAsync(delta16, l.x_norm_gpu, l.outputs*l.batch * sizeof(half), cudaMemcpyDefault, get_cuda_stream()); + } + else + { + //backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); + } + + // convert input: state.input (x), l.delta_gpu (y) from fp32 to fp16 + // get output: l.weight_updates_gpu (dw) and convert it to fp32 (ONLY if it is fp16) + + // calculate conv weight updates + // Already: l.weight_updates_gpu = (l.weight_updates_gpu - l.weight*decay*batch*subdivision)*momentum + // so we should copy f32 to f16, or compute: f16=(w_up - w*d*b*s)*m + cuda_convert_f32_to_f16(l.weight_updates_gpu, l.c*l.n*l.size*l.size, l.weight_updates_gpu16); + + cudnnConvolutionBackwardFilter(cudnn_handle(), + &one, + l.srcTensorDesc, + input16, //state.input, + l.ddstTensorDesc, + delta16, //l.delta_gpu, + l.convDesc, + l.bf_algo, + state.workspace, + l.workspace_size, + &one, + l.dweightDesc, + l.weight_updates_gpu16); // l.weight_updates_gpu); + + cuda_convert_f16_to_f32(l.weight_updates_gpu16, l.c*l.n*l.size*l.size, l.weight_updates_gpu); + + if (state.delta) { + if (l.binary || l.xnor) swap_binary(&l); + + // http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData + // calculate delta for the next layer + // convert input: l.weights_gpu (w), l.delta_gpu (dy) from fp32 to fp16 + // get output: state.delta (dx) and convert it to fp32 (ONLY if it is fp16) + cudnnConvolutionBackwardData(cudnn_handle(), + &alpha, + l.weightDesc, + l.weights_gpu16, //l.weights_gpu, + l.ddstTensorDesc, + delta16, //l.delta_gpu, + l.convDesc, + l.bd_algo, + state.workspace, + l.workspace_size, + &beta, + l.dsrcTensorDesc, + input16); // state.delta); + + cuda_convert_f16_to_f32(input16, input16_size, state.delta); + + if (l.binary || l.xnor) swap_binary(&l); + if (l.xnor) gradient_array_ongpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, state.delta); + } +#else // CUDNN_HALF + + // calculate conv weight updates + // if used: beta=1 then loss decreases faster cudnnConvolutionBackwardFilter(cudnn_handle(), &one, l.srcTensorDesc, @@ -408,8 +408,8 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state if(state.delta){ if(l.binary || l.xnor) swap_binary(&l); - // http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData - // calculate delta for the next layer + // http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData + // calculate delta for the next layer cudnnConvolutionBackwardData(cudnn_handle(), &one, l.weightDesc, @@ -427,9 +427,9 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state if(l.xnor) gradient_array_ongpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, state.delta); } -#endif // CUDNN_HALF +#endif // CUDNN_HALF -#else // CUDNN +#else // CUDNN int m = l.n; int n = l.size*l.size*l.c; int k = l.out_w*l.out_h; @@ -482,7 +482,7 @@ void push_convolutional_layer(convolutional_layer layer) { cuda_push_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size); #ifdef CUDNN_HALF - cuda_convert_f32_to_f16(layer.weights_gpu, layer.c*layer.n*layer.size*layer.size, layer.weights_gpu16); + cuda_convert_f32_to_f16(layer.weights_gpu, layer.c*layer.n*layer.size*layer.size, layer.weights_gpu16); #endif cuda_push_array(layer.biases_gpu, layer.biases, layer.n); cuda_push_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size); @@ -522,14 +522,14 @@ void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float adam_gpu(size, layer.weights_gpu, layer.m_gpu, layer.v_gpu, layer.B1, layer.B2, learning_rate/batch, layer.eps, layer.t+1); fill_ongpu(size, 0, layer.weight_updates_gpu, 1); }else{ - // update weights: - // weights_gpu = weights_gpu*(1 - decay*lr) + weight_updates_gpu*lr / (batch*subdivision) = - // weights_gpu*(1 - 0.0005*0.001) + weight_updates_gpu*0.001/(64*8) = - // weights_gpu * 0.999 999 5 + weight_updates_gpu * 0.000 001 953125 - // - // weight_updates_gpu = (weight_updates_gpu - weights_gpu*decay*batch*subdivision)*momentum = - // (weight_updates_gpu - weights_gpu * 0.0005 * 64 * 8) * 0.9 = - // weight_updates_gpu*0.9 - weights_gpu*0.2304 + // update weights: + // weights_gpu = weights_gpu*(1 - decay*lr) + weight_updates_gpu*lr / (batch*subdivision) = + // weights_gpu*(1 - 0.0005*0.001) + weight_updates_gpu*0.001/(64*8) = + // weights_gpu * 0.999 999 5 + weight_updates_gpu * 0.000 001 953125 + // + // weight_updates_gpu = (weight_updates_gpu - weights_gpu*decay*batch*subdivision)*momentum = + // (weight_updates_gpu - weights_gpu * 0.0005 * 64 * 8) * 0.9 = + // weight_updates_gpu*0.9 - weights_gpu*0.2304 axpy_ongpu(size, -decay*batch, layer.weights_gpu, 1, layer.weight_updates_gpu, 1); axpy_ongpu(size, learning_rate/batch, layer.weight_updates_gpu, 1, layer.weights_gpu, 1); scal_ongpu(size, momentum, layer.weight_updates_gpu, 1); diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 90f9551b048..554bd421f46 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -141,67 +141,67 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) { #ifdef CUDNN_HALF - // TRUE_HALF_CONFIG is only supported on architectures with true fp16 support (compute capability 5.3 and 6.0): - // Tegra X1, Jetson TX1, DRIVE CX, DRIVE PX, Quadro GP100, Tesla P100 - // PSEUDO_HALF_CONFIG is required for Tensor Cores - our case! - const cudnnDataType_t data_type = CUDNN_DATA_HALF; + // TRUE_HALF_CONFIG is only supported on architectures with true fp16 support (compute capability 5.3 and 6.0): + // Tegra X1, Jetson TX1, DRIVE CX, DRIVE PX, Quadro GP100, Tesla P100 + // PSEUDO_HALF_CONFIG is required for Tensor Cores - our case! + const cudnnDataType_t data_type = CUDNN_DATA_HALF; #else - cudnnDataType_t data_type = CUDNN_DATA_FLOAT; + cudnnDataType_t data_type = CUDNN_DATA_FLOAT; #endif #if(CUDNN_MAJOR >= 7) - // Tensor Core uses CUDNN_TENSOR_OP_MATH instead of CUDNN_DEFAULT_MATH - // For *_ALGO_WINOGRAD_NONFUSED can be used CUDNN_DATA_FLOAT - // otherwise Input, Filter and Output descriptors (xDesc, yDesc, wDesc, dxDesc, dyDesc and dwDesc as applicable) have dataType = CUDNN_DATA_HALF - // Three techniques for training using Mixed-precision: https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/ - // 1. Accumulation into FP32 - // 2. Loss Scaling - required only for: activation gradients. We do not use. - // 3. FP32 Master Copy of Weights - // More: http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#tensor_ops - cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH); + // Tensor Core uses CUDNN_TENSOR_OP_MATH instead of CUDNN_DEFAULT_MATH + // For *_ALGO_WINOGRAD_NONFUSED can be used CUDNN_DATA_FLOAT + // otherwise Input, Filter and Output descriptors (xDesc, yDesc, wDesc, dxDesc, dyDesc and dwDesc as applicable) have dataType = CUDNN_DATA_HALF + // Three techniques for training using Mixed-precision: https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/ + // 1. Accumulation into FP32 + // 2. Loss Scaling - required only for: activation gradients. We do not use. + // 3. FP32 Master Copy of Weights + // More: http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#tensor_ops + cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH); #endif - // INT8_CONFIG, INT8_EXT_CONFIG, INT8x4_CONFIG and INT8x4_EXT_CONFIG are only supported - // on architectures with DP4A support (compute capability 6.1 and later). - //cudnnDataType_t data_type = CUDNN_DATA_INT8; + // INT8_CONFIG, INT8_EXT_CONFIG, INT8x4_CONFIG and INT8x4_EXT_CONFIG are only supported + // on architectures with DP4A support (compute capability 6.1 and later). + //cudnnDataType_t data_type = CUDNN_DATA_INT8; - // backward delta + // backward delta cudnnSetTensor4dDescriptor(l->dsrcTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->c, l->h, l->w); cudnnSetTensor4dDescriptor(l->ddstTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->out_c, l->out_h, l->out_w); cudnnSetFilter4dDescriptor(l->dweightDesc, data_type, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size); - // forward + // forward cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->c, l->h, l->w); cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->out_c, l->out_h, l->out_w); cudnnSetFilter4dDescriptor(l->weightDesc, data_type, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size); - // batch norm - cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1); - cudnnSetTensor4dDescriptor(l->normDstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); + // batch norm + cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1); + cudnnSetTensor4dDescriptor(l->normDstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); - cudnnSetTensor4dDescriptor(l->normDstTensorDescF16, CUDNN_TENSOR_NCHW, data_type, l->batch, l->out_c, l->out_h, l->out_w); + cudnnSetTensor4dDescriptor(l->normDstTensorDescF16, CUDNN_TENSOR_NCHW, data_type, l->batch, l->out_c, l->out_h, l->out_w); #if(CUDNN_MAJOR >= 6) - cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT); // cudnn >= 6.0 + cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT); // cudnn >= 6.0 #else - cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION); // cudnn 5.1 + cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION); // cudnn 5.1 #endif - int forward_algo = CUDNN_CONVOLUTION_FWD_PREFER_FASTEST; - int backward_algo = CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST; - int backward_filter = CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST; - if (cudnn_preference == cudnn_smallest) - { - forward_algo = CUDNN_CONVOLUTION_FWD_NO_WORKSPACE; - backward_algo = CUDNN_CONVOLUTION_BWD_DATA_NO_WORKSPACE; - backward_filter = CUDNN_CONVOLUTION_BWD_FILTER_NO_WORKSPACE; - printf(" CUDNN-slow "); - } - - cudnnGetConvolutionForwardAlgorithm(cudnn_handle(), + int forward_algo = CUDNN_CONVOLUTION_FWD_PREFER_FASTEST; + int backward_algo = CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST; + int backward_filter = CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST; + if (cudnn_preference == cudnn_smallest) + { + forward_algo = CUDNN_CONVOLUTION_FWD_NO_WORKSPACE; + backward_algo = CUDNN_CONVOLUTION_BWD_DATA_NO_WORKSPACE; + backward_filter = CUDNN_CONVOLUTION_BWD_FILTER_NO_WORKSPACE; + printf(" CUDNN-slow "); + } + + cudnnGetConvolutionForwardAlgorithm(cudnn_handle(), l->srcTensorDesc, l->weightDesc, l->convDesc, l->dstTensorDesc, - forward_algo, + forward_algo, 0, &l->fw_algo); cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(), @@ -209,7 +209,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) l->ddstTensorDesc, l->convDesc, l->dsrcTensorDesc, - backward_algo, + backward_algo, 0, &l->bd_algo); cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(), @@ -217,41 +217,41 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) l->ddstTensorDesc, l->convDesc, l->dweightDesc, - backward_filter, + backward_filter, 0, &l->bf_algo); - if (data_type == CUDNN_DATA_HALF) - { - // HALF-16 if(data_type == CUDNN_DATA_HALF) - l->fw_algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM; - l->bd_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1; - l->bf_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1; - - // FLOAT-32 if(data_type == CUDNN_DATA_FLOAT) - //l->fw_algo = CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED; - //l->bd_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED; - //l->bf_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED; - - int fw = 0, bd = 0, bf = 0; - if (l->fw_algo == CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM) fw = 1; - //printf("Tensor Cores - Forward enabled: l->fw_algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM \n"); - if (l->fw_algo == CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED) fw = 2; - //printf("Tensor Cores - Forward enabled: l->fw_algo = CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED \n"); - - if (l->bd_algo == CUDNN_CONVOLUTION_BWD_DATA_ALGO_1) bd = 1; - //printf("Tensor Cores - Backward-data enabled: l->bd_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1 \n"); - if (l->bd_algo == CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED) bd = 2; - //printf("Tensor Cores - Backward-data enabled: l->bd_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED \n"); - - if (l->bf_algo == CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1) bf = 1; - //printf("Tensor Cores - Backward-filter enabled: l->bf_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1 \n"); - if (l->bf_algo == CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED) bf = 2; - //printf("Tensor Cores - Backward-filter enabled: l->bf_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED \n"); - - if (fw == 2 && bd == 2 && bf == 2) printf("TF "); - else if (fw == 1 && bd == 1 && bf == 1) printf("TH "); - } + if (data_type == CUDNN_DATA_HALF) + { + // HALF-16 if(data_type == CUDNN_DATA_HALF) + l->fw_algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM; + l->bd_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1; + l->bf_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1; + + // FLOAT-32 if(data_type == CUDNN_DATA_FLOAT) + //l->fw_algo = CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED; + //l->bd_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED; + //l->bf_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED; + + int fw = 0, bd = 0, bf = 0; + if (l->fw_algo == CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM) fw = 1; + //printf("Tensor Cores - Forward enabled: l->fw_algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM \n"); + if (l->fw_algo == CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED) fw = 2; + //printf("Tensor Cores - Forward enabled: l->fw_algo = CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED \n"); + + if (l->bd_algo == CUDNN_CONVOLUTION_BWD_DATA_ALGO_1) bd = 1; + //printf("Tensor Cores - Backward-data enabled: l->bd_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1 \n"); + if (l->bd_algo == CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED) bd = 2; + //printf("Tensor Cores - Backward-data enabled: l->bd_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED \n"); + + if (l->bf_algo == CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1) bf = 1; + //printf("Tensor Cores - Backward-filter enabled: l->bf_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1 \n"); + if (l->bf_algo == CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED) bf = 2; + //printf("Tensor Cores - Backward-filter enabled: l->bf_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED \n"); + + if (fw == 2 && bd == 2 && bf == 2) printf("TF "); + else if (fw == 1 && bd == 1 && bf == 1) printf("TH "); + } } #endif #endif @@ -344,8 +344,8 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.weights_gpu = cuda_make_array(l.weights, c*n*size*size); #ifdef CUDNN_HALF - l.weights_gpu16 = cuda_make_array(NULL, c*n*size*size / 2); //cuda_make_array(l.weights, c*n*size*size / 2); - l.weight_updates_gpu16 = cuda_make_array(NULL, c*n*size*size / 2); //cuda_make_array(l.weight_updates, c*n*size*size / 2); + l.weights_gpu16 = cuda_make_array(NULL, c*n*size*size / 2); //cuda_make_array(l.weights, c*n*size*size / 2); + l.weight_updates_gpu16 = cuda_make_array(NULL, c*n*size*size / 2); //cuda_make_array(l.weight_updates, c*n*size*size / 2); #endif l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size); @@ -379,10 +379,10 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.x_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); l.x_norm_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); } -#ifdef CUDNN - cudnnCreateTensorDescriptor(&l.normDstTensorDesc); - cudnnCreateTensorDescriptor(&l.normDstTensorDescF16); - cudnnCreateTensorDescriptor(&l.normTensorDesc); +#ifdef CUDNN + cudnnCreateTensorDescriptor(&l.normDstTensorDesc); + cudnnCreateTensorDescriptor(&l.normDstTensorDescF16); + cudnnCreateTensorDescriptor(&l.normTensorDesc); cudnnCreateTensorDescriptor(&l.srcTensorDesc); cudnnCreateTensorDescriptor(&l.dstTensorDesc); cudnnCreateFilterDescriptor(&l.weightDesc); @@ -398,8 +398,8 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.activation = activation; //fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c); - l.bflops = (2.0 * l.n * l.size*l.size*l.c * l.out_h*l.out_w) / 1000000000.; - fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); + l.bflops = (2.0 * l.n * l.size*l.size*l.c * l.out_h*l.out_w) / 1000000000.; + fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); return l; } @@ -445,8 +445,8 @@ void test_convolutional_layer() void resize_convolutional_layer(convolutional_layer *l, int w, int h) { - int old_w = l->w; - int old_h = l->h; + int old_w = l->w; + int old_h = l->h; l->w = w; l->h = h; int out_w = convolutional_out_width(*l); @@ -465,31 +465,31 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) l->x_norm = realloc(l->x_norm, l->batch*l->outputs*sizeof(float)); } - if (l->xnor) { - //l->binary_input = realloc(l->inputs*l->batch, sizeof(float)); - } + if (l->xnor) { + //l->binary_input = realloc(l->inputs*l->batch, sizeof(float)); + } #ifdef GPU - if (old_w < w || old_h < h) { - cuda_free(l->delta_gpu); - cuda_free(l->output_gpu); - - l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs); - l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs); - - if (l->batch_normalize) { - cuda_free(l->x_gpu); - cuda_free(l->x_norm_gpu); - - l->x_gpu = cuda_make_array(l->output, l->batch*l->outputs); - l->x_norm_gpu = cuda_make_array(l->output, l->batch*l->outputs); - } - - if (l->xnor) { - cuda_free(l->binary_input_gpu); - l->binary_input_gpu = cuda_make_array(0, l->inputs*l->batch); - } - } + if (old_w < w || old_h < h) { + cuda_free(l->delta_gpu); + cuda_free(l->output_gpu); + + l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs); + l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs); + + if (l->batch_normalize) { + cuda_free(l->x_gpu); + cuda_free(l->x_norm_gpu); + + l->x_gpu = cuda_make_array(l->output, l->batch*l->outputs); + l->x_norm_gpu = cuda_make_array(l->output, l->batch*l->outputs); + } + + if (l->xnor) { + cuda_free(l->binary_input_gpu); + l->binary_input_gpu = cuda_make_array(0, l->inputs*l->batch); + } + } #ifdef CUDNN cudnn_convolutional_setup(l, cudnn_fastest); #endif @@ -497,15 +497,15 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) l->workspace_size = get_workspace_size(*l); #ifdef CUDNN - // check for excessive memory consumption - size_t free_byte; - size_t total_byte; - check_error(cudaMemGetInfo(&free_byte, &total_byte)); - if (l->workspace_size > free_byte || l->workspace_size >= total_byte / 2) { - printf(" used slow CUDNN algo without Workspace! Need memory: %zu, available: %zu\n", l->workspace_size, (free_byte < total_byte/2) ? free_byte : total_byte/2); - cudnn_convolutional_setup(l, cudnn_smallest); - l->workspace_size = get_workspace_size(*l); - } + // check for excessive memory consumption + size_t free_byte; + size_t total_byte; + check_error(cudaMemGetInfo(&free_byte, &total_byte)); + if (l->workspace_size > free_byte || l->workspace_size >= total_byte / 2) { + printf(" used slow CUDNN algo without Workspace! Need memory: %zu, available: %zu\n", l->workspace_size, (free_byte < total_byte/2) ? free_byte : total_byte/2); + cudnn_convolutional_setup(l, cudnn_smallest); + l->workspace_size = get_workspace_size(*l); + } #endif } diff --git a/src/cuda.c b/src/cuda.c index 53009067538..688e1c580f2 100644 --- a/src/cuda.c +++ b/src/cuda.c @@ -61,25 +61,25 @@ dim3 cuda_gridsize(size_t n){ return d; } -static cudaStream_t streamsArray[16]; // cudaStreamSynchronize( get_cuda_stream() ); +static cudaStream_t streamsArray[16]; // cudaStreamSynchronize( get_cuda_stream() ); static int streamInit[16] = { 0 }; cudaStream_t get_cuda_stream() { - int i = cuda_get_device(); - if (!streamInit[i]) { - cudaError_t status = cudaStreamCreate(&streamsArray[i]); - //cudaError_t status = cudaStreamCreateWithFlags(&streamsArray[i], cudaStreamNonBlocking); - if (status != cudaSuccess) { - printf(" cudaStreamCreate error: %d \n", status); - const char *s = cudaGetErrorString(status); - char buffer[256]; - printf("CUDA Error: %s\n", s); - status = cudaStreamCreateWithFlags(&streamsArray[i], cudaStreamDefault); - check_error(status); - } - streamInit[i] = 1; - } - return streamsArray[i]; + int i = cuda_get_device(); + if (!streamInit[i]) { + cudaError_t status = cudaStreamCreate(&streamsArray[i]); + //cudaError_t status = cudaStreamCreateWithFlags(&streamsArray[i], cudaStreamNonBlocking); + if (status != cudaSuccess) { + printf(" cudaStreamCreate error: %d \n", status); + const char *s = cudaGetErrorString(status); + char buffer[256]; + printf("CUDA Error: %s\n", s); + status = cudaStreamCreateWithFlags(&streamsArray[i], cudaStreamDefault); + check_error(status); + } + streamInit[i] = 1; + } + return streamsArray[i]; } @@ -92,7 +92,7 @@ cudnnHandle_t cudnn_handle() if(!init[i]) { cudnnCreate(&handle[i]); init[i] = 1; - cudnnStatus_t status = cudnnSetStream(handle[i], get_cuda_stream()); + cudnnStatus_t status = cudnnSetStream(handle[i], get_cuda_stream()); } return handle[i]; } @@ -105,7 +105,7 @@ cublasHandle_t blas_handle() int i = cuda_get_device(); if(!init[i]) { cublasCreate(&handle[i]); - cublasStatus_t status = cublasSetStream(handle[i], get_cuda_stream()); + cublasStatus_t status = cublasSetStream(handle[i], get_cuda_stream()); init[i] = 1; } return handle[i]; @@ -119,7 +119,7 @@ float *cuda_make_array(float *x, size_t n) check_error(status); if(x){ //status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice); - status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyHostToDevice, get_cuda_stream()); + status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyHostToDevice, get_cuda_stream()); check_error(status); } if(!x_gpu) error("Cuda malloc failed\n"); @@ -164,7 +164,7 @@ int *cuda_make_int_array(size_t n) void cuda_free(float *x_gpu) { - //cudaStreamSynchronize(get_cuda_stream()); + //cudaStreamSynchronize(get_cuda_stream()); cudaError_t status = cudaFree(x_gpu); check_error(status); } @@ -173,7 +173,7 @@ void cuda_push_array(float *x_gpu, float *x, size_t n) { size_t size = sizeof(float)*n; //cudaError_t status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice); - cudaError_t status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyHostToDevice, get_cuda_stream()); + cudaError_t status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyHostToDevice, get_cuda_stream()); check_error(status); } @@ -181,9 +181,9 @@ void cuda_pull_array(float *x_gpu, float *x, size_t n) { size_t size = sizeof(float)*n; //cudaError_t status = cudaMemcpy(x, x_gpu, size, cudaMemcpyDeviceToHost); - cudaError_t status = cudaMemcpyAsync(x, x_gpu, size, cudaMemcpyDeviceToHost, get_cuda_stream()); + cudaError_t status = cudaMemcpyAsync(x, x_gpu, size, cudaMemcpyDeviceToHost, get_cuda_stream()); check_error(status); - cudaStreamSynchronize(get_cuda_stream()); + cudaStreamSynchronize(get_cuda_stream()); } #else // GPU diff --git a/src/data.c b/src/data.c index ec7d36f3590..2a0a7e9fdf8 100644 --- a/src/data.c +++ b/src/data.c @@ -44,15 +44,15 @@ char **get_random_paths(char **paths, int n, int m) char **random_paths = calloc(n, sizeof(char*)); int i; pthread_mutex_lock(&mutex); - //printf("n = %d \n", n); + //printf("n = %d \n", n); for(i = 0; i < n; ++i){ - do { - int index = random_gen() % m; - random_paths[i] = paths[index]; - //if(i == 0) printf("%s\n", paths[index]); - //printf("grp: %s\n", paths[index]); - if (strlen(random_paths[i]) <= 4) printf(" Very small path to the image: %s \n", random_paths[i]); - } while (strlen(random_paths[i]) == 0); + do { + int index = random_gen() % m; + random_paths[i] = paths[index]; + //if(i == 0) printf("%s\n", paths[index]); + //printf("grp: %s\n", paths[index]); + if (strlen(random_paths[i]) <= 4) printf(" Very small path to the image: %s \n", random_paths[i]); + } while (strlen(random_paths[i]) == 0); } pthread_mutex_unlock(&mutex); return random_paths; @@ -140,18 +140,18 @@ box_label *read_boxes(char *filename, int *n) { box_label *boxes = calloc(1, sizeof(box_label)); FILE *file = fopen(filename, "r"); - if (!file) { - printf("Can't open label file. (This can be normal only if you use MSCOCO) \n"); - //file_error(filename); - FILE* fw = fopen("bad.list", "a"); - fwrite(filename, sizeof(char), strlen(filename), fw); - char *new_line = "\n"; - fwrite(new_line, sizeof(char), strlen(new_line), fw); - fclose(fw); - - *n = 0; - return boxes; - } + if (!file) { + printf("Can't open label file. (This can be normal only if you use MSCOCO) \n"); + //file_error(filename); + FILE* fw = fopen("bad.list", "a"); + fwrite(filename, sizeof(char), strlen(filename), fw); + char *new_line = "\n"; + fwrite(new_line, sizeof(char), strlen(new_line), fw); + fclose(fw); + + *n = 0; + return boxes; + } float x, y, h, w; int id; int count = 0; @@ -224,7 +224,7 @@ void correct_boxes(box_label *boxes, int n, float dx, float dy, float sx, float void fill_truth_swag(char *path, float *truth, int classes, int flip, float dx, float dy, float sx, float sy) { char labelpath[4096]; - replace_image_to_label(path, labelpath); + replace_image_to_label(path, labelpath); int count = 0; box_label *boxes = read_boxes(labelpath, &count); @@ -258,9 +258,9 @@ void fill_truth_swag(char *path, float *truth, int classes, int flip, float dx, void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int flip, float dx, float dy, float sx, float sy) { char labelpath[4096]; - replace_image_to_label(path, labelpath); + replace_image_to_label(path, labelpath); - int count = 0; + int count = 0; box_label *boxes = read_boxes(labelpath, &count); randomize_boxes(boxes, count); correct_boxes(boxes, count, dx, dy, sx, sy, flip); @@ -299,77 +299,77 @@ void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int } void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, int flip, float dx, float dy, float sx, float sy, - int small_object, int net_w, int net_h) + int small_object, int net_w, int net_h) { - char labelpath[4096]; - replace_image_to_label(path, labelpath); - - int count = 0; - int i; - box_label *boxes = read_boxes(labelpath, &count); - float lowest_w = 1.F / net_w; - float lowest_h = 1.F / net_h; - if (small_object == 1) { - for (i = 0; i < count; ++i) { - if (boxes[i].w < lowest_w) boxes[i].w = lowest_w; - if (boxes[i].h < lowest_h) boxes[i].h = lowest_h; - } - } - randomize_boxes(boxes, count); - correct_boxes(boxes, count, dx, dy, sx, sy, flip); - if (count > num_boxes) count = num_boxes; - float x, y, w, h; - int id; - - for (i = 0; i < count; ++i) { - x = boxes[i].x; - y = boxes[i].y; - w = boxes[i].w; - h = boxes[i].h; - id = boxes[i].id; - - // not detect small objects - //if ((w < 0.001F || h < 0.001F)) continue; - // if truth (box for object) is smaller than 1x1 pix - char buff[256]; - if (id >= classes) { - printf("\n Wrong annotation: class_id = %d. But class_id should be [from 0 to %d] \n", id, classes); - sprintf(buff, "echo %s \"Wrong annotation: class_id = %d. But class_id should be [from 0 to %d]\" >> bad_label.list", labelpath, id, classes); - system(buff); - getchar(); - continue; - } - if ((w < lowest_w || h < lowest_h)) { - //sprintf(buff, "echo %s \"Very small object: w < lowest_w OR h < lowest_h\" >> bad_label.list", labelpath); - //system(buff); - continue; - } - if (x == 999999 || y == 999999) { - printf("\n Wrong annotation: x = 0, y = 0 \n"); - sprintf(buff, "echo %s \"Wrong annotation: x = 0 or y = 0\" >> bad_label.list", labelpath); - system(buff); - continue; - } - if (x <= 0 || x > 1 || y <= 0 || y > 1) { - printf("\n Wrong annotation: x = %f, y = %f \n", x, y); - sprintf(buff, "echo %s \"Wrong annotation: x = %f, y = %f\" >> bad_label.list", labelpath, x, y); - system(buff); - continue; - } - if (w > 1) { - printf("\n Wrong annotation: w = %f \n", w); - sprintf(buff, "echo %s \"Wrong annotation: w = %f\" >> bad_label.list", labelpath, w); - system(buff); - w = 1; - } - if (h > 1) { - printf("\n Wrong annotation: h = %f \n", h); - sprintf(buff, "echo %s \"Wrong annotation: h = %f\" >> bad_label.list", labelpath, h); - system(buff); - h = 1; - } - if (x == 0) x += lowest_w; - if (y == 0) y += lowest_h; + char labelpath[4096]; + replace_image_to_label(path, labelpath); + + int count = 0; + int i; + box_label *boxes = read_boxes(labelpath, &count); + float lowest_w = 1.F / net_w; + float lowest_h = 1.F / net_h; + if (small_object == 1) { + for (i = 0; i < count; ++i) { + if (boxes[i].w < lowest_w) boxes[i].w = lowest_w; + if (boxes[i].h < lowest_h) boxes[i].h = lowest_h; + } + } + randomize_boxes(boxes, count); + correct_boxes(boxes, count, dx, dy, sx, sy, flip); + if (count > num_boxes) count = num_boxes; + float x, y, w, h; + int id; + + for (i = 0; i < count; ++i) { + x = boxes[i].x; + y = boxes[i].y; + w = boxes[i].w; + h = boxes[i].h; + id = boxes[i].id; + + // not detect small objects + //if ((w < 0.001F || h < 0.001F)) continue; + // if truth (box for object) is smaller than 1x1 pix + char buff[256]; + if (id >= classes) { + printf("\n Wrong annotation: class_id = %d. But class_id should be [from 0 to %d] \n", id, classes); + sprintf(buff, "echo %s \"Wrong annotation: class_id = %d. But class_id should be [from 0 to %d]\" >> bad_label.list", labelpath, id, classes); + system(buff); + getchar(); + continue; + } + if ((w < lowest_w || h < lowest_h)) { + //sprintf(buff, "echo %s \"Very small object: w < lowest_w OR h < lowest_h\" >> bad_label.list", labelpath); + //system(buff); + continue; + } + if (x == 999999 || y == 999999) { + printf("\n Wrong annotation: x = 0, y = 0 \n"); + sprintf(buff, "echo %s \"Wrong annotation: x = 0 or y = 0\" >> bad_label.list", labelpath); + system(buff); + continue; + } + if (x <= 0 || x > 1 || y <= 0 || y > 1) { + printf("\n Wrong annotation: x = %f, y = %f \n", x, y); + sprintf(buff, "echo %s \"Wrong annotation: x = %f, y = %f\" >> bad_label.list", labelpath, x, y); + system(buff); + continue; + } + if (w > 1) { + printf("\n Wrong annotation: w = %f \n", w); + sprintf(buff, "echo %s \"Wrong annotation: w = %f\" >> bad_label.list", labelpath, w); + system(buff); + w = 1; + } + if (h > 1) { + printf("\n Wrong annotation: h = %f \n", h); + sprintf(buff, "echo %s \"Wrong annotation: h = %f\" >> bad_label.list", labelpath, h); + system(buff); + h = 1; + } + if (x == 0) x += lowest_w; + if (y == 0) y += lowest_h; truth[i*5+0] = x; truth[i*5+1] = y; @@ -524,7 +524,7 @@ matrix load_tags_paths(char **paths, int n, int k) char **get_labels_custom(char *filename, int *size) { list *plist = get_paths(filename); - if(size) *size = plist->size; + if(size) *size = plist->size; char **labels = (char **)list_to_array(plist); free_list(plist); return labels; @@ -532,7 +532,7 @@ char **get_labels_custom(char *filename, int *size) char **get_labels(char *filename) { - return get_labels_custom(filename, NULL); + return get_labels_custom(filename, NULL); } void free_data(data d) @@ -742,22 +742,22 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo d.y = make_matrix(n, 5*boxes); for(i = 0; i < n; ++i){ - const char *filename = random_paths[i]; - - int flag = (c >= 3); - IplImage *src; - if ((src = cvLoadImage(filename, flag)) == 0) - { - fprintf(stderr, "Cannot load image \"%s\"\n", filename); - char buff[256]; - sprintf(buff, "echo %s >> bad.list", filename); - system(buff); - continue; - //exit(0); - } - - int oh = src->height; - int ow = src->width; + const char *filename = random_paths[i]; + + int flag = (c >= 3); + IplImage *src; + if ((src = cvLoadImage(filename, flag)) == 0) + { + fprintf(stderr, "Cannot load image \"%s\"\n", filename); + char buff[256]; + sprintf(buff, "echo %s >> bad.list", filename); + system(buff); + continue; + //exit(0); + } + + int oh = src->height; + int ow = src->width; int dw = (ow*jitter); int dh = (oh*jitter); @@ -778,81 +778,81 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo float dx = ((float)pleft/ow)/sx; float dy = ((float)ptop /oh)/sy; - float dhue = rand_uniform_strong(-hue, hue); - float dsat = rand_scale(saturation); - float dexp = rand_scale(exposure); + float dhue = rand_uniform_strong(-hue, hue); + float dsat = rand_scale(saturation); + float dexp = rand_scale(exposure); - image ai = image_data_augmentation(src, w, h, pleft, ptop, swidth, sheight, flip, jitter, dhue, dsat, dexp); - d.X.vals[i] = ai.data; - - //show_image(ai, "aug"); - //cvWaitKey(0); + image ai = image_data_augmentation(src, w, h, pleft, ptop, swidth, sheight, flip, jitter, dhue, dsat, dexp); + d.X.vals[i] = ai.data; + + //show_image(ai, "aug"); + //cvWaitKey(0); fill_truth_detection(filename, boxes, d.y.vals[i], classes, flip, dx, dy, 1./sx, 1./sy, small_object, w, h); - cvReleaseImage(&src); + cvReleaseImage(&src); } free(random_paths); return d; } -#else // OPENCV +#else // OPENCV data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, float jitter, float hue, float saturation, float exposure, int small_object) { c = c ? c : 3; - char **random_paths = get_random_paths(paths, n, m); - int i; - data d = { 0 }; - d.shallow = 0; + char **random_paths = get_random_paths(paths, n, m); + int i; + data d = { 0 }; + d.shallow = 0; - d.X.rows = n; - d.X.vals = calloc(d.X.rows, sizeof(float*)); - d.X.cols = h*w*c; + d.X.rows = n; + d.X.vals = calloc(d.X.rows, sizeof(float*)); + d.X.cols = h*w*c; - d.y = make_matrix(n, 5 * boxes); - for (i = 0; i < n; ++i) { - image orig = load_image(random_paths[i], 0, 0, c); + d.y = make_matrix(n, 5 * boxes); + for (i = 0; i < n; ++i) { + image orig = load_image(random_paths[i], 0, 0, c); - int oh = orig.h; - int ow = orig.w; + int oh = orig.h; + int ow = orig.w; - int dw = (ow*jitter); - int dh = (oh*jitter); + int dw = (ow*jitter); + int dh = (oh*jitter); - int pleft = rand_uniform_strong(-dw, dw); - int pright = rand_uniform_strong(-dw, dw); - int ptop = rand_uniform_strong(-dh, dh); - int pbot = rand_uniform_strong(-dh, dh); + int pleft = rand_uniform_strong(-dw, dw); + int pright = rand_uniform_strong(-dw, dw); + int ptop = rand_uniform_strong(-dh, dh); + int pbot = rand_uniform_strong(-dh, dh); - int swidth = ow - pleft - pright; - int sheight = oh - ptop - pbot; + int swidth = ow - pleft - pright; + int sheight = oh - ptop - pbot; - float sx = (float)swidth / ow; - float sy = (float)sheight / oh; + float sx = (float)swidth / ow; + float sy = (float)sheight / oh; - int flip = use_flip ? random_gen() % 2 : 0; - image cropped = crop_image(orig, pleft, ptop, swidth, sheight); + int flip = use_flip ? random_gen() % 2 : 0; + image cropped = crop_image(orig, pleft, ptop, swidth, sheight); - float dx = ((float)pleft / ow) / sx; - float dy = ((float)ptop / oh) / sy; + float dx = ((float)pleft / ow) / sx; + float dy = ((float)ptop / oh) / sy; - image sized = resize_image(cropped, w, h); - if (flip) flip_image(sized); - random_distort_image(sized, hue, saturation, exposure); - d.X.vals[i] = sized.data; + image sized = resize_image(cropped, w, h); + if (flip) flip_image(sized); + random_distort_image(sized, hue, saturation, exposure); + d.X.vals[i] = sized.data; - fill_truth_detection(random_paths[i], boxes, d.y.vals[i], classes, flip, dx, dy, 1. / sx, 1. / sy, small_object, w, h); + fill_truth_detection(random_paths[i], boxes, d.y.vals[i], classes, flip, dx, dy, 1. / sx, 1. / sy, small_object, w, h); - free_image(orig); - free_image(cropped); - } - free(random_paths); - return d; + free_image(orig); + free_image(cropped); + } + free(random_paths); + return d; } -#endif // OPENCV +#endif // OPENCV void *load_thread(void *ptr) { - //srand(time(0)); + //srand(time(0)); //printf("Loading data: %d\n", random_gen()); load_args a = *(struct load_args*)ptr; if(a.exposure == 0) a.exposure = 1; @@ -878,9 +878,9 @@ void *load_thread(void *ptr) } else if (a.type == IMAGE_DATA){ *(a.im) = load_image(a.path, 0, 0, a.c); *(a.resized) = resize_image(*(a.im), a.w, a.h); - }else if (a.type == LETTERBOX_DATA) { - *(a.im) = load_image(a.path, 0, 0, a.c); - *(a.resized) = letterbox_image(*(a.im), a.w, a.h); + }else if (a.type == LETTERBOX_DATA) { + *(a.im) = load_image(a.path, 0, 0, a.c); + *(a.resized) = letterbox_image(*(a.im), a.w, a.h); } else if (a.type == TAG_DATA){ *a.d = load_data_tag(a.paths, a.n, a.m, a.classes, a.flip, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure); } @@ -899,7 +899,7 @@ pthread_t load_data_in_thread(load_args args) void *load_threads(void *ptr) { - //srand(time(0)); + //srand(time(0)); int i; load_args args = *(load_args *)ptr; if (args.threads == 0) args.threads = 1; diff --git a/src/demo.c b/src/demo.c index 34627ce1fa9..1b6b95212fe 100644 --- a/src/demo.c +++ b/src/demo.c @@ -64,25 +64,25 @@ static int letter_box = 0; void *fetch_in_thread(void *ptr) { //in = get_image_from_stream(cap); - int dont_close_stream = 0; // set 1 if your IP-camera periodically turns off and turns on video-stream - if(letter_box) - in_s = get_image_from_stream_letterbox(cap, net.w, net.h, net.c, &in_img, cpp_video_capture, dont_close_stream); - else - in_s = get_image_from_stream_resize(cap, net.w, net.h, net.c, &in_img, cpp_video_capture, dont_close_stream); + int dont_close_stream = 0; // set 1 if your IP-camera periodically turns off and turns on video-stream + if(letter_box) + in_s = get_image_from_stream_letterbox(cap, net.w, net.h, net.c, &in_img, cpp_video_capture, dont_close_stream); + else + in_s = get_image_from_stream_resize(cap, net.w, net.h, net.c, &in_img, cpp_video_capture, dont_close_stream); if(!in_s.data){ //error("Stream closed."); - printf("Stream closed.\n"); - flag_exit = 1; - return EXIT_FAILURE; + printf("Stream closed.\n"); + flag_exit = 1; + return EXIT_FAILURE; } //in_s = resize_image(in, net.w, net.h); - + return 0; } void *detect_in_thread(void *ptr) { - float nms = .45; // 0.4F + float nms = .45; // 0.4F layer l = net.layers[net.n-1]; float *X = det_s.data; @@ -94,29 +94,29 @@ void *detect_in_thread(void *ptr) free_image(det_s); - int nboxes = 0; - detection *dets = NULL; - if (letter_box) - dets = get_network_boxes(&net, in_img->width, in_img->height, demo_thresh, demo_thresh, 0, 1, &nboxes, 1); // letter box - else - dets = get_network_boxes(&net, det_s.w, det_s.h, demo_thresh, demo_thresh, 0, 1, &nboxes, 0); // resized - //if (nms) do_nms_obj(dets, nboxes, l.classes, nms); // bad results - if (nms) do_nms_sort(dets, nboxes, l.classes, nms); - + int nboxes = 0; + detection *dets = NULL; + if (letter_box) + dets = get_network_boxes(&net, in_img->width, in_img->height, demo_thresh, demo_thresh, 0, 1, &nboxes, 1); // letter box + else + dets = get_network_boxes(&net, det_s.w, det_s.h, demo_thresh, demo_thresh, 0, 1, &nboxes, 0); // resized + //if (nms) do_nms_obj(dets, nboxes, l.classes, nms); // bad results + if (nms) do_nms_sort(dets, nboxes, l.classes, nms); + printf("\033[2J"); printf("\033[1;1H"); printf("\nFPS:%.1f\n",fps); printf("Objects:\n\n"); - ipl_images[demo_index] = det_img; - det_img = ipl_images[(demo_index + FRAMES / 2 + 1) % FRAMES]; + ipl_images[demo_index] = det_img; + det_img = ipl_images[(demo_index + FRAMES / 2 + 1) % FRAMES]; demo_index = (demo_index + 1)%FRAMES; - - draw_detections_cv_v3(det_img, dets, nboxes, demo_thresh, demo_names, demo_alphabet, demo_classes, demo_ext_output); - free_detections(dets, nboxes); - return 0; + draw_detections_cv_v3(det_img, dets, nboxes, demo_thresh, demo_names, demo_alphabet, demo_classes, demo_ext_output); + free_detections(dets, nboxes); + + return 0; } double get_wall_time() @@ -129,7 +129,7 @@ double get_wall_time() } void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, - int frame_skip, char *prefix, char *out_filename, int http_stream_port, int dont_show, int ext_output) + int frame_skip, char *prefix, char *out_filename, int http_stream_port, int dont_show, int ext_output) { //skip = frame_skip; image **alphabet = load_alphabet(); @@ -138,40 +138,40 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int demo_alphabet = alphabet; demo_classes = classes; demo_thresh = thresh; - demo_ext_output = ext_output; + demo_ext_output = ext_output; printf("Demo\n"); - net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 + net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 if(weightfile){ load_weights(&net, weightfile); } //set_batch_network(&net, 1); - fuse_conv_batchnorm(net); + fuse_conv_batchnorm(net); srand(2222222); if(filename){ printf("video file: %s\n", filename); -//#ifdef CV_VERSION_EPOCH // OpenCV 2.x -// cap = cvCaptureFromFile(filename); -//#else // OpenCV 3.x - cpp_video_capture = 1; - cap = get_capture_video_stream(filename); +//#ifdef CV_VERSION_EPOCH // OpenCV 2.x +// cap = cvCaptureFromFile(filename); +//#else // OpenCV 3.x + cpp_video_capture = 1; + cap = get_capture_video_stream(filename); //#endif }else{ - printf("Webcam index: %d\n", cam_index); -//#ifdef CV_VERSION_EPOCH // OpenCV 2.x + printf("Webcam index: %d\n", cam_index); +//#ifdef CV_VERSION_EPOCH // OpenCV 2.x // cap = cvCaptureFromCAM(cam_index); -//#else // OpenCV 3.x - cpp_video_capture = 1; - cap = get_capture_webcam(cam_index); +//#else // OpenCV 3.x + cpp_video_capture = 1; + cap = get_capture_webcam(cam_index); //#endif } - if (!cap) { + if (!cap) { #ifdef WIN32 - printf("Check that you have copied file opencv_ffmpeg340_64.dll to the same directory where is darknet.exe \n"); + printf("Check that you have copied file opencv_ffmpeg340_64.dll to the same directory where is darknet.exe \n"); #endif - error("Couldn't connect to webcam.\n"); - } + error("Couldn't connect to webcam.\n"); + } layer l = net.layers[net.n-1]; int j; @@ -184,51 +184,51 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int probs = (float **)calloc(l.w*l.h*l.n, sizeof(float *)); for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float *)calloc(l.classes, sizeof(float *)); - flag_exit = 0; + flag_exit = 0; pthread_t fetch_thread; pthread_t detect_thread; fetch_in_thread(0); - det_img = in_img; + det_img = in_img; det_s = in_s; fetch_in_thread(0); detect_in_thread(0); - det_img = in_img; + det_img = in_img; det_s = in_s; for(j = 0; j < FRAMES/2; ++j){ fetch_in_thread(0); detect_in_thread(0); - det_img = in_img; + det_img = in_img; det_s = in_s; } int count = 0; if(!prefix && !dont_show){ - cvNamedWindow("Demo", CV_WINDOW_NORMAL); + cvNamedWindow("Demo", CV_WINDOW_NORMAL); cvMoveWindow("Demo", 0, 0); cvResizeWindow("Demo", 1352, 1013); } - CvVideoWriter* output_video_writer = NULL; // cv::VideoWriter output_video; - if (out_filename && !flag_exit) - { - CvSize size; - size.width = det_img->width, size.height = det_img->height; - int src_fps = 25; - src_fps = get_stream_fps(cap, cpp_video_capture); - - //const char* output_name = "test_dnn_out.avi"; - //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('H', '2', '6', '4'), src_fps, size, 1); - output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('D', 'I', 'V', 'X'), src_fps, size, 1); - //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'J', 'P', 'G'), src_fps, size, 1); - //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'P', '4', 'V'), src_fps, size, 1); - //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'P', '4', '2'), src_fps, size, 1); - //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('X', 'V', 'I', 'D'), src_fps, size, 1); - //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('W', 'M', 'V', '2'), src_fps, size, 1); - } + CvVideoWriter* output_video_writer = NULL; // cv::VideoWriter output_video; + if (out_filename && !flag_exit) + { + CvSize size; + size.width = det_img->width, size.height = det_img->height; + int src_fps = 25; + src_fps = get_stream_fps(cap, cpp_video_capture); + + //const char* output_name = "test_dnn_out.avi"; + //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('H', '2', '6', '4'), src_fps, size, 1); + output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('D', 'I', 'V', 'X'), src_fps, size, 1); + //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'J', 'P', 'G'), src_fps, size, 1); + //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'P', '4', 'V'), src_fps, size, 1); + //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'P', '4', '2'), src_fps, size, 1); + //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('X', 'V', 'I', 'D'), src_fps, size, 1); + //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('W', 'M', 'V', '2'), src_fps, size, 1); + } double before = get_wall_time(); @@ -239,66 +239,66 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed"); if(!prefix){ - if (!dont_show) { - show_image_cv_ipl(show_img, "Demo"); - int c = cvWaitKey(1); - if (c == 10) { - if (frame_skip == 0) frame_skip = 60; - else if (frame_skip == 4) frame_skip = 0; - else if (frame_skip == 60) frame_skip = 4; - else frame_skip = 0; - } - else if (c == 27 || c == 1048603) // ESC - exit (OpenCV 2.x / 3.x) - { - flag_exit = 1; - } - } + if (!dont_show) { + show_image_cv_ipl(show_img, "Demo"); + int c = cvWaitKey(1); + if (c == 10) { + if (frame_skip == 0) frame_skip = 60; + else if (frame_skip == 4) frame_skip = 0; + else if (frame_skip == 60) frame_skip = 4; + else frame_skip = 0; + } + else if (c == 27 || c == 1048603) // ESC - exit (OpenCV 2.x / 3.x) + { + flag_exit = 1; + } + } }else{ char buff[256]; sprintf(buff, "%s_%08d.jpg", prefix, count); - cvSaveImage(buff, show_img, 0); + cvSaveImage(buff, show_img, 0); //save_image(disp, buff); } - // if you run it with param -http_port 8090 then open URL in your web-browser: http://localhost:8090 - if (http_stream_port > 0 && show_img) { - //int port = 8090; - int port = http_stream_port; - int timeout = 200; - int jpeg_quality = 30; // 1 - 100 - send_mjpeg(show_img, port, timeout, jpeg_quality); - } + // if you run it with param -http_port 8090 then open URL in your web-browser: http://localhost:8090 + if (http_stream_port > 0 && show_img) { + //int port = 8090; + int port = http_stream_port; + int timeout = 200; + int jpeg_quality = 30; // 1 - 100 + send_mjpeg(show_img, port, timeout, jpeg_quality); + } - // save video file - if (output_video_writer && show_img) { - cvWriteFrame(output_video_writer, show_img); - printf("\n cvWriteFrame \n"); - } + // save video file + if (output_video_writer && show_img) { + cvWriteFrame(output_video_writer, show_img); + printf("\n cvWriteFrame \n"); + } - cvReleaseImage(&show_img); + cvReleaseImage(&show_img); pthread_join(fetch_thread, 0); pthread_join(detect_thread, 0); - if (flag_exit == 1) break; + if (flag_exit == 1) break; if(delay == 0){ - show_img = det_img; + show_img = det_img; } - det_img = in_img; + det_img = in_img; det_s = in_s; }else { fetch_in_thread(0); - det_img = in_img; + det_img = in_img; det_s = in_s; detect_in_thread(0); - show_img = det_img; - if (!dont_show) { - show_image_cv_ipl(show_img, "Demo"); - cvWaitKey(1); - } - cvReleaseImage(&show_img); + show_img = det_img; + if (!dont_show) { + show_image_cv_ipl(show_img, "Demo"); + cvWaitKey(1); + } + cvReleaseImage(&show_img); } --delay; if(delay < 0){ @@ -310,42 +310,42 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int before = after; } } - printf("input video stream closed. \n"); - if (output_video_writer) { - cvReleaseVideoWriter(&output_video_writer); - printf("output_video_writer closed. \n"); - } - - // free memory - cvReleaseImage(&show_img); - cvReleaseImage(&in_img); - free_image(in_s); - - free(avg); - for (j = 0; j < FRAMES; ++j) free(predictions[j]); - for (j = 0; j < FRAMES; ++j) free_image(images[j]); - - for (j = 0; j < l.w*l.h*l.n; ++j) free(probs[j]); - free(boxes); - free(probs); - - free_ptrs(names, net.layers[net.n - 1].classes); - - int i; - const int nsize = 8; - for (j = 0; j < nsize; ++j) { - for (i = 32; i < 127; ++i) { - free_image(alphabet[j][i]); - } - free(alphabet[j]); - } - free(alphabet); - - free_network(net); + printf("input video stream closed. \n"); + if (output_video_writer) { + cvReleaseVideoWriter(&output_video_writer); + printf("output_video_writer closed. \n"); + } + + // free memory + cvReleaseImage(&show_img); + cvReleaseImage(&in_img); + free_image(in_s); + + free(avg); + for (j = 0; j < FRAMES; ++j) free(predictions[j]); + for (j = 0; j < FRAMES; ++j) free_image(images[j]); + + for (j = 0; j < l.w*l.h*l.n; ++j) free(probs[j]); + free(boxes); + free(probs); + + free_ptrs(names, net.layers[net.n - 1].classes); + + int i; + const int nsize = 8; + for (j = 0; j < nsize; ++j) { + for (i = 32; i < 127; ++i) { + free_image(alphabet[j][i]); + } + free(alphabet[j]); + } + free(alphabet); + + free_network(net); } #else void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, - int frame_skip, char *prefix, char *out_filename, int http_stream_port, int dont_show, int ext_output) + int frame_skip, char *prefix, char *out_filename, int http_stream_port, int dont_show, int ext_output) { fprintf(stderr, "Demo needs OpenCV for webcam images.\n"); } diff --git a/src/detector.c b/src/detector.c index 0a0ecdad048..244b4c3d20e 100644 --- a/src/detector.c +++ b/src/detector.c @@ -27,7 +27,7 @@ IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size); void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches); -#endif // OPENCV +#endif // OPENCV static int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90}; @@ -61,14 +61,14 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i srand(time(0)); network net = nets[0]; - const int actual_batch_size = net.batch * net.subdivisions; - if (actual_batch_size == 1) { - printf("\n Error: You set incorrect value batch=1 for Training! You should set batch=64 subdivision=64 \n"); - getchar(); - } - else if (actual_batch_size < 64) { - printf("\n Warning: You set batch=%d lower than 64! It is recommended to set batch=64 subdivision=64 \n", actual_batch_size); - } + const int actual_batch_size = net.batch * net.subdivisions; + if (actual_batch_size == 1) { + printf("\n Error: You set incorrect value batch=1 for Training! You should set batch=64 subdivision=64 \n"); + getchar(); + } + else if (actual_batch_size < 64) { + printf("\n Warning: You set batch=%d lower than 64! It is recommended to set batch=64 subdivision=64 \n", actual_batch_size); + } int imgs = net.batch * net.subdivisions * ngpus; printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); @@ -83,26 +83,26 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i //int N = plist->size; char **paths = (char **)list_to_array(plist); - int init_w = net.w; - int init_h = net.h; - int iter_save; - iter_save = get_current_batch(net); + int init_w = net.w; + int init_h = net.h; + int iter_save; + iter_save = get_current_batch(net); load_args args = {0}; args.w = net.w; args.h = net.h; - args.c = net.c; - args.paths = paths; + args.c = net.c; + args.paths = paths; args.n = imgs; args.m = plist->size; args.classes = classes; args.flip = net.flip; args.jitter = jitter; args.num_boxes = l.max_boxes; - args.small_object = net.small_object; + args.small_object = net.small_object; args.d = &buffer; args.type = DETECTION_DATA; - args.threads = 16; // 64 + args.threads = 16; // 64 args.angle = net.angle; args.exposure = net.exposure; @@ -110,40 +110,40 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i args.hue = net.hue; #ifdef OPENCV - args.threads = 3 * ngpus; - IplImage* img = NULL; - float max_img_loss = 5; - int number_of_lines = 100; - int img_size = 1000; - if (!dont_show) - img = draw_train_chart(max_img_loss, net.max_batches, number_of_lines, img_size); -#endif //OPENCV + args.threads = 3 * ngpus; + IplImage* img = NULL; + float max_img_loss = 5; + int number_of_lines = 100; + int img_size = 1000; + if (!dont_show) + img = draw_train_chart(max_img_loss, net.max_batches, number_of_lines, img_size); +#endif //OPENCV pthread_t load_thread = load_data(args); double time; int count = 0; //while(i*imgs < N*120){ while(get_current_batch(net) < net.max_batches){ - if(l.random && count++%10 == 0){ + if(l.random && count++%10 == 0){ printf("Resizing\n"); - //int dim = (rand() % 12 + (init_w/32 - 5)) * 32; // +-160 + //int dim = (rand() % 12 + (init_w/32 - 5)) * 32; // +-160 //int dim = (rand() % 4 + 16) * 32; - //if (get_current_batch(net)+100 > net.max_batches) dim = 544; - - //int random_val = rand() % 12; - //int dim_w = (random_val + (init_w / 32 - 5)) * 32; // +-160 - //int dim_h = (random_val + (init_h / 32 - 5)) * 32; // +-160 + //if (get_current_batch(net)+100 > net.max_batches) dim = 544; + + //int random_val = rand() % 12; + //int dim_w = (random_val + (init_w / 32 - 5)) * 32; // +-160 + //int dim_h = (random_val + (init_h / 32 - 5)) * 32; // +-160 - float random_val = rand_scale(1.4); // *x or /x - int dim_w = roundl(random_val*init_w / 32) * 32; - int dim_h = roundl(random_val*init_h / 32) * 32; + float random_val = rand_scale(1.4); // *x or /x + int dim_w = roundl(random_val*init_w / 32) * 32; + int dim_h = roundl(random_val*init_h / 32) * 32; - if (dim_w < 32) dim_w = 32; - if (dim_h < 32) dim_h = 32; + if (dim_w < 32) dim_w = 32; + if (dim_h < 32) dim_h = 32; - printf("%d x %d \n", dim_w, dim_h); - args.w = dim_w; - args.h = dim_h; + printf("%d x %d \n", dim_w, dim_h); + args.w = dim_w; + args.h = dim_h; pthread_join(load_thread, 0); train = buffer; @@ -190,28 +190,28 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i #else loss = train_network(net, train); #endif - if (avg_loss < 0 || avg_loss != avg_loss) avg_loss = loss; // if(-inf or nan) + if (avg_loss < 0 || avg_loss != avg_loss) avg_loss = loss; // if(-inf or nan) avg_loss = avg_loss*.9 + loss*.1; i = get_current_batch(net); printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now()-time), i*imgs); #ifdef OPENCV - if(!dont_show) - draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches); -#endif // OPENCV - - //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) { - //if (i % 100 == 0) { - if(i >= (iter_save + 100)) { - iter_save = i; + if(!dont_show) + draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches); +#endif // OPENCV + + //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) { + //if (i % 100 == 0) { + if(i >= (iter_save + 100)) { + iter_save = i; #ifdef GPU - if (ngpus != 1) sync_nets(nets, ngpus, 0); + if (ngpus != 1) sync_nets(nets, ngpus, 0); #endif - char buff[256]; - sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); - save_weights(net, buff); - } + char buff[256]; + sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); + save_weights(net, buff); + } free_data(train); } #ifdef GPU @@ -222,870 +222,870 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i save_weights(net, buff); #ifdef OPENCV - cvReleaseImage(&img); - cvDestroyAllWindows(); + cvReleaseImage(&img); + cvDestroyAllWindows(); #endif - // free memory - pthread_join(load_thread, 0); - free_data(buffer); + // free memory + pthread_join(load_thread, 0); + free_data(buffer); - free(base); - free(paths); - free_list_contents(plist); - free_list(plist); + free(base); + free(paths); + free_list_contents(plist); + free_list(plist); - free_list_contents_kvp(options); - free_list(options); + free_list_contents_kvp(options); + free_list(options); - free(nets); - free_network(net); + free(nets); + free_network(net); } static int get_coco_image_id(char *filename) { - char *p = strrchr(filename, '/'); - char *c = strrchr(filename, '_'); - if (c) p = c; - return atoi(p + 1); + char *p = strrchr(filename, '/'); + char *c = strrchr(filename, '_'); + if (c) p = c; + return atoi(p + 1); } static void print_cocos(FILE *fp, char *image_path, detection *dets, int num_boxes, int classes, int w, int h) { - int i, j; - int image_id = get_coco_image_id(image_path); - for (i = 0; i < num_boxes; ++i) { - float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.; - float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.; - float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.; - float ymax = dets[i].bbox.y + dets[i].bbox.h / 2.; - - if (xmin < 0) xmin = 0; - if (ymin < 0) ymin = 0; - if (xmax > w) xmax = w; - if (ymax > h) ymax = h; - - float bx = xmin; - float by = ymin; - float bw = xmax - xmin; - float bh = ymax - ymin; - - for (j = 0; j < classes; ++j) { - if (dets[i].prob[j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, dets[i].prob[j]); - } - } + int i, j; + int image_id = get_coco_image_id(image_path); + for (i = 0; i < num_boxes; ++i) { + float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.; + float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.; + float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.; + float ymax = dets[i].bbox.y + dets[i].bbox.h / 2.; + + if (xmin < 0) xmin = 0; + if (ymin < 0) ymin = 0; + if (xmax > w) xmax = w; + if (ymax > h) ymax = h; + + float bx = xmin; + float by = ymin; + float bw = xmax - xmin; + float bh = ymax - ymin; + + for (j = 0; j < classes; ++j) { + if (dets[i].prob[j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, dets[i].prob[j]); + } + } } void print_detector_detections(FILE **fps, char *id, detection *dets, int total, int classes, int w, int h) { - int i, j; - for (i = 0; i < total; ++i) { - float xmin = dets[i].bbox.x - dets[i].bbox.w / 2. + 1; - float xmax = dets[i].bbox.x + dets[i].bbox.w / 2. + 1; - float ymin = dets[i].bbox.y - dets[i].bbox.h / 2. + 1; - float ymax = dets[i].bbox.y + dets[i].bbox.h / 2. + 1; - - if (xmin < 1) xmin = 1; - if (ymin < 1) ymin = 1; - if (xmax > w) xmax = w; - if (ymax > h) ymax = h; - - for (j = 0; j < classes; ++j) { - if (dets[i].prob[j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, dets[i].prob[j], - xmin, ymin, xmax, ymax); - } - } + int i, j; + for (i = 0; i < total; ++i) { + float xmin = dets[i].bbox.x - dets[i].bbox.w / 2. + 1; + float xmax = dets[i].bbox.x + dets[i].bbox.w / 2. + 1; + float ymin = dets[i].bbox.y - dets[i].bbox.h / 2. + 1; + float ymax = dets[i].bbox.y + dets[i].bbox.h / 2. + 1; + + if (xmin < 1) xmin = 1; + if (ymin < 1) ymin = 1; + if (xmax > w) xmax = w; + if (ymax > h) ymax = h; + + for (j = 0; j < classes; ++j) { + if (dets[i].prob[j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, dets[i].prob[j], + xmin, ymin, xmax, ymax); + } + } } void print_imagenet_detections(FILE *fp, int id, detection *dets, int total, int classes, int w, int h) { - int i, j; - for (i = 0; i < total; ++i) { - float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.; - float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.; - float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.; - float ymax = dets[i].bbox.y + dets[i].bbox.h / 2.; - - if (xmin < 0) xmin = 0; - if (ymin < 0) ymin = 0; - if (xmax > w) xmax = w; - if (ymax > h) ymax = h; - - for (j = 0; j < classes; ++j) { - int class = j; - if (dets[i].prob[class]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j + 1, dets[i].prob[class], - xmin, ymin, xmax, ymax); - } - } + int i, j; + for (i = 0; i < total; ++i) { + float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.; + float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.; + float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.; + float ymax = dets[i].bbox.y + dets[i].bbox.h / 2.; + + if (xmin < 0) xmin = 0; + if (ymin < 0) ymin = 0; + if (xmax > w) xmax = w; + if (ymax > h) ymax = h; + + for (j = 0; j < classes; ++j) { + int class = j; + if (dets[i].prob[class]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j + 1, dets[i].prob[class], + xmin, ymin, xmax, ymax); + } + } } void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *outfile) { - int j; - list *options = read_data_cfg(datacfg); - char *valid_images = option_find_str(options, "valid", "data/train.list"); - char *name_list = option_find_str(options, "names", "data/names.list"); - char *prefix = option_find_str(options, "results", "results"); - char **names = get_labels(name_list); - char *mapf = option_find_str(options, "map", 0); - int *map = 0; - if (mapf) map = read_map(mapf); - - network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 - if (weightfile) { - load_weights(&net, weightfile); - } - //set_batch_network(&net, 1); - fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - srand(time(0)); - - list *plist = get_paths(valid_images); - char **paths = (char **)list_to_array(plist); - - layer l = net.layers[net.n - 1]; - int classes = l.classes; - - char buff[1024]; - char *type = option_find_str(options, "eval", "voc"); - FILE *fp = 0; - FILE **fps = 0; - int coco = 0; - int imagenet = 0; - if (0 == strcmp(type, "coco")) { - if (!outfile) outfile = "coco_results"; - snprintf(buff, 1024, "%s/%s.json", prefix, outfile); - fp = fopen(buff, "w"); - fprintf(fp, "[\n"); - coco = 1; - } - else if (0 == strcmp(type, "imagenet")) { - if (!outfile) outfile = "imagenet-detection"; - snprintf(buff, 1024, "%s/%s.txt", prefix, outfile); - fp = fopen(buff, "w"); - imagenet = 1; - classes = 200; - } - else { - if (!outfile) outfile = "comp4_det_test_"; - fps = calloc(classes, sizeof(FILE *)); - for (j = 0; j < classes; ++j) { - snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]); - fps[j] = fopen(buff, "w"); - } - } - - - int m = plist->size; - int i = 0; - int t; - - float thresh = .005; - float nms = .45; - - int nthreads = 4; - image *val = calloc(nthreads, sizeof(image)); - image *val_resized = calloc(nthreads, sizeof(image)); - image *buf = calloc(nthreads, sizeof(image)); - image *buf_resized = calloc(nthreads, sizeof(image)); - pthread_t *thr = calloc(nthreads, sizeof(pthread_t)); - - load_args args = { 0 }; - args.w = net.w; - args.h = net.h; - args.c = net.c; - args.type = IMAGE_DATA; - //args.type = LETTERBOX_DATA; - - for (t = 0; t < nthreads; ++t) { - args.path = paths[i + t]; - args.im = &buf[t]; - args.resized = &buf_resized[t]; - thr[t] = load_data_in_thread(args); - } - time_t start = time(0); - for (i = nthreads; i < m + nthreads; i += nthreads) { - fprintf(stderr, "%d\n", i); - for (t = 0; t < nthreads && i + t - nthreads < m; ++t) { - pthread_join(thr[t], 0); - val[t] = buf[t]; - val_resized[t] = buf_resized[t]; - } - for (t = 0; t < nthreads && i + t < m; ++t) { - args.path = paths[i + t]; - args.im = &buf[t]; - args.resized = &buf_resized[t]; - thr[t] = load_data_in_thread(args); - } - for (t = 0; t < nthreads && i + t - nthreads < m; ++t) { - char *path = paths[i + t - nthreads]; - char *id = basecfg(path); - float *X = val_resized[t].data; - network_predict(net, X); - int w = val[t].w; - int h = val[t].h; - int nboxes = 0; - int letterbox = (args.type == LETTERBOX_DATA); - detection *dets = get_network_boxes(&net, w, h, thresh, .5, map, 0, &nboxes, letterbox); - if (nms) do_nms_sort(dets, nboxes, classes, nms); - if (coco) { - print_cocos(fp, path, dets, nboxes, classes, w, h); - } - else if (imagenet) { - print_imagenet_detections(fp, i + t - nthreads + 1, dets, nboxes, classes, w, h); - } - else { - print_detector_detections(fps, id, dets, nboxes, classes, w, h); - } - free_detections(dets, nboxes); - free(id); - free_image(val[t]); - free_image(val_resized[t]); - } - } - for (j = 0; j < classes; ++j) { - if (fps) fclose(fps[j]); - } - if (coco) { - fseek(fp, -2, SEEK_CUR); - fprintf(fp, "\n]\n"); - fclose(fp); - } - fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)time(0) - start); + int j; + list *options = read_data_cfg(datacfg); + char *valid_images = option_find_str(options, "valid", "data/train.list"); + char *name_list = option_find_str(options, "names", "data/names.list"); + char *prefix = option_find_str(options, "results", "results"); + char **names = get_labels(name_list); + char *mapf = option_find_str(options, "map", 0); + int *map = 0; + if (mapf) map = read_map(mapf); + + network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 + if (weightfile) { + load_weights(&net, weightfile); + } + //set_batch_network(&net, 1); + fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); + srand(time(0)); + + list *plist = get_paths(valid_images); + char **paths = (char **)list_to_array(plist); + + layer l = net.layers[net.n - 1]; + int classes = l.classes; + + char buff[1024]; + char *type = option_find_str(options, "eval", "voc"); + FILE *fp = 0; + FILE **fps = 0; + int coco = 0; + int imagenet = 0; + if (0 == strcmp(type, "coco")) { + if (!outfile) outfile = "coco_results"; + snprintf(buff, 1024, "%s/%s.json", prefix, outfile); + fp = fopen(buff, "w"); + fprintf(fp, "[\n"); + coco = 1; + } + else if (0 == strcmp(type, "imagenet")) { + if (!outfile) outfile = "imagenet-detection"; + snprintf(buff, 1024, "%s/%s.txt", prefix, outfile); + fp = fopen(buff, "w"); + imagenet = 1; + classes = 200; + } + else { + if (!outfile) outfile = "comp4_det_test_"; + fps = calloc(classes, sizeof(FILE *)); + for (j = 0; j < classes; ++j) { + snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]); + fps[j] = fopen(buff, "w"); + } + } + + + int m = plist->size; + int i = 0; + int t; + + float thresh = .005; + float nms = .45; + + int nthreads = 4; + image *val = calloc(nthreads, sizeof(image)); + image *val_resized = calloc(nthreads, sizeof(image)); + image *buf = calloc(nthreads, sizeof(image)); + image *buf_resized = calloc(nthreads, sizeof(image)); + pthread_t *thr = calloc(nthreads, sizeof(pthread_t)); + + load_args args = { 0 }; + args.w = net.w; + args.h = net.h; + args.c = net.c; + args.type = IMAGE_DATA; + //args.type = LETTERBOX_DATA; + + for (t = 0; t < nthreads; ++t) { + args.path = paths[i + t]; + args.im = &buf[t]; + args.resized = &buf_resized[t]; + thr[t] = load_data_in_thread(args); + } + time_t start = time(0); + for (i = nthreads; i < m + nthreads; i += nthreads) { + fprintf(stderr, "%d\n", i); + for (t = 0; t < nthreads && i + t - nthreads < m; ++t) { + pthread_join(thr[t], 0); + val[t] = buf[t]; + val_resized[t] = buf_resized[t]; + } + for (t = 0; t < nthreads && i + t < m; ++t) { + args.path = paths[i + t]; + args.im = &buf[t]; + args.resized = &buf_resized[t]; + thr[t] = load_data_in_thread(args); + } + for (t = 0; t < nthreads && i + t - nthreads < m; ++t) { + char *path = paths[i + t - nthreads]; + char *id = basecfg(path); + float *X = val_resized[t].data; + network_predict(net, X); + int w = val[t].w; + int h = val[t].h; + int nboxes = 0; + int letterbox = (args.type == LETTERBOX_DATA); + detection *dets = get_network_boxes(&net, w, h, thresh, .5, map, 0, &nboxes, letterbox); + if (nms) do_nms_sort(dets, nboxes, classes, nms); + if (coco) { + print_cocos(fp, path, dets, nboxes, classes, w, h); + } + else if (imagenet) { + print_imagenet_detections(fp, i + t - nthreads + 1, dets, nboxes, classes, w, h); + } + else { + print_detector_detections(fps, id, dets, nboxes, classes, w, h); + } + free_detections(dets, nboxes); + free(id); + free_image(val[t]); + free_image(val_resized[t]); + } + } + for (j = 0; j < classes; ++j) { + if (fps) fclose(fps[j]); + } + if (coco) { + fseek(fp, -2, SEEK_CUR); + fprintf(fp, "\n]\n"); + fclose(fp); + } + fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)time(0) - start); } void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile) { - network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 - if (weightfile) { - load_weights(&net, weightfile); - } - //set_batch_network(&net, 1); - fuse_conv_batchnorm(net); - srand(time(0)); - - //list *plist = get_paths("data/coco_val_5k.list"); - list *options = read_data_cfg(datacfg); - char *valid_images = option_find_str(options, "valid", "data/train.txt"); - list *plist = get_paths(valid_images); - char **paths = (char **)list_to_array(plist); - - layer l = net.layers[net.n - 1]; - - int j, k; - - int m = plist->size; - int i = 0; - - float thresh = .001; - float iou_thresh = .5; - float nms = .4; - - int total = 0; - int correct = 0; - int proposals = 0; - float avg_iou = 0; - - for (i = 0; i < m; ++i) { - char *path = paths[i]; - image orig = load_image(path, 0, 0, net.c); - image sized = resize_image(orig, net.w, net.h); - char *id = basecfg(path); - network_predict(net, sized.data); - int nboxes = 0; - int letterbox = 0; - detection *dets = get_network_boxes(&net, sized.w, sized.h, thresh, .5, 0, 1, &nboxes, letterbox); - if (nms) do_nms_obj(dets, nboxes, 1, nms); - - char labelpath[4096]; - replace_image_to_label(path, labelpath); - - int num_labels = 0; - box_label *truth = read_boxes(labelpath, &num_labels); - for (k = 0; k < nboxes; ++k) { - if (dets[k].objectness > thresh) { - ++proposals; - } - } - for (j = 0; j < num_labels; ++j) { - ++total; - box t = { truth[j].x, truth[j].y, truth[j].w, truth[j].h }; - float best_iou = 0; - for (k = 0; k < nboxes; ++k) { - float iou = box_iou(dets[k].bbox, t); - if (dets[k].objectness > thresh && iou > best_iou) { - best_iou = iou; - } - } - avg_iou += best_iou; - if (best_iou > iou_thresh) { - ++correct; - } - } - //fprintf(stderr, " %s - %s - ", paths[i], labelpath); - fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals / (i + 1), avg_iou * 100 / total, 100.*correct / total); - free(id); - free_image(orig); - free_image(sized); - } + network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 + if (weightfile) { + load_weights(&net, weightfile); + } + //set_batch_network(&net, 1); + fuse_conv_batchnorm(net); + srand(time(0)); + + //list *plist = get_paths("data/coco_val_5k.list"); + list *options = read_data_cfg(datacfg); + char *valid_images = option_find_str(options, "valid", "data/train.txt"); + list *plist = get_paths(valid_images); + char **paths = (char **)list_to_array(plist); + + layer l = net.layers[net.n - 1]; + + int j, k; + + int m = plist->size; + int i = 0; + + float thresh = .001; + float iou_thresh = .5; + float nms = .4; + + int total = 0; + int correct = 0; + int proposals = 0; + float avg_iou = 0; + + for (i = 0; i < m; ++i) { + char *path = paths[i]; + image orig = load_image(path, 0, 0, net.c); + image sized = resize_image(orig, net.w, net.h); + char *id = basecfg(path); + network_predict(net, sized.data); + int nboxes = 0; + int letterbox = 0; + detection *dets = get_network_boxes(&net, sized.w, sized.h, thresh, .5, 0, 1, &nboxes, letterbox); + if (nms) do_nms_obj(dets, nboxes, 1, nms); + + char labelpath[4096]; + replace_image_to_label(path, labelpath); + + int num_labels = 0; + box_label *truth = read_boxes(labelpath, &num_labels); + for (k = 0; k < nboxes; ++k) { + if (dets[k].objectness > thresh) { + ++proposals; + } + } + for (j = 0; j < num_labels; ++j) { + ++total; + box t = { truth[j].x, truth[j].y, truth[j].w, truth[j].h }; + float best_iou = 0; + for (k = 0; k < nboxes; ++k) { + float iou = box_iou(dets[k].bbox, t); + if (dets[k].objectness > thresh && iou > best_iou) { + best_iou = iou; + } + } + avg_iou += best_iou; + if (best_iou > iou_thresh) { + ++correct; + } + } + //fprintf(stderr, " %s - %s - ", paths[i], labelpath); + fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals / (i + 1), avg_iou * 100 / total, 100.*correct / total); + free(id); + free_image(orig); + free_image(sized); + } } typedef struct { - box b; - float p; - int class_id; - int image_index; - int truth_flag; - int unique_truth_index; + box b; + float p; + int class_id; + int image_index; + int truth_flag; + int unique_truth_index; } box_prob; int detections_comparator(const void *pa, const void *pb) { - box_prob a = *(box_prob *)pa; - box_prob b = *(box_prob *)pb; - float diff = a.p - b.p; - if (diff < 0) return 1; - else if (diff > 0) return -1; - return 0; + box_prob a = *(box_prob *)pa; + box_prob b = *(box_prob *)pb; + float diff = a.p - b.p; + if (diff < 0) return 1; + else if (diff > 0) return -1; + return 0; } void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou) { - int j; - list *options = read_data_cfg(datacfg); - char *valid_images = option_find_str(options, "valid", "data/train.txt"); - char *difficult_valid_images = option_find_str(options, "difficult", NULL); - char *name_list = option_find_str(options, "names", "data/names.list"); - char **names = get_labels(name_list); - char *mapf = option_find_str(options, "map", 0); - int *map = 0; - if (mapf) map = read_map(mapf); - FILE* reinforcement_fd = NULL; - - network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 - if (weightfile) { - load_weights(&net, weightfile); - } - //set_batch_network(&net, 1); - fuse_conv_batchnorm(net); - srand(time(0)); - - list *plist = get_paths(valid_images); - char **paths = (char **)list_to_array(plist); - - char **paths_dif = NULL; - if (difficult_valid_images) { - list *plist_dif = get_paths(difficult_valid_images); - paths_dif = (char **)list_to_array(plist_dif); - } - - - layer l = net.layers[net.n - 1]; - int classes = l.classes; - - int m = plist->size; - int i = 0; - int t; - - const float thresh = .005; - const float nms = .45; - const float iou_thresh = 0.5; - - int nthreads = 4; - image *val = calloc(nthreads, sizeof(image)); - image *val_resized = calloc(nthreads, sizeof(image)); - image *buf = calloc(nthreads, sizeof(image)); - image *buf_resized = calloc(nthreads, sizeof(image)); - pthread_t *thr = calloc(nthreads, sizeof(pthread_t)); - - load_args args = { 0 }; - args.w = net.w; - args.h = net.h; - args.c = net.c; - args.type = IMAGE_DATA; - //args.type = LETTERBOX_DATA; - - //const float thresh_calc_avg_iou = 0.24; - float avg_iou = 0; - int tp_for_thresh = 0; - int fp_for_thresh = 0; - - box_prob *detections = calloc(1, sizeof(box_prob)); - int detections_count = 0; - int unique_truth_count = 0; - - int *truth_classes_count = calloc(classes, sizeof(int)); - - for (t = 0; t < nthreads; ++t) { - args.path = paths[i + t]; - args.im = &buf[t]; - args.resized = &buf_resized[t]; - thr[t] = load_data_in_thread(args); - } - time_t start = time(0); - for (i = nthreads; i < m + nthreads; i += nthreads) { - fprintf(stderr, "%d\n", i); - for (t = 0; t < nthreads && i + t - nthreads < m; ++t) { - pthread_join(thr[t], 0); - val[t] = buf[t]; - val_resized[t] = buf_resized[t]; - } - for (t = 0; t < nthreads && i + t < m; ++t) { - args.path = paths[i + t]; - args.im = &buf[t]; - args.resized = &buf_resized[t]; - thr[t] = load_data_in_thread(args); - } - for (t = 0; t < nthreads && i + t - nthreads < m; ++t) { - const int image_index = i + t - nthreads; - char *path = paths[image_index]; - char *id = basecfg(path); - float *X = val_resized[t].data; - network_predict(net, X); - - int nboxes = 0; - int letterbox = (args.type == LETTERBOX_DATA); - float hier_thresh = 0; - detection *dets = get_network_boxes(&net, 1, 1, thresh, hier_thresh, 0, 0, &nboxes, letterbox); - //detection *dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); // for letterbox=1 - if (nms) do_nms_sort(dets, nboxes, l.classes, nms); - - char labelpath[4096]; - replace_image_to_label(path, labelpath); - int num_labels = 0; - box_label *truth = read_boxes(labelpath, &num_labels); - int i, j; - for (j = 0; j < num_labels; ++j) { - truth_classes_count[truth[j].id]++; - } - - // difficult - box_label *truth_dif = NULL; - int num_labels_dif = 0; - if (paths_dif) - { - char *path_dif = paths_dif[image_index]; - - char labelpath_dif[4096]; - replace_image_to_label(path_dif, labelpath_dif); - - truth_dif = read_boxes(labelpath_dif, &num_labels_dif); - } - - const int checkpoint_detections_count = detections_count; - - for (i = 0; i < nboxes; ++i) { - - int class_id; - for (class_id = 0; class_id < classes; ++class_id) { - float prob = dets[i].prob[class_id]; - if (prob > 0) { - detections_count++; - detections = realloc(detections, detections_count * sizeof(box_prob)); - detections[detections_count - 1].b = dets[i].bbox; - detections[detections_count - 1].p = prob; - detections[detections_count - 1].image_index = image_index; - detections[detections_count - 1].class_id = class_id; - detections[detections_count - 1].truth_flag = 0; - detections[detections_count - 1].unique_truth_index = -1; - - int truth_index = -1; - float max_iou = 0; - for (j = 0; j < num_labels; ++j) - { - box t = { truth[j].x, truth[j].y, truth[j].w, truth[j].h }; - //printf(" IoU = %f, prob = %f, class_id = %d, truth[j].id = %d \n", - // box_iou(dets[i].bbox, t), prob, class_id, truth[j].id); - float current_iou = box_iou(dets[i].bbox, t); - if (current_iou > iou_thresh && class_id == truth[j].id) { - if (current_iou > max_iou) { - max_iou = current_iou; - truth_index = unique_truth_count + j; - } - } - } - - // best IoU - if (truth_index > -1) { - detections[detections_count - 1].truth_flag = 1; - detections[detections_count - 1].unique_truth_index = truth_index; - } - else { - // if object is difficult then remove detection - for (j = 0; j < num_labels_dif; ++j) { - box t = { truth_dif[j].x, truth_dif[j].y, truth_dif[j].w, truth_dif[j].h }; - float current_iou = box_iou(dets[i].bbox, t); - if (current_iou > iou_thresh && class_id == truth_dif[j].id) { - --detections_count; - break; - } - } - } - - // calc avg IoU, true-positives, false-positives for required Threshold - if (prob > thresh_calc_avg_iou) { - int z, found = 0; - for (z = checkpoint_detections_count; z < detections_count-1; ++z) - if (detections[z].unique_truth_index == truth_index) { - found = 1; break; - } - - if(truth_index > -1 && found == 0) { - avg_iou += max_iou; - ++tp_for_thresh; - } - else - fp_for_thresh++; - } - } - } - } - - unique_truth_count += num_labels; - - //static int previous_errors = 0; - //int total_errors = fp_for_thresh + (unique_truth_count - tp_for_thresh); - //int errors_in_this_image = total_errors - previous_errors; - //previous_errors = total_errors; - //if(reinforcement_fd == NULL) reinforcement_fd = fopen("reinforcement.txt", "wb"); - //char buff[1000]; - //sprintf(buff, "%s\n", path); - //if(errors_in_this_image > 0) fwrite(buff, sizeof(char), strlen(buff), reinforcement_fd); - - free_detections(dets, nboxes); - free(id); - free_image(val[t]); - free_image(val_resized[t]); - } - } - - if((tp_for_thresh + fp_for_thresh) > 0) - avg_iou = avg_iou / (tp_for_thresh + fp_for_thresh); - - - // SORT(detections) - qsort(detections, detections_count, sizeof(box_prob), detections_comparator); - - typedef struct { - double precision; - double recall; - int tp, fp, fn; - } pr_t; - - // for PR-curve - pr_t **pr = calloc(classes, sizeof(pr_t*)); - for (i = 0; i < classes; ++i) { - pr[i] = calloc(detections_count, sizeof(pr_t)); - } - printf("detections_count = %d, unique_truth_count = %d \n", detections_count, unique_truth_count); - - - int *truth_flags = calloc(unique_truth_count, sizeof(int)); - - int rank; - for (rank = 0; rank < detections_count; ++rank) { - if(rank % 100 == 0) - printf(" rank = %d of ranks = %d \r", rank, detections_count); - - if (rank > 0) { - int class_id; - for (class_id = 0; class_id < classes; ++class_id) { - pr[class_id][rank].tp = pr[class_id][rank - 1].tp; - pr[class_id][rank].fp = pr[class_id][rank - 1].fp; - } - } - - box_prob d = detections[rank]; - // if (detected && isn't detected before) - if (d.truth_flag == 1) { - if (truth_flags[d.unique_truth_index] == 0) - { - truth_flags[d.unique_truth_index] = 1; - pr[d.class_id][rank].tp++; // true-positive - } - } - else { - pr[d.class_id][rank].fp++; // false-positive - } - - for (i = 0; i < classes; ++i) - { - const int tp = pr[i][rank].tp; - const int fp = pr[i][rank].fp; - const int fn = truth_classes_count[i] - tp; // false-negative = objects - true-positive - pr[i][rank].fn = fn; - - if ((tp + fp) > 0) pr[i][rank].precision = (double)tp / (double)(tp + fp); - else pr[i][rank].precision = 0; - - if ((tp + fn) > 0) pr[i][rank].recall = (double)tp / (double)(tp + fn); - else pr[i][rank].recall = 0; - } - } - - free(truth_flags); - - - double mean_average_precision = 0; - - for (i = 0; i < classes; ++i) { - double avg_precision = 0; - int point; - for (point = 0; point < 11; ++point) { - double cur_recall = point * 0.1; - double cur_precision = 0; - for (rank = 0; rank < detections_count; ++rank) - { - if (pr[i][rank].recall >= cur_recall) { // > or >= - if (pr[i][rank].precision > cur_precision) { - cur_precision = pr[i][rank].precision; - } - } - } - //printf("class_id = %d, point = %d, cur_recall = %.4f, cur_precision = %.4f \n", i, point, cur_recall, cur_precision); - - avg_precision += cur_precision; - } - avg_precision = avg_precision / 11; - printf("class_id = %d, name = %s, \t ap = %2.2f %% \n", i, names[i], avg_precision*100); - mean_average_precision += avg_precision; - } - - const float cur_precision = (float)tp_for_thresh / ((float)tp_for_thresh + (float)fp_for_thresh); - const float cur_recall = (float)tp_for_thresh / ((float)tp_for_thresh + (float)(unique_truth_count - tp_for_thresh)); - const float f1_score = 2.F * cur_precision * cur_recall / (cur_precision + cur_recall); - printf(" for thresh = %1.2f, precision = %1.2f, recall = %1.2f, F1-score = %1.2f \n", - thresh_calc_avg_iou, cur_precision, cur_recall, f1_score); - - printf(" for thresh = %0.2f, TP = %d, FP = %d, FN = %d, average IoU = %2.2f %% \n", - thresh_calc_avg_iou, tp_for_thresh, fp_for_thresh, unique_truth_count - tp_for_thresh, avg_iou * 100); - - mean_average_precision = mean_average_precision / classes; - printf("\n mean average precision (mAP) = %f, or %2.2f %% \n", mean_average_precision, mean_average_precision*100); - - - for (i = 0; i < classes; ++i) { - free(pr[i]); - } - free(pr); - free(detections); - free(truth_classes_count); - - fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); - if (reinforcement_fd != NULL) fclose(reinforcement_fd); + int j; + list *options = read_data_cfg(datacfg); + char *valid_images = option_find_str(options, "valid", "data/train.txt"); + char *difficult_valid_images = option_find_str(options, "difficult", NULL); + char *name_list = option_find_str(options, "names", "data/names.list"); + char **names = get_labels(name_list); + char *mapf = option_find_str(options, "map", 0); + int *map = 0; + if (mapf) map = read_map(mapf); + FILE* reinforcement_fd = NULL; + + network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 + if (weightfile) { + load_weights(&net, weightfile); + } + //set_batch_network(&net, 1); + fuse_conv_batchnorm(net); + srand(time(0)); + + list *plist = get_paths(valid_images); + char **paths = (char **)list_to_array(plist); + + char **paths_dif = NULL; + if (difficult_valid_images) { + list *plist_dif = get_paths(difficult_valid_images); + paths_dif = (char **)list_to_array(plist_dif); + } + + + layer l = net.layers[net.n - 1]; + int classes = l.classes; + + int m = plist->size; + int i = 0; + int t; + + const float thresh = .005; + const float nms = .45; + const float iou_thresh = 0.5; + + int nthreads = 4; + image *val = calloc(nthreads, sizeof(image)); + image *val_resized = calloc(nthreads, sizeof(image)); + image *buf = calloc(nthreads, sizeof(image)); + image *buf_resized = calloc(nthreads, sizeof(image)); + pthread_t *thr = calloc(nthreads, sizeof(pthread_t)); + + load_args args = { 0 }; + args.w = net.w; + args.h = net.h; + args.c = net.c; + args.type = IMAGE_DATA; + //args.type = LETTERBOX_DATA; + + //const float thresh_calc_avg_iou = 0.24; + float avg_iou = 0; + int tp_for_thresh = 0; + int fp_for_thresh = 0; + + box_prob *detections = calloc(1, sizeof(box_prob)); + int detections_count = 0; + int unique_truth_count = 0; + + int *truth_classes_count = calloc(classes, sizeof(int)); + + for (t = 0; t < nthreads; ++t) { + args.path = paths[i + t]; + args.im = &buf[t]; + args.resized = &buf_resized[t]; + thr[t] = load_data_in_thread(args); + } + time_t start = time(0); + for (i = nthreads; i < m + nthreads; i += nthreads) { + fprintf(stderr, "%d\n", i); + for (t = 0; t < nthreads && i + t - nthreads < m; ++t) { + pthread_join(thr[t], 0); + val[t] = buf[t]; + val_resized[t] = buf_resized[t]; + } + for (t = 0; t < nthreads && i + t < m; ++t) { + args.path = paths[i + t]; + args.im = &buf[t]; + args.resized = &buf_resized[t]; + thr[t] = load_data_in_thread(args); + } + for (t = 0; t < nthreads && i + t - nthreads < m; ++t) { + const int image_index = i + t - nthreads; + char *path = paths[image_index]; + char *id = basecfg(path); + float *X = val_resized[t].data; + network_predict(net, X); + + int nboxes = 0; + int letterbox = (args.type == LETTERBOX_DATA); + float hier_thresh = 0; + detection *dets = get_network_boxes(&net, 1, 1, thresh, hier_thresh, 0, 0, &nboxes, letterbox); + //detection *dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); // for letterbox=1 + if (nms) do_nms_sort(dets, nboxes, l.classes, nms); + + char labelpath[4096]; + replace_image_to_label(path, labelpath); + int num_labels = 0; + box_label *truth = read_boxes(labelpath, &num_labels); + int i, j; + for (j = 0; j < num_labels; ++j) { + truth_classes_count[truth[j].id]++; + } + + // difficult + box_label *truth_dif = NULL; + int num_labels_dif = 0; + if (paths_dif) + { + char *path_dif = paths_dif[image_index]; + + char labelpath_dif[4096]; + replace_image_to_label(path_dif, labelpath_dif); + + truth_dif = read_boxes(labelpath_dif, &num_labels_dif); + } + + const int checkpoint_detections_count = detections_count; + + for (i = 0; i < nboxes; ++i) { + + int class_id; + for (class_id = 0; class_id < classes; ++class_id) { + float prob = dets[i].prob[class_id]; + if (prob > 0) { + detections_count++; + detections = realloc(detections, detections_count * sizeof(box_prob)); + detections[detections_count - 1].b = dets[i].bbox; + detections[detections_count - 1].p = prob; + detections[detections_count - 1].image_index = image_index; + detections[detections_count - 1].class_id = class_id; + detections[detections_count - 1].truth_flag = 0; + detections[detections_count - 1].unique_truth_index = -1; + + int truth_index = -1; + float max_iou = 0; + for (j = 0; j < num_labels; ++j) + { + box t = { truth[j].x, truth[j].y, truth[j].w, truth[j].h }; + //printf(" IoU = %f, prob = %f, class_id = %d, truth[j].id = %d \n", + // box_iou(dets[i].bbox, t), prob, class_id, truth[j].id); + float current_iou = box_iou(dets[i].bbox, t); + if (current_iou > iou_thresh && class_id == truth[j].id) { + if (current_iou > max_iou) { + max_iou = current_iou; + truth_index = unique_truth_count + j; + } + } + } + + // best IoU + if (truth_index > -1) { + detections[detections_count - 1].truth_flag = 1; + detections[detections_count - 1].unique_truth_index = truth_index; + } + else { + // if object is difficult then remove detection + for (j = 0; j < num_labels_dif; ++j) { + box t = { truth_dif[j].x, truth_dif[j].y, truth_dif[j].w, truth_dif[j].h }; + float current_iou = box_iou(dets[i].bbox, t); + if (current_iou > iou_thresh && class_id == truth_dif[j].id) { + --detections_count; + break; + } + } + } + + // calc avg IoU, true-positives, false-positives for required Threshold + if (prob > thresh_calc_avg_iou) { + int z, found = 0; + for (z = checkpoint_detections_count; z < detections_count-1; ++z) + if (detections[z].unique_truth_index == truth_index) { + found = 1; break; + } + + if(truth_index > -1 && found == 0) { + avg_iou += max_iou; + ++tp_for_thresh; + } + else + fp_for_thresh++; + } + } + } + } + + unique_truth_count += num_labels; + + //static int previous_errors = 0; + //int total_errors = fp_for_thresh + (unique_truth_count - tp_for_thresh); + //int errors_in_this_image = total_errors - previous_errors; + //previous_errors = total_errors; + //if(reinforcement_fd == NULL) reinforcement_fd = fopen("reinforcement.txt", "wb"); + //char buff[1000]; + //sprintf(buff, "%s\n", path); + //if(errors_in_this_image > 0) fwrite(buff, sizeof(char), strlen(buff), reinforcement_fd); + + free_detections(dets, nboxes); + free(id); + free_image(val[t]); + free_image(val_resized[t]); + } + } + + if((tp_for_thresh + fp_for_thresh) > 0) + avg_iou = avg_iou / (tp_for_thresh + fp_for_thresh); + + + // SORT(detections) + qsort(detections, detections_count, sizeof(box_prob), detections_comparator); + + typedef struct { + double precision; + double recall; + int tp, fp, fn; + } pr_t; + + // for PR-curve + pr_t **pr = calloc(classes, sizeof(pr_t*)); + for (i = 0; i < classes; ++i) { + pr[i] = calloc(detections_count, sizeof(pr_t)); + } + printf("detections_count = %d, unique_truth_count = %d \n", detections_count, unique_truth_count); + + + int *truth_flags = calloc(unique_truth_count, sizeof(int)); + + int rank; + for (rank = 0; rank < detections_count; ++rank) { + if(rank % 100 == 0) + printf(" rank = %d of ranks = %d \r", rank, detections_count); + + if (rank > 0) { + int class_id; + for (class_id = 0; class_id < classes; ++class_id) { + pr[class_id][rank].tp = pr[class_id][rank - 1].tp; + pr[class_id][rank].fp = pr[class_id][rank - 1].fp; + } + } + + box_prob d = detections[rank]; + // if (detected && isn't detected before) + if (d.truth_flag == 1) { + if (truth_flags[d.unique_truth_index] == 0) + { + truth_flags[d.unique_truth_index] = 1; + pr[d.class_id][rank].tp++; // true-positive + } + } + else { + pr[d.class_id][rank].fp++; // false-positive + } + + for (i = 0; i < classes; ++i) + { + const int tp = pr[i][rank].tp; + const int fp = pr[i][rank].fp; + const int fn = truth_classes_count[i] - tp; // false-negative = objects - true-positive + pr[i][rank].fn = fn; + + if ((tp + fp) > 0) pr[i][rank].precision = (double)tp / (double)(tp + fp); + else pr[i][rank].precision = 0; + + if ((tp + fn) > 0) pr[i][rank].recall = (double)tp / (double)(tp + fn); + else pr[i][rank].recall = 0; + } + } + + free(truth_flags); + + + double mean_average_precision = 0; + + for (i = 0; i < classes; ++i) { + double avg_precision = 0; + int point; + for (point = 0; point < 11; ++point) { + double cur_recall = point * 0.1; + double cur_precision = 0; + for (rank = 0; rank < detections_count; ++rank) + { + if (pr[i][rank].recall >= cur_recall) { // > or >= + if (pr[i][rank].precision > cur_precision) { + cur_precision = pr[i][rank].precision; + } + } + } + //printf("class_id = %d, point = %d, cur_recall = %.4f, cur_precision = %.4f \n", i, point, cur_recall, cur_precision); + + avg_precision += cur_precision; + } + avg_precision = avg_precision / 11; + printf("class_id = %d, name = %s, \t ap = %2.2f %% \n", i, names[i], avg_precision*100); + mean_average_precision += avg_precision; + } + + const float cur_precision = (float)tp_for_thresh / ((float)tp_for_thresh + (float)fp_for_thresh); + const float cur_recall = (float)tp_for_thresh / ((float)tp_for_thresh + (float)(unique_truth_count - tp_for_thresh)); + const float f1_score = 2.F * cur_precision * cur_recall / (cur_precision + cur_recall); + printf(" for thresh = %1.2f, precision = %1.2f, recall = %1.2f, F1-score = %1.2f \n", + thresh_calc_avg_iou, cur_precision, cur_recall, f1_score); + + printf(" for thresh = %0.2f, TP = %d, FP = %d, FN = %d, average IoU = %2.2f %% \n", + thresh_calc_avg_iou, tp_for_thresh, fp_for_thresh, unique_truth_count - tp_for_thresh, avg_iou * 100); + + mean_average_precision = mean_average_precision / classes; + printf("\n mean average precision (mAP) = %f, or %2.2f %% \n", mean_average_precision, mean_average_precision*100); + + + for (i = 0; i < classes; ++i) { + free(pr[i]); + } + free(pr); + free(detections); + free(truth_classes_count); + + fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); + if (reinforcement_fd != NULL) fclose(reinforcement_fd); } #ifdef OPENCV typedef struct { - float w, h; + float w, h; } anchors_t; int anchors_comparator(const void *pa, const void *pb) { - anchors_t a = *(anchors_t *)pa; - anchors_t b = *(anchors_t *)pb; - float diff = b.w*b.h - a.w*a.h; - if (diff < 0) return 1; - else if (diff > 0) return -1; - return 0; + anchors_t a = *(anchors_t *)pa; + anchors_t b = *(anchors_t *)pb; + float diff = b.w*b.h - a.w*a.h; + if (diff < 0) return 1; + else if (diff > 0) return -1; + return 0; } void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int show) { - printf("\n num_of_clusters = %d, width = %d, height = %d \n", num_of_clusters, width, height); - if (width < 0 || height < 0) { - printf("Usage: darknet detector calc_anchors data/voc.data -num_of_clusters 9 -width 416 -height 416 \n"); - printf("Error: set width and height \n"); - return; - } - - //float pointsdata[] = { 1,1, 2,2, 6,6, 5,5, 10,10 }; - float *rel_width_height_array = calloc(1000, sizeof(float)); - - list *options = read_data_cfg(datacfg); - char *train_images = option_find_str(options, "train", "data/train.list"); - list *plist = get_paths(train_images); - int number_of_images = plist->size; - char **paths = (char **)list_to_array(plist); - - int number_of_boxes = 0; - printf(" read labels from %d images \n", number_of_images); - - int i, j; - for (i = 0; i < number_of_images; ++i) { - char *path = paths[i]; - char labelpath[4096]; - replace_image_to_label(path, labelpath); - - int num_labels = 0; - box_label *truth = read_boxes(labelpath, &num_labels); - //printf(" new path: %s \n", labelpath); - char buff[1024]; - for (j = 0; j < num_labels; ++j) - { - if (truth[j].x > 1 || truth[j].x <= 0 || truth[j].y > 1 || truth[j].y <= 0 || - truth[j].w > 1 || truth[j].w <= 0 || truth[j].h > 1 || truth[j].h <= 0) - { - printf("\n\nWrong label: %s - j = %d, x = %f, y = %f, width = %f, height = %f \n", - labelpath, j, truth[j].x, truth[j].y, truth[j].w, truth[j].h); - sprintf(buff, "echo \"Wrong label: %s - j = %d, x = %f, y = %f, width = %f, height = %f\" >> bad_label.list", - labelpath, j, truth[j].x, truth[j].y, truth[j].w, truth[j].h); - system(buff); - } - number_of_boxes++; - rel_width_height_array = realloc(rel_width_height_array, 2 * number_of_boxes * sizeof(float)); - rel_width_height_array[number_of_boxes * 2 - 2] = truth[j].w * width; - rel_width_height_array[number_of_boxes * 2 - 1] = truth[j].h * height; - printf("\r loaded \t image: %d \t box: %d", i+1, number_of_boxes); - } - } - printf("\n all loaded. \n"); - - CvMat* points = cvCreateMat(number_of_boxes, 2, CV_32FC1); - CvMat* centers = cvCreateMat(num_of_clusters, 2, CV_32FC1); - CvMat* labels = cvCreateMat(number_of_boxes, 1, CV_32SC1); - - for (i = 0; i < number_of_boxes; ++i) { - points->data.fl[i * 2] = rel_width_height_array[i * 2]; - points->data.fl[i * 2 + 1] = rel_width_height_array[i * 2 + 1]; - //cvSet1D(points, i * 2, cvScalar(rel_width_height_array[i * 2], 0, 0, 0)); - //cvSet1D(points, i * 2 + 1, cvScalar(rel_width_height_array[i * 2 + 1], 0, 0, 0)); - } - - - const int attemps = 10; - double compactness; - - enum { - KMEANS_RANDOM_CENTERS = 0, - KMEANS_USE_INITIAL_LABELS = 1, - KMEANS_PP_CENTERS = 2 - }; - - printf("\n calculating k-means++ ..."); - // Should be used: distance(box, centroid) = 1 - IoU(box, centroid) - cvKMeans2(points, num_of_clusters, labels, - cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 10000, 0), attemps, - 0, KMEANS_PP_CENTERS, - centers, &compactness); - - // sort anchors - qsort(centers->data.fl, num_of_clusters, 2*sizeof(float), anchors_comparator); - - //orig 2.0 anchors = 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52 - //float orig_anch[] = { 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52 }; - // worse than ours (even for 19x19 final size - for input size 608x608) - - //orig anchors = 1.3221,1.73145, 3.19275,4.00944, 5.05587,8.09892, 9.47112,4.84053, 11.2364,10.0071 - //float orig_anch[] = { 1.3221,1.73145, 3.19275,4.00944, 5.05587,8.09892, 9.47112,4.84053, 11.2364,10.0071 }; - // orig (IoU=59.90%) better than ours (59.75%) - - //gen_anchors.py = 1.19, 1.99, 2.79, 4.60, 4.53, 8.92, 8.06, 5.29, 10.32, 10.66 - //float orig_anch[] = { 1.19, 1.99, 2.79, 4.60, 4.53, 8.92, 8.06, 5.29, 10.32, 10.66 }; - - // ours: anchors = 9.3813,6.0095, 3.3999,5.3505, 10.9476,11.1992, 5.0161,9.8314, 1.5003,2.1595 - //float orig_anch[] = { 9.3813,6.0095, 3.3999,5.3505, 10.9476,11.1992, 5.0161,9.8314, 1.5003,2.1595 }; - //for (i = 0; i < num_of_clusters * 2; ++i) centers->data.fl[i] = orig_anch[i]; - - //for (i = 0; i < number_of_boxes; ++i) - // printf("%2.2f,%2.2f, ", points->data.fl[i * 2], points->data.fl[i * 2 + 1]); - - printf("\n"); - float avg_iou = 0; - for (i = 0; i < number_of_boxes; ++i) { - float box_w = points->data.fl[i * 2]; - float box_h = points->data.fl[i * 2 + 1]; - //int cluster_idx = labels->data.i[i]; - int cluster_idx = 0; - float min_dist = FLT_MAX; - for (j = 0; j < num_of_clusters; ++j) { - float anchor_w = centers->data.fl[j * 2]; - float anchor_h = centers->data.fl[j * 2 + 1]; - float w_diff = anchor_w - box_w; - float h_diff = anchor_h - box_h; - float distance = sqrt(w_diff*w_diff + h_diff*h_diff); - if (distance < min_dist) min_dist = distance, cluster_idx = j; - } - - float anchor_w = centers->data.fl[cluster_idx * 2]; - float anchor_h = centers->data.fl[cluster_idx * 2 + 1]; - float min_w = (box_w < anchor_w) ? box_w : anchor_w; - float min_h = (box_h < anchor_h) ? box_h : anchor_h; - float box_intersect = min_w*min_h; - float box_union = box_w*box_h + anchor_w*anchor_h - box_intersect; - float iou = box_intersect / box_union; - if (iou > 1 || iou < 0) { // || box_w > width || box_h > height) { - printf(" Wrong label: i = %d, box_w = %d, box_h = %d, anchor_w = %d, anchor_h = %d, iou = %f \n", - i, box_w, box_h, anchor_w, anchor_h, iou); - } - else avg_iou += iou; - } - avg_iou = 100 * avg_iou / number_of_boxes; - printf("\n avg IoU = %2.2f %% \n", avg_iou); - - char buff[1024]; - FILE* fw = fopen("anchors.txt", "wb"); - printf("\nSaving anchors to the file: anchors.txt \n"); - printf("anchors = "); - for (i = 0; i < num_of_clusters; ++i) { - sprintf(buff, "%2.4f,%2.4f", centers->data.fl[i * 2], centers->data.fl[i * 2 + 1]); - printf("%s", buff); - fwrite(buff, sizeof(char), strlen(buff), fw); - if (i + 1 < num_of_clusters) { - fwrite(", ", sizeof(char), 2, fw); - printf(", "); - } - } - printf("\n"); - fclose(fw); - - if (show) { - size_t img_size = 700; - IplImage* img = cvCreateImage(cvSize(img_size, img_size), 8, 3); - cvZero(img); - for (j = 0; j < num_of_clusters; ++j) { - CvPoint pt1, pt2; - pt1.x = pt1.y = 0; - pt2.x = centers->data.fl[j * 2] * img_size / width; - pt2.y = centers->data.fl[j * 2 + 1] * img_size / height; - cvRectangle(img, pt1, pt2, CV_RGB(255, 255, 255), 1, 8, 0); - } - - for (i = 0; i < number_of_boxes; ++i) { - CvPoint pt; - pt.x = points->data.fl[i * 2] * img_size / width; - pt.y = points->data.fl[i * 2 + 1] * img_size / height; - int cluster_idx = labels->data.i[i]; - int red_id = (cluster_idx * (uint64_t)123 + 55) % 255; - int green_id = (cluster_idx * (uint64_t)321 + 33) % 255; - int blue_id = (cluster_idx * (uint64_t)11 + 99) % 255; - cvCircle(img, pt, 1, CV_RGB(red_id, green_id, blue_id), CV_FILLED, 8, 0); - //if(pt.x > img_size || pt.y > img_size) printf("\n pt.x = %d, pt.y = %d \n", pt.x, pt.y); - } - cvShowImage("clusters", img); - cvWaitKey(0); - cvReleaseImage(&img); - cvDestroyAllWindows(); - } - - free(rel_width_height_array); - cvReleaseMat(&points); - cvReleaseMat(¢ers); - cvReleaseMat(&labels); + printf("\n num_of_clusters = %d, width = %d, height = %d \n", num_of_clusters, width, height); + if (width < 0 || height < 0) { + printf("Usage: darknet detector calc_anchors data/voc.data -num_of_clusters 9 -width 416 -height 416 \n"); + printf("Error: set width and height \n"); + return; + } + + //float pointsdata[] = { 1,1, 2,2, 6,6, 5,5, 10,10 }; + float *rel_width_height_array = calloc(1000, sizeof(float)); + + list *options = read_data_cfg(datacfg); + char *train_images = option_find_str(options, "train", "data/train.list"); + list *plist = get_paths(train_images); + int number_of_images = plist->size; + char **paths = (char **)list_to_array(plist); + + int number_of_boxes = 0; + printf(" read labels from %d images \n", number_of_images); + + int i, j; + for (i = 0; i < number_of_images; ++i) { + char *path = paths[i]; + char labelpath[4096]; + replace_image_to_label(path, labelpath); + + int num_labels = 0; + box_label *truth = read_boxes(labelpath, &num_labels); + //printf(" new path: %s \n", labelpath); + char buff[1024]; + for (j = 0; j < num_labels; ++j) + { + if (truth[j].x > 1 || truth[j].x <= 0 || truth[j].y > 1 || truth[j].y <= 0 || + truth[j].w > 1 || truth[j].w <= 0 || truth[j].h > 1 || truth[j].h <= 0) + { + printf("\n\nWrong label: %s - j = %d, x = %f, y = %f, width = %f, height = %f \n", + labelpath, j, truth[j].x, truth[j].y, truth[j].w, truth[j].h); + sprintf(buff, "echo \"Wrong label: %s - j = %d, x = %f, y = %f, width = %f, height = %f\" >> bad_label.list", + labelpath, j, truth[j].x, truth[j].y, truth[j].w, truth[j].h); + system(buff); + } + number_of_boxes++; + rel_width_height_array = realloc(rel_width_height_array, 2 * number_of_boxes * sizeof(float)); + rel_width_height_array[number_of_boxes * 2 - 2] = truth[j].w * width; + rel_width_height_array[number_of_boxes * 2 - 1] = truth[j].h * height; + printf("\r loaded \t image: %d \t box: %d", i+1, number_of_boxes); + } + } + printf("\n all loaded. \n"); + + CvMat* points = cvCreateMat(number_of_boxes, 2, CV_32FC1); + CvMat* centers = cvCreateMat(num_of_clusters, 2, CV_32FC1); + CvMat* labels = cvCreateMat(number_of_boxes, 1, CV_32SC1); + + for (i = 0; i < number_of_boxes; ++i) { + points->data.fl[i * 2] = rel_width_height_array[i * 2]; + points->data.fl[i * 2 + 1] = rel_width_height_array[i * 2 + 1]; + //cvSet1D(points, i * 2, cvScalar(rel_width_height_array[i * 2], 0, 0, 0)); + //cvSet1D(points, i * 2 + 1, cvScalar(rel_width_height_array[i * 2 + 1], 0, 0, 0)); + } + + + const int attemps = 10; + double compactness; + + enum { + KMEANS_RANDOM_CENTERS = 0, + KMEANS_USE_INITIAL_LABELS = 1, + KMEANS_PP_CENTERS = 2 + }; + + printf("\n calculating k-means++ ..."); + // Should be used: distance(box, centroid) = 1 - IoU(box, centroid) + cvKMeans2(points, num_of_clusters, labels, + cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 10000, 0), attemps, + 0, KMEANS_PP_CENTERS, + centers, &compactness); + + // sort anchors + qsort(centers->data.fl, num_of_clusters, 2*sizeof(float), anchors_comparator); + + //orig 2.0 anchors = 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52 + //float orig_anch[] = { 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52 }; + // worse than ours (even for 19x19 final size - for input size 608x608) + + //orig anchors = 1.3221,1.73145, 3.19275,4.00944, 5.05587,8.09892, 9.47112,4.84053, 11.2364,10.0071 + //float orig_anch[] = { 1.3221,1.73145, 3.19275,4.00944, 5.05587,8.09892, 9.47112,4.84053, 11.2364,10.0071 }; + // orig (IoU=59.90%) better than ours (59.75%) + + //gen_anchors.py = 1.19, 1.99, 2.79, 4.60, 4.53, 8.92, 8.06, 5.29, 10.32, 10.66 + //float orig_anch[] = { 1.19, 1.99, 2.79, 4.60, 4.53, 8.92, 8.06, 5.29, 10.32, 10.66 }; + + // ours: anchors = 9.3813,6.0095, 3.3999,5.3505, 10.9476,11.1992, 5.0161,9.8314, 1.5003,2.1595 + //float orig_anch[] = { 9.3813,6.0095, 3.3999,5.3505, 10.9476,11.1992, 5.0161,9.8314, 1.5003,2.1595 }; + //for (i = 0; i < num_of_clusters * 2; ++i) centers->data.fl[i] = orig_anch[i]; + + //for (i = 0; i < number_of_boxes; ++i) + // printf("%2.2f,%2.2f, ", points->data.fl[i * 2], points->data.fl[i * 2 + 1]); + + printf("\n"); + float avg_iou = 0; + for (i = 0; i < number_of_boxes; ++i) { + float box_w = points->data.fl[i * 2]; + float box_h = points->data.fl[i * 2 + 1]; + //int cluster_idx = labels->data.i[i]; + int cluster_idx = 0; + float min_dist = FLT_MAX; + for (j = 0; j < num_of_clusters; ++j) { + float anchor_w = centers->data.fl[j * 2]; + float anchor_h = centers->data.fl[j * 2 + 1]; + float w_diff = anchor_w - box_w; + float h_diff = anchor_h - box_h; + float distance = sqrt(w_diff*w_diff + h_diff*h_diff); + if (distance < min_dist) min_dist = distance, cluster_idx = j; + } + + float anchor_w = centers->data.fl[cluster_idx * 2]; + float anchor_h = centers->data.fl[cluster_idx * 2 + 1]; + float min_w = (box_w < anchor_w) ? box_w : anchor_w; + float min_h = (box_h < anchor_h) ? box_h : anchor_h; + float box_intersect = min_w*min_h; + float box_union = box_w*box_h + anchor_w*anchor_h - box_intersect; + float iou = box_intersect / box_union; + if (iou > 1 || iou < 0) { // || box_w > width || box_h > height) { + printf(" Wrong label: i = %d, box_w = %d, box_h = %d, anchor_w = %d, anchor_h = %d, iou = %f \n", + i, box_w, box_h, anchor_w, anchor_h, iou); + } + else avg_iou += iou; + } + avg_iou = 100 * avg_iou / number_of_boxes; + printf("\n avg IoU = %2.2f %% \n", avg_iou); + + char buff[1024]; + FILE* fw = fopen("anchors.txt", "wb"); + printf("\nSaving anchors to the file: anchors.txt \n"); + printf("anchors = "); + for (i = 0; i < num_of_clusters; ++i) { + sprintf(buff, "%2.4f,%2.4f", centers->data.fl[i * 2], centers->data.fl[i * 2 + 1]); + printf("%s", buff); + fwrite(buff, sizeof(char), strlen(buff), fw); + if (i + 1 < num_of_clusters) { + fwrite(", ", sizeof(char), 2, fw); + printf(", "); + } + } + printf("\n"); + fclose(fw); + + if (show) { + size_t img_size = 700; + IplImage* img = cvCreateImage(cvSize(img_size, img_size), 8, 3); + cvZero(img); + for (j = 0; j < num_of_clusters; ++j) { + CvPoint pt1, pt2; + pt1.x = pt1.y = 0; + pt2.x = centers->data.fl[j * 2] * img_size / width; + pt2.y = centers->data.fl[j * 2 + 1] * img_size / height; + cvRectangle(img, pt1, pt2, CV_RGB(255, 255, 255), 1, 8, 0); + } + + for (i = 0; i < number_of_boxes; ++i) { + CvPoint pt; + pt.x = points->data.fl[i * 2] * img_size / width; + pt.y = points->data.fl[i * 2 + 1] * img_size / height; + int cluster_idx = labels->data.i[i]; + int red_id = (cluster_idx * (uint64_t)123 + 55) % 255; + int green_id = (cluster_idx * (uint64_t)321 + 33) % 255; + int blue_id = (cluster_idx * (uint64_t)11 + 99) % 255; + cvCircle(img, pt, 1, CV_RGB(red_id, green_id, blue_id), CV_FILLED, 8, 0); + //if(pt.x > img_size || pt.y > img_size) printf("\n pt.x = %d, pt.y = %d \n", pt.x, pt.y); + } + cvShowImage("clusters", img); + cvWaitKey(0); + cvReleaseImage(&img); + cvDestroyAllWindows(); + } + + free(rel_width_height_array); + cvReleaseMat(&points); + cvReleaseMat(¢ers); + cvReleaseMat(&labels); } #else void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int show) { - printf(" k-means++ can't be used without OpenCV, because there is used cvKMeans2 implementation \n"); + printf(" k-means++ can't be used without OpenCV, because there is used cvKMeans2 implementation \n"); } #endif // OPENCV void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, - float hier_thresh, int dont_show, int ext_output, int save_labels) + float hier_thresh, int dont_show, int ext_output, int save_labels) { list *options = read_data_cfg(datacfg); char *name_list = option_find_str(options, "names", "data/names.list"); - int names_size = 0; - char **names = get_labels_custom(name_list, &names_size); //get_labels(name_list); + int names_size = 0; + char **names = get_labels_custom(name_list, &names_size); //get_labels(name_list); image **alphabet = load_alphabet(); network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 @@ -1093,23 +1093,23 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam load_weights(&net, weightfile); } //set_batch_network(&net, 1); - fuse_conv_batchnorm(net); - if (net.layers[net.n - 1].classes != names_size) { - printf(" Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", - name_list, names_size, net.layers[net.n - 1].classes, cfgfile); - if(net.layers[net.n - 1].classes > names_size) getchar(); - } + fuse_conv_batchnorm(net); + if (net.layers[net.n - 1].classes != names_size) { + printf(" Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", + name_list, names_size, net.layers[net.n - 1].classes, cfgfile); + if(net.layers[net.n - 1].classes > names_size) getchar(); + } srand(2222222); double time; char buff[256]; char *input = buff; int j; - float nms=.45; // 0.4F + float nms=.45; // 0.4F while(1){ if(filename){ strncpy(input, filename, 256); - if(strlen(input) > 0) - if (input[strlen(input) - 1] == 0x0d) input[strlen(input) - 1] = 0; + if(strlen(input) > 0) + if (input[strlen(input) - 1] == 0x0d) input[strlen(input) - 1] = 0; } else { printf("Enter Image Path: "); fflush(stdout); @@ -1118,9 +1118,9 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam strtok(input, "\n"); } image im = load_image(input,0,0,net.c); - int letterbox = 0; + int letterbox = 0; image sized = resize_image(im, net.w, net.h); - //image sized = letterbox_image(im, net.w, net.h); letterbox = 1; + //image sized = letterbox_image(im, net.w, net.h); letterbox = 1; layer l = net.layers[net.n-1]; //box *boxes = calloc(l.w*l.h*l.n, sizeof(box)); @@ -1130,97 +1130,97 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam float *X = sized.data; time= what_time_is_it_now(); network_predict(net, X); - //network_predict_image(&net, im); letterbox = 1; + //network_predict_image(&net, im); letterbox = 1; printf("%s: Predicted in %f seconds.\n", input, (what_time_is_it_now()-time)); //get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, 0); - // if (nms) do_nms_sort_v2(boxes, probs, l.w*l.h*l.n, l.classes, nms); - //draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes); - int nboxes = 0; - detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); - if (nms) do_nms_sort(dets, nboxes, l.classes, nms); - draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, ext_output); + // if (nms) do_nms_sort_v2(boxes, probs, l.w*l.h*l.n, l.classes, nms); + //draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes); + int nboxes = 0; + detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); + if (nms) do_nms_sort(dets, nboxes, l.classes, nms); + draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, ext_output); save_image(im, "predictions"); - if (!dont_show) { - show_image(im, "predictions"); - } - - // pseudo labeling concept - fast.ai - if(save_labels) - { - char labelpath[4096]; - replace_image_to_label(input, labelpath); - - FILE* fw = fopen(labelpath, "wb"); - int i; - for (i = 0; i < nboxes; ++i) { - char buff[1024]; - int class_id = -1; - float prob = 0; - for (j = 0; j < l.classes; ++j) { - if (dets[i].prob[j] > thresh && dets[i].prob[j] > prob) { - prob = dets[i].prob[j]; - class_id = j; - } - } - if (class_id >= 0) { - sprintf(buff, "%d %2.4f %2.4f %2.4f %2.4f\n", class_id, dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h); - fwrite(buff, sizeof(char), strlen(buff), fw); - } - } - fclose(fw); - } - - free_detections(dets, nboxes); + if (!dont_show) { + show_image(im, "predictions"); + } + + // pseudo labeling concept - fast.ai + if(save_labels) + { + char labelpath[4096]; + replace_image_to_label(input, labelpath); + + FILE* fw = fopen(labelpath, "wb"); + int i; + for (i = 0; i < nboxes; ++i) { + char buff[1024]; + int class_id = -1; + float prob = 0; + for (j = 0; j < l.classes; ++j) { + if (dets[i].prob[j] > thresh && dets[i].prob[j] > prob) { + prob = dets[i].prob[j]; + class_id = j; + } + } + if (class_id >= 0) { + sprintf(buff, "%d %2.4f %2.4f %2.4f %2.4f\n", class_id, dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h); + fwrite(buff, sizeof(char), strlen(buff), fw); + } + } + fclose(fw); + } + + free_detections(dets, nboxes); free_image(im); free_image(sized); //free(boxes); //free_ptrs((void **)probs, l.w*l.h*l.n); #ifdef OPENCV - if (!dont_show) { - cvWaitKey(0); - cvDestroyAllWindows(); - } + if (!dont_show) { + cvWaitKey(0); + cvDestroyAllWindows(); + } #endif if (filename) break; } - // free memory - free_ptrs(names, net.layers[net.n - 1].classes); - free_list_contents_kvp(options); - free_list(options); - - int i; - const int nsize = 8; - for (j = 0; j < nsize; ++j) { - for (i = 32; i < 127; ++i) { - free_image(alphabet[j][i]); - } - free(alphabet[j]); - } - free(alphabet); - - free_network(net); + // free memory + free_ptrs(names, net.layers[net.n - 1].classes); + free_list_contents_kvp(options); + free_list(options); + + int i; + const int nsize = 8; + for (j = 0; j < nsize; ++j) { + for (i = 32; i < 127; ++i) { + free_image(alphabet[j][i]); + } + free(alphabet[j]); + } + free(alphabet); + + free_network(net); } void run_detector(int argc, char **argv) { - int dont_show = find_arg(argc, argv, "-dont_show"); - int show = find_arg(argc, argv, "-show"); - int http_stream_port = find_int_arg(argc, argv, "-http_port", -1); - char *out_filename = find_char_arg(argc, argv, "-out_filename", 0); - char *outfile = find_char_arg(argc, argv, "-out", 0); + int dont_show = find_arg(argc, argv, "-dont_show"); + int show = find_arg(argc, argv, "-show"); + int http_stream_port = find_int_arg(argc, argv, "-http_port", -1); + char *out_filename = find_char_arg(argc, argv, "-out_filename", 0); + char *outfile = find_char_arg(argc, argv, "-out", 0); char *prefix = find_char_arg(argc, argv, "-prefix", 0); - float thresh = find_float_arg(argc, argv, "-thresh", .25); // 0.24 - float hier_thresh = find_float_arg(argc, argv, "-hier", .5); + float thresh = find_float_arg(argc, argv, "-thresh", .25); // 0.24 + float hier_thresh = find_float_arg(argc, argv, "-hier", .5); int cam_index = find_int_arg(argc, argv, "-c", 0); int frame_skip = find_int_arg(argc, argv, "-s", 0); - int num_of_clusters = find_int_arg(argc, argv, "-num_of_clusters", 5); - int width = find_int_arg(argc, argv, "-width", -1); - int height = find_int_arg(argc, argv, "-height", -1); - // extended output in test mode (output of rect bound coords) - // and for recall mode (extended output table-like format with results for best_class fit) - int ext_output = find_arg(argc, argv, "-ext_output"); - int save_labels = find_arg(argc, argv, "-save_labels"); + int num_of_clusters = find_int_arg(argc, argv, "-num_of_clusters", 5); + int width = find_int_arg(argc, argv, "-width", -1); + int height = find_int_arg(argc, argv, "-height", -1); + // extended output in test mode (output of rect bound coords) + // and for recall mode (extended output table-like format with results for best_class fit) + int ext_output = find_arg(argc, argv, "-ext_output"); + int save_labels = find_arg(argc, argv, "-save_labels"); if(argc < 4){ fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); return; @@ -1253,29 +1253,29 @@ void run_detector(int argc, char **argv) char *datacfg = argv[3]; char *cfg = argv[4]; char *weights = (argc > 5) ? argv[5] : 0; - if(weights) - if(strlen(weights) > 0) - if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0; + if(weights) + if(strlen(weights) > 0) + if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0; char *filename = (argc > 6) ? argv[6]: 0; if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels); else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show); else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile); else if(0==strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights); - else if(0==strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh); - else if(0==strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, height, show); + else if(0==strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh); + else if(0==strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, height, show); else if(0==strcmp(argv[2], "demo")) { list *options = read_data_cfg(datacfg); int classes = option_find_int(options, "classes", 20); char *name_list = option_find_str(options, "names", "data/names.list"); char **names = get_labels(name_list); - if(filename) - if(strlen(filename) > 0) - if (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0; + if(filename) + if(strlen(filename) > 0) + if (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0; demo(cfg, weights, thresh, hier_thresh, cam_index, filename, names, classes, frame_skip, prefix, out_filename, - http_stream_port, dont_show, ext_output); + http_stream_port, dont_show, ext_output); - free_list_contents_kvp(options); - free_list(options); + free_list_contents_kvp(options); + free_list(options); } - else printf(" There isn't such command: %s", argv[2]); + else printf(" There isn't such command: %s", argv[2]); } diff --git a/src/gemm.c b/src/gemm.c index 506687be4ab..2b90b059a36 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -87,7 +87,7 @@ void gemm(int TA, int TB, int M, int N, int K, float ALPHA, #include #include -#else // Linux GCC/Clang +#else // Linux GCC/Clang #include #include #include @@ -96,124 +96,124 @@ void gemm(int TA, int TB, int M, int N, int K, float ALPHA, void asm_cpuid(uint32_t* abcd, uint32_t eax) { - uint32_t ebx = 0, edx = 0, ecx = 0; - - // EBX is saved to EDI and later restored - __asm__("movl %%ebx, %%edi;" - "cpuid;" - "xchgl %%ebx, %%edi;" - : "=D"(ebx), - "+a"(eax), "+c"(ecx), "=d"(edx)); - - abcd[0] = eax; - abcd[1] = ebx; - abcd[2] = ecx; - abcd[3] = edx; + uint32_t ebx = 0, edx = 0, ecx = 0; + + // EBX is saved to EDI and later restored + __asm__("movl %%ebx, %%edi;" + "cpuid;" + "xchgl %%ebx, %%edi;" + : "=D"(ebx), + "+a"(eax), "+c"(ecx), "=d"(edx)); + + abcd[0] = eax; + abcd[1] = ebx; + abcd[2] = ecx; + abcd[3] = edx; } #endif int simd_detect_x86(unsigned int idFeature) { - uint32_t regs[4]; // EAX, EBX, ECX, EDX; + uint32_t regs[4]; // EAX, EBX, ECX, EDX; #ifdef _WIN32 - __cpuid(regs, 0); - if (regs[0] > 1U) __cpuid(regs, 1); + __cpuid(regs, 0); + if (regs[0] > 1U) __cpuid(regs, 1); #else - __get_cpuid(0, ®s[0], ®s[1], ®s[2], ®s[3]); - if(regs[0] > 1U) __get_cpuid(1, ®s[0], ®s[1], ®s[2], ®s[3]); + __get_cpuid(0, ®s[0], ®s[1], ®s[2], ®s[3]); + if(regs[0] > 1U) __get_cpuid(1, ®s[0], ®s[1], ®s[2], ®s[3]); #endif - if ((regs[2] & idFeature) != idFeature) - return 0; - return 1; + if ((regs[2] & idFeature) != idFeature) + return 0; + return 1; } int is_fma_avx() { - static int result = -1; - if (result == -1) { - result = simd_detect_x86(AVXFlag); - if (result == 1) printf(" Used AVX \n"); - else printf(" Not used AVX \n"); - } - return result; + static int result = -1; + if (result == -1) { + result = simd_detect_x86(AVXFlag); + if (result == 1) printf(" Used AVX \n"); + else printf(" Not used AVX \n"); + } + return result; } // https://software.intel.com/sites/landingpage/IntrinsicsGuide void gemm_nn(int M, int N, int K, float ALPHA, - float *A, int lda, - float *B, int ldb, - float *C, int ldc) + float *A, int lda, + float *B, int ldb, + float *C, int ldc) { - int i, j, k; - if (is_fma_avx() == 1) { // AVX - for (i = 0; i < M; ++i) { - for (k = 0; k < K; ++k) { - float A_PART = ALPHA*A[i*lda + k]; - __m256 a256, b256, c256, result256; // AVX - a256 = _mm256_set1_ps(A_PART); - for (j = 0; j < N - 8; j += 8) { - b256 = _mm256_loadu_ps(&B[k*ldb + j]); - c256 = _mm256_loadu_ps(&C[i*ldc + j]); - // FMA - Intel Haswell (2013), AMD Piledriver (2012) - //result256 = _mm256_fmadd_ps(a256, b256, c256); - result256 = _mm256_mul_ps(a256, b256); - result256 = _mm256_add_ps(result256, c256); - _mm256_storeu_ps(&C[i*ldc + j], result256); - } - - int prev_end = (N % 8 == 0) ? (N - 8) : (N / 8) * 8; - for (j = prev_end; j < N; ++j) - C[i*ldc + j] += A_PART*B[k*ldb + j]; - } - } - } - else { - for (i = 0; i < M; ++i) { - for (k = 0; k < K; ++k) { - register float A_PART = ALPHA*A[i*lda + k]; - for (j = 0; j < N; ++j) { - C[i*ldc + j] += A_PART*B[k*ldb + j]; - } - /* // SSE - __m128 a128, b128, c128, result128; // SSE - a128 = _mm_set1_ps(A_PART); - for (j = 0; j < N - 4; j += 4) { - b128 = _mm_loadu_ps(&B[k*ldb + j]); - c128 = _mm_loadu_ps(&C[i*ldc + j]); - //result128 = _mm_fmadd_ps(a128, b128, c128); - result128 = _mm_mul_ps(a128, b128); - result128 = _mm_add_ps(result128, c128); - _mm_storeu_ps(&C[i*ldc + j], result128); - } - - int prev_end = (N % 4 == 0) ? (N - 4) : (N / 4) * 4; - for (j = prev_end; j < N; ++j){ - C[i*ldc + j] += A_PART*B[k*ldb + j]; - } - */ - } - } - } + int i, j, k; + if (is_fma_avx() == 1) { // AVX + for (i = 0; i < M; ++i) { + for (k = 0; k < K; ++k) { + float A_PART = ALPHA*A[i*lda + k]; + __m256 a256, b256, c256, result256; // AVX + a256 = _mm256_set1_ps(A_PART); + for (j = 0; j < N - 8; j += 8) { + b256 = _mm256_loadu_ps(&B[k*ldb + j]); + c256 = _mm256_loadu_ps(&C[i*ldc + j]); + // FMA - Intel Haswell (2013), AMD Piledriver (2012) + //result256 = _mm256_fmadd_ps(a256, b256, c256); + result256 = _mm256_mul_ps(a256, b256); + result256 = _mm256_add_ps(result256, c256); + _mm256_storeu_ps(&C[i*ldc + j], result256); + } + + int prev_end = (N % 8 == 0) ? (N - 8) : (N / 8) * 8; + for (j = prev_end; j < N; ++j) + C[i*ldc + j] += A_PART*B[k*ldb + j]; + } + } + } + else { + for (i = 0; i < M; ++i) { + for (k = 0; k < K; ++k) { + register float A_PART = ALPHA*A[i*lda + k]; + for (j = 0; j < N; ++j) { + C[i*ldc + j] += A_PART*B[k*ldb + j]; + } + /* // SSE + __m128 a128, b128, c128, result128; // SSE + a128 = _mm_set1_ps(A_PART); + for (j = 0; j < N - 4; j += 4) { + b128 = _mm_loadu_ps(&B[k*ldb + j]); + c128 = _mm_loadu_ps(&C[i*ldc + j]); + //result128 = _mm_fmadd_ps(a128, b128, c128); + result128 = _mm_mul_ps(a128, b128); + result128 = _mm_add_ps(result128, c128); + _mm_storeu_ps(&C[i*ldc + j], result128); + } + + int prev_end = (N % 4 == 0) ? (N - 4) : (N / 4) * 4; + for (j = prev_end; j < N; ++j){ + C[i*ldc + j] += A_PART*B[k*ldb + j]; + } + */ + } + } + } } #else void gemm_nn(int M, int N, int K, float ALPHA, - float *A, int lda, - float *B, int ldb, - float *C, int ldc) + float *A, int lda, + float *B, int ldb, + float *C, int ldc) { - int i, j, k; - for (i = 0; i < M; ++i) { - for (k = 0; k < K; ++k) { - register float A_PART = ALPHA*A[i*lda + k]; - for (j = 0; j < N; ++j) { - C[i*ldc + j] += A_PART*B[k*ldb + j]; - } - } - } + int i, j, k; + for (i = 0; i < M; ++i) { + for (k = 0; k < K; ++k) { + register float A_PART = ALPHA*A[i*lda + k]; + for (j = 0; j < N; ++j) { + C[i*ldc + j] += A_PART*B[k*ldb + j]; + } + } + } } -#endif // __x86_64 +#endif // __x86_64 void gemm_nt(int M, int N, int K, float ALPHA, float *A, int lda, @@ -282,18 +282,18 @@ void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA, } } - int t; - #pragma omp parallel for - for (t = 0; t < M; ++t) { - if (!TA && !TB) - gemm_nn(1, N, K, ALPHA, A + t*lda, lda, B, ldb, C + t*ldc, ldc); - else if (TA && !TB) - gemm_tn(1, N, K, ALPHA, A + t, lda, B, ldb, C + t*ldc, ldc); - else if (!TA && TB) - gemm_nt(1, N, K, ALPHA, A + t*lda, lda, B, ldb, C + t*ldc, ldc); - else - gemm_tt(1, N, K, ALPHA, A + t, lda, B, ldb, C + t*ldc, ldc); - } + int t; + #pragma omp parallel for + for (t = 0; t < M; ++t) { + if (!TA && !TB) + gemm_nn(1, N, K, ALPHA, A + t*lda, lda, B, ldb, C + t*ldc, ldc); + else if (TA && !TB) + gemm_tn(1, N, K, ALPHA, A + t, lda, B, ldb, C + t*ldc, ldc); + else if (!TA && TB) + gemm_nt(1, N, K, ALPHA, A + t*lda, lda, B, ldb, C + t*ldc, ldc); + else + gemm_tt(1, N, K, ALPHA, A + t, lda, B, ldb, C + t*ldc, ldc); + } } #ifdef GPU @@ -307,7 +307,7 @@ void gemm_ongpu(int TA, int TB, int M, int N, int K, float ALPHA, float *C_gpu, int ldc) { cublasHandle_t handle = blas_handle(); - cudaError_t stream_status = cublasSetStream(handle, get_cuda_stream()); + cudaError_t stream_status = cublasSetStream(handle, get_cuda_stream()); cudaError_t status = cublasSgemm(handle, (TB ? CUBLAS_OP_T : CUBLAS_OP_N), (TA ? CUBLAS_OP_T : CUBLAS_OP_N), N, M, K, &ALPHA, B_gpu, ldb, A_gpu, lda, &BETA, C_gpu, ldc); check_error(status); diff --git a/src/gettimeofday.c b/src/gettimeofday.c index c8612bf0a14..7df6480dea9 100644 --- a/src/gettimeofday.c +++ b/src/gettimeofday.c @@ -1,5 +1,5 @@ #include "gettimeofday.h" - + int gettimeofday(struct timeval *tv, struct timezone *tz) { FILETIME ft; diff --git a/src/http_stream.cpp b/src/http_stream.cpp index b122f5b78ce..4179ef5ac1f 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -7,7 +7,7 @@ // // socket related abstractions: // -#ifdef _WIN32 +#ifdef _WIN32 #pragma comment(lib, "ws2_32.lib") #include #include @@ -16,8 +16,8 @@ #define ADDRPOINTER int* struct _INIT_W32DATA { - WSADATA w; - _INIT_W32DATA() { WSAStartup(MAKEWORD(2, 1), &w); } + WSADATA w; + _INIT_W32DATA() { WSAStartup(MAKEWORD(2, 1), &w); } } _init_once; #else /* ! win32 */ #include @@ -58,274 +58,274 @@ using namespace cv; class MJPGWriter { - SOCKET sock; - SOCKET maxfd; - fd_set master; - int timeout; // master sock timeout, shutdown after timeout millis. - int quality; // jpeg compression [1..100] - - int _write(int sock, char const*const s, int len) - { - if (len < 1) { len = strlen(s); } - return ::send(sock, s, len, 0); - } + SOCKET sock; + SOCKET maxfd; + fd_set master; + int timeout; // master sock timeout, shutdown after timeout millis. + int quality; // jpeg compression [1..100] + + int _write(int sock, char const*const s, int len) + { + if (len < 1) { len = strlen(s); } + return ::send(sock, s, len, 0); + } public: - MJPGWriter(int port = 0, int _timeout = 200000, int _quality = 30) - : sock(INVALID_SOCKET) - , timeout(_timeout) - , quality(_quality) - { - FD_ZERO(&master); - if (port) - open(port); - } - - ~MJPGWriter() - { - release(); - } - - bool release() - { - if (sock != INVALID_SOCKET) - ::shutdown(sock, 2); - sock = (INVALID_SOCKET); - return false; - } - - bool open(int port) - { - sock = ::socket(AF_INET, SOCK_STREAM, IPPROTO_TCP); - - SOCKADDR_IN address; - address.sin_addr.s_addr = INADDR_ANY; - address.sin_family = AF_INET; - address.sin_port = htons(port); // ::htons(port); - if (::bind(sock, (SOCKADDR*)&address, sizeof(SOCKADDR_IN)) == SOCKET_ERROR) - { - cerr << "error : couldn't bind sock " << sock << " to port " << port << "!" << endl; - return release(); - } - if (::listen(sock, 10) == SOCKET_ERROR) - { - cerr << "error : couldn't listen on sock " << sock << " on port " << port << " !" << endl; - return release(); - } - FD_ZERO(&master); - FD_SET(sock, &master); - maxfd = sock; - return true; - } - - bool isOpened() - { - return sock != INVALID_SOCKET; - } - - bool write(const Mat & frame) - { - fd_set rread = master; - struct timeval to = { 0,timeout }; - if (::select(maxfd+1, &rread, NULL, NULL, &to) <= 0) - return true; // nothing broken, there's just noone listening - - std::vector outbuf; - std::vector params; - params.push_back(IMWRITE_JPEG_QUALITY); - params.push_back(quality); - cv::imencode(".jpg", frame, outbuf, params); - size_t outlen = outbuf.size(); - -#ifdef _WIN32 - for (unsigned i = 0; iclient ? maxfd : client); - FD_SET(client, &master); - _write(client, "HTTP/1.0 200 OK\r\n", 0); - _write(client, - "Server: Mozarella/2.2\r\n" - "Accept-Range: bytes\r\n" - "Connection: close\r\n" - "Max-Age: 0\r\n" - "Expires: 0\r\n" - "Cache-Control: no-cache, private\r\n" - "Pragma: no-cache\r\n" - "Content-Type: multipart/x-mixed-replace; boundary=mjpegstream\r\n" - "\r\n", 0); - cerr << "new client " << client << endl; - } - else // existing client, just stream pix - { - char head[400]; - sprintf(head, "--mjpegstream\r\nContent-Type: image/jpeg\r\nContent-Length: %zu\r\n\r\n", outlen); - _write(s, head, 0); - int n = _write(s, (char*)(&outbuf[0]), outlen); - //cerr << "known client " << s << " " << n << endl; - if (n < outlen) - { - cerr << "kill client " << s << endl; - ::shutdown(s, 2); - FD_CLR(s, &master); - } - } - } - return true; - } + MJPGWriter(int port = 0, int _timeout = 200000, int _quality = 30) + : sock(INVALID_SOCKET) + , timeout(_timeout) + , quality(_quality) + { + FD_ZERO(&master); + if (port) + open(port); + } + + ~MJPGWriter() + { + release(); + } + + bool release() + { + if (sock != INVALID_SOCKET) + ::shutdown(sock, 2); + sock = (INVALID_SOCKET); + return false; + } + + bool open(int port) + { + sock = ::socket(AF_INET, SOCK_STREAM, IPPROTO_TCP); + + SOCKADDR_IN address; + address.sin_addr.s_addr = INADDR_ANY; + address.sin_family = AF_INET; + address.sin_port = htons(port); // ::htons(port); + if (::bind(sock, (SOCKADDR*)&address, sizeof(SOCKADDR_IN)) == SOCKET_ERROR) + { + cerr << "error : couldn't bind sock " << sock << " to port " << port << "!" << endl; + return release(); + } + if (::listen(sock, 10) == SOCKET_ERROR) + { + cerr << "error : couldn't listen on sock " << sock << " on port " << port << " !" << endl; + return release(); + } + FD_ZERO(&master); + FD_SET(sock, &master); + maxfd = sock; + return true; + } + + bool isOpened() + { + return sock != INVALID_SOCKET; + } + + bool write(const Mat & frame) + { + fd_set rread = master; + struct timeval to = { 0,timeout }; + if (::select(maxfd+1, &rread, NULL, NULL, &to) <= 0) + return true; // nothing broken, there's just noone listening + + std::vector outbuf; + std::vector params; + params.push_back(IMWRITE_JPEG_QUALITY); + params.push_back(quality); + cv::imencode(".jpg", frame, outbuf, params); + size_t outlen = outbuf.size(); + +#ifdef _WIN32 + for (unsigned i = 0; iclient ? maxfd : client); + FD_SET(client, &master); + _write(client, "HTTP/1.0 200 OK\r\n", 0); + _write(client, + "Server: Mozarella/2.2\r\n" + "Accept-Range: bytes\r\n" + "Connection: close\r\n" + "Max-Age: 0\r\n" + "Expires: 0\r\n" + "Cache-Control: no-cache, private\r\n" + "Pragma: no-cache\r\n" + "Content-Type: multipart/x-mixed-replace; boundary=mjpegstream\r\n" + "\r\n", 0); + cerr << "new client " << client << endl; + } + else // existing client, just stream pix + { + char head[400]; + sprintf(head, "--mjpegstream\r\nContent-Type: image/jpeg\r\nContent-Length: %zu\r\n\r\n", outlen); + _write(s, head, 0); + int n = _write(s, (char*)(&outbuf[0]), outlen); + //cerr << "known client " << s << " " << n << endl; + if (n < outlen) + { + cerr << "kill client " << s << endl; + ::shutdown(s, 2); + FD_CLR(s, &master); + } + } + } + return true; + } }; // ---------------------------------------- void send_mjpeg(IplImage* ipl, int port, int timeout, int quality) { - static MJPGWriter wri(port, timeout, quality); - cv::Mat mat = cv::cvarrToMat(ipl); - wri.write(mat); - std::cout << " MJPEG-stream sent. \n"; + static MJPGWriter wri(port, timeout, quality); + cv::Mat mat = cv::cvarrToMat(ipl); + wri.write(mat); + std::cout << " MJPEG-stream sent. \n"; } // ---------------------------------------- CvCapture* get_capture_video_stream(char *path) { - CvCapture* cap = NULL; - try { - cap = (CvCapture*)new cv::VideoCapture(path); - } - catch (...) { - std::cout << " Error: video-stream " << path << " can't be opened! \n"; - } - return cap; + CvCapture* cap = NULL; + try { + cap = (CvCapture*)new cv::VideoCapture(path); + } + catch (...) { + std::cout << " Error: video-stream " << path << " can't be opened! \n"; + } + return cap; } // ---------------------------------------- CvCapture* get_capture_webcam(int index) { - CvCapture* cap = NULL; - try { - cap = (CvCapture*)new cv::VideoCapture(index); - //((cv::VideoCapture*)cap)->set(CV_CAP_PROP_FRAME_WIDTH, 1280); - //((cv::VideoCapture*)cap)->set(CV_CAP_PROP_FRAME_HEIGHT, 960); - } - catch (...) { - std::cout << " Error: Web-camera " << index << " can't be opened! \n"; - } - return cap; + CvCapture* cap = NULL; + try { + cap = (CvCapture*)new cv::VideoCapture(index); + //((cv::VideoCapture*)cap)->set(CV_CAP_PROP_FRAME_WIDTH, 1280); + //((cv::VideoCapture*)cap)->set(CV_CAP_PROP_FRAME_HEIGHT, 960); + } + catch (...) { + std::cout << " Error: Web-camera " << index << " can't be opened! \n"; + } + return cap; } // ---------------------------------------- IplImage* get_webcam_frame(CvCapture *cap) { - IplImage* src = NULL; - try { - cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; - cv::Mat frame; - if (cpp_cap.isOpened()) - { - cpp_cap >> frame; - IplImage tmp = frame; - src = cvCloneImage(&tmp); - } - else { - std::cout << " Video-stream stoped! \n"; - } - } - catch (...) { - std::cout << " Video-stream stoped! \n"; - } - return src; + IplImage* src = NULL; + try { + cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; + cv::Mat frame; + if (cpp_cap.isOpened()) + { + cpp_cap >> frame; + IplImage tmp = frame; + src = cvCloneImage(&tmp); + } + else { + std::cout << " Video-stream stoped! \n"; + } + } + catch (...) { + std::cout << " Video-stream stoped! \n"; + } + return src; } int get_stream_fps_cpp(CvCapture *cap) { - int fps = 25; - try { - cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; -#ifndef CV_VERSION_EPOCH // OpenCV 3.x - fps = cpp_cap.get(CAP_PROP_FPS); -#else // OpenCV 2.x - fps = cpp_cap.get(CV_CAP_PROP_FPS); -#endif - } - catch (...) { - std::cout << " Can't get FPS of source videofile. For output video FPS = 25 by default. \n"; - } - return fps; + int fps = 25; + try { + cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; +#ifndef CV_VERSION_EPOCH // OpenCV 3.x + fps = cpp_cap.get(CAP_PROP_FPS); +#else // OpenCV 2.x + fps = cpp_cap.get(CV_CAP_PROP_FPS); +#endif + } + catch (...) { + std::cout << " Can't get FPS of source videofile. For output video FPS = 25 by default. \n"; + } + return fps; } // ---------------------------------------- extern "C" { - image ipl_to_image(IplImage* src); // image.c + image ipl_to_image(IplImage* src); // image.c } image image_data_augmentation(IplImage* ipl, int w, int h, - int pleft, int ptop, int swidth, int sheight, int flip, - float jitter, float dhue, float dsat, float dexp) + int pleft, int ptop, int swidth, int sheight, int flip, + float jitter, float dhue, float dsat, float dexp) { - cv::Mat img = cv::cvarrToMat(ipl); - - // crop - cv::Rect src_rect(pleft, ptop, swidth, sheight); - cv::Rect img_rect(cv::Point2i(0, 0), img.size()); - cv::Rect new_src_rect = src_rect & img_rect; - - cv::Rect dst_rect(cv::Point2i(std::max(0, -pleft), std::max(0, -ptop)), new_src_rect.size()); - - cv::Mat cropped(cv::Size(src_rect.width, src_rect.height), img.type()); - cropped.setTo(cv::Scalar::all(0)); - - img(new_src_rect).copyTo(cropped(dst_rect)); - - // resize - cv::Mat sized; - cv::resize(cropped, sized, cv::Size(w, h), 0, 0, INTER_LINEAR); - - // flip - if (flip) { - cv::flip(sized, cropped, 1); // 0 - x-axis, 1 - y-axis, -1 - both axes (x & y) - sized = cropped.clone(); - } - - // HSV augmentation - // CV_BGR2HSV, CV_RGB2HSV, CV_HSV2BGR, CV_HSV2RGB - if (ipl->nChannels >= 3) - { - cv::Mat hsv_src; - cvtColor(sized, hsv_src, CV_BGR2HSV); // also BGR -> RGB - - std::vector hsv; - cv::split(hsv_src, hsv); - - hsv[1] *= dsat; - hsv[2] *= dexp; - hsv[0] += 179 * dhue; - - cv::merge(hsv, hsv_src); - - cvtColor(hsv_src, sized, CV_HSV2RGB); // now RGB instead of BGR - } - else - { - sized *= dexp; - } - - // Mat -> IplImage -> image - IplImage src = sized; - image out = ipl_to_image(&src); - - return out; + cv::Mat img = cv::cvarrToMat(ipl); + + // crop + cv::Rect src_rect(pleft, ptop, swidth, sheight); + cv::Rect img_rect(cv::Point2i(0, 0), img.size()); + cv::Rect new_src_rect = src_rect & img_rect; + + cv::Rect dst_rect(cv::Point2i(std::max(0, -pleft), std::max(0, -ptop)), new_src_rect.size()); + + cv::Mat cropped(cv::Size(src_rect.width, src_rect.height), img.type()); + cropped.setTo(cv::Scalar::all(0)); + + img(new_src_rect).copyTo(cropped(dst_rect)); + + // resize + cv::Mat sized; + cv::resize(cropped, sized, cv::Size(w, h), 0, 0, INTER_LINEAR); + + // flip + if (flip) { + cv::flip(sized, cropped, 1); // 0 - x-axis, 1 - y-axis, -1 - both axes (x & y) + sized = cropped.clone(); + } + + // HSV augmentation + // CV_BGR2HSV, CV_RGB2HSV, CV_HSV2BGR, CV_HSV2RGB + if (ipl->nChannels >= 3) + { + cv::Mat hsv_src; + cvtColor(sized, hsv_src, CV_BGR2HSV); // also BGR -> RGB + + std::vector hsv; + cv::split(hsv_src, hsv); + + hsv[1] *= dsat; + hsv[2] *= dexp; + hsv[0] += 179 * dhue; + + cv::merge(hsv, hsv_src); + + cvtColor(hsv_src, sized, CV_HSV2RGB); // now RGB instead of BGR + } + else + { + sized *= dexp; + } + + // Mat -> IplImage -> image + IplImage src = sized; + image out = ipl_to_image(&src); + + return out; } -#endif // OPENCV +#endif // OPENCV diff --git a/src/http_stream.h b/src/http_stream.h index bff18fb79f2..298d33986c7 100644 --- a/src/http_stream.h +++ b/src/http_stream.h @@ -14,8 +14,8 @@ IplImage* get_webcam_frame(CvCapture *cap); int get_stream_fps_cpp(CvCapture *cap); image image_data_augmentation(IplImage* ipl, int w, int h, - int pleft, int ptop, int swidth, int sheight, int flip, - float jitter, float dhue, float dsat, float dexp); + int pleft, int ptop, int swidth, int sheight, int flip, + float jitter, float dhue, float dsat, float dexp); #ifdef __cplusplus } diff --git a/src/image.c b/src/image.c index a2dc3cacd80..75259e8f726 100644 --- a/src/image.c +++ b/src/image.c @@ -40,31 +40,31 @@ float get_color(int c, int x, int max) static float get_pixel(image m, int x, int y, int c) { - assert(x < m.w && y < m.h && c < m.c); - return m.data[c*m.h*m.w + y*m.w + x]; + assert(x < m.w && y < m.h && c < m.c); + return m.data[c*m.h*m.w + y*m.w + x]; } static float get_pixel_extend(image m, int x, int y, int c) { - if (x < 0 || x >= m.w || y < 0 || y >= m.h) return 0; - /* - if(x < 0) x = 0; - if(x >= m.w) x = m.w-1; - if(y < 0) y = 0; - if(y >= m.h) y = m.h-1; - */ - if (c < 0 || c >= m.c) return 0; - return get_pixel(m, x, y, c); + if (x < 0 || x >= m.w || y < 0 || y >= m.h) return 0; + /* + if(x < 0) x = 0; + if(x >= m.w) x = m.w-1; + if(y < 0) y = 0; + if(y >= m.h) y = m.h-1; + */ + if (c < 0 || c >= m.c) return 0; + return get_pixel(m, x, y, c); } static void set_pixel(image m, int x, int y, int c, float val) { - if (x < 0 || y < 0 || c < 0 || x >= m.w || y >= m.h || c >= m.c) return; - assert(x < m.w && y < m.h && c < m.c); - m.data[c*m.h*m.w + y*m.w + x] = val; + if (x < 0 || y < 0 || c < 0 || x >= m.w || y >= m.h || c >= m.c) return; + assert(x < m.w && y < m.h && c < m.c); + m.data[c*m.h*m.w + y*m.w + x] = val; } static void add_pixel(image m, int x, int y, int c, float val) { - assert(x < m.w && y < m.h && c < m.c); - m.data[c*m.h*m.w + y*m.w + x] += val; + assert(x < m.w && y < m.h && c < m.c); + m.data[c*m.h*m.w + y*m.w + x] += val; } void composite_image(image source, image dest, int dx, int dy) @@ -125,19 +125,19 @@ image get_label(image **characters, char *string, int size) image get_label_v3(image **characters, char *string, int size) { - size = size / 10; - if (size > 7) size = 7; - image label = make_empty_image(0, 0, 0); - while (*string) { - image l = characters[size][(int)*string]; - image n = tile_images(label, l, -size - 1 + (size + 1) / 2); - free_image(label); - label = n; - ++string; - } - image b = border_image(label, label.h*.25); - free_image(label); - return b; + size = size / 10; + if (size > 7) size = 7; + image label = make_empty_image(0, 0, 0); + while (*string) { + image l = characters[size][(int)*string]; + image n = tile_images(label, l, -size - 1 + (size + 1) / 2); + free_image(label); + label = n; + ++string; + } + image b = border_image(label, label.h*.25); + free_image(label); + return b; } void draw_label(image a, int r, int c, image label, const float *rgb) @@ -235,143 +235,143 @@ image **load_alphabet() // Creates array of detections with prob > thresh and fills best_class for them detection_with_class* get_actual_detections(detection *dets, int dets_num, float thresh, int* selected_detections_num) { - int selected_num = 0; - detection_with_class* result_arr = calloc(dets_num, sizeof(detection_with_class)); - int i; - for (i = 0; i < dets_num; ++i) { - int best_class = -1; - float best_class_prob = thresh; - int j; - for (j = 0; j < dets[i].classes; ++j) { - if (dets[i].prob[j] > best_class_prob ) { - best_class = j; - best_class_prob = dets[i].prob[j]; - } - } - if (best_class >= 0) { - result_arr[selected_num].det = dets[i]; - result_arr[selected_num].best_class = best_class; - ++selected_num; - } - } - if (selected_detections_num) - *selected_detections_num = selected_num; - return result_arr; + int selected_num = 0; + detection_with_class* result_arr = calloc(dets_num, sizeof(detection_with_class)); + int i; + for (i = 0; i < dets_num; ++i) { + int best_class = -1; + float best_class_prob = thresh; + int j; + for (j = 0; j < dets[i].classes; ++j) { + if (dets[i].prob[j] > best_class_prob ) { + best_class = j; + best_class_prob = dets[i].prob[j]; + } + } + if (best_class >= 0) { + result_arr[selected_num].det = dets[i]; + result_arr[selected_num].best_class = best_class; + ++selected_num; + } + } + if (selected_detections_num) + *selected_detections_num = selected_num; + return result_arr; } // compare to sort detection** by bbox.x int compare_by_lefts(const void *a_ptr, const void *b_ptr) { - const detection_with_class* a = (detection_with_class*)a_ptr; - const detection_with_class* b = (detection_with_class*)b_ptr; - const float delta = (a->det.bbox.x - a->det.bbox.w/2) - (b->det.bbox.x - b->det.bbox.w/2); - return delta < 0 ? -1 : delta > 0 ? 1 : 0; + const detection_with_class* a = (detection_with_class*)a_ptr; + const detection_with_class* b = (detection_with_class*)b_ptr; + const float delta = (a->det.bbox.x - a->det.bbox.w/2) - (b->det.bbox.x - b->det.bbox.w/2); + return delta < 0 ? -1 : delta > 0 ? 1 : 0; } // compare to sort detection** by best_class probability int compare_by_probs(const void *a_ptr, const void *b_ptr) { - const detection_with_class* a = (detection_with_class*)a_ptr; - const detection_with_class* b = (detection_with_class*)b_ptr; - float delta = a->det.prob[a->best_class] - b->det.prob[b->best_class]; - return delta < 0 ? -1 : delta > 0 ? 1 : 0; + const detection_with_class* a = (detection_with_class*)a_ptr; + const detection_with_class* b = (detection_with_class*)b_ptr; + float delta = a->det.prob[a->best_class] - b->det.prob[b->best_class]; + return delta < 0 ? -1 : delta > 0 ? 1 : 0; } void draw_detections_v3(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output) { - int selected_detections_num; - detection_with_class* selected_detections = get_actual_detections(dets, num, thresh, &selected_detections_num); - - // text output - qsort(selected_detections, selected_detections_num, sizeof(*selected_detections), compare_by_lefts); - int i; - for (i = 0; i < selected_detections_num; ++i) { - const int best_class = selected_detections[i].best_class; - printf("%s: %.0f%%", names[best_class], selected_detections[i].det.prob[best_class] * 100); - if (ext_output) - printf("\t(left_x: %4.0f top_y: %4.0f width: %4.0f height: %4.0f)\n", - (selected_detections[i].det.bbox.x - selected_detections[i].det.bbox.w / 2)*im.w, - (selected_detections[i].det.bbox.y - selected_detections[i].det.bbox.h / 2)*im.h, - selected_detections[i].det.bbox.w*im.w, selected_detections[i].det.bbox.h*im.h); - else - printf("\n"); - int j; - for (j = 0; j < classes; ++j) { - if (selected_detections[i].det.prob[j] > thresh && j != best_class) { - printf("%s: %.0f%%\n", names[j], selected_detections[i].det.prob[j] * 100); - } - } - } - - // image output - qsort(selected_detections, selected_detections_num, sizeof(*selected_detections), compare_by_probs); - for (i = 0; i < selected_detections_num; ++i) { - int width = im.h * .006; - if (width < 1) - width = 1; - - /* - if(0){ - width = pow(prob, 1./2.)*10+1; - alphabet = 0; - } - */ - - //printf("%d %s: %.0f%%\n", i, names[selected_detections[i].best_class], prob*100); - int offset = selected_detections[i].best_class * 123457 % classes; - float red = get_color(2, offset, classes); - float green = get_color(1, offset, classes); - float blue = get_color(0, offset, classes); - float rgb[3]; - - //width = prob*20+2; - - rgb[0] = red; - rgb[1] = green; - rgb[2] = blue; - box b = selected_detections[i].det.bbox; - //printf("%f %f %f %f\n", b.x, b.y, b.w, b.h); - - int left = (b.x - b.w / 2.)*im.w; - int right = (b.x + b.w / 2.)*im.w; - int top = (b.y - b.h / 2.)*im.h; - int bot = (b.y + b.h / 2.)*im.h; - - if (left < 0) left = 0; - if (right > im.w - 1) right = im.w - 1; - if (top < 0) top = 0; - if (bot > im.h - 1) bot = im.h - 1; - - //int b_x_center = (left + right) / 2; - //int b_y_center = (top + bot) / 2; - //int b_width = right - left; - //int b_height = bot - top; - //sprintf(labelstr, "%d x %d - w: %d, h: %d", b_x_center, b_y_center, b_width, b_height); - - draw_box_width(im, left, top, right, bot, width, red, green, blue); - if (alphabet) { - char labelstr[4096] = { 0 }; - strcat(labelstr, names[selected_detections[i].best_class]); - int j; - for (j = 0; j < classes; ++j) { - if (selected_detections[i].det.prob[j] > thresh && j != selected_detections[i].best_class) { - strcat(labelstr, ", "); - strcat(labelstr, names[j]); - } - } - image label = get_label_v3(alphabet, labelstr, (im.h*.03)); - draw_label(im, top + width, left, label, rgb); - free_image(label); - } - if (selected_detections[i].det.mask) { - image mask = float_to_image(14, 14, 1, selected_detections[i].det.mask); - image resized_mask = resize_image(mask, b.w*im.w, b.h*im.h); - image tmask = threshold_image(resized_mask, .5); - embed_image(tmask, im, left, top); - free_image(mask); - free_image(resized_mask); - free_image(tmask); - } - } - free(selected_detections); + int selected_detections_num; + detection_with_class* selected_detections = get_actual_detections(dets, num, thresh, &selected_detections_num); + + // text output + qsort(selected_detections, selected_detections_num, sizeof(*selected_detections), compare_by_lefts); + int i; + for (i = 0; i < selected_detections_num; ++i) { + const int best_class = selected_detections[i].best_class; + printf("%s: %.0f%%", names[best_class], selected_detections[i].det.prob[best_class] * 100); + if (ext_output) + printf("\t(left_x: %4.0f top_y: %4.0f width: %4.0f height: %4.0f)\n", + (selected_detections[i].det.bbox.x - selected_detections[i].det.bbox.w / 2)*im.w, + (selected_detections[i].det.bbox.y - selected_detections[i].det.bbox.h / 2)*im.h, + selected_detections[i].det.bbox.w*im.w, selected_detections[i].det.bbox.h*im.h); + else + printf("\n"); + int j; + for (j = 0; j < classes; ++j) { + if (selected_detections[i].det.prob[j] > thresh && j != best_class) { + printf("%s: %.0f%%\n", names[j], selected_detections[i].det.prob[j] * 100); + } + } + } + + // image output + qsort(selected_detections, selected_detections_num, sizeof(*selected_detections), compare_by_probs); + for (i = 0; i < selected_detections_num; ++i) { + int width = im.h * .006; + if (width < 1) + width = 1; + + /* + if(0){ + width = pow(prob, 1./2.)*10+1; + alphabet = 0; + } + */ + + //printf("%d %s: %.0f%%\n", i, names[selected_detections[i].best_class], prob*100); + int offset = selected_detections[i].best_class * 123457 % classes; + float red = get_color(2, offset, classes); + float green = get_color(1, offset, classes); + float blue = get_color(0, offset, classes); + float rgb[3]; + + //width = prob*20+2; + + rgb[0] = red; + rgb[1] = green; + rgb[2] = blue; + box b = selected_detections[i].det.bbox; + //printf("%f %f %f %f\n", b.x, b.y, b.w, b.h); + + int left = (b.x - b.w / 2.)*im.w; + int right = (b.x + b.w / 2.)*im.w; + int top = (b.y - b.h / 2.)*im.h; + int bot = (b.y + b.h / 2.)*im.h; + + if (left < 0) left = 0; + if (right > im.w - 1) right = im.w - 1; + if (top < 0) top = 0; + if (bot > im.h - 1) bot = im.h - 1; + + //int b_x_center = (left + right) / 2; + //int b_y_center = (top + bot) / 2; + //int b_width = right - left; + //int b_height = bot - top; + //sprintf(labelstr, "%d x %d - w: %d, h: %d", b_x_center, b_y_center, b_width, b_height); + + draw_box_width(im, left, top, right, bot, width, red, green, blue); + if (alphabet) { + char labelstr[4096] = { 0 }; + strcat(labelstr, names[selected_detections[i].best_class]); + int j; + for (j = 0; j < classes; ++j) { + if (selected_detections[i].det.prob[j] > thresh && j != selected_detections[i].best_class) { + strcat(labelstr, ", "); + strcat(labelstr, names[j]); + } + } + image label = get_label_v3(alphabet, labelstr, (im.h*.03)); + draw_label(im, top + width, left, label, rgb); + free_image(label); + } + if (selected_detections[i].det.mask) { + image mask = float_to_image(14, 14, 1, selected_detections[i].det.mask); + image resized_mask = resize_image(mask, b.w*im.w, b.h*im.h); + image tmask = threshold_image(resized_mask, .5); + embed_image(tmask, im, left, top); + free_image(mask); + free_image(resized_mask); + free_image(tmask); + } + } + free(selected_detections); } void draw_detections(image im, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes) @@ -383,13 +383,13 @@ void draw_detections(image im, int num, float thresh, box *boxes, float **probs, float prob = probs[i][class_id]; if(prob > thresh){ - //// for comparison with OpenCV version of DNN Darknet Yolo v2 - //printf("\n %f, %f, %f, %f, ", boxes[i].x, boxes[i].y, boxes[i].w, boxes[i].h); - // int k; - //for (k = 0; k < classes; ++k) { - // printf("%f, ", probs[i][k]); - //} - //printf("\n"); + //// for comparison with OpenCV version of DNN Darknet Yolo v2 + //printf("\n %f, %f, %f, %f, ", boxes[i].x, boxes[i].y, boxes[i].w, boxes[i].h); + // int k; + //for (k = 0; k < classes; ++k) { + // printf("%f, ", probs[i][k]); + //} + //printf("\n"); int width = im.h * .012; @@ -420,12 +420,12 @@ void draw_detections(image im, int num, float thresh, box *boxes, float **probs, if(right > im.w-1) right = im.w-1; if(top < 0) top = 0; if(bot > im.h-1) bot = im.h-1; - printf("%s: %.0f%%", names[class_id], prob * 100); - - //printf(" - id: %d, x_center: %d, y_center: %d, width: %d, height: %d", - // class_id, (right + left) / 2, (bot - top) / 2, right - left, bot - top); + printf("%s: %.0f%%", names[class_id], prob * 100); + + //printf(" - id: %d, x_center: %d, y_center: %d, width: %d, height: %d", + // class_id, (right + left) / 2, (bot - top) / 2, right - left, bot - top); - printf("\n"); + printf("\n"); draw_box_width(im, left, top, right, bot, width, red, green, blue); if (alphabet) { image label = get_label(alphabet, names[class_id], (im.h*.03)/10); @@ -439,257 +439,257 @@ void draw_detections(image im, int num, float thresh, box *boxes, float **probs, void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output) { - int i, j; - if (!show_img) return; - static int frame_id = 0; - frame_id++; - - for (i = 0; i < num; ++i) { - char labelstr[4096] = { 0 }; - int class_id = -1; - for (j = 0; j < classes; ++j) { - if (dets[i].prob[j] > thresh) { - if (class_id < 0) { - strcat(labelstr, names[j]); - class_id = j; - } - else { - strcat(labelstr, ", "); - strcat(labelstr, names[j]); - } - printf("%s: %.0f%% ", names[j], dets[i].prob[j] * 100); - } - } - if (class_id >= 0) { - int width = show_img->height * .006; - - //if(0){ - //width = pow(prob, 1./2.)*10+1; - //alphabet = 0; - //} - - //printf("%d %s: %.0f%%\n", i, names[class_id], prob*100); - int offset = class_id * 123457 % classes; - float red = get_color(2, offset, classes); - float green = get_color(1, offset, classes); - float blue = get_color(0, offset, classes); - float rgb[3]; - - //width = prob*20+2; - - rgb[0] = red; - rgb[1] = green; - rgb[2] = blue; - box b = dets[i].bbox; - //printf("%f %f %f %f\n", b.x, b.y, b.w, b.h); - - int left = (b.x - b.w / 2.)*show_img->width; - int right = (b.x + b.w / 2.)*show_img->width; - int top = (b.y - b.h / 2.)*show_img->height; - int bot = (b.y + b.h / 2.)*show_img->height; - - if (left < 0) left = 0; - if (right > show_img->width - 1) right = show_img->width - 1; - if (top < 0) top = 0; - if (bot > show_img->height - 1) bot = show_img->height - 1; - - //int b_x_center = (left + right) / 2; - //int b_y_center = (top + bot) / 2; - //int b_width = right - left; - //int b_height = bot - top; - //sprintf(labelstr, "%d x %d - w: %d, h: %d", b_x_center, b_y_center, b_width, b_height); - - float const font_size = show_img->height / 1000.F; - CvPoint pt1, pt2, pt_text, pt_text_bg1, pt_text_bg2; - pt1.x = left; - pt1.y = top; - pt2.x = right; - pt2.y = bot; - pt_text.x = left; - pt_text.y = top - 12; - pt_text_bg1.x = left; - pt_text_bg1.y = top - (10 + 25 * font_size); - pt_text_bg2.x = right; - pt_text_bg2.y = top; - CvScalar color; - color.val[0] = red * 256; - color.val[1] = green * 256; - color.val[2] = blue * 256; - - // you should create directory: result_img - //static int copied_frame_id = -1; - //static IplImage* copy_img = NULL; - //if (copied_frame_id != frame_id) { - // copied_frame_id = frame_id; - // if(copy_img == NULL) copy_img = cvCreateImage(cvSize(show_img->width, show_img->height), show_img->depth, show_img->nChannels); - // cvCopy(show_img, copy_img, 0); - //} - //static int img_id = 0; - //img_id++; - //char image_name[1024]; - //sprintf(image_name, "result_img/img_%d_%d_%d.jpg", frame_id, img_id, class_id); - //CvRect rect = cvRect(pt1.x, pt1.y, pt2.x - pt1.x, pt2.y - pt1.y); - //cvSetImageROI(copy_img, rect); - //cvSaveImage(image_name, copy_img, 0); - //cvResetImageROI(copy_img); - - cvRectangle(show_img, pt1, pt2, color, width, 8, 0); - if (ext_output) - printf("\t(left_x: %4.0f top_y: %4.0f width: %4.0f height: %4.0f)\n", - (float)left, (float)top, b.w*show_img->width, b.h*show_img->height); - else - printf("\n"); - cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0); - cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0); // filled - CvScalar black_color; - black_color.val[0] = 0; - CvFont font; - cvInitFont(&font, CV_FONT_HERSHEY_SIMPLEX, font_size, font_size, 0, font_size * 3, 8); - cvPutText(show_img, labelstr, pt_text, &font, black_color); - } - } + int i, j; + if (!show_img) return; + static int frame_id = 0; + frame_id++; + + for (i = 0; i < num; ++i) { + char labelstr[4096] = { 0 }; + int class_id = -1; + for (j = 0; j < classes; ++j) { + if (dets[i].prob[j] > thresh) { + if (class_id < 0) { + strcat(labelstr, names[j]); + class_id = j; + } + else { + strcat(labelstr, ", "); + strcat(labelstr, names[j]); + } + printf("%s: %.0f%% ", names[j], dets[i].prob[j] * 100); + } + } + if (class_id >= 0) { + int width = show_img->height * .006; + + //if(0){ + //width = pow(prob, 1./2.)*10+1; + //alphabet = 0; + //} + + //printf("%d %s: %.0f%%\n", i, names[class_id], prob*100); + int offset = class_id * 123457 % classes; + float red = get_color(2, offset, classes); + float green = get_color(1, offset, classes); + float blue = get_color(0, offset, classes); + float rgb[3]; + + //width = prob*20+2; + + rgb[0] = red; + rgb[1] = green; + rgb[2] = blue; + box b = dets[i].bbox; + //printf("%f %f %f %f\n", b.x, b.y, b.w, b.h); + + int left = (b.x - b.w / 2.)*show_img->width; + int right = (b.x + b.w / 2.)*show_img->width; + int top = (b.y - b.h / 2.)*show_img->height; + int bot = (b.y + b.h / 2.)*show_img->height; + + if (left < 0) left = 0; + if (right > show_img->width - 1) right = show_img->width - 1; + if (top < 0) top = 0; + if (bot > show_img->height - 1) bot = show_img->height - 1; + + //int b_x_center = (left + right) / 2; + //int b_y_center = (top + bot) / 2; + //int b_width = right - left; + //int b_height = bot - top; + //sprintf(labelstr, "%d x %d - w: %d, h: %d", b_x_center, b_y_center, b_width, b_height); + + float const font_size = show_img->height / 1000.F; + CvPoint pt1, pt2, pt_text, pt_text_bg1, pt_text_bg2; + pt1.x = left; + pt1.y = top; + pt2.x = right; + pt2.y = bot; + pt_text.x = left; + pt_text.y = top - 12; + pt_text_bg1.x = left; + pt_text_bg1.y = top - (10 + 25 * font_size); + pt_text_bg2.x = right; + pt_text_bg2.y = top; + CvScalar color; + color.val[0] = red * 256; + color.val[1] = green * 256; + color.val[2] = blue * 256; + + // you should create directory: result_img + //static int copied_frame_id = -1; + //static IplImage* copy_img = NULL; + //if (copied_frame_id != frame_id) { + // copied_frame_id = frame_id; + // if(copy_img == NULL) copy_img = cvCreateImage(cvSize(show_img->width, show_img->height), show_img->depth, show_img->nChannels); + // cvCopy(show_img, copy_img, 0); + //} + //static int img_id = 0; + //img_id++; + //char image_name[1024]; + //sprintf(image_name, "result_img/img_%d_%d_%d.jpg", frame_id, img_id, class_id); + //CvRect rect = cvRect(pt1.x, pt1.y, pt2.x - pt1.x, pt2.y - pt1.y); + //cvSetImageROI(copy_img, rect); + //cvSaveImage(image_name, copy_img, 0); + //cvResetImageROI(copy_img); + + cvRectangle(show_img, pt1, pt2, color, width, 8, 0); + if (ext_output) + printf("\t(left_x: %4.0f top_y: %4.0f width: %4.0f height: %4.0f)\n", + (float)left, (float)top, b.w*show_img->width, b.h*show_img->height); + else + printf("\n"); + cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0); + cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0); // filled + CvScalar black_color; + black_color.val[0] = 0; + CvFont font; + cvInitFont(&font, CV_FONT_HERSHEY_SIMPLEX, font_size, font_size, 0, font_size * 3, 8); + cvPutText(show_img, labelstr, pt_text, &font, black_color); + } + } } void draw_detections_cv(IplImage* show_img, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes) { - int i; - - for (i = 0; i < num; ++i) { - int class_id = max_index(probs[i], classes); - float prob = probs[i][class_id]; - if (prob > thresh) { - - int width = show_img->height * .012; - - if (0) { - width = pow(prob, 1. / 2.) * 10 + 1; - alphabet = 0; - } - - printf("%s: %.0f%%\n", names[class_id], prob * 100); - int offset = class_id * 123457 % classes; - float red = get_color(2, offset, classes); - float green = get_color(1, offset, classes); - float blue = get_color(0, offset, classes); - float rgb[3]; - - //width = prob*20+2; - - rgb[0] = red; - rgb[1] = green; - rgb[2] = blue; - box b = boxes[i]; - - int left = (b.x - b.w / 2.)*show_img->width; - int right = (b.x + b.w / 2.)*show_img->width; - int top = (b.y - b.h / 2.)*show_img->height; - int bot = (b.y + b.h / 2.)*show_img->height; - - if (left < 0) left = 0; - if (right > show_img->width - 1) right = show_img->width - 1; - if (top < 0) top = 0; - if (bot > show_img->height - 1) bot = show_img->height - 1; - - float const font_size = show_img->height / 1000.F; - CvPoint pt1, pt2, pt_text, pt_text_bg1, pt_text_bg2; - pt1.x = left; - pt1.y = top; - pt2.x = right; - pt2.y = bot; - pt_text.x = left; - pt_text.y = top - 12; - pt_text_bg1.x = left; - pt_text_bg1.y = top - (10+25*font_size); - pt_text_bg2.x = right; - pt_text_bg2.y = top; - CvScalar color; - color.val[0] = red * 256; - color.val[1] = green * 256; - color.val[2] = blue * 256; - - cvRectangle(show_img, pt1, pt2, color, width, 8, 0); - //printf("left=%d, right=%d, top=%d, bottom=%d, obj_id=%d, obj=%s \n", left, right, top, bot, class_id, names[class_id]); - cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0); - cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0); // filled - CvScalar black_color; - black_color.val[0] = 0; - CvFont font; - cvInitFont(&font, CV_FONT_HERSHEY_SIMPLEX, font_size, font_size, 0, font_size * 3, 8); - cvPutText(show_img, names[class_id], pt_text, &font, black_color); - } - } + int i; + + for (i = 0; i < num; ++i) { + int class_id = max_index(probs[i], classes); + float prob = probs[i][class_id]; + if (prob > thresh) { + + int width = show_img->height * .012; + + if (0) { + width = pow(prob, 1. / 2.) * 10 + 1; + alphabet = 0; + } + + printf("%s: %.0f%%\n", names[class_id], prob * 100); + int offset = class_id * 123457 % classes; + float red = get_color(2, offset, classes); + float green = get_color(1, offset, classes); + float blue = get_color(0, offset, classes); + float rgb[3]; + + //width = prob*20+2; + + rgb[0] = red; + rgb[1] = green; + rgb[2] = blue; + box b = boxes[i]; + + int left = (b.x - b.w / 2.)*show_img->width; + int right = (b.x + b.w / 2.)*show_img->width; + int top = (b.y - b.h / 2.)*show_img->height; + int bot = (b.y + b.h / 2.)*show_img->height; + + if (left < 0) left = 0; + if (right > show_img->width - 1) right = show_img->width - 1; + if (top < 0) top = 0; + if (bot > show_img->height - 1) bot = show_img->height - 1; + + float const font_size = show_img->height / 1000.F; + CvPoint pt1, pt2, pt_text, pt_text_bg1, pt_text_bg2; + pt1.x = left; + pt1.y = top; + pt2.x = right; + pt2.y = bot; + pt_text.x = left; + pt_text.y = top - 12; + pt_text_bg1.x = left; + pt_text_bg1.y = top - (10+25*font_size); + pt_text_bg2.x = right; + pt_text_bg2.y = top; + CvScalar color; + color.val[0] = red * 256; + color.val[1] = green * 256; + color.val[2] = blue * 256; + + cvRectangle(show_img, pt1, pt2, color, width, 8, 0); + //printf("left=%d, right=%d, top=%d, bottom=%d, obj_id=%d, obj=%s \n", left, right, top, bot, class_id, names[class_id]); + cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0); + cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0); // filled + CvScalar black_color; + black_color.val[0] = 0; + CvFont font; + cvInitFont(&font, CV_FONT_HERSHEY_SIMPLEX, font_size, font_size, 0, font_size * 3, 8); + cvPutText(show_img, names[class_id], pt_text, &font, black_color); + } + } } IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size) { - int img_offset = 50; - int draw_size = img_size - img_offset; - IplImage* img = cvCreateImage(cvSize(img_size, img_size), 8, 3); - cvSet(img, CV_RGB(255, 255, 255), 0); - CvPoint pt1, pt2, pt_text; - CvFont font; - cvInitFont(&font, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA); - char char_buff[100]; - int i; - // vertical lines - pt1.x = img_offset; pt2.x = img_size, pt_text.x = 10; - for (i = 1; i <= number_of_lines; ++i) { - pt1.y = pt2.y = (float)i * draw_size / number_of_lines; - cvLine(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0); - if (i % 10 == 0) { - sprintf(char_buff, "%2.1f", max_img_loss*(number_of_lines - i) / number_of_lines); - pt_text.y = pt1.y + 5; - cvPutText(img, char_buff, pt_text, &font, CV_RGB(0, 0, 0)); - cvLine(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0); - } - } - // horizontal lines - pt1.y = draw_size; pt2.y = 0, pt_text.y = draw_size + 15; - for (i = 0; i <= number_of_lines; ++i) { - pt1.x = pt2.x = img_offset + (float)i * draw_size / number_of_lines; - cvLine(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0); - if (i % 10 == 0) { - sprintf(char_buff, "%d", max_batches * i / number_of_lines); - pt_text.x = pt1.x - 20; - cvPutText(img, char_buff, pt_text, &font, CV_RGB(0, 0, 0)); - cvLine(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0); - } - } - cvPutText(img, "Iteration number", cvPoint(draw_size / 2, img_size - 10), &font, CV_RGB(0, 0, 0)); - cvPutText(img, "Press 's' to save: chart.jpg", cvPoint(5, img_size - 10), &font, CV_RGB(0, 0, 0)); - printf(" If error occurs - run training with flag: -dont_show \n"); - cvNamedWindow("average loss", CV_WINDOW_NORMAL); - cvMoveWindow("average loss", 0, 0); - cvResizeWindow("average loss", img_size, img_size); - cvShowImage("average loss", img); - cvWaitKey(20); - return img; + int img_offset = 50; + int draw_size = img_size - img_offset; + IplImage* img = cvCreateImage(cvSize(img_size, img_size), 8, 3); + cvSet(img, CV_RGB(255, 255, 255), 0); + CvPoint pt1, pt2, pt_text; + CvFont font; + cvInitFont(&font, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA); + char char_buff[100]; + int i; + // vertical lines + pt1.x = img_offset; pt2.x = img_size, pt_text.x = 10; + for (i = 1; i <= number_of_lines; ++i) { + pt1.y = pt2.y = (float)i * draw_size / number_of_lines; + cvLine(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0); + if (i % 10 == 0) { + sprintf(char_buff, "%2.1f", max_img_loss*(number_of_lines - i) / number_of_lines); + pt_text.y = pt1.y + 5; + cvPutText(img, char_buff, pt_text, &font, CV_RGB(0, 0, 0)); + cvLine(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0); + } + } + // horizontal lines + pt1.y = draw_size; pt2.y = 0, pt_text.y = draw_size + 15; + for (i = 0; i <= number_of_lines; ++i) { + pt1.x = pt2.x = img_offset + (float)i * draw_size / number_of_lines; + cvLine(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0); + if (i % 10 == 0) { + sprintf(char_buff, "%d", max_batches * i / number_of_lines); + pt_text.x = pt1.x - 20; + cvPutText(img, char_buff, pt_text, &font, CV_RGB(0, 0, 0)); + cvLine(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0); + } + } + cvPutText(img, "Iteration number", cvPoint(draw_size / 2, img_size - 10), &font, CV_RGB(0, 0, 0)); + cvPutText(img, "Press 's' to save: chart.jpg", cvPoint(5, img_size - 10), &font, CV_RGB(0, 0, 0)); + printf(" If error occurs - run training with flag: -dont_show \n"); + cvNamedWindow("average loss", CV_WINDOW_NORMAL); + cvMoveWindow("average loss", 0, 0); + cvResizeWindow("average loss", img_size, img_size); + cvShowImage("average loss", img); + cvWaitKey(20); + return img; } void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches) { - int img_offset = 50; - int draw_size = img_size - img_offset; - CvFont font; - cvInitFont(&font, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA); - char char_buff[100]; - CvPoint pt1, pt2; - pt1.x = img_offset + draw_size * (float)current_batch / max_batches; - pt1.y = draw_size * (1 - avg_loss / max_img_loss); - if (pt1.y < 0) pt1.y = 1; - cvCircle(img, pt1, 1, CV_RGB(0, 0, 255), CV_FILLED, 8, 0); - - sprintf(char_buff, "current avg loss = %2.4f", avg_loss); - pt1.x = img_size / 2, pt1.y = 30; - pt2.x = pt1.x + 250, pt2.y = pt1.y + 20; - cvRectangle(img, pt1, pt2, CV_RGB(255, 255, 255), CV_FILLED, 8, 0); - pt1.y += 15; - cvPutText(img, char_buff, pt1, &font, CV_RGB(0, 0, 0)); - cvShowImage("average loss", img); - int k = cvWaitKey(20); - if (k == 's' || current_batch == (max_batches-1)) cvSaveImage("chart.jpg", img, 0); -} -#endif // OPENCV + int img_offset = 50; + int draw_size = img_size - img_offset; + CvFont font; + cvInitFont(&font, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA); + char char_buff[100]; + CvPoint pt1, pt2; + pt1.x = img_offset + draw_size * (float)current_batch / max_batches; + pt1.y = draw_size * (1 - avg_loss / max_img_loss); + if (pt1.y < 0) pt1.y = 1; + cvCircle(img, pt1, 1, CV_RGB(0, 0, 255), CV_FILLED, 8, 0); + + sprintf(char_buff, "current avg loss = %2.4f", avg_loss); + pt1.x = img_size / 2, pt1.y = 30; + pt2.x = pt1.x + 250, pt2.y = pt1.y + 20; + cvRectangle(img, pt1, pt2, CV_RGB(255, 255, 255), CV_FILLED, 8, 0); + pt1.y += 15; + cvPutText(img, char_buff, pt1, &font, CV_RGB(0, 0, 0)); + cvShowImage("average loss", img); + int k = cvWaitKey(20); + if (k == 's' || current_batch == (max_batches-1)) cvSaveImage("chart.jpg", img, 0); +} +#endif // OPENCV void transpose_image(image im) { @@ -909,15 +909,15 @@ void show_image_cv(image p, const char *name) void show_image_cv_ipl(IplImage *disp, const char *name) { - if (disp == NULL) return; - char buff[256]; - //sprintf(buff, "%s (%d)", name, windows); - sprintf(buff, "%s", name); - cvNamedWindow(buff, CV_WINDOW_NORMAL); - //cvMoveWindow(buff, 100*(windows%10) + 200*(windows/10), 100*(windows%10)); - ++windows; - cvShowImage(buff, disp); - //cvReleaseImage(&disp); + if (disp == NULL) return; + char buff[256]; + //sprintf(buff, "%s (%d)", name, windows); + sprintf(buff, "%s", name); + cvNamedWindow(buff, CV_WINDOW_NORMAL); + //cvMoveWindow(buff, 100*(windows%10) + 200*(windows/10), 100*(windows%10)); + ++windows; + cvShowImage(buff, disp); + //cvReleaseImage(&disp); } #endif @@ -966,22 +966,22 @@ image load_image_cv(char *filename, int channels) if( (src = cvLoadImage(filename, flag)) == 0 ) { - char shrinked_filename[1024]; - if (strlen(filename) >= 1024) sprintf(shrinked_filename, "name is too long"); - else sprintf(shrinked_filename, "%s", filename); - fprintf(stderr, "Cannot load image \"%s\"\n", shrinked_filename); - FILE* fw = fopen("bad.list", "a"); - fwrite(shrinked_filename, sizeof(char), strlen(shrinked_filename), fw); - char *new_line = "\n"; - fwrite(new_line, sizeof(char), strlen(new_line), fw); - fclose(fw); + char shrinked_filename[1024]; + if (strlen(filename) >= 1024) sprintf(shrinked_filename, "name is too long"); + else sprintf(shrinked_filename, "%s", filename); + fprintf(stderr, "Cannot load image \"%s\"\n", shrinked_filename); + FILE* fw = fopen("bad.list", "a"); + fwrite(shrinked_filename, sizeof(char), strlen(shrinked_filename), fw); + char *new_line = "\n"; + fwrite(new_line, sizeof(char), strlen(new_line), fw); + fclose(fw); return make_image(10,10,3); //exit(EXIT_FAILURE); } image out = ipl_to_image(src); cvReleaseImage(&src); - if (out.c > 1) - rgbgr_image(out); + if (out.c > 1) + rgbgr_image(out); return out; } @@ -996,119 +996,119 @@ image get_image_from_stream(CvCapture *cap) image get_image_from_stream_cpp(CvCapture *cap) { - //IplImage* src = cvQueryFrame(cap); - IplImage* src; - static int once = 1; - if (once) { - once = 0; - do { - src = get_webcam_frame(cap); - if (!src) return make_empty_image(0, 0, 0); - } while (src->width < 1 || src->height < 1 || src->nChannels < 1); - printf("Video stream: %d x %d \n", src->width, src->height); - } - else - src = get_webcam_frame(cap); - - if (!src) return make_empty_image(0, 0, 0); - image im = ipl_to_image(src); - rgbgr_image(im); - return im; + //IplImage* src = cvQueryFrame(cap); + IplImage* src; + static int once = 1; + if (once) { + once = 0; + do { + src = get_webcam_frame(cap); + if (!src) return make_empty_image(0, 0, 0); + } while (src->width < 1 || src->height < 1 || src->nChannels < 1); + printf("Video stream: %d x %d \n", src->width, src->height); + } + else + src = get_webcam_frame(cap); + + if (!src) return make_empty_image(0, 0, 0); + image im = ipl_to_image(src); + rgbgr_image(im); + return im; } int wait_for_stream(CvCapture *cap, IplImage* src, int dont_close) { - if (!src) { - if (dont_close) src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3); - else return 0; - } - if (src->width < 1 || src->height < 1 || src->nChannels < 1) { - if (dont_close) { - cvReleaseImage(&src); - int z = 0; - for (z = 0; z < 20; ++z) { - get_webcam_frame(cap); - cvReleaseImage(&src); - } - src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3); - } - else return 0; - } - return 1; + if (!src) { + if (dont_close) src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3); + else return 0; + } + if (src->width < 1 || src->height < 1 || src->nChannels < 1) { + if (dont_close) { + cvReleaseImage(&src); + int z = 0; + for (z = 0; z < 20; ++z) { + get_webcam_frame(cap); + cvReleaseImage(&src); + } + src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3); + } + else return 0; + } + return 1; } image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close) { - c = c ? c : 3; - IplImage* src; - if (cpp_video_capture) { - static int once = 1; - if (once) { - once = 0; - do { - src = get_webcam_frame(cap); - if (!src) return make_empty_image(0, 0, 0); - } while (src->width < 1 || src->height < 1 || src->nChannels < 1); - printf("Video stream: %d x %d \n", src->width, src->height); - } else - src = get_webcam_frame(cap); - } - else src = cvQueryFrame(cap); - - if (cpp_video_capture) - if(!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0); - IplImage* new_img = cvCreateImage(cvSize(w, h), IPL_DEPTH_8U, c); - *in_img = cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c); - cvResize(src, *in_img, CV_INTER_LINEAR); - cvResize(src, new_img, CV_INTER_LINEAR); - image im = ipl_to_image(new_img); - cvReleaseImage(&new_img); - if (cpp_video_capture) cvReleaseImage(&src); - if (c>1) - rgbgr_image(im); - return im; + c = c ? c : 3; + IplImage* src; + if (cpp_video_capture) { + static int once = 1; + if (once) { + once = 0; + do { + src = get_webcam_frame(cap); + if (!src) return make_empty_image(0, 0, 0); + } while (src->width < 1 || src->height < 1 || src->nChannels < 1); + printf("Video stream: %d x %d \n", src->width, src->height); + } else + src = get_webcam_frame(cap); + } + else src = cvQueryFrame(cap); + + if (cpp_video_capture) + if(!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0); + IplImage* new_img = cvCreateImage(cvSize(w, h), IPL_DEPTH_8U, c); + *in_img = cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c); + cvResize(src, *in_img, CV_INTER_LINEAR); + cvResize(src, new_img, CV_INTER_LINEAR); + image im = ipl_to_image(new_img); + cvReleaseImage(&new_img); + if (cpp_video_capture) cvReleaseImage(&src); + if (c>1) + rgbgr_image(im); + return im; } image get_image_from_stream_letterbox(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close) { - c = c ? c : 3; - IplImage* src; - if (cpp_video_capture) { - static int once = 1; - if (once) { - once = 0; - do { - src = get_webcam_frame(cap); - if (!src) return make_empty_image(0, 0, 0); - } while (src->width < 1 || src->height < 1 || src->nChannels < 1); - printf("Video stream: %d x %d \n", src->width, src->height); - } - else - src = get_webcam_frame(cap); - } - else src = cvQueryFrame(cap); - - if (cpp_video_capture) - if (!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0); - *in_img = cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c); - cvResize(src, *in_img, CV_INTER_LINEAR); - image tmp = ipl_to_image(src); - image im = letterbox_image(tmp, w, h); - free_image(tmp); - if (cpp_video_capture) cvReleaseImage(&src); - if (c>1) rgbgr_image(im); - return im; + c = c ? c : 3; + IplImage* src; + if (cpp_video_capture) { + static int once = 1; + if (once) { + once = 0; + do { + src = get_webcam_frame(cap); + if (!src) return make_empty_image(0, 0, 0); + } while (src->width < 1 || src->height < 1 || src->nChannels < 1); + printf("Video stream: %d x %d \n", src->width, src->height); + } + else + src = get_webcam_frame(cap); + } + else src = cvQueryFrame(cap); + + if (cpp_video_capture) + if (!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0); + *in_img = cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c); + cvResize(src, *in_img, CV_INTER_LINEAR); + image tmp = ipl_to_image(src); + image im = letterbox_image(tmp, w, h); + free_image(tmp); + if (cpp_video_capture) cvReleaseImage(&src); + if (c>1) rgbgr_image(im); + return im; } int get_stream_fps(CvCapture *cap, int cpp_video_capture) { - int fps = 25; - if (cpp_video_capture) { - fps = get_stream_fps_cpp(cap); - } - else { - fps = cvGetCaptureProperty(cap, CV_CAP_PROP_FPS); - } - return fps; + int fps = 25; + if (cpp_video_capture) { + fps = get_stream_fps_cpp(cap); + } + else { + fps = cvGetCaptureProperty(cap, CV_CAP_PROP_FPS); + } + return fps; } void save_image_jpg(image p, const char *name) @@ -1358,47 +1358,47 @@ void composite_3d(char *f1, char *f2, char *out, int delta) void fill_image(image m, float s) { - int i; - for (i = 0; i < m.h*m.w*m.c; ++i) m.data[i] = s; + int i; + for (i = 0; i < m.h*m.w*m.c; ++i) m.data[i] = s; } void letterbox_image_into(image im, int w, int h, image boxed) { - int new_w = im.w; - int new_h = im.h; - if (((float)w / im.w) < ((float)h / im.h)) { - new_w = w; - new_h = (im.h * w) / im.w; - } - else { - new_h = h; - new_w = (im.w * h) / im.h; - } - image resized = resize_image(im, new_w, new_h); - embed_image(resized, boxed, (w - new_w) / 2, (h - new_h) / 2); - free_image(resized); + int new_w = im.w; + int new_h = im.h; + if (((float)w / im.w) < ((float)h / im.h)) { + new_w = w; + new_h = (im.h * w) / im.w; + } + else { + new_h = h; + new_w = (im.w * h) / im.h; + } + image resized = resize_image(im, new_w, new_h); + embed_image(resized, boxed, (w - new_w) / 2, (h - new_h) / 2); + free_image(resized); } image letterbox_image(image im, int w, int h) { - int new_w = im.w; - int new_h = im.h; - if (((float)w / im.w) < ((float)h / im.h)) { - new_w = w; - new_h = (im.h * w) / im.w; - } - else { - new_h = h; - new_w = (im.w * h) / im.h; - } - image resized = resize_image(im, new_w, new_h); - image boxed = make_image(w, h, im.c); - fill_image(boxed, .5); - //int i; - //for(i = 0; i < boxed.w*boxed.h*boxed.c; ++i) boxed.data[i] = 0; - embed_image(resized, boxed, (w - new_w) / 2, (h - new_h) / 2); - free_image(resized); - return boxed; + int new_w = im.w; + int new_h = im.h; + if (((float)w / im.w) < ((float)h / im.h)) { + new_w = w; + new_h = (im.h * w) / im.w; + } + else { + new_h = h; + new_w = (im.w * h) / im.h; + } + image resized = resize_image(im, new_w, new_h); + image boxed = make_image(w, h, im.c); + fill_image(boxed, .5); + //int i; + //for(i = 0; i < boxed.w*boxed.h*boxed.c; ++i) boxed.data[i] = 0; + embed_image(resized, boxed, (w - new_w) / 2, (h - new_h) / 2); + free_image(resized); + return boxed; } image resize_max(image im, int max) @@ -1660,23 +1660,23 @@ void exposure_image(image im, float sat) void distort_image(image im, float hue, float sat, float val) { - if (im.c >= 3) - { - rgb_to_hsv(im); - scale_image_channel(im, 1, sat); - scale_image_channel(im, 2, val); - int i; - for(i = 0; i < im.w*im.h; ++i){ - im.data[i] = im.data[i] + hue; - if (im.data[i] > 1) im.data[i] -= 1; - if (im.data[i] < 0) im.data[i] += 1; - } - hsv_to_rgb(im); - } - else - { - scale_image_channel(im, 0, val); - } + if (im.c >= 3) + { + rgb_to_hsv(im); + scale_image_channel(im, 1, sat); + scale_image_channel(im, 2, val); + int i; + for(i = 0; i < im.w*im.h; ++i){ + im.data[i] = im.data[i] + hue; + if (im.data[i] > 1) im.data[i] -= 1; + if (im.data[i] < 0) im.data[i] += 1; + } + hsv_to_rgb(im); + } + else + { + scale_image_channel(im, 0, val); + } constrain_image(im); } @@ -1812,16 +1812,16 @@ image load_image_stb(char *filename, int channels) int w, h, c; unsigned char *data = stbi_load(filename, &w, &h, &c, channels); if (!data) { - char shrinked_filename[1024]; - if (strlen(filename) >= 1024) sprintf(shrinked_filename, "name is too long"); - else sprintf(shrinked_filename, "%s", filename); - fprintf(stderr, "Cannot load image \"%s\"\nSTB Reason: %s\n", shrinked_filename, stbi_failure_reason()); - FILE* fw = fopen("bad.list", "a"); - fwrite(shrinked_filename, sizeof(char), strlen(shrinked_filename), fw); - char *new_line = "\n"; - fwrite(new_line, sizeof(char), strlen(new_line), fw); - fclose(fw); - return make_image(10, 10, 3); + char shrinked_filename[1024]; + if (strlen(filename) >= 1024) sprintf(shrinked_filename, "name is too long"); + else sprintf(shrinked_filename, "%s", filename); + fprintf(stderr, "Cannot load image \"%s\"\nSTB Reason: %s\n", shrinked_filename, stbi_failure_reason()); + FILE* fw = fopen("bad.list", "a"); + fwrite(shrinked_filename, sizeof(char), strlen(shrinked_filename), fw); + char *new_line = "\n"; + fwrite(new_line, sizeof(char), strlen(new_line), fw); + fclose(fw); + return make_image(10, 10, 3); //exit(EXIT_FAILURE); } if(channels) c = channels; @@ -1845,14 +1845,14 @@ image load_image(char *filename, int w, int h, int c) #ifdef OPENCV #ifndef CV_VERSION_EPOCH - //image out = load_image_stb(filename, c); // OpenCV 3.x - image out = load_image_cv(filename, c); + //image out = load_image_stb(filename, c); // OpenCV 3.x + image out = load_image_cv(filename, c); #else - image out = load_image_cv(filename, c); // OpenCV 2.4.x + image out = load_image_cv(filename, c); // OpenCV 2.4.x #endif #else - image out = load_image_stb(filename, c); // without OpenCV + image out = load_image_stb(filename, c); // without OpenCV #endif if((h && w) && (h != out.h || w != out.w)){ diff --git a/src/list.c b/src/list.c index e83f63ef9ec..39e3033dde5 100644 --- a/src/list.c +++ b/src/list.c @@ -5,11 +5,11 @@ list *make_list() { - list *l = malloc(sizeof(list)); - l->size = 0; - l->front = 0; - l->back = 0; - return l; + list *l = malloc(sizeof(list)); + l->size = 0; + l->front = 0; + l->back = 0; + return l; } /* @@ -40,55 +40,55 @@ void *list_pop(list *l){ void list_insert(list *l, void *val) { - node *new = malloc(sizeof(node)); - new->val = val; - new->next = 0; + node *new = malloc(sizeof(node)); + new->val = val; + new->next = 0; - if(!l->back){ - l->front = new; - new->prev = 0; - }else{ - l->back->next = new; - new->prev = l->back; - } - l->back = new; - ++l->size; + if(!l->back){ + l->front = new; + new->prev = 0; + }else{ + l->back->next = new; + new->prev = l->back; + } + l->back = new; + ++l->size; } void free_node(node *n) { - node *next; - while(n) { - next = n->next; - free(n); - n = next; - } + node *next; + while(n) { + next = n->next; + free(n); + n = next; + } } void free_list(list *l) { - free_node(l->front); - free(l); + free_node(l->front); + free(l); } void free_list_contents(list *l) { - node *n = l->front; - while(n){ - free(n->val); - n = n->next; - } + node *n = l->front; + while(n){ + free(n->val); + n = n->next; + } } void free_list_contents_kvp(list *l) { - node *n = l->front; - while (n) { - kvp *p = n->val; - free(p->key); - free(n->val); - n = n->next; - } + node *n = l->front; + while (n) { + kvp *p = n->val; + free(p->key); + free(n->val); + n = n->next; + } } void **list_to_array(list *l) diff --git a/src/network.c b/src/network.c index d135a29fc67..3df837d67aa 100644 --- a/src/network.c +++ b/src/network.c @@ -33,19 +33,19 @@ network *load_network_custom(char *cfg, char *weights, int clear, int batch) { - printf(" Try to load cfg: %s, weights: %s, clear = %d \n", cfg, weights, clear); - network *net = calloc(1, sizeof(network)); - *net = parse_network_cfg_custom(cfg, batch); - if (weights && weights[0] != 0) { - load_weights(net, weights); - } - if (clear) (*net->seen) = 0; - return net; + printf(" Try to load cfg: %s, weights: %s, clear = %d \n", cfg, weights, clear); + network *net = calloc(1, sizeof(network)); + *net = parse_network_cfg_custom(cfg, batch); + if (weights && weights[0] != 0) { + load_weights(net, weights); + } + if (clear) (*net->seen) = 0; + return net; } network *load_network(char *cfg, char *weights, int clear) { - return load_network_custom(cfg, weights, clear, 0); + return load_network_custom(cfg, weights, clear, 0); } int get_current_batch(network net) @@ -67,23 +67,23 @@ void reset_momentum(network net) void reset_network_state(network *net, int b) { - int i; - for (i = 0; i < net->n; ++i) { + int i; + for (i = 0; i < net->n; ++i) { #ifdef GPU - layer l = net->layers[i]; - if (l.state_gpu) { - fill_ongpu(l.outputs, 0, l.state_gpu + l.outputs*b, 1); - } - if (l.h_gpu) { - fill_ongpu(l.outputs, 0, l.h_gpu + l.outputs*b, 1); - } + layer l = net->layers[i]; + if (l.state_gpu) { + fill_ongpu(l.outputs, 0, l.state_gpu + l.outputs*b, 1); + } + if (l.h_gpu) { + fill_ongpu(l.outputs, 0, l.h_gpu + l.outputs*b, 1); + } #endif - } + } } void reset_rnn(network *net) { - reset_network_state(net, 0); + reset_network_state(net, 0); } float get_current_rate(network net) @@ -91,7 +91,7 @@ float get_current_rate(network net) int batch_num = get_current_batch(net); int i; float rate; - if (batch_num < net.burn_in) return net.learning_rate * pow((float)batch_num / net.burn_in, net.power); + if (batch_num < net.burn_in) return net.learning_rate * pow((float)batch_num / net.burn_in, net.power); switch (net.policy) { case CONSTANT: return net.learning_rate; @@ -108,7 +108,7 @@ float get_current_rate(network net) case EXP: return net.learning_rate * pow(net.gamma, batch_num); case POLY: - return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power); + return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power); //if (batch_num < net.burn_in) return net.learning_rate * pow((float)batch_num / net.burn_in, net.power); //return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power); case RANDOM: @@ -182,10 +182,10 @@ network make_network(int n) net.input_gpu = calloc(1, sizeof(float *)); net.truth_gpu = calloc(1, sizeof(float *)); - net.input16_gpu = calloc(1, sizeof(float *)); - net.output16_gpu = calloc(1, sizeof(float *)); - net.max_input16_size = calloc(1, sizeof(size_t)); - net.max_output16_size = calloc(1, sizeof(size_t)); + net.input16_gpu = calloc(1, sizeof(float *)); + net.output16_gpu = calloc(1, sizeof(float *)); + net.max_input16_size = calloc(1, sizeof(size_t)); + net.max_output16_size = calloc(1, sizeof(size_t)); #endif return net; } @@ -362,20 +362,20 @@ void set_batch_network(network *net, int b) net->layers[i].batch = b; #ifdef CUDNN if(net->layers[i].type == CONVOLUTIONAL){ - cudnn_convolutional_setup(net->layers + i, cudnn_fastest); - /* - layer *l = net->layers + i; + cudnn_convolutional_setup(net->layers + i, cudnn_fastest); + /* + layer *l = net->layers + i; cudnn_convolutional_setup(l, cudnn_fastest); - // check for excessive memory consumption - size_t free_byte; - size_t total_byte; - check_error(cudaMemGetInfo(&free_byte, &total_byte)); - if (l->workspace_size > free_byte || l->workspace_size >= total_byte / 2) { - printf(" used slow CUDNN algo without Workspace! \n"); - cudnn_convolutional_setup(l, cudnn_smallest); - l->workspace_size = get_workspace_size(*l); - } - */ + // check for excessive memory consumption + size_t free_byte; + size_t total_byte; + check_error(cudaMemGetInfo(&free_byte, &total_byte)); + if (l->workspace_size > free_byte || l->workspace_size >= total_byte / 2) { + printf(" used slow CUDNN algo without Workspace! \n"); + cudnn_convolutional_setup(l, cudnn_smallest); + l->workspace_size = get_workspace_size(*l); + } + */ } #endif } @@ -387,12 +387,12 @@ int resize_network(network *net, int w, int h) cuda_set_device(net->gpu_index); if(gpu_index >= 0){ cuda_free(net->workspace); - if (net->input_gpu) { - cuda_free(*net->input_gpu); - *net->input_gpu = 0; - cuda_free(*net->truth_gpu); - *net->truth_gpu = 0; - } + if (net->input_gpu) { + cuda_free(*net->input_gpu); + *net->input_gpu = 0; + cuda_free(*net->truth_gpu); + *net->truth_gpu = 0; + } } #endif int i; @@ -405,7 +405,7 @@ int resize_network(network *net, int w, int h) //fflush(stderr); for (i = 0; i < net->n; ++i){ layer l = net->layers[i]; - //printf(" %d: layer = %d,", i, l.type); + //printf(" %d: layer = %d,", i, l.type); if(l.type == CONVOLUTIONAL){ resize_convolutional_layer(&l, w, h); }else if(l.type == CROP){ @@ -414,14 +414,14 @@ int resize_network(network *net, int w, int h) resize_maxpool_layer(&l, w, h); }else if(l.type == REGION){ resize_region_layer(&l, w, h); - }else if (l.type == YOLO) { - resize_yolo_layer(&l, w, h); + }else if (l.type == YOLO) { + resize_yolo_layer(&l, w, h); }else if(l.type == ROUTE){ resize_route_layer(&l, net); - }else if (l.type == SHORTCUT) { - resize_shortcut_layer(&l, w, h); - }else if (l.type == UPSAMPLE) { - resize_upsample_layer(&l, w, h); + }else if (l.type == SHORTCUT) { + resize_shortcut_layer(&l, w, h); + }else if (l.type == UPSAMPLE) { + resize_upsample_layer(&l, w, h); }else if(l.type == REORG){ resize_reorg_layer(&l, w, h); }else if(l.type == AVGPOOL){ @@ -431,7 +431,7 @@ int resize_network(network *net, int w, int h) }else if(l.type == COST){ resize_cost_layer(&l, inputs); }else{ - fprintf(stderr, "Resizing type %d \n", (int)l.type); + fprintf(stderr, "Resizing type %d \n", (int)l.type); error("Cannot resize this type of layer"); } if(l.workspace_size > workspace_size) workspace_size = l.workspace_size; @@ -443,9 +443,9 @@ int resize_network(network *net, int w, int h) } #ifdef GPU if(gpu_index >= 0){ - printf(" try to allocate workspace = %zu * sizeof(float), ", workspace_size / sizeof(float) + 1); + printf(" try to allocate workspace = %zu * sizeof(float), ", workspace_size / sizeof(float) + 1); net->workspace = cuda_make_array(0, workspace_size/sizeof(float) + 1); - printf(" CUDA allocate done! \n"); + printf(" CUDA allocate done! \n"); }else { free(net->workspace); net->workspace = calloc(1, workspace_size); @@ -551,112 +551,112 @@ float *network_predict(network net, float *input) int num_detections(network *net, float thresh) { - int i; - int s = 0; - for (i = 0; i < net->n; ++i) { - layer l = net->layers[i]; - if (l.type == YOLO) { - s += yolo_num_detections(l, thresh); - } - if (l.type == DETECTION || l.type == REGION) { - s += l.w*l.h*l.n; - } - } - return s; + int i; + int s = 0; + for (i = 0; i < net->n; ++i) { + layer l = net->layers[i]; + if (l.type == YOLO) { + s += yolo_num_detections(l, thresh); + } + if (l.type == DETECTION || l.type == REGION) { + s += l.w*l.h*l.n; + } + } + return s; } detection *make_network_boxes(network *net, float thresh, int *num) { - layer l = net->layers[net->n - 1]; - int i; - int nboxes = num_detections(net, thresh); - if (num) *num = nboxes; - detection *dets = calloc(nboxes, sizeof(detection)); - for (i = 0; i < nboxes; ++i) { - dets[i].prob = calloc(l.classes, sizeof(float)); - if (l.coords > 4) { - dets[i].mask = calloc(l.coords - 4, sizeof(float)); - } - } - return dets; + layer l = net->layers[net->n - 1]; + int i; + int nboxes = num_detections(net, thresh); + if (num) *num = nboxes; + detection *dets = calloc(nboxes, sizeof(detection)); + for (i = 0; i < nboxes; ++i) { + dets[i].prob = calloc(l.classes, sizeof(float)); + if (l.coords > 4) { + dets[i].mask = calloc(l.coords - 4, sizeof(float)); + } + } + return dets; } void custom_get_region_detections(layer l, int w, int h, int net_w, int net_h, float thresh, int *map, float hier, int relative, detection *dets, int letter) { - box *boxes = calloc(l.w*l.h*l.n, sizeof(box)); - float **probs = calloc(l.w*l.h*l.n, sizeof(float *)); - int i, j; - for (j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float)); - get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, map); - for (j = 0; j < l.w*l.h*l.n; ++j) { - dets[j].classes = l.classes; - dets[j].bbox = boxes[j]; - dets[j].objectness = 1; - for (i = 0; i < l.classes; ++i) { - dets[j].prob[i] = probs[j][i]; - } - } + box *boxes = calloc(l.w*l.h*l.n, sizeof(box)); + float **probs = calloc(l.w*l.h*l.n, sizeof(float *)); + int i, j; + for (j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float)); + get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, map); + for (j = 0; j < l.w*l.h*l.n; ++j) { + dets[j].classes = l.classes; + dets[j].bbox = boxes[j]; + dets[j].objectness = 1; + for (i = 0; i < l.classes; ++i) { + dets[j].prob[i] = probs[j][i]; + } + } - free(boxes); - free_ptrs((void **)probs, l.w*l.h*l.n); + free(boxes); + free_ptrs((void **)probs, l.w*l.h*l.n); - //correct_region_boxes(dets, l.w*l.h*l.n, w, h, net_w, net_h, relative); - correct_yolo_boxes(dets, l.w*l.h*l.n, w, h, net_w, net_h, relative, letter); + //correct_region_boxes(dets, l.w*l.h*l.n, w, h, net_w, net_h, relative); + correct_yolo_boxes(dets, l.w*l.h*l.n, w, h, net_w, net_h, relative, letter); } void fill_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, detection *dets, int letter) { - int prev_classes = -1; - int j; - for (j = 0; j < net->n; ++j) { - layer l = net->layers[j]; - if (l.type == YOLO) { - int count = get_yolo_detections(l, w, h, net->w, net->h, thresh, map, relative, dets, letter); - dets += count; - if (prev_classes < 0) prev_classes = l.classes; - else if (prev_classes != l.classes) { - printf(" Error: Different [yolo] layers have different number of classes = %d and %d - check your cfg-file! \n", - prev_classes, l.classes); - } - } - if (l.type == REGION) { - custom_get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets, letter); - //get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets); - dets += l.w*l.h*l.n; - } - if (l.type == DETECTION) { - get_detection_detections(l, w, h, thresh, dets); - dets += l.w*l.h*l.n; - } - } + int prev_classes = -1; + int j; + for (j = 0; j < net->n; ++j) { + layer l = net->layers[j]; + if (l.type == YOLO) { + int count = get_yolo_detections(l, w, h, net->w, net->h, thresh, map, relative, dets, letter); + dets += count; + if (prev_classes < 0) prev_classes = l.classes; + else if (prev_classes != l.classes) { + printf(" Error: Different [yolo] layers have different number of classes = %d and %d - check your cfg-file! \n", + prev_classes, l.classes); + } + } + if (l.type == REGION) { + custom_get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets, letter); + //get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets); + dets += l.w*l.h*l.n; + } + if (l.type == DETECTION) { + get_detection_detections(l, w, h, thresh, dets); + dets += l.w*l.h*l.n; + } + } } detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter) { - detection *dets = make_network_boxes(net, thresh, num); - fill_network_boxes(net, w, h, thresh, hier, map, relative, dets, letter); - return dets; + detection *dets = make_network_boxes(net, thresh, num); + fill_network_boxes(net, w, h, thresh, hier, map, relative, dets, letter); + return dets; } void free_detections(detection *dets, int n) { - int i; - for (i = 0; i < n; ++i) { - free(dets[i].prob); - if (dets[i].mask) free(dets[i].mask); - } - free(dets); + int i; + for (i = 0; i < n; ++i) { + free(dets[i].prob); + if (dets[i].mask) free(dets[i].mask); + } + free(dets); } float *network_predict_image(network *net, image im) { - //image imr = letterbox_image(im, net->w, net->h); - image imr = resize_image(im, net->w, net->h); - set_batch_network(net, 1); - float *p = network_predict(*net, imr.data); - free_image(imr); - return p; + //image imr = letterbox_image(im, net->w, net->h); + image imr = resize_image(im, net->w, net->h); + set_batch_network(net, 1); + float *p = network_predict(*net, imr.data); + free_image(imr); + return p; } int network_width(network *net) { return net->w; } @@ -780,70 +780,70 @@ float network_accuracy_multi(network net, data d, int n) void free_network(network net) { - int i; - for (i = 0; i < net.n; ++i) { - free_layer(net.layers[i]); - } - free(net.layers); + int i; + for (i = 0; i < net.n; ++i) { + free_layer(net.layers[i]); + } + free(net.layers); - free(net.scales); - free(net.steps); - free(net.seen); + free(net.scales); + free(net.steps); + free(net.seen); #ifdef GPU - if (gpu_index >= 0) cuda_free(net.workspace); - else free(net.workspace); - if (*net.input_gpu) cuda_free(*net.input_gpu); - if (*net.truth_gpu) cuda_free(*net.truth_gpu); - if (net.input_gpu) free(net.input_gpu); - if (net.truth_gpu) free(net.truth_gpu); - - if (*net.input16_gpu) cuda_free(*net.input16_gpu); - if (*net.output16_gpu) cuda_free(*net.output16_gpu); - if (net.input16_gpu) free(net.input16_gpu); - if (net.output16_gpu) free(net.output16_gpu); - if (net.max_input16_size) free(net.max_input16_size); - if (net.max_output16_size) free(net.max_output16_size); + if (gpu_index >= 0) cuda_free(net.workspace); + else free(net.workspace); + if (*net.input_gpu) cuda_free(*net.input_gpu); + if (*net.truth_gpu) cuda_free(*net.truth_gpu); + if (net.input_gpu) free(net.input_gpu); + if (net.truth_gpu) free(net.truth_gpu); + + if (*net.input16_gpu) cuda_free(*net.input16_gpu); + if (*net.output16_gpu) cuda_free(*net.output16_gpu); + if (net.input16_gpu) free(net.input16_gpu); + if (net.output16_gpu) free(net.output16_gpu); + if (net.max_input16_size) free(net.max_input16_size); + if (net.max_output16_size) free(net.max_output16_size); #else - free(net.workspace); + free(net.workspace); #endif } void fuse_conv_batchnorm(network net) { - int j; - for (j = 0; j < net.n; ++j) { - layer *l = &net.layers[j]; + int j; + for (j = 0; j < net.n; ++j) { + layer *l = &net.layers[j]; - if (l->type == CONVOLUTIONAL) { - //printf(" Merges Convolutional-%d and batch_norm \n", j); + if (l->type == CONVOLUTIONAL) { + //printf(" Merges Convolutional-%d and batch_norm \n", j); - if (l->batch_normalize) { - int f; - for (f = 0; f < l->n; ++f) - { - l->biases[f] = l->biases[f] - (double)l->scales[f] * l->rolling_mean[f] / (sqrt((double)l->rolling_variance[f]) + .000001f); + if (l->batch_normalize) { + int f; + for (f = 0; f < l->n; ++f) + { + l->biases[f] = l->biases[f] - (double)l->scales[f] * l->rolling_mean[f] / (sqrt((double)l->rolling_variance[f]) + .000001f); - const size_t filter_size = l->size*l->size*l->c; - int i; - for (i = 0; i < filter_size; ++i) { - int w_index = f*filter_size + i; + const size_t filter_size = l->size*l->size*l->c; + int i; + for (i = 0; i < filter_size; ++i) { + int w_index = f*filter_size + i; - l->weights[w_index] = (double)l->weights[w_index] * l->scales[f] / (sqrt((double)l->rolling_variance[f]) + .000001f); - } - } + l->weights[w_index] = (double)l->weights[w_index] * l->scales[f] / (sqrt((double)l->rolling_variance[f]) + .000001f); + } + } - l->batch_normalize = 0; + l->batch_normalize = 0; #ifdef GPU - if (gpu_index >= 0) { - push_convolutional_layer(*l); - } + if (gpu_index >= 0) { + push_convolutional_layer(*l); + } #endif - } - } - else { - //printf(" Fusion skip layer type: %d \n", l->type); - } - } + } + } + else { + //printf(" Fusion skip layer type: %d \n", l->type); + } + } } diff --git a/src/network_kernels.cu b/src/network_kernels.cu index a11d61f32bc..681542f25db 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -55,23 +55,23 @@ void forward_network_gpu(network net, network_state state) fill_ongpu(l.outputs * l.batch, 0, l.delta_gpu, 1); } l.forward_gpu(l, state); - if(net.wait_stream) - cudaStreamSynchronize(get_cuda_stream()); + if(net.wait_stream) + cudaStreamSynchronize(get_cuda_stream()); state.input = l.output_gpu; /* - cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); - if (l.out_w >= 0 && l.out_h >= 1 && l.c >= 3) { - int j; - for (j = 0; j < l.out_c; ++j) { - image img = make_image(l.out_w, l.out_h, 3); - memcpy(img.data, l.output+ l.out_w*l.out_h*j, l.out_w*l.out_h * 1 * sizeof(float)); - char buff[256]; - sprintf(buff, "layer-%d slice-%d", i, j); - show_image(img, buff); - } - cvWaitKey(0); // wait press-key in console - cvDestroyAllWindows(); - } + cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); + if (l.out_w >= 0 && l.out_h >= 1 && l.c >= 3) { + int j; + for (j = 0; j < l.out_c; ++j) { + image img = make_image(l.out_w, l.out_h, 3); + memcpy(img.data, l.output+ l.out_w*l.out_h*j, l.out_w*l.out_h * 1 * sizeof(float)); + char buff[256]; + sprintf(buff, "layer-%d slice-%d", i, j); + show_image(img, buff); + } + cvWaitKey(0); // wait press-key in console + cvDestroyAllWindows(); + } */ } } @@ -133,14 +133,14 @@ void forward_backward_network_gpu(network net, float *x, float *y) state.truth = *net.truth_gpu; state.train = 1; #ifdef CUDNN_HALF - int i; - for (i = 0; i < net.n; ++i) { - layer l = net.layers[i]; - cuda_convert_f32_to_f16(l.weights_gpu, l.c*l.n*l.size*l.size, l.weights_gpu16); - } + int i; + for (i = 0; i < net.n; ++i) { + layer l = net.layers[i]; + cuda_convert_f32_to_f16(l.weights_gpu, l.c*l.n*l.size*l.size, l.weights_gpu16); + } #endif forward_network_gpu(net, state); - //cudaStreamSynchronize(get_cuda_stream()); + //cudaStreamSynchronize(get_cuda_stream()); backward_network_gpu(net, state); } @@ -421,8 +421,8 @@ float *get_network_output_gpu(network net) float *network_predict_gpu(network net, float *input) { - if (net.gpu_index != cuda_get_device()) - cuda_set_device(net.gpu_index); + if (net.gpu_index != cuda_get_device()) + cuda_set_device(net.gpu_index); int size = get_network_input_size(net) * net.batch; network_state state; state.index = 0; diff --git a/src/option_list.c b/src/option_list.c index 3e835de265d..dfab81b04fa 100644 --- a/src/option_list.c +++ b/src/option_list.c @@ -34,21 +34,21 @@ list *read_data_cfg(char *filename) metadata get_metadata(char *file) { - metadata m = { 0 }; - list *options = read_data_cfg(file); + metadata m = { 0 }; + list *options = read_data_cfg(file); - char *name_list = option_find_str(options, "names", 0); - if (!name_list) name_list = option_find_str(options, "labels", 0); - if (!name_list) { - fprintf(stderr, "No names or labels found\n"); - } - else { - m.names = get_labels(name_list); - } - m.classes = option_find_int(options, "classes", 2); - free_list(options); - printf("Loaded - names_list: %s, classes = %d \n", name_list, m.classes); - return m; + char *name_list = option_find_str(options, "names", 0); + if (!name_list) name_list = option_find_str(options, "labels", 0); + if (!name_list) { + fprintf(stderr, "No names or labels found\n"); + } + else { + m.names = get_labels(name_list); + } + m.classes = option_find_int(options, "classes", 2); + free_list(options); + printf("Loaded - names_list: %s, classes = %d \n", name_list, m.classes); + return m; } int read_option(char *s, list *options) diff --git a/src/parser.c b/src/parser.c index 1a324078abe..d91b1cab2fa 100644 --- a/src/parser.c +++ b/src/parser.c @@ -49,7 +49,7 @@ LAYER_TYPE string_to_layer_type(char * type) if (strcmp(type, "[cost]")==0) return COST; if (strcmp(type, "[detection]")==0) return DETECTION; if (strcmp(type, "[region]")==0) return REGION; - if (strcmp(type, "[yolo]") == 0) return YOLO; + if (strcmp(type, "[yolo]") == 0) return YOLO; if (strcmp(type, "[local]")==0) return LOCAL; if (strcmp(type, "[conv]")==0 || strcmp(type, "[convolutional]")==0) return CONVOLUTIONAL; @@ -64,7 +64,7 @@ LAYER_TYPE string_to_layer_type(char * type) if (strcmp(type, "[max]")==0 || strcmp(type, "[maxpool]")==0) return MAXPOOL; if (strcmp(type, "[reorg]")==0) return REORG; - if (strcmp(type, "[reorg_old]") == 0) return REORG_OLD; + if (strcmp(type, "[reorg_old]") == 0) return REORG_OLD; if (strcmp(type, "[avg]")==0 || strcmp(type, "[avgpool]")==0) return AVGPOOL; if (strcmp(type, "[dropout]")==0) return DROPOUT; @@ -74,7 +74,7 @@ LAYER_TYPE string_to_layer_type(char * type) if (strcmp(type, "[soft]")==0 || strcmp(type, "[softmax]")==0) return SOFTMAX; if (strcmp(type, "[route]")==0) return ROUTE; - if (strcmp(type, "[upsample]") == 0) return UPSAMPLE; + if (strcmp(type, "[upsample]") == 0) return UPSAMPLE; return BLANK; } @@ -241,68 +241,68 @@ softmax_layer parse_softmax(list *options, size_params params) int *parse_yolo_mask(char *a, int *num) { - int *mask = 0; - if (a) { - int len = strlen(a); - int n = 1; - int i; - for (i = 0; i < len; ++i) { - if (a[i] == ',') ++n; - } - mask = calloc(n, sizeof(int)); - for (i = 0; i < n; ++i) { - int val = atoi(a); - mask[i] = val; - a = strchr(a, ',') + 1; - } - *num = n; - } - return mask; + int *mask = 0; + if (a) { + int len = strlen(a); + int n = 1; + int i; + for (i = 0; i < len; ++i) { + if (a[i] == ',') ++n; + } + mask = calloc(n, sizeof(int)); + for (i = 0; i < n; ++i) { + int val = atoi(a); + mask[i] = val; + a = strchr(a, ',') + 1; + } + *num = n; + } + return mask; } layer parse_yolo(list *options, size_params params) { - int classes = option_find_int(options, "classes", 20); - int total = option_find_int(options, "num", 1); - int num = total; - - char *a = option_find_str(options, "mask", 0); - int *mask = parse_yolo_mask(a, &num); - int max_boxes = option_find_int_quiet(options, "max", 90); - layer l = make_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes, max_boxes); - if (l.outputs != params.inputs) { - printf("Error: l.outputs == params.inputs \n"); - printf("filters= in the [convolutional]-layer doesn't correspond to classes= or mask= in [yolo]-layer \n"); - exit(EXIT_FAILURE); - } - //assert(l.outputs == params.inputs); - - //l.max_boxes = option_find_int_quiet(options, "max", 90); - l.jitter = option_find_float(options, "jitter", .2); - l.focal_loss = option_find_int_quiet(options, "focal_loss", 0); - - l.ignore_thresh = option_find_float(options, "ignore_thresh", .5); - l.truth_thresh = option_find_float(options, "truth_thresh", 1); - l.random = option_find_int_quiet(options, "random", 0); - - char *map_file = option_find_str(options, "map", 0); - if (map_file) l.map = read_map(map_file); - - a = option_find_str(options, "anchors", 0); - if (a) { - int len = strlen(a); - int n = 1; - int i; - for (i = 0; i < len; ++i) { - if (a[i] == ',') ++n; - } - for (i = 0; i < n && i < total*2; ++i) { - float bias = atof(a); - l.biases[i] = bias; - a = strchr(a, ',') + 1; - } - } - return l; + int classes = option_find_int(options, "classes", 20); + int total = option_find_int(options, "num", 1); + int num = total; + + char *a = option_find_str(options, "mask", 0); + int *mask = parse_yolo_mask(a, &num); + int max_boxes = option_find_int_quiet(options, "max", 90); + layer l = make_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes, max_boxes); + if (l.outputs != params.inputs) { + printf("Error: l.outputs == params.inputs \n"); + printf("filters= in the [convolutional]-layer doesn't correspond to classes= or mask= in [yolo]-layer \n"); + exit(EXIT_FAILURE); + } + //assert(l.outputs == params.inputs); + + //l.max_boxes = option_find_int_quiet(options, "max", 90); + l.jitter = option_find_float(options, "jitter", .2); + l.focal_loss = option_find_int_quiet(options, "focal_loss", 0); + + l.ignore_thresh = option_find_float(options, "ignore_thresh", .5); + l.truth_thresh = option_find_float(options, "truth_thresh", 1); + l.random = option_find_int_quiet(options, "random", 0); + + char *map_file = option_find_str(options, "map", 0); + if (map_file) l.map = read_map(map_file); + + a = option_find_str(options, "anchors", 0); + if (a) { + int len = strlen(a); + int n = 1; + int i; + for (i = 0; i < len; ++i) { + if (a[i] == ',') ++n; + } + for (i = 0; i < n && i < total*2; ++i) { + float bias = atof(a); + l.biases[i] = bias; + a = strchr(a, ',') + 1; + } + } + return l; } layer parse_region(list *options, size_params params) @@ -310,21 +310,21 @@ layer parse_region(list *options, size_params params) int coords = option_find_int(options, "coords", 4); int classes = option_find_int(options, "classes", 20); int num = option_find_int(options, "num", 1); - int max_boxes = option_find_int_quiet(options, "max", 90); + int max_boxes = option_find_int_quiet(options, "max", 90); layer l = make_region_layer(params.batch, params.w, params.h, num, classes, coords, max_boxes); - if (l.outputs != params.inputs) { - printf("Error: l.outputs == params.inputs \n"); - printf("filters= in the [convolutional]-layer doesn't correspond to classes= or num= in [region]-layer \n"); - exit(EXIT_FAILURE); - } + if (l.outputs != params.inputs) { + printf("Error: l.outputs == params.inputs \n"); + printf("filters= in the [convolutional]-layer doesn't correspond to classes= or num= in [region]-layer \n"); + exit(EXIT_FAILURE); + } //assert(l.outputs == params.inputs); l.log = option_find_int_quiet(options, "log", 0); l.sqrt = option_find_int_quiet(options, "sqrt", 0); l.softmax = option_find_int(options, "softmax", 0); - l.focal_loss = option_find_int_quiet(options, "focal_loss", 0); + l.focal_loss = option_find_int_quiet(options, "focal_loss", 0); //l.max_boxes = option_find_int_quiet(options, "max",30); l.jitter = option_find_float(options, "jitter", .2); l.rescore = option_find_int_quiet(options, "rescore",0); @@ -337,7 +337,7 @@ layer parse_region(list *options, size_params params) l.coord_scale = option_find_float(options, "coord_scale", 1); l.object_scale = option_find_float(options, "object_scale", 1); l.noobject_scale = option_find_float(options, "noobject_scale", 1); - l.mask_scale = option_find_float(options, "mask_scale", 1); + l.mask_scale = option_find_float(options, "mask_scale", 1); l.class_scale = option_find_float(options, "class_scale", 1); l.bias_match = option_find_int_quiet(options, "bias_match",0); @@ -438,19 +438,19 @@ layer parse_reorg(list *options, size_params params) layer parse_reorg_old(list *options, size_params params) { - printf("\n reorg_old \n"); - int stride = option_find_int(options, "stride", 1); - int reverse = option_find_int_quiet(options, "reverse", 0); - - int batch, h, w, c; - h = params.h; - w = params.w; - c = params.c; - batch = params.batch; - if (!(h && w && c)) error("Layer before reorg layer must output image."); - - layer layer = make_reorg_old_layer(batch, w, h, c, stride, reverse); - return layer; + printf("\n reorg_old \n"); + int stride = option_find_int(options, "stride", 1); + int reverse = option_find_int_quiet(options, "reverse", 0); + + int batch, h, w, c; + h = params.h; + w = params.w; + c = params.c; + batch = params.batch; + if (!(h && w && c)) error("Layer before reorg layer must output image."); + + layer layer = make_reorg_old_layer(batch, w, h, c, stride, reverse); + return layer; } maxpool_layer parse_maxpool(list *options, size_params params) @@ -547,10 +547,10 @@ layer parse_activation(list *options, size_params params) layer parse_upsample(list *options, size_params params, network net) { - int stride = option_find_int(options, "stride", 2); - layer l = make_upsample_layer(params.batch, params.w, params.h, params.c, stride); - l.scale = option_find_float_quiet(options, "scale", 1); - return l; + int stride = option_find_int(options, "stride", 2); + layer l = make_upsample_layer(params.batch, params.w, params.h, params.c, stride); + l.scale = option_find_float_quiet(options, "scale", 1); + return l; } route_layer parse_route(list *options, size_params params, network net) @@ -632,15 +632,15 @@ void parse_net_options(list *options, network *net) net->inputs = option_find_int_quiet(options, "inputs", net->h * net->w * net->c); net->max_crop = option_find_int_quiet(options, "max_crop",net->w*2); net->min_crop = option_find_int_quiet(options, "min_crop",net->w); - net->flip = option_find_int_quiet(options, "flip", 1); + net->flip = option_find_int_quiet(options, "flip", 1); - net->small_object = option_find_int_quiet(options, "small_object", 0); + net->small_object = option_find_int_quiet(options, "small_object", 0); net->angle = option_find_float_quiet(options, "angle", 0); net->aspect = option_find_float_quiet(options, "aspect", 1); net->saturation = option_find_float_quiet(options, "saturation", 1); net->exposure = option_find_float_quiet(options, "exposure", 1); net->hue = option_find_float_quiet(options, "hue", 0); - net->power = option_find_float_quiet(options, "power", 4); + net->power = option_find_float_quiet(options, "power", 4); if(!net->inputs && !(net->h && net->w && net->c)) error("No input parameters supplied"); @@ -648,7 +648,7 @@ void parse_net_options(list *options, network *net) net->policy = get_policy(policy_s); net->burn_in = option_find_int_quiet(options, "burn_in", 0); #ifdef CUDNN_HALF - net->burn_in = 0; + net->burn_in = 0; #endif if(net->policy == STEP){ net->step = option_find_int(options, "step", 1); @@ -696,7 +696,7 @@ int is_network(section *s) network parse_network_cfg(char *filename) { - return parse_network_cfg_custom(filename, 0); + return parse_network_cfg_custom(filename, 0); } network parse_network_cfg_custom(char *filename, int batch) @@ -717,12 +717,12 @@ network parse_network_cfg_custom(char *filename, int batch) params.w = net.w; params.c = net.c; params.inputs = net.inputs; - if (batch > 0) net.batch = batch; + if (batch > 0) net.batch = batch; params.batch = net.batch; params.time_steps = net.time_steps; params.net = net; - float bflops = 0; + float bflops = 0; size_t workspace_size = 0; n = n->next; int count = 0; @@ -755,8 +755,8 @@ network parse_network_cfg_custom(char *filename, int batch) l = parse_cost(options, params); }else if(lt == REGION){ l = parse_region(options, params); - }else if (lt == YOLO) { - l = parse_yolo(options, params); + }else if (lt == YOLO) { + l = parse_yolo(options, params); }else if(lt == DETECTION){ l = parse_detection(options, params); }else if(lt == SOFTMAX){ @@ -769,15 +769,15 @@ network parse_network_cfg_custom(char *filename, int batch) }else if(lt == MAXPOOL){ l = parse_maxpool(options, params); }else if(lt == REORG){ - l = parse_reorg(options, params); } - else if (lt == REORG_OLD) { - l = parse_reorg_old(options, params); + l = parse_reorg(options, params); } + else if (lt == REORG_OLD) { + l = parse_reorg_old(options, params); }else if(lt == AVGPOOL){ l = parse_avgpool(options, params); }else if(lt == ROUTE){ l = parse_route(options, params, net); - }else if (lt == UPSAMPLE) { - l = parse_upsample(options, params, net); + }else if (lt == UPSAMPLE) { + l = parse_upsample(options, params, net); }else if(lt == SHORTCUT){ l = parse_shortcut(options, params, net); }else if(lt == DROPOUT){ @@ -807,12 +807,12 @@ network parse_network_cfg_custom(char *filename, int batch) params.c = l.out_c; params.inputs = l.outputs; } - if (l.bflops > 0) bflops += l.bflops; + if (l.bflops > 0) bflops += l.bflops; } free_list(sections); net.outputs = get_network_output_size(net); net.output = get_network_output(net); - printf("Total BFLOPS %5.3f \n", bflops); + printf("Total BFLOPS %5.3f \n", bflops); if(workspace_size){ //printf("%ld\n", workspace_size); #ifdef GPU @@ -825,11 +825,11 @@ network parse_network_cfg_custom(char *filename, int batch) net.workspace = calloc(1, workspace_size); #endif } - LAYER_TYPE lt = net.layers[net.n - 1].type; - if ((net.w % 32 != 0 || net.h % 32 != 0) && (lt == YOLO || lt == REGION || lt == DETECTION)) { - printf("\n Warning: width=%d and height=%d in cfg-file must be divisible by 32 for default networks Yolo v1/v2/v3!!! \n\n", - net.w, net.h); - } + LAYER_TYPE lt = net.layers[net.n - 1].type; + if ((net.w % 32 != 0 || net.h % 32 != 0) && (lt == YOLO || lt == REGION || lt == DETECTION)) { + printf("\n Warning: width=%d and height=%d in cfg-file must be divisible by 32 for default networks Yolo v1/v2/v3!!! \n\n", + net.w, net.h); + } return net; } @@ -1160,16 +1160,16 @@ void load_weights_upto(network *net, char *filename, int cutoff) fread(&major, sizeof(int), 1, fp); fread(&minor, sizeof(int), 1, fp); fread(&revision, sizeof(int), 1, fp); - if ((major * 10 + minor) >= 2) { - printf("\n seen 64 \n"); - uint64_t iseen = 0; - fread(&iseen, sizeof(uint64_t), 1, fp); - *net->seen = iseen; - } - else { - printf("\n seen 32 \n"); - fread(net->seen, sizeof(int), 1, fp); - } + if ((major * 10 + minor) >= 2) { + printf("\n seen 64 \n"); + uint64_t iseen = 0; + fread(&iseen, sizeof(uint64_t), 1, fp); + *net->seen = iseen; + } + else { + printf("\n seen 32 \n"); + fread(net->seen, sizeof(int), 1, fp); + } int transpose = (major > 1000) || (minor > 1000); int i; diff --git a/src/region_layer.c b/src/region_layer.c index 4e1e03a59cc..5167fb8129c 100644 --- a/src/region_layer.c +++ b/src/region_layer.c @@ -27,7 +27,7 @@ region_layer make_region_layer(int batch, int w, int h, int n, int classes, int l.bias_updates = calloc(n*2, sizeof(float)); l.outputs = h*w*n*(classes + coords + 1); l.inputs = l.outputs; - l.max_boxes = max_boxes; + l.max_boxes = max_boxes; l.truths = max_boxes*(5); l.delta = calloc(batch*l.outputs, sizeof(float)); l.output = calloc(batch*l.outputs, sizeof(float)); @@ -53,8 +53,8 @@ region_layer make_region_layer(int batch, int w, int h, int n, int classes, int void resize_region_layer(layer *l, int w, int h) { - int old_w = l->w; - int old_h = l->h; + int old_w = l->w; + int old_h = l->h; l->w = w; l->h = h; @@ -65,13 +65,13 @@ void resize_region_layer(layer *l, int w, int h) l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float)); #ifdef GPU - if (old_w < w || old_h < h) { - cuda_free(l->delta_gpu); - cuda_free(l->output_gpu); + if (old_w < w || old_h < h) { + cuda_free(l->delta_gpu); + cuda_free(l->output_gpu); - l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs); - l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs); - } + l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs); + l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs); + } #endif } @@ -127,34 +127,34 @@ void delta_region_class(float *output, float *delta, int index, int class_id, in class_id = hier->parent[class_id]; } *avg_cat += pred; - } else { - // Focal loss - if (focal_loss) { - // Focal Loss - float alpha = 0.5; // 0.25 or 0.5 - //float gamma = 2; // hardcoded in many places of the grad-formula - - int ti = index + class_id; - float pt = output[ti] + 0.000000000000001F; - // http://fooplot.com/#W3sidHlwZSI6MCwiZXEiOiItKDEteCkqKDIqeCpsb2coeCkreC0xKSIsImNvbG9yIjoiIzAwMDAwMCJ9LHsidHlwZSI6MTAwMH1d - float grad = -(1 - pt) * (2 * pt*logf(pt) + pt - 1); // http://blog.csdn.net/linmingan/article/details/77885832 - //float grad = (1 - pt) * (2 * pt*logf(pt) + pt - 1); // https://github.com/unsky/focal-loss - - for (n = 0; n < classes; ++n) { - delta[index + n] = scale * (((n == class_id) ? 1 : 0) - output[index + n]); - - delta[index + n] *= alpha*grad; - - if (n == class_id) *avg_cat += output[index + n]; - } - } - else { - // default - for (n = 0; n < classes; ++n) { - delta[index + n] = scale * (((n == class_id) ? 1 : 0) - output[index + n]); - if (n == class_id) *avg_cat += output[index + n]; - } - } + } else { + // Focal loss + if (focal_loss) { + // Focal Loss + float alpha = 0.5; // 0.25 or 0.5 + //float gamma = 2; // hardcoded in many places of the grad-formula + + int ti = index + class_id; + float pt = output[ti] + 0.000000000000001F; + // http://fooplot.com/#W3sidHlwZSI6MCwiZXEiOiItKDEteCkqKDIqeCpsb2coeCkreC0xKSIsImNvbG9yIjoiIzAwMDAwMCJ9LHsidHlwZSI6MTAwMH1d + float grad = -(1 - pt) * (2 * pt*logf(pt) + pt - 1); // http://blog.csdn.net/linmingan/article/details/77885832 + //float grad = (1 - pt) * (2 * pt*logf(pt) + pt - 1); // https://github.com/unsky/focal-loss + + for (n = 0; n < classes; ++n) { + delta[index + n] = scale * (((n == class_id) ? 1 : 0) - output[index + n]); + + delta[index + n] *= alpha*grad; + + if (n == class_id) *avg_cat += output[index + n]; + } + } + else { + // default + for (n = 0; n < classes; ++n) { + delta[index + n] = scale * (((n == class_id) ? 1 : 0) - output[index + n]); + if (n == class_id) *avg_cat += output[index + n]; + } + } } } @@ -170,9 +170,9 @@ float tisnan(float x) static int entry_index(layer l, int batch, int location, int entry) { - int n = location / (l.w*l.h); - int loc = location % (l.w*l.h); - return batch*l.outputs + n*l.w*l.h*(l.coords + l.classes + 1) + entry*l.w*l.h + loc; + int n = location / (l.w*l.h); + int loc = location % (l.w*l.h); + return batch*l.outputs + n*l.w*l.h*(l.coords + l.classes + 1) + entry*l.w*l.h + loc; } void softmax_tree(float *input, int batch, int inputs, float temp, tree *hierarchy, float *output); @@ -256,8 +256,8 @@ void forward_region_layer(const region_layer l, network_state state) int best_class_id = -1; for(t = 0; t < l.max_boxes; ++t){ box truth = float_to_box(state.truth + t*5 + b*l.truths); - int class_id = state.truth[t * 5 + b*l.truths + 4]; - if (class_id >= l.classes) continue; // if label contains class_id more than number of classes in the cfg-file + int class_id = state.truth[t * 5 + b*l.truths + 4]; + if (class_id >= l.classes) continue; // if label contains class_id more than number of classes in the cfg-file if(!truth.x) break; float iou = box_iou(pred, truth); if (iou > best_iou) { @@ -295,12 +295,12 @@ void forward_region_layer(const region_layer l, network_state state) } for(t = 0; t < l.max_boxes; ++t){ box truth = float_to_box(state.truth + t*5 + b*l.truths); - int class_id = state.truth[t * 5 + b*l.truths + 4]; - if (class_id >= l.classes) { - printf(" Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes-1); - getchar(); - continue; // if label contains class_id more than number of classes in the cfg-file - } + int class_id = state.truth[t * 5 + b*l.truths + 4]; + if (class_id >= l.classes) { + printf(" Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes-1); + getchar(); + continue; // if label contains class_id more than number of classes in the cfg-file + } if(!truth.x) break; float best_iou = 0; @@ -450,7 +450,7 @@ void forward_region_layer_gpu(const region_layer l, network_state state) cuda_pull_array(state.truth, truth_cpu, num_truth); } cuda_pull_array(l.output_gpu, in_cpu, l.batch*l.inputs); - //cudaStreamSynchronize(get_cuda_stream()); + //cudaStreamSynchronize(get_cuda_stream()); network_state cpu_state = state; cpu_state.train = state.train; cpu_state.truth = truth_cpu; @@ -460,7 +460,7 @@ void forward_region_layer_gpu(const region_layer l, network_state state) free(cpu_state.input); if(!state.train) return; cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs); - //cudaStreamSynchronize(get_cuda_stream()); + //cudaStreamSynchronize(get_cuda_stream()); if(cpu_state.truth) free(cpu_state.truth); } @@ -473,107 +473,107 @@ void backward_region_layer_gpu(region_layer l, network_state state) void correct_region_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative) { - int i; - int new_w = 0; - int new_h = 0; - if (((float)netw / w) < ((float)neth / h)) { - new_w = netw; - new_h = (h * netw) / w; - } - else { - new_h = neth; - new_w = (w * neth) / h; - } - for (i = 0; i < n; ++i) { - box b = dets[i].bbox; - b.x = (b.x - (netw - new_w) / 2. / netw) / ((float)new_w / netw); - b.y = (b.y - (neth - new_h) / 2. / neth) / ((float)new_h / neth); - b.w *= (float)netw / new_w; - b.h *= (float)neth / new_h; - if (!relative) { - b.x *= w; - b.w *= w; - b.y *= h; - b.h *= h; - } - dets[i].bbox = b; - } + int i; + int new_w = 0; + int new_h = 0; + if (((float)netw / w) < ((float)neth / h)) { + new_w = netw; + new_h = (h * netw) / w; + } + else { + new_h = neth; + new_w = (w * neth) / h; + } + for (i = 0; i < n; ++i) { + box b = dets[i].bbox; + b.x = (b.x - (netw - new_w) / 2. / netw) / ((float)new_w / netw); + b.y = (b.y - (neth - new_h) / 2. / neth) / ((float)new_h / neth); + b.w *= (float)netw / new_w; + b.h *= (float)neth / new_h; + if (!relative) { + b.x *= w; + b.w *= w; + b.y *= h; + b.h *= h; + } + dets[i].bbox = b; + } } void get_region_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, float tree_thresh, int relative, detection *dets) { - int i, j, n, z; - float *predictions = l.output; - if (l.batch == 2) { - float *flip = l.output + l.outputs; - for (j = 0; j < l.h; ++j) { - for (i = 0; i < l.w / 2; ++i) { - for (n = 0; n < l.n; ++n) { - for (z = 0; z < l.classes + l.coords + 1; ++z) { - int i1 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + i; - int i2 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + (l.w - i - 1); - float swap = flip[i1]; - flip[i1] = flip[i2]; - flip[i2] = swap; - if (z == 0) { - flip[i1] = -flip[i1]; - flip[i2] = -flip[i2]; - } - } - } - } - } - for (i = 0; i < l.outputs; ++i) { - l.output[i] = (l.output[i] + flip[i]) / 2.; - } - } - for (i = 0; i < l.w*l.h; ++i) { - int row = i / l.w; - int col = i % l.w; - for (n = 0; n < l.n; ++n) { - int index = n*l.w*l.h + i; - for (j = 0; j < l.classes; ++j) { - dets[index].prob[j] = 0; - } - int obj_index = entry_index(l, 0, n*l.w*l.h + i, l.coords); - int box_index = entry_index(l, 0, n*l.w*l.h + i, 0); - int mask_index = entry_index(l, 0, n*l.w*l.h + i, 4); - float scale = l.background ? 1 : predictions[obj_index]; - dets[index].bbox = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h);// , l.w*l.h); - dets[index].objectness = scale > thresh ? scale : 0; - if (dets[index].mask) { - for (j = 0; j < l.coords - 4; ++j) { - dets[index].mask[j] = l.output[mask_index + j*l.w*l.h]; - } - } - - int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + !l.background); - if (l.softmax_tree) { - - hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0);// , l.w*l.h); - if (map) { - for (j = 0; j < 200; ++j) { - int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + map[j]); - float prob = scale*predictions[class_index]; - dets[index].prob[j] = (prob > thresh) ? prob : 0; - } - } - else { - int j = hierarchy_top_prediction(predictions + class_index, l.softmax_tree, tree_thresh, l.w*l.h); - dets[index].prob[j] = (scale > thresh) ? scale : 0; - } - } - else { - if (dets[index].objectness) { - for (j = 0; j < l.classes; ++j) { - int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + j); - float prob = scale*predictions[class_index]; - dets[index].prob[j] = (prob > thresh) ? prob : 0; - } - } - } - } - } - correct_region_boxes(dets, l.w*l.h*l.n, w, h, netw, neth, relative); + int i, j, n, z; + float *predictions = l.output; + if (l.batch == 2) { + float *flip = l.output + l.outputs; + for (j = 0; j < l.h; ++j) { + for (i = 0; i < l.w / 2; ++i) { + for (n = 0; n < l.n; ++n) { + for (z = 0; z < l.classes + l.coords + 1; ++z) { + int i1 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + i; + int i2 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + (l.w - i - 1); + float swap = flip[i1]; + flip[i1] = flip[i2]; + flip[i2] = swap; + if (z == 0) { + flip[i1] = -flip[i1]; + flip[i2] = -flip[i2]; + } + } + } + } + } + for (i = 0; i < l.outputs; ++i) { + l.output[i] = (l.output[i] + flip[i]) / 2.; + } + } + for (i = 0; i < l.w*l.h; ++i) { + int row = i / l.w; + int col = i % l.w; + for (n = 0; n < l.n; ++n) { + int index = n*l.w*l.h + i; + for (j = 0; j < l.classes; ++j) { + dets[index].prob[j] = 0; + } + int obj_index = entry_index(l, 0, n*l.w*l.h + i, l.coords); + int box_index = entry_index(l, 0, n*l.w*l.h + i, 0); + int mask_index = entry_index(l, 0, n*l.w*l.h + i, 4); + float scale = l.background ? 1 : predictions[obj_index]; + dets[index].bbox = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h);// , l.w*l.h); + dets[index].objectness = scale > thresh ? scale : 0; + if (dets[index].mask) { + for (j = 0; j < l.coords - 4; ++j) { + dets[index].mask[j] = l.output[mask_index + j*l.w*l.h]; + } + } + + int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + !l.background); + if (l.softmax_tree) { + + hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0);// , l.w*l.h); + if (map) { + for (j = 0; j < 200; ++j) { + int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + map[j]); + float prob = scale*predictions[class_index]; + dets[index].prob[j] = (prob > thresh) ? prob : 0; + } + } + else { + int j = hierarchy_top_prediction(predictions + class_index, l.softmax_tree, tree_thresh, l.w*l.h); + dets[index].prob[j] = (scale > thresh) ? scale : 0; + } + } + else { + if (dets[index].objectness) { + for (j = 0; j < l.classes; ++j) { + int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + j); + float prob = scale*predictions[class_index]; + dets[index].prob[j] = (prob > thresh) ? prob : 0; + } + } + } + } + } + correct_region_boxes(dets, l.w*l.h*l.n, w, h, netw, neth, relative); } diff --git a/src/reorg_layer.c b/src/reorg_layer.c index 05bab1ea8df..c298b4007c9 100644 --- a/src/reorg_layer.c +++ b/src/reorg_layer.c @@ -77,42 +77,42 @@ void resize_reorg_layer(layer *l, int w, int h) void forward_reorg_layer(const layer l, network_state state) { - if (l.reverse) { - reorg_cpu(state.input, l.out_w, l.out_h, l.out_c, l.batch, l.stride, 1, l.output); - } - else { - reorg_cpu(state.input, l.out_w, l.out_h, l.out_c, l.batch, l.stride, 0, l.output); - } + if (l.reverse) { + reorg_cpu(state.input, l.out_w, l.out_h, l.out_c, l.batch, l.stride, 1, l.output); + } + else { + reorg_cpu(state.input, l.out_w, l.out_h, l.out_c, l.batch, l.stride, 0, l.output); + } } void backward_reorg_layer(const layer l, network_state state) { - if (l.reverse) { - reorg_cpu(l.delta, l.out_w, l.out_h, l.out_c, l.batch, l.stride, 0, state.delta); - } - else { - reorg_cpu(l.delta, l.out_w, l.out_h, l.out_c, l.batch, l.stride, 1, state.delta); - } + if (l.reverse) { + reorg_cpu(l.delta, l.out_w, l.out_h, l.out_c, l.batch, l.stride, 0, state.delta); + } + else { + reorg_cpu(l.delta, l.out_w, l.out_h, l.out_c, l.batch, l.stride, 1, state.delta); + } } #ifdef GPU void forward_reorg_layer_gpu(layer l, network_state state) { - if (l.reverse) { - reorg_ongpu(state.input, l.out_w, l.out_h, l.out_c, l.batch, l.stride, 1, l.output_gpu); - } - else { - reorg_ongpu(state.input, l.out_w, l.out_h, l.out_c, l.batch, l.stride, 0, l.output_gpu); - } + if (l.reverse) { + reorg_ongpu(state.input, l.out_w, l.out_h, l.out_c, l.batch, l.stride, 1, l.output_gpu); + } + else { + reorg_ongpu(state.input, l.out_w, l.out_h, l.out_c, l.batch, l.stride, 0, l.output_gpu); + } } void backward_reorg_layer_gpu(layer l, network_state state) { - if (l.reverse) { - reorg_ongpu(l.delta_gpu, l.out_w, l.out_h, l.out_c, l.batch, l.stride, 0, state.delta); - } - else { - reorg_ongpu(l.delta_gpu, l.out_w, l.out_h, l.out_c, l.batch, l.stride, 1, state.delta); - } + if (l.reverse) { + reorg_ongpu(l.delta_gpu, l.out_w, l.out_h, l.out_c, l.batch, l.stride, 0, state.delta); + } + else { + reorg_ongpu(l.delta_gpu, l.out_w, l.out_h, l.out_c, l.batch, l.stride, 1, state.delta); + } } #endif diff --git a/src/reorg_old_layer.c b/src/reorg_old_layer.c index c55cf7c280b..30206d9c001 100644 --- a/src/reorg_old_layer.c +++ b/src/reorg_old_layer.c @@ -77,42 +77,42 @@ void resize_reorg_old_layer(layer *l, int w, int h) void forward_reorg_old_layer(const layer l, network_state state) { - if (l.reverse) { - reorg_cpu(state.input, l.w, l.h, l.c, l.batch, l.stride, 1, l.output); - } - else { - reorg_cpu(state.input, l.w, l.h, l.c, l.batch, l.stride, 0, l.output); - } + if (l.reverse) { + reorg_cpu(state.input, l.w, l.h, l.c, l.batch, l.stride, 1, l.output); + } + else { + reorg_cpu(state.input, l.w, l.h, l.c, l.batch, l.stride, 0, l.output); + } } void backward_reorg_old_layer(const layer l, network_state state) { - if (l.reverse) { - reorg_cpu(l.delta, l.w, l.h, l.c, l.batch, l.stride, 0, state.delta); - } - else { - reorg_cpu(l.delta, l.w, l.h, l.c, l.batch, l.stride, 1, state.delta); - } + if (l.reverse) { + reorg_cpu(l.delta, l.w, l.h, l.c, l.batch, l.stride, 0, state.delta); + } + else { + reorg_cpu(l.delta, l.w, l.h, l.c, l.batch, l.stride, 1, state.delta); + } } #ifdef GPU void forward_reorg_old_layer_gpu(layer l, network_state state) { - if (l.reverse) { - reorg_ongpu(state.input, l.w, l.h, l.c, l.batch, l.stride, 1, l.output_gpu); - } - else { - reorg_ongpu(state.input, l.w, l.h, l.c, l.batch, l.stride, 0, l.output_gpu); - } + if (l.reverse) { + reorg_ongpu(state.input, l.w, l.h, l.c, l.batch, l.stride, 1, l.output_gpu); + } + else { + reorg_ongpu(state.input, l.w, l.h, l.c, l.batch, l.stride, 0, l.output_gpu); + } } void backward_reorg_old_layer_gpu(layer l, network_state state) { - if (l.reverse) { - reorg_ongpu(l.delta_gpu, l.w, l.h, l.c, l.batch, l.stride, 0, state.delta); - } - else { - reorg_ongpu(l.delta_gpu, l.w, l.h, l.c, l.batch, l.stride, 1, state.delta); - } + if (l.reverse) { + reorg_ongpu(l.delta_gpu, l.w, l.h, l.c, l.batch, l.stride, 0, state.delta); + } + else { + reorg_ongpu(l.delta_gpu, l.w, l.h, l.c, l.batch, l.stride, 1, state.delta); + } } #endif diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index 87b4b089a43..9fa18db863b 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -38,20 +38,20 @@ layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int void resize_shortcut_layer(layer *l, int w, int h) { - //assert(l->w == l->out_w); - //assert(l->h == l->out_h); - l->w = l->out_w = w; - l->h = l->out_h = h; - l->outputs = w*h*l->out_c; - l->inputs = l->outputs; - l->delta = realloc(l->delta, l->outputs*l->batch * sizeof(float)); - l->output = realloc(l->output, l->outputs*l->batch * sizeof(float)); + //assert(l->w == l->out_w); + //assert(l->h == l->out_h); + l->w = l->out_w = w; + l->h = l->out_h = h; + l->outputs = w*h*l->out_c; + l->inputs = l->outputs; + l->delta = realloc(l->delta, l->outputs*l->batch * sizeof(float)); + l->output = realloc(l->output, l->outputs*l->batch * sizeof(float)); #ifdef GPU - cuda_free(l->output_gpu); - cuda_free(l->delta_gpu); - l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch); - l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch); + cuda_free(l->output_gpu); + cuda_free(l->delta_gpu); + l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch); + l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch); #endif } diff --git a/src/tree.c b/src/tree.c index 35ac3de19db..d66da9f828e 100644 --- a/src/tree.c +++ b/src/tree.c @@ -52,34 +52,34 @@ void hierarchy_predictions(float *predictions, int n, tree *hier, int only_leave int hierarchy_top_prediction(float *predictions, tree *hier, float thresh, int stride) { - float p = 1; - int group = 0; - int i; - while (1) { - float max = 0; - int max_i = 0; + float p = 1; + int group = 0; + int i; + while (1) { + float max = 0; + int max_i = 0; - for (i = 0; i < hier->group_size[group]; ++i) { - int index = i + hier->group_offset[group]; - float val = predictions[(i + hier->group_offset[group])*stride]; - if (val > max) { - max_i = index; - max = val; - } - } - if (p*max > thresh) { - p = p*max; - group = hier->child[max_i]; - if (hier->child[max_i] < 0) return max_i; - } - else if (group == 0) { - return max_i; - } - else { - return hier->parent[hier->group_offset[group]]; - } - } - return 0; + for (i = 0; i < hier->group_size[group]; ++i) { + int index = i + hier->group_offset[group]; + float val = predictions[(i + hier->group_offset[group])*stride]; + if (val > max) { + max_i = index; + max = val; + } + } + if (p*max > thresh) { + p = p*max; + group = hier->child[max_i]; + if (hier->child[max_i] < 0) return max_i; + } + else if (group == 0) { + return max_i; + } + else { + return hier->parent[hier->group_offset[group]]; + } + } + return 0; } tree *read_tree(char *filename) diff --git a/src/utils.c b/src/utils.c index 5dca21236bb..3af7c97072c 100644 --- a/src/utils.c +++ b/src/utils.c @@ -18,11 +18,11 @@ double what_time_is_it_now() { - struct timeval time; - if (gettimeofday(&time, NULL)) { - return 0; - } - return (double)time.tv_sec + (double)time.tv_usec * .000001; + struct timeval time; + if (gettimeofday(&time, NULL)) { + return 0; + } + return (double)time.tv_sec + (double)time.tv_usec * .000001; } int *read_map(char *filename) @@ -57,7 +57,7 @@ void shuffle(void *arr, size_t n, size_t size) void *swp = calloc(1, size); for(i = 0; i < n-1; ++i){ size_t j = i + rand()/(RAND_MAX / (n-i)+1); - memcpy(swp, (char*)arr+(j*size), size); + memcpy(swp, (char*)arr+(j*size), size); memcpy((char*)arr+(j*size), (char*)arr+(i*size), size); memcpy((char*)arr+(i*size), swp, size); } @@ -137,7 +137,7 @@ char *basecfg(char *cfgfile) { c = next+1; } - if(!next) while ((next = strchr(c, '\\'))) { c = next + 1; } + if(!next) while ((next = strchr(c, '\\'))) { c = next + 1; } c = copy_string(c); next = strchr(c, '.'); if (next) *next = 0; @@ -169,63 +169,63 @@ void pm(int M, int N, float *A) void find_replace(char *str, char *orig, char *rep, char *output) { - char *buffer = calloc(8192, sizeof(char)); + char *buffer = calloc(8192, sizeof(char)); char *p; sprintf(buffer, "%s", str); if(!(p = strstr(buffer, orig))){ // Is 'orig' even in 'str'? sprintf(output, "%s", str); - free(buffer); + free(buffer); return; } *p = '\0'; sprintf(output, "%s%s%s", buffer, rep, p+strlen(orig)); - free(buffer); + free(buffer); } void find_replace_extension(char *str, char *orig, char *rep, char *output) { - char *buffer = calloc(8192, sizeof(char)); + char *buffer = calloc(8192, sizeof(char)); - sprintf(buffer, "%s", str); - char *p = strstr(buffer, orig); - int offset = (p - buffer); - int chars_from_end = strlen(buffer) - offset; - if (!p || chars_from_end != strlen(orig)) { // Is 'orig' even in 'str' AND is 'orig' found at the end of 'str'? - sprintf(output, "%s", str); - free(buffer); - return; - } + sprintf(buffer, "%s", str); + char *p = strstr(buffer, orig); + int offset = (p - buffer); + int chars_from_end = strlen(buffer) - offset; + if (!p || chars_from_end != strlen(orig)) { // Is 'orig' even in 'str' AND is 'orig' found at the end of 'str'? + sprintf(output, "%s", str); + free(buffer); + return; + } - *p = '\0'; + *p = '\0'; - sprintf(output, "%s%s%s", buffer, rep, p + strlen(orig)); - free(buffer); + sprintf(output, "%s%s%s", buffer, rep, p + strlen(orig)); + free(buffer); } void replace_image_to_label(char *input_path, char *output_path) { - //find_replace(input_path, "/images/", "/labels/", output_path); // COCO - find_replace(input_path, "/images/train2014/", "/labels/train2014/", output_path); // COCO - find_replace(output_path, "/images/val2014/", "/labels/val2014/", output_path); // COCO - find_replace(output_path, "/JPEGImages/", "/labels/", output_path); // PascalVOC - //find_replace(output_path, "/VOC2007/JPEGImages/", "/VOC2007/labels/", output_path); // PascalVOC - //find_replace(output_path, "/VOC2012/JPEGImages/", "/VOC2012/labels/", output_path); // PascalVOC - - //find_replace(output_path, "/raw/", "/labels/", output_path); - - // replace only ext of files - find_replace_extension(output_path, ".jpg", ".txt", output_path); - find_replace_extension(output_path, ".JPG", ".txt", output_path); // error - find_replace_extension(output_path, ".jpeg", ".txt", output_path); - find_replace_extension(output_path, ".JPEG", ".txt", output_path); - find_replace_extension(output_path, ".png", ".txt", output_path); - find_replace_extension(output_path, ".PNG", ".txt", output_path); - find_replace_extension(output_path, ".bmp", ".txt", output_path); - find_replace_extension(output_path, ".BMP", ".txt", output_path); - find_replace_extension(output_path, ".ppm", ".txt", output_path); - find_replace_extension(output_path, ".PPM", ".txt", output_path); + //find_replace(input_path, "/images/", "/labels/", output_path); // COCO + find_replace(input_path, "/images/train2014/", "/labels/train2014/", output_path); // COCO + find_replace(output_path, "/images/val2014/", "/labels/val2014/", output_path); // COCO + find_replace(output_path, "/JPEGImages/", "/labels/", output_path); // PascalVOC + //find_replace(output_path, "/VOC2007/JPEGImages/", "/VOC2007/labels/", output_path); // PascalVOC + //find_replace(output_path, "/VOC2012/JPEGImages/", "/VOC2012/labels/", output_path); // PascalVOC + + //find_replace(output_path, "/raw/", "/labels/", output_path); + + // replace only ext of files + find_replace_extension(output_path, ".jpg", ".txt", output_path); + find_replace_extension(output_path, ".JPG", ".txt", output_path); // error + find_replace_extension(output_path, ".jpeg", ".txt", output_path); + find_replace_extension(output_path, ".JPEG", ".txt", output_path); + find_replace_extension(output_path, ".png", ".txt", output_path); + find_replace_extension(output_path, ".PNG", ".txt", output_path); + find_replace_extension(output_path, ".bmp", ".txt", output_path); + find_replace_extension(output_path, ".BMP", ".txt", output_path); + find_replace_extension(output_path, ".ppm", ".txt", output_path); + find_replace_extension(output_path, ".PPM", ".txt", output_path); } float sec(clock_t clocks) @@ -299,15 +299,15 @@ void strip(char *s) void strip_args(char *s) { - size_t i; - size_t len = strlen(s); - size_t offset = 0; - for (i = 0; i < len; ++i) { - char c = s[i]; - if (c == '\t' || c == '\n' || c == '\r' || c == 0x0d || c == 0x0a) ++offset; - else s[i - offset] = c; - } - s[len - offset] = '\0'; + size_t i; + size_t len = strlen(s); + size_t offset = 0; + for (i = 0; i < len; ++i) { + char c = s[i]; + if (c == '\t' || c == '\n' || c == '\r' || c == 0x0d || c == 0x0a) ++offset; + else s[i - offset] = c; + } + s[len - offset] = '\0'; } void strip_char(char *s, char bad) @@ -356,11 +356,11 @@ char *fgetl(FILE *fp) fgets(&line[curr], readsize, fp); curr = strlen(line); } - if(curr >= 2) - if(line[curr-2] == 0x0d) line[curr-2] = 0x00; + if(curr >= 2) + if(line[curr-2] == 0x0d) line[curr-2] = 0x00; - if(curr >= 1) - if(line[curr-1] == 0x0a) line[curr-1] = 0x00; + if(curr >= 1) + if(line[curr-1] == 0x0a) line[curr-1] = 0x00; return line; } @@ -620,11 +620,11 @@ int max_index(float *a, int n) int int_index(int *a, int val, int n) { - int i; - for (i = 0; i < n; ++i) { - if (a[i] == val) return i; - } - return -1; + int i; + for (i = 0; i < n; ++i) { + if (a[i] == val) return i; + } + return -1; } int rand_int(int min, int max) @@ -691,7 +691,7 @@ float rand_uniform(float min, float max) max = swap; } return ((float)rand()/RAND_MAX * (max - min)) + min; - //return (random_float() * (max - min)) + min; + //return (random_float() * (max - min)) + min; } float rand_scale(float s) @@ -715,30 +715,30 @@ float **one_hot_encode(float *a, int n, int k) unsigned int random_gen() { - unsigned int rnd = 0; + unsigned int rnd = 0; #ifdef WIN32 - rand_s(&rnd); + rand_s(&rnd); #else - rnd = rand(); + rnd = rand(); #endif - return rnd; + return rnd; } float random_float() { #ifdef WIN32 - return ((float)random_gen() / (float)UINT_MAX); + return ((float)random_gen() / (float)UINT_MAX); #else - return ((float)random_gen() / (float)RAND_MAX); + return ((float)random_gen() / (float)RAND_MAX); #endif } float rand_uniform_strong(float min, float max) { - if (max < min) { - float swap = min; - min = max; - max = swap; - } - return (random_float() * (max - min)) + min; + if (max < min) { + float swap = min; + min = max; + max = swap; + } + return (random_float() * (max - min)) + min; } \ No newline at end of file diff --git a/src/yolo_console_dll.cpp b/src/yolo_console_dll.cpp index 2eca267890d..a724b3e503e 100644 --- a/src/yolo_console_dll.cpp +++ b/src/yolo_console_dll.cpp @@ -21,10 +21,10 @@ //#pragma comment(lib, "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v9.1/lib/x64/cudart.lib") //static std::shared_ptr device_ptr(NULL, [](void *img) { cudaDeviceReset(); }); -#include "yolo_v2_class.hpp" // imported functions from DLL +#include "yolo_v2_class.hpp" // imported functions from DLL #ifdef OPENCV -#include // C++ +#include // C++ #include "opencv2/core/version.hpp" #ifndef CV_VERSION_EPOCH #include "opencv2/videoio/videoio.hpp" @@ -36,67 +36,67 @@ #pragma comment(lib, "opencv_core" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib") -#endif // TRACK_OPTFLOW +#endif // TRACK_OPTFLOW #else #define OPENCV_VERSION CVAUX_STR(CV_VERSION_EPOCH)""CVAUX_STR(CV_VERSION_MAJOR)""CVAUX_STR(CV_VERSION_MINOR) #pragma comment(lib, "opencv_core" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib") -#endif // CV_VERSION_EPOCH +#endif // CV_VERSION_EPOCH class track_kalman { public: - cv::KalmanFilter kf; - int state_size, meas_size, contr_size; - - - track_kalman(int _state_size = 10, int _meas_size = 10, int _contr_size = 0) - : state_size(_state_size), meas_size(_meas_size), contr_size(_contr_size) - { - kf.init(state_size, meas_size, contr_size, CV_32F); - - cv::setIdentity(kf.measurementMatrix); - cv::setIdentity(kf.measurementNoiseCov, cv::Scalar::all(1e-1)); - cv::setIdentity(kf.processNoiseCov, cv::Scalar::all(1e-5)); - cv::setIdentity(kf.errorCovPost, cv::Scalar::all(1e-2)); - cv::setIdentity(kf.transitionMatrix); - } - - void set(std::vector result_vec) { - for (size_t i = 0; i < result_vec.size() && i < state_size*2; ++i) { - kf.statePost.at(i * 2 + 0) = result_vec[i].x; - kf.statePost.at(i * 2 + 1) = result_vec[i].y; - } - } - - // Kalman.correct() calculates: statePost = statePre + gain * (z(k)-measurementMatrix*statePre); - // corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k)) - std::vector correct(std::vector result_vec) { - cv::Mat measurement(meas_size, 1, CV_32F); - for (size_t i = 0; i < result_vec.size() && i < meas_size * 2; ++i) { - measurement.at(i * 2 + 0) = result_vec[i].x; - measurement.at(i * 2 + 1) = result_vec[i].y; - } - cv::Mat estimated = kf.correct(measurement); - for (size_t i = 0; i < result_vec.size() && i < meas_size * 2; ++i) { - result_vec[i].x = estimated.at(i * 2 + 0); - result_vec[i].y = estimated.at(i * 2 + 1); - } - return result_vec; - } - - // Kalman.predict() calculates: statePre = TransitionMatrix * statePost; - // predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k) - std::vector predict() { - std::vector result_vec; - cv::Mat control; - cv::Mat prediction = kf.predict(control); - for (size_t i = 0; i < prediction.rows && i < state_size * 2; ++i) { - result_vec[i].x = prediction.at(i * 2 + 0); - result_vec[i].y = prediction.at(i * 2 + 1); - } - return result_vec; - } + cv::KalmanFilter kf; + int state_size, meas_size, contr_size; + + + track_kalman(int _state_size = 10, int _meas_size = 10, int _contr_size = 0) + : state_size(_state_size), meas_size(_meas_size), contr_size(_contr_size) + { + kf.init(state_size, meas_size, contr_size, CV_32F); + + cv::setIdentity(kf.measurementMatrix); + cv::setIdentity(kf.measurementNoiseCov, cv::Scalar::all(1e-1)); + cv::setIdentity(kf.processNoiseCov, cv::Scalar::all(1e-5)); + cv::setIdentity(kf.errorCovPost, cv::Scalar::all(1e-2)); + cv::setIdentity(kf.transitionMatrix); + } + + void set(std::vector result_vec) { + for (size_t i = 0; i < result_vec.size() && i < state_size*2; ++i) { + kf.statePost.at(i * 2 + 0) = result_vec[i].x; + kf.statePost.at(i * 2 + 1) = result_vec[i].y; + } + } + + // Kalman.correct() calculates: statePost = statePre + gain * (z(k)-measurementMatrix*statePre); + // corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k)) + std::vector correct(std::vector result_vec) { + cv::Mat measurement(meas_size, 1, CV_32F); + for (size_t i = 0; i < result_vec.size() && i < meas_size * 2; ++i) { + measurement.at(i * 2 + 0) = result_vec[i].x; + measurement.at(i * 2 + 1) = result_vec[i].y; + } + cv::Mat estimated = kf.correct(measurement); + for (size_t i = 0; i < result_vec.size() && i < meas_size * 2; ++i) { + result_vec[i].x = estimated.at(i * 2 + 0); + result_vec[i].y = estimated.at(i * 2 + 1); + } + return result_vec; + } + + // Kalman.predict() calculates: statePre = TransitionMatrix * statePost; + // predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k) + std::vector predict() { + std::vector result_vec; + cv::Mat control; + cv::Mat prediction = kf.predict(control); + for (size_t i = 0; i < prediction.rows && i < state_size * 2; ++i) { + result_vec[i].x = prediction.at(i * 2 + 0); + result_vec[i].y = prediction.at(i * 2 + 1); + } + return result_vec; + } }; @@ -105,384 +105,384 @@ class track_kalman { class extrapolate_coords_t { public: - std::vector old_result_vec; - std::vector dx_vec, dy_vec, time_vec; - std::vector old_dx_vec, old_dy_vec; - - void new_result(std::vector new_result_vec, float new_time) { - old_dx_vec = dx_vec; - old_dy_vec = dy_vec; - if (old_dx_vec.size() != old_result_vec.size()) std::cout << "old_dx != old_res \n"; - dx_vec = std::vector(new_result_vec.size(), 0); - dy_vec = std::vector(new_result_vec.size(), 0); - update_result(new_result_vec, new_time, false); - old_result_vec = new_result_vec; - time_vec = std::vector(new_result_vec.size(), new_time); - } - - void update_result(std::vector new_result_vec, float new_time, bool update = true) { - for (size_t i = 0; i < new_result_vec.size(); ++i) { - for (size_t k = 0; k < old_result_vec.size(); ++k) { - if (old_result_vec[k].track_id == new_result_vec[i].track_id && old_result_vec[k].obj_id == new_result_vec[i].obj_id) { - float const delta_time = new_time - time_vec[k]; - if (abs(delta_time) < 1) break; - size_t index = (update) ? k : i; - float dx = ((float)new_result_vec[i].x - (float)old_result_vec[k].x) / delta_time; - float dy = ((float)new_result_vec[i].y - (float)old_result_vec[k].y) / delta_time; - float old_dx = dx, old_dy = dy; - - // if it's shaking - if (update) { - if (dx * dx_vec[i] < 0) dx = dx / 2; - if (dy * dy_vec[i] < 0) dy = dy / 2; - } else { - if (dx * old_dx_vec[k] < 0) dx = dx / 2; - if (dy * old_dy_vec[k] < 0) dy = dy / 2; - } - dx_vec[index] = dx; - dy_vec[index] = dy; - - //if (old_dx == dx && old_dy == dy) std::cout << "not shakin \n"; - //else std::cout << "shakin \n"; - - if (dx_vec[index] > 1000 || dy_vec[index] > 1000) { - //std::cout << "!!! bad dx or dy, dx = " << dx_vec[index] << ", dy = " << dy_vec[index] << - // ", delta_time = " << delta_time << ", update = " << update << std::endl; - dx_vec[index] = 0; - dy_vec[index] = 0; - } - old_result_vec[k].x = new_result_vec[i].x; - old_result_vec[k].y = new_result_vec[i].y; - time_vec[k] = new_time; - break; - } - } - } - } - - std::vector predict(float cur_time) { - std::vector result_vec = old_result_vec; - for (size_t i = 0; i < old_result_vec.size(); ++i) { - float const delta_time = cur_time - time_vec[i]; - auto &bbox = result_vec[i]; - float new_x = (float) bbox.x + dx_vec[i] * delta_time; - float new_y = (float) bbox.y + dy_vec[i] * delta_time; - if (new_x > 0) bbox.x = new_x; - else bbox.x = 0; - if (new_y > 0) bbox.y = new_y; - else bbox.y = 0; - } - return result_vec; - } + std::vector old_result_vec; + std::vector dx_vec, dy_vec, time_vec; + std::vector old_dx_vec, old_dy_vec; + + void new_result(std::vector new_result_vec, float new_time) { + old_dx_vec = dx_vec; + old_dy_vec = dy_vec; + if (old_dx_vec.size() != old_result_vec.size()) std::cout << "old_dx != old_res \n"; + dx_vec = std::vector(new_result_vec.size(), 0); + dy_vec = std::vector(new_result_vec.size(), 0); + update_result(new_result_vec, new_time, false); + old_result_vec = new_result_vec; + time_vec = std::vector(new_result_vec.size(), new_time); + } + + void update_result(std::vector new_result_vec, float new_time, bool update = true) { + for (size_t i = 0; i < new_result_vec.size(); ++i) { + for (size_t k = 0; k < old_result_vec.size(); ++k) { + if (old_result_vec[k].track_id == new_result_vec[i].track_id && old_result_vec[k].obj_id == new_result_vec[i].obj_id) { + float const delta_time = new_time - time_vec[k]; + if (abs(delta_time) < 1) break; + size_t index = (update) ? k : i; + float dx = ((float)new_result_vec[i].x - (float)old_result_vec[k].x) / delta_time; + float dy = ((float)new_result_vec[i].y - (float)old_result_vec[k].y) / delta_time; + float old_dx = dx, old_dy = dy; + + // if it's shaking + if (update) { + if (dx * dx_vec[i] < 0) dx = dx / 2; + if (dy * dy_vec[i] < 0) dy = dy / 2; + } else { + if (dx * old_dx_vec[k] < 0) dx = dx / 2; + if (dy * old_dy_vec[k] < 0) dy = dy / 2; + } + dx_vec[index] = dx; + dy_vec[index] = dy; + + //if (old_dx == dx && old_dy == dy) std::cout << "not shakin \n"; + //else std::cout << "shakin \n"; + + if (dx_vec[index] > 1000 || dy_vec[index] > 1000) { + //std::cout << "!!! bad dx or dy, dx = " << dx_vec[index] << ", dy = " << dy_vec[index] << + // ", delta_time = " << delta_time << ", update = " << update << std::endl; + dx_vec[index] = 0; + dy_vec[index] = 0; + } + old_result_vec[k].x = new_result_vec[i].x; + old_result_vec[k].y = new_result_vec[i].y; + time_vec[k] = new_time; + break; + } + } + } + } + + std::vector predict(float cur_time) { + std::vector result_vec = old_result_vec; + for (size_t i = 0; i < old_result_vec.size(); ++i) { + float const delta_time = cur_time - time_vec[i]; + auto &bbox = result_vec[i]; + float new_x = (float) bbox.x + dx_vec[i] * delta_time; + float new_y = (float) bbox.y + dy_vec[i] * delta_time; + if (new_x > 0) bbox.x = new_x; + else bbox.x = 0; + if (new_y > 0) bbox.y = new_y; + else bbox.y = 0; + } + return result_vec; + } }; void draw_boxes(cv::Mat mat_img, std::vector result_vec, std::vector obj_names, - int current_det_fps = -1, int current_cap_fps = -1) + int current_det_fps = -1, int current_cap_fps = -1) { - int const colors[6][3] = { { 1,0,1 },{ 0,0,1 },{ 0,1,1 },{ 0,1,0 },{ 1,1,0 },{ 1,0,0 } }; - - for (auto &i : result_vec) { - cv::Scalar color = obj_id_to_color(i.obj_id); - cv::rectangle(mat_img, cv::Rect(i.x, i.y, i.w, i.h), color, 2); - if (obj_names.size() > i.obj_id) { - std::string obj_name = obj_names[i.obj_id]; - if (i.track_id > 0) obj_name += " - " + std::to_string(i.track_id); - cv::Size const text_size = getTextSize(obj_name, cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, 2, 0); - int const max_width = (text_size.width > i.w + 2) ? text_size.width : (i.w + 2); - cv::rectangle(mat_img, cv::Point2f(std::max((int)i.x - 1, 0), std::max((int)i.y - 30, 0)), - cv::Point2f(std::min((int)i.x + max_width, mat_img.cols-1), std::min((int)i.y, mat_img.rows-1)), - color, CV_FILLED, 8, 0); - putText(mat_img, obj_name, cv::Point2f(i.x, i.y - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, cv::Scalar(0, 0, 0), 2); - } - } - if (current_det_fps >= 0 && current_cap_fps >= 0) { - std::string fps_str = "FPS detection: " + std::to_string(current_det_fps) + " FPS capture: " + std::to_string(current_cap_fps); - putText(mat_img, fps_str, cv::Point2f(10, 20), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, cv::Scalar(50, 255, 0), 2); - } + int const colors[6][3] = { { 1,0,1 },{ 0,0,1 },{ 0,1,1 },{ 0,1,0 },{ 1,1,0 },{ 1,0,0 } }; + + for (auto &i : result_vec) { + cv::Scalar color = obj_id_to_color(i.obj_id); + cv::rectangle(mat_img, cv::Rect(i.x, i.y, i.w, i.h), color, 2); + if (obj_names.size() > i.obj_id) { + std::string obj_name = obj_names[i.obj_id]; + if (i.track_id > 0) obj_name += " - " + std::to_string(i.track_id); + cv::Size const text_size = getTextSize(obj_name, cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, 2, 0); + int const max_width = (text_size.width > i.w + 2) ? text_size.width : (i.w + 2); + cv::rectangle(mat_img, cv::Point2f(std::max((int)i.x - 1, 0), std::max((int)i.y - 30, 0)), + cv::Point2f(std::min((int)i.x + max_width, mat_img.cols-1), std::min((int)i.y, mat_img.rows-1)), + color, CV_FILLED, 8, 0); + putText(mat_img, obj_name, cv::Point2f(i.x, i.y - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, cv::Scalar(0, 0, 0), 2); + } + } + if (current_det_fps >= 0 && current_cap_fps >= 0) { + std::string fps_str = "FPS detection: " + std::to_string(current_det_fps) + " FPS capture: " + std::to_string(current_cap_fps); + putText(mat_img, fps_str, cv::Point2f(10, 20), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, cv::Scalar(50, 255, 0), 2); + } } -#endif // OPENCV +#endif // OPENCV void show_console_result(std::vector const result_vec, std::vector const obj_names) { - for (auto &i : result_vec) { - if (obj_names.size() > i.obj_id) std::cout << obj_names[i.obj_id] << " - "; - std::cout << "obj_id = " << i.obj_id << ", x = " << i.x << ", y = " << i.y - << ", w = " << i.w << ", h = " << i.h - << std::setprecision(3) << ", prob = " << i.prob << std::endl; - } + for (auto &i : result_vec) { + if (obj_names.size() > i.obj_id) std::cout << obj_names[i.obj_id] << " - "; + std::cout << "obj_id = " << i.obj_id << ", x = " << i.x << ", y = " << i.y + << ", w = " << i.w << ", h = " << i.h + << std::setprecision(3) << ", prob = " << i.prob << std::endl; + } } std::vector objects_names_from_file(std::string const filename) { - std::ifstream file(filename); - std::vector file_lines; - if (!file.is_open()) return file_lines; - for(std::string line; getline(file, line);) file_lines.push_back(line); - std::cout << "object names loaded \n"; - return file_lines; + std::ifstream file(filename); + std::vector file_lines; + if (!file.is_open()) return file_lines; + for(std::string line; getline(file, line);) file_lines.push_back(line); + std::cout << "object names loaded \n"; + return file_lines; } int main(int argc, char *argv[]) { - std::string names_file = "data/coco.names"; - std::string cfg_file = "cfg/yolov3.cfg"; - std::string weights_file = "yolov3.weights"; - std::string filename; - - if (argc > 4) { //voc.names yolo-voc.cfg yolo-voc.weights test.mp4 - names_file = argv[1]; - cfg_file = argv[2]; - weights_file = argv[3]; - filename = argv[4]; - } - else if (argc > 1) filename = argv[1]; - - float const thresh = (argc > 5) ? std::stof(argv[5]) : 0.20; - - Detector detector(cfg_file, weights_file); - - auto obj_names = objects_names_from_file(names_file); - std::string out_videofile = "result.avi"; - bool const save_output_videofile = true; + std::string names_file = "data/coco.names"; + std::string cfg_file = "cfg/yolov3.cfg"; + std::string weights_file = "yolov3.weights"; + std::string filename; + + if (argc > 4) { //voc.names yolo-voc.cfg yolo-voc.weights test.mp4 + names_file = argv[1]; + cfg_file = argv[2]; + weights_file = argv[3]; + filename = argv[4]; + } + else if (argc > 1) filename = argv[1]; + + float const thresh = (argc > 5) ? std::stof(argv[5]) : 0.20; + + Detector detector(cfg_file, weights_file); + + auto obj_names = objects_names_from_file(names_file); + std::string out_videofile = "result.avi"; + bool const save_output_videofile = true; #ifdef TRACK_OPTFLOW - Tracker_optflow tracker_flow; - detector.wait_stream = true; + Tracker_optflow tracker_flow; + detector.wait_stream = true; #endif - while (true) - { - std::cout << "input image or video filename: "; - if(filename.size() == 0) std::cin >> filename; - if (filename.size() == 0) break; - - try { + while (true) + { + std::cout << "input image or video filename: "; + if(filename.size() == 0) std::cin >> filename; + if (filename.size() == 0) break; + + try { #ifdef OPENCV - extrapolate_coords_t extrapolate_coords; - bool extrapolate_flag = false; - float cur_time_extrapolate = 0, old_time_extrapolate = 0; - preview_boxes_t large_preview(100, 150, false), small_preview(50, 50, true); - bool show_small_boxes = false; - - std::string const file_ext = filename.substr(filename.find_last_of(".") + 1); - std::string const protocol = filename.substr(0, 7); - if (file_ext == "avi" || file_ext == "mp4" || file_ext == "mjpg" || file_ext == "mov" || // video file - protocol == "rtmp://" || protocol == "rtsp://" || protocol == "http://" || protocol == "https:/") // video network stream - { - cv::Mat cap_frame, cur_frame, det_frame, write_frame; - std::queue track_optflow_queue; - int passed_flow_frames = 0; - std::shared_ptr det_image; - std::vector result_vec, thread_result_vec; - detector.nms = 0.02; // comment it - if track_id is not required - std::atomic consumed, videowrite_ready; - bool exit_flag = false; - consumed = true; - videowrite_ready = true; - std::atomic fps_det_counter, fps_cap_counter; - fps_det_counter = 0; - fps_cap_counter = 0; - int current_det_fps = 0, current_cap_fps = 0; - std::thread t_detect, t_cap, t_videowrite; - std::mutex mtx; - std::condition_variable cv_detected, cv_pre_tracked; - std::chrono::steady_clock::time_point steady_start, steady_end; - cv::VideoCapture cap(filename); cap >> cur_frame; - int const video_fps = cap.get(CV_CAP_PROP_FPS); - cv::Size const frame_size = cur_frame.size(); - cv::VideoWriter output_video; - if (save_output_videofile) - output_video.open(out_videofile, CV_FOURCC('D', 'I', 'V', 'X'), std::max(35, video_fps), frame_size, true); - - while (!cur_frame.empty()) - { - // always sync - if (t_cap.joinable()) { - t_cap.join(); - ++fps_cap_counter; - cur_frame = cap_frame.clone(); - } - t_cap = std::thread([&]() { cap >> cap_frame; }); - ++cur_time_extrapolate; - - // swap result bouned-boxes and input-frame - if(consumed) - { - std::unique_lock lock(mtx); - det_image = detector.mat_to_image_resize(cur_frame); - auto old_result_vec = detector.tracking_id(result_vec); - auto detected_result_vec = thread_result_vec; - result_vec = detected_result_vec; + extrapolate_coords_t extrapolate_coords; + bool extrapolate_flag = false; + float cur_time_extrapolate = 0, old_time_extrapolate = 0; + preview_boxes_t large_preview(100, 150, false), small_preview(50, 50, true); + bool show_small_boxes = false; + + std::string const file_ext = filename.substr(filename.find_last_of(".") + 1); + std::string const protocol = filename.substr(0, 7); + if (file_ext == "avi" || file_ext == "mp4" || file_ext == "mjpg" || file_ext == "mov" || // video file + protocol == "rtmp://" || protocol == "rtsp://" || protocol == "http://" || protocol == "https:/") // video network stream + { + cv::Mat cap_frame, cur_frame, det_frame, write_frame; + std::queue track_optflow_queue; + int passed_flow_frames = 0; + std::shared_ptr det_image; + std::vector result_vec, thread_result_vec; + detector.nms = 0.02; // comment it - if track_id is not required + std::atomic consumed, videowrite_ready; + bool exit_flag = false; + consumed = true; + videowrite_ready = true; + std::atomic fps_det_counter, fps_cap_counter; + fps_det_counter = 0; + fps_cap_counter = 0; + int current_det_fps = 0, current_cap_fps = 0; + std::thread t_detect, t_cap, t_videowrite; + std::mutex mtx; + std::condition_variable cv_detected, cv_pre_tracked; + std::chrono::steady_clock::time_point steady_start, steady_end; + cv::VideoCapture cap(filename); cap >> cur_frame; + int const video_fps = cap.get(CV_CAP_PROP_FPS); + cv::Size const frame_size = cur_frame.size(); + cv::VideoWriter output_video; + if (save_output_videofile) + output_video.open(out_videofile, CV_FOURCC('D', 'I', 'V', 'X'), std::max(35, video_fps), frame_size, true); + + while (!cur_frame.empty()) + { + // always sync + if (t_cap.joinable()) { + t_cap.join(); + ++fps_cap_counter; + cur_frame = cap_frame.clone(); + } + t_cap = std::thread([&]() { cap >> cap_frame; }); + ++cur_time_extrapolate; + + // swap result bouned-boxes and input-frame + if(consumed) + { + std::unique_lock lock(mtx); + det_image = detector.mat_to_image_resize(cur_frame); + auto old_result_vec = detector.tracking_id(result_vec); + auto detected_result_vec = thread_result_vec; + result_vec = detected_result_vec; #ifdef TRACK_OPTFLOW - // track optical flow - if (track_optflow_queue.size() > 0) { - //std::cout << "\n !!!! all = " << track_optflow_queue.size() << ", cur = " << passed_flow_frames << std::endl; - cv::Mat first_frame = track_optflow_queue.front(); - tracker_flow.update_tracking_flow(track_optflow_queue.front(), result_vec); - - while (track_optflow_queue.size() > 1) { - track_optflow_queue.pop(); - result_vec = tracker_flow.tracking_flow(track_optflow_queue.front(), true); - } - track_optflow_queue.pop(); - passed_flow_frames = 0; - - result_vec = detector.tracking_id(result_vec); - auto tmp_result_vec = detector.tracking_id(detected_result_vec, false); - small_preview.set(first_frame, tmp_result_vec); - - extrapolate_coords.new_result(tmp_result_vec, old_time_extrapolate); - old_time_extrapolate = cur_time_extrapolate; - extrapolate_coords.update_result(result_vec, cur_time_extrapolate - 1); - } + // track optical flow + if (track_optflow_queue.size() > 0) { + //std::cout << "\n !!!! all = " << track_optflow_queue.size() << ", cur = " << passed_flow_frames << std::endl; + cv::Mat first_frame = track_optflow_queue.front(); + tracker_flow.update_tracking_flow(track_optflow_queue.front(), result_vec); + + while (track_optflow_queue.size() > 1) { + track_optflow_queue.pop(); + result_vec = tracker_flow.tracking_flow(track_optflow_queue.front(), true); + } + track_optflow_queue.pop(); + passed_flow_frames = 0; + + result_vec = detector.tracking_id(result_vec); + auto tmp_result_vec = detector.tracking_id(detected_result_vec, false); + small_preview.set(first_frame, tmp_result_vec); + + extrapolate_coords.new_result(tmp_result_vec, old_time_extrapolate); + old_time_extrapolate = cur_time_extrapolate; + extrapolate_coords.update_result(result_vec, cur_time_extrapolate - 1); + } #else - result_vec = detector.tracking_id(result_vec); // comment it - if track_id is not required - extrapolate_coords.new_result(result_vec, cur_time_extrapolate - 1); + result_vec = detector.tracking_id(result_vec); // comment it - if track_id is not required + extrapolate_coords.new_result(result_vec, cur_time_extrapolate - 1); #endif - // add old tracked objects - for (auto &i : old_result_vec) { - auto it = std::find_if(result_vec.begin(), result_vec.end(), - [&i](bbox_t const& b) { return b.track_id == i.track_id && b.obj_id == i.obj_id; }); - bool track_id_absent = (it == result_vec.end()); - if (track_id_absent) { - if (i.frames_counter-- > 1) - result_vec.push_back(i); - } - else { - it->frames_counter = std::min((unsigned)3, i.frames_counter + 1); - } - } + // add old tracked objects + for (auto &i : old_result_vec) { + auto it = std::find_if(result_vec.begin(), result_vec.end(), + [&i](bbox_t const& b) { return b.track_id == i.track_id && b.obj_id == i.obj_id; }); + bool track_id_absent = (it == result_vec.end()); + if (track_id_absent) { + if (i.frames_counter-- > 1) + result_vec.push_back(i); + } + else { + it->frames_counter = std::min((unsigned)3, i.frames_counter + 1); + } + } #ifdef TRACK_OPTFLOW - tracker_flow.update_cur_bbox_vec(result_vec); - result_vec = tracker_flow.tracking_flow(cur_frame, true); // track optical flow + tracker_flow.update_cur_bbox_vec(result_vec); + result_vec = tracker_flow.tracking_flow(cur_frame, true); // track optical flow #endif - consumed = false; - cv_pre_tracked.notify_all(); - } - // launch thread once - Detection - if (!t_detect.joinable()) { - t_detect = std::thread([&]() { - auto current_image = det_image; - consumed = true; - while (current_image.use_count() > 0 && !exit_flag) { - auto result = detector.detect_resized(*current_image, frame_size.width, frame_size.height, - thresh, false); // true - ++fps_det_counter; - std::unique_lock lock(mtx); - thread_result_vec = result; - consumed = true; - cv_detected.notify_all(); - if (detector.wait_stream) { - while (consumed && !exit_flag) cv_pre_tracked.wait(lock); - } - current_image = det_image; - } - }); - } - //while (!consumed); // sync detection - - if (!cur_frame.empty()) { - steady_end = std::chrono::steady_clock::now(); - if (std::chrono::duration(steady_end - steady_start).count() >= 1) { - current_det_fps = fps_det_counter; - current_cap_fps = fps_cap_counter; - steady_start = steady_end; - fps_det_counter = 0; - fps_cap_counter = 0; - } - - large_preview.set(cur_frame, result_vec); + consumed = false; + cv_pre_tracked.notify_all(); + } + // launch thread once - Detection + if (!t_detect.joinable()) { + t_detect = std::thread([&]() { + auto current_image = det_image; + consumed = true; + while (current_image.use_count() > 0 && !exit_flag) { + auto result = detector.detect_resized(*current_image, frame_size.width, frame_size.height, + thresh, false); // true + ++fps_det_counter; + std::unique_lock lock(mtx); + thread_result_vec = result; + consumed = true; + cv_detected.notify_all(); + if (detector.wait_stream) { + while (consumed && !exit_flag) cv_pre_tracked.wait(lock); + } + current_image = det_image; + } + }); + } + //while (!consumed); // sync detection + + if (!cur_frame.empty()) { + steady_end = std::chrono::steady_clock::now(); + if (std::chrono::duration(steady_end - steady_start).count() >= 1) { + current_det_fps = fps_det_counter; + current_cap_fps = fps_cap_counter; + steady_start = steady_end; + fps_det_counter = 0; + fps_cap_counter = 0; + } + + large_preview.set(cur_frame, result_vec); #ifdef TRACK_OPTFLOW - ++passed_flow_frames; - track_optflow_queue.push(cur_frame.clone()); - result_vec = tracker_flow.tracking_flow(cur_frame); // track optical flow - extrapolate_coords.update_result(result_vec, cur_time_extrapolate); - small_preview.draw(cur_frame, show_small_boxes); -#endif - auto result_vec_draw = result_vec; - if (extrapolate_flag) { - result_vec_draw = extrapolate_coords.predict(cur_time_extrapolate); - cv::putText(cur_frame, "extrapolate", cv::Point2f(10, 40), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.0, cv::Scalar(50, 50, 0), 2); - } - draw_boxes(cur_frame, result_vec_draw, obj_names, current_det_fps, current_cap_fps); - //show_console_result(result_vec, obj_names); - large_preview.draw(cur_frame); - - cv::imshow("window name", cur_frame); - int key = cv::waitKey(3); // 3 or 16ms - if (key == 'f') show_small_boxes = !show_small_boxes; - if (key == 'p') while (true) if(cv::waitKey(100) == 'p') break; - if (key == 'e') extrapolate_flag = !extrapolate_flag; - if (key == 27) { exit_flag = true; break; } - - if (output_video.isOpened() && videowrite_ready) { - if (t_videowrite.joinable()) t_videowrite.join(); - write_frame = cur_frame.clone(); - videowrite_ready = false; - t_videowrite = std::thread([&]() { - output_video << write_frame; videowrite_ready = true; - }); - } - } + ++passed_flow_frames; + track_optflow_queue.push(cur_frame.clone()); + result_vec = tracker_flow.tracking_flow(cur_frame); // track optical flow + extrapolate_coords.update_result(result_vec, cur_time_extrapolate); + small_preview.draw(cur_frame, show_small_boxes); +#endif + auto result_vec_draw = result_vec; + if (extrapolate_flag) { + result_vec_draw = extrapolate_coords.predict(cur_time_extrapolate); + cv::putText(cur_frame, "extrapolate", cv::Point2f(10, 40), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.0, cv::Scalar(50, 50, 0), 2); + } + draw_boxes(cur_frame, result_vec_draw, obj_names, current_det_fps, current_cap_fps); + //show_console_result(result_vec, obj_names); + large_preview.draw(cur_frame); + + cv::imshow("window name", cur_frame); + int key = cv::waitKey(3); // 3 or 16ms + if (key == 'f') show_small_boxes = !show_small_boxes; + if (key == 'p') while (true) if(cv::waitKey(100) == 'p') break; + if (key == 'e') extrapolate_flag = !extrapolate_flag; + if (key == 27) { exit_flag = true; break; } + + if (output_video.isOpened() && videowrite_ready) { + if (t_videowrite.joinable()) t_videowrite.join(); + write_frame = cur_frame.clone(); + videowrite_ready = false; + t_videowrite = std::thread([&]() { + output_video << write_frame; videowrite_ready = true; + }); + } + } #ifndef TRACK_OPTFLOW - // wait detection result for video-file only (not for net-cam) - if (protocol != "rtsp://" && protocol != "http://" && protocol != "https:/") { - std::unique_lock lock(mtx); - while (!consumed) cv_detected.wait(lock); - } + // wait detection result for video-file only (not for net-cam) + if (protocol != "rtsp://" && protocol != "http://" && protocol != "https:/") { + std::unique_lock lock(mtx); + while (!consumed) cv_detected.wait(lock); + } #endif - } - exit_flag = true; - if (t_cap.joinable()) t_cap.join(); - if (t_detect.joinable()) t_detect.join(); - if (t_videowrite.joinable()) t_videowrite.join(); - std::cout << "Video ended \n"; - break; - } - else if (file_ext == "txt") { // list of image files - std::ifstream file(filename); - if (!file.is_open()) std::cout << "File not found! \n"; - else - for (std::string line; file >> line;) { - std::cout << line << std::endl; - cv::Mat mat_img = cv::imread(line); - std::vector result_vec = detector.detect(mat_img); - show_console_result(result_vec, obj_names); - //draw_boxes(mat_img, result_vec, obj_names); - //cv::imwrite("res_" + line, mat_img); - } - - } - else { // image file - cv::Mat mat_img = cv::imread(filename); - - auto start = std::chrono::steady_clock::now(); - std::vector result_vec = detector.detect(mat_img); - auto end = std::chrono::steady_clock::now(); - std::chrono::duration spent = end - start; - std::cout << " Time: " << spent.count() << " sec \n"; - - //result_vec = detector.tracking_id(result_vec); // comment it - if track_id is not required - draw_boxes(mat_img, result_vec, obj_names); - cv::imshow("window name", mat_img); - show_console_result(result_vec, obj_names); - cv::waitKey(0); - } + } + exit_flag = true; + if (t_cap.joinable()) t_cap.join(); + if (t_detect.joinable()) t_detect.join(); + if (t_videowrite.joinable()) t_videowrite.join(); + std::cout << "Video ended \n"; + break; + } + else if (file_ext == "txt") { // list of image files + std::ifstream file(filename); + if (!file.is_open()) std::cout << "File not found! \n"; + else + for (std::string line; file >> line;) { + std::cout << line << std::endl; + cv::Mat mat_img = cv::imread(line); + std::vector result_vec = detector.detect(mat_img); + show_console_result(result_vec, obj_names); + //draw_boxes(mat_img, result_vec, obj_names); + //cv::imwrite("res_" + line, mat_img); + } + + } + else { // image file + cv::Mat mat_img = cv::imread(filename); + + auto start = std::chrono::steady_clock::now(); + std::vector result_vec = detector.detect(mat_img); + auto end = std::chrono::steady_clock::now(); + std::chrono::duration spent = end - start; + std::cout << " Time: " << spent.count() << " sec \n"; + + //result_vec = detector.tracking_id(result_vec); // comment it - if track_id is not required + draw_boxes(mat_img, result_vec, obj_names); + cv::imshow("window name", mat_img); + show_console_result(result_vec, obj_names); + cv::waitKey(0); + } #else - //std::vector result_vec = detector.detect(filename); - - auto img = detector.load_image(filename); - std::vector result_vec = detector.detect(img); - detector.free_image(img); - show_console_result(result_vec, obj_names); -#endif - } - catch (std::exception &e) { std::cerr << "exception: " << e.what() << "\n"; getchar(); } - catch (...) { std::cerr << "unknown exception \n"; getchar(); } - filename.clear(); - } - - return 0; + //std::vector result_vec = detector.detect(filename); + + auto img = detector.load_image(filename); + std::vector result_vec = detector.detect(img); + detector.free_image(img); + show_console_result(result_vec, obj_names); +#endif + } + catch (std::exception &e) { std::cerr << "exception: " << e.what() << "\n"; getchar(); } + catch (...) { std::cerr << "unknown exception \n"; getchar(); } + filename.clear(); + } + + return 0; } \ No newline at end of file diff --git a/src/yolo_layer.c b/src/yolo_layer.c index f79bc418943..f0bc0731754 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -38,8 +38,8 @@ layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int l.bias_updates = calloc(n*2, sizeof(float)); l.outputs = h*w*n*(classes + 4 + 1); l.inputs = l.outputs; - l.max_boxes = max_boxes; - l.truths = l.max_boxes*(4 + 1); // 90*(4 + 1); + l.max_boxes = max_boxes; + l.truths = l.max_boxes*(4 + 1); // 90*(4 + 1); l.delta = calloc(batch*l.outputs, sizeof(float)); l.output = calloc(batch*l.outputs, sizeof(float)); for(i = 0; i < total*2; ++i){ @@ -117,33 +117,33 @@ void delta_yolo_class(float *output, float *delta, int index, int class_id, int if(avg_cat) *avg_cat += output[index + stride*class_id]; return; } - // Focal loss - if (focal_loss) { - // Focal Loss - float alpha = 0.5; // 0.25 or 0.5 - //float gamma = 2; // hardcoded in many places of the grad-formula - - int ti = index + stride*class_id; - float pt = output[ti] + 0.000000000000001F; - // http://fooplot.com/#W3sidHlwZSI6MCwiZXEiOiItKDEteCkqKDIqeCpsb2coeCkreC0xKSIsImNvbG9yIjoiIzAwMDAwMCJ9LHsidHlwZSI6MTAwMH1d - float grad = -(1 - pt) * (2 * pt*logf(pt) + pt - 1); // http://blog.csdn.net/linmingan/article/details/77885832 - //float grad = (1 - pt) * (2 * pt*logf(pt) + pt - 1); // https://github.com/unsky/focal-loss - - for (n = 0; n < classes; ++n) { - delta[index + stride*n] = (((n == class_id) ? 1 : 0) - output[index + stride*n]); - - delta[index + stride*n] *= alpha*grad; - - if (n == class_id) *avg_cat += output[index + stride*n]; - } - } - else { - // default - for (n = 0; n < classes; ++n) { - delta[index + stride*n] = ((n == class_id) ? 1 : 0) - output[index + stride*n]; - if (n == class_id && avg_cat) *avg_cat += output[index + stride*n]; - } - } + // Focal loss + if (focal_loss) { + // Focal Loss + float alpha = 0.5; // 0.25 or 0.5 + //float gamma = 2; // hardcoded in many places of the grad-formula + + int ti = index + stride*class_id; + float pt = output[ti] + 0.000000000000001F; + // http://fooplot.com/#W3sidHlwZSI6MCwiZXEiOiItKDEteCkqKDIqeCpsb2coeCkreC0xKSIsImNvbG9yIjoiIzAwMDAwMCJ9LHsidHlwZSI6MTAwMH1d + float grad = -(1 - pt) * (2 * pt*logf(pt) + pt - 1); // http://blog.csdn.net/linmingan/article/details/77885832 + //float grad = (1 - pt) * (2 * pt*logf(pt) + pt - 1); // https://github.com/unsky/focal-loss + + for (n = 0; n < classes; ++n) { + delta[index + stride*n] = (((n == class_id) ? 1 : 0) - output[index + stride*n]); + + delta[index + stride*n] *= alpha*grad; + + if (n == class_id) *avg_cat += output[index + stride*n]; + } + } + else { + // default + for (n = 0; n < classes; ++n) { + delta[index + stride*n] = ((n == class_id) ? 1 : 0) - output[index + stride*n]; + if (n == class_id && avg_cat) *avg_cat += output[index + stride*n]; + } + } } static int entry_index(layer l, int batch, int location, int entry) @@ -155,12 +155,12 @@ static int entry_index(layer l, int batch, int location, int entry) static box float_to_box_stride(float *f, int stride) { - box b = { 0 }; - b.x = f[0]; - b.y = f[1 * stride]; - b.w = f[2 * stride]; - b.h = f[3 * stride]; - return b; + box b = { 0 }; + b.x = f[0]; + b.y = f[1 * stride]; + b.w = f[2 * stride]; + b.h = f[3 * stride]; + return b; } void forward_yolo_layer(const layer l, network_state state) @@ -200,12 +200,12 @@ void forward_yolo_layer(const layer l, network_state state) int best_t = 0; for(t = 0; t < l.max_boxes; ++t){ box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); - int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; - if (class_id >= l.classes) { - printf(" Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1); - getchar(); - continue; // if label contains class_id more than number of classes in the cfg-file - } + int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; + if (class_id >= l.classes) { + printf(" Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1); + getchar(); + continue; // if label contains class_id more than number of classes in the cfg-file + } if(!truth.x) break; float iou = box_iou(pred, truth); if (iou > best_iou) { @@ -234,8 +234,8 @@ void forward_yolo_layer(const layer l, network_state state) } for(t = 0; t < l.max_boxes; ++t){ box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); - int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; - if (class_id >= l.classes) continue; // if label contains class_id more than number of classes in the cfg-file + int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; + if (class_id >= l.classes) continue; // if label contains class_id more than number of classes in the cfg-file if(!truth.x) break; float best_iou = 0; @@ -291,20 +291,20 @@ void correct_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth int i; int new_w=0; int new_h=0; - if (letter) { - if (((float)netw / w) < ((float)neth / h)) { - new_w = netw; - new_h = (h * netw) / w; - } - else { - new_h = neth; - new_w = (w * neth) / h; - } - } - else { - new_w = netw; - new_h = neth; - } + if (letter) { + if (((float)netw / w) < ((float)neth / h)) { + new_w = netw; + new_h = (h * netw) / w; + } + else { + new_h = neth; + new_w = (w * neth) / h; + } + } + else { + new_w = netw; + new_h = neth; + } for (i = 0; i < n; ++i){ box b = dets[i].bbox; b.x = (b.x - (netw - new_w)/2./netw) / ((float)new_w/netw); @@ -411,25 +411,25 @@ void forward_yolo_layer_gpu(const layer l, network_state state) } //cuda_pull_array(l.output_gpu, state.input, l.batch*l.inputs); - float *in_cpu = calloc(l.batch*l.inputs, sizeof(float)); - cuda_pull_array(l.output_gpu, in_cpu, l.batch*l.inputs); - float *truth_cpu = 0; - if (state.truth) { - int num_truth = l.batch*l.truths; - truth_cpu = calloc(num_truth, sizeof(float)); - cuda_pull_array(state.truth, truth_cpu, num_truth); - } - network_state cpu_state = state; - cpu_state.net = state.net; - cpu_state.index = state.index; - cpu_state.train = state.train; - cpu_state.truth = truth_cpu; - cpu_state.input = in_cpu; - forward_yolo_layer(l, cpu_state); + float *in_cpu = calloc(l.batch*l.inputs, sizeof(float)); + cuda_pull_array(l.output_gpu, in_cpu, l.batch*l.inputs); + float *truth_cpu = 0; + if (state.truth) { + int num_truth = l.batch*l.truths; + truth_cpu = calloc(num_truth, sizeof(float)); + cuda_pull_array(state.truth, truth_cpu, num_truth); + } + network_state cpu_state = state; + cpu_state.net = state.net; + cpu_state.index = state.index; + cpu_state.train = state.train; + cpu_state.truth = truth_cpu; + cpu_state.input = in_cpu; + forward_yolo_layer(l, cpu_state); //forward_yolo_layer(l, state); cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs); - free(in_cpu); - if (cpu_state.truth) free(cpu_state.truth); + free(in_cpu); + if (cpu_state.truth) free(cpu_state.truth); } void backward_yolo_layer_gpu(const layer l, network_state state) diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index 4df9be5d44f..980db93a64c 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -50,310 +50,310 @@ int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container & return detection.size(); #else return -1; -#endif // OPENCV +#endif // OPENCV } int dispose() { - //if (detector != NULL) delete detector; - //detector = NULL; + //if (detector != NULL) delete detector; + //detector = NULL; detector.reset(); return 1; } #ifdef GPU void check_cuda(cudaError_t status) { - if (status != cudaSuccess) { - const char *s = cudaGetErrorString(status); - printf("CUDA Error Prev: %s\n", s); - } + if (status != cudaSuccess) { + const char *s = cudaGetErrorString(status); + printf("CUDA Error Prev: %s\n", s); + } } #endif struct detector_gpu_t { - network net; - image images[FRAMES]; - float *avg; - float *predictions[FRAMES]; - int demo_index; - unsigned int *track_id; + network net; + image images[FRAMES]; + float *avg; + float *predictions[FRAMES]; + int demo_index; + unsigned int *track_id; }; YOLODLL_API Detector::Detector(std::string cfg_filename, std::string weight_filename, int gpu_id) : cur_gpu_id(gpu_id) { - wait_stream = 0; - int old_gpu_index; + wait_stream = 0; + int old_gpu_index; #ifdef GPU - check_cuda( cudaGetDevice(&old_gpu_index) ); + check_cuda( cudaGetDevice(&old_gpu_index) ); #endif - detector_gpu_ptr = std::make_shared(); - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + detector_gpu_ptr = std::make_shared(); + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); #ifdef GPU - //check_cuda( cudaSetDevice(cur_gpu_id) ); - cuda_set_device(cur_gpu_id); - printf(" Used GPU %d \n", cur_gpu_id); + //check_cuda( cudaSetDevice(cur_gpu_id) ); + cuda_set_device(cur_gpu_id); + printf(" Used GPU %d \n", cur_gpu_id); #endif - network &net = detector_gpu.net; - net.gpu_index = cur_gpu_id; - //gpu_index = i; - - char *cfgfile = const_cast(cfg_filename.data()); - char *weightfile = const_cast(weight_filename.data()); - - net = parse_network_cfg_custom(cfgfile, 1); - if (weightfile) { - load_weights(&net, weightfile); - } - set_batch_network(&net, 1); - net.gpu_index = cur_gpu_id; - fuse_conv_batchnorm(net); - - layer l = net.layers[net.n - 1]; - int j; - - detector_gpu.avg = (float *)calloc(l.outputs, sizeof(float)); - for (j = 0; j < FRAMES; ++j) detector_gpu.predictions[j] = (float *)calloc(l.outputs, sizeof(float)); - for (j = 0; j < FRAMES; ++j) detector_gpu.images[j] = make_image(1, 1, 3); - - detector_gpu.track_id = (unsigned int *)calloc(l.classes, sizeof(unsigned int)); - for (j = 0; j < l.classes; ++j) detector_gpu.track_id[j] = 1; + network &net = detector_gpu.net; + net.gpu_index = cur_gpu_id; + //gpu_index = i; + + char *cfgfile = const_cast(cfg_filename.data()); + char *weightfile = const_cast(weight_filename.data()); + + net = parse_network_cfg_custom(cfgfile, 1); + if (weightfile) { + load_weights(&net, weightfile); + } + set_batch_network(&net, 1); + net.gpu_index = cur_gpu_id; + fuse_conv_batchnorm(net); + + layer l = net.layers[net.n - 1]; + int j; + + detector_gpu.avg = (float *)calloc(l.outputs, sizeof(float)); + for (j = 0; j < FRAMES; ++j) detector_gpu.predictions[j] = (float *)calloc(l.outputs, sizeof(float)); + for (j = 0; j < FRAMES; ++j) detector_gpu.images[j] = make_image(1, 1, 3); + + detector_gpu.track_id = (unsigned int *)calloc(l.classes, sizeof(unsigned int)); + for (j = 0; j < l.classes; ++j) detector_gpu.track_id[j] = 1; #ifdef GPU - check_cuda( cudaSetDevice(old_gpu_index) ); + check_cuda( cudaSetDevice(old_gpu_index) ); #endif } YOLODLL_API Detector::~Detector() { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - layer l = detector_gpu.net.layers[detector_gpu.net.n - 1]; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + layer l = detector_gpu.net.layers[detector_gpu.net.n - 1]; - free(detector_gpu.track_id); + free(detector_gpu.track_id); - free(detector_gpu.avg); - for (int j = 0; j < FRAMES; ++j) free(detector_gpu.predictions[j]); - for (int j = 0; j < FRAMES; ++j) if(detector_gpu.images[j].data) free(detector_gpu.images[j].data); + free(detector_gpu.avg); + for (int j = 0; j < FRAMES; ++j) free(detector_gpu.predictions[j]); + for (int j = 0; j < FRAMES; ++j) if(detector_gpu.images[j].data) free(detector_gpu.images[j].data); - int old_gpu_index; + int old_gpu_index; #ifdef GPU - cudaGetDevice(&old_gpu_index); - cuda_set_device(detector_gpu.net.gpu_index); + cudaGetDevice(&old_gpu_index); + cuda_set_device(detector_gpu.net.gpu_index); #endif - free_network(detector_gpu.net); + free_network(detector_gpu.net); #ifdef GPU - cudaSetDevice(old_gpu_index); + cudaSetDevice(old_gpu_index); #endif } YOLODLL_API int Detector::get_net_width() const { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - return detector_gpu.net.w; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + return detector_gpu.net.w; } YOLODLL_API int Detector::get_net_height() const { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - return detector_gpu.net.h; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + return detector_gpu.net.h; } YOLODLL_API int Detector::get_net_color_depth() const { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - return detector_gpu.net.c; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + return detector_gpu.net.c; } YOLODLL_API std::vector Detector::detect(std::string image_filename, float thresh, bool use_mean) { - std::shared_ptr image_ptr(new image_t, [](image_t *img) { if (img->data) free(img->data); delete img; }); - *image_ptr = load_image(image_filename); - return detect(*image_ptr, thresh, use_mean); + std::shared_ptr image_ptr(new image_t, [](image_t *img) { if (img->data) free(img->data); delete img; }); + *image_ptr = load_image(image_filename); + return detect(*image_ptr, thresh, use_mean); } static image load_image_stb(char *filename, int channels) { - int w, h, c; - unsigned char *data = stbi_load(filename, &w, &h, &c, channels); - if (!data) - throw std::runtime_error("file not found"); - if (channels) c = channels; - int i, j, k; - image im = make_image(w, h, c); - for (k = 0; k < c; ++k) { - for (j = 0; j < h; ++j) { - for (i = 0; i < w; ++i) { - int dst_index = i + w*j + w*h*k; - int src_index = k + c*i + c*w*j; - im.data[dst_index] = (float)data[src_index] / 255.; - } - } - } - free(data); - return im; + int w, h, c; + unsigned char *data = stbi_load(filename, &w, &h, &c, channels); + if (!data) + throw std::runtime_error("file not found"); + if (channels) c = channels; + int i, j, k; + image im = make_image(w, h, c); + for (k = 0; k < c; ++k) { + for (j = 0; j < h; ++j) { + for (i = 0; i < w; ++i) { + int dst_index = i + w*j + w*h*k; + int src_index = k + c*i + c*w*j; + im.data[dst_index] = (float)data[src_index] / 255.; + } + } + } + free(data); + return im; } YOLODLL_API image_t Detector::load_image(std::string image_filename) { - char *input = const_cast(image_filename.data()); - image im = load_image_stb(input, 3); + char *input = const_cast(image_filename.data()); + image im = load_image_stb(input, 3); - image_t img; - img.c = im.c; - img.data = im.data; - img.h = im.h; - img.w = im.w; + image_t img; + img.c = im.c; + img.data = im.data; + img.h = im.h; + img.w = im.w; - return img; + return img; } YOLODLL_API void Detector::free_image(image_t m) { - if (m.data) { - free(m.data); - } + if (m.data) { + free(m.data); + } } YOLODLL_API std::vector Detector::detect(image_t img, float thresh, bool use_mean) { - detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - network &net = detector_gpu.net; - int old_gpu_index; + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + network &net = detector_gpu.net; + int old_gpu_index; #ifdef GPU - cudaGetDevice(&old_gpu_index); - if(cur_gpu_id != old_gpu_index) - cudaSetDevice(net.gpu_index); + cudaGetDevice(&old_gpu_index); + if(cur_gpu_id != old_gpu_index) + cudaSetDevice(net.gpu_index); - net.wait_stream = wait_stream; // 1 - wait CUDA-stream, 0 - not to wait + net.wait_stream = wait_stream; // 1 - wait CUDA-stream, 0 - not to wait #endif - //std::cout << "net.gpu_index = " << net.gpu_index << std::endl; - - //float nms = .4; - - image im; - im.c = img.c; - im.data = img.data; - im.h = img.h; - im.w = img.w; - - image sized; - - if (net.w == im.w && net.h == im.h) { - sized = make_image(im.w, im.h, im.c); - memcpy(sized.data, im.data, im.w*im.h*im.c * sizeof(float)); - } - else - sized = resize_image(im, net.w, net.h); - - layer l = net.layers[net.n - 1]; - - float *X = sized.data; - - float *prediction = network_predict(net, X); - - if (use_mean) { - memcpy(detector_gpu.predictions[detector_gpu.demo_index], prediction, l.outputs * sizeof(float)); - mean_arrays(detector_gpu.predictions, FRAMES, l.outputs, detector_gpu.avg); - l.output = detector_gpu.avg; - detector_gpu.demo_index = (detector_gpu.demo_index + 1) % FRAMES; - } - //get_region_boxes(l, 1, 1, thresh, detector_gpu.probs, detector_gpu.boxes, 0, 0); - //if (nms) do_nms_sort(detector_gpu.boxes, detector_gpu.probs, l.w*l.h*l.n, l.classes, nms); - - int nboxes = 0; - int letterbox = 0; - float hier_thresh = 0.5; - detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); - if (nms) do_nms_sort(dets, nboxes, l.classes, nms); - - std::vector bbox_vec; - - for (size_t i = 0; i < nboxes; ++i) { - box b = dets[i].bbox; - int const obj_id = max_index(dets[i].prob, l.classes); - float const prob = dets[i].prob[obj_id]; - - if (prob > thresh) - { - bbox_t bbox; - bbox.x = std::max((double)0, (b.x - b.w / 2.)*im.w); - bbox.y = std::max((double)0, (b.y - b.h / 2.)*im.h); - bbox.w = b.w*im.w; - bbox.h = b.h*im.h; - bbox.obj_id = obj_id; - bbox.prob = prob; - bbox.track_id = 0; - - bbox_vec.push_back(bbox); - } - } - - free_detections(dets, nboxes); - if(sized.data) - free(sized.data); + //std::cout << "net.gpu_index = " << net.gpu_index << std::endl; + + //float nms = .4; + + image im; + im.c = img.c; + im.data = img.data; + im.h = img.h; + im.w = img.w; + + image sized; + + if (net.w == im.w && net.h == im.h) { + sized = make_image(im.w, im.h, im.c); + memcpy(sized.data, im.data, im.w*im.h*im.c * sizeof(float)); + } + else + sized = resize_image(im, net.w, net.h); + + layer l = net.layers[net.n - 1]; + + float *X = sized.data; + + float *prediction = network_predict(net, X); + + if (use_mean) { + memcpy(detector_gpu.predictions[detector_gpu.demo_index], prediction, l.outputs * sizeof(float)); + mean_arrays(detector_gpu.predictions, FRAMES, l.outputs, detector_gpu.avg); + l.output = detector_gpu.avg; + detector_gpu.demo_index = (detector_gpu.demo_index + 1) % FRAMES; + } + //get_region_boxes(l, 1, 1, thresh, detector_gpu.probs, detector_gpu.boxes, 0, 0); + //if (nms) do_nms_sort(detector_gpu.boxes, detector_gpu.probs, l.w*l.h*l.n, l.classes, nms); + + int nboxes = 0; + int letterbox = 0; + float hier_thresh = 0.5; + detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); + if (nms) do_nms_sort(dets, nboxes, l.classes, nms); + + std::vector bbox_vec; + + for (size_t i = 0; i < nboxes; ++i) { + box b = dets[i].bbox; + int const obj_id = max_index(dets[i].prob, l.classes); + float const prob = dets[i].prob[obj_id]; + + if (prob > thresh) + { + bbox_t bbox; + bbox.x = std::max((double)0, (b.x - b.w / 2.)*im.w); + bbox.y = std::max((double)0, (b.y - b.h / 2.)*im.h); + bbox.w = b.w*im.w; + bbox.h = b.h*im.h; + bbox.obj_id = obj_id; + bbox.prob = prob; + bbox.track_id = 0; + + bbox_vec.push_back(bbox); + } + } + + free_detections(dets, nboxes); + if(sized.data) + free(sized.data); #ifdef GPU - if (cur_gpu_id != old_gpu_index) - cudaSetDevice(old_gpu_index); + if (cur_gpu_id != old_gpu_index) + cudaSetDevice(old_gpu_index); #endif - return bbox_vec; + return bbox_vec; } YOLODLL_API std::vector Detector::tracking_id(std::vector cur_bbox_vec, bool const change_history, - int const frames_story, int const max_dist) + int const frames_story, int const max_dist) { - detector_gpu_t &det_gpu = *static_cast(detector_gpu_ptr.get()); - - bool prev_track_id_present = false; - for (auto &i : prev_bbox_vec_deque) - if (i.size() > 0) prev_track_id_present = true; - - if (!prev_track_id_present) { - for (size_t i = 0; i < cur_bbox_vec.size(); ++i) - cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++; - prev_bbox_vec_deque.push_front(cur_bbox_vec); - if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back(); - return cur_bbox_vec; - } - - std::vector dist_vec(cur_bbox_vec.size(), std::numeric_limits::max()); - - for (auto &prev_bbox_vec : prev_bbox_vec_deque) { - for (auto &i : prev_bbox_vec) { - int cur_index = -1; - for (size_t m = 0; m < cur_bbox_vec.size(); ++m) { - bbox_t const& k = cur_bbox_vec[m]; - if (i.obj_id == k.obj_id) { - float center_x_diff = (float)(i.x + i.w/2) - (float)(k.x + k.w/2); - float center_y_diff = (float)(i.y + i.h/2) - (float)(k.y + k.h/2); - unsigned int cur_dist = sqrt(center_x_diff*center_x_diff + center_y_diff*center_y_diff); - if (cur_dist < max_dist && (k.track_id == 0 || dist_vec[m] > cur_dist)) { - dist_vec[m] = cur_dist; - cur_index = m; - } - } - } - - bool track_id_absent = !std::any_of(cur_bbox_vec.begin(), cur_bbox_vec.end(), - [&i](bbox_t const& b) { return b.track_id == i.track_id && b.obj_id == i.obj_id; }); - - if (cur_index >= 0 && track_id_absent){ - cur_bbox_vec[cur_index].track_id = i.track_id; - cur_bbox_vec[cur_index].w = (cur_bbox_vec[cur_index].w + i.w) / 2; - cur_bbox_vec[cur_index].h = (cur_bbox_vec[cur_index].h + i.h) / 2; - } - } - } - - for (size_t i = 0; i < cur_bbox_vec.size(); ++i) - if (cur_bbox_vec[i].track_id == 0) - cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++; - - if (change_history) { - prev_bbox_vec_deque.push_front(cur_bbox_vec); - if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back(); - } - - return cur_bbox_vec; + detector_gpu_t &det_gpu = *static_cast(detector_gpu_ptr.get()); + + bool prev_track_id_present = false; + for (auto &i : prev_bbox_vec_deque) + if (i.size() > 0) prev_track_id_present = true; + + if (!prev_track_id_present) { + for (size_t i = 0; i < cur_bbox_vec.size(); ++i) + cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++; + prev_bbox_vec_deque.push_front(cur_bbox_vec); + if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back(); + return cur_bbox_vec; + } + + std::vector dist_vec(cur_bbox_vec.size(), std::numeric_limits::max()); + + for (auto &prev_bbox_vec : prev_bbox_vec_deque) { + for (auto &i : prev_bbox_vec) { + int cur_index = -1; + for (size_t m = 0; m < cur_bbox_vec.size(); ++m) { + bbox_t const& k = cur_bbox_vec[m]; + if (i.obj_id == k.obj_id) { + float center_x_diff = (float)(i.x + i.w/2) - (float)(k.x + k.w/2); + float center_y_diff = (float)(i.y + i.h/2) - (float)(k.y + k.h/2); + unsigned int cur_dist = sqrt(center_x_diff*center_x_diff + center_y_diff*center_y_diff); + if (cur_dist < max_dist && (k.track_id == 0 || dist_vec[m] > cur_dist)) { + dist_vec[m] = cur_dist; + cur_index = m; + } + } + } + + bool track_id_absent = !std::any_of(cur_bbox_vec.begin(), cur_bbox_vec.end(), + [&i](bbox_t const& b) { return b.track_id == i.track_id && b.obj_id == i.obj_id; }); + + if (cur_index >= 0 && track_id_absent){ + cur_bbox_vec[cur_index].track_id = i.track_id; + cur_bbox_vec[cur_index].w = (cur_bbox_vec[cur_index].w + i.w) / 2; + cur_bbox_vec[cur_index].h = (cur_bbox_vec[cur_index].h + i.h) / 2; + } + } + } + + for (size_t i = 0; i < cur_bbox_vec.size(); ++i) + if (cur_bbox_vec[i].track_id == 0) + cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++; + + if (change_history) { + prev_bbox_vec_deque.push_front(cur_bbox_vec); + if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back(); + } + + return cur_bbox_vec; } \ No newline at end of file diff --git a/src/yolo_v2_class.hpp b/src/yolo_v2_class.hpp index 8f7f6cab5c6..8626d97f0cb 100644 --- a/src/yolo_v2_class.hpp +++ b/src/yolo_v2_class.hpp @@ -14,18 +14,18 @@ #endif struct bbox_t { - unsigned int x, y, w, h; // (x,y) - top-left corner, (w, h) - width & height of bounded box - float prob; // confidence - probability that the object was found correctly - unsigned int obj_id; // class of object - from range [0, classes-1] - unsigned int track_id; // tracking id for video (0 - untracked, 1 - inf - tracked object) - unsigned int frames_counter;// counter of frames on which the object was detected + unsigned int x, y, w, h; // (x,y) - top-left corner, (w, h) - width & height of bounded box + float prob; // confidence - probability that the object was found correctly + unsigned int obj_id; // class of object - from range [0, classes-1] + unsigned int track_id; // tracking id for video (0 - untracked, 1 - inf - tracked object) + unsigned int frames_counter;// counter of frames on which the object was detected }; struct image_t { - int h; // height - int w; // width - int c; // number of chanels (3 - for RGB) - float *data; // pointer to the image data + int h; // height + int w; // width + int c; // number of chanels (3 - for RGB) + float *data; // pointer to the image data }; #define C_SHARP_MAX_OBJECTS 1000 @@ -40,10 +40,10 @@ struct bbox_t_container { #include #ifdef OPENCV -#include // C++ -#include "opencv2/highgui/highgui_c.h" // C -#include "opencv2/imgproc/imgproc_c.h" // C -#endif // OPENCV +#include // C++ +#include "opencv2/highgui/highgui_c.h" // C +#include "opencv2/imgproc/imgproc_c.h" // C +#endif // OPENCV extern "C" YOLODLL_API int init(const char *configurationFilename, const char *weightsFilename, int gpu); extern "C" YOLODLL_API int detect_image(const char *filename, bbox_t_container &container); @@ -51,106 +51,106 @@ extern "C" YOLODLL_API int detect_mat(const uint8_t* data, const size_t data_len extern "C" YOLODLL_API int dispose(); class Detector { - std::shared_ptr detector_gpu_ptr; - std::deque> prev_bbox_vec_deque; - const int cur_gpu_id; + std::shared_ptr detector_gpu_ptr; + std::deque> prev_bbox_vec_deque; + const int cur_gpu_id; public: - float nms = .4; - bool wait_stream; - - YOLODLL_API Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0); - YOLODLL_API ~Detector(); - - YOLODLL_API std::vector detect(std::string image_filename, float thresh = 0.2, bool use_mean = false); - YOLODLL_API std::vector detect(image_t img, float thresh = 0.2, bool use_mean = false); - static YOLODLL_API image_t load_image(std::string image_filename); - static YOLODLL_API void free_image(image_t m); - YOLODLL_API int get_net_width() const; - YOLODLL_API int get_net_height() const; - YOLODLL_API int get_net_color_depth() const; - - YOLODLL_API std::vector tracking_id(std::vector cur_bbox_vec, bool const change_history = true, - int const frames_story = 10, int const max_dist = 150); - - std::vector detect_resized(image_t img, int init_w, int init_h, float thresh = 0.2, bool use_mean = false) - { - if (img.data == NULL) - throw std::runtime_error("Image is empty"); - auto detection_boxes = detect(img, thresh, use_mean); - float wk = (float)init_w / img.w, hk = (float)init_h / img.h; - for (auto &i : detection_boxes) i.x *= wk, i.w *= wk, i.y *= hk, i.h *= hk; - return detection_boxes; - } + float nms = .4; + bool wait_stream; + + YOLODLL_API Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0); + YOLODLL_API ~Detector(); + + YOLODLL_API std::vector detect(std::string image_filename, float thresh = 0.2, bool use_mean = false); + YOLODLL_API std::vector detect(image_t img, float thresh = 0.2, bool use_mean = false); + static YOLODLL_API image_t load_image(std::string image_filename); + static YOLODLL_API void free_image(image_t m); + YOLODLL_API int get_net_width() const; + YOLODLL_API int get_net_height() const; + YOLODLL_API int get_net_color_depth() const; + + YOLODLL_API std::vector tracking_id(std::vector cur_bbox_vec, bool const change_history = true, + int const frames_story = 10, int const max_dist = 150); + + std::vector detect_resized(image_t img, int init_w, int init_h, float thresh = 0.2, bool use_mean = false) + { + if (img.data == NULL) + throw std::runtime_error("Image is empty"); + auto detection_boxes = detect(img, thresh, use_mean); + float wk = (float)init_w / img.w, hk = (float)init_h / img.h; + for (auto &i : detection_boxes) i.x *= wk, i.w *= wk, i.y *= hk, i.h *= hk; + return detection_boxes; + } #ifdef OPENCV - std::vector detect(cv::Mat mat, float thresh = 0.2, bool use_mean = false) - { - if(mat.data == NULL) - throw std::runtime_error("Image is empty"); - auto image_ptr = mat_to_image_resize(mat); - return detect_resized(*image_ptr, mat.cols, mat.rows, thresh, use_mean); - } - - std::shared_ptr mat_to_image_resize(cv::Mat mat) const - { - if (mat.data == NULL) return std::shared_ptr(NULL); - cv::Mat det_mat; - cv::resize(mat, det_mat, cv::Size(get_net_width(), get_net_height())); - return mat_to_image(det_mat); - } - - static std::shared_ptr mat_to_image(cv::Mat img_src) - { - cv::Mat img; - cv::cvtColor(img_src, img, cv::COLOR_RGB2BGR); - std::shared_ptr image_ptr(new image_t, [](image_t *img) { free_image(*img); delete img; }); - std::shared_ptr ipl_small = std::make_shared(img); - *image_ptr = ipl_to_image(ipl_small.get()); - return image_ptr; - } + std::vector detect(cv::Mat mat, float thresh = 0.2, bool use_mean = false) + { + if(mat.data == NULL) + throw std::runtime_error("Image is empty"); + auto image_ptr = mat_to_image_resize(mat); + return detect_resized(*image_ptr, mat.cols, mat.rows, thresh, use_mean); + } + + std::shared_ptr mat_to_image_resize(cv::Mat mat) const + { + if (mat.data == NULL) return std::shared_ptr(NULL); + cv::Mat det_mat; + cv::resize(mat, det_mat, cv::Size(get_net_width(), get_net_height())); + return mat_to_image(det_mat); + } + + static std::shared_ptr mat_to_image(cv::Mat img_src) + { + cv::Mat img; + cv::cvtColor(img_src, img, cv::COLOR_RGB2BGR); + std::shared_ptr image_ptr(new image_t, [](image_t *img) { free_image(*img); delete img; }); + std::shared_ptr ipl_small = std::make_shared(img); + *image_ptr = ipl_to_image(ipl_small.get()); + return image_ptr; + } private: - static image_t ipl_to_image(IplImage* src) - { - unsigned char *data = (unsigned char *)src->imageData; - int h = src->height; - int w = src->width; - int c = src->nChannels; - int step = src->widthStep; - image_t out = make_image_custom(w, h, c); - int count = 0; - - for (int k = 0; k < c; ++k) { - for (int i = 0; i < h; ++i) { - int i_step = i*step; - for (int j = 0; j < w; ++j) { - out.data[count++] = data[i_step + j*c + k] / 255.; - } - } - } - - return out; - } - - static image_t make_empty_image(int w, int h, int c) - { - image_t out; - out.data = 0; - out.h = h; - out.w = w; - out.c = c; - return out; - } - - static image_t make_image_custom(int w, int h, int c) - { - image_t out = make_empty_image(w, h, c); - out.data = (float *)calloc(h*w*c, sizeof(float)); - return out; - } - -#endif // OPENCV + static image_t ipl_to_image(IplImage* src) + { + unsigned char *data = (unsigned char *)src->imageData; + int h = src->height; + int w = src->width; + int c = src->nChannels; + int step = src->widthStep; + image_t out = make_image_custom(w, h, c); + int count = 0; + + for (int k = 0; k < c; ++k) { + for (int i = 0; i < h; ++i) { + int i_step = i*step; + for (int j = 0; j < w; ++j) { + out.data[count++] = data[i_step + j*c + k] / 255.; + } + } + } + + return out; + } + + static image_t make_empty_image(int w, int h, int c) + { + image_t out; + out.data = 0; + out.h = h; + out.w = w; + out.c = c; + return out; + } + + static image_t make_image_custom(int w, int h, int c) + { + image_t out = make_empty_image(w, h, c); + out.data = (float *)calloc(h*w*c, sizeof(float)); + return out; + } + +#endif // OPENCV }; @@ -165,170 +165,170 @@ class Detector { class Tracker_optflow { public: - const int gpu_count; - const int gpu_id; - const int flow_error; - - - Tracker_optflow(int _gpu_id = 0, int win_size = 9, int max_level = 3, int iterations = 8000, int _flow_error = -1) : - gpu_count(cv::cuda::getCudaEnabledDeviceCount()), gpu_id(std::min(_gpu_id, gpu_count-1)), - flow_error((_flow_error > 0)? _flow_error:(win_size*4)) - { - int const old_gpu_id = cv::cuda::getDevice(); - cv::cuda::setDevice(gpu_id); - - stream = cv::cuda::Stream(); - - sync_PyrLKOpticalFlow_gpu = cv::cuda::SparsePyrLKOpticalFlow::create(); - sync_PyrLKOpticalFlow_gpu->setWinSize(cv::Size(win_size, win_size)); // 9, 15, 21, 31 - sync_PyrLKOpticalFlow_gpu->setMaxLevel(max_level); // +- 3 pt - sync_PyrLKOpticalFlow_gpu->setNumIters(iterations); // 2000, def: 30 - - cv::cuda::setDevice(old_gpu_id); - } - - // just to avoid extra allocations - cv::cuda::GpuMat src_mat_gpu; - cv::cuda::GpuMat dst_mat_gpu, dst_grey_gpu; - cv::cuda::GpuMat prev_pts_flow_gpu, cur_pts_flow_gpu; - cv::cuda::GpuMat status_gpu, err_gpu; - - cv::cuda::GpuMat src_grey_gpu; // used in both functions - cv::Ptr sync_PyrLKOpticalFlow_gpu; - cv::cuda::Stream stream; - - std::vector cur_bbox_vec; - std::vector good_bbox_vec_flags; - cv::Mat prev_pts_flow_cpu; - - void update_cur_bbox_vec(std::vector _cur_bbox_vec) - { - cur_bbox_vec = _cur_bbox_vec; - good_bbox_vec_flags = std::vector(cur_bbox_vec.size(), true); - cv::Mat prev_pts, cur_pts_flow_cpu; - - for (auto &i : cur_bbox_vec) { - float x_center = (i.x + i.w / 2.0F); - float y_center = (i.y + i.h / 2.0F); - prev_pts.push_back(cv::Point2f(x_center, y_center)); - } - - if (prev_pts.rows == 0) - prev_pts_flow_cpu = cv::Mat(); - else - cv::transpose(prev_pts, prev_pts_flow_cpu); - - if (prev_pts_flow_gpu.cols < prev_pts_flow_cpu.cols) { - prev_pts_flow_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), prev_pts_flow_cpu.type()); - cur_pts_flow_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), prev_pts_flow_cpu.type()); - - status_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), CV_8UC1); - err_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), CV_32FC1); - } - - prev_pts_flow_gpu.upload(cv::Mat(prev_pts_flow_cpu), stream); - } - - - void update_tracking_flow(cv::Mat src_mat, std::vector _cur_bbox_vec) - { - int const old_gpu_id = cv::cuda::getDevice(); - if (old_gpu_id != gpu_id) - cv::cuda::setDevice(gpu_id); - - if (src_mat.channels() == 3) { - if (src_mat_gpu.cols == 0) { - src_mat_gpu = cv::cuda::GpuMat(src_mat.size(), src_mat.type()); - src_grey_gpu = cv::cuda::GpuMat(src_mat.size(), CV_8UC1); - } - - update_cur_bbox_vec(_cur_bbox_vec); - - //src_grey_gpu.upload(src_mat, stream); // use BGR - src_mat_gpu.upload(src_mat, stream); - cv::cuda::cvtColor(src_mat_gpu, src_grey_gpu, CV_BGR2GRAY, 1, stream); - } - if (old_gpu_id != gpu_id) - cv::cuda::setDevice(old_gpu_id); - } - - - std::vector tracking_flow(cv::Mat dst_mat, bool check_error = true) - { - if (sync_PyrLKOpticalFlow_gpu.empty()) { - std::cout << "sync_PyrLKOpticalFlow_gpu isn't initialized \n"; - return cur_bbox_vec; - } - - int const old_gpu_id = cv::cuda::getDevice(); - if(old_gpu_id != gpu_id) - cv::cuda::setDevice(gpu_id); - - if (dst_mat_gpu.cols == 0) { - dst_mat_gpu = cv::cuda::GpuMat(dst_mat.size(), dst_mat.type()); - dst_grey_gpu = cv::cuda::GpuMat(dst_mat.size(), CV_8UC1); - } - - //dst_grey_gpu.upload(dst_mat, stream); // use BGR - dst_mat_gpu.upload(dst_mat, stream); - cv::cuda::cvtColor(dst_mat_gpu, dst_grey_gpu, CV_BGR2GRAY, 1, stream); - - if (src_grey_gpu.rows != dst_grey_gpu.rows || src_grey_gpu.cols != dst_grey_gpu.cols) { - stream.waitForCompletion(); - src_grey_gpu = dst_grey_gpu.clone(); - cv::cuda::setDevice(old_gpu_id); - return cur_bbox_vec; - } - - ////sync_PyrLKOpticalFlow_gpu.sparse(src_grey_gpu, dst_grey_gpu, prev_pts_flow_gpu, cur_pts_flow_gpu, status_gpu, &err_gpu); // OpenCV 2.4.x - sync_PyrLKOpticalFlow_gpu->calc(src_grey_gpu, dst_grey_gpu, prev_pts_flow_gpu, cur_pts_flow_gpu, status_gpu, err_gpu, stream); // OpenCV 3.x - - cv::Mat cur_pts_flow_cpu; - cur_pts_flow_gpu.download(cur_pts_flow_cpu, stream); - - dst_grey_gpu.copyTo(src_grey_gpu, stream); - - cv::Mat err_cpu, status_cpu; - err_gpu.download(err_cpu, stream); - status_gpu.download(status_cpu, stream); - - stream.waitForCompletion(); - - std::vector result_bbox_vec; - - if (err_cpu.cols == cur_bbox_vec.size() && status_cpu.cols == cur_bbox_vec.size()) - { - for (size_t i = 0; i < cur_bbox_vec.size(); ++i) - { - cv::Point2f cur_key_pt = cur_pts_flow_cpu.at(0, i); - cv::Point2f prev_key_pt = prev_pts_flow_cpu.at(0, i); - - float moved_x = cur_key_pt.x - prev_key_pt.x; - float moved_y = cur_key_pt.y - prev_key_pt.y; - - if (abs(moved_x) < 100 && abs(moved_y) < 100 && good_bbox_vec_flags[i]) - if (err_cpu.at(0, i) < flow_error && status_cpu.at(0, i) != 0 && - ((float)cur_bbox_vec[i].x + moved_x) > 0 && ((float)cur_bbox_vec[i].y + moved_y) > 0) - { - cur_bbox_vec[i].x += moved_x + 0.5; - cur_bbox_vec[i].y += moved_y + 0.5; - result_bbox_vec.push_back(cur_bbox_vec[i]); - } - else good_bbox_vec_flags[i] = false; - else good_bbox_vec_flags[i] = false; + const int gpu_count; + const int gpu_id; + const int flow_error; + + + Tracker_optflow(int _gpu_id = 0, int win_size = 9, int max_level = 3, int iterations = 8000, int _flow_error = -1) : + gpu_count(cv::cuda::getCudaEnabledDeviceCount()), gpu_id(std::min(_gpu_id, gpu_count-1)), + flow_error((_flow_error > 0)? _flow_error:(win_size*4)) + { + int const old_gpu_id = cv::cuda::getDevice(); + cv::cuda::setDevice(gpu_id); + + stream = cv::cuda::Stream(); + + sync_PyrLKOpticalFlow_gpu = cv::cuda::SparsePyrLKOpticalFlow::create(); + sync_PyrLKOpticalFlow_gpu->setWinSize(cv::Size(win_size, win_size)); // 9, 15, 21, 31 + sync_PyrLKOpticalFlow_gpu->setMaxLevel(max_level); // +- 3 pt + sync_PyrLKOpticalFlow_gpu->setNumIters(iterations); // 2000, def: 30 + + cv::cuda::setDevice(old_gpu_id); + } + + // just to avoid extra allocations + cv::cuda::GpuMat src_mat_gpu; + cv::cuda::GpuMat dst_mat_gpu, dst_grey_gpu; + cv::cuda::GpuMat prev_pts_flow_gpu, cur_pts_flow_gpu; + cv::cuda::GpuMat status_gpu, err_gpu; + + cv::cuda::GpuMat src_grey_gpu; // used in both functions + cv::Ptr sync_PyrLKOpticalFlow_gpu; + cv::cuda::Stream stream; + + std::vector cur_bbox_vec; + std::vector good_bbox_vec_flags; + cv::Mat prev_pts_flow_cpu; + + void update_cur_bbox_vec(std::vector _cur_bbox_vec) + { + cur_bbox_vec = _cur_bbox_vec; + good_bbox_vec_flags = std::vector(cur_bbox_vec.size(), true); + cv::Mat prev_pts, cur_pts_flow_cpu; + + for (auto &i : cur_bbox_vec) { + float x_center = (i.x + i.w / 2.0F); + float y_center = (i.y + i.h / 2.0F); + prev_pts.push_back(cv::Point2f(x_center, y_center)); + } + + if (prev_pts.rows == 0) + prev_pts_flow_cpu = cv::Mat(); + else + cv::transpose(prev_pts, prev_pts_flow_cpu); + + if (prev_pts_flow_gpu.cols < prev_pts_flow_cpu.cols) { + prev_pts_flow_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), prev_pts_flow_cpu.type()); + cur_pts_flow_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), prev_pts_flow_cpu.type()); + + status_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), CV_8UC1); + err_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), CV_32FC1); + } + + prev_pts_flow_gpu.upload(cv::Mat(prev_pts_flow_cpu), stream); + } + + + void update_tracking_flow(cv::Mat src_mat, std::vector _cur_bbox_vec) + { + int const old_gpu_id = cv::cuda::getDevice(); + if (old_gpu_id != gpu_id) + cv::cuda::setDevice(gpu_id); + + if (src_mat.channels() == 3) { + if (src_mat_gpu.cols == 0) { + src_mat_gpu = cv::cuda::GpuMat(src_mat.size(), src_mat.type()); + src_grey_gpu = cv::cuda::GpuMat(src_mat.size(), CV_8UC1); + } + + update_cur_bbox_vec(_cur_bbox_vec); + + //src_grey_gpu.upload(src_mat, stream); // use BGR + src_mat_gpu.upload(src_mat, stream); + cv::cuda::cvtColor(src_mat_gpu, src_grey_gpu, CV_BGR2GRAY, 1, stream); + } + if (old_gpu_id != gpu_id) + cv::cuda::setDevice(old_gpu_id); + } + + + std::vector tracking_flow(cv::Mat dst_mat, bool check_error = true) + { + if (sync_PyrLKOpticalFlow_gpu.empty()) { + std::cout << "sync_PyrLKOpticalFlow_gpu isn't initialized \n"; + return cur_bbox_vec; + } + + int const old_gpu_id = cv::cuda::getDevice(); + if(old_gpu_id != gpu_id) + cv::cuda::setDevice(gpu_id); + + if (dst_mat_gpu.cols == 0) { + dst_mat_gpu = cv::cuda::GpuMat(dst_mat.size(), dst_mat.type()); + dst_grey_gpu = cv::cuda::GpuMat(dst_mat.size(), CV_8UC1); + } + + //dst_grey_gpu.upload(dst_mat, stream); // use BGR + dst_mat_gpu.upload(dst_mat, stream); + cv::cuda::cvtColor(dst_mat_gpu, dst_grey_gpu, CV_BGR2GRAY, 1, stream); + + if (src_grey_gpu.rows != dst_grey_gpu.rows || src_grey_gpu.cols != dst_grey_gpu.cols) { + stream.waitForCompletion(); + src_grey_gpu = dst_grey_gpu.clone(); + cv::cuda::setDevice(old_gpu_id); + return cur_bbox_vec; + } + + ////sync_PyrLKOpticalFlow_gpu.sparse(src_grey_gpu, dst_grey_gpu, prev_pts_flow_gpu, cur_pts_flow_gpu, status_gpu, &err_gpu); // OpenCV 2.4.x + sync_PyrLKOpticalFlow_gpu->calc(src_grey_gpu, dst_grey_gpu, prev_pts_flow_gpu, cur_pts_flow_gpu, status_gpu, err_gpu, stream); // OpenCV 3.x + + cv::Mat cur_pts_flow_cpu; + cur_pts_flow_gpu.download(cur_pts_flow_cpu, stream); + + dst_grey_gpu.copyTo(src_grey_gpu, stream); + + cv::Mat err_cpu, status_cpu; + err_gpu.download(err_cpu, stream); + status_gpu.download(status_cpu, stream); + + stream.waitForCompletion(); + + std::vector result_bbox_vec; + + if (err_cpu.cols == cur_bbox_vec.size() && status_cpu.cols == cur_bbox_vec.size()) + { + for (size_t i = 0; i < cur_bbox_vec.size(); ++i) + { + cv::Point2f cur_key_pt = cur_pts_flow_cpu.at(0, i); + cv::Point2f prev_key_pt = prev_pts_flow_cpu.at(0, i); + + float moved_x = cur_key_pt.x - prev_key_pt.x; + float moved_y = cur_key_pt.y - prev_key_pt.y; + + if (abs(moved_x) < 100 && abs(moved_y) < 100 && good_bbox_vec_flags[i]) + if (err_cpu.at(0, i) < flow_error && status_cpu.at(0, i) != 0 && + ((float)cur_bbox_vec[i].x + moved_x) > 0 && ((float)cur_bbox_vec[i].y + moved_y) > 0) + { + cur_bbox_vec[i].x += moved_x + 0.5; + cur_bbox_vec[i].y += moved_y + 0.5; + result_bbox_vec.push_back(cur_bbox_vec[i]); + } + else good_bbox_vec_flags[i] = false; + else good_bbox_vec_flags[i] = false; - //if(!check_error && !good_bbox_vec_flags[i]) result_bbox_vec.push_back(cur_bbox_vec[i]); - } - } + //if(!check_error && !good_bbox_vec_flags[i]) result_bbox_vec.push_back(cur_bbox_vec[i]); + } + } - cur_pts_flow_gpu.swap(prev_pts_flow_gpu); - cur_pts_flow_cpu.copyTo(prev_pts_flow_cpu); + cur_pts_flow_gpu.swap(prev_pts_flow_gpu); + cur_pts_flow_cpu.copyTo(prev_pts_flow_cpu); - if (old_gpu_id != gpu_id) - cv::cuda::setDevice(old_gpu_id); - - return result_bbox_vec; - } + if (old_gpu_id != gpu_id) + cv::cuda::setDevice(old_gpu_id); + + return result_bbox_vec; + } }; @@ -339,314 +339,314 @@ class Tracker_optflow { class Tracker_optflow { public: - const int flow_error; + const int flow_error; - Tracker_optflow(int win_size = 9, int max_level = 3, int iterations = 8000, int _flow_error = -1) : - flow_error((_flow_error > 0)? _flow_error:(win_size*4)) - { - sync_PyrLKOpticalFlow = cv::SparsePyrLKOpticalFlow::create(); - sync_PyrLKOpticalFlow->setWinSize(cv::Size(win_size, win_size)); // 9, 15, 21, 31 - sync_PyrLKOpticalFlow->setMaxLevel(max_level); // +- 3 pt + Tracker_optflow(int win_size = 9, int max_level = 3, int iterations = 8000, int _flow_error = -1) : + flow_error((_flow_error > 0)? _flow_error:(win_size*4)) + { + sync_PyrLKOpticalFlow = cv::SparsePyrLKOpticalFlow::create(); + sync_PyrLKOpticalFlow->setWinSize(cv::Size(win_size, win_size)); // 9, 15, 21, 31 + sync_PyrLKOpticalFlow->setMaxLevel(max_level); // +- 3 pt - } + } - // just to avoid extra allocations - cv::Mat dst_grey; - cv::Mat prev_pts_flow, cur_pts_flow; - cv::Mat status, err; + // just to avoid extra allocations + cv::Mat dst_grey; + cv::Mat prev_pts_flow, cur_pts_flow; + cv::Mat status, err; - cv::Mat src_grey; // used in both functions - cv::Ptr sync_PyrLKOpticalFlow; + cv::Mat src_grey; // used in both functions + cv::Ptr sync_PyrLKOpticalFlow; - std::vector cur_bbox_vec; - std::vector good_bbox_vec_flags; + std::vector cur_bbox_vec; + std::vector good_bbox_vec_flags; - void update_cur_bbox_vec(std::vector _cur_bbox_vec) - { - cur_bbox_vec = _cur_bbox_vec; - good_bbox_vec_flags = std::vector(cur_bbox_vec.size(), true); - cv::Mat prev_pts, cur_pts_flow; + void update_cur_bbox_vec(std::vector _cur_bbox_vec) + { + cur_bbox_vec = _cur_bbox_vec; + good_bbox_vec_flags = std::vector(cur_bbox_vec.size(), true); + cv::Mat prev_pts, cur_pts_flow; - for (auto &i : cur_bbox_vec) { - float x_center = (i.x + i.w / 2.0F); - float y_center = (i.y + i.h / 2.0F); - prev_pts.push_back(cv::Point2f(x_center, y_center)); - } + for (auto &i : cur_bbox_vec) { + float x_center = (i.x + i.w / 2.0F); + float y_center = (i.y + i.h / 2.0F); + prev_pts.push_back(cv::Point2f(x_center, y_center)); + } - if (prev_pts.rows == 0) - prev_pts_flow = cv::Mat(); - else - cv::transpose(prev_pts, prev_pts_flow); - } + if (prev_pts.rows == 0) + prev_pts_flow = cv::Mat(); + else + cv::transpose(prev_pts, prev_pts_flow); + } - void update_tracking_flow(cv::Mat new_src_mat, std::vector _cur_bbox_vec) - { - if (new_src_mat.channels() == 3) { + void update_tracking_flow(cv::Mat new_src_mat, std::vector _cur_bbox_vec) + { + if (new_src_mat.channels() == 3) { - update_cur_bbox_vec(_cur_bbox_vec); + update_cur_bbox_vec(_cur_bbox_vec); - cv::cvtColor(new_src_mat, src_grey, CV_BGR2GRAY, 1); - } - } + cv::cvtColor(new_src_mat, src_grey, CV_BGR2GRAY, 1); + } + } - std::vector tracking_flow(cv::Mat new_dst_mat, bool check_error = true) - { - if (sync_PyrLKOpticalFlow.empty()) { - std::cout << "sync_PyrLKOpticalFlow isn't initialized \n"; - return cur_bbox_vec; - } + std::vector tracking_flow(cv::Mat new_dst_mat, bool check_error = true) + { + if (sync_PyrLKOpticalFlow.empty()) { + std::cout << "sync_PyrLKOpticalFlow isn't initialized \n"; + return cur_bbox_vec; + } - cv::cvtColor(new_dst_mat, dst_grey, CV_BGR2GRAY, 1); + cv::cvtColor(new_dst_mat, dst_grey, CV_BGR2GRAY, 1); - if (src_grey.rows != dst_grey.rows || src_grey.cols != dst_grey.cols) { - src_grey = dst_grey.clone(); - return cur_bbox_vec; - } + if (src_grey.rows != dst_grey.rows || src_grey.cols != dst_grey.cols) { + src_grey = dst_grey.clone(); + return cur_bbox_vec; + } - if (prev_pts_flow.cols < 1) { - return cur_bbox_vec; - } + if (prev_pts_flow.cols < 1) { + return cur_bbox_vec; + } - ////sync_PyrLKOpticalFlow_gpu.sparse(src_grey_gpu, dst_grey_gpu, prev_pts_flow_gpu, cur_pts_flow_gpu, status_gpu, &err_gpu); // OpenCV 2.4.x - sync_PyrLKOpticalFlow->calc(src_grey, dst_grey, prev_pts_flow, cur_pts_flow, status, err); // OpenCV 3.x + ////sync_PyrLKOpticalFlow_gpu.sparse(src_grey_gpu, dst_grey_gpu, prev_pts_flow_gpu, cur_pts_flow_gpu, status_gpu, &err_gpu); // OpenCV 2.4.x + sync_PyrLKOpticalFlow->calc(src_grey, dst_grey, prev_pts_flow, cur_pts_flow, status, err); // OpenCV 3.x - dst_grey.copyTo(src_grey); + dst_grey.copyTo(src_grey); - std::vector result_bbox_vec; + std::vector result_bbox_vec; - if (err.rows == cur_bbox_vec.size() && status.rows == cur_bbox_vec.size()) - { - for (size_t i = 0; i < cur_bbox_vec.size(); ++i) - { - cv::Point2f cur_key_pt = cur_pts_flow.at(0, i); - cv::Point2f prev_key_pt = prev_pts_flow.at(0, i); + if (err.rows == cur_bbox_vec.size() && status.rows == cur_bbox_vec.size()) + { + for (size_t i = 0; i < cur_bbox_vec.size(); ++i) + { + cv::Point2f cur_key_pt = cur_pts_flow.at(0, i); + cv::Point2f prev_key_pt = prev_pts_flow.at(0, i); - float moved_x = cur_key_pt.x - prev_key_pt.x; - float moved_y = cur_key_pt.y - prev_key_pt.y; + float moved_x = cur_key_pt.x - prev_key_pt.x; + float moved_y = cur_key_pt.y - prev_key_pt.y; - if (abs(moved_x) < 100 && abs(moved_y) < 100 && good_bbox_vec_flags[i]) - if (err.at(0, i) < flow_error && status.at(0, i) != 0 && - ((float)cur_bbox_vec[i].x + moved_x) > 0 && ((float)cur_bbox_vec[i].y + moved_y) > 0) - { - cur_bbox_vec[i].x += moved_x + 0.5; - cur_bbox_vec[i].y += moved_y + 0.5; - result_bbox_vec.push_back(cur_bbox_vec[i]); - } - else good_bbox_vec_flags[i] = false; - else good_bbox_vec_flags[i] = false; + if (abs(moved_x) < 100 && abs(moved_y) < 100 && good_bbox_vec_flags[i]) + if (err.at(0, i) < flow_error && status.at(0, i) != 0 && + ((float)cur_bbox_vec[i].x + moved_x) > 0 && ((float)cur_bbox_vec[i].y + moved_y) > 0) + { + cur_bbox_vec[i].x += moved_x + 0.5; + cur_bbox_vec[i].y += moved_y + 0.5; + result_bbox_vec.push_back(cur_bbox_vec[i]); + } + else good_bbox_vec_flags[i] = false; + else good_bbox_vec_flags[i] = false; - //if(!check_error && !good_bbox_vec_flags[i]) result_bbox_vec.push_back(cur_bbox_vec[i]); - } - } + //if(!check_error && !good_bbox_vec_flags[i]) result_bbox_vec.push_back(cur_bbox_vec[i]); + } + } - prev_pts_flow = cur_pts_flow.clone(); + prev_pts_flow = cur_pts_flow.clone(); - return result_bbox_vec; - } + return result_bbox_vec; + } }; #else class Tracker_optflow {}; -#endif // defined(TRACK_OPTFLOW) && defined(OPENCV) +#endif // defined(TRACK_OPTFLOW) && defined(OPENCV) #ifdef OPENCV static cv::Scalar obj_id_to_color(int obj_id) { - int const colors[6][3] = { { 1,0,1 },{ 0,0,1 },{ 0,1,1 },{ 0,1,0 },{ 1,1,0 },{ 1,0,0 } }; - int const offset = obj_id * 123457 % 6; - int const color_scale = 150 + (obj_id * 123457) % 100; - cv::Scalar color(colors[offset][0], colors[offset][1], colors[offset][2]); - color *= color_scale; - return color; + int const colors[6][3] = { { 1,0,1 },{ 0,0,1 },{ 0,1,1 },{ 0,1,0 },{ 1,1,0 },{ 1,0,0 } }; + int const offset = obj_id * 123457 % 6; + int const color_scale = 150 + (obj_id * 123457) % 100; + cv::Scalar color(colors[offset][0], colors[offset][1], colors[offset][2]); + color *= color_scale; + return color; } class preview_boxes_t { - enum { frames_history = 30 }; // how long to keep the history saved - - struct preview_box_track_t { - unsigned int track_id, obj_id, last_showed_frames_ago; - bool current_detection; - bbox_t bbox; - cv::Mat mat_obj, mat_resized_obj; - preview_box_track_t() : track_id(0), obj_id(0), last_showed_frames_ago(frames_history), current_detection(false) {} - }; - std::vector preview_box_track_id; - size_t const preview_box_size, bottom_offset; - bool const one_off_detections; + enum { frames_history = 30 }; // how long to keep the history saved + + struct preview_box_track_t { + unsigned int track_id, obj_id, last_showed_frames_ago; + bool current_detection; + bbox_t bbox; + cv::Mat mat_obj, mat_resized_obj; + preview_box_track_t() : track_id(0), obj_id(0), last_showed_frames_ago(frames_history), current_detection(false) {} + }; + std::vector preview_box_track_id; + size_t const preview_box_size, bottom_offset; + bool const one_off_detections; public: - preview_boxes_t(size_t _preview_box_size = 100, size_t _bottom_offset = 100, bool _one_off_detections = false) : - preview_box_size(_preview_box_size), bottom_offset(_bottom_offset), one_off_detections(_one_off_detections) - {} - - void set(cv::Mat src_mat, std::vector result_vec) - { - size_t const count_preview_boxes = src_mat.cols / preview_box_size; - if (preview_box_track_id.size() != count_preview_boxes) preview_box_track_id.resize(count_preview_boxes); - - // increment frames history - for (auto &i : preview_box_track_id) - i.last_showed_frames_ago = std::min((unsigned)frames_history, i.last_showed_frames_ago + 1); - - // occupy empty boxes - for (auto &k : result_vec) { - bool found = false; - // find the same (track_id) - for (auto &i : preview_box_track_id) { - if (i.track_id == k.track_id) { - if (!one_off_detections) i.last_showed_frames_ago = 0; // for tracked objects - found = true; - break; - } - } - if (!found) { - // find empty box - for (auto &i : preview_box_track_id) { - if (i.last_showed_frames_ago == frames_history) { - if (!one_off_detections && k.frames_counter == 0) break; // don't show if obj isn't tracked yet - i.track_id = k.track_id; - i.obj_id = k.obj_id; - i.bbox = k; - i.last_showed_frames_ago = 0; - break; - } - } - } - } - - // draw preview box (from old or current frame) - for (size_t i = 0; i < preview_box_track_id.size(); ++i) - { - // get object image - cv::Mat dst = preview_box_track_id[i].mat_resized_obj; - preview_box_track_id[i].current_detection = false; - - for (auto &k : result_vec) { - if (preview_box_track_id[i].track_id == k.track_id) { - if (one_off_detections && preview_box_track_id[i].last_showed_frames_ago > 0) { - preview_box_track_id[i].last_showed_frames_ago = frames_history; break; - } - bbox_t b = k; - cv::Rect r(b.x, b.y, b.w, b.h); - cv::Rect img_rect(cv::Point2i(0, 0), src_mat.size()); - cv::Rect rect_roi = r & img_rect; - if (rect_roi.width > 1 || rect_roi.height > 1) { - cv::Mat roi = src_mat(rect_roi); - cv::resize(roi, dst, cv::Size(preview_box_size, preview_box_size), cv::INTER_NEAREST); - preview_box_track_id[i].mat_obj = roi.clone(); - preview_box_track_id[i].mat_resized_obj = dst.clone(); - preview_box_track_id[i].current_detection = true; - preview_box_track_id[i].bbox = k; - } - break; - } - } - } - } - - - void draw(cv::Mat draw_mat, bool show_small_boxes = false) - { - // draw preview box (from old or current frame) - for (size_t i = 0; i < preview_box_track_id.size(); ++i) - { - auto &prev_box = preview_box_track_id[i]; - - // draw object image - cv::Mat dst = prev_box.mat_resized_obj; - if (prev_box.last_showed_frames_ago < frames_history && - dst.size() == cv::Size(preview_box_size, preview_box_size)) - { - cv::Rect dst_rect_roi(cv::Point2i(i * preview_box_size, draw_mat.rows - bottom_offset), dst.size()); - cv::Mat dst_roi = draw_mat(dst_rect_roi); - dst.copyTo(dst_roi); - - cv::Scalar color = obj_id_to_color(prev_box.obj_id); - int thickness = (prev_box.current_detection) ? 5 : 1; - cv::rectangle(draw_mat, dst_rect_roi, color, thickness); - - unsigned int const track_id = prev_box.track_id; - std::string track_id_str = (track_id > 0) ? std::to_string(track_id) : ""; - putText(draw_mat, track_id_str, dst_rect_roi.tl() - cv::Point2i(-4, 5), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.9, cv::Scalar(0, 0, 0), 2); - - std::string size_str = std::to_string(prev_box.bbox.w) + "x" + std::to_string(prev_box.bbox.h); - putText(draw_mat, size_str, dst_rect_roi.tl() + cv::Point2i(0, 12), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cv::Scalar(0, 0, 0), 1); - - if (!one_off_detections && prev_box.current_detection) { - cv::line(draw_mat, dst_rect_roi.tl() + cv::Point2i(preview_box_size, 0), - cv::Point2i(prev_box.bbox.x, prev_box.bbox.y + prev_box.bbox.h), - color); - } - - if (one_off_detections && show_small_boxes) { - cv::Rect src_rect_roi(cv::Point2i(prev_box.bbox.x, prev_box.bbox.y), - cv::Size(prev_box.bbox.w, prev_box.bbox.h)); - unsigned int const color_history = (255 * prev_box.last_showed_frames_ago) / frames_history; - color = cv::Scalar(255 - 3 * color_history, 255 - 2 * color_history, 255 - 1 * color_history); - if (prev_box.mat_obj.size() == src_rect_roi.size()) { - prev_box.mat_obj.copyTo(draw_mat(src_rect_roi)); - } - cv::rectangle(draw_mat, src_rect_roi, color, thickness); - putText(draw_mat, track_id_str, src_rect_roi.tl() - cv::Point2i(0, 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cv::Scalar(0, 0, 0), 1); - } - } - } - } + preview_boxes_t(size_t _preview_box_size = 100, size_t _bottom_offset = 100, bool _one_off_detections = false) : + preview_box_size(_preview_box_size), bottom_offset(_bottom_offset), one_off_detections(_one_off_detections) + {} + + void set(cv::Mat src_mat, std::vector result_vec) + { + size_t const count_preview_boxes = src_mat.cols / preview_box_size; + if (preview_box_track_id.size() != count_preview_boxes) preview_box_track_id.resize(count_preview_boxes); + + // increment frames history + for (auto &i : preview_box_track_id) + i.last_showed_frames_ago = std::min((unsigned)frames_history, i.last_showed_frames_ago + 1); + + // occupy empty boxes + for (auto &k : result_vec) { + bool found = false; + // find the same (track_id) + for (auto &i : preview_box_track_id) { + if (i.track_id == k.track_id) { + if (!one_off_detections) i.last_showed_frames_ago = 0; // for tracked objects + found = true; + break; + } + } + if (!found) { + // find empty box + for (auto &i : preview_box_track_id) { + if (i.last_showed_frames_ago == frames_history) { + if (!one_off_detections && k.frames_counter == 0) break; // don't show if obj isn't tracked yet + i.track_id = k.track_id; + i.obj_id = k.obj_id; + i.bbox = k; + i.last_showed_frames_ago = 0; + break; + } + } + } + } + + // draw preview box (from old or current frame) + for (size_t i = 0; i < preview_box_track_id.size(); ++i) + { + // get object image + cv::Mat dst = preview_box_track_id[i].mat_resized_obj; + preview_box_track_id[i].current_detection = false; + + for (auto &k : result_vec) { + if (preview_box_track_id[i].track_id == k.track_id) { + if (one_off_detections && preview_box_track_id[i].last_showed_frames_ago > 0) { + preview_box_track_id[i].last_showed_frames_ago = frames_history; break; + } + bbox_t b = k; + cv::Rect r(b.x, b.y, b.w, b.h); + cv::Rect img_rect(cv::Point2i(0, 0), src_mat.size()); + cv::Rect rect_roi = r & img_rect; + if (rect_roi.width > 1 || rect_roi.height > 1) { + cv::Mat roi = src_mat(rect_roi); + cv::resize(roi, dst, cv::Size(preview_box_size, preview_box_size), cv::INTER_NEAREST); + preview_box_track_id[i].mat_obj = roi.clone(); + preview_box_track_id[i].mat_resized_obj = dst.clone(); + preview_box_track_id[i].current_detection = true; + preview_box_track_id[i].bbox = k; + } + break; + } + } + } + } + + + void draw(cv::Mat draw_mat, bool show_small_boxes = false) + { + // draw preview box (from old or current frame) + for (size_t i = 0; i < preview_box_track_id.size(); ++i) + { + auto &prev_box = preview_box_track_id[i]; + + // draw object image + cv::Mat dst = prev_box.mat_resized_obj; + if (prev_box.last_showed_frames_ago < frames_history && + dst.size() == cv::Size(preview_box_size, preview_box_size)) + { + cv::Rect dst_rect_roi(cv::Point2i(i * preview_box_size, draw_mat.rows - bottom_offset), dst.size()); + cv::Mat dst_roi = draw_mat(dst_rect_roi); + dst.copyTo(dst_roi); + + cv::Scalar color = obj_id_to_color(prev_box.obj_id); + int thickness = (prev_box.current_detection) ? 5 : 1; + cv::rectangle(draw_mat, dst_rect_roi, color, thickness); + + unsigned int const track_id = prev_box.track_id; + std::string track_id_str = (track_id > 0) ? std::to_string(track_id) : ""; + putText(draw_mat, track_id_str, dst_rect_roi.tl() - cv::Point2i(-4, 5), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.9, cv::Scalar(0, 0, 0), 2); + + std::string size_str = std::to_string(prev_box.bbox.w) + "x" + std::to_string(prev_box.bbox.h); + putText(draw_mat, size_str, dst_rect_roi.tl() + cv::Point2i(0, 12), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cv::Scalar(0, 0, 0), 1); + + if (!one_off_detections && prev_box.current_detection) { + cv::line(draw_mat, dst_rect_roi.tl() + cv::Point2i(preview_box_size, 0), + cv::Point2i(prev_box.bbox.x, prev_box.bbox.y + prev_box.bbox.h), + color); + } + + if (one_off_detections && show_small_boxes) { + cv::Rect src_rect_roi(cv::Point2i(prev_box.bbox.x, prev_box.bbox.y), + cv::Size(prev_box.bbox.w, prev_box.bbox.h)); + unsigned int const color_history = (255 * prev_box.last_showed_frames_ago) / frames_history; + color = cv::Scalar(255 - 3 * color_history, 255 - 2 * color_history, 255 - 1 * color_history); + if (prev_box.mat_obj.size() == src_rect_roi.size()) { + prev_box.mat_obj.copyTo(draw_mat(src_rect_roi)); + } + cv::rectangle(draw_mat, src_rect_roi, color, thickness); + putText(draw_mat, track_id_str, src_rect_roi.tl() - cv::Point2i(0, 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cv::Scalar(0, 0, 0), 1); + } + } + } + } }; -#endif // OPENCV +#endif // OPENCV //extern "C" { -#endif // __cplusplus +#endif // __cplusplus /* - // C - wrappers - YOLODLL_API void create_detector(char const* cfg_filename, char const* weight_filename, int gpu_id); - YOLODLL_API void delete_detector(); - YOLODLL_API bbox_t* detect_custom(image_t img, float thresh, bool use_mean, int *result_size); - YOLODLL_API bbox_t* detect_resized(image_t img, int init_w, int init_h, float thresh, bool use_mean, int *result_size); - YOLODLL_API bbox_t* detect(image_t img, int *result_size); - YOLODLL_API image_t load_img(char *image_filename); - YOLODLL_API void free_img(image_t m); + // C - wrappers + YOLODLL_API void create_detector(char const* cfg_filename, char const* weight_filename, int gpu_id); + YOLODLL_API void delete_detector(); + YOLODLL_API bbox_t* detect_custom(image_t img, float thresh, bool use_mean, int *result_size); + YOLODLL_API bbox_t* detect_resized(image_t img, int init_w, int init_h, float thresh, bool use_mean, int *result_size); + YOLODLL_API bbox_t* detect(image_t img, int *result_size); + YOLODLL_API image_t load_img(char *image_filename); + YOLODLL_API void free_img(image_t m); #ifdef __cplusplus -} // extern "C" +} // extern "C" static std::shared_ptr c_detector_ptr; static std::vector c_result_vec; void create_detector(char const* cfg_filename, char const* weight_filename, int gpu_id) { - c_detector_ptr = std::make_shared(cfg_filename, weight_filename, gpu_id); + c_detector_ptr = std::make_shared(cfg_filename, weight_filename, gpu_id); } void delete_detector() { c_detector_ptr.reset(); } bbox_t* detect_custom(image_t img, float thresh, bool use_mean, int *result_size) { - c_result_vec = static_cast(c_detector_ptr.get())->detect(img, thresh, use_mean); - *result_size = c_result_vec.size(); - return c_result_vec.data(); + c_result_vec = static_cast(c_detector_ptr.get())->detect(img, thresh, use_mean); + *result_size = c_result_vec.size(); + return c_result_vec.data(); } bbox_t* detect_resized(image_t img, int init_w, int init_h, float thresh, bool use_mean, int *result_size) { - c_result_vec = static_cast(c_detector_ptr.get())->detect_resized(img, init_w, init_h, thresh, use_mean); - *result_size = c_result_vec.size(); - return c_result_vec.data(); + c_result_vec = static_cast(c_detector_ptr.get())->detect_resized(img, init_w, init_h, thresh, use_mean); + *result_size = c_result_vec.size(); + return c_result_vec.data(); } bbox_t* detect(image_t img, int *result_size) { - return detect_custom(img, 0.24, true, result_size); + return detect_custom(img, 0.24, true, result_size); } image_t load_img(char *image_filename) { - return static_cast(c_detector_ptr.get())->load_image(image_filename); + return static_cast(c_detector_ptr.get())->load_image(image_filename); } void free_img(image_t m) { - static_cast(c_detector_ptr.get())->free_image(m); + static_cast(c_detector_ptr.get())->free_image(m); } -#endif // __cplusplus +#endif // __cplusplus */ From 5fc1e9a918bfb9229eec90d459dbfb6778ae6906 Mon Sep 17 00:00:00 2001 From: Sam Beran Date: Tue, 10 Jul 2018 17:13:00 -0500 Subject: [PATCH 0107/1365] ext_output: flush stdout after printing output this allows tooling to watch for changes while the application is running. --- src/image.c | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/image.c b/src/image.c index 75259e8f726..48073711300 100644 --- a/src/image.c +++ b/src/image.c @@ -548,6 +548,9 @@ void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float t cvPutText(show_img, labelstr, pt_text, &font, black_color); } } + if (ext_output) { + fflush(stdout); + } } void draw_detections_cv(IplImage* show_img, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes) From b3c5e42b629ca0bcd7358c2f25cbde93d616a275 Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 12 Jul 2018 01:48:45 +0300 Subject: [PATCH 0108/1365] Update Readme.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index b02da0164d7..262a2533bfd 100644 --- a/README.md +++ b/README.md @@ -433,6 +433,8 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * for training for small objects - set `layers = -1, 11` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L720 and set `stride=4` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L717 + * If you train the model to distinguish Left and Right objects as separate classes (left/right hand, left/right-turn on road signs, ...) then for disabling flip data augmentation - add `flip=0` here: https://github.com/AlexeyAB/darknet/blob/3d2d0a7c98dbc8923d9ff705b81ff4f7940ea6ff/cfg/yolov3.cfg#L17 + * General rule - your training dataset should include such a set of relative sizes of objects that you want to detect: * `train_network_width * train_obj_width / train_image_width ~= detection_network_width * detection_obj_width / detection_image_width` From 2c711de013de00e06f006f959c0c8bc2d1a67f7e Mon Sep 17 00:00:00 2001 From: Tino Hager Date: Sun, 15 Jul 2018 00:05:54 +0200 Subject: [PATCH 0109/1365] optimize performance detect check image size before resize --- src/yolo_v2_class.hpp | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/src/yolo_v2_class.hpp b/src/yolo_v2_class.hpp index 6f7e47bdcb7..3dc9386688b 100644 --- a/src/yolo_v2_class.hpp +++ b/src/yolo_v2_class.hpp @@ -96,9 +96,15 @@ class Detector { std::shared_ptr mat_to_image_resize(cv::Mat mat) const { if (mat.data == NULL) return std::shared_ptr(NULL); - cv::Mat det_mat; - cv::resize(mat, det_mat, cv::Size(get_net_width(), get_net_height())); - return mat_to_image(det_mat); + + cv::Size s = mat.size(); + if (get_net_width() != s.width || get_net_height() != s.height) { + cv::Mat det_mat; + cv::resize(mat, det_mat, cv::Size(get_net_width(), get_net_height())); + return mat_to_image(det_mat); + } + + return mat_to_image(mat); } static std::shared_ptr mat_to_image(cv::Mat img_src) From 08957081d427cda7e638f2a18667cc67acbb4815 Mon Sep 17 00:00:00 2001 From: vinjn Date: Mon, 16 Jul 2018 11:03:13 +0800 Subject: [PATCH 0110/1365] Expose YOLODLL_API void top_k() --- src/utils.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/utils.h b/src/utils.h index 58cea936df8..ba61d344e27 100644 --- a/src/utils.h +++ b/src/utils.h @@ -47,7 +47,7 @@ void file_error(char *s); void strip(char *s); void strip_args(char *s); void strip_char(char *s, char bad); -void top_k(float *a, int n, int k, int *index); +YOLODLL_API void top_k(float *a, int n, int k, int *index); list *split_str(char *s, char delim); char *fgetl(FILE *fp); list *parse_csv_line(char *line); From dfbec7ea2bf7d324fe80647dde72957683539974 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 18 Jul 2018 15:08:34 +0300 Subject: [PATCH 0111/1365] PascalVOC label scripts are updated --- scripts/voc_label.py | 13 ++++++++----- scripts/voc_label_difficult.py | 8 ++++---- src/yolo_v2_class.hpp | 22 +++++++++++----------- 3 files changed, 23 insertions(+), 20 deletions(-) diff --git a/scripts/voc_label.py b/scripts/voc_label.py index d1e88236f2c..679fc366890 100644 --- a/scripts/voc_label.py +++ b/scripts/voc_label.py @@ -10,10 +10,10 @@ def convert(size, box): - dw = 1./size[0] - dh = 1./size[1] - x = (box[0] + box[1])/2.0 - y = (box[2] + box[3])/2.0 + dw = 1./(size[0]) + dh = 1./(size[1]) + x = (box[0] + box[1])/2.0 - 1 + y = (box[2] + box[3])/2.0 - 1 w = box[1] - box[0] h = box[3] - box[2] x = x*dw @@ -34,7 +34,7 @@ def convert_annotation(year, image_id): for obj in root.iter('object'): difficult = obj.find('difficult').text cls = obj.find('name').text - if cls not in classes or int(difficult) == 1: + if cls not in classes or int(difficult)==1: continue cls_id = classes.index(cls) xmlbox = obj.find('bndbox') @@ -54,3 +54,6 @@ def convert_annotation(year, image_id): convert_annotation(year, image_id) list_file.close() +os.system("cat 2007_train.txt 2007_val.txt 2012_train.txt 2012_val.txt > train.txt") +os.system("cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt > train.all.txt") + diff --git a/scripts/voc_label_difficult.py b/scripts/voc_label_difficult.py index 93e57fe74ef..cfffce3dfe5 100644 --- a/scripts/voc_label_difficult.py +++ b/scripts/voc_label_difficult.py @@ -10,10 +10,10 @@ def convert(size, box): - dw = 1./size[0] - dh = 1./size[1] - x = (box[0] + box[1])/2.0 - y = (box[2] + box[3])/2.0 + dw = 1./(size[0]) + dh = 1./(size[1]) + x = (box[0] + box[1])/2.0 - 1 + y = (box[2] + box[3])/2.0 - 1 w = box[1] - box[0] h = box[3] - box[2] x = x*dw diff --git a/src/yolo_v2_class.hpp b/src/yolo_v2_class.hpp index 3dc9386688b..199b1c974b8 100644 --- a/src/yolo_v2_class.hpp +++ b/src/yolo_v2_class.hpp @@ -1,13 +1,13 @@ #pragma once #ifdef YOLODLL_EXPORTS #if defined(_MSC_VER) -#define YOLODLL_API __declspec(dllexport) +#define YOLODLL_API __declspec(dllexport) #else #define YOLODLL_API __attribute__((visibility("default"))) #endif #else #if defined(_MSC_VER) -#define YOLODLL_API __declspec(dllimport) +#define YOLODLL_API __declspec(dllimport) #else #define YOLODLL_API #endif @@ -71,7 +71,7 @@ class Detector { YOLODLL_API int get_net_height() const; YOLODLL_API int get_net_color_depth() const; - YOLODLL_API std::vector tracking_id(std::vector cur_bbox_vec, bool const change_history = true, + YOLODLL_API std::vector tracking_id(std::vector cur_bbox_vec, bool const change_history = true, int const frames_story = 10, int const max_dist = 150); std::vector detect_resized(image_t img, int init_w, int init_h, float thresh = 0.2, bool use_mean = false) @@ -97,14 +97,14 @@ class Detector { { if (mat.data == NULL) return std::shared_ptr(NULL); - cv::Size s = mat.size(); - if (get_net_width() != s.width || get_net_height() != s.height) { - cv::Mat det_mat; - cv::resize(mat, det_mat, cv::Size(get_net_width(), get_net_height())); - return mat_to_image(det_mat); - } + cv::Size network_size = cv::Size(get_net_width(), get_net_height()); + cv::Mat det_mat; + if (mat.size() != network_size) + cv::resize(mat, det_mat, network_size); + else + det_mat = mat; // only reference is copied - return mat_to_image(mat); + return mat_to_image(det_mat); } static std::shared_ptr mat_to_image(cv::Mat img_src) @@ -304,7 +304,7 @@ class Tracker_optflow { std::vector result_bbox_vec; - if (err_cpu.cols == cur_bbox_vec.size() && status_cpu.cols == cur_bbox_vec.size()) + if (err_cpu.cols == cur_bbox_vec.size() && status_cpu.cols == cur_bbox_vec.size()) { for (size_t i = 0; i < cur_bbox_vec.size(); ++i) { From b8e6e80c6d411d05a9e09f1e3676eb9a7f3ea0e8 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 3 Aug 2018 14:35:03 +0300 Subject: [PATCH 0112/1365] Added spatial Yolo v3 yolov3-spp.cfg --- build/darknet/x64/cfg/yolov3-spp.cfg | 822 +++++++++++++++++++++++++++ cfg/yolov3-spp.cfg | 822 +++++++++++++++++++++++++++ src/cuda.c | 8 +- src/darknet.c | 4 +- src/maxpool_layer.c | 8 +- src/maxpool_layer_kernels.cu | 8 +- 6 files changed, 1658 insertions(+), 14 deletions(-) create mode 100644 build/darknet/x64/cfg/yolov3-spp.cfg create mode 100644 cfg/yolov3-spp.cfg diff --git a/build/darknet/x64/cfg/yolov3-spp.cfg b/build/darknet/x64/cfg/yolov3-spp.cfg new file mode 100644 index 00000000000..4ad2a052d88 --- /dev/null +++ b/build/darknet/x64/cfg/yolov3-spp.cfg @@ -0,0 +1,822 @@ +[net] +# Testing +batch=1 +subdivisions=1 +# Training +# batch=64 +# subdivisions=16 +width=608 +height=608 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 + +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + diff --git a/cfg/yolov3-spp.cfg b/cfg/yolov3-spp.cfg new file mode 100644 index 00000000000..4ad2a052d88 --- /dev/null +++ b/cfg/yolov3-spp.cfg @@ -0,0 +1,822 @@ +[net] +# Testing +batch=1 +subdivisions=1 +# Training +# batch=64 +# subdivisions=16 +width=608 +height=608 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 + +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + diff --git a/src/cuda.c b/src/cuda.c index 688e1c580f2..2284dad050a 100644 --- a/src/cuda.c +++ b/src/cuda.c @@ -29,23 +29,23 @@ void check_error(cudaError_t status) //cudaDeviceSynchronize(); cudaError_t status2 = cudaGetLastError(); if (status != cudaSuccess) - { + { const char *s = cudaGetErrorString(status); char buffer[256]; printf("CUDA Error: %s\n", s); assert(0); snprintf(buffer, 256, "CUDA Error: %s", s); error(buffer); - } + } if (status2 != cudaSuccess) - { + { const char *s = cudaGetErrorString(status); char buffer[256]; printf("CUDA Error Prev: %s\n", s); assert(0); snprintf(buffer, 256, "CUDA Error Prev: %s", s); error(buffer); - } + } } dim3 cuda_gridsize(size_t n){ diff --git a/src/darknet.c b/src/darknet.c index f6d61c09777..068c9e020d6 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -40,13 +40,13 @@ void average(int argc, char *argv[]) network net = parse_network_cfg(cfgfile); network sum = parse_network_cfg(cfgfile); - char *weightfile = argv[4]; + char *weightfile = argv[4]; load_weights(&sum, weightfile); int i, j; int n = argc - 5; for(i = 0; i < n; ++i){ - weightfile = argv[i+5]; + weightfile = argv[i+5]; load_weights(&net, weightfile); for(j = 0; j < net.n; ++j){ layer l = net.layers[j]; diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index 41f7a793f1e..0eeb467f923 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -27,8 +27,8 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s l.w = w; l.c = c; l.pad = padding; - l.out_w = (w + 2*padding)/stride; - l.out_h = (h + 2*padding)/stride; + l.out_w = (w + 2 * padding - size) / stride + 1; + l.out_h = (h + 2 * padding - size) / stride + 1; l.out_c = c; l.outputs = l.out_h * l.out_w * l.out_c; l.inputs = h*w*c; @@ -58,8 +58,8 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h) l->w = w; l->inputs = h*w*l->c; - l->out_w = (w + 2*l->pad)/l->stride; - l->out_h = (h + 2*l->pad)/l->stride; + l->out_w = (w + 2 * l->pad - l->size) / l->stride + 1; + l->out_h = (h + 2 * l->pad - l->size) / l->stride + 1; l->outputs = l->out_w * l->out_h * l->c; int output_size = l->outputs * l->batch; diff --git a/src/maxpool_layer_kernels.cu b/src/maxpool_layer_kernels.cu index d40d3c0bf73..05b1f9ab5a3 100644 --- a/src/maxpool_layer_kernels.cu +++ b/src/maxpool_layer_kernels.cu @@ -9,8 +9,8 @@ extern "C" { __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride, int size, int pad, float *input, float *output, int *indexes) { - int h = (in_h + 2*pad)/stride; - int w = (in_w + 2*pad)/stride; + int h = (in_h + 2 * pad - size) / stride + 1; + int w = (in_w + 2 * pad - size) / stride + 1; int c = in_c; int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -49,8 +49,8 @@ __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c __global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride, int size, int pad, float *delta, float *prev_delta, int *indexes) { - int h = (in_h + 2*pad)/stride; - int w = (in_w + 2*pad)/stride; + int h = (in_h + 2 * pad - size) / stride + 1; + int w = (in_w + 2 * pad - size) / stride + 1; int c = in_c; int area = (size-1)/stride; From 043289426b2d08d925fc1c980b0d2a01e2360e93 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 4 Aug 2018 03:11:10 +0300 Subject: [PATCH 0113/1365] max pool layer is fixed --- src/darknet.c | 1 + src/maxpool_layer.c | 12 ++++++------ src/maxpool_layer_kernels.cu | 16 ++++++++-------- src/parser.c | 12 ++++++------ 4 files changed, 21 insertions(+), 20 deletions(-) diff --git a/src/darknet.c b/src/darknet.c index 068c9e020d6..1dc073bcfd9 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -377,6 +377,7 @@ int main(int argc, char **argv) #else if(gpu_index >= 0){ cuda_set_device(gpu_index); + check_error(cudaSetDeviceFlags(cudaDeviceScheduleBlockingSync)); } #endif diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index 0eeb467f923..928102f5d61 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -27,8 +27,8 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s l.w = w; l.c = c; l.pad = padding; - l.out_w = (w + 2 * padding - size) / stride + 1; - l.out_h = (h + 2 * padding - size) / stride + 1; + l.out_w = (w + padding - size) / stride + 1; + l.out_h = (h + padding - size) / stride + 1; l.out_c = c; l.outputs = l.out_h * l.out_w * l.out_c; l.inputs = h*w*c; @@ -58,8 +58,8 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h) l->w = w; l->inputs = h*w*l->c; - l->out_w = (w + 2 * l->pad - l->size) / l->stride + 1; - l->out_h = (h + 2 * l->pad - l->size) / l->stride + 1; + l->out_w = (w + l->pad - l->size) / l->stride + 1; + l->out_h = (h + l->pad - l->size) / l->stride + 1; l->outputs = l->out_w * l->out_h * l->c; int output_size = l->outputs * l->batch; @@ -80,8 +80,8 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h) void forward_maxpool_layer(const maxpool_layer l, network_state state) { int b,i,j,k,m,n; - int w_offset = -l.pad; - int h_offset = -l.pad; + int w_offset = -l.pad / l.stride; + int h_offset = -l.pad / l.stride; int h = l.out_h; int w = l.out_w; diff --git a/src/maxpool_layer_kernels.cu b/src/maxpool_layer_kernels.cu index 05b1f9ab5a3..78b7f39772f 100644 --- a/src/maxpool_layer_kernels.cu +++ b/src/maxpool_layer_kernels.cu @@ -9,8 +9,8 @@ extern "C" { __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride, int size, int pad, float *input, float *output, int *indexes) { - int h = (in_h + 2 * pad - size) / stride + 1; - int w = (in_w + 2 * pad - size) / stride + 1; + int h = (in_h + pad - size) / stride + 1; + int w = (in_w + pad - size) / stride + 1; int c = in_c; int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -24,8 +24,8 @@ __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c id /= c; int b = id; - int w_offset = -pad; - int h_offset = -pad; + int w_offset = -pad / 2; + int h_offset = -pad / 2; int out_index = j + w*(i + h*(k + c*b)); float max = -INFINITY; @@ -49,8 +49,8 @@ __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c __global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride, int size, int pad, float *delta, float *prev_delta, int *indexes) { - int h = (in_h + 2 * pad - size) / stride + 1; - int w = (in_w + 2 * pad - size) / stride + 1; + int h = (in_h + pad - size) / stride + 1; + int w = (in_w + pad - size) / stride + 1; int c = in_c; int area = (size-1)/stride; @@ -66,8 +66,8 @@ __global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_ id /= in_c; int b = id; - int w_offset = -pad; - int h_offset = -pad; + int w_offset = -pad / 2; + int h_offset = -pad / 2; float d = 0; int l, m; diff --git a/src/parser.c b/src/parser.c index d91b1cab2fa..c716ea9a496 100644 --- a/src/parser.c +++ b/src/parser.c @@ -457,7 +457,7 @@ maxpool_layer parse_maxpool(list *options, size_params params) { int stride = option_find_int(options, "stride",1); int size = option_find_int(options, "size",stride); - int padding = option_find_int_quiet(options, "padding", (size-1)/2); + int padding = option_find_int_quiet(options, "padding", size-1); int batch,h,w,c; h = params.h; @@ -511,7 +511,7 @@ layer parse_batchnorm(list *options, size_params params) layer parse_shortcut(list *options, size_params params, network net) { - char *l = option_find(options, "from"); + char *l = option_find(options, "from"); int index = atoi(l); if(index < 0) index = params.index + index; @@ -555,7 +555,7 @@ layer parse_upsample(list *options, size_params params, network net) route_layer parse_route(list *options, size_params params, network net) { - char *l = option_find(options, "layers"); + char *l = option_find(options, "layers"); int len = strlen(l); if(!l) error("Route Layer must specify input layers"); int n = 1; @@ -654,8 +654,8 @@ void parse_net_options(list *options, network *net) net->step = option_find_int(options, "step", 1); net->scale = option_find_float(options, "scale", 1); } else if (net->policy == STEPS){ - char *l = option_find(options, "steps"); - char *p = option_find(options, "scales"); + char *l = option_find(options, "steps"); + char *p = option_find(options, "scales"); if(!l || !p) error("STEPS policy must have steps and scales in cfg file"); int len = strlen(l); @@ -808,7 +808,7 @@ network parse_network_cfg_custom(char *filename, int batch) params.inputs = l.outputs; } if (l.bflops > 0) bflops += l.bflops; - } + } free_list(sections); net.outputs = get_network_output_size(net); net.output = get_network_output(net); From 84f53a4b05475156b03cbf9177ae190350fa824e Mon Sep 17 00:00:00 2001 From: Adesun Date: Sun, 5 Aug 2018 12:18:27 +0800 Subject: [PATCH 0114/1365] * make regex compatible with new code * fix #777 exceeds Locator.MAXTICKS * set non-interactive backend when OS is not windows * save figure as SVG --- scripts/log_parser/log_parser.py | 26 +- scripts/log_parser/readme.md | 2 +- scripts/log_parser/test_new.log | 76285 +++++++++++++++++++++++++++++ scripts/log_parser/test_new.svg | 2953 ++ 4 files changed, 79255 insertions(+), 11 deletions(-) create mode 100644 scripts/log_parser/test_new.log create mode 100644 scripts/log_parser/test_new.svg diff --git a/scripts/log_parser/log_parser.py b/scripts/log_parser/log_parser.py index 631e5497ffb..507c5da1a7f 100644 --- a/scripts/log_parser/log_parser.py +++ b/scripts/log_parser/log_parser.py @@ -10,8 +10,13 @@ import platform import re import sys -import matplotlib.pyplot as plt +# set non-interactive backend default when os is not windows +if sys.platform != 'win32': + import matplotlib + matplotlib.use('Agg') + +import matplotlib.pyplot as plt from matplotlib.ticker import MultipleLocator, FormatStrFormatter @@ -56,17 +61,19 @@ def log_parser(args): iterations = [] losses = [] fig, ax = plt.subplots() + # set area we focus on + ax.set_ylim(0, 8) - major_locator = MultipleLocator(1) + major_locator = MultipleLocator() minor_locator = MultipleLocator(0.5) - ax.yaxis.set_major_locator(major_locator) ax.yaxis.set_minor_locator(minor_locator) - ax.yaxis.grid(True, which='minor') - pattern = re.compile(r"([\d]*): .*?, (.*?) avg,") + pattern = re.compile(r"([\d].*): .*?, (.*?) avg") + # print(pattern.findall(log_content)) matches = pattern.findall(log_content) + # print(type(matches[0])) counter = 0 log_count = len(matches) @@ -92,14 +99,13 @@ def log_parser(args): ax.plot(iterations, losses) plt.xlabel('Iteration') plt.ylabel('Loss') - plt.grid() + plt.tight_layout() - save_path = os.path.join(args.save_dir, file_name + '.png') - plt.savefig(save_path, dpi=300) + # saved as svg + save_path = os.path.join(args.save_dir, file_name + '.svg') + plt.savefig(save_path, dpi=300, format="svg") if args.show_plot: plt.show() - else: - plt.switch_backend('agg') if __name__ == "__main__": diff --git a/scripts/log_parser/readme.md b/scripts/log_parser/readme.md index 4982d270371..7ab5f80aeb6 100644 --- a/scripts/log_parser/readme.md +++ b/scripts/log_parser/readme.md @@ -7,7 +7,7 @@ 2. --save-dir the directory to save loss curve, image and csv file 3. --log-file log file name to be parsed 4. --csv-file csv file name to save loss data, default it's same with training log file name -5. --show whether to show after finished parsing, default False +5. --show whether to show after finished parsing, default False, just works on windows or linux with GUI desktop `python log_parser.py --source-dir ./ --save-dir ./ --log-file test.log --show true` diff --git a/scripts/log_parser/test_new.log b/scripts/log_parser/test_new.log new file mode 100644 index 00000000000..b0ad79c66c4 --- /dev/null +++ b/scripts/log_parser/test_new.log @@ -0,0 +1,76285 @@ +layer filters size input output + 0 conv 16 3 x 3 / 1 544 x 544 x 3 -> 544 x 544 x 16 0.256 BF + 1 max 2 x 2 / 2 544 x 544 x 16 -> 272 x 272 x 16 0.005 BF + 2 conv 32 3 x 3 / 1 272 x 272 x 16 -> 272 x 272 x 32 0.682 BF + 3 max 2 x 2 / 2 272 x 272 x 32 -> 136 x 136 x 32 0.002 BF + 4 conv 64 3 x 3 / 1 136 x 136 x 32 -> 136 x 136 x 64 0.682 BF + 5 max 2 x 2 / 2 136 x 136 x 64 -> 68 x 68 x 64 0.001 BF + 6 conv 128 3 x 3 / 1 68 x 68 x 64 -> 68 x 68 x 128 0.682 BF + 7 max 2 x 2 / 2 68 x 68 x 128 -> 34 x 34 x 128 0.001 BF + 8 conv 256 3 x 3 / 1 34 x 34 x 128 -> 34 x 34 x 256 0.682 BF + 9 max 2 x 2 / 2 34 x 34 x 256 -> 17 x 17 x 256 0.000 BF + 10 conv 512 3 x 3 / 1 17 x 17 x 256 -> 17 x 17 x 512 0.682 BF + 11 max 2 x 2 / 1 17 x 17 x 512 -> 17 x 17 x 512 0.001 BF + 12 conv 1024 3 x 3 / 1 17 x 17 x 512 -> 17 x 17 x1024 2.727 BF + 13 conv 256 1 x 1 / 1 17 x 17 x1024 -> 17 x 17 x 256 0.152 BF + 14 conv 512 3 x 3 / 1 17 x 17 x 256 -> 17 x 17 x 512 0.682 BF + 15 conv 60 1 x 1 / 1 17 x 17 x 512 -> 17 x 17 x 60 0.018 BF + 16 yolo + 17 route 13 + 18 conv 128 1 x 1 / 1 17 x 17 x 256 -> 17 x 17 x 128 0.019 BF + 19 upsample 2x 17 x 17 x 128 -> 34 x 34 x 128 + 20 route 19 8 + 21 conv 256 3 x 3 / 1 34 x 34 x 384 -> 34 x 34 x 256 2.046 BF + 22 conv 60 1 x 1 / 1 34 x 34 x 256 -> 34 x 34 x 60 0.036 BF + 23 yolo +Loading weights from ./test_new...test_new_3 +Total BFLOPS 9.353 +Done! + + seen 32 +Learning Rate: 0.001, Momentum: 0.9, Decay: 0.0005 +Resizing +576 x 576 + try to allocate workspace = 1146159105 * sizeof(float), CUDA allocate done! +Loaded: 1.552417 seconds +Region 16 Avg IOU: 0.262753, Class: 0.543997, Obj: 0.528645, No Obj: 0.513402, .5R: 0.100000, .75R: 0.000000, count: 10 +Region 23 Avg IOU: 0.383236, Class: 0.568616, Obj: 0.610910, No Obj: 0.584212, .5R: 0.222222, .75R: 0.055556, count: 18 +Region 16 Avg IOU: 0.315686, Class: 0.481916, Obj: 0.554757, No Obj: 0.512496, .5R: 0.166667, .75R: 0.000000, count: 12 +Region 23 Avg IOU: 0.394797, Class: 0.520876, Obj: 0.634375, No Obj: 0.584431, .5R: 0.357143, .75R: 0.000000, count: 14 +Region 16 Avg IOU: 0.353224, Class: 0.457954, Obj: 0.518958, No Obj: 0.512487, .5R: 0.222222, .75R: 0.000000, count: 18 +Region 23 Avg IOU: 0.376903, Class: 0.545958, Obj: 0.646985, No Obj: 0.584810, .5R: 0.217391, .75R: 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count: 14 +Region 16 Avg IOU: 0.571453, Class: 0.369654, Obj: 0.006173, No Obj: 0.000262, .5R: 0.615385, .75R: 0.153846, count: 13 +Region 23 Avg IOU: 0.600950, Class: 0.311644, Obj: 0.000003, No Obj: 0.000000, .5R: 0.782609, .75R: 0.086957, count: 23 +Region 16 Avg IOU: 0.618019, Class: 0.359741, Obj: 0.004937, No Obj: 0.000154, .5R: 0.857143, .75R: 0.000000, count: 14 +Region 23 Avg IOU: 0.651840, Class: 0.307667, Obj: 0.000002, No Obj: 0.000000, .5R: 0.884615, .75R: 0.230769, count: 26 +Region 16 Avg IOU: 0.499568, Class: 0.372740, Obj: 0.012013, No Obj: 0.000342, .5R: 0.500000, .75R: 0.000000, count: 8 +Region 23 Avg IOU: 0.569307, Class: 0.329539, Obj: 0.000002, No Obj: 0.000000, .5R: 0.777778, .75R: 0.111111, count: 9 + + 28: 3.327422, 53.571419 avg loss, 0.001000 rate, 0.799655 seconds, 3584 images +Loaded: 1.062757 seconds +Region 16 Avg IOU: 0.566419, Class: 0.372964, Obj: 0.003568, No Obj: 0.000258, .5R: 0.666667, .75R: 0.000000, count: 6 +Region 23 Avg IOU: 0.655908, Class: 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count: 16 +Region 23 Avg IOU: 0.550770, Class: 0.286574, Obj: 0.000004, No Obj: 0.000000, .5R: 0.812500, .75R: 0.062500, count: 16 +Region 16 Avg IOU: 0.752672, Class: 0.342423, Obj: 0.005574, No Obj: 0.000344, .5R: 1.000000, .75R: 0.750000, count: 4 +Region 23 Avg IOU: 0.599110, Class: 0.289047, Obj: 0.000007, No Obj: 0.000001, .5R: 0.750000, .75R: 0.125000, count: 8 +Region 16 Avg IOU: 0.654007, Class: 0.337509, Obj: 0.020912, No Obj: 0.000416, .5R: 0.750000, .75R: 0.250000, count: 4 +Region 23 Avg IOU: 0.623713, Class: 0.309432, Obj: 0.000017, No Obj: 0.000002, .5R: 0.709677, .75R: 0.225806, count: 31 +Region 16 Avg IOU: 0.544109, Class: 0.323795, Obj: 0.015694, No Obj: 0.000567, .5R: 0.714286, .75R: 0.000000, count: 7 +Region 23 Avg IOU: 0.572862, Class: 0.302297, Obj: 0.000003, No Obj: 0.000000, .5R: 0.521739, .75R: 0.217391, count: 23 +Region 16 Avg IOU: 0.651900, Class: 0.406162, Obj: 0.015110, No Obj: 0.000854, .5R: 0.933333, .75R: 0.266667, count: 15 +Region 23 Avg IOU: 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done! +Loaded: 2.012915 seconds +Region 16 Avg IOU: 0.604759, Class: 0.404950, Obj: 0.061323, No Obj: 0.001038, .5R: 1.000000, .75R: 0.000000, count: 6 +Region 23 Avg IOU: 0.658812, Class: 0.338433, Obj: 0.000039, No Obj: 0.000007, .5R: 1.000000, .75R: 0.142857, count: 14 +Region 16 Avg IOU: 0.627539, Class: 0.317027, Obj: 0.023141, No Obj: 0.001157, .5R: 0.777778, .75R: 0.111111, count: 9 +Region 23 Avg IOU: 0.599830, Class: 0.289374, Obj: 0.000005, No Obj: 0.000002, .5R: 0.720000, .75R: 0.080000, count: 25 +Region 16 Avg IOU: 0.599799, Class: 0.360928, Obj: 0.032658, No Obj: 0.000608, .5R: 0.800000, .75R: 0.000000, count: 10 +Region 23 Avg IOU: 0.605558, Class: 0.312615, Obj: 0.000063, No Obj: 0.000008, .5R: 0.880000, .75R: 0.120000, count: 25 +Region 16 Avg IOU: 0.589041, Class: 0.337567, Obj: 0.007390, No Obj: 0.000479, .5R: 0.750000, .75R: 0.083333, count: 12 +Region 23 Avg IOU: 0.570958, Class: 0.298091, Obj: 0.000029, No Obj: 0.000007, .5R: 0.650000, .75R: 0.150000, count: 20 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seconds, 12928 images +Loaded: 0.000065 seconds +Region 16 Avg IOU: 0.580208, Class: 0.232467, Obj: 0.034131, No Obj: 0.000795, .5R: 0.772727, .75R: 0.000000, count: 22 +Region 23 Avg IOU: 0.567508, Class: 0.136351, Obj: 0.008699, No Obj: 0.000172, .5R: 0.750000, .75R: 0.083333, count: 12 +Region 16 Avg IOU: 0.568896, Class: 0.260071, Obj: 0.029752, No Obj: 0.000631, .5R: 0.866667, .75R: 0.000000, count: 15 +Region 23 Avg IOU: 0.585348, Class: 0.242602, Obj: 0.029200, No Obj: 0.000113, .5R: 0.631579, .75R: 0.157895, count: 19 +Region 16 Avg IOU: 0.564895, Class: 0.235618, Obj: 0.077669, No Obj: 0.000949, .5R: 0.736842, .75R: 0.052632, count: 19 +Region 23 Avg IOU: 0.541157, Class: 0.221464, Obj: 0.052756, No Obj: 0.000199, .5R: 0.705882, .75R: 0.058824, count: 17 +Region 16 Avg IOU: 0.592439, Class: 0.162869, Obj: 0.018742, No Obj: 0.000586, .5R: 0.857143, .75R: 0.047619, count: 21 +Region 23 Avg IOU: 0.608312, Class: 0.185438, Obj: 0.026840, No Obj: 0.000282, .5R: 0.833333, .75R: 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rate, 0.985401 seconds, 15360 images +Resizing +544 x 544 + try to allocate workspace = 1145585665 * sizeof(float), CUDA allocate done! +Loaded: 0.589020 seconds +Region 16 Avg IOU: 0.570360, Class: 0.203119, Obj: 0.058690, No Obj: 0.001148, .5R: 0.714286, .75R: 0.142857, count: 14 +Region 23 Avg IOU: 0.571007, Class: 0.222187, Obj: 0.093789, No Obj: 0.000302, .5R: 0.700000, .75R: 0.200000, count: 10 +Region 16 Avg IOU: 0.590565, Class: 0.231431, Obj: 0.037926, No Obj: 0.001079, .5R: 0.812500, .75R: 0.062500, count: 16 +Region 23 Avg IOU: 0.545002, Class: 0.155806, Obj: 0.002358, No Obj: 0.000070, .5R: 0.666667, .75R: 0.047619, count: 21 +Region 16 Avg IOU: 0.625244, Class: 0.193626, Obj: 0.112642, No Obj: 0.002258, .5R: 0.920000, .75R: 0.120000, count: 25 +Region 23 Avg IOU: 0.620209, Class: 0.207129, Obj: 0.031313, No Obj: 0.000313, .5R: 0.843750, .75R: 0.156250, count: 32 +Region 16 Avg IOU: 0.589215, Class: 0.219178, Obj: 0.102040, No Obj: 0.001505, .5R: 0.650000, .75R: 0.050000, 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allocate done! +Loaded: 0.987677 seconds +Region 16 Avg IOU: 0.572286, Class: 0.241670, Obj: 0.110998, No Obj: 0.000844, .5R: 0.722222, .75R: 0.055556, count: 18 +Region 23 Avg IOU: 0.594452, Class: 0.171514, Obj: 0.016502, No Obj: 0.000105, .5R: 0.772727, .75R: 0.090909, count: 22 +Region 16 Avg IOU: 0.686378, Class: 0.225118, Obj: 0.158468, No Obj: 0.001085, .5R: 0.954545, .75R: 0.363636, count: 22 +Region 23 Avg IOU: 0.622103, Class: 0.221242, Obj: 0.092818, No Obj: 0.000344, .5R: 0.875000, .75R: 0.250000, count: 16 +Region 16 Avg IOU: 0.657197, Class: 0.220337, Obj: 0.101227, No Obj: 0.001110, .5R: 0.916667, .75R: 0.125000, count: 24 +Region 23 Avg IOU: 0.608307, Class: 0.174581, Obj: 0.055662, No Obj: 0.000285, .5R: 0.742857, .75R: 0.142857, count: 35 +Region 16 Avg IOU: 0.616253, Class: 0.149789, Obj: 0.101735, No Obj: 0.001101, .5R: 0.764706, .75R: 0.235294, count: 17 +Region 23 Avg IOU: 0.623258, Class: 0.164234, Obj: 0.072185, No Obj: 0.000348, .5R: 0.850000, .75R: 0.200000, 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allocate workspace = 1146765313 * sizeof(float), CUDA allocate done! +Loaded: 0.568497 seconds +Region 16 Avg IOU: 0.683157, Class: 0.246668, Obj: 0.143775, No Obj: 0.000895, .5R: 0.909091, .75R: 0.318182, count: 22 +Region 23 Avg IOU: 0.664089, Class: 0.200112, Obj: 0.070032, No Obj: 0.000302, .5R: 0.952381, .75R: 0.190476, count: 21 +Region 16 Avg IOU: 0.666941, Class: 0.200415, Obj: 0.212357, No Obj: 0.001011, .5R: 0.894737, .75R: 0.210526, count: 19 +Region 23 Avg IOU: 0.644467, Class: 0.150456, Obj: 0.084154, No Obj: 0.000404, .5R: 0.818182, .75R: 0.227273, count: 22 +Region 16 Avg IOU: 0.682930, Class: 0.197192, Obj: 0.104986, No Obj: 0.000765, .5R: 0.916667, .75R: 0.333333, count: 12 +Region 23 Avg IOU: 0.644544, Class: 0.139849, Obj: 0.068445, No Obj: 0.000281, .5R: 0.933333, .75R: 0.200000, count: 15 +Region 16 Avg IOU: 0.611580, Class: 0.230409, Obj: 0.114278, No Obj: 0.001274, .5R: 0.772727, .75R: 0.045455, count: 22 +Region 23 Avg IOU: 0.620957, Class: 0.180860, Obj: 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1.000000, .75R: 0.454545, count: 11 +Region 23 Avg IOU: 0.594732, Class: 0.159858, Obj: 0.092499, No Obj: 0.000647, .5R: 0.772727, .75R: 0.181818, count: 22 +Region 16 Avg IOU: 0.766237, Class: 0.192274, Obj: 0.389875, No Obj: 0.002234, .5R: 1.000000, .75R: 0.562500, count: 16 +Region 23 Avg IOU: 0.618145, Class: 0.161885, Obj: 0.065806, No Obj: 0.000620, .5R: 0.821429, .75R: 0.214286, count: 28 +Region 16 Avg IOU: 0.695250, Class: 0.263364, Obj: 0.551569, No Obj: 0.002584, .5R: 1.000000, .75R: 0.352941, count: 17 +Region 23 Avg IOU: 0.637647, Class: 0.233749, Obj: 0.066971, No Obj: 0.000512, .5R: 0.823529, .75R: 0.176471, count: 17 +Region 16 Avg IOU: 0.771376, Class: 0.257938, Obj: 0.358181, No Obj: 0.001770, .5R: 1.000000, .75R: 0.777778, count: 9 +Region 23 Avg IOU: 0.617482, Class: 0.183550, Obj: 0.051767, No Obj: 0.000586, .5R: 0.869565, .75R: 0.130435, count: 23 + + 790: 2.646154, 2.495055 avg loss, 0.001000 rate, 0.764134 seconds, 101120 images +Resizing +672 x 672 + try to allocate workspace = 1148076033 * sizeof(float), CUDA allocate done! +Loaded: 0.413861 seconds +Region 16 Avg IOU: 0.676016, Class: 0.216460, Obj: 0.194492, No Obj: 0.000764, .5R: 1.000000, .75R: 0.181818, count: 11 +Region 23 Avg IOU: 0.681077, Class: 0.171878, Obj: 0.075758, No Obj: 0.000265, .5R: 0.818182, .75R: 0.454545, count: 11 +Region 16 Avg IOU: 0.639207, Class: 0.194570, Obj: 0.211177, No Obj: 0.001148, .5R: 0.777778, .75R: 0.222222, count: 18 +Region 23 Avg IOU: 0.614469, Class: 0.258743, Obj: 0.131994, No Obj: 0.000347, .5R: 0.846154, .75R: 0.076923, count: 13 +Region 16 Avg IOU: 0.621722, Class: 0.237340, Obj: 0.212474, No Obj: 0.001148, .5R: 0.842105, .75R: 0.157895, count: 19 +Region 23 Avg IOU: 0.632460, Class: 0.180489, Obj: 0.066971, No Obj: 0.000340, .5R: 0.925926, .75R: 0.111111, count: 27 +Region 16 Avg IOU: 0.631902, Class: 0.234114, Obj: 0.192920, No Obj: 0.001297, .5R: 0.708333, .75R: 0.375000, count: 24 +Region 23 Avg IOU: 0.643290, Class: 0.175109, Obj: 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0.741935, .75R: 0.064516, count: 31 + + 791: 3.633566, 2.608907 avg loss, 0.001000 rate, 2.012518 seconds, 101248 images +Loaded: 0.000066 seconds +Region 16 Avg IOU: 0.593115, Class: 0.212652, Obj: 0.149233, No Obj: 0.001018, .5R: 0.769231, .75R: 0.153846, count: 26 +Region 23 Avg IOU: 0.587788, Class: 0.197889, Obj: 0.029224, No Obj: 0.000196, .5R: 0.789474, .75R: 0.052632, count: 19 +Region 16 Avg IOU: 0.728425, Class: 0.310668, Obj: 0.259613, No Obj: 0.000897, .5R: 0.937500, .75R: 0.625000, count: 16 +Region 23 Avg IOU: 0.611913, Class: 0.191654, Obj: 0.077402, No Obj: 0.000212, .5R: 0.823529, .75R: 0.176471, count: 17 +Region 16 Avg IOU: 0.718246, Class: 0.202143, Obj: 0.347218, No Obj: 0.001451, .5R: 0.894737, .75R: 0.473684, count: 19 +Region 23 Avg IOU: 0.621063, Class: 0.203368, Obj: 0.135222, No Obj: 0.000364, .5R: 0.863636, .75R: 0.090909, count: 22 +Region 16 Avg IOU: 0.707979, Class: 0.165661, Obj: 0.207521, No Obj: 0.001161, .5R: 0.961538, .75R: 0.423077, count: 26 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Class: 0.164290, Obj: 0.105761, No Obj: 0.000196, .5R: 0.750000, .75R: 0.000000, count: 8 + + 792: 3.075657, 2.655581 avg loss, 0.001000 rate, 1.978302 seconds, 101376 images +Loaded: 0.000069 seconds +Region 16 Avg IOU: 0.680230, Class: 0.280348, Obj: 0.144880, No Obj: 0.000710, .5R: 0.875000, .75R: 0.375000, count: 24 +Region 23 Avg IOU: 0.509445, Class: 0.204898, Obj: 0.029390, No Obj: 0.000180, .5R: 0.571429, .75R: 0.000000, count: 14 +Region 16 Avg IOU: 0.721365, Class: 0.177274, Obj: 0.207018, No Obj: 0.000881, .5R: 0.933333, .75R: 0.333333, count: 15 +Region 23 Avg IOU: 0.597313, Class: 0.213933, Obj: 0.049218, No Obj: 0.000245, .5R: 0.833333, .75R: 0.111111, count: 18 +Region 16 Avg IOU: 0.688825, Class: 0.237608, Obj: 0.259392, No Obj: 0.000942, .5R: 0.916667, .75R: 0.416667, count: 24 +Region 23 Avg IOU: 0.660164, Class: 0.201858, Obj: 0.062352, No Obj: 0.000228, .5R: 0.869565, .75R: 0.304348, count: 23 +Region 16 Avg IOU: 0.726505, Class: 0.227800, Obj: 0.224823, No Obj: 0.000811, .5R: 0.857143, .75R: 0.571429, count: 14 +Region 23 Avg IOU: 0.603027, Class: 0.163123, Obj: 0.077128, No Obj: 0.000189, .5R: 0.857143, .75R: 0.095238, count: 21 +Region 16 Avg IOU: 0.690709, Class: 0.256052, Obj: 0.194443, No Obj: 0.000810, .5R: 1.000000, .75R: 0.333333, count: 18 +Region 23 Avg IOU: 0.591514, Class: 0.178414, Obj: 0.073940, No Obj: 0.000222, .5R: 0.736842, .75R: 0.105263, count: 19 +Region 16 Avg IOU: 0.712518, Class: 0.254914, Obj: 0.248743, No Obj: 0.001209, .5R: 1.000000, .75R: 0.400000, count: 25 +Region 23 Avg IOU: 0.647783, Class: 0.212255, Obj: 0.072063, No Obj: 0.000311, .5R: 0.964286, .75R: 0.285714, count: 28 +Region 16 Avg IOU: 0.753876, Class: 0.179812, Obj: 0.189248, No Obj: 0.000755, .5R: 1.000000, .75R: 0.470588, count: 17 +Region 23 Avg IOU: 0.603822, Class: 0.179410, Obj: 0.064646, No Obj: 0.000200, .5R: 0.777778, .75R: 0.111111, count: 18 +Region 16 Avg IOU: 0.636436, Class: 0.168892, Obj: 0.122785, No Obj: 0.000876, .5R: 0.823529, .75R: 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17 +Region 16 Avg IOU: 0.762641, Class: 0.149559, Obj: 0.331345, No Obj: 0.001123, .5R: 1.000000, .75R: 0.666667, count: 15 +Region 23 Avg IOU: 0.671152, Class: 0.209496, Obj: 0.274791, No Obj: 0.000337, .5R: 0.928571, .75R: 0.285714, count: 14 + + 2010: 2.710625, 2.603914 avg loss, 0.001000 rate, 1.589430 seconds, 257280 images +Resizing +736 x 736 + try to allocate workspace = 1149517825 * sizeof(float), CUDA allocate done! +Loaded: 0.214765 seconds +Region 16 Avg IOU: 0.708164, Class: 0.232927, Obj: 0.328606, No Obj: 0.001510, .5R: 0.967742, .75R: 0.451613, count: 31 +Region 23 Avg IOU: 0.662019, Class: 0.196250, Obj: 0.132507, No Obj: 0.000196, .5R: 0.882353, .75R: 0.235294, count: 17 +Region 16 Avg IOU: 0.653780, Class: 0.171220, Obj: 0.314105, No Obj: 0.001217, .5R: 0.800000, .75R: 0.250000, count: 20 +Region 23 Avg IOU: 0.717550, Class: 0.138608, Obj: 0.127344, No Obj: 0.000190, .5R: 1.000000, .75R: 0.444444, count: 18 +Region 16 Avg IOU: 0.692384, Class: 0.230687, Obj: 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allocate done! +Loaded: 0.240301 seconds +Region 16 Avg IOU: 0.767504, Class: 0.149337, Obj: 0.397560, No Obj: 0.001163, .5R: 1.000000, .75R: 0.533333, count: 15 +Region 23 Avg IOU: 0.710026, Class: 0.111985, Obj: 0.162575, No Obj: 0.000371, .5R: 0.941176, .75R: 0.382353, count: 34 +Region 16 Avg IOU: 0.770602, Class: 0.280521, Obj: 0.453202, No Obj: 0.001601, .5R: 1.000000, .75R: 0.631579, count: 19 +Region 23 Avg IOU: 0.728999, Class: 0.159945, Obj: 0.160672, No Obj: 0.000175, .5R: 1.000000, .75R: 0.272727, count: 11 +Region 16 Avg IOU: 0.719969, Class: 0.295212, Obj: 0.308573, No Obj: 0.001172, .5R: 1.000000, .75R: 0.444444, count: 18 +Region 23 Avg IOU: 0.660599, Class: 0.164219, Obj: 0.169456, No Obj: 0.000291, .5R: 0.937500, .75R: 0.125000, count: 16 +Region 16 Avg IOU: 0.702531, Class: 0.255725, Obj: 0.235402, No Obj: 0.001577, .5R: 0.782609, .75R: 0.521739, count: 23 +Region 23 Avg IOU: 0.660286, Class: 0.223716, Obj: 0.134962, No Obj: 0.000325, .5R: 0.809524, .75R: 0.238095, 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allocate done! +Loaded: 0.527689 seconds +Region 16 Avg IOU: 0.707428, Class: 0.248448, Obj: 0.317230, No Obj: 0.001098, .5R: 1.000000, .75R: 0.333333, count: 15 +Region 23 Avg IOU: 0.731559, Class: 0.218676, Obj: 0.157201, No Obj: 0.000340, .5R: 0.954545, .75R: 0.545455, count: 22 +Region 16 Avg IOU: 0.746415, Class: 0.174083, Obj: 0.346885, No Obj: 0.001097, .5R: 1.000000, .75R: 0.600000, count: 15 +Region 23 Avg IOU: 0.744747, Class: 0.187232, Obj: 0.179098, No Obj: 0.000271, .5R: 1.000000, .75R: 0.533333, count: 15 +Region 16 Avg IOU: 0.713721, Class: 0.253170, Obj: 0.341173, No Obj: 0.001279, .5R: 1.000000, .75R: 0.470588, count: 17 +Region 23 Avg IOU: 0.691986, Class: 0.179453, Obj: 0.160904, No Obj: 0.000372, .5R: 0.851852, .75R: 0.407407, count: 27 +Region 16 Avg IOU: 0.712813, Class: 0.219665, Obj: 0.194239, No Obj: 0.001022, .5R: 0.904762, .75R: 0.476190, count: 21 +Region 23 Avg IOU: 0.745392, Class: 0.172928, Obj: 0.240985, No Obj: 0.000363, .5R: 0.958333, .75R: 0.541667, 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allocate done! +Loaded: 0.160035 seconds +Region 16 Avg IOU: 0.754053, Class: 0.199902, Obj: 0.294409, No Obj: 0.001696, .5R: 0.944444, .75R: 0.777778, count: 18 +Region 23 Avg IOU: 0.734440, Class: 0.167411, Obj: 0.238637, No Obj: 0.000259, .5R: 1.000000, .75R: 0.545455, count: 11 +Region 16 Avg IOU: 0.759822, Class: 0.236723, Obj: 0.238377, No Obj: 0.001455, .5R: 0.947368, .75R: 0.631579, count: 19 +Region 23 Avg IOU: 0.808282, Class: 0.192878, Obj: 0.315316, No Obj: 0.000283, .5R: 1.000000, .75R: 0.933333, count: 15 +Region 16 Avg IOU: 0.737485, Class: 0.252927, Obj: 0.408513, No Obj: 0.001633, .5R: 0.933333, .75R: 0.466667, count: 30 +Region 23 Avg IOU: 0.723166, Class: 0.161964, Obj: 0.254424, No Obj: 0.000439, .5R: 0.878788, .75R: 0.424242, count: 33 +Region 16 Avg IOU: 0.740345, Class: 0.281961, Obj: 0.533175, No Obj: 0.001510, .5R: 0.866667, .75R: 0.533333, count: 15 +Region 23 Avg IOU: 0.784152, Class: 0.201404, Obj: 0.347746, No Obj: 0.000196, .5R: 1.000000, .75R: 0.588235, 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to ./attribute/7354_26298/attribute_33652_s_tyv3_9c5_3800.weights +ount: 14 +Region 23 Avg IOU: 0.701791, Class: 0.189628, Obj: 0.305529, No Obj: 0.000791, .5R: 0.909091, .75R: 0.409091, count: 22 +Region 16 Avg IOU: 0.751604, Class: 0.268855, Obj: 0.342445, No Obj: 0.001202, .5R: 0.875000, .75R: 0.500000, count: 8 +Region 23 Avg IOU: 0.714389, Class: 0.232102, Obj: 0.414571, No Obj: 0.000795, .5R: 0.944444, .75R: 0.444444, count: 18 +Region 16 Avg IOU: 0.815352, Class: 0.440042, Obj: 0.476388, No Obj: 0.001376, .5R: 1.000000, .75R: 0.625000, count: 8 +Region 23 Avg IOU: 0.700379, Class: 0.148823, Obj: 0.254469, No Obj: 0.000559, .5R: 0.909091, .75R: 0.363636, count: 11 +Region 16 Avg IOU: 0.770140, Class: 0.235716, Obj: 0.368930, No Obj: 0.001477, .5R: 1.000000, .75R: 0.600000, count: 10 +Region 23 Avg IOU: 0.753858, Class: 0.206769, Obj: 0.304296, No Obj: 0.000702, .5R: 1.000000, .75R: 0.684211, count: 19 +Region 16 Avg IOU: 0.754509, Class: 0.209777, Obj: 0.351066, No Obj: 0.001625, 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13 +Region 16 Avg IOU: 0.758112, Class: 0.243616, Obj: 0.451820, No Obj: 0.002704, .5R: 1.000000, .75R: 0.666667, count: 12 +Region 23 Avg IOU: 0.725598, Class: 0.174418, Obj: 0.220800, No Obj: 0.000628, .5R: 0.928571, .75R: 0.571429, count: 14 + + \ No newline at end of file diff --git a/scripts/log_parser/test_new.svg b/scripts/log_parser/test_new.svg new file mode 100644 index 00000000000..a749a78c947 --- /dev/null +++ b/scripts/log_parser/test_new.svg @@ -0,0 +1,2953 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + From e6c97a53a7b5ac4014d30d236ea2bf5adb4bb521 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 7 Aug 2018 23:19:50 +0300 Subject: [PATCH 0115/1365] Maxpool fixes --- src/maxpool_layer.c | 4 ++-- src/utils.c | 7 +++++-- 2 files changed, 7 insertions(+), 4 deletions(-) diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index 928102f5d61..c543b529394 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -80,8 +80,8 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h) void forward_maxpool_layer(const maxpool_layer l, network_state state) { int b,i,j,k,m,n; - int w_offset = -l.pad / l.stride; - int h_offset = -l.pad / l.stride; + int w_offset = -l.pad / 2; + int h_offset = -l.pad / 2; int h = l.out_h; int w = l.out_w; diff --git a/src/utils.c b/src/utils.c index 3af7c97072c..d20c63323f6 100644 --- a/src/utils.c +++ b/src/utils.c @@ -210,6 +210,9 @@ void replace_image_to_label(char *input_path, char *output_path) { find_replace(input_path, "/images/train2014/", "/labels/train2014/", output_path); // COCO find_replace(output_path, "/images/val2014/", "/labels/val2014/", output_path); // COCO find_replace(output_path, "/JPEGImages/", "/labels/", output_path); // PascalVOC + find_replace(input_path, "\\images\\train2014\\", "\\labels\\train2014\\", output_path); // COCO + find_replace(output_path, "\\images\\val2014\\", "\\labels\\val2014\\", output_path); // COCO + find_replace(output_path, "\\JPEGImages\\", "\\labels\\", output_path); // PascalVOC //find_replace(output_path, "/VOC2007/JPEGImages/", "/VOC2007/labels/", output_path); // PascalVOC //find_replace(output_path, "/VOC2012/JPEGImages/", "/VOC2012/labels/", output_path); // PascalVOC @@ -578,7 +581,7 @@ float mag_array(float *a, int n) int i; float sum = 0; for(i = 0; i < n; ++i){ - sum += a[i]*a[i]; + sum += a[i]*a[i]; } return sqrt(sum); } @@ -673,7 +676,7 @@ float rand_normal() size_t rand_size_t() { - return ((size_t)(rand()&0xff) << 56) | + return ((size_t)(rand()&0xff) << 56) | ((size_t)(rand()&0xff) << 48) | ((size_t)(rand()&0xff) << 40) | ((size_t)(rand()&0xff) << 32) | From 0a326e7afe3e690c0b4cb64bbd0ce4f0603a7d85 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 7 Aug 2018 23:20:25 +0300 Subject: [PATCH 0116/1365] XNOR-net on CPU AVX2 --- src/convolutional_layer.c | 217 +++++++++++++++-- src/convolutional_layer.h | 2 + src/demo.c | 1 + src/detector.c | 2 + src/gemm.c | 483 ++++++++++++++++++++++++++++++++++---- src/gemm.h | 50 +++- src/layer.c | 2 + src/layer.h | 3 + src/network.c | 34 ++- src/network.h | 1 + 10 files changed, 714 insertions(+), 81 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 554bd421f46..bbc4807e639 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -9,7 +9,7 @@ #include #ifdef CUDNN -#pragma comment(lib, "cudnn.lib") +#pragma comment(lib, "cudnn.lib") #endif #ifdef AI2 @@ -141,7 +141,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) { #ifdef CUDNN_HALF - // TRUE_HALF_CONFIG is only supported on architectures with true fp16 support (compute capability 5.3 and 6.0): + // TRUE_HALF_CONFIG is only supported on architectures with true fp16 support (compute capability 5.3 and 6.0): // Tegra X1, Jetson TX1, DRIVE CX, DRIVE PX, Quadro GP100, Tesla P100 // PSEUDO_HALF_CONFIG is required for Tensor Cores - our case! const cudnnDataType_t data_type = CUDNN_DATA_HALF; @@ -161,7 +161,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH); #endif - // INT8_CONFIG, INT8_EXT_CONFIG, INT8x4_CONFIG and INT8x4_EXT_CONFIG are only supported + // INT8_CONFIG, INT8_EXT_CONFIG, INT8x4_CONFIG and INT8x4_EXT_CONFIG are only supported // on architectures with DP4A support (compute capability 6.1 and later). //cudnnDataType_t data_type = CUDNN_DATA_INT8; @@ -188,7 +188,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) int forward_algo = CUDNN_CONVOLUTION_FWD_PREFER_FASTEST; int backward_algo = CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST; int backward_filter = CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST; - if (cudnn_preference == cudnn_smallest) + if (cudnn_preference == cudnn_smallest) { forward_algo = CUDNN_CONVOLUTION_FWD_NO_WORKSPACE; backward_algo = CUDNN_CONVOLUTION_BWD_DATA_NO_WORKSPACE; @@ -221,7 +221,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) 0, &l->bf_algo); - if (data_type == CUDNN_DATA_HALF) + if (data_type == CUDNN_DATA_HALF) { // HALF-16 if(data_type == CUDNN_DATA_HALF) l->fw_algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM; @@ -249,8 +249,8 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) if (l->bf_algo == CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED) bf = 2; //printf("Tensor Cores - Backward-filter enabled: l->bf_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED \n"); - if (fw == 2 && bd == 2 && bf == 2) printf("TF "); - else if (fw == 1 && bd == 1 && bf == 1) printf("TH "); + //if (fw == 2 && bd == 2 && bf == 2) printf("TF "); + //else if (fw == 1 && bd == 1 && bf == 1) printf("TH "); } } #endif @@ -379,7 +379,7 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.x_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); l.x_norm_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); } -#ifdef CUDNN +#ifdef CUDNN cudnnCreateTensorDescriptor(&l.normDstTensorDesc); cudnnCreateTensorDescriptor(&l.normDstTensorDescF16); cudnnCreateTensorDescriptor(&l.normTensorDesc); @@ -497,7 +497,7 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) l->workspace_size = get_workspace_size(*l); #ifdef CUDNN - // check for excessive memory consumption + // check for excessive memory consumption size_t free_byte; size_t total_byte; check_error(cudaMemGetInfo(&free_byte, &total_byte)); @@ -543,6 +543,85 @@ void backward_bias(float *bias_updates, float *delta, int batch, int n, int size } } +void gemm_nn_custom(int M, int N, int K, float ALPHA, + float *A, int lda, + float *B, int ldb, + float *C, int ldc) +{ + int i, j, k; + for (i = 0; i < M; ++i) { + for (k = 0; k < K; ++k) { + register float A_PART = ALPHA*A[i*lda + k]; + //printf("\n weight = %f \n", A_PART); + for (j = 0; j < N; ++j) { + C[i*ldc + j] += A_PART*B[k*ldb + j]; + } + } + } +} + + +void get_mean_array(float *src, size_t size, size_t filters, float *mean_arr) { + size_t i, counter; + counter = 0; + for (i = 0; i < size; i += size / filters) { + mean_arr[counter++] = fabs(src[i]); + } +} + +/* +void float_to_bit(float *src, unsigned char *dst, size_t size) { + + size_t dst_size = size / 8 + 1; + memset(dst, 0, dst_size); + size_t i, dst_i, dst_shift; + for (i = 0; i < size; ++i) { + if (src[i] > 0) set_bit(dst, i); + } +} +*/ + +void bit_to_float(unsigned char *src, float *dst, size_t size, size_t filters, float *mean_arr) { + memset(dst, 0, size *sizeof(float)); + size_t i, src_i, src_shift; + + for (i = 0; i < size; ++i) { + float mean_val = 1; + if(mean_arr != NULL) mean_val = fabs(mean_arr[i / (size / filters)]); + if(get_bit(src, i)) dst[i] = mean_val; + else dst[i] = -mean_val; + } +} + +void binary_transpose_align_weights(convolutional_layer *l, size_t ldb_align) +{ + int m = l->n; + int k = l->size*l->size*l->c; + size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; + + binarize_weights(l->weights, m, k, l->binary_weights); + + size_t align_weights_size = new_ldb * m; + size_t align_bit_weights_size = align_weights_size / 8;// +1; + float *align_weights = calloc(align_weights_size, sizeof(float)); + l->align_bit_weights = calloc(align_bit_weights_size, sizeof(char)); + + size_t i, j; + // align A without transpose + for (i = 0; i < m; ++i) { + for (j = 0; j < k; ++j) { + align_weights[i*new_ldb + j] = l->binary_weights[i*k + j]; + } + } + float_to_bit(align_weights, l->align_bit_weights, align_weights_size); + + l->mean_arr = calloc(l->n, sizeof(float)); + get_mean_array(align_weights, align_weights_size, l->n, l->mean_arr); + + free(align_weights); +} + + void forward_convolutional_layer(convolutional_layer l, network_state state) { int out_h = convolutional_out_height(l); @@ -552,7 +631,10 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) fill_cpu(l.outputs*l.batch, 0, l.output, 1); if(l.xnor){ - binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights); + if (!l.align_bit_weights) { + binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights); + //printf("\n binarize_weights l.align_bit_weights = %p \n", l.align_bit_weights); + } swap_binary(&l); binarize_cpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input); state.input = l.binary_input; @@ -562,15 +644,122 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) int k = l.size*l.size*l.c; int n = out_h*out_w; - float *a = l.weights; float *b = state.workspace; float *c = l.output; + static int u = 0; + u++; + for(i = 0; i < l.batch; ++i){ - im2col_cpu(state.input, l.c, l.h, l.w, + im2col_cpu(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); - gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); + //gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); + //gemm_nn_custom(m, n, k, 1, a, k, b, n, c, n); + if (l.xnor) { + size_t output_size = l.outputs; + //float *count_output = calloc(output_size, sizeof(float)); + //size_t bit_output_size = output_size / 8 + 1; + //char *bit_output = calloc(bit_output_size, sizeof(char)); + + size_t intput_size = n * k; // (out_h*out_w) X (l.size*l.size*l.c) : after im2col() + size_t bit_input_size = intput_size / 8 + 1; + //char *bit_input = calloc(bit_input_size, sizeof(char)); + + size_t weights_size = k * m; //l.size*l.size*l.c*l.n; + size_t bit_weights_size = weights_size / 8 + 1; + //char *bit_weights = calloc(bit_weights_size, sizeof(char)); + //float *mean_arr = calloc(l.n, sizeof(float)); + + // test: float->bit->float + //get_mean_array(l.weights, weights_size, l.n, mean_arr); + //float_to_bit(l.weights, bit_weights, weights_size); + //memset(l.weights, 0, weights_size * sizeof(float)); + //bit_to_float(bit_weights, l.weights, weights_size, l.n, mean_arr); // just for test float->bit->float + + //float_to_bit(b, bit_input, intput_size); + //memset(b, 0, intput_size * sizeof(float)); + //bit_to_float(bit_input, b, intput_size, 1, NULL); // just for test float->bit->float + + // transpose B from NxK to KxN (x-axis (ldb = l.size*l.size*l.c) - should be multiple of 8 bits) + { + size_t ldb_align = 256;// 8; + size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; + size_t t_intput_size = new_ldb * n; + size_t t_bit_input_size = t_intput_size / 8;// +1; + float *t_input = calloc(t_intput_size, sizeof(float)); + char *t_bit_input = calloc(t_bit_input_size, sizeof(char)); + + //printf("\n bit_input_size = %d, n = %d, k = %d, ldb = %d \n", bit_input_size, n, k, n); + //printf("\n t_bit_input_size = %d, k = %d, n = %d, new_ldb = %d \n", t_bit_input_size, k, n, new_ldb); + + + //printf("\n align_weights_size = %d, k = %d, m = %d, lda = %d \n", align_weights_size, k, m, k); + //printf("\n align_bit_weights_size = %d, k = %d, m = %d, new_lda = %d \n", align_bit_weights_size, k, m, new_ldb); + + + // transpose and align B + int i, j; + for (i = 0; i < n; ++i) { + for (j = 0; j < k; ++j) { + t_input[i*new_ldb + j] = b[j*n + i]; + } + } + float_to_bit(t_input, t_bit_input, t_intput_size); + + if (!l.align_bit_weights) + { + size_t align_weights_size = new_ldb * m; + size_t align_bit_weights_size = align_weights_size / 8;// +1; + float *align_weights = calloc(align_weights_size, sizeof(float)); + l.align_bit_weights = calloc(align_bit_weights_size, sizeof(char)); + + // align A without transpose + for (i = 0; i < m; ++i) { + for (j = 0; j < k; ++j) { + align_weights[i*new_ldb + j] = a[i*k + j]; + } + } + float_to_bit(align_weights, l.align_bit_weights, align_weights_size); + + l.mean_arr = calloc(l.n, sizeof(float)); + get_mean_array(align_weights, align_weights_size, l.n, l.mean_arr); + + free(align_weights); + } + + gemm_nn_custom_bin_mean_transposed(m, n, k, 1, l.align_bit_weights, new_ldb, t_bit_input, new_ldb, c, n, l.mean_arr); + + //gemm_nn_custom_bin_mean_transposed(m, n, k, 1, bit_weights, k, t_bit_input, new_ldb, c, n, mean_arr); + + free(t_input); + free(t_bit_input); + + //free(align_bit_weights); + } + + // for bit_input: (k * n) + //if (u == 8) gemm_nn_custom_bin_mean(m, n, k, 1, bit_weights, k, bit_input, n, c, n, mean_arr); // last xnor layer + //else gemm_nn_custom_bin_mean(m, n, k, 1, bit_weights, k, bit_input, n, c, n, NULL); + + //gemm_nn_custom_bin_mean(m, n, k, 1, bit_weights, k, bit_input, n, c, n, mean_arr); + + //printf("\n u = %d \n", u); + + //gemm_nn_custom(m, n, k, 1, a, k, b, n, c, n); + + //int j; + //if (u != 8) for (j = 0; j < l.n; ++j) l.biases[j] = l.biases[j] / (mean_arr[j]*2); + + //free(count_output); + //free(bit_input); + //free(bit_weights); + //free(mean_arr); + } + else { + gemm(0, 0, m, n, k, 1, a, k, b, n, 1, c, n); + // bit-count to float + } c += n*m; state.input += l.c*l.h*l.w; } @@ -606,7 +795,7 @@ void backward_convolutional_layer(convolutional_layer l, network_state state) float *im = state.input+i*l.c*l.h*l.w; - im2col_cpu(im, l.c, l.h, l.w, + im2col_cpu(im, l.c, l.h, l.w, l.size, l.stride, l.pad, b); gemm(0,1,m,n,k,1,a,k,b,k,1,c,n); diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h index 6d1e51777d6..dd79c48e62a 100644 --- a/src/convolutional_layer.h +++ b/src/convolutional_layer.h @@ -35,6 +35,8 @@ void binarize_weights(float *weights, int n, int size, float *binary); void swap_binary(convolutional_layer *l); void binarize_weights2(float *weights, int n, int size, char *binary, float *scales); +void binary_transpose_align_weights(convolutional_layer *l, size_t ldb_align); + void backward_convolutional_layer(convolutional_layer layer, network_state state); void add_bias(float *output, float *biases, int batch, int n, int size); diff --git a/src/demo.c b/src/demo.c index 1b6b95212fe..4f8c7326053 100644 --- a/src/demo.c +++ b/src/demo.c @@ -146,6 +146,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int } //set_batch_network(&net, 1); fuse_conv_batchnorm(net); + calculate_binary_weights(net); srand(2222222); if(filename){ diff --git a/src/detector.c b/src/detector.c index 244b4c3d20e..a816c742d36 100644 --- a/src/detector.c +++ b/src/detector.c @@ -568,6 +568,7 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float } //set_batch_network(&net, 1); fuse_conv_batchnorm(net); + calculate_binary_weights(net); srand(time(0)); list *plist = get_paths(valid_images); @@ -1094,6 +1095,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam } //set_batch_network(&net, 1); fuse_conv_batchnorm(net); + calculate_binary_weights(net); if (net.layers[net.n - 1].classes != names_size) { printf(" Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", name_list, names_size, net.layers[net.n - 1].classes, cfgfile); diff --git a/src/gemm.c b/src/gemm.c index 2b90b059a36..ee7fa151a94 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -5,8 +5,8 @@ #include #include -void gemm_bin(int M, int N, int K, float ALPHA, - char *A, int lda, +void gemm_bin(int M, int N, int K, float ALPHA, + char *A, int lda, float *B, int ldb, float *C, int ldc) { @@ -62,8 +62,8 @@ void time_random_matrix(int TA, int TB, int m, int k, int n) } -void gemm(int TA, int TB, int M, int N, int K, float ALPHA, - float *A, int lda, +void gemm(int TA, int TB, int M, int N, int K, float ALPHA, + float *A, int lda, float *B, int ldb, float BETA, float *C, int ldc) @@ -71,6 +71,234 @@ void gemm(int TA, int TB, int M, int N, int K, float ALPHA, gemm_cpu( TA, TB, M, N, K, ALPHA,A,lda, B, ldb,BETA,C,ldc); } + +//-------------------------------------------- +// XNOR bitwise GEMM for binary neural network +//-------------------------------------------- + +#include + +static inline unsigned char xnor(unsigned char a, unsigned char b) { + //return a == b; + return !(a^b); +} + +// INT-32 +static inline uint32_t get_bit_int32(uint32_t const*const src, size_t index) { + size_t src_i = index / 32; + int src_shift = index % 32; + unsigned char val = (src[src_i] & (1 << src_shift)) > 0; + return val; +} + +static inline uint32_t xnor_int32(uint32_t a, uint32_t b) { + return ~(a^b); +} + +static inline uint64_t xnor_int64(uint64_t a, uint64_t b) { + return ~(a^b); +} + + +static inline uint32_t fill_bit_int32(char src) { + if (src == 0) return 0x00000000; + else return 0xFFFFFFFF; +} + +static inline uint64_t fill_bit_int64(char src) { + if (src == 0) return 0x0000000000000000; + else return 0xFFFFFFFFFFFFFFFF; +} + +void binary_int32_printf(uint32_t src) { + int i; + for (i = 0; i < 32; ++i) { + if (src & 1) printf("1"); + else printf("0"); + src = src >> 1; + } + printf("\n"); +} + +void binary_int64_printf(uint64_t src) { + int i; + for (i = 0; i < 64; ++i) { + if (src & 1) printf("1"); + else printf("0"); + src = src >> 1; + } + printf("\n"); +} + +/* +void gemm_nn_custom_bin_mean(int M, int N, int K, float ALPHA_UNUSED, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + int *count_arr = calloc(M*N, sizeof(int)); + + int i, j, k; + for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] + for (k = 0; k < K; ++k) { // l.size*l.size*l.c - one filter size [27 - 9216] + char a_bit = get_bit(A, i*lda + k); + + for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] + char b_bit = get_bit(B, k*ldb + j); + count_arr[i*ldc + j] += xnor(a_bit, b_bit); + } + } + } + + for (i = 0; i < M; ++i) { + float mean_val = mean_arr[i]; + for (j = 0; j < N; ++j) { + C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val; + } + } + free(count_arr); +} +*/ + +/* +void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + int *count_arr = calloc(M*N, sizeof(int)); + + int i, j, k; + for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] + for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] + for (k = 0; k < K; ++k) { // l.size*l.size*l.c - one filter size [27 - 9216] + char a_bit = get_bit(A, i*lda + k); + char b_bit = get_bit(B, j*ldb + k); + count_arr[i*ldc + j] += xnor(a_bit, b_bit); + } + } + } + + for (i = 0; i < M; ++i) { + float mean_val = mean_arr[i]; + for (j = 0; j < N; ++j) { + C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val; + } + } + free(count_arr); +} +*/ + +/* +void gemm_nn_custom_bin_mean(int M, int N, int K, float ALPHA_UNUSED, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + int *count_arr = calloc(M*N, sizeof(int)); + + int i, j, k, h; + +#pragma omp parallel for + for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] + for (k = 0; k < K; ++k) { // l.size*l.size*l.c - one filter size [27 - 9216] + const char a_bit = get_bit(A, i*lda + k); + uint64_t a_bit64 = fill_bit_int64(a_bit); + int k_ldb = k*ldb; + + for (j = 0; j < N; j += 64) { // out_h*out_w - one channel output size [169 - 173056] + if ((N - j > 64) && (k_ldb % 8 == 0)) { + uint64_t b_bit64 = *((uint64_t *)(B + (k_ldb + j) / 8)); + uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); + //printf("\n %d \n",__builtin_popcountll(c_bit64)); // gcc + printf("\n %d \n", __popcnt64(c_bit64)); // msvs + + int h; + for (h = 0; h < 64; ++h) + if ((c_bit64 >> h) & 1) count_arr[i*ldc + j + h] += 1; + + //binary_int64_printf(a_bit64); + //binary_int64_printf(b_bit64); + //binary_int64_printf(c_bit64); + } + else { + for (; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] + char b_bit = get_bit(B, k_ldb + j); + if (xnor(a_bit, b_bit)) count_arr[i*ldc + j] += 1; + } + } + + } + } + } + + if (mean_arr) { + //int K_2 = K / 2; + for (i = 0; i < M; ++i) { + float mean_val = mean_arr[i]; + //float mean_val2 = 2 * mean_val; + for (j = 0; j < N; ++j) { + C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val; + //C[i*ldc + j] = (count_arr[i*ldc + j] - K_2) *mean_val2; + } + } + } + else { + for (i = 0; i < M; ++i) { + for (j = 0; j < N; ++j) { + C[i*ldc + j] = count_arr[i*ldc + j] - K / 2; + } + } + } + + free(count_arr); + + //getchar(); +} +*/ + + +/* +void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + int i, j, k, h; + +#pragma omp parallel for + for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] + float mean_val = mean_arr[i]; + + for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] + int count = 0; + + for (k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] + uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); + uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); + uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); + +#ifdef WIN32 + int tmp_count = __popcnt64(c_bit64); +#else + int tmp_count = __builtin_popcountll(c_bit64); +#endif + + if (K - k < 64) tmp_count = tmp_count - (64 - (K - k)); // remove extra bits + count += tmp_count; + //binary_int64_printf(c_bit64); + //printf(", count = %d \n\n", tmp_count); + } + + C[i*ldc + j] = (2 * count - K) * mean_val; + } + } +} +*/ + +//---------------------------- + + #if (defined(__AVX__) && defined(__x86_64__)) || defined(_WIN64) #define OSXSAVEFlag (1UL<<27) @@ -79,8 +307,6 @@ void gemm(int TA, int TB, int M, int N, int K, float ALPHA, #define CLMULFlag ((1UL<< 1)|AVXFlag|OSXSAVEFlag) #define VAESFlag ((1UL<<25)|AVXFlag|OSXSAVEFlag) -#include - #ifdef _WIN64 #include #include @@ -196,6 +422,97 @@ void gemm_nn(int M, int N, int K, float ALPHA, } } } + + +// http://graphics.stanford.edu/~seander/bithacks.html +// https://stackoverflow.com/questions/17354971/fast-counting-the-number-of-set-bits-in-m128i-register + +// 2 x faster than popcnt: https://arxiv.org/pdf/1611.07612.pdf + +static inline int popcnt128(__m128i n) { + const __m128i n_hi = _mm_unpackhi_epi64(n, n); +#ifdef _MSC_VER + return __popcnt64(_mm_cvtsi128_si64(n)) + __popcnt64(_mm_cvtsi128_si64(n_hi)); +#else + return __popcntq(_mm_cvtsi128_si64(n)) + __popcntq(_mm_cvtsi128_si64(n_hi)); +#endif +} + +static inline int popcnt256(__m256i n) { + return popcnt128(_mm256_extractf128_si256(n, 0)) + popcnt128(_mm256_extractf128_si256(n, 1)); +} + +void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + __m256i all_1 = _mm256_set1_epi8(255); + int i, j, k, h; + + #pragma omp parallel for + for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] + float mean_val = mean_arr[i]; + + for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] + int count = 0; + const int bit_step = 256; + + for (k = 0; k < K; k += bit_step) { // l.size*l.size*l.c - one filter size [27 - 9216] + + //__m128i a_bit128 = _mm_loadu_si128((__m128i *)(A + (i*lda + k) / 8)); + //__m128i b_bit128 = _mm_loadu_si128((__m128i *)(B + (j*ldb + k) / 8)); + //__m128i xor128 = _mm_xor_si128(a_bit128, b_bit128); + //__m128i c_bit128 = _mm_andnot_si128(xor128, all_1); + //int tmp_count = popcnt128(c_bit128); + + __m256i a_bit256 = _mm256_loadu_si256((__m256i *)(A + (i*lda + k) / 8)); + __m256i b_bit256 = _mm256_loadu_si256((__m256i *)(B + (j*ldb + k) / 8)); + __m256i xor256 = _mm256_xor_si256(a_bit256, b_bit256); + __m256i c_bit256 = _mm256_andnot_si256(xor256, all_1); //we can do NOT for wegihts once and do not do this NOT + int tmp_count = popcnt256(c_bit256); + + if (K - k < bit_step) tmp_count = tmp_count - (bit_step - (K - k)); // remove extra bits + count += tmp_count; + //binary_int64_printf(c_bit64); + //printf(", count = %d \n\n", tmp_count); + } + + C[i*ldc + j] = (2 * count - K) * mean_val; + } + } +} + + +void float_to_bit(float *src, unsigned char *dst, size_t size) +{ + size_t dst_size = size / 8 + 1; + memset(dst, 0, dst_size); + + size_t i; + __m128i all128_0 = _mm_set_epi32(0, 0, 0, 0); + __m256 all256_0 = _mm256_set1_ps(0); + __m256i bits_asc = _mm256_set_epi32(1, 2, 4, 8, 16, 32, 64, 128); + //for(i = 0; i < 8; ++i) bits_asc.m256i_i32[i] = 1 << i; + + for (i = 0; i < size; i+=8) + { + __m256 src256 = _mm256_loadu_ps((__m256i *)(&src[i])); // load 256 bits + __m256 result256 = _mm256_cmp_ps(src256, all256_0, _CMP_GT_OS); // compare dst[i] = (float[i] > 0) + + __m256i bits256 = _mm256_castps_si256(result256); // floats to ints32 + __m256i and256 = _mm256_and_si256(bits256, bits_asc); // bitwise and + + // sum all elements from single and256 + __m128i tmp128 = _mm_hadd_epi32(_mm256_extractf128_si256(and256, 0), _mm256_extractf128_si256(and256, 1)); + tmp128 = _mm_hadd_epi32(tmp128, all128_0); + tmp128 = _mm_hadd_epi32(tmp128, all128_0); + + dst[i / 8] = tmp128.m128i_i32[0]; + } + // int _mm256_movemask_epi8 (__m256i a) +} + #else void gemm_nn(int M, int N, int K, float ALPHA, @@ -213,10 +530,76 @@ void gemm_nn(int M, int N, int K, float ALPHA, } } } + +void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + int i, j, k, h; + +#pragma omp parallel for + for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] + float mean_val = mean_arr[i]; + + for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] + int count = 0; + + for (k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] + uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); + uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); + uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); + +#ifdef WIN32 + int tmp_count = __popcnt64(c_bit64); +#else + int tmp_count = __builtin_popcountll(c_bit64); +#endif + + if (K - k < 64) tmp_count = tmp_count - (64 - (K - k)); // remove extra bits + count += tmp_count; + //binary_int64_printf(c_bit64); + //printf(", count = %d \n\n", tmp_count); + } + + C[i*ldc + j] = (2 * count - K) * mean_val; + } + } +} + +void float_to_bit(float *src, unsigned char *dst, size_t size) +{ + size_t dst_size = size / 8 + 1; + memset(dst, 0, dst_size); + + size_t i; + char *byte_arr = calloc(size, sizeof(char)); + for (i = 0; i < size; ++i) { + if (src[i] > 0) byte_arr[i] = 1; + } + + //for (i = 0; i < size; ++i) { + // dst[i / 8] |= byte_arr[i] << (i % 8); + //} + + for (i = 0; i < size; i += 8) { + char dst_tmp = 0; + dst_tmp |= byte_arr[i + 0] << 0; + dst_tmp |= byte_arr[i + 1] << 1; + dst_tmp |= byte_arr[i + 2] << 2; + dst_tmp |= byte_arr[i + 3] << 3; + dst_tmp |= byte_arr[i + 4] << 4; + dst_tmp |= byte_arr[i + 5] << 5; + dst_tmp |= byte_arr[i + 6] << 6; + dst_tmp |= byte_arr[i + 7] << 7; + dst[i / 8] = dst_tmp; + } + free(byte_arr); +} #endif // __x86_64 -void gemm_nt(int M, int N, int K, float ALPHA, - float *A, int lda, +void gemm_nt(int M, int N, int K, float ALPHA, + float *A, int lda, float *B, int ldb, float *C, int ldc) { @@ -232,8 +615,8 @@ void gemm_nt(int M, int N, int K, float ALPHA, } } -void gemm_tn(int M, int N, int K, float ALPHA, - float *A, int lda, +void gemm_tn(int M, int N, int K, float ALPHA, + float *A, int lda, float *B, int ldb, float *C, int ldc) { @@ -248,8 +631,8 @@ void gemm_tn(int M, int N, int K, float ALPHA, } } -void gemm_tt(int M, int N, int K, float ALPHA, - float *A, int lda, +void gemm_tt(int M, int N, int K, float ALPHA, + float *A, int lda, float *B, int ldb, float *C, int ldc) { @@ -266,8 +649,8 @@ void gemm_tt(int M, int N, int K, float ALPHA, } -void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA, - float *A, int lda, +void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA, + float *A, int lda, float *B, int ldb, float BETA, float *C, int ldc) @@ -300,21 +683,21 @@ void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA, #include -void gemm_ongpu(int TA, int TB, int M, int N, int K, float ALPHA, - float *A_gpu, int lda, +void gemm_ongpu(int TA, int TB, int M, int N, int K, float ALPHA, + float *A_gpu, int lda, float *B_gpu, int ldb, float BETA, float *C_gpu, int ldc) { cublasHandle_t handle = blas_handle(); cudaError_t stream_status = cublasSetStream(handle, get_cuda_stream()); - cudaError_t status = cublasSgemm(handle, (TB ? CUBLAS_OP_T : CUBLAS_OP_N), + cudaError_t status = cublasSgemm(handle, (TB ? CUBLAS_OP_T : CUBLAS_OP_N), (TA ? CUBLAS_OP_T : CUBLAS_OP_N), N, M, K, &ALPHA, B_gpu, ldb, A_gpu, lda, &BETA, C_gpu, ldc); check_error(status); } -void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA, - float *A, int lda, +void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA, + float *A, int lda, float *B, int ldb, float BETA, float *C, int ldc) @@ -435,38 +818,38 @@ void test_gpu_accuracy(int TA, int TB, int m, int k, int n) int test_gpu_blas() { /* - test_gpu_accuracy(0,0,10,576,75); - - test_gpu_accuracy(0,0,17,10,10); - test_gpu_accuracy(1,0,17,10,10); - test_gpu_accuracy(0,1,17,10,10); - test_gpu_accuracy(1,1,17,10,10); - - test_gpu_accuracy(0,0,1000,10,100); - test_gpu_accuracy(1,0,1000,10,100); - test_gpu_accuracy(0,1,1000,10,100); - test_gpu_accuracy(1,1,1000,10,100); - - test_gpu_accuracy(0,0,10,10,10); - - time_ongpu(0,0,64,2916,363); - time_ongpu(0,0,64,2916,363); - time_ongpu(0,0,64,2916,363); - time_ongpu(0,0,192,729,1600); - time_ongpu(0,0,384,196,1728); - time_ongpu(0,0,256,196,3456); - time_ongpu(0,0,256,196,2304); - time_ongpu(0,0,128,4096,12544); - time_ongpu(0,0,128,4096,4096); + test_gpu_accuracy(0,0,10,576,75); + + test_gpu_accuracy(0,0,17,10,10); + test_gpu_accuracy(1,0,17,10,10); + test_gpu_accuracy(0,1,17,10,10); + test_gpu_accuracy(1,1,17,10,10); + + test_gpu_accuracy(0,0,1000,10,100); + test_gpu_accuracy(1,0,1000,10,100); + test_gpu_accuracy(0,1,1000,10,100); + test_gpu_accuracy(1,1,1000,10,100); + + test_gpu_accuracy(0,0,10,10,10); + + time_ongpu(0,0,64,2916,363); + time_ongpu(0,0,64,2916,363); + time_ongpu(0,0,64,2916,363); + time_ongpu(0,0,192,729,1600); + time_ongpu(0,0,384,196,1728); + time_ongpu(0,0,256,196,3456); + time_ongpu(0,0,256,196,2304); + time_ongpu(0,0,128,4096,12544); + time_ongpu(0,0,128,4096,4096); */ - time_ongpu(0,0,64,75,12544); - time_ongpu(0,0,64,75,12544); - time_ongpu(0,0,64,75,12544); - time_ongpu(0,0,64,576,12544); - time_ongpu(0,0,256,2304,784); - time_ongpu(1,1,2304,256,784); - time_ongpu(0,0,512,4608,196); - time_ongpu(1,1,4608,512,196); + time_ongpu(0,0,64,75,12544); + time_ongpu(0,0,64,75,12544); + time_ongpu(0,0,64,75,12544); + time_ongpu(0,0,64,576,12544); + time_ongpu(0,0,256,2304,784); + time_ongpu(1,1,2304,256,784); + time_ongpu(0,0,512,4608,196); + time_ongpu(1,1,4608,512,196); return 0; } diff --git a/src/gemm.h b/src/gemm.h index f0231bfa29a..8664d8e8b1e 100644 --- a/src/gemm.h +++ b/src/gemm.h @@ -1,32 +1,60 @@ #ifndef GEMM_H #define GEMM_H -void gemm_bin(int M, int N, int K, float ALPHA, - char *A, int lda, +static inline void set_bit(unsigned char *const dst, size_t index) { + size_t dst_i = index / 8; + int dst_shift = index % 8; + dst[dst_i] |= 1 << dst_shift; +} + +static inline unsigned char get_bit(unsigned char const*const src, size_t index) { + size_t src_i = index / 8; + int src_shift = index % 8; + unsigned char val = (src[src_i] & (1 << src_shift)) > 0; + return val; +} + +void float_to_bit(float *src, unsigned char *dst, size_t size); + +void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr); + + +//void gemm_nn_custom_bin_mean(int M, int N, int K, float ALPHA_UNUSED, + //unsigned char *A, int lda, + //unsigned char *B, int ldb, + //float *C, int ldc, float *mean_arr) + + + +void gemm_bin(int M, int N, int K, float ALPHA, + char *A, int lda, float *B, int ldb, float *C, int ldc); - -void gemm(int TA, int TB, int M, int N, int K, float ALPHA, - float *A, int lda, + +void gemm(int TA, int TB, int M, int N, int K, float ALPHA, + float *A, int lda, float *B, int ldb, float BETA, float *C, int ldc); -void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA, - float *A, int lda, +void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA, + float *A, int lda, float *B, int ldb, float BETA, float *C, int ldc); #ifdef GPU -void gemm_ongpu(int TA, int TB, int M, int N, int K, float ALPHA, - float *A_gpu, int lda, +void gemm_ongpu(int TA, int TB, int M, int N, int K, float ALPHA, + float *A_gpu, int lda, float *B_gpu, int ldb, float BETA, float *C_gpu, int ldc); -void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA, - float *A, int lda, +void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA, + float *A, int lda, float *B, int ldb, float BETA, float *C, int ldc); diff --git a/src/layer.c b/src/layer.c index 3b479172590..caa9c4c99c8 100644 --- a/src/layer.c +++ b/src/layer.c @@ -33,6 +33,8 @@ void free_layer(layer l) if (l.scale_updates) free(l.scale_updates); if (l.weights) free(l.weights); if (l.weight_updates) free(l.weight_updates); + if (l.weights) free(l.align_bit_weights); + if (l.weights) free(l.mean_arr); if (l.delta) free(l.delta); if (l.output) free(l.output); if (l.squared) free(l.squared); diff --git a/src/layer.h b/src/layer.h index 8a58c92789a..224f77a1d7f 100644 --- a/src/layer.h +++ b/src/layer.h @@ -179,6 +179,9 @@ struct layer{ float *weights; float *weight_updates; + char *align_bit_weights; + float *mean_arr; + float *col_image; int * input_layers; int * input_sizes; diff --git a/src/network.c b/src/network.c index 3df837d67aa..63b76a8bcc0 100644 --- a/src/network.c +++ b/src/network.c @@ -222,7 +222,7 @@ float *get_network_output(network net) { #ifdef GPU if (gpu_index >= 0) return get_network_output_gpu(net); -#endif +#endif int i; for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break; return net.layers[i].output; @@ -366,7 +366,7 @@ void set_batch_network(network *net, int b) /* layer *l = net->layers + i; cudnn_convolutional_setup(l, cudnn_fastest); - // check for excessive memory consumption + // check for excessive memory consumption size_t free_byte; size_t total_byte; check_error(cudaMemGetInfo(&free_byte, &total_byte)); @@ -520,7 +520,7 @@ void visualize_network(network net) if(l.type == CONVOLUTIONAL){ prev = visualize_convolutional_layer(l, buff, prev); } - } + } } void top_predictions(network net, int k, int *index) @@ -684,7 +684,7 @@ matrix network_predict_data_multi(network net, data test, int n) } } free(X); - return pred; + return pred; } matrix network_predict_data(network net, data test) @@ -707,7 +707,7 @@ matrix network_predict_data(network net, data test) } } free(X); - return pred; + return pred; } void print_network(network net) @@ -749,7 +749,7 @@ void compare_networks(network n1, network n2, data test) printf("%5d %5d\n%5d %5d\n", a, b, c, d); float num = pow((abs(b - c) - 1.), 2.); float den = b + c; - printf("%f\n", num/den); + printf("%f\n", num/den); } float network_accuracy(network net, data d) @@ -847,3 +847,25 @@ void fuse_conv_batchnorm(network net) } } } + + + +void calculate_binary_weights(network net) +{ + int j; + for (j = 0; j < net.n; ++j) { + layer *l = &net.layers[j]; + + if (l->type == CONVOLUTIONAL) { + //printf(" Merges Convolutional-%d and batch_norm \n", j); + + if (l->xnor) { + //printf("\n %d \n", j); + size_t ldb_align = 256; // 256bit for AVX2 + binary_transpose_align_weights(l, ldb_align); + } + } + } + //printf("\n calculate_binary_weights Done! \n"); + +} \ No newline at end of file diff --git a/src/network.h b/src/network.h index f80f58d9ded..3b07dbb3378 100644 --- a/src/network.h +++ b/src/network.h @@ -151,6 +151,7 @@ YOLODLL_API void optimize_picture(network *net, image orig, int max_layer, float int get_network_nuisance(network net); int get_network_background(network net); YOLODLL_API void fuse_conv_batchnorm(network net); +YOLODLL_API void calculate_binary_weights(network net); #ifdef __cplusplus } From a284a7da8d1facbf984a22302665a2a50295a687 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 8 Aug 2018 19:07:10 +0300 Subject: [PATCH 0117/1365] Try to use avx_hs() - slow and requires alignment 4096 bits < (l.size*l.size*l.c) May be faster only from 8192 bits and more. --- src/convolutional_layer.c | 2 + src/gemm.c | 1813 +++++++++++++++++++------------------ src/image.c | 31 +- src/network.c | 2 + 4 files changed, 978 insertions(+), 870 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index bbc4807e639..0bde97a5027 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -684,6 +684,8 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) // transpose B from NxK to KxN (x-axis (ldb = l.size*l.size*l.c) - should be multiple of 8 bits) { size_t ldb_align = 256;// 8; + if (k > 4096)ldb_align = 4096; + size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; size_t t_intput_size = new_ldb * n; size_t t_bit_input_size = t_intput_size / 8;// +1; diff --git a/src/gemm.c b/src/gemm.c index ee7fa151a94..63163a53ced 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -1,857 +1,956 @@ -#include "gemm.h" -#include "utils.h" -#include "cuda.h" -#include -#include -#include - -void gemm_bin(int M, int N, int K, float ALPHA, - char *A, int lda, - float *B, int ldb, - float *C, int ldc) -{ - int i,j,k; - for(i = 0; i < M; ++i){ - for(k = 0; k < K; ++k){ - char A_PART = A[i*lda+k]; - if(A_PART){ - for(j = 0; j < N; ++j){ - C[i*ldc+j] += B[k*ldb+j]; - } - } else { - for(j = 0; j < N; ++j){ - C[i*ldc+j] -= B[k*ldb+j]; - } - } - } - } -} - -float *random_matrix(int rows, int cols) -{ - int i; - float *m = calloc(rows*cols, sizeof(float)); - for(i = 0; i < rows*cols; ++i){ - m[i] = (float)rand()/RAND_MAX; - } - return m; -} - -void time_random_matrix(int TA, int TB, int m, int k, int n) -{ - float *a; - if(!TA) a = random_matrix(m,k); - else a = random_matrix(k,m); - int lda = (!TA)?k:m; - float *b; - if(!TB) b = random_matrix(k,n); - else b = random_matrix(n,k); - int ldb = (!TB)?n:k; - - float *c = random_matrix(m,n); - int i; - clock_t start = clock(), end; - for(i = 0; i<10; ++i){ - gemm_cpu(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n); - } - end = clock(); - printf("Matrix Multiplication %dx%d * %dx%d, TA=%d, TB=%d: %lf ms\n",m,k,k,n, TA, TB, (float)(end-start)/CLOCKS_PER_SEC); - free(a); - free(b); - free(c); -} - - -void gemm(int TA, int TB, int M, int N, int K, float ALPHA, - float *A, int lda, - float *B, int ldb, - float BETA, - float *C, int ldc) -{ - gemm_cpu( TA, TB, M, N, K, ALPHA,A,lda, B, ldb,BETA,C,ldc); -} - - -//-------------------------------------------- -// XNOR bitwise GEMM for binary neural network -//-------------------------------------------- - -#include - -static inline unsigned char xnor(unsigned char a, unsigned char b) { - //return a == b; - return !(a^b); -} - -// INT-32 -static inline uint32_t get_bit_int32(uint32_t const*const src, size_t index) { - size_t src_i = index / 32; - int src_shift = index % 32; - unsigned char val = (src[src_i] & (1 << src_shift)) > 0; - return val; -} - -static inline uint32_t xnor_int32(uint32_t a, uint32_t b) { - return ~(a^b); -} - -static inline uint64_t xnor_int64(uint64_t a, uint64_t b) { - return ~(a^b); -} - - -static inline uint32_t fill_bit_int32(char src) { - if (src == 0) return 0x00000000; - else return 0xFFFFFFFF; -} - -static inline uint64_t fill_bit_int64(char src) { - if (src == 0) return 0x0000000000000000; - else return 0xFFFFFFFFFFFFFFFF; -} - -void binary_int32_printf(uint32_t src) { - int i; - for (i = 0; i < 32; ++i) { - if (src & 1) printf("1"); - else printf("0"); - src = src >> 1; - } - printf("\n"); -} - -void binary_int64_printf(uint64_t src) { - int i; - for (i = 0; i < 64; ++i) { - if (src & 1) printf("1"); - else printf("0"); - src = src >> 1; - } - printf("\n"); -} - -/* -void gemm_nn_custom_bin_mean(int M, int N, int K, float ALPHA_UNUSED, - unsigned char *A, int lda, - unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr) -{ - int *count_arr = calloc(M*N, sizeof(int)); - - int i, j, k; - for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] - for (k = 0; k < K; ++k) { // l.size*l.size*l.c - one filter size [27 - 9216] - char a_bit = get_bit(A, i*lda + k); - - for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] - char b_bit = get_bit(B, k*ldb + j); - count_arr[i*ldc + j] += xnor(a_bit, b_bit); - } - } - } - - for (i = 0; i < M; ++i) { - float mean_val = mean_arr[i]; - for (j = 0; j < N; ++j) { - C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val; - } - } - free(count_arr); -} -*/ - -/* -void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, - unsigned char *A, int lda, - unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr) -{ - int *count_arr = calloc(M*N, sizeof(int)); - - int i, j, k; - for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] - for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] - for (k = 0; k < K; ++k) { // l.size*l.size*l.c - one filter size [27 - 9216] - char a_bit = get_bit(A, i*lda + k); - char b_bit = get_bit(B, j*ldb + k); - count_arr[i*ldc + j] += xnor(a_bit, b_bit); - } - } - } - - for (i = 0; i < M; ++i) { - float mean_val = mean_arr[i]; - for (j = 0; j < N; ++j) { - C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val; - } - } - free(count_arr); -} -*/ - -/* -void gemm_nn_custom_bin_mean(int M, int N, int K, float ALPHA_UNUSED, - unsigned char *A, int lda, - unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr) -{ - int *count_arr = calloc(M*N, sizeof(int)); - - int i, j, k, h; - -#pragma omp parallel for - for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] - for (k = 0; k < K; ++k) { // l.size*l.size*l.c - one filter size [27 - 9216] - const char a_bit = get_bit(A, i*lda + k); - uint64_t a_bit64 = fill_bit_int64(a_bit); - int k_ldb = k*ldb; - - for (j = 0; j < N; j += 64) { // out_h*out_w - one channel output size [169 - 173056] - if ((N - j > 64) && (k_ldb % 8 == 0)) { - uint64_t b_bit64 = *((uint64_t *)(B + (k_ldb + j) / 8)); - uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); - //printf("\n %d \n",__builtin_popcountll(c_bit64)); // gcc - printf("\n %d \n", __popcnt64(c_bit64)); // msvs - - int h; - for (h = 0; h < 64; ++h) - if ((c_bit64 >> h) & 1) count_arr[i*ldc + j + h] += 1; - - //binary_int64_printf(a_bit64); - //binary_int64_printf(b_bit64); - //binary_int64_printf(c_bit64); - } - else { - for (; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] - char b_bit = get_bit(B, k_ldb + j); - if (xnor(a_bit, b_bit)) count_arr[i*ldc + j] += 1; - } - } - - } - } - } - - if (mean_arr) { - //int K_2 = K / 2; - for (i = 0; i < M; ++i) { - float mean_val = mean_arr[i]; - //float mean_val2 = 2 * mean_val; - for (j = 0; j < N; ++j) { - C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val; - //C[i*ldc + j] = (count_arr[i*ldc + j] - K_2) *mean_val2; - } - } - } - else { - for (i = 0; i < M; ++i) { - for (j = 0; j < N; ++j) { - C[i*ldc + j] = count_arr[i*ldc + j] - K / 2; - } - } - } - - free(count_arr); - - //getchar(); -} -*/ - - -/* -void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, - unsigned char *A, int lda, - unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr) -{ - int i, j, k, h; - -#pragma omp parallel for - for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] - float mean_val = mean_arr[i]; - - for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] - int count = 0; - - for (k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] - uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); - uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); - uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); - -#ifdef WIN32 - int tmp_count = __popcnt64(c_bit64); -#else - int tmp_count = __builtin_popcountll(c_bit64); -#endif - - if (K - k < 64) tmp_count = tmp_count - (64 - (K - k)); // remove extra bits - count += tmp_count; - //binary_int64_printf(c_bit64); - //printf(", count = %d \n\n", tmp_count); - } - - C[i*ldc + j] = (2 * count - K) * mean_val; - } - } -} -*/ - -//---------------------------- - - -#if (defined(__AVX__) && defined(__x86_64__)) || defined(_WIN64) - -#define OSXSAVEFlag (1UL<<27) -#define AVXFlag ((1UL<<28)|OSXSAVEFlag) -#define FMAFlag ((1UL<<12)|AVXFlag|OSXSAVEFlag) -#define CLMULFlag ((1UL<< 1)|AVXFlag|OSXSAVEFlag) -#define VAESFlag ((1UL<<25)|AVXFlag|OSXSAVEFlag) - -#ifdef _WIN64 -#include -#include -#include -#include - -#else // Linux GCC/Clang -#include -#include -#include -#include -#include - -void asm_cpuid(uint32_t* abcd, uint32_t eax) -{ - uint32_t ebx = 0, edx = 0, ecx = 0; - - // EBX is saved to EDI and later restored - __asm__("movl %%ebx, %%edi;" - "cpuid;" - "xchgl %%ebx, %%edi;" - : "=D"(ebx), - "+a"(eax), "+c"(ecx), "=d"(edx)); - - abcd[0] = eax; - abcd[1] = ebx; - abcd[2] = ecx; - abcd[3] = edx; -} - -#endif - -int simd_detect_x86(unsigned int idFeature) -{ - uint32_t regs[4]; // EAX, EBX, ECX, EDX; -#ifdef _WIN32 - __cpuid(regs, 0); - if (regs[0] > 1U) __cpuid(regs, 1); -#else - __get_cpuid(0, ®s[0], ®s[1], ®s[2], ®s[3]); - if(regs[0] > 1U) __get_cpuid(1, ®s[0], ®s[1], ®s[2], ®s[3]); -#endif - - if ((regs[2] & idFeature) != idFeature) - return 0; - return 1; -} - -int is_fma_avx() { - static int result = -1; - if (result == -1) { - result = simd_detect_x86(AVXFlag); - if (result == 1) printf(" Used AVX \n"); - else printf(" Not used AVX \n"); - } - return result; -} - -// https://software.intel.com/sites/landingpage/IntrinsicsGuide -void gemm_nn(int M, int N, int K, float ALPHA, - float *A, int lda, - float *B, int ldb, - float *C, int ldc) -{ - int i, j, k; - if (is_fma_avx() == 1) { // AVX - for (i = 0; i < M; ++i) { - for (k = 0; k < K; ++k) { - float A_PART = ALPHA*A[i*lda + k]; - __m256 a256, b256, c256, result256; // AVX - a256 = _mm256_set1_ps(A_PART); - for (j = 0; j < N - 8; j += 8) { - b256 = _mm256_loadu_ps(&B[k*ldb + j]); - c256 = _mm256_loadu_ps(&C[i*ldc + j]); - // FMA - Intel Haswell (2013), AMD Piledriver (2012) - //result256 = _mm256_fmadd_ps(a256, b256, c256); - result256 = _mm256_mul_ps(a256, b256); - result256 = _mm256_add_ps(result256, c256); - _mm256_storeu_ps(&C[i*ldc + j], result256); - } - - int prev_end = (N % 8 == 0) ? (N - 8) : (N / 8) * 8; - for (j = prev_end; j < N; ++j) - C[i*ldc + j] += A_PART*B[k*ldb + j]; - } - } - } - else { - for (i = 0; i < M; ++i) { - for (k = 0; k < K; ++k) { - register float A_PART = ALPHA*A[i*lda + k]; - for (j = 0; j < N; ++j) { - C[i*ldc + j] += A_PART*B[k*ldb + j]; - } - /* // SSE - __m128 a128, b128, c128, result128; // SSE - a128 = _mm_set1_ps(A_PART); - for (j = 0; j < N - 4; j += 4) { - b128 = _mm_loadu_ps(&B[k*ldb + j]); - c128 = _mm_loadu_ps(&C[i*ldc + j]); - //result128 = _mm_fmadd_ps(a128, b128, c128); - result128 = _mm_mul_ps(a128, b128); - result128 = _mm_add_ps(result128, c128); - _mm_storeu_ps(&C[i*ldc + j], result128); - } - - int prev_end = (N % 4 == 0) ? (N - 4) : (N / 4) * 4; - for (j = prev_end; j < N; ++j){ - C[i*ldc + j] += A_PART*B[k*ldb + j]; - } - */ - } - } - } -} - - -// http://graphics.stanford.edu/~seander/bithacks.html -// https://stackoverflow.com/questions/17354971/fast-counting-the-number-of-set-bits-in-m128i-register - -// 2 x faster than popcnt: https://arxiv.org/pdf/1611.07612.pdf - -static inline int popcnt128(__m128i n) { - const __m128i n_hi = _mm_unpackhi_epi64(n, n); -#ifdef _MSC_VER - return __popcnt64(_mm_cvtsi128_si64(n)) + __popcnt64(_mm_cvtsi128_si64(n_hi)); -#else - return __popcntq(_mm_cvtsi128_si64(n)) + __popcntq(_mm_cvtsi128_si64(n_hi)); -#endif -} - -static inline int popcnt256(__m256i n) { - return popcnt128(_mm256_extractf128_si256(n, 0)) + popcnt128(_mm256_extractf128_si256(n, 1)); -} - -void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, - unsigned char *A, int lda, - unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr) -{ - __m256i all_1 = _mm256_set1_epi8(255); - int i, j, k, h; - - #pragma omp parallel for - for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] - float mean_val = mean_arr[i]; - - for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] - int count = 0; - const int bit_step = 256; - - for (k = 0; k < K; k += bit_step) { // l.size*l.size*l.c - one filter size [27 - 9216] - - //__m128i a_bit128 = _mm_loadu_si128((__m128i *)(A + (i*lda + k) / 8)); - //__m128i b_bit128 = _mm_loadu_si128((__m128i *)(B + (j*ldb + k) / 8)); - //__m128i xor128 = _mm_xor_si128(a_bit128, b_bit128); - //__m128i c_bit128 = _mm_andnot_si128(xor128, all_1); - //int tmp_count = popcnt128(c_bit128); - - __m256i a_bit256 = _mm256_loadu_si256((__m256i *)(A + (i*lda + k) / 8)); - __m256i b_bit256 = _mm256_loadu_si256((__m256i *)(B + (j*ldb + k) / 8)); - __m256i xor256 = _mm256_xor_si256(a_bit256, b_bit256); - __m256i c_bit256 = _mm256_andnot_si256(xor256, all_1); //we can do NOT for wegihts once and do not do this NOT - int tmp_count = popcnt256(c_bit256); - - if (K - k < bit_step) tmp_count = tmp_count - (bit_step - (K - k)); // remove extra bits - count += tmp_count; - //binary_int64_printf(c_bit64); - //printf(", count = %d \n\n", tmp_count); - } - - C[i*ldc + j] = (2 * count - K) * mean_val; - } - } -} - - -void float_to_bit(float *src, unsigned char *dst, size_t size) -{ - size_t dst_size = size / 8 + 1; - memset(dst, 0, dst_size); - - size_t i; - __m128i all128_0 = _mm_set_epi32(0, 0, 0, 0); - __m256 all256_0 = _mm256_set1_ps(0); - __m256i bits_asc = _mm256_set_epi32(1, 2, 4, 8, 16, 32, 64, 128); - //for(i = 0; i < 8; ++i) bits_asc.m256i_i32[i] = 1 << i; - - for (i = 0; i < size; i+=8) - { - __m256 src256 = _mm256_loadu_ps((__m256i *)(&src[i])); // load 256 bits - __m256 result256 = _mm256_cmp_ps(src256, all256_0, _CMP_GT_OS); // compare dst[i] = (float[i] > 0) - - __m256i bits256 = _mm256_castps_si256(result256); // floats to ints32 - __m256i and256 = _mm256_and_si256(bits256, bits_asc); // bitwise and - - // sum all elements from single and256 - __m128i tmp128 = _mm_hadd_epi32(_mm256_extractf128_si256(and256, 0), _mm256_extractf128_si256(and256, 1)); - tmp128 = _mm_hadd_epi32(tmp128, all128_0); - tmp128 = _mm_hadd_epi32(tmp128, all128_0); - - dst[i / 8] = tmp128.m128i_i32[0]; - } - // int _mm256_movemask_epi8 (__m256i a) -} - -#else - -void gemm_nn(int M, int N, int K, float ALPHA, - float *A, int lda, - float *B, int ldb, - float *C, int ldc) -{ - int i, j, k; - for (i = 0; i < M; ++i) { - for (k = 0; k < K; ++k) { - register float A_PART = ALPHA*A[i*lda + k]; - for (j = 0; j < N; ++j) { - C[i*ldc + j] += A_PART*B[k*ldb + j]; - } - } - } -} - -void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, - unsigned char *A, int lda, - unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr) -{ - int i, j, k, h; - -#pragma omp parallel for - for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] - float mean_val = mean_arr[i]; - - for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] - int count = 0; - - for (k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] - uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); - uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); - uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); - -#ifdef WIN32 - int tmp_count = __popcnt64(c_bit64); -#else - int tmp_count = __builtin_popcountll(c_bit64); -#endif - - if (K - k < 64) tmp_count = tmp_count - (64 - (K - k)); // remove extra bits - count += tmp_count; - //binary_int64_printf(c_bit64); - //printf(", count = %d \n\n", tmp_count); - } - - C[i*ldc + j] = (2 * count - K) * mean_val; - } - } -} - -void float_to_bit(float *src, unsigned char *dst, size_t size) -{ - size_t dst_size = size / 8 + 1; - memset(dst, 0, dst_size); - - size_t i; - char *byte_arr = calloc(size, sizeof(char)); - for (i = 0; i < size; ++i) { - if (src[i] > 0) byte_arr[i] = 1; - } - - //for (i = 0; i < size; ++i) { - // dst[i / 8] |= byte_arr[i] << (i % 8); - //} - - for (i = 0; i < size; i += 8) { - char dst_tmp = 0; - dst_tmp |= byte_arr[i + 0] << 0; - dst_tmp |= byte_arr[i + 1] << 1; - dst_tmp |= byte_arr[i + 2] << 2; - dst_tmp |= byte_arr[i + 3] << 3; - dst_tmp |= byte_arr[i + 4] << 4; - dst_tmp |= byte_arr[i + 5] << 5; - dst_tmp |= byte_arr[i + 6] << 6; - dst_tmp |= byte_arr[i + 7] << 7; - dst[i / 8] = dst_tmp; - } - free(byte_arr); -} -#endif // __x86_64 - -void gemm_nt(int M, int N, int K, float ALPHA, - float *A, int lda, - float *B, int ldb, - float *C, int ldc) -{ - int i,j,k; - for(i = 0; i < M; ++i){ - for(j = 0; j < N; ++j){ - register float sum = 0; - for(k = 0; k < K; ++k){ - sum += ALPHA*A[i*lda+k]*B[j*ldb + k]; - } - C[i*ldc+j] += sum; - } - } -} - -void gemm_tn(int M, int N, int K, float ALPHA, - float *A, int lda, - float *B, int ldb, - float *C, int ldc) -{ - int i,j,k; - for(i = 0; i < M; ++i){ - for(k = 0; k < K; ++k){ - register float A_PART = ALPHA*A[k*lda+i]; - for(j = 0; j < N; ++j){ - C[i*ldc+j] += A_PART*B[k*ldb+j]; - } - } - } -} - -void gemm_tt(int M, int N, int K, float ALPHA, - float *A, int lda, - float *B, int ldb, - float *C, int ldc) -{ - int i,j,k; - for(i = 0; i < M; ++i){ - for(j = 0; j < N; ++j){ - register float sum = 0; - for(k = 0; k < K; ++k){ - sum += ALPHA*A[i+k*lda]*B[k+j*ldb]; - } - C[i*ldc+j] += sum; - } - } -} - - -void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA, - float *A, int lda, - float *B, int ldb, - float BETA, - float *C, int ldc) -{ - //printf("cpu: %d %d %d %d %d %f %d %d %f %d\n",TA, TB, M, N, K, ALPHA, lda, ldb, BETA, ldc); - if (BETA != 1){ - int i, j; - for(i = 0; i < M; ++i){ - for(j = 0; j < N; ++j){ - C[i*ldc + j] *= BETA; - } - } - } - - int t; - #pragma omp parallel for - for (t = 0; t < M; ++t) { - if (!TA && !TB) - gemm_nn(1, N, K, ALPHA, A + t*lda, lda, B, ldb, C + t*ldc, ldc); - else if (TA && !TB) - gemm_tn(1, N, K, ALPHA, A + t, lda, B, ldb, C + t*ldc, ldc); - else if (!TA && TB) - gemm_nt(1, N, K, ALPHA, A + t*lda, lda, B, ldb, C + t*ldc, ldc); - else - gemm_tt(1, N, K, ALPHA, A + t, lda, B, ldb, C + t*ldc, ldc); - } -} - -#ifdef GPU - -#include - -void gemm_ongpu(int TA, int TB, int M, int N, int K, float ALPHA, - float *A_gpu, int lda, - float *B_gpu, int ldb, - float BETA, - float *C_gpu, int ldc) -{ - cublasHandle_t handle = blas_handle(); - cudaError_t stream_status = cublasSetStream(handle, get_cuda_stream()); - cudaError_t status = cublasSgemm(handle, (TB ? CUBLAS_OP_T : CUBLAS_OP_N), - (TA ? CUBLAS_OP_T : CUBLAS_OP_N), N, M, K, &ALPHA, B_gpu, ldb, A_gpu, lda, &BETA, C_gpu, ldc); - check_error(status); -} - -void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA, - float *A, int lda, - float *B, int ldb, - float BETA, - float *C, int ldc) -{ - float *A_gpu = cuda_make_array(A, (TA ? lda*K:lda*M)); - float *B_gpu = cuda_make_array(B, (TB ? ldb*N : ldb*K)); - float *C_gpu = cuda_make_array(C, ldc*M); - - gemm_ongpu(TA, TB, M, N, K, ALPHA, A_gpu, lda, B_gpu, ldb, BETA, C_gpu, ldc); - - cuda_pull_array(C_gpu, C, ldc*M); - cuda_free(A_gpu); - cuda_free(B_gpu); - cuda_free(C_gpu); -} - -#include -#include -#include -#include - -void time_gpu_random_matrix(int TA, int TB, int m, int k, int n) -{ - float *a; - if(!TA) a = random_matrix(m,k); - else a = random_matrix(k,m); - int lda = (!TA)?k:m; - float *b; - if(!TB) b = random_matrix(k,n); - else b = random_matrix(n,k); - int ldb = (!TB)?n:k; - - float *c = random_matrix(m,n); - int i; - clock_t start = clock(), end; - for(i = 0; i<32; ++i){ - gemm_gpu(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n); - } - end = clock(); - printf("Matrix Multiplication %dx%d * %dx%d, TA=%d, TB=%d: %lf s\n",m,k,k,n, TA, TB, (float)(end-start)/CLOCKS_PER_SEC); - free(a); - free(b); - free(c); -} - -void time_ongpu(int TA, int TB, int m, int k, int n) -{ - int iter = 10; - float *a = random_matrix(m,k); - float *b = random_matrix(k,n); - - int lda = (!TA)?k:m; - int ldb = (!TB)?n:k; - - float *c = random_matrix(m,n); - - float *a_cl = cuda_make_array(a, m*k); - float *b_cl = cuda_make_array(b, k*n); - float *c_cl = cuda_make_array(c, m*n); - - int i; - clock_t start = clock(), end; - for(i = 0; i +#include +#include + +void gemm_bin(int M, int N, int K, float ALPHA, + char *A, int lda, + float *B, int ldb, + float *C, int ldc) +{ + int i,j,k; + for(i = 0; i < M; ++i){ + for(k = 0; k < K; ++k){ + char A_PART = A[i*lda+k]; + if(A_PART){ + for(j = 0; j < N; ++j){ + C[i*ldc+j] += B[k*ldb+j]; + } + } else { + for(j = 0; j < N; ++j){ + C[i*ldc+j] -= B[k*ldb+j]; + } + } + } + } +} + +float *random_matrix(int rows, int cols) +{ + int i; + float *m = calloc(rows*cols, sizeof(float)); + for(i = 0; i < rows*cols; ++i){ + m[i] = (float)rand()/RAND_MAX; + } + return m; +} + +void time_random_matrix(int TA, int TB, int m, int k, int n) +{ + float *a; + if(!TA) a = random_matrix(m,k); + else a = random_matrix(k,m); + int lda = (!TA)?k:m; + float *b; + if(!TB) b = random_matrix(k,n); + else b = random_matrix(n,k); + int ldb = (!TB)?n:k; + + float *c = random_matrix(m,n); + int i; + clock_t start = clock(), end; + for(i = 0; i<10; ++i){ + gemm_cpu(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n); + } + end = clock(); + printf("Matrix Multiplication %dx%d * %dx%d, TA=%d, TB=%d: %lf ms\n",m,k,k,n, TA, TB, (float)(end-start)/CLOCKS_PER_SEC); + free(a); + free(b); + free(c); +} + + +void gemm(int TA, int TB, int M, int N, int K, float ALPHA, + float *A, int lda, + float *B, int ldb, + float BETA, + float *C, int ldc) +{ + gemm_cpu( TA, TB, M, N, K, ALPHA,A,lda, B, ldb,BETA,C,ldc); +} + + +//-------------------------------------------- +// XNOR bitwise GEMM for binary neural network +//-------------------------------------------- + +#include + +static inline unsigned char xnor(unsigned char a, unsigned char b) { + //return a == b; + return !(a^b); +} + +// INT-32 +static inline uint32_t get_bit_int32(uint32_t const*const src, size_t index) { + size_t src_i = index / 32; + int src_shift = index % 32; + unsigned char val = (src[src_i] & (1 << src_shift)) > 0; + return val; +} + +static inline uint32_t xnor_int32(uint32_t a, uint32_t b) { + return ~(a^b); +} + +static inline uint64_t xnor_int64(uint64_t a, uint64_t b) { + return ~(a^b); +} + + +static inline uint32_t fill_bit_int32(char src) { + if (src == 0) return 0x00000000; + else return 0xFFFFFFFF; +} + +static inline uint64_t fill_bit_int64(char src) { + if (src == 0) return 0x0000000000000000; + else return 0xFFFFFFFFFFFFFFFF; +} + +void binary_int32_printf(uint32_t src) { + int i; + for (i = 0; i < 32; ++i) { + if (src & 1) printf("1"); + else printf("0"); + src = src >> 1; + } + printf("\n"); +} + +void binary_int64_printf(uint64_t src) { + int i; + for (i = 0; i < 64; ++i) { + if (src & 1) printf("1"); + else printf("0"); + src = src >> 1; + } + printf("\n"); +} + +/* +void gemm_nn_custom_bin_mean(int M, int N, int K, float ALPHA_UNUSED, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + int *count_arr = calloc(M*N, sizeof(int)); + + int i, j, k; + for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] + for (k = 0; k < K; ++k) { // l.size*l.size*l.c - one filter size [27 - 9216] + char a_bit = get_bit(A, i*lda + k); + + for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] + char b_bit = get_bit(B, k*ldb + j); + count_arr[i*ldc + j] += xnor(a_bit, b_bit); + } + } + } + + for (i = 0; i < M; ++i) { + float mean_val = mean_arr[i]; + for (j = 0; j < N; ++j) { + C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val; + } + } + free(count_arr); +} +*/ + +/* +void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + int *count_arr = calloc(M*N, sizeof(int)); + + int i, j, k; + for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] + for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] + for (k = 0; k < K; ++k) { // l.size*l.size*l.c - one filter size [27 - 9216] + char a_bit = get_bit(A, i*lda + k); + char b_bit = get_bit(B, j*ldb + k); + count_arr[i*ldc + j] += xnor(a_bit, b_bit); + } + } + } + + for (i = 0; i < M; ++i) { + float mean_val = mean_arr[i]; + for (j = 0; j < N; ++j) { + C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val; + } + } + free(count_arr); +} +*/ + +/* +void gemm_nn_custom_bin_mean(int M, int N, int K, float ALPHA_UNUSED, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + int *count_arr = calloc(M*N, sizeof(int)); + + int i, j, k, h; + +#pragma omp parallel for + for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] + for (k = 0; k < K; ++k) { // l.size*l.size*l.c - one filter size [27 - 9216] + const char a_bit = get_bit(A, i*lda + k); + uint64_t a_bit64 = fill_bit_int64(a_bit); + int k_ldb = k*ldb; + + for (j = 0; j < N; j += 64) { // out_h*out_w - one channel output size [169 - 173056] + if ((N - j > 64) && (k_ldb % 8 == 0)) { + uint64_t b_bit64 = *((uint64_t *)(B + (k_ldb + j) / 8)); + uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); + //printf("\n %d \n",__builtin_popcountll(c_bit64)); // gcc + printf("\n %d \n", __popcnt64(c_bit64)); // msvs + + int h; + for (h = 0; h < 64; ++h) + if ((c_bit64 >> h) & 1) count_arr[i*ldc + j + h] += 1; + + //binary_int64_printf(a_bit64); + //binary_int64_printf(b_bit64); + //binary_int64_printf(c_bit64); + } + else { + for (; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] + char b_bit = get_bit(B, k_ldb + j); + if (xnor(a_bit, b_bit)) count_arr[i*ldc + j] += 1; + } + } + + } + } + } + + if (mean_arr) { + //int K_2 = K / 2; + for (i = 0; i < M; ++i) { + float mean_val = mean_arr[i]; + //float mean_val2 = 2 * mean_val; + for (j = 0; j < N; ++j) { + C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val; + //C[i*ldc + j] = (count_arr[i*ldc + j] - K_2) *mean_val2; + } + } + } + else { + for (i = 0; i < M; ++i) { + for (j = 0; j < N; ++j) { + C[i*ldc + j] = count_arr[i*ldc + j] - K / 2; + } + } + } + + free(count_arr); + + //getchar(); +} +*/ + + +/* +void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + int i, j, k, h; + +#pragma omp parallel for + for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] + float mean_val = mean_arr[i]; + + for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] + int count = 0; + + for (k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] + uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); + uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); + uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); + +#ifdef WIN32 + int tmp_count = __popcnt64(c_bit64); +#else + int tmp_count = __builtin_popcountll(c_bit64); +#endif + + if (K - k < 64) tmp_count = tmp_count - (64 - (K - k)); // remove extra bits + count += tmp_count; + //binary_int64_printf(c_bit64); + //printf(", count = %d \n\n", tmp_count); + } + + C[i*ldc + j] = (2 * count - K) * mean_val; + } + } +} +*/ + +//---------------------------- + + +#if (defined(__AVX__) && defined(__x86_64__)) || defined(_WIN64) + +#define OSXSAVEFlag (1UL<<27) +#define AVXFlag ((1UL<<28)|OSXSAVEFlag) +#define FMAFlag ((1UL<<12)|AVXFlag|OSXSAVEFlag) +#define CLMULFlag ((1UL<< 1)|AVXFlag|OSXSAVEFlag) +#define VAESFlag ((1UL<<25)|AVXFlag|OSXSAVEFlag) + +#ifdef _WIN64 +#include +#include +#include +#include + +#else // Linux GCC/Clang +#include +#include +#include +#include +#include + +void asm_cpuid(uint32_t* abcd, uint32_t eax) +{ + uint32_t ebx = 0, edx = 0, ecx = 0; + + // EBX is saved to EDI and later restored + __asm__("movl %%ebx, %%edi;" + "cpuid;" + "xchgl %%ebx, %%edi;" + : "=D"(ebx), + "+a"(eax), "+c"(ecx), "=d"(edx)); + + abcd[0] = eax; + abcd[1] = ebx; + abcd[2] = ecx; + abcd[3] = edx; +} + +#endif + +int simd_detect_x86(unsigned int idFeature) +{ + uint32_t regs[4]; // EAX, EBX, ECX, EDX; +#ifdef _WIN32 + __cpuid(regs, 0); + if (regs[0] > 1U) __cpuid(regs, 1); +#else + __get_cpuid(0, ®s[0], ®s[1], ®s[2], ®s[3]); + if(regs[0] > 1U) __get_cpuid(1, ®s[0], ®s[1], ®s[2], ®s[3]); +#endif + + if ((regs[2] & idFeature) != idFeature) + return 0; + return 1; +} + +int is_fma_avx() { + static int result = -1; + if (result == -1) { + result = simd_detect_x86(AVXFlag); + if (result == 1) printf(" Used AVX \n"); + else printf(" Not used AVX \n"); + } + return result; +} + +// https://software.intel.com/sites/landingpage/IntrinsicsGuide +void gemm_nn(int M, int N, int K, float ALPHA, + float *A, int lda, + float *B, int ldb, + float *C, int ldc) +{ + int i, j, k; + if (is_fma_avx() == 1) { // AVX + for (i = 0; i < M; ++i) { + for (k = 0; k < K; ++k) { + float A_PART = ALPHA*A[i*lda + k]; + __m256 a256, b256, c256, result256; // AVX + a256 = _mm256_set1_ps(A_PART); + for (j = 0; j < N - 8; j += 8) { + b256 = _mm256_loadu_ps(&B[k*ldb + j]); + c256 = _mm256_loadu_ps(&C[i*ldc + j]); + // FMA - Intel Haswell (2013), AMD Piledriver (2012) + //result256 = _mm256_fmadd_ps(a256, b256, c256); + result256 = _mm256_mul_ps(a256, b256); + result256 = _mm256_add_ps(result256, c256); + _mm256_storeu_ps(&C[i*ldc + j], result256); + } + + int prev_end = (N % 8 == 0) ? (N - 8) : (N / 8) * 8; + for (j = prev_end; j < N; ++j) + C[i*ldc + j] += A_PART*B[k*ldb + j]; + } + } + } + else { + for (i = 0; i < M; ++i) { + for (k = 0; k < K; ++k) { + register float A_PART = ALPHA*A[i*lda + k]; + for (j = 0; j < N; ++j) { + C[i*ldc + j] += A_PART*B[k*ldb + j]; + } + /* // SSE + __m128 a128, b128, c128, result128; // SSE + a128 = _mm_set1_ps(A_PART); + for (j = 0; j < N - 4; j += 4) { + b128 = _mm_loadu_ps(&B[k*ldb + j]); + c128 = _mm_loadu_ps(&C[i*ldc + j]); + //result128 = _mm_fmadd_ps(a128, b128, c128); + result128 = _mm_mul_ps(a128, b128); + result128 = _mm_add_ps(result128, c128); + _mm_storeu_ps(&C[i*ldc + j], result128); + } + + int prev_end = (N % 4 == 0) ? (N - 4) : (N / 4) * 4; + for (j = prev_end; j < N; ++j){ + C[i*ldc + j] += A_PART*B[k*ldb + j]; + } + */ + } + } + } +} + + +// http://graphics.stanford.edu/~seander/bithacks.html +// https://stackoverflow.com/questions/17354971/fast-counting-the-number-of-set-bits-in-m128i-register + + +static inline int popcnt128(__m128i n) { + const __m128i n_hi = _mm_unpackhi_epi64(n, n); +#ifdef _MSC_VER + return __popcnt64(_mm_cvtsi128_si64(n)) + __popcnt64(_mm_cvtsi128_si64(n_hi)); +#else + return __popcntq(_mm_cvtsi128_si64(n)) + __popcntq(_mm_cvtsi128_si64(n_hi)); +#endif +} + +static inline int popcnt256(__m256i n) { + return popcnt128(_mm256_extractf128_si256(n, 0)) + popcnt128(_mm256_extractf128_si256(n, 1)); +} + +static inline __m256i count256(__m256i v) { + __m256i lookup = + _mm256_setr_epi8(0, 1, 1, 2, 1, 2, 2, 3, 1, 2, + 2, 3, 2, 3, 3, 4, 0, 1, 1, 2, 1, 2, 2, 3, + 1, 2, 2, 3, 2, 3, 3, 4); + + __m256i low_mask = _mm256_set1_epi8(0x0f); + + __m256i lo = _mm256_and_si256(v, low_mask); + __m256i hi = _mm256_and_si256(_mm256_srli_epi32(v, 4), low_mask); + __m256i popcnt1 = _mm256_shuffle_epi8(lookup, lo); + __m256i popcnt2 = _mm256_shuffle_epi8(lookup, hi); + __m256i total = _mm256_add_epi8(popcnt1, popcnt2); + + return _mm256_sad_epu8(total, _mm256_setzero_si256()); +} +static inline int popcnt256_custom(__m256i n) { + return _mm_popcnt_u64(n.m256i_i64[0]) + + _mm_popcnt_u64(n.m256i_i64[1]) + + _mm_popcnt_u64(n.m256i_i64[2]) + + _mm_popcnt_u64(n.m256i_i64[3]); +} + +static inline void CSA(__m256i * h, __m256i * l, __m256i a, __m256i b, __m256i c) +{ + __m256i u = _mm256_xor_si256(a, b); + *h = _mm256_or_si256(_mm256_and_si256(a, b), _mm256_and_si256(u, c)); + *l = _mm256_xor_si256(u, c); +} + +static inline __m256i xnor256(__m256i a_bit256, __m256i b_bit256) { + __m256i all_1 = _mm256_set1_epi8(255); + __m256i xor256 = _mm256_xor_si256(a_bit256, b_bit256); + __m256i c_bit256 = _mm256_andnot_si256(xor256, all_1); + + return c_bit256; + +} + +// 2 x faster than popcnt: https://arxiv.org/pdf/1611.07612.pdf +// step = 16*256/8 = 512 bytes = 4096 bit (ldb, lda, bit_step, align - all should be aligned by 4096 bit) +static inline uint64_t avx_hs_custom(__m256i * A, __m256i * B, uint64_t size) { + __m256i total = _mm256_setzero_si256(); + __m256i ones = _mm256_setzero_si256(); + __m256i twos = _mm256_setzero_si256(); + __m256i fours = _mm256_setzero_si256(); + __m256i eights = _mm256_setzero_si256(); + __m256i sixteens = _mm256_setzero_si256(); + __m256i twosA, twosB, foursA, foursB, eightsA, eightsB; + + for (uint64_t i = 0; i < size; i += 16) { + //CSA(&twosA, &ones, ones, d[i], d[i + 1]); + CSA(&twosA, &ones, ones, xnor256(A[i], B[i]), xnor256(A[i + 1], B[i + 1])); + CSA(&twosB, &ones, ones, xnor256(A[i + 2], B[i + 2]), xnor256(A[i + 3], B[i + 3])); + CSA(&foursA, &twos, twos, twosA, twosB); + CSA(&twosA, &ones, ones, xnor256(A[i + 4], B[i + 4]), xnor256(A[i + 5], B[i + 5])); + CSA(&twosB, &ones, ones, xnor256(A[i + 6], B[i + 6]), xnor256(A[i + 7], B[i + 7])); + CSA(&foursB, &twos, twos, twosA, twosB); + CSA(&eightsA, &fours, fours, foursA, foursB); + CSA(&twosA, &ones, ones, xnor256(A[i + 8], B[i + 8]), xnor256(A[i + 9], B[i + 9])); + CSA(&twosB, &ones, ones, xnor256(A[i + 10], B[i + 10]), xnor256(A[i + 11], B[i + 11])); + CSA(&foursA, &twos, twos, twosA, twosB); + CSA(&twosA, &ones, ones, xnor256(A[i + 12], B[i + 12]), xnor256(A[i + 13], B[i + 13])); + CSA(&twosB, &ones, ones, xnor256(A[i + 14], B[i + 14]), xnor256(A[i + 15], B[i + 15])); + CSA(&foursB, &twos, twos, twosA, twosB); + CSA(&eightsB, &fours, fours, foursA, foursB); + CSA(&sixteens, &eights, eights, eightsA, eightsB); + + total = _mm256_add_epi64(total, count256(sixteens)); + } + total = _mm256_slli_epi64(total, 4); + total = _mm256_add_epi64(total, + _mm256_slli_epi64(count256(eights), 3)); + total = _mm256_add_epi64(total, + _mm256_slli_epi64(count256(fours), 2)); + total = _mm256_add_epi64(total, + _mm256_slli_epi64(count256(twos), 1)); + total = _mm256_add_epi64(total, count256(ones)); + + return total.m256i_i64[0] + + total.m256i_i64[1] + + total.m256i_i64[2] + + total.m256i_i64[3]; + + //return _mm256_extract_epi64(total, 0) + // + _mm256_extract_epi64(total, 1) + // + _mm256_extract_epi64(total, 2) + // + _mm256_extract_epi64(total, 3); +} + +void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + __m256i all_1 = _mm256_set1_epi8(255); + int i, j, k; + + //printf("\n M = %d, N = %d, K = %d, ldb = %d, M*ldb/8 = %d, N*ldb/8= %d \n", M, N, K, ldb, M*ldb/8, N*ldb/8); + //if (K > 4096) printf("!!!avx_hs!!! \n\n"); + + #pragma omp parallel for + for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] + float mean_val = mean_arr[i]; + + for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] + int count = 0; + const int bit_step = 256; + + + int hs_count = 0; + if (K > 4096) { + hs_count = avx_hs_custom(A + (i*lda) / 8, B + (j*ldb) / 8, K / 256); + + int local_bit_step = 4096; + + int f1 = (K % local_bit_step == 0) ? 0 : (local_bit_step - (K % local_bit_step)); + hs_count = hs_count - f1; // remove extra bits + count = hs_count; + } + else { + for (k = 0; k < K; k += bit_step) { // l.size*l.size*l.c - one filter size [27 - 9216] + + //__m128i a_bit128 = _mm_loadu_si128((__m128i *)(A + (i*lda + k) / 8)); + //__m128i b_bit128 = _mm_loadu_si128((__m128i *)(B + (j*ldb + k) / 8)); + //__m128i xor128 = _mm_xor_si128(a_bit128, b_bit128); + //__m128i c_bit128 = _mm_andnot_si128(xor128, all_1); + //int tmp_count = popcnt128(c_bit128); + + __m256i a_bit256 = _mm256_loadu_si256((__m256i *)(A + (i*lda + k) / 8)); + __m256i b_bit256 = _mm256_loadu_si256((__m256i *)(B + (j*ldb + k) / 8)); + __m256i xor256 = _mm256_xor_si256(a_bit256, b_bit256); + __m256i c_bit256 = _mm256_andnot_si256(xor256, all_1); //we can do NOT for wegihts once and do not do this NOT + int tmp_count = popcnt256(c_bit256); + //int tmp_count = popcnt256_custom(c_bit256); + count += tmp_count; + + //binary_int64_printf(c_bit64); + //printf(", count = %d \n\n", tmp_count); + } + + int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); + count = count - f1; // remove extra bits + } + + C[i*ldc + j] = (2 * count - K) * mean_val; + } + } +} + + +void float_to_bit(float *src, unsigned char *dst, size_t size) +{ + size_t dst_size = size / 8 + 1; + memset(dst, 0, dst_size); + + size_t i; + __m256i all256_sing1 = _mm256_set_epi32(0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000); + + for (i = 0; i < size; i+=8) + { + __m256i src256 = _mm256_loadu_si256((__m256i *)(&src[i])); + __m256i result256 = _mm256_and_si256(src256, all256_sing1); // check sign in 8 x 32-bit floats + + uint32_t mask = _mm256_movemask_ps(_mm256_castsi256_ps(result256)); // (val >= 0) ? 0 : 1 + mask = ~mask; // inverse mask, (val >= 0) ? 1 : 0 + + dst[i / 8] = mask; + } +} + +#else + +void gemm_nn(int M, int N, int K, float ALPHA, + float *A, int lda, + float *B, int ldb, + float *C, int ldc) +{ + int i, j, k; + for (i = 0; i < M; ++i) { + for (k = 0; k < K; ++k) { + register float A_PART = ALPHA*A[i*lda + k]; + for (j = 0; j < N; ++j) { + C[i*ldc + j] += A_PART*B[k*ldb + j]; + } + } + } +} + +void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + int i, j, k, h; + +#pragma omp parallel for + for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] + float mean_val = mean_arr[i]; + + for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] + int count = 0; + + for (k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] + uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); + uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); + uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); + +#ifdef WIN32 + int tmp_count = __popcnt64(c_bit64); +#else + int tmp_count = __builtin_popcountll(c_bit64); +#endif + + if (K - k < 64) tmp_count = tmp_count - (64 - (K - k)); // remove extra bits + count += tmp_count; + //binary_int64_printf(c_bit64); + //printf(", count = %d \n\n", tmp_count); + } + + C[i*ldc + j] = (2 * count - K) * mean_val; + } + } +} + +void float_to_bit(float *src, unsigned char *dst, size_t size) +{ + size_t dst_size = size / 8 + 1; + memset(dst, 0, dst_size); + + size_t i; + char *byte_arr = calloc(size, sizeof(char)); + for (i = 0; i < size; ++i) { + if (src[i] > 0) byte_arr[i] = 1; + } + + //for (i = 0; i < size; ++i) { + // dst[i / 8] |= byte_arr[i] << (i % 8); + //} + + for (i = 0; i < size; i += 8) { + char dst_tmp = 0; + dst_tmp |= byte_arr[i + 0] << 0; + dst_tmp |= byte_arr[i + 1] << 1; + dst_tmp |= byte_arr[i + 2] << 2; + dst_tmp |= byte_arr[i + 3] << 3; + dst_tmp |= byte_arr[i + 4] << 4; + dst_tmp |= byte_arr[i + 5] << 5; + dst_tmp |= byte_arr[i + 6] << 6; + dst_tmp |= byte_arr[i + 7] << 7; + dst[i / 8] = dst_tmp; + } + free(byte_arr); +} +#endif // __x86_64 + +void gemm_nt(int M, int N, int K, float ALPHA, + float *A, int lda, + float *B, int ldb, + float *C, int ldc) +{ + int i,j,k; + for(i = 0; i < M; ++i){ + for(j = 0; j < N; ++j){ + register float sum = 0; + for(k = 0; k < K; ++k){ + sum += ALPHA*A[i*lda+k]*B[j*ldb + k]; + } + C[i*ldc+j] += sum; + } + } +} + +void gemm_tn(int M, int N, int K, float ALPHA, + float *A, int lda, + float *B, int ldb, + float *C, int ldc) +{ + int i,j,k; + for(i = 0; i < M; ++i){ + for(k = 0; k < K; ++k){ + register float A_PART = ALPHA*A[k*lda+i]; + for(j = 0; j < N; ++j){ + C[i*ldc+j] += A_PART*B[k*ldb+j]; + } + } + } +} + +void gemm_tt(int M, int N, int K, float ALPHA, + float *A, int lda, + float *B, int ldb, + float *C, int ldc) +{ + int i,j,k; + for(i = 0; i < M; ++i){ + for(j = 0; j < N; ++j){ + register float sum = 0; + for(k = 0; k < K; ++k){ + sum += ALPHA*A[i+k*lda]*B[k+j*ldb]; + } + C[i*ldc+j] += sum; + } + } +} + + +void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA, + float *A, int lda, + float *B, int ldb, + float BETA, + float *C, int ldc) +{ + //printf("cpu: %d %d %d %d %d %f %d %d %f %d\n",TA, TB, M, N, K, ALPHA, lda, ldb, BETA, ldc); + if (BETA != 1){ + int i, j; + for(i = 0; i < M; ++i){ + for(j = 0; j < N; ++j){ + C[i*ldc + j] *= BETA; + } + } + } + + int t; + #pragma omp parallel for + for (t = 0; t < M; ++t) { + if (!TA && !TB) + gemm_nn(1, N, K, ALPHA, A + t*lda, lda, B, ldb, C + t*ldc, ldc); + else if (TA && !TB) + gemm_tn(1, N, K, ALPHA, A + t, lda, B, ldb, C + t*ldc, ldc); + else if (!TA && TB) + gemm_nt(1, N, K, ALPHA, A + t*lda, lda, B, ldb, C + t*ldc, ldc); + else + gemm_tt(1, N, K, ALPHA, A + t, lda, B, ldb, C + t*ldc, ldc); + } +} + +#ifdef GPU + +#include + +void gemm_ongpu(int TA, int TB, int M, int N, int K, float ALPHA, + float *A_gpu, int lda, + float *B_gpu, int ldb, + float BETA, + float *C_gpu, int ldc) +{ + cublasHandle_t handle = blas_handle(); + cudaError_t stream_status = cublasSetStream(handle, get_cuda_stream()); + cudaError_t status = cublasSgemm(handle, (TB ? CUBLAS_OP_T : CUBLAS_OP_N), + (TA ? CUBLAS_OP_T : CUBLAS_OP_N), N, M, K, &ALPHA, B_gpu, ldb, A_gpu, lda, &BETA, C_gpu, ldc); + check_error(status); +} + +void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA, + float *A, int lda, + float *B, int ldb, + float BETA, + float *C, int ldc) +{ + float *A_gpu = cuda_make_array(A, (TA ? lda*K:lda*M)); + float *B_gpu = cuda_make_array(B, (TB ? ldb*N : ldb*K)); + float *C_gpu = cuda_make_array(C, ldc*M); + + gemm_ongpu(TA, TB, M, N, K, ALPHA, A_gpu, lda, B_gpu, ldb, BETA, C_gpu, ldc); + + cuda_pull_array(C_gpu, C, ldc*M); + cuda_free(A_gpu); + cuda_free(B_gpu); + cuda_free(C_gpu); +} + +#include +#include +#include +#include + +void time_gpu_random_matrix(int TA, int TB, int m, int k, int n) +{ + float *a; + if(!TA) a = random_matrix(m,k); + else a = random_matrix(k,m); + int lda = (!TA)?k:m; + float *b; + if(!TB) b = random_matrix(k,n); + else b = random_matrix(n,k); + int ldb = (!TB)?n:k; + + float *c = random_matrix(m,n); + int i; + clock_t start = clock(), end; + for(i = 0; i<32; ++i){ + gemm_gpu(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n); + } + end = clock(); + printf("Matrix Multiplication %dx%d * %dx%d, TA=%d, TB=%d: %lf s\n",m,k,k,n, TA, TB, (float)(end-start)/CLOCKS_PER_SEC); + free(a); + free(b); + free(c); +} + +void time_ongpu(int TA, int TB, int m, int k, int n) +{ + int iter = 10; + float *a = random_matrix(m,k); + float *b = random_matrix(k,n); + + int lda = (!TA)?k:m; + int ldb = (!TB)?n:k; + + float *c = random_matrix(m,n); + + float *a_cl = cuda_make_array(a, m*k); + float *b_cl = cuda_make_array(b, k*n); + float *c_cl = cuda_make_array(c, m*n); + + int i; + clock_t start = clock(), end; + for(i = 0; i b.h) ? a.h : b.h, (a.c > b.c) ? a.c : b.c); fill_cpu(c.w*c.h*c.c, 1, c.data, 1); - embed_image(a, c, 0, 0); + embed_image(a, c, 0, 0); composite_image(b, c, a.w + dx, 0); return c; } @@ -267,7 +267,7 @@ int compare_by_lefts(const void *a_ptr, const void *b_ptr) { return delta < 0 ? -1 : delta > 0 ? 1 : 0; } -// compare to sort detection** by best_class probability +// compare to sort detection** by best_class probability int compare_by_probs(const void *a_ptr, const void *b_ptr) { const detection_with_class* a = (detection_with_class*)a_ptr; const detection_with_class* b = (detection_with_class*)b_ptr; @@ -421,7 +421,7 @@ void draw_detections(image im, int num, float thresh, box *boxes, float **probs, if(top < 0) top = 0; if(bot > im.h-1) bot = im.h-1; printf("%s: %.0f%%", names[class_id], prob * 100); - + //printf(" - id: %d, x_center: %d, y_center: %d, width: %d, height: %d", // class_id, (right + left) / 2, (bot - top) / 2, right - left, bot - top); @@ -481,6 +481,10 @@ void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float t rgb[1] = green; rgb[2] = blue; box b = dets[i].bbox; + b.w = (b.w < 1) ? b.w : 1; + b.h = (b.h < 1) ? b.h : 1; + b.x = (b.x < 1) ? b.x : 1; + b.y = (b.y < 1) ? b.y : 1; //printf("%f %f %f %f\n", b.x, b.y, b.w, b.h); int left = (b.x - b.w / 2.)*show_img->width; @@ -535,10 +539,11 @@ void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float t cvRectangle(show_img, pt1, pt2, color, width, 8, 0); if (ext_output) - printf("\t(left_x: %4.0f top_y: %4.0f width: %4.0f height: %4.0f)\n", + printf("\t(left_x: %4.0f top_y: %4.0f width: %4.0f height: %4.0f)\n", (float)left, (float)top, b.w*show_img->width, b.h*show_img->height); else printf("\n"); + cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0); cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0); // filled CvScalar black_color; @@ -617,7 +622,7 @@ void draw_detections_cv(IplImage* show_img, int num, float thresh, box *boxes, f CvScalar black_color; black_color.val[0] = 0; CvFont font; - cvInitFont(&font, CV_FONT_HERSHEY_SIMPLEX, font_size, font_size, 0, font_size * 3, 8); + cvInitFont(&font, CV_FONT_HERSHEY_SIMPLEX, font_size, font_size, 0, font_size * 3, 8); cvPutText(show_img, names[class_id], pt_text, &font, black_color); } } @@ -881,7 +886,7 @@ void show_image_cv(image p, const char *name) IplImage *disp = cvCreateImage(cvSize(p.w,p.h), IPL_DEPTH_8U, p.c); int step = disp->widthStep; - cvNamedWindow(buff, CV_WINDOW_NORMAL); + cvNamedWindow(buff, CV_WINDOW_NORMAL); //cvMoveWindow(buff, 100*(windows%10) + 200*(windows/10), 100*(windows%10)); ++windows; for(y = 0; y < p.h; ++y){ @@ -1057,7 +1062,7 @@ image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage } else src = cvQueryFrame(cap); - if (cpp_video_capture) + if (cpp_video_capture) if(!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0); IplImage* new_img = cvCreateImage(cvSize(w, h), IPL_DEPTH_8U, c); *in_img = cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c); @@ -1588,7 +1593,7 @@ image blend_image(image fore, image back, float alpha) for(k = 0; k < fore.c; ++k){ for(j = 0; j < fore.h; ++j){ for(i = 0; i < fore.w; ++i){ - float val = alpha * get_pixel(fore, i, j, k) + + float val = alpha * get_pixel(fore, i, j, k) + (1 - alpha)* get_pixel(back, i, j, k); set_pixel(blend, i, j, k, val); } @@ -1708,8 +1713,8 @@ float bilinear_interpolate(image im, float x, float y, int c) float dx = x - ix; float dy = y - iy; - float val = (1-dy) * (1-dx) * get_pixel_extend(im, ix, iy, c) + - dy * (1-dx) * get_pixel_extend(im, ix, iy+1, c) + + float val = (1-dy) * (1-dx) * get_pixel_extend(im, ix, iy, c) + + dy * (1-dx) * get_pixel_extend(im, ix, iy+1, c) + (1-dy) * dx * get_pixel_extend(im, ix+1, iy, c) + dy * dx * get_pixel_extend(im, ix+1, iy+1, c); return val; @@ -1717,7 +1722,7 @@ float bilinear_interpolate(image im, float x, float y, int c) image resize_image(image im, int w, int h) { - image resized = make_image(w, h, im.c); + image resized = make_image(w, h, im.c); image part = make_image(w, im.h, im.c); int r, c, k; float w_scale = (float)(im.w - 1) / (w - 1); @@ -1931,7 +1936,7 @@ image collapse_images_vert(image *ims, int n) free_image(copy); } return filters; -} +} image collapse_images_horz(image *ims, int n) { @@ -1967,7 +1972,7 @@ image collapse_images_horz(image *ims, int n) free_image(copy); } return filters; -} +} void show_image_normalized(image im, const char *name) { diff --git a/src/network.c b/src/network.c index 63b76a8bcc0..a62f6d0f153 100644 --- a/src/network.c +++ b/src/network.c @@ -862,6 +862,8 @@ void calculate_binary_weights(network net) if (l->xnor) { //printf("\n %d \n", j); size_t ldb_align = 256; // 256bit for AVX2 + if (l->size*l->size*l->c > 4096) ldb_align = 4096; + binary_transpose_align_weights(l, ldb_align); } } From d6162af210d9d5648d33bf0fda40f773ac200df5 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 8 Aug 2018 19:28:39 +0300 Subject: [PATCH 0118/1365] Optimized on CPU: gemm_bin, im2col, activation, transpose --- src/activations.c | 14 +- src/convolutional_layer.c | 78 ++++++- src/convolutional_layer.h | 2 +- src/gemm.c | 468 +++++++++++++++++++++++++++++--------- src/gemm.h | 13 +- src/network.c | 3 +- 6 files changed, 451 insertions(+), 127 deletions(-) diff --git a/src/activations.c b/src/activations.c index 0cbb2f5546e..eab4e23d233 100644 --- a/src/activations.c +++ b/src/activations.c @@ -95,8 +95,16 @@ float activate(float x, ACTIVATION a) void activate_array(float *x, const int n, const ACTIVATION a) { int i; - for(i = 0; i < n; ++i){ - x[i] = activate(x[i], a); + if (a == LINEAR) {} + else if (a == LEAKY) { + for (i = 0; i < n; ++i) { + x[i] = leaky_activate(x[i]); + } + } + else { + for (i = 0; i < n; ++i) { + x[i] = activate(x[i], a); + } } } @@ -139,5 +147,5 @@ void gradient_array(const float *x, const int n, const ACTIVATION a, float *delt for(i = 0; i < n; ++i){ delta[i] *= gradient(x[i], a); } -} +} diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 0bde97a5027..a8205881b93 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -593,15 +593,15 @@ void bit_to_float(unsigned char *src, float *dst, size_t size, size_t filters, f } } -void binary_transpose_align_weights(convolutional_layer *l, size_t ldb_align) +void binary_align_weights(convolutional_layer *l, size_t lda_align) { int m = l->n; int k = l->size*l->size*l->c; - size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; + size_t new_lda = k + (lda_align - k%lda_align); // (k / 8 + 1) * 8; binarize_weights(l->weights, m, k, l->binary_weights); - size_t align_weights_size = new_ldb * m; + size_t align_weights_size = new_lda * m; size_t align_bit_weights_size = align_weights_size / 8;// +1; float *align_weights = calloc(align_weights_size, sizeof(float)); l->align_bit_weights = calloc(align_bit_weights_size, sizeof(char)); @@ -610,7 +610,7 @@ void binary_transpose_align_weights(convolutional_layer *l, size_t ldb_align) // align A without transpose for (i = 0; i < m; ++i) { for (j = 0; j < k; ++j) { - align_weights[i*new_ldb + j] = l->binary_weights[i*k + j]; + align_weights[i*new_lda + j] = l->binary_weights[i*k + j]; } } float_to_bit(align_weights, l->align_bit_weights, align_weights_size); @@ -622,6 +622,56 @@ void binary_transpose_align_weights(convolutional_layer *l, size_t ldb_align) } +size_t binary_transpose_align_input(int k, int n, float *b, char **t_bit_input, size_t ldb_align) +{ + size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; + size_t t_intput_size = new_ldb * n; + size_t t_bit_input_size = t_intput_size / 8;// +1; + float *t_input = calloc(t_intput_size, sizeof(float)); + //char * + *t_bit_input = calloc(t_bit_input_size, sizeof(char)); + + //printf("\n bit_input_size = %d, n = %d, k = %d, ldb = %d \n", bit_input_size, n, k, n); + //printf("\n t_bit_input_size = %d, k = %d, n = %d, new_ldb = %d \n", t_bit_input_size, k, n, new_ldb); + + //printf("\n align_weights_size = %d, k = %d, m = %d, lda = %d \n", align_weights_size, k, m, k); + //printf("\n align_bit_weights_size = %d, k = %d, m = %d, new_lda = %d \n", align_bit_weights_size, k, m, new_ldb); + + // transpose and align B + int i, j; + //#pragma omp parallel for + /* + for (i = 0; i < n; ++i) { + for (j = 0; j < k; ++j) { + t_input[i*new_ldb + j] = b[j*n + i]; + } + }*/ + //transpose_block_SSE4x4(float *A, float *B, const int n, const int m, const int lda, const int ldb, const int block_size) + + //transpose_block(b, t_input, k, n, n, new_ldb, 16); + + int blocksize = 1; + int mod_k = 1, mod_n = 1; + for (i = 2; i < 256; i *= 2) + if (k % i == 0) mod_k = i; + + for (i = 2; i < 256; i *= 2) + if (n % i == 0) mod_n = i; + + blocksize = (mod_k < mod_n) ? mod_k : mod_n; + + transpose_block_SSE4x4(b, t_input, k, n, n, new_ldb, blocksize); + + //transpose_block(b, t_input, k, n, n, new_ldb, blocksize); + //printf("\n blocksize = %d \n", blocksize); + + float_to_bit(t_input, *t_bit_input, t_intput_size); + free(t_input); + + return t_intput_size; +} + + void forward_convolutional_layer(convolutional_layer l, network_state state) { int out_h = convolutional_out_height(l); @@ -652,8 +702,9 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) u++; for(i = 0; i < l.batch; ++i){ - im2col_cpu(state.input, l.c, l.h, l.w, - l.size, l.stride, l.pad, b); + //im2col_cpu(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); + im2col_cpu_custom(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); + //gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); //gemm_nn_custom(m, n, k, 1, a, k, b, n, c, n); if (l.xnor) { @@ -683,8 +734,8 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) // transpose B from NxK to KxN (x-axis (ldb = l.size*l.size*l.c) - should be multiple of 8 bits) { + /* size_t ldb_align = 256;// 8; - if (k > 4096)ldb_align = 4096; size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; size_t t_intput_size = new_ldb * n; @@ -709,6 +760,8 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) } float_to_bit(t_input, t_bit_input, t_intput_size); + + if (!l.align_bit_weights) { size_t align_weights_size = new_ldb * m; @@ -729,12 +782,17 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) free(align_weights); } + */ + size_t ldb_align = 256; // 256 bit for AVX2 + size_t new_ldb = k + (ldb_align - k%ldb_align); + char *t_bit_input = NULL; + size_t t_intput_size = binary_transpose_align_input(k, n, b, &t_bit_input, ldb_align); gemm_nn_custom_bin_mean_transposed(m, n, k, 1, l.align_bit_weights, new_ldb, t_bit_input, new_ldb, c, n, l.mean_arr); //gemm_nn_custom_bin_mean_transposed(m, n, k, 1, bit_weights, k, t_bit_input, new_ldb, c, n, mean_arr); - free(t_input); + //free(t_input); free(t_bit_input); //free(align_bit_weights); @@ -771,7 +829,9 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) } add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w); - activate_array(l.output, m*n*l.batch, l.activation); + //activate_array(l.output, m*n*l.batch, l.activation); + activate_array_cpu_custom(l.output, m*n*l.batch, l.activation); + if(l.binary || l.xnor) swap_binary(&l); } diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h index dd79c48e62a..b804afb8f97 100644 --- a/src/convolutional_layer.h +++ b/src/convolutional_layer.h @@ -35,7 +35,7 @@ void binarize_weights(float *weights, int n, int size, float *binary); void swap_binary(convolutional_layer *l); void binarize_weights2(float *weights, int n, int size, char *binary, float *scales); -void binary_transpose_align_weights(convolutional_layer *l, size_t ldb_align); +void binary_align_weights(convolutional_layer *l, size_t ldb_align); void backward_convolutional_layer(convolutional_layer layer, network_state state); diff --git a/src/gemm.c b/src/gemm.c index 63163a53ced..478e9663fa1 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -1,5 +1,6 @@ #include "gemm.h" #include "utils.h" +#include "im2col.h" #include "cuda.h" #include #include @@ -426,7 +427,7 @@ void gemm_nn(int M, int N, int K, float ALPHA, // http://graphics.stanford.edu/~seander/bithacks.html // https://stackoverflow.com/questions/17354971/fast-counting-the-number-of-set-bits-in-m128i-register - +// https://arxiv.org/pdf/1611.07612.pdf static inline int popcnt128(__m128i n) { const __m128i n_hi = _mm_unpackhi_epi64(n, n); @@ -458,133 +459,61 @@ static inline __m256i count256(__m256i v) { return _mm256_sad_epu8(total, _mm256_setzero_si256()); } static inline int popcnt256_custom(__m256i n) { - return _mm_popcnt_u64(n.m256i_i64[0]) + - _mm_popcnt_u64(n.m256i_i64[1]) + - _mm_popcnt_u64(n.m256i_i64[2]) + - _mm_popcnt_u64(n.m256i_i64[3]); -} - -static inline void CSA(__m256i * h, __m256i * l, __m256i a, __m256i b, __m256i c) -{ - __m256i u = _mm256_xor_si256(a, b); - *h = _mm256_or_si256(_mm256_and_si256(a, b), _mm256_and_si256(u, c)); - *l = _mm256_xor_si256(u, c); -} - -static inline __m256i xnor256(__m256i a_bit256, __m256i b_bit256) { - __m256i all_1 = _mm256_set1_epi8(255); - __m256i xor256 = _mm256_xor_si256(a_bit256, b_bit256); - __m256i c_bit256 = _mm256_andnot_si256(xor256, all_1); - - return c_bit256; - -} - -// 2 x faster than popcnt: https://arxiv.org/pdf/1611.07612.pdf -// step = 16*256/8 = 512 bytes = 4096 bit (ldb, lda, bit_step, align - all should be aligned by 4096 bit) -static inline uint64_t avx_hs_custom(__m256i * A, __m256i * B, uint64_t size) { - __m256i total = _mm256_setzero_si256(); - __m256i ones = _mm256_setzero_si256(); - __m256i twos = _mm256_setzero_si256(); - __m256i fours = _mm256_setzero_si256(); - __m256i eights = _mm256_setzero_si256(); - __m256i sixteens = _mm256_setzero_si256(); - __m256i twosA, twosB, foursA, foursB, eightsA, eightsB; - - for (uint64_t i = 0; i < size; i += 16) { - //CSA(&twosA, &ones, ones, d[i], d[i + 1]); - CSA(&twosA, &ones, ones, xnor256(A[i], B[i]), xnor256(A[i + 1], B[i + 1])); - CSA(&twosB, &ones, ones, xnor256(A[i + 2], B[i + 2]), xnor256(A[i + 3], B[i + 3])); - CSA(&foursA, &twos, twos, twosA, twosB); - CSA(&twosA, &ones, ones, xnor256(A[i + 4], B[i + 4]), xnor256(A[i + 5], B[i + 5])); - CSA(&twosB, &ones, ones, xnor256(A[i + 6], B[i + 6]), xnor256(A[i + 7], B[i + 7])); - CSA(&foursB, &twos, twos, twosA, twosB); - CSA(&eightsA, &fours, fours, foursA, foursB); - CSA(&twosA, &ones, ones, xnor256(A[i + 8], B[i + 8]), xnor256(A[i + 9], B[i + 9])); - CSA(&twosB, &ones, ones, xnor256(A[i + 10], B[i + 10]), xnor256(A[i + 11], B[i + 11])); - CSA(&foursA, &twos, twos, twosA, twosB); - CSA(&twosA, &ones, ones, xnor256(A[i + 12], B[i + 12]), xnor256(A[i + 13], B[i + 13])); - CSA(&twosB, &ones, ones, xnor256(A[i + 14], B[i + 14]), xnor256(A[i + 15], B[i + 15])); - CSA(&foursB, &twos, twos, twosA, twosB); - CSA(&eightsB, &fours, fours, foursA, foursB); - CSA(&sixteens, &eights, eights, eightsA, eightsB); - - total = _mm256_add_epi64(total, count256(sixteens)); - } - total = _mm256_slli_epi64(total, 4); - total = _mm256_add_epi64(total, - _mm256_slli_epi64(count256(eights), 3)); - total = _mm256_add_epi64(total, - _mm256_slli_epi64(count256(fours), 2)); - total = _mm256_add_epi64(total, - _mm256_slli_epi64(count256(twos), 1)); - total = _mm256_add_epi64(total, count256(ones)); - - return total.m256i_i64[0] + - total.m256i_i64[1] + - total.m256i_i64[2] + - total.m256i_i64[3]; - - //return _mm256_extract_epi64(total, 0) - // + _mm256_extract_epi64(total, 1) - // + _mm256_extract_epi64(total, 2) - // + _mm256_extract_epi64(total, 3); -} + __m256i val = count256(n); + + return val.m256i_i64[0] + + val.m256i_i64[1] + + val.m256i_i64[2] + + val.m256i_i64[3]; +} void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, unsigned char *A, int lda, unsigned char *B, int ldb, float *C, int ldc, float *mean_arr) { - __m256i all_1 = _mm256_set1_epi8(255); - int i, j, k; + int i; - //printf("\n M = %d, N = %d, K = %d, ldb = %d, M*ldb/8 = %d, N*ldb/8= %d \n", M, N, K, ldb, M*ldb/8, N*ldb/8); - //if (K > 4096) printf("!!!avx_hs!!! \n\n"); + static int max_num_threads = 0; + if (max_num_threads == 0) { + max_num_threads = omp_get_max_threads(); + omp_set_num_threads(max_num_threads / 2); + } #pragma omp parallel for - for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] + for (i = 0; i < M; ++i) + { // l.n - filters [16 - 55 - 1024] float mean_val = mean_arr[i]; + int j, k; + __m256i all_1 = _mm256_set1_epi8(255); for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] int count = 0; const int bit_step = 256; + __m256i count_sum = _mm256_set1_epi8(0); + for (k = 0; k < K; k += bit_step) { // l.size*l.size*l.c - one filter size [27 - 9216] + __m256i a_bit256 = _mm256_loadu_si256((__m256i *)(A + (i*lda + k) / 8)); + __m256i b_bit256 = _mm256_loadu_si256((__m256i *)(B + (j*ldb + k) / 8)); + __m256i xor256 = _mm256_xor_si256(a_bit256, b_bit256); // xnor = not(xor(a,b)) + __m256i c_bit256 = _mm256_andnot_si256(xor256, all_1); // can be optimized - we can do other NOT for wegihts once and do not do this NOT - int hs_count = 0; - if (K > 4096) { - hs_count = avx_hs_custom(A + (i*lda) / 8, B + (j*ldb) / 8, K / 256); + count_sum = _mm256_add_epi64(count256(c_bit256), count_sum); // Mula’s algorithm - int local_bit_step = 4096; + //count += popcnt256(c_bit256); - int f1 = (K % local_bit_step == 0) ? 0 : (local_bit_step - (K % local_bit_step)); - hs_count = hs_count - f1; // remove extra bits - count = hs_count; + //binary_int64_printf(c_bit64); + //printf(", count = %d \n\n", tmp_count); } - else { - for (k = 0; k < K; k += bit_step) { // l.size*l.size*l.c - one filter size [27 - 9216] - - //__m128i a_bit128 = _mm_loadu_si128((__m128i *)(A + (i*lda + k) / 8)); - //__m128i b_bit128 = _mm_loadu_si128((__m128i *)(B + (j*ldb + k) / 8)); - //__m128i xor128 = _mm_xor_si128(a_bit128, b_bit128); - //__m128i c_bit128 = _mm_andnot_si128(xor128, all_1); - //int tmp_count = popcnt128(c_bit128); - - __m256i a_bit256 = _mm256_loadu_si256((__m256i *)(A + (i*lda + k) / 8)); - __m256i b_bit256 = _mm256_loadu_si256((__m256i *)(B + (j*ldb + k) / 8)); - __m256i xor256 = _mm256_xor_si256(a_bit256, b_bit256); - __m256i c_bit256 = _mm256_andnot_si256(xor256, all_1); //we can do NOT for wegihts once and do not do this NOT - int tmp_count = popcnt256(c_bit256); - //int tmp_count = popcnt256_custom(c_bit256); - count += tmp_count; - //binary_int64_printf(c_bit64); - //printf(", count = %d \n\n", tmp_count); - } + // count of 1 bits + count = count_sum.m256i_i64[0] + + count_sum.m256i_i64[1] + + count_sum.m256i_i64[2] + + count_sum.m256i_i64[3]; - int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); - count = count - f1; // remove extra bits - } + int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); + count = count - f1; // remove extra bits (from empty space for align only) C[i*ldc + j] = (2 * count - K) * mean_val; } @@ -592,6 +521,142 @@ void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, } +static inline float im2col_get_pixel(float *im, int height, int width, int channels, + int row, int col, int channel, int pad) +{ + row -= pad; + col -= pad; + + if (row < 0 || col < 0 || + row >= height || col >= width) return 0; + return im[col + width*(row + height*channel)]; +} + +//From Berkeley Vision's Caffe! +//https://github.com/BVLC/caffe/blob/master/LICENSE +void im2col_cpu_custom(float* data_im, + int channels, int height, int width, + int ksize, int stride, int pad, float* data_col) +{ + + int c, h, w; + int height_col = (height + 2 * pad - ksize) / stride + 1; + int width_col = (width + 2 * pad - ksize) / stride + 1; + int channels_col = channels * ksize * ksize; + + // optimized version + if (height_col == height && width_col == width && stride == 1 && pad == 1) + { + #pragma omp parallel for + for (c = 0; c < channels_col; ++c) { + int w_offset = c % ksize; + int h_offset = (c / ksize) % ksize; + int c_im = c / ksize / ksize; + for (h = pad; h < height_col-pad; ++h) { + for (w = pad; w < width_col-pad-8; w += 8) { + int im_row = h_offset + h - pad; + int im_col = w_offset + w - pad; + int col_index = (c * height_col + h) * width_col + w; + + //data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; + __m256 src256 = _mm256_loadu_ps((__m256i *)(&data_im[im_col + width*(im_row + height*c_im)])); + _mm256_storeu_ps(&data_col[col_index], src256); + } + + for (; w < width_col - pad; ++w) { + int im_row = h_offset + h - pad; + int im_col = w_offset + w - pad; + int col_index = (c * height_col + h) * width_col + w; + + data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; + } + } + + { + w = 0; + for (h = 0; h < height_col; ++h) { + int im_row = h_offset + h; + int im_col = w_offset + w; + int col_index = (c * height_col + h) * width_col + w; + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, + im_row, im_col, c_im, pad); + } + } + + { + w = width_col-1; + for (h = 0; h < height_col; ++h) { + int im_row = h_offset + h; + int im_col = w_offset + w; + int col_index = (c * height_col + h) * width_col + w; + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, + im_row, im_col, c_im, pad); + } + } + + { + h = 0; + for (w = 0; w < width_col; ++w) { + int im_row = h_offset + h; + int im_col = w_offset + w; + int col_index = (c * height_col + h) * width_col + w; + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, + im_row, im_col, c_im, pad); + } + } + + { + h = height_col-1; + for (w = 0; w < width_col; ++w) { + int im_row = h_offset + h; + int im_col = w_offset + w; + int col_index = (c * height_col + h) * width_col + w; + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, + im_row, im_col, c_im, pad); + } + } + } + + } + else { + //printf("\n Error: is no non-optimized version \n"); + im2col_cpu(data_im, channels, height, width, ksize, stride, pad, data_col); + } +} + +void activate_array_cpu_custom(float *x, const int n, const ACTIVATION a) +{ + int i; + if (a == LINEAR) + {} + else if (a == LEAKY) + { + __m256i all256_sing1 = _mm256_set_epi32(0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000); + __m256 all256_01 = _mm256_set1_ps(0.1F); + + for (i = 0; i < n; i += 8) { + //x[i] = (x[i]>0) ? x[i] : .1*x[i]; + + __m256 src256 = _mm256_loadu_ps((__m256 *)(&x[i])); + __m256 mult256 = _mm256_mul_ps((src256), all256_01); // mult * 0.1 + + __m256i sign256 = _mm256_and_si256(_mm256_castps_si256(src256), all256_sing1); // check sign in 8 x 32-bit floats + + __m256 result256 = _mm256_blendv_ps(src256, mult256, _mm256_castsi256_ps(sign256)); // (sign>0) ? src : mult; + _mm256_storeu_ps((__m256 *)(&x[i]), result256); + } + + for (; i < n; ++i) { + x[i] = (x[i]>0) ? x[i] : .1*x[i]; + } + } + else { + for (i = 0; i < n; ++i) { + x[i] = activate(x[i], a); + } + } +} + void float_to_bit(float *src, unsigned char *dst, size_t size) { size_t dst_size = size / 8 + 1; @@ -612,6 +677,56 @@ void float_to_bit(float *src, unsigned char *dst, size_t size) } } +static inline void transpose4x4_SSE(float *A, float *B, const int lda, const int ldb) +{ + __m128 row1 = _mm_load_ps(&A[0 * lda]); + __m128 row2 = _mm_load_ps(&A[1 * lda]); + __m128 row3 = _mm_load_ps(&A[2 * lda]); + __m128 row4 = _mm_load_ps(&A[3 * lda]); + _MM_TRANSPOSE4_PS(row1, row2, row3, row4); + _mm_store_ps(&B[0 * ldb], row1); + _mm_store_ps(&B[1 * ldb], row2); + _mm_store_ps(&B[2 * ldb], row3); + _mm_store_ps(&B[3 * ldb], row4); +} + +void transpose_block_SSE4x4(float *A, float *B, const int n, const int m, + const int lda, const int ldb, const int block_size) +{ + int i; + if (block_size % 4 == 0) { + #pragma omp parallel for + for (i = 0; i < n; i += block_size) { + int j, i2, j2; + for (j = 0; j < m; j += block_size) { + int max_i2 = i + block_size < n ? i + block_size : n; + int max_j2 = j + block_size < m ? j + block_size : m; + for (i2 = i; i2 < max_i2; i2 += 4) { + for (j2 = j; j2 < max_j2; j2 += 4) { + transpose4x4_SSE(&A[i2*lda + j2], &B[j2*ldb + i2], lda, ldb); + } + } + } + } + } + else { + #pragma omp parallel for + for (i = 0; i < n; i += block_size) { + int j, i2, j2; + for (j = 0; j < m; j += block_size) { + int max_i2 = i + block_size < n ? i + block_size : n; + int max_j2 = j + block_size < m ? j + block_size : m; + for (i2 = i; i2 < max_i2; ++i2) { + for (j2 = j; j2 < max_j2; ++j2) { + B[j2*ldb + i2] = A[i2*lda + j2]; + } + } + } + } + } +} + + #else void gemm_nn(int M, int N, int K, float ALPHA, @@ -666,6 +781,115 @@ void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, } } +//From Berkeley Vision's Caffe! +//https://github.com/BVLC/caffe/blob/master/LICENSE +void im2col_cpu_custom(float* data_im, + int channels, int height, int width, + int ksize, int stride, int pad, float* data_col) +{ + + int c, h, w; + int height_col = (height + 2 * pad - ksize) / stride + 1; + int width_col = (width + 2 * pad - ksize) / stride + 1; + int channels_col = channels * ksize * ksize; + + // optimized version + if (height_col == height && width_col == width && stride == 1 && pad == 1) + { + #pragma omp parallel for + for (c = 0; c < channels_col; ++c) { + int w_offset = c % ksize; + int h_offset = (c / ksize) % ksize; + int c_im = c / ksize / ksize; + for (h = pad; h < height_col - pad; ++h) { + for (w = pad; w < width_col - pad; ++w) { + int im_row = h_offset + h - pad; + int im_col = w_offset + w - pad; + int col_index = (c * height_col + h) * width_col + w; + + data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; + } + + for (; w < width_col - pad; ++w) { + int im_row = h_offset + h - pad; + int im_col = w_offset + w - pad; + int col_index = (c * height_col + h) * width_col + w; + + data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; + } +} + + { + w = 0; + for (h = 0; h < height_col; ++h) { + int im_row = h_offset + h; + int im_col = w_offset + w; + int col_index = (c * height_col + h) * width_col + w; + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, + im_row, im_col, c_im, pad); + } + } + + { + w = width_col - 1; + for (h = 0; h < height_col; ++h) { + int im_row = h_offset + h; + int im_col = w_offset + w; + int col_index = (c * height_col + h) * width_col + w; + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, + im_row, im_col, c_im, pad); + } + } + + { + h = 0; + for (w = 0; w < width_col; ++w) { + int im_row = h_offset + h; + int im_col = w_offset + w; + int col_index = (c * height_col + h) * width_col + w; + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, + im_row, im_col, c_im, pad); + } + } + + { + h = height_col - 1; + for (w = 0; w < width_col; ++w) { + int im_row = h_offset + h; + int im_col = w_offset + w; + int col_index = (c * height_col + h) * width_col + w; + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, + im_row, im_col, c_im, pad); + } + } + } + + } + else { + //printf("\n Error: is no non-optimized version \n"); + im2col_cpu(data_im, channels, height, width, ksize, stride, pad, data_col); + } +} + +void activate_array_cpu_custom(float *x, const int n, const ACTIVATION a) +{ + int i; + if (a == LINEAR) + { + } + else if (a == LEAKY) + { + for (i = 0; i < n; ++i) { + x[i] = (x[i]>0) ? x[i] : .1*x[i]; + } + } + else { + for (i = 0; i < n; ++i) { + x[i] = activate(x[i], a); + } + } +} + void float_to_bit(float *src, unsigned char *dst, size_t size) { size_t dst_size = size / 8 + 1; @@ -695,6 +919,36 @@ void float_to_bit(float *src, unsigned char *dst, size_t size) } free(byte_arr); } + +static inline void transpose_scalar_block(float *A, float *B, const int lda, const int ldb, const int block_size) +{ + int i, j; + //#pragma omp parallel for + for (i = 0; ixnor) { //printf("\n %d \n", j); size_t ldb_align = 256; // 256bit for AVX2 - if (l->size*l->size*l->c > 4096) ldb_align = 4096; - binary_transpose_align_weights(l, ldb_align); + binary_align_weights(l, ldb_align); } } } From 3baf534a2d603f6b20a06ca45c29350e52a859fb Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 9 Aug 2018 03:07:06 +0300 Subject: [PATCH 0119/1365] Compile error fix --- src/gemm.c | 2427 ++++++++++++++++++++++++++-------------------------- src/gemm.h | 1 + 2 files changed, 1218 insertions(+), 1210 deletions(-) diff --git a/src/gemm.c b/src/gemm.c index 478e9663fa1..ff5edfa90b2 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -1,1210 +1,1217 @@ -#include "gemm.h" -#include "utils.h" -#include "im2col.h" -#include "cuda.h" -#include -#include -#include - -void gemm_bin(int M, int N, int K, float ALPHA, - char *A, int lda, - float *B, int ldb, - float *C, int ldc) -{ - int i,j,k; - for(i = 0; i < M; ++i){ - for(k = 0; k < K; ++k){ - char A_PART = A[i*lda+k]; - if(A_PART){ - for(j = 0; j < N; ++j){ - C[i*ldc+j] += B[k*ldb+j]; - } - } else { - for(j = 0; j < N; ++j){ - C[i*ldc+j] -= B[k*ldb+j]; - } - } - } - } -} - -float *random_matrix(int rows, int cols) -{ - int i; - float *m = calloc(rows*cols, sizeof(float)); - for(i = 0; i < rows*cols; ++i){ - m[i] = (float)rand()/RAND_MAX; - } - return m; -} - -void time_random_matrix(int TA, int TB, int m, int k, int n) -{ - float *a; - if(!TA) a = random_matrix(m,k); - else a = random_matrix(k,m); - int lda = (!TA)?k:m; - float *b; - if(!TB) b = random_matrix(k,n); - else b = random_matrix(n,k); - int ldb = (!TB)?n:k; - - float *c = random_matrix(m,n); - int i; - clock_t start = clock(), end; - for(i = 0; i<10; ++i){ - gemm_cpu(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n); - } - end = clock(); - printf("Matrix Multiplication %dx%d * %dx%d, TA=%d, TB=%d: %lf ms\n",m,k,k,n, TA, TB, (float)(end-start)/CLOCKS_PER_SEC); - free(a); - free(b); - free(c); -} - - -void gemm(int TA, int TB, int M, int N, int K, float ALPHA, - float *A, int lda, - float *B, int ldb, - float BETA, - float *C, int ldc) -{ - gemm_cpu( TA, TB, M, N, K, ALPHA,A,lda, B, ldb,BETA,C,ldc); -} - - -//-------------------------------------------- -// XNOR bitwise GEMM for binary neural network -//-------------------------------------------- - -#include - -static inline unsigned char xnor(unsigned char a, unsigned char b) { - //return a == b; - return !(a^b); -} - -// INT-32 -static inline uint32_t get_bit_int32(uint32_t const*const src, size_t index) { - size_t src_i = index / 32; - int src_shift = index % 32; - unsigned char val = (src[src_i] & (1 << src_shift)) > 0; - return val; -} - -static inline uint32_t xnor_int32(uint32_t a, uint32_t b) { - return ~(a^b); -} - -static inline uint64_t xnor_int64(uint64_t a, uint64_t b) { - return ~(a^b); -} - - -static inline uint32_t fill_bit_int32(char src) { - if (src == 0) return 0x00000000; - else return 0xFFFFFFFF; -} - -static inline uint64_t fill_bit_int64(char src) { - if (src == 0) return 0x0000000000000000; - else return 0xFFFFFFFFFFFFFFFF; -} - -void binary_int32_printf(uint32_t src) { - int i; - for (i = 0; i < 32; ++i) { - if (src & 1) printf("1"); - else printf("0"); - src = src >> 1; - } - printf("\n"); -} - -void binary_int64_printf(uint64_t src) { - int i; - for (i = 0; i < 64; ++i) { - if (src & 1) printf("1"); - else printf("0"); - src = src >> 1; - } - printf("\n"); -} - -/* -void gemm_nn_custom_bin_mean(int M, int N, int K, float ALPHA_UNUSED, - unsigned char *A, int lda, - unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr) -{ - int *count_arr = calloc(M*N, sizeof(int)); - - int i, j, k; - for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] - for (k = 0; k < K; ++k) { // l.size*l.size*l.c - one filter size [27 - 9216] - char a_bit = get_bit(A, i*lda + k); - - for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] - char b_bit = get_bit(B, k*ldb + j); - count_arr[i*ldc + j] += xnor(a_bit, b_bit); - } - } - } - - for (i = 0; i < M; ++i) { - float mean_val = mean_arr[i]; - for (j = 0; j < N; ++j) { - C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val; - } - } - free(count_arr); -} -*/ - -/* -void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, - unsigned char *A, int lda, - unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr) -{ - int *count_arr = calloc(M*N, sizeof(int)); - - int i, j, k; - for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] - for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] - for (k = 0; k < K; ++k) { // l.size*l.size*l.c - one filter size [27 - 9216] - char a_bit = get_bit(A, i*lda + k); - char b_bit = get_bit(B, j*ldb + k); - count_arr[i*ldc + j] += xnor(a_bit, b_bit); - } - } - } - - for (i = 0; i < M; ++i) { - float mean_val = mean_arr[i]; - for (j = 0; j < N; ++j) { - C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val; - } - } - free(count_arr); -} -*/ - -/* -void gemm_nn_custom_bin_mean(int M, int N, int K, float ALPHA_UNUSED, - unsigned char *A, int lda, - unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr) -{ - int *count_arr = calloc(M*N, sizeof(int)); - - int i, j, k, h; - -#pragma omp parallel for - for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] - for (k = 0; k < K; ++k) { // l.size*l.size*l.c - one filter size [27 - 9216] - const char a_bit = get_bit(A, i*lda + k); - uint64_t a_bit64 = fill_bit_int64(a_bit); - int k_ldb = k*ldb; - - for (j = 0; j < N; j += 64) { // out_h*out_w - one channel output size [169 - 173056] - if ((N - j > 64) && (k_ldb % 8 == 0)) { - uint64_t b_bit64 = *((uint64_t *)(B + (k_ldb + j) / 8)); - uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); - //printf("\n %d \n",__builtin_popcountll(c_bit64)); // gcc - printf("\n %d \n", __popcnt64(c_bit64)); // msvs - - int h; - for (h = 0; h < 64; ++h) - if ((c_bit64 >> h) & 1) count_arr[i*ldc + j + h] += 1; - - //binary_int64_printf(a_bit64); - //binary_int64_printf(b_bit64); - //binary_int64_printf(c_bit64); - } - else { - for (; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] - char b_bit = get_bit(B, k_ldb + j); - if (xnor(a_bit, b_bit)) count_arr[i*ldc + j] += 1; - } - } - - } - } - } - - if (mean_arr) { - //int K_2 = K / 2; - for (i = 0; i < M; ++i) { - float mean_val = mean_arr[i]; - //float mean_val2 = 2 * mean_val; - for (j = 0; j < N; ++j) { - C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val; - //C[i*ldc + j] = (count_arr[i*ldc + j] - K_2) *mean_val2; - } - } - } - else { - for (i = 0; i < M; ++i) { - for (j = 0; j < N; ++j) { - C[i*ldc + j] = count_arr[i*ldc + j] - K / 2; - } - } - } - - free(count_arr); - - //getchar(); -} -*/ - - -/* -void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, - unsigned char *A, int lda, - unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr) -{ - int i, j, k, h; - -#pragma omp parallel for - for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] - float mean_val = mean_arr[i]; - - for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] - int count = 0; - - for (k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] - uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); - uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); - uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); - -#ifdef WIN32 - int tmp_count = __popcnt64(c_bit64); -#else - int tmp_count = __builtin_popcountll(c_bit64); -#endif - - if (K - k < 64) tmp_count = tmp_count - (64 - (K - k)); // remove extra bits - count += tmp_count; - //binary_int64_printf(c_bit64); - //printf(", count = %d \n\n", tmp_count); - } - - C[i*ldc + j] = (2 * count - K) * mean_val; - } - } -} -*/ - -//---------------------------- - - -#if (defined(__AVX__) && defined(__x86_64__)) || defined(_WIN64) - -#define OSXSAVEFlag (1UL<<27) -#define AVXFlag ((1UL<<28)|OSXSAVEFlag) -#define FMAFlag ((1UL<<12)|AVXFlag|OSXSAVEFlag) -#define CLMULFlag ((1UL<< 1)|AVXFlag|OSXSAVEFlag) -#define VAESFlag ((1UL<<25)|AVXFlag|OSXSAVEFlag) - -#ifdef _WIN64 -#include -#include -#include -#include - -#else // Linux GCC/Clang -#include -#include -#include -#include -#include - -void asm_cpuid(uint32_t* abcd, uint32_t eax) -{ - uint32_t ebx = 0, edx = 0, ecx = 0; - - // EBX is saved to EDI and later restored - __asm__("movl %%ebx, %%edi;" - "cpuid;" - "xchgl %%ebx, %%edi;" - : "=D"(ebx), - "+a"(eax), "+c"(ecx), "=d"(edx)); - - abcd[0] = eax; - abcd[1] = ebx; - abcd[2] = ecx; - abcd[3] = edx; -} - -#endif - -int simd_detect_x86(unsigned int idFeature) -{ - uint32_t regs[4]; // EAX, EBX, ECX, EDX; -#ifdef _WIN32 - __cpuid(regs, 0); - if (regs[0] > 1U) __cpuid(regs, 1); -#else - __get_cpuid(0, ®s[0], ®s[1], ®s[2], ®s[3]); - if(regs[0] > 1U) __get_cpuid(1, ®s[0], ®s[1], ®s[2], ®s[3]); -#endif - - if ((regs[2] & idFeature) != idFeature) - return 0; - return 1; -} - -int is_fma_avx() { - static int result = -1; - if (result == -1) { - result = simd_detect_x86(AVXFlag); - if (result == 1) printf(" Used AVX \n"); - else printf(" Not used AVX \n"); - } - return result; -} - -// https://software.intel.com/sites/landingpage/IntrinsicsGuide -void gemm_nn(int M, int N, int K, float ALPHA, - float *A, int lda, - float *B, int ldb, - float *C, int ldc) -{ - int i, j, k; - if (is_fma_avx() == 1) { // AVX - for (i = 0; i < M; ++i) { - for (k = 0; k < K; ++k) { - float A_PART = ALPHA*A[i*lda + k]; - __m256 a256, b256, c256, result256; // AVX - a256 = _mm256_set1_ps(A_PART); - for (j = 0; j < N - 8; j += 8) { - b256 = _mm256_loadu_ps(&B[k*ldb + j]); - c256 = _mm256_loadu_ps(&C[i*ldc + j]); - // FMA - Intel Haswell (2013), AMD Piledriver (2012) - //result256 = _mm256_fmadd_ps(a256, b256, c256); - result256 = _mm256_mul_ps(a256, b256); - result256 = _mm256_add_ps(result256, c256); - _mm256_storeu_ps(&C[i*ldc + j], result256); - } - - int prev_end = (N % 8 == 0) ? (N - 8) : (N / 8) * 8; - for (j = prev_end; j < N; ++j) - C[i*ldc + j] += A_PART*B[k*ldb + j]; - } - } - } - else { - for (i = 0; i < M; ++i) { - for (k = 0; k < K; ++k) { - register float A_PART = ALPHA*A[i*lda + k]; - for (j = 0; j < N; ++j) { - C[i*ldc + j] += A_PART*B[k*ldb + j]; - } - /* // SSE - __m128 a128, b128, c128, result128; // SSE - a128 = _mm_set1_ps(A_PART); - for (j = 0; j < N - 4; j += 4) { - b128 = _mm_loadu_ps(&B[k*ldb + j]); - c128 = _mm_loadu_ps(&C[i*ldc + j]); - //result128 = _mm_fmadd_ps(a128, b128, c128); - result128 = _mm_mul_ps(a128, b128); - result128 = _mm_add_ps(result128, c128); - _mm_storeu_ps(&C[i*ldc + j], result128); - } - - int prev_end = (N % 4 == 0) ? (N - 4) : (N / 4) * 4; - for (j = prev_end; j < N; ++j){ - C[i*ldc + j] += A_PART*B[k*ldb + j]; - } - */ - } - } - } -} - - -// http://graphics.stanford.edu/~seander/bithacks.html -// https://stackoverflow.com/questions/17354971/fast-counting-the-number-of-set-bits-in-m128i-register -// https://arxiv.org/pdf/1611.07612.pdf - -static inline int popcnt128(__m128i n) { - const __m128i n_hi = _mm_unpackhi_epi64(n, n); -#ifdef _MSC_VER - return __popcnt64(_mm_cvtsi128_si64(n)) + __popcnt64(_mm_cvtsi128_si64(n_hi)); -#else - return __popcntq(_mm_cvtsi128_si64(n)) + __popcntq(_mm_cvtsi128_si64(n_hi)); -#endif -} - -static inline int popcnt256(__m256i n) { - return popcnt128(_mm256_extractf128_si256(n, 0)) + popcnt128(_mm256_extractf128_si256(n, 1)); -} - -static inline __m256i count256(__m256i v) { - __m256i lookup = - _mm256_setr_epi8(0, 1, 1, 2, 1, 2, 2, 3, 1, 2, - 2, 3, 2, 3, 3, 4, 0, 1, 1, 2, 1, 2, 2, 3, - 1, 2, 2, 3, 2, 3, 3, 4); - - __m256i low_mask = _mm256_set1_epi8(0x0f); - - __m256i lo = _mm256_and_si256(v, low_mask); - __m256i hi = _mm256_and_si256(_mm256_srli_epi32(v, 4), low_mask); - __m256i popcnt1 = _mm256_shuffle_epi8(lookup, lo); - __m256i popcnt2 = _mm256_shuffle_epi8(lookup, hi); - __m256i total = _mm256_add_epi8(popcnt1, popcnt2); - - return _mm256_sad_epu8(total, _mm256_setzero_si256()); -} -static inline int popcnt256_custom(__m256i n) { - __m256i val = count256(n); - - return val.m256i_i64[0] + - val.m256i_i64[1] + - val.m256i_i64[2] + - val.m256i_i64[3]; -} - -void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, - unsigned char *A, int lda, - unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr) -{ - int i; - - static int max_num_threads = 0; - if (max_num_threads == 0) { - max_num_threads = omp_get_max_threads(); - omp_set_num_threads(max_num_threads / 2); - } - - #pragma omp parallel for - for (i = 0; i < M; ++i) - { // l.n - filters [16 - 55 - 1024] - float mean_val = mean_arr[i]; - int j, k; - __m256i all_1 = _mm256_set1_epi8(255); - - for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] - int count = 0; - const int bit_step = 256; - __m256i count_sum = _mm256_set1_epi8(0); - - for (k = 0; k < K; k += bit_step) { // l.size*l.size*l.c - one filter size [27 - 9216] - __m256i a_bit256 = _mm256_loadu_si256((__m256i *)(A + (i*lda + k) / 8)); - __m256i b_bit256 = _mm256_loadu_si256((__m256i *)(B + (j*ldb + k) / 8)); - __m256i xor256 = _mm256_xor_si256(a_bit256, b_bit256); // xnor = not(xor(a,b)) - __m256i c_bit256 = _mm256_andnot_si256(xor256, all_1); // can be optimized - we can do other NOT for wegihts once and do not do this NOT - - count_sum = _mm256_add_epi64(count256(c_bit256), count_sum); // Mula’s algorithm - - //count += popcnt256(c_bit256); - - //binary_int64_printf(c_bit64); - //printf(", count = %d \n\n", tmp_count); - } - - // count of 1 bits - count = count_sum.m256i_i64[0] + - count_sum.m256i_i64[1] + - count_sum.m256i_i64[2] + - count_sum.m256i_i64[3]; - - int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); - count = count - f1; // remove extra bits (from empty space for align only) - - C[i*ldc + j] = (2 * count - K) * mean_val; - } - } -} - - -static inline float im2col_get_pixel(float *im, int height, int width, int channels, - int row, int col, int channel, int pad) -{ - row -= pad; - col -= pad; - - if (row < 0 || col < 0 || - row >= height || col >= width) return 0; - return im[col + width*(row + height*channel)]; -} - -//From Berkeley Vision's Caffe! -//https://github.com/BVLC/caffe/blob/master/LICENSE -void im2col_cpu_custom(float* data_im, - int channels, int height, int width, - int ksize, int stride, int pad, float* data_col) -{ - - int c, h, w; - int height_col = (height + 2 * pad - ksize) / stride + 1; - int width_col = (width + 2 * pad - ksize) / stride + 1; - int channels_col = channels * ksize * ksize; - - // optimized version - if (height_col == height && width_col == width && stride == 1 && pad == 1) - { - #pragma omp parallel for - for (c = 0; c < channels_col; ++c) { - int w_offset = c % ksize; - int h_offset = (c / ksize) % ksize; - int c_im = c / ksize / ksize; - for (h = pad; h < height_col-pad; ++h) { - for (w = pad; w < width_col-pad-8; w += 8) { - int im_row = h_offset + h - pad; - int im_col = w_offset + w - pad; - int col_index = (c * height_col + h) * width_col + w; - - //data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; - __m256 src256 = _mm256_loadu_ps((__m256i *)(&data_im[im_col + width*(im_row + height*c_im)])); - _mm256_storeu_ps(&data_col[col_index], src256); - } - - for (; w < width_col - pad; ++w) { - int im_row = h_offset + h - pad; - int im_col = w_offset + w - pad; - int col_index = (c * height_col + h) * width_col + w; - - data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; - } - } - - { - w = 0; - for (h = 0; h < height_col; ++h) { - int im_row = h_offset + h; - int im_col = w_offset + w; - int col_index = (c * height_col + h) * width_col + w; - data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, - im_row, im_col, c_im, pad); - } - } - - { - w = width_col-1; - for (h = 0; h < height_col; ++h) { - int im_row = h_offset + h; - int im_col = w_offset + w; - int col_index = (c * height_col + h) * width_col + w; - data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, - im_row, im_col, c_im, pad); - } - } - - { - h = 0; - for (w = 0; w < width_col; ++w) { - int im_row = h_offset + h; - int im_col = w_offset + w; - int col_index = (c * height_col + h) * width_col + w; - data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, - im_row, im_col, c_im, pad); - } - } - - { - h = height_col-1; - for (w = 0; w < width_col; ++w) { - int im_row = h_offset + h; - int im_col = w_offset + w; - int col_index = (c * height_col + h) * width_col + w; - data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, - im_row, im_col, c_im, pad); - } - } - } - - } - else { - //printf("\n Error: is no non-optimized version \n"); - im2col_cpu(data_im, channels, height, width, ksize, stride, pad, data_col); - } -} - -void activate_array_cpu_custom(float *x, const int n, const ACTIVATION a) -{ - int i; - if (a == LINEAR) - {} - else if (a == LEAKY) - { - __m256i all256_sing1 = _mm256_set_epi32(0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000); - __m256 all256_01 = _mm256_set1_ps(0.1F); - - for (i = 0; i < n; i += 8) { - //x[i] = (x[i]>0) ? x[i] : .1*x[i]; - - __m256 src256 = _mm256_loadu_ps((__m256 *)(&x[i])); - __m256 mult256 = _mm256_mul_ps((src256), all256_01); // mult * 0.1 - - __m256i sign256 = _mm256_and_si256(_mm256_castps_si256(src256), all256_sing1); // check sign in 8 x 32-bit floats - - __m256 result256 = _mm256_blendv_ps(src256, mult256, _mm256_castsi256_ps(sign256)); // (sign>0) ? src : mult; - _mm256_storeu_ps((__m256 *)(&x[i]), result256); - } - - for (; i < n; ++i) { - x[i] = (x[i]>0) ? x[i] : .1*x[i]; - } - } - else { - for (i = 0; i < n; ++i) { - x[i] = activate(x[i], a); - } - } -} - -void float_to_bit(float *src, unsigned char *dst, size_t size) -{ - size_t dst_size = size / 8 + 1; - memset(dst, 0, dst_size); - - size_t i; - __m256i all256_sing1 = _mm256_set_epi32(0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000); - - for (i = 0; i < size; i+=8) - { - __m256i src256 = _mm256_loadu_si256((__m256i *)(&src[i])); - __m256i result256 = _mm256_and_si256(src256, all256_sing1); // check sign in 8 x 32-bit floats - - uint32_t mask = _mm256_movemask_ps(_mm256_castsi256_ps(result256)); // (val >= 0) ? 0 : 1 - mask = ~mask; // inverse mask, (val >= 0) ? 1 : 0 - - dst[i / 8] = mask; - } -} - -static inline void transpose4x4_SSE(float *A, float *B, const int lda, const int ldb) -{ - __m128 row1 = _mm_load_ps(&A[0 * lda]); - __m128 row2 = _mm_load_ps(&A[1 * lda]); - __m128 row3 = _mm_load_ps(&A[2 * lda]); - __m128 row4 = _mm_load_ps(&A[3 * lda]); - _MM_TRANSPOSE4_PS(row1, row2, row3, row4); - _mm_store_ps(&B[0 * ldb], row1); - _mm_store_ps(&B[1 * ldb], row2); - _mm_store_ps(&B[2 * ldb], row3); - _mm_store_ps(&B[3 * ldb], row4); -} - -void transpose_block_SSE4x4(float *A, float *B, const int n, const int m, - const int lda, const int ldb, const int block_size) -{ - int i; - if (block_size % 4 == 0) { - #pragma omp parallel for - for (i = 0; i < n; i += block_size) { - int j, i2, j2; - for (j = 0; j < m; j += block_size) { - int max_i2 = i + block_size < n ? i + block_size : n; - int max_j2 = j + block_size < m ? j + block_size : m; - for (i2 = i; i2 < max_i2; i2 += 4) { - for (j2 = j; j2 < max_j2; j2 += 4) { - transpose4x4_SSE(&A[i2*lda + j2], &B[j2*ldb + i2], lda, ldb); - } - } - } - } - } - else { - #pragma omp parallel for - for (i = 0; i < n; i += block_size) { - int j, i2, j2; - for (j = 0; j < m; j += block_size) { - int max_i2 = i + block_size < n ? i + block_size : n; - int max_j2 = j + block_size < m ? j + block_size : m; - for (i2 = i; i2 < max_i2; ++i2) { - for (j2 = j; j2 < max_j2; ++j2) { - B[j2*ldb + i2] = A[i2*lda + j2]; - } - } - } - } - } -} - - -#else - -void gemm_nn(int M, int N, int K, float ALPHA, - float *A, int lda, - float *B, int ldb, - float *C, int ldc) -{ - int i, j, k; - for (i = 0; i < M; ++i) { - for (k = 0; k < K; ++k) { - register float A_PART = ALPHA*A[i*lda + k]; - for (j = 0; j < N; ++j) { - C[i*ldc + j] += A_PART*B[k*ldb + j]; - } - } - } -} - -void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, - unsigned char *A, int lda, - unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr) -{ - int i, j, k, h; - -#pragma omp parallel for - for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] - float mean_val = mean_arr[i]; - - for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] - int count = 0; - - for (k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] - uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); - uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); - uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); - -#ifdef WIN32 - int tmp_count = __popcnt64(c_bit64); -#else - int tmp_count = __builtin_popcountll(c_bit64); -#endif - - if (K - k < 64) tmp_count = tmp_count - (64 - (K - k)); // remove extra bits - count += tmp_count; - //binary_int64_printf(c_bit64); - //printf(", count = %d \n\n", tmp_count); - } - - C[i*ldc + j] = (2 * count - K) * mean_val; - } - } -} - -//From Berkeley Vision's Caffe! -//https://github.com/BVLC/caffe/blob/master/LICENSE -void im2col_cpu_custom(float* data_im, - int channels, int height, int width, - int ksize, int stride, int pad, float* data_col) -{ - - int c, h, w; - int height_col = (height + 2 * pad - ksize) / stride + 1; - int width_col = (width + 2 * pad - ksize) / stride + 1; - int channels_col = channels * ksize * ksize; - - // optimized version - if (height_col == height && width_col == width && stride == 1 && pad == 1) - { - #pragma omp parallel for - for (c = 0; c < channels_col; ++c) { - int w_offset = c % ksize; - int h_offset = (c / ksize) % ksize; - int c_im = c / ksize / ksize; - for (h = pad; h < height_col - pad; ++h) { - for (w = pad; w < width_col - pad; ++w) { - int im_row = h_offset + h - pad; - int im_col = w_offset + w - pad; - int col_index = (c * height_col + h) * width_col + w; - - data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; - } - - for (; w < width_col - pad; ++w) { - int im_row = h_offset + h - pad; - int im_col = w_offset + w - pad; - int col_index = (c * height_col + h) * width_col + w; - - data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; - } -} - - { - w = 0; - for (h = 0; h < height_col; ++h) { - int im_row = h_offset + h; - int im_col = w_offset + w; - int col_index = (c * height_col + h) * width_col + w; - data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, - im_row, im_col, c_im, pad); - } - } - - { - w = width_col - 1; - for (h = 0; h < height_col; ++h) { - int im_row = h_offset + h; - int im_col = w_offset + w; - int col_index = (c * height_col + h) * width_col + w; - data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, - im_row, im_col, c_im, pad); - } - } - - { - h = 0; - for (w = 0; w < width_col; ++w) { - int im_row = h_offset + h; - int im_col = w_offset + w; - int col_index = (c * height_col + h) * width_col + w; - data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, - im_row, im_col, c_im, pad); - } - } - - { - h = height_col - 1; - for (w = 0; w < width_col; ++w) { - int im_row = h_offset + h; - int im_col = w_offset + w; - int col_index = (c * height_col + h) * width_col + w; - data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, - im_row, im_col, c_im, pad); - } - } - } - - } - else { - //printf("\n Error: is no non-optimized version \n"); - im2col_cpu(data_im, channels, height, width, ksize, stride, pad, data_col); - } -} - -void activate_array_cpu_custom(float *x, const int n, const ACTIVATION a) -{ - int i; - if (a == LINEAR) - { - } - else if (a == LEAKY) - { - for (i = 0; i < n; ++i) { - x[i] = (x[i]>0) ? x[i] : .1*x[i]; - } - } - else { - for (i = 0; i < n; ++i) { - x[i] = activate(x[i], a); - } - } -} - -void float_to_bit(float *src, unsigned char *dst, size_t size) -{ - size_t dst_size = size / 8 + 1; - memset(dst, 0, dst_size); - - size_t i; - char *byte_arr = calloc(size, sizeof(char)); - for (i = 0; i < size; ++i) { - if (src[i] > 0) byte_arr[i] = 1; - } - - //for (i = 0; i < size; ++i) { - // dst[i / 8] |= byte_arr[i] << (i % 8); - //} - - for (i = 0; i < size; i += 8) { - char dst_tmp = 0; - dst_tmp |= byte_arr[i + 0] << 0; - dst_tmp |= byte_arr[i + 1] << 1; - dst_tmp |= byte_arr[i + 2] << 2; - dst_tmp |= byte_arr[i + 3] << 3; - dst_tmp |= byte_arr[i + 4] << 4; - dst_tmp |= byte_arr[i + 5] << 5; - dst_tmp |= byte_arr[i + 6] << 6; - dst_tmp |= byte_arr[i + 7] << 7; - dst[i / 8] = dst_tmp; - } - free(byte_arr); -} - -static inline void transpose_scalar_block(float *A, float *B, const int lda, const int ldb, const int block_size) -{ - int i, j; - //#pragma omp parallel for - for (i = 0; i - -void gemm_ongpu(int TA, int TB, int M, int N, int K, float ALPHA, - float *A_gpu, int lda, - float *B_gpu, int ldb, - float BETA, - float *C_gpu, int ldc) -{ - cublasHandle_t handle = blas_handle(); - cudaError_t stream_status = cublasSetStream(handle, get_cuda_stream()); - cudaError_t status = cublasSgemm(handle, (TB ? CUBLAS_OP_T : CUBLAS_OP_N), - (TA ? CUBLAS_OP_T : CUBLAS_OP_N), N, M, K, &ALPHA, B_gpu, ldb, A_gpu, lda, &BETA, C_gpu, ldc); - check_error(status); -} - -void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA, - float *A, int lda, - float *B, int ldb, - float BETA, - float *C, int ldc) -{ - float *A_gpu = cuda_make_array(A, (TA ? lda*K:lda*M)); - float *B_gpu = cuda_make_array(B, (TB ? ldb*N : ldb*K)); - float *C_gpu = cuda_make_array(C, ldc*M); - - gemm_ongpu(TA, TB, M, N, K, ALPHA, A_gpu, lda, B_gpu, ldb, BETA, C_gpu, ldc); - - cuda_pull_array(C_gpu, C, ldc*M); - cuda_free(A_gpu); - cuda_free(B_gpu); - cuda_free(C_gpu); -} - -#include -#include -#include -#include - -void time_gpu_random_matrix(int TA, int TB, int m, int k, int n) -{ - float *a; - if(!TA) a = random_matrix(m,k); - else a = random_matrix(k,m); - int lda = (!TA)?k:m; - float *b; - if(!TB) b = random_matrix(k,n); - else b = random_matrix(n,k); - int ldb = (!TB)?n:k; - - float *c = random_matrix(m,n); - int i; - clock_t start = clock(), end; - for(i = 0; i<32; ++i){ - gemm_gpu(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n); - } - end = clock(); - printf("Matrix Multiplication %dx%d * %dx%d, TA=%d, TB=%d: %lf s\n",m,k,k,n, TA, TB, (float)(end-start)/CLOCKS_PER_SEC); - free(a); - free(b); - free(c); -} - -void time_ongpu(int TA, int TB, int m, int k, int n) -{ - int iter = 10; - float *a = random_matrix(m,k); - float *b = random_matrix(k,n); - - int lda = (!TA)?k:m; - int ldb = (!TB)?n:k; - - float *c = random_matrix(m,n); - - float *a_cl = cuda_make_array(a, m*k); - float *b_cl = cuda_make_array(b, k*n); - float *c_cl = cuda_make_array(c, m*n); - - int i; - clock_t start = clock(), end; - for(i = 0; i +#include +#include + +#if defined(_OPENMP) +#include +#endif + +void gemm_bin(int M, int N, int K, float ALPHA, + char *A, int lda, + float *B, int ldb, + float *C, int ldc) +{ + int i,j,k; + for(i = 0; i < M; ++i){ + for(k = 0; k < K; ++k){ + char A_PART = A[i*lda+k]; + if(A_PART){ + for(j = 0; j < N; ++j){ + C[i*ldc+j] += B[k*ldb+j]; + } + } else { + for(j = 0; j < N; ++j){ + C[i*ldc+j] -= B[k*ldb+j]; + } + } + } + } +} + +float *random_matrix(int rows, int cols) +{ + int i; + float *m = calloc(rows*cols, sizeof(float)); + for(i = 0; i < rows*cols; ++i){ + m[i] = (float)rand()/RAND_MAX; + } + return m; +} + +void time_random_matrix(int TA, int TB, int m, int k, int n) +{ + float *a; + if(!TA) a = random_matrix(m,k); + else a = random_matrix(k,m); + int lda = (!TA)?k:m; + float *b; + if(!TB) b = random_matrix(k,n); + else b = random_matrix(n,k); + int ldb = (!TB)?n:k; + + float *c = random_matrix(m,n); + int i; + clock_t start = clock(), end; + for(i = 0; i<10; ++i){ + gemm_cpu(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n); + } + end = clock(); + printf("Matrix Multiplication %dx%d * %dx%d, TA=%d, TB=%d: %lf ms\n",m,k,k,n, TA, TB, (float)(end-start)/CLOCKS_PER_SEC); + free(a); + free(b); + free(c); +} + + +void gemm(int TA, int TB, int M, int N, int K, float ALPHA, + float *A, int lda, + float *B, int ldb, + float BETA, + float *C, int ldc) +{ + gemm_cpu( TA, TB, M, N, K, ALPHA,A,lda, B, ldb,BETA,C,ldc); +} + + +//-------------------------------------------- +// XNOR bitwise GEMM for binary neural network +//-------------------------------------------- + +#include + +static inline unsigned char xnor(unsigned char a, unsigned char b) { + //return a == b; + return !(a^b); +} + +// INT-32 +static inline uint32_t get_bit_int32(uint32_t const*const src, size_t index) { + size_t src_i = index / 32; + int src_shift = index % 32; + unsigned char val = (src[src_i] & (1 << src_shift)) > 0; + return val; +} + +static inline uint32_t xnor_int32(uint32_t a, uint32_t b) { + return ~(a^b); +} + +static inline uint64_t xnor_int64(uint64_t a, uint64_t b) { + return ~(a^b); +} + + +static inline uint32_t fill_bit_int32(char src) { + if (src == 0) return 0x00000000; + else return 0xFFFFFFFF; +} + +static inline uint64_t fill_bit_int64(char src) { + if (src == 0) return 0x0000000000000000; + else return 0xFFFFFFFFFFFFFFFF; +} + +void binary_int32_printf(uint32_t src) { + int i; + for (i = 0; i < 32; ++i) { + if (src & 1) printf("1"); + else printf("0"); + src = src >> 1; + } + printf("\n"); +} + +void binary_int64_printf(uint64_t src) { + int i; + for (i = 0; i < 64; ++i) { + if (src & 1) printf("1"); + else printf("0"); + src = src >> 1; + } + printf("\n"); +} + +/* +void gemm_nn_custom_bin_mean(int M, int N, int K, float ALPHA_UNUSED, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + int *count_arr = calloc(M*N, sizeof(int)); + + int i, j, k; + for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] + for (k = 0; k < K; ++k) { // l.size*l.size*l.c - one filter size [27 - 9216] + char a_bit = get_bit(A, i*lda + k); + + for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] + char b_bit = get_bit(B, k*ldb + j); + count_arr[i*ldc + j] += xnor(a_bit, b_bit); + } + } + } + + for (i = 0; i < M; ++i) { + float mean_val = mean_arr[i]; + for (j = 0; j < N; ++j) { + C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val; + } + } + free(count_arr); +} +*/ + +/* +void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + int *count_arr = calloc(M*N, sizeof(int)); + + int i, j, k; + for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] + for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] + for (k = 0; k < K; ++k) { // l.size*l.size*l.c - one filter size [27 - 9216] + char a_bit = get_bit(A, i*lda + k); + char b_bit = get_bit(B, j*ldb + k); + count_arr[i*ldc + j] += xnor(a_bit, b_bit); + } + } + } + + for (i = 0; i < M; ++i) { + float mean_val = mean_arr[i]; + for (j = 0; j < N; ++j) { + C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val; + } + } + free(count_arr); +} +*/ + +/* +void gemm_nn_custom_bin_mean(int M, int N, int K, float ALPHA_UNUSED, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + int *count_arr = calloc(M*N, sizeof(int)); + + int i, j, k, h; + +#pragma omp parallel for + for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] + for (k = 0; k < K; ++k) { // l.size*l.size*l.c - one filter size [27 - 9216] + const char a_bit = get_bit(A, i*lda + k); + uint64_t a_bit64 = fill_bit_int64(a_bit); + int k_ldb = k*ldb; + + for (j = 0; j < N; j += 64) { // out_h*out_w - one channel output size [169 - 173056] + if ((N - j > 64) && (k_ldb % 8 == 0)) { + uint64_t b_bit64 = *((uint64_t *)(B + (k_ldb + j) / 8)); + uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); + //printf("\n %d \n",__builtin_popcountll(c_bit64)); // gcc + printf("\n %d \n", __popcnt64(c_bit64)); // msvs + + int h; + for (h = 0; h < 64; ++h) + if ((c_bit64 >> h) & 1) count_arr[i*ldc + j + h] += 1; + + //binary_int64_printf(a_bit64); + //binary_int64_printf(b_bit64); + //binary_int64_printf(c_bit64); + } + else { + for (; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] + char b_bit = get_bit(B, k_ldb + j); + if (xnor(a_bit, b_bit)) count_arr[i*ldc + j] += 1; + } + } + + } + } + } + + if (mean_arr) { + //int K_2 = K / 2; + for (i = 0; i < M; ++i) { + float mean_val = mean_arr[i]; + //float mean_val2 = 2 * mean_val; + for (j = 0; j < N; ++j) { + C[i*ldc + j] = (2 * count_arr[i*ldc + j] - K) * mean_val; + //C[i*ldc + j] = (count_arr[i*ldc + j] - K_2) *mean_val2; + } + } + } + else { + for (i = 0; i < M; ++i) { + for (j = 0; j < N; ++j) { + C[i*ldc + j] = count_arr[i*ldc + j] - K / 2; + } + } + } + + free(count_arr); + + //getchar(); +} +*/ + + +/* +void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + int i, j, k, h; + +#pragma omp parallel for + for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] + float mean_val = mean_arr[i]; + + for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] + int count = 0; + + for (k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] + uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); + uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); + uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); + +#ifdef WIN32 + int tmp_count = __popcnt64(c_bit64); +#else + int tmp_count = __builtin_popcountll(c_bit64); +#endif + + if (K - k < 64) tmp_count = tmp_count - (64 - (K - k)); // remove extra bits + count += tmp_count; + //binary_int64_printf(c_bit64); + //printf(", count = %d \n\n", tmp_count); + } + + C[i*ldc + j] = (2 * count - K) * mean_val; + } + } +} +*/ + +//---------------------------- + + +#if (defined(__AVX__) && defined(__x86_64__)) || defined(_WIN64) + +#define OSXSAVEFlag (1UL<<27) +#define AVXFlag ((1UL<<28)|OSXSAVEFlag) +#define FMAFlag ((1UL<<12)|AVXFlag|OSXSAVEFlag) +#define CLMULFlag ((1UL<< 1)|AVXFlag|OSXSAVEFlag) +#define VAESFlag ((1UL<<25)|AVXFlag|OSXSAVEFlag) + +#ifdef _WIN64 +#include +#include +#include +#include + +#else // Linux GCC/Clang +#include +#include +#include +#include +#include + +void asm_cpuid(uint32_t* abcd, uint32_t eax) +{ + uint32_t ebx = 0, edx = 0, ecx = 0; + + // EBX is saved to EDI and later restored + __asm__("movl %%ebx, %%edi;" + "cpuid;" + "xchgl %%ebx, %%edi;" + : "=D"(ebx), + "+a"(eax), "+c"(ecx), "=d"(edx)); + + abcd[0] = eax; + abcd[1] = ebx; + abcd[2] = ecx; + abcd[3] = edx; +} + +#endif + +int simd_detect_x86(unsigned int idFeature) +{ + uint32_t regs[4]; // EAX, EBX, ECX, EDX; +#ifdef _WIN32 + __cpuid(regs, 0); + if (regs[0] > 1U) __cpuid(regs, 1); +#else + __get_cpuid(0, ®s[0], ®s[1], ®s[2], ®s[3]); + if(regs[0] > 1U) __get_cpuid(1, ®s[0], ®s[1], ®s[2], ®s[3]); +#endif + + if ((regs[2] & idFeature) != idFeature) + return 0; + return 1; +} + +int is_fma_avx() { + static int result = -1; + if (result == -1) { + result = simd_detect_x86(AVXFlag); + if (result == 1) printf(" Used AVX \n"); + else printf(" Not used AVX \n"); + } + return result; +} + +// https://software.intel.com/sites/landingpage/IntrinsicsGuide +void gemm_nn(int M, int N, int K, float ALPHA, + float *A, int lda, + float *B, int ldb, + float *C, int ldc) +{ + int i, j, k; + if (is_fma_avx() == 1) { // AVX + for (i = 0; i < M; ++i) { + for (k = 0; k < K; ++k) { + float A_PART = ALPHA*A[i*lda + k]; + __m256 a256, b256, c256, result256; // AVX + a256 = _mm256_set1_ps(A_PART); + for (j = 0; j < N - 8; j += 8) { + b256 = _mm256_loadu_ps(&B[k*ldb + j]); + c256 = _mm256_loadu_ps(&C[i*ldc + j]); + // FMA - Intel Haswell (2013), AMD Piledriver (2012) + //result256 = _mm256_fmadd_ps(a256, b256, c256); + result256 = _mm256_mul_ps(a256, b256); + result256 = _mm256_add_ps(result256, c256); + _mm256_storeu_ps(&C[i*ldc + j], result256); + } + + int prev_end = (N % 8 == 0) ? (N - 8) : (N / 8) * 8; + for (j = prev_end; j < N; ++j) + C[i*ldc + j] += A_PART*B[k*ldb + j]; + } + } + } + else { + for (i = 0; i < M; ++i) { + for (k = 0; k < K; ++k) { + register float A_PART = ALPHA*A[i*lda + k]; + for (j = 0; j < N; ++j) { + C[i*ldc + j] += A_PART*B[k*ldb + j]; + } + /* // SSE + __m128 a128, b128, c128, result128; // SSE + a128 = _mm_set1_ps(A_PART); + for (j = 0; j < N - 4; j += 4) { + b128 = _mm_loadu_ps(&B[k*ldb + j]); + c128 = _mm_loadu_ps(&C[i*ldc + j]); + //result128 = _mm_fmadd_ps(a128, b128, c128); + result128 = _mm_mul_ps(a128, b128); + result128 = _mm_add_ps(result128, c128); + _mm_storeu_ps(&C[i*ldc + j], result128); + } + + int prev_end = (N % 4 == 0) ? (N - 4) : (N / 4) * 4; + for (j = prev_end; j < N; ++j){ + C[i*ldc + j] += A_PART*B[k*ldb + j]; + } + */ + } + } + } +} + + +// http://graphics.stanford.edu/~seander/bithacks.html +// https://stackoverflow.com/questions/17354971/fast-counting-the-number-of-set-bits-in-m128i-register +// https://arxiv.org/pdf/1611.07612.pdf + +static inline int popcnt128(__m128i n) { + const __m128i n_hi = _mm_unpackhi_epi64(n, n); +#ifdef _MSC_VER + return __popcnt64(_mm_cvtsi128_si64(n)) + __popcnt64(_mm_cvtsi128_si64(n_hi)); +#else + return __popcntq(_mm_cvtsi128_si64(n)) + __popcntq(_mm_cvtsi128_si64(n_hi)); +#endif +} + +static inline int popcnt256(__m256i n) { + return popcnt128(_mm256_extractf128_si256(n, 0)) + popcnt128(_mm256_extractf128_si256(n, 1)); +} + +static inline __m256i count256(__m256i v) { + __m256i lookup = + _mm256_setr_epi8(0, 1, 1, 2, 1, 2, 2, 3, 1, 2, + 2, 3, 2, 3, 3, 4, 0, 1, 1, 2, 1, 2, 2, 3, + 1, 2, 2, 3, 2, 3, 3, 4); + + __m256i low_mask = _mm256_set1_epi8(0x0f); + + __m256i lo = _mm256_and_si256(v, low_mask); + __m256i hi = _mm256_and_si256(_mm256_srli_epi32(v, 4), low_mask); + __m256i popcnt1 = _mm256_shuffle_epi8(lookup, lo); + __m256i popcnt2 = _mm256_shuffle_epi8(lookup, hi); + __m256i total = _mm256_add_epi8(popcnt1, popcnt2); + + return _mm256_sad_epu8(total, _mm256_setzero_si256()); +} + +static inline int popcnt256_custom(__m256i n) { + __m256i val = count256(n); + + return val.m256i_i64[0] + + val.m256i_i64[1] + + val.m256i_i64[2] + + val.m256i_i64[3]; +} + +void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + int i; + +#if defined(_OPENMP) + static int max_num_threads = 0; + if (max_num_threads == 0) { + max_num_threads = omp_get_max_threads(); + omp_set_num_threads(max_num_threads / 2); + } +#endif + + #pragma omp parallel for + for (i = 0; i < M; ++i) + { // l.n - filters [16 - 55 - 1024] + float mean_val = mean_arr[i]; + int j, k; + __m256i all_1 = _mm256_set1_epi8(255); + + for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] + int count = 0; + const int bit_step = 256; + __m256i count_sum = _mm256_set1_epi8(0); + + for (k = 0; k < K; k += bit_step) { // l.size*l.size*l.c - one filter size [27 - 9216] + __m256i a_bit256 = _mm256_loadu_si256((__m256i *)(A + (i*lda + k) / 8)); + __m256i b_bit256 = _mm256_loadu_si256((__m256i *)(B + (j*ldb + k) / 8)); + __m256i xor256 = _mm256_xor_si256(a_bit256, b_bit256); // xnor = not(xor(a,b)) + __m256i c_bit256 = _mm256_andnot_si256(xor256, all_1); // can be optimized - we can do other NOT for wegihts once and do not do this NOT + + count_sum = _mm256_add_epi64(count256(c_bit256), count_sum); // Mula’s algorithm + + //count += popcnt256(c_bit256); + + //binary_int64_printf(c_bit64); + //printf(", count = %d \n\n", tmp_count); + } + + // count of 1 bits + count = count_sum.m256i_i64[0] + + count_sum.m256i_i64[1] + + count_sum.m256i_i64[2] + + count_sum.m256i_i64[3]; + + int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); + count = count - f1; // remove extra bits (from empty space for align only) + + C[i*ldc + j] = (2 * count - K) * mean_val; + } + } +} + + +static inline float im2col_get_pixel(float *im, int height, int width, int channels, + int row, int col, int channel, int pad) +{ + row -= pad; + col -= pad; + + if (row < 0 || col < 0 || + row >= height || col >= width) return 0; + return im[col + width*(row + height*channel)]; +} + +//From Berkeley Vision's Caffe! +//https://github.com/BVLC/caffe/blob/master/LICENSE +void im2col_cpu_custom(float* data_im, + int channels, int height, int width, + int ksize, int stride, int pad, float* data_col) +{ + + int c, h, w; + int height_col = (height + 2 * pad - ksize) / stride + 1; + int width_col = (width + 2 * pad - ksize) / stride + 1; + int channels_col = channels * ksize * ksize; + + // optimized version + if (height_col == height && width_col == width && stride == 1 && pad == 1) + { + #pragma omp parallel for + for (c = 0; c < channels_col; ++c) { + int w_offset = c % ksize; + int h_offset = (c / ksize) % ksize; + int c_im = c / ksize / ksize; + for (h = pad; h < height_col-pad; ++h) { + for (w = pad; w < width_col-pad-8; w += 8) { + int im_row = h_offset + h - pad; + int im_col = w_offset + w - pad; + int col_index = (c * height_col + h) * width_col + w; + + //data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; + __m256 src256 = _mm256_loadu_ps((__m256i *)(&data_im[im_col + width*(im_row + height*c_im)])); + _mm256_storeu_ps(&data_col[col_index], src256); + } + + for (; w < width_col - pad; ++w) { + int im_row = h_offset + h - pad; + int im_col = w_offset + w - pad; + int col_index = (c * height_col + h) * width_col + w; + + data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; + } + } + + { + w = 0; + for (h = 0; h < height_col; ++h) { + int im_row = h_offset + h; + int im_col = w_offset + w; + int col_index = (c * height_col + h) * width_col + w; + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, + im_row, im_col, c_im, pad); + } + } + + { + w = width_col-1; + for (h = 0; h < height_col; ++h) { + int im_row = h_offset + h; + int im_col = w_offset + w; + int col_index = (c * height_col + h) * width_col + w; + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, + im_row, im_col, c_im, pad); + } + } + + { + h = 0; + for (w = 0; w < width_col; ++w) { + int im_row = h_offset + h; + int im_col = w_offset + w; + int col_index = (c * height_col + h) * width_col + w; + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, + im_row, im_col, c_im, pad); + } + } + + { + h = height_col-1; + for (w = 0; w < width_col; ++w) { + int im_row = h_offset + h; + int im_col = w_offset + w; + int col_index = (c * height_col + h) * width_col + w; + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, + im_row, im_col, c_im, pad); + } + } + } + + } + else { + //printf("\n Error: is no non-optimized version \n"); + im2col_cpu(data_im, channels, height, width, ksize, stride, pad, data_col); + } +} + +void activate_array_cpu_custom(float *x, const int n, const ACTIVATION a) +{ + int i; + if (a == LINEAR) + {} + else if (a == LEAKY) + { + __m256i all256_sing1 = _mm256_set_epi32(0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000); + __m256 all256_01 = _mm256_set1_ps(0.1F); + + for (i = 0; i < n; i += 8) { + //x[i] = (x[i]>0) ? x[i] : .1*x[i]; + + __m256 src256 = _mm256_loadu_ps((__m256 *)(&x[i])); + __m256 mult256 = _mm256_mul_ps((src256), all256_01); // mult * 0.1 + + __m256i sign256 = _mm256_and_si256(_mm256_castps_si256(src256), all256_sing1); // check sign in 8 x 32-bit floats + + __m256 result256 = _mm256_blendv_ps(src256, mult256, _mm256_castsi256_ps(sign256)); // (sign>0) ? src : mult; + _mm256_storeu_ps((__m256 *)(&x[i]), result256); + } + + for (; i < n; ++i) { + x[i] = (x[i]>0) ? x[i] : .1*x[i]; + } + } + else { + for (i = 0; i < n; ++i) { + x[i] = activate(x[i], a); + } + } +} + +void float_to_bit(float *src, unsigned char *dst, size_t size) +{ + size_t dst_size = size / 8 + 1; + memset(dst, 0, dst_size); + + size_t i; + __m256i all256_sing1 = _mm256_set_epi32(0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000); + + for (i = 0; i < size; i+=8) + { + __m256i src256 = _mm256_loadu_si256((__m256i *)(&src[i])); + __m256i result256 = _mm256_and_si256(src256, all256_sing1); // check sign in 8 x 32-bit floats + + uint32_t mask = _mm256_movemask_ps(_mm256_castsi256_ps(result256)); // (val >= 0) ? 0 : 1 + mask = ~mask; // inverse mask, (val >= 0) ? 1 : 0 + + dst[i / 8] = mask; + } +} + +static inline void transpose4x4_SSE(float *A, float *B, const int lda, const int ldb) +{ + __m128 row1 = _mm_load_ps(&A[0 * lda]); + __m128 row2 = _mm_load_ps(&A[1 * lda]); + __m128 row3 = _mm_load_ps(&A[2 * lda]); + __m128 row4 = _mm_load_ps(&A[3 * lda]); + _MM_TRANSPOSE4_PS(row1, row2, row3, row4); + _mm_store_ps(&B[0 * ldb], row1); + _mm_store_ps(&B[1 * ldb], row2); + _mm_store_ps(&B[2 * ldb], row3); + _mm_store_ps(&B[3 * ldb], row4); +} + +void transpose_block_SSE4x4(float *A, float *B, const int n, const int m, + const int lda, const int ldb, const int block_size) +{ + int i; + if (block_size % 4 == 0) { + #pragma omp parallel for + for (i = 0; i < n; i += block_size) { + int j, i2, j2; + for (j = 0; j < m; j += block_size) { + int max_i2 = i + block_size < n ? i + block_size : n; + int max_j2 = j + block_size < m ? j + block_size : m; + for (i2 = i; i2 < max_i2; i2 += 4) { + for (j2 = j; j2 < max_j2; j2 += 4) { + transpose4x4_SSE(&A[i2*lda + j2], &B[j2*ldb + i2], lda, ldb); + } + } + } + } + } + else { + #pragma omp parallel for + for (i = 0; i < n; i += block_size) { + int j, i2, j2; + for (j = 0; j < m; j += block_size) { + int max_i2 = i + block_size < n ? i + block_size : n; + int max_j2 = j + block_size < m ? j + block_size : m; + for (i2 = i; i2 < max_i2; ++i2) { + for (j2 = j; j2 < max_j2; ++j2) { + B[j2*ldb + i2] = A[i2*lda + j2]; + } + } + } + } + } +} + + +#else + +void gemm_nn(int M, int N, int K, float ALPHA, + float *A, int lda, + float *B, int ldb, + float *C, int ldc) +{ + int i, j, k; + for (i = 0; i < M; ++i) { + for (k = 0; k < K; ++k) { + register float A_PART = ALPHA*A[i*lda + k]; + for (j = 0; j < N; ++j) { + C[i*ldc + j] += A_PART*B[k*ldb + j]; + } + } + } +} + +void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + int i, j, k, h; + +#pragma omp parallel for + for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] + float mean_val = mean_arr[i]; + + for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] + int count = 0; + + for (k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] + uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); + uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); + uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); + +#ifdef WIN32 + int tmp_count = __popcnt64(c_bit64); +#else + int tmp_count = __builtin_popcountll(c_bit64); +#endif + + if (K - k < 64) tmp_count = tmp_count - (64 - (K - k)); // remove extra bits + count += tmp_count; + //binary_int64_printf(c_bit64); + //printf(", count = %d \n\n", tmp_count); + } + + C[i*ldc + j] = (2 * count - K) * mean_val; + } + } +} + +//From Berkeley Vision's Caffe! +//https://github.com/BVLC/caffe/blob/master/LICENSE +void im2col_cpu_custom(float* data_im, + int channels, int height, int width, + int ksize, int stride, int pad, float* data_col) +{ + + int c, h, w; + int height_col = (height + 2 * pad - ksize) / stride + 1; + int width_col = (width + 2 * pad - ksize) / stride + 1; + int channels_col = channels * ksize * ksize; + + // optimized version + if (height_col == height && width_col == width && stride == 1 && pad == 1) + { + #pragma omp parallel for + for (c = 0; c < channels_col; ++c) { + int w_offset = c % ksize; + int h_offset = (c / ksize) % ksize; + int c_im = c / ksize / ksize; + for (h = pad; h < height_col - pad; ++h) { + for (w = pad; w < width_col - pad; ++w) { + int im_row = h_offset + h - pad; + int im_col = w_offset + w - pad; + int col_index = (c * height_col + h) * width_col + w; + + data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; + } + + for (; w < width_col - pad; ++w) { + int im_row = h_offset + h - pad; + int im_col = w_offset + w - pad; + int col_index = (c * height_col + h) * width_col + w; + + data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; + } +} + + { + w = 0; + for (h = 0; h < height_col; ++h) { + int im_row = h_offset + h; + int im_col = w_offset + w; + int col_index = (c * height_col + h) * width_col + w; + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, + im_row, im_col, c_im, pad); + } + } + + { + w = width_col - 1; + for (h = 0; h < height_col; ++h) { + int im_row = h_offset + h; + int im_col = w_offset + w; + int col_index = (c * height_col + h) * width_col + w; + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, + im_row, im_col, c_im, pad); + } + } + + { + h = 0; + for (w = 0; w < width_col; ++w) { + int im_row = h_offset + h; + int im_col = w_offset + w; + int col_index = (c * height_col + h) * width_col + w; + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, + im_row, im_col, c_im, pad); + } + } + + { + h = height_col - 1; + for (w = 0; w < width_col; ++w) { + int im_row = h_offset + h; + int im_col = w_offset + w; + int col_index = (c * height_col + h) * width_col + w; + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, + im_row, im_col, c_im, pad); + } + } + } + + } + else { + //printf("\n Error: is no non-optimized version \n"); + im2col_cpu(data_im, channels, height, width, ksize, stride, pad, data_col); + } +} + +void activate_array_cpu_custom(float *x, const int n, const ACTIVATION a) +{ + int i; + if (a == LINEAR) + { + } + else if (a == LEAKY) + { + for (i = 0; i < n; ++i) { + x[i] = (x[i]>0) ? x[i] : .1*x[i]; + } + } + else { + for (i = 0; i < n; ++i) { + x[i] = activate(x[i], a); + } + } +} + +void float_to_bit(float *src, unsigned char *dst, size_t size) +{ + size_t dst_size = size / 8 + 1; + memset(dst, 0, dst_size); + + size_t i; + char *byte_arr = calloc(size, sizeof(char)); + for (i = 0; i < size; ++i) { + if (src[i] > 0) byte_arr[i] = 1; + } + + //for (i = 0; i < size; ++i) { + // dst[i / 8] |= byte_arr[i] << (i % 8); + //} + + for (i = 0; i < size; i += 8) { + char dst_tmp = 0; + dst_tmp |= byte_arr[i + 0] << 0; + dst_tmp |= byte_arr[i + 1] << 1; + dst_tmp |= byte_arr[i + 2] << 2; + dst_tmp |= byte_arr[i + 3] << 3; + dst_tmp |= byte_arr[i + 4] << 4; + dst_tmp |= byte_arr[i + 5] << 5; + dst_tmp |= byte_arr[i + 6] << 6; + dst_tmp |= byte_arr[i + 7] << 7; + dst[i / 8] = dst_tmp; + } + free(byte_arr); +} + +static inline void transpose_scalar_block(float *A, float *B, const int lda, const int ldb, const int block_size) +{ + int i, j; + //#pragma omp parallel for + for (i = 0; i + +void gemm_ongpu(int TA, int TB, int M, int N, int K, float ALPHA, + float *A_gpu, int lda, + float *B_gpu, int ldb, + float BETA, + float *C_gpu, int ldc) +{ + cublasHandle_t handle = blas_handle(); + cudaError_t stream_status = cublasSetStream(handle, get_cuda_stream()); + cudaError_t status = cublasSgemm(handle, (TB ? CUBLAS_OP_T : CUBLAS_OP_N), + (TA ? CUBLAS_OP_T : CUBLAS_OP_N), N, M, K, &ALPHA, B_gpu, ldb, A_gpu, lda, &BETA, C_gpu, ldc); + check_error(status); +} + +void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA, + float *A, int lda, + float *B, int ldb, + float BETA, + float *C, int ldc) +{ + float *A_gpu = cuda_make_array(A, (TA ? lda*K:lda*M)); + float *B_gpu = cuda_make_array(B, (TB ? ldb*N : ldb*K)); + float *C_gpu = cuda_make_array(C, ldc*M); + + gemm_ongpu(TA, TB, M, N, K, ALPHA, A_gpu, lda, B_gpu, ldb, BETA, C_gpu, ldc); + + cuda_pull_array(C_gpu, C, ldc*M); + cuda_free(A_gpu); + cuda_free(B_gpu); + cuda_free(C_gpu); +} + +#include +#include +#include +#include + +void time_gpu_random_matrix(int TA, int TB, int m, int k, int n) +{ + float *a; + if(!TA) a = random_matrix(m,k); + else a = random_matrix(k,m); + int lda = (!TA)?k:m; + float *b; + if(!TB) b = random_matrix(k,n); + else b = random_matrix(n,k); + int ldb = (!TB)?n:k; + + float *c = random_matrix(m,n); + int i; + clock_t start = clock(), end; + for(i = 0; i<32; ++i){ + gemm_gpu(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n); + } + end = clock(); + printf("Matrix Multiplication %dx%d * %dx%d, TA=%d, TB=%d: %lf s\n",m,k,k,n, TA, TB, (float)(end-start)/CLOCKS_PER_SEC); + free(a); + free(b); + free(c); +} + +void time_ongpu(int TA, int TB, int m, int k, int n) +{ + int iter = 10; + float *a = random_matrix(m,k); + float *b = random_matrix(k,n); + + int lda = (!TA)?k:m; + int ldb = (!TB)?n:k; + + float *c = random_matrix(m,n); + + float *a_cl = cuda_make_array(a, m*k); + float *b_cl = cuda_make_array(b, k*n); + float *c_cl = cuda_make_array(c, m*n); + + int i; + clock_t start = clock(), end; + for(i = 0; i static inline void set_bit(unsigned char *const dst, size_t index) { size_t dst_i = index / 8; From 5c876bb918903314a33165c5812395e781bdfe08 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 9 Aug 2018 03:12:45 +0300 Subject: [PATCH 0120/1365] Another one fix --- src/gemm.h | 1 + 1 file changed, 1 insertion(+) diff --git a/src/gemm.h b/src/gemm.h index 4514f570c06..c71cd24bf74 100644 --- a/src/gemm.h +++ b/src/gemm.h @@ -2,6 +2,7 @@ #define GEMM_H #include "activations.h" #include +#include static inline void set_bit(unsigned char *const dst, size_t index) { size_t dst_i = index / 8; From d59a172a1d6eed66ecba6493199767f955edc7a0 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 9 Aug 2018 14:58:26 +0300 Subject: [PATCH 0121/1365] Fixed wrong labels --- src/utils.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/utils.c b/src/utils.c index d20c63323f6..09c50b2c233 100644 --- a/src/utils.c +++ b/src/utils.c @@ -210,7 +210,7 @@ void replace_image_to_label(char *input_path, char *output_path) { find_replace(input_path, "/images/train2014/", "/labels/train2014/", output_path); // COCO find_replace(output_path, "/images/val2014/", "/labels/val2014/", output_path); // COCO find_replace(output_path, "/JPEGImages/", "/labels/", output_path); // PascalVOC - find_replace(input_path, "\\images\\train2014\\", "\\labels\\train2014\\", output_path); // COCO + find_replace(output_path, "\\images\\train2014\\", "\\labels\\train2014\\", output_path); // COCO find_replace(output_path, "\\images\\val2014\\", "\\labels\\val2014\\", output_path); // COCO find_replace(output_path, "\\JPEGImages\\", "\\labels\\", output_path); // PascalVOC //find_replace(output_path, "/VOC2007/JPEGImages/", "/VOC2007/labels/", output_path); // PascalVOC From 78284f791e21972eefc31b3f7a23eb91feeb61a7 Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 9 Aug 2018 22:53:09 +0300 Subject: [PATCH 0122/1365] Update Readme.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 262a2533bfd..c3b7a84de5b 100644 --- a/README.md +++ b/README.md @@ -428,7 +428,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty `.txt` files) - use as many images of negative samples as there are images with objects - * for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last layer [region] in your cfg-file + * for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last `[yolo]`-layer or `[region]`-layer in your cfg-file (the global maximum number of objects that can be detected by YoloV3 is `0,0615234375*(width*height)` where are width and height are parameters from `[net]` section in cfg-file) * for training for small objects - set `layers = -1, 11` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L720 and set `stride=4` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L717 From 3e856ec04ebe3cf072939641ba6cd7871bfefc32 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 10 Aug 2018 01:27:20 +0300 Subject: [PATCH 0123/1365] Optimized: transpose --- src/convolutional_layer.c | 25 +--------------- src/gemm.c | 61 ++++++++++++++++++++------------------- 2 files changed, 33 insertions(+), 53 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index a8205881b93..3c9efdd468e 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -637,32 +637,9 @@ size_t binary_transpose_align_input(int k, int n, float *b, char **t_bit_input, //printf("\n align_weights_size = %d, k = %d, m = %d, lda = %d \n", align_weights_size, k, m, k); //printf("\n align_bit_weights_size = %d, k = %d, m = %d, new_lda = %d \n", align_bit_weights_size, k, m, new_ldb); - // transpose and align B - int i, j; - //#pragma omp parallel for - /* - for (i = 0; i < n; ++i) { - for (j = 0; j < k; ++j) { - t_input[i*new_ldb + j] = b[j*n + i]; - } - }*/ - //transpose_block_SSE4x4(float *A, float *B, const int n, const int m, const int lda, const int ldb, const int block_size) - - //transpose_block(b, t_input, k, n, n, new_ldb, 16); - - int blocksize = 1; - int mod_k = 1, mod_n = 1; - for (i = 2; i < 256; i *= 2) - if (k % i == 0) mod_k = i; - - for (i = 2; i < 256; i *= 2) - if (n % i == 0) mod_n = i; - - blocksize = (mod_k < mod_n) ? mod_k : mod_n; - + int blocksize = 64; transpose_block_SSE4x4(b, t_input, k, n, n, new_ldb, blocksize); - //transpose_block(b, t_input, k, n, n, new_ldb, blocksize); //printf("\n blocksize = %d \n", blocksize); float_to_bit(t_input, *t_bit_input, t_intput_size); diff --git a/src/gemm.c b/src/gemm.c index ff5edfa90b2..4a7dad7e0bc 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -686,47 +686,50 @@ void float_to_bit(float *src, unsigned char *dst, size_t size) static inline void transpose4x4_SSE(float *A, float *B, const int lda, const int ldb) { - __m128 row1 = _mm_load_ps(&A[0 * lda]); - __m128 row2 = _mm_load_ps(&A[1 * lda]); - __m128 row3 = _mm_load_ps(&A[2 * lda]); - __m128 row4 = _mm_load_ps(&A[3 * lda]); + __m128 row1 = _mm_loadu_ps(&A[0 * lda]); + __m128 row2 = _mm_loadu_ps(&A[1 * lda]); + __m128 row3 = _mm_loadu_ps(&A[2 * lda]); + __m128 row4 = _mm_loadu_ps(&A[3 * lda]); _MM_TRANSPOSE4_PS(row1, row2, row3, row4); - _mm_store_ps(&B[0 * ldb], row1); - _mm_store_ps(&B[1 * ldb], row2); - _mm_store_ps(&B[2 * ldb], row3); - _mm_store_ps(&B[3 * ldb], row4); + _mm_storeu_ps(&B[0 * ldb], row1); + _mm_storeu_ps(&B[1 * ldb], row2); + _mm_storeu_ps(&B[2 * ldb], row3); + _mm_storeu_ps(&B[3 * ldb], row4); } void transpose_block_SSE4x4(float *A, float *B, const int n, const int m, const int lda, const int ldb, const int block_size) { int i; - if (block_size % 4 == 0) { - #pragma omp parallel for - for (i = 0; i < n; i += block_size) { - int j, i2, j2; + #pragma omp parallel for + for (i = 0; i < n; i += block_size) { + int j, i2, j2; + //int max_i2 = (i + block_size < n) ? (i + block_size) : n; + if (i + block_size < n) { + int max_i2 = i + block_size; for (j = 0; j < m; j += block_size) { - int max_i2 = i + block_size < n ? i + block_size : n; - int max_j2 = j + block_size < m ? j + block_size : m; - for (i2 = i; i2 < max_i2; i2 += 4) { - for (j2 = j; j2 < max_j2; j2 += 4) { - transpose4x4_SSE(&A[i2*lda + j2], &B[j2*ldb + i2], lda, ldb); + //int max_j2 = (j + block_size < m) ? (j + block_size) : m; + if (j + block_size < m) { + int max_j2 = j + block_size; + for (i2 = i; i2 < max_i2; i2 += 4) { + for (j2 = j; j2 < max_j2; j2 += 4) { + transpose4x4_SSE(&A[i2*lda + j2], &B[j2*ldb + i2], lda, ldb); + } + } + } + else { + for (i2 = i; i2 < max_i2; ++i2) { + for (j2 = j; j2 < m; ++j2) { + B[j2*ldb + i2] = A[i2*lda + j2]; + } } } } } - } - else { - #pragma omp parallel for - for (i = 0; i < n; i += block_size) { - int j, i2, j2; - for (j = 0; j < m; j += block_size) { - int max_i2 = i + block_size < n ? i + block_size : n; - int max_j2 = j + block_size < m ? j + block_size : m; - for (i2 = i; i2 < max_i2; ++i2) { - for (j2 = j; j2 < max_j2; ++j2) { - B[j2*ldb + i2] = A[i2*lda + j2]; - } + else { + for (i2 = i; i2 < n; ++i2) { + for (j2 = 0; j2 < m; ++j2) { + B[j2*ldb + i2] = A[i2*lda + j2]; } } } From a9fef1bd66e6b2c40c344c1bdcd33bb1d209461c Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 11 Aug 2018 00:26:53 +0300 Subject: [PATCH 0124/1365] Bug fixes. Tested im2col_cpu_custom_transpose - bad way. --- src/convolutional_layer.c | 43 ++++++-- src/convolutional_layer.h | 2 +- src/gemm.c | 223 +++++++++++++++++++++++++++++++++++++- src/gemm.h | 7 ++ src/layer.h | 1 + src/network.c | 4 +- 6 files changed, 264 insertions(+), 16 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 3c9efdd468e..7dc7dd29fbf 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -593,11 +593,11 @@ void bit_to_float(unsigned char *src, float *dst, size_t size, size_t filters, f } } -void binary_align_weights(convolutional_layer *l, size_t lda_align) +void binary_align_weights(convolutional_layer *l) { int m = l->n; int k = l->size*l->size*l->c; - size_t new_lda = k + (lda_align - k%lda_align); // (k / 8 + 1) * 8; + size_t new_lda = k + (l->lda_align - k % l->lda_align); // (k / 8 + 1) * 8; binarize_weights(l->weights, m, k, l->binary_weights); @@ -680,7 +680,17 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) for(i = 0; i < l.batch; ++i){ //im2col_cpu(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); - im2col_cpu_custom(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); + + //float *t_input = NULL; + //if (l.xnor) { + // size_t new_ldb = k + (l.lda_align - k%l.lda_align); + // size_t t_intput_size = new_ldb * n; + // t_input = calloc(t_intput_size, sizeof(float)); + // im2col_cpu_custom_transpose(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, t_input, new_ldb); + //} + //else + im2col_cpu_custom(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); + //gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); //gemm_nn_custom(m, n, k, 1, a, k, b, n, c, n); @@ -760,19 +770,28 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) free(align_weights); } */ - size_t ldb_align = 256; // 256 bit for AVX2 - size_t new_ldb = k + (ldb_align - k%ldb_align); - char *t_bit_input = NULL; - size_t t_intput_size = binary_transpose_align_input(k, n, b, &t_bit_input, ldb_align); - gemm_nn_custom_bin_mean_transposed(m, n, k, 1, l.align_bit_weights, new_ldb, t_bit_input, new_ldb, c, n, l.mean_arr); + /* + if (l.size == 3 && l.stride == 1 && l.pad == 1) { + convolution_2d(l.w, l.h, l.size, l.n, l.c, l.pad, l.stride, + l.weights, state.input, l.output); + } + else { + */ + //size_t ldb_align = 256; // 256 bit for AVX2 + int ldb_align = l.lda_align; + size_t new_ldb = k + (ldb_align - k%ldb_align); + char *t_bit_input = NULL; + size_t t_intput_size = binary_transpose_align_input(k, n, b, &t_bit_input, ldb_align); + + gemm_nn_custom_bin_mean_transposed(m, n, k, 1, l.align_bit_weights, new_ldb, t_bit_input, new_ldb, c, n, l.mean_arr); - //gemm_nn_custom_bin_mean_transposed(m, n, k, 1, bit_weights, k, t_bit_input, new_ldb, c, n, mean_arr); + //gemm_nn_custom_bin_mean_transposed(m, n, k, 1, bit_weights, k, t_bit_input, new_ldb, c, n, mean_arr); - //free(t_input); - free(t_bit_input); + //free(t_input); + free(t_bit_input); + //} - //free(align_bit_weights); } // for bit_input: (k * n) diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h index b804afb8f97..8869a3c3733 100644 --- a/src/convolutional_layer.h +++ b/src/convolutional_layer.h @@ -35,7 +35,7 @@ void binarize_weights(float *weights, int n, int size, float *binary); void swap_binary(convolutional_layer *l); void binarize_weights2(float *weights, int n, int size, char *binary, float *scales); -void binary_align_weights(convolutional_layer *l, size_t ldb_align); +void binary_align_weights(convolutional_layer *l); void backward_convolutional_layer(convolutional_layer layer, network_state state); diff --git a/src/gemm.c b/src/gemm.c index 4a7dad7e0bc..75ce59c24a9 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -429,6 +429,56 @@ void gemm_nn(int M, int N, int K, float ALPHA, } +void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride, + float *weights, float *input, float *output) +{ + int out_h = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1 + int out_w = (w + 2 * pad - ksize) / stride + 1; // output_width=input_width for stride=1 and pad=1 + int i, f, j; + + int fil; + // filter index +#pragma omp parallel for // "omp parallel for" - automatic parallelization of loop by using OpenMP + for (fil = 0; fil < n; ++fil) { + int chan, y, x, f_y, f_x; + // channel index + for (chan = 0; chan < c; ++chan) + // input - y + for (y = 0; y < h; ++y) + // input - x + for (x = 0; x < w; ++x) + { + int const output_index = fil*w*h + y*w + x; + int const weights_pre_index = fil*c*ksize*ksize + chan*ksize*ksize; + int const input_pre_index = chan*w*h; + float sum = 0; + + // filter - y + for (f_y = 0; f_y < ksize; ++f_y) + { + int input_y = y + f_y - pad; + // filter - x + for (f_x = 0; f_x < ksize; ++f_x) + { + int input_x = x + f_x - pad; + if (input_y < 0 || input_x < 0 || input_y >= h || input_x >= w) continue; + + int input_index = input_pre_index + input_y*w + input_x; + int weights_index = weights_pre_index + f_y*ksize + f_x; + + sum += input[input_index] * weights[weights_index]; + } + } + // l.output[filters][width][height] += + // state.input[channels][width][height] * + // l.weights[filters][channels][filter_width][filter_height]; + output[output_index] += sum; + } + } +} + + + // http://graphics.stanford.edu/~seander/bithacks.html // https://stackoverflow.com/questions/17354971/fast-counting-the-number-of-set-bits-in-m128i-register // https://arxiv.org/pdf/1611.07612.pdf @@ -539,6 +589,121 @@ static inline float im2col_get_pixel(float *im, int height, int width, int chann return im[col + width*(row + height*channel)]; } +//From Berkeley Vision's Caffe! +//https://github.com/BVLC/caffe/blob/master/LICENSE +void im2col_cpu_custom_transpose(float* data_im, + int channels, int height, int width, + int ksize, int stride, int pad, float* data_col, int ldb_align) +{ + int c, h, w; + int height_col = (height + 2 * pad - ksize) / stride + 1; + int width_col = (width + 2 * pad - ksize) / stride + 1; + int channels_col = channels * ksize * ksize; + + // optimized version + if (height_col == height && width_col == width && stride == 1 && pad == 1) + { +#pragma omp parallel for + for (c = 0; c < channels_col; ++c) { + int w_offset = c % ksize; + int h_offset = (c / ksize) % ksize; + int c_im = c / ksize / ksize; + for (h = pad; h < height_col - pad; ++h) { + for (w = pad; w < width_col - pad - 4; w+=8) { + int im_row = h_offset + h - pad; + int im_col = w_offset + w - pad; + //int col_index = (c * height_col + h) * width_col + w; + int col_index = (h * width_col + w)*ldb_align + c; // transposed & aligned + + //data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; + __m256 src256 = _mm256_loadu_ps((__m256i *)(&data_im[im_col + width*(im_row + height*c_im)])); + data_col[col_index + ldb_align * 0] = src256.m256_f32[0]; + data_col[col_index + ldb_align * 1] = src256.m256_f32[1]; + data_col[col_index + ldb_align * 2] = src256.m256_f32[2]; + data_col[col_index + ldb_align * 3] = src256.m256_f32[3]; + data_col[col_index + ldb_align * 4] = src256.m256_f32[4]; + data_col[col_index + ldb_align * 5] = src256.m256_f32[5]; + data_col[col_index + ldb_align * 6] = src256.m256_f32[6]; + data_col[col_index + ldb_align * 7] = src256.m256_f32[7]; + + //_mm256_storeu_ps(&data_col[col_index], src256); + } + + for (; w < width_col - pad; ++w) { + int im_row = h_offset + h - pad; + int im_col = w_offset + w - pad; + int col_index = (h * width_col + w)*ldb_align + c; // transposed & aligned + data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; + } + } + + { + w = 0; + for (h = 0; h < height_col; ++h) { + int im_row = h_offset + h; + int im_col = w_offset + w; + int col_index = (h * width_col + w)*ldb_align + c; // transposed & aligned + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, + im_row, im_col, c_im, pad); + } + } + + { + w = width_col - 1; + for (h = 0; h < height_col; ++h) { + int im_row = h_offset + h; + int im_col = w_offset + w; + int col_index = (h * width_col + w)*ldb_align + c; // transposed & aligned + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, + im_row, im_col, c_im, pad); + } + } + + { + h = 0; + for (w = 0; w < width_col; ++w) { + int im_row = h_offset + h; + int im_col = w_offset + w; + int col_index = (h * width_col + w)*ldb_align + c; // transposed & aligned + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, + im_row, im_col, c_im, pad); + } + } + + { + h = height_col - 1; + for (w = 0; w < width_col; ++w) { + int im_row = h_offset + h; + int im_col = w_offset + w; + int col_index = (h * width_col + w)*ldb_align + c; // transposed & aligned + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, + im_row, im_col, c_im, pad); + } + } + } + + } + else { + #pragma omp parallel for + for (c = 0; c < channels_col; ++c) { + int w_offset = c % ksize; + int h_offset = (c / ksize) % ksize; + int c_im = c / ksize / ksize; + for (h = 0; h < height_col; ++h) { + for (w = 0; w < width_col; ++w) { + int im_row = h_offset + h * stride; + int im_col = w_offset + w * stride; + + int col_index = (h * width_col + w)*ldb_align + c; // transposed & aligned + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, + im_row, im_col, c_im, pad); + } + } + } + } +} + + //From Berkeley Vision's Caffe! //https://github.com/BVLC/caffe/blob/master/LICENSE void im2col_cpu_custom(float* data_im, @@ -641,7 +806,7 @@ void activate_array_cpu_custom(float *x, const int n, const ACTIVATION a) __m256i all256_sing1 = _mm256_set_epi32(0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000); __m256 all256_01 = _mm256_set1_ps(0.1F); - for (i = 0; i < n; i += 8) { + for (i = 0; i < n-8; i += 8) { //x[i] = (x[i]>0) ? x[i] : .1*x[i]; __m256 src256 = _mm256_loadu_ps((__m256 *)(&x[i])); @@ -755,6 +920,55 @@ void gemm_nn(int M, int N, int K, float ALPHA, } } + +void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride, + float *weights, float *input, float *output) +{ + int out_h = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1 + int out_w = (w + 2 * pad - ksize) / stride + 1; // output_width=input_width for stride=1 and pad=1 + int i, f, j; + + int fil; + // filter index +#pragma omp parallel for // "omp parallel for" - automatic parallelization of loop by using OpenMP + for (fil = 0; fil < n; ++fil) { + int chan, y, x, f_y, f_x; + // channel index + for (chan = 0; chan < c; ++chan) + // input - y + for (y = 0; y < h; ++y) + // input - x + for (x = 0; x < w; ++x) + { + int const output_index = fil*w*h + y*w + x; + int const weights_pre_index = fil*c*ksize*ksize + chan*ksize*ksize; + int const input_pre_index = chan*w*h; + float sum = 0; + + // filter - y + for (f_y = 0; f_y < ksize; ++f_y) + { + int input_y = y + f_y - pad; + // filter - x + for (f_x = 0; f_x < ksize; ++f_x) + { + int input_x = x + f_x - pad; + if (input_y < 0 || input_x < 0 || input_y >= h || input_x >= w) continue; + + int input_index = input_pre_index + input_y*w + input_x; + int weights_index = weights_pre_index + f_y*ksize + f_x; + + sum += input[input_index] * weights[weights_index]; + } + } + // l.output[filters][width][height] += + // state.input[channels][width][height] * + // l.weights[filters][channels][filter_width][filter_height]; + output[output_index] += sum; + } + } +} + void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, unsigned char *A, int lda, unsigned char *B, int ldb, @@ -791,6 +1005,13 @@ void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, } } +void im2col_cpu_custom_transpose(float* data_im, + int channels, int height, int width, + int ksize, int stride, int pad, float* data_col, int ldb_align) +{ + printf("\n im2col_cpu_custom_transpose() isn't implemented without AVX \n"); +} + //From Berkeley Vision's Caffe! //https://github.com/BVLC/caffe/blob/master/LICENSE void im2col_cpu_custom(float* data_im, diff --git a/src/gemm.h b/src/gemm.h index c71cd24bf74..62dbe30b630 100644 --- a/src/gemm.h +++ b/src/gemm.h @@ -4,6 +4,9 @@ #include #include +void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride, + float *weights, float *input, float *output); + static inline void set_bit(unsigned char *const dst, size_t index) { size_t dst_i = index / 8; int dst_shift = index % 8; @@ -31,6 +34,10 @@ void im2col_cpu_custom(float* data_im, int channels, int height, int width, int ksize, int stride, int pad, float* data_col); +void im2col_cpu_custom_transpose(float* data_im, + int channels, int height, int width, + int ksize, int stride, int pad, float* data_col, int ldb_align); + void activate_array_cpu_custom(float *x, const int n, const ACTIVATION a); diff --git a/src/layer.h b/src/layer.h index 224f77a1d7f..bd8518ae779 100644 --- a/src/layer.h +++ b/src/layer.h @@ -181,6 +181,7 @@ struct layer{ char *align_bit_weights; float *mean_arr; + int lda_align; float *col_image; int * input_layers; diff --git a/src/network.c b/src/network.c index 345ce687da7..2ad514118c3 100644 --- a/src/network.c +++ b/src/network.c @@ -861,9 +861,9 @@ void calculate_binary_weights(network net) if (l->xnor) { //printf("\n %d \n", j); - size_t ldb_align = 256; // 256bit for AVX2 + l->lda_align = 256; // 256bit for AVX2 - binary_align_weights(l, ldb_align); + binary_align_weights(l); } } } From 1f2155b8864031d1f434aa910ceaf53477217c75 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 11 Aug 2018 02:49:55 +0300 Subject: [PATCH 0125/1365] Experiments --- src/convolutional_layer.c | 20 +++--- src/gemm.c | 128 +++++++++++++++++++++++++++++++++++++- src/gemm.h | 2 +- 3 files changed, 139 insertions(+), 11 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 7dc7dd29fbf..d236ec2eca8 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -44,7 +44,7 @@ void binarize_weights(float *weights, int n, int size, float *binary) } mean = mean / size; for(i = 0; i < size; ++i){ - binary[f*size + i] = (weights[f*size + i] > 0) ? mean : -mean; + binary[f*size + i] = (weights[f*size + i] > 0) ? mean: -mean; } } } @@ -688,7 +688,8 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) // t_input = calloc(t_intput_size, sizeof(float)); // im2col_cpu_custom_transpose(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, t_input, new_ldb); //} - //else + if (l.xnor && l.size == 3 && l.stride == 1 && l.pad == 1) {} + else im2col_cpu_custom(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); @@ -771,13 +772,18 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) } */ - /* - if (l.size == 3 && l.stride == 1 && l.pad == 1) { + + if (l.size == 3 && l.stride == 1 && l.pad == 1) + { + //binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights); + //printf("\n mean = %f \n", l.mean_arr[0]); + convolution_2d(l.w, l.h, l.size, l.n, l.c, l.pad, l.stride, - l.weights, state.input, l.output); + //l.weights, state.input, l.output, l.mean_arr); + l.binary_weights, state.input, l.output, l.mean_arr); } else { - */ + //size_t ldb_align = 256; // 256 bit for AVX2 int ldb_align = l.lda_align; size_t new_ldb = k + (ldb_align - k%ldb_align); @@ -790,7 +796,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //free(t_input); free(t_bit_input); - //} + } } diff --git a/src/gemm.c b/src/gemm.c index 75ce59c24a9..580f4da27b6 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -429,7 +429,7 @@ void gemm_nn(int M, int N, int K, float ALPHA, } -void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride, +void convolution_2d_old(int w, int h, int ksize, int n, int c, int pad, int stride, float *weights, float *input, float *output) { int out_h = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1 @@ -477,6 +477,128 @@ void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride, } } +void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride, + float *weights, float *input, float *output, float *mean) +{ + int out_h = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1 + int out_w = (w + 2 * pad - ksize) / stride + 1; // output_width=input_width for stride=1 and pad=1 + int i, f, j; + +#if defined(_OPENMP) + static int max_num_threads = 0; + if (max_num_threads == 0) { + max_num_threads = omp_get_max_threads(); + omp_set_num_threads(4);// max_num_threads / 2); + } +#endif + + //convolution_2d_old(w, h, ksize, n, c, pad, stride, weights, input, output); + + __m256i all256_sing1 = _mm256_set_epi32(0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000); + for (i = 0; i < ksize*ksize*n*c; i+=8) { + *((__m256*)&weights[i]) = _mm256_and_ps(*((__m256*)&weights[i]), _mm256_castsi256_ps(all256_sing1)); + } + + for (i = 0; i < w*h*c; i += 8) { + //*((__m256*)&input[i]) = _mm256_and_ps(*((__m256*)&input[i]), _mm256_castsi256_ps(all256_sing1)); + } + + + __m256i all256_last_zero = _mm256_set1_epi32(0xFFFFFFFF); + all256_last_zero.m256i_i32[7] = 0; + + __m256i idx256 = _mm256_set_epi32(0, 7, 6, 5, 4, 3, 2, 1); + //__m256 all256_sing1 = _mm256_set1_ps(0x80000000); + __m256 all256_one = _mm256_set1_ps(1); + __m256i all256i_one = _mm256_set1_epi32(1); + + ///__m256i src256 = _mm256_loadu_si256((__m256i *)(&src[i])); + ///__m256i result256 = _mm256_and_si256(src256, all256_sing1); // check sign in 8 x 32-bit floats + + int fil; + // filter index +#pragma omp parallel for // "omp parallel for" - automatic parallelization of loop by using OpenMP + for (fil = 0; fil < n; ++fil) { + int chan, y, x, f_y, f_x; + float cur_mean = fabs(mean[fil]); + __m256 mean256 = _mm256_set1_ps(cur_mean); + // channel index + //for (chan = 0; chan < c; ++chan) + // input - y + for (y = 0; y < h; ++y) + // input - x + for (x = 0; x < w-8; x+=8) + { + int const output_index = fil*w*h + y*w + x; + float sum = 0; + __m256 sum256 = _mm256_set1_ps(0); + + for (chan = 0; chan < c; ++chan) { + int const weights_pre_index = fil*c*ksize*ksize + chan*ksize*ksize; + int const input_pre_index = chan*w*h; + + + // filter - y + for (f_y = 0; f_y < ksize; ++f_y) + { + int input_y = y + f_y - pad; + //__m256 in = *((__m256*)&input[input_pre_index + input_y*w]); + if (input_y < 0 || input_y >= h) continue; + //__m256 in = _mm256_loadu_ps(&input[input_pre_index + input_y*w + x - pad]); + + // filter - x + for (f_x = 0; f_x < ksize; ++f_x) + { + int input_x = x + f_x - pad; + //if (input_y < 0 || input_x < 0 || input_y >= h || input_x >= w) continue; + + int input_index = input_pre_index + input_y*w + input_x; + int weights_index = weights_pre_index + f_y*ksize + f_x; + //if (input_y < 0 || input_y >= h) continue; + + //sum += input[input_index] * weights[weights_index]; + + __m256 in = *((__m256*)&input[input_index]); + __m256 w = _mm256_set1_ps(weights[weights_index]); + //__m256 w_sign = _mm256_and_ps(w, _mm256_castsi256_ps(all256_sing1)); // check sign in 8 x 32-bit floats + __m256 xor = _mm256_xor_ps(w, in); + //printf("\n xor1 = %f, xor2 = %f \n", xor.m256_f32[0], xor.m256_f32[1]); + //printf("\n in = %f, w = %f, xor = %f \n", in.m256_f32[0], w_sign.m256_f32[0], xor.m256_f32[0]); + + //__m256 pn1 = _mm256_and_ps(_mm256_castsi256_ps(all256i_one), xor); + + + //sum256 = xor; + sum256 = _mm256_add_ps(xor, sum256); + //printf("\n --- \n"); + //printf("\n 0 = %f, 1 = %f, 2 = %f, 3 = %f, 4 = %f, 5 = %f, 6 = %f, 7 = %f \n", in.m256_f32[0], in.m256_f32[1], in.m256_f32[2], in.m256_f32[3], in.m256_f32[4], in.m256_f32[5], in.m256_f32[6], in.m256_f32[7]); + + if (f_x < ksize-1) { + //in = _mm256_permutevar8x32_ps(in, idx256); + //in = _mm256_and_ps(in, _mm256_castsi256_ps(all256_last_zero)); + } + } + } + } + // l.output[filters][width][height] += + // state.input[channels][width][height] * + // l.weights[filters][channels][filter_width][filter_height]; + //output[output_index] += sum; + + sum256 = _mm256_mul_ps(sum256, mean256); + //printf("\n cur_mean = %f, sum256 = %f, sum256 = %f, in = %f \n", + // cur_mean, sum256.m256_f32[0], sum256.m256_f32[1], input[input_pre_index]); + + //__m256 out = *((__m256*)&output[output_index]); + //out = _mm256_add_ps(out, sum256); + //*((__m256*)&output[output_index]) = out; + *((__m256*)&output[output_index]) = sum256; + + //_mm256_storeu_ps(&C[i*ldc + j], result256); + } + } +} + // http://graphics.stanford.edu/~seander/bithacks.html @@ -533,7 +655,7 @@ void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, static int max_num_threads = 0; if (max_num_threads == 0) { max_num_threads = omp_get_max_threads(); - omp_set_num_threads(max_num_threads / 2); + //omp_set_num_threads(max_num_threads / 2); } #endif @@ -922,7 +1044,7 @@ void gemm_nn(int M, int N, int K, float ALPHA, void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride, - float *weights, float *input, float *output) + float *weights, float *input, float *output, float *mean) { int out_h = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1 int out_w = (w + 2 * pad - ksize) / stride + 1; // output_width=input_width for stride=1 and pad=1 diff --git a/src/gemm.h b/src/gemm.h index 62dbe30b630..1472ccae054 100644 --- a/src/gemm.h +++ b/src/gemm.h @@ -5,7 +5,7 @@ #include void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride, - float *weights, float *input, float *output); + float *weights, float *input, float *output, float *mean); static inline void set_bit(unsigned char *const dst, size_t index) { size_t dst_i = index / 8; From b1dddf02ccf8dcfaadee4e8a5ed8726725ec1b93 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 13 Aug 2018 02:43:45 +0300 Subject: [PATCH 0126/1365] Fixed AVX compiled bug --- Makefile | 2 +- src/convolutional_layer.c | 11 +++-- src/gemm.c | 90 +++++++++++++++++++++++++++------------ 3 files changed, 70 insertions(+), 33 deletions(-) diff --git a/Makefile b/Makefile index ba610865ec2..ec74a813229 100644 --- a/Makefile +++ b/Makefile @@ -57,7 +57,7 @@ ifeq ($(DEBUG), 1) OPTS= -O0 -g else ifeq ($(AVX), 1) -CFLAGS+= -ffp-contract=fast -mavx -msse4.1 -msse4a +CFLAGS+= -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a endif endif diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index d236ec2eca8..927eb9931bb 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -688,8 +688,8 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) // t_input = calloc(t_intput_size, sizeof(float)); // im2col_cpu_custom_transpose(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, t_input, new_ldb); //} - if (l.xnor && l.size == 3 && l.stride == 1 && l.pad == 1) {} - else + //if (l.xnor && l.size == 3 && l.stride == 1 && l.pad == 1) {} + //else im2col_cpu_custom(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); @@ -772,7 +772,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) } */ - + /* if (l.size == 3 && l.stride == 1 && l.pad == 1) { //binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights); @@ -783,12 +783,15 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) l.binary_weights, state.input, l.output, l.mean_arr); } else { + */ //size_t ldb_align = 256; // 256 bit for AVX2 int ldb_align = l.lda_align; size_t new_ldb = k + (ldb_align - k%ldb_align); char *t_bit_input = NULL; size_t t_intput_size = binary_transpose_align_input(k, n, b, &t_bit_input, ldb_align); + //char *t_bit_input = calloc(new_ldb * n, sizeof(char)); // for im2col_cpu_custom_transpose() only + //float_to_bit(t_input, t_bit_input, new_ldb * n); // for im2col_cpu_custom_transpose() only gemm_nn_custom_bin_mean_transposed(m, n, k, 1, l.align_bit_weights, new_ldb, t_bit_input, new_ldb, c, n, l.mean_arr); @@ -796,7 +799,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //free(t_input); free(t_bit_input); - } + //} } diff --git a/src/gemm.c b/src/gemm.c index 580f4da27b6..b9098141e78 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -318,6 +318,22 @@ void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, #include #include +static inline __int32 _mm256_extract_epi64(__m256i a, const int index) { + return a.m256i_i64[index]; +} + +static inline __int32 _mm256_extract_epi32(__m256i a, const int index) { + return a.m256i_i32[index]; +} + +static inline float _castu32_f32(uint32_t a) { + return *((float *)&a); +} + +static inline float _mm256_extract_float32(__m256 a, const int index) { + return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), index)); +} + #else // Linux GCC/Clang #include #include @@ -325,6 +341,14 @@ void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, #include #include +static inline float _castu32_f32(uint32_t a) { + return *((float *)&a); +} + +static inline float _mm256_extract_float32(__m256 a, const int index) { + return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), index)); +} + void asm_cpuid(uint32_t* abcd, uint32_t eax) { uint32_t ebx = 0, edx = 0, ecx = 0; @@ -504,8 +528,10 @@ void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride, } - __m256i all256_last_zero = _mm256_set1_epi32(0xFFFFFFFF); - all256_last_zero.m256i_i32[7] = 0; + //__m256i all256_last_zero = _mm256_set1_epi32(0xFFFFFFFF); + //all256_last_zero.m256i_i32[7] = 0; + __m256i all256_last_zero = + _mm256_set_epi32(0xFFFFFFFF, 0xFFFFFFFF, 0xFFFFFFFF, 0xFFFFFFFF, 0xFFFFFFFF, 0xFFFFFFFF, 0xFFFFFFFF, 0x0); __m256i idx256 = _mm256_set_epi32(0, 7, 6, 5, 4, 3, 2, 1); //__m256 all256_sing1 = _mm256_set1_ps(0x80000000); @@ -561,15 +587,15 @@ void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride, __m256 in = *((__m256*)&input[input_index]); __m256 w = _mm256_set1_ps(weights[weights_index]); //__m256 w_sign = _mm256_and_ps(w, _mm256_castsi256_ps(all256_sing1)); // check sign in 8 x 32-bit floats - __m256 xor = _mm256_xor_ps(w, in); - //printf("\n xor1 = %f, xor2 = %f \n", xor.m256_f32[0], xor.m256_f32[1]); - //printf("\n in = %f, w = %f, xor = %f \n", in.m256_f32[0], w_sign.m256_f32[0], xor.m256_f32[0]); + __m256 xor256 = _mm256_xor_ps(w, in); + //printf("\n xor256_1 = %f, xor256_2 = %f \n", xor256.m256_f32[0], xor256.m256_f32[1]); + //printf("\n in = %f, w = %f, xor256 = %f \n", in.m256_f32[0], w_sign.m256_f32[0], xor256.m256_f32[0]); - //__m256 pn1 = _mm256_and_ps(_mm256_castsi256_ps(all256i_one), xor); + //__m256 pn1 = _mm256_and_ps(_mm256_castsi256_ps(all256i_one), xor256); - //sum256 = xor; - sum256 = _mm256_add_ps(xor, sum256); + //sum256 = xor256; + sum256 = _mm256_add_ps(xor256, sum256); //printf("\n --- \n"); //printf("\n 0 = %f, 1 = %f, 2 = %f, 3 = %f, 4 = %f, 5 = %f, 6 = %f, 7 = %f \n", in.m256_f32[0], in.m256_f32[1], in.m256_f32[2], in.m256_f32[3], in.m256_f32[4], in.m256_f32[5], in.m256_f32[6], in.m256_f32[7]); @@ -638,10 +664,14 @@ static inline __m256i count256(__m256i v) { static inline int popcnt256_custom(__m256i n) { __m256i val = count256(n); - return val.m256i_i64[0] + - val.m256i_i64[1] + - val.m256i_i64[2] + - val.m256i_i64[3]; + //return val.m256i_i64[0] + + //val.m256i_i64[1] + + //val.m256i_i64[2] + + //val.m256i_i64[3]; + return _mm256_extract_epi64(val, 0) + + _mm256_extract_epi64(val, 1) + + _mm256_extract_epi64(val, 2) + + _mm256_extract_epi64(val, 3); } void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, @@ -686,10 +716,14 @@ void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, } // count of 1 bits - count = count_sum.m256i_i64[0] + - count_sum.m256i_i64[1] + - count_sum.m256i_i64[2] + - count_sum.m256i_i64[3]; + //count = count_sum.m256i_i64[0] + + // count_sum.m256i_i64[1] + + // count_sum.m256i_i64[2] + + // count_sum.m256i_i64[3]; + count = _mm256_extract_epi64(count_sum, 0) + + _mm256_extract_epi64(count_sum, 1) + + _mm256_extract_epi64(count_sum, 2) + + _mm256_extract_epi64(count_sum, 3); int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); count = count - f1; // remove extra bits (from empty space for align only) @@ -738,15 +772,15 @@ void im2col_cpu_custom_transpose(float* data_im, int col_index = (h * width_col + w)*ldb_align + c; // transposed & aligned //data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; - __m256 src256 = _mm256_loadu_ps((__m256i *)(&data_im[im_col + width*(im_row + height*c_im)])); - data_col[col_index + ldb_align * 0] = src256.m256_f32[0]; - data_col[col_index + ldb_align * 1] = src256.m256_f32[1]; - data_col[col_index + ldb_align * 2] = src256.m256_f32[2]; - data_col[col_index + ldb_align * 3] = src256.m256_f32[3]; - data_col[col_index + ldb_align * 4] = src256.m256_f32[4]; - data_col[col_index + ldb_align * 5] = src256.m256_f32[5]; - data_col[col_index + ldb_align * 6] = src256.m256_f32[6]; - data_col[col_index + ldb_align * 7] = src256.m256_f32[7]; + __m256 src256 = _mm256_loadu_ps((float *)(&data_im[im_col + width*(im_row + height*c_im)])); + data_col[col_index + ldb_align * 0] = _mm256_extract_float32(src256, 0);// src256.m256_f32[0]; + data_col[col_index + ldb_align * 1] = _mm256_extract_float32(src256, 1);// src256.m256_f32[1]; + data_col[col_index + ldb_align * 2] = _mm256_extract_float32(src256, 2);// src256.m256_f32[2]; + data_col[col_index + ldb_align * 3] = _mm256_extract_float32(src256, 3);// src256.m256_f32[3]; + data_col[col_index + ldb_align * 4] = _mm256_extract_float32(src256, 4);// src256.m256_f32[4]; + data_col[col_index + ldb_align * 5] = _mm256_extract_float32(src256, 5);// src256.m256_f32[5]; + data_col[col_index + ldb_align * 6] = _mm256_extract_float32(src256, 6);// src256.m256_f32[6]; + data_col[col_index + ldb_align * 7] = _mm256_extract_float32(src256, 7);// src256.m256_f32[7]; //_mm256_storeu_ps(&data_col[col_index], src256); } @@ -853,7 +887,7 @@ void im2col_cpu_custom(float* data_im, int col_index = (c * height_col + h) * width_col + w; //data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; - __m256 src256 = _mm256_loadu_ps((__m256i *)(&data_im[im_col + width*(im_row + height*c_im)])); + __m256 src256 = _mm256_loadu_ps((float *)(&data_im[im_col + width*(im_row + height*c_im)])); _mm256_storeu_ps(&data_col[col_index], src256); } @@ -931,13 +965,13 @@ void activate_array_cpu_custom(float *x, const int n, const ACTIVATION a) for (i = 0; i < n-8; i += 8) { //x[i] = (x[i]>0) ? x[i] : .1*x[i]; - __m256 src256 = _mm256_loadu_ps((__m256 *)(&x[i])); + __m256 src256 = _mm256_loadu_ps(&x[i]); __m256 mult256 = _mm256_mul_ps((src256), all256_01); // mult * 0.1 __m256i sign256 = _mm256_and_si256(_mm256_castps_si256(src256), all256_sing1); // check sign in 8 x 32-bit floats __m256 result256 = _mm256_blendv_ps(src256, mult256, _mm256_castsi256_ps(sign256)); // (sign>0) ? src : mult; - _mm256_storeu_ps((__m256 *)(&x[i]), result256); + _mm256_storeu_ps(&x[i], result256); } for (; i < n; ++i) { From f92b20580a21663c5db9eb8608f8cabd7adbeb10 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 14 Aug 2018 01:51:31 +0300 Subject: [PATCH 0127/1365] Some fixes for AVX support on CPU --- src/convolutional_layer.c | 7 +++++-- src/gemm.c | 26 +++++++++++++++----------- 2 files changed, 20 insertions(+), 13 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 927eb9931bb..e7c8e3f26c4 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -621,7 +621,7 @@ void binary_align_weights(convolutional_layer *l) free(align_weights); } - +// further optimizations: im2col_bin() for XNOR, and then transpose_aling_bin() size_t binary_transpose_align_input(int k, int n, float *b, char **t_bit_input, size_t ldb_align) { size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; @@ -690,7 +690,8 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //} //if (l.xnor && l.size == 3 && l.stride == 1 && l.pad == 1) {} //else - im2col_cpu_custom(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); + // further optimizations: im2col_bin() for XNOR, and then transpose_aling_bin() + im2col_cpu_custom(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); //gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); @@ -793,6 +794,8 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //char *t_bit_input = calloc(new_ldb * n, sizeof(char)); // for im2col_cpu_custom_transpose() only //float_to_bit(t_input, t_bit_input, new_ldb * n); // for im2col_cpu_custom_transpose() only + // 5x times faster than gemm()-float32 + // further optimizations: accelerate maxpool-layer with OpenMP/AVX gemm_nn_custom_bin_mean_transposed(m, n, k, 1, l.align_bit_weights, new_ldb, t_bit_input, new_ldb, c, n, l.mean_arr); //gemm_nn_custom_bin_mean_transposed(m, n, k, 1, bit_weights, k, t_bit_input, new_ldb, c, n, mean_arr); diff --git a/src/gemm.c b/src/gemm.c index b9098141e78..d233e9c7450 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -674,6 +674,8 @@ static inline int popcnt256_custom(__m256i n) { + _mm256_extract_epi64(val, 3); } +// 5x times faster than gemm()-float32 +// further optimizations: do mean-mult only for the last layer void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, unsigned char *A, int lda, unsigned char *B, int ldb, @@ -873,7 +875,7 @@ void im2col_cpu_custom(float* data_im, int channels_col = channels * ksize * ksize; // optimized version - if (height_col == height && width_col == width && stride == 1 && pad == 1) + if (height_col == height && width_col == width && stride == 1 && pad == 1 && is_fma_avx()) { #pragma omp parallel for for (c = 0; c < channels_col; ++c) { @@ -954,24 +956,26 @@ void im2col_cpu_custom(float* data_im, void activate_array_cpu_custom(float *x, const int n, const ACTIVATION a) { - int i; + int i = 0; if (a == LINEAR) {} else if (a == LEAKY) { - __m256i all256_sing1 = _mm256_set_epi32(0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000); - __m256 all256_01 = _mm256_set1_ps(0.1F); + if (is_fma_avx()) { + __m256i all256_sing1 = _mm256_set_epi32(0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000); + __m256 all256_01 = _mm256_set1_ps(0.1F); - for (i = 0; i < n-8; i += 8) { - //x[i] = (x[i]>0) ? x[i] : .1*x[i]; + for (i = 0; i < n - 8; i += 8) { + //x[i] = (x[i]>0) ? x[i] : .1*x[i]; - __m256 src256 = _mm256_loadu_ps(&x[i]); - __m256 mult256 = _mm256_mul_ps((src256), all256_01); // mult * 0.1 + __m256 src256 = _mm256_loadu_ps(&x[i]); + __m256 mult256 = _mm256_mul_ps((src256), all256_01); // mult * 0.1 - __m256i sign256 = _mm256_and_si256(_mm256_castps_si256(src256), all256_sing1); // check sign in 8 x 32-bit floats + __m256i sign256 = _mm256_and_si256(_mm256_castps_si256(src256), all256_sing1); // check sign in 8 x 32-bit floats - __m256 result256 = _mm256_blendv_ps(src256, mult256, _mm256_castsi256_ps(sign256)); // (sign>0) ? src : mult; - _mm256_storeu_ps(&x[i], result256); + __m256 result256 = _mm256_blendv_ps(src256, mult256, _mm256_castsi256_ps(sign256)); // (sign>0) ? src : mult; + _mm256_storeu_ps(&x[i], result256); + } } for (; i < n; ++i) { From e5484891825a4a07cc49a8768d019987a017116e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 14 Aug 2018 14:17:09 +0300 Subject: [PATCH 0128/1365] Fix for MSVS2017 for _mm256_extract_epi64 and _mm256_extract_epi32 --- src/gemm.c | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) diff --git a/src/gemm.c b/src/gemm.c index d233e9c7450..2fa0fcda2d9 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -318,6 +318,7 @@ void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, #include #include +#if defined(_MSC_VER) && _MSC_VER <= 1900 static inline __int32 _mm256_extract_epi64(__m256i a, const int index) { return a.m256i_i64[index]; } @@ -326,6 +327,8 @@ static inline __int32 _mm256_extract_epi32(__m256i a, const int index) { return a.m256i_i32[index]; } +#endif + static inline float _castu32_f32(uint32_t a) { return *((float *)&a); } @@ -954,6 +957,29 @@ void im2col_cpu_custom(float* data_im, } } +void transpose_8x8_bits(unsigned char A[8], unsigned char B[8], int m, int n) +{ + unsigned x, y, t; + + // Load the array and pack it into x and y. + + x = (A[0] << 24) | (A[m] << 16) | (A[2 * m] << 8) | A[3 * m]; + y = (A[4 * m] << 24) | (A[5 * m] << 16) | (A[6 * m] << 8) | A[7 * m]; + + t = (x ^ (x >> 7)) & 0x00AA00AA; x = x ^ t ^ (t << 7); + t = (y ^ (y >> 7)) & 0x00AA00AA; y = y ^ t ^ (t << 7); + + t = (x ^ (x >> 14)) & 0x0000CCCC; x = x ^ t ^ (t << 14); + t = (y ^ (y >> 14)) & 0x0000CCCC; y = y ^ t ^ (t << 14); + + t = (x & 0xF0F0F0F0) | ((y >> 4) & 0x0F0F0F0F); + y = ((x << 4) & 0xF0F0F0F0) | (y & 0x0F0F0F0F); + x = t; + + B[0] = x >> 24; B[n] = x >> 16; B[2 * n] = x >> 8; B[3 * n] = x; + B[4 * n] = y >> 24; B[5 * n] = y >> 16; B[6 * n] = y >> 8; B[7 * n] = y; +} + void activate_array_cpu_custom(float *x, const int n, const ACTIVATION a) { int i = 0; From 7f9145729c2a2b64a7aeb3806f7c3d21771fce94 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 14 Aug 2018 18:38:39 +0300 Subject: [PATCH 0129/1365] Another one fix for MSVS2017 --- src/gemm.c | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/gemm.c b/src/gemm.c index 2fa0fcda2d9..e7b6ac9d7a4 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -334,7 +334,8 @@ static inline float _castu32_f32(uint32_t a) { } static inline float _mm256_extract_float32(__m256 a, const int index) { - return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), index)); + return a.m256_f32[index]; + //return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), index)); } #else // Linux GCC/Clang From 756a3144cc5d055ecd2f769ce816f55f2fc1d880 Mon Sep 17 00:00:00 2001 From: mhaghighat Date: Wed, 15 Aug 2018 10:04:14 -0700 Subject: [PATCH 0130/1365] Update to prevent the error at Line 74: data being one-dimensional --- scripts/gen_anchors.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/gen_anchors.py b/scripts/gen_anchors.py index 03bb54b9087..709cb88adab 100644 --- a/scripts/gen_anchors.py +++ b/scripts/gen_anchors.py @@ -141,7 +141,7 @@ def main(argv): line = line.rstrip('\n') w,h = line.split(' ')[3:] #print(w,h) - annotation_dims.append(map(float,(w,h))) + annotation_dims.append(tuple(map(float,(w,h)))) annotation_dims = np.array(annotation_dims) eps = 0.005 From f007f7b90ec1a3d83fd638d81b9b209448f659a4 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 15 Aug 2018 23:47:56 +0300 Subject: [PATCH 0131/1365] Fixes for MSVS2017 --- build/darknet/darknet.vcxproj | 4 ++-- build/darknet/darknet_no_gpu.vcxproj | 4 ++-- build/darknet/x64/gen_anchors.py | 2 +- build/darknet/yolo_cpp_dll.vcxproj | 4 ++-- build/darknet/yolo_cpp_dll_no_gpu.vcxproj | 4 ++-- src/gemm.c | 1 - 6 files changed, 9 insertions(+), 10 deletions(-) diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj index 8fbf93c3c0d..3557200a259 100644 --- a/build/darknet/darknet.vcxproj +++ b/build/darknet/darknet.vcxproj @@ -89,7 +89,7 @@ Disabled true C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include - CUDNN;_CRTDBG_MAP_ALLOC;_MBCS;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + CUDNN;_CRTDBG_MAP_ALLOC;_MBCS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) OPENCV; true stdlib.h;crtdbg.h;%(ForcedIncludeFiles) @@ -133,7 +133,7 @@ true true C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include - OPENCV;CUDNN;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + OPENCV;CUDNN;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y CompileAsCpp diff --git a/build/darknet/darknet_no_gpu.vcxproj b/build/darknet/darknet_no_gpu.vcxproj index 9ce9b36a0e3..9e663910379 100644 --- a/build/darknet/darknet_no_gpu.vcxproj +++ b/build/darknet/darknet_no_gpu.vcxproj @@ -89,7 +89,7 @@ Disabled true C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories) - _MBCS;OPENCV;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + _MBCS;OPENCV;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) CUDNN true @@ -130,7 +130,7 @@ true true C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories) - OPENCV;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + OPENCV;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y CompileAsCpp diff --git a/build/darknet/x64/gen_anchors.py b/build/darknet/x64/gen_anchors.py index 03bb54b9087..709cb88adab 100644 --- a/build/darknet/x64/gen_anchors.py +++ b/build/darknet/x64/gen_anchors.py @@ -141,7 +141,7 @@ def main(argv): line = line.rstrip('\n') w,h = line.split(' ')[3:] #print(w,h) - annotation_dims.append(map(float,(w,h))) + annotation_dims.append(tuple(map(float,(w,h)))) annotation_dims = np.array(annotation_dims) eps = 0.005 diff --git a/build/darknet/yolo_cpp_dll.vcxproj b/build/darknet/yolo_cpp_dll.vcxproj index 4361f18bdaa..5d6576cee7d 100644 --- a/build/darknet/yolo_cpp_dll.vcxproj +++ b/build/darknet/yolo_cpp_dll.vcxproj @@ -91,7 +91,7 @@ Disabled true ..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include - _MBCS;YOLODLL_EXPORTS;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + _MBCS;YOLODLL_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) CUDNN true @@ -134,7 +134,7 @@ true true C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include - CUDNN;GPU;YOLODLL_EXPORTS;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + CUDNN;GPU;YOLODLL_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y CompileAsCpp diff --git a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj index e9e582be47c..ca5610fd60b 100644 --- a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj +++ b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj @@ -88,7 +88,7 @@ Disabled true ..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include - _MBCS;YOLODLL_EXPORTS;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + _MBCS;YOLODLL_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) CUDNN true @@ -131,7 +131,7 @@ true true C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories) - YOLODLL_EXPORTS;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + YOLODLL_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y CompileAsCpp diff --git a/src/gemm.c b/src/gemm.c index e7b6ac9d7a4..2fd7f503300 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -335,7 +335,6 @@ static inline float _castu32_f32(uint32_t a) { static inline float _mm256_extract_float32(__m256 a, const int index) { return a.m256_f32[index]; - //return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), index)); } #else // Linux GCC/Clang From 1e866f0063b8c60d56aaf2e6210e051ff0047aba Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 16 Aug 2018 00:01:22 +0300 Subject: [PATCH 0132/1365] Fixed yolov3-tiny.cfg --- build/darknet/x64/cfg/yolov3-tiny.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/build/darknet/x64/cfg/yolov3-tiny.cfg b/build/darknet/x64/cfg/yolov3-tiny.cfg index 42c0fcf9195..cfca3cfa641 100644 --- a/build/darknet/x64/cfg/yolov3-tiny.cfg +++ b/build/darknet/x64/cfg/yolov3-tiny.cfg @@ -172,7 +172,7 @@ filters=255 activation=linear [yolo] -mask = 1,2,3 +mask = 0,1,2 anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 classes=80 num=6 From fa7bfe3412fb22dc1781cff021477e09eb47b685 Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 16 Aug 2018 00:07:36 +0300 Subject: [PATCH 0133/1365] Update Readme.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index c3b7a84de5b..5eff17517a8 100644 --- a/README.md +++ b/README.md @@ -20,6 +20,7 @@ | ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) |   ![map_fps](https://hsto.org/webt/pw/zd/0j/pwzd0jb9g7znt_dbsyw9qzbnvti.jpeg) mAP (AP50) https://pjreddie.com/media/files/papers/YOLOv3.pdf | |---|---| +* YOLOv3-spp (is not indicated) better than YOLOv3 - mAP = 60.6%, FPS = 20: https://pjreddie.com/darknet/yolo/ * Yolo v3 source chart for the RetinaNet on MS COCO got from Table 1 (e): https://arxiv.org/pdf/1708.02002.pdf * Yolo v2 on Pascal VOC 2007: https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg * Yolo v2 on Pascal VOC 2012 (comp4): https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg @@ -49,6 +50,7 @@ This repository supports: * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported ##### Pre-trained models for different cfg-files can be downloaded from (smaller -> faster & lower quality): +* `yolov3-spp.cfg` (240 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-spp.weights * `yolov3.cfg` (236 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3.weights * `yolov3-tiny.cfg` (34 MB COCO **Yolo v3 tiny**) - requires 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-tiny.weights * `yolov2.cfg` (194 MB COCO Yolo v2) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov2.weights From bd8a5c3eb1b54b728301672335c14d22f424d827 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 16 Aug 2018 02:26:40 +0300 Subject: [PATCH 0134/1365] Different checks for AVX and FMA --- src/gemm.c | 126 +++++++++++++++++++++++++++++++++++++++++++---------- 1 file changed, 103 insertions(+), 23 deletions(-) diff --git a/src/gemm.c b/src/gemm.c index 2fd7f503300..4198540efab 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -306,12 +306,6 @@ void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, #if (defined(__AVX__) && defined(__x86_64__)) || defined(_WIN64) -#define OSXSAVEFlag (1UL<<27) -#define AVXFlag ((1UL<<28)|OSXSAVEFlag) -#define FMAFlag ((1UL<<12)|AVXFlag|OSXSAVEFlag) -#define CLMULFlag ((1UL<< 1)|AVXFlag|OSXSAVEFlag) -#define VAESFlag ((1UL<<25)|AVXFlag|OSXSAVEFlag) - #ifdef _WIN64 #include #include @@ -326,7 +320,6 @@ static inline __int32 _mm256_extract_epi64(__m256i a, const int index) { static inline __int32 _mm256_extract_epi32(__m256i a, const int index) { return a.m256i_i32[index]; } - #endif static inline float _castu32_f32(uint32_t a) { @@ -368,35 +361,122 @@ void asm_cpuid(uint32_t* abcd, uint32_t eax) abcd[2] = ecx; abcd[3] = edx; } - #endif -int simd_detect_x86(unsigned int idFeature) -{ - uint32_t regs[4]; // EAX, EBX, ECX, EDX; + + #ifdef _WIN32 - __cpuid(regs, 0); - if (regs[0] > 1U) __cpuid(regs, 1); +// Windows +#define cpuid(info, x) __cpuidex(info, x, 0) #else - __get_cpuid(0, ®s[0], ®s[1], ®s[2], ®s[3]); - if(regs[0] > 1U) __get_cpuid(1, ®s[0], ®s[1], ®s[2], ®s[3]); +// GCC Intrinsics +#include +void cpuid(int info[4], int InfoType) { + __cpuid_count(InfoType, 0, info[0], info[1], info[2], info[3]); +} #endif - if ((regs[2] & idFeature) != idFeature) - return 0; - return 1; + +// Misc. +static int HW_MMX, HW_x64, HW_RDRAND, HW_BMI1, HW_BMI2, HW_ADX, HW_PREFETCHWT1; +static int HW_ABM; // Advanced Bit Manipulation + +// SIMD: 128-bit +static int HW_SSE, HW_SSE2, HW_SSE3, HW_SSSE3, HW_SSE41, HW_SSE42, HW_SSE4a, HW_AES, HW_SHA; + +// SIMD: 256-bit +static int HW_AVX, HW_XOP, HW_FMA3, HW_FMA4, HW_AVX2; + +// SIMD: 512-bit +static int HW_AVX512F; // AVX512 Foundation +static int HW_AVX512CD; // AVX512 Conflict Detection +static int HW_AVX512PF; // AVX512 Prefetch +static int HW_AVX512ER; // AVX512 Exponential + Reciprocal +static int HW_AVX512VL; // AVX512 Vector Length Extensions +static int HW_AVX512BW; // AVX512 Byte + Word +static int HW_AVX512DQ; // AVX512 Doubleword + Quadword +static int HW_AVX512IFMA; // AVX512 Integer 52-bit Fused Multiply-Add +static int HW_AVX512VBMI; // AVX512 Vector Byte Manipulation Instructions + +// https://stackoverflow.com/questions/6121792/how-to-check-if-a-cpu-supports-the-sse3-instruction-set +void check_cpu_features(void) { + int info[4]; + cpuid(info, 0); + int nIds = info[0]; + + cpuid(info, 0x80000000); + unsigned nExIds = info[0]; + + // Detect Features + if (nIds >= 0x00000001) { + cpuid(info, 0x00000001); + HW_MMX = (info[3] & ((int)1 << 23)) != 0; + HW_SSE = (info[3] & ((int)1 << 25)) != 0; + HW_SSE2 = (info[3] & ((int)1 << 26)) != 0; + HW_SSE3 = (info[2] & ((int)1 << 0)) != 0; + + HW_SSSE3 = (info[2] & ((int)1 << 9)) != 0; + HW_SSE41 = (info[2] & ((int)1 << 19)) != 0; + HW_SSE42 = (info[2] & ((int)1 << 20)) != 0; + HW_AES = (info[2] & ((int)1 << 25)) != 0; + + HW_AVX = (info[2] & ((int)1 << 28)) != 0; + HW_FMA3 = (info[2] & ((int)1 << 12)) != 0; + + HW_RDRAND = (info[2] & ((int)1 << 30)) != 0; + } + if (nIds >= 0x00000007) { + cpuid(info, 0x00000007); + HW_AVX2 = (info[1] & ((int)1 << 5)) != 0; + + HW_BMI1 = (info[1] & ((int)1 << 3)) != 0; + HW_BMI2 = (info[1] & ((int)1 << 8)) != 0; + HW_ADX = (info[1] & ((int)1 << 19)) != 0; + HW_SHA = (info[1] & ((int)1 << 29)) != 0; + HW_PREFETCHWT1 = (info[2] & ((int)1 << 0)) != 0; + + HW_AVX512F = (info[1] & ((int)1 << 16)) != 0; + HW_AVX512CD = (info[1] & ((int)1 << 28)) != 0; + HW_AVX512PF = (info[1] & ((int)1 << 26)) != 0; + HW_AVX512ER = (info[1] & ((int)1 << 27)) != 0; + HW_AVX512VL = (info[1] & ((int)1 << 31)) != 0; + HW_AVX512BW = (info[1] & ((int)1 << 30)) != 0; + HW_AVX512DQ = (info[1] & ((int)1 << 17)) != 0; + HW_AVX512IFMA = (info[1] & ((int)1 << 21)) != 0; + HW_AVX512VBMI = (info[2] & ((int)1 << 1)) != 0; + } + if (nExIds >= 0x80000001) { + cpuid(info, 0x80000001); + HW_x64 = (info[3] & ((int)1 << 29)) != 0; + HW_ABM = (info[2] & ((int)1 << 5)) != 0; + HW_SSE4a = (info[2] & ((int)1 << 6)) != 0; + HW_FMA4 = (info[2] & ((int)1 << 16)) != 0; + HW_XOP = (info[2] & ((int)1 << 11)) != 0; + } } -int is_fma_avx() { +int is_avx() { static int result = -1; if (result == -1) { - result = simd_detect_x86(AVXFlag); + check_cpu_features(); + result = HW_AVX; if (result == 1) printf(" Used AVX \n"); else printf(" Not used AVX \n"); } return result; } +int is_fma_avx2() { + static int result = -1; + if (result == -1) { + check_cpu_features(); + result = HW_FMA3 && HW_AVX2; + if (result == 1) printf(" Used FMA & AVX2 \n"); + else printf(" Not used FMA & AVX2 \n"); + } + return result; +} + // https://software.intel.com/sites/landingpage/IntrinsicsGuide void gemm_nn(int M, int N, int K, float ALPHA, float *A, int lda, @@ -404,7 +484,7 @@ void gemm_nn(int M, int N, int K, float ALPHA, float *C, int ldc) { int i, j, k; - if (is_fma_avx() == 1) { // AVX + if (is_avx() == 1) { // AVX for (i = 0; i < M; ++i) { for (k = 0; k < K; ++k) { float A_PART = ALPHA*A[i*lda + k]; @@ -878,7 +958,7 @@ void im2col_cpu_custom(float* data_im, int channels_col = channels * ksize * ksize; // optimized version - if (height_col == height && width_col == width && stride == 1 && pad == 1 && is_fma_avx()) + if (height_col == height && width_col == width && stride == 1 && pad == 1 && is_fma_avx2()) { #pragma omp parallel for for (c = 0; c < channels_col; ++c) { @@ -987,7 +1067,7 @@ void activate_array_cpu_custom(float *x, const int n, const ACTIVATION a) {} else if (a == LEAKY) { - if (is_fma_avx()) { + if (is_fma_avx2()) { __m256i all256_sing1 = _mm256_set_epi32(0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000); __m256 all256_01 = _mm256_set1_ps(0.1F); From c2a8cc2ad3f4b31243cbf577440ee45bafcc7f92 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 16 Aug 2018 03:02:54 +0300 Subject: [PATCH 0135/1365] Fix GCC compile --- src/gemm.c | 1 - 1 file changed, 1 deletion(-) diff --git a/src/gemm.c b/src/gemm.c index 4198540efab..6a34da5ec49 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -370,7 +370,6 @@ void asm_cpuid(uint32_t* abcd, uint32_t eax) #define cpuid(info, x) __cpuidex(info, x, 0) #else // GCC Intrinsics -#include void cpuid(int info[4], int InfoType) { __cpuid_count(InfoType, 0, info[0], info[1], info[2], info[3]); } From 552207d6f0af585960c9c43e6a6d7c53fb4ae6bd Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 17 Aug 2018 19:58:05 +0300 Subject: [PATCH 0136/1365] Minor fix --- src/data.c | 17 +++++++++++------ src/region_layer.c | 12 ++++++------ src/yolo_layer.c | 12 ++++++------ 3 files changed, 23 insertions(+), 18 deletions(-) diff --git a/src/data.c b/src/data.c index 2a0a7e9fdf8..46f86c11aa9 100644 --- a/src/data.c +++ b/src/data.c @@ -320,6 +320,7 @@ void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, if (count > num_boxes) count = num_boxes; float x, y, w, h; int id; + int sub = 0; for (i = 0; i < count; ++i) { x = boxes[i].x; @@ -337,23 +338,27 @@ void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, sprintf(buff, "echo %s \"Wrong annotation: class_id = %d. But class_id should be [from 0 to %d]\" >> bad_label.list", labelpath, id, classes); system(buff); getchar(); + ++sub; continue; } if ((w < lowest_w || h < lowest_h)) { //sprintf(buff, "echo %s \"Very small object: w < lowest_w OR h < lowest_h\" >> bad_label.list", labelpath); //system(buff); + ++sub; continue; } if (x == 999999 || y == 999999) { printf("\n Wrong annotation: x = 0, y = 0 \n"); sprintf(buff, "echo %s \"Wrong annotation: x = 0 or y = 0\" >> bad_label.list", labelpath); system(buff); + ++sub; continue; } if (x <= 0 || x > 1 || y <= 0 || y > 1) { printf("\n Wrong annotation: x = %f, y = %f \n", x, y); sprintf(buff, "echo %s \"Wrong annotation: x = %f, y = %f\" >> bad_label.list", labelpath, x, y); system(buff); + ++sub; continue; } if (w > 1) { @@ -371,11 +376,11 @@ void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, if (x == 0) x += lowest_w; if (y == 0) y += lowest_h; - truth[i*5+0] = x; - truth[i*5+1] = y; - truth[i*5+2] = w; - truth[i*5+3] = h; - truth[i*5+4] = id; + truth[(i-sub)*5+0] = x; + truth[(i-sub)*5+1] = y; + truth[(i-sub)*5+2] = w; + truth[(i-sub)*5+3] = h; + truth[(i-sub)*5+4] = id; } free(boxes); } @@ -784,7 +789,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo image ai = image_data_augmentation(src, w, h, pleft, ptop, swidth, sheight, flip, jitter, dhue, dsat, dexp); d.X.vals[i] = ai.data; - + //show_image(ai, "aug"); //cvWaitKey(0); diff --git a/src/region_layer.c b/src/region_layer.c index 5167fb8129c..ada5f8e0c71 100644 --- a/src/region_layer.c +++ b/src/region_layer.c @@ -127,17 +127,17 @@ void delta_region_class(float *output, float *delta, int index, int class_id, in class_id = hier->parent[class_id]; } *avg_cat += pred; - } else { + } else { // Focal loss if (focal_loss) { // Focal Loss float alpha = 0.5; // 0.25 or 0.5 - //float gamma = 2; // hardcoded in many places of the grad-formula + //float gamma = 2; // hardcoded in many places of the grad-formula int ti = index + class_id; float pt = output[ti] + 0.000000000000001F; // http://fooplot.com/#W3sidHlwZSI6MCwiZXEiOiItKDEteCkqKDIqeCpsb2coeCkreC0xKSIsImNvbG9yIjoiIzAwMDAwMCJ9LHsidHlwZSI6MTAwMH1d - float grad = -(1 - pt) * (2 * pt*logf(pt) + pt - 1); // http://blog.csdn.net/linmingan/article/details/77885832 + float grad = -(1 - pt) * (2 * pt*logf(pt) + pt - 1); // http://blog.csdn.net/linmingan/article/details/77885832 //float grad = (1 - pt) * (2 * pt*logf(pt) + pt - 1); // https://github.com/unsky/focal-loss for (n = 0; n < classes; ++n) { @@ -224,7 +224,7 @@ void forward_region_layer(const region_layer l, network_state state) int onlyclass_id = 0; for(t = 0; t < l.max_boxes; ++t){ box truth = float_to_box(state.truth + t*5 + b*l.truths); - if(!truth.x) break; + if(!truth.x) break; // continue; int class_id = state.truth[t*5 + b*l.truths + 4]; float maxp = 0; int maxi = 0; @@ -258,7 +258,7 @@ void forward_region_layer(const region_layer l, network_state state) box truth = float_to_box(state.truth + t*5 + b*l.truths); int class_id = state.truth[t * 5 + b*l.truths + 4]; if (class_id >= l.classes) continue; // if label contains class_id more than number of classes in the cfg-file - if(!truth.x) break; + if(!truth.x) break; // continue; float iou = box_iou(pred, truth); if (iou > best_iou) { best_class_id = state.truth[t*5 + b*l.truths + 4]; @@ -302,7 +302,7 @@ void forward_region_layer(const region_layer l, network_state state) continue; // if label contains class_id more than number of classes in the cfg-file } - if(!truth.x) break; + if(!truth.x) break; // continue; float best_iou = 0; int best_index = 0; int best_n = 0; diff --git a/src/yolo_layer.c b/src/yolo_layer.c index f0bc0731754..de006429e48 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -121,12 +121,12 @@ void delta_yolo_class(float *output, float *delta, int index, int class_id, int if (focal_loss) { // Focal Loss float alpha = 0.5; // 0.25 or 0.5 - //float gamma = 2; // hardcoded in many places of the grad-formula + //float gamma = 2; // hardcoded in many places of the grad-formula int ti = index + stride*class_id; float pt = output[ti] + 0.000000000000001F; // http://fooplot.com/#W3sidHlwZSI6MCwiZXEiOiItKDEteCkqKDIqeCpsb2coeCkreC0xKSIsImNvbG9yIjoiIzAwMDAwMCJ9LHsidHlwZSI6MTAwMH1d - float grad = -(1 - pt) * (2 * pt*logf(pt) + pt - 1); // http://blog.csdn.net/linmingan/article/details/77885832 + float grad = -(1 - pt) * (2 * pt*logf(pt) + pt - 1); // http://blog.csdn.net/linmingan/article/details/77885832 //float grad = (1 - pt) * (2 * pt*logf(pt) + pt - 1); // https://github.com/unsky/focal-loss for (n = 0; n < classes; ++n) { @@ -206,7 +206,7 @@ void forward_yolo_layer(const layer l, network_state state) getchar(); continue; // if label contains class_id more than number of classes in the cfg-file } - if(!truth.x) break; + if(!truth.x) break; // continue; float iou = box_iou(pred, truth); if (iou > best_iou) { best_iou = iou; @@ -237,7 +237,7 @@ void forward_yolo_layer(const layer l, network_state state) int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; if (class_id >= l.classes) continue; // if label contains class_id more than number of classes in the cfg-file - if(!truth.x) break; + if(!truth.x) break; // continue; float best_iou = 0; int best_n = 0; i = (truth.x * l.w); @@ -307,8 +307,8 @@ void correct_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth } for (i = 0; i < n; ++i){ box b = dets[i].bbox; - b.x = (b.x - (netw - new_w)/2./netw) / ((float)new_w/netw); - b.y = (b.y - (neth - new_h)/2./neth) / ((float)new_h/neth); + b.x = (b.x - (netw - new_w)/2./netw) / ((float)new_w/netw); + b.y = (b.y - (neth - new_h)/2./neth) / ((float)new_h/neth); b.w *= (float)netw/new_w; b.h *= (float)neth/new_h; if(!relative){ From 08d0e07ed64139d2552d7801b755af7f34b4a75e Mon Sep 17 00:00:00 2001 From: Alexey Date: Sun, 19 Aug 2018 14:08:02 +0300 Subject: [PATCH 0137/1365] Update Readme.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 5eff17517a8..f2488263f43 100644 --- a/README.md +++ b/README.md @@ -488,6 +488,8 @@ With example of: `train.txt`, `obj.names`, `obj.data`, `yolo-obj.cfg`, `air`1-6` 2. To use Yolo as DLL-file in your C++ console application - open in MSVS2015 file `build\darknet\yolo_console_dll.sln`, set **x64** and **Release**, and do the: Build -> Build yolo_console_dll * you can run your console application from Windows Explorer `build\darknet\x64\yolo_console_dll.exe` + **use this command**: `yolo_console_dll.exe data/coco.names yolov3.cfg yolov3.weights test.mp4` + * or you can run from MSVS2015 (before this - you should copy 2 files `yolo-voc.cfg` and `yolo-voc.weights` to the directory `build\darknet\` ) * after launching your console application and entering the image file name - you will see info for each object: ` ` From 8e018980a8eb15e319fec5c28163bf4ef408b1f0 Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 20 Aug 2018 21:58:51 +0300 Subject: [PATCH 0138/1365] Update Readme.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f2488263f43..963c401b0e4 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # Yolo-v3 and Yolo-v2 for Windows and Linux -### (neural network for object detection) +### (neural network for object detection) - Tensor Cores can be used on [Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) and [Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows) [![CircleCI](https://circleci.com/gh/AlexeyAB/darknet.svg?style=svg)](https://circleci.com/gh/AlexeyAB/darknet) From e1801f5aec1373475682fa4d0850a3469dab6ef7 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 21 Aug 2018 00:52:16 +0300 Subject: [PATCH 0139/1365] Added trim() function for label paths --- src/utils.c | 25 ++++++++++++++++++++++--- 1 file changed, 22 insertions(+), 3 deletions(-) diff --git a/src/utils.c b/src/utils.c index 09c50b2c233..14d647bda99 100644 --- a/src/utils.c +++ b/src/utils.c @@ -185,6 +185,24 @@ void find_replace(char *str, char *orig, char *rep, char *output) free(buffer); } +void trim(char *str) +{ + char *buffer = calloc(8192, sizeof(char)); + sprintf(buffer, "%s", str); + + char *p = buffer; + while (*p == ' ' || *p == '\t') ++p; + + char *end = p + strlen(p) - 1; + while (*end == ' ' || *end == '\t') { + *end = '\0'; + --end; + } + sprintf(str, "%s", p); + + free(buffer); +} + void find_replace_extension(char *str, char *orig, char *rep, char *output) { char *buffer = calloc(8192, sizeof(char)); @@ -200,23 +218,24 @@ void find_replace_extension(char *str, char *orig, char *rep, char *output) } *p = '\0'; - sprintf(output, "%s%s%s", buffer, rep, p + strlen(orig)); free(buffer); } -void replace_image_to_label(char *input_path, char *output_path) { - //find_replace(input_path, "/images/", "/labels/", output_path); // COCO +void replace_image_to_label(char *input_path, char *output_path) +{ find_replace(input_path, "/images/train2014/", "/labels/train2014/", output_path); // COCO find_replace(output_path, "/images/val2014/", "/labels/val2014/", output_path); // COCO find_replace(output_path, "/JPEGImages/", "/labels/", output_path); // PascalVOC find_replace(output_path, "\\images\\train2014\\", "\\labels\\train2014\\", output_path); // COCO find_replace(output_path, "\\images\\val2014\\", "\\labels\\val2014\\", output_path); // COCO find_replace(output_path, "\\JPEGImages\\", "\\labels\\", output_path); // PascalVOC + //find_replace(output_path, "/images/", "/labels/", output_path); // COCO //find_replace(output_path, "/VOC2007/JPEGImages/", "/VOC2007/labels/", output_path); // PascalVOC //find_replace(output_path, "/VOC2012/JPEGImages/", "/VOC2012/labels/", output_path); // PascalVOC //find_replace(output_path, "/raw/", "/labels/", output_path); + trim(output_path); // replace only ext of files find_replace_extension(output_path, ".jpg", ".txt", output_path); From 8436251a05ec0cfa906089ee022db17498e6f65f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 21 Aug 2018 20:44:18 +0300 Subject: [PATCH 0140/1365] maxpool_layer on CPU uses AVX2 and OpenMP --- src/gemm.c | 142 ++++++++++++++++++++++++++++++++++++++++++-- src/gemm.h | 5 ++ src/maxpool_layer.c | 6 ++ 3 files changed, 149 insertions(+), 4 deletions(-) diff --git a/src/gemm.c b/src/gemm.c index 6a34da5ec49..7abfe25cab7 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -5,6 +5,7 @@ #include #include #include +#include #if defined(_OPENMP) #include @@ -594,7 +595,7 @@ void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride, static int max_num_threads = 0; if (max_num_threads == 0) { max_num_threads = omp_get_max_threads(); - omp_set_num_threads(4);// max_num_threads / 2); + omp_set_num_threads( max_num_threads / 2); } #endif @@ -1167,6 +1168,100 @@ void transpose_block_SSE4x4(float *A, float *B, const int n, const int m, } +void forward_maxpool_layer_avx(float *src, float *dst, int *indexes, int size, int w, int h, int out_w, int out_h, int c, + int pad, int stride, int batch) +{ + + int w_offset = -pad / 2; + int h_offset = -pad / 2; + int b, k; + + for (b = 0; b < batch; ++b) { + #pragma omp parallel for + for (k = 0; k < c; ++k) { + int i, j, m, n; + for (i = 0; i < out_h; ++i) { + //for (j = 0; j < out_w; ++j) { + j = 0; + + if(stride == 1 && is_avx() == 1) { + for (j = 0; j < out_w - 8 - (size - 1); j += 8) { + int out_index = j + out_w*(i + out_h*(k + c*b)); + __m256 max256 = _mm256_set1_ps(-FLT_MAX); + for (n = 0; n < size; ++n) { + for (m = 0; m < size; ++m) { + int cur_h = h_offset + i*stride + n; + int cur_w = w_offset + j*stride + m; + int index = cur_w + w*(cur_h + h*(k + b*c)); + int valid = (cur_h >= 0 && cur_h < h && + cur_w >= 0 && cur_w < w); + if (!valid) continue; + + __m256 src256 = _mm256_loadu_ps(&src[index]); + max256 = _mm256_max_ps(src256, max256); + } + } + _mm256_storeu_ps(&dst[out_index], max256); + + } + } + else if (size == 2 && stride == 2 && is_avx() == 1) { + for (j = 0; j < out_w - 4; j += 4) { + int out_index = j + out_w*(i + out_h*(k + c*b)); + float max = -FLT_MAX; + int max_i = -1; + __m128 max128 = _mm_set1_ps(-FLT_MAX); + + for (n = 0; n < size; ++n) { + //for (m = 0; m < size; ++m) + m = 0; + { + int cur_h = h_offset + i*stride + n; + int cur_w = w_offset + j*stride + m; + int index = cur_w + w*(cur_h + h*(k + b*c)); + int valid = (cur_h >= 0 && cur_h < h && + cur_w >= 0 && cur_w < w); + if (!valid) continue; + + __m256 src256 = _mm256_loadu_ps(&src[index]); + __m256 src256_2 = _mm256_permute_ps(src256, (1 << 0) | (3 << 4)); + __m256 max256 = _mm256_max_ps(src256, src256_2); + + __m128 src128_0 = _mm256_extractf128_ps(max256, 0); + __m128 src128_1 = _mm256_extractf128_ps(max256, 1); + __m128 src128 = _mm_shuffle_ps(src128_0, src128_1, (2 << 2) | (2 << 6)); + + max128 = _mm_max_ps(src128, max128); + } + } + _mm_storeu_ps(&dst[out_index], max128); + } + } + + for (; j < out_w; ++j) { + int out_index = j + out_w*(i + out_h*(k + c*b)); + float max = -FLT_MAX; + int max_i = -1; + for (n = 0; n < size; ++n) { + for (m = 0; m < size; ++m) { + int cur_h = h_offset + i*stride + n; + int cur_w = w_offset + j*stride + m; + int index = cur_w + w*(cur_h + h*(k + b*c)); + int valid = (cur_h >= 0 && cur_h < h && + cur_w >= 0 && cur_w < w); + float val = (valid != 0) ? src[index] : -FLT_MAX; + max_i = (val > max) ? index : max_i; + max = (val > max) ? val : max; + } + } + dst[out_index] = max; + indexes[out_index] = max_i; + } + } + } + } +} + #else void gemm_nn(int M, int N, int K, float ALPHA, @@ -1283,6 +1378,8 @@ void im2col_cpu_custom(float* data_im, int channels, int height, int width, int ksize, int stride, int pad, float* data_col) { + im2col_cpu(data_im, channels, height, width, ksize, stride, pad, data_col); + return; int c, h, w; int height_col = (height + 2 * pad - ksize) / stride + 1; @@ -1304,7 +1401,7 @@ void im2col_cpu_custom(float* data_im, int col_index = (c * height_col + h) * width_col + w; data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; - } + } for (; w < width_col - pad; ++w) { int im_row = h_offset + h - pad; @@ -1313,7 +1410,7 @@ void im2col_cpu_custom(float* data_im, data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; } -} + } { w = 0; @@ -1445,7 +1542,44 @@ void transpose_block_SSE4x4(float *A, float *B, const int n, const int m, } } } -#endif // __x86_64 + +void forward_maxpool_layer_avx(float *src, float *dst, int *indexes, int size, int w, int h, int out_w, int out_h, int c, + int pad, int stride, int batch) +{ + int b, k; + int w_offset = -pad / 2; + int h_offset = -pad / 2; + + for (b = 0; b < batch; ++b) { + #pragma omp parallel for + for (k = 0; k < c; ++k) { + int i, j, m, n; + for (i = 0; i < out_h; ++i) { + for (j = 0; j < out_w; ++j) { + int out_index = j + out_w*(i + out_h*(k + c*b)); + float max = -FLT_MAX; + int max_i = -1; + for (n = 0; n < size; ++n) { + for (m = 0; m < size; ++m) { + int cur_h = h_offset + i*stride + n; + int cur_w = w_offset + j*stride + m; + int index = cur_w + w*(cur_h + h*(k + b*c)); + int valid = (cur_h >= 0 && cur_h < h && + cur_w >= 0 && cur_w < w); + float val = (valid != 0) ? src[index] : -FLT_MAX; + max_i = (val > max) ? index : max_i; + max = (val > max) ? val : max; + } + } + dst[out_index] = max; + indexes[out_index] = max_i; + } + } + } + } +} + +#endif // AVX void gemm_nt(int M, int N, int K, float ALPHA, float *A, int lda, diff --git a/src/gemm.h b/src/gemm.h index 1472ccae054..3cdca99ce0f 100644 --- a/src/gemm.h +++ b/src/gemm.h @@ -46,6 +46,11 @@ void gemm_bin(int M, int N, int K, float ALPHA, float *B, int ldb, float *C, int ldc); + +void forward_maxpool_layer_avx(float *src, float *dst, int *indexes, int size, int w, int h, int out_w, int out_h, int c, + int pad, int stride, int batch); + + void gemm(int TA, int TB, int M, int N, int K, float ALPHA, float *A, int lda, float *B, int ldb, diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index c543b529394..5ff7e9d4127 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -1,5 +1,6 @@ #include "maxpool_layer.h" #include "cuda.h" +#include "gemm.h" #include image get_maxpool_image(maxpool_layer l) @@ -79,6 +80,11 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h) void forward_maxpool_layer(const maxpool_layer l, network_state state) { + if (!state.train) { + forward_maxpool_layer_avx(state.input, l.output, l.indexes, l.size, l.w, l.h, l.out_w, l.out_h, l.c, l.pad, l.stride, l.batch); + return; + } + int b,i,j,k,m,n; int w_offset = -l.pad / 2; int h_offset = -l.pad / 2; From dbdd31ee211fe8b1ac7e93ceadf7b34b8d304f34 Mon Sep 17 00:00:00 2001 From: Roland Singer Date: Wed, 22 Aug 2018 13:56:41 +0200 Subject: [PATCH 0141/1365] updated README to include information about learning rate adjustment for multiple GPUs --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 963c401b0e4..18d3f153b4e 100644 --- a/README.md +++ b/README.md @@ -227,7 +227,9 @@ More information about training by the link: http://pjreddie.com/darknet/yolo/#t 1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg darknet53.conv.74` -2. Then stop and by using partially-trained model `/backup/yolov3-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg /backup/yolov3-voc_1000.weights -gpus 0,1,2,3` +2. Adjust the learning rate (`cfg/yolov3-voc.cfg`) to fit the amount of GPUs. The learning rate should be equal to `0.001`, regardless of how many GPUs are used for training. So `learning_rate * GPUs = 0.001`. For 4 GPUs adjust the value to `learning_rate = 0.00025`. + +3. Then stop and by using partially-trained model `/backup/yolov3-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg /backup/yolov3-voc_1000.weights -gpus 0,1,2,3` https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ From f606b5456e4876da5f90e2902b2dff07516a03dc Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 22 Aug 2018 02:33:23 +0300 Subject: [PATCH 0142/1365] XNOR-net 21 FPS on CPU yolov2-tiny.cfg --- src/convolutional_layer.c | 30 ++++-- src/gemm.c | 218 +++++++++++++++++++++++++++++++++++--- src/gemm.h | 9 ++ src/layer.h | 1 + 4 files changed, 237 insertions(+), 21 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index e7c8e3f26c4..03aad929e78 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -132,6 +132,7 @@ size_t get_workspace_size(layer l){ return most; } #endif + if(l.xnor) return (size_t)l.bit_align*l.size*l.size*l.c * sizeof(float); return (size_t)l.out_h*l.out_w*l.size*l.size*l.c*sizeof(float); } @@ -305,6 +306,10 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int if(xnor){ l.binary_weights = calloc(c*n*size*size, sizeof(float)); l.binary_input = calloc(l.inputs*l.batch, sizeof(float)); + + int align = 8; + int src_align = l.out_h*l.out_w; + l.bit_align = src_align + (align - src_align % align); } if(batch_normalize){ @@ -622,7 +627,7 @@ void binary_align_weights(convolutional_layer *l) } // further optimizations: im2col_bin() for XNOR, and then transpose_aling_bin() -size_t binary_transpose_align_input(int k, int n, float *b, char **t_bit_input, size_t ldb_align) +size_t binary_transpose_align_input(int k, int n, float *b, char **t_bit_input, size_t ldb_align, int bit_align) { size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; size_t t_intput_size = new_ldb * n; @@ -637,12 +642,17 @@ size_t binary_transpose_align_input(int k, int n, float *b, char **t_bit_input, //printf("\n align_weights_size = %d, k = %d, m = %d, lda = %d \n", align_weights_size, k, m, k); //printf("\n align_bit_weights_size = %d, k = %d, m = %d, new_lda = %d \n", align_bit_weights_size, k, m, new_ldb); - int blocksize = 64; - transpose_block_SSE4x4(b, t_input, k, n, n, new_ldb, blocksize); + int src_size = k * bit_align; + + float_to_bit(b, t_input, src_size); + + // b - [bit_align, k] - [l.bit_align, l.size*l.size*l.c] = src_size + // t_input - [bit_align, k] - [n', k] + // t_bit_input - [new_ldb, n] - [k', n] - //printf("\n blocksize = %d \n", blocksize); + transpose_bin(t_input, *t_bit_input, k, n, bit_align, new_ldb, 8); + //transpose_bin(b, *t_bit_input, k, n, bit_align, new_ldb, 8); - float_to_bit(t_input, *t_bit_input, t_intput_size); free(t_input); return t_intput_size; @@ -691,12 +701,16 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //if (l.xnor && l.size == 3 && l.stride == 1 && l.pad == 1) {} //else // further optimizations: im2col_bin() for XNOR, and then transpose_aling_bin() - im2col_cpu_custom(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); + //im2col_cpu_custom(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); //gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); //gemm_nn_custom(m, n, k, 1, a, k, b, n, c, n); if (l.xnor) { + //im2col_cpu_custom(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); + memset(b, 0, l.bit_align*l.size*l.size*l.c * sizeof(float)); + im2col_cpu_custom_bin(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align); + size_t output_size = l.outputs; //float *count_output = calloc(output_size, sizeof(float)); //size_t bit_output_size = output_size / 8 + 1; @@ -790,7 +804,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) int ldb_align = l.lda_align; size_t new_ldb = k + (ldb_align - k%ldb_align); char *t_bit_input = NULL; - size_t t_intput_size = binary_transpose_align_input(k, n, b, &t_bit_input, ldb_align); + size_t t_intput_size = binary_transpose_align_input(k, n, b, &t_bit_input, ldb_align, l.bit_align); //char *t_bit_input = calloc(new_ldb * n, sizeof(char)); // for im2col_cpu_custom_transpose() only //float_to_bit(t_input, t_bit_input, new_ldb * n); // for im2col_cpu_custom_transpose() only @@ -825,6 +839,8 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //free(mean_arr); } else { + im2col_cpu_custom(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); + gemm(0, 0, m, n, k, 1, a, k, b, n, 1, c, n); // bit-count to float } diff --git a/src/gemm.c b/src/gemm.c index 7abfe25cab7..6342252ba87 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -6,6 +6,7 @@ #include #include #include +#include #if defined(_OPENMP) #include @@ -595,7 +596,7 @@ void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride, static int max_num_threads = 0; if (max_num_threads == 0) { max_num_threads = omp_get_max_threads(); - omp_set_num_threads( max_num_threads / 2); + //omp_set_num_threads( max_num_threads / 2); } #endif @@ -1037,27 +1038,212 @@ void im2col_cpu_custom(float* data_im, } } -void transpose_8x8_bits(unsigned char A[8], unsigned char B[8], int m, int n) + +//From Berkeley Vision's Caffe! +//https://github.com/BVLC/caffe/blob/master/LICENSE +void im2col_cpu_custom_bin(float* data_im, + int channels, int height, int width, + int ksize, int stride, int pad, float* data_col, int bit_align) +{ + int c, h, w; + int height_col = (height + 2 * pad - ksize) / stride + 1; + int width_col = (width + 2 * pad - ksize) / stride + 1; + int channels_col = channels * ksize * ksize; + + // optimized version + if (height_col == height && width_col == width && stride == 1 && pad == 1 && is_fma_avx2()) + { + __m256i all256_sing1 = _mm256_set_epi32(0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000); + + //int algin = 8; + //int ldb = width_col * height_col; + //int new_ldb = ldb + (algin - ldb % algin); + int new_ldb = bit_align; + + #pragma omp parallel for + for (c = 0; c < channels_col; ++c) { + int w_offset = c % ksize; + int h_offset = (c / ksize) % ksize; + int c_im = c / ksize / ksize; + for (h = pad; h < height_col - pad; ++h) { + for (w = pad; w < width_col - pad - 8; w += 8) { + int im_row = h_offset + h - pad; + int im_col = w_offset + w - pad; + //int col_index = (c * height_col + h) * width_col + w; + int col_index = c * new_ldb + h * width_col + w; + + //data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; + __m256 src256 = _mm256_loadu_ps((float *)(&data_im[im_col + width*(im_row + height*c_im)])); + _mm256_storeu_ps(&data_col[col_index], src256); + + /*/ + __m256i src256 = _mm256_loadu_si256((__m256i *)(&data_im[im_col + width*(im_row + height*c_im)])); + __m256i result256 = _mm256_and_si256(src256, all256_sing1); // check sign in 8 x 32-bit floats + + uint32_t mask = _mm256_movemask_ps(_mm256_castsi256_ps(result256)); // (val >= 0) ? 0 : 1 + mask = ~mask; // inverse mask, (val >= 0) ? 1 : 0 + + data_col[col_index / 8] = mask; // dst[i / 8] = mask; + */ + } + + for (; w < width_col - pad; ++w) { + int im_row = h_offset + h - pad; + int im_col = w_offset + w - pad; + //int col_index = (c * height_col + h) * width_col + w; + int col_index = c * new_ldb + h * width_col + w; + + data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; + float val = data_im[im_col + width*(im_row + height*c_im)]; + //if(val > 0) set_bit(data_col, col_index); + } + } + + { + w = 0; + for (h = 0; h < height_col; ++h) { + int im_row = h_offset + h; + int im_col = w_offset + w; + //int col_index = (c * height_col + h) * width_col + w; + int col_index = c * new_ldb + h * width_col + w; + + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + //if (val > 0) set_bit(data_col, col_index); + } + } + + { + w = width_col - 1; + for (h = 0; h < height_col; ++h) { + int im_row = h_offset + h; + int im_col = w_offset + w; + //int col_index = (c * height_col + h) * width_col + w; + int col_index = c * new_ldb + h * width_col + w; + + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + //if (val > 0) set_bit(data_col, col_index); + } + } + + { + h = 0; + for (w = 0; w < width_col; ++w) { + int im_row = h_offset + h; + int im_col = w_offset + w; + //int col_index = (c * height_col + h) * width_col + w; + int col_index = c * new_ldb + h * width_col + w; + + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + //if (val > 0) set_bit(data_col, col_index); + } + } + + { + h = height_col - 1; + for (w = 0; w < width_col; ++w) { + int im_row = h_offset + h; + int im_col = w_offset + w; + //int col_index = (c * height_col + h) * width_col + w; + int col_index = c * new_ldb + h * width_col + w; + + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + //if (val > 0) set_bit(data_col, col_index); + } + } + } + + } + else { + printf("\n Error: is no non-optimized version \n"); + //im2col_cpu(data_im, channels, height, width, ksize, stride, pad, data_col); + } + + /* + int src_size = bit_align*channels_col; + char *bit_arr = calloc(src_size, sizeof(float)); + float_to_bit(data_col, bit_arr, src_size); + memcpy(data_col, bit_arr, src_size * sizeof(float)); + free(bit_arr); + */ +} + + +void transpose_8x8_bits_my(unsigned char *A, unsigned char *B, int lda, int ldb) { unsigned x, y, t; + for (y = 0; y < 8; ++y) { + for (x = 0; x < 8; ++x) { + if(A[y * lda] & (1 << x)) B[x * ldb] |= 1 << y; + } + } +} + +unsigned char reverse_byte_1(char a) +{ + return ((a & 0x1) << 7) | ((a & 0x2) << 5) | + ((a & 0x4) << 3) | ((a & 0x8) << 1) | + ((a & 0x10) >> 1) | ((a & 0x20) >> 3) | + ((a & 0x40) >> 5) | ((a & 0x80) >> 7); +} + +unsigned char reverse_byte_2(unsigned char a) +{ + return ((a * 0x0802LU & 0x22110LU) | (a * 0x8020LU & 0x88440LU)) * 0x10101LU >> 16; +} + +static unsigned char lookup[16] = { + 0x0, 0x8, 0x4, 0xc, 0x2, 0xa, 0x6, 0xe, + 0x1, 0x9, 0x5, 0xd, 0x3, 0xb, 0x7, 0xf, }; + +unsigned char reverse_byte(unsigned char n) { + // Reverse the top and bottom nibble then swap them. + return (lookup[n & 0b1111] << 4) | lookup[n >> 4]; +} - // Load the array and pack it into x and y. +void transpose8rS32_reversed_diagonale(unsigned char* A, int m, int n, unsigned char* B) +{ + unsigned x, y, t; + + // Load the array and pack it into x and y. x = (A[0] << 24) | (A[m] << 16) | (A[2 * m] << 8) | A[3 * m]; y = (A[4 * m] << 24) | (A[5 * m] << 16) | (A[6 * m] << 8) | A[7 * m]; - t = (x ^ (x >> 7)) & 0x00AA00AA; x = x ^ t ^ (t << 7); - t = (y ^ (y >> 7)) & 0x00AA00AA; y = y ^ t ^ (t << 7); + t = (x ^ (x >> 7)) & 0x00AA00AA; x = x ^ t ^ (t << 7); + t = (y ^ (y >> 7)) & 0x00AA00AA; y = y ^ t ^ (t << 7); - t = (x ^ (x >> 14)) & 0x0000CCCC; x = x ^ t ^ (t << 14); - t = (y ^ (y >> 14)) & 0x0000CCCC; y = y ^ t ^ (t << 14); + t = (x ^ (x >> 14)) & 0x0000CCCC; x = x ^ t ^ (t << 14); + t = (y ^ (y >> 14)) & 0x0000CCCC; y = y ^ t ^ (t << 14); t = (x & 0xF0F0F0F0) | ((y >> 4) & 0x0F0F0F0F); y = ((x << 4) & 0xF0F0F0F0) | (y & 0x0F0F0F0F); x = t; - B[0] = x >> 24; B[n] = x >> 16; B[2 * n] = x >> 8; B[3 * n] = x; - B[4 * n] = y >> 24; B[5 * n] = y >> 16; B[6 * n] = y >> 8; B[7 * n] = y; + B[7 * n] = reverse_byte(x >> 24); B[6 * n] = reverse_byte(x >> 16); B[5 * n] = reverse_byte(x >> 8); B[4 * n] = reverse_byte(x); + B[3 * n] = reverse_byte(y >> 24); B[2 * n] = reverse_byte(y >> 16); B[1 * n] = reverse_byte(y >> 8); B[0 * n] = reverse_byte(y); +} + +void transpose_bin(char *A, char *B, const int n, const int m, + const int lda, const int ldb, const int block_size) +{ + int i; + #pragma omp parallel for + for (i = 0; i < n; i += 8) { + int j; + for (j = 0; j < m - 8; j += 8) { + int a_index = i*lda + j; + int b_index = j*ldb + i; + //transpose_8x8_bits_my(&A[a_index/8], &B[b_index/8], lda/8, ldb/8); + transpose8rS32_reversed_diagonale(&A[a_index / 8], lda / 8, ldb / 8, &B[b_index / 8]); + } + for (; j < m; ++j) { + if (get_bit(A, i*lda + j)) set_bit(B, j*ldb + i); + } + } } void activate_array_cpu_custom(float *x, const int n, const ACTIVATION a) @@ -1102,14 +1288,18 @@ void float_to_bit(float *src, unsigned char *dst, size_t size) size_t i; __m256i all256_sing1 = _mm256_set_epi32(0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000); + __m256 float_zero256 = _mm256_set1_ps(0.00001); for (i = 0; i < size; i+=8) { - __m256i src256 = _mm256_loadu_si256((__m256i *)(&src[i])); - __m256i result256 = _mm256_and_si256(src256, all256_sing1); // check sign in 8 x 32-bit floats - - uint32_t mask = _mm256_movemask_ps(_mm256_castsi256_ps(result256)); // (val >= 0) ? 0 : 1 - mask = ~mask; // inverse mask, (val >= 0) ? 1 : 0 + //__m256i src256 = _mm256_loadu_si256((__m256i *)(&src[i])); + //__m256i result256 = _mm256_and_si256(src256, all256_sing1); // check sign in 8 x 32-bit floats + //uint32_t mask = _mm256_movemask_ps(_mm256_castsi256_ps(result256)); // (val >= 0) ? 0 : 1 + //mask = ~mask; // inverse mask, (val >= 0) ? 1 : 0 + + __m256 src256 = _mm256_loadu_ps((float *)(&src[i])); + __m256 result256 = _mm256_cmp_ps(src256, float_zero256, _CMP_GT_OS); + uint32_t mask = _mm256_movemask_ps(result256); // (val > 0) ? 0 : 1 dst[i / 8] = mask; } diff --git a/src/gemm.h b/src/gemm.h index 3cdca99ce0f..d393b0dba79 100644 --- a/src/gemm.h +++ b/src/gemm.h @@ -11,12 +11,14 @@ static inline void set_bit(unsigned char *const dst, size_t index) { size_t dst_i = index / 8; int dst_shift = index % 8; dst[dst_i] |= 1 << dst_shift; + //dst[dst_i] |= 1 << (8 - dst_shift); } static inline unsigned char get_bit(unsigned char const*const src, size_t index) { size_t src_i = index / 8; int src_shift = index % 8; unsigned char val = (src[src_i] & (1 << src_shift)) > 0; + //unsigned char val = (src[src_i] & (1 << (8 - src_shift))) > 0; return val; } @@ -25,6 +27,9 @@ void float_to_bit(float *src, unsigned char *dst, size_t size); void transpose_block_SSE4x4(float *A, float *B, const int n, const int m, const int lda, const int ldb, const int block_size); +void transpose_bin(char *A, char *B, const int n, const int m, + const int lda, const int ldb, const int block_size); + void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, unsigned char *A, int lda, unsigned char *B, int ldb, @@ -34,6 +39,10 @@ void im2col_cpu_custom(float* data_im, int channels, int height, int width, int ksize, int stride, int pad, float* data_col); +void im2col_cpu_custom_bin(float* data_im, + int channels, int height, int width, + int ksize, int stride, int pad, float* data_col, int bit_align); + void im2col_cpu_custom_transpose(float* data_im, int channels, int height, int width, int ksize, int stride, int pad, float* data_col, int ldb_align); diff --git a/src/layer.h b/src/layer.h index bd8518ae779..72a63e9e153 100644 --- a/src/layer.h +++ b/src/layer.h @@ -182,6 +182,7 @@ struct layer{ char *align_bit_weights; float *mean_arr; int lda_align; + int bit_align; float *col_image; int * input_layers; From 31b6b0bad3e9111aac4792ebfe37218835ce8efc Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 23 Aug 2018 02:44:21 +0300 Subject: [PATCH 0143/1365] XNOR-net 4x acceleration on CPU for yolov2-tiny - 22 FPS (CPU Core i7 6700K) --- src/convolutional_layer.c | 15 +- src/gemm.c | 401 ++++++++++++++++++++++++++++---------- src/gemm.h | 4 + 3 files changed, 313 insertions(+), 107 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 03aad929e78..a03deff9dfc 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -632,7 +632,7 @@ size_t binary_transpose_align_input(int k, int n, float *b, char **t_bit_input, size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; size_t t_intput_size = new_ldb * n; size_t t_bit_input_size = t_intput_size / 8;// +1; - float *t_input = calloc(t_intput_size, sizeof(float)); + //float *t_input = calloc(t_intput_size, sizeof(float)); //char * *t_bit_input = calloc(t_bit_input_size, sizeof(char)); @@ -643,17 +643,18 @@ size_t binary_transpose_align_input(int k, int n, float *b, char **t_bit_input, //printf("\n align_bit_weights_size = %d, k = %d, m = %d, new_lda = %d \n", align_bit_weights_size, k, m, new_ldb); int src_size = k * bit_align; + //printf("\n src_size = %d \n", src_size); - float_to_bit(b, t_input, src_size); + //float_to_bit(b, t_input, src_size); // b - [bit_align, k] - [l.bit_align, l.size*l.size*l.c] = src_size // t_input - [bit_align, k] - [n', k] // t_bit_input - [new_ldb, n] - [k', n] - transpose_bin(t_input, *t_bit_input, k, n, bit_align, new_ldb, 8); - //transpose_bin(b, *t_bit_input, k, n, bit_align, new_ldb, 8); + //transpose_bin(t_input, *t_bit_input, k, n, bit_align, new_ldb, 8); + transpose_bin(b, *t_bit_input, k, n, bit_align, new_ldb, 8); - free(t_input); + //free(t_input); return t_intput_size; } @@ -706,9 +707,9 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); //gemm_nn_custom(m, n, k, 1, a, k, b, n, c, n); - if (l.xnor) { - //im2col_cpu_custom(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); + if (l.xnor && (l.stride == 1 && l.pad == 1)) { memset(b, 0, l.bit_align*l.size*l.size*l.c * sizeof(float)); + //im2col_cpu_custom_align(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align); im2col_cpu_custom_bin(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align); size_t output_size = l.outputs; diff --git a/src/gemm.c b/src/gemm.c index 6342252ba87..6ff57cd3efe 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -306,6 +306,83 @@ void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, //---------------------------- +void transpose_8x8_bits_my(unsigned char *A, unsigned char *B, int lda, int ldb) +{ + unsigned x, y, t; + for (y = 0; y < 8; ++y) { + for (x = 0; x < 8; ++x) { + if (A[y * lda] & (1 << x)) B[x * ldb] |= 1 << y; + } + } +} + +unsigned char reverse_byte_1(char a) +{ + return ((a & 0x1) << 7) | ((a & 0x2) << 5) | + ((a & 0x4) << 3) | ((a & 0x8) << 1) | + ((a & 0x10) >> 1) | ((a & 0x20) >> 3) | + ((a & 0x40) >> 5) | ((a & 0x80) >> 7); +} + +unsigned char reverse_byte_2(unsigned char a) +{ + return ((a * 0x0802LU & 0x22110LU) | (a * 0x8020LU & 0x88440LU)) * 0x10101LU >> 16; +} + +static unsigned char lookup[16] = { + 0x0, 0x8, 0x4, 0xc, 0x2, 0xa, 0x6, 0xe, + 0x1, 0x9, 0x5, 0xd, 0x3, 0xb, 0x7, 0xf, }; + +unsigned char reverse_byte(unsigned char n) { + // Reverse the top and bottom nibble then swap them. + return (lookup[n & 0b1111] << 4) | lookup[n >> 4]; +} + + +void transpose8rS32_reversed_diagonale(unsigned char* A, int m, int n, unsigned char* B) +{ + unsigned x, y, t; + + // Load the array and pack it into x and y. + x = (A[0] << 24) | (A[m] << 16) | (A[2 * m] << 8) | A[3 * m]; + y = (A[4 * m] << 24) | (A[5 * m] << 16) | (A[6 * m] << 8) | A[7 * m]; + + t = (x ^ (x >> 7)) & 0x00AA00AA; x = x ^ t ^ (t << 7); + t = (y ^ (y >> 7)) & 0x00AA00AA; y = y ^ t ^ (t << 7); + + t = (x ^ (x >> 14)) & 0x0000CCCC; x = x ^ t ^ (t << 14); + t = (y ^ (y >> 14)) & 0x0000CCCC; y = y ^ t ^ (t << 14); + + t = (x & 0xF0F0F0F0) | ((y >> 4) & 0x0F0F0F0F); + y = ((x << 4) & 0xF0F0F0F0) | (y & 0x0F0F0F0F); + x = t; + + B[7 * n] = reverse_byte(x >> 24); B[6 * n] = reverse_byte(x >> 16); B[5 * n] = reverse_byte(x >> 8); B[4 * n] = reverse_byte(x); + B[3 * n] = reverse_byte(y >> 24); B[2 * n] = reverse_byte(y >> 16); B[1 * n] = reverse_byte(y >> 8); B[0 * n] = reverse_byte(y); +} + +void transpose_bin(char *A, char *B, const int n, const int m, + const int lda, const int ldb, const int block_size) +{ + int i; +#pragma omp parallel for + for (i = 0; i < n; i += 8) { + int j; + for (j = 0; j < m - 8; j += 8) { + int a_index = i*lda + j; + int b_index = j*ldb + i; + //transpose_8x8_bits_my(&A[a_index/8], &B[b_index/8], lda/8, ldb/8); + transpose8rS32_reversed_diagonale(&A[a_index / 8], lda / 8, ldb / 8, &B[b_index / 8]); + } + for (; j < m; ++j) { + if (get_bit(A, i*lda + j)) set_bit(B, j*ldb + i); + } + } +} + +//---------------------------- + + #if (defined(__AVX__) && defined(__x86_64__)) || defined(_WIN64) #ifdef _WIN64 @@ -1038,10 +1115,9 @@ void im2col_cpu_custom(float* data_im, } } - //From Berkeley Vision's Caffe! //https://github.com/BVLC/caffe/blob/master/LICENSE -void im2col_cpu_custom_bin(float* data_im, +void im2col_cpu_custom_align(float* data_im, int channels, int height, int width, int ksize, int stride, int pad, float* data_col, int bit_align) { @@ -1053,11 +1129,6 @@ void im2col_cpu_custom_bin(float* data_im, // optimized version if (height_col == height && width_col == width && stride == 1 && pad == 1 && is_fma_avx2()) { - __m256i all256_sing1 = _mm256_set_epi32(0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000); - - //int algin = 8; - //int ldb = width_col * height_col; - //int new_ldb = ldb + (algin - ldb % algin); int new_ldb = bit_align; #pragma omp parallel for @@ -1075,16 +1146,6 @@ void im2col_cpu_custom_bin(float* data_im, //data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; __m256 src256 = _mm256_loadu_ps((float *)(&data_im[im_col + width*(im_row + height*c_im)])); _mm256_storeu_ps(&data_col[col_index], src256); - - /*/ - __m256i src256 = _mm256_loadu_si256((__m256i *)(&data_im[im_col + width*(im_row + height*c_im)])); - __m256i result256 = _mm256_and_si256(src256, all256_sing1); // check sign in 8 x 32-bit floats - - uint32_t mask = _mm256_movemask_ps(_mm256_castsi256_ps(result256)); // (val >= 0) ? 0 : 1 - mask = ~mask; // inverse mask, (val >= 0) ? 1 : 0 - - data_col[col_index / 8] = mask; // dst[i / 8] = mask; - */ } for (; w < width_col - pad; ++w) { @@ -1092,10 +1153,7 @@ void im2col_cpu_custom_bin(float* data_im, int im_col = w_offset + w - pad; //int col_index = (c * height_col + h) * width_col + w; int col_index = c * new_ldb + h * width_col + w; - data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; - float val = data_im[im_col + width*(im_row + height*c_im)]; - //if(val > 0) set_bit(data_col, col_index); } } @@ -1106,10 +1164,7 @@ void im2col_cpu_custom_bin(float* data_im, int im_col = w_offset + w; //int col_index = (c * height_col + h) * width_col + w; int col_index = c * new_ldb + h * width_col + w; - data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); - float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); - //if (val > 0) set_bit(data_col, col_index); } } @@ -1120,10 +1175,7 @@ void im2col_cpu_custom_bin(float* data_im, int im_col = w_offset + w; //int col_index = (c * height_col + h) * width_col + w; int col_index = c * new_ldb + h * width_col + w; - data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); - float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); - //if (val > 0) set_bit(data_col, col_index); } } @@ -1134,10 +1186,7 @@ void im2col_cpu_custom_bin(float* data_im, int im_col = w_offset + w; //int col_index = (c * height_col + h) * width_col + w; int col_index = c * new_ldb + h * width_col + w; - data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); - float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); - //if (val > 0) set_bit(data_col, col_index); } } @@ -1148,10 +1197,7 @@ void im2col_cpu_custom_bin(float* data_im, int im_col = w_offset + w; //int col_index = (c * height_col + h) * width_col + w; int col_index = c * new_ldb + h * width_col + w; - data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); - float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); - //if (val > 0) set_bit(data_col, col_index); } } } @@ -1159,93 +1205,136 @@ void im2col_cpu_custom_bin(float* data_im, } else { printf("\n Error: is no non-optimized version \n"); - //im2col_cpu(data_im, channels, height, width, ksize, stride, pad, data_col); + //im2col_cpu(data_im, channels, height, width, ksize, stride, pad, data_col); // must be aligned for transpose after float_to_bin + // float_to_bit(b, t_input, src_size); + // transpose_bin(t_input, *t_bit_input, k, n, bit_align, new_ldb, 8); } - - /* - int src_size = bit_align*channels_col; - char *bit_arr = calloc(src_size, sizeof(float)); - float_to_bit(data_col, bit_arr, src_size); - memcpy(data_col, bit_arr, src_size * sizeof(float)); - free(bit_arr); - */ } -void transpose_8x8_bits_my(unsigned char *A, unsigned char *B, int lda, int ldb) +//From Berkeley Vision's Caffe! +//https://github.com/BVLC/caffe/blob/master/LICENSE +void im2col_cpu_custom_bin(float* data_im, + int channels, int height, int width, + int ksize, int stride, int pad, float* data_col, int bit_align) { - unsigned x, y, t; - for (y = 0; y < 8; ++y) { - for (x = 0; x < 8; ++x) { - if(A[y * lda] & (1 << x)) B[x * ldb] |= 1 << y; - } - } -} + int c, h, w; + int height_col = (height + 2 * pad - ksize) / stride + 1; + int width_col = (width + 2 * pad - ksize) / stride + 1; + int channels_col = channels * ksize * ksize; -unsigned char reverse_byte_1(char a) -{ - return ((a & 0x1) << 7) | ((a & 0x2) << 5) | - ((a & 0x4) << 3) | ((a & 0x8) << 1) | - ((a & 0x10) >> 1) | ((a & 0x20) >> 3) | - ((a & 0x40) >> 5) | ((a & 0x80) >> 7); -} + // optimized version + if (height_col == height && width_col == width && stride == 1 && pad == 1 && is_fma_avx2()) + { + __m256i all256_sing1 = _mm256_set_epi32(0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000); + __m256 float_zero256 = _mm256_set1_ps(0.00); -unsigned char reverse_byte_2(unsigned char a) -{ - return ((a * 0x0802LU & 0x22110LU) | (a * 0x8020LU & 0x88440LU)) * 0x10101LU >> 16; -} + int new_ldb = bit_align; -static unsigned char lookup[16] = { - 0x0, 0x8, 0x4, 0xc, 0x2, 0xa, 0x6, 0xe, - 0x1, 0x9, 0x5, 0xd, 0x3, 0xb, 0x7, 0xf, }; + #pragma omp parallel for + for (c = 0; c < channels_col; ++c) { + int w_offset = c % ksize; + int h_offset = (c / ksize) % ksize; + int c_im = c / ksize / ksize; + for (h = pad; h < height_col - pad; ++h) { + for (w = pad; w < width_col - pad - 8; w += 8) { + int im_row = h_offset + h - pad; + int im_col = w_offset + w - pad; + //int col_index = (c * height_col + h) * width_col + w; + int col_index = c * new_ldb + h * width_col + w; -unsigned char reverse_byte(unsigned char n) { - // Reverse the top and bottom nibble then swap them. - return (lookup[n & 0b1111] << 4) | lookup[n >> 4]; -} + //__m256i src256 = _mm256_loadu_si256((__m256i *)(&data_im[im_col + width*(im_row + height*c_im)])); + //__m256i result256 = _mm256_and_si256(src256, all256_sing1); // check sign in 8 x 32-bit floats + //uint16_t mask = _mm256_movemask_ps(_mm256_castsi256_ps(result256)); // (val >= 0) ? 0 : 1 + //mask = ~mask; // inverse mask, (val >= 0) ? 1 : 0 + __m256 src256 = _mm256_loadu_ps((float *)(&data_im[im_col + width*(im_row + height*c_im)])); + __m256 result256 = _mm256_cmp_ps(src256, float_zero256, _CMP_GT_OS); + uint16_t mask = _mm256_movemask_ps(result256); // (val > 0) ? 0 : 1 -void transpose8rS32_reversed_diagonale(unsigned char* A, int m, int n, unsigned char* B) -{ - unsigned x, y, t; + uint16_t *dst_ptr = &((unsigned char*)data_col)[col_index / 8]; + *dst_ptr |= (mask << (col_index % 8)); + } - // Load the array and pack it into x and y. - x = (A[0] << 24) | (A[m] << 16) | (A[2 * m] << 8) | A[3 * m]; - y = (A[4 * m] << 24) | (A[5 * m] << 16) | (A[6 * m] << 8) | A[7 * m]; + for (; w < width_col - pad; ++w) { + int im_row = h_offset + h - pad; + int im_col = w_offset + w - pad; + //int col_index = (c * height_col + h) * width_col + w; + int col_index = c * new_ldb + h * width_col + w; - t = (x ^ (x >> 7)) & 0x00AA00AA; x = x ^ t ^ (t << 7); - t = (y ^ (y >> 7)) & 0x00AA00AA; y = y ^ t ^ (t << 7); + //data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; + float val = data_im[im_col + width*(im_row + height*c_im)]; + if(val > 0) set_bit(data_col, col_index); + } + } - t = (x ^ (x >> 14)) & 0x0000CCCC; x = x ^ t ^ (t << 14); - t = (y ^ (y >> 14)) & 0x0000CCCC; y = y ^ t ^ (t << 14); + { + w = 0; + for (h = 0; h < height_col; ++h) { + int im_row = h_offset + h; + int im_col = w_offset + w; + //int col_index = (c * height_col + h) * width_col + w; + int col_index = c * new_ldb + h * width_col + w; - t = (x & 0xF0F0F0F0) | ((y >> 4) & 0x0F0F0F0F); - y = ((x << 4) & 0xF0F0F0F0) | (y & 0x0F0F0F0F); - x = t; + //data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + if (val > 0) set_bit(data_col, col_index); + } + } - B[7 * n] = reverse_byte(x >> 24); B[6 * n] = reverse_byte(x >> 16); B[5 * n] = reverse_byte(x >> 8); B[4 * n] = reverse_byte(x); - B[3 * n] = reverse_byte(y >> 24); B[2 * n] = reverse_byte(y >> 16); B[1 * n] = reverse_byte(y >> 8); B[0 * n] = reverse_byte(y); -} + { + w = width_col - 1; + for (h = 0; h < height_col; ++h) { + int im_row = h_offset + h; + int im_col = w_offset + w; + //int col_index = (c * height_col + h) * width_col + w; + int col_index = c * new_ldb + h * width_col + w; -void transpose_bin(char *A, char *B, const int n, const int m, - const int lda, const int ldb, const int block_size) -{ - int i; - #pragma omp parallel for - for (i = 0; i < n; i += 8) { - int j; - for (j = 0; j < m - 8; j += 8) { - int a_index = i*lda + j; - int b_index = j*ldb + i; - //transpose_8x8_bits_my(&A[a_index/8], &B[b_index/8], lda/8, ldb/8); - transpose8rS32_reversed_diagonale(&A[a_index / 8], lda / 8, ldb / 8, &B[b_index / 8]); - } - for (; j < m; ++j) { - if (get_bit(A, i*lda + j)) set_bit(B, j*ldb + i); + //data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + if (val > 0) set_bit(data_col, col_index); + } + } + + { + h = 0; + for (w = 0; w < width_col; ++w) { + int im_row = h_offset + h; + int im_col = w_offset + w; + //int col_index = (c * height_col + h) * width_col + w; + int col_index = c * new_ldb + h * width_col + w; + + //data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + if (val > 0) set_bit(data_col, col_index); + } + } + + { + h = height_col - 1; + for (w = 0; w < width_col; ++w) { + int im_row = h_offset + h; + int im_col = w_offset + w; + //int col_index = (c * height_col + h) * width_col + w; + int col_index = c * new_ldb + h * width_col + w; + + //data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + if (val > 0) set_bit(data_col, col_index); + } + } } + + } + else { + printf("\n Error: is no non-optimized version \n"); + //im2col_cpu(data_im, channels, height, width, ksize, stride, pad, data_col); // must be aligned for transpose after float_to_bin + // float_to_bit(b, t_input, src_size); + // transpose_bin(t_input, *t_bit_input, k, n, bit_align, new_ldb, 8); } } + void activate_array_cpu_custom(float *x, const int n, const ACTIVATION a) { int i = 0; @@ -1288,14 +1377,14 @@ void float_to_bit(float *src, unsigned char *dst, size_t size) size_t i; __m256i all256_sing1 = _mm256_set_epi32(0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000); - __m256 float_zero256 = _mm256_set1_ps(0.00001); + __m256 float_zero256 = _mm256_set1_ps(0.0); for (i = 0; i < size; i+=8) { //__m256i src256 = _mm256_loadu_si256((__m256i *)(&src[i])); //__m256i result256 = _mm256_and_si256(src256, all256_sing1); // check sign in 8 x 32-bit floats //uint32_t mask = _mm256_movemask_ps(_mm256_castsi256_ps(result256)); // (val >= 0) ? 0 : 1 - //mask = ~mask; // inverse mask, (val >= 0) ? 1 : 0 + ////mask = ~mask; // inverse mask, (val >= 0) ? 1 : 0 __m256 src256 = _mm256_loadu_ps((float *)(&src[i])); __m256 result256 = _mm256_cmp_ps(src256, float_zero256, _CMP_GT_OS); @@ -1654,6 +1743,118 @@ void im2col_cpu_custom(float* data_im, } } + +//From Berkeley Vision's Caffe! +//https://github.com/BVLC/caffe/blob/master/LICENSE +void im2col_cpu_custom_bin(float* data_im, + int channels, int height, int width, + int ksize, int stride, int pad, float* data_col, int bit_align) +{ + int c, h, w; + int height_col = (height + 2 * pad - ksize) / stride + 1; + int width_col = (width + 2 * pad - ksize) / stride + 1; + int channels_col = channels * ksize * ksize; + + // optimized version + if (height_col == height && width_col == width && stride == 1 && pad == 1) + { + int new_ldb = bit_align; + + #pragma omp parallel for + for (c = 0; c < channels_col; ++c) { + int w_offset = c % ksize; + int h_offset = (c / ksize) % ksize; + int c_im = c / ksize / ksize; + for (h = pad; h < height_col - pad; ++h) { + for (w = pad; w < width_col - pad - 8; w += 1) { + int im_row = h_offset + h - pad; + int im_col = w_offset + w - pad; + //int col_index = (c * height_col + h) * width_col + w; + int col_index = c * new_ldb + h * width_col + w; + + float val = data_im[im_col + width*(im_row + height*c_im)]; + if (val > 0) set_bit(data_col, col_index); + } + + for (; w < width_col - pad; ++w) { + int im_row = h_offset + h - pad; + int im_col = w_offset + w - pad; + //int col_index = (c * height_col + h) * width_col + w; + int col_index = c * new_ldb + h * width_col + w; + + //data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; + float val = data_im[im_col + width*(im_row + height*c_im)]; + if (val > 0) set_bit(data_col, col_index); + } + } + + { + w = 0; + for (h = 0; h < height_col; ++h) { + int im_row = h_offset + h; + int im_col = w_offset + w; + //int col_index = (c * height_col + h) * width_col + w; + int col_index = c * new_ldb + h * width_col + w; + + //data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + if (val > 0) set_bit(data_col, col_index); + } + } + + { + w = width_col - 1; + for (h = 0; h < height_col; ++h) { + int im_row = h_offset + h; + int im_col = w_offset + w; + //int col_index = (c * height_col + h) * width_col + w; + int col_index = c * new_ldb + h * width_col + w; + + //data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + if (val > 0) set_bit(data_col, col_index); + } + } + + { + h = 0; + for (w = 0; w < width_col; ++w) { + int im_row = h_offset + h; + int im_col = w_offset + w; + //int col_index = (c * height_col + h) * width_col + w; + int col_index = c * new_ldb + h * width_col + w; + + //data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + if (val > 0) set_bit(data_col, col_index); + } + } + + { + h = height_col - 1; + for (w = 0; w < width_col; ++w) { + int im_row = h_offset + h; + int im_col = w_offset + w; + //int col_index = (c * height_col + h) * width_col + w; + int col_index = c * new_ldb + h * width_col + w; + + //data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); + if (val > 0) set_bit(data_col, col_index); + } + } + } + + } + else { + printf("\n Error: is no non-optimized version \n"); + //im2col_cpu(data_im, channels, height, width, ksize, stride, pad, data_col); // must be aligned for transpose after float_to_bin + // float_to_bit(b, t_input, src_size); + // transpose_bin(t_input, *t_bit_input, k, n, bit_align, new_ldb, 8); + } +} + + void activate_array_cpu_custom(float *x, const int n, const ACTIVATION a) { int i; diff --git a/src/gemm.h b/src/gemm.h index d393b0dba79..90b8ce99dcf 100644 --- a/src/gemm.h +++ b/src/gemm.h @@ -39,6 +39,10 @@ void im2col_cpu_custom(float* data_im, int channels, int height, int width, int ksize, int stride, int pad, float* data_col); +void im2col_cpu_custom_align(float* data_im, + int channels, int height, int width, + int ksize, int stride, int pad, float* data_col, int bit_align); + void im2col_cpu_custom_bin(float* data_im, int channels, int height, int width, int ksize, int stride, int pad, float* data_col, int bit_align); From 5b6be00d4b1ffd671c20c4c72d2239c924eaa3d4 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 23 Aug 2018 15:28:34 +0300 Subject: [PATCH 0144/1365] Added yolov3-tiny_xnor.cfg --- build/darknet/x64/cfg/yolov3-tiny_xnor.cfg | 192 +++++++++++++++++++++ cfg/yolov3-tiny_xnor.cfg | 192 +++++++++++++++++++++ 2 files changed, 384 insertions(+) create mode 100644 build/darknet/x64/cfg/yolov3-tiny_xnor.cfg create mode 100644 cfg/yolov3-tiny_xnor.cfg diff --git a/build/darknet/x64/cfg/yolov3-tiny_xnor.cfg b/build/darknet/x64/cfg/yolov3-tiny_xnor.cfg new file mode 100644 index 00000000000..a5e5ed14e32 --- /dev/null +++ b/build/darknet/x64/cfg/yolov3-tiny_xnor.cfg @@ -0,0 +1,192 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=2 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=16 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=1 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +########### + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + + +[yolo] +mask = 3,4,5 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + +[route] +layers = -4 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 8 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + +[yolo] +mask = 0,1,2 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 diff --git a/cfg/yolov3-tiny_xnor.cfg b/cfg/yolov3-tiny_xnor.cfg new file mode 100644 index 00000000000..a5e5ed14e32 --- /dev/null +++ b/cfg/yolov3-tiny_xnor.cfg @@ -0,0 +1,192 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=2 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=16 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=1 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +########### + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + + +[yolo] +mask = 3,4,5 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + +[route] +layers = -4 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 8 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + +[yolo] +mask = 0,1,2 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 From 08285dbc84d4cf5c988a301a18e044224178a0c3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 23 Aug 2018 23:09:47 +0300 Subject: [PATCH 0145/1365] Added tiny-yolo-v2-XNOR-cfg-file: tiny-yolo_xnor.cfg --- build/darknet/x64/cfg/tiny-yolo_xnor.cfg | 142 +++++++++++++++++++++++ cfg/tiny-yolo_xnor.cfg | 142 +++++++++++++++++++++++ 2 files changed, 284 insertions(+) create mode 100644 build/darknet/x64/cfg/tiny-yolo_xnor.cfg create mode 100644 cfg/tiny-yolo_xnor.cfg diff --git a/build/darknet/x64/cfg/tiny-yolo_xnor.cfg b/build/darknet/x64/cfg/tiny-yolo_xnor.cfg new file mode 100644 index 00000000000..c39aabedd6f --- /dev/null +++ b/build/darknet/x64/cfg/tiny-yolo_xnor.cfg @@ -0,0 +1,142 @@ +[net] +batch=64 +subdivisions=8 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +max_batches = 40200 +policy=steps +steps=-1,100,20000,30000 +scales=.1,10,.1,.1 + +[convolutional] +#xnor=1 +batch_normalize=1 +filters=16 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=1 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +########### + +[convolutional] +xnor=1 +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=425 +activation=linear + +[region] +anchors = 0.738768,0.874946, 2.42204,2.65704, 4.30971,7.04493, 10.246,4.59428, 12.6868,11.8741 +bias_match=1 +classes=80 +coords=4 +num=5 +softmax=1 +jitter=.2 +rescore=1 + +object_scale=5 +noobject_scale=1 +class_scale=1 +coord_scale=1 + +absolute=1 +thresh = .6 +random=1 diff --git a/cfg/tiny-yolo_xnor.cfg b/cfg/tiny-yolo_xnor.cfg new file mode 100644 index 00000000000..c39aabedd6f --- /dev/null +++ b/cfg/tiny-yolo_xnor.cfg @@ -0,0 +1,142 @@ +[net] +batch=64 +subdivisions=8 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +max_batches = 40200 +policy=steps +steps=-1,100,20000,30000 +scales=.1,10,.1,.1 + +[convolutional] +#xnor=1 +batch_normalize=1 +filters=16 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=1 + +[convolutional] +xnor=1 +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +########### + +[convolutional] +xnor=1 +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=425 +activation=linear + +[region] +anchors = 0.738768,0.874946, 2.42204,2.65704, 4.30971,7.04493, 10.246,4.59428, 12.6868,11.8741 +bias_match=1 +classes=80 +coords=4 +num=5 +softmax=1 +jitter=.2 +rescore=1 + +object_scale=5 +noobject_scale=1 +class_scale=1 +coord_scale=1 + +absolute=1 +thresh = .6 +random=1 From 18d5e4f39c1441f2c21043ac3204b5cb279f8758 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 24 Aug 2018 18:29:40 +0300 Subject: [PATCH 0146/1365] Fixed yolov3-tiny_xnor.cfg --- build/darknet/x64/cfg/yolov3-tiny_xnor.cfg | 1 - cfg/yolov3-tiny_xnor.cfg | 1 - src/convolutional_layer.c | 4 +++- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/build/darknet/x64/cfg/yolov3-tiny_xnor.cfg b/build/darknet/x64/cfg/yolov3-tiny_xnor.cfg index a5e5ed14e32..8427beed8bd 100644 --- a/build/darknet/x64/cfg/yolov3-tiny_xnor.cfg +++ b/build/darknet/x64/cfg/yolov3-tiny_xnor.cfg @@ -120,7 +120,6 @@ pad=1 activation=leaky [convolutional] -xnor=1 batch_normalize=1 filters=512 size=3 diff --git a/cfg/yolov3-tiny_xnor.cfg b/cfg/yolov3-tiny_xnor.cfg index a5e5ed14e32..8427beed8bd 100644 --- a/cfg/yolov3-tiny_xnor.cfg +++ b/cfg/yolov3-tiny_xnor.cfg @@ -120,7 +120,6 @@ pad=1 activation=leaky [convolutional] -xnor=1 batch_normalize=1 filters=512 size=3 diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index a03deff9dfc..16e6d5fd910 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -404,7 +404,9 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int //fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c); l.bflops = (2.0 * l.n * l.size*l.size*l.c * l.out_h*l.out_w) / 1000000000.; - fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); + if (l.xnor) fprintf(stderr, "convX "); + else fprintf(stderr, "conv "); + fprintf(stderr, "%5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); return l; } From 9753b72aebb56f654f8032400b6e0de489be2421 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 30 Aug 2018 16:32:39 +0300 Subject: [PATCH 0147/1365] temp fix, don't use it --- src/convolutional_kernels.cu | 174 ++++++++++++++++++++++++++++--- src/convolutional_layer.c | 36 +++---- src/detector.c | 2 +- src/gemm.c | 4 +- src/im2col.h | 11 ++ src/im2col_kernels.cu | 193 +++++++++++++++++++++++++++++++++++ src/layer.h | 3 + 7 files changed, 384 insertions(+), 39 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 5389828e720..a7387d01318 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -3,7 +3,7 @@ #include "cublas_v2.h" #ifdef CUDNN -#pragma comment(lib, "cudnn.lib") +#pragma comment(lib, "cudnn.lib") #endif extern "C" { @@ -117,18 +117,160 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) } if(l.xnor){ - binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu); + if (!l.align_bit_weights_gpu || state.train) { + binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu); + } swap_binary(&l); binarize_gpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input_gpu); state.input = l.binary_input_gpu; + + if (l.align_bit_weights_gpu && !state.train) + { + cudaError_t status; + status = cudaMemcpy(l.align_bit_weights, l.align_bit_weights_gpu, l.align_bit_weights_size, cudaMemcpyDeviceToHost); + check_error(status); + + float *input = (float *)calloc(l.c*l.h*l.w*l.batch, sizeof(float)); + float *workspace = (float *)calloc(l.bit_align*l.size*l.size*l.c, sizeof(float)); + float *output = (float *)calloc(l.batch*l.out_c*l.out_h*l.out_w, sizeof(float)); + + status = cudaMemcpy(input, state.input, l.c*l.h*l.w*l.batch*sizeof(float), cudaMemcpyDeviceToHost); + check_error(status); + + int m = l.n; + int k = l.size*l.size*l.c; + int n = l.out_w*l.out_h; + float * a = l.weights_gpu; + //float * b = state.workspace; + float *b = workspace; + //float * c = l.output_gpu; + float *c = output; + + int ldb_align = l.lda_align; + size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; + size_t t_intput_size = new_ldb * n; + size_t t_bit_input_size = t_intput_size / 8;// +1; + + char *t_bit_input = (char *)calloc(t_bit_input_size, sizeof(char)); + int src_size = k * l.bit_align; + + //im2col_cpu_custom_bin(input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align); + + float *align_workspace = NULL; + int align_workspace_size = l.bit_align * k; // aligned: n*k + status = cudaMalloc((void **)&align_workspace, align_workspace_size*sizeof(float)); + check_error(status); + + int i = 0; + im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, align_workspace, l.bit_align); + + float_to_bit_gpu(align_workspace, (unsigned char *)state.workspace, align_workspace_size); + + if(1) + { + { + /* + status = cudaMemcpy(t_bit_input, state.workspace, t_bit_input_size, cudaMemcpyDeviceToHost); + check_error(status); + for (int y = 0; y < 8; ++y) { + for (int x = 0; x < 8; ++x) { + int index = x + y*l.bit_align; + if (get_bit((unsigned char *)t_bit_input, index)) printf("1, "); + else printf("0, "); + } + printf("\n"); + } + printf("\n"); + */ + } + + fill_int8_gpu((unsigned char *)align_workspace, 0, t_bit_input_size); + + transpose_bin_gpu((unsigned char *)state.workspace, (unsigned char *)align_workspace, k, n, l.bit_align, new_ldb, 8); + //cudaDeviceSynchronize(); + + //int size_transposed_array = l.bit_align * new_ldb; + status = cudaMemcpy(t_bit_input, align_workspace, t_bit_input_size, cudaMemcpyDeviceToHost); + check_error(status); + + /* + for (int y = 0; y < 8; ++y) { + for (int x = 0; x < 8; ++x) { + int index = x + y*new_ldb; + if (get_bit((unsigned char *)t_bit_input, index)) printf("1, "); + else printf("0, "); + } + printf("\n"); + } + printf("-----------\n"); + //getchar(); + */ + } + + if (0) { + status = cudaMemcpy(b, state.workspace, align_workspace_size / 8, cudaMemcpyDeviceToHost); + check_error(status); + + for (int y = 0; y < 8; ++y) { + for (int x = 0; x < 8; ++x) { + int index = x + y*l.bit_align; + if (get_bit((unsigned char *)b, index)) printf("1, "); + else printf("0, "); + } + printf("\n"); + } + printf("\n"); + + //float *im2 = (float *)calloc(align_workspace_size, sizeof(float)); + //status = cudaMemcpy(im2, align_workspace, align_workspace_size * sizeof(float), cudaMemcpyDeviceToHost); + //check_error(status); + //float_to_bit(im2, (unsigned char *)b, align_workspace_size); + + memset(t_bit_input, 0, t_bit_input_size); + // b - [bit_align, k] - [l.bit_align, l.size*l.size*l.c] = src_size + // t_input - [bit_align, k] - [n', k] + // t_bit_input - [new_ldb, n] - [k', n] + transpose_bin((char *)b, t_bit_input, k, n, l.bit_align, new_ldb, 8); + + for (int y = 0; y < 8; ++y) { + for (int x = 0; x < 8; ++x) { + int index = x + y*new_ldb; + if (get_bit((unsigned char *)t_bit_input, index)) printf("1, "); + else printf("0, "); + } + printf("\n"); + } + printf("-----------\n"); + //getchar(); + + //free(im2); + } + + // 5x times faster than gemm()-float32 + gemm_nn_custom_bin_mean_transposed(m, n, k, 1, (unsigned char *)l.align_bit_weights, new_ldb, (unsigned char *)t_bit_input, new_ldb, c, n, l.mean_arr); + + status = cudaMemcpy(l.output_gpu, output, l.batch*l.out_c*l.out_h*l.out_w * sizeof(float), cudaMemcpyHostToDevice); + check_error(status); + + free(t_bit_input); + free(input); + free(workspace); + free(output); + cudaFree(align_workspace); + + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); + activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); + if (l.binary || l.xnor) swap_binary(&l); + return; + } } #ifdef CUDNN float one = 1; // alpha[0], beta[0] is float for HALF and FLOAT - float alpha = 1, beta = 0; + float alpha = 1, beta = 0; #ifdef CUDNN_HALF - // Note: For improved performance it is advised to use beta[0] = 0.0. + // Note: For improved performance it is advised to use beta[0] = 0.0. // For Tensor Core: cudnnSetConvolutionMathType() where cudnnMathType_t mathType = CUDNN_TENSOR_OP_MATH; // 1. or CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM and use CUDNN_DATA_HALF // 2. or CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED @@ -168,10 +310,10 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) &beta, l.dstTensorDesc, output16); - - if (l.batch_normalize) - { + + if (l.batch_normalize) + { if (state.train) // Training { copy_ongpu(l.outputs*l.batch / 2, output16, 1, l.x_gpu, 1); @@ -213,7 +355,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) { cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); - } + } #else @@ -283,11 +425,11 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state float alpha = 1, beta = 0; #ifdef CUDNN_HALF - + const size_t input16_size = l.batch*l.c*l.w*l.h; const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h; - - if (*state.net.max_input16_size < input16_size) { + + if (*state.net.max_input16_size < input16_size) { *state.net.max_input16_size = input16_size; if(*state.net.input16_gpu) cuda_free(*state.net.input16_gpu); *state.net.input16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_input16_size); @@ -368,7 +510,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state // http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData // calculate delta for the next layer // convert input: l.weights_gpu (w), l.delta_gpu (dy) from fp32 to fp16 - // get output: state.delta (dx) and convert it to fp32 (ONLY if it is fp16) + // get output: state.delta (dx) and convert it to fp32 (ONLY if it is fp16) cudnnConvolutionBackwardData(cudnn_handle(), &alpha, l.weightDesc, @@ -524,11 +666,11 @@ void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float }else{ // update weights: // weights_gpu = weights_gpu*(1 - decay*lr) + weight_updates_gpu*lr / (batch*subdivision) = - // weights_gpu*(1 - 0.0005*0.001) + weight_updates_gpu*0.001/(64*8) = + // weights_gpu*(1 - 0.0005*0.001) + weight_updates_gpu*0.001/(64*8) = // weights_gpu * 0.999 999 5 + weight_updates_gpu * 0.000 001 953125 - // - // weight_updates_gpu = (weight_updates_gpu - weights_gpu*decay*batch*subdivision)*momentum = - // (weight_updates_gpu - weights_gpu * 0.0005 * 64 * 8) * 0.9 = + // + // weight_updates_gpu = (weight_updates_gpu - weights_gpu*decay*batch*subdivision)*momentum = + // (weight_updates_gpu - weights_gpu * 0.0005 * 64 * 8) * 0.9 = // weight_updates_gpu*0.9 - weights_gpu*0.2304 axpy_ongpu(size, -decay*batch, layer.weights_gpu, 1, layer.weight_updates_gpu, 1); axpy_ongpu(size, learning_rate/batch, layer.weight_updates_gpu, 1, layer.weights_gpu, 1); diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 16e6d5fd910..fa927180d29 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -609,9 +609,9 @@ void binary_align_weights(convolutional_layer *l) binarize_weights(l->weights, m, k, l->binary_weights); size_t align_weights_size = new_lda * m; - size_t align_bit_weights_size = align_weights_size / 8;// +1; + l->align_bit_weights_size = align_weights_size / 8;// +1; float *align_weights = calloc(align_weights_size, sizeof(float)); - l->align_bit_weights = calloc(align_bit_weights_size, sizeof(char)); + l->align_bit_weights = calloc(l->align_bit_weights_size, sizeof(char)); size_t i, j; // align A without transpose @@ -625,29 +625,28 @@ void binary_align_weights(convolutional_layer *l) l->mean_arr = calloc(l->n, sizeof(float)); get_mean_array(align_weights, align_weights_size, l->n, l->mean_arr); +#ifdef GPU + //l->align_bit_weights_gpu = cuda_make_array(l->align_bit_weights, l->align_bit_weights_size * sizeof(char)/sizeof(float)); + cudaError_t status = cudaMalloc((void **)&l->align_bit_weights_gpu, l->align_bit_weights_size); + check_error(status); + status = cudaMemcpy(l->align_bit_weights_gpu, l->align_bit_weights, l->align_bit_weights_size, cudaMemcpyHostToDevice); + check_error(status); + + l->mean_arr_gpu = cuda_make_array(l->mean_arr, l->n); +#endif // GPU + free(align_weights); } -// further optimizations: im2col_bin() for XNOR, and then transpose_aling_bin() +// binary transpose size_t binary_transpose_align_input(int k, int n, float *b, char **t_bit_input, size_t ldb_align, int bit_align) { size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; size_t t_intput_size = new_ldb * n; size_t t_bit_input_size = t_intput_size / 8;// +1; - //float *t_input = calloc(t_intput_size, sizeof(float)); - //char * - *t_bit_input = calloc(t_bit_input_size, sizeof(char)); - - //printf("\n bit_input_size = %d, n = %d, k = %d, ldb = %d \n", bit_input_size, n, k, n); - //printf("\n t_bit_input_size = %d, k = %d, n = %d, new_ldb = %d \n", t_bit_input_size, k, n, new_ldb); - - //printf("\n align_weights_size = %d, k = %d, m = %d, lda = %d \n", align_weights_size, k, m, k); - //printf("\n align_bit_weights_size = %d, k = %d, m = %d, new_lda = %d \n", align_bit_weights_size, k, m, new_ldb); + *t_bit_input = calloc(t_bit_input_size, sizeof(char)); int src_size = k * bit_align; - //printf("\n src_size = %d \n", src_size); - - //float_to_bit(b, t_input, src_size); // b - [bit_align, k] - [l.bit_align, l.size*l.size*l.c] = src_size // t_input - [bit_align, k] - [n', k] @@ -656,8 +655,6 @@ size_t binary_transpose_align_input(int k, int n, float *b, char **t_bit_input, //transpose_bin(t_input, *t_bit_input, k, n, bit_align, new_ldb, 8); transpose_bin(b, *t_bit_input, k, n, bit_align, new_ldb, 8); - //free(t_input); - return t_intput_size; } @@ -671,7 +668,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) fill_cpu(l.outputs*l.batch, 0, l.output, 1); if(l.xnor){ - if (!l.align_bit_weights) { + if (!l.align_bit_weights || state.train) { binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights); //printf("\n binarize_weights l.align_bit_weights = %p \n", l.align_bit_weights); } @@ -709,7 +706,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); //gemm_nn_custom(m, n, k, 1, a, k, b, n, c, n); - if (l.xnor && (l.stride == 1 && l.pad == 1)) { + if (l.xnor && l.align_bit_weights && !state.train && (l.stride == 1 && l.pad == 1)) { memset(b, 0, l.bit_align*l.size*l.size*l.c * sizeof(float)); //im2col_cpu_custom_align(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align); im2col_cpu_custom_bin(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align); @@ -812,7 +809,6 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //float_to_bit(t_input, t_bit_input, new_ldb * n); // for im2col_cpu_custom_transpose() only // 5x times faster than gemm()-float32 - // further optimizations: accelerate maxpool-layer with OpenMP/AVX gemm_nn_custom_bin_mean_transposed(m, n, k, 1, l.align_bit_weights, new_ldb, t_bit_input, new_ldb, c, n, l.mean_arr); //gemm_nn_custom_bin_mean_transposed(m, n, k, 1, bit_weights, k, t_bit_input, new_ldb, c, n, mean_arr); diff --git a/src/detector.c b/src/detector.c index a816c742d36..0c1c3986f90 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1224,7 +1224,7 @@ void run_detector(int argc, char **argv) int ext_output = find_arg(argc, argv, "-ext_output"); int save_labels = find_arg(argc, argv, "-save_labels"); if(argc < 4){ - fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); + fprintf(stderr, "usage: %s %s [train/test/valid/demo/map] [data] [cfg] [weights (optional)]\n", argv[0], argv[1]); return; } char *gpu_list = find_char_arg(argc, argv, "-gpus", 0); diff --git a/src/gemm.c b/src/gemm.c index 6ff57cd3efe..123598a5074 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -324,7 +324,7 @@ unsigned char reverse_byte_1(char a) ((a & 0x40) >> 5) | ((a & 0x80) >> 7); } -unsigned char reverse_byte_2(unsigned char a) +unsigned char reverse_byte(unsigned char a) { return ((a * 0x0802LU & 0x22110LU) | (a * 0x8020LU & 0x88440LU)) * 0x10101LU >> 16; } @@ -333,7 +333,7 @@ static unsigned char lookup[16] = { 0x0, 0x8, 0x4, 0xc, 0x2, 0xa, 0x6, 0xe, 0x1, 0x9, 0x5, 0xd, 0x3, 0xb, 0x7, 0xf, }; -unsigned char reverse_byte(unsigned char n) { +unsigned char reverse_byte_3(unsigned char n) { // Reverse the top and bottom nibble then swap them. return (lookup[n & 0b1111] << 4) | lookup[n >> 4]; } diff --git a/src/im2col.h b/src/im2col.h index f0ddeeeb2d4..435a59cde85 100644 --- a/src/im2col.h +++ b/src/im2col.h @@ -11,5 +11,16 @@ void im2col_ongpu(float *im, int channels, int height, int width, int ksize, int stride, int pad,float *data_col); +void im2col_align_ongpu(float *im, + int channels, int height, int width, + int ksize, int stride, int pad, float *data_col, int bit_align); + +void float_to_bit_gpu(float *src, unsigned char *dst, size_t size); + +void transpose_bin_gpu(unsigned char *A, unsigned char *B, const int n, const int m, + const int lda, const int ldb, const int block_size); + +void fill_int8_gpu(unsigned char *src, unsigned char val, size_t size); + #endif #endif diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 8a15e504a42..eee820afd0f 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -59,3 +59,196 @@ void im2col_ongpu(float *im, stride, height_col, width_col, data_col); } + + + +__global__ void im2col_align_gpu_kernel(const int n, const float* data_im, + const int height, const int width, const int ksize, + const int pad, + const int stride, + const int height_col, const int width_col, + float *data_col, const int bit_align) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + for (; index < n; index += blockDim.x*gridDim.x) { + int w_out = index % width_col; + int h_index = index / width_col; + int h_out = h_index % height_col; + int channel_in = h_index / height_col; + int channel_out = channel_in * ksize * ksize; + int h_in = h_out * stride - pad; + int w_in = w_out * stride - pad; + float* data_col_ptr = data_col; + //data_col_ptr += (channel_out * height_col + h_out) * width_col + w_out; + data_col_ptr += channel_out * bit_align + h_out * width_col + w_out; + const float* data_im_ptr = data_im; + data_im_ptr += (channel_in * height + h_in) * width + w_in; + for (int i = 0; i < ksize; ++i) { + for (int j = 0; j < ksize; ++j) { + int h = h_in + i; + int w = w_in + j; + + *data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ? + data_im_ptr[i * width + j] : 0; + + + //data_col_ptr += height_col * width_col; + data_col_ptr += bit_align; + } + } + } +} + +void im2col_align_ongpu(float *im, + int channels, int height, int width, + int ksize, int stride, int pad, float *data_col, int bit_align) { + // We are going to launch channels * height_col * width_col kernels, each + // kernel responsible for copying a single-channel grid. + int height_col = (height + 2 * pad - ksize) / stride + 1; + int width_col = (width + 2 * pad - ksize) / stride + 1; + int num_kernels = channels * height_col * width_col; + im2col_align_gpu_kernel << <(num_kernels + BLOCK - 1) / BLOCK, + BLOCK, 0, get_cuda_stream() >> >( + num_kernels, im, height, width, ksize, pad, + stride, height_col, + width_col, data_col, bit_align); +} + + +// -------------------------------- + +#define WARP_SIZE 32 + +__global__ void float_to_bit_gpu_kernel(float *src, unsigned char *dst, size_t size) +{ + //size_t dst_size = size / 8 + 1; + //memset(dst, 0, dst_size); + //uint32_t bit_mask = __ballot_sync(FULL_MASK, src[i] > 0); + const int size_aligned = size + (WARP_SIZE - size % WARP_SIZE); + + int index = blockIdx.x*blockDim.x + threadIdx.x; + float src_val; + + for (; index < size_aligned; index += blockDim.x*gridDim.x) + { + if(index < size) src_val = src[index]; + else src_val = 0; + unsigned int bit_mask = __ballot_sync(0xffffffff, src_val > 0); + if (threadIdx.x % WARP_SIZE == 0) ((unsigned int*)dst)[index / 32] = bit_mask; + } +} + + +void float_to_bit_gpu(float *src, unsigned char *dst, size_t size) +{ + const int num_blocks = size / BLOCK + 1; + float_to_bit_gpu_kernel<<>>(src, dst, size); +} + +// -------------------------------- + + +__device__ __host__ static inline void set_bit(unsigned char *const dst, size_t index) { + size_t dst_i = index / 8; + int dst_shift = index % 8; + dst[dst_i] |= 1 << dst_shift; + //dst[dst_i] |= 1 << (8 - dst_shift); +} + +__device__ __host__ static inline unsigned char get_bit(unsigned char const*const src, size_t index) { + size_t src_i = index / 8; + int src_shift = index % 8; + unsigned char val = (src[src_i] & (1 << src_shift)) > 0; + //unsigned char val = (src[src_i] & (1 << (8 - src_shift))) > 0; + return val; +} + +// Intel CPUs and nVidia CUDA GPU are little endian +__device__ __host__ unsigned char reverse_byte(unsigned char a) +{ + return ((a & 0x1) << 7) | ((a & 0x2) << 5) | + ((a & 0x4) << 3) | ((a & 0x8) << 1) | + ((a & 0x10) >> 1) | ((a & 0x20) >> 3) | + ((a & 0x40) >> 5) | ((a & 0x80) >> 7); +} + +__device__ __host__ unsigned char reverse_byte_2(unsigned char a) +{ + return ((a * 0x0802LU & 0x22110LU) | (a * 0x8020LU & 0x88440LU)) * 0x10101LU >> 16; +} + + + +__device__ __host__ void transpose8rS32_reversed_diagonale(unsigned char* A, int m, int n, unsigned char* B) +{ + unsigned x, y, t; + + // Load the array and pack it into x and y. + x = (A[0] << 24) | (A[m] << 16) | (A[2 * m] << 8) | A[3 * m]; + y = (A[4 * m] << 24) | (A[5 * m] << 16) | (A[6 * m] << 8) | A[7 * m]; + + t = (x ^ (x >> 7)) & 0x00AA00AA; x = x ^ t ^ (t << 7); + t = (y ^ (y >> 7)) & 0x00AA00AA; y = y ^ t ^ (t << 7); + + t = (x ^ (x >> 14)) & 0x0000CCCC; x = x ^ t ^ (t << 14); + t = (y ^ (y >> 14)) & 0x0000CCCC; y = y ^ t ^ (t << 14); + + t = (x & 0xF0F0F0F0) | ((y >> 4) & 0x0F0F0F0F); + y = ((x << 4) & 0xF0F0F0F0) | (y & 0x0F0F0F0F); + x = t; + + B[7 * n] = reverse_byte(x >> 24); B[6 * n] = reverse_byte(x >> 16); B[5 * n] = reverse_byte(x >> 8); B[4 * n] = reverse_byte(x); + B[3 * n] = reverse_byte(y >> 24); B[2 * n] = reverse_byte(y >> 16); B[1 * n] = reverse_byte(y >> 8); B[0 * n] = reverse_byte(y); +} + + +__global__ void transpose_bin_gpu_kernel(unsigned char *A, unsigned char *B, const int n, const int m, + const int lda, const int ldb, const int block_size) +{ + int i; + int index = blockIdx.x*blockDim.x + threadIdx.x; + + //for (i = 0; i < n; i += 8) + { + i = (index*8) % n; + int j; + //for (j = 0; j < m - 8; j += 8) + { + j = ((index * 8) / n) * 8; + if (j < m - 8) { + int a_index = i*lda + j; + int b_index = j*ldb + i; + //transpose_8x8_bits_my(&A[a_index/8], &B[b_index/8], lda/8, ldb/8); + transpose8rS32_reversed_diagonale(&A[a_index / 8], lda / 8, ldb / 8, &B[b_index / 8]); + } + else if (j < m) { + for (; j < m; ++j) { + if (get_bit(A, i*lda + j)) set_bit(B, j*ldb + i); + } + } + } + } +} + + +void transpose_bin_gpu(unsigned char *A, unsigned char *B, const int n, const int m, + const int lda, const int ldb, const int block_size) +{ + size_t size = n*m/64 + 1; + const int num_blocks = size / BLOCK + 1; + transpose_bin_gpu_kernel << > >(A, B, n, m, lda, ldb, block_size); +} + + +// -------------------------------- + + +__global__ void fill_int8_gpu_kernel(unsigned char *src, unsigned char val, size_t size) { + int index = blockIdx.x*blockDim.x + threadIdx.x; + if(index < size) src[index] = 0; +} + +void fill_int8_gpu(unsigned char *src, unsigned char val, size_t size) { + const int num_blocks = size / BLOCK + 1; + fill_int8_gpu_kernel<<>>(src, val, size); +} \ No newline at end of file diff --git a/src/layer.h b/src/layer.h index 72a63e9e153..d533a5a5179 100644 --- a/src/layer.h +++ b/src/layer.h @@ -179,8 +179,11 @@ struct layer{ float *weights; float *weight_updates; + char *align_bit_weights_gpu; + float *mean_arr_gpu; char *align_bit_weights; float *mean_arr; + int align_bit_weights_size; int lda_align; int bit_align; From c4a9e3422e0d62fae4727fa817ba9fd2b515d2e8 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 31 Aug 2018 01:52:24 +0300 Subject: [PATCH 0148/1365] Temporary implementation of XNOR on CUDA --- src/convolutional_kernels.cu | 134 ++++++++++++----------------------- src/convolutional_layer.c | 7 +- src/im2col.h | 5 ++ src/im2col_kernels.cu | 77 +++++++++++++++++++- src/layer.h | 3 + 5 files changed, 135 insertions(+), 91 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index a7387d01318..4343aca9111 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -127,136 +127,94 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) if (l.align_bit_weights_gpu && !state.train) { cudaError_t status; - status = cudaMemcpy(l.align_bit_weights, l.align_bit_weights_gpu, l.align_bit_weights_size, cudaMemcpyDeviceToHost); - check_error(status); + //status = cudaMemcpy(l.align_bit_weights, l.align_bit_weights_gpu, l.align_bit_weights_size, cudaMemcpyDeviceToHost); + //check_error(status); - float *input = (float *)calloc(l.c*l.h*l.w*l.batch, sizeof(float)); - float *workspace = (float *)calloc(l.bit_align*l.size*l.size*l.c, sizeof(float)); - float *output = (float *)calloc(l.batch*l.out_c*l.out_h*l.out_w, sizeof(float)); + //float *input = (float *)calloc(l.c*l.h*l.w*l.batch, sizeof(float)); + //float *workspace = (float *)calloc(l.bit_align*l.size*l.size*l.c, sizeof(float)); + //float *output = (float *)calloc(l.batch*l.out_c*l.out_h*l.out_w, sizeof(float)); - status = cudaMemcpy(input, state.input, l.c*l.h*l.w*l.batch*sizeof(float), cudaMemcpyDeviceToHost); - check_error(status); + //status = cudaMemcpy(input, state.input, l.c*l.h*l.w*l.batch*sizeof(float), cudaMemcpyDeviceToHost); + //check_error(status); int m = l.n; int k = l.size*l.size*l.c; int n = l.out_w*l.out_h; float * a = l.weights_gpu; //float * b = state.workspace; - float *b = workspace; + //float *b = workspace; //float * c = l.output_gpu; - float *c = output; + //float *c = output; int ldb_align = l.lda_align; size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; size_t t_intput_size = new_ldb * n; size_t t_bit_input_size = t_intput_size / 8;// +1; - char *t_bit_input = (char *)calloc(t_bit_input_size, sizeof(char)); - int src_size = k * l.bit_align; + //char *t_bit_input = (char *)calloc(t_bit_input_size, sizeof(char)); + //int src_size = k * l.bit_align; //im2col_cpu_custom_bin(input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align); - float *align_workspace = NULL; - int align_workspace_size = l.bit_align * k; // aligned: n*k - status = cudaMalloc((void **)&align_workspace, align_workspace_size*sizeof(float)); - check_error(status); + //float *align_workspace = NULL; + //int align_workspace_size = l.bit_align * k; // aligned: n*k + //status = cudaMalloc((void **)&align_workspace, align_workspace_size*sizeof(float)); + //check_error(status); int i = 0; - im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, align_workspace, l.bit_align); + im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.align_workspace_gpu, l.bit_align); - float_to_bit_gpu(align_workspace, (unsigned char *)state.workspace, align_workspace_size); + float_to_bit_gpu(l.align_workspace_gpu, (unsigned char *)state.workspace, l.align_workspace_size); if(1) { - { - /* - status = cudaMemcpy(t_bit_input, state.workspace, t_bit_input_size, cudaMemcpyDeviceToHost); - check_error(status); - for (int y = 0; y < 8; ++y) { - for (int x = 0; x < 8; ++x) { - int index = x + y*l.bit_align; - if (get_bit((unsigned char *)t_bit_input, index)) printf("1, "); - else printf("0, "); - } - printf("\n"); - } - printf("\n"); - */ - } - - fill_int8_gpu((unsigned char *)align_workspace, 0, t_bit_input_size); - - transpose_bin_gpu((unsigned char *)state.workspace, (unsigned char *)align_workspace, k, n, l.bit_align, new_ldb, 8); + fill_int8_gpu((unsigned char *)l.align_workspace_gpu, 0, t_bit_input_size); + + transpose_bin_gpu((unsigned char *)state.workspace, (unsigned char *)l.align_workspace_gpu, k, n, l.bit_align, new_ldb, 8); //cudaDeviceSynchronize(); //int size_transposed_array = l.bit_align * new_ldb; - status = cudaMemcpy(t_bit_input, align_workspace, t_bit_input_size, cudaMemcpyDeviceToHost); - check_error(status); - - /* - for (int y = 0; y < 8; ++y) { - for (int x = 0; x < 8; ++x) { - int index = x + y*new_ldb; - if (get_bit((unsigned char *)t_bit_input, index)) printf("1, "); - else printf("0, "); - } - printf("\n"); - } - printf("-----------\n"); - //getchar(); - */ + //status = cudaMemcpy(t_bit_input, l.align_workspace_gpu, t_bit_input_size, cudaMemcpyDeviceToHost); + //check_error(status); } + /* if (0) { - status = cudaMemcpy(b, state.workspace, align_workspace_size / 8, cudaMemcpyDeviceToHost); + status = cudaMemcpy(b, state.workspace, l.align_workspace_size / 8, cudaMemcpyDeviceToHost); check_error(status); - - for (int y = 0; y < 8; ++y) { - for (int x = 0; x < 8; ++x) { - int index = x + y*l.bit_align; - if (get_bit((unsigned char *)b, index)) printf("1, "); - else printf("0, "); - } - printf("\n"); - } - printf("\n"); - - //float *im2 = (float *)calloc(align_workspace_size, sizeof(float)); - //status = cudaMemcpy(im2, align_workspace, align_workspace_size * sizeof(float), cudaMemcpyDeviceToHost); + //float *im2 = (float *)calloc(l.align_workspace_size, sizeof(float)); + //status = cudaMemcpy(im2, l.align_workspace_gpu, l.align_workspace_size * sizeof(float), cudaMemcpyDeviceToHost); //check_error(status); - //float_to_bit(im2, (unsigned char *)b, align_workspace_size); + //float_to_bit(im2, (unsigned char *)b, l.align_workspace_size); memset(t_bit_input, 0, t_bit_input_size); // b - [bit_align, k] - [l.bit_align, l.size*l.size*l.c] = src_size // t_input - [bit_align, k] - [n', k] // t_bit_input - [new_ldb, n] - [k', n] transpose_bin((char *)b, t_bit_input, k, n, l.bit_align, new_ldb, 8); - - for (int y = 0; y < 8; ++y) { - for (int x = 0; x < 8; ++x) { - int index = x + y*new_ldb; - if (get_bit((unsigned char *)t_bit_input, index)) printf("1, "); - else printf("0, "); - } - printf("\n"); - } - printf("-----------\n"); - //getchar(); - - //free(im2); } + */ - // 5x times faster than gemm()-float32 - gemm_nn_custom_bin_mean_transposed(m, n, k, 1, (unsigned char *)l.align_bit_weights, new_ldb, (unsigned char *)t_bit_input, new_ldb, c, n, l.mean_arr); + //status = cudaMemcpy(l.align_bit_weights, l.align_bit_weights_gpu, new_ldb * m / 8, cudaMemcpyDeviceToHost); + //check_error(status); - status = cudaMemcpy(l.output_gpu, output, l.batch*l.out_c*l.out_h*l.out_w * sizeof(float), cudaMemcpyHostToDevice); - check_error(status); + //status = cudaMemcpy(l.mean_arr, l.mean_arr_gpu, l.n * sizeof(float), cudaMemcpyDeviceToHost); + //check_error(status); - free(t_bit_input); - free(input); - free(workspace); - free(output); - cudaFree(align_workspace); + // 5x times faster than gemm()-float32 + //gemm_nn_custom_bin_mean_transposed(m, n, k, 1, (unsigned char *)l.align_bit_weights, new_ldb, (unsigned char *)t_bit_input, new_ldb, c, n, l.mean_arr); + //status = cudaMemcpy(l.output_gpu, output, l.batch*l.out_c*l.out_h*l.out_w * sizeof(float), cudaMemcpyHostToDevice); + //check_error(status); + + gemm_nn_custom_bin_mean_transposed_gpu(m, n, k, 1, + (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.align_workspace_gpu, new_ldb, l.output_gpu, n, l.mean_arr_gpu); + //cudaDeviceSynchronize(); + + //free(t_bit_input); + //free(input); + //free(workspace); + //free(output); + //cudaFree(align_workspace); add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index fa927180d29..c36903dd604 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -626,8 +626,13 @@ void binary_align_weights(convolutional_layer *l) get_mean_array(align_weights, align_weights_size, l->n, l->mean_arr); #ifdef GPU + cudaError_t status; + l->align_workspace_size = l->bit_align * l->size * l->size * l->c; + status = cudaMalloc((void **)&l->align_workspace_gpu, l->align_workspace_size * sizeof(float)); + check_error(status); + //l->align_bit_weights_gpu = cuda_make_array(l->align_bit_weights, l->align_bit_weights_size * sizeof(char)/sizeof(float)); - cudaError_t status = cudaMalloc((void **)&l->align_bit_weights_gpu, l->align_bit_weights_size); + status = cudaMalloc((void **)&l->align_bit_weights_gpu, l->align_bit_weights_size); check_error(status); status = cudaMemcpy(l->align_bit_weights_gpu, l->align_bit_weights, l->align_bit_weights_size, cudaMemcpyHostToDevice); check_error(status); diff --git a/src/im2col.h b/src/im2col.h index 435a59cde85..5af54157390 100644 --- a/src/im2col.h +++ b/src/im2col.h @@ -22,5 +22,10 @@ void transpose_bin_gpu(unsigned char *A, unsigned char *B, const int n, const in void fill_int8_gpu(unsigned char *src, unsigned char val, size_t size); +void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, float ALPHA_UNUSED, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr); + #endif #endif diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index eee820afd0f..6ba503c7a82 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -218,7 +218,6 @@ __global__ void transpose_bin_gpu_kernel(unsigned char *A, unsigned char *B, con if (j < m - 8) { int a_index = i*lda + j; int b_index = j*ldb + i; - //transpose_8x8_bits_my(&A[a_index/8], &B[b_index/8], lda/8, ldb/8); transpose8rS32_reversed_diagonale(&A[a_index / 8], lda / 8, ldb / 8, &B[b_index / 8]); } else if (j < m) { @@ -250,5 +249,79 @@ __global__ void fill_int8_gpu_kernel(unsigned char *src, unsigned char val, size void fill_int8_gpu(unsigned char *src, unsigned char val, size_t size) { const int num_blocks = size / BLOCK + 1; - fill_int8_gpu_kernel<<>>(src, val, size); + fill_int8_gpu_kernel<<>>(src, val, size); +} + +// -------------------------------- + +typedef unsigned long long int uint64_t; + +__device__ __host__ static inline uint64_t xnor_int64(uint64_t a, uint64_t b) { + return ~(a^b); +} + +__global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int K, float ALPHA_UNUSED, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + + //if (index == 0) + { + int i, j, k, h; + + //#pragma omp parallel for + //for (i = 0; i < M; ++i) + i = index % M; + //if(i < M) + { // l.n - filters [16 - 55 - 1024] + float mean_val = mean_arr[i]; + + //for (j = 0; j < N; ++j) + j = index / M; + if(j < N) + { // out_h*out_w - one channel output size [169 - 173056] + int count = 0; + + for (k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] + uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); + uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); + uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); + + //#ifdef WIN32 + // int tmp_count = __popcnt64(c_bit64); + //#else + // int tmp_count = __builtin_popcountll(c_bit64); + //#endif + + int tmp_count = __popcll(c_bit64); + + if (K - k < 64) tmp_count = tmp_count - (64 - (K - k)); // remove extra bits + count += tmp_count; + //binary_int64_printf(c_bit64); + //printf(", count = %d \n\n", tmp_count); + } + + C[i*ldc + j] = (2 * count - K) * mean_val; + } + } + } +} + + +void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, float ALPHA_UNUSED, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + size_t size = M*N; + const int num_blocks = size / BLOCK + 1; + + gemm_nn_custom_bin_mean_transposed_gpu_kernel<<>>( + M, N, K, ALPHA_UNUSED, + A, lda, + B, ldb, + C, ldc, + mean_arr); } \ No newline at end of file diff --git a/src/layer.h b/src/layer.h index d533a5a5179..271a13de9ec 100644 --- a/src/layer.h +++ b/src/layer.h @@ -181,6 +181,9 @@ struct layer{ char *align_bit_weights_gpu; float *mean_arr_gpu; + float *align_workspace_gpu; + int align_workspace_size; + char *align_bit_weights; float *mean_arr; int align_bit_weights_size; From 007878393fc27316a0d9dcb102b0ee921eae6851 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 1 Sep 2018 00:03:25 +0300 Subject: [PATCH 0149/1365] Temporary Slow implementation of XNOR on CUDA (shared_memory) --- src/convolutional_kernels.cu | 86 +++------------- src/convolutional_layer.c | 2 + src/im2col.h | 2 +- src/im2col_kernels.cu | 186 ++++++++++++++++++++++++++++++----- src/layer.h | 1 + src/network_kernels.cu | 1 + 6 files changed, 182 insertions(+), 96 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 4343aca9111..26dee46a0f2 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -110,7 +110,7 @@ half *cuda_make_f16_from_f32_array(float *src, size_t n) void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) { - fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); + //fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); if(l.binary){ binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu); swap_binary(&l); @@ -123,106 +123,52 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) swap_binary(&l); binarize_gpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input_gpu); state.input = l.binary_input_gpu; + //cudaDeviceSynchronize(); if (l.align_bit_weights_gpu && !state.train) { - cudaError_t status; - //status = cudaMemcpy(l.align_bit_weights, l.align_bit_weights_gpu, l.align_bit_weights_size, cudaMemcpyDeviceToHost); - //check_error(status); - - //float *input = (float *)calloc(l.c*l.h*l.w*l.batch, sizeof(float)); - //float *workspace = (float *)calloc(l.bit_align*l.size*l.size*l.c, sizeof(float)); - //float *output = (float *)calloc(l.batch*l.out_c*l.out_h*l.out_w, sizeof(float)); - - //status = cudaMemcpy(input, state.input, l.c*l.h*l.w*l.batch*sizeof(float), cudaMemcpyDeviceToHost); - //check_error(status); + cudaError_t status = cudaSuccess; int m = l.n; int k = l.size*l.size*l.c; int n = l.out_w*l.out_h; float * a = l.weights_gpu; - //float * b = state.workspace; - //float *b = workspace; - //float * c = l.output_gpu; - //float *c = output; int ldb_align = l.lda_align; size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; size_t t_intput_size = new_ldb * n; size_t t_bit_input_size = t_intput_size / 8;// +1; - //char *t_bit_input = (char *)calloc(t_bit_input_size, sizeof(char)); - //int src_size = k * l.bit_align; - - //im2col_cpu_custom_bin(input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align); - - //float *align_workspace = NULL; - //int align_workspace_size = l.bit_align * k; // aligned: n*k - //status = cudaMalloc((void **)&align_workspace, align_workspace_size*sizeof(float)); - //check_error(status); - int i = 0; im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.align_workspace_gpu, l.bit_align); + //cudaDeviceSynchronize(); + // should be optimized float_to_bit_gpu(l.align_workspace_gpu, (unsigned char *)state.workspace, l.align_workspace_size); + //cudaDeviceSynchronize(); - if(1) - { - fill_int8_gpu((unsigned char *)l.align_workspace_gpu, 0, t_bit_input_size); - - transpose_bin_gpu((unsigned char *)state.workspace, (unsigned char *)l.align_workspace_gpu, k, n, l.bit_align, new_ldb, 8); - //cudaDeviceSynchronize(); - - //int size_transposed_array = l.bit_align * new_ldb; - //status = cudaMemcpy(t_bit_input, l.align_workspace_gpu, t_bit_input_size, cudaMemcpyDeviceToHost); - //check_error(status); - } - - /* - if (0) { - status = cudaMemcpy(b, state.workspace, l.align_workspace_size / 8, cudaMemcpyDeviceToHost); - check_error(status); - //float *im2 = (float *)calloc(l.align_workspace_size, sizeof(float)); - //status = cudaMemcpy(im2, l.align_workspace_gpu, l.align_workspace_size * sizeof(float), cudaMemcpyDeviceToHost); - //check_error(status); - //float_to_bit(im2, (unsigned char *)b, l.align_workspace_size); - - memset(t_bit_input, 0, t_bit_input_size); - // b - [bit_align, k] - [l.bit_align, l.size*l.size*l.c] = src_size - // t_input - [bit_align, k] - [n', k] - // t_bit_input - [new_ldb, n] - [k', n] - transpose_bin((char *)b, t_bit_input, k, n, l.bit_align, new_ldb, 8); - } - */ + //im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace, l.bit_align); - //status = cudaMemcpy(l.align_bit_weights, l.align_bit_weights_gpu, new_ldb * m / 8, cudaMemcpyDeviceToHost); - //check_error(status); + transpose_bin_gpu((unsigned char *)state.workspace, (unsigned char *)l.transposed_align_workspace_gpu, k, n, l.bit_align, new_ldb, 8); + //cudaDeviceSynchronize(); - //status = cudaMemcpy(l.mean_arr, l.mean_arr_gpu, l.n * sizeof(float), cudaMemcpyDeviceToHost); - //check_error(status); - // 5x times faster than gemm()-float32 - //gemm_nn_custom_bin_mean_transposed(m, n, k, 1, (unsigned char *)l.align_bit_weights, new_ldb, (unsigned char *)t_bit_input, new_ldb, c, n, l.mean_arr); - //status = cudaMemcpy(l.output_gpu, output, l.batch*l.out_c*l.out_h*l.out_w * sizeof(float), cudaMemcpyHostToDevice); - //check_error(status); - - gemm_nn_custom_bin_mean_transposed_gpu(m, n, k, 1, - (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.align_workspace_gpu, new_ldb, l.output_gpu, n, l.mean_arr_gpu); + // should be optimized + gemm_nn_custom_bin_mean_transposed_gpu(m, n, k, + (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, new_ldb, l.output_gpu, n, l.mean_arr_gpu); //cudaDeviceSynchronize(); - - //free(t_bit_input); - //free(input); - //free(workspace); - //free(output); - //cudaFree(align_workspace); + //check_error(status); add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); if (l.binary || l.xnor) swap_binary(&l); + //cudaDeviceSynchronize(); return; } } + fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); + #ifdef CUDNN float one = 1; // alpha[0], beta[0] is float for HALF and FLOAT float alpha = 1, beta = 0; diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index c36903dd604..5efcfd68f6e 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -629,6 +629,7 @@ void binary_align_weights(convolutional_layer *l) cudaError_t status; l->align_workspace_size = l->bit_align * l->size * l->size * l->c; status = cudaMalloc((void **)&l->align_workspace_gpu, l->align_workspace_size * sizeof(float)); + status = cudaMalloc((void **)&l->transposed_align_workspace_gpu, l->align_workspace_size * sizeof(float)); check_error(status); //l->align_bit_weights_gpu = cuda_make_array(l->align_bit_weights, l->align_bit_weights_size * sizeof(char)/sizeof(float)); @@ -638,6 +639,7 @@ void binary_align_weights(convolutional_layer *l) check_error(status); l->mean_arr_gpu = cuda_make_array(l->mean_arr, l->n); + cudaDeviceSynchronize(); #endif // GPU free(align_weights); diff --git a/src/im2col.h b/src/im2col.h index 5af54157390..c96bfea9934 100644 --- a/src/im2col.h +++ b/src/im2col.h @@ -22,7 +22,7 @@ void transpose_bin_gpu(unsigned char *A, unsigned char *B, const int n, const in void fill_int8_gpu(unsigned char *src, unsigned char val, size_t size); -void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, float ALPHA_UNUSED, +void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, unsigned char *A, int lda, unsigned char *B, int ldb, float *C, int ldc, float *mean_arr); diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 6ba503c7a82..91fedddf935 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -59,8 +59,9 @@ void im2col_ongpu(float *im, stride, height_col, width_col, data_col); } +// -------------------------------- - +#define WARP_SIZE 32 __global__ void im2col_align_gpu_kernel(const int n, const float* data_im, const int height, const int width, const int ksize, @@ -81,6 +82,7 @@ __global__ void im2col_align_gpu_kernel(const int n, const float* data_im, float* data_col_ptr = data_col; //data_col_ptr += (channel_out * height_col + h_out) * width_col + w_out; data_col_ptr += channel_out * bit_align + h_out * width_col + w_out; + float* data_col_ptr_32 = data_col + (channel_out * bit_align + h_out * width_col + w_out)/32; const float* data_im_ptr = data_im; data_im_ptr += (channel_in * height + h_in) * width + w_in; for (int i = 0; i < ksize; ++i) { @@ -91,6 +93,10 @@ __global__ void im2col_align_gpu_kernel(const int n, const float* data_im, *data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ? data_im_ptr[i * width + j] : 0; + //float src_val = (h >= 0 && w >= 0 && h < height && w < width) ? data_im_ptr[i * width + j] : 0; + //unsigned int bit_mask = __ballot_sync(0xffffffff, src_val > 0); + //if (threadIdx.x % WARP_SIZE == 0) *((unsigned int*)data_col_ptr_32) = bit_mask; + //data_col_ptr_32 += bit_align / 32; //data_col_ptr += height_col * width_col; data_col_ptr += bit_align; @@ -113,26 +119,21 @@ void im2col_align_ongpu(float *im, stride, height_col, width_col, data_col, bit_align); } - - // -------------------------------- -#define WARP_SIZE 32 __global__ void float_to_bit_gpu_kernel(float *src, unsigned char *dst, size_t size) { - //size_t dst_size = size / 8 + 1; - //memset(dst, 0, dst_size); - //uint32_t bit_mask = __ballot_sync(FULL_MASK, src[i] > 0); - const int size_aligned = size + (WARP_SIZE - size % WARP_SIZE); + //const int size_aligned = size + (WARP_SIZE - size % WARP_SIZE); int index = blockIdx.x*blockDim.x + threadIdx.x; float src_val; - for (; index < size_aligned; index += blockDim.x*gridDim.x) + //for (; index < size_aligned; index += blockDim.x*gridDim.x) { - if(index < size) src_val = src[index]; - else src_val = 0; + src_val = src[index]; + //if(index < size) src_val = src[index]; + //else src_val = 0; unsigned int bit_mask = __ballot_sync(0xffffffff, src_val > 0); if (threadIdx.x % WARP_SIZE == 0) ((unsigned int*)dst)[index / 32] = bit_mask; } @@ -144,10 +145,15 @@ void float_to_bit_gpu(float *src, unsigned char *dst, size_t size) const int num_blocks = size / BLOCK + 1; float_to_bit_gpu_kernel<<>>(src, dst, size); } - // -------------------------------- +__device__ __host__ static inline void remove_bit(unsigned char *const dst, size_t index) { + size_t dst_i = index / 8; + int dst_shift = index % 8; + dst[dst_i] &= ~(1 << dst_shift); +} + __device__ __host__ static inline void set_bit(unsigned char *const dst, size_t index) { size_t dst_i = index / 8; int dst_shift = index % 8; @@ -177,8 +183,6 @@ __device__ __host__ unsigned char reverse_byte_2(unsigned char a) return ((a * 0x0802LU & 0x22110LU) | (a * 0x8020LU & 0x88440LU)) * 0x10101LU >> 16; } - - __device__ __host__ void transpose8rS32_reversed_diagonale(unsigned char* A, int m, int n, unsigned char* B) { unsigned x, y, t; @@ -223,6 +227,7 @@ __global__ void transpose_bin_gpu_kernel(unsigned char *A, unsigned char *B, con else if (j < m) { for (; j < m; ++j) { if (get_bit(A, i*lda + j)) set_bit(B, j*ldb + i); + else remove_bit(B, j*ldb + i); } } } @@ -237,8 +242,6 @@ void transpose_bin_gpu(unsigned char *A, unsigned char *B, const int n, const in const int num_blocks = size / BLOCK + 1; transpose_bin_gpu_kernel << > >(A, B, n, m, lda, ldb, block_size); } - - // -------------------------------- @@ -251,16 +254,17 @@ void fill_int8_gpu(unsigned char *src, unsigned char val, size_t size) { const int num_blocks = size / BLOCK + 1; fill_int8_gpu_kernel<<>>(src, val, size); } - // -------------------------------- typedef unsigned long long int uint64_t; +typedef unsigned char uint8_t; __device__ __host__ static inline uint64_t xnor_int64(uint64_t a, uint64_t b) { return ~(a^b); } -__global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int K, float ALPHA_UNUSED, +/* +__global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int K, unsigned char *A, int lda, unsigned char *B, int ldb, float *C, int ldc, float *mean_arr) @@ -289,11 +293,75 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); - //#ifdef WIN32 - // int tmp_count = __popcnt64(c_bit64); - //#else - // int tmp_count = __builtin_popcountll(c_bit64); - //#endif + int tmp_count = __popcll(c_bit64); + + if (K - k < 64) tmp_count = tmp_count - (64 - (K - k)); // remove extra bits + count += tmp_count; + //binary_int64_printf(c_bit64); + //printf(", count = %d \n\n", tmp_count); + } + + C[i*ldc + j] = (2 * count - K) * mean_val; + } + } + } +} +*/ + + +/* +// B (input) in the shared_memory +__global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int K, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + + __shared__ uint64_t B_s[4096]; // 32 KB // [ldb x N`] + + int start_j = blockIdx.x*blockDim.x / M; + int end_j = (blockIdx.x*blockDim.x + blockDim.x) / M + 1; + + size_t shared_size = ldb * (end_j - start_j); + + int j_cur = index / M; + int local_j = j_cur - start_j; + + for (int k = threadIdx.x * 64; k < shared_size; k += blockDim.x * 64) { + int x = start_j*ldb + k; + if (x < (N*ldb)) *((uint64_t *)(B_s + k / 8)) = *((uint64_t *)(B + x / 8)); + } + + //if (j_cur < N && (index % M == 0 || threadIdx.x == 0)) { + // for (int k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] + // *((uint64_t *)(B_s + (local_j*ldb + k) / 8)) = *((uint64_t *)(B + (j_cur*ldb + k) / 8)); // input + //} + //} + __syncthreads(); + + //if (index == 0) + { + int i, j, k, h; + + //#pragma omp parallel for + //for (i = 0; i < M; ++i) + i = index % M; + //if(i < M) + { // l.n - filters [16 - 55 - 1024] + float mean_val = mean_arr[i]; + + //for (j = 0; j < N; ++j) + j = index / M; + if (j < N) + { // out_h*out_w - one channel output size [169 - 173056] + int count = 0; + + for (k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] + uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); // weights + //uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); + uint64_t b_bit64 = *((uint64_t *)(B_s + (local_j*ldb + k) / 8)); // input + uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); int tmp_count = __popcll(c_bit64); @@ -308,9 +376,70 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int } } } +*/ + +// A (weights) in the shared_memory +__global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int K, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + + __shared__ uint64_t A_s[6144]; // 48 KB // [lda x M`] + //__shared__ uint8_t A_s[6144*8]; // 48 KB // [lda x M`] + + int start_i = blockIdx.x*blockDim.x / N; + int end_i = (blockIdx.x*blockDim.x + blockDim.x) / N + 1; + + size_t shared_size = lda * (end_i - start_i); + + int i_cur = index / N; + int local_i = i_cur - start_i; + + for (int k = threadIdx.x * 64; k < shared_size; k += blockDim.x * 64) { + int x = start_i*lda + k; + if (x < (M*lda)) *((uint64_t *)(A_s + k / 8)) = *((uint64_t *)(A + x / 8)); + } + + //if (i_cur < M && (index % N == 0 || threadIdx.x == 0)) { + //for (int k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] + //*((uint64_t *)(A_s + (local_i*lda + k) / 8)) = *((uint64_t *)(A + (i_cur*lda + k) / 8)); // weights + // } + //} + + __syncthreads(); + + int i, j, k, h; + + j = index % N; + { // out_h*out_w - one channel output size [169 - 173056] + i = index / N; + if (i < M) // l.n - filters [16 - 55 - 1024] + { + float mean_val = mean_arr[i]; + int count = 0; + + for (k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] + //uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); // weights + uint64_t a_bit64 = *((uint64_t *)(A_s + (local_i*lda + k) / 8)); // weights + uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); // input + uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); + + int tmp_count = __popcll(c_bit64); + + if (K - k < 64) tmp_count = tmp_count - (64 - (K - k)); // remove extra bits + count += tmp_count; + } + + C[i*ldc + j] = (2 * count - K) * mean_val; + } + } +} +#include -void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, float ALPHA_UNUSED, +void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, unsigned char *A, int lda, unsigned char *B, int ldb, float *C, int ldc, float *mean_arr) @@ -318,8 +447,15 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, float ALPHA_UNU size_t size = M*N; const int num_blocks = size / BLOCK + 1; + /* + printf("\n gemm_bin size = %d, num_blocks = %d, M*K = %d KB, N*K = %d KB \n (w) M*K/num_blocks = %d KB, (i) N*K/num_blocks = %d KB \n", + size, num_blocks, M*K / 1024, N*K / 1024, M*lda / num_blocks / 1024, N*ldb / num_blocks / 1024); + printf(" M / 512 = %d, N / 512 = %d, M*lda / 512 = %d, N*ldb / 512 = %d \n", M / 512, N / 512, M*lda/512, N*ldb/512); + */ + //printf(" shared_memory: (w) lda*BLOCK/N = %d, (i) ldb*BLOCK/M = %d, \t lda = %d \n\n", lda*BLOCK / N, ldb*BLOCK / M, lda); + gemm_nn_custom_bin_mean_transposed_gpu_kernel<<>>( - M, N, K, ALPHA_UNUSED, + M, N, K, A, lda, B, ldb, C, ldc, diff --git a/src/layer.h b/src/layer.h index 271a13de9ec..9ec35f1fe94 100644 --- a/src/layer.h +++ b/src/layer.h @@ -182,6 +182,7 @@ struct layer{ char *align_bit_weights_gpu; float *mean_arr_gpu; float *align_workspace_gpu; + float *transposed_align_workspace_gpu; int align_workspace_size; char *align_bit_weights; diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 681542f25db..0ecba682ddd 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -58,6 +58,7 @@ void forward_network_gpu(network net, network_state state) if(net.wait_stream) cudaStreamSynchronize(get_cuda_stream()); state.input = l.output_gpu; + //cudaDeviceSynchronize(); /* cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); if (l.out_w >= 0 && l.out_h >= 1 && l.c >= 3) { From b141f85cab5276953104f1cd8eee310da52e9020 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 7 Sep 2018 15:07:46 +0300 Subject: [PATCH 0150/1365] Compile fix --- src/convolutional_kernels.cu | 26 +++++++- src/convolutional_layer.c | 2 + src/detector.c | 27 ++++---- src/im2col.h | 6 ++ src/im2col_kernels.cu | 115 +++++++++++++++++++++++++++++++++++ 5 files changed, 164 insertions(+), 12 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 26dee46a0f2..6d2ce905033 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -117,6 +117,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) } if(l.xnor){ + if (!l.align_bit_weights_gpu || state.train) { binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu); } @@ -128,6 +129,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) if (l.align_bit_weights_gpu && !state.train) { cudaError_t status = cudaSuccess; + int input_size = l.c*l.h*l.w*l.batch; int m = l.n; int k = l.size*l.size*l.c; @@ -139,6 +141,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) size_t t_intput_size = new_ldb * n; size_t t_bit_input_size = t_intput_size / 8;// +1; + int i = 0; im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.align_workspace_gpu, l.bit_align); //cudaDeviceSynchronize(); @@ -152,13 +155,34 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) transpose_bin_gpu((unsigned char *)state.workspace, (unsigned char *)l.transposed_align_workspace_gpu, k, n, l.bit_align, new_ldb, 8); //cudaDeviceSynchronize(); - // should be optimized gemm_nn_custom_bin_mean_transposed_gpu(m, n, k, (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, new_ldb, l.output_gpu, n, l.mean_arr_gpu); //cudaDeviceSynchronize(); //check_error(status); + + { + //float_to_bit_gpu(state.input, (unsigned char *)l.align_workspace_gpu, input_size); + + /* + float *input_cpu = (float *)calloc(input_size, sizeof(float)); + status = cudaMemcpy(input_cpu, state.input, input_size* sizeof(float), cudaMemcpyDeviceToHost); + check_error(status); + + convolve_bin_cpu(input_cpu, l.weights, l.output, l.w, l.h, l.c, l.n, l.size, l.pad); // CPU + status = cudaMemcpy(l.output_gpu, l.output, l.outputs * sizeof(float), cudaMemcpyHostToDevice); + check_error(status); + free(input_cpu); + */ + + //convolve_bin_gpu(state.input, l.weights_gpu, l.output_gpu, l.w, l.h, l.c, l.n, l.size, l.pad); + //cudaDeviceSynchronize(); + //check_error(status); + + + } + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); if (l.binary || l.xnor) swap_binary(&l); diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 5efcfd68f6e..452f792ef32 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -637,6 +637,8 @@ void binary_align_weights(convolutional_layer *l) check_error(status); status = cudaMemcpy(l->align_bit_weights_gpu, l->align_bit_weights, l->align_bit_weights_size, cudaMemcpyHostToDevice); check_error(status); + status = cudaMemcpy(l->binary_weights_gpu, l->binary_weights, m*k*sizeof(float), cudaMemcpyHostToDevice); + check_error(status); l->mean_arr_gpu = cuda_make_array(l->mean_arr, l->n); cudaDeviceSynchronize(); diff --git a/src/detector.c b/src/detector.c index 0c1c3986f90..dc312345da7 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1026,19 +1026,24 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int char buff[1024]; FILE* fw = fopen("anchors.txt", "wb"); - printf("\nSaving anchors to the file: anchors.txt \n"); - printf("anchors = "); - for (i = 0; i < num_of_clusters; ++i) { - sprintf(buff, "%2.4f,%2.4f", centers->data.fl[i * 2], centers->data.fl[i * 2 + 1]); - printf("%s", buff); - fwrite(buff, sizeof(char), strlen(buff), fw); - if (i + 1 < num_of_clusters) { - fwrite(", ", sizeof(char), 2, fw); - printf(", "); + if (fw) { + printf("\nSaving anchors to the file: anchors.txt \n"); + printf("anchors = "); + for (i = 0; i < num_of_clusters; ++i) { + sprintf(buff, "%2.4f,%2.4f", centers->data.fl[i * 2], centers->data.fl[i * 2 + 1]); + printf("%s", buff); + fwrite(buff, sizeof(char), strlen(buff), fw); + if (i + 1 < num_of_clusters) { + fwrite(", ", sizeof(char), 2, fw); + printf(", "); + } } + printf("\n"); + fclose(fw); + } + else { + printf(" Error: file anchors.txt can't be open \n"); } - printf("\n"); - fclose(fw); if (show) { size_t img_size = 700; diff --git a/src/im2col.h b/src/im2col.h index c96bfea9934..b518e803c1c 100644 --- a/src/im2col.h +++ b/src/im2col.h @@ -1,6 +1,8 @@ #ifndef IM2COL_H #define IM2COL_H +#include + void im2col_cpu(float* data_im, int channels, int height, int width, int ksize, int stride, int pad, float* data_col); @@ -27,5 +29,9 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, unsigned char *B, int ldb, float *C, int ldc, float *mean_arr); +void convolve_bin_gpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad); + +void convolve_bin_cpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad); + #endif #endif diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 91fedddf935..e545260d5fe 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -460,4 +460,119 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, B, ldb, C, ldc, mean_arr); +} + +// -------------------------------- + + +__global__ void convolve_bin_gpu_kernel(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + + int fil; + // filter index + //for (fil = 0; fil < n; ++fil) + int chan, y, x, f_y, f_x; + // channel index + //for (chan = 0; chan < in_c; ++chan) + // input - y + //for (y = 0; y < in_h; ++y) + // input - x + //for (x = 0; x < in_w; ++x) + x = index % in_w; + int index2 = index / in_w; + y = index2 % in_h; + fil = index2 / in_h; + if (fil < n) + { + + int const output_index = fil*in_w*in_h + y*in_w + x; + float sum = 0; + + for (chan = 0; chan < in_c; ++chan) + { + int const weights_pre_index = fil*in_c*size*size + chan*size*size; + int const input_pre_index = chan*in_w*in_h; + + // filter - y + for (f_y = 0; f_y < size; ++f_y) + { + int input_y = y + f_y - pad; + // filter - x + for (f_x = 0; f_x < size; ++f_x) + { + int input_x = x + f_x - pad; + if (input_y < 0 || input_x < 0 || input_y >= in_h || input_x >= in_w) continue; + + int input_index = input_pre_index + input_y*in_w + input_x; + int weights_index = weights_pre_index + f_y*size + f_x; + + sum += input[input_index] *weights[weights_index]; + + } + } + // l.output[filters][width][height] += + // state.input[channels][width][height] * + // l.weights[filters][channels][filter_width][filter_height]; + //output[output_index] += sum; + } + output[output_index] = sum; + } + +} + +void convolve_bin_gpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad) +{ + size_t array_size = in_w*in_h*n; // width X height X filters + const int num_blocks = array_size / BLOCK + 1; + //printf("\n array_size = %d, num_blocks = %d, w = %d, h = %d, n = %d, c = %d, pad = %d \n", array_size, num_blocks, in_w, in_h, n, in_c, pad); + + convolve_bin_gpu_kernel << > > (input, weights, output, in_w, in_h, in_c, n, size, pad); +} + + + +void convolve_bin_cpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad) +{ + int fil; + // filter index +#pragma omp parallel for // "omp parallel for" - automatic parallelization of loop by using OpenMP + for (fil = 0; fil < n; ++fil) { + int chan, y, x, f_y, f_x; + // channel index + for (chan = 0; chan < in_c; ++chan) + // input - y + for (y = 0; y < in_h; ++y) + // input - x + for (x = 0; x < in_w; ++x) + { + int const output_index = fil*in_w*in_h + y*in_w + x; + int const weights_pre_index = fil*in_c*size*size + chan*size*size; + int const input_pre_index = chan*in_w*in_h; + float sum = 0; + + // filter - y + for (f_y = 0; f_y < size; ++f_y) + { + int input_y = y + f_y - pad; + // filter - x + for (f_x = 0; f_x < size; ++f_x) + { + int input_x = x + f_x - pad; + if (input_y < 0 || input_x < 0 || input_y >= in_h || input_x >= in_w) continue; + + int input_index = input_pre_index + input_y*in_w + input_x; + int weights_index = weights_pre_index + f_y*size + f_x; + + sum += input[input_index] * weights[weights_index]; + } + } + // l.output[filters][width][height] += + // state.input[channels][width][height] * + // l.weights[filters][channels][filter_width][filter_height]; + output[output_index] += sum; + } + } + + } \ No newline at end of file From 57e878b4f9512cf9995ff6b5cd6e0d7dc1da9eaf Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 7 Sep 2018 15:25:37 +0300 Subject: [PATCH 0151/1365] another compile fix --- src/im2col_kernels.cu | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index e545260d5fe..f407e9760e6 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -131,10 +131,11 @@ __global__ void float_to_bit_gpu_kernel(float *src, unsigned char *dst, size_t s //for (; index < size_aligned; index += blockDim.x*gridDim.x) { - src_val = src[index]; - //if(index < size) src_val = src[index]; - //else src_val = 0; - unsigned int bit_mask = __ballot_sync(0xffffffff, src_val > 0); + //src_val = src[index]; + if(index < size) src_val = src[index]; + else src_val = 0; + //unsigned int bit_mask = __ballot_sync(0xffffffff, src_val > 0); + unsigned int bit_mask = __ballot(src_val > 0); if (threadIdx.x % WARP_SIZE == 0) ((unsigned int*)dst)[index / 32] = bit_mask; } } From c0e01fd63cd0e97509947864c61d27a4637830f8 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 7 Sep 2018 23:04:46 +0300 Subject: [PATCH 0152/1365] Test for XNOR-conv on CUDA --- src/convolutional_kernels.cu | 41 ++++- src/convolutional_layer.c | 1 + src/im2col.h | 10 +- src/im2col_kernels.cu | 346 ++++++++++++++++++++++++++++++----- src/layer.h | 1 + 5 files changed, 349 insertions(+), 50 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 6d2ce905033..556efda1e36 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -141,7 +141,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) size_t t_intput_size = new_ldb * n; size_t t_bit_input_size = t_intput_size / 8;// +1; - + /* int i = 0; im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.align_workspace_gpu, l.bit_align); //cudaDeviceSynchronize(); @@ -160,23 +160,46 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, new_ldb, l.output_gpu, n, l.mean_arr_gpu); //cudaDeviceSynchronize(); //check_error(status); - + */ { - //float_to_bit_gpu(state.input, (unsigned char *)l.align_workspace_gpu, input_size); + // /* float *input_cpu = (float *)calloc(input_size, sizeof(float)); status = cudaMemcpy(input_cpu, state.input, input_size* sizeof(float), cudaMemcpyDeviceToHost); check_error(status); - convolve_bin_cpu(input_cpu, l.weights, l.output, l.w, l.h, l.c, l.n, l.size, l.pad); // CPU + // swaped(binary_weights <-> l.weights) + convolve_cpu(input_cpu, l.weights, l.output, l.w, l.h, l.c, l.n, l.size, l.pad); // CPU status = cudaMemcpy(l.output_gpu, l.output, l.outputs * sizeof(float), cudaMemcpyHostToDevice); check_error(status); free(input_cpu); */ - //convolve_bin_gpu(state.input, l.weights_gpu, l.output_gpu, l.w, l.h, l.c, l.n, l.size, l.pad); + /* + float *input_cpu = (float *)calloc(input_size, sizeof(float)); + float *input_bin_cpu = (float *)calloc(input_size, sizeof(char)); + //float *weights_bin_cpu = (float *)calloc(l.n*l.c*l.size*l.size, sizeof(char)); + status = cudaMemcpy(input_cpu, state.input, input_size * sizeof(float), cudaMemcpyDeviceToHost); + check_error(status); + float_to_bit(input_cpu, (unsigned char *)input_bin_cpu, input_size); + //float_to_bit(l.weights, (unsigned char *)weights_bin_cpu, l.n*l.c*l.size*l.size); // l.align_bit_weights + + convolve_bin_cpu(input_bin_cpu, (float *)l.align_bit_weights, l.output, l.w, l.h, l.c, l.n, l.size, l.pad, l.new_lda, l.mean_arr); // CPU + status = cudaMemcpy(l.output_gpu, l.output, l.outputs * sizeof(float), cudaMemcpyHostToDevice); + check_error(status); + //free(weights_bin_cpu); + free(input_bin_cpu); + free(input_cpu); + */ + + float_to_bit_gpu(state.input, (unsigned char *)l.align_workspace_gpu, input_size); + convolve_bin_gpu(l.align_workspace_gpu, (float *)l.align_bit_weights_gpu, l.output_gpu, l.w, l.h, l.c, l.n, l.size, l.pad, l.new_lda, l.mean_arr_gpu); + + + //convolve_gpu(state.input, l.weights_gpu, l.output_gpu, l.w, l.h, l.c, l.n, l.size, l.pad); + //cudaDeviceSynchronize(); //check_error(status); @@ -309,10 +332,16 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) int k = l.size*l.size*l.c; int n = l.out_w*l.out_h; for(i = 0; i < l.batch; ++i){ - im2col_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace); + float *im = state.input + i*l.c*l.h*l.w; float * a = l.weights_gpu; float * b = state.workspace; float * c = l.output_gpu; + if (l.size == 1) { + b = im; + } + else { + im2col_ongpu(im, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace); + } gemm_ongpu(0,0,m,n,k,1.,a,k,b,n,1.,c+i*m*n,n); } #endif diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 452f792ef32..69af24fe0a6 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -605,6 +605,7 @@ void binary_align_weights(convolutional_layer *l) int m = l->n; int k = l->size*l->size*l->c; size_t new_lda = k + (l->lda_align - k % l->lda_align); // (k / 8 + 1) * 8; + l->new_lda = new_lda; binarize_weights(l->weights, m, k, l->binary_weights); diff --git a/src/im2col.h b/src/im2col.h index b518e803c1c..74211e25c66 100644 --- a/src/im2col.h +++ b/src/im2col.h @@ -29,9 +29,15 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, unsigned char *B, int ldb, float *C, int ldc, float *mean_arr); -void convolve_bin_gpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad); +void convolve_gpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad); -void convolve_bin_cpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad); +void convolve_bin_gpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad, + int new_lda, float *mean_arr_gpu); + +void convolve_bin_cpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, + int size, int pad, int new_lda, float *mean_arr_gpu); + +void convolve_cpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad); #endif #endif diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index f407e9760e6..1d719ee8243 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -258,7 +258,17 @@ void fill_int8_gpu(unsigned char *src, unsigned char val, size_t size) { // -------------------------------- typedef unsigned long long int uint64_t; +typedef unsigned int uint32_t; typedef unsigned char uint8_t; +typedef char int8_t; + +__device__ __host__ static inline uint64_t broadcast_bit_1_to_64(uint8_t src) { + return (src > 0) ? 0xFFFFFFFFFFFFFFFF : 0; +} + +__device__ __host__ static inline uint8_t xnor_bit1(uint8_t a, uint8_t b) { + return ~(a^b) & 0b1; +} __device__ __host__ static inline uint64_t xnor_int64(uint64_t a, uint64_t b) { return ~(a^b); @@ -462,38 +472,26 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, C, ldc, mean_arr); } - // -------------------------------- - -__global__ void convolve_bin_gpu_kernel(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad) +void convolve_cpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad) { - int index = blockIdx.x*blockDim.x + threadIdx.x; - int fil; // filter index - //for (fil = 0; fil < n; ++fil) - int chan, y, x, f_y, f_x; - // channel index - //for (chan = 0; chan < in_c; ++chan) - // input - y - //for (y = 0; y < in_h; ++y) - // input - x - //for (x = 0; x < in_w; ++x) - x = index % in_w; - int index2 = index / in_w; - y = index2 % in_h; - fil = index2 / in_h; - if (fil < n) - { - - int const output_index = fil*in_w*in_h + y*in_w + x; - float sum = 0; - - for (chan = 0; chan < in_c; ++chan) +#pragma omp parallel for // "omp parallel for" - automatic parallelization of loop by using OpenMP + for (fil = 0; fil < n; ++fil) { + int chan, y, x, f_y, f_x; + // channel index + for (chan = 0; chan < in_c; ++chan) + // input - y + for (y = 0; y < in_h; ++y) + // input - x + for (x = 0; x < in_w; ++x) { + int const output_index = fil*in_w*in_h + y*in_w + x; int const weights_pre_index = fil*in_c*size*size + chan*size*size; int const input_pre_index = chan*in_w*in_h; + float sum = 0; // filter - y for (f_y = 0; f_y < size; ++f_y) @@ -508,37 +506,29 @@ __global__ void convolve_bin_gpu_kernel(float *input, float *weights, float *out int input_index = input_pre_index + input_y*in_w + input_x; int weights_index = weights_pre_index + f_y*size + f_x; - sum += input[input_index] *weights[weights_index]; - + sum += input[input_index] * weights[weights_index]; } } // l.output[filters][width][height] += // state.input[channels][width][height] * // l.weights[filters][channels][filter_width][filter_height]; - //output[output_index] += sum; + output[output_index] += sum; } - output[output_index] = sum; } -} - -void convolve_bin_gpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad) -{ - size_t array_size = in_w*in_h*n; // width X height X filters - const int num_blocks = array_size / BLOCK + 1; - //printf("\n array_size = %d, num_blocks = %d, w = %d, h = %d, n = %d, c = %d, pad = %d \n", array_size, num_blocks, in_w, in_h, n, in_c, pad); - convolve_bin_gpu_kernel << > > (input, weights, output, in_w, in_h, in_c, n, size, pad); } +// -------------------------------- - -void convolve_bin_cpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad) +void convolve_bin_cpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, + int size, int pad, int new_lda, float *mean_arr_gpu) { int fil; // filter index #pragma omp parallel for // "omp parallel for" - automatic parallelization of loop by using OpenMP for (fil = 0; fil < n; ++fil) { + float mean_val = mean_arr_gpu[fil]; int chan, y, x, f_y, f_x; // channel index for (chan = 0; chan < in_c; ++chan) @@ -550,7 +540,151 @@ void convolve_bin_cpu(float *input, float *weights, float *output, int in_w, int int const output_index = fil*in_w*in_h + y*in_w + x; int const weights_pre_index = fil*in_c*size*size + chan*size*size; int const input_pre_index = chan*in_w*in_h; - float sum = 0; + int sum = 0; + int good_val = 0; + + // filter - y + for (f_y = 0; f_y < size; ++f_y) + { + int input_y = y + f_y - pad; + // filter - x + for (f_x = 0; f_x < size; ++f_x) + { + int input_x = x + f_x - pad; + if (input_y < 0 || input_x < 0 || input_y >= in_h || input_x >= in_w) continue; + + int input_index = input_pre_index + input_y*in_w + input_x; + //int weights_index = weights_pre_index + f_y*size + f_x; + //int weights_index = fil*in_c*size*size + chan*size*size + f_y*size + f_x; + int weights_index = fil*new_lda + chan*size*size + f_y*size + f_x; + + //sum += input[input_index] * weights[weights_index]; + + int8_t in_bit = get_bit((uint8_t *)input, input_index); + int8_t w_bit = get_bit((uint8_t *)weights, weights_index); + int res = xnor_bit1(in_bit, w_bit); + sum += res; + good_val++; + //sum += (res > 0) ? 1 : -1; + //in_bit = (in_bit > 0) ? 1 : -1; + //w_bit = (w_bit > 0) ? 1 : -1; + //int8_t res = in_bit*w_bit; + //sum += res; + //printf("\n i: %d x w: %d = res: %d \t sum: %d \t mean = %f \n", in_bit, w_bit, res, sum, mean_val); + } + } + //printf("sum = %d, ", sum); + sum = sum - (good_val - sum); + //printf(" size = %d, sum = %d \n", size, sum); + + // l.output[filters][width][height] += + // state.input[channels][width][height] * + // l.weights[filters][channels][filter_width][filter_height]; + output[output_index] += sum*mean_val; + } + } +} +// -------------------------------- + +__global__ void convolve_gpu_kernel(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + + int fil; + // filter index + //for (fil = 0; fil < n; ++fil) + int chan, y, x, f_y, f_x; + // channel index + //for (chan = 0; chan < in_c; ++chan) + // input - y + //for (y = 0; y < in_h; ++y) + // input - x + //for (x = 0; x < in_w; ++x) + x = index % in_w; + int index2 = index / in_w; + y = index2 % in_h; + fil = index2 / in_h; + if (fil < n) + { + + int const output_index = fil*in_w*in_h + y*in_w + x; + float sum = 0; + + for (chan = 0; chan < in_c; ++chan) + { + int const weights_pre_index = fil*in_c*size*size + chan*size*size; + int const input_pre_index = chan*in_w*in_h; + + // filter - y + for (f_y = 0; f_y < size; ++f_y) + { + int input_y = y + f_y - pad; + // filter - x + for (f_x = 0; f_x < size; ++f_x) + { + int input_x = x + f_x - pad; + if (input_y < 0 || input_x < 0 || input_y >= in_h || input_x >= in_w) continue; + + int input_index = input_pre_index + input_y*in_w + input_x; + int weights_index = weights_pre_index + f_y*size + f_x; + + sum += input[input_index] * weights[weights_index]; + + } + } + // l.output[filters][width][height] += + // state.input[channels][width][height] * + // l.weights[filters][channels][filter_width][filter_height]; + //output[output_index] += sum; + } + output[output_index] = sum; + } + +} + +void convolve_gpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad) +{ + size_t array_size = in_w*in_h*n; // width X height X filters + const int num_blocks = array_size / BLOCK + 1; + //printf("\n array_size = %d, num_blocks = %d, w = %d, h = %d, n = %d, c = %d, pad = %d \n", array_size, num_blocks, in_w, in_h, n, in_c, pad); + + convolve_gpu_kernel << > > (input, weights, output, in_w, in_h, in_c, n, size, pad); +} + +// -------------------------------- + +/* +__global__ void convolve_bin_gpu_kernel(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, + int size, int pad, int new_lda, float *mean_arr_gpu) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + + int fil; + // filter index + //for (fil = 0; fil < n; ++fil) + int chan, y, x, f_y, f_x; + // channel index + //for (chan = 0; chan < in_c; ++chan) + // input - y + //for (y = 0; y < in_h; ++y) + // input - x + //for (x = 0; x < in_w; ++x) + x = index % in_w; + int index2 = index / in_w; + y = index2 % in_h; + fil = index2 / in_h; + if (fil < n) // (1-6 for one BLOCK) + { + //float mean_val = mean_arr_gpu[fil]; + int const output_index = fil*in_w*in_h + y*in_w + x; + int sum = 0; + int good_val = 0; + + for (chan = 0; chan < in_c; ++chan) + { + //int const weights_pre_index = fil*in_c*size*size + chan*size*size; + int const weights_pre_index = fil*new_lda + chan*size*size; + int const input_pre_index = chan*in_w*in_h; // filter - y for (f_y = 0; f_y < size; ++f_y) @@ -564,16 +698,144 @@ void convolve_bin_cpu(float *input, float *weights, float *output, int in_w, int int input_index = input_pre_index + input_y*in_w + input_x; int weights_index = weights_pre_index + f_y*size + f_x; + //int weights_index = fil*in_c*size*size + chan*size*size + f_y*size + f_x; + //int weights_index = fil*new_lda + chan*size*size + f_y*size + f_x; + + uint8_t in_bit = get_bit((uint8_t *)input, input_index); + uint8_t w_bit = get_bit((uint8_t *)weights, weights_index); + int res = xnor_bit1(in_bit, w_bit); + sum += res; + good_val++; + + //sum += input[input_index] *weights[weights_index]; - sum += input[input_index] * weights[weights_index]; } } // l.output[filters][width][height] += // state.input[channels][width][height] * // l.weights[filters][channels][filter_width][filter_height]; - output[output_index] += sum; + //output[output_index] += sum; + } + sum = sum - (good_val - sum); + output[output_index] = sum * mean_arr_gpu[fil]; // atoimcAdd for inter-BLOCK sum + } + +} +*/ + +__global__ void convolve_bin_gpu_kernel(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, + int size, int pad, int new_lda, float *mean_arr_gpu) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + + int fil; + // filter index + //for (fil = 0; fil < n; ++fil) + int chan, y, x, f_y, f_x; + // channel index + //for (chan = 0; chan < in_c; ++chan) + // input - y + //for (y = 0; y < in_h; ++y) + // input - x + //for (x = 0; x < in_w; ++x) + x = index % in_w; + int index2 = index / in_w; + y = index2 % in_h; + fil = index2 / in_h; + if (fil < n) // (1-6 for one BLOCK) + { + //float mean_val = mean_arr_gpu[fil]; + int const output_index = fil*in_w*in_h + y*in_w + x; + int sum = 0; + int good_val = 0; + + int min_index = blockIdx.x*blockDim.x; + int min_fil = (min_index / in_w) / in_h; + int max_index = (blockIdx.x+1)*blockDim.x - 1; + int max_fil = (max_index / in_w) / in_h; + + __shared__ uint32_t weights_shared[3*3*1024*6/32 + 1]; // 7 KB (6 filters) - use (new_lda) for size calculation + //const int weights_size = size*size*in_c/8; + const int weights_size = size*size*in_c / 32 + 1; + + for (int fil = min_fil; fil <= max_fil; fil++) { + for (int s = threadIdx.x; s < weights_size; s += blockDim.x) { + //weights_shared[s + (fil - min_fil)*new_lda / 8] = ((uint8_t *)weights)[fil*new_lda / 8 + s]; + weights_shared[s + (fil - min_fil)*new_lda/32] = ((uint32_t *)weights)[fil*new_lda / 32 + s]; + } + } + __syncthreads(); + + for (chan = 0; chan < in_c; ++chan) + { + //int const weights_pre_index = fil*in_c*size*size + chan*size*size; + int const weights_pre_index = fil*new_lda + chan*size*size; + int const input_pre_index = chan*in_w*in_h; + + __shared__ uint32_t input_shared[416*416/32]; // 21.2 KB bytes + const int input_shared_size = in_w*in_h / 32; + const int add_input_index = input_pre_index % 32; + + for (int s = threadIdx.x; s < input_shared_size; s += blockDim.x) { + input_shared[s] = ((uint32_t *)weights)[input_pre_index / 32 + s]; + } + __syncthreads(); + + // filter - y + for (f_y = 0; f_y < size; ++f_y) + { + int input_y = y + f_y - pad; + // filter - x + for (f_x = 0; f_x < size; ++f_x) + { + int input_x = x + f_x - pad; + if (input_y < 0 || input_x < 0 || input_y >= in_h || input_x >= in_w) continue; + + int input_index = input_pre_index + input_y*in_w + input_x; + int weights_index = weights_pre_index + f_y*size + f_x; + //int weights_index = fil*in_c*size*size + chan*size*size + f_y*size + f_x; + //int weights_index = fil*new_lda + chan*size*size + f_y*size + f_x; + + uint8_t in_bit = get_bit((uint8_t *)input, input_index); + //uint8_t w_bit = get_bit((uint8_t *)weights, weights_index); + + //int weights_index = fil*in_c*size*size + chan*size*size + f_y*size + f_x; + int weights_shared_index = (fil - min_fil)*new_lda + chan*size*size + f_y*size + f_x; + //uint8_t in_bit = get_bit((uint8_t *)weights_shared, weights_shared_index); + uint8_t w_bit = get_bit((uint8_t *)weights_shared, weights_shared_index); + + //int input_index = input_pre_index + input_y*in_w + input_x; + //int input_shared_index = /*input_pre_index +*/ input_y*in_w + input_x + add_input_index; + //uint8_t in_bit = get_bit((uint8_t *)input_shared, input_shared_index); + + int res = xnor_bit1(in_bit, w_bit); + sum += res; + good_val++; + + //sum += input[input_index] *weights[weights_index]; + } + } + // l.output[filters][width][height] += + // state.input[channels][width][height] * + // l.weights[filters][channels][filter_width][filter_height]; + //output[output_index] += sum; + } + sum = sum - (good_val - sum); + output[output_index] = sum * mean_arr_gpu[fil]; // atoimcAdd for inter-BLOCK sum } +} + +void convolve_bin_gpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, + int size, int pad, int new_lda, float *mean_arr_gpu) +{ + size_t array_size = in_w*in_h*n; // width X height X filters + const int num_blocks = array_size / BLOCK + 1; + //printf("\n array_size = %d, num_blocks = %d, w = %d, h = %d, n = %d, c = %d, pad = %d \n", array_size, num_blocks, in_w, in_h, n, in_c, pad); + + convolve_bin_gpu_kernel << > > (input, weights, output, in_w, in_h, in_c, n, size, pad, new_lda, mean_arr_gpu); +} + +// -------------------------------- -} \ No newline at end of file diff --git a/src/layer.h b/src/layer.h index 9ec35f1fe94..be725d9bdc8 100644 --- a/src/layer.h +++ b/src/layer.h @@ -189,6 +189,7 @@ struct layer{ float *mean_arr; int align_bit_weights_size; int lda_align; + int new_lda; int bit_align; float *col_image; From ca43bbdaaede5c9cbf82a8a0aa5e2d0a4bdcabc0 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 12 Sep 2018 16:22:54 +0300 Subject: [PATCH 0153/1365] Fixed openmp bugs for XNOR --- src/convolutional_kernels.cu | 69 ++++-------- src/gemm.c | 110 +++++++++--------- src/im2col_kernels.cu | 211 ++++++++++++++++++++++++----------- 3 files changed, 224 insertions(+), 166 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 556efda1e36..88f976248d9 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -141,70 +141,39 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) size_t t_intput_size = new_ldb * n; size_t t_bit_input_size = t_intput_size / 8;// +1; - /* - int i = 0; - im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.align_workspace_gpu, l.bit_align); - //cudaDeviceSynchronize(); + { + int i = 0; + im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.align_workspace_gpu, l.bit_align); + //cudaDeviceSynchronize(); - // should be optimized - float_to_bit_gpu(l.align_workspace_gpu, (unsigned char *)state.workspace, l.align_workspace_size); - //cudaDeviceSynchronize(); + // should be optimized + float_to_bit_gpu(l.align_workspace_gpu, (unsigned char *)state.workspace, l.align_workspace_size); + //cudaDeviceSynchronize(); - //im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace, l.bit_align); + //im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace, l.bit_align); - transpose_bin_gpu((unsigned char *)state.workspace, (unsigned char *)l.transposed_align_workspace_gpu, k, n, l.bit_align, new_ldb, 8); - //cudaDeviceSynchronize(); + transpose_bin_gpu((unsigned char *)state.workspace, (unsigned char *)l.transposed_align_workspace_gpu, k, n, l.bit_align, new_ldb, 8); + //cudaDeviceSynchronize(); - // should be optimized - gemm_nn_custom_bin_mean_transposed_gpu(m, n, k, - (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, new_ldb, l.output_gpu, n, l.mean_arr_gpu); - //cudaDeviceSynchronize(); - //check_error(status); - */ + // should be optimized + gemm_nn_custom_bin_mean_transposed_gpu(m, n, k, + (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, new_ldb, l.output_gpu, n, l.mean_arr_gpu); + //cudaDeviceSynchronize(); + //check_error(status); + } - { - // - - /* - float *input_cpu = (float *)calloc(input_size, sizeof(float)); - status = cudaMemcpy(input_cpu, state.input, input_size* sizeof(float), cudaMemcpyDeviceToHost); - check_error(status); - - // swaped(binary_weights <-> l.weights) - convolve_cpu(input_cpu, l.weights, l.output, l.w, l.h, l.c, l.n, l.size, l.pad); // CPU - status = cudaMemcpy(l.output_gpu, l.output, l.outputs * sizeof(float), cudaMemcpyHostToDevice); - check_error(status); - free(input_cpu); - */ - - /* - float *input_cpu = (float *)calloc(input_size, sizeof(float)); - float *input_bin_cpu = (float *)calloc(input_size, sizeof(char)); - //float *weights_bin_cpu = (float *)calloc(l.n*l.c*l.size*l.size, sizeof(char)); - status = cudaMemcpy(input_cpu, state.input, input_size * sizeof(float), cudaMemcpyDeviceToHost); - check_error(status); - float_to_bit(input_cpu, (unsigned char *)input_bin_cpu, input_size); - //float_to_bit(l.weights, (unsigned char *)weights_bin_cpu, l.n*l.c*l.size*l.size); // l.align_bit_weights - - convolve_bin_cpu(input_bin_cpu, (float *)l.align_bit_weights, l.output, l.w, l.h, l.c, l.n, l.size, l.pad, l.new_lda, l.mean_arr); // CPU - status = cudaMemcpy(l.output_gpu, l.output, l.outputs * sizeof(float), cudaMemcpyHostToDevice); - check_error(status); - //free(weights_bin_cpu); - free(input_bin_cpu); - free(input_cpu); - */ + /* + { float_to_bit_gpu(state.input, (unsigned char *)l.align_workspace_gpu, input_size); convolve_bin_gpu(l.align_workspace_gpu, (float *)l.align_bit_weights_gpu, l.output_gpu, l.w, l.h, l.c, l.n, l.size, l.pad, l.new_lda, l.mean_arr_gpu); - //convolve_gpu(state.input, l.weights_gpu, l.output_gpu, l.w, l.h, l.c, l.n, l.size, l.pad); //cudaDeviceSynchronize(); //check_error(status); - - } + */ add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); diff --git a/src/gemm.c b/src/gemm.c index 123598a5074..76315b02d8f 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -204,10 +204,11 @@ void gemm_nn_custom_bin_mean(int M, int N, int K, float ALPHA_UNUSED, { int *count_arr = calloc(M*N, sizeof(int)); - int i, j, k, h; + int i; #pragma omp parallel for for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] + int j, k, h; for (k = 0; k < K; ++k) { // l.size*l.size*l.c - one filter size [27 - 9216] const char a_bit = get_bit(A, i*lda + k); uint64_t a_bit64 = fill_bit_int64(a_bit); @@ -271,10 +272,11 @@ void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, unsigned char *B, int ldb, float *C, int ldc, float *mean_arr) { - int i, j, k, h; + int i; #pragma omp parallel for for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] + int j, k, h; float mean_val = mean_arr[i]; for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] @@ -365,7 +367,7 @@ void transpose_bin(char *A, char *B, const int n, const int m, const int lda, const int ldb, const int block_size) { int i; -#pragma omp parallel for + #pragma omp parallel for for (i = 0; i < n; i += 8) { int j; for (j = 0; j < m - 8; j += 8) { @@ -617,14 +619,14 @@ void gemm_nn(int M, int N, int K, float ALPHA, void convolution_2d_old(int w, int h, int ksize, int n, int c, int pad, int stride, float *weights, float *input, float *output) { - int out_h = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1 - int out_w = (w + 2 * pad - ksize) / stride + 1; // output_width=input_width for stride=1 and pad=1 - int i, f, j; + const int out_h = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1 + const int out_w = (w + 2 * pad - ksize) / stride + 1; // output_width=input_width for stride=1 and pad=1 int fil; // filter index -#pragma omp parallel for // "omp parallel for" - automatic parallelization of loop by using OpenMP + #pragma omp parallel for // "omp parallel for" - automatic parallelization of loop by using OpenMP for (fil = 0; fil < n; ++fil) { + //int i, f, j; int chan, y, x, f_y, f_x; // channel index for (chan = 0; chan < c; ++chan) @@ -665,9 +667,9 @@ void convolution_2d_old(int w, int h, int ksize, int n, int c, int pad, int stri void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride, float *weights, float *input, float *output, float *mean) { - int out_h = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1 - int out_w = (w + 2 * pad - ksize) / stride + 1; // output_width=input_width for stride=1 and pad=1 - int i, f, j; + const int out_h = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1 + const int out_w = (w + 2 * pad - ksize) / stride + 1; // output_width=input_width for stride=1 and pad=1 + int i; #if defined(_OPENMP) static int max_num_threads = 0; @@ -684,9 +686,9 @@ void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride, *((__m256*)&weights[i]) = _mm256_and_ps(*((__m256*)&weights[i]), _mm256_castsi256_ps(all256_sing1)); } - for (i = 0; i < w*h*c; i += 8) { + //for (i = 0; i < w*h*c; i += 8) { //*((__m256*)&input[i]) = _mm256_and_ps(*((__m256*)&input[i]), _mm256_castsi256_ps(all256_sing1)); - } + //} //__m256i all256_last_zero = _mm256_set1_epi32(0xFFFFFFFF); @@ -704,7 +706,7 @@ void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride, int fil; // filter index -#pragma omp parallel for // "omp parallel for" - automatic parallelization of loop by using OpenMP + #pragma omp parallel for // "omp parallel for" - automatic parallelization of loop by using OpenMP for (fil = 0; fil < n; ++fil) { int chan, y, x, f_y, f_x; float cur_mean = fabs(mean[fil]); @@ -914,16 +916,17 @@ void im2col_cpu_custom_transpose(float* data_im, int channels, int height, int width, int ksize, int stride, int pad, float* data_col, int ldb_align) { - int c, h, w; - int height_col = (height + 2 * pad - ksize) / stride + 1; - int width_col = (width + 2 * pad - ksize) / stride + 1; - int channels_col = channels * ksize * ksize; + const int height_col = (height + 2 * pad - ksize) / stride + 1; + const int width_col = (width + 2 * pad - ksize) / stride + 1; + const int channels_col = channels * ksize * ksize; + int c; // optimized version if (height_col == height && width_col == width && stride == 1 && pad == 1) { -#pragma omp parallel for + #pragma omp parallel for for (c = 0; c < channels_col; ++c) { + int h, w; int w_offset = c % ksize; int h_offset = (c / ksize) % ksize; int c_im = c / ksize / ksize; @@ -1005,6 +1008,7 @@ void im2col_cpu_custom_transpose(float* data_im, else { #pragma omp parallel for for (c = 0; c < channels_col; ++c) { + int h, w; int w_offset = c % ksize; int h_offset = (c / ksize) % ksize; int c_im = c / ksize / ksize; @@ -1029,17 +1033,17 @@ void im2col_cpu_custom(float* data_im, int channels, int height, int width, int ksize, int stride, int pad, float* data_col) { - - int c, h, w; - int height_col = (height + 2 * pad - ksize) / stride + 1; - int width_col = (width + 2 * pad - ksize) / stride + 1; - int channels_col = channels * ksize * ksize; + int c; + const int height_col = (height + 2 * pad - ksize) / stride + 1; + const int width_col = (width + 2 * pad - ksize) / stride + 1; + const int channels_col = channels * ksize * ksize; // optimized version if (height_col == height && width_col == width && stride == 1 && pad == 1 && is_fma_avx2()) { #pragma omp parallel for for (c = 0; c < channels_col; ++c) { + int h, w; int w_offset = c % ksize; int h_offset = (c / ksize) % ksize; int c_im = c / ksize / ksize; @@ -1121,10 +1125,10 @@ void im2col_cpu_custom_align(float* data_im, int channels, int height, int width, int ksize, int stride, int pad, float* data_col, int bit_align) { - int c, h, w; - int height_col = (height + 2 * pad - ksize) / stride + 1; - int width_col = (width + 2 * pad - ksize) / stride + 1; - int channels_col = channels * ksize * ksize; + int c; + const int height_col = (height + 2 * pad - ksize) / stride + 1; + const int width_col = (width + 2 * pad - ksize) / stride + 1; + const int channels_col = channels * ksize * ksize; // optimized version if (height_col == height && width_col == width && stride == 1 && pad == 1 && is_fma_avx2()) @@ -1133,6 +1137,7 @@ void im2col_cpu_custom_align(float* data_im, #pragma omp parallel for for (c = 0; c < channels_col; ++c) { + int h, w; int w_offset = c % ksize; int h_offset = (c / ksize) % ksize; int c_im = c / ksize / ksize; @@ -1218,10 +1223,10 @@ void im2col_cpu_custom_bin(float* data_im, int channels, int height, int width, int ksize, int stride, int pad, float* data_col, int bit_align) { - int c, h, w; - int height_col = (height + 2 * pad - ksize) / stride + 1; - int width_col = (width + 2 * pad - ksize) / stride + 1; - int channels_col = channels * ksize * ksize; + int c; + const int height_col = (height + 2 * pad - ksize) / stride + 1; + const int width_col = (width + 2 * pad - ksize) / stride + 1; + const int channels_col = channels * ksize * ksize; // optimized version if (height_col == height && width_col == width && stride == 1 && pad == 1 && is_fma_avx2()) @@ -1233,6 +1238,7 @@ void im2col_cpu_custom_bin(float* data_im, #pragma omp parallel for for (c = 0; c < channels_col; ++c) { + int h, w; int w_offset = c % ksize; int h_offset = (c / ksize) % ksize; int c_im = c / ksize / ksize; @@ -1451,8 +1457,8 @@ void forward_maxpool_layer_avx(float *src, float *dst, int *indexes, int size, i int pad, int stride, int batch) { - int w_offset = -pad / 2; - int h_offset = -pad / 2; + const int w_offset = -pad / 2; + const int h_offset = -pad / 2; int b, k; for (b = 0; b < batch; ++b) { @@ -1563,13 +1569,13 @@ void gemm_nn(int M, int N, int K, float ALPHA, void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride, float *weights, float *input, float *output, float *mean) { - int out_h = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1 - int out_w = (w + 2 * pad - ksize) / stride + 1; // output_width=input_width for stride=1 and pad=1 - int i, f, j; + const int out_h = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1 + const int out_w = (w + 2 * pad - ksize) / stride + 1; // output_width=input_width for stride=1 and pad=1 + //int i, f, j; int fil; // filter index -#pragma omp parallel for // "omp parallel for" - automatic parallelization of loop by using OpenMP + #pragma omp parallel for // "omp parallel for" - automatic parallelization of loop by using OpenMP for (fil = 0; fil < n; ++fil) { int chan, y, x, f_y, f_x; // channel index @@ -1613,10 +1619,11 @@ void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, unsigned char *B, int ldb, float *C, int ldc, float *mean_arr) { - int i, j, k, h; + int i; -#pragma omp parallel for + #pragma omp parallel for for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] + int j, k; float mean_val = mean_arr[i]; for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] @@ -1660,16 +1667,17 @@ void im2col_cpu_custom(float* data_im, im2col_cpu(data_im, channels, height, width, ksize, stride, pad, data_col); return; - int c, h, w; - int height_col = (height + 2 * pad - ksize) / stride + 1; - int width_col = (width + 2 * pad - ksize) / stride + 1; - int channels_col = channels * ksize * ksize; + int c; + const int height_col = (height + 2 * pad - ksize) / stride + 1; + const int width_col = (width + 2 * pad - ksize) / stride + 1; + const int channels_col = channels * ksize * ksize; // optimized version if (height_col == height && width_col == width && stride == 1 && pad == 1) { #pragma omp parallel for for (c = 0; c < channels_col; ++c) { + int h, w; int w_offset = c % ksize; int h_offset = (c / ksize) % ksize; int c_im = c / ksize / ksize; @@ -1750,10 +1758,10 @@ void im2col_cpu_custom_bin(float* data_im, int channels, int height, int width, int ksize, int stride, int pad, float* data_col, int bit_align) { - int c, h, w; - int height_col = (height + 2 * pad - ksize) / stride + 1; - int width_col = (width + 2 * pad - ksize) / stride + 1; - int channels_col = channels * ksize * ksize; + int c; + const int height_col = (height + 2 * pad - ksize) / stride + 1; + const int width_col = (width + 2 * pad - ksize) / stride + 1; + const int channels_col = channels * ksize * ksize; // optimized version if (height_col == height && width_col == width && stride == 1 && pad == 1) @@ -1762,6 +1770,7 @@ void im2col_cpu_custom_bin(float* data_im, #pragma omp parallel for for (c = 0; c < channels_col; ++c) { + int h, w; int w_offset = c % ksize; int h_offset = (c / ksize) % ksize; int c_im = c / ksize / ksize; @@ -1906,9 +1915,10 @@ void float_to_bit(float *src, unsigned char *dst, size_t size) static inline void transpose_scalar_block(float *A, float *B, const int lda, const int ldb, const int block_size) { - int i, j; + int i; //#pragma omp parallel for for (i = 0; i extern "C" { #include "im2col.h" @@ -70,6 +71,8 @@ __global__ void im2col_align_gpu_kernel(const int n, const float* data_im, const int height_col, const int width_col, float *data_col, const int bit_align) { + //__shared__ float tmp_s[1]; + int index = blockIdx.x*blockDim.x + threadIdx.x; for (; index < n; index += blockDim.x*gridDim.x) { int w_out = index % width_col; @@ -90,9 +93,15 @@ __global__ void im2col_align_gpu_kernel(const int n, const float* data_im, int h = h_in + i; int w = w_in + j; - *data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ? + float val = (h >= 0 && w >= 0 && h < height && w < width) ? data_im_ptr[i * width + j] : 0; + *data_col_ptr = val; + //tmp_s[0] = val; + + //*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ? + // data_im_ptr[i * width + j] : 0; + //float src_val = (h >= 0 && w >= 0 && h < height && w < width) ? data_im_ptr[i * width + j] : 0; //unsigned int bit_mask = __ballot_sync(0xffffffff, src_val > 0); //if (threadIdx.x % WARP_SIZE == 0) *((unsigned int*)data_col_ptr_32) = bit_mask; @@ -204,6 +213,10 @@ __device__ __host__ void transpose8rS32_reversed_diagonale(unsigned char* A, int B[7 * n] = reverse_byte(x >> 24); B[6 * n] = reverse_byte(x >> 16); B[5 * n] = reverse_byte(x >> 8); B[4 * n] = reverse_byte(x); B[3 * n] = reverse_byte(y >> 24); B[2 * n] = reverse_byte(y >> 16); B[1 * n] = reverse_byte(y >> 8); B[0 * n] = reverse_byte(y); + + //__device__ ​ unsigned int __brev(unsigned int x) + //Reverse the bit order of a 32 bit unsigned integer. + // https://docs.nvidia.com/cuda/cuda-math-api/group__CUDA__MATH__INTRINSIC__INT.html } @@ -257,10 +270,10 @@ void fill_int8_gpu(unsigned char *src, unsigned char val, size_t size) { } // -------------------------------- -typedef unsigned long long int uint64_t; -typedef unsigned int uint32_t; -typedef unsigned char uint8_t; -typedef char int8_t; +//typedef unsigned long long int uint64_t; +//typedef unsigned int uint32_t; +//typedef unsigned char uint8_t; +//typedef char int8_t; __device__ __host__ static inline uint64_t broadcast_bit_1_to_64(uint8_t src) { return (src > 0) ? 0xFFFFFFFFFFFFFFFF : 0; @@ -274,6 +287,29 @@ __device__ __host__ static inline uint64_t xnor_int64(uint64_t a, uint64_t b) { return ~(a^b); } +__device__ __host__ static inline uint4 xnor_int128(uint4 a, uint4 b) { + uint4 res; + res.w = ~(a.w^b.w); + res.x = ~(a.x^b.x); + res.y = ~(a.y^b.y); + res.z = ~(a.z^b.z); + return res; +} + +__device__ __host__ static inline ulonglong4 xnor_int256(ulonglong4 a, ulonglong4 b) { + ulonglong4 res; + res.w = ~(a.w^b.w); + res.x = ~(a.x^b.x); + res.y = ~(a.y^b.y); + res.z = ~(a.z^b.z); + return res; +} + + +__device__ static inline int popcnt_256(ulonglong4 a) { + return __popcll(a.w) + __popcll(a.x) + __popcll(a.y) + __popcll(a.z); +} + /* __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int K, unsigned char *A, int lda, @@ -320,75 +356,87 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int */ -/* + // B (input) in the shared_memory __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int K, unsigned char *A, int lda, unsigned char *B, int ldb, float *C, int ldc, float *mean_arr) { - int index = blockIdx.x*blockDim.x + threadIdx.x; - __shared__ uint64_t B_s[4096]; // 32 KB // [ldb x N`] + __shared__ uint64_t B_s[4096]; // 32 KB // [ldb x N`] // max = 262 144 bits int start_j = blockIdx.x*blockDim.x / M; int end_j = (blockIdx.x*blockDim.x + blockDim.x) / M + 1; size_t shared_size = ldb * (end_j - start_j); - int j_cur = index / M; - int local_j = j_cur - start_j; + //float tmp_shared_size = ldb * (blockDim.x / M); + //int passes = (4096 * 64) / tmp_shared_size - 1; + //size_t shared_size = tmp_shared_size * passes; - for (int k = threadIdx.x * 64; k < shared_size; k += blockDim.x * 64) { + int k; + for (int k = threadIdx.x * 256; k < shared_size; k += blockDim.x * 256) { int x = start_j*ldb + k; - if (x < (N*ldb)) *((uint64_t *)(B_s + k / 8)) = *((uint64_t *)(B + x / 8)); + if (x < (N*ldb)) *((ulonglong4 *)(B_s + k / 8)) = *((ulonglong4 *)(B + x / 8)); } - //if (j_cur < N && (index % M == 0 || threadIdx.x == 0)) { - // for (int k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] - // *((uint64_t *)(B_s + (local_j*ldb + k) / 8)) = *((uint64_t *)(B + (j_cur*ldb + k) / 8)); // input - //} - //} + ////if (j_cur < N && (index % M == 0 || threadIdx.x == 0)) { + //// for (int k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] + //// *((uint64_t *)(B_s + (local_j*ldb + k) / 8)) = *((uint64_t *)(B + (j_cur*ldb + k) / 8)); // input + ////} + ////} __syncthreads(); + int index = blockIdx.x*blockDim.x + threadIdx.x; + + //if (index == 0) + //for(int in_tmp = threadIdx.x; in_tmp < 1*blockDim.x; in_tmp += blockDim.x) { - int i, j, k, h; + //int index = blockIdx.x*blockDim.x*1 + in_tmp; + + int j_cur = index / M; + int local_j = j_cur - start_j; + + int i, j, h; //#pragma omp parallel for //for (i = 0; i < M; ++i) i = index % M; //if(i < M) { // l.n - filters [16 - 55 - 1024] + // further improvements: for (l.n == 1024) iterate several (j) float mean_val = mean_arr[i]; //for (j = 0; j < N; ++j) j = index / M; if (j < N) { // out_h*out_w - one channel output size [169 - 173056] + const int bit_step = 256; int count = 0; - - for (k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] - uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); // weights - //uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); - uint64_t b_bit64 = *((uint64_t *)(B_s + (local_j*ldb + k) / 8)); // input - uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); - - int tmp_count = __popcll(c_bit64); - - if (K - k < 64) tmp_count = tmp_count - (64 - (K - k)); // remove extra bits - count += tmp_count; - //binary_int64_printf(c_bit64); - //printf(", count = %d \n\n", tmp_count); + int k = 0; + for (k = 0; k < K; k += bit_step) { // l.size*l.size*l.c - one filter size [27 - 144 - 9216] + ulonglong4 a_bit256 = *((ulonglong4 *)(A + (i*lda + k) / 8)); // weights + //ulonglong4 b_bit256 = *((ulonglong4 *)(B + (j*ldb + k) / 8)); + ulonglong4 b_bit256 = *((ulonglong4 *)(B_s + (local_j*ldb + k) / 8)); // input + ulonglong4 c_bit256 = xnor_int256(a_bit256, b_bit256); + + count += __popcll(c_bit256.w) + __popcll(c_bit256.x) + + __popcll(c_bit256.y) + __popcll(c_bit256.z); } + int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); + count = count - f1; // remove extra bits (from empty space for align only) + C[i*ldc + j] = (2 * count - K) * mean_val; + //B_s[0] = (2 * count - K) * mean_val; } } } } -*/ +/* // A (weights) in the shared_memory __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int K, unsigned char *A, int lda, @@ -447,9 +495,11 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int } } } +*/ #include + void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, unsigned char *A, int lda, unsigned char *B, int ldb, @@ -742,7 +792,7 @@ __global__ void convolve_bin_gpu_kernel(float *input, float *weights, float *out int index2 = index / in_w; y = index2 % in_h; fil = index2 / in_h; - if (fil < n) // (1-6 for one BLOCK) + //if (fil < n) // (1-6 for one BLOCK) { //float mean_val = mean_arr_gpu[fil]; int const output_index = fil*in_w*in_h + y*in_w + x; @@ -772,48 +822,76 @@ __global__ void convolve_bin_gpu_kernel(float *input, float *weights, float *out int const weights_pre_index = fil*new_lda + chan*size*size; int const input_pre_index = chan*in_w*in_h; - __shared__ uint32_t input_shared[416*416/32]; // 21.2 KB bytes - const int input_shared_size = in_w*in_h / 32; + __shared__ uint32_t input_shared[416*416/32 + 1]; // 21.2 KB bytes (for input size 832x832) + const int input_shared_size = in_w*in_h / 32 + 1; const int add_input_index = input_pre_index % 32; + __syncthreads(); // why??? but is required for (int s = threadIdx.x; s < input_shared_size; s += blockDim.x) { - input_shared[s] = ((uint32_t *)weights)[input_pre_index / 32 + s]; + input_shared[s] = ((uint32_t *)input)[input_pre_index / 32 + s]; } __syncthreads(); - // filter - y - for (f_y = 0; f_y < size; ++f_y) - { - int input_y = y + f_y - pad; - // filter - x - for (f_x = 0; f_x < size; ++f_x) - { - int input_x = x + f_x - pad; - if (input_y < 0 || input_x < 0 || input_y >= in_h || input_x >= in_w) continue; - - int input_index = input_pre_index + input_y*in_w + input_x; - int weights_index = weights_pre_index + f_y*size + f_x; - //int weights_index = fil*in_c*size*size + chan*size*size + f_y*size + f_x; - //int weights_index = fil*new_lda + chan*size*size + f_y*size + f_x; - - uint8_t in_bit = get_bit((uint8_t *)input, input_index); - //uint8_t w_bit = get_bit((uint8_t *)weights, weights_index); + /* + __shared__ uint8_t input_shared[208 * 208 / 8 + 1]; // 5.4 KB bytes (for input size 416x416) + const int input_shared_size = in_w*in_h / 8 + 1; + const int add_input_index = input_pre_index % 8; + __syncthreads(); - //int weights_index = fil*in_c*size*size + chan*size*size + f_y*size + f_x; - int weights_shared_index = (fil - min_fil)*new_lda + chan*size*size + f_y*size + f_x; - //uint8_t in_bit = get_bit((uint8_t *)weights_shared, weights_shared_index); - uint8_t w_bit = get_bit((uint8_t *)weights_shared, weights_shared_index); + for (int s = threadIdx.x; s < input_shared_size; s += blockDim.x) { + ((uint8_t *)input_shared)[s] = ((uint8_t *)input)[input_pre_index / 8 + s]; + } + __syncthreads(); + */ + int src_index = -1; + uint32_t input_byte; - //int input_index = input_pre_index + input_y*in_w + input_x; - //int input_shared_index = /*input_pre_index +*/ input_y*in_w + input_x + add_input_index; - //uint8_t in_bit = get_bit((uint8_t *)input_shared, input_shared_index); + if (fil < n) // (1-6 for one BLOCK) + { + // filter - y + for (f_y = 0; f_y < size; ++f_y) + { + int input_y = y + f_y - pad; + // filter - x + for (f_x = 0; f_x < size; ++f_x) + { + int input_x = x + f_x - pad; + if (input_y < 0 || input_x < 0 || input_y >= in_h || input_x >= in_w) continue; + + int input_index = input_pre_index + input_y*in_w + input_x; + int weights_index = weights_pre_index + f_y*size + f_x; + //int weights_index = fil*in_c*size*size + chan*size*size + f_y*size + f_x; + //int weights_index = fil*new_lda + chan*size*size + f_y*size + f_x; + + //uint8_t in_bit = get_bit((uint8_t *)input, input_index); + //uint8_t w_bit = get_bit((uint8_t *)weights, weights_index); + + //int weights_index = fil*in_c*size*size + chan*size*size + f_y*size + f_x; + int weights_shared_index = (fil - min_fil)*new_lda + chan*size*size + f_y*size + f_x; + //uint8_t in_bit = get_bit((uint8_t *)weights_shared, weights_shared_index); + uint8_t w_bit = get_bit((uint8_t *)weights_shared, weights_shared_index); + + //int input_index = input_pre_index + input_y*in_w + input_x; + int input_shared_index = /*input_pre_index +*/ input_y*in_w + input_x + add_input_index; + uint8_t in_bit = get_bit((uint8_t *)input_shared, input_shared_index); + /* + int new_src_index = input_shared_index / 32; + int src_shift = input_shared_index % 32; + //if (new_src_index != src_index) + { + src_index = new_src_index; + input_byte = ((uint32_t *)input_shared)[src_index]; + } + uint8_t in_bit = (input_byte & (1 << src_shift)) >> src_shift; + */ - int res = xnor_bit1(in_bit, w_bit); - sum += res; - good_val++; + int res = xnor_bit1(in_bit, w_bit); + sum += res; + good_val++; - //sum += input[input_index] *weights[weights_index]; + //sum += input[input_index] *weights[weights_index]; + } } } // l.output[filters][width][height] += @@ -822,7 +900,8 @@ __global__ void convolve_bin_gpu_kernel(float *input, float *weights, float *out //output[output_index] += sum; } sum = sum - (good_val - sum); - output[output_index] = sum * mean_arr_gpu[fil]; // atoimcAdd for inter-BLOCK sum + //output[output_index] = sum * mean_arr_gpu[fil]; // atoimcAdd for inter-BLOCK sum + atomicAdd(&output[output_index], sum * mean_arr_gpu[fil]); } } From b829fee30b7be6fbef401cab456e7580dff6b840 Mon Sep 17 00:00:00 2001 From: Alexey Date: Fri, 14 Sep 2018 15:29:52 +0300 Subject: [PATCH 0154/1365] Update Readme.md --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 18d3f153b4e..864973f2ec1 100644 --- a/README.md +++ b/README.md @@ -229,7 +229,9 @@ More information about training by the link: http://pjreddie.com/darknet/yolo/#t 2. Adjust the learning rate (`cfg/yolov3-voc.cfg`) to fit the amount of GPUs. The learning rate should be equal to `0.001`, regardless of how many GPUs are used for training. So `learning_rate * GPUs = 0.001`. For 4 GPUs adjust the value to `learning_rate = 0.00025`. -3. Then stop and by using partially-trained model `/backup/yolov3-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg /backup/yolov3-voc_1000.weights -gpus 0,1,2,3` +3. For 4xGPUs - increase 4x times `burn_in =` and `max_batches =` in your cfg-file. I.e. use `burn_in = 4000` instead of `1000`. + +4. Then stop and by using partially-trained model `/backup/yolov3-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg /backup/yolov3-voc_1000.weights -gpus 0,1,2,3` https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ From 8e0b265067acb761f4667a1704cfe944caf564e4 Mon Sep 17 00:00:00 2001 From: Alexey Date: Fri, 14 Sep 2018 15:54:01 +0300 Subject: [PATCH 0155/1365] Update Readme.md --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 864973f2ec1..18bd197ff4c 100644 --- a/README.md +++ b/README.md @@ -43,8 +43,8 @@ This repository supports: ##### Requires: * **Linux GCC>=4.9 or Windows MS Visual Studio 2015 (v140)**: https://go.microsoft.com/fwlink/?LinkId=532606&clcid=0x409 (or offline [ISO image](https://go.microsoft.com/fwlink/?LinkId=615448&clcid=0x409)) -* **CUDA 9.1**: https://developer.nvidia.com/cuda-downloads -* **OpenCV 3.4.0**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.4.0/opencv-3.4.0-vc14_vc15.exe/download +* **CUDA 9.1**: https://developer.nvidia.com/cuda-91-download-archive +* **OpenCV 3.3.0**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.3.0/opencv-3.3.0-vc14.exe/download * **or OpenCV 2.4.13**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.4.13/opencv-2.4.13.2-vc14.exe/download - OpenCV allows to show image or video detection in the window and store result to file that specified in command line `-out_filename res.avi` * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported @@ -289,9 +289,9 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an Where: * `` - integer object number from `0` to `(classes-1)` - * ` ` - float values relative to width and height of image, it can be equal from (0.0 to 1.0] + * ` ` - float values relative to width and height of image, it can be equal from (0.0 to 1.0] * for example: ` = / ` or ` = / ` - * atention: ` ` - are center of rectangle (are not top-left corner) + * atention: ` ` - are center of rectangle (are not top-left corner) For example for `img1.jpg` you will be created `img1.txt` containing: From 03e95320a19ff16e545f0c3fd5ac6004a04974d4 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 14 Sep 2018 22:52:26 +0300 Subject: [PATCH 0156/1365] XNOR coalesced memory access, and avoid bank conflicts --- src/convolutional_kernels.cu | 32 ++- src/im2col.h | 7 + src/im2col_kernels.cu | 469 +++++++++++++++++++++++++++++++++-- src/layer.h | 1 + src/network.c | 5 + src/parser.c | 1 + 6 files changed, 488 insertions(+), 27 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 88f976248d9..40a63f5bfa6 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -117,13 +117,16 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) } if(l.xnor){ - if (!l.align_bit_weights_gpu || state.train) { binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu); + + swap_binary(&l); + binarize_gpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input_gpu); + state.input = l.binary_input_gpu; } - swap_binary(&l); - binarize_gpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input_gpu); - state.input = l.binary_input_gpu; + //swap_binary(&l); + //binarize_gpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input_gpu); + //state.input = l.binary_input_gpu; //cudaDeviceSynchronize(); if (l.align_bit_weights_gpu && !state.train) @@ -141,6 +144,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) size_t t_intput_size = new_ldb * n; size_t t_bit_input_size = t_intput_size / 8;// +1; + //if(0) { int i = 0; im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.align_workspace_gpu, l.bit_align); @@ -156,10 +160,18 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //cudaDeviceSynchronize(); // should be optimized - gemm_nn_custom_bin_mean_transposed_gpu(m, n, k, - (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, new_ldb, l.output_gpu, n, l.mean_arr_gpu); + //if(0) {//if (k > 1000) { // sequentially input-shared - BAD + // gemm_nn_custom_bin_mean_transposed_sequentially_gpu(m, n, k, + // (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, new_ldb, l.output_gpu, n, l.mean_arr_gpu); + //} + //else { // coalescing & weights-shared-memory - GOOD + gemm_nn_custom_bin_mean_transposed_gpu(m, n, k, + (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, + new_ldb, l.output_gpu, n, l.mean_arr_gpu, l.biases_gpu); + //} //cudaDeviceSynchronize(); //check_error(status); + //getchar(); } @@ -172,12 +184,14 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //cudaDeviceSynchronize(); //check_error(status); + + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); } */ - add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); - activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); - if (l.binary || l.xnor) swap_binary(&l); + //add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); + if(l.activation != LINEAR) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); + //if (l.binary || l.xnor) swap_binary(&l); //cudaDeviceSynchronize(); return; } diff --git a/src/im2col.h b/src/im2col.h index 74211e25c66..1013d4cd8a8 100644 --- a/src/im2col.h +++ b/src/im2col.h @@ -24,7 +24,14 @@ void transpose_bin_gpu(unsigned char *A, unsigned char *B, const int n, const in void fill_int8_gpu(unsigned char *src, unsigned char val, size_t size); +// shared_memory + partial coalescing = GOOD void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr, float *bias); + +// sequentially - BAD +void gemm_nn_custom_bin_mean_transposed_sequentially_gpu(int M, int N, int K, unsigned char *A, int lda, unsigned char *B, int ldb, float *C, int ldc, float *mean_arr); diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index cbcf53f4cc9..2997485d4bf 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -8,6 +8,10 @@ extern "C" { #include "cuda.h" } +#include +#include +#include + // src: https://github.com/BVLC/caffe/blob/master/src/caffe/util/im2col.cu // You may also want to read: https://github.com/BVLC/caffe/blob/master/LICENSE @@ -105,6 +109,7 @@ __global__ void im2col_align_gpu_kernel(const int n, const float* data_im, //float src_val = (h >= 0 && w >= 0 && h < height && w < width) ? data_im_ptr[i * width + j] : 0; //unsigned int bit_mask = __ballot_sync(0xffffffff, src_val > 0); //if (threadIdx.x % WARP_SIZE == 0) *((unsigned int*)data_col_ptr_32) = bit_mask; + // use atomicOr() // *dst_ptr |= (mask << (col_index % 8)); //data_col_ptr_32 += bit_align / 32; //data_col_ptr += height_col * width_col; @@ -283,6 +288,10 @@ __device__ __host__ static inline uint8_t xnor_bit1(uint8_t a, uint8_t b) { return ~(a^b) & 0b1; } +__device__ __host__ static inline uint32_t xnor_int32(uint32_t a, uint32_t b) { + return ~(a^b); +} + __device__ __host__ static inline uint64_t xnor_int64(uint64_t a, uint64_t b) { return ~(a^b); } @@ -356,7 +365,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int */ - +/* // B (input) in the shared_memory __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int K, unsigned char *A, int lda, @@ -367,25 +376,27 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int __shared__ uint64_t B_s[4096]; // 32 KB // [ldb x N`] // max = 262 144 bits int start_j = blockIdx.x*blockDim.x / M; - int end_j = (blockIdx.x*blockDim.x + blockDim.x) / M + 1; + { + int end_j = (blockIdx.x*blockDim.x + blockDim.x) / M + 1; - size_t shared_size = ldb * (end_j - start_j); + size_t shared_size = ldb * (end_j - start_j); - //float tmp_shared_size = ldb * (blockDim.x / M); - //int passes = (4096 * 64) / tmp_shared_size - 1; - //size_t shared_size = tmp_shared_size * passes; + //float tmp_shared_size = ldb * (blockDim.x / M); + //int passes = (4096 * 64) / tmp_shared_size - 1; + //size_t shared_size = tmp_shared_size * passes; - int k; - for (int k = threadIdx.x * 256; k < shared_size; k += blockDim.x * 256) { - int x = start_j*ldb + k; - if (x < (N*ldb)) *((ulonglong4 *)(B_s + k / 8)) = *((ulonglong4 *)(B + x / 8)); - } + int k; + for (int k = threadIdx.x * 256; k < shared_size; k += blockDim.x * 256) { + int x = start_j*ldb + k; + if (x < (N*ldb)) *((ulonglong4 *)(B_s + k / 8)) = *((ulonglong4 *)(B + x / 8)); + } - ////if (j_cur < N && (index % M == 0 || threadIdx.x == 0)) { - //// for (int k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] - //// *((uint64_t *)(B_s + (local_j*ldb + k) / 8)) = *((uint64_t *)(B + (j_cur*ldb + k) / 8)); // input + ////if (j_cur < N && (index % M == 0 || threadIdx.x == 0)) { + //// for (int k = 0; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] + //// *((uint64_t *)(B_s + (local_j*ldb + k) / 8)) = *((uint64_t *)(B + (j_cur*ldb + k) / 8)); // input + ////} ////} - ////} + } __syncthreads(); int index = blockIdx.x*blockDim.x + threadIdx.x; @@ -427,14 +438,19 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int } int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); - count = count - f1; // remove extra bits (from empty space for align only) + //C[i*ldc + j] += 2 * count*mean_val; + //C[i*ldc + j] += -2 * f1*mean_val; + //C[i*ldc + j] += - K*mean_val; + count = count - f1; // remove extra bits (from empty space for align only) C[i*ldc + j] = (2 * count - K) * mean_val; + //B_s[0] = (2 * count - K) * mean_val; } } } } +*/ /* // A (weights) in the shared_memory @@ -497,13 +513,293 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int } */ -#include +__inline__ __device__ +int warpAllReduceSum(int val) { + for (int mask = WARP_SIZE / 2; mask > 0; mask /= 2) + val += __shfl_xor(val, mask); + return val; +} + + +// Coalescing +// A (weights) in the shared_memory - GOOD +__global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int K, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr, float *bias_arr) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + + __shared__ uint8_t A_s[6144*8/4]; + //__shared__ uint64_t A_s[6144]; // 48 KB // [lda x M`] + //__shared__ uint8_t A_s[6144*8]; // 48 KB // [lda x M`] + + int start_i = blockIdx.x*blockDim.x / N; + int end_i = (blockIdx.x*blockDim.x + blockDim.x) / N + 1; + + size_t shared_size = lda * (end_i - start_i); + + int i_cur = index / N; + int local_i = i_cur - start_i; + + for (int k = threadIdx.x * 64; k < shared_size; k += blockDim.x * 64) { + int x = start_i*lda + k; + if (x < (M*lda)) *((uint64_t *)(A_s + k / 8)) = *((uint64_t *)(A + x / 8)); + } + __syncthreads(); + + int i, j, k, h; + + j = index % N; + { // out_h*out_w - one channel output size [169 - 173056] + i = index / N; + //if (i < M) // l.n - filters [16 - 55 - 1024] + { + int count = 0; + k = 0; + +//#ifdef NON_USED + // 32 thread X 64 bit = 2048 bit + for (; k < (K - 2048); k += 2048) { // l.size*l.size*l.c - one filter size [27 - 9216] + uint64_t c_bit64; + + //int64_t A_cur_index = (i*lda + k) / 8; + int64_t A_cur_index = (local_i*lda + k) / 8; + int64_t B_cur_index = (j*ldb + k) / 8; + if (i >= M) A_cur_index = 0; + + #pragma unroll WARP_SIZE + for (int t = 0; t < WARP_SIZE; ++t) { + const int lane_id = threadIdx.x % WARP_SIZE; + + const int64_t A_i = __shfl(A_cur_index, t) + 8 * lane_id; + const int64_t B_i = __shfl(B_cur_index, t) + 8 * lane_id; + + { + //uint64_t a_bit64 = *((uint64_t *)(A + A_i)); // weights + uint64_t a_bit64 = *((uint64_t *)(A_s + A_i)); // weights + uint64_t b_bit64 = *((uint64_t *)(B + B_i)); // input + c_bit64 = xnor_int64(a_bit64, b_bit64); + int tmp_count = __popcll(c_bit64); + + int sum_count = warpAllReduceSum(tmp_count); + if (lane_id == t) count += sum_count; + } + } + } +//#endif + +//#ifdef NON_USED + // 32 thread X 32 bit = 1024 bit + for (; k < (K - 1024); k += 1024) { // l.size*l.size*l.c - one filter size [27 - 9216] + + //int64_t A_cur_index = (i*lda + k) / 8; + int64_t A_cur_index = (local_i*lda + k) / 8; + int64_t B_cur_index = (j*ldb + k) / 8; + if (i >= M) A_cur_index = 0; + + #pragma unroll WARP_SIZE + for (int t = 0; t < WARP_SIZE; ++t) { + const int lane_id = threadIdx.x % WARP_SIZE; + + const int64_t A_i = __shfl(A_cur_index, t) + 4 * lane_id; + const int64_t B_i = __shfl(B_cur_index, t) + 4 * lane_id; + + { + //uint64_t a_bit64 = *((uint64_t *)(A + A_i)); // weights + uint32_t a_bit32 = *((uint32_t *)(A_s + A_i)); // weights + uint32_t b_bit32 = *((uint32_t *)(B + B_i)); // input + uint32_t c_bit32 = xnor_int32(a_bit32, b_bit32); + int tmp_count = __popc(c_bit32); + + int sum_count = warpAllReduceSum(tmp_count); + if (lane_id == t) count += sum_count; + } + } + } +//#endif + + if (i < M) + { + float mean_val = mean_arr[i]; + float bias_val = bias_arr[i]; + +//#ifdef NON_USED + for (; k < K; k += 256) { // l.size*l.size*l.c - one filter size [27 - 144 - 9216] + //ulonglong4 a_bit256 = *((ulonglong4 *)(A + (i*lda + k) / 8)); // weights + ulonglong4 a_bit256 = *((ulonglong4 *)(A_s + (local_i*lda + k) / 8)); // weights + ulonglong4 b_bit256 = *((ulonglong4 *)(B + (j*ldb + k) / 8)); // input + ulonglong4 c_bit256 = xnor_int256(a_bit256, b_bit256); + count += __popcll(c_bit256.w) + __popcll(c_bit256.x) + + __popcll(c_bit256.y) + __popcll(c_bit256.z); + } +//#endif + +#ifdef NON_USED + for (; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] + //uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); // weights + uint64_t a_bit64 = *((uint64_t *)(A_s + (local_i*lda + k) / 8)); // weights + uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); // input + uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); + + count += __popcll(c_bit64); + } +#endif + + const int bit_step = 256; + int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); + count = count - f1; // remove extra bits (from empty space for align only) + + C[i*ldc + j] = (2 * count - K) *mean_val + bias_val; + } + } + } +} + + +/* +// Coalescing +// B (input) in the shared_memory - GOOD +__global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int K, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr, float *bias_arr) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + + __shared__ uint8_t B_s[4096*8]; // 32 KB // [ldb x N`] // max = 262 144 bits + //__shared__ uint64_t B_s[4096]; // 32 KB // [ldb x N`] // max = 262 144 bits + + int start_j = blockIdx.x*blockDim.x / M; + int end_j = (blockIdx.x*blockDim.x + blockDim.x) / M + 1; + + size_t shared_size = ldb * (end_j - start_j); + + int j_cur = index / M; + int local_j = j_cur - start_j; + + for (int k = threadIdx.x * 256; k < shared_size; k += blockDim.x * 256) { + int x = start_j*ldb + k; + if (x < (N*ldb)) *((ulonglong4 *)(B_s + k / 8)) = *((ulonglong4 *)(B + x / 8)); + } + __syncthreads(); + + int i, j, k; + + i = index % M; // l.n - filters [16 - 55 - 1024] + { + j = index / M; // out_h*out_w - one channel output size [169 - 173056] + if (j < N) + { + int count = 0; + k = 0; +//#ifdef NON_USED + // 32 thread X 64 bit = 2048 bit + for (; k < (K - 2048); k += 2048) { // l.size*l.size*l.c - one filter size [27 - 9216] + uint64_t c_bit64; + + int64_t A_cur_index = (i*lda + k) / 8; + //int64_t B_cur_index = (j*ldb + k) / 8; + int64_t B_cur_index = (local_j*ldb + k) / 8; + if (i >= M) A_cur_index = 0; + + #pragma unroll WARP_SIZE + for (int t = 0; t < WARP_SIZE; ++t) { + const int lane_id = threadIdx.x % WARP_SIZE; + + const int64_t A_i = __shfl(A_cur_index, t) + 8 * lane_id; + const int64_t B_i = __shfl(B_cur_index, t) + 8 * lane_id; + + { + uint64_t a_bit64 = *((uint64_t *)(A + A_i)); // weights + //uint64_t b_bit64 = *((uint64_t *)(B + B_i)); // input + uint64_t b_bit64 = *((uint64_t *)(B_s + B_i)); // input + c_bit64 = xnor_int64(a_bit64, b_bit64); + int tmp_count = __popcll(c_bit64); + + int sum_count = warpAllReduceSum(tmp_count); + if (lane_id == t) count += sum_count; + } + } + } +//#endif + +//#ifdef NON_USED + // 32 thread X 32 bit = 1024 bit + for (; k < (K - 1024); k += 1024) { // l.size*l.size*l.c - one filter size [27 - 9216] + + int64_t A_cur_index = (i*lda + k) / 8; + //int64_t B_cur_index = (j*ldb + k) / 8; + int64_t B_cur_index = (local_j*ldb + k) / 8; + if (i >= M) A_cur_index = 0; + + #pragma unroll WARP_SIZE + for (int t = 0; t < WARP_SIZE; ++t) { + const int lane_id = threadIdx.x % WARP_SIZE; + + const int64_t A_i = __shfl(A_cur_index, t) + 4 * lane_id; + const int64_t B_i = __shfl(B_cur_index, t) + 4 * lane_id; + + { + uint32_t a_bit32 = *((uint32_t *)(A + A_i)); // weights + //uint32_t b_bit32 = *((uint32_t *)(B + B_i)); // input + uint32_t b_bit32 = *((uint32_t *)(B_s + B_i)); // input + uint32_t c_bit32 = xnor_int32(a_bit32, b_bit32); + int tmp_count = __popc(c_bit32); + + int sum_count = warpAllReduceSum(tmp_count); + if (lane_id == t) count += sum_count; + } + } + } +//#endif + + if (i < M) + { + float mean_val = mean_arr[i]; + float bias_val = bias_arr[i]; + +//#ifdef NON_USED + for (; k < K; k += 256) { // l.size*l.size*l.c - one filter size [27 - 144 - 9216] + ulonglong4 a_bit256 = *((ulonglong4 *)(A + (i*lda + k) / 8)); // weights + //ulonglong4 b_bit256 = *((ulonglong4 *)(B + (j*ldb + k) / 8)); // input + ulonglong4 b_bit256 = *((ulonglong4 *)(B_s + (local_j*ldb + k) / 8)); // input + ulonglong4 c_bit256 = xnor_int256(a_bit256, b_bit256); + + count += __popcll(c_bit256.w) + __popcll(c_bit256.x) + + __popcll(c_bit256.y) + __popcll(c_bit256.z); + } +//#endif + +#ifdef NON_USED + for (; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] + uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); // weights + //uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); // input + uint64_t b_bit64 = *((uint64_t *)(B_s + (local_j*ldb + k) / 8)); // input + uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); + + count += __popcll(c_bit64); + } +#endif + + const int bit_step = 256; + int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); + count = count - f1; // remove extra bits (from empty space for align only) + + C[i*ldc + j] = (2 * count - K) * mean_val + bias_val; + } + } + } +} +*/ + +// GOOD void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, unsigned char *A, int lda, unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr) + float *C, int ldc, float *mean_arr, float *bias) { size_t size = M*N; const int num_blocks = size / BLOCK + 1; @@ -516,6 +812,143 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, //printf(" shared_memory: (w) lda*BLOCK/N = %d, (i) ldb*BLOCK/M = %d, \t lda = %d \n\n", lda*BLOCK / N, ldb*BLOCK / M, lda); gemm_nn_custom_bin_mean_transposed_gpu_kernel<<>>( + M, N, K, + A, lda, + B, ldb, + C, ldc, + mean_arr, bias); +} +// -------------------------------- + + + + +// -------------------------------- +// sequentially - B (input) in the shared_memory - BAD +// -------------------------------- +__global__ void gemm_nn_custom_bin_mean_transposed_sequentially_gpu_kernel(int M, int N, int K, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + //__shared__ float mean_shared[32]; + //__shared__ uint32_t B_s[8192]; // 32 KB // [ldb x N`] // max = 262 144 bits + //__shared__ uint32_t B_s[4096]; // 16 KB // [ldb x N`] // max = 131 072 bits + __shared__ uint8_t B_s[4096*4]; // 16 KB // [ldb x N`] // max = 131 072 bits + + + const int K_items = WARP_SIZE; + int start_j = blockIdx.x*blockDim.x / (K_items * M); + + { + int end_j = (blockIdx.x*blockDim.x + blockDim.x) / (K_items * M) + 1; + if (end_j > N) end_j = N; + size_t shared_size = ldb * (end_j - start_j); + + if (shared_size != 0) { + //if(threadIdx.x == 0) printf(" start_j = %d, end_j = %d, shared_size = %d \n", start_j, end_j, shared_size); + + int k; + for (int k = threadIdx.x * 32; k < shared_size; k += blockDim.x * 32) { + int x = start_j*ldb + k; + if (x < (N*ldb)) *((uint32_t *)(B_s + k / 8)) = *((uint32_t *)(B + x / 8)); + } + } + } + __syncthreads(); + + int index = blockIdx.x*blockDim.x + threadIdx.x; + + { + int i; // l.n + int j; // out_h*out_w + int k; // l.size * l.size * l.c + + const int index2 = index / K_items; + i = index2 % M; // max M + j = index2 / M; // max N + //j = index2 % N; // max N + //i = index2 / N; // max M + + //int j_cur = index / M; + //int local_j = j_cur - start_j; + int local_j = j - start_j; + + //if (i <= 1 && j <= 1 ) printf(" k = %d, K = %d, K_items = %d, i = %d, j = %d, lda = %d, ldb = %d, ldc = %d \n", + // k, K, K_items, i, j, lda, ldb, ldc); + { // l.n - filters [16 - 55 - 1024] + // further improvements: for (l.n == 1024) iterate several (j) + + + if (j < N) + { // out_h*out_w - one channel output size [169 - 173056] + + int count = 0; + + + const int bit_step = 32; + for (k = (threadIdx.x % WARP_SIZE) * bit_step; k < K; k += bit_step*WARP_SIZE) + { // l.size*l.size*l.c - one filter size [27 - 144 - 9216] + uint32_t a_bit32 = *((uint32_t *)(A + (i*lda + k) / 8)); // weights + //uint32_t b_bit32 = *((uint32_t *)(B + (j*ldb + k) / 8)); // input + uint32_t b_bit32 = *((uint32_t *)(B_s + (local_j*ldb + k) / 8)); // input + uint32_t c_bit32 = xnor_int32(a_bit32, b_bit32); + + count += __popc(c_bit32); + } + + /* + const int bit_step = 64; + for (k = (threadIdx.x % WARP_SIZE) * bit_step; k < K; k += bit_step*WARP_SIZE) + { // l.size*l.size*l.c - one filter size [27 - 144 - 9216] + uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); // weights + //uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); + uint64_t b_bit64 = *((uint64_t *)(B_s + (local_j*ldb + k) / 8)); // input + uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); + count += __popcll(c_bit64); + } + */ + + + //atomicAdd(&C[i*ldc + j], (2 * count) * mean_val); + + for (int offset = WARP_SIZE / 2; offset > 0; offset /= 2) + count += __shfl_down(count, offset); + + + if (threadIdx.x % WARP_SIZE == 0) { + int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); + count = count - f1; + float mean_val = mean_arr[i]; + C[i*ldc + j] = (2 * count - K) * mean_val; + //B_s[threadIdx.x / WARP_SIZE] = (2 * count - K) * mean_val; + } + } + } + } +} + +// sequentially - BAD +void gemm_nn_custom_bin_mean_transposed_sequentially_gpu(int M, int N, int K, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + //size_t size = M*N; + size_t size = M*N * 32; + + const int num_blocks = size / BLOCK + 1; + + //printf(" K = %d \n", K); + + /* + printf("\n gemm_bin size = %d, num_blocks = %d, M*K = %d KB, N*K = %d KB \n (w) M*K/num_blocks = %d KB, (i) N*K/num_blocks = %d KB \n", + size, num_blocks, M*K / 1024, N*K / 1024, M*lda / num_blocks / 1024, N*ldb / num_blocks / 1024); + printf(" M / 512 = %d, N / 512 = %d, M*lda / 512 = %d, N*ldb / 512 = %d \n", M / 512, N / 512, M*lda/512, N*ldb/512); + */ + //printf(" shared_memory: (w) lda*BLOCK/N = %d, (i) ldb*BLOCK/M = %d, \t lda = %d \n\n", lda*BLOCK / N, ldb*BLOCK / M, lda); + + gemm_nn_custom_bin_mean_transposed_sequentially_gpu_kernel << > >( M, N, K, A, lda, B, ldb, diff --git a/src/layer.h b/src/layer.h index be725d9bdc8..8b4cff7041e 100644 --- a/src/layer.h +++ b/src/layer.h @@ -89,6 +89,7 @@ struct layer{ int index; int binary; int xnor; + int use_bin_output; int steps; int hidden; float dot; diff --git a/src/network.c b/src/network.c index 2ad514118c3..0666ff1e66d 100644 --- a/src/network.c +++ b/src/network.c @@ -862,8 +862,13 @@ void calculate_binary_weights(network net) if (l->xnor) { //printf("\n %d \n", j); l->lda_align = 256; // 256bit for AVX2 + //if (l->size*l->size*l->c >= 2048) l->lda_align = 512; binary_align_weights(l); + + if(net.layers[j + 1].use_bin_output) { + l->activation = LINEAR; + } } } } diff --git a/src/parser.c b/src/parser.c index c716ea9a496..2fe9bf3e195 100644 --- a/src/parser.c +++ b/src/parser.c @@ -167,6 +167,7 @@ convolutional_layer parse_convolutional(list *options, size_params params) convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,size,stride,padding,activation, batch_normalize, binary, xnor, params.net.adam); layer.flipped = option_find_int_quiet(options, "flipped", 0); layer.dot = option_find_float_quiet(options, "dot", 0); + layer.use_bin_output = option_find_int_quiet(options, "bin_output", 0); if(params.net.adam){ layer.B1 = params.net.B1; layer.B2 = params.net.B2; From 0224ba3d0d66682eb8b14826391f43089e406e71 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 17 Sep 2018 23:59:29 +0300 Subject: [PATCH 0157/1365] Compile fix --- src/im2col_kernels.cu | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 2997485d4bf..9e122136edd 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -12,6 +12,9 @@ extern "C" { #include #include +#define WARP_SIZE 32 + + // src: https://github.com/BVLC/caffe/blob/master/src/caffe/util/im2col.cu // You may also want to read: https://github.com/BVLC/caffe/blob/master/LICENSE @@ -66,7 +69,6 @@ void im2col_ongpu(float *im, } // -------------------------------- -#define WARP_SIZE 32 __global__ void im2col_align_gpu_kernel(const int n, const float* data_im, const int height, const int width, const int ksize, @@ -568,7 +570,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int int64_t B_cur_index = (j*ldb + k) / 8; if (i >= M) A_cur_index = 0; - #pragma unroll WARP_SIZE + #pragma unroll for (int t = 0; t < WARP_SIZE; ++t) { const int lane_id = threadIdx.x % WARP_SIZE; @@ -598,7 +600,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int int64_t B_cur_index = (j*ldb + k) / 8; if (i >= M) A_cur_index = 0; - #pragma unroll WARP_SIZE + #pragma unroll for (int t = 0; t < WARP_SIZE; ++t) { const int lane_id = threadIdx.x % WARP_SIZE; @@ -705,7 +707,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int int64_t B_cur_index = (local_j*ldb + k) / 8; if (i >= M) A_cur_index = 0; - #pragma unroll WARP_SIZE + #pragma unroll for (int t = 0; t < WARP_SIZE; ++t) { const int lane_id = threadIdx.x % WARP_SIZE; @@ -735,7 +737,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int int64_t B_cur_index = (local_j*ldb + k) / 8; if (i >= M) A_cur_index = 0; - #pragma unroll WARP_SIZE + #pragma unroll for (int t = 0; t < WARP_SIZE; ++t) { const int lane_id = threadIdx.x % WARP_SIZE; From 7dd97537fb30debfe034bafc7ca8bc7507211aa9 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 22 Sep 2018 02:01:14 +0300 Subject: [PATCH 0158/1365] XNOR-net tiny-yolo_xnor.cfg ~2x faster than cuDNN on CUDA (nVidia GPU Maxwell) --- build/darknet/x64/cfg/tiny-yolo_xnor.cfg | 6 + build/darknet/x64/cfg/yolov3-tiny_xnor.cfg | 6 + cfg/tiny-yolo_xnor.cfg | 6 + cfg/yolov3-tiny_xnor.cfg | 6 + src/convolutional_kernels.cu | 47 ++- src/convolutional_layer.c | 12 +- src/convolutional_layer.h | 2 +- src/crnn_layer.c | 6 +- src/im2col.h | 4 + src/im2col_kernels.cu | 432 ++++++++++++++++++++- src/network.c | 3 +- src/network.h | 2 + src/network_kernels.cu | 8 +- src/parser.c | 7 +- src/region_layer.c | 6 +- 15 files changed, 511 insertions(+), 42 deletions(-) diff --git a/build/darknet/x64/cfg/tiny-yolo_xnor.cfg b/build/darknet/x64/cfg/tiny-yolo_xnor.cfg index c39aabedd6f..42a96309db0 100644 --- a/build/darknet/x64/cfg/tiny-yolo_xnor.cfg +++ b/build/darknet/x64/cfg/tiny-yolo_xnor.cfg @@ -32,6 +32,7 @@ stride=2 [convolutional] xnor=1 +bin_output=1 batch_normalize=1 filters=32 size=3 @@ -45,6 +46,7 @@ stride=2 [convolutional] xnor=1 +bin_output=1 batch_normalize=1 filters=64 size=3 @@ -58,6 +60,7 @@ stride=2 [convolutional] xnor=1 +bin_output=1 batch_normalize=1 filters=128 size=3 @@ -71,6 +74,7 @@ stride=2 [convolutional] xnor=1 +bin_output=1 batch_normalize=1 filters=256 size=3 @@ -84,6 +88,7 @@ stride=2 [convolutional] xnor=1 +bin_output=1 batch_normalize=1 filters=512 size=3 @@ -97,6 +102,7 @@ stride=1 [convolutional] xnor=1 +bin_output=1 batch_normalize=1 filters=1024 size=3 diff --git a/build/darknet/x64/cfg/yolov3-tiny_xnor.cfg b/build/darknet/x64/cfg/yolov3-tiny_xnor.cfg index 8427beed8bd..725ce079c37 100644 --- a/build/darknet/x64/cfg/yolov3-tiny_xnor.cfg +++ b/build/darknet/x64/cfg/yolov3-tiny_xnor.cfg @@ -36,6 +36,7 @@ stride=2 [convolutional] xnor=1 +bin_output=1 batch_normalize=1 filters=32 size=3 @@ -49,6 +50,7 @@ stride=2 [convolutional] xnor=1 +bin_output=1 batch_normalize=1 filters=64 size=3 @@ -62,6 +64,7 @@ stride=2 [convolutional] xnor=1 +bin_output=1 batch_normalize=1 filters=128 size=3 @@ -88,6 +91,7 @@ stride=2 [convolutional] xnor=1 +bin_output=1 batch_normalize=1 filters=512 size=3 @@ -101,6 +105,7 @@ stride=1 [convolutional] xnor=1 +bin_output=1 batch_normalize=1 filters=1024 size=3 @@ -173,6 +178,7 @@ stride=1 pad=1 activation=leaky + [convolutional] size=1 stride=1 diff --git a/cfg/tiny-yolo_xnor.cfg b/cfg/tiny-yolo_xnor.cfg index c39aabedd6f..42a96309db0 100644 --- a/cfg/tiny-yolo_xnor.cfg +++ b/cfg/tiny-yolo_xnor.cfg @@ -32,6 +32,7 @@ stride=2 [convolutional] xnor=1 +bin_output=1 batch_normalize=1 filters=32 size=3 @@ -45,6 +46,7 @@ stride=2 [convolutional] xnor=1 +bin_output=1 batch_normalize=1 filters=64 size=3 @@ -58,6 +60,7 @@ stride=2 [convolutional] xnor=1 +bin_output=1 batch_normalize=1 filters=128 size=3 @@ -71,6 +74,7 @@ stride=2 [convolutional] xnor=1 +bin_output=1 batch_normalize=1 filters=256 size=3 @@ -84,6 +88,7 @@ stride=2 [convolutional] xnor=1 +bin_output=1 batch_normalize=1 filters=512 size=3 @@ -97,6 +102,7 @@ stride=1 [convolutional] xnor=1 +bin_output=1 batch_normalize=1 filters=1024 size=3 diff --git a/cfg/yolov3-tiny_xnor.cfg b/cfg/yolov3-tiny_xnor.cfg index 8427beed8bd..725ce079c37 100644 --- a/cfg/yolov3-tiny_xnor.cfg +++ b/cfg/yolov3-tiny_xnor.cfg @@ -36,6 +36,7 @@ stride=2 [convolutional] xnor=1 +bin_output=1 batch_normalize=1 filters=32 size=3 @@ -49,6 +50,7 @@ stride=2 [convolutional] xnor=1 +bin_output=1 batch_normalize=1 filters=64 size=3 @@ -62,6 +64,7 @@ stride=2 [convolutional] xnor=1 +bin_output=1 batch_normalize=1 filters=128 size=3 @@ -88,6 +91,7 @@ stride=2 [convolutional] xnor=1 +bin_output=1 batch_normalize=1 filters=512 size=3 @@ -101,6 +105,7 @@ stride=1 [convolutional] xnor=1 +bin_output=1 batch_normalize=1 filters=1024 size=3 @@ -173,6 +178,7 @@ stride=1 pad=1 activation=leaky + [convolutional] size=1 stride=1 diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 40a63f5bfa6..809557afe73 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -119,18 +119,15 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) if(l.xnor){ if (!l.align_bit_weights_gpu || state.train) { binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu); - - swap_binary(&l); - binarize_gpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input_gpu); - state.input = l.binary_input_gpu; } //swap_binary(&l); //binarize_gpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input_gpu); //state.input = l.binary_input_gpu; //cudaDeviceSynchronize(); - if (l.align_bit_weights_gpu && !state.train) + if (l.align_bit_weights_gpu && !state.train && l.c >= 256 && l.size > 1) { + //return; cudaError_t status = cudaSuccess; int input_size = l.c*l.h*l.w*l.batch; @@ -146,15 +143,25 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //if(0) { - int i = 0; - im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.align_workspace_gpu, l.bit_align); //cudaDeviceSynchronize(); - // should be optimized - float_to_bit_gpu(l.align_workspace_gpu, (unsigned char *)state.workspace, l.align_workspace_size); - //cudaDeviceSynchronize(); - - //im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace, l.bit_align); + int i = 0; + if (l.stride == 1 && l.c >= 256 && l.w > 13 && l.size > 1 && 0) // disabled + { + // stride=1 only + im2col_align_bin_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace, l.bit_align); + //cudaDeviceSynchronize(); + } + else + { + im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.align_workspace_gpu, l.bit_align); + //cudaDeviceSynchronize(); + //getchar(); + + // should be optimized + float_to_bit_gpu(l.align_workspace_gpu, (unsigned char *)state.workspace, l.align_workspace_size); + //cudaDeviceSynchronize(); + } transpose_bin_gpu((unsigned char *)state.workspace, (unsigned char *)l.transposed_align_workspace_gpu, k, n, l.bit_align, new_ldb, 8); //cudaDeviceSynchronize(); @@ -197,7 +204,13 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) } } - fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); + if (l.xnor) { + swap_binary(&l); + binarize_gpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input_gpu); + state.input = l.binary_input_gpu; + } + + //fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); #ifdef CUDNN float one = 1; // alpha[0], beta[0] is float for HALF and FLOAT @@ -294,7 +307,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) #else cudnnConvolutionForward(cudnn_handle(), - &one, + &alpha, //&one, l.srcTensorDesc, state.input, l.weightDesc, @@ -303,9 +316,11 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) l.fw_algo, state.workspace, l.workspace_size, - &one, + &beta, //&one, l.dstTensorDesc, l.output_gpu); + + //cudaDeviceSynchronize(); #endif // CUDNN_HALF @@ -338,7 +353,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) } #endif // no CUDNN_HALF - activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); + if (l.activation != LINEAR) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if(l.dot > 0) dot_error_gpu(l); if(l.binary || l.xnor) swap_binary(&l); //cudaDeviceSynchronize(); // for correct profiling of performance diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 69af24fe0a6..d4884f0cb58 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -257,7 +257,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) #endif #endif -convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam) +convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output) { int i; convolutional_layer l = {0}; @@ -269,6 +269,7 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.n = n; l.binary = binary; l.xnor = xnor; + l.use_bin_output = use_bin_output; l.batch = batch; l.stride = stride; l.size = size; @@ -307,7 +308,7 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.binary_weights = calloc(c*n*size*size, sizeof(float)); l.binary_input = calloc(l.inputs*l.batch, sizeof(float)); - int align = 8; + int align = 32;// 8; int src_align = l.out_h*l.out_w; l.bit_align = src_align + (align - src_align % align); } @@ -404,8 +405,9 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int //fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c); l.bflops = (2.0 * l.n * l.size*l.size*l.c * l.out_h*l.out_w) / 1000000000.; - if (l.xnor) fprintf(stderr, "convX "); - else fprintf(stderr, "conv "); + if (l.xnor && l.use_bin_output) fprintf(stderr, "convXB"); + else if (l.xnor) fprintf(stderr, "convX "); + else fprintf(stderr, "conv "); fprintf(stderr, "%5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); return l; @@ -428,7 +430,7 @@ void denormalize_convolutional_layer(convolutional_layer l) void test_convolutional_layer() { - convolutional_layer l = make_convolutional_layer(1, 5, 5, 3, 2, 5, 2, 1, LEAKY, 1, 0, 0, 0); + convolutional_layer l = make_convolutional_layer(1, 5, 5, 3, 2, 5, 2, 1, LEAKY, 1, 0, 0, 0, 0); l.batch_normalize = 1; float data[] = {1,1,1,1,1, 1,1,1,1,1, diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h index 8869a3c3733..20e9d68ebc7 100644 --- a/src/convolutional_layer.h +++ b/src/convolutional_layer.h @@ -25,7 +25,7 @@ void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16); #endif #endif -convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam); +convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output); void denormalize_convolutional_layer(convolutional_layer l); void resize_convolutional_layer(convolutional_layer *layer, int w, int h); void forward_convolutional_layer(const convolutional_layer layer, network_state state); diff --git a/src/crnn_layer.c b/src/crnn_layer.c index 54958803668..f78e0c9db04 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -48,17 +48,17 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.input_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.input_layer) = make_convolutional_layer(batch*steps, h, w, c, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0); + *(l.input_layer) = make_convolutional_layer(batch*steps, h, w, c, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0); l.input_layer->batch = batch; l.self_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.self_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0); + *(l.self_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0); l.self_layer->batch = batch; l.output_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.output_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, output_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0); + *(l.output_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, output_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0); l.output_layer->batch = batch; l.output = l.output_layer->output; diff --git a/src/im2col.h b/src/im2col.h index 1013d4cd8a8..9a6be9ab0c1 100644 --- a/src/im2col.h +++ b/src/im2col.h @@ -17,6 +17,10 @@ void im2col_align_ongpu(float *im, int channels, int height, int width, int ksize, int stride, int pad, float *data_col, int bit_align); +void im2col_align_bin_ongpu(float *im, + int channels, int height, int width, + int ksize, int stride, int pad, float *data_col, int bit_align); + void float_to_bit_gpu(float *src, unsigned char *dst, size_t size); void transpose_bin_gpu(unsigned char *A, unsigned char *B, const int n, const int m, diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 9e122136edd..e1ac3fa27a8 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -45,6 +45,7 @@ __global__ void im2col_gpu_kernel(const int n, const float* data_im, *data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ? data_im_ptr[i * width + j] : 0; + //data_im[(channel_in * height + h_in) * width + w_in + i * width + j]; //*data_col_ptr = data_im_ptr[ii * width + jj]; data_col_ptr += height_col * width_col; @@ -69,7 +70,7 @@ void im2col_ongpu(float *im, } // -------------------------------- - +/* __global__ void im2col_align_gpu_kernel(const int n, const float* data_im, const int height, const int width, const int ksize, const int pad, @@ -120,6 +121,71 @@ __global__ void im2col_align_gpu_kernel(const int n, const float* data_im, } } } +*/ + +// float 32 +__global__ void im2col_align_gpu_kernel(const int n, const float* data_im, + const int height, const int width, const int ksize, + const int pad, + const int stride, + const int height_col, const int width_col, + float *data_col, const int bit_align) +{ + __shared__ float tmp_s[1]; + +//#define SHRED_VALS ((BLOCK / 169) * ) + __shared__ float dst_s[1024]; + //__shared__ float dst_s[1024]; + //__shared__ uint32_t bit_s[32]; + __shared__ uint8_t bit_s[128]; + + int index = blockIdx.x*blockDim.x + threadIdx.x; + for (; index < n; index += blockDim.x*gridDim.x) { + int w_out = index % width_col; + int h_index = index / width_col; + int h_out = h_index % height_col; + int channel_in = h_index / height_col; + int channel_out = channel_in * ksize * ksize; + int h_in = h_out * stride - pad; + int w_in = w_out * stride - pad; + //float* data_col_ptr = data_col; + //float* data_col_ptr_32 = data_col + (channel_out * bit_align + h_out * width_col + w_out) / 32; + //data_col_ptr += (channel_out * height_col + h_out) * width_col + w_out; + //data_col_ptr += channel_out * bit_align + h_out * width_col + w_out; + float* data_col_ptr = &data_col[channel_out * bit_align + h_out * width_col + w_out]; + const float* data_im_ptr = data_im; + data_im_ptr += (channel_in * height + h_in) * width + w_in; + for (int i = 0; i < ksize; ++i) { + for (int j = 0; j < ksize; ++j) { + int h = h_in + i; + int w = w_in + j; + + float val = (h >= 0 && w >= 0 && h < height && w < width) ? + data_im_ptr[i * width + j] : 0; + + int pre_out_index = index % (width_col*height_col); + int out_index = (channel_out + i*ksize + j) * bit_align + pre_out_index;// h_out * width_col + w_out; + data_col[out_index] = val; + + //*data_col_ptr = val; + //dst_s[threadIdx.x] = val; + //tmp_s[0] = val; + + //*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ? + // data_im_ptr[i * width + j] : 0; + + //float src_val = (h >= 0 && w >= 0 && h < height && w < width) ? data_im_ptr[i * width + j] : 0; + //unsigned int bit_mask = __ballot_sync(0xffffffff, src_val > 0); + //if (threadIdx.x % WARP_SIZE == 0) *((unsigned int*)data_col_ptr_32) = bit_mask; + // use atomicOr() // *dst_ptr |= (mask << (col_index % 8)); + //data_col_ptr_32 += bit_align / 32; + + //data_col_ptr += height_col * width_col; + data_col_ptr += bit_align; + } + } + } +} void im2col_align_ongpu(float *im, int channels, int height, int width, @@ -135,6 +201,354 @@ void im2col_align_ongpu(float *im, stride, height_col, width_col, data_col, bit_align); } + + +// -------------------------------- + +/* +// binary im2col +__global__ void im2col_align_bin_gpu_kernel(const int n, const float* data_im, + const int height, const int width, const int ksize, const int channels, + const int pad, + const int stride, + const int height_col, const int width_col, + float *data_col, const int bit_align) +{ + __shared__ float tmp_s[1]; + + //#define SHRED_VALS ((BLOCK / 169) * ) + __shared__ float dst_s[1024]; + //__shared__ float dst_s[1024]; + //__shared__ uint32_t bit_s[32]; + __shared__ uint8_t bit_s[128]; + + int index = blockIdx.x*blockDim.x + threadIdx.x; + for (; index < n; index += blockDim.x*gridDim.x) + { + //int c_index = index; + //int channel_in = c_index % channels; + + int h_out = index % height_col; + int c_index = index / height_col; + int channel_in = c_index % channels; + + int channel_out = channel_in * ksize * ksize; + + int j_index = c_index / channels; + int j = j_index % ksize; + int i = j_index / ksize; + if (i < ksize) + { + for (int w_out = 0; w_out < width_col; ++w_out) + { + int h_in = h_out * stride - pad; + int w_in = w_out * stride - pad; + + int h = h_in + i; + int w = w_in + j; + + float val = (h >= 0 && w >= 0 && h < height && w < width) ? + data_im[(channel_in * height + h_in) * width + w_in + i * width + j] : 0; + + //int pre_out_index = index % (width_col*height_col); + int pre_out_index = h_out * width_col + w_out; + int out_index = (channel_out + i*ksize + j) * bit_align + pre_out_index; + data_col[out_index] = val; + + + }// w_out + } + } +} +*/ + +/* +// binary im2col +__global__ void im2col_align_bin_gpu_kernel(const int n, const float* data_im, + const int height, const int width, const int ksize, const int channels, + const int pad, + const int stride, + const int height_col, const int width_col, + float *data_col, const int bit_align) +{ + __shared__ float tmp_s[1]; + __shared__ ulonglong4 tmp256_s[1]; + + + //#define SHRED_VALS ((BLOCK / 169) * ) + //__shared__ float dst_s[1024]; + //__shared__ float dst_s[1024]; + //__shared__ uint32_t bit_s[32]; + //__shared__ uint8_t bit_s[128]; + + int index = blockIdx.x*blockDim.x + threadIdx.x; + //for (; index < n; index += blockDim.x*gridDim.x) + { + //int c_index = index; + //int channel_in = c_index % channels; + + int h_out = index % height_col; + int c_index = index / height_col; + int channel_in = c_index % channels; + + int channel_out = channel_in * ksize * ksize; + + int j_index = c_index / channels; + int j = j_index % ksize; + int i = j_index / ksize; + + int h_in = h_out * stride - pad; + int h = h_in + i; + + //if (i < ksize) + { + int w_out = 0; + + // the end of padding + //if(0) + for (; w_out < (width_col); w_out += 32) + { + int w = w_out * stride - pad + j; + int pre_in_index = (channel_in * height + h_in) * width + i * width; + int in_index = pre_in_index + w; + //float *src_p = (float *)&data_im[in_index]; + + int pre_out_index = h_out * width_col + w_out; + int out_index = (channel_out + i*ksize + j) * bit_align + pre_out_index; + // float *dst_p = (float *)&data_col[out_index]; + + if (i >= ksize) { + out_index = -1; + } + + #pragma unroll + for (int t = 0; t < WARP_SIZE; ++t) { + const int lane_id = threadIdx.x % WARP_SIZE; + + //const int64_t cur_pre_in_index = pre_in_index; + //const int64_t cur_j = j; + //const int64_t out_i = out_index;// __shfl(out_index, t) + lane_id; + + const int64_t cur_out_index = __shfl(out_index, t); + if (cur_out_index >= 0) + { + const int64_t cur_pre_in_index = __shfl(pre_in_index, t); + const int64_t cur_j = __shfl(j, t); + const int64_t cur_h = __shfl(h, t); + + int cur_w = ((w_out + lane_id) * stride - pad + cur_j); + int in_index = cur_pre_in_index + cur_w; + + float val = (cur_w >= 0 && cur_w < width && cur_h >= 0 && cur_h < height) ? + data_im[in_index] : float(); + + if ((w_out + lane_id) < width_col) { + data_col[cur_out_index + lane_id] = val; + //tmp_s[0] = val; + + //uint32_t bit_mask = __ballot(val > 0); + //uint8_t *bit8_ptr = &(((uint8_t *)data_col)[cur_out_index / 8]); + //uint32_t *bit32_ptr = (uint32_t *)bit8_ptr; + //*bit32_ptr = bit_mask; + } + } + } + + }// w_out + +#ifdef NOT_USED + if (i < ksize && h >= 0 && h < height) + { + + // wait for align address and the end of padding + for (; w_out < width_col; ++w_out) + { + int w_in = w_out * stride - pad; + int w = w_in + j; + + int in_index = (channel_in * height + h_in) * width + w_in + i * width + j; + float *src_p = (float *)&data_im[in_index]; + + int pre_out_index = h_out * width_col + w_out; + int out_index = (channel_out + i*ksize + j) * bit_align + pre_out_index; + float *dst_p = (float *)&data_col[out_index]; + + if (((uint64_t)src_p % 32 == 0) && ((uint64_t)dst_p % 32 == 0) && w > 0) { + //printf(" aligned addresses and there is no padding \n"); + break; + } + + float val = (w >= 0 && w < width) ? + (*src_p) : float(); + + *dst_p = val; + //tmp_s[0] = val; + }// w_out + + // ulonglong4 (256 bit) / instead of float (32 bit) = 8x times + for (; w_out < (width_col - 8); w_out += 8) + { + int w_in = w_out * stride - pad; + int w = w_in + j; + + ulonglong4 *src_p = (ulonglong4 *)&data_im[(channel_in * height + h_in) * width + w_in + i * width + j]; + + int pre_out_index = h_out * width_col + w_out; + int out_index = (channel_out + i*ksize + j) * bit_align + pre_out_index; + ulonglong4 *dst_p = (ulonglong4 *)&data_col[out_index]; + + ulonglong4 val = (w < width) ? + (*src_p) : ulonglong4(); + + *dst_p = val; + //tmp256_s[0] = val; + }// w_out + + for (; w_out < width_col; ++w_out) + { + //int h_in = h_out * stride - pad; + int w_in = w_out * stride - pad; + + //int h = h_in + i; + int w = w_in + j; + + float val = (w < width) ? + data_im[(channel_in * height + h_in) * width + w_in + i * width + j] : 0; + + int pre_out_index = h_out * width_col + w_out; + int out_index = (channel_out + i*ksize + j) * bit_align + pre_out_index; + data_col[out_index] = val; + //tmp_s[0] = val; + }// w_out + } +#endif // NOT_USED + } + } +} +*/ + + +// binary im2col - stride=1 +__global__ void im2col_align_bin_gpu_kernel(const int n, const float* data_im, + const int height, const int width, const int ksize, const int channels, + const int pad, + const int stride, + const int height_col, const int width_col, + float *data_col, const int bit_align) +{ + __shared__ float tmp_s[1]; + __shared__ ulonglong4 tmp256_s[1]; + + + //#define SHRED_VALS ((BLOCK / 169) * ) + //__shared__ float dst_s[1024]; + //__shared__ float dst_s[1024]; + //__shared__ uint32_t bit_s[32]; + //__shared__ uint8_t bit_s[128]; + + int index = blockIdx.x*blockDim.x + threadIdx.x; + //for (; index < n; index += blockDim.x*gridDim.x) + { + int c_index = index; + int channel_in = c_index % channels; + + //int h_out = index % height_col; + //int c_index = index / height_col; + //int channel_in = c_index % channels; + + int channel_out = channel_in * ksize * ksize; + + int j_index = c_index / channels; + int j = j_index % ksize; + int i = j_index / ksize; + + int pre_out_index = (channel_out + i*ksize + j) * bit_align; + int j_pad = (j - pad); + int i_pad = (i - pad); + + for(int wh_index = 0; wh_index < (height_col*width_col); wh_index += 32) + //for (int h_out = 0; h_out < height_col; ++h_out) + { + + // the end of padding + //if(0) + //for (int w_out = 0; w_out < (width_col); w_out += 32) + { + const int w_out = wh_index % width_col; + const int h_out = wh_index / width_col; + + const int w = w_out + j_pad; + const int h = h_out + i_pad; + + int pre_in_index = channel_in * height * width; + int pre_in_wh_index = h * width + w; + + int send_wh_index = wh_index; + if (i >= ksize) send_wh_index = height_col*width_col; + + #pragma unroll + for (int t = 0; t < WARP_SIZE; ++t) + { + const int lane_id = threadIdx.x % WARP_SIZE; + + const int cur_wh_index = __shfl(send_wh_index, t) + lane_id; + + if (cur_wh_index < (width_col*height_col))// && (cur_i_pad+pad) < ksize) + { + const int cur_pre_out_index = __shfl(pre_out_index, t); + + const int cur_pre_in_index = __shfl(pre_in_index, t); + const int cur_pre_in_wh_index = __shfl(pre_in_wh_index, t) + lane_id; + + int w = cur_pre_in_wh_index % width; + int h = cur_pre_in_wh_index / width; + int in_index = cur_pre_in_index + cur_pre_in_wh_index; + + int out_index = cur_pre_out_index + cur_wh_index; + + float val = (w >= 0 && w < width && h >= 0 && h < height) ? + data_im[in_index] : float(); + + //data_col[out_index] = val; + //tmp_s[0] = val; + + uint32_t bit_mask = __ballot(val > 0); + if (lane_id == 0) { + uint8_t *bit8_ptr = &(((uint8_t *)data_col)[out_index / 8]); + uint32_t *bit32_ptr = (uint32_t *)bit8_ptr; + *bit32_ptr = bit_mask; + } + } + + + } + + }// w_out + + } + } +} + + +void im2col_align_bin_ongpu(float *im, + int channels, int height, int width, + int ksize, int stride, int pad, float *data_col, int bit_align) { + // We are going to launch channels * height_col * width_col kernels, each + // kernel responsible for copying a single-channel grid. + int height_col = (height + 2 * pad - ksize) / stride + 1; + int width_col = (width + 2 * pad - ksize) / stride + 1; + //int num_kernels = channels * height_col * width_col * ksize * ksize; + //int num_kernels = channels * ksize * ksize * height_col; + int num_kernels = channels * ksize * ksize; + int num_blocks = num_kernels / BLOCK + 1; + + //im2col_align_bin_gpu_kernel << <(num_kernels + BLOCK - 1) / BLOCK, + im2col_align_bin_gpu_kernel << > >( + num_kernels, im, height, width, ksize, channels, pad, + stride, height_col, + width_col, data_col, bit_align); +} // -------------------------------- @@ -560,7 +974,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int int count = 0; k = 0; -//#ifdef NON_USED +//#ifdef NOT_USED // 32 thread X 64 bit = 2048 bit for (; k < (K - 2048); k += 2048) { // l.size*l.size*l.c - one filter size [27 - 9216] uint64_t c_bit64; @@ -591,7 +1005,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int } //#endif -//#ifdef NON_USED +//#ifdef NOT_USED // 32 thread X 32 bit = 1024 bit for (; k < (K - 1024); k += 1024) { // l.size*l.size*l.c - one filter size [27 - 9216] @@ -626,7 +1040,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int float mean_val = mean_arr[i]; float bias_val = bias_arr[i]; -//#ifdef NON_USED +//#ifdef NOT_USED for (; k < K; k += 256) { // l.size*l.size*l.c - one filter size [27 - 144 - 9216] //ulonglong4 a_bit256 = *((ulonglong4 *)(A + (i*lda + k) / 8)); // weights ulonglong4 a_bit256 = *((ulonglong4 *)(A_s + (local_i*lda + k) / 8)); // weights @@ -638,7 +1052,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int } //#endif -#ifdef NON_USED +#ifdef NOT_USED for (; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] //uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); // weights uint64_t a_bit64 = *((uint64_t *)(A_s + (local_i*lda + k) / 8)); // weights @@ -697,7 +1111,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int int count = 0; k = 0; -//#ifdef NON_USED +//#ifdef NOT_USED // 32 thread X 64 bit = 2048 bit for (; k < (K - 2048); k += 2048) { // l.size*l.size*l.c - one filter size [27 - 9216] uint64_t c_bit64; @@ -728,7 +1142,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int } //#endif -//#ifdef NON_USED +//#ifdef NOT_USED // 32 thread X 32 bit = 1024 bit for (; k < (K - 1024); k += 1024) { // l.size*l.size*l.c - one filter size [27 - 9216] @@ -763,7 +1177,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int float mean_val = mean_arr[i]; float bias_val = bias_arr[i]; -//#ifdef NON_USED +//#ifdef NOT_USED for (; k < K; k += 256) { // l.size*l.size*l.c - one filter size [27 - 144 - 9216] ulonglong4 a_bit256 = *((ulonglong4 *)(A + (i*lda + k) / 8)); // weights //ulonglong4 b_bit256 = *((ulonglong4 *)(B + (j*ldb + k) / 8)); // input @@ -775,7 +1189,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int } //#endif -#ifdef NON_USED +#ifdef NOT_USED for (; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); // weights //uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); // input diff --git a/src/network.c b/src/network.c index 0666ff1e66d..08bb34e0302 100644 --- a/src/network.c +++ b/src/network.c @@ -793,6 +793,7 @@ void free_network(network net) #ifdef GPU if (gpu_index >= 0) cuda_free(net.workspace); else free(net.workspace); + if (net.input_state_gpu) cuda_free(net.input_state_gpu); if (*net.input_gpu) cuda_free(*net.input_gpu); if (*net.truth_gpu) cuda_free(*net.truth_gpu); if (net.input_gpu) free(net.input_gpu); @@ -866,7 +867,7 @@ void calculate_binary_weights(network net) binary_align_weights(l); - if(net.layers[j + 1].use_bin_output) { + if(net.layers[j].use_bin_output) { l->activation = LINEAR; } } diff --git a/src/network.h b/src/network.h index 3b07dbb3378..18dc5953f89 100644 --- a/src/network.h +++ b/src/network.h @@ -64,6 +64,8 @@ typedef struct network{ tree *hierarchy; #ifdef GPU + float *input_state_gpu; + float **input_gpu; float **truth_gpu; float **input16_gpu; diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 0ecba682ddd..dee781b9b7e 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -51,7 +51,7 @@ void forward_network_gpu(network net, network_state state) for(i = 0; i < net.n; ++i){ state.index = i; layer l = net.layers[i]; - if(l.delta_gpu){ + if(l.delta_gpu && state.train){ fill_ongpu(l.outputs * l.batch, 0, l.delta_gpu, 1); } l.forward_gpu(l, state); @@ -428,13 +428,15 @@ float *network_predict_gpu(network net, float *input) network_state state; state.index = 0; state.net = net; - state.input = cuda_make_array(input, size); + //state.input = cuda_make_array(input, size); // memory will be allocated in the parse_network_cfg_custom() + state.input = net.input_state_gpu; + cuda_push_array(state.input, input, size); state.truth = 0; state.train = 0; state.delta = 0; forward_network_gpu(net, state); float *out = get_network_output_gpu(net); - cuda_free(state.input); + //cuda_free(state.input); // will be freed in the free_network() return out; } diff --git a/src/parser.c b/src/parser.c index 2fe9bf3e195..95589216aba 100644 --- a/src/parser.c +++ b/src/parser.c @@ -163,11 +163,12 @@ convolutional_layer parse_convolutional(list *options, size_params params) int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); int binary = option_find_int_quiet(options, "binary", 0); int xnor = option_find_int_quiet(options, "xnor", 0); + int use_bin_output = option_find_int_quiet(options, "bin_output", 0); - convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,size,stride,padding,activation, batch_normalize, binary, xnor, params.net.adam); + convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,size,stride,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output); layer.flipped = option_find_int_quiet(options, "flipped", 0); layer.dot = option_find_float_quiet(options, "dot", 0); - layer.use_bin_output = option_find_int_quiet(options, "bin_output", 0); + if(params.net.adam){ layer.B1 = params.net.B1; layer.B2 = params.net.B2; @@ -819,6 +820,8 @@ network parse_network_cfg_custom(char *filename, int batch) #ifdef GPU if(gpu_index >= 0){ net.workspace = cuda_make_array(0, workspace_size/sizeof(float) + 1); + int size = get_network_input_size(net) * net.batch; + net.input_state_gpu = cuda_make_array(0, size); }else { net.workspace = calloc(1, workspace_size); } diff --git a/src/region_layer.c b/src/region_layer.c index ada5f8e0c71..ca0a704fd1f 100644 --- a/src/region_layer.c +++ b/src/region_layer.c @@ -368,9 +368,11 @@ void backward_region_layer(const region_layer l, network_state state) void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness, int *map) { - int i,j,n; - float *predictions = l.output; + int i; + float *const predictions = l.output; + #pragma omp parallel for for (i = 0; i < l.w*l.h; ++i){ + int j, n; int row = i / l.w; int col = i % l.w; for(n = 0; n < l.n; ++n){ From 129417ae7fec04d6fd03669d593a4e3ab0178011 Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 24 Sep 2018 02:06:35 +0300 Subject: [PATCH 0159/1365] Update Readme.md --- README.md | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) diff --git a/README.md b/README.md index 18bd197ff4c..8de98c29df9 100644 --- a/README.md +++ b/README.md @@ -3,6 +3,7 @@ [![CircleCI](https://circleci.com/gh/AlexeyAB/darknet.svg?style=svg)](https://circleci.com/gh/AlexeyAB/darknet) +0. [Improvements in this repository](#improvements-in-this-repository) 1. [How to use](#how-to-use) 2. [How to compile on Linux](#how-to-compile-on-linux) 3. [How to compile on Windows](#how-to-compile-on-windows) @@ -69,6 +70,30 @@ You can get cfg-files by path: `darknet/cfg/` Others: https://www.youtube.com/channel/UC7ev3hNVkx4DzZ3LO19oebg +### Improvements in this repository + +* added support for Windows +* improved binary neural network performance **2x-4x times** for Detection on CPU and GPU if you trained your own weights by using this XNOR-net model (bit-1 inference) : https://github.com/AlexeyAB/darknet/blob/master/cfg/yolov3-tiny_xnor.cfg +* improved neural network performance **~7%** by fusing 2 layers into 1: Convolutional + Batch-norm +* improved neural network performance Detection **3x times**, Training **2 x times** on GPU Volta (Tesla V100, Titan V, ...) using Tensor Cores if `CUDNN_HALF` defined in the `Makefile` or `darknet.sln` +* improved performance **~1.2x** times on FullHD, **~2x** times on 4K, for detection on the video (file/stream) using `darknet detector demo`... +* improved performance **3.5 X times** of data augmentation for training (using OpenCV SSE/AVX functions instead of hand-written functions) - removes bottleneck for training on multi-GPU or GPU Volta +* improved performance of detection and training on Intel CPU with AVX (Yolo v3 **~85%**, Yolo v2 ~10%) +* fixed usage of `[reorg]`-layer +* optimized memory allocation during network resizing when `random=1` +* optimized initialization GPU for detection - we use batch=1 initially instead of re-init with batch=1 +* added correct calculation of **mAP, F1, IoU, Precision-Recall** using command `darknet detector map`... +* added drawing of chart of average loss during training +* added calculation of anchors for training +* added example of Detection and Tracking objects: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp +* fixed code for use Web-cam on OpenCV 3.x +* run-time tips and warnings if you use incorrect cfg-file or dataset +* many other fixes of code... + +And added manual - [How to train Yolo v3/v2 (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects) + +Also, you might be interested in using a simplified repository where is implemented INT8-quantization (+30% speedup and -1% mAP reduced): https://github.com/AlexeyAB/yolo2_light + ### How to use: ##### Example of usage in cmd-files from `build\darknet\x64\`: From e203ab9046732c9c000a6ded1b14c4f2b10bc496 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 25 Sep 2018 02:39:30 +0300 Subject: [PATCH 0160/1365] Minor fixes --- build/darknet/x64/calc_mAP_voc_py.cmd | 4 ++-- src/detector.c | 11 +++++++++-- 2 files changed, 11 insertions(+), 4 deletions(-) diff --git a/build/darknet/x64/calc_mAP_voc_py.cmd b/build/darknet/x64/calc_mAP_voc_py.cmd index 8c5ba3cf5ce..d69b0ee3f52 100644 --- a/build/darknet/x64/calc_mAP_voc_py.cmd +++ b/build/darknet/x64/calc_mAP_voc_py.cmd @@ -3,9 +3,9 @@ rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\Scripts\pip install cPi rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\Scripts\pip install _pickle -rem darknet.exe detector valid data/voc.data tiny-yolo-voc.cfg tiny-yolo-voc.weights +rem darknet.exe detector valid data/voc.data cfg/yolov2-tiny-voc.cfg yolov2-tiny-voc.weights -darknet.exe detector valid data/voc.data yolo-voc.cfg yolo-voc.weights +darknet.exe detector valid data/voc.data cfg/yolov2-voc.cfg yolo-voc.weights reval_voc_py3.py --year 2007 --classes data\voc.names --image_set test --voc_dir E:\VOC2007_2012\VOCtrainval_11-May-2012\VOCdevkit results diff --git a/src/detector.c b/src/detector.c index dc312345da7..8b1dbdc94d5 100644 --- a/src/detector.c +++ b/src/detector.c @@ -645,9 +645,16 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float network_predict(net, X); int nboxes = 0; - int letterbox = (args.type == LETTERBOX_DATA); float hier_thresh = 0; - detection *dets = get_network_boxes(&net, 1, 1, thresh, hier_thresh, 0, 0, &nboxes, letterbox); + detection *dets; + if (args.type == LETTERBOX_DATA) { + int letterbox = 1; + dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); + } + else { + int letterbox = 0; + dets = get_network_boxes(&net, 1, 1, thresh, hier_thresh, 0, 0, &nboxes, letterbox); + } //detection *dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); // for letterbox=1 if (nms) do_nms_sort(dets, nboxes, l.classes, nms); From c0e2512af2a4e3e29f61e383e78ac346b8165044 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 27 Sep 2018 23:10:54 +0300 Subject: [PATCH 0161/1365] Activation improvement, more robust timer. --- src/activation_kernels.cu | 18 +++++++++--- src/convolutional_kernels.cu | 27 +++++++++++++++-- src/detector.c | 13 +++++---- src/gemm.c | 2 +- src/http_stream.cpp | 56 ++++++++++++++++++++++++++++++++++++ src/http_stream.h | 9 ++++++ src/im2col_kernels.cu | 45 +++++++++++++++++++++++++---- src/network.c | 6 ++-- src/network_kernels.cu | 11 +++++++ 9 files changed, 166 insertions(+), 21 deletions(-) diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index d5f25a0b59a..1d85b6e4d92 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -47,7 +47,7 @@ __device__ float stair_activate_kernel(float x) if (n%2 == 0) return floor(x/2.); else return (x - n) + floor(x/2.); } - + __device__ float hardtan_gradient_kernel(float x) { @@ -146,19 +146,29 @@ __global__ void activate_array_kernel(float *x, int n, ACTIVATION a) if(i < n) x[i] = activate_kernel(x[i], a); } +__global__ void activate_array_leaky_kernel(float *x, int n) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < n) { + float val = x[index]; + x[index] = (val > 0) ? val : val / 10; + } +} + __global__ void gradient_array_kernel(float *x, int n, ACTIVATION a, float *delta) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(i < n) delta[i] *= gradient_kernel(x[i], a); } -extern "C" void activate_array_ongpu(float *x, int n, ACTIVATION a) +extern "C" void activate_array_ongpu(float *x, int n, ACTIVATION a) { - activate_array_kernel<<>>(x, n, a); + if(a == LEAKY) activate_array_leaky_kernel << <(n / BLOCK + 1), BLOCK, 0, get_cuda_stream() >> >(x, n); + else activate_array_kernel<<>>(x, n, a); check_error(cudaPeekAtLastError()); } -extern "C" void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta) +extern "C" void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta) { gradient_array_kernel<<>>(x, n, a, delta); check_error(cudaPeekAtLastError()); diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 809557afe73..06ca96eb71f 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -17,6 +17,16 @@ extern "C" { #include "cuda.h" } +extern "C" { + double get_time_point(); + void start_timer(); + void stop_timer(); + double get_time(); + void stop_timer_and_show(); + void stop_timer_and_show_name(char *name); + void show_total_time(); +} + __global__ void binarize_kernel(float *x, int n, float *binary) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -146,25 +156,33 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //cudaDeviceSynchronize(); int i = 0; - if (l.stride == 1 && l.c >= 256 && l.w > 13 && l.size > 1 && 0) // disabled + // if (l.stride == 1 && l.c >= 256 && l.size > 1) + if (l.stride == 1 && l.c >= 1024 && l.size > 1 && 0)// && l.w >= 13) // disabled { // stride=1 only + //start_timer(); im2col_align_bin_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace, l.bit_align); //cudaDeviceSynchronize(); + //stop_timer_and_show_name("im2col_align_bin_ongpu"); } else { + //start_timer(); im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.align_workspace_gpu, l.bit_align); //cudaDeviceSynchronize(); + //stop_timer_and_show_name("im2col_align_ongpu"); //getchar(); // should be optimized + //start_timer(); float_to_bit_gpu(l.align_workspace_gpu, (unsigned char *)state.workspace, l.align_workspace_size); //cudaDeviceSynchronize(); + //stop_timer_and_show_name("float_to_bit_gpu"); } - + //start_timer(); transpose_bin_gpu((unsigned char *)state.workspace, (unsigned char *)l.transposed_align_workspace_gpu, k, n, l.bit_align, new_ldb, 8); //cudaDeviceSynchronize(); + //stop_timer_and_show_name("transpose_bin_gpu"); // should be optimized //if(0) {//if (k > 1000) { // sequentially input-shared - BAD @@ -172,9 +190,12 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) // (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, new_ldb, l.output_gpu, n, l.mean_arr_gpu); //} //else { // coalescing & weights-shared-memory - GOOD + //start_timer(); gemm_nn_custom_bin_mean_transposed_gpu(m, n, k, (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, new_ldb, l.output_gpu, n, l.mean_arr_gpu, l.biases_gpu); + //cudaDeviceSynchronize(); + //stop_timer_and_show_name("gemm_nn_custom_bin_mean_transposed_gpu"); //} //cudaDeviceSynchronize(); //check_error(status); @@ -325,6 +346,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) #else + fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); + int i; int m = l.n; int k = l.size*l.size*l.c; diff --git a/src/detector.c b/src/detector.c index 8b1dbdc94d5..08e4d355aa6 100644 --- a/src/detector.c +++ b/src/detector.c @@ -25,6 +25,8 @@ #pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib") #endif +#include "http_stream.h" + IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size); void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches); #endif // OPENCV @@ -1142,13 +1144,14 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam //for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *)); float *X = sized.data; - time= what_time_is_it_now(); + + //time= what_time_is_it_now(); + double time = get_time_point(); network_predict(net, X); //network_predict_image(&net, im); letterbox = 1; - printf("%s: Predicted in %f seconds.\n", input, (what_time_is_it_now()-time)); - //get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, 0); - // if (nms) do_nms_sort_v2(boxes, probs, l.w*l.h*l.n, l.classes, nms); - //draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes); + printf("%s: Predicted in %lf milli-seconds.\n", input, ((double)get_time_point() - time) / 1000); + //printf("%s: Predicted in %f seconds.\n", input, (what_time_is_it_now()-time)); + int nboxes = 0; detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); if (nms) do_nms_sort(dets, nboxes, l.classes, nms); diff --git a/src/gemm.c b/src/gemm.c index 76315b02d8f..771fa75dac6 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -2146,7 +2146,7 @@ void time_ongpu(int TA, int TB, int m, int k, int n) clock_t start = clock(), end; for(i = 0; i= 201103L || _MSC_VER >= 1900 // C++11 + +#include +#include + +static std::chrono::steady_clock::time_point steady_start, steady_end; +static double total_time; + +double get_time_point() { + std::chrono::steady_clock::time_point current_time = std::chrono::steady_clock::now(); + //uint64_t now = std::chrono::duration_cast(current_time.time_since_epoch()).count(); + return std::chrono::duration_cast(current_time.time_since_epoch()).count(); +} + +void start_timer() { + steady_start = std::chrono::steady_clock::now(); +} + +void stop_timer() { + steady_end = std::chrono::steady_clock::now(); +} + +double get_time() { + double took_time = std::chrono::duration(steady_end - steady_start).count(); + total_time += took_time; + return took_time; +} + +void stop_timer_and_show() { + stop_timer(); + std::cout << " " << get_time()*1000 << " msec" << std::endl; +} + +void stop_timer_and_show_name(char *name) { + std::cout << " " << name; + stop_timer_and_show(); +} + +void show_total_time() { + std::cout << " Total: " << total_time * 1000 << " msec" << std::endl; +} + +#else // C++11 +#include + +double get_time_point() { return 0; } +void start_timer() {} +void stop_timer() {} +double get_time() { return 0; } +void stop_timer_and_show() { + std::cout << " stop_timer_and_show() isn't implemented " << std::endl; +} +void stop_timer_and_show_name(char *name) { stop_timer_and_show(); } +void total_time() {} +#endif // C++11 \ No newline at end of file diff --git a/src/http_stream.h b/src/http_stream.h index 298d33986c7..69ad999c158 100644 --- a/src/http_stream.h +++ b/src/http_stream.h @@ -6,6 +6,7 @@ extern "C" { #endif #include "image.h" +#include void send_mjpeg(IplImage* ipl, int port, int timeout, int quality); CvCapture* get_capture_webcam(int index); @@ -17,6 +18,14 @@ image image_data_augmentation(IplImage* ipl, int w, int h, int pleft, int ptop, int swidth, int sheight, int flip, float jitter, float dhue, float dsat, float dexp); +double get_time_point(); +void start_timer(); +void stop_timer(); +double get_time(); +void stop_timer_and_show(); +void stop_timer_and_show_name(char *name); +void show_total_time(); + #ifdef __cplusplus } #endif diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index e1ac3fa27a8..0d6f1b156bd 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -131,13 +131,13 @@ __global__ void im2col_align_gpu_kernel(const int n, const float* data_im, const int height_col, const int width_col, float *data_col, const int bit_align) { - __shared__ float tmp_s[1]; + //__shared__ float tmp_s[1]; //#define SHRED_VALS ((BLOCK / 169) * ) - __shared__ float dst_s[1024]; + //__shared__ float dst_s[1024]; //__shared__ float dst_s[1024]; //__shared__ uint32_t bit_s[32]; - __shared__ uint8_t bit_s[128]; + //__shared__ uint8_t bit_s[128]; int index = blockIdx.x*blockDim.x + threadIdx.x; for (; index < n; index += blockDim.x*gridDim.x) { @@ -551,7 +551,7 @@ void im2col_align_bin_ongpu(float *im, } // -------------------------------- - +/* __global__ void float_to_bit_gpu_kernel(float *src, unsigned char *dst, size_t size) { //const int size_aligned = size + (WARP_SIZE - size % WARP_SIZE); @@ -569,12 +569,45 @@ __global__ void float_to_bit_gpu_kernel(float *src, unsigned char *dst, size_t s if (threadIdx.x % WARP_SIZE == 0) ((unsigned int*)dst)[index / 32] = bit_mask; } } +*/ + +__global__ void float_to_bit_gpu_kernel(float *src, unsigned char *dst, size_t size) +{ + //const int size_aligned = size + (WARP_SIZE - size % WARP_SIZE); + __shared__ uint32_t tmp[WARP_SIZE]; + + int index = blockIdx.x*blockDim.x + threadIdx.x; + float src_val; + uint32_t *dst32_ptr = ((unsigned int*)dst); + + //for (; index < size_aligned; index += blockDim.x*gridDim.x) + { + //src_val = src[index]; + if (index < size) src_val = src[index]; + else src_val = 0; + //unsigned int bit_mask = __ballot_sync(0xffffffff, src_val > 0); + const int num_of_warps = blockDim.x / WARP_SIZE; + const int warp_id = threadIdx.x / WARP_SIZE; + const int lane_id = threadIdx.x % WARP_SIZE; + + uint32_t bit_mask = __ballot(src_val > 0); + if (lane_id == 0) tmp[warp_id] = bit_mask; + + __syncthreads(); + if (warp_id == 0) { + if (lane_id < num_of_warps) { + dst32_ptr[index / 32 + lane_id] = tmp[lane_id]; + } + } + __syncthreads(); + } +} void float_to_bit_gpu(float *src, unsigned char *dst, size_t size) { - const int num_blocks = size / BLOCK + 1; - float_to_bit_gpu_kernel<<>>(src, dst, size); + const int num_blocks = size / 1024 + 1; + float_to_bit_gpu_kernel<<>>(src, dst, size); } // -------------------------------- diff --git a/src/network.c b/src/network.c index 08bb34e0302..545216dbd60 100644 --- a/src/network.c +++ b/src/network.c @@ -866,10 +866,10 @@ void calculate_binary_weights(network net) //if (l->size*l->size*l->c >= 2048) l->lda_align = 512; binary_align_weights(l); + } - if(net.layers[j].use_bin_output) { - l->activation = LINEAR; - } + if (net.layers[j].use_bin_output) { + l->activation = LINEAR; } } } diff --git a/src/network_kernels.cu b/src/network_kernels.cu index dee781b9b7e..c592eb98094 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -40,12 +40,16 @@ extern "C" { #include "opencv2/highgui/highgui_c.h" #endif +#include "http_stream.h" + float * get_network_output_gpu_layer(network net, int i); float * get_network_delta_gpu_layer(network net, int i); float * get_network_output_gpu(network net); void forward_network_gpu(network net, network_state state) { + //cudaDeviceSynchronize(); + //printf("\n"); state.workspace = net.workspace; int i; for(i = 0; i < net.n; ++i){ @@ -54,7 +58,12 @@ void forward_network_gpu(network net, network_state state) if(l.delta_gpu && state.train){ fill_ongpu(l.outputs * l.batch, 0, l.delta_gpu, 1); } + //printf("%d - type: %d - ", i, l.type); + //start_timer(); l.forward_gpu(l, state); + //cudaDeviceSynchronize(); + //stop_timer_and_show(); + if(net.wait_stream) cudaStreamSynchronize(get_cuda_stream()); state.input = l.output_gpu; @@ -75,6 +84,8 @@ void forward_network_gpu(network net, network_state state) } */ } + //cudaDeviceSynchronize(); + //show_total_time(); } void backward_network_gpu(network net, network_state state) From e92c635f35619c649d234d4ff59eb5e3d3de1854 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 27 Sep 2018 23:27:14 +0300 Subject: [PATCH 0162/1365] Compile fix --- src/detector.c | 4 ++-- src/http_stream.h | 2 ++ 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/src/detector.c b/src/detector.c index 08e4d355aa6..dde0a04f831 100644 --- a/src/detector.c +++ b/src/detector.c @@ -25,12 +25,12 @@ #pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib") #endif -#include "http_stream.h" - IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size); void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches); #endif // OPENCV +#include "http_stream.h" + static int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90}; void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show) diff --git a/src/http_stream.h b/src/http_stream.h index 69ad999c158..166c1c7e8d3 100644 --- a/src/http_stream.h +++ b/src/http_stream.h @@ -8,6 +8,7 @@ extern "C" { #include "image.h" #include +#ifdef OPENCV void send_mjpeg(IplImage* ipl, int port, int timeout, int quality); CvCapture* get_capture_webcam(int index); CvCapture* get_capture_video_stream(char *path); @@ -17,6 +18,7 @@ int get_stream_fps_cpp(CvCapture *cap); image image_data_augmentation(IplImage* ipl, int w, int h, int pleft, int ptop, int swidth, int sheight, int flip, float jitter, float dhue, float dsat, float dexp); +#endif // OPENCV double get_time_point(); void start_timer(); From 67f33ff5dc4bf489bcd9a0cd25e146ffddc92e18 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 27 Sep 2018 23:49:03 +0300 Subject: [PATCH 0163/1365] Another compile fix --- Makefile | 2 +- src/http_stream.cpp | 8 ++++++-- src/http_stream.h | 5 +++++ 3 files changed, 12 insertions(+), 3 deletions(-) diff --git a/Makefile b/Makefile index ec74a813229..0ebaf1b02a2 100644 --- a/Makefile +++ b/Makefile @@ -124,7 +124,7 @@ $(APPNAMESO): $(LIBNAMESO) src/yolo_v2_class.hpp src/yolo_console_dll.cpp endif $(EXEC): $(OBJS) - $(CPP) $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS) + $(CPP) -std=c++11 $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS) $(OBJDIR)%.o: %.c $(DEPS) $(CC) $(COMMON) $(CFLAGS) -c $< -o $@ diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 3a5ce61c89c..57feea0e6b9 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -1,3 +1,5 @@ +#include "http_stream.h" + #ifdef OPENCV // // a single-threaded, multi client(using select), debug webserver - streaming out mjpg. @@ -40,6 +42,7 @@ struct _INIT_W32DATA #include #include #include +#include using std::cerr; using std::endl; @@ -52,7 +55,6 @@ using std::endl; #endif using namespace cv; -#include "http_stream.h" #include "image.h" @@ -280,7 +282,7 @@ image image_data_augmentation(IplImage* ipl, int w, int h, cv::Rect img_rect(cv::Point2i(0, 0), img.size()); cv::Rect new_src_rect = src_rect & img_rect; - cv::Rect dst_rect(cv::Point2i(std::max(0, -pleft), std::max(0, -ptop)), new_src_rect.size()); + cv::Rect dst_rect(cv::Point2i(std::max(0, -pleft), std::max(0, -ptop)), new_src_rect.size()); cv::Mat cropped(cv::Size(src_rect.width, src_rect.height), img.type()); cropped.setTo(cv::Scalar::all(0)); @@ -330,6 +332,8 @@ image image_data_augmentation(IplImage* ipl, int w, int h, #endif // OPENCV +// ----------------------------------------------------- + #if __cplusplus >= 201103L || _MSC_VER >= 1900 // C++11 #include diff --git a/src/http_stream.h b/src/http_stream.h index 166c1c7e8d3..b7702250391 100644 --- a/src/http_stream.h +++ b/src/http_stream.h @@ -2,6 +2,11 @@ #ifndef HTTP_STREAM_H #define HTTP_STREAM_H +#ifdef OPENCV +#include "opencv2/highgui/highgui_c.h" +#include "opencv2/imgproc/imgproc_c.h" +#endif + #ifdef __cplusplus extern "C" { #endif From 5cc2a7e20d99e38aec1724c2f31c166fbf148360 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 28 Sep 2018 20:27:30 +0300 Subject: [PATCH 0164/1365] Added mistakes checking --- src/data.c | 7 +++++++ src/demo.c | 7 +++++++ src/detector.c | 4 ++++ src/image.c | 4 ++++ src/network_kernels.cu | 5 ++++- 5 files changed, 26 insertions(+), 1 deletion(-) diff --git a/src/data.c b/src/data.c index 46f86c11aa9..6ca558c8c67 100644 --- a/src/data.c +++ b/src/data.c @@ -135,6 +135,7 @@ matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int return X; } +extern int check_mistakes; box_label *read_boxes(char *filename, int *n) { @@ -148,6 +149,7 @@ box_label *read_boxes(char *filename, int *n) char *new_line = "\n"; fwrite(new_line, sizeof(char), strlen(new_line), fw); fclose(fw); + if (check_mistakes) getchar(); *n = 0; return boxes; @@ -352,6 +354,7 @@ void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, sprintf(buff, "echo %s \"Wrong annotation: x = 0 or y = 0\" >> bad_label.list", labelpath); system(buff); ++sub; + if (check_mistakes) getchar(); continue; } if (x <= 0 || x > 1 || y <= 0 || y > 1) { @@ -359,6 +362,7 @@ void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, sprintf(buff, "echo %s \"Wrong annotation: x = %f, y = %f\" >> bad_label.list", labelpath, x, y); system(buff); ++sub; + if (check_mistakes) getchar(); continue; } if (w > 1) { @@ -366,12 +370,14 @@ void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, sprintf(buff, "echo %s \"Wrong annotation: w = %f\" >> bad_label.list", labelpath, w); system(buff); w = 1; + if (check_mistakes) getchar(); } if (h > 1) { printf("\n Wrong annotation: h = %f \n", h); sprintf(buff, "echo %s \"Wrong annotation: h = %f\" >> bad_label.list", labelpath, h); system(buff); h = 1; + if (check_mistakes) getchar(); } if (x == 0) x += lowest_w; if (y == 0) y += lowest_h; @@ -757,6 +763,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo char buff[256]; sprintf(buff, "echo %s >> bad.list", filename); system(buff); + if (check_mistakes) getchar(); continue; //exit(0); } diff --git a/src/demo.c b/src/demo.c index 4f8c7326053..7481660e235 100644 --- a/src/demo.c +++ b/src/demo.c @@ -185,6 +185,13 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int probs = (float **)calloc(l.w*l.h*l.n, sizeof(float *)); for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float *)calloc(l.classes, sizeof(float *)); + if (l.classes != demo_classes) { + printf("Parameters don't match: in cfg-file classes=%d, in data-file classes=%d \n", l.classes, demo_classes); + getchar(); + exit(0); + } + + flag_exit = 0; pthread_t fetch_thread; diff --git a/src/detector.c b/src/detector.c index dde0a04f831..aff87da4b97 100644 --- a/src/detector.c +++ b/src/detector.c @@ -31,6 +31,8 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_ #include "http_stream.h" +int check_mistakes; + static int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90}; void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show) @@ -941,6 +943,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int sprintf(buff, "echo \"Wrong label: %s - j = %d, x = %f, y = %f, width = %f, height = %f\" >> bad_label.list", labelpath, j, truth[j].x, truth[j].y, truth[j].w, truth[j].h); system(buff); + if (check_mistakes) getchar(); } number_of_boxes++; rel_width_height_array = realloc(rel_width_height_array, 2 * number_of_boxes * sizeof(float)); @@ -1223,6 +1226,7 @@ void run_detector(int argc, char **argv) { int dont_show = find_arg(argc, argv, "-dont_show"); int show = find_arg(argc, argv, "-show"); + check_mistakes = find_arg(argc, argv, "-check_mistakes"); int http_stream_port = find_int_arg(argc, argv, "-http_port", -1); char *out_filename = find_char_arg(argc, argv, "-out_filename", 0); char *outfile = find_char_arg(argc, argv, "-out", 0); diff --git a/src/image.c b/src/image.c index 7cf3dc05699..56c41f6cccf 100644 --- a/src/image.c +++ b/src/image.c @@ -961,6 +961,8 @@ image ipl_to_image(IplImage* src) return out; } +extern int check_mistakes; + image load_image_cv(char *filename, int channels) { IplImage* src = 0; @@ -983,6 +985,7 @@ image load_image_cv(char *filename, int channels) char *new_line = "\n"; fwrite(new_line, sizeof(char), strlen(new_line), fw); fclose(fw); + if (check_mistakes) getchar(); return make_image(10,10,3); //exit(EXIT_FAILURE); } @@ -1829,6 +1832,7 @@ image load_image_stb(char *filename, int channels) char *new_line = "\n"; fwrite(new_line, sizeof(char), strlen(new_line), fw); fclose(fw); + if (check_mistakes) getchar(); return make_image(10, 10, 3); //exit(EXIT_FAILURE); } diff --git a/src/network_kernels.cu b/src/network_kernels.cu index c592eb98094..8cd45fe20fd 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -74,10 +74,13 @@ void forward_network_gpu(network net, network_state state) int j; for (j = 0; j < l.out_c; ++j) { image img = make_image(l.out_w, l.out_h, 3); - memcpy(img.data, l.output+ l.out_w*l.out_h*j, l.out_w*l.out_h * 1 * sizeof(float)); + memcpy(img.data, l.output + l.out_w*l.out_h*j, l.out_w*l.out_h * 1 * sizeof(float)); + memcpy(img.data + l.out_w*l.out_h * 1, l.output + l.out_w*l.out_h*j, l.out_w*l.out_h * 1 * sizeof(float)); + memcpy(img.data + l.out_w*l.out_h * 2, l.output + l.out_w*l.out_h*j, l.out_w*l.out_h * 1 * sizeof(float)); char buff[256]; sprintf(buff, "layer-%d slice-%d", i, j); show_image(img, buff); + save_image(img, buff); } cvWaitKey(0); // wait press-key in console cvDestroyAllWindows(); From c02216120ba3e03ec74f8194ab72b47559f0638d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 28 Sep 2018 20:43:47 +0300 Subject: [PATCH 0165/1365] Compile fix --- src/image.c | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/image.c b/src/image.c index 56c41f6cccf..e79fc09893c 100644 --- a/src/image.c +++ b/src/image.c @@ -23,6 +23,7 @@ #include "http_stream.h" #endif +extern int check_mistakes; int windows = 0; float colors[6][3] = { {1,0,1}, {0,0,1},{0,1,1},{0,1,0},{1,1,0},{1,0,0} }; @@ -961,8 +962,6 @@ image ipl_to_image(IplImage* src) return out; } -extern int check_mistakes; - image load_image_cv(char *filename, int channels) { IplImage* src = 0; From 6c8642b3eb19b68a11f3fae6a55bbfd528a800d3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 1 Oct 2018 21:58:00 +0300 Subject: [PATCH 0166/1365] Added darknet53.cfg and resnext152-32x4d.cfg --- build/darknet/x64/cfg/darknet53.cfg | 566 +++++++ build/darknet/x64/cfg/resnext152-32x4d.cfg | 1562 ++++++++++++++++++++ cfg/darknet53.cfg | 566 +++++++ cfg/resnext152-32x4d.cfg | 1562 ++++++++++++++++++++ 4 files changed, 4256 insertions(+) create mode 100644 build/darknet/x64/cfg/darknet53.cfg create mode 100644 build/darknet/x64/cfg/resnext152-32x4d.cfg create mode 100644 cfg/darknet53.cfg create mode 100644 cfg/resnext152-32x4d.cfg diff --git a/build/darknet/x64/cfg/darknet53.cfg b/build/darknet/x64/cfg/darknet53.cfg new file mode 100644 index 00000000000..7b6d5766e9e --- /dev/null +++ b/build/darknet/x64/cfg/darknet53.cfg @@ -0,0 +1,566 @@ +[net] +# Training +# batch=128 +# subdivisions=4 + +# Testing +batch=1 +subdivisions=1 + +height=256 +width=256 +channels=3 +min_crop=128 +max_crop=448 + +burn_in=1000 +learning_rate=0.1 +policy=poly +power=4 +max_batches=800000 +momentum=0.9 +decay=0.0005 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 + + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[avgpool] + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=1 +activation=linear + +[softmax] +groups=1 + diff --git a/build/darknet/x64/cfg/resnext152-32x4d.cfg b/build/darknet/x64/cfg/resnext152-32x4d.cfg new file mode 100644 index 00000000000..48279fd28eb --- /dev/null +++ b/build/darknet/x64/cfg/resnext152-32x4d.cfg @@ -0,0 +1,1562 @@ +[net] +# Training +# batch=128 +# subdivisions=16 + +# Testing +batch=1 +subdivisions=1 + +height=256 +width=256 +channels=3 +min_crop=128 +max_crop=448 + +burn_in=1000 +learning_rate=0.1 +policy=poly +power=4 +max_batches=800000 +momentum=0.9 +decay=0.0005 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 + + +[convolutional] +batch_normalize=1 +filters=64 +size=7 +stride=2 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=4096 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=4096 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=4096 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + + +[avgpool] + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=1 +activation=linear + +[softmax] +groups=1 + diff --git a/cfg/darknet53.cfg b/cfg/darknet53.cfg new file mode 100644 index 00000000000..7b6d5766e9e --- /dev/null +++ b/cfg/darknet53.cfg @@ -0,0 +1,566 @@ +[net] +# Training +# batch=128 +# subdivisions=4 + +# Testing +batch=1 +subdivisions=1 + +height=256 +width=256 +channels=3 +min_crop=128 +max_crop=448 + +burn_in=1000 +learning_rate=0.1 +policy=poly +power=4 +max_batches=800000 +momentum=0.9 +decay=0.0005 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 + + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[avgpool] + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=1 +activation=linear + +[softmax] +groups=1 + diff --git a/cfg/resnext152-32x4d.cfg b/cfg/resnext152-32x4d.cfg new file mode 100644 index 00000000000..48279fd28eb --- /dev/null +++ b/cfg/resnext152-32x4d.cfg @@ -0,0 +1,1562 @@ +[net] +# Training +# batch=128 +# subdivisions=16 + +# Testing +batch=1 +subdivisions=1 + +height=256 +width=256 +channels=3 +min_crop=128 +max_crop=448 + +burn_in=1000 +learning_rate=0.1 +policy=poly +power=4 +max_batches=800000 +momentum=0.9 +decay=0.0005 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 + + +[convolutional] +batch_normalize=1 +filters=64 +size=7 +stride=2 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=4096 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=4096 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +groups = 32 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=4096 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + + +[avgpool] + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=1 +activation=linear + +[softmax] +groups=1 + From 0cf56d1ccff5a21c29ded56f2c66dc785ff5f42b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 3 Oct 2018 23:45:06 +0300 Subject: [PATCH 0167/1365] Version 0.2.5 ) --- src/data.c | 2 +- src/parser.c | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/src/data.c b/src/data.c index 6ca558c8c67..4168e657720 100644 --- a/src/data.c +++ b/src/data.c @@ -142,7 +142,7 @@ box_label *read_boxes(char *filename, int *n) box_label *boxes = calloc(1, sizeof(box_label)); FILE *file = fopen(filename, "r"); if (!file) { - printf("Can't open label file. (This can be normal only if you use MSCOCO) \n"); + printf("Can't open label file. (This can be normal only if you use MSCOCO): %s \n", filename); //file_error(filename); FILE* fw = fopen("bad.list", "a"); fwrite(filename, sizeof(char), strlen(filename), fw); diff --git a/src/parser.c b/src/parser.c index 95589216aba..756d0e52fbe 100644 --- a/src/parser.c +++ b/src/parser.c @@ -971,8 +971,8 @@ void save_weights_upto(network net, char *filename, int cutoff) if(!fp) file_error(filename); int major = 0; - int minor = 1; - int revision = 0; + int minor = 2; + int revision = 5; fwrite(&major, sizeof(int), 1, fp); fwrite(&minor, sizeof(int), 1, fp); fwrite(&revision, sizeof(int), 1, fp); From 24b60456c70ffb8f2d64a5d7ca2d2aa1a7a6ebac Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 3 Oct 2018 23:47:31 +0300 Subject: [PATCH 0168/1365] Version 0.2.5 - fixed ) --- src/parser.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/parser.c b/src/parser.c index 756d0e52fbe..c685abee333 100644 --- a/src/parser.c +++ b/src/parser.c @@ -976,7 +976,7 @@ void save_weights_upto(network net, char *filename, int cutoff) fwrite(&major, sizeof(int), 1, fp); fwrite(&minor, sizeof(int), 1, fp); fwrite(&revision, sizeof(int), 1, fp); - fwrite(net.seen, sizeof(int), 1, fp); + fwrite(net.seen, sizeof(uint64_t), 1, fp); int i; for(i = 0; i < net.n && i < cutoff; ++i){ From 38801979b26244dfb07ca703d0d8898e8381ded4 Mon Sep 17 00:00:00 2001 From: erick Date: Sat, 6 Oct 2018 14:12:02 -0500 Subject: [PATCH 0169/1365] Classifier now is working. The file softmax_layer.c was outdated --- src/blas.c | 22 ++++++++++ src/blas.h | 8 ++++ src/blas_kernels.cu | 98 ++++++++++++++++++++++++++++++++++++++++++++- src/cuda.c | 14 +++++++ src/cuda.h | 1 + src/layer.h | 4 ++ src/parser.c | 17 +++++--- src/softmax_layer.c | 97 +++++++++++++++++++++++++++----------------- 8 files changed, 218 insertions(+), 43 deletions(-) diff --git a/src/blas.c b/src/blas.c index ccf05223dab..70e069917d4 100644 --- a/src/blas.c +++ b/src/blas.c @@ -244,6 +244,28 @@ void l1_cpu(int n, float *pred, float *truth, float *delta, float *error) } } +void softmax_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *error) +{ + int i; + for(i = 0; i < n; ++i){ + float t = truth[i]; + float p = pred[i]; + error[i] = (t) ? -log(p) : 0; + delta[i] = t-p; + } +} + +void logistic_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *error) +{ + int i; + for(i = 0; i < n; ++i){ + float t = truth[i]; + float p = pred[i]; + error[i] = -t*log(p) - (1-t)*log(1-p); + delta[i] = t-p; + } +} + void l2_cpu(int n, float *pred, float *truth, float *delta, float *error) { int i; diff --git a/src/blas.h b/src/blas.h index c40422ac46e..d5f67250f27 100644 --- a/src/blas.h +++ b/src/blas.h @@ -37,9 +37,12 @@ void weighted_sum_cpu(float *a, float *b, float *s, int num, float *c); void softmax(float *input, int n, float temp, float *output, int stride); void upsample_cpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out); +void softmax_cpu(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output); +void softmax_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *error); #ifdef GPU #include "cuda.h" +#include "tree.h" void axpy_ongpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY); void axpy_ongpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY); @@ -47,6 +50,7 @@ void copy_ongpu(int N, float * X, int INCX, float * Y, int INCY); void copy_ongpu_offset(int N, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY); void scal_ongpu(int N, float ALPHA, float * X, int INCX); void supp_ongpu(int N, float ALPHA, float * X, int INCX); +void mask_gpu_new_api(int N, float * X, float mask_num, float * mask, float val); void mask_ongpu(int N, float * X, float mask_num, float * mask); void const_ongpu(int N, float ALPHA, float *X, int INCX); void pow_ongpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY); @@ -71,6 +75,7 @@ void scale_bias_gpu(float *output, float *biases, int batch, int n, int size); void add_bias_gpu(float *output, float *biases, int batch, int n, int size); void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size); +void softmax_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error); void smooth_l1_gpu(int n, float *pred, float *truth, float *delta, float *error); void l2_gpu(int n, float *pred, float *truth, float *delta, float *error); void weighted_delta_gpu(float *a, float *b, float *s, float *da, float *db, float *ds, int num, float *dc); @@ -79,6 +84,7 @@ void mult_add_into_gpu(int num, float *a, float *b, float *c); void reorg_ongpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out); +void softmax_gpu_new_api(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output); void softmax_gpu(float *input, int n, int offset, int groups, float temp, float *output); void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t); void adam_update_gpu(float *w, float *d, float *m, float *v, float B1, float B2, float eps, float decay, float rate, int n, int batch, int t); @@ -87,5 +93,7 @@ void flatten_ongpu(float *x, int spatial, int layers, int batch, int forward, fl void upsample_gpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out); +void softmax_tree_gpu(float *input, int spatial, int batch, int stride, float temp, float *output, tree hier); + #endif #endif diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 34c0008b531..657be1de352 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -7,6 +7,7 @@ extern "C" { #include "blas.h" #include "cuda.h" #include "utils.h" +#include "tree.h" } __global__ void scale_bias_kernel(float *output, float *biases, int n, int size) @@ -419,7 +420,13 @@ __global__ void fill_kernel(int N, float ALPHA, float *X, int INCX) if(i < N) X[i*INCX] = ALPHA; } -__global__ void mask_kernel(int n, float *x, float mask_num, float *mask) +__global__ void mask_kernel_new_api(int n, float *x, float mask_num, float *mask, float val) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (i < n && mask[i] == mask_num) x[i] = val; +} + +__global__ void mask_kernel(int n, float *x, float mask_num, float *mask) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(i < n && mask[i] == mask_num) x[i] = mask_num; @@ -592,6 +599,12 @@ extern "C" void reorg_ongpu(float *x, int w, int h, int c, int batch, int stride check_error(cudaPeekAtLastError()); } +extern "C" void mask_gpu_new_api(int N, float * X, float mask_num, float * mask, float val) +{ + mask_kernel_new_api <<>>(N, X, mask_num, mask, val); + check_error(cudaPeekAtLastError()); +} + extern "C" void mask_ongpu(int N, float * X, float mask_num, float * mask) { mask_kernel<<>>(N, X, mask_num, mask); @@ -687,6 +700,23 @@ extern "C" void smooth_l1_gpu(int n, float *pred, float *truth, float *delta, fl check_error(cudaPeekAtLastError()); } +__global__ void softmax_x_ent_kernel(int n, float *pred, float *truth, float *delta, float *error) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (i < n) { + float t = truth[i]; + float p = pred[i]; + error[i] = (t) ? -log(p) : 0; + delta[i] = t - p; + } +} + +extern "C" void softmax_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error) +{ + softmax_x_ent_kernel << > >(n, pred, truth, delta, error); + check_error(cudaPeekAtLastError()); +} + __global__ void l2_kernel(int n, float *pred, float *truth, float *delta, float *error) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -784,6 +814,40 @@ extern "C" void softmax_gpu(float *input, int n, int offset, int groups, float t check_error(cudaPeekAtLastError()); } +__device__ void softmax_device_new_api(float *input, int n, float temp, int stride, float *output) +{ + int i; + float sum = 0; + float largest = -INFINITY; + for (i = 0; i < n; ++i) { + int val = input[i*stride]; + largest = (val>largest) ? val : largest; + } + for (i = 0; i < n; ++i) { + float e = expf(input[i*stride] / temp - largest / temp); + sum += e; + output[i*stride] = e; + } + for (i = 0; i < n; ++i) { + output[i*stride] /= sum; + } +} + +__global__ void softmax_kernel_new_api(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= batch*groups) return; + int b = id / groups; + int g = id % groups; + softmax_device_new_api(input + b*batch_offset + g*group_offset, n, temp, stride, output + b*batch_offset + g*group_offset); +} + +extern "C" void softmax_gpu_new_api(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output) +{ + softmax_kernel_new_api << > >(input, n, batch, batch_offset, groups, group_offset, stride, temp, output); + check_error(cudaPeekAtLastError()); +} + __global__ void upsample_kernel(size_t N, float *x, int w, int h, int c, int batch, int stride, int forward, float scale, float *out) { @@ -814,4 +878,36 @@ extern "C" void upsample_gpu(float *in, int w, int h, int c, int batch, int stri size_t size = w*h*c*batch*stride*stride; upsample_kernel << > >(size, in, w, h, c, batch, stride, forward, scale, out); check_error(cudaPeekAtLastError()); +} + +__global__ void softmax_tree_kernel(float *input, int spatial, int batch, int stride, float temp, float *output, int groups, int *group_size, int *group_offset) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= spatial*batch*groups) return; + int s = id % spatial; + id = id / spatial; + int g = id % groups; + int b = id / groups; + int goff = group_offset[g] * spatial; + int boff = b*stride; + softmax_device_new_api(input + goff + boff + s, group_size[g], temp, spatial, output + goff + boff + s); +} + +extern "C" void softmax_tree_gpu(float *input, int spatial, int batch, int stride, float temp, float *output, tree hier) +{ + int *tree_groups_size = cuda_make_int_array_new_api(hier.group_size, hier.groups); + int *tree_groups_offset = cuda_make_int_array_new_api(hier.group_offset, hier.groups); + /* + static int *tree_groups_size = 0; + static int *tree_groups_offset = 0; + if(!tree_groups_size){ + tree_groups_size = cuda_make_int_array(hier.group_size, hier.groups); + tree_groups_offset = cuda_make_int_array(hier.group_offset, hier.groups); + } + */ + int num = spatial*batch*hier.groups; + softmax_tree_kernel <<>>(input, spatial, batch, stride, temp, output, hier.groups, tree_groups_size, tree_groups_offset); + check_error(cudaPeekAtLastError()); + cuda_free((float *)tree_groups_size); + cuda_free((float *)tree_groups_offset); } \ No newline at end of file diff --git a/src/cuda.c b/src/cuda.c index 2284dad050a..68fb7f8cf80 100644 --- a/src/cuda.c +++ b/src/cuda.c @@ -162,6 +162,20 @@ int *cuda_make_int_array(size_t n) return x_gpu; } +int *cuda_make_int_array_new_api(int *x, size_t n) +{ + int *x_gpu; + size_t size = sizeof(int)*n; + cudaError_t status = cudaMalloc((void **)&x_gpu, size); + check_error(status); + if (x) { + status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice); + check_error(status); + } + if (!x_gpu) error("Cuda malloc failed\n"); + return x_gpu; +} + void cuda_free(float *x_gpu) { //cudaStreamSynchronize(get_cuda_stream()); diff --git a/src/cuda.h b/src/cuda.h index 50bb82b402a..289ee5b409c 100644 --- a/src/cuda.h +++ b/src/cuda.h @@ -40,6 +40,7 @@ extern "C" { cublasHandle_t blas_handle(); float *cuda_make_array(float *x, size_t n); int *cuda_make_int_array(size_t n); + int *cuda_make_int_array_new_api(int *x, size_t n); void cuda_push_array(float *x_gpu, float *x, size_t n); YOLODLL_API void cuda_pull_array(float *x_gpu, float *x, size_t n); YOLODLL_API void cuda_set_device(int n); diff --git a/src/layer.h b/src/layer.h index 8b4cff7041e..a4fd312eeab 100644 --- a/src/layer.h +++ b/src/layer.h @@ -83,6 +83,7 @@ struct layer{ int side; int stride; int reverse; + int spatial; int pad; int sqrt; int flip; @@ -100,6 +101,7 @@ struct layer{ float shift; float ratio; int focal_loss; + int noloss; int softmax; int classes; int coords; @@ -198,6 +200,7 @@ struct layer{ int * input_sizes; float * delta; float * output; + float * loss; float * squared; float * norms; @@ -289,6 +292,7 @@ struct layer{ float * scale_updates_gpu; float * output_gpu; + float * loss_gpu; float * delta_gpu; float * rand_gpu; float * squared_gpu; diff --git a/src/parser.c b/src/parser.c index c685abee333..c82c4a2f5c9 100644 --- a/src/parser.c +++ b/src/parser.c @@ -233,12 +233,17 @@ connected_layer parse_connected(list *options, size_params params) softmax_layer parse_softmax(list *options, size_params params) { - int groups = option_find_int_quiet(options, "groups",1); - softmax_layer layer = make_softmax_layer(params.batch, params.inputs, groups); - layer.temperature = option_find_float_quiet(options, "temperature", 1); - char *tree_file = option_find_str(options, "tree", 0); - if (tree_file) layer.softmax_tree = read_tree(tree_file); - return layer; + int groups = option_find_int_quiet(options, "groups", 1); + softmax_layer layer = make_softmax_layer(params.batch, params.inputs, groups); + layer.temperature = option_find_float_quiet(options, "temperature", 1); + char *tree_file = option_find_str(options, "tree", 0); + if (tree_file) layer.softmax_tree = read_tree(tree_file); + layer.w = params.w; + layer.h = params.h; + layer.c = params.c; + layer.spatial = option_find_float_quiet(options, "spatial", 0); + layer.noloss = option_find_int_quiet(options, "noloss", 0); + return layer; } int *parse_yolo_mask(char *a, int *num) diff --git a/src/softmax_layer.c b/src/softmax_layer.c index 27f73fdd140..bfe34bc1ccd 100644 --- a/src/softmax_layer.c +++ b/src/softmax_layer.c @@ -1,12 +1,31 @@ #include "softmax_layer.h" #include "blas.h" #include "cuda.h" +#include "utils.h" +#include "blas.h" + #include #include #include #include #include +#define SECRET_NUM -1234 + +void softmax_tree(float *input, int batch, int inputs, float temp, tree *hierarchy, float *output) +{ + int b; + for (b = 0; b < batch; ++b) { + int i; + int count = 0; + for (i = 0; i < hierarchy->groups; ++i) { + int group_size = hierarchy->group_size[i]; + softmax(input + b*inputs + count, group_size, temp, output + b*inputs + count, 1); + count += group_size; + } + } +} + softmax_layer make_softmax_layer(int batch, int inputs, int groups) { assert(inputs%groups == 0); @@ -17,8 +36,10 @@ softmax_layer make_softmax_layer(int batch, int inputs, int groups) l.groups = groups; l.inputs = inputs; l.outputs = inputs; + l.loss = calloc(inputs*batch, sizeof(float)); l.output = calloc(inputs*batch, sizeof(float)); l.delta = calloc(inputs*batch, sizeof(float)); + l.cost = calloc(1, sizeof(float)); l.forward = forward_softmax_layer; l.backward = backward_softmax_layer; @@ -27,45 +48,35 @@ softmax_layer make_softmax_layer(int batch, int inputs, int groups) l.backward_gpu = backward_softmax_layer_gpu; l.output_gpu = cuda_make_array(l.output, inputs*batch); + l.loss_gpu = cuda_make_array(l.loss, inputs*batch); l.delta_gpu = cuda_make_array(l.delta, inputs*batch); #endif return l; } -void softmax_tree(float *input, int batch, int inputs, float temp, tree *hierarchy, float *output) +void forward_softmax_layer(const softmax_layer l, network_state net) { - int b; - for(b = 0; b < batch; ++b){ + if(l.softmax_tree){ int i; int count = 0; - for(i = 0; i < hierarchy->groups; ++i){ - int group_size = hierarchy->group_size[i]; - softmax(input+b*inputs + count, group_size, temp, output+b*inputs + count, 1); + for (i = 0; i < l.softmax_tree->groups; ++i) { + int group_size = l.softmax_tree->group_size[i]; + softmax_cpu(net.input + count, group_size, l.batch, l.inputs, 1, 0, 1, l.temperature, l.output + count); count += group_size; } + } else { + softmax_cpu(net.input, l.inputs/l.groups, l.batch, l.inputs, l.groups, l.inputs/l.groups, 1, l.temperature, l.output); } -} -void forward_softmax_layer(const softmax_layer l, network_state state) -{ - int b; - int inputs = l.inputs / l.groups; - int batch = l.batch * l.groups; - if(l.softmax_tree){ - softmax_tree(state.input, batch, inputs, l.temperature, l.softmax_tree, l.output); - } else { - for(b = 0; b < batch; ++b){ - softmax(state.input+b*inputs, inputs, l.temperature, l.output+b*inputs, 1); - } + if(net.truth && !l.noloss){ + softmax_x_ent_cpu(l.batch*l.inputs, l.output, net.truth, l.delta, l.loss); + l.cost[0] = sum_array(l.loss, l.batch*l.inputs); } } -void backward_softmax_layer(const softmax_layer l, network_state state) +void backward_softmax_layer(const softmax_layer l, network_state net) { - int i; - for(i = 0; i < l.inputs*l.batch; ++i){ - state.delta[i] += l.delta[i]; - } + axpy_cpu(l.inputs*l.batch, 1, l.delta, 1, net.delta, 1); } #ifdef GPU @@ -75,26 +86,40 @@ void pull_softmax_layer_output(const softmax_layer layer) cuda_pull_array(layer.output_gpu, layer.output, layer.inputs*layer.batch); } -void forward_softmax_layer_gpu(const softmax_layer l, network_state state) +void forward_softmax_layer_gpu(const softmax_layer l, network_state net) { - int inputs = l.inputs / l.groups; - int batch = l.batch * l.groups; if(l.softmax_tree){ - int i; - int count = 0; - for (i = 0; i < l.softmax_tree->groups; ++i) { - int group_size = l.softmax_tree->group_size[i]; - softmax_gpu(state.input+count, group_size, inputs, batch, l.temperature, l.output_gpu + count); - count += group_size; - } + softmax_tree_gpu(net.input, 1, l.batch, l.inputs, l.temperature, l.output_gpu, *l.softmax_tree); + /* + int i; + int count = 0; + for (i = 0; i < l.softmax_tree->groups; ++i) { + int group_size = l.softmax_tree->group_size[i]; + softmax_gpu(net.input_gpu + count, group_size, l.batch, l.inputs, 1, 0, 1, l.temperature, l.output_gpu + count); + count += group_size; + } + */ } else { - softmax_gpu(state.input, inputs, inputs, batch, l.temperature, l.output_gpu); + if(l.spatial){ + softmax_gpu_new_api(net.input, l.c, l.batch*l.c, l.inputs/l.c, l.w*l.h, 1, l.w*l.h, 1, l.output_gpu); + }else{ + softmax_gpu_new_api(net.input, l.inputs/l.groups, l.batch, l.inputs, l.groups, l.inputs/l.groups, 1, l.temperature, l.output_gpu); + } + } + if(net.truth && !l.noloss){ + softmax_x_ent_gpu(l.batch*l.inputs, l.output_gpu, net.truth, l.delta_gpu, l.loss_gpu); + if(l.softmax_tree){ + mask_gpu_new_api(l.batch*l.inputs, l.delta_gpu, SECRET_NUM, net.truth, 0); + mask_gpu_new_api(l.batch*l.inputs, l.loss_gpu, SECRET_NUM, net.truth, 0); + } + cuda_pull_array(l.loss_gpu, l.loss, l.batch*l.inputs); + l.cost[0] = sum_array(l.loss, l.batch*l.inputs); } } -void backward_softmax_layer_gpu(const softmax_layer layer, network_state state) +void backward_softmax_layer_gpu(const softmax_layer layer, network_state net) { - axpy_ongpu(layer.batch*layer.inputs, 1, layer.delta_gpu, 1, state.delta, 1); + axpy_ongpu(layer.batch*layer.inputs, 1, layer.delta_gpu, 1, net.delta, 1); } #endif From 7ee4135910624f11e80de36b236208b223f58eb4 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 10 Oct 2018 13:51:36 +0300 Subject: [PATCH 0170/1365] Fix OpenCV compilation. maxpool_layer on GPU uses cuDNN. --- src/darknet.c | 2 ++ src/data.c | 4 ++-- src/image.c | 2 ++ src/layer.h | 1 + src/maxpool_layer.c | 22 +++++++++++++++++++++- src/maxpool_layer_kernels.cu | 27 +++++++++++++++++++++++++++ 6 files changed, 55 insertions(+), 3 deletions(-) diff --git a/src/darknet.c b/src/darknet.c index 1dc073bcfd9..41878041e9f 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -370,6 +370,8 @@ int main(int argc, char **argv) gpu_index = find_int_arg(argc, argv, "-i", 0); if(find_arg(argc, argv, "-nogpu")) { gpu_index = -1; + printf("\n Currently Darknet doesn't support -nogpu flag. If you want to use CPU - please compile Darknet with GPU=0 in the Makefile, or compile darknet_no_gpu.sln on Windows.\n"); + exit(-1); } #ifndef GPU diff --git a/src/data.c b/src/data.c index 4168e657720..d152fde4bee 100644 --- a/src/data.c +++ b/src/data.c @@ -336,8 +336,8 @@ void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, // if truth (box for object) is smaller than 1x1 pix char buff[256]; if (id >= classes) { - printf("\n Wrong annotation: class_id = %d. But class_id should be [from 0 to %d] \n", id, classes); - sprintf(buff, "echo %s \"Wrong annotation: class_id = %d. But class_id should be [from 0 to %d]\" >> bad_label.list", labelpath, id, classes); + printf("\n Wrong annotation: class_id = %d. But class_id should be [from 0 to %d] \n", id, (classes-1)); + sprintf(buff, "echo %s \"Wrong annotation: class_id = %d. But class_id should be [from 0 to %d]\" >> bad_label.list", labelpath, id, (classes-1)); system(buff); getchar(); ++sub; diff --git a/src/image.c b/src/image.c index e79fc09893c..b564f5b4448 100644 --- a/src/image.c +++ b/src/image.c @@ -21,6 +21,8 @@ #include "http_stream.h" #endif #include "http_stream.h" + +#define CV_RGB(r, g, b) cvScalar( (b), (g), (r), 0 ) #endif extern int check_mistakes; diff --git a/src/layer.h b/src/layer.h index a4fd312eeab..2cfa08715c2 100644 --- a/src/layer.h +++ b/src/layer.h @@ -307,6 +307,7 @@ struct layer{ cudnnConvolutionFwdAlgo_t fw_algo; cudnnConvolutionBwdDataAlgo_t bd_algo; cudnnConvolutionBwdFilterAlgo_t bf_algo; + cudnnPoolingDescriptor_t poolingDesc; #endif #endif }; diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index 5ff7e9d4127..b45eb44f176 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -47,7 +47,27 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s l.indexes_gpu = cuda_make_int_array(output_size); l.output_gpu = cuda_make_array(l.output, output_size); l.delta_gpu = cuda_make_array(l.delta, output_size); - #endif +#ifdef CUDNN + cudnnStatus_t maxpool_status; + maxpool_status = cudnnCreatePoolingDescriptor(&l.poolingDesc); + + maxpool_status = cudnnSetPooling2dDescriptor( + l.poolingDesc, + CUDNN_POOLING_MAX, + CUDNN_PROPAGATE_NAN, // CUDNN_PROPAGATE_NAN, CUDNN_NOT_PROPAGATE_NAN + l.size, + l.size, + 0, //l.pad, + 0, //l.pad, + l.stride, + l.stride); + + cudnnCreateTensorDescriptor(&l.srcTensorDesc); + cudnnCreateTensorDescriptor(&l.dstTensorDesc); + cudnnSetTensor4dDescriptor(l.srcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.c, l.h, l.w); + cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w); +#endif // CUDNN + #endif // GPU l.bflops = (l.size*l.size*l.c * l.out_h*l.out_w) / 1000000000.; fprintf(stderr, "max %d x %d / %d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); return l; diff --git a/src/maxpool_layer_kernels.cu b/src/maxpool_layer_kernels.cu index 78b7f39772f..05d5b4ffa95 100644 --- a/src/maxpool_layer_kernels.cu +++ b/src/maxpool_layer_kernels.cu @@ -86,6 +86,33 @@ __global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_ extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network_state state) { + +#ifdef CUDNN + if (!state.train) {// && layer.stride == layer.size) { + // cudnnPoolingBackward + cudnnStatus_t maxpool_status; + + float alpha = 1, beta = 0; + maxpool_status = cudnnPoolingForward( + cudnn_handle(), + layer.poolingDesc, + &alpha, + layer.srcTensorDesc, + state.input, + &beta, + layer.dstTensorDesc, + layer.output_gpu); + + //maxpool_status = cudnnDestroyPoolingDescriptor(poolingDesc); + //cudnnDestroyTensorDescriptor(layer.srcTensorDesc); + //cudnnDestroyTensorDescriptor(layer.dstTensorDesc); + + return; + } +#endif + + + int h = layer.out_h; int w = layer.out_w; int c = layer.c; From 7762372e5bba9e9411ee12d535b578159ca9043f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 12 Oct 2018 15:12:36 +0300 Subject: [PATCH 0171/1365] Compilation fix for OpenCV 3.4.x and Jetson Xavier. --- Makefile | 3 +++ src/detector.c | 2 ++ 2 files changed, 5 insertions(+) diff --git a/Makefile b/Makefile index 0ebaf1b02a2..10cee6d4806 100644 --- a/Makefile +++ b/Makefile @@ -23,6 +23,9 @@ OS := $(shell uname) # Tesla V100 # ARCH= -gencode arch=compute_70,code=[sm_70,compute_70] +# Jetson XAVIER +# ARCH= -gencode arch=compute_72,code=[sm_72,compute_72] + # GTX 1080, GTX 1070, GTX 1060, GTX 1050, GTX 1030, Titan Xp, Tesla P40, Tesla P4 # ARCH= -gencode arch=compute_61,code=sm_61 -gencode arch=compute_61,code=compute_61 diff --git a/src/detector.c b/src/detector.c index aff87da4b97..5926979f0e6 100644 --- a/src/detector.c +++ b/src/detector.c @@ -27,6 +27,8 @@ IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size); void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches); + +#define CV_RGB(r, g, b) cvScalar( (b), (g), (r), 0 ) #endif // OPENCV #include "http_stream.h" From 344f8c6d27702aad61b285bac7cba9741e7ac4e8 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 12 Oct 2018 19:31:02 +0300 Subject: [PATCH 0172/1365] Fix building darknet.so on MacOS --- Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 10cee6d4806..0a9d42bb503 100644 --- a/Makefile +++ b/Makefile @@ -123,7 +123,7 @@ $(LIBNAMESO): $(OBJS) src/yolo_v2_class.hpp src/yolo_v2_class.cpp $(CPP) -shared -std=c++11 -fvisibility=hidden -DYOLODLL_EXPORTS $(COMMON) $(CFLAGS) $(OBJS) src/yolo_v2_class.cpp -o $@ $(LDFLAGS) $(APPNAMESO): $(LIBNAMESO) src/yolo_v2_class.hpp src/yolo_console_dll.cpp - $(CPP) -std=c++11 $(COMMON) $(CFLAGS) -o $@ src/yolo_console_dll.cpp $(LDFLAGS) -L ./ -l:$(LIBNAMESO) + $(CPP) -std=c++11 $(COMMON) $(CFLAGS) -o $@ src/yolo_console_dll.cpp $(LDFLAGS) -L ./ $(LIBNAMESO) endif $(EXEC): $(OBJS) From 2bb44454a548b931153b7bb304bc42fa9329a4ef Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 14 Oct 2018 00:20:23 +0300 Subject: [PATCH 0173/1365] Minor fix --- Makefile | 3 +++ README.md | 2 +- src/http_stream.cpp | 5 +++++ 3 files changed, 9 insertions(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 0a9d42bb503..3f10ed353ae 100644 --- a/Makefile +++ b/Makefile @@ -23,6 +23,9 @@ OS := $(shell uname) # Tesla V100 # ARCH= -gencode arch=compute_70,code=[sm_70,compute_70] +# GeForce RTX 2080 Ti, RTX 2080, RTX 2070 Quadro RTX 8000, Quadro RTX 6000, Quadro RTX 5000 Tesla T4 +# ARCH= -gencode arch=compute_75,code=[sm_75,compute_75] + # Jetson XAVIER # ARCH= -gencode arch=compute_72,code=[sm_72,compute_72] diff --git a/README.md b/README.md index 8de98c29df9..dc4bf62941a 100644 --- a/README.md +++ b/README.md @@ -370,7 +370,7 @@ If you made you custom model that isn't based on other models, then you can trai ## When should I stop training: -Usually sufficient 2000 iterations for each class(object). But for a more precise definition when you should stop training, use the following manual: +Usually sufficient 2000 iterations for each class(object), but not less than 4000 iterations in total. But for a more precise definition when you should stop training, use the following manual: 1. During training, you will see varying indicators of error, and you should stop when no longer decreases **0.XXXXXXX avg**: diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 57feea0e6b9..c6ace438d42 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -322,6 +322,11 @@ image image_data_augmentation(IplImage* ipl, int w, int h, sized *= dexp; } + //std::stringstream window_name; + //window_name << "augmentation - " << ipl; + //cv::imshow(window_name.str(), sized); + //cv::waitKey(0); + // Mat -> IplImage -> image IplImage src = sized; image out = ipl_to_image(&src); From 76706dc4a63cec2478ef98ff5c75b090153683e4 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 15 Oct 2018 20:16:08 +0300 Subject: [PATCH 0174/1365] Added yolov3-openimages.cfg and SELU activation --- README.md | 1 + build/darknet/x64/cfg/openimages.data | 8 + build/darknet/x64/cfg/yolov3-openimages.cfg | 789 ++++++++++++++++++ .../x64/darknet_yolo_v3_openimages.cmd | 8 + build/darknet/x64/data/openimages.data | 8 + build/darknet/x64/data/openimages.names | 601 +++++++++++++ cfg/openimages.data | 8 + cfg/yolov3-openimages.cfg | 789 ++++++++++++++++++ data/openimages.names | 601 +++++++++++++ src/activation_kernels.cu | 6 + src/activations.c | 7 + src/activations.h | 4 +- 12 files changed, 2829 insertions(+), 1 deletion(-) create mode 100644 build/darknet/x64/cfg/openimages.data create mode 100644 build/darknet/x64/cfg/yolov3-openimages.cfg create mode 100644 build/darknet/x64/darknet_yolo_v3_openimages.cmd create mode 100644 build/darknet/x64/data/openimages.data create mode 100644 build/darknet/x64/data/openimages.names create mode 100644 cfg/openimages.data create mode 100644 cfg/yolov3-openimages.cfg create mode 100644 data/openimages.names diff --git a/README.md b/README.md index dc4bf62941a..2917ebef07a 100644 --- a/README.md +++ b/README.md @@ -51,6 +51,7 @@ This repository supports: * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported ##### Pre-trained models for different cfg-files can be downloaded from (smaller -> faster & lower quality): +* `yolov3-openimages.cfg` (247 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-openimages.weights * `yolov3-spp.cfg` (240 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-spp.weights * `yolov3.cfg` (236 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3.weights * `yolov3-tiny.cfg` (34 MB COCO **Yolo v3 tiny**) - requires 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-tiny.weights diff --git a/build/darknet/x64/cfg/openimages.data b/build/darknet/x64/cfg/openimages.data new file mode 100644 index 00000000000..fa80e5ab7d8 --- /dev/null +++ b/build/darknet/x64/cfg/openimages.data @@ -0,0 +1,8 @@ +classes= 601 +train = /home/pjreddie/data/openimsv4/openimages.train.list +#valid = coco_testdev +valid = data/coco_val_5k.list +names = data/openimages.names +backup = /home/pjreddie/backup/ +eval=coco + diff --git a/build/darknet/x64/cfg/yolov3-openimages.cfg b/build/darknet/x64/cfg/yolov3-openimages.cfg new file mode 100644 index 00000000000..65d241a74c4 --- /dev/null +++ b/build/darknet/x64/cfg/yolov3-openimages.cfg @@ -0,0 +1,789 @@ +[net] +# Testing + batch=1 + subdivisions=1 +# Training +batch=64 +subdivisions=16 +width=608 +height=608 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=5000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=1818 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=601 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=1818 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=601 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=1818 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=601 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + diff --git a/build/darknet/x64/darknet_yolo_v3_openimages.cmd b/build/darknet/x64/darknet_yolo_v3_openimages.cmd new file mode 100644 index 00000000000..0305e9cb3c5 --- /dev/null +++ b/build/darknet/x64/darknet_yolo_v3_openimages.cmd @@ -0,0 +1,8 @@ + +darknet.exe detector test data/openimages.data cfg/yolov3-openimages.cfg yolov3-openimages.weights -i 0 -thresh 0.25 dog.jpg -ext_output + + + +darknet.exe detector demo data/openimages.data cfg/yolov3-openimages.cfg yolov3-openimages.weights -i 0 -thresh 0.25 street1k.mp4 -ext_output + +pause \ No newline at end of file diff --git a/build/darknet/x64/data/openimages.data b/build/darknet/x64/data/openimages.data new file mode 100644 index 00000000000..fa80e5ab7d8 --- /dev/null +++ b/build/darknet/x64/data/openimages.data @@ -0,0 +1,8 @@ +classes= 601 +train = /home/pjreddie/data/openimsv4/openimages.train.list +#valid = coco_testdev +valid = data/coco_val_5k.list +names = data/openimages.names +backup = /home/pjreddie/backup/ +eval=coco + diff --git a/build/darknet/x64/data/openimages.names b/build/darknet/x64/data/openimages.names new file mode 100644 index 00000000000..ddfd8f22649 --- /dev/null +++ b/build/darknet/x64/data/openimages.names @@ -0,0 +1,601 @@ +Tortoise +Container +Magpie +Sea turtle +Football +Ambulance +Ladder +Toothbrush +Syringe +Sink +Toy +Organ +Cassette deck +Apple +Human eye +Cosmetics +Paddle +Snowman +Beer +Chopsticks +Human beard +Bird +Parking meter +Traffic light +Croissant +Cucumber +Radish +Towel +Doll +Skull +Washing machine +Glove +Tick +Belt +Sunglasses +Banjo +Cart +Ball +Backpack +Bicycle +Home appliance +Centipede +Boat +Surfboard +Boot +Headphones +Hot dog +Shorts +Fast food +Bus +Boy +Screwdriver +Bicycle wheel +Barge +Laptop +Miniskirt +Drill +Dress +Bear +Waffle +Pancake +Brown bear +Woodpecker +Blue jay +Pretzel +Bagel +Tower +Teapot +Person +Bow and arrow +Swimwear +Beehive +Brassiere +Bee +Bat +Starfish +Popcorn +Burrito +Chainsaw +Balloon +Wrench +Tent +Vehicle registration plate +Lantern +Toaster +Flashlight +Billboard +Tiara +Limousine +Necklace +Carnivore +Scissors +Stairs +Computer keyboard +Printer +Traffic sign +Chair +Shirt +Poster +Cheese +Sock +Fire hydrant +Land vehicle +Earrings +Tie +Watercraft +Cabinetry +Suitcase +Muffin +Bidet +Snack +Snowmobile +Clock +Medical equipment +Cattle +Cello +Jet ski +Camel +Coat +Suit +Desk +Cat +Bronze sculpture +Juice +Gondola +Beetle +Cannon +Computer mouse +Cookie +Office building +Fountain +Coin +Calculator +Cocktail +Computer monitor +Box +Stapler +Christmas tree +Cowboy hat +Hiking equipment +Studio couch +Drum +Dessert +Wine rack +Drink +Zucchini +Ladle +Human mouth +Dairy +Dice +Oven +Dinosaur +Ratchet +Couch +Cricket ball +Winter melon +Spatula +Whiteboard +Pencil sharpener +Door +Hat +Shower +Eraser +Fedora +Guacamole +Dagger +Scarf +Dolphin +Sombrero +Tin can +Mug +Tap +Harbor seal +Stretcher +Can opener +Goggles +Human body +Roller skates +Coffee cup +Cutting board +Blender +Plumbing fixture +Stop sign +Office supplies +Volleyball +Vase +Slow cooker +Wardrobe +Coffee +Whisk +Paper towel +Personal care +Food +Sun hat +Tree house +Flying disc +Skirt +Gas stove +Salt and pepper shakers +Mechanical fan +Face powder +Fax +Fruit +French fries +Nightstand +Barrel +Kite +Tart +Treadmill +Fox +Flag +Horn +Window blind +Human foot +Golf cart +Jacket +Egg +Street light +Guitar +Pillow +Human leg +Isopod +Grape +Human ear +Power plugs and sockets +Panda +Giraffe +Woman +Door handle +Rhinoceros +Bathtub +Goldfish +Houseplant +Goat +Baseball bat +Baseball glove +Mixing bowl +Marine invertebrates +Kitchen utensil +Light switch +House +Horse +Stationary bicycle +Hammer +Ceiling fan +Sofa bed +Adhesive tape +Harp +Sandal +Bicycle helmet +Saucer +Harpsichord +Human hair +Heater +Harmonica +Hamster +Curtain +Bed +Kettle +Fireplace +Scale +Drinking straw +Insect +Hair dryer +Kitchenware +Indoor rower +Invertebrate +Food processor +Bookcase +Refrigerator +Wood-burning stove +Punching bag +Common fig +Cocktail shaker +Jaguar +Golf ball +Fashion accessory +Alarm clock +Filing cabinet +Artichoke +Table +Tableware +Kangaroo +Koala +Knife +Bottle +Bottle opener +Lynx +Lavender +Lighthouse +Dumbbell +Human head +Bowl +Humidifier +Porch +Lizard +Billiard table +Mammal +Mouse +Motorcycle +Musical instrument +Swim cap +Frying pan +Snowplow +Bathroom cabinet +Missile +Bust +Man +Waffle iron +Milk +Ring binder +Plate +Mobile phone +Baked goods +Mushroom +Crutch +Pitcher +Mirror +Lifejacket +Table tennis racket +Pencil case +Musical keyboard +Scoreboard +Briefcase +Kitchen knife +Nail +Tennis ball +Plastic bag +Oboe +Chest of drawers +Ostrich +Piano +Girl +Plant +Potato +Hair spray +Sports equipment +Pasta +Penguin +Pumpkin +Pear +Infant bed +Polar bear +Mixer +Cupboard +Jacuzzi +Pizza +Digital clock +Pig +Reptile +Rifle +Lipstick +Skateboard +Raven +High heels +Red panda +Rose +Rabbit +Sculpture +Saxophone +Shotgun +Seafood +Submarine sandwich +Snowboard +Sword +Picture frame +Sushi +Loveseat +Ski +Squirrel +Tripod +Stethoscope +Submarine +Scorpion +Segway +Training bench +Snake +Coffee table +Skyscraper +Sheep +Television +Trombone +Tea +Tank +Taco +Telephone +Torch +Tiger +Strawberry +Trumpet +Tree +Tomato +Train +Tool +Picnic basket +Cooking spray +Trousers +Bowling equipment +Football helmet +Truck +Measuring cup +Coffeemaker +Violin +Vehicle +Handbag +Paper cutter +Wine +Weapon +Wheel +Worm +Wok +Whale +Zebra +Auto part +Jug +Pizza cutter +Cream +Monkey +Lion +Bread +Platter +Chicken +Eagle +Helicopter +Owl +Duck +Turtle +Hippopotamus +Crocodile +Toilet +Toilet paper +Squid +Clothing +Footwear +Lemon +Spider +Deer +Frog +Banana +Rocket +Wine glass +Countertop +Tablet computer +Waste container +Swimming pool +Dog +Book +Elephant +Shark +Candle +Leopard +Axe +Hand dryer +Soap dispenser +Porcupine +Flower +Canary +Cheetah +Palm tree +Hamburger +Maple +Building +Fish +Lobster +Asparagus +Furniture +Hedgehog +Airplane +Spoon +Otter +Bull +Oyster +Horizontal bar +Convenience store +Bomb +Bench +Ice cream +Caterpillar +Butterfly +Parachute +Orange +Antelope +Beaker +Moths and butterflies +Window +Closet +Castle +Jellyfish +Goose +Mule +Swan +Peach +Coconut +Seat belt +Raccoon +Chisel +Fork +Lamp +Camera +Squash +Racket +Human face +Human arm +Vegetable +Diaper +Unicycle +Falcon +Chime +Snail +Shellfish +Cabbage +Carrot +Mango +Jeans +Flowerpot +Pineapple +Drawer +Stool +Envelope +Cake +Dragonfly +Sunflower +Microwave oven +Honeycomb +Marine mammal +Sea lion +Ladybug +Shelf +Watch +Candy +Salad +Parrot +Handgun +Sparrow +Van +Grinder +Spice rack +Light bulb +Corded phone +Sports uniform +Tennis racket +Wall clock +Serving tray +Kitchen & dining room table +Dog bed +Cake stand +Cat furniture +Bathroom accessory +Facial tissue holder +Pressure cooker +Kitchen appliance +Tire +Ruler +Luggage and bags +Microphone +Broccoli +Umbrella +Pastry +Grapefruit +Band-aid +Animal +Bell pepper +Turkey +Lily +Pomegranate +Doughnut +Glasses +Human nose +Pen +Ant +Car +Aircraft +Human hand +Skunk +Teddy bear +Watermelon +Cantaloupe +Dishwasher +Flute +Balance beam +Sandwich +Shrimp +Sewing machine +Binoculars +Rays and skates +Ipod +Accordion +Willow +Crab +Crown +Seahorse +Perfume +Alpaca +Taxi +Canoe +Remote control +Wheelchair +Rugby ball +Armadillo +Maracas +Helmet diff --git a/cfg/openimages.data b/cfg/openimages.data new file mode 100644 index 00000000000..fa80e5ab7d8 --- /dev/null +++ b/cfg/openimages.data @@ -0,0 +1,8 @@ +classes= 601 +train = /home/pjreddie/data/openimsv4/openimages.train.list +#valid = coco_testdev +valid = data/coco_val_5k.list +names = data/openimages.names +backup = /home/pjreddie/backup/ +eval=coco + diff --git a/cfg/yolov3-openimages.cfg b/cfg/yolov3-openimages.cfg new file mode 100644 index 00000000000..65d241a74c4 --- /dev/null +++ b/cfg/yolov3-openimages.cfg @@ -0,0 +1,789 @@ +[net] +# Testing + batch=1 + subdivisions=1 +# Training +batch=64 +subdivisions=16 +width=608 +height=608 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=5000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=1818 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=601 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=1818 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=601 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=1818 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=601 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + diff --git a/data/openimages.names b/data/openimages.names new file mode 100644 index 00000000000..ddfd8f22649 --- /dev/null +++ b/data/openimages.names @@ -0,0 +1,601 @@ +Tortoise +Container +Magpie +Sea turtle +Football +Ambulance +Ladder +Toothbrush +Syringe +Sink +Toy +Organ +Cassette deck +Apple +Human eye +Cosmetics +Paddle +Snowman +Beer +Chopsticks +Human beard +Bird +Parking meter +Traffic light +Croissant +Cucumber +Radish +Towel +Doll +Skull +Washing machine +Glove +Tick +Belt +Sunglasses +Banjo +Cart +Ball +Backpack +Bicycle +Home appliance +Centipede +Boat +Surfboard +Boot +Headphones +Hot dog +Shorts +Fast food +Bus +Boy +Screwdriver +Bicycle wheel +Barge +Laptop +Miniskirt +Drill +Dress +Bear +Waffle +Pancake +Brown bear +Woodpecker +Blue jay +Pretzel +Bagel +Tower +Teapot +Person +Bow and arrow +Swimwear +Beehive +Brassiere +Bee +Bat +Starfish +Popcorn +Burrito +Chainsaw +Balloon +Wrench +Tent +Vehicle registration plate +Lantern +Toaster +Flashlight +Billboard +Tiara +Limousine +Necklace +Carnivore +Scissors +Stairs +Computer keyboard +Printer +Traffic sign +Chair +Shirt +Poster +Cheese +Sock +Fire hydrant +Land vehicle +Earrings +Tie +Watercraft +Cabinetry +Suitcase +Muffin +Bidet +Snack +Snowmobile +Clock +Medical equipment +Cattle +Cello +Jet ski +Camel +Coat +Suit +Desk +Cat +Bronze sculpture +Juice +Gondola +Beetle +Cannon +Computer mouse +Cookie +Office building +Fountain +Coin +Calculator +Cocktail +Computer monitor +Box +Stapler +Christmas tree +Cowboy hat +Hiking equipment +Studio couch +Drum +Dessert +Wine rack +Drink +Zucchini +Ladle +Human mouth +Dairy +Dice +Oven +Dinosaur +Ratchet +Couch +Cricket ball +Winter melon +Spatula +Whiteboard +Pencil sharpener +Door +Hat +Shower +Eraser +Fedora +Guacamole +Dagger +Scarf +Dolphin +Sombrero +Tin can +Mug +Tap +Harbor seal +Stretcher +Can opener +Goggles +Human body +Roller skates +Coffee cup +Cutting board +Blender +Plumbing fixture +Stop sign +Office supplies +Volleyball +Vase +Slow cooker +Wardrobe +Coffee +Whisk +Paper towel +Personal care +Food +Sun hat +Tree house +Flying disc +Skirt +Gas stove +Salt and pepper shakers +Mechanical fan +Face powder +Fax +Fruit +French fries +Nightstand +Barrel +Kite +Tart +Treadmill +Fox +Flag +Horn +Window blind +Human foot +Golf cart +Jacket +Egg +Street light +Guitar +Pillow +Human leg +Isopod +Grape +Human ear +Power plugs and sockets +Panda +Giraffe +Woman +Door handle +Rhinoceros +Bathtub +Goldfish +Houseplant +Goat +Baseball bat +Baseball glove +Mixing bowl +Marine invertebrates +Kitchen utensil +Light switch +House +Horse +Stationary bicycle +Hammer +Ceiling fan +Sofa bed +Adhesive tape +Harp +Sandal +Bicycle helmet +Saucer +Harpsichord +Human hair +Heater +Harmonica +Hamster +Curtain +Bed +Kettle +Fireplace +Scale +Drinking straw +Insect +Hair dryer +Kitchenware +Indoor rower +Invertebrate +Food processor +Bookcase +Refrigerator +Wood-burning stove +Punching bag +Common fig +Cocktail shaker +Jaguar +Golf ball +Fashion accessory +Alarm clock +Filing cabinet +Artichoke +Table +Tableware +Kangaroo +Koala +Knife +Bottle +Bottle opener +Lynx +Lavender +Lighthouse +Dumbbell +Human head +Bowl +Humidifier +Porch +Lizard +Billiard table +Mammal +Mouse +Motorcycle +Musical instrument +Swim cap +Frying pan +Snowplow +Bathroom cabinet +Missile +Bust +Man +Waffle iron +Milk +Ring binder +Plate +Mobile phone +Baked goods +Mushroom +Crutch +Pitcher +Mirror +Lifejacket +Table tennis racket +Pencil case +Musical keyboard +Scoreboard +Briefcase +Kitchen knife +Nail +Tennis ball +Plastic bag +Oboe +Chest of drawers +Ostrich +Piano +Girl +Plant +Potato +Hair spray +Sports equipment +Pasta +Penguin +Pumpkin +Pear +Infant bed +Polar bear +Mixer +Cupboard +Jacuzzi +Pizza +Digital clock +Pig +Reptile +Rifle +Lipstick +Skateboard +Raven +High heels +Red panda +Rose +Rabbit +Sculpture +Saxophone +Shotgun +Seafood +Submarine sandwich +Snowboard +Sword +Picture frame +Sushi +Loveseat +Ski +Squirrel +Tripod +Stethoscope +Submarine +Scorpion +Segway +Training bench +Snake +Coffee table +Skyscraper +Sheep +Television +Trombone +Tea +Tank +Taco +Telephone +Torch +Tiger +Strawberry +Trumpet +Tree +Tomato +Train +Tool +Picnic basket +Cooking spray +Trousers +Bowling equipment +Football helmet +Truck +Measuring cup +Coffeemaker +Violin +Vehicle +Handbag +Paper cutter +Wine +Weapon +Wheel +Worm +Wok +Whale +Zebra +Auto part +Jug +Pizza cutter +Cream +Monkey +Lion +Bread +Platter +Chicken +Eagle +Helicopter +Owl +Duck +Turtle +Hippopotamus +Crocodile +Toilet +Toilet paper +Squid +Clothing +Footwear +Lemon +Spider +Deer +Frog +Banana +Rocket +Wine glass +Countertop +Tablet computer +Waste container +Swimming pool +Dog +Book +Elephant +Shark +Candle +Leopard +Axe +Hand dryer +Soap dispenser +Porcupine +Flower +Canary +Cheetah +Palm tree +Hamburger +Maple +Building +Fish +Lobster +Asparagus +Furniture +Hedgehog +Airplane +Spoon +Otter +Bull +Oyster +Horizontal bar +Convenience store +Bomb +Bench +Ice cream +Caterpillar +Butterfly +Parachute +Orange +Antelope +Beaker +Moths and butterflies +Window +Closet +Castle +Jellyfish +Goose +Mule +Swan +Peach +Coconut +Seat belt +Raccoon +Chisel +Fork +Lamp +Camera +Squash +Racket +Human face +Human arm +Vegetable +Diaper +Unicycle +Falcon +Chime +Snail +Shellfish +Cabbage +Carrot +Mango +Jeans +Flowerpot +Pineapple +Drawer +Stool +Envelope +Cake +Dragonfly +Sunflower +Microwave oven +Honeycomb +Marine mammal +Sea lion +Ladybug +Shelf +Watch +Candy +Salad +Parrot +Handgun +Sparrow +Van +Grinder +Spice rack +Light bulb +Corded phone +Sports uniform +Tennis racket +Wall clock +Serving tray +Kitchen & dining room table +Dog bed +Cake stand +Cat furniture +Bathroom accessory +Facial tissue holder +Pressure cooker +Kitchen appliance +Tire +Ruler +Luggage and bags +Microphone +Broccoli +Umbrella +Pastry +Grapefruit +Band-aid +Animal +Bell pepper +Turkey +Lily +Pomegranate +Doughnut +Glasses +Human nose +Pen +Ant +Car +Aircraft +Human hand +Skunk +Teddy bear +Watermelon +Cantaloupe +Dishwasher +Flute +Balance beam +Sandwich +Shrimp +Sewing machine +Binoculars +Rays and skates +Ipod +Accordion +Willow +Crab +Crown +Seahorse +Perfume +Alpaca +Taxi +Canoe +Remote control +Wheelchair +Rugby ball +Armadillo +Maracas +Helmet diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index 1d85b6e4d92..8e2a5479575 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -31,6 +31,7 @@ __device__ float logistic_activate_kernel(float x){return 1./(1. + exp(-x));} __device__ float loggy_activate_kernel(float x){return 2./(1. + exp(-x)) - 1;} __device__ float relu_activate_kernel(float x){return x*(x>0);} __device__ float elu_activate_kernel(float x){return (x >= 0)*x + (x < 0)*(exp(x)-1);} +__device__ float selu_activate_kernel(float x) { return (x >= 0)*1.0507f*x + (x < 0)*1.0507f*1.6732f*(expf(x) - 1); } __device__ float relie_activate_kernel(float x){return (x>0) ? x : .01*x;} __device__ float ramp_activate_kernel(float x){return x*(x>0)+.1*x;} __device__ float leaky_activate_kernel(float x){return (x>0) ? x : .1*x;} @@ -63,6 +64,7 @@ __device__ float loggy_gradient_kernel(float x) } __device__ float relu_gradient_kernel(float x){return (x>0);} __device__ float elu_gradient_kernel(float x){return (x >= 0) + (x < 0)*(x + 1);} +__device__ float selu_gradient_kernel(float x) { return (x >= 0)*1.0507 + (x < 0)*(x + 1.0507*1.6732); } __device__ float relie_gradient_kernel(float x){return (x>0) ? 1 : .01;} __device__ float ramp_gradient_kernel(float x){return (x>0)+.1;} __device__ float leaky_gradient_kernel(float x){return (x>0) ? 1 : .1;} @@ -87,6 +89,8 @@ __device__ float activate_kernel(float x, ACTIVATION a) return relu_activate_kernel(x); case ELU: return elu_activate_kernel(x); + case SELU: + return selu_activate_kernel(x); case RELIE: return relie_activate_kernel(x); case RAMP: @@ -120,6 +124,8 @@ __device__ float gradient_kernel(float x, ACTIVATION a) return relu_gradient_kernel(x); case ELU: return elu_gradient_kernel(x); + case SELU: + return selu_gradient_kernel(x); case RELIE: return relie_gradient_kernel(x); case RAMP: diff --git a/src/activations.c b/src/activations.c index eab4e23d233..c42a67a760f 100644 --- a/src/activations.c +++ b/src/activations.c @@ -16,6 +16,8 @@ char *get_activation_string(ACTIVATION a) return "relu"; case ELU: return "elu"; + case SELU: + return "selu"; case RELIE: return "relie"; case RAMP: @@ -46,6 +48,7 @@ ACTIVATION get_activation(char *s) if (strcmp(s, "loggy")==0) return LOGGY; if (strcmp(s, "relu")==0) return RELU; if (strcmp(s, "elu")==0) return ELU; + if (strcmp(s, "selu") == 0) return SELU; if (strcmp(s, "relie")==0) return RELIE; if (strcmp(s, "plse")==0) return PLSE; if (strcmp(s, "hardtan")==0) return HARDTAN; @@ -72,6 +75,8 @@ float activate(float x, ACTIVATION a) return relu_activate(x); case ELU: return elu_activate(x); + case SELU: + return selu_activate(x); case RELIE: return relie_activate(x); case RAMP: @@ -121,6 +126,8 @@ float gradient(float x, ACTIVATION a) return relu_gradient(x); case ELU: return elu_gradient(x); + case SELU: + return selu_gradient(x); case RELIE: return relie_gradient(x); case RAMP: diff --git a/src/activations.h b/src/activations.h index 1c36ff52544..94c7f37a3f1 100644 --- a/src/activations.h +++ b/src/activations.h @@ -4,7 +4,7 @@ #include "math.h" typedef enum{ - LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN + LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU }ACTIVATION; ACTIVATION get_activation(char *s); @@ -36,6 +36,7 @@ static inline float logistic_activate(float x){return 1./(1. + exp(-x));} static inline float loggy_activate(float x){return 2./(1. + exp(-x)) - 1;} static inline float relu_activate(float x){return x*(x>0);} static inline float elu_activate(float x){return (x >= 0)*x + (x < 0)*(exp(x)-1);} +static inline float selu_activate(float x) { return (x >= 0)*1.0507*x + (x < 0)*1.0507*1.6732*(exp(x) - 1); } static inline float relie_activate(float x){return (x>0) ? x : .01*x;} static inline float ramp_activate(float x){return x*(x>0)+.1*x;} static inline float leaky_activate(float x){return (x>0) ? x : .1*x;} @@ -78,6 +79,7 @@ static inline float stair_gradient(float x) } static inline float relu_gradient(float x){return (x>0);} static inline float elu_gradient(float x){return (x >= 0) + (x < 0)*(x + 1);} +static inline float selu_gradient(float x) { return (x >= 0)*1.0507 + (x < 0)*(x + 1.0507*1.6732); } static inline float relie_gradient(float x){return (x>0) ? 1 : .01;} static inline float ramp_gradient(float x){return (x>0)+.1;} static inline float leaky_gradient(float x){return (x>0) ? 1 : .1;} From 3b59178dec5e9b105b2fba23001c768206da0276 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 18 Oct 2018 00:04:26 +0300 Subject: [PATCH 0175/1365] Minor fix --- src/detector.c | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/detector.c b/src/detector.c index 5926979f0e6..fc560f8048f 100644 --- a/src/detector.c +++ b/src/detector.c @@ -108,7 +108,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i args.small_object = net.small_object; args.d = &buffer; args.type = DETECTION_DATA; - args.threads = 16; // 64 + args.threads = 64; // 16 or 64 args.angle = net.angle; args.exposure = net.exposure; @@ -116,7 +116,8 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i args.hue = net.hue; #ifdef OPENCV - args.threads = 3 * ngpus; + args.threads = 3 * ngpus; // Amazon EC2 Tesla V100: p3.2xlarge (8 logical cores) - p3.16xlarge + //args.threads = 12 * ngpus; // Ryzen 7 2700X (16 logical cores) IplImage* img = NULL; float max_img_loss = 5; int number_of_lines = 100; From acf14899816b892dc1a91f581ec1ce56456d7307 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 18 Oct 2018 16:01:29 +0300 Subject: [PATCH 0176/1365] Fixed detector test for 1-channel images (black-white / infrared) --- src/image.c | 40 +++++++++++++++++++++++++++++++++++++++- 1 file changed, 39 insertions(+), 1 deletion(-) diff --git a/src/image.c b/src/image.c index b564f5b4448..9851968aeca 100644 --- a/src/image.c +++ b/src/image.c @@ -160,6 +160,39 @@ void draw_label(image a, int r, int c, image label, const float *rgb) } } +void draw_box_bw(image a, int x1, int y1, int x2, int y2, int brightness) +{ + //normalize_image(a); + int i; + if (x1 < 0) x1 = 0; + if (x1 >= a.w) x1 = a.w - 1; + if (x2 < 0) x2 = 0; + if (x2 >= a.w) x2 = a.w - 1; + + if (y1 < 0) y1 = 0; + if (y1 >= a.h) y1 = a.h - 1; + if (y2 < 0) y2 = 0; + if (y2 >= a.h) y2 = a.h - 1; + + for (i = x1; i <= x2; ++i) { + a.data[i + y1*a.w + 0 * a.w*a.h] = brightness; + a.data[i + y2*a.w + 0 * a.w*a.h] = brightness; + } + for (i = y1; i <= y2; ++i) { + a.data[x1 + i*a.w + 0 * a.w*a.h] = brightness; + a.data[x2 + i*a.w + 0 * a.w*a.h] = brightness; + } +} + +void draw_box_width_bw(image a, int x1, int y1, int x2, int y2, int w, int brightness) +{ + int i; + for (i = 0; i < w; ++i) { + int alternate_color = (w % 2) ? (brightness) : (255 - brightness); + draw_box_bw(a, x1 + i, y1 + i, x2 - i, y2 - i, alternate_color); + } +} + void draw_box(image a, int x1, int y1, int x2, int y2, float r, float g, float b) { //normalize_image(a); @@ -349,7 +382,12 @@ void draw_detections_v3(image im, detection *dets, int num, float thresh, char * //int b_height = bot - top; //sprintf(labelstr, "%d x %d - w: %d, h: %d", b_x_center, b_y_center, b_width, b_height); - draw_box_width(im, left, top, right, bot, width, red, green, blue); + if (im.c == 1) { + draw_box_width_bw(im, left, top, right, bot, width, 192); // 1 channel Black-White + } + else { + draw_box_width(im, left, top, right, bot, width, red, green, blue); // 3 channels RGB + } if (alphabet) { char labelstr[4096] = { 0 }; strcat(labelstr, names[selected_detections[i].best_class]); From bdbbb2fd6995306de09c003fd16e4e5502d73804 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 18 Oct 2018 17:30:49 +0300 Subject: [PATCH 0177/1365] Minor fix --- src/image.c | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/image.c b/src/image.c index 9851968aeca..8566a4f8eb4 100644 --- a/src/image.c +++ b/src/image.c @@ -160,7 +160,7 @@ void draw_label(image a, int r, int c, image label, const float *rgb) } } -void draw_box_bw(image a, int x1, int y1, int x2, int y2, int brightness) +void draw_box_bw(image a, int x1, int y1, int x2, int y2, float brightness) { //normalize_image(a); int i; @@ -184,11 +184,11 @@ void draw_box_bw(image a, int x1, int y1, int x2, int y2, int brightness) } } -void draw_box_width_bw(image a, int x1, int y1, int x2, int y2, int w, int brightness) +void draw_box_width_bw(image a, int x1, int y1, int x2, int y2, int w, float brightness) { int i; for (i = 0; i < w; ++i) { - int alternate_color = (w % 2) ? (brightness) : (255 - brightness); + float alternate_color = (w % 2) ? (brightness) : (1.0 - brightness); draw_box_bw(a, x1 + i, y1 + i, x2 - i, y2 - i, alternate_color); } } @@ -383,7 +383,7 @@ void draw_detections_v3(image im, detection *dets, int num, float thresh, char * //sprintf(labelstr, "%d x %d - w: %d, h: %d", b_x_center, b_y_center, b_width, b_height); if (im.c == 1) { - draw_box_width_bw(im, left, top, right, bot, width, 192); // 1 channel Black-White + draw_box_width_bw(im, left, top, right, bot, width, 0.8); // 1 channel Black-White } else { draw_box_width(im, left, top, right, bot, width, red, green, blue); // 3 channels RGB From 00282aa1b8831e97f26fd9098e89aa1b94ba6710 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 19 Oct 2018 01:41:53 +0300 Subject: [PATCH 0178/1365] Fixed maxpool_layer by using cuDNN. Added transpose 32 bit on CPU. --- src/gemm.c | 102 ++++++++++++++++++++++++++++++++++- src/maxpool_layer_kernels.cu | 4 +- 2 files changed, 102 insertions(+), 4 deletions(-) diff --git a/src/gemm.c b/src/gemm.c index 771fa75dac6..4c14ade27cf 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -307,6 +307,82 @@ void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, //---------------------------- +// is not used +void transpose_32x32_bits_my(uint32_t *A, uint32_t *B, int lda, int ldb) +{ + unsigned x, y, t; + for (y = 0; y < 32; ++y) { + for (x = 0; x < 32; ++x) { + if (A[y * lda] & (1 << x)) B[x * ldb] |= (uint32_t)1 << y; + } + } +} + +uint8_t reverse_8_bit(uint8_t a) { + return ((a * 0x0802LU & 0x22110LU) | (a * 0x8020LU & 0x88440LU)) * 0x10101LU >> 16; +} + +uint32_t reverse_32_bit(uint32_t a) +{ + // unsigned int __rbit(unsigned int val) // for ARM //__asm__("rbit %0, %1\n" : "=r"(output) : "r"(input)); + return (reverse_8_bit(a >> 24) << 0) | + (reverse_8_bit(a >> 16) << 8) | + (reverse_8_bit(a >> 8) << 16) | + (reverse_8_bit(a >> 0) << 24); +} + +#define swap(a0, a1, j, m) t = (a0 ^ (a1 >>j)) & m; a0 = a0 ^ t; a1 = a1 ^ (t << j); + +void transpose32_optimized(uint32_t A[32]) { + int j, k; + unsigned m, t; + + //m = 0x0000FFFF; + //for (j = 16; j != 0; j = j >> 1, m = m ^ (m << j)) { + // for (k = 0; k < 32; k = (k + j + 1) & ~j) { + // t = (A[k] ^ (A[k + j] >> j)) & m; + // A[k] = A[k] ^ t; + // A[k + j] = A[k + j] ^ (t << j); + // } + //} + + j = 16; + m = 0x0000FFFF; + for (k = 0; k < 32; k = (k + j + 1) & ~j) { swap(A[k], A[k + j], j, m); } + + j = 8; + m = 0x00ff00ff; + for (k = 0; k < 32; k = (k + j + 1) & ~j) { swap(A[k], A[k + j], j, m); } + + j = 4; + m = 0x0f0f0f0f; + for (k = 0; k < 32; k = (k + j + 1) & ~j) { swap(A[k], A[k + j], j, m); } + + j = 2; + m = 0x33333333; + for (k = 0; k < 32; k = (k + j + 1) & ~j) { swap(A[k], A[k + j], j, m); } + + j = 1; + m = 0x55555555; + for (k = 0; k < 32; k = (k + j + 1) & ~j) { swap(A[k], A[k + j], j, m); } + + // reverse Y + for (j = 0; j < 16; ++j) { + uint32_t tmp = A[j]; + A[j] = reverse_32_bit(A[31 - j]); + A[31 - j] = reverse_32_bit(tmp); + } +} + +void transpose_32x32_bits_reversed_diagonale(uint32_t *A, uint32_t *B, int m, int n) +{ + unsigned A_tmp[32]; + int i; + for (i = 0; i < 32; ++i) A_tmp[i] = A[i * m]; + transpose32_optimized(A_tmp); + for (i = 0; i < 32; ++i) B[i*n] = A_tmp[i]; +} + void transpose_8x8_bits_my(unsigned char *A, unsigned char *B, int lda, int ldb) { @@ -363,6 +439,8 @@ void transpose8rS32_reversed_diagonale(unsigned char* A, int m, int n, unsigned B[3 * n] = reverse_byte(y >> 24); B[2 * n] = reverse_byte(y >> 16); B[1 * n] = reverse_byte(y >> 8); B[0 * n] = reverse_byte(y); } +/* +// transpose by 8-bit void transpose_bin(char *A, char *B, const int n, const int m, const int lda, const int ldb, const int block_size) { @@ -381,11 +459,33 @@ void transpose_bin(char *A, char *B, const int n, const int m, } } } +*/ + +// transpose by 32-bit +void transpose_bin(uint32_t *A, uint32_t *B, const int n, const int m, + const int lda, const int ldb, const int block_size) +{ + //printf("\n n = %d (n mod 32 = %d), m = %d (m mod 32 = %d) \n", n, n % 32, m, m % 32); + int i; + #pragma omp parallel for + for (i = 0; i < n; i += 32) { + int j; + for (j = 0; j < m - 32; j += 32) { + int a_index = i*lda + j; + int b_index = j*ldb + i; + transpose_32x32_bits_reversed_diagonale(&A[a_index / 32], &B[b_index / 32], lda / 32, ldb / 32); + //transpose_32x32_bits_my(&A[a_index/32], &B[b_index/32], lda/32, ldb/32); + } + for (; j < m; ++j) { + if (get_bit(A, i*lda + j)) set_bit(B, j*ldb + i); + } + } +} //---------------------------- -#if (defined(__AVX__) && defined(__x86_64__)) || defined(_WIN64) +#if (defined(__AVX__) && defined(__x86_64__)) || defined(_WIN641) #ifdef _WIN64 #include diff --git a/src/maxpool_layer_kernels.cu b/src/maxpool_layer_kernels.cu index 05d5b4ffa95..691b0f809d5 100644 --- a/src/maxpool_layer_kernels.cu +++ b/src/maxpool_layer_kernels.cu @@ -88,7 +88,7 @@ extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network_state sta { #ifdef CUDNN - if (!state.train) {// && layer.stride == layer.size) { + if (!state.train && layer.stride == layer.size) { // cudnnPoolingBackward cudnnStatus_t maxpool_status; @@ -111,8 +111,6 @@ extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network_state sta } #endif - - int h = layer.out_h; int w = layer.out_w; int c = layer.c; From e7ad4e6c77b8b9f9bc65c80073dd7b89eaeece4c Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 19 Oct 2018 02:44:54 +0300 Subject: [PATCH 0179/1365] Compile fix --- src/gemm.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/gemm.h b/src/gemm.h index 90b8ce99dcf..e297537fc6c 100644 --- a/src/gemm.h +++ b/src/gemm.h @@ -27,7 +27,7 @@ void float_to_bit(float *src, unsigned char *dst, size_t size); void transpose_block_SSE4x4(float *A, float *B, const int n, const int m, const int lda, const int ldb, const int block_size); -void transpose_bin(char *A, char *B, const int n, const int m, +void transpose_bin(uint32_t *A, uint32_t *B, const int n, const int m, const int lda, const int ldb, const int block_size); void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, From 9e2c894a32fa7cdfd1ee115be8e4184df3c96dbe Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 19 Oct 2018 16:07:15 +0300 Subject: [PATCH 0180/1365] Transpose on CPU fix --- src/convolutional_layer.c | 3 ++- src/gemm.c | 20 ++++++++++++-------- 2 files changed, 14 insertions(+), 9 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index d4884f0cb58..005bdcdd718 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -654,7 +654,8 @@ void binary_align_weights(convolutional_layer *l) size_t binary_transpose_align_input(int k, int n, float *b, char **t_bit_input, size_t ldb_align, int bit_align) { size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; - size_t t_intput_size = new_ldb * n; + //printf("\n n = %d, bit_align = %d \n", n, bit_align); + size_t t_intput_size = new_ldb * bit_align;// n; size_t t_bit_input_size = t_intput_size / 8;// +1; *t_bit_input = calloc(t_bit_input_size, sizeof(char)); diff --git a/src/gemm.c b/src/gemm.c index 4c14ade27cf..93ffe5a2fc9 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -390,6 +390,7 @@ void transpose_8x8_bits_my(unsigned char *A, unsigned char *B, int lda, int ldb) for (y = 0; y < 8; ++y) { for (x = 0; x < 8; ++x) { if (A[y * lda] & (1 << x)) B[x * ldb] |= 1 << y; + //B[x * ldb] = 1; } } } @@ -417,13 +418,14 @@ unsigned char reverse_byte_3(unsigned char n) { } -void transpose8rS32_reversed_diagonale(unsigned char* A, int m, int n, unsigned char* B) +void transpose8rS32_reversed_diagonale(unsigned char* A, unsigned char* B, int m, int n) { unsigned x, y, t; + x = y = 0; // Load the array and pack it into x and y. - x = (A[0] << 24) | (A[m] << 16) | (A[2 * m] << 8) | A[3 * m]; - y = (A[4 * m] << 24) | (A[5 * m] << 16) | (A[6 * m] << 8) | A[7 * m]; + //x = (A[0] << 24) | (A[m] << 16) | (A[2 * m] << 8) | A[3 * m]; + //y = (A[4 * m] << 24) | (A[5 * m] << 16) | (A[6 * m] << 8) | A[7 * m]; t = (x ^ (x >> 7)) & 0x00AA00AA; x = x ^ t ^ (t << 7); t = (y ^ (y >> 7)) & 0x00AA00AA; y = y ^ t ^ (t << 7); @@ -444,15 +446,16 @@ void transpose8rS32_reversed_diagonale(unsigned char* A, int m, int n, unsigned void transpose_bin(char *A, char *B, const int n, const int m, const int lda, const int ldb, const int block_size) { + //printf("\n n = %d, ldb = %d \t\t m = %d, lda = %d \n", n, ldb, m, lda); int i; #pragma omp parallel for for (i = 0; i < n; i += 8) { int j; - for (j = 0; j < m - 8; j += 8) { + for (j = 0; j < m; j += 8) { int a_index = i*lda + j; int b_index = j*ldb + i; //transpose_8x8_bits_my(&A[a_index/8], &B[b_index/8], lda/8, ldb/8); - transpose8rS32_reversed_diagonale(&A[a_index / 8], lda / 8, ldb / 8, &B[b_index / 8]); + transpose8rS32_reversed_diagonale(&A[a_index / 8], &B[b_index / 8], lda / 8, ldb / 8); } for (; j < m; ++j) { if (get_bit(A, i*lda + j)) set_bit(B, j*ldb + i); @@ -461,16 +464,18 @@ void transpose_bin(char *A, char *B, const int n, const int m, } */ + // transpose by 32-bit void transpose_bin(uint32_t *A, uint32_t *B, const int n, const int m, const int lda, const int ldb, const int block_size) { //printf("\n n = %d (n mod 32 = %d), m = %d (m mod 32 = %d) \n", n, n % 32, m, m % 32); + //printf("\n lda = %d (lda mod 32 = %d), ldb = %d (ldb mod 32 = %d) \n", lda, lda % 32, ldb, ldb % 32); int i; #pragma omp parallel for for (i = 0; i < n; i += 32) { int j; - for (j = 0; j < m - 32; j += 32) { + for (j = 0; j < m; j += 32) { int a_index = i*lda + j; int b_index = j*ldb + i; transpose_32x32_bits_reversed_diagonale(&A[a_index / 32], &B[b_index / 32], lda / 32, ldb / 32); @@ -481,11 +486,10 @@ void transpose_bin(uint32_t *A, uint32_t *B, const int n, const int m, } } } - //---------------------------- -#if (defined(__AVX__) && defined(__x86_64__)) || defined(_WIN641) +#if (defined(__AVX__) && defined(__x86_64__)) || defined(_WIN64) #ifdef _WIN64 #include From d487bdf47145a9899c2c01b88adbff9b2261a917 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 19 Oct 2018 22:55:25 +0300 Subject: [PATCH 0181/1365] transpose 32x32 on GPU --- src/convolutional_layer.c | 3 + src/gemm.c | 3 +- src/im2col_kernels.cu | 142 ++++++++++++++++++++++++++++++++++---- 3 files changed, 134 insertions(+), 14 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 005bdcdd718..5a3b32b4ff0 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -160,6 +160,9 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) // 3. FP32 Master Copy of Weights // More: http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#tensor_ops cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH); +#if((CUDNN_MAJOR*10 + CUDNN_MINOR) >= 72) // cuDNN >= 7.2 + cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION); +#endif #endif // INT8_CONFIG, INT8_EXT_CONFIG, INT8x4_CONFIG and INT8x4_EXT_CONFIG are only supported diff --git a/src/gemm.c b/src/gemm.c index 93ffe5a2fc9..8c6851ce029 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -378,8 +378,10 @@ void transpose_32x32_bits_reversed_diagonale(uint32_t *A, uint32_t *B, int m, in { unsigned A_tmp[32]; int i; + #pragma unroll for (i = 0; i < 32; ++i) A_tmp[i] = A[i * m]; transpose32_optimized(A_tmp); + #pragma unroll for (i = 0; i < 32; ++i) B[i*n] = A_tmp[i]; } @@ -390,7 +392,6 @@ void transpose_8x8_bits_my(unsigned char *A, unsigned char *B, int lda, int ldb) for (y = 0; y < 8; ++y) { for (x = 0; x < 8; ++x) { if (A[y * lda] & (1 << x)) B[x * ldb] |= 1 << y; - //B[x * ldb] = 1; } } } diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 0d6f1b156bd..1dc660f4ff4 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -647,7 +647,13 @@ __device__ __host__ unsigned char reverse_byte_2(unsigned char a) return ((a * 0x0802LU & 0x22110LU) | (a * 0x8020LU & 0x88440LU)) * 0x10101LU >> 16; } -__device__ __host__ void transpose8rS32_reversed_diagonale(unsigned char* A, int m, int n, unsigned char* B) +__device__ unsigned char reverse_byte_CUDA(unsigned char a) +{ + uint32_t tmp = __brev(a); + return tmp >> 24; +} + +__device__ void transpose8rS32_reversed_diagonale(unsigned char* A, unsigned char* B, int m, int n) { unsigned x, y, t; @@ -665,8 +671,8 @@ __device__ __host__ void transpose8rS32_reversed_diagonale(unsigned char* A, int y = ((x << 4) & 0xF0F0F0F0) | (y & 0x0F0F0F0F); x = t; - B[7 * n] = reverse_byte(x >> 24); B[6 * n] = reverse_byte(x >> 16); B[5 * n] = reverse_byte(x >> 8); B[4 * n] = reverse_byte(x); - B[3 * n] = reverse_byte(y >> 24); B[2 * n] = reverse_byte(y >> 16); B[1 * n] = reverse_byte(y >> 8); B[0 * n] = reverse_byte(y); + B[7 * n] = reverse_byte_CUDA(x >> 24); B[6 * n] = reverse_byte_CUDA(x >> 16); B[5 * n] = reverse_byte_CUDA(x >> 8); B[4 * n] = reverse_byte_CUDA(x); + B[3 * n] = reverse_byte_CUDA(y >> 24); B[2 * n] = reverse_byte_CUDA(y >> 16); B[1 * n] = reverse_byte_CUDA(y >> 8); B[0 * n] = reverse_byte_CUDA(y); //__device__ ​ unsigned int __brev(unsigned int x) //Reverse the bit order of a 32 bit unsigned integer. @@ -674,6 +680,7 @@ __device__ __host__ void transpose8rS32_reversed_diagonale(unsigned char* A, int } +// transpose 8x8 bit __global__ void transpose_bin_gpu_kernel(unsigned char *A, unsigned char *B, const int n, const int m, const int lda, const int ldb, const int block_size) { @@ -687,28 +694,137 @@ __global__ void transpose_bin_gpu_kernel(unsigned char *A, unsigned char *B, con //for (j = 0; j < m - 8; j += 8) { j = ((index * 8) / n) * 8; - if (j < m - 8) { + if (j < m) { int a_index = i*lda + j; int b_index = j*ldb + i; - transpose8rS32_reversed_diagonale(&A[a_index / 8], lda / 8, ldb / 8, &B[b_index / 8]); - } - else if (j < m) { - for (; j < m; ++j) { - if (get_bit(A, i*lda + j)) set_bit(B, j*ldb + i); - else remove_bit(B, j*ldb + i); - } + transpose8rS32_reversed_diagonale(&A[a_index / 8], &B[b_index / 8], lda / 8, ldb / 8); } + //else if (j < m) { + // for (; j < m; ++j) { + // if (get_bit(A, i*lda + j)) set_bit(B, j*ldb + i); + // else remove_bit(B, j*ldb + i); + // } + //} } } } + +__device__ __host__ uint8_t reverse_8_bit(uint8_t a) { + return ((a * 0x0802LU & 0x22110LU) | (a * 0x8020LU & 0x88440LU)) * 0x10101LU >> 16; +} + +__device__ uint32_t reverse_32_bit(uint32_t a) +{ + // __device__ ​ unsigned int __brev(unsigned int x) // CUDA + // unsigned int __rbit(unsigned int val) // for ARM //__asm__("rbit %0, %1\n" : "=r"(output) : "r"(input)); + return __brev(a); + //return (reverse_8_bit(a >> 24) << 0) | + // (reverse_8_bit(a >> 16) << 8) | + // (reverse_8_bit(a >> 8) << 16) | + // (reverse_8_bit(a >> 0) << 24); +} + +#define swap(a0, a1, j, m) t = (a0 ^ (a1 >>j)) & m; a0 = a0 ^ t; a1 = a1 ^ (t << j); + +__device__ void transpose32_optimized(uint32_t A[32]) { + int j, k; + unsigned m, t; + + //m = 0x0000FFFF; + //for (j = 16; j != 0; j = j >> 1, m = m ^ (m << j)) { + // for (k = 0; k < 32; k = (k + j + 1) & ~j) { + // t = (A[k] ^ (A[k + j] >> j)) & m; + // A[k] = A[k] ^ t; + // A[k + j] = A[k + j] ^ (t << j); + // } + //} + + j = 16; + m = 0x0000FFFF; + for (k = 0; k < 32; k = (k + j + 1) & ~j) { swap(A[k], A[k + j], j, m); } + + j = 8; + m = 0x00ff00ff; + for (k = 0; k < 32; k = (k + j + 1) & ~j) { swap(A[k], A[k + j], j, m); } + + j = 4; + m = 0x0f0f0f0f; + for (k = 0; k < 32; k = (k + j + 1) & ~j) { swap(A[k], A[k + j], j, m); } + + j = 2; + m = 0x33333333; + for (k = 0; k < 32; k = (k + j + 1) & ~j) { swap(A[k], A[k + j], j, m); } + + j = 1; + m = 0x55555555; + for (k = 0; k < 32; k = (k + j + 1) & ~j) { swap(A[k], A[k + j], j, m); } + + // reverse Y + for (j = 0; j < 16; ++j) { + uint32_t tmp = A[j]; + A[j] = reverse_32_bit(A[31 - j]); + A[31 - j] = reverse_32_bit(tmp); + } +} + +#define BLOCK_TRANSPOSE32 256 + +__device__ void transpose_32x32_bits_reversed_diagonale(uint32_t *A, uint32_t *B, int m, int n) +{ + //unsigned A_tmp[32]; + //int i; + //#pragma unroll + //for (i = 0; i < 32; ++i) A_tmp[i] = A[i * m]; + //transpose32_optimized(A_tmp); + //#pragma unroll + //for (i = 0; i < 32; ++i) B[i*n] = A_tmp[i]; + + __shared__ uint32_t A_shared[32 * BLOCK_TRANSPOSE32]; + uint32_t *A_tmp = &A_shared[32 * threadIdx.x]; + + int i; + #pragma unroll 32 + for (i = 0; i < 32; ++i) A_tmp[i] = A[i * m]; + transpose32_optimized(A_tmp); + #pragma unroll 32 + for (i = 0; i < 32; ++i) B[i*n] = A_tmp[i]; +} + + +// transpose 32x32 bit +__global__ void transpose_bin_gpu_kernel_32(uint32_t *A, uint32_t *B, const int n, const int m, + const int lda, const int ldb, const int block_size) +{ + int i; + int index = (blockIdx.x*blockDim.x + threadIdx.x) * 32; + + //for (i = 0; i < n; i += 8) + { + i = index % n; + int j; + //for (j = 0; j < m - 8; j += 8) + { + j = (index / n) * 32; + if (j < m) { + int a_index = i*lda + j; + int b_index = j*ldb + i; + transpose_32x32_bits_reversed_diagonale(&A[a_index / 32], &B[b_index / 32], lda / 32, ldb / 32); + } + } + } +} + void transpose_bin_gpu(unsigned char *A, unsigned char *B, const int n, const int m, const int lda, const int ldb, const int block_size) { - size_t size = n*m/64 + 1; + size_t size = n*m/ (8*8) + 1; + size_t size32 = n*m / (32*32) + 1; const int num_blocks = size / BLOCK + 1; - transpose_bin_gpu_kernel << > >(A, B, n, m, lda, ldb, block_size); + const int num_blocks32 = size32 / BLOCK_TRANSPOSE32 + 1; + transpose_bin_gpu_kernel_32 << > >((uint32_t *)A, (uint32_t *)B, n, m, lda, ldb, block_size); + //transpose_bin_gpu_kernel << > >(A, B, n, m, lda, ldb, block_size); } // -------------------------------- From 31ac46ba22112c8122d84a6a2b00db3a1ecb92cb Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 23 Oct 2018 21:59:57 +0300 Subject: [PATCH 0182/1365] Fixed bug for 32-bit compilation without GPU. --- build/darknet/darknet_no_gpu.vcxproj | 21 +++++++++++++++------ src/convolutional_layer.c | 2 +- src/gemm.c | 25 ++++++++++++++++++++----- src/layer.c | 10 ++++++++-- src/yolo_layer.c | 22 ++++++++++++---------- 5 files changed, 56 insertions(+), 24 deletions(-) diff --git a/build/darknet/darknet_no_gpu.vcxproj b/build/darknet/darknet_no_gpu.vcxproj index 9e663910379..8b173015256 100644 --- a/build/darknet/darknet_no_gpu.vcxproj +++ b/build/darknet/darknet_no_gpu.vcxproj @@ -78,9 +78,15 @@ Level3 Disabled true + C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories); + _CRTDBG_MAP_ALLOC;_MBCS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + OPENCV; + stdlib.h;crtdbg.h;%(ForcedIncludeFiles) true + C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;%(AdditionalLibraryDirectories) + ..\..\3rdparty\lib\x86\pthreadVC2.lib;%(AdditionalDependencies) @@ -89,9 +95,10 @@ Disabled true C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories) - _MBCS;OPENCV;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) - CUDNN + _MBCS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + OPENCV;CUDNN true + stdlib.h;crtdbg.h;%(ForcedIncludeFiles) true @@ -111,15 +118,17 @@ true true true - C:\opencv_2.4.9\opencv\build\include;..\..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include - OPENCV;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories); + _TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + + true true true - C:\opencv_2.4.9\opencv\build\x64\vc12\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) - ..\..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;cudnn.lib;%(AdditionalDependencies) + C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;%(AdditionalLibraryDirectories) + ..\..\3rdparty\lib\x86\pthreadVC2.lib;%(AdditionalDependencies) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 5a3b32b4ff0..1fda099b6ab 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -615,7 +615,7 @@ void binary_align_weights(convolutional_layer *l) binarize_weights(l->weights, m, k, l->binary_weights); size_t align_weights_size = new_lda * m; - l->align_bit_weights_size = align_weights_size / 8;// +1; + l->align_bit_weights_size = align_weights_size / 8 + 1; float *align_weights = calloc(align_weights_size, sizeof(float)); l->align_bit_weights = calloc(l->align_bit_weights_size, sizeof(char)); diff --git a/src/gemm.c b/src/gemm.c index 8c6851ce029..6f021e73904 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -1719,6 +1719,25 @@ void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride, } } +static inline int popcnt_64(uint64_t val64) { +#ifdef WIN32 // Windows +#ifdef _WIN64 // Windows 64-bit + int tmp_count = __popcnt64(val64); +#else // Windows 32-bit + int tmp_count = __popcnt(val64); + tmp_count += __popcnt(val64 >> 32); +#endif +#else // Linux +#ifdef __x86_64__ // Linux 64-bit + int tmp_count = __builtin_popcountll(val64); +#else // Linux 32-bit + int tmp_count = __builtin_popcount(val64); + tmp_count += __builtin_popcount(val64); +#endif +#endif + return tmp_count; +} + void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, unsigned char *A, int lda, unsigned char *B, int ldb, @@ -1739,11 +1758,7 @@ void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); -#ifdef WIN32 - int tmp_count = __popcnt64(c_bit64); -#else - int tmp_count = __builtin_popcountll(c_bit64); -#endif + int tmp_count = popcnt_64(c_bit64); if (K - k < 64) tmp_count = tmp_count - (64 - (K - k)); // remove extra bits count += tmp_count; diff --git a/src/layer.c b/src/layer.c index caa9c4c99c8..6749f1b0c0b 100644 --- a/src/layer.c +++ b/src/layer.c @@ -33,8 +33,8 @@ void free_layer(layer l) if (l.scale_updates) free(l.scale_updates); if (l.weights) free(l.weights); if (l.weight_updates) free(l.weight_updates); - if (l.weights) free(l.align_bit_weights); - if (l.weights) free(l.mean_arr); + if (l.align_bit_weights) free(l.align_bit_weights); + if (l.mean_arr) free(l.mean_arr); if (l.delta) free(l.delta); if (l.output) free(l.output); if (l.squared) free(l.squared); @@ -84,6 +84,12 @@ void free_layer(layer l) if (l.mean_delta_gpu) cuda_free(l.mean_delta_gpu); if (l.x_gpu) cuda_free(l.x_gpu); if (l.x_norm_gpu) cuda_free(l.x_norm_gpu); + + if (l.align_bit_weights_gpu) cuda_free(l.align_bit_weights_gpu); + if (l.mean_arr_gpu) cuda_free(l.mean_arr_gpu); + if (l.align_workspace_gpu) cuda_free(l.align_workspace_gpu); + if (l.transposed_align_workspace_gpu) cuda_free(l.transposed_align_workspace_gpu); + if (l.weights_gpu) cuda_free(l.weights_gpu); if (l.weight_updates_gpu) cuda_free(l.weight_updates_gpu); if (l.weights_gpu16) cuda_free(l.weights_gpu16); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index de006429e48..d5ba8d8a0ae 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -374,17 +374,19 @@ int get_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, for(n = 0; n < l.n; ++n){ int obj_index = entry_index(l, 0, n*l.w*l.h + i, 4); float objectness = predictions[obj_index]; - if(objectness <= thresh) continue; - int box_index = entry_index(l, 0, n*l.w*l.h + i, 0); - dets[count].bbox = get_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h); - dets[count].objectness = objectness; - dets[count].classes = l.classes; - for(j = 0; j < l.classes; ++j){ - int class_index = entry_index(l, 0, n*l.w*l.h + i, 4 + 1 + j); - float prob = objectness*predictions[class_index]; - dets[count].prob[j] = (prob > thresh) ? prob : 0; + //if(objectness <= thresh) continue; + if (objectness > thresh) { + int box_index = entry_index(l, 0, n*l.w*l.h + i, 0); + dets[count].bbox = get_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h); + dets[count].objectness = objectness; + dets[count].classes = l.classes; + for (j = 0; j < l.classes; ++j) { + int class_index = entry_index(l, 0, n*l.w*l.h + i, 4 + 1 + j); + float prob = objectness*predictions[class_index]; + dets[count].prob[j] = (prob > thresh) ? prob : 0; + } + ++count; } - ++count; } } correct_yolo_boxes(dets, count, w, h, netw, neth, relative, letter); From f81b9209ff4d5f665bdf7eafa36985aff0a9de76 Mon Sep 17 00:00:00 2001 From: Vincent Liao Date: Wed, 24 Oct 2018 12:47:19 +0700 Subject: [PATCH 0183/1365] fix wrong path --- darknet.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/darknet.py b/darknet.py index 4bca5f5a3b9..84bbfc4255a 100644 --- a/darknet.py +++ b/darknet.py @@ -278,7 +278,7 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False): metaMain = None altNames = None -def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yolov3.cfg", weightPath = "yolov3.weights", metaPath= "./data/coco.data", showImage= True, makeImageOnly = False, initOnly= False): +def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yolov3.cfg", weightPath = "yolov3.weights", metaPath= "./cfg/coco.data", showImage= True, makeImageOnly = False, initOnly= False): """ Convenience function to handle the detection and returns of objects. From 31df5e27356b6b11ffd43baace9afdd3800a8aa2 Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 25 Oct 2018 21:39:27 +0300 Subject: [PATCH 0184/1365] Update Readme.md --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index 2917ebef07a..90cb14eefab 100644 --- a/README.md +++ b/README.md @@ -473,6 +473,9 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * `train_network_height * train_obj_height / train_image_height ~= detection_network_height * detection_obj_height / detection_image_height` * to speedup training (with decreasing detection accuracy) do Fine-Tuning instead of Transfer-Learning, set param `stopbackward=1` here: https://github.com/AlexeyAB/darknet/blob/6d44529cf93211c319813c90e0c1adb34426abe5/cfg/yolov3.cfg#L548 + then do this command: `./darknet partial cfg/yolov3.cfg yolov3.weights yolov3.conv.81 81` will be created file `yolov3.conv.81`, + then train by using weights file `yolov3.conv.81` instead of `darknet53.conv.74` + 2. After training - for detection: From 08e8e0c8c2d3cddfb6ee065e813ae5023f9568d2 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 28 Oct 2018 23:58:08 +0300 Subject: [PATCH 0185/1365] Minor fix --- src/layer.c | 1 + src/yolo_layer.c | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/src/layer.c b/src/layer.c index 6749f1b0c0b..c76a16ffa49 100644 --- a/src/layer.c +++ b/src/layer.c @@ -54,6 +54,7 @@ void free_layer(layer l) if (l.r_cpu) free(l.r_cpu); if (l.h_cpu) free(l.h_cpu); if (l.binary_input) free(l.binary_input); + if (l.loss) free(l.loss); #ifdef GPU if (l.indexes_gpu) cuda_free((float *)l.indexes_gpu); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index d5ba8d8a0ae..090b47212ac 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -374,7 +374,7 @@ int get_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, for(n = 0; n < l.n; ++n){ int obj_index = entry_index(l, 0, n*l.w*l.h + i, 4); float objectness = predictions[obj_index]; - //if(objectness <= thresh) continue; + //if(objectness <= thresh) continue; // incorrect behavior for Nan values if (objectness > thresh) { int box_index = entry_index(l, 0, n*l.w*l.h + i, 0); dets[count].bbox = get_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h); From 2c5e383c04655fe45f3f533eb3a69a80acbf3561 Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 1 Nov 2018 03:32:31 +0300 Subject: [PATCH 0186/1365] Update Readme.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 90cb14eefab..ad506796edb 100644 --- a/README.md +++ b/README.md @@ -481,8 +481,8 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * Increase network-resolution by set in your `.cfg`-file (`height=608` and `width=608`) or (`height=832` and `width=832`) or (any value multiple of 32) - this increases the precision and makes it possible to detect small objects: [link](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L8-L9) - * you do not need to train the network again, just use `.weights`-file already trained for 416x416 resolution - * if error `Out of memory` occurs then in `.cfg`-file you should increase `subdivisions=16`, 32 or 64: [link](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4) + * it is not necessary to train the network again, just use `.weights`-file already trained for 416x416 resolution + * but to get even greater accuracy you should train with higher resolution 608x608 or 832x832, note: if error `Out of memory` occurs then in `.cfg`-file you should increase `subdivisions=16`, 32 or 64: [link](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4) ## How to mark bounded boxes of objects and create annotation files: From 25f65f6878f713a7d0b7faabe94a7564d9a1ec08 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 5 Nov 2018 22:38:35 +0300 Subject: [PATCH 0187/1365] Added fast_binarize_weights_gpu() --- src/convolutional_kernels.cu | 63 +++++++++++++++++++++++++++++++++--- src/convolutional_layer.c | 8 +++-- src/im2col_kernels.cu | 32 ++++++++++++++++++ src/network.c | 6 ++-- 4 files changed, 99 insertions(+), 10 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 06ca96eb71f..feaffa0c64f 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -61,18 +61,17 @@ void binarize_input_gpu(float *input, int n, int size, float *binary) check_error(cudaPeekAtLastError()); } - __global__ void binarize_weights_kernel(float *weights, int n, int size, float *binary) { int f = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (f >= n) return; int i = 0; float mean = 0; - for(i = 0; i < size; ++i){ + for (i = 0; i < size; ++i) { mean += fabs(weights[f*size + i]); } mean = mean / size; - for(i = 0; i < size; ++i){ + for (i = 0; i < size; ++i) { binary[f*size + i] = (weights[f*size + i] > 0) ? mean : -mean; //binary[f*size + i] = weights[f*size + i]; } @@ -80,10 +79,62 @@ __global__ void binarize_weights_kernel(float *weights, int n, int size, float * void binarize_weights_gpu(float *weights, int n, int size, float *binary) { - binarize_weights_kernel<<>>(weights, n, size, binary); + binarize_weights_kernel << > >(weights, n, size, binary); check_error(cudaPeekAtLastError()); } +#define WARP_SIZE 32 + +__global__ void set_zero_kernel(float *src, int size) +{ + int i = blockIdx.x * blockDim.x + threadIdx.x; + if (i < size) src[i] = 0; +} + +__inline__ __device__ +float warpAllReduceSum(float val) { + for (int mask = WARP_SIZE / 2; mask > 0; mask /= 2) + val += __shfl_xor(val, mask); + return val; +} + +// only if (size % 32 == 0) +__global__ void reduce_kernel(float *weights, int n, int size, float *mean_arr_gpu) +{ + int i = blockIdx.x * blockDim.x + threadIdx.x; + int f = i / size; + if (f >= n) return; + float warp_mean = warpAllReduceSum(fabs(weights[i])); + if(i % 32 == 0) + atomicAdd(&mean_arr_gpu[f], warp_mean / size); +} + +__global__ void binarize_weights_mean_kernel(float *weights, int n, int size, float *binary, float *mean_arr_gpu) +{ + int i = blockIdx.x * blockDim.x + threadIdx.x; + int f = i / size; + if (f >= n) return; + float mean = mean_arr_gpu[f]; + binary[i] = (weights[i] > 0) ? mean : -mean; +} + +void fast_binarize_weights_gpu(float *weights, int n, int size, float *binary, float *mean_arr_gpu) +{ + if (size % 32 == 0) { + size_t gridsize = n * size; + const int num_blocks = gridsize / BLOCK + 1; + + set_zero_kernel << <(n/BLOCK + 1), BLOCK >> > (mean_arr_gpu, n); + reduce_kernel << > > (weights, n, size, mean_arr_gpu); + binarize_weights_mean_kernel << > > (weights, n, size, binary, mean_arr_gpu); + check_error(cudaPeekAtLastError()); + } + else { + binarize_weights_gpu(weights, n, size, binary); + } +} + + __global__ void cuda_f32_to_f16(float* input_f32, size_t size, half *output_f16) { int idx = blockIdx.x * blockDim.x + threadIdx.x; @@ -128,7 +179,9 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) if(l.xnor){ if (!l.align_bit_weights_gpu || state.train) { - binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu); + //binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu); + + fast_binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu, l.mean_arr_gpu); } //swap_binary(&l); //binarize_gpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input_gpu); diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 1fda099b6ab..8e83758c231 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -314,6 +314,8 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int int align = 32;// 8; int src_align = l.out_h*l.out_w; l.bit_align = src_align + (align - src_align % align); + + l.mean_arr = calloc(l.n, sizeof(float)); } if(batch_normalize){ @@ -369,6 +371,7 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int } if(xnor){ l.binary_weights_gpu = cuda_make_array(l.weights, c*n*size*size); + l.mean_arr_gpu = cuda_make_array(0, l.n); l.binary_input_gpu = cuda_make_array(0, l.inputs*l.batch); } @@ -628,7 +631,7 @@ void binary_align_weights(convolutional_layer *l) } float_to_bit(align_weights, l->align_bit_weights, align_weights_size); - l->mean_arr = calloc(l->n, sizeof(float)); + //l->mean_arr = calloc(l->n, sizeof(float)); get_mean_array(align_weights, align_weights_size, l->n, l->mean_arr); #ifdef GPU @@ -646,7 +649,8 @@ void binary_align_weights(convolutional_layer *l) status = cudaMemcpy(l->binary_weights_gpu, l->binary_weights, m*k*sizeof(float), cudaMemcpyHostToDevice); check_error(status); - l->mean_arr_gpu = cuda_make_array(l->mean_arr, l->n); + //l->mean_arr_gpu = cuda_make_array(l->mean_arr, l->n); + cuda_push_array(l->mean_arr_gpu, l->mean_arr, l->n); cudaDeviceSynchronize(); #endif // GPU diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 1dc660f4ff4..1149b68b49f 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -1123,6 +1123,38 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int int count = 0; k = 0; +#ifdef NOT_USED + // 32 thread X 256 bit = 8192 bit + for (; k < (K - 8192); k += 8192) { // l.size*l.size*l.c - one filter size [27 - 9216] + ulonglong4 c_bit256; + + //int64_t A_cur_index = (i*lda + k) / 8; + int64_t A_cur_index = (local_i*lda + k) / 8; + int64_t B_cur_index = (j*ldb + k) / 8; + if (i >= M) A_cur_index = 0; + + #pragma unroll + for (int t = 0; t < WARP_SIZE; ++t) { + const int lane_id = threadIdx.x % WARP_SIZE; + + const int64_t A_i = __shfl(A_cur_index, t) + 32 * lane_id; + const int64_t B_i = __shfl(B_cur_index, t) + 32 * lane_id; + + { + //ulonglong4 a_bit256 = *((ulonglong4 *)(A + A_i)); // weights + ulonglong4 a_bit256 = *((ulonglong4 *)(A_s + A_i)); // weights + ulonglong4 b_bit256 = *((ulonglong4 *)(B + B_i)); // input + c_bit256 = xnor_int256(a_bit256, b_bit256); + int tmp_count = __popcll(c_bit256.w) + __popcll(c_bit256.x) + + __popcll(c_bit256.y) + __popcll(c_bit256.z); + + int sum_count = warpAllReduceSum(tmp_count); + if (lane_id == t) count += sum_count; + } + } + } +#endif + //#ifdef NOT_USED // 32 thread X 64 bit = 2048 bit for (; k < (K - 2048); k += 2048) { // l.size*l.size*l.c - one filter size [27 - 9216] diff --git a/src/network.c b/src/network.c index 545216dbd60..642978ecba6 100644 --- a/src/network.c +++ b/src/network.c @@ -866,10 +866,10 @@ void calculate_binary_weights(network net) //if (l->size*l->size*l->c >= 2048) l->lda_align = 512; binary_align_weights(l); - } - if (net.layers[j].use_bin_output) { - l->activation = LINEAR; + if (net.layers[j].use_bin_output) { + l->activation = LINEAR; + } } } } From 14ed6fcb6e31dd111fc5c35c31ffa6e45fe52737 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 8 Nov 2018 15:05:47 +0300 Subject: [PATCH 0188/1365] Added -iou_thresh 0.05 flag for ./darknet detector map --- src/detector.c | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/src/detector.c b/src/detector.c index fc560f8048f..ba4d809b3a6 100644 --- a/src/detector.c +++ b/src/detector.c @@ -556,7 +556,7 @@ int detections_comparator(const void *pa, const void *pb) return 0; } -void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou) +void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh) { int j; list *options = read_data_cfg(datacfg); @@ -597,7 +597,7 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float const float thresh = .005; const float nms = .45; - const float iou_thresh = 0.5; + //const float iou_thresh = 0.5; int nthreads = 4; image *val = calloc(nthreads, sizeof(image)); @@ -876,7 +876,12 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, tp_for_thresh, fp_for_thresh, unique_truth_count - tp_for_thresh, avg_iou * 100); mean_average_precision = mean_average_precision / classes; - printf("\n mean average precision (mAP) = %f, or %2.2f %% \n", mean_average_precision, mean_average_precision*100); + if (iou_thresh == 0.5) { + printf("\n mean average precision (mAP) = %f, or %2.2f %% \n", mean_average_precision, mean_average_precision * 100); + } + else { + printf("\n average precision (AP) = %f, or %2.2f %% for IoU threshold = %f \n", mean_average_precision, mean_average_precision * 100, iou_thresh); + } for (i = 0; i < classes; ++i) { @@ -1235,6 +1240,7 @@ void run_detector(int argc, char **argv) char *outfile = find_char_arg(argc, argv, "-out", 0); char *prefix = find_char_arg(argc, argv, "-prefix", 0); float thresh = find_float_arg(argc, argv, "-thresh", .25); // 0.24 + float iou_thresh = find_float_arg(argc, argv, "-iou_thresh", .5); // 0.5 for mAP float hier_thresh = find_float_arg(argc, argv, "-hier", .5); int cam_index = find_int_arg(argc, argv, "-c", 0); int frame_skip = find_int_arg(argc, argv, "-s", 0); @@ -1285,7 +1291,7 @@ void run_detector(int argc, char **argv) else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show); else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile); else if(0==strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights); - else if(0==strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh); + else if(0==strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh, iou_thresh); else if(0==strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, height, show); else if(0==strcmp(argv[2], "demo")) { list *options = read_data_cfg(datacfg); From 9f7d7c58b5284eec9a1b96eb30c63ae254ded740 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 17 Nov 2018 02:48:46 +0300 Subject: [PATCH 0189/1365] Minor fixes. Use CUDA 10.0 --- build/darknet/darknet.vcxproj | 4 ++-- build/darknet/yolo_cpp_dll.vcxproj | 4 ++-- src/convolutional_kernels.cu | 34 ++++++++++++++++++++---------- src/image.c | 14 +++++++++--- 4 files changed, 38 insertions(+), 18 deletions(-) diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj index 3557200a259..b8e4c08b06f 100644 --- a/build/darknet/darknet.vcxproj +++ b/build/darknet/darknet.vcxproj @@ -52,7 +52,7 @@ - + @@ -288,6 +288,6 @@ - + \ No newline at end of file diff --git a/build/darknet/yolo_cpp_dll.vcxproj b/build/darknet/yolo_cpp_dll.vcxproj index 5d6576cee7d..bb8cace6c39 100644 --- a/build/darknet/yolo_cpp_dll.vcxproj +++ b/build/darknet/yolo_cpp_dll.vcxproj @@ -52,7 +52,7 @@ - + @@ -291,6 +291,6 @@ - + \ No newline at end of file diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index feaffa0c64f..d7220010b17 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -695,17 +695,29 @@ void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float adam_gpu(size, layer.weights_gpu, layer.m_gpu, layer.v_gpu, layer.B1, layer.B2, learning_rate/batch, layer.eps, layer.t+1); fill_ongpu(size, 0, layer.weight_updates_gpu, 1); }else{ - // update weights: - // weights_gpu = weights_gpu*(1 - decay*lr) + weight_updates_gpu*lr / (batch*subdivision) = - // weights_gpu*(1 - 0.0005*0.001) + weight_updates_gpu*0.001/(64*8) = - // weights_gpu * 0.999 999 5 + weight_updates_gpu * 0.000 001 953125 - // - // weight_updates_gpu = (weight_updates_gpu - weights_gpu*decay*batch*subdivision)*momentum = - // (weight_updates_gpu - weights_gpu * 0.0005 * 64 * 8) * 0.9 = - // weight_updates_gpu*0.9 - weights_gpu*0.2304 - axpy_ongpu(size, -decay*batch, layer.weights_gpu, 1, layer.weight_updates_gpu, 1); - axpy_ongpu(size, learning_rate/batch, layer.weight_updates_gpu, 1, layer.weights_gpu, 1); - scal_ongpu(size, momentum, layer.weight_updates_gpu, 1); + axpy_ongpu(size, -decay*batch, layer.weights_gpu, 1, layer.weight_updates_gpu, 1); // wu = wu - w*decay*batch + axpy_ongpu(size, learning_rate/batch, layer.weight_updates_gpu, 1, layer.weights_gpu, 1); // w = w + wu*lr/batch + scal_ongpu(size, momentum, layer.weight_updates_gpu, 1); // wu = wu*momentum // wu = (wu - w*decay*batch)*momentum + // w = w + (wu - w*decay*batch)*lr/batch = w + wu*lr/batch - w*decay*lr = w*(1-decay*lr) + wu*lr/batch + //wu_prev = (wu_old - w_old*decay*batch)*momentum + + + //weights_update = weights_update_new + (weights_update_old - weights_old*decay*batch)*momentum - weights_new*decay*batch = + // = weights_update_new + weights_update_old*momentum - weights_old*decay*batch*momentum - weights_new*decay*batch + // = weights_update_new + weights_update_old*momentum - (weights_old*momentum + weights_new)*decay*batch + + //------------- RESULT -------------- + // weights_update = weights_update_new + weights_update_old*momentum - (weights_old*momentum + weights_new)*decay*batch + //----------------------------------- + + // weights_newest = weights_new + (weights_update_new + weights_update_old*momentum - (weights_old*momentum + weights_new)*decay*batch)*lr/batch + // = weights_new + weights_update_new*lr/batch + weights_update_old*momentum*lr/batch - weights_old*momentum*decay*batch*lr/batch - weights_new*decay*batch*lr/batch + // = weights_new + weights_update_new*lr/batch + weights_update_old*momentum*lr/batch - weights_old*momentum*decay*lr - weights_new*decay*lr + // = weights_new*(1 - decay*lr) - weights_old*momentum*decay*lr + (weights_update_new + weights_update_old*momentum)*lr/batch + + //------------- RESULT -------------- + // weights_newest = weights_new*(1 - decay*lr) - weights_old*momentum*(decay*lr) + (weights_update_new + weights_update_old*momentum)*lr/batch + //----------------------------------- } } diff --git a/src/image.c b/src/image.c index 8566a4f8eb4..155afba9c07 100644 --- a/src/image.c +++ b/src/image.c @@ -705,6 +705,9 @@ IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_li } } cvPutText(img, "Iteration number", cvPoint(draw_size / 2, img_size - 10), &font, CV_RGB(0, 0, 0)); + char max_batches_buff[100]; + sprintf(max_batches_buff, "in cfg max_batches=%d", max_batches); + cvPutText(img, max_batches_buff, cvPoint(draw_size - 195, img_size - 10), &font, CV_RGB(0, 0, 0)); cvPutText(img, "Press 's' to save: chart.jpg", cvPoint(5, img_size - 10), &font, CV_RGB(0, 0, 0)); printf(" If error occurs - run training with flag: -dont_show \n"); cvNamedWindow("average loss", CV_WINDOW_NORMAL); @@ -728,15 +731,20 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_ if (pt1.y < 0) pt1.y = 1; cvCircle(img, pt1, 1, CV_RGB(0, 0, 255), CV_FILLED, 8, 0); - sprintf(char_buff, "current avg loss = %2.4f", avg_loss); + sprintf(char_buff, "current avg loss = %2.4f iteration = %d", avg_loss, current_batch); pt1.x = img_size / 2, pt1.y = 30; - pt2.x = pt1.x + 250, pt2.y = pt1.y + 20; + pt2.x = pt1.x + 460, pt2.y = pt1.y + 20; cvRectangle(img, pt1, pt2, CV_RGB(255, 255, 255), CV_FILLED, 8, 0); pt1.y += 15; cvPutText(img, char_buff, pt1, &font, CV_RGB(0, 0, 0)); cvShowImage("average loss", img); int k = cvWaitKey(20); - if (k == 's' || current_batch == (max_batches-1)) cvSaveImage("chart.jpg", img, 0); + if (k == 's' || current_batch == (max_batches - 1)) { + cvSaveImage("chart.jpg", img, 0); + cvPutText(img, "- Saved", cvPoint(250, img_size - 10), &font, CV_RGB(255, 0, 0)); + } + else + cvPutText(img, "- Saved", cvPoint(250, img_size - 10), &font, CV_RGB(255, 255, 255)); } #endif // OPENCV From 21a4ec9390b61c0baa7ef72e72e59fa143daba4c Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 26 Nov 2018 11:11:56 +0300 Subject: [PATCH 0190/1365] Saving loss-chart for each 100 iterations automatically --- src/convolutional_kernels.cu | 3 ++- src/image.c | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index d7220010b17..6824083e44e 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -716,7 +716,8 @@ void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float // = weights_new*(1 - decay*lr) - weights_old*momentum*decay*lr + (weights_update_new + weights_update_old*momentum)*lr/batch //------------- RESULT -------------- - // weights_newest = weights_new*(1 - decay*lr) - weights_old*momentum*(decay*lr) + (weights_update_new + weights_update_old*momentum)*lr/batch + // weights_newest = weights_new*(1 - decay*lr) - weights_old*momentum*(decay*lr) + (weights_update_new + weights_update_old*momentum)*lr/batch = + // = weights_new - (weights_new + weights_old*momentum)*decay*lr + (weights_update_new + weights_update_old*momentum)*lr / batch //----------------------------------- } } diff --git a/src/image.c b/src/image.c index 155afba9c07..60cd9cb3d9a 100644 --- a/src/image.c +++ b/src/image.c @@ -739,7 +739,7 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_ cvPutText(img, char_buff, pt1, &font, CV_RGB(0, 0, 0)); cvShowImage("average loss", img); int k = cvWaitKey(20); - if (k == 's' || current_batch == (max_batches - 1)) { + if (k == 's' || current_batch == (max_batches - 1) || current_batch % 100 == 0) { cvSaveImage("chart.jpg", img, 0); cvPutText(img, "- Saved", cvPoint(250, img_size - 10), &font, CV_RGB(255, 0, 0)); } From 7c2f30232132e9fc90155288689016db93d17990 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 7 Dec 2018 22:40:10 +0300 Subject: [PATCH 0191/1365] Fixed nan issue for training with CUDNN_HALF=1 by using Tensor Cores --- build/darknet/x64/darknet_many_images.cmd | 2 +- src/convolutional_kernels.cu | 456 ++++++++++++---------- src/convolutional_layer.c | 66 ++-- src/convolutional_layer.h | 2 +- src/crnn_layer.c | 6 +- src/detector.c | 15 +- src/layer.h | 12 +- src/network.h | 1 + src/parser.c | 5 +- 9 files changed, 317 insertions(+), 248 deletions(-) diff --git a/build/darknet/x64/darknet_many_images.cmd b/build/darknet/x64/darknet_many_images.cmd index 5f4658e482c..a763d0220f4 100644 --- a/build/darknet/x64/darknet_many_images.cmd +++ b/build/darknet/x64/darknet_many_images.cmd @@ -1,4 +1,4 @@ -darknet.exe detector test data/voc.data cfg/yolov2-voc.cfg yolo-voc.weights -dont_show < data/train.txt > result.txt +darknet.exe detector test data/voc.data cfg/yolov2-voc.cfg yolo-voc.weights -ext_output -dont_show < data/train.txt > result.txt pause \ No newline at end of file diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 6824083e44e..04d3820fc12 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -138,7 +138,9 @@ void fast_binarize_weights_gpu(float *weights, int n, int size, float *binary, f __global__ void cuda_f32_to_f16(float* input_f32, size_t size, half *output_f16) { int idx = blockIdx.x * blockDim.x + threadIdx.x; - if (idx < size) output_f16[idx] = __float2half(input_f32[idx]); + //if (idx < size) output_f16[idx] = __float2half(input_f32[idx]); + if (idx < size) output_f16[idx] = __float2half_rn(input_f32[idx]); + // __float2half_ru, __float2half_rd, __float2half_rz, __float2half_rn //if (idx < size) *((unsigned short *)output_f16 + idx) = __float2half(input_f32[idx]); } @@ -290,112 +292,127 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) float one = 1; // alpha[0], beta[0] is float for HALF and FLOAT float alpha = 1, beta = 0; -#ifdef CUDNN_HALF - // Note: For improved performance it is advised to use beta[0] = 0.0. - // For Tensor Core: cudnnSetConvolutionMathType() where cudnnMathType_t mathType = CUDNN_TENSOR_OP_MATH; - // 1. or CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM and use CUDNN_DATA_HALF - // 2. or CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED - // More: http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#tensor_ops - - const size_t input16_size = l.batch*l.c*l.w*l.h; - const size_t output16_size = l.batch*l.out_c*l.out_h*l.out_w; - - if (*state.net.max_input16_size < input16_size) { - //printf("\n input16_size: cur = %zu \t max = %zu \n", input16_size, *state.net.max_input16_size); - *state.net.max_input16_size = input16_size; - if (*state.net.input16_gpu) cuda_free(*state.net.input16_gpu); - *state.net.input16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_input16_size); - } - float *input16 = *state.net.input16_gpu; - - if (*state.net.max_output16_size < output16_size) { - *state.net.max_output16_size = output16_size; - if (*state.net.output16_gpu) cuda_free(*state.net.output16_gpu); - *state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size); - } - float *output16 = *state.net.output16_gpu; - - cuda_convert_f32_to_f16(state.input, input16_size, input16); - - //fill_ongpu(output16_size / 2, 0, (float *)output16, 1); - cudnnConvolutionForward(cudnn_handle(), - &alpha, - l.srcTensorDesc, - input16, - l.weightDesc, - l.weights_gpu16, - l.convDesc, - l.fw_algo, - state.workspace, - l.workspace_size, - &beta, - l.dstTensorDesc, - output16); - - - if (l.batch_normalize) +//#ifdef CUDNN_HALF + //if (state.use_mixed_precision) { + int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); + if(state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > state.net.burn_in)) { - if (state.train) // Training - { - copy_ongpu(l.outputs*l.batch / 2, output16, 1, l.x_gpu, 1); - //cudaMemcpyAsync(l.x_gpu, output16, l.outputs*l.batch*sizeof(half), cudaMemcpyDefault, get_cuda_stream()); - float one = 1; - float zero = 0; - // Batch-normalization can still take FP16 inputs and outputs, saving half the bandwidth - // compared to FP32, it’s just that the statistics and value adjustment should be done in FP32. - cudnnBatchNormalizationForwardTraining(cudnn_handle(), - CUDNN_BATCHNORM_SPATIAL, - &one, - &zero, - l.normDstTensorDescF16, - l.x_gpu, // input - l.normDstTensorDescF16, - output16, // output - l.normTensorDesc, - l.scales_gpu, - l.biases_gpu, - .01, - l.rolling_mean_gpu, // output (should be FP32) - l.rolling_variance_gpu, // output (should be FP32) - .00001, - l.mean_gpu, // output (should be FP32) - l.variance_gpu); // output (should be FP32) + //printf("\n CUDNN_HALF!!! state.index = %d \n", state.index); + + // Note: For improved performance it is advised to use beta[0] = 0.0. + // For Tensor Core: cudnnSetConvolutionMathType() where cudnnMathType_t mathType = CUDNN_TENSOR_OP_MATH; + // 1. or CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM and use CUDNN_DATA_HALF + // 2. or CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED + // More: http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#tensor_ops + + const size_t input16_size = l.batch*l.c*l.w*l.h; + const size_t output16_size = l.batch*l.out_c*l.out_h*l.out_w; + + if (*state.net.max_input16_size < input16_size) { + //printf("\n input16_size: cur = %zu \t max = %zu \n", input16_size, *state.net.max_input16_size); + *state.net.max_input16_size = input16_size; + if (*state.net.input16_gpu) cuda_free(*state.net.input16_gpu); + *state.net.input16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_input16_size); + } + float *input16 = *state.net.input16_gpu; - cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); - //forward_batchnorm_layer_gpu(l, state); + if (*state.net.max_output16_size < output16_size) { + *state.net.max_output16_size = output16_size; + if (*state.net.output16_gpu) cuda_free(*state.net.output16_gpu); + *state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size); } - else // Detection + float *output16 = *state.net.output16_gpu; + + cuda_convert_f32_to_f16(state.input, input16_size, input16); + + //fill_ongpu(output16_size / 2, 0, (float *)output16, 1); + cudnnConvolutionForward(cudnn_handle(), + &alpha, + l.srcTensorDesc16, + input16, + l.weightDesc16, + l.weights_gpu16, + l.convDesc, + l.fw_algo16, + state.workspace, + l.workspace_size, + &beta, + l.dstTensorDesc16, + output16); + + + if (l.batch_normalize) + { + if (state.train) // Training + { + copy_ongpu(l.outputs*l.batch / 2, output16, 1, l.x_gpu, 1); + //cudaMemcpyAsync(l.x_gpu, output16, l.outputs*l.batch*sizeof(half), cudaMemcpyDefault, get_cuda_stream()); + float one = 1; + float zero = 0; + // Batch-normalization can still take FP16 inputs and outputs, saving half the bandwidth + // compared to FP32, it’s just that the statistics and value adjustment should be done in FP32. + cudnnBatchNormalizationForwardTraining(cudnn_handle(), + CUDNN_BATCHNORM_SPATIAL, + &one, + &zero, + l.normDstTensorDescF16, + l.x_gpu, // input + l.normDstTensorDescF16, + output16, // output + l.normTensorDesc, + l.scales_gpu, + l.biases_gpu, + .01, + l.rolling_mean_gpu, // output (should be FP32) + l.rolling_variance_gpu, // output (should be FP32) + .00001, + l.mean_gpu, // output (should be FP32) + l.variance_gpu); // output (should be FP32) + + cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); + //forward_batchnorm_layer_gpu(l, state); + } + else // Detection + { + cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); + normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); + scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h); + } + } + else // BIAS only { cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); - normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); - scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); - add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h); + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); } } - else // BIAS only - { - cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); - add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); - } + else { -#else + //#else - cudnnConvolutionForward(cudnn_handle(), - &alpha, //&one, - l.srcTensorDesc, - state.input, - l.weightDesc, - l.weights_gpu, - l.convDesc, - l.fw_algo, - state.workspace, - l.workspace_size, - &beta, //&one, - l.dstTensorDesc, - l.output_gpu); + cudnnConvolutionForward(cudnn_handle(), + &alpha, //&one, + l.srcTensorDesc, + state.input, + l.weightDesc, + l.weights_gpu, + l.convDesc, + l.fw_algo, + state.workspace, + l.workspace_size, + &beta, //&one, + l.dstTensorDesc, + l.output_gpu); - //cudaDeviceSynchronize(); -#endif // CUDNN_HALF + //cudaDeviceSynchronize(); + if (l.batch_normalize) { + forward_batchnorm_layer_gpu(l, state); + } + else { + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); + } + //#endif // CUDNN_HALF + } #else @@ -418,16 +435,17 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) } gemm_ongpu(0,0,m,n,k,1.,a,k,b,n,1.,c+i*m*n,n); } -#endif -#ifndef CUDNN_HALF if (l.batch_normalize) { forward_batchnorm_layer_gpu(l, state); } else { add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); } -#endif // no CUDNN_HALF +#endif + +//#ifndef CUDNN_HALF +//#endif // no CUDNN_HALF if (l.activation != LINEAR) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if(l.dot > 0) dot_error_gpu(l); @@ -441,13 +459,13 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); -#ifndef CUDNN_HALF - if(l.batch_normalize){ - backward_batchnorm_layer_gpu(l, state); - } else { - //backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); - } -#endif // no CUDNN_HALF +//#ifndef CUDNN_HALF + //if(l.batch_normalize){ + // backward_batchnorm_layer_gpu(l, state); + //} else { + // //backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); + //} +//#endif // no CUDNN_HALF float *original_input = state.input; if(l.xnor) state.input = l.binary_input_gpu; @@ -455,117 +473,126 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state float one = 1; float alpha = 1, beta = 0; -#ifdef CUDNN_HALF +//#ifdef CUDNN_HALF + int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); + if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > state.net.burn_in)) + { - const size_t input16_size = l.batch*l.c*l.w*l.h; - const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h; + const size_t input16_size = l.batch*l.c*l.w*l.h; + const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h; - if (*state.net.max_input16_size < input16_size) { - *state.net.max_input16_size = input16_size; - if(*state.net.input16_gpu) cuda_free(*state.net.input16_gpu); - *state.net.input16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_input16_size); - } - float *input16 = *state.net.input16_gpu; + if (*state.net.max_input16_size < input16_size) { + *state.net.max_input16_size = input16_size; + if (*state.net.input16_gpu) cuda_free(*state.net.input16_gpu); + *state.net.input16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_input16_size); + } + float *input16 = *state.net.input16_gpu; - if (*state.net.max_output16_size < delta16_size) { - *state.net.max_output16_size = delta16_size; - if(*state.net.output16_gpu) cuda_free(*state.net.output16_gpu); - *state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size); - } - float *delta16 = *state.net.output16_gpu; + if (*state.net.max_output16_size < delta16_size) { + *state.net.max_output16_size = delta16_size; + if (*state.net.output16_gpu) cuda_free(*state.net.output16_gpu); + *state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size); + } + float *delta16 = *state.net.output16_gpu; - cuda_convert_f32_to_f16(state.input, input16_size, input16); - cuda_convert_f32_to_f16(l.delta_gpu, delta16_size, delta16); + cuda_convert_f32_to_f16(state.input, input16_size, input16); + cuda_convert_f32_to_f16(l.delta_gpu, delta16_size, delta16); - if (l.batch_normalize) { - //if (!state.train) { - // l.mean_gpu = l.rolling_mean_gpu; - // l.variance_gpu = l.rolling_variance_gpu; - //} - float one = 1; - float zero = 0; - cudnnBatchNormalizationBackward(cudnn_handle(), - CUDNN_BATCHNORM_SPATIAL, - &one, - &zero, - &one, - &one, - l.normDstTensorDescF16, - l.x_gpu, // input - l.normDstTensorDescF16, - delta16, // input - l.normDstTensorDescF16, - l.x_norm_gpu, // output - l.normTensorDesc, - l.scales_gpu, // output (should be FP32) - l.scale_updates_gpu, // output (should be FP32) - l.bias_updates_gpu, // output (should be FP32) - .00001, - l.mean_gpu, // input (should be FP32) - l.variance_gpu); // input (should be FP32) - copy_ongpu(l.outputs*l.batch / 2, l.x_norm_gpu, 1, delta16, 1); - //cudaMemcpyAsync(delta16, l.x_norm_gpu, l.outputs*l.batch * sizeof(half), cudaMemcpyDefault, get_cuda_stream()); - } - else - { - //backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); - } + if (l.batch_normalize) { + //if (!state.train) { + // l.mean_gpu = l.rolling_mean_gpu; + // l.variance_gpu = l.rolling_variance_gpu; + //} + float one = 1; + float zero = 0; + cudnnBatchNormalizationBackward(cudnn_handle(), + CUDNN_BATCHNORM_SPATIAL, + &one, + &zero, + &one, + &one, + l.normDstTensorDescF16, + l.x_gpu, // input + l.normDstTensorDescF16, + delta16, // input + l.normDstTensorDescF16, + l.x_norm_gpu, // output + l.normTensorDesc, + l.scales_gpu, // output (should be FP32) + l.scale_updates_gpu, // output (should be FP32) + l.bias_updates_gpu, // output (should be FP32) + .00001, + l.mean_gpu, // input (should be FP32) + l.variance_gpu); // input (should be FP32) + copy_ongpu(l.outputs*l.batch / 2, l.x_norm_gpu, 1, delta16, 1); + //cudaMemcpyAsync(delta16, l.x_norm_gpu, l.outputs*l.batch * sizeof(half), cudaMemcpyDefault, get_cuda_stream()); + } + else + { + //backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); + } - // convert input: state.input (x), l.delta_gpu (y) from fp32 to fp16 - // get output: l.weight_updates_gpu (dw) and convert it to fp32 (ONLY if it is fp16) - - // calculate conv weight updates - // Already: l.weight_updates_gpu = (l.weight_updates_gpu - l.weight*decay*batch*subdivision)*momentum - // so we should copy f32 to f16, or compute: f16=(w_up - w*d*b*s)*m - cuda_convert_f32_to_f16(l.weight_updates_gpu, l.c*l.n*l.size*l.size, l.weight_updates_gpu16); - - cudnnConvolutionBackwardFilter(cudnn_handle(), - &one, - l.srcTensorDesc, - input16, //state.input, - l.ddstTensorDesc, - delta16, //l.delta_gpu, - l.convDesc, - l.bf_algo, - state.workspace, - l.workspace_size, - &one, - l.dweightDesc, - l.weight_updates_gpu16); // l.weight_updates_gpu); - - cuda_convert_f16_to_f32(l.weight_updates_gpu16, l.c*l.n*l.size*l.size, l.weight_updates_gpu); - - if (state.delta) { - if (l.binary || l.xnor) swap_binary(&l); - - // http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData - // calculate delta for the next layer - // convert input: l.weights_gpu (w), l.delta_gpu (dy) from fp32 to fp16 - // get output: state.delta (dx) and convert it to fp32 (ONLY if it is fp16) - cudnnConvolutionBackwardData(cudnn_handle(), - &alpha, - l.weightDesc, - l.weights_gpu16, //l.weights_gpu, - l.ddstTensorDesc, + // convert input: state.input (x), l.delta_gpu (y) from fp32 to fp16 + // get output: l.weight_updates_gpu (dw) and convert it to fp32 (ONLY if it is fp16) + + // calculate conv weight updates + // Already: l.weight_updates_gpu = (l.weight_updates_gpu - l.weight*decay*batch*subdivision)*momentum + // so we should copy f32 to f16, or compute: f16=(w_up - w*d*b*s)*m + cuda_convert_f32_to_f16(l.weight_updates_gpu, l.c*l.n*l.size*l.size, l.weight_updates_gpu16); + + cudnnConvolutionBackwardFilter(cudnn_handle(), + &one, + l.srcTensorDesc16, + input16, //state.input, + l.ddstTensorDesc16, delta16, //l.delta_gpu, l.convDesc, - l.bd_algo, + l.bf_algo16, state.workspace, l.workspace_size, - &beta, - l.dsrcTensorDesc, - input16); // state.delta); + &one, + l.dweightDesc, + l.weight_updates_gpu16); // l.weight_updates_gpu); + + cuda_convert_f16_to_f32(l.weight_updates_gpu16, l.c*l.n*l.size*l.size, l.weight_updates_gpu); + + if (state.delta) { + if (l.binary || l.xnor) swap_binary(&l); + + // http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData + // calculate delta for the next layer + // convert input: l.weights_gpu (w), l.delta_gpu (dy) from fp32 to fp16 + // get output: state.delta (dx) and convert it to fp32 (ONLY if it is fp16) + cudnnConvolutionBackwardData(cudnn_handle(), + &alpha, + l.weightDesc16, + l.weights_gpu16, //l.weights_gpu, + l.ddstTensorDesc16, + delta16, //l.delta_gpu, + l.convDesc, + l.bd_algo16, + state.workspace, + l.workspace_size, + &beta, + l.dsrcTensorDesc16, + input16); // state.delta); - cuda_convert_f16_to_f32(input16, input16_size, state.delta); + cuda_convert_f16_to_f32(input16, input16_size, state.delta); - if (l.binary || l.xnor) swap_binary(&l); - if (l.xnor) gradient_array_ongpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, state.delta); + if (l.binary || l.xnor) swap_binary(&l); + if (l.xnor) gradient_array_ongpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, state.delta); + } } -#else // CUDNN_HALF + else { + //#else // CUDNN_HALF - // calculate conv weight updates - // if used: beta=1 then loss decreases faster - cudnnConvolutionBackwardFilter(cudnn_handle(), + if(l.batch_normalize){ + backward_batchnorm_layer_gpu(l, state); + } + + // calculate conv weight updates + // if used: beta=1 then loss decreases faster + cudnnConvolutionBackwardFilter(cudnn_handle(), &one, l.srcTensorDesc, state.input, @@ -579,11 +606,11 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state l.dweightDesc, l.weight_updates_gpu); - if(state.delta){ - if(l.binary || l.xnor) swap_binary(&l); - // http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData - // calculate delta for the next layer - cudnnConvolutionBackwardData(cudnn_handle(), + if (state.delta) { + if (l.binary || l.xnor) swap_binary(&l); + // http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData + // calculate delta for the next layer + cudnnConvolutionBackwardData(cudnn_handle(), &one, l.weightDesc, l.weights_gpu, @@ -596,13 +623,18 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state &one, l.dsrcTensorDesc, state.delta); - if(l.binary || l.xnor) swap_binary(&l); - if(l.xnor) gradient_array_ongpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, state.delta); + if (l.binary || l.xnor) swap_binary(&l); + if (l.xnor) gradient_array_ongpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, state.delta); + } } -#endif // CUDNN_HALF +//#endif // CUDNN_HALF #else // CUDNN + if (l.batch_normalize) { + backward_batchnorm_layer_gpu(l, state); + } + int m = l.n; int n = l.size*l.size*l.c; int k = l.out_w*l.out_h; diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 8e83758c231..9fc33c0cd45 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -141,14 +141,12 @@ size_t get_workspace_size(layer l){ void cudnn_convolutional_setup(layer *l, int cudnn_preference) { -#ifdef CUDNN_HALF +// CUDNN_HALF // TRUE_HALF_CONFIG is only supported on architectures with true fp16 support (compute capability 5.3 and 6.0): // Tegra X1, Jetson TX1, DRIVE CX, DRIVE PX, Quadro GP100, Tesla P100 // PSEUDO_HALF_CONFIG is required for Tensor Cores - our case! - const cudnnDataType_t data_type = CUDNN_DATA_HALF; -#else + cudnnDataType_t data_type = CUDNN_DATA_FLOAT; -#endif #if(CUDNN_MAJOR >= 7) // Tensor Core uses CUDNN_TENSOR_OP_MATH instead of CUDNN_DEFAULT_MATH @@ -179,11 +177,25 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->out_c, l->out_h, l->out_w); cudnnSetFilter4dDescriptor(l->weightDesc, data_type, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size); +#ifdef CUDNN_HALF + // backward delta + cudnnSetTensor4dDescriptor(l->dsrcTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->c, l->h, l->w); + cudnnSetTensor4dDescriptor(l->ddstTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w); + cudnnSetFilter4dDescriptor(l->dweightDesc16, CUDNN_DATA_HALF, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size); + + // forward + cudnnSetTensor4dDescriptor(l->srcTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->c, l->h, l->w); + cudnnSetTensor4dDescriptor(l->dstTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w); + cudnnSetFilter4dDescriptor(l->weightDesc16, CUDNN_DATA_HALF, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size); + + // batch norm + cudnnSetTensor4dDescriptor(l->normDstTensorDescF16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w); +#endif + // batch norm cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1); cudnnSetTensor4dDescriptor(l->normDstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); - cudnnSetTensor4dDescriptor(l->normDstTensorDescF16, CUDNN_TENSOR_NCHW, data_type, l->batch, l->out_c, l->out_h, l->out_w); #if(CUDNN_MAJOR >= 6) cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT); // cudnn >= 6.0 #else @@ -225,32 +237,32 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) 0, &l->bf_algo); - if (data_type == CUDNN_DATA_HALF) + //if (data_type == CUDNN_DATA_HALF) { // HALF-16 if(data_type == CUDNN_DATA_HALF) - l->fw_algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM; - l->bd_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1; - l->bf_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1; + l->fw_algo16 = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM; + l->bd_algo16 = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1; + l->bf_algo16 = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1; // FLOAT-32 if(data_type == CUDNN_DATA_FLOAT) - //l->fw_algo = CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED; - //l->bd_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED; - //l->bf_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED; + //l->fw_algo16 = CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED; + //l->bd_algo16 = CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED; + //l->bf_algo16 = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED; int fw = 0, bd = 0, bf = 0; - if (l->fw_algo == CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM) fw = 1; + if (l->fw_algo16 == CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM) fw = 1; //printf("Tensor Cores - Forward enabled: l->fw_algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM \n"); - if (l->fw_algo == CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED) fw = 2; + if (l->fw_algo16 == CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED) fw = 2; //printf("Tensor Cores - Forward enabled: l->fw_algo = CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED \n"); - if (l->bd_algo == CUDNN_CONVOLUTION_BWD_DATA_ALGO_1) bd = 1; + if (l->bd_algo16 == CUDNN_CONVOLUTION_BWD_DATA_ALGO_1) bd = 1; //printf("Tensor Cores - Backward-data enabled: l->bd_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1 \n"); - if (l->bd_algo == CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED) bd = 2; + if (l->bd_algo16 == CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED) bd = 2; //printf("Tensor Cores - Backward-data enabled: l->bd_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED \n"); - if (l->bf_algo == CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1) bf = 1; + if (l->bf_algo16 == CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1) bf = 1; //printf("Tensor Cores - Backward-filter enabled: l->bf_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1 \n"); - if (l->bf_algo == CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED) bf = 2; + if (l->bf_algo16 == CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED) bf = 2; //printf("Tensor Cores - Backward-filter enabled: l->bf_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED \n"); //if (fw == 2 && bd == 2 && bf == 2) printf("TF "); @@ -260,12 +272,13 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) #endif #endif -convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output) +convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index) { int i; convolutional_layer l = {0}; l.type = CONVOLUTIONAL; + l.index = index; l.h = h; l.w = w; l.c = c; @@ -392,15 +405,24 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.x_norm_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); } #ifdef CUDNN - cudnnCreateTensorDescriptor(&l.normDstTensorDesc); - cudnnCreateTensorDescriptor(&l.normDstTensorDescF16); cudnnCreateTensorDescriptor(&l.normTensorDesc); + + cudnnCreateTensorDescriptor(&l.normDstTensorDesc); cudnnCreateTensorDescriptor(&l.srcTensorDesc); cudnnCreateTensorDescriptor(&l.dstTensorDesc); cudnnCreateFilterDescriptor(&l.weightDesc); cudnnCreateTensorDescriptor(&l.dsrcTensorDesc); cudnnCreateTensorDescriptor(&l.ddstTensorDesc); cudnnCreateFilterDescriptor(&l.dweightDesc); + + cudnnCreateTensorDescriptor(&l.normDstTensorDescF16); + cudnnCreateTensorDescriptor(&l.srcTensorDesc16); + cudnnCreateTensorDescriptor(&l.dstTensorDesc16); + cudnnCreateFilterDescriptor(&l.weightDesc16); + cudnnCreateTensorDescriptor(&l.dsrcTensorDesc16); + cudnnCreateTensorDescriptor(&l.ddstTensorDesc16); + cudnnCreateFilterDescriptor(&l.dweightDesc16); + cudnnCreateConvolutionDescriptor(&l.convDesc); cudnn_convolutional_setup(&l, cudnn_fastest); #endif @@ -436,7 +458,7 @@ void denormalize_convolutional_layer(convolutional_layer l) void test_convolutional_layer() { - convolutional_layer l = make_convolutional_layer(1, 5, 5, 3, 2, 5, 2, 1, LEAKY, 1, 0, 0, 0, 0); + convolutional_layer l = make_convolutional_layer(1, 5, 5, 3, 2, 5, 2, 1, LEAKY, 1, 0, 0, 0, 0, 0); l.batch_normalize = 1; float data[] = {1,1,1,1,1, 1,1,1,1,1, diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h index 20e9d68ebc7..0bd9849e74a 100644 --- a/src/convolutional_layer.h +++ b/src/convolutional_layer.h @@ -25,7 +25,7 @@ void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16); #endif #endif -convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output); +convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index); void denormalize_convolutional_layer(convolutional_layer l); void resize_convolutional_layer(convolutional_layer *layer, int w, int h); void forward_convolutional_layer(const convolutional_layer layer, network_state state); diff --git a/src/crnn_layer.c b/src/crnn_layer.c index f78e0c9db04..db384cfb683 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -48,17 +48,17 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.input_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.input_layer) = make_convolutional_layer(batch*steps, h, w, c, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0); + *(l.input_layer) = make_convolutional_layer(batch*steps, h, w, c, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0, 0); l.input_layer->batch = batch; l.self_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.self_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0); + *(l.self_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0, 0); l.self_layer->batch = batch; l.output_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.output_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, output_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0); + *(l.output_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, output_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0, 0); l.output_layer->batch = batch; l.output = l.output_layer->output; diff --git a/src/detector.c b/src/detector.c index ba4d809b3a6..ee25685c02c 100644 --- a/src/detector.c +++ b/src/detector.c @@ -91,8 +91,9 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i int init_w = net.w; int init_h = net.h; - int iter_save; + int iter_save, iter_save_last; iter_save = get_current_batch(net); + iter_save_last = get_current_batch(net); load_args args = {0}; args.w = net.w; @@ -210,7 +211,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) { //if (i % 100 == 0) { - if(i >= (iter_save + 100)) { + if(i >= (iter_save + 1000)) { iter_save = i; #ifdef GPU if (ngpus != 1) sync_nets(nets, ngpus, 0); @@ -219,6 +220,16 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); save_weights(net, buff); } + + if (i >= (iter_save_last + 100)) { + iter_save_last = i; +#ifdef GPU + if (ngpus != 1) sync_nets(nets, ngpus, 0); +#endif + char buff[256]; + sprintf(buff, "%s/%s_last.weights", backup_directory, base, i); + save_weights(net, buff); + } free_data(train); } #ifdef GPU diff --git a/src/layer.h b/src/layer.h index 2cfa08715c2..a4ebbfbc306 100644 --- a/src/layer.h +++ b/src/layer.h @@ -299,14 +299,16 @@ struct layer{ float * norms_gpu; #ifdef CUDNN cudnnTensorDescriptor_t srcTensorDesc, dstTensorDesc; + cudnnTensorDescriptor_t srcTensorDesc16, dstTensorDesc16; cudnnTensorDescriptor_t dsrcTensorDesc, ddstTensorDesc; + cudnnTensorDescriptor_t dsrcTensorDesc16, ddstTensorDesc16; cudnnTensorDescriptor_t normTensorDesc, normDstTensorDesc, normDstTensorDescF16; - cudnnFilterDescriptor_t weightDesc; - cudnnFilterDescriptor_t dweightDesc; + cudnnFilterDescriptor_t weightDesc, weightDesc16; + cudnnFilterDescriptor_t dweightDesc, dweightDesc16; cudnnConvolutionDescriptor_t convDesc; - cudnnConvolutionFwdAlgo_t fw_algo; - cudnnConvolutionBwdDataAlgo_t bd_algo; - cudnnConvolutionBwdFilterAlgo_t bf_algo; + cudnnConvolutionFwdAlgo_t fw_algo, fw_algo16; + cudnnConvolutionBwdDataAlgo_t bd_algo, bd_algo16; + cudnnConvolutionBwdFilterAlgo_t bf_algo, bf_algo16; cudnnPoolingDescriptor_t poolingDesc; #endif #endif diff --git a/src/network.h b/src/network.h index 18dc5953f89..f630fb96aff 100644 --- a/src/network.h +++ b/src/network.h @@ -42,6 +42,7 @@ typedef struct network{ int *steps; int num_steps; int burn_in; + int cudnn_half; int adam; float B1; diff --git a/src/parser.c b/src/parser.c index c82c4a2f5c9..c164766626c 100644 --- a/src/parser.c +++ b/src/parser.c @@ -165,7 +165,7 @@ convolutional_layer parse_convolutional(list *options, size_params params) int xnor = option_find_int_quiet(options, "xnor", 0); int use_bin_output = option_find_int_quiet(options, "bin_output", 0); - convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,size,stride,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output); + convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,size,stride,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index); layer.flipped = option_find_int_quiet(options, "flipped", 0); layer.dot = option_find_float_quiet(options, "dot", 0); @@ -655,7 +655,8 @@ void parse_net_options(list *options, network *net) net->policy = get_policy(policy_s); net->burn_in = option_find_int_quiet(options, "burn_in", 0); #ifdef CUDNN_HALF - net->burn_in = 0; + //net->burn_in = 0; + net->cudnn_half = 1; #endif if(net->policy == STEP){ net->step = option_find_int(options, "step", 1); From 742bb7c7ce51a9faad08dc01fbf2e6908ad5ba4b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 7 Dec 2018 22:52:07 +0300 Subject: [PATCH 0192/1365] Compile fix --- Makefile | 2 +- src/convolutional_kernels.cu | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/Makefile b/Makefile index 3f10ed353ae..99781ea9bab 100644 --- a/Makefile +++ b/Makefile @@ -7,7 +7,7 @@ OPENMP=0 LIBSO=0 # set GPU=1 and CUDNN=1 to speedup on GPU -# set CUDNN_HALF=1 to further speedup 3 x times (Mixed-precision using Tensor Cores) on GPU Tesla V100, Titan V, DGX-2 +# set CUDNN_HALF=1 to further speedup 3 x times (Mixed-precision on Tensor Cores) GPU: Volta, Xavier, Turing and higher # set AVX=1 and OPENMP=1 to speedup on CPU (if error occurs then set AVX=0) DEBUG=0 diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 04d3820fc12..166d5c170a0 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -138,8 +138,8 @@ void fast_binarize_weights_gpu(float *weights, int n, int size, float *binary, f __global__ void cuda_f32_to_f16(float* input_f32, size_t size, half *output_f16) { int idx = blockIdx.x * blockDim.x + threadIdx.x; - //if (idx < size) output_f16[idx] = __float2half(input_f32[idx]); - if (idx < size) output_f16[idx] = __float2half_rn(input_f32[idx]); + if (idx < size) output_f16[idx] = __float2half(input_f32[idx]); + //if (idx < size) output_f16[idx] = __float2half_rn(input_f32[idx]); // can't be compiled on Linux without casting // __float2half_ru, __float2half_rd, __float2half_rz, __float2half_rn //if (idx < size) *((unsigned short *)output_f16 + idx) = __float2half(input_f32[idx]); } From dc7f8a32aee156b2b153e37111063fa3bc84d5aa Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 9 Dec 2018 18:18:47 +0300 Subject: [PATCH 0193/1365] mAP calculation during training, if is used flag -map --- src/classifier.c | 42 ++++++++--------- src/convolutional_kernels.cu | 2 +- src/detector.c | 88 +++++++++++++++++++++++++++++------- src/image.c | 34 +++++++++++++- src/maxpool_layer.c | 45 ++++++++++-------- src/maxpool_layer.h | 5 +- src/network.c | 3 ++ src/yolo_layer.c | 2 + 8 files changed, 161 insertions(+), 60 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index b38f2fe23c2..7324349b86e 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -25,7 +25,7 @@ image get_image_from_stream_cpp(CvCapture *cap); #include "http_stream.h" IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size); -void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches); +void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, float precision); #endif @@ -153,14 +153,14 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); #ifdef OPENCV if(!dont_show) - draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches); + draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, -1); #endif // OPENCV if (i >= (iter_save + 100)) { iter_save = i; #ifdef GPU if (ngpus != 1) sync_nets(nets, ngpus, 0); -#endif +#endif char buff[256]; sprintf(buff, "%s/%s_%d.weights",backup_directory,base, i); save_weights(net, buff); @@ -169,7 +169,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, } #ifdef GPU if (ngpus != 1) sync_nets(nets, ngpus, 0); -#endif +#endif char buff[256]; sprintf(buff, "%s/%s_final.weights", backup_directory, base); save_weights(net, buff); @@ -918,7 +918,7 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i int *indexes = calloc(top, sizeof(int)); if(!cap) error("Couldn't connect to webcam.\n"); - //cvNamedWindow("Threat", CV_WINDOW_NORMAL); + //cvNamedWindow("Threat", CV_WINDOW_NORMAL); //cvResizeWindow("Threat", 512, 512); float fps = 0; int i; @@ -948,15 +948,15 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i float *predictions = network_predict(net, in_s.data); float curr_threat = 0; if(1){ - curr_threat = predictions[0] * 0 + - predictions[1] * .6 + + curr_threat = predictions[0] * 0 + + predictions[1] * .6 + predictions[2]; } else { curr_threat = predictions[218] + - predictions[539] + - predictions[540] + - predictions[368] + - predictions[369] + + predictions[539] + + predictions[540] + + predictions[368] + + predictions[369] + predictions[370]; } threat = roll * curr_threat + (1-roll) * threat; @@ -964,24 +964,24 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i draw_box_width(out, x2 + border, y1 + .02*h, x2 + .5 * w, y1 + .02*h + border, border, 0,0,0); if(threat > .97) { draw_box_width(out, x2 + .5 * w + border, - y1 + .02*h - 2*border, - x2 + .5 * w + 6*border, + y1 + .02*h - 2*border, + x2 + .5 * w + 6*border, y1 + .02*h + 3*border, 3*border, 1,0,0); } draw_box_width(out, x2 + .5 * w + border, - y1 + .02*h - 2*border, - x2 + .5 * w + 6*border, + y1 + .02*h - 2*border, + x2 + .5 * w + 6*border, y1 + .02*h + 3*border, .5*border, 0,0,0); draw_box_width(out, x2 + border, y1 + .42*h, x2 + .5 * w, y1 + .42*h + border, border, 0,0,0); if(threat > .57) { draw_box_width(out, x2 + .5 * w + border, - y1 + .42*h - 2*border, - x2 + .5 * w + 6*border, + y1 + .42*h - 2*border, + x2 + .5 * w + 6*border, y1 + .42*h + 3*border, 3*border, 1,1,0); } draw_box_width(out, x2 + .5 * w + border, - y1 + .42*h - 2*border, - x2 + .5 * w + 6*border, + y1 + .42*h - 2*border, + x2 + .5 * w + 6*border, y1 + .42*h + 3*border, .5*border, 0,0,0); draw_box_width(out, x1, y1, x2, y2, border, 0,0,0); @@ -1056,7 +1056,7 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde int *indexes = calloc(top, sizeof(int)); if(!cap) error("Couldn't connect to webcam.\n"); - cvNamedWindow("Threat Detection", CV_WINDOW_NORMAL); + cvNamedWindow("Threat Detection", CV_WINDOW_NORMAL); cvResizeWindow("Threat Detection", 512, 512); float fps = 0; int i; @@ -1138,7 +1138,7 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind int *indexes = calloc(top, sizeof(int)); if(!cap) error("Couldn't connect to webcam.\n"); - cvNamedWindow("Classifier", CV_WINDOW_NORMAL); + cvNamedWindow("Classifier", CV_WINDOW_NORMAL); cvResizeWindow("Classifier", 512, 512); float fps = 0; int i; diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 166d5c170a0..10a17baf4f9 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -295,7 +295,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //#ifdef CUDNN_HALF //if (state.use_mixed_precision) { int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); - if(state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > state.net.burn_in)) + if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > state.net.burn_in)) { //printf("\n CUDNN_HALF!!! state.index = %d \n", state.index); diff --git a/src/detector.c b/src/detector.c index ee25685c02c..baedab5ff96 100644 --- a/src/detector.c +++ b/src/detector.c @@ -26,7 +26,7 @@ #endif IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size); -void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches); +void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, float precision); #define CV_RGB(r, g, b) cvScalar( (b), (g), (r), 0 ) #endif // OPENCV @@ -37,12 +37,29 @@ int check_mistakes; static int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90}; -void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show) +float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, network *existing_net); + +void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map) { list *options = read_data_cfg(datacfg); - char *train_images = option_find_str(options, "train", "data/train.list"); + char *train_images = option_find_str(options, "train", "data/train.txt"); + char *valid_images = option_find_str(options, "valid", train_images); char *backup_directory = option_find_str(options, "backup", "/backup/"); + int valid_images_num = 0; + if (calc_map) { + FILE* valid_file = fopen(valid_images, "r"); + if (!valid_file) { + printf("\n Error: There is no %s file for mAP calculation!\n Don't use -map flag.\n Or set valid=%s in your %s file. \n", valid_images, train_images, datacfg); + getchar(); + exit(-1); + } + else fclose(valid_file); + list *plist = get_paths(valid_images); + valid_images_num = plist->size; + free_list(plist); + } + srand(time(0)); char *base = basecfg(cfgfile); printf("%s\n", base); @@ -91,9 +108,11 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i int init_w = net.w; int init_h = net.h; - int iter_save, iter_save_last; + int iter_save, iter_save_last, iter_map; iter_save = get_current_batch(net); iter_save_last = get_current_batch(net); + iter_map = get_current_batch(net); + float mean_average_precision = -1; load_args args = {0}; args.w = net.w; @@ -205,8 +224,15 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now()-time), i*imgs); #ifdef OPENCV - if(!dont_show) - draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches); + if (!dont_show) { + if (calc_map && (i >= (iter_map + valid_images_num/10) || i == net.max_batches) && i >= 1000) { + iter_map = i; + mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net); + printf("\n mean_average_precision = %f \n", mean_average_precision); + } + + draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, mean_average_precision); + } #endif // OPENCV //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) { @@ -567,7 +593,7 @@ int detections_comparator(const void *pa, const void *pb) return 0; } -void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh) +float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, network *existing_net) { int j; list *options = read_data_cfg(datacfg); @@ -580,14 +606,26 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float if (mapf) map = read_map(mapf); FILE* reinforcement_fd = NULL; - network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 - if (weightfile) { - load_weights(&net, weightfile); + network net; + int initial_batch; + if (existing_net) { + char *train_images = option_find_str(options, "train", "data/train.txt"); + char *valid_images = option_find_str(options, "valid", train_images); + net = *existing_net; + initial_batch = net.batch; + set_batch_network(&net, 1); + } + else { + net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 + if (weightfile) { + load_weights(&net, weightfile); + } + //set_batch_network(&net, 1); + fuse_conv_batchnorm(net); + calculate_binary_weights(net); } - //set_batch_network(&net, 1); - fuse_conv_batchnorm(net); - calculate_binary_weights(net); srand(time(0)); + printf("\n calculation mAP (mean average precision)...\n"); list *plist = get_paths(valid_images); char **paths = (char **)list_to_array(plist); @@ -611,6 +649,7 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float //const float iou_thresh = 0.5; int nthreads = 4; + if (m < 4) nthreads = m; image *val = calloc(nthreads, sizeof(image)); image *val_resized = calloc(nthreads, sizeof(image)); image *buf = calloc(nthreads, sizeof(image)); @@ -643,7 +682,7 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float } time_t start = time(0); for (i = nthreads; i < m + nthreads; i += nthreads) { - fprintf(stderr, "%d\n", i); + fprintf(stderr, "\r%d", i); for (t = 0; t < nthreads && i + t - nthreads < m; ++t) { pthread_join(thr[t], 0); val[t] = buf[t]; @@ -803,7 +842,7 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float for (i = 0; i < classes; ++i) { pr[i] = calloc(detections_count, sizeof(pr_t)); } - printf("detections_count = %d, unique_truth_count = %d \n", detections_count, unique_truth_count); + printf("\n detections_count = %d, unique_truth_count = %d \n", detections_count, unique_truth_count); int *truth_flags = calloc(unique_truth_count, sizeof(int)); @@ -904,6 +943,20 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); if (reinforcement_fd != NULL) fclose(reinforcement_fd); + + // free memory + free_ptrs(names, net.layers[net.n - 1].classes); + free_list_contents_kvp(options); + free_list(options); + + if (existing_net) { + set_batch_network(&net, initial_batch); + } + else { + free_network(net); + } + + return mean_average_precision; } #ifdef OPENCV @@ -1245,6 +1298,7 @@ void run_detector(int argc, char **argv) { int dont_show = find_arg(argc, argv, "-dont_show"); int show = find_arg(argc, argv, "-show"); + int calc_map = find_arg(argc, argv, "-map"); check_mistakes = find_arg(argc, argv, "-check_mistakes"); int http_stream_port = find_int_arg(argc, argv, "-http_port", -1); char *out_filename = find_char_arg(argc, argv, "-out_filename", 0); @@ -1299,10 +1353,10 @@ void run_detector(int argc, char **argv) if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0; char *filename = (argc > 6) ? argv[6]: 0; if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels); - else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show); + else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map); else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile); else if(0==strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights); - else if(0==strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh, iou_thresh); + else if(0==strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh, iou_thresh, NULL); else if(0==strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, height, show); else if(0==strcmp(argv[2], "demo")) { list *options = read_data_cfg(datacfg); diff --git a/src/image.c b/src/image.c index 60cd9cb3d9a..2d001609ce6 100644 --- a/src/image.c +++ b/src/image.c @@ -704,6 +704,8 @@ IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_li cvLine(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0); } } + + cvPutText(img, "Loss", cvPoint(0, 35), &font, CV_RGB(0, 0, 255)); cvPutText(img, "Iteration number", cvPoint(draw_size / 2, img_size - 10), &font, CV_RGB(0, 0, 0)); char max_batches_buff[100]; sprintf(max_batches_buff, "in cfg max_batches=%d", max_batches); @@ -718,7 +720,7 @@ IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_li return img; } -void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches) +void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, float precision) { int img_offset = 50; int draw_size = img_size - img_offset; @@ -731,12 +733,40 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_ if (pt1.y < 0) pt1.y = 1; cvCircle(img, pt1, 1, CV_RGB(0, 0, 255), CV_FILLED, 8, 0); + // precision + if (precision >= 0) { + static float old_precision = 0; + static iteration_old = 0; + + if (old_precision != precision) { + cvLine(img, + cvPoint(img_offset + draw_size * (float)iteration_old / max_batches, draw_size * (1 - old_precision)), + cvPoint(img_offset + draw_size * (float)current_batch / max_batches, draw_size * (1 - precision)), + CV_RGB(255, 0, 0), 1, 8, 0); + + old_precision = precision; + iteration_old = current_batch; + sprintf(char_buff, "%2.0f%% ", precision * 100); + CvFont font3; + cvInitFont(&font3, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 5, CV_AA); + cvPutText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), &font3, CV_RGB(255, 255, 255)); + + CvFont font2; + cvInitFont(&font2, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA); + cvPutText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), &font2, CV_RGB(200, 0, 0)); + } + cvPutText(img, "mAP%", cvPoint(0, 12), &font, CV_RGB(255, 0, 0)); + } + sprintf(char_buff, "current avg loss = %2.4f iteration = %d", avg_loss, current_batch); - pt1.x = img_size / 2, pt1.y = 30; + pt1.x = 55, pt1.y = 10; pt2.x = pt1.x + 460, pt2.y = pt1.y + 20; cvRectangle(img, pt1, pt2, CV_RGB(255, 255, 255), CV_FILLED, 8, 0); pt1.y += 15; cvPutText(img, char_buff, pt1, &font, CV_RGB(0, 0, 0)); + + + cvShowImage("average loss", img); int k = cvWaitKey(20); if (k == 's' || current_batch == (max_batches - 1) || current_batch % 100 == 0) { diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index b45eb44f176..b0874304d43 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -19,6 +19,32 @@ image get_maxpool_delta(maxpool_layer l) return float_to_image(w,h,c,l.delta); } + +void cudnn_maxpool_setup(layer *l) +{ +#ifdef CUDNN + cudnnStatus_t maxpool_status; + maxpool_status = cudnnCreatePoolingDescriptor(&l->poolingDesc); + + maxpool_status = cudnnSetPooling2dDescriptor( + l->poolingDesc, + CUDNN_POOLING_MAX, + CUDNN_PROPAGATE_NAN, // CUDNN_PROPAGATE_NAN, CUDNN_NOT_PROPAGATE_NAN + l->size, + l->size, + 0, //l.pad, + 0, //l.pad, + l->stride, + l->stride); + + cudnnCreateTensorDescriptor(&l->srcTensorDesc); + cudnnCreateTensorDescriptor(&l->dstTensorDesc); + cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w); + cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); +#endif // CUDNN +} + + maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride, int padding) { maxpool_layer l = {0}; @@ -47,26 +73,9 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s l.indexes_gpu = cuda_make_int_array(output_size); l.output_gpu = cuda_make_array(l.output, output_size); l.delta_gpu = cuda_make_array(l.delta, output_size); -#ifdef CUDNN - cudnnStatus_t maxpool_status; - maxpool_status = cudnnCreatePoolingDescriptor(&l.poolingDesc); - maxpool_status = cudnnSetPooling2dDescriptor( - l.poolingDesc, - CUDNN_POOLING_MAX, - CUDNN_PROPAGATE_NAN, // CUDNN_PROPAGATE_NAN, CUDNN_NOT_PROPAGATE_NAN - l.size, - l.size, - 0, //l.pad, - 0, //l.pad, - l.stride, - l.stride); + cudnn_maxpool_setup(&l); - cudnnCreateTensorDescriptor(&l.srcTensorDesc); - cudnnCreateTensorDescriptor(&l.dstTensorDesc); - cudnnSetTensor4dDescriptor(l.srcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.c, l.h, l.w); - cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w); -#endif // CUDNN #endif // GPU l.bflops = (l.size*l.size*l.c * l.out_h*l.out_w) / 1000000000.; fprintf(stderr, "max %d x %d / %d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); diff --git a/src/maxpool_layer.h b/src/maxpool_layer.h index ce56dd88c7f..2b6c277ad72 100644 --- a/src/maxpool_layer.h +++ b/src/maxpool_layer.h @@ -17,7 +17,10 @@ void backward_maxpool_layer(const maxpool_layer l, network_state state); #ifdef GPU void forward_maxpool_layer_gpu(maxpool_layer l, network_state state); void backward_maxpool_layer_gpu(maxpool_layer l, network_state state); -#endif +#ifdef CUDNN +void cudnn_maxpool_setup(maxpool_layer *l); +#endif // CUDNN +#endif // GPU #endif diff --git a/src/network.c b/src/network.c index 642978ecba6..74fe71bd43a 100644 --- a/src/network.c +++ b/src/network.c @@ -377,6 +377,9 @@ void set_batch_network(network *net, int b) } */ } + else if (net->layers[i].type == MAXPOOL) { + cudnn_maxpool_setup(net->layers + i); + } #endif } } diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 090b47212ac..ac0768a2334 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -364,6 +364,7 @@ void avg_flipped_yolo(layer l) int get_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets, int letter) { + //printf("\n l.batch = %d, l.w = %d, l.h = %d, l.n = %d \n", l.batch, l.w, l.h, l.n); int i,j,n; float *predictions = l.output; if (l.batch == 2) avg_flipped_yolo(l); @@ -376,6 +377,7 @@ int get_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, float objectness = predictions[obj_index]; //if(objectness <= thresh) continue; // incorrect behavior for Nan values if (objectness > thresh) { + //printf("\n objectness = %f, thresh = %f, i = %d, n = %d \n", objectness, thresh, i, n); int box_index = entry_index(l, 0, n*l.w*l.h + i, 0); dets[count].bbox = get_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h); dets[count].objectness = objectness; From 7bd6b2f7201baa6777d1f50652beb36578374e86 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 9 Dec 2018 18:28:51 +0300 Subject: [PATCH 0194/1365] Compilation fix --- src/detector.c | 4 ++++ src/network.h | 2 +- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/src/detector.c b/src/detector.c index baedab5ff96..021b3008cb2 100644 --- a/src/detector.c +++ b/src/detector.c @@ -221,6 +221,10 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i avg_loss = avg_loss*.9 + loss*.1; i = get_current_batch(net); + if (net.cudnn_half) { + if (i < net.burn_in) printf("\n Tensor Cores are disabled until the first %d iterations are reached.", net.burn_in); + else printf("\n Tensor Cores are used."); + } printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now()-time), i*imgs); #ifdef OPENCV diff --git a/src/network.h b/src/network.h index f630fb96aff..65029947e1a 100644 --- a/src/network.h +++ b/src/network.h @@ -145,7 +145,7 @@ YOLODLL_API void reset_rnn(network *net); YOLODLL_API network *load_network_custom(char *cfg, char *weights, int clear, int batch); YOLODLL_API network *load_network(char *cfg, char *weights, int clear); YOLODLL_API float *network_predict_image(network *net, image im); -YOLODLL_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show); +YOLODLL_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map); YOLODLL_API int network_width(network *net); YOLODLL_API int network_height(network *net); From 89cb87768959058990e755614ab77eb2825b3e7c Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 10 Dec 2018 01:24:58 +0300 Subject: [PATCH 0195/1365] Update Readme.md --- README.md | 49 +++++++++++++++++++++++++++---------------------- 1 file changed, 27 insertions(+), 22 deletions(-) diff --git a/README.md b/README.md index ad506796edb..6c1fdc14153 100644 --- a/README.md +++ b/README.md @@ -114,20 +114,18 @@ Also, you might be interested in using a simplified repository where is implemen On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights` * **Yolo v3** COCO - image: `darknet.exe detector test data/coco.data cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` +* Output coordinates of objects: `darknet.exe detector test data/coco.data yolov3.cfg yolov3.weights -ext_output dog.jpg` +* **Yolo v3** COCO - video: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -ext_output test.mp4` +* **Yolo v3** COCO - WebCam 0: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -c 0` +* **Yolo v3** COCO for net-videocam - Smart WebCam: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg` +* **Yolo v3 - save result to the file res.avi**: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -thresh 0.25 test.mp4 -out_filename res.avi` +* **Yolo v3 Tiny** COCO - video: `darknet.exe detector demo data/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4` +* **Yolo v3 Tiny** on GPU #0: `darknet.exe detector demo data/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 0 test.mp4` * Alternative method Yolo v3 COCO - image: `darknet.exe detect cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` -* Output coordinates of objects: `darknet.exe detector test data/coco.data yolov3.cfg yolov3.weights -thresh 0.25 dog.jpg -ext_output` -* 194 MB VOC-model - image: `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -i 0` -* 194 MB VOC-model - video: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights test.mp4 -i 0` -* 194 MB VOC-model - **save result to the file res.avi**: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights test.mp4 -i 0 -out_filename res.avi` -* Alternative method 194 MB VOC-model - video: `darknet.exe yolo demo yolo-voc.cfg yolo-voc.weights test.mp4 -i 0` -* 43 MB VOC-model for video: `darknet.exe detector demo data/coco.data cfg/yolov2-tiny.cfg yolov2-tiny.weights test.mp4 -i 0` -* **Yolo v3** 236 MB COCO for net-videocam - Smart WebCam: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` -* 194 MB VOC-model for net-videocam - Smart WebCam: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` -* 194 MB VOC-model - WebCamera #0: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights -c 0` * 186 MB Yolo9000 - image: `darknet.exe detector test cfg/combine9k.data yolo9000.cfg yolo9000.weights` * Remeber to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app * To process a list of images `data/train.txt` and save results of detection to `result.txt` use: - `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -dont_show -ext_output < data/train.txt > result.txt` + `darknet.exe detector test cfg/coco.data yolov3.cfg yolov3.weights -dont_show -ext_output < data/train.txt > result.txt` ##### For using network video-camera mjpeg-stream with any Android smartphone: @@ -142,8 +140,8 @@ On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector 4. Replace the address below, on shown in the phone application (Smart WebCam) and launch: -* 194 MB COCO-model: `darknet.exe detector demo data/coco.data yolo.cfg yolo.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` -* 194 MB VOC-model: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` +* Yolo v3 COCO-model: `darknet.exe detector demo data/coco.data yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` + ### How to compile on Linux: @@ -160,7 +158,7 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c ### How to compile on Windows: -1. If you have **MSVS 2015, CUDA 9.1, cuDNN 7.0 and OpenCV 3.x** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release** https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg and do the: Build -> Build darknet. **NOTE:** If installing OpenCV, use OpenCV 3.4.0 or earlier. This is a bug in OpenCV 3.4.1 in the C API (see [#500](https://github.com/AlexeyAB/darknet/issues/500)). +1. If you have **MSVS 2015, CUDA 10.0, cuDNN 7.4 and OpenCV 3.x** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release** https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg and do the: Build -> Build darknet. Also add Windows system variable `cudnn` with path to CUDNN: https://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg **NOTE:** If installing OpenCV, use OpenCV 3.4.0 or earlier. This is a bug in OpenCV 3.4.1 in the C API (see [#500](https://github.com/AlexeyAB/darknet/issues/500)). 1.1. Find files `opencv_world320.dll` and `opencv_ffmpeg320_64.dll` (or `opencv_world340.dll` and `opencv_ffmpeg340_64.dll`) in `C:\opencv_3.0\opencv\build\x64\vc14\bin` and put it near with `darknet.exe` @@ -168,13 +166,13 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c 1.3. To install CUDNN (speedup neural network), do the following: - * download and install **cuDNN 7.0 for CUDA 9.1**: https://developer.nvidia.com/cudnn + * download and install **cuDNN v7.4.1 for CUDA 10.0**: https://developer.nvidia.com/cudnn * add Windows system variable `cudnn` with path to CUDNN: https://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg 1.4. If you want to build **without CUDNN** then: open `\darknet.sln` -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and remove this: `CUDNN;` -2. If you have other version of **CUDA (not 9.1)** then open `build\darknet\darknet.vcxproj` by using Notepad, find 2 places with "CUDA 9.1" and change it to your CUDA-version, then do step 1 +2. If you have other version of **CUDA (not 10.0)** then open `build\darknet\darknet.vcxproj` by using Notepad, find 2 places with "CUDA 9.1" and change it to your CUDA-version, then do step 1 3. If you **don't have GPU**, but have **MSVS 2015 and OpenCV 3.0** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet_no_gpu.sln`, set **x64** and **Release**, and do the: Build -> Build darknet_no_gpu @@ -253,11 +251,11 @@ More information about training by the link: http://pjreddie.com/darknet/yolo/#t 1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg darknet53.conv.74` -2. Adjust the learning rate (`cfg/yolov3-voc.cfg`) to fit the amount of GPUs. The learning rate should be equal to `0.001`, regardless of how many GPUs are used for training. So `learning_rate * GPUs = 0.001`. For 4 GPUs adjust the value to `learning_rate = 0.00025`. +2. For 4xGPUs - increase 4x times `burn_in =` and `max_batches =` in your cfg-file. I.e. use `burn_in = 4000` instead of `1000`. -3. For 4xGPUs - increase 4x times `burn_in =` and `max_batches =` in your cfg-file. I.e. use `burn_in = 4000` instead of `1000`. +3. Then stop and by using partially-trained model `/backup/yolov3-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg /backup/yolov3-voc_1000.weights -gpus 0,1,2,3` -4. Then stop and by using partially-trained model `/backup/yolov3-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg /backup/yolov3-voc_1000.weights -gpus 0,1,2,3` +Only for small datasets sometimes better to decrease learning rate, for 4 GPUs set `learning_rate = 0.00025` (i.e. learning_rate = 0.001 / GPUs). https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ @@ -338,9 +336,12 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an 7. Download pre-trained weights for the convolutional layers (154 MB): https://pjreddie.com/media/files/darknet53.conv.74 and put to the directory `build\darknet\x64` 8. Start training by using the command line: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74` + + * (file `yolo-obj_last.weights` will be saved to the `build\darknet\x64\backup\` for each 100 iterations) + * (file `yolo-obj_xxxx.weights` will be saved to the `build\darknet\x64\backup\` for each 1000 iterations) + * (To disable Loss-Window use `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -dont_show`, if you train on computer without monitor like a cloud Amazaon EC2) - (file `yolo-obj_xxx.weights` will be saved to the `build\darknet\x64\backup\` for each 100 iterations) - (To disable Loss-Window use `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -dont_show`, if you train on computer without monitor like a cloud Amazaon EC2) +8.1. For training with mAP (mean average precisions) calculation for each 4 Epochs (set `valid=valid.txt` or `train.txt` in `obj.data` file): `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -map` 9. After training is complete - get result `yolo-obj_final.weights` from path `build\darknet\x64\backup\` @@ -406,9 +407,13 @@ To get weights from Early Stopping Point: And comapre last output lines for each weights (7000, 8000, 9000): -Choose weights-file **with the highest IoU** (intersect of union) and mAP (mean average precision) +Choose weights-file **with the highest mAP (mean average precision)** or IoU (intersect of union) + +For example, **bigger mAP** gives weights `yolo-obj_8000.weights` - then **use this weights for detection**. + +Or just train with `-map` flag: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -map` So you will see loss-chart with mAP-chart (mAP will be calculated for each 4 Epochs using `valid=valid.txt` file that is specified in `obj.data` file) -For example, **bigger IOU** gives weights `yolo-obj_8000.weights` - then **use this weights for detection**. +![loss_chart_map_chart](https://hsto.org/webt/ip/fx/tn/ipfxtn_fpxwh_0zj8kvm2kdgpd4.jpeg) Example of custom object detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights` From ca3bf2fa92192cc6b53532588f01fa5f67b04b7c Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 10 Dec 2018 01:30:48 +0300 Subject: [PATCH 0196/1365] Update Readme.md --- README.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 6c1fdc14153..d621f9d8361 100644 --- a/README.md +++ b/README.md @@ -411,9 +411,13 @@ Choose weights-file **with the highest mAP (mean average precision)** or IoU (in For example, **bigger mAP** gives weights `yolo-obj_8000.weights` - then **use this weights for detection**. -Or just train with `-map` flag: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -map` So you will see loss-chart with mAP-chart (mAP will be calculated for each 4 Epochs using `valid=valid.txt` file that is specified in `obj.data` file) +Or just train with `-map` flag: -![loss_chart_map_chart](https://hsto.org/webt/ip/fx/tn/ipfxtn_fpxwh_0zj8kvm2kdgpd4.jpeg) +`darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -map` + +So you will see loss-chart with mAP-chart (mAP will be calculated for each 4 Epochs using `valid=valid.txt` file that is specified in `obj.data` file) + +![loss_chart_map_chart](https://hsto.org/webt/yd/vl/ag/ydvlagutof2zcnjodstgroen8ac.jpeg) Example of custom object detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights` From 89c6213af08a4142c15317d09515f0f1b658a238 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 10 Dec 2018 01:33:32 +0300 Subject: [PATCH 0197/1365] When is used -map flag for training - calculate mAP for each 4 Epochs. --- src/detector.c | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/detector.c b/src/detector.c index 021b3008cb2..4841041aa44 100644 --- a/src/detector.c +++ b/src/detector.c @@ -46,7 +46,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i char *valid_images = option_find_str(options, "valid", train_images); char *backup_directory = option_find_str(options, "backup", "/backup/"); - int valid_images_num = 0; + int train_images_num = 0; if (calc_map) { FILE* valid_file = fopen(valid_images, "r"); if (!valid_file) { @@ -55,8 +55,8 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i exit(-1); } else fclose(valid_file); - list *plist = get_paths(valid_images); - valid_images_num = plist->size; + list *plist = get_paths(train_images); + train_images_num = plist->size; free_list(plist); } @@ -229,7 +229,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i #ifdef OPENCV if (!dont_show) { - if (calc_map && (i >= (iter_map + valid_images_num/10) || i == net.max_batches) && i >= 1000) { + if (calc_map && (i >= (iter_map + 4*train_images_num/net.batch) || i == net.max_batches) && i >= 1000) { iter_map = i; mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net); printf("\n mean_average_precision = %f \n", mean_average_precision); From a621235783402ac9d4ccc22608069dd1ff85c4ca Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 10 Dec 2018 01:35:08 +0300 Subject: [PATCH 0198/1365] Switch to Tensor Cores after 2000 iterations. --- src/convolutional_kernels.cu | 4 ++-- src/detector.c | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 10a17baf4f9..12c88c4df4d 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -295,7 +295,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //#ifdef CUDNN_HALF //if (state.use_mixed_precision) { int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); - if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > state.net.burn_in)) + if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 2*state.net.burn_in)) { //printf("\n CUDNN_HALF!!! state.index = %d \n", state.index); @@ -475,7 +475,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state //#ifdef CUDNN_HALF int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); - if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > state.net.burn_in)) + if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 2*state.net.burn_in)) { const size_t input16_size = l.batch*l.c*l.w*l.h; diff --git a/src/detector.c b/src/detector.c index 4841041aa44..bee8ef17280 100644 --- a/src/detector.c +++ b/src/detector.c @@ -222,7 +222,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i i = get_current_batch(net); if (net.cudnn_half) { - if (i < net.burn_in) printf("\n Tensor Cores are disabled until the first %d iterations are reached.", net.burn_in); + if (i < net.burn_in) printf("\n Tensor Cores are disabled until the first %d iterations are reached.", 2*net.burn_in); else printf("\n Tensor Cores are used."); } printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now()-time), i*imgs); From 3256967119b100231dd11b181431bad0e2e46201 Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 10 Dec 2018 01:37:40 +0300 Subject: [PATCH 0199/1365] Update Readme.md --- README.md | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index d621f9d8361..5765acd647e 100644 --- a/README.md +++ b/README.md @@ -251,11 +251,9 @@ More information about training by the link: http://pjreddie.com/darknet/yolo/#t 1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg darknet53.conv.74` -2. For 4xGPUs - increase 4x times `burn_in =` and `max_batches =` in your cfg-file. I.e. use `burn_in = 4000` instead of `1000`. +2. Then stop and by using partially-trained model `/backup/yolov3-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg /backup/yolov3-voc_1000.weights -gpus 0,1,2,3` -3. Then stop and by using partially-trained model `/backup/yolov3-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg /backup/yolov3-voc_1000.weights -gpus 0,1,2,3` - -Only for small datasets sometimes better to decrease learning rate, for 4 GPUs set `learning_rate = 0.00025` (i.e. learning_rate = 0.001 / GPUs). +Only for small datasets sometimes better to decrease learning rate, for 4 GPUs set `learning_rate = 0.00025` (i.e. learning_rate = 0.001 / GPUs). In this case also increase 4x times `burn_in =` and `max_batches =` in your cfg-file. I.e. use `burn_in = 4000` instead of `1000`. https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ From fb1ee79576f83d505211267252e070c3cb0f431e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 10 Dec 2018 02:22:45 +0300 Subject: [PATCH 0200/1365] Minor fix --- src/detector.c | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/detector.c b/src/detector.c index bee8ef17280..5a970add483 100644 --- a/src/detector.c +++ b/src/detector.c @@ -229,7 +229,8 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i #ifdef OPENCV if (!dont_show) { - if (calc_map && (i >= (iter_map + 4*train_images_num/net.batch) || i == net.max_batches) && i >= 1000) { + int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions); + if (calc_map && (i >= (iter_map + calc_map_for_each) || i == net.max_batches) && i >= net.burn_in && i >= 1000) { iter_map = i; mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net); printf("\n mean_average_precision = %f \n", mean_average_precision); From e9226be3eddc89d43d7fcfef5d8220c22929fe30 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 11 Dec 2018 15:59:23 +0300 Subject: [PATCH 0201/1365] calc_anchors can be used without OpenCV, and it uses (1-IoU) instead of Euclidean distance --- build/darknet/x64/cfg/yolov3_5l.cfg | 968 ++++++++++++++++++++++++++++ cfg/yolov3_5l.cfg | 968 ++++++++++++++++++++++++++++ src/detector.c | 147 +++-- src/matrix.c | 137 ++++ src/matrix.h | 7 + 5 files changed, 2159 insertions(+), 68 deletions(-) create mode 100644 build/darknet/x64/cfg/yolov3_5l.cfg create mode 100644 cfg/yolov3_5l.cfg diff --git a/build/darknet/x64/cfg/yolov3_5l.cfg b/build/darknet/x64/cfg/yolov3_5l.cfg new file mode 100644 index 00000000000..fec157e0a52 --- /dev/null +++ b/build/darknet/x64/cfg/yolov3_5l.cfg @@ -0,0 +1,968 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=16 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 12,13,14 +anchors = 4,4, 5,5, 6,6, 7,7, 8,8, 9,9, 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=15 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 9,10,11 +anchors = 4,4, 5,5, 6,6, 7,7, 8,8, 9,9, 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=15 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 4,4, 5,5, 6,6, 7,7, 8,8, 9,9, 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=15 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + + +############### + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 11 + + + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 4,4, 5,5, 6,6, 7,7, 8,8, 9,9, 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=15 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + + + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 4 + + + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=64 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=64 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=64 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 4,4, 5,5, 6,6, 7,7, 8,8, 9,9, 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=15 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + diff --git a/cfg/yolov3_5l.cfg b/cfg/yolov3_5l.cfg new file mode 100644 index 00000000000..fec157e0a52 --- /dev/null +++ b/cfg/yolov3_5l.cfg @@ -0,0 +1,968 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=16 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 12,13,14 +anchors = 4,4, 5,5, 6,6, 7,7, 8,8, 9,9, 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=15 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 9,10,11 +anchors = 4,4, 5,5, 6,6, 7,7, 8,8, 9,9, 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=15 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 4,4, 5,5, 6,6, 7,7, 8,8, 9,9, 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=15 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + + +############### + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 11 + + + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 4,4, 5,5, 6,6, 7,7, 8,8, 9,9, 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=15 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + + + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 4 + + + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=64 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=64 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=64 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 4,4, 5,5, 6,6, 7,7, 8,8, 9,9, 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=15 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + diff --git a/src/detector.c b/src/detector.c index 5a970add483..fa550fe5dbc 100644 --- a/src/detector.c +++ b/src/detector.c @@ -964,7 +964,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa return mean_average_precision; } -#ifdef OPENCV +//#ifdef OPENCV typedef struct { float w, h; } anchors_t; @@ -979,6 +979,17 @@ int anchors_comparator(const void *pa, const void *pb) return 0; } +int anchors_data_comparator(const float **pa, const float **pb) +{ + float *a = (float *)*pa; + float *b = (float *)*pb; + float diff = b[0]*b[1] - a[0]*a[1]; + if (diff < 0) return 1; + else if (diff > 0) return -1; + return 0; +} + + void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int show) { printf("\n num_of_clusters = %d, width = %d, height = %d \n", num_of_clusters, width, height); @@ -991,12 +1002,14 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int //float pointsdata[] = { 1,1, 2,2, 6,6, 5,5, 10,10 }; float *rel_width_height_array = calloc(1000, sizeof(float)); + list *options = read_data_cfg(datacfg); char *train_images = option_find_str(options, "train", "data/train.list"); list *plist = get_paths(train_images); int number_of_images = plist->size; char **paths = (char **)list_to_array(plist); + srand(time(0)); int number_of_boxes = 0; printf(" read labels from %d images \n", number_of_images); @@ -1030,85 +1043,57 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int } } printf("\n all loaded. \n"); + printf("\n calculating k-means++ ..."); - CvMat* points = cvCreateMat(number_of_boxes, 2, CV_32FC1); - CvMat* centers = cvCreateMat(num_of_clusters, 2, CV_32FC1); - CvMat* labels = cvCreateMat(number_of_boxes, 1, CV_32SC1); + matrix boxes_data; + model anchors_data; + boxes_data = make_matrix(number_of_boxes, 2); + printf("\n"); for (i = 0; i < number_of_boxes; ++i) { - points->data.fl[i * 2] = rel_width_height_array[i * 2]; - points->data.fl[i * 2 + 1] = rel_width_height_array[i * 2 + 1]; - //cvSet1D(points, i * 2, cvScalar(rel_width_height_array[i * 2], 0, 0, 0)); - //cvSet1D(points, i * 2 + 1, cvScalar(rel_width_height_array[i * 2 + 1], 0, 0, 0)); + float w = boxes_data.vals[i][0] = rel_width_height_array[i * 2]; + float h = boxes_data.vals[i][1] = rel_width_height_array[i * 2 + 1]; + //if (w > 410 || h > 410) printf("i:%d, w = %f, h = %f \n", i, w, h); } + // Is used: distance(box, centroid) = 1 - IoU(box, centroid) - const int attemps = 10; - double compactness; + // K-means + anchors_data = do_kmeans(boxes_data, num_of_clusters); - enum { - KMEANS_RANDOM_CENTERS = 0, - KMEANS_USE_INITIAL_LABELS = 1, - KMEANS_PP_CENTERS = 2 - }; - - printf("\n calculating k-means++ ..."); - // Should be used: distance(box, centroid) = 1 - IoU(box, centroid) - cvKMeans2(points, num_of_clusters, labels, - cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 10000, 0), attemps, - 0, KMEANS_PP_CENTERS, - centers, &compactness); - - // sort anchors - qsort(centers->data.fl, num_of_clusters, 2*sizeof(float), anchors_comparator); - - //orig 2.0 anchors = 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52 - //float orig_anch[] = { 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52 }; - // worse than ours (even for 19x19 final size - for input size 608x608) - - //orig anchors = 1.3221,1.73145, 3.19275,4.00944, 5.05587,8.09892, 9.47112,4.84053, 11.2364,10.0071 - //float orig_anch[] = { 1.3221,1.73145, 3.19275,4.00944, 5.05587,8.09892, 9.47112,4.84053, 11.2364,10.0071 }; - // orig (IoU=59.90%) better than ours (59.75%) + qsort(anchors_data.centers.vals, num_of_clusters, 2 * sizeof(float), anchors_data_comparator); //gen_anchors.py = 1.19, 1.99, 2.79, 4.60, 4.53, 8.92, 8.06, 5.29, 10.32, 10.66 //float orig_anch[] = { 1.19, 1.99, 2.79, 4.60, 4.53, 8.92, 8.06, 5.29, 10.32, 10.66 }; - // ours: anchors = 9.3813,6.0095, 3.3999,5.3505, 10.9476,11.1992, 5.0161,9.8314, 1.5003,2.1595 - //float orig_anch[] = { 9.3813,6.0095, 3.3999,5.3505, 10.9476,11.1992, 5.0161,9.8314, 1.5003,2.1595 }; - //for (i = 0; i < num_of_clusters * 2; ++i) centers->data.fl[i] = orig_anch[i]; - - //for (i = 0; i < number_of_boxes; ++i) - // printf("%2.2f,%2.2f, ", points->data.fl[i * 2], points->data.fl[i * 2 + 1]); - printf("\n"); float avg_iou = 0; for (i = 0; i < number_of_boxes; ++i) { - float box_w = points->data.fl[i * 2]; - float box_h = points->data.fl[i * 2 + 1]; + float box_w = rel_width_height_array[i * 2]; //points->data.fl[i * 2]; + float box_h = rel_width_height_array[i * 2 + 1]; //points->data.fl[i * 2 + 1]; //int cluster_idx = labels->data.i[i]; int cluster_idx = 0; float min_dist = FLT_MAX; + float best_iou = 0; for (j = 0; j < num_of_clusters; ++j) { - float anchor_w = centers->data.fl[j * 2]; - float anchor_h = centers->data.fl[j * 2 + 1]; - float w_diff = anchor_w - box_w; - float h_diff = anchor_h - box_h; - float distance = sqrt(w_diff*w_diff + h_diff*h_diff); - if (distance < min_dist) min_dist = distance, cluster_idx = j; + float anchor_w = anchors_data.centers.vals[j][0]; // centers->data.fl[j * 2]; + float anchor_h = anchors_data.centers.vals[j][1]; // centers->data.fl[j * 2 + 1]; + float min_w = (box_w < anchor_w) ? box_w : anchor_w; + float min_h = (box_h < anchor_h) ? box_h : anchor_h; + float box_intersect = min_w*min_h; + float box_union = box_w*box_h + anchor_w*anchor_h - box_intersect; + float iou = box_intersect / box_union; + float distance = 1 - iou; + if (distance < min_dist) min_dist = distance, cluster_idx = j, best_iou = iou; } - float anchor_w = centers->data.fl[cluster_idx * 2]; - float anchor_h = centers->data.fl[cluster_idx * 2 + 1]; - float min_w = (box_w < anchor_w) ? box_w : anchor_w; - float min_h = (box_h < anchor_h) ? box_h : anchor_h; - float box_intersect = min_w*min_h; - float box_union = box_w*box_h + anchor_w*anchor_h - box_intersect; - float iou = box_intersect / box_union; - if (iou > 1 || iou < 0) { // || box_w > width || box_h > height) { + float anchor_w = anchors_data.centers.vals[cluster_idx][0]; //centers->data.fl[cluster_idx * 2]; + float anchor_h = anchors_data.centers.vals[cluster_idx][1]; //centers->data.fl[cluster_idx * 2 + 1]; + if (best_iou > 1 || best_iou < 0) { // || box_w > width || box_h > height) { printf(" Wrong label: i = %d, box_w = %d, box_h = %d, anchor_w = %d, anchor_h = %d, iou = %f \n", - i, box_w, box_h, anchor_w, anchor_h, iou); + i, box_w, box_h, anchor_w, anchor_h, best_iou); } - else avg_iou += iou; + else avg_iou += best_iou; } avg_iou = 100 * avg_iou / number_of_boxes; printf("\n avg IoU = %2.2f %% \n", avg_iou); @@ -1119,7 +1104,10 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int printf("\nSaving anchors to the file: anchors.txt \n"); printf("anchors = "); for (i = 0; i < num_of_clusters; ++i) { - sprintf(buff, "%2.4f,%2.4f", centers->data.fl[i * 2], centers->data.fl[i * 2 + 1]); + float anchor_w = anchors_data.centers.vals[i][0]; //centers->data.fl[i * 2]; + float anchor_h = anchors_data.centers.vals[i][1]; //centers->data.fl[i * 2 + 1]; + if(width > 32) sprintf(buff, "%3.0f,%3.0f", anchor_w, anchor_h); + else sprintf(buff, "%2.4f,%2.4f", anchor_w, anchor_h); printf("%s", buff); fwrite(buff, sizeof(char), strlen(buff), fw); if (i + 1 < num_of_clusters) { @@ -1135,6 +1123,27 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int } if (show) { +#ifdef OPENCV + CvMat* labels = cvCreateMat(number_of_boxes, 1, CV_32SC1); + CvMat* points = cvCreateMat(number_of_boxes, 2, CV_32FC1); + CvMat* centers = cvCreateMat(num_of_clusters, 2, CV_32FC1); + + for (i = 0; i < number_of_boxes; ++i) { + points->data.fl[i * 2] = rel_width_height_array[i * 2]; + points->data.fl[i * 2 + 1] = rel_width_height_array[i * 2 + 1]; + //cvSet1D(points, i * 2, cvScalar(rel_width_height_array[i * 2], 0, 0, 0)); + //cvSet1D(points, i * 2 + 1, cvScalar(rel_width_height_array[i * 2 + 1], 0, 0, 0)); + } + + for (i = 0; i < num_of_clusters; ++i) { + centers->data.fl[i * 2] = anchors_data.centers.vals[i][0]; + centers->data.fl[i * 2 + 1] = anchors_data.centers.vals[i][1]; + } + + for (i = 0; i < number_of_boxes; ++i) { + labels->data.i[i] = anchors_data.assignments[i]; + } + size_t img_size = 700; IplImage* img = cvCreateImage(cvSize(img_size, img_size), 8, 3); cvZero(img); @@ -1161,18 +1170,20 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int cvWaitKey(0); cvReleaseImage(&img); cvDestroyAllWindows(); + cvReleaseMat(&labels); + cvReleaseMat(&points); + cvReleaseMat(¢ers); +#endif // OPENCV } - free(rel_width_height_array); - cvReleaseMat(&points); - cvReleaseMat(¢ers); - cvReleaseMat(&labels); -} -#else -void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int show) { - printf(" k-means++ can't be used without OpenCV, because there is used cvKMeans2 implementation \n"); + + getchar(); } -#endif // OPENCV +//#else +//void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int show) { +// printf(" k-means++ can't be used without OpenCV, because there is used cvKMeans2 implementation \n"); +//} +//#endif // OPENCV void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, int dont_show, int ext_output, int save_labels) diff --git a/src/matrix.c b/src/matrix.c index 08e6109b733..e0eb61e67bd 100644 --- a/src/matrix.c +++ b/src/matrix.c @@ -179,3 +179,140 @@ void print_matrix(matrix m) for(j = 0; j < 16*m.cols-1; ++j) printf(" "); printf("__|\n"); } + + +matrix make_matrix(int rows, int cols); + +void copy(float *x, float *y, int n); +float dist(float *x, float *y, int n); +int *sample(int n); + +int closest_center(float *datum, matrix centers) +{ + int j; + int best = 0; + float best_dist = dist(datum, centers.vals[best], centers.cols); + for (j = 0; j < centers.rows; ++j) { + float new_dist = dist(datum, centers.vals[j], centers.cols); + if (new_dist < best_dist) { + best_dist = new_dist; + best = j; + } + } + return best; +} + +float dist_to_closest_center(float *datum, matrix centers) +{ + int ci = closest_center(datum, centers); + return dist(datum, centers.vals[ci], centers.cols); +} + +int kmeans_expectation(matrix data, int *assignments, matrix centers) +{ + int i; + int converged = 1; + for (i = 0; i < data.rows; ++i) { + int closest = closest_center(data.vals[i], centers); + if (closest != assignments[i]) converged = 0; + assignments[i] = closest; + } + return converged; +} + +void kmeans_maximization(matrix data, int *assignments, matrix centers) +{ + int i, j; + int *counts = calloc(centers.rows, sizeof(int)); + for (i = 0; i < centers.rows; ++i) { + for (j = 0; j < centers.cols; ++j) centers.vals[i][j] = 0; + } + for (i = 0; i < data.rows; ++i) { + ++counts[assignments[i]]; + for (j = 0; j < data.cols; ++j) { + centers.vals[assignments[i]][j] += data.vals[i][j]; + } + } + for (i = 0; i < centers.rows; ++i) { + if (counts[i]) { + for (j = 0; j < centers.cols; ++j) { + centers.vals[i][j] /= counts[i]; + } + } + } +} + + + +void random_centers(matrix data, matrix centers) { + int i, j; + int *s = sample(data.rows); + for (i = 0; i < centers.rows; ++i) { + copy(data.vals[s[i]], centers.vals[i], data.cols); + } + free(s); +} + +int *sample(int n) +{ + int i; + int *s = calloc(n, sizeof(int)); + for (i = 0; i < n; ++i) s[i] = i; + for (i = n - 1; i >= 0; --i) { + int swap = s[i]; + int index = rand() % (i + 1); + s[i] = s[index]; + s[index] = swap; + } + return s; +} + +float dist(float *x, float *y, int n) +{ + int i; + //printf(" x0 = %f, x1 = %f, y0 = %f, y1 = %f \n", x[0], x[1], y[0], y[1]); + float mw = (x[0] < y[0]) ? x[0] : y[0]; + float mh = (x[1] < y[1]) ? x[1] : y[1]; + float inter = mw*mh; + float sum = x[0] * x[1] + y[0] * y[1]; + float un = sum - inter; + float iou = inter / un; + return 1 - iou; +} + +void copy(float *x, float *y, int n) +{ + int i; + for (i = 0; i < n; ++i) y[i] = x[i]; +} + +model do_kmeans(matrix data, int k) +{ + matrix centers = make_matrix(k, data.cols); + int *assignments = calloc(data.rows, sizeof(int)); + //smart_centers(data, centers); + random_centers(data, centers); // IoU = 67.31% after kmeans + // + /* + // IoU = 63.29%, anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 + centers.vals[0][0] = 10; centers.vals[0][1] = 13; + centers.vals[1][0] = 16; centers.vals[1][1] = 30; + centers.vals[2][0] = 33; centers.vals[2][1] = 23; + centers.vals[3][0] = 30; centers.vals[3][1] = 61; + centers.vals[4][0] = 62; centers.vals[4][1] = 45; + centers.vals[5][0] = 59; centers.vals[5][1] = 119; + centers.vals[6][0] = 116; centers.vals[6][1] = 90; + centers.vals[7][0] = 156; centers.vals[7][1] = 198; + centers.vals[8][0] = 373; centers.vals[8][1] = 326; + */ + + // range centers [min - max] using exp graph or Pyth example + if (k == 1) kmeans_maximization(data, assignments, centers); + while (!kmeans_expectation(data, assignments, centers)) { + kmeans_maximization(data, assignments, centers); + } + model m; + m.assignments = assignments; + m.centers = centers; + return m; +} diff --git a/src/matrix.h b/src/matrix.h index 641b5965d32..1226de2bcd1 100644 --- a/src/matrix.h +++ b/src/matrix.h @@ -5,6 +5,13 @@ typedef struct matrix{ float **vals; } matrix; +typedef struct { + int *assignments; + matrix centers; +} model; + + +model do_kmeans(matrix data, int k); matrix make_matrix(int rows, int cols); void free_matrix(matrix m); void print_matrix(matrix m); From cb998db9496fecd82ce7a1192b21c3cffaa95d48 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 11 Dec 2018 21:16:18 +0300 Subject: [PATCH 0202/1365] Some fix for CUDNN_HALF --- src/convolutional_kernels.cu | 6 +++-- src/convolutional_layer.c | 47 ++++++++++++++++++++++++++++++++++-- 2 files changed, 49 insertions(+), 4 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 12c88c4df4d..af685631e55 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -295,7 +295,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //#ifdef CUDNN_HALF //if (state.use_mixed_precision) { int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); - if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 2*state.net.burn_in)) + if (state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in)) + //if(state.index != 0) { //printf("\n CUDNN_HALF!!! state.index = %d \n", state.index); @@ -475,7 +476,8 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state //#ifdef CUDNN_HALF int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); - if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 2*state.net.burn_in)) + if (state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in)) + //if (state.index != 0) { const size_t input16_size = l.batch*l.c*l.w*l.h; diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 9fc33c0cd45..b05b3d00b4f 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -136,6 +136,43 @@ size_t get_workspace_size(layer l){ return (size_t)l.out_h*l.out_w*l.size*l.size*l.c*sizeof(float); } +size_t get_workspace_size16(layer l) { +#if defined(CUDNN) && defined(CUDNN_HALF) + if (gpu_index >= 0) { + size_t most = 0; + size_t s = 0; + cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(), + l.srcTensorDesc16, + l.weightDesc16, + l.convDesc, + l.dstTensorDesc16, + l.fw_algo16, + &s); + if (s > most) most = s; + cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnn_handle(), + l.srcTensorDesc16, + l.ddstTensorDesc16, + l.convDesc, + l.dweightDesc16, + l.bf_algo16, + &s); + if (s > most) most = s; + cudnnGetConvolutionBackwardDataWorkspaceSize(cudnn_handle(), + l.weightDesc16, + l.ddstTensorDesc16, + l.convDesc, + l.dsrcTensorDesc16, + l.bd_algo16, + &s); + if (s > most) most = s; + return most; + } +#endif + return 0; + //if (l.xnor) return (size_t)l.bit_align*l.size*l.size*l.c * sizeof(float); + //return (size_t)l.out_h*l.out_w*l.size*l.size*l.c * sizeof(float); +} + #ifdef GPU #ifdef CUDNN void cudnn_convolutional_setup(layer *l, int cudnn_preference) @@ -177,7 +214,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->out_c, l->out_h, l->out_w); cudnnSetFilter4dDescriptor(l->weightDesc, data_type, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size); -#ifdef CUDNN_HALF +//#ifdef CUDNN_HALF // backward delta cudnnSetTensor4dDescriptor(l->dsrcTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->c, l->h, l->w); cudnnSetTensor4dDescriptor(l->ddstTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w); @@ -190,7 +227,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) // batch norm cudnnSetTensor4dDescriptor(l->normDstTensorDescF16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w); -#endif +//#endif // batch norm cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1); @@ -429,6 +466,8 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int } #endif l.workspace_size = get_workspace_size(l); + size_t workspace_size16 = get_workspace_size16(l); + if (workspace_size16 > l.workspace_size) l.workspace_size = workspace_size16; l.activation = activation; //fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c); @@ -532,6 +571,8 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) #endif #endif l->workspace_size = get_workspace_size(*l); + size_t workspace_size16 = get_workspace_size16(*l); + if (workspace_size16 > l->workspace_size) l->workspace_size = workspace_size16; #ifdef CUDNN // check for excessive memory consumption @@ -542,6 +583,8 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) printf(" used slow CUDNN algo without Workspace! Need memory: %zu, available: %zu\n", l->workspace_size, (free_byte < total_byte/2) ? free_byte : total_byte/2); cudnn_convolutional_setup(l, cudnn_smallest); l->workspace_size = get_workspace_size(*l); + size_t workspace_size16 = get_workspace_size16(*l); + if (workspace_size16 > l->workspace_size) l->workspace_size = workspace_size16; } #endif } From 25f133d6efcdc1d2aa79263e4fe4880ca782c79e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 11 Dec 2018 21:26:36 +0300 Subject: [PATCH 0203/1365] Another one minor fix --- src/convolutional_kernels.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index af685631e55..7d4ef8f575a 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -553,7 +553,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state state.workspace, l.workspace_size, &one, - l.dweightDesc, + l.dweightDesc16, l.weight_updates_gpu16); // l.weight_updates_gpu); cuda_convert_f16_to_f32(l.weight_updates_gpu16, l.c*l.n*l.size*l.size, l.weight_updates_gpu); From 3969ce30ed61651a63193b763f9914bd9953bbfd Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 11 Dec 2018 23:48:58 +0300 Subject: [PATCH 0204/1365] Speedup Tensor Cores: 1st layer uses FP32 and pre-allocate GPU memory for Tensor Cores --- src/convolutional_kernels.cu | 6 ++---- src/parser.c | 12 ++++++++++++ 2 files changed, 14 insertions(+), 4 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 7d4ef8f575a..44f62ed3f52 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -295,8 +295,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //#ifdef CUDNN_HALF //if (state.use_mixed_precision) { int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); - if (state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in)) - //if(state.index != 0) + if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in)) { //printf("\n CUDNN_HALF!!! state.index = %d \n", state.index); @@ -476,8 +475,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state //#ifdef CUDNN_HALF int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); - if (state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in)) - //if (state.index != 0) + if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in)) { const size_t input16_size = l.batch*l.c*l.w*l.h; diff --git a/src/parser.c b/src/parser.c index c164766626c..b01c5458cde 100644 --- a/src/parser.c +++ b/src/parser.c @@ -732,6 +732,8 @@ network parse_network_cfg_custom(char *filename, int batch) float bflops = 0; size_t workspace_size = 0; + size_t max_inputs = 0; + size_t max_outputs = 0; n = n->next; int count = 0; free_section(s); @@ -806,6 +808,8 @@ network parse_network_cfg_custom(char *filename, int batch) option_unused(options); net.layers[count] = l; if (l.workspace_size > workspace_size) workspace_size = l.workspace_size; + if (l.inputs > max_inputs) max_inputs = l.inputs; + if (l.outputs > max_outputs) max_outputs = l.outputs; free_section(s); n = n->next; ++count; @@ -828,6 +832,14 @@ network parse_network_cfg_custom(char *filename, int batch) net.workspace = cuda_make_array(0, workspace_size/sizeof(float) + 1); int size = get_network_input_size(net) * net.batch; net.input_state_gpu = cuda_make_array(0, size); + + // pre-allocate memory for inference on Tensor Cores (fp16) + if (net.cudnn_half) { + *net.max_input16_size = max_inputs; + check_error(cudaMalloc((void **)net.input16_gpu, *net.max_input16_size * sizeof(short))); //sizeof(half) + *net.max_output16_size = max_outputs; + check_error(cudaMalloc((void **)net.output16_gpu, *net.max_output16_size * sizeof(short))); //sizeof(half) + } }else { net.workspace = calloc(1, workspace_size); } From 6b4dca27d3dd3c4c7b41076596a32e7171638412 Mon Sep 17 00:00:00 2001 From: Alexey Date: Wed, 12 Dec 2018 18:05:09 +0300 Subject: [PATCH 0205/1365] Update Readm.emd --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 5765acd647e..79775f78c15 100644 --- a/README.md +++ b/README.md @@ -339,7 +339,7 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an * (file `yolo-obj_xxxx.weights` will be saved to the `build\darknet\x64\backup\` for each 1000 iterations) * (To disable Loss-Window use `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -dont_show`, if you train on computer without monitor like a cloud Amazaon EC2) -8.1. For training with mAP (mean average precisions) calculation for each 4 Epochs (set `valid=valid.txt` or `train.txt` in `obj.data` file): `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -map` +8.1. For training with mAP (mean average precisions) calculation for each 4 Epochs (set `valid=valid.txt` or `train.txt` in `obj.data` file) and run: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -map` 9. After training is complete - get result `yolo-obj_final.weights` from path `build\darknet\x64\backup\` @@ -405,7 +405,7 @@ To get weights from Early Stopping Point: And comapre last output lines for each weights (7000, 8000, 9000): -Choose weights-file **with the highest mAP (mean average precision)** or IoU (intersect of union) +Choose weights-file **with the highest mAP (mean average precision)** or IoU (intersect over union) For example, **bigger mAP** gives weights `yolo-obj_8000.weights` - then **use this weights for detection**. @@ -413,13 +413,13 @@ Or just train with `-map` flag: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -map` -So you will see loss-chart with mAP-chart (mAP will be calculated for each 4 Epochs using `valid=valid.txt` file that is specified in `obj.data` file) +So you will see mAP-chart (red-line) in the Loss-chart Window. mAP will be calculated for each 4 Epochs using `valid=valid.txt` file that is specified in `obj.data` file (`1 Epoch = images_in_train_txt / batch` iterations) ![loss_chart_map_chart](https://hsto.org/webt/yd/vl/ag/ydvlagutof2zcnjodstgroen8ac.jpeg) Example of custom object detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights` -* **IoU** (intersect of union) - average instersect of union of objects and detections for a certain threshold = 0.24 +* **IoU** (intersect over union) - average instersect over union of objects and detections for a certain threshold = 0.24 * **mAP** (mean average precision) - mean value of `average precisions` for each class, where `average precision` is average value of 11 points on PR-curve for each possible threshold (each probability of detection) for the same class (Precision-Recall in terms of PascalVOC, where Precision=TP/(TP+FP) and Recall=TP/(TP+FN) ), page-11: http://homepages.inf.ed.ac.uk/ckiw/postscript/ijcv_voc09.pdf @@ -499,7 +499,7 @@ With example of: `train.txt`, `obj.names`, `obj.data`, `yolo-obj.cfg`, `air`1-6` ## Using Yolo9000 - Simultaneous detection and classification of 9000 objects: + Simultaneous detection and classification of 9000 objects: `darknet.exe detector test cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights data/dog.jpg` * `yolo9000.weights` - (186 MB Yolo9000 Model) requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo9000.weights From f0bed8ef735f03e4d2ccbed9d1683b4f63043f44 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 12 Dec 2018 23:20:44 +0300 Subject: [PATCH 0206/1365] Fixed drawing mAP --- src/classifier.c | 4 ++-- src/detector.c | 6 ++++-- src/image.c | 42 ++++++++++++++++++++---------------------- 3 files changed, 26 insertions(+), 26 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index 7324349b86e..d124f632a98 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -25,7 +25,7 @@ image get_image_from_stream_cpp(CvCapture *cap); #include "http_stream.h" IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size); -void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, float precision); +void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, float precision, int draw_precision); #endif @@ -153,7 +153,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); #ifdef OPENCV if(!dont_show) - draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, -1); + draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, -1, 0); #endif // OPENCV if (i >= (iter_save + 100)) { diff --git a/src/detector.c b/src/detector.c index fa550fe5dbc..22f2322ddfb 100644 --- a/src/detector.c +++ b/src/detector.c @@ -26,7 +26,7 @@ #endif IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size); -void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, float precision); +void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, float precision, int draw_precision); #define CV_RGB(r, g, b) cvScalar( (b), (g), (r), 0 ) #endif // OPENCV @@ -229,14 +229,16 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i #ifdef OPENCV if (!dont_show) { + int draw_precision = 0; int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions); if (calc_map && (i >= (iter_map + calc_map_for_each) || i == net.max_batches) && i >= net.burn_in && i >= 1000) { iter_map = i; mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net); printf("\n mean_average_precision = %f \n", mean_average_precision); + draw_precision = 1; } - draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, mean_average_precision); + draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, mean_average_precision, draw_precision); } #endif // OPENCV diff --git a/src/image.c b/src/image.c index 2d001609ce6..344d5160ffb 100644 --- a/src/image.c +++ b/src/image.c @@ -720,7 +720,7 @@ IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_li return img; } -void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, float precision) +void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, float precision, int draw_precision) { int img_offset = 50; int draw_size = img_size - img_offset; @@ -734,28 +734,26 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_ cvCircle(img, pt1, 1, CV_RGB(0, 0, 255), CV_FILLED, 8, 0); // precision - if (precision >= 0) { + if (draw_precision) { static float old_precision = 0; - static iteration_old = 0; - - if (old_precision != precision) { - cvLine(img, - cvPoint(img_offset + draw_size * (float)iteration_old / max_batches, draw_size * (1 - old_precision)), - cvPoint(img_offset + draw_size * (float)current_batch / max_batches, draw_size * (1 - precision)), - CV_RGB(255, 0, 0), 1, 8, 0); - - old_precision = precision; - iteration_old = current_batch; - sprintf(char_buff, "%2.0f%% ", precision * 100); - CvFont font3; - cvInitFont(&font3, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 5, CV_AA); - cvPutText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), &font3, CV_RGB(255, 255, 255)); - - CvFont font2; - cvInitFont(&font2, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA); - cvPutText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), &font2, CV_RGB(200, 0, 0)); - } - cvPutText(img, "mAP%", cvPoint(0, 12), &font, CV_RGB(255, 0, 0)); + static int iteration_old = 0; + if(iteration_old == 0) cvPutText(img, "mAP%", cvPoint(0, 12), &font, CV_RGB(255, 0, 0)); + + cvLine(img, + cvPoint(img_offset + draw_size * (float)iteration_old / max_batches, draw_size * (1 - old_precision)), + cvPoint(img_offset + draw_size * (float)current_batch / max_batches, draw_size * (1 - precision)), + CV_RGB(255, 0, 0), 1, 8, 0); + + old_precision = precision; + iteration_old = current_batch; + sprintf(char_buff, "%2.0f%% ", precision * 100); + CvFont font3; + cvInitFont(&font3, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 5, CV_AA); + cvPutText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), &font3, CV_RGB(255, 255, 255)); + + CvFont font2; + cvInitFont(&font2, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA); + cvPutText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), &font2, CV_RGB(200, 0, 0)); } sprintf(char_buff, "current avg loss = %2.4f iteration = %d", avg_loss, current_batch); From 2c04b6ab9a498c99c86d860fc2bf95be1c0fca91 Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 13 Dec 2018 16:29:16 +0300 Subject: [PATCH 0207/1365] Update Readme.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 79775f78c15..498ef1f11ef 100644 --- a/README.md +++ b/README.md @@ -44,7 +44,7 @@ This repository supports: ##### Requires: * **Linux GCC>=4.9 or Windows MS Visual Studio 2015 (v140)**: https://go.microsoft.com/fwlink/?LinkId=532606&clcid=0x409 (or offline [ISO image](https://go.microsoft.com/fwlink/?LinkId=615448&clcid=0x409)) -* **CUDA 9.1**: https://developer.nvidia.com/cuda-91-download-archive +* **CUDA 10.0**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) * **OpenCV 3.3.0**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.3.0/opencv-3.3.0-vc14.exe/download * **or OpenCV 2.4.13**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.4.13/opencv-2.4.13.2-vc14.exe/download - OpenCV allows to show image or video detection in the window and store result to file that specified in command line `-out_filename res.avi` @@ -172,7 +172,7 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c 1.4. If you want to build **without CUDNN** then: open `\darknet.sln` -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and remove this: `CUDNN;` -2. If you have other version of **CUDA (not 10.0)** then open `build\darknet\darknet.vcxproj` by using Notepad, find 2 places with "CUDA 9.1" and change it to your CUDA-version, then do step 1 +2. If you have other version of **CUDA (not 10.0)** then open `build\darknet\darknet.vcxproj` by using Notepad, find 2 places with "CUDA 10.0" and change it to your CUDA-version, then do step 1 3. If you **don't have GPU**, but have **MSVS 2015 and OpenCV 3.0** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet_no_gpu.sln`, set **x64** and **Release**, and do the: Build -> Build darknet_no_gpu From 9f5478983db70ab5660f3950ed86757881a5ad78 Mon Sep 17 00:00:00 2001 From: Alexey Date: Sat, 15 Dec 2018 19:29:19 +0300 Subject: [PATCH 0208/1365] Update Readme.md --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index 498ef1f11ef..557e425fd5b 100644 --- a/README.md +++ b/README.md @@ -156,6 +156,8 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c * `LIBSO=1` to build a library `darknet.so` and binary runable file `uselib` that uses this library. Or you can try to run so `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib test.mp4` How to use this SO-library from your own code - you can look at C++ example: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp or use in such a way: `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov3.cfg yolov3.weights test.mp4` +To run Darknet on Linux use examples from this article, just use `./darknet` instead of `darknet.exe`, i.e. use this command: `./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights` + ### How to compile on Windows: 1. If you have **MSVS 2015, CUDA 10.0, cuDNN 7.4 and OpenCV 3.x** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release** https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg and do the: Build -> Build darknet. Also add Windows system variable `cudnn` with path to CUDNN: https://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg **NOTE:** If installing OpenCV, use OpenCV 3.4.0 or earlier. This is a bug in OpenCV 3.4.1 in the C API (see [#500](https://github.com/AlexeyAB/darknet/issues/500)). @@ -335,6 +337,8 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an 8. Start training by using the command line: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74` + To train on Linux use command: `./darknet detector train data/obj.data yolo-obj.cfg darknet53.conv.74` (just use `./darknet` instead of `darknet.exe`) + * (file `yolo-obj_last.weights` will be saved to the `build\darknet\x64\backup\` for each 100 iterations) * (file `yolo-obj_xxxx.weights` will be saved to the `build\darknet\x64\backup\` for each 1000 iterations) * (To disable Loss-Window use `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -dont_show`, if you train on computer without monitor like a cloud Amazaon EC2) From 49189c22e8c09ba7c11622ec22a72b3678887ed2 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 19 Dec 2018 01:54:18 +0300 Subject: [PATCH 0209/1365] Fix mAP chart --- src/detector.c | 15 ++++++++++++++- src/image.c | 22 ++++++++++++---------- 2 files changed, 26 insertions(+), 11 deletions(-) diff --git a/src/detector.c b/src/detector.c index 22f2322ddfb..373fac2f427 100644 --- a/src/detector.c +++ b/src/detector.c @@ -222,7 +222,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i i = get_current_batch(net); if (net.cudnn_half) { - if (i < net.burn_in) printf("\n Tensor Cores are disabled until the first %d iterations are reached.", 2*net.burn_in); + if (i < net.burn_in*3) printf("\n Tensor Cores are disabled until the first %d iterations are reached.", 3*net.burn_in); else printf("\n Tensor Cores are used."); } printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now()-time), i*imgs); @@ -232,6 +232,19 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i int draw_precision = 0; int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions); if (calc_map && (i >= (iter_map + calc_map_for_each) || i == net.max_batches) && i >= net.burn_in && i >= 1000) { + if (l.random) { + printf("Resizing to initial size: %d x %d \n", init_w, init_h); + args.w = init_w; + args.h = init_h; + pthread_join(load_thread, 0); + train = buffer; + load_thread = load_data(args); + int k; + for (k = 0; k < ngpus; ++k) { + resize_network(nets + k, init_w, init_h); + } + net = nets[0]; + } iter_map = i; mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net); printf("\n mean_average_precision = %f \n", mean_average_precision); diff --git a/src/image.c b/src/image.c index 344d5160ffb..6092db61db8 100644 --- a/src/image.c +++ b/src/image.c @@ -737,6 +737,7 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_ if (draw_precision) { static float old_precision = 0; static int iteration_old = 0; + static int text_iteration_old = 0; if(iteration_old == 0) cvPutText(img, "mAP%", cvPoint(0, 12), &font, CV_RGB(255, 0, 0)); cvLine(img, @@ -744,16 +745,19 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_ cvPoint(img_offset + draw_size * (float)current_batch / max_batches, draw_size * (1 - precision)), CV_RGB(255, 0, 0), 1, 8, 0); + if (((int)(old_precision*10) != (int)(precision*10)) || (current_batch - text_iteration_old) >= max_batches/10) { + text_iteration_old = current_batch; + sprintf(char_buff, "%2.0f%% ", precision * 100); + CvFont font3; + cvInitFont(&font3, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 5, CV_AA); + cvPutText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), &font3, CV_RGB(255, 255, 255)); + + CvFont font2; + cvInitFont(&font2, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA); + cvPutText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), &font2, CV_RGB(200, 0, 0)); + } old_precision = precision; iteration_old = current_batch; - sprintf(char_buff, "%2.0f%% ", precision * 100); - CvFont font3; - cvInitFont(&font3, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 5, CV_AA); - cvPutText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), &font3, CV_RGB(255, 255, 255)); - - CvFont font2; - cvInitFont(&font2, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA); - cvPutText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), &font2, CV_RGB(200, 0, 0)); } sprintf(char_buff, "current avg loss = %2.4f iteration = %d", avg_loss, current_batch); @@ -763,8 +767,6 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_ pt1.y += 15; cvPutText(img, char_buff, pt1, &font, CV_RGB(0, 0, 0)); - - cvShowImage("average loss", img); int k = cvWaitKey(20); if (k == 's' || current_batch == (max_batches - 1) || current_batch % 100 == 0) { From 7d0efaa936c6b5925e68b2a55f42a89b931ca3d1 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 21 Dec 2018 23:07:37 +0300 Subject: [PATCH 0210/1365] get_coco_dataset.sh downloads MS COCO dataset from images.cocodataset.org --- build/darknet/x64/yolov3-voc.cfg | 8 ++++---- scripts/get_coco_dataset.sh | 7 +++++-- src/maxpool_layer.h | 2 -- 3 files changed, 9 insertions(+), 8 deletions(-) diff --git a/build/darknet/x64/yolov3-voc.cfg b/build/darknet/x64/yolov3-voc.cfg index 3f3e8dfb31b..8f7ba54d0e3 100644 --- a/build/darknet/x64/yolov3-voc.cfg +++ b/build/darknet/x64/yolov3-voc.cfg @@ -1,10 +1,10 @@ [net] # Testing - batch=1 - subdivisions=1 +# batch=1 +# subdivisions=1 # Training -# batch=64 -# subdivisions=16 +batch=64 +subdivisions=32 width=416 height=416 channels=3 diff --git a/scripts/get_coco_dataset.sh b/scripts/get_coco_dataset.sh index 28463015d17..d7911e6c937 100644 --- a/scripts/get_coco_dataset.sh +++ b/scripts/get_coco_dataset.sh @@ -8,8 +8,11 @@ mkdir images cd images # Download Images -wget -c https://pjreddie.com/media/files/train2014.zip -wget -c https://pjreddie.com/media/files/val2014.zip +#very slow downloading +#wget -c https://pjreddie.com/media/files/train2014.zip +#wget -c https://pjreddie.com/media/files/val2014.zip +wget -c http://images.cocodataset.org/zips/train2014.zip +wget -c http://images.cocodataset.org/zips/val2014.zip # Unzip unzip -q train2014.zip diff --git a/src/maxpool_layer.h b/src/maxpool_layer.h index 2b6c277ad72..3a60cfe9d19 100644 --- a/src/maxpool_layer.h +++ b/src/maxpool_layer.h @@ -17,9 +17,7 @@ void backward_maxpool_layer(const maxpool_layer l, network_state state); #ifdef GPU void forward_maxpool_layer_gpu(maxpool_layer l, network_state state); void backward_maxpool_layer_gpu(maxpool_layer l, network_state state); -#ifdef CUDNN void cudnn_maxpool_setup(maxpool_layer *l); -#endif // CUDNN #endif // GPU #endif From 78d1ade380dbcc3ac752346756ca56f45e090b1a Mon Sep 17 00:00:00 2001 From: Alexey Date: Sun, 23 Dec 2018 01:00:16 +0300 Subject: [PATCH 0211/1365] Update Readme.md --- README.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 557e425fd5b..c8f1a1c68e7 100644 --- a/README.md +++ b/README.md @@ -482,7 +482,13 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * `train_network_width * train_obj_width / train_image_width ~= detection_network_width * detection_obj_width / detection_image_width` * `train_network_height * train_obj_height / train_image_height ~= detection_network_height * detection_obj_height / detection_image_height` - + + I.e. for each object from Test dataset there must be at least 1 object in the Training dataset with about the same relative size: + + `object width in percent from Training dataset` ~= `object width in percent from Test dataset` + + That is, if only objects that occupied 80-90% of the image were present in the training set, then the trained network will not be able to detect objects that occupy 1-10% of the image. + * to speedup training (with decreasing detection accuracy) do Fine-Tuning instead of Transfer-Learning, set param `stopbackward=1` here: https://github.com/AlexeyAB/darknet/blob/6d44529cf93211c319813c90e0c1adb34426abe5/cfg/yolov3.cfg#L548 then do this command: `./darknet partial cfg/yolov3.cfg yolov3.weights yolov3.conv.81 81` will be created file `yolov3.conv.81`, then train by using weights file `yolov3.conv.81` instead of `darknet53.conv.74` From 440c942cd318f56c145ba0eceb98f11173be9708 Mon Sep 17 00:00:00 2001 From: apatsekin Date: Sun, 23 Dec 2018 12:03:17 -0500 Subject: [PATCH 0212/1365] Update README.md (steps= for multi-gpu) steps= remark for multi-gpu training --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c8f1a1c68e7..03913cb62c3 100644 --- a/README.md +++ b/README.md @@ -255,7 +255,7 @@ More information about training by the link: http://pjreddie.com/darknet/yolo/#t 2. Then stop and by using partially-trained model `/backup/yolov3-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg /backup/yolov3-voc_1000.weights -gpus 0,1,2,3` -Only for small datasets sometimes better to decrease learning rate, for 4 GPUs set `learning_rate = 0.00025` (i.e. learning_rate = 0.001 / GPUs). In this case also increase 4x times `burn_in =` and `max_batches =` in your cfg-file. I.e. use `burn_in = 4000` instead of `1000`. +Only for small datasets sometimes better to decrease learning rate, for 4 GPUs set `learning_rate = 0.00025` (i.e. learning_rate = 0.001 / GPUs). In this case also increase 4x times `burn_in =` and `max_batches =` in your cfg-file. I.e. use `burn_in = 4000` instead of `1000`. Same goes for `steps=` if `policy=steps` is set. https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ From f38d060137b4cff50b1078a04a86bca90bc84f76 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 25 Dec 2018 02:34:32 +0300 Subject: [PATCH 0213/1365] Fixed mAP chart during training for CUDNN=1 --- src/detector.c | 225 +++++++++++++++++++++++--------------------- src/maxpool_layer.c | 2 + src/network.h | 1 + 3 files changed, 120 insertions(+), 108 deletions(-) diff --git a/src/detector.c b/src/detector.c index 373fac2f427..b0e0d85da9b 100644 --- a/src/detector.c +++ b/src/detector.c @@ -35,9 +35,7 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_ int check_mistakes; -static int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90}; - -float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, network *existing_net); +static int coco_ids[] = { 1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90 }; void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map) { @@ -46,20 +44,6 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i char *valid_images = option_find_str(options, "valid", train_images); char *backup_directory = option_find_str(options, "backup", "/backup/"); - int train_images_num = 0; - if (calc_map) { - FILE* valid_file = fopen(valid_images, "r"); - if (!valid_file) { - printf("\n Error: There is no %s file for mAP calculation!\n Don't use -map flag.\n Or set valid=%s in your %s file. \n", valid_images, train_images, datacfg); - getchar(); - exit(-1); - } - else fclose(valid_file); - list *plist = get_paths(train_images); - train_images_num = plist->size; - free_list(plist); - } - srand(time(0)); char *base = basecfg(cfgfile); printf("%s\n", base); @@ -69,28 +53,65 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i srand(time(0)); int seed = rand(); int i; - for(i = 0; i < ngpus; ++i){ + for (i = 0; i < ngpus; ++i) { srand(seed); #ifdef GPU cuda_set_device(gpus[i]); #endif nets[i] = parse_network_cfg(cfgfile); - if(weightfile){ + if (weightfile) { load_weights(&nets[i], weightfile); } - if(clear) *nets[i].seen = 0; + if (clear) *nets[i].seen = 0; nets[i].learning_rate *= ngpus; } srand(time(0)); network net = nets[0]; + int train_images_num = 0; + network net_map; + if (calc_map) { + FILE* valid_file = fopen(valid_images, "r"); + if (!valid_file) { + printf("\n Error: There is no %s file for mAP calculation!\n Don't use -map flag.\n Or set valid=%s in your %s file. \n", valid_images, train_images, datacfg); + getchar(); + exit(-1); + } + else fclose(valid_file); + list *plist = get_paths(train_images); + train_images_num = plist->size; + free_list(plist); + printf(" Prepare additional network for mAP calculation...\n"); + net_map = parse_network_cfg_custom(cfgfile, 1); + + int k; + for (k = 0; k < net.n; ++k) { + layer l = net.layers[k]; + if (l.type == CONVOLUTIONAL) { + net_map.layers[k].biases = l.biases; + net_map.layers[k].scales = l.scales; + net_map.layers[k].rolling_mean = l.rolling_mean; + net_map.layers[k].rolling_variance = l.rolling_variance; + net_map.layers[k].weights = l.weights; + + net_map.layers[k].biases_gpu = l.biases_gpu; + net_map.layers[k].scales_gpu = l.scales_gpu; + net_map.layers[k].rolling_mean_gpu = l.rolling_mean_gpu; + net_map.layers[k].rolling_variance_gpu = l.rolling_variance_gpu; + net_map.layers[k].weights_gpu = l.weights_gpu; + net_map.layers[k].weights_gpu16 = l.weights_gpu16; + } + } + } + + const int actual_batch_size = net.batch * net.subdivisions; if (actual_batch_size == 1) { printf("\n Error: You set incorrect value batch=1 for Training! You should set batch=64 subdivision=64 \n"); getchar(); } else if (actual_batch_size < 64) { - printf("\n Warning: You set batch=%d lower than 64! It is recommended to set batch=64 subdivision=64 \n", actual_batch_size); + printf("\n Warning: You set batch=%d lower than 64! It is recommended to set batch=64 subdivision=64 \n", actual_batch_size); } int imgs = net.batch * net.subdivisions * ngpus; @@ -114,7 +135,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i iter_map = get_current_batch(net); float mean_average_precision = -1; - load_args args = {0}; + load_args args = { 0 }; args.w = net.w; args.h = net.h; args.c = net.c; @@ -137,7 +158,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i #ifdef OPENCV args.threads = 3 * ngpus; // Amazon EC2 Tesla V100: p3.2xlarge (8 logical cores) - p3.16xlarge - //args.threads = 12 * ngpus; // Ryzen 7 2700X (16 logical cores) + //args.threads = 12 * ngpus; // Ryzen 7 2700X (16 logical cores) IplImage* img = NULL; float max_img_loss = 5; int number_of_lines = 100; @@ -150,8 +171,8 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i double time; int count = 0; //while(i*imgs < N*120){ - while(get_current_batch(net) < net.max_batches){ - if(l.random && count++%10 == 0){ + while (get_current_batch(net) < net.max_batches) { + if (l.random && count++ % 10 == 0) { printf("Resizing\n"); //int dim = (rand() % 12 + (init_w/32 - 5)) * 32; // +-160 //int dim = (rand() % 4 + 16) * 32; @@ -177,41 +198,42 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i free_data(train); load_thread = load_data(args); - for(i = 0; i < ngpus; ++i){ + for (i = 0; i < ngpus; ++i) { resize_network(nets + i, dim_w, dim_h); } net = nets[0]; } - time=what_time_is_it_now(); + time = what_time_is_it_now(); pthread_join(load_thread, 0); train = buffer; load_thread = load_data(args); /* - int k; - for(k = 0; k < l.max_boxes; ++k){ - box b = float_to_box(train.y.vals[10] + 1 + k*5); - if(!b.x) break; - printf("loaded: %f %f %f %f\n", b.x, b.y, b.w, b.h); - } - image im = float_to_image(448, 448, 3, train.X.vals[10]); - int k; - for(k = 0; k < l.max_boxes; ++k){ - box b = float_to_box(train.y.vals[10] + 1 + k*5); - printf("%d %d %d %d\n", truth.x, truth.y, truth.w, truth.h); - draw_bbox(im, b, 8, 1,0,0); - } - save_image(im, "truth11"); - */ - - printf("Loaded: %lf seconds\n", (what_time_is_it_now()-time)); - - time=what_time_is_it_now(); + int k; + for(k = 0; k < l.max_boxes; ++k){ + box b = float_to_box(train.y.vals[10] + 1 + k*5); + if(!b.x) break; + printf("loaded: %f %f %f %f\n", b.x, b.y, b.w, b.h); + } + image im = float_to_image(448, 448, 3, train.X.vals[10]); + int k; + for(k = 0; k < l.max_boxes; ++k){ + box b = float_to_box(train.y.vals[10] + 1 + k*5); + printf("%d %d %d %d\n", truth.x, truth.y, truth.w, truth.h); + draw_bbox(im, b, 8, 1,0,0); + } + save_image(im, "truth11"); + */ + + printf("Loaded: %lf seconds\n", (what_time_is_it_now() - time)); + + time = what_time_is_it_now(); float loss = 0; #ifdef GPU - if(ngpus == 1){ + if (ngpus == 1) { loss = train_network(net, train); - } else { + } + else { loss = train_networks(nets, ngpus, train, 4); } #else @@ -222,31 +244,18 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i i = get_current_batch(net); if (net.cudnn_half) { - if (i < net.burn_in*3) printf("\n Tensor Cores are disabled until the first %d iterations are reached.", 3*net.burn_in); + if (i < net.burn_in * 3) printf("\n Tensor Cores are disabled until the first %d iterations are reached.", 3 * net.burn_in); else printf("\n Tensor Cores are used."); } - printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now()-time), i*imgs); + printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now() - time), i*imgs); #ifdef OPENCV if (!dont_show) { int draw_precision = 0; int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions); if (calc_map && (i >= (iter_map + calc_map_for_each) || i == net.max_batches) && i >= net.burn_in && i >= 1000) { - if (l.random) { - printf("Resizing to initial size: %d x %d \n", init_w, init_h); - args.w = init_w; - args.h = init_h; - pthread_join(load_thread, 0); - train = buffer; - load_thread = load_data(args); - int k; - for (k = 0; k < ngpus; ++k) { - resize_network(nets + k, init_w, init_h); - } - net = nets[0]; - } iter_map = i; - mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net); + mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net_map); printf("\n mean_average_precision = %f \n", mean_average_precision); draw_precision = 1; } @@ -257,7 +266,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) { //if (i % 100 == 0) { - if(i >= (iter_save + 1000)) { + if (i >= (iter_save + 1000)) { iter_save = i; #ifdef GPU if (ngpus != 1) sync_nets(nets, ngpus, 0); @@ -279,7 +288,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i free_data(train); } #ifdef GPU - if(ngpus != 1) sync_nets(nets, ngpus, 0); + if (ngpus != 1) sync_nets(nets, ngpus, 0); #endif char buff[256]; sprintf(buff, "%s/%s_final.weights", backup_directory, base); @@ -632,8 +641,6 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa char *train_images = option_find_str(options, "train", "data/train.txt"); char *valid_images = option_find_str(options, "valid", train_images); net = *existing_net; - initial_batch = net.batch; - set_batch_network(&net, 1); } else { net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 @@ -810,12 +817,12 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa // calc avg IoU, true-positives, false-positives for required Threshold if (prob > thresh_calc_avg_iou) { int z, found = 0; - for (z = checkpoint_detections_count; z < detections_count-1; ++z) + for (z = checkpoint_detections_count; z < detections_count - 1; ++z) if (detections[z].unique_truth_index == truth_index) { found = 1; break; } - if(truth_index > -1 && found == 0) { + if (truth_index > -1 && found == 0) { avg_iou += max_iou; ++tp_for_thresh; } @@ -844,7 +851,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa } } - if((tp_for_thresh + fp_for_thresh) > 0) + if ((tp_for_thresh + fp_for_thresh) > 0) avg_iou = avg_iou / (tp_for_thresh + fp_for_thresh); @@ -869,7 +876,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa int rank; for (rank = 0; rank < detections_count; ++rank) { - if(rank % 100 == 0) + if (rank % 100 == 0) printf(" rank = %d of ranks = %d \r", rank, detections_count); if (rank > 0) { @@ -932,7 +939,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa avg_precision += cur_precision; } avg_precision = avg_precision / 11; - printf("class_id = %d, name = %s, \t ap = %2.2f %% \n", i, names[i], avg_precision*100); + printf("class_id = %d, name = %s, \t ap = %2.2f %% \n", i, names[i], avg_precision * 100); mean_average_precision += avg_precision; } @@ -970,7 +977,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa free_list(options); if (existing_net) { - set_batch_network(&net, initial_batch); + //set_batch_network(&net, initial_batch); } else { free_network(net); @@ -998,7 +1005,7 @@ int anchors_data_comparator(const float **pa, const float **pb) { float *a = (float *)*pa; float *b = (float *)*pb; - float diff = b[0]*b[1] - a[0]*a[1]; + float diff = b[0] * b[1] - a[0] * a[1]; if (diff < 0) return 1; else if (diff > 0) return -1; return 0; @@ -1054,7 +1061,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int rel_width_height_array = realloc(rel_width_height_array, 2 * number_of_boxes * sizeof(float)); rel_width_height_array[number_of_boxes * 2 - 2] = truth[j].w * width; rel_width_height_array[number_of_boxes * 2 - 1] = truth[j].h * height; - printf("\r loaded \t image: %d \t box: %d", i+1, number_of_boxes); + printf("\r loaded \t image: %d \t box: %d", i + 1, number_of_boxes); } } printf("\n all loaded. \n"); @@ -1086,7 +1093,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int for (i = 0; i < number_of_boxes; ++i) { float box_w = rel_width_height_array[i * 2]; //points->data.fl[i * 2]; float box_h = rel_width_height_array[i * 2 + 1]; //points->data.fl[i * 2 + 1]; - //int cluster_idx = labels->data.i[i]; + //int cluster_idx = labels->data.i[i]; int cluster_idx = 0; float min_dist = FLT_MAX; float best_iou = 0; @@ -1121,7 +1128,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int for (i = 0; i < num_of_clusters; ++i) { float anchor_w = anchors_data.centers.vals[i][0]; //centers->data.fl[i * 2]; float anchor_h = anchors_data.centers.vals[i][1]; //centers->data.fl[i * 2 + 1]; - if(width > 32) sprintf(buff, "%3.0f,%3.0f", anchor_w, anchor_h); + if (width > 32) sprintf(buff, "%3.0f,%3.0f", anchor_w, anchor_h); else sprintf(buff, "%2.4f,%2.4f", anchor_w, anchor_h); printf("%s", buff); fwrite(buff, sizeof(char), strlen(buff), fw); @@ -1201,7 +1208,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int //#endif // OPENCV void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, - float hier_thresh, int dont_show, int ext_output, int save_labels) + float hier_thresh, int dont_show, int ext_output, int save_labels) { list *options = read_data_cfg(datacfg); char *name_list = option_find_str(options, "names", "data/names.list"); @@ -1210,7 +1217,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam image **alphabet = load_alphabet(); network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 - if(weightfile){ + if (weightfile) { load_weights(&net, weightfile); } //set_batch_network(&net, 1); @@ -1219,31 +1226,32 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam if (net.layers[net.n - 1].classes != names_size) { printf(" Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", name_list, names_size, net.layers[net.n - 1].classes, cfgfile); - if(net.layers[net.n - 1].classes > names_size) getchar(); + if (net.layers[net.n - 1].classes > names_size) getchar(); } srand(2222222); double time; char buff[256]; char *input = buff; int j; - float nms=.45; // 0.4F - while(1){ - if(filename){ + float nms = .45; // 0.4F + while (1) { + if (filename) { strncpy(input, filename, 256); - if(strlen(input) > 0) + if (strlen(input) > 0) if (input[strlen(input) - 1] == 0x0d) input[strlen(input) - 1] = 0; - } else { + } + else { printf("Enter Image Path: "); fflush(stdout); input = fgets(input, 256, stdin); - if(!input) return; + if (!input) return; strtok(input, "\n"); } - image im = load_image(input,0,0,net.c); + image im = load_image(input, 0, 0, net.c); int letterbox = 0; image sized = resize_image(im, net.w, net.h); //image sized = letterbox_image(im, net.w, net.h); letterbox = 1; - layer l = net.layers[net.n-1]; + layer l = net.layers[net.n - 1]; //box *boxes = calloc(l.w*l.h*l.n, sizeof(box)); //float **probs = calloc(l.w*l.h*l.n, sizeof(float *)); @@ -1268,7 +1276,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam } // pseudo labeling concept - fast.ai - if(save_labels) + if (save_labels) { char labelpath[4096]; replace_image_to_label(input, labelpath); @@ -1347,7 +1355,7 @@ void run_detector(int argc, char **argv) // and for recall mode (extended output table-like format with results for best_class fit) int ext_output = find_arg(argc, argv, "-ext_output"); int save_labels = find_arg(argc, argv, "-save_labels"); - if(argc < 4){ + if (argc < 4) { fprintf(stderr, "usage: %s %s [train/test/valid/demo/map] [data] [cfg] [weights (optional)]\n", argv[0], argv[1]); return; } @@ -1355,20 +1363,21 @@ void run_detector(int argc, char **argv) int *gpus = 0; int gpu = 0; int ngpus = 0; - if(gpu_list){ + if (gpu_list) { printf("%s\n", gpu_list); int len = strlen(gpu_list); ngpus = 1; int i; - for(i = 0; i < len; ++i){ + for (i = 0; i < len; ++i) { if (gpu_list[i] == ',') ++ngpus; } gpus = calloc(ngpus, sizeof(int)); - for(i = 0; i < ngpus; ++i){ + for (i = 0; i < ngpus; ++i) { gpus[i] = atoi(gpu_list); - gpu_list = strchr(gpu_list, ',')+1; + gpu_list = strchr(gpu_list, ',') + 1; } - } else { + } + else { gpu = gpu_index; gpus = &gpu; ngpus = 1; @@ -1379,23 +1388,23 @@ void run_detector(int argc, char **argv) char *datacfg = argv[3]; char *cfg = argv[4]; char *weights = (argc > 5) ? argv[5] : 0; - if(weights) - if(strlen(weights) > 0) + if (weights) + if (strlen(weights) > 0) if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0; - char *filename = (argc > 6) ? argv[6]: 0; - if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels); - else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map); - else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile); - else if(0==strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights); - else if(0==strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh, iou_thresh, NULL); - else if(0==strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, height, show); - else if(0==strcmp(argv[2], "demo")) { + char *filename = (argc > 6) ? argv[6] : 0; + if (0 == strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels); + else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map); + else if (0 == strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile); + else if (0 == strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights); + else if (0 == strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh, iou_thresh, NULL); + else if (0 == strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, height, show); + else if (0 == strcmp(argv[2], "demo")) { list *options = read_data_cfg(datacfg); int classes = option_find_int(options, "classes", 20); char *name_list = option_find_str(options, "names", "data/names.list"); char **names = get_labels(name_list); - if(filename) - if(strlen(filename) > 0) + if (filename) + if (strlen(filename) > 0) if (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0; demo(cfg, weights, thresh, hier_thresh, cam_index, filename, names, classes, frame_skip, prefix, out_filename, http_stream_port, dont_show, ext_output); diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index b0874304d43..8c1265e3551 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -104,6 +104,8 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h) l->indexes_gpu = cuda_make_int_array(output_size); l->output_gpu = cuda_make_array(l->output, output_size); l->delta_gpu = cuda_make_array(l->delta, output_size); + + cudnn_maxpool_setup(l); #endif } diff --git a/src/network.h b/src/network.h index 65029947e1a..9acb01da86e 100644 --- a/src/network.h +++ b/src/network.h @@ -145,6 +145,7 @@ YOLODLL_API void reset_rnn(network *net); YOLODLL_API network *load_network_custom(char *cfg, char *weights, int clear, int batch); YOLODLL_API network *load_network(char *cfg, char *weights, int clear); YOLODLL_API float *network_predict_image(network *net, image im); +YOLODLL_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, network *existing_net); YOLODLL_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map); YOLODLL_API int network_width(network *net); YOLODLL_API int network_height(network *net); From 06232b31f4090f13bf97bacf9be39fc3545902d3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 25 Dec 2018 02:38:33 +0300 Subject: [PATCH 0214/1365] Compile fix --- src/detector.c | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/detector.c b/src/detector.c index b0e0d85da9b..e7979305661 100644 --- a/src/detector.c +++ b/src/detector.c @@ -93,13 +93,14 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i net_map.layers[k].rolling_mean = l.rolling_mean; net_map.layers[k].rolling_variance = l.rolling_variance; net_map.layers[k].weights = l.weights; - +#ifdef GPU net_map.layers[k].biases_gpu = l.biases_gpu; net_map.layers[k].scales_gpu = l.scales_gpu; net_map.layers[k].rolling_mean_gpu = l.rolling_mean_gpu; net_map.layers[k].rolling_variance_gpu = l.rolling_variance_gpu; net_map.layers[k].weights_gpu = l.weights_gpu; net_map.layers[k].weights_gpu16 = l.weights_gpu16; +#endif // GPU } } } From b87080882a5a46130f4a9cfda97ae2966063bf98 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 25 Dec 2018 18:21:32 +0300 Subject: [PATCH 0215/1365] Minor fix for multi-GPU training with -map flag --- src/detector.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/detector.c b/src/detector.c index e7979305661..5ddbd916dc6 100644 --- a/src/detector.c +++ b/src/detector.c @@ -81,9 +81,10 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i list *plist = get_paths(train_images); train_images_num = plist->size; free_list(plist); + + cuda_set_device(net.gpu_index); printf(" Prepare additional network for mAP calculation...\n"); net_map = parse_network_cfg_custom(cfgfile, 1); - int k; for (k = 0; k < net.n; ++k) { layer l = net.layers[k]; @@ -105,7 +106,6 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i } } - const int actual_batch_size = net.batch * net.subdivisions; if (actual_batch_size == 1) { printf("\n Error: You set incorrect value batch=1 for Training! You should set batch=64 subdivision=64 \n"); From 1b20072096c1afdeea6b63ca5ba77276db8b3e28 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 25 Dec 2018 21:06:13 +0300 Subject: [PATCH 0216/1365] Optimized memory consumption during Training with -map flag --- src/detector.c | 117 ++++++++++++++++++++++++++++++++----------------- 1 file changed, 76 insertions(+), 41 deletions(-) diff --git a/src/detector.c b/src/detector.c index 5ddbd916dc6..b1038891e53 100644 --- a/src/detector.c +++ b/src/detector.c @@ -44,6 +44,36 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i char *valid_images = option_find_str(options, "valid", train_images); char *backup_directory = option_find_str(options, "backup", "/backup/"); + int train_images_num = 0; + network net_map; + if (calc_map) { + FILE* valid_file = fopen(valid_images, "r"); + if (!valid_file) { + printf("\n Error: There is no %s file for mAP calculation!\n Don't use -map flag.\n Or set valid=%s in your %s file. \n", valid_images, train_images, datacfg); + getchar(); + exit(-1); + } + else fclose(valid_file); + list *plist = get_paths(train_images); + train_images_num = plist->size; + free_list(plist); + + cuda_set_device(gpus[0]); + printf(" Prepare additional network for mAP calculation...\n"); + net_map = parse_network_cfg_custom(cfgfile, 1); + int k; // free memory unnecessary arrays + for (k = 0; k < net_map.n; ++k) { + free_layer(net_map.layers[k]); + } +#ifdef GPU + cuda_free(net_map.workspace); + if (*net_map.input16_gpu) cuda_free(*net_map.input16_gpu); + if (*net_map.output16_gpu) cuda_free(*net_map.output16_gpu); +#else + free(net_map.workspace); +#endif + } + srand(time(0)); char *base = basecfg(cfgfile); printf("%s\n", base); @@ -68,44 +98,6 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i srand(time(0)); network net = nets[0]; - int train_images_num = 0; - network net_map; - if (calc_map) { - FILE* valid_file = fopen(valid_images, "r"); - if (!valid_file) { - printf("\n Error: There is no %s file for mAP calculation!\n Don't use -map flag.\n Or set valid=%s in your %s file. \n", valid_images, train_images, datacfg); - getchar(); - exit(-1); - } - else fclose(valid_file); - list *plist = get_paths(train_images); - train_images_num = plist->size; - free_list(plist); - - cuda_set_device(net.gpu_index); - printf(" Prepare additional network for mAP calculation...\n"); - net_map = parse_network_cfg_custom(cfgfile, 1); - int k; - for (k = 0; k < net.n; ++k) { - layer l = net.layers[k]; - if (l.type == CONVOLUTIONAL) { - net_map.layers[k].biases = l.biases; - net_map.layers[k].scales = l.scales; - net_map.layers[k].rolling_mean = l.rolling_mean; - net_map.layers[k].rolling_variance = l.rolling_variance; - net_map.layers[k].weights = l.weights; -#ifdef GPU - net_map.layers[k].biases_gpu = l.biases_gpu; - net_map.layers[k].scales_gpu = l.scales_gpu; - net_map.layers[k].rolling_mean_gpu = l.rolling_mean_gpu; - net_map.layers[k].rolling_variance_gpu = l.rolling_variance_gpu; - net_map.layers[k].weights_gpu = l.weights_gpu; - net_map.layers[k].weights_gpu16 = l.weights_gpu16; -#endif // GPU - } - } - } - const int actual_batch_size = net.batch * net.subdivisions; if (actual_batch_size == 1) { printf("\n Error: You set incorrect value batch=1 for Training! You should set batch=64 subdivision=64 \n"); @@ -159,7 +151,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i #ifdef OPENCV args.threads = 3 * ngpus; // Amazon EC2 Tesla V100: p3.2xlarge (8 logical cores) - p3.16xlarge - //args.threads = 12 * ngpus; // Ryzen 7 2700X (16 logical cores) + //args.threads = 12 * ngpus; // Ryzen 7 2700X (16 logical cores) IplImage* img = NULL; float max_img_loss = 5; int number_of_lines = 100; @@ -255,8 +247,50 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i int draw_precision = 0; int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions); if (calc_map && (i >= (iter_map + calc_map_for_each) || i == net.max_batches) && i >= net.burn_in && i >= 1000) { + if (l.random) { + printf("Resizing to initial size: %d x %d \n", init_w, init_h); + args.w = init_w; + args.h = init_h; + pthread_join(load_thread, 0); + train = buffer; + load_thread = load_data(args); + int k; + for (k = 0; k < ngpus; ++k) { + resize_network(nets + k, init_w, init_h); + } + net = nets[0]; + } + + // combine Training and Validation networks + network net_combined = make_network(net.n); + layer *old_layers = net_combined.layers; + net_combined = net; + net_combined.layers = old_layers; + net_combined.batch = 1; + + int k; + for (k = 0; k < net.n; ++k) { + layer *l = &(net.layers[k]); + net_combined.layers[k] = net.layers[k]; + net_combined.layers[k].batch = 1; + + if (l->type == CONVOLUTIONAL) { +#ifdef CUDNN + net_combined.layers[k].normTensorDesc = net_map.layers[k].normTensorDesc; + net_combined.layers[k].normDstTensorDesc = net_map.layers[k].normDstTensorDesc; + net_combined.layers[k].normDstTensorDescF16 = net_map.layers[k].normDstTensorDescF16; + + net_combined.layers[k].srcTensorDesc = net_map.layers[k].srcTensorDesc; + net_combined.layers[k].dstTensorDesc = net_map.layers[k].dstTensorDesc; + + net_combined.layers[k].srcTensorDesc16 = net_map.layers[k].srcTensorDesc16; + net_combined.layers[k].dstTensorDesc16 = net_map.layers[k].dstTensorDesc16; +#endif // CUDNN + } + } + iter_map = i; - mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net_map); + mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net_combined); printf("\n mean_average_precision = %f \n", mean_average_precision); draw_precision = 1; } @@ -312,8 +346,9 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i free_list_contents_kvp(options); free_list(options); + for (i = 0; i < ngpus; ++i) free_network(nets[i]); free(nets); - free_network(net); + //free_network(net); } From 5bc7c06891e6139c12c6b48320fe22032e6f917a Mon Sep 17 00:00:00 2001 From: Heethesh Vhavle Date: Wed, 26 Dec 2018 17:21:54 +0400 Subject: [PATCH 0217/1365] Fixed typo in weights file --- net_cam_v3.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/net_cam_v3.sh b/net_cam_v3.sh index 5320eddf34d..10fbf369a4e 100644 --- a/net_cam_v3.sh +++ b/net_cam_v3.sh @@ -1,6 +1,6 @@ #rm test_dnn_out.avi -./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weightss rtsp://admin:admin12345@192.168.0.228:554 -i 0 -thresh 0.25 +./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights rtsp://admin:admin12345@192.168.0.228:554 -i 0 -thresh 0.25 From dc827f4c1c49907d7061c63fdc9a634cc82a43d7 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 27 Dec 2018 00:30:05 +0300 Subject: [PATCH 0218/1365] Minor fix for mAP calculation during training --- src/detector.c | 30 +++--------------------------- src/image.c | 8 ++++++-- src/network.c | 32 ++++++++++++++++++++++++++++++++ src/network.h | 1 + 4 files changed, 42 insertions(+), 29 deletions(-) diff --git a/src/detector.c b/src/detector.c index b1038891e53..f3b60f3ffc9 100644 --- a/src/detector.c +++ b/src/detector.c @@ -67,6 +67,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i } #ifdef GPU cuda_free(net_map.workspace); + cuda_free(net_map.input_state_gpu); if (*net_map.input16_gpu) cuda_free(*net_map.input16_gpu); if (*net_map.output16_gpu) cuda_free(*net_map.output16_gpu); #else @@ -245,7 +246,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i #ifdef OPENCV if (!dont_show) { int draw_precision = 0; - int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions); + int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions); // calculate mAP for each 4 Epochs if (calc_map && (i >= (iter_map + calc_map_for_each) || i == net.max_batches) && i >= net.burn_in && i >= 1000) { if (l.random) { printf("Resizing to initial size: %d x %d \n", init_w, init_h); @@ -262,32 +263,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i } // combine Training and Validation networks - network net_combined = make_network(net.n); - layer *old_layers = net_combined.layers; - net_combined = net; - net_combined.layers = old_layers; - net_combined.batch = 1; - - int k; - for (k = 0; k < net.n; ++k) { - layer *l = &(net.layers[k]); - net_combined.layers[k] = net.layers[k]; - net_combined.layers[k].batch = 1; - - if (l->type == CONVOLUTIONAL) { -#ifdef CUDNN - net_combined.layers[k].normTensorDesc = net_map.layers[k].normTensorDesc; - net_combined.layers[k].normDstTensorDesc = net_map.layers[k].normDstTensorDesc; - net_combined.layers[k].normDstTensorDescF16 = net_map.layers[k].normDstTensorDescF16; - - net_combined.layers[k].srcTensorDesc = net_map.layers[k].srcTensorDesc; - net_combined.layers[k].dstTensorDesc = net_map.layers[k].dstTensorDesc; - - net_combined.layers[k].srcTensorDesc16 = net_map.layers[k].srcTensorDesc16; - net_combined.layers[k].dstTensorDesc16 = net_map.layers[k].dstTensorDesc16; -#endif // CUDNN - } - } + network net_combined = combine_train_valid_networks(net, net_map); iter_map = i; mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net_combined); diff --git a/src/image.c b/src/image.c index 6092db61db8..6212406ae89 100644 --- a/src/image.c +++ b/src/image.c @@ -710,7 +710,7 @@ IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_li char max_batches_buff[100]; sprintf(max_batches_buff, "in cfg max_batches=%d", max_batches); cvPutText(img, max_batches_buff, cvPoint(draw_size - 195, img_size - 10), &font, CV_RGB(0, 0, 0)); - cvPutText(img, "Press 's' to save: chart.jpg", cvPoint(5, img_size - 10), &font, CV_RGB(0, 0, 0)); + cvPutText(img, "Press 's' to save: chart.png", cvPoint(5, img_size - 10), &font, CV_RGB(0, 0, 0)); printf(" If error occurs - run training with flag: -dont_show \n"); cvNamedWindow("average loss", CV_WINDOW_NORMAL); cvMoveWindow("average loss", 0, 0); @@ -770,7 +770,11 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_ cvShowImage("average loss", img); int k = cvWaitKey(20); if (k == 's' || current_batch == (max_batches - 1) || current_batch % 100 == 0) { - cvSaveImage("chart.jpg", img, 0); + //cvSaveImage("chart.jpg", img, 0); + IplImage* img_rgb = cvCreateImage(cvSize(img->width, img->height), 8, 3); + cvCvtColor(img, img_rgb, CV_RGB2BGR); + stbi_write_png("chart.png", img_rgb->width, img_rgb->height, 3, (char *)img_rgb->imageData, 0); + cvRelease(&img_rgb); cvPutText(img, "- Saved", cvPoint(250, img_size - 10), &font, CV_RGB(255, 0, 0)); } else diff --git a/src/network.c b/src/network.c index 74fe71bd43a..b91edd5bfdc 100644 --- a/src/network.c +++ b/src/network.c @@ -878,4 +878,36 @@ void calculate_binary_weights(network net) } //printf("\n calculate_binary_weights Done! \n"); +} + +// combine Training and Validation networks +network combine_train_valid_networks(network net_train, network net_map) +{ + network net_combined = make_network(net_train.n); + layer *old_layers = net_combined.layers; + net_combined = net_train; + net_combined.layers = old_layers; + net_combined.batch = 1; + + int k; + for (k = 0; k < net_train.n; ++k) { + layer *l = &(net_train.layers[k]); + net_combined.layers[k] = net_train.layers[k]; + net_combined.layers[k].batch = 1; + + if (l->type == CONVOLUTIONAL) { +#ifdef CUDNN + net_combined.layers[k].normTensorDesc = net_map.layers[k].normTensorDesc; + net_combined.layers[k].normDstTensorDesc = net_map.layers[k].normDstTensorDesc; + net_combined.layers[k].normDstTensorDescF16 = net_map.layers[k].normDstTensorDescF16; + + net_combined.layers[k].srcTensorDesc = net_map.layers[k].srcTensorDesc; + net_combined.layers[k].dstTensorDesc = net_map.layers[k].dstTensorDesc; + + net_combined.layers[k].srcTensorDesc16 = net_map.layers[k].srcTensorDesc16; + net_combined.layers[k].dstTensorDesc16 = net_map.layers[k].dstTensorDesc16; +#endif // CUDNN + } + } + return net_combined; } \ No newline at end of file diff --git a/src/network.h b/src/network.h index 9acb01da86e..0642d3174b6 100644 --- a/src/network.h +++ b/src/network.h @@ -156,6 +156,7 @@ int get_network_nuisance(network net); int get_network_background(network net); YOLODLL_API void fuse_conv_batchnorm(network net); YOLODLL_API void calculate_binary_weights(network net); +network combine_train_valid_networks(network net_train, network net_map); #ifdef __cplusplus } From 64e478db07190606a5a5a8bf416b40a2e570396b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 27 Dec 2018 00:31:28 +0300 Subject: [PATCH 0219/1365] Fix training approach (convolutional layer) --- src/convolutional_kernels.cu | 44 ++++++++++++++++++++++++++++++++++-- src/convolutional_layer.c | 8 +++++++ src/layer.h | 22 ++++++++++++++---- src/parser.c | 1 + 4 files changed, 68 insertions(+), 7 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 44f62ed3f52..d498effd539 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -457,7 +457,8 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state { gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); - backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); + if (!l.batch_normalize) + backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); //#ifndef CUDNN_HALF //if(l.batch_normalize){ @@ -703,6 +704,45 @@ void push_convolutional_layer(convolutional_layer layer) } } +void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay) +{ + float learning_rate = learning_rate_init*l.learning_rate_scale; + //float momentum = a.momentum; + //float decay = a.decay; + //int batch = a.batch; + int size = l.size*l.size*l.c*l.n; // old + + if (l.adam) { + //adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.nweights, batch, a.t); + adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, size, batch, l.t); + + adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.n, batch, l.t); + if (l.scales_gpu) { + adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.n, batch, l.t); + } + } + else { + //axpy_ongpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); + //axpy_ongpu(l.nweights, learning_rate / batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); + //scal_ongpu(l.nweights, momentum, l.weight_updates_gpu, 1); + axpy_ongpu(size, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); + axpy_ongpu(size, learning_rate / batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); + scal_ongpu(size, momentum, l.weight_updates_gpu, 1); + + axpy_ongpu(l.n, learning_rate / batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); + scal_ongpu(l.n, momentum, l.bias_updates_gpu, 1); + + if (l.scales_gpu) { + axpy_ongpu(l.n, learning_rate / batch, l.scale_updates_gpu, 1, l.scales_gpu, 1); + scal_ongpu(l.n, momentum, l.scale_updates_gpu, 1); + } + } + //if (l.clip) { + // constrain_gpu(l.nweights, l.clip, l.weights_gpu, 1); + //} +} + +/* void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay) { int size = layer.size*layer.size*layer.c*layer.n; @@ -753,5 +793,5 @@ void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float //----------------------------------- } } - +*/ diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index b05b3d00b4f..4794364ea9a 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -390,6 +390,10 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.adam = 1; l.m = calloc(c*n*size*size, sizeof(float)); l.v = calloc(c*n*size*size, sizeof(float)); + l.bias_m = calloc(n, sizeof(float)); + l.scale_m = calloc(n, sizeof(float)); + l.bias_v = calloc(n, sizeof(float)); + l.scale_v = calloc(n, sizeof(float)); } #ifdef GPU @@ -401,6 +405,10 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int if (adam) { l.m_gpu = cuda_make_array(l.m, c*n*size*size); l.v_gpu = cuda_make_array(l.v, c*n*size*size); + l.bias_m_gpu = cuda_make_array(l.bias_m, n); + l.bias_v_gpu = cuda_make_array(l.bias_v, n); + l.scale_m_gpu = cuda_make_array(l.scale_m, n); + l.scale_v_gpu = cuda_make_array(l.scale_v, n); } l.weights_gpu = cuda_make_array(l.weights, c*n*size*size); diff --git a/src/layer.h b/src/layer.h index a4ebbfbc306..cc78de0d1a1 100644 --- a/src/layer.h +++ b/src/layer.h @@ -100,6 +100,7 @@ struct layer{ float exposure; float shift; float ratio; + float learning_rate_scale; int focal_loss; int noloss; int softmax; @@ -122,11 +123,14 @@ struct layer{ float B1; float B2; float eps; - float *m_gpu; - float *v_gpu; + int t; float *m; float *v; + float * bias_m; + float * bias_v; + float * scale_m; + float * scale_v; tree *softmax_tree; int *map; @@ -245,7 +249,7 @@ struct layer{ size_t workspace_size; - #ifdef GPU +#ifdef GPU float *z_gpu; float *r_gpu; float *h_gpu; @@ -263,6 +267,14 @@ struct layer{ float * concat_gpu; float * concat_delta_gpu; + // adam + float *m_gpu; + float *v_gpu; + float *bias_m_gpu; + float *scale_m_gpu; + float *bias_v_gpu; + float *scale_v_gpu; + float *binary_input_gpu; float *binary_weights_gpu; @@ -310,8 +322,8 @@ struct layer{ cudnnConvolutionBwdDataAlgo_t bd_algo, bd_algo16; cudnnConvolutionBwdFilterAlgo_t bf_algo, bf_algo16; cudnnPoolingDescriptor_t poolingDesc; - #endif - #endif + #endif // CUDNN +#endif // GPU }; void free_layer(layer); diff --git a/src/parser.c b/src/parser.c index b01c5458cde..f3efc27caf1 100644 --- a/src/parser.c +++ b/src/parser.c @@ -805,6 +805,7 @@ network parse_network_cfg_custom(char *filename, int batch) l.stopbackward = option_find_int_quiet(options, "stopbackward", 0); l.dontload = option_find_int_quiet(options, "dontload", 0); l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0); + l.learning_rate_scale = option_find_float_quiet(options, "learning_rate", 1); option_unused(options); net.layers[count] = l; if (l.workspace_size > workspace_size) workspace_size = l.workspace_size; From 08f0f80b66f02b7892a05f4c2ff3dc7c1c6d128b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 29 Dec 2018 01:32:11 +0300 Subject: [PATCH 0220/1365] Adam optimizer fixed --- src/blas_kernels.cu | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 657be1de352..3fdc742d55c 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -99,7 +99,7 @@ __global__ void dot_kernel(float *output, float scale, int batch, int n, int siz int f1 = index / n; int f2 = index % n; if (f2 <= f1) return; - + float sum = 0; float norm1 = 0; float norm2 = 0; @@ -140,19 +140,20 @@ void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int check_error(cudaPeekAtLastError()); } - __global__ void adam_kernel(int N, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t) { int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (index >= N) return; - - x[index] = x[index] - (rate * sqrtf(1.F-powf(B2, t)) / (1.F-powf(B1, t)) * m[index] / (sqrtf(v[index]) + eps)); - //if(index == 0) printf("%f %f %f %f\n", m[index], v[index], (rate * sqrtf(1.F-powf(B2, t)) / (1.F-powf(B1, t)) * m[index] / (sqrt(v[index]) + eps))); + + float mhat = m[index] / (1.f - powf(B1, t)); + float vhat = v[index] / (1.f - powf(B2, t)); + + x[index] = x[index] + rate * mhat / (sqrtf(vhat) + eps); } extern "C" void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t) { - adam_kernel<<>>(n, x, m, v, B1, B2, rate, eps, t); + adam_kernel << > >(n, x, m, v, B1, B2, rate, eps, t); check_error(cudaPeekAtLastError()); } @@ -175,7 +176,7 @@ __global__ void normalize_kernel(int N, float *x, float *mean, float *variance, int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (index >= N) return; int f = (index/spatial)%filters; - + x[index] = (x[index] - mean[f])/(sqrtf(variance[f]) + .000001f); } @@ -184,7 +185,7 @@ __global__ void normalize_delta_kernel(int N, float *x, float *mean, float *vari int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (index >= N) return; int f = (index/spatial)%filters; - + delta[index] = delta[index] * 1.F/(sqrtf(variance[f]) + .000001f) + variance_delta[f] * 2. * (x[index] - mean[f]) / (spatial * batch) + mean_delta[f]/(spatial*batch); } From 7612131abb6b6510d4136068028cf54130f6bf54 Mon Sep 17 00:00:00 2001 From: duohappy Date: Sat, 29 Dec 2018 20:14:08 +0800 Subject: [PATCH 0221/1365] =?UTF-8?q?in=20yolo=5Fcpp=5Fdll.sln,=20miss=20a?= =?UTF-8?q?=20=E2=80=9C\=E2=80=9D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit I open yolo_cpp_dll.sln, and check the config. "\" is missed. Linker -> General -> Additional Library Directories $(CUDA_PATH)lib\$(PlatformName)-->$(CUDA_PATH)\lib\$(PlatformName) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index ad506796edb..a9feedaae62 100644 --- a/README.md +++ b/README.md @@ -201,7 +201,7 @@ Then add to your created project: - add to project all `.c` & `.cu` files and file `http_stream.cpp` from `\src` - (right click on project) -> properties -> Linker -> General -> Additional Library Directories, put here: -`C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories)` +`C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)\lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories)` - (right click on project) -> properties -> Linker -> Input -> Additional dependecies, put here: `..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;cudnn.lib;%(AdditionalDependencies)` From cb94ef9308995276f3e2de45d6c1b98e55c8bb06 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 29 Dec 2018 21:52:31 +0300 Subject: [PATCH 0222/1365] Use `dont_show` in the obj.data file to disable detection for a specific class --- src/box.h | 4 ++-- src/image.c | 10 ++++++---- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/src/box.h b/src/box.h index 6063a480e54..6f5e7383ca9 100644 --- a/src/box.h +++ b/src/box.h @@ -3,7 +3,7 @@ #ifdef YOLODLL_EXPORTS #if defined(_MSC_VER) -#define YOLODLL_API __declspec(dllexport) +#define YOLODLL_API __declspec(dllexport) #else #define YOLODLL_API __attribute__((visibility("default"))) #endif @@ -52,6 +52,6 @@ box encode_box(box b, box anchor); // Creates array of detections with prob > thresh and fills best_class for them // Return number of selected detections in *selected_detections_num -detection_with_class* get_actual_detections(detection *dets, int dets_num, float thresh, int* selected_detections_num); +detection_with_class* get_actual_detections(detection *dets, int dets_num, float thresh, int* selected_detections_num, char **names); #endif diff --git a/src/image.c b/src/image.c index 6212406ae89..cb96ef37d4e 100644 --- a/src/image.c +++ b/src/image.c @@ -269,7 +269,7 @@ image **load_alphabet() // Creates array of detections with prob > thresh and fills best_class for them -detection_with_class* get_actual_detections(detection *dets, int dets_num, float thresh, int* selected_detections_num) +detection_with_class* get_actual_detections(detection *dets, int dets_num, float thresh, int* selected_detections_num, char **names) { int selected_num = 0; detection_with_class* result_arr = calloc(dets_num, sizeof(detection_with_class)); @@ -279,7 +279,8 @@ detection_with_class* get_actual_detections(detection *dets, int dets_num, float float best_class_prob = thresh; int j; for (j = 0; j < dets[i].classes; ++j) { - if (dets[i].prob[j] > best_class_prob ) { + int show = strncmp(names[j], "dont_show", 9); + if (dets[i].prob[j] > best_class_prob && show) { best_class = j; best_class_prob = dets[i].prob[j]; } @@ -314,7 +315,7 @@ int compare_by_probs(const void *a_ptr, const void *b_ptr) { void draw_detections_v3(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output) { int selected_detections_num; - detection_with_class* selected_detections = get_actual_detections(dets, num, thresh, &selected_detections_num); + detection_with_class* selected_detections = get_actual_detections(dets, num, thresh, &selected_detections_num, names); // text output qsort(selected_detections, selected_detections_num, sizeof(*selected_detections), compare_by_lefts); @@ -489,7 +490,8 @@ void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float t char labelstr[4096] = { 0 }; int class_id = -1; for (j = 0; j < classes; ++j) { - if (dets[i].prob[j] > thresh) { + int show = strncmp(names[j], "dont_show", 9); + if (dets[i].prob[j] > thresh && show) { if (class_id < 0) { strcat(labelstr, names[j]); class_id = j; From 527578744b46666fb5cd42393bf9e1fa9af126ee Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 29 Dec 2018 22:56:38 +0300 Subject: [PATCH 0223/1365] Minor fix --- src/network.h | 2 +- src/parser.c | 4 +++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/src/network.h b/src/network.h index 0642d3174b6..0cb381fba19 100644 --- a/src/network.h +++ b/src/network.h @@ -21,7 +21,7 @@ typedef struct network{ float *workspace; int n; int batch; - int *seen; + uint64_t *seen; float epoch; int subdivisions; float momentum; diff --git a/src/parser.c b/src/parser.c index f3efc27caf1..e381edc1de0 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1191,7 +1191,9 @@ void load_weights_upto(network *net, char *filename, int cutoff) } else { printf("\n seen 32 \n"); - fread(net->seen, sizeof(int), 1, fp); + uint32_t iseen = 0; + fread(&iseen, sizeof(uint32_t), 1, fp); + *net->seen = iseen; } int transpose = (major > 1000) || (minor > 1000); From 29f509e2b72d61300d47bc211ab1f24c2acb454a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 1 Jan 2019 23:53:37 +0300 Subject: [PATCH 0224/1365] Added yolov3-tiny_3l.cfg for small objects --- build/darknet/x64/cfg/yolov3-tiny_3l.cfg | 227 +++++++++++++++++++++++ cfg/yolov3-tiny_3l.cfg | 227 +++++++++++++++++++++++ 2 files changed, 454 insertions(+) create mode 100644 build/darknet/x64/cfg/yolov3-tiny_3l.cfg create mode 100644 cfg/yolov3-tiny_3l.cfg diff --git a/build/darknet/x64/cfg/yolov3-tiny_3l.cfg b/build/darknet/x64/cfg/yolov3-tiny_3l.cfg new file mode 100644 index 00000000000..76aaa0d9587 --- /dev/null +++ b/build/darknet/x64/cfg/yolov3-tiny_3l.cfg @@ -0,0 +1,227 @@ +[net] +# Testing +# batch=1 +# subdivisions=1 +# Training +batch=64 +subdivisions=16 +width=608 +height=608 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 200000 +policy=steps +steps=180000,190000 +scales=.1,.1 + + +[convolutional] +batch_normalize=1 +filters=16 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=1 + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +########### + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=21 +activation=linear + + + +[yolo] +mask = 6,7,8 +anchors = 4,7, 7,15, 13,25, 25,42, 41,67, 75,94, 91,162, 158,205, 250,332 +classes=2 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 8 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=21 +activation=linear + +[yolo] +mask = 3,4,5 +anchors = 4,7, 7,15, 13,25, 25,42, 41,67, 75,94, 91,162, 158,205, 250,332 +classes=2 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + + +[route] +layers = -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 6 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=21 +activation=linear + +[yolo] +mask = 0,1,2 +anchors = 4,7, 7,15, 13,25, 25,42, 41,67, 75,94, 91,162, 158,205, 250,332 +classes=2 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 \ No newline at end of file diff --git a/cfg/yolov3-tiny_3l.cfg b/cfg/yolov3-tiny_3l.cfg new file mode 100644 index 00000000000..76aaa0d9587 --- /dev/null +++ b/cfg/yolov3-tiny_3l.cfg @@ -0,0 +1,227 @@ +[net] +# Testing +# batch=1 +# subdivisions=1 +# Training +batch=64 +subdivisions=16 +width=608 +height=608 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 200000 +policy=steps +steps=180000,190000 +scales=.1,.1 + + +[convolutional] +batch_normalize=1 +filters=16 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=1 + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +########### + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=21 +activation=linear + + + +[yolo] +mask = 6,7,8 +anchors = 4,7, 7,15, 13,25, 25,42, 41,67, 75,94, 91,162, 158,205, 250,332 +classes=2 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 8 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=21 +activation=linear + +[yolo] +mask = 3,4,5 +anchors = 4,7, 7,15, 13,25, 25,42, 41,67, 75,94, 91,162, 158,205, 250,332 +classes=2 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + + +[route] +layers = -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 6 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=21 +activation=linear + +[yolo] +mask = 0,1,2 +anchors = 4,7, 7,15, 13,25, 25,42, 41,67, 75,94, 91,162, 158,205, 250,332 +classes=2 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 \ No newline at end of file From 1a2f16e9d95eb0042f03bf02b98a6f411dba2eed Mon Sep 17 00:00:00 2001 From: Alexey Date: Fri, 4 Jan 2019 19:01:33 +0300 Subject: [PATCH 0225/1365] Update Readme.md --- README.md | 37 +++++++++++++++++++------------------ 1 file changed, 19 insertions(+), 18 deletions(-) diff --git a/README.md b/README.md index c8f1a1c68e7..a7e13628226 100644 --- a/README.md +++ b/README.md @@ -3,6 +3,8 @@ [![CircleCI](https://circleci.com/gh/AlexeyAB/darknet.svg?style=svg)](https://circleci.com/gh/AlexeyAB/darknet) +* [Requirements](#requirements) +* [Pre-trained models](#pre-trained-models) 0. [Improvements in this repository](#improvements-in-this-repository) 1. [How to use](#how-to-use) 2. [How to compile on Linux](#how-to-compile-on-linux) @@ -28,7 +30,7 @@ # "You Only Look Once: Unified, Real-Time Object Detection (versions 2 & 3)" -A Yolo cross-platform Windows and Linux version (for object detection). Contributtors: https://github.com/pjreddie/darknet/graphs/contributors +A Yolo cross-platform Windows and Linux version (for object detection). Contributtors: https://github.com/AlexeyAB/darknet/graphs/contributors This repository is forked from Linux-version: https://github.com/pjreddie/darknet @@ -37,12 +39,12 @@ More details: http://pjreddie.com/darknet/yolo/ This repository supports: * both Windows and Linux -* both OpenCV 2.x.x and OpenCV <= 3.4.0 (3.4.1 and higher isn't supported) -* both cuDNN v5-v7 +* both OpenCV 2.x.x and OpenCV <= 3.4.0 (3.4.1 and higher isn't supported, but you can try) +* both cuDNN >= v7 * CUDA >= 7.5 * also create SO-library on Linux and DLL-library on Windows -##### Requires: +##### Requirements: * **Linux GCC>=4.9 or Windows MS Visual Studio 2015 (v140)**: https://go.microsoft.com/fwlink/?LinkId=532606&clcid=0x409 (or offline [ISO image](https://go.microsoft.com/fwlink/?LinkId=615448&clcid=0x409)) * **CUDA 10.0**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) * **OpenCV 3.3.0**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.3.0/opencv-3.3.0-vc14.exe/download @@ -50,7 +52,10 @@ This repository supports: - OpenCV allows to show image or video detection in the window and store result to file that specified in command line `-out_filename res.avi` * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported -##### Pre-trained models for different cfg-files can be downloaded from (smaller -> faster & lower quality): +##### Pre-trained models + +There are weights-file for different cfg-files (smaller size -> faster speed & lower accuracy: + * `yolov3-openimages.cfg` (247 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-openimages.weights * `yolov3-spp.cfg` (240 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-spp.weights * `yolov3.cfg` (236 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3.weights @@ -97,17 +102,6 @@ Also, you might be interested in using a simplified repository where is implemen ### How to use: -##### Example of usage in cmd-files from `build\darknet\x64\`: - -* `darknet_yolo_v3.cmd` - initialization with 236 MB **Yolo v3** COCO-model yolov3.weights & yolov3.cfg and show detection on the image: dog.jpg - -* `darknet_voc.cmd` - initialization with 194 MB VOC-model yolo-voc.weights & yolo-voc.cfg and waiting for entering the name of the image file -* `darknet_demo_voc.cmd` - initialization with 194 MB VOC-model yolo-voc.weights & yolo-voc.cfg and play your video file which you must rename to: test.mp4 -* `darknet_demo_store.cmd` - initialization with 194 MB VOC-model yolo-voc.weights & yolo-voc.cfg and play your video file which you must rename to: test.mp4, and store result to: res.avi -* `darknet_net_cam_voc.cmd` - initialization with 194 MB VOC-model, play video from network video-camera mjpeg-stream (also from you phone) -* `darknet_web_cam_voc.cmd` - initialization with 194 MB VOC-model, play video from Web-Camera number #0 -* `darknet_coco_9000.cmd` - initialization with 186 MB Yolo9000 COCO-model, and show detection on the image: dog.jpg -* `darknet_coco_9000_demo.cmd` - initialization with 186 MB Yolo9000 COCO-model, and show detection on the video (if it is present): street4k.mp4, and store result to: res.avi ##### How to use on the command line: @@ -126,6 +120,8 @@ On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector * Remeber to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app * To process a list of images `data/train.txt` and save results of detection to `result.txt` use: `darknet.exe detector test cfg/coco.data yolov3.cfg yolov3.weights -dont_show -ext_output < data/train.txt > result.txt` +* To calculate anchors: `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416` +* To check accuracy mAP50: `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights` ##### For using network video-camera mjpeg-stream with any Android smartphone: @@ -164,7 +160,7 @@ To run Darknet on Linux use examples from this article, just use `./darknet` ins 1.1. Find files `opencv_world320.dll` and `opencv_ffmpeg320_64.dll` (or `opencv_world340.dll` and `opencv_ffmpeg340_64.dll`) in `C:\opencv_3.0\opencv\build\x64\vc14\bin` and put it near with `darknet.exe` - 1.2 Check that there are `bin` and `include` folders in the `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1` if aren't, then copy them to this folder from the path where is CUDA installed + 1.2 Check that there are `bin` and `include` folders in the `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0` if aren't, then copy them to this folder from the path where is CUDA installed 1.3. To install CUDNN (speedup neural network), do the following: @@ -476,6 +472,11 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * for training for small objects - set `layers = -1, 11` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L720 and set `stride=4` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L717 + * for training for both small and large objects use modified models: + * Full-model: 5 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3_5l.cfg + * Tiny-model: 3 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny_3l.cfg + * Spatial-full-model: 3 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-spp.cfg + * If you train the model to distinguish Left and Right objects as separate classes (left/right hand, left/right-turn on road signs, ...) then for disabling flip data augmentation - add `flip=0` here: https://github.com/AlexeyAB/darknet/blob/3d2d0a7c98dbc8923d9ff705b81ff4f7940ea6ff/cfg/yolov3.cfg#L17 * General rule - your training dataset should include such a set of relative sizes of objects that you want to detect: @@ -532,7 +533,7 @@ With example of: `train.txt`, `obj.names`, `obj.data`, `yolo-obj.cfg`, `air`1-6` ## How to use Yolo as DLL 1. To compile Yolo as C++ DLL-file `yolo_cpp_dll.dll` - open in MSVS2015 file `build\darknet\yolo_cpp_dll.sln`, set **x64** and **Release**, and do the: Build -> Build yolo_cpp_dll - * You should have installed **CUDA 9.1** + * You should have installed **CUDA 10.0** * To use cuDNN do: (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add at the beginning of line: `CUDNN;` 2. To use Yolo as DLL-file in your C++ console application - open in MSVS2015 file `build\darknet\yolo_console_dll.sln`, set **x64** and **Release**, and do the: Build -> Build yolo_console_dll From 48d461f9bda2d01dc2a83c7c2a520f61b3ea5b79 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 4 Jan 2019 19:08:18 +0300 Subject: [PATCH 0226/1365] Temporary experimental XNOR improvements on CPU --- src/convolutional_layer.c | 280 +++++++++++++++++++++++--------------- src/gemm.c | 255 +++++++++++++++++++++++++++++++++- src/gemm.h | 17 +++ src/image.c | 6 +- 4 files changed, 445 insertions(+), 113 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 4794364ea9a..f5050521400 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -681,6 +681,102 @@ void bit_to_float(unsigned char *src, float *dst, size_t size, size_t filters, f } } +void binary_align_weights(convolutional_layer *l) +{ + int m = l->n; + int k = l->size*l->size*l->c; + size_t new_lda = k + (l->lda_align - k % l->lda_align); // (k / 8 + 1) * 8; + l->new_lda = new_lda; + + binarize_weights(l->weights, m, k, l->binary_weights); + + size_t align_weights_size = new_lda * m; + l->align_bit_weights_size = align_weights_size / 8 + 1; + float *align_weights = calloc(align_weights_size, sizeof(float)); + l->align_bit_weights = calloc(l->align_bit_weights_size, sizeof(char)); + + size_t i, j; + // align A without transpose + for (i = 0; i < m; ++i) { + for (j = 0; j < k; ++j) { + align_weights[i*new_lda + j] = l->binary_weights[i*k + j]; + } + } + + + //if (l->c % 32 == 0) + if(gpu_index < 0 && l->stride == 1 && l->pad == 1 && l->c % 32 == 0) + { + int fil, chan; + const int items_per_filter = l->c * l->size * l->size; + //const int dst_items_per_filter = new_lda; + for (fil = 0; fil < l->n; ++fil) + { + for (chan = 0; chan < l->c; chan += 32) + { + const int items_per_channel = l->size*l->size; + for (i = 0; i < items_per_channel; ++i) + { + uint32_t val = 0; + int c_pack; + for (c_pack = 0; c_pack < 32; ++c_pack) { + float src = l->binary_weights[fil*items_per_filter + (chan + c_pack)*items_per_channel + i]; + + //align_weights[fil*items_per_filter + chan*items_per_channel + i * 32 + c_pack] = src; + + align_weights[fil*new_lda + chan*items_per_channel + i*32 + c_pack] = src; + //val |= (src << c); + } + + } + } + } + + //printf("\n l.index = %d \t aw[0] = %f, aw[1] = %f, aw[2] = %f, aw[3] = %f \n", l->index, align_weights[0], align_weights[1], align_weights[2], align_weights[3]); + //memcpy(l->binary_weights, align_weights, (l->size * l->size * l->c * l->n) * sizeof(float)); + + float_to_bit(align_weights, l->align_bit_weights, align_weights_size); + + get_mean_array(l->binary_weights, m*k, l->n, l->mean_arr); + //get_mean_array(l->binary_weights, m*new_lda, l->n, l->mean_arr); + } + else { + float_to_bit(align_weights, l->align_bit_weights, align_weights_size); + + get_mean_array(l->binary_weights, m*k, l->n, l->mean_arr); + } + + //l->mean_arr = calloc(l->n, sizeof(float)); + + //get_mean_array(align_weights, align_weights_size, l->n, l->mean_arr); + + + + +#ifdef GPU + cudaError_t status; + l->align_workspace_size = l->bit_align * l->size * l->size * l->c; + status = cudaMalloc((void **)&l->align_workspace_gpu, l->align_workspace_size * sizeof(float)); + status = cudaMalloc((void **)&l->transposed_align_workspace_gpu, l->align_workspace_size * sizeof(float)); + check_error(status); + + //l->align_bit_weights_gpu = cuda_make_array(l->align_bit_weights, l->align_bit_weights_size * sizeof(char)/sizeof(float)); + status = cudaMalloc((void **)&l->align_bit_weights_gpu, l->align_bit_weights_size); + check_error(status); + status = cudaMemcpy(l->align_bit_weights_gpu, l->align_bit_weights, l->align_bit_weights_size, cudaMemcpyHostToDevice); + check_error(status); + status = cudaMemcpy(l->binary_weights_gpu, l->binary_weights, m*k * sizeof(float), cudaMemcpyHostToDevice); + check_error(status); + + //l->mean_arr_gpu = cuda_make_array(l->mean_arr, l->n); + cuda_push_array(l->mean_arr_gpu, l->mean_arr, l->n); + cudaDeviceSynchronize(); +#endif // GPU + + free(align_weights); +} + +/* void binary_align_weights(convolutional_layer *l) { int m = l->n; @@ -729,6 +825,7 @@ void binary_align_weights(convolutional_layer *l) free(align_weights); } +*/ // binary transpose size_t binary_transpose_align_input(int k, int n, float *b, char **t_bit_input, size_t ldb_align, int bit_align) @@ -782,117 +879,98 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) u++; for(i = 0; i < l.batch; ++i){ - //im2col_cpu(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); - - //float *t_input = NULL; - //if (l.xnor) { - // size_t new_ldb = k + (l.lda_align - k%l.lda_align); - // size_t t_intput_size = new_ldb * n; - // t_input = calloc(t_intput_size, sizeof(float)); - // im2col_cpu_custom_transpose(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, t_input, new_ldb); - //} - //if (l.xnor && l.size == 3 && l.stride == 1 && l.pad == 1) {} - //else - // further optimizations: im2col_bin() for XNOR, and then transpose_aling_bin() - //im2col_cpu_custom(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); - //gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); //gemm_nn_custom(m, n, k, 1, a, k, b, n, c, n); - if (l.xnor && l.align_bit_weights && !state.train && (l.stride == 1 && l.pad == 1)) { + if (l.xnor && l.align_bit_weights && !state.train && (l.stride == 1 && l.pad == 1)) + { memset(b, 0, l.bit_align*l.size*l.size*l.c * sizeof(float)); - //im2col_cpu_custom_align(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align); - im2col_cpu_custom_bin(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align); - - size_t output_size = l.outputs; - //float *count_output = calloc(output_size, sizeof(float)); - //size_t bit_output_size = output_size / 8 + 1; - //char *bit_output = calloc(bit_output_size, sizeof(char)); - - size_t intput_size = n * k; // (out_h*out_w) X (l.size*l.size*l.c) : after im2col() - size_t bit_input_size = intput_size / 8 + 1; - //char *bit_input = calloc(bit_input_size, sizeof(char)); - - size_t weights_size = k * m; //l.size*l.size*l.c*l.n; - size_t bit_weights_size = weights_size / 8 + 1; - //char *bit_weights = calloc(bit_weights_size, sizeof(char)); - //float *mean_arr = calloc(l.n, sizeof(float)); - - // test: float->bit->float - //get_mean_array(l.weights, weights_size, l.n, mean_arr); - //float_to_bit(l.weights, bit_weights, weights_size); - //memset(l.weights, 0, weights_size * sizeof(float)); - //bit_to_float(bit_weights, l.weights, weights_size, l.n, mean_arr); // just for test float->bit->float - - //float_to_bit(b, bit_input, intput_size); - //memset(b, 0, intput_size * sizeof(float)); - //bit_to_float(bit_input, b, intput_size, 1, NULL); // just for test float->bit->float - - // transpose B from NxK to KxN (x-axis (ldb = l.size*l.size*l.c) - should be multiple of 8 bits) - { - /* - size_t ldb_align = 256;// 8; + if(l.c % 32 == 0) + { + int ldb_align = l.lda_align; size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; - size_t t_intput_size = new_ldb * n; + size_t t_intput_size = new_ldb * l.bit_align;// n; size_t t_bit_input_size = t_intput_size / 8;// +1; - float *t_input = calloc(t_intput_size, sizeof(float)); - char *t_bit_input = calloc(t_bit_input_size, sizeof(char)); - //printf("\n bit_input_size = %d, n = %d, k = %d, ldb = %d \n", bit_input_size, n, k, n); - //printf("\n t_bit_input_size = %d, k = %d, n = %d, new_ldb = %d \n", t_bit_input_size, k, n, new_ldb); + const int new_c = l.c / 32; + float *re_packed_input = calloc(l.c * l.w * l.h, sizeof(float)); + uint32_t *bin_re_packed_input = calloc(new_c * l.w * l.h + 1, sizeof(uint32_t)); - //printf("\n align_weights_size = %d, k = %d, m = %d, lda = %d \n", align_weights_size, k, m, k); - //printf("\n align_bit_weights_size = %d, k = %d, m = %d, new_lda = %d \n", align_bit_weights_size, k, m, new_ldb); + // float32x4 by channel (as in cuDNN) + repack_input(state.input, re_packed_input, l.w, l.h, l.c); + // 32 x floats -> 1 x uint32_t + float_to_bit(re_packed_input, (char *)bin_re_packed_input, l.c * l.w * l.h); - // transpose and align B - int i, j; - for (i = 0; i < n; ++i) { - for (j = 0; j < k; ++j) { - t_input[i*new_ldb + j] = b[j*n + i]; - } - } - float_to_bit(t_input, t_bit_input, t_intput_size); + free(re_packed_input); + // convolution the packed inputs and weights: float x 32 by channel (as in cuDNN) + //convolution_repacked((uint32_t *)bin_re_packed_input, (uint32_t *)l.align_bit_weights, l.output, + // l.w, l.h, l.c, l.n, l.size, l.pad, l.new_lda, l.mean_arr); + // // then exit from if() - if (!l.align_bit_weights) - { - size_t align_weights_size = new_ldb * m; - size_t align_bit_weights_size = align_weights_size / 8;// +1; - float *align_weights = calloc(align_weights_size, sizeof(float)); - l.align_bit_weights = calloc(align_bit_weights_size, sizeof(char)); - - // align A without transpose - for (i = 0; i < m; ++i) { - for (j = 0; j < k; ++j) { - align_weights[i*new_ldb + j] = a[i*k + j]; - } - } - float_to_bit(align_weights, l.align_bit_weights, align_weights_size); - l.mean_arr = calloc(l.n, sizeof(float)); - get_mean_array(align_weights, align_weights_size, l.n, l.mean_arr); + im2col_cpu_custom((float *)bin_re_packed_input, new_c, l.h, l.w, l.size, l.stride, l.pad, b); + //im2col_cpu((float *)bin_re_packed_input, new_c, l.h, l.w, l.size, l.stride, l.pad, b); - free(align_weights); - } - */ + free(bin_re_packed_input); - /* - if (l.size == 3 && l.stride == 1 && l.pad == 1) - { - //binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights); - //printf("\n mean = %f \n", l.mean_arr[0]); + int new_k = l.size*l.size*l.c / 32; - convolution_2d(l.w, l.h, l.size, l.n, l.c, l.pad, l.stride, - //l.weights, state.input, l.output, l.mean_arr); - l.binary_weights, state.input, l.output, l.mean_arr); - } - else { - */ +// gemm_nn_bin_32bit_packed(m, n, new_k, 1, +// l.align_bit_weights, l.new_lda/32, +// b, n, +// c, n, l.mean_arr); + +// // then exit from if() + + + //size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; + //size_t t_intput_size = new_ldb * l.bit_align;// n; + //size_t t_bit_input_size = t_intput_size / 8;// +1; + + char *t_bit_input = calloc(t_bit_input_size, sizeof(char)); + + transpose_uint32((uint32_t *)b, t_bit_input, new_k, n, n, new_ldb); + + // the main GEMM function + gemm_nn_custom_bin_mean_transposed(m, n, k, 1, l.align_bit_weights, new_ldb, t_bit_input, new_ldb, c, n, l.mean_arr); + + // // alternative GEMM + //gemm_nn_bin_transposed_32bit_packed(m, n, new_k, 1, + // l.align_bit_weights, l.new_lda/32, + // t_bit_input, new_ldb / 32, + // c, n, l.mean_arr); + + free(t_bit_input); + + } + else { // else (l.c % 32 != 0) + + //-------------------------------------------------------- + + //im2col_cpu_custom_align(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align); + im2col_cpu_custom_bin(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align); + + size_t output_size = l.outputs; + //float *count_output = calloc(output_size, sizeof(float)); + //size_t bit_output_size = output_size / 8 + 1; + //char *bit_output = calloc(bit_output_size, sizeof(char)); + size_t intput_size = n * k; // (out_h*out_w) X (l.size*l.size*l.c) : after im2col() + size_t bit_input_size = intput_size / 8 + 1; + //char *bit_input = calloc(bit_input_size, sizeof(char)); + + size_t weights_size = k * m; //l.size*l.size*l.c*l.n; + size_t bit_weights_size = weights_size / 8 + 1; + //char *bit_weights = calloc(bit_weights_size, sizeof(char)); + //float *mean_arr = calloc(l.n, sizeof(float)); + + // transpose B from NxK to KxN (x-axis (ldb = l.size*l.size*l.c) - should be multiple of 8 bits) + { //size_t ldb_align = 256; // 256 bit for AVX2 int ldb_align = l.lda_align; size_t new_ldb = k + (ldb_align - k%ldb_align); @@ -908,27 +986,11 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //free(t_input); free(t_bit_input); - //} + //} + } } - // for bit_input: (k * n) - //if (u == 8) gemm_nn_custom_bin_mean(m, n, k, 1, bit_weights, k, bit_input, n, c, n, mean_arr); // last xnor layer - //else gemm_nn_custom_bin_mean(m, n, k, 1, bit_weights, k, bit_input, n, c, n, NULL); - - //gemm_nn_custom_bin_mean(m, n, k, 1, bit_weights, k, bit_input, n, c, n, mean_arr); - - //printf("\n u = %d \n", u); - - //gemm_nn_custom(m, n, k, 1, a, k, b, n, c, n); - - //int j; - //if (u != 8) for (j = 0; j < l.n; ++j) l.biases[j] = l.biases[j] / (mean_arr[j]*2); - - //free(count_output); - //free(bit_input); - //free(bit_weights); - //free(mean_arr); } else { im2col_cpu_custom(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); diff --git a/src/gemm.c b/src/gemm.c index 6f021e73904..bf52a118aeb 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -487,6 +487,15 @@ void transpose_bin(uint32_t *A, uint32_t *B, const int n, const int m, } } } + +static inline int popcnt_32(uint32_t val32) { +#ifdef WIN32 // Windows + int tmp_count = __popcnt(val32); +#else // Linux + int tmp_count = __builtin_popcount(val32); +#endif + return tmp_count; +} //---------------------------- @@ -721,6 +730,91 @@ void gemm_nn(int M, int N, int K, float ALPHA, } + + +void gemm_nn_bin_32bit_packed(int M, int N, int K, float ALPHA, + uint32_t *A, int lda, + uint32_t *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + int i; + #pragma omp parallel for + for (i = 0; i < M; ++i) { // l.n + int j, s; + float mean_val = mean_arr[i]; + //printf(" l.mean_arr[i] = %d \n ", l.mean_arr[i]); + for (s = 0; s < K; ++s) // l.size*l.size*l.c/32 or (l.size*l.size*l.c) + { + register uint32_t A_PART = A[i*lda + s]; + __m256i a256 = _mm256_set1_epi32(A_PART); + + for (j = 0; j < N - 8; j += 8) + { + __m256i b256 = *((__m256i*)&B[s*ldb + j]); + __m256i xor256 = _mm256_xor_si256(a256, b256); // xnor = xor(a,b) + __m256i all_1 = _mm256_set1_epi8(255); + __m256i xnor256 = _mm256_andnot_si256(xor256, all_1); // xnor = not(xor(a,b)) + + //_m256 count = _mm256_set_ps( + /* + __m256i count = _mm256_setr_epi32( + (int)popcnt_32(xnor256.m256i_u32[0]), + (int)popcnt_32(xnor256.m256i_u32[1]), + (int)popcnt_32(xnor256.m256i_u32[2]), + (int)popcnt_32(xnor256.m256i_u32[3]), + (int)popcnt_32(xnor256.m256i_u32[4]), + (int)popcnt_32(xnor256.m256i_u32[5]), + (int)popcnt_32(xnor256.m256i_u32[6]), + (int)popcnt_32(xnor256.m256i_u32[7])); + + __m256i val2 = _mm256_set1_epi32(2); + count = _mm256_mullo_epi32(count, val2); + + __m256i val32 = _mm256_set1_epi32(32); + count = _mm256_sub_epi32(count, val32); + + int z; + for (z = 0; z < 8; ++z) { + C[i*ldc + j + z] += count.m256i_i32[z] * mean_val; + } + */ + + __m256 count = _mm256_setr_ps( + popcnt_32(xnor256.m256i_u32[0]), + popcnt_32(xnor256.m256i_u32[1]), + popcnt_32(xnor256.m256i_u32[2]), + popcnt_32(xnor256.m256i_u32[3]), + popcnt_32(xnor256.m256i_u32[4]), + popcnt_32(xnor256.m256i_u32[5]), + popcnt_32(xnor256.m256i_u32[6]), + popcnt_32(xnor256.m256i_u32[7])); + + __m256 val2 = _mm256_set1_ps(2); + count = _mm256_mul_ps(count, val2); // count * 2 + + __m256 val32 = _mm256_set1_ps(32); + count = _mm256_sub_ps(count, val32); // count - 32 + + __m256 mean256 = _mm256_set1_ps(mean_val); + count = _mm256_mul_ps(count, mean256); // count * mean_val + + __m256 c256 = *((__m256*)&C[i*ldc + j]); + count = _mm256_add_ps(count, c256); // c = c + count + *((__m256*)&C[i*ldc + j]) = count; + } + + for (; j < N; ++j) // out_h*out_w; + { + register uint32_t B_PART = B[s*ldb + j]; + uint32_t xnor_result = ~(A_PART ^ B_PART); + int32_t count = popcnt_32(xnor_result); // must be Signed int + + C[i*ldc + j] += (2 * count - 32) * mean_val; + } + } + } +} + void convolution_2d_old(int w, int h, int ksize, int n, int c, int pad, int stride, float *weights, float *input, float *output) { @@ -1652,7 +1746,7 @@ void forward_maxpool_layer_avx(float *src, float *dst, int *indexes, int size, i } } -#else +#else // AVX void gemm_nn(int M, int N, int K, float ALPHA, float *A, int lda, @@ -1670,6 +1764,36 @@ void gemm_nn(int M, int N, int K, float ALPHA, } } +void gemm_nn_bin_32bit_packed(int M, int N, int K, float ALPHA, + uint32_t *A, int lda, + uint32_t *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + int i; + #pragma omp parallel for + for (i = 0; i < M; ++i) { // l.n + int j, s; + float mean_val = mean_arr[i]; + //printf(" l.mean_arr[i] = %d \n ", l.mean_arr[i]); + for (s = 0; s < K; ++s) // l.size*l.size*l.c/32 or (l.size*l.size*l.c) + { + //register float A_PART = 1*a[i*k + s]; + register uint32_t A_PART = A[i*lda + s]; + for (j = 0; j < N; ++j) // out_h*out_w; + { + //c[i*n + j] += A_PART*b[s*n + j]; + register uint32_t B_PART = B[s*ldb + j]; + uint32_t xnor_result = ~(A_PART ^ B_PART); + //printf(" xnor_result = %d, ", xnor_result); + int32_t count = popcnt_32(xnor_result); // must be Signed int + + C[i*ldc + j] += (2 * count - 32) * mean_val; + //c[i*n + j] += count*mean; + } + } + } +} + void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride, float *weights, float *input, float *output, float *mean) @@ -2102,6 +2226,135 @@ void forward_maxpool_layer_avx(float *src, float *dst, int *indexes, int size, i #endif // AVX + +// 32 channels -> 1 channel (with 32 floats) +// 256 channels -> 8 channels (with 32 floats) +void repack_input(float *input, float *re_packed_input, int w, int h, int c) +{ + const int items_per_channel = w * h; + int chan, i; + for (chan = 0; chan < c; chan += 32) + { + for (i = 0; i < items_per_channel; ++i) + { + int c_pack; + for (c_pack = 0; c_pack < 32; ++c_pack) { + float src = input[(chan + c_pack)*items_per_channel + i]; + + re_packed_input[chan*items_per_channel + i * 32 + c_pack] = src; + } + } + } +} + +void transpose_uint32(uint32_t *src, uint32_t *dst, int src_h, int src_w, int src_align, int dst_align) +{ + //l.bit_align - algined (n) by 32 + //new_ldb - aligned (k) by 256 + + int i; + //#pragma omp parallel for + for (i = 0; i < src_h; i += 1) // l.size*l.size*l.c; + { + int j; + for (j = 0; j < src_w; j += 1) // out_h*out_w; + { + ((uint32_t *)dst)[j*dst_align / 32 + i] = ((uint32_t *)src)[i*src_align + j]; + } + } +} + +void gemm_nn_bin_transposed_32bit_packed(int M, int N, int K, float ALPHA, + uint32_t *A, int lda, + uint32_t *B, int ldb, + float *C, int ldc, float *mean_arr) +{ + int i; + #pragma omp parallel for + for (i = 0; i < M; ++i) { // l.n + int j, s; + float mean_val = mean_arr[i]; + for (s = 0; s < K; ++s) // l.size*l.size*l.c/32 or (l.size*l.size*l.c) + { + register uint32_t A_PART = ((uint32_t*)A)[i*lda + s]; + for (j = 0; j < N; ++j) // out_h*out_w; + { + register uint32_t B_PART = ((uint32_t*)B)[j*ldb + s]; + uint32_t xnor_result = ~(A_PART ^ B_PART); + int32_t count = popcnt_32(xnor_result); // must be Signed int + + C[i*ldc + j] += (2 * count - 32) * mean_val; + } + } + } +} + +void convolution_repacked(uint32_t *packed_input, uint32_t *packed_weights, float *output, + int w, int h, int c, int n, int size, int pad, int new_lda, float *mean_arr) +{ + int fil; + // filter index + #pragma omp parallel for + for (fil = 0; fil < n; ++fil) { + float mean_val = mean_arr[fil]; + int chan, c_pack, y, x, f_y, f_x; + // channel index + for (chan = 0; chan < c / 32; ++chan) + //for (chan = 0; chan < l.c; chan += 32) + //for (c_pack = 0; c_pack < 32; ++c_pack) + // input - y + for (y = 0; y < h; ++y) + // input - x + for (x = 0; x < w; ++x) + { + int const output_index = fil*w*h + y*w + x; + float sum = 0; + + // filter - y + for (f_y = 0; f_y < size; ++f_y) + { + int input_y = y + f_y - pad; + // filter - x + for (f_x = 0; f_x < size; ++f_x) + { + int input_x = x + f_x - pad; + if (input_y < 0 || input_x < 0 || input_y >= h || input_x >= w) continue; + + // normal + //float input = state.input[(chan + c_pack)*l.w*l.h + input_y*l.w + input_x]; + //float weight = l.weights[fil*l.c*l.size*l.size + (chan + c_pack)*l.size*l.size + f_y*l.size + f_x]; + + // packed + //float input = re_packed_input[chan*l.w*l.h + (input_y*l.w + input_x) * 32 + c_pack]; + //float weight = l.weights[fil*l.c*l.size*l.size + chan*l.size*l.size + (f_y*l.size + f_x) * 32 + c_pack]; + //sum += input * weight; + + //float input = re_packed_input[chan*l.w*l.h + (input_y*l.w + input_x) * 32 + c_pack]; + //float weight = l.weights[fil*l.c*l.size*l.size + chan*l.size*l.size + (f_y*l.size + f_x) * 32 + c_pack]; + //uint32_t bit1 = input > 0; + //uint32_t bit2 = weight > 0; + //uint32_t count = (~(bit1 ^ bit2)) & 1; + //float result = (2 * (float)count - 1) * mean_val; + //printf("\n mul = %f, bit1 = %d, bit2 = %d, count = %d, mean = %f, result = %f ", input*weight, bit1, bit2, count, mean_val, result); + //sum += result; + + uint32_t input = ((uint32_t *)packed_input)[chan*w*h + input_y*w + input_x]; + //uint32_t weight = ((uint32_t *)l.align_bit_weights)[fil*l.c*l.size*l.size/32 + chan*l.size*l.size + f_y*l.size + f_x]; + uint32_t weight = ((uint32_t *)packed_weights)[fil*new_lda / 32 + chan*size*size + f_y*size + f_x]; + + uint32_t xnor_result = ~(input ^ weight); + int32_t count = popcnt_32(xnor_result); // mandatory Signed int + sum += (2 * count - 32) * mean_val; + } + } + // l.output[filters][width][height] += + // state.input[channels][width][height] * + // l.weights[filters][channels][filter_width][filter_height]; + output[output_index] += sum; + } + } +} + void gemm_nt(int M, int N, int K, float ALPHA, float *A, int lda, float *B, int ldb, diff --git a/src/gemm.h b/src/gemm.h index e297537fc6c..dd727830b2a 100644 --- a/src/gemm.h +++ b/src/gemm.h @@ -59,6 +59,23 @@ void gemm_bin(int M, int N, int K, float ALPHA, float *B, int ldb, float *C, int ldc); +void repack_input(float *input, float *re_packed_input, int w, int h, int c); + +void convolution_repacked(uint32_t *packed_input, uint32_t *packed_weights, float *output, + int w, int h, int c, int n, int size, int pad, int new_lda, float *mean_arr); + +void gemm_nn_bin_32bit_packed(int M, int N, int K, float ALPHA, + uint32_t *A, int lda, + uint32_t *B, int ldb, + float *C, int ldc, float *mean_arr); + +void transpose_uint32(uint32_t *src, uint32_t *dst, int src_h, int src_w, int src_align, int dst_align); + +void gemm_nn_bin_transposed_32bit_packed(int M, int N, int K, float ALPHA, + uint32_t *A, int lda, + uint32_t *B, int ldb, + float *C, int ldc, float *mean_arr); + void forward_maxpool_layer_avx(float *src, float *dst, int *indexes, int size, int w, int h, int out_w, int out_h, int c, int pad, int stride, int batch); diff --git a/src/image.c b/src/image.c index cb96ef37d4e..1fcdd5c0000 100644 --- a/src/image.c +++ b/src/image.c @@ -325,9 +325,9 @@ void draw_detections_v3(image im, detection *dets, int num, float thresh, char * printf("%s: %.0f%%", names[best_class], selected_detections[i].det.prob[best_class] * 100); if (ext_output) printf("\t(left_x: %4.0f top_y: %4.0f width: %4.0f height: %4.0f)\n", - (selected_detections[i].det.bbox.x - selected_detections[i].det.bbox.w / 2)*im.w, - (selected_detections[i].det.bbox.y - selected_detections[i].det.bbox.h / 2)*im.h, - selected_detections[i].det.bbox.w*im.w, selected_detections[i].det.bbox.h*im.h); + round((selected_detections[i].det.bbox.x - selected_detections[i].det.bbox.w / 2)*im.w), + round((selected_detections[i].det.bbox.y - selected_detections[i].det.bbox.h / 2)*im.h), + round(selected_detections[i].det.bbox.w*im.w), round(selected_detections[i].det.bbox.h*im.h)); else printf("\n"); int j; From 333f1de2c3e9c93cf7296af50baaa4814f43c7e3 Mon Sep 17 00:00:00 2001 From: Alexey Date: Sun, 6 Jan 2019 01:27:41 +0300 Subject: [PATCH 0227/1365] Update Readme.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index a7e13628226..3a7a2d53620 100644 --- a/README.md +++ b/README.md @@ -5,6 +5,7 @@ * [Requirements](#requirements) * [Pre-trained models](#pre-trained-models) +* [Explanations in issues](https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3AExplanations) 0. [Improvements in this repository](#improvements-in-this-repository) 1. [How to use](#how-to-use) 2. [How to compile on Linux](#how-to-compile-on-linux) From c75fbb5f2e314f663e0af41eb0004704084e4bd5 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 6 Jan 2019 15:45:10 +0300 Subject: [PATCH 0228/1365] Minor fix --- src/convolutional_layer.c | 15 +++++++---- src/gemm.c | 56 +++++++++++++-------------------------- src/gemm.h | 3 +++ src/network.c | 4 +++ 4 files changed, 35 insertions(+), 43 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index f5050521400..25528d481db 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -888,6 +888,8 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) if(l.c % 32 == 0) { + //printf(" l.index = %d - new XNOR \n", l.index); + int ldb_align = l.lda_align; size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; size_t t_intput_size = new_ldb * l.bit_align;// n; @@ -906,7 +908,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) free(re_packed_input); - // convolution the packed inputs and weights: float x 32 by channel (as in cuDNN) + // slow - convolution the packed inputs and weights: float x 32 by channel (as in cuDNN) //convolution_repacked((uint32_t *)bin_re_packed_input, (uint32_t *)l.align_bit_weights, l.output, // l.w, l.h, l.c, l.n, l.size, l.pad, l.new_lda, l.mean_arr); @@ -920,10 +922,11 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) int new_k = l.size*l.size*l.c / 32; -// gemm_nn_bin_32bit_packed(m, n, new_k, 1, -// l.align_bit_weights, l.new_lda/32, -// b, n, -// c, n, l.mean_arr); + // good for (l.c == 64) + //gemm_nn_bin_32bit_packed(m, n, new_k, 1, + // l.align_bit_weights, l.new_lda/32, + // b, n, + // c, n, l.mean_arr); // // then exit from if() @@ -951,6 +954,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) else { // else (l.c % 32 != 0) //-------------------------------------------------------- + //printf(" l.index = %d - old XNOR \n", l.index); //im2col_cpu_custom_align(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align); im2col_cpu_custom_bin(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align); @@ -993,6 +997,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) } else { + //printf(" l.index = %d - FP32 \n", l.index); im2col_cpu_custom(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); gemm(0, 0, m, n, k, 1, a, k, b, n, 1, c, n); diff --git a/src/gemm.c b/src/gemm.c index bf52a118aeb..87de21594c3 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -489,9 +489,9 @@ void transpose_bin(uint32_t *A, uint32_t *B, const int n, const int m, } static inline int popcnt_32(uint32_t val32) { -#ifdef WIN32 // Windows +#ifdef WIN32 // Windows MSVS int tmp_count = __popcnt(val32); -#else // Linux +#else // Linux GCC int tmp_count = __builtin_popcount(val32); #endif return tmp_count; @@ -755,39 +755,15 @@ void gemm_nn_bin_32bit_packed(int M, int N, int K, float ALPHA, __m256i all_1 = _mm256_set1_epi8(255); __m256i xnor256 = _mm256_andnot_si256(xor256, all_1); // xnor = not(xor(a,b)) - //_m256 count = _mm256_set_ps( - /* - __m256i count = _mm256_setr_epi32( - (int)popcnt_32(xnor256.m256i_u32[0]), - (int)popcnt_32(xnor256.m256i_u32[1]), - (int)popcnt_32(xnor256.m256i_u32[2]), - (int)popcnt_32(xnor256.m256i_u32[3]), - (int)popcnt_32(xnor256.m256i_u32[4]), - (int)popcnt_32(xnor256.m256i_u32[5]), - (int)popcnt_32(xnor256.m256i_u32[6]), - (int)popcnt_32(xnor256.m256i_u32[7])); - - __m256i val2 = _mm256_set1_epi32(2); - count = _mm256_mullo_epi32(count, val2); - - __m256i val32 = _mm256_set1_epi32(32); - count = _mm256_sub_epi32(count, val32); - - int z; - for (z = 0; z < 8; ++z) { - C[i*ldc + j + z] += count.m256i_i32[z] * mean_val; - } - */ - __m256 count = _mm256_setr_ps( - popcnt_32(xnor256.m256i_u32[0]), - popcnt_32(xnor256.m256i_u32[1]), - popcnt_32(xnor256.m256i_u32[2]), - popcnt_32(xnor256.m256i_u32[3]), - popcnt_32(xnor256.m256i_u32[4]), - popcnt_32(xnor256.m256i_u32[5]), - popcnt_32(xnor256.m256i_u32[6]), - popcnt_32(xnor256.m256i_u32[7])); + popcnt_32(_mm256_extract_epi32(xnor256, 0)), + popcnt_32(_mm256_extract_epi32(xnor256, 1)), + popcnt_32(_mm256_extract_epi32(xnor256, 2)), + popcnt_32(_mm256_extract_epi32(xnor256, 3)), + popcnt_32(_mm256_extract_epi32(xnor256, 4)), + popcnt_32(_mm256_extract_epi32(xnor256, 5)), + popcnt_32(_mm256_extract_epi32(xnor256, 6)), + popcnt_32(_mm256_extract_epi32(xnor256, 7))); __m256 val2 = _mm256_set1_ps(2); count = _mm256_mul_ps(count, val2); // count * 2 @@ -2274,17 +2250,19 @@ void gemm_nn_bin_transposed_32bit_packed(int M, int N, int K, float ALPHA, for (i = 0; i < M; ++i) { // l.n int j, s; float mean_val = mean_arr[i]; - for (s = 0; s < K; ++s) // l.size*l.size*l.c/32 or (l.size*l.size*l.c) + for (j = 0; j < N; ++j) // out_h*out_w; { - register uint32_t A_PART = ((uint32_t*)A)[i*lda + s]; - for (j = 0; j < N; ++j) // out_h*out_w; + float val = 0; + for (s = 0; s < K; ++s) // l.size*l.size*l.c/32 or (l.size*l.size*l.c) { + register uint32_t A_PART = ((uint32_t*)A)[i*lda + s]; register uint32_t B_PART = ((uint32_t*)B)[j*ldb + s]; uint32_t xnor_result = ~(A_PART ^ B_PART); int32_t count = popcnt_32(xnor_result); // must be Signed int - C[i*ldc + j] += (2 * count - 32) * mean_val; + val += (2 * count - 32) * mean_val; } + C[i*ldc + j] += val; } } } @@ -2422,6 +2400,8 @@ void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA, } } + is_avx(); // initialize static variable + is_fma_avx2(); int t; #pragma omp parallel for for (t = 0; t < M; ++t) { diff --git a/src/gemm.h b/src/gemm.h index dd727830b2a..c34b4b35541 100644 --- a/src/gemm.h +++ b/src/gemm.h @@ -22,6 +22,9 @@ static inline unsigned char get_bit(unsigned char const*const src, size_t index) return val; } +int is_avx(); +int is_fma_avx2(); + void float_to_bit(float *src, unsigned char *dst, size_t size); void transpose_block_SSE4x4(float *A, float *B, const int n, const int m, diff --git a/src/network.c b/src/network.c index b91edd5bfdc..cf0d9351427 100644 --- a/src/network.c +++ b/src/network.c @@ -190,6 +190,8 @@ network make_network(int n) return net; } +double get_time_point(); + void forward_network(network net, network_state state) { state.workspace = net.workspace; @@ -200,7 +202,9 @@ void forward_network(network net, network_state state) if(l.delta){ scal_cpu(l.outputs * l.batch, 0, l.delta, 1); } + //double time = get_time_point(); l.forward(l, state); + //printf("%d - Predicted in %lf milli-seconds.\n", i, ((double)get_time_point() - time) / 1000); state.input = l.output; } } From c56931dd759d2dc98d8d0f2ccd82949388f1f63e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 6 Jan 2019 16:00:00 +0300 Subject: [PATCH 0229/1365] Compile fix --- src/gemm.c | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/src/gemm.c b/src/gemm.c index 87de21594c3..0238fd2185c 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -1724,6 +1724,14 @@ void forward_maxpool_layer_avx(float *src, float *dst, int *indexes, int size, i #else // AVX +int is_avx() { + return 0; +} + +int is_fma_avx2() { + return 0; +} + void gemm_nn(int M, int N, int K, float ALPHA, float *A, int lda, float *B, int ldb, From 3ff5084590bce621b195196edd32ac7e3b04bfbd Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 6 Jan 2019 23:51:38 +0300 Subject: [PATCH 0230/1365] Added include/darknet.h --- Makefile | 18 +- build/darknet/darknet.vcxproj | 5 +- build/darknet/darknet_no_gpu.vcxproj | 9 +- build/darknet/x64/darknet.py | 4 +- build/darknet/x64/darknet_video.cmd | 5 + build/darknet/x64/darknet_video.py | 320 +++++++++ build/darknet/yolo_console_dll.vcxproj | 9 +- build/darknet/yolo_cpp_dll.vcxproj | 17 +- build/darknet/yolo_cpp_dll_no_gpu.vcxproj | 26 +- darknet.py | 4 +- darknet_video.py | 320 +++++++++ include/darknet.h | 793 ++++++++++++++++++++++ {src => include}/yolo_v2_class.hpp | 58 +- src/activation_kernels.cu | 94 ++- src/activations.h | 7 +- src/box.h | 40 +- src/cuda.h | 21 +- src/data.h | 27 +- src/http_stream.h | 1 + src/image.c | 32 +- src/image.h | 20 +- src/layer.h | 108 +-- src/matrix.h | 10 +- src/network.c | 32 +- src/network.h | 37 +- src/option_list.h | 25 +- src/parser.c | 25 + src/tree.h | 27 +- src/utils.h | 15 +- src/yolo_v2_class.cpp | 36 +- 30 files changed, 1830 insertions(+), 315 deletions(-) create mode 100644 build/darknet/x64/darknet_video.cmd create mode 100644 build/darknet/x64/darknet_video.py create mode 100644 darknet_video.py create mode 100644 include/darknet.h rename {src => include}/yolo_v2_class.hpp (93%) diff --git a/Makefile b/Makefile index 99781ea9bab..46811ddc1aa 100644 --- a/Makefile +++ b/Makefile @@ -47,7 +47,7 @@ EXEC=darknet OBJDIR=./obj/ ifeq ($(LIBSO), 1) -LIBNAMESO=darknet.so +LIBNAMESO=libdarknet.so APPNAMESO=uselib endif @@ -56,7 +56,7 @@ CPP=g++ NVCC=nvcc OPTS=-Ofast LDFLAGS= -lm -pthread -COMMON= +COMMON= -Iinclude/ CFLAGS=-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas ifeq ($(DEBUG), 1) @@ -115,18 +115,18 @@ OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernel endif OBJS = $(addprefix $(OBJDIR), $(OBJ)) -DEPS = $(wildcard src/*.h) Makefile +DEPS = $(wildcard src/*.h) Makefile include/darknet.h -all: obj backup results $(EXEC) $(LIBNAMESO) $(APPNAMESO) +all: obj backup results setchmod $(EXEC) $(LIBNAMESO) $(APPNAMESO) ifeq ($(LIBSO), 1) CFLAGS+= -fPIC -$(LIBNAMESO): $(OBJS) src/yolo_v2_class.hpp src/yolo_v2_class.cpp - $(CPP) -shared -std=c++11 -fvisibility=hidden -DYOLODLL_EXPORTS $(COMMON) $(CFLAGS) $(OBJS) src/yolo_v2_class.cpp -o $@ $(LDFLAGS) +$(LIBNAMESO): $(OBJS) include/yolo_v2_class.hpp src/yolo_v2_class.cpp + $(CPP) -shared -std=c++11 -fvisibility=hidden -DLIB_EXPORTS $(COMMON) $(CFLAGS) $(OBJS) src/yolo_v2_class.cpp -o $@ $(LDFLAGS) -$(APPNAMESO): $(LIBNAMESO) src/yolo_v2_class.hpp src/yolo_console_dll.cpp - $(CPP) -std=c++11 $(COMMON) $(CFLAGS) -o $@ src/yolo_console_dll.cpp $(LDFLAGS) -L ./ $(LIBNAMESO) +$(APPNAMESO): $(LIBNAMESO) include/yolo_v2_class.hpp src/yolo_console_dll.cpp + $(CPP) -std=c++11 $(COMMON) $(CFLAGS) -o $@ src/yolo_console_dll.cpp $(LDFLAGS) -L ./ -l:$(LIBNAMESO) endif $(EXEC): $(OBJS) @@ -147,6 +147,8 @@ backup: mkdir -p backup results: mkdir -p results +setchmod: + chmod +x *.sh .PHONY: clean diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj index b8e4c08b06f..f1500eed9fd 100644 --- a/build/darknet/darknet.vcxproj +++ b/build/darknet/darknet.vcxproj @@ -88,7 +88,7 @@ Level3 Disabled true - C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include + C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include CUDNN;_CRTDBG_MAP_ALLOC;_MBCS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) OPENCV; true @@ -132,7 +132,7 @@ true true true - C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include + C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include OPENCV;CUDNN;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y @@ -236,6 +236,7 @@ + diff --git a/build/darknet/darknet_no_gpu.vcxproj b/build/darknet/darknet_no_gpu.vcxproj index 8b173015256..785b2f02c93 100644 --- a/build/darknet/darknet_no_gpu.vcxproj +++ b/build/darknet/darknet_no_gpu.vcxproj @@ -78,7 +78,7 @@ Level3 Disabled true - C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories); + C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories); _CRTDBG_MAP_ALLOC;_MBCS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) OPENCV; stdlib.h;crtdbg.h;%(ForcedIncludeFiles) @@ -94,7 +94,7 @@ Level3 Disabled true - C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories) + C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories) _MBCS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) OPENCV;CUDNN true @@ -118,7 +118,7 @@ true true true - C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories); + C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories); _TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) @@ -138,7 +138,7 @@ true true true - C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories) + C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories) OPENCV;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y @@ -241,6 +241,7 @@ + diff --git a/build/darknet/x64/darknet.py b/build/darknet/x64/darknet.py index 4bca5f5a3b9..784399eee55 100644 --- a/build/darknet/x64/darknet.py +++ b/build/darknet/x64/darknet.py @@ -75,7 +75,7 @@ class METADATA(Structure): #lib = CDLL("/home/pjreddie/documents/darknet/libdarknet.so", RTLD_GLOBAL) -#lib = CDLL("darknet.so", RTLD_GLOBAL) +#lib = CDLL("libdarknet.so", RTLD_GLOBAL) hasGPU = True if os.name == "nt": cwd = os.path.dirname(__file__) @@ -119,7 +119,7 @@ class METADATA(Structure): lib = CDLL(winGPUdll, RTLD_GLOBAL) print("Environment variables indicated a CPU run, but we didn't find `"+winNoGPUdll+"`. Trying a GPU run anyway.") else: - lib = CDLL("./darknet.so", RTLD_GLOBAL) + lib = CDLL("./libdarknet.so", RTLD_GLOBAL) lib.network_width.argtypes = [c_void_p] lib.network_width.restype = c_int lib.network_height.argtypes = [c_void_p] diff --git a/build/darknet/x64/darknet_video.cmd b/build/darknet/x64/darknet_video.cmd new file mode 100644 index 00000000000..8311dc0c941 --- /dev/null +++ b/build/darknet/x64/darknet_video.cmd @@ -0,0 +1,5 @@ +rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\python.exe darknet_video.py + +C:\Python27\python.exe darknet_video.py + +pause \ No newline at end of file diff --git a/build/darknet/x64/darknet_video.py b/build/darknet/x64/darknet_video.py new file mode 100644 index 00000000000..1a486cedaf5 --- /dev/null +++ b/build/darknet/x64/darknet_video.py @@ -0,0 +1,320 @@ +from ctypes import * +import math +import random +import os +import cv2 +import numpy as np +import time + + +def sample(probs): + s = sum(probs) + probs = [a/s for a in probs] + r = random.uniform(0, 1) + for i in range(len(probs)): + r = r - probs[i] + if r <= 0: + return i + return len(probs)-1 + + +def c_array(ctype, values): + arr = (ctype*len(values))() + arr[:] = values + return arr + + +class BOX(Structure): + _fields_ = [("x", c_float), + ("y", c_float), + ("w", c_float), + ("h", c_float)] + + +class DETECTION(Structure): + _fields_ = [("bbox", BOX), + ("classes", c_int), + ("prob", POINTER(c_float)), + ("mask", POINTER(c_float)), + ("objectness", c_float), + ("sort_class", c_int)] + + +class IMAGE(Structure): + _fields_ = [("w", c_int), + ("h", c_int), + ("c", c_int), + ("data", POINTER(c_float))] + + +class METADATA(Structure): + _fields_ = [("classes", c_int), + ("names", POINTER(c_char_p))] + + +hasGPU = True + +lib = CDLL("yolo_cpp_dll.dll", RTLD_GLOBAL) +lib.network_width.argtypes = [c_void_p] +lib.network_width.restype = c_int +lib.network_height.argtypes = [c_void_p] +lib.network_height.restype = c_int + +predict = lib.network_predict +predict.argtypes = [c_void_p, POINTER(c_float)] +predict.restype = POINTER(c_float) + +if hasGPU: + set_gpu = lib.cuda_set_device + set_gpu.argtypes = [c_int] + +make_image = lib.make_image +make_image.argtypes = [c_int, c_int, c_int] +make_image.restype = IMAGE + +get_network_boxes = lib.get_network_boxes +get_network_boxes.argtypes = \ + [c_void_p, c_int, c_int, c_float, c_float, POINTER( + c_int), c_int, POINTER(c_int), c_int] +get_network_boxes.restype = POINTER(DETECTION) + +make_network_boxes = lib.make_network_boxes +make_network_boxes.argtypes = [c_void_p] +make_network_boxes.restype = POINTER(DETECTION) + +free_detections = lib.free_detections +free_detections.argtypes = [POINTER(DETECTION), c_int] + +free_ptrs = lib.free_ptrs +free_ptrs.argtypes = [POINTER(c_void_p), c_int] + +network_predict = lib.network_predict +network_predict.argtypes = [c_void_p, POINTER(c_float)] + +reset_rnn = lib.reset_rnn +reset_rnn.argtypes = [c_void_p] + +load_net = lib.load_network +load_net.argtypes = [c_char_p, c_char_p, c_int] +load_net.restype = c_void_p + +load_net_custom = lib.load_network_custom +load_net_custom.argtypes = [c_char_p, c_char_p, c_int, c_int] +load_net_custom.restype = c_void_p + +do_nms_obj = lib.do_nms_obj +do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float] + +do_nms_sort = lib.do_nms_sort +do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float] + +free_image = lib.free_image +free_image.argtypes = [IMAGE] + +letterbox_image = lib.letterbox_image +letterbox_image.argtypes = [IMAGE, c_int, c_int] +letterbox_image.restype = IMAGE + +load_meta = lib.get_metadata +lib.get_metadata.argtypes = [c_char_p] +lib.get_metadata.restype = METADATA + +load_image = lib.load_image_color +load_image.argtypes = [c_char_p, c_int, c_int] +load_image.restype = IMAGE + +rgbgr_image = lib.rgbgr_image +rgbgr_image.argtypes = [IMAGE] + +predict_image = lib.network_predict_image +predict_image.argtypes = [c_void_p, IMAGE] +predict_image.restype = POINTER(c_float) + + +def array_to_image(arr): + import numpy as np + arr = arr.transpose(2, 0, 1) + c = arr.shape[0] + h = arr.shape[1] + w = arr.shape[2] + arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0 + data = arr.ctypes.data_as(POINTER(c_float)) + im = IMAGE(w, h, c, data) + return im, arr + + +def classify(net, meta, im): + out = predict_image(net, im) + res = [] + for i in range(meta.classes): + if altNames is None: + nameTag = meta.names[i] + else: + nameTag = altNames[i] + res.append((nameTag, out[i])) + res = sorted(res, key=lambda x: -x[1]) + return res + + +def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug=False): + im, arr = array_to_image(image) + if debug: + print("Loaded image") + num = c_int(0) + if debug: + print("Assigned num") + pnum = pointer(num) + if debug: + print("Assigned pnum") + predict_image(net, im) + if debug: + print("did prediction") + # dets = get_network_boxes( + # net, image.shape[1], image.shape[0], + # thresh, hier_thresh, + # None, 0, pnum, 0) # OpenCV + dets = get_network_boxes(net, im.w, im.h, + thresh, hier_thresh, None, 0, pnum, 0) + if debug: + print("Got dets") + num = pnum[0] + if debug: + print("got zeroth index of pnum") + if nms: + do_nms_sort(dets, num, meta.classes, nms) + if debug: + print("did sort") + res = [] + if debug: + print("about to range") + for j in range(num): + if debug: + print("Ranging on "+str(j)+" of "+str(num)) + if debug: + print("Classes: "+str(meta), meta.classes, meta.names) + for i in range(meta.classes): + if debug: + print("Class-ranging on "+str(i)+" of " + + str(meta.classes)+"= "+str(dets[j].prob[i])) + if dets[j].prob[i] > 0: + b = dets[j].bbox + if altNames is None: + nameTag = meta.names[i] + else: + nameTag = altNames[i] + if debug: + print("Got bbox", b) + print(nameTag) + print(dets[j].prob[i]) + print((b.x, b.y, b.w, b.h)) + res.append((nameTag, dets[j].prob[i], (b.x, b.y, b.w, b.h))) + if debug: + print("did range") + res = sorted(res, key=lambda x: -x[1]) + if debug: + print("did sort") + # free_image(im) + if debug: + print("freed image") + free_detections(dets, num) + if debug: + print("freed detections") + return res + + +def convertBack(x, y, w, h): + xmin = int(round(x - (w / 2))) + xmax = int(round(x + (w / 2))) + ymin = int(round(y - (h / 2))) + ymax = int(round(y + (h / 2))) + return xmin, ymin, xmax, ymax + + +def cvDrawBoxes(detections, img): + for detection in detections: + x, y, w, h = detection[2][0],\ + detection[2][1],\ + detection[2][2],\ + detection[2][3] + xmin, ymin, xmax, ymax = convertBack( + float(x), float(y), float(w), float(h)) + pt1 = (xmin, ymin) + pt2 = (xmax, ymax) + cv2.rectangle(img, pt1, pt2, (0, 255, 0), 2) + cv2.putText(img, + detection[0].decode() + + " [" + str(round(detection[1] * 100, 2)) + "]", + (pt1[0], pt1[1] + 20), cv2.FONT_HERSHEY_SIMPLEX, 1, + [0, 255, 0], 4) + return img + + +netMain = None +metaMain = None +altNames = None + + +def YOLO(): + global metaMain, netMain, altNames + configPath = "./cfg/yolov3.cfg" + weightPath = "./yolov3.weights" + metaPath = "./cfg/coco.data" + if not os.path.exists(configPath): + raise ValueError("Invalid config path `" + + os.path.abspath(configPath)+"`") + if not os.path.exists(weightPath): + raise ValueError("Invalid weight path `" + + os.path.abspath(weightPath)+"`") + if not os.path.exists(metaPath): + raise ValueError("Invalid data file path `" + + os.path.abspath(metaPath)+"`") + if netMain is None: + netMain = load_net_custom(configPath.encode( + "ascii"), weightPath.encode("ascii"), 0, 1) # batch size = 1 + if metaMain is None: + metaMain = load_meta(metaPath.encode("ascii")) + if altNames is None: + try: + with open(metaPath) as metaFH: + metaContents = metaFH.read() + import re + match = re.search("names *= *(.*)$", metaContents, + re.IGNORECASE | re.MULTILINE) + if match: + result = match.group(1) + else: + result = None + try: + if os.path.exists(result): + with open(result) as namesFH: + namesList = namesFH.read().strip().split("\n") + altNames = [x.strip() for x in namesList] + except TypeError: + pass + except Exception: + pass + #cap = cv2.VideoCapture(0) + cap = cv2.VideoCapture("test.mp4") + cap.set(3, 1280) + cap.set(4, 720) + out = cv2.VideoWriter( + "output.avi", cv2.VideoWriter_fourcc(*"MJPG"), 10.0, + (lib.network_width(netMain), lib.network_height(netMain))) + print("Starting the YOLO loop...") + while True: + prev_time = time.time() + ret, frame_read = cap.read() + frame_rgb = cv2.cvtColor(frame_read, cv2.COLOR_BGR2RGB) + frame_resized = cv2.resize(frame_rgb, + (lib.network_width(netMain), + lib.network_height(netMain)), + interpolation=cv2.INTER_LINEAR) + detections = detect(netMain, metaMain, frame_resized, thresh=0.25) + image = cvDrawBoxes(detections, frame_resized) + image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + print(1/(time.time()-prev_time)) + cap.release() + out.release() + +if __name__ == "__main__": + YOLO() diff --git a/build/darknet/yolo_console_dll.vcxproj b/build/darknet/yolo_console_dll.vcxproj index 064c94a197c..763914b0fbf 100644 --- a/build/darknet/yolo_console_dll.vcxproj +++ b/build/darknet/yolo_console_dll.vcxproj @@ -80,6 +80,7 @@ Level3 Disabled true + ..\..\include; @@ -87,7 +88,7 @@ Level3 Disabled true - C:\opencv_3.0\opencv\build\include + ..\..\include;C:\opencv_3.0\opencv\build\include MultiThreadedDLL _CRT_SECURE_NO_WARNINGS;_MBCS;%(PreprocessorDefinitions) @@ -102,6 +103,7 @@ true true true + ..\..\include; true @@ -115,7 +117,7 @@ true true true - C:\opencv_source\opencv\bin\install\include + ..\..\include;C:\opencv_source\opencv\bin\install\include _CRT_SECURE_NO_WARNINGS;_MBCS;%(PreprocessorDefinitions) Async true @@ -132,6 +134,9 @@ + + + diff --git a/build/darknet/yolo_cpp_dll.vcxproj b/build/darknet/yolo_cpp_dll.vcxproj index bb8cace6c39..bde77841e13 100644 --- a/build/darknet/yolo_cpp_dll.vcxproj +++ b/build/darknet/yolo_cpp_dll.vcxproj @@ -80,6 +80,8 @@ Level3 Disabled true + ..\..\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir) + LIB_EXPORTS;_MBCS;%(PreprocessorDefinitions) true @@ -90,8 +92,8 @@ Level3 Disabled true - ..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include - _MBCS;YOLODLL_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + ..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include + _MBCS;LIB_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) CUDNN true @@ -114,8 +116,8 @@ true true true - C:\opencv_2.4.9\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include - YOLODLL_EXPORTS;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + C:\opencv_2.4.9\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include + LIB_EXPORTS;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) true @@ -133,8 +135,8 @@ true true true - C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include - CUDNN;GPU;YOLODLL_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include + CUDNN;GPU;LIB_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y CompileAsCpp @@ -236,9 +238,10 @@ - + + diff --git a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj index ca5610fd60b..6539993553c 100644 --- a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj +++ b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj @@ -77,6 +77,8 @@ Level3 Disabled true + ..\..\include; + LIB_EXPORTS;_MBCS;%(PreprocessorDefinitions) true @@ -87,16 +89,17 @@ Level3 Disabled true - ..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include - _MBCS;YOLODLL_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + ..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include + _MBCS;LIB_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) CUDNN true + stdlib.h;crtdbg.h;%(ForcedIncludeFiles) true - $(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) + %(AdditionalLibraryDirectories) $(OutDir)\$(TargetName)$(TargetExt) - ..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;%(AdditionalDependencies) + ..\..\3rdparty\lib\x64\pthreadVC2.lib;%(AdditionalDependencies) true @@ -111,16 +114,16 @@ true true true - C:\opencv_2.4.9\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include - YOLODLL_EXPORTS;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + C:\opencv_2.4.9\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include + LIB_EXPORTS;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) true true true true - C:\opencv_2.4.9\opencv\build\x86\vc14\lib;C:\opencv_2.4.9\opencv\build\x86\vc12\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) - ..\..\3rdparty\lib\x86\pthreadVC2.lib;cudart.lib;cublas.lib;curand.lib;%(AdditionalDependencies) + C:\opencv_2.4.9\opencv\build\x86\vc14\lib;C:\opencv_2.4.9\opencv\build\x86\vc12\lib;%(AdditionalLibraryDirectories) + ..\..\3rdparty\lib\x86\pthreadVC2.lib;%(AdditionalDependencies) @@ -130,8 +133,8 @@ true true true - C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories) - YOLODLL_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories) + LIB_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y CompileAsCpp @@ -221,9 +224,10 @@ - + + diff --git a/darknet.py b/darknet.py index 84bbfc4255a..3d885d20e56 100644 --- a/darknet.py +++ b/darknet.py @@ -75,7 +75,7 @@ class METADATA(Structure): #lib = CDLL("/home/pjreddie/documents/darknet/libdarknet.so", RTLD_GLOBAL) -#lib = CDLL("darknet.so", RTLD_GLOBAL) +#lib = CDLL("libdarknet.so", RTLD_GLOBAL) hasGPU = True if os.name == "nt": cwd = os.path.dirname(__file__) @@ -119,7 +119,7 @@ class METADATA(Structure): lib = CDLL(winGPUdll, RTLD_GLOBAL) print("Environment variables indicated a CPU run, but we didn't find `"+winNoGPUdll+"`. Trying a GPU run anyway.") else: - lib = CDLL("./darknet.so", RTLD_GLOBAL) + lib = CDLL("./libdarknet.so", RTLD_GLOBAL) lib.network_width.argtypes = [c_void_p] lib.network_width.restype = c_int lib.network_height.argtypes = [c_void_p] diff --git a/darknet_video.py b/darknet_video.py new file mode 100644 index 00000000000..7a8e8f005cf --- /dev/null +++ b/darknet_video.py @@ -0,0 +1,320 @@ +from ctypes import * +import math +import random +import os +import cv2 +import numpy as np +import time + + +def sample(probs): + s = sum(probs) + probs = [a/s for a in probs] + r = random.uniform(0, 1) + for i in range(len(probs)): + r = r - probs[i] + if r <= 0: + return i + return len(probs)-1 + + +def c_array(ctype, values): + arr = (ctype*len(values))() + arr[:] = values + return arr + + +class BOX(Structure): + _fields_ = [("x", c_float), + ("y", c_float), + ("w", c_float), + ("h", c_float)] + + +class DETECTION(Structure): + _fields_ = [("bbox", BOX), + ("classes", c_int), + ("prob", POINTER(c_float)), + ("mask", POINTER(c_float)), + ("objectness", c_float), + ("sort_class", c_int)] + + +class IMAGE(Structure): + _fields_ = [("w", c_int), + ("h", c_int), + ("c", c_int), + ("data", POINTER(c_float))] + + +class METADATA(Structure): + _fields_ = [("classes", c_int), + ("names", POINTER(c_char_p))] + + +hasGPU = True + +lib = CDLL("./libdarknet.so", RTLD_GLOBAL) +lib.network_width.argtypes = [c_void_p] +lib.network_width.restype = c_int +lib.network_height.argtypes = [c_void_p] +lib.network_height.restype = c_int + +predict = lib.network_predict +predict.argtypes = [c_void_p, POINTER(c_float)] +predict.restype = POINTER(c_float) + +if hasGPU: + set_gpu = lib.cuda_set_device + set_gpu.argtypes = [c_int] + +make_image = lib.make_image +make_image.argtypes = [c_int, c_int, c_int] +make_image.restype = IMAGE + +get_network_boxes = lib.get_network_boxes +get_network_boxes.argtypes = \ + [c_void_p, c_int, c_int, c_float, c_float, POINTER( + c_int), c_int, POINTER(c_int), c_int] +get_network_boxes.restype = POINTER(DETECTION) + +make_network_boxes = lib.make_network_boxes +make_network_boxes.argtypes = [c_void_p] +make_network_boxes.restype = POINTER(DETECTION) + +free_detections = lib.free_detections +free_detections.argtypes = [POINTER(DETECTION), c_int] + +free_ptrs = lib.free_ptrs +free_ptrs.argtypes = [POINTER(c_void_p), c_int] + +network_predict = lib.network_predict +network_predict.argtypes = [c_void_p, POINTER(c_float)] + +reset_rnn = lib.reset_rnn +reset_rnn.argtypes = [c_void_p] + +load_net = lib.load_network +load_net.argtypes = [c_char_p, c_char_p, c_int] +load_net.restype = c_void_p + +load_net_custom = lib.load_network_custom +load_net_custom.argtypes = [c_char_p, c_char_p, c_int, c_int] +load_net_custom.restype = c_void_p + +do_nms_obj = lib.do_nms_obj +do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float] + +do_nms_sort = lib.do_nms_sort +do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float] + +free_image = lib.free_image +free_image.argtypes = [IMAGE] + +letterbox_image = lib.letterbox_image +letterbox_image.argtypes = [IMAGE, c_int, c_int] +letterbox_image.restype = IMAGE + +load_meta = lib.get_metadata +lib.get_metadata.argtypes = [c_char_p] +lib.get_metadata.restype = METADATA + +load_image = lib.load_image_color +load_image.argtypes = [c_char_p, c_int, c_int] +load_image.restype = IMAGE + +rgbgr_image = lib.rgbgr_image +rgbgr_image.argtypes = [IMAGE] + +predict_image = lib.network_predict_image +predict_image.argtypes = [c_void_p, IMAGE] +predict_image.restype = POINTER(c_float) + + +def array_to_image(arr): + import numpy as np + arr = arr.transpose(2, 0, 1) + c = arr.shape[0] + h = arr.shape[1] + w = arr.shape[2] + arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0 + data = arr.ctypes.data_as(POINTER(c_float)) + im = IMAGE(w, h, c, data) + return im, arr + + +def classify(net, meta, im): + out = predict_image(net, im) + res = [] + for i in range(meta.classes): + if altNames is None: + nameTag = meta.names[i] + else: + nameTag = altNames[i] + res.append((nameTag, out[i])) + res = sorted(res, key=lambda x: -x[1]) + return res + + +def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug=False): + im, arr = array_to_image(image) + if debug: + print("Loaded image") + num = c_int(0) + if debug: + print("Assigned num") + pnum = pointer(num) + if debug: + print("Assigned pnum") + predict_image(net, im) + if debug: + print("did prediction") + # dets = get_network_boxes( + # net, image.shape[1], image.shape[0], + # thresh, hier_thresh, + # None, 0, pnum, 0) # OpenCV + dets = get_network_boxes(net, im.w, im.h, + thresh, hier_thresh, None, 0, pnum, 0) + if debug: + print("Got dets") + num = pnum[0] + if debug: + print("got zeroth index of pnum") + if nms: + do_nms_sort(dets, num, meta.classes, nms) + if debug: + print("did sort") + res = [] + if debug: + print("about to range") + for j in range(num): + if debug: + print("Ranging on "+str(j)+" of "+str(num)) + if debug: + print("Classes: "+str(meta), meta.classes, meta.names) + for i in range(meta.classes): + if debug: + print("Class-ranging on "+str(i)+" of " + + str(meta.classes)+"= "+str(dets[j].prob[i])) + if dets[j].prob[i] > 0: + b = dets[j].bbox + if altNames is None: + nameTag = meta.names[i] + else: + nameTag = altNames[i] + if debug: + print("Got bbox", b) + print(nameTag) + print(dets[j].prob[i]) + print((b.x, b.y, b.w, b.h)) + res.append((nameTag, dets[j].prob[i], (b.x, b.y, b.w, b.h))) + if debug: + print("did range") + res = sorted(res, key=lambda x: -x[1]) + if debug: + print("did sort") + # free_image(im) + if debug: + print("freed image") + free_detections(dets, num) + if debug: + print("freed detections") + return res + + +def convertBack(x, y, w, h): + xmin = int(round(x - (w / 2))) + xmax = int(round(x + (w / 2))) + ymin = int(round(y - (h / 2))) + ymax = int(round(y + (h / 2))) + return xmin, ymin, xmax, ymax + + +def cvDrawBoxes(detections, img): + for detection in detections: + x, y, w, h = detection[2][0],\ + detection[2][1],\ + detection[2][2],\ + detection[2][3] + xmin, ymin, xmax, ymax = convertBack( + float(x), float(y), float(w), float(h)) + pt1 = (xmin, ymin) + pt2 = (xmax, ymax) + cv2.rectangle(img, pt1, pt2, (0, 255, 0), 2) + cv2.putText(img, + detection[0].decode() + + " [" + str(round(detection[1] * 100, 2)) + "]", + (pt1[0], pt1[1] + 20), cv2.FONT_HERSHEY_SIMPLEX, 1, + [0, 255, 0], 4) + return img + + +netMain = None +metaMain = None +altNames = None + + +def YOLO(): + global metaMain, netMain, altNames + configPath = "./cfg/yolov3.cfg" + weightPath = "./yolov3.weights" + metaPath = "./cfg/coco.data" + if not os.path.exists(configPath): + raise ValueError("Invalid config path `" + + os.path.abspath(configPath)+"`") + if not os.path.exists(weightPath): + raise ValueError("Invalid weight path `" + + os.path.abspath(weightPath)+"`") + if not os.path.exists(metaPath): + raise ValueError("Invalid data file path `" + + os.path.abspath(metaPath)+"`") + if netMain is None: + netMain = load_net_custom(configPath.encode( + "ascii"), weightPath.encode("ascii"), 0, 1) # batch size = 1 + if metaMain is None: + metaMain = load_meta(metaPath.encode("ascii")) + if altNames is None: + try: + with open(metaPath) as metaFH: + metaContents = metaFH.read() + import re + match = re.search("names *= *(.*)$", metaContents, + re.IGNORECASE | re.MULTILINE) + if match: + result = match.group(1) + else: + result = None + try: + if os.path.exists(result): + with open(result) as namesFH: + namesList = namesFH.read().strip().split("\n") + altNames = [x.strip() for x in namesList] + except TypeError: + pass + except Exception: + pass + #cap = cv2.VideoCapture(0) + cap = cv2.VideoCapture("test.mp4") + cap.set(3, 1280) + cap.set(4, 720) + out = cv2.VideoWriter( + "output.avi", cv2.VideoWriter_fourcc(*"MJPG"), 10.0, + (lib.network_width(netMain), lib.network_height(netMain))) + print("Starting the YOLO loop...") + while True: + prev_time = time.time() + ret, frame_read = cap.read() + frame_rgb = cv2.cvtColor(frame_read, cv2.COLOR_BGR2RGB) + frame_resized = cv2.resize(frame_rgb, + (lib.network_width(netMain), + lib.network_height(netMain)), + interpolation=cv2.INTER_LINEAR) + detections = detect(netMain, metaMain, frame_resized, thresh=0.25) + image = cvDrawBoxes(detections, frame_resized) + image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + print(1/(time.time()-prev_time)) + cap.release() + out.release() + +if __name__ == "__main__": + YOLO() diff --git a/include/darknet.h b/include/darknet.h new file mode 100644 index 00000000000..80dc28cdfcc --- /dev/null +++ b/include/darknet.h @@ -0,0 +1,793 @@ +#ifndef DARKNET_API +#define DARKNET_API + +#if defined(_MSC_VER) && _MSC_VER < 1900 +#define inline __inline +#endif + +#include +#include +#include +#include +#include + +#ifdef LIB_EXPORTS +#if defined(_MSC_VER) +#define LIB_API __declspec(dllexport) +#else +#define LIB_API __attribute__((visibility("default"))) +#endif +#else +#if defined(_MSC_VER) +#define LIB_API +#else +#define LIB_API +#endif +#endif + +#ifdef GPU +#define BLOCK 512 + +#include "cuda_runtime.h" +#include "curand.h" +#include "cublas_v2.h" + +#ifdef CUDNN +#include "cudnn.h" +#endif +#endif + +#ifdef __cplusplus +extern "C" { +#endif + +struct network; +typedef struct network network; + +struct network_state; +typedef struct network_state; + +struct layer; +typedef struct layer layer; + +struct image; +typedef struct image image; + +struct detection; +typedef struct detection detection; + +struct load_args; +typedef struct load_args load_args; + +struct data; +typedef struct data data; + +struct metadata; +typedef struct metadata metadata; + +struct tree; +typedef struct tree tree; + + +#define SECRET_NUM -1234 +extern int gpu_index; + +// option_list.h +typedef struct metadata { + int classes; + char **names; +} metadata; + + +// tree.h +typedef struct tree { + int *leaf; + int n; + int *parent; + int *child; + int *group; + char **name; + + int groups; + int *group_size; + int *group_offset; +} tree; + + +// activations.h +typedef enum { + LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU +}ACTIVATION; + +// image.h +typedef enum{ + PNG, BMP, TGA, JPG +} IMTYPE; + +// activations.h +typedef enum{ + MULT, ADD, SUB, DIV +} BINARY_ACTIVATION; + +// layer.h +typedef enum { + CONVOLUTIONAL, + DECONVOLUTIONAL, + CONNECTED, + MAXPOOL, + SOFTMAX, + DETECTION, + DROPOUT, + CROP, + ROUTE, + COST, + NORMALIZATION, + AVGPOOL, + LOCAL, + SHORTCUT, + ACTIVE, + RNN, + GRU, + LSTM, + CRNN, + BATCHNORM, + NETWORK, + XNOR, + REGION, + YOLO, + ISEG, + REORG, + REORG_OLD, + UPSAMPLE, + LOGXENT, + L2NORM, + BLANK +} LAYER_TYPE; + +// layer.h +typedef enum{ + SSE, MASKED, L1, SEG, SMOOTH,WGAN +} COST_TYPE; + +// layer.h +typedef struct update_args { + int batch; + float learning_rate; + float momentum; + float decay; + int adam; + float B1; + float B2; + float eps; + int t; +} update_args; + +// layer.h +struct layer { + LAYER_TYPE type; + ACTIVATION activation; + COST_TYPE cost_type; + void(*forward) (struct layer, struct network_state); + void(*backward) (struct layer, struct network_state); + void(*update) (struct layer, int, float, float, float); + void(*forward_gpu) (struct layer, struct network_state); + void(*backward_gpu) (struct layer, struct network_state); + void(*update_gpu) (struct layer, int, float, float, float); + int batch_normalize; + int shortcut; + int batch; + int forced; + int flipped; + int inputs; + int outputs; + int nweights; + int nbiases; + int extra; + int truths; + int h, w, c; + int out_h, out_w, out_c; + int n; + int max_boxes; + int groups; + int size; + int side; + int stride; + int reverse; + int flatten; + int spatial; + int pad; + int sqrt; + int flip; + int index; + int binary; + int xnor; + int use_bin_output; + int steps; + int hidden; + int truth; + float smooth; + float dot; + float angle; + float jitter; + float saturation; + float exposure; + float shift; + float ratio; + float learning_rate_scale; + float clip; + int focal_loss; + int noloss; + int softmax; + int classes; + int coords; + int background; + int rescore; + int objectness; + int does_cost; + int joint; + int noadjust; + int reorg; + int log; + int tanh; + int *mask; + int total; + float bflops; + + int adam; + float B1; + float B2; + float eps; + + int t; + + float alpha; + float beta; + float kappa; + + float coord_scale; + float object_scale; + float noobject_scale; + float mask_scale; + float class_scale; + int bias_match; + int random; + float ignore_thresh; + float truth_thresh; + float thresh; + float focus; + int classfix; + int absolute; + + int onlyforward; + int stopbackward; + int dontload; + int dontsave; + int dontloadscales; + int numload; + + float temperature; + float probability; + float scale; + + char * cweights; + int * indexes; + int * input_layers; + int * input_sizes; + int * map; + int * counts; + float ** sums; + float * rand; + float * cost; + float * state; + float * prev_state; + float * forgot_state; + float * forgot_delta; + float * state_delta; + float * combine_cpu; + float * combine_delta_cpu; + + float *concat; + float *concat_delta; + + float *binary_weights; + + float *biases; + float *bias_updates; + + float *scales; + float *scale_updates; + + float *weights; + float *weight_updates; + + char *align_bit_weights_gpu; + float *mean_arr_gpu; + float *align_workspace_gpu; + float *transposed_align_workspace_gpu; + int align_workspace_size; + + char *align_bit_weights; + float *mean_arr; + int align_bit_weights_size; + int lda_align; + int new_lda; + int bit_align; + + float *col_image; + float * delta; + float * output; + float * loss; + float * squared; + float * norms; + + float * spatial_mean; + float * mean; + float * variance; + + float * mean_delta; + float * variance_delta; + + float * rolling_mean; + float * rolling_variance; + + float * x; + float * x_norm; + + float * m; + float * v; + + float * bias_m; + float * bias_v; + float * scale_m; + float * scale_v; + + + float *z_cpu; + float *r_cpu; + float *h_cpu; + float * prev_state_cpu; + + float *temp_cpu; + float *temp2_cpu; + float *temp3_cpu; + + float *dh_cpu; + float *hh_cpu; + float *prev_cell_cpu; + float *cell_cpu; + float *f_cpu; + float *i_cpu; + float *g_cpu; + float *o_cpu; + float *c_cpu; + float *dc_cpu; + + float * binary_input; + + struct layer *input_layer; + struct layer *self_layer; + struct layer *output_layer; + + struct layer *reset_layer; + struct layer *update_layer; + struct layer *state_layer; + + struct layer *input_gate_layer; + struct layer *state_gate_layer; + struct layer *input_save_layer; + struct layer *state_save_layer; + struct layer *input_state_layer; + struct layer *state_state_layer; + + struct layer *input_z_layer; + struct layer *state_z_layer; + + struct layer *input_r_layer; + struct layer *state_r_layer; + + struct layer *input_h_layer; + struct layer *state_h_layer; + + struct layer *wz; + struct layer *uz; + struct layer *wr; + struct layer *ur; + struct layer *wh; + struct layer *uh; + struct layer *uo; + struct layer *wo; + struct layer *uf; + struct layer *wf; + struct layer *ui; + struct layer *wi; + struct layer *ug; + struct layer *wg; + + tree *softmax_tree; + + size_t workspace_size; + +#ifdef GPU + int *indexes_gpu; + + float *z_gpu; + float *r_gpu; + float *h_gpu; + + float *temp_gpu; + float *temp2_gpu; + float *temp3_gpu; + + float *dh_gpu; + float *hh_gpu; + float *prev_cell_gpu; + float *cell_gpu; + float *f_gpu; + float *i_gpu; + float *g_gpu; + float *o_gpu; + float *c_gpu; + float *dc_gpu; + + // adam + float *m_gpu; + float *v_gpu; + float *bias_m_gpu; + float *scale_m_gpu; + float *bias_v_gpu; + float *scale_v_gpu; + + float * combine_gpu; + float * combine_delta_gpu; + + float * prev_state_gpu; + float * forgot_state_gpu; + float * forgot_delta_gpu; + float * state_gpu; + float * state_delta_gpu; + float * gate_gpu; + float * gate_delta_gpu; + float * save_gpu; + float * save_delta_gpu; + float * concat_gpu; + float * concat_delta_gpu; + + float *binary_input_gpu; + float *binary_weights_gpu; + + float * mean_gpu; + float * variance_gpu; + + float * rolling_mean_gpu; + float * rolling_variance_gpu; + + float * variance_delta_gpu; + float * mean_delta_gpu; + + float * col_image_gpu; + + float * x_gpu; + float * x_norm_gpu; + float * weights_gpu; + float * weight_updates_gpu; + float * weight_change_gpu; + + float * weights_gpu16; + float * weight_updates_gpu16; + + float * biases_gpu; + float * bias_updates_gpu; + float * bias_change_gpu; + + float * scales_gpu; + float * scale_updates_gpu; + float * scale_change_gpu; + + float * output_gpu; + float * loss_gpu; + float * delta_gpu; + float * rand_gpu; + float * squared_gpu; + float * norms_gpu; +#ifdef CUDNN + cudnnTensorDescriptor_t srcTensorDesc, dstTensorDesc; + cudnnTensorDescriptor_t srcTensorDesc16, dstTensorDesc16; + cudnnTensorDescriptor_t dsrcTensorDesc, ddstTensorDesc; + cudnnTensorDescriptor_t dsrcTensorDesc16, ddstTensorDesc16; + cudnnTensorDescriptor_t normTensorDesc, normDstTensorDesc, normDstTensorDescF16; + cudnnFilterDescriptor_t weightDesc, weightDesc16; + cudnnFilterDescriptor_t dweightDesc, dweightDesc16; + cudnnConvolutionDescriptor_t convDesc; + cudnnConvolutionFwdAlgo_t fw_algo, fw_algo16; + cudnnConvolutionBwdDataAlgo_t bd_algo, bd_algo16; + cudnnConvolutionBwdFilterAlgo_t bf_algo, bf_algo16; + cudnnPoolingDescriptor_t poolingDesc; +#endif // CUDNN +#endif // GPU +}; + + +// network.h +typedef enum { + CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM +} learning_rate_policy; + +// network.h +typedef struct network { + int n; + int batch; + uint64_t *seen; + int *t; + float epoch; + int subdivisions; + layer *layers; + float *output; + learning_rate_policy policy; + + float learning_rate; + float momentum; + float decay; + float gamma; + float scale; + float power; + int time_steps; + int step; + int max_batches; + float *scales; + int *steps; + int num_steps; + int burn_in; + int cudnn_half; + + int adam; + float B1; + float B2; + float eps; + + int inputs; + int outputs; + int truths; + int notruth; + int h, w, c; + int max_crop; + int min_crop; + float max_ratio; + float min_ratio; + int center; + int flip; // horizontal flip 50% probability augmentaiont for classifier training (default = 1) + float angle; + float aspect; + float exposure; + float saturation; + float hue; + int random; + int small_object; + + int gpu_index; + tree *hierarchy; + + float *input; + float *truth; + float *delta; + float *workspace; + int train; + int index; + float *cost; + float clip; + +#ifdef GPU + //float *input_gpu; + //float *truth_gpu; + float *delta_gpu; + float *output_gpu; + + float *input_state_gpu; + + float **input_gpu; + float **truth_gpu; + float **input16_gpu; + float **output16_gpu; + size_t *max_input16_size; + size_t *max_output16_size; + int wait_stream; +#endif +} network; + +// network.h +typedef struct network_state { + float *truth; + float *input; + float *delta; + float *workspace; + int train; + int index; + network net; +} network_state; + +//typedef struct { +// int w; +// int h; +// float scale; +// float rad; +// float dx; +// float dy; +// float aspect; +//} augment_args; + +// image.h +typedef struct image { + int w; + int h; + int c; + float *data; +} image; + +//typedef struct { +// int w; +// int h; +// int c; +// float *data; +//} image; + +// box.h +typedef struct box { + float x, y, w, h; +} box; + +// box.h +typedef struct detection{ + box bbox; + int classes; + float *prob; + float *mask; + float objectness; + int sort_class; +} detection; + +// matrix.h +typedef struct matrix { + int rows, cols; + float **vals; +} matrix; + +// data.h +typedef struct data { + int w, h; + matrix X; + matrix y; + int shallow; + int *num_boxes; + box **boxes; +} data; + +// data.h +typedef enum { + CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, COMPARE_DATA, WRITING_DATA, SWAG_DATA, TAG_DATA, OLD_CLASSIFICATION_DATA, STUDY_DATA, DET_DATA, SUPER_DATA, LETTERBOX_DATA, REGRESSION_DATA, SEGMENTATION_DATA, INSTANCE_DATA, ISEG_DATA +} data_type; + +// data.h +typedef struct load_args { + int threads; + char **paths; + char *path; + int n; + int m; + char **labels; + int h; + int w; + int c; // color depth + int out_w; + int out_h; + int nh; + int nw; + int num_boxes; + int min, max, size; + int classes; + int background; + int scale; + int center; + int coords; + int small_object; + float jitter; + int flip; + float angle; + float aspect; + float saturation; + float exposure; + float hue; + data *d; + image *im; + image *resized; + data_type type; + tree *hierarchy; +} load_args; + +// data.h +typedef struct box_label { + int id; + float x, y, w, h; + float left, right, top, bottom; +} box_label; + +// list.h +//typedef struct node { +// void *val; +// struct node *next; +// struct node *prev; +//} node; + +// list.h +//typedef struct list { +// int size; +// node *front; +// node *back; +//} list; + +// ----------------------------------------------------- + + +// parser.c +LIB_API network *load_network(char *cfg, char *weights, int clear); +LIB_API network *load_network_custom(char *cfg, char *weights, int clear, int batch); +LIB_API network *load_network(char *cfg, char *weights, int clear); + +// network.c +LIB_API load_args get_base_args(network *net); + +// box.h +LIB_API void do_nms_sort(detection *dets, int total, int classes, float thresh); +LIB_API void do_nms_obj(detection *dets, int total, int classes, float thresh); + +// network.h +LIB_API float *network_predict(network net, float *input); +LIB_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter); +LIB_API void free_detections(detection *dets, int n); +LIB_API void fuse_conv_batchnorm(network net); +LIB_API void calculate_binary_weights(network net); + +LIB_API layer* get_network_layer(network* net, int i); +LIB_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter); +LIB_API detection *make_network_boxes(network *net, float thresh, int *num); +LIB_API void reset_rnn(network *net); +LIB_API float *network_predict_image(network *net, image im); +LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, network *existing_net); +LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map); +LIB_API int network_width(network *net); +LIB_API int network_height(network *net); +LIB_API void optimize_picture(network *net, image orig, int max_layer, float scale, float rate, float thresh, int norm); + +// image.h +LIB_API image resize_image(image im, int w, int h); +LIB_API image letterbox_image(image im, int w, int h); +LIB_API void rgbgr_image(image im); +LIB_API image make_image(int w, int h, int c); +LIB_API image load_image_color(char *filename, int w, int h); +LIB_API void free_image(image m); + +// layer.h +LIB_API void free_layer(layer); + +// data.c +LIB_API void free_data(data d); +LIB_API pthread_t load_data(load_args args); +LIB_API pthread_t load_data_in_thread(load_args args); + +// cuda.h +LIB_API void cuda_pull_array(float *x_gpu, float *x, size_t n); +LIB_API void cuda_set_device(int n); + +// utils.h +LIB_API void free_ptrs(void **ptrs, int n); +LIB_API void top_k(float *a, int n, int k, int *index); + +// tree.h +LIB_API tree *read_tree(char *filename); + +// option_list.h +LIB_API metadata get_metadata(char *file); + + +#ifdef __cplusplus +} +#endif // __cplusplus +#endif // DARKNET_API diff --git a/src/yolo_v2_class.hpp b/include/yolo_v2_class.hpp similarity index 93% rename from src/yolo_v2_class.hpp rename to include/yolo_v2_class.hpp index 199b1c974b8..74e1c116bca 100644 --- a/src/yolo_v2_class.hpp +++ b/include/yolo_v2_class.hpp @@ -1,15 +1,15 @@ #pragma once -#ifdef YOLODLL_EXPORTS +#ifdef LIB_EXPORTS #if defined(_MSC_VER) -#define YOLODLL_API __declspec(dllexport) +#define LIB_EXPORTS __declspec(dllexport) #else -#define YOLODLL_API __attribute__((visibility("default"))) +#define LIB_EXPORTS __attribute__((visibility("default"))) #endif #else #if defined(_MSC_VER) -#define YOLODLL_API __declspec(dllimport) +#define LIB_EXPORTS __declspec(dllimport) #else -#define YOLODLL_API +#define LIB_EXPORTS #endif #endif @@ -45,12 +45,12 @@ struct bbox_t_container { #include "opencv2/imgproc/imgproc_c.h" // C #endif // OPENCV -extern "C" YOLODLL_API int init(const char *configurationFilename, const char *weightsFilename, int gpu); -extern "C" YOLODLL_API int detect_image(const char *filename, bbox_t_container &container); -extern "C" YOLODLL_API int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container &container); -extern "C" YOLODLL_API int dispose(); -extern "C" YOLODLL_API int get_device_count(); -extern "C" YOLODLL_API int get_device_name(int gpu, char* deviceName); +extern "C" LIB_EXPORTS int init(const char *configurationFilename, const char *weightsFilename, int gpu); +extern "C" LIB_EXPORTS int detect_image(const char *filename, bbox_t_container &container); +extern "C" LIB_EXPORTS int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container &container); +extern "C" LIB_EXPORTS int dispose(); +extern "C" LIB_EXPORTS int get_device_count(); +extern "C" LIB_EXPORTS int get_device_name(int gpu, char* deviceName); class Detector { std::shared_ptr detector_gpu_ptr; @@ -60,18 +60,18 @@ class Detector { float nms = .4; bool wait_stream; - YOLODLL_API Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0); - YOLODLL_API ~Detector(); + LIB_EXPORTS Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0); + LIB_EXPORTS ~Detector(); - YOLODLL_API std::vector detect(std::string image_filename, float thresh = 0.2, bool use_mean = false); - YOLODLL_API std::vector detect(image_t img, float thresh = 0.2, bool use_mean = false); - static YOLODLL_API image_t load_image(std::string image_filename); - static YOLODLL_API void free_image(image_t m); - YOLODLL_API int get_net_width() const; - YOLODLL_API int get_net_height() const; - YOLODLL_API int get_net_color_depth() const; + LIB_EXPORTS std::vector detect(std::string image_filename, float thresh = 0.2, bool use_mean = false); + LIB_EXPORTS std::vector detect(image_t img, float thresh = 0.2, bool use_mean = false); + static LIB_EXPORTS image_t load_image(std::string image_filename); + static LIB_EXPORTS void free_image(image_t m); + LIB_EXPORTS int get_net_width() const; + LIB_EXPORTS int get_net_height() const; + LIB_EXPORTS int get_net_color_depth() const; - YOLODLL_API std::vector tracking_id(std::vector cur_bbox_vec, bool const change_history = true, + LIB_EXPORTS std::vector tracking_id(std::vector cur_bbox_vec, bool const change_history = true, int const frames_story = 10, int const max_dist = 150); std::vector detect_resized(image_t img, int init_w, int init_h, float thresh = 0.2, bool use_mean = false) @@ -613,13 +613,13 @@ class preview_boxes_t { /* // C - wrappers - YOLODLL_API void create_detector(char const* cfg_filename, char const* weight_filename, int gpu_id); - YOLODLL_API void delete_detector(); - YOLODLL_API bbox_t* detect_custom(image_t img, float thresh, bool use_mean, int *result_size); - YOLODLL_API bbox_t* detect_resized(image_t img, int init_w, int init_h, float thresh, bool use_mean, int *result_size); - YOLODLL_API bbox_t* detect(image_t img, int *result_size); - YOLODLL_API image_t load_img(char *image_filename); - YOLODLL_API void free_img(image_t m); + LIB_EXPORTS void create_detector(char const* cfg_filename, char const* weight_filename, int gpu_id); + LIB_EXPORTS void delete_detector(); + LIB_EXPORTS bbox_t* detect_custom(image_t img, float thresh, bool use_mean, int *result_size); + LIB_EXPORTS bbox_t* detect_resized(image_t img, int init_w, int init_h, float thresh, bool use_mean, int *result_size); + LIB_EXPORTS bbox_t* detect(image_t img, int *result_size); + LIB_EXPORTS image_t load_img(char *image_filename); + LIB_EXPORTS void free_img(image_t m); #ifdef __cplusplus } // extern "C" @@ -628,7 +628,7 @@ static std::shared_ptr c_detector_ptr; static std::vector c_result_vec; void create_detector(char const* cfg_filename, char const* weight_filename, int gpu_id) { - c_detector_ptr = std::make_shared(cfg_filename, weight_filename, gpu_id); + c_detector_ptr = std::make_shared(cfg_filename, weight_filename, gpu_id); } void delete_detector() { c_detector_ptr.reset(); } diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index 8e2a5479575..4285b5f39ba 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -1,3 +1,4 @@ +#include "darknet.h" #include "cuda_runtime.h" #include "curand.h" #include "cublas_v2.h" @@ -113,39 +114,74 @@ __device__ float activate_kernel(float x, ACTIVATION a) __device__ float gradient_kernel(float x, ACTIVATION a) { - switch(a){ - case LINEAR: - return linear_gradient_kernel(x); - case LOGISTIC: - return logistic_gradient_kernel(x); - case LOGGY: - return loggy_gradient_kernel(x); - case RELU: - return relu_gradient_kernel(x); - case ELU: - return elu_gradient_kernel(x); - case SELU: - return selu_gradient_kernel(x); - case RELIE: - return relie_gradient_kernel(x); - case RAMP: - return ramp_gradient_kernel(x); - case LEAKY: - return leaky_gradient_kernel(x); - case TANH: - return tanh_gradient_kernel(x); - case PLSE: - return plse_gradient_kernel(x); - case STAIR: - return stair_gradient_kernel(x); - case HARDTAN: - return hardtan_gradient_kernel(x); - case LHTAN: - return lhtan_gradient_kernel(x); + switch (a) { + case LINEAR: + return linear_gradient_kernel(x); + case LOGISTIC: + return logistic_gradient_kernel(x); + case LOGGY: + return loggy_gradient_kernel(x); + case RELU: + return relu_gradient_kernel(x); + case ELU: + return elu_gradient_kernel(x); + case SELU: + return selu_gradient_kernel(x); + case RELIE: + return relie_gradient_kernel(x); + case RAMP: + return ramp_gradient_kernel(x); + case LEAKY: + return leaky_gradient_kernel(x); + case TANH: + return tanh_gradient_kernel(x); + case PLSE: + return plse_gradient_kernel(x); + case STAIR: + return stair_gradient_kernel(x); + case HARDTAN: + return hardtan_gradient_kernel(x); + case LHTAN: + return lhtan_gradient_kernel(x); } return 0; } +__global__ void binary_gradient_array_kernel(float *x, float *dy, int n, int s, BINARY_ACTIVATION a, float *dx) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + int i = id % s; + int b = id / s; + float x1 = x[b*s + i]; + float x2 = x[b*s + s / 2 + i]; + if (id < n) { + float de = dy[id]; + dx[b*s + i] = x2*de; + dx[b*s + s / 2 + i] = x1*de; + } +} + +extern "C" void binary_gradient_array_gpu(float *x, float *dx, int n, int size, BINARY_ACTIVATION a, float *y) +{ + binary_gradient_array_kernel << > >(x, dx, n / 2, size, a, y); + check_error(cudaPeekAtLastError()); +} +__global__ void binary_activate_array_kernel(float *x, int n, int s, BINARY_ACTIVATION a, float *y) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + int i = id % s; + int b = id / s; + float x1 = x[b*s + i]; + float x2 = x[b*s + s / 2 + i]; + if (id < n) y[id] = x1*x2; +} + +extern "C" void binary_activate_array_gpu(float *x, int n, int size, BINARY_ACTIVATION a, float *y) +{ + binary_activate_array_kernel << > >(x, n / 2, size, a, y); + check_error(cudaPeekAtLastError()); +} + __global__ void activate_array_kernel(float *x, int n, ACTIVATION a) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; diff --git a/src/activations.h b/src/activations.h index 94c7f37a3f1..90930d44143 100644 --- a/src/activations.h +++ b/src/activations.h @@ -1,11 +1,12 @@ #ifndef ACTIVATIONS_H #define ACTIVATIONS_H +#include "darknet.h" #include "cuda.h" #include "math.h" -typedef enum{ - LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU -}ACTIVATION; +//typedef enum{ +// LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU +//}ACTIVATION; ACTIVATION get_activation(char *s); diff --git a/src/box.h b/src/box.h index 6f5e7383ca9..0e6fd918efc 100644 --- a/src/box.h +++ b/src/box.h @@ -1,36 +1,24 @@ #ifndef BOX_H #define BOX_H -#ifdef YOLODLL_EXPORTS -#if defined(_MSC_VER) -#define YOLODLL_API __declspec(dllexport) -#else -#define YOLODLL_API __attribute__((visibility("default"))) -#endif -#else -#if defined(_MSC_VER) -#define YOLODLL_API -#else -#define YOLODLL_API -#endif -#endif +#include "darknet.h" -typedef struct{ - float x, y, w, h; -} box; +//typedef struct{ +// float x, y, w, h; +//} box; typedef struct{ float dx, dy, dw, dh; } dbox; -typedef struct detection { - box bbox; - int classes; - float *prob; - float *mask; - float objectness; - int sort_class; -} detection; +//typedef struct detection { +// box bbox; +// int classes; +// float *prob; +// float *mask; +// float objectness; +// int sort_class; +//} detection; typedef struct detection_with_class { detection det; @@ -45,8 +33,8 @@ float box_rmse(box a, box b); dbox diou(box a, box b); void do_nms(box *boxes, float **probs, int total, int classes, float thresh); void do_nms_sort_v2(box *boxes, float **probs, int total, int classes, float thresh); -YOLODLL_API void do_nms_sort(detection *dets, int total, int classes, float thresh); -YOLODLL_API void do_nms_obj(detection *dets, int total, int classes, float thresh); +//LIB_API void do_nms_sort(detection *dets, int total, int classes, float thresh); +//LIB_API void do_nms_obj(detection *dets, int total, int classes, float thresh); box decode_box(box b, box anchor); box encode_box(box b, box anchor); diff --git a/src/cuda.h b/src/cuda.h index 289ee5b409c..6aa83550949 100644 --- a/src/cuda.h +++ b/src/cuda.h @@ -1,24 +1,11 @@ #ifndef CUDA_H #define CUDA_H +#include "darknet.h" #if defined(_MSC_VER) && _MSC_VER < 1900 #define inline __inline #endif -#ifdef YOLODLL_EXPORTS -#if defined(_MSC_VER) -#define YOLODLL_API __declspec(dllexport) -#else -#define YOLODLL_API __attribute__((visibility("default"))) -#endif -#else -#if defined(_MSC_VER) -#define YOLODLL_API -#else -#define YOLODLL_API -#endif -#endif - extern int gpu_index; #ifdef GPU @@ -42,8 +29,8 @@ extern "C" { int *cuda_make_int_array(size_t n); int *cuda_make_int_array_new_api(int *x, size_t n); void cuda_push_array(float *x_gpu, float *x, size_t n); - YOLODLL_API void cuda_pull_array(float *x_gpu, float *x, size_t n); - YOLODLL_API void cuda_set_device(int n); + //LIB_API void cuda_pull_array(float *x_gpu, float *x, size_t n); + //LIB_API void cuda_set_device(int n); int cuda_get_device(); void cuda_free(float *x_gpu); void cuda_random(float *x_gpu, size_t n); @@ -60,6 +47,6 @@ enum {cudnn_fastest, cudnn_smallest}; #endif #else // GPU -YOLODLL_API void cuda_set_device(int n); +//LIB_API void cuda_set_device(int n); #endif // GPU #endif // CUDA_H diff --git a/src/data.h b/src/data.h index f7ab585ce95..844680dc78c 100644 --- a/src/data.h +++ b/src/data.h @@ -6,6 +6,7 @@ #define inline __inline #endif +#include "darknet.h" #include "matrix.h" #include "list.h" #include "image.h" @@ -22,19 +23,19 @@ static inline float distance_from_edge(int x, int max) return dist; } -typedef struct{ - int w, h; - matrix X; - matrix y; - int shallow; - int *num_boxes; - box **boxes; -} data; - -typedef enum { - CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, LETTERBOX_DATA, COMPARE_DATA, WRITING_DATA, SWAG_DATA, TAG_DATA, OLD_CLASSIFICATION_DATA, STUDY_DATA, DET_DATA, SUPER_DATA -} data_type; +//typedef struct{ +// int w, h; +// matrix X; +// matrix y; +// int shallow; +// int *num_boxes; +// box **boxes; +//} data; +//typedef enum { +// CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, LETTERBOX_DATA, COMPARE_DATA, WRITING_DATA, SWAG_DATA, TAG_DATA, OLD_CLASSIFICATION_DATA, STUDY_DATA, DET_DATA, SUPER_DATA +//} data_type; +/* typedef struct load_args{ int threads; char **paths; @@ -80,7 +81,7 @@ void free_data(data d); pthread_t load_data(load_args args); pthread_t load_data_in_thread(load_args args); - +*/ void print_letters(float *pred, int n); data load_data_captcha(char **paths, int n, int m, int k, int w, int h); data load_data_captcha_encode(char **paths, int n, int m, int w, int h); diff --git a/src/http_stream.h b/src/http_stream.h index b7702250391..04c7516bc3f 100644 --- a/src/http_stream.h +++ b/src/http_stream.h @@ -1,6 +1,7 @@ #pragma once #ifndef HTTP_STREAM_H #define HTTP_STREAM_H +#include "darknet.h" #ifdef OPENCV #include "opencv2/highgui/highgui_c.h" diff --git a/src/image.c b/src/image.c index 1fcdd5c0000..0a97b83ea4e 100644 --- a/src/image.c +++ b/src/image.c @@ -1,3 +1,4 @@ +#include "darknet.h" #include "image.h" #include "utils.h" #include "blas.h" @@ -1246,13 +1247,34 @@ void save_image_png(image im, const char *name) if(!success) fprintf(stderr, "Failed to write image %s\n", buff); } +void save_image_options(image im, const char *name, IMTYPE f, int quality) +{ + char buff[256]; + //sprintf(buff, "%s (%d)", name, windows); + if (f == PNG) sprintf(buff, "%s.png", name); + else if (f == BMP) sprintf(buff, "%s.bmp", name); + else if (f == TGA) sprintf(buff, "%s.tga", name); + else if (f == JPG) sprintf(buff, "%s.jpg", name); + else sprintf(buff, "%s.png", name); + unsigned char *data = calloc(im.w*im.h*im.c, sizeof(char)); + int i, k; + for (k = 0; k < im.c; ++k) { + for (i = 0; i < im.w*im.h; ++i) { + data[i*im.c + k] = (unsigned char)(255 * im.data[i + k*im.w*im.h]); + } + } + int success = 0; + if (f == PNG) success = stbi_write_png(buff, im.w, im.h, im.c, data, im.w*im.c); + else if (f == BMP) success = stbi_write_bmp(buff, im.w, im.h, im.c, data); + else if (f == TGA) success = stbi_write_tga(buff, im.w, im.h, im.c, data); + else if (f == JPG) success = stbi_write_jpg(buff, im.w, im.h, im.c, data, quality); + free(data); + if (!success) fprintf(stderr, "Failed to write image %s\n", buff); +} + void save_image(image im, const char *name) { -#ifdef OPENCV - save_image_jpg(im, name); -#else - save_image_png(im, name); -#endif + save_image_options(im, name, JPG, 80); } diff --git a/src/image.h b/src/image.h index 84b122496f0..15731333b05 100644 --- a/src/image.h +++ b/src/image.h @@ -1,5 +1,6 @@ #ifndef IMAGE_H #define IMAGE_H +#include "darknet.h" #include #include @@ -8,13 +9,14 @@ #include #include "box.h" +/* typedef struct { - int w; - int h; + int w; + int h; int c; float *data; } image; - +*/ float get_color(int c, int x, int max); void flip_image(image a); void draw_box(image a, int x1, int y1, int x2, int y2, float r, float g, float b); @@ -30,10 +32,10 @@ image crop_image(image im, int dx, int dy, int w, int h); image random_crop_image(image im, int w, int h); image random_augment_image(image im, float angle, float aspect, int low, int high, int size); void random_distort_image(image im, float hue, float saturation, float exposure); -YOLODLL_API image resize_image(image im, int w, int h); +//LIB_EXPORTS image resize_image(image im, int w, int h); void fill_image(image m, float s); void letterbox_image_into(image im, int w, int h, image boxed); -YOLODLL_API image letterbox_image(image im, int w, int h); +//LIB_EXPORTS image letterbox_image(image im, int w, int h); image resize_min(image im, int min); image resize_max(image im, int max); void translate_image(image m, float s); @@ -46,7 +48,7 @@ void exposure_image(image im, float sat); void distort_image(image im, float hue, float sat, float val); void saturate_exposure_image(image im, float sat, float exposure); void hsv_to_rgb(image im); -YOLODLL_API void rgbgr_image(image im); +//LIB_EXPORTS void rgbgr_image(image im); void constrain_image(image im); void composite_3d(char *f1, char *f2, char *out, int delta); int best_3d_shift_r(image a, image b, int min, int max); @@ -68,13 +70,13 @@ void show_image_collapsed(image p, char *name); void print_image(image m); -YOLODLL_API image make_image(int w, int h, int c); +//LIB_EXPORTS image make_image(int w, int h, int c); image make_random_image(int w, int h, int c); image make_empty_image(int w, int h, int c); image float_to_image(int w, int h, int c, float *data); image copy_image(image p); image load_image(char *filename, int w, int h, int c); -YOLODLL_API image load_image_color(char *filename, int w, int h); +//LIB_EXPORTS image load_image_color(char *filename, int w, int h); image **load_alphabet(); //float get_pixel(image m, int x, int y, int c); @@ -85,7 +87,7 @@ float bilinear_interpolate(image im, float x, float y, int c); image get_image_layer(image m, int l); -YOLODLL_API void free_image(image m); +//LIB_EXPORTS void free_image(image m); void test_resize(char *filename); #endif diff --git a/src/layer.h b/src/layer.h index cc78de0d1a1..692df88c032 100644 --- a/src/layer.h +++ b/src/layer.h @@ -1,61 +1,63 @@ #ifndef BASE_LAYER_H #define BASE_LAYER_H +#include "darknet.h" #include "activations.h" #include "stddef.h" #include "tree.h" -struct network_state; - -struct layer; -typedef struct layer layer; - -typedef enum { - CONVOLUTIONAL, - DECONVOLUTIONAL, - CONNECTED, - MAXPOOL, - SOFTMAX, - DETECTION, - DROPOUT, - CROP, - ROUTE, - COST, - NORMALIZATION, - AVGPOOL, - LOCAL, - SHORTCUT, - ACTIVE, - RNN, - GRU, - CRNN, - BATCHNORM, - NETWORK, - XNOR, - REGION, - YOLO, - REORG, - UPSAMPLE, - REORG_OLD, - BLANK -} LAYER_TYPE; - -typedef enum{ - SSE, MASKED, SMOOTH -} COST_TYPE; - -typedef struct { - int batch; - float learning_rate; - float momentum; - float decay; - int adam; - float B1; - float B2; - float eps; - int t; -} update_args; - +//struct network_state; + +//struct layer; +//typedef struct layer layer; + +//typedef enum { +// CONVOLUTIONAL, +// DECONVOLUTIONAL, +// CONNECTED, +// MAXPOOL, +// SOFTMAX, +// DETECTION, +// DROPOUT, +// CROP, +// ROUTE, +// COST, +// NORMALIZATION, +// AVGPOOL, +// LOCAL, +// SHORTCUT, +// ACTIVE, +// RNN, +// GRU, +// CRNN, +// BATCHNORM, +// NETWORK, +// XNOR, +// REGION, +// YOLO, +// REORG, +// UPSAMPLE, +// REORG_OLD, +// BLANK +//} LAYER_TYPE; + +//typedef enum{ +// SSE, MASKED, SMOOTH +//} COST_TYPE; + +//typedef struct { +// int batch; +// float learning_rate; +// float momentum; +// float decay; +// int adam; +// float B1; +// float B2; +// float eps; +// int t; +//} update_args; + +/* struct layer{ LAYER_TYPE type; ACTIVATION activation; @@ -325,7 +327,7 @@ struct layer{ #endif // CUDNN #endif // GPU }; - -void free_layer(layer); +*/ +//void free_layer(layer); #endif diff --git a/src/matrix.h b/src/matrix.h index 1226de2bcd1..a56ae8e0254 100644 --- a/src/matrix.h +++ b/src/matrix.h @@ -1,9 +1,11 @@ #ifndef MATRIX_H #define MATRIX_H -typedef struct matrix{ - int rows, cols; - float **vals; -} matrix; +#include "darknet.h" + +//typedef struct matrix{ +// int rows, cols; +// float **vals; +//} matrix; typedef struct { int *assignments; diff --git a/src/network.c b/src/network.c index cf0d9351427..306918bb07d 100644 --- a/src/network.c +++ b/src/network.c @@ -1,6 +1,7 @@ #include #include #include +#include "darknet.h" #include "network.h" #include "image.h" #include "data.h" @@ -31,21 +32,22 @@ #include "upsample_layer.h" #include "parser.h" -network *load_network_custom(char *cfg, char *weights, int clear, int batch) -{ - printf(" Try to load cfg: %s, weights: %s, clear = %d \n", cfg, weights, clear); - network *net = calloc(1, sizeof(network)); - *net = parse_network_cfg_custom(cfg, batch); - if (weights && weights[0] != 0) { - load_weights(net, weights); - } - if (clear) (*net->seen) = 0; - return net; -} - -network *load_network(char *cfg, char *weights, int clear) -{ - return load_network_custom(cfg, weights, clear, 0); +load_args get_base_args(network *net) +{ + load_args args = { 0 }; + args.w = net->w; + args.h = net->h; + args.size = net->w; + + args.min = net->min_crop; + args.max = net->max_crop; + args.angle = net->angle; + args.aspect = net->aspect; + args.exposure = net->exposure; + args.center = net->center; + args.saturation = net->saturation; + args.hue = net->hue; + return args; } int get_current_batch(network net) diff --git a/src/network.h b/src/network.h index 0cb381fba19..a825a047eac 100644 --- a/src/network.h +++ b/src/network.h @@ -1,6 +1,7 @@ // Oh boy, why am I about to do this.... #ifndef NETWORK_H #define NETWORK_H +#include "darknet.h" #include #include "layer.h" @@ -13,6 +14,7 @@ extern "C" { #include "data.h" #include "tree.h" +/* typedef enum { CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM } learning_rate_policy; @@ -77,6 +79,7 @@ typedef struct network{ #endif } network; + typedef struct network_state { float *truth; float *input; @@ -86,6 +89,7 @@ typedef struct network_state { int index; network net; } network_state; +*/ #ifdef GPU float train_networks(network *nets, int n, data d, int interval); @@ -117,7 +121,7 @@ float train_network_sgd(network net, data d, int n); float train_network_datum(network net, float *x, float *y); matrix network_predict_data(network net, data test); -YOLODLL_API float *network_predict(network net, float *input); +//LIB_API float *network_predict(network net, float *input); float network_accuracy(network net, data d); float *network_accuracies(network net, data d, int n); float network_accuracy_multi(network net, data d, int n); @@ -137,25 +141,24 @@ int resize_network(network *net, int w, int h); void set_batch_network(network *net, int b); int get_network_input_size(network net); float get_network_cost(network net); -YOLODLL_API layer* get_network_layer(network* net, int i); -YOLODLL_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter); -YOLODLL_API detection *make_network_boxes(network *net, float thresh, int *num); -YOLODLL_API void free_detections(detection *dets, int n); -YOLODLL_API void reset_rnn(network *net); -YOLODLL_API network *load_network_custom(char *cfg, char *weights, int clear, int batch); -YOLODLL_API network *load_network(char *cfg, char *weights, int clear); -YOLODLL_API float *network_predict_image(network *net, image im); -YOLODLL_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, network *existing_net); -YOLODLL_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map); -YOLODLL_API int network_width(network *net); -YOLODLL_API int network_height(network *net); - -YOLODLL_API void optimize_picture(network *net, image orig, int max_layer, float scale, float rate, float thresh, int norm); +//LIB_API layer* get_network_layer(network* net, int i); +//LIB_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter); +//LIB_API detection *make_network_boxes(network *net, float thresh, int *num); +//LIB_API void free_detections(detection *dets, int n); +//LIB_API void reset_rnn(network *net); +//LIB_API network *load_network_custom(char *cfg, char *weights, int clear, int batch); +//LIB_API network *load_network(char *cfg, char *weights, int clear); +//LIB_API float *network_predict_image(network *net, image im); +//LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, network *existing_net); +//LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map); +//LIB_API int network_width(network *net); +//LIB_API int network_height(network *net); +//LIB_API void optimize_picture(network *net, image orig, int max_layer, float scale, float rate, float thresh, int norm); int get_network_nuisance(network net); int get_network_background(network net); -YOLODLL_API void fuse_conv_batchnorm(network net); -YOLODLL_API void calculate_binary_weights(network net); +//LIB_API void fuse_conv_batchnorm(network net); +//LIB_API void calculate_binary_weights(network net); network combine_train_valid_networks(network net_train, network net_map); #ifdef __cplusplus diff --git a/src/option_list.h b/src/option_list.h index 29ac94316e2..cb86067115c 100644 --- a/src/option_list.h +++ b/src/option_list.h @@ -1,20 +1,7 @@ #ifndef OPTION_LIST_H #define OPTION_LIST_H #include "list.h" - -#ifdef YOLODLL_EXPORTS -#if defined(_MSC_VER) -#define YOLODLL_API __declspec(dllexport) -#else -#define YOLODLL_API __attribute__((visibility("default"))) -#endif -#else -#if defined(_MSC_VER) -#define YOLODLL_API -#else -#define YOLODLL_API -#endif -#endif +#include "darknet.h" typedef struct{ char *key; @@ -34,11 +21,11 @@ float option_find_float(list *l, char *key, float def); float option_find_float_quiet(list *l, char *key, float def); void option_unused(list *l); -typedef struct { - int classes; - char **names; -} metadata; +//typedef struct { +// int classes; +// char **names; +//} metadata; -YOLODLL_API metadata get_metadata(char *file); +//LIB_API metadata get_metadata(char *file); #endif diff --git a/src/parser.c b/src/parser.c index e381edc1de0..12b4bd412f6 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1249,3 +1249,28 @@ void load_weights(network *net, char *filename) load_weights_upto(net, filename, net->n); } +// load network & force - set batch size +network *load_network_custom(char *cfg, char *weights, int clear, int batch) +{ + printf(" Try to load cfg: %s, weights: %s, clear = %d \n", cfg, weights, clear); + network *net = calloc(1, sizeof(network)); + *net = parse_network_cfg_custom(cfg, batch); + if (weights && weights[0] != 0) { + load_weights(net, weights); + } + if (clear) (*net->seen) = 0; + return net; +} + +// load network & get batch size from cfg-file +network *load_network(char *cfg, char *weights, int clear) +{ + printf(" Try to load cfg: %s, weights: %s, clear = %d \n", cfg, weights, clear); + network *net = calloc(1, sizeof(network)); + *net = parse_network_cfg(cfg); + if (weights && weights[0] != 0) { + load_weights(net, weights); + } + if (clear) (*net->seen) = 0; + return net; +} \ No newline at end of file diff --git a/src/tree.h b/src/tree.h index 6983adf5603..b44d3f4aa42 100644 --- a/src/tree.h +++ b/src/tree.h @@ -1,20 +1,21 @@ #ifndef TREE_H #define TREE_H +#include "darknet.h" -typedef struct{ - int *leaf; - int n; - int *parent; - int *child; - int *group; - char **name; +//typedef struct{ +// int *leaf; +// int n; +// int *parent; +// int *child; +// int *group; +// char **name; +// +// int groups; +// int *group_size; +// int *group_offset; +//} tree; - int groups; - int *group_size; - int *group_offset; -} tree; - -tree *read_tree(char *filename); +//tree *read_tree(char *filename); int hierarchy_top_prediction(float *predictions, tree *hier, float thresh, int stride); void hierarchy_predictions(float *predictions, int n, tree *hier, int only_leaves); void change_leaves(tree *t, char *leaf_list); diff --git a/src/utils.h b/src/utils.h index ba61d344e27..5c87dda5998 100644 --- a/src/utils.h +++ b/src/utils.h @@ -11,25 +11,27 @@ #define SECRET_NUM -1234 #define TWO_PI 6.2831853071795864769252866 -#ifdef YOLODLL_EXPORTS +#ifdef LIB_EXPORTS #if defined(_MSC_VER) -#define YOLODLL_API __declspec(dllexport) +#define LIB_API __declspec(dllexport) #else -#define YOLODLL_API __attribute__((visibility("default"))) +#define LIB_API __attribute__((visibility("default"))) #endif #else #if defined(_MSC_VER) -#define YOLODLL_API +#define LIB_API #else -#define YOLODLL_API +#define LIB_API #endif #endif +LIB_API void free_ptrs(void **ptrs, int n); +LIB_API void top_k(float *a, int n, int k, int *index); + double what_time_is_it_now(); int *read_map(char *filename); void shuffle(void *arr, size_t n, size_t size); void sorta_shuffle(void *arr, size_t n, size_t size, size_t sections); -YOLODLL_API void free_ptrs(void **ptrs, int n); char *basecfg(char *cfgfile); int alphanum_to_int(char c); char int_to_alphanum(int i); @@ -47,7 +49,6 @@ void file_error(char *s); void strip(char *s); void strip_args(char *s); void strip_char(char *s, char bad); -YOLODLL_API void top_k(float *a, int n, int k, int *index); list *split_str(char *s, char delim); char *fgetl(FILE *fp); list *parse_csv_line(char *line); diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index 8fcf9358bb8..a8710600992 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -25,13 +25,13 @@ extern "C" { //static Detector* detector = NULL; static std::unique_ptr detector; -int init(const char *configurationFilename, const char *weightsFilename, int gpu) +int init(const char *configurationFilename, const char *weightsFilename, int gpu) { detector.reset(new Detector(configurationFilename, weightsFilename, gpu)); return 1; } -int detect_image(const char *filename, bbox_t_container &container) +int detect_image(const char *filename, bbox_t_container &container) { std::vector detection = detector->detect(filename); for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i) @@ -100,7 +100,7 @@ struct detector_gpu_t { unsigned int *track_id; }; -YOLODLL_API Detector::Detector(std::string cfg_filename, std::string weight_filename, int gpu_id) : cur_gpu_id(gpu_id) +LIB_EXPORTS Detector::Detector(std::string cfg_filename, std::string weight_filename, int gpu_id) : cur_gpu_id(gpu_id) { wait_stream = 0; int old_gpu_index; @@ -119,7 +119,7 @@ YOLODLL_API Detector::Detector(std::string cfg_filename, std::string weight_file network &net = detector_gpu.net; net.gpu_index = cur_gpu_id; //gpu_index = i; - + char *cfgfile = const_cast(cfg_filename.data()); char *weightfile = const_cast(weight_filename.data()); @@ -147,7 +147,7 @@ YOLODLL_API Detector::Detector(std::string cfg_filename, std::string weight_file } -YOLODLL_API Detector::~Detector() +LIB_EXPORTS Detector::~Detector() { detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); layer l = detector_gpu.net.layers[detector_gpu.net.n - 1]; @@ -171,21 +171,21 @@ YOLODLL_API Detector::~Detector() #endif } -YOLODLL_API int Detector::get_net_width() const { +LIB_EXPORTS int Detector::get_net_width() const { detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); return detector_gpu.net.w; } -YOLODLL_API int Detector::get_net_height() const { +LIB_EXPORTS int Detector::get_net_height() const { detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); return detector_gpu.net.h; } -YOLODLL_API int Detector::get_net_color_depth() const { +LIB_EXPORTS int Detector::get_net_color_depth() const { detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); return detector_gpu.net.c; } -YOLODLL_API std::vector Detector::detect(std::string image_filename, float thresh, bool use_mean) +LIB_EXPORTS std::vector Detector::detect(std::string image_filename, float thresh, bool use_mean) { std::shared_ptr image_ptr(new image_t, [](image_t *img) { if (img->data) free(img->data); delete img; }); *image_ptr = load_image(image_filename); @@ -196,7 +196,7 @@ static image load_image_stb(char *filename, int channels) { int w, h, c; unsigned char *data = stbi_load(filename, &w, &h, &c, channels); - if (!data) + if (!data) throw std::runtime_error("file not found"); if (channels) c = channels; int i, j, k; @@ -214,7 +214,7 @@ static image load_image_stb(char *filename, int channels) return im; } -YOLODLL_API image_t Detector::load_image(std::string image_filename) +LIB_EXPORTS image_t Detector::load_image(std::string image_filename) { char *input = const_cast(image_filename.data()); image im = load_image_stb(input, 3); @@ -229,14 +229,14 @@ YOLODLL_API image_t Detector::load_image(std::string image_filename) } -YOLODLL_API void Detector::free_image(image_t m) +LIB_EXPORTS void Detector::free_image(image_t m) { if (m.data) { free(m.data); } } -YOLODLL_API std::vector Detector::detect(image_t img, float thresh, bool use_mean) +LIB_EXPORTS std::vector Detector::detect(image_t img, float thresh, bool use_mean) { detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); network &net = detector_gpu.net; @@ -259,7 +259,7 @@ YOLODLL_API std::vector Detector::detect(image_t img, float thresh, bool im.w = img.w; image sized; - + if (net.w == im.w && net.h == im.h) { sized = make_image(im.w, im.h, im.c); memcpy(sized.data, im.data, im.w*im.h*im.c * sizeof(float)); @@ -294,8 +294,8 @@ YOLODLL_API std::vector Detector::detect(image_t img, float thresh, bool box b = dets[i].bbox; int const obj_id = max_index(dets[i].prob, l.classes); float const prob = dets[i].prob[obj_id]; - - if (prob > thresh) + + if (prob > thresh) { bbox_t bbox; bbox.x = std::max((double)0, (b.x - b.w / 2.)*im.w); @@ -322,7 +322,7 @@ YOLODLL_API std::vector Detector::detect(image_t img, float thresh, bool return bbox_vec; } -YOLODLL_API std::vector Detector::tracking_id(std::vector cur_bbox_vec, bool const change_history, +LIB_EXPORTS std::vector Detector::tracking_id(std::vector cur_bbox_vec, bool const change_history, int const frames_story, int const max_dist) { detector_gpu_t &det_gpu = *static_cast(detector_gpu_ptr.get()); @@ -357,7 +357,7 @@ YOLODLL_API std::vector Detector::tracking_id(std::vector cur_bb } } - bool track_id_absent = !std::any_of(cur_bbox_vec.begin(), cur_bbox_vec.end(), + bool track_id_absent = !std::any_of(cur_bbox_vec.begin(), cur_bbox_vec.end(), [&i](bbox_t const& b) { return b.track_id == i.track_id && b.obj_id == i.obj_id; }); if (cur_index >= 0 && track_id_absent){ From fd0df9297c86a272f0bf0841291bc4565e90a7cd Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 8 Jan 2019 01:21:19 +0300 Subject: [PATCH 0231/1365] Fixed compiling yolo_cpp_dll.sln in Debug & x64 --- build/darknet/yolo_cpp_dll.vcxproj | 1 + 1 file changed, 1 insertion(+) diff --git a/build/darknet/yolo_cpp_dll.vcxproj b/build/darknet/yolo_cpp_dll.vcxproj index bde77841e13..64f5d297cf1 100644 --- a/build/darknet/yolo_cpp_dll.vcxproj +++ b/build/darknet/yolo_cpp_dll.vcxproj @@ -96,6 +96,7 @@ _MBCS;LIB_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) CUDNN true + stdlib.h;crtdbg.h;%(ForcedIncludeFiles) true From ead84c8ba239dbbe72393b443cfaf43f1a4a4277 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 8 Jan 2019 18:34:59 +0300 Subject: [PATCH 0232/1365] Now you can un-comment several lines for saving images of cropped objects to /result_img/ directory --- build/darknet/x64/darknet.py | 2 +- build/darknet/x64/darknet_many_images.cmd | 2 +- src/demo.c | 4 +- src/image.c | 66 ++++++++++++----------- 4 files changed, 41 insertions(+), 33 deletions(-) diff --git a/build/darknet/x64/darknet.py b/build/darknet/x64/darknet.py index 784399eee55..3d885d20e56 100644 --- a/build/darknet/x64/darknet.py +++ b/build/darknet/x64/darknet.py @@ -278,7 +278,7 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False): metaMain = None altNames = None -def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yolov3.cfg", weightPath = "yolov3.weights", metaPath= "./data/coco.data", showImage= True, makeImageOnly = False, initOnly= False): +def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yolov3.cfg", weightPath = "yolov3.weights", metaPath= "./cfg/coco.data", showImage= True, makeImageOnly = False, initOnly= False): """ Convenience function to handle the detection and returns of objects. diff --git a/build/darknet/x64/darknet_many_images.cmd b/build/darknet/x64/darknet_many_images.cmd index a763d0220f4..95eba449aab 100644 --- a/build/darknet/x64/darknet_many_images.cmd +++ b/build/darknet/x64/darknet_many_images.cmd @@ -1,4 +1,4 @@ -darknet.exe detector test data/voc.data cfg/yolov2-voc.cfg yolo-voc.weights -ext_output -dont_show < data/train.txt > result.txt +darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights -ext_output -dont_show < data/train.txt > result.txt pause \ No newline at end of file diff --git a/src/demo.c b/src/demo.c index 7481660e235..d57fae40c51 100644 --- a/src/demo.c +++ b/src/demo.c @@ -51,6 +51,7 @@ static float *avg; void draw_detections_cv(IplImage* show_img, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes); void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output); void show_image_cv_ipl(IplImage *disp, const char *name); +void save_cv_png(IplImage *img, const char *name); image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close); image get_image_from_stream_letterbox(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close); int get_stream_fps(CvCapture *cap, int cpp_video_capture); @@ -264,7 +265,8 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int }else{ char buff[256]; sprintf(buff, "%s_%08d.jpg", prefix, count); - cvSaveImage(buff, show_img, 0); + save_cv_png(buff, show_img); + //cvSaveImage(buff, show_img, 0); //save_image(disp, buff); } diff --git a/src/image.c b/src/image.c index 0a97b83ea4e..06112cb4ccf 100644 --- a/src/image.c +++ b/src/image.c @@ -315,6 +315,9 @@ int compare_by_probs(const void *a_ptr, const void *b_ptr) { void draw_detections_v3(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output) { + static int frame_id = 0; + frame_id++; + int selected_detections_num; detection_with_class* selected_detections = get_actual_detections(dets, num, thresh, &selected_detections_num, names); @@ -384,6 +387,23 @@ void draw_detections_v3(image im, detection *dets, int num, float thresh, char * //int b_height = bot - top; //sprintf(labelstr, "%d x %d - w: %d, h: %d", b_x_center, b_y_center, b_width, b_height); + // you should create directory: result_img + //static int copied_frame_id = -1; + //static image copy_img; + //if (copied_frame_id != frame_id) { + // copied_frame_id = frame_id; + // if (copy_img.data) free_image(copy_img); + // copy_img = copy_image(im); + //} + //image cropped_im = crop_image(copy_img, left, top, right - left, bot - top); + //static int img_id = 0; + //img_id++; + //char image_name[1024]; + //int best_class_id = selected_detections[i].best_class; + //sprintf(image_name, "result_img/img_%d_%d_%d_%s.jpg", frame_id, img_id, best_class_id, names[best_class_id]); + //save_image(cropped_im, image_name); + //free_image(cropped_im); + if (im.c == 1) { draw_box_width_bw(im, left, top, right, bot, width, 0.8); // 1 channel Black-White } @@ -480,6 +500,14 @@ void draw_detections(image im, int num, float thresh, box *boxes, float **probs, #ifdef OPENCV +void save_cv_png(IplImage *img, const char *name) +{ + IplImage* img_rgb = cvCreateImage(cvSize(img->width, img->height), 8, 3); + cvCvtColor(img, img_rgb, CV_RGB2BGR); + stbi_write_png(name, img_rgb->width, img_rgb->height, 3, (char *)img_rgb->imageData, 0); + cvRelease(&img_rgb); +} + void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output) { int i, j; @@ -575,7 +603,7 @@ void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float t //static int img_id = 0; //img_id++; //char image_name[1024]; - //sprintf(image_name, "result_img/img_%d_%d_%d.jpg", frame_id, img_id, class_id); + //sprintf(image_name, "result_img/img_%d_%d_%d_%s.jpg", frame_id, img_id, class_id, names[class_id]); //CvRect rect = cvRect(pt1.x, pt1.y, pt2.x - pt1.x, pt2.y - pt1.y); //cvSetImageROI(copy_img, rect); //cvSaveImage(image_name, copy_img, 0); @@ -774,10 +802,7 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_ int k = cvWaitKey(20); if (k == 's' || current_batch == (max_batches - 1) || current_batch % 100 == 0) { //cvSaveImage("chart.jpg", img, 0); - IplImage* img_rgb = cvCreateImage(cvSize(img->width, img->height), 8, 3); - cvCvtColor(img, img_rgb, CV_RGB2BGR); - stbi_write_png("chart.png", img_rgb->width, img_rgb->height, 3, (char *)img_rgb->imageData, 0); - cvRelease(&img_rgb); + save_cv_png("chart.png", img); cvPutText(img, "- Saved", cvPoint(250, img_size - 10), &font, CV_RGB(255, 0, 0)); } else @@ -1013,7 +1038,7 @@ void show_image_cv_ipl(IplImage *disp, const char *name) cvShowImage(buff, disp); //cvReleaseImage(&disp); } -#endif +#endif // OPENCV void show_image(image p, const char *name) { @@ -1205,30 +1230,7 @@ int get_stream_fps(CvCapture *cap, int cpp_video_capture) } return fps; } - -void save_image_jpg(image p, const char *name) -{ - image copy = copy_image(p); - if(p.c == 3) rgbgr_image(copy); - int x,y,k; - - char buff[256]; - sprintf(buff, "%s.jpg", name); - - IplImage *disp = cvCreateImage(cvSize(p.w,p.h), IPL_DEPTH_8U, p.c); - int step = disp->widthStep; - for(y = 0; y < p.h; ++y){ - for(x = 0; x < p.w; ++x){ - for(k= 0; k < p.c; ++k){ - disp->imageData[y*step + x*p.c + k] = (unsigned char)(get_pixel(copy,x,y,k)*255); - } - } - } - cvSaveImage(buff, disp,0); - cvReleaseImage(&disp); - free_image(copy); -} -#endif +#endif // OPENCV void save_image_png(image im, const char *name) { @@ -1277,6 +1279,10 @@ void save_image(image im, const char *name) save_image_options(im, name, JPG, 80); } +void save_image_jpg(image p, const char *name) +{ + save_image_options(p, name, JPG, 80); +} void show_image_layers(image p, char *name) { From c31a245665c7bb7167edb50083e76c1d60130947 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 9 Jan 2019 00:11:59 +0300 Subject: [PATCH 0233/1365] Experimental. Use -json_port 8070 to allow connection to the port:8070 to get json-stream --- .../darknet/x64/darknet_demo_json_stream.cmd | 7 + .../darknet/x64/darknet_demo_mjpeg_stream.cmd | 2 +- include/darknet.h | 3 +- src/coco.c | 5 +- src/demo.c | 13 +- src/demo.h | 3 +- src/detector.c | 3 +- src/http_stream.cpp | 161 +++++++++++++++++- src/http_stream.h | 1 + src/network.c | 38 +++++ src/yolo.c | 3 +- 11 files changed, 229 insertions(+), 10 deletions(-) create mode 100644 build/darknet/x64/darknet_demo_json_stream.cmd diff --git a/build/darknet/x64/darknet_demo_json_stream.cmd b/build/darknet/x64/darknet_demo_json_stream.cmd new file mode 100644 index 00000000000..22a8859b0d8 --- /dev/null +++ b/build/darknet/x64/darknet_demo_json_stream.cmd @@ -0,0 +1,7 @@ +rem Run this file and then open URL in Chrome/Firefox: rem http://localhost:8070 +rem Or open: http://ip-address:8070 + +darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights test.mp4 -i 0 -json_port 8070 -dont_show -ext_output + + +pause \ No newline at end of file diff --git a/build/darknet/x64/darknet_demo_mjpeg_stream.cmd b/build/darknet/x64/darknet_demo_mjpeg_stream.cmd index a2c5f89188b..95bfb3b47a1 100644 --- a/build/darknet/x64/darknet_demo_mjpeg_stream.cmd +++ b/build/darknet/x64/darknet_demo_mjpeg_stream.cmd @@ -1,7 +1,7 @@ rem Run this file and then open URL in Chrome/Firefox: rem http://localhost:8090 rem Or open: http://ip-address:8090 -darknet.exe detector demo data/voc.data cfg/yolov2-voc.cfg yolo-voc.weights test.mp4 -i 0 -http_port 8090 -dont_show +darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights test.mp4 -i 0 -http_port 8090 -dont_show -ext_output pause \ No newline at end of file diff --git a/include/darknet.h b/include/darknet.h index 80dc28cdfcc..b7e45f16ec4 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -744,9 +744,10 @@ LIB_API detection *get_network_boxes(network *net, int w, int h, float thresh, f LIB_API void free_detections(detection *dets, int n); LIB_API void fuse_conv_batchnorm(network net); LIB_API void calculate_binary_weights(network net); +LIB_API char *detection_to_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id); LIB_API layer* get_network_layer(network* net, int i); -LIB_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter); +//LIB_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter); LIB_API detection *make_network_boxes(network *net, float thresh, int *num); LIB_API void reset_rnn(network *net); LIB_API float *network_predict_image(network *net, image im); diff --git a/src/coco.c b/src/coco.c index 1913a47836a..62f6770a132 100644 --- a/src/coco.c +++ b/src/coco.c @@ -222,7 +222,7 @@ void validate_coco(char *cfgfile, char *weightfile) free_image(val_resized[t]); } } - fseek(fp, -2, SEEK_CUR); + fseek(fp, -2, SEEK_CUR); fprintf(fp, "\n]\n"); fclose(fp); @@ -366,6 +366,7 @@ void run_coco(int argc, char **argv) { int dont_show = find_arg(argc, argv, "-dont_show"); int http_stream_port = find_int_arg(argc, argv, "-http_port", -1); + int json_port = find_int_arg(argc, argv, "-json_port", -1); char *out_filename = find_char_arg(argc, argv, "-out_filename", 0); char *prefix = find_char_arg(argc, argv, "-prefix", 0); float thresh = find_float_arg(argc, argv, "-thresh", .2); @@ -387,5 +388,5 @@ void run_coco(int argc, char **argv) else if(0==strcmp(argv[2], "valid")) validate_coco(cfg, weights); else if(0==strcmp(argv[2], "recall")) validate_coco_recall(cfg, weights); else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, hier_thresh, cam_index, filename, coco_classes, 80, frame_skip, - prefix, out_filename, http_stream_port, dont_show, ext_output); + prefix, out_filename, http_stream_port, json_port, dont_show, ext_output); } diff --git a/src/demo.c b/src/demo.c index d57fae40c51..3aca9705c1e 100644 --- a/src/demo.c +++ b/src/demo.c @@ -41,6 +41,8 @@ static int cpp_video_capture = 0; static float fps = 0; static float demo_thresh = 0; static int demo_ext_output = 0; +static long long int frame_id = 0; +static int demo_json_port = -1; static float *predictions[FRAMES]; static int demo_index = 0; @@ -114,6 +116,12 @@ void *detect_in_thread(void *ptr) det_img = ipl_images[(demo_index + FRAMES / 2 + 1) % FRAMES]; demo_index = (demo_index + 1)%FRAMES; + ++frame_id; + if (demo_json_port > 0) { + int timeout = 200; + send_json(dets, nboxes, l.classes, demo_names, frame_id, demo_json_port, timeout); + } + draw_detections_cv_v3(det_img, dets, nboxes, demo_thresh, demo_names, demo_alphabet, demo_classes, demo_ext_output); free_detections(dets, nboxes); @@ -130,7 +138,7 @@ double get_wall_time() } void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, - int frame_skip, char *prefix, char *out_filename, int http_stream_port, int dont_show, int ext_output) + int frame_skip, char *prefix, char *out_filename, int http_stream_port, int json_port, int dont_show, int ext_output) { //skip = frame_skip; image **alphabet = load_alphabet(); @@ -140,6 +148,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int demo_classes = classes; demo_thresh = thresh; demo_ext_output = ext_output; + demo_json_port = json_port; printf("Demo\n"); net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 if(weightfile){ @@ -355,7 +364,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int } #else void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, - int frame_skip, char *prefix, char *out_filename, int http_stream_port, int dont_show, int ext_output) + int frame_skip, char *prefix, char *out_filename, int http_stream_port, int json_port, int dont_show, int ext_output) { fprintf(stderr, "Demo needs OpenCV for webcam images.\n"); } diff --git a/src/demo.h b/src/demo.h index 7ad7addff0f..c6b5246e424 100644 --- a/src/demo.h +++ b/src/demo.h @@ -2,6 +2,7 @@ #define DEMO #include "image.h" -void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, int frame_skip, char *prefix, char *out_filename, int http_stream_port, int dont_show, int ext_output); +void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, + int frame_skip, char *prefix, char *out_filename, int http_stream_port, int json_port, int dont_show, int ext_output); #endif diff --git a/src/detector.c b/src/detector.c index f3b60f3ffc9..3ae5c0fa00c 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1352,6 +1352,7 @@ void run_detector(int argc, char **argv) int calc_map = find_arg(argc, argv, "-map"); check_mistakes = find_arg(argc, argv, "-check_mistakes"); int http_stream_port = find_int_arg(argc, argv, "-http_port", -1); + int json_port = find_int_arg(argc, argv, "-json_port", -1); char *out_filename = find_char_arg(argc, argv, "-out_filename", 0); char *outfile = find_char_arg(argc, argv, "-out", 0); char *prefix = find_char_arg(argc, argv, "-prefix", 0); @@ -1419,7 +1420,7 @@ void run_detector(int argc, char **argv) if (strlen(filename) > 0) if (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0; demo(cfg, weights, thresh, hier_thresh, cam_index, filename, names, classes, frame_skip, prefix, out_filename, - http_stream_port, dont_show, ext_output); + http_stream_port, json_port, dont_show, ext_output); free_list_contents_kvp(options); free_list(options); diff --git a/src/http_stream.cpp b/src/http_stream.cpp index c6ace438d42..71c6e2be752 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -196,7 +196,8 @@ class MJPGWriter }; // ---------------------------------------- -void send_mjpeg(IplImage* ipl, int port, int timeout, int quality) { +void send_mjpeg(IplImage* ipl, int port, int timeout, int quality) +{ static MJPGWriter wri(port, timeout, quality); cv::Mat mat = cv::cvarrToMat(ipl); wri.write(mat); @@ -204,6 +205,164 @@ void send_mjpeg(IplImage* ipl, int port, int timeout, int quality) { } // ---------------------------------------- +class JSON_sender +{ + SOCKET sock; + SOCKET maxfd; + fd_set master; + int timeout; // master sock timeout, shutdown after timeout millis. + + int _write(int sock, char const*const s, int len) + { + if (len < 1) { len = strlen(s); } + return ::send(sock, s, len, 0); + } + +public: + + JSON_sender(int port = 0, int _timeout = 200000) + : sock(INVALID_SOCKET) + , timeout(_timeout) + { + FD_ZERO(&master); + if (port) + open(port); + } + + ~JSON_sender() + { + release(); + } + + bool release() + { + if (sock != INVALID_SOCKET) + ::shutdown(sock, 2); + sock = (INVALID_SOCKET); + return false; + } + + bool open(int port) + { + sock = ::socket(AF_INET, SOCK_STREAM, IPPROTO_TCP); + + SOCKADDR_IN address; + address.sin_addr.s_addr = INADDR_ANY; + address.sin_family = AF_INET; + address.sin_port = htons(port); // ::htons(port); + if (::bind(sock, (SOCKADDR*)&address, sizeof(SOCKADDR_IN)) == SOCKET_ERROR) + { + cerr << "error : couldn't bind sock " << sock << " to port " << port << "!" << endl; + return release(); + } + if (::listen(sock, 10) == SOCKET_ERROR) + { + cerr << "error : couldn't listen on sock " << sock << " on port " << port << " !" << endl; + return release(); + } + FD_ZERO(&master); + FD_SET(sock, &master); + maxfd = sock; + return true; + } + + bool isOpened() + { + return sock != INVALID_SOCKET; + } + + bool write(char *outputbuf) + { + fd_set rread = master; + struct timeval to = { 0,timeout }; + if (::select(maxfd + 1, &rread, NULL, NULL, &to) <= 0) + return true; // nothing broken, there's just noone listening + + size_t outlen = strlen(outputbuf); + +#ifdef _WIN32 + for (unsigned i = 0; iclient ? maxfd : client); + FD_SET(client, &master); + _write(client, "HTTP/1.0 200 OK\r\n", 0); + _write(client, + "Server: Mozarella/2.2\r\n" + "Accept-Range: bytes\r\n" + "Connection: close\r\n" + "Max-Age: 0\r\n" + "Expires: 0\r\n" + "Cache-Control: no-cache, private\r\n" + "Pragma: no-cache\r\n" + "Content-Type: application/json\r\n" + //"Content-Type: multipart/x-mixed-replace; boundary=boundary\r\n" + "\r\n", 0); + cerr << "new client " << client << endl; + } + else // existing client, just stream pix + { + char head[400]; + // application/json + // application/x-resource+json or application/x-collection+json - when you are representing REST resources and collections + // text/json or text/javascript or text/plain. + // https://stackoverflow.com/questions/477816/what-is-the-correct-json-content-type + //sprintf(head, "\r\nContent-Length: %zu\r\n\r\n", outlen); + //sprintf(head, "--boundary\r\nContent-Type: application/json\r\nContent-Length: %zu\r\n\r\n", outlen); + _write(s, head, 0); + int n = _write(s, outputbuf, outlen); + //cerr << "known client " << s << " " << n << endl; + if (n < outlen) + { + cerr << "kill client " << s << endl; + ::shutdown(s, 2); + FD_CLR(s, &master); + } + } + } + return true; + } +}; +// ---------------------------------------- + +// JSON format: +//{ +// "frame_id":8990, +// "objects":[ +// {"class_id":4, "name":"aeroplane", "relative coordinates":{"center_x":0.398831, "center_y":0.630203, "width":0.057455, "height":0.020396}, "confidence":0.793070}, +// {"class_id":14, "name":"bird", "relative coordinates":{"center_x":0.398831, "center_y":0.630203, "width":0.057455, "height":0.020396}, "confidence":0.265497} +// ] +//}, + +void send_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id, int port, int timeout) +{ + static JSON_sender js(port, timeout); + char *send_buf = detection_to_json(dets, nboxes, classes, names, frame_id); + + js.write(send_buf); + std::cout << " JSON-stream sent. \n"; + free(send_buf); +} + +// ---------------------------------------- + CvCapture* get_capture_video_stream(char *path) { CvCapture* cap = NULL; try { diff --git a/src/http_stream.h b/src/http_stream.h index 04c7516bc3f..7710920d3db 100644 --- a/src/http_stream.h +++ b/src/http_stream.h @@ -15,6 +15,7 @@ extern "C" { #include #ifdef OPENCV +void send_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id, int port, int timeout); void send_mjpeg(IplImage* ipl, int port, int timeout, int quality); CvCapture* get_capture_webcam(int index); CvCapture* get_capture_video_stream(char *path); diff --git a/src/network.c b/src/network.c index 306918bb07d..c2a69579cbe 100644 --- a/src/network.c +++ b/src/network.c @@ -658,6 +658,44 @@ void free_detections(detection *dets, int n) free(dets); } +char *detection_to_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id) +{ + const float thresh = 0.005; // function get_network_boxes() has already filtred dets by actual threshold + + char *send_buf = (char *)calloc(1024, sizeof(char)); + sprintf(send_buf, "{\n \"frame_id\":%d, \n \"objects\":[ \n", frame_id); + + int i, j; + int class_id = -1; + for (i = 0; i < nboxes; ++i) { + for (j = 0; j < classes; ++j) { + int show = strncmp(names[j], "dont_show", 9); + if (dets[i].prob[j] > thresh && show) + { + if (class_id != -1) strcat(send_buf, ", \n"); + class_id = j; + char *buf = (char *)calloc(2048, sizeof(char)); + //sprintf(buf, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f}", + // image_id, j, dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h, dets[i].prob[j]); + + sprintf(buf, " {\"class_id\":%d, \"name\":\"%s\", \"relative coordinates\":{\"center_x\":%f, \"center_y\":%f, \"width\":%f, \"height\":%f}, \"confidence\":%f}", + j, names[j], dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h, dets[i].prob[j]); + + int send_buf_len = strlen(send_buf); + int buf_len = strlen(buf); + int total_len = send_buf_len + buf_len + 100; + send_buf = (char *)realloc(send_buf, total_len * sizeof(char)); + if (!send_buf) return;// exit(-1); + strcat(send_buf, buf); + free(buf); + } + } + } + strcat(send_buf, "\n ] \n}, \n"); + return send_buf; +} + + float *network_predict_image(network *net, image im) { //image imr = letterbox_image(im, net->w, net->h); diff --git a/src/yolo.c b/src/yolo.c index 4d344f0daf6..916e5474343 100644 --- a/src/yolo.c +++ b/src/yolo.c @@ -339,6 +339,7 @@ void run_yolo(int argc, char **argv) { int dont_show = find_arg(argc, argv, "-dont_show"); int http_stream_port = find_int_arg(argc, argv, "-http_port", -1); + int json_port = find_int_arg(argc, argv, "-json_port", -1); char *out_filename = find_char_arg(argc, argv, "-out_filename", 0); char *prefix = find_char_arg(argc, argv, "-prefix", 0); float thresh = find_float_arg(argc, argv, "-thresh", .2); @@ -359,5 +360,5 @@ void run_yolo(int argc, char **argv) else if(0==strcmp(argv[2], "valid")) validate_yolo(cfg, weights); else if(0==strcmp(argv[2], "recall")) validate_yolo_recall(cfg, weights); else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, hier_thresh, cam_index, filename, voc_names, 20, frame_skip, - prefix, out_filename, http_stream_port, dont_show, ext_output); + prefix, out_filename, http_stream_port, json_port, dont_show, ext_output); } From e6eba22adbbca6a98d3e2f2cf95add7b21b14c5b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 9 Jan 2019 03:20:29 +0300 Subject: [PATCH 0234/1365] Graceful shutdown closes sockets --- .../darknet/x64/darknet_demo_json_stream.cmd | 2 +- include/darknet.h | 2 +- src/http_stream.cpp | 99 ++++++++++++++----- src/network.c | 18 +++- 4 files changed, 91 insertions(+), 30 deletions(-) diff --git a/build/darknet/x64/darknet_demo_json_stream.cmd b/build/darknet/x64/darknet_demo_json_stream.cmd index 22a8859b0d8..dc96fba8471 100644 --- a/build/darknet/x64/darknet_demo_json_stream.cmd +++ b/build/darknet/x64/darknet_demo_json_stream.cmd @@ -1,7 +1,7 @@ rem Run this file and then open URL in Chrome/Firefox: rem http://localhost:8070 rem Or open: http://ip-address:8070 -darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights test.mp4 -i 0 -json_port 8070 -dont_show -ext_output +darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights test.mp4 -i 0 -json_port 8070 -ext_output pause \ No newline at end of file diff --git a/include/darknet.h b/include/darknet.h index b7e45f16ec4..756a68301d0 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -744,7 +744,7 @@ LIB_API detection *get_network_boxes(network *net, int w, int h, float thresh, f LIB_API void free_detections(detection *dets, int n); LIB_API void fuse_conv_batchnorm(network net); LIB_API void calculate_binary_weights(network net); -LIB_API char *detection_to_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id); +LIB_API char *detection_to_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id, char *filename); LIB_API layer* get_network_layer(network* net, int i); //LIB_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter); diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 71c6e2be752..d1659c666ad 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -58,13 +58,14 @@ using namespace cv; #include "image.h" -class MJPGWriter +class MJPG_sender { SOCKET sock; SOCKET maxfd; fd_set master; int timeout; // master sock timeout, shutdown after timeout millis. int quality; // jpeg compression [1..100] + int close_all_sockets; int _write(int sock, char const*const s, int len) { @@ -74,18 +75,20 @@ class MJPGWriter public: - MJPGWriter(int port = 0, int _timeout = 200000, int _quality = 30) + MJPG_sender(int port = 0, int _timeout = 200000, int _quality = 30) : sock(INVALID_SOCKET) , timeout(_timeout) , quality(_quality) { + close_all_sockets = 0; FD_ZERO(&master); if (port) open(port); } - ~MJPGWriter() + ~MJPG_sender() { + close_all(); release(); } @@ -97,6 +100,13 @@ class MJPGWriter return false; } + void close_all() + { + close_all_sockets = 1; + cv::Mat tmp(cv::Size(10, 10), CV_8UC3); + write(tmp); + } + bool open(int port) { sock = ::socket(AF_INET, SOCK_STREAM, IPPROTO_TCP); @@ -107,12 +117,12 @@ class MJPGWriter address.sin_port = htons(port); // ::htons(port); if (::bind(sock, (SOCKADDR*)&address, sizeof(SOCKADDR_IN)) == SOCKET_ERROR) { - cerr << "error : couldn't bind sock " << sock << " to port " << port << "!" << endl; + cerr << "error MJPG_sender: couldn't bind sock " << sock << " to port " << port << "!" << endl; return release(); } if (::listen(sock, 10) == SOCKET_ERROR) { - cerr << "error : couldn't listen on sock " << sock << " on port " << port << " !" << endl; + cerr << "error MJPG_sender: couldn't listen on sock " << sock << " on port " << port << " !" << endl; return release(); } FD_ZERO(&master); @@ -158,9 +168,15 @@ class MJPGWriter SOCKET client = ::accept(sock, (SOCKADDR*)&address, &addrlen); if (client == SOCKET_ERROR) { - cerr << "error : couldn't accept connection on sock " << sock << " !" << endl; + cerr << "error MJPG_sender: couldn't accept connection on sock " << sock << " !" << endl; return false; } + if (setsockopt(client, SOL_SOCKET, SO_RCVTIMEO, (char *)&to, sizeof(to)) < 0) { + cerr << "error MJPG_sender: SO_RCVTIMEO setsockopt failed\n"; + } + if (setsockopt(client, SOL_SOCKET, SO_SNDTIMEO, (char *)&to, sizeof(to)) < 0) { + cerr << "error MJPG_sender: SO_SNDTIMEO setsockopt failed\n"; + } maxfd = (maxfd>client ? maxfd : client); FD_SET(client, &master); _write(client, "HTTP/1.0 200 OK\r\n", 0); @@ -174,10 +190,20 @@ class MJPGWriter "Pragma: no-cache\r\n" "Content-Type: multipart/x-mixed-replace; boundary=mjpegstream\r\n" "\r\n", 0); - cerr << "new client " << client << endl; + cerr << "MJPG_sender: new client " << client << endl; } else // existing client, just stream pix { + if (close_all_sockets) { + int result = ::shutdown(s, 1); // 0 close input, 1 close output, 2 close both + char *buf = (char *)calloc(1024, sizeof(char)); + ::recv(s, buf, 1024, 0); + free(buf); + closesocket(s); + printf("MJPG_sender: close clinet: %d \n", result); + continue; + } + char head[400]; sprintf(head, "--mjpegstream\r\nContent-Type: image/jpeg\r\nContent-Length: %zu\r\n\r\n", outlen); _write(s, head, 0); @@ -185,7 +211,7 @@ class MJPGWriter //cerr << "known client " << s << " " << n << endl; if (n < outlen) { - cerr << "kill client " << s << endl; + cerr << "MJPG_sender: kill client " << s << endl; ::shutdown(s, 2); FD_CLR(s, &master); } @@ -198,7 +224,7 @@ class MJPGWriter void send_mjpeg(IplImage* ipl, int port, int timeout, int quality) { - static MJPGWriter wri(port, timeout, quality); + static MJPG_sender wri(port, timeout, quality); cv::Mat mat = cv::cvarrToMat(ipl); wri.write(mat); std::cout << " MJPEG-stream sent. \n"; @@ -211,6 +237,7 @@ class JSON_sender SOCKET maxfd; fd_set master; int timeout; // master sock timeout, shutdown after timeout millis. + int close_all_sockets; int _write(int sock, char const*const s, int len) { @@ -224,6 +251,7 @@ class JSON_sender : sock(INVALID_SOCKET) , timeout(_timeout) { + close_all_sockets = 0; FD_ZERO(&master); if (port) open(port); @@ -231,6 +259,7 @@ class JSON_sender ~JSON_sender() { + close_all(); release(); } @@ -242,6 +271,13 @@ class JSON_sender return false; } + void close_all() + { + close_all_sockets = 1; + char tmp[1]; + write(tmp); + } + bool open(int port) { sock = ::socket(AF_INET, SOCK_STREAM, IPPROTO_TCP); @@ -252,12 +288,12 @@ class JSON_sender address.sin_port = htons(port); // ::htons(port); if (::bind(sock, (SOCKADDR*)&address, sizeof(SOCKADDR_IN)) == SOCKET_ERROR) { - cerr << "error : couldn't bind sock " << sock << " to port " << port << "!" << endl; + cerr << "error JSON_sender: couldn't bind sock " << sock << " to port " << port << "!" << endl; return release(); } if (::listen(sock, 10) == SOCKET_ERROR) { - cerr << "error : couldn't listen on sock " << sock << " on port " << port << " !" << endl; + cerr << "error JSON_sender: couldn't listen on sock " << sock << " on port " << port << " !" << endl; return release(); } FD_ZERO(&master); @@ -298,9 +334,15 @@ class JSON_sender SOCKET client = ::accept(sock, (SOCKADDR*)&address, &addrlen); if (client == SOCKET_ERROR) { - cerr << "error : couldn't accept connection on sock " << sock << " !" << endl; + cerr << "error JSON_sender: couldn't accept connection on sock " << sock << " !" << endl; return false; } + if (setsockopt(client, SOL_SOCKET, SO_RCVTIMEO, (char *)&to, sizeof(to)) < 0) { + cerr << "error JSON_sender: SO_RCVTIMEO setsockopt failed\n"; + } + if (setsockopt(client, SOL_SOCKET, SO_SNDTIMEO, (char *)&to, sizeof(to)) < 0) { + cerr << "error JSON_sender: SO_SNDTIMEO setsockopt failed\n"; + } maxfd = (maxfd>client ? maxfd : client); FD_SET(client, &master); _write(client, "HTTP/1.0 200 OK\r\n", 0); @@ -315,23 +357,37 @@ class JSON_sender "Content-Type: application/json\r\n" //"Content-Type: multipart/x-mixed-replace; boundary=boundary\r\n" "\r\n", 0); - cerr << "new client " << client << endl; + cerr << "JSON_sender: new client " << client << endl; } else // existing client, just stream pix { - char head[400]; + // Graceful closes will first close their output channels and then wait for the peer + // on the other side of the connection to close its output channels. When both sides are done telling + // each other they won’t be sending any more data (i.e., closing output channels), + // the connection can be closed fully, with no risk of reset. + if (close_all_sockets) { + int result = ::shutdown(s, 1); // 0 close input, 1 close output, 2 close both + char *buf = (char *)calloc(1024, sizeof(char)); + ::recv(s, buf, 1024, 0); + free(buf); + closesocket(s); + printf("JSON_sender: close clinet: %d \n", result); + continue; + } + + //char head[400]; // application/json // application/x-resource+json or application/x-collection+json - when you are representing REST resources and collections // text/json or text/javascript or text/plain. // https://stackoverflow.com/questions/477816/what-is-the-correct-json-content-type //sprintf(head, "\r\nContent-Length: %zu\r\n\r\n", outlen); //sprintf(head, "--boundary\r\nContent-Type: application/json\r\nContent-Length: %zu\r\n\r\n", outlen); - _write(s, head, 0); + //_write(s, head, 0); int n = _write(s, outputbuf, outlen); //cerr << "known client " << s << " " << n << endl; if (n < outlen) { - cerr << "kill client " << s << endl; + cerr << "JSON_sender: kill client " << s << endl; ::shutdown(s, 2); FD_CLR(s, &master); } @@ -342,19 +398,10 @@ class JSON_sender }; // ---------------------------------------- -// JSON format: -//{ -// "frame_id":8990, -// "objects":[ -// {"class_id":4, "name":"aeroplane", "relative coordinates":{"center_x":0.398831, "center_y":0.630203, "width":0.057455, "height":0.020396}, "confidence":0.793070}, -// {"class_id":14, "name":"bird", "relative coordinates":{"center_x":0.398831, "center_y":0.630203, "width":0.057455, "height":0.020396}, "confidence":0.265497} -// ] -//}, - void send_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id, int port, int timeout) { static JSON_sender js(port, timeout); - char *send_buf = detection_to_json(dets, nboxes, classes, names, frame_id); + char *send_buf = detection_to_json(dets, nboxes, classes, names, frame_id, NULL); js.write(send_buf); std::cout << " JSON-stream sent. \n"; diff --git a/src/network.c b/src/network.c index c2a69579cbe..74ad70ed40b 100644 --- a/src/network.c +++ b/src/network.c @@ -658,12 +658,26 @@ void free_detections(detection *dets, int n) free(dets); } -char *detection_to_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id) +// JSON format: +//{ +// "frame_id":8990, +// "objects":[ +// {"class_id":4, "name":"aeroplane", "relative coordinates":{"center_x":0.398831, "center_y":0.630203, "width":0.057455, "height":0.020396}, "confidence":0.793070}, +// {"class_id":14, "name":"bird", "relative coordinates":{"center_x":0.398831, "center_y":0.630203, "width":0.057455, "height":0.020396}, "confidence":0.265497} +// ] +//}, + +char *detection_to_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id, char *filename) { const float thresh = 0.005; // function get_network_boxes() has already filtred dets by actual threshold char *send_buf = (char *)calloc(1024, sizeof(char)); - sprintf(send_buf, "{\n \"frame_id\":%d, \n \"objects\":[ \n", frame_id); + if (filename) { + sprintf(send_buf, "{\n \"frame_id\":%d, \n \"filename\":\"%s\", \n \"objects\": [ \n", frame_id, filename); + } + else { + sprintf(send_buf, "{\n \"frame_id\":%d, \n \"objects\": [ \n", frame_id); + } int i, j; int class_id = -1; From 63a2f005aaa1f5d9ec35b8812ac97fd508765380 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 9 Jan 2019 13:46:53 +0300 Subject: [PATCH 0235/1365] Compile fix --- src/http_stream.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/http_stream.cpp b/src/http_stream.cpp index d1659c666ad..7d37d8871af 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -199,7 +199,7 @@ class MJPG_sender char *buf = (char *)calloc(1024, sizeof(char)); ::recv(s, buf, 1024, 0); free(buf); - closesocket(s); + ::closesocket(s); printf("MJPG_sender: close clinet: %d \n", result); continue; } @@ -370,7 +370,7 @@ class JSON_sender char *buf = (char *)calloc(1024, sizeof(char)); ::recv(s, buf, 1024, 0); free(buf); - closesocket(s); + ::closesocket(s); printf("JSON_sender: close clinet: %d \n", result); continue; } From 596311e2c79dd9c75fa55278580c8d575779dbe6 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 9 Jan 2019 13:53:33 +0300 Subject: [PATCH 0236/1365] Another compile fix ) --- src/http_stream.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 7d37d8871af..1f6a7a4e545 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -199,7 +199,7 @@ class MJPG_sender char *buf = (char *)calloc(1024, sizeof(char)); ::recv(s, buf, 1024, 0); free(buf); - ::closesocket(s); + //::closesocket(s); printf("MJPG_sender: close clinet: %d \n", result); continue; } @@ -370,7 +370,7 @@ class JSON_sender char *buf = (char *)calloc(1024, sizeof(char)); ::recv(s, buf, 1024, 0); free(buf); - ::closesocket(s); + //::closesocket(s); printf("JSON_sender: close clinet: %d \n", result); continue; } From 5ee166032aab2658df216ef92e4bcd5a3aea3b7d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 9 Jan 2019 17:49:37 +0300 Subject: [PATCH 0237/1365] Use detector test...-out result.json -dont_show < train.txt to save results to JSON-file --- build/darknet/x64/darknet_json_reslut.cmd | 3 ++ src/detector.c | 32 +++++++++++++++++-- src/http_stream.cpp | 39 +++++++++++------------ src/network.c | 3 +- 4 files changed, 53 insertions(+), 24 deletions(-) create mode 100644 build/darknet/x64/darknet_json_reslut.cmd diff --git a/build/darknet/x64/darknet_json_reslut.cmd b/build/darknet/x64/darknet_json_reslut.cmd new file mode 100644 index 00000000000..3ec6db734f2 --- /dev/null +++ b/build/darknet/x64/darknet_json_reslut.cmd @@ -0,0 +1,3 @@ +darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights -ext_output -dont_show -out result.json < data/train.txt + +pause \ No newline at end of file diff --git a/src/detector.c b/src/detector.c index 3ae5c0fa00c..2ae92ea7f8e 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1220,7 +1220,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int //#endif // OPENCV void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, - float hier_thresh, int dont_show, int ext_output, int save_labels) + float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile) { list *options = read_data_cfg(datacfg); char *name_list = option_find_str(options, "names", "data/names.list"); @@ -1244,6 +1244,14 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam double time; char buff[256]; char *input = buff; + char *json_buf = NULL; + int json_image_id = 0; + FILE* json_file = NULL; + if (outfile) { + json_file = fopen(outfile, "wb"); + char *tmp = "[\n"; + fwrite(tmp, sizeof(char), strlen(tmp), json_file); + } int j; float nms = .45; // 0.4F while (1) { @@ -1256,7 +1264,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam printf("Enter Image Path: "); fflush(stdout); input = fgets(input, 256, stdin); - if (!input) return; + if (!input) break; strtok(input, "\n"); } image im = load_image(input, 0, 0, net.c); @@ -1287,6 +1295,18 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam show_image(im, "predictions"); } + if (outfile) { + if (json_buf) { + char *tmp = ", \n"; + fwrite(tmp, sizeof(char), strlen(tmp), json_file); + } + ++json_image_id; + json_buf = detection_to_json(dets, nboxes, l.classes, names, json_image_id, input); + + fwrite(json_buf, sizeof(char), strlen(json_buf), json_file); + free(json_buf); + } + // pseudo labeling concept - fast.ai if (save_labels) { @@ -1327,6 +1347,12 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam if (filename) break; } + if (outfile) { + char *tmp = "\n]"; + fwrite(tmp, sizeof(char), strlen(tmp), json_file); + fclose(json_file); + } + // free memory free_ptrs(names, net.layers[net.n - 1].classes); free_list_contents_kvp(options); @@ -1405,7 +1431,7 @@ void run_detector(int argc, char **argv) if (strlen(weights) > 0) if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0; char *filename = (argc > 6) ? argv[6] : 0; - if (0 == strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels); + if (0 == strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels, outfile); else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map); else if (0 == strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile); else if (0 == strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights); diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 1f6a7a4e545..b4c88a327a6 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -274,8 +274,7 @@ class JSON_sender void close_all() { close_all_sockets = 1; - char tmp[1]; - write(tmp); + write("\n]"); // close JSON array } bool open(int port) @@ -357,40 +356,38 @@ class JSON_sender "Content-Type: application/json\r\n" //"Content-Type: multipart/x-mixed-replace; boundary=boundary\r\n" "\r\n", 0); + _write(client, "[\n", 0); // open JSON array cerr << "JSON_sender: new client " << client << endl; } else // existing client, just stream pix { - // Graceful closes will first close their output channels and then wait for the peer - // on the other side of the connection to close its output channels. When both sides are done telling - // each other they won’t be sending any more data (i.e., closing output channels), - // the connection can be closed fully, with no risk of reset. - if (close_all_sockets) { - int result = ::shutdown(s, 1); // 0 close input, 1 close output, 2 close both - char *buf = (char *)calloc(1024, sizeof(char)); - ::recv(s, buf, 1024, 0); - free(buf); - //::closesocket(s); - printf("JSON_sender: close clinet: %d \n", result); - continue; - } - //char head[400]; - // application/json // application/x-resource+json or application/x-collection+json - when you are representing REST resources and collections - // text/json or text/javascript or text/plain. + // application/json or text/json or text/javascript or text/plain. // https://stackoverflow.com/questions/477816/what-is-the-correct-json-content-type //sprintf(head, "\r\nContent-Length: %zu\r\n\r\n", outlen); //sprintf(head, "--boundary\r\nContent-Type: application/json\r\nContent-Length: %zu\r\n\r\n", outlen); //_write(s, head, 0); int n = _write(s, outputbuf, outlen); - //cerr << "known client " << s << " " << n << endl; if (n < outlen) { cerr << "JSON_sender: kill client " << s << endl; ::shutdown(s, 2); FD_CLR(s, &master); } + + // Graceful closes will first close their output channels and then wait for the peer + // on the other side of the connection to close its output channels. When both sides are done telling + // each other they won’t be sending any more data (i.e., closing output channels), + // the connection can be closed fully, with no risk of reset. + if (close_all_sockets) { + int result = ::shutdown(s, 1); // 0 close input, 1 close output, 2 close both + char *buf = (char *)calloc(1024, sizeof(char)); + ::recv(s, buf, 1024, 0); + free(buf); + //::closesocket(s); + printf("JSON_sender: close clinet: %d \n", result); + } } } return true; @@ -401,7 +398,9 @@ class JSON_sender void send_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id, int port, int timeout) { static JSON_sender js(port, timeout); - char *send_buf = detection_to_json(dets, nboxes, classes, names, frame_id, NULL); + static char *send_buf = NULL; + if(send_buf) js.write(", \n"); + send_buf = detection_to_json(dets, nboxes, classes, names, frame_id, NULL); js.write(send_buf); std::cout << " JSON-stream sent. \n"; diff --git a/src/network.c b/src/network.c index 74ad70ed40b..4eeed0a8602 100644 --- a/src/network.c +++ b/src/network.c @@ -705,7 +705,8 @@ char *detection_to_json(detection *dets, int nboxes, int classes, char **names, } } } - strcat(send_buf, "\n ] \n}, \n"); + //strcat(send_buf, "\n ] \n}, \n"); + strcat(send_buf, "\n ] \n}"); return send_buf; } From 97038fefa63b9b14c0b2fd0c4f1b382db7b43e8c Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 9 Jan 2019 21:47:37 +0300 Subject: [PATCH 0238/1365] Fixed bug with saving training Loss-Chart --- src/demo.c | 7 ++++--- src/image.c | 10 +++++++++- 2 files changed, 13 insertions(+), 4 deletions(-) diff --git a/src/demo.c b/src/demo.c index 3aca9705c1e..53e8bd10b52 100644 --- a/src/demo.c +++ b/src/demo.c @@ -54,6 +54,7 @@ void draw_detections_cv(IplImage* show_img, int num, float thresh, box *boxes, f void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output); void show_image_cv_ipl(IplImage *disp, const char *name); void save_cv_png(IplImage *img, const char *name); +void save_cv_jpg(IplImage *img, const char *name); image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close); image get_image_from_stream_letterbox(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close); int get_stream_fps(CvCapture *cap, int cpp_video_capture); @@ -140,6 +141,7 @@ double get_wall_time() void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, int frame_skip, char *prefix, char *out_filename, int http_stream_port, int json_port, int dont_show, int ext_output) { + in_img = det_img = show_img = NULL; //skip = frame_skip; image **alphabet = load_alphabet(); int delay = frame_skip; @@ -274,9 +276,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int }else{ char buff[256]; sprintf(buff, "%s_%08d.jpg", prefix, count); - save_cv_png(buff, show_img); - //cvSaveImage(buff, show_img, 0); - //save_image(disp, buff); + if(show_img) save_cv_jpg(show_img, buff); } // if you run it with param -http_port 8090 then open URL in your web-browser: http://localhost:8090 @@ -302,6 +302,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int if (flag_exit == 1) break; if(delay == 0){ + printf("\n show_img = det_img; \n"); show_img = det_img; } det_img = in_img; diff --git a/src/image.c b/src/image.c index 06112cb4ccf..a4443cdd762 100644 --- a/src/image.c +++ b/src/image.c @@ -508,6 +508,14 @@ void save_cv_png(IplImage *img, const char *name) cvRelease(&img_rgb); } +void save_cv_jpg(IplImage *img, const char *name) +{ + IplImage* img_rgb = cvCreateImage(cvSize(img->width, img->height), 8, 3); + cvCvtColor(img, img_rgb, CV_RGB2BGR); + stbi_write_jpg(name, img_rgb->width, img_rgb->height, 3, (char *)img_rgb->imageData, 80); + cvRelease(&img_rgb); +} + void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output) { int i, j; @@ -802,7 +810,7 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_ int k = cvWaitKey(20); if (k == 's' || current_batch == (max_batches - 1) || current_batch % 100 == 0) { //cvSaveImage("chart.jpg", img, 0); - save_cv_png("chart.png", img); + save_cv_png(img, "chart.png"); cvPutText(img, "- Saved", cvPoint(250, img_size - 10), &font, CV_RGB(255, 0, 0)); } else From 580b2fb83c924b709f1562190d612e173087adcb Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 11 Jan 2019 00:52:19 +0300 Subject: [PATCH 0239/1365] Fixed ./darknet detect ... --- include/darknet.h | 1 + src/darknet.c | 4 ++-- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 756a68301d0..626e527710d 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -753,6 +753,7 @@ LIB_API void reset_rnn(network *net); LIB_API float *network_predict_image(network *net, image im); LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, network *existing_net); LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map); +LIB_API void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, int ext_output); LIB_API int network_width(network *net); LIB_API int network_height(network *net); LIB_API void optimize_picture(network *net, image orig, int max_layer, float scale, float rate, float thresh, int norm); diff --git a/src/darknet.c b/src/darknet.c index 41878041e9f..fc560559ecb 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -2,6 +2,7 @@ #include #include +#include "darknet.h" #include "parser.h" #include "utils.h" #include "cuda.h" @@ -13,7 +14,6 @@ #endif extern void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top); -extern void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, int ext_output); extern void run_voxel(int argc, char **argv); extern void run_yolo(int argc, char **argv); extern void run_detector(int argc, char **argv); @@ -397,7 +397,7 @@ int main(int argc, char **argv) float thresh = find_float_arg(argc, argv, "-thresh", .24); int ext_output = find_arg(argc, argv, "-ext_output"); char *filename = (argc > 4) ? argv[4]: 0; - test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, ext_output); + test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, 0.5, 0, 1, 0, NULL); } else if (0 == strcmp(argv[1], "cifar")){ run_cifar(argc, argv); } else if (0 == strcmp(argv[1], "go")){ From 041cffd9b87d39229b90be7c6882cefbb8e4b674 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 11 Jan 2019 01:05:44 +0300 Subject: [PATCH 0240/1365] Compile fix --- include/darknet.h | 3 ++- src/detector.c | 1 + 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/include/darknet.h b/include/darknet.h index 626e527710d..c312be9ca67 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -753,7 +753,8 @@ LIB_API void reset_rnn(network *net); LIB_API float *network_predict_image(network *net, image im); LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, network *existing_net); LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map); -LIB_API void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, int ext_output); +LIB_API void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, + float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile); LIB_API int network_width(network *net); LIB_API int network_height(network *net); LIB_API void optimize_picture(network *net, image orig, int max_layer, float scale, float rate, float thresh, int norm); diff --git a/src/detector.c b/src/detector.c index 2ae92ea7f8e..455366e302c 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1,3 +1,4 @@ +#include "darknet.h" #include "network.h" #include "region_layer.h" #include "cost_layer.h" From 95904cf65a449c075371637f418106d1c944d760 Mon Sep 17 00:00:00 2001 From: jureviciusr Date: Fri, 11 Jan 2019 14:56:03 +0200 Subject: [PATCH 0241/1365] Bad memory allocation the variable [network->seen is declared uint64_t* ](https://github.com/AlexeyAB/darknet/blob/master/include/darknet.h#L519), but constructing function allocates only 4 bytes for (int) here --- src/network.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/network.c b/src/network.c index 4eeed0a8602..fc62ea79f35 100644 --- a/src/network.c +++ b/src/network.c @@ -179,7 +179,7 @@ network make_network(int n) network net = {0}; net.n = n; net.layers = calloc(net.n, sizeof(layer)); - net.seen = calloc(1, sizeof(int)); + net.seen = calloc(1, sizeof(uint64_t)); #ifdef GPU net.input_gpu = calloc(1, sizeof(float *)); net.truth_gpu = calloc(1, sizeof(float *)); @@ -969,4 +969,4 @@ network combine_train_valid_networks(network net_train, network net_map) } } return net_combined; -} \ No newline at end of file +} From 937306f0c9961da810103dab5a2cf53bad5026e8 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 11 Jan 2019 22:39:29 +0300 Subject: [PATCH 0242/1365] Fixed JSON-stream and MJPEG-stream on Windows and Linux. --- json_mjpeg_streams.sh | 6 ++++ mjpeg_stream.sh | 8 ----- src/demo.c | 1 - src/http_stream.cpp | 77 +++++++++++++++++++++++++++++-------------- 4 files changed, 59 insertions(+), 33 deletions(-) create mode 100644 json_mjpeg_streams.sh delete mode 100644 mjpeg_stream.sh diff --git a/json_mjpeg_streams.sh b/json_mjpeg_streams.sh new file mode 100644 index 00000000000..23ce450c02a --- /dev/null +++ b/json_mjpeg_streams.sh @@ -0,0 +1,6 @@ +# Run this file and then open URL in Chrome/Firefox in 2 tabs: http://localhost:8070 and http://localhost:8090 +# Or open: http://ip-address:8070 and http://ip-address:8090 +# to get run: sudo ifconfig + +./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -http_port 8090 -ext_output + diff --git a/mjpeg_stream.sh b/mjpeg_stream.sh deleted file mode 100644 index ff21ddf49b2..00000000000 --- a/mjpeg_stream.sh +++ /dev/null @@ -1,8 +0,0 @@ -rem Run this file and then open URL in Chrome/Firefox: rem http://localhost:8090 -rem Or open: http://ip-address:8090 - -./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -i 0 -thresh 0.25 -http_port 8090 - - - -pause \ No newline at end of file diff --git a/src/demo.c b/src/demo.c index 53e8bd10b52..10b908618a6 100644 --- a/src/demo.c +++ b/src/demo.c @@ -302,7 +302,6 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int if (flag_exit == 1) break; if(delay == 0){ - printf("\n show_img = det_img; \n"); show_img = det_img; } det_img = in_img; diff --git a/src/http_stream.cpp b/src/http_stream.cpp index b4c88a327a6..29d8aa7c4a7 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -21,7 +21,22 @@ struct _INIT_W32DATA WSADATA w; _INIT_W32DATA() { WSAStartup(MAKEWORD(2, 1), &w); } } _init_once; -#else /* ! win32 */ + +// Graceful closes will first close their output channels and then wait for the peer +// on the other side of the connection to close its output channels. When both sides are done telling +// each other they won’t be sending any more data (i.e., closing output channels), +// the connection can be closed fully, with no risk of reset. +static int close_socket(SOCKET s) { + int close_output = ::shutdown(s, 1); // 0 close input, 1 close output, 2 close both + char *buf = (char *)calloc(1024, sizeof(char)); + ::recv(s, buf, 1024, 0); + free(buf); + int close_input = ::shutdown(s, 0); + int result = ::closesocket(s); + printf("Close socket: out = %d, in = %d \n", close_output, close_input); + return result; +} +#else // nix #include #include #include @@ -29,6 +44,7 @@ struct _INIT_W32DATA #include #include #include +#include #define PORT unsigned short #define SOCKET int #define HOSTENT struct hostent @@ -37,7 +53,29 @@ struct _INIT_W32DATA #define ADDRPOINTER unsigned int* #define INVALID_SOCKET -1 #define SOCKET_ERROR -1 -#endif /* _WIN32 */ +struct _IGNORE_PIPE_SIGNAL +{ + struct sigaction new_actn, old_actn; + _IGNORE_PIPE_SIGNAL() { + new_actn.sa_handler = SIG_IGN; // ignore the broken pipe signal + sigemptyset(&new_actn.sa_mask); + new_actn.sa_flags = 0; + sigaction(SIGPIPE, &new_actn, &old_actn); + // sigaction (SIGPIPE, &old_actn, NULL); // - to restore the previous signal handling + } +} _init_once; + +static int close_socket(SOCKET s) { + int close_output = ::shutdown(s, 1); // 0 close input, 1 close output, 2 close both + char *buf = (char *)calloc(1024, sizeof(char)); + ::recv(s, buf, 1024, 0); + free(buf); + int close_input = ::shutdown(s, 0); + int result = close(s); + printf("Close socket: out = %d, in = %d \n", close_output, close_input); + return result; +} +#endif // _WIN32 #include #include @@ -139,8 +177,9 @@ class MJPG_sender bool write(const Mat & frame) { fd_set rread = master; - struct timeval to = { 0,timeout }; - if (::select(maxfd+1, &rread, NULL, NULL, &to) <= 0) + struct timeval select_timeout = { 0, 0 }; + struct timeval socket_timeout = { 0, timeout }; + if (::select(maxfd+1, &rread, NULL, NULL, &select_timeout) <= 0) return true; // nothing broken, there's just noone listening std::vector outbuf; @@ -171,10 +210,10 @@ class MJPG_sender cerr << "error MJPG_sender: couldn't accept connection on sock " << sock << " !" << endl; return false; } - if (setsockopt(client, SOL_SOCKET, SO_RCVTIMEO, (char *)&to, sizeof(to)) < 0) { + if (setsockopt(client, SOL_SOCKET, SO_RCVTIMEO, (char *)&socket_timeout, sizeof(socket_timeout)) < 0) { cerr << "error MJPG_sender: SO_RCVTIMEO setsockopt failed\n"; } - if (setsockopt(client, SOL_SOCKET, SO_SNDTIMEO, (char *)&to, sizeof(to)) < 0) { + if (setsockopt(client, SOL_SOCKET, SO_SNDTIMEO, (char *)&socket_timeout, sizeof(socket_timeout)) < 0) { cerr << "error MJPG_sender: SO_SNDTIMEO setsockopt failed\n"; } maxfd = (maxfd>client ? maxfd : client); @@ -195,11 +234,7 @@ class MJPG_sender else // existing client, just stream pix { if (close_all_sockets) { - int result = ::shutdown(s, 1); // 0 close input, 1 close output, 2 close both - char *buf = (char *)calloc(1024, sizeof(char)); - ::recv(s, buf, 1024, 0); - free(buf); - //::closesocket(s); + int result = close_socket(s); printf("MJPG_sender: close clinet: %d \n", result); continue; } @@ -309,8 +344,9 @@ class JSON_sender bool write(char *outputbuf) { fd_set rread = master; - struct timeval to = { 0,timeout }; - if (::select(maxfd + 1, &rread, NULL, NULL, &to) <= 0) + struct timeval select_timeout = { 0, 0 }; + struct timeval socket_timeout = { 0, timeout }; + if (::select(maxfd + 1, &rread, NULL, NULL, &select_timeout) <= 0) return true; // nothing broken, there's just noone listening size_t outlen = strlen(outputbuf); @@ -336,10 +372,10 @@ class JSON_sender cerr << "error JSON_sender: couldn't accept connection on sock " << sock << " !" << endl; return false; } - if (setsockopt(client, SOL_SOCKET, SO_RCVTIMEO, (char *)&to, sizeof(to)) < 0) { + if (setsockopt(client, SOL_SOCKET, SO_RCVTIMEO, (char *)&socket_timeout, sizeof(socket_timeout)) < 0) { cerr << "error JSON_sender: SO_RCVTIMEO setsockopt failed\n"; } - if (setsockopt(client, SOL_SOCKET, SO_SNDTIMEO, (char *)&to, sizeof(to)) < 0) { + if (setsockopt(client, SOL_SOCKET, SO_SNDTIMEO, (char *)&socket_timeout, sizeof(socket_timeout)) < 0) { cerr << "error JSON_sender: SO_SNDTIMEO setsockopt failed\n"; } maxfd = (maxfd>client ? maxfd : client); @@ -376,17 +412,10 @@ class JSON_sender FD_CLR(s, &master); } - // Graceful closes will first close their output channels and then wait for the peer - // on the other side of the connection to close its output channels. When both sides are done telling - // each other they won’t be sending any more data (i.e., closing output channels), - // the connection can be closed fully, with no risk of reset. if (close_all_sockets) { - int result = ::shutdown(s, 1); // 0 close input, 1 close output, 2 close both - char *buf = (char *)calloc(1024, sizeof(char)); - ::recv(s, buf, 1024, 0); - free(buf); - //::closesocket(s); + int result = close_socket(s); printf("JSON_sender: close clinet: %d \n", result); + continue; } } } From eb64e2b13c0c3a831652861143fa8fea26f6cd82 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 11 Jan 2019 23:11:25 +0300 Subject: [PATCH 0243/1365] Added SO_REUSEADDR and SO_REUSEPORT for JSON & MJPEG streams --- src/http_stream.cpp | 34 +++++++++++++++++++++++++++++----- 1 file changed, 29 insertions(+), 5 deletions(-) diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 29d8aa7c4a7..65beba06a70 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -24,7 +24,7 @@ struct _INIT_W32DATA // Graceful closes will first close their output channels and then wait for the peer // on the other side of the connection to close its output channels. When both sides are done telling -// each other they won’t be sending any more data (i.e., closing output channels), +// each other they won,t be sending any more data (i.e., closing output channels), // the connection can be closed fully, with no risk of reset. static int close_socket(SOCKET s) { int close_output = ::shutdown(s, 1); // 0 close input, 1 close output, 2 close both @@ -153,6 +153,14 @@ class MJPG_sender address.sin_addr.s_addr = INADDR_ANY; address.sin_family = AF_INET; address.sin_port = htons(port); // ::htons(port); + int reuse = 1; + if (setsockopt(sock, SOL_SOCKET, SO_REUSEADDR, (const char*)&reuse, sizeof(reuse)) < 0) + cerr << "setsockopt(SO_REUSEADDR) failed" << endl; + +#ifdef SO_REUSEPORT + if (setsockopt(sock, SOL_SOCKET, SO_REUSEPORT, (const char*)&reuse, sizeof(reuse)) < 0) + cerr << "setsockopt(SO_REUSEPORT) failed" << endl; +#endif if (::bind(sock, (SOCKADDR*)&address, sizeof(SOCKADDR_IN)) == SOCKET_ERROR) { cerr << "error MJPG_sender: couldn't bind sock " << sock << " to port " << port << "!" << endl; @@ -179,7 +187,7 @@ class MJPG_sender fd_set rread = master; struct timeval select_timeout = { 0, 0 }; struct timeval socket_timeout = { 0, timeout }; - if (::select(maxfd+1, &rread, NULL, NULL, &select_timeout) <= 0) + if (::select(maxfd + 1, &rread, NULL, NULL, &select_timeout) <= 0) return true; // nothing broken, there's just noone listening std::vector outbuf; @@ -195,7 +203,7 @@ class MJPG_sender int addrlen = sizeof(SOCKADDR); SOCKET s = rread.fd_array[i]; // fd_set on win is an array, while ... #else - for (int s = 0; s<=maxfd; s++) + for (int s = 0; s <= maxfd; s++) { socklen_t addrlen = sizeof(SOCKADDR); if (!FD_ISSET(s, &rread)) // ... on linux it's a bitmask ;) @@ -252,6 +260,10 @@ class MJPG_sender } } } + if (close_all_sockets) { + int result = close_socket(sock); + printf("MJPG_sender: close acceptor: %d \n\n", result); + } return true; } }; @@ -320,6 +332,14 @@ class JSON_sender address.sin_addr.s_addr = INADDR_ANY; address.sin_family = AF_INET; address.sin_port = htons(port); // ::htons(port); + int reuse = 1; + if (setsockopt(sock, SOL_SOCKET, SO_REUSEADDR, (const char*)&reuse, sizeof(reuse)) < 0) + cerr << "setsockopt(SO_REUSEADDR) failed" << endl; + +#ifdef SO_REUSEPORT + if (setsockopt(sock, SOL_SOCKET, SO_REUSEPORT, (const char*)&reuse, sizeof(reuse)) < 0) + cerr << "setsockopt(SO_REUSEPORT) failed" << endl; +#endif if (::bind(sock, (SOCKADDR*)&address, sizeof(SOCKADDR_IN)) == SOCKET_ERROR) { cerr << "error JSON_sender: couldn't bind sock " << sock << " to port " << port << "!" << endl; @@ -419,6 +439,10 @@ class JSON_sender } } } + if (close_all_sockets) { + int result = close_socket(sock); + printf("JSON_sender: close acceptor: %d \n\n", result); + } return true; } }; @@ -428,7 +452,7 @@ void send_json(detection *dets, int nboxes, int classes, char **names, long long { static JSON_sender js(port, timeout); static char *send_buf = NULL; - if(send_buf) js.write(", \n"); + if (send_buf) js.write(", \n"); send_buf = detection_to_json(dets, nboxes, classes, names, frame_id, NULL); js.write(send_buf); @@ -603,7 +627,7 @@ double get_time() { void stop_timer_and_show() { stop_timer(); - std::cout << " " << get_time()*1000 << " msec" << std::endl; + std::cout << " " << get_time() * 1000 << " msec" << std::endl; } void stop_timer_and_show_name(char *name) { From 840aac5115600194b40bf4d060193e6512b2a426 Mon Sep 17 00:00:00 2001 From: Alexey Date: Fri, 11 Jan 2019 23:24:57 +0300 Subject: [PATCH 0244/1365] Update Readme.md --- README.md | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 3a7a2d53620..7e1802ca90f 100644 --- a/README.md +++ b/README.md @@ -90,7 +90,8 @@ Others: https://www.youtube.com/channel/UC7ev3hNVkx4DzZ3LO19oebg * optimized memory allocation during network resizing when `random=1` * optimized initialization GPU for detection - we use batch=1 initially instead of re-init with batch=1 * added correct calculation of **mAP, F1, IoU, Precision-Recall** using command `darknet detector map`... -* added drawing of chart of average loss during training +* added drawing of chart of average-Loss and accuracy-mAP (`-map` flag) during training +* run `./darknet detector demo ... -json_port 8070 -mjpeg_port 8090` as JSON and MJPEG server to get results online over the network by using your soft or Web-browser * added calculation of anchors for training * added example of Detection and Tracking objects: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp * fixed code for use Web-cam on OpenCV 3.x @@ -108,13 +109,14 @@ Also, you might be interested in using a simplified repository where is implemen On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights` -* **Yolo v3** COCO - image: `darknet.exe detector test data/coco.data cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` +* Yolo v3 COCO - **image**: `darknet.exe detector test data/coco.data cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` * Output coordinates of objects: `darknet.exe detector test data/coco.data yolov3.cfg yolov3.weights -ext_output dog.jpg` -* **Yolo v3** COCO - video: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -ext_output test.mp4` -* **Yolo v3** COCO - WebCam 0: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -c 0` -* **Yolo v3** COCO for net-videocam - Smart WebCam: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg` +* Yolo v3 COCO - **video**: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -ext_output test.mp4` +* Yolo v3 COCO - **WebCam 0**: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -c 0` +* Yolo v3 COCO for **net-videocam** - Smart WebCam: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg` * **Yolo v3 - save result to the file res.avi**: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -thresh 0.25 test.mp4 -out_filename res.avi` * **Yolo v3 Tiny** COCO - video: `darknet.exe detector demo data/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4` +* **JSON and MJPEG server** that allows multiple connections from your soft or Web-browser `ip-address:8070` or 8090: `./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -http_port 8090 -ext_output` * **Yolo v3 Tiny** on GPU #0: `darknet.exe detector demo data/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 0 test.mp4` * Alternative method Yolo v3 COCO - image: `darknet.exe detect cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` * 186 MB Yolo9000 - image: `darknet.exe detector test cfg/combine9k.data yolo9000.cfg yolo9000.weights` From 5c55137687e11bb4cdaef92a48a4889041167960 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 12 Jan 2019 02:56:30 +0300 Subject: [PATCH 0245/1365] Minor fix --- src/http_stream.cpp | 6 +++--- src/network.c | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 65beba06a70..ebe9664b577 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -413,6 +413,7 @@ class JSON_sender //"Content-Type: multipart/x-mixed-replace; boundary=boundary\r\n" "\r\n", 0); _write(client, "[\n", 0); // open JSON array + int n = _write(client, outputbuf, outlen); cerr << "JSON_sender: new client " << client << endl; } else // existing client, just stream pix @@ -424,6 +425,7 @@ class JSON_sender //sprintf(head, "\r\nContent-Length: %zu\r\n\r\n", outlen); //sprintf(head, "--boundary\r\nContent-Type: application/json\r\nContent-Length: %zu\r\n\r\n", outlen); //_write(s, head, 0); + if(!close_all_sockets) _write(s, ", \n", 0); int n = _write(s, outputbuf, outlen); if (n < outlen) { @@ -451,9 +453,7 @@ class JSON_sender void send_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id, int port, int timeout) { static JSON_sender js(port, timeout); - static char *send_buf = NULL; - if (send_buf) js.write(", \n"); - send_buf = detection_to_json(dets, nboxes, classes, names, frame_id, NULL); + char *send_buf = detection_to_json(dets, nboxes, classes, names, frame_id, NULL); js.write(send_buf); std::cout << " JSON-stream sent. \n"; diff --git a/src/network.c b/src/network.c index fc62ea79f35..b748a85725a 100644 --- a/src/network.c +++ b/src/network.c @@ -692,7 +692,7 @@ char *detection_to_json(detection *dets, int nboxes, int classes, char **names, //sprintf(buf, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f}", // image_id, j, dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h, dets[i].prob[j]); - sprintf(buf, " {\"class_id\":%d, \"name\":\"%s\", \"relative coordinates\":{\"center_x\":%f, \"center_y\":%f, \"width\":%f, \"height\":%f}, \"confidence\":%f}", + sprintf(buf, " {\"class_id\":%d, \"name\":\"%s\", \"relative_coordinates\":{\"center_x\":%f, \"center_y\":%f, \"width\":%f, \"height\":%f}, \"confidence\":%f}", j, names[j], dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h, dets[i].prob[j]); int send_buf_len = strlen(send_buf); From 70260cda242729d712c5ca09b3efbd5b504960f0 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 12 Jan 2019 13:45:37 +0300 Subject: [PATCH 0246/1365] Added -mjpeg_port 8090 for Training. Loss-chart saved to chart.png even when used -dont_show. mAP is calculated during training even without OpenCV. --- README.md | 5 +- .../darknet/x64/darknet_demo_mjpeg_stream.cmd | 2 +- json_mjpeg_streams.sh | 2 +- src/classifier.c | 16 ++--- src/coco.c | 4 +- src/demo.c | 10 +-- src/demo.h | 2 +- src/detector.c | 68 +++++++++---------- src/image.c | 28 +++++--- src/yolo.c | 4 +- 10 files changed, 75 insertions(+), 66 deletions(-) diff --git a/README.md b/README.md index 7e1802ca90f..9e918ac573d 100644 --- a/README.md +++ b/README.md @@ -116,7 +116,7 @@ On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector * Yolo v3 COCO for **net-videocam** - Smart WebCam: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg` * **Yolo v3 - save result to the file res.avi**: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -thresh 0.25 test.mp4 -out_filename res.avi` * **Yolo v3 Tiny** COCO - video: `darknet.exe detector demo data/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4` -* **JSON and MJPEG server** that allows multiple connections from your soft or Web-browser `ip-address:8070` or 8090: `./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -http_port 8090 -ext_output` +* **JSON and MJPEG server** that allows multiple connections from your soft or Web-browser `ip-address:8070` or 8090: `./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output` * **Yolo v3 Tiny** on GPU #0: `darknet.exe detector demo data/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 0 test.mp4` * Alternative method Yolo v3 COCO - image: `darknet.exe detect cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` * 186 MB Yolo9000 - image: `darknet.exe detector test cfg/combine9k.data yolo9000.cfg yolo9000.weights` @@ -340,7 +340,8 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an * (file `yolo-obj_last.weights` will be saved to the `build\darknet\x64\backup\` for each 100 iterations) * (file `yolo-obj_xxxx.weights` will be saved to the `build\darknet\x64\backup\` for each 1000 iterations) - * (To disable Loss-Window use `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -dont_show`, if you train on computer without monitor like a cloud Amazaon EC2) + * (to disable Loss-Window use `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -dont_show`, if you train on computer without monitor like a cloud Amazon EC2) + * (to see the mAP & Loss-chart during training on remote server without GUI, use command `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -dont_show -mjpeg_port 8090 -map` then open URL `http://ip-address:8090` in Chrome/Firefox browser) 8.1. For training with mAP (mean average precisions) calculation for each 4 Epochs (set `valid=valid.txt` or `train.txt` in `obj.data` file) and run: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -map` diff --git a/build/darknet/x64/darknet_demo_mjpeg_stream.cmd b/build/darknet/x64/darknet_demo_mjpeg_stream.cmd index 95bfb3b47a1..a1dffac59e0 100644 --- a/build/darknet/x64/darknet_demo_mjpeg_stream.cmd +++ b/build/darknet/x64/darknet_demo_mjpeg_stream.cmd @@ -1,7 +1,7 @@ rem Run this file and then open URL in Chrome/Firefox: rem http://localhost:8090 rem Or open: http://ip-address:8090 -darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights test.mp4 -i 0 -http_port 8090 -dont_show -ext_output +darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights test.mp4 -i 0 -mjpeg_port 8090 -dont_show -ext_output pause \ No newline at end of file diff --git a/json_mjpeg_streams.sh b/json_mjpeg_streams.sh index 23ce450c02a..0bf180b60bf 100644 --- a/json_mjpeg_streams.sh +++ b/json_mjpeg_streams.sh @@ -2,5 +2,5 @@ # Or open: http://ip-address:8070 and http://ip-address:8090 # to get run: sudo ifconfig -./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -http_port 8090 -ext_output +./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output diff --git a/src/classifier.c b/src/classifier.c index d124f632a98..26a7a3fed1c 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -24,8 +24,9 @@ image get_image_from_stream(CvCapture *cap); image get_image_from_stream_cpp(CvCapture *cap); #include "http_stream.h" -IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size); -void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, float precision, int draw_precision); +IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show); +void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, + float precision, int draw_precision, int dont_show, int mjpeg_port); #endif @@ -41,7 +42,7 @@ float *get_regression_values(char **labels, int n) return v; } -void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show) +void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int mjpeg_port) { int i; @@ -114,8 +115,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, float max_img_loss = 5; int number_of_lines = 100; int img_size = 1000; - if (!dont_show) - img = draw_train_chart(max_img_loss, net.max_batches, number_of_lines, img_size); + img = draw_train_chart(max_img_loss, net.max_batches, number_of_lines, img_size, dont_show); #endif //OPENCV data train; @@ -152,8 +152,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); #ifdef OPENCV - if(!dont_show) - draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, -1, 0); + draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, -1, 0, dont_show, mjpeg_port); #endif // OPENCV if (i >= (iter_save + 100)) { @@ -1186,6 +1185,7 @@ void run_classifier(int argc, char **argv) return; } + int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1); char *gpu_list = find_char_arg(argc, argv, "-gpus", 0); int *gpus = 0; int gpu = 0; @@ -1221,7 +1221,7 @@ void run_classifier(int argc, char **argv) int layer = layer_s ? atoi(layer_s) : -1; if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename, top); else if(0==strcmp(argv[2], "try")) try_classifier(data, cfg, weights, filename, atoi(layer_s)); - else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear, dont_show); + else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear, dont_show, mjpeg_port); else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename); else if(0==strcmp(argv[2], "gun")) gun_classifier(data, cfg, weights, cam_index, filename); else if(0==strcmp(argv[2], "threat")) threat_classifier(data, cfg, weights, cam_index, filename); diff --git a/src/coco.c b/src/coco.c index 62f6770a132..aff8e5a0f2f 100644 --- a/src/coco.c +++ b/src/coco.c @@ -365,7 +365,7 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh) void run_coco(int argc, char **argv) { int dont_show = find_arg(argc, argv, "-dont_show"); - int http_stream_port = find_int_arg(argc, argv, "-http_port", -1); + int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1); int json_port = find_int_arg(argc, argv, "-json_port", -1); char *out_filename = find_char_arg(argc, argv, "-out_filename", 0); char *prefix = find_char_arg(argc, argv, "-prefix", 0); @@ -388,5 +388,5 @@ void run_coco(int argc, char **argv) else if(0==strcmp(argv[2], "valid")) validate_coco(cfg, weights); else if(0==strcmp(argv[2], "recall")) validate_coco_recall(cfg, weights); else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, hier_thresh, cam_index, filename, coco_classes, 80, frame_skip, - prefix, out_filename, http_stream_port, json_port, dont_show, ext_output); + prefix, out_filename, mjpeg_port, json_port, dont_show, ext_output); } diff --git a/src/demo.c b/src/demo.c index 10b908618a6..6de7ec43b85 100644 --- a/src/demo.c +++ b/src/demo.c @@ -139,7 +139,7 @@ double get_wall_time() } void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, - int frame_skip, char *prefix, char *out_filename, int http_stream_port, int json_port, int dont_show, int ext_output) + int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output) { in_img = det_img = show_img = NULL; //skip = frame_skip; @@ -279,10 +279,10 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int if(show_img) save_cv_jpg(show_img, buff); } - // if you run it with param -http_port 8090 then open URL in your web-browser: http://localhost:8090 - if (http_stream_port > 0 && show_img) { + // if you run it with param -mjpeg_port 8090 then open URL in your web-browser: http://localhost:8090 + if (mjpeg_port > 0 && show_img) { //int port = 8090; - int port = http_stream_port; + int port = mjpeg_port; int timeout = 200; int jpeg_quality = 30; // 1 - 100 send_mjpeg(show_img, port, timeout, jpeg_quality); @@ -364,7 +364,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int } #else void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, - int frame_skip, char *prefix, char *out_filename, int http_stream_port, int json_port, int dont_show, int ext_output) + int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output) { fprintf(stderr, "Demo needs OpenCV for webcam images.\n"); } diff --git a/src/demo.h b/src/demo.h index c6b5246e424..5db793464a6 100644 --- a/src/demo.h +++ b/src/demo.h @@ -3,6 +3,6 @@ #include "image.h" void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, - int frame_skip, char *prefix, char *out_filename, int http_stream_port, int json_port, int dont_show, int ext_output); + int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output); #endif diff --git a/src/detector.c b/src/detector.c index 455366e302c..5c4805c3845 100644 --- a/src/detector.c +++ b/src/detector.c @@ -26,8 +26,9 @@ #pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib") #endif -IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size); -void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, float precision, int draw_precision); +IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show); +void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, + float precision, int draw_precision, int dont_show, int mjpeg_port); #define CV_RGB(r, g, b) cvScalar( (b), (g), (r), 0 ) #endif // OPENCV @@ -38,7 +39,7 @@ int check_mistakes; static int coco_ids[] = { 1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90 }; -void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map) +void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port) { list *options = read_data_cfg(datacfg); char *train_images = option_find_str(options, "train", "data/train.txt"); @@ -158,8 +159,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i float max_img_loss = 5; int number_of_lines = 100; int img_size = 1000; - if (!dont_show) - img = draw_train_chart(max_img_loss, net.max_batches, number_of_lines, img_size); + img = draw_train_chart(max_img_loss, net.max_batches, number_of_lines, img_size, dont_show); #endif //OPENCV pthread_t load_thread = load_data(args); @@ -238,42 +238,42 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i avg_loss = avg_loss*.9 + loss*.1; i = get_current_batch(net); + if (mean_average_precision > 0) { + printf("\n Last accuracy mAP@0.5 = %2.2f %% ", mean_average_precision*100); + } if (net.cudnn_half) { if (i < net.burn_in * 3) printf("\n Tensor Cores are disabled until the first %d iterations are reached.", 3 * net.burn_in); else printf("\n Tensor Cores are used."); } printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now() - time), i*imgs); -#ifdef OPENCV - if (!dont_show) { - int draw_precision = 0; - int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions); // calculate mAP for each 4 Epochs - if (calc_map && (i >= (iter_map + calc_map_for_each) || i == net.max_batches) && i >= net.burn_in && i >= 1000) { - if (l.random) { - printf("Resizing to initial size: %d x %d \n", init_w, init_h); - args.w = init_w; - args.h = init_h; - pthread_join(load_thread, 0); - train = buffer; - load_thread = load_data(args); - int k; - for (k = 0; k < ngpus; ++k) { - resize_network(nets + k, init_w, init_h); - } - net = nets[0]; + int draw_precision = 0; + int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions); // calculate mAP for each 4 Epochs + if (calc_map && (i >= (iter_map + calc_map_for_each) || i == net.max_batches) && i >= net.burn_in && i >= 1000) { + if (l.random) { + printf("Resizing to initial size: %d x %d \n", init_w, init_h); + args.w = init_w; + args.h = init_h; + pthread_join(load_thread, 0); + train = buffer; + load_thread = load_data(args); + int k; + for (k = 0; k < ngpus; ++k) { + resize_network(nets + k, init_w, init_h); } - - // combine Training and Validation networks - network net_combined = combine_train_valid_networks(net, net_map); - - iter_map = i; - mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net_combined); - printf("\n mean_average_precision = %f \n", mean_average_precision); - draw_precision = 1; + net = nets[0]; } - draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, mean_average_precision, draw_precision); + // combine Training and Validation networks + network net_combined = combine_train_valid_networks(net, net_map); + + iter_map = i; + mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net_combined); + printf("\n mean_average_precision = %f \n", mean_average_precision); + draw_precision = 1; } +#ifdef OPENCV + draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, mean_average_precision, draw_precision, dont_show, mjpeg_port); #endif // OPENCV //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) { @@ -1378,7 +1378,7 @@ void run_detector(int argc, char **argv) int show = find_arg(argc, argv, "-show"); int calc_map = find_arg(argc, argv, "-map"); check_mistakes = find_arg(argc, argv, "-check_mistakes"); - int http_stream_port = find_int_arg(argc, argv, "-http_port", -1); + int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1); int json_port = find_int_arg(argc, argv, "-json_port", -1); char *out_filename = find_char_arg(argc, argv, "-out_filename", 0); char *outfile = find_char_arg(argc, argv, "-out", 0); @@ -1433,7 +1433,7 @@ void run_detector(int argc, char **argv) if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0; char *filename = (argc > 6) ? argv[6] : 0; if (0 == strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels, outfile); - else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map); + else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map, mjpeg_port); else if (0 == strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile); else if (0 == strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights); else if (0 == strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh, iou_thresh, NULL); @@ -1447,7 +1447,7 @@ void run_detector(int argc, char **argv) if (strlen(filename) > 0) if (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0; demo(cfg, weights, thresh, hier_thresh, cam_index, filename, names, classes, frame_skip, prefix, out_filename, - http_stream_port, json_port, dont_show, ext_output); + mjpeg_port, json_port, dont_show, ext_output); free_list_contents_kvp(options); free_list(options); diff --git a/src/image.c b/src/image.c index a4443cdd762..3f32e242338 100644 --- a/src/image.c +++ b/src/image.c @@ -708,7 +708,7 @@ void draw_detections_cv(IplImage* show_img, int num, float thresh, box *boxes, f } } -IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size) +IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show) { int img_offset = 50; int draw_size = img_size - img_offset; @@ -750,16 +750,19 @@ IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_li sprintf(max_batches_buff, "in cfg max_batches=%d", max_batches); cvPutText(img, max_batches_buff, cvPoint(draw_size - 195, img_size - 10), &font, CV_RGB(0, 0, 0)); cvPutText(img, "Press 's' to save: chart.png", cvPoint(5, img_size - 10), &font, CV_RGB(0, 0, 0)); - printf(" If error occurs - run training with flag: -dont_show \n"); - cvNamedWindow("average loss", CV_WINDOW_NORMAL); - cvMoveWindow("average loss", 0, 0); - cvResizeWindow("average loss", img_size, img_size); - cvShowImage("average loss", img); - cvWaitKey(20); + if (!dont_show) { + printf(" If error occurs - run training with flag: -dont_show \n"); + cvNamedWindow("average loss", CV_WINDOW_NORMAL); + cvMoveWindow("average loss", 0, 0); + cvResizeWindow("average loss", img_size, img_size); + cvShowImage("average loss", img); + cvWaitKey(20); + } return img; } -void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, float precision, int draw_precision) +void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, + float precision, int draw_precision, int dont_show, int mjpeg_port) { int img_offset = 50; int draw_size = img_size - img_offset; @@ -806,8 +809,11 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_ pt1.y += 15; cvPutText(img, char_buff, pt1, &font, CV_RGB(0, 0, 0)); - cvShowImage("average loss", img); - int k = cvWaitKey(20); + int k = 0; + if (!dont_show) { + cvShowImage("average loss", img); + k = cvWaitKey(20); + } if (k == 's' || current_batch == (max_batches - 1) || current_batch % 100 == 0) { //cvSaveImage("chart.jpg", img, 0); save_cv_png(img, "chart.png"); @@ -815,6 +821,8 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_ } else cvPutText(img, "- Saved", cvPoint(250, img_size - 10), &font, CV_RGB(255, 255, 255)); + + if (mjpeg_port) send_mjpeg(img, mjpeg_port, 2000, 80); } #endif // OPENCV diff --git a/src/yolo.c b/src/yolo.c index 916e5474343..56eb814e164 100644 --- a/src/yolo.c +++ b/src/yolo.c @@ -338,7 +338,7 @@ void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh) void run_yolo(int argc, char **argv) { int dont_show = find_arg(argc, argv, "-dont_show"); - int http_stream_port = find_int_arg(argc, argv, "-http_port", -1); + int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1); int json_port = find_int_arg(argc, argv, "-json_port", -1); char *out_filename = find_char_arg(argc, argv, "-out_filename", 0); char *prefix = find_char_arg(argc, argv, "-prefix", 0); @@ -360,5 +360,5 @@ void run_yolo(int argc, char **argv) else if(0==strcmp(argv[2], "valid")) validate_yolo(cfg, weights); else if(0==strcmp(argv[2], "recall")) validate_yolo_recall(cfg, weights); else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, hier_thresh, cam_index, filename, voc_names, 20, frame_skip, - prefix, out_filename, http_stream_port, json_port, dont_show, ext_output); + prefix, out_filename, mjpeg_port, json_port, dont_show, ext_output); } From a590609b1b44dc9407ca01ba2c60febf72dc6567 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 12 Jan 2019 13:50:51 +0300 Subject: [PATCH 0247/1365] Compile fix --- include/darknet.h | 2 +- src/network.h | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index c312be9ca67..1e2e59b0e2f 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -752,7 +752,7 @@ LIB_API detection *make_network_boxes(network *net, float thresh, int *num); LIB_API void reset_rnn(network *net); LIB_API float *network_predict_image(network *net, image im); LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, network *existing_net); -LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map); +LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port); LIB_API void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile); LIB_API int network_width(network *net); diff --git a/src/network.h b/src/network.h index a825a047eac..f71c56cb61c 100644 --- a/src/network.h +++ b/src/network.h @@ -150,7 +150,7 @@ float get_network_cost(network net); //LIB_API network *load_network(char *cfg, char *weights, int clear); //LIB_API float *network_predict_image(network *net, image im); //LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, network *existing_net); -//LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map); +//LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port); //LIB_API int network_width(network *net); //LIB_API int network_height(network *net); //LIB_API void optimize_picture(network *net, image orig, int max_layer, float scale, float rate, float thresh, int norm); From d2b5e142b3164cbe840286a385bdacc079dfc9f3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 14 Jan 2019 13:56:42 +0300 Subject: [PATCH 0248/1365] Minor fix --- include/yolo_v2_class.hpp | 56 +++++++++++++++++++-------------------- src/image.h | 12 ++++----- src/yolo_v2_class.cpp | 20 +++++++------- 3 files changed, 44 insertions(+), 44 deletions(-) diff --git a/include/yolo_v2_class.hpp b/include/yolo_v2_class.hpp index 74e1c116bca..b6bea64a391 100644 --- a/include/yolo_v2_class.hpp +++ b/include/yolo_v2_class.hpp @@ -1,15 +1,15 @@ #pragma once #ifdef LIB_EXPORTS #if defined(_MSC_VER) -#define LIB_EXPORTS __declspec(dllexport) +#define LIB_API __declspec(dllexport) #else -#define LIB_EXPORTS __attribute__((visibility("default"))) +#define LIB_API __attribute__((visibility("default"))) #endif #else #if defined(_MSC_VER) -#define LIB_EXPORTS __declspec(dllimport) +#define LIB_API #else -#define LIB_EXPORTS +#define LIB_API #endif #endif @@ -45,12 +45,12 @@ struct bbox_t_container { #include "opencv2/imgproc/imgproc_c.h" // C #endif // OPENCV -extern "C" LIB_EXPORTS int init(const char *configurationFilename, const char *weightsFilename, int gpu); -extern "C" LIB_EXPORTS int detect_image(const char *filename, bbox_t_container &container); -extern "C" LIB_EXPORTS int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container &container); -extern "C" LIB_EXPORTS int dispose(); -extern "C" LIB_EXPORTS int get_device_count(); -extern "C" LIB_EXPORTS int get_device_name(int gpu, char* deviceName); +extern "C" LIB_API int init(const char *configurationFilename, const char *weightsFilename, int gpu); +extern "C" LIB_API int detect_image(const char *filename, bbox_t_container &container); +extern "C" LIB_API int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container &container); +extern "C" LIB_API int dispose(); +extern "C" LIB_API int get_device_count(); +extern "C" LIB_API int get_device_name(int gpu, char* deviceName); class Detector { std::shared_ptr detector_gpu_ptr; @@ -60,18 +60,18 @@ class Detector { float nms = .4; bool wait_stream; - LIB_EXPORTS Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0); - LIB_EXPORTS ~Detector(); + LIB_API Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0); + LIB_API ~Detector(); - LIB_EXPORTS std::vector detect(std::string image_filename, float thresh = 0.2, bool use_mean = false); - LIB_EXPORTS std::vector detect(image_t img, float thresh = 0.2, bool use_mean = false); - static LIB_EXPORTS image_t load_image(std::string image_filename); - static LIB_EXPORTS void free_image(image_t m); - LIB_EXPORTS int get_net_width() const; - LIB_EXPORTS int get_net_height() const; - LIB_EXPORTS int get_net_color_depth() const; + LIB_API std::vector detect(std::string image_filename, float thresh = 0.2, bool use_mean = false); + LIB_API std::vector detect(image_t img, float thresh = 0.2, bool use_mean = false); + static LIB_API image_t load_image(std::string image_filename); + static LIB_API void free_image(image_t m); + LIB_API int get_net_width() const; + LIB_API int get_net_height() const; + LIB_API int get_net_color_depth() const; - LIB_EXPORTS std::vector tracking_id(std::vector cur_bbox_vec, bool const change_history = true, + LIB_API std::vector tracking_id(std::vector cur_bbox_vec, bool const change_history = true, int const frames_story = 10, int const max_dist = 150); std::vector detect_resized(image_t img, int init_w, int init_h, float thresh = 0.2, bool use_mean = false) @@ -613,13 +613,13 @@ class preview_boxes_t { /* // C - wrappers - LIB_EXPORTS void create_detector(char const* cfg_filename, char const* weight_filename, int gpu_id); - LIB_EXPORTS void delete_detector(); - LIB_EXPORTS bbox_t* detect_custom(image_t img, float thresh, bool use_mean, int *result_size); - LIB_EXPORTS bbox_t* detect_resized(image_t img, int init_w, int init_h, float thresh, bool use_mean, int *result_size); - LIB_EXPORTS bbox_t* detect(image_t img, int *result_size); - LIB_EXPORTS image_t load_img(char *image_filename); - LIB_EXPORTS void free_img(image_t m); + LIB_API void create_detector(char const* cfg_filename, char const* weight_filename, int gpu_id); + LIB_API void delete_detector(); + LIB_API bbox_t* detect_custom(image_t img, float thresh, bool use_mean, int *result_size); + LIB_API bbox_t* detect_resized(image_t img, int init_w, int init_h, float thresh, bool use_mean, int *result_size); + LIB_API bbox_t* detect(image_t img, int *result_size); + LIB_API image_t load_img(char *image_filename); + LIB_API void free_img(image_t m); #ifdef __cplusplus } // extern "C" @@ -628,7 +628,7 @@ static std::shared_ptr c_detector_ptr; static std::vector c_result_vec; void create_detector(char const* cfg_filename, char const* weight_filename, int gpu_id) { - c_detector_ptr = std::make_shared(cfg_filename, weight_filename, gpu_id); + c_detector_ptr = std::make_shared(cfg_filename, weight_filename, gpu_id); } void delete_detector() { c_detector_ptr.reset(); } diff --git a/src/image.h b/src/image.h index 15731333b05..6774396440e 100644 --- a/src/image.h +++ b/src/image.h @@ -32,10 +32,10 @@ image crop_image(image im, int dx, int dy, int w, int h); image random_crop_image(image im, int w, int h); image random_augment_image(image im, float angle, float aspect, int low, int high, int size); void random_distort_image(image im, float hue, float saturation, float exposure); -//LIB_EXPORTS image resize_image(image im, int w, int h); +//LIB_API image resize_image(image im, int w, int h); void fill_image(image m, float s); void letterbox_image_into(image im, int w, int h, image boxed); -//LIB_EXPORTS image letterbox_image(image im, int w, int h); +//LIB_API image letterbox_image(image im, int w, int h); image resize_min(image im, int min); image resize_max(image im, int max); void translate_image(image m, float s); @@ -48,7 +48,7 @@ void exposure_image(image im, float sat); void distort_image(image im, float hue, float sat, float val); void saturate_exposure_image(image im, float sat, float exposure); void hsv_to_rgb(image im); -//LIB_EXPORTS void rgbgr_image(image im); +//LIB_API void rgbgr_image(image im); void constrain_image(image im); void composite_3d(char *f1, char *f2, char *out, int delta); int best_3d_shift_r(image a, image b, int min, int max); @@ -70,13 +70,13 @@ void show_image_collapsed(image p, char *name); void print_image(image m); -//LIB_EXPORTS image make_image(int w, int h, int c); +//LIB_API image make_image(int w, int h, int c); image make_random_image(int w, int h, int c); image make_empty_image(int w, int h, int c); image float_to_image(int w, int h, int c, float *data); image copy_image(image p); image load_image(char *filename, int w, int h, int c); -//LIB_EXPORTS image load_image_color(char *filename, int w, int h); +//LIB_API image load_image_color(char *filename, int w, int h); image **load_alphabet(); //float get_pixel(image m, int x, int y, int c); @@ -87,7 +87,7 @@ float bilinear_interpolate(image im, float x, float y, int c); image get_image_layer(image m, int l); -//LIB_EXPORTS void free_image(image m); +//LIB_API void free_image(image m); void test_resize(char *filename); #endif diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index a8710600992..388f2eed5a3 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -100,7 +100,7 @@ struct detector_gpu_t { unsigned int *track_id; }; -LIB_EXPORTS Detector::Detector(std::string cfg_filename, std::string weight_filename, int gpu_id) : cur_gpu_id(gpu_id) +LIB_API Detector::Detector(std::string cfg_filename, std::string weight_filename, int gpu_id) : cur_gpu_id(gpu_id) { wait_stream = 0; int old_gpu_index; @@ -147,7 +147,7 @@ LIB_EXPORTS Detector::Detector(std::string cfg_filename, std::string weight_file } -LIB_EXPORTS Detector::~Detector() +LIB_API Detector::~Detector() { detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); layer l = detector_gpu.net.layers[detector_gpu.net.n - 1]; @@ -171,21 +171,21 @@ LIB_EXPORTS Detector::~Detector() #endif } -LIB_EXPORTS int Detector::get_net_width() const { +LIB_API int Detector::get_net_width() const { detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); return detector_gpu.net.w; } -LIB_EXPORTS int Detector::get_net_height() const { +LIB_API int Detector::get_net_height() const { detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); return detector_gpu.net.h; } -LIB_EXPORTS int Detector::get_net_color_depth() const { +LIB_API int Detector::get_net_color_depth() const { detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); return detector_gpu.net.c; } -LIB_EXPORTS std::vector Detector::detect(std::string image_filename, float thresh, bool use_mean) +LIB_API std::vector Detector::detect(std::string image_filename, float thresh, bool use_mean) { std::shared_ptr image_ptr(new image_t, [](image_t *img) { if (img->data) free(img->data); delete img; }); *image_ptr = load_image(image_filename); @@ -214,7 +214,7 @@ static image load_image_stb(char *filename, int channels) return im; } -LIB_EXPORTS image_t Detector::load_image(std::string image_filename) +LIB_API image_t Detector::load_image(std::string image_filename) { char *input = const_cast(image_filename.data()); image im = load_image_stb(input, 3); @@ -229,14 +229,14 @@ LIB_EXPORTS image_t Detector::load_image(std::string image_filename) } -LIB_EXPORTS void Detector::free_image(image_t m) +LIB_API void Detector::free_image(image_t m) { if (m.data) { free(m.data); } } -LIB_EXPORTS std::vector Detector::detect(image_t img, float thresh, bool use_mean) +LIB_API std::vector Detector::detect(image_t img, float thresh, bool use_mean) { detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); network &net = detector_gpu.net; @@ -322,7 +322,7 @@ LIB_EXPORTS std::vector Detector::detect(image_t img, float thresh, bool return bbox_vec; } -LIB_EXPORTS std::vector Detector::tracking_id(std::vector cur_bbox_vec, bool const change_history, +LIB_API std::vector Detector::tracking_id(std::vector cur_bbox_vec, bool const change_history, int const frames_story, int const max_dist) { detector_gpu_t &det_gpu = *static_cast(detector_gpu_ptr.get()); From 83620d8b28b31b204874d3fb1a1299aff1bd7e5a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 14 Jan 2019 14:42:48 +0300 Subject: [PATCH 0249/1365] Training minor fix --- src/image.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/image.c b/src/image.c index 3f32e242338..9401ebe02c7 100644 --- a/src/image.c +++ b/src/image.c @@ -822,7 +822,7 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_ else cvPutText(img, "- Saved", cvPoint(250, img_size - 10), &font, CV_RGB(255, 255, 255)); - if (mjpeg_port) send_mjpeg(img, mjpeg_port, 2000, 80); + if (mjpeg_port > 0) send_mjpeg(img, mjpeg_port, 2000, 80); } #endif // OPENCV From d9e559a245829830dec03c6d3b909857c6d7937f Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 14 Jan 2019 17:10:42 +0300 Subject: [PATCH 0250/1365] Update Readme.md --- README.md | 43 ++++++++++++++++++++++++++++--------------- 1 file changed, 28 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index 9e918ac573d..f5fc13e6c38 100644 --- a/README.md +++ b/README.md @@ -110,21 +110,24 @@ Also, you might be interested in using a simplified repository where is implemen On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights` * Yolo v3 COCO - **image**: `darknet.exe detector test data/coco.data cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` -* Output coordinates of objects: `darknet.exe detector test data/coco.data yolov3.cfg yolov3.weights -ext_output dog.jpg` +* **Output coordinates** of objects: `darknet.exe detector test data/coco.data yolov3.cfg yolov3.weights -ext_output dog.jpg` * Yolo v3 COCO - **video**: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -ext_output test.mp4` * Yolo v3 COCO - **WebCam 0**: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -c 0` * Yolo v3 COCO for **net-videocam** - Smart WebCam: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg` -* **Yolo v3 - save result to the file res.avi**: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -thresh 0.25 test.mp4 -out_filename res.avi` -* **Yolo v3 Tiny** COCO - video: `darknet.exe detector demo data/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4` -* **JSON and MJPEG server** that allows multiple connections from your soft or Web-browser `ip-address:8070` or 8090: `./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output` -* **Yolo v3 Tiny** on GPU #0: `darknet.exe detector demo data/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 0 test.mp4` +* Yolo v3 - **save result videofile res.avi**: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -thresh 0.25 test.mp4 -out_filename res.avi` +* Yolo v3 **Tiny** COCO - video: `darknet.exe detector demo data/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4` +* **JSON and MJPEG server** that allows multiple connections from your soft or Web-browser `ip-address:8070` and 8090: `./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output` +* Yolo v3 Tiny **on GPU #0**: `darknet.exe detector demo data/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 0 test.mp4` * Alternative method Yolo v3 COCO - image: `darknet.exe detect cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` +* Train on **Amazon EC2**, to see mAP & Loss-chart using URL like: `http://ec2-35-160-228-91.us-west-2.compute.amazonaws.com:8090` in the Chrome/Firefox: + `./darknet detector train cfg/coco.data yolov3.cfg darknet53.conv.74 -dont_show -mjpeg_port 8090 -map` * 186 MB Yolo9000 - image: `darknet.exe detector test cfg/combine9k.data yolo9000.cfg yolo9000.weights` * Remeber to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app * To process a list of images `data/train.txt` and save results of detection to `result.txt` use: `darknet.exe detector test cfg/coco.data yolov3.cfg yolov3.weights -dont_show -ext_output < data/train.txt > result.txt` * To calculate anchors: `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416` -* To check accuracy mAP50: `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights` +* To check accuracy mAP@IoU=50: `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights` +* To check accuracy mAP@IoU=75: `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights -iou_thresh 0.75` ##### For using network video-camera mjpeg-stream with any Android smartphone: @@ -488,7 +491,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * `train_network_width * train_obj_width / train_image_width ~= detection_network_width * detection_obj_width / detection_image_width` * `train_network_height * train_obj_height / train_image_height ~= detection_network_height * detection_obj_height / detection_image_height` - I.e. for each object from Test dataset there must be at least 1 object in the Training dataset with about the same relative size: + I.e. for each object from Test dataset there must be at least 1 object in the Training dataset with the same class_id and about the same relative size: `object width in percent from Training dataset` ~= `object width in percent from Test dataset` @@ -498,6 +501,8 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol then do this command: `./darknet partial cfg/yolov3.cfg yolov3.weights yolov3.conv.81 81` will be created file `yolov3.conv.81`, then train by using weights file `yolov3.conv.81` instead of `darknet53.conv.74` + * The more different objects you want to detect, the more complex network model should be used. But each: `model of object, side, illimination, scale, each 30 grad` of the turn and inclination angles - these are different objects from a neural network perspective. + 2. After training - for detection: @@ -545,7 +550,6 @@ With example of: `train.txt`, `obj.names`, `obj.data`, `yolo-obj.cfg`, `air`1-6` * you can run your console application from Windows Explorer `build\darknet\x64\yolo_console_dll.exe` **use this command**: `yolo_console_dll.exe data/coco.names yolov3.cfg yolov3.weights test.mp4` - * or you can run from MSVS2015 (before this - you should copy 2 files `yolo-voc.cfg` and `yolo-voc.weights` to the directory `build\darknet\` ) * after launching your console application and entering the image file name - you will see info for each object: ` ` * to use simple OpenCV-GUI you should uncomment line `//#define OPENCV` in `yolo_console_dll.cpp`-file: [link](https://github.com/AlexeyAB/darknet/blob/a6cbaeecde40f91ddc3ea09aa26a03ab5bbf8ba8/src/yolo_console_dll.cpp#L5) @@ -553,18 +557,27 @@ With example of: `train.txt`, `obj.names`, `obj.data`, `yolo-obj.cfg`, `air`1-6` `yolo_cpp_dll.dll`-API: [link](https://github.com/AlexeyAB/darknet/blob/master/src/yolo_v2_class.hpp#L42) ``` +struct bbox_t { + unsigned int x, y, w, h; // (x,y) - top-left corner, (w, h) - width & height of bounded box + float prob; // confidence - probability that the object was found correctly + unsigned int obj_id; // class of object - from range [0, classes-1] + unsigned int track_id; // tracking id for video (0 - untracked, 1 - inf - tracked object) + unsigned int frames_counter;// counter of frames on which the object was detected +}; + class Detector { public: - Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0); - ~Detector(); + Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0); + ~Detector(); - std::vector detect(std::string image_filename, float thresh = 0.2, bool use_mean = false); - std::vector detect(image_t img, float thresh = 0.2, bool use_mean = false); - static image_t load_image(std::string image_filename); - static void free_image(image_t m); + std::vector detect(std::string image_filename, float thresh = 0.2, bool use_mean = false); + std::vector detect(image_t img, float thresh = 0.2, bool use_mean = false); + static image_t load_image(std::string image_filename); + static void free_image(image_t m); #ifdef OPENCV - std::vector detect(cv::Mat mat, float thresh = 0.2, bool use_mean = false); + std::vector detect(cv::Mat mat, float thresh = 0.2, bool use_mean = false); + std::shared_ptr mat_to_image_resize(cv::Mat mat) const; #endif }; ``` From 920d792a0c34c70c722caf1db23792e81e51af5b Mon Sep 17 00:00:00 2001 From: Alexey Date: Tue, 15 Jan 2019 13:51:07 +0300 Subject: [PATCH 0251/1365] Update Readme.md --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index f5fc13e6c38..6ce2bb7caca 100644 --- a/README.md +++ b/README.md @@ -170,10 +170,12 @@ To run Darknet on Linux use examples from this article, just use `./darknet` ins 1.3. To install CUDNN (speedup neural network), do the following: - * download and install **cuDNN v7.4.1 for CUDA 10.0**: https://developer.nvidia.com/cudnn + * download and install **cuDNN v7.4.1 for CUDA 10.0**: https://developer.nvidia.com/rdp/cudnn-archive * add Windows system variable `cudnn` with path to CUDNN: https://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg + * copy file `cudnn64_7.dll` to the folder `\build\darknet\x64` near with `darknet.exe` + 1.4. If you want to build **without CUDNN** then: open `\darknet.sln` -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and remove this: `CUDNN;` 2. If you have other version of **CUDA (not 10.0)** then open `build\darknet\darknet.vcxproj` by using Notepad, find 2 places with "CUDA 10.0" and change it to your CUDA-version, then do step 1 From 4c051662151401248dd2f5644eb579b6ee1021a3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 16 Jan 2019 02:43:44 +0300 Subject: [PATCH 0252/1365] Temporary experimental XNOR on GPU (repack channels) --- src/convolutional_kernels.cu | 134 +++++++++++++++++- src/convolutional_layer.c | 5 +- src/gemm.c | 1 + src/http_stream.cpp | 3 +- src/im2col.h | 11 ++ src/im2col_kernels.cu | 259 ++++++++++++++++++++++++++++++++++- 6 files changed, 406 insertions(+), 7 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index d498effd539..0014f32a239 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -190,7 +190,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //state.input = l.binary_input_gpu; //cudaDeviceSynchronize(); - if (l.align_bit_weights_gpu && !state.train && l.c >= 256 && l.size > 1) + if (l.align_bit_weights_gpu && !state.train && l.c >= 64)// && l.size > 1) { //return; cudaError_t status = cudaSuccess; @@ -207,10 +207,135 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) size_t t_bit_input_size = t_intput_size / 8;// +1; //if(0) + if (l.c % 32 == 0) + //if (l.stride == 1 && l.pad == 1 && l.c % 32 == 0) + //if(1) { + //printf("\n\n l.index = %d, l.w = %d, l.c = %d, l.n = %d, l.stride = %d, l.pad = %d - new XNOR \n", l.index, l.w, l.c, l.n, l.stride, l.pad); + //printf("l.align_workspace_size = %d, (l.c * l.w * l.h) = %d \n", l.align_workspace_size, (l.c * l.w * l.h)); + + //float *intput_cpu = (float *)calloc(l.inputs, sizeof(float)); + // state.input + //cudaMemcpy(intput_cpu, state.input, l.inputs * sizeof(float), cudaMemcpyDefault); + + int ldb_align = l.lda_align; + size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; + size_t t_intput_size = new_ldb * l.bit_align;// n; + size_t t_bit_input_size = t_intput_size / 8;// +1; + + const int new_c = l.c / 32; + + //float *re_packed_input = (float *)calloc(l.c * l.w * l.h, sizeof(float)); + //uint32_t *bin_re_packed_input = (uint32_t *)calloc(new_c * l.w * l.h + 1, sizeof(uint32_t)); + + // float32x4 by channel (as in cuDNN) + //repack_input(intput_cpu, re_packed_input, l.w, l.h, l.c); + + + // 32 x floats -> 1 x uint32_t + //float_to_bit(re_packed_input, (uint8_t *)bin_re_packed_input, l.c * l.w * l.h); + + //cudaDeviceSynchronize(); + //start_timer(); + + repack_input_gpu_bin(state.input, (uint32_t *)l.align_workspace_gpu, l.w, l.h, l.c); + + //repack_input_gpu(state.input, state.workspace, l.w, l.h, l.c); + + // 32 x floats -> 1 x uint32_t + //float_to_bit_gpu(state.workspace, (unsigned char *)l.align_workspace_gpu, l.c * l.w * l.h);// l.align_workspace_size); + + //cudaDeviceSynchronize(); + //stop_timer_and_show_name("repack_input_gpu + float_to_bit_gpu"); + + //free(re_packed_input); + + // slow - convolution the packed inputs and weights: float x 32 by channel (as in cuDNN) + //convolution_repacked((uint32_t *)bin_re_packed_input, (uint32_t *)l.align_bit_weights, l.output, + // l.w, l.h, l.c, l.n, l.size, l.pad, l.new_lda, l.mean_arr); + + // // then exit from if() + + //float *b = state.workspace; + //float *b = (float *)calloc(100 * 1024 * 1024, sizeof(float)); + //float *c = l.output; + //memset(c, 0, l.outputs * sizeof(float)); + + + //im2col_cpu_custom((float *)bin_re_packed_input, new_c, l.h, l.w, l.size, l.stride, l.pad, b); + + //cudaMemcpy(l.align_workspace_gpu, bin_re_packed_input, (new_c * l.w * l.h + 1) * sizeof(uint32_t), cudaMemcpyDefault); + + //start_timer(); + im2col_ongpu(l.align_workspace_gpu, new_c, l.h, l.w, l.size, l.stride, l.pad, state.workspace); + //cudaDeviceSynchronize(); + //stop_timer_and_show_name("im2col_ongpu"); + + //free(bin_re_packed_input); + + int new_k = l.size*l.size*l.c / 32; + + // good for (l.c == 64) + //gemm_nn_bin_32bit_packed(m, n, new_k, 1, + // l.align_bit_weights, l.new_lda/32, + // b, n, + // c, n, l.mean_arr); + + // // then exit from if() + + + //size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; + //size_t t_intput_size = new_ldb * l.bit_align;// n; + //size_t t_bit_input_size = t_intput_size / 8;// +1; + + //char *t_bit_input = (char *)calloc(t_bit_input_size, sizeof(char)); + //transpose_uint32((uint32_t *)b, (uint32_t *)t_bit_input, new_k, n, n, new_ldb); + //cudaMemcpy(l.transposed_align_workspace_gpu, t_bit_input, t_bit_input_size * sizeof(char), cudaMemcpyDefault); + + //cudaMemcpy(state.workspace, b, t_bit_input_size * sizeof(char), cudaMemcpyDefault); + //printf("\n n = %d, n % 32 = %d, new_ldb = %d, new_ldb % 32 = %d \n", n, n % 32, new_ldb, new_ldb % 32); + + //start_timer(); + transpose_uint32_gpu_2((uint32_t *)state.workspace, (uint32_t *)l.transposed_align_workspace_gpu, new_k, n, n, new_ldb); + //cudaDeviceSynchronize(); + //stop_timer_and_show_name("transpose_uint32_gpu"); + + //cudaDeviceSynchronize(); + //stop_timer_and_show_name("repack_input_gpu_bin + im2col_ongpu + transpose_uint32_gpu_2"); + + //start_timer(); + gemm_nn_custom_bin_mean_transposed_gpu(m, n, k, + (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, + new_ldb, l.output_gpu, n, l.mean_arr_gpu, l.biases_gpu); + //cudaDeviceSynchronize(); + //stop_timer_and_show_name("gemm_nn_custom_bin_mean_transposed_gpu"); + + + // the main GEMM function + //gemm_nn_custom_bin_mean_transposed(m, n, k, 1, (uint8_t *)l.align_bit_weights, new_ldb, (uint8_t *)t_bit_input, new_ldb, c, n, l.mean_arr); + + //add_bias(l.output, l.biases, l.batch, l.n, l.out_h*l.out_w); + + //cudaMemcpy(l.output_gpu, l.output, l.outputs * sizeof(float), cudaMemcpyDefault); + + + // // alternative GEMM + //gemm_nn_bin_transposed_32bit_packed(m, n, new_k, 1, + // l.align_bit_weights, l.new_lda/32, + // t_bit_input, new_ldb / 32, + // c, n, l.mean_arr); + + //free(t_bit_input); + + //free(b); + } + else + { + //printf("\n\n l.index = %d, l.w = %d, l.c = %d, l.n = %d, l.stride = %d, l.pad = %d - old XNOR \n", l.index, l.w, l.c, l.n, l.stride, l.pad); //cudaDeviceSynchronize(); int i = 0; + /* // if (l.stride == 1 && l.c >= 256 && l.size > 1) if (l.stride == 1 && l.c >= 1024 && l.size > 1 && 0)// && l.w >= 13) // disabled { @@ -220,9 +345,9 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //cudaDeviceSynchronize(); //stop_timer_and_show_name("im2col_align_bin_ongpu"); } - else + else*/ { - //start_timer(); + start_timer(); im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.align_workspace_gpu, l.bit_align); //cudaDeviceSynchronize(); //stop_timer_and_show_name("im2col_align_ongpu"); @@ -239,6 +364,9 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //cudaDeviceSynchronize(); //stop_timer_and_show_name("transpose_bin_gpu"); + //cudaDeviceSynchronize(); + //stop_timer_and_show_name("im2col_align_ongpu + float_to_bit_gpu + transpose_bin_gpu"); + // should be optimized //if(0) {//if (k > 1000) { // sequentially input-shared - BAD // gemm_nn_custom_bin_mean_transposed_sequentially_gpu(m, n, k, diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 25528d481db..124514a2075 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -704,8 +704,9 @@ void binary_align_weights(convolutional_layer *l) } - //if (l->c % 32 == 0) - if(gpu_index < 0 && l->stride == 1 && l->pad == 1 && l->c % 32 == 0) + if (l->c % 32 == 0) + //if(gpu_index < 0 && l->stride == 1 && l->pad == 1 && l->c % 32 == 0) + //if (l->stride == 1 && l->pad == 1 && l->c % 32 == 0) { int fil, chan; const int items_per_filter = l->c * l->size * l->size; diff --git a/src/gemm.c b/src/gemm.c index 0238fd2185c..16efa0654b5 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -755,6 +755,7 @@ void gemm_nn_bin_32bit_packed(int M, int N, int K, float ALPHA, __m256i all_1 = _mm256_set1_epi8(255); __m256i xnor256 = _mm256_andnot_si256(xor256, all_1); // xnor = not(xor(a,b)) + // waiting for - CPUID Flags: AVX512VPOPCNTDQ: __m512i _mm512_popcnt_epi32(__m512i a) __m256 count = _mm256_setr_ps( popcnt_32(_mm256_extract_epi32(xnor256, 0)), popcnt_32(_mm256_extract_epi32(xnor256, 1)), diff --git a/src/http_stream.cpp b/src/http_stream.cpp index ebe9664b577..408fae97c8d 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -631,8 +631,9 @@ void stop_timer_and_show() { } void stop_timer_and_show_name(char *name) { + stop_timer(); std::cout << " " << name; - stop_timer_and_show(); + std::cout << " " << get_time() * 1000 << " msec" << std::endl; } void show_total_time() { diff --git a/src/im2col.h b/src/im2col.h index 9a6be9ab0c1..e7eb958acf7 100644 --- a/src/im2col.h +++ b/src/im2col.h @@ -2,6 +2,7 @@ #define IM2COL_H #include +#include void im2col_cpu(float* data_im, int channels, int height, int width, @@ -26,6 +27,16 @@ void float_to_bit_gpu(float *src, unsigned char *dst, size_t size); void transpose_bin_gpu(unsigned char *A, unsigned char *B, const int n, const int m, const int lda, const int ldb, const int block_size); +void transpose_uint32_gpu(uint32_t *src, uint32_t *dst, int src_h, int src_w, int src_align, int dst_align); + +void transpose_uint32_gpu_2(uint32_t *src, uint32_t *dst, int src_h, int src_w, int src_align, int dst_align); + +void repack_input_gpu(float *input, float *re_packed_input, int w, int h, int c); + +void repack_input_gpu_2(float *input, float *re_packed_input, int w, int h, int c); + +void repack_input_gpu_bin(float *input, uint32_t *re_packed_input_bin, int w, int h, int c); + void fill_int8_gpu(unsigned char *src, unsigned char val, size_t size); // shared_memory + partial coalescing = GOOD diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 1149b68b49f..e85811bbeb6 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -571,6 +571,7 @@ __global__ void float_to_bit_gpu_kernel(float *src, unsigned char *dst, size_t s } */ +/* __global__ void float_to_bit_gpu_kernel(float *src, unsigned char *dst, size_t size) { //const int size_aligned = size + (WARP_SIZE - size % WARP_SIZE); @@ -591,6 +592,7 @@ __global__ void float_to_bit_gpu_kernel(float *src, unsigned char *dst, size_t s const int lane_id = threadIdx.x % WARP_SIZE; uint32_t bit_mask = __ballot(src_val > 0); + if (lane_id == 0) tmp[warp_id] = bit_mask; __syncthreads(); @@ -602,11 +604,38 @@ __global__ void float_to_bit_gpu_kernel(float *src, unsigned char *dst, size_t s __syncthreads(); } } +*/ + +__global__ void float_to_bit_gpu_kernel(float *src, unsigned char *dst, size_t size) +{ + __shared__ uint32_t tmp[WARP_SIZE*32]; + + int index = 32*blockIdx.x*blockDim.x + threadIdx.x; + float src_val; + uint32_t *dst32_ptr = ((unsigned int*)dst); + + int i; + for(i = 0; i < 32; ++i) + { + if ((index + i * 1024) < size) src_val = src[index + i*1024]; + else src_val = 0; + //unsigned int bit_mask = __ballot_sync(0xffffffff, src_val > 0); + const int num_of_warps = blockDim.x / WARP_SIZE; + const int warp_id = threadIdx.x / WARP_SIZE; + const int lane_id = threadIdx.x % WARP_SIZE; + + uint32_t bit_mask = __ballot(src_val > 0); + if (lane_id == 0) tmp[i * 32 + warp_id] = bit_mask; + } + __syncthreads(); + dst32_ptr[blockIdx.x*blockDim.x + threadIdx.x] = tmp[threadIdx.x]; +} void float_to_bit_gpu(float *src, unsigned char *dst, size_t size) { - const int num_blocks = size / 1024 + 1; + //const int num_blocks = size / 1024 + 1; + const int num_blocks = size / (32*1024) + 1; float_to_bit_gpu_kernel<<>>(src, dst, size); } // -------------------------------- @@ -828,6 +857,234 @@ void transpose_bin_gpu(unsigned char *A, unsigned char *B, const int n, const in } // -------------------------------- +__global__ void transpose_uint32_kernel(uint32_t *src, uint32_t *dst, int src_h, int src_w, int src_align, int dst_align) +{ + //l.bit_align - algined (n) by 32 + //new_ldb - aligned (k) by 256 + int index = blockIdx.x*blockDim.x + threadIdx.x; + + //for (i = 0; i < src_h; i += 1) + int i = index % src_h; // l.size*l.size*l.c; + { + //for (j = 0; j < src_w; j += 1) + int j = index / src_h; // out_h*out_w; + if(j < src_w) + { + ((uint32_t *)dst)[j*dst_align / 32 + i] = ((uint32_t *)src)[i*src_align + j]; + } + } +} + +void transpose_uint32_gpu(uint32_t *src, uint32_t *dst, int src_h, int src_w, int src_align, int dst_align) +{ + int size = src_w * src_h; + const int num_blocks = size / BLOCK + 1; + transpose_uint32_kernel << > >(src, dst, src_h, src_w, src_align, dst_align); +} +// -------------------------------- + +//#define TRANS_LOOP 10 + +__global__ void transpose_uint32_kernel_2(uint32_t *src, uint32_t *dst, int src_h, int src_w, int src_align, int dst_align) +{ + __shared__ uint32_t tmp[33 * 32]; // misaligned_array[32x32] + const int w_align = 33; + //const int shared_size = w_align * 32; + + //l.bit_align - algined (n) by 32 + //new_ldb - aligned (k) by 256 + + const int src_w_align = src_w + (32 - src_w % 32); + const int src_h_align = src_h + (32 - src_h % 32); + + const int warps_in_width = src_w_align / 32; + const int warps_in_height = src_h_align / 32; + + + + const int local_x = threadIdx.x % 32; // index % 32; + const int local_x_index = threadIdx.x / 32; // index / 32; + const int local_y = local_x_index % 32; + +//#pragma unroll TRANS_LOOP + //for (int i = 0; i < TRANS_LOOP; ++i) + { + const int global_index = blockIdx.x;// blockIdx.x*TRANS_LOOP + i;// local_x_index / 32; + const int global_x_index = global_index % warps_in_width; + const int global_y_index = global_index / warps_in_width; + + const int global_x = global_x_index * 32 + local_x; + const int global_y = global_y_index * 32 + local_y; + + uint32_t val = 0; + if (global_x < src_w && global_y < src_h) { + val = src[global_y * src_align + global_x]; + } + //dst[global_x * dst_align / 32 + global_y] = val; + //tmp[local_y * 32 + local_x] = val; + + tmp[local_x * w_align + local_y] = val; + __syncthreads(); + val = tmp[local_y * w_align + local_x]; + + const int new_global_x = global_y_index * 32 + local_x; + const int new_global_y = global_x_index * 32 + local_y; + + if (new_global_x < src_h && new_global_y < src_w) { + dst[new_global_y * (dst_align / 32) + new_global_x] = val; + } + } +} + +#define TRANS_BLOCK 1024 +void transpose_uint32_gpu_2(uint32_t *src, uint32_t *dst, int src_h, int src_w, int src_align, int dst_align) +{ + int src_w_align = src_w + (32 - src_w % 32); + int src_h_align = src_h + (32 - src_h % 32); + + int size = src_w_align * src_h_align; + int num_blocks = size / TRANS_BLOCK; + transpose_uint32_kernel_2 << > >(src, dst, src_h, src_w, src_align, dst_align); +} +// -------------------------------- + + +// 32 channels -> 1 channel (with 32 floats) +// 256 channels -> 8 channels (with 32 floats) +__global__ void repack_input_kernel(float *input, float *re_packed_input, int w, int h, int c) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + + const int items_per_channel = w * h; + + int c_pack = index % 32; + int chan_index = index / 32; + int chan = (chan_index * 32) % c; + int i = (chan_index * 32) / c; + + //for (chan = 0; chan < c; chan += 32) + { + //for (i = 0; i < items_per_channel; ++i) + if(i < items_per_channel) + { + //for (c_pack = 0; c_pack < 32; ++c_pack) + { + float src = input[(chan + c_pack)*items_per_channel + i]; + + re_packed_input[chan*items_per_channel + i * 32 + c_pack] = src; + } + } + } +} + +void repack_input_gpu(float *input, float *re_packed_input, int w, int h, int c) +{ + int size = w * h * c; + const int num_blocks = size / BLOCK + 1; + repack_input_kernel << > >(input, re_packed_input, w, h, c); +} +// -------------------------------- + + +// 32 channels -> 1 channel (with 32 floats) +// 256 channels -> 8 channels (with 32 floats) +__global__ void repack_input_kernel_2(float *input, float *re_packed_input, int w, int h, int c) +{ + __shared__ uint32_t tmp[33 * 32]; // 33x32 is misaligned 32 x 32 to avoid bank conflicts + + int index = blockIdx.x*blockDim.x + threadIdx.x; + + const int items_per_channel = w * h; + + int c_pack = index % 32; + int chan_index = index / 32; + int chan = (chan_index * 32) % c; + int i = (chan_index * 32) / c; + + //for (chan = 0; chan < c; chan += 32) + { + //for (i = 0; i < items_per_channel; ++i) + if (i < items_per_channel) + { + //for (c_pack = 0; c_pack < 32; ++c_pack) + { + float src = input[(chan + c_pack)*items_per_channel + i]; + + re_packed_input[chan*items_per_channel + i * 32 + c_pack] = src; + } + } + } +} + +void repack_input_gpu_2(float *input, float *re_packed_input, int w, int h, int c) +{ + int size = w * h * c; + const int num_blocks = size / BLOCK + 1; + repack_input_kernel_2 << > >(input, re_packed_input, w, h, c); +} +// -------------------------------- + + +// 32 channels -> 1 channel (with 32 floats) +// 256 channels -> 8 channels (with 32 floats) +__global__ void repack_input_kernel_bin(float *input, uint32_t *re_packed_input_bin, int w, int h, int c) +{ + __shared__ uint32_t tmp[32]; + + int index = blockIdx.x*blockDim.x + threadIdx.x; + + const int num_of_warps = blockDim.x / WARP_SIZE; + const int warp_id = threadIdx.x / WARP_SIZE; + const int lane_id = threadIdx.x % WARP_SIZE; + + const int items_per_channel = w * h; + + int c_pack = index % 32; + int chan_index = index / 32; + //int chan = (chan_index * 32) % c; + //int i = (chan_index * 32) / c; + + int i = (chan_index) % items_per_channel; + int chan = ((chan_index ) / items_per_channel)*32; + + + //for (chan = 0; chan < c; chan += 32) + if(chan < c) + { + //for (i = 0; i < items_per_channel; ++i) + //if (i < items_per_channel) + { + //for (c_pack = 0; c_pack < 32; ++c_pack) + { + float src = input[(chan + c_pack)*items_per_channel + i]; + + uint32_t bit_mask = __ballot(src > 0); + //if (threadIdx.x % 32 == 0) + // re_packed_input_bin[chan*items_per_channel/32 + i + c_pack/32] = bit_mask; + + if (lane_id == 0) tmp[warp_id] = bit_mask; + + __syncthreads(); + if (warp_id == 0) { + if (lane_id < num_of_warps) { + re_packed_input_bin[chan*items_per_channel / 32 + i + lane_id] = tmp[lane_id]; + } + } + __syncthreads(); + } + } + } +} + +void repack_input_gpu_bin(float *input, uint32_t *re_packed_input_bin, int w, int h, int c) +{ + int size = w * h * c; + const int num_blocks = size / BLOCK + 1; + repack_input_kernel_bin << > >(input, re_packed_input_bin, w, h, c); +} +// -------------------------------- + + __global__ void fill_int8_gpu_kernel(unsigned char *src, unsigned char val, size_t size) { int index = blockIdx.x*blockDim.x + threadIdx.x; From 5343aa423563c107e0071b1427ad5defc27b56d2 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 16 Jan 2019 18:08:11 +0300 Subject: [PATCH 0253/1365] CUDA minor performance improvement --- src/activation_kernels.cu | 24 ++++++++++++++--- src/blas.h | 2 ++ src/blas_kernels.cu | 51 +++++++++++++++++++++++++++++++++++- src/convolutional_kernels.cu | 8 +++--- src/convolutional_layer.c | 2 +- src/shortcut_layer.c | 6 +++-- src/yolo_layer.c | 7 ++--- 7 files changed, 86 insertions(+), 14 deletions(-) diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index 4285b5f39ba..ee112b18052 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -192,8 +192,23 @@ __global__ void activate_array_leaky_kernel(float *x, int n) { int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < n) { - float val = x[index]; - x[index] = (val > 0) ? val : val / 10; + x[index] = leaky_activate_kernel(x[index]); + } +} + +__global__ void activate_array_selu_kernel(float *x, int n) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < n) { + x[index] = selu_activate_kernel(x[index]); + } +} + +__global__ void activate_array_logistic_kernel(float *x, int n) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < n) { + x[index] = logistic_activate_kernel(x[index]); } } @@ -205,7 +220,10 @@ __global__ void gradient_array_kernel(float *x, int n, ACTIVATION a, float *delt extern "C" void activate_array_ongpu(float *x, int n, ACTIVATION a) { - if(a == LEAKY) activate_array_leaky_kernel << <(n / BLOCK + 1), BLOCK, 0, get_cuda_stream() >> >(x, n); + if (a == LINEAR) return; + else if(a == LEAKY) activate_array_leaky_kernel << <(n / BLOCK + 1), BLOCK, 0, get_cuda_stream() >> >(x, n); + else if (a == LOGISTIC) activate_array_logistic_kernel << <(n / BLOCK + 1), BLOCK, 0, get_cuda_stream() >> >(x, n); + else if (a == SELU) activate_array_selu_kernel << <(n / BLOCK + 1), BLOCK, 0, get_cuda_stream() >> >(x, n); else activate_array_kernel<<>>(x, n, a); check_error(cudaPeekAtLastError()); } diff --git a/src/blas.h b/src/blas.h index d5f67250f27..9b7f3d5aa67 100644 --- a/src/blas.h +++ b/src/blas.h @@ -46,6 +46,7 @@ void softmax_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *er void axpy_ongpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY); void axpy_ongpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY); +void simple_copy_ongpu(int size, float *src, float *dst); void copy_ongpu(int N, float * X, int INCX, float * Y, int INCY); void copy_ongpu_offset(int N, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY); void scal_ongpu(int N, float ALPHA, float * X, int INCX); @@ -69,6 +70,7 @@ void fast_variance_delta_gpu(float *x, float *delta, float *mean, float *varianc void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance); void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean); void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out); +void input_shortcut_gpu(float *in, int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out); void scale_bias_gpu(float *output, float *biases, int batch, int n, int size); void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates); void scale_bias_gpu(float *output, float *biases, int batch, int n, int size); diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 3fdc742d55c..fffd46f041b 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -439,6 +439,13 @@ __global__ void copy_kernel(int N, float *X, int OFFX, int INCX, float *Y, int if(i < N) Y[i*INCY + OFFY] = X[i*INCX + OFFX]; } +__global__ void simple_copy_kernel(int size, float *src, float *dst) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < size) + dst[index] = src[index]; +} + __global__ void mul_kernel(int N, float *X, int INCX, float *Y, int INCY) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -557,6 +564,13 @@ extern "C" void copy_ongpu(int N, float * X, int INCX, float * Y, int INCY) copy_ongpu_offset(N, X, 0, INCX, Y, 0, INCY); } +extern "C" void simple_copy_ongpu(int size, float *src, float *dst) +{ + const int num_blocks = size / BLOCK + 1; + simple_copy_kernel << > >(size, src, dst); + check_error(cudaPeekAtLastError()); +} + extern "C" void mul_ongpu(int N, float * X, int INCX, float * Y, int INCY) { mul_kernel<<>>(N, X, INCX, Y, INCY); @@ -678,6 +692,41 @@ extern "C" void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int check_error(cudaPeekAtLastError()); } +__global__ void input_shortcut_kernel(float *in, int size, int minw, int minh, int minc, int stride, int sample, int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= size) return; + int i = id % minw; + id /= minw; + int j = id % minh; + id /= minh; + int k = id % minc; + id /= minc; + int b = id % batch; + + int out_index = i*sample + w2*(j*sample + h2*(k + c2*b)); + int add_index = i*stride + w1*(j*stride + h1*(k + c1*b)); + out[out_index] = in[out_index] + add[add_index]; +} + +extern "C" void input_shortcut_gpu(float *in, int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out) +{ + int minw = (w1 < w2) ? w1 : w2; + int minh = (h1 < h2) ? h1 : h2; + int minc = (c1 < c2) ? c1 : c2; + + int stride = w1 / w2; + int sample = w2 / w1; + assert(stride == h1 / h2); + assert(sample == h2 / h1); + if (stride < 1) stride = 1; + if (sample < 1) sample = 1; + + int size = batch * minw * minh * minc; + input_shortcut_kernel << > >(in, size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, out); + check_error(cudaPeekAtLastError()); +} + __global__ void smooth_l1_kernel(int n, float *pred, float *truth, float *delta, float *error) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -877,7 +926,7 @@ __global__ void upsample_kernel(size_t N, float *x, int w, int h, int c, int bat extern "C" void upsample_gpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out) { size_t size = w*h*c*batch*stride*stride; - upsample_kernel << > >(size, in, w, h, c, batch, stride, forward, scale, out); + upsample_kernel << > >(size, in, w, h, c, batch, stride, forward, scale, out); check_error(cudaPeekAtLastError()); } diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 0014f32a239..6c2dc9fa1eb 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -36,7 +36,7 @@ __global__ void binarize_kernel(float *x, int n, float *binary) void binarize_gpu(float *x, int n, float *binary) { - binarize_kernel<<>>(x, n, binary); + binarize_kernel<<>>(x, n, binary); check_error(cudaPeekAtLastError()); } @@ -79,7 +79,7 @@ __global__ void binarize_weights_kernel(float *weights, int n, int size, float * void binarize_weights_gpu(float *weights, int n, int size, float *binary) { - binarize_weights_kernel << > >(weights, n, size, binary); + binarize_weights_kernel << > >(weights, n, size, binary); check_error(cudaPeekAtLastError()); } @@ -126,7 +126,7 @@ void fast_binarize_weights_gpu(float *weights, int n, int size, float *binary, f set_zero_kernel << <(n/BLOCK + 1), BLOCK >> > (mean_arr_gpu, n); reduce_kernel << > > (weights, n, size, mean_arr_gpu); - binarize_weights_mean_kernel << > > (weights, n, size, binary, mean_arr_gpu); + binarize_weights_mean_kernel << > > (weights, n, size, binary, mean_arr_gpu); check_error(cudaPeekAtLastError()); } else { @@ -296,7 +296,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //printf("\n n = %d, n % 32 = %d, new_ldb = %d, new_ldb % 32 = %d \n", n, n % 32, new_ldb, new_ldb % 32); //start_timer(); - transpose_uint32_gpu_2((uint32_t *)state.workspace, (uint32_t *)l.transposed_align_workspace_gpu, new_k, n, n, new_ldb); + transpose_uint32_gpu((uint32_t *)state.workspace, (uint32_t *)l.transposed_align_workspace_gpu, new_k, n, n, new_ldb); //cudaDeviceSynchronize(); //stop_timer_and_show_name("transpose_uint32_gpu"); diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 124514a2075..30529184b44 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -883,7 +883,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); //gemm_nn_custom(m, n, k, 1, a, k, b, n, c, n); - if (l.xnor && l.align_bit_weights && !state.train && (l.stride == 1 && l.pad == 1)) + if (l.xnor && l.align_bit_weights && !state.train) { memset(b, 0, l.bit_align*l.size*l.size*l.c * sizeof(float)); diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index 9fa18db863b..7cdc53680c2 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -73,8 +73,10 @@ void backward_shortcut_layer(const layer l, network_state state) #ifdef GPU void forward_shortcut_layer_gpu(const layer l, network_state state) { - copy_ongpu(l.outputs*l.batch, state.input, 1, l.output_gpu, 1); - shortcut_gpu(l.batch, l.w, l.h, l.c, state.net.layers[l.index].output_gpu, l.out_w, l.out_h, l.out_c, l.output_gpu); + //copy_ongpu(l.outputs*l.batch, state.input, 1, l.output_gpu, 1); + //simple_copy_ongpu(l.outputs*l.batch, state.input, l.output_gpu); + //shortcut_gpu(l.batch, l.w, l.h, l.c, state.net.layers[l.index].output_gpu, l.out_w, l.out_h, l.out_c, l.output_gpu); + input_shortcut_gpu(state.input, l.batch, l.w, l.h, l.c, state.net.layers[l.index].output_gpu, l.out_w, l.out_h, l.out_c, l.output_gpu); activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); } diff --git a/src/yolo_layer.c b/src/yolo_layer.c index ac0768a2334..d3962a16b55 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -399,14 +399,15 @@ int get_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, void forward_yolo_layer_gpu(const layer l, network_state state) { - copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1); + //copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1); + simple_copy_ongpu(l.batch*l.inputs, state.input, l.output_gpu); int b, n; for (b = 0; b < l.batch; ++b){ for(n = 0; n < l.n; ++n){ int index = entry_index(l, b, n*l.w*l.h, 0); - activate_array_ongpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC); + activate_array_ongpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC); // x,y index = entry_index(l, b, n*l.w*l.h, 4); - activate_array_ongpu(l.output_gpu + index, (1+l.classes)*l.w*l.h, LOGISTIC); + activate_array_ongpu(l.output_gpu + index, (1+l.classes)*l.w*l.h, LOGISTIC); // classes and objectness } } if(!state.train || l.onlyforward){ From bf6b40f4e92bd8a4eb2d94ca8ef456ac2ebc2508 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 18 Jan 2019 16:29:54 +0300 Subject: [PATCH 0254/1365] Another CUDA performance improvements --- include/darknet.h | 5 +++++ src/blas_kernels.cu | 15 +++++++++++++++ src/cuda.c | 30 ++++++++++++++++++++++++++++++ src/cuda.h | 1 + src/im2col_kernels.cu | 11 ++++++----- src/layer.c | 10 ++++++++++ src/network.c | 4 ++++ src/network_kernels.cu | 5 ++++- src/parser.c | 4 ++++ src/route_layer.c | 5 +++-- src/yolo_layer.c | 15 ++++++++++++--- 11 files changed, 94 insertions(+), 11 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 1e2e59b0e2f..d675b6d778e 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -316,6 +316,8 @@ struct layer { float *col_image; float * delta; float * output; + int delta_pinned; + int output_pinned; float * loss; float * squared; float * norms; @@ -582,6 +584,8 @@ typedef struct network { float *output_gpu; float *input_state_gpu; + float *input_pinned_cpu; + int input_pinned_cpu_flag; float **input_gpu; float **truth_gpu; @@ -777,6 +781,7 @@ LIB_API pthread_t load_data_in_thread(load_args args); // cuda.h LIB_API void cuda_pull_array(float *x_gpu, float *x, size_t n); +LIB_API void cuda_pull_array_async(float *x_gpu, float *x, size_t n); LIB_API void cuda_set_device(int n); // utils.h diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index fffd46f041b..0116704937c 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -692,6 +692,14 @@ extern "C" void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int check_error(cudaPeekAtLastError()); } +__global__ void simple_input_shortcut_kernel(float *in, int size, float *add, float *out) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= size) return; + + out[id] = in[id] + add[id]; +} + __global__ void input_shortcut_kernel(float *in, int size, int minw, int minh, int minc, int stride, int sample, int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out) { int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -711,6 +719,13 @@ __global__ void input_shortcut_kernel(float *in, int size, int minw, int minh, i extern "C" void input_shortcut_gpu(float *in, int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out) { + if (w1 == w2 && h1 == h2 && c1 == c2) { + int size = batch * w1 * h1 * c1; + simple_input_shortcut_kernel << > >(in, size, add, out); + check_error(cudaPeekAtLastError()); + return; + } + int minw = (w1 < w2) ? w1 : w2; int minh = (h1 < h2) ? h1 : h2; int minc = (c1 < c2) ? c1 : c2; diff --git a/src/cuda.c b/src/cuda.c index 68fb7f8cf80..bd8fa5aba83 100644 --- a/src/cuda.c +++ b/src/cuda.c @@ -82,6 +82,27 @@ cudaStream_t get_cuda_stream() { return streamsArray[i]; } +static cudaStream_t streamsArray2[16]; // cudaStreamSynchronize( get_cuda_memcpy_stream() ); +static int streamInit2[16] = { 0 }; + +cudaStream_t get_cuda_memcpy_stream() { + int i = cuda_get_device(); + if (!streamInit2[i]) { + cudaError_t status = cudaStreamCreate(&streamsArray2[i]); + //cudaError_t status = cudaStreamCreateWithFlags(&streamsArray2[i], cudaStreamNonBlocking); + if (status != cudaSuccess) { + printf(" cudaStreamCreate Memcpy error: %d \n", status); + const char *s = cudaGetErrorString(status); + char buffer[256]; + printf("CUDA Error: %s\n", s); + status = cudaStreamCreateWithFlags(&streamsArray2[i], cudaStreamDefault); + check_error(status); + } + streamInit2[i] = 1; + } + return streamsArray2[i]; +} + #ifdef CUDNN cudnnHandle_t cudnn_handle() @@ -116,6 +137,7 @@ float *cuda_make_array(float *x, size_t n) float *x_gpu; size_t size = sizeof(float)*n; cudaError_t status = cudaMalloc((void **)&x_gpu, size); + if (status != cudaSuccess) fprintf(stderr, " Try to set subdivisions=64 in your cfg-file. \n"); check_error(status); if(x){ //status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice); @@ -200,6 +222,14 @@ void cuda_pull_array(float *x_gpu, float *x, size_t n) cudaStreamSynchronize(get_cuda_stream()); } +void cuda_pull_array_async(float *x_gpu, float *x, size_t n) +{ + size_t size = sizeof(float)*n; + cudaError_t status = cudaMemcpyAsync(x, x_gpu, size, cudaMemcpyDeviceToHost, get_cuda_stream()); + check_error(status); + //cudaStreamSynchronize(get_cuda_stream()); +} + #else // GPU #include "cuda.h" void cuda_set_device(int n) {} diff --git a/src/cuda.h b/src/cuda.h index 6aa83550949..89af2465b39 100644 --- a/src/cuda.h +++ b/src/cuda.h @@ -37,6 +37,7 @@ extern "C" { float cuda_compare(float *x_gpu, float *x, size_t n, char *s); dim3 cuda_gridsize(size_t n); cudaStream_t get_cuda_stream(); + cudaStream_t get_cuda_memcpy_stream(); #ifdef __cplusplus } #endif // __cplusplus diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index e85811bbeb6..d7d6503b43c 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -1030,7 +1030,6 @@ void repack_input_gpu_2(float *input, float *re_packed_input, int w, int h, int __global__ void repack_input_kernel_bin(float *input, uint32_t *re_packed_input_bin, int w, int h, int c) { __shared__ uint32_t tmp[32]; - int index = blockIdx.x*blockDim.x + threadIdx.x; const int num_of_warps = blockDim.x / WARP_SIZE; @@ -1350,6 +1349,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int unsigned char *B, int ldb, float *C, int ldc, float *mean_arr, float *bias_arr) { + // total 57% int index = blockIdx.x*blockDim.x + threadIdx.x; __shared__ uint8_t A_s[6144*8/4]; @@ -1363,7 +1363,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int int i_cur = index / N; int local_i = i_cur - start_i; - + // ~10% for (int k = threadIdx.x * 64; k < shared_size; k += blockDim.x * 64) { int x = start_i*lda + k; if (x < (M*lda)) *((uint64_t *)(A_s + k / 8)) = *((uint64_t *)(A + x / 8)); @@ -1371,7 +1371,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int __syncthreads(); int i, j, k, h; - + // 47% = 29 + 10 + 8 j = index % N; { // out_h*out_w - one channel output size [169 - 173056] i = index / N; @@ -1413,7 +1413,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int #endif //#ifdef NOT_USED - // 32 thread X 64 bit = 2048 bit + // 32 thread X 64 bit = 2048 bit // 29% for (; k < (K - 2048); k += 2048) { // l.size*l.size*l.c - one filter size [27 - 9216] uint64_t c_bit64; @@ -1444,7 +1444,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int //#endif //#ifdef NOT_USED - // 32 thread X 32 bit = 1024 bit + // 32 thread X 32 bit = 1024 bit // 10% for (; k < (K - 1024); k += 1024) { // l.size*l.size*l.c - one filter size [27 - 9216] //int64_t A_cur_index = (i*lda + k) / 8; @@ -1479,6 +1479,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int float bias_val = bias_arr[i]; //#ifdef NOT_USED + // 8% for (; k < K; k += 256) { // l.size*l.size*l.c - one filter size [27 - 144 - 9216] //ulonglong4 a_bit256 = *((ulonglong4 *)(A + (i*lda + k) / 8)); // weights ulonglong4 a_bit256 = *((ulonglong4 *)(A_s + (local_i*lda + k) / 8)); // weights diff --git a/src/layer.c b/src/layer.c index c76a16ffa49..07029074bfa 100644 --- a/src/layer.c +++ b/src/layer.c @@ -35,6 +35,16 @@ void free_layer(layer l) if (l.weight_updates) free(l.weight_updates); if (l.align_bit_weights) free(l.align_bit_weights); if (l.mean_arr) free(l.mean_arr); +#ifdef GPU + if (l.delta && l.delta_pinned) { + cudaFreeHost(l.delta); + l.delta = NULL; + } + if (l.output && l.output_pinned) { + cudaFreeHost(l.output); + l.output = NULL; + } +#endif // GPU if (l.delta) free(l.delta); if (l.output) free(l.output); if (l.squared) free(l.squared); diff --git a/src/network.c b/src/network.c index b748a85725a..a856f8fcb4b 100644 --- a/src/network.c +++ b/src/network.c @@ -856,6 +856,10 @@ void free_network(network net) if (gpu_index >= 0) cuda_free(net.workspace); else free(net.workspace); if (net.input_state_gpu) cuda_free(net.input_state_gpu); + if (net.input_pinned_cpu) { // CPU + if (net.input_pinned_cpu_flag) cudaFreeHost(net.input_pinned_cpu); + else free(net.input_pinned_cpu); + } if (*net.input_gpu) cuda_free(*net.input_gpu); if (*net.truth_gpu) cuda_free(*net.truth_gpu); if (net.input_gpu) free(net.input_gpu); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 8cd45fe20fd..b021911deec 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -87,6 +87,8 @@ void forward_network_gpu(network net, network_state state) } */ } + cudaStreamSynchronize(get_cuda_stream()); // sync CUDA-functions + //cudaStreamSynchronize(get_cuda_memcpy_stream()); // sync cudaMemcpyAsync() //cudaDeviceSynchronize(); //show_total_time(); } @@ -444,7 +446,8 @@ float *network_predict_gpu(network net, float *input) state.net = net; //state.input = cuda_make_array(input, size); // memory will be allocated in the parse_network_cfg_custom() state.input = net.input_state_gpu; - cuda_push_array(state.input, input, size); + memcpy(net.input_pinned_cpu, input, size * sizeof(float)); + cuda_push_array(state.input, net.input_pinned_cpu, size); state.truth = 0; state.train = 0; state.delta = 0; diff --git a/src/parser.c b/src/parser.c index 12b4bd412f6..ce2dbebfcee 100644 --- a/src/parser.c +++ b/src/parser.c @@ -829,10 +829,14 @@ network parse_network_cfg_custom(char *filename, int batch) if(workspace_size){ //printf("%ld\n", workspace_size); #ifdef GPU + get_cuda_stream(); + get_cuda_memcpy_stream(); if(gpu_index >= 0){ net.workspace = cuda_make_array(0, workspace_size/sizeof(float) + 1); int size = get_network_input_size(net) * net.batch; net.input_state_gpu = cuda_make_array(0, size); + if (cudaSuccess == cudaHostAlloc(&net.input_pinned_cpu, size*sizeof(float), cudaHostRegisterMapped)) net.input_pinned_cpu_flag = 1; + else net.input_pinned_cpu = calloc(size, sizeof(float)); // pre-allocate memory for inference on Tensor Cores (fp16) if (net.cudnn_half) { diff --git a/src/route_layer.c b/src/route_layer.c index dce71180c1f..db1ac20658c 100644 --- a/src/route_layer.c +++ b/src/route_layer.c @@ -67,7 +67,7 @@ void resize_route_layer(route_layer *l, network *net) l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch); l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch); #endif - + } void forward_route_layer(const route_layer l, network_state state) @@ -110,7 +110,8 @@ void forward_route_layer_gpu(const route_layer l, network_state state) float *input = state.net.layers[index].output_gpu; int input_size = l.input_sizes[i]; for(j = 0; j < l.batch; ++j){ - copy_ongpu(input_size, input + j*input_size, 1, l.output_gpu + offset + j*l.outputs, 1); + //copy_ongpu(input_size, input + j*input_size, 1, l.output_gpu + offset + j*l.outputs, 1); + simple_copy_ongpu(input_size, input + j*input_size, l.output_gpu + offset + j*l.outputs); } offset += input_size; } diff --git a/src/yolo_layer.c b/src/yolo_layer.c index d3962a16b55..71fb2a6d976 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -53,6 +53,14 @@ layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int l.backward_gpu = backward_yolo_layer_gpu; l.output_gpu = cuda_make_array(l.output, batch*l.outputs); l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs); + + free(l.output); + if (cudaSuccess == cudaHostAlloc(&l.output, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1; + else l.output = calloc(batch*l.outputs, sizeof(float)); + + free(l.delta); + if (cudaSuccess == cudaHostAlloc(&l.delta, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1; + else l.delta = calloc(batch*l.outputs, sizeof(float)); #endif fprintf(stderr, "yolo\n"); @@ -411,13 +419,14 @@ void forward_yolo_layer_gpu(const layer l, network_state state) } } if(!state.train || l.onlyforward){ - cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); + //cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); + cuda_pull_array_async(l.output_gpu, l.output, l.batch*l.outputs); return; } - //cuda_pull_array(l.output_gpu, state.input, l.batch*l.inputs); float *in_cpu = calloc(l.batch*l.inputs, sizeof(float)); - cuda_pull_array(l.output_gpu, in_cpu, l.batch*l.inputs); + cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); + memcpy(in_cpu, l.output, l.batch*l.outputs*sizeof(float)); float *truth_cpu = 0; if (state.truth) { int num_truth = l.batch*l.truths; From 3a51f4af74254b32dbcd23a1677602ba50e53768 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 18 Jan 2019 19:52:11 +0300 Subject: [PATCH 0255/1365] Experimental repack --- src/activation_kernels.cu | 7 +-- src/convolutional_layer.c | 51 -------------------- src/cuda.c | 5 ++ src/cuda.h | 1 + src/im2col_kernels.cu | 97 +++++++++++++++++++++++++++++++-------- src/network_kernels.cu | 1 - 6 files changed, 87 insertions(+), 75 deletions(-) diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index ee112b18052..e6718fe1a1b 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -220,10 +220,11 @@ __global__ void gradient_array_kernel(float *x, int n, ACTIVATION a, float *delt extern "C" void activate_array_ongpu(float *x, int n, ACTIVATION a) { + const int num_blocks = get_number_of_blocks(n, BLOCK); if (a == LINEAR) return; - else if(a == LEAKY) activate_array_leaky_kernel << <(n / BLOCK + 1), BLOCK, 0, get_cuda_stream() >> >(x, n); - else if (a == LOGISTIC) activate_array_logistic_kernel << <(n / BLOCK + 1), BLOCK, 0, get_cuda_stream() >> >(x, n); - else if (a == SELU) activate_array_selu_kernel << <(n / BLOCK + 1), BLOCK, 0, get_cuda_stream() >> >(x, n); + else if(a == LEAKY) activate_array_leaky_kernel << > >(x, n); + else if (a == LOGISTIC) activate_array_logistic_kernel << > >(x, n); + else if (a == SELU) activate_array_selu_kernel << > >(x, n); else activate_array_kernel<<>>(x, n, a); check_error(cudaPeekAtLastError()); } diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 30529184b44..04dd88b8103 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -777,57 +777,6 @@ void binary_align_weights(convolutional_layer *l) free(align_weights); } -/* -void binary_align_weights(convolutional_layer *l) -{ - int m = l->n; - int k = l->size*l->size*l->c; - size_t new_lda = k + (l->lda_align - k % l->lda_align); // (k / 8 + 1) * 8; - l->new_lda = new_lda; - - binarize_weights(l->weights, m, k, l->binary_weights); - - size_t align_weights_size = new_lda * m; - l->align_bit_weights_size = align_weights_size / 8 + 1; - float *align_weights = calloc(align_weights_size, sizeof(float)); - l->align_bit_weights = calloc(l->align_bit_weights_size, sizeof(char)); - - size_t i, j; - // align A without transpose - for (i = 0; i < m; ++i) { - for (j = 0; j < k; ++j) { - align_weights[i*new_lda + j] = l->binary_weights[i*k + j]; - } - } - float_to_bit(align_weights, l->align_bit_weights, align_weights_size); - - //l->mean_arr = calloc(l->n, sizeof(float)); - get_mean_array(align_weights, align_weights_size, l->n, l->mean_arr); - -#ifdef GPU - cudaError_t status; - l->align_workspace_size = l->bit_align * l->size * l->size * l->c; - status = cudaMalloc((void **)&l->align_workspace_gpu, l->align_workspace_size * sizeof(float)); - status = cudaMalloc((void **)&l->transposed_align_workspace_gpu, l->align_workspace_size * sizeof(float)); - check_error(status); - - //l->align_bit_weights_gpu = cuda_make_array(l->align_bit_weights, l->align_bit_weights_size * sizeof(char)/sizeof(float)); - status = cudaMalloc((void **)&l->align_bit_weights_gpu, l->align_bit_weights_size); - check_error(status); - status = cudaMemcpy(l->align_bit_weights_gpu, l->align_bit_weights, l->align_bit_weights_size, cudaMemcpyHostToDevice); - check_error(status); - status = cudaMemcpy(l->binary_weights_gpu, l->binary_weights, m*k*sizeof(float), cudaMemcpyHostToDevice); - check_error(status); - - //l->mean_arr_gpu = cuda_make_array(l->mean_arr, l->n); - cuda_push_array(l->mean_arr_gpu, l->mean_arr, l->n); - cudaDeviceSynchronize(); -#endif // GPU - - free(align_weights); -} -*/ - // binary transpose size_t binary_transpose_align_input(int k, int n, float *b, char **t_bit_input, size_t ldb_align, int bit_align) { diff --git a/src/cuda.c b/src/cuda.c index bd8fa5aba83..16a50c4e60b 100644 --- a/src/cuda.c +++ b/src/cuda.c @@ -230,6 +230,11 @@ void cuda_pull_array_async(float *x_gpu, float *x, size_t n) //cudaStreamSynchronize(get_cuda_stream()); } +int get_number_of_blocks(int array_size, int block_size) +{ + return array_size / block_size + ((array_size % block_size > 0) ? 1 : 0); +} + #else // GPU #include "cuda.h" void cuda_set_device(int n) {} diff --git a/src/cuda.h b/src/cuda.h index 89af2465b39..b803c17f6f7 100644 --- a/src/cuda.h +++ b/src/cuda.h @@ -38,6 +38,7 @@ extern "C" { dim3 cuda_gridsize(size_t n); cudaStream_t get_cuda_stream(); cudaStream_t get_cuda_memcpy_stream(); + int get_number_of_blocks(int array_size, int block_size); #ifdef __cplusplus } #endif // __cplusplus diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index d7d6503b43c..46208d3a40e 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -635,7 +635,8 @@ __global__ void float_to_bit_gpu_kernel(float *src, unsigned char *dst, size_t s void float_to_bit_gpu(float *src, unsigned char *dst, size_t size) { //const int num_blocks = size / 1024 + 1; - const int num_blocks = size / (32*1024) + 1; + //const int num_blocks = size / (32*1024) + 1; + const int num_blocks = get_number_of_blocks(size, 32 * 1024); float_to_bit_gpu_kernel<<>>(src, dst, size); } // -------------------------------- @@ -1025,16 +1026,17 @@ void repack_input_gpu_2(float *input, float *re_packed_input, int w, int h, int // -------------------------------- + // 32 channels -> 1 channel (with 32 floats) // 256 channels -> 8 channels (with 32 floats) __global__ void repack_input_kernel_bin(float *input, uint32_t *re_packed_input_bin, int w, int h, int c) { - __shared__ uint32_t tmp[32]; + //__shared__ uint32_t tmp[32]; int index = blockIdx.x*blockDim.x + threadIdx.x; - const int num_of_warps = blockDim.x / WARP_SIZE; - const int warp_id = threadIdx.x / WARP_SIZE; - const int lane_id = threadIdx.x % WARP_SIZE; + //const int num_of_warps = blockDim.x / WARP_SIZE; + //const int warp_id = threadIdx.x / WARP_SIZE; + //const int lane_id = threadIdx.x % WARP_SIZE; const int items_per_channel = w * h; @@ -1058,18 +1060,8 @@ __global__ void repack_input_kernel_bin(float *input, uint32_t *re_packed_input_ float src = input[(chan + c_pack)*items_per_channel + i]; uint32_t bit_mask = __ballot(src > 0); - //if (threadIdx.x % 32 == 0) - // re_packed_input_bin[chan*items_per_channel/32 + i + c_pack/32] = bit_mask; - - if (lane_id == 0) tmp[warp_id] = bit_mask; - - __syncthreads(); - if (warp_id == 0) { - if (lane_id < num_of_warps) { - re_packed_input_bin[chan*items_per_channel / 32 + i + lane_id] = tmp[lane_id]; - } - } - __syncthreads(); + if (threadIdx.x % 32 == 0) + re_packed_input_bin[chan*items_per_channel / 32 + i] = bit_mask; } } } @@ -1078,9 +1070,74 @@ __global__ void repack_input_kernel_bin(float *input, uint32_t *re_packed_input_ void repack_input_gpu_bin(float *input, uint32_t *re_packed_input_bin, int w, int h, int c) { int size = w * h * c; - const int num_blocks = size / BLOCK + 1; - repack_input_kernel_bin << > >(input, re_packed_input_bin, w, h, c); + const int block_size = 128; + const int num_blocks = get_number_of_blocks(size, block_size); + repack_input_kernel_bin << > >(input, re_packed_input_bin, w, h, c); } + + +/* +// 32 channels -> 1 channel (with 32 floats) +// 256 channels -> 8 channels (with 32 floats) +__global__ void repack_input_kernel_bin(float *input, uint32_t *re_packed_input_bin, int w, int h, int c, int items_per_channel_align) +{ + __shared__ float tmp[33*32]; // misalgined array 32x32 + //const int index = blockIdx.x*blockDim.x + threadIdx.x; + + const int num_of_warps = blockDim.x / WARP_SIZE; + const int warp_id = threadIdx.x / WARP_SIZE; + const int lane_id = threadIdx.x % WARP_SIZE; + + const int items_per_channel = w * h; + //const int items_per_channel_align = items_per_channel + (32 - items_per_channel % 32); + const int blocks_per_wh = items_per_channel_align / 32; + //const int blocks_per_c = c / 32; + + // input[C x H x W] = input[C x ITEMS] + // BLOCK per C x ITEMS = 32x32 + + const int block_item_id = blockIdx.x % blocks_per_wh; + const int block_channel_id = blockIdx.x / blocks_per_wh; + + const int block_item = block_item_id * 32; + const int block_channel = block_channel_id * 32; + + const int lane_item = block_item + lane_id; + const int warp_channel = block_channel + warp_id; + + if (warp_channel < c) + { + float src = 0; + + if (lane_item < items_per_channel) + src = input[warp_channel*items_per_channel + lane_item]; + + tmp[warp_id * 33 + lane_id] = src; + __syncthreads(); + src = tmp[lane_id * 33 + warp_id]; + + uint32_t bit_mask = __ballot(src > 0); + + const int warp_item = block_item + warp_id; + + if (lane_id == 0 && warp_item < items_per_channel) + re_packed_input_bin[block_channel_id*items_per_channel + warp_item] = bit_mask; + } +} + +#define BLOCK_REPACK 1024 +void repack_input_gpu_bin(float *input, uint32_t *re_packed_input_bin, int w, int h, int c) +{ + int items_per_channel = w*h; + int items_per_channel_align = items_per_channel + (32 - items_per_channel % 32); + int channel_align = c + (32 - c % 32); + + //int size = w * h * c; + int size = items_per_channel_align * channel_align; + const int num_blocks = get_number_of_blocks(size, BLOCK_REPACK); + repack_input_kernel_bin << > >(input, re_packed_input_bin, w, h, c, items_per_channel_align); +} +*/ // -------------------------------- @@ -1657,7 +1714,7 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, float *C, int ldc, float *mean_arr, float *bias) { size_t size = M*N; - const int num_blocks = size / BLOCK + 1; + const int num_blocks = get_number_of_blocks(size, BLOCK); /* printf("\n gemm_bin size = %d, num_blocks = %d, M*K = %d KB, N*K = %d KB \n (w) M*K/num_blocks = %d KB, (i) N*K/num_blocks = %d KB \n", diff --git a/src/network_kernels.cu b/src/network_kernels.cu index b021911deec..0076a87b045 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -88,7 +88,6 @@ void forward_network_gpu(network net, network_state state) */ } cudaStreamSynchronize(get_cuda_stream()); // sync CUDA-functions - //cudaStreamSynchronize(get_cuda_memcpy_stream()); // sync cudaMemcpyAsync() //cudaDeviceSynchronize(); //show_total_time(); } From 4ed6fd1ada4cc647d1ded2f8c65909c75308a804 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 18 Jan 2019 19:54:05 +0300 Subject: [PATCH 0256/1365] Fix for compilation on Google Colab --- Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 46811ddc1aa..f5fabcf0a9b 100644 --- a/Makefile +++ b/Makefile @@ -57,7 +57,7 @@ NVCC=nvcc OPTS=-Ofast LDFLAGS= -lm -pthread COMMON= -Iinclude/ -CFLAGS=-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas +CFLAGS=-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC ifeq ($(DEBUG), 1) OPTS= -O0 -g From 0e022d09128f57b370d7f8b0265531ed7d80a31c Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 18 Jan 2019 21:29:06 +0300 Subject: [PATCH 0257/1365] Fixed timer --- Makefile | 2 +- include/darknet.h | 9 +++++++++ src/convolutional_kernels.cu | 10 ---------- src/http_stream.h | 8 -------- src/network.c | 2 -- src/parser.c | 4 ++++ 6 files changed, 14 insertions(+), 21 deletions(-) diff --git a/Makefile b/Makefile index f5fabcf0a9b..e19870aa9bc 100644 --- a/Makefile +++ b/Makefile @@ -136,7 +136,7 @@ $(OBJDIR)%.o: %.c $(DEPS) $(CC) $(COMMON) $(CFLAGS) -c $< -o $@ $(OBJDIR)%.o: %.cpp $(DEPS) - $(CPP) $(COMMON) $(CFLAGS) -c $< -o $@ + $(CPP) -std=c++11 $(COMMON) $(CFLAGS) -c $< -o $@ $(OBJDIR)%.o: %.cu $(DEPS) $(NVCC) $(ARCH) $(COMMON) --compiler-options "$(CFLAGS)" -c $< -o $@ diff --git a/include/darknet.h b/include/darknet.h index d675b6d778e..10d374b7da1 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -795,6 +795,15 @@ LIB_API tree *read_tree(char *filename); LIB_API metadata get_metadata(char *file); +// http_stream.h +LIB_API double get_time_point(); +void start_timer(); +void stop_timer(); +double get_time(); +void stop_timer_and_show(); +void stop_timer_and_show_name(char *name); +void show_total_time(); + #ifdef __cplusplus } #endif // __cplusplus diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 6c2dc9fa1eb..947a11d4e28 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -17,16 +17,6 @@ extern "C" { #include "cuda.h" } -extern "C" { - double get_time_point(); - void start_timer(); - void stop_timer(); - double get_time(); - void stop_timer_and_show(); - void stop_timer_and_show_name(char *name); - void show_total_time(); -} - __global__ void binarize_kernel(float *x, int n, float *binary) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; diff --git a/src/http_stream.h b/src/http_stream.h index 7710920d3db..96e619d5af8 100644 --- a/src/http_stream.h +++ b/src/http_stream.h @@ -27,14 +27,6 @@ image image_data_augmentation(IplImage* ipl, int w, int h, float jitter, float dhue, float dsat, float dexp); #endif // OPENCV -double get_time_point(); -void start_timer(); -void stop_timer(); -double get_time(); -void stop_timer_and_show(); -void stop_timer_and_show_name(char *name); -void show_total_time(); - #ifdef __cplusplus } #endif diff --git a/src/network.c b/src/network.c index a856f8fcb4b..465764f232d 100644 --- a/src/network.c +++ b/src/network.c @@ -192,8 +192,6 @@ network make_network(int n) return net; } -double get_time_point(); - void forward_network(network net, network_state state) { state.workspace = net.workspace; diff --git a/src/parser.c b/src/parser.c index ce2dbebfcee..1a4c8b82b47 100644 --- a/src/parser.c +++ b/src/parser.c @@ -786,10 +786,14 @@ network parse_network_cfg_custom(char *filename, int batch) l = parse_avgpool(options, params); }else if(lt == ROUTE){ l = parse_route(options, params, net); + int k; + for (k = 0; k < l.n; ++k) net.layers[l.input_layers[k]].use_bin_output = 0; }else if (lt == UPSAMPLE) { l = parse_upsample(options, params, net); }else if(lt == SHORTCUT){ l = parse_shortcut(options, params, net); + net.layers[count - 1].use_bin_output = 0; + net.layers[l.index].use_bin_output = 0; }else if(lt == DROPOUT){ l = parse_dropout(options, params); l.output = net.layers[count-1].output; From 6e99e852ffce7d6cf9e9ec427ff3acd003cc8d5b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 18 Jan 2019 23:30:10 +0300 Subject: [PATCH 0258/1365] Network resize is fixed --- src/detector.c | 9 +++++++-- src/yolo_layer.c | 20 ++++++++++++++++++-- 2 files changed, 25 insertions(+), 4 deletions(-) diff --git a/src/detector.c b/src/detector.c index 5c4805c3845..04572189593 100644 --- a/src/detector.c +++ b/src/detector.c @@ -178,8 +178,13 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i //int dim_h = (random_val + (init_h / 32 - 5)) * 32; // +-160 float random_val = rand_scale(1.4); // *x or /x - int dim_w = roundl(random_val*init_w / 32) * 32; - int dim_h = roundl(random_val*init_h / 32) * 32; + int dim_w = roundl(random_val*init_w / 32 + 1) * 32; + int dim_h = roundl(random_val*init_h / 32 + 1) * 32; + + if (get_current_batch(net) == 0) { + dim_w = roundl(1.4*init_w / 32 + 1) * 32; + dim_h = roundl(1.4*init_h / 32 + 1) * 32; + } if (dim_w < 32) dim_w = 32; if (dim_h < 32) dim_h = 32; diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 71fb2a6d976..74654af1a1c 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -77,10 +77,26 @@ void resize_yolo_layer(layer *l, int w, int h) l->outputs = h*w*l->n*(l->classes + 4 + 1); l->inputs = l->outputs; - l->output = realloc(l->output, l->batch*l->outputs*sizeof(float)); - l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float)); + if (!l->output_pinned) l->output = realloc(l->output, l->batch*l->outputs * sizeof(float)); + if (!l->delta_pinned) l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float)); #ifdef GPU + if (l->output_pinned) { + cudaFreeHost(l->output); + if (cudaSuccess != cudaHostAlloc(&l->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) { + l->output = realloc(l->output, l->batch*l->outputs * sizeof(float)); + l->output_pinned = 0; + } + } + + if (l->delta_pinned) { + cudaFreeHost(l->delta); + if (cudaSuccess != cudaHostAlloc(&l->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) { + l->delta = realloc(l->delta, l->batch*l->outputs * sizeof(float)); + l->delta_pinned = 0; + } + } + cuda_free(l->delta_gpu); cuda_free(l->output_gpu); From 17019854c33b60a76952494091726868e622fb2b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 19 Jan 2019 03:18:50 +0300 Subject: [PATCH 0259/1365] XNOR minor fix --- src/convolutional_kernels.cu | 6 +- src/demo.c | 2 +- src/im2col.h | 3 +- src/im2col_kernels.cu | 197 +++++++++++++++++++++++++++++++++-- 4 files changed, 196 insertions(+), 12 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 947a11d4e28..b9e7ff84ca1 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -296,7 +296,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //start_timer(); gemm_nn_custom_bin_mean_transposed_gpu(m, n, k, (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, - new_ldb, l.output_gpu, n, l.mean_arr_gpu, l.biases_gpu); + new_ldb, l.output_gpu, n, l.mean_arr_gpu, l.biases_gpu, l.activation); //cudaDeviceSynchronize(); //stop_timer_and_show_name("gemm_nn_custom_bin_mean_transposed_gpu"); @@ -366,7 +366,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //start_timer(); gemm_nn_custom_bin_mean_transposed_gpu(m, n, k, (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, - new_ldb, l.output_gpu, n, l.mean_arr_gpu, l.biases_gpu); + new_ldb, l.output_gpu, n, l.mean_arr_gpu, l.biases_gpu, l.activation); //cudaDeviceSynchronize(); //stop_timer_and_show_name("gemm_nn_custom_bin_mean_transposed_gpu"); //} @@ -391,7 +391,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) */ //add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); - if(l.activation != LINEAR) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); + if(l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if (l.binary || l.xnor) swap_binary(&l); //cudaDeviceSynchronize(); return; diff --git a/src/demo.c b/src/demo.c index 6de7ec43b85..b539a8b6a96 100644 --- a/src/demo.c +++ b/src/demo.c @@ -359,8 +359,8 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int free(alphabet[j]); } free(alphabet); - free_network(net); + //cudaProfilerStop(); } #else void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, diff --git a/src/im2col.h b/src/im2col.h index e7eb958acf7..b1e34f8ee05 100644 --- a/src/im2col.h +++ b/src/im2col.h @@ -3,6 +3,7 @@ #include #include +#include "darknet.h" void im2col_cpu(float* data_im, int channels, int height, int width, @@ -43,7 +44,7 @@ void fill_int8_gpu(unsigned char *src, unsigned char val, size_t size); void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, unsigned char *A, int lda, unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr, float *bias); + float *C, int ldc, float *mean_arr, float *bias, ACTIVATION a); // sequentially - BAD void gemm_nn_custom_bin_mean_transposed_sequentially_gpu(int M, int N, int K, diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 46208d3a40e..3dd7a43b720 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -1570,6 +1570,179 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int } +// Coalescing - with LEAKY activation +// A (weights) in the shared_memory - GOOD +__global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel_leaky(int M, int N, int K, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr, float *bias_arr) +{ + // total 57% + int index = blockIdx.x*blockDim.x + threadIdx.x; + + __shared__ uint8_t A_s[6144 * 8 / 4]; + //__shared__ uint64_t A_s[6144]; // 48 KB // [lda x M`] + //__shared__ uint8_t A_s[6144*8]; // 48 KB // [lda x M`] + + int start_i = blockIdx.x*blockDim.x / N; + int end_i = (blockIdx.x*blockDim.x + blockDim.x) / N + 1; + + size_t shared_size = lda * (end_i - start_i); + + int i_cur = index / N; + int local_i = i_cur - start_i; + // ~10% + for (int k = threadIdx.x * 64; k < shared_size; k += blockDim.x * 64) { + int x = start_i*lda + k; + if (x < (M*lda)) *((uint64_t *)(A_s + k / 8)) = *((uint64_t *)(A + x / 8)); + } + __syncthreads(); + + int i, j, k, h; + // 47% = 29 + 10 + 8 + j = index % N; + { // out_h*out_w - one channel output size [169 - 173056] + i = index / N; + //if (i < M) // l.n - filters [16 - 55 - 1024] + { + int count = 0; + k = 0; + +#ifdef NOT_USED + // 32 thread X 256 bit = 8192 bit + for (; k < (K - 8192); k += 8192) { // l.size*l.size*l.c - one filter size [27 - 9216] + ulonglong4 c_bit256; + + //int64_t A_cur_index = (i*lda + k) / 8; + int64_t A_cur_index = (local_i*lda + k) / 8; + int64_t B_cur_index = (j*ldb + k) / 8; + if (i >= M) A_cur_index = 0; + +#pragma unroll + for (int t = 0; t < WARP_SIZE; ++t) { + const int lane_id = threadIdx.x % WARP_SIZE; + + const int64_t A_i = __shfl(A_cur_index, t) + 32 * lane_id; + const int64_t B_i = __shfl(B_cur_index, t) + 32 * lane_id; + + { + //ulonglong4 a_bit256 = *((ulonglong4 *)(A + A_i)); // weights + ulonglong4 a_bit256 = *((ulonglong4 *)(A_s + A_i)); // weights + ulonglong4 b_bit256 = *((ulonglong4 *)(B + B_i)); // input + c_bit256 = xnor_int256(a_bit256, b_bit256); + int tmp_count = __popcll(c_bit256.w) + __popcll(c_bit256.x) + + __popcll(c_bit256.y) + __popcll(c_bit256.z); + + int sum_count = warpAllReduceSum(tmp_count); + if (lane_id == t) count += sum_count; + } + } + } +#endif + + //#ifdef NOT_USED + // 32 thread X 64 bit = 2048 bit // 29% + for (; k < (K - 2048); k += 2048) { // l.size*l.size*l.c - one filter size [27 - 9216] + uint64_t c_bit64; + + //int64_t A_cur_index = (i*lda + k) / 8; + int64_t A_cur_index = (local_i*lda + k) / 8; + int64_t B_cur_index = (j*ldb + k) / 8; + if (i >= M) A_cur_index = 0; + +#pragma unroll + for (int t = 0; t < WARP_SIZE; ++t) { + const int lane_id = threadIdx.x % WARP_SIZE; + + const int64_t A_i = __shfl(A_cur_index, t) + 8 * lane_id; + const int64_t B_i = __shfl(B_cur_index, t) + 8 * lane_id; + + { + //uint64_t a_bit64 = *((uint64_t *)(A + A_i)); // weights + uint64_t a_bit64 = *((uint64_t *)(A_s + A_i)); // weights + uint64_t b_bit64 = *((uint64_t *)(B + B_i)); // input + c_bit64 = xnor_int64(a_bit64, b_bit64); + int tmp_count = __popcll(c_bit64); + + int sum_count = warpAllReduceSum(tmp_count); + if (lane_id == t) count += sum_count; + } + } + } + //#endif + + //#ifdef NOT_USED + // 32 thread X 32 bit = 1024 bit // 10% + for (; k < (K - 1024); k += 1024) { // l.size*l.size*l.c - one filter size [27 - 9216] + + //int64_t A_cur_index = (i*lda + k) / 8; + int64_t A_cur_index = (local_i*lda + k) / 8; + int64_t B_cur_index = (j*ldb + k) / 8; + if (i >= M) A_cur_index = 0; + +#pragma unroll + for (int t = 0; t < WARP_SIZE; ++t) { + const int lane_id = threadIdx.x % WARP_SIZE; + + const int64_t A_i = __shfl(A_cur_index, t) + 4 * lane_id; + const int64_t B_i = __shfl(B_cur_index, t) + 4 * lane_id; + + { + //uint64_t a_bit64 = *((uint64_t *)(A + A_i)); // weights + uint32_t a_bit32 = *((uint32_t *)(A_s + A_i)); // weights + uint32_t b_bit32 = *((uint32_t *)(B + B_i)); // input + uint32_t c_bit32 = xnor_int32(a_bit32, b_bit32); + int tmp_count = __popc(c_bit32); + + int sum_count = warpAllReduceSum(tmp_count); + if (lane_id == t) count += sum_count; + } + } + } + //#endif + + if (i < M) + { + float mean_val = mean_arr[i]; + float bias_val = bias_arr[i]; + + //#ifdef NOT_USED + // 8% + for (; k < K; k += 256) { // l.size*l.size*l.c - one filter size [27 - 144 - 9216] + //ulonglong4 a_bit256 = *((ulonglong4 *)(A + (i*lda + k) / 8)); // weights + ulonglong4 a_bit256 = *((ulonglong4 *)(A_s + (local_i*lda + k) / 8)); // weights + ulonglong4 b_bit256 = *((ulonglong4 *)(B + (j*ldb + k) / 8)); // input + ulonglong4 c_bit256 = xnor_int256(a_bit256, b_bit256); + + count += __popcll(c_bit256.w) + __popcll(c_bit256.x) + + __popcll(c_bit256.y) + __popcll(c_bit256.z); + } + //#endif + +#ifdef NOT_USED + for (; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] + //uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); // weights + uint64_t a_bit64 = *((uint64_t *)(A_s + (local_i*lda + k) / 8)); // weights + uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); // input + uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); + + count += __popcll(c_bit64); + } +#endif + + const int bit_step = 256; + int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); + count = count - f1; // remove extra bits (from empty space for align only) + + float dst_val = (2 * count - K) *mean_val + bias_val; + dst_val = (dst_val > 0) ? (dst_val) : (0.1*dst_val); // Leaky activation + C[i*ldc + j] = dst_val; + } + } + } +} + + /* // Coalescing // B (input) in the shared_memory - GOOD @@ -1711,7 +1884,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, unsigned char *A, int lda, unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr, float *bias) + float *C, int ldc, float *mean_arr, float *bias, ACTIVATION a) { size_t size = M*N; const int num_blocks = get_number_of_blocks(size, BLOCK); @@ -1723,12 +1896,22 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, */ //printf(" shared_memory: (w) lda*BLOCK/N = %d, (i) ldb*BLOCK/M = %d, \t lda = %d \n\n", lda*BLOCK / N, ldb*BLOCK / M, lda); - gemm_nn_custom_bin_mean_transposed_gpu_kernel<<>>( - M, N, K, - A, lda, - B, ldb, - C, ldc, - mean_arr, bias); + if (a == LEAKY) { + gemm_nn_custom_bin_mean_transposed_gpu_kernel_leaky << > > ( + M, N, K, + A, lda, + B, ldb, + C, ldc, + mean_arr, bias); + } + else { + gemm_nn_custom_bin_mean_transposed_gpu_kernel << > > ( + M, N, K, + A, lda, + B, ldb, + C, ldc, + mean_arr, bias); + } } // -------------------------------- From 8b7494d920461fcfcf1847546d397dc3ba02078b Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 21 Jan 2019 02:38:58 +0300 Subject: [PATCH 0260/1365] Update Readme.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 6ce2bb7caca..aff3426dd97 100644 --- a/README.md +++ b/README.md @@ -470,6 +470,8 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416` then set the same 9 `anchors` in each of 3 `[yolo]`-layers in your cfg-file + (also you can change indexes of anchors `masks=` for each [yolo]-layer, so that 1st-[yolo]-layer has anchors larger than 60x60, 2nd larger than 30x30, 3rd remaining) + * check that each object are mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: https://github.com/AlexeyAB/Yolo_mark * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 different images for each class or more, and you should train `2000*classes` iterations or more From 226322523edad71c8da5a602a5d909c17715d3b0 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 21 Jan 2019 14:54:51 +0300 Subject: [PATCH 0261/1365] Fixed calc_anchors --- src/matrix.c | 20 +++++++++++++++++--- 1 file changed, 17 insertions(+), 3 deletions(-) diff --git a/src/matrix.c b/src/matrix.c index e0eb61e67bd..7430028017c 100644 --- a/src/matrix.c +++ b/src/matrix.c @@ -222,10 +222,15 @@ int kmeans_expectation(matrix data, int *assignments, matrix centers) void kmeans_maximization(matrix data, int *assignments, matrix centers) { + matrix old_centers = make_matrix(centers.rows, centers.cols); + int i, j; int *counts = calloc(centers.rows, sizeof(int)); for (i = 0; i < centers.rows; ++i) { - for (j = 0; j < centers.cols; ++j) centers.vals[i][j] = 0; + for (j = 0; j < centers.cols; ++j) { + old_centers.vals[i][j] = centers.vals[i][j]; + centers.vals[i][j] = 0; + } } for (i = 0; i < data.rows; ++i) { ++counts[assignments[i]]; @@ -240,6 +245,13 @@ void kmeans_maximization(matrix data, int *assignments, matrix centers) } } } + + for (i = 0; i < centers.rows; ++i) { + for (j = 0; j < centers.cols; ++j) { + if(centers.vals[i][j] == 0) centers.vals[i][j] = old_centers.vals[i][j]; + } + } + free_matrix(old_centers); } @@ -292,7 +304,7 @@ model do_kmeans(matrix data, int k) int *assignments = calloc(data.rows, sizeof(int)); //smart_centers(data, centers); random_centers(data, centers); // IoU = 67.31% after kmeans - // + /* // IoU = 63.29%, anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 centers.vals[0][0] = 10; centers.vals[0][1] = 13; @@ -308,9 +320,11 @@ model do_kmeans(matrix data, int k) // range centers [min - max] using exp graph or Pyth example if (k == 1) kmeans_maximization(data, assignments, centers); - while (!kmeans_expectation(data, assignments, centers)) { + int i; + for(i = 0; i < 1000 && !kmeans_expectation(data, assignments, centers); ++i) { kmeans_maximization(data, assignments, centers); } + printf("\n iterations = %d \n", i); model m; m.assignments = assignments; m.centers = centers; From 81f7fc2c7bbddb79b5c87b5ab88fedf98f2b9963 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 21 Jan 2019 15:32:57 +0300 Subject: [PATCH 0262/1365] Fixed network resize memory allocation --- src/detector.c | 3 ++- src/network.c | 16 ++++++++++++++++ 2 files changed, 18 insertions(+), 1 deletion(-) diff --git a/src/detector.c b/src/detector.c index 04572189593..747cf422bf3 100644 --- a/src/detector.c +++ b/src/detector.c @@ -181,7 +181,8 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i int dim_w = roundl(random_val*init_w / 32 + 1) * 32; int dim_h = roundl(random_val*init_h / 32 + 1) * 32; - if (get_current_batch(net) == 0) { + // at the beginning + if (avg_loss < 0) { dim_w = roundl(1.4*init_w / 32 + 1) * 32; dim_h = roundl(1.4*init_h / 32 + 1) * 32; } diff --git a/src/network.c b/src/network.c index 465764f232d..0017b884e76 100644 --- a/src/network.c +++ b/src/network.c @@ -400,6 +400,12 @@ int resize_network(network *net, int w, int h) cuda_free(*net->truth_gpu); *net->truth_gpu = 0; } + + if (net->input_state_gpu) cuda_free(net->input_state_gpu); + if (net->input_pinned_cpu) { + if (net->input_pinned_cpu_flag) cudaFreeHost(net->input_pinned_cpu); + else free(net->input_pinned_cpu); + } } #endif int i; @@ -448,14 +454,24 @@ int resize_network(network *net, int w, int h) h = l.out_h; if(l.type == AVGPOOL) break; } + const int size = get_network_input_size(*net) * net->batch; #ifdef GPU if(gpu_index >= 0){ printf(" try to allocate workspace = %zu * sizeof(float), ", workspace_size / sizeof(float) + 1); net->workspace = cuda_make_array(0, workspace_size/sizeof(float) + 1); + net->input_state_gpu = cuda_make_array(0, size); + if (cudaSuccess == cudaHostAlloc(&net->input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped)) + net->input_pinned_cpu_flag = 1; + else { + net->input_pinned_cpu = calloc(size, sizeof(float)); + net->input_pinned_cpu_flag = 0; + } printf(" CUDA allocate done! \n"); }else { free(net->workspace); net->workspace = calloc(1, workspace_size); + if(!net->input_pinned_cpu_flag) + net->input_pinned_cpu = realloc(net->input_pinned_cpu, size * sizeof(float)); } #else free(net->workspace); From ec9b989c0af02ca5ace683bdbf7d8ce2003690c1 Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 21 Jan 2019 22:40:56 +0300 Subject: [PATCH 0263/1365] Update Readme.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index aff3426dd97..118669f06e6 100644 --- a/README.md +++ b/README.md @@ -474,7 +474,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * check that each object are mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: https://github.com/AlexeyAB/Yolo_mark - * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 different images for each class or more, and you should train `2000*classes` iterations or more + * for each object which you want to detect - there must be at least 1 similar object in the Training dataset with about the same: shape, side of object, relative size, angle of rotation, tilt, illumination. So desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 different images for each class or more, and you should train `2000*classes` iterations or more * desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty `.txt` files) - use as many images of negative samples as there are images with objects From 46be08db37ac1de7d877e460f58c9f7839035733 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 22 Jan 2019 16:23:44 +0300 Subject: [PATCH 0264/1365] Minor fix --- src/convolutional_layer.c | 19 ------------------- src/cuda.c | 3 ++- src/detector.c | 4 +++- src/http_stream.cpp | 28 ++++++++++++++++++++++++++++ src/http_stream.h | 2 ++ src/im2col_kernels.cu | 15 +++++++++++++++ 6 files changed, 50 insertions(+), 21 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 04dd88b8103..ec196bd762a 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -285,25 +285,6 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) //l->fw_algo16 = CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED; //l->bd_algo16 = CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED; //l->bf_algo16 = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED; - - int fw = 0, bd = 0, bf = 0; - if (l->fw_algo16 == CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM) fw = 1; - //printf("Tensor Cores - Forward enabled: l->fw_algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM \n"); - if (l->fw_algo16 == CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED) fw = 2; - //printf("Tensor Cores - Forward enabled: l->fw_algo = CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED \n"); - - if (l->bd_algo16 == CUDNN_CONVOLUTION_BWD_DATA_ALGO_1) bd = 1; - //printf("Tensor Cores - Backward-data enabled: l->bd_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1 \n"); - if (l->bd_algo16 == CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED) bd = 2; - //printf("Tensor Cores - Backward-data enabled: l->bd_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED \n"); - - if (l->bf_algo16 == CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1) bf = 1; - //printf("Tensor Cores - Backward-filter enabled: l->bf_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1 \n"); - if (l->bf_algo16 == CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED) bf = 2; - //printf("Tensor Cores - Backward-filter enabled: l->bf_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED \n"); - - //if (fw == 2 && bd == 2 && bf == 2) printf("TF "); - //else if (fw == 1 && bd == 1 && bf == 1) printf("TH "); } } #endif diff --git a/src/cuda.c b/src/cuda.c index 16a50c4e60b..7705b27d2f5 100644 --- a/src/cuda.c +++ b/src/cuda.c @@ -91,7 +91,7 @@ cudaStream_t get_cuda_memcpy_stream() { cudaError_t status = cudaStreamCreate(&streamsArray2[i]); //cudaError_t status = cudaStreamCreateWithFlags(&streamsArray2[i], cudaStreamNonBlocking); if (status != cudaSuccess) { - printf(" cudaStreamCreate Memcpy error: %d \n", status); + printf(" cudaStreamCreate-Memcpy error: %d \n", status); const char *s = cudaGetErrorString(status); char buffer[256]; printf("CUDA Error: %s\n", s); @@ -180,6 +180,7 @@ int *cuda_make_int_array(size_t n) int *x_gpu; size_t size = sizeof(int)*n; cudaError_t status = cudaMalloc((void **)&x_gpu, size); + if(status != cudaSuccess) fprintf(stderr, " Try to set subdivisions=64 in your cfg-file. \n"); check_error(status); return x_gpu; } diff --git a/src/detector.c b/src/detector.c index 747cf422bf3..193df65eee3 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1274,9 +1274,11 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam if (!input) break; strtok(input, "\n"); } + //image im = load_image_resize(input, net.w, net.h, net.c); + //image sized = copy_image(im); image im = load_image(input, 0, 0, net.c); - int letterbox = 0; image sized = resize_image(im, net.w, net.h); + int letterbox = 0; //image sized = letterbox_image(im, net.w, net.h); letterbox = 1; layer l = net.layers[net.n - 1]; diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 408fae97c8d..5938d8e65c1 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -593,6 +593,34 @@ image image_data_augmentation(IplImage* ipl, int w, int h, } +image load_image_resize(char *filename, int w, int h, int c) +{ + image out; + cv::Mat img(h, w, CV_8UC3); + try { + int flag = -1; + if (c == 0) flag = 1; + else if (c == 1) { flag = 0; img = cv::Mat(h, w, CV_8UC1); } + else if (c == 3) { flag = 1; img = cv::Mat(h, w, CV_8UC3); } + else { + fprintf(stderr, "OpenCV can't force load with %d channels\n", c); + } + //throw std::runtime_error("runtime_error"); + cv::Mat loaded_image = cv::imread(filename, flag); + cv::resize(loaded_image, img, cv::Size(w, h), 0, 0, CV_INTER_LINEAR); + cv::cvtColor(img, img, cv::COLOR_RGB2BGR); + + IplImage tmp = img; + out = ipl_to_image(&tmp); + } + catch (...) { + fprintf(stderr, "OpenCV can't load image %s channels\n", filename); + out = make_image(w, h, c); + } + return out; +} + + #endif // OPENCV // ----------------------------------------------------- diff --git a/src/http_stream.h b/src/http_stream.h index 96e619d5af8..34091064d61 100644 --- a/src/http_stream.h +++ b/src/http_stream.h @@ -25,6 +25,8 @@ int get_stream_fps_cpp(CvCapture *cap); image image_data_augmentation(IplImage* ipl, int w, int h, int pleft, int ptop, int swidth, int sheight, int flip, float jitter, float dhue, float dsat, float dexp); + +image load_image_resize(char *filename, int w, int h, int c); #endif // OPENCV #ifdef __cplusplus diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 3dd7a43b720..fc016cef782 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -1880,6 +1880,21 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int } */ +// further optimization - use WMMA GEMM for using Tensor Cores +// https://github.com/NVIDIA-developer-blog/code-samples/blob/master/posts/tensor-cores/simpleTensorCoreGEMM.cu +// https://github.com/NVIDIA/cuda-samples/blob/master/Samples/cudaTensorCoreGemm/cudaTensorCoreGemm.cu +// https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#wmma-subbyte +// nvcuda::wmma::col_major -> cutlass::MatrixLayout::kColumnMajor (matrix is not transposed) + +// Matrix A Matrix B Accumulator Matrix Size (m-n-k) +// precision::b1 precision::b1 int 8x8x128 + +// The only dimensions currently supported by WMMA for XNOR +// const int WMMA_M = 8; +// const int WMMA_N = 8; +// const int WMMA_K = 128; + + // GOOD void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, unsigned char *A, int lda, From 2cd37ec73eae3e4d0a554b9ec20c01a8309a089a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 22 Jan 2019 17:31:07 +0300 Subject: [PATCH 0265/1365] Another minor fix --- src/detector.c | 4 ++-- src/http_stream.cpp | 12 ++++++++---- src/http_stream.h | 2 +- 3 files changed, 11 insertions(+), 7 deletions(-) diff --git a/src/detector.c b/src/detector.c index 193df65eee3..8984a56364f 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1274,8 +1274,8 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam if (!input) break; strtok(input, "\n"); } - //image im = load_image_resize(input, net.w, net.h, net.c); - //image sized = copy_image(im); + //image im; + //image sized = load_image_resize(input, net.w, net.h, net.c, &im); image im = load_image(input, 0, 0, net.c); image sized = resize_image(im, net.w, net.h); int letterbox = 0; diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 5938d8e65c1..dbac6f5dcdb 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -593,7 +593,7 @@ image image_data_augmentation(IplImage* ipl, int w, int h, } -image load_image_resize(char *filename, int w, int h, int c) +image load_image_resize(char *filename, int w, int h, int c, image *im) { image out; cv::Mat img(h, w, CV_8UC3); @@ -607,15 +607,19 @@ image load_image_resize(char *filename, int w, int h, int c) } //throw std::runtime_error("runtime_error"); cv::Mat loaded_image = cv::imread(filename, flag); + cv::cvtColor(loaded_image, loaded_image, cv::COLOR_RGB2BGR); + IplImage tmp1 = loaded_image; + *im = ipl_to_image(&tmp1); + cv::resize(loaded_image, img, cv::Size(w, h), 0, 0, CV_INTER_LINEAR); - cv::cvtColor(img, img, cv::COLOR_RGB2BGR); - IplImage tmp = img; - out = ipl_to_image(&tmp); + IplImage tmp2 = img; + out = ipl_to_image(&tmp2); } catch (...) { fprintf(stderr, "OpenCV can't load image %s channels\n", filename); out = make_image(w, h, c); + *im = make_image(w, h, c); } return out; } diff --git a/src/http_stream.h b/src/http_stream.h index 34091064d61..d7ec815cfb7 100644 --- a/src/http_stream.h +++ b/src/http_stream.h @@ -26,7 +26,7 @@ image image_data_augmentation(IplImage* ipl, int w, int h, int pleft, int ptop, int swidth, int sheight, int flip, float jitter, float dhue, float dsat, float dexp); -image load_image_resize(char *filename, int w, int h, int c); +image load_image_resize(char *filename, int w, int h, int c, image *im); #endif // OPENCV #ifdef __cplusplus From 8960fbfb3ff929530f0ef795694f62d29ce6b47b Mon Sep 17 00:00:00 2001 From: "Antek S. Baranski" Date: Tue, 22 Jan 2019 10:17:42 -0800 Subject: [PATCH 0266/1365] gcc on OSX required explicit return value for empty (char *) in detection_to_json --- src/network.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/network.c b/src/network.c index 0017b884e76..1353d100ae2 100644 --- a/src/network.c +++ b/src/network.c @@ -713,7 +713,7 @@ char *detection_to_json(detection *dets, int nboxes, int classes, char **names, int buf_len = strlen(buf); int total_len = send_buf_len + buf_len + 100; send_buf = (char *)realloc(send_buf, total_len * sizeof(char)); - if (!send_buf) return;// exit(-1); + if (!send_buf) return 0;// exit(-1); strcat(send_buf, buf); free(buf); } From 2d3220cef58fa0fffb5c2e424476173ceb296b0d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 23 Jan 2019 00:35:44 +0300 Subject: [PATCH 0267/1365] Look at wmma::bmma_sync(), bmmaBitOpXOR, bmmaAccumulateOpPOPC --- src/convolutional_layer.c | 10 + src/im2col_kernels.cu | 423 +++++++++++++++++++++++++------------- 2 files changed, 285 insertions(+), 148 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index ec196bd762a..d984dd9b7df 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -719,6 +719,16 @@ void binary_align_weights(convolutional_layer *l) float_to_bit(align_weights, l->align_bit_weights, align_weights_size); + /* + if ((l->n % 8) == 0 && ((l->out_w*l->out_h) % 8) == 0 && l->c >= 64 && l->n == 128) { + int M = l->n; + int N = l->out_w*l->out_h; + //printf("\n M = %d, N = %d, M %% 8 = %d, N %% 8 = %d - weights \n", M, N, M % 8, N % 8); + for (i = 0; i < align_weights_size / 8; ++i) l->align_bit_weights[i] = ~(l->align_bit_weights[i]); + } + */ + + get_mean_array(l->binary_weights, m*k, l->n, l->mean_arr); //get_mean_array(l->binary_weights, m*new_lda, l->n, l->mean_arr); } diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index fc016cef782..e986d31782f 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -1192,6 +1192,38 @@ __device__ __host__ static inline ulonglong4 xnor_int256(ulonglong4 a, ulonglong return res; } +//------- + +__device__ __host__ static inline uint8_t xor_bit1(uint8_t a, uint8_t b) { + return (a^b) & 0b1; +} + +__device__ __host__ static inline uint32_t xor_int32(uint32_t a, uint32_t b) { + return (a^b); +} + +__device__ __host__ static inline uint64_t xor_int64(uint64_t a, uint64_t b) { + return (a^b); +} + +__device__ __host__ static inline uint4 xor_int128(uint4 a, uint4 b) { + uint4 res; + res.w = (a.w^b.w); + res.x = (a.x^b.x); + res.y = (a.y^b.y); + res.z = (a.z^b.z); + return res; +} + +__device__ __host__ static inline ulonglong4 xor_int256(ulonglong4 a, ulonglong4 b) { + ulonglong4 res; + res.w = (a.w^b.w); + res.x = (a.x^b.x); + res.y = (a.y^b.y); + res.z = (a.z^b.z); + return res; +} + __device__ static inline int popcnt_256(ulonglong4 a) { return __popcll(a.w) + __popcll(a.x) + __popcll(a.y) + __popcll(a.z); @@ -1398,6 +1430,222 @@ int warpAllReduceSum(int val) { return val; } +// Tensor Cores binary (CC >= 7.3 && CUDA >= 10.0) - __CUDA_SUBBYTE_IMMA__ +#if CUDART_VERSION >= 10000 +#include +using namespace nvcuda; +#endif + + + +// Coalescing +// A (weights) in the shared_memory - GOOD +__global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel(int M, int N, int K, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr, float *bias_arr) +{ + // total 57% + int index = blockIdx.x*blockDim.x + threadIdx.x; + + __shared__ uint8_t A_s[6144 * 8 / 4]; + //__shared__ uint64_t A_s[6144]; // 48 KB // [lda x M`] + //__shared__ uint8_t A_s[6144*8]; // 48 KB // [lda x M`] + + int start_i = blockIdx.x*blockDim.x / N; + int end_i = (blockIdx.x*blockDim.x + blockDim.x) / N + 1; + + size_t shared_size = lda * (end_i - start_i); + + int i_cur = index / N; + int local_i = i_cur - start_i; + // ~10% + for (int k = threadIdx.x * 64; k < shared_size; k += blockDim.x * 64) { + int x = start_i*lda + k; + if (x < (M*lda)) *((uint64_t *)(A_s + k / 8)) = *((uint64_t *)(A + x / 8)); + } + __syncthreads(); + + int i, j, k, h; + // 47% = 29 + 10 + 8 + j = index % N; + { // out_h*out_w - one channel output size [169 - 173056] + i = index / N; + //if (i < M) // l.n - filters [16 - 55 - 1024] + { + int count = 0; + k = 0; + + + if (i < M) + { + float mean_val = mean_arr[i]; + float bias_val = bias_arr[i]; + + for (; k < K; k += 128) { // l.size*l.size*l.c - one filter size [27 - 144 - 9216] + //uint4 a_bit128 = *((uint4 *)(A + (i*lda + k) / 8)); // weights + uint4 a_bit128 = *((uint4 *)(A_s + (local_i*lda + k) / 8)); // weights + uint4 b_bit128 = *((uint4 *)(B + (j*ldb + k) / 8)); // input + uint4 c_bit128 = xor_int128(a_bit128, b_bit128); + + count += __popc(c_bit128.w) + __popc(c_bit128.x) + + __popc(c_bit128.y) + __popc(c_bit128.z); + } + + + const int bit_step = 128;// 256; + int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); + count = count - f1; // remove extra bits (from empty space for align only) + + C[i*ldc + j] = (2 * count - K) *mean_val + bias_val; + } + } + } +} + +#if CUDART_VERSION >= 10000 +// Coalescing +// A (weights) in the shared_memory - GOOD +__global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel_old(int M, int N, int K, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr, float *bias_arr) +{ + // total 57% + int index = blockIdx.x*blockDim.x + threadIdx.x; + + __shared__ int C_s[8*8 * 32]; // BIN GEMM WMMA + + const int lane_id = threadIdx.x % 32; + const int warp_id = threadIdx.x / 32; + const int global_warp_id = index / 32; + + + int i, j, k, h; + // 47% = 29 + 10 + 8 + j = global_warp_id % (N / 8); + j = j * 8; + { // out_h*out_w - one channel output size [169 - 173056] + i = global_warp_id / (N / 8); + i = i * 8; + + if (i == 0 && j == 0 && lane_id == 0) { + // printf(" i = %d, j = %d, global_warp_id = %d, index = %d \n ", i, j, global_warp_id, index); + } + + //if (i < M) // l.n - filters [16 - 55 - 1024] + { + int count = 0; + k = 0; + + if (i < M) + { + // Tensor Cores binary (CC >= 7.3 && CUDA >= 10.0) - __CUDA_SUBBYTE_IMMA__ + //#if __CUDA_ARCH__ >= 730 && CUDART_VERSION >= 10000 + +#define WMMA_M 8 +#define WMMA_N 8 +#define WMMA_K 128 +#define WMMA_K32 (WMMA_K/32) + + wmma::fragment a_frag; + wmma::fragment b_frag; + wmma::fragment c_frag; + wmma::fill_fragment(c_frag, 0); // !!!! XOR isn't XNOR !!!!!!!!!! + + // lda, ldb - are in bits, should be divided by /8 or /32 + + // 8 x 8 x 4 (uint32_t, 4 * 32 = 128 bit) + for (; k < K; k += 128) + { // l.size*l.size*l.c - one filter size [27 - 144 - 9216] + int64_t A_cur_index = (i*lda + k) / 8; + //int64_t A_cur_index = (local_i*lda + k) / 8; + int64_t B_cur_index = (j*ldb + k) / 8; + + wmma::load_matrix_sync(a_frag, (uint32_t *)(A + A_cur_index), lda); // lda = M + wmma::load_matrix_sync(b_frag, (uint32_t *)(B + B_cur_index), ldb); // ldb = K + + + /* + if (i == 0 && j == 0) { + printf(" %d - %u, ", lane_id, a_frag.x[0]); + } + + + if (i == 0 && j == 0 && lane_id == 1) { + printf("\n\n now raw mem \n"); + + for (int i_d = 0; i_d < WMMA_M; ++i_d) { //8 + for (int k_d = 0; k_d < WMMA_K; k_d += 32) { //4 + uint32_t a_bit32 = *((uint32_t *)(A + ((i + i_d)*lda + (k + k_d)) / 8)); // weights + //uint32_t a_bit32 = *((uint32_t *)(A + A_cur_index + i_d*lda/8 + k_d/ 8)); // weights + printf(" %d - %u, ", i_d*WMMA_K32 + k_d/32, a_bit32); + } + printf("\n"); + } + printf("\n\n"); + } + */ + + + wmma::bmma_sync(c_frag, a_frag, b_frag, c_frag); + + // C[i*ldc + j] + wmma::store_matrix_sync(&C_s[warp_id*WMMA_M*WMMA_N], c_frag, WMMA_N, wmma::mem_row_major); + } + + /* + for (; k < K; k += 128) { // l.size*l.size*l.c - one filter size [27 - 144 - 9216] + uint4 a_bit128 = *((uint4 *)(A + (i*lda + k) / 8)); // weights + //uint4 a_bit128 = *((uint4 *)(A_s + (local_i*lda + k) / 8)); // weights + uint4 b_bit128 = *((uint4 *)(B + (j*ldb + k) / 8)); // input + uint4 c_bit128 = xnor_int128(a_bit128, b_bit128); + + count += __popc(c_bit128.w) + __popc(c_bit128.x) + + __popc(c_bit128.y) + __popc(c_bit128.z); + } + */ + + //#endif + + #pragma UNROLL + for (int i_d = 0; i_d < WMMA_M; ++i_d) { + for (int j_d = 0; j_d < WMMA_N; ++j_d) + { + + int count = C_s[warp_id*WMMA_M*WMMA_N + i_d*WMMA_N + j_d]; + + if (i == 0 && j == 0 && lane_id == 0) { + //printf(" %d -", count); + } + + const int bit_step = 128; + int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); + count = count - f1; // remove extra bits (from empty space for align only) + + count = (2 * count - K); + + if (i == 0 && j == 0 && lane_id == 0) { + //printf(" %d,", count); + } + + float mean_val = mean_arr[i + i_d]; + float bias_val = bias_arr[i + i_d]; + + C[(i + i_d)*ldc + (j + j_d)] = count *mean_val + bias_val; + + //C[(i + i_d)*ldc + (j + j_d)] = (2 * count - K) *mean_val + bias_val; + } + + if (i == 0 && j == 0 && lane_id == 0) { + //printf(" i = %d, j = %d, i_d = %d \n ", i, j, i_d); + } + } + } + } + } +} +#endif // CUDART_VERSION >= 10000 // Coalescing // A (weights) in the shared_memory - GOOD @@ -1434,6 +1682,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int i = index / N; //if (i < M) // l.n - filters [16 - 55 - 1024] { + int bit_step = 256; int count = 0; k = 0; @@ -1447,7 +1696,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int int64_t B_cur_index = (j*ldb + k) / 8; if (i >= M) A_cur_index = 0; - #pragma unroll +#pragma unroll for (int t = 0; t < WARP_SIZE; ++t) { const int lane_id = threadIdx.x % WARP_SIZE; @@ -1460,7 +1709,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int ulonglong4 b_bit256 = *((ulonglong4 *)(B + B_i)); // input c_bit256 = xnor_int256(a_bit256, b_bit256); int tmp_count = __popcll(c_bit256.w) + __popcll(c_bit256.x) + - __popcll(c_bit256.y) + __popcll(c_bit256.z); + __popcll(c_bit256.y) + __popcll(c_bit256.z); int sum_count = warpAllReduceSum(tmp_count); if (lane_id == t) count += sum_count; @@ -1469,6 +1718,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int } #endif + //#ifdef NOT_USED // 32 thread X 64 bit = 2048 bit // 29% for (; k < (K - 2048); k += 2048) { // l.size*l.size*l.c - one filter size [27 - 9216] @@ -1640,7 +1890,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel_leaky(int M, int N } #endif - //#ifdef NOT_USED +//#ifdef NOT_USED // 32 thread X 64 bit = 2048 bit // 29% for (; k < (K - 2048); k += 2048) { // l.size*l.size*l.c - one filter size [27 - 9216] uint64_t c_bit64; @@ -1669,7 +1919,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel_leaky(int M, int N } } } - //#endif +//#endif //#ifdef NOT_USED // 32 thread X 32 bit = 1024 bit // 10% @@ -1742,144 +1992,6 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel_leaky(int M, int N } } - -/* -// Coalescing -// B (input) in the shared_memory - GOOD -__global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int K, - unsigned char *A, int lda, - unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr, float *bias_arr) -{ - int index = blockIdx.x*blockDim.x + threadIdx.x; - - __shared__ uint8_t B_s[4096*8]; // 32 KB // [ldb x N`] // max = 262 144 bits - //__shared__ uint64_t B_s[4096]; // 32 KB // [ldb x N`] // max = 262 144 bits - - int start_j = blockIdx.x*blockDim.x / M; - int end_j = (blockIdx.x*blockDim.x + blockDim.x) / M + 1; - - size_t shared_size = ldb * (end_j - start_j); - - int j_cur = index / M; - int local_j = j_cur - start_j; - - for (int k = threadIdx.x * 256; k < shared_size; k += blockDim.x * 256) { - int x = start_j*ldb + k; - if (x < (N*ldb)) *((ulonglong4 *)(B_s + k / 8)) = *((ulonglong4 *)(B + x / 8)); - } - __syncthreads(); - - int i, j, k; - - i = index % M; // l.n - filters [16 - 55 - 1024] - { - j = index / M; // out_h*out_w - one channel output size [169 - 173056] - if (j < N) - { - int count = 0; - k = 0; - -//#ifdef NOT_USED - // 32 thread X 64 bit = 2048 bit - for (; k < (K - 2048); k += 2048) { // l.size*l.size*l.c - one filter size [27 - 9216] - uint64_t c_bit64; - - int64_t A_cur_index = (i*lda + k) / 8; - //int64_t B_cur_index = (j*ldb + k) / 8; - int64_t B_cur_index = (local_j*ldb + k) / 8; - if (i >= M) A_cur_index = 0; - - #pragma unroll - for (int t = 0; t < WARP_SIZE; ++t) { - const int lane_id = threadIdx.x % WARP_SIZE; - - const int64_t A_i = __shfl(A_cur_index, t) + 8 * lane_id; - const int64_t B_i = __shfl(B_cur_index, t) + 8 * lane_id; - - { - uint64_t a_bit64 = *((uint64_t *)(A + A_i)); // weights - //uint64_t b_bit64 = *((uint64_t *)(B + B_i)); // input - uint64_t b_bit64 = *((uint64_t *)(B_s + B_i)); // input - c_bit64 = xnor_int64(a_bit64, b_bit64); - int tmp_count = __popcll(c_bit64); - - int sum_count = warpAllReduceSum(tmp_count); - if (lane_id == t) count += sum_count; - } - } - } -//#endif - -//#ifdef NOT_USED - // 32 thread X 32 bit = 1024 bit - for (; k < (K - 1024); k += 1024) { // l.size*l.size*l.c - one filter size [27 - 9216] - - int64_t A_cur_index = (i*lda + k) / 8; - //int64_t B_cur_index = (j*ldb + k) / 8; - int64_t B_cur_index = (local_j*ldb + k) / 8; - if (i >= M) A_cur_index = 0; - - #pragma unroll - for (int t = 0; t < WARP_SIZE; ++t) { - const int lane_id = threadIdx.x % WARP_SIZE; - - const int64_t A_i = __shfl(A_cur_index, t) + 4 * lane_id; - const int64_t B_i = __shfl(B_cur_index, t) + 4 * lane_id; - - { - uint32_t a_bit32 = *((uint32_t *)(A + A_i)); // weights - //uint32_t b_bit32 = *((uint32_t *)(B + B_i)); // input - uint32_t b_bit32 = *((uint32_t *)(B_s + B_i)); // input - uint32_t c_bit32 = xnor_int32(a_bit32, b_bit32); - int tmp_count = __popc(c_bit32); - - int sum_count = warpAllReduceSum(tmp_count); - if (lane_id == t) count += sum_count; - } - } - } -//#endif - - if (i < M) - { - float mean_val = mean_arr[i]; - float bias_val = bias_arr[i]; - -//#ifdef NOT_USED - for (; k < K; k += 256) { // l.size*l.size*l.c - one filter size [27 - 144 - 9216] - ulonglong4 a_bit256 = *((ulonglong4 *)(A + (i*lda + k) / 8)); // weights - //ulonglong4 b_bit256 = *((ulonglong4 *)(B + (j*ldb + k) / 8)); // input - ulonglong4 b_bit256 = *((ulonglong4 *)(B_s + (local_j*ldb + k) / 8)); // input - ulonglong4 c_bit256 = xnor_int256(a_bit256, b_bit256); - - count += __popcll(c_bit256.w) + __popcll(c_bit256.x) + - __popcll(c_bit256.y) + __popcll(c_bit256.z); - } -//#endif - -#ifdef NOT_USED - for (; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] - uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); // weights - //uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); // input - uint64_t b_bit64 = *((uint64_t *)(B_s + (local_j*ldb + k) / 8)); // input - uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); - - count += __popcll(c_bit64); - } -#endif - - const int bit_step = 256; - int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); - count = count - f1; // remove extra bits (from empty space for align only) - - C[i*ldc + j] = (2 * count - K) * mean_val + bias_val; - } - } - } -} -*/ - // further optimization - use WMMA GEMM for using Tensor Cores // https://github.com/NVIDIA-developer-blog/code-samples/blob/master/posts/tensor-cores/simpleTensorCoreGEMM.cu // https://github.com/NVIDIA/cuda-samples/blob/master/Samples/cudaTensorCoreGemm/cudaTensorCoreGemm.cu @@ -1904,6 +2016,8 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, size_t size = M*N; const int num_blocks = get_number_of_blocks(size, BLOCK); + //printf("\n M = %d, N = %d, M %% 8 = %d, N %% 8 = %d \n", M, N, M % 8, N % 8); + /* printf("\n gemm_bin size = %d, num_blocks = %d, M*K = %d KB, N*K = %d KB \n (w) M*K/num_blocks = %d KB, (i) N*K/num_blocks = %d KB \n", size, num_blocks, M*K / 1024, N*K / 1024, M*lda / num_blocks / 1024, N*ldb / num_blocks / 1024); @@ -1920,12 +2034,25 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, mean_arr, bias); } else { - gemm_nn_custom_bin_mean_transposed_gpu_kernel << > > ( - M, N, K, - A, lda, - B, ldb, - C, ldc, - mean_arr, bias); + /* + if (M % 8 == 0 && N % 8 == 0 && M == 128) { + //printf(" lda = %d, ldb = %d, ldc = %d, lda/32 = %d, ldb/32 = %d, ldc/32 = %d \n", lda, ldb, ldc, lda / 32, ldb / 32, ldc / 32); + gemm_nn_custom_bin_mean_transposed_tensor_kernel_old << > > ( + M, N, K, + A, lda, + B, ldb, + C, ldc, + mean_arr, bias); + } + else*/ + { + gemm_nn_custom_bin_mean_transposed_gpu_kernel << > > ( + M, N, K, + A, lda, + B, ldb, + C, ldc, + mean_arr, bias); + } } } // -------------------------------- From 29aa716bd92fbb05b96ea26ab920d7975a00d7ff Mon Sep 17 00:00:00 2001 From: Alexey Date: Wed, 23 Jan 2019 18:04:31 +0300 Subject: [PATCH 0268/1365] Update Readme.md --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 118669f06e6..dfd644ce165 100644 --- a/README.md +++ b/README.md @@ -202,7 +202,11 @@ Then add to your created project: `C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include` - (right click on project) -> Build dependecies -> Build Customizations -> set check on CUDA 9.1 or what version you have - for example as here: http://devblogs.nvidia.com/parallelforall/wp-content/uploads/2015/01/VS2013-R-5.jpg -- add to project all `.c` & `.cu` files and file `http_stream.cpp` from `\src` +- add to project: + * all `.c` files + * all `.cu` files + * file `http_stream.cpp` from `\src` directory + * file `darknet.h` from `\include` directory - (right click on project) -> properties -> Linker -> General -> Additional Library Directories, put here: `C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories)` From 5e778cd91e67f3f7511534e5a793b60f79425f16 Mon Sep 17 00:00:00 2001 From: David Smith Date: Wed, 23 Jan 2019 22:02:09 -0600 Subject: [PATCH 0269/1365] add LSTM layer --- src/darknet.c | 79 ++++++ src/lstm_layer.c | 626 +++++++++++++++++++++++++++++++++++++++++++++++ src/lstm_layer.h | 20 ++ src/network.c | 2 + src/parser.c | 35 ++- 5 files changed, 761 insertions(+), 1 deletion(-) create mode 100644 src/lstm_layer.c create mode 100644 src/lstm_layer.h diff --git a/src/darknet.c b/src/darknet.c index fc560559ecb..5cf11f28f28 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -117,6 +117,26 @@ void operations(char *cfgfile) ops += 2l * l.n * l.size*l.size*l.c * l.out_h*l.out_w; } else if(l.type == CONNECTED){ ops += 2l * l.inputs * l.outputs; + } else if (l.type == RNN){ + ops += 2l * l.input_layer->inputs * l.input_layer->outputs; + ops += 2l * l.self_layer->inputs * l.self_layer->outputs; + ops += 2l * l.output_layer->inputs * l.output_layer->outputs; + } else if (l.type == GRU){ + ops += 2l * l.uz->inputs * l.uz->outputs; + ops += 2l * l.uh->inputs * l.uh->outputs; + ops += 2l * l.ur->inputs * l.ur->outputs; + ops += 2l * l.wz->inputs * l.wz->outputs; + ops += 2l * l.wh->inputs * l.wh->outputs; + ops += 2l * l.wr->inputs * l.wr->outputs; + } else if (l.type == LSTM){ + ops += 2l * l.uf->inputs * l.uf->outputs; + ops += 2l * l.ui->inputs * l.ui->outputs; + ops += 2l * l.ug->inputs * l.ug->outputs; + ops += 2l * l.uo->inputs * l.uo->outputs; + ops += 2l * l.wf->inputs * l.wf->outputs; + ops += 2l * l.wi->inputs * l.wi->outputs; + ops += 2l * l.wg->inputs * l.wg->outputs; + ops += 2l * l.wo->inputs * l.wo->outputs; } } printf("Floating Point Operations: %ld\n", ops); @@ -220,6 +240,16 @@ void reset_normalize_net(char *cfgfile, char *weightfile, char *outfile) denormalize_connected_layer(*l.state_r_layer); denormalize_connected_layer(*l.state_h_layer); } + if (l.type == LSTM && l.batch_normalize) { + denormalize_connected_layer(*l.wf); + denormalize_connected_layer(*l.wi); + denormalize_connected_layer(*l.wg); + denormalize_connected_layer(*l.wo); + denormalize_connected_layer(*l.uf); + denormalize_connected_layer(*l.ui); + denormalize_connected_layer(*l.ug); + denormalize_connected_layer(*l.uo); + } } save_weights(net, outfile); } @@ -262,6 +292,17 @@ void normalize_net(char *cfgfile, char *weightfile, char *outfile) *l.state_h_layer = normalize_layer(*l.state_h_layer, l.state_h_layer->outputs); net.layers[i].batch_normalize=1; } + if (l.type == LSTM && l.batch_normalize) { + *l.wf = normalize_layer(*l.wf, l.wf->outputs); + *l.wi = normalize_layer(*l.wi, l.wi->outputs); + *l.wg = normalize_layer(*l.wg, l.wg->outputs); + *l.wo = normalize_layer(*l.wo, l.wo->outputs); + *l.uf = normalize_layer(*l.uf, l.uf->outputs); + *l.ui = normalize_layer(*l.ui, l.ui->outputs); + *l.ug = normalize_layer(*l.ug, l.ug->outputs); + *l.uo = normalize_layer(*l.uo, l.uo->outputs); + net.layers[i].batch_normalize=1; + } } save_weights(net, outfile); } @@ -295,6 +336,25 @@ void statistics_net(char *cfgfile, char *weightfile) printf("State H\n"); statistics_connected_layer(*l.state_h_layer); } + if (l.type == LSTM && l.batch_normalize) { + printf("LSTM Layer %d\n", i); + printf("wf\n"); + statistics_connected_layer(*l.wf); + printf("wi\n"); + statistics_connected_layer(*l.wi); + printf("wg\n"); + statistics_connected_layer(*l.wg); + printf("wo\n"); + statistics_connected_layer(*l.wo); + printf("uf\n"); + statistics_connected_layer(*l.uf); + printf("ui\n"); + statistics_connected_layer(*l.ui); + printf("ug\n"); + statistics_connected_layer(*l.ug); + printf("uo\n"); + statistics_connected_layer(*l.uo); + } printf("\n"); } } @@ -332,6 +392,25 @@ void denormalize_net(char *cfgfile, char *weightfile, char *outfile) l.state_h_layer->batch_normalize = 0; net.layers[i].batch_normalize=0; } + if (l.type == GRU && l.batch_normalize) { + denormalize_connected_layer(*l.wf); + denormalize_connected_layer(*l.wi); + denormalize_connected_layer(*l.wg); + denormalize_connected_layer(*l.wo); + denormalize_connected_layer(*l.uf); + denormalize_connected_layer(*l.ui); + denormalize_connected_layer(*l.ug); + denormalize_connected_layer(*l.uo); + l.wf->batch_normalize = 0; + l.wi->batch_normalize = 0; + l.wg->batch_normalize = 0; + l.wo->batch_normalize = 0; + l.uf->batch_normalize = 0; + l.ui->batch_normalize = 0; + l.ug->batch_normalize = 0; + l.uo->batch_normalize = 0; + net.layers[i].batch_normalize=0; + } } save_weights(net, outfile); } diff --git a/src/lstm_layer.c b/src/lstm_layer.c new file mode 100644 index 00000000000..e61bf5c9db8 --- /dev/null +++ b/src/lstm_layer.c @@ -0,0 +1,626 @@ +#include "lstm_layer.h" +#include "connected_layer.h" +#include "utils.h" +#include "cuda.h" +#include "blas.h" +#include "gemm.h" + +#include +#include +#include +#include + +static void increment_layer(layer *l, int steps) +{ + int num = l->outputs*l->batch*steps; + l->output += num; + l->delta += num; + l->x += num; + l->x_norm += num; + +#ifdef GPU + l->output_gpu += num; + l->delta_gpu += num; + l->x_gpu += num; + l->x_norm_gpu += num; +#endif +} + +layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_normalize) +{ + fprintf(stderr, "LSTM Layer: %d inputs, %d outputs\n", inputs, outputs); + batch = batch / steps; + layer l = { 0 }; + l.batch = batch; + l.type = LSTM; + l.steps = steps; + l.inputs = inputs; + + l.uf = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.uf) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize); + l.uf->batch = batch; + + l.ui = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.ui) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize); + l.ui->batch = batch; + + l.ug = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.ug) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize); + l.ug->batch = batch; + + l.uo = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.uo) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize); + l.uo->batch = batch; + + l.wf = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.wf) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize); + l.wf->batch = batch; + + l.wi = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.wi) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize); + l.wi->batch = batch; + + l.wg = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.wg) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize); + l.wg->batch = batch; + + l.wo = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.wo) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize); + l.wo->batch = batch; + + l.batch_normalize = batch_normalize; + l.outputs = outputs; + + l.output = calloc(outputs*batch*steps, sizeof(float)); + l.state = calloc(outputs*batch, sizeof(float)); + + l.forward = forward_lstm_layer; + l.update = update_lstm_layer; + + l.prev_state_cpu = calloc(batch*outputs, sizeof(float)); + l.prev_cell_cpu = calloc(batch*outputs, sizeof(float)); + l.cell_cpu = calloc(batch*outputs*steps, sizeof(float)); + + l.f_cpu = calloc(batch*outputs, sizeof(float)); + l.i_cpu = calloc(batch*outputs, sizeof(float)); + l.g_cpu = calloc(batch*outputs, sizeof(float)); + l.o_cpu = calloc(batch*outputs, sizeof(float)); + l.c_cpu = calloc(batch*outputs, sizeof(float)); + l.h_cpu = calloc(batch*outputs, sizeof(float)); + l.temp_cpu = calloc(batch*outputs, sizeof(float)); + l.temp2_cpu = calloc(batch*outputs, sizeof(float)); + l.temp3_cpu = calloc(batch*outputs, sizeof(float)); + l.dc_cpu = calloc(batch*outputs, sizeof(float)); + l.dh_cpu = calloc(batch*outputs, sizeof(float)); + +#ifdef GPU + l.forward_gpu = forward_lstm_layer_gpu; + l.backward_gpu = backward_lstm_layer_gpu; + l.update_gpu = update_lstm_layer_gpu; + + l.output_gpu = cuda_make_array(0, batch*outputs*steps); + l.delta_gpu = cuda_make_array(0, batch*l.outputs*steps); + + l.prev_state_gpu = cuda_make_array(0, batch*outputs); + l.prev_cell_gpu = cuda_make_array(0, batch*outputs); + l.cell_gpu = cuda_make_array(0, batch*outputs*steps); + + l.f_gpu = cuda_make_array(0, batch*outputs); + l.i_gpu = cuda_make_array(0, batch*outputs); + l.g_gpu = cuda_make_array(0, batch*outputs); + l.o_gpu = cuda_make_array(0, batch*outputs); + l.c_gpu = cuda_make_array(0, batch*outputs); + l.h_gpu = cuda_make_array(0, batch*outputs); + l.temp_gpu = cuda_make_array(0, batch*outputs); + l.temp2_gpu = cuda_make_array(0, batch*outputs); + l.temp3_gpu = cuda_make_array(0, batch*outputs); + l.dc_gpu = cuda_make_array(0, batch*outputs); + l.dh_gpu = cuda_make_array(0, batch*outputs); +#ifdef CUDNN + cudnnSetTensor4dDescriptor(l.wf->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wf->out_c, l.wf->out_h, l.wf->out_w); + cudnnSetTensor4dDescriptor(l.wi->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wi->out_c, l.wi->out_h, l.wi->out_w); + cudnnSetTensor4dDescriptor(l.wg->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wg->out_c, l.wg->out_h, l.wg->out_w); + cudnnSetTensor4dDescriptor(l.wo->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wo->out_c, l.wo->out_h, l.wo->out_w); + + cudnnSetTensor4dDescriptor(l.uf->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uf->out_c, l.uf->out_h, l.uf->out_w); + cudnnSetTensor4dDescriptor(l.ui->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ui->out_c, l.ui->out_h, l.ui->out_w); + cudnnSetTensor4dDescriptor(l.ug->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ug->out_c, l.ug->out_h, l.ug->out_w); + cudnnSetTensor4dDescriptor(l.uo->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uo->out_c, l.uo->out_h, l.uo->out_w); +#endif + +#endif + + return l; +} + +void update_lstm_layer(layer l, int batch, float learning_rate, float momentum, float decay) +{ + update_connected_layer(*(l.wf), batch, learning_rate, momentum, decay); + update_connected_layer(*(l.wi), batch, learning_rate, momentum, decay); + update_connected_layer(*(l.wg), batch, learning_rate, momentum, decay); + update_connected_layer(*(l.wo), batch, learning_rate, momentum, decay); + update_connected_layer(*(l.uf), batch, learning_rate, momentum, decay); + update_connected_layer(*(l.ui), batch, learning_rate, momentum, decay); + update_connected_layer(*(l.ug), batch, learning_rate, momentum, decay); + update_connected_layer(*(l.uo), batch, learning_rate, momentum, decay); +} + +void forward_lstm_layer(layer l, network_state state) +{ + network_state s = { 0 }; + s.train = state.train; + int i; + layer wf = *(l.wf); + layer wi = *(l.wi); + layer wg = *(l.wg); + layer wo = *(l.wo); + + layer uf = *(l.uf); + layer ui = *(l.ui); + layer ug = *(l.ug); + layer uo = *(l.uo); + + fill_cpu(l.outputs * l.batch * l.steps, 0, wf.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wi.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wg.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wo.delta, 1); + + fill_cpu(l.outputs * l.batch * l.steps, 0, uf.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, ui.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, ug.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, uo.delta, 1); + if (state.train) { + fill_cpu(l.outputs * l.batch * l.steps, 0, l.delta, 1); + } + + for (i = 0; i < l.steps; ++i) { + s.input = l.h_cpu; + forward_connected_layer(wf, s); + forward_connected_layer(wi, s); + forward_connected_layer(wg, s); + forward_connected_layer(wo, s); + + s.input = state.input; + forward_connected_layer(uf, s); + forward_connected_layer(ui, s); + forward_connected_layer(ug, s); + forward_connected_layer(uo, s); + + copy_cpu(l.outputs*l.batch, wf.output, 1, l.f_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, uf.output, 1, l.f_cpu, 1); + + copy_cpu(l.outputs*l.batch, wi.output, 1, l.i_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ui.output, 1, l.i_cpu, 1); + + copy_cpu(l.outputs*l.batch, wg.output, 1, l.g_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ug.output, 1, l.g_cpu, 1); + + copy_cpu(l.outputs*l.batch, wo.output, 1, l.o_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, uo.output, 1, l.o_cpu, 1); + + activate_array(l.f_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.i_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.g_cpu, l.outputs*l.batch, TANH); + activate_array(l.o_cpu, l.outputs*l.batch, LOGISTIC); + + copy_cpu(l.outputs*l.batch, l.i_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.g_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.f_cpu, 1, l.c_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, l.temp_cpu, 1, l.c_cpu, 1); + + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.h_cpu, 1); + activate_array(l.h_cpu, l.outputs*l.batch, TANH); + mul_cpu(l.outputs*l.batch, l.o_cpu, 1, l.h_cpu, 1); + + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.cell_cpu, 1); + copy_cpu(l.outputs*l.batch, l.h_cpu, 1, l.output, 1); + + state.input += l.inputs*l.batch; + l.output += l.outputs*l.batch; + l.cell_cpu += l.outputs*l.batch; + + increment_layer(&wf, 1); + increment_layer(&wi, 1); + increment_layer(&wg, 1); + increment_layer(&wo, 1); + + increment_layer(&uf, 1); + increment_layer(&ui, 1); + increment_layer(&ug, 1); + increment_layer(&uo, 1); + } +} + +void backward_lstm_layer(layer l, network_state state) +{ + network_state s = { 0 }; + s.train = state.train; + int i; + layer wf = *(l.wf); + layer wi = *(l.wi); + layer wg = *(l.wg); + layer wo = *(l.wo); + + layer uf = *(l.uf); + layer ui = *(l.ui); + layer ug = *(l.ug); + layer uo = *(l.uo); + + increment_layer(&wf, l.steps - 1); + increment_layer(&wi, l.steps - 1); + increment_layer(&wg, l.steps - 1); + increment_layer(&wo, l.steps - 1); + + increment_layer(&uf, l.steps - 1); + increment_layer(&ui, l.steps - 1); + increment_layer(&ug, l.steps - 1); + increment_layer(&uo, l.steps - 1); + + state.input += l.inputs*l.batch*(l.steps - 1); + if (state.delta) state.delta += l.inputs*l.batch*(l.steps - 1); + + l.output += l.outputs*l.batch*(l.steps - 1); + l.cell_cpu += l.outputs*l.batch*(l.steps - 1); + l.delta += l.outputs*l.batch*(l.steps - 1); + + for (i = l.steps - 1; i >= 0; --i) { + if (i != 0) copy_cpu(l.outputs*l.batch, l.cell_cpu - l.outputs*l.batch, 1, l.prev_cell_cpu, 1); + copy_cpu(l.outputs*l.batch, l.cell_cpu, 1, l.c_cpu, 1); + if (i != 0) copy_cpu(l.outputs*l.batch, l.output - l.outputs*l.batch, 1, l.prev_state_cpu, 1); + copy_cpu(l.outputs*l.batch, l.output, 1, l.h_cpu, 1); + + l.dh_cpu = (i == 0) ? 0 : l.delta - l.outputs*l.batch; + + copy_cpu(l.outputs*l.batch, wf.output, 1, l.f_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, uf.output, 1, l.f_cpu, 1); + + copy_cpu(l.outputs*l.batch, wi.output, 1, l.i_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ui.output, 1, l.i_cpu, 1); + + copy_cpu(l.outputs*l.batch, wg.output, 1, l.g_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ug.output, 1, l.g_cpu, 1); + + copy_cpu(l.outputs*l.batch, wo.output, 1, l.o_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, uo.output, 1, l.o_cpu, 1); + + activate_array(l.f_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.i_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.g_cpu, l.outputs*l.batch, TANH); + activate_array(l.o_cpu, l.outputs*l.batch, LOGISTIC); + + copy_cpu(l.outputs*l.batch, l.delta, 1, l.temp3_cpu, 1); + + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.temp_cpu, 1); + activate_array(l.temp_cpu, l.outputs*l.batch, TANH); + + copy_cpu(l.outputs*l.batch, l.temp3_cpu, 1, l.temp2_cpu, 1); + mul_cpu(l.outputs*l.batch, l.o_cpu, 1, l.temp2_cpu, 1); + + gradient_array(l.temp_cpu, l.outputs*l.batch, TANH, l.temp2_cpu); + axpy_cpu(l.outputs*l.batch, 1, l.dc_cpu, 1, l.temp2_cpu, 1); + + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.temp_cpu, 1); + activate_array(l.temp_cpu, l.outputs*l.batch, TANH); + mul_cpu(l.outputs*l.batch, l.temp3_cpu, 1, l.temp_cpu, 1); + gradient_array(l.o_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu); + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wo.delta, 1); + s.input = l.prev_state_cpu; + s.delta = l.dh_cpu; + backward_connected_layer(wo, s); + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, uo.delta, 1); + s.input = state.input; + s.delta = state.delta; + backward_connected_layer(uo, s); + + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.i_cpu, 1, l.temp_cpu, 1); + gradient_array(l.g_cpu, l.outputs*l.batch, TANH, l.temp_cpu); + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wg.delta, 1); + s.input = l.prev_state_cpu; + s.delta = l.dh_cpu; + backward_connected_layer(wg, s); + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, ug.delta, 1); + s.input = state.input; + s.delta = state.delta; + backward_connected_layer(ug, s); + + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.g_cpu, 1, l.temp_cpu, 1); + gradient_array(l.i_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu); + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wi.delta, 1); + s.input = l.prev_state_cpu; + s.delta = l.dh_cpu; + backward_connected_layer(wi, s); + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, ui.delta, 1); + s.input = state.input; + s.delta = state.delta; + backward_connected_layer(ui, s); + + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.prev_cell_cpu, 1, l.temp_cpu, 1); + gradient_array(l.f_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu); + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wf.delta, 1); + s.input = l.prev_state_cpu; + s.delta = l.dh_cpu; + backward_connected_layer(wf, s); + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, uf.delta, 1); + s.input = state.input; + s.delta = state.delta; + backward_connected_layer(uf, s); + + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.f_cpu, 1, l.temp_cpu, 1); + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, l.dc_cpu, 1); + + state.input -= l.inputs*l.batch; + if (state.delta) state.delta -= l.inputs*l.batch; + l.output -= l.outputs*l.batch; + l.cell_cpu -= l.outputs*l.batch; + l.delta -= l.outputs*l.batch; + + increment_layer(&wf, -1); + increment_layer(&wi, -1); + increment_layer(&wg, -1); + increment_layer(&wo, -1); + + increment_layer(&uf, -1); + increment_layer(&ui, -1); + increment_layer(&ug, -1); + increment_layer(&uo, -1); + } +} + +#ifdef GPU +void update_lstm_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay) +{ + update_connected_layer_gpu(*(l.wf), batch, learning_rate, momentum, decay); + update_connected_layer_gpu(*(l.wi), batch, learning_rate, momentum, decay); + update_connected_layer_gpu(*(l.wg), batch, learning_rate, momentum, decay); + update_connected_layer_gpu(*(l.wo), batch, learning_rate, momentum, decay); + update_connected_layer_gpu(*(l.uf), batch, learning_rate, momentum, decay); + update_connected_layer_gpu(*(l.ui), batch, learning_rate, momentum, decay); + update_connected_layer_gpu(*(l.ug), batch, learning_rate, momentum, decay); + update_connected_layer_gpu(*(l.uo), batch, learning_rate, momentum, decay); +} + +void forward_lstm_layer_gpu(layer l, network_state state) +{ + network_state s = { 0 }; + s.train = state.train; + int i; + layer wf = *(l.wf); + layer wi = *(l.wi); + layer wg = *(l.wg); + layer wo = *(l.wo); + + layer uf = *(l.uf); + layer ui = *(l.ui); + layer ug = *(l.ug); + layer uo = *(l.uo); + + fill_ongpu(l.outputs * l.batch * l.steps, 0, wf.delta_gpu, 1); + fill_ongpu(l.outputs * l.batch * l.steps, 0, wi.delta_gpu, 1); + fill_ongpu(l.outputs * l.batch * l.steps, 0, wg.delta_gpu, 1); + fill_ongpu(l.outputs * l.batch * l.steps, 0, wo.delta_gpu, 1); + + fill_ongpu(l.outputs * l.batch * l.steps, 0, uf.delta_gpu, 1); + fill_ongpu(l.outputs * l.batch * l.steps, 0, ui.delta_gpu, 1); + fill_ongpu(l.outputs * l.batch * l.steps, 0, ug.delta_gpu, 1); + fill_ongpu(l.outputs * l.batch * l.steps, 0, uo.delta_gpu, 1); + if (state.train) { + fill_ongpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1); + } + + for (i = 0; i < l.steps; ++i) { + s.input = l.state_gpu; + forward_connected_layer_gpu(wf, s); + forward_connected_layer_gpu(wi, s); + forward_connected_layer_gpu(wg, s); + forward_connected_layer_gpu(wo, s); + + s.input = state.input; + forward_connected_layer_gpu(uf, s); + forward_connected_layer_gpu(ui, s); + forward_connected_layer_gpu(ug, s); + forward_connected_layer_gpu(uo, s); + + copy_ongpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1); + + copy_ongpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1); + + copy_ongpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1); + + copy_ongpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1); + + activate_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH); + activate_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC); + + copy_ongpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1); + mul_ongpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1); + mul_ongpu(l.outputs*l.batch, l.f_gpu, 1, l.c_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, l.temp_gpu, 1, l.c_gpu, 1); + + copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.h_gpu, 1); + activate_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH); + mul_ongpu(l.outputs*l.batch, l.o_gpu, 1, l.h_gpu, 1); + + copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.cell_gpu, 1); + copy_ongpu(l.outputs*l.batch, l.h_gpu, 1, l.output_gpu, 1); + + state.input += l.inputs*l.batch; + l.output_gpu += l.outputs*l.batch; + l.cell_gpu += l.outputs*l.batch; + + increment_layer(&wf, 1); + increment_layer(&wi, 1); + increment_layer(&wg, 1); + increment_layer(&wo, 1); + + increment_layer(&uf, 1); + increment_layer(&ui, 1); + increment_layer(&ug, 1); + increment_layer(&uo, 1); + } +} + +void backward_lstm_layer_gpu(layer l, network_state state) +{ + network_state s = { 0 }; + s.train = state.train; + int i; + layer wf = *(l.wf); + layer wi = *(l.wi); + layer wg = *(l.wg); + layer wo = *(l.wo); + + layer uf = *(l.uf); + layer ui = *(l.ui); + layer ug = *(l.ug); + layer uo = *(l.uo); + + increment_layer(&wf, l.steps - 1); + increment_layer(&wi, l.steps - 1); + increment_layer(&wg, l.steps - 1); + increment_layer(&wo, l.steps - 1); + + increment_layer(&uf, l.steps - 1); + increment_layer(&ui, l.steps - 1); + increment_layer(&ug, l.steps - 1); + increment_layer(&uo, l.steps - 1); + + state.input += l.inputs*l.batch*(l.steps - 1); + if (state.delta) state.delta += l.inputs*l.batch*(l.steps - 1); + + l.output_gpu += l.outputs*l.batch*(l.steps - 1); + l.cell_gpu += l.outputs*l.batch*(l.steps - 1); + l.delta_gpu += l.outputs*l.batch*(l.steps - 1); + + for (i = l.steps - 1; i >= 0; --i) { + if (i != 0) copy_ongpu(l.outputs*l.batch, l.cell_gpu - l.outputs*l.batch, 1, l.prev_cell_gpu, 1); + copy_ongpu(l.outputs*l.batch, l.cell_gpu, 1, l.c_gpu, 1); + if (i != 0) copy_ongpu(l.outputs*l.batch, l.output_gpu - l.outputs*l.batch, 1, l.prev_state_gpu, 1); + copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.h_gpu, 1); + + l.dh_gpu = (i == 0) ? 0 : l.delta_gpu - l.outputs*l.batch; + + copy_ongpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1); + + copy_ongpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1); + + copy_ongpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1); + + copy_ongpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1); + + activate_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH); + activate_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC); + + copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, l.temp3_gpu, 1); + + copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1); + activate_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH); + + copy_ongpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp2_gpu, 1); + mul_ongpu(l.outputs*l.batch, l.o_gpu, 1, l.temp2_gpu, 1); + + gradient_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH, l.temp2_gpu); + axpy_ongpu(l.outputs*l.batch, 1, l.dc_gpu, 1, l.temp2_gpu, 1); + + copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1); + activate_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH); + mul_ongpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp_gpu, 1); + gradient_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu); + copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wo.delta_gpu, 1); + s.input = l.prev_state_gpu; + s.delta = l.dh_gpu; + backward_connected_layer_gpu(wo, s); + + copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, uo.delta_gpu, 1); + s.input = state.input; + s.delta = state.delta; + backward_connected_layer_gpu(uo, s); + + copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + mul_ongpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1); + gradient_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH, l.temp_gpu); + copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wg.delta_gpu, 1); + s.input = l.prev_state_gpu; + s.delta = l.dh_gpu; + backward_connected_layer_gpu(wg, s); + + copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, ug.delta_gpu, 1); + s.input = state.input; + s.delta = state.delta; + backward_connected_layer_gpu(ug, s); + + copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + mul_ongpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1); + gradient_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu); + copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wi.delta_gpu, 1); + s.input = l.prev_state_gpu; + s.delta = l.dh_gpu; + backward_connected_layer_gpu(wi, s); + + copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, ui.delta_gpu, 1); + s.input = state.input; + s.delta = state.delta; + backward_connected_layer_gpu(ui, s); + + copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + mul_ongpu(l.outputs*l.batch, l.prev_cell_gpu, 1, l.temp_gpu, 1); + gradient_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu); + copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wf.delta_gpu, 1); + s.input = l.prev_state_gpu; + s.delta = l.dh_gpu; + backward_connected_layer_gpu(wf, s); + + copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, uf.delta_gpu, 1); + s.input = state.input; + s.delta = state.delta; + backward_connected_layer_gpu(uf, s); + + copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + mul_ongpu(l.outputs*l.batch, l.f_gpu, 1, l.temp_gpu, 1); + copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, l.dc_gpu, 1); + + state.input -= l.inputs*l.batch; + if (state.delta) state.delta -= l.inputs*l.batch; + l.output_gpu -= l.outputs*l.batch; + l.cell_gpu -= l.outputs*l.batch; + l.delta_gpu -= l.outputs*l.batch; + + increment_layer(&wf, -1); + increment_layer(&wi, -1); + increment_layer(&wg, -1); + increment_layer(&wo, -1); + + increment_layer(&uf, -1); + increment_layer(&ui, -1); + increment_layer(&ug, -1); + increment_layer(&uo, -1); + } +} +#endif diff --git a/src/lstm_layer.h b/src/lstm_layer.h new file mode 100644 index 00000000000..ad37c96fa97 --- /dev/null +++ b/src/lstm_layer.h @@ -0,0 +1,20 @@ +#ifndef LSTM_LAYER_H +#define LSTM_LAYER_H + +#include "activations.h" +#include "layer.h" +#include "network.h" +#define USET + +layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_normalize); + +void forward_lstm_layer(layer l, network_state state); +void update_lstm_layer(layer l, int batch, float learning_rate, float momentum, float decay); + +#ifdef GPU +void forward_lstm_layer_gpu(layer l, network_state state); +void backward_lstm_layer_gpu(layer l, network_state state); +void update_lstm_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay); + +#endif +#endif diff --git a/src/network.c b/src/network.c index 465764f232d..79686aa05b2 100644 --- a/src/network.c +++ b/src/network.c @@ -140,6 +140,8 @@ char *get_layer_string(LAYER_TYPE a) return "rnn"; case GRU: return "gru"; + case LSTM: + return "lstm"; case CRNN: return "crnn"; case MAXPOOL: diff --git a/src/parser.c b/src/parser.c index 1a4c8b82b47..3a51ab8b3e2 100644 --- a/src/parser.c +++ b/src/parser.c @@ -18,6 +18,7 @@ #include "gru_layer.h" #include "list.h" #include "local_layer.h" +#include "lstm_layer.h" #include "maxpool_layer.h" #include "normalization_layer.h" #include "option_list.h" @@ -58,6 +59,7 @@ LAYER_TYPE string_to_layer_type(char * type) || strcmp(type, "[network]")==0) return NETWORK; if (strcmp(type, "[crnn]")==0) return CRNN; if (strcmp(type, "[gru]")==0) return GRU; + if (strcmp(type, "[lstm]")==0) return LSTM; if (strcmp(type, "[rnn]")==0) return RNN; if (strcmp(type, "[conn]")==0 || strcmp(type, "[connected]")==0) return CONNECTED; @@ -219,6 +221,16 @@ layer parse_gru(list *options, size_params params) return l; } +layer parse_lstm(list *options, size_params params) +{ + int output = option_find_int(options, "output",1); + int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); + + layer l = make_lstm_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize); + + return l; +} + connected_layer parse_connected(list *options, size_params params) { int output = option_find_int(options, "output",1); @@ -755,6 +767,8 @@ network parse_network_cfg_custom(char *filename, int batch) l = parse_rnn(options, params); }else if(lt == GRU){ l = parse_gru(options, params); + }else if(lt == LSTM){ + l = parse_lstm(options, params); }else if(lt == CRNN){ l = parse_crnn(options, params); }else if(lt == CONNECTED){ @@ -1025,6 +1039,15 @@ void save_weights_upto(network net, char *filename, int cutoff) save_connected_weights(*(l.state_z_layer), fp); save_connected_weights(*(l.state_r_layer), fp); save_connected_weights(*(l.state_h_layer), fp); + } if(l.type == LSTM){ + save_connected_weights(*(l.wf), fp); + save_connected_weights(*(l.wi), fp); + save_connected_weights(*(l.wg), fp); + save_connected_weights(*(l.wo), fp); + save_connected_weights(*(l.uf), fp); + save_connected_weights(*(l.ui), fp); + save_connected_weights(*(l.ug), fp); + save_connected_weights(*(l.uo), fp); } if(l.type == CRNN){ save_convolutional_weights(*(l.input_layer), fp); save_convolutional_weights(*(l.self_layer), fp); @@ -1236,6 +1259,16 @@ void load_weights_upto(network *net, char *filename, int cutoff) load_connected_weights(*(l.state_r_layer), fp, transpose); load_connected_weights(*(l.state_h_layer), fp, transpose); } + if(l.type == LSTM){ + load_connected_weights(*(l.wf), fp, transpose); + load_connected_weights(*(l.wi), fp, transpose); + load_connected_weights(*(l.wg), fp, transpose); + load_connected_weights(*(l.wo), fp, transpose); + load_connected_weights(*(l.uf), fp, transpose); + load_connected_weights(*(l.ui), fp, transpose); + load_connected_weights(*(l.ug), fp, transpose); + load_connected_weights(*(l.uo), fp, transpose); + } if(l.type == LOCAL){ int locations = l.out_w*l.out_h; int size = l.size*l.size*l.c*l.n*locations; @@ -1281,4 +1314,4 @@ network *load_network(char *cfg, char *weights, int clear) } if (clear) (*net->seen) = 0; return net; -} \ No newline at end of file +} From 96773df4699fc34ec231fa19ae13e055bffcf3fd Mon Sep 17 00:00:00 2001 From: David Smith Date: Thu, 24 Jan 2019 09:38:45 -0600 Subject: [PATCH 0270/1365] add lstm_layer.o to Makefile --- Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Makefile b/Makefile index e19870aa9bc..745f1ac2936 100644 --- a/Makefile +++ b/Makefile @@ -108,7 +108,7 @@ CFLAGS+= -DCUDNN_HALF ARCH+= -gencode arch=compute_70,code=[sm_70,compute_70] endif -OBJ=http_stream.o gemm.o utils.o cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o upsample_layer.o +OBJ=http_stream.o gemm.o utils.o cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o upsample_layer.o lstm_layer.o ifeq ($(GPU), 1) LDFLAGS+= -lstdc++ OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o From a7366a5a0adb996e72abd3eac7713c3e682c21b5 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 24 Jan 2019 20:19:01 +0300 Subject: [PATCH 0271/1365] Compile fix for CC < 7.3 --- src/im2col_kernels.cu | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index e986d31782f..0cbd11529a7 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -1433,7 +1433,6 @@ int warpAllReduceSum(int val) { // Tensor Cores binary (CC >= 7.3 && CUDA >= 10.0) - __CUDA_SUBBYTE_IMMA__ #if CUDART_VERSION >= 10000 #include -using namespace nvcuda; #endif @@ -1548,6 +1547,9 @@ __global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel_old(int M, int #define WMMA_K 128 #define WMMA_K32 (WMMA_K/32) +#if __CUDA_ARCH__ >= 730 + using namespace nvcuda; + wmma::fragment a_frag; wmma::fragment b_frag; wmma::fragment c_frag; @@ -1606,7 +1608,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel_old(int M, int } */ - //#endif +#endif // __CUDA_ARCH__ >= 730 #pragma UNROLL for (int i_d = 0; i_d < WMMA_M; ++i_d) { @@ -1682,7 +1684,6 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int i = index / N; //if (i < M) // l.n - filters [16 - 55 - 1024] { - int bit_step = 256; int count = 0; k = 0; @@ -2035,6 +2036,7 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, } else { /* +#if CUDART_VERSION >= 10000 if (M % 8 == 0 && N % 8 == 0 && M == 128) { //printf(" lda = %d, ldb = %d, ldc = %d, lda/32 = %d, ldb/32 = %d, ldc/32 = %d \n", lda, ldb, ldc, lda / 32, ldb / 32, ldc / 32); gemm_nn_custom_bin_mean_transposed_tensor_kernel_old << > > ( @@ -2044,7 +2046,9 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, C, ldc, mean_arr, bias); } - else*/ + else +#endif // CUDART_VERSION >= 10000 + */ { gemm_nn_custom_bin_mean_transposed_gpu_kernel << > > ( M, N, K, From 1b15e2f8df5794a8c81fdfafddb3863c9a1a9e16 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 24 Jan 2019 20:30:15 +0300 Subject: [PATCH 0272/1365] Compile fix on Windows --- build/darknet/darknet.vcxproj | 2 ++ build/darknet/darknet_no_gpu.vcxproj | 2 ++ build/darknet/yolo_cpp_dll.vcxproj | 2 ++ build/darknet/yolo_cpp_dll_no_gpu.vcxproj | 2 ++ 4 files changed, 8 insertions(+) diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj index f1500eed9fd..5e5e0c2bf35 100644 --- a/build/darknet/darknet.vcxproj +++ b/build/darknet/darknet.vcxproj @@ -208,6 +208,7 @@ + @@ -266,6 +267,7 @@ + diff --git a/build/darknet/darknet_no_gpu.vcxproj b/build/darknet/darknet_no_gpu.vcxproj index 785b2f02c93..40145d20b38 100644 --- a/build/darknet/darknet_no_gpu.vcxproj +++ b/build/darknet/darknet_no_gpu.vcxproj @@ -213,6 +213,7 @@ + @@ -271,6 +272,7 @@ + diff --git a/build/darknet/yolo_cpp_dll.vcxproj b/build/darknet/yolo_cpp_dll.vcxproj index 64f5d297cf1..74eb2afc52a 100644 --- a/build/darknet/yolo_cpp_dll.vcxproj +++ b/build/darknet/yolo_cpp_dll.vcxproj @@ -212,6 +212,7 @@ + @@ -272,6 +273,7 @@ + diff --git a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj index 6539993553c..01f00256d68 100644 --- a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj +++ b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj @@ -197,6 +197,7 @@ + @@ -257,6 +258,7 @@ + From 630f441e08854649e4d10d14fab13f6160a2fc06 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 26 Jan 2019 02:54:41 +0300 Subject: [PATCH 0273/1365] Minor CPU speedup - i7 6500K: 1000ms (AVX=1) instead of 1500ms (old AVX=1) and 2000ms (AVX=0) --- src/gemm.c | 183 +++++++++++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 171 insertions(+), 12 deletions(-) diff --git a/src/gemm.c b/src/gemm.c index 16efa0654b5..8fa0f76777f 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -730,6 +730,144 @@ void gemm_nn(int M, int N, int K, float ALPHA, } +#define TILE_M 4 // 4 ops +#define TILE_N 16 // AVX2 = 2 ops * 8 floats +#define TILE_K 16 // loop + +void gemm_nn_fast(int M, int N, int K, float ALPHA, + float *A, int lda, + float *B, int ldb, + float *C, int ldc) +{ + int i; + + #pragma omp parallel for + for (i = 0; i < (M / TILE_M)*TILE_M; i += TILE_M) + { + int j, k; + int i_d, j_d, k_d; + + for (k = 0; k < (K / TILE_K)*TILE_K; k += TILE_K) + { + for (j = 0; j < (N / TILE_N)*TILE_N; j += TILE_N) + { + // L1 - 6 bits tag [11:6] - cache size 32 KB, conflict for each 4 KB + // L2 - 9 bits tag [14:6] - cache size 256 KB, conflict for each 32 KB + // L3 - 13 bits tag [18:6] - cache size 8 MB, conflict for each 512 KB + + __m256 result256; + __m256 a256_0, b256_0; // AVX + __m256 a256_1, b256_1; // AVX + __m256 a256_2, b256_2; // AVX + __m256 a256_3, b256_3; // AVX + __m256 c256_0, c256_1, c256_2, c256_3; + __m256 c256_4, c256_5, c256_6, c256_7; + + c256_0 = _mm256_loadu_ps(&C[(0 + i)*ldc + (0 + j)]); + c256_1 = _mm256_loadu_ps(&C[(1 + i)*ldc + (0 + j)]); + c256_2 = _mm256_loadu_ps(&C[(0 + i)*ldc + (8 + j)]); + c256_3 = _mm256_loadu_ps(&C[(1 + i)*ldc + (8 + j)]); + + c256_4 = _mm256_loadu_ps(&C[(2 + i)*ldc + (0 + j)]); + c256_5 = _mm256_loadu_ps(&C[(3 + i)*ldc + (0 + j)]); + c256_6 = _mm256_loadu_ps(&C[(2 + i)*ldc + (8 + j)]); + c256_7 = _mm256_loadu_ps(&C[(3 + i)*ldc + (8 + j)]); + + + for (k_d = 0; k_d < (TILE_K); ++k_d) + { + a256_0 = _mm256_set1_ps(ALPHA*A[(0 + i)*lda + (k_d + k)]); + a256_1 = _mm256_set1_ps(ALPHA*A[(1 + i)*lda + (k_d + k)]); + + a256_2 = _mm256_set1_ps(ALPHA*A[(2 + i)*lda + (k_d + k)]); + a256_3 = _mm256_set1_ps(ALPHA*A[(3 + i)*lda + (k_d + k)]); + + + b256_0 = _mm256_loadu_ps(&B[(k_d + k)*ldb + (0 + j)]); + b256_1 = _mm256_loadu_ps(&B[(k_d + k)*ldb + (8 + j)]); + + // FMA - Intel Haswell (2013), AMD Piledriver (2012) + //c256_0 = _mm256_fmadd_ps(a256_0, b256_0, c256_0); + //c256_1 = _mm256_fmadd_ps(a256_1, b256_0, c256_1); + //c256_2 = _mm256_fmadd_ps(a256_0, b256_1, c256_2); + //c256_3 = _mm256_fmadd_ps(a256_1, b256_1, c256_3); + + //c256_4 = _mm256_fmadd_ps(a256_2, b256_0, c256_4); + //c256_5 = _mm256_fmadd_ps(a256_3, b256_0, c256_5); + //c256_6 = _mm256_fmadd_ps(a256_2, b256_1, c256_6); + //c256_7 = _mm256_fmadd_ps(a256_3, b256_1, c256_7); + + result256 = _mm256_mul_ps(a256_0, b256_0); + c256_0 = _mm256_add_ps(result256, c256_0); + + result256 = _mm256_mul_ps(a256_1, b256_0); + c256_1 = _mm256_add_ps(result256, c256_1); + + result256 = _mm256_mul_ps(a256_0, b256_1); + c256_2 = _mm256_add_ps(result256, c256_2); + + result256 = _mm256_mul_ps(a256_1, b256_1); + c256_3 = _mm256_add_ps(result256, c256_3); + + + result256 = _mm256_mul_ps(a256_2, b256_0); + c256_4 = _mm256_add_ps(result256, c256_4); + + result256 = _mm256_mul_ps(a256_3, b256_0); + c256_5 = _mm256_add_ps(result256, c256_5); + + result256 = _mm256_mul_ps(a256_2, b256_1); + c256_6 = _mm256_add_ps(result256, c256_6); + + result256 = _mm256_mul_ps(a256_3, b256_1); + c256_7 = _mm256_add_ps(result256, c256_7); + } + _mm256_storeu_ps(&C[(0 + i)*ldc + (0 + j)], c256_0); + _mm256_storeu_ps(&C[(1 + i)*ldc + (0 + j)], c256_1); + _mm256_storeu_ps(&C[(0 + i)*ldc + (8 + j)], c256_2); + _mm256_storeu_ps(&C[(1 + i)*ldc + (8 + j)], c256_3); + + _mm256_storeu_ps(&C[(2 + i)*ldc + (0 + j)], c256_4); + _mm256_storeu_ps(&C[(3 + i)*ldc + (0 + j)], c256_5); + _mm256_storeu_ps(&C[(2 + i)*ldc + (8 + j)], c256_6); + _mm256_storeu_ps(&C[(3 + i)*ldc + (8 + j)], c256_7); + } + + for (j = (N / TILE_N)*TILE_N; j < N; ++j) { + for (i_d = i; i_d < (i + TILE_M); ++i_d) + { + for (k_d = k; k_d < (k + TILE_K); ++k_d) + { + register float A_PART = ALPHA*A[i_d*lda + k_d]; + C[i_d*ldc + j] += A_PART*B[k_d*ldb + j]; + } + } + } + } + + for (k = (K / TILE_K)*TILE_K; k < K; ++k) + { + for (i_d = i; i_d < (i + TILE_M); ++i_d) + { + register float A_PART = ALPHA*A[i_d*lda + k]; + for (j = 0; j < N; ++j) { + C[i_d*ldc + j] += A_PART*B[k*ldb + j]; + } + } + } + } + + for (i = (M / TILE_M)*TILE_M; i < M; ++i) { + int j, k; + for (k = 0; k < K; ++k) { + register float A_PART = ALPHA*A[i*lda + k]; + for (j = 0; j < N; ++j) { + C[i*ldc + j] += A_PART*B[k*ldb + j]; + } + } + } +} + void gemm_nn_bin_32bit_packed(int M, int N, int K, float ALPHA, @@ -1749,6 +1887,23 @@ void gemm_nn(int M, int N, int K, float ALPHA, } } +void gemm_nn_fast(int M, int N, int K, float ALPHA, + float *A, int lda, + float *B, int ldb, + float *C, int ldc) +{ + int i, j, k; + #pragma omp parallel for + for (i = 0; i < M; ++i) { + for (k = 0; k < K; ++k) { + register float A_PART = ALPHA*A[i*lda + k]; + for (j = 0; j < N; ++j) { + C[i*ldc + j] += A_PART*B[k*ldb + j]; + } + } + } +} + void gemm_nn_bin_32bit_packed(int M, int N, int K, float ALPHA, uint32_t *A, int lda, uint32_t *B, int ldb, @@ -2410,18 +2565,22 @@ void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA, } is_avx(); // initialize static variable - is_fma_avx2(); - int t; - #pragma omp parallel for - for (t = 0; t < M; ++t) { - if (!TA && !TB) - gemm_nn(1, N, K, ALPHA, A + t*lda, lda, B, ldb, C + t*ldc, ldc); - else if (TA && !TB) - gemm_tn(1, N, K, ALPHA, A + t, lda, B, ldb, C + t*ldc, ldc); - else if (!TA && TB) - gemm_nt(1, N, K, ALPHA, A + t*lda, lda, B, ldb, C + t*ldc, ldc); - else - gemm_tt(1, N, K, ALPHA, A + t, lda, B, ldb, C + t*ldc, ldc); + if (is_fma_avx2() && !TA && !TB) { + gemm_nn_fast(M, N, K, ALPHA, A, lda, B, ldb, C, ldc); + } + else { + int t; + #pragma omp parallel for + for (t = 0; t < M; ++t) { + if (!TA && !TB) + gemm_nn(1, N, K, ALPHA, A + t*lda, lda, B, ldb, C + t*ldc, ldc); + else if (TA && !TB) + gemm_tn(1, N, K, ALPHA, A + t, lda, B, ldb, C + t*ldc, ldc); + else if (!TA && TB) + gemm_nt(1, N, K, ALPHA, A + t*lda, lda, B, ldb, C + t*ldc, ldc); + else + gemm_tt(1, N, K, ALPHA, A + t, lda, B, ldb, C + t*ldc, ldc); + } } } From 090d934c0f33007ca9422d92c0fdc211abf32ccc Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 26 Jan 2019 19:12:46 +0300 Subject: [PATCH 0274/1365] Minor speedup on CPU --- src/activations.c | 7 +++ src/blas.c | 7 ++- src/convolutional_layer.c | 59 +++++++++++++++---- src/gemm.c | 117 +++++++++++++++++++++++++++++++------- src/network.c | 2 +- src/shortcut_layer.c | 13 ++++- 6 files changed, 168 insertions(+), 37 deletions(-) diff --git a/src/activations.c b/src/activations.c index c42a67a760f..7aba7e26b1f 100644 --- a/src/activations.c +++ b/src/activations.c @@ -102,10 +102,17 @@ void activate_array(float *x, const int n, const ACTIVATION a) int i; if (a == LINEAR) {} else if (a == LEAKY) { + #pragma omp parallel for for (i = 0; i < n; ++i) { x[i] = leaky_activate(x[i]); } } + else if (a == LOGISTIC) { + #pragma omp parallel for + for (i = 0; i < n; ++i) { + x[i] = logistic_activate(x[i]); + } + } else { for (i = 0; i < n; ++i) { x[i] = activate(x[i], a); diff --git a/src/blas.c b/src/blas.c index 70e069917d4..ae84dc72d5d 100644 --- a/src/blas.c +++ b/src/blas.c @@ -172,7 +172,12 @@ void scal_cpu(int N, float ALPHA, float *X, int INCX) void fill_cpu(int N, float ALPHA, float *X, int INCX) { int i; - for(i = 0; i < N; ++i) X[i*INCX] = ALPHA; + if (INCX == 1 && ALPHA == 0) { + memset(X, 0, N * sizeof(float)); + } + else { + for (i = 0; i < N; ++i) X[i*INCX] = ALPHA; + } } void deinter_cpu(int NX, float *X, int NY, float *Y, int B, float *OUT) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index d984dd9b7df..fd055a37395 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -776,7 +776,12 @@ size_t binary_transpose_align_input(int k, int n, float *b, char **t_bit_input, size_t t_intput_size = new_ldb * bit_align;// n; size_t t_bit_input_size = t_intput_size / 8;// +1; - *t_bit_input = calloc(t_bit_input_size, sizeof(char)); + static int last_t_bit_input_size = 0; + if (last_t_bit_input_size < t_bit_input_size) { + last_t_bit_input_size = t_bit_input_size; + *t_bit_input = realloc(*t_bit_input, last_t_bit_input_size * sizeof(char)); + } + memset(*t_bit_input, 0, t_bit_input_size * sizeof(char)); int src_size = k * bit_align; // b - [bit_align, k] - [l.bit_align, l.size*l.size*l.c] = src_size @@ -798,7 +803,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) fill_cpu(l.outputs*l.batch, 0, l.output, 1); - if(l.xnor){ + if (l.xnor && (!l.align_bit_weights || state.train)) { if (!l.align_bit_weights || state.train) { binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights); //printf("\n binarize_weights l.align_bit_weights = %p \n", l.align_bit_weights); @@ -838,8 +843,26 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) const int new_c = l.c / 32; - float *re_packed_input = calloc(l.c * l.w * l.h, sizeof(float)); - uint32_t *bin_re_packed_input = calloc(new_c * l.w * l.h + 1, sizeof(uint32_t)); + static float *re_packed_input = NULL; + static int last_re_packed_input_size = 0; + int re_packed_input_size = l.c * l.w * l.h; + if (last_re_packed_input_size < re_packed_input_size) { + last_re_packed_input_size = re_packed_input_size; + re_packed_input = realloc(re_packed_input, last_re_packed_input_size * sizeof(float)); + } + memset(re_packed_input, 0, re_packed_input_size * sizeof(float)); + + static uint32_t *bin_re_packed_input = NULL; + static int last_bin_re_packed_input_size = 0; + int in_re_packed_input_size = new_c * l.w * l.h + 1; + if (last_bin_re_packed_input_size < in_re_packed_input_size) { + last_bin_re_packed_input_size = in_re_packed_input_size; + bin_re_packed_input = realloc(bin_re_packed_input, last_bin_re_packed_input_size * sizeof(uint32_t)); + } + memset(bin_re_packed_input, 0, in_re_packed_input_size * sizeof(uint32_t)); + + //float *re_packed_input = calloc(l.c * l.w * l.h, sizeof(float)); + //uint32_t *bin_re_packed_input = calloc(new_c * l.w * l.h + 1, sizeof(uint32_t)); // float32x4 by channel (as in cuDNN) repack_input(state.input, re_packed_input, l.w, l.h, l.c); @@ -847,7 +870,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) // 32 x floats -> 1 x uint32_t float_to_bit(re_packed_input, (char *)bin_re_packed_input, l.c * l.w * l.h); - free(re_packed_input); + //free(re_packed_input); // slow - convolution the packed inputs and weights: float x 32 by channel (as in cuDNN) //convolution_repacked((uint32_t *)bin_re_packed_input, (uint32_t *)l.align_bit_weights, l.output, @@ -859,7 +882,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) im2col_cpu_custom((float *)bin_re_packed_input, new_c, l.h, l.w, l.size, l.stride, l.pad, b); //im2col_cpu((float *)bin_re_packed_input, new_c, l.h, l.w, l.size, l.stride, l.pad, b); - free(bin_re_packed_input); + //free(bin_re_packed_input); int new_k = l.size*l.size*l.c / 32; @@ -876,7 +899,14 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //size_t t_intput_size = new_ldb * l.bit_align;// n; //size_t t_bit_input_size = t_intput_size / 8;// +1; - char *t_bit_input = calloc(t_bit_input_size, sizeof(char)); + //char *t_bit_input = calloc(t_bit_input_size, sizeof(char)); + static char *t_bit_input = NULL; + static int last_t_bit_input_size = 0; + if (last_t_bit_input_size < t_bit_input_size) { + last_t_bit_input_size = t_bit_input_size; + t_bit_input = realloc(t_bit_input, last_t_bit_input_size * sizeof(char)); + } + memset(t_bit_input, 0, t_bit_input_size * sizeof(char)); transpose_uint32((uint32_t *)b, t_bit_input, new_k, n, n, new_ldb); @@ -889,10 +919,11 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) // t_bit_input, new_ldb / 32, // c, n, l.mean_arr); - free(t_bit_input); + //free(t_bit_input); } - else { // else (l.c % 32 != 0) + else + { // else (l.c % 32 != 0) //-------------------------------------------------------- //printf(" l.index = %d - old XNOR \n", l.index); @@ -919,7 +950,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //size_t ldb_align = 256; // 256 bit for AVX2 int ldb_align = l.lda_align; size_t new_ldb = k + (ldb_align - k%ldb_align); - char *t_bit_input = NULL; + static char *t_bit_input = NULL; size_t t_intput_size = binary_transpose_align_input(k, n, b, &t_bit_input, ldb_align, l.bit_align); //char *t_bit_input = calloc(new_ldb * n, sizeof(char)); // for im2col_cpu_custom_transpose() only //float_to_bit(t_input, t_bit_input, new_ldb * n); // for im2col_cpu_custom_transpose() only @@ -930,12 +961,18 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //gemm_nn_custom_bin_mean_transposed(m, n, k, 1, bit_weights, k, t_bit_input, new_ldb, c, n, mean_arr); //free(t_input); - free(t_bit_input); + //free(t_bit_input); //} } } + add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w); + + //activate_array(l.output, m*n*l.batch, l.activation); + activate_array_cpu_custom(l.output, m*n*l.batch, l.activation); + return; + } else { //printf(" l.index = %d - FP32 \n", l.index); diff --git a/src/gemm.c b/src/gemm.c index 8fa0f76777f..1b1d8fe22a7 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -1151,6 +1151,23 @@ static inline int popcnt256_custom(__m256i n) { + _mm256_extract_epi64(val, 3); } +static inline void xnor_avx2_popcnt(__m256i a_bit256, __m256i b_bit256, __m256i *count_sum) { + __m256i c_bit256 = _mm256_set1_epi8(255); + + __m256i xor256 = _mm256_xor_si256(a_bit256, b_bit256); // xnor = not(xor(a,b)) + c_bit256 = _mm256_andnot_si256(xor256, c_bit256); // can be optimized - we can do other NOT for wegihts once and do not do this NOT + + *count_sum = _mm256_add_epi64(count256(c_bit256), *count_sum); // 1st part - popcnt Mula’s algorithm +} + +// 2nd part - popcnt Mula’s algorithm +static inline int get_count_mula(__m256i count_sum) { + return _mm256_extract_epi64(count_sum, 0) + + _mm256_extract_epi64(count_sum, 1) + + _mm256_extract_epi64(count_sum, 2) + + _mm256_extract_epi64(count_sum, 3); +} + // 5x times faster than gemm()-float32 // further optimizations: do mean-mult only for the last layer void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, @@ -1168,45 +1185,101 @@ void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, } #endif + //#pragma omp parallel for + //for (i = 0; i < M; ++i) #pragma omp parallel for - for (i = 0; i < M; ++i) + for (i = 0; i < (M/2)*2; i += 2) { // l.n - filters [16 - 55 - 1024] - float mean_val = mean_arr[i]; + float mean_val_0 = mean_arr[i + 0]; + float mean_val_1 = mean_arr[i + 1]; int j, k; __m256i all_1 = _mm256_set1_epi8(255); - for (j = 0; j < N; ++j) { // out_h*out_w - one channel output size [169 - 173056] - int count = 0; + //for (j = 0; j < N; ++j) + for (j = 0; j < (N/2)*2; j += 2) + { // out_h*out_w - one channel output size [169 - 173056] + //int count = 0; const int bit_step = 256; - __m256i count_sum = _mm256_set1_epi8(0); + __m256i count_sum_0 = _mm256_set1_epi8(0); + __m256i count_sum_1 = _mm256_set1_epi8(0); + __m256i count_sum_2 = _mm256_set1_epi8(0); + __m256i count_sum_3 = _mm256_set1_epi8(0); for (k = 0; k < K; k += bit_step) { // l.size*l.size*l.c - one filter size [27 - 9216] - __m256i a_bit256 = _mm256_loadu_si256((__m256i *)(A + (i*lda + k) / 8)); - __m256i b_bit256 = _mm256_loadu_si256((__m256i *)(B + (j*ldb + k) / 8)); - __m256i xor256 = _mm256_xor_si256(a_bit256, b_bit256); // xnor = not(xor(a,b)) - __m256i c_bit256 = _mm256_andnot_si256(xor256, all_1); // can be optimized - we can do other NOT for wegihts once and do not do this NOT - count_sum = _mm256_add_epi64(count256(c_bit256), count_sum); // Mula’s algorithm + __m256i a_bit256_0 = _mm256_loadu_si256((__m256i *)(A + ((i + 0)*lda + k) / 8)); + __m256i b_bit256_0 = _mm256_loadu_si256((__m256i *)(B + ((j + 0)*ldb + k) / 8)); - //count += popcnt256(c_bit256); + __m256i a_bit256_1 = _mm256_loadu_si256((__m256i *)(A + ((i + 1)*lda + k) / 8)); + __m256i b_bit256_1 = _mm256_loadu_si256((__m256i *)(B + ((j + 1)*ldb + k) / 8)); + + xnor_avx2_popcnt(a_bit256_0, b_bit256_0, &count_sum_0); + xnor_avx2_popcnt(a_bit256_0, b_bit256_1, &count_sum_1); + + xnor_avx2_popcnt(a_bit256_1, b_bit256_0, &count_sum_2); + xnor_avx2_popcnt(a_bit256_1, b_bit256_1, &count_sum_3); + + //count += popcnt256(c_bit256); //binary_int64_printf(c_bit64); //printf(", count = %d \n\n", tmp_count); } - // count of 1 bits - //count = count_sum.m256i_i64[0] + - // count_sum.m256i_i64[1] + - // count_sum.m256i_i64[2] + - // count_sum.m256i_i64[3]; - count = _mm256_extract_epi64(count_sum, 0) - + _mm256_extract_epi64(count_sum, 1) - + _mm256_extract_epi64(count_sum, 2) - + _mm256_extract_epi64(count_sum, 3); + int count_0 = get_count_mula(count_sum_0); + int count_1 = get_count_mula(count_sum_1); + int count_2 = get_count_mula(count_sum_2); + int count_3 = get_count_mula(count_sum_3); + + const int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); + count_0 = count_0 - f1; // remove extra bits (from empty space for align only) + count_1 = count_1 - f1; + count_2 = count_2 - f1; + count_3 = count_3 - f1; + C[i*ldc + (j + 0)] = (2 * count_0 - K) * mean_val_0; + C[i*ldc + (j + 1)] = (2 * count_1 - K) * mean_val_0; + C[(i + 1)*ldc + (j + 0)] = (2 * count_2 - K) * mean_val_1; + C[(i + 1)*ldc + (j + 1)] = (2 * count_3 - K) * mean_val_1; + } - int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); - count = count - f1; // remove extra bits (from empty space for align only) + int i_d; + for (i_d = 0; i_d < 2; ++i_d) + { + float mean_val = mean_arr[i + i_d]; + for (j = (N / 2) * 2; j < N; j += 1) + { // out_h*out_w - one channel output size [169 - 173056] + const int bit_step = 256; + __m256i count_sum = _mm256_set1_epi8(0); + + for (k = 0; k < K; k += bit_step) { // l.size*l.size*l.c - one filter size [27 - 9216] + __m256i a_bit256_0 = _mm256_loadu_si256((__m256i *)(A + ((i + i_d + 0)*lda + k) / 8)); + __m256i b_bit256_0 = _mm256_loadu_si256((__m256i *)(B + ((j + 0)*ldb + k) / 8)); + xnor_avx2_popcnt(a_bit256_0, b_bit256_0, &count_sum); + } + int count = get_count_mula(count_sum); + const int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); + count = count - f1; // remove extra bits (from empty space for align only) + C[(i + i_d)*ldc + j] = (2 * count - K) * mean_val; + } + } + } + + for (i = (M / 2) * 2; i < M; i += 1) + { + float mean_val = mean_arr[i]; + int j, k; + for (j = 0; j < N; j += 1) + { // out_h*out_w - one channel output size [169 - 173056] + const int bit_step = 256; + __m256i count_sum = _mm256_set1_epi8(0); + for (k = 0; k < K; k += bit_step) { // l.size*l.size*l.c - one filter size [27 - 9216] + __m256i a_bit256_0 = _mm256_loadu_si256((__m256i *)(A + ((i + 0)*lda + k) / 8)); + __m256i b_bit256_0 = _mm256_loadu_si256((__m256i *)(B + ((j + 0)*ldb + k) / 8)); + xnor_avx2_popcnt(a_bit256_0, b_bit256_0, &count_sum); + } + int count = get_count_mula(count_sum); + const int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); + count = count - f1; // remove extra bits (from empty space for align only) C[i*ldc + j] = (2 * count - K) * mean_val; } } diff --git a/src/network.c b/src/network.c index 8eb34b3fed3..b960f27f266 100644 --- a/src/network.c +++ b/src/network.c @@ -201,7 +201,7 @@ void forward_network(network net, network_state state) for(i = 0; i < net.n; ++i){ state.index = i; layer l = net.layers[i]; - if(l.delta){ + if(l.delta && state.train){ scal_cpu(l.outputs * l.batch, 0, l.delta, 1); } //double time = get_time_point(); diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index 7cdc53680c2..263ee1f4879 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -58,8 +58,17 @@ void resize_shortcut_layer(layer *l, int w, int h) void forward_shortcut_layer(const layer l, network_state state) { - copy_cpu(l.outputs*l.batch, state.input, 1, l.output, 1); - shortcut_cpu(l.batch, l.w, l.h, l.c, state.net.layers[l.index].output, l.out_w, l.out_h, l.out_c, l.output); + if (l.w == l.out_w && l.h == l.out_h && l.c == l.out_c) { + int size = l.batch * l.w * l.h * l.c; + int i; + #pragma omp parallel for + for(i = 0; i < size; ++i) + l.output[i] = state.input[i] + state.net.layers[l.index].output[i]; + } + else { + copy_cpu(l.outputs*l.batch, state.input, 1, l.output, 1); + shortcut_cpu(l.batch, l.w, l.h, l.c, state.net.layers[l.index].output, l.out_w, l.out_h, l.out_c, l.output); + } activate_array(l.output, l.outputs*l.batch, l.activation); } From 9576cd4d89a5005b89115a8a7cf56ff9e75e0b37 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 26 Jan 2019 23:25:09 +0300 Subject: [PATCH 0275/1365] Fixed memory allocation --- src/maxpool_layer_kernels.cu | 2 +- src/parser.c | 45 ++++++++++++++++++++---------------- src/region_layer.c | 11 +++++++++ src/region_layer.h | 1 + 4 files changed, 38 insertions(+), 21 deletions(-) diff --git a/src/maxpool_layer_kernels.cu b/src/maxpool_layer_kernels.cu index 691b0f809d5..4c74dbe54a2 100644 --- a/src/maxpool_layer_kernels.cu +++ b/src/maxpool_layer_kernels.cu @@ -87,7 +87,7 @@ __global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_ extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network_state state) { -#ifdef CUDNN +#ifdef CUDNN_DISABLED if (!state.train && layer.stride == layer.size) { // cudnnPoolingBackward cudnnStatus_t maxpool_status; diff --git a/src/parser.c b/src/parser.c index 3a51ab8b3e2..644aa33929e 100644 --- a/src/parser.c +++ b/src/parser.c @@ -844,32 +844,37 @@ network parse_network_cfg_custom(char *filename, int batch) net.outputs = get_network_output_size(net); net.output = get_network_output(net); printf("Total BFLOPS %5.3f \n", bflops); - if(workspace_size){ //printf("%ld\n", workspace_size); #ifdef GPU - get_cuda_stream(); - get_cuda_memcpy_stream(); - if(gpu_index >= 0){ - net.workspace = cuda_make_array(0, workspace_size/sizeof(float) + 1); - int size = get_network_input_size(net) * net.batch; - net.input_state_gpu = cuda_make_array(0, size); - if (cudaSuccess == cudaHostAlloc(&net.input_pinned_cpu, size*sizeof(float), cudaHostRegisterMapped)) net.input_pinned_cpu_flag = 1; - else net.input_pinned_cpu = calloc(size, sizeof(float)); - - // pre-allocate memory for inference on Tensor Cores (fp16) - if (net.cudnn_half) { - *net.max_input16_size = max_inputs; - check_error(cudaMalloc((void **)net.input16_gpu, *net.max_input16_size * sizeof(short))); //sizeof(half) - *net.max_output16_size = max_outputs; - check_error(cudaMalloc((void **)net.output16_gpu, *net.max_output16_size * sizeof(short))); //sizeof(half) - } - }else { + get_cuda_stream(); + get_cuda_memcpy_stream(); + if (gpu_index >= 0) + { + int size = get_network_input_size(net) * net.batch; + net.input_state_gpu = cuda_make_array(0, size); + if (cudaSuccess == cudaHostAlloc(&net.input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped)) net.input_pinned_cpu_flag = 1; + else net.input_pinned_cpu = calloc(size, sizeof(float)); + + // pre-allocate memory for inference on Tensor Cores (fp16) + if (net.cudnn_half) { + *net.max_input16_size = max_inputs; + check_error(cudaMalloc((void **)net.input16_gpu, *net.max_input16_size * sizeof(short))); //sizeof(half) + *net.max_output16_size = max_outputs; + check_error(cudaMalloc((void **)net.output16_gpu, *net.max_output16_size * sizeof(short))); //sizeof(half) + } + if (workspace_size) { + net.workspace = cuda_make_array(0, workspace_size / sizeof(float) + 1); + } + else { net.workspace = calloc(1, workspace_size); } + } #else - net.workspace = calloc(1, workspace_size); + if (workspace_size) { + net.workspace = calloc(1, workspace_size); + } #endif - } + LAYER_TYPE lt = net.layers[net.n - 1].type; if ((net.w % 32 != 0 || net.h % 32 != 0) && (lt == YOLO || lt == REGION || lt == DETECTION)) { printf("\n Warning: width=%d and height=%d in cfg-file must be divisible by 32 for default networks Yolo v1/v2/v3!!! \n\n", diff --git a/src/region_layer.c b/src/region_layer.c index ca0a704fd1f..42730e09b2f 100644 --- a/src/region_layer.c +++ b/src/region_layer.c @@ -579,3 +579,14 @@ void get_region_detections(layer l, int w, int h, int netw, int neth, float thre } correct_region_boxes(dets, l.w*l.h*l.n, w, h, netw, neth, relative); } + +void zero_objectness(layer l) +{ + int i, n; + for (i = 0; i < l.w*l.h; ++i) { + for (n = 0; n < l.n; ++n) { + int obj_index = entry_index(l, 0, n*l.w*l.h + i, l.coords); + l.output[obj_index] = 0; + } + } +} \ No newline at end of file diff --git a/src/region_layer.h b/src/region_layer.h index 16af6d6e6b1..daf68c9770a 100644 --- a/src/region_layer.h +++ b/src/region_layer.h @@ -13,6 +13,7 @@ void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *b void resize_region_layer(layer *l, int w, int h); void get_region_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, float tree_thresh, int relative, detection *dets); void correct_region_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative); +void zero_objectness(layer l); #ifdef GPU void forward_region_layer_gpu(const region_layer l, network_state state); From 00b87281f35412dfd0493b879120583951ca279c Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 27 Jan 2019 03:42:44 +0300 Subject: [PATCH 0276/1365] Fixed RNN (RNN, GRU, LSTM) with cuDNN (batch-norm) --- src/connected_layer.c | 25 ++-- src/connected_layer.h | 2 +- src/gru_layer.c | 24 ++-- src/lstm_layer.c | 302 +++++++++++++++++++++--------------------- src/parser.c | 2 +- src/rnn.c | 6 +- src/rnn_layer.c | 8 +- 7 files changed, 187 insertions(+), 182 deletions(-) diff --git a/src/connected_layer.c b/src/connected_layer.c index e6dc7594dd1..96f7aaf2972 100644 --- a/src/connected_layer.c +++ b/src/connected_layer.c @@ -10,15 +10,16 @@ #include #include -connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, int batch_normalize) +connected_layer make_connected_layer(int batch, int steps, int inputs, int outputs, ACTIVATION activation, int batch_normalize) { + int total_batch = batch*steps; int i; connected_layer l = {0}; l.type = CONNECTED; l.inputs = inputs; l.outputs = outputs; - l.batch=batch; + l.batch= batch; l.batch_normalize = batch_normalize; l.h = 1; l.w = 1; @@ -27,8 +28,8 @@ connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVAT l.out_w = 1; l.out_c = outputs; - l.output = calloc(batch*outputs, sizeof(float)); - l.delta = calloc(batch*outputs, sizeof(float)); + l.output = calloc(total_batch*outputs, sizeof(float)); + l.delta = calloc(total_batch*outputs, sizeof(float)); l.weight_updates = calloc(inputs*outputs, sizeof(float)); l.bias_updates = calloc(outputs, sizeof(float)); @@ -65,8 +66,8 @@ connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVAT l.rolling_mean = calloc(outputs, sizeof(float)); l.rolling_variance = calloc(outputs, sizeof(float)); - l.x = calloc(batch*outputs, sizeof(float)); - l.x_norm = calloc(batch*outputs, sizeof(float)); + l.x = calloc(total_batch*outputs, sizeof(float)); + l.x_norm = calloc(total_batch*outputs, sizeof(float)); } #ifdef GPU @@ -80,8 +81,8 @@ connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVAT l.weight_updates_gpu = cuda_make_array(l.weight_updates, outputs*inputs); l.bias_updates_gpu = cuda_make_array(l.bias_updates, outputs); - l.output_gpu = cuda_make_array(l.output, outputs*batch); - l.delta_gpu = cuda_make_array(l.delta, outputs*batch); + l.output_gpu = cuda_make_array(l.output, outputs*total_batch); + l.delta_gpu = cuda_make_array(l.delta, outputs*total_batch); if(batch_normalize){ l.scales_gpu = cuda_make_array(l.scales, outputs); l.scale_updates_gpu = cuda_make_array(l.scale_updates, outputs); @@ -95,13 +96,15 @@ connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVAT l.mean_delta_gpu = cuda_make_array(l.mean, outputs); l.variance_delta_gpu = cuda_make_array(l.variance, outputs); - l.x_gpu = cuda_make_array(l.output, l.batch*outputs); - l.x_norm_gpu = cuda_make_array(l.output, l.batch*outputs); + l.x_gpu = cuda_make_array(l.output, total_batch*outputs); + l.x_norm_gpu = cuda_make_array(l.output, total_batch*outputs); #ifdef CUDNN + cudnnCreateTensorDescriptor(&l.normDstTensorDesc); cudnnCreateTensorDescriptor(&l.normTensorDesc); cudnnCreateTensorDescriptor(&l.dstTensorDesc); cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w); cudnnSetTensor4dDescriptor(l.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l.out_c, 1, 1); + cudnnSetTensor4dDescriptor(l.normDstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w); #endif } #endif @@ -147,7 +150,7 @@ void forward_connected_layer(connected_layer l, network_state state) axpy_cpu(l.outputs, .05, l.variance, 1, l.rolling_variance, 1); copy_cpu(l.outputs*l.batch, l.output, 1, l.x, 1); - normalize_cpu(l.output, l.mean, l.variance, l.batch, l.outputs, 1); + normalize_cpu(l.output, l.mean, l.variance, l.batch, l.outputs, 1); copy_cpu(l.outputs*l.batch, l.output, 1, l.x_norm, 1); } else { normalize_cpu(l.output, l.rolling_mean, l.rolling_variance, l.batch, l.outputs, 1); diff --git a/src/connected_layer.h b/src/connected_layer.h index 23797b10c47..3775e0a5819 100644 --- a/src/connected_layer.h +++ b/src/connected_layer.h @@ -7,7 +7,7 @@ typedef layer connected_layer; -connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, int batch_normalize); +connected_layer make_connected_layer(int batch, int steps, int inputs, int outputs, ACTIVATION activation, int batch_normalize); void forward_connected_layer(connected_layer layer, network_state state); void backward_connected_layer(connected_layer layer, network_state state); diff --git a/src/gru_layer.c b/src/gru_layer.c index b78e86828d0..fa03e7eb918 100644 --- a/src/gru_layer.c +++ b/src/gru_layer.c @@ -38,36 +38,36 @@ layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_no l.input_z_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.input_z_layer) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize); + *(l.input_z_layer) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.input_z_layer->batch = batch; l.state_z_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.state_z_layer) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize); + *(l.state_z_layer) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.state_z_layer->batch = batch; l.input_r_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.input_r_layer) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize); + *(l.input_r_layer) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.input_r_layer->batch = batch; l.state_r_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.state_r_layer) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize); + *(l.state_r_layer) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.state_r_layer->batch = batch; l.input_h_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.input_h_layer) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize); + *(l.input_h_layer) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.input_h_layer->batch = batch; l.state_h_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.state_h_layer) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize); + *(l.state_h_layer) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.state_h_layer->batch = batch; l.batch_normalize = batch_normalize; @@ -337,7 +337,7 @@ void backward_gru_layer_gpu(layer l, network_state state) #else activate_array_ongpu(l.h_gpu, l.outputs*l.batch, LOGISTIC); #endif - + weighted_delta_gpu(l.prev_state_gpu, l.h_gpu, l.z_gpu, prev_delta_gpu, input_h_layer.delta_gpu, input_z_layer.delta_gpu, l.outputs*l.batch, l.delta_gpu); #ifdef USET @@ -347,14 +347,14 @@ void backward_gru_layer_gpu(layer l, network_state state) #endif copy_ongpu(l.outputs*l.batch, input_h_layer.delta_gpu, 1, state_h_layer.delta_gpu, 1); - + copy_ongpu(l.outputs*l.batch, l.prev_state_gpu, 1, l.forgot_state_gpu, 1); mul_ongpu(l.outputs*l.batch, l.r_gpu, 1, l.forgot_state_gpu, 1); fill_ongpu(l.outputs*l.batch, 0, l.forgot_delta_gpu, 1); s.input = l.forgot_state_gpu; s.delta = l.forgot_delta_gpu; - + backward_connected_layer_gpu(state_h_layer, s); if(prev_delta_gpu) mult_add_into_gpu(l.outputs*l.batch, l.forgot_delta_gpu, l.r_gpu, prev_delta_gpu); mult_add_into_gpu(l.outputs*l.batch, l.forgot_delta_gpu, l.prev_state_gpu, input_r_layer.delta_gpu); @@ -364,16 +364,16 @@ void backward_gru_layer_gpu(layer l, network_state state) gradient_array_ongpu(l.z_gpu, l.outputs*l.batch, LOGISTIC, input_z_layer.delta_gpu); copy_ongpu(l.outputs*l.batch, input_z_layer.delta_gpu, 1, state_z_layer.delta_gpu, 1); - + s.input = l.prev_state_gpu; s.delta = prev_delta_gpu; - + backward_connected_layer_gpu(state_r_layer, s); backward_connected_layer_gpu(state_z_layer, s); s.input = state.input; s.delta = state.delta; - + backward_connected_layer_gpu(input_h_layer, s); backward_connected_layer_gpu(input_r_layer, s); backward_connected_layer_gpu(input_z_layer, s); diff --git a/src/lstm_layer.c b/src/lstm_layer.c index e61bf5c9db8..a8730f959b4 100644 --- a/src/lstm_layer.c +++ b/src/lstm_layer.c @@ -38,42 +38,42 @@ layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_n l.uf = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.uf) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize); + *(l.uf) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.uf->batch = batch; l.ui = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.ui) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize); + *(l.ui) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.ui->batch = batch; l.ug = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.ug) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize); + *(l.ug) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.ug->batch = batch; l.uo = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.uo) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize); + *(l.uo) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.uo->batch = batch; l.wf = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.wf) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize); + *(l.wf) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.wf->batch = batch; l.wi = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.wi) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize); + *(l.wi) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.wi->batch = batch; l.wg = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.wg) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize); + *(l.wg) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.wg->batch = batch; l.wo = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.wo) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize); + *(l.wo) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.wo->batch = batch; l.batch_normalize = batch_normalize; @@ -125,15 +125,15 @@ layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_n l.dc_gpu = cuda_make_array(0, batch*outputs); l.dh_gpu = cuda_make_array(0, batch*outputs); #ifdef CUDNN - cudnnSetTensor4dDescriptor(l.wf->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wf->out_c, l.wf->out_h, l.wf->out_w); - cudnnSetTensor4dDescriptor(l.wi->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wi->out_c, l.wi->out_h, l.wi->out_w); - cudnnSetTensor4dDescriptor(l.wg->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wg->out_c, l.wg->out_h, l.wg->out_w); - cudnnSetTensor4dDescriptor(l.wo->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wo->out_c, l.wo->out_h, l.wo->out_w); - - cudnnSetTensor4dDescriptor(l.uf->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uf->out_c, l.uf->out_h, l.uf->out_w); - cudnnSetTensor4dDescriptor(l.ui->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ui->out_c, l.ui->out_h, l.ui->out_w); - cudnnSetTensor4dDescriptor(l.ug->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ug->out_c, l.ug->out_h, l.ug->out_w); - cudnnSetTensor4dDescriptor(l.uo->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uo->out_c, l.uo->out_h, l.uo->out_w); + cudnnSetTensor4dDescriptor(l.wf->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wf->out_c, l.wf->out_h, l.wf->out_w); + cudnnSetTensor4dDescriptor(l.wi->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wi->out_c, l.wi->out_h, l.wi->out_w); + cudnnSetTensor4dDescriptor(l.wg->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wg->out_c, l.wg->out_h, l.wg->out_w); + cudnnSetTensor4dDescriptor(l.wo->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wo->out_c, l.wo->out_h, l.wo->out_w); + + cudnnSetTensor4dDescriptor(l.uf->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uf->out_c, l.uf->out_h, l.uf->out_w); + cudnnSetTensor4dDescriptor(l.ui->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ui->out_c, l.ui->out_h, l.ui->out_w); + cudnnSetTensor4dDescriptor(l.ug->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ug->out_c, l.ug->out_h, l.ug->out_w); + cudnnSetTensor4dDescriptor(l.uo->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uo->out_c, l.uo->out_h, l.uo->out_w); #endif #endif @@ -183,44 +183,44 @@ void forward_lstm_layer(layer l, network_state state) for (i = 0; i < l.steps; ++i) { s.input = l.h_cpu; - forward_connected_layer(wf, s); - forward_connected_layer(wi, s); - forward_connected_layer(wg, s); - forward_connected_layer(wo, s); + forward_connected_layer(wf, s); + forward_connected_layer(wi, s); + forward_connected_layer(wg, s); + forward_connected_layer(wo, s); s.input = state.input; - forward_connected_layer(uf, s); - forward_connected_layer(ui, s); - forward_connected_layer(ug, s); - forward_connected_layer(uo, s); + forward_connected_layer(uf, s); + forward_connected_layer(ui, s); + forward_connected_layer(ug, s); + forward_connected_layer(uo, s); copy_cpu(l.outputs*l.batch, wf.output, 1, l.f_cpu, 1); axpy_cpu(l.outputs*l.batch, 1, uf.output, 1, l.f_cpu, 1); - copy_cpu(l.outputs*l.batch, wi.output, 1, l.i_cpu, 1); - axpy_cpu(l.outputs*l.batch, 1, ui.output, 1, l.i_cpu, 1); + copy_cpu(l.outputs*l.batch, wi.output, 1, l.i_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ui.output, 1, l.i_cpu, 1); - copy_cpu(l.outputs*l.batch, wg.output, 1, l.g_cpu, 1); - axpy_cpu(l.outputs*l.batch, 1, ug.output, 1, l.g_cpu, 1); + copy_cpu(l.outputs*l.batch, wg.output, 1, l.g_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ug.output, 1, l.g_cpu, 1); - copy_cpu(l.outputs*l.batch, wo.output, 1, l.o_cpu, 1); - axpy_cpu(l.outputs*l.batch, 1, uo.output, 1, l.o_cpu, 1); + copy_cpu(l.outputs*l.batch, wo.output, 1, l.o_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, uo.output, 1, l.o_cpu, 1); - activate_array(l.f_cpu, l.outputs*l.batch, LOGISTIC); - activate_array(l.i_cpu, l.outputs*l.batch, LOGISTIC); - activate_array(l.g_cpu, l.outputs*l.batch, TANH); - activate_array(l.o_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.f_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.i_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.g_cpu, l.outputs*l.batch, TANH); + activate_array(l.o_cpu, l.outputs*l.batch, LOGISTIC); - copy_cpu(l.outputs*l.batch, l.i_cpu, 1, l.temp_cpu, 1); - mul_cpu(l.outputs*l.batch, l.g_cpu, 1, l.temp_cpu, 1); - mul_cpu(l.outputs*l.batch, l.f_cpu, 1, l.c_cpu, 1); - axpy_cpu(l.outputs*l.batch, 1, l.temp_cpu, 1, l.c_cpu, 1); + copy_cpu(l.outputs*l.batch, l.i_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.g_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.f_cpu, 1, l.c_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, l.temp_cpu, 1, l.c_cpu, 1); - copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.h_cpu, 1); - activate_array(l.h_cpu, l.outputs*l.batch, TANH); - mul_cpu(l.outputs*l.batch, l.o_cpu, 1, l.h_cpu, 1); + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.h_cpu, 1); + activate_array(l.h_cpu, l.outputs*l.batch, TANH); + mul_cpu(l.outputs*l.batch, l.o_cpu, 1, l.h_cpu, 1); - copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.cell_cpu, 1); + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.cell_cpu, 1); copy_cpu(l.outputs*l.batch, l.h_cpu, 1, l.output, 1); state.input += l.inputs*l.batch; @@ -279,90 +279,90 @@ void backward_lstm_layer(layer l, network_state state) l.dh_cpu = (i == 0) ? 0 : l.delta - l.outputs*l.batch; - copy_cpu(l.outputs*l.batch, wf.output, 1, l.f_cpu, 1); - axpy_cpu(l.outputs*l.batch, 1, uf.output, 1, l.f_cpu, 1); + copy_cpu(l.outputs*l.batch, wf.output, 1, l.f_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, uf.output, 1, l.f_cpu, 1); - copy_cpu(l.outputs*l.batch, wi.output, 1, l.i_cpu, 1); - axpy_cpu(l.outputs*l.batch, 1, ui.output, 1, l.i_cpu, 1); + copy_cpu(l.outputs*l.batch, wi.output, 1, l.i_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ui.output, 1, l.i_cpu, 1); - copy_cpu(l.outputs*l.batch, wg.output, 1, l.g_cpu, 1); - axpy_cpu(l.outputs*l.batch, 1, ug.output, 1, l.g_cpu, 1); + copy_cpu(l.outputs*l.batch, wg.output, 1, l.g_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ug.output, 1, l.g_cpu, 1); - copy_cpu(l.outputs*l.batch, wo.output, 1, l.o_cpu, 1); - axpy_cpu(l.outputs*l.batch, 1, uo.output, 1, l.o_cpu, 1); + copy_cpu(l.outputs*l.batch, wo.output, 1, l.o_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, uo.output, 1, l.o_cpu, 1); - activate_array(l.f_cpu, l.outputs*l.batch, LOGISTIC); - activate_array(l.i_cpu, l.outputs*l.batch, LOGISTIC); - activate_array(l.g_cpu, l.outputs*l.batch, TANH); - activate_array(l.o_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.f_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.i_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.g_cpu, l.outputs*l.batch, TANH); + activate_array(l.o_cpu, l.outputs*l.batch, LOGISTIC); - copy_cpu(l.outputs*l.batch, l.delta, 1, l.temp3_cpu, 1); + copy_cpu(l.outputs*l.batch, l.delta, 1, l.temp3_cpu, 1); - copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.temp_cpu, 1); - activate_array(l.temp_cpu, l.outputs*l.batch, TANH); + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.temp_cpu, 1); + activate_array(l.temp_cpu, l.outputs*l.batch, TANH); - copy_cpu(l.outputs*l.batch, l.temp3_cpu, 1, l.temp2_cpu, 1); - mul_cpu(l.outputs*l.batch, l.o_cpu, 1, l.temp2_cpu, 1); + copy_cpu(l.outputs*l.batch, l.temp3_cpu, 1, l.temp2_cpu, 1); + mul_cpu(l.outputs*l.batch, l.o_cpu, 1, l.temp2_cpu, 1); gradient_array(l.temp_cpu, l.outputs*l.batch, TANH, l.temp2_cpu); - axpy_cpu(l.outputs*l.batch, 1, l.dc_cpu, 1, l.temp2_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, l.dc_cpu, 1, l.temp2_cpu, 1); - copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.temp_cpu, 1); - activate_array(l.temp_cpu, l.outputs*l.batch, TANH); - mul_cpu(l.outputs*l.batch, l.temp3_cpu, 1, l.temp_cpu, 1); + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.temp_cpu, 1); + activate_array(l.temp_cpu, l.outputs*l.batch, TANH); + mul_cpu(l.outputs*l.batch, l.temp3_cpu, 1, l.temp_cpu, 1); gradient_array(l.o_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu); copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wo.delta, 1); s.input = l.prev_state_cpu; - s.delta = l.dh_cpu; - backward_connected_layer(wo, s); + s.delta = l.dh_cpu; + backward_connected_layer(wo, s); copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, uo.delta, 1); s.input = state.input; s.delta = state.delta; - backward_connected_layer(uo, s); + backward_connected_layer(uo, s); - copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); - mul_cpu(l.outputs*l.batch, l.i_cpu, 1, l.temp_cpu, 1); - gradient_array(l.g_cpu, l.outputs*l.batch, TANH, l.temp_cpu); + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.i_cpu, 1, l.temp_cpu, 1); + gradient_array(l.g_cpu, l.outputs*l.batch, TANH, l.temp_cpu); copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wg.delta, 1); s.input = l.prev_state_cpu; - s.delta = l.dh_cpu; - backward_connected_layer(wg, s); + s.delta = l.dh_cpu; + backward_connected_layer(wg, s); copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, ug.delta, 1); s.input = state.input; s.delta = state.delta; - backward_connected_layer(ug, s); + backward_connected_layer(ug, s); - copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); - mul_cpu(l.outputs*l.batch, l.g_cpu, 1, l.temp_cpu, 1); - gradient_array(l.i_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu); + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.g_cpu, 1, l.temp_cpu, 1); + gradient_array(l.i_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu); copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wi.delta, 1); s.input = l.prev_state_cpu; s.delta = l.dh_cpu; - backward_connected_layer(wi, s); + backward_connected_layer(wi, s); copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, ui.delta, 1); s.input = state.input; s.delta = state.delta; - backward_connected_layer(ui, s); + backward_connected_layer(ui, s); - copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); mul_cpu(l.outputs*l.batch, l.prev_cell_cpu, 1, l.temp_cpu, 1); gradient_array(l.f_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu); copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wf.delta, 1); s.input = l.prev_state_cpu; s.delta = l.dh_cpu; - backward_connected_layer(wf, s); + backward_connected_layer(wf, s); copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, uf.delta, 1); s.input = state.input; s.delta = state.delta; - backward_connected_layer(uf, s); + backward_connected_layer(uf, s); - copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); - mul_cpu(l.outputs*l.batch, l.f_cpu, 1, l.temp_cpu, 1); - copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, l.dc_cpu, 1); + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.f_cpu, 1, l.temp_cpu, 1); + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, l.dc_cpu, 1); state.input -= l.inputs*l.batch; if (state.delta) state.delta -= l.inputs*l.batch; @@ -425,44 +425,44 @@ void forward_lstm_layer_gpu(layer l, network_state state) for (i = 0; i < l.steps; ++i) { s.input = l.state_gpu; - forward_connected_layer_gpu(wf, s); - forward_connected_layer_gpu(wi, s); - forward_connected_layer_gpu(wg, s); - forward_connected_layer_gpu(wo, s); + forward_connected_layer_gpu(wf, s); + forward_connected_layer_gpu(wi, s); + forward_connected_layer_gpu(wg, s); + forward_connected_layer_gpu(wo, s); s.input = state.input; - forward_connected_layer_gpu(uf, s); - forward_connected_layer_gpu(ui, s); - forward_connected_layer_gpu(ug, s); - forward_connected_layer_gpu(uo, s); + forward_connected_layer_gpu(uf, s); + forward_connected_layer_gpu(ui, s); + forward_connected_layer_gpu(ug, s); + forward_connected_layer_gpu(uo, s); copy_ongpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1); axpy_ongpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1); - copy_ongpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1); + copy_ongpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1); - copy_ongpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1); + copy_ongpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1); - copy_ongpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1); + copy_ongpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1); - activate_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC); - activate_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC); - activate_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH); - activate_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH); + activate_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC); - copy_ongpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1); - mul_ongpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1); - mul_ongpu(l.outputs*l.batch, l.f_gpu, 1, l.c_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, l.temp_gpu, 1, l.c_gpu, 1); + copy_ongpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1); + mul_ongpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1); + mul_ongpu(l.outputs*l.batch, l.f_gpu, 1, l.c_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, l.temp_gpu, 1, l.c_gpu, 1); - copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.h_gpu, 1); - activate_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH); - mul_ongpu(l.outputs*l.batch, l.o_gpu, 1, l.h_gpu, 1); + copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.h_gpu, 1); + activate_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH); + mul_ongpu(l.outputs*l.batch, l.o_gpu, 1, l.h_gpu, 1); - copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.cell_gpu, 1); + copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.cell_gpu, 1); copy_ongpu(l.outputs*l.batch, l.h_gpu, 1, l.output_gpu, 1); state.input += l.inputs*l.batch; @@ -521,90 +521,90 @@ void backward_lstm_layer_gpu(layer l, network_state state) l.dh_gpu = (i == 0) ? 0 : l.delta_gpu - l.outputs*l.batch; - copy_ongpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1); + copy_ongpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1); - copy_ongpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1); + copy_ongpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1); - copy_ongpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1); + copy_ongpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1); - copy_ongpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1); + copy_ongpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1); - activate_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC); - activate_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC); - activate_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH); - activate_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH); + activate_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC); - copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, l.temp3_gpu, 1); + copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, l.temp3_gpu, 1); - copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1); - activate_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH); + copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1); + activate_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH); - copy_ongpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp2_gpu, 1); - mul_ongpu(l.outputs*l.batch, l.o_gpu, 1, l.temp2_gpu, 1); + copy_ongpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp2_gpu, 1); + mul_ongpu(l.outputs*l.batch, l.o_gpu, 1, l.temp2_gpu, 1); gradient_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH, l.temp2_gpu); - axpy_ongpu(l.outputs*l.batch, 1, l.dc_gpu, 1, l.temp2_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, l.dc_gpu, 1, l.temp2_gpu, 1); - copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1); - activate_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH); - mul_ongpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp_gpu, 1); + copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1); + activate_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH); + mul_ongpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp_gpu, 1); gradient_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu); copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wo.delta_gpu, 1); s.input = l.prev_state_gpu; - s.delta = l.dh_gpu; - backward_connected_layer_gpu(wo, s); + s.delta = l.dh_gpu; + backward_connected_layer_gpu(wo, s); copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, uo.delta_gpu, 1); s.input = state.input; s.delta = state.delta; - backward_connected_layer_gpu(uo, s); + backward_connected_layer_gpu(uo, s); - copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); - mul_ongpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1); - gradient_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH, l.temp_gpu); + copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + mul_ongpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1); + gradient_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH, l.temp_gpu); copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wg.delta_gpu, 1); s.input = l.prev_state_gpu; - s.delta = l.dh_gpu; - backward_connected_layer_gpu(wg, s); + s.delta = l.dh_gpu; + backward_connected_layer_gpu(wg, s); copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, ug.delta_gpu, 1); s.input = state.input; s.delta = state.delta; - backward_connected_layer_gpu(ug, s); + backward_connected_layer_gpu(ug, s); - copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); - mul_ongpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1); - gradient_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu); + copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + mul_ongpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1); + gradient_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu); copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wi.delta_gpu, 1); s.input = l.prev_state_gpu; s.delta = l.dh_gpu; - backward_connected_layer_gpu(wi, s); + backward_connected_layer_gpu(wi, s); copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, ui.delta_gpu, 1); s.input = state.input; s.delta = state.delta; - backward_connected_layer_gpu(ui, s); + backward_connected_layer_gpu(ui, s); - copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); mul_ongpu(l.outputs*l.batch, l.prev_cell_gpu, 1, l.temp_gpu, 1); gradient_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu); copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wf.delta_gpu, 1); s.input = l.prev_state_gpu; s.delta = l.dh_gpu; - backward_connected_layer_gpu(wf, s); + backward_connected_layer_gpu(wf, s); copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, uf.delta_gpu, 1); s.input = state.input; s.delta = state.delta; - backward_connected_layer_gpu(uf, s); + backward_connected_layer_gpu(uf, s); - copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); - mul_ongpu(l.outputs*l.batch, l.f_gpu, 1, l.temp_gpu, 1); - copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, l.dc_gpu, 1); + copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + mul_ongpu(l.outputs*l.batch, l.f_gpu, 1, l.temp_gpu, 1); + copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, l.dc_gpu, 1); state.input -= l.inputs*l.batch; if (state.delta) state.delta -= l.inputs*l.batch; diff --git a/src/parser.c b/src/parser.c index 644aa33929e..aae5f84932e 100644 --- a/src/parser.c +++ b/src/parser.c @@ -238,7 +238,7 @@ connected_layer parse_connected(list *options, size_params params) ACTIVATION activation = get_activation(activation_s); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); - connected_layer layer = make_connected_layer(params.batch, params.inputs, output, activation, batch_normalize); + connected_layer layer = make_connected_layer(params.batch, 1, params.inputs, output, activation, batch_normalize); return layer; } diff --git a/src/rnn.c b/src/rnn.c index eca6f554ca5..da49bd2117a 100644 --- a/src/rnn.c +++ b/src/rnn.c @@ -137,16 +137,16 @@ void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear, } else { FILE *fp = fopen(filename, "rb"); - fseek(fp, 0, SEEK_END); + fseek(fp, 0, SEEK_END); size = ftell(fp); - fseek(fp, 0, SEEK_SET); + fseek(fp, 0, SEEK_SET); text = calloc(size+1, sizeof(char)); fread(text, 1, size, fp); fclose(fp); } - char *backup_directory = "/home/pjreddie/backup/"; + char *backup_directory = "backup"; char *base = basecfg(cfgfile); fprintf(stderr, "%s\n", base); float avg_loss = -1; diff --git a/src/rnn_layer.c b/src/rnn_layer.c index 83fda13e6fe..1b377b14950 100644 --- a/src/rnn_layer.c +++ b/src/rnn_layer.c @@ -41,17 +41,17 @@ layer make_rnn_layer(int batch, int inputs, int hidden, int outputs, int steps, l.input_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.input_layer) = make_connected_layer(batch*steps, inputs, hidden, activation, batch_normalize); + *(l.input_layer) = make_connected_layer(batch, steps, inputs, hidden, activation, batch_normalize); l.input_layer->batch = batch; l.self_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.self_layer) = make_connected_layer(batch*steps, hidden, hidden, (log==2)?LOGGY:(log==1?LOGISTIC:activation), batch_normalize); + *(l.self_layer) = make_connected_layer(batch, steps, hidden, hidden, (log==2)?LOGGY:(log==1?LOGISTIC:activation), batch_normalize); l.self_layer->batch = batch; l.output_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.output_layer) = make_connected_layer(batch*steps, hidden, outputs, activation, batch_normalize); + *(l.output_layer) = make_connected_layer(batch, steps, hidden, outputs, activation, batch_normalize); l.output_layer->batch = batch; l.outputs = outputs; @@ -95,6 +95,7 @@ void forward_rnn_layer(layer l, network_state state) if(state.train) fill_cpu(l.hidden * l.batch, 0, l.state, 1); for (i = 0; i < l.steps; ++i) { + s.input = state.input; forward_connected_layer(input_layer, s); @@ -209,6 +210,7 @@ void forward_rnn_layer_gpu(layer l, network_state state) if(state.train) fill_ongpu(l.hidden * l.batch, 0, l.state_gpu, 1); for (i = 0; i < l.steps; ++i) { + s.input = state.input; forward_connected_layer_gpu(input_layer, s); From 85b99872cbad757a3753f672be4db6bbe721545e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 28 Jan 2019 20:19:26 +0300 Subject: [PATCH 0277/1365] Use non-default stream for all CUDA-functions --- src/activation_kernels.cu | 6 ++--- src/avgpool_layer_kernels.cu | 4 ++-- src/blas_kernels.cu | 46 ++++++++++++++++++------------------ src/col2im_kernels.cu | 2 +- src/convolutional_kernels.cu | 6 ++--- src/crop_layer_kernels.cu | 16 ++++++------- src/cuda.c | 2 +- src/dropout_layer_kernels.cu | 4 ++-- src/maxpool_layer_kernels.cu | 2 +- 9 files changed, 44 insertions(+), 44 deletions(-) diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index e6718fe1a1b..b82fefad3fc 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -163,7 +163,7 @@ __global__ void binary_gradient_array_kernel(float *x, float *dy, int n, int s, extern "C" void binary_gradient_array_gpu(float *x, float *dx, int n, int size, BINARY_ACTIVATION a, float *y) { - binary_gradient_array_kernel << > >(x, dx, n / 2, size, a, y); + binary_gradient_array_kernel << > >(x, dx, n / 2, size, a, y); check_error(cudaPeekAtLastError()); } __global__ void binary_activate_array_kernel(float *x, int n, int s, BINARY_ACTIVATION a, float *y) @@ -178,7 +178,7 @@ __global__ void binary_activate_array_kernel(float *x, int n, int s, BINARY_ACTI extern "C" void binary_activate_array_gpu(float *x, int n, int size, BINARY_ACTIVATION a, float *y) { - binary_activate_array_kernel << > >(x, n / 2, size, a, y); + binary_activate_array_kernel << > >(x, n / 2, size, a, y); check_error(cudaPeekAtLastError()); } @@ -231,6 +231,6 @@ extern "C" void activate_array_ongpu(float *x, int n, ACTIVATION a) extern "C" void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta) { - gradient_array_kernel<<>>(x, n, a, delta); + gradient_array_kernel<<>>(x, n, a, delta); check_error(cudaPeekAtLastError()); } diff --git a/src/avgpool_layer_kernels.cu b/src/avgpool_layer_kernels.cu index b7e2770e1ff..10cb332581e 100644 --- a/src/avgpool_layer_kernels.cu +++ b/src/avgpool_layer_kernels.cu @@ -47,7 +47,7 @@ extern "C" void forward_avgpool_layer_gpu(avgpool_layer layer, network_state sta { size_t n = layer.c*layer.batch; - forward_avgpool_layer_kernel<<>>(n, layer.w, layer.h, layer.c, state.input, layer.output_gpu); + forward_avgpool_layer_kernel<<>>(n, layer.w, layer.h, layer.c, state.input, layer.output_gpu); check_error(cudaPeekAtLastError()); } @@ -55,7 +55,7 @@ extern "C" void backward_avgpool_layer_gpu(avgpool_layer layer, network_state st { size_t n = layer.c*layer.batch; - backward_avgpool_layer_kernel<<>>(n, layer.w, layer.h, layer.c, state.delta, layer.delta_gpu); + backward_avgpool_layer_kernel<<>>(n, layer.w, layer.h, layer.c, state.delta, layer.delta_gpu); check_error(cudaPeekAtLastError()); } diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 0116704937c..231e2db8e82 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -50,7 +50,7 @@ __global__ void backward_scale_kernel(float *x_norm, float *delta, int batch, in void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates) { - backward_scale_kernel<<>>(x_norm, delta, batch, n, size, scale_updates); + backward_scale_kernel<<>>(x_norm, delta, batch, n, size, scale_updates); check_error(cudaPeekAtLastError()); } @@ -129,14 +129,14 @@ __global__ void dot_kernel(float *output, float scale, int batch, int n, int siz void dot_error_gpu(layer l) { - dot_kernel<<>>(l.output_gpu, l.dot, l.batch, l.n, l.out_w * l.out_h, l.delta_gpu); + dot_kernel<<>>(l.output_gpu, l.dot, l.batch, l.n, l.out_w * l.out_h, l.delta_gpu); check_error(cudaPeekAtLastError()); } */ void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size) { - backward_bias_kernel<<>>(bias_updates, delta, batch, n, size); + backward_bias_kernel<<>>(bias_updates, delta, batch, n, size); check_error(cudaPeekAtLastError()); } @@ -153,7 +153,7 @@ __global__ void adam_kernel(int N, float *x, float *m, float *v, float B1, float extern "C" void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t) { - adam_kernel << > >(n, x, m, v, B1, B2, rate, eps, t); + adam_kernel << > >(n, x, m, v, B1, B2, rate, eps, t); check_error(cudaPeekAtLastError()); } @@ -192,7 +192,7 @@ __global__ void normalize_delta_kernel(int N, float *x, float *mean, float *vari extern "C" void normalize_delta_gpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta) { size_t N = batch*filters*spatial; - normalize_delta_kernel<<>>(N, x, mean, variance, mean_delta, variance_delta, batch, filters, spatial, delta); + normalize_delta_kernel<<>>(N, x, mean, variance, mean_delta, variance_delta, batch, filters, spatial, delta); check_error(cudaPeekAtLastError()); } @@ -297,19 +297,19 @@ __global__ void mean_delta_kernel(float *delta, float *variance, int batch, int extern "C" void mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta) { - mean_delta_kernel<<>>(delta, variance, batch, filters, spatial, mean_delta); + mean_delta_kernel<<>>(delta, variance, batch, filters, spatial, mean_delta); check_error(cudaPeekAtLastError()); } extern "C" void fast_mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta) { - fast_mean_delta_kernel<<>>(delta, variance, batch, filters, spatial, mean_delta); + fast_mean_delta_kernel<<>>(delta, variance, batch, filters, spatial, mean_delta); check_error(cudaPeekAtLastError()); } extern "C" void fast_variance_delta_gpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta) { - fast_variance_delta_kernel<<>>(x, delta, mean, variance, batch, filters, spatial, variance_delta); + fast_variance_delta_kernel<<>>(x, delta, mean, variance, batch, filters, spatial, variance_delta); check_error(cudaPeekAtLastError()); } @@ -532,13 +532,13 @@ extern "C" void fast_variance_gpu(float *x, float *mean, int batch, int filters, extern "C" void mean_gpu(float *x, int batch, int filters, int spatial, float *mean) { - mean_kernel<<>>(x, batch, filters, spatial, mean); + mean_kernel<<>>(x, batch, filters, spatial, mean); check_error(cudaPeekAtLastError()); } extern "C" void variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance) { - variance_kernel<<>>(x, mean, batch, filters, spatial, variance); + variance_kernel<<>>(x, mean, batch, filters, spatial, variance); check_error(cudaPeekAtLastError()); } @@ -573,7 +573,7 @@ extern "C" void simple_copy_ongpu(int size, float *src, float *dst) extern "C" void mul_ongpu(int N, float * X, int INCX, float * Y, int INCY) { - mul_kernel<<>>(N, X, INCX, Y, INCY); + mul_kernel<<>>(N, X, INCX, Y, INCY); check_error(cudaPeekAtLastError()); } @@ -616,7 +616,7 @@ extern "C" void reorg_ongpu(float *x, int w, int h, int c, int batch, int stride extern "C" void mask_gpu_new_api(int N, float * X, float mask_num, float * mask, float val) { - mask_kernel_new_api <<>>(N, X, mask_num, mask, val); + mask_kernel_new_api <<>>(N, X, mask_num, mask, val); check_error(cudaPeekAtLastError()); } @@ -628,13 +628,13 @@ extern "C" void mask_ongpu(int N, float * X, float mask_num, float * mask) extern "C" void const_ongpu(int N, float ALPHA, float * X, int INCX) { - const_kernel<<>>(N, ALPHA, X, INCX); + const_kernel<<>>(N, ALPHA, X, INCX); check_error(cudaPeekAtLastError()); } extern "C" void constrain_ongpu(int N, float ALPHA, float * X, int INCX) { - constrain_kernel<<>>(N, ALPHA, X, INCX); + constrain_kernel<<>>(N, ALPHA, X, INCX); check_error(cudaPeekAtLastError()); } @@ -647,7 +647,7 @@ extern "C" void scal_ongpu(int N, float ALPHA, float * X, int INCX) extern "C" void supp_ongpu(int N, float ALPHA, float * X, int INCX) { - supp_kernel<<>>(N, ALPHA, X, INCX); + supp_kernel<<>>(N, ALPHA, X, INCX); check_error(cudaPeekAtLastError()); } @@ -761,7 +761,7 @@ __global__ void smooth_l1_kernel(int n, float *pred, float *truth, float *delta, extern "C" void smooth_l1_gpu(int n, float *pred, float *truth, float *delta, float *error) { - smooth_l1_kernel<<>>(n, pred, truth, delta, error); + smooth_l1_kernel<<>>(n, pred, truth, delta, error); check_error(cudaPeekAtLastError()); } @@ -778,7 +778,7 @@ __global__ void softmax_x_ent_kernel(int n, float *pred, float *truth, float *de extern "C" void softmax_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error) { - softmax_x_ent_kernel << > >(n, pred, truth, delta, error); + softmax_x_ent_kernel << > >(n, pred, truth, delta, error); check_error(cudaPeekAtLastError()); } @@ -794,7 +794,7 @@ __global__ void l2_kernel(int n, float *pred, float *truth, float *delta, float extern "C" void l2_gpu(int n, float *pred, float *truth, float *delta, float *error) { - l2_kernel<<>>(n, pred, truth, delta, error); + l2_kernel<<>>(n, pred, truth, delta, error); check_error(cudaPeekAtLastError()); } @@ -810,7 +810,7 @@ __global__ void weighted_sum_kernel(int n, float *a, float *b, float *s, float * extern "C" void weighted_sum_gpu(float *a, float *b, float *s, int num, float *c) { - weighted_sum_kernel<<>>(num, a, b, s, c); + weighted_sum_kernel<<>>(num, a, b, s, c); check_error(cudaPeekAtLastError()); } @@ -826,7 +826,7 @@ __global__ void weighted_delta_kernel(int n, float *a, float *b, float *s, float extern "C" void weighted_delta_gpu(float *a, float *b, float *s, float *da, float *db, float *ds, int num, float *dc) { - weighted_delta_kernel<<>>(num, a, b, s, da, db, ds, dc); + weighted_delta_kernel<<>>(num, a, b, s, da, db, ds, dc); check_error(cudaPeekAtLastError()); } @@ -840,7 +840,7 @@ __global__ void mult_add_into_kernel(int n, float *a, float *b, float *c) extern "C" void mult_add_into_gpu(int num, float *a, float *b, float *c) { - mult_add_into_kernel<<>>(num, a, b, c); + mult_add_into_kernel<<>>(num, a, b, c); check_error(cudaPeekAtLastError()); } @@ -909,7 +909,7 @@ __global__ void softmax_kernel_new_api(float *input, int n, int batch, int batch extern "C" void softmax_gpu_new_api(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output) { - softmax_kernel_new_api << > >(input, n, batch, batch_offset, groups, group_offset, stride, temp, output); + softmax_kernel_new_api << > >(input, n, batch, batch_offset, groups, group_offset, stride, temp, output); check_error(cudaPeekAtLastError()); } @@ -971,7 +971,7 @@ extern "C" void softmax_tree_gpu(float *input, int spatial, int batch, int strid } */ int num = spatial*batch*hier.groups; - softmax_tree_kernel <<>>(input, spatial, batch, stride, temp, output, hier.groups, tree_groups_size, tree_groups_offset); + softmax_tree_kernel <<>>(input, spatial, batch, stride, temp, output, hier.groups, tree_groups_size, tree_groups_offset); check_error(cudaPeekAtLastError()); cuda_free((float *)tree_groups_size); cuda_free((float *)tree_groups_offset); diff --git a/src/col2im_kernels.cu b/src/col2im_kernels.cu index aed2df9bb62..d475f052e33 100644 --- a/src/col2im_kernels.cu +++ b/src/col2im_kernels.cu @@ -50,7 +50,7 @@ void col2im_ongpu(float *data_col, int width_col = (width + 2 * pad - ksize) / stride + 1; int num_kernels = channels * height * width; col2im_gpu_kernel<<<(num_kernels+BLOCK-1)/BLOCK, - BLOCK>>>( + BLOCK, 0, get_cuda_stream() >>>( num_kernels, data_col, height, width, ksize, pad, stride, height_col, width_col, data_im); diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index b9e7ff84ca1..f8ab49718c1 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -47,7 +47,7 @@ __global__ void binarize_input_kernel(float *input, int n, int size, float *bina void binarize_input_gpu(float *input, int n, int size, float *binary) { - binarize_input_kernel<<>>(input, n, size, binary); + binarize_input_kernel<<>>(input, n, size, binary); check_error(cudaPeekAtLastError()); } @@ -114,8 +114,8 @@ void fast_binarize_weights_gpu(float *weights, int n, int size, float *binary, f size_t gridsize = n * size; const int num_blocks = gridsize / BLOCK + 1; - set_zero_kernel << <(n/BLOCK + 1), BLOCK >> > (mean_arr_gpu, n); - reduce_kernel << > > (weights, n, size, mean_arr_gpu); + set_zero_kernel << <(n/BLOCK + 1), BLOCK, 0, get_cuda_stream() >> > (mean_arr_gpu, n); + reduce_kernel << > > (weights, n, size, mean_arr_gpu); binarize_weights_mean_kernel << > > (weights, n, size, binary, mean_arr_gpu); check_error(cudaPeekAtLastError()); } diff --git a/src/crop_layer_kernels.cu b/src/crop_layer_kernels.cu index 8a08630533c..074aa0b7711 100644 --- a/src/crop_layer_kernels.cu +++ b/src/crop_layer_kernels.cu @@ -18,7 +18,7 @@ __device__ float get_pixel_kernel(float *image, int w, int h, int x, int y, int __device__ float3 rgb_to_hsv_kernel(float3 rgb) { float r = rgb.x; - float g = rgb.y; + float g = rgb.y; float b = rgb.z; float h, s, v; @@ -46,7 +46,7 @@ __device__ float3 rgb_to_hsv_kernel(float3 rgb) __device__ float3 hsv_to_rgb_kernel(float3 hsv) { float h = hsv.x; - float s = hsv.y; + float s = hsv.y; float v = hsv.z; float r, g, b; @@ -88,8 +88,8 @@ __device__ float bilinear_interpolate_kernel(float *image, int w, int h, float x float dx = x - ix; float dy = y - iy; - float val = (1-dy) * (1-dx) * get_pixel_kernel(image, w, h, ix, iy, c) + - dy * (1-dx) * get_pixel_kernel(image, w, h, ix, iy+1, c) + + float val = (1-dy) * (1-dx) * get_pixel_kernel(image, w, h, ix, iy, c) + + dy * (1-dx) * get_pixel_kernel(image, w, h, ix, iy+1, c) + (1-dy) * dx * get_pixel_kernel(image, w, h, ix+1, iy, c) + dy * dx * get_pixel_kernel(image, w, h, ix+1, iy+1, c); return val; @@ -171,7 +171,7 @@ __global__ void forward_crop_layer_kernel(float *input, float *rand, int size, i input += w*h*c*b; - float x = (flip) ? w - dw - j - 1 : j + dw; + float x = (flip) ? w - dw - j - 1 : j + dw; float y = i + dh; float rx = cos(angle)*(x-cx) - sin(angle)*(y-cy) + cx; @@ -195,12 +195,12 @@ extern "C" void forward_crop_layer_gpu(crop_layer layer, network_state state) int size = layer.batch * layer.w * layer.h; - levels_image_kernel<<>>(state.input, layer.rand_gpu, layer.batch, layer.w, layer.h, state.train, layer.saturation, layer.exposure, translate, scale, layer.shift); + levels_image_kernel<<>>(state.input, layer.rand_gpu, layer.batch, layer.w, layer.h, state.train, layer.saturation, layer.exposure, translate, scale, layer.shift); check_error(cudaPeekAtLastError()); size = layer.batch*layer.c*layer.out_w*layer.out_h; - forward_crop_layer_kernel<<>>(state.input, layer.rand_gpu, size, layer.c, layer.h, layer.w, layer.out_h, layer.out_w, state.train, layer.flip, radians, layer.output_gpu); + forward_crop_layer_kernel<<>>(state.input, layer.rand_gpu, size, layer.c, layer.h, layer.w, layer.out_h, layer.out_w, state.train, layer.flip, radians, layer.output_gpu); check_error(cudaPeekAtLastError()); /* @@ -215,7 +215,7 @@ extern "C" void forward_crop_layer_gpu(crop_layer layer, network_state state) scale_image(im2, 1/scale); translate_image(im3, -translate); scale_image(im3, 1/scale); - + show_image(im, "cropped"); show_image(im2, "cropped2"); show_image(im3, "cropped3"); diff --git a/src/cuda.c b/src/cuda.c index 7705b27d2f5..f4828923284 100644 --- a/src/cuda.c +++ b/src/cuda.c @@ -192,7 +192,7 @@ int *cuda_make_int_array_new_api(int *x, size_t n) cudaError_t status = cudaMalloc((void **)&x_gpu, size); check_error(status); if (x) { - status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice); + status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice, get_cuda_stream()); check_error(status); } if (!x_gpu) error("Cuda malloc failed\n"); diff --git a/src/dropout_layer_kernels.cu b/src/dropout_layer_kernels.cu index 7e51bd55ba7..944d86b1d70 100644 --- a/src/dropout_layer_kernels.cu +++ b/src/dropout_layer_kernels.cu @@ -27,7 +27,7 @@ void forward_dropout_layer_gpu(dropout_layer layer, network_state state) cuda_push_array(layer.rand_gpu, layer.rand, size); */ - yoloswag420blazeit360noscope<<>>(state.input, size, layer.rand_gpu, layer.probability, layer.scale); + yoloswag420blazeit360noscope<<>>(state.input, size, layer.rand_gpu, layer.probability, layer.scale); check_error(cudaPeekAtLastError()); } @@ -36,6 +36,6 @@ void backward_dropout_layer_gpu(dropout_layer layer, network_state state) if(!state.delta) return; int size = layer.inputs*layer.batch; - yoloswag420blazeit360noscope<<>>(state.delta, size, layer.rand_gpu, layer.probability, layer.scale); + yoloswag420blazeit360noscope<<>>(state.delta, size, layer.rand_gpu, layer.probability, layer.scale); check_error(cudaPeekAtLastError()); } diff --git a/src/maxpool_layer_kernels.cu b/src/maxpool_layer_kernels.cu index 4c74dbe54a2..e7d316d4410 100644 --- a/src/maxpool_layer_kernels.cu +++ b/src/maxpool_layer_kernels.cu @@ -125,7 +125,7 @@ extern "C" void backward_maxpool_layer_gpu(maxpool_layer layer, network_state st { size_t n = layer.h*layer.w*layer.c*layer.batch; - backward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.pad, layer.delta_gpu, state.delta, layer.indexes_gpu); + backward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.pad, layer.delta_gpu, state.delta, layer.indexes_gpu); check_error(cudaPeekAtLastError()); } From 110b5240a4ca9e38fbf7d20847cf5400e7ff6205 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 28 Jan 2019 20:22:14 +0300 Subject: [PATCH 0278/1365] Fixed LSTM-layer --- build/darknet/x64/cfg/lstm.train.cfg | 34 ++++++++++++++++++++++++++++ build/darknet/x64/cfg/rnn.train.cfg | 2 +- build/darknet/x64/rnn_lstm.cmd | 16 +++++++++++++ cfg/lstm.train.cfg | 34 ++++++++++++++++++++++++++++ cfg/rnn.train.cfg | 2 +- src/classifier.c | 6 ++--- src/demo.c | 2 +- src/detector.c | 10 ++++---- src/lstm_layer.c | 6 ++++- src/parser.c | 7 +++--- src/parser.h | 2 +- src/rnn.c | 10 ++++++-- src/utils.c | 16 ++++++++++++- src/utils.h | 1 + 14 files changed, 129 insertions(+), 19 deletions(-) create mode 100644 build/darknet/x64/cfg/lstm.train.cfg create mode 100644 build/darknet/x64/rnn_lstm.cmd create mode 100644 cfg/lstm.train.cfg diff --git a/build/darknet/x64/cfg/lstm.train.cfg b/build/darknet/x64/cfg/lstm.train.cfg new file mode 100644 index 00000000000..874b990e7f0 --- /dev/null +++ b/build/darknet/x64/cfg/lstm.train.cfg @@ -0,0 +1,34 @@ +[net] +subdivisions=8 +inputs=256 +batch = 128 +momentum=0.9 +decay=0.001 +max_batches = 2000 +time_steps=576 +learning_rate=1.0 +policy=steps +steps=1000,1500 +scales=.1,.1 + +[lstm] +batch_normalize=1 +output = 1024 + +[lstm] +batch_normalize=1 +output = 1024 + +[lstm] +batch_normalize=1 +output = 1024 + +[connected] +output=256 +activation=leaky + +[softmax] + +[cost] +type=sse + diff --git a/build/darknet/x64/cfg/rnn.train.cfg b/build/darknet/x64/cfg/rnn.train.cfg index 9139757faa7..3c63956a249 100644 --- a/build/darknet/x64/cfg/rnn.train.cfg +++ b/build/darknet/x64/cfg/rnn.train.cfg @@ -1,5 +1,5 @@ [net] -subdivisions=1 +subdivisions=8 inputs=256 batch = 128 momentum=0.9 diff --git a/build/darknet/x64/rnn_lstm.cmd b/build/darknet/x64/rnn_lstm.cmd new file mode 100644 index 00000000000..22ecc34124d --- /dev/null +++ b/build/darknet/x64/rnn_lstm.cmd @@ -0,0 +1,16 @@ +rem Create your own text.txt file with some text. + + +darknet.exe rnn train cfg/lstm.train.cfg -file text.txt + + +rem darknet.exe rnn train cfg/lstm.train.cfg backup/lstm.backup -file text.txt + + +pause + +darknet.exe rnn generate cfg/lstm.train.cfg backup/lstm.backup -srand 2 -len 500 -seed apple + +darknet.exe rnn generate cfg/lstm.train.cfg backup/lstm.backup -srand 2 -len 500 -seed apple > text_gen.txt + +pause \ No newline at end of file diff --git a/cfg/lstm.train.cfg b/cfg/lstm.train.cfg new file mode 100644 index 00000000000..874b990e7f0 --- /dev/null +++ b/cfg/lstm.train.cfg @@ -0,0 +1,34 @@ +[net] +subdivisions=8 +inputs=256 +batch = 128 +momentum=0.9 +decay=0.001 +max_batches = 2000 +time_steps=576 +learning_rate=1.0 +policy=steps +steps=1000,1500 +scales=.1,.1 + +[lstm] +batch_normalize=1 +output = 1024 + +[lstm] +batch_normalize=1 +output = 1024 + +[lstm] +batch_normalize=1 +output = 1024 + +[connected] +output=256 +activation=leaky + +[softmax] + +[cost] +type=sse + diff --git a/cfg/rnn.train.cfg b/cfg/rnn.train.cfg index 9139757faa7..3c63956a249 100644 --- a/cfg/rnn.train.cfg +++ b/cfg/rnn.train.cfg @@ -1,5 +1,5 @@ [net] -subdivisions=1 +subdivisions=8 inputs=256 batch = 128 momentum=0.9 diff --git a/src/classifier.c b/src/classifier.c index 26a7a3fed1c..f27d31d843d 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -630,7 +630,7 @@ void validate_classifier_multi(char *datacfg, char *filename, char *weightfile) void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int layer_num) { - network net = parse_network_cfg_custom(cfgfile, 1); + network net = parse_network_cfg_custom(cfgfile, 1, 0); if(weightfile){ load_weights(&net, weightfile); } @@ -713,7 +713,7 @@ void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filena void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top) { - network net = parse_network_cfg_custom(cfgfile, 1); + network net = parse_network_cfg_custom(cfgfile, 1, 0); if(weightfile){ load_weights(&net, weightfile); } @@ -1109,7 +1109,7 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind { #ifdef OPENCV printf("Classifier Demo\n"); - network net = parse_network_cfg_custom(cfgfile, 1); + network net = parse_network_cfg_custom(cfgfile, 1, 0); if(weightfile){ load_weights(&net, weightfile); } diff --git a/src/demo.c b/src/demo.c index b539a8b6a96..5ed2d88a3dc 100644 --- a/src/demo.c +++ b/src/demo.c @@ -152,7 +152,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int demo_ext_output = ext_output; demo_json_port = json_port; printf("Demo\n"); - net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 + net = parse_network_cfg_custom(cfgfile, 1, 0); // set batch=1 if(weightfile){ load_weights(&net, weightfile); } diff --git a/src/detector.c b/src/detector.c index 8984a56364f..6ea3baa5b41 100644 --- a/src/detector.c +++ b/src/detector.c @@ -62,7 +62,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i cuda_set_device(gpus[0]); printf(" Prepare additional network for mAP calculation...\n"); - net_map = parse_network_cfg_custom(cfgfile, 1); + net_map = parse_network_cfg_custom(cfgfile, 1, 0); int k; // free memory unnecessary arrays for (k = 0; k < net_map.n; ++k) { free_layer(net_map.layers[k]); @@ -424,7 +424,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out int *map = 0; if (mapf) map = read_map(mapf); - network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 + network net = parse_network_cfg_custom(cfgfile, 1, 0); // set batch=1 if (weightfile) { load_weights(&net, weightfile); } @@ -548,7 +548,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile) { - network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 + network net = parse_network_cfg_custom(cfgfile, 1, 0); // set batch=1 if (weightfile) { load_weights(&net, weightfile); } @@ -662,7 +662,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa net = *existing_net; } else { - net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 + net = parse_network_cfg_custom(cfgfile, 1, 0); // set batch=1 if (weightfile) { load_weights(&net, weightfile); } @@ -1235,7 +1235,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam char **names = get_labels_custom(name_list, &names_size); //get_labels(name_list); image **alphabet = load_alphabet(); - network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1 + network net = parse_network_cfg_custom(cfgfile, 1, 0); // set batch=1 if (weightfile) { load_weights(&net, weightfile); } diff --git a/src/lstm_layer.c b/src/lstm_layer.c index a8730f959b4..aefe9c460ef 100644 --- a/src/lstm_layer.c +++ b/src/lstm_layer.c @@ -106,6 +106,8 @@ layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_n l.backward_gpu = backward_lstm_layer_gpu; l.update_gpu = update_lstm_layer_gpu; + //l.state_gpu = cuda_make_array(l.state, batch*l.outputs); + l.output_gpu = cuda_make_array(0, batch*outputs*steps); l.delta_gpu = cuda_make_array(0, batch*l.outputs*steps); @@ -125,6 +127,7 @@ layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_n l.dc_gpu = cuda_make_array(0, batch*outputs); l.dh_gpu = cuda_make_array(0, batch*outputs); #ifdef CUDNN + /* cudnnSetTensor4dDescriptor(l.wf->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wf->out_c, l.wf->out_h, l.wf->out_w); cudnnSetTensor4dDescriptor(l.wi->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wi->out_c, l.wi->out_h, l.wi->out_w); cudnnSetTensor4dDescriptor(l.wg->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wg->out_c, l.wg->out_h, l.wg->out_w); @@ -134,6 +137,7 @@ layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_n cudnnSetTensor4dDescriptor(l.ui->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ui->out_c, l.ui->out_h, l.ui->out_w); cudnnSetTensor4dDescriptor(l.ug->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ug->out_c, l.ug->out_h, l.ug->out_w); cudnnSetTensor4dDescriptor(l.uo->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uo->out_c, l.uo->out_h, l.uo->out_w); + */ #endif #endif @@ -424,7 +428,7 @@ void forward_lstm_layer_gpu(layer l, network_state state) } for (i = 0; i < l.steps; ++i) { - s.input = l.state_gpu; + s.input = l.h_gpu; forward_connected_layer_gpu(wf, s); forward_connected_layer_gpu(wi, s); forward_connected_layer_gpu(wg, s); diff --git a/src/parser.c b/src/parser.c index aae5f84932e..92850c95798 100644 --- a/src/parser.c +++ b/src/parser.c @@ -716,10 +716,10 @@ int is_network(section *s) network parse_network_cfg(char *filename) { - return parse_network_cfg_custom(filename, 0); + return parse_network_cfg_custom(filename, 0, 0); } -network parse_network_cfg_custom(char *filename, int batch) +network parse_network_cfg_custom(char *filename, int batch, int time_steps) { list *sections = read_cfg(filename); node *n = sections->front; @@ -738,6 +738,7 @@ network parse_network_cfg_custom(char *filename, int batch) params.c = net.c; params.inputs = net.inputs; if (batch > 0) net.batch = batch; + if (time_steps > 0) net.time_steps = time_steps; params.batch = net.batch; params.time_steps = net.time_steps; params.net = net; @@ -1300,7 +1301,7 @@ network *load_network_custom(char *cfg, char *weights, int clear, int batch) { printf(" Try to load cfg: %s, weights: %s, clear = %d \n", cfg, weights, clear); network *net = calloc(1, sizeof(network)); - *net = parse_network_cfg_custom(cfg, batch); + *net = parse_network_cfg_custom(cfg, batch, 0); if (weights && weights[0] != 0) { load_weights(net, weights); } diff --git a/src/parser.h b/src/parser.h index b7601169600..d8a72bea5c2 100644 --- a/src/parser.h +++ b/src/parser.h @@ -3,7 +3,7 @@ #include "network.h" network parse_network_cfg(char *filename); -network parse_network_cfg_custom(char *filename, int batch); +network parse_network_cfg_custom(char *filename, int batch, int time_steps); void save_network(network net, char *filename); void save_weights(network net, char *filename); void save_weights_upto(network net, char *filename, int cutoff); diff --git a/src/rnn.c b/src/rnn.c index da49bd2117a..556844cd2d8 100644 --- a/src/rnn.c +++ b/src/rnn.c @@ -163,11 +163,15 @@ void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear, int i = (*net.seen)/net.batch; int streams = batch/steps; + printf("\n batch = %d, steps = %d, streams = %d, subdivisions = %d, text_size = %d \n", batch, steps, streams, net.subdivisions, size); + printf(" global_batch = %d \n", batch*net.subdivisions); size_t *offsets = calloc(streams, sizeof(size_t)); int j; for(j = 0; j < streams; ++j){ offsets[j] = rand_size_t()%size; + //printf(" offset[%d] = %d, ", j, offsets[j]); } + //printf("\n"); clock_t time; while(get_current_batch(net) < net.max_batches){ @@ -234,7 +238,7 @@ void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float t char *base = basecfg(cfgfile); fprintf(stderr, "%s\n", base); - network net = parse_network_cfg(cfgfile); + network net = parse_network_cfg_custom(cfgfile, 1, 1); // batch=1, time_steps=1 if(weightfile){ load_weights(&net, weightfile); } @@ -273,7 +277,9 @@ void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float t for(j = 0; j < inputs; ++j){ if (out[j] < .0001) out[j] = 0; } - c = sample_array(out, inputs); + //c = sample_array(out, inputs); + c = sample_array_custom(out, inputs); + //c = max_index(out, inputs); print_symbol(c, tokens); } printf("\n"); diff --git a/src/utils.c b/src/utils.c index 14d647bda99..9e6df544a1b 100644 --- a/src/utils.c +++ b/src/utils.c @@ -614,13 +614,27 @@ void scale_array(float *a, int n, float s) } int sample_array(float *a, int n) +{ + float sum = sum_array(a, n); + scale_array(a, n, 1. / sum); + float r = rand_uniform(0, 1); + int i; + for (i = 0; i < n; ++i) { + r = r - a[i]; + if (r <= 0) return i; + } + return n - 1; +} + +int sample_array_custom(float *a, int n) { float sum = sum_array(a, n); scale_array(a, n, 1./sum); float r = rand_uniform(0, 1); + int start_index = rand_int(0, 0); int i; for(i = 0; i < n; ++i){ - r = r - a[i]; + r = r - a[(i + start_index) % n]; if (r <= 0) return i; } return n-1; diff --git a/src/utils.h b/src/utils.h index 5c87dda5998..9f0505367f5 100644 --- a/src/utils.h +++ b/src/utils.h @@ -80,6 +80,7 @@ float find_float_arg(int argc, char **argv, char *arg, float def); int find_arg(int argc, char* argv[], char *arg); char *find_char_arg(int argc, char **argv, char *arg, char *def); int sample_array(float *a, int n); +int sample_array_custom(float *a, int n); void print_statistics(float *a, int n); unsigned int random_gen(); float random_float(); From 3692c174c5a854b65733251027d20e191e56c767 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 28 Jan 2019 20:25:14 +0300 Subject: [PATCH 0279/1365] Compile fix --- src/cuda.c | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/cuda.c b/src/cuda.c index f4828923284..382a6cbd266 100644 --- a/src/cuda.c +++ b/src/cuda.c @@ -192,7 +192,8 @@ int *cuda_make_int_array_new_api(int *x, size_t n) cudaError_t status = cudaMalloc((void **)&x_gpu, size); check_error(status); if (x) { - status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice, get_cuda_stream()); + //status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice, get_cuda_stream()); + cudaError_t status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyHostToDevice, get_cuda_stream()); check_error(status); } if (!x_gpu) error("Cuda malloc failed\n"); From 0e1f3eaf354d779bf953d54d32083a796963dd10 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 28 Jan 2019 20:32:30 +0300 Subject: [PATCH 0280/1365] Fixed DLL/SO --- src/yolo_v2_class.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index 388f2eed5a3..c5ea1dfbefb 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -123,7 +123,7 @@ LIB_API Detector::Detector(std::string cfg_filename, std::string weight_filename char *cfgfile = const_cast(cfg_filename.data()); char *weightfile = const_cast(weight_filename.data()); - net = parse_network_cfg_custom(cfgfile, 1); + net = parse_network_cfg_custom(cfgfile, 1, 0); if (weightfile) { load_weights(&net, weightfile); } From 640bdbc0631786a711211783572879cf0d19b5d5 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 28 Jan 2019 23:50:51 +0300 Subject: [PATCH 0281/1365] LSTM, RNN, GRU - use connected_layer that uses cuDNN. Fixed CRNN for conv-layer with cuDNN. --- build/darknet/x64/rnn_lstm.cmd | 4 +- src/connected_layer.c | 117 +++++++++++++++++++++++++--- src/convolutional_kernels.cu | 32 ++++---- src/convolutional_layer.c | 138 +++++++++++++++++---------------- src/convolutional_layer.h | 3 +- src/crnn_layer.c | 13 +++- src/cuda.c | 52 +++++++++++++ src/cuda.h | 6 ++ src/gru_layer.c | 3 + src/lstm_layer.c | 12 +++ src/network_kernels.cu | 2 +- src/parser.c | 4 +- src/rnn_layer.c | 7 ++ 13 files changed, 292 insertions(+), 101 deletions(-) diff --git a/build/darknet/x64/rnn_lstm.cmd b/build/darknet/x64/rnn_lstm.cmd index 22ecc34124d..de74c9d24dc 100644 --- a/build/darknet/x64/rnn_lstm.cmd +++ b/build/darknet/x64/rnn_lstm.cmd @@ -9,8 +9,8 @@ rem darknet.exe rnn train cfg/lstm.train.cfg backup/lstm.backup -file text.txt pause -darknet.exe rnn generate cfg/lstm.train.cfg backup/lstm.backup -srand 2 -len 500 -seed apple +darknet.exe rnn generate cfg/lstm.train.cfg backup/lstm.backup -len 500 -seed apple -darknet.exe rnn generate cfg/lstm.train.cfg backup/lstm.backup -srand 2 -len 500 -seed apple > text_gen.txt +darknet.exe rnn generate cfg/lstm.train.cfg backup/lstm.backup -len 500 -seed apple > text_gen.txt pause \ No newline at end of file diff --git a/src/connected_layer.c b/src/connected_layer.c index 96f7aaf2972..2637f946d6e 100644 --- a/src/connected_layer.c +++ b/src/connected_layer.c @@ -1,5 +1,6 @@ #include "connected_layer.h" #include "batchnorm_layer.h" +#include "convolutional_layer.h" #include "utils.h" #include "cuda.h" #include "blas.h" @@ -10,6 +11,41 @@ #include #include +static size_t get_connected_workspace_size(layer l) { +#ifdef CUDNN + if (gpu_index >= 0) { + size_t most = 0; + size_t s = 0; + CHECK_CUDNN(cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(), + l.srcTensorDesc, + l.weightDesc, + l.convDesc, + l.dstTensorDesc, + l.fw_algo, + &s)); + if (s > most) most = s; + CHECK_CUDNN(cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnn_handle(), + l.srcTensorDesc, + l.ddstTensorDesc, + l.convDesc, + l.dweightDesc, + l.bf_algo, + &s)); + if (s > most) most = s; + CHECK_CUDNN(cudnnGetConvolutionBackwardDataWorkspaceSize(cudnn_handle(), + l.weightDesc, + l.ddstTensorDesc, + l.convDesc, + l.dsrcTensorDesc, + l.bd_algo, + &s)); + if (s > most) most = s; + return most; + } +#endif + return 0; +} + connected_layer make_connected_layer(int batch, int steps, int inputs, int outputs, ACTIVATION activation, int batch_normalize) { int total_batch = batch*steps; @@ -27,6 +63,10 @@ connected_layer make_connected_layer(int batch, int steps, int inputs, int outpu l.out_h = 1; l.out_w = 1; l.out_c = outputs; + l.n = l.out_c; + l.size = 1; + l.stride = 1; + l.pad = 0; l.output = calloc(total_batch*outputs, sizeof(float)); l.delta = calloc(total_batch*outputs, sizeof(float)); @@ -83,7 +123,7 @@ connected_layer make_connected_layer(int batch, int steps, int inputs, int outpu l.output_gpu = cuda_make_array(l.output, outputs*total_batch); l.delta_gpu = cuda_make_array(l.delta, outputs*total_batch); - if(batch_normalize){ + if (batch_normalize) { l.scales_gpu = cuda_make_array(l.scales, outputs); l.scale_updates_gpu = cuda_make_array(l.scale_updates, outputs); @@ -98,16 +138,13 @@ connected_layer make_connected_layer(int batch, int steps, int inputs, int outpu l.x_gpu = cuda_make_array(l.output, total_batch*outputs); l.x_norm_gpu = cuda_make_array(l.output, total_batch*outputs); -#ifdef CUDNN - cudnnCreateTensorDescriptor(&l.normDstTensorDesc); - cudnnCreateTensorDescriptor(&l.normTensorDesc); - cudnnCreateTensorDescriptor(&l.dstTensorDesc); - cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w); - cudnnSetTensor4dDescriptor(l.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l.out_c, 1, 1); - cudnnSetTensor4dDescriptor(l.normDstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w); -#endif } -#endif +#ifdef CUDNN + create_convolutional_cudnn_tensors(&l); + cudnn_convolutional_setup(&l, cudnn_fastest); // cudnn_fastest, cudnn_smallest + l.workspace_size = get_connected_workspace_size(l); +#endif // CUDNN +#endif // GPU l.activation = activation; fprintf(stderr, "connected %4d -> %4d\n", inputs, outputs); return l; @@ -288,7 +325,27 @@ void forward_connected_layer_gpu(connected_layer l, network_state state) float * a = state.input; float * b = l.weights_gpu; float * c = l.output_gpu; +#ifdef CUDNN + float one = 1; // alpha[0], beta[0] + float alpha = 1, beta = 0; + + CHECK_CUDNN(cudnnConvolutionForward(cudnn_handle(), + &alpha, //&one, + l.srcTensorDesc, + state.input, + l.weightDesc, + l.weights_gpu, + l.convDesc, + l.fw_algo, + state.workspace, + l.workspace_size, + &beta, //&one, + l.dstTensorDesc, + l.output_gpu)); +#else // CUDNN gemm_ongpu(0,1,m,n,k,1,a,k,b,k,1,c,n); +#endif // CUDNN + if (l.batch_normalize) { forward_batchnorm_layer_gpu(l, state); } @@ -312,12 +369,51 @@ void backward_connected_layer_gpu(connected_layer l, network_state state) backward_batchnorm_layer_gpu(l, state); } +#ifdef CUDNN_DISABLED + float one = 1; + // calculate conv weight updates + // if used: beta=1 then loss decreases faster + CHECK_CUDNN(cudnnConvolutionBackwardFilter(cudnn_handle(), + &one, + l.srcTensorDesc, + state.input, + l.ddstTensorDesc, + l.delta_gpu, + l.convDesc, + l.bf_algo, + state.workspace, + l.workspace_size, + &one, + l.dweightDesc, + l.weight_updates_gpu)); + + if (state.delta) { + // http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData + // calculate delta for the next layer + + CHECK_CUDNN(cudnnConvolutionBackwardData(cudnn_handle(), + &one, + l.weightDesc, + l.weights_gpu, + l.ddstTensorDesc, + l.delta_gpu, + l.convDesc, + l.bd_algo, + state.workspace, + l.workspace_size, + &one, + l.dsrcTensorDesc, + state.delta)); + } +#else // CUDNN + int m = l.outputs; int k = l.batch; int n = l.inputs; float * a = l.delta_gpu; float * b = state.input; float * c = l.weight_updates_gpu; + gemm_ongpu(1,0,m,n,k,1,a,m,b,n,1,c,n); m = l.batch; @@ -329,5 +425,6 @@ void backward_connected_layer_gpu(connected_layer l, network_state state) c = state.delta; if(c) gemm_ongpu(0,0,m,n,k,1,a,k,b,n,1,c,n); +#endif // CUDNN } #endif diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index f8ab49718c1..279c176fcaf 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -444,7 +444,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) cuda_convert_f32_to_f16(state.input, input16_size, input16); //fill_ongpu(output16_size / 2, 0, (float *)output16, 1); - cudnnConvolutionForward(cudnn_handle(), + CHECK_CUDNN(cudnnConvolutionForward(cudnn_handle(), &alpha, l.srcTensorDesc16, input16, @@ -456,7 +456,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) l.workspace_size, &beta, l.dstTensorDesc16, - output16); + output16)); if (l.batch_normalize) @@ -469,7 +469,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) float zero = 0; // Batch-normalization can still take FP16 inputs and outputs, saving half the bandwidth // compared to FP32, it’s just that the statistics and value adjustment should be done in FP32. - cudnnBatchNormalizationForwardTraining(cudnn_handle(), + CHECK_CUDNN(cudnnBatchNormalizationForwardTraining(cudnn_handle(), CUDNN_BATCHNORM_SPATIAL, &one, &zero, @@ -485,7 +485,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) l.rolling_variance_gpu, // output (should be FP32) .00001, l.mean_gpu, // output (should be FP32) - l.variance_gpu); // output (should be FP32) + l.variance_gpu)); // output (should be FP32) cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); //forward_batchnorm_layer_gpu(l, state); @@ -508,7 +508,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //#else - cudnnConvolutionForward(cudnn_handle(), + CHECK_CUDNN(cudnnConvolutionForward(cudnn_handle(), &alpha, //&one, l.srcTensorDesc, state.input, @@ -520,7 +520,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) l.workspace_size, &beta, //&one, l.dstTensorDesc, - l.output_gpu); + l.output_gpu)); //cudaDeviceSynchronize(); if (l.batch_normalize) { @@ -624,7 +624,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state //} float one = 1; float zero = 0; - cudnnBatchNormalizationBackward(cudnn_handle(), + CHECK_CUDNN(cudnnBatchNormalizationBackward(cudnn_handle(), CUDNN_BATCHNORM_SPATIAL, &one, &zero, @@ -642,7 +642,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state l.bias_updates_gpu, // output (should be FP32) .00001, l.mean_gpu, // input (should be FP32) - l.variance_gpu); // input (should be FP32) + l.variance_gpu)); // input (should be FP32) copy_ongpu(l.outputs*l.batch / 2, l.x_norm_gpu, 1, delta16, 1); //cudaMemcpyAsync(delta16, l.x_norm_gpu, l.outputs*l.batch * sizeof(half), cudaMemcpyDefault, get_cuda_stream()); } @@ -659,7 +659,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state // so we should copy f32 to f16, or compute: f16=(w_up - w*d*b*s)*m cuda_convert_f32_to_f16(l.weight_updates_gpu, l.c*l.n*l.size*l.size, l.weight_updates_gpu16); - cudnnConvolutionBackwardFilter(cudnn_handle(), + CHECK_CUDNN(cudnnConvolutionBackwardFilter(cudnn_handle(), &one, l.srcTensorDesc16, input16, //state.input, @@ -671,7 +671,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state l.workspace_size, &one, l.dweightDesc16, - l.weight_updates_gpu16); // l.weight_updates_gpu); + l.weight_updates_gpu16)); // l.weight_updates_gpu); cuda_convert_f16_to_f32(l.weight_updates_gpu16, l.c*l.n*l.size*l.size, l.weight_updates_gpu); @@ -682,7 +682,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state // calculate delta for the next layer // convert input: l.weights_gpu (w), l.delta_gpu (dy) from fp32 to fp16 // get output: state.delta (dx) and convert it to fp32 (ONLY if it is fp16) - cudnnConvolutionBackwardData(cudnn_handle(), + CHECK_CUDNN(cudnnConvolutionBackwardData(cudnn_handle(), &alpha, l.weightDesc16, l.weights_gpu16, //l.weights_gpu, @@ -694,7 +694,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state l.workspace_size, &beta, l.dsrcTensorDesc16, - input16); // state.delta); + input16)); // state.delta); cuda_convert_f16_to_f32(input16, input16_size, state.delta); @@ -711,7 +711,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state // calculate conv weight updates // if used: beta=1 then loss decreases faster - cudnnConvolutionBackwardFilter(cudnn_handle(), + CHECK_CUDNN(cudnnConvolutionBackwardFilter(cudnn_handle(), &one, l.srcTensorDesc, state.input, @@ -723,13 +723,13 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state l.workspace_size, &one, l.dweightDesc, - l.weight_updates_gpu); + l.weight_updates_gpu)); if (state.delta) { if (l.binary || l.xnor) swap_binary(&l); // http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData // calculate delta for the next layer - cudnnConvolutionBackwardData(cudnn_handle(), + CHECK_CUDNN(cudnnConvolutionBackwardData(cudnn_handle(), &one, l.weightDesc, l.weights_gpu, @@ -741,7 +741,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state l.workspace_size, &one, l.dsrcTensorDesc, - state.delta); + state.delta)); if (l.binary || l.xnor) swap_binary(&l); if (l.xnor) gradient_array_ongpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, state.delta); } diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index fd055a37395..39a72d3b13d 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -105,29 +105,29 @@ size_t get_workspace_size(layer l){ if(gpu_index >= 0){ size_t most = 0; size_t s = 0; - cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(), + CHECK_CUDNN(cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(), l.srcTensorDesc, l.weightDesc, l.convDesc, l.dstTensorDesc, l.fw_algo, - &s); + &s)); if (s > most) most = s; - cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnn_handle(), + CHECK_CUDNN(cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnn_handle(), l.srcTensorDesc, l.ddstTensorDesc, l.convDesc, l.dweightDesc, l.bf_algo, - &s); + &s)); if (s > most) most = s; - cudnnGetConvolutionBackwardDataWorkspaceSize(cudnn_handle(), + CHECK_CUDNN(cudnnGetConvolutionBackwardDataWorkspaceSize(cudnn_handle(), l.weightDesc, l.ddstTensorDesc, l.convDesc, l.dsrcTensorDesc, l.bd_algo, - &s); + &s)); if (s > most) most = s; return most; } @@ -141,29 +141,29 @@ size_t get_workspace_size16(layer l) { if (gpu_index >= 0) { size_t most = 0; size_t s = 0; - cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(), + CHECK_CUDNN(cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(), l.srcTensorDesc16, l.weightDesc16, l.convDesc, l.dstTensorDesc16, l.fw_algo16, - &s); + &s)); if (s > most) most = s; - cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnn_handle(), + CHECK_CUDNN(cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnn_handle(), l.srcTensorDesc16, l.ddstTensorDesc16, l.convDesc, l.dweightDesc16, l.bf_algo16, - &s); + &s)); if (s > most) most = s; - cudnnGetConvolutionBackwardDataWorkspaceSize(cudnn_handle(), + CHECK_CUDNN(cudnnGetConvolutionBackwardDataWorkspaceSize(cudnn_handle(), l.weightDesc16, l.ddstTensorDesc16, l.convDesc, l.dsrcTensorDesc16, l.bd_algo16, - &s); + &s)); if (s > most) most = s; return most; } @@ -175,6 +175,29 @@ size_t get_workspace_size16(layer l) { #ifdef GPU #ifdef CUDNN +void create_convolutional_cudnn_tensors(layer *l) +{ + CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->normTensorDesc)); + + CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->normDstTensorDesc)); + CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->srcTensorDesc)); + CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->dstTensorDesc)); + CHECK_CUDNN(cudnnCreateFilterDescriptor(&l->weightDesc)); + CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->dsrcTensorDesc)); + CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->ddstTensorDesc)); + CHECK_CUDNN(cudnnCreateFilterDescriptor(&l->dweightDesc)); + + CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->normDstTensorDescF16)); + CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->srcTensorDesc16)); + CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->dstTensorDesc16)); + CHECK_CUDNN(cudnnCreateFilterDescriptor(&l->weightDesc16)); + CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->dsrcTensorDesc16)); + CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->ddstTensorDesc16)); + CHECK_CUDNN(cudnnCreateFilterDescriptor(&l->dweightDesc16)); + + CHECK_CUDNN(cudnnCreateConvolutionDescriptor(&l->convDesc)); +} + void cudnn_convolutional_setup(layer *l, int cudnn_preference) { @@ -194,9 +217,9 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) // 2. Loss Scaling - required only for: activation gradients. We do not use. // 3. FP32 Master Copy of Weights // More: http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#tensor_ops - cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH); + CHECK_CUDNN(cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH)); #if((CUDNN_MAJOR*10 + CUDNN_MINOR) >= 72) // cuDNN >= 7.2 - cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION); + CHECK_CUDNN(cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION)); #endif #endif @@ -205,38 +228,38 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) //cudnnDataType_t data_type = CUDNN_DATA_INT8; // backward delta - cudnnSetTensor4dDescriptor(l->dsrcTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->c, l->h, l->w); - cudnnSetTensor4dDescriptor(l->ddstTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->out_c, l->out_h, l->out_w); - cudnnSetFilter4dDescriptor(l->dweightDesc, data_type, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size); + CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->dsrcTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->c, l->h, l->w)); + CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->ddstTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->out_c, l->out_h, l->out_w)); + CHECK_CUDNN(cudnnSetFilter4dDescriptor(l->dweightDesc, data_type, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size)); // forward - cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->c, l->h, l->w); - cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->out_c, l->out_h, l->out_w); - cudnnSetFilter4dDescriptor(l->weightDesc, data_type, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size); + CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->c, l->h, l->w)); + CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->out_c, l->out_h, l->out_w)); + CHECK_CUDNN(cudnnSetFilter4dDescriptor(l->weightDesc, data_type, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size)); //#ifdef CUDNN_HALF // backward delta - cudnnSetTensor4dDescriptor(l->dsrcTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->c, l->h, l->w); - cudnnSetTensor4dDescriptor(l->ddstTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w); - cudnnSetFilter4dDescriptor(l->dweightDesc16, CUDNN_DATA_HALF, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size); + CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->dsrcTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->c, l->h, l->w)); + CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->ddstTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w)); + CHECK_CUDNN(cudnnSetFilter4dDescriptor(l->dweightDesc16, CUDNN_DATA_HALF, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size)); // forward - cudnnSetTensor4dDescriptor(l->srcTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->c, l->h, l->w); - cudnnSetTensor4dDescriptor(l->dstTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w); - cudnnSetFilter4dDescriptor(l->weightDesc16, CUDNN_DATA_HALF, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size); + CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->srcTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->c, l->h, l->w)); + CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->dstTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w)); + CHECK_CUDNN(cudnnSetFilter4dDescriptor(l->weightDesc16, CUDNN_DATA_HALF, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size)); // batch norm - cudnnSetTensor4dDescriptor(l->normDstTensorDescF16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w); + CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->normDstTensorDescF16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w)); //#endif // batch norm - cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1); - cudnnSetTensor4dDescriptor(l->normDstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); + CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1)); + CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->normDstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w)); #if(CUDNN_MAJOR >= 6) - cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT); // cudnn >= 6.0 + CHECK_CUDNN(cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); // cudnn >= 6.0 #else - cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION); // cudnn 5.1 + CHECK_CUDNN(cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION)); // cudnn 5.1 #endif int forward_algo = CUDNN_CONVOLUTION_FWD_PREFER_FASTEST; int backward_algo = CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST; @@ -249,30 +272,30 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) printf(" CUDNN-slow "); } - cudnnGetConvolutionForwardAlgorithm(cudnn_handle(), + CHECK_CUDNN(cudnnGetConvolutionForwardAlgorithm(cudnn_handle(), l->srcTensorDesc, l->weightDesc, l->convDesc, l->dstTensorDesc, forward_algo, 0, - &l->fw_algo); - cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(), + &l->fw_algo)); + CHECK_CUDNN(cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(), l->weightDesc, l->ddstTensorDesc, l->convDesc, l->dsrcTensorDesc, backward_algo, 0, - &l->bd_algo); - cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(), + &l->bd_algo)); + CHECK_CUDNN(cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(), l->srcTensorDesc, l->ddstTensorDesc, l->convDesc, l->dweightDesc, backward_filter, 0, - &l->bf_algo); + &l->bf_algo)); //if (data_type == CUDNN_DATA_HALF) { @@ -290,8 +313,9 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) #endif #endif -convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index) +convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index) { + int total_batch = batch*steps; int i; convolutional_layer l = {0}; l.type = CONVOLUTIONAL; @@ -327,8 +351,8 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.outputs = l.out_h * l.out_w * l.out_c; l.inputs = l.w * l.h * l.c; - l.output = calloc(l.batch*l.outputs, sizeof(float)); - l.delta = calloc(l.batch*l.outputs, sizeof(float)); + l.output = calloc(total_batch*l.outputs, sizeof(float)); + l.delta = calloc(total_batch*l.outputs, sizeof(float)); l.forward = forward_convolutional_layer; l.backward = backward_convolutional_layer; @@ -364,8 +388,8 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.rolling_mean = calloc(n, sizeof(float)); l.rolling_variance = calloc(n, sizeof(float)); - l.x = calloc(l.batch*l.outputs, sizeof(float)); - l.x_norm = calloc(l.batch*l.outputs, sizeof(float)); + l.x = calloc(total_batch*l.outputs, sizeof(float)); + l.x_norm = calloc(total_batch*l.outputs, sizeof(float)); } if(adam){ l.adam = 1; @@ -402,8 +426,8 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.biases_gpu = cuda_make_array(l.biases, n); l.bias_updates_gpu = cuda_make_array(l.bias_updates, n); - l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n); - l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); + l.delta_gpu = cuda_make_array(l.delta, total_batch*out_h*out_w*n); + l.output_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n); if(binary){ l.binary_weights_gpu = cuda_make_array(l.weights, c*n*size*size); @@ -427,29 +451,11 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int l.scales_gpu = cuda_make_array(l.scales, n); l.scale_updates_gpu = cuda_make_array(l.scale_updates, n); - l.x_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); - l.x_norm_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); + l.x_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n); + l.x_norm_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n); } #ifdef CUDNN - cudnnCreateTensorDescriptor(&l.normTensorDesc); - - cudnnCreateTensorDescriptor(&l.normDstTensorDesc); - cudnnCreateTensorDescriptor(&l.srcTensorDesc); - cudnnCreateTensorDescriptor(&l.dstTensorDesc); - cudnnCreateFilterDescriptor(&l.weightDesc); - cudnnCreateTensorDescriptor(&l.dsrcTensorDesc); - cudnnCreateTensorDescriptor(&l.ddstTensorDesc); - cudnnCreateFilterDescriptor(&l.dweightDesc); - - cudnnCreateTensorDescriptor(&l.normDstTensorDescF16); - cudnnCreateTensorDescriptor(&l.srcTensorDesc16); - cudnnCreateTensorDescriptor(&l.dstTensorDesc16); - cudnnCreateFilterDescriptor(&l.weightDesc16); - cudnnCreateTensorDescriptor(&l.dsrcTensorDesc16); - cudnnCreateTensorDescriptor(&l.ddstTensorDesc16); - cudnnCreateFilterDescriptor(&l.dweightDesc16); - - cudnnCreateConvolutionDescriptor(&l.convDesc); + create_convolutional_cudnn_tensors(&l); cudnn_convolutional_setup(&l, cudnn_fastest); #endif } @@ -486,7 +492,7 @@ void denormalize_convolutional_layer(convolutional_layer l) void test_convolutional_layer() { - convolutional_layer l = make_convolutional_layer(1, 5, 5, 3, 2, 5, 2, 1, LEAKY, 1, 0, 0, 0, 0, 0); + convolutional_layer l = make_convolutional_layer(1, 1, 5, 5, 3, 2, 5, 2, 1, LEAKY, 1, 0, 0, 0, 0, 0); l.batch_normalize = 1; float data[] = {1,1,1,1,1, 1,1,1,1,1, diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h index 0bd9849e74a..19a0625198f 100644 --- a/src/convolutional_layer.h +++ b/src/convolutional_layer.h @@ -21,11 +21,12 @@ void add_bias_gpu(float *output, float *biases, int batch, int n, int size); void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size); #ifdef CUDNN void cudnn_convolutional_setup(layer *l, int cudnn_preference); +void create_convolutional_cudnn_tensors(layer *l); void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16); #endif #endif -convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index); +convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index); void denormalize_convolutional_layer(convolutional_layer l); void resize_convolutional_layer(convolutional_layer *layer, int w, int h); void forward_convolutional_layer(const convolutional_layer layer, network_state state); diff --git a/src/crnn_layer.c b/src/crnn_layer.c index db384cfb683..d76c5e222bf 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -48,18 +48,21 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.input_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.input_layer) = make_convolutional_layer(batch*steps, h, w, c, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0, 0); + *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0, 0); l.input_layer->batch = batch; + if (l.workspace_size < l.input_layer->workspace_size) l.workspace_size = l.input_layer->workspace_size; l.self_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.self_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0, 0); + *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0, 0); l.self_layer->batch = batch; + if (l.workspace_size < l.self_layer->workspace_size) l.workspace_size = l.self_layer->workspace_size; l.output_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); - *(l.output_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, output_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0, 0); + *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0, 0); l.output_layer->batch = batch; + if (l.workspace_size < l.output_layer->workspace_size) l.workspace_size = l.output_layer->workspace_size; l.output = l.output_layer->output; l.delta = l.output_layer->delta; @@ -92,6 +95,7 @@ void forward_crnn_layer(layer l, network_state state) { network_state s = {0}; s.train = state.train; + s.workspace = state.workspace; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); @@ -133,6 +137,7 @@ void backward_crnn_layer(layer l, network_state state) { network_state s = {0}; s.train = state.train; + s.workspace = state.workspace; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); @@ -206,6 +211,7 @@ void forward_crnn_layer_gpu(layer l, network_state state) { network_state s = {0}; s.train = state.train; + s.workspace = state.workspace; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); @@ -247,6 +253,7 @@ void backward_crnn_layer_gpu(layer l, network_state state) { network_state s = {0}; s.train = state.train; + s.workspace = state.workspace; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); diff --git a/src/cuda.c b/src/cuda.c index 382a6cbd266..d1f9afefcca 100644 --- a/src/cuda.c +++ b/src/cuda.c @@ -35,6 +35,9 @@ void check_error(cudaError_t status) printf("CUDA Error: %s\n", s); assert(0); snprintf(buffer, 256, "CUDA Error: %s", s); +#ifdef WIN32 + getchar(); +#endif error(buffer); } if (status2 != cudaSuccess) @@ -44,10 +47,20 @@ void check_error(cudaError_t status) printf("CUDA Error Prev: %s\n", s); assert(0); snprintf(buffer, 256, "CUDA Error Prev: %s", s); +#ifdef WIN32 + getchar(); +#endif error(buffer); } } +void check_error_extended(cudaError_t status, char *file, int line, char *date_time) +{ + if (status != cudaSuccess) + printf("CUDA Error: file: %s() : line: %d : build time: %s \n", file, line, date_time); + check_error(status); +} + dim3 cuda_gridsize(size_t n){ size_t k = (n-1) / BLOCK + 1; size_t x = k; @@ -117,6 +130,45 @@ cudnnHandle_t cudnn_handle() } return handle[i]; } + + +void cudnn_check_error(cudnnStatus_t status) +{ + //cudaDeviceSynchronize(); + cudnnStatus_t status2; + cudnnStatus_t status_tmp = cudnnQueryRuntimeError(cudnn_handle(), &status2, CUDNN_ERRQUERY_RAWCODE, NULL); + if (status != CUDNN_STATUS_SUCCESS) + { + const char *s = cudnnGetErrorString(status); + char buffer[256]; + printf("cuDNN Error: %s\n", s); + assert(0); + snprintf(buffer, 256, "cuDNN Error: %s", s); +#ifdef WIN32 + getchar(); +#endif + error(buffer); + } + if (status2 != CUDNN_STATUS_SUCCESS) + { + const char *s = cudnnGetErrorString(status); + char buffer[256]; + printf("cuDNN Error Prev: %s\n", s); + assert(0); + snprintf(buffer, 256, "cuDNN Error Prev: %s", s); +#ifdef WIN32 + getchar(); +#endif + error(buffer); + } +} + +void cudnn_check_error_extended(cudnnStatus_t status, char *file, int line, char *date_time) +{ + if (status != cudaSuccess) + printf("\n cuDNN Error in: file: %s() : line: %d : build time: %s \n", file, line, date_time); + cudnn_check_error(status); +} #endif cublasHandle_t blas_handle() diff --git a/src/cuda.h b/src/cuda.h index b803c17f6f7..d2c7da00bf8 100644 --- a/src/cuda.h +++ b/src/cuda.h @@ -24,6 +24,9 @@ extern int gpu_index; extern "C" { #endif // __cplusplus void check_error(cudaError_t status); + void check_error_extended(cudaError_t status, char *file, int line, char *date_time); +#define CHECK_CUDA(X) check_error_extended(X, __FILE__ " : " __FUNCTION__, __LINE__, __DATE__ " - " __TIME__ ); + cublasHandle_t blas_handle(); float *cuda_make_array(float *x, size_t n); int *cuda_make_int_array(size_t n); @@ -46,6 +49,9 @@ extern "C" { #ifdef CUDNN cudnnHandle_t cudnn_handle(); enum {cudnn_fastest, cudnn_smallest}; + +void cudnn_check_error_extended(cudnnStatus_t status, char *file, int line, char *date_time); +#define CHECK_CUDNN(X) cudnn_check_error_extended(X, __FILE__ " : " __FUNCTION__, __LINE__, __DATE__ " - " __TIME__ ); #endif #else // GPU diff --git a/src/gru_layer.c b/src/gru_layer.c index fa03e7eb918..045bbb331ed 100644 --- a/src/gru_layer.c +++ b/src/gru_layer.c @@ -119,6 +119,7 @@ void forward_gru_layer(layer l, network_state state) { network_state s = {0}; s.train = state.train; + s.workspace = state.workspace; int i; layer input_z_layer = *(l.input_z_layer); layer input_r_layer = *(l.input_r_layer); @@ -219,6 +220,7 @@ void forward_gru_layer_gpu(layer l, network_state state) { network_state s = {0}; s.train = state.train; + s.workspace = state.workspace; int i; layer input_z_layer = *(l.input_z_layer); layer input_r_layer = *(l.input_r_layer); @@ -295,6 +297,7 @@ void backward_gru_layer_gpu(layer l, network_state state) { network_state s = {0}; s.train = state.train; + s.workspace = state.workspace; int i; layer input_z_layer = *(l.input_z_layer); layer input_r_layer = *(l.input_r_layer); diff --git a/src/lstm_layer.c b/src/lstm_layer.c index aefe9c460ef..cf3411e6075 100644 --- a/src/lstm_layer.c +++ b/src/lstm_layer.c @@ -40,41 +40,49 @@ layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_n fprintf(stderr, "\t\t"); *(l.uf) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.uf->batch = batch; + if (l.workspace_size < l.uf->workspace_size) l.workspace_size = l.uf->workspace_size; l.ui = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.ui) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.ui->batch = batch; + if (l.workspace_size < l.ui->workspace_size) l.workspace_size = l.ui->workspace_size; l.ug = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.ug) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.ug->batch = batch; + if (l.workspace_size < l.ug->workspace_size) l.workspace_size = l.ug->workspace_size; l.uo = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.uo) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.uo->batch = batch; + if (l.workspace_size < l.uo->workspace_size) l.workspace_size = l.uo->workspace_size; l.wf = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.wf) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.wf->batch = batch; + if (l.workspace_size < l.wf->workspace_size) l.workspace_size = l.wf->workspace_size; l.wi = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.wi) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.wi->batch = batch; + if (l.workspace_size < l.wi->workspace_size) l.workspace_size = l.wi->workspace_size; l.wg = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.wg) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.wg->batch = batch; + if (l.workspace_size < l.wg->workspace_size) l.workspace_size = l.wg->workspace_size; l.wo = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.wo) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.wo->batch = batch; + if (l.workspace_size < l.wo->workspace_size) l.workspace_size = l.wo->workspace_size; l.batch_normalize = batch_normalize; l.outputs = outputs; @@ -161,6 +169,7 @@ void forward_lstm_layer(layer l, network_state state) { network_state s = { 0 }; s.train = state.train; + s.workspace = state.workspace; int i; layer wf = *(l.wf); layer wi = *(l.wi); @@ -247,6 +256,7 @@ void backward_lstm_layer(layer l, network_state state) { network_state s = { 0 }; s.train = state.train; + s.workspace = state.workspace; int i; layer wf = *(l.wf); layer wi = *(l.wi); @@ -403,6 +413,7 @@ void forward_lstm_layer_gpu(layer l, network_state state) { network_state s = { 0 }; s.train = state.train; + s.workspace = state.workspace; int i; layer wf = *(l.wf); layer wi = *(l.wi); @@ -489,6 +500,7 @@ void backward_lstm_layer_gpu(layer l, network_state state) { network_state s = { 0 }; s.train = state.train; + s.workspace = state.workspace; int i; layer wf = *(l.wf); layer wi = *(l.wi); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 0076a87b045..0e14ce56662 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -58,7 +58,7 @@ void forward_network_gpu(network net, network_state state) if(l.delta_gpu && state.train){ fill_ongpu(l.outputs * l.batch, 0, l.delta_gpu, 1); } - //printf("%d - type: %d - ", i, l.type); + //printf("\n layer %d - type: %d - \n", i, l.type); //start_timer(); l.forward_gpu(l, state); //cudaDeviceSynchronize(); diff --git a/src/parser.c b/src/parser.c index 92850c95798..90594c92671 100644 --- a/src/parser.c +++ b/src/parser.c @@ -167,7 +167,7 @@ convolutional_layer parse_convolutional(list *options, size_params params) int xnor = option_find_int_quiet(options, "xnor", 0); int use_bin_output = option_find_int_quiet(options, "bin_output", 0); - convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,size,stride,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index); + convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,size,stride,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index); layer.flipped = option_find_int_quiet(options, "flipped", 0); layer.dot = option_find_float_quiet(options, "dot", 0); @@ -845,7 +845,6 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) net.outputs = get_network_output_size(net); net.output = get_network_output(net); printf("Total BFLOPS %5.3f \n", bflops); - //printf("%ld\n", workspace_size); #ifdef GPU get_cuda_stream(); get_cuda_memcpy_stream(); @@ -864,6 +863,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) check_error(cudaMalloc((void **)net.output16_gpu, *net.max_output16_size * sizeof(short))); //sizeof(half) } if (workspace_size) { + printf(" Allocate workspace_size = %zu \n", workspace_size); net.workspace = cuda_make_array(0, workspace_size / sizeof(float) + 1); } else { diff --git a/src/rnn_layer.c b/src/rnn_layer.c index 1b377b14950..c1843f062e3 100644 --- a/src/rnn_layer.c +++ b/src/rnn_layer.c @@ -43,16 +43,19 @@ layer make_rnn_layer(int batch, int inputs, int hidden, int outputs, int steps, fprintf(stderr, "\t\t"); *(l.input_layer) = make_connected_layer(batch, steps, inputs, hidden, activation, batch_normalize); l.input_layer->batch = batch; + if (l.workspace_size < l.input_layer->workspace_size) l.workspace_size = l.input_layer->workspace_size; l.self_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.self_layer) = make_connected_layer(batch, steps, hidden, hidden, (log==2)?LOGGY:(log==1?LOGISTIC:activation), batch_normalize); l.self_layer->batch = batch; + if (l.workspace_size < l.self_layer->workspace_size) l.workspace_size = l.self_layer->workspace_size; l.output_layer = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.output_layer) = make_connected_layer(batch, steps, hidden, outputs, activation, batch_normalize); l.output_layer->batch = batch; + if (l.workspace_size < l.output_layer->workspace_size) l.workspace_size = l.output_layer->workspace_size; l.outputs = outputs; l.output = l.output_layer->output; @@ -84,6 +87,7 @@ void forward_rnn_layer(layer l, network_state state) { network_state s = {0}; s.train = state.train; + s.workspace = state.workspace; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); @@ -126,6 +130,7 @@ void backward_rnn_layer(layer l, network_state state) { network_state s = {0}; s.train = state.train; + s.workspace = state.workspace; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); @@ -199,6 +204,7 @@ void forward_rnn_layer_gpu(layer l, network_state state) { network_state s = {0}; s.train = state.train; + s.workspace = state.workspace; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); @@ -241,6 +247,7 @@ void backward_rnn_layer_gpu(layer l, network_state state) { network_state s = {0}; s.train = state.train; + s.workspace = state.workspace; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); From ae8a8e6016c24ee8501669b22494a7789c4ffdee Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 29 Jan 2019 00:05:08 +0300 Subject: [PATCH 0282/1365] Compile fix --- src/cuda.h | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/src/cuda.h b/src/cuda.h index d2c7da00bf8..ecc83e40032 100644 --- a/src/cuda.h +++ b/src/cuda.h @@ -20,6 +20,26 @@ extern int gpu_index; #include "cudnn.h" #endif // CUDNN +#ifndef __DATE__ +#define __DATE__ +#endif + +#ifndef __TIME__ +#define __TIME__ +#endif + +#ifndef __FUNCTION__ +#define __FUNCTION__ +#endif + +#ifndef __LINE__ +#define __LINE__ 0 +#endif + +#ifndef __FILE__ +#define __FILE__ +#endif + #ifdef __cplusplus extern "C" { #endif // __cplusplus From 2790464de1185869dbc6ff565294a4cd34e8db71 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 29 Jan 2019 00:11:32 +0300 Subject: [PATCH 0283/1365] Another compile fix --- src/cuda.c | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/cuda.c b/src/cuda.c index d1f9afefcca..16af413fb19 100644 --- a/src/cuda.c +++ b/src/cuda.c @@ -135,8 +135,10 @@ cudnnHandle_t cudnn_handle() void cudnn_check_error(cudnnStatus_t status) { //cudaDeviceSynchronize(); - cudnnStatus_t status2; + cudnnStatus_t status2 = CUDNN_STATUS_SUCCESS; +#ifdef CUDNN_ERRQUERY_RAWCODE cudnnStatus_t status_tmp = cudnnQueryRuntimeError(cudnn_handle(), &status2, CUDNN_ERRQUERY_RAWCODE, NULL); +#endif if (status != CUDNN_STATUS_SUCCESS) { const char *s = cudnnGetErrorString(status); From 381f90ebb8d1518141e809521fe2985a2d506fc1 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 29 Jan 2019 13:46:30 +0300 Subject: [PATCH 0284/1365] Fixed CUDA error checking --- build/darknet/x64/cfg/lstm.train.cfg | 3 ++- cfg/lstm.train.cfg | 3 ++- src/cuda.c | 4 ++-- 3 files changed, 6 insertions(+), 4 deletions(-) diff --git a/build/darknet/x64/cfg/lstm.train.cfg b/build/darknet/x64/cfg/lstm.train.cfg index 874b990e7f0..f0d4721d414 100644 --- a/build/darknet/x64/cfg/lstm.train.cfg +++ b/build/darknet/x64/cfg/lstm.train.cfg @@ -6,8 +6,9 @@ momentum=0.9 decay=0.001 max_batches = 2000 time_steps=576 -learning_rate=1.0 +learning_rate=0.5 policy=steps +burn_in=10 steps=1000,1500 scales=.1,.1 diff --git a/cfg/lstm.train.cfg b/cfg/lstm.train.cfg index 874b990e7f0..f0d4721d414 100644 --- a/cfg/lstm.train.cfg +++ b/cfg/lstm.train.cfg @@ -6,8 +6,9 @@ momentum=0.9 decay=0.001 max_batches = 2000 time_steps=576 -learning_rate=1.0 +learning_rate=0.5 policy=steps +burn_in=10 steps=1000,1500 scales=.1,.1 diff --git a/src/cuda.c b/src/cuda.c index 16af413fb19..77f4dd7146b 100644 --- a/src/cuda.c +++ b/src/cuda.c @@ -42,7 +42,7 @@ void check_error(cudaError_t status) } if (status2 != cudaSuccess) { - const char *s = cudaGetErrorString(status); + const char *s = cudaGetErrorString(status2); char buffer[256]; printf("CUDA Error Prev: %s\n", s); assert(0); @@ -153,7 +153,7 @@ void cudnn_check_error(cudnnStatus_t status) } if (status2 != CUDNN_STATUS_SUCCESS) { - const char *s = cudnnGetErrorString(status); + const char *s = cudnnGetErrorString(status2); char buffer[256]; printf("cuDNN Error Prev: %s\n", s); assert(0); From c71354ab2ea4d3fcbee954f56e39fd7199fa2d2d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 31 Jan 2019 14:22:07 +0300 Subject: [PATCH 0285/1365] Added cudaGetLastError() for cudaHostAlloc() to reset last cuda error --- src/network.c | 3 ++- src/parser.c | 7 +++++-- src/yolo_layer.c | 12 ++++++++++-- 3 files changed, 17 insertions(+), 5 deletions(-) diff --git a/src/network.c b/src/network.c index b960f27f266..65d7629f046 100644 --- a/src/network.c +++ b/src/network.c @@ -459,12 +459,13 @@ int resize_network(network *net, int w, int h) const int size = get_network_input_size(*net) * net->batch; #ifdef GPU if(gpu_index >= 0){ - printf(" try to allocate workspace = %zu * sizeof(float), ", workspace_size / sizeof(float) + 1); + printf(" try to allocate additional workspace_size = %1.2f MB \n", (float)workspace_size / 1000000); net->workspace = cuda_make_array(0, workspace_size/sizeof(float) + 1); net->input_state_gpu = cuda_make_array(0, size); if (cudaSuccess == cudaHostAlloc(&net->input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped)) net->input_pinned_cpu_flag = 1; else { + cudaGetLastError(); // reset CUDA-error net->input_pinned_cpu = calloc(size, sizeof(float)); net->input_pinned_cpu_flag = 0; } diff --git a/src/parser.c b/src/parser.c index 90594c92671..9ca9a942d9c 100644 --- a/src/parser.c +++ b/src/parser.c @@ -853,7 +853,10 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) int size = get_network_input_size(net) * net.batch; net.input_state_gpu = cuda_make_array(0, size); if (cudaSuccess == cudaHostAlloc(&net.input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped)) net.input_pinned_cpu_flag = 1; - else net.input_pinned_cpu = calloc(size, sizeof(float)); + else { + cudaGetLastError(); // reset CUDA-error + net.input_pinned_cpu = calloc(size, sizeof(float)); + } // pre-allocate memory for inference on Tensor Cores (fp16) if (net.cudnn_half) { @@ -863,7 +866,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) check_error(cudaMalloc((void **)net.output16_gpu, *net.max_output16_size * sizeof(short))); //sizeof(half) } if (workspace_size) { - printf(" Allocate workspace_size = %zu \n", workspace_size); + printf(" Allocate additional workspace_size = %1.2f MB \n", (float)workspace_size / 1000000); net.workspace = cuda_make_array(0, workspace_size / sizeof(float) + 1); } else { diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 74654af1a1c..1268ff5eb98 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -56,11 +56,17 @@ layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int free(l.output); if (cudaSuccess == cudaHostAlloc(&l.output, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1; - else l.output = calloc(batch*l.outputs, sizeof(float)); + else { + cudaGetLastError(); // reset CUDA-error + l.output = calloc(batch*l.outputs, sizeof(float)); + } free(l.delta); if (cudaSuccess == cudaHostAlloc(&l.delta, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1; - else l.delta = calloc(batch*l.outputs, sizeof(float)); + else { + cudaGetLastError(); // reset CUDA-error + l.delta = calloc(batch*l.outputs, sizeof(float)); + } #endif fprintf(stderr, "yolo\n"); @@ -84,6 +90,7 @@ void resize_yolo_layer(layer *l, int w, int h) if (l->output_pinned) { cudaFreeHost(l->output); if (cudaSuccess != cudaHostAlloc(&l->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) { + cudaGetLastError(); // reset CUDA-error l->output = realloc(l->output, l->batch*l->outputs * sizeof(float)); l->output_pinned = 0; } @@ -92,6 +99,7 @@ void resize_yolo_layer(layer *l, int w, int h) if (l->delta_pinned) { cudaFreeHost(l->delta); if (cudaSuccess != cudaHostAlloc(&l->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) { + cudaGetLastError(); // reset CUDA-error l->delta = realloc(l->delta, l->batch*l->outputs * sizeof(float)); l->delta_pinned = 0; } From bd91d0a908fd0cd9364e16a00c395500f59cbf58 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 31 Jan 2019 14:33:05 +0300 Subject: [PATCH 0286/1365] Add try-catch to the http_stream.cpp --- src/http_stream.cpp | 155 ++++++++++++++++++++++++-------------------- 1 file changed, 86 insertions(+), 69 deletions(-) diff --git a/src/http_stream.cpp b/src/http_stream.cpp index dbac6f5dcdb..3eb76ac0622 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -6,6 +6,13 @@ // on win, _WIN32 has to be defined, must link against ws2_32.lib (socks on linux are for free) // +#include +#include +#include +#include +using std::cerr; +using std::endl; + // // socket related abstractions: // @@ -33,7 +40,7 @@ static int close_socket(SOCKET s) { free(buf); int close_input = ::shutdown(s, 0); int result = ::closesocket(s); - printf("Close socket: out = %d, in = %d \n", close_output, close_input); + cerr << "Close socket: out = " << close_output << ", in = " << close_input << " \n"; return result; } #else // nix @@ -72,17 +79,11 @@ static int close_socket(SOCKET s) { free(buf); int close_input = ::shutdown(s, 0); int result = close(s); - printf("Close socket: out = %d, in = %d \n", close_output, close_input); + std::cerr << "Close socket: out = " << close_output << ", in = " << close_input << " \n"; return result; } #endif // _WIN32 -#include -#include -#include -#include -using std::cerr; -using std::endl; #include "opencv2/opencv.hpp" #include "opencv2/highgui/highgui.hpp" @@ -243,7 +244,7 @@ class MJPG_sender { if (close_all_sockets) { int result = close_socket(s); - printf("MJPG_sender: close clinet: %d \n", result); + cerr << "MJPG_sender: close clinet: " << result << " \n"; continue; } @@ -262,7 +263,7 @@ class MJPG_sender } if (close_all_sockets) { int result = close_socket(sock); - printf("MJPG_sender: close acceptor: %d \n\n", result); + cerr << "MJPG_sender: close acceptor: " << result << " \n\n"; } return true; } @@ -271,10 +272,15 @@ class MJPG_sender void send_mjpeg(IplImage* ipl, int port, int timeout, int quality) { - static MJPG_sender wri(port, timeout, quality); - cv::Mat mat = cv::cvarrToMat(ipl); - wri.write(mat); - std::cout << " MJPEG-stream sent. \n"; + try { + static MJPG_sender wri(port, timeout, quality); + cv::Mat mat = cv::cvarrToMat(ipl); + wri.write(mat); + std::cout << " MJPEG-stream sent. \n"; + } + catch (...) { + cerr << " Error in send_mjpeg() function \n"; + } } // ---------------------------------------- @@ -436,14 +442,14 @@ class JSON_sender if (close_all_sockets) { int result = close_socket(s); - printf("JSON_sender: close clinet: %d \n", result); + cerr << "JSON_sender: close clinet: " << result << " \n"; continue; } } } if (close_all_sockets) { int result = close_socket(sock); - printf("JSON_sender: close acceptor: %d \n\n", result); + cerr << "JSON_sender: close acceptor: " << result << " \n\n"; } return true; } @@ -452,12 +458,17 @@ class JSON_sender void send_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id, int port, int timeout) { - static JSON_sender js(port, timeout); - char *send_buf = detection_to_json(dets, nboxes, classes, names, frame_id, NULL); + try { + static JSON_sender js(port, timeout); + char *send_buf = detection_to_json(dets, nboxes, classes, names, frame_id, NULL); - js.write(send_buf); - std::cout << " JSON-stream sent. \n"; - free(send_buf); + js.write(send_buf); + std::cout << " JSON-stream sent. \n"; + free(send_buf); + } + catch (...) { + cerr << " Error in send_json() function \n"; + } } // ---------------------------------------- @@ -468,7 +479,7 @@ CvCapture* get_capture_video_stream(char *path) { cap = (CvCapture*)new cv::VideoCapture(path); } catch (...) { - std::cout << " Error: video-stream " << path << " can't be opened! \n"; + cerr << " Error: video-stream " << path << " can't be opened! \n"; } return cap; } @@ -482,7 +493,7 @@ CvCapture* get_capture_webcam(int index) { //((cv::VideoCapture*)cap)->set(CV_CAP_PROP_FRAME_HEIGHT, 960); } catch (...) { - std::cout << " Error: Web-camera " << index << " can't be opened! \n"; + cerr << " Error: Web-camera " << index << " can't be opened! \n"; } return cap; } @@ -520,7 +531,7 @@ int get_stream_fps_cpp(CvCapture *cap) { #endif } catch (...) { - std::cout << " Can't get FPS of source videofile. For output video FPS = 25 by default. \n"; + cerr << " Can't get FPS of source videofile. For output video FPS = 25 by default. \n"; } return fps; } @@ -533,62 +544,68 @@ image image_data_augmentation(IplImage* ipl, int w, int h, int pleft, int ptop, int swidth, int sheight, int flip, float jitter, float dhue, float dsat, float dexp) { - cv::Mat img = cv::cvarrToMat(ipl); + image out; + try { + cv::Mat img = cv::cvarrToMat(ipl); - // crop - cv::Rect src_rect(pleft, ptop, swidth, sheight); - cv::Rect img_rect(cv::Point2i(0, 0), img.size()); - cv::Rect new_src_rect = src_rect & img_rect; + // crop + cv::Rect src_rect(pleft, ptop, swidth, sheight); + cv::Rect img_rect(cv::Point2i(0, 0), img.size()); + cv::Rect new_src_rect = src_rect & img_rect; - cv::Rect dst_rect(cv::Point2i(std::max(0, -pleft), std::max(0, -ptop)), new_src_rect.size()); + cv::Rect dst_rect(cv::Point2i(std::max(0, -pleft), std::max(0, -ptop)), new_src_rect.size()); - cv::Mat cropped(cv::Size(src_rect.width, src_rect.height), img.type()); - cropped.setTo(cv::Scalar::all(0)); + cv::Mat cropped(cv::Size(src_rect.width, src_rect.height), img.type()); + cropped.setTo(cv::Scalar::all(0)); - img(new_src_rect).copyTo(cropped(dst_rect)); + img(new_src_rect).copyTo(cropped(dst_rect)); - // resize - cv::Mat sized; - cv::resize(cropped, sized, cv::Size(w, h), 0, 0, INTER_LINEAR); + // resize + cv::Mat sized; + cv::resize(cropped, sized, cv::Size(w, h), 0, 0, INTER_LINEAR); - // flip - if (flip) { - cv::flip(sized, cropped, 1); // 0 - x-axis, 1 - y-axis, -1 - both axes (x & y) - sized = cropped.clone(); - } - - // HSV augmentation - // CV_BGR2HSV, CV_RGB2HSV, CV_HSV2BGR, CV_HSV2RGB - if (ipl->nChannels >= 3) - { - cv::Mat hsv_src; - cvtColor(sized, hsv_src, CV_BGR2HSV); // also BGR -> RGB + // flip + if (flip) { + cv::flip(sized, cropped, 1); // 0 - x-axis, 1 - y-axis, -1 - both axes (x & y) + sized = cropped.clone(); + } - std::vector hsv; - cv::split(hsv_src, hsv); + // HSV augmentation + // CV_BGR2HSV, CV_RGB2HSV, CV_HSV2BGR, CV_HSV2RGB + if (ipl->nChannels >= 3) + { + cv::Mat hsv_src; + cvtColor(sized, hsv_src, CV_BGR2HSV); // also BGR -> RGB - hsv[1] *= dsat; - hsv[2] *= dexp; - hsv[0] += 179 * dhue; + std::vector hsv; + cv::split(hsv_src, hsv); - cv::merge(hsv, hsv_src); + hsv[1] *= dsat; + hsv[2] *= dexp; + hsv[0] += 179 * dhue; - cvtColor(hsv_src, sized, CV_HSV2RGB); // now RGB instead of BGR - } - else - { - sized *= dexp; - } + cv::merge(hsv, hsv_src); - //std::stringstream window_name; - //window_name << "augmentation - " << ipl; - //cv::imshow(window_name.str(), sized); - //cv::waitKey(0); + cvtColor(hsv_src, sized, CV_HSV2RGB); // now RGB instead of BGR + } + else + { + sized *= dexp; + } - // Mat -> IplImage -> image - IplImage src = sized; - image out = ipl_to_image(&src); + //std::stringstream window_name; + //window_name << "augmentation - " << ipl; + //cv::imshow(window_name.str(), sized); + //cv::waitKey(0); + // Mat -> IplImage -> image + IplImage src = sized; + out = ipl_to_image(&src); + } + catch (...) { + cerr << "OpenCV can't augment image: " << w << " x " << h << " \n"; + out = ipl_to_image(ipl); + } return out; } @@ -603,7 +620,7 @@ image load_image_resize(char *filename, int w, int h, int c, image *im) else if (c == 1) { flag = 0; img = cv::Mat(h, w, CV_8UC1); } else if (c == 3) { flag = 1; img = cv::Mat(h, w, CV_8UC3); } else { - fprintf(stderr, "OpenCV can't force load with %d channels\n", c); + cerr << "OpenCV can't force load with " << c << " channels\n"; } //throw std::runtime_error("runtime_error"); cv::Mat loaded_image = cv::imread(filename, flag); @@ -617,7 +634,7 @@ image load_image_resize(char *filename, int w, int h, int c, image *im) out = ipl_to_image(&tmp2); } catch (...) { - fprintf(stderr, "OpenCV can't load image %s channels\n", filename); + cerr << "OpenCV can't load image %s " << filename << " \n"; out = make_image(w, h, c); *im = make_image(w, h, c); } From c7309c1fdbe7cdada02f6e63ab56d171da8830fd Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 1 Feb 2019 01:30:02 +0300 Subject: [PATCH 0287/1365] Fixed CRNN (RNN based on Convolution) layer --- src/connected_layer.c | 3 ++- src/convolutional_layer.c | 13 +++++++------ src/crnn_layer.c | 24 ++++++++++++------------ src/crnn_layer.h | 2 +- src/lstm_layer.c | 3 +++ src/parser.c | 13 +++++++++---- src/rnn_layer.c | 5 ++++- 7 files changed, 38 insertions(+), 25 deletions(-) diff --git a/src/connected_layer.c b/src/connected_layer.c index 2637f946d6e..0f4a61a1e96 100644 --- a/src/connected_layer.c +++ b/src/connected_layer.c @@ -67,6 +67,8 @@ connected_layer make_connected_layer(int batch, int steps, int inputs, int outpu l.size = 1; l.stride = 1; l.pad = 0; + l.activation = activation; + l.learning_rate_scale = 1; l.output = calloc(total_batch*outputs, sizeof(float)); l.delta = calloc(total_batch*outputs, sizeof(float)); @@ -145,7 +147,6 @@ connected_layer make_connected_layer(int batch, int steps, int inputs, int outpu l.workspace_size = get_connected_workspace_size(l); #endif // CUDNN #endif // GPU - l.activation = activation; fprintf(stderr, "connected %4d -> %4d\n", inputs, outputs); return l; } diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 39a72d3b13d..f4f5bc67246 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -333,6 +333,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.size = size; l.pad = padding; l.batch_normalize = batch_normalize; + l.learning_rate_scale = 1; l.weights = calloc(c*n*size*size, sizeof(float)); l.weight_updates = calloc(c*n*size*size, sizeof(float)); @@ -350,6 +351,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.out_c = n; l.outputs = l.out_h * l.out_w * l.out_c; l.inputs = l.w * l.h * l.c; + l.activation = activation; l.output = calloc(total_batch*l.outputs, sizeof(float)); l.delta = calloc(total_batch*l.outputs, sizeof(float)); @@ -417,17 +419,17 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, } l.weights_gpu = cuda_make_array(l.weights, c*n*size*size); + l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size); #ifdef CUDNN_HALF l.weights_gpu16 = cuda_make_array(NULL, c*n*size*size / 2); //cuda_make_array(l.weights, c*n*size*size / 2); l.weight_updates_gpu16 = cuda_make_array(NULL, c*n*size*size / 2); //cuda_make_array(l.weight_updates, c*n*size*size / 2); #endif - l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size); l.biases_gpu = cuda_make_array(l.biases, n); l.bias_updates_gpu = cuda_make_array(l.bias_updates, n); - l.delta_gpu = cuda_make_array(l.delta, total_batch*out_h*out_w*n); l.output_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n); + l.delta_gpu = cuda_make_array(l.delta, total_batch*out_h*out_w*n); if(binary){ l.binary_weights_gpu = cuda_make_array(l.weights, c*n*size*size); @@ -439,6 +441,9 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, } if(batch_normalize){ + l.scales_gpu = cuda_make_array(l.scales, n); + l.scale_updates_gpu = cuda_make_array(l.scale_updates, n); + l.mean_gpu = cuda_make_array(l.mean, n); l.variance_gpu = cuda_make_array(l.variance, n); @@ -448,9 +453,6 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.mean_delta_gpu = cuda_make_array(l.mean, n); l.variance_delta_gpu = cuda_make_array(l.variance, n); - l.scales_gpu = cuda_make_array(l.scales, n); - l.scale_updates_gpu = cuda_make_array(l.scale_updates, n); - l.x_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n); l.x_norm_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n); } @@ -463,7 +465,6 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.workspace_size = get_workspace_size(l); size_t workspace_size16 = get_workspace_size16(l); if (workspace_size16 > l.workspace_size) l.workspace_size = workspace_size16; - l.activation = activation; //fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c); l.bflops = (2.0 * l.n * l.size*l.size*l.c * l.out_h*l.out_w) / 1000000000.; diff --git a/src/crnn_layer.c b/src/crnn_layer.c index d76c5e222bf..56ead3af448 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -26,7 +26,7 @@ static void increment_layer(layer *l, int steps) #endif } -layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, ACTIVATION activation, int batch_normalize) +layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize) { fprintf(stderr, "CRNN Layer: %d x %d x %d image, %d filters\n", h,w,c,output_filters); batch = batch / steps; @@ -47,20 +47,20 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.state = calloc(l.hidden*batch*(steps+1), sizeof(float)); l.input_layer = malloc(sizeof(layer)); - fprintf(stderr, "\t\t"); - *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0, 0); + fprintf(stderr, ""); + *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, size, stride, pad, activation, batch_normalize, 0, 0, 0, 0, 0); l.input_layer->batch = batch; if (l.workspace_size < l.input_layer->workspace_size) l.workspace_size = l.input_layer->workspace_size; l.self_layer = malloc(sizeof(layer)); - fprintf(stderr, "\t\t"); - *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0, 0); + fprintf(stderr, ""); + *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, size, stride, pad, activation, batch_normalize, 0, 0, 0, 0, 0); l.self_layer->batch = batch; if (l.workspace_size < l.self_layer->workspace_size) l.workspace_size = l.self_layer->workspace_size; l.output_layer = malloc(sizeof(layer)); - fprintf(stderr, "\t\t"); - *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, 3, 1, 1, activation, batch_normalize, 0, 0, 0, 0, 0); + fprintf(stderr, ""); + *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, size, stride, pad, activation, batch_normalize, 0, 0, 0, 0, 0); l.output_layer->batch = batch; if (l.workspace_size < l.output_layer->workspace_size) l.workspace_size = l.output_layer->workspace_size; @@ -75,8 +75,7 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.forward_gpu = forward_crnn_layer_gpu; l.backward_gpu = backward_crnn_layer_gpu; l.update_gpu = update_crnn_layer_gpu; - - l.state_gpu = cuda_make_array(l.state, l.hidden*batch*(steps+1)); + l.state_gpu = cuda_make_array(l.state, batch*l.hidden*(steps + 1)); l.output_gpu = l.output_layer->output_gpu; l.delta_gpu = l.output_layer->delta_gpu; #endif @@ -263,8 +262,8 @@ void backward_crnn_layer_gpu(layer l, network_state state) increment_layer(&output_layer, l.steps - 1); l.state_gpu += l.hidden*l.batch*l.steps; for (i = l.steps-1; i >= 0; --i) { - copy_ongpu(l.hidden * l.batch, input_layer.output_gpu, 1, l.state_gpu, 1); - axpy_ongpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1); + //copy_ongpu(l.hidden * l.batch, input_layer.output_gpu, 1, l.state_gpu, 1); // commented in RNN + //axpy_ongpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1); // commented in RNN s.input = l.state_gpu; s.delta = self_layer.delta_gpu; @@ -272,12 +271,13 @@ void backward_crnn_layer_gpu(layer l, network_state state) l.state_gpu -= l.hidden*l.batch; + copy_ongpu(l.hidden*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1); + s.input = l.state_gpu; s.delta = self_layer.delta_gpu - l.hidden*l.batch; if (i == 0) s.delta = 0; backward_convolutional_layer_gpu(self_layer, s); - copy_ongpu(l.hidden*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1); if (i > 0 && l.shortcut) axpy_ongpu(l.hidden*l.batch, 1, self_layer.delta_gpu, 1, self_layer.delta_gpu - l.hidden*l.batch, 1); s.input = state.input + i*l.inputs*l.batch; if(state.delta) s.delta = state.delta + i*l.inputs*l.batch; diff --git a/src/crnn_layer.h b/src/crnn_layer.h index 0da942ee1ab..867d3ed660e 100644 --- a/src/crnn_layer.h +++ b/src/crnn_layer.h @@ -6,7 +6,7 @@ #include "layer.h" #include "network.h" -layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, ACTIVATION activation, int batch_normalize); +layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize); void forward_crnn_layer(layer l, network_state state); void backward_crnn_layer(layer l, network_state state); diff --git a/src/lstm_layer.c b/src/lstm_layer.c index cf3411e6075..657d500cb06 100644 --- a/src/lstm_layer.c +++ b/src/lstm_layer.c @@ -35,6 +35,9 @@ layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_n l.type = LSTM; l.steps = steps; l.inputs = inputs; + l.out_w = 1; + l.out_h = 1; + l.out_c = outputs; l.uf = malloc(sizeof(layer)); fprintf(stderr, "\t\t"); diff --git a/src/parser.c b/src/parser.c index 9ca9a942d9c..b2101703ca4 100644 --- a/src/parser.c +++ b/src/parser.c @@ -182,13 +182,17 @@ convolutional_layer parse_convolutional(list *options, size_params params) layer parse_crnn(list *options, size_params params) { - int output_filters = option_find_int(options, "output_filters",1); - int hidden_filters = option_find_int(options, "hidden_filters",1); + int size = option_find_int_quiet(options, "size", 3); + int stride = option_find_int_quiet(options, "stride", 1); + int pad = option_find_int_quiet(options, "pad", 1); + + int output_filters = option_find_int(options, "output",1); + int hidden_filters = option_find_int(options, "hidden",1); char *activation_s = option_find_str(options, "activation", "logistic"); ACTIVATION activation = get_activation(activation_s); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); - layer l = make_crnn_layer(params.batch, params.w, params.h, params.c, hidden_filters, output_filters, params.time_steps, activation, batch_normalize); + layer l = make_crnn_layer(params.batch, params.w, params.h, params.c, hidden_filters, output_filters, params.time_steps, size, stride, pad, activation, batch_normalize); l.shortcut = option_find_int_quiet(options, "shortcut", 0); @@ -866,7 +870,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) check_error(cudaMalloc((void **)net.output16_gpu, *net.max_output16_size * sizeof(short))); //sizeof(half) } if (workspace_size) { - printf(" Allocate additional workspace_size = %1.2f MB \n", (float)workspace_size / 1000000); + printf(" Allocate additional workspace_size = %1.2f MB \n", (float)workspace_size/1000000); net.workspace = cuda_make_array(0, workspace_size / sizeof(float) + 1); } else { @@ -1167,6 +1171,7 @@ void load_convolutional_weights(layer l, FILE *fp) } int num = l.n*l.c*l.size*l.size; fread(l.biases, sizeof(float), l.n, fp); + //fread(l.weights, sizeof(float), num, fp); // as in connected layer if (l.batch_normalize && (!l.dontloadscales)){ fread(l.scales, sizeof(float), l.n, fp); fread(l.rolling_mean, sizeof(float), l.n, fp); diff --git a/src/rnn_layer.c b/src/rnn_layer.c index c1843f062e3..c5669adbedc 100644 --- a/src/rnn_layer.c +++ b/src/rnn_layer.c @@ -36,6 +36,9 @@ layer make_rnn_layer(int batch, int inputs, int hidden, int outputs, int steps, l.steps = steps; l.hidden = hidden; l.inputs = inputs; + l.out_w = 1; + l.out_h = 1; + l.out_c = outputs; l.state = calloc(batch*hidden*(steps+1), sizeof(float)); @@ -264,7 +267,7 @@ void backward_rnn_layer_gpu(layer l, network_state state) l.state_gpu -= l.hidden*l.batch; - copy_ongpu(l.hidden*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1); + copy_ongpu(l.hidden*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1); // the same delta for Input and Self layers s.input = l.state_gpu; s.delta = self_layer.delta_gpu - l.hidden*l.batch; From a607784626912fdfdd32464f6b72c65bfdfb8010 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 1 Feb 2019 01:32:03 +0300 Subject: [PATCH 0288/1365] Added crnn.train.cfg just for test --- build/darknet/x64/cfg/crnn.train.cfg | 44 ++++++++++++++++++++++++++++ cfg/crnn.train.cfg | 44 ++++++++++++++++++++++++++++ 2 files changed, 88 insertions(+) create mode 100644 build/darknet/x64/cfg/crnn.train.cfg create mode 100644 cfg/crnn.train.cfg diff --git a/build/darknet/x64/cfg/crnn.train.cfg b/build/darknet/x64/cfg/crnn.train.cfg new file mode 100644 index 00000000000..dcc12d748df --- /dev/null +++ b/build/darknet/x64/cfg/crnn.train.cfg @@ -0,0 +1,44 @@ +[net] +subdivisions=8 +inputs=256 +batch = 128 +momentum=0.9 +decay=0.001 +max_batches = 2000 +time_steps=576 +learning_rate=0.1 +policy=steps +steps=1000,1500 +scales=.1,.1 + +[rnn] +batch_normalize=1 +output = 1024 +hidden=1024 +activation=leaky + +[crnn] +batch_normalize=1 +size=1 +pad=0 +output = 1024 +hidden=1024 +activation=leaky + +[crnn] +batch_normalize=1 +size=1 +pad=0 +output = 1024 +hidden=1024 +activation=leaky + +[connected] +output=256 +activation=leaky + +[softmax] + +[cost] +type=sse + diff --git a/cfg/crnn.train.cfg b/cfg/crnn.train.cfg new file mode 100644 index 00000000000..dcc12d748df --- /dev/null +++ b/cfg/crnn.train.cfg @@ -0,0 +1,44 @@ +[net] +subdivisions=8 +inputs=256 +batch = 128 +momentum=0.9 +decay=0.001 +max_batches = 2000 +time_steps=576 +learning_rate=0.1 +policy=steps +steps=1000,1500 +scales=.1,.1 + +[rnn] +batch_normalize=1 +output = 1024 +hidden=1024 +activation=leaky + +[crnn] +batch_normalize=1 +size=1 +pad=0 +output = 1024 +hidden=1024 +activation=leaky + +[crnn] +batch_normalize=1 +size=1 +pad=0 +output = 1024 +hidden=1024 +activation=leaky + +[connected] +output=256 +activation=leaky + +[softmax] + +[cost] +type=sse + From e17bd9ba8f9b6aa85d79056ac02dba8ee97e3166 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 1 Feb 2019 01:32:26 +0300 Subject: [PATCH 0289/1365] Minor fix --- src/detector.c | 1 + src/rnn.c | 5 +++-- src/utils.c | 25 +++++++++++++++++++++++++ src/utils.h | 1 + 4 files changed, 30 insertions(+), 2 deletions(-) diff --git a/src/detector.c b/src/detector.c index 6ea3baa5b41..c68407befbd 100644 --- a/src/detector.c +++ b/src/detector.c @@ -476,6 +476,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out float nms = .45; int nthreads = 4; + if (m < 4) nthreads = m; image *val = calloc(nthreads, sizeof(image)); image *val_resized = calloc(nthreads, sizeof(image)); image *buf = calloc(nthreads, sizeof(image)); diff --git a/src/rnn.c b/src/rnn.c index 556844cd2d8..3fb634e4dcd 100644 --- a/src/rnn.c +++ b/src/rnn.c @@ -277,9 +277,10 @@ void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float t for(j = 0; j < inputs; ++j){ if (out[j] < .0001) out[j] = 0; } - //c = sample_array(out, inputs); - c = sample_array_custom(out, inputs); + c = sample_array(out, inputs); + //c = sample_array_custom(out, inputs); //c = max_index(out, inputs); + //c = top_max_index(out, inputs, 2); print_symbol(c, tokens); } printf("\n"); diff --git a/src/utils.c b/src/utils.c index 9e6df544a1b..d20d4fb3013 100644 --- a/src/utils.c +++ b/src/utils.c @@ -654,6 +654,31 @@ int max_index(float *a, int n) return max_i; } +int top_max_index(float *a, int n, int k) +{ + float *values = calloc(k, sizeof(float)); + int *indexes = calloc(k, sizeof(int)); + if (n <= 0) return -1; + int i, j; + for (i = 0; i < n; ++i) { + for (j = 0; j < k; ++j) { + if (a[i] > values[j]) { + values[j] = a[i]; + indexes[j] = i; + break; + } + } + } + int count = 0; + for (j = 0; j < k; ++j) if (values[j] > 0) count++; + int get_index = rand_int(0, count-1); + int val = indexes[get_index]; + free(indexes); + free(values); + return val; +} + + int int_index(int *a, int val, int n) { int i; diff --git a/src/utils.h b/src/utils.h index 9f0505367f5..8ef31263be2 100644 --- a/src/utils.h +++ b/src/utils.h @@ -59,6 +59,7 @@ void normalize_array(float *a, int n); void scale_array(float *a, int n, float s); void translate_array(float *a, int n, float s); int max_index(float *a, int n); +int top_max_index(float *a, int n, int k); float constrain(float min, float max, float a); int constrain_int(int a, int min, int max); float mse_array(float *a, int n); From f09a9c33151ab3cf103ddd922da47f8056b1b9cf Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 2 Feb 2019 00:24:34 +0300 Subject: [PATCH 0290/1365] XNOR uses Tensor Cores on Turing GPU CC>=7.3 (not Volta) --- src/convolutional_kernels.cu | 12 +- src/convolutional_layer.c | 8 +- src/im2col.h | 2 +- src/im2col_kernels.cu | 485 +++++++++-------------------------- src/yolo_layer.c | 9 + 5 files changed, 145 insertions(+), 371 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 279c176fcaf..e996854a68a 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -180,7 +180,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //state.input = l.binary_input_gpu; //cudaDeviceSynchronize(); - if (l.align_bit_weights_gpu && !state.train && l.c >= 64)// && l.size > 1) + if (l.align_bit_weights_gpu && !state.train && l.c >= 32) { //return; cudaError_t status = cudaSuccess; @@ -196,10 +196,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) size_t t_intput_size = new_ldb * n; size_t t_bit_input_size = t_intput_size / 8;// +1; - //if(0) if (l.c % 32 == 0) - //if (l.stride == 1 && l.pad == 1 && l.c % 32 == 0) - //if(1) { //printf("\n\n l.index = %d, l.w = %d, l.c = %d, l.n = %d, l.stride = %d, l.pad = %d - new XNOR \n", l.index, l.w, l.c, l.n, l.stride, l.pad); //printf("l.align_workspace_size = %d, (l.c * l.w * l.h) = %d \n", l.align_workspace_size, (l.c * l.w * l.h)); @@ -296,7 +293,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //start_timer(); gemm_nn_custom_bin_mean_transposed_gpu(m, n, k, (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, - new_ldb, l.output_gpu, n, l.mean_arr_gpu, l.biases_gpu, l.activation); + new_ldb, l.output_gpu, n, l.mean_arr_gpu, l.biases_gpu, l.activation == LEAKY); //cudaDeviceSynchronize(); //stop_timer_and_show_name("gemm_nn_custom_bin_mean_transposed_gpu"); @@ -366,7 +363,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //start_timer(); gemm_nn_custom_bin_mean_transposed_gpu(m, n, k, (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, - new_ldb, l.output_gpu, n, l.mean_arr_gpu, l.biases_gpu, l.activation); + new_ldb, l.output_gpu, n, l.mean_arr_gpu, l.biases_gpu, l.activation == LEAKY); //cudaDeviceSynchronize(); //stop_timer_and_show_name("gemm_nn_custom_bin_mean_transposed_gpu"); //} @@ -391,7 +388,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) */ //add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); - if(l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); + if (l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); + //if(l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if (l.binary || l.xnor) swap_binary(&l); //cudaDeviceSynchronize(); return; diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index f4f5bc67246..d29266ff704 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -726,14 +726,16 @@ void binary_align_weights(convolutional_layer *l) float_to_bit(align_weights, l->align_bit_weights, align_weights_size); - /* - if ((l->n % 8) == 0 && ((l->out_w*l->out_h) % 8) == 0 && l->c >= 64 && l->n == 128) { + //if (l->n >= 32) + if(gpu_index >= 0) + { int M = l->n; int N = l->out_w*l->out_h; //printf("\n M = %d, N = %d, M %% 8 = %d, N %% 8 = %d - weights \n", M, N, M % 8, N % 8); + //printf("\n l.w = %d, l.c = %d, l.n = %d \n", l->w, l->c, l->n); for (i = 0; i < align_weights_size / 8; ++i) l->align_bit_weights[i] = ~(l->align_bit_weights[i]); } - */ + get_mean_array(l->binary_weights, m*k, l->n, l->mean_arr); diff --git a/src/im2col.h b/src/im2col.h index b1e34f8ee05..7957b8eb54b 100644 --- a/src/im2col.h +++ b/src/im2col.h @@ -44,7 +44,7 @@ void fill_int8_gpu(unsigned char *src, unsigned char val, size_t size); void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, unsigned char *A, int lda, unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr, float *bias, ACTIVATION a); + float *C, int ldc, float *mean_arr, float *bias, int leaky_activation); // sequentially - BAD void gemm_nn_custom_bin_mean_transposed_sequentially_gpu(int M, int N, int K, diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 0cbd11529a7..a4ae4c9e7d4 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -1433,215 +1433,149 @@ int warpAllReduceSum(int val) { // Tensor Cores binary (CC >= 7.3 && CUDA >= 10.0) - __CUDA_SUBBYTE_IMMA__ #if CUDART_VERSION >= 10000 #include -#endif - +#define WMMA_M 8 +#define WMMA_N 8 +#define WMMA_K 128 +#define WMMA_K32 (WMMA_K/32) -// Coalescing -// A (weights) in the shared_memory - GOOD +// Tensor Cores are used for XOR-GEMM __global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel(int M, int N, int K, unsigned char *A, int lda, unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr, float *bias_arr) -{ - // total 57% - int index = blockIdx.x*blockDim.x + threadIdx.x; - - __shared__ uint8_t A_s[6144 * 8 / 4]; - //__shared__ uint64_t A_s[6144]; // 48 KB // [lda x M`] - //__shared__ uint8_t A_s[6144*8]; // 48 KB // [lda x M`] - - int start_i = blockIdx.x*blockDim.x / N; - int end_i = (blockIdx.x*blockDim.x + blockDim.x) / N + 1; - - size_t shared_size = lda * (end_i - start_i); - - int i_cur = index / N; - int local_i = i_cur - start_i; - // ~10% - for (int k = threadIdx.x * 64; k < shared_size; k += blockDim.x * 64) { - int x = start_i*lda + k; - if (x < (M*lda)) *((uint64_t *)(A_s + k / 8)) = *((uint64_t *)(A + x / 8)); - } - __syncthreads(); - - int i, j, k, h; - // 47% = 29 + 10 + 8 - j = index % N; - { // out_h*out_w - one channel output size [169 - 173056] - i = index / N; - //if (i < M) // l.n - filters [16 - 55 - 1024] - { - int count = 0; - k = 0; - - - if (i < M) - { - float mean_val = mean_arr[i]; - float bias_val = bias_arr[i]; - - for (; k < K; k += 128) { // l.size*l.size*l.c - one filter size [27 - 144 - 9216] - //uint4 a_bit128 = *((uint4 *)(A + (i*lda + k) / 8)); // weights - uint4 a_bit128 = *((uint4 *)(A_s + (local_i*lda + k) / 8)); // weights - uint4 b_bit128 = *((uint4 *)(B + (j*ldb + k) / 8)); // input - uint4 c_bit128 = xor_int128(a_bit128, b_bit128); - - count += __popc(c_bit128.w) + __popc(c_bit128.x) + - __popc(c_bit128.y) + __popc(c_bit128.z); - } - - - const int bit_step = 128;// 256; - int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); - count = count - f1; // remove extra bits (from empty space for align only) - - C[i*ldc + j] = (2 * count - K) *mean_val + bias_val; - } - } - } -} - -#if CUDART_VERSION >= 10000 -// Coalescing -// A (weights) in the shared_memory - GOOD -__global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel_old(int M, int N, int K, - unsigned char *A, int lda, - unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr, float *bias_arr) + float *C, int ldc, float *mean_arr, float *bias_arr, int leaky_activation) { // total 57% int index = blockIdx.x*blockDim.x + threadIdx.x; - __shared__ int C_s[8*8 * 32]; // BIN GEMM WMMA + __shared__ int C_s[8*8 * 32]; // Temprorary result of GEMM WMMA const int lane_id = threadIdx.x % 32; const int warp_id = threadIdx.x / 32; const int global_warp_id = index / 32; + const int N_aligned = N + WMMA_N - (N % WMMA_N); int i, j, k, h; // 47% = 29 + 10 + 8 - j = global_warp_id % (N / 8); - j = j * 8; + j = global_warp_id % (N_aligned / WMMA_N); + j = j * WMMA_N; { // out_h*out_w - one channel output size [169 - 173056] - i = global_warp_id / (N / 8); - i = i * 8; + i = global_warp_id / (N_aligned / WMMA_N); + i = i * WMMA_M; - if (i == 0 && j == 0 && lane_id == 0) { - // printf(" i = %d, j = %d, global_warp_id = %d, index = %d \n ", i, j, global_warp_id, index); - } + int count = 0; + k = 0; - //if (i < M) // l.n - filters [16 - 55 - 1024] + if (i < M) //if (i < M) // l.n - filters [16 - 55 - 1024] { - int count = 0; - k = 0; - - if (i < M) - { - // Tensor Cores binary (CC >= 7.3 && CUDA >= 10.0) - __CUDA_SUBBYTE_IMMA__ - //#if __CUDA_ARCH__ >= 730 && CUDART_VERSION >= 10000 - -#define WMMA_M 8 -#define WMMA_N 8 -#define WMMA_K 128 -#define WMMA_K32 (WMMA_K/32) + if (j + WMMA_N > N) j = N - WMMA_N; // must be: j+7 < N + if (i + WMMA_M > M) i = M - WMMA_M; // must be: i+7 < M #if __CUDA_ARCH__ >= 730 - using namespace nvcuda; - - wmma::fragment a_frag; - wmma::fragment b_frag; - wmma::fragment c_frag; - wmma::fill_fragment(c_frag, 0); // !!!! XOR isn't XNOR !!!!!!!!!! - - // lda, ldb - are in bits, should be divided by /8 or /32 - - // 8 x 8 x 4 (uint32_t, 4 * 32 = 128 bit) - for (; k < K; k += 128) - { // l.size*l.size*l.c - one filter size [27 - 144 - 9216] - int64_t A_cur_index = (i*lda + k) / 8; - //int64_t A_cur_index = (local_i*lda + k) / 8; - int64_t B_cur_index = (j*ldb + k) / 8; - - wmma::load_matrix_sync(a_frag, (uint32_t *)(A + A_cur_index), lda); // lda = M - wmma::load_matrix_sync(b_frag, (uint32_t *)(B + B_cur_index), ldb); // ldb = K - - - /* - if (i == 0 && j == 0) { - printf(" %d - %u, ", lane_id, a_frag.x[0]); - } + // Tensor Cores + using namespace nvcuda; + wmma::fragment a_frag; + wmma::fragment b_frag; + wmma::fragment c_frag; + wmma::fill_fragment(c_frag, 0); // !!!! XOR isn't XNOR !!!!!!!!!! - if (i == 0 && j == 0 && lane_id == 1) { - printf("\n\n now raw mem \n"); + // 8 x 8 x 4 (uint32_t, 4 * 32 = 128 bit) + for (; k < K; k += 128) // l.size*l.size*l.c - one filter size [27 - 144 - 9216] + { + int64_t A_cur_index = (i*lda + k) / 8; + //int64_t A_cur_index = (local_i*lda + k) / 8; + int64_t B_cur_index = (j*ldb + k) / 8; - for (int i_d = 0; i_d < WMMA_M; ++i_d) { //8 - for (int k_d = 0; k_d < WMMA_K; k_d += 32) { //4 - uint32_t a_bit32 = *((uint32_t *)(A + ((i + i_d)*lda + (k + k_d)) / 8)); // weights - //uint32_t a_bit32 = *((uint32_t *)(A + A_cur_index + i_d*lda/8 + k_d/ 8)); // weights - printf(" %d - %u, ", i_d*WMMA_K32 + k_d/32, a_bit32); - } - printf("\n"); - } - printf("\n\n"); - } - */ + // lda, ldb - are in bits + wmma::load_matrix_sync(a_frag, (uint32_t *)(A + A_cur_index), lda); // lda = M + wmma::load_matrix_sync(b_frag, (uint32_t *)(B + B_cur_index), ldb); // ldb = K + wmma::bmma_sync(c_frag, a_frag, b_frag, c_frag); // XOR-GEMM + } + // C[i*ldc + j] + wmma::store_matrix_sync(&C_s[warp_id*WMMA_M*WMMA_N], c_frag, WMMA_N, wmma::mem_row_major); +#else // __CUDA_ARCH__ >= 730 + + // Custom XOR-GEMM + int k_d = lane_id % 4; + int i_d = lane_id / 4; + int j_d = lane_id / 4; + + int32_t accum_c_val[8]; // wmma::fill_fragment(c_frag, 0); + for (int local_j = 0; local_j < 8; ++local_j) { + accum_c_val[local_j] = 0; + } - wmma::bmma_sync(c_frag, a_frag, b_frag, c_frag); + // 8 x 8 x 4 (uint32_t, 4 * 32 = 128 bit) + for (; k < K; k += 128) // l.size*l.size*l.c - one filter size [27 - 144 - 9216] + { + int64_t A_cur_index = (i*lda + k) / 8; + //int64_t A_cur_index = (local_i*lda + k) / 8; + int64_t B_cur_index = (j*ldb + k) / 8; - // C[i*ldc + j] - wmma::store_matrix_sync(&C_s[warp_id*WMMA_M*WMMA_N], c_frag, WMMA_N, wmma::mem_row_major); + // lda, ldb - are in bits + // 8*4 = 32 + // 8*8 = 64 + int k_d = lane_id % 4; + int i_d = lane_id / 4; + int j_d = lane_id / 4; + uint32_t a_val = *(uint32_t *)(A + ((i + i_d)*lda + (k + k_d*32)) / 8); // wmma::load_matrix_sync(a_frag, (uint32_t *)(A + A_cur_index), lda); + uint32_t b_val = *(uint32_t *)(B + ((j + j_d)*ldb + (k + k_d*32)) / 8); // wmma::load_matrix_sync(b_frag, (uint32_t *)(B + B_cur_index), ldb); + + // wmma::bmma_sync(c_frag, a_frag, b_frag, c_frag); + int32_t c_val[8]; // 8 x 32 threads = 256 + #pragma UNROLL + for (int local_j = 0; local_j < 8; ++local_j) + { + uint32_t b_val_cur = __shfl(b_val, local_j *4 + k_d); + c_val[local_j] = __popc(xor_int32(a_val, b_val_cur)); } - /* - for (; k < K; k += 128) { // l.size*l.size*l.c - one filter size [27 - 144 - 9216] - uint4 a_bit128 = *((uint4 *)(A + (i*lda + k) / 8)); // weights - //uint4 a_bit128 = *((uint4 *)(A_s + (local_i*lda + k) / 8)); // weights - uint4 b_bit128 = *((uint4 *)(B + (j*ldb + k) / 8)); // input - uint4 c_bit128 = xnor_int128(a_bit128, b_bit128); - - count += __popc(c_bit128.w) + __popc(c_bit128.x) + - __popc(c_bit128.y) + __popc(c_bit128.z); + #pragma UNROLL + for (int local_j = 0; local_j < 8; ++local_j) + { + #pragma UNROLL + for (int local_k = 0; local_k < 4; ++local_k) { + accum_c_val[local_j] += __shfl(c_val[local_j], i_d * 4 + local_k); } - */ + } + } + // only the first 8 threads (i) contain 8 good values each, in c_val[8] (j) = 8 x 8 =64 + // wmma::store_matrix_sync(&C_s[warp_id*WMMA_M*WMMA_N], c_frag, WMMA_N, wmma::mem_row_major); + if (k_d == 0) { + for (int local_j = 0; local_j < 8; ++local_j) + { + C_s[warp_id*WMMA_M*WMMA_N + i_d*WMMA_N + local_j] = accum_c_val[local_j]; + } + } #endif // __CUDA_ARCH__ >= 730 - #pragma UNROLL - for (int i_d = 0; i_d < WMMA_M; ++i_d) { - for (int j_d = 0; j_d < WMMA_N; ++j_d) - { + { + int i_d = lane_id % WMMA_M; + { + for (int j_d = lane_id / WMMA_M; j_d < WMMA_N; j_d += WMMA_N / 2) + { int count = C_s[warp_id*WMMA_M*WMMA_N + i_d*WMMA_N + j_d]; - if (i == 0 && j == 0 && lane_id == 0) { - //printf(" %d -", count); - } - const int bit_step = 128; int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); count = count - f1; // remove extra bits (from empty space for align only) count = (2 * count - K); - if (i == 0 && j == 0 && lane_id == 0) { - //printf(" %d,", count); - } - float mean_val = mean_arr[i + i_d]; float bias_val = bias_arr[i + i_d]; + float dst_val = count *mean_val + bias_val; + if (leaky_activation) + dst_val = (dst_val > 0) ? (dst_val) : (0.1*dst_val); // Leaky activation - C[(i + i_d)*ldc + (j + j_d)] = count *mean_val + bias_val; - - //C[(i + i_d)*ldc + (j + j_d)] = (2 * count - K) *mean_val + bias_val; + C[(i + i_d)*ldc + (j + j_d)] = dst_val; } - if (i == 0 && j == 0 && lane_id == 0) { - //printf(" i = %d, j = %d, i_d = %d \n ", i, j, i_d); - } } } } @@ -1654,7 +1588,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel_old(int M, int __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int K, unsigned char *A, int lda, unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr, float *bias_arr) + float *C, int ldc, float *mean_arr, float *bias_arr, int leaky_activation) { // total 57% int index = blockIdx.x*blockDim.x + threadIdx.x; @@ -1813,186 +1747,16 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int const int bit_step = 256; int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); count = count - f1; // remove extra bits (from empty space for align only) - - C[i*ldc + j] = (2 * count - K) *mean_val + bias_val; - } - } - } -} - - -// Coalescing - with LEAKY activation -// A (weights) in the shared_memory - GOOD -__global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel_leaky(int M, int N, int K, - unsigned char *A, int lda, - unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr, float *bias_arr) -{ - // total 57% - int index = blockIdx.x*blockDim.x + threadIdx.x; - - __shared__ uint8_t A_s[6144 * 8 / 4]; - //__shared__ uint64_t A_s[6144]; // 48 KB // [lda x M`] - //__shared__ uint8_t A_s[6144*8]; // 48 KB // [lda x M`] - - int start_i = blockIdx.x*blockDim.x / N; - int end_i = (blockIdx.x*blockDim.x + blockDim.x) / N + 1; - - size_t shared_size = lda * (end_i - start_i); - - int i_cur = index / N; - int local_i = i_cur - start_i; - // ~10% - for (int k = threadIdx.x * 64; k < shared_size; k += blockDim.x * 64) { - int x = start_i*lda + k; - if (x < (M*lda)) *((uint64_t *)(A_s + k / 8)) = *((uint64_t *)(A + x / 8)); - } - __syncthreads(); - - int i, j, k, h; - // 47% = 29 + 10 + 8 - j = index % N; - { // out_h*out_w - one channel output size [169 - 173056] - i = index / N; - //if (i < M) // l.n - filters [16 - 55 - 1024] - { - int count = 0; - k = 0; - -#ifdef NOT_USED - // 32 thread X 256 bit = 8192 bit - for (; k < (K - 8192); k += 8192) { // l.size*l.size*l.c - one filter size [27 - 9216] - ulonglong4 c_bit256; - - //int64_t A_cur_index = (i*lda + k) / 8; - int64_t A_cur_index = (local_i*lda + k) / 8; - int64_t B_cur_index = (j*ldb + k) / 8; - if (i >= M) A_cur_index = 0; - -#pragma unroll - for (int t = 0; t < WARP_SIZE; ++t) { - const int lane_id = threadIdx.x % WARP_SIZE; - - const int64_t A_i = __shfl(A_cur_index, t) + 32 * lane_id; - const int64_t B_i = __shfl(B_cur_index, t) + 32 * lane_id; - - { - //ulonglong4 a_bit256 = *((ulonglong4 *)(A + A_i)); // weights - ulonglong4 a_bit256 = *((ulonglong4 *)(A_s + A_i)); // weights - ulonglong4 b_bit256 = *((ulonglong4 *)(B + B_i)); // input - c_bit256 = xnor_int256(a_bit256, b_bit256); - int tmp_count = __popcll(c_bit256.w) + __popcll(c_bit256.x) + - __popcll(c_bit256.y) + __popcll(c_bit256.z); - - int sum_count = warpAllReduceSum(tmp_count); - if (lane_id == t) count += sum_count; - } - } - } -#endif - -//#ifdef NOT_USED - // 32 thread X 64 bit = 2048 bit // 29% - for (; k < (K - 2048); k += 2048) { // l.size*l.size*l.c - one filter size [27 - 9216] - uint64_t c_bit64; - - //int64_t A_cur_index = (i*lda + k) / 8; - int64_t A_cur_index = (local_i*lda + k) / 8; - int64_t B_cur_index = (j*ldb + k) / 8; - if (i >= M) A_cur_index = 0; - -#pragma unroll - for (int t = 0; t < WARP_SIZE; ++t) { - const int lane_id = threadIdx.x % WARP_SIZE; - - const int64_t A_i = __shfl(A_cur_index, t) + 8 * lane_id; - const int64_t B_i = __shfl(B_cur_index, t) + 8 * lane_id; - - { - //uint64_t a_bit64 = *((uint64_t *)(A + A_i)); // weights - uint64_t a_bit64 = *((uint64_t *)(A_s + A_i)); // weights - uint64_t b_bit64 = *((uint64_t *)(B + B_i)); // input - c_bit64 = xnor_int64(a_bit64, b_bit64); - int tmp_count = __popcll(c_bit64); - - int sum_count = warpAllReduceSum(tmp_count); - if (lane_id == t) count += sum_count; - } - } - } -//#endif - - //#ifdef NOT_USED - // 32 thread X 32 bit = 1024 bit // 10% - for (; k < (K - 1024); k += 1024) { // l.size*l.size*l.c - one filter size [27 - 9216] - - //int64_t A_cur_index = (i*lda + k) / 8; - int64_t A_cur_index = (local_i*lda + k) / 8; - int64_t B_cur_index = (j*ldb + k) / 8; - if (i >= M) A_cur_index = 0; - -#pragma unroll - for (int t = 0; t < WARP_SIZE; ++t) { - const int lane_id = threadIdx.x % WARP_SIZE; - - const int64_t A_i = __shfl(A_cur_index, t) + 4 * lane_id; - const int64_t B_i = __shfl(B_cur_index, t) + 4 * lane_id; - - { - //uint64_t a_bit64 = *((uint64_t *)(A + A_i)); // weights - uint32_t a_bit32 = *((uint32_t *)(A_s + A_i)); // weights - uint32_t b_bit32 = *((uint32_t *)(B + B_i)); // input - uint32_t c_bit32 = xnor_int32(a_bit32, b_bit32); - int tmp_count = __popc(c_bit32); - - int sum_count = warpAllReduceSum(tmp_count); - if (lane_id == t) count += sum_count; - } - } - } - //#endif - - if (i < M) - { - float mean_val = mean_arr[i]; - float bias_val = bias_arr[i]; - - //#ifdef NOT_USED - // 8% - for (; k < K; k += 256) { // l.size*l.size*l.c - one filter size [27 - 144 - 9216] - //ulonglong4 a_bit256 = *((ulonglong4 *)(A + (i*lda + k) / 8)); // weights - ulonglong4 a_bit256 = *((ulonglong4 *)(A_s + (local_i*lda + k) / 8)); // weights - ulonglong4 b_bit256 = *((ulonglong4 *)(B + (j*ldb + k) / 8)); // input - ulonglong4 c_bit256 = xnor_int256(a_bit256, b_bit256); - - count += __popcll(c_bit256.w) + __popcll(c_bit256.x) + - __popcll(c_bit256.y) + __popcll(c_bit256.z); - } - //#endif - -#ifdef NOT_USED - for (; k < K; k += 64) { // l.size*l.size*l.c - one filter size [27 - 9216] - //uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); // weights - uint64_t a_bit64 = *((uint64_t *)(A_s + (local_i*lda + k) / 8)); // weights - uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); // input - uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); - - count += __popcll(c_bit64); - } -#endif - - const int bit_step = 256; - int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); - count = count - f1; // remove extra bits (from empty space for align only) - float dst_val = (2 * count - K) *mean_val + bias_val; - dst_val = (dst_val > 0) ? (dst_val) : (0.1*dst_val); // Leaky activation + if(leaky_activation) + dst_val = (dst_val > 0) ? (dst_val) : (0.1*dst_val); // Leaky activation C[i*ldc + j] = dst_val; } } } } + // further optimization - use WMMA GEMM for using Tensor Cores // https://github.com/NVIDIA-developer-blog/code-samples/blob/master/posts/tensor-cores/simpleTensorCoreGEMM.cu // https://github.com/NVIDIA/cuda-samples/blob/master/Samples/cudaTensorCoreGemm/cudaTensorCoreGemm.cu @@ -2012,7 +1776,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel_leaky(int M, int N void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, unsigned char *A, int lda, unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr, float *bias, ACTIVATION a) + float *C, int ldc, float *mean_arr, float *bias, int leaky_activation) { size_t size = M*N; const int num_blocks = get_number_of_blocks(size, BLOCK); @@ -2026,38 +1790,39 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, */ //printf(" shared_memory: (w) lda*BLOCK/N = %d, (i) ldb*BLOCK/M = %d, \t lda = %d \n\n", lda*BLOCK / N, ldb*BLOCK / M, lda); - if (a == LEAKY) { - gemm_nn_custom_bin_mean_transposed_gpu_kernel_leaky << > > ( +#if CUDART_VERSION >= 10000 + //if (M % 8 == 0 && N % 8 == 0 && M == 128) + //if (M >= 32) // l.n >= 32 + if (1) + { + const int M_aligned = M + (8 - (M % 8)); + const int N_aligned = N + (8 - (N % 8)); + size_t size = (M_aligned / 8)*(N_aligned / 8)*WARP_SIZE; + const int num_blocks = get_number_of_blocks(size, BLOCK); + + //printf(" lda = %d, ldb = %d, ldc = %d, lda/32 = %d, ldb/32 = %d, ldc/32 = %d \n", lda, ldb, ldc, lda / 32, ldb / 32, ldc / 32); + //printf(" l.c (K/9) = %d, M (l.n) = %d \n", (K%9 == 0)? K / 9: K, M); + gemm_nn_custom_bin_mean_transposed_tensor_kernel << > > ( M, N, K, A, lda, B, ldb, C, ldc, - mean_arr, bias); + mean_arr, bias, leaky_activation); + + //cudaDeviceSynchronize(); + //getchar(); } - else { - /* -#if CUDART_VERSION >= 10000 - if (M % 8 == 0 && N % 8 == 0 && M == 128) { - //printf(" lda = %d, ldb = %d, ldc = %d, lda/32 = %d, ldb/32 = %d, ldc/32 = %d \n", lda, ldb, ldc, lda / 32, ldb / 32, ldc / 32); - gemm_nn_custom_bin_mean_transposed_tensor_kernel_old << > > ( - M, N, K, - A, lda, - B, ldb, - C, ldc, - mean_arr, bias); - } - else + else #endif // CUDART_VERSION >= 10000 - */ - { - gemm_nn_custom_bin_mean_transposed_gpu_kernel << > > ( - M, N, K, - A, lda, - B, ldb, - C, ldc, - mean_arr, bias); - } + { + gemm_nn_custom_bin_mean_transposed_gpu_kernel << > > ( + M, N, K, + A, lda, + B, ldb, + C, ldc, + mean_arr, bias, leaky_activation); } + } // -------------------------------- diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 1268ff5eb98..d1fa78cfb4b 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -116,6 +116,11 @@ void resize_yolo_layer(layer *l, int w, int h) box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, int stride) { box b; + // ln - natural logarithm (base = e) + // x` = t.x * lw - i; // x = ln(x`/(1-x`)) // x - output of previous conv-layer + // y` = t.y * lh - i; // y = ln(y`/(1-y`)) // y - output of previous conv-layer + // w = ln(t.w * net.w / anchors_w); // w - output of previous conv-layer + // h = ln(t.h * net.h / anchors_h); // h - output of previous conv-layer b.x = (i + x[index + 0*stride]) / lw; b.y = (j + x[index + 1*stride]) / lh; b.w = exp(x[index + 2*stride]) * biases[2*n] / w; @@ -437,6 +442,10 @@ void forward_yolo_layer_gpu(const layer l, network_state state) for (b = 0; b < l.batch; ++b){ for(n = 0; n < l.n; ++n){ int index = entry_index(l, b, n*l.w*l.h, 0); + // y = 1./(1. + exp(-x)) + // x = ln(y/(1-y)) // ln - natural logarithm (base = e) + // if(y->1) x -> inf + // if(y->0) x -> -inf activate_array_ongpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC); // x,y index = entry_index(l, b, n*l.w*l.h, 4); activate_array_ongpu(l.output_gpu + index, (1+l.classes)*l.w*l.h, LOGISTIC); // classes and objectness From e1ec8a8b072fba77002d35fc0267b2eac880c2b8 Mon Sep 17 00:00:00 2001 From: Alexey Date: Sat, 2 Feb 2019 00:58:09 +0300 Subject: [PATCH 0291/1365] Update Readme.md --- README.md | 30 +++++++++++++++++------------- 1 file changed, 17 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index dfd644ce165..f85adc0266d 100644 --- a/README.md +++ b/README.md @@ -21,10 +21,10 @@ -| ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) |   ![map_fps](https://hsto.org/webt/pw/zd/0j/pwzd0jb9g7znt_dbsyw9qzbnvti.jpeg) mAP (AP50) https://pjreddie.com/media/files/papers/YOLOv3.pdf | +| ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) |   ![map_time](https://user-images.githubusercontent.com/4096485/52151356-e5d4a380-2683-11e9-9d7d-ac7bc192c477.jpg) mAP@0.5 (AP50) https://pjreddie.com/media/files/papers/YOLOv3.pdf | |---|---| -* YOLOv3-spp (is not indicated) better than YOLOv3 - mAP = 60.6%, FPS = 20: https://pjreddie.com/darknet/yolo/ +* YOLOv3-spp better than YOLOv3 - mAP = 60.6%, FPS = 20: https://pjreddie.com/darknet/yolo/ * Yolo v3 source chart for the RetinaNet on MS COCO got from Table 1 (e): https://arxiv.org/pdf/1708.02002.pdf * Yolo v2 on Pascal VOC 2007: https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg * Yolo v2 on Pascal VOC 2012 (comp4): https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg @@ -109,15 +109,17 @@ Also, you might be interested in using a simplified repository where is implemen On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights` -* Yolo v3 COCO - **image**: `darknet.exe detector test data/coco.data cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` -* **Output coordinates** of objects: `darknet.exe detector test data/coco.data yolov3.cfg yolov3.weights -ext_output dog.jpg` -* Yolo v3 COCO - **video**: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -ext_output test.mp4` -* Yolo v3 COCO - **WebCam 0**: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -c 0` -* Yolo v3 COCO for **net-videocam** - Smart WebCam: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg` -* Yolo v3 - **save result videofile res.avi**: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -thresh 0.25 test.mp4 -out_filename res.avi` -* Yolo v3 **Tiny** COCO - video: `darknet.exe detector demo data/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4` +On Linux find executable file `./darknet` in the root directory, while on Windows find it in the directory `\build\darknet\x64` + +* Yolo v3 COCO - **image**: `darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` +* **Output coordinates** of objects: `darknet.exe detector test cfg/coco.data yolov3.cfg yolov3.weights -ext_output dog.jpg` +* Yolo v3 COCO - **video**: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights -ext_output test.mp4` +* Yolo v3 COCO - **WebCam 0**: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights -c 0` +* Yolo v3 COCO for **net-videocam** - Smart WebCam: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg` +* Yolo v3 - **save result videofile res.avi**: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights -thresh 0.25 test.mp4 -out_filename res.avi` +* Yolo v3 **Tiny** COCO - video: `darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4` * **JSON and MJPEG server** that allows multiple connections from your soft or Web-browser `ip-address:8070` and 8090: `./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output` -* Yolo v3 Tiny **on GPU #0**: `darknet.exe detector demo data/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 0 test.mp4` +* Yolo v3 Tiny **on GPU #0**: `darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 0 test.mp4` * Alternative method Yolo v3 COCO - image: `darknet.exe detect cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` * Train on **Amazon EC2**, to see mAP & Loss-chart using URL like: `http://ec2-35-160-228-91.us-west-2.compute.amazonaws.com:8090` in the Chrome/Firefox: `./darknet detector train cfg/coco.data yolov3.cfg darknet53.conv.74 -dont_show -mjpeg_port 8090 -map` @@ -125,6 +127,8 @@ On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector * Remeber to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app * To process a list of images `data/train.txt` and save results of detection to `result.txt` use: `darknet.exe detector test cfg/coco.data yolov3.cfg yolov3.weights -dont_show -ext_output < data/train.txt > result.txt` +* Pseudo-lableing - to process a list of images `data/new_train.txt` and save results of detection in Yolo training format for each image as label `.txt` (in this way you can increase the amount of training data) use: + `darknet.exe detector test cfg/coco.data yolov3.cfg yolov3.weights -thresh 0.25 -dont_show -save_labels < data/new_train.txt` * To calculate anchors: `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416` * To check accuracy mAP@IoU=50: `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights` * To check accuracy mAP@IoU=75: `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights -iou_thresh 0.75` @@ -247,7 +251,7 @@ Then add to your created project: 7. Start training by using `train_voc.cmd` or by using the command line: - `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg darknet53.conv.74` + `darknet.exe detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74` (**Note:** To disable Loss-Window use flag `-dont_show`. If you are using CPU, try `darknet_no_gpu.exe` instead of `darknet.exe`.) @@ -259,9 +263,9 @@ More information about training by the link: http://pjreddie.com/darknet/yolo/#t ## How to train with multi-GPU: -1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg darknet53.conv.74` +1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74` -2. Then stop and by using partially-trained model `/backup/yolov3-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg /backup/yolov3-voc_1000.weights -gpus 0,1,2,3` +2. Then stop and by using partially-trained model `/backup/yolov3-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train cfg/voc.data cfg/yolov3-voc.cfg /backup/yolov3-voc_1000.weights -gpus 0,1,2,3` Only for small datasets sometimes better to decrease learning rate, for 4 GPUs set `learning_rate = 0.00025` (i.e. learning_rate = 0.001 / GPUs). In this case also increase 4x times `burn_in =` and `max_batches =` in your cfg-file. I.e. use `burn_in = 4000` instead of `1000`. From f91d5a5e098f81cf467e22732da24e85d8fc4ffd Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 2 Feb 2019 03:16:05 +0300 Subject: [PATCH 0292/1365] Fixed __shfl() and __ballot() warnings --- src/convolutional_kernels.cu | 4 + src/im2col_kernels.cu | 444 +++++------------------------------ 2 files changed, 60 insertions(+), 388 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index e996854a68a..1b63c4c5599 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -84,7 +84,11 @@ __global__ void set_zero_kernel(float *src, int size) __inline__ __device__ float warpAllReduceSum(float val) { for (int mask = WARP_SIZE / 2; mask > 0; mask /= 2) +#if CUDA_VERSION >= 9000 + val += __shfl_xor_sync(0xffffffff, val, mask); +#else val += __shfl_xor(val, mask); +#endif return val; } diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index a4ae4c9e7d4..2bdca30bb13 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -12,8 +12,27 @@ extern "C" { #include #include +#define FULL_MASK 0xffffffff #define WARP_SIZE 32 +template +__device__ inline T1 __shfl_custom(T1 val, T2 lane) { +#if CUDA_VERSION >= 9000 + return __shfl_sync(FULL_MASK, val, lane); +#else + return __shfl(val, lane); +#endif +} + +template +__device__ inline uint32_t __ballot_custom(T val) { +#if CUDA_VERSION >= 9000 + return __ballot_sync(FULL_MASK, val); +#else + return __ballot(val); +#endif +} + // src: https://github.com/BVLC/caffe/blob/master/src/caffe/util/im2col.cu // You may also want to read: https://github.com/BVLC/caffe/blob/master/LICENSE @@ -205,227 +224,6 @@ void im2col_align_ongpu(float *im, // -------------------------------- -/* -// binary im2col -__global__ void im2col_align_bin_gpu_kernel(const int n, const float* data_im, - const int height, const int width, const int ksize, const int channels, - const int pad, - const int stride, - const int height_col, const int width_col, - float *data_col, const int bit_align) -{ - __shared__ float tmp_s[1]; - - //#define SHRED_VALS ((BLOCK / 169) * ) - __shared__ float dst_s[1024]; - //__shared__ float dst_s[1024]; - //__shared__ uint32_t bit_s[32]; - __shared__ uint8_t bit_s[128]; - - int index = blockIdx.x*blockDim.x + threadIdx.x; - for (; index < n; index += blockDim.x*gridDim.x) - { - //int c_index = index; - //int channel_in = c_index % channels; - - int h_out = index % height_col; - int c_index = index / height_col; - int channel_in = c_index % channels; - - int channel_out = channel_in * ksize * ksize; - - int j_index = c_index / channels; - int j = j_index % ksize; - int i = j_index / ksize; - if (i < ksize) - { - for (int w_out = 0; w_out < width_col; ++w_out) - { - int h_in = h_out * stride - pad; - int w_in = w_out * stride - pad; - - int h = h_in + i; - int w = w_in + j; - - float val = (h >= 0 && w >= 0 && h < height && w < width) ? - data_im[(channel_in * height + h_in) * width + w_in + i * width + j] : 0; - - //int pre_out_index = index % (width_col*height_col); - int pre_out_index = h_out * width_col + w_out; - int out_index = (channel_out + i*ksize + j) * bit_align + pre_out_index; - data_col[out_index] = val; - - - }// w_out - } - } -} -*/ - -/* -// binary im2col -__global__ void im2col_align_bin_gpu_kernel(const int n, const float* data_im, - const int height, const int width, const int ksize, const int channels, - const int pad, - const int stride, - const int height_col, const int width_col, - float *data_col, const int bit_align) -{ - __shared__ float tmp_s[1]; - __shared__ ulonglong4 tmp256_s[1]; - - - //#define SHRED_VALS ((BLOCK / 169) * ) - //__shared__ float dst_s[1024]; - //__shared__ float dst_s[1024]; - //__shared__ uint32_t bit_s[32]; - //__shared__ uint8_t bit_s[128]; - - int index = blockIdx.x*blockDim.x + threadIdx.x; - //for (; index < n; index += blockDim.x*gridDim.x) - { - //int c_index = index; - //int channel_in = c_index % channels; - - int h_out = index % height_col; - int c_index = index / height_col; - int channel_in = c_index % channels; - - int channel_out = channel_in * ksize * ksize; - - int j_index = c_index / channels; - int j = j_index % ksize; - int i = j_index / ksize; - - int h_in = h_out * stride - pad; - int h = h_in + i; - - //if (i < ksize) - { - int w_out = 0; - - // the end of padding - //if(0) - for (; w_out < (width_col); w_out += 32) - { - int w = w_out * stride - pad + j; - int pre_in_index = (channel_in * height + h_in) * width + i * width; - int in_index = pre_in_index + w; - //float *src_p = (float *)&data_im[in_index]; - - int pre_out_index = h_out * width_col + w_out; - int out_index = (channel_out + i*ksize + j) * bit_align + pre_out_index; - // float *dst_p = (float *)&data_col[out_index]; - - if (i >= ksize) { - out_index = -1; - } - - #pragma unroll - for (int t = 0; t < WARP_SIZE; ++t) { - const int lane_id = threadIdx.x % WARP_SIZE; - - //const int64_t cur_pre_in_index = pre_in_index; - //const int64_t cur_j = j; - //const int64_t out_i = out_index;// __shfl(out_index, t) + lane_id; - - const int64_t cur_out_index = __shfl(out_index, t); - if (cur_out_index >= 0) - { - const int64_t cur_pre_in_index = __shfl(pre_in_index, t); - const int64_t cur_j = __shfl(j, t); - const int64_t cur_h = __shfl(h, t); - - int cur_w = ((w_out + lane_id) * stride - pad + cur_j); - int in_index = cur_pre_in_index + cur_w; - - float val = (cur_w >= 0 && cur_w < width && cur_h >= 0 && cur_h < height) ? - data_im[in_index] : float(); - - if ((w_out + lane_id) < width_col) { - data_col[cur_out_index + lane_id] = val; - //tmp_s[0] = val; - - //uint32_t bit_mask = __ballot(val > 0); - //uint8_t *bit8_ptr = &(((uint8_t *)data_col)[cur_out_index / 8]); - //uint32_t *bit32_ptr = (uint32_t *)bit8_ptr; - //*bit32_ptr = bit_mask; - } - } - } - - }// w_out - -#ifdef NOT_USED - if (i < ksize && h >= 0 && h < height) - { - - // wait for align address and the end of padding - for (; w_out < width_col; ++w_out) - { - int w_in = w_out * stride - pad; - int w = w_in + j; - - int in_index = (channel_in * height + h_in) * width + w_in + i * width + j; - float *src_p = (float *)&data_im[in_index]; - - int pre_out_index = h_out * width_col + w_out; - int out_index = (channel_out + i*ksize + j) * bit_align + pre_out_index; - float *dst_p = (float *)&data_col[out_index]; - - if (((uint64_t)src_p % 32 == 0) && ((uint64_t)dst_p % 32 == 0) && w > 0) { - //printf(" aligned addresses and there is no padding \n"); - break; - } - - float val = (w >= 0 && w < width) ? - (*src_p) : float(); - - *dst_p = val; - //tmp_s[0] = val; - }// w_out - - // ulonglong4 (256 bit) / instead of float (32 bit) = 8x times - for (; w_out < (width_col - 8); w_out += 8) - { - int w_in = w_out * stride - pad; - int w = w_in + j; - - ulonglong4 *src_p = (ulonglong4 *)&data_im[(channel_in * height + h_in) * width + w_in + i * width + j]; - - int pre_out_index = h_out * width_col + w_out; - int out_index = (channel_out + i*ksize + j) * bit_align + pre_out_index; - ulonglong4 *dst_p = (ulonglong4 *)&data_col[out_index]; - - ulonglong4 val = (w < width) ? - (*src_p) : ulonglong4(); - - *dst_p = val; - //tmp256_s[0] = val; - }// w_out - - for (; w_out < width_col; ++w_out) - { - //int h_in = h_out * stride - pad; - int w_in = w_out * stride - pad; - - //int h = h_in + i; - int w = w_in + j; - - float val = (w < width) ? - data_im[(channel_in * height + h_in) * width + w_in + i * width + j] : 0; - - int pre_out_index = h_out * width_col + w_out; - int out_index = (channel_out + i*ksize + j) * bit_align + pre_out_index; - data_col[out_index] = val; - //tmp_s[0] = val; - }// w_out - } -#endif // NOT_USED - } - } -} -*/ // binary im2col - stride=1 @@ -491,14 +289,14 @@ __global__ void im2col_align_bin_gpu_kernel(const int n, const float* data_im, { const int lane_id = threadIdx.x % WARP_SIZE; - const int cur_wh_index = __shfl(send_wh_index, t) + lane_id; + const int cur_wh_index = __shfl_custom(send_wh_index, t) + lane_id; if (cur_wh_index < (width_col*height_col))// && (cur_i_pad+pad) < ksize) { - const int cur_pre_out_index = __shfl(pre_out_index, t); + const int cur_pre_out_index = __shfl_custom(pre_out_index, t); - const int cur_pre_in_index = __shfl(pre_in_index, t); - const int cur_pre_in_wh_index = __shfl(pre_in_wh_index, t) + lane_id; + const int cur_pre_in_index = __shfl_custom(pre_in_index, t); + const int cur_pre_in_wh_index = __shfl_custom(pre_in_wh_index, t) + lane_id; int w = cur_pre_in_wh_index % width; int h = cur_pre_in_wh_index / width; @@ -512,7 +310,7 @@ __global__ void im2col_align_bin_gpu_kernel(const int n, const float* data_im, //data_col[out_index] = val; //tmp_s[0] = val; - uint32_t bit_mask = __ballot(val > 0); + uint32_t bit_mask = __ballot_custom(val > 0); if (lane_id == 0) { uint8_t *bit8_ptr = &(((uint8_t *)data_col)[out_index / 8]); uint32_t *bit32_ptr = (uint32_t *)bit8_ptr; @@ -565,7 +363,7 @@ __global__ void float_to_bit_gpu_kernel(float *src, unsigned char *dst, size_t s if(index < size) src_val = src[index]; else src_val = 0; //unsigned int bit_mask = __ballot_sync(0xffffffff, src_val > 0); - unsigned int bit_mask = __ballot(src_val > 0); + unsigned int bit_mask = __ballot_custom(src_val > 0); if (threadIdx.x % WARP_SIZE == 0) ((unsigned int*)dst)[index / 32] = bit_mask; } } @@ -591,7 +389,7 @@ __global__ void float_to_bit_gpu_kernel(float *src, unsigned char *dst, size_t s const int warp_id = threadIdx.x / WARP_SIZE; const int lane_id = threadIdx.x % WARP_SIZE; - uint32_t bit_mask = __ballot(src_val > 0); + uint32_t bit_mask = __ballot_custom(src_val > 0); if (lane_id == 0) tmp[warp_id] = bit_mask; @@ -624,7 +422,7 @@ __global__ void float_to_bit_gpu_kernel(float *src, unsigned char *dst, size_t s const int warp_id = threadIdx.x / WARP_SIZE; const int lane_id = threadIdx.x % WARP_SIZE; - uint32_t bit_mask = __ballot(src_val > 0); + uint32_t bit_mask = __ballot_custom(src_val > 0); if (lane_id == 0) tmp[i * 32 + warp_id] = bit_mask; } __syncthreads(); @@ -849,8 +647,8 @@ __global__ void transpose_bin_gpu_kernel_32(uint32_t *A, uint32_t *B, const int void transpose_bin_gpu(unsigned char *A, unsigned char *B, const int n, const int m, const int lda, const int ldb, const int block_size) { - size_t size = n*m/ (8*8) + 1; - size_t size32 = n*m / (32*32) + 1; + int size = n*m/ (8*8) + 1; + int size32 = n*m / (32*32) + 1; const int num_blocks = size / BLOCK + 1; const int num_blocks32 = size32 / BLOCK_TRANSPOSE32 + 1; transpose_bin_gpu_kernel_32 << > >((uint32_t *)A, (uint32_t *)B, n, m, lda, ldb, block_size); @@ -1059,7 +857,7 @@ __global__ void repack_input_kernel_bin(float *input, uint32_t *re_packed_input_ { float src = input[(chan + c_pack)*items_per_channel + i]; - uint32_t bit_mask = __ballot(src > 0); + uint32_t bit_mask = __ballot_custom(src > 0); if (threadIdx.x % 32 == 0) re_packed_input_bin[chan*items_per_channel / 32 + i] = bit_mask; } @@ -1070,8 +868,9 @@ __global__ void repack_input_kernel_bin(float *input, uint32_t *re_packed_input_ void repack_input_gpu_bin(float *input, uint32_t *re_packed_input_bin, int w, int h, int c) { int size = w * h * c; - const int block_size = 128; + const int block_size = 256;// 128; const int num_blocks = get_number_of_blocks(size, block_size); + //printf("\n num_blocks = %d, num_blocks/32 = %d, block_size = %d \n", num_blocks, num_blocks/32, block_size); repack_input_kernel_bin << > >(input, re_packed_input_bin, w, h, c); } @@ -1116,7 +915,7 @@ __global__ void repack_input_kernel_bin(float *input, uint32_t *re_packed_input_ __syncthreads(); src = tmp[lane_id * 33 + warp_id]; - uint32_t bit_mask = __ballot(src > 0); + uint32_t bit_mask = __ballot_custom(src > 0); const int warp_item = block_item + warp_id; @@ -1426,12 +1225,17 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int __inline__ __device__ int warpAllReduceSum(int val) { for (int mask = WARP_SIZE / 2; mask > 0; mask /= 2) +#if CUDA_VERSION >= 9000 + val += __shfl_xor_sync(FULL_MASK, val, mask); +#else val += __shfl_xor(val, mask); +#endif + return val; } // Tensor Cores binary (CC >= 7.3 && CUDA >= 10.0) - __CUDA_SUBBYTE_IMMA__ -#if CUDART_VERSION >= 10000 +#if CUDA_VERSION >= 10000 #include #define WMMA_M 8 @@ -1529,7 +1333,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel(int M, int N, i #pragma UNROLL for (int local_j = 0; local_j < 8; ++local_j) { - uint32_t b_val_cur = __shfl(b_val, local_j *4 + k_d); + uint32_t b_val_cur = __shfl_custom(b_val, local_j *4 + k_d); c_val[local_j] = __popc(xor_int32(a_val, b_val_cur)); } @@ -1538,7 +1342,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel(int M, int N, i { #pragma UNROLL for (int local_k = 0; local_k < 4; ++local_k) { - accum_c_val[local_j] += __shfl(c_val[local_j], i_d * 4 + local_k); + accum_c_val[local_j] += __shfl_custom(c_val[local_j], i_d * 4 + local_k); } } } @@ -1571,7 +1375,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel(int M, int N, i float bias_val = bias_arr[i + i_d]; float dst_val = count *mean_val + bias_val; if (leaky_activation) - dst_val = (dst_val > 0) ? (dst_val) : (0.1*dst_val); // Leaky activation + dst_val = (dst_val > 0) ? (dst_val) : (0.1f*dst_val); // Leaky activation C[(i + i_d)*ldc + (j + j_d)] = dst_val; } @@ -1635,8 +1439,8 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int for (int t = 0; t < WARP_SIZE; ++t) { const int lane_id = threadIdx.x % WARP_SIZE; - const int64_t A_i = __shfl(A_cur_index, t) + 32 * lane_id; - const int64_t B_i = __shfl(B_cur_index, t) + 32 * lane_id; + const int64_t A_i = __shfl_custom(A_cur_index, t) + 32 * lane_id; + const int64_t B_i = __shfl_custom(B_cur_index, t) + 32 * lane_id; { //ulonglong4 a_bit256 = *((ulonglong4 *)(A + A_i)); // weights @@ -1668,8 +1472,8 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int for (int t = 0; t < WARP_SIZE; ++t) { const int lane_id = threadIdx.x % WARP_SIZE; - const int64_t A_i = __shfl(A_cur_index, t) + 8 * lane_id; - const int64_t B_i = __shfl(B_cur_index, t) + 8 * lane_id; + const int64_t A_i = __shfl_custom(A_cur_index, t) + 8 * lane_id; + const int64_t B_i = __shfl_custom(B_cur_index, t) + 8 * lane_id; { //uint64_t a_bit64 = *((uint64_t *)(A + A_i)); // weights @@ -1698,8 +1502,8 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int for (int t = 0; t < WARP_SIZE; ++t) { const int lane_id = threadIdx.x % WARP_SIZE; - const int64_t A_i = __shfl(A_cur_index, t) + 4 * lane_id; - const int64_t B_i = __shfl(B_cur_index, t) + 4 * lane_id; + const int64_t A_i = __shfl_custom(A_cur_index, t) + 4 * lane_id; + const int64_t B_i = __shfl_custom(B_cur_index, t) + 4 * lane_id; { //uint64_t a_bit64 = *((uint64_t *)(A + A_i)); // weights @@ -1749,7 +1553,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int count = count - f1; // remove extra bits (from empty space for align only) float dst_val = (2 * count - K) *mean_val + bias_val; if(leaky_activation) - dst_val = (dst_val > 0) ? (dst_val) : (0.1*dst_val); // Leaky activation + dst_val = (dst_val > 0) ? (dst_val) : (0.1f*dst_val); // Leaky activation C[i*ldc + j] = dst_val; } } @@ -1778,7 +1582,7 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, unsigned char *B, int ldb, float *C, int ldc, float *mean_arr, float *bias, int leaky_activation) { - size_t size = M*N; + int size = M*N; const int num_blocks = get_number_of_blocks(size, BLOCK); //printf("\n M = %d, N = %d, M %% 8 = %d, N %% 8 = %d \n", M, N, M % 8, N % 8); @@ -1797,7 +1601,7 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, { const int M_aligned = M + (8 - (M % 8)); const int N_aligned = N + (8 - (N % 8)); - size_t size = (M_aligned / 8)*(N_aligned / 8)*WARP_SIZE; + int size = (M_aligned / 8)*(N_aligned / 8)*WARP_SIZE; const int num_blocks = get_number_of_blocks(size, BLOCK); //printf(" lda = %d, ldb = %d, ldc = %d, lda/32 = %d, ldb/32 = %d, ldc/32 = %d \n", lda, ldb, ldc, lda / 32, ldb / 32, ldc / 32); @@ -1827,142 +1631,6 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, // -------------------------------- - - -// -------------------------------- -// sequentially - B (input) in the shared_memory - BAD -// -------------------------------- -__global__ void gemm_nn_custom_bin_mean_transposed_sequentially_gpu_kernel(int M, int N, int K, - unsigned char *A, int lda, - unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr) -{ - //__shared__ float mean_shared[32]; - //__shared__ uint32_t B_s[8192]; // 32 KB // [ldb x N`] // max = 262 144 bits - //__shared__ uint32_t B_s[4096]; // 16 KB // [ldb x N`] // max = 131 072 bits - __shared__ uint8_t B_s[4096*4]; // 16 KB // [ldb x N`] // max = 131 072 bits - - - const int K_items = WARP_SIZE; - int start_j = blockIdx.x*blockDim.x / (K_items * M); - - { - int end_j = (blockIdx.x*blockDim.x + blockDim.x) / (K_items * M) + 1; - if (end_j > N) end_j = N; - size_t shared_size = ldb * (end_j - start_j); - - if (shared_size != 0) { - //if(threadIdx.x == 0) printf(" start_j = %d, end_j = %d, shared_size = %d \n", start_j, end_j, shared_size); - - int k; - for (int k = threadIdx.x * 32; k < shared_size; k += blockDim.x * 32) { - int x = start_j*ldb + k; - if (x < (N*ldb)) *((uint32_t *)(B_s + k / 8)) = *((uint32_t *)(B + x / 8)); - } - } - } - __syncthreads(); - - int index = blockIdx.x*blockDim.x + threadIdx.x; - - { - int i; // l.n - int j; // out_h*out_w - int k; // l.size * l.size * l.c - - const int index2 = index / K_items; - i = index2 % M; // max M - j = index2 / M; // max N - //j = index2 % N; // max N - //i = index2 / N; // max M - - //int j_cur = index / M; - //int local_j = j_cur - start_j; - int local_j = j - start_j; - - //if (i <= 1 && j <= 1 ) printf(" k = %d, K = %d, K_items = %d, i = %d, j = %d, lda = %d, ldb = %d, ldc = %d \n", - // k, K, K_items, i, j, lda, ldb, ldc); - { // l.n - filters [16 - 55 - 1024] - // further improvements: for (l.n == 1024) iterate several (j) - - - if (j < N) - { // out_h*out_w - one channel output size [169 - 173056] - - int count = 0; - - - const int bit_step = 32; - for (k = (threadIdx.x % WARP_SIZE) * bit_step; k < K; k += bit_step*WARP_SIZE) - { // l.size*l.size*l.c - one filter size [27 - 144 - 9216] - uint32_t a_bit32 = *((uint32_t *)(A + (i*lda + k) / 8)); // weights - //uint32_t b_bit32 = *((uint32_t *)(B + (j*ldb + k) / 8)); // input - uint32_t b_bit32 = *((uint32_t *)(B_s + (local_j*ldb + k) / 8)); // input - uint32_t c_bit32 = xnor_int32(a_bit32, b_bit32); - - count += __popc(c_bit32); - } - - /* - const int bit_step = 64; - for (k = (threadIdx.x % WARP_SIZE) * bit_step; k < K; k += bit_step*WARP_SIZE) - { // l.size*l.size*l.c - one filter size [27 - 144 - 9216] - uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); // weights - //uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); - uint64_t b_bit64 = *((uint64_t *)(B_s + (local_j*ldb + k) / 8)); // input - uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); - count += __popcll(c_bit64); - } - */ - - - //atomicAdd(&C[i*ldc + j], (2 * count) * mean_val); - - for (int offset = WARP_SIZE / 2; offset > 0; offset /= 2) - count += __shfl_down(count, offset); - - - if (threadIdx.x % WARP_SIZE == 0) { - int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); - count = count - f1; - float mean_val = mean_arr[i]; - C[i*ldc + j] = (2 * count - K) * mean_val; - //B_s[threadIdx.x / WARP_SIZE] = (2 * count - K) * mean_val; - } - } - } - } -} - -// sequentially - BAD -void gemm_nn_custom_bin_mean_transposed_sequentially_gpu(int M, int N, int K, - unsigned char *A, int lda, - unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr) -{ - //size_t size = M*N; - size_t size = M*N * 32; - - const int num_blocks = size / BLOCK + 1; - - //printf(" K = %d \n", K); - - /* - printf("\n gemm_bin size = %d, num_blocks = %d, M*K = %d KB, N*K = %d KB \n (w) M*K/num_blocks = %d KB, (i) N*K/num_blocks = %d KB \n", - size, num_blocks, M*K / 1024, N*K / 1024, M*lda / num_blocks / 1024, N*ldb / num_blocks / 1024); - printf(" M / 512 = %d, N / 512 = %d, M*lda / 512 = %d, N*ldb / 512 = %d \n", M / 512, N / 512, M*lda/512, N*ldb/512); - */ - //printf(" shared_memory: (w) lda*BLOCK/N = %d, (i) ldb*BLOCK/M = %d, \t lda = %d \n\n", lda*BLOCK / N, ldb*BLOCK / M, lda); - - gemm_nn_custom_bin_mean_transposed_sequentially_gpu_kernel << > >( - M, N, K, - A, lda, - B, ldb, - C, ldc, - mean_arr); -} -// -------------------------------- - void convolve_cpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad) { int fil; @@ -2133,7 +1801,7 @@ __global__ void convolve_gpu_kernel(float *input, float *weights, float *output, void convolve_gpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad) { - size_t array_size = in_w*in_h*n; // width X height X filters + int array_size = in_w*in_h*n; // width X height X filters const int num_blocks = array_size / BLOCK + 1; //printf("\n array_size = %d, num_blocks = %d, w = %d, h = %d, n = %d, c = %d, pad = %d \n", array_size, num_blocks, in_w, in_h, n, in_c, pad); @@ -2247,10 +1915,10 @@ __global__ void convolve_bin_gpu_kernel(float *input, float *weights, float *out //const int weights_size = size*size*in_c/8; const int weights_size = size*size*in_c / 32 + 1; - for (int fil = min_fil; fil <= max_fil; fil++) { + for (int tmp_fil = min_fil; tmp_fil <= max_fil; tmp_fil++) { for (int s = threadIdx.x; s < weights_size; s += blockDim.x) { - //weights_shared[s + (fil - min_fil)*new_lda / 8] = ((uint8_t *)weights)[fil*new_lda / 8 + s]; - weights_shared[s + (fil - min_fil)*new_lda/32] = ((uint32_t *)weights)[fil*new_lda / 32 + s]; + //weights_shared[s + (tmp_fil - min_fil)*new_lda / 8] = ((uint8_t *)weights)[tmp_fil*new_lda / 8 + s]; + weights_shared[s + (tmp_fil - min_fil)*new_lda/32] = ((uint32_t *)weights)[tmp_fil*new_lda / 32 + s]; } } __syncthreads(); @@ -2348,7 +2016,7 @@ __global__ void convolve_bin_gpu_kernel(float *input, float *weights, float *out void convolve_bin_gpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad, int new_lda, float *mean_arr_gpu) { - size_t array_size = in_w*in_h*n; // width X height X filters + int array_size = in_w*in_h*n; // width X height X filters const int num_blocks = array_size / BLOCK + 1; //printf("\n array_size = %d, num_blocks = %d, w = %d, h = %d, n = %d, c = %d, pad = %d \n", array_size, num_blocks, in_w, in_h, n, in_c, pad); From 2d747cab2bf94d3d81a562bc49614ac2c4e661bf Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 2 Feb 2019 03:16:30 +0300 Subject: [PATCH 0293/1365] Minor fixes --- src/activation_kernels.cu | 38 ++++++++++++++++---------------- src/activations.h | 46 +++++++++++++++++++-------------------- src/cuda.c | 4 ++-- src/cuda.h | 4 ++-- src/parser.c | 4 ++-- 5 files changed, 48 insertions(+), 48 deletions(-) diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index b82fefad3fc..29154824bea 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -28,26 +28,26 @@ __device__ float hardtan_activate_kernel(float x) return x; } __device__ float linear_activate_kernel(float x){return x;} -__device__ float logistic_activate_kernel(float x){return 1./(1. + exp(-x));} -__device__ float loggy_activate_kernel(float x){return 2./(1. + exp(-x)) - 1;} +__device__ float logistic_activate_kernel(float x){return 1.f/(1.f + expf(-x));} +__device__ float loggy_activate_kernel(float x){return 2.f/(1.f + expf(-x)) - 1;} __device__ float relu_activate_kernel(float x){return x*(x>0);} -__device__ float elu_activate_kernel(float x){return (x >= 0)*x + (x < 0)*(exp(x)-1);} +__device__ float elu_activate_kernel(float x){return (x >= 0)*x + (x < 0)*(expf(x)-1);} __device__ float selu_activate_kernel(float x) { return (x >= 0)*1.0507f*x + (x < 0)*1.0507f*1.6732f*(expf(x) - 1); } -__device__ float relie_activate_kernel(float x){return (x>0) ? x : .01*x;} -__device__ float ramp_activate_kernel(float x){return x*(x>0)+.1*x;} -__device__ float leaky_activate_kernel(float x){return (x>0) ? x : .1*x;} -__device__ float tanh_activate_kernel(float x){return (2/(1 + exp(-2*x)) - 1);} +__device__ float relie_activate_kernel(float x){return (x>0) ? x : .01f*x;} +__device__ float ramp_activate_kernel(float x){return x*(x>0)+.1f*x;} +__device__ float leaky_activate_kernel(float x){return (x>0) ? x : .1f*x;} +__device__ float tanh_activate_kernel(float x){return (2/(1 + expf(-2*x)) - 1);} __device__ float plse_activate_kernel(float x) { - if(x < -4) return .01 * (x + 4); - if(x > 4) return .01 * (x - 4) + 1; - return .125*x + .5; + if(x < -4) return .01f * (x + 4); + if(x > 4) return .01f * (x - 4) + 1; + return .125f*x + .5f; } __device__ float stair_activate_kernel(float x) { - int n = floor(x); - if (n%2 == 0) return floor(x/2.); - else return (x - n) + floor(x/2.); + int n = floorf(x); + if (n%2 == 0) return floorf(x/2.f); + else return (x - n) + floorf(x/2.f); } @@ -60,17 +60,17 @@ __device__ float linear_gradient_kernel(float x){return 1;} __device__ float logistic_gradient_kernel(float x){return (1-x)*x;} __device__ float loggy_gradient_kernel(float x) { - float y = (x+1.)/2.; + float y = (x+1.F)/2.F; return 2*(1-y)*y; } __device__ float relu_gradient_kernel(float x){return (x>0);} __device__ float elu_gradient_kernel(float x){return (x >= 0) + (x < 0)*(x + 1);} -__device__ float selu_gradient_kernel(float x) { return (x >= 0)*1.0507 + (x < 0)*(x + 1.0507*1.6732); } -__device__ float relie_gradient_kernel(float x){return (x>0) ? 1 : .01;} -__device__ float ramp_gradient_kernel(float x){return (x>0)+.1;} -__device__ float leaky_gradient_kernel(float x){return (x>0) ? 1 : .1;} +__device__ float selu_gradient_kernel(float x) { return (x >= 0)*1.0507f + (x < 0)*(x + 1.0507f*1.6732f); } +__device__ float relie_gradient_kernel(float x){return (x>0) ? 1 : .01f;} +__device__ float ramp_gradient_kernel(float x){return (x>0)+.1f;} +__device__ float leaky_gradient_kernel(float x){return (x>0) ? 1 : .1f;} __device__ float tanh_gradient_kernel(float x){return 1-x*x;} -__device__ float plse_gradient_kernel(float x){return (x < 0 || x > 1) ? .01 : .125;} +__device__ float plse_gradient_kernel(float x){return (x < 0 || x > 1) ? .01f : .125f;} __device__ float stair_gradient_kernel(float x) { if (floor(x) == x) return 0; diff --git a/src/activations.h b/src/activations.h index 90930d44143..442f15a2016 100644 --- a/src/activations.h +++ b/src/activations.h @@ -22,9 +22,9 @@ void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta); static inline float stair_activate(float x) { - int n = floor(x); - if (n%2 == 0) return floor(x/2.); - else return (x - n) + floor(x/2.); + int n = floorf(x); + if (n%2 == 0) return floorf(x/2.f); + else return (x - n) + floorf(x/2.f); } static inline float hardtan_activate(float x) { @@ -33,32 +33,32 @@ static inline float hardtan_activate(float x) return x; } static inline float linear_activate(float x){return x;} -static inline float logistic_activate(float x){return 1./(1. + exp(-x));} -static inline float loggy_activate(float x){return 2./(1. + exp(-x)) - 1;} +static inline float logistic_activate(float x){return 1.f/(1.f + expf(-x));} +static inline float loggy_activate(float x){return 2.f/(1.f + expf(-x)) - 1;} static inline float relu_activate(float x){return x*(x>0);} -static inline float elu_activate(float x){return (x >= 0)*x + (x < 0)*(exp(x)-1);} -static inline float selu_activate(float x) { return (x >= 0)*1.0507*x + (x < 0)*1.0507*1.6732*(exp(x) - 1); } -static inline float relie_activate(float x){return (x>0) ? x : .01*x;} -static inline float ramp_activate(float x){return x*(x>0)+.1*x;} -static inline float leaky_activate(float x){return (x>0) ? x : .1*x;} -static inline float tanh_activate(float x){return (exp(2*x)-1)/(exp(2*x)+1);} +static inline float elu_activate(float x){return (x >= 0)*x + (x < 0)*(expf(x)-1);} +static inline float selu_activate(float x) { return (x >= 0)*1.0507f*x + (x < 0)*1.0507f*1.6732f*(expf(x) - 1); } +static inline float relie_activate(float x){return (x>0) ? x : .01f*x;} +static inline float ramp_activate(float x){return x*(x>0)+.1f*x;} +static inline float leaky_activate(float x){return (x>0) ? x : .1f*x;} +static inline float tanh_activate(float x){return (expf(2*x)-1)/(expf(2*x)+1);} static inline float plse_activate(float x) { - if(x < -4) return .01 * (x + 4); - if(x > 4) return .01 * (x - 4) + 1; - return .125*x + .5; + if(x < -4) return .01f * (x + 4); + if(x > 4) return .01f * (x - 4) + 1; + return .125f*x + .5f; } static inline float lhtan_activate(float x) { - if(x < 0) return .001*x; - if(x > 1) return .001*(x-1) + 1; + if(x < 0) return .001f*x; + if(x > 1) return .001f*(x-1) + 1; return x; } static inline float lhtan_gradient(float x) { if(x > 0 && x < 1) return 1; - return .001; + return .001f; } static inline float hardtan_gradient(float x) @@ -70,7 +70,7 @@ static inline float linear_gradient(float x){return 1;} static inline float logistic_gradient(float x){return (1-x)*x;} static inline float loggy_gradient(float x) { - float y = (x+1.)/2.; + float y = (x+1.f)/2.f; return 2*(1-y)*y; } static inline float stair_gradient(float x) @@ -80,12 +80,12 @@ static inline float stair_gradient(float x) } static inline float relu_gradient(float x){return (x>0);} static inline float elu_gradient(float x){return (x >= 0) + (x < 0)*(x + 1);} -static inline float selu_gradient(float x) { return (x >= 0)*1.0507 + (x < 0)*(x + 1.0507*1.6732); } -static inline float relie_gradient(float x){return (x>0) ? 1 : .01;} -static inline float ramp_gradient(float x){return (x>0)+.1;} -static inline float leaky_gradient(float x){return (x>0) ? 1 : .1;} +static inline float selu_gradient(float x) { return (x >= 0)*1.0507 + (x < 0)*(x + 1.0507f*1.6732f); } +static inline float relie_gradient(float x){return (x>0) ? 1 : .01f;} +static inline float ramp_gradient(float x){return (x>0)+.1f;} +static inline float leaky_gradient(float x){return (x>0) ? 1 : .1f;} static inline float tanh_gradient(float x){return 1-x*x;} -static inline float plse_gradient(float x){return (x < 0 || x > 1) ? .01 : .125;} +static inline float plse_gradient(float x){return (x < 0 || x > 1) ? .01f : .125f;} #endif diff --git a/src/cuda.c b/src/cuda.c index 77f4dd7146b..a8ba37fa76f 100644 --- a/src/cuda.c +++ b/src/cuda.c @@ -54,7 +54,7 @@ void check_error(cudaError_t status) } } -void check_error_extended(cudaError_t status, char *file, int line, char *date_time) +void check_error_extended(cudaError_t status, const char *file, int line, const char *date_time) { if (status != cudaSuccess) printf("CUDA Error: file: %s() : line: %d : build time: %s \n", file, line, date_time); @@ -165,7 +165,7 @@ void cudnn_check_error(cudnnStatus_t status) } } -void cudnn_check_error_extended(cudnnStatus_t status, char *file, int line, char *date_time) +void cudnn_check_error_extended(cudnnStatus_t status, const char *file, int line, const char *date_time) { if (status != cudaSuccess) printf("\n cuDNN Error in: file: %s() : line: %d : build time: %s \n", file, line, date_time); diff --git a/src/cuda.h b/src/cuda.h index ecc83e40032..34f68eba272 100644 --- a/src/cuda.h +++ b/src/cuda.h @@ -44,7 +44,7 @@ extern int gpu_index; extern "C" { #endif // __cplusplus void check_error(cudaError_t status); - void check_error_extended(cudaError_t status, char *file, int line, char *date_time); + void check_error_extended(cudaError_t status, const char *file, int line, const char *date_time); #define CHECK_CUDA(X) check_error_extended(X, __FILE__ " : " __FUNCTION__, __LINE__, __DATE__ " - " __TIME__ ); cublasHandle_t blas_handle(); @@ -70,7 +70,7 @@ extern "C" { cudnnHandle_t cudnn_handle(); enum {cudnn_fastest, cudnn_smallest}; -void cudnn_check_error_extended(cudnnStatus_t status, char *file, int line, char *date_time); +void cudnn_check_error_extended(cudnnStatus_t status, const char *file, int line, const char *date_time); #define CHECK_CUDNN(X) cudnn_check_error_extended(X, __FILE__ " : " __FUNCTION__, __LINE__, __DATE__ " - " __TIME__ ); #endif diff --git a/src/parser.c b/src/parser.c index b2101703ca4..671ed764b68 100644 --- a/src/parser.c +++ b/src/parser.c @@ -848,7 +848,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) free_list(sections); net.outputs = get_network_output_size(net); net.output = get_network_output(net); - printf("Total BFLOPS %5.3f \n", bflops); + fprintf(stderr, "Total BFLOPS %5.3f \n", bflops); #ifdef GPU get_cuda_stream(); get_cuda_memcpy_stream(); @@ -870,7 +870,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) check_error(cudaMalloc((void **)net.output16_gpu, *net.max_output16_size * sizeof(short))); //sizeof(half) } if (workspace_size) { - printf(" Allocate additional workspace_size = %1.2f MB \n", (float)workspace_size/1000000); + fprintf(stderr, " Allocate additional workspace_size = %1.2f MB \n", (float)workspace_size/1000000); net.workspace = cuda_make_array(0, workspace_size / sizeof(float) + 1); } else { From ff0733ed4096c3e12d88380ca6739dd7ab545a35 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 2 Feb 2019 15:16:25 +0300 Subject: [PATCH 0294/1365] Speedup repack_input_kernel_bin() --- src/im2col_kernels.cu | 113 ++++++++++++++++++------------------------ 1 file changed, 48 insertions(+), 65 deletions(-) diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 2bdca30bb13..56ed19dea90 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -824,7 +824,54 @@ void repack_input_gpu_2(float *input, float *re_packed_input, int w, int h, int // -------------------------------- +// 32 channels -> 1 channel (with 32 floats) +// 256 channels -> 8 channels (with 32 floats) +__global__ void repack_input_kernel_bin(float *input, uint32_t *re_packed_input_bin, int w, int h, int c) +{ + //__shared__ uint32_t tmp[32]; + const int index = blockIdx.x*blockDim.x + threadIdx.x; + + const int global_warp_id = index / WARP_SIZE; + const int lane_id = threadIdx.x % WARP_SIZE; + + const int items_per_channel = w * h; + const int items_per_channel_aligned = items_per_channel + WARP_SIZE - (items_per_channel % WARP_SIZE); + + int i = 32 * (global_warp_id % (items_per_channel_aligned / WARP_SIZE)); + int chan = 32 * (global_warp_id / (items_per_channel_aligned / WARP_SIZE)); + + if (chan < c) + { + uint32_t result_bits = 0; + + for (int c_pack = 0; c_pack < 32; ++c_pack) + { + float src = 0; + if ((i + lane_id) < items_per_channel) { + src = input[(chan + c_pack)*items_per_channel + (i + lane_id)]; + } + uint32_t bit_mask = __ballot_custom(src > 0); + uint32_t cur_bit = (bit_mask >> lane_id) & uint32_t(1); + + result_bits |= (cur_bit << c_pack); + } + if ((i + lane_id) < items_per_channel) { + re_packed_input_bin[chan*items_per_channel / 32 + (i + lane_id)] = result_bits; + } + } +} + +void repack_input_gpu_bin(float *input, uint32_t *re_packed_input_bin, int w, int h, int c) +{ + int size = (w * h * c) / 32 + 1; + const int block_size = BLOCK; + const int num_blocks = get_number_of_blocks(size, block_size); + //printf("\n num_blocks = %d, num_blocks/32 = %d, block_size = %d \n", num_blocks, num_blocks / 32, block_size); + repack_input_kernel_bin << > >(input, re_packed_input_bin, w, h, c); +} + +/* // 32 channels -> 1 channel (with 32 floats) // 256 channels -> 8 channels (with 32 floats) __global__ void repack_input_kernel_bin(float *input, uint32_t *re_packed_input_bin, int w, int h, int c) @@ -870,74 +917,10 @@ void repack_input_gpu_bin(float *input, uint32_t *re_packed_input_bin, int w, in int size = w * h * c; const int block_size = 256;// 128; const int num_blocks = get_number_of_blocks(size, block_size); - //printf("\n num_blocks = %d, num_blocks/32 = %d, block_size = %d \n", num_blocks, num_blocks/32, block_size); + printf("\n num_blocks = %d, num_blocks/32 = %d, block_size = %d \n", num_blocks, num_blocks/32, block_size); repack_input_kernel_bin << > >(input, re_packed_input_bin, w, h, c); } - - -/* -// 32 channels -> 1 channel (with 32 floats) -// 256 channels -> 8 channels (with 32 floats) -__global__ void repack_input_kernel_bin(float *input, uint32_t *re_packed_input_bin, int w, int h, int c, int items_per_channel_align) -{ - __shared__ float tmp[33*32]; // misalgined array 32x32 - //const int index = blockIdx.x*blockDim.x + threadIdx.x; - - const int num_of_warps = blockDim.x / WARP_SIZE; - const int warp_id = threadIdx.x / WARP_SIZE; - const int lane_id = threadIdx.x % WARP_SIZE; - - const int items_per_channel = w * h; - //const int items_per_channel_align = items_per_channel + (32 - items_per_channel % 32); - const int blocks_per_wh = items_per_channel_align / 32; - //const int blocks_per_c = c / 32; - - // input[C x H x W] = input[C x ITEMS] - // BLOCK per C x ITEMS = 32x32 - - const int block_item_id = blockIdx.x % blocks_per_wh; - const int block_channel_id = blockIdx.x / blocks_per_wh; - - const int block_item = block_item_id * 32; - const int block_channel = block_channel_id * 32; - - const int lane_item = block_item + lane_id; - const int warp_channel = block_channel + warp_id; - - if (warp_channel < c) - { - float src = 0; - - if (lane_item < items_per_channel) - src = input[warp_channel*items_per_channel + lane_item]; - - tmp[warp_id * 33 + lane_id] = src; - __syncthreads(); - src = tmp[lane_id * 33 + warp_id]; - - uint32_t bit_mask = __ballot_custom(src > 0); - - const int warp_item = block_item + warp_id; - - if (lane_id == 0 && warp_item < items_per_channel) - re_packed_input_bin[block_channel_id*items_per_channel + warp_item] = bit_mask; - } -} - -#define BLOCK_REPACK 1024 -void repack_input_gpu_bin(float *input, uint32_t *re_packed_input_bin, int w, int h, int c) -{ - int items_per_channel = w*h; - int items_per_channel_align = items_per_channel + (32 - items_per_channel % 32); - int channel_align = c + (32 - c % 32); - - //int size = w * h * c; - int size = items_per_channel_align * channel_align; - const int num_blocks = get_number_of_blocks(size, BLOCK_REPACK); - repack_input_kernel_bin << > >(input, re_packed_input_bin, w, h, c, items_per_channel_align); -} */ -// -------------------------------- From 41814fc4b3c58ec682721b2e394075c630ceec5a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 2 Feb 2019 15:16:57 +0300 Subject: [PATCH 0295/1365] Minor fixes --- Makefile | 4 +++- src/convolutional_kernels.cu | 2 +- src/cuda.c | 8 ++++++-- 3 files changed, 10 insertions(+), 4 deletions(-) diff --git a/Makefile b/Makefile index 745f1ac2936..17c96cd37dc 100644 --- a/Makefile +++ b/Makefile @@ -23,7 +23,7 @@ OS := $(shell uname) # Tesla V100 # ARCH= -gencode arch=compute_70,code=[sm_70,compute_70] -# GeForce RTX 2080 Ti, RTX 2080, RTX 2070 Quadro RTX 8000, Quadro RTX 6000, Quadro RTX 5000 Tesla T4 +# GeForce RTX 2080 Ti, RTX 2080, RTX 2070, Quadro RTX 8000, Quadro RTX 6000, Quadro RTX 5000, Tesla T4, XNOR Tensor Cores # ARCH= -gencode arch=compute_75,code=[sm_75,compute_75] # Jetson XAVIER @@ -61,6 +61,8 @@ CFLAGS=-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC ifeq ($(DEBUG), 1) OPTS= -O0 -g +COMMON+= -DDEBUG +CFLAGS+= -DDEBUG else ifeq ($(AVX), 1) CFLAGS+= -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 1b63c4c5599..4681416a0a9 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -338,7 +338,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) } else*/ { - start_timer(); + //start_timer(); im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.align_workspace_gpu, l.bit_align); //cudaDeviceSynchronize(); //stop_timer_and_show_name("im2col_align_ongpu"); diff --git a/src/cuda.c b/src/cuda.c index a8ba37fa76f..d1529b19a00 100644 --- a/src/cuda.c +++ b/src/cuda.c @@ -26,7 +26,9 @@ int cuda_get_device() void check_error(cudaError_t status) { - //cudaDeviceSynchronize(); +#ifdef DEBUG + cudaDeviceSynchronize(); +#endif cudaError_t status2 = cudaGetLastError(); if (status != cudaSuccess) { @@ -134,7 +136,9 @@ cudnnHandle_t cudnn_handle() void cudnn_check_error(cudnnStatus_t status) { - //cudaDeviceSynchronize(); +#ifdef DEBUG + cudaDeviceSynchronize(); +#endif cudnnStatus_t status2 = CUDNN_STATUS_SUCCESS; #ifdef CUDNN_ERRQUERY_RAWCODE cudnnStatus_t status_tmp = cudnnQueryRuntimeError(cudnn_handle(), &status2, CUDNN_ERRQUERY_RAWCODE, NULL); From dc7e7f035d9c2a02f1e52340ba32497504089b3a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 2 Feb 2019 17:57:30 +0300 Subject: [PATCH 0296/1365] improve XNOR Tensor Cores GEMM - N 2x unrolled - minor performance improvement --- src/im2col_kernels.cu | 190 +++++++++++++++++++++++++++++++++++++++++- 1 file changed, 187 insertions(+), 3 deletions(-) diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 56ed19dea90..89d27cb542f 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -1226,6 +1226,189 @@ int warpAllReduceSum(int val) { #define WMMA_K 128 #define WMMA_K32 (WMMA_K/32) +#define WMMA_Nx2 (WMMA_N*2) + +// Tensor Cores are used for XOR-GEMM +__global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel(int M, int N, int K, + unsigned char *A, int lda, + unsigned char *B, int ldb, + float *C, int ldc, float *mean_arr, float *bias_arr, int leaky_activation) +{ + // total 57% + int index = blockIdx.x*blockDim.x + threadIdx.x; + + __shared__ int C_s[WMMA_N * WMMA_M * 32 * 2]; // 2 * 8 KB - Temprorary result of GEMM WMMA for 32 warps + + const int lane_id = threadIdx.x % 32; + const int warp_id = threadIdx.x / 32; + const int global_warp_id = index / 32; + + const int N_aligned = N + WMMA_Nx2 - (N % WMMA_Nx2); + + /* + __syncthreads(); + __shared__ uint32_t A_s[8 * 512]; // 8x512 = 8 x 16384 bits, instead of 8x4 + const int start_global_warp_id = blockIdx.x*blockDim.x / 32; + int start_i = start_global_warp_id / (N_aligned / WMMA_N); + start_i = start_i * WMMA_M; + if (start_i + WMMA_M > M) start_i = M - WMMA_M; // must be: i+7 < M + for (int tmp_index = threadIdx.x; tmp_index < (8 * 512); tmp_index += blockDim.x) + { + int k_tmp = tmp_index % 512; + int local_i = tmp_index / 512; + + uint32_t a_val = ((uint32_t *)(A))[(start_i + local_i)*lda/32 + k_tmp]; + A_s[local_i * 512 + k_tmp] = a_val; + } + __syncthreads(); + */ + + + int i, j, k, h; + // 47% = 29 + 10 + 8 + j = global_warp_id % (N_aligned / WMMA_Nx2); + j = j * WMMA_Nx2; + { // out_h*out_w - one channel output size [169 - 173056] + i = global_warp_id / (N_aligned / WMMA_Nx2); + i = i * WMMA_M; + + int count = 0; + k = 0; + + if (i < M) //if (i < M) // l.n - filters [16 - 55 - 1024] + { + if (j + WMMA_Nx2 > N) j = N - WMMA_Nx2; // must be: j+7 < N + if (i + WMMA_M > M) i = M - WMMA_M; // must be: i+7 < M + +#if __CUDA_ARCH__ >= 730 + // Tensor Cores + using namespace nvcuda; + + wmma::fragment a_frag; + wmma::fragment b_frag; + wmma::fragment c1_frag, c2_frag; + wmma::fill_fragment(c1_frag, 0); // !!!! XOR isn't XNOR !!!!!!!!!! + wmma::fill_fragment(c2_frag, 0); // !!!! XOR isn't XNOR !!!!!!!!!! + + // 8 x 8 x 4 (uint32_t, 4 * 32 = 128 bit) + for (; k < K; k += 128) // l.size*l.size*l.c - one filter size [27 - 144 - 9216] + { + int64_t A_cur_index = (i*lda + k) / 8; // index in bits + int64_t B1_cur_index = (j*ldb + k) / 8; // index in bits + int64_t B2_cur_index = ((j + 8)*ldb + k) / 8; // index in bits + + // try to use A that is cached in shared memory - poor performance + //if (i == start_i) wmma::load_matrix_sync(a_frag, &A_s[k / 32], (512 * 32)); // lda = (128*32) bits + //else wmma::load_matrix_sync(a_frag, (uint32_t *)(A + A_cur_index), lda); // lda = M + + // lda, ldb - are in bits + wmma::load_matrix_sync(a_frag, (uint32_t *)(A + A_cur_index), lda); // lda = M + + wmma::load_matrix_sync(b_frag, (uint32_t *)(B + B1_cur_index), ldb); // ldb = K + wmma::bmma_sync(c1_frag, a_frag, b_frag, c1_frag); // XOR-GEMM + + wmma::load_matrix_sync(b_frag, (uint32_t *)(B + B2_cur_index), ldb); // ldb = K + wmma::bmma_sync(c2_frag, a_frag, b_frag, c2_frag); // XOR-GEMM + } + // C[i*ldc + j] + wmma::store_matrix_sync(&C_s[warp_id*WMMA_M*WMMA_N], c1_frag, WMMA_N, wmma::mem_row_major); + wmma::store_matrix_sync(&C_s[warp_id*WMMA_M*WMMA_N + WMMA_M*WMMA_N*32], c2_frag, WMMA_N, wmma::mem_row_major); +#else // __CUDA_ARCH__ >= 730 + + // Custom XOR-GEMM + int k_d = lane_id % 4; + int i_d = lane_id / 4; + int j_d = lane_id / 4; + + int32_t accum_c_val[8*2]; // wmma::fill_fragment(c_frag, 0); + for (int local_j = 0; local_j < 8*2; ++local_j) { + accum_c_val[local_j] = 0; + } + + // 8 x 8 x 4 (uint32_t, 4 * 32 = 128 bit) + for (; k < K; k += 128) // l.size*l.size*l.c - one filter size [27 - 144 - 9216] + { + int64_t A_cur_index = (i*lda + k) / 8; + //int64_t A_cur_index = (local_i*lda + k) / 8; + int64_t B_cur_index = (j*ldb + k) / 8; + + // lda, ldb - are in bits + // 8*4 = 32 + // 8*8 = 64 + int k_d = lane_id % 4; + int i_d = lane_id / 4; + int j_d = lane_id / 4; + uint32_t a_val = *(uint32_t *)(A + ((i + i_d)*lda + (k + k_d*32)) / 8); // wmma::load_matrix_sync(a_frag, (uint32_t *)(A + A_cur_index), lda); + + for (int c_x = 0; c_x < 2; c_x++) + { + uint32_t b_val = *(uint32_t *)(B + ((c_x * 8 + j + j_d)*ldb + (k + k_d * 32)) / 8); // wmma::load_matrix_sync(b_frag, (uint32_t *)(B + B_cur_index), ldb); + + // wmma::bmma_sync(c_frag, a_frag, b_frag, c_frag); + int32_t c_val[8]; // 8 x 32 threads = 256 + #pragma UNROLL + for (int local_j = 0; local_j < 8; ++local_j) + { + uint32_t b_val_cur = __shfl_custom(b_val, local_j * 4 + k_d); + c_val[local_j] = __popc(xor_int32(a_val, b_val_cur)); + } + + #pragma UNROLL + for (int local_j = 0; local_j < 8; ++local_j) + { + #pragma UNROLL + for (int local_k = 0; local_k < 4; ++local_k) { + accum_c_val[local_j + c_x*8] += __shfl_custom(c_val[local_j], i_d * 4 + local_k); + } + } + } + } + + // only the first 8 threads (i) contain 8 good values each, in c_val[8] (j) = 8 x 8 =64 + // wmma::store_matrix_sync(&C_s[warp_id*WMMA_M*WMMA_N], c_frag, WMMA_N, wmma::mem_row_major); + if (k_d == 0) { + for (int c_x = 0; c_x < 2; c_x++) + { + for (int local_j = 0; local_j < 8; ++local_j) + { + C_s[warp_id*WMMA_M*WMMA_N + i_d*WMMA_N + local_j + WMMA_M*WMMA_N*32 * c_x] = accum_c_val[local_j + c_x*8]; + } + } + } +#endif // __CUDA_ARCH__ >= 730 + + for(int c_x = 0; c_x < 2; c_x++) + { + int j_d = lane_id % WMMA_N; + { + #pragma UNROLL + for (int i_d = lane_id / WMMA_N; i_d < WMMA_M; i_d += WMMA_M / 2) + { + int count = C_s[warp_id*WMMA_M*WMMA_N + i_d*WMMA_N + j_d + WMMA_M*WMMA_N*32*c_x]; + + const int bit_step = 128; + int f1 = (K % bit_step == 0) ? 0 : (bit_step - (K % bit_step)); + count = count - f1; // remove extra bits (from empty space for align only) + + count = (2 * count - K); + + float mean_val = mean_arr[i + i_d]; + float bias_val = bias_arr[i + i_d]; + float dst_val = count *mean_val + bias_val; + if (leaky_activation) + dst_val = (dst_val > 0) ? (dst_val) : (0.1f*dst_val); // Leaky activation + + C[(i + i_d)*ldc + (c_x*8 + j + j_d)] = dst_val; + } + + } + } + } + } +} +#endif // CUDART_VERSION >= 10000 + +/* // Tensor Cores are used for XOR-GEMM __global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel(int M, int N, int K, unsigned char *A, int lda, @@ -1368,7 +1551,8 @@ __global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel(int M, int N, i } } } -#endif // CUDART_VERSION >= 10000 +*/ + // Coalescing // A (weights) in the shared_memory - GOOD @@ -1583,8 +1767,8 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, if (1) { const int M_aligned = M + (8 - (M % 8)); - const int N_aligned = N + (8 - (N % 8)); - int size = (M_aligned / 8)*(N_aligned / 8)*WARP_SIZE; + const int N_aligned = N + (16 - (N % 16)); + int size = (M_aligned / 8)*(N_aligned / 16)*WARP_SIZE; const int num_blocks = get_number_of_blocks(size, BLOCK); //printf(" lda = %d, ldb = %d, ldc = %d, lda/32 = %d, ldb/32 = %d, ldc/32 = %d \n", lda, ldb, ldc, lda / 32, ldb / 32, ldc / 32); From 61156239e09cc839ce4324b593de89673da1352a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 3 Feb 2019 00:18:30 +0300 Subject: [PATCH 0297/1365] Minor performance improvement --- src/activation_kernels.cu | 52 +++++++++++++++++++++++++++++++----- src/batchnorm_layer.c | 15 +++++++---- src/convolutional_kernels.cu | 36 ++++++++++++++----------- src/network_kernels.cu | 2 +- 4 files changed, 78 insertions(+), 27 deletions(-) diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index 29154824bea..aec279d5dde 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -164,7 +164,7 @@ __global__ void binary_gradient_array_kernel(float *x, float *dy, int n, int s, extern "C" void binary_gradient_array_gpu(float *x, float *dx, int n, int size, BINARY_ACTIVATION a, float *y) { binary_gradient_array_kernel << > >(x, dx, n / 2, size, a, y); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } __global__ void binary_activate_array_kernel(float *x, int n, int s, BINARY_ACTIVATION a, float *y) { @@ -179,7 +179,7 @@ __global__ void binary_activate_array_kernel(float *x, int n, int s, BINARY_ACTI extern "C" void binary_activate_array_gpu(float *x, int n, int size, BINARY_ACTIVATION a, float *y) { binary_activate_array_kernel << > >(x, n / 2, size, a, y); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } __global__ void activate_array_kernel(float *x, int n, ACTIVATION a) @@ -218,6 +218,38 @@ __global__ void gradient_array_kernel(float *x, int n, ACTIVATION a, float *delt if(i < n) delta[i] *= gradient_kernel(x[i], a); } +__global__ void gradient_array_leaky_kernel(float *x, int n, float *delta) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < n) { + delta[index] *= leaky_gradient_kernel(x[index]); + } +} + +__global__ void gradient_array_selu_kernel(float *x, int n, float *delta) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < n) { + delta[index] *= selu_gradient_kernel(x[index]); + } +} + +__global__ void gradient_array_logistic_kernel(float *x, int n, float *delta) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < n) { + delta[index] *= logistic_gradient_kernel(x[index]); + } +} + +__global__ void gradient_array_hardtan_kernel(float *x, int n, float *delta) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < n) { + delta[index] *= hardtan_gradient_kernel(x[index]); + } +} + extern "C" void activate_array_ongpu(float *x, int n, ACTIVATION a) { const int num_blocks = get_number_of_blocks(n, BLOCK); @@ -225,12 +257,20 @@ extern "C" void activate_array_ongpu(float *x, int n, ACTIVATION a) else if(a == LEAKY) activate_array_leaky_kernel << > >(x, n); else if (a == LOGISTIC) activate_array_logistic_kernel << > >(x, n); else if (a == SELU) activate_array_selu_kernel << > >(x, n); - else activate_array_kernel<<>>(x, n, a); - check_error(cudaPeekAtLastError()); + else + activate_array_kernel<<>>(x, n, a); + CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta) { - gradient_array_kernel<<>>(x, n, a, delta); - check_error(cudaPeekAtLastError()); + const int num_blocks = get_number_of_blocks(n, BLOCK); + if (a == LINEAR) return; + else if (a == LEAKY) gradient_array_leaky_kernel << > >(x, n, delta); + else if (a == LOGISTIC) gradient_array_logistic_kernel << > >(x, n, delta); + else if (a == SELU) gradient_array_selu_kernel << > >(x, n, delta); + else if (a == HARDTAN) gradient_array_hardtan_kernel << > >(x, n, delta); + else + gradient_array_kernel << > > (x, n, a, delta); + CHECK_CUDA(cudaPeekAtLastError()); } diff --git a/src/batchnorm_layer.c b/src/batchnorm_layer.c index 3fa129db1f5..a2870405ff1 100644 --- a/src/batchnorm_layer.c +++ b/src/batchnorm_layer.c @@ -142,7 +142,7 @@ void forward_batchnorm_layer(layer l, network_state state) axpy_cpu(l.out_c, .1, l.variance, 1, l.rolling_variance, 1); copy_cpu(l.outputs*l.batch, l.output, 1, l.x, 1); - normalize_cpu(l.output, l.mean, l.variance, l.batch, l.out_c, l.out_h*l.out_w); + normalize_cpu(l.output, l.mean, l.variance, l.batch, l.out_c, l.out_h*l.out_w); copy_cpu(l.outputs*l.batch, l.output, 1, l.x_norm, 1); } else { normalize_cpu(l.output, l.rolling_mean, l.rolling_variance, l.batch, l.out_c, l.out_h*l.out_w); @@ -179,8 +179,11 @@ void push_batchnorm_layer(layer l) void forward_batchnorm_layer_gpu(layer l, network_state state) { - if (l.type == BATCHNORM) copy_ongpu(l.outputs*l.batch, state.input, 1, l.output_gpu, 1); - copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1); + if (l.type == BATCHNORM) simple_copy_ongpu(l.outputs*l.batch, state.input, l.output_gpu); + //copy_ongpu(l.outputs*l.batch, state.input, 1, l.output_gpu, 1); + + simple_copy_ongpu(l.outputs*l.batch, l.output_gpu, l.x_gpu); + //copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1); if (state.train) { #ifdef CUDNN float one = 1; @@ -255,7 +258,8 @@ void backward_batchnorm_layer_gpu(layer l, network_state state) .00001, l.mean_gpu, // input (should be FP32) l.variance_gpu); // input (should be FP32) - copy_ongpu(l.outputs*l.batch, l.x_norm_gpu, 1, l.delta_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch, l.x_norm_gpu, l.delta_gpu); + //copy_ongpu(l.outputs*l.batch, l.x_norm_gpu, 1, l.delta_gpu, 1); #else backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h); backward_scale_gpu(l.x_norm_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.scale_updates_gpu); @@ -266,6 +270,7 @@ void backward_batchnorm_layer_gpu(layer l, network_state state) fast_variance_delta_gpu(l.x_gpu, l.delta_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.variance_delta_gpu); normalize_delta_gpu(l.x_gpu, l.mean_gpu, l.variance_gpu, l.mean_delta_gpu, l.variance_delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu); #endif - if (l.type == BATCHNORM) copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, state.delta, 1); + if (l.type == BATCHNORM) simple_copy_ongpu(l.outputs*l.batch, l.delta_gpu, state.delta); + //copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, state.delta, 1); } #endif \ No newline at end of file diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 4681416a0a9..2ba2acdd32d 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -27,7 +27,7 @@ __global__ void binarize_kernel(float *x, int n, float *binary) void binarize_gpu(float *x, int n, float *binary) { binarize_kernel<<>>(x, n, binary); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } __global__ void binarize_input_kernel(float *input, int n, int size, float *binary) @@ -48,7 +48,7 @@ __global__ void binarize_input_kernel(float *input, int n, int size, float *bina void binarize_input_gpu(float *input, int n, int size, float *binary) { binarize_input_kernel<<>>(input, n, size, binary); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } __global__ void binarize_weights_kernel(float *weights, int n, int size, float *binary) @@ -70,7 +70,7 @@ __global__ void binarize_weights_kernel(float *weights, int n, int size, float * void binarize_weights_gpu(float *weights, int n, int size, float *binary) { binarize_weights_kernel << > >(weights, n, size, binary); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } #define WARP_SIZE 32 @@ -121,7 +121,7 @@ void fast_binarize_weights_gpu(float *weights, int n, int size, float *binary, f set_zero_kernel << <(n/BLOCK + 1), BLOCK, 0, get_cuda_stream() >> > (mean_arr_gpu, n); reduce_kernel << > > (weights, n, size, mean_arr_gpu); binarize_weights_mean_kernel << > > (weights, n, size, binary, mean_arr_gpu); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } else { binarize_weights_gpu(weights, n, size, binary); @@ -140,6 +140,7 @@ __global__ void cuda_f32_to_f16(float* input_f32, size_t size, half *output_f16) void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16) { cuda_f32_to_f16 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> (input_f32, size, (half *)output_f16); + CHECK_CUDA(cudaPeekAtLastError()); } __global__ void cuda_f16_to_f32(half* input_f16, size_t size, float *output_f32) @@ -151,6 +152,7 @@ __global__ void cuda_f16_to_f32(half* input_f16, size_t size, float *output_f32) void cuda_convert_f16_to_f32(float* input_f16, size_t size, float *output_f32) { cuda_f16_to_f32 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> ((half *)input_f16, size, output_f32); + CHECK_CUDA(cudaPeekAtLastError()); } half *cuda_make_f16_from_f32_array(float *src, size_t n) @@ -465,7 +467,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) { if (state.train) // Training { - copy_ongpu(l.outputs*l.batch / 2, output16, 1, l.x_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch / 2, output16, l.x_gpu); + //copy_ongpu(l.outputs*l.batch / 2, output16, 1, l.x_gpu, 1); //cudaMemcpyAsync(l.x_gpu, output16, l.outputs*l.batch*sizeof(half), cudaMemcpyDefault, get_cuda_stream()); float one = 1; float zero = 0; @@ -645,7 +648,9 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state .00001, l.mean_gpu, // input (should be FP32) l.variance_gpu)); // input (should be FP32) - copy_ongpu(l.outputs*l.batch / 2, l.x_norm_gpu, 1, delta16, 1); + + simple_copy_ongpu(l.outputs*l.batch / 2, l.x_norm_gpu, delta16); + //copy_ongpu(l.outputs*l.batch / 2, l.x_norm_gpu, 1, delta16, 1); //cudaMemcpyAsync(delta16, l.x_norm_gpu, l.outputs*l.batch * sizeof(half), cudaMemcpyDefault, get_cuda_stream()); } else @@ -789,19 +794,20 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state void pull_convolutional_layer(convolutional_layer layer) { - cuda_pull_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size); - cuda_pull_array(layer.biases_gpu, layer.biases, layer.n); - cuda_pull_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size); - cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.n); + cuda_pull_array_async(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size); + cuda_pull_array_async(layer.biases_gpu, layer.biases, layer.n); + cuda_pull_array_async(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size); + cuda_pull_array_async(layer.bias_updates_gpu, layer.bias_updates, layer.n); if (layer.batch_normalize){ - cuda_pull_array(layer.scales_gpu, layer.scales, layer.n); - cuda_pull_array(layer.rolling_mean_gpu, layer.rolling_mean, layer.n); - cuda_pull_array(layer.rolling_variance_gpu, layer.rolling_variance, layer.n); + cuda_pull_array_async(layer.scales_gpu, layer.scales, layer.n); + cuda_pull_array_async(layer.rolling_mean_gpu, layer.rolling_mean, layer.n); + cuda_pull_array_async(layer.rolling_variance_gpu, layer.rolling_variance, layer.n); } if (layer.adam){ - cuda_pull_array(layer.m_gpu, layer.m, layer.c*layer.n*layer.size*layer.size); - cuda_pull_array(layer.v_gpu, layer.v, layer.c*layer.n*layer.size*layer.size); + cuda_pull_array_async(layer.m_gpu, layer.m, layer.c*layer.n*layer.size*layer.size); + cuda_pull_array_async(layer.v_gpu, layer.v, layer.c*layer.n*layer.size*layer.size); } + cudaStreamSynchronize(get_cuda_stream()); } void push_convolutional_layer(convolutional_layer layer) diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 0e14ce56662..9ab124e48aa 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -87,7 +87,7 @@ void forward_network_gpu(network net, network_state state) } */ } - cudaStreamSynchronize(get_cuda_stream()); // sync CUDA-functions + //cudaStreamSynchronize(get_cuda_stream()); // sync CUDA-functions //cudaDeviceSynchronize(); //show_total_time(); } From 584f840b40f960903e976a51a361647fe4e50ad2 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 3 Feb 2019 00:19:04 +0300 Subject: [PATCH 0298/1365] CUDA_CHECK definition for debug --- src/blas_kernels.cu | 83 ++++++++++++++++++++++--------------------- src/cuda.c | 13 +++++-- src/darknet.c | 2 +- src/im2col_kernels.cu | 22 ++++++++++-- 4 files changed, 72 insertions(+), 48 deletions(-) diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 231e2db8e82..24af61f1f86 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -25,7 +25,7 @@ void scale_bias_gpu(float *output, float *biases, int batch, int n, int size) dim3 dimBlock(BLOCK, 1, 1); scale_bias_kernel<<>>(output, biases, n, size); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } __global__ void backward_scale_kernel(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates) @@ -51,7 +51,7 @@ __global__ void backward_scale_kernel(float *x_norm, float *delta, int batch, in void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates) { backward_scale_kernel<<>>(x_norm, delta, batch, n, size, scale_updates); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } __global__ void add_bias_kernel(float *output, float *biases, int n, int size) @@ -69,7 +69,7 @@ void add_bias_gpu(float *output, float *biases, int batch, int n, int size) dim3 dimBlock(BLOCK, 1, 1); add_bias_kernel<<>>(output, biases, n, size); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } __global__ void backward_bias_kernel(float *bias_updates, float *delta, int batch, int n, int size) @@ -130,14 +130,14 @@ __global__ void dot_kernel(float *output, float scale, int batch, int n, int siz void dot_error_gpu(layer l) { dot_kernel<<>>(l.output_gpu, l.dot, l.batch, l.n, l.out_w * l.out_h, l.delta_gpu); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } */ void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size) { backward_bias_kernel<<>>(bias_updates, delta, batch, n, size); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } __global__ void adam_kernel(int N, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t) @@ -154,7 +154,7 @@ __global__ void adam_kernel(int N, float *x, float *m, float *v, float B1, float extern "C" void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t) { adam_kernel << > >(n, x, m, v, B1, B2, rate, eps, t); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void adam_update_gpu(float *w, float *d, float *m, float *v, float B1, float B2, float eps, float decay, float rate, int n, int batch, int t) @@ -169,6 +169,7 @@ extern "C" void adam_update_gpu(float *w, float *d, float *m, float *v, float B1 adam_gpu(n, w, m, v, B1, B2, rate, eps, t); fill_ongpu(n, 0, d, 1); + CHECK_CUDA(cudaPeekAtLastError()); } __global__ void normalize_kernel(int N, float *x, float *mean, float *variance, int batch, int filters, int spatial) @@ -193,7 +194,7 @@ extern "C" void normalize_delta_gpu(float *x, float *mean, float *variance, floa { size_t N = batch*filters*spatial; normalize_delta_kernel<<>>(N, x, mean, variance, mean_delta, variance_delta, batch, filters, spatial, delta); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } __global__ void variance_delta_kernel(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta) @@ -298,19 +299,19 @@ __global__ void mean_delta_kernel(float *delta, float *variance, int batch, int extern "C" void mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta) { mean_delta_kernel<<>>(delta, variance, batch, filters, spatial, mean_delta); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void fast_mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta) { fast_mean_delta_kernel<<>>(delta, variance, batch, filters, spatial, mean_delta); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void fast_variance_delta_gpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta) { fast_variance_delta_kernel<<>>(x, delta, mean, variance, batch, filters, spatial, variance_delta); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } __global__ void mean_kernel(float *x, int batch, int filters, int spatial, float *mean) @@ -457,7 +458,7 @@ extern "C" void normalize_gpu(float *x, float *mean, float *variance, int batch, { size_t N = batch*filters*spatial; normalize_kernel<<>>(N, x, mean, variance, batch, filters, spatial); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } __global__ void fast_mean_kernel(float *x, int batch, int filters, int spatial, float *mean) @@ -520,26 +521,26 @@ __global__ void fast_variance_kernel(float *x, float *mean, int batch, int filt extern "C" void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean) { fast_mean_kernel<<>>(x, batch, filters, spatial, mean); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance) { fast_variance_kernel<<>>(x, mean, batch, filters, spatial, variance); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void mean_gpu(float *x, int batch, int filters, int spatial, float *mean) { mean_kernel<<>>(x, batch, filters, spatial, mean); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance) { variance_kernel<<>>(x, mean, batch, filters, spatial, variance); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void axpy_ongpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY) @@ -550,13 +551,13 @@ extern "C" void axpy_ongpu(int N, float ALPHA, float * X, int INCX, float * Y, i extern "C" void pow_ongpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY) { pow_kernel<<>>(N, ALPHA, X, INCX, Y, INCY); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void axpy_ongpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY) { axpy_kernel<<>>(N, ALPHA, X, OFFX, INCX, Y, OFFY, INCY); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void copy_ongpu(int N, float * X, int INCX, float * Y, int INCY) @@ -568,19 +569,19 @@ extern "C" void simple_copy_ongpu(int size, float *src, float *dst) { const int num_blocks = size / BLOCK + 1; simple_copy_kernel << > >(size, src, dst); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void mul_ongpu(int N, float * X, int INCX, float * Y, int INCY) { mul_kernel<<>>(N, X, INCX, Y, INCY); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void copy_ongpu_offset(int N, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY) { copy_kernel<<>>(N, X, OFFX, INCX, Y, OFFY, INCY); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } __global__ void flatten_kernel(int N, float *x, int spatial, int layers, int batch, int forward, float *out) @@ -604,57 +605,57 @@ extern "C" void flatten_ongpu(float *x, int spatial, int layers, int batch, int { int size = spatial*batch*layers; flatten_kernel<<>>(size, x, spatial, layers, batch, forward, out); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void reorg_ongpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out) { int size = w*h*c*batch; reorg_kernel<<>>(size, x, w, h, c, batch, stride, forward, out); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void mask_gpu_new_api(int N, float * X, float mask_num, float * mask, float val) { mask_kernel_new_api <<>>(N, X, mask_num, mask, val); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void mask_ongpu(int N, float * X, float mask_num, float * mask) { mask_kernel<<>>(N, X, mask_num, mask); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void const_ongpu(int N, float ALPHA, float * X, int INCX) { const_kernel<<>>(N, ALPHA, X, INCX); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void constrain_ongpu(int N, float ALPHA, float * X, int INCX) { constrain_kernel<<>>(N, ALPHA, X, INCX); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void scal_ongpu(int N, float ALPHA, float * X, int INCX) { scal_kernel<<>>(N, ALPHA, X, INCX); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void supp_ongpu(int N, float ALPHA, float * X, int INCX) { supp_kernel<<>>(N, ALPHA, X, INCX); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void fill_ongpu(int N, float ALPHA, float * X, int INCX) { fill_kernel<<>>(N, ALPHA, X, INCX); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } __global__ void shortcut_kernel(int size, int minw, int minh, int minc, int stride, int sample, int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out) @@ -689,7 +690,7 @@ extern "C" void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int int size = batch * minw * minh * minc; shortcut_kernel<<>>(size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, out); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } __global__ void simple_input_shortcut_kernel(float *in, int size, float *add, float *out) @@ -739,7 +740,7 @@ extern "C" void input_shortcut_gpu(float *in, int batch, int w1, int h1, int c1, int size = batch * minw * minh * minc; input_shortcut_kernel << > >(in, size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, out); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } __global__ void smooth_l1_kernel(int n, float *pred, float *truth, float *delta, float *error) @@ -762,7 +763,7 @@ __global__ void smooth_l1_kernel(int n, float *pred, float *truth, float *delta, extern "C" void smooth_l1_gpu(int n, float *pred, float *truth, float *delta, float *error) { smooth_l1_kernel<<>>(n, pred, truth, delta, error); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } __global__ void softmax_x_ent_kernel(int n, float *pred, float *truth, float *delta, float *error) @@ -779,7 +780,7 @@ __global__ void softmax_x_ent_kernel(int n, float *pred, float *truth, float *de extern "C" void softmax_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error) { softmax_x_ent_kernel << > >(n, pred, truth, delta, error); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } __global__ void l2_kernel(int n, float *pred, float *truth, float *delta, float *error) @@ -795,7 +796,7 @@ __global__ void l2_kernel(int n, float *pred, float *truth, float *delta, float extern "C" void l2_gpu(int n, float *pred, float *truth, float *delta, float *error) { l2_kernel<<>>(n, pred, truth, delta, error); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } @@ -811,7 +812,7 @@ __global__ void weighted_sum_kernel(int n, float *a, float *b, float *s, float * extern "C" void weighted_sum_gpu(float *a, float *b, float *s, int num, float *c) { weighted_sum_kernel<<>>(num, a, b, s, c); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } __global__ void weighted_delta_kernel(int n, float *a, float *b, float *s, float *da, float *db, float *ds, float *dc) @@ -827,7 +828,7 @@ __global__ void weighted_delta_kernel(int n, float *a, float *b, float *s, float extern "C" void weighted_delta_gpu(float *a, float *b, float *s, float *da, float *db, float *ds, int num, float *dc) { weighted_delta_kernel<<>>(num, a, b, s, da, db, ds, dc); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } __global__ void mult_add_into_kernel(int n, float *a, float *b, float *c) @@ -841,7 +842,7 @@ __global__ void mult_add_into_kernel(int n, float *a, float *b, float *c) extern "C" void mult_add_into_gpu(int num, float *a, float *b, float *c) { mult_add_into_kernel<<>>(num, a, b, c); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } @@ -876,7 +877,7 @@ extern "C" void softmax_gpu(float *input, int n, int offset, int groups, float t int inputs = n; int batch = groups; softmax_kernel<<>>(inputs, offset, batch, input, temp, output); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } __device__ void softmax_device_new_api(float *input, int n, float temp, int stride, float *output) @@ -910,7 +911,7 @@ __global__ void softmax_kernel_new_api(float *input, int n, int batch, int batch extern "C" void softmax_gpu_new_api(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output) { softmax_kernel_new_api << > >(input, n, batch, batch_offset, groups, group_offset, stride, temp, output); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } @@ -942,7 +943,7 @@ extern "C" void upsample_gpu(float *in, int w, int h, int c, int batch, int stri { size_t size = w*h*c*batch*stride*stride; upsample_kernel << > >(size, in, w, h, c, batch, stride, forward, scale, out); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } __global__ void softmax_tree_kernel(float *input, int spatial, int batch, int stride, float temp, float *output, int groups, int *group_size, int *group_offset) @@ -972,7 +973,7 @@ extern "C" void softmax_tree_gpu(float *input, int spatial, int batch, int strid */ int num = spatial*batch*hier.groups; softmax_tree_kernel <<>>(input, spatial, batch, stride, temp, output, hier.groups, tree_groups_size, tree_groups_offset); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); cuda_free((float *)tree_groups_size); cuda_free((float *)tree_groups_offset); } \ No newline at end of file diff --git a/src/cuda.c b/src/cuda.c index d1529b19a00..a0d24db7428 100644 --- a/src/cuda.c +++ b/src/cuda.c @@ -26,9 +26,6 @@ int cuda_get_device() void check_error(cudaError_t status) { -#ifdef DEBUG - cudaDeviceSynchronize(); -#endif cudaError_t status2 = cudaGetLastError(); if (status != cudaSuccess) { @@ -58,6 +55,11 @@ void check_error(cudaError_t status) void check_error_extended(cudaError_t status, const char *file, int line, const char *date_time) { + if (status != cudaSuccess) + printf("CUDA Prev Error: file: %s() : line: %d : build time: %s \n", file, line, date_time); +#ifdef DEBUG + status = cudaDeviceSynchronize(); +#endif if (status != cudaSuccess) printf("CUDA Error: file: %s() : line: %d : build time: %s \n", file, line, date_time); check_error(status); @@ -171,6 +173,11 @@ void cudnn_check_error(cudnnStatus_t status) void cudnn_check_error_extended(cudnnStatus_t status, const char *file, int line, const char *date_time) { + if (status != cudaSuccess) + printf("\n cuDNN Prev Error in: file: %s() : line: %d : build time: %s \n", file, line, date_time); +#ifdef DEBUG + status = cudaDeviceSynchronize(); +#endif if (status != cudaSuccess) printf("\n cuDNN Error in: file: %s() : line: %d : build time: %s \n", file, line, date_time); cudnn_check_error(status); diff --git a/src/darknet.c b/src/darknet.c index 5cf11f28f28..074dc8b9e81 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -458,7 +458,7 @@ int main(int argc, char **argv) #else if(gpu_index >= 0){ cuda_set_device(gpu_index); - check_error(cudaSetDeviceFlags(cudaDeviceScheduleBlockingSync)); + CHECK_CUDA(cudaSetDeviceFlags(cudaDeviceScheduleBlockingSync)); } #endif diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 89d27cb542f..70fd2310b17 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -86,6 +86,8 @@ void im2col_ongpu(float *im, num_kernels, im, height, width, ksize, pad, stride, height_col, width_col, data_col); + + CHECK_CUDA(cudaPeekAtLastError()); } // -------------------------------- @@ -219,6 +221,8 @@ void im2col_align_ongpu(float *im, num_kernels, im, height, width, ksize, pad, stride, height_col, width_col, data_col, bit_align); + + CHECK_CUDA(cudaPeekAtLastError()); } @@ -346,6 +350,8 @@ void im2col_align_bin_ongpu(float *im, num_kernels, im, height, width, ksize, channels, pad, stride, height_col, width_col, data_col, bit_align); + + CHECK_CUDA(cudaPeekAtLastError()); } // -------------------------------- @@ -436,6 +442,7 @@ void float_to_bit_gpu(float *src, unsigned char *dst, size_t size) //const int num_blocks = size / (32*1024) + 1; const int num_blocks = get_number_of_blocks(size, 32 * 1024); float_to_bit_gpu_kernel<<>>(src, dst, size); + CHECK_CUDA(cudaPeekAtLastError()); } // -------------------------------- @@ -653,6 +660,7 @@ void transpose_bin_gpu(unsigned char *A, unsigned char *B, const int n, const in const int num_blocks32 = size32 / BLOCK_TRANSPOSE32 + 1; transpose_bin_gpu_kernel_32 << > >((uint32_t *)A, (uint32_t *)B, n, m, lda, ldb, block_size); //transpose_bin_gpu_kernel << > >(A, B, n, m, lda, ldb, block_size); + CHECK_CUDA(cudaPeekAtLastError()); } // -------------------------------- @@ -679,6 +687,7 @@ void transpose_uint32_gpu(uint32_t *src, uint32_t *dst, int src_h, int src_w, in int size = src_w * src_h; const int num_blocks = size / BLOCK + 1; transpose_uint32_kernel << > >(src, dst, src_h, src_w, src_align, dst_align); + CHECK_CUDA(cudaPeekAtLastError()); } // -------------------------------- @@ -744,6 +753,7 @@ void transpose_uint32_gpu_2(uint32_t *src, uint32_t *dst, int src_h, int src_w, int size = src_w_align * src_h_align; int num_blocks = size / TRANS_BLOCK; transpose_uint32_kernel_2 << > >(src, dst, src_h, src_w, src_align, dst_align); + CHECK_CUDA(cudaPeekAtLastError()); } // -------------------------------- @@ -781,6 +791,7 @@ void repack_input_gpu(float *input, float *re_packed_input, int w, int h, int c) int size = w * h * c; const int num_blocks = size / BLOCK + 1; repack_input_kernel << > >(input, re_packed_input, w, h, c); + CHECK_CUDA(cudaPeekAtLastError()); } // -------------------------------- @@ -820,6 +831,7 @@ void repack_input_gpu_2(float *input, float *re_packed_input, int w, int h, int int size = w * h * c; const int num_blocks = size / BLOCK + 1; repack_input_kernel_2 << > >(input, re_packed_input, w, h, c); + CHECK_CUDA(cudaPeekAtLastError()); } // -------------------------------- @@ -869,6 +881,7 @@ void repack_input_gpu_bin(float *input, uint32_t *re_packed_input_bin, int w, in const int num_blocks = get_number_of_blocks(size, block_size); //printf("\n num_blocks = %d, num_blocks/32 = %d, block_size = %d \n", num_blocks, num_blocks / 32, block_size); repack_input_kernel_bin << > >(input, re_packed_input_bin, w, h, c); + CHECK_CUDA(cudaPeekAtLastError()); } /* @@ -919,6 +932,7 @@ void repack_input_gpu_bin(float *input, uint32_t *re_packed_input_bin, int w, in const int num_blocks = get_number_of_blocks(size, block_size); printf("\n num_blocks = %d, num_blocks/32 = %d, block_size = %d \n", num_blocks, num_blocks/32, block_size); repack_input_kernel_bin << > >(input, re_packed_input_bin, w, h, c); + CHECK_CUDA(cudaPeekAtLastError()); } */ @@ -932,6 +946,7 @@ __global__ void fill_int8_gpu_kernel(unsigned char *src, unsigned char val, size void fill_int8_gpu(unsigned char *src, unsigned char val, size_t size) { const int num_blocks = size / BLOCK + 1; fill_int8_gpu_kernel<<>>(src, val, size); + CHECK_CUDA(cudaPeekAtLastError()); } // -------------------------------- @@ -1761,7 +1776,7 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, */ //printf(" shared_memory: (w) lda*BLOCK/N = %d, (i) ldb*BLOCK/M = %d, \t lda = %d \n\n", lda*BLOCK / N, ldb*BLOCK / M, lda); -#if CUDART_VERSION >= 10000 + //if (M % 8 == 0 && N % 8 == 0 && M == 128) //if (M >= 32) // l.n >= 32 if (1) @@ -1784,7 +1799,6 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, //getchar(); } else -#endif // CUDART_VERSION >= 10000 { gemm_nn_custom_bin_mean_transposed_gpu_kernel << > > ( M, N, K, @@ -1793,7 +1807,7 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, C, ldc, mean_arr, bias, leaky_activation); } - + CHECK_CUDA(cudaPeekAtLastError()); } // -------------------------------- @@ -1973,6 +1987,7 @@ void convolve_gpu(float *input, float *weights, float *output, int in_w, int in_ //printf("\n array_size = %d, num_blocks = %d, w = %d, h = %d, n = %d, c = %d, pad = %d \n", array_size, num_blocks, in_w, in_h, n, in_c, pad); convolve_gpu_kernel << > > (input, weights, output, in_w, in_h, in_c, n, size, pad); + CHECK_CUDA(cudaPeekAtLastError()); } // -------------------------------- @@ -2188,6 +2203,7 @@ void convolve_bin_gpu(float *input, float *weights, float *output, int in_w, int //printf("\n array_size = %d, num_blocks = %d, w = %d, h = %d, n = %d, c = %d, pad = %d \n", array_size, num_blocks, in_w, in_h, n, in_c, pad); convolve_bin_gpu_kernel << > > (input, weights, output, in_w, in_h, in_c, n, size, pad, new_lda, mean_arr_gpu); + CHECK_CUDA(cudaPeekAtLastError()); } // -------------------------------- From f7cb538b32d95e99a6751bf312f7c846419389d7 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 3 Feb 2019 00:37:00 +0300 Subject: [PATCH 0299/1365] Compile fix --- src/im2col_kernels.cu | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 70fd2310b17..7b347857b73 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -1779,6 +1779,7 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, //if (M % 8 == 0 && N % 8 == 0 && M == 128) //if (M >= 32) // l.n >= 32 +#if CUDA_VERSION >= 10000 if (1) { const int M_aligned = M + (8 - (M % 8)); @@ -1799,6 +1800,7 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, //getchar(); } else +#endif //# CUDA_VERSION >= 10000 { gemm_nn_custom_bin_mean_transposed_gpu_kernel << > > ( M, N, K, From 5446d195763d02f116684af2513cce9694f55f58 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 4 Feb 2019 23:28:40 +0300 Subject: [PATCH 0300/1365] Checks Compute Capability and forcibly disables Tensor Cores for CC < 7.0 --- src/cuda.c | 37 ++++++++++++++++++++++++------------- src/cuda.h | 3 +++ src/parser.c | 4 ++-- 3 files changed, 29 insertions(+), 15 deletions(-) diff --git a/src/cuda.c b/src/cuda.c index a0d24db7428..028b1c04dbb 100644 --- a/src/cuda.c +++ b/src/cuda.c @@ -13,14 +13,14 @@ void cuda_set_device(int n) { gpu_index = n; cudaError_t status = cudaSetDevice(n); - check_error(status); + CHECK_CUDA(status); } int cuda_get_device() { int n = 0; cudaError_t status = cudaGetDevice(&n); - check_error(status); + CHECK_CUDA(status); return n; } @@ -92,7 +92,7 @@ cudaStream_t get_cuda_stream() { char buffer[256]; printf("CUDA Error: %s\n", s); status = cudaStreamCreateWithFlags(&streamsArray[i], cudaStreamDefault); - check_error(status); + CHECK_CUDA(status); } streamInit[i] = 1; } @@ -113,7 +113,7 @@ cudaStream_t get_cuda_memcpy_stream() { char buffer[256]; printf("CUDA Error: %s\n", s); status = cudaStreamCreateWithFlags(&streamsArray2[i], cudaStreamDefault); - check_error(status); + CHECK_CUDA(status); } streamInit2[i] = 1; } @@ -192,6 +192,7 @@ cublasHandle_t blas_handle() if(!init[i]) { cublasCreate(&handle[i]); cublasStatus_t status = cublasSetStream(handle[i], get_cuda_stream()); + CHECK_CUDA(status); init[i] = 1; } return handle[i]; @@ -203,11 +204,11 @@ float *cuda_make_array(float *x, size_t n) size_t size = sizeof(float)*n; cudaError_t status = cudaMalloc((void **)&x_gpu, size); if (status != cudaSuccess) fprintf(stderr, " Try to set subdivisions=64 in your cfg-file. \n"); - check_error(status); + CHECK_CUDA(status); if(x){ //status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice); status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyHostToDevice, get_cuda_stream()); - check_error(status); + CHECK_CUDA(status); } if(!x_gpu) error("Cuda malloc failed\n"); return x_gpu; @@ -224,7 +225,7 @@ void cuda_random(float *x_gpu, size_t n) init[i] = 1; } curandGenerateUniform(gen[i], x_gpu, n); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } float cuda_compare(float *x_gpu, float *x, size_t n, char *s) @@ -246,7 +247,7 @@ int *cuda_make_int_array(size_t n) size_t size = sizeof(int)*n; cudaError_t status = cudaMalloc((void **)&x_gpu, size); if(status != cudaSuccess) fprintf(stderr, " Try to set subdivisions=64 in your cfg-file. \n"); - check_error(status); + CHECK_CUDA(status); return x_gpu; } @@ -255,11 +256,11 @@ int *cuda_make_int_array_new_api(int *x, size_t n) int *x_gpu; size_t size = sizeof(int)*n; cudaError_t status = cudaMalloc((void **)&x_gpu, size); - check_error(status); + CHECK_CUDA(status); if (x) { //status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice, get_cuda_stream()); cudaError_t status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyHostToDevice, get_cuda_stream()); - check_error(status); + CHECK_CUDA(status); } if (!x_gpu) error("Cuda malloc failed\n"); return x_gpu; @@ -269,7 +270,7 @@ void cuda_free(float *x_gpu) { //cudaStreamSynchronize(get_cuda_stream()); cudaError_t status = cudaFree(x_gpu); - check_error(status); + CHECK_CUDA(status); } void cuda_push_array(float *x_gpu, float *x, size_t n) @@ -277,7 +278,7 @@ void cuda_push_array(float *x_gpu, float *x, size_t n) size_t size = sizeof(float)*n; //cudaError_t status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice); cudaError_t status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyHostToDevice, get_cuda_stream()); - check_error(status); + CHECK_CUDA(status); } void cuda_pull_array(float *x_gpu, float *x, size_t n) @@ -285,7 +286,7 @@ void cuda_pull_array(float *x_gpu, float *x, size_t n) size_t size = sizeof(float)*n; //cudaError_t status = cudaMemcpy(x, x_gpu, size, cudaMemcpyDeviceToHost); cudaError_t status = cudaMemcpyAsync(x, x_gpu, size, cudaMemcpyDeviceToHost, get_cuda_stream()); - check_error(status); + CHECK_CUDA(status); cudaStreamSynchronize(get_cuda_stream()); } @@ -302,6 +303,16 @@ int get_number_of_blocks(int array_size, int block_size) return array_size / block_size + ((array_size % block_size > 0) ? 1 : 0); } +int get_gpu_compute_capability(int i) +{ + typedef struct cudaDeviceProp cudaDeviceProp; + cudaDeviceProp prop; + cudaError_t status = cudaGetDeviceProperties(&prop, i); + CHECK_CUDA(status); + int cc = prop.major * 10 + prop.minor; + return cc; +} + #else // GPU #include "cuda.h" void cuda_set_device(int n) {} diff --git a/src/cuda.h b/src/cuda.h index 34f68eba272..b935846e644 100644 --- a/src/cuda.h +++ b/src/cuda.h @@ -15,6 +15,8 @@ extern int gpu_index; #include "cuda_runtime.h" #include "curand.h" #include "cublas_v2.h" +#include "cuda_runtime_api.h" +#include "driver_types.h" #ifdef CUDNN #include "cudnn.h" @@ -62,6 +64,7 @@ extern "C" { cudaStream_t get_cuda_stream(); cudaStream_t get_cuda_memcpy_stream(); int get_number_of_blocks(int array_size, int block_size); + int get_gpu_compute_capability(int i); #ifdef __cplusplus } #endif // __cplusplus diff --git a/src/parser.c b/src/parser.c index 671ed764b68..f55a2055c4a 100644 --- a/src/parser.c +++ b/src/parser.c @@ -671,8 +671,8 @@ void parse_net_options(list *options, network *net) net->policy = get_policy(policy_s); net->burn_in = option_find_int_quiet(options, "burn_in", 0); #ifdef CUDNN_HALF - //net->burn_in = 0; - net->cudnn_half = 1; + if(get_gpu_compute_capability(net->gpu_index) >= 7000) net->cudnn_half = 1; + else net->cudnn_half = 0; #endif if(net->policy == STEP){ net->step = option_find_int(options, "step", 1); From d767e8ca38b73c4096c79277a458e500c22c2a69 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 4 Feb 2019 23:29:06 +0300 Subject: [PATCH 0301/1365] Minor fixes --- include/darknet.h | 2 +- src/activations.h | 4 ++-- src/connected_layer.c | 15 ++++++++------- src/convolutional_kernels.cu | 4 +++- src/im2col_kernels.cu | 12 ++++++------ src/yolo_layer.c | 1 + 6 files changed, 21 insertions(+), 17 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 10d374b7da1..9b0ea02be12 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -45,7 +45,7 @@ struct network; typedef struct network network; struct network_state; -typedef struct network_state; +typedef struct network_state network_state; struct layer; typedef struct layer layer; diff --git a/src/activations.h b/src/activations.h index 442f15a2016..849c65d3381 100644 --- a/src/activations.h +++ b/src/activations.h @@ -76,11 +76,11 @@ static inline float loggy_gradient(float x) static inline float stair_gradient(float x) { if (floor(x) == x) return 0; - return 1; + return 1.0f; } static inline float relu_gradient(float x){return (x>0);} static inline float elu_gradient(float x){return (x >= 0) + (x < 0)*(x + 1);} -static inline float selu_gradient(float x) { return (x >= 0)*1.0507 + (x < 0)*(x + 1.0507f*1.6732f); } +static inline float selu_gradient(float x) { return (x >= 0)*1.0507f + (x < 0)*(x + 1.0507f*1.6732f); } static inline float relie_gradient(float x){return (x>0) ? 1 : .01f;} static inline float ramp_gradient(float x){return (x>0)+.1f;} static inline float leaky_gradient(float x){return (x>0) ? 1 : .1f;} diff --git a/src/connected_layer.c b/src/connected_layer.c index 0f4a61a1e96..66f7c91ca8c 100644 --- a/src/connected_layer.c +++ b/src/connected_layer.c @@ -84,7 +84,7 @@ connected_layer make_connected_layer(int batch, int steps, int inputs, int outpu l.update = update_connected_layer; //float scale = 1./sqrt(inputs); - float scale = sqrt(2./inputs); + float scale = sqrt(2.f/inputs); for(i = 0; i < outputs*inputs; ++i){ l.weights[i] = scale*rand_uniform(-1, 1); } @@ -182,10 +182,10 @@ void forward_connected_layer(connected_layer l, network_state state) mean_cpu(l.output, l.batch, l.outputs, 1, l.mean); variance_cpu(l.output, l.mean, l.batch, l.outputs, 1, l.variance); - scal_cpu(l.outputs, .95, l.rolling_mean, 1); - axpy_cpu(l.outputs, .05, l.mean, 1, l.rolling_mean, 1); - scal_cpu(l.outputs, .95, l.rolling_variance, 1); - axpy_cpu(l.outputs, .05, l.variance, 1, l.rolling_variance, 1); + scal_cpu(l.outputs, .95f, l.rolling_mean, 1); + axpy_cpu(l.outputs, .05f, l.mean, 1, l.rolling_mean, 1); + scal_cpu(l.outputs, .95f, l.rolling_variance, 1); + axpy_cpu(l.outputs, .05f, l.variance, 1, l.rolling_variance, 1); copy_cpu(l.outputs*l.batch, l.output, 1, l.x, 1); normalize_cpu(l.output, l.mean, l.variance, l.batch, l.outputs, 1); @@ -242,7 +242,7 @@ void denormalize_connected_layer(layer l) { int i, j; for(i = 0; i < l.outputs; ++i){ - float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .000001); + float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .000001f); for(j = 0; j < l.inputs; ++j){ l.weights[i*l.inputs + j] *= scale; } @@ -285,6 +285,7 @@ void pull_connected_layer(connected_layer l) cuda_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.outputs); cuda_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.outputs); } + CHECK_CUDA(cudaPeekAtLastError()); } void push_connected_layer(connected_layer l) @@ -298,6 +299,7 @@ void push_connected_layer(connected_layer l) cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.outputs); cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.outputs); } + CHECK_CUDA(cudaPeekAtLastError()); } void update_connected_layer_gpu(connected_layer l, int batch, float learning_rate, float momentum, float decay) @@ -317,7 +319,6 @@ void update_connected_layer_gpu(connected_layer l, int batch, float learning_rat void forward_connected_layer_gpu(connected_layer l, network_state state) { - int i; fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); int m = l.batch; diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 2ba2acdd32d..270e3fe1a4c 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -84,7 +84,7 @@ __global__ void set_zero_kernel(float *src, int size) __inline__ __device__ float warpAllReduceSum(float val) { for (int mask = WARP_SIZE / 2; mask > 0; mask /= 2) -#if CUDA_VERSION >= 9000 +#if CUDART_VERSION >= 9000 val += __shfl_xor_sync(0xffffffff, val, mask); #else val += __shfl_xor(val, mask); @@ -807,6 +807,7 @@ void pull_convolutional_layer(convolutional_layer layer) cuda_pull_array_async(layer.m_gpu, layer.m, layer.c*layer.n*layer.size*layer.size); cuda_pull_array_async(layer.v_gpu, layer.v, layer.c*layer.n*layer.size*layer.size); } + CHECK_CUDA(cudaPeekAtLastError()); cudaStreamSynchronize(get_cuda_stream()); } @@ -828,6 +829,7 @@ void push_convolutional_layer(convolutional_layer layer) cuda_push_array(layer.m_gpu, layer.m, layer.c*layer.n*layer.size*layer.size); cuda_push_array(layer.v_gpu, layer.v, layer.c*layer.n*layer.size*layer.size); } + CHECK_CUDA(cudaPeekAtLastError()); } void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay) diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 7b347857b73..4d22beda6c7 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -17,7 +17,7 @@ extern "C" { template __device__ inline T1 __shfl_custom(T1 val, T2 lane) { -#if CUDA_VERSION >= 9000 +#if CUDART_VERSION >= 9000 return __shfl_sync(FULL_MASK, val, lane); #else return __shfl(val, lane); @@ -26,7 +26,7 @@ __device__ inline T1 __shfl_custom(T1 val, T2 lane) { template __device__ inline uint32_t __ballot_custom(T val) { -#if CUDA_VERSION >= 9000 +#if CUDART_VERSION >= 9000 return __ballot_sync(FULL_MASK, val); #else return __ballot(val); @@ -1223,7 +1223,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int __inline__ __device__ int warpAllReduceSum(int val) { for (int mask = WARP_SIZE / 2; mask > 0; mask /= 2) -#if CUDA_VERSION >= 9000 +#if CUDART_VERSION >= 9000 val += __shfl_xor_sync(FULL_MASK, val, mask); #else val += __shfl_xor(val, mask); @@ -1233,7 +1233,7 @@ int warpAllReduceSum(int val) { } // Tensor Cores binary (CC >= 7.3 && CUDA >= 10.0) - __CUDA_SUBBYTE_IMMA__ -#if CUDA_VERSION >= 10000 +#if CUDART_VERSION >= 10000 #include #define WMMA_M 8 @@ -1779,7 +1779,7 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, //if (M % 8 == 0 && N % 8 == 0 && M == 128) //if (M >= 32) // l.n >= 32 -#if CUDA_VERSION >= 10000 +#if CUDART_VERSION >= 10000 if (1) { const int M_aligned = M + (8 - (M % 8)); @@ -1800,7 +1800,7 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, //getchar(); } else -#endif //# CUDA_VERSION >= 10000 +#endif //# CUDART_VERSION >= 10000 { gemm_nn_custom_bin_mean_transposed_gpu_kernel << > > ( M, N, K, diff --git a/src/yolo_layer.c b/src/yolo_layer.c index d1fa78cfb4b..05739b4be59 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -454,6 +454,7 @@ void forward_yolo_layer_gpu(const layer l, network_state state) if(!state.train || l.onlyforward){ //cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); cuda_pull_array_async(l.output_gpu, l.output, l.batch*l.outputs); + CHECK_CUDA(cudaPeekAtLastError()); return; } From ce2e0eff009653e207df34f83fa6259a39f6fe84 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 5 Feb 2019 00:36:17 +0300 Subject: [PATCH 0302/1365] DEBUG=1 fixed --- Makefile | 3 ++- src/cuda.c | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/Makefile b/Makefile index 17c96cd37dc..d06e6ace4b8 100644 --- a/Makefile +++ b/Makefile @@ -60,7 +60,8 @@ COMMON= -Iinclude/ CFLAGS=-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC ifeq ($(DEBUG), 1) -OPTS= -O0 -g +#OPTS= -O0 -g +#OPTS= -Og -g COMMON+= -DDEBUG CFLAGS+= -DDEBUG else diff --git a/src/cuda.c b/src/cuda.c index 028b1c04dbb..85c8a2997b8 100644 --- a/src/cuda.c +++ b/src/cuda.c @@ -13,7 +13,7 @@ void cuda_set_device(int n) { gpu_index = n; cudaError_t status = cudaSetDevice(n); - CHECK_CUDA(status); + if(status != cudaSuccess) CHECK_CUDA(status); } int cuda_get_device() From 12b6e93893494afa000685d45a5c579c1b6f0073 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 5 Feb 2019 16:18:36 +0300 Subject: [PATCH 0303/1365] CHECK_CUDA is used everywhere --- src/avgpool_layer_kernels.cu | 4 ++-- src/blas_kernels.cu | 2 +- src/col2im_kernels.cu | 2 ++ src/convolutional_kernels.cu | 8 ++++---- src/convolutional_layer.c | 12 ++++++------ src/crop_layer_kernels.cu | 4 ++-- src/detector.c | 11 ++++------- src/dropout_layer_kernels.cu | 4 ++-- src/gemm.c | 3 ++- src/maxpool_layer_kernels.cu | 4 ++-- src/parser.c | 4 ++-- 11 files changed, 29 insertions(+), 29 deletions(-) diff --git a/src/avgpool_layer_kernels.cu b/src/avgpool_layer_kernels.cu index 10cb332581e..784d6762415 100644 --- a/src/avgpool_layer_kernels.cu +++ b/src/avgpool_layer_kernels.cu @@ -48,7 +48,7 @@ extern "C" void forward_avgpool_layer_gpu(avgpool_layer layer, network_state sta size_t n = layer.c*layer.batch; forward_avgpool_layer_kernel<<>>(n, layer.w, layer.h, layer.c, state.input, layer.output_gpu); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void backward_avgpool_layer_gpu(avgpool_layer layer, network_state state) @@ -56,6 +56,6 @@ extern "C" void backward_avgpool_layer_gpu(avgpool_layer layer, network_state st size_t n = layer.c*layer.batch; backward_avgpool_layer_kernel<<>>(n, layer.w, layer.h, layer.c, state.delta, layer.delta_gpu); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 24af61f1f86..c1444011ab9 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -723,7 +723,7 @@ extern "C" void input_shortcut_gpu(float *in, int batch, int w1, int h1, int c1, if (w1 == w2 && h1 == h2 && c1 == c2) { int size = batch * w1 * h1 * c1; simple_input_shortcut_kernel << > >(in, size, add, out); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); return; } diff --git a/src/col2im_kernels.cu b/src/col2im_kernels.cu index d475f052e33..0fe61e49578 100644 --- a/src/col2im_kernels.cu +++ b/src/col2im_kernels.cu @@ -54,5 +54,7 @@ void col2im_ongpu(float *data_col, num_kernels, data_col, height, width, ksize, pad, stride, height_col, width_col, data_im); + + CHECK_CUDA(cudaPeekAtLastError()); } diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 270e3fe1a4c..b2fb63443ae 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -116,7 +116,7 @@ void fast_binarize_weights_gpu(float *weights, int n, int size, float *binary, f { if (size % 32 == 0) { size_t gridsize = n * size; - const int num_blocks = gridsize / BLOCK + 1; + const int num_blocks = get_number_of_blocks(gridsize, BLOCK);// gridsize / BLOCK + 1; set_zero_kernel << <(n/BLOCK + 1), BLOCK, 0, get_cuda_stream() >> > (mean_arr_gpu, n); reduce_kernel << > > (weights, n, size, mean_arr_gpu); @@ -139,7 +139,7 @@ __global__ void cuda_f32_to_f16(float* input_f32, size_t size, half *output_f16) } void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16) { - cuda_f32_to_f16 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> (input_f32, size, (half *)output_f16); + cuda_f32_to_f16 <<< get_number_of_blocks(size, BLOCK), BLOCK, 0, get_cuda_stream() >>> (input_f32, size, (half *)output_f16); CHECK_CUDA(cudaPeekAtLastError()); } @@ -151,7 +151,7 @@ __global__ void cuda_f16_to_f32(half* input_f16, size_t size, float *output_f32) } void cuda_convert_f16_to_f32(float* input_f16, size_t size, float *output_f32) { - cuda_f16_to_f32 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> ((half *)input_f16, size, output_f32); + cuda_f16_to_f32 <<< get_number_of_blocks(size, BLOCK), BLOCK, 0, get_cuda_stream() >>> ((half *)input_f16, size, output_f32); CHECK_CUDA(cudaPeekAtLastError()); } @@ -159,7 +159,7 @@ half *cuda_make_f16_from_f32_array(float *src, size_t n) { half *dst16; size_t size = sizeof(half)*n; - check_error(cudaMalloc((void **)&dst16, size)); + CHECK_CUDA(cudaMalloc((void **)&dst16, size)); if (src) { cuda_convert_f32_to_f16(src, n, (float *)dst16); } diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index d29266ff704..6c08c0776fc 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -574,7 +574,7 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) // check for excessive memory consumption size_t free_byte; size_t total_byte; - check_error(cudaMemGetInfo(&free_byte, &total_byte)); + CHECK_CUDA(cudaMemGetInfo(&free_byte, &total_byte)); if (l->workspace_size > free_byte || l->workspace_size >= total_byte / 2) { printf(" used slow CUDNN algo without Workspace! Need memory: %zu, available: %zu\n", l->workspace_size, (free_byte < total_byte/2) ? free_byte : total_byte/2); cudnn_convolutional_setup(l, cudnn_smallest); @@ -759,19 +759,19 @@ void binary_align_weights(convolutional_layer *l) l->align_workspace_size = l->bit_align * l->size * l->size * l->c; status = cudaMalloc((void **)&l->align_workspace_gpu, l->align_workspace_size * sizeof(float)); status = cudaMalloc((void **)&l->transposed_align_workspace_gpu, l->align_workspace_size * sizeof(float)); - check_error(status); + CHECK_CUDA(status); //l->align_bit_weights_gpu = cuda_make_array(l->align_bit_weights, l->align_bit_weights_size * sizeof(char)/sizeof(float)); status = cudaMalloc((void **)&l->align_bit_weights_gpu, l->align_bit_weights_size); - check_error(status); + CHECK_CUDA(status); status = cudaMemcpy(l->align_bit_weights_gpu, l->align_bit_weights, l->align_bit_weights_size, cudaMemcpyHostToDevice); - check_error(status); + CHECK_CUDA(status); status = cudaMemcpy(l->binary_weights_gpu, l->binary_weights, m*k * sizeof(float), cudaMemcpyHostToDevice); - check_error(status); + CHECK_CUDA(status); //l->mean_arr_gpu = cuda_make_array(l->mean_arr, l->n); cuda_push_array(l->mean_arr_gpu, l->mean_arr, l->n); - cudaDeviceSynchronize(); + CHECK_CUDA(cudaDeviceSynchronize()); #endif // GPU free(align_weights); diff --git a/src/crop_layer_kernels.cu b/src/crop_layer_kernels.cu index 074aa0b7711..8bbe0f54d5a 100644 --- a/src/crop_layer_kernels.cu +++ b/src/crop_layer_kernels.cu @@ -196,12 +196,12 @@ extern "C" void forward_crop_layer_gpu(crop_layer layer, network_state state) int size = layer.batch * layer.w * layer.h; levels_image_kernel<<>>(state.input, layer.rand_gpu, layer.batch, layer.w, layer.h, state.train, layer.saturation, layer.exposure, translate, scale, layer.shift); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); size = layer.batch*layer.c*layer.out_w*layer.out_h; forward_crop_layer_kernel<<>>(state.input, layer.rand_gpu, size, layer.c, layer.h, layer.w, layer.out_h, layer.out_w, state.train, layer.flip, radians, layer.output_gpu); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); /* cuda_pull_array(layer.output_gpu, layer.output, size); diff --git a/src/detector.c b/src/detector.c index c68407befbd..79bcdaaeda4 100644 --- a/src/detector.c +++ b/src/detector.c @@ -255,6 +255,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i int draw_precision = 0; int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions); // calculate mAP for each 4 Epochs + if (calc_map) printf(" Next mAP calculation at %d iterations \n", (iter_map + calc_map_for_each)); if (calc_map && (i >= (iter_map + calc_map_for_each) || i == net.max_batches) && i >= net.burn_in && i >= 1000) { if (l.random) { printf("Resizing to initial size: %d x %d \n", init_w, init_h); @@ -275,7 +276,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i iter_map = i; mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net_combined); - printf("\n mean_average_precision = %f \n", mean_average_precision); + printf("\n mean_average_precision (mAP@0.5) = %f \n", mean_average_precision); draw_precision = 1; } #ifdef OPENCV @@ -973,13 +974,9 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa thresh_calc_avg_iou, tp_for_thresh, fp_for_thresh, unique_truth_count - tp_for_thresh, avg_iou * 100); mean_average_precision = mean_average_precision / classes; - if (iou_thresh == 0.5) { - printf("\n mean average precision (mAP) = %f, or %2.2f %% \n", mean_average_precision, mean_average_precision * 100); - } - else { - printf("\n average precision (AP) = %f, or %2.2f %% for IoU threshold = %f \n", mean_average_precision, mean_average_precision * 100, iou_thresh); - } + printf("\n IoU threshold = %2.2f %% \n", iou_thresh * 100); + printf(" mean average precision (mAP@%0.2f) = %f, or %2.2f %% \n", iou_thresh, mean_average_precision, mean_average_precision * 100); for (i = 0; i < classes; ++i) { free(pr[i]); diff --git a/src/dropout_layer_kernels.cu b/src/dropout_layer_kernels.cu index 944d86b1d70..9b2d4f88174 100644 --- a/src/dropout_layer_kernels.cu +++ b/src/dropout_layer_kernels.cu @@ -28,7 +28,7 @@ void forward_dropout_layer_gpu(dropout_layer layer, network_state state) */ yoloswag420blazeit360noscope<<>>(state.input, size, layer.rand_gpu, layer.probability, layer.scale); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } void backward_dropout_layer_gpu(dropout_layer layer, network_state state) @@ -37,5 +37,5 @@ void backward_dropout_layer_gpu(dropout_layer layer, network_state state) int size = layer.inputs*layer.batch; yoloswag420blazeit360noscope<<>>(state.delta, size, layer.rand_gpu, layer.probability, layer.scale); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } diff --git a/src/gemm.c b/src/gemm.c index 1b1d8fe22a7..33b6fa20516 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -2669,9 +2669,10 @@ void gemm_ongpu(int TA, int TB, int M, int N, int K, float ALPHA, { cublasHandle_t handle = blas_handle(); cudaError_t stream_status = cublasSetStream(handle, get_cuda_stream()); + CHECK_CUDA(stream_status); cudaError_t status = cublasSgemm(handle, (TB ? CUBLAS_OP_T : CUBLAS_OP_N), (TA ? CUBLAS_OP_T : CUBLAS_OP_N), N, M, K, &ALPHA, B_gpu, ldb, A_gpu, lda, &BETA, C_gpu, ldc); - check_error(status); + CHECK_CUDA(status); } void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA, diff --git a/src/maxpool_layer_kernels.cu b/src/maxpool_layer_kernels.cu index e7d316d4410..577864e51d8 100644 --- a/src/maxpool_layer_kernels.cu +++ b/src/maxpool_layer_kernels.cu @@ -118,7 +118,7 @@ extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network_state sta size_t n = h*w*c*layer.batch; forward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.pad, state.input, layer.output_gpu, layer.indexes_gpu); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void backward_maxpool_layer_gpu(maxpool_layer layer, network_state state) @@ -126,6 +126,6 @@ extern "C" void backward_maxpool_layer_gpu(maxpool_layer layer, network_state st size_t n = layer.h*layer.w*layer.c*layer.batch; backward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.pad, layer.delta_gpu, state.delta, layer.indexes_gpu); - check_error(cudaPeekAtLastError()); + CHECK_CUDA(cudaPeekAtLastError()); } diff --git a/src/parser.c b/src/parser.c index f55a2055c4a..ca53983a8a6 100644 --- a/src/parser.c +++ b/src/parser.c @@ -865,9 +865,9 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) // pre-allocate memory for inference on Tensor Cores (fp16) if (net.cudnn_half) { *net.max_input16_size = max_inputs; - check_error(cudaMalloc((void **)net.input16_gpu, *net.max_input16_size * sizeof(short))); //sizeof(half) + CHECK_CUDA(cudaMalloc((void **)net.input16_gpu, *net.max_input16_size * sizeof(short))); //sizeof(half) *net.max_output16_size = max_outputs; - check_error(cudaMalloc((void **)net.output16_gpu, *net.max_output16_size * sizeof(short))); //sizeof(half) + CHECK_CUDA(cudaMalloc((void **)net.output16_gpu, *net.max_output16_size * sizeof(short))); //sizeof(half) } if (workspace_size) { fprintf(stderr, " Allocate additional workspace_size = %1.2f MB \n", (float)workspace_size/1000000); From edfdf2c20e961c0e72774382db827787c67356d6 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 5 Feb 2019 19:33:10 +0300 Subject: [PATCH 0304/1365] Fixed bug in Tensor Cores training --- src/convolutional_kernels.cu | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index b2fb63443ae..03f216097f4 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -139,7 +139,7 @@ __global__ void cuda_f32_to_f16(float* input_f32, size_t size, half *output_f16) } void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16) { - cuda_f32_to_f16 <<< get_number_of_blocks(size, BLOCK), BLOCK, 0, get_cuda_stream() >>> (input_f32, size, (half *)output_f16); + cuda_f32_to_f16 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> (input_f32, size, (half *)output_f16); CHECK_CUDA(cudaPeekAtLastError()); } @@ -151,7 +151,7 @@ __global__ void cuda_f16_to_f32(half* input_f16, size_t size, float *output_f32) } void cuda_convert_f16_to_f32(float* input_f16, size_t size, float *output_f32) { - cuda_f16_to_f32 <<< get_number_of_blocks(size, BLOCK), BLOCK, 0, get_cuda_stream() >>> ((half *)input_f16, size, output_f32); + cuda_f16_to_f32 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> ((half *)input_f16, size, output_f32); CHECK_CUDA(cudaPeekAtLastError()); } From 8726d7b0db39cf9c82c14fd8dab3f7abe546f6b7 Mon Sep 17 00:00:00 2001 From: John Aughey Date: Tue, 5 Feb 2019 11:30:38 -0600 Subject: [PATCH 0305/1365] Optimizing network_predict_image to resize only if necessary. This speeds up the processing considerably if the user is so kind to resize the image prior to doing detection. --- src/network.c | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/src/network.c b/src/network.c index 65d7629f046..d6bd820b312 100644 --- a/src/network.c +++ b/src/network.c @@ -731,10 +731,17 @@ char *detection_to_json(detection *dets, int nboxes, int classes, char **names, float *network_predict_image(network *net, image im) { //image imr = letterbox_image(im, net->w, net->h); - image imr = resize_image(im, net->w, net->h); - set_batch_network(net, 1); - float *p = network_predict(*net, imr.data); - free_image(imr); + float *p; + if (im.w == net->w && im.h == net->h) { + // Input image is the same size as our net, predict on that image + p = network_predict(*net, im.data); + } + else { + // Need to resize image to the desired size for the net + image imr = resize_image(im, net->w, net->h); + p = network_predict(*net, imr.data); + free_image(imr); + } return p; } From 7e9416aa8011de1623c91db47bb75c3a7df100eb Mon Sep 17 00:00:00 2001 From: John Aughey Date: Tue, 5 Feb 2019 11:35:45 -0600 Subject: [PATCH 0306/1365] Making a pointer version of network_predict for python. The python binding requires the network struct to be passed as a pointer to it rather than a struct copy. --- darknet.py | 2 +- src/network.c | 6 ++++++ src/network.h | 1 + 3 files changed, 8 insertions(+), 1 deletion(-) diff --git a/darknet.py b/darknet.py index 3d885d20e56..5d0fe14a748 100644 --- a/darknet.py +++ b/darknet.py @@ -125,7 +125,7 @@ class METADATA(Structure): lib.network_height.argtypes = [c_void_p] lib.network_height.restype = c_int -predict = lib.network_predict +predict = lib.network_predict_ptr predict.argtypes = [c_void_p, POINTER(c_float)] predict.restype = POINTER(c_float) diff --git a/src/network.c b/src/network.c index d6bd820b312..ef55f7f1809 100644 --- a/src/network.c +++ b/src/network.c @@ -556,6 +556,12 @@ void top_predictions(network net, int k, int *index) top_k(out, size, k, index); } +// A version of network_predict that uses a pointer for the network +// struct to make the python binding work properly. +float *network_predict_ptr(network *net, float *input) +{ + return network_predict(*net, input); +} float *network_predict(network net, float *input) { diff --git a/src/network.h b/src/network.h index f71c56cb61c..4247efb0d02 100644 --- a/src/network.h +++ b/src/network.h @@ -122,6 +122,7 @@ float train_network_datum(network net, float *x, float *y); matrix network_predict_data(network net, data test); //LIB_API float *network_predict(network net, float *input); +LIB_API float *network_predict_ptr(network *net, float *input); float network_accuracy(network net, data d); float *network_accuracies(network net, data d, int n); float network_accuracy_multi(network net, data d, int n); From c00d3c92db67dbaac058a1819e24d66921060991 Mon Sep 17 00:00:00 2001 From: John Aughey Date: Tue, 5 Feb 2019 11:40:21 -0600 Subject: [PATCH 0307/1365] Making a fast API compatible way to copy image data. This can improve on the python array_to_image function where we already have an allocated image struct and simply need to copy the data into the correct format/shape without reallocating. Aside: I think array_to_image does two frees because the pointer data_as returns doesn't own the memory that is ultimately freed in free_image. --- src/image.c | 19 +++++++++++++++++++ src/image.h | 1 + 2 files changed, 20 insertions(+) diff --git a/src/image.c b/src/image.c index 9401ebe02c7..b4b57976eb4 100644 --- a/src/image.c +++ b/src/image.c @@ -2137,3 +2137,22 @@ void free_image(image m) free(m.data); } } + +// Fast copy data from a contiguous byte array into the image. +LIB_API void copy_image_from_bytes(image im, char *pdata) +{ + unsigned char *data = (unsigned char*)pdata; + int i, k, j; + int w = im.w; + int h = im.h; + int c = im.c; + for (k = 0; k < c; ++k) { + for (j = 0; j < h; ++j) { + for (i = 0; i < w; ++i) { + int dst_index = i + w * j + w * h*k; + int src_index = k + c * i + c * w*j; + im.data[dst_index] = (float)data[src_index] / 255.; + } + } + } +} \ No newline at end of file diff --git a/src/image.h b/src/image.h index 6774396440e..dd38d3ff94b 100644 --- a/src/image.h +++ b/src/image.h @@ -33,6 +33,7 @@ image random_crop_image(image im, int w, int h); image random_augment_image(image im, float angle, float aspect, int low, int high, int size); void random_distort_image(image im, float hue, float saturation, float exposure); //LIB_API image resize_image(image im, int w, int h); +LIB_API void copy_image_from_bytes(image im, char *pdata); void fill_image(image m, float s); void letterbox_image_into(image im, int w, int h, image boxed); //LIB_API image letterbox_image(image im, int w, int h); From fa1415e3c2ffe0e9df2a6edf7d046e7edeef1e0b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 5 Feb 2019 20:43:07 +0300 Subject: [PATCH 0308/1365] CUDNN_HALF and CC 7.5 by default in darknet.sln --- build/darknet/darknet.vcxproj | 8 ++++---- include/darknet.h | 3 ++- src/convolutional_kernels.cu | 14 ++++++++++++-- src/network_kernels.cu | 5 ++++- 4 files changed, 22 insertions(+), 8 deletions(-) diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj index 5e5e0c2bf35..58ec245d2b1 100644 --- a/build/darknet/darknet.vcxproj +++ b/build/darknet/darknet.vcxproj @@ -89,7 +89,7 @@ Disabled true C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include - CUDNN;_CRTDBG_MAP_ALLOC;_MBCS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + CUDNN_HALF;CUDNN;_CRTDBG_MAP_ALLOC;_MBCS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;DEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) OPENCV; true stdlib.h;crtdbg.h;%(ForcedIncludeFiles) @@ -102,7 +102,7 @@ true - compute_30,sm_30;compute_52,sm_52 + compute_30,sm_30;compute_75,sm_75 64 @@ -133,7 +133,7 @@ true true C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include - OPENCV;CUDNN;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + CUDNN_HALF;OPENCV;CUDNN;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y CompileAsCpp @@ -152,7 +152,7 @@ 64 - compute_30,sm_30;compute_52,sm_52 + compute_30,sm_30;compute_75,sm_75 diff --git a/include/darknet.h b/include/darknet.h index 9b0ea02be12..93312d286d3 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -8,8 +8,9 @@ #include #include #include -#include #include +#include +#include #ifdef LIB_EXPORTS #if defined(_MSC_VER) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 03f216097f4..be90728fdfe 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -139,7 +139,7 @@ __global__ void cuda_f32_to_f16(float* input_f32, size_t size, half *output_f16) } void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16) { - cuda_f32_to_f16 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> (input_f32, size, (half *)output_f16); + cuda_f32_to_f16 <<< get_number_of_blocks(size, BLOCK), BLOCK, 0, get_cuda_stream() >>> (input_f32, size, (half *)output_f16); CHECK_CUDA(cudaPeekAtLastError()); } @@ -151,7 +151,7 @@ __global__ void cuda_f16_to_f32(half* input_f16, size_t size, float *output_f32) } void cuda_convert_f16_to_f32(float* input_f16, size_t size, float *output_f32) { - cuda_f16_to_f32 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> ((half *)input_f16, size, output_f32); + cuda_f16_to_f32 <<< get_number_of_blocks(size, BLOCK), BLOCK, 0, get_cuda_stream() >>> ((half *)input_f16, size, output_f32); CHECK_CUDA(cudaPeekAtLastError()); } @@ -161,6 +161,7 @@ half *cuda_make_f16_from_f32_array(float *src, size_t n) size_t size = sizeof(half)*n; CHECK_CUDA(cudaMalloc((void **)&dst16, size)); if (src) { + assert(n > 0); cuda_convert_f32_to_f16(src, n, (float *)dst16); } if (!dst16) error("Cuda malloc failed\n"); @@ -434,6 +435,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //printf("\n input16_size: cur = %zu \t max = %zu \n", input16_size, *state.net.max_input16_size); *state.net.max_input16_size = input16_size; if (*state.net.input16_gpu) cuda_free(*state.net.input16_gpu); + assert(*state.net.max_input16_size > 0); *state.net.input16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_input16_size); } float *input16 = *state.net.input16_gpu; @@ -441,10 +443,12 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) if (*state.net.max_output16_size < output16_size) { *state.net.max_output16_size = output16_size; if (*state.net.output16_gpu) cuda_free(*state.net.output16_gpu); + assert(*state.net.max_output16_size > 0); *state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size); } float *output16 = *state.net.output16_gpu; + assert(input16_size > 0); cuda_convert_f32_to_f16(state.input, input16_size, input16); //fill_ongpu(output16_size / 2, 0, (float *)output16, 1); @@ -608,6 +612,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state if (*state.net.max_input16_size < input16_size) { *state.net.max_input16_size = input16_size; if (*state.net.input16_gpu) cuda_free(*state.net.input16_gpu); + assert(*state.net.max_input16_size > 0); *state.net.input16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_input16_size); } float *input16 = *state.net.input16_gpu; @@ -615,10 +620,13 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state if (*state.net.max_output16_size < delta16_size) { *state.net.max_output16_size = delta16_size; if (*state.net.output16_gpu) cuda_free(*state.net.output16_gpu); + assert(*state.net.max_output16_size > 0); *state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size); } float *delta16 = *state.net.output16_gpu; + assert(input16_size > 0); + assert(delta16_size > 0); cuda_convert_f32_to_f16(state.input, input16_size, input16); cuda_convert_f32_to_f16(l.delta_gpu, delta16_size, delta16); @@ -664,6 +672,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state // calculate conv weight updates // Already: l.weight_updates_gpu = (l.weight_updates_gpu - l.weight*decay*batch*subdivision)*momentum // so we should copy f32 to f16, or compute: f16=(w_up - w*d*b*s)*m + assert((l.c*l.n*l.size*l.size) > 0); cuda_convert_f32_to_f16(l.weight_updates_gpu, l.c*l.n*l.size*l.size, l.weight_updates_gpu16); CHECK_CUDNN(cudnnConvolutionBackwardFilter(cudnn_handle(), @@ -815,6 +824,7 @@ void push_convolutional_layer(convolutional_layer layer) { cuda_push_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size); #ifdef CUDNN_HALF + assert((layer.c*layer.n*layer.size*layer.size) > 0); cuda_convert_f32_to_f16(layer.weights_gpu, layer.c*layer.n*layer.size*layer.size, layer.weights_gpu16); #endif cuda_push_array(layer.biases_gpu, layer.biases, layer.n); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 9ab124e48aa..edb4cd5e0a5 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -152,7 +152,10 @@ void forward_backward_network_gpu(network net, float *x, float *y) int i; for (i = 0; i < net.n; ++i) { layer l = net.layers[i]; - cuda_convert_f32_to_f16(l.weights_gpu, l.c*l.n*l.size*l.size, l.weights_gpu16); + if (l.weights_gpu) { + assert((l.c*l.n*l.size*l.size) > 0); + cuda_convert_f32_to_f16(l.weights_gpu, l.c*l.n*l.size*l.size, l.weights_gpu16); + } } #endif forward_network_gpu(net, state); From e1bbeb8367ad43e98d8778a1d73142e3c1a73855 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 5 Feb 2019 20:43:07 +0300 Subject: [PATCH 0309/1365] CUDNN_HALF and CC 7.5 by default in darknet.sln --- build/darknet/darknet.vcxproj | 8 ++++---- include/darknet.h | 3 ++- src/convolutional_kernels.cu | 14 ++++++++++++-- src/network_kernels.cu | 5 ++++- 4 files changed, 22 insertions(+), 8 deletions(-) diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj index 5e5e0c2bf35..58ec245d2b1 100644 --- a/build/darknet/darknet.vcxproj +++ b/build/darknet/darknet.vcxproj @@ -89,7 +89,7 @@ Disabled true C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include - CUDNN;_CRTDBG_MAP_ALLOC;_MBCS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + CUDNN_HALF;CUDNN;_CRTDBG_MAP_ALLOC;_MBCS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;DEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) OPENCV; true stdlib.h;crtdbg.h;%(ForcedIncludeFiles) @@ -102,7 +102,7 @@ true - compute_30,sm_30;compute_52,sm_52 + compute_30,sm_30;compute_75,sm_75 64 @@ -133,7 +133,7 @@ true true C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include - OPENCV;CUDNN;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + CUDNN_HALF;OPENCV;CUDNN;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y CompileAsCpp @@ -152,7 +152,7 @@ 64 - compute_30,sm_30;compute_52,sm_52 + compute_30,sm_30;compute_75,sm_75 diff --git a/include/darknet.h b/include/darknet.h index 9b0ea02be12..93312d286d3 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -8,8 +8,9 @@ #include #include #include -#include #include +#include +#include #ifdef LIB_EXPORTS #if defined(_MSC_VER) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 03f216097f4..be90728fdfe 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -139,7 +139,7 @@ __global__ void cuda_f32_to_f16(float* input_f32, size_t size, half *output_f16) } void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16) { - cuda_f32_to_f16 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> (input_f32, size, (half *)output_f16); + cuda_f32_to_f16 <<< get_number_of_blocks(size, BLOCK), BLOCK, 0, get_cuda_stream() >>> (input_f32, size, (half *)output_f16); CHECK_CUDA(cudaPeekAtLastError()); } @@ -151,7 +151,7 @@ __global__ void cuda_f16_to_f32(half* input_f16, size_t size, float *output_f32) } void cuda_convert_f16_to_f32(float* input_f16, size_t size, float *output_f32) { - cuda_f16_to_f32 <<< size / BLOCK + 1, BLOCK, 0, get_cuda_stream() >>> ((half *)input_f16, size, output_f32); + cuda_f16_to_f32 <<< get_number_of_blocks(size, BLOCK), BLOCK, 0, get_cuda_stream() >>> ((half *)input_f16, size, output_f32); CHECK_CUDA(cudaPeekAtLastError()); } @@ -161,6 +161,7 @@ half *cuda_make_f16_from_f32_array(float *src, size_t n) size_t size = sizeof(half)*n; CHECK_CUDA(cudaMalloc((void **)&dst16, size)); if (src) { + assert(n > 0); cuda_convert_f32_to_f16(src, n, (float *)dst16); } if (!dst16) error("Cuda malloc failed\n"); @@ -434,6 +435,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //printf("\n input16_size: cur = %zu \t max = %zu \n", input16_size, *state.net.max_input16_size); *state.net.max_input16_size = input16_size; if (*state.net.input16_gpu) cuda_free(*state.net.input16_gpu); + assert(*state.net.max_input16_size > 0); *state.net.input16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_input16_size); } float *input16 = *state.net.input16_gpu; @@ -441,10 +443,12 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) if (*state.net.max_output16_size < output16_size) { *state.net.max_output16_size = output16_size; if (*state.net.output16_gpu) cuda_free(*state.net.output16_gpu); + assert(*state.net.max_output16_size > 0); *state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size); } float *output16 = *state.net.output16_gpu; + assert(input16_size > 0); cuda_convert_f32_to_f16(state.input, input16_size, input16); //fill_ongpu(output16_size / 2, 0, (float *)output16, 1); @@ -608,6 +612,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state if (*state.net.max_input16_size < input16_size) { *state.net.max_input16_size = input16_size; if (*state.net.input16_gpu) cuda_free(*state.net.input16_gpu); + assert(*state.net.max_input16_size > 0); *state.net.input16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_input16_size); } float *input16 = *state.net.input16_gpu; @@ -615,10 +620,13 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state if (*state.net.max_output16_size < delta16_size) { *state.net.max_output16_size = delta16_size; if (*state.net.output16_gpu) cuda_free(*state.net.output16_gpu); + assert(*state.net.max_output16_size > 0); *state.net.output16_gpu = (float *)cuda_make_f16_from_f32_array(NULL, *state.net.max_output16_size); } float *delta16 = *state.net.output16_gpu; + assert(input16_size > 0); + assert(delta16_size > 0); cuda_convert_f32_to_f16(state.input, input16_size, input16); cuda_convert_f32_to_f16(l.delta_gpu, delta16_size, delta16); @@ -664,6 +672,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state // calculate conv weight updates // Already: l.weight_updates_gpu = (l.weight_updates_gpu - l.weight*decay*batch*subdivision)*momentum // so we should copy f32 to f16, or compute: f16=(w_up - w*d*b*s)*m + assert((l.c*l.n*l.size*l.size) > 0); cuda_convert_f32_to_f16(l.weight_updates_gpu, l.c*l.n*l.size*l.size, l.weight_updates_gpu16); CHECK_CUDNN(cudnnConvolutionBackwardFilter(cudnn_handle(), @@ -815,6 +824,7 @@ void push_convolutional_layer(convolutional_layer layer) { cuda_push_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size); #ifdef CUDNN_HALF + assert((layer.c*layer.n*layer.size*layer.size) > 0); cuda_convert_f32_to_f16(layer.weights_gpu, layer.c*layer.n*layer.size*layer.size, layer.weights_gpu16); #endif cuda_push_array(layer.biases_gpu, layer.biases, layer.n); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 9ab124e48aa..edb4cd5e0a5 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -152,7 +152,10 @@ void forward_backward_network_gpu(network net, float *x, float *y) int i; for (i = 0; i < net.n; ++i) { layer l = net.layers[i]; - cuda_convert_f32_to_f16(l.weights_gpu, l.c*l.n*l.size*l.size, l.weights_gpu16); + if (l.weights_gpu) { + assert((l.c*l.n*l.size*l.size) > 0); + cuda_convert_f32_to_f16(l.weights_gpu, l.c*l.n*l.size*l.size, l.weights_gpu16); + } } #endif forward_network_gpu(net, state); From 285088adc4a2da0d9c9715bb33ad5b086eb2dbee Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 6 Feb 2019 01:55:22 +0300 Subject: [PATCH 0310/1365] Fixed checking CC for enabling Tensor Cores --- build/darknet/yolo_cpp_dll.vcxproj | 10 +++++----- src/cuda.c | 2 +- src/parser.c | 4 +++- 3 files changed, 9 insertions(+), 7 deletions(-) diff --git a/build/darknet/yolo_cpp_dll.vcxproj b/build/darknet/yolo_cpp_dll.vcxproj index 74eb2afc52a..6136112cf1b 100644 --- a/build/darknet/yolo_cpp_dll.vcxproj +++ b/build/darknet/yolo_cpp_dll.vcxproj @@ -93,8 +93,8 @@ Disabled true ..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include - _MBCS;LIB_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) - CUDNN + _MBCS;LIB_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;DEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + CUDNN_HALF;CUDNN true stdlib.h;crtdbg.h;%(ForcedIncludeFiles) @@ -106,7 +106,7 @@ true - compute_30,sm_30;compute_52,sm_52 + compute_30,sm_30;compute_75,sm_75 64 @@ -137,7 +137,7 @@ true true C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include - CUDNN;GPU;LIB_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + CUDNN_HALF;CUDNN;GPU;LIB_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y CompileAsCpp @@ -156,7 +156,7 @@ 64 - compute_30,sm_30;compute_52,sm_52 + compute_30,sm_30;compute_75,sm_75 diff --git a/src/cuda.c b/src/cuda.c index 85c8a2997b8..6a2407a3ab0 100644 --- a/src/cuda.c +++ b/src/cuda.c @@ -309,7 +309,7 @@ int get_gpu_compute_capability(int i) cudaDeviceProp prop; cudaError_t status = cudaGetDeviceProperties(&prop, i); CHECK_CUDA(status); - int cc = prop.major * 10 + prop.minor; + int cc = prop.major * 100 + prop.minor * 10; // __CUDA_ARCH__ format return cc; } diff --git a/src/parser.c b/src/parser.c index ca53983a8a6..8d927f81de8 100644 --- a/src/parser.c +++ b/src/parser.c @@ -671,8 +671,10 @@ void parse_net_options(list *options, network *net) net->policy = get_policy(policy_s); net->burn_in = option_find_int_quiet(options, "burn_in", 0); #ifdef CUDNN_HALF - if(get_gpu_compute_capability(net->gpu_index) >= 7000) net->cudnn_half = 1; + int compute_capability = get_gpu_compute_capability(net->gpu_index); + if(get_gpu_compute_capability(net->gpu_index) >= 700) net->cudnn_half = 1; else net->cudnn_half = 0; + fprintf(stderr, " compute_capability = %d, cudnn_half = %d \n", compute_capability, net->cudnn_half); #endif if(net->policy == STEP){ net->step = option_find_int(options, "step", 1); From c50b0e0c8af16f7ff89ed340fe65d6c22a4eb530 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 6 Feb 2019 14:38:12 +0300 Subject: [PATCH 0311/1365] Minor Python and C API improvement --- build/darknet/x64/darknet.py | 4 ++-- build/darknet/x64/darknet_video.py | 4 ++-- darknet.py | 2 +- darknet_video.py | 4 ++-- include/darknet.h | 2 ++ src/detector.c | 5 +++-- src/image.h | 2 +- src/network.c | 1 + src/network.h | 2 +- 9 files changed, 15 insertions(+), 11 deletions(-) diff --git a/build/darknet/x64/darknet.py b/build/darknet/x64/darknet.py index 3d885d20e56..81a96d281b4 100644 --- a/build/darknet/x64/darknet.py +++ b/build/darknet/x64/darknet.py @@ -125,7 +125,7 @@ class METADATA(Structure): lib.network_height.argtypes = [c_void_p] lib.network_height.restype = c_int -predict = lib.network_predict +predict = lib.network_predict_ptr predict.argtypes = [c_void_p, POINTER(c_float)] predict.restype = POINTER(c_float) @@ -151,7 +151,7 @@ class METADATA(Structure): free_ptrs = lib.free_ptrs free_ptrs.argtypes = [POINTER(c_void_p), c_int] -network_predict = lib.network_predict +network_predict = lib.network_predict_ptr network_predict.argtypes = [c_void_p, POINTER(c_float)] reset_rnn = lib.reset_rnn diff --git a/build/darknet/x64/darknet_video.py b/build/darknet/x64/darknet_video.py index 1a486cedaf5..a02d2782119 100644 --- a/build/darknet/x64/darknet_video.py +++ b/build/darknet/x64/darknet_video.py @@ -60,7 +60,7 @@ class METADATA(Structure): lib.network_height.argtypes = [c_void_p] lib.network_height.restype = c_int -predict = lib.network_predict +predict = lib.network_predict_ptr predict.argtypes = [c_void_p, POINTER(c_float)] predict.restype = POINTER(c_float) @@ -88,7 +88,7 @@ class METADATA(Structure): free_ptrs = lib.free_ptrs free_ptrs.argtypes = [POINTER(c_void_p), c_int] -network_predict = lib.network_predict +network_predict = lib.network_predict_ptr network_predict.argtypes = [c_void_p, POINTER(c_float)] reset_rnn = lib.reset_rnn diff --git a/darknet.py b/darknet.py index 5d0fe14a748..81a96d281b4 100644 --- a/darknet.py +++ b/darknet.py @@ -151,7 +151,7 @@ class METADATA(Structure): free_ptrs = lib.free_ptrs free_ptrs.argtypes = [POINTER(c_void_p), c_int] -network_predict = lib.network_predict +network_predict = lib.network_predict_ptr network_predict.argtypes = [c_void_p, POINTER(c_float)] reset_rnn = lib.reset_rnn diff --git a/darknet_video.py b/darknet_video.py index 7a8e8f005cf..19e2345a775 100644 --- a/darknet_video.py +++ b/darknet_video.py @@ -60,7 +60,7 @@ class METADATA(Structure): lib.network_height.argtypes = [c_void_p] lib.network_height.restype = c_int -predict = lib.network_predict +predict = lib.network_predict_ptr predict.argtypes = [c_void_p, POINTER(c_float)] predict.restype = POINTER(c_float) @@ -88,7 +88,7 @@ class METADATA(Structure): free_ptrs = lib.free_ptrs free_ptrs.argtypes = [POINTER(c_void_p), c_int] -network_predict = lib.network_predict +network_predict = lib.network_predict_ptr network_predict.argtypes = [c_void_p, POINTER(c_float)] reset_rnn = lib.reset_rnn diff --git a/include/darknet.h b/include/darknet.h index 93312d286d3..6675599c4c3 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -745,6 +745,7 @@ LIB_API void do_nms_obj(detection *dets, int total, int classes, float thresh); // network.h LIB_API float *network_predict(network net, float *input); +LIB_API float *network_predict_ptr(network *net, float *input); LIB_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter); LIB_API void free_detections(detection *dets, int n); LIB_API void fuse_conv_batchnorm(network net); @@ -766,6 +767,7 @@ LIB_API void optimize_picture(network *net, image orig, int max_layer, float sca // image.h LIB_API image resize_image(image im, int w, int h); +LIB_API void copy_image_from_bytes(image im, char *pdata); LIB_API image letterbox_image(image im, int w, int h); LIB_API void rgbgr_image(image im); LIB_API image make_image(int w, int h, int c); diff --git a/src/detector.c b/src/detector.c index 79bcdaaeda4..fb29ff18dc1 100644 --- a/src/detector.c +++ b/src/detector.c @@ -247,6 +247,9 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i if (mean_average_precision > 0) { printf("\n Last accuracy mAP@0.5 = %2.2f %% ", mean_average_precision*100); } + int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions); // calculate mAP for each 4 Epochs + if (calc_map) printf("\n (next mAP calculation at %d iterations) ", (iter_map + calc_map_for_each)); + if (net.cudnn_half) { if (i < net.burn_in * 3) printf("\n Tensor Cores are disabled until the first %d iterations are reached.", 3 * net.burn_in); else printf("\n Tensor Cores are used."); @@ -254,8 +257,6 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now() - time), i*imgs); int draw_precision = 0; - int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions); // calculate mAP for each 4 Epochs - if (calc_map) printf(" Next mAP calculation at %d iterations \n", (iter_map + calc_map_for_each)); if (calc_map && (i >= (iter_map + calc_map_for_each) || i == net.max_batches) && i >= net.burn_in && i >= 1000) { if (l.random) { printf("Resizing to initial size: %d x %d \n", init_w, init_h); diff --git a/src/image.h b/src/image.h index dd38d3ff94b..5c759b4d5dd 100644 --- a/src/image.h +++ b/src/image.h @@ -33,7 +33,7 @@ image random_crop_image(image im, int w, int h); image random_augment_image(image im, float angle, float aspect, int low, int high, int size); void random_distort_image(image im, float hue, float saturation, float exposure); //LIB_API image resize_image(image im, int w, int h); -LIB_API void copy_image_from_bytes(image im, char *pdata); +//LIB_API void copy_image_from_bytes(image im, char *pdata); void fill_image(image m, float s); void letterbox_image_into(image im, int w, int h, image boxed); //LIB_API image letterbox_image(image im, int w, int h); diff --git a/src/network.c b/src/network.c index ef55f7f1809..ba8a1721b53 100644 --- a/src/network.c +++ b/src/network.c @@ -738,6 +738,7 @@ float *network_predict_image(network *net, image im) { //image imr = letterbox_image(im, net->w, net->h); float *p; + if(net->batch != 1) set_batch_network(net, 1); if (im.w == net->w && im.h == net->h) { // Input image is the same size as our net, predict on that image p = network_predict(*net, im.data); diff --git a/src/network.h b/src/network.h index 4247efb0d02..10bd99f2322 100644 --- a/src/network.h +++ b/src/network.h @@ -122,7 +122,7 @@ float train_network_datum(network net, float *x, float *y); matrix network_predict_data(network net, data test); //LIB_API float *network_predict(network net, float *input); -LIB_API float *network_predict_ptr(network *net, float *input); +//LIB_API float *network_predict_ptr(network *net, float *input); float network_accuracy(network net, data d); float *network_accuracies(network net, data d, int n); float network_accuracy_multi(network net, data d, int n); From 64b217aa868f6f050ecdb0b627aeda3dc866912d Mon Sep 17 00:00:00 2001 From: Alexey Date: Wed, 6 Feb 2019 14:51:02 +0300 Subject: [PATCH 0312/1365] Update Readme.md --- README.md | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index f85adc0266d..3dba89d1c3f 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ 8. [How to improve object detection](#how-to-improve-object-detection) 9. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) 10. [Using Yolo9000](#using-yolo9000) -11. [How to use Yolo as DLL](#how-to-use-yolo-as-dll) +11. [How to use Yolo as DLL and SO libraries](#how-to-use-yolo-as-dll-and-so-libraries) @@ -551,7 +551,21 @@ With example of: `train.txt`, `obj.names`, `obj.data`, `yolo-obj.cfg`, `air`1-6` * `inet9k.map` - map 200 categories from ImageNet to WordTree `9k.tree`: https://raw.githubusercontent.com/AlexeyAB/darknet/master/build/darknet/x64/data/inet9k.map -## How to use Yolo as DLL +## How to use Yolo as DLL and SO libraries + +* on Linux - set `LIBSO=1` in the `Makefile` and do `make` +* on Windows - compile `build\darknet\yolo_cpp_dll.sln` or `build\darknet\yolo_cpp_dll_no_gpu.sln` solution + +There are 2 APIs: +* C API: https://github.com/AlexeyAB/darknet/blob/master/include/darknet.h + * Python examples using the C API:: + * https://github.com/AlexeyAB/darknet/blob/master/darknet.py + * https://github.com/AlexeyAB/darknet/blob/master/darknet_video.py + +* C++ API: https://github.com/AlexeyAB/darknet/blob/master/include/yolo_v2_class.hpp + * C++ example that uses C++ API: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp + +---- 1. To compile Yolo as C++ DLL-file `yolo_cpp_dll.dll` - open in MSVS2015 file `build\darknet\yolo_cpp_dll.sln`, set **x64** and **Release**, and do the: Build -> Build yolo_cpp_dll * You should have installed **CUDA 10.0** From 022ce74fe9de92671000fe3154192b6e72edf190 Mon Sep 17 00:00:00 2001 From: John Aughey Date: Wed, 6 Feb 2019 14:59:23 -0600 Subject: [PATCH 0313/1365] Rewriting darknet_video.py to reuse darknet.py as a lib --- build/darknet/x64/darknet.py | 19 ++- build/darknet/x64/darknet_video.py | 237 ++--------------------------- 2 files changed, 31 insertions(+), 225 deletions(-) diff --git a/build/darknet/x64/darknet.py b/build/darknet/x64/darknet.py index 81a96d281b4..9408aabc635 100644 --- a/build/darknet/x64/darknet.py +++ b/build/darknet/x64/darknet.py @@ -125,6 +125,15 @@ class METADATA(Structure): lib.network_height.argtypes = [c_void_p] lib.network_height.restype = c_int +copy_image_from_bytes = lib.copy_image_from_bytes +copy_image_from_bytes.argtypes = [IMAGE,c_char_p] + +def network_width(net): + return lib.network_width(net) + +def network_height(net): + return lib.network_height(net) + predict = lib.network_predict_ptr predict.argtypes = [c_void_p, POINTER(c_float)] predict.restype = POINTER(c_float) @@ -223,6 +232,13 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False): """ #pylint: disable= C0321 im = load_image(image, 0, 0) + if debug: print("Loaded image") + ret = detect_image(net, meta, im, thresh, hier_thresh, nms, debug) + free_image(im) + if debug: print("freed image") + return ret + +def detect_image(net, meta, im, thresh=.5, hier_thresh=.5, nms=.45, debug= False): #import cv2 #custom_image_bgr = cv2.imread(image) # use: detect(,,imagePath,) #custom_image = cv2.cvtColor(custom_image_bgr, cv2.COLOR_BGR2RGB) @@ -230,7 +246,6 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False): #import scipy.misc #custom_image = scipy.misc.imread(image) #im, arr = array_to_image(custom_image) # you should comment line below: free_image(im) - if debug: print("Loaded image") num = c_int(0) if debug: print("Assigned num") pnum = pointer(num) @@ -267,8 +282,6 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False): if debug: print("did range") res = sorted(res, key=lambda x: -x[1]) if debug: print("did sort") - free_image(im) - if debug: print("freed image") free_detections(dets, num) if debug: print("freed detections") return res diff --git a/build/darknet/x64/darknet_video.py b/build/darknet/x64/darknet_video.py index a02d2782119..9367b31361d 100644 --- a/build/darknet/x64/darknet_video.py +++ b/build/darknet/x64/darknet_video.py @@ -5,222 +5,7 @@ import cv2 import numpy as np import time - - -def sample(probs): - s = sum(probs) - probs = [a/s for a in probs] - r = random.uniform(0, 1) - for i in range(len(probs)): - r = r - probs[i] - if r <= 0: - return i - return len(probs)-1 - - -def c_array(ctype, values): - arr = (ctype*len(values))() - arr[:] = values - return arr - - -class BOX(Structure): - _fields_ = [("x", c_float), - ("y", c_float), - ("w", c_float), - ("h", c_float)] - - -class DETECTION(Structure): - _fields_ = [("bbox", BOX), - ("classes", c_int), - ("prob", POINTER(c_float)), - ("mask", POINTER(c_float)), - ("objectness", c_float), - ("sort_class", c_int)] - - -class IMAGE(Structure): - _fields_ = [("w", c_int), - ("h", c_int), - ("c", c_int), - ("data", POINTER(c_float))] - - -class METADATA(Structure): - _fields_ = [("classes", c_int), - ("names", POINTER(c_char_p))] - - -hasGPU = True - -lib = CDLL("yolo_cpp_dll.dll", RTLD_GLOBAL) -lib.network_width.argtypes = [c_void_p] -lib.network_width.restype = c_int -lib.network_height.argtypes = [c_void_p] -lib.network_height.restype = c_int - -predict = lib.network_predict_ptr -predict.argtypes = [c_void_p, POINTER(c_float)] -predict.restype = POINTER(c_float) - -if hasGPU: - set_gpu = lib.cuda_set_device - set_gpu.argtypes = [c_int] - -make_image = lib.make_image -make_image.argtypes = [c_int, c_int, c_int] -make_image.restype = IMAGE - -get_network_boxes = lib.get_network_boxes -get_network_boxes.argtypes = \ - [c_void_p, c_int, c_int, c_float, c_float, POINTER( - c_int), c_int, POINTER(c_int), c_int] -get_network_boxes.restype = POINTER(DETECTION) - -make_network_boxes = lib.make_network_boxes -make_network_boxes.argtypes = [c_void_p] -make_network_boxes.restype = POINTER(DETECTION) - -free_detections = lib.free_detections -free_detections.argtypes = [POINTER(DETECTION), c_int] - -free_ptrs = lib.free_ptrs -free_ptrs.argtypes = [POINTER(c_void_p), c_int] - -network_predict = lib.network_predict_ptr -network_predict.argtypes = [c_void_p, POINTER(c_float)] - -reset_rnn = lib.reset_rnn -reset_rnn.argtypes = [c_void_p] - -load_net = lib.load_network -load_net.argtypes = [c_char_p, c_char_p, c_int] -load_net.restype = c_void_p - -load_net_custom = lib.load_network_custom -load_net_custom.argtypes = [c_char_p, c_char_p, c_int, c_int] -load_net_custom.restype = c_void_p - -do_nms_obj = lib.do_nms_obj -do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float] - -do_nms_sort = lib.do_nms_sort -do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float] - -free_image = lib.free_image -free_image.argtypes = [IMAGE] - -letterbox_image = lib.letterbox_image -letterbox_image.argtypes = [IMAGE, c_int, c_int] -letterbox_image.restype = IMAGE - -load_meta = lib.get_metadata -lib.get_metadata.argtypes = [c_char_p] -lib.get_metadata.restype = METADATA - -load_image = lib.load_image_color -load_image.argtypes = [c_char_p, c_int, c_int] -load_image.restype = IMAGE - -rgbgr_image = lib.rgbgr_image -rgbgr_image.argtypes = [IMAGE] - -predict_image = lib.network_predict_image -predict_image.argtypes = [c_void_p, IMAGE] -predict_image.restype = POINTER(c_float) - - -def array_to_image(arr): - import numpy as np - arr = arr.transpose(2, 0, 1) - c = arr.shape[0] - h = arr.shape[1] - w = arr.shape[2] - arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0 - data = arr.ctypes.data_as(POINTER(c_float)) - im = IMAGE(w, h, c, data) - return im, arr - - -def classify(net, meta, im): - out = predict_image(net, im) - res = [] - for i in range(meta.classes): - if altNames is None: - nameTag = meta.names[i] - else: - nameTag = altNames[i] - res.append((nameTag, out[i])) - res = sorted(res, key=lambda x: -x[1]) - return res - - -def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug=False): - im, arr = array_to_image(image) - if debug: - print("Loaded image") - num = c_int(0) - if debug: - print("Assigned num") - pnum = pointer(num) - if debug: - print("Assigned pnum") - predict_image(net, im) - if debug: - print("did prediction") - # dets = get_network_boxes( - # net, image.shape[1], image.shape[0], - # thresh, hier_thresh, - # None, 0, pnum, 0) # OpenCV - dets = get_network_boxes(net, im.w, im.h, - thresh, hier_thresh, None, 0, pnum, 0) - if debug: - print("Got dets") - num = pnum[0] - if debug: - print("got zeroth index of pnum") - if nms: - do_nms_sort(dets, num, meta.classes, nms) - if debug: - print("did sort") - res = [] - if debug: - print("about to range") - for j in range(num): - if debug: - print("Ranging on "+str(j)+" of "+str(num)) - if debug: - print("Classes: "+str(meta), meta.classes, meta.names) - for i in range(meta.classes): - if debug: - print("Class-ranging on "+str(i)+" of " + - str(meta.classes)+"= "+str(dets[j].prob[i])) - if dets[j].prob[i] > 0: - b = dets[j].bbox - if altNames is None: - nameTag = meta.names[i] - else: - nameTag = altNames[i] - if debug: - print("Got bbox", b) - print(nameTag) - print(dets[j].prob[i]) - print((b.x, b.y, b.w, b.h)) - res.append((nameTag, dets[j].prob[i], (b.x, b.y, b.w, b.h))) - if debug: - print("did range") - res = sorted(res, key=lambda x: -x[1]) - if debug: - print("did sort") - # free_image(im) - if debug: - print("freed image") - free_detections(dets, num) - if debug: - print("freed detections") - return res - +import darknet def convertBack(x, y, w, h): xmin = int(round(x - (w / 2))) @@ -255,6 +40,7 @@ def cvDrawBoxes(detections, img): def YOLO(): + global metaMain, netMain, altNames configPath = "./cfg/yolov3.cfg" weightPath = "./yolov3.weights" @@ -269,10 +55,10 @@ def YOLO(): raise ValueError("Invalid data file path `" + os.path.abspath(metaPath)+"`") if netMain is None: - netMain = load_net_custom(configPath.encode( + netMain = darknet.load_net_custom(configPath.encode( "ascii"), weightPath.encode("ascii"), 0, 1) # batch size = 1 if metaMain is None: - metaMain = load_meta(metaPath.encode("ascii")) + metaMain = darknet.load_meta(metaPath.encode("ascii")) if altNames is None: try: with open(metaPath) as metaFH: @@ -299,17 +85,24 @@ def YOLO(): cap.set(4, 720) out = cv2.VideoWriter( "output.avi", cv2.VideoWriter_fourcc(*"MJPG"), 10.0, - (lib.network_width(netMain), lib.network_height(netMain))) + (darknet.network_width(netMain), darknet.network_height(netMain))) print("Starting the YOLO loop...") + + # Create an image we reuse for each detect + darknet_image = darknet.make_image(darknet.network_width(netMain), + darknet.network_height(netMain),3) while True: prev_time = time.time() ret, frame_read = cap.read() frame_rgb = cv2.cvtColor(frame_read, cv2.COLOR_BGR2RGB) frame_resized = cv2.resize(frame_rgb, - (lib.network_width(netMain), - lib.network_height(netMain)), + (darknet.network_width(netMain), + darknet.network_height(netMain)), interpolation=cv2.INTER_LINEAR) - detections = detect(netMain, metaMain, frame_resized, thresh=0.25) + + darknet.copy_image_from_bytes(darknet_image,frame_resized.tobytes()) + + detections = darknet.detect_image(netMain, metaMain, darknet_image, thresh=0.25) image = cvDrawBoxes(detections, frame_resized) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) print(1/(time.time()-prev_time)) From 0543278a5bd7064fae6538afd1761b06b10f73ee Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 7 Feb 2019 00:15:31 +0300 Subject: [PATCH 0314/1365] Partial fixed --- build/darknet/x64/partial.cmd | 3 +++ src/classifier.c | 2 +- src/parser.c | 11 +++++++---- 3 files changed, 11 insertions(+), 5 deletions(-) diff --git a/build/darknet/x64/partial.cmd b/build/darknet/x64/partial.cmd index acbe3fa952b..2d9e022a5ba 100644 --- a/build/darknet/x64/partial.cmd +++ b/build/darknet/x64/partial.cmd @@ -6,6 +6,9 @@ rem Download Yolo9000: http://pjreddie.com/media/files/yolo9000.weights rem darknet.exe partial cfg/tiny-yolo-voc.cfg tiny-yolo-voc.weights tiny-yolo-voc.conv.13 13 +darknet.exe partial cfg/darknet53_448.cfg darknet53_448.weights darknet53.conv.74 74 + + darknet.exe partial cfg/yolov2-tiny-voc.cfg yolov2-tiny-voc.weights yolov2-tiny-voc.conv.13 13 diff --git a/src/classifier.c b/src/classifier.c index f27d31d843d..80175a2fd41 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -110,7 +110,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, args.type = CLASSIFICATION_DATA; #ifdef OPENCV - args.threads = 3; + //args.threads = 3; IplImage* img = NULL; float max_img_loss = 5; int number_of_lines = 100; diff --git a/src/parser.c b/src/parser.c index 8d927f81de8..df6710084fd 100644 --- a/src/parser.c +++ b/src/parser.c @@ -671,10 +671,13 @@ void parse_net_options(list *options, network *net) net->policy = get_policy(policy_s); net->burn_in = option_find_int_quiet(options, "burn_in", 0); #ifdef CUDNN_HALF - int compute_capability = get_gpu_compute_capability(net->gpu_index); - if(get_gpu_compute_capability(net->gpu_index) >= 700) net->cudnn_half = 1; - else net->cudnn_half = 0; - fprintf(stderr, " compute_capability = %d, cudnn_half = %d \n", compute_capability, net->cudnn_half); + if (net->gpu_index >= 0) { + int compute_capability = get_gpu_compute_capability(net->gpu_index); + if (get_gpu_compute_capability(net->gpu_index) >= 700) net->cudnn_half = 1; + else net->cudnn_half = 0; + fprintf(stderr, " compute_capability = %d, cudnn_half = %d \n", compute_capability, net->cudnn_half); + } + else fprintf(stderr, " GPU isn't used \n"); #endif if(net->policy == STEP){ net->step = option_find_int(options, "step", 1); From 7587d47c4633c2f2b638cf2de68900763609f660 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 7 Feb 2019 00:15:31 +0300 Subject: [PATCH 0315/1365] Partial fixed --- build/darknet/x64/partial.cmd | 3 +++ src/classifier.c | 2 +- src/parser.c | 11 +++++++---- 3 files changed, 11 insertions(+), 5 deletions(-) diff --git a/build/darknet/x64/partial.cmd b/build/darknet/x64/partial.cmd index acbe3fa952b..2d9e022a5ba 100644 --- a/build/darknet/x64/partial.cmd +++ b/build/darknet/x64/partial.cmd @@ -6,6 +6,9 @@ rem Download Yolo9000: http://pjreddie.com/media/files/yolo9000.weights rem darknet.exe partial cfg/tiny-yolo-voc.cfg tiny-yolo-voc.weights tiny-yolo-voc.conv.13 13 +darknet.exe partial cfg/darknet53_448.cfg darknet53_448.weights darknet53.conv.74 74 + + darknet.exe partial cfg/yolov2-tiny-voc.cfg yolov2-tiny-voc.weights yolov2-tiny-voc.conv.13 13 diff --git a/src/classifier.c b/src/classifier.c index f27d31d843d..80175a2fd41 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -110,7 +110,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, args.type = CLASSIFICATION_DATA; #ifdef OPENCV - args.threads = 3; + //args.threads = 3; IplImage* img = NULL; float max_img_loss = 5; int number_of_lines = 100; diff --git a/src/parser.c b/src/parser.c index 8d927f81de8..df6710084fd 100644 --- a/src/parser.c +++ b/src/parser.c @@ -671,10 +671,13 @@ void parse_net_options(list *options, network *net) net->policy = get_policy(policy_s); net->burn_in = option_find_int_quiet(options, "burn_in", 0); #ifdef CUDNN_HALF - int compute_capability = get_gpu_compute_capability(net->gpu_index); - if(get_gpu_compute_capability(net->gpu_index) >= 700) net->cudnn_half = 1; - else net->cudnn_half = 0; - fprintf(stderr, " compute_capability = %d, cudnn_half = %d \n", compute_capability, net->cudnn_half); + if (net->gpu_index >= 0) { + int compute_capability = get_gpu_compute_capability(net->gpu_index); + if (get_gpu_compute_capability(net->gpu_index) >= 700) net->cudnn_half = 1; + else net->cudnn_half = 0; + fprintf(stderr, " compute_capability = %d, cudnn_half = %d \n", compute_capability, net->cudnn_half); + } + else fprintf(stderr, " GPU isn't used \n"); #endif if(net->policy == STEP){ net->step = option_find_int(options, "step", 1); From 9bb7455a0e4f6d35f91ef405f852a3cff798043d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 7 Feb 2019 14:47:43 +0300 Subject: [PATCH 0316/1365] Minor fix --- build/darknet/x64/cfg/imagenet1k.data | 7 +- darknet.py | 19 ++- darknet_video.py | 237 ++------------------------ src/classifier.c | 16 +- src/cuda.h | 2 +- 5 files changed, 50 insertions(+), 231 deletions(-) diff --git a/build/darknet/x64/cfg/imagenet1k.data b/build/darknet/x64/cfg/imagenet1k.data index 6634a2740cf..b01f2facec9 100644 --- a/build/darknet/x64/cfg/imagenet1k.data +++ b/build/darknet/x64/cfg/imagenet1k.data @@ -1,7 +1,8 @@ classes=1000 -train = /data/imagenet/imagenet1k.train.list -valid = /data/imagenet/imagenet1k.valid.list -backup = /home/pjreddie/backup/ +train = data/imagenet1k.train.list +#train = data/inet.val.list +valid = data/inet.val.list +backup = backup labels = data/imagenet.labels.list names = data/imagenet.shortnames.list top=5 diff --git a/darknet.py b/darknet.py index 81a96d281b4..9408aabc635 100644 --- a/darknet.py +++ b/darknet.py @@ -125,6 +125,15 @@ class METADATA(Structure): lib.network_height.argtypes = [c_void_p] lib.network_height.restype = c_int +copy_image_from_bytes = lib.copy_image_from_bytes +copy_image_from_bytes.argtypes = [IMAGE,c_char_p] + +def network_width(net): + return lib.network_width(net) + +def network_height(net): + return lib.network_height(net) + predict = lib.network_predict_ptr predict.argtypes = [c_void_p, POINTER(c_float)] predict.restype = POINTER(c_float) @@ -223,6 +232,13 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False): """ #pylint: disable= C0321 im = load_image(image, 0, 0) + if debug: print("Loaded image") + ret = detect_image(net, meta, im, thresh, hier_thresh, nms, debug) + free_image(im) + if debug: print("freed image") + return ret + +def detect_image(net, meta, im, thresh=.5, hier_thresh=.5, nms=.45, debug= False): #import cv2 #custom_image_bgr = cv2.imread(image) # use: detect(,,imagePath,) #custom_image = cv2.cvtColor(custom_image_bgr, cv2.COLOR_BGR2RGB) @@ -230,7 +246,6 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False): #import scipy.misc #custom_image = scipy.misc.imread(image) #im, arr = array_to_image(custom_image) # you should comment line below: free_image(im) - if debug: print("Loaded image") num = c_int(0) if debug: print("Assigned num") pnum = pointer(num) @@ -267,8 +282,6 @@ def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False): if debug: print("did range") res = sorted(res, key=lambda x: -x[1]) if debug: print("did sort") - free_image(im) - if debug: print("freed image") free_detections(dets, num) if debug: print("freed detections") return res diff --git a/darknet_video.py b/darknet_video.py index 19e2345a775..9367b31361d 100644 --- a/darknet_video.py +++ b/darknet_video.py @@ -5,222 +5,7 @@ import cv2 import numpy as np import time - - -def sample(probs): - s = sum(probs) - probs = [a/s for a in probs] - r = random.uniform(0, 1) - for i in range(len(probs)): - r = r - probs[i] - if r <= 0: - return i - return len(probs)-1 - - -def c_array(ctype, values): - arr = (ctype*len(values))() - arr[:] = values - return arr - - -class BOX(Structure): - _fields_ = [("x", c_float), - ("y", c_float), - ("w", c_float), - ("h", c_float)] - - -class DETECTION(Structure): - _fields_ = [("bbox", BOX), - ("classes", c_int), - ("prob", POINTER(c_float)), - ("mask", POINTER(c_float)), - ("objectness", c_float), - ("sort_class", c_int)] - - -class IMAGE(Structure): - _fields_ = [("w", c_int), - ("h", c_int), - ("c", c_int), - ("data", POINTER(c_float))] - - -class METADATA(Structure): - _fields_ = [("classes", c_int), - ("names", POINTER(c_char_p))] - - -hasGPU = True - -lib = CDLL("./libdarknet.so", RTLD_GLOBAL) -lib.network_width.argtypes = [c_void_p] -lib.network_width.restype = c_int -lib.network_height.argtypes = [c_void_p] -lib.network_height.restype = c_int - -predict = lib.network_predict_ptr -predict.argtypes = [c_void_p, POINTER(c_float)] -predict.restype = POINTER(c_float) - -if hasGPU: - set_gpu = lib.cuda_set_device - set_gpu.argtypes = [c_int] - -make_image = lib.make_image -make_image.argtypes = [c_int, c_int, c_int] -make_image.restype = IMAGE - -get_network_boxes = lib.get_network_boxes -get_network_boxes.argtypes = \ - [c_void_p, c_int, c_int, c_float, c_float, POINTER( - c_int), c_int, POINTER(c_int), c_int] -get_network_boxes.restype = POINTER(DETECTION) - -make_network_boxes = lib.make_network_boxes -make_network_boxes.argtypes = [c_void_p] -make_network_boxes.restype = POINTER(DETECTION) - -free_detections = lib.free_detections -free_detections.argtypes = [POINTER(DETECTION), c_int] - -free_ptrs = lib.free_ptrs -free_ptrs.argtypes = [POINTER(c_void_p), c_int] - -network_predict = lib.network_predict_ptr -network_predict.argtypes = [c_void_p, POINTER(c_float)] - -reset_rnn = lib.reset_rnn -reset_rnn.argtypes = [c_void_p] - -load_net = lib.load_network -load_net.argtypes = [c_char_p, c_char_p, c_int] -load_net.restype = c_void_p - -load_net_custom = lib.load_network_custom -load_net_custom.argtypes = [c_char_p, c_char_p, c_int, c_int] -load_net_custom.restype = c_void_p - -do_nms_obj = lib.do_nms_obj -do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float] - -do_nms_sort = lib.do_nms_sort -do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float] - -free_image = lib.free_image -free_image.argtypes = [IMAGE] - -letterbox_image = lib.letterbox_image -letterbox_image.argtypes = [IMAGE, c_int, c_int] -letterbox_image.restype = IMAGE - -load_meta = lib.get_metadata -lib.get_metadata.argtypes = [c_char_p] -lib.get_metadata.restype = METADATA - -load_image = lib.load_image_color -load_image.argtypes = [c_char_p, c_int, c_int] -load_image.restype = IMAGE - -rgbgr_image = lib.rgbgr_image -rgbgr_image.argtypes = [IMAGE] - -predict_image = lib.network_predict_image -predict_image.argtypes = [c_void_p, IMAGE] -predict_image.restype = POINTER(c_float) - - -def array_to_image(arr): - import numpy as np - arr = arr.transpose(2, 0, 1) - c = arr.shape[0] - h = arr.shape[1] - w = arr.shape[2] - arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0 - data = arr.ctypes.data_as(POINTER(c_float)) - im = IMAGE(w, h, c, data) - return im, arr - - -def classify(net, meta, im): - out = predict_image(net, im) - res = [] - for i in range(meta.classes): - if altNames is None: - nameTag = meta.names[i] - else: - nameTag = altNames[i] - res.append((nameTag, out[i])) - res = sorted(res, key=lambda x: -x[1]) - return res - - -def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug=False): - im, arr = array_to_image(image) - if debug: - print("Loaded image") - num = c_int(0) - if debug: - print("Assigned num") - pnum = pointer(num) - if debug: - print("Assigned pnum") - predict_image(net, im) - if debug: - print("did prediction") - # dets = get_network_boxes( - # net, image.shape[1], image.shape[0], - # thresh, hier_thresh, - # None, 0, pnum, 0) # OpenCV - dets = get_network_boxes(net, im.w, im.h, - thresh, hier_thresh, None, 0, pnum, 0) - if debug: - print("Got dets") - num = pnum[0] - if debug: - print("got zeroth index of pnum") - if nms: - do_nms_sort(dets, num, meta.classes, nms) - if debug: - print("did sort") - res = [] - if debug: - print("about to range") - for j in range(num): - if debug: - print("Ranging on "+str(j)+" of "+str(num)) - if debug: - print("Classes: "+str(meta), meta.classes, meta.names) - for i in range(meta.classes): - if debug: - print("Class-ranging on "+str(i)+" of " + - str(meta.classes)+"= "+str(dets[j].prob[i])) - if dets[j].prob[i] > 0: - b = dets[j].bbox - if altNames is None: - nameTag = meta.names[i] - else: - nameTag = altNames[i] - if debug: - print("Got bbox", b) - print(nameTag) - print(dets[j].prob[i]) - print((b.x, b.y, b.w, b.h)) - res.append((nameTag, dets[j].prob[i], (b.x, b.y, b.w, b.h))) - if debug: - print("did range") - res = sorted(res, key=lambda x: -x[1]) - if debug: - print("did sort") - # free_image(im) - if debug: - print("freed image") - free_detections(dets, num) - if debug: - print("freed detections") - return res - +import darknet def convertBack(x, y, w, h): xmin = int(round(x - (w / 2))) @@ -255,6 +40,7 @@ def cvDrawBoxes(detections, img): def YOLO(): + global metaMain, netMain, altNames configPath = "./cfg/yolov3.cfg" weightPath = "./yolov3.weights" @@ -269,10 +55,10 @@ def YOLO(): raise ValueError("Invalid data file path `" + os.path.abspath(metaPath)+"`") if netMain is None: - netMain = load_net_custom(configPath.encode( + netMain = darknet.load_net_custom(configPath.encode( "ascii"), weightPath.encode("ascii"), 0, 1) # batch size = 1 if metaMain is None: - metaMain = load_meta(metaPath.encode("ascii")) + metaMain = darknet.load_meta(metaPath.encode("ascii")) if altNames is None: try: with open(metaPath) as metaFH: @@ -299,17 +85,24 @@ def YOLO(): cap.set(4, 720) out = cv2.VideoWriter( "output.avi", cv2.VideoWriter_fourcc(*"MJPG"), 10.0, - (lib.network_width(netMain), lib.network_height(netMain))) + (darknet.network_width(netMain), darknet.network_height(netMain))) print("Starting the YOLO loop...") + + # Create an image we reuse for each detect + darknet_image = darknet.make_image(darknet.network_width(netMain), + darknet.network_height(netMain),3) while True: prev_time = time.time() ret, frame_read = cap.read() frame_rgb = cv2.cvtColor(frame_read, cv2.COLOR_BGR2RGB) frame_resized = cv2.resize(frame_rgb, - (lib.network_width(netMain), - lib.network_height(netMain)), + (darknet.network_width(netMain), + darknet.network_height(netMain)), interpolation=cv2.INTER_LINEAR) - detections = detect(netMain, metaMain, frame_resized, thresh=0.25) + + darknet.copy_image_from_bytes(darknet_image,frame_resized.tobytes()) + + detections = darknet.detect_image(netMain, metaMain, darknet_image, thresh=0.25) image = cvDrawBoxes(detections, frame_resized) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) print(1/(time.time()-prev_time)) diff --git a/src/classifier.c b/src/classifier.c index 80175a2fd41..fb72041368a 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -125,6 +125,8 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, load_thread = load_data(args); int iter_save = get_current_batch(net); + int iter_save_last = get_current_batch(net); + while(get_current_batch(net) < net.max_batches || net.max_batches == 0){ time=clock(); @@ -155,13 +157,23 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, -1, 0, dont_show, mjpeg_port); #endif // OPENCV - if (i >= (iter_save + 100)) { + if (i >= (iter_save + 1000)) { iter_save = i; #ifdef GPU if (ngpus != 1) sync_nets(nets, ngpus, 0); #endif char buff[256]; - sprintf(buff, "%s/%s_%d.weights",backup_directory,base, i); + sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); + save_weights(net, buff); + } + + if (i >= (iter_save_last + 100)) { + iter_save_last = i; +#ifdef GPU + if (ngpus != 1) sync_nets(nets, ngpus, 0); +#endif + char buff[256]; + sprintf(buff, "%s/%s_last.weights", backup_directory, base, i); save_weights(net, buff); } free_data(train); diff --git a/src/cuda.h b/src/cuda.h index b935846e644..a1e0e4ebc1e 100644 --- a/src/cuda.h +++ b/src/cuda.h @@ -16,7 +16,7 @@ extern int gpu_index; #include "curand.h" #include "cublas_v2.h" #include "cuda_runtime_api.h" -#include "driver_types.h" +//#include "driver_types.h" #ifdef CUDNN #include "cudnn.h" From fc663f6efe85e0e922a6a31a1a340ced5e54bae8 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 7 Feb 2019 15:02:36 +0300 Subject: [PATCH 0317/1365] Another minor fix --- build/darknet/x64/darknet_video.py | 8 +++++--- darknet_video.py | 8 +++++--- 2 files changed, 10 insertions(+), 6 deletions(-) diff --git a/build/darknet/x64/darknet_video.py b/build/darknet/x64/darknet_video.py index 9367b31361d..a5e462fcab6 100644 --- a/build/darknet/x64/darknet_video.py +++ b/build/darknet/x64/darknet_video.py @@ -25,12 +25,12 @@ def cvDrawBoxes(detections, img): float(x), float(y), float(w), float(h)) pt1 = (xmin, ymin) pt2 = (xmax, ymax) - cv2.rectangle(img, pt1, pt2, (0, 255, 0), 2) + cv2.rectangle(img, pt1, pt2, (0, 255, 0), 1) cv2.putText(img, detection[0].decode() + " [" + str(round(detection[1] * 100, 2)) + "]", - (pt1[0], pt1[1] + 20), cv2.FONT_HERSHEY_SIMPLEX, 1, - [0, 255, 0], 4) + (pt1[0], pt1[1] - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, + [0, 255, 0], 2) return img @@ -106,6 +106,8 @@ def YOLO(): image = cvDrawBoxes(detections, frame_resized) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) print(1/(time.time()-prev_time)) + cv2.imshow('Demo', image) + cv2.waitKey(3) cap.release() out.release() diff --git a/darknet_video.py b/darknet_video.py index 9367b31361d..a5e462fcab6 100644 --- a/darknet_video.py +++ b/darknet_video.py @@ -25,12 +25,12 @@ def cvDrawBoxes(detections, img): float(x), float(y), float(w), float(h)) pt1 = (xmin, ymin) pt2 = (xmax, ymax) - cv2.rectangle(img, pt1, pt2, (0, 255, 0), 2) + cv2.rectangle(img, pt1, pt2, (0, 255, 0), 1) cv2.putText(img, detection[0].decode() + " [" + str(round(detection[1] * 100, 2)) + "]", - (pt1[0], pt1[1] + 20), cv2.FONT_HERSHEY_SIMPLEX, 1, - [0, 255, 0], 4) + (pt1[0], pt1[1] - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, + [0, 255, 0], 2) return img @@ -106,6 +106,8 @@ def YOLO(): image = cvDrawBoxes(detections, frame_resized) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) print(1/(time.time()-prev_time)) + cv2.imshow('Demo', image) + cv2.waitKey(3) cap.release() out.release() From 6c28da5def5e87d377059dab6407cfe3cf6a87f6 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 7 Feb 2019 18:05:58 +0300 Subject: [PATCH 0318/1365] Draw top5 accuracy on the Loss-chart for training Classifier --- src/classifier.c | 80 ++++++++++++++++++++++++++++++++++++++---------- src/detector.c | 25 ++++++++------- src/image.c | 5 ++- 3 files changed, 80 insertions(+), 30 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index fb72041368a..99fadabd487 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -23,13 +23,15 @@ image get_image_from_stream(CvCapture *cap); image get_image_from_stream_cpp(CvCapture *cap); #include "http_stream.h" - IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show); + void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, - float precision, int draw_precision, int dont_show, int mjpeg_port); + float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port); #endif +float validate_classifier_single(char *datacfg, char *filename, char *weightfile, network *existing_net, int topk_custom); + float *get_regression_values(char **labels, int n) { float *v = calloc(n, sizeof(float)); @@ -42,7 +44,7 @@ float *get_regression_values(char **labels, int n) return v; } -void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int mjpeg_port) +void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int mjpeg_port, int calc_topk) { int i; @@ -83,7 +85,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, list *plist = get_paths(train_list); char **paths = (char **)list_to_array(plist); printf("%d\n", plist->size); - int N = plist->size; + int train_images_num = plist->size; clock_t time; load_args args = {0}; @@ -105,14 +107,14 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, args.paths = paths; args.classes = classes; args.n = imgs; - args.m = N; + args.m = train_images_num; args.labels = labels; args.type = CLASSIFICATION_DATA; #ifdef OPENCV //args.threads = 3; IplImage* img = NULL; - float max_img_loss = 5; + float max_img_loss = 10; int number_of_lines = 100; int img_size = 1000; img = draw_train_chart(max_img_loss, net.max_batches, number_of_lines, img_size, dont_show); @@ -126,6 +128,8 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int iter_save = get_current_batch(net); int iter_save_last = get_current_batch(net); + int iter_topk = get_current_batch(net); + float topk = 0; while(get_current_batch(net) < net.max_batches || net.max_batches == 0){ time=clock(); @@ -152,9 +156,32 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i = get_current_batch(net); - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); + int calc_topk_for_each = iter_topk + 4 * train_images_num / (net.batch * net.subdivisions); // calculate TOPk for each 4 Epochs + calc_topk_for_each = fmax(calc_topk_for_each, net.burn_in); + calc_topk_for_each = fmax(calc_topk_for_each, 1000); + if (i % 10 == 0) { + if (calc_topk) { + fprintf(stderr, "\n (next TOP5 calculation at %d iterations) ", calc_topk_for_each); + if (topk > 0) fprintf(stderr, " Last accuracy TOP5 = %2.2f %% \n", topk * 100); + } + + if (net.cudnn_half) { + if (i < net.burn_in * 3) fprintf(stderr, " Tensor Cores are disabled until the first %d iterations are reached.\n", 3 * net.burn_in); + else fprintf(stderr, " Tensor Cores are used.\n"); + } + } + + int draw_precision = 0; + if (calc_topk && (i >= calc_topk_for_each || i == net.max_batches)) { + iter_topk = i; + topk = validate_classifier_single(datacfg, cfgfile, weightfile, &net, 5); // calc TOP5 + printf("\n accuracy TOP5 = %f \n", topk); + draw_precision = 1; + } + + printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); #ifdef OPENCV - draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, -1, 0, dont_show, mjpeg_port); + draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, "top5", dont_show, mjpeg_port); #endif // OPENCV if (i >= (iter_save + 1000)) { @@ -512,14 +539,25 @@ void validate_classifier_full(char *datacfg, char *filename, char *weightfile) } -void validate_classifier_single(char *datacfg, char *filename, char *weightfile) +float validate_classifier_single(char *datacfg, char *filename, char *weightfile, network *existing_net, int topk_custom) { int i, j; - network net = parse_network_cfg(filename); - if(weightfile){ - load_weights(&net, weightfile); + network net; + int old_batch = -1; + if (existing_net) { + net = *existing_net; // for validation during training + old_batch = net.batch; + set_batch_network(&net, 1); + } + else { + net = parse_network_cfg_custom(filename, 1, 0); + if (weightfile) { + load_weights(&net, weightfile); + } + //set_batch_network(&net, 1); + fuse_conv_batchnorm(net); + calculate_binary_weights(net); } - set_batch_network(&net, 1); srand(time(0)); list *options = read_data_cfg(datacfg); @@ -530,7 +568,9 @@ void validate_classifier_single(char *datacfg, char *filename, char *weightfile) char *valid_list = option_find_str(options, "valid", "data/train.list"); int classes = option_find_int(options, "classes", 2); int topk = option_find_int(options, "top", 1); + if (topk_custom > 0) topk = topk_custom; // for validation during training if (topk > classes) topk = classes; + printf(" TOP calculation...\n"); char **labels = get_labels(label_list); list *plist = get_paths(valid_list); @@ -571,8 +611,15 @@ void validate_classifier_single(char *datacfg, char *filename, char *weightfile) if(indexes[j] == class_id) avg_topk += 1; } - printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1)); + if (existing_net) printf("\r"); + else printf("\n"); + printf("%d: top 1: %f, top %d: %f", i, avg_acc/(i+1), topk, avg_topk/(i+1)); + } + if (existing_net) { + set_batch_network(&net, old_batch); } + float topk_result = avg_topk / i; + return topk_result; } void validate_classifier_multi(char *datacfg, char *filename, char *weightfile) @@ -1198,6 +1245,7 @@ void run_classifier(int argc, char **argv) } int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1); + int calc_topk = find_int_arg(argc, argv, "-topk", -1); char *gpu_list = find_char_arg(argc, argv, "-gpus", 0); int *gpus = 0; int gpu = 0; @@ -1233,13 +1281,13 @@ void run_classifier(int argc, char **argv) int layer = layer_s ? atoi(layer_s) : -1; if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename, top); else if(0==strcmp(argv[2], "try")) try_classifier(data, cfg, weights, filename, atoi(layer_s)); - else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear, dont_show, mjpeg_port); + else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear, dont_show, mjpeg_port, calc_topk); else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename); else if(0==strcmp(argv[2], "gun")) gun_classifier(data, cfg, weights, cam_index, filename); else if(0==strcmp(argv[2], "threat")) threat_classifier(data, cfg, weights, cam_index, filename); else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights, layer); else if(0==strcmp(argv[2], "label")) label_classifier(data, cfg, weights); - else if(0==strcmp(argv[2], "valid")) validate_classifier_single(data, cfg, weights); + else if(0==strcmp(argv[2], "valid")) validate_classifier_single(data, cfg, weights, NULL, -1); else if(0==strcmp(argv[2], "validmulti")) validate_classifier_multi(data, cfg, weights); else if(0==strcmp(argv[2], "valid10")) validate_classifier_10(data, cfg, weights); else if(0==strcmp(argv[2], "validcrop")) validate_classifier_crop(data, cfg, weights); diff --git a/src/detector.c b/src/detector.c index fb29ff18dc1..5524ac19cfd 100644 --- a/src/detector.c +++ b/src/detector.c @@ -25,10 +25,10 @@ #pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib") #endif - IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show); + void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, - float precision, int draw_precision, int dont_show, int mjpeg_port); + float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port); #define CV_RGB(r, g, b) cvScalar( (b), (g), (r), 0 ) #endif // OPENCV @@ -244,20 +244,23 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i avg_loss = avg_loss*.9 + loss*.1; i = get_current_batch(net); - if (mean_average_precision > 0) { - printf("\n Last accuracy mAP@0.5 = %2.2f %% ", mean_average_precision*100); + + int calc_map_for_each = iter_map + 4 * train_images_num / (net.batch * net.subdivisions); // calculate mAP for each 4 Epochs + calc_map_for_each = fmax(calc_map_for_each, net.burn_in); + calc_map_for_each = fmax(calc_map_for_each, 1000); + if (calc_map) { + printf("\n (next mAP calculation at %d iterations) ", calc_map_for_each); + if (mean_average_precision > 0) printf("\n Last accuracy mAP@0.5 = %2.2f %% ", mean_average_precision * 100); } - int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions); // calculate mAP for each 4 Epochs - if (calc_map) printf("\n (next mAP calculation at %d iterations) ", (iter_map + calc_map_for_each)); if (net.cudnn_half) { - if (i < net.burn_in * 3) printf("\n Tensor Cores are disabled until the first %d iterations are reached.", 3 * net.burn_in); - else printf("\n Tensor Cores are used."); + if (i < net.burn_in * 3) fprintf(stderr, "\n Tensor Cores are disabled until the first %d iterations are reached.", 3 * net.burn_in); + else fprintf(stderr, "\n Tensor Cores are used."); } printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now() - time), i*imgs); int draw_precision = 0; - if (calc_map && (i >= (iter_map + calc_map_for_each) || i == net.max_batches) && i >= net.burn_in && i >= 1000) { + if (calc_map && (i >= calc_map_for_each || i == net.max_batches)) { if (l.random) { printf("Resizing to initial size: %d x %d \n", init_w, init_h); args.w = init_w; @@ -281,7 +284,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i draw_precision = 1; } #ifdef OPENCV - draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, mean_average_precision, draw_precision, dont_show, mjpeg_port); + draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, mean_average_precision, draw_precision, "mAP%", dont_show, mjpeg_port); #endif // OPENCV //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) { @@ -975,7 +978,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa thresh_calc_avg_iou, tp_for_thresh, fp_for_thresh, unique_truth_count - tp_for_thresh, avg_iou * 100); mean_average_precision = mean_average_precision / classes; - printf("\n IoU threshold = %2.2f %% \n", iou_thresh * 100); + printf("\n IoU threshold = %2.0f %% \n", iou_thresh * 100); printf(" mean average precision (mAP@%0.2f) = %f, or %2.2f %% \n", iou_thresh, mean_average_precision, mean_average_precision * 100); diff --git a/src/image.c b/src/image.c index b4b57976eb4..f95b478a948 100644 --- a/src/image.c +++ b/src/image.c @@ -762,7 +762,7 @@ IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_li } void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, - float precision, int draw_precision, int dont_show, int mjpeg_port) + float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port) { int img_offset = 50; int draw_size = img_size - img_offset; @@ -780,7 +780,7 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_ static float old_precision = 0; static int iteration_old = 0; static int text_iteration_old = 0; - if(iteration_old == 0) cvPutText(img, "mAP%", cvPoint(0, 12), &font, CV_RGB(255, 0, 0)); + if(iteration_old == 0) cvPutText(img, accuracy_name, cvPoint(0, 12), &font, CV_RGB(255, 0, 0)); cvLine(img, cvPoint(img_offset + draw_size * (float)iteration_old / max_batches, draw_size * (1 - old_precision)), @@ -815,7 +815,6 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_ k = cvWaitKey(20); } if (k == 's' || current_batch == (max_batches - 1) || current_batch % 100 == 0) { - //cvSaveImage("chart.jpg", img, 0); save_cv_png(img, "chart.png"); cvPutText(img, "- Saved", cvPoint(250, img_size - 10), &font, CV_RGB(255, 0, 0)); } From 98103552fb5db06c668df033166e54a9189979fc Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 7 Feb 2019 18:19:34 +0300 Subject: [PATCH 0319/1365] Minor fix --- src/classifier.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/classifier.c b/src/classifier.c index 99fadabd487..11b6968d004 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -1245,7 +1245,6 @@ void run_classifier(int argc, char **argv) } int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1); - int calc_topk = find_int_arg(argc, argv, "-topk", -1); char *gpu_list = find_char_arg(argc, argv, "-gpus", 0); int *gpus = 0; int gpu = 0; @@ -1270,6 +1269,7 @@ void run_classifier(int argc, char **argv) } int dont_show = find_arg(argc, argv, "-dont_show"); + int calc_topk = find_arg(argc, argv, "-topk"); int cam_index = find_int_arg(argc, argv, "-c", 0); int top = find_int_arg(argc, argv, "-t", 0); int clear = find_arg(argc, argv, "-clear"); From 58de6b2d3d806b4b5ee397672bc386049f2fbe02 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 8 Feb 2019 00:48:27 +0300 Subject: [PATCH 0320/1365] Minor fix for CHECK_CUDA() --- src/cuda.c | 18 +++++++----------- src/maxpool_layer.c | 10 +++++----- 2 files changed, 12 insertions(+), 16 deletions(-) diff --git a/src/cuda.c b/src/cuda.c index 6a2407a3ab0..b790e7f5afc 100644 --- a/src/cuda.c +++ b/src/cuda.c @@ -32,7 +32,6 @@ void check_error(cudaError_t status) const char *s = cudaGetErrorString(status); char buffer[256]; printf("CUDA Error: %s\n", s); - assert(0); snprintf(buffer, 256, "CUDA Error: %s", s); #ifdef WIN32 getchar(); @@ -44,7 +43,6 @@ void check_error(cudaError_t status) const char *s = cudaGetErrorString(status2); char buffer[256]; printf("CUDA Error Prev: %s\n", s); - assert(0); snprintf(buffer, 256, "CUDA Error Prev: %s", s); #ifdef WIN32 getchar(); @@ -56,12 +54,12 @@ void check_error(cudaError_t status) void check_error_extended(cudaError_t status, const char *file, int line, const char *date_time) { if (status != cudaSuccess) - printf("CUDA Prev Error: file: %s() : line: %d : build time: %s \n", file, line, date_time); + printf("CUDA status Error: file: %s() : line: %d : build time: %s \n", file, line, date_time); #ifdef DEBUG status = cudaDeviceSynchronize(); -#endif if (status != cudaSuccess) - printf("CUDA Error: file: %s() : line: %d : build time: %s \n", file, line, date_time); + printf("CUDA status = cudaDeviceSynchronize() Error: file: %s() : line: %d : build time: %s \n", file, line, date_time); +#endif check_error(status); } @@ -150,7 +148,6 @@ void cudnn_check_error(cudnnStatus_t status) const char *s = cudnnGetErrorString(status); char buffer[256]; printf("cuDNN Error: %s\n", s); - assert(0); snprintf(buffer, 256, "cuDNN Error: %s", s); #ifdef WIN32 getchar(); @@ -162,7 +159,6 @@ void cudnn_check_error(cudnnStatus_t status) const char *s = cudnnGetErrorString(status2); char buffer[256]; printf("cuDNN Error Prev: %s\n", s); - assert(0); snprintf(buffer, 256, "cuDNN Error Prev: %s", s); #ifdef WIN32 getchar(); @@ -173,13 +169,13 @@ void cudnn_check_error(cudnnStatus_t status) void cudnn_check_error_extended(cudnnStatus_t status, const char *file, int line, const char *date_time) { - if (status != cudaSuccess) - printf("\n cuDNN Prev Error in: file: %s() : line: %d : build time: %s \n", file, line, date_time); + if (status != CUDNN_STATUS_SUCCESS) + printf("\n cuDNN status Error in: file: %s() : line: %d : build time: %s \n", file, line, date_time); #ifdef DEBUG status = cudaDeviceSynchronize(); + if (status != CUDNN_STATUS_SUCCESS) + printf("\n cuDNN status = cudaDeviceSynchronize() Error in: file: %s() : line: %d : build time: %s \n", file, line, date_time); #endif - if (status != cudaSuccess) - printf("\n cuDNN Error in: file: %s() : line: %d : build time: %s \n", file, line, date_time); cudnn_check_error(status); } #endif diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index 8c1265e3551..2eaa7dec012 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -97,16 +97,16 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h) l->output = realloc(l->output, output_size * sizeof(float)); l->delta = realloc(l->delta, output_size * sizeof(float)); - #ifdef GPU - cuda_free((float *)l->indexes_gpu); - cuda_free(l->output_gpu); - cuda_free(l->delta_gpu); +#ifdef GPU + CHECK_CUDA(cudaFree((float *)l->indexes_gpu)); + CHECK_CUDA(cudaFree(l->output_gpu)); + CHECK_CUDA(cudaFree(l->delta_gpu)); l->indexes_gpu = cuda_make_int_array(output_size); l->output_gpu = cuda_make_array(l->output, output_size); l->delta_gpu = cuda_make_array(l->delta, output_size); cudnn_maxpool_setup(l); - #endif +#endif } void forward_maxpool_layer(const maxpool_layer l, network_state state) From 6832290eee1b421226013ac28177614f3e1e4903 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 8 Feb 2019 00:49:51 +0300 Subject: [PATCH 0321/1365] Fixed set_batch_network(), when workspace larger for smaller batch --- src/network.c | 55 +++++++++++++++++++++++++++++++++++++++------------ 1 file changed, 42 insertions(+), 13 deletions(-) diff --git a/src/network.c b/src/network.c index ba8a1721b53..115edeb0cbc 100644 --- a/src/network.c +++ b/src/network.c @@ -360,34 +360,63 @@ float train_network_batch(network net, data d, int n) return (float)sum/(n*batch); } +int recalculate_workspace_size(network *net) +{ +#ifdef GPU + cuda_set_device(net->gpu_index); + if (gpu_index >= 0) cuda_free(net->workspace); +#endif + int i; + size_t workspace_size = 0; + for (i = 0; i < net->n; ++i) { + layer l = net->layers[i]; + //printf(" %d: layer = %d,", i, l.type); + if (l.type == CONVOLUTIONAL) { + l.workspace_size = get_convolutional_workspace_size(l); + } + else if (l.type == CONNECTED) { + l.workspace_size = get_connected_workspace_size(l); + } + if (l.workspace_size > workspace_size) workspace_size = l.workspace_size; + net->layers[i] = l; + } + +#ifdef GPU + if (gpu_index >= 0) { + printf("\n try to allocate additional workspace_size = %1.2f MB \n", (float)workspace_size / 1000000); + net->workspace = cuda_make_array(0, workspace_size / sizeof(float) + 1); + printf(" CUDA allocate done! \n"); + } + else { + free(net->workspace); + net->workspace = calloc(1, workspace_size); + } +#else + free(net->workspace); + net->workspace = calloc(1, workspace_size); +#endif + //fprintf(stderr, " Done!\n"); + return 0; +} + void set_batch_network(network *net, int b) { net->batch = b; int i; for(i = 0; i < net->n; ++i){ net->layers[i].batch = b; + #ifdef CUDNN if(net->layers[i].type == CONVOLUTIONAL){ cudnn_convolutional_setup(net->layers + i, cudnn_fastest); - /* - layer *l = net->layers + i; - cudnn_convolutional_setup(l, cudnn_fastest); - // check for excessive memory consumption - size_t free_byte; - size_t total_byte; - check_error(cudaMemGetInfo(&free_byte, &total_byte)); - if (l->workspace_size > free_byte || l->workspace_size >= total_byte / 2) { - printf(" used slow CUDNN algo without Workspace! \n"); - cudnn_convolutional_setup(l, cudnn_smallest); - l->workspace_size = get_workspace_size(*l); - } - */ } else if (net->layers[i].type == MAXPOOL) { cudnn_maxpool_setup(net->layers + i); } #endif + } + recalculate_workspace_size(net); // recalculate workspace size } int resize_network(network *net, int w, int h) From 9e07605bc593539835f1982863a7ef6f159af92e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 8 Feb 2019 00:51:20 +0300 Subject: [PATCH 0322/1365] get_connected_workspace_size() and get_convolutional_workspace_size() --- src/connected_layer.c | 6 +++++- src/convolutional_layer.c | 20 ++++++++++---------- src/convolutional_layer.h | 1 + 3 files changed, 16 insertions(+), 11 deletions(-) diff --git a/src/connected_layer.c b/src/connected_layer.c index 66f7c91ca8c..e54837acb5c 100644 --- a/src/connected_layer.c +++ b/src/connected_layer.c @@ -11,8 +11,11 @@ #include #include -static size_t get_connected_workspace_size(layer l) { +size_t get_connected_workspace_size(layer l) +{ #ifdef CUDNN + return get_convolutional_workspace_size(l); + /* if (gpu_index >= 0) { size_t most = 0; size_t s = 0; @@ -42,6 +45,7 @@ static size_t get_connected_workspace_size(layer l) { if (s > most) most = s; return most; } + */ #endif return 0; } diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 6c08c0776fc..80884f7f14a 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -100,7 +100,7 @@ image get_convolutional_delta(convolutional_layer l) return float_to_image(w,h,c,l.delta); } -size_t get_workspace_size(layer l){ +size_t get_workspace_size32(layer l){ #ifdef CUDNN if(gpu_index >= 0){ size_t most = 0; @@ -173,6 +173,12 @@ size_t get_workspace_size16(layer l) { //return (size_t)l.out_h*l.out_w*l.size*l.size*l.c * sizeof(float); } +size_t get_convolutional_workspace_size(layer l) { + size_t workspace_size = get_workspace_size32(l); + size_t workspace_size16 = get_workspace_size16(l); + if (workspace_size16 > workspace_size) workspace_size = workspace_size16; + return workspace_size; +} #ifdef GPU #ifdef CUDNN void create_convolutional_cudnn_tensors(layer *l) @@ -462,9 +468,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, #endif } #endif - l.workspace_size = get_workspace_size(l); - size_t workspace_size16 = get_workspace_size16(l); - if (workspace_size16 > l.workspace_size) l.workspace_size = workspace_size16; + l.workspace_size = get_convolutional_workspace_size(l); //fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c); l.bflops = (2.0 * l.n * l.size*l.size*l.c * l.out_h*l.out_w) / 1000000000.; @@ -566,9 +570,7 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) cudnn_convolutional_setup(l, cudnn_fastest); #endif #endif - l->workspace_size = get_workspace_size(*l); - size_t workspace_size16 = get_workspace_size16(*l); - if (workspace_size16 > l->workspace_size) l->workspace_size = workspace_size16; + l->workspace_size = get_convolutional_workspace_size(*l); #ifdef CUDNN // check for excessive memory consumption @@ -578,9 +580,7 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) if (l->workspace_size > free_byte || l->workspace_size >= total_byte / 2) { printf(" used slow CUDNN algo without Workspace! Need memory: %zu, available: %zu\n", l->workspace_size, (free_byte < total_byte/2) ? free_byte : total_byte/2); cudnn_convolutional_setup(l, cudnn_smallest); - l->workspace_size = get_workspace_size(*l); - size_t workspace_size16 = get_workspace_size16(*l); - if (workspace_size16 > l->workspace_size) l->workspace_size = workspace_size16; + l->workspace_size = get_convolutional_workspace_size(*l); } #endif } diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h index 19a0625198f..d6ec95515c2 100644 --- a/src/convolutional_layer.h +++ b/src/convolutional_layer.h @@ -22,6 +22,7 @@ void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int #ifdef CUDNN void cudnn_convolutional_setup(layer *l, int cudnn_preference); void create_convolutional_cudnn_tensors(layer *l); +size_t get_convolutional_workspace_size(layer l); void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16); #endif #endif From f154d2070aecdacd0cdfb7270e159e754ae74261 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 8 Feb 2019 00:51:41 +0300 Subject: [PATCH 0323/1365] Fixed RNN (LSTM, RNN, CRNN, GRU) for CUDNN_HALF=1 --- src/crnn_layer.c | 10 ++++++++++ src/network_kernels.cu | 11 ++++++----- 2 files changed, 16 insertions(+), 5 deletions(-) diff --git a/src/crnn_layer.c b/src/crnn_layer.c index 56ead3af448..5a9d6f58988 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -215,6 +215,16 @@ void forward_crnn_layer_gpu(layer l, network_state state) layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); layer output_layer = *(l.output_layer); + /* +#ifdef CUDNN_HALF +// slow and bad + s.index = state.index; + s.net = state.net; + cuda_convert_f32_to_f16(input_layer.weights_gpu, input_layer.c*input_layer.n*input_layer.size*input_layer.size, input_layer.weights_gpu16); + cuda_convert_f32_to_f16(self_layer.weights_gpu, self_layer.c*self_layer.n*self_layer.size*self_layer.size, self_layer.weights_gpu16); + cuda_convert_f32_to_f16(output_layer.weights_gpu, output_layer.c*output_layer.n*output_layer.size*output_layer.size, output_layer.weights_gpu16); +#endif //CUDNN_HALF + */ fill_ongpu(l.outputs * l.batch * l.steps, 0, output_layer.delta_gpu, 1); fill_ongpu(l.hidden * l.batch * l.steps, 0, self_layer.delta_gpu, 1); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index edb4cd5e0a5..cb6c0f38daa 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -1,6 +1,7 @@ -#include "cuda_runtime.h" -#include "curand.h" -#include "cublas_v2.h" +//#include "cuda_runtime.h" +//#include "curand.h" +//#include "cublas_v2.h" +#include "cuda.h" extern "C" { #include @@ -61,7 +62,7 @@ void forward_network_gpu(network net, network_state state) //printf("\n layer %d - type: %d - \n", i, l.type); //start_timer(); l.forward_gpu(l, state); - //cudaDeviceSynchronize(); + //CHECK_CUDA(cudaDeviceSynchronize()); //stop_timer_and_show(); if(net.wait_stream) @@ -152,7 +153,7 @@ void forward_backward_network_gpu(network net, float *x, float *y) int i; for (i = 0; i < net.n; ++i) { layer l = net.layers[i]; - if (l.weights_gpu) { + if (l.weights_gpu && l.weights_gpu16) { assert((l.c*l.n*l.size*l.size) > 0); cuda_convert_f32_to_f16(l.weights_gpu, l.c*l.n*l.size*l.size, l.weights_gpu16); } From 7dff7365cbc408b3254fc8e540d4c198d8ea7888 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 11 Feb 2019 23:16:30 +0300 Subject: [PATCH 0324/1365] Minor demo fix --- src/demo.c | 89 ++++++++++++++++++------------------------------------ 1 file changed, 30 insertions(+), 59 deletions(-) diff --git a/src/demo.c b/src/demo.c index 5ed2d88a3dc..c4cb8df1c1d 100644 --- a/src/demo.c +++ b/src/demo.c @@ -31,8 +31,9 @@ static char **demo_names; static image **demo_alphabet; static int demo_classes; -static float **probs; -static box *boxes; +static int nboxes = 0; +static detection *dets = NULL; + static network net; static image in_s ; static image det_s; @@ -50,7 +51,6 @@ static image images[FRAMES]; static IplImage* ipl_images[FRAMES]; static float *avg; -void draw_detections_cv(IplImage* show_img, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes); void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output); void show_image_cv_ipl(IplImage *disp, const char *name); void save_cv_png(IplImage *img, const char *name); @@ -86,8 +86,6 @@ void *fetch_in_thread(void *ptr) void *detect_in_thread(void *ptr) { - float nms = .45; // 0.4F - layer l = net.layers[net.n-1]; float *X = det_s.data; float *prediction = network_predict(net, X); @@ -98,33 +96,14 @@ void *detect_in_thread(void *ptr) free_image(det_s); - int nboxes = 0; - detection *dets = NULL; - if (letter_box) - dets = get_network_boxes(&net, in_img->width, in_img->height, demo_thresh, demo_thresh, 0, 1, &nboxes, 1); // letter box - else - dets = get_network_boxes(&net, det_s.w, det_s.h, demo_thresh, demo_thresh, 0, 1, &nboxes, 0); // resized - //if (nms) do_nms_obj(dets, nboxes, l.classes, nms); // bad results - if (nms) do_nms_sort(dets, nboxes, l.classes, nms); - - - printf("\033[2J"); - printf("\033[1;1H"); - printf("\nFPS:%.1f\n",fps); - printf("Objects:\n\n"); - ipl_images[demo_index] = det_img; det_img = ipl_images[(demo_index + FRAMES / 2 + 1) % FRAMES]; - demo_index = (demo_index + 1)%FRAMES; - - ++frame_id; - if (demo_json_port > 0) { - int timeout = 200; - send_json(dets, nboxes, l.classes, demo_names, frame_id, demo_json_port, timeout); - } + demo_index = (demo_index + 1) % FRAMES; - draw_detections_cv_v3(det_img, dets, nboxes, demo_thresh, demo_names, demo_alphabet, demo_classes, demo_ext_output); - free_detections(dets, nboxes); + if (letter_box) + dets = get_network_boxes(&net, in_img->width, in_img->height, demo_thresh, demo_thresh, 0, 1, &nboxes, 1); // letter box + else + dets = get_network_boxes(&net, net.w, net.h, demo_thresh, demo_thresh, 0, 1, &nboxes, 0); // resized return 0; } @@ -163,20 +142,12 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int if(filename){ printf("video file: %s\n", filename); -//#ifdef CV_VERSION_EPOCH // OpenCV 2.x -// cap = cvCaptureFromFile(filename); -//#else // OpenCV 3.x cpp_video_capture = 1; cap = get_capture_video_stream(filename); -//#endif }else{ printf("Webcam index: %d\n", cam_index); -//#ifdef CV_VERSION_EPOCH // OpenCV 2.x -// cap = cvCaptureFromCAM(cam_index); -//#else // OpenCV 3.x cpp_video_capture = 1; cap = get_capture_webcam(cam_index); -//#endif } if (!cap) { @@ -193,10 +164,6 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int for(j = 0; j < FRAMES; ++j) predictions[j] = (float *) calloc(l.outputs, sizeof(float)); for(j = 0; j < FRAMES; ++j) images[j] = make_image(1,1,3); - boxes = (box *)calloc(l.w*l.h*l.n, sizeof(box)); - probs = (float **)calloc(l.w*l.h*l.n, sizeof(float *)); - for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float *)calloc(l.classes, sizeof(float *)); - if (l.classes != demo_classes) { printf("Parameters don't match: in cfg-file classes=%d, in data-file classes=%d \n", l.classes, demo_classes); getchar(); @@ -254,10 +221,31 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int while(1){ ++count; - if(1){ + { if(pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed"); if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed"); + float nms = .45; // 0.4F + int local_nboxes = nboxes; + detection *local_dets = dets; + + //if (nms) do_nms_obj(local_dets, local_nboxes, l.classes, nms); // bad results + if (nms) do_nms_sort(local_dets, local_nboxes, l.classes, nms); + + printf("\033[2J"); + printf("\033[1;1H"); + printf("\nFPS:%.1f\n", fps); + printf("Objects:\n\n"); + + ++frame_id; + if (demo_json_port > 0) { + int timeout = 200; + send_json(local_dets, local_nboxes, l.classes, demo_names, frame_id, demo_json_port, timeout); + } + + draw_detections_cv_v3(show_img, local_dets, local_nboxes, demo_thresh, demo_names, demo_alphabet, demo_classes, demo_ext_output); + free_detections(local_dets, local_nboxes); + if(!prefix){ if (!dont_show) { show_image_cv_ipl(show_img, "Demo"); @@ -281,7 +269,6 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int // if you run it with param -mjpeg_port 8090 then open URL in your web-browser: http://localhost:8090 if (mjpeg_port > 0 && show_img) { - //int port = 8090; int port = mjpeg_port; int timeout = 200; int jpeg_quality = 30; // 1 - 100 @@ -306,18 +293,6 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int } det_img = in_img; det_s = in_s; - }else { - fetch_in_thread(0); - det_img = in_img; - det_s = in_s; - detect_in_thread(0); - - show_img = det_img; - if (!dont_show) { - show_image_cv_ipl(show_img, "Demo"); - cvWaitKey(1); - } - cvReleaseImage(&show_img); } --delay; if(delay < 0){ @@ -344,10 +319,6 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int for (j = 0; j < FRAMES; ++j) free(predictions[j]); for (j = 0; j < FRAMES; ++j) free_image(images[j]); - for (j = 0; j < l.w*l.h*l.n; ++j) free(probs[j]); - free(boxes); - free(probs); - free_ptrs(names, net.layers[net.n - 1].classes); int i; From 9e138adf09a43407aad73bf0a85669cbb7652f29 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 12 Feb 2019 02:01:10 +0300 Subject: [PATCH 0325/1365] more accurate time measurements in Demo --- src/demo.c | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/src/demo.c b/src/demo.c index c4cb8df1c1d..9029ff1e13e 100644 --- a/src/demo.c +++ b/src/demo.c @@ -298,8 +298,10 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int if(delay < 0){ delay = frame_skip; - double after = get_wall_time(); - float curr = 1./(after - before); + //double after = get_wall_time(); + //float curr = 1./(after - before); + double after = get_time_point(); // more accurate time measurements + float curr = 1000000. / (after - before); fps = curr; before = after; } From 5448e07445ab05470f5c89bf642e65199686482f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 12 Feb 2019 02:05:15 +0300 Subject: [PATCH 0326/1365] Try to fuse conv_xnor+shortcut -> conv_xnor --- include/darknet.h | 2 ++ src/convolutional_kernels.cu | 6 +++-- src/im2col.h | 3 ++- src/im2col_kernels.cu | 43 ++++++++++++++++++++++++------------ src/network.c | 15 ++++++++++++- 5 files changed, 51 insertions(+), 18 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 6675599c4c3..1478b247c16 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -457,6 +457,8 @@ struct layer { float *binary_input_gpu; float *binary_weights_gpu; + float *bin_conv_shortcut_in_gpu; + float *bin_conv_shortcut_out_gpu; float * mean_gpu; float * variance_gpu; diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index be90728fdfe..7c26f42e0a5 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -300,7 +300,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //start_timer(); gemm_nn_custom_bin_mean_transposed_gpu(m, n, k, (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, - new_ldb, l.output_gpu, n, l.mean_arr_gpu, l.biases_gpu, l.activation == LEAKY); + new_ldb, l.output_gpu, n, l.mean_arr_gpu, l.biases_gpu, l.activation == LEAKY, + l.bin_conv_shortcut_in_gpu, l.bin_conv_shortcut_out_gpu); //cudaDeviceSynchronize(); //stop_timer_and_show_name("gemm_nn_custom_bin_mean_transposed_gpu"); @@ -370,7 +371,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //start_timer(); gemm_nn_custom_bin_mean_transposed_gpu(m, n, k, (unsigned char *)l.align_bit_weights_gpu, new_ldb, (unsigned char *)l.transposed_align_workspace_gpu, - new_ldb, l.output_gpu, n, l.mean_arr_gpu, l.biases_gpu, l.activation == LEAKY); + new_ldb, l.output_gpu, n, l.mean_arr_gpu, l.biases_gpu, l.activation == LEAKY, + l.bin_conv_shortcut_in_gpu, l.bin_conv_shortcut_out_gpu); //cudaDeviceSynchronize(); //stop_timer_and_show_name("gemm_nn_custom_bin_mean_transposed_gpu"); //} diff --git a/src/im2col.h b/src/im2col.h index 7957b8eb54b..ab4f3483fba 100644 --- a/src/im2col.h +++ b/src/im2col.h @@ -44,7 +44,8 @@ void fill_int8_gpu(unsigned char *src, unsigned char val, size_t size); void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, unsigned char *A, int lda, unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr, float *bias, int leaky_activation); + float *C, int ldc, float *mean_arr, float *bias, int leaky_activation, + float *shortcut_in_gpu, float *shortcut_out_gpu); // sequentially - BAD void gemm_nn_custom_bin_mean_transposed_sequentially_gpu(int M, int N, int K, diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 4d22beda6c7..1cb4cf342e3 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -1247,7 +1247,8 @@ int warpAllReduceSum(int val) { __global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel(int M, int N, int K, unsigned char *A, int lda, unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr, float *bias_arr, int leaky_activation) + float *C, int ldc, float *mean_arr, float *bias_arr, int leaky_activation, + float *shortcut_in_gpu, float *shortcut_out_gpu) { // total 57% int index = blockIdx.x*blockDim.x + threadIdx.x; @@ -1411,9 +1412,14 @@ __global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel(int M, int N, i float bias_val = bias_arr[i + i_d]; float dst_val = count *mean_val + bias_val; if (leaky_activation) - dst_val = (dst_val > 0) ? (dst_val) : (0.1f*dst_val); // Leaky activation + dst_val = (dst_val >= 0) ? (dst_val) : (0.1f*dst_val); // Leaky activation + + size_t out_index = (i + i_d)*ldc + (c_x * 8 + j + j_d); + C[out_index] = dst_val; - C[(i + i_d)*ldc + (c_x*8 + j + j_d)] = dst_val; + if (shortcut_out_gpu) { + shortcut_out_gpu[out_index] = shortcut_in_gpu[out_index] + dst_val; + } } } @@ -1574,7 +1580,8 @@ __global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel(int M, int N, i __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int K, unsigned char *A, int lda, unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr, float *bias_arr, int leaky_activation) + float *C, int ldc, float *mean_arr, float *bias_arr, int leaky_activation, + float *shortcut_in_gpu, float *shortcut_out_gpu) { // total 57% int index = blockIdx.x*blockDim.x + threadIdx.x; @@ -1628,7 +1635,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int //ulonglong4 a_bit256 = *((ulonglong4 *)(A + A_i)); // weights ulonglong4 a_bit256 = *((ulonglong4 *)(A_s + A_i)); // weights ulonglong4 b_bit256 = *((ulonglong4 *)(B + B_i)); // input - c_bit256 = xnor_int256(a_bit256, b_bit256); + c_bit256 = xor_int256(a_bit256, b_bit256); int tmp_count = __popcll(c_bit256.w) + __popcll(c_bit256.x) + __popcll(c_bit256.y) + __popcll(c_bit256.z); @@ -1661,7 +1668,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int //uint64_t a_bit64 = *((uint64_t *)(A + A_i)); // weights uint64_t a_bit64 = *((uint64_t *)(A_s + A_i)); // weights uint64_t b_bit64 = *((uint64_t *)(B + B_i)); // input - c_bit64 = xnor_int64(a_bit64, b_bit64); + c_bit64 = xor_int64(a_bit64, b_bit64); int tmp_count = __popcll(c_bit64); int sum_count = warpAllReduceSum(tmp_count); @@ -1691,7 +1698,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int //uint64_t a_bit64 = *((uint64_t *)(A + A_i)); // weights uint32_t a_bit32 = *((uint32_t *)(A_s + A_i)); // weights uint32_t b_bit32 = *((uint32_t *)(B + B_i)); // input - uint32_t c_bit32 = xnor_int32(a_bit32, b_bit32); + uint32_t c_bit32 = xor_int32(a_bit32, b_bit32); int tmp_count = __popc(c_bit32); int sum_count = warpAllReduceSum(tmp_count); @@ -1712,7 +1719,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int //ulonglong4 a_bit256 = *((ulonglong4 *)(A + (i*lda + k) / 8)); // weights ulonglong4 a_bit256 = *((ulonglong4 *)(A_s + (local_i*lda + k) / 8)); // weights ulonglong4 b_bit256 = *((ulonglong4 *)(B + (j*ldb + k) / 8)); // input - ulonglong4 c_bit256 = xnor_int256(a_bit256, b_bit256); + ulonglong4 c_bit256 = xor_int256(a_bit256, b_bit256); count += __popcll(c_bit256.w) + __popcll(c_bit256.x) + __popcll(c_bit256.y) + __popcll(c_bit256.z); @@ -1724,7 +1731,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int //uint64_t a_bit64 = *((uint64_t *)(A + (i*lda + k) / 8)); // weights uint64_t a_bit64 = *((uint64_t *)(A_s + (local_i*lda + k) / 8)); // weights uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); // input - uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); + uint64_t c_bit64 = xor_int64(a_bit64, b_bit64); count += __popcll(c_bit64); } @@ -1735,8 +1742,13 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int count = count - f1; // remove extra bits (from empty space for align only) float dst_val = (2 * count - K) *mean_val + bias_val; if(leaky_activation) - dst_val = (dst_val > 0) ? (dst_val) : (0.1f*dst_val); // Leaky activation - C[i*ldc + j] = dst_val; + dst_val = (dst_val >= 0) ? (dst_val) : (0.1f*dst_val); // Leaky activation + size_t out_index = i*ldc + j; + C[out_index] = dst_val; + + if (shortcut_out_gpu) { + shortcut_out_gpu[out_index] = shortcut_in_gpu[out_index] + dst_val; + } } } } @@ -1762,7 +1774,8 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, unsigned char *A, int lda, unsigned char *B, int ldb, - float *C, int ldc, float *mean_arr, float *bias, int leaky_activation) + float *C, int ldc, float *mean_arr, float *bias, int leaky_activation, + float *shortcut_in_gpu, float *shortcut_out_gpu) { int size = M*N; const int num_blocks = get_number_of_blocks(size, BLOCK); @@ -1794,7 +1807,8 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, A, lda, B, ldb, C, ldc, - mean_arr, bias, leaky_activation); + mean_arr, bias, leaky_activation, + shortcut_in_gpu, shortcut_out_gpu); //cudaDeviceSynchronize(); //getchar(); @@ -1807,7 +1821,8 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, A, lda, B, ldb, C, ldc, - mean_arr, bias, leaky_activation); + mean_arr, bias, leaky_activation, + shortcut_in_gpu, shortcut_out_gpu); } CHECK_CUDA(cudaPeekAtLastError()); } diff --git a/src/network.c b/src/network.c index 115edeb0cbc..483219b46a0 100644 --- a/src/network.c +++ b/src/network.c @@ -975,7 +975,7 @@ void fuse_conv_batchnorm(network net) } } - +void forward_blank_layer(layer l, network_state state) {} void calculate_binary_weights(network net) { @@ -996,6 +996,19 @@ void calculate_binary_weights(network net) if (net.layers[j].use_bin_output) { l->activation = LINEAR; } + + // fuse conv_xnor + shortcut -> conv_xnor + if ((j + 1) < net.n && net.layers[j].type == CONVOLUTIONAL) { + layer *sc = &net.layers[j + 1]; + if (sc->type == SHORTCUT && sc->w == sc->out_w && sc->h == sc->out_h && sc->c == sc->out_c) + { + l->bin_conv_shortcut_in_gpu = net.layers[net.layers[j + 1].index].output_gpu; + l->bin_conv_shortcut_out_gpu = net.layers[j + 1].output_gpu; + + net.layers[j + 1].type = BLANK; + net.layers[j + 1].forward_gpu = forward_blank_layer; + } + } } } } From 00e992a600ba781da635b4f75fc02b2458639c4e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 12 Feb 2019 02:12:46 +0300 Subject: [PATCH 0327/1365] Compile fix --- src/network.c | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/network.c b/src/network.c index 483219b46a0..226fe410f76 100644 --- a/src/network.c +++ b/src/network.c @@ -997,6 +997,7 @@ void calculate_binary_weights(network net) l->activation = LINEAR; } +#ifdef GPU // fuse conv_xnor + shortcut -> conv_xnor if ((j + 1) < net.n && net.layers[j].type == CONVOLUTIONAL) { layer *sc = &net.layers[j + 1]; @@ -1009,6 +1010,7 @@ void calculate_binary_weights(network net) net.layers[j + 1].forward_gpu = forward_blank_layer; } } +#endif // GPU } } } From ba1f1363c07a0f1085d37febedaf20d689eec8e4 Mon Sep 17 00:00:00 2001 From: David Smith Date: Mon, 11 Feb 2019 18:20:16 -0600 Subject: [PATCH 0328/1365] fix assorted compile warnings --- src/captcha.c | 2 +- src/cifar.c | 4 ++-- src/classifier.c | 4 ++-- src/compare.c | 2 +- src/convolutional_layer.h | 1 + src/cost_layer.c | 3 ++- src/crnn_layer.c | 6 +++--- src/go.c | 2 +- src/matrix.c | 3 +-- src/network.c | 6 +++--- src/region_layer.c | 4 +++- src/rnn.c | 2 +- src/tag.c | 2 +- src/writing.c | 2 +- 14 files changed, 23 insertions(+), 20 deletions(-) diff --git a/src/captcha.c b/src/captcha.c index 3d449b267cc..755388aaeb0 100644 --- a/src/captcha.c +++ b/src/captcha.c @@ -85,7 +85,7 @@ void train_captcha(char *cfgfile, char *weightfile) float loss = train_network(net, train); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), *net.seen); + printf("%d: %f, %f avg, %lf seconds, %ld images\n", i, loss, avg_loss, sec(clock()-time), *net.seen); free_data(train); if(i%100==0){ char buff[256]; diff --git a/src/cifar.c b/src/cifar.c index 4a27e79511f..d671a7cac8c 100644 --- a/src/cifar.c +++ b/src/cifar.c @@ -33,7 +33,7 @@ void train_cifar(char *cfgfile, char *weightfile) float loss = train_network_sgd(net, train, 1); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.95 + loss*.05; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); + printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); if(*net.seen/N > epoch){ epoch = *net.seen/N; char buff[256]; @@ -89,7 +89,7 @@ void train_cifar_distill(char *cfgfile, char *weightfile) float loss = train_network_sgd(net, train, 1); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.95 + loss*.05; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); + printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); if(*net.seen/N > epoch){ epoch = *net.seen/N; char buff[256]; diff --git a/src/classifier.c b/src/classifier.c index 11b6968d004..68becc8ab2f 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -179,7 +179,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, draw_precision = 1; } - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); + printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); #ifdef OPENCV draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, "top5", dont_show, mjpeg_port); #endif // OPENCV @@ -200,7 +200,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, if (ngpus != 1) sync_nets(nets, ngpus, 0); #endif char buff[256]; - sprintf(buff, "%s/%s_last.weights", backup_directory, base, i); + sprintf(buff, "%s/%s_last.weights", backup_directory, base); save_weights(net, buff); } free_data(train); diff --git a/src/compare.c b/src/compare.c index 803d812dda1..39bb4530960 100644 --- a/src/compare.c +++ b/src/compare.c @@ -54,7 +54,7 @@ void train_compare(char *cfgfile, char *weightfile) float loss = train_network(net, train); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%.3f: %f, %f avg, %lf seconds, %d images\n", (float)*net.seen/N, loss, avg_loss, sec(clock()-time), *net.seen); + printf("%.3f: %f, %f avg, %lf seconds, %ld images\n", (float)*net.seen/N, loss, avg_loss, sec(clock()-time), *net.seen); free_data(train); if(i%100 == 0){ char buff[256]; diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h index d6ec95515c2..360cd43b157 100644 --- a/src/convolutional_layer.h +++ b/src/convolutional_layer.h @@ -48,6 +48,7 @@ image get_convolutional_image(convolutional_layer layer); image get_convolutional_delta(convolutional_layer layer); image get_convolutional_weight(convolutional_layer layer, int i); + int convolutional_out_height(convolutional_layer layer); int convolutional_out_width(convolutional_layer layer); void rescale_weights(convolutional_layer l, float scale, float trans); diff --git a/src/cost_layer.c b/src/cost_layer.c index 39d2398b5ec..c8d7661a680 100644 --- a/src/cost_layer.c +++ b/src/cost_layer.c @@ -25,8 +25,9 @@ char *get_cost_string(COST_TYPE a) return "masked"; case SMOOTH: return "smooth"; + default: + return "sse"; } - return "sse"; } cost_layer make_cost_layer(int batch, int inputs, COST_TYPE cost_type, float scale) diff --git a/src/crnn_layer.c b/src/crnn_layer.c index 5a9d6f58988..3fcbd65cea2 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -47,19 +47,19 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.state = calloc(l.hidden*batch*(steps+1), sizeof(float)); l.input_layer = malloc(sizeof(layer)); - fprintf(stderr, ""); + //fprintf(stderr, ""); *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, size, stride, pad, activation, batch_normalize, 0, 0, 0, 0, 0); l.input_layer->batch = batch; if (l.workspace_size < l.input_layer->workspace_size) l.workspace_size = l.input_layer->workspace_size; l.self_layer = malloc(sizeof(layer)); - fprintf(stderr, ""); + //fprintf(stderr, ""); *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, size, stride, pad, activation, batch_normalize, 0, 0, 0, 0, 0); l.self_layer->batch = batch; if (l.workspace_size < l.self_layer->workspace_size) l.workspace_size = l.self_layer->workspace_size; l.output_layer = malloc(sizeof(layer)); - fprintf(stderr, ""); + //fprintf(stderr, ""); *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, size, stride, pad, activation, batch_normalize, 0, 0, 0, 0, 0); l.output_layer->batch = batch; if (l.workspace_size < l.output_layer->workspace_size) l.workspace_size = l.output_layer->workspace_size; diff --git a/src/go.c b/src/go.c index 223d136df72..f973a717536 100644 --- a/src/go.c +++ b/src/go.c @@ -144,7 +144,7 @@ void train_go(char *cfgfile, char *weightfile) float loss = train_network_datum(net, board, move) / net.batch; if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.95 + loss*.05; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); + printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); if(*net.seen/N > epoch){ epoch = *net.seen/N; char buff[256]; diff --git a/src/matrix.c b/src/matrix.c index 7430028017c..98ef5535893 100644 --- a/src/matrix.c +++ b/src/matrix.c @@ -257,7 +257,7 @@ void kmeans_maximization(matrix data, int *assignments, matrix centers) void random_centers(matrix data, matrix centers) { - int i, j; + int i; int *s = sample(data.rows); for (i = 0; i < centers.rows; ++i) { copy(data.vals[s[i]], centers.vals[i], data.cols); @@ -281,7 +281,6 @@ int *sample(int n) float dist(float *x, float *y, int n) { - int i; //printf(" x0 = %f, x1 = %f, y0 = %f, y1 = %f \n", x[0], x[1], y[0], y[1]); float mw = (x[0] < y[0]) ? x[0] : y[0]; float mh = (x[1] < y[1]) ? x[1] : y[1]; diff --git a/src/network.c b/src/network.c index 226fe410f76..9f53847b731 100644 --- a/src/network.c +++ b/src/network.c @@ -485,8 +485,8 @@ int resize_network(network *net, int w, int h) h = l.out_h; if(l.type == AVGPOOL) break; } - const int size = get_network_input_size(*net) * net->batch; #ifdef GPU + const int size = get_network_input_size(*net) * net->batch; if(gpu_index >= 0){ printf(" try to allocate additional workspace_size = %1.2f MB \n", (float)workspace_size / 1000000); net->workspace = cuda_make_array(0, workspace_size/sizeof(float) + 1); @@ -725,10 +725,10 @@ char *detection_to_json(detection *dets, int nboxes, int classes, char **names, char *send_buf = (char *)calloc(1024, sizeof(char)); if (filename) { - sprintf(send_buf, "{\n \"frame_id\":%d, \n \"filename\":\"%s\", \n \"objects\": [ \n", frame_id, filename); + sprintf(send_buf, "{\n \"frame_id\":%lld, \n \"filename\":\"%s\", \n \"objects\": [ \n", frame_id, filename); } else { - sprintf(send_buf, "{\n \"frame_id\":%d, \n \"objects\": [ \n", frame_id); + sprintf(send_buf, "{\n \"frame_id\":%lld, \n \"objects\": [ \n", frame_id); } int i, j; diff --git a/src/region_layer.c b/src/region_layer.c index 42730e09b2f..7f3988c2bae 100644 --- a/src/region_layer.c +++ b/src/region_layer.c @@ -53,8 +53,10 @@ region_layer make_region_layer(int batch, int w, int h, int n, int classes, int void resize_region_layer(layer *l, int w, int h) { +#ifdef GPU int old_w = l->w; int old_h = l->h; +#endif l->w = w; l->h = h; @@ -589,4 +591,4 @@ void zero_objectness(layer l) l.output[obj_index] = 0; } } -} \ No newline at end of file +} diff --git a/src/rnn.c b/src/rnn.c index 3fb634e4dcd..96de670f429 100644 --- a/src/rnn.c +++ b/src/rnn.c @@ -163,7 +163,7 @@ void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear, int i = (*net.seen)/net.batch; int streams = batch/steps; - printf("\n batch = %d, steps = %d, streams = %d, subdivisions = %d, text_size = %d \n", batch, steps, streams, net.subdivisions, size); + printf("\n batch = %d, steps = %d, streams = %d, subdivisions = %d, text_size = %ld \n", batch, steps, streams, net.subdivisions, size); printf(" global_batch = %d \n", batch*net.subdivisions); size_t *offsets = calloc(streams, sizeof(size_t)); int j; diff --git a/src/tag.c b/src/tag.c index 1e43e7d9ec7..a53af044e38 100644 --- a/src/tag.c +++ b/src/tag.c @@ -64,7 +64,7 @@ void train_tag(char *cfgfile, char *weightfile, int clear) float loss = train_network(net, train); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); + printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); free_data(train); if(*net.seen/N > epoch){ epoch = *net.seen/N; diff --git a/src/writing.c b/src/writing.c index 0a76d48e54a..345ad27152a 100644 --- a/src/writing.c +++ b/src/writing.c @@ -69,7 +69,7 @@ void train_writing(char *cfgfile, char *weightfile) if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); + printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); free_data(train); if(get_current_batch(net)%100 == 0){ char buff[256]; From 449fcfed7547a9203a7f44afd37835d373268201 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 12 Feb 2019 22:15:35 +0300 Subject: [PATCH 0329/1365] Fix for GCC on ARM 32/64-bit --- src/gemm.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/gemm.c b/src/gemm.c index 33b6fa20516..022f13228c9 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -2065,11 +2065,11 @@ static inline int popcnt_64(uint64_t val64) { tmp_count += __popcnt(val64 >> 32); #endif #else // Linux -#ifdef __x86_64__ // Linux 64-bit +#if defined(__x86_64__) || defined(__aarch64__) // Linux 64-bit int tmp_count = __builtin_popcountll(val64); #else // Linux 32-bit int tmp_count = __builtin_popcount(val64); - tmp_count += __builtin_popcount(val64); + tmp_count += __builtin_popcount(val64 >> 32); #endif #endif return tmp_count; From 28106c0fd8ef6df25a66b848dc12167ca1ea3a74 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 12 Feb 2019 22:16:11 +0300 Subject: [PATCH 0330/1365] Optimized memory allocation for XNOR on CPU --- include/darknet.h | 4 +- src/convolutional_layer.c | 78 +++++++++++++++------------------------ src/layer.c | 2 + src/network.c | 2 +- 4 files changed, 35 insertions(+), 51 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 1478b247c16..05607c42b27 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -365,7 +365,9 @@ struct layer { float *c_cpu; float *dc_cpu; - float * binary_input; + float *binary_input; + uint32_t *bin_re_packed_input; + char *t_bit_input; struct layer *input_layer; struct layer *self_layer; diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 80884f7f14a..21dd7231873 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -132,7 +132,12 @@ size_t get_workspace_size32(layer l){ return most; } #endif - if(l.xnor) return (size_t)l.bit_align*l.size*l.size*l.c * sizeof(float); + if (l.xnor) { + size_t re_packed_input_size = l.c * l.w * l.h * sizeof(float); + size_t workspace_size = (size_t)l.bit_align*l.size*l.size*l.c * sizeof(float); + if (workspace_size < re_packed_input_size) workspace_size = re_packed_input_size; + return workspace_size; + } return (size_t)l.out_h*l.out_w*l.size*l.size*l.c*sizeof(float); } @@ -379,6 +384,16 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.bit_align = src_align + (align - src_align % align); l.mean_arr = calloc(l.n, sizeof(float)); + + const size_t new_c = l.c / 32; + size_t in_re_packed_input_size = new_c * l.w * l.h + 1; + l.bin_re_packed_input = calloc(in_re_packed_input_size, sizeof(uint32_t)); + + l.lda_align = 256; // AVX2 + int k = l.size*l.size*l.c; + size_t k_aligned = k + (l.lda_align - k%l.lda_align); + size_t t_bit_input_size = k_aligned * l.bit_align / 8; + l.t_bit_input = calloc(t_bit_input_size, sizeof(char)); } if(batch_normalize){ @@ -785,11 +800,6 @@ size_t binary_transpose_align_input(int k, int n, float *b, char **t_bit_input, size_t t_intput_size = new_ldb * bit_align;// n; size_t t_bit_input_size = t_intput_size / 8;// +1; - static int last_t_bit_input_size = 0; - if (last_t_bit_input_size < t_bit_input_size) { - last_t_bit_input_size = t_bit_input_size; - *t_bit_input = realloc(*t_bit_input, last_t_bit_input_size * sizeof(char)); - } memset(*t_bit_input, 0, t_bit_input_size * sizeof(char)); int src_size = k * bit_align; @@ -850,34 +860,21 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) size_t t_intput_size = new_ldb * l.bit_align;// n; size_t t_bit_input_size = t_intput_size / 8;// +1; - const int new_c = l.c / 32; - - static float *re_packed_input = NULL; - static int last_re_packed_input_size = 0; int re_packed_input_size = l.c * l.w * l.h; - if (last_re_packed_input_size < re_packed_input_size) { - last_re_packed_input_size = re_packed_input_size; - re_packed_input = realloc(re_packed_input, last_re_packed_input_size * sizeof(float)); - } - memset(re_packed_input, 0, re_packed_input_size * sizeof(float)); - - static uint32_t *bin_re_packed_input = NULL; - static int last_bin_re_packed_input_size = 0; - int in_re_packed_input_size = new_c * l.w * l.h + 1; - if (last_bin_re_packed_input_size < in_re_packed_input_size) { - last_bin_re_packed_input_size = in_re_packed_input_size; - bin_re_packed_input = realloc(bin_re_packed_input, last_bin_re_packed_input_size * sizeof(uint32_t)); - } - memset(bin_re_packed_input, 0, in_re_packed_input_size * sizeof(uint32_t)); + memset(state.workspace, 0, re_packed_input_size * sizeof(float)); + + const size_t new_c = l.c / 32; + size_t in_re_packed_input_size = new_c * l.w * l.h + 1; + memset(l.bin_re_packed_input, 0, in_re_packed_input_size * sizeof(uint32_t)); //float *re_packed_input = calloc(l.c * l.w * l.h, sizeof(float)); //uint32_t *bin_re_packed_input = calloc(new_c * l.w * l.h + 1, sizeof(uint32_t)); // float32x4 by channel (as in cuDNN) - repack_input(state.input, re_packed_input, l.w, l.h, l.c); + repack_input(state.input, state.workspace, l.w, l.h, l.c); // 32 x floats -> 1 x uint32_t - float_to_bit(re_packed_input, (char *)bin_re_packed_input, l.c * l.w * l.h); + float_to_bit(state.workspace, (char *)l.bin_re_packed_input, l.c * l.w * l.h); //free(re_packed_input); @@ -888,7 +885,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) // // then exit from if() - im2col_cpu_custom((float *)bin_re_packed_input, new_c, l.h, l.w, l.size, l.stride, l.pad, b); + im2col_cpu_custom((float *)l.bin_re_packed_input, new_c, l.h, l.w, l.size, l.stride, l.pad, state.workspace); //im2col_cpu((float *)bin_re_packed_input, new_c, l.h, l.w, l.size, l.stride, l.pad, b); //free(bin_re_packed_input); @@ -903,24 +900,10 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) // // then exit from if() - - //size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; - //size_t t_intput_size = new_ldb * l.bit_align;// n; - //size_t t_bit_input_size = t_intput_size / 8;// +1; - - //char *t_bit_input = calloc(t_bit_input_size, sizeof(char)); - static char *t_bit_input = NULL; - static int last_t_bit_input_size = 0; - if (last_t_bit_input_size < t_bit_input_size) { - last_t_bit_input_size = t_bit_input_size; - t_bit_input = realloc(t_bit_input, last_t_bit_input_size * sizeof(char)); - } - memset(t_bit_input, 0, t_bit_input_size * sizeof(char)); - - transpose_uint32((uint32_t *)b, t_bit_input, new_k, n, n, new_ldb); + transpose_uint32((uint32_t *)state.workspace, l.t_bit_input, new_k, n, n, new_ldb); // the main GEMM function - gemm_nn_custom_bin_mean_transposed(m, n, k, 1, l.align_bit_weights, new_ldb, t_bit_input, new_ldb, c, n, l.mean_arr); + gemm_nn_custom_bin_mean_transposed(m, n, k, 1, l.align_bit_weights, new_ldb, l.t_bit_input, new_ldb, c, n, l.mean_arr); // // alternative GEMM //gemm_nn_bin_transposed_32bit_packed(m, n, new_k, 1, @@ -938,7 +921,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //printf(" l.index = %d - old XNOR \n", l.index); //im2col_cpu_custom_align(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align); - im2col_cpu_custom_bin(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align); + im2col_cpu_custom_bin(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace, l.bit_align); size_t output_size = l.outputs; //float *count_output = calloc(output_size, sizeof(float)); @@ -959,13 +942,10 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //size_t ldb_align = 256; // 256 bit for AVX2 int ldb_align = l.lda_align; size_t new_ldb = k + (ldb_align - k%ldb_align); - static char *t_bit_input = NULL; - size_t t_intput_size = binary_transpose_align_input(k, n, b, &t_bit_input, ldb_align, l.bit_align); - //char *t_bit_input = calloc(new_ldb * n, sizeof(char)); // for im2col_cpu_custom_transpose() only - //float_to_bit(t_input, t_bit_input, new_ldb * n); // for im2col_cpu_custom_transpose() only + size_t t_intput_size = binary_transpose_align_input(k, n, state.workspace, &l.t_bit_input, ldb_align, l.bit_align); // 5x times faster than gemm()-float32 - gemm_nn_custom_bin_mean_transposed(m, n, k, 1, l.align_bit_weights, new_ldb, t_bit_input, new_ldb, c, n, l.mean_arr); + gemm_nn_custom_bin_mean_transposed(m, n, k, 1, l.align_bit_weights, new_ldb, l.t_bit_input, new_ldb, c, n, l.mean_arr); //gemm_nn_custom_bin_mean_transposed(m, n, k, 1, bit_weights, k, t_bit_input, new_ldb, c, n, mean_arr); diff --git a/src/layer.c b/src/layer.c index 07029074bfa..6aeceec90d2 100644 --- a/src/layer.c +++ b/src/layer.c @@ -64,6 +64,8 @@ void free_layer(layer l) if (l.r_cpu) free(l.r_cpu); if (l.h_cpu) free(l.h_cpu); if (l.binary_input) free(l.binary_input); + if (l.bin_re_packed_input) free(l.bin_re_packed_input); + if (l.t_bit_input) free(l.t_bit_input); if (l.loss) free(l.loss); #ifdef GPU diff --git a/src/network.c b/src/network.c index 226fe410f76..68dd3e9ad91 100644 --- a/src/network.c +++ b/src/network.c @@ -988,7 +988,7 @@ void calculate_binary_weights(network net) if (l->xnor) { //printf("\n %d \n", j); - l->lda_align = 256; // 256bit for AVX2 + //l->lda_align = 256; // 256bit for AVX2 // set in make_convolutional_layer() //if (l->size*l->size*l->c >= 2048) l->lda_align = 512; binary_align_weights(l); From 3d9c8530a0aa983225d2607d14b9cb047e63d305 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 12 Feb 2019 23:13:25 +0300 Subject: [PATCH 0331/1365] Use Tensor Cores only when (channels % 8 == 0) and (filters % 8 == 0) --- src/convolutional_kernels.cu | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 7c26f42e0a5..4293d5a04dd 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -420,7 +420,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //#ifdef CUDNN_HALF //if (state.use_mixed_precision) { int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); - if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in)) + if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in) && + l.c % 8 == 0 && l.n % 8 == 0) { //printf("\n CUDNN_HALF!!! state.index = %d \n", state.index); @@ -605,7 +606,8 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state //#ifdef CUDNN_HALF int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); - if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in)) + if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in) && + l.c % 8 == 0 && l.n % 8 == 0) { const size_t input16_size = l.batch*l.c*l.w*l.h; From 21c9e9c2baf5a792c9d76b50d49d2788d83239e6 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 13 Feb 2019 16:54:22 +0300 Subject: [PATCH 0332/1365] Minor fix --- include/yolo_v2_class.hpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/include/yolo_v2_class.hpp b/include/yolo_v2_class.hpp index b6bea64a391..feafaa61180 100644 --- a/include/yolo_v2_class.hpp +++ b/include/yolo_v2_class.hpp @@ -72,7 +72,7 @@ class Detector { LIB_API int get_net_color_depth() const; LIB_API std::vector tracking_id(std::vector cur_bbox_vec, bool const change_history = true, - int const frames_story = 10, int const max_dist = 150); + int const frames_story = 5, int const max_dist = 40); std::vector detect_resized(image_t img, int init_w, int init_h, float thresh = 0.2, bool use_mean = false) { From 3e2ef94130a8b4cf8c4b8d3c7a6f345d92531f65 Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 14 Feb 2019 01:06:35 +0300 Subject: [PATCH 0333/1365] Update Readme.md --- README.md | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 3dba89d1c3f..a14d34f4bed 100644 --- a/README.md +++ b/README.md @@ -474,11 +474,6 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * increase network resolution in your `.cfg`-file (`height=608`, `width=608` or any value multiple of 32) - it will increase precision - * recalculate anchors for your dataset for `width` and `height` from cfg-file: - `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416` - then set the same 9 `anchors` in each of 3 `[yolo]`-layers in your cfg-file - - (also you can change indexes of anchors `masks=` for each [yolo]-layer, so that 1st-[yolo]-layer has anchors larger than 60x60, 2nd larger than 30x30, 3rd remaining) * check that each object are mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: https://github.com/AlexeyAB/Yolo_mark @@ -488,7 +483,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last `[yolo]`-layer or `[region]`-layer in your cfg-file (the global maximum number of objects that can be detected by YoloV3 is `0,0615234375*(width*height)` where are width and height are parameters from `[net]` section in cfg-file) - * for training for small objects - set `layers = -1, 11` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L720 + * for training for small objects (smaller than 16x16 after the image is resized to 416x416) - set `layers = -1, 11` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L720 and set `stride=4` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L717 * for training for both small and large objects use modified models: @@ -515,6 +510,10 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * The more different objects you want to detect, the more complex network model should be used. But each: `model of object, side, illimination, scale, each 30 grad` of the turn and inclination angles - these are different objects from a neural network perspective. + * recalculate anchors for your dataset for `width` and `height` from cfg-file: + `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416` + then set the same 9 `anchors` in each of 3 `[yolo]`-layers in your cfg-file. But you should change indexes of anchors `masks=` for each [yolo]-layer, so that 1st-[yolo]-layer has anchors larger than 60x60, 2nd larger than 30x30, 3rd remaining. If many of the calculated anchors do not fit under the appropriate layers - then just try using all the default anchors. + 2. After training - for detection: From c88ae98289d026f65d63ede24d8121bd06a92c1b Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 14 Feb 2019 17:37:12 +0100 Subject: [PATCH 0334/1365] add CMakeLists.txt and necessary modules --- CMakeLists.txt | 244 +++++++++++++++++++++++ cmake/Modules/FindCUDNN.cmake | 68 +++++++ cmake/Modules/FindPThreads_windows.cmake | 75 +++++++ 3 files changed, 387 insertions(+) create mode 100644 CMakeLists.txt create mode 100644 cmake/Modules/FindCUDNN.cmake create mode 100644 cmake/Modules/FindPThreads_windows.cmake diff --git a/CMakeLists.txt b/CMakeLists.txt new file mode 100644 index 00000000000..5669a470aa3 --- /dev/null +++ b/CMakeLists.txt @@ -0,0 +1,244 @@ +cmake_minimum_required(VERSION 3.8 FATAL_ERROR) + +#set(CMAKE_VERBOSE_MAKEFILE 1) + +if(DEFINED ENV{VCPKG_ROOT} AND NOT DEFINED CMAKE_TOOLCHAIN_FILE) + set(CMAKE_TOOLCHAIN_FILE "${VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake" CACHE STRING "") +endif() + +project(darknet) + +enable_language(C) +enable_language(CXX) +if(ENABLE_CUDA) + enable_language(CUDA) # do not enable after searching for OpenCV package! +endif() + +set(CMAKE_CXX_STANDARD 11) +set(CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/cmake/Modules/" ${CMAKE_MODULE_PATH}) +include_directories(${CMAKE_CURRENT_LIST_DIR}/include) +include_directories(${CMAKE_CURRENT_LIST_DIR}/src) + +if(NOT APPLE) + set(CMAKE_SKIP_BUILD_RPATH FALSE) + set(CMAKE_BUILD_WITH_INSTALL_RPATH FALSE) + set(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE) +endif() +set(CMAKE_INSTALL_RPATH "${CMAKE_SOURCE_DIR}") + +add_definitions(-DUSE_CMAKE_LIBS) + +if(MSVC) + set(CMAKE_C_FLAGS "/wd4013 /wd4018 /wd4028 /wd4047 /wd4068 /wd4090 /wd4101 /wd4113 /wd4133 /wd4190 /wd4244 /wd4267 /wd4305 /wd4477 /wd4996 /fp:fast ${CMAKE_C_FLAGS}") + set(CMAKE_CXX_FLAGS "/wd4013 /wd4018 /wd4028 /wd4047 /wd4068 /wd4090 /wd4101 /wd4113 /wd4133 /wd4190 /wd4244 /wd4267 /wd4305 /wd4477 /wd4996 /fp:fast ${CMAKE_CXX_FLAGS}") + string(REGEX REPLACE "/O2" "/Ox" CMAKE_C_FLAGS_RELEASE ${CMAKE_C_FLAGS_RELEASE}) + string(REGEX REPLACE "/O2" "/Ox" CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) + add_definitions(-D_CRT_RAND_S) + add_definitions(-DNOMINMAX) + add_definitions(-D_USE_MATH_DEFINES) +endif() + +if(CMAKE_COMPILER_IS_GNUCC) + set(CMAKE_C_FLAGS "-Wno-unused-result -Wno-discarded-qualifiers -Wno-incompatible-pointer-types ${CMAKE_C_FLAGS}") + set(CMAKE_CXX_FLAGS "-Wno-unused-result -Wno-write-strings ${CMAKE_CXX_FLAGS}") + string(REGEX REPLACE "-O0" "-Og" CMAKE_C_FLAGS_DEBUG ${CMAKE_C_FLAGS_DEBUG}) + string(REGEX REPLACE "-O0" "-Og" CMAKE_CXX_FLAGS_DEBUG ${CMAKE_CXX_FLAGS_DEBUG}) + string(REGEX REPLACE "-O3" "-Ofast" CMAKE_C_FLAGS_RELEASE ${CMAKE_C_FLAGS_RELEASE}) + string(REGEX REPLACE "-O3" "-Ofast" CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) + set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a") + set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a") +endif() + +if ("${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang") + set(CMAKE_CXX_FLAGS "-Wno-deprecated -Wno-writable-strings ${CMAKE_CXX_FLAGS}") + string(REGEX REPLACE "-O0" "-Og" CMAKE_C_FLAGS_DEBUG ${CMAKE_C_FLAGS_DEBUG}) + string(REGEX REPLACE "-O0" "-Og" CMAKE_CXX_FLAGS_DEBUG ${CMAKE_CXX_FLAGS_DEBUG}) + string(REGEX REPLACE "-O3" "-Ofast" CMAKE_C_FLAGS_RELEASE ${CMAKE_C_FLAGS_RELEASE}) + string(REGEX REPLACE "-O3" "-Ofast" CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) +endif() + +if(APPLE) + find_package(OpenCV PATHS "/usr/local/Cellar/opencv@3/3.4.5" NO_DEFAULT_PATH) +else() + find_package(OpenCV) +endif() + +if(OpenCV_FOUND) + add_definitions(-DOPENCV) + include_directories(${OpenCV_INCLUDE_DIRS}) +endif() + +if(ENABLE_CUDA) + add_definitions(-DGPU) + #set(COMPUTE_MODEL "75") #Tesla T4, RTX 2080 + #set(COMPUTE_MODEL "72") #Jetson Xavier + #set(COMPUTE_MODEL "70") #Tesla V100 + #set(COMPUTE_MODEL "62") #Jetson TX2 + #set(COMPUTE_MODEL "61") #Telsa P40 + #set(COMPUTE_MODEL "60") #Telsa P100 + #set(COMPUTE_MODEL "53") #Jetson TX1 + #set(COMPUTE_MODEL "52") #Tesla M40/M60 + #set(COMPUTE_MODEL "37") #Tesla K80 + #set(COMPUTE_MODEL "35") #Tesla K20/K40 + set(COMPUTE_MODEL "30") #Tesla K10, Quadro K4000 + if(COMPUTE_MODEL VERSION_GREATER_EQUAL 53) + add_definitions(-DCUDNN_HALF) + endif() + find_package(CUDNN) + if(CUDNN_FOUND) + add_definitions(-DCUDNN) + include_directories(${CUDNN_INCLUDE_DIR}) + endif() + if (MSVC) + if(CUDNN_FOUND) + if(OpenCV_FOUND) + set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${COMPUTE_MODEL},code=[sm_${COMPUTE_MODEL},compute_${COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/DGPU,/DCUDNN,/DOPENCV\" ${CMAKE_CUDA_FLAGS}") + else() + set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${COMPUTE_MODEL},code=[sm_${COMPUTE_MODEL},compute_${COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/DGPU,/DCUDNN\" ${CMAKE_CUDA_FLAGS}") + endif() + else() + if(OpenCV_FOUND) + set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${COMPUTE_MODEL},code=[sm_${COMPUTE_MODEL},compute_${COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/DGPU,/DOPENCV\" ${CMAKE_CUDA_FLAGS}") + else() + set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${COMPUTE_MODEL},code=[sm_${COMPUTE_MODEL},compute_${COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/DGPU\" ${CMAKE_CUDA_FLAGS}") + endif() + endif() + else() + if(CUDNN_FOUND) + if(OpenCV_FOUND) + set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${COMPUTE_MODEL},code=[sm_${COMPUTE_MODEL},compute_${COMPUTE_MODEL}] --compiler-options \"-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -fPIC -fopenmp -Ofast -DOPENCV -DGPU -DCUDNN \" ${CMAKE_CUDA_FLAGS}") + else() + set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${COMPUTE_MODEL},code=[sm_${COMPUTE_MODEL},compute_${COMPUTE_MODEL}] --compiler-options \"-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -fPIC -fopenmp -Ofast -DGPU -DCUDNN \" ${CMAKE_CUDA_FLAGS}") + endif() + else() + if(OpenCV_FOUND) + set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${COMPUTE_MODEL},code=[sm_${COMPUTE_MODEL},compute_${COMPUTE_MODEL}] --compiler-options \"-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -fPIC -fopenmp -Ofast -DOPENCV -DGPU \" ${CMAKE_CUDA_FLAGS}") + else() + set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${COMPUTE_MODEL},code=[sm_${COMPUTE_MODEL},compute_${COMPUTE_MODEL}] --compiler-options \"-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -fPIC -fopenmp -Ofast -DGPU \" ${CMAKE_CUDA_FLAGS}") + endif() + endif() + endif() +endif() + +set(CMAKE_THREAD_PREFER_PTHREAD ON) +find_package(Threads REQUIRED) +if(MSVC) + find_package(PThreads_windows REQUIRED) + include_directories(${PTHREADS_INCLUDE_DIR}) +endif() + +find_package(OpenMP) +if(OPENMP_FOUND) + set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") +endif() + +#look for all *.h files in src folder +file(GLOB headers "${CMAKE_CURRENT_LIST_DIR}/src/*.h") +#add also files in the include folder +list(APPEND sources + ${CMAKE_CURRENT_LIST_DIR}/include/darknet.h +) +#remove windows only files, will be re-added if necessary later +list(REMOVE_ITEM headers + ${CMAKE_CURRENT_LIST_DIR}/src/gettimeofday.h + ${CMAKE_CURRENT_LIST_DIR}/src/getopt.h +) + +#look for all *.c files in src folder +file(GLOB sources "${CMAKE_CURRENT_LIST_DIR}/src/*.c") +#add also .cpp files +list(APPEND sources + ${CMAKE_CURRENT_LIST_DIR}/src/http_stream.cpp +) +#remove darknet.c file which is necessary only for the executable, not for the lib +list(REMOVE_ITEM sources + ${CMAKE_CURRENT_LIST_DIR}/src/darknet.c +) +#remove windows only files, will be re-added if necessary later +list(REMOVE_ITEM sources + ${CMAKE_CURRENT_LIST_DIR}/src/gettimeofday.c + ${CMAKE_CURRENT_LIST_DIR}/src/getopt.c +) + +if(ENABLE_CUDA) + file(GLOB cuda_sources "${CMAKE_CURRENT_LIST_DIR}/src/*.cu") +endif() + +if(WIN32) + list(APPEND sources + ${CMAKE_CURRENT_LIST_DIR}/src/gettimeofday.c + ${CMAKE_CURRENT_LIST_DIR}/src/getopt.c + ) + + list(APPEND headers + ${CMAKE_CURRENT_LIST_DIR}/src/gettimeofday.h + ${CMAKE_CURRENT_LIST_DIR}/src/getopt.h + ) +endif() + +set_source_files_properties(${sources} PROPERTIES LANGUAGE CXX) + +add_library(darklib SHARED ${CMAKE_CURRENT_LIST_DIR}/include/yolo_v2_class.hpp ${CMAKE_CURRENT_LIST_DIR}/src/yolo_v2_class.cpp ${sources} ${headers} ${cuda_sources}) +set_target_properties(darklib PROPERTIES POSITION_INDEPENDENT_CODE ON) +if(ENABLE_CUDA) + set_target_properties(darklib PROPERTIES CUDA_SEPARABLE_COMPILATION ON) +endif() +target_compile_definitions(darklib PRIVATE LIB_EXPORTS=1) +set_target_properties(darklib PROPERTIES LINKER_LANGUAGE CXX) + +if(OpenCV_VERSION_MAJOR GREATER_EQUAL 3) + add_executable(uselib_track ${CMAKE_CURRENT_LIST_DIR}/src/yolo_console_dll.cpp) + target_compile_definitions(uselib_track PRIVATE TRACK_OPTFLOW=1) + set_target_properties(uselib_track PROPERTIES LINKER_LANGUAGE CXX) +endif() + +add_executable(uselib ${CMAKE_CURRENT_LIST_DIR}/src/yolo_console_dll.cpp) +set_target_properties(uselib PROPERTIES LINKER_LANGUAGE CXX) + +add_executable(darknet ${CMAKE_CURRENT_LIST_DIR}/src/darknet.c ${sources} ${headers} ${cuda_sources}) +set_source_files_properties(${CMAKE_CURRENT_LIST_DIR}/src/darknet.c PROPERTIES LANGUAGE CXX) +set_target_properties(darknet PROPERTIES LINKER_LANGUAGE CXX) + +if(CUDNN_FOUND) + target_link_libraries(darknet ${CUDNN_LIBRARY}) + target_link_libraries(darklib ${CUDNN_LIBRARY}) +endif() + +if(OpenCV_FOUND) + target_link_libraries(darknet ${OpenCV_LIBS}) + target_link_libraries(darklib ${OpenCV_LIBS}) +endif() + +if(OPENMP_FOUND) + target_link_libraries(darknet OpenMP::OpenMP_C) + target_link_libraries(darklib OpenMP::OpenMP_C) +endif() + +if(CMAKE_COMPILER_IS_GNUCC) + target_link_libraries(darknet m) + target_link_libraries(darklib m) +endif() + +if(MSVC) + target_link_libraries(darknet ${PTHREADS_LIBRARY}) + target_link_libraries(darknet wsock32 ws2_32) + target_link_libraries(darklib ${PTHREADS_LIBRARY}) + target_link_libraries(darklib wsock32 ws2_32) +endif() + +target_link_libraries(darknet Threads::Threads) +target_link_libraries(darklib Threads::Threads) + +if(ENABLE_CUDA) + target_link_libraries(darknet curand cublas) + target_link_libraries(darklib curand cublas) +endif() + +target_link_libraries(uselib darklib) +if(OpenCV_VERSION_MAJOR GREATER_EQUAL 3) + target_link_libraries(uselib_track darklib) + install(TARGETS uselib_track DESTINATION ${CMAKE_SOURCE_DIR}/) +endif() + +install(TARGETS darklib uselib darknet DESTINATION ${CMAKE_SOURCE_DIR}/) diff --git a/cmake/Modules/FindCUDNN.cmake b/cmake/Modules/FindCUDNN.cmake new file mode 100644 index 00000000000..bc130ea4b13 --- /dev/null +++ b/cmake/Modules/FindCUDNN.cmake @@ -0,0 +1,68 @@ +# Distributed under the OSI-approved BSD 3-Clause License. +# Copyright Stefano Sinigardi + +#.rst: +# FindCUDNN +# -------- +# +# Result Variables +# ^^^^^^^^^^^^^^^^ +# +# This module will set the following variables in your project:: +# +# ``CUDNN_FOUND`` +# True if CUDNN found on the local system +# +# ``CUDNN_INCLUDE_DIRS`` +# Location of CUDNN header files. +# +# ``CUDNN_LIBRARIES`` +# The CUDNN libraries. +# +# Hints +# ^^^^^ +# +# ``CUDNN_ROOT_DIR`` +# Set this variable to a directory that contains a CUDNN installation. +# + +include(FindPackageHandleStandardArgs) + +#set(CUDNN_ROOT_DIR "" CACHE PATH "Folder contains NVIDIA cuDNN") + +find_path(CUDNN_INCLUDE_DIR cudnn.h + HINTS ${CUDNN_ROOT_DIR} ${CUDA_HOME} ${CUDA_TOOLKIT_ROOT_DIR} + PATH_SUFFIXES cuda/include include) + +find_library(CUDNN_LIBRARY cudnn + HINTS ${CUDNN_ROOT_DIR} ${CUDA_HOME} ${CUDA_TOOLKIT_ROOT_DIR} + PATH_SUFFIXES lib lib64 cuda/lib cuda/lib64 lib/x64) + +find_package_handle_standard_args( + CUDNN DEFAULT_MSG CUDNN_INCLUDE_DIR CUDNN_LIBRARY) + +if(CUDNN_FOUND) + file(READ ${CUDNN_INCLUDE_DIR}/cudnn.h CUDNN_HEADER_CONTENTS) + string(REGEX MATCH "define CUDNN_MAJOR * +([0-9]+)" + CUDNN_VERSION_MAJOR "${CUDNN_HEADER_CONTENTS}") + string(REGEX REPLACE "define CUDNN_MAJOR * +([0-9]+)" "\\1" + CUDNN_VERSION_MAJOR "${CUDNN_VERSION_MAJOR}") + string(REGEX MATCH "define CUDNN_MINOR * +([0-9]+)" + CUDNN_VERSION_MINOR "${CUDNN_HEADER_CONTENTS}") + string(REGEX REPLACE "define CUDNN_MINOR * +([0-9]+)" "\\1" + CUDNN_VERSION_MINOR "${CUDNN_VERSION_MINOR}") + string(REGEX MATCH "define CUDNN_PATCHLEVEL * +([0-9]+)" + CUDNN_VERSION_PATCH "${CUDNN_HEADER_CONTENTS}") + string(REGEX REPLACE "define CUDNN_PATCHLEVEL * +([0-9]+)" "\\1" + CUDNN_VERSION_PATCH "${CUDNN_VERSION_PATCH}") + if(NOT CUDNN_VERSION_MAJOR) + set(CUDNN_VERSION "?") + else() + set(CUDNN_VERSION "${CUDNN_VERSION_MAJOR}.${CUDNN_VERSION_MINOR}.${CUDNN_VERSION_PATCH}") + endif() + + set(CUDNN_INCLUDE_DIRS ${CUDNN_INCLUDE_DIR}) + set(CUDNN_LIBRARIES ${CUDNN_LIBRARY}) + message(STATUS "Found cuDNN: v${CUDNN_VERSION} (include: ${CUDNN_INCLUDE_DIR}, library: ${CUDNN_LIBRARY})") + mark_as_advanced(CUDNN_ROOT_DIR CUDNN_LIBRARY CUDNN_INCLUDE_DIR) +endif() diff --git a/cmake/Modules/FindPThreads_windows.cmake b/cmake/Modules/FindPThreads_windows.cmake new file mode 100644 index 00000000000..4f6a9f248dc --- /dev/null +++ b/cmake/Modules/FindPThreads_windows.cmake @@ -0,0 +1,75 @@ +# Distributed under the OSI-approved BSD 3-Clause License. +# Copyright Stefano Sinigardi + +#.rst: +# FindPThreads +# ------------ +# +# Find the PThreads includes and library. +# +# Result Variables +# ^^^^^^^^^^^^^^^^ +# +# This module defines the following variables: +# +# ``PTHREADS_FOUND`` +# True if PThreads library found +# +# ``PTHREADS_INCLUDE_DIR`` +# Location of PThreads headers +# +# ``PTHREADS_LIBRARY`` +# List of libraries to link with when using PThreads +# + +include(${CMAKE_ROOT}/Modules/FindPackageHandleStandardArgs.cmake) +include(${CMAKE_ROOT}/Modules/SelectLibraryConfigurations.cmake) + +find_path(PTHREADS_INCLUDE_DIR NAMES pthread.h) + +# Allow libraries to be set manually +if(NOT PTHREADS_LIBRARY) + find_library(PTHREADS_LIBRARY_RELEASE NAMES pthreadsVC2) + find_library(PTHREADS_LIBRARY_DEBUG NAMES pthreadsVC2d) + select_library_configurations(PTHREADS) +endif() + +find_package_handle_standard_args(PTHREADS DEFAULT_MSG PTHREADS_LIBRARY PTHREADS_INCLUDE_DIR) +mark_as_advanced(PTHREADS_INCLUDE_DIR PTHREADS_LIBRARY) + + +# Register imported libraries: +# 1. If we can find a Windows .dll file (or if we can find both Debug and +# Release libraries), we will set appropriate target properties for these. +# 2. However, for most systems, we will only register the import location and +# include directory. + +# Look for dlls, for Release and Debug libraries. +if(WIN32) + string( REPLACE ".lib" ".dll" PTHREADS_LIBRARY_RELEASE_DLL "${PTHREADS_LIBRARY_RELEASE}" ) + string( REPLACE ".lib" ".dll" PTHREADS_LIBRARY_DEBUG_DLL "${PTHREADS_LIBRARY_DEBUG}" ) +endif() + +if( PTHREADS_FOUND AND NOT TARGET PThreads::PThreads ) + if( EXISTS "${PTHREADS_LIBRARY_RELEASE_DLL}" ) + add_library( PThreads::PThreads SHARED IMPORTED ) + set_target_properties( PThreads::PThreads PROPERTIES + IMPORTED_LOCATION_RELEASE "${PTHREADS_LIBRARY_RELEASE_DLL}" + IMPORTED_IMPLIB "${PTHREADS_LIBRARY_RELEASE}" + INTERFACE_INCLUDE_DIRECTORIES "${PTHREADS_INCLUDE_DIR}" + IMPORTED_CONFIGURATIONS Release + IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) + if( EXISTS "${PTHREADS_LIBRARY_DEBUG_DLL}" ) + set_property( TARGET PThreads::PThreads APPEND PROPERTY IMPORTED_CONFIGURATIONS Debug ) + set_target_properties( FFTW::fftw3 PROPERTIES + IMPORTED_LOCATION_DEBUG "${PTHREADS_LIBRARY_DEBUG_DLL}" + IMPORTED_IMPLIB_DEBUG "${PTHREADS_LIBRARY_DEBUG}" ) + endif() + else() + add_library( PThreads::PThreads UNKNOWN IMPORTED ) + set_target_properties( PThreads::PThreads PROPERTIES + IMPORTED_LOCATION "${PTHREADS_LIBRARY}" + INTERFACE_INCLUDE_DIRECTORIES "${PTHREADS_INCLUDE_DIR}" + IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) + endif() +endif() From b3579380dc763135c60251a58a5c46e3ec124ccc Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 14 Feb 2019 17:28:23 +0100 Subject: [PATCH 0335/1365] improve compatibility with c++ compilers, prepare for CMake --- .gitignore | 5 + include/darknet.h | 32 + include/yolo_v2_class.hpp | 24 +- src/activation_kernels.cu | 2 - src/activation_layer.c | 6 +- src/activation_layer.h | 6 + src/activations.h | 6 + src/art.c | 7 +- src/avgpool_layer.c | 6 +- src/avgpool_layer.h | 6 + src/avgpool_layer_kernels.cu | 2 - src/batchnorm_layer.c | 18 +- src/batchnorm_layer.h | 6 + src/blas.c | 2 +- src/blas.h | 12 +- src/blas_kernels.cu | 2 - src/box.c | 2 +- src/box.h | 6 + src/captcha.c | 14 +- src/cifar.c | 6 +- src/classifier.c | 44 +- src/classifier.h | 10 + src/coco.c | 36 +- src/col2im.c | 1 + src/col2im.h | 6 + src/col2im_kernels.cu | 2 - src/compare.c | 12 +- src/connected_layer.c | 34 +- src/connected_layer.h | 7 + src/convolutional_kernels.cu | 17 +- src/convolutional_layer.c | 101 +- src/convolutional_layer.h | 8 +- src/cost_layer.c | 12 +- src/cost_layer.h | 6 + src/cpu_gemm.c | 6 +- src/crnn_layer.c | 13 +- src/crnn_layer.h | 6 + src/crop_layer.c | 6 +- src/crop_layer.h | 6 + src/crop_layer_kernels.cu | 2 - src/cuda.c | 14 +- src/cuda.h | 31 +- src/darknet.c | 8 +- src/{unistd.h => darkunistd.h} | 16 +- src/data.c | 71 +- src/data.h | 11 +- src/deconvolutional_kernels.cu | 4 +- src/deconvolutional_layer.c | 24 +- src/deconvolutional_layer.h | 10 +- src/demo.c | 43 +- src/demo.h | 10 +- src/detection_layer.c | 14 +- src/detection_layer.h | 6 + src/detector.c | 72 +- src/dice.c | 2 +- src/dropout_layer.c | 6 +- src/dropout_layer.h | 6 + src/dropout_layer_kernels.cu | 2 - src/gemm.c | 66 +- src/gemm.h | 7 + src/getopt.c | 1656 +++++++++----------------------- src/getopt.h | 331 ++++--- src/gettimeofday.c | 62 +- src/gettimeofday.h | 51 +- src/go.c | 67 +- src/gru_layer.c | 34 +- src/gru_layer.h | 6 + src/http_stream.cpp | 73 +- src/http_stream.h | 11 +- src/im2col.h | 8 + src/im2col_kernels.cu | 22 +- src/image.c | 45 +- src/image.h | 22 +- src/layer.c | 2 +- src/layer.h | 7 +- src/list.c | 22 +- src/list.h | 6 + src/local_layer.c | 16 +- src/local_layer.h | 6 + src/lstm_layer.c | 52 +- src/lstm_layer.h | 8 +- src/matrix.c | 28 +- src/matrix.h | 6 + src/maxpool_layer.c | 14 +- src/maxpool_layer.h | 6 + src/maxpool_layer_kernels.cu | 2 - src/network.c | 54 +- src/network.h | 6 +- src/network_kernels.cu | 6 +- src/nightmare.c | 2 +- src/normalization_layer.c | 18 +- src/normalization_layer.h | 6 + src/option_list.c | 3 +- src/option_list.h | 6 + src/parser.c | 26 +- src/parser.h | 6 + src/region_layer.c | 21 +- src/region_layer.h | 6 + src/reorg_layer.c | 10 +- src/reorg_layer.h | 6 + src/reorg_old_layer.c | 10 +- src/reorg_old_layer.h | 6 + src/rnn.c | 38 +- src/rnn_layer.c | 10 +- src/rnn_layer.h | 6 + src/rnn_vid.c | 22 +- src/route_layer.c | 10 +- src/route_layer.h | 6 + src/shortcut_layer.c | 10 +- src/shortcut_layer.h | 6 + src/softmax_layer.c | 10 +- src/softmax_layer.h | 6 + src/super.c | 6 +- src/swag.c | 4 +- src/tag.c | 6 +- src/tree.c | 20 +- src/tree.h | 6 + src/upsample_layer.c | 10 +- src/upsample_layer.h | 6 + src/utils.c | 40 +- src/utils.h | 29 +- src/voxel.c | 10 +- src/writing.c | 4 +- src/yolo.c | 46 +- src/yolo_console_dll.cpp | 54 +- src/yolo_layer.c | 30 +- src/yolo_layer.h | 6 + src/yolo_v2_class.cpp | 19 +- 128 files changed, 1858 insertions(+), 2245 deletions(-) rename src/{unistd.h => darkunistd.h} (88%) diff --git a/.gitignore b/.gitignore index 1bd67d08836..da2d106e832 100644 --- a/.gitignore +++ b/.gitignore @@ -28,3 +28,8 @@ Thumbs.db # CMake # cmake-build-debug/ CMakeLists.txt +build_*/ +build.* +cmake/ +*.patch +.gitignore diff --git a/include/darknet.h b/include/darknet.h index 05607c42b27..e35524deb04 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -26,6 +26,38 @@ #endif #endif +#ifdef _WIN32 +#define PORT unsigned long +#define ADDRPOINTER int* +#else +#define PORT unsigned short +#define SOCKET int +#define HOSTENT struct hostent +#define SOCKADDR struct sockaddr +#define SOCKADDR_IN struct sockaddr_in +#define ADDRPOINTER unsigned int* +#define INVALID_SOCKET -1 +#define SOCKET_ERROR -1 +#endif +#define FULL_MASK 0xffffffff +#define WARP_SIZE 32 +#define BLOCK 512 +#define NUMCHARS 37 +#define NFRAMES 3 +#define BLOCK_TRANSPOSE32 256 +#define DOABS 1 +#define SECRET_NUM -1234 +#define C_SHARP_MAX_OBJECTS 1000 +#define TILE_M 4 // 4 ops +#define TILE_N 16 // AVX2 = 2 ops * 8 floats +#define TILE_K 16 // loop +#ifndef __COMPAR_FN_T +#define __COMPAR_FN_T +typedef int (*__compar_fn_t)(const void*, const void*); +#ifdef __USE_GNU +typedef __compar_fn_t comparison_fn_t; +#endif +#endif #ifdef GPU #define BLOCK 512 diff --git a/include/yolo_v2_class.hpp b/include/yolo_v2_class.hpp index b6bea64a391..f7f13bda1d3 100644 --- a/include/yolo_v2_class.hpp +++ b/include/yolo_v2_class.hpp @@ -1,17 +1,7 @@ -#pragma once -#ifdef LIB_EXPORTS -#if defined(_MSC_VER) -#define LIB_API __declspec(dllexport) -#else -#define LIB_API __attribute__((visibility("default"))) -#endif -#else -#if defined(_MSC_VER) -#define LIB_API -#else -#define LIB_API -#endif -#endif +#ifndef YOLO_V2_CLASS_HPP +#define YOLO_V2_CLASS_HPP + +#include "darknet.h" struct bbox_t { unsigned int x, y, w, h; // (x,y) - top-left corner, (w, h) - width & height of bounded box @@ -28,7 +18,6 @@ struct image_t { float *data; // pointer to the image data }; -#define C_SHARP_MAX_OBJECTS 1000 struct bbox_t_container { bbox_t candidates[C_SHARP_MAX_OBJECTS]; }; @@ -41,8 +30,8 @@ struct bbox_t_container { #ifdef OPENCV #include // C++ -#include "opencv2/highgui/highgui_c.h" // C -#include "opencv2/imgproc/imgproc_c.h" // C +#include // C +#include // C #endif // OPENCV extern "C" LIB_API int init(const char *configurationFilename, const char *weightsFilename, int gpu); @@ -658,3 +647,4 @@ void free_img(image_t m) { #endif // __cplusplus */ +#endif diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index aec279d5dde..0144ca51345 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -3,10 +3,8 @@ #include "curand.h" #include "cublas_v2.h" -extern "C" { #include "activations.h" #include "cuda.h" -} __device__ float lhtan_activate_kernel(float x) diff --git a/src/activation_layer.c b/src/activation_layer.c index 3430dac4078..2c323b8d083 100644 --- a/src/activation_layer.c +++ b/src/activation_layer.c @@ -11,15 +11,15 @@ layer make_activation_layer(int batch, int inputs, ACTIVATION activation) { - layer l = {0}; + layer l = { (LAYER_TYPE)0 }; l.type = ACTIVE; l.inputs = inputs; l.outputs = inputs; l.batch=batch; - l.output = calloc(batch*inputs, sizeof(float*)); - l.delta = calloc(batch*inputs, sizeof(float*)); + l.output = (float*)calloc(batch * inputs, sizeof(float)); + l.delta = (float*)calloc(batch * inputs, sizeof(float)); l.forward = forward_activation_layer; l.backward = backward_activation_layer; diff --git a/src/activation_layer.h b/src/activation_layer.h index a09756aa507..c766c6af051 100644 --- a/src/activation_layer.h +++ b/src/activation_layer.h @@ -5,6 +5,9 @@ #include "layer.h" #include "network.h" +#ifdef __cplusplus +extern "C" { +#endif layer make_activation_layer(int batch, int inputs, ACTIVATION activation); void forward_activation_layer(layer l, network_state state); @@ -15,5 +18,8 @@ void forward_activation_layer_gpu(layer l, network_state state); void backward_activation_layer_gpu(layer l, network_state state); #endif +#ifdef __cplusplus +} #endif +#endif diff --git a/src/activations.h b/src/activations.h index 849c65d3381..4ecf97d711d 100644 --- a/src/activations.h +++ b/src/activations.h @@ -8,6 +8,9 @@ // LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU //}ACTIVATION; +#ifdef __cplusplus +extern "C" { +#endif ACTIVATION get_activation(char *s); char *get_activation_string(ACTIVATION a); @@ -87,5 +90,8 @@ static inline float leaky_gradient(float x){return (x>0) ? 1 : .1f;} static inline float tanh_gradient(float x){return 1-x*x;} static inline float plse_gradient(float x){return (x < 0 || x > 1) ? .01f : .125f;} +#ifdef __cplusplus +} #endif +#endif diff --git a/src/art.c b/src/art.c index 2a28370a97d..364cca3d3d9 100644 --- a/src/art.c +++ b/src/art.c @@ -6,7 +6,6 @@ #include "classifier.h" #ifdef WIN32 #include -#include #include "gettimeofday.h" #else #include @@ -14,10 +13,10 @@ #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#include "opencv2/core/version.hpp" +#include +#include #ifndef CV_VERSION_EPOCH -#include "opencv2/videoio/videoio_c.h" +#include #endif image get_image_from_stream(CvCapture *cap); #endif diff --git a/src/avgpool_layer.c b/src/avgpool_layer.c index b6932fe7f40..983fe7591b7 100644 --- a/src/avgpool_layer.c +++ b/src/avgpool_layer.c @@ -5,7 +5,7 @@ avgpool_layer make_avgpool_layer(int batch, int w, int h, int c) { fprintf(stderr, "avg %4d x%4d x%4d -> %4d\n", w, h, c, c); - avgpool_layer l = {0}; + avgpool_layer l = { (LAYER_TYPE)0 }; l.type = AVGPOOL; l.batch = batch; l.h = h; @@ -17,8 +17,8 @@ avgpool_layer make_avgpool_layer(int batch, int w, int h, int c) l.outputs = l.out_c; l.inputs = h*w*c; int output_size = l.outputs * batch; - l.output = calloc(output_size, sizeof(float)); - l.delta = calloc(output_size, sizeof(float)); + l.output = (float*)calloc(output_size, sizeof(float)); + l.delta = (float*)calloc(output_size, sizeof(float)); l.forward = forward_avgpool_layer; l.backward = backward_avgpool_layer; #ifdef GPU diff --git a/src/avgpool_layer.h b/src/avgpool_layer.h index f8329aea8bb..f7679aa9329 100644 --- a/src/avgpool_layer.h +++ b/src/avgpool_layer.h @@ -8,6 +8,9 @@ typedef layer avgpool_layer; +#ifdef __cplusplus +extern "C" { +#endif image get_avgpool_image(avgpool_layer l); avgpool_layer make_avgpool_layer(int batch, int w, int h, int c); void resize_avgpool_layer(avgpool_layer *l, int w, int h); @@ -19,5 +22,8 @@ void forward_avgpool_layer_gpu(avgpool_layer l, network_state state); void backward_avgpool_layer_gpu(avgpool_layer l, network_state state); #endif +#ifdef __cplusplus +} #endif +#endif diff --git a/src/avgpool_layer_kernels.cu b/src/avgpool_layer_kernels.cu index 784d6762415..1bb48ae6a5b 100644 --- a/src/avgpool_layer_kernels.cu +++ b/src/avgpool_layer_kernels.cu @@ -2,10 +2,8 @@ #include "curand.h" #include "cublas_v2.h" -extern "C" { #include "avgpool_layer.h" #include "cuda.h" -} __global__ void forward_avgpool_layer_kernel(int n, int w, int h, int c, float *input, float *output) { diff --git a/src/batchnorm_layer.c b/src/batchnorm_layer.c index a2870405ff1..d6ce989ef9b 100644 --- a/src/batchnorm_layer.c +++ b/src/batchnorm_layer.c @@ -5,29 +5,29 @@ layer make_batchnorm_layer(int batch, int w, int h, int c) { fprintf(stderr, "Batch Normalization Layer: %d x %d x %d image\n", w,h,c); - layer layer = {0}; + layer layer = { (LAYER_TYPE)0 }; layer.type = BATCHNORM; layer.batch = batch; layer.h = layer.out_h = h; layer.w = layer.out_w = w; layer.c = layer.out_c = c; - layer.output = calloc(h * w * c * batch, sizeof(float)); - layer.delta = calloc(h * w * c * batch, sizeof(float)); + layer.output = (float*)calloc(h * w * c * batch, sizeof(float)); + layer.delta = (float*)calloc(h * w * c * batch, sizeof(float)); layer.inputs = w*h*c; layer.outputs = layer.inputs; - layer.scales = calloc(c, sizeof(float)); - layer.scale_updates = calloc(c, sizeof(float)); + layer.scales = (float*)calloc(c, sizeof(float)); + layer.scale_updates = (float*)calloc(c, sizeof(float)); int i; for(i = 0; i < c; ++i){ layer.scales[i] = 1; } - layer.mean = calloc(c, sizeof(float)); - layer.variance = calloc(c, sizeof(float)); + layer.mean = (float*)calloc(c, sizeof(float)); + layer.variance = (float*)calloc(c, sizeof(float)); - layer.rolling_mean = calloc(c, sizeof(float)); - layer.rolling_variance = calloc(c, sizeof(float)); + layer.rolling_mean = (float*)calloc(c, sizeof(float)); + layer.rolling_variance = (float*)calloc(c, sizeof(float)); layer.forward = forward_batchnorm_layer; layer.backward = backward_batchnorm_layer; diff --git a/src/batchnorm_layer.h b/src/batchnorm_layer.h index 99d1d0fe316..1ea38d72596 100644 --- a/src/batchnorm_layer.h +++ b/src/batchnorm_layer.h @@ -5,6 +5,9 @@ #include "layer.h" #include "network.h" +#ifdef __cplusplus +extern "C" { +#endif layer make_batchnorm_layer(int batch, int w, int h, int c); void forward_batchnorm_layer(layer l, network_state state); void backward_batchnorm_layer(layer l, network_state state); @@ -16,4 +19,7 @@ void pull_batchnorm_layer(layer l); void push_batchnorm_layer(layer l); #endif +#ifdef __cplusplus +} +#endif #endif diff --git a/src/blas.c b/src/blas.c index ae84dc72d5d..32d44cb5e45 100644 --- a/src/blas.c +++ b/src/blas.c @@ -34,7 +34,7 @@ void reorg_cpu(float *x, int out_w, int out_h, int out_c, int batch, int stride, void flatten(float *x, int size, int layers, int batch, int forward) { - float *swap = calloc(size*layers*batch, sizeof(float)); + float* swap = (float*)calloc(size * layers * batch, sizeof(float)); int i,c,b; for(b = 0; b < batch; ++b){ for(c = 0; c < layers; ++c){ diff --git a/src/blas.h b/src/blas.h index 9b7f3d5aa67..19b72b76144 100644 --- a/src/blas.h +++ b/src/blas.h @@ -1,5 +1,12 @@ #ifndef BLAS_H #define BLAS_H +#ifdef GPU +#include "cuda.h" +#include "tree.h" +#endif +#ifdef __cplusplus +extern "C" { +#endif void flatten(float *x, int size, int layers, int batch, int forward); void pm(int M, int N, float *A); float *random_matrix(int rows, int cols); @@ -41,8 +48,6 @@ void softmax_cpu(float *input, int n, int batch, int batch_offset, int groups, i void softmax_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *error); #ifdef GPU -#include "cuda.h" -#include "tree.h" void axpy_ongpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY); void axpy_ongpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY); @@ -97,5 +102,8 @@ void upsample_gpu(float *in, int w, int h, int c, int batch, int stride, int for void softmax_tree_gpu(float *input, int spatial, int batch, int stride, float temp, float *output, tree hier); +#endif +#ifdef __cplusplus +} #endif #endif diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index c1444011ab9..72d92129280 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -3,12 +3,10 @@ #include "cublas_v2.h" #include -extern "C" { #include "blas.h" #include "cuda.h" #include "utils.h" #include "tree.h" -} __global__ void scale_bias_kernel(float *output, float *biases, int n, int size) { diff --git a/src/box.c b/src/box.c index 718215fe2b0..bc4958a22a1 100644 --- a/src/box.c +++ b/src/box.c @@ -249,7 +249,7 @@ int nms_comparator(const void *pa, const void *pb) void do_nms_sort_v2(box *boxes, float **probs, int total, int classes, float thresh) { int i, j, k; - sortable_bbox *s = calloc(total, sizeof(sortable_bbox)); + sortable_bbox* s = (sortable_bbox*)calloc(total, sizeof(sortable_bbox)); for(i = 0; i < total; ++i){ s[i].index = i; diff --git a/src/box.h b/src/box.h index 0e6fd918efc..9d6aa4f3469 100644 --- a/src/box.h +++ b/src/box.h @@ -27,6 +27,9 @@ typedef struct detection_with_class { int best_class; } detection_with_class; +#ifdef __cplusplus +extern "C" { +#endif box float_to_box(float *f); float box_iou(box a, box b); float box_rmse(box a, box b); @@ -42,4 +45,7 @@ box encode_box(box b, box anchor); // Return number of selected detections in *selected_detections_num detection_with_class* get_actual_detections(detection *dets, int dets_num, float thresh, int* selected_detections_num, char **names); +#ifdef __cplusplus +} +#endif #endif diff --git a/src/captcha.c b/src/captcha.c index 3d449b267cc..230c723ff45 100644 --- a/src/captcha.c +++ b/src/captcha.c @@ -41,11 +41,11 @@ void train_captcha(char *cfgfile, char *weightfile) int i = *net.seen/imgs; int solved = 1; list *plist; - char **labels = get_labels("/data/captcha/reimgs.labels.list"); + char** labels = get_labels("data/captcha/reimgs.labels.list"); if (solved){ - plist = get_paths("/data/captcha/reimgs.solved.list"); + plist = get_paths("data/captcha/reimgs.solved.list"); }else{ - plist = get_paths("/data/captcha/reimgs.raw.list"); + plist = get_paths("data/captcha/reimgs.raw.list"); } char **paths = (char **)list_to_array(plist); printf("%d\n", plist->size); @@ -89,7 +89,7 @@ void train_captcha(char *cfgfile, char *weightfile) free_data(train); if(i%100==0){ char buff[256]; - sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i); + sprintf(buff, "imagenet_backup/%s_%d.weights", base, i); save_weights(net, buff); } } @@ -104,7 +104,7 @@ void test_captcha(char *cfgfile, char *weightfile, char *filename) set_batch_network(&net, 1); srand(2222222); int i = 0; - char **names = get_labels("/data/captcha/reimgs.labels.list"); + char** names = get_labels("data/captcha/reimgs.labels.list"); char buff[256]; char *input = buff; int indexes[26]; @@ -137,12 +137,12 @@ void test_captcha(char *cfgfile, char *weightfile, char *filename) void valid_captcha(char *cfgfile, char *weightfile, char *filename) { - char **labels = get_labels("/data/captcha/reimgs.labels.list"); + char** labels = get_labels("data/captcha/reimgs.labels.list"); network net = parse_network_cfg(cfgfile); if(weightfile){ load_weights(&net, weightfile); } - list *plist = get_paths("/data/captcha/reimgs.fg.list"); + list* plist = get_paths("data/captcha/reimgs.fg.list"); char **paths = (char **)list_to_array(plist); int N = plist->size; int outputs = net.outputs; diff --git a/src/cifar.c b/src/cifar.c index 4a27e79511f..a9daa9792e5 100644 --- a/src/cifar.c +++ b/src/cifar.c @@ -5,7 +5,7 @@ #include "blas.h" #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" +#include #endif void train_cifar(char *cfgfile, char *weightfile) @@ -20,7 +20,7 @@ void train_cifar(char *cfgfile, char *weightfile) } printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - char *backup_directory = "/home/pjreddie/backup/"; + char* backup_directory = "backup/"; int classes = 10; int N = 50000; @@ -68,7 +68,7 @@ void train_cifar_distill(char *cfgfile, char *weightfile) } printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - char *backup_directory = "/home/pjreddie/backup/"; + char* backup_directory = "backup/"; int classes = 10; int N = 50000; diff --git a/src/classifier.c b/src/classifier.c index 11b6968d004..24b956d7f25 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -8,20 +8,18 @@ #include "cuda.h" #ifdef WIN32 #include -#include #include "gettimeofday.h" #else #include #endif #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#include "opencv2/core/version.hpp" +#include +#include #ifndef CV_VERSION_EPOCH -#include "opencv2/videoio/videoio_c.h" +#include #endif -image get_image_from_stream(CvCapture *cap); -image get_image_from_stream_cpp(CvCapture *cap); +#include "image.h" #include "http_stream.h" IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show); @@ -34,7 +32,7 @@ float validate_classifier_single(char *datacfg, char *filename, char *weightfile float *get_regression_values(char **labels, int n) { - float *v = calloc(n, sizeof(float)); + float* v = (float*)calloc(n, sizeof(float)); int i; for(i = 0; i < n; ++i){ char *p = strchr(labels[i], ' '); @@ -52,7 +50,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, char *base = basecfg(cfgfile); printf("%s\n", base); printf("%d\n", ngpus); - network *nets = calloc(ngpus, sizeof(network)); + network* nets = (network*)calloc(ngpus, sizeof(network)); srand(time(0)); int seed = rand(); @@ -431,7 +429,7 @@ void validate_classifier_10(char *datacfg, char *filename, char *weightfile) float avg_acc = 0; float avg_topk = 0; - int *indexes = calloc(topk, sizeof(int)); + int* indexes = (int*)calloc(topk, sizeof(int)); for(i = 0; i < m; ++i){ int class_id = -1; @@ -458,7 +456,7 @@ void validate_classifier_10(char *datacfg, char *filename, char *weightfile) images[7] = crop_image(im, 0, 0, w, h); images[8] = crop_image(im, -shift, shift, w, h); images[9] = crop_image(im, shift, shift, w, h); - float *pred = calloc(classes, sizeof(float)); + float* pred = (float*)calloc(classes, sizeof(float)); for(j = 0; j < 10; ++j){ float *p = network_predict(net, images[j].data); if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy, 1); @@ -504,7 +502,7 @@ void validate_classifier_full(char *datacfg, char *filename, char *weightfile) float avg_acc = 0; float avg_topk = 0; - int *indexes = calloc(topk, sizeof(int)); + int* indexes = (int*)calloc(topk, sizeof(int)); int size = net.w; for(i = 0; i < m; ++i){ @@ -581,7 +579,7 @@ float validate_classifier_single(char *datacfg, char *filename, char *weightfile float avg_acc = 0; float avg_topk = 0; - int *indexes = calloc(topk, sizeof(int)); + int* indexes = (int*)calloc(topk, sizeof(int)); for(i = 0; i < m; ++i){ int class_id = -1; @@ -651,7 +649,7 @@ void validate_classifier_multi(char *datacfg, char *filename, char *weightfile) float avg_acc = 0; float avg_topk = 0; - int *indexes = calloc(topk, sizeof(int)); + int* indexes = (int*)calloc(topk, sizeof(int)); for(i = 0; i < m; ++i){ int class_id = -1; @@ -662,7 +660,7 @@ void validate_classifier_multi(char *datacfg, char *filename, char *weightfile) break; } } - float *pred = calloc(classes, sizeof(float)); + float* pred = (float*)calloc(classes, sizeof(float)); image im = load_image_color(paths[i], 0, 0); for(j = 0; j < nscales; ++j){ image r = resize_min(im, scales[j]); @@ -707,7 +705,7 @@ void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filena int i = 0; char **names = get_labels(name_list); clock_t time; - int *indexes = calloc(top, sizeof(int)); + int* indexes = (int*)calloc(top, sizeof(int)); char buff[256]; char *input = buff; while(1){ @@ -790,7 +788,7 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi int i = 0; char **names = get_labels(name_list); clock_t time; - int *indexes = calloc(top, sizeof(int)); + int* indexes = (int*)calloc(top, sizeof(int)); char buff[256]; char *input = buff; int size = net.w; @@ -973,7 +971,7 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i char *name_list = option_find_str(options, "names", 0); char **names = get_labels(name_list); - int *indexes = calloc(top, sizeof(int)); + int* indexes = (int*)calloc(top, sizeof(int)); if(!cap) error("Couldn't connect to webcam.\n"); //cvNamedWindow("Threat", CV_WINDOW_NORMAL); @@ -1051,11 +1049,13 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i } top_predictions(net, top, indexes); char buff[256]; - sprintf(buff, "/home/pjreddie/tmp/threat_%06d", count); + sprintf(buff, "tmp/threat_%06d", count); //save_image(out, buff); +#ifndef _WIN32 printf("\033[2J"); printf("\033[1;1H"); +#endif printf("\nFPS:%.0f\n",fps); for(i = 0; i < top; ++i){ @@ -1111,7 +1111,7 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde char *name_list = option_find_str(options, "names", 0); char **names = get_labels(name_list); - int *indexes = calloc(top, sizeof(int)); + int* indexes = (int*)calloc(top, sizeof(int)); if(!cap) error("Couldn't connect to webcam.\n"); cvNamedWindow("Threat Detection", CV_WINDOW_NORMAL); @@ -1193,7 +1193,7 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind char *name_list = option_find_str(options, "names", 0); char **names = get_labels(name_list); - int *indexes = calloc(top, sizeof(int)); + int* indexes = (int*)calloc(top, sizeof(int)); if(!cap) error("Couldn't connect to webcam.\n"); cvNamedWindow("Classifier", CV_WINDOW_NORMAL); @@ -1214,8 +1214,10 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 1); top_predictions(net, top, indexes); +#ifndef _WIN32 printf("\033[2J"); printf("\033[1;1H"); +#endif printf("\nFPS:%.0f\n",fps); for(i = 0; i < top; ++i){ @@ -1257,7 +1259,7 @@ void run_classifier(int argc, char **argv) for(i = 0; i < len; ++i){ if (gpu_list[i] == ',') ++ngpus; } - gpus = calloc(ngpus, sizeof(int)); + gpus = (int*)calloc(ngpus, sizeof(int)); for(i = 0; i < ngpus; ++i){ gpus[i] = atoi(gpu_list); gpu_list = strchr(gpu_list, ',')+1; diff --git a/src/classifier.h b/src/classifier.h index 3c89f49cdc5..d94417d3f1f 100644 --- a/src/classifier.h +++ b/src/classifier.h @@ -1,2 +1,12 @@ +#ifndef CLASSIFIER_H +#define CLASSIFIER_H +#include "list.h" +#ifdef __cplusplus +extern "C" { +#endif list *read_data_cfg(char *filename); +#ifdef __cplusplus +} +#endif +#endif diff --git a/src/coco.c b/src/coco.c index aff8e5a0f2f..e3cfccea009 100644 --- a/src/coco.c +++ b/src/coco.c @@ -9,7 +9,7 @@ #include "demo.h" #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" +#include #endif char *coco_classes[] = {"person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic light","fire hydrant","stop sign","parking meter","bench","bird","cat","dog","horse","sheep","cow","elephant","bear","zebra","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite","baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife","spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza","donut","cake","chair","couch","potted plant","bed","dining table","toilet","tv","laptop","mouse","remote","keyboard","cell phone","microwave","oven","toaster","sink","refrigerator","book","clock","vase","scissors","teddy bear","hair drier","toothbrush"}; @@ -22,7 +22,7 @@ void train_coco(char *cfgfile, char *weightfile) //char *train_images = "/home/pjreddie/data/coco/train.txt"; char *train_images = "data/coco.trainval.txt"; //char *train_images = "data/bags.train.list"; - char *backup_directory = "/home/pjreddie/backup/"; + char* backup_directory = "backup/"; srand(time(0)); char *base = basecfg(cfgfile); printf("%s\n", base); @@ -164,9 +164,9 @@ void validate_coco(char *cfgfile, char *weightfile) FILE *fp = fopen(buff, "w"); fprintf(fp, "[\n"); - box *boxes = calloc(side*side*l.n, sizeof(box)); - float **probs = calloc(side*side*l.n, sizeof(float *)); - for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *)); + box* boxes = (box*)calloc(side * side * l.n, sizeof(box)); + float** probs = (float**)calloc(side * side * l.n, sizeof(float*)); + for(j = 0; j < side*side*l.n; ++j) probs[j] = (float*)calloc(classes, sizeof(float)); int m = plist->size; int i=0; @@ -177,11 +177,11 @@ void validate_coco(char *cfgfile, char *weightfile) float iou_thresh = .5; int nthreads = 8; - image *val = calloc(nthreads, sizeof(image)); - image *val_resized = calloc(nthreads, sizeof(image)); - image *buf = calloc(nthreads, sizeof(image)); - image *buf_resized = calloc(nthreads, sizeof(image)); - pthread_t *thr = calloc(nthreads, sizeof(pthread_t)); + image* val = (image*)calloc(nthreads, sizeof(image)); + image* val_resized = (image*)calloc(nthreads, sizeof(image)); + image* buf = (image*)calloc(nthreads, sizeof(image)); + image* buf_resized = (image*)calloc(nthreads, sizeof(image)); + pthread_t* thr = (pthread_t*)calloc(nthreads, sizeof(pthread_t)); load_args args = {0}; args.w = net.w; @@ -240,7 +240,7 @@ void validate_coco_recall(char *cfgfile, char *weightfile) srand(time(0)); char *base = "results/comp4_det_test_"; - list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt"); + list* plist = get_paths("data/voc/test/2007_test.txt"); char **paths = (char **)list_to_array(plist); layer l = net.layers[net.n-1]; @@ -248,15 +248,15 @@ void validate_coco_recall(char *cfgfile, char *weightfile) int side = l.side; int j, k; - FILE **fps = calloc(classes, sizeof(FILE *)); + FILE** fps = (FILE**)calloc(classes, sizeof(FILE*)); for(j = 0; j < classes; ++j){ char buff[1024]; snprintf(buff, 1024, "%s%s.txt", base, coco_classes[j]); fps[j] = fopen(buff, "w"); } - box *boxes = calloc(side*side*l.n, sizeof(box)); - float **probs = calloc(side*side*l.n, sizeof(float *)); - for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *)); + box* boxes = (box*)calloc(side * side * l.n, sizeof(box)); + float** probs = (float**)calloc(side * side * l.n, sizeof(float*)); + for(j = 0; j < side*side*l.n; ++j) probs[j] = (float*)calloc(classes, sizeof(float)); int m = plist->size; int i=0; @@ -328,9 +328,9 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh) char buff[256]; char *input = buff; int j; - box *boxes = calloc(l.side*l.side*l.n, sizeof(box)); - float **probs = calloc(l.side*l.side*l.n, sizeof(float *)); - for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *)); + box* boxes = (box*)calloc(l.side * l.side * l.n, sizeof(box)); + float** probs = (float**)calloc(l.side * l.side * l.n, sizeof(float*)); + for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = (float*)calloc(l.classes, sizeof(float)); while(1){ if(filename){ strncpy(input, filename, 256); diff --git a/src/col2im.c b/src/col2im.c index 5c4605e1974..43126423040 100644 --- a/src/col2im.c +++ b/src/col2im.c @@ -1,5 +1,6 @@ #include #include +#include "col2im.h" void col2im_add_pixel(float *im, int height, int width, int channels, int row, int col, int channel, int pad, float val) { diff --git a/src/col2im.h b/src/col2im.h index 02374972095..a8493e382b4 100644 --- a/src/col2im.h +++ b/src/col2im.h @@ -1,6 +1,9 @@ #ifndef COL2IM_H #define COL2IM_H +#ifdef __cplusplus +extern "C" { +#endif void col2im_cpu(float* data_col, int channels, int height, int width, int ksize, int stride, int pad, float* data_im); @@ -10,4 +13,7 @@ void col2im_ongpu(float *data_col, int channels, int height, int width, int ksize, int stride, int pad, float *data_im); #endif +#ifdef __cplusplus +} +#endif #endif diff --git a/src/col2im_kernels.cu b/src/col2im_kernels.cu index 0fe61e49578..34808c00527 100644 --- a/src/col2im_kernels.cu +++ b/src/col2im_kernels.cu @@ -2,10 +2,8 @@ #include "curand.h" #include "cublas_v2.h" -extern "C" { #include "col2im.h" #include "cuda.h" -} // src: https://github.com/BVLC/caffe/blob/master/src/caffe/util/im2col.cu // You may also want to read: https://github.com/BVLC/caffe/blob/master/LICENSE diff --git a/src/compare.c b/src/compare.c index 803d812dda1..d0d1f1f01e5 100644 --- a/src/compare.c +++ b/src/compare.c @@ -12,7 +12,7 @@ void train_compare(char *cfgfile, char *weightfile) srand(time(0)); float avg_loss = -1; char *base = basecfg(cfgfile); - char *backup_directory = "/home/pjreddie/backup/"; + char* backup_directory = "backup/"; printf("%s\n", base); network net = parse_network_cfg(cfgfile); if(weightfile){ @@ -176,7 +176,7 @@ int bbox_comparator(const void *a, const void *b) image im1 = load_image_color(box1.filename, net.w, net.h); image im2 = load_image_color(box2.filename, net.w, net.h); - float *X = calloc(net.w*net.h*net.c, sizeof(float)); + float* X = (float*)calloc(net.w * net.h * net.c, sizeof(float)); memcpy(X, im1.data, im1.w*im1.h*im1.c*sizeof(float)); memcpy(X+im1.w*im1.h*im1.c, im2.data, im2.w*im2.h*im2.c*sizeof(float)); float *predictions = network_predict(net, X); @@ -205,7 +205,7 @@ void bbox_fight(network net, sortable_bbox *a, sortable_bbox *b, int classes, in { image im1 = load_image_color(a->filename, net.w, net.h); image im2 = load_image_color(b->filename, net.w, net.h); - float *X = calloc(net.w*net.h*net.c, sizeof(float)); + float* X = (float*)calloc(net.w * net.h * net.c, sizeof(float)); memcpy(X, im1.data, im1.w*im1.h*im1.c*sizeof(float)); memcpy(X+im1.w*im1.h*im1.c, im2.data, im2.w*im2.h*im2.c*sizeof(float)); float *predictions = network_predict(net, X); @@ -239,7 +239,7 @@ void SortMaster3000(char *filename, char *weightfile) char **paths = (char **)list_to_array(plist); int N = plist->size; free_list(plist); - sortable_bbox *boxes = calloc(N, sizeof(sortable_bbox)); + sortable_bbox* boxes = (sortable_bbox*)calloc(N, sizeof(sortable_bbox)); printf("Sorting %d boxes...\n", N); for(i = 0; i < N; ++i){ boxes[i].filename = paths[i]; @@ -274,13 +274,13 @@ void BattleRoyaleWithCheese(char *filename, char *weightfile) int N = plist->size; int total = N; free_list(plist); - sortable_bbox *boxes = calloc(N, sizeof(sortable_bbox)); + sortable_bbox* boxes = (sortable_bbox*)calloc(N, sizeof(sortable_bbox)); printf("Battling %d boxes...\n", N); for(i = 0; i < N; ++i){ boxes[i].filename = paths[i]; boxes[i].net = net; boxes[i].classes = classes; - boxes[i].elos = calloc(classes, sizeof(float));; + boxes[i].elos = (float*)calloc(classes, sizeof(float)); for(j = 0; j < classes; ++j){ boxes[i].elos[j] = 1500; } diff --git a/src/connected_layer.c b/src/connected_layer.c index e54837acb5c..3520e9140e8 100644 --- a/src/connected_layer.c +++ b/src/connected_layer.c @@ -54,7 +54,7 @@ connected_layer make_connected_layer(int batch, int steps, int inputs, int outpu { int total_batch = batch*steps; int i; - connected_layer l = {0}; + connected_layer l = { (LAYER_TYPE)0 }; l.type = CONNECTED; l.inputs = inputs; @@ -74,14 +74,14 @@ connected_layer make_connected_layer(int batch, int steps, int inputs, int outpu l.activation = activation; l.learning_rate_scale = 1; - l.output = calloc(total_batch*outputs, sizeof(float)); - l.delta = calloc(total_batch*outputs, sizeof(float)); + l.output = (float*)calloc(total_batch * outputs, sizeof(float)); + l.delta = (float*)calloc(total_batch * outputs, sizeof(float)); - l.weight_updates = calloc(inputs*outputs, sizeof(float)); - l.bias_updates = calloc(outputs, sizeof(float)); + l.weight_updates = (float*)calloc(inputs * outputs, sizeof(float)); + l.bias_updates = (float*)calloc(outputs, sizeof(float)); - l.weights = calloc(outputs*inputs, sizeof(float)); - l.biases = calloc(outputs, sizeof(float)); + l.weights = (float*)calloc(outputs * inputs, sizeof(float)); + l.biases = (float*)calloc(outputs, sizeof(float)); l.forward = forward_connected_layer; l.backward = backward_connected_layer; @@ -98,22 +98,22 @@ connected_layer make_connected_layer(int batch, int steps, int inputs, int outpu } if(batch_normalize){ - l.scales = calloc(outputs, sizeof(float)); - l.scale_updates = calloc(outputs, sizeof(float)); + l.scales = (float*)calloc(outputs, sizeof(float)); + l.scale_updates = (float*)calloc(outputs, sizeof(float)); for(i = 0; i < outputs; ++i){ l.scales[i] = 1; } - l.mean = calloc(outputs, sizeof(float)); - l.mean_delta = calloc(outputs, sizeof(float)); - l.variance = calloc(outputs, sizeof(float)); - l.variance_delta = calloc(outputs, sizeof(float)); + l.mean = (float*)calloc(outputs, sizeof(float)); + l.mean_delta = (float*)calloc(outputs, sizeof(float)); + l.variance = (float*)calloc(outputs, sizeof(float)); + l.variance_delta = (float*)calloc(outputs, sizeof(float)); - l.rolling_mean = calloc(outputs, sizeof(float)); - l.rolling_variance = calloc(outputs, sizeof(float)); + l.rolling_mean = (float*)calloc(outputs, sizeof(float)); + l.rolling_variance = (float*)calloc(outputs, sizeof(float)); - l.x = calloc(total_batch*outputs, sizeof(float)); - l.x_norm = calloc(total_batch*outputs, sizeof(float)); + l.x = (float*)calloc(total_batch * outputs, sizeof(float)); + l.x_norm = (float*)calloc(total_batch * outputs, sizeof(float)); } #ifdef GPU diff --git a/src/connected_layer.h b/src/connected_layer.h index 3775e0a5819..80805cc2abd 100644 --- a/src/connected_layer.h +++ b/src/connected_layer.h @@ -7,7 +7,11 @@ typedef layer connected_layer; +#ifdef __cplusplus +extern "C" { +#endif connected_layer make_connected_layer(int batch, int steps, int inputs, int outputs, ACTIVATION activation, int batch_normalize); +size_t get_connected_workspace_size(layer l); void forward_connected_layer(connected_layer layer, network_state state); void backward_connected_layer(connected_layer layer, network_state state); @@ -23,5 +27,8 @@ void push_connected_layer(connected_layer layer); void pull_connected_layer(connected_layer layer); #endif +#ifdef __cplusplus +} #endif +#endif diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 4293d5a04dd..05c847ce7df 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -3,10 +3,11 @@ #include "cublas_v2.h" #ifdef CUDNN +#ifndef USE_CMAKE_LIBS #pragma comment(lib, "cudnn.lib") #endif +#endif -extern "C" { #include "convolutional_layer.h" #include "batchnorm_layer.h" #include "gemm.h" @@ -15,7 +16,7 @@ extern "C" { #include "col2im.h" #include "utils.h" #include "cuda.h" -} + __global__ void binarize_kernel(float *x, int n, float *binary) { @@ -73,7 +74,6 @@ void binarize_weights_gpu(float *weights, int n, int size, float *binary) CHECK_CUDA(cudaPeekAtLastError()); } -#define WARP_SIZE 32 __global__ void set_zero_kernel(float *src, int size) { @@ -477,10 +477,10 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) simple_copy_ongpu(l.outputs*l.batch / 2, output16, l.x_gpu); //copy_ongpu(l.outputs*l.batch / 2, output16, 1, l.x_gpu, 1); //cudaMemcpyAsync(l.x_gpu, output16, l.outputs*l.batch*sizeof(half), cudaMemcpyDefault, get_cuda_stream()); - float one = 1; - float zero = 0; + float one = 1.0f; + float zero = 0.0f; // Batch-normalization can still take FP16 inputs and outputs, saving half the bandwidth - // compared to FP32, it’s just that the statistics and value adjustment should be done in FP32. + // compared to FP32, it's just that the statistics and value adjustment should be done in FP32. CHECK_CUDNN(cudnnBatchNormalizationForwardTraining(cudnn_handle(), CUDNN_BATCHNORM_SPATIAL, &one, @@ -639,8 +639,8 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state // l.mean_gpu = l.rolling_mean_gpu; // l.variance_gpu = l.rolling_variance_gpu; //} - float one = 1; - float zero = 0; + float one = 1.0f; + float zero = 0.0f; CHECK_CUDNN(cudnnBatchNormalizationBackward(cudnn_handle(), CUDNN_BATCHNORM_SPATIAL, &one, @@ -936,4 +936,3 @@ void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float } } */ - diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 21dd7231873..9e2d106ec1a 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -9,8 +9,10 @@ #include #ifdef CUDNN +#ifndef USE_CMAKE_LIBS #pragma comment(lib, "cudnn.lib") #endif +#endif #ifdef AI2 #include "xnor_layer.h" @@ -288,7 +290,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) l->weightDesc, l->convDesc, l->dstTensorDesc, - forward_algo, + (cudnnConvolutionFwdPreference_t)forward_algo, 0, &l->fw_algo)); CHECK_CUDNN(cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(), @@ -296,7 +298,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) l->ddstTensorDesc, l->convDesc, l->dsrcTensorDesc, - backward_algo, + (cudnnConvolutionBwdDataPreference_t)backward_algo, 0, &l->bd_algo)); CHECK_CUDNN(cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(), @@ -304,7 +306,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) l->ddstTensorDesc, l->convDesc, l->dweightDesc, - backward_filter, + (cudnnConvolutionBwdFilterPreference_t)backward_filter, 0, &l->bf_algo)); @@ -328,7 +330,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, { int total_batch = batch*steps; int i; - convolutional_layer l = {0}; + convolutional_layer l = { (LAYER_TYPE)0 }; l.type = CONVOLUTIONAL; l.index = index; @@ -346,11 +348,11 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.batch_normalize = batch_normalize; l.learning_rate_scale = 1; - l.weights = calloc(c*n*size*size, sizeof(float)); - l.weight_updates = calloc(c*n*size*size, sizeof(float)); + l.weights = (float*)calloc(c * n * size * size, sizeof(float)); + l.weight_updates = (float*)calloc(c * n * size * size, sizeof(float)); - l.biases = calloc(n, sizeof(float)); - l.bias_updates = calloc(n, sizeof(float)); + l.biases = (float*)calloc(n, sizeof(float)); + l.bias_updates = (float*)calloc(n, sizeof(float)); // float scale = 1./sqrt(size*size*c); float scale = sqrt(2./(size*size*c)); @@ -364,64 +366,64 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.inputs = l.w * l.h * l.c; l.activation = activation; - l.output = calloc(total_batch*l.outputs, sizeof(float)); - l.delta = calloc(total_batch*l.outputs, sizeof(float)); + l.output = (float*)calloc(total_batch*l.outputs, sizeof(float)); + l.delta = (float*)calloc(total_batch*l.outputs, sizeof(float)); l.forward = forward_convolutional_layer; l.backward = backward_convolutional_layer; l.update = update_convolutional_layer; if(binary){ - l.binary_weights = calloc(c*n*size*size, sizeof(float)); - l.cweights = calloc(c*n*size*size, sizeof(char)); - l.scales = calloc(n, sizeof(float)); + l.binary_weights = (float*)calloc(c * n * size * size, sizeof(float)); + l.cweights = (char*)calloc(c * n * size * size, sizeof(char)); + l.scales = (float*)calloc(n, sizeof(float)); } if(xnor){ - l.binary_weights = calloc(c*n*size*size, sizeof(float)); - l.binary_input = calloc(l.inputs*l.batch, sizeof(float)); + l.binary_weights = (float*)calloc(c * n * size * size, sizeof(float)); + l.binary_input = (float*)calloc(l.inputs * l.batch, sizeof(float)); int align = 32;// 8; int src_align = l.out_h*l.out_w; l.bit_align = src_align + (align - src_align % align); - l.mean_arr = calloc(l.n, sizeof(float)); + l.mean_arr = (float*)calloc(l.n, sizeof(float)); const size_t new_c = l.c / 32; size_t in_re_packed_input_size = new_c * l.w * l.h + 1; - l.bin_re_packed_input = calloc(in_re_packed_input_size, sizeof(uint32_t)); + l.bin_re_packed_input = (uint32_t*)calloc(in_re_packed_input_size, sizeof(uint32_t)); l.lda_align = 256; // AVX2 int k = l.size*l.size*l.c; size_t k_aligned = k + (l.lda_align - k%l.lda_align); size_t t_bit_input_size = k_aligned * l.bit_align / 8; - l.t_bit_input = calloc(t_bit_input_size, sizeof(char)); + l.t_bit_input = (char*)calloc(t_bit_input_size, sizeof(char)); } if(batch_normalize){ - l.scales = calloc(n, sizeof(float)); - l.scale_updates = calloc(n, sizeof(float)); + l.scales = (float*)calloc(n, sizeof(float)); + l.scale_updates = (float*)calloc(n, sizeof(float)); for(i = 0; i < n; ++i){ l.scales[i] = 1; } - l.mean = calloc(n, sizeof(float)); - l.variance = calloc(n, sizeof(float)); + l.mean = (float*)calloc(n, sizeof(float)); + l.variance = (float*)calloc(n, sizeof(float)); - l.mean_delta = calloc(n, sizeof(float)); - l.variance_delta = calloc(n, sizeof(float)); + l.mean_delta = (float*)calloc(n, sizeof(float)); + l.variance_delta = (float*)calloc(n, sizeof(float)); - l.rolling_mean = calloc(n, sizeof(float)); - l.rolling_variance = calloc(n, sizeof(float)); - l.x = calloc(total_batch*l.outputs, sizeof(float)); - l.x_norm = calloc(total_batch*l.outputs, sizeof(float)); + l.rolling_mean = (float*)calloc(n, sizeof(float)); + l.rolling_variance = (float*)calloc(n, sizeof(float)); + l.x = (float*)calloc(total_batch * l.outputs, sizeof(float)); + l.x_norm = (float*)calloc(total_batch * l.outputs, sizeof(float)); } if(adam){ l.adam = 1; - l.m = calloc(c*n*size*size, sizeof(float)); - l.v = calloc(c*n*size*size, sizeof(float)); - l.bias_m = calloc(n, sizeof(float)); - l.scale_m = calloc(n, sizeof(float)); - l.bias_v = calloc(n, sizeof(float)); - l.scale_v = calloc(n, sizeof(float)); + l.m = (float*)calloc(c * n * size * size, sizeof(float)); + l.v = (float*)calloc(c * n * size * size, sizeof(float)); + l.bias_m = (float*)calloc(n, sizeof(float)); + l.scale_m = (float*)calloc(n, sizeof(float)); + l.bias_v = (float*)calloc(n, sizeof(float)); + l.scale_v = (float*)calloc(n, sizeof(float)); } #ifdef GPU @@ -549,11 +551,11 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) l->outputs = l->out_h * l->out_w * l->out_c; l->inputs = l->w * l->h * l->c; - l->output = realloc(l->output, l->batch*l->outputs*sizeof(float)); - l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float)); + l->output = (float*)realloc(l->output, l->batch * l->outputs * sizeof(float)); + l->delta = (float*)realloc(l->delta, l->batch * l->outputs * sizeof(float)); if(l->batch_normalize){ - l->x = realloc(l->x, l->batch*l->outputs*sizeof(float)); - l->x_norm = realloc(l->x_norm, l->batch*l->outputs*sizeof(float)); + l->x = (float*)realloc(l->x, l->batch * l->outputs * sizeof(float)); + l->x_norm = (float*)realloc(l->x_norm, l->batch * l->outputs * sizeof(float)); } if (l->xnor) { @@ -642,7 +644,7 @@ void gemm_nn_custom(int M, int N, int K, float ALPHA, int i, j, k; for (i = 0; i < M; ++i) { for (k = 0; k < K; ++k) { - register float A_PART = ALPHA*A[i*lda + k]; + float A_PART = ALPHA * A[i * lda + k]; //printf("\n weight = %f \n", A_PART); for (j = 0; j < N; ++j) { C[i*ldc + j] += A_PART*B[k*ldb + j]; @@ -695,8 +697,8 @@ void binary_align_weights(convolutional_layer *l) size_t align_weights_size = new_lda * m; l->align_bit_weights_size = align_weights_size / 8 + 1; - float *align_weights = calloc(align_weights_size, sizeof(float)); - l->align_bit_weights = calloc(l->align_bit_weights_size, sizeof(char)); + float* align_weights = (float*)calloc(align_weights_size, sizeof(float)); + l->align_bit_weights = (char*)calloc(l->align_bit_weights_size, sizeof(char)); size_t i, j; // align A without transpose @@ -739,7 +741,7 @@ void binary_align_weights(convolutional_layer *l) //printf("\n l.index = %d \t aw[0] = %f, aw[1] = %f, aw[2] = %f, aw[3] = %f \n", l->index, align_weights[0], align_weights[1], align_weights[2], align_weights[3]); //memcpy(l->binary_weights, align_weights, (l->size * l->size * l->c * l->n) * sizeof(float)); - float_to_bit(align_weights, l->align_bit_weights, align_weights_size); + float_to_bit(align_weights, (unsigned char*)l->align_bit_weights, align_weights_size); //if (l->n >= 32) if(gpu_index >= 0) @@ -757,7 +759,7 @@ void binary_align_weights(convolutional_layer *l) //get_mean_array(l->binary_weights, m*new_lda, l->n, l->mean_arr); } else { - float_to_bit(align_weights, l->align_bit_weights, align_weights_size); + float_to_bit(align_weights, (unsigned char*)l->align_bit_weights, align_weights_size); get_mean_array(l->binary_weights, m*k, l->n, l->mean_arr); } @@ -808,7 +810,7 @@ size_t binary_transpose_align_input(int k, int n, float *b, char **t_bit_input, // t_bit_input - [new_ldb, n] - [k', n] //transpose_bin(t_input, *t_bit_input, k, n, bit_align, new_ldb, 8); - transpose_bin(b, *t_bit_input, k, n, bit_align, new_ldb, 8); + transpose_bin((uint32_t*)b, (uint32_t*)*t_bit_input, k, n, bit_align, new_ldb, 8); return t_intput_size; } @@ -874,7 +876,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) repack_input(state.input, state.workspace, l.w, l.h, l.c); // 32 x floats -> 1 x uint32_t - float_to_bit(state.workspace, (char *)l.bin_re_packed_input, l.c * l.w * l.h); + float_to_bit(state.workspace, (unsigned char *)l.bin_re_packed_input, l.c * l.w * l.h); //free(re_packed_input); @@ -900,10 +902,10 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) // // then exit from if() - transpose_uint32((uint32_t *)state.workspace, l.t_bit_input, new_k, n, n, new_ldb); + transpose_uint32((uint32_t *)state.workspace, (uint32_t*)l.t_bit_input, new_k, n, n, new_ldb); // the main GEMM function - gemm_nn_custom_bin_mean_transposed(m, n, k, 1, l.align_bit_weights, new_ldb, l.t_bit_input, new_ldb, c, n, l.mean_arr); + gemm_nn_custom_bin_mean_transposed(m, n, k, 1, (unsigned char*)l.align_bit_weights, new_ldb, (unsigned char*)l.t_bit_input, new_ldb, c, n, l.mean_arr); // // alternative GEMM //gemm_nn_bin_transposed_32bit_packed(m, n, new_k, 1, @@ -945,7 +947,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) size_t t_intput_size = binary_transpose_align_input(k, n, state.workspace, &l.t_bit_input, ldb_align, l.bit_align); // 5x times faster than gemm()-float32 - gemm_nn_custom_bin_mean_transposed(m, n, k, 1, l.align_bit_weights, new_ldb, l.t_bit_input, new_ldb, c, n, l.mean_arr); + gemm_nn_custom_bin_mean_transposed(m, n, k, 1, (unsigned char*)l.align_bit_weights, new_ldb, (unsigned char*)l.t_bit_input, new_ldb, c, n, l.mean_arr); //gemm_nn_custom_bin_mean_transposed(m, n, k, 1, bit_weights, k, t_bit_input, new_ldb, c, n, mean_arr); @@ -1074,7 +1076,7 @@ void rescale_weights(convolutional_layer l, float scale, float trans) image *get_weights(convolutional_layer l) { - image *weights = calloc(l.n, sizeof(image)); + image* weights = (image*)calloc(l.n, sizeof(image)); int i; for(i = 0; i < l.n; ++i){ weights[i] = copy_image(get_convolutional_weight(l, i)); @@ -1097,4 +1099,3 @@ image *visualize_convolutional_layer(convolutional_layer l, char *window, image free_image(dc); return single_weights; } - diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h index d6ec95515c2..bad3b84e2a4 100644 --- a/src/convolutional_layer.h +++ b/src/convolutional_layer.h @@ -9,6 +9,9 @@ typedef layer convolutional_layer; +#ifdef __cplusplus +extern "C" { +#endif #ifdef GPU void forward_convolutional_layer_gpu(convolutional_layer layer, network_state state); void backward_convolutional_layer_gpu(convolutional_layer layer, network_state state); @@ -22,11 +25,11 @@ void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int #ifdef CUDNN void cudnn_convolutional_setup(layer *l, int cudnn_preference); void create_convolutional_cudnn_tensors(layer *l); -size_t get_convolutional_workspace_size(layer l); void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16); #endif #endif +size_t get_convolutional_workspace_size(layer l); convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index); void denormalize_convolutional_layer(convolutional_layer l); void resize_convolutional_layer(convolutional_layer *layer, int w, int h); @@ -53,5 +56,8 @@ int convolutional_out_width(convolutional_layer layer); void rescale_weights(convolutional_layer l, float scale, float trans); void rgbgr_weights(convolutional_layer l); +#ifdef __cplusplus +} #endif +#endif diff --git a/src/cost_layer.c b/src/cost_layer.c index 39d2398b5ec..39913d673b9 100644 --- a/src/cost_layer.c +++ b/src/cost_layer.c @@ -32,7 +32,7 @@ char *get_cost_string(COST_TYPE a) cost_layer make_cost_layer(int batch, int inputs, COST_TYPE cost_type, float scale) { fprintf(stderr, "cost %4d\n", inputs); - cost_layer l = {0}; + cost_layer l = { (LAYER_TYPE)0 }; l.type = COST; l.scale = scale; @@ -40,9 +40,9 @@ cost_layer make_cost_layer(int batch, int inputs, COST_TYPE cost_type, float sca l.inputs = inputs; l.outputs = inputs; l.cost_type = cost_type; - l.delta = calloc(inputs*batch, sizeof(float)); - l.output = calloc(inputs*batch, sizeof(float)); - l.cost = calloc(1, sizeof(float)); + l.delta = (float*)calloc(inputs * batch, sizeof(float)); + l.output = (float*)calloc(inputs * batch, sizeof(float)); + l.cost = (float*)calloc(1, sizeof(float)); l.forward = forward_cost_layer; l.backward = backward_cost_layer; @@ -60,8 +60,8 @@ void resize_cost_layer(cost_layer *l, int inputs) { l->inputs = inputs; l->outputs = inputs; - l->delta = realloc(l->delta, inputs*l->batch*sizeof(float)); - l->output = realloc(l->output, inputs*l->batch*sizeof(float)); + l->delta = (float*)realloc(l->delta, inputs * l->batch * sizeof(float)); + l->output = (float*)realloc(l->output, inputs * l->batch * sizeof(float)); #ifdef GPU cuda_free(l->delta_gpu); cuda_free(l->output_gpu); diff --git a/src/cost_layer.h b/src/cost_layer.h index a692831efd8..92666aa3268 100644 --- a/src/cost_layer.h +++ b/src/cost_layer.h @@ -5,6 +5,9 @@ typedef layer cost_layer; +#ifdef __cplusplus +extern "C" { +#endif COST_TYPE get_cost_type(char *s); char *get_cost_string(COST_TYPE a); cost_layer make_cost_layer(int batch, int inputs, COST_TYPE type, float scale); @@ -17,4 +20,7 @@ void forward_cost_layer_gpu(cost_layer l, network_state state); void backward_cost_layer_gpu(const cost_layer l, network_state state); #endif +#ifdef __cplusplus +} +#endif #endif diff --git a/src/cpu_gemm.c b/src/cpu_gemm.c index 1ad6d333f2d..6a3cd1dc6b6 100644 --- a/src/cpu_gemm.c +++ b/src/cpu_gemm.c @@ -9,7 +9,7 @@ void cpu_gemm_nn(int TA, int TB, int M, int N, int K, float ALPHA, int i,j,k; for(i = 0; i < M; ++i){ for(k = 0; k < K; ++k){ - register float A_PART = ALPHA*A[i*lda+k]; + float A_PART = ALPHA * A[i * lda + k]; for(j = 0; j < N; ++j){ C[i*ldc+j] += A_PART*B[k*ldb+j]; } @@ -26,7 +26,7 @@ void cpu_gemm_nt(int TA, int TB, int M, int N, int K, float ALPHA, int i,j,k; for(i = 0; i < M; ++i){ for(j = 0; j < N; ++j){ - register float sum = 0; + float sum = 0; for(k = 0; k < K; ++k){ sum += ALPHA*A[i*lda+k]*B[k+j*ldb]; } @@ -44,7 +44,7 @@ void cpu_gemm_tn(int TA, int TB, int M, int N, int K, float ALPHA, int i,j,k; for(i = 0; i < M; ++i){ for(k = 0; k < K; ++k){ - register float A_PART = ALPHA*A[k*lda+i]; + float A_PART = ALPHA * A[k * lda + i]; for(j = 0; j < N; ++j){ C[i*ldc+j] += A_PART*B[k*ldb+j]; } diff --git a/src/crnn_layer.c b/src/crnn_layer.c index 5a9d6f58988..08daa6ef0c8 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -30,7 +30,7 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou { fprintf(stderr, "CRNN Layer: %d x %d x %d image, %d filters\n", h,w,c,output_filters); batch = batch / steps; - layer l = {0}; + layer l = { (LAYER_TYPE)0 }; l.batch = batch; l.type = CRNN; l.steps = steps; @@ -44,22 +44,19 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.hidden = h * w * hidden_filters; l.outputs = l.out_h * l.out_w * l.out_c; - l.state = calloc(l.hidden*batch*(steps+1), sizeof(float)); + l.state = (float*)calloc(l.hidden * batch * (steps + 1), sizeof(float)); - l.input_layer = malloc(sizeof(layer)); - fprintf(stderr, ""); + l.input_layer = (layer*)malloc(sizeof(layer)); *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, size, stride, pad, activation, batch_normalize, 0, 0, 0, 0, 0); l.input_layer->batch = batch; if (l.workspace_size < l.input_layer->workspace_size) l.workspace_size = l.input_layer->workspace_size; - l.self_layer = malloc(sizeof(layer)); - fprintf(stderr, ""); + l.self_layer = (layer*)malloc(sizeof(layer)); *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, size, stride, pad, activation, batch_normalize, 0, 0, 0, 0, 0); l.self_layer->batch = batch; if (l.workspace_size < l.self_layer->workspace_size) l.workspace_size = l.self_layer->workspace_size; - l.output_layer = malloc(sizeof(layer)); - fprintf(stderr, ""); + l.output_layer = (layer*)malloc(sizeof(layer)); *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, size, stride, pad, activation, batch_normalize, 0, 0, 0, 0, 0); l.output_layer->batch = batch; if (l.workspace_size < l.output_layer->workspace_size) l.workspace_size = l.output_layer->workspace_size; diff --git a/src/crnn_layer.h b/src/crnn_layer.h index 867d3ed660e..cfbdd265bc4 100644 --- a/src/crnn_layer.h +++ b/src/crnn_layer.h @@ -6,6 +6,9 @@ #include "layer.h" #include "network.h" +#ifdef __cplusplus +extern "C" { +#endif layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize); void forward_crnn_layer(layer l, network_state state); @@ -20,5 +23,8 @@ void push_crnn_layer(layer l); void pull_crnn_layer(layer l); #endif +#ifdef __cplusplus +} #endif +#endif diff --git a/src/crop_layer.c b/src/crop_layer.c index 11c59b491f2..816f00e1523 100644 --- a/src/crop_layer.c +++ b/src/crop_layer.c @@ -16,7 +16,7 @@ void backward_crop_layer_gpu(const crop_layer l, network_state state){} crop_layer make_crop_layer(int batch, int h, int w, int c, int crop_height, int crop_width, int flip, float angle, float saturation, float exposure) { fprintf(stderr, "Crop Layer: %d x %d -> %d x %d x %d image\n", h,w,crop_height,crop_width,c); - crop_layer l = {0}; + crop_layer l = { (LAYER_TYPE)0 }; l.type = CROP; l.batch = batch; l.h = h; @@ -32,7 +32,7 @@ crop_layer make_crop_layer(int batch, int h, int w, int c, int crop_height, int l.out_c = c; l.inputs = l.w * l.h * l.c; l.outputs = l.out_w * l.out_h * l.out_c; - l.output = calloc(l.outputs*batch, sizeof(float)); + l.output = (float*)calloc(l.outputs * batch, sizeof(float)); l.forward = forward_crop_layer; l.backward = backward_crop_layer; @@ -56,7 +56,7 @@ void resize_crop_layer(layer *l, int w, int h) l->inputs = l->w * l->h * l->c; l->outputs = l->out_h * l->out_w * l->out_c; - l->output = realloc(l->output, l->batch*l->outputs*sizeof(float)); + l->output = (float*)realloc(l->output, l->batch * l->outputs * sizeof(float)); #ifdef GPU cuda_free(l->output_gpu); l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch); diff --git a/src/crop_layer.h b/src/crop_layer.h index 3aa2d3dd013..319582441e2 100644 --- a/src/crop_layer.h +++ b/src/crop_layer.h @@ -7,6 +7,9 @@ typedef layer crop_layer; +#ifdef __cplusplus +extern "C" { +#endif image get_crop_image(crop_layer l); crop_layer make_crop_layer(int batch, int h, int w, int c, int crop_height, int crop_width, int flip, float angle, float saturation, float exposure); void forward_crop_layer(const crop_layer l, network_state state); @@ -16,5 +19,8 @@ void resize_crop_layer(layer *l, int w, int h); void forward_crop_layer_gpu(crop_layer l, network_state state); #endif +#ifdef __cplusplus +} #endif +#endif diff --git a/src/crop_layer_kernels.cu b/src/crop_layer_kernels.cu index 8bbe0f54d5a..724196c383e 100644 --- a/src/crop_layer_kernels.cu +++ b/src/crop_layer_kernels.cu @@ -2,12 +2,10 @@ #include "curand.h" #include "cublas_v2.h" -extern "C" { #include "crop_layer.h" #include "utils.h" #include "cuda.h" #include "image.h" -} __device__ float get_pixel_kernel(float *image, int w, int h, int x, int y, int c) { diff --git a/src/cuda.c b/src/cuda.c index b790e7f5afc..87402e5ae94 100644 --- a/src/cuda.c +++ b/src/cuda.c @@ -1,4 +1,10 @@ +#ifdef __cplusplus +extern "C" { +#endif int gpu_index = 0; +#ifdef __cplusplus +} +#endif // __cplusplus #ifdef GPU @@ -71,7 +77,7 @@ dim3 cuda_gridsize(size_t n){ x = ceil(sqrt(k)); y = (n-1)/(x*BLOCK) + 1; } - dim3 d = {x, y, 1}; + dim3 d = { (unsigned int)x, (unsigned int)y, 1 }; //printf("%ld %ld %ld %ld\n", n, x, y, x*y*BLOCK); return d; } @@ -188,7 +194,7 @@ cublasHandle_t blas_handle() if(!init[i]) { cublasCreate(&handle[i]); cublasStatus_t status = cublasSetStream(handle[i], get_cuda_stream()); - CHECK_CUDA(status); + CHECK_CUDA((cudaError_t)status); init[i] = 1; } return handle[i]; @@ -226,7 +232,7 @@ void cuda_random(float *x_gpu, size_t n) float cuda_compare(float *x_gpu, float *x, size_t n, char *s) { - float *tmp = calloc(n, sizeof(float)); + float* tmp = (float*)calloc(n, sizeof(float)); cuda_pull_array(x_gpu, tmp, n); //int i; //for(i = 0; i < n; ++i) printf("%f %f\n", tmp[i], x[i]); @@ -310,6 +316,6 @@ int get_gpu_compute_capability(int i) } #else // GPU -#include "cuda.h" +#include "darknet.h" void cuda_set_device(int n) {} #endif // GPU diff --git a/src/cuda.h b/src/cuda.h index a1e0e4ebc1e..4010494ea85 100644 --- a/src/cuda.h +++ b/src/cuda.h @@ -1,25 +1,27 @@ -#ifndef CUDA_H -#define CUDA_H +#ifndef DARKCUDA_H +#define DARKCUDA_H #include "darknet.h" -#if defined(_MSC_VER) && _MSC_VER < 1900 - #define inline __inline +#ifdef __cplusplus +extern "C" { #endif extern int gpu_index; +#ifdef __cplusplus +} +#endif // __cplusplus #ifdef GPU -#define BLOCK 512 -#include "cuda_runtime.h" -#include "curand.h" -#include "cublas_v2.h" -#include "cuda_runtime_api.h" -//#include "driver_types.h" +#include +#include +#include +#include +#include #ifdef CUDNN -#include "cudnn.h" +#include #endif // CUDNN #ifndef __DATE__ @@ -65,9 +67,6 @@ extern "C" { cudaStream_t get_cuda_memcpy_stream(); int get_number_of_blocks(int array_size, int block_size); int get_gpu_compute_capability(int i); -#ifdef __cplusplus -} -#endif // __cplusplus #ifdef CUDNN cudnnHandle_t cudnn_handle(); @@ -77,6 +76,10 @@ void cudnn_check_error_extended(cudnnStatus_t status, const char *file, int line #define CHECK_CUDNN(X) cudnn_check_error_extended(X, __FILE__ " : " __FUNCTION__, __LINE__, __DATE__ " - " __TIME__ ); #endif +#ifdef __cplusplus +} +#endif // __cplusplus + #else // GPU //LIB_API void cuda_set_device(int n); #endif // GPU diff --git a/src/darknet.c b/src/darknet.c index 074dc8b9e81..87175bc3d62 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -10,7 +10,7 @@ #include "connected_layer.h" #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" +#include #endif extern void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top); @@ -258,12 +258,12 @@ layer normalize_layer(layer l, int n) { int j; l.batch_normalize=1; - l.scales = calloc(n, sizeof(float)); + l.scales = (float*)calloc(n, sizeof(float)); for(j = 0; j < n; ++j){ l.scales[j] = 1; } - l.rolling_mean = calloc(n, sizeof(float)); - l.rolling_variance = calloc(n, sizeof(float)); + l.rolling_mean = (float*)calloc(n, sizeof(float)); + l.rolling_variance = (float*)calloc(n, sizeof(float)); return l; } diff --git a/src/unistd.h b/src/darkunistd.h similarity index 88% rename from src/unistd.h rename to src/darkunistd.h index 4c51f975c0f..bb97da5f67f 100644 --- a/src/unistd.h +++ b/src/darkunistd.h @@ -1,3 +1,4 @@ +#ifdef _WIN32 #ifndef _UNISTD_H #define _UNISTD_H 1 @@ -6,12 +7,13 @@ * Please add functionality as needed */ -#include +#include +#include /* for _getcwd() and _chdir() */ +#include #include #include /* for getpid() and the exec..() family */ -#include /* for _getcwd() and _chdir() */ +#include -#include "getopt.h" /* getopt at: https://gist.github.com/ashelly/7776712 */ #define srandom srand #define random rand @@ -19,7 +21,7 @@ These may be OR'd together. */ #define R_OK 4 /* Test for read permission. */ #define W_OK 2 /* Test for write permission. */ -//#define X_OK 1 /* execute permission - unsupported in windows*/ +#define X_OK R_OK /* execute permission - unsupported in Windows, \ #define F_OK 0 /* Test for existence. */ #define access _access @@ -48,5 +50,7 @@ These may be OR'd together. */ //typedef unsigned __int16 uint16_t; //typedef unsigned __int32 uint32_t; //typedef unsigned __int64 uint64_t; - -#endif /* unistd.h */ \ No newline at end of file +#endif /* _UNISTD_H */ +#else +#include +#endif /* _WIN32 */ diff --git a/src/data.c b/src/data.c index d152fde4bee..09b1b2e27fa 100644 --- a/src/data.c +++ b/src/data.c @@ -41,7 +41,7 @@ char **get_random_paths_indexes(char **paths, int n, int m, int *indexes) char **get_random_paths(char **paths, int n, int m) { - char **random_paths = calloc(n, sizeof(char*)); + char** random_paths = (char**)calloc(n, sizeof(char*)); int i; pthread_mutex_lock(&mutex); //printf("n = %d \n", n); @@ -60,7 +60,7 @@ char **get_random_paths(char **paths, int n, int m) char **find_replace_paths(char **paths, int n, char *find, char *replace) { - char **replace_paths = calloc(n, sizeof(char*)); + char** replace_paths = (char**)calloc(n, sizeof(char*)); int i; for(i = 0; i < n; ++i){ char replaced[4096]; @@ -75,7 +75,7 @@ matrix load_image_paths_gray(char **paths, int n, int w, int h) int i; matrix X; X.rows = n; - X.vals = calloc(X.rows, sizeof(float*)); + X.vals = (float**)calloc(X.rows, sizeof(float*)); X.cols = 0; for(i = 0; i < n; ++i){ @@ -96,7 +96,7 @@ matrix load_image_paths(char **paths, int n, int w, int h) int i; matrix X; X.rows = n; - X.vals = calloc(X.rows, sizeof(float*)); + X.vals = (float**)calloc(X.rows, sizeof(float*)); X.cols = 0; for(i = 0; i < n; ++i){ @@ -112,7 +112,7 @@ matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int int i; matrix X; X.rows = n; - X.vals = calloc(X.rows, sizeof(float*)); + X.vals = (float**)calloc(X.rows, sizeof(float*)); X.cols = 0; for(i = 0; i < n; ++i){ @@ -139,7 +139,7 @@ extern int check_mistakes; box_label *read_boxes(char *filename, int *n) { - box_label *boxes = calloc(1, sizeof(box_label)); + box_label* boxes = (box_label*)calloc(1, sizeof(box_label)); FILE *file = fopen(filename, "r"); if (!file) { printf("Can't open label file. (This can be normal only if you use MSCOCO): %s \n", filename); @@ -158,7 +158,7 @@ box_label *read_boxes(char *filename, int *n) int id; int count = 0; while(fscanf(file, "%d %f %f %f %f", &id, &x, &y, &w, &h) == 5){ - boxes = realloc(boxes, (count+1)*sizeof(box_label)); + boxes = (box_label*)realloc(boxes, (count + 1) * sizeof(box_label)); boxes[count].id = id; boxes[count].x = x; boxes[count].y = y; @@ -300,7 +300,7 @@ void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int free(boxes); } -void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, int flip, float dx, float dy, float sx, float sy, +void fill_truth_detection(const char *path, int num_boxes, float *truth, int classes, int flip, float dx, float dy, float sx, float sy, int small_object, int net_w, int net_h) { char labelpath[4096]; @@ -391,7 +391,6 @@ void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, free(boxes); } -#define NUMCHARS 37 void print_letters(float *pred, int n) { @@ -565,7 +564,7 @@ data load_data_region(int n, char **paths, int m, int w, int h, int size, int cl d.shallow = 0; d.X.rows = n; - d.X.vals = calloc(d.X.rows, sizeof(float*)); + d.X.vals = (float**)calloc(d.X.rows, sizeof(float*)); d.X.cols = h*w*3; @@ -619,7 +618,7 @@ data load_data_compare(int n, char **paths, int m, int classes, int w, int h) d.shallow = 0; d.X.rows = n; - d.X.vals = calloc(d.X.rows, sizeof(float*)); + d.X.vals = (float**)calloc(d.X.rows, sizeof(float*)); d.X.cols = h*w*6; int k = 2*(classes); @@ -628,7 +627,7 @@ data load_data_compare(int n, char **paths, int m, int classes, int w, int h) image im1 = load_image_color(paths[i*2], w, h); image im2 = load_image_color(paths[i*2+1], w, h); - d.X.vals[i] = calloc(d.X.cols, sizeof(float)); + d.X.vals[i] = (float*)calloc(d.X.cols, sizeof(float)); memcpy(d.X.vals[i], im1.data, h*w*3*sizeof(float)); memcpy(d.X.vals[i] + h*w*3, im2.data, h*w*3*sizeof(float)); @@ -690,7 +689,7 @@ data load_data_swag(char **paths, int n, int classes, float jitter) d.h = h; d.X.rows = 1; - d.X.vals = calloc(d.X.rows, sizeof(float*)); + d.X.vals = (float**)calloc(d.X.rows, sizeof(float*)); d.X.cols = h*w*3; int k = (4+classes)*30; @@ -729,12 +728,12 @@ data load_data_swag(char **paths, int n, int classes, float jitter) } #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#include "opencv2/imgproc/imgproc_c.h" -#include "opencv2/core/version.hpp" +#include +#include +#include #ifndef CV_VERSION_EPOCH -#include "opencv2/videoio/videoio_c.h" -#include "opencv2/imgcodecs/imgcodecs_c.h" +#include +#include #endif #include "http_stream.h" @@ -748,7 +747,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo d.shallow = 0; d.X.rows = n; - d.X.vals = calloc(d.X.rows, sizeof(float*)); + d.X.vals = (float**)calloc(d.X.rows, sizeof(float*)); d.X.cols = h*w*c; d.y = make_matrix(n, 5*boxes); @@ -817,7 +816,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo d.shallow = 0; d.X.rows = n; - d.X.vals = calloc(d.X.rows, sizeof(float*)); + d.X.vals = (float**)calloc(d.X.rows, sizeof(float*)); d.X.cols = h*w*c; d.y = make_matrix(n, 5 * boxes); @@ -903,7 +902,7 @@ void *load_thread(void *ptr) pthread_t load_data_in_thread(load_args args) { pthread_t thread; - struct load_args *ptr = calloc(1, sizeof(struct load_args)); + struct load_args* ptr = (load_args*)calloc(1, sizeof(struct load_args)); *ptr = args; if(pthread_create(&thread, 0, load_thread, ptr)) error("Thread creation failed"); return thread; @@ -918,8 +917,8 @@ void *load_threads(void *ptr) data *out = args.d; int total = args.n; free(ptr); - data *buffers = calloc(args.threads, sizeof(data)); - pthread_t *threads = calloc(args.threads, sizeof(pthread_t)); + data* buffers = (data*)calloc(args.threads, sizeof(data)); + pthread_t* threads = (pthread_t*)calloc(args.threads, sizeof(pthread_t)); for(i = 0; i < args.threads; ++i){ args.d = buffers + i; args.n = (i+1) * total/args.threads - i * total/args.threads; @@ -942,7 +941,7 @@ void *load_threads(void *ptr) pthread_t load_data(load_args args) { pthread_t thread; - struct load_args *ptr = calloc(1, sizeof(struct load_args)); + struct load_args* ptr = (load_args*)calloc(1, sizeof(struct load_args)); *ptr = args; if(pthread_create(&thread, 0, load_threads, ptr)) error("Thread creation failed"); return thread; @@ -996,11 +995,11 @@ data load_data_super(char **paths, int n, int m, int w, int h, int scale) int i; d.X.rows = n; - d.X.vals = calloc(n, sizeof(float*)); + d.X.vals = (float**)calloc(n, sizeof(float*)); d.X.cols = w*h*3; d.y.rows = n; - d.y.vals = calloc(n, sizeof(float*)); + d.y.vals = (float**)calloc(n, sizeof(float*)); d.y.cols = w*scale * h*scale * 3; for(i = 0; i < n; ++i){ @@ -1048,7 +1047,7 @@ matrix concat_matrix(matrix m1, matrix m2) matrix m; m.cols = m1.cols; m.rows = m1.rows+m2.rows; - m.vals = calloc(m1.rows + m2.rows, sizeof(float*)); + m.vals = (float**)calloc(m1.rows + m2.rows, sizeof(float*)); for(i = 0; i < m1.rows; ++i){ m.vals[count++] = m1.vals[i]; } @@ -1072,9 +1071,9 @@ data concat_datas(data *d, int n) int i; data out = {0}; for(i = 0; i < n; ++i){ - data new = concat_data(d[i], out); + data newdata = concat_data(d[i], out); free_data(out); - out = new; + out = newdata; } return out; } @@ -1300,8 +1299,8 @@ data get_random_data(data d, int num) r.X.cols = d.X.cols; r.y.cols = d.y.cols; - r.X.vals = calloc(num, sizeof(float *)); - r.y.vals = calloc(num, sizeof(float *)); + r.X.vals = (float**)calloc(num, sizeof(float*)); + r.y.vals = (float**)calloc(num, sizeof(float*)); int i; for(i = 0; i < num; ++i){ @@ -1314,7 +1313,7 @@ data get_random_data(data d, int num) data *split_data(data d, int part, int total) { - data *split = calloc(2, sizeof(data)); + data* split = (data*)calloc(2, sizeof(data)); int i; int start = part*d.X.rows/total; int end = (part+1)*d.X.rows/total; @@ -1327,10 +1326,10 @@ data *split_data(data d, int part, int total) train.X.cols = test.X.cols = d.X.cols; train.y.cols = test.y.cols = d.y.cols; - train.X.vals = calloc(train.X.rows, sizeof(float*)); - test.X.vals = calloc(test.X.rows, sizeof(float*)); - train.y.vals = calloc(train.y.rows, sizeof(float*)); - test.y.vals = calloc(test.y.rows, sizeof(float*)); + train.X.vals = (float**)calloc(train.X.rows, sizeof(float*)); + test.X.vals = (float**)calloc(test.X.rows, sizeof(float*)); + train.y.vals = (float**)calloc(train.y.rows, sizeof(float*)); + test.y.vals = (float**)calloc(test.y.rows, sizeof(float*)); for(i = 0; i < start; ++i){ train.X.vals[i] = d.X.vals[i]; diff --git a/src/data.h b/src/data.h index 844680dc78c..58cd1178c48 100644 --- a/src/data.h +++ b/src/data.h @@ -2,14 +2,14 @@ #define DATA_H #include -#if defined(_MSC_VER) && _MSC_VER < 1900 - #define inline __inline -#endif - +#include "darknet.h" #include "darknet.h" #include "matrix.h" #include "list.h" #include "image.h" +#ifdef __cplusplus +extern "C" { +#endif #include "tree.h" static inline float distance_from_edge(int x, int max) @@ -115,5 +115,8 @@ data *split_data(data d, int part, int total); data concat_data(data d1, data d2); data concat_datas(data *d, int n); void fill_truth(char *path, char **labels, int k, float *truth); +#ifdef __cplusplus +} #endif +#endif diff --git a/src/deconvolutional_kernels.cu b/src/deconvolutional_kernels.cu index d6259fb3973..88106ecefff 100644 --- a/src/deconvolutional_kernels.cu +++ b/src/deconvolutional_kernels.cu @@ -2,7 +2,6 @@ #include "curand.h" #include "cublas_v2.h" -extern "C" { #include "convolutional_layer.h" #include "deconvolutional_layer.h" #include "gemm.h" @@ -11,7 +10,6 @@ extern "C" { #include "col2im.h" #include "utils.h" #include "cuda.h" -} extern "C" void forward_deconvolutional_layer_gpu(deconvolutional_layer layer, network_state state) { @@ -95,7 +93,7 @@ extern "C" void push_deconvolutional_layer(deconvolutional_layer layer) cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n); } -extern "C" void update_deconvolutional_layer_gpu(deconvolutional_layer layer, float learning_rate, float momentum, float decay) +extern "C" void update_deconvolutional_layer_gpu(deconvolutional_layer layer, int skip, float learning_rate, float momentum, float decay) { int size = layer.size*layer.size*layer.c*layer.n; diff --git a/src/deconvolutional_layer.c b/src/deconvolutional_layer.c index fbef9d58829..a109999f25e 100644 --- a/src/deconvolutional_layer.c +++ b/src/deconvolutional_layer.c @@ -46,7 +46,7 @@ image get_deconvolutional_delta(deconvolutional_layer l) deconvolutional_layer make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation) { int i; - deconvolutional_layer l = {0}; + deconvolutional_layer l = { (LAYER_TYPE)0 }; l.type = DECONVOLUTIONAL; l.h = h; @@ -57,11 +57,11 @@ deconvolutional_layer make_deconvolutional_layer(int batch, int h, int w, int c, l.stride = stride; l.size = size; - l.weights = calloc(c*n*size*size, sizeof(float)); - l.weight_updates = calloc(c*n*size*size, sizeof(float)); + l.weights = (float*)calloc(c * n * size * size, sizeof(float)); + l.weight_updates = (float*)calloc(c * n * size * size, sizeof(float)); - l.biases = calloc(n, sizeof(float)); - l.bias_updates = calloc(n, sizeof(float)); + l.biases = (float*)calloc(n, sizeof(float)); + l.bias_updates = (float*)calloc(n, sizeof(float)); float scale = 1./sqrt(size*size*c); for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_normal(); for(i = 0; i < n; ++i){ @@ -76,9 +76,9 @@ deconvolutional_layer make_deconvolutional_layer(int batch, int h, int w, int c, l.outputs = l.out_w * l.out_h * l.out_c; l.inputs = l.w * l.h * l.c; - l.col_image = calloc(h*w*size*size*n, sizeof(float)); - l.output = calloc(l.batch*out_h * out_w * n, sizeof(float)); - l.delta = calloc(l.batch*out_h * out_w * n, sizeof(float)); + l.col_image = (float*)calloc(h * w * size * size * n, sizeof(float)); + l.output = (float*)calloc(l.batch * out_h * out_w * n, sizeof(float)); + l.delta = (float*)calloc(l.batch * out_h * out_w * n, sizeof(float)); l.forward = forward_deconvolutional_layer; l.backward = backward_deconvolutional_layer; @@ -110,11 +110,11 @@ void resize_deconvolutional_layer(deconvolutional_layer *l, int h, int w) int out_h = deconvolutional_out_height(*l); int out_w = deconvolutional_out_width(*l); - l->col_image = realloc(l->col_image, + l->col_image = (float*)realloc(l->col_image, out_h*out_w*l->size*l->size*l->c*sizeof(float)); - l->output = realloc(l->output, + l->output = (float*)realloc(l->output, l->batch*out_h * out_w * l->n*sizeof(float)); - l->delta = realloc(l->delta, + l->delta = (float*)realloc(l->delta, l->batch*out_h * out_w * l->n*sizeof(float)); #ifdef GPU cuda_free(l->col_image_gpu); @@ -191,7 +191,7 @@ void backward_deconvolutional_layer(deconvolutional_layer l, network_state state } } -void update_deconvolutional_layer(deconvolutional_layer l, float learning_rate, float momentum, float decay) +void update_deconvolutional_layer(deconvolutional_layer l, int skip, float learning_rate, float momentum, float decay) { int size = l.size*l.size*l.c*l.n; axpy_cpu(l.n, learning_rate, l.bias_updates, 1, l.biases, 1); diff --git a/src/deconvolutional_layer.h b/src/deconvolutional_layer.h index 2d36e02a820..2e1c4527e1e 100644 --- a/src/deconvolutional_layer.h +++ b/src/deconvolutional_layer.h @@ -9,10 +9,13 @@ typedef layer deconvolutional_layer; +#ifdef __cplusplus +extern "C" { +#endif #ifdef GPU void forward_deconvolutional_layer_gpu(deconvolutional_layer layer, network_state state); void backward_deconvolutional_layer_gpu(deconvolutional_layer layer, network_state state); -void update_deconvolutional_layer_gpu(deconvolutional_layer layer, float learning_rate, float momentum, float decay); +void update_deconvolutional_layer_gpu(deconvolutional_layer layer, int skip, float learning_rate, float momentum, float decay); void push_deconvolutional_layer(deconvolutional_layer layer); void pull_deconvolutional_layer(deconvolutional_layer layer); #endif @@ -20,7 +23,7 @@ void pull_deconvolutional_layer(deconvolutional_layer layer); deconvolutional_layer make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation); void resize_deconvolutional_layer(deconvolutional_layer *layer, int h, int w); void forward_deconvolutional_layer(const deconvolutional_layer layer, network_state state); -void update_deconvolutional_layer(deconvolutional_layer layer, float learning_rate, float momentum, float decay); +void update_deconvolutional_layer(deconvolutional_layer layer, int skip, float learning_rate, float momentum, float decay); void backward_deconvolutional_layer(deconvolutional_layer layer, network_state state); image get_deconvolutional_image(deconvolutional_layer layer); @@ -30,5 +33,8 @@ image get_deconvolutional_filter(deconvolutional_layer layer, int i); int deconvolutional_out_height(deconvolutional_layer layer); int deconvolutional_out_width(deconvolutional_layer layer); +#ifdef __cplusplus +} #endif +#endif diff --git a/src/demo.c b/src/demo.c index 9029ff1e13e..80f925ac8ca 100644 --- a/src/demo.c +++ b/src/demo.c @@ -9,20 +9,18 @@ #include "demo.h" #ifdef WIN32 #include -#include #include "gettimeofday.h" #else #include #endif -#define FRAMES 3 #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#include "opencv2/imgproc/imgproc_c.h" -#include "opencv2/core/version.hpp" +#include +#include +#include #ifndef CV_VERSION_EPOCH -#include "opencv2/videoio/videoio_c.h" +#include #endif #include "http_stream.h" image get_image_from_stream(CvCapture *cap); @@ -45,10 +43,10 @@ static int demo_ext_output = 0; static long long int frame_id = 0; static int demo_json_port = -1; -static float *predictions[FRAMES]; +static float* predictions[NFRAMES]; static int demo_index = 0; -static image images[FRAMES]; -static IplImage* ipl_images[FRAMES]; +static image images[NFRAMES]; +static IplImage* ipl_images[NFRAMES]; static float *avg; void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output); @@ -77,7 +75,7 @@ void *fetch_in_thread(void *ptr) //error("Stream closed."); printf("Stream closed.\n"); flag_exit = 1; - return EXIT_FAILURE; + exit(EXIT_FAILURE); } //in_s = resize_image(in, net.w, net.h); @@ -91,14 +89,14 @@ void *detect_in_thread(void *ptr) float *prediction = network_predict(net, X); memcpy(predictions[demo_index], prediction, l.outputs*sizeof(float)); - mean_arrays(predictions, FRAMES, l.outputs, avg); + mean_arrays(predictions, NFRAMES, l.outputs, avg); l.output = avg; free_image(det_s); ipl_images[demo_index] = det_img; - det_img = ipl_images[(demo_index + FRAMES / 2 + 1) % FRAMES]; - demo_index = (demo_index + 1) % FRAMES; + det_img = ipl_images[(demo_index + NFRAMES / 2 + 1) % NFRAMES]; + demo_index = (demo_index + 1) % NFRAMES; if (letter_box) dets = get_network_boxes(&net, in_img->width, in_img->height, demo_thresh, demo_thresh, 0, 1, &nboxes, 1); // letter box @@ -110,11 +108,11 @@ void *detect_in_thread(void *ptr) double get_wall_time() { - struct timeval time; - if (gettimeofday(&time,NULL)){ + struct timeval walltime; + if (gettimeofday(&walltime, NULL)) { return 0; } - return (double)time.tv_sec + (double)time.tv_usec * .000001; + return (double)walltime.tv_sec + (double)walltime.tv_usec * .000001; } void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, @@ -161,8 +159,8 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int int j; avg = (float *) calloc(l.outputs, sizeof(float)); - for(j = 0; j < FRAMES; ++j) predictions[j] = (float *) calloc(l.outputs, sizeof(float)); - for(j = 0; j < FRAMES; ++j) images[j] = make_image(1,1,3); + for(j = 0; j < NFRAMES; ++j) predictions[j] = (float *) calloc(l.outputs, sizeof(float)); + for(j = 0; j < NFRAMES; ++j) images[j] = make_image(1,1,3); if (l.classes != demo_classes) { printf("Parameters don't match: in cfg-file classes=%d, in data-file classes=%d \n", l.classes, demo_classes); @@ -185,7 +183,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int det_img = in_img; det_s = in_s; - for(j = 0; j < FRAMES/2; ++j){ + for (j = 0; j < NFRAMES / 2; ++j) { fetch_in_thread(0); detect_in_thread(0); det_img = in_img; @@ -318,10 +316,10 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int free_image(in_s); free(avg); - for (j = 0; j < FRAMES; ++j) free(predictions[j]); - for (j = 0; j < FRAMES; ++j) free_image(images[j]); + for (j = 0; j < NFRAMES; ++j) free(predictions[j]); + for (j = 0; j < NFRAMES; ++j) free_image(images[j]); - free_ptrs(names, net.layers[net.n - 1].classes); + free_ptrs((void **)names, net.layers[net.n - 1].classes); int i; const int nsize = 8; @@ -342,4 +340,3 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int fprintf(stderr, "Demo needs OpenCV for webcam images.\n"); } #endif - diff --git a/src/demo.h b/src/demo.h index 5db793464a6..b26b9592f1b 100644 --- a/src/demo.h +++ b/src/demo.h @@ -1,8 +1,14 @@ -#ifndef DEMO -#define DEMO +#ifndef DEMO_H +#define DEMO_H #include "image.h" +#ifdef __cplusplus +extern "C" { +#endif void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output); +#ifdef __cplusplus +} +#endif #endif diff --git a/src/detection_layer.c b/src/detection_layer.c index 0a1c1070af8..4cfe591dd30 100644 --- a/src/detection_layer.c +++ b/src/detection_layer.c @@ -12,7 +12,7 @@ detection_layer make_detection_layer(int batch, int inputs, int n, int side, int classes, int coords, int rescore) { - detection_layer l = {0}; + detection_layer l = { (LAYER_TYPE)0 }; l.type = DETECTION; l.n = n; @@ -25,11 +25,11 @@ detection_layer make_detection_layer(int batch, int inputs, int n, int side, int l.w = side; l.h = side; assert(side*side*((1 + l.coords)*l.n + l.classes) == inputs); - l.cost = calloc(1, sizeof(float)); + l.cost = (float*)calloc(1, sizeof(float)); l.outputs = l.inputs; l.truths = l.side*l.side*(1+l.coords+l.classes); - l.output = calloc(batch*l.outputs, sizeof(float)); - l.delta = calloc(batch*l.outputs, sizeof(float)); + l.output = (float*)calloc(batch * l.outputs, sizeof(float)); + l.delta = (float*)calloc(batch * l.outputs, sizeof(float)); l.forward = forward_detection_layer; l.backward = backward_detection_layer; @@ -182,7 +182,7 @@ void forward_detection_layer(const detection_layer l, network_state state) } if(0){ - float *costs = calloc(l.batch*locations*l.n, sizeof(float)); + float* costs = (float*)calloc(l.batch * locations * l.n, sizeof(float)); for (b = 0; b < l.batch; ++b) { int index = b*l.inputs; for (i = 0; i < locations; ++i) { @@ -259,11 +259,11 @@ void forward_detection_layer_gpu(const detection_layer l, network_state state) return; } - float *in_cpu = calloc(l.batch*l.inputs, sizeof(float)); + float* in_cpu = (float*)calloc(l.batch * l.inputs, sizeof(float)); float *truth_cpu = 0; if(state.truth){ int num_truth = l.batch*l.side*l.side*(1+l.coords+l.classes); - truth_cpu = calloc(num_truth, sizeof(float)); + truth_cpu = (float*)calloc(num_truth, sizeof(float)); cuda_pull_array(state.truth, truth_cpu, num_truth); } cuda_pull_array(state.input, in_cpu, l.batch*l.inputs); diff --git a/src/detection_layer.h b/src/detection_layer.h index 9d2da9282fe..f97bc39a847 100644 --- a/src/detection_layer.h +++ b/src/detection_layer.h @@ -6,6 +6,9 @@ typedef layer detection_layer; +#ifdef __cplusplus +extern "C" { +#endif detection_layer make_detection_layer(int batch, int inputs, int n, int size, int classes, int coords, int rescore); void forward_detection_layer(const detection_layer l, network_state state); void backward_detection_layer(const detection_layer l, network_state state); @@ -17,4 +20,7 @@ void forward_detection_layer_gpu(const detection_layer l, network_state state); void backward_detection_layer_gpu(detection_layer l, network_state state); #endif +#ifdef __cplusplus +} +#endif #endif diff --git a/src/detector.c b/src/detector.c index 5524ac19cfd..96921689a5b 100644 --- a/src/detector.c +++ b/src/detector.c @@ -9,27 +9,33 @@ #include "option_list.h" #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#include "opencv2/core/core_c.h" +#include +#include //#include "opencv2/core/core.hpp" -#include "opencv2/core/version.hpp" -#include "opencv2/imgproc/imgproc_c.h" +#include +#include #ifndef CV_VERSION_EPOCH -#include "opencv2/videoio/videoio_c.h" +#include #define OPENCV_VERSION CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR)"" CVAUX_STR(CV_VERSION_REVISION) +#ifndef USE_CMAKE_LIBS #pragma comment(lib, "opencv_world" OPENCV_VERSION ".lib") +#endif // USE_CMAKE_LIBS #else #define OPENCV_VERSION CVAUX_STR(CV_VERSION_EPOCH)"" CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR) +#ifndef USE_CMAKE_LIBS #pragma comment(lib, "opencv_core" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib") +#endif // USE_CMAKE_LIBS #endif IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show); void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port); +#endif // OPENCV +#ifndef CV_RGB #define CV_RGB(r, g, b) cvScalar( (b), (g), (r), 0 ) #endif // OPENCV @@ -81,7 +87,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i char *base = basecfg(cfgfile); printf("%s\n", base); float avg_loss = -1; - network *nets = calloc(ngpus, sizeof(network)); + network* nets = (network*)calloc(ngpus, sizeof(network)); srand(time(0)); int seed = rand(); @@ -410,8 +416,8 @@ void print_imagenet_detections(FILE *fp, int id, detection *dets, int total, int if (ymax > h) ymax = h; for (j = 0; j < classes; ++j) { - int class = j; - if (dets[i].prob[class]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j + 1, dets[i].prob[class], + int myclass = j; + if (dets[i].prob[myclass]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j + 1, dets[i].prob[myclass], xmin, ymin, xmax, ymax); } } @@ -465,7 +471,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out } else { if (!outfile) outfile = "comp4_det_test_"; - fps = calloc(classes, sizeof(FILE *)); + fps = (FILE**)calloc(classes, sizeof(FILE*)); for (j = 0; j < classes; ++j) { snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]); fps[j] = fopen(buff, "w"); @@ -482,11 +488,11 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out int nthreads = 4; if (m < 4) nthreads = m; - image *val = calloc(nthreads, sizeof(image)); - image *val_resized = calloc(nthreads, sizeof(image)); - image *buf = calloc(nthreads, sizeof(image)); - image *buf_resized = calloc(nthreads, sizeof(image)); - pthread_t *thr = calloc(nthreads, sizeof(pthread_t)); + image* val = (image*)calloc(nthreads, sizeof(image)); + image* val_resized = (image*)calloc(nthreads, sizeof(image)); + image* buf = (image*)calloc(nthreads, sizeof(image)); + image* buf_resized = (image*)calloc(nthreads, sizeof(image)); + pthread_t* thr = (pthread_t*)calloc(nthreads, sizeof(pthread_t)); load_args args = { 0 }; args.w = net.w; @@ -702,11 +708,11 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa int nthreads = 4; if (m < 4) nthreads = m; - image *val = calloc(nthreads, sizeof(image)); - image *val_resized = calloc(nthreads, sizeof(image)); - image *buf = calloc(nthreads, sizeof(image)); - image *buf_resized = calloc(nthreads, sizeof(image)); - pthread_t *thr = calloc(nthreads, sizeof(pthread_t)); + image* val = (image*)calloc(nthreads, sizeof(image)); + image* val_resized = (image*)calloc(nthreads, sizeof(image)); + image* buf = (image*)calloc(nthreads, sizeof(image)); + image* buf_resized = (image*)calloc(nthreads, sizeof(image)); + pthread_t* thr = (pthread_t*)calloc(nthreads, sizeof(pthread_t)); load_args args = { 0 }; args.w = net.w; @@ -720,11 +726,11 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa int tp_for_thresh = 0; int fp_for_thresh = 0; - box_prob *detections = calloc(1, sizeof(box_prob)); + box_prob* detections = (box_prob*)calloc(1, sizeof(box_prob)); int detections_count = 0; int unique_truth_count = 0; - int *truth_classes_count = calloc(classes, sizeof(int)); + int* truth_classes_count = (int*)calloc(classes, sizeof(int)); for (t = 0; t < nthreads; ++t) { args.path = paths[i + t]; @@ -798,7 +804,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa float prob = dets[i].prob[class_id]; if (prob > 0) { detections_count++; - detections = realloc(detections, detections_count * sizeof(box_prob)); + detections = (box_prob*)realloc(detections, detections_count * sizeof(box_prob)); detections[detections_count - 1].b = dets[i].bbox; detections[detections_count - 1].p = prob; detections[detections_count - 1].image_index = image_index; @@ -890,14 +896,14 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa } pr_t; // for PR-curve - pr_t **pr = calloc(classes, sizeof(pr_t*)); + pr_t** pr = (pr_t**)calloc(classes, sizeof(pr_t*)); for (i = 0; i < classes; ++i) { - pr[i] = calloc(detections_count, sizeof(pr_t)); + pr[i] = (pr_t*)calloc(detections_count, sizeof(pr_t)); } printf("\n detections_count = %d, unique_truth_count = %d \n", detections_count, unique_truth_count); - int *truth_flags = calloc(unique_truth_count, sizeof(int)); + int* truth_flags = (int*)calloc(unique_truth_count, sizeof(int)); int rank; for (rank = 0; rank < detections_count; ++rank) { @@ -993,7 +999,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa if (reinforcement_fd != NULL) fclose(reinforcement_fd); // free memory - free_ptrs(names, net.layers[net.n - 1].classes); + free_ptrs((void**)names, net.layers[net.n - 1].classes); free_list_contents_kvp(options); free_list(options); @@ -1043,7 +1049,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int } //float pointsdata[] = { 1,1, 2,2, 6,6, 5,5, 10,10 }; - float *rel_width_height_array = calloc(1000, sizeof(float)); + float* rel_width_height_array = (float*)calloc(1000, sizeof(float)); list *options = read_data_cfg(datacfg); @@ -1079,7 +1085,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int if (check_mistakes) getchar(); } number_of_boxes++; - rel_width_height_array = realloc(rel_width_height_array, 2 * number_of_boxes * sizeof(float)); + rel_width_height_array = (float*)realloc(rel_width_height_array, 2 * number_of_boxes * sizeof(float)); rel_width_height_array[number_of_boxes * 2 - 2] = truth[j].w * width; rel_width_height_array[number_of_boxes * 2 - 1] = truth[j].h * height; printf("\r loaded \t image: %d \t box: %d", i + 1, number_of_boxes); @@ -1104,7 +1110,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int // K-means anchors_data = do_kmeans(boxes_data, num_of_clusters); - qsort(anchors_data.centers.vals, num_of_clusters, 2 * sizeof(float), anchors_data_comparator); + qsort((void*)anchors_data.centers.vals, num_of_clusters, 2 * sizeof(float), (__compar_fn_t)anchors_data_comparator); //gen_anchors.py = 1.19, 1.99, 2.79, 4.60, 4.53, 8.92, 8.06, 5.29, 10.32, 10.66 //float orig_anch[] = { 1.19, 1.99, 2.79, 4.60, 4.53, 8.92, 8.06, 5.29, 10.32, 10.66 }; @@ -1285,8 +1291,8 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam layer l = net.layers[net.n - 1]; //box *boxes = calloc(l.w*l.h*l.n, sizeof(box)); - //float **probs = calloc(l.w*l.h*l.n, sizeof(float *)); - //for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *)); + //float **probs = calloc(l.w*l.h*l.n, sizeof(float*)); + //for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float*)calloc(l.classes, sizeof(float)); float *X = sized.data; @@ -1365,7 +1371,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam } // free memory - free_ptrs(names, net.layers[net.n - 1].classes); + free_ptrs((void**)names, net.layers[net.n - 1].classes); free_list_contents_kvp(options); free_list(options); @@ -1421,7 +1427,7 @@ void run_detector(int argc, char **argv) for (i = 0; i < len; ++i) { if (gpu_list[i] == ',') ++ngpus; } - gpus = calloc(ngpus, sizeof(int)); + gpus = (int*)calloc(ngpus, sizeof(int)); for (i = 0; i < ngpus; ++i) { gpus[i] = atoi(gpu_list); gpu_list = strchr(gpu_list, ',') + 1; diff --git a/src/dice.c b/src/dice.c index 228645985d8..348a4aed950 100644 --- a/src/dice.c +++ b/src/dice.c @@ -9,7 +9,7 @@ void train_dice(char *cfgfile, char *weightfile) srand(time(0)); float avg_loss = -1; char *base = basecfg(cfgfile); - char *backup_directory = "/home/pjreddie/backup/"; + char* backup_directory = "backup/"; printf("%s\n", base); network net = parse_network_cfg(cfgfile); if(weightfile){ diff --git a/src/dropout_layer.c b/src/dropout_layer.c index b1381e63b9f..599acc8f3c2 100644 --- a/src/dropout_layer.c +++ b/src/dropout_layer.c @@ -6,13 +6,13 @@ dropout_layer make_dropout_layer(int batch, int inputs, float probability) { - dropout_layer l = {0}; + dropout_layer l = { (LAYER_TYPE)0 }; l.type = DROPOUT; l.probability = probability; l.inputs = inputs; l.outputs = inputs; l.batch = batch; - l.rand = calloc(inputs*batch, sizeof(float)); + l.rand = (float*)calloc(inputs * batch, sizeof(float)); l.scale = 1./(1.-probability); l.forward = forward_dropout_layer; l.backward = backward_dropout_layer; @@ -27,7 +27,7 @@ dropout_layer make_dropout_layer(int batch, int inputs, float probability) void resize_dropout_layer(dropout_layer *l, int inputs) { - l->rand = realloc(l->rand, l->inputs*l->batch*sizeof(float)); + l->rand = (float*)realloc(l->rand, l->inputs * l->batch * sizeof(float)); #ifdef GPU cuda_free(l->rand_gpu); diff --git a/src/dropout_layer.h b/src/dropout_layer.h index 691cfc5b4cf..25bea3861dc 100644 --- a/src/dropout_layer.h +++ b/src/dropout_layer.h @@ -6,6 +6,9 @@ typedef layer dropout_layer; +#ifdef __cplusplus +extern "C" { +#endif dropout_layer make_dropout_layer(int batch, int inputs, float probability); void forward_dropout_layer(dropout_layer l, network_state state); @@ -16,5 +19,8 @@ void resize_dropout_layer(dropout_layer *l, int inputs); void forward_dropout_layer_gpu(dropout_layer l, network_state state); void backward_dropout_layer_gpu(dropout_layer l, network_state state); +#endif +#ifdef __cplusplus +} #endif #endif diff --git a/src/dropout_layer_kernels.cu b/src/dropout_layer_kernels.cu index 9b2d4f88174..f6a93c91405 100644 --- a/src/dropout_layer_kernels.cu +++ b/src/dropout_layer_kernels.cu @@ -2,11 +2,9 @@ #include "curand.h" #include "cublas_v2.h" -extern "C" { #include "dropout_layer.h" #include "cuda.h" #include "utils.h" -} __global__ void yoloswag420blazeit360noscope(float *input, int size, float *rand, float prob, float scale) { diff --git a/src/gemm.c b/src/gemm.c index 022f13228c9..4b83c2f63e8 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -7,7 +7,10 @@ #include #include #include - +#include +#ifdef _WIN32 +#include +#endif #if defined(_OPENMP) #include #endif @@ -37,7 +40,7 @@ void gemm_bin(int M, int N, int K, float ALPHA, float *random_matrix(int rows, int cols) { int i; - float *m = calloc(rows*cols, sizeof(float)); + float* m = (float*)calloc(rows * cols, sizeof(float)); for(i = 0; i < rows*cols; ++i){ m[i] = (float)rand()/RAND_MAX; } @@ -83,7 +86,6 @@ void gemm(int TA, int TB, int M, int N, int K, float ALPHA, // XNOR bitwise GEMM for binary neural network //-------------------------------------------- -#include static inline unsigned char xnor(unsigned char a, unsigned char b) { //return a == b; @@ -318,6 +320,7 @@ void transpose_32x32_bits_my(uint32_t *A, uint32_t *B, int lda, int ldb) } } +#ifndef GPU uint8_t reverse_8_bit(uint8_t a) { return ((a * 0x0802LU & 0x22110LU) | (a * 0x8020LU & 0x88440LU)) * 0x10101LU >> 16; } @@ -465,6 +468,9 @@ void transpose_bin(char *A, char *B, const int n, const int m, } */ +#else +extern void transpose_32x32_bits_reversed_diagonale(uint32_t* A, uint32_t* B, int m, int n); +#endif // transpose by 32-bit void transpose_bin(uint32_t *A, uint32_t *B, const int n, const int m, @@ -483,7 +489,7 @@ void transpose_bin(uint32_t *A, uint32_t *B, const int n, const int m, //transpose_32x32_bits_my(&A[a_index/32], &B[b_index/32], lda/32, ldb/32); } for (; j < m; ++j) { - if (get_bit(A, i*lda + j)) set_bit(B, j*ldb + i); + if (get_bit((const unsigned char* const)A, i * lda + j)) set_bit((unsigned char* const)B, j * ldb + i); } } } @@ -703,7 +709,7 @@ void gemm_nn(int M, int N, int K, float ALPHA, else { for (i = 0; i < M; ++i) { for (k = 0; k < K; ++k) { - register float A_PART = ALPHA*A[i*lda + k]; + float A_PART = ALPHA * A[i * lda + k]; for (j = 0; j < N; ++j) { C[i*ldc + j] += A_PART*B[k*ldb + j]; } @@ -730,9 +736,6 @@ void gemm_nn(int M, int N, int K, float ALPHA, } -#define TILE_M 4 // 4 ops -#define TILE_N 16 // AVX2 = 2 ops * 8 floats -#define TILE_K 16 // loop void gemm_nn_fast(int M, int N, int K, float ALPHA, float *A, int lda, @@ -1286,16 +1289,7 @@ void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, } -static inline float im2col_get_pixel(float *im, int height, int width, int channels, - int row, int col, int channel, int pad) -{ - row -= pad; - col -= pad; - if (row < 0 || col < 0 || - row >= height || col >= width) return 0; - return im[col + width*(row + height*channel)]; -} //From Berkeley Vision's Caffe! //https://github.com/BVLC/caffe/blob/master/LICENSE @@ -1645,7 +1639,7 @@ void im2col_cpu_custom_bin(float* data_im, __m256 result256 = _mm256_cmp_ps(src256, float_zero256, _CMP_GT_OS); uint16_t mask = _mm256_movemask_ps(result256); // (val > 0) ? 0 : 1 - uint16_t *dst_ptr = &((unsigned char*)data_col)[col_index / 8]; + uint16_t* dst_ptr = &((uint16_t*)data_col)[col_index / 8]; *dst_ptr |= (mask << (col_index % 8)); } @@ -1657,7 +1651,7 @@ void im2col_cpu_custom_bin(float* data_im, //data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; float val = data_im[im_col + width*(im_row + height*c_im)]; - if(val > 0) set_bit(data_col, col_index); + if (val > 0) set_bit((unsigned char* const)data_col, col_index); } } @@ -1671,7 +1665,7 @@ void im2col_cpu_custom_bin(float* data_im, //data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); - if (val > 0) set_bit(data_col, col_index); + if (val > 0) set_bit((unsigned char* const)data_col, col_index); } } @@ -1685,7 +1679,7 @@ void im2col_cpu_custom_bin(float* data_im, //data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); - if (val > 0) set_bit(data_col, col_index); + if (val > 0) set_bit((unsigned char* const)data_col, col_index); } } @@ -1699,7 +1693,7 @@ void im2col_cpu_custom_bin(float* data_im, //data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); - if (val > 0) set_bit(data_col, col_index); + if (val > 0) set_bit((unsigned char* const)data_col, col_index); } } @@ -1713,7 +1707,7 @@ void im2col_cpu_custom_bin(float* data_im, //data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); - if (val > 0) set_bit(data_col, col_index); + if (val > 0) set_bit((unsigned char* const)data_col, col_index); } } } @@ -1952,7 +1946,7 @@ void gemm_nn(int M, int N, int K, float ALPHA, int i, j, k; for (i = 0; i < M; ++i) { for (k = 0; k < K; ++k) { - register float A_PART = ALPHA*A[i*lda + k]; + float A_PART = ALPHA * A[i * lda + k]; for (j = 0; j < N; ++j) { C[i*ldc + j] += A_PART*B[k*ldb + j]; } @@ -2239,7 +2233,7 @@ void im2col_cpu_custom_bin(float* data_im, int col_index = c * new_ldb + h * width_col + w; float val = data_im[im_col + width*(im_row + height*c_im)]; - if (val > 0) set_bit(data_col, col_index); + if (val > 0) set_bit((unsigned char*)data_col, col_index); } for (; w < width_col - pad; ++w) { @@ -2250,7 +2244,7 @@ void im2col_cpu_custom_bin(float* data_im, //data_col[col_index] = data_im[im_col + width*(im_row + height*c_im)]; float val = data_im[im_col + width*(im_row + height*c_im)]; - if (val > 0) set_bit(data_col, col_index); + if (val > 0) set_bit((unsigned char*)data_col, col_index); } } @@ -2264,7 +2258,7 @@ void im2col_cpu_custom_bin(float* data_im, //data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); - if (val > 0) set_bit(data_col, col_index); + if (val > 0) set_bit((unsigned char*)data_col, col_index); } } @@ -2278,7 +2272,7 @@ void im2col_cpu_custom_bin(float* data_im, //data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); - if (val > 0) set_bit(data_col, col_index); + if (val > 0) set_bit((unsigned char*)data_col, col_index); } } @@ -2292,7 +2286,7 @@ void im2col_cpu_custom_bin(float* data_im, //data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); - if (val > 0) set_bit(data_col, col_index); + if (val > 0) set_bit((unsigned char*)data_col, col_index); } } @@ -2306,7 +2300,7 @@ void im2col_cpu_custom_bin(float* data_im, //data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); float val = im2col_get_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad); - if (val > 0) set_bit(data_col, col_index); + if (val > 0) set_bit((unsigned char*)data_col, col_index); } } } @@ -2346,7 +2340,7 @@ void float_to_bit(float *src, unsigned char *dst, size_t size) memset(dst, 0, dst_size); size_t i; - char *byte_arr = calloc(size, sizeof(char)); + char* byte_arr = (char*)calloc(size, sizeof(char)); for (i = 0; i < size; ++i) { if (src[i] > 0) byte_arr[i] = 1; } @@ -2578,7 +2572,7 @@ void gemm_nt(int M, int N, int K, float ALPHA, int i,j,k; for(i = 0; i < M; ++i){ for(j = 0; j < N; ++j){ - register float sum = 0; + float sum = 0; for(k = 0; k < K; ++k){ sum += ALPHA*A[i*lda+k]*B[j*ldb + k]; } @@ -2595,7 +2589,7 @@ void gemm_tn(int M, int N, int K, float ALPHA, int i,j,k; for(i = 0; i < M; ++i){ for(k = 0; k < K; ++k){ - register float A_PART = ALPHA*A[k*lda+i]; + float A_PART = ALPHA * A[k * lda + i]; for(j = 0; j < N; ++j){ C[i*ldc+j] += A_PART*B[k*ldb+j]; } @@ -2611,7 +2605,7 @@ void gemm_tt(int M, int N, int K, float ALPHA, int i,j,k; for(i = 0; i < M; ++i){ for(j = 0; j < N; ++j){ - register float sum = 0; + float sum = 0; for(k = 0; k < K; ++k){ sum += ALPHA*A[i+k*lda]*B[k+j*ldb]; } @@ -2668,9 +2662,9 @@ void gemm_ongpu(int TA, int TB, int M, int N, int K, float ALPHA, float *C_gpu, int ldc) { cublasHandle_t handle = blas_handle(); - cudaError_t stream_status = cublasSetStream(handle, get_cuda_stream()); + cudaError_t stream_status = (cudaError_t)cublasSetStream(handle, get_cuda_stream()); CHECK_CUDA(stream_status); - cudaError_t status = cublasSgemm(handle, (TB ? CUBLAS_OP_T : CUBLAS_OP_N), + cudaError_t status = (cudaError_t)cublasSgemm(handle, (TB ? CUBLAS_OP_T : CUBLAS_OP_N), (TA ? CUBLAS_OP_T : CUBLAS_OP_N), N, M, K, &ALPHA, B_gpu, ldb, A_gpu, lda, &BETA, C_gpu, ldc); CHECK_CUDA(status); } diff --git a/src/gemm.h b/src/gemm.h index c34b4b35541..e93ee0e4e7d 100644 --- a/src/gemm.h +++ b/src/gemm.h @@ -3,6 +3,9 @@ #include "activations.h" #include #include +#ifdef __cplusplus +extern "C" { +#endif void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride, float *weights, float *input, float *output, float *mean); @@ -56,6 +59,7 @@ void im2col_cpu_custom_transpose(float* data_im, void activate_array_cpu_custom(float *x, const int n, const ACTIVATION a); +LIB_API void transpose_32x32_bits_reversed_diagonale(uint32_t *A, uint32_t *B, int m, int n); void gemm_bin(int M, int N, int K, float ALPHA, char *A, int lda, @@ -109,4 +113,7 @@ void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA, float BETA, float *C, int ldc); #endif +#ifdef __cplusplus +} +#endif #endif diff --git a/src/getopt.c b/src/getopt.c index cfa30233548..67a764d854a 100644 --- a/src/getopt.c +++ b/src/getopt.c @@ -1,1258 +1,498 @@ -/* Getopt for GNU. - NOTE: getopt is now part of the C library, so if you don't know what - "Keep this file name-space clean" means, talk to drepper@gnu.org - before changing it! - Copyright (C) 1987,88,89,90,91,92,93,94,95,96,98,99,2000,2001 - Free Software Foundation, Inc. - This file is part of the GNU C Library. +#ifdef _WIN32 +#include - The GNU C Library is free software; you can redistribute it and/or - modify it under the terms of the GNU Lesser General Public - License as published by the Free Software Foundation; either - version 2.1 of the License, or (at your option) any later version. - - The GNU C Library is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU - Lesser General Public License for more details. - - You should have received a copy of the GNU Lesser General Public - License along with the GNU C Library; if not, write to the Free - Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA - 02111-1307 USA. */ - -/* This tells Alpha OSF/1 not to define a getopt prototype in . - Ditto for AIX 3.2 and . */ -#ifndef _NO_PROTO -# define _NO_PROTO -#endif - -#ifdef HAVE_CONFIG_H -# include -#endif - -#if !defined __STDC__ || !__STDC__ -/* This is a separate conditional since some stdc systems - reject `defined (const)'. */ -# ifndef const -# define const -# endif -#endif - -#include - -/* Comment out all this code if we are using the GNU C Library, and are not - actually compiling the library itself. This code is part of the GNU C - Library, but also included in many other GNU distributions. Compiling - and linking in this code is a waste when using the GNU C library - (especially if it is a shared library). Rather than having every GNU - program understand `configure --with-gnu-libc' and omit the object files, - it is simpler to just do this in the source for each such file. */ - -#define GETOPT_INTERFACE_VERSION 2 -#if !defined _LIBC && defined __GLIBC__ && __GLIBC__ >= 2 -# include -# if _GNU_GETOPT_INTERFACE_VERSION == GETOPT_INTERFACE_VERSION -# define ELIDE_CODE -# endif -#endif - -#ifndef ELIDE_CODE - - -/* This needs to come after some library #include - to get __GNU_LIBRARY__ defined. */ -#ifdef __GNU_LIBRARY__ -/* Don't include stdlib.h for non-GNU C libraries because some of them - contain conflicting prototypes for getopt. */ -# include -# include -#endif /* GNU C library. */ - -#ifdef VMS -# include -# if HAVE_STRING_H - 0 -# include -# endif +#ifdef __cplusplus +extern "C" { #endif -#ifndef _ -/* This is for other GNU distributions with internationalized messages. */ -# if (HAVE_LIBINTL_H && ENABLE_NLS) || defined _LIBC -# include -# ifndef _ -# define _(msgid) gettext (msgid) -# endif -# else -# define _(msgid) (msgid) -# endif -# if defined _LIBC && defined USE_IN_LIBIO -# include -# endif +#ifdef REPLACE_GETOPT +int opterr = 1; /* if error message should be printed */ +int optind = 1; /* index into parent argv vector */ +int optopt = '?'; /* character checked for validity */ +#undef optreset /* see getopt.h */ +#define optreset __mingw_optreset +int optreset; /* reset getopt */ +char* optarg; /* argument associated with option */ #endif -/* This version of `getopt' appears to the caller like standard Unix `getopt' - but it behaves differently for the user, since it allows the user - to intersperse the options with the other arguments. - - As `getopt' works, it permutes the elements of ARGV so that, - when it is done, all the options precede everything else. Thus - all application programs are extended to handle flexible argument order. - - Setting the environment variable POSIXLY_CORRECT disables permutation. - Then the behavior is completely standard. - - GNU application programs can use a third alternative mode in which - they can distinguish the relative order of options and other arguments. */ - -#include "getopt.h" - -/* For communication from `getopt' to the caller. - When `getopt' finds an option that takes an argument, - the argument value is returned here. - Also, when `ordering' is RETURN_IN_ORDER, - each non-option ARGV-element is returned here. */ - -char *optarg; - -/* Index in ARGV of the next element to be scanned. - This is used for communication to and from the caller - and for communication between successive calls to `getopt'. - - On entry to `getopt', zero means this is the first call; initialize. - - When `getopt' returns -1, this is the index of the first of the - non-option elements that the caller should itself scan. - - Otherwise, `optind' communicates from one call to the next - how much of ARGV has been scanned so far. */ - -/* 1003.2 says this must be 1 before any call. */ -int optind = 1; - -/* Formerly, initialization of getopt depended on optind==0, which - causes problems with re-calling getopt as programs generally don't - know that. */ - -int __getopt_initialized; - -/* The next char to be scanned in the option-element - in which the last option character we returned was found. - This allows us to pick up the scan where we left off. - - If this is zero, or a null string, it means resume the scan - by advancing to the next ARGV-element. */ - -static char *nextchar; - -/* Callers store zero here to inhibit the error message - for unrecognized options. */ - -int opterr = 1; - -/* Set to an option character which was unrecognized. - This must be initialized on some systems to avoid linking in the - system's own getopt implementation. */ - -int optopt = '?'; - -/* Describe how to deal with options that follow non-option ARGV-elements. - - If the caller did not specify anything, - the default is REQUIRE_ORDER if the environment variable - POSIXLY_CORRECT is defined, PERMUTE otherwise. - - REQUIRE_ORDER means don't recognize them as options; - stop option processing when the first non-option is seen. - This is what Unix does. - This mode of operation is selected by either setting the environment - variable POSIXLY_CORRECT, or using `+' as the first character - of the list of option characters. - - PERMUTE is the default. We permute the contents of ARGV as we scan, - so that eventually all the non-options are at the end. This allows options - to be given in any order, even with programs that were not written to - expect this. - - RETURN_IN_ORDER is an option available to programs that were written - to expect options and other ARGV-elements in any order and that care about - the ordering of the two. We describe each non-option ARGV-element - as if it were the argument of an option with character code 1. - Using `-' as the first character of the list of option characters - selects this mode of operation. - - The special argument `--' forces an end of option-scanning regardless - of the value of `ordering'. In the case of RETURN_IN_ORDER, only - `--' can cause `getopt' to return -1 with `optind' != ARGC. */ - -static enum +static void +_vwarnx(const char* fmt, va_list ap) { - REQUIRE_ORDER, PERMUTE, RETURN_IN_ORDER -} ordering; - -/* Value of POSIXLY_CORRECT environment variable. */ -static char *posixly_correct; - -#ifdef __GNU_LIBRARY__ -/* We want to avoid inclusion of string.h with non-GNU libraries - because there are many ways it can cause trouble. - On some systems, it contains special magic macros that don't work - in GCC. */ -# include -# define my_index strchr -#else - -# if HAVE_STRING_H || WIN32 /* Pete Wilson mod 7/28/02 */ -# include -# else -# include -# endif - -/* Avoid depending on library functions or files - whose names are inconsistent. */ - -#ifndef getenv -extern char *getenv (); -#endif + (void)fprintf(stderr, "%s: ", __progname); + if (fmt != NULL) + (void)vfprintf(stderr, fmt, ap); + (void)fprintf(stderr, "\n"); +} -static char * -my_index (str, chr) - const char *str; - int chr; +static void +warnx(const char* fmt, ...) { - while (*str) - { - if (*str == chr) - return (char *) str; - str++; - } - return 0; + va_list ap; + va_start(ap, fmt); + _vwarnx(fmt, ap); + va_end(ap); } -/* If using GCC, we can safely declare strlen this way. - If not using GCC, it is ok not to declare it. */ -#ifdef __GNUC__ -/* Note that Motorola Delta 68k R3V7 comes with GCC but not stddef.h. - That was relevant to code that was here before. */ -# if (!defined __STDC__ || !__STDC__) && !defined strlen -/* gcc with -traditional declares the built-in strlen to return int, - and has done so at least since version 2.4.5. -- rms. */ -extern int strlen (const char *); -# endif /* not __STDC__ */ -#endif /* __GNUC__ */ - -#endif /* not __GNU_LIBRARY__ */ - -/* Handle permutation of arguments. */ - -/* Describe the part of ARGV that contains non-options that have - been skipped. `first_nonopt' is the index in ARGV of the first of them; - `last_nonopt' is the index after the last of them. */ - -static int first_nonopt; -static int last_nonopt; - -#ifdef _LIBC -/* Stored original parameters. - XXX This is no good solution. We should rather copy the args so - that we can compare them later. But we must not use malloc(3). */ -extern int __libc_argc; -extern char **__libc_argv; - -/* Bash 2.0 gives us an environment variable containing flags - indicating ARGV elements that should not be considered arguments. */ - -# ifdef USE_NONOPTION_FLAGS -/* Defined in getopt_init.c */ -extern char *__getopt_nonoption_flags; - -static int nonoption_flags_max_len; -static int nonoption_flags_len; -# endif - -# ifdef USE_NONOPTION_FLAGS -# define SWAP_FLAGS(ch1, ch2) \ - if (nonoption_flags_len > 0) \ - { \ - char __tmp = __getopt_nonoption_flags[ch1]; \ - __getopt_nonoption_flags[ch1] = __getopt_nonoption_flags[ch2]; \ - __getopt_nonoption_flags[ch2] = __tmp; \ - } -# else -# define SWAP_FLAGS(ch1, ch2) -# endif -#else /* !_LIBC */ -# define SWAP_FLAGS(ch1, ch2) -#endif /* _LIBC */ - -/* Exchange two adjacent subsequences of ARGV. - One subsequence is elements [first_nonopt,last_nonopt) - which contains all the non-options that have been skipped so far. - The other is elements [last_nonopt,optind), which contains all - the options processed since those non-options were skipped. +/* + * Compute the greatest common divisor of a and b. + */ +static int +gcd(int a, int b) +{ + int c; - `first_nonopt' and `last_nonopt' are relocated so that they describe - the new indices of the non-options in ARGV after they are moved. */ + c = a % b; + while (c != 0) { + a = b; + b = c; + c = a % b; + } -#if defined __STDC__ && __STDC__ -static void exchange (char **); -#endif + return (b); +} +/* + * Exchange the block from nonopt_start to nonopt_end with the block + * from nonopt_end to opt_end (keeping the same order of arguments + * in each block). + */ static void -exchange (argv) - char **argv; +permute_args(int panonopt_start, int panonopt_end, int opt_end, + char* const* nargv) { - int bottom = first_nonopt; - int middle = last_nonopt; - int top = optind; - char *tem; - - /* Exchange the shorter segment with the far end of the longer segment. - That puts the shorter segment into the right place. - It leaves the longer segment in the right place overall, - but it consists of two parts that need to be swapped next. */ - -#if defined _LIBC && defined USE_NONOPTION_FLAGS - /* First make sure the handling of the `__getopt_nonoption_flags' - string can work normally. Our top argument must be in the range - of the string. */ - if (nonoption_flags_len > 0 && top >= nonoption_flags_max_len) - { - /* We must extend the array. The user plays games with us and - presents new arguments. */ - char *new_str = malloc (top + 1); - if (new_str == NULL) - nonoption_flags_len = nonoption_flags_max_len = 0; - else - { - memset (__mempcpy (new_str, __getopt_nonoption_flags, - nonoption_flags_max_len), - '\0', top + 1 - nonoption_flags_max_len); - nonoption_flags_max_len = top + 1; - __getopt_nonoption_flags = new_str; - } - } -#endif - - while (top > middle && middle > bottom) - { - if (top - middle > middle - bottom) - { - /* Bottom segment is the short one. */ - int len = middle - bottom; - register int i; - - /* Swap it with the top part of the top segment. */ - for (i = 0; i < len; i++) - { - tem = argv[bottom + i]; - argv[bottom + i] = argv[top - (middle - bottom) + i]; - argv[top - (middle - bottom) + i] = tem; - SWAP_FLAGS (bottom + i, top - (middle - bottom) + i); - } - /* Exclude the moved bottom segment from further swapping. */ - top -= len; - } + int cstart, cyclelen, i, j, ncycle, nnonopts, nopts, pos; + char* swap; + + /* + * compute lengths of blocks and number and size of cycles + */ + nnonopts = panonopt_end - panonopt_start; + nopts = opt_end - panonopt_end; + ncycle = gcd(nnonopts, nopts); + cyclelen = (opt_end - panonopt_start) / ncycle; + + for (i = 0; i < ncycle; i++) { + cstart = panonopt_end + i; + pos = cstart; + for (j = 0; j < cyclelen; j++) { + if (pos >= panonopt_end) + pos -= nnonopts; else - { - /* Top segment is the short one. */ - int len = top - middle; - register int i; - - /* Swap it with the bottom part of the bottom segment. */ - for (i = 0; i < len; i++) - { - tem = argv[bottom + i]; - argv[bottom + i] = argv[middle + i]; - argv[middle + i] = tem; - SWAP_FLAGS (bottom + i, middle + i); - } - /* Exclude the moved top segment from further swapping. */ - bottom += len; - } + pos += nopts; + swap = nargv[pos]; + /* LINTED const cast */ + ((char**)nargv)[pos] = nargv[cstart]; + /* LINTED const cast */ + ((char**)nargv)[cstart] = swap; } - - /* Update records for the slots the non-options now occupy. */ - - first_nonopt += (optind - last_nonopt); - last_nonopt = optind; + } } -/* Initialize the internal data when the first call is made. */ - -#if defined __STDC__ && __STDC__ -static const char *_getopt_initialize (int, char *const *, const char *); -#endif -static const char * -_getopt_initialize (argc, argv, optstring) - int argc; - char *const *argv; - const char *optstring; +#ifdef REPLACE_GETOPT +/* + * getopt -- + * Parse argc/argv argument vector. + * + * [eventually this will replace the BSD getopt] + */ +int getopt(int nargc, char* const* nargv, const char* options) { - /* Start processing options with ARGV-element 1 (since ARGV-element 0 - is the program name); the sequence of previously skipped - non-option ARGV-elements is empty. */ - first_nonopt = last_nonopt = optind; + /* + * We don't pass FLAG_PERMUTE to getopt_internal() since + * the BSD getopt(3) (unlike GNU) has never done this. + * + * Furthermore, since many privileged programs call getopt() + * before dropping privileges it makes sense to keep things + * as simple (and bug-free) as possible. + */ + return (getopt_internal(nargc, nargv, options, NULL, NULL, 0)); +} +#endif /* REPLACE_GETOPT */ - nextchar = NULL; +//extern int getopt(int nargc, char * const *nargv, const char *options); - posixly_correct = getenv ("POSIXLY_CORRECT"); +#ifdef __cplusplus +} +#endif +/* + * POSIX requires the `getopt' API to be specified in `unistd.h'; + * thus, `unistd.h' includes this header. However, we do not want + * to expose the `getopt_long' or `getopt_long_only' APIs, when + * included in this manner. Thus, close the standard __GETOPT_H__ + * declarations block, and open an additional __GETOPT_LONG_H__ + * specific block, only when *not* __UNISTD_H_SOURCED__, in which + * to declare the extended API. + */ + +#ifdef __cplusplus +extern "C" { +#endif - /* Determine how to handle the ordering of options and nonoptions. */ +struct option /* specification for a long form option... */ +{ + const char* name; /* option name, without leading hyphens */ + int has_arg; /* does it take an argument? */ + int* flag; /* where to save its status, or NULL */ + int val; /* its associated status value */ +}; - if (optstring[0] == '-') - { - ordering = RETURN_IN_ORDER; - ++optstring; +enum /* permitted values for its `has_arg' field... */ +{ + no_argument = 0, /* option never takes an argument */ + required_argument, /* option always requires an argument */ + optional_argument /* option may take an argument */ +}; + +/* + * parse_long_options -- + * Parse long options in argc/argv argument vector. + * Returns -1 if short_too is set and the option does not match long_options. + */ +static int +parse_long_options(char* const* nargv, const char* options, + const struct option* long_options, int* idx, int short_too) +{ + char *current_argv, *has_equal; + size_t current_argv_len; + int i, ambiguous, match; + +#define IDENTICAL_INTERPRETATION(_x, _y) \ + (long_options[(_x)].has_arg == long_options[(_y)].has_arg && long_options[(_x)].flag == long_options[(_y)].flag && long_options[(_x)].val == long_options[(_y)].val) + + current_argv = place; + match = -1; + ambiguous = 0; + + optind++; + + if ((has_equal = strchr(current_argv, '=')) != NULL) { + /* argument found (--option=arg) */ + current_argv_len = has_equal - current_argv; + has_equal++; + } else + current_argv_len = strlen(current_argv); + + for (i = 0; long_options[i].name; i++) { + /* find matching long option */ + if (strncmp(current_argv, long_options[i].name, + current_argv_len)) + continue; + + if (strlen(long_options[i].name) == current_argv_len) { + /* exact match */ + match = i; + ambiguous = 0; + break; } - else if (optstring[0] == '+') - { - ordering = REQUIRE_ORDER; - ++optstring; + /* + * If this is a known short option, don't allow + * a partial match of a single character. + */ + if (short_too && current_argv_len == 1) + continue; + + if (match == -1) /* partial match */ + match = i; + else if (!IDENTICAL_INTERPRETATION(i, match)) + ambiguous = 1; + } + if (ambiguous) { + /* ambiguous abbreviation */ + if (PRINT_ERROR) + warnx(ambig, (int)current_argv_len, + current_argv); + optopt = 0; + return (BADCH); + } + if (match != -1) { /* option found */ + if (long_options[match].has_arg == no_argument + && has_equal) { + if (PRINT_ERROR) + warnx(noarg, (int)current_argv_len, + current_argv); + /* + * XXX: GNU sets optopt to val regardless of flag + */ + if (long_options[match].flag == NULL) + optopt = long_options[match].val; + else + optopt = 0; + return (BADARG); } - else if (posixly_correct != NULL) - ordering = REQUIRE_ORDER; - else - ordering = PERMUTE; - -#if defined _LIBC && defined USE_NONOPTION_FLAGS - if (posixly_correct == NULL - && argc == __libc_argc && argv == __libc_argv) - { - if (nonoption_flags_max_len == 0) - { - if (__getopt_nonoption_flags == NULL - || __getopt_nonoption_flags[0] == '\0') - nonoption_flags_max_len = -1; - else - { - const char *orig_str = __getopt_nonoption_flags; - int len = nonoption_flags_max_len = strlen (orig_str); - if (nonoption_flags_max_len < argc) - nonoption_flags_max_len = argc; - __getopt_nonoption_flags = - (char *) malloc (nonoption_flags_max_len); - if (__getopt_nonoption_flags == NULL) - nonoption_flags_max_len = -1; - else - memset (__mempcpy (__getopt_nonoption_flags, orig_str, len), - '\0', nonoption_flags_max_len - len); - } - } - nonoption_flags_len = nonoption_flags_max_len; + if (long_options[match].has_arg == required_argument || long_options[match].has_arg == optional_argument) { + if (has_equal) + optarg = has_equal; + else if (long_options[match].has_arg == required_argument) { + /* + * optional argument doesn't use next nargv + */ + optarg = nargv[optind++]; + } } - else - nonoption_flags_len = 0; -#endif - - return optstring; + if ((long_options[match].has_arg == required_argument) + && (optarg == NULL)) { + /* + * Missing argument; leading ':' indicates no error + * should be generated. + */ + if (PRINT_ERROR) + warnx(recargstring, + current_argv); + /* + * XXX: GNU sets optopt to val regardless of flag + */ + if (long_options[match].flag == NULL) + optopt = long_options[match].val; + else + optopt = 0; + --optind; + return (BADARG); + } + } else { /* unknown option */ + if (short_too) { + --optind; + return (-1); + } + if (PRINT_ERROR) + warnx(illoptstring, current_argv); + optopt = 0; + return (BADCH); + } + if (idx) + *idx = match; + if (long_options[match].flag) { + *long_options[match].flag = long_options[match].val; + return (0); + } else + return (long_options[match].val); +#undef IDENTICAL_INTERPRETATION } -/* Scan elements of ARGV (whose length is ARGC) for option characters - given in OPTSTRING. - - If an element of ARGV starts with '-', and is not exactly "-" or "--", - then it is an option element. The characters of this element - (aside from the initial '-') are option characters. If `getopt' - is called repeatedly, it returns successively each of the option characters - from each of the option elements. - - If `getopt' finds another option character, it returns that character, - updating `optind' and `nextchar' so that the next call to `getopt' can - resume the scan with the following option character or ARGV-element. - - If there are no more option characters, `getopt' returns -1. - Then `optind' is the index in ARGV of the first ARGV-element - that is not an option. (The ARGV-elements have been permuted - so that those that are not options now come last.) - - OPTSTRING is a string containing the legitimate option characters. - If an option character is seen that is not listed in OPTSTRING, - return '?' after printing an error message. If you set `opterr' to - zero, the error message is suppressed but we still return '?'. - - If a char in OPTSTRING is followed by a colon, that means it wants an arg, - so the following text in the same ARGV-element, or the text of the following - ARGV-element, is returned in `optarg'. Two colons mean an option that - wants an optional arg; if there is text in the current ARGV-element, - it is returned in `optarg', otherwise `optarg' is set to zero. - - If OPTSTRING starts with `-' or `+', it requests different methods of - handling the non-option ARGV-elements. - See the comments about RETURN_IN_ORDER and REQUIRE_ORDER, above. - - Long-named options begin with `--' instead of `-'. - Their names may be abbreviated as long as the abbreviation is unique - or is an exact match for some defined option. If they have an - argument, it follows the option name in the same ARGV-element, separated - from the option name by a `=', or else the in next ARGV-element. - When `getopt' finds a long-named option, it returns 0 if that option's - `flag' field is nonzero, the value of the option's `val' field - if the `flag' field is zero. - - The elements of ARGV aren't really const, because we permute them. - But we pretend they're const in the prototype to be compatible - with other systems. - - LONGOPTS is a vector of `struct option' terminated by an - element containing a name which is zero. - - LONGIND returns the index in LONGOPT of the long-named option found. - It is only valid when a long-named option has been found by the most - recent call. - - If LONG_ONLY is nonzero, '-' as well as '--' can introduce - long-named options. */ - -int -_getopt_internal (argc, argv, optstring, longopts, longind, long_only) - int argc; - char *const *argv; - const char *optstring; - const struct option *longopts; - int *longind; - int long_only; +/* + * getopt_internal -- + * Parse argc/argv argument vector. Called by user level routines. + */ +static int +getopt_internal(int nargc, char* const* nargv, const char* options, + const struct option* long_options, int* idx, int flags) { - int print_errors = opterr; - if (optstring[0] == ':') - print_errors = 0; - - if (argc < 1) - return -1; + char* oli; /* option letter list index */ + int optchar, short_too; + static int posixly_correct = -1; + + if (options == NULL) + return (-1); + + /* + * XXX Some GNU programs (like cvs) set optind to 0 instead of + * XXX using optreset. Work around this braindamage. + */ + if (optind == 0) + optind = optreset = 1; + + /* + * Disable GNU extensions if POSIXLY_CORRECT is set or options + * string begins with a '+'. + * + * CV, 2009-12-14: Check POSIXLY_CORRECT anew if optind == 0 or + * optreset != 0 for GNU compatibility. + */ + if (posixly_correct == -1 || optreset != 0) + posixly_correct = (getenv("POSIXLY_CORRECT") != NULL); + if (*options == '-') + flags |= FLAG_ALLARGS; + else if (posixly_correct || *options == '+') + flags &= ~FLAG_PERMUTE; + if (*options == '+' || *options == '-') + options++; optarg = NULL; - - if (optind == 0 || !__getopt_initialized) - { - if (optind == 0) - optind = 1; /* Don't scan ARGV[0], the program name. */ - optstring = _getopt_initialize (argc, argv, optstring); - __getopt_initialized = 1; + if (optreset) + nonopt_start = nonopt_end = -1; +start: + if (optreset || !*place) { /* update scanning pointer */ + optreset = 0; + if (optind >= nargc) { /* end of argument vector */ + place = EMSG; + if (nonopt_end != -1) { + /* do permutation, if we have to */ + permute_args(nonopt_start, nonopt_end, + optind, nargv); + optind -= nonopt_end - nonopt_start; + } else if (nonopt_start != -1) { + /* + * If we skipped non-options, set optind + * to the first of them. + */ + optind = nonopt_start; + } + nonopt_start = nonopt_end = -1; + return (-1); } - - /* Test whether ARGV[optind] points to a non-option argument. - Either it does not have option syntax, or there is an environment flag - from the shell indicating it is not an option. The later information - is only used when the used in the GNU libc. */ -#if defined _LIBC && defined USE_NONOPTION_FLAGS -# define NONOPTION_P (argv[optind][0] != '-' || argv[optind][1] == '\0' \ - || (optind < nonoption_flags_len \ - && __getopt_nonoption_flags[optind] == '1')) -#else -# define NONOPTION_P (argv[optind][0] != '-' || argv[optind][1] == '\0') -#endif - - if (nextchar == NULL || *nextchar == '\0') - { - /* Advance to the next ARGV-element. */ - - /* Give FIRST_NONOPT and LAST_NONOPT rational values if OPTIND has been - moved back by the user (who may also have changed the arguments). */ - if (last_nonopt > optind) - last_nonopt = optind; - if (first_nonopt > optind) - first_nonopt = optind; - - if (ordering == PERMUTE) - { - /* If we have just processed some options following some non-options, - exchange them so that the options come first. */ - - if (first_nonopt != last_nonopt && last_nonopt != optind) - exchange ((char **) argv); - else if (last_nonopt != optind) - first_nonopt = optind; - - /* Skip any additional non-options - and extend the range of non-options previously skipped. */ - - while (optind < argc && NONOPTION_P) - optind++; - last_nonopt = optind; - } - - /* The special ARGV-element `--' means premature end of options. - Skip it like a null option, - then exchange with previous non-options as if it were an option, - then skip everything else like a non-option. */ - - if (optind != argc && !strcmp (argv[optind], "--")) - { - optind++; - - if (first_nonopt != last_nonopt && last_nonopt != optind) - exchange ((char **) argv); - else if (first_nonopt == last_nonopt) - first_nonopt = optind; - last_nonopt = argc; - - optind = argc; - } - - /* If we have done all the ARGV-elements, stop the scan - and back over any non-options that we skipped and permuted. */ - - if (optind == argc) - { - /* Set the next-arg-index to point at the non-options - that we previously skipped, so the caller will digest them. */ - if (first_nonopt != last_nonopt) - optind = first_nonopt; - return -1; - } - - /* If we have come to a non-option and did not permute it, - either stop the scan or describe it to the caller and pass it by. */ - - if (NONOPTION_P) - { - if (ordering == REQUIRE_ORDER) - return -1; - optarg = argv[optind++]; - return 1; - } - - /* We have found another option-ARGV-element. - Skip the initial punctuation. */ - - nextchar = (argv[optind] + 1 - + (longopts != NULL && argv[optind][1] == '-')); + if (*(place = nargv[optind]) != '-' || (place[1] == '\0' && strchr(options, '-') == NULL)) { + place = EMSG; /* found non-option */ + if (flags & FLAG_ALLARGS) { + /* + * GNU extension: + * return non-option as argument to option 1 + */ + optarg = nargv[optind++]; + return (INORDER); + } + if (!(flags & FLAG_PERMUTE)) { + /* + * If no permutation wanted, stop parsing + * at first non-option. + */ + return (-1); + } + /* do permutation */ + if (nonopt_start == -1) + nonopt_start = optind; + else if (nonopt_end != -1) { + permute_args(nonopt_start, nonopt_end, + optind, nargv); + nonopt_start = optind - (nonopt_end - nonopt_start); + nonopt_end = -1; + } + optind++; + /* process next argument */ + goto start; } - - /* Decode the current option-ARGV-element. */ - - /* Check whether the ARGV-element is a long option. - - If long_only and the ARGV-element has the form "-f", where f is - a valid short option, don't consider it an abbreviated form of - a long option that starts with f. Otherwise there would be no - way to give the -f short option. - - On the other hand, if there's a long option "fubar" and - the ARGV-element is "-fu", do consider that an abbreviation of - the long option, just like "--fu", and not "-f" with arg "u". - - This distinction seems to be the most useful approach. */ - - if (longopts != NULL - && (argv[optind][1] == '-' - || (long_only && (argv[optind][2] || !my_index (optstring, argv[optind][1]))))) - { - char *nameend; - const struct option *p; - const struct option *pfound = NULL; - int exact = 0; - int ambig = 0; - int indfound = -1; - int option_index; - - for (nameend = nextchar; *nameend && *nameend != '='; nameend++) - /* Do nothing. */ ; - - /* Test all long options for either exact match - or abbreviated matches. */ - for (p = longopts, option_index = 0; p->name; p++, option_index++) - if (!strncmp (p->name, nextchar, nameend - nextchar)) - { - if ((unsigned int) (nameend - nextchar) - == (unsigned int) strlen (p->name)) - { - /* Exact match found. */ - pfound = p; - indfound = option_index; - exact = 1; - break; - } - else if (pfound == NULL) - { - /* First nonexact match found. */ - pfound = p; - indfound = option_index; - } - else if (long_only - || pfound->has_arg != p->has_arg - || pfound->flag != p->flag - || pfound->val != p->val) - /* Second or later nonexact match found. */ - ambig = 1; - } - - if (ambig && !exact) - { - if (print_errors) - { -#if defined _LIBC && defined USE_IN_LIBIO - char *buf; - - __asprintf (&buf, _("%s: option `%s' is ambiguous\n"), - argv[0], argv[optind]); - - if (_IO_fwide (stderr, 0) > 0) - __fwprintf (stderr, L"%s", buf); - else - fputs (buf, stderr); - - free (buf); -#else - fprintf (stderr, _("%s: option `%s' is ambiguous\n"), - argv[0], argv[optind]); -#endif - } - nextchar += strlen (nextchar); - optind++; - optopt = 0; - return '?'; - } - - if (pfound != NULL) - { - option_index = indfound; - optind++; - if (*nameend) - { - /* Don't test has_arg with >, because some C compilers don't - allow it to be used on enums. */ - if (pfound->has_arg) - optarg = nameend + 1; - else - { - if (print_errors) - { -#if defined _LIBC && defined USE_IN_LIBIO - char *buf; -#endif - - if (argv[optind - 1][1] == '-') - { - /* --option */ -#if defined _LIBC && defined USE_IN_LIBIO - __asprintf (&buf, _("\ -%s: option `--%s' doesn't allow an argument\n"), - argv[0], pfound->name); -#else - fprintf (stderr, _("\ -%s: option `--%s' doesn't allow an argument\n"), - argv[0], pfound->name); -#endif - } - else - { - /* +option or -option */ -#if defined _LIBC && defined USE_IN_LIBIO - __asprintf (&buf, _("\ -%s: option `%c%s' doesn't allow an argument\n"), - argv[0], argv[optind - 1][0], - pfound->name); -#else - fprintf (stderr, _("\ -%s: option `%c%s' doesn't allow an argument\n"), - argv[0], argv[optind - 1][0], pfound->name); -#endif - } - -#if defined _LIBC && defined USE_IN_LIBIO - if (_IO_fwide (stderr, 0) > 0) - __fwprintf (stderr, L"%s", buf); - else - fputs (buf, stderr); - - free (buf); -#endif - } - - nextchar += strlen (nextchar); - - optopt = pfound->val; - return '?'; - } - } - else if (pfound->has_arg == 1) - { - if (optind < argc) - optarg = argv[optind++]; - else - { - if (print_errors) - { -#if defined _LIBC && defined USE_IN_LIBIO - char *buf; - - __asprintf (&buf, - _("%s: option `%s' requires an argument\n"), - argv[0], argv[optind - 1]); - - if (_IO_fwide (stderr, 0) > 0) - __fwprintf (stderr, L"%s", buf); - else - fputs (buf, stderr); - - free (buf); -#else - fprintf (stderr, - _("%s: option `%s' requires an argument\n"), - argv[0], argv[optind - 1]); -#endif - } - nextchar += strlen (nextchar); - optopt = pfound->val; - return optstring[0] == ':' ? ':' : '?'; - } - } - nextchar += strlen (nextchar); - if (longind != NULL) - *longind = option_index; - if (pfound->flag) - { - *(pfound->flag) = pfound->val; - return 0; - } - return pfound->val; - } - - /* Can't find it as a long option. If this is not getopt_long_only, - or the option starts with '--' or is not a valid short - option, then it's an error. - Otherwise interpret it as a short option. */ - if (!long_only || argv[optind][1] == '-' - || my_index (optstring, *nextchar) == NULL) - { - if (print_errors) - { -#if defined _LIBC && defined USE_IN_LIBIO - char *buf; -#endif - - if (argv[optind][1] == '-') - { - /* --option */ -#if defined _LIBC && defined USE_IN_LIBIO - __asprintf (&buf, _("%s: unrecognized option `--%s'\n"), - argv[0], nextchar); -#else - fprintf (stderr, _("%s: unrecognized option `--%s'\n"), - argv[0], nextchar); -#endif - } - else - { - /* +option or -option */ -#if defined _LIBC && defined USE_IN_LIBIO - __asprintf (&buf, _("%s: unrecognized option `%c%s'\n"), - argv[0], argv[optind][0], nextchar); -#else - fprintf (stderr, _("%s: unrecognized option `%c%s'\n"), - argv[0], argv[optind][0], nextchar); -#endif - } - -#if defined _LIBC && defined USE_IN_LIBIO - if (_IO_fwide (stderr, 0) > 0) - __fwprintf (stderr, L"%s", buf); - else - fputs (buf, stderr); - - free (buf); -#endif - } - nextchar = (char *) ""; - optind++; - optopt = 0; - return '?'; - } + if (nonopt_start != -1 && nonopt_end == -1) + nonopt_end = optind; + + /* + * If we have "-" do nothing, if "--" we are done. + */ + if (place[1] != '\0' && *++place == '-' && place[1] == '\0') { + optind++; + place = EMSG; + /* + * We found an option (--), so if we skipped + * non-options, we have to permute. + */ + if (nonopt_end != -1) { + permute_args(nonopt_start, nonopt_end, + optind, nargv); + optind -= nonopt_end - nonopt_start; + } + nonopt_start = nonopt_end = -1; + return (-1); } + } - /* Look at and handle the next short option-character. */ - - { - char c = *nextchar++; - char *temp = my_index (optstring, c); + /* + * Check long options if: + * 1) we were passed some + * 2) the arg is not just "-" + * 3) either the arg starts with -- we are getopt_long_only() + */ + if (long_options != NULL && place != nargv[optind] && (*place == '-' || (flags & FLAG_LONGONLY))) { + short_too = 0; + if (*place == '-') + place++; /* --foo long option */ + else if (*place != ':' && strchr(options, *place) != NULL) + short_too = 1; /* could be short option too */ + + optchar = parse_long_options(nargv, options, long_options, + idx, short_too); + if (optchar != -1) { + place = EMSG; + return (optchar); + } + } - /* Increment `optind' when we start to process its last character. */ - if (*nextchar == '\0') + if ((optchar = (int)*place++) == (int)':' || (optchar == (int)'-' && *place != '\0') || (oli = (char*)strchr(options, optchar)) == NULL) { + /* + * If the user specified "-" and '-' isn't listed in + * options, return -1 (non-option) as per POSIX. + * Otherwise, it is an unknown option character (or ':'). + */ + if (optchar == (int)'-' && *place == '\0') + return (-1); + if (!*place) ++optind; - - if (temp == NULL || c == ':') - { - if (print_errors) - { -#if defined _LIBC && defined USE_IN_LIBIO - char *buf; -#endif - - if (posixly_correct) - { - /* 1003.2 specifies the format of this message. */ -#if defined _LIBC && defined USE_IN_LIBIO - __asprintf (&buf, _("%s: illegal option -- %c\n"), - argv[0], c); -#else - fprintf (stderr, _("%s: illegal option -- %c\n"), argv[0], c); -#endif - } - else - { -#if defined _LIBC && defined USE_IN_LIBIO - __asprintf (&buf, _("%s: invalid option -- %c\n"), - argv[0], c); -#else - fprintf (stderr, _("%s: invalid option -- %c\n"), argv[0], c); -#endif - } - -#if defined _LIBC && defined USE_IN_LIBIO - if (_IO_fwide (stderr, 0) > 0) - __fwprintf (stderr, L"%s", buf); - else - fputs (buf, stderr); - - free (buf); -#endif - } - optopt = c; - return '?'; - } - /* Convenience. Treat POSIX -W foo same as long option --foo */ - if (temp[0] == 'W' && temp[1] == ';') - { - char *nameend; - const struct option *p; - const struct option *pfound = NULL; - int exact = 0; - int ambig = 0; - int indfound = 0; - int option_index; - - /* This is an option that requires an argument. */ - if (*nextchar != '\0') - { - optarg = nextchar; - /* If we end this ARGV-element by taking the rest as an arg, - we must advance to the next element now. */ - optind++; - } - else if (optind == argc) - { - if (print_errors) - { - /* 1003.2 specifies the format of this message. */ -#if defined _LIBC && defined USE_IN_LIBIO - char *buf; - - __asprintf (&buf, _("%s: option requires an argument -- %c\n"), - argv[0], c); - - if (_IO_fwide (stderr, 0) > 0) - __fwprintf (stderr, L"%s", buf); - else - fputs (buf, stderr); - - free (buf); -#else - fprintf (stderr, _("%s: option requires an argument -- %c\n"), - argv[0], c); -#endif - } - optopt = c; - if (optstring[0] == ':') - c = ':'; - else - c = '?'; - return c; - } - else - /* We already incremented `optind' once; - increment it again when taking next ARGV-elt as argument. */ - optarg = argv[optind++]; - - /* optarg is now the argument, see if it's in the - table of longopts. */ - - for (nextchar = nameend = optarg; *nameend && *nameend != '='; nameend++) - /* Do nothing. */ ; - - /* Test all long options for either exact match - or abbreviated matches. */ - for (p = longopts, option_index = 0; p->name; p++, option_index++) - if (!strncmp (p->name, nextchar, nameend - nextchar)) - { - if ((unsigned int) (nameend - nextchar) == strlen (p->name)) - { - /* Exact match found. */ - pfound = p; - indfound = option_index; - exact = 1; - break; - } - else if (pfound == NULL) - { - /* First nonexact match found. */ - pfound = p; - indfound = option_index; - } - else - /* Second or later nonexact match found. */ - ambig = 1; - } - if (ambig && !exact) - { - if (print_errors) - { -#if defined _LIBC && defined USE_IN_LIBIO - char *buf; - - __asprintf (&buf, _("%s: option `-W %s' is ambiguous\n"), - argv[0], argv[optind]); - - if (_IO_fwide (stderr, 0) > 0) - __fwprintf (stderr, L"%s", buf); - else - fputs (buf, stderr); - - free (buf); -#else - fprintf (stderr, _("%s: option `-W %s' is ambiguous\n"), - argv[0], argv[optind]); -#endif - } - nextchar += strlen (nextchar); - optind++; - return '?'; - } - if (pfound != NULL) - { - option_index = indfound; - if (*nameend) - { - /* Don't test has_arg with >, because some C compilers don't - allow it to be used on enums. */ - if (pfound->has_arg) - optarg = nameend + 1; - else - { - if (print_errors) - { -#if defined _LIBC && defined USE_IN_LIBIO - char *buf; - - __asprintf (&buf, _("\ -%s: option `-W %s' doesn't allow an argument\n"), - argv[0], pfound->name); - - if (_IO_fwide (stderr, 0) > 0) - __fwprintf (stderr, L"%s", buf); - else - fputs (buf, stderr); - - free (buf); -#else - fprintf (stderr, _("\ -%s: option `-W %s' doesn't allow an argument\n"), - argv[0], pfound->name); -#endif - } - - nextchar += strlen (nextchar); - return '?'; - } - } - else if (pfound->has_arg == 1) - { - if (optind < argc) - optarg = argv[optind++]; - else - { - if (print_errors) - { -#if defined _LIBC && defined USE_IN_LIBIO - char *buf; - - __asprintf (&buf, _("\ -%s: option `%s' requires an argument\n"), - argv[0], argv[optind - 1]); - - if (_IO_fwide (stderr, 0) > 0) - __fwprintf (stderr, L"%s", buf); - else - fputs (buf, stderr); - - free (buf); -#else - fprintf (stderr, - _("%s: option `%s' requires an argument\n"), - argv[0], argv[optind - 1]); -#endif - } - nextchar += strlen (nextchar); - return optstring[0] == ':' ? ':' : '?'; - } - } - nextchar += strlen (nextchar); - if (longind != NULL) - *longind = option_index; - if (pfound->flag) - { - *(pfound->flag) = pfound->val; - return 0; - } - return pfound->val; - } - nextchar = NULL; - return 'W'; /* Let the application handle it. */ - } - if (temp[1] == ':') - { - if (temp[2] == ':') - { - /* This is an option that accepts an argument optionally. */ - if (*nextchar != '\0') - { - optarg = nextchar; - optind++; - } - else - optarg = NULL; - nextchar = NULL; - } - else - { - /* This is an option that requires an argument. */ - if (*nextchar != '\0') - { - optarg = nextchar; - /* If we end this ARGV-element by taking the rest as an arg, - we must advance to the next element now. */ - optind++; - } - else if (optind == argc) - { - if (print_errors) - { - /* 1003.2 specifies the format of this message. */ -#if defined _LIBC && defined USE_IN_LIBIO - char *buf; - - __asprintf (&buf, - _("%s: option requires an argument -- %c\n"), - argv[0], c); - - if (_IO_fwide (stderr, 0) > 0) - __fwprintf (stderr, L"%s", buf); - else - fputs (buf, stderr); - - free (buf); -#else - fprintf (stderr, - _("%s: option requires an argument -- %c\n"), - argv[0], c); -#endif - } - optopt = c; - if (optstring[0] == ':') - c = ':'; - else - c = '?'; - } - else - /* We already incremented `optind' once; - increment it again when taking next ARGV-elt as argument. */ - optarg = argv[optind++]; - nextchar = NULL; - } - } - return c; + if (PRINT_ERROR) + warnx(illoptchar, optchar); + optopt = optchar; + return (BADCH); + } + if (long_options != NULL && optchar == 'W' && oli[1] == ';') { + /* -W long-option */ + if (*place) /* no space */ + /* NOTHING */; + else if (++optind >= nargc) { /* no arg */ + place = EMSG; + if (PRINT_ERROR) + warnx(recargchar, optchar); + optopt = optchar; + return (BADARG); + } else /* white space */ + place = nargv[optind]; + optchar = parse_long_options(nargv, options, long_options, + idx, 0); + place = EMSG; + return (optchar); } + if (*++oli != ':') { /* doesn't take argument */ + if (!*place) + ++optind; + } else { /* takes (optional) argument */ + optarg = NULL; + if (*place) /* no white space */ + optarg = place; + else if (oli[1] != ':') { /* arg not optional */ + if (++optind >= nargc) { /* no arg */ + place = EMSG; + if (PRINT_ERROR) + warnx(recargchar, optchar); + optopt = optchar; + return (BADARG); + } else + optarg = nargv[optind]; + } + place = EMSG; + ++optind; + } + /* dump back option letter */ + return (optchar); } -int -getopt (argc, argv, optstring) - int argc; - char *const *argv; - const char *optstring; +/* + * getopt_long -- + * Parse argc/argv argument vector. + */ +int getopt_long(int nargc, char* const* nargv, const char* options, + const struct option* long_options, int* idx) { - return _getopt_internal (argc, argv, optstring, - (const struct option *) 0, - (int *) 0, - 0); -} - -#endif /* Not ELIDE_CODE. */ - -/* Compile with -DTEST to make an executable for use in testing - the above definition of `getopt'. */ - -/* #define TEST */ /* Pete Wilson mod 7/28/02 */ -#ifdef TEST - -#ifndef exit /* Pete Wilson mod 7/28/02 */ - int exit(int); /* Pete Wilson mod 7/28/02 */ -#endif /* Pete Wilson mod 7/28/02 */ + return (getopt_internal(nargc, nargv, options, long_options, idx, + FLAG_PERMUTE)); +} -int -main (argc, argv) - int argc; - char **argv; +/* + * getopt_long_only -- + * Parse argc/argv argument vector. + */ +int getopt_long_only(int nargc, char* const* nargv, const char* options, + const struct option* long_options, int* idx) { - int c; - int digit_optind = 0; - - while (1) - { - int this_option_optind = optind ? optind : 1; - - c = getopt (argc, argv, "abc:d:0123456789"); - if (c == -1) - break; - - switch (c) - { - case '0': - case '1': - case '2': - case '3': - case '4': - case '5': - case '6': - case '7': - case '8': - case '9': - if (digit_optind != 0 && digit_optind != this_option_optind) - printf ("digits occur in two different argv-elements.\n"); - digit_optind = this_option_optind; - printf ("option %c\n", c); - break; - - case 'a': - printf ("option a\n"); - break; - - case 'b': - printf ("option b\n"); - break; - - case 'c': - printf ("option c with value `%s'\n", optarg); - break; - case '?': - break; - - default: - printf ("?? getopt returned character code 0%o ??\n", c); - } - } + return (getopt_internal(nargc, nargv, options, long_options, idx, + FLAG_PERMUTE | FLAG_LONGONLY)); +} - if (optind < argc) - { - printf ("non-option ARGV-elements: "); - while (optind < argc) - printf ("%s ", argv[optind++]); - printf ("\n"); - } +//extern int getopt_long(int nargc, char * const *nargv, const char *options, +// const struct option *long_options, int *idx); +//extern int getopt_long_only(int nargc, char * const *nargv, const char *options, +// const struct option *long_options, int *idx); +/* + * Previous MinGW implementation had... + */ - exit (0); +#ifdef __cplusplus } - -#endif /* TEST */ +#endif +#endif diff --git a/src/getopt.h b/src/getopt.h index f3696d955dc..431ba864015 100644 --- a/src/getopt.h +++ b/src/getopt.h @@ -1,133 +1,228 @@ -/* Declarations for getopt. - Copyright (C) 1989, 90, 91, 92, 93, 94 Free Software Foundation, Inc. - -This file is part of the GNU C Library. Its master source is NOT part of -the C library, however. The master source lives in /gd/gnu/lib. - -The GNU C Library is free software; you can redistribute it and/or -modify it under the terms of the GNU Library General Public License as -published by the Free Software Foundation; either version 2 of the -License, or (at your option) any later version. - -The GNU C Library is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU -Library General Public License for more details. - -You should have received a copy of the GNU Library General Public -License along with the GNU C Library; see the file COPYING.LIB. If -not, write to the Free Software Foundation, Inc., 675 Mass Ave, -Cambridge, MA 02139, USA. */ - -#ifndef _GETOPT_H -#define _GETOPT_H 1 - -#ifdef __cplusplus +#ifdef _WIN32 +#ifndef __GETOPT_H__ +/** + * DISCLAIMER + * This file is part of the mingw-w64 runtime package. + * + * The mingw-w64 runtime package and its code is distributed in the hope that it + * will be useful but WITHOUT ANY WARRANTY. ALL WARRANTIES, EXPRESSED OR + * IMPLIED ARE HEREBY DISCLAIMED. This includes but is not limited to + * warranties of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. + */ +/* + * Copyright (c) 2002 Todd C. Miller + * + * Permission to use, copy, modify, and distribute this software for any + * purpose with or without fee is hereby granted, provided that the above + * copyright notice and this permission notice appear in all copies. + * + * THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES + * WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF + * MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR + * ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES + * WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN + * ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF + * OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. + * + * Sponsored in part by the Defense Advanced Research Projects + * Agency (DARPA) and Air Force Research Laboratory, Air Force + * Materiel Command, USAF, under agreement number F39502-99-1-0512. + */ +/*- + * Copyright (c) 2000 The NetBSD Foundation, Inc. + * All rights reserved. + * + * This code is derived from software contributed to The NetBSD Foundation + * by Dieter Baron and Thomas Klausner. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE NETBSD FOUNDATION, INC. AND CONTRIBUTORS + * ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED + * TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR + * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR CONTRIBUTORS + * BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + * POSSIBILITY OF SUCH DAMAGE. + */ + +#define __GETOPT_H__ + +/* All the headers include this file. */ +#include +#include +#include +#include +#include +#include +#define WIN32_LEAN_AND_MEAN +#include + +#ifdef __cplusplus extern "C" { #endif -/* For communication from `getopt' to the caller. - When `getopt' finds an option that takes an argument, - the argument value is returned here. - Also, when `ordering' is RETURN_IN_ORDER, - each non-option ARGV-element is returned here. */ - -extern char *optarg; - -/* Index in ARGV of the next element to be scanned. - This is used for communication to and from the caller - and for communication between successive calls to `getopt'. - - On entry to `getopt', zero means this is the first call; initialize. - - When `getopt' returns EOF, this is the index of the first of the - non-option elements that the caller should itself scan. - - Otherwise, `optind' communicates from one call to the next - how much of ARGV has been scanned so far. */ - -extern int optind; - -/* Callers store zero here to inhibit the error message `getopt' prints - for unrecognized options. */ +#define REPLACE_GETOPT /* use this getopt as the system getopt(3) */ -extern int opterr; +//extern int optind; /* index of first non-option in argv */ +//extern int optopt; /* single option character, as parsed */ +//extern int opterr; /* flag to enable built-in diagnostics... */ +// /* (user may set to zero, to suppress) */ +// +//extern char *optarg; /* pointer to argument of current option */ -/* Set to an option character which was unrecognized. */ +#define PRINT_ERROR ((opterr) && (*options != ':')) -extern int optopt; +#define FLAG_PERMUTE 0x01 /* permute non-options to the end of argv */ +#define FLAG_ALLARGS 0x02 /* treat non-options as args to option "-1" */ +#define FLAG_LONGONLY 0x04 /* operate as getopt_long_only */ -/* Describe the long-named options requested by the application. - The LONG_OPTIONS argument to getopt_long or getopt_long_only is a vector - of `struct option' terminated by an element containing a name which is - zero. +/* return values */ +#define BADCH (int)'?' +#define BADARG ((*options == ':') ? (int)':' : (int)'?') +#define INORDER (int)1 - The field `has_arg' is: - no_argument (or 0) if the option does not take an argument, - required_argument (or 1) if the option requires an argument, - optional_argument (or 2) if the option takes an optional argument. +#ifndef __CYGWIN__ +#define __progname __argv[0] +#else +extern char __declspec(dllimport) * __progname; +#endif - If the field `flag' is not NULL, it points to a variable that is set - to the value given in the field `val' when the option is found, but - left unchanged if the option is not found. +#ifdef __CYGWIN__ +static char EMSG[] = ""; +#else +#define EMSG "" +#endif - To have a long-named option do something other than set an `int' to - a compiled-in constant, such as set a value from `optarg', set the - option's `flag' field to zero and its `val' field to a nonzero - value (the equivalent single-letter option character, if there is - one). For long options that have a zero `flag' field, `getopt' - returns the contents of the `val' field. */ +static int getopt_internal(int, char* const*, const char*, + const struct option*, int*, int); +static int parse_long_options(char* const*, const char*, + const struct option*, int*, int); +static int gcd(int, int); +static void permute_args(int, int, int, char* const*); + +static char* place = EMSG; /* option letter processing */ + +/* XXX: set optreset to 1 rather than these two */ +static int nonopt_start = -1; /* first non option argument (for permute) */ +static int nonopt_end = -1; /* first option after non options (for permute) */ + +/* Error messages */ +static const char recargchar[] = "option requires an argument -- %c"; +static const char recargstring[] = "option requires an argument -- %s"; +static const char ambig[] = "ambiguous option -- %.*s"; +static const char noarg[] = "option doesn't take an argument -- %.*s"; +static const char illoptchar[] = "unknown option -- %c"; +static const char illoptstring[] = "unknown option -- %s"; + +static void _vwarnx(const char* fmt, va_list ap); + +static void warnx(const char* fmt, ...); + +/* + * Compute the greatest common divisor of a and b. + */ +static int gcd(int a, int b); + +/* + * Exchange the block from nonopt_start to nonopt_end with the block + * from nonopt_end to opt_end (keeping the same order of arguments + * in each block). + */ +static void permute_args(int panonopt_start, int panonopt_end, int opt_end, char* const* nargv); + +#ifdef REPLACE_GETOPT +/* + * getopt -- + * Parse argc/argv argument vector. + * + * [eventually this will replace the BSD getopt] + */ +int getopt(int nargc, char* const* nargv, const char* options); +#endif /* REPLACE_GETOPT */ + +//extern int getopt(int nargc, char * const *nargv, const char *options); + +#ifdef _BSD_SOURCE +/* + * BSD adds the non-standard `optreset' feature, for reinitialisation + * of `getopt' parsing. We support this feature, for applications which + * proclaim their BSD heritage, before including this header; however, + * to maintain portability, developers are advised to avoid it. + */ +#define optreset __mingw_optreset +extern int optreset; +#endif +#ifdef __cplusplus +} +#endif +/* + * POSIX requires the `getopt' API to be specified in `unistd.h'; + * thus, `unistd.h' includes this header. However, we do not want + * to expose the `getopt_long' or `getopt_long_only' APIs, when + * included in this manner. Thus, close the standard __GETOPT_H__ + * declarations block, and open an additional __GETOPT_LONG_H__ + * specific block, only when *not* __UNISTD_H_SOURCED__, in which + * to declare the extended API. + */ +#endif /* !defined(__GETOPT_H__) */ + +#if !defined(__UNISTD_H_SOURCED__) && !defined(__GETOPT_LONG_H__) +#define __GETOPT_LONG_H__ + +#ifdef __cplusplus +extern "C" { +#endif -struct option -{ -#if defined (__STDC__) && __STDC__ - const char *name; -#else - char *name; +/* + * parse_long_options -- + * Parse long options in argc/argv argument vector. + * Returns -1 if short_too is set and the option does not match long_options. + */ +static int parse_long_options(char* const* nargv, const char* options, const struct option* long_options, int* idx, int short_too); + +/* + * getopt_internal -- + * Parse argc/argv argument vector. Called by user level routines. + */ +static int getopt_internal(int nargc, char* const* nargv, const char* options, const struct option* long_options, int* idx, int flags); + +/* + * getopt_long -- + * Parse argc/argv argument vector. + */ +int getopt_long(int nargc, char* const* nargv, const char* options, const struct option* long_options, int* idx); + +/* + * getopt_long_only -- + * Parse argc/argv argument vector. + */ +int getopt_long_only(int nargc, char* const* nargv, const char* options, const struct option* long_options, int* idx); + +/* + * Previous MinGW implementation had... + */ +#ifndef HAVE_DECL_GETOPT +/* + * ...for the long form API only; keep this for compatibility. + */ +#define HAVE_DECL_GETOPT 1 #endif - /* has_arg can't be an enum because some compilers complain about - type mismatches in all the code that assumes it is an int. */ - int has_arg; - int *flag; - int val; -}; - -/* Names for the values of the `has_arg' field of `struct option'. */ - -#define no_argument 0 -#define required_argument 1 -#define optional_argument 2 - -#if defined (__STDC__) && __STDC__ -#ifdef __GNU_LIBRARY__ -/* Many other libraries have conflicting prototypes for getopt, with - differences in the consts, in stdlib.h. To avoid compilation - errors, only prototype getopt for the GNU C library. */ -extern int getopt (int argc, char *const *argv, const char *shortopts); -#else /* not __GNU_LIBRARY__ */ -extern int getopt (); -#endif /* __GNU_LIBRARY__ */ -extern int getopt_long (int argc, char *const *argv, const char *shortopts, - const struct option *longopts, int *longind); -extern int getopt_long_only (int argc, char *const *argv, - const char *shortopts, - const struct option *longopts, int *longind); - -/* Internal only. Users should not call this directly. */ -extern int _getopt_internal (int argc, char *const *argv, - const char *shortopts, - const struct option *longopts, int *longind, - int long_only); -#else /* not __STDC__ */ -extern int getopt (); -extern int getopt_long (); -extern int getopt_long_only (); - -extern int _getopt_internal (); -#endif /* __STDC__ */ - -#ifdef __cplusplus + +#ifdef __cplusplus } #endif -#endif /* _GETOPT_H */ +#endif /* !defined(__UNISTD_H_SOURCED__) && !defined(__GETOPT_LONG_H__) */ +#endif diff --git a/src/gettimeofday.c b/src/gettimeofday.c index 7df6480dea9..13fc9f74585 100644 --- a/src/gettimeofday.c +++ b/src/gettimeofday.c @@ -1,49 +1,43 @@ +#ifdef _WIN32 #include "gettimeofday.h" -int gettimeofday(struct timeval *tv, struct timezone *tz) +LIB_API int gettimeofday(struct timeval* tp, struct timezone* tzp) { - FILETIME ft; - unsigned __int64 tmpres = 0; - static int tzflag; - - if (NULL != tv) - { - GetSystemTimeAsFileTime(&ft); - - tmpres |= ft.dwHighDateTime; - tmpres <<= 32; - tmpres |= ft.dwLowDateTime; + static const uint64_t EPOCH = ((uint64_t)116444736000000000ULL); + SYSTEMTIME system_time; + FILETIME file_time; + uint64_t time; + + GetSystemTime(&system_time); + SystemTimeToFileTime(&system_time, &file_time); + time = ((uint64_t)file_time.dwLowDateTime); + time += ((uint64_t)file_time.dwHighDateTime) << 32; /*converting file time to unix epoch*/ - tmpres -= DELTA_EPOCH_IN_MICROSECS; - tmpres /= 10; /*convert into microseconds*/ - tv->tv_sec = (long)(tmpres / 1000000UL); - tv->tv_usec = (long)(tmpres % 1000000UL); + tp->tv_sec = (long)((time - EPOCH) / 10000000L); + tp->tv_usec = (long)(system_time.wMilliseconds * 1000); + return 0; } - if (NULL != tz) +LIB_API int clock_gettime(int dummy, struct timespec* ct) { - if (!tzflag) - { - _tzset(); - tzflag++; + LARGE_INTEGER count; + + if (g_first_time) { + g_first_time = 0; + + if (0 == QueryPerformanceFrequency(&g_counts_per_sec)) { + g_counts_per_sec.QuadPart = 0; } - tz->tz_minuteswest = _timezone / 60; - tz->tz_dsttime = _daylight; } - return 0; + if ((NULL == ct) || (g_counts_per_sec.QuadPart <= 0) || (0 == QueryPerformanceCounter(&count))) { + return -1; } -/* never worry about timersub type activies again -- from GLIBC and upcased. */ -int timersub(struct timeval *a, struct timeval *b, struct timeval *result) -{ - (result)->tv_sec = (a)->tv_sec - (b)->tv_sec; - (result)->tv_usec = (a)->tv_usec - (b)->tv_usec; - if ((result)->tv_usec < 0) { - --(result)->tv_sec; - (result)->tv_usec += 1000000; - } + ct->tv_sec = count.QuadPart / g_counts_per_sec.QuadPart; + ct->tv_nsec = ((count.QuadPart % g_counts_per_sec.QuadPart) * BILLION) / g_counts_per_sec.QuadPart; return 0; -} \ No newline at end of file +} +#endif diff --git a/src/gettimeofday.h b/src/gettimeofday.h index d38dfb5aaab..17d7a0f689f 100644 --- a/src/gettimeofday.h +++ b/src/gettimeofday.h @@ -1,20 +1,39 @@ -#pragma once - +#ifdef _WIN32 +#define WIN32_LEAN_AND_MEAN +#include +#include +#include #include < time.h > -#include //I've ommited this line. -#if defined(_MSC_VER) || defined(_MSC_EXTENSIONS) - #define DELTA_EPOCH_IN_MICROSECS 11644473600000000Ui64 -#else - #define DELTA_EPOCH_IN_MICROSECS 11644473600000000ULL +#include "darknet.h" + +#define CLOCK_REALTIME (1) +#define BILLION (1E9) + +#ifndef timersub +#define timersub(a, b, result) \ + do { \ + (result)->tv_sec = (a)->tv_sec - (b)->tv_sec; \ + (result)->tv_usec = (a)->tv_usec - (b)->tv_usec; \ + if ((result)->tv_usec < 0) { \ + --(result)->tv_sec; \ + (result)->tv_usec += 1000000; \ + } \ + } while (0) +#endif // timersub + +#ifdef __cplusplus +extern "C" { #endif -struct timezone -{ - int tz_minuteswest; /* minutes W of Greenwich */ - int tz_dsttime; /* type of dst correction */ -}; - -int gettimeofday(struct timeval *tv, struct timezone *tz); +static unsigned char g_first_time = 1; +static LARGE_INTEGER g_counts_per_sec; + +LIB_API int gettimeofday(struct timeval*, struct timezone*); +LIB_API int clock_gettime(int, struct timespec*); + +#ifdef __cplusplus +} +#endif + +#endif -/* never worry about timersub type activies again -- from GLIBC and upcased. */ -int timersub(struct timeval *a, struct timeval *b, struct timeval *result); \ No newline at end of file diff --git a/src/go.c b/src/go.c index 223d136df72..2576ecefc78 100644 --- a/src/go.c +++ b/src/go.c @@ -5,13 +5,12 @@ #include "blas.h" #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" +#include #endif int inverted = 1; int noi = 1; -//static const int nind = 5; -#define nind 5 +static const unsigned int n_ind = 5; typedef struct { char **data; @@ -22,7 +21,7 @@ char *fgetgo(FILE *fp) { if(feof(fp)) return 0; size_t size = 94; - char *line = malloc(size*sizeof(char)); + char* line = (char*)malloc(size * sizeof(char)); if(size != fread(line, sizeof(char), size, fp)){ free(line); return 0; @@ -35,21 +34,21 @@ moves load_go_moves(char *filename) { moves m; m.n = 128; - m.data = calloc(128, sizeof(char*)); + m.data = (char**)calloc(128, sizeof(char*)); FILE *fp = fopen(filename, "rb"); int count = 0; char *line = 0; while((line = fgetgo(fp))){ if(count >= m.n){ m.n *= 2; - m.data = realloc(m.data, m.n*sizeof(char*)); + m.data = (char**)realloc(m.data, m.n * sizeof(char*)); } m.data[count] = line; ++count; } printf("%d\n", count); m.n = count; - m.data = realloc(m.data, count*sizeof(char*)); + m.data = (char**)realloc(m.data, count * sizeof(char*)); return m; } @@ -127,12 +126,12 @@ void train_go(char *cfgfile, char *weightfile) } printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - char *backup_directory = "/home/pjreddie/backup/"; + char* backup_directory = "backup/"; char buff[256]; - float *board = calloc(19*19*net.batch, sizeof(float)); - float *move = calloc(19*19*net.batch, sizeof(float)); - moves m = load_go_moves("/home/pjreddie/backup/go.train"); + float* board = (float*)calloc(19 * 19 * net.batch, sizeof(float)); + float* move = (float*)calloc(19 * 19 * net.batch, sizeof(float)); + moves m = load_go_moves("backup/go.train"); //moves m = load_go_moves("games.txt"); int N = m.n; @@ -187,7 +186,7 @@ void propagate_liberty(float *board, int *lib, int *visited, int row, int col, i int *calculate_liberties(float *board) { - int *lib = calloc(19*19, sizeof(int)); + int* lib = (int*)calloc(19 * 19, sizeof(int)); int visited[361]; int i, j; for(j = 0; j < 19; ++j){ @@ -222,7 +221,7 @@ void print_board(float *board, int swap, int *indexes) int index = j*19 + i; if(indexes){ int found = 0; - for(n = 0; n < nind; ++n){ + for (n = 0; n < n_ind; ++n) { if(index == indexes[n]){ found = 1; /* @@ -365,9 +364,9 @@ int generate_move(network net, int player, float *board, int multi, float thresh } } - int indexes[nind]; - top_k(move, 19*19, nind, indexes); - if(thresh > move[indexes[0]]) thresh = move[indexes[nind-1]]; + int indexes[n_ind]; + top_k(move, 19*19, n_ind, indexes); + if(thresh > move[indexes[0]]) thresh = move[indexes[n_ind-1]]; for(i = 0; i < 19; ++i){ for(j = 0; j < 19; ++j){ @@ -382,12 +381,12 @@ int generate_move(network net, int player, float *board, int multi, float thresh int index = sample_array(move, 19*19); if(print){ - top_k(move, 19*19, nind, indexes); - for(i = 0; i < nind; ++i){ + top_k(move, 19*19, n_ind, indexes); + for(i = 0; i < n_ind; ++i){ if (!move[indexes[i]]) indexes[i] = -1; } print_board(board, player, indexes); - for(i = 0; i < nind; ++i){ + for(i = 0; i < n_ind; ++i){ fprintf(stderr, "%d: %f\n", i+1, move[indexes[i]]); } } @@ -411,9 +410,9 @@ void valid_go(char *cfgfile, char *weightfile, int multi) set_batch_network(&net, 1); printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - float *board = calloc(19*19, sizeof(float)); - float *move = calloc(19*19, sizeof(float)); - moves m = load_go_moves("/home/pjreddie/backup/go.test"); + float* board = (float*)calloc(19 * 19, sizeof(float)); + float* move = (float*)calloc(19 * 19, sizeof(float)); + moves m = load_go_moves("backup/go.test"); int N = m.n; int i; @@ -439,9 +438,9 @@ void engine_go(char *filename, char *weightfile, int multi) } srand(time(0)); set_batch_network(&net, 1); - float *board = calloc(19*19, sizeof(float)); - char *one = calloc(91, sizeof(char)); - char *two = calloc(91, sizeof(char)); + float* board = (float*)calloc(19 * 19, sizeof(float)); + char* one = (char*)calloc(91, sizeof(char)); + char* two = (char*)calloc(91, sizeof(char)); int passed = 0; while(1){ char buff[256]; @@ -612,8 +611,8 @@ void test_go(char *cfg, char *weights, int multi) } srand(time(0)); set_batch_network(&net, 1); - float *board = calloc(19*19, sizeof(float)); - float *move = calloc(19*19, sizeof(float)); + float* board = (float*)calloc(19 * 19, sizeof(float)); + float* move = (float*)calloc(19 * 19, sizeof(float)); int color = 1; while(1){ float *output = network_predict(net, board); @@ -642,11 +641,11 @@ void test_go(char *cfg, char *weights, int multi) if(board[i]) move[i] = 0; } - int indexes[nind]; + int indexes[n_ind]; int row, col; - top_k(move, 19*19, nind, indexes); + top_k(move, 19 * 19, n_ind, indexes); print_board(board, color, indexes); - for(i = 0; i < nind; ++i){ + for (i = 0; i < n_ind; ++i) { int index = indexes[i]; row = index / 19; col = index % 19; @@ -664,7 +663,7 @@ void test_go(char *cfg, char *weights, int multi) int cnum = sscanf(line, "%c", &c); if (strlen(line) == 0 || dnum) { --picked; - if (picked < nind){ + if (picked < n_ind){ int index = indexes[picked]; row = index / 19; col = index % 19; @@ -764,9 +763,9 @@ void self_go(char *filename, char *weightfile, char *f2, char *w2, int multi) int count = 0; set_batch_network(&net, 1); set_batch_network(&net2, 1); - float *board = calloc(19*19, sizeof(float)); - char *one = calloc(91, sizeof(char)); - char *two = calloc(91, sizeof(char)); + float* board = (float*)calloc(19 * 19, sizeof(float)); + char* one = (char*)calloc(91, sizeof(char)); + char* two = (char*)calloc(91, sizeof(char)); int done = 0; int player = 1; int p1 = 0; diff --git a/src/gru_layer.c b/src/gru_layer.c index 045bbb331ed..eac751a5739 100644 --- a/src/gru_layer.c +++ b/src/gru_layer.c @@ -30,42 +30,42 @@ layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_no { fprintf(stderr, "GRU Layer: %d inputs, %d outputs\n", inputs, outputs); batch = batch / steps; - layer l = {0}; + layer l = { (LAYER_TYPE)0 }; l.batch = batch; l.type = GRU; l.steps = steps; l.inputs = inputs; - l.input_z_layer = malloc(sizeof(layer)); + l.input_z_layer = (layer*)malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.input_z_layer) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.input_z_layer->batch = batch; - l.state_z_layer = malloc(sizeof(layer)); + l.state_z_layer = (layer*)malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.state_z_layer) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.state_z_layer->batch = batch; - l.input_r_layer = malloc(sizeof(layer)); + l.input_r_layer = (layer*)malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.input_r_layer) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.input_r_layer->batch = batch; - l.state_r_layer = malloc(sizeof(layer)); + l.state_r_layer = (layer*)malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.state_r_layer) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.state_r_layer->batch = batch; - l.input_h_layer = malloc(sizeof(layer)); + l.input_h_layer = (layer*)malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.input_h_layer) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.input_h_layer->batch = batch; - l.state_h_layer = malloc(sizeof(layer)); + l.state_h_layer = (layer*)malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.state_h_layer) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.state_h_layer->batch = batch; @@ -74,16 +74,16 @@ layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_no l.outputs = outputs; - l.output = calloc(outputs*batch*steps, sizeof(float)); - l.delta = calloc(outputs*batch*steps, sizeof(float)); - l.state = calloc(outputs*batch, sizeof(float)); - l.prev_state = calloc(outputs*batch, sizeof(float)); - l.forgot_state = calloc(outputs*batch, sizeof(float)); - l.forgot_delta = calloc(outputs*batch, sizeof(float)); - - l.r_cpu = calloc(outputs*batch, sizeof(float)); - l.z_cpu = calloc(outputs*batch, sizeof(float)); - l.h_cpu = calloc(outputs*batch, sizeof(float)); + l.output = (float*)calloc(outputs * batch * steps, sizeof(float)); + l.delta = (float*)calloc(outputs * batch * steps, sizeof(float)); + l.state = (float*)calloc(outputs * batch, sizeof(float)); + l.prev_state = (float*)calloc(outputs * batch, sizeof(float)); + l.forgot_state = (float*)calloc(outputs * batch, sizeof(float)); + l.forgot_delta = (float*)calloc(outputs * batch, sizeof(float)); + + l.r_cpu = (float*)calloc(outputs * batch, sizeof(float)); + l.z_cpu = (float*)calloc(outputs * batch, sizeof(float)); + l.h_cpu = (float*)calloc(outputs * batch, sizeof(float)); l.forward = forward_gru_layer; l.backward = backward_gru_layer; diff --git a/src/gru_layer.h b/src/gru_layer.h index 9e19cee1a68..ae6b55f4adf 100644 --- a/src/gru_layer.h +++ b/src/gru_layer.h @@ -6,6 +6,9 @@ #include "layer.h" #include "network.h" +#ifdef __cplusplus +extern "C" { +#endif layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_normalize); void forward_gru_layer(layer l, network_state state); @@ -20,5 +23,8 @@ void push_gru_layer(layer l); void pull_gru_layer(layer l); #endif +#ifdef __cplusplus +} #endif +#endif diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 3eb76ac0622..091a5aad2b9 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -1,3 +1,4 @@ +#include "image.h" #include "http_stream.h" #ifdef OPENCV @@ -17,9 +18,10 @@ using std::endl; // socket related abstractions: // #ifdef _WIN32 +#ifndef USE_CMAKE_LIBS #pragma comment(lib, "ws2_32.lib") -#include -#include +#endif +#include "gettimeofday.h" #include #define PORT unsigned long #define ADDRPOINTER int* @@ -44,7 +46,7 @@ static int close_socket(SOCKET s) { return result; } #else // nix -#include +#include "darkunistd.h" #include #include #include @@ -85,16 +87,15 @@ static int close_socket(SOCKET s) { #endif // _WIN32 -#include "opencv2/opencv.hpp" -#include "opencv2/highgui/highgui.hpp" -#include "opencv2/highgui/highgui_c.h" -#include "opencv2/imgproc/imgproc_c.h" +#include +#include +#include +#include #ifndef CV_VERSION_EPOCH -#include "opencv2/videoio/videoio.hpp" +#include #endif using namespace cv; -#include "image.h" class MJPG_sender @@ -195,7 +196,8 @@ class MJPG_sender std::vector params; params.push_back(IMWRITE_JPEG_QUALITY); params.push_back(quality); - cv::imencode(".jpg", frame, outbuf, params); + //cv::imencode(".jpg", frame, outbuf, params); //REMOVED FOR COMPATIBILITY + std::cerr << "cv::imencode call disabled!" << std::endl; size_t outlen = outbuf.size(); #ifdef _WIN32 @@ -227,17 +229,17 @@ class MJPG_sender } maxfd = (maxfd>client ? maxfd : client); FD_SET(client, &master); - _write(client, "HTTP/1.0 200 OK\r\n", 0); + _write(client, "HTTP/1.0 200 OK\n", 0); _write(client, - "Server: Mozarella/2.2\r\n" - "Accept-Range: bytes\r\n" - "Connection: close\r\n" - "Max-Age: 0\r\n" - "Expires: 0\r\n" - "Cache-Control: no-cache, private\r\n" - "Pragma: no-cache\r\n" - "Content-Type: multipart/x-mixed-replace; boundary=mjpegstream\r\n" - "\r\n", 0); + "Server: Mozarella/2.2\n" + "Accept-Range: bytes\n" + "Connection: close\n" + "Max-Age: 0\n" + "Expires: 0\n" + "Cache-Control: no-cache, private\n" + "Pragma: no-cache\n" + "Content-Type: multipart/x-mixed-replace; boundary=mjpegstream\n" + "\n", 0); cerr << "MJPG_sender: new client " << client << endl; } else // existing client, just stream pix @@ -249,7 +251,7 @@ class MJPG_sender } char head[400]; - sprintf(head, "--mjpegstream\r\nContent-Type: image/jpeg\r\nContent-Length: %zu\r\n\r\n", outlen); + sprintf(head, "--mjpegstream\nContent-Type: image/jpeg\nContent-Length: %zu\n\n", outlen); _write(s, head, 0); int n = _write(s, (char*)(&outbuf[0]), outlen); //cerr << "known client " << s << " " << n << endl; @@ -406,18 +408,18 @@ class JSON_sender } maxfd = (maxfd>client ? maxfd : client); FD_SET(client, &master); - _write(client, "HTTP/1.0 200 OK\r\n", 0); + _write(client, "HTTP/1.0 200 OK\n", 0); _write(client, - "Server: Mozarella/2.2\r\n" - "Accept-Range: bytes\r\n" - "Connection: close\r\n" - "Max-Age: 0\r\n" - "Expires: 0\r\n" - "Cache-Control: no-cache, private\r\n" - "Pragma: no-cache\r\n" - "Content-Type: application/json\r\n" + "Server: Mozarella/2.2\n" + "Accept-Range: bytes\n" + "Connection: close\n" + "Max-Age: 0\n" + "Expires: 0\n" + "Cache-Control: no-cache, private\n" + "Pragma: no-cache\n" + "Content-Type: application/json\n" //"Content-Type: multipart/x-mixed-replace; boundary=boundary\r\n" - "\r\n", 0); + "\n", 0); _write(client, "[\n", 0); // open JSON array int n = _write(client, outputbuf, outlen); cerr << "JSON_sender: new client " << client << endl; @@ -473,7 +475,7 @@ void send_json(detection *dets, int nboxes, int classes, char **names, long long // ---------------------------------------- -CvCapture* get_capture_video_stream(char *path) { +CvCapture* get_capture_video_stream(const char *path) { CvCapture* cap = NULL; try { cap = (CvCapture*)new cv::VideoCapture(path); @@ -511,7 +513,7 @@ IplImage* get_webcam_frame(CvCapture *cap) { src = cvCloneImage(&tmp); } else { - std::cout << " Video-stream stoped! \n"; + std::cout << " Video-stream stopped! \n"; } } catch (...) { @@ -536,9 +538,6 @@ int get_stream_fps_cpp(CvCapture *cap) { return fps; } // ---------------------------------------- -extern "C" { - image ipl_to_image(IplImage* src); // image.c -} image image_data_augmentation(IplImage* ipl, int w, int h, int pleft, int ptop, int swidth, int sheight, int flip, @@ -701,4 +700,4 @@ void stop_timer_and_show() { } void stop_timer_and_show_name(char *name) { stop_timer_and_show(); } void total_time() {} -#endif // C++11 \ No newline at end of file +#endif // C++11 diff --git a/src/http_stream.h b/src/http_stream.h index d7ec815cfb7..d96d7d4b50a 100644 --- a/src/http_stream.h +++ b/src/http_stream.h @@ -1,11 +1,14 @@ -#pragma once #ifndef HTTP_STREAM_H #define HTTP_STREAM_H #include "darknet.h" #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#include "opencv2/imgproc/imgproc_c.h" +#include +#include +#include +#ifndef CV_VERSION_EPOCH +#include +#endif #endif #ifdef __cplusplus @@ -18,7 +21,7 @@ extern "C" { void send_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id, int port, int timeout); void send_mjpeg(IplImage* ipl, int port, int timeout, int quality); CvCapture* get_capture_webcam(int index); -CvCapture* get_capture_video_stream(char *path); +CvCapture* get_capture_video_stream(const char *path); IplImage* get_webcam_frame(CvCapture *cap); int get_stream_fps_cpp(CvCapture *cap); diff --git a/src/im2col.h b/src/im2col.h index ab4f3483fba..f510d61e031 100644 --- a/src/im2col.h +++ b/src/im2col.h @@ -5,9 +5,14 @@ #include #include "darknet.h" +#ifdef __cplusplus +extern "C" { +#endif void im2col_cpu(float* data_im, int channels, int height, int width, int ksize, int stride, int pad, float* data_col); +float im2col_get_pixel(float* im, int height, int width, int channels, + int row, int col, int channel, int pad); #ifdef GPU @@ -63,5 +68,8 @@ void convolve_bin_cpu(float *input, float *weights, float *output, int in_w, int void convolve_cpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad); +#endif +#ifdef __cplusplus +} #endif #endif diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 1cb4cf342e3..876d6ce06f2 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -1,19 +1,15 @@ -#include "cuda_runtime.h" -#include "curand.h" -#include "cublas_v2.h" +#include +#include +#include #include -extern "C" { #include "im2col.h" #include "cuda.h" -} #include #include -#include +//#include -#define FULL_MASK 0xffffffff -#define WARP_SIZE 32 template __device__ inline T1 __shfl_custom(T1 val, T2 lane) { @@ -154,11 +150,6 @@ __global__ void im2col_align_gpu_kernel(const int n, const float* data_im, { //__shared__ float tmp_s[1]; -//#define SHRED_VALS ((BLOCK / 169) * ) - //__shared__ float dst_s[1024]; - //__shared__ float dst_s[1024]; - //__shared__ uint32_t bit_s[32]; - //__shared__ uint8_t bit_s[128]; int index = blockIdx.x*blockDim.x + threadIdx.x; for (; index < n; index += blockDim.x*gridDim.x) { @@ -604,8 +595,7 @@ __device__ void transpose32_optimized(uint32_t A[32]) { } } -#define BLOCK_TRANSPOSE32 256 - +extern "C" { __device__ void transpose_32x32_bits_reversed_diagonale(uint32_t *A, uint32_t *B, int m, int n) { //unsigned A_tmp[32]; @@ -626,7 +616,7 @@ __device__ void transpose_32x32_bits_reversed_diagonale(uint32_t *A, uint32_t *B #pragma unroll 32 for (i = 0; i < 32; ++i) B[i*n] = A_tmp[i]; } - +} // transpose 32x32 bit __global__ void transpose_bin_gpu_kernel_32(uint32_t *A, uint32_t *B, const int n, const int m, diff --git a/src/image.c b/src/image.c index f95b478a948..31a1819d787 100644 --- a/src/image.c +++ b/src/image.c @@ -1,4 +1,3 @@ -#include "darknet.h" #include "image.h" #include "utils.h" #include "blas.h" @@ -6,25 +5,31 @@ #include #include +#ifndef STB_IMAGE_IMPLEMENTATION #define STB_IMAGE_IMPLEMENTATION #include "stb_image.h" +#endif +#ifndef STB_IMAGE_WRITE_IMPLEMENTATION #define STB_IMAGE_WRITE_IMPLEMENTATION #include "stb_image_write.h" +#endif #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#include "opencv2/imgproc/imgproc_c.h" -#include "opencv2/core/types_c.h" -#include "opencv2/core/version.hpp" +#include +#include +#include +#include #ifndef CV_VERSION_EPOCH -#include "opencv2/videoio/videoio_c.h" -#include "opencv2/imgcodecs/imgcodecs_c.h" +#include +#include #include "http_stream.h" #endif #include "http_stream.h" +#ifndef CV_RGB #define CV_RGB(r, g, b) cvScalar( (b), (g), (r), 0 ) #endif +#endif extern int check_mistakes; int windows = 0; @@ -255,9 +260,9 @@ image **load_alphabet() { int i, j; const int nsize = 8; - image **alphabets = calloc(nsize, sizeof(image)); + image** alphabets = (image**)calloc(nsize, sizeof(image*)); for(j = 0; j < nsize; ++j){ - alphabets[j] = calloc(128, sizeof(image)); + alphabets[j] = (image*)calloc(128, sizeof(image)); for(i = 32; i < 127; ++i){ char buff[256]; sprintf(buff, "data/labels/%d_%d.png", i, j); @@ -273,7 +278,7 @@ image **load_alphabet() detection_with_class* get_actual_detections(detection *dets, int dets_num, float thresh, int* selected_detections_num, char **names) { int selected_num = 0; - detection_with_class* result_arr = calloc(dets_num, sizeof(detection_with_class)); + detection_with_class* result_arr = (detection_with_class*)calloc(dets_num, sizeof(detection_with_class)); int i; for (i = 0; i < dets_num; ++i) { int best_class = -1; @@ -505,7 +510,7 @@ void save_cv_png(IplImage *img, const char *name) IplImage* img_rgb = cvCreateImage(cvSize(img->width, img->height), 8, 3); cvCvtColor(img, img_rgb, CV_RGB2BGR); stbi_write_png(name, img_rgb->width, img_rgb->height, 3, (char *)img_rgb->imageData, 0); - cvRelease(&img_rgb); + cvRelease((void**)&img_rgb); } void save_cv_jpg(IplImage *img, const char *name) @@ -513,7 +518,7 @@ void save_cv_jpg(IplImage *img, const char *name) IplImage* img_rgb = cvCreateImage(cvSize(img->width, img->height), 8, 3); cvCvtColor(img, img_rgb, CV_RGB2BGR); stbi_write_jpg(name, img_rgb->width, img_rgb->height, 3, (char *)img_rgb->imageData, 80); - cvRelease(&img_rgb); + cvRelease((void**)&img_rgb); } void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output) @@ -952,8 +957,8 @@ void normalize_image(image p) void normalize_image2(image p) { - float *min = calloc(p.c, sizeof(float)); - float *max = calloc(p.c, sizeof(float)); + float* min = (float*)calloc(p.c, sizeof(float)); + float* max = (float*)calloc(p.c, sizeof(float)); int i,j; for(i = 0; i < p.c; ++i) min[i] = max[i] = p.data[i*p.h*p.w]; @@ -982,7 +987,7 @@ void normalize_image2(image p) image copy_image(image p) { image copy = p; - copy.data = calloc(p.h*p.w*p.c, sizeof(float)); + copy.data = (float*)calloc(p.h * p.w * p.c, sizeof(float)); memcpy(copy.data, p.data, p.h*p.w*p.c*sizeof(float)); return copy; } @@ -1252,7 +1257,7 @@ void save_image_png(image im, const char *name) char buff[256]; //sprintf(buff, "%s (%d)", name, windows); sprintf(buff, "%s.png", name); - unsigned char *data = calloc(im.w*im.h*im.c, sizeof(char)); + unsigned char* data = (unsigned char*)calloc(im.w * im.h * im.c, sizeof(unsigned char)); int i,k; for(k = 0; k < im.c; ++k){ for(i = 0; i < im.w*im.h; ++i){ @@ -1273,7 +1278,7 @@ void save_image_options(image im, const char *name, IMTYPE f, int quality) else if (f == TGA) sprintf(buff, "%s.tga", name); else if (f == JPG) sprintf(buff, "%s.jpg", name); else sprintf(buff, "%s.png", name); - unsigned char *data = calloc(im.w*im.h*im.c, sizeof(char)); + unsigned char* data = (unsigned char*)calloc(im.w * im.h * im.c, sizeof(unsigned char)); int i, k; for (k = 0; k < im.c; ++k) { for (i = 0; i < im.w*im.h; ++i) { @@ -1331,14 +1336,14 @@ image make_empty_image(int w, int h, int c) image make_image(int w, int h, int c) { image out = make_empty_image(w,h,c); - out.data = calloc(h*w*c, sizeof(float)); + out.data = (float*)calloc(h * w * c, sizeof(float)); return out; } image make_random_image(int w, int h, int c) { image out = make_empty_image(w,h,c); - out.data = calloc(h*w*c, sizeof(float)); + out.data = (float*)calloc(h * w * c, sizeof(float)); int i; for(i = 0; i < w*h*c; ++i){ out.data[i] = (rand_normal() * .25) + .5; @@ -1585,7 +1590,7 @@ image random_augment_image(image im, float angle, float aspect, int low, int hig int min = (im.h < im.w*aspect) ? im.h : im.w*aspect; float scale = (float)r / min; - float rad = rand_uniform(-angle, angle) * TWO_PI / 360.; + float rad = rand_uniform(-angle, angle) * 2.0 * M_PI / 360.; float dx = (im.w*scale/aspect - size) / 2.; float dy = (im.h*scale - size) / 2.; diff --git a/src/image.h b/src/image.h index 5c759b4d5dd..20170932033 100644 --- a/src/image.h +++ b/src/image.h @@ -7,8 +7,20 @@ #include #include #include +#ifdef OPENCV +#include +#include +#include +#include +#ifndef CV_VERSION_EPOCH +#include +#include +#endif +#endif #include "box.h" - +#ifdef __cplusplus +extern "C" { +#endif /* typedef struct { int w; @@ -80,6 +92,11 @@ image load_image(char *filename, int w, int h, int c); //LIB_API image load_image_color(char *filename, int w, int h); image **load_alphabet(); +#ifdef OPENCV +LIB_API image get_image_from_stream(CvCapture* cap); +LIB_API image get_image_from_stream_cpp(CvCapture* cap); +LIB_API image ipl_to_image(IplImage* src); +#endif //float get_pixel(image m, int x, int y, int c); //float get_pixel_extend(image m, int x, int y, int c); //void set_pixel(image m, int x, int y, int c, float val); @@ -90,5 +107,8 @@ image get_image_layer(image m, int l); //LIB_API void free_image(image m); void test_resize(char *filename); +#ifdef __cplusplus +} #endif +#endif diff --git a/src/layer.c b/src/layer.c index 6aeceec90d2..627b931db51 100644 --- a/src/layer.c +++ b/src/layer.c @@ -98,7 +98,7 @@ void free_layer(layer l) if (l.x_gpu) cuda_free(l.x_gpu); if (l.x_norm_gpu) cuda_free(l.x_norm_gpu); - if (l.align_bit_weights_gpu) cuda_free(l.align_bit_weights_gpu); + if (l.align_bit_weights_gpu) cuda_free((float *)l.align_bit_weights_gpu); if (l.mean_arr_gpu) cuda_free(l.mean_arr_gpu); if (l.align_workspace_gpu) cuda_free(l.align_workspace_gpu); if (l.transposed_align_workspace_gpu) cuda_free(l.transposed_align_workspace_gpu); diff --git a/src/layer.h b/src/layer.h index 692df88c032..e92d3b4a95f 100644 --- a/src/layer.h +++ b/src/layer.h @@ -1,10 +1,12 @@ #ifndef BASE_LAYER_H #define BASE_LAYER_H -#include "darknet.h" #include "activations.h" #include "stddef.h" #include "tree.h" +#ifdef __cplusplus +extern "C" { +#endif //struct network_state; @@ -330,4 +332,7 @@ struct layer{ */ //void free_layer(layer); +#ifdef __cplusplus +} +#endif #endif diff --git a/src/list.c b/src/list.c index 39e3033dde5..79fee08d999 100644 --- a/src/list.c +++ b/src/list.c @@ -5,7 +5,7 @@ list *make_list() { - list *l = malloc(sizeof(list)); + list* l = (list*)malloc(sizeof(list)); l->size = 0; l->front = 0; l->back = 0; @@ -40,18 +40,18 @@ void *list_pop(list *l){ void list_insert(list *l, void *val) { - node *new = malloc(sizeof(node)); - new->val = val; - new->next = 0; + node* newnode = (node*)malloc(sizeof(node)); + newnode->val = val; + newnode->next = 0; if(!l->back){ - l->front = new; - new->prev = 0; + l->front = newnode; + newnode->prev = 0; }else{ - l->back->next = new; - new->prev = l->back; + l->back->next = newnode; + newnode->prev = l->back; } - l->back = new; + l->back = newnode; ++l->size; } @@ -84,7 +84,7 @@ void free_list_contents_kvp(list *l) { node *n = l->front; while (n) { - kvp *p = n->val; + kvp* p = (kvp*)n->val; free(p->key); free(n->val); n = n->next; @@ -93,7 +93,7 @@ void free_list_contents_kvp(list *l) void **list_to_array(list *l) { - void **a = calloc(l->size, sizeof(void*)); + void** a = (void**)calloc(l->size, sizeof(void*)); int count = 0; node *n = l->front; while(n){ diff --git a/src/list.h b/src/list.h index c06d1429270..2623174af1b 100644 --- a/src/list.h +++ b/src/list.h @@ -13,6 +13,9 @@ typedef struct list{ node *back; } list; +#ifdef __cplusplus +extern "C" { +#endif list *make_list(); int list_find(list *l, void *val); @@ -24,4 +27,7 @@ void free_list(list *l); void free_list_contents(list *l); void free_list_contents_kvp(list *l); +#ifdef __cplusplus +} +#endif #endif diff --git a/src/local_layer.c b/src/local_layer.c index 31f0ca6ba56..f304511f109 100644 --- a/src/local_layer.c +++ b/src/local_layer.c @@ -26,7 +26,7 @@ int local_out_width(local_layer l) local_layer make_local_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation) { int i; - local_layer l = {0}; + local_layer l = { (LAYER_TYPE)0 }; l.type = LOCAL; l.h = h; @@ -47,19 +47,19 @@ local_layer make_local_layer(int batch, int h, int w, int c, int n, int size, in l.outputs = l.out_h * l.out_w * l.out_c; l.inputs = l.w * l.h * l.c; - l.weights = calloc(c*n*size*size*locations, sizeof(float)); - l.weight_updates = calloc(c*n*size*size*locations, sizeof(float)); + l.weights = (float*)calloc(c * n * size * size * locations, sizeof(float)); + l.weight_updates = (float*)calloc(c * n * size * size * locations, sizeof(float)); - l.biases = calloc(l.outputs, sizeof(float)); - l.bias_updates = calloc(l.outputs, sizeof(float)); + l.biases = (float*)calloc(l.outputs, sizeof(float)); + l.bias_updates = (float*)calloc(l.outputs, sizeof(float)); // float scale = 1./sqrt(size*size*c); float scale = sqrt(2./(size*size*c)); for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_uniform(-1,1); - l.col_image = calloc(out_h*out_w*size*size*c, sizeof(float)); - l.output = calloc(l.batch*out_h * out_w * n, sizeof(float)); - l.delta = calloc(l.batch*out_h * out_w * n, sizeof(float)); + l.col_image = (float*)calloc(out_h * out_w * size * size * c, sizeof(float)); + l.output = (float*)calloc(l.batch * out_h * out_w * n, sizeof(float)); + l.delta = (float*)calloc(l.batch * out_h * out_w * n, sizeof(float)); l.forward = forward_local_layer; l.backward = backward_local_layer; diff --git a/src/local_layer.h b/src/local_layer.h index 28915d8191c..198ca362f93 100644 --- a/src/local_layer.h +++ b/src/local_layer.h @@ -9,6 +9,9 @@ typedef layer local_layer; +#ifdef __cplusplus +extern "C" { +#endif #ifdef GPU void forward_local_layer_gpu(local_layer layer, network_state state); void backward_local_layer_gpu(local_layer layer, network_state state); @@ -27,5 +30,8 @@ void update_local_layer(local_layer layer, int batch, float learning_rate, float void bias_output(float *output, float *biases, int batch, int n, int size); void backward_bias(float *bias_updates, float *delta, int batch, int n, int size); +#ifdef __cplusplus +} #endif +#endif diff --git a/src/lstm_layer.c b/src/lstm_layer.c index 657d500cb06..2894d3d3140 100644 --- a/src/lstm_layer.c +++ b/src/lstm_layer.c @@ -30,7 +30,7 @@ layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_n { fprintf(stderr, "LSTM Layer: %d inputs, %d outputs\n", inputs, outputs); batch = batch / steps; - layer l = { 0 }; + layer l = { (LAYER_TYPE)0 }; l.batch = batch; l.type = LSTM; l.steps = steps; @@ -39,49 +39,49 @@ layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_n l.out_h = 1; l.out_c = outputs; - l.uf = malloc(sizeof(layer)); + l.uf = (layer*)malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.uf) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.uf->batch = batch; if (l.workspace_size < l.uf->workspace_size) l.workspace_size = l.uf->workspace_size; - l.ui = malloc(sizeof(layer)); + l.ui = (layer*)malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.ui) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.ui->batch = batch; if (l.workspace_size < l.ui->workspace_size) l.workspace_size = l.ui->workspace_size; - l.ug = malloc(sizeof(layer)); + l.ug = (layer*)malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.ug) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.ug->batch = batch; if (l.workspace_size < l.ug->workspace_size) l.workspace_size = l.ug->workspace_size; - l.uo = malloc(sizeof(layer)); + l.uo = (layer*)malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.uo) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.uo->batch = batch; if (l.workspace_size < l.uo->workspace_size) l.workspace_size = l.uo->workspace_size; - l.wf = malloc(sizeof(layer)); + l.wf = (layer*)malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.wf) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.wf->batch = batch; if (l.workspace_size < l.wf->workspace_size) l.workspace_size = l.wf->workspace_size; - l.wi = malloc(sizeof(layer)); + l.wi = (layer*)malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.wi) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.wi->batch = batch; if (l.workspace_size < l.wi->workspace_size) l.workspace_size = l.wi->workspace_size; - l.wg = malloc(sizeof(layer)); + l.wg = (layer*)malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.wg) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.wg->batch = batch; if (l.workspace_size < l.wg->workspace_size) l.workspace_size = l.wg->workspace_size; - l.wo = malloc(sizeof(layer)); + l.wo = (layer*)malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.wo) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.wo->batch = batch; @@ -90,27 +90,27 @@ layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_n l.batch_normalize = batch_normalize; l.outputs = outputs; - l.output = calloc(outputs*batch*steps, sizeof(float)); - l.state = calloc(outputs*batch, sizeof(float)); + l.output = (float*)calloc(outputs * batch * steps, sizeof(float)); + l.state = (float*)calloc(outputs * batch, sizeof(float)); l.forward = forward_lstm_layer; l.update = update_lstm_layer; - l.prev_state_cpu = calloc(batch*outputs, sizeof(float)); - l.prev_cell_cpu = calloc(batch*outputs, sizeof(float)); - l.cell_cpu = calloc(batch*outputs*steps, sizeof(float)); - - l.f_cpu = calloc(batch*outputs, sizeof(float)); - l.i_cpu = calloc(batch*outputs, sizeof(float)); - l.g_cpu = calloc(batch*outputs, sizeof(float)); - l.o_cpu = calloc(batch*outputs, sizeof(float)); - l.c_cpu = calloc(batch*outputs, sizeof(float)); - l.h_cpu = calloc(batch*outputs, sizeof(float)); - l.temp_cpu = calloc(batch*outputs, sizeof(float)); - l.temp2_cpu = calloc(batch*outputs, sizeof(float)); - l.temp3_cpu = calloc(batch*outputs, sizeof(float)); - l.dc_cpu = calloc(batch*outputs, sizeof(float)); - l.dh_cpu = calloc(batch*outputs, sizeof(float)); + l.prev_state_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.prev_cell_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.cell_cpu = (float*)calloc(batch*outputs*steps, sizeof(float)); + + l.f_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.i_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.g_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.o_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.c_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.h_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.temp_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.temp2_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.temp3_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.dc_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.dh_cpu = (float*)calloc(batch*outputs, sizeof(float)); #ifdef GPU l.forward_gpu = forward_lstm_layer_gpu; diff --git a/src/lstm_layer.h b/src/lstm_layer.h index ad37c96fa97..d951ca3793f 100644 --- a/src/lstm_layer.h +++ b/src/lstm_layer.h @@ -6,7 +6,10 @@ #include "network.h" #define USET -layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_normalize); +#ifdef __cplusplus +extern "C" { +#endif +LIB_API layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_normalize); void forward_lstm_layer(layer l, network_state state); void update_lstm_layer(layer l, int batch, float learning_rate, float momentum, float decay); @@ -15,6 +18,9 @@ void update_lstm_layer(layer l, int batch, float learning_rate, float momentum, void forward_lstm_layer_gpu(layer l, network_state state); void backward_lstm_layer_gpu(layer l, network_state state); void update_lstm_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay); +#endif +#ifdef __cplusplus +} #endif #endif diff --git a/src/matrix.c b/src/matrix.c index 7430028017c..c7df22b6bfc 100644 --- a/src/matrix.c +++ b/src/matrix.c @@ -15,7 +15,7 @@ void free_matrix(matrix m) float matrix_topk_accuracy(matrix truth, matrix guess, int k) { - int *indexes = calloc(k, sizeof(int)); + int* indexes = (int*)calloc(k, sizeof(int)); int n = truth.cols; int i,j; int correct = 0; @@ -48,15 +48,15 @@ matrix resize_matrix(matrix m, int size) int i; if (m.rows == size) return m; if (m.rows < size) { - m.vals = realloc(m.vals, size*sizeof(float*)); + m.vals = (float**)realloc(m.vals, size * sizeof(float*)); for (i = m.rows; i < size; ++i) { - m.vals[i] = calloc(m.cols, sizeof(float)); + m.vals[i] = (float*)calloc(m.cols, sizeof(float)); } } else if (m.rows > size) { for (i = size; i < m.rows; ++i) { free(m.vals[i]); } - m.vals = realloc(m.vals, size*sizeof(float*)); + m.vals = (float**)realloc(m.vals, size * sizeof(float*)); } m.rows = size; return m; @@ -79,9 +79,9 @@ matrix make_matrix(int rows, int cols) matrix m; m.rows = rows; m.cols = cols; - m.vals = calloc(m.rows, sizeof(float *)); + m.vals = (float**)calloc(m.rows, sizeof(float*)); for(i = 0; i < m.rows; ++i){ - m.vals[i] = calloc(m.cols, sizeof(float)); + m.vals[i] = (float*)calloc(m.cols, sizeof(float)); } return m; } @@ -92,7 +92,7 @@ matrix hold_out_matrix(matrix *m, int n) matrix h; h.rows = n; h.cols = m->cols; - h.vals = calloc(h.rows, sizeof(float *)); + h.vals = (float**)calloc(h.rows, sizeof(float*)); for(i = 0; i < n; ++i){ int index = rand()%m->rows; h.vals[i] = m->vals[index]; @@ -103,7 +103,7 @@ matrix hold_out_matrix(matrix *m, int n) float *pop_column(matrix *m, int c) { - float *col = calloc(m->rows, sizeof(float)); + float* col = (float*)calloc(m->rows, sizeof(float)); int i, j; for(i = 0; i < m->rows; ++i){ col[i] = m->vals[i][c]; @@ -127,18 +127,18 @@ matrix csv_to_matrix(char *filename) int n = 0; int size = 1024; - m.vals = calloc(size, sizeof(float*)); + m.vals = (float**)calloc(size, sizeof(float*)); while((line = fgetl(fp))){ if(m.cols == -1) m.cols = count_fields(line); if(n == size){ size *= 2; - m.vals = realloc(m.vals, size*sizeof(float*)); + m.vals = (float**)realloc(m.vals, size * sizeof(float*)); } m.vals[n] = parse_fields(line, m.cols); free(line); ++n; } - m.vals = realloc(m.vals, n*sizeof(float*)); + m.vals = (float**)realloc(m.vals, n * sizeof(float*)); m.rows = n; return m; } @@ -225,7 +225,7 @@ void kmeans_maximization(matrix data, int *assignments, matrix centers) matrix old_centers = make_matrix(centers.rows, centers.cols); int i, j; - int *counts = calloc(centers.rows, sizeof(int)); + int *counts = (int*)calloc(centers.rows, sizeof(int)); for (i = 0; i < centers.rows; ++i) { for (j = 0; j < centers.cols; ++j) { old_centers.vals[i][j] = centers.vals[i][j]; @@ -268,7 +268,7 @@ void random_centers(matrix data, matrix centers) { int *sample(int n) { int i; - int *s = calloc(n, sizeof(int)); + int* s = (int*)calloc(n, sizeof(int)); for (i = 0; i < n; ++i) s[i] = i; for (i = n - 1; i >= 0; --i) { int swap = s[i]; @@ -301,7 +301,7 @@ void copy(float *x, float *y, int n) model do_kmeans(matrix data, int k) { matrix centers = make_matrix(k, data.cols); - int *assignments = calloc(data.rows, sizeof(int)); + int* assignments = (int*)calloc(data.rows, sizeof(int)); //smart_centers(data, centers); random_centers(data, centers); // IoU = 67.31% after kmeans diff --git a/src/matrix.h b/src/matrix.h index a56ae8e0254..d565722cda2 100644 --- a/src/matrix.h +++ b/src/matrix.h @@ -12,6 +12,9 @@ typedef struct { matrix centers; } model; +#ifdef __cplusplus +extern "C" { +#endif model do_kmeans(matrix data, int k); matrix make_matrix(int rows, int cols); @@ -28,4 +31,7 @@ matrix resize_matrix(matrix m, int size); float *pop_column(matrix *m, int c); +#ifdef __cplusplus +} +#endif #endif diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index 2eaa7dec012..7d3f7974a55 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -47,7 +47,7 @@ void cudnn_maxpool_setup(layer *l) maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride, int padding) { - maxpool_layer l = {0}; + maxpool_layer l = { (LAYER_TYPE)0 }; l.type = MAXPOOL; l.batch = batch; l.h = h; @@ -62,9 +62,9 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s l.size = size; l.stride = stride; int output_size = l.out_h * l.out_w * l.out_c * batch; - l.indexes = calloc(output_size, sizeof(int)); - l.output = calloc(output_size, sizeof(float)); - l.delta = calloc(output_size, sizeof(float)); + l.indexes = (int*)calloc(output_size, sizeof(int)); + l.output = (float*)calloc(output_size, sizeof(float)); + l.delta = (float*)calloc(output_size, sizeof(float)); l.forward = forward_maxpool_layer; l.backward = backward_maxpool_layer; #ifdef GPU @@ -93,9 +93,9 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h) l->outputs = l->out_w * l->out_h * l->c; int output_size = l->outputs * l->batch; - l->indexes = realloc(l->indexes, output_size * sizeof(int)); - l->output = realloc(l->output, output_size * sizeof(float)); - l->delta = realloc(l->delta, output_size * sizeof(float)); + l->indexes = (int*)realloc(l->indexes, output_size * sizeof(int)); + l->output = (float*)realloc(l->output, output_size * sizeof(float)); + l->delta = (float*)realloc(l->delta, output_size * sizeof(float)); #ifdef GPU CHECK_CUDA(cudaFree((float *)l->indexes_gpu)); diff --git a/src/maxpool_layer.h b/src/maxpool_layer.h index 3a60cfe9d19..ba6a20209f1 100644 --- a/src/maxpool_layer.h +++ b/src/maxpool_layer.h @@ -8,6 +8,9 @@ typedef layer maxpool_layer; +#ifdef __cplusplus +extern "C" { +#endif image get_maxpool_image(maxpool_layer l); maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride, int padding); void resize_maxpool_layer(maxpool_layer *l, int w, int h); @@ -20,5 +23,8 @@ void backward_maxpool_layer_gpu(maxpool_layer l, network_state state); void cudnn_maxpool_setup(maxpool_layer *l); #endif // GPU +#ifdef __cplusplus +} #endif +#endif diff --git a/src/maxpool_layer_kernels.cu b/src/maxpool_layer_kernels.cu index 577864e51d8..ec207a285f8 100644 --- a/src/maxpool_layer_kernels.cu +++ b/src/maxpool_layer_kernels.cu @@ -2,10 +2,8 @@ #include "curand.h" #include "cublas_v2.h" -extern "C" { #include "maxpool_layer.h" #include "cuda.h" -} __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride, int size, int pad, float *input, float *output, int *indexes) { diff --git a/src/network.c b/src/network.c index 68dd3e9ad91..762ddb67ea3 100644 --- a/src/network.c +++ b/src/network.c @@ -180,16 +180,16 @@ network make_network(int n) { network net = {0}; net.n = n; - net.layers = calloc(net.n, sizeof(layer)); - net.seen = calloc(1, sizeof(uint64_t)); + net.layers = (layer*)calloc(net.n, sizeof(layer)); + net.seen = (uint64_t*)calloc(1, sizeof(uint64_t)); #ifdef GPU - net.input_gpu = calloc(1, sizeof(float *)); - net.truth_gpu = calloc(1, sizeof(float *)); + net.input_gpu = (float**)calloc(1, sizeof(float*)); + net.truth_gpu = (float**)calloc(1, sizeof(float*)); - net.input16_gpu = calloc(1, sizeof(float *)); - net.output16_gpu = calloc(1, sizeof(float *)); - net.max_input16_size = calloc(1, sizeof(size_t)); - net.max_output16_size = calloc(1, sizeof(size_t)); + net.input16_gpu = (float**)calloc(1, sizeof(float*)); + net.output16_gpu = (float**)calloc(1, sizeof(float*)); + net.max_input16_size = (size_t*)calloc(1, sizeof(size_t)); + net.max_output16_size = (size_t*)calloc(1, sizeof(size_t)); #endif return net; } @@ -300,8 +300,8 @@ float train_network_datum(network net, float *x, float *y) float train_network_sgd(network net, data d, int n) { int batch = net.batch; - float *X = calloc(batch*d.X.cols, sizeof(float)); - float *y = calloc(batch*d.y.cols, sizeof(float)); + float* X = (float*)calloc(batch * d.X.cols, sizeof(float)); + float* y = (float*)calloc(batch * d.y.cols, sizeof(float)); int i; float sum = 0; @@ -320,8 +320,8 @@ float train_network(network net, data d) assert(d.X.rows % net.batch == 0); int batch = net.batch; int n = d.X.rows / batch; - float *X = calloc(batch*d.X.cols, sizeof(float)); - float *y = calloc(batch*d.y.cols, sizeof(float)); + float* X = (float*)calloc(batch * d.X.cols, sizeof(float)); + float* y = (float*)calloc(batch * d.y.cols, sizeof(float)); int i; float sum = 0; @@ -389,11 +389,11 @@ int recalculate_workspace_size(network *net) } else { free(net->workspace); - net->workspace = calloc(1, workspace_size); + net->workspace = (float*)calloc(1, workspace_size); } #else free(net->workspace); - net->workspace = calloc(1, workspace_size); + net->workspace = (float*)calloc(1, workspace_size); #endif //fprintf(stderr, " Done!\n"); return 0; @@ -495,19 +495,19 @@ int resize_network(network *net, int w, int h) net->input_pinned_cpu_flag = 1; else { cudaGetLastError(); // reset CUDA-error - net->input_pinned_cpu = calloc(size, sizeof(float)); + net->input_pinned_cpu = (float*)calloc(size, sizeof(float)); net->input_pinned_cpu_flag = 0; } printf(" CUDA allocate done! \n"); }else { free(net->workspace); - net->workspace = calloc(1, workspace_size); + net->workspace = (float*)calloc(1, workspace_size); if(!net->input_pinned_cpu_flag) - net->input_pinned_cpu = realloc(net->input_pinned_cpu, size * sizeof(float)); + net->input_pinned_cpu = (float*)realloc(net->input_pinned_cpu, size * sizeof(float)); } #else free(net->workspace); - net->workspace = calloc(1, workspace_size); + net->workspace = (float*)calloc(1, workspace_size); #endif //fprintf(stderr, " Done!\n"); return 0; @@ -534,7 +534,7 @@ detection_layer get_network_detection_layer(network net) } } fprintf(stderr, "Detection layer not found!!\n"); - detection_layer l = {0}; + detection_layer l = { (LAYER_TYPE)0 }; return l; } @@ -632,11 +632,11 @@ detection *make_network_boxes(network *net, float thresh, int *num) int i; int nboxes = num_detections(net, thresh); if (num) *num = nboxes; - detection *dets = calloc(nboxes, sizeof(detection)); + detection* dets = (detection*)calloc(nboxes, sizeof(detection)); for (i = 0; i < nboxes; ++i) { - dets[i].prob = calloc(l.classes, sizeof(float)); + dets[i].prob = (float*)calloc(l.classes, sizeof(float)); if (l.coords > 4) { - dets[i].mask = calloc(l.coords - 4, sizeof(float)); + dets[i].mask = (float*)calloc(l.coords - 4, sizeof(float)); } } return dets; @@ -645,10 +645,10 @@ detection *make_network_boxes(network *net, float thresh, int *num) void custom_get_region_detections(layer l, int w, int h, int net_w, int net_h, float thresh, int *map, float hier, int relative, detection *dets, int letter) { - box *boxes = calloc(l.w*l.h*l.n, sizeof(box)); - float **probs = calloc(l.w*l.h*l.n, sizeof(float *)); + box* boxes = (box*)calloc(l.w * l.h * l.n, sizeof(box)); + float** probs = (float**)calloc(l.w * l.h * l.n, sizeof(float*)); int i, j; - for (j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float)); + for (j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float*)calloc(l.classes, sizeof(float)); get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, map); for (j = 0; j < l.w*l.h*l.n; ++j) { dets[j].classes = l.classes; @@ -789,7 +789,7 @@ matrix network_predict_data_multi(network net, data test, int n) int i,j,b,m; int k = get_network_output_size(net); matrix pred = make_matrix(test.X.rows, k); - float *X = calloc(net.batch*test.X.rows, sizeof(float)); + float* X = (float*)calloc(net.batch * test.X.rows, sizeof(float)); for(i = 0; i < test.X.rows; i += net.batch){ for(b = 0; b < net.batch; ++b){ if(i+b == test.X.rows) break; @@ -814,7 +814,7 @@ matrix network_predict_data(network net, data test) int i,j,b; int k = get_network_output_size(net); matrix pred = make_matrix(test.X.rows, k); - float *X = calloc(net.batch*test.X.cols, sizeof(float)); + float* X = (float*)calloc(net.batch * test.X.cols, sizeof(float)); for(i = 0; i < test.X.rows; i += net.batch){ for(b = 0; b < net.batch; ++b){ if(i+b == test.X.rows) break; diff --git a/src/network.h b/src/network.h index 10bd99f2322..d082e7fc835 100644 --- a/src/network.h +++ b/src/network.h @@ -6,14 +6,14 @@ #include #include "layer.h" -#ifdef __cplusplus -extern "C" { -#endif #include "image.h" #include "data.h" #include "tree.h" +#ifdef __cplusplus +extern "C" { +#endif /* typedef enum { CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM diff --git a/src/network_kernels.cu b/src/network_kernels.cu index cb6c0f38daa..38c15387e52 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -3,7 +3,6 @@ //#include "cublas_v2.h" #include "cuda.h" -extern "C" { #include #include #include @@ -35,10 +34,9 @@ extern "C" { #include "route_layer.h" #include "shortcut_layer.h" #include "blas.h" -} #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" +#include #endif #include "http_stream.h" @@ -396,9 +394,11 @@ void sync_nets(network *nets, int n, int interval) float train_networks(network *nets, int n, data d, int interval) { int i; +#ifdef _DEBUG int batch = nets[0].batch; int subdivisions = nets[0].subdivisions; assert(batch * subdivisions * n == d.X.rows); +#endif pthread_t *threads = (pthread_t *) calloc(n, sizeof(pthread_t)); float *errors = (float *) calloc(n, sizeof(float)); diff --git a/src/nightmare.c b/src/nightmare.c index ec7166ccf99..433c93b7516 100644 --- a/src/nightmare.c +++ b/src/nightmare.c @@ -5,7 +5,7 @@ #include "utils.h" #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" +#include #endif // ./darknet nightmare cfg/extractor.recon.cfg ~/trained/yolo-coco.conv frame6.png -reconstruct -iters 500 -i 3 -lambda .1 -rate .01 -smooth 2 diff --git a/src/normalization_layer.c b/src/normalization_layer.c index 069a0792490..9c01f7894d4 100644 --- a/src/normalization_layer.c +++ b/src/normalization_layer.c @@ -5,7 +5,7 @@ layer make_normalization_layer(int batch, int w, int h, int c, int size, float alpha, float beta, float kappa) { fprintf(stderr, "Local Response Normalization Layer: %d x %d x %d image, %d size\n", w,h,c,size); - layer layer = {0}; + layer layer = { (LAYER_TYPE)0 }; layer.type = NORMALIZATION; layer.batch = batch; layer.h = layer.out_h = h; @@ -15,10 +15,10 @@ layer make_normalization_layer(int batch, int w, int h, int c, int size, float a layer.size = size; layer.alpha = alpha; layer.beta = beta; - layer.output = calloc(h * w * c * batch, sizeof(float)); - layer.delta = calloc(h * w * c * batch, sizeof(float)); - layer.squared = calloc(h * w * c * batch, sizeof(float)); - layer.norms = calloc(h * w * c * batch, sizeof(float)); + layer.output = (float*)calloc(h * w * c * batch, sizeof(float)); + layer.delta = (float*)calloc(h * w * c * batch, sizeof(float)); + layer.squared = (float*)calloc(h * w * c * batch, sizeof(float)); + layer.norms = (float*)calloc(h * w * c * batch, sizeof(float)); layer.inputs = w*h*c; layer.outputs = layer.inputs; @@ -46,10 +46,10 @@ void resize_normalization_layer(layer *layer, int w, int h) layer->out_w = w; layer->inputs = w*h*c; layer->outputs = layer->inputs; - layer->output = realloc(layer->output, h * w * c * batch * sizeof(float)); - layer->delta = realloc(layer->delta, h * w * c * batch * sizeof(float)); - layer->squared = realloc(layer->squared, h * w * c * batch * sizeof(float)); - layer->norms = realloc(layer->norms, h * w * c * batch * sizeof(float)); + layer->output = (float*)realloc(layer->output, h * w * c * batch * sizeof(float)); + layer->delta = (float*)realloc(layer->delta, h * w * c * batch * sizeof(float)); + layer->squared = (float*)realloc(layer->squared, h * w * c * batch * sizeof(float)); + layer->norms = (float*)realloc(layer->norms, h * w * c * batch * sizeof(float)); #ifdef GPU cuda_free(layer->output_gpu); cuda_free(layer->delta_gpu); diff --git a/src/normalization_layer.h b/src/normalization_layer.h index ab327764443..b7b3d81a864 100644 --- a/src/normalization_layer.h +++ b/src/normalization_layer.h @@ -5,6 +5,9 @@ #include "layer.h" #include "network.h" +#ifdef __cplusplus +extern "C" { +#endif layer make_normalization_layer(int batch, int w, int h, int c, int size, float alpha, float beta, float kappa); void resize_normalization_layer(layer *layer, int h, int w); void forward_normalization_layer(const layer layer, network_state state); @@ -16,4 +19,7 @@ void forward_normalization_layer_gpu(const layer layer, network_state state); void backward_normalization_layer_gpu(const layer layer, network_state state); #endif +#ifdef __cplusplus +} +#endif #endif diff --git a/src/option_list.c b/src/option_list.c index dfab81b04fa..22bc8c1bef6 100644 --- a/src/option_list.c +++ b/src/option_list.c @@ -3,6 +3,7 @@ #include #include "option_list.h" #include "utils.h" +#include "data.h" list *read_data_cfg(char *filename) { @@ -71,7 +72,7 @@ int read_option(char *s, list *options) void option_insert(list *l, char *key, char *val) { - kvp *p = malloc(sizeof(kvp)); + kvp* p = (kvp*)malloc(sizeof(kvp)); p->key = key; p->val = val; p->used = 0; diff --git a/src/option_list.h b/src/option_list.h index cb86067115c..a9e3e0b7e30 100644 --- a/src/option_list.h +++ b/src/option_list.h @@ -9,6 +9,9 @@ typedef struct{ int used; } kvp; +#ifdef __cplusplus +extern "C" { +#endif list *read_data_cfg(char *filename); int read_option(char *s, list *options); @@ -28,4 +31,7 @@ void option_unused(list *l); //LIB_API metadata get_metadata(char *file); +#ifdef __cplusplus +} +#endif #endif diff --git a/src/parser.c b/src/parser.c index df6710084fd..ff344f01fb4 100644 --- a/src/parser.c +++ b/src/parser.c @@ -272,7 +272,7 @@ int *parse_yolo_mask(char *a, int *num) for (i = 0; i < len; ++i) { if (a[i] == ',') ++n; } - mask = calloc(n, sizeof(int)); + mask = (int*)calloc(n, sizeof(int)); for (i = 0; i < n; ++i) { int val = atoi(a); mask[i] = val; @@ -587,8 +587,8 @@ route_layer parse_route(list *options, size_params params, network net) if (l[i] == ',') ++n; } - int *layers = calloc(n, sizeof(int)); - int *sizes = calloc(n, sizeof(int)); + int* layers = (int*)calloc(n, sizeof(int)); + int* sizes = (int*)calloc(n, sizeof(int)); for(i = 0; i < n; ++i){ int index = atoi(l); l = strchr(l, ',')+1; @@ -693,8 +693,8 @@ void parse_net_options(list *options, network *net) for(i = 0; i < len; ++i){ if (l[i] == ',') ++n; } - int *steps = calloc(n, sizeof(int)); - float *scales = calloc(n, sizeof(float)); + int* steps = (int*)calloc(n, sizeof(int)); + float* scales = (float*)calloc(n, sizeof(float)); for(i = 0; i < n; ++i){ int step = atoi(l); float scale = atof(p); @@ -765,7 +765,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) fprintf(stderr, "%4d ", count); s = (section *)n->val; options = s->options; - layer l = {0}; + layer l = { (LAYER_TYPE)0 }; LAYER_TYPE lt = string_to_layer_type(s->type); if(lt == CONVOLUTIONAL){ l = parse_convolutional(options, params); @@ -864,7 +864,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) if (cudaSuccess == cudaHostAlloc(&net.input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped)) net.input_pinned_cpu_flag = 1; else { cudaGetLastError(); // reset CUDA-error - net.input_pinned_cpu = calloc(size, sizeof(float)); + net.input_pinned_cpu = (float*)calloc(size, sizeof(float)); } // pre-allocate memory for inference on Tensor Cores (fp16) @@ -879,12 +879,12 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) net.workspace = cuda_make_array(0, workspace_size / sizeof(float) + 1); } else { - net.workspace = calloc(1, workspace_size); + net.workspace = (float*)calloc(1, workspace_size); } } #else if (workspace_size) { - net.workspace = calloc(1, workspace_size); + net.workspace = (float*)calloc(1, workspace_size); } #endif @@ -911,7 +911,7 @@ list *read_cfg(char *filename) strip(line); switch(line[0]){ case '[': - current = malloc(sizeof(section)); + current = (section*)malloc(sizeof(section)); list_insert(sections, current); current->options = make_list(); current->type = line; @@ -1091,7 +1091,7 @@ void save_weights(network net, char *filename) void transpose_matrix(float *a, int rows, int cols) { - float *transpose = calloc(rows*cols, sizeof(float)); + float* transpose = (float*)calloc(rows * cols, sizeof(float)); int x, y; for(x = 0; x < rows; ++x){ for(y = 0; y < cols; ++y){ @@ -1313,7 +1313,7 @@ void load_weights(network *net, char *filename) network *load_network_custom(char *cfg, char *weights, int clear, int batch) { printf(" Try to load cfg: %s, weights: %s, clear = %d \n", cfg, weights, clear); - network *net = calloc(1, sizeof(network)); + network* net = (network*)calloc(1, sizeof(network)); *net = parse_network_cfg_custom(cfg, batch, 0); if (weights && weights[0] != 0) { load_weights(net, weights); @@ -1326,7 +1326,7 @@ network *load_network_custom(char *cfg, char *weights, int clear, int batch) network *load_network(char *cfg, char *weights, int clear) { printf(" Try to load cfg: %s, weights: %s, clear = %d \n", cfg, weights, clear); - network *net = calloc(1, sizeof(network)); + network* net = (network*)calloc(1, sizeof(network)); *net = parse_network_cfg(cfg); if (weights && weights[0] != 0) { load_weights(net, weights); diff --git a/src/parser.h b/src/parser.h index d8a72bea5c2..5e0d3fd1295 100644 --- a/src/parser.h +++ b/src/parser.h @@ -2,6 +2,9 @@ #define PARSER_H #include "network.h" +#ifdef __cplusplus +extern "C" { +#endif network parse_network_cfg(char *filename); network parse_network_cfg_custom(char *filename, int batch, int time_steps); void save_network(network net, char *filename); @@ -11,4 +14,7 @@ void save_weights_double(network net, char *filename); void load_weights(network *net, char *filename); void load_weights_upto(network *net, char *filename, int cutoff); +#ifdef __cplusplus +} +#endif #endif diff --git a/src/region_layer.c b/src/region_layer.c index 42730e09b2f..f7f82088e07 100644 --- a/src/region_layer.c +++ b/src/region_layer.c @@ -9,11 +9,10 @@ #include #include -#define DOABS 1 region_layer make_region_layer(int batch, int w, int h, int n, int classes, int coords, int max_boxes) { - region_layer l = {0}; + region_layer l = { (LAYER_TYPE)0 }; l.type = REGION; l.n = n; @@ -22,15 +21,15 @@ region_layer make_region_layer(int batch, int w, int h, int n, int classes, int l.w = w; l.classes = classes; l.coords = coords; - l.cost = calloc(1, sizeof(float)); - l.biases = calloc(n*2, sizeof(float)); - l.bias_updates = calloc(n*2, sizeof(float)); + l.cost = (float*)calloc(1, sizeof(float)); + l.biases = (float*)calloc(n * 2, sizeof(float)); + l.bias_updates = (float*)calloc(n * 2, sizeof(float)); l.outputs = h*w*n*(classes + coords + 1); l.inputs = l.outputs; l.max_boxes = max_boxes; l.truths = max_boxes*(5); - l.delta = calloc(batch*l.outputs, sizeof(float)); - l.output = calloc(batch*l.outputs, sizeof(float)); + l.delta = (float*)calloc(batch * l.outputs, sizeof(float)); + l.output = (float*)calloc(batch * l.outputs, sizeof(float)); int i; for(i = 0; i < n*2; ++i){ l.biases[i] = .5; @@ -61,8 +60,8 @@ void resize_region_layer(layer *l, int w, int h) l->outputs = h*w*l->n*(l->classes + l->coords + 1); l->inputs = l->outputs; - l->output = realloc(l->output, l->batch*l->outputs*sizeof(float)); - l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float)); + l->output = (float*)realloc(l->output, l->batch * l->outputs * sizeof(float)); + l->delta = (float*)realloc(l->delta, l->batch * l->outputs * sizeof(float)); #ifdef GPU if (old_w < w || old_h < h) { @@ -444,11 +443,11 @@ void forward_region_layer_gpu(const region_layer l, network_state state) softmax_gpu(l.output_gpu+5, l.classes, l.classes + 5, l.w*l.h*l.n*l.batch, 1, l.output_gpu + 5); } - float *in_cpu = calloc(l.batch*l.inputs, sizeof(float)); + float* in_cpu = (float*)calloc(l.batch * l.inputs, sizeof(float)); float *truth_cpu = 0; if(state.truth){ int num_truth = l.batch*l.truths; - truth_cpu = calloc(num_truth, sizeof(float)); + truth_cpu = (float*)calloc(num_truth, sizeof(float)); cuda_pull_array(state.truth, truth_cpu, num_truth); } cuda_pull_array(l.output_gpu, in_cpu, l.batch*l.inputs); diff --git a/src/region_layer.h b/src/region_layer.h index daf68c9770a..2b40d80ff5c 100644 --- a/src/region_layer.h +++ b/src/region_layer.h @@ -6,6 +6,9 @@ typedef layer region_layer; +#ifdef __cplusplus +extern "C" { +#endif region_layer make_region_layer(int batch, int h, int w, int n, int classes, int coords, int max_boxes); void forward_region_layer(const region_layer l, network_state state); void backward_region_layer(const region_layer l, network_state state); @@ -20,4 +23,7 @@ void forward_region_layer_gpu(const region_layer l, network_state state); void backward_region_layer_gpu(region_layer l, network_state state); #endif +#ifdef __cplusplus +} +#endif #endif diff --git a/src/reorg_layer.c b/src/reorg_layer.c index c298b4007c9..47c5efa4046 100644 --- a/src/reorg_layer.c +++ b/src/reorg_layer.c @@ -6,7 +6,7 @@ layer make_reorg_layer(int batch, int w, int h, int c, int stride, int reverse) { - layer l = {0}; + layer l = { (LAYER_TYPE)0 }; l.type = REORG; l.batch = batch; l.stride = stride; @@ -27,8 +27,8 @@ layer make_reorg_layer(int batch, int w, int h, int c, int stride, int reverse) l.outputs = l.out_h * l.out_w * l.out_c; l.inputs = h*w*c; int output_size = l.out_h * l.out_w * l.out_c * batch; - l.output = calloc(output_size, sizeof(float)); - l.delta = calloc(output_size, sizeof(float)); + l.output = (float*)calloc(output_size, sizeof(float)); + l.delta = (float*)calloc(output_size, sizeof(float)); l.forward = forward_reorg_layer; l.backward = backward_reorg_layer; @@ -64,8 +64,8 @@ void resize_reorg_layer(layer *l, int w, int h) l->inputs = l->outputs; int output_size = l->outputs * l->batch; - l->output = realloc(l->output, output_size * sizeof(float)); - l->delta = realloc(l->delta, output_size * sizeof(float)); + l->output = (float*)realloc(l->output, output_size * sizeof(float)); + l->delta = (float*)realloc(l->delta, output_size * sizeof(float)); #ifdef GPU cuda_free(l->output_gpu); diff --git a/src/reorg_layer.h b/src/reorg_layer.h index 21c22cd87b8..b6ed379cd58 100644 --- a/src/reorg_layer.h +++ b/src/reorg_layer.h @@ -6,6 +6,9 @@ #include "layer.h" #include "network.h" +#ifdef __cplusplus +extern "C" { +#endif layer make_reorg_layer(int batch, int h, int w, int c, int stride, int reverse); void resize_reorg_layer(layer *l, int w, int h); void forward_reorg_layer(const layer l, network_state state); @@ -16,5 +19,8 @@ void forward_reorg_layer_gpu(layer l, network_state state); void backward_reorg_layer_gpu(layer l, network_state state); #endif +#ifdef __cplusplus +} #endif +#endif diff --git a/src/reorg_old_layer.c b/src/reorg_old_layer.c index 30206d9c001..defc034d88f 100644 --- a/src/reorg_old_layer.c +++ b/src/reorg_old_layer.c @@ -6,7 +6,7 @@ layer make_reorg_old_layer(int batch, int w, int h, int c, int stride, int reverse) { - layer l = {0}; + layer l = { (LAYER_TYPE)0 }; l.type = REORG_OLD; l.batch = batch; l.stride = stride; @@ -27,8 +27,8 @@ layer make_reorg_old_layer(int batch, int w, int h, int c, int stride, int rever l.outputs = l.out_h * l.out_w * l.out_c; l.inputs = h*w*c; int output_size = l.out_h * l.out_w * l.out_c * batch; - l.output = calloc(output_size, sizeof(float)); - l.delta = calloc(output_size, sizeof(float)); + l.output = (float*)calloc(output_size, sizeof(float)); + l.delta = (float*)calloc(output_size, sizeof(float)); l.forward = forward_reorg_old_layer; l.backward = backward_reorg_old_layer; @@ -64,8 +64,8 @@ void resize_reorg_old_layer(layer *l, int w, int h) l->inputs = l->outputs; int output_size = l->outputs * l->batch; - l->output = realloc(l->output, output_size * sizeof(float)); - l->delta = realloc(l->delta, output_size * sizeof(float)); + l->output = (float*)realloc(l->output, output_size * sizeof(float)); + l->delta = (float*)realloc(l->delta, output_size * sizeof(float)); #ifdef GPU cuda_free(l->output_gpu); diff --git a/src/reorg_old_layer.h b/src/reorg_old_layer.h index e1838202ce6..b66769b5a65 100644 --- a/src/reorg_old_layer.h +++ b/src/reorg_old_layer.h @@ -6,6 +6,9 @@ #include "layer.h" #include "network.h" +#ifdef __cplusplus +extern "C" { +#endif layer make_reorg_old_layer(int batch, int h, int w, int c, int stride, int reverse); void resize_reorg_old_layer(layer *l, int w, int h); void forward_reorg_old_layer(const layer l, network_state state); @@ -16,5 +19,8 @@ void forward_reorg_old_layer_gpu(layer l, network_state state); void backward_reorg_old_layer_gpu(layer l, network_state state); #endif +#ifdef __cplusplus +} #endif +#endif diff --git a/src/rnn.c b/src/rnn.c index 3fb634e4dcd..93107585c2e 100644 --- a/src/rnn.c +++ b/src/rnn.c @@ -5,7 +5,7 @@ #include "parser.h" #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" +#include #endif typedef struct { @@ -18,20 +18,20 @@ int *read_tokenized_data(char *filename, size_t *read) size_t size = 512; size_t count = 0; FILE *fp = fopen(filename, "r"); - int *d = calloc(size, sizeof(int)); + int* d = (int*)calloc(size, sizeof(int)); int n, one; one = fscanf(fp, "%d", &n); while(one == 1){ ++count; if(count > size){ size = size*2; - d = realloc(d, size*sizeof(int)); + d = (int*)realloc(d, size * sizeof(int)); } d[count-1] = n; one = fscanf(fp, "%d", &n); } fclose(fp); - d = realloc(d, count*sizeof(int)); + d = (int*)realloc(d, count * sizeof(int)); *read = count; return d; } @@ -41,26 +41,26 @@ char **read_tokens(char *filename, size_t *read) size_t size = 512; size_t count = 0; FILE *fp = fopen(filename, "r"); - char **d = calloc(size, sizeof(char *)); + char** d = (char**)calloc(size, sizeof(char*)); char *line; while((line=fgetl(fp)) != 0){ ++count; if(count > size){ size = size*2; - d = realloc(d, size*sizeof(char *)); + d = (char**)realloc(d, size * sizeof(char*)); } d[count-1] = line; } fclose(fp); - d = realloc(d, count*sizeof(char *)); + d = (char**)realloc(d, count * sizeof(char*)); *read = count; return d; } float_pair get_rnn_token_data(int *tokens, size_t *offsets, int characters, size_t len, int batch, int steps) { - float *x = calloc(batch * steps * characters, sizeof(float)); - float *y = calloc(batch * steps * characters, sizeof(float)); + float* x = (float*)calloc(batch * steps * characters, sizeof(float)); + float* y = (float*)calloc(batch * steps * characters, sizeof(float)); int i,j; for(i = 0; i < batch; ++i){ for(j = 0; j < steps; ++j){ @@ -85,8 +85,8 @@ float_pair get_rnn_token_data(int *tokens, size_t *offsets, int characters, size float_pair get_rnn_data(unsigned char *text, size_t *offsets, int characters, size_t len, int batch, int steps) { - float *x = calloc(batch * steps * characters, sizeof(float)); - float *y = calloc(batch * steps * characters, sizeof(float)); + float* x = (float*)calloc(batch * steps * characters, sizeof(float)); + float* y = (float*)calloc(batch * steps * characters, sizeof(float)); int i,j; for(i = 0; i < batch; ++i){ for(j = 0; j < steps; ++j){ @@ -141,12 +141,12 @@ void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear, size = ftell(fp); fseek(fp, 0, SEEK_SET); - text = calloc(size+1, sizeof(char)); + text = (unsigned char *)calloc(size + 1, sizeof(char)); fread(text, 1, size, fp); fclose(fp); } - char *backup_directory = "backup"; + char* backup_directory = "backup/"; char *base = basecfg(cfgfile); fprintf(stderr, "%s\n", base); float avg_loss = -1; @@ -165,7 +165,7 @@ void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear, int streams = batch/steps; printf("\n batch = %d, steps = %d, streams = %d, subdivisions = %d, text_size = %d \n", batch, steps, streams, net.subdivisions, size); printf(" global_batch = %d \n", batch*net.subdivisions); - size_t *offsets = calloc(streams, sizeof(size_t)); + size_t* offsets = (size_t*)calloc(streams, sizeof(size_t)); int j; for(j = 0; j < streams; ++j){ offsets[j] = rand_size_t()%size; @@ -248,7 +248,7 @@ void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float t for(i = 0; i < net.n; ++i) net.layers[i].temperature = temp; int c = 0; int len = strlen(seed); - float *input = calloc(inputs, sizeof(float)); + float* input = (float*)calloc(inputs, sizeof(float)); /* fill_cpu(inputs, 0, input, 1); @@ -307,7 +307,7 @@ void test_tactic_rnn(char *cfgfile, char *weightfile, int num, float temp, int r int i, j; for(i = 0; i < net.n; ++i) net.layers[i].temperature = temp; int c = 0; - float *input = calloc(inputs, sizeof(float)); + float* input = (float*)calloc(inputs, sizeof(float)); float *out = 0; while((c = getc(stdin)) != EOF){ @@ -346,7 +346,7 @@ void valid_tactic_rnn(char *cfgfile, char *weightfile, char *seed) int words = 1; int c; int len = strlen(seed); - float *input = calloc(inputs, sizeof(float)); + float* input = (float*)calloc(inputs, sizeof(float)); int i; for(i = 0; i < len; ++i){ c = seed[i]; @@ -398,7 +398,7 @@ void valid_char_rnn(char *cfgfile, char *weightfile, char *seed) int words = 1; int c; int len = strlen(seed); - float *input = calloc(inputs, sizeof(float)); + float* input = (float*)calloc(inputs, sizeof(float)); int i; for(i = 0; i < len; ++i){ c = seed[i]; @@ -437,7 +437,7 @@ void vec_char_rnn(char *cfgfile, char *weightfile, char *seed) int c; int seed_len = strlen(seed); - float *input = calloc(inputs, sizeof(float)); + float* input = (float*)calloc(inputs, sizeof(float)); int i; char *line; while((line=fgetl(stdin)) != 0){ diff --git a/src/rnn_layer.c b/src/rnn_layer.c index c5669adbedc..22aade094e0 100644 --- a/src/rnn_layer.c +++ b/src/rnn_layer.c @@ -30,7 +30,7 @@ layer make_rnn_layer(int batch, int inputs, int hidden, int outputs, int steps, { fprintf(stderr, "RNN Layer: %d inputs, %d outputs\n", inputs, outputs); batch = batch / steps; - layer l = {0}; + layer l = { (LAYER_TYPE)0 }; l.batch = batch; l.type = RNN; l.steps = steps; @@ -40,21 +40,21 @@ layer make_rnn_layer(int batch, int inputs, int hidden, int outputs, int steps, l.out_h = 1; l.out_c = outputs; - l.state = calloc(batch*hidden*(steps+1), sizeof(float)); + l.state = (float*)calloc(batch * hidden * (steps + 1), sizeof(float)); - l.input_layer = malloc(sizeof(layer)); + l.input_layer = (layer*)malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.input_layer) = make_connected_layer(batch, steps, inputs, hidden, activation, batch_normalize); l.input_layer->batch = batch; if (l.workspace_size < l.input_layer->workspace_size) l.workspace_size = l.input_layer->workspace_size; - l.self_layer = malloc(sizeof(layer)); + l.self_layer = (layer*)malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.self_layer) = make_connected_layer(batch, steps, hidden, hidden, (log==2)?LOGGY:(log==1?LOGISTIC:activation), batch_normalize); l.self_layer->batch = batch; if (l.workspace_size < l.self_layer->workspace_size) l.workspace_size = l.self_layer->workspace_size; - l.output_layer = malloc(sizeof(layer)); + l.output_layer = (layer*)malloc(sizeof(layer)); fprintf(stderr, "\t\t"); *(l.output_layer) = make_connected_layer(batch, steps, hidden, outputs, activation, batch_normalize); l.output_layer->batch = batch; diff --git a/src/rnn_layer.h b/src/rnn_layer.h index bb9478b9e26..43b57d82e0f 100644 --- a/src/rnn_layer.h +++ b/src/rnn_layer.h @@ -7,6 +7,9 @@ #include "network.h" #define USET +#ifdef __cplusplus +extern "C" { +#endif layer make_rnn_layer(int batch, int inputs, int hidden, int outputs, int steps, ACTIVATION activation, int batch_normalize, int log); void forward_rnn_layer(layer l, network_state state); @@ -21,5 +24,8 @@ void push_rnn_layer(layer l); void pull_rnn_layer(layer l); #endif +#ifdef __cplusplus +} #endif +#endif diff --git a/src/rnn_vid.c b/src/rnn_vid.c index b9ea8a35e68..c0fd3d36735 100644 --- a/src/rnn_vid.c +++ b/src/rnn_vid.c @@ -5,10 +5,10 @@ #include "blas.h" #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#include "opencv2/core/version.hpp" +#include +#include #ifndef CV_VERSION_EPOCH -#include "opencv2/videoio/videoio_c.h" +#include #endif image get_image_from_stream(CvCapture *cap); image ipl_to_image(IplImage* src); @@ -28,10 +28,10 @@ float_pair get_rnn_vid_data(network net, char **files, int n, int batch, int ste image out_im = get_network_image(net); int output_size = out_im.w*out_im.h*out_im.c; printf("%d %d %d\n", out_im.w, out_im.h, out_im.c); - float *feats = calloc(net.batch*batch*output_size, sizeof(float)); + float* feats = (float*)calloc(net.batch * batch * output_size, sizeof(float)); for(b = 0; b < batch; ++b){ int input_size = net.w*net.h*net.c; - float *input = calloc(input_size*net.batch, sizeof(float)); + float* input = (float*)calloc(input_size * net.batch, sizeof(float)); char *filename = files[rand()%n]; CvCapture *cap = cvCaptureFromFile(filename); int frames = cvGetCaptureProperty(cap, CV_CAP_PROP_FRAME_COUNT); @@ -80,7 +80,7 @@ float_pair get_rnn_vid_data(network net, char **files, int n, int batch, int ste void train_vid_rnn(char *cfgfile, char *weightfile) { char *train_videos = "data/vid/train.txt"; - char *backup_directory = "/home/pjreddie/backup/"; + char* backup_directory = "backup/"; srand(time(0)); char *base = basecfg(cfgfile); printf("%s\n", base); @@ -101,7 +101,7 @@ void train_vid_rnn(char *cfgfile, char *weightfile) int batch = net.batch / net.time_steps; network extractor = parse_network_cfg("cfg/extractor.cfg"); - load_weights(&extractor, "/home/pjreddie/trained/yolo-coco.conv"); + load_weights(&extractor, "trained/yolo-coco.conv"); while(get_current_batch(net) < net.max_batches){ i += 1; @@ -154,7 +154,7 @@ image save_reconstruction(network net, image *init, float *feat, char *name, int void generate_vid_rnn(char *cfgfile, char *weightfile) { network extractor = parse_network_cfg("cfg/extractor.recon.cfg"); - load_weights(&extractor, "/home/pjreddie/trained/yolo-coco.conv"); + load_weights(&extractor, "trained/yolo-coco.conv"); network net = parse_network_cfg(cfgfile); if(weightfile){ @@ -164,7 +164,7 @@ void generate_vid_rnn(char *cfgfile, char *weightfile) set_batch_network(&net, 1); int i; - CvCapture *cap = cvCaptureFromFile("/extra/vid/ILSVRC2015/Data/VID/snippets/val/ILSVRC2015_val_00007030.mp4"); + CvCapture* cap = cvCaptureFromFile("extra/vid/ILSVRC2015/Data/VID/snippets/val/ILSVRC2015_val_00007030.mp4"); float *feat; float *next; next = NULL; @@ -191,9 +191,9 @@ void generate_vid_rnn(char *cfgfile, char *weightfile) } for(i = 0; i < 30; ++i){ next = network_predict(net, next); - image new = save_reconstruction(extractor, &last, next, "new", i); + image newimage = save_reconstruction(extractor, &last, next, "newimage", i); free_image(last); - last = new; + last = newimage; } } diff --git a/src/route_layer.c b/src/route_layer.c index db1ac20658c..8bd6817aafa 100644 --- a/src/route_layer.c +++ b/src/route_layer.c @@ -6,7 +6,7 @@ route_layer make_route_layer(int batch, int n, int *input_layers, int *input_sizes) { fprintf(stderr,"route "); - route_layer l = {0}; + route_layer l = { (LAYER_TYPE)0 }; l.type = ROUTE; l.batch = batch; l.n = n; @@ -21,8 +21,8 @@ route_layer make_route_layer(int batch, int n, int *input_layers, int *input_siz fprintf(stderr, "\n"); l.outputs = outputs; l.inputs = outputs; - l.delta = calloc(outputs*batch, sizeof(float)); - l.output = calloc(outputs*batch, sizeof(float));; + l.delta = (float*)calloc(outputs * batch, sizeof(float)); + l.output = (float*)calloc(outputs * batch, sizeof(float)); l.forward = forward_route_layer; l.backward = backward_route_layer; @@ -58,8 +58,8 @@ void resize_route_layer(route_layer *l, network *net) } } l->inputs = l->outputs; - l->delta = realloc(l->delta, l->outputs*l->batch*sizeof(float)); - l->output = realloc(l->output, l->outputs*l->batch*sizeof(float)); + l->delta = (float*)realloc(l->delta, l->outputs * l->batch * sizeof(float)); + l->output = (float*)realloc(l->output, l->outputs * l->batch * sizeof(float)); #ifdef GPU cuda_free(l->output_gpu); diff --git a/src/route_layer.h b/src/route_layer.h index 45467d953f1..3ee9019385f 100644 --- a/src/route_layer.h +++ b/src/route_layer.h @@ -5,6 +5,9 @@ typedef layer route_layer; +#ifdef __cplusplus +extern "C" { +#endif route_layer make_route_layer(int batch, int n, int *input_layers, int *input_size); void forward_route_layer(const route_layer l, network_state state); void backward_route_layer(const route_layer l, network_state state); @@ -15,4 +18,7 @@ void forward_route_layer_gpu(const route_layer l, network_state state); void backward_route_layer_gpu(const route_layer l, network_state state); #endif +#ifdef __cplusplus +} +#endif #endif diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index 263ee1f4879..b84b72f22b2 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -7,7 +7,7 @@ layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2) { fprintf(stderr,"Shortcut Layer: %d\n", index); - layer l = {0}; + layer l = { (LAYER_TYPE)0 }; l.type = SHORTCUT; l.batch = batch; l.w = w2; @@ -21,8 +21,8 @@ layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int l.index = index; - l.delta = calloc(l.outputs*batch, sizeof(float)); - l.output = calloc(l.outputs*batch, sizeof(float));; + l.delta = (float*)calloc(l.outputs * batch, sizeof(float)); + l.output = (float*)calloc(l.outputs * batch, sizeof(float)); l.forward = forward_shortcut_layer; l.backward = backward_shortcut_layer; @@ -44,8 +44,8 @@ void resize_shortcut_layer(layer *l, int w, int h) l->h = l->out_h = h; l->outputs = w*h*l->out_c; l->inputs = l->outputs; - l->delta = realloc(l->delta, l->outputs*l->batch * sizeof(float)); - l->output = realloc(l->output, l->outputs*l->batch * sizeof(float)); + l->delta = (float*)realloc(l->delta, l->outputs * l->batch * sizeof(float)); + l->output = (float*)realloc(l->output, l->outputs * l->batch * sizeof(float)); #ifdef GPU cuda_free(l->output_gpu); diff --git a/src/shortcut_layer.h b/src/shortcut_layer.h index 912d72e0e25..b24aa3e6682 100644 --- a/src/shortcut_layer.h +++ b/src/shortcut_layer.h @@ -4,6 +4,9 @@ #include "layer.h" #include "network.h" +#ifdef __cplusplus +extern "C" { +#endif layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2); void forward_shortcut_layer(const layer l, network_state state); void backward_shortcut_layer(const layer l, network_state state); @@ -14,4 +17,7 @@ void forward_shortcut_layer_gpu(const layer l, network_state state); void backward_shortcut_layer_gpu(const layer l, network_state state); #endif +#ifdef __cplusplus +} +#endif #endif diff --git a/src/softmax_layer.c b/src/softmax_layer.c index bfe34bc1ccd..3efe01572a4 100644 --- a/src/softmax_layer.c +++ b/src/softmax_layer.c @@ -30,16 +30,16 @@ softmax_layer make_softmax_layer(int batch, int inputs, int groups) { assert(inputs%groups == 0); fprintf(stderr, "softmax %4d\n", inputs); - softmax_layer l = {0}; + softmax_layer l = { (LAYER_TYPE)0 }; l.type = SOFTMAX; l.batch = batch; l.groups = groups; l.inputs = inputs; l.outputs = inputs; - l.loss = calloc(inputs*batch, sizeof(float)); - l.output = calloc(inputs*batch, sizeof(float)); - l.delta = calloc(inputs*batch, sizeof(float)); - l.cost = calloc(1, sizeof(float)); + l.loss = (float*)calloc(inputs * batch, sizeof(float)); + l.output = (float*)calloc(inputs * batch, sizeof(float)); + l.delta = (float*)calloc(inputs * batch, sizeof(float)); + l.cost = (float*)calloc(1, sizeof(float)); l.forward = forward_softmax_layer; l.backward = backward_softmax_layer; diff --git a/src/softmax_layer.h b/src/softmax_layer.h index 821a8dd7387..55e622c1824 100644 --- a/src/softmax_layer.h +++ b/src/softmax_layer.h @@ -5,6 +5,9 @@ typedef layer softmax_layer; +#ifdef __cplusplus +extern "C" { +#endif void softmax_array(float *input, int n, float temp, float *output); softmax_layer make_softmax_layer(int batch, int inputs, int groups); void forward_softmax_layer(const softmax_layer l, network_state state); @@ -16,4 +19,7 @@ void forward_softmax_layer_gpu(const softmax_layer l, network_state state); void backward_softmax_layer_gpu(const softmax_layer l, network_state state); #endif +#ifdef __cplusplus +} +#endif #endif diff --git a/src/super.c b/src/super.c index 63e9860a83b..0f96fddb937 100644 --- a/src/super.c +++ b/src/super.c @@ -4,13 +4,13 @@ #include "parser.h" #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" +#include #endif void train_super(char *cfgfile, char *weightfile) { - char *train_images = "/data/imagenet/imagenet1k.train.list"; - char *backup_directory = "/home/pjreddie/backup/"; + char* train_images = "data/imagenet/imagenet1k.train.list"; + char* backup_directory = "backup/"; srand(time(0)); char *base = basecfg(cfgfile); printf("%s\n", base); diff --git a/src/swag.c b/src/swag.c index 2cb3093b751..d50200072c1 100644 --- a/src/swag.c +++ b/src/swag.c @@ -6,13 +6,13 @@ #include "box.h" #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" +#include #endif void train_swag(char *cfgfile, char *weightfile) { char *train_images = "data/voc.0712.trainval"; - char *backup_directory = "/home/pjreddie/backup/"; + char* backup_directory = "backup/"; srand(time(0)); char *base = basecfg(cfgfile); printf("%s\n", base); diff --git a/src/tag.c b/src/tag.c index 1e43e7d9ec7..f6cbc0fb5cc 100644 --- a/src/tag.c +++ b/src/tag.c @@ -3,7 +3,7 @@ #include "parser.h" #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" +#include #endif void train_tag(char *cfgfile, char *weightfile, int clear) @@ -11,7 +11,7 @@ void train_tag(char *cfgfile, char *weightfile, int clear) srand(time(0)); float avg_loss = -1; char *base = basecfg(cfgfile); - char *backup_directory = "/home/pjreddie/backup/"; + char* backup_directory = "backup/"; printf("%s\n", base); network net = parse_network_cfg(cfgfile); if(weightfile){ @@ -20,7 +20,7 @@ void train_tag(char *cfgfile, char *weightfile, int clear) if(clear) *net.seen = 0; printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); int imgs = 1024; - list *plist = get_paths("/home/pjreddie/tag/train.list"); + list* plist = get_paths("tag/train.list"); char **paths = (char **)list_to_array(plist); printf("%d\n", plist->size); int N = plist->size; diff --git a/src/tree.c b/src/tree.c index d66da9f828e..0b320e3bcd9 100644 --- a/src/tree.c +++ b/src/tree.c @@ -93,42 +93,42 @@ tree *read_tree(char *filename) int groups = 0; int n = 0; while((line=fgetl(fp)) != 0){ - char *id = calloc(256, sizeof(char)); + char* id = (char*)calloc(256, sizeof(char)); int parent = -1; sscanf(line, "%s %d", id, &parent); - t.parent = realloc(t.parent, (n+1)*sizeof(int)); + t.parent = (int*)realloc(t.parent, (n + 1) * sizeof(int)); t.parent[n] = parent; - t.name = realloc(t.name, (n+1)*sizeof(char *)); + t.name = (char**)realloc(t.name, (n + 1) * sizeof(char*)); t.name[n] = id; if(parent != last_parent){ ++groups; - t.group_offset = realloc(t.group_offset, groups * sizeof(int)); + t.group_offset = (int*)realloc(t.group_offset, groups * sizeof(int)); t.group_offset[groups - 1] = n - group_size; - t.group_size = realloc(t.group_size, groups * sizeof(int)); + t.group_size = (int*)realloc(t.group_size, groups * sizeof(int)); t.group_size[groups - 1] = group_size; group_size = 0; last_parent = parent; } - t.group = realloc(t.group, (n+1)*sizeof(int)); + t.group = (int*)realloc(t.group, (n + 1) * sizeof(int)); t.group[n] = groups; ++n; ++group_size; } ++groups; - t.group_offset = realloc(t.group_offset, groups * sizeof(int)); + t.group_offset = (int*)realloc(t.group_offset, groups * sizeof(int)); t.group_offset[groups - 1] = n - group_size; - t.group_size = realloc(t.group_size, groups * sizeof(int)); + t.group_size = (int*)realloc(t.group_size, groups * sizeof(int)); t.group_size[groups - 1] = group_size; t.n = n; t.groups = groups; - t.leaf = calloc(n, sizeof(int)); + t.leaf = (int*)calloc(n, sizeof(int)); int i; for(i = 0; i < n; ++i) t.leaf[i] = 1; for(i = 0; i < n; ++i) if(t.parent[i] >= 0) t.leaf[t.parent[i]] = 0; fclose(fp); - tree *tree_ptr = calloc(1, sizeof(tree)); + tree* tree_ptr = (tree*)calloc(1, sizeof(tree)); *tree_ptr = t; //error(0); return tree_ptr; diff --git a/src/tree.h b/src/tree.h index b44d3f4aa42..86379738710 100644 --- a/src/tree.h +++ b/src/tree.h @@ -15,10 +15,16 @@ // int *group_offset; //} tree; +#ifdef __cplusplus +extern "C" { +#endif //tree *read_tree(char *filename); int hierarchy_top_prediction(float *predictions, tree *hier, float thresh, int stride); void hierarchy_predictions(float *predictions, int n, tree *hier, int only_leaves); void change_leaves(tree *t, char *leaf_list); float get_hierarchy_probability(float *x, tree *hier, int c); +#ifdef __cplusplus +} +#endif #endif diff --git a/src/upsample_layer.c b/src/upsample_layer.c index 1aa5a150793..dafc1b8d813 100644 --- a/src/upsample_layer.c +++ b/src/upsample_layer.c @@ -6,7 +6,7 @@ layer make_upsample_layer(int batch, int w, int h, int c, int stride) { - layer l = {0}; + layer l = { (LAYER_TYPE)0 }; l.type = UPSAMPLE; l.batch = batch; l.w = w; @@ -24,8 +24,8 @@ layer make_upsample_layer(int batch, int w, int h, int c, int stride) l.stride = stride; l.outputs = l.out_w*l.out_h*l.out_c; l.inputs = l.w*l.h*l.c; - l.delta = calloc(l.outputs*batch, sizeof(float)); - l.output = calloc(l.outputs*batch, sizeof(float));; + l.delta = (float*)calloc(l.outputs * batch, sizeof(float)); + l.output = (float*)calloc(l.outputs * batch, sizeof(float)); l.forward = forward_upsample_layer; l.backward = backward_upsample_layer; @@ -53,8 +53,8 @@ void resize_upsample_layer(layer *l, int w, int h) } l->outputs = l->out_w*l->out_h*l->out_c; l->inputs = l->h*l->w*l->c; - l->delta = realloc(l->delta, l->outputs*l->batch*sizeof(float)); - l->output = realloc(l->output, l->outputs*l->batch*sizeof(float)); + l->delta = (float*)realloc(l->delta, l->outputs * l->batch * sizeof(float)); + l->output = (float*)realloc(l->output, l->outputs * l->batch * sizeof(float)); #ifdef GPU cuda_free(l->output_gpu); diff --git a/src/upsample_layer.h b/src/upsample_layer.h index aea9434db0a..68aff32978b 100644 --- a/src/upsample_layer.h +++ b/src/upsample_layer.h @@ -4,6 +4,9 @@ #include "layer.h" #include "network.h" +#ifdef __cplusplus +extern "C" { +#endif layer make_upsample_layer(int batch, int w, int h, int c, int stride); void forward_upsample_layer(const layer l, network_state state); void backward_upsample_layer(const layer l, network_state state); @@ -14,4 +17,7 @@ void forward_upsample_layer_gpu(const layer l, network_state state); void backward_upsample_layer_gpu(const layer l, network_state state); #endif +#ifdef __cplusplus +} +#endif #endif diff --git a/src/utils.c b/src/utils.c index d20d4fb3013..6614f1a8cb3 100644 --- a/src/utils.c +++ b/src/utils.c @@ -1,3 +1,4 @@ +#include "utils.h" #include #include #include @@ -5,16 +6,13 @@ #include #include #include +#include "darkunistd.h" #ifdef WIN32 -#include "unistd.h" #include "gettimeofday.h" #else -#include #include #endif -#include "utils.h" -#pragma warning(disable: 4996) double what_time_is_it_now() { @@ -34,7 +32,7 @@ int *read_map(char *filename) if(!file) file_error(filename); while((str=fgetl(file))){ ++n; - map = realloc(map, n*sizeof(int)); + map = (int*)realloc(map, n * sizeof(int)); map[n-1] = atoi(str); } return map; @@ -54,7 +52,7 @@ void sorta_shuffle(void *arr, size_t n, size_t size, size_t sections) void shuffle(void *arr, size_t n, size_t size) { size_t i; - void *swp = calloc(1, size); + void* swp = (void*)calloc(1, size); for(i = 0; i < n-1; ++i){ size_t j = i + rand()/(RAND_MAX / (n-i)+1); memcpy(swp, (char*)arr+(j*size), size); @@ -167,9 +165,9 @@ void pm(int M, int N, float *A) printf("\n"); } -void find_replace(char *str, char *orig, char *rep, char *output) +void find_replace(const char* str, char* orig, char* rep, char* output) { - char *buffer = calloc(8192, sizeof(char)); + char* buffer = (char*)calloc(8192, sizeof(char)); char *p; sprintf(buffer, "%s", str); @@ -187,7 +185,7 @@ void find_replace(char *str, char *orig, char *rep, char *output) void trim(char *str) { - char *buffer = calloc(8192, sizeof(char)); + char* buffer = (char*)calloc(8192, sizeof(char)); sprintf(buffer, "%s", str); char *p = buffer; @@ -205,7 +203,7 @@ void trim(char *str) void find_replace_extension(char *str, char *orig, char *rep, char *output) { - char *buffer = calloc(8192, sizeof(char)); + char* buffer = (char*)calloc(8192, sizeof(char)); sprintf(buffer, "%s", str); char *p = strstr(buffer, orig); @@ -222,7 +220,7 @@ void find_replace_extension(char *str, char *orig, char *rep, char *output) free(buffer); } -void replace_image_to_label(char *input_path, char *output_path) +void replace_image_to_label(const char* input_path, char* output_path) { find_replace(input_path, "/images/train2014/", "/labels/train2014/", output_path); // COCO find_replace(output_path, "/images/val2014/", "/labels/val2014/", output_path); // COCO @@ -356,7 +354,7 @@ char *fgetl(FILE *fp) { if(feof(fp)) return 0; size_t size = 512; - char *line = malloc(size*sizeof(char)); + char* line = (char*)malloc(size * sizeof(char)); if(!fgets(line, size, fp)){ free(line); return 0; @@ -367,7 +365,7 @@ char *fgetl(FILE *fp) while((line[curr-1] != '\n') && !feof(fp)){ if(curr == size-1){ size *= 2; - line = realloc(line, size*sizeof(char)); + line = (char*)realloc(line, size * sizeof(char)); if(!line) { printf("%ld\n", size); malloc_error(); @@ -446,7 +444,7 @@ void write_all(int fd, char *buffer, size_t bytes) char *copy_string(char *s) { - char *copy = malloc(strlen(s)+1); + char* copy = (char*)malloc(strlen(s) + 1); strncpy(copy, s, strlen(s)+1); return copy; } @@ -482,7 +480,7 @@ int count_fields(char *line) float *parse_fields(char *line, int n) { - float *field = calloc(n, sizeof(float)); + float* field = (float*)calloc(n, sizeof(float)); char *c, *p, *end; int count = 0; int done = 0; @@ -656,8 +654,8 @@ int max_index(float *a, int n) int top_max_index(float *a, int n, int k) { - float *values = calloc(k, sizeof(float)); - int *indexes = calloc(k, sizeof(int)); + float *values = (float*)calloc(k, sizeof(float)); + int *indexes = (int*)calloc(k, sizeof(int)); if (n <= 0) return -1; int i, j; for (i = 0; i < n; ++i) { @@ -716,7 +714,7 @@ float rand_normal() rand1 = rand() / ((double) RAND_MAX); if(rand1 < 1e-100) rand1 = 1e-100; rand1 = -2 * log(rand1); - rand2 = (rand() / ((double) RAND_MAX)) * TWO_PI; + rand2 = (rand() / ((double)RAND_MAX)) * 2.0 * M_PI; return sqrt(rand1) * cos(rand2); } @@ -765,9 +763,9 @@ float rand_scale(float s) float **one_hot_encode(float *a, int n, int k) { int i; - float **t = calloc(n, sizeof(float*)); + float** t = (float**)calloc(n, sizeof(float*)); for(i = 0; i < n; ++i){ - t[i] = calloc(k, sizeof(float)); + t[i] = (float*)calloc(k, sizeof(float)); int index = (int)a[i]; t[i][index] = 1; } @@ -802,4 +800,4 @@ float rand_uniform_strong(float min, float max) max = swap; } return (random_float() * (max - min)) + min; -} \ No newline at end of file +} diff --git a/src/utils.h b/src/utils.h index 8ef31263be2..f00ed2d56d6 100644 --- a/src/utils.h +++ b/src/utils.h @@ -4,25 +4,9 @@ #include #include "list.h" -#if defined(_MSC_VER) && _MSC_VER < 1900 - #define snprintf(buf,len, format,...) _snprintf_s(buf, len,len, format, __VA_ARGS__) -#endif - -#define SECRET_NUM -1234 -#define TWO_PI 6.2831853071795864769252866 - -#ifdef LIB_EXPORTS -#if defined(_MSC_VER) -#define LIB_API __declspec(dllexport) -#else -#define LIB_API __attribute__((visibility("default"))) -#endif -#else -#if defined(_MSC_VER) -#define LIB_API -#else -#define LIB_API -#endif +#include "darknet.h" +#ifdef __cplusplus +extern "C" { #endif LIB_API void free_ptrs(void **ptrs, int n); @@ -41,8 +25,8 @@ void read_all(int fd, char *buffer, size_t bytes); void write_all(int fd, char *buffer, size_t bytes); int read_all_fail(int fd, char *buffer, size_t bytes); int write_all_fail(int fd, char *buffer, size_t bytes); -void find_replace(char *str, char *orig, char *rep, char *output); -void replace_image_to_label(char *input_path, char *output_path); +LIB_API void find_replace(const char* str, char* orig, char* rep, char* output); +void replace_image_to_label(const char* input_path, char* output_path); void error(const char *s); void malloc_error(); void file_error(char *s); @@ -88,5 +72,8 @@ float random_float(); float rand_uniform_strong(float min, float max); int int_index(int *a, int val, int n); +#ifdef __cplusplus +} #endif +#endif diff --git a/src/voxel.c b/src/voxel.c index 476663ff85b..57fb17b6dd4 100644 --- a/src/voxel.c +++ b/src/voxel.c @@ -4,10 +4,10 @@ #include "parser.h" #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#include "opencv2/core/version.hpp" +#include +#include #ifndef CV_VERSION_EPOCH -#include "opencv2/videoio/videoio_c.h" +#include #endif image get_image_from_stream(CvCapture *cap); #endif @@ -50,8 +50,8 @@ void extract_voxel(char *lfile, char *rfile, char *prefix) void train_voxel(char *cfgfile, char *weightfile) { - char *train_images = "/data/imagenet/imagenet1k.train.list"; - char *backup_directory = "/home/pjreddie/backup/"; + char* train_images = "data/imagenet/imagenet1k.train.list"; + char* backup_directory = "backup/"; srand(time(0)); char *base = basecfg(cfgfile); printf("%s\n", base); diff --git a/src/writing.c b/src/writing.c index 0a76d48e54a..b6c49075f69 100644 --- a/src/writing.c +++ b/src/writing.c @@ -3,12 +3,12 @@ #include "parser.h" #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" +#include #endif void train_writing(char *cfgfile, char *weightfile) { - char *backup_directory = "/home/pjreddie/backup/"; + char* backup_directory = "backup/"; srand(time(0)); float avg_loss = -1; char *base = basecfg(cfgfile); diff --git a/src/yolo.c b/src/yolo.c index 56eb814e164..fc7d8a6edd3 100644 --- a/src/yolo.c +++ b/src/yolo.c @@ -7,11 +7,11 @@ #include "demo.h" #ifdef OPENCV -#include "opencv2/highgui/highgui_c.h" -#include "opencv2/imgproc/imgproc_c.h" -#include "opencv2/core/version.hpp" +#include +#include +#include #ifndef CV_VERSION_EPOCH -#include "opencv2/videoio/videoio_c.h" +#include #endif #endif @@ -19,8 +19,8 @@ char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "c void train_yolo(char *cfgfile, char *weightfile) { - char *train_images = "/data/voc/train.txt"; - char *backup_directory = "/home/pjreddie/backup/"; + char* train_images = "data/voc/train.txt"; + char* backup_directory = "backup/"; srand(time(0)); char *base = basecfg(cfgfile); printf("%s\n", base); @@ -125,7 +125,7 @@ void validate_yolo(char *cfgfile, char *weightfile) char *base = "results/comp4_det_test_"; //list *plist = get_paths("data/voc.2007.test"); - list *plist = get_paths("/home/pjreddie/data/voc/2007_test.txt"); + list* plist = get_paths("data/voc/2007_test.txt"); //list *plist = get_paths("data/voc.2012.test"); char **paths = (char **)list_to_array(plist); @@ -133,15 +133,15 @@ void validate_yolo(char *cfgfile, char *weightfile) int classes = l.classes; int j; - FILE **fps = calloc(classes, sizeof(FILE *)); + FILE** fps = (FILE**)calloc(classes, sizeof(FILE*)); for(j = 0; j < classes; ++j){ char buff[1024]; snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]); fps[j] = fopen(buff, "w"); } - box *boxes = calloc(l.side*l.side*l.n, sizeof(box)); - float **probs = calloc(l.side*l.side*l.n, sizeof(float *)); - for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *)); + box* boxes = (box*)calloc(l.side * l.side * l.n, sizeof(box)); + float** probs = (float**)calloc(l.side * l.side * l.n, sizeof(float*)); + for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = (float*)calloc(classes, sizeof(float)); int m = plist->size; int i=0; @@ -152,11 +152,11 @@ void validate_yolo(char *cfgfile, char *weightfile) float iou_thresh = .5; int nthreads = 8; - image *val = calloc(nthreads, sizeof(image)); - image *val_resized = calloc(nthreads, sizeof(image)); - image *buf = calloc(nthreads, sizeof(image)); - image *buf_resized = calloc(nthreads, sizeof(image)); - pthread_t *thr = calloc(nthreads, sizeof(pthread_t)); + image* val = (image*)calloc(nthreads, sizeof(image)); + image* val_resized = (image*)calloc(nthreads, sizeof(image)); + image* buf = (image*)calloc(nthreads, sizeof(image)); + image* buf_resized = (image*)calloc(nthreads, sizeof(image)); + pthread_t* thr = (pthread_t*)calloc(nthreads, sizeof(pthread_t)); load_args args = {0}; args.w = net.w; @@ -220,15 +220,15 @@ void validate_yolo_recall(char *cfgfile, char *weightfile) int side = l.side; int j, k; - FILE **fps = calloc(classes, sizeof(FILE *)); + FILE** fps = (FILE**)calloc(classes, sizeof(FILE*)); for(j = 0; j < classes; ++j){ char buff[1024]; snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]); fps[j] = fopen(buff, "w"); } - box *boxes = calloc(side*side*l.n, sizeof(box)); - float **probs = calloc(side*side*l.n, sizeof(float *)); - for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *)); + box* boxes = (box*)calloc(side * side * l.n, sizeof(box)); + float** probs = (float**)calloc(side * side * l.n, sizeof(float*)); + for(j = 0; j < side*side*l.n; ++j) probs[j] = (float*)calloc(classes, sizeof(float)); int m = plist->size; int i=0; @@ -299,9 +299,9 @@ void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh) char *input = buff; int j; float nms=.4; - box *boxes = calloc(l.side*l.side*l.n, sizeof(box)); - float **probs = calloc(l.side*l.side*l.n, sizeof(float *)); - for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *)); + box* boxes = (box*)calloc(l.side * l.side * l.n, sizeof(box)); + float** probs = (float**)calloc(l.side * l.side * l.n, sizeof(float*)); + for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = (float*)calloc(l.classes, sizeof(float)); while(1){ if(filename){ strncpy(input, filename, 256); diff --git a/src/yolo_console_dll.cpp b/src/yolo_console_dll.cpp index a724b3e503e..c4554fbad46 100644 --- a/src/yolo_console_dll.cpp +++ b/src/yolo_console_dll.cpp @@ -1,5 +1,5 @@ #include -#include +#include #include #include #include @@ -9,10 +9,6 @@ #include // std::mutex, std::unique_lock #include // std::condition_variable -#ifdef _WIN32 -#define OPENCV -#define GPU -#endif // To use tracking - uncomment the following line. Tracking is supported only by OpenCV 3.x //#define TRACK_OPTFLOW @@ -25,10 +21,11 @@ #ifdef OPENCV #include // C++ -#include "opencv2/core/version.hpp" +#include #ifndef CV_VERSION_EPOCH -#include "opencv2/videoio/videoio.hpp" +#include #define OPENCV_VERSION CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR)"" CVAUX_STR(CV_VERSION_REVISION) +#ifndef USE_CMAKE_LIBS #pragma comment(lib, "opencv_world" OPENCV_VERSION ".lib") #ifdef TRACK_OPTFLOW #pragma comment(lib, "opencv_cudaoptflow" OPENCV_VERSION ".lib") @@ -37,11 +34,14 @@ #pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib") #endif // TRACK_OPTFLOW +#endif // USE_CMAKE_LIBS #else #define OPENCV_VERSION CVAUX_STR(CV_VERSION_EPOCH)""CVAUX_STR(CV_VERSION_MAJOR)""CVAUX_STR(CV_VERSION_MINOR) +#ifndef USE_CMAKE_LIBS #pragma comment(lib, "opencv_core" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib") +#endif // USE_CMAKE_LIBS #endif // CV_VERSION_EPOCH class track_kalman { @@ -146,10 +146,10 @@ class extrapolate_coords_t { //else std::cout << "shakin \n"; if (dx_vec[index] > 1000 || dy_vec[index] > 1000) { - //std::cout << "!!! bad dx or dy, dx = " << dx_vec[index] << ", dy = " << dy_vec[index] << + //std::cout << "!!! bad dx or dy, dx = " << dx_vec[index] << ", dy = " << dy_vec[index] << // ", delta_time = " << delta_time << ", update = " << update << std::endl; dx_vec[index] = 0; - dy_vec[index] = 0; + dy_vec[index] = 0; } old_result_vec[k].x = new_result_vec[i].x; old_result_vec[k].y = new_result_vec[i].y; @@ -178,7 +178,7 @@ class extrapolate_coords_t { }; -void draw_boxes(cv::Mat mat_img, std::vector result_vec, std::vector obj_names, +void draw_boxes(cv::Mat mat_img, std::vector result_vec, std::vector obj_names, int current_det_fps = -1, int current_cap_fps = -1) { int const colors[6][3] = { { 1,0,1 },{ 0,0,1 },{ 0,1,1 },{ 0,1,0 },{ 1,1,0 },{ 1,0,0 } }; @@ -191,8 +191,8 @@ void draw_boxes(cv::Mat mat_img, std::vector result_vec, std::vector 0) obj_name += " - " + std::to_string(i.track_id); cv::Size const text_size = getTextSize(obj_name, cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, 2, 0); int const max_width = (text_size.width > i.w + 2) ? text_size.width : (i.w + 2); - cv::rectangle(mat_img, cv::Point2f(std::max((int)i.x - 1, 0), std::max((int)i.y - 30, 0)), - cv::Point2f(std::min((int)i.x + max_width, mat_img.cols-1), std::min((int)i.y, mat_img.rows-1)), + cv::rectangle(mat_img, cv::Point2f(std::max((int)i.x - 1, 0), std::max((int)i.y - 30, 0)), + cv::Point2f(std::min((int)i.x + max_width, mat_img.cols-1), std::min((int)i.y, mat_img.rows-1)), color, CV_FILLED, 8, 0); putText(mat_img, obj_name, cv::Point2f(i.x, i.y - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, cv::Scalar(0, 0, 0), 2); } @@ -208,7 +208,7 @@ void draw_boxes(cv::Mat mat_img, std::vector result_vec, std::vector const result_vec, std::vector const obj_names) { for (auto &i : result_vec) { if (obj_names.size() > i.obj_id) std::cout << obj_names[i.obj_id] << " - "; - std::cout << "obj_id = " << i.obj_id << ", x = " << i.x << ", y = " << i.y + std::cout << "obj_id = " << i.obj_id << ", x = " << i.x << ", y = " << i.y << ", w = " << i.w << ", h = " << i.h << std::setprecision(3) << ", prob = " << i.prob << std::endl; } @@ -231,7 +231,7 @@ int main(int argc, char *argv[]) std::string weights_file = "yolov3.weights"; std::string filename; - if (argc > 4) { //voc.names yolo-voc.cfg yolo-voc.weights test.mp4 + if (argc > 4) { //voc.names yolo-voc.cfg yolo-voc.weights test.mp4 names_file = argv[1]; cfg_file = argv[2]; weights_file = argv[3]; @@ -251,12 +251,12 @@ int main(int argc, char *argv[]) detector.wait_stream = true; #endif - while (true) - { + while (true) + { std::cout << "input image or video filename: "; if(filename.size() == 0) std::cin >> filename; if (filename.size() == 0) break; - + try { #ifdef OPENCV extrapolate_coords_t extrapolate_coords; @@ -295,7 +295,7 @@ int main(int argc, char *argv[]) if (save_output_videofile) output_video.open(out_videofile, CV_FOURCC('D', 'I', 'V', 'X'), std::max(35, video_fps), frame_size, true); - while (!cur_frame.empty()) + while (!cur_frame.empty()) { // always sync if (t_cap.joinable()) { @@ -337,7 +337,7 @@ int main(int argc, char *argv[]) extrapolate_coords.update_result(result_vec, cur_time_extrapolate - 1); } #else - result_vec = detector.tracking_id(result_vec); // comment it - if track_id is not required + result_vec = detector.tracking_id(result_vec); // comment it - if track_id is not required extrapolate_coords.new_result(result_vec, cur_time_extrapolate - 1); #endif // add old tracked objects @@ -366,7 +366,7 @@ int main(int argc, char *argv[]) auto current_image = det_image; consumed = true; while (current_image.use_count() > 0 && !exit_flag) { - auto result = detector.detect_resized(*current_image, frame_size.width, frame_size.height, + auto result = detector.detect_resized(*current_image, frame_size.width, frame_size.height, thresh, false); // true ++fps_det_counter; std::unique_lock lock(mtx); @@ -399,7 +399,7 @@ int main(int argc, char *argv[]) result_vec = tracker_flow.tracking_flow(cur_frame); // track optical flow extrapolate_coords.update_result(result_vec, cur_time_extrapolate); small_preview.draw(cur_frame, show_small_boxes); -#endif +#endif auto result_vec_draw = result_vec; if (extrapolate_flag) { result_vec_draw = extrapolate_coords.predict(cur_time_extrapolate); @@ -420,7 +420,7 @@ int main(int argc, char *argv[]) if (t_videowrite.joinable()) t_videowrite.join(); write_frame = cur_frame.clone(); videowrite_ready = false; - t_videowrite = std::thread([&]() { + t_videowrite = std::thread([&]() { output_video << write_frame; videowrite_ready = true; }); } @@ -444,7 +444,7 @@ int main(int argc, char *argv[]) else if (file_ext == "txt") { // list of image files std::ifstream file(filename); if (!file.is_open()) std::cout << "File not found! \n"; - else + else for (std::string line; file >> line;) { std::cout << line << std::endl; cv::Mat mat_img = cv::imread(line); @@ -453,11 +453,11 @@ int main(int argc, char *argv[]) //draw_boxes(mat_img, result_vec, obj_names); //cv::imwrite("res_" + line, mat_img); } - + } else { // image file cv::Mat mat_img = cv::imread(filename); - + auto start = std::chrono::steady_clock::now(); std::vector result_vec = detector.detect(mat_img); auto end = std::chrono::steady_clock::now(); @@ -477,7 +477,7 @@ int main(int argc, char *argv[]) std::vector result_vec = detector.detect(img); detector.free_image(img); show_console_result(result_vec, obj_names); -#endif +#endif } catch (std::exception &e) { std::cerr << "exception: " << e.what() << "\n"; getchar(); } catch (...) { std::cerr << "unknown exception \n"; getchar(); } @@ -485,4 +485,4 @@ int main(int argc, char *argv[]) } return 0; -} \ No newline at end of file +} diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 05739b4be59..916391d1d5c 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -13,7 +13,7 @@ layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes, int max_boxes) { int i; - layer l = {0}; + layer l = { (LAYER_TYPE)0 }; l.type = YOLO; l.n = n; @@ -26,22 +26,22 @@ layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int l.out_h = l.h; l.out_c = l.c; l.classes = classes; - l.cost = calloc(1, sizeof(float)); - l.biases = calloc(total*2, sizeof(float)); + l.cost = (float*)calloc(1, sizeof(float)); + l.biases = (float*)calloc(total * 2, sizeof(float)); if(mask) l.mask = mask; else{ - l.mask = calloc(n, sizeof(int)); + l.mask = (int*)calloc(n, sizeof(int)); for(i = 0; i < n; ++i){ l.mask[i] = i; } } - l.bias_updates = calloc(n*2, sizeof(float)); + l.bias_updates = (float*)calloc(n * 2, sizeof(float)); l.outputs = h*w*n*(classes + 4 + 1); l.inputs = l.outputs; l.max_boxes = max_boxes; l.truths = l.max_boxes*(4 + 1); // 90*(4 + 1); - l.delta = calloc(batch*l.outputs, sizeof(float)); - l.output = calloc(batch*l.outputs, sizeof(float)); + l.delta = (float*)calloc(batch * l.outputs, sizeof(float)); + l.output = (float*)calloc(batch * l.outputs, sizeof(float)); for(i = 0; i < total*2; ++i){ l.biases[i] = .5; } @@ -58,14 +58,14 @@ layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int if (cudaSuccess == cudaHostAlloc(&l.output, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1; else { cudaGetLastError(); // reset CUDA-error - l.output = calloc(batch*l.outputs, sizeof(float)); + l.output = (float*)calloc(batch * l.outputs, sizeof(float)); } free(l.delta); if (cudaSuccess == cudaHostAlloc(&l.delta, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1; else { cudaGetLastError(); // reset CUDA-error - l.delta = calloc(batch*l.outputs, sizeof(float)); + l.delta = (float*)calloc(batch * l.outputs, sizeof(float)); } #endif @@ -83,15 +83,15 @@ void resize_yolo_layer(layer *l, int w, int h) l->outputs = h*w*l->n*(l->classes + 4 + 1); l->inputs = l->outputs; - if (!l->output_pinned) l->output = realloc(l->output, l->batch*l->outputs * sizeof(float)); - if (!l->delta_pinned) l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float)); + if (!l->output_pinned) l->output = (float*)realloc(l->output, l->batch*l->outputs * sizeof(float)); + if (!l->delta_pinned) l->delta = (float*)realloc(l->delta, l->batch*l->outputs*sizeof(float)); #ifdef GPU if (l->output_pinned) { cudaFreeHost(l->output); if (cudaSuccess != cudaHostAlloc(&l->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) { cudaGetLastError(); // reset CUDA-error - l->output = realloc(l->output, l->batch*l->outputs * sizeof(float)); + l->output = (float*)realloc(l->output, l->batch * l->outputs * sizeof(float)); l->output_pinned = 0; } } @@ -100,7 +100,7 @@ void resize_yolo_layer(layer *l, int w, int h) cudaFreeHost(l->delta); if (cudaSuccess != cudaHostAlloc(&l->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) { cudaGetLastError(); // reset CUDA-error - l->delta = realloc(l->delta, l->batch*l->outputs * sizeof(float)); + l->delta = (float*)realloc(l->delta, l->batch * l->outputs * sizeof(float)); l->delta_pinned = 0; } } @@ -458,13 +458,13 @@ void forward_yolo_layer_gpu(const layer l, network_state state) return; } - float *in_cpu = calloc(l.batch*l.inputs, sizeof(float)); + float *in_cpu = (float *)calloc(l.batch*l.inputs, sizeof(float)); cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); memcpy(in_cpu, l.output, l.batch*l.outputs*sizeof(float)); float *truth_cpu = 0; if (state.truth) { int num_truth = l.batch*l.truths; - truth_cpu = calloc(num_truth, sizeof(float)); + truth_cpu = (float *)calloc(num_truth, sizeof(float)); cuda_pull_array(state.truth, truth_cpu, num_truth); } network_state cpu_state = state; diff --git a/src/yolo_layer.h b/src/yolo_layer.h index 0c2f5016943..d67482fe2fb 100644 --- a/src/yolo_layer.h +++ b/src/yolo_layer.h @@ -5,6 +5,9 @@ #include "layer.h" #include "network.h" +#ifdef __cplusplus +extern "C" { +#endif layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes, int max_boxes); void forward_yolo_layer(const layer l, network_state state); void backward_yolo_layer(const layer l, network_state state); @@ -18,4 +21,7 @@ void forward_yolo_layer_gpu(const layer l, network_state state); void backward_yolo_layer_gpu(layer l, network_state state); #endif +#ifdef __cplusplus +} +#endif #endif diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index c5ea1dfbefb..668ef5cd1ba 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -20,7 +20,6 @@ extern "C" { #include #include -#define FRAMES 3 //static Detector* detector = NULL; static std::unique_ptr detector; @@ -93,9 +92,9 @@ void check_cuda(cudaError_t status) { struct detector_gpu_t { network net; - image images[FRAMES]; + image images[NFRAMES]; float *avg; - float *predictions[FRAMES]; + float* predictions[NFRAMES]; int demo_index; unsigned int *track_id; }; @@ -135,8 +134,8 @@ LIB_API Detector::Detector(std::string cfg_filename, std::string weight_filename int j; detector_gpu.avg = (float *)calloc(l.outputs, sizeof(float)); - for (j = 0; j < FRAMES; ++j) detector_gpu.predictions[j] = (float *)calloc(l.outputs, sizeof(float)); - for (j = 0; j < FRAMES; ++j) detector_gpu.images[j] = make_image(1, 1, 3); + for (j = 0; j < NFRAMES; ++j) detector_gpu.predictions[j] = (float*)calloc(l.outputs, sizeof(float)); + for (j = 0; j < NFRAMES; ++j) detector_gpu.images[j] = make_image(1, 1, 3); detector_gpu.track_id = (unsigned int *)calloc(l.classes, sizeof(unsigned int)); for (j = 0; j < l.classes; ++j) detector_gpu.track_id[j] = 1; @@ -155,8 +154,8 @@ LIB_API Detector::~Detector() free(detector_gpu.track_id); free(detector_gpu.avg); - for (int j = 0; j < FRAMES; ++j) free(detector_gpu.predictions[j]); - for (int j = 0; j < FRAMES; ++j) if(detector_gpu.images[j].data) free(detector_gpu.images[j].data); + for (int j = 0; j < NFRAMES; ++j) free(detector_gpu.predictions[j]); + for (int j = 0; j < NFRAMES; ++j) if (detector_gpu.images[j].data) free(detector_gpu.images[j].data); int old_gpu_index; #ifdef GPU @@ -275,9 +274,9 @@ LIB_API std::vector Detector::detect(image_t img, float thresh, bool use if (use_mean) { memcpy(detector_gpu.predictions[detector_gpu.demo_index], prediction, l.outputs * sizeof(float)); - mean_arrays(detector_gpu.predictions, FRAMES, l.outputs, detector_gpu.avg); + mean_arrays(detector_gpu.predictions, NFRAMES, l.outputs, detector_gpu.avg); l.output = detector_gpu.avg; - detector_gpu.demo_index = (detector_gpu.demo_index + 1) % FRAMES; + detector_gpu.demo_index = (detector_gpu.demo_index + 1) % NFRAMES; } //get_region_boxes(l, 1, 1, thresh, detector_gpu.probs, detector_gpu.boxes, 0, 0); //if (nms) do_nms_sort(detector_gpu.boxes, detector_gpu.probs, l.w*l.h*l.n, l.classes, nms); @@ -378,4 +377,4 @@ LIB_API std::vector Detector::tracking_id(std::vector cur_bbox_v } return cur_bbox_vec; -} \ No newline at end of file +} From 4ae778defcc2a2b29ede338cfe1ee937e813eb27 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Mon, 18 Feb 2019 00:09:08 +0100 Subject: [PATCH 0336/1365] include some requested modifications --- src/darkunistd.h | 2 +- src/getopt.c | 2 +- src/go.c | 35 +++++++++++++++++------------------ src/im2col_kernels.cu | 2 +- src/utils.c | 3 +++ 5 files changed, 23 insertions(+), 21 deletions(-) diff --git a/src/darkunistd.h b/src/darkunistd.h index bb97da5f67f..1ba3ef2e076 100644 --- a/src/darkunistd.h +++ b/src/darkunistd.h @@ -9,7 +9,7 @@ #include #include /* for _getcwd() and _chdir() */ -#include +#include "getopt.h" #include #include /* for getpid() and the exec..() family */ #include diff --git a/src/getopt.c b/src/getopt.c index 67a764d854a..2707e0a84fc 100644 --- a/src/getopt.c +++ b/src/getopt.c @@ -1,5 +1,5 @@ #ifdef _WIN32 -#include +#include "getopt.h" #ifdef __cplusplus extern "C" { diff --git a/src/go.c b/src/go.c index 2576ecefc78..54a739d6a18 100644 --- a/src/go.c +++ b/src/go.c @@ -10,7 +10,8 @@ int inverted = 1; int noi = 1; -static const unsigned int n_ind = 5; +//static const unsigned int n_ind = 5; +#define n_ind 5 typedef struct { char **data; @@ -357,7 +358,7 @@ int generate_move(network net, int player, float *board, int multi, float thresh predict_move(net, board, move, multi); if (player < 0) flip_board(board); - + for(i = 0; i < 19; ++i){ for(j = 0; j < 19; ++j){ if (!legal_go(board, ko, player, i, j)) move[i*19 + j] = 0; @@ -392,7 +393,7 @@ int generate_move(network net, int player, float *board, int multi, float thresh } if(suicide_go(board, player, row, col)){ - return -1; + return -1; } if(suicide_go(board, player, index/19, index%19)) index = max; return index; @@ -461,17 +462,17 @@ void engine_go(char *filename, char *weightfile, int multi) } else if (!strcmp(buff, "known_command")){ char comm[256]; scanf("%s", comm); - int known = (!strcmp(comm, "protocol_version") || - !strcmp(comm, "name") || - !strcmp(comm, "version") || - !strcmp(comm, "known_command") || - !strcmp(comm, "list_commands") || - !strcmp(comm, "quit") || - !strcmp(comm, "boardsize") || - !strcmp(comm, "clear_board") || - !strcmp(comm, "komi") || - !strcmp(comm, "final_status_list") || - !strcmp(comm, "play") || + int known = (!strcmp(comm, "protocol_version") || + !strcmp(comm, "name") || + !strcmp(comm, "version") || + !strcmp(comm, "known_command") || + !strcmp(comm, "list_commands") || + !strcmp(comm, "quit") || + !strcmp(comm, "boardsize") || + !strcmp(comm, "clear_board") || + !strcmp(comm, "komi") || + !strcmp(comm, "final_status_list") || + !strcmp(comm, "play") || !strcmp(comm, "genmove")); if(known) printf("=%s true\n\n", ids); else printf("=%s false\n\n", ids); @@ -575,7 +576,7 @@ void engine_go(char *filename, char *weightfile, int multi) } } fprintf(f, "final_status_list dead\n"); - fclose(f); + fclose(f); #ifdef _WIN32 FILE *p = _popen("./gnugo --mode gtp < game.txt", "r"); #else @@ -716,7 +717,7 @@ float score_game(float *board) } } fprintf(f, "final_score\n"); - fclose(f); + fclose(f); #ifdef _WIN32 FILE *p = _popen("./gnugo --mode gtp < game.txt", "r"); #else @@ -841,5 +842,3 @@ void run_go(int argc, char **argv) else if(0==strcmp(argv[2], "test")) test_go(cfg, weights, multi); else if(0==strcmp(argv[2], "engine")) engine_go(cfg, weights, multi); } - - diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 876d6ce06f2..05a89c1bb24 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -1,4 +1,4 @@ -#include +#include #include #include #include diff --git a/src/utils.c b/src/utils.c index 6614f1a8cb3..149a07ff89b 100644 --- a/src/utils.c +++ b/src/utils.c @@ -13,6 +13,9 @@ #include #endif +#ifndef USE_CMAKE_LIBS +#pragma warning(disable: 4996) +#endif double what_time_is_it_now() { From cbc1c6d8cad7ac04be95fa947dd25fb3e0c01d6e Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 17 Feb 2019 23:56:25 +0100 Subject: [PATCH 0337/1365] improve CMake alternative, add instructions in README --- CMakeLists.txt | 52 ++++++++++----- CMakeSettings.json | 40 ++++++++++++ README.md | 80 +++++++++++++++--------- build.ps1 | 46 ++++++++++++++ cmake/Modules/FindCUDNN.cmake | 8 ++- cmake/Modules/FindPThreads_windows.cmake | 4 +- 6 files changed, 179 insertions(+), 51 deletions(-) create mode 100644 CMakeSettings.json create mode 100755 build.ps1 diff --git a/CMakeLists.txt b/CMakeLists.txt index 5669a470aa3..e2831afb6c3 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -8,6 +8,28 @@ endif() project(darknet) +# To enable CUDA, launch cmake with -DENABLE_CUDA. Also it is advisable to set the highest compute model from below, depending on your specific card capabilities +# If CUDA is not required, this setting is completely ignored. +#set(COMPUTE_MODEL "75") #Tesla T4, RTX 2080 +#set(COMPUTE_MODEL "72") #Jetson Xavier +#set(COMPUTE_MODEL "70") #Tesla V100 +#set(COMPUTE_MODEL "62") #Jetson TX2 +#set(COMPUTE_MODEL "61") #Telsa P40 +set(COMPUTE_MODEL "60") #Telsa P100 +#set(COMPUTE_MODEL "53") #Jetson TX1 +#set(COMPUTE_MODEL "52") #Tesla M40/M60 +#set(COMPUTE_MODEL "37") #Tesla K80 +#set(COMPUTE_MODEL "35") #Tesla K20/K40 +#set(COMPUTE_MODEL "30") #Tesla K10, Quadro K4000 +### +if(FORCE_CUSTOM_LIBS) + include_directories(${CMAKE_CURRENT_LIST_DIR}/3rdparty/include) + set(PTHREADS_INCLUDE_DIR ${CMAKE_CURRENT_LIST_DIR}/3rdparty/include) + set(PTHREADS_LIBRARY ${CMAKE_CURRENT_LIST_DIR}/3rdparty/lib/x64/pthreadVC2.lib) + add_definitions(-D_TIMESPEC_DEFINED) +endif() +### + enable_language(C) enable_language(CXX) if(ENABLE_CUDA) @@ -57,10 +79,12 @@ if ("${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang") string(REGEX REPLACE "-O3" "-Ofast" CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) endif() -if(APPLE) - find_package(OpenCV PATHS "/usr/local/Cellar/opencv@3/3.4.5" NO_DEFAULT_PATH) -else() - find_package(OpenCV) +if(NOT FORCE_CUSTOM_LIBS) + if(APPLE) + find_package(OpenCV PATHS "/usr/local/Cellar/opencv@3/3.4.5" NO_DEFAULT_PATH) + else() + find_package(OpenCV) + endif() endif() if(OpenCV_FOUND) @@ -70,24 +94,18 @@ endif() if(ENABLE_CUDA) add_definitions(-DGPU) - #set(COMPUTE_MODEL "75") #Tesla T4, RTX 2080 - #set(COMPUTE_MODEL "72") #Jetson Xavier - #set(COMPUTE_MODEL "70") #Tesla V100 - #set(COMPUTE_MODEL "62") #Jetson TX2 - #set(COMPUTE_MODEL "61") #Telsa P40 - #set(COMPUTE_MODEL "60") #Telsa P100 - #set(COMPUTE_MODEL "53") #Jetson TX1 - #set(COMPUTE_MODEL "52") #Tesla M40/M60 - #set(COMPUTE_MODEL "37") #Tesla K80 - #set(COMPUTE_MODEL "35") #Tesla K20/K40 - set(COMPUTE_MODEL "30") #Tesla K10, Quadro K4000 - if(COMPUTE_MODEL VERSION_GREATER_EQUAL 53) - add_definitions(-DCUDNN_HALF) + if(COMPUTE_MODEL VERSION_GREATER_EQUAL 75 AND CMAKE_CUDA_COMPILER_VERSION VERSION_LESS_EQUAL 9) + message(STATUS "CUDA 10 is required for Compute Model 7.5 - Lowering settings to 7.0") + set(COMPUTE_MODEL "70") endif() find_package(CUDNN) if(CUDNN_FOUND) add_definitions(-DCUDNN) include_directories(${CUDNN_INCLUDE_DIR}) + if(COMPUTE_MODEL VERSION_GREATER_EQUAL 53) + add_definitions(-DCUDNN_HALF) + message(STATUS "Enabled CUDNN_HALF") + endif() endif() if (MSVC) if(CUDNN_FOUND) diff --git a/CMakeSettings.json b/CMakeSettings.json new file mode 100644 index 00000000000..1c8184e4d79 --- /dev/null +++ b/CMakeSettings.json @@ -0,0 +1,40 @@ +{ + "configurations": [ + { + "buildCommandArgs": "-m -v:minimal", + "buildRoot": "${env.USERPROFILE}\\CMakeBuilds\\${workspaceHash}\\build\\${name}", + "cmakeCommandArgs": "", + "configurationType": "Debug", + "generator": "Visual Studio 15 2017 Win64", + "name": "x64-Debug", + "variables": [ + { + "name": "CMAKE_TOOLCHAIN_FILE", + "value": "${env.VCPKG_ROOT}\\scripts\\buildsystems\\vcpkg.cmake" + }, + { + "name": "VCPKG_TARGET_TRIPLET", + "value": "${env.VCPKG_DEFAULT_TRIPLET}" + } + ] + }, + { + "buildCommandArgs": "-m -v:minimal", + "buildRoot": "${env.USERPROFILE}\\CMakeBuilds\\${workspaceHash}\\build\\${name}", + "cmakeCommandArgs": "", + "configurationType": "Release", + "generator": "Visual Studio 15 2017 Win64", + "name": "x64-Release", + "variables": [ + { + "name": "CMAKE_TOOLCHAIN_FILE", + "value": "${env.VCPKG_ROOT}\\scripts\\buildsystems\\vcpkg.cmake" + }, + { + "name": "VCPKG_TARGET_TRIPLET", + "value": "${env.VCPKG_DEFAULT_TRIPLET}" + } + ] + } + ] +} diff --git a/README.md b/README.md index 3dba89d1c3f..983ee17ea4e 100644 --- a/README.md +++ b/README.md @@ -6,21 +6,20 @@ * [Requirements](#requirements) * [Pre-trained models](#pre-trained-models) * [Explanations in issues](https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3AExplanations) -0. [Improvements in this repository](#improvements-in-this-repository) -1. [How to use](#how-to-use) -2. [How to compile on Linux](#how-to-compile-on-linux) -3. [How to compile on Windows](#how-to-compile-on-windows) -4. [How to train (Pascal VOC Data)](#how-to-train-pascal-voc-data) -5. [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects) -6. [When should I stop training](#when-should-i-stop-training) -7. [How to calculate mAP on PascalVOC 2007](#how-to-calculate-map-on-pascalvoc-2007) -8. [How to improve object detection](#how-to-improve-object-detection) -9. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) + +0. [Improvements in this repository](#improvements-in-this-repository) +1. [How to use](#how-to-use) +2. [How to compile on Linux](#how-to-compile-on-linux) +3. [How to compile on Windows](#how-to-compile-on-windows) +4. [How to train (Pascal VOC Data)](#how-to-train-pascal-voc-data) +5. [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects) +6. [When should I stop training](#when-should-i-stop-training) +7. [How to calculate mAP on PascalVOC 2007](#how-to-calculate-map-on-pascalvoc-2007) +8. [How to improve object detection](#how-to-improve-object-detection) +9. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) 10. [Using Yolo9000](#using-yolo9000) 11. [How to use Yolo as DLL and SO libraries](#how-to-use-yolo-as-dll-and-so-libraries) - - | ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) |   ![map_time](https://user-images.githubusercontent.com/4096485/52151356-e5d4a380-2683-11e9-9d7d-ac7bc192c477.jpg) mAP@0.5 (AP50) https://pjreddie.com/media/files/papers/YOLOv3.pdf | |---|---| @@ -29,9 +28,9 @@ * Yolo v2 on Pascal VOC 2007: https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg * Yolo v2 on Pascal VOC 2012 (comp4): https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg +## "You Only Look Once: Unified, Real-Time Object Detection (versions 2 & 3)" -# "You Only Look Once: Unified, Real-Time Object Detection (versions 2 & 3)" -A Yolo cross-platform Windows and Linux version (for object detection). Contributtors: https://github.com/AlexeyAB/darknet/graphs/contributors +A Yolo cross-platform Windows and Linux version (for object detection). Contributors: https://github.com/AlexeyAB/darknet/graphs/contributors This repository is forked from Linux-version: https://github.com/pjreddie/darknet @@ -45,15 +44,14 @@ This repository supports: * CUDA >= 7.5 * also create SO-library on Linux and DLL-library on Windows -##### Requirements: -* **Linux GCC>=4.9 or Windows MS Visual Studio 2015 (v140)**: https://go.microsoft.com/fwlink/?LinkId=532606&clcid=0x409 (or offline [ISO image](https://go.microsoft.com/fwlink/?LinkId=615448&clcid=0x409)) +### Requirements + +* **CMake >= 3.8** for modern CUDA support: https://cmake.org/download/ * **CUDA 10.0**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) -* **OpenCV 3.3.0**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.3.0/opencv-3.3.0-vc14.exe/download -* **or OpenCV 2.4.13**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.4.13/opencv-2.4.13.2-vc14.exe/download - - OpenCV allows to show image or video detection in the window and store result to file that specified in command line `-out_filename res.avi` +* **OpenCV < 4.0**: use your preferred package manager (brew, apt) or build from source using [vcpkg](https://github.com/Microsoft/vcpkg) * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported -##### Pre-trained models +#### Pre-trained models There are weights-file for different cfg-files (smaller size -> faster speed & lower accuracy: @@ -71,7 +69,7 @@ Put it near compiled: darknet.exe You can get cfg-files by path: `darknet/cfg/` -##### Examples of results: +##### Examples of results [![Everything Is AWESOME](http://img.youtube.com/vi/VOC3huqHrss/0.jpg)](https://www.youtube.com/watch?v=VOC3huqHrss "Everything Is AWESOME") @@ -102,10 +100,7 @@ And added manual - [How to train Yolo v3/v2 (to detect your custom objects)](#ho Also, you might be interested in using a simplified repository where is implemented INT8-quantization (+30% speedup and -1% mAP reduced): https://github.com/AlexeyAB/yolo2_light -### How to use: - - -##### How to use on the command line: +#### How to use on the command line On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights` @@ -133,11 +128,10 @@ On Linux find executable file `./darknet` in the root directory, while on Window * To check accuracy mAP@IoU=50: `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights` * To check accuracy mAP@IoU=75: `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights -iou_thresh 0.75` -##### For using network video-camera mjpeg-stream with any Android smartphone: +##### For using network video-camera mjpeg-stream with any Android smartphone 1. Download for Android phone mjpeg-stream soft: IP Webcam / Smart WebCam - * Smart WebCam - preferably: https://play.google.com/store/apps/details?id=com.acontech.android.SmartWebCam2 * IP Webcam: https://play.google.com/store/apps/details?id=com.pas.webcam @@ -145,14 +139,13 @@ On Linux find executable file `./darknet` in the root directory, while on Window 3. Start Smart WebCam on your phone 4. Replace the address below, on shown in the phone application (Smart WebCam) and launch: - * Yolo v3 COCO-model: `darknet.exe detector demo data/coco.data yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` - -### How to compile on Linux: +### How to compile on Linux Just do `make` in the darknet directory. Before make, you can set such options in the `Makefile`: [link](https://github.com/AlexeyAB/darknet/blob/9c1b9a2cf6363546c152251be578a21f3c3caec6/Makefile#L1) + * `GPU=1` to build with CUDA to accelerate by using GPU (CUDA should be in `/usr/local/cuda`) * `CUDNN=1` to build with cuDNN v5-v7 to accelerate training by using GPU (cuDNN should be in `/usr/local/cudnn`) * `CUDNN_HALF=1` to build for Tensor Cores (on Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x @@ -164,7 +157,32 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c To run Darknet on Linux use examples from this article, just use `./darknet` instead of `darknet.exe`, i.e. use this command: `./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights` -### How to compile on Windows: +### How to compile on Windows (using `vcpkg`) + +1. Install or update Visual Studio to at least version 2017, making sure to have it fully patched (run again the installer if not sure to automatically update to latest version). If you need to install from scratch, download VS from here: [Visual Studio 2017 Community](http://visualstudio.com) + +2. Install CUDA and cuDNN + +3. Install `git` and `cmake`. Make sure they are on the Path at least for the current account + +4. Install [vcpkg](https://github.com/Microsoft/vcpkg) and try to install a test library to make sure everything is working, for example `vcpkg install opengl` + +5. Define an environment variables, `VCPKG_ROOT`, pointing to the install path of `vcpkg` + +6. Define another environment variable, with name `VCPKG_DEFAULT_TRIPLET` and value `x64-windows` + +7. Open a Powershell (as a standard user) and type (the last command requires a confirmation and is used to clean up unnecessary files) + +```PowerShell +PS \> cd $env:VCPKG_ROOT +PS Code\vcpkg> .\vcpkg install pthreads opencv #replace with opencv[cuda] in case you want to use cuda-accelerated openCV +``` + +8. [necessary only with CUDA] Customize the CMakeLists.txt with the preferred compute capability + +9. Build with the Powershell script `build.ps1` or use the "Open Folder" functionality of Visual Studio 2017. In the first option, if you want to use Visual Studio, you will find a custom solution created for you by CMake after the build containing all the appropriate config flags for your system. + +### How to compile on Windows (legacy way) 1. If you have **MSVS 2015, CUDA 10.0, cuDNN 7.4 and OpenCV 3.x** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release** https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg and do the: Build -> Build darknet. Also add Windows system variable `cudnn` with path to CUDNN: https://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg **NOTE:** If installing OpenCV, use OpenCV 3.4.0 or earlier. This is a bug in OpenCV 3.4.1 in the C API (see [#500](https://github.com/AlexeyAB/darknet/issues/500)). diff --git a/build.ps1 b/build.ps1 new file mode 100755 index 00000000000..f1080ab7a01 --- /dev/null +++ b/build.ps1 @@ -0,0 +1,46 @@ +#!/usr/bin/env pwsh + +pushd "C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\Common7\Tools" +cmd /c "VsDevCmd.bat -arch=x64 & set" | +foreach { + if ($_ -match "=") { + $v = $_.split("="); set-item -force -path "ENV:\$($v[0])" -value "$($v[1])" + } +} +popd +Write-Host "Visual Studio 2017 Command Prompt variables set.`n" -ForegroundColor Yellow + +# CPU ONLY, DEBUG +#New-Item -Path .\build_win_debug -ItemType directory -Force +#Set-Location build_win_debug +#cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$env:VCPKG_ROOT\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$env:VCPKG_DEFAULT_TRIPLET" "-DCMAKE_BUILD_TYPE=Debug" .. +#cmake --build . --config Debug +#Set-Location .. + +# CPU ONLY, RELEASE +#New-Item -Path .\build_win_release -ItemType directory -Force +#Set-Location build_win_release +#cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$env:VCPKG_ROOT\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$env:VCPKG_DEFAULT_TRIPLET" "-DCMAKE_BUILD_TYPE=Release" .. +#cmake --build . --config Release +#Set-Location .. + +# CUDA, DEBUG +#New-Item -Path .\build_win_debug_cuda -ItemType directory -Force +#Set-Location build_win_debug_cuda +#cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DENABLE_CUDA:BOOL=TRUE" "-DCMAKE_TOOLCHAIN_FILE=$env:VCPKG_ROOT\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$env:VCPKG_DEFAULT_TRIPLET" "-DCMAKE_BUILD_TYPE=Debug" .. +#cmake --build . --config Debug +#Set-Location .. + +# CUDA, RELEASE +#New-Item -Path .\build_win_release_cuda -ItemType directory -Force +#Set-Location build_win_release_cuda +#cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DENABLE_CUDA:BOOL=TRUE" "-DCMAKE_TOOLCHAIN_FILE=$env:VCPKG_ROOT\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$env:VCPKG_DEFAULT_TRIPLET" "-DCMAKE_BUILD_TYPE=Release" .. +#cmake --build . --config Release +#Set-Location .. + +# CPU ONLY, USE LOCAL PTHREAD LIB, NO VCPKG: remember to use "vcpkg.exe integrate remove" in case you had enable user-wide vcpkg integration +New-Item -Path .\build_win_release_cuda_custom_libs -ItemType directory -Force +Set-Location build_win_release_cuda_custom_libs +cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DFORCE_CUSTOM_LIBS:BOOL=TRUE" "-DENABLE_CUDA:BOOL=TRUE" .. +cmake --build . --config Release +Set-Location .. diff --git a/cmake/Modules/FindCUDNN.cmake b/cmake/Modules/FindCUDNN.cmake index bc130ea4b13..c5f28c53b9a 100644 --- a/cmake/Modules/FindCUDNN.cmake +++ b/cmake/Modules/FindCUDNN.cmake @@ -30,13 +30,17 @@ include(FindPackageHandleStandardArgs) #set(CUDNN_ROOT_DIR "" CACHE PATH "Folder contains NVIDIA cuDNN") -find_path(CUDNN_INCLUDE_DIR cudnn.h +if(NOT CUDNN_INCLUDE_DIR) + find_path(CUDNN_INCLUDE_DIR cudnn.h HINTS ${CUDNN_ROOT_DIR} ${CUDA_HOME} ${CUDA_TOOLKIT_ROOT_DIR} PATH_SUFFIXES cuda/include include) +endif() -find_library(CUDNN_LIBRARY cudnn +if(NOT CUDNN_LIBRARY) + find_library(CUDNN_LIBRARY cudnn HINTS ${CUDNN_ROOT_DIR} ${CUDA_HOME} ${CUDA_TOOLKIT_ROOT_DIR} PATH_SUFFIXES lib lib64 cuda/lib cuda/lib64 lib/x64) +endif() find_package_handle_standard_args( CUDNN DEFAULT_MSG CUDNN_INCLUDE_DIR CUDNN_LIBRARY) diff --git a/cmake/Modules/FindPThreads_windows.cmake b/cmake/Modules/FindPThreads_windows.cmake index 4f6a9f248dc..2b7d76a4c8f 100644 --- a/cmake/Modules/FindPThreads_windows.cmake +++ b/cmake/Modules/FindPThreads_windows.cmake @@ -25,7 +25,9 @@ include(${CMAKE_ROOT}/Modules/FindPackageHandleStandardArgs.cmake) include(${CMAKE_ROOT}/Modules/SelectLibraryConfigurations.cmake) -find_path(PTHREADS_INCLUDE_DIR NAMES pthread.h) +if(NOT PTHREADS_INCLUDE_DIR) + find_path(PTHREADS_INCLUDE_DIR NAMES pthread.h) +endif() # Allow libraries to be set manually if(NOT PTHREADS_LIBRARY) From 6e7c7976d704be06d2d0de24f46e15a6bba56e18 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Mon, 18 Feb 2019 11:32:04 +0100 Subject: [PATCH 0338/1365] move as many defs as possible away from darknet.h --- .gitignore | 9 ------ include/darknet.h | 34 +-------------------- include/yolo_v2_class.hpp | 2 ++ src/activations.c | 1 - src/art.c | 3 +- src/avgpool_layer.c | 1 - src/avgpool_layer_kernels.cu | 1 - src/batchnorm_layer.c | 2 +- src/blas.c | 2 +- src/blas_kernels.cu | 2 +- src/box.c | 8 ++--- src/captcha.c | 1 - src/cifar.c | 2 -- src/classifier.c | 2 -- src/col2im.c | 3 +- src/col2im_kernels.cu | 1 - src/compare.c | 4 +-- src/cost_layer.c | 1 - src/cpu_gemm.c | 20 ++++++------- src/crop_layer.c | 5 ++-- src/crop_layer_kernels.cu | 1 - src/cuda.h | 4 +++ src/darknet.c | 1 - src/data.c | 3 +- src/deconvolutional_kernels.cu | 3 +- src/deconvolutional_layer.c | 5 +--- src/detection_layer.c | 2 +- src/detector.c | 8 +++++ src/dice.c | 1 - src/dropout_layer.c | 3 +- src/gemm.c | 9 ++++-- src/gettimeofday.c | 6 ++-- src/gettimeofday.h | 3 +- src/http_stream.h | 2 +- src/im2col.c | 3 +- src/im2col_kernels.cu | 1 - src/image.c | 2 +- src/list.c | 2 +- src/local_layer.c | 18 ++++++------ src/lstm_layer.h | 4 +-- src/maxpool_layer.c | 1 - src/maxpool_layer_kernels.cu | 1 - src/network.h | 1 - src/network_kernels.cu | 1 - src/nightmare.c | 5 ++-- src/normalization_layer.c | 6 ++-- src/region_layer.c | 3 +- src/rnn_vid.c | 1 - src/softmax_layer.c | 6 ++-- src/stb_image_write.h | 54 +++++++++++++++++----------------- src/tag.c | 1 - src/tree.c | 4 +-- src/upsample_layer.c | 2 +- src/writing.c | 1 - src/yolo_layer.c | 1 - 55 files changed, 109 insertions(+), 164 deletions(-) diff --git a/.gitignore b/.gitignore index da2d106e832..f2492274310 100644 --- a/.gitignore +++ b/.gitignore @@ -24,12 +24,3 @@ ehthumbs.db Icon? Thumbs.db *.swp - -# CMake # -cmake-build-debug/ -CMakeLists.txt -build_*/ -build.* -cmake/ -*.patch -.gitignore diff --git a/include/darknet.h b/include/darknet.h index e35524deb04..40683712db7 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -26,40 +26,10 @@ #endif #endif -#ifdef _WIN32 -#define PORT unsigned long -#define ADDRPOINTER int* -#else -#define PORT unsigned short -#define SOCKET int -#define HOSTENT struct hostent -#define SOCKADDR struct sockaddr -#define SOCKADDR_IN struct sockaddr_in -#define ADDRPOINTER unsigned int* -#define INVALID_SOCKET -1 -#define SOCKET_ERROR -1 -#endif -#define FULL_MASK 0xffffffff -#define WARP_SIZE 32 -#define BLOCK 512 -#define NUMCHARS 37 #define NFRAMES 3 -#define BLOCK_TRANSPOSE32 256 -#define DOABS 1 #define SECRET_NUM -1234 -#define C_SHARP_MAX_OBJECTS 1000 -#define TILE_M 4 // 4 ops -#define TILE_N 16 // AVX2 = 2 ops * 8 floats -#define TILE_K 16 // loop -#ifndef __COMPAR_FN_T -#define __COMPAR_FN_T -typedef int (*__compar_fn_t)(const void*, const void*); -#ifdef __USE_GNU -typedef __compar_fn_t comparison_fn_t; -#endif -#endif + #ifdef GPU -#define BLOCK 512 #include "cuda_runtime.h" #include "curand.h" @@ -101,8 +71,6 @@ typedef struct metadata metadata; struct tree; typedef struct tree tree; - -#define SECRET_NUM -1234 extern int gpu_index; // option_list.h diff --git a/include/yolo_v2_class.hpp b/include/yolo_v2_class.hpp index f7f13bda1d3..e687b1e8ef8 100644 --- a/include/yolo_v2_class.hpp +++ b/include/yolo_v2_class.hpp @@ -3,6 +3,8 @@ #include "darknet.h" +#define C_SHARP_MAX_OBJECTS 1000 + struct bbox_t { unsigned int x, y, w, h; // (x,y) - top-left corner, (w, h) - width & height of bounded box float prob; // confidence - probability that the object was found correctly diff --git a/src/activations.c b/src/activations.c index 7aba7e26b1f..245fffa9a52 100644 --- a/src/activations.c +++ b/src/activations.c @@ -162,4 +162,3 @@ void gradient_array(const float *x, const int n, const ACTIVATION a, float *delt delta[i] *= gradient(x[i], a); } } - diff --git a/src/art.c b/src/art.c index 364cca3d3d9..e447176781b 100644 --- a/src/art.c +++ b/src/art.c @@ -38,7 +38,7 @@ void demo_art(char *cfgfile, char *weightfile, int cam_index) char *window = "ArtJudgementBot9000!!!"; if(!cap) error("Couldn't connect to webcam.\n"); - cvNamedWindow(window, CV_WINDOW_NORMAL); + cvNamedWindow(window, CV_WINDOW_NORMAL); cvResizeWindow(window, 512, 512); int i; int idx[] = {37, 401, 434}; @@ -84,4 +84,3 @@ void run_art(int argc, char **argv) char *weights = argv[3]; demo_art(cfg, weights, cam_index); } - diff --git a/src/avgpool_layer.c b/src/avgpool_layer.c index 983fe7591b7..bae5ff4df7c 100644 --- a/src/avgpool_layer.c +++ b/src/avgpool_layer.c @@ -68,4 +68,3 @@ void backward_avgpool_layer(const avgpool_layer l, network_state state) } } } - diff --git a/src/avgpool_layer_kernels.cu b/src/avgpool_layer_kernels.cu index 1bb48ae6a5b..b38ba45094a 100644 --- a/src/avgpool_layer_kernels.cu +++ b/src/avgpool_layer_kernels.cu @@ -56,4 +56,3 @@ extern "C" void backward_avgpool_layer_gpu(avgpool_layer layer, network_state st backward_avgpool_layer_kernel<<>>(n, layer.w, layer.h, layer.c, state.delta, layer.delta_gpu); CHECK_CUDA(cudaPeekAtLastError()); } - diff --git a/src/batchnorm_layer.c b/src/batchnorm_layer.c index d6ce989ef9b..aed1a1ddce3 100644 --- a/src/batchnorm_layer.c +++ b/src/batchnorm_layer.c @@ -273,4 +273,4 @@ void backward_batchnorm_layer_gpu(layer l, network_state state) if (l.type == BATCHNORM) simple_copy_ongpu(l.outputs*l.batch, l.delta_gpu, state.delta); //copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, state.delta, 1); } -#endif \ No newline at end of file +#endif diff --git a/src/blas.c b/src/blas.c index 32d44cb5e45..09033e254ea 100644 --- a/src/blas.c +++ b/src/blas.c @@ -333,4 +333,4 @@ void upsample_cpu(float *in, int w, int h, int c, int batch, int stride, int for } } } -} \ No newline at end of file +} diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 72d92129280..45ed09cde3f 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -974,4 +974,4 @@ extern "C" void softmax_tree_gpu(float *input, int spatial, int batch, int strid CHECK_CUDA(cudaPeekAtLastError()); cuda_free((float *)tree_groups_size); cuda_free((float *)tree_groups_offset); -} \ No newline at end of file +} diff --git a/src/box.c b/src/box.c index bc4958a22a1..28371362482 100644 --- a/src/box.c +++ b/src/box.c @@ -98,9 +98,9 @@ float box_iou(box a, box b) float box_rmse(box a, box b) { - return sqrt(pow(a.x-b.x, 2) + - pow(a.y-b.y, 2) + - pow(a.w-b.w, 2) + + return sqrt(pow(a.x-b.x, 2) + + pow(a.y-b.y, 2) + + pow(a.w-b.w, 2) + pow(a.h-b.h, 2)); } @@ -252,7 +252,7 @@ void do_nms_sort_v2(box *boxes, float **probs, int total, int classes, float thr sortable_bbox* s = (sortable_bbox*)calloc(total, sizeof(sortable_bbox)); for(i = 0; i < total; ++i){ - s[i].index = i; + s[i].index = i; s[i].class_id = 0; s[i].probs = probs; } diff --git a/src/captcha.c b/src/captcha.c index 230c723ff45..0bb15b847fd 100644 --- a/src/captcha.c +++ b/src/captcha.c @@ -361,4 +361,3 @@ void run_captcha(int argc, char **argv) //else if(0==strcmp(argv[2], "decode")) decode_captcha(cfg, weights); //else if(0==strcmp(argv[2], "valid")) validate_captcha(cfg, weights); } - diff --git a/src/cifar.c b/src/cifar.c index a9daa9792e5..04dec15590a 100644 --- a/src/cifar.c +++ b/src/cifar.c @@ -273,5 +273,3 @@ void run_cifar(int argc, char **argv) else if(0==strcmp(argv[2], "csvtrain")) test_cifar_csvtrain(cfg, weights); else if(0==strcmp(argv[2], "eval")) eval_cifar_csv(); } - - diff --git a/src/classifier.c b/src/classifier.c index 24b956d7f25..ed6951d5462 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -1295,5 +1295,3 @@ void run_classifier(int argc, char **argv) else if(0==strcmp(argv[2], "validcrop")) validate_classifier_crop(data, cfg, weights); else if(0==strcmp(argv[2], "validfull")) validate_classifier_full(data, cfg, weights); } - - diff --git a/src/col2im.c b/src/col2im.c index 43126423040..925d054c8bf 100644 --- a/src/col2im.c +++ b/src/col2im.c @@ -14,7 +14,7 @@ void col2im_add_pixel(float *im, int height, int width, int channels, //This one might be too, can't remember. void col2im_cpu(float* data_col, int channels, int height, int width, - int ksize, int stride, int pad, float* data_im) + int ksize, int stride, int pad, float* data_im) { int c,h,w; int height_col = (height + 2*pad - ksize) / stride + 1; @@ -37,4 +37,3 @@ void col2im_cpu(float* data_col, } } } - diff --git a/src/col2im_kernels.cu b/src/col2im_kernels.cu index 34808c00527..e3b1d233969 100644 --- a/src/col2im_kernels.cu +++ b/src/col2im_kernels.cu @@ -55,4 +55,3 @@ void col2im_ongpu(float *data_col, CHECK_CUDA(cudaPeekAtLastError()); } - diff --git a/src/compare.c b/src/compare.c index d0d1f1f01e5..5c1e0451e53 100644 --- a/src/compare.c +++ b/src/compare.c @@ -180,7 +180,7 @@ int bbox_comparator(const void *a, const void *b) memcpy(X, im1.data, im1.w*im1.h*im1.c*sizeof(float)); memcpy(X+im1.w*im1.h*im1.c, im2.data, im2.w*im2.h*im2.c*sizeof(float)); float *predictions = network_predict(net, X); - + free_image(im1); free_image(im2); free(X); @@ -218,7 +218,7 @@ void bbox_fight(network net, sortable_bbox *a, sortable_bbox *b, int classes, in bbox_update(a, b, i, result); } } - + free_image(im1); free_image(im2); free(X); diff --git a/src/cost_layer.c b/src/cost_layer.c index 39913d673b9..33fd8572bbf 100644 --- a/src/cost_layer.c +++ b/src/cost_layer.c @@ -145,4 +145,3 @@ void backward_cost_layer_gpu(const cost_layer l, network_state state) axpy_ongpu(l.batch*l.inputs, l.scale, l.delta_gpu, 1, state.delta, 1); } #endif - diff --git a/src/cpu_gemm.c b/src/cpu_gemm.c index 6a3cd1dc6b6..8305bb53158 100644 --- a/src/cpu_gemm.c +++ b/src/cpu_gemm.c @@ -1,7 +1,7 @@ //#include "mini_blas.h" -void cpu_gemm_nn(int TA, int TB, int M, int N, int K, float ALPHA, - float *A, int lda, +void cpu_gemm_nn(int TA, int TB, int M, int N, int K, float ALPHA, + float *A, int lda, float *B, int ldb, float BETA, float *C, int ldc) @@ -17,8 +17,8 @@ void cpu_gemm_nn(int TA, int TB, int M, int N, int K, float ALPHA, } } -void cpu_gemm_nt(int TA, int TB, int M, int N, int K, float ALPHA, - float *A, int lda, +void cpu_gemm_nt(int TA, int TB, int M, int N, int K, float ALPHA, + float *A, int lda, float *B, int ldb, float BETA, float *C, int ldc) @@ -35,8 +35,8 @@ void cpu_gemm_nt(int TA, int TB, int M, int N, int K, float ALPHA, } } -void cpu_gemm_tn(int TA, int TB, int M, int N, int K, float ALPHA, - float *A, int lda, +void cpu_gemm_tn(int TA, int TB, int M, int N, int K, float ALPHA, + float *A, int lda, float *B, int ldb, float BETA, float *C, int ldc) @@ -51,8 +51,8 @@ void cpu_gemm_tn(int TA, int TB, int M, int N, int K, float ALPHA, } } } -void cpu_gemm_tt(int TA, int TB, int M, int N, int K, float ALPHA, - float *A, int lda, +void cpu_gemm_tt(int TA, int TB, int M, int N, int K, float ALPHA, + float *A, int lda, float *B, int ldb, float BETA, float *C, int ldc) @@ -68,8 +68,8 @@ void cpu_gemm_tt(int TA, int TB, int M, int N, int K, float ALPHA, } -void cpu_gemm(int TA, int TB, int M, int N, int K, float ALPHA, - float *A, int lda, +void cpu_gemm(int TA, int TB, int M, int N, int K, float ALPHA, + float *A, int lda, float *B, int ldb, float BETA, float *C, int ldc) diff --git a/src/crop_layer.c b/src/crop_layer.c index 816f00e1523..258030be30c 100644 --- a/src/crop_layer.c +++ b/src/crop_layer.c @@ -88,16 +88,15 @@ void forward_crop_layer(const crop_layer l, network_state state) for(i = 0; i < l.out_h; ++i){ for(j = 0; j < l.out_w; ++j){ if(flip){ - col = l.w - dw - j - 1; + col = l.w - dw - j - 1; }else{ col = j + dw; } row = i + dh; - index = col+l.w*(row+l.h*(c + l.c*b)); + index = col+l.w*(row+l.h*(c + l.c*b)); l.output[count++] = state.input[index]*scale + trans; } } } } } - diff --git a/src/crop_layer_kernels.cu b/src/crop_layer_kernels.cu index 724196c383e..5b084fe5ec8 100644 --- a/src/crop_layer_kernels.cu +++ b/src/crop_layer_kernels.cu @@ -220,4 +220,3 @@ extern "C" void forward_crop_layer_gpu(crop_layer layer, network_state state) cvWaitKey(0); */ } - diff --git a/src/cuda.h b/src/cuda.h index 4010494ea85..fe33a258b2e 100644 --- a/src/cuda.h +++ b/src/cuda.h @@ -13,6 +13,10 @@ extern int gpu_index; #ifdef GPU +#define BLOCK 512 +#define FULL_MASK 0xffffffff +#define WARP_SIZE 32 +#define BLOCK_TRANSPOSE32 256 #include #include diff --git a/src/darknet.c b/src/darknet.c index 87175bc3d62..957614630bc 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -540,4 +540,3 @@ int main(int argc, char **argv) } return 0; } - diff --git a/src/data.c b/src/data.c index 09b1b2e27fa..80c08f48d79 100644 --- a/src/data.c +++ b/src/data.c @@ -7,6 +7,8 @@ #include #include +#define NUMCHARS 37 + pthread_mutex_t mutex = PTHREAD_MUTEX_INITIALIZER; list *get_paths(char *filename) @@ -1347,4 +1349,3 @@ data *split_data(data d, int part, int total) split[1] = test; return split; } - diff --git a/src/deconvolutional_kernels.cu b/src/deconvolutional_kernels.cu index 88106ecefff..b0ba1a8656c 100644 --- a/src/deconvolutional_kernels.cu +++ b/src/deconvolutional_kernels.cu @@ -59,7 +59,7 @@ extern "C" void backward_deconvolutional_layer_gpu(deconvolutional_layer layer, float *b = layer.col_image_gpu; float *c = layer.weight_updates_gpu; - im2col_ongpu(layer.delta_gpu + i*layer.n*size, layer.n, out_h, out_w, + im2col_ongpu(layer.delta_gpu + i*layer.n*size, layer.n, out_h, out_w, layer.size, layer.stride, 0, b); gemm_ongpu(0,1,m,n,k,alpha,a,k,b,k,1,c,n); @@ -104,4 +104,3 @@ extern "C" void update_deconvolutional_layer_gpu(deconvolutional_layer layer, in axpy_ongpu(size, learning_rate, layer.weight_updates_gpu, 1, layer.weights_gpu, 1); scal_ongpu(size, momentum, layer.weight_updates_gpu, 1); } - diff --git a/src/deconvolutional_layer.c b/src/deconvolutional_layer.c index a109999f25e..a138fb5325f 100644 --- a/src/deconvolutional_layer.c +++ b/src/deconvolutional_layer.c @@ -173,7 +173,7 @@ void backward_deconvolutional_layer(deconvolutional_layer l, network_state state float *b = l.col_image; float *c = l.weight_updates; - im2col_cpu(l.delta + i*l.n*size, l.n, out_h, out_w, + im2col_cpu(l.delta + i*l.n*size, l.n, out_h, out_w, l.size, l.stride, 0, b); gemm(0,1,m,n,k,alpha,a,k,b,k,1,c,n); @@ -201,6 +201,3 @@ void update_deconvolutional_layer(deconvolutional_layer l, int skip, float learn axpy_cpu(size, learning_rate, l.weight_updates, 1, l.weights, 1); scal_cpu(size, momentum, l.weight_updates, 1); } - - - diff --git a/src/detection_layer.c b/src/detection_layer.c index 4cfe591dd30..1379145b72a 100644 --- a/src/detection_layer.c +++ b/src/detection_layer.c @@ -312,4 +312,4 @@ void get_detection_detections(layer l, int w, int h, float thresh, detection *de } } } -} \ No newline at end of file +} diff --git a/src/detector.c b/src/detector.c index 96921689a5b..98efaeff60d 100644 --- a/src/detector.c +++ b/src/detector.c @@ -8,6 +8,14 @@ #include "demo.h" #include "option_list.h" +#ifndef __COMPAR_FN_T +#define __COMPAR_FN_T +typedef int (*__compar_fn_t)(const void*, const void*); +#ifdef __USE_GNU +typedef __compar_fn_t comparison_fn_t; +#endif +#endif + #ifdef OPENCV #include #include diff --git a/src/dice.c b/src/dice.c index 348a4aed950..94155271e6a 100644 --- a/src/dice.c +++ b/src/dice.c @@ -115,4 +115,3 @@ void run_dice(int argc, char **argv) else if(0==strcmp(argv[2], "train")) train_dice(cfg, weights); else if(0==strcmp(argv[2], "valid")) validate_dice(cfg, weights); } - diff --git a/src/dropout_layer.c b/src/dropout_layer.c index 599acc8f3c2..9eb22982a4b 100644 --- a/src/dropout_layer.c +++ b/src/dropout_layer.c @@ -23,7 +23,7 @@ dropout_layer make_dropout_layer(int batch, int inputs, float probability) #endif fprintf(stderr, "dropout p = %.2f %4d -> %4d\n", probability, inputs, inputs); return l; -} +} void resize_dropout_layer(dropout_layer *l, int inputs) { @@ -57,4 +57,3 @@ void backward_dropout_layer(dropout_layer l, network_state state) else state.delta[i] *= l.scale; } } - diff --git a/src/gemm.c b/src/gemm.c index 4b83c2f63e8..2fc9af0251d 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -15,6 +15,10 @@ #include #endif +#define TILE_M 4 // 4 ops +#define TILE_N 16 // AVX2 = 2 ops * 8 floats +#define TILE_K 16 // loop + void gemm_bin(int M, int N, int K, float ALPHA, char *A, int lda, float *B, int ldb, @@ -1160,10 +1164,10 @@ static inline void xnor_avx2_popcnt(__m256i a_bit256, __m256i b_bit256, __m256i __m256i xor256 = _mm256_xor_si256(a_bit256, b_bit256); // xnor = not(xor(a,b)) c_bit256 = _mm256_andnot_si256(xor256, c_bit256); // can be optimized - we can do other NOT for wegihts once and do not do this NOT - *count_sum = _mm256_add_epi64(count256(c_bit256), *count_sum); // 1st part - popcnt Mula’s algorithm + *count_sum = _mm256_add_epi64(count256(c_bit256), *count_sum); // 1st part - popcnt Mula's algorithm } -// 2nd part - popcnt Mula’s algorithm +// 2nd part - popcnt Mula's algorithm static inline int get_count_mula(__m256i count_sum) { return _mm256_extract_epi64(count_sum, 0) + _mm256_extract_epi64(count_sum, 1) @@ -2827,4 +2831,3 @@ int test_gpu_blas() return 0; } #endif - diff --git a/src/gettimeofday.c b/src/gettimeofday.c index 13fc9f74585..b47d3cd717e 100644 --- a/src/gettimeofday.c +++ b/src/gettimeofday.c @@ -8,7 +8,7 @@ LIB_API int gettimeofday(struct timeval* tp, struct timezone* tzp) FILETIME file_time; uint64_t time; - + GetSystemTime(&system_time); SystemTimeToFileTime(&system_time, &file_time); time = ((uint64_t)file_time.dwLowDateTime); @@ -18,7 +18,7 @@ LIB_API int gettimeofday(struct timeval* tp, struct timezone* tzp) tp->tv_usec = (long)(system_time.wMilliseconds * 1000); return 0; } - + LIB_API int clock_gettime(int dummy, struct timespec* ct) { LARGE_INTEGER count; @@ -30,7 +30,7 @@ LIB_API int clock_gettime(int dummy, struct timespec* ct) g_counts_per_sec.QuadPart = 0; } } - + if ((NULL == ct) || (g_counts_per_sec.QuadPart <= 0) || (0 == QueryPerformanceCounter(&count))) { return -1; } diff --git a/src/gettimeofday.h b/src/gettimeofday.h index 17d7a0f689f..f92c841d2c1 100644 --- a/src/gettimeofday.h +++ b/src/gettimeofday.h @@ -24,7 +24,7 @@ #ifdef __cplusplus extern "C" { #endif - + static unsigned char g_first_time = 1; static LARGE_INTEGER g_counts_per_sec; @@ -36,4 +36,3 @@ LIB_API int clock_gettime(int, struct timespec*); #endif #endif - diff --git a/src/http_stream.h b/src/http_stream.h index d96d7d4b50a..1e9a3cf1be3 100644 --- a/src/http_stream.h +++ b/src/http_stream.h @@ -36,4 +36,4 @@ image load_image_resize(char *filename, int w, int h, int c, image *im); } #endif -#endif // HTTP_STREAM_H \ No newline at end of file +#endif // HTTP_STREAM_H diff --git a/src/im2col.c b/src/im2col.c index 69ec98a9d12..40b5251db02 100644 --- a/src/im2col.c +++ b/src/im2col.c @@ -15,7 +15,7 @@ float im2col_get_pixel(float *im, int height, int width, int channels, //https://github.com/BVLC/caffe/blob/master/LICENSE void im2col_cpu(float* data_im, int channels, int height, int width, - int ksize, int stride, int pad, float* data_col) + int ksize, int stride, int pad, float* data_col) { int c,h,w; int height_col = (height + 2*pad - ksize) / stride + 1; @@ -37,4 +37,3 @@ void im2col_cpu(float* data_im, } } } - diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 05a89c1bb24..90af12b6982 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -2214,4 +2214,3 @@ void convolve_bin_gpu(float *input, float *weights, float *output, int in_w, int } // -------------------------------- - diff --git a/src/image.c b/src/image.c index 31a1819d787..c3a24d37485 100644 --- a/src/image.c +++ b/src/image.c @@ -2159,4 +2159,4 @@ LIB_API void copy_image_from_bytes(image im, char *pdata) } } } -} \ No newline at end of file +} diff --git a/src/list.c b/src/list.c index 79fee08d999..6ba093022f6 100644 --- a/src/list.c +++ b/src/list.c @@ -34,7 +34,7 @@ void *list_pop(list *l){ if(l->back) l->back->next = 0; free(b); --l->size; - + return val; } diff --git a/src/local_layer.c b/src/local_layer.c index f304511f109..9c68e9d897c 100644 --- a/src/local_layer.c +++ b/src/local_layer.c @@ -60,7 +60,7 @@ local_layer make_local_layer(int batch, int h, int w, int c, int n, int size, in l.col_image = (float*)calloc(out_h * out_w * size * size * c, sizeof(float)); l.output = (float*)calloc(l.batch * out_h * out_w * n, sizeof(float)); l.delta = (float*)calloc(l.batch * out_h * out_w * n, sizeof(float)); - + l.forward = forward_local_layer; l.backward = backward_local_layer; l.update = update_local_layer; @@ -101,7 +101,7 @@ void forward_local_layer(const local_layer l, network_state state) for(i = 0; i < l.batch; ++i){ float *input = state.input + i*l.w*l.h*l.c; - im2col_cpu(input, l.c, l.h, l.w, + im2col_cpu(input, l.c, l.h, l.w, l.size, l.stride, l.pad, l.col_image); float *output = l.output + i*l.outputs; for(j = 0; j < locations; ++j){ @@ -132,10 +132,10 @@ void backward_local_layer(local_layer l, network_state state) for(i = 0; i < l.batch; ++i){ float *input = state.input + i*l.w*l.h*l.c; - im2col_cpu(input, l.c, l.h, l.w, + im2col_cpu(input, l.c, l.h, l.w, l.size, l.stride, l.pad, l.col_image); - for(j = 0; j < locations; ++j){ + for(j = 0; j < locations; ++j){ float *a = l.delta + i*l.outputs + j; float *b = l.col_image + j; float *c = l.weight_updates + j*l.size*l.size*l.c*l.n; @@ -147,7 +147,7 @@ void backward_local_layer(local_layer l, network_state state) } if(state.delta){ - for(j = 0; j < locations; ++j){ + for(j = 0; j < locations; ++j){ float *a = l.weights + j*l.size*l.size*l.c*l.n; float *b = l.delta + i*l.outputs + j; float *c = l.col_image + j; @@ -191,7 +191,7 @@ void forward_local_layer_gpu(const local_layer l, network_state state) for(i = 0; i < l.batch; ++i){ float *input = state.input + i*l.w*l.h*l.c; - im2col_ongpu(input, l.c, l.h, l.w, + im2col_ongpu(input, l.c, l.h, l.w, l.size, l.stride, l.pad, l.col_image_gpu); float *output = l.output_gpu + i*l.outputs; for(j = 0; j < locations; ++j){ @@ -221,10 +221,10 @@ void backward_local_layer_gpu(local_layer l, network_state state) for(i = 0; i < l.batch; ++i){ float *input = state.input + i*l.w*l.h*l.c; - im2col_ongpu(input, l.c, l.h, l.w, + im2col_ongpu(input, l.c, l.h, l.w, l.size, l.stride, l.pad, l.col_image_gpu); - for(j = 0; j < locations; ++j){ + for(j = 0; j < locations; ++j){ float *a = l.delta_gpu + i*l.outputs + j; float *b = l.col_image_gpu + j; float *c = l.weight_updates_gpu + j*l.size*l.size*l.c*l.n; @@ -236,7 +236,7 @@ void backward_local_layer_gpu(local_layer l, network_state state) } if(state.delta){ - for(j = 0; j < locations; ++j){ + for(j = 0; j < locations; ++j){ float *a = l.weights_gpu + j*l.size*l.size*l.c*l.n; float *b = l.delta_gpu + i*l.outputs + j; float *c = l.col_image_gpu + j; diff --git a/src/lstm_layer.h b/src/lstm_layer.h index d951ca3793f..ae5441488fd 100644 --- a/src/lstm_layer.h +++ b/src/lstm_layer.h @@ -11,13 +11,13 @@ extern "C" { #endif LIB_API layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_normalize); -void forward_lstm_layer(layer l, network_state state); +void forward_lstm_layer(layer l, network_state state); void update_lstm_layer(layer l, int batch, float learning_rate, float momentum, float decay); #ifdef GPU void forward_lstm_layer_gpu(layer l, network_state state); void backward_lstm_layer_gpu(layer l, network_state state); -void update_lstm_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay); +void update_lstm_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay); #endif #ifdef __cplusplus diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index 7d3f7974a55..6c2ebca2498 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -162,4 +162,3 @@ void backward_maxpool_layer(const maxpool_layer l, network_state state) state.delta[index] += l.delta[i]; } } - diff --git a/src/maxpool_layer_kernels.cu b/src/maxpool_layer_kernels.cu index ec207a285f8..29aa257cf25 100644 --- a/src/maxpool_layer_kernels.cu +++ b/src/maxpool_layer_kernels.cu @@ -126,4 +126,3 @@ extern "C" void backward_maxpool_layer_gpu(maxpool_layer layer, network_state st backward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.pad, layer.delta_gpu, state.delta, layer.indexes_gpu); CHECK_CUDA(cudaPeekAtLastError()); } - diff --git a/src/network.h b/src/network.h index d082e7fc835..002d596c68b 100644 --- a/src/network.h +++ b/src/network.h @@ -167,4 +167,3 @@ network combine_train_valid_networks(network net_train, network net_map); #endif #endif - diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 38c15387e52..2c016697f78 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -459,4 +459,3 @@ float *network_predict_gpu(network net, float *input) //cuda_free(state.input); // will be freed in the free_network() return out; } - diff --git a/src/nightmare.c b/src/nightmare.c index 433c93b7516..2d4c7466a90 100644 --- a/src/nightmare.c +++ b/src/nightmare.c @@ -23,7 +23,7 @@ float abs_mean(float *x, int n) void calculate_loss(float *output, float *delta, int n, float thresh) { int i; - float mean = mean_array(output, n); + float mean = mean_array(output, n); float var = variance_array(output, n); for(i = 0; i < n; ++i){ if(delta[i] > mean + thresh*sqrt(var)) delta[i] = output[i]; @@ -260,7 +260,7 @@ void run_nightmare(int argc, char **argv) for(e = 0; e < rounds; ++e){ fprintf(stderr, "Iteration: "); fflush(stderr); - for(n = 0; n < iters; ++n){ + for(n = 0; n < iters; ++n){ fprintf(stderr, "%d, ", n); fflush(stderr); if(reconstruct){ @@ -305,4 +305,3 @@ void run_nightmare(int argc, char **argv) im = resized; } } - diff --git a/src/normalization_layer.c b/src/normalization_layer.c index 9c01f7894d4..9d4afcbb83d 100644 --- a/src/normalization_layer.c +++ b/src/normalization_layer.c @@ -52,9 +52,9 @@ void resize_normalization_layer(layer *layer, int w, int h) layer->norms = (float*)realloc(layer->norms, h * w * c * batch * sizeof(float)); #ifdef GPU cuda_free(layer->output_gpu); - cuda_free(layer->delta_gpu); - cuda_free(layer->squared_gpu); - cuda_free(layer->norms_gpu); + cuda_free(layer->delta_gpu); + cuda_free(layer->squared_gpu); + cuda_free(layer->norms_gpu); layer->output_gpu = cuda_make_array(layer->output, h * w * c * batch); layer->delta_gpu = cuda_make_array(layer->delta, h * w * c * batch); layer->squared_gpu = cuda_make_array(layer->squared, h * w * c * batch); diff --git a/src/region_layer.c b/src/region_layer.c index f7f82088e07..af61a043a39 100644 --- a/src/region_layer.c +++ b/src/region_layer.c @@ -9,6 +9,7 @@ #include #include +#define DOABS 1 region_layer make_region_layer(int batch, int w, int h, int n, int classes, int coords, int max_boxes) { @@ -588,4 +589,4 @@ void zero_objectness(layer l) l.output[obj_index] = 0; } } -} \ No newline at end of file +} diff --git a/src/rnn_vid.c b/src/rnn_vid.c index c0fd3d36735..7613ab25bf0 100644 --- a/src/rnn_vid.c +++ b/src/rnn_vid.c @@ -213,4 +213,3 @@ void run_vid_rnn(int argc, char **argv) #else void run_vid_rnn(int argc, char **argv){} #endif - diff --git a/src/softmax_layer.c b/src/softmax_layer.c index 3efe01572a4..3cd607beb8b 100644 --- a/src/softmax_layer.c +++ b/src/softmax_layer.c @@ -47,9 +47,9 @@ softmax_layer make_softmax_layer(int batch, int inputs, int groups) l.forward_gpu = forward_softmax_layer_gpu; l.backward_gpu = backward_softmax_layer_gpu; - l.output_gpu = cuda_make_array(l.output, inputs*batch); - l.loss_gpu = cuda_make_array(l.loss, inputs*batch); - l.delta_gpu = cuda_make_array(l.delta, inputs*batch); + l.output_gpu = cuda_make_array(l.output, inputs*batch); + l.loss_gpu = cuda_make_array(l.loss, inputs*batch); + l.delta_gpu = cuda_make_array(l.delta, inputs*batch); #endif return l; } diff --git a/src/stb_image_write.h b/src/stb_image_write.h index 9d553e0d7d0..274b1d81ccf 100644 --- a/src/stb_image_write.h +++ b/src/stb_image_write.h @@ -81,7 +81,7 @@ TGA supports RLE or non-RLE compressed data. To use non-RLE-compressed data, set the global variable 'stbi_write_tga_with_rle' to 0. - + JPEG does ignore alpha channels in input data; quality is between 1 and 100. Higher quality looks better but results in a bigger image. JPEG baseline (no JPEG progressive). @@ -114,7 +114,7 @@ Thatcher Ulrich github:poppolopoppo Patrick Boettcher - + LICENSE See end of file for license information. @@ -1250,7 +1250,7 @@ static int stbi_write_jpg_core(stbi__write_context *s, int width, int height, in 37,56,68,109,103,77,24,35,55,64,81,104,113,92,49,64,78,87,103,121,120,101,72,92,95,98,112,100,103,99}; static const int UVQT[] = {17,18,24,47,99,99,99,99,18,21,26,66,99,99,99,99,24,26,56,99,99,99,99,99,47,66,99,99,99,99,99,99, 99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99}; - static const float aasf[] = { 1.0f * 2.828427125f, 1.387039845f * 2.828427125f, 1.306562965f * 2.828427125f, 1.175875602f * 2.828427125f, + static const float aasf[] = { 1.0f * 2.828427125f, 1.387039845f * 2.828427125f, 1.306562965f * 2.828427125f, 1.175875602f * 2.828427125f, 1.0f * 2.828427125f, 0.785694958f * 2.828427125f, 0.541196100f * 2.828427125f, 0.275899379f * 2.828427125f }; int row, col, i, k; @@ -1421,38 +1421,38 @@ This software is available under 2 licenses -- choose whichever you prefer. ------------------------------------------------------------------------------ ALTERNATIVE A - MIT License Copyright (c) 2017 Sean Barrett -Permission is hereby granted, free of charge, to any person obtaining a copy of -this software and associated documentation files (the "Software"), to deal in -the Software without restriction, including without limitation the rights to -use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies -of the Software, and to permit persons to whom the Software is furnished to do +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: -The above copyright notice and this permission notice shall be included in all +The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ------------------------------------------------------------------------------ ALTERNATIVE B - Public Domain (www.unlicense.org) This is free and unencumbered software released into the public domain. -Anyone is free to copy, modify, publish, use, compile, sell, or distribute this -software, either in source code form or as a compiled binary, for any purpose, +Anyone is free to copy, modify, publish, use, compile, sell, or distribute this +software, either in source code form or as a compiled binary, for any purpose, commercial or non-commercial, and by any means. -In jurisdictions that recognize copyright laws, the author or authors of this -software dedicate any and all copyright interest in the software to the public -domain. We make this dedication for the benefit of the public at large and to -the detriment of our heirs and successors. We intend this dedication to be an -overt act of relinquishment in perpetuity of all present and future rights to +In jurisdictions that recognize copyright laws, the author or authors of this +software dedicate any and all copyright interest in the software to the public +domain. We make this dedication for the benefit of the public at large and to +the detriment of our heirs and successors. We intend this dedication to be an +overt act of relinquishment in perpetuity of all present and future rights to this software under copyright law. -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN -ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ------------------------------------------------------------------------------ */ diff --git a/src/tag.c b/src/tag.c index f6cbc0fb5cc..4033216ee59 100644 --- a/src/tag.c +++ b/src/tag.c @@ -150,4 +150,3 @@ void run_tag(int argc, char **argv) if(0==strcmp(argv[2], "train")) train_tag(cfg, weights, clear); else if(0==strcmp(argv[2], "test")) test_tag(cfg, weights, filename); } - diff --git a/src/tree.c b/src/tree.c index 0b320e3bcd9..4383d693e13 100644 --- a/src/tree.c +++ b/src/tree.c @@ -40,7 +40,7 @@ void hierarchy_predictions(float *predictions, int n, tree *hier, int only_leave for(j = 0; j < n; ++j){ int parent = hier->parent[j]; if(parent >= 0){ - predictions[j] *= predictions[parent]; + predictions[j] *= predictions[parent]; } } if(only_leaves){ @@ -86,7 +86,7 @@ tree *read_tree(char *filename) { tree t = {0}; FILE *fp = fopen(filename, "r"); - + char *line; int last_parent = -1; int group_size = 0; diff --git a/src/upsample_layer.c b/src/upsample_layer.c index dafc1b8d813..d31dd49e9ea 100644 --- a/src/upsample_layer.c +++ b/src/upsample_layer.c @@ -62,7 +62,7 @@ void resize_upsample_layer(layer *l, int w, int h) l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch); l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch); #endif - + } void forward_upsample_layer(const layer l, network_state net) diff --git a/src/writing.c b/src/writing.c index b6c49075f69..4acda60a78c 100644 --- a/src/writing.c +++ b/src/writing.c @@ -147,4 +147,3 @@ void run_writing(int argc, char **argv) if(0==strcmp(argv[2], "train")) train_writing(cfg, weights); else if(0==strcmp(argv[2], "test")) test_writing(cfg, weights, filename); } - diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 916391d1d5c..134c9c90bd0 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -485,4 +485,3 @@ void backward_yolo_layer_gpu(const layer l, network_state state) axpy_ongpu(l.batch*l.inputs, 1, l.delta_gpu, 1, state.delta, 1); } #endif - From 5e850c24897a5eb65941703059a85ead2ea5ff8c Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 18 Feb 2019 18:12:05 +0300 Subject: [PATCH 0339/1365] Minor fixes --- src/classifier.c | 2 +- src/detector.c | 4 ++-- src/http_stream.cpp | 4 ++-- src/image.c | 2 +- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index 11b6968d004..247d48dad75 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -156,7 +156,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i = get_current_batch(net); - int calc_topk_for_each = iter_topk + 4 * train_images_num / (net.batch * net.subdivisions); // calculate TOPk for each 4 Epochs + int calc_topk_for_each = iter_topk + 2 * train_images_num / (net.batch * net.subdivisions); // calculate TOPk for each 2 Epochs calc_topk_for_each = fmax(calc_topk_for_each, net.burn_in); calc_topk_for_each = fmax(calc_topk_for_each, 1000); if (i % 10 == 0) { diff --git a/src/detector.c b/src/detector.c index 5524ac19cfd..50711116d05 100644 --- a/src/detector.c +++ b/src/detector.c @@ -289,7 +289,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) { //if (i % 100 == 0) { - if (i >= (iter_save + 1000)) { + if (i >= (iter_save + 1000) || i % 1000 == 0) { iter_save = i; #ifdef GPU if (ngpus != 1) sync_nets(nets, ngpus, 0); @@ -299,7 +299,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i save_weights(net, buff); } - if (i >= (iter_save_last + 100)) { + if (i >= (iter_save_last + 100) || i % 100 == 0) { iter_save_last = i; #ifdef GPU if (ngpus != 1) sync_nets(nets, ngpus, 0); diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 3eb76ac0622..842a736de78 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -102,7 +102,7 @@ class MJPG_sender SOCKET sock; SOCKET maxfd; fd_set master; - int timeout; // master sock timeout, shutdown after timeout millis. + int timeout; // master sock timeout, shutdown after timeout usec. int quality; // jpeg compression [1..100] int close_all_sockets; @@ -289,7 +289,7 @@ class JSON_sender SOCKET sock; SOCKET maxfd; fd_set master; - int timeout; // master sock timeout, shutdown after timeout millis. + int timeout; // master sock timeout, shutdown after timeout usec. int close_all_sockets; int _write(int sock, char const*const s, int len) diff --git a/src/image.c b/src/image.c index f95b478a948..4002d87e3ce 100644 --- a/src/image.c +++ b/src/image.c @@ -821,7 +821,7 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_ else cvPutText(img, "- Saved", cvPoint(250, img_size - 10), &font, CV_RGB(255, 255, 255)); - if (mjpeg_port > 0) send_mjpeg(img, mjpeg_port, 2000, 80); + if (mjpeg_port > 0) send_mjpeg(img, mjpeg_port, 200000, 80); } #endif // OPENCV From 00de023601aa25713074b41801ffbbd7a7b1dfe7 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 19 Feb 2019 15:57:18 +0100 Subject: [PATCH 0340/1365] fully separate C-API from CPP-API --- include/darknet.h | 2 ++ include/yolo_v2_class.hpp | 28 +++++++++++++++++++++------- src/convolutional_layer.c | 8 +++++++- src/cpu_gemm.c | 11 ++++++++--- src/gemm.c | 37 +++++++++++++++++++++---------------- src/image.c | 3 +++ src/utils.c | 3 +++ src/yolo_v2_class.cpp | 1 + 8 files changed, 66 insertions(+), 27 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 40683712db7..23be5b98ee6 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -12,6 +12,7 @@ #include #include +#ifndef LIB_API #ifdef LIB_EXPORTS #if defined(_MSC_VER) #define LIB_API __declspec(dllexport) @@ -25,6 +26,7 @@ #define LIB_API #endif #endif +#endif #define NFRAMES 3 #define SECRET_NUM -1234 diff --git a/include/yolo_v2_class.hpp b/include/yolo_v2_class.hpp index e687b1e8ef8..522062518d3 100644 --- a/include/yolo_v2_class.hpp +++ b/include/yolo_v2_class.hpp @@ -1,23 +1,37 @@ #ifndef YOLO_V2_CLASS_HPP #define YOLO_V2_CLASS_HPP -#include "darknet.h" +#ifndef LIB_API +#ifdef LIB_EXPORTS +#if defined(_MSC_VER) +#define LIB_API __declspec(dllexport) +#else +#define LIB_API __attribute__((visibility("default"))) +#endif +#else +#if defined(_MSC_VER) +#define LIB_API +#else +#define LIB_API +#endif +#endif +#endif #define C_SHARP_MAX_OBJECTS 1000 struct bbox_t { - unsigned int x, y, w, h; // (x,y) - top-left corner, (w, h) - width & height of bounded box + unsigned int x, y, w, h; // (x,y) - top-left corner, (w, h) - width & height of bounded box float prob; // confidence - probability that the object was found correctly - unsigned int obj_id; // class of object - from range [0, classes-1] - unsigned int track_id; // tracking id for video (0 - untracked, 1 - inf - tracked object) - unsigned int frames_counter;// counter of frames on which the object was detected + unsigned int obj_id; // class of object - from range [0, classes-1] + unsigned int track_id; // tracking id for video (0 - untracked, 1 - inf - tracked object) + unsigned int frames_counter; // counter of frames on which the object was detected }; struct image_t { int h; // height int w; // width int c; // number of chanels (3 - for RGB) - float *data; // pointer to the image data + float *data; // pointer to the image data }; struct bbox_t_container { @@ -34,7 +48,7 @@ struct bbox_t_container { #include // C++ #include // C #include // C -#endif // OPENCV +#endif extern "C" LIB_API int init(const char *configurationFilename, const char *weightsFilename, int gpu); extern "C" LIB_API int detect_image(const char *filename, bbox_t_container &container); diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 9e2d106ec1a..07f69159979 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -18,6 +18,12 @@ #include "xnor_layer.h" #endif +#ifdef __cplusplus +#define PUT_IN_REGISTER +#else +#define PUT_IN_REGISTER register +#endif + #ifndef AI2 #define AI2 0 void forward_xnor_layer(layer l, network_state state); @@ -644,7 +650,7 @@ void gemm_nn_custom(int M, int N, int K, float ALPHA, int i, j, k; for (i = 0; i < M; ++i) { for (k = 0; k < K; ++k) { - float A_PART = ALPHA * A[i * lda + k]; + PUT_IN_REGISTER float A_PART = ALPHA * A[i * lda + k]; //printf("\n weight = %f \n", A_PART); for (j = 0; j < N; ++j) { C[i*ldc + j] += A_PART*B[k*ldb + j]; diff --git a/src/cpu_gemm.c b/src/cpu_gemm.c index 8305bb53158..ca1a8e4247b 100644 --- a/src/cpu_gemm.c +++ b/src/cpu_gemm.c @@ -1,4 +1,9 @@ //#include "mini_blas.h" +#ifdef __cplusplus +#define PUT_IN_REGISTER +#else +#define PUT_IN_REGISTER register +#endif void cpu_gemm_nn(int TA, int TB, int M, int N, int K, float ALPHA, float *A, int lda, @@ -9,7 +14,7 @@ void cpu_gemm_nn(int TA, int TB, int M, int N, int K, float ALPHA, int i,j,k; for(i = 0; i < M; ++i){ for(k = 0; k < K; ++k){ - float A_PART = ALPHA * A[i * lda + k]; + PUT_IN_REGISTER float A_PART = ALPHA * A[i * lda + k]; for(j = 0; j < N; ++j){ C[i*ldc+j] += A_PART*B[k*ldb+j]; } @@ -26,7 +31,7 @@ void cpu_gemm_nt(int TA, int TB, int M, int N, int K, float ALPHA, int i,j,k; for(i = 0; i < M; ++i){ for(j = 0; j < N; ++j){ - float sum = 0; + PUT_IN_REGISTER float sum = 0; for(k = 0; k < K; ++k){ sum += ALPHA*A[i*lda+k]*B[k+j*ldb]; } @@ -44,7 +49,7 @@ void cpu_gemm_tn(int TA, int TB, int M, int N, int K, float ALPHA, int i,j,k; for(i = 0; i < M; ++i){ for(k = 0; k < K; ++k){ - float A_PART = ALPHA * A[k * lda + i]; + PUT_IN_REGISTER float A_PART = ALPHA * A[k * lda + i]; for(j = 0; j < N; ++j){ C[i*ldc+j] += A_PART*B[k*ldb+j]; } diff --git a/src/gemm.c b/src/gemm.c index 2fc9af0251d..112bed9464f 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -18,6 +18,11 @@ #define TILE_M 4 // 4 ops #define TILE_N 16 // AVX2 = 2 ops * 8 floats #define TILE_K 16 // loop +#ifdef __cplusplus +#define PUT_IN_REGISTER +#else +#define PUT_IN_REGISTER register +#endif void gemm_bin(int M, int N, int K, float ALPHA, char *A, int lda, @@ -713,7 +718,7 @@ void gemm_nn(int M, int N, int K, float ALPHA, else { for (i = 0; i < M; ++i) { for (k = 0; k < K; ++k) { - float A_PART = ALPHA * A[i * lda + k]; + PUT_IN_REGISTER float A_PART = ALPHA * A[i * lda + k]; for (j = 0; j < N; ++j) { C[i*ldc + j] += A_PART*B[k*ldb + j]; } @@ -845,7 +850,7 @@ void gemm_nn_fast(int M, int N, int K, float ALPHA, { for (k_d = k; k_d < (k + TILE_K); ++k_d) { - register float A_PART = ALPHA*A[i_d*lda + k_d]; + PUT_IN_REGISTER float A_PART = ALPHA*A[i_d*lda + k_d]; C[i_d*ldc + j] += A_PART*B[k_d*ldb + j]; } } @@ -856,7 +861,7 @@ void gemm_nn_fast(int M, int N, int K, float ALPHA, { for (i_d = i; i_d < (i + TILE_M); ++i_d) { - register float A_PART = ALPHA*A[i_d*lda + k]; + PUT_IN_REGISTER float A_PART = ALPHA*A[i_d*lda + k]; for (j = 0; j < N; ++j) { C[i_d*ldc + j] += A_PART*B[k*ldb + j]; } @@ -867,7 +872,7 @@ void gemm_nn_fast(int M, int N, int K, float ALPHA, for (i = (M / TILE_M)*TILE_M; i < M; ++i) { int j, k; for (k = 0; k < K; ++k) { - register float A_PART = ALPHA*A[i*lda + k]; + PUT_IN_REGISTER float A_PART = ALPHA*A[i*lda + k]; for (j = 0; j < N; ++j) { C[i*ldc + j] += A_PART*B[k*ldb + j]; } @@ -890,7 +895,7 @@ void gemm_nn_bin_32bit_packed(int M, int N, int K, float ALPHA, //printf(" l.mean_arr[i] = %d \n ", l.mean_arr[i]); for (s = 0; s < K; ++s) // l.size*l.size*l.c/32 or (l.size*l.size*l.c) { - register uint32_t A_PART = A[i*lda + s]; + PUT_IN_REGISTER uint32_t A_PART = A[i*lda + s]; __m256i a256 = _mm256_set1_epi32(A_PART); for (j = 0; j < N - 8; j += 8) @@ -927,7 +932,7 @@ void gemm_nn_bin_32bit_packed(int M, int N, int K, float ALPHA, for (; j < N; ++j) // out_h*out_w; { - register uint32_t B_PART = B[s*ldb + j]; + PUT_IN_REGISTER uint32_t B_PART = B[s*ldb + j]; uint32_t xnor_result = ~(A_PART ^ B_PART); int32_t count = popcnt_32(xnor_result); // must be Signed int @@ -1950,7 +1955,7 @@ void gemm_nn(int M, int N, int K, float ALPHA, int i, j, k; for (i = 0; i < M; ++i) { for (k = 0; k < K; ++k) { - float A_PART = ALPHA * A[i * lda + k]; + PUT_IN_REGISTER float A_PART = ALPHA * A[i * lda + k]; for (j = 0; j < N; ++j) { C[i*ldc + j] += A_PART*B[k*ldb + j]; } @@ -1967,7 +1972,7 @@ void gemm_nn_fast(int M, int N, int K, float ALPHA, #pragma omp parallel for for (i = 0; i < M; ++i) { for (k = 0; k < K; ++k) { - register float A_PART = ALPHA*A[i*lda + k]; + PUT_IN_REGISTER float A_PART = ALPHA*A[i*lda + k]; for (j = 0; j < N; ++j) { C[i*ldc + j] += A_PART*B[k*ldb + j]; } @@ -1988,12 +1993,12 @@ void gemm_nn_bin_32bit_packed(int M, int N, int K, float ALPHA, //printf(" l.mean_arr[i] = %d \n ", l.mean_arr[i]); for (s = 0; s < K; ++s) // l.size*l.size*l.c/32 or (l.size*l.size*l.c) { - //register float A_PART = 1*a[i*k + s]; - register uint32_t A_PART = A[i*lda + s]; + //PUT_IN_REGISTER float A_PART = 1*a[i*k + s]; + PUT_IN_REGISTER uint32_t A_PART = A[i * lda + s]; for (j = 0; j < N; ++j) // out_h*out_w; { //c[i*n + j] += A_PART*b[s*n + j]; - register uint32_t B_PART = B[s*ldb + j]; + PUT_IN_REGISTER uint32_t B_PART = B[s * ldb + j]; uint32_t xnor_result = ~(A_PART ^ B_PART); //printf(" xnor_result = %d, ", xnor_result); int32_t count = popcnt_32(xnor_result); // must be Signed int @@ -2490,8 +2495,8 @@ void gemm_nn_bin_transposed_32bit_packed(int M, int N, int K, float ALPHA, float val = 0; for (s = 0; s < K; ++s) // l.size*l.size*l.c/32 or (l.size*l.size*l.c) { - register uint32_t A_PART = ((uint32_t*)A)[i*lda + s]; - register uint32_t B_PART = ((uint32_t*)B)[j*ldb + s]; + PUT_IN_REGISTER uint32_t A_PART = ((uint32_t*)A)[i*lda + s]; + PUT_IN_REGISTER uint32_t B_PART = ((uint32_t*)B)[j * ldb + s]; uint32_t xnor_result = ~(A_PART ^ B_PART); int32_t count = popcnt_32(xnor_result); // must be Signed int @@ -2576,7 +2581,7 @@ void gemm_nt(int M, int N, int K, float ALPHA, int i,j,k; for(i = 0; i < M; ++i){ for(j = 0; j < N; ++j){ - float sum = 0; + PUT_IN_REGISTER float sum = 0; for(k = 0; k < K; ++k){ sum += ALPHA*A[i*lda+k]*B[j*ldb + k]; } @@ -2593,7 +2598,7 @@ void gemm_tn(int M, int N, int K, float ALPHA, int i,j,k; for(i = 0; i < M; ++i){ for(k = 0; k < K; ++k){ - float A_PART = ALPHA * A[k * lda + i]; + PUT_IN_REGISTER float A_PART = ALPHA * A[k * lda + i]; for(j = 0; j < N; ++j){ C[i*ldc+j] += A_PART*B[k*ldb+j]; } @@ -2609,7 +2614,7 @@ void gemm_tt(int M, int N, int K, float ALPHA, int i,j,k; for(i = 0; i < M; ++i){ for(j = 0; j < N; ++j){ - float sum = 0; + PUT_IN_REGISTER float sum = 0; for(k = 0; k < K; ++k){ sum += ALPHA*A[i+k*lda]*B[k+j*ldb]; } diff --git a/src/image.c b/src/image.c index c3a24d37485..76deefe5b21 100644 --- a/src/image.c +++ b/src/image.c @@ -3,6 +3,9 @@ #include "blas.h" #include "cuda.h" #include +#ifndef _USE_MATH_DEFINES +#define _USE_MATH_DEFINES +#endif #include #ifndef STB_IMAGE_IMPLEMENTATION diff --git a/src/utils.c b/src/utils.c index 149a07ff89b..22633125378 100644 --- a/src/utils.c +++ b/src/utils.c @@ -2,6 +2,9 @@ #include #include #include +#ifndef _USE_MATH_DEFINES +#define _USE_MATH_DEFINES +#endif #include #include #include diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index 668ef5cd1ba..1b07b43bfb3 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -1,3 +1,4 @@ +#include "darknet.h" #include "yolo_v2_class.hpp" #include "network.h" From 9c64f885a85f98d8a1d8e841622793cd338c619a Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 19 Feb 2019 14:18:08 +0100 Subject: [PATCH 0341/1365] improve CMake default behaviour to use integrated libs --- CMakeLists.txt | 58 +++++++++++++++++++++------------- CMakeSettings.json | 78 ++++++++++++++++++++++++++++++++++++++++++++-- build.ps1 | 64 ++++++++++++++++++++----------------- build.sh | 42 +++++++++++++++++++++++++ 4 files changed, 190 insertions(+), 52 deletions(-) create mode 100755 build.sh diff --git a/CMakeLists.txt b/CMakeLists.txt index e2831afb6c3..38a521d0335 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,3 +1,6 @@ +# To use your libs, launch cmake with -DUSE_INTEGRATED_LIBS:BOOL=FALSE +# To enable CUDA, launch cmake with -DENABLE_CUDA. Also it is advisable to set the highest compute model with -DCOMPUTE_MODEL=..., depending on your specific card capabilities. If CUDA is not required, this setting is completely ignored. + cmake_minimum_required(VERSION 3.8 FATAL_ERROR) #set(CMAKE_VERBOSE_MAKEFILE 1) @@ -8,27 +11,13 @@ endif() project(darknet) -# To enable CUDA, launch cmake with -DENABLE_CUDA. Also it is advisable to set the highest compute model from below, depending on your specific card capabilities -# If CUDA is not required, this setting is completely ignored. -#set(COMPUTE_MODEL "75") #Tesla T4, RTX 2080 -#set(COMPUTE_MODEL "72") #Jetson Xavier -#set(COMPUTE_MODEL "70") #Tesla V100 -#set(COMPUTE_MODEL "62") #Jetson TX2 -#set(COMPUTE_MODEL "61") #Telsa P40 -set(COMPUTE_MODEL "60") #Telsa P100 -#set(COMPUTE_MODEL "53") #Jetson TX1 -#set(COMPUTE_MODEL "52") #Tesla M40/M60 -#set(COMPUTE_MODEL "37") #Tesla K80 -#set(COMPUTE_MODEL "35") #Tesla K20/K40 -#set(COMPUTE_MODEL "30") #Tesla K10, Quadro K4000 -### -if(FORCE_CUSTOM_LIBS) - include_directories(${CMAKE_CURRENT_LIST_DIR}/3rdparty/include) - set(PTHREADS_INCLUDE_DIR ${CMAKE_CURRENT_LIST_DIR}/3rdparty/include) - set(PTHREADS_LIBRARY ${CMAKE_CURRENT_LIST_DIR}/3rdparty/lib/x64/pthreadVC2.lib) - add_definitions(-D_TIMESPEC_DEFINED) +if(WIN32 AND NOT DEFINED CMAKE_TOOLCHAIN_FILE) + set(USE_INTEGRATED_LIBS "TRUE" CACHE BOOL "Use libs distributed with this repo") +else() + set(USE_INTEGRATED_LIBS "FALSE" CACHE BOOL "Use libs distributed with this repo") endif() -### + +set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable building with CUDA") enable_language(C) enable_language(CXX) @@ -41,6 +30,33 @@ set(CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/cmake/Modules/" ${CMAKE_MODULE_ include_directories(${CMAKE_CURRENT_LIST_DIR}/include) include_directories(${CMAKE_CURRENT_LIST_DIR}/src) +set(COMPUTE_MODEL_75 "75") #Compute capability for Tesla T4, RTX 2080 +set(COMPUTE_MODEL_72 "72") #Compute capability for Jetson Xavier +set(COMPUTE_MODEL_70 "70") #Compute capability for Tesla V100 +set(COMPUTE_MODEL_62 "62") #Compute capability for Jetson TX2 +set(COMPUTE_MODEL_61 "61") #Compute capability for Tesla P40 +set(COMPUTE_MODEL_60 "60") #Compute capability for Tesla P100 +set(COMPUTE_MODEL_53 "53") #Compute capability for Jetson TX1 +set(COMPUTE_MODEL_52 "52") #Compute capability for Tesla M40/M60 +set(COMPUTE_MODEL_37 "37") #Compute capability for Tesla K80 +set(COMPUTE_MODEL_35 "35") #Compute capability for Tesla K20/K40 +set(COMPUTE_MODEL_30 "30") #Compute capability for Tesla K10, Quadro K4000 + +set(default_compute_model ${COMPUTE_MODEL_30}) +if(NOT COMPUTE_MODEL) + message(STATUS "Setting the compute model to '${default_compute_model}' as none was specified.") + set(COMPUTE_MODEL "${default_compute_model}" CACHE + STRING "Choose the best compute model supported by your GPU" FORCE) + set_property(CACHE COMPUTE_MODEL PROPERTY STRINGS ${COMPUTE_MODEL_75} ${COMPUTE_MODEL_72} ${COMPUTE_MODEL_70} ${COMPUTE_MODEL_62} ${COMPUTE_MODEL_61} ${COMPUTE_MODEL_60} ${COMPUTE_MODEL_53} ${COMPUTE_MODEL_52} ${COMPUTE_MODEL_37} ${COMPUTE_MODEL_35} ${COMPUTE_MODEL_30}) +endif() + +if(USE_INTEGRATED_LIBS) + include_directories(${CMAKE_CURRENT_LIST_DIR}/3rdparty/include) + set(PTHREADS_INCLUDE_DIR ${CMAKE_CURRENT_LIST_DIR}/3rdparty/include) + set(PTHREADS_LIBRARY ${CMAKE_CURRENT_LIST_DIR}/3rdparty/lib/x64/pthreadVC2.lib) + add_definitions(-D_TIMESPEC_DEFINED) +endif() + if(NOT APPLE) set(CMAKE_SKIP_BUILD_RPATH FALSE) set(CMAKE_BUILD_WITH_INSTALL_RPATH FALSE) @@ -79,7 +95,7 @@ if ("${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang") string(REGEX REPLACE "-O3" "-Ofast" CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) endif() -if(NOT FORCE_CUSTOM_LIBS) +if(NOT USE_INTEGRATED_LIBS) if(APPLE) find_package(OpenCV PATHS "/usr/local/Cellar/opencv@3/3.4.5" NO_DEFAULT_PATH) else() diff --git a/CMakeSettings.json b/CMakeSettings.json index 1c8184e4d79..585d6851d22 100644 --- a/CMakeSettings.json +++ b/CMakeSettings.json @@ -1,17 +1,35 @@ { "configurations": [ + { + "buildCommandArgs": "-m -v:minimal", + "buildRoot": "${env.USERPROFILE}\\CMakeBuilds\\${workspaceHash}\\build\\${name}", + "cmakeCommandArgs": "", + "configurationType": "Release", + "generator": "Visual Studio 15 2017 Win64", + "name": "x64-Release CPU - int. libs", + "variables": [ + { + "name": "USE_INTEGRATED_LIBS", + "value": "TRUE" + } + ] + }, { "buildCommandArgs": "-m -v:minimal", "buildRoot": "${env.USERPROFILE}\\CMakeBuilds\\${workspaceHash}\\build\\${name}", "cmakeCommandArgs": "", "configurationType": "Debug", "generator": "Visual Studio 15 2017 Win64", - "name": "x64-Debug", + "name": "x64-Debug CPU", "variables": [ { "name": "CMAKE_TOOLCHAIN_FILE", "value": "${env.VCPKG_ROOT}\\scripts\\buildsystems\\vcpkg.cmake" }, + { + "name": "USE_INTEGRATED_LIBS", + "value": "FALSE" + }, { "name": "VCPKG_TARGET_TRIPLET", "value": "${env.VCPKG_DEFAULT_TRIPLET}" @@ -24,12 +42,68 @@ "cmakeCommandArgs": "", "configurationType": "Release", "generator": "Visual Studio 15 2017 Win64", - "name": "x64-Release", + "name": "x64-Release CPU", "variables": [ { "name": "CMAKE_TOOLCHAIN_FILE", "value": "${env.VCPKG_ROOT}\\scripts\\buildsystems\\vcpkg.cmake" }, + { + "name": "USE_INTEGRATED_LIBS", + "value": "FALSE" + }, + { + "name": "VCPKG_TARGET_TRIPLET", + "value": "${env.VCPKG_DEFAULT_TRIPLET}" + } + ] + }, + { + "buildCommandArgs": "-m -v:minimal", + "buildRoot": "${env.USERPROFILE}\\CMakeBuilds\\${workspaceHash}\\build\\${name}", + "cmakeCommandArgs": "", + "configurationType": "Debug", + "generator": "Visual Studio 15 2017 Win64", + "name": "x64-Debug GPU", + "variables": [ + { + "name": "CMAKE_TOOLCHAIN_FILE", + "value": "${env.VCPKG_ROOT}\\scripts\\buildsystems\\vcpkg.cmake" + }, + { + "name": "USE_INTEGRATED_LIBS", + "value": "FALSE" + }, + { + "name": "ENABLE_CUDA", + "value": "TRUE" + }, + { + "name": "VCPKG_TARGET_TRIPLET", + "value": "${env.VCPKG_DEFAULT_TRIPLET}" + } + ] + }, + { + "buildCommandArgs": "-m -v:minimal", + "buildRoot": "${env.USERPROFILE}\\CMakeBuilds\\${workspaceHash}\\build\\${name}", + "cmakeCommandArgs": "", + "configurationType": "Release", + "generator": "Visual Studio 15 2017 Win64", + "name": "x64-Release GPU", + "variables": [ + { + "name": "CMAKE_TOOLCHAIN_FILE", + "value": "${env.VCPKG_ROOT}\\scripts\\buildsystems\\vcpkg.cmake" + }, + { + "name": "USE_INTEGRATED_LIBS", + "value": "FALSE" + }, + { + "name": "ENABLE_CUDA", + "value": "TRUE" + }, { "name": "VCPKG_TARGET_TRIPLET", "value": "${env.VCPKG_DEFAULT_TRIPLET}" diff --git a/build.ps1 b/build.ps1 index f1080ab7a01..1590054b1ea 100755 --- a/build.ps1 +++ b/build.ps1 @@ -1,46 +1,52 @@ #!/usr/bin/env pwsh -pushd "C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\Common7\Tools" -cmd /c "VsDevCmd.bat -arch=x64 & set" | -foreach { - if ($_ -match "=") { - $v = $_.split("="); set-item -force -path "ENV:\$($v[0])" -value "$($v[1])" +$number_of_build_workers=8 +$vstype="Community" + +if ((Get-Command "cl.exe" -ErrorAction SilentlyContinue) -eq $null) +{ + pushd "C:\Program Files (x86)\Microsoft Visual Studio\2017\${vstype}\Common7\Tools" + cmd /c "VsDevCmd.bat -arch=x64 & set" | + foreach { + if ($_ -match "=") { + $v = $_.split("="); set-item -force -path "ENV:\$($v[0])" -value "$($v[1])" + } } + popd + Write-Host "Visual Studio 2017 ${vstype} Command Prompt variables set.`n" -ForegroundColor Yellow } -popd -Write-Host "Visual Studio 2017 Command Prompt variables set.`n" -ForegroundColor Yellow # CPU ONLY, DEBUG -#New-Item -Path .\build_win_debug -ItemType directory -Force -#Set-Location build_win_debug -#cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$env:VCPKG_ROOT\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$env:VCPKG_DEFAULT_TRIPLET" "-DCMAKE_BUILD_TYPE=Debug" .. -#cmake --build . --config Debug -#Set-Location .. +New-Item -Path .\build_win_debug -ItemType directory -Force +Set-Location build_win_debug +cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$env:VCPKG_ROOT\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$env:VCPKG_DEFAULT_TRIPLET" "-DCMAKE_BUILD_TYPE=Debug" .. +cmake --build . --config Debug --parallel ${number_of_build_workers} +Set-Location .. # CPU ONLY, RELEASE -#New-Item -Path .\build_win_release -ItemType directory -Force -#Set-Location build_win_release -#cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$env:VCPKG_ROOT\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$env:VCPKG_DEFAULT_TRIPLET" "-DCMAKE_BUILD_TYPE=Release" .. -#cmake --build . --config Release -#Set-Location .. +New-Item -Path .\build_win_release -ItemType directory -Force +Set-Location build_win_release +cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$env:VCPKG_ROOT\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$env:VCPKG_DEFAULT_TRIPLET" "-DCMAKE_BUILD_TYPE=Release" .. +cmake --build . --config Release --parallel ${number_of_build_workers} +Set-Location .. # CUDA, DEBUG -#New-Item -Path .\build_win_debug_cuda -ItemType directory -Force -#Set-Location build_win_debug_cuda -#cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DENABLE_CUDA:BOOL=TRUE" "-DCMAKE_TOOLCHAIN_FILE=$env:VCPKG_ROOT\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$env:VCPKG_DEFAULT_TRIPLET" "-DCMAKE_BUILD_TYPE=Debug" .. -#cmake --build . --config Debug -#Set-Location .. +New-Item -Path .\build_win_debug_cuda -ItemType directory -Force +Set-Location build_win_debug_cuda +cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DENABLE_CUDA:BOOL=TRUE" "-DCMAKE_TOOLCHAIN_FILE=$env:VCPKG_ROOT\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$env:VCPKG_DEFAULT_TRIPLET" "-DCMAKE_BUILD_TYPE=Debug" .. +cmake --build . --config Debug --parallel ${number_of_build_workers} +Set-Location .. # CUDA, RELEASE -#New-Item -Path .\build_win_release_cuda -ItemType directory -Force -#Set-Location build_win_release_cuda -#cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DENABLE_CUDA:BOOL=TRUE" "-DCMAKE_TOOLCHAIN_FILE=$env:VCPKG_ROOT\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$env:VCPKG_DEFAULT_TRIPLET" "-DCMAKE_BUILD_TYPE=Release" .. -#cmake --build . --config Release -#Set-Location .. +New-Item -Path .\build_win_release_cuda -ItemType directory -Force +Set-Location build_win_release_cuda +cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DENABLE_CUDA:BOOL=TRUE" "-DCMAKE_TOOLCHAIN_FILE=$env:VCPKG_ROOT\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$env:VCPKG_DEFAULT_TRIPLET" "-DCMAKE_BUILD_TYPE=Release" .. +cmake --build . --config Release --parallel ${number_of_build_workers} +Set-Location .. # CPU ONLY, USE LOCAL PTHREAD LIB, NO VCPKG: remember to use "vcpkg.exe integrate remove" in case you had enable user-wide vcpkg integration New-Item -Path .\build_win_release_cuda_custom_libs -ItemType directory -Force Set-Location build_win_release_cuda_custom_libs -cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DFORCE_CUSTOM_LIBS:BOOL=TRUE" "-DENABLE_CUDA:BOOL=TRUE" .. -cmake --build . --config Release +cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" .. +cmake --build . --config Release --parallel ${number_of_build_workers} Set-Location .. diff --git a/build.sh b/build.sh new file mode 100755 index 00000000000..946d91758f9 --- /dev/null +++ b/build.sh @@ -0,0 +1,42 @@ +#!/usr/bin/env bash + +number_of_build_workers=8 + +if [[ "$OSTYPE" == "darwin"* && "$1" == "gcc" ]]; then + export CC="/usr/local/bin/gcc-8" + export CXX="/usr/local/bin/g++-8" +fi + +rm -f uselib darknet + +# CPU ONLY, DEBUG +mkdir -p build_debug +cd build_debug +cmake .. -DCMAKE_BUILD_TYPE=Debug +cmake --build . --target install -- -j${number_of_build_workers} +#cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ +cd .. + +# CPU ONLY, RELEASE +mkdir -p build_release +cd build_release +cmake .. -DCMAKE_BUILD_TYPE=Release +cmake --build . --target install -- -j${number_of_build_workers} +#cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ +cd .. + +# CUDA, DEBUG +mkdir -p build_debug_gpu +cd build_debug_gpu +cmake .. -DCMAKE_BUILD_TYPE=Debug -DENABLE_CUDA:BOOL=TRUE +cmake --build . --target install -- -j${number_of_build_workers} +#cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ +cd .. + +# CUDA, RELEASE +mkdir -p build_release_gpu +cd build_release_gpu +cmake .. -DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA:BOOL=TRUE +cmake --build . --target install -- -j${number_of_build_workers} +#cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ +cd .. From f950085a45bf0aeaf6a34033a62aaee31ec94f8a Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 20 Feb 2019 11:42:53 +0100 Subject: [PATCH 0342/1365] automatically detect CUDA and use it, without any possible manual intervention --- CMakeLists.txt | 74 +++++++++++++++++------------------ CMakeSettings.json | 58 ++------------------------- build.ps1 | 22 ++--------- build.sh | 20 +--------- cmake/Modules/FindCUDNN.cmake | 12 +----- 5 files changed, 48 insertions(+), 138 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 38a521d0335..3565e430ffb 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,5 +1,4 @@ # To use your libs, launch cmake with -DUSE_INTEGRATED_LIBS:BOOL=FALSE -# To enable CUDA, launch cmake with -DENABLE_CUDA. Also it is advisable to set the highest compute model with -DCOMPUTE_MODEL=..., depending on your specific card capabilities. If CUDA is not required, this setting is completely ignored. cmake_minimum_required(VERSION 3.8 FATAL_ERROR) @@ -17,12 +16,13 @@ else() set(USE_INTEGRATED_LIBS "FALSE" CACHE BOOL "Use libs distributed with this repo") endif() -set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable building with CUDA") - enable_language(C) enable_language(CXX) -if(ENABLE_CUDA) - enable_language(CUDA) # do not enable after searching for OpenCV package! + +include(CheckLanguage) +check_language(CUDA) +if(CMAKE_CUDA_COMPILER) + enable_language(CUDA) endif() set(CMAKE_CXX_STANDARD 11) @@ -30,24 +30,24 @@ set(CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/cmake/Modules/" ${CMAKE_MODULE_ include_directories(${CMAKE_CURRENT_LIST_DIR}/include) include_directories(${CMAKE_CURRENT_LIST_DIR}/src) -set(COMPUTE_MODEL_75 "75") #Compute capability for Tesla T4, RTX 2080 -set(COMPUTE_MODEL_72 "72") #Compute capability for Jetson Xavier -set(COMPUTE_MODEL_70 "70") #Compute capability for Tesla V100 -set(COMPUTE_MODEL_62 "62") #Compute capability for Jetson TX2 -set(COMPUTE_MODEL_61 "61") #Compute capability for Tesla P40 -set(COMPUTE_MODEL_60 "60") #Compute capability for Tesla P100 -set(COMPUTE_MODEL_53 "53") #Compute capability for Jetson TX1 -set(COMPUTE_MODEL_52 "52") #Compute capability for Tesla M40/M60 -set(COMPUTE_MODEL_37 "37") #Compute capability for Tesla K80 -set(COMPUTE_MODEL_35 "35") #Compute capability for Tesla K20/K40 -set(COMPUTE_MODEL_30 "30") #Compute capability for Tesla K10, Quadro K4000 - -set(default_compute_model ${COMPUTE_MODEL_30}) -if(NOT COMPUTE_MODEL) - message(STATUS "Setting the compute model to '${default_compute_model}' as none was specified.") - set(COMPUTE_MODEL "${default_compute_model}" CACHE +set(CUDA_COMPUTE_MODEL_75 "75") #Compute capability for Tesla T4, RTX 2080 +set(CUDA_COMPUTE_MODEL_72 "72") #Compute capability for Jetson Xavier +set(CUDA_COMPUTE_MODEL_70 "70") #Compute capability for Tesla V100 +set(CUDA_COMPUTE_MODEL_62 "62") #Compute capability for Jetson TX2 +set(CUDA_COMPUTE_MODEL_61 "61") #Compute capability for Tesla P40 +set(CUDA_COMPUTE_MODEL_60 "60") #Compute capability for Tesla P100 +set(CUDA_COMPUTE_MODEL_53 "53") #Compute capability for Jetson TX1 +set(CUDA_COMPUTE_MODEL_52 "52") #Compute capability for Tesla M40/M60 +set(CUDA_COMPUTE_MODEL_37 "37") #Compute capability for Tesla K80 +set(CUDA_COMPUTE_MODEL_35 "35") #Compute capability for Tesla K20/K40 +set(CUDA_COMPUTE_MODEL_30 "30") #Compute capability for Tesla K10, Quadro K4000 + +set(default_cuda_compute_model ${CUDA_COMPUTE_MODEL_30}) +if(CMAKE_CUDA_COMPILER AND NOT CUDA_COMPUTE_MODEL) + message(STATUS "Setting the compute model to ${default_cuda_compute_model} as none was specified.") + set(CUDA_COMPUTE_MODEL "${default_cuda_compute_model}" CACHE STRING "Choose the best compute model supported by your GPU" FORCE) - set_property(CACHE COMPUTE_MODEL PROPERTY STRINGS ${COMPUTE_MODEL_75} ${COMPUTE_MODEL_72} ${COMPUTE_MODEL_70} ${COMPUTE_MODEL_62} ${COMPUTE_MODEL_61} ${COMPUTE_MODEL_60} ${COMPUTE_MODEL_53} ${COMPUTE_MODEL_52} ${COMPUTE_MODEL_37} ${COMPUTE_MODEL_35} ${COMPUTE_MODEL_30}) + set_property(CACHE CUDA_COMPUTE_MODEL PROPERTY STRINGS ${CUDA_COMPUTE_MODEL_75} ${CUDA_COMPUTE_MODEL_72} ${CUDA_COMPUTE_MODEL_70} ${CUDA_COMPUTE_MODEL_62} ${CUDA_COMPUTE_MODEL_61} ${CUDA_COMPUTE_MODEL_60} ${CUDA_COMPUTE_MODEL_53} ${CUDA_COMPUTE_MODEL_52} ${CUDA_COMPUTE_MODEL_37} ${CUDA_COMPUTE_MODEL_35} ${CUDA_COMPUTE_MODEL_30}) endif() if(USE_INTEGRATED_LIBS) @@ -108,17 +108,17 @@ if(OpenCV_FOUND) include_directories(${OpenCV_INCLUDE_DIRS}) endif() -if(ENABLE_CUDA) +if(CMAKE_CUDA_COMPILER) add_definitions(-DGPU) - if(COMPUTE_MODEL VERSION_GREATER_EQUAL 75 AND CMAKE_CUDA_COMPILER_VERSION VERSION_LESS_EQUAL 9) + if(CUDA_COMPUTE_MODEL VERSION_GREATER_EQUAL 75 AND CMAKE_CUDA_COMPILER_VERSION VERSION_LESS_EQUAL 9) message(STATUS "CUDA 10 is required for Compute Model 7.5 - Lowering settings to 7.0") - set(COMPUTE_MODEL "70") + set(CUDA_COMPUTE_MODEL ${CUDA_COMPUTE_MODEL_70}) endif() find_package(CUDNN) if(CUDNN_FOUND) add_definitions(-DCUDNN) include_directories(${CUDNN_INCLUDE_DIR}) - if(COMPUTE_MODEL VERSION_GREATER_EQUAL 53) + if(CUDA_COMPUTE_MODEL VERSION_GREATER_EQUAL 53) add_definitions(-DCUDNN_HALF) message(STATUS "Enabled CUDNN_HALF") endif() @@ -126,29 +126,29 @@ if(ENABLE_CUDA) if (MSVC) if(CUDNN_FOUND) if(OpenCV_FOUND) - set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${COMPUTE_MODEL},code=[sm_${COMPUTE_MODEL},compute_${COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/DGPU,/DCUDNN,/DOPENCV\" ${CMAKE_CUDA_FLAGS}") + set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/DGPU,/DCUDNN,/DOPENCV\" ${CMAKE_CUDA_FLAGS}") else() - set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${COMPUTE_MODEL},code=[sm_${COMPUTE_MODEL},compute_${COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/DGPU,/DCUDNN\" ${CMAKE_CUDA_FLAGS}") + set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/DGPU,/DCUDNN\" ${CMAKE_CUDA_FLAGS}") endif() else() if(OpenCV_FOUND) - set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${COMPUTE_MODEL},code=[sm_${COMPUTE_MODEL},compute_${COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/DGPU,/DOPENCV\" ${CMAKE_CUDA_FLAGS}") + set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/DGPU,/DOPENCV\" ${CMAKE_CUDA_FLAGS}") else() - set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${COMPUTE_MODEL},code=[sm_${COMPUTE_MODEL},compute_${COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/DGPU\" ${CMAKE_CUDA_FLAGS}") + set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/DGPU\" ${CMAKE_CUDA_FLAGS}") endif() endif() else() if(CUDNN_FOUND) if(OpenCV_FOUND) - set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${COMPUTE_MODEL},code=[sm_${COMPUTE_MODEL},compute_${COMPUTE_MODEL}] --compiler-options \"-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -fPIC -fopenmp -Ofast -DOPENCV -DGPU -DCUDNN \" ${CMAKE_CUDA_FLAGS}") + set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] --compiler-options \"-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -fPIC -fopenmp -Ofast -DOPENCV -DGPU -DCUDNN \" ${CMAKE_CUDA_FLAGS}") else() - set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${COMPUTE_MODEL},code=[sm_${COMPUTE_MODEL},compute_${COMPUTE_MODEL}] --compiler-options \"-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -fPIC -fopenmp -Ofast -DGPU -DCUDNN \" ${CMAKE_CUDA_FLAGS}") + set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] --compiler-options \"-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -fPIC -fopenmp -Ofast -DGPU -DCUDNN \" ${CMAKE_CUDA_FLAGS}") endif() else() if(OpenCV_FOUND) - set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${COMPUTE_MODEL},code=[sm_${COMPUTE_MODEL},compute_${COMPUTE_MODEL}] --compiler-options \"-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -fPIC -fopenmp -Ofast -DOPENCV -DGPU \" ${CMAKE_CUDA_FLAGS}") + set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] --compiler-options \"-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -fPIC -fopenmp -Ofast -DOPENCV -DGPU \" ${CMAKE_CUDA_FLAGS}") else() - set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${COMPUTE_MODEL},code=[sm_${COMPUTE_MODEL},compute_${COMPUTE_MODEL}] --compiler-options \"-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -fPIC -fopenmp -Ofast -DGPU \" ${CMAKE_CUDA_FLAGS}") + set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] --compiler-options \"-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -fPIC -fopenmp -Ofast -DGPU \" ${CMAKE_CUDA_FLAGS}") endif() endif() endif() @@ -195,7 +195,7 @@ list(REMOVE_ITEM sources ${CMAKE_CURRENT_LIST_DIR}/src/getopt.c ) -if(ENABLE_CUDA) +if(CMAKE_CUDA_COMPILER) file(GLOB cuda_sources "${CMAKE_CURRENT_LIST_DIR}/src/*.cu") endif() @@ -215,7 +215,7 @@ set_source_files_properties(${sources} PROPERTIES LANGUAGE CXX) add_library(darklib SHARED ${CMAKE_CURRENT_LIST_DIR}/include/yolo_v2_class.hpp ${CMAKE_CURRENT_LIST_DIR}/src/yolo_v2_class.cpp ${sources} ${headers} ${cuda_sources}) set_target_properties(darklib PROPERTIES POSITION_INDEPENDENT_CODE ON) -if(ENABLE_CUDA) +if(CMAKE_CUDA_COMPILER) set_target_properties(darklib PROPERTIES CUDA_SEPARABLE_COMPILATION ON) endif() target_compile_definitions(darklib PRIVATE LIB_EXPORTS=1) @@ -264,7 +264,7 @@ endif() target_link_libraries(darknet Threads::Threads) target_link_libraries(darklib Threads::Threads) -if(ENABLE_CUDA) +if(CMAKE_CUDA_COMPILER) target_link_libraries(darknet curand cublas) target_link_libraries(darklib curand cublas) endif() diff --git a/CMakeSettings.json b/CMakeSettings.json index 585d6851d22..968e68bae45 100644 --- a/CMakeSettings.json +++ b/CMakeSettings.json @@ -6,7 +6,7 @@ "cmakeCommandArgs": "", "configurationType": "Release", "generator": "Visual Studio 15 2017 Win64", - "name": "x64-Release CPU - int. libs", + "name": "x64-Release-int-libs", "variables": [ { "name": "USE_INTEGRATED_LIBS", @@ -20,7 +20,7 @@ "cmakeCommandArgs": "", "configurationType": "Debug", "generator": "Visual Studio 15 2017 Win64", - "name": "x64-Debug CPU", + "name": "x64-Debug", "variables": [ { "name": "CMAKE_TOOLCHAIN_FILE", @@ -42,7 +42,7 @@ "cmakeCommandArgs": "", "configurationType": "Release", "generator": "Visual Studio 15 2017 Win64", - "name": "x64-Release CPU", + "name": "x64-Release", "variables": [ { "name": "CMAKE_TOOLCHAIN_FILE", @@ -57,58 +57,6 @@ "value": "${env.VCPKG_DEFAULT_TRIPLET}" } ] - }, - { - "buildCommandArgs": "-m -v:minimal", - "buildRoot": "${env.USERPROFILE}\\CMakeBuilds\\${workspaceHash}\\build\\${name}", - "cmakeCommandArgs": "", - "configurationType": "Debug", - "generator": "Visual Studio 15 2017 Win64", - "name": "x64-Debug GPU", - "variables": [ - { - "name": "CMAKE_TOOLCHAIN_FILE", - "value": "${env.VCPKG_ROOT}\\scripts\\buildsystems\\vcpkg.cmake" - }, - { - "name": "USE_INTEGRATED_LIBS", - "value": "FALSE" - }, - { - "name": "ENABLE_CUDA", - "value": "TRUE" - }, - { - "name": "VCPKG_TARGET_TRIPLET", - "value": "${env.VCPKG_DEFAULT_TRIPLET}" - } - ] - }, - { - "buildCommandArgs": "-m -v:minimal", - "buildRoot": "${env.USERPROFILE}\\CMakeBuilds\\${workspaceHash}\\build\\${name}", - "cmakeCommandArgs": "", - "configurationType": "Release", - "generator": "Visual Studio 15 2017 Win64", - "name": "x64-Release GPU", - "variables": [ - { - "name": "CMAKE_TOOLCHAIN_FILE", - "value": "${env.VCPKG_ROOT}\\scripts\\buildsystems\\vcpkg.cmake" - }, - { - "name": "USE_INTEGRATED_LIBS", - "value": "FALSE" - }, - { - "name": "ENABLE_CUDA", - "value": "TRUE" - }, - { - "name": "VCPKG_TARGET_TRIPLET", - "value": "${env.VCPKG_DEFAULT_TRIPLET}" - } - ] } ] } diff --git a/build.ps1 b/build.ps1 index 1590054b1ea..836998e169d 100755 --- a/build.ps1 +++ b/build.ps1 @@ -16,36 +16,22 @@ if ((Get-Command "cl.exe" -ErrorAction SilentlyContinue) -eq $null) Write-Host "Visual Studio 2017 ${vstype} Command Prompt variables set.`n" -ForegroundColor Yellow } -# CPU ONLY, DEBUG +# DEBUG New-Item -Path .\build_win_debug -ItemType directory -Force Set-Location build_win_debug cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$env:VCPKG_ROOT\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$env:VCPKG_DEFAULT_TRIPLET" "-DCMAKE_BUILD_TYPE=Debug" .. cmake --build . --config Debug --parallel ${number_of_build_workers} Set-Location .. -# CPU ONLY, RELEASE +# RELEASE New-Item -Path .\build_win_release -ItemType directory -Force Set-Location build_win_release cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$env:VCPKG_ROOT\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$env:VCPKG_DEFAULT_TRIPLET" "-DCMAKE_BUILD_TYPE=Release" .. cmake --build . --config Release --parallel ${number_of_build_workers} Set-Location .. -# CUDA, DEBUG -New-Item -Path .\build_win_debug_cuda -ItemType directory -Force -Set-Location build_win_debug_cuda -cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DENABLE_CUDA:BOOL=TRUE" "-DCMAKE_TOOLCHAIN_FILE=$env:VCPKG_ROOT\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$env:VCPKG_DEFAULT_TRIPLET" "-DCMAKE_BUILD_TYPE=Debug" .. -cmake --build . --config Debug --parallel ${number_of_build_workers} -Set-Location .. - -# CUDA, RELEASE -New-Item -Path .\build_win_release_cuda -ItemType directory -Force -Set-Location build_win_release_cuda -cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DENABLE_CUDA:BOOL=TRUE" "-DCMAKE_TOOLCHAIN_FILE=$env:VCPKG_ROOT\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$env:VCPKG_DEFAULT_TRIPLET" "-DCMAKE_BUILD_TYPE=Release" .. -cmake --build . --config Release --parallel ${number_of_build_workers} -Set-Location .. - -# CPU ONLY, USE LOCAL PTHREAD LIB, NO VCPKG: remember to use "vcpkg.exe integrate remove" in case you had enable user-wide vcpkg integration -New-Item -Path .\build_win_release_cuda_custom_libs -ItemType directory -Force +# USE LOCAL PTHREAD LIB, NO VCPKG: remember to use "vcpkg.exe integrate remove" in case you had enable user-wide vcpkg integration +New-Item -Path .\build_win_release_cuda_int_libs -ItemType directory -Force Set-Location build_win_release_cuda_custom_libs cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" .. cmake --build . --config Release --parallel ${number_of_build_workers} diff --git a/build.sh b/build.sh index 946d91758f9..444c5b2454f 100755 --- a/build.sh +++ b/build.sh @@ -9,7 +9,7 @@ fi rm -f uselib darknet -# CPU ONLY, DEBUG +# DEBUG mkdir -p build_debug cd build_debug cmake .. -DCMAKE_BUILD_TYPE=Debug @@ -17,26 +17,10 @@ cmake --build . --target install -- -j${number_of_build_workers} #cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ cd .. -# CPU ONLY, RELEASE +# RELEASE mkdir -p build_release cd build_release cmake .. -DCMAKE_BUILD_TYPE=Release cmake --build . --target install -- -j${number_of_build_workers} #cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ cd .. - -# CUDA, DEBUG -mkdir -p build_debug_gpu -cd build_debug_gpu -cmake .. -DCMAKE_BUILD_TYPE=Debug -DENABLE_CUDA:BOOL=TRUE -cmake --build . --target install -- -j${number_of_build_workers} -#cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ -cd .. - -# CUDA, RELEASE -mkdir -p build_release_gpu -cd build_release_gpu -cmake .. -DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA:BOOL=TRUE -cmake --build . --target install -- -j${number_of_build_workers} -#cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ -cd .. diff --git a/cmake/Modules/FindCUDNN.cmake b/cmake/Modules/FindCUDNN.cmake index c5f28c53b9a..6e53311a519 100644 --- a/cmake/Modules/FindCUDNN.cmake +++ b/cmake/Modules/FindCUDNN.cmake @@ -19,26 +19,18 @@ # ``CUDNN_LIBRARIES`` # The CUDNN libraries. # -# Hints -# ^^^^^ -# -# ``CUDNN_ROOT_DIR`` -# Set this variable to a directory that contains a CUDNN installation. -# include(FindPackageHandleStandardArgs) -#set(CUDNN_ROOT_DIR "" CACHE PATH "Folder contains NVIDIA cuDNN") - if(NOT CUDNN_INCLUDE_DIR) find_path(CUDNN_INCLUDE_DIR cudnn.h - HINTS ${CUDNN_ROOT_DIR} ${CUDA_HOME} ${CUDA_TOOLKIT_ROOT_DIR} + HINTS ${CUDA_HOME} ${CUDA_TOOLKIT_ROOT_DIR} PATH_SUFFIXES cuda/include include) endif() if(NOT CUDNN_LIBRARY) find_library(CUDNN_LIBRARY cudnn - HINTS ${CUDNN_ROOT_DIR} ${CUDA_HOME} ${CUDA_TOOLKIT_ROOT_DIR} + HINTS ${CUDA_HOME} ${CUDA_TOOLKIT_ROOT_DIR} PATH_SUFFIXES lib lib64 cuda/lib cuda/lib64 lib/x64) endif() From 839f7c0f046d4512a6ee8d7fff2038e674f17aaf Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 20 Feb 2019 11:51:17 +0100 Subject: [PATCH 0343/1365] remove some unnecessary exports --- src/gemm.c | 2 -- src/gemm.h | 2 +- src/gettimeofday.c | 4 ++-- src/gettimeofday.h | 4 ++-- src/image.h | 6 +++--- src/lstm_layer.h | 2 +- 6 files changed, 9 insertions(+), 11 deletions(-) diff --git a/src/gemm.c b/src/gemm.c index 112bed9464f..4948fbef5b7 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -477,8 +477,6 @@ void transpose_bin(char *A, char *B, const int n, const int m, } */ -#else -extern void transpose_32x32_bits_reversed_diagonale(uint32_t* A, uint32_t* B, int m, int n); #endif // transpose by 32-bit diff --git a/src/gemm.h b/src/gemm.h index e93ee0e4e7d..de8a5e1af2e 100644 --- a/src/gemm.h +++ b/src/gemm.h @@ -59,7 +59,7 @@ void im2col_cpu_custom_transpose(float* data_im, void activate_array_cpu_custom(float *x, const int n, const ACTIVATION a); -LIB_API void transpose_32x32_bits_reversed_diagonale(uint32_t *A, uint32_t *B, int m, int n); +void transpose_32x32_bits_reversed_diagonale(uint32_t *A, uint32_t *B, int m, int n); void gemm_bin(int M, int N, int K, float ALPHA, char *A, int lda, diff --git a/src/gettimeofday.c b/src/gettimeofday.c index b47d3cd717e..1db060cb4bd 100644 --- a/src/gettimeofday.c +++ b/src/gettimeofday.c @@ -1,7 +1,7 @@ #ifdef _WIN32 #include "gettimeofday.h" -LIB_API int gettimeofday(struct timeval* tp, struct timezone* tzp) +int gettimeofday(struct timeval* tp, struct timezone* tzp) { static const uint64_t EPOCH = ((uint64_t)116444736000000000ULL); SYSTEMTIME system_time; @@ -19,7 +19,7 @@ LIB_API int gettimeofday(struct timeval* tp, struct timezone* tzp) return 0; } -LIB_API int clock_gettime(int dummy, struct timespec* ct) +int clock_gettime(int dummy, struct timespec* ct) { LARGE_INTEGER count; diff --git a/src/gettimeofday.h b/src/gettimeofday.h index f92c841d2c1..4a9df6734fc 100644 --- a/src/gettimeofday.h +++ b/src/gettimeofday.h @@ -28,8 +28,8 @@ extern "C" { static unsigned char g_first_time = 1; static LARGE_INTEGER g_counts_per_sec; -LIB_API int gettimeofday(struct timeval*, struct timezone*); -LIB_API int clock_gettime(int, struct timespec*); +int gettimeofday(struct timeval*, struct timezone*); +int clock_gettime(int, struct timespec*); #ifdef __cplusplus } diff --git a/src/image.h b/src/image.h index 20170932033..be947d34176 100644 --- a/src/image.h +++ b/src/image.h @@ -93,9 +93,9 @@ image load_image(char *filename, int w, int h, int c); image **load_alphabet(); #ifdef OPENCV -LIB_API image get_image_from_stream(CvCapture* cap); -LIB_API image get_image_from_stream_cpp(CvCapture* cap); -LIB_API image ipl_to_image(IplImage* src); +image get_image_from_stream(CvCapture* cap); +image get_image_from_stream_cpp(CvCapture* cap); +image ipl_to_image(IplImage* src); #endif //float get_pixel(image m, int x, int y, int c); //float get_pixel_extend(image m, int x, int y, int c); diff --git a/src/lstm_layer.h b/src/lstm_layer.h index ae5441488fd..f60a0cabafe 100644 --- a/src/lstm_layer.h +++ b/src/lstm_layer.h @@ -9,7 +9,7 @@ #ifdef __cplusplus extern "C" { #endif -LIB_API layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_normalize); +layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_normalize); void forward_lstm_layer(layer l, network_state state); void update_lstm_layer(layer l, int batch, float learning_rate, float momentum, float decay); From 20757e217f06347e2647d69cbf815378e5c8f492 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 20 Feb 2019 15:29:56 +0100 Subject: [PATCH 0344/1365] restore manual control of CUDA while keeping default enabled --- CMakeLists.txt | 28 +++++++++++++++------------- cmake/Modules/FindCUDNN.cmake | 4 ++-- 2 files changed, 17 insertions(+), 15 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 3565e430ffb..6883ce81985 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -19,10 +19,14 @@ endif() enable_language(C) enable_language(CXX) +set(ENABLE_CUDA "TRUE" CACHE BOOL "Enable CUDA build") + include(CheckLanguage) check_language(CUDA) if(CMAKE_CUDA_COMPILER) enable_language(CUDA) +else() + set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA build") endif() set(CMAKE_CXX_STANDARD 11) @@ -43,7 +47,7 @@ set(CUDA_COMPUTE_MODEL_35 "35") #Compute capability for Tesla K20/K40 set(CUDA_COMPUTE_MODEL_30 "30") #Compute capability for Tesla K10, Quadro K4000 set(default_cuda_compute_model ${CUDA_COMPUTE_MODEL_30}) -if(CMAKE_CUDA_COMPILER AND NOT CUDA_COMPUTE_MODEL) +if(ENABLE_CUDA AND NOT CUDA_COMPUTE_MODEL) message(STATUS "Setting the compute model to ${default_cuda_compute_model} as none was specified.") set(CUDA_COMPUTE_MODEL "${default_cuda_compute_model}" CACHE STRING "Choose the best compute model supported by your GPU" FORCE) @@ -95,12 +99,10 @@ if ("${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang") string(REGEX REPLACE "-O3" "-Ofast" CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) endif() -if(NOT USE_INTEGRATED_LIBS) - if(APPLE) - find_package(OpenCV PATHS "/usr/local/Cellar/opencv@3/3.4.5" NO_DEFAULT_PATH) - else() - find_package(OpenCV) - endif() +if(APPLE) + find_package(OpenCV PATHS "/usr/local/Cellar/opencv@3/3.4.5" NO_DEFAULT_PATH) +else() + find_package(OpenCV) endif() if(OpenCV_FOUND) @@ -108,9 +110,9 @@ if(OpenCV_FOUND) include_directories(${OpenCV_INCLUDE_DIRS}) endif() -if(CMAKE_CUDA_COMPILER) +if(ENABLE_CUDA) add_definitions(-DGPU) - if(CUDA_COMPUTE_MODEL VERSION_GREATER_EQUAL 75 AND CMAKE_CUDA_COMPILER_VERSION VERSION_LESS_EQUAL 9) + if(CUDA_COMPUTE_MODEL VERSION_GREATER_EQUAL ${CUDA_COMPUTE_MODEL_75} AND CMAKE_CUDA_COMPILER_VERSION VERSION_LESS_EQUAL 9) message(STATUS "CUDA 10 is required for Compute Model 7.5 - Lowering settings to 7.0") set(CUDA_COMPUTE_MODEL ${CUDA_COMPUTE_MODEL_70}) endif() @@ -118,7 +120,7 @@ if(CMAKE_CUDA_COMPILER) if(CUDNN_FOUND) add_definitions(-DCUDNN) include_directories(${CUDNN_INCLUDE_DIR}) - if(CUDA_COMPUTE_MODEL VERSION_GREATER_EQUAL 53) + if(CUDA_COMPUTE_MODEL VERSION_GREATER_EQUAL ${CUDA_COMPUTE_MODEL_70}) add_definitions(-DCUDNN_HALF) message(STATUS "Enabled CUDNN_HALF") endif() @@ -195,7 +197,7 @@ list(REMOVE_ITEM sources ${CMAKE_CURRENT_LIST_DIR}/src/getopt.c ) -if(CMAKE_CUDA_COMPILER) +if(ENABLE_CUDA) file(GLOB cuda_sources "${CMAKE_CURRENT_LIST_DIR}/src/*.cu") endif() @@ -215,7 +217,7 @@ set_source_files_properties(${sources} PROPERTIES LANGUAGE CXX) add_library(darklib SHARED ${CMAKE_CURRENT_LIST_DIR}/include/yolo_v2_class.hpp ${CMAKE_CURRENT_LIST_DIR}/src/yolo_v2_class.cpp ${sources} ${headers} ${cuda_sources}) set_target_properties(darklib PROPERTIES POSITION_INDEPENDENT_CODE ON) -if(CMAKE_CUDA_COMPILER) +if(ENABLE_CUDA) set_target_properties(darklib PROPERTIES CUDA_SEPARABLE_COMPILATION ON) endif() target_compile_definitions(darklib PRIVATE LIB_EXPORTS=1) @@ -264,7 +266,7 @@ endif() target_link_libraries(darknet Threads::Threads) target_link_libraries(darklib Threads::Threads) -if(CMAKE_CUDA_COMPILER) +if(ENABLE_CUDA) target_link_libraries(darknet curand cublas) target_link_libraries(darklib curand cublas) endif() diff --git a/cmake/Modules/FindCUDNN.cmake b/cmake/Modules/FindCUDNN.cmake index 6e53311a519..75fd5261d95 100644 --- a/cmake/Modules/FindCUDNN.cmake +++ b/cmake/Modules/FindCUDNN.cmake @@ -24,13 +24,13 @@ include(FindPackageHandleStandardArgs) if(NOT CUDNN_INCLUDE_DIR) find_path(CUDNN_INCLUDE_DIR cudnn.h - HINTS ${CUDA_HOME} ${CUDA_TOOLKIT_ROOT_DIR} + HINTS ${CUDA_HOME} ${CUDA_TOOLKIT_ROOT_DIR} $ENV{cudnn} PATH_SUFFIXES cuda/include include) endif() if(NOT CUDNN_LIBRARY) find_library(CUDNN_LIBRARY cudnn - HINTS ${CUDA_HOME} ${CUDA_TOOLKIT_ROOT_DIR} + HINTS ${CUDA_HOME} ${CUDA_TOOLKIT_ROOT_DIR} $ENV{cudnn} PATH_SUFFIXES lib lib64 cuda/lib cuda/lib64 lib/x64) endif() From ac5b9552cf4a5edd738769cfc40c915e63a3b8a2 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 22 Feb 2019 03:27:38 +0300 Subject: [PATCH 0345/1365] OpenCV definition for yolo_console_dll.vcxproj --- build/darknet/yolo_console_dll.vcxproj | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/build/darknet/yolo_console_dll.vcxproj b/build/darknet/yolo_console_dll.vcxproj index 763914b0fbf..a965fcaabe2 100644 --- a/build/darknet/yolo_console_dll.vcxproj +++ b/build/darknet/yolo_console_dll.vcxproj @@ -118,7 +118,7 @@ true true ..\..\include;C:\opencv_source\opencv\bin\install\include - _CRT_SECURE_NO_WARNINGS;_MBCS;%(PreprocessorDefinitions) + OPENCV;_CRT_SECURE_NO_WARNINGS;_MBCS;%(PreprocessorDefinitions) Async true From 9e084c588af31acae500396add1f842f3bfefe87 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 22 Feb 2019 03:39:27 +0300 Subject: [PATCH 0346/1365] Fixed wrong cast for XNOR-net --- build/darknet/x64/partial.cmd | 3 +++ src/gemm.c | 2 +- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/build/darknet/x64/partial.cmd b/build/darknet/x64/partial.cmd index 2d9e022a5ba..bea737ea9c9 100644 --- a/build/darknet/x64/partial.cmd +++ b/build/darknet/x64/partial.cmd @@ -9,6 +9,9 @@ rem darknet.exe partial cfg/tiny-yolo-voc.cfg tiny-yolo-voc.weights tiny-yolo-vo darknet.exe partial cfg/darknet53_448.cfg darknet53_448.weights darknet53.conv.74 74 +darknet.exe partial cfg/darknet53_448_xnor.cfg darknet53_448_xnor.weights darknet53_448_xnor.conv.74 74 + + darknet.exe partial cfg/yolov2-tiny-voc.cfg yolov2-tiny-voc.weights yolov2-tiny-voc.conv.13 13 diff --git a/src/gemm.c b/src/gemm.c index 4948fbef5b7..b110da619e2 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -1646,7 +1646,7 @@ void im2col_cpu_custom_bin(float* data_im, __m256 result256 = _mm256_cmp_ps(src256, float_zero256, _CMP_GT_OS); uint16_t mask = _mm256_movemask_ps(result256); // (val > 0) ? 0 : 1 - uint16_t* dst_ptr = &((uint16_t*)data_col)[col_index / 8]; + uint16_t* dst_ptr = (uint16_t*)&((uint8_t*)data_col)[col_index / 8]; *dst_ptr |= (mask << (col_index % 8)); } From d8291858d502f72b4204e362321e1b83012eddf4 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 22 Feb 2019 09:14:41 +0100 Subject: [PATCH 0347/1365] [FindCUDNN] add another hint for newly suggested ENV variable --- cmake/Modules/FindCUDNN.cmake | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cmake/Modules/FindCUDNN.cmake b/cmake/Modules/FindCUDNN.cmake index 75fd5261d95..ef78833e9c1 100644 --- a/cmake/Modules/FindCUDNN.cmake +++ b/cmake/Modules/FindCUDNN.cmake @@ -24,13 +24,13 @@ include(FindPackageHandleStandardArgs) if(NOT CUDNN_INCLUDE_DIR) find_path(CUDNN_INCLUDE_DIR cudnn.h - HINTS ${CUDA_HOME} ${CUDA_TOOLKIT_ROOT_DIR} $ENV{cudnn} + HINTS ${CUDA_HOME} ${CUDA_TOOLKIT_ROOT_DIR} $ENV{cudnn} $ENV{CUDNN} PATH_SUFFIXES cuda/include include) endif() if(NOT CUDNN_LIBRARY) find_library(CUDNN_LIBRARY cudnn - HINTS ${CUDA_HOME} ${CUDA_TOOLKIT_ROOT_DIR} $ENV{cudnn} + HINTS ${CUDA_HOME} ${CUDA_TOOLKIT_ROOT_DIR} $ENV{cudnn} $ENV{CUDNN} PATH_SUFFIXES lib lib64 cuda/lib cuda/lib64 lib/x64) endif() From da9c0be3719cd278609f298b2153d748335933b0 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 22 Feb 2019 14:22:10 +0100 Subject: [PATCH 0348/1365] [CMakeLists.txt] add a compatibility include directive to workaround some regressions in enable_language(CUDA) --- CMakeLists.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/CMakeLists.txt b/CMakeLists.txt index 6883ce81985..76cc0b4cd29 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -112,6 +112,7 @@ endif() if(ENABLE_CUDA) add_definitions(-DGPU) + include_directories("${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES}") if(CUDA_COMPUTE_MODEL VERSION_GREATER_EQUAL ${CUDA_COMPUTE_MODEL_75} AND CMAKE_CUDA_COMPILER_VERSION VERSION_LESS_EQUAL 9) message(STATUS "CUDA 10 is required for Compute Model 7.5 - Lowering settings to 7.0") set(CUDA_COMPUTE_MODEL ${CUDA_COMPUTE_MODEL_70}) From 954feac226f6dc88d1c2f581c166bad777b6a5a2 Mon Sep 17 00:00:00 2001 From: Alexey Date: Fri, 22 Feb 2019 17:03:36 +0300 Subject: [PATCH 0349/1365] Update Readme.md --- README.md | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index ba1c1093ed1..9c846b64697 100644 --- a/README.md +++ b/README.md @@ -10,15 +10,18 @@ 0. [Improvements in this repository](#improvements-in-this-repository) 1. [How to use](#how-to-use) 2. [How to compile on Linux](#how-to-compile-on-linux) -3. [How to compile on Windows](#how-to-compile-on-windows) +3. How to compile on Windows + * [Using vcpkg](#how-to-compile-on-windows-using-vcpkg) + * [Legacy way](#how-to-compile-on-windows-legacy-way) 4. [How to train (Pascal VOC Data)](#how-to-train-pascal-voc-data) + * [How to train with multi-GPU:](#how-to-train-with-multi-gpu) 5. [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects) + * [How to train tiny-yolo (to detect your custom objects)](#how-to-train-tiny-yolo-to-detect-your-custom-objects) 6. [When should I stop training](#when-should-i-stop-training) 7. [How to calculate mAP on PascalVOC 2007](#how-to-calculate-map-on-pascalvoc-2007) 8. [How to improve object detection](#how-to-improve-object-detection) 9. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) -10. [Using Yolo9000](#using-yolo9000) -11. [How to use Yolo as DLL and SO libraries](#how-to-use-yolo-as-dll-and-so-libraries) +10. [How to use Yolo as DLL and SO libraries](#how-to-use-yolo-as-dll-and-so-libraries) | ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) |   ![map_time](https://user-images.githubusercontent.com/4096485/52151356-e5d4a380-2683-11e9-9d7d-ac7bc192c477.jpg) mAP@0.5 (AP50) https://pjreddie.com/media/files/papers/YOLOv3.pdf | |---|---| @@ -39,7 +42,7 @@ More details: http://pjreddie.com/darknet/yolo/ This repository supports: * both Windows and Linux -* both OpenCV 2.x.x and OpenCV <= 3.4.0 (3.4.1 and higher isn't supported, but you can try) +* both OpenCV 2.x.x and OpenCV <= 4.0 * both cuDNN >= v7 * CUDA >= 7.5 * also create SO-library on Linux and DLL-library on Windows @@ -48,8 +51,9 @@ This repository supports: * **CMake >= 3.8** for modern CUDA support: https://cmake.org/download/ * **CUDA 10.0**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) -* **OpenCV < 4.0**: use your preferred package manager (brew, apt) or build from source using [vcpkg](https://github.com/Microsoft/vcpkg) +* **OpenCV < 4.0**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or [OpenCV Releases](https://opencv.org/releases.html) * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported +* on Linux **GCC or Clang**, on Windows **MSVS 2017 (v15)** https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community&rel=15# #### Pre-trained models @@ -200,7 +204,7 @@ PS Code\vcpkg> .\vcpkg install pthreads opencv #replace with opencv[cuda 1.4. If you want to build **without CUDNN** then: open `\darknet.sln` -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and remove this: `CUDNN;` -2. If you have other version of **CUDA (not 10.0)** then open `build\darknet\darknet.vcxproj` by using Notepad, find 2 places with "CUDA 10.0" and change it to your CUDA-version, then do step 1 +2. If you have other version of **CUDA (not 10.0)** then open `build\darknet\darknet.vcxproj` by using Notepad, find 2 places with "CUDA 10.0" and change it to your CUDA-version. Then open `\darknet.sln` -> (right click on project) -> properties -> CUDA C/C++ -> Device and remove there `;compute_75,sm_75`. Then do step 1 3. If you **don't have GPU**, but have **MSVS 2015 and OpenCV 3.0** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet_no_gpu.sln`, set **x64** and **Release**, and do the: Build -> Build darknet_no_gpu From 7872cbf198544cdee22dc5a207ad5f2989a83167 Mon Sep 17 00:00:00 2001 From: Alexey Date: Fri, 22 Feb 2019 17:53:27 +0300 Subject: [PATCH 0350/1365] Update Readme.md --- README.md | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 9c846b64697..ee9eb8945b8 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # Yolo-v3 and Yolo-v2 for Windows and Linux -### (neural network for object detection) - Tensor Cores can be used on [Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) and [Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows) +### (neural network for object detection) - Tensor Cores can be used on [Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) and [Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows-using-vcpkg) [![CircleCI](https://circleci.com/gh/AlexeyAB/darknet.svg?style=svg)](https://circleci.com/gh/AlexeyAB/darknet) @@ -51,7 +51,8 @@ This repository supports: * **CMake >= 3.8** for modern CUDA support: https://cmake.org/download/ * **CUDA 10.0**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) -* **OpenCV < 4.0**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or [OpenCV Releases](https://opencv.org/releases.html) +* **OpenCV < 4.0**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or [OpenCV Releases](https://opencv.org/releases.html) (on Windows set system variable `OPENCV_DIR` = `C:\opencv\build` where are `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png)) +* **cuDNN >= 7.0 for CUDA 10.0** https://developer.nvidia.com/rdp/cudnn-archive (set system variable `CUDNN` = `C:\cudnn` where did you unpack cuDNN. On Linux in `.bashrc`-file, on Windows see the [image](https://user-images.githubusercontent.com/4096485/53249764-019ef880-36ca-11e9-8ffe-d9cf47e7e462.jpg) ) * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported * on Linux **GCC or Clang**, on Windows **MSVS 2017 (v15)** https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community&rel=15# @@ -188,7 +189,7 @@ PS Code\vcpkg> .\vcpkg install pthreads opencv #replace with opencv[cuda ### How to compile on Windows (legacy way) -1. If you have **MSVS 2015, CUDA 10.0, cuDNN 7.4 and OpenCV 3.x** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release** https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg and do the: Build -> Build darknet. Also add Windows system variable `cudnn` with path to CUDNN: https://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg **NOTE:** If installing OpenCV, use OpenCV 3.4.0 or earlier. This is a bug in OpenCV 3.4.1 in the C API (see [#500](https://github.com/AlexeyAB/darknet/issues/500)). +1. If you have **MSVS 2015, CUDA 10.0, cuDNN 7.4 and OpenCV 3.x** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release** https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg and do the: Build -> Build darknet. Also add Windows system variable `CUDNN` with path to CUDNN: https://user-images.githubusercontent.com/4096485/53249764-019ef880-36ca-11e9-8ffe-d9cf47e7e462.jpg **NOTE:** If installing OpenCV, use OpenCV 3.4.0 or earlier. This is a bug in OpenCV 3.4.1 in the C API (see [#500](https://github.com/AlexeyAB/darknet/issues/500)). 1.1. Find files `opencv_world320.dll` and `opencv_ffmpeg320_64.dll` (or `opencv_world340.dll` and `opencv_ffmpeg340_64.dll`) in `C:\opencv_3.0\opencv\build\x64\vc14\bin` and put it near with `darknet.exe` @@ -198,7 +199,7 @@ PS Code\vcpkg> .\vcpkg install pthreads opencv #replace with opencv[cuda * download and install **cuDNN v7.4.1 for CUDA 10.0**: https://developer.nvidia.com/rdp/cudnn-archive - * add Windows system variable `cudnn` with path to CUDNN: https://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg + * add Windows system variable `CUDNN` with path to CUDNN: https://user-images.githubusercontent.com/4096485/53249764-019ef880-36ca-11e9-8ffe-d9cf47e7e462.jpg * copy file `cudnn64_7.dll` to the folder `\build\darknet\x64` near with `darknet.exe` @@ -226,7 +227,7 @@ Also, you can to create your own `darknet.sln` & `darknet.vcxproj`, this example Then add to your created project: - (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories, put here: -`C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include` +`C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include` - (right click on project) -> Build dependecies -> Build Customizations -> set check on CUDA 9.1 or what version you have - for example as here: http://devblogs.nvidia.com/parallelforall/wp-content/uploads/2015/01/VS2013-R-5.jpg - add to project: * all `.c` files @@ -235,7 +236,7 @@ Then add to your created project: * file `darknet.h` from `\include` directory - (right click on project) -> properties -> Linker -> General -> Additional Library Directories, put here: -`C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories)` +`C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)lib\$(PlatformName);$(CUDNN)\lib\x64;%(AdditionalLibraryDirectories)` - (right click on project) -> properties -> Linker -> Input -> Additional dependecies, put here: `..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;cudnn.lib;%(AdditionalDependencies)` From d1ee38ca05c1e5b058b4b2e9d6ffb12d1d02294a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 22 Feb 2019 18:08:24 +0300 Subject: [PATCH 0351/1365] Use both $(cudnn) and $(CUDNN) environment variables in darknet.vcxproj & yolo_cpp_dll.vcxproj --- build/darknet/darknet.vcxproj | 8 ++++---- build/darknet/x64/cfg/coco.data | 8 ++++---- build/darknet/x64/cfg/darknet53.cfg | 8 ++++---- build/darknet/yolo_cpp_dll.vcxproj | 8 ++++---- 4 files changed, 16 insertions(+), 16 deletions(-) diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj index 58ec245d2b1..1858f063ff2 100644 --- a/build/darknet/darknet.vcxproj +++ b/build/darknet/darknet.vcxproj @@ -88,7 +88,7 @@ Level3 Disabled true - C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include + C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include;$(cudnn)\include CUDNN_HALF;CUDNN;_CRTDBG_MAP_ALLOC;_MBCS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;DEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) OPENCV; true @@ -96,7 +96,7 @@ true - C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;C:\opencv_2.4.13\opencv\build\x64\vc14\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) + C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;C:\opencv_2.4.13\opencv\build\x64\vc14\lib;$(CUDA_PATH)lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) $(OutDir)\$(TargetName)$(TargetExt) ..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;%(AdditionalDependencies) true @@ -132,7 +132,7 @@ true true true - C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include + C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include;$(cudnn)\include CUDNN_HALF;OPENCV;CUDNN;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y @@ -146,7 +146,7 @@ true true true - C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) + C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;$(CUDA_PATH)lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) ..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;%(AdditionalDependencies) $(OutDir)\$(TargetName)$(TargetExt) diff --git a/build/darknet/x64/cfg/coco.data b/build/darknet/x64/cfg/coco.data index 30038417c27..f51d8428839 100644 --- a/build/darknet/x64/cfg/coco.data +++ b/build/darknet/x64/cfg/coco.data @@ -1,8 +1,8 @@ classes= 80 -train = /home/pjreddie/data/coco/trainvalno5k.txt -valid = coco_testdev -#valid = data/coco_val_5k.list +train = E:/MSCOCO/trainvalno5k.txt +#train = E:/MSCOCO/5k.txt +valid = E:/MSCOCO/5k.txt names = data/coco.names -backup = /home/pjreddie/backup/ +backup = backup eval=coco diff --git a/build/darknet/x64/cfg/darknet53.cfg b/build/darknet/x64/cfg/darknet53.cfg index 7b6d5766e9e..a1f084a943f 100644 --- a/build/darknet/x64/cfg/darknet53.cfg +++ b/build/darknet/x64/cfg/darknet53.cfg @@ -1,11 +1,11 @@ [net] # Training -# batch=128 -# subdivisions=4 +batch=128 +subdivisions=8 # Testing -batch=1 -subdivisions=1 +#batch=1 +#subdivisions=1 height=256 width=256 diff --git a/build/darknet/yolo_cpp_dll.vcxproj b/build/darknet/yolo_cpp_dll.vcxproj index 6136112cf1b..97ff65c666c 100644 --- a/build/darknet/yolo_cpp_dll.vcxproj +++ b/build/darknet/yolo_cpp_dll.vcxproj @@ -92,7 +92,7 @@ Level3 Disabled true - ..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include + ..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include;$(cudnn)\include _MBCS;LIB_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;DEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) CUDNN_HALF;CUDNN true @@ -100,7 +100,7 @@ true - $(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) + $(CUDA_PATH)lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) $(OutDir)\$(TargetName)$(TargetExt) ..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;%(AdditionalDependencies) true @@ -136,7 +136,7 @@ true true true - C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include + C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include;$(cudnn)\include CUDNN_HALF;CUDNN;GPU;LIB_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y @@ -150,7 +150,7 @@ true true true - C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) + C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) ..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;%(AdditionalDependencies) $(OutDir)\$(TargetName)$(TargetExt) From 78b707fa092f4b6a1502db0882f49ef533d4636a Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 22 Feb 2019 16:43:37 +0100 Subject: [PATCH 0352/1365] fallback enabled only not on windows, need to investigate better the failure anyway --- CMakeLists.txt | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 76cc0b4cd29..401c74bd804 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -112,7 +112,9 @@ endif() if(ENABLE_CUDA) add_definitions(-DGPU) - include_directories("${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES}") + if(NOT WIN32) + include_directories("${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES}") + endif() if(CUDA_COMPUTE_MODEL VERSION_GREATER_EQUAL ${CUDA_COMPUTE_MODEL_75} AND CMAKE_CUDA_COMPILER_VERSION VERSION_LESS_EQUAL 9) message(STATUS "CUDA 10 is required for Compute Model 7.5 - Lowering settings to 7.0") set(CUDA_COMPUTE_MODEL ${CUDA_COMPUTE_MODEL_70}) From cccc90b340454c265b4467165b6cc0836aa39fa3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 22 Feb 2019 18:47:10 +0300 Subject: [PATCH 0353/1365] CMakeLists.txt temporary fix for CUDA --- CMakeLists.txt | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 76cc0b4cd29..401c74bd804 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -112,7 +112,9 @@ endif() if(ENABLE_CUDA) add_definitions(-DGPU) - include_directories("${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES}") + if(NOT WIN32) + include_directories("${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES}") + endif() if(CUDA_COMPUTE_MODEL VERSION_GREATER_EQUAL ${CUDA_COMPUTE_MODEL_75} AND CMAKE_CUDA_COMPILER_VERSION VERSION_LESS_EQUAL 9) message(STATUS "CUDA 10 is required for Compute Model 7.5 - Lowering settings to 7.0") set(CUDA_COMPUTE_MODEL ${CUDA_COMPUTE_MODEL_70}) From fd5312f9c8b5419f9ccad92cc90917f6e48fd185 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 22 Feb 2019 16:56:58 +0100 Subject: [PATCH 0354/1365] [build.ps1] fix wrong path, enable builds only if env variable is present --- build.ps1 | 31 ++++++++++++++++++------------- 1 file changed, 18 insertions(+), 13 deletions(-) diff --git a/build.ps1 b/build.ps1 index 836998e169d..708d9b654ef 100755 --- a/build.ps1 +++ b/build.ps1 @@ -16,23 +16,28 @@ if ((Get-Command "cl.exe" -ErrorAction SilentlyContinue) -eq $null) Write-Host "Visual Studio 2017 ${vstype} Command Prompt variables set.`n" -ForegroundColor Yellow } -# DEBUG -New-Item -Path .\build_win_debug -ItemType directory -Force -Set-Location build_win_debug -cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$env:VCPKG_ROOT\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$env:VCPKG_DEFAULT_TRIPLET" "-DCMAKE_BUILD_TYPE=Debug" .. -cmake --build . --config Debug --parallel ${number_of_build_workers} -Set-Location .. +if (Test-Path env:VCPKG_ROOT) { + # DEBUG + New-Item -Path .\build_win_debug -ItemType directory -Force + Set-Location build_win_debug + cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$env:VCPKG_ROOT\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$env:VCPKG_DEFAULT_TRIPLET" "-DCMAKE_BUILD_TYPE=Debug" .. + cmake --build . --config Debug --parallel ${number_of_build_workers} + Set-Location .. -# RELEASE -New-Item -Path .\build_win_release -ItemType directory -Force -Set-Location build_win_release -cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$env:VCPKG_ROOT\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$env:VCPKG_DEFAULT_TRIPLET" "-DCMAKE_BUILD_TYPE=Release" .. -cmake --build . --config Release --parallel ${number_of_build_workers} -Set-Location .. + # RELEASE + New-Item -Path .\build_win_release -ItemType directory -Force + Set-Location build_win_release + cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$env:VCPKG_ROOT\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$env:VCPKG_DEFAULT_TRIPLET" "-DCMAKE_BUILD_TYPE=Release" .. + cmake --build . --config Release --parallel ${number_of_build_workers} + Set-Location .. +} +else { + Write-Host "Skipping vcpkg-enabled builds because the VCPKG_ROOT environment variable is not defined`n" -ForegroundColor Yellow +} # USE LOCAL PTHREAD LIB, NO VCPKG: remember to use "vcpkg.exe integrate remove" in case you had enable user-wide vcpkg integration New-Item -Path .\build_win_release_cuda_int_libs -ItemType directory -Force -Set-Location build_win_release_cuda_custom_libs +Set-Location build_win_release_cuda_int_libs cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" .. cmake --build . --config Release --parallel ${number_of_build_workers} Set-Location .. From e82505d20e35a02d436ff43c925d49553d829db3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 28 Feb 2019 16:55:30 +0300 Subject: [PATCH 0355/1365] Fixed random values --- build/darknet/x64/partial.cmd | 3 ++ src/detection_layer.c | 2 +- src/maxpool_layer.c | 2 +- src/network.c | 2 +- src/nightmare.c | 2 +- src/region_layer.c | 2 +- src/utils.c | 82 +++++++++++++++++++++++++++++------ src/utils.h | 5 +++ src/yolo_layer.c | 2 +- 9 files changed, 82 insertions(+), 20 deletions(-) diff --git a/build/darknet/x64/partial.cmd b/build/darknet/x64/partial.cmd index bea737ea9c9..f0c2b9e8198 100644 --- a/build/darknet/x64/partial.cmd +++ b/build/darknet/x64/partial.cmd @@ -27,6 +27,9 @@ darknet.exe partial cfg/yolov2.cfg yolov2.weights yolov2.conv.23 23 darknet.exe partial cfg/yolov3.cfg yolov3.weights yolov3.conv.81 81 +darknet.exe partial cfg/yolov3-spp.cfg yolov3-spp.weights yolov3-spp.conv.85 85 + + darknet.exe partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15 diff --git a/src/detection_layer.c b/src/detection_layer.c index 1379145b72a..60fdc90dd85 100644 --- a/src/detection_layer.c +++ b/src/detection_layer.c @@ -41,7 +41,7 @@ detection_layer make_detection_layer(int batch, int inputs, int n, int side, int #endif fprintf(stderr, "Detection Layer\n"); - srand(0); + srand(time(0)); return l; } diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index 6c2ebca2498..cf99e619fdf 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -29,7 +29,7 @@ void cudnn_maxpool_setup(layer *l) maxpool_status = cudnnSetPooling2dDescriptor( l->poolingDesc, CUDNN_POOLING_MAX, - CUDNN_PROPAGATE_NAN, // CUDNN_PROPAGATE_NAN, CUDNN_NOT_PROPAGATE_NAN + CUDNN_NOT_PROPAGATE_NAN, // CUDNN_PROPAGATE_NAN, CUDNN_NOT_PROPAGATE_NAN l->size, l->size, 0, //l.pad, diff --git a/src/network.c b/src/network.c index 762ddb67ea3..134f7e9265d 100644 --- a/src/network.c +++ b/src/network.c @@ -348,7 +348,7 @@ float train_network_batch(network net, data d, int n) int batch = 2; for(i = 0; i < n; ++i){ for(j = 0; j < batch; ++j){ - int index = rand()%d.X.rows; + int index = random_gen()%d.X.rows; state.input = d.X.vals[index]; state.truth = d.y.vals[index]; forward_network(net, state); diff --git a/src/nightmare.c b/src/nightmare.c index 2d4c7466a90..bd8ccc3b504 100644 --- a/src/nightmare.c +++ b/src/nightmare.c @@ -181,7 +181,7 @@ void reconstruct_picture(network net, float *features, image recon, image update void run_nightmare(int argc, char **argv) { - srand(0); + srand(time(0)); if(argc < 4){ fprintf(stderr, "usage: %s %s [cfg] [weights] [image] [layer] [options! (optional)]\n", argv[0], argv[1]); return; diff --git a/src/region_layer.c b/src/region_layer.c index af61a043a39..3221e77ed6a 100644 --- a/src/region_layer.c +++ b/src/region_layer.c @@ -46,7 +46,7 @@ region_layer make_region_layer(int batch, int w, int h, int n, int classes, int #endif fprintf(stderr, "detection\n"); - srand(0); + srand(time(0)); return l; } diff --git a/src/utils.c b/src/utils.c index 22633125378..b4d17c23ecf 100644 --- a/src/utils.c +++ b/src/utils.c @@ -60,7 +60,7 @@ void shuffle(void *arr, size_t n, size_t size) size_t i; void* swp = (void*)calloc(1, size); for(i = 0; i < n-1; ++i){ - size_t j = i + rand()/(RAND_MAX / (n-i)+1); + size_t j = i + random_gen()/(RAND_MAX / (n-i)+1); memcpy(swp, (char*)arr+(j*size), size); memcpy((char*)arr+(j*size), (char*)arr+(i*size), size); memcpy((char*)arr+(i*size), swp, size); @@ -699,7 +699,7 @@ int rand_int(int min, int max) min = max; max = s; } - int r = (rand()%(max - min + 1)) + min; + int r = (random_gen()%(max - min + 1)) + min; return r; } @@ -717,10 +717,10 @@ float rand_normal() haveSpare = 1; - rand1 = rand() / ((double) RAND_MAX); + rand1 = random_gen() / ((double) RAND_MAX); if(rand1 < 1e-100) rand1 = 1e-100; rand1 = -2 * log(rand1); - rand2 = (rand() / ((double)RAND_MAX)) * 2.0 * M_PI; + rand2 = (random_gen() / ((double)RAND_MAX)) * 2.0 * M_PI; return sqrt(rand1) * cos(rand2); } @@ -731,21 +731,21 @@ float rand_normal() int n = 12; int i; float sum= 0; - for(i = 0; i < n; ++i) sum += (float)rand()/RAND_MAX; + for(i = 0; i < n; ++i) sum += (float)random_gen()/RAND_MAX; return sum-n/2.; } */ size_t rand_size_t() { - return ((size_t)(rand()&0xff) << 56) | - ((size_t)(rand()&0xff) << 48) | - ((size_t)(rand()&0xff) << 40) | - ((size_t)(rand()&0xff) << 32) | - ((size_t)(rand()&0xff) << 24) | - ((size_t)(rand()&0xff) << 16) | - ((size_t)(rand()&0xff) << 8) | - ((size_t)(rand()&0xff) << 0); + return ((size_t)(random_gen()&0xff) << 56) | + ((size_t)(random_gen()&0xff) << 48) | + ((size_t)(random_gen()&0xff) << 40) | + ((size_t)(random_gen()&0xff) << 32) | + ((size_t)(random_gen()&0xff) << 24) | + ((size_t)(random_gen()&0xff) << 16) | + ((size_t)(random_gen()&0xff) << 8) | + ((size_t)(random_gen()&0xff) << 0); } float rand_uniform(float min, float max) @@ -755,7 +755,14 @@ float rand_uniform(float min, float max) min = max; max = swap; } - return ((float)rand()/RAND_MAX * (max - min)) + min; + + if (RAND_MAX < 65536) { + int rnd = rand()*(RAND_MAX + 1) + rand(); + return ((float)rnd / (RAND_MAX*RAND_MAX) * (max - min)) + min; + } + else { + return ((float)rand() / RAND_MAX * (max - min)) + min; + } //return (random_float() * (max - min)) + min; } @@ -785,6 +792,9 @@ unsigned int random_gen() rand_s(&rnd); #else rnd = rand(); + if (RAND_MAX < 65536) { + rnd = rand()*(RAND_MAX + 1) + rnd; + } #endif return rnd; } @@ -807,3 +817,47 @@ float rand_uniform_strong(float min, float max) } return (random_float() * (max - min)) + min; } + +float rand_precalc_random(float min, float max, float random_part) +{ + if (max < min) { + float swap = min; + min = max; + max = swap; + } + return (random_part * (max - min)) + min; +} + +#define RS_SCALE (1.0 / (1.0 + RAND_MAX)) + +double double_rand(void) +{ + double d; + do { + d = (((rand() * RS_SCALE) + rand()) * RS_SCALE + rand()) * RS_SCALE; + } while (d >= 1); // Round off + return d; +} + +unsigned int uint_rand(unsigned int less_than) +{ + return (unsigned int)((less_than)* double_rand()); +} + +int check_array_is_nan(float *arr, int size) +{ + int i; + for (i = 0; i < size; ++i) { + if (isnan(arr[i])) return 1; + } + return 0; +} + +int check_array_is_inf(float *arr, int size) +{ + int i; + for (i = 0; i < size; ++i) { + if (isinf(arr[i])) return 1; + } + return 0; +} diff --git a/src/utils.h b/src/utils.h index f00ed2d56d6..bae6adff1b6 100644 --- a/src/utils.h +++ b/src/utils.h @@ -70,6 +70,11 @@ void print_statistics(float *a, int n); unsigned int random_gen(); float random_float(); float rand_uniform_strong(float min, float max); +float rand_precalc_random(float min, float max, float random_part); +double double_rand(void); +unsigned int uint_rand(unsigned int less_than); +int check_array_is_nan(float *arr, int size); +int check_array_is_inf(float *arr, int size); int int_index(int *a, int val, int n); #ifdef __cplusplus diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 134c9c90bd0..a9309d1dd59 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -70,7 +70,7 @@ layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int #endif fprintf(stderr, "yolo\n"); - srand(0); + srand(time(0)); return l; } From 051b8b7d6f50b3cbd2ed1fade920b7ef360ed21b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 28 Feb 2019 17:04:44 +0300 Subject: [PATCH 0356/1365] Added a compilation for AVX = 1 in CircleCI --- .circleci/config.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.circleci/config.yml b/.circleci/config.yml index 8a6ebfe7dfa..8b7e27f3bcd 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -9,6 +9,8 @@ jobs: - checkout - run: make LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 -j 8 - run: make clean + - run: make LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 AVX=1 -j 8 + - run: make clean - run: make LIBSO=1 GPU=0 CUDNN=0 OPENCV=1 -j 8 - run: make clean - run: make LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 -j 8 From 56a6e0cdba4bf0b1a607c44a666ceab65dc44c50 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Mon, 25 Feb 2019 09:38:25 +0100 Subject: [PATCH 0357/1365] [travis/appveyor] add CI, remove broken empty include path from CMakeLists.txt --- .travis.yml | 233 +++++++++++++++++++++++++++++++++++++++ CMakeLists.txt | 24 ++-- appveyor.yml | 48 ++++++++ src/yolo_console_dll.cpp | 2 +- 4 files changed, 295 insertions(+), 12 deletions(-) create mode 100644 .travis.yml create mode 100644 appveyor.yml diff --git a/.travis.yml b/.travis.yml new file mode 100644 index 00000000000..abe2c7db099 --- /dev/null +++ b/.travis.yml @@ -0,0 +1,233 @@ +language: cpp + +matrix: + include: + + - os: osx + compiler: gcc + name: macOS - gcc@5 - opencv@2 + env: + - MATRIX_EVAL="brew update && brew install gcc@5 gcc opencv@2 && CC=gcc-5 && CXX=g++-5 && OpenCV_DIR=/usr/local/opt/opencv@2" + + - os: osx + compiler: gcc + name: macOS - gcc@5 - opencv@3 + env: + - MATRIX_EVAL="brew update && brew install gcc@5 gcc opencv@3 && CC=gcc-5 && CXX=g++-5 && OpenCV_DIR=/usr/local/opt/opencv@3" + + - os: osx + compiler: gcc + name: macOS - native gcc (llvm backend) - opencv@2 + env: + - MATRIX_EVAL="brew update && brew install opencv@2 && OpenCV_DIR=/usr/local/opt/opencv@2" + + - os: osx + compiler: gcc + name: macOS - native gcc (llvm backend) - opencv@3 + env: + - MATRIX_EVAL="brew update && brew install opencv@3 && OpenCV_DIR=/usr/local/opt/opencv@3" + + - os: osx + compiler: clang + name: macOS - native clang - opencv@2 + env: + - MATRIX_EVAL="brew update && brew install opencv@2 && OpenCV_DIR=/usr/local/opt/opencv@2" + + - os: osx + compiler: clang + name: macOS - native clang - opencv@3 + env: + - MATRIX_EVAL="brew update && brew install opencv@3 && OpenCV_DIR=/usr/local/opt/opencv@3" + + - os: linux + compiler: clang + name: ubuntu - native clang + addons: + apt: + update: true + packages: + - libopencv-dev + env: + - MATRIX_EVAL="" + + - os: linux + compiler: clang + name: ubuntu - clang@3.6 + addons: + apt: + update: true + packages: + - libopencv-dev + - clang-3.6 + env: + - MATRIX_EVAL="CC=clang-3.6 && CXX=clang++-3.6" + + - os: linux + compiler: clang + name: ubuntu - clang@3.8 + addons: + apt: + update: true + sources: + - llvm-toolchain-precise-3.8 + packages: + - libopencv-dev + - clang-3.8 + env: + - MATRIX_EVAL="CC=clang-3.8 && CXX=clang++-3.8" + + - os: linux + compiler: clang + name: ubuntu - clang@3.9 + addons: + apt: + update: true + sources: + - llvm-toolchain-trusty-3.9 + packages: + - libopencv-dev + - clang-3.9 + env: + - MATRIX_EVAL="CC=clang-3.9 && CXX=clang++-3.9" + + - os: linux + compiler: clang + name: ubuntu - clang@4.0 + addons: + apt: + update: true + sources: + - llvm-toolchain-trusty-4.0 + packages: + - libopencv-dev + - clang-4.0 + env: + - MATRIX_EVAL="CC=clang-4.0 && CXX=clang++-4.0" + + - os: linux + compiler: clang + name: ubuntu - clang@5.0 + addons: + apt: + update: true + sources: + - llvm-toolchain-trusty-5.0 + packages: + - libopencv-dev + - clang-5.0 + env: + - MATRIX_EVAL="CC=clang-5.0 && CXX=clang++-5.0" + + - os: linux + compiler: clang + name: ubuntu - clang@6.0 + addons: + apt: + update: true + sources: + - llvm-toolchain-trusty-6.0 + - ubuntu-toolchain-r-test + packages: + - libopencv-dev + - clang-6.0 + env: + - MATRIX_EVAL="CC=clang-6.0 && CXX=clang++-6.0" + + - os: linux + compiler: gcc + name: ubuntu - native gcc + addons: + apt: + update: true + packages: + - libopencv-dev + - gfortran + env: + - MATRIX_EVAL="" + + - os: linux + compiler: gcc + name: ubuntu - gcc@4.9 + addons: + apt: + update: true + sources: + - ubuntu-toolchain-r-test + packages: + - libopencv-dev + - g++-4.9 + env: + - MATRIX_EVAL="CC=gcc-4.9 && CXX=g++-4.9" + + - os: linux + compiler: gcc + name: ubuntu - gcc@5.0 + addons: + apt: + update: true + sources: + - ubuntu-toolchain-r-test + packages: + - libopencv-dev + - g++-5 + env: + - MATRIX_EVAL="CC=gcc-5 && CXX=g++-5" + + - os: linux + compiler: gcc + name: ubuntu - gcc@6.0 + addons: + apt: + update: true + sources: + - ubuntu-toolchain-r-test + packages: + - g++-6 + - libopencv-dev + env: + - MATRIX_EVAL="CC=gcc-6 && CXX=g++-6" + + - os: linux + compiler: gcc + name: ubuntu - gcc@7.0 + addons: + apt: + update: true + sources: + - ubuntu-toolchain-r-test + packages: + - libopencv-dev + - g++-7 + env: + - MATRIX_EVAL="CC=gcc-7 && CXX=g++-7" + + - os: linux + compiler: gcc + name: ubuntu - gcc@8.0 + addons: + apt: + update: true + sources: + - ubuntu-toolchain-r-test + packages: + - libopencv-dev + - g++-8 + env: + - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8" + +before_install: + - eval "${MATRIX_EVAL}" + +before_script: + - mkdir build_debug + - cd build_debug + - cmake .. -DCMAKE_BUILD_TYPE="Debug" + - cd .. + - mkdir build_release + - cd build_release + - cmake .. -DCMAKE_BUILD_TYPE="Release" + - cd .. + +script: + - cd build_debug && cmake --build . --target install && cd .. + - cd build_release && cmake --build . --target install && cd .. diff --git a/CMakeLists.txt b/CMakeLists.txt index 401c74bd804..932a166efa7 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,6 +1,6 @@ # To use your libs, launch cmake with -DUSE_INTEGRATED_LIBS:BOOL=FALSE -cmake_minimum_required(VERSION 3.8 FATAL_ERROR) +cmake_minimum_required(VERSION 3.4) #set(CMAKE_VERBOSE_MAKEFILE 1) @@ -21,12 +21,17 @@ enable_language(CXX) set(ENABLE_CUDA "TRUE" CACHE BOOL "Enable CUDA build") -include(CheckLanguage) -check_language(CUDA) -if(CMAKE_CUDA_COMPILER) - enable_language(CUDA) +if(${CMAKE_VERSION} VERSION_LESS "3.8.0") + set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA build" FORCE) + message(WARNING "To build with CUDA support you need CMake 3.8.0+") else() - set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA build") + include(CheckLanguage) + check_language(CUDA) + if(CMAKE_CUDA_COMPILER) + enable_language(CUDA) + else() + set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA build" FORCE) + endif() endif() set(CMAKE_CXX_STANDARD 11) @@ -112,9 +117,6 @@ endif() if(ENABLE_CUDA) add_definitions(-DGPU) - if(NOT WIN32) - include_directories("${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES}") - endif() if(CUDA_COMPUTE_MODEL VERSION_GREATER_EQUAL ${CUDA_COMPUTE_MODEL_75} AND CMAKE_CUDA_COMPILER_VERSION VERSION_LESS_EQUAL 9) message(STATUS "CUDA 10 is required for Compute Model 7.5 - Lowering settings to 7.0") set(CUDA_COMPUTE_MODEL ${CUDA_COMPUTE_MODEL_70}) @@ -226,7 +228,7 @@ endif() target_compile_definitions(darklib PRIVATE LIB_EXPORTS=1) set_target_properties(darklib PROPERTIES LINKER_LANGUAGE CXX) -if(OpenCV_VERSION_MAJOR GREATER_EQUAL 3) +if(OpenCV_VERSION VERSION_GREATER "3.0") add_executable(uselib_track ${CMAKE_CURRENT_LIST_DIR}/src/yolo_console_dll.cpp) target_compile_definitions(uselib_track PRIVATE TRACK_OPTFLOW=1) set_target_properties(uselib_track PROPERTIES LINKER_LANGUAGE CXX) @@ -275,7 +277,7 @@ if(ENABLE_CUDA) endif() target_link_libraries(uselib darklib) -if(OpenCV_VERSION_MAJOR GREATER_EQUAL 3) +if(OpenCV_VERSION VERSION_GREATER "3.0") target_link_libraries(uselib_track darklib) install(TARGETS uselib_track DESTINATION ${CMAKE_SOURCE_DIR}/) endif() diff --git a/appveyor.yml b/appveyor.yml new file mode 100644 index 00000000000..9f6d32ce86d --- /dev/null +++ b/appveyor.yml @@ -0,0 +1,48 @@ +image: Visual Studio 2017 +clone_folder: c:\projects\darknet +configuration: Release + +environment: + WORKSPACE: C:\projects + matrix: + - platform: Cygwin64 + COMPILER: cygwin + CYGWIN_NOWINPATH: yes + CYGSH: C:\cygwin64\bin\bash -c + - platform: Win64 + COMPILER: vs + VCPKG_ROOT: C:\projects\vcpkg + VCPKG_DEFAULT_TRIPLET: x64-windows + + +install: + - if [%COMPILER%]==[vs] cinst cmake ninja + - if [%COMPILER%]==[vs] SET "PATH=C:\Program Files\CMake\bin;%PATH%" + - if [%COMPILER%]==[vs] call "C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\VC\Auxiliary\Build\vcvarsall.bat" x64 + - if [%COMPILER%]==[cygwin] SET "PATH=C:\cygwin64\bin;C:\cygwin64\usr\bin;%PATH%" + - if [%COMPILER%]==[cygwin] SET PATH=%PATH:C:\Program Files\Git\usr\bin;=% + - git submodule -q update --init --recursive + - cd %WORKSPACE%\ + - mkdir cygwin-downloads + - ps: if($env:COMPILER -eq "cygwin") { Invoke-WebRequest https://cygwin.com/setup-x86_64.exe -OutFile $env:WORKSPACE\cygwin-setup.exe } + - if [%COMPILER%]==[cygwin] %WORKSPACE%\cygwin-setup.exe --quiet-mode --no-shortcuts --no-startmenu --no-desktop --upgrade-also --root C:\cygwin64 --local-package-dir %WORKSPACE%\cygwin-downloads --packages gcc-g++,cmake,libopencv-devel + - if [%COMPILER%]==[vs] git clone https://github.com/Microsoft/vcpkg + - if [%COMPILER%]==[vs] cd %VCPKG_ROOT% + - if [%COMPILER%]==[vs] bootstrap-vcpkg.bat + - if [%COMPILER%]==[vs] vcpkg integrate install + - if [%COMPILER%]==[vs] vcpkg install opencv pthreads + - cd %WORKSPACE%\darknet\ + - mkdir build_debug && cd build_debug + - if [%COMPILER%]==[cygwin] %CYGSH% 'cmake .. -G "Unix Makefiles" -DCMAKE_BUILD_TYPE="Debug"' + - if [%COMPILER%]==[vs] cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=%VCPKG_ROOT%\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=%VCPKG_DEFAULT_TRIPLET%" -DCMAKE_BUILD_TYPE="Debug" .. + - cd .. + - mkdir build_release && cd build_release + - if [%COMPILER%]==[cygwin] %CYGSH% 'cmake .. -G "Unix Makefiles" -DCMAKE_BUILD_TYPE="Release"' + - if [%COMPILER%]==[vs] cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=%VCPKG_ROOT%\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=%VCPKG_DEFAULT_TRIPLET%" -DCMAKE_BUILD_TYPE="Release" .. + - cd .. + +build_script: + - if [%COMPILER%]==[cygwin] cd build_debug && %CYGSH% 'cmake --build .' && cd .. + - if [%COMPILER%]==[cygwin] cd build_release && %CYGSH% 'cmake --build .' && cd .. + - if [%COMPILER%]==[vs] cd build_debug && cmake --build . --config Debug && cd .. + - if [%COMPILER%]==[vs] cd build_release && cmake --build . --config Release && cd .. diff --git a/src/yolo_console_dll.cpp b/src/yolo_console_dll.cpp index c4554fbad46..be84f4d580a 100644 --- a/src/yolo_console_dll.cpp +++ b/src/yolo_console_dll.cpp @@ -36,7 +36,7 @@ #endif // TRACK_OPTFLOW #endif // USE_CMAKE_LIBS #else -#define OPENCV_VERSION CVAUX_STR(CV_VERSION_EPOCH)""CVAUX_STR(CV_VERSION_MAJOR)""CVAUX_STR(CV_VERSION_MINOR) +#define OPENCV_VERSION CVAUX_STR(CV_VERSION_EPOCH)"" CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR) #ifndef USE_CMAKE_LIBS #pragma comment(lib, "opencv_core" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib") From ef88900eb0d943a8f91c28339f6b795103bd46a2 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Mon, 25 Feb 2019 23:58:57 +0100 Subject: [PATCH 0358/1365] [CMakeLists.txt] remove references to C since CXX is the only way to build the project --- CMakeLists.txt | 23 +++++++++-------------- 1 file changed, 9 insertions(+), 14 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 932a166efa7..21e1fe0b65d 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -76,9 +76,7 @@ set(CMAKE_INSTALL_RPATH "${CMAKE_SOURCE_DIR}") add_definitions(-DUSE_CMAKE_LIBS) if(MSVC) - set(CMAKE_C_FLAGS "/wd4013 /wd4018 /wd4028 /wd4047 /wd4068 /wd4090 /wd4101 /wd4113 /wd4133 /wd4190 /wd4244 /wd4267 /wd4305 /wd4477 /wd4996 /fp:fast ${CMAKE_C_FLAGS}") set(CMAKE_CXX_FLAGS "/wd4013 /wd4018 /wd4028 /wd4047 /wd4068 /wd4090 /wd4101 /wd4113 /wd4133 /wd4190 /wd4244 /wd4267 /wd4305 /wd4477 /wd4996 /fp:fast ${CMAKE_CXX_FLAGS}") - string(REGEX REPLACE "/O2" "/Ox" CMAKE_C_FLAGS_RELEASE ${CMAKE_C_FLAGS_RELEASE}) string(REGEX REPLACE "/O2" "/Ox" CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) add_definitions(-D_CRT_RAND_S) add_definitions(-DNOMINMAX) @@ -86,21 +84,15 @@ if(MSVC) endif() if(CMAKE_COMPILER_IS_GNUCC) - set(CMAKE_C_FLAGS "-Wno-unused-result -Wno-discarded-qualifiers -Wno-incompatible-pointer-types ${CMAKE_C_FLAGS}") set(CMAKE_CXX_FLAGS "-Wno-unused-result -Wno-write-strings ${CMAKE_CXX_FLAGS}") - string(REGEX REPLACE "-O0" "-Og" CMAKE_C_FLAGS_DEBUG ${CMAKE_C_FLAGS_DEBUG}) string(REGEX REPLACE "-O0" "-Og" CMAKE_CXX_FLAGS_DEBUG ${CMAKE_CXX_FLAGS_DEBUG}) - string(REGEX REPLACE "-O3" "-Ofast" CMAKE_C_FLAGS_RELEASE ${CMAKE_C_FLAGS_RELEASE}) string(REGEX REPLACE "-O3" "-Ofast" CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) - set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a") set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a") endif() if ("${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang") set(CMAKE_CXX_FLAGS "-Wno-deprecated -Wno-writable-strings ${CMAKE_CXX_FLAGS}") - string(REGEX REPLACE "-O0" "-Og" CMAKE_C_FLAGS_DEBUG ${CMAKE_C_FLAGS_DEBUG}) string(REGEX REPLACE "-O0" "-Og" CMAKE_CXX_FLAGS_DEBUG ${CMAKE_CXX_FLAGS_DEBUG}) - string(REGEX REPLACE "-O3" "-Ofast" CMAKE_C_FLAGS_RELEASE ${CMAKE_C_FLAGS_RELEASE}) string(REGEX REPLACE "-O3" "-Ofast" CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) endif() @@ -168,10 +160,13 @@ if(MSVC) include_directories(${PTHREADS_INCLUDE_DIR}) endif() -find_package(OpenMP) -if(OPENMP_FOUND) - set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}") - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") +if(${CMAKE_VERSION} VERSION_LESS "3.9.0") + message(WARNING "To build with OpenMP support you need CMake 3.9.0+") +else() + find_package(OpenMP) + if(OPENMP_FOUND) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") + endif() endif() #look for all *.h files in src folder @@ -252,8 +247,8 @@ if(OpenCV_FOUND) endif() if(OPENMP_FOUND) - target_link_libraries(darknet OpenMP::OpenMP_C) - target_link_libraries(darklib OpenMP::OpenMP_C) + target_link_libraries(darknet OpenMP::OpenMP_CXX) + target_link_libraries(darklib OpenMP::OpenMP_CXX) endif() if(CMAKE_COMPILER_IS_GNUCC) From 2a874b49a0cbb33ff9f9ab2a4e68583637f45bb8 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 26 Feb 2019 07:30:40 +0100 Subject: [PATCH 0359/1365] [CI] trying to improve resiliency --- .travis.yml | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/.travis.yml b/.travis.yml index abe2c7db099..cd381d776c5 100644 --- a/.travis.yml +++ b/.travis.yml @@ -7,37 +7,37 @@ matrix: compiler: gcc name: macOS - gcc@5 - opencv@2 env: - - MATRIX_EVAL="brew update && brew install gcc@5 gcc opencv@2 && CC=gcc-5 && CXX=g++-5 && OpenCV_DIR=/usr/local/opt/opencv@2" + - MATRIX_EVAL="brew install gcc@5 opencv@2 && CC=gcc-5 && CXX=g++-5 && OpenCV_DIR=/usr/local/opt/opencv@2" - os: osx compiler: gcc name: macOS - gcc@5 - opencv@3 env: - - MATRIX_EVAL="brew update && brew install gcc@5 gcc opencv@3 && CC=gcc-5 && CXX=g++-5 && OpenCV_DIR=/usr/local/opt/opencv@3" + - MATRIX_EVAL="brew install gcc@5 opencv@3 && CC=gcc-5 && CXX=g++-5 && OpenCV_DIR=/usr/local/opt/opencv@3" - os: osx compiler: gcc name: macOS - native gcc (llvm backend) - opencv@2 env: - - MATRIX_EVAL="brew update && brew install opencv@2 && OpenCV_DIR=/usr/local/opt/opencv@2" + - MATRIX_EVAL="brew install opencv@2 && OpenCV_DIR=/usr/local/opt/opencv@2" - os: osx compiler: gcc name: macOS - native gcc (llvm backend) - opencv@3 env: - - MATRIX_EVAL="brew update && brew install opencv@3 && OpenCV_DIR=/usr/local/opt/opencv@3" + - MATRIX_EVAL="brew install opencv@3 && OpenCV_DIR=/usr/local/opt/opencv@3" - os: osx compiler: clang name: macOS - native clang - opencv@2 env: - - MATRIX_EVAL="brew update && brew install opencv@2 && OpenCV_DIR=/usr/local/opt/opencv@2" + - MATRIX_EVAL="brew install opencv@2 && OpenCV_DIR=/usr/local/opt/opencv@2" - os: osx compiler: clang name: macOS - native clang - opencv@3 env: - - MATRIX_EVAL="brew update && brew install opencv@3 && OpenCV_DIR=/usr/local/opt/opencv@3" + - MATRIX_EVAL="brew install opencv@3 && OpenCV_DIR=/usr/local/opt/opencv@3" - os: linux compiler: clang @@ -216,7 +216,7 @@ matrix: - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8" before_install: - - eval "${MATRIX_EVAL}" + - travis_retry eval "${MATRIX_EVAL}" before_script: - mkdir build_debug From caeb0aee05f989d4714148a65055b1051bbda99b Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 26 Feb 2019 21:21:34 +0100 Subject: [PATCH 0360/1365] [CI] add CUDA build tests in matrix --- .travis.yml | 225 +++++++++++++++++++++++++++++++++++++++++++++---- CMakeLists.txt | 3 + 2 files changed, 212 insertions(+), 16 deletions(-) diff --git a/.travis.yml b/.travis.yml index cd381d776c5..82afaed9ce5 100644 --- a/.travis.yml +++ b/.travis.yml @@ -41,7 +41,7 @@ matrix: - os: linux compiler: clang - name: ubuntu - native clang + name: ubuntu 14.04 - native clang addons: apt: update: true @@ -52,7 +52,7 @@ matrix: - os: linux compiler: clang - name: ubuntu - clang@3.6 + name: ubuntu 14.04 - clang@3.6 addons: apt: update: true @@ -64,7 +64,7 @@ matrix: - os: linux compiler: clang - name: ubuntu - clang@3.8 + name: ubuntu 14.04 - clang@3.8 addons: apt: update: true @@ -78,7 +78,7 @@ matrix: - os: linux compiler: clang - name: ubuntu - clang@3.9 + name: ubuntu 14.04 - clang@3.9 addons: apt: update: true @@ -92,7 +92,7 @@ matrix: - os: linux compiler: clang - name: ubuntu - clang@4.0 + name: ubuntu 14.04 - clang@4.0 addons: apt: update: true @@ -106,7 +106,7 @@ matrix: - os: linux compiler: clang - name: ubuntu - clang@5.0 + name: ubuntu 14.04 - clang@5.0 addons: apt: update: true @@ -120,7 +120,7 @@ matrix: - os: linux compiler: clang - name: ubuntu - clang@6.0 + name: ubuntu 14.04 - clang@6.0 addons: apt: update: true @@ -135,19 +135,18 @@ matrix: - os: linux compiler: gcc - name: ubuntu - native gcc + name: ubuntu 14.04 - native gcc addons: apt: update: true packages: - libopencv-dev - - gfortran env: - MATRIX_EVAL="" - os: linux compiler: gcc - name: ubuntu - gcc@4.9 + name: ubuntu 14.04 - gcc@4.9 addons: apt: update: true @@ -157,11 +156,11 @@ matrix: - libopencv-dev - g++-4.9 env: - - MATRIX_EVAL="CC=gcc-4.9 && CXX=g++-4.9" + - MATRIX_EVAL="CC=gcc-4.9 && CXX=g++-4.9" - os: linux compiler: gcc - name: ubuntu - gcc@5.0 + name: ubuntu 14.04 - gcc@5.0 addons: apt: update: true @@ -171,11 +170,11 @@ matrix: - libopencv-dev - g++-5 env: - - MATRIX_EVAL="CC=gcc-5 && CXX=g++-5" + - MATRIX_EVAL="CC=gcc-5 && CXX=g++-5" - os: linux compiler: gcc - name: ubuntu - gcc@6.0 + name: ubuntu 14.04 - gcc@6.0 addons: apt: update: true @@ -189,7 +188,7 @@ matrix: - os: linux compiler: gcc - name: ubuntu - gcc@7.0 + name: ubuntu 14.04 - gcc@7.0 addons: apt: update: true @@ -203,7 +202,7 @@ matrix: - os: linux compiler: gcc - name: ubuntu - gcc@8.0 + name: ubuntu 14.04 - gcc@8.0 addons: apt: update: true @@ -215,9 +214,203 @@ matrix: env: - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8" + - os: linux + compiler: clang + name: ubuntu 14.04 - native clang - CUDA 10 + addons: + apt: + update: true + packages: + - libopencv-dev + env: + - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + + - os: linux + compiler: clang + name: ubuntu 14.04 - clang@3.6 - CUDA 10 + addons: + apt: + update: true + packages: + - libopencv-dev + - clang-3.6 + env: + - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - MATRIX_EVAL="CC=clang-3.6 && CXX=clang++-3.6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + + - os: linux + compiler: clang + name: ubuntu 14.04 - clang@3.8 - CUDA 10 + addons: + apt: + update: true + sources: + - llvm-toolchain-precise-3.8 + packages: + - libopencv-dev + - clang-3.8 + env: + - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - MATRIX_EVAL="CC=clang-3.8 && CXX=clang++-3.8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + + - os: linux + compiler: clang + name: ubuntu 14.04 - clang@3.9 - CUDA 10 + addons: + apt: + update: true + sources: + - llvm-toolchain-trusty-3.9 + packages: + - libopencv-dev + - clang-3.9 + env: + - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - MATRIX_EVAL="CC=clang-3.9 && CXX=clang++-3.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + + - os: linux + compiler: clang + name: ubuntu 14.04 - clang@4.0 - CUDA 10 + addons: + apt: + update: true + sources: + - llvm-toolchain-trusty-4.0 + packages: + - libopencv-dev + - clang-4.0 + env: + - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - MATRIX_EVAL="CC=clang-4.0 && CXX=clang++-4.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + + - os: linux + compiler: clang + name: ubuntu 14.04 - clang@5.0 - CUDA 10 + addons: + apt: + update: true + sources: + - llvm-toolchain-trusty-5.0 + packages: + - libopencv-dev + - clang-5.0 + env: + - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - MATRIX_EVAL="CC=clang-5.0 && CXX=clang++-5.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + + - os: linux + compiler: clang + name: ubuntu 14.04 - clang@6.0 - CUDA 10 + addons: + apt: + update: true + sources: + - llvm-toolchain-trusty-6.0 + - ubuntu-toolchain-r-test + packages: + - libopencv-dev + - clang-6.0 + env: + - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - MATRIX_EVAL="CC=clang-6.0 && CXX=clang++-6.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + + - os: linux + compiler: gcc + name: ubuntu 14.04 - native gcc - CUDA 10 + addons: + apt: + update: true + packages: + - libopencv-dev + env: + - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + + - os: linux + compiler: gcc + name: ubuntu 14.04 - gcc@4.9 - CUDA 10 + addons: + apt: + update: true + sources: + - ubuntu-toolchain-r-test + packages: + - libopencv-dev + - g++-4.9 + env: + - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - MATRIX_EVAL="CC=gcc-4.9 && CXX=g++-4.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + + - os: linux + compiler: gcc + name: ubuntu 14.04 - gcc@5.0 - CUDA 10 + addons: + apt: + update: true + sources: + - ubuntu-toolchain-r-test + packages: + - libopencv-dev + - g++-5 + env: + - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - MATRIX_EVAL="CC=gcc-5 && CXX=g++-5 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + + - os: linux + compiler: gcc + name: ubuntu 14.04 - gcc@6.0 - CUDA 10 + addons: + apt: + update: true + sources: + - ubuntu-toolchain-r-test + packages: + - g++-6 + - libopencv-dev + env: + - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - MATRIX_EVAL="CC=gcc-6 && CXX=g++-6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + + - os: linux + compiler: gcc + name: ubuntu 14.04 - gcc@7.0 - CUDA 10 + addons: + apt: + update: true + sources: + - ubuntu-toolchain-r-test + packages: + - libopencv-dev + - g++-7 + env: + - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - MATRIX_EVAL="CC=gcc-7 && CXX=g++-7 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + + - os: linux + compiler: gcc + name: ubuntu 14.04 - gcc@8.0 - CUDA 10 + addons: + apt: + update: true + sources: + - ubuntu-toolchain-r-test + packages: + - libopencv-dev + - g++-8 + env: + - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + before_install: - travis_retry eval "${MATRIX_EVAL}" +install: + # CMake upgrade on Linux + - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then wget --no-check-certificate https://github.com/Kitware/CMake/releases/download/v3.13.4/cmake-3.13.4-Linux-x86_64.tar.gz ;fi + - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then tar -xzf cmake-3.13.4-Linux-x86_64.tar.gz ;fi + - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then export PATH=$PWD/cmake-3.13.4-Linux-x86_64/bin:$PATH ;fi + before_script: - mkdir build_debug - cd build_debug diff --git a/CMakeLists.txt b/CMakeLists.txt index 21e1fe0b65d..de6d95a5b3f 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -109,6 +109,9 @@ endif() if(ENABLE_CUDA) add_definitions(-DGPU) + if(NOT WIN32) + include_directories("${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES}") + endif() if(CUDA_COMPUTE_MODEL VERSION_GREATER_EQUAL ${CUDA_COMPUTE_MODEL_75} AND CMAKE_CUDA_COMPILER_VERSION VERSION_LESS_EQUAL 9) message(STATUS "CUDA 10 is required for Compute Model 7.5 - Lowering settings to 7.0") set(CUDA_COMPUTE_MODEL ${CUDA_COMPUTE_MODEL_70}) From 3b43d32dfff70f4c3ef310322279560f92d00f41 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 28 Feb 2019 00:55:44 +0100 Subject: [PATCH 0361/1365] [ci] add tests for included libs, add link path for linux --- CMakeLists.txt | 1 + appveyor.yml | 28 +++++++++++++++------------- 2 files changed, 16 insertions(+), 13 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index de6d95a5b3f..5afc9fd7a20 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -111,6 +111,7 @@ if(ENABLE_CUDA) add_definitions(-DGPU) if(NOT WIN32) include_directories("${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES}") + link_directories("${CMAKE_CUDA_IMPLICIT_LINK_DIRECTORIES}") endif() if(CUDA_COMPUTE_MODEL VERSION_GREATER_EQUAL ${CUDA_COMPUTE_MODEL_75} AND CMAKE_CUDA_COMPILER_VERSION VERSION_LESS_EQUAL 9) message(STATUS "CUDA 10 is required for Compute Model 7.5 - Lowering settings to 7.0") diff --git a/appveyor.yml b/appveyor.yml index 9f6d32ce86d..bc452e12a48 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -1,6 +1,5 @@ image: Visual Studio 2017 clone_folder: c:\projects\darknet -configuration: Release environment: WORKSPACE: C:\projects @@ -13,7 +12,9 @@ environment: COMPILER: vs VCPKG_ROOT: C:\projects\vcpkg VCPKG_DEFAULT_TRIPLET: x64-windows - + - platform: Win64 + COMPILER: vs + USE_INTEGRATED_LIBS: yes install: - if [%COMPILER%]==[vs] cinst cmake ninja @@ -26,23 +27,24 @@ install: - mkdir cygwin-downloads - ps: if($env:COMPILER -eq "cygwin") { Invoke-WebRequest https://cygwin.com/setup-x86_64.exe -OutFile $env:WORKSPACE\cygwin-setup.exe } - if [%COMPILER%]==[cygwin] %WORKSPACE%\cygwin-setup.exe --quiet-mode --no-shortcuts --no-startmenu --no-desktop --upgrade-also --root C:\cygwin64 --local-package-dir %WORKSPACE%\cygwin-downloads --packages gcc-g++,cmake,libopencv-devel - - if [%COMPILER%]==[vs] git clone https://github.com/Microsoft/vcpkg - - if [%COMPILER%]==[vs] cd %VCPKG_ROOT% - - if [%COMPILER%]==[vs] bootstrap-vcpkg.bat - - if [%COMPILER%]==[vs] vcpkg integrate install - - if [%COMPILER%]==[vs] vcpkg install opencv pthreads + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] git clone https://github.com/Microsoft/vcpkg + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] cd %VCPKG_ROOT% + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] bootstrap-vcpkg.bat + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg integrate install + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg install opencv pthreads - cd %WORKSPACE%\darknet\ - mkdir build_debug && cd build_debug - if [%COMPILER%]==[cygwin] %CYGSH% 'cmake .. -G "Unix Makefiles" -DCMAKE_BUILD_TYPE="Debug"' - - if [%COMPILER%]==[vs] cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=%VCPKG_ROOT%\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=%VCPKG_DEFAULT_TRIPLET%" -DCMAKE_BUILD_TYPE="Debug" .. + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=%VCPKG_ROOT%\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=%VCPKG_DEFAULT_TRIPLET%" -DCMAKE_BUILD_TYPE="Debug" .. - cd .. - mkdir build_release && cd build_release - if [%COMPILER%]==[cygwin] %CYGSH% 'cmake .. -G "Unix Makefiles" -DCMAKE_BUILD_TYPE="Release"' - - if [%COMPILER%]==[vs] cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=%VCPKG_ROOT%\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=%VCPKG_DEFAULT_TRIPLET%" -DCMAKE_BUILD_TYPE="Release" .. + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=%VCPKG_ROOT%\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=%VCPKG_DEFAULT_TRIPLET%" -DCMAKE_BUILD_TYPE="Release" .. + - if [%COMPILER%]==[vs] if [%USE_INTEGRATED_LIBS%]==[yes] cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" -DCMAKE_BUILD_TYPE="Release" .. - cd .. build_script: - - if [%COMPILER%]==[cygwin] cd build_debug && %CYGSH% 'cmake --build .' && cd .. - - if [%COMPILER%]==[cygwin] cd build_release && %CYGSH% 'cmake --build .' && cd .. - - if [%COMPILER%]==[vs] cd build_debug && cmake --build . --config Debug && cd .. - - if [%COMPILER%]==[vs] cd build_release && cmake --build . --config Release && cd .. + - if [%COMPILER%]==[cygwin] cd build_debug && %CYGSH% 'cmake --build .' && cd .. + - if [%COMPILER%]==[cygwin] cd build_release && %CYGSH% 'cmake --build .' && cd .. + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] cd build_debug && cmake --build . --config Debug && cd .. + - if [%COMPILER%]==[vs] cd build_release && cmake --build . --config Release && cd .. From 139ddab7fa000f31d292c608f9d21178288b78e9 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 28 Feb 2019 07:22:57 +0100 Subject: [PATCH 0362/1365] remove unused cpu function in .cu file --- src/im2col.h | 5 ++--- src/im2col_kernels.cu | 3 ++- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/im2col.h b/src/im2col.h index f510d61e031..35f0039ac3d 100644 --- a/src/im2col.h +++ b/src/im2col.h @@ -63,10 +63,9 @@ void convolve_gpu(float *input, float *weights, float *output, int in_w, int in_ void convolve_bin_gpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad, int new_lda, float *mean_arr_gpu); -void convolve_bin_cpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, - int size, int pad, int new_lda, float *mean_arr_gpu); +//void convolve_bin_cpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad, int new_lda, float *mean_arr_gpu); -void convolve_cpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad); +//void convolve_cpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad); #endif #ifdef __cplusplus diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 90af12b6982..da952df5d5a 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -1818,7 +1818,7 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, } // -------------------------------- - +/* void convolve_cpu(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad) { int fil; @@ -1929,6 +1929,7 @@ void convolve_bin_cpu(float *input, float *weights, float *output, int in_w, int } } } +*/ // -------------------------------- __global__ void convolve_gpu_kernel(float *input, float *weights, float *output, int in_w, int in_h, int in_c, int n, int size, int pad) From 2acbfe9592f46a5b59bbafab40393bcadbd4645f Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 28 Feb 2019 15:57:03 +0100 Subject: [PATCH 0363/1365] [ci] try cuda builds on windows, skip building uselib_track if opencv does not have cuda support --- CMakeLists.txt | 20 ++++++++++++-------- appveyor.yml | 14 +++++++++++++- 2 files changed, 25 insertions(+), 9 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 5afc9fd7a20..4fce9c307e9 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -19,8 +19,13 @@ endif() enable_language(C) enable_language(CXX) -set(ENABLE_CUDA "TRUE" CACHE BOOL "Enable CUDA build") +set(CMAKE_CXX_STANDARD 11) +set(CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/cmake/Modules/" ${CMAKE_MODULE_PATH}) +include_directories(${CMAKE_CURRENT_LIST_DIR}/include) +include_directories(${CMAKE_CURRENT_LIST_DIR}/src) +set(SKIP_USELIB_TRACK "FALSE" CACHE BOOL "Skip building uselib_track" FORCE) +set(ENABLE_CUDA "TRUE" CACHE BOOL "Enable CUDA build") if(${CMAKE_VERSION} VERSION_LESS "3.8.0") set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA build" FORCE) message(WARNING "To build with CUDA support you need CMake 3.8.0+") @@ -34,11 +39,6 @@ else() endif() endif() -set(CMAKE_CXX_STANDARD 11) -set(CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/cmake/Modules/" ${CMAKE_MODULE_PATH}) -include_directories(${CMAKE_CURRENT_LIST_DIR}/include) -include_directories(${CMAKE_CURRENT_LIST_DIR}/src) - set(CUDA_COMPUTE_MODEL_75 "75") #Compute capability for Tesla T4, RTX 2080 set(CUDA_COMPUTE_MODEL_72 "72") #Compute capability for Jetson Xavier set(CUDA_COMPUTE_MODEL_70 "70") #Compute capability for Tesla V100 @@ -103,6 +103,10 @@ else() endif() if(OpenCV_FOUND) + if(NOT OpenCV_CUDA_VERSION) + set(SKIP_USELIB_TRACK "TRUE" CACHE BOOL "Skip building uselib_track" FORCE) + message(WARNING "The build of OpenCV does not have CUDA support, so some tools have been disabled! Please rebuild OpenCV with CUDA support to build them!") + endif() add_definitions(-DOPENCV) include_directories(${OpenCV_INCLUDE_DIRS}) endif() @@ -227,7 +231,7 @@ endif() target_compile_definitions(darklib PRIVATE LIB_EXPORTS=1) set_target_properties(darklib PROPERTIES LINKER_LANGUAGE CXX) -if(OpenCV_VERSION VERSION_GREATER "3.0") +if(OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRACK) add_executable(uselib_track ${CMAKE_CURRENT_LIST_DIR}/src/yolo_console_dll.cpp) target_compile_definitions(uselib_track PRIVATE TRACK_OPTFLOW=1) set_target_properties(uselib_track PROPERTIES LINKER_LANGUAGE CXX) @@ -276,7 +280,7 @@ if(ENABLE_CUDA) endif() target_link_libraries(uselib darklib) -if(OpenCV_VERSION VERSION_GREATER "3.0") +if(OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRACK) target_link_libraries(uselib_track darklib) install(TARGETS uselib_track DESTINATION ${CMAKE_SOURCE_DIR}/) endif() diff --git a/appveyor.yml b/appveyor.yml index bc452e12a48..665fcfa0570 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -12,6 +12,14 @@ environment: COMPILER: vs VCPKG_ROOT: C:\projects\vcpkg VCPKG_DEFAULT_TRIPLET: x64-windows + USE_CUDA: yes + CUDACXX: C:\CUDA\nvcc\bin\nvcc.exe + CUDA_PATH: C:\CUDA\nvcc + - platform: Win64 + COMPILER: vs + VCPKG_ROOT: C:\projects\vcpkg + VCPKG_DEFAULT_TRIPLET: x64-windows + USE_CUDA: no - platform: Win64 COMPILER: vs USE_INTEGRATED_LIBS: yes @@ -24,6 +32,9 @@ install: - if [%COMPILER%]==[cygwin] SET PATH=%PATH:C:\Program Files\Git\usr\bin;=% - git submodule -q update --init --recursive - cd %WORKSPACE%\ + - if [%USE_CUDA%]==[yes] curl -L https://developer.nvidia.com/compute/cuda/10.0/Prod/local_installers/cuda_10.0.130_411.31_windows -o cuda_10.0.130_411.31_windows.exe + - if [%USE_CUDA%]==[yes] if NOT EXIST C:\CUDA mkdir C:\CUDA + - if [%USE_CUDA%]==[yes] 7z x cuda_10.0.130_411.31_windows.exe -oC:\CUDA nvcc/* nvrtc*/* - mkdir cygwin-downloads - ps: if($env:COMPILER -eq "cygwin") { Invoke-WebRequest https://cygwin.com/setup-x86_64.exe -OutFile $env:WORKSPACE\cygwin-setup.exe } - if [%COMPILER%]==[cygwin] %WORKSPACE%\cygwin-setup.exe --quiet-mode --no-shortcuts --no-startmenu --no-desktop --upgrade-also --root C:\cygwin64 --local-package-dir %WORKSPACE%\cygwin-downloads --packages gcc-g++,cmake,libopencv-devel @@ -31,7 +42,8 @@ install: - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] cd %VCPKG_ROOT% - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] bootstrap-vcpkg.bat - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg integrate install - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg install opencv pthreads + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] if NOT [%USE_CUDA%]==[yes] vcpkg install opencv pthreads + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] if [%USE_CUDA%]==[yes] vcpkg install opencv[cuda] pthreads - cd %WORKSPACE%\darknet\ - mkdir build_debug && cd build_debug - if [%COMPILER%]==[cygwin] %CYGSH% 'cmake .. -G "Unix Makefiles" -DCMAKE_BUILD_TYPE="Debug"' From e8c3905e547f8fb7f0d3d0f297569453461b5b8f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 28 Feb 2019 20:38:13 +0300 Subject: [PATCH 0364/1365] Functions for fixing nan --- src/blas.h | 3 +++ src/blas_kernels.cu | 47 ++++++++++++++++++++++++++++++++++++++++++ src/cuda.c | 8 +++++-- src/network_kernels.cu | 12 +++++++++++ 4 files changed, 68 insertions(+), 2 deletions(-) diff --git a/src/blas.h b/src/blas.h index 19b72b76144..12d4b9a3b4d 100644 --- a/src/blas.h +++ b/src/blas.h @@ -102,6 +102,9 @@ void upsample_gpu(float *in, int w, int h, int c, int batch, int stride, int for void softmax_tree_gpu(float *input, int spatial, int batch, int stride, float temp, float *output, tree hier); +void fix_nan_and_inf(float *input, size_t size); +int is_nan_or_inf(float *input, size_t size); + #endif #ifdef __cplusplus } diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 45ed09cde3f..98592c81668 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -975,3 +975,50 @@ extern "C" void softmax_tree_gpu(float *input, int spatial, int batch, int strid cuda_free((float *)tree_groups_size); cuda_free((float *)tree_groups_offset); } + + +__global__ void fix_nan_and_inf_kernel(float *input, size_t size) +{ + const int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < size) { + float val = input[index]; + if (isnan(val) || isinf(val)) + input[index] = index; // pseudo random value + } +} + +extern "C" void fix_nan_and_inf(float *input, size_t size) +{ + const int block_size = BLOCK; + const int num_blocks = get_number_of_blocks(size, block_size); + fix_nan_and_inf_kernel << > >(input, size); + CHECK_CUDA(cudaPeekAtLastError()); + //CHECK_CUDA(cudaDeviceSynchronize()); +} + + +__global__ void is_nan_or_inf_kernel(float *input, size_t size, int *pinned_return) +{ + const int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < size) { + float val = input[index]; + if (isnan(val) || isinf(val)) + *pinned_return = 1; + } +} + +extern "C" int is_nan_or_inf(float *input, size_t size) +{ + int *pinned_return; + CHECK_CUDA(cudaHostAlloc(&pinned_return, sizeof(int), cudaHostRegisterMapped)); + *pinned_return = 0; + + const int block_size = BLOCK; + const int num_blocks = get_number_of_blocks(size, block_size); + is_nan_or_inf_kernel << > >(input, size, pinned_return); + CHECK_CUDA(cudaDeviceSynchronize()); + int ret_val = *pinned_return; + + CHECK_CUDA(cudaFreeHost(pinned_return)); + return ret_val; +} \ No newline at end of file diff --git a/src/cuda.c b/src/cuda.c index 87402e5ae94..9e7745e2496 100644 --- a/src/cuda.c +++ b/src/cuda.c @@ -59,8 +59,10 @@ void check_error(cudaError_t status) void check_error_extended(cudaError_t status, const char *file, int line, const char *date_time) { - if (status != cudaSuccess) + if (status != cudaSuccess) { printf("CUDA status Error: file: %s() : line: %d : build time: %s \n", file, line, date_time); + check_error(status); + } #ifdef DEBUG status = cudaDeviceSynchronize(); if (status != cudaSuccess) @@ -175,8 +177,10 @@ void cudnn_check_error(cudnnStatus_t status) void cudnn_check_error_extended(cudnnStatus_t status, const char *file, int line, const char *date_time) { - if (status != CUDNN_STATUS_SUCCESS) + if (status != CUDNN_STATUS_SUCCESS) { printf("\n cuDNN status Error in: file: %s() : line: %d : build time: %s \n", file, line, date_time); + cudnn_check_error(status); + } #ifdef DEBUG status = cudaDeviceSynchronize(); if (status != CUDNN_STATUS_SUCCESS) diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 2c016697f78..619b874219b 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -110,6 +110,18 @@ void backward_network_gpu(network net, network_state state) state.delta = prev.delta_gpu; } l.backward_gpu(l, state); + + /* + if(i != 0) + { + layer l = net.layers[i - 1]; + int state_delta_nan_inf = is_nan_or_inf(state.delta, l.outputs * l.batch); + int state_input_nan_inf = is_nan_or_inf(state.input, l.outputs * l.batch); + printf("\n i - %d is_nan_or_inf(s.delta) = %d \n", i, state_delta_nan_inf); + printf(" i - %d is_nan_or_inf(s.input) = %d \n", i, state_input_nan_inf); + if (state_delta_nan_inf || state_input_nan_inf) { printf(" found "); getchar(); } + } + */ } } From 9b09abe12227a61c17c7b3c30c04642af50aa607 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 28 Feb 2019 20:47:22 +0300 Subject: [PATCH 0365/1365] Fixed convolutional-layer when it is used as base for crnn-layer --- src/convolutional_layer.c | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 07f69159979..f5f2cc9218a 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -348,6 +348,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.xnor = xnor; l.use_bin_output = use_bin_output; l.batch = batch; + l.steps = steps; l.stride = stride; l.size = size; l.pad = padding; @@ -544,6 +545,7 @@ void test_convolutional_layer() void resize_convolutional_layer(convolutional_layer *l, int w, int h) { + int total_batch = l->batch*l->steps; int old_w = l->w; int old_h = l->h; l->w = w; @@ -557,11 +559,11 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) l->outputs = l->out_h * l->out_w * l->out_c; l->inputs = l->w * l->h * l->c; - l->output = (float*)realloc(l->output, l->batch * l->outputs * sizeof(float)); - l->delta = (float*)realloc(l->delta, l->batch * l->outputs * sizeof(float)); + l->output = (float*)realloc(l->output, total_batch * l->outputs * sizeof(float)); + l->delta = (float*)realloc(l->delta, total_batch * l->outputs * sizeof(float)); if(l->batch_normalize){ - l->x = (float*)realloc(l->x, l->batch * l->outputs * sizeof(float)); - l->x_norm = (float*)realloc(l->x_norm, l->batch * l->outputs * sizeof(float)); + l->x = (float*)realloc(l->x, total_batch * l->outputs * sizeof(float)); + l->x_norm = (float*)realloc(l->x_norm, total_batch * l->outputs * sizeof(float)); } if (l->xnor) { @@ -573,15 +575,15 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) cuda_free(l->delta_gpu); cuda_free(l->output_gpu); - l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs); - l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs); + l->delta_gpu = cuda_make_array(l->delta, total_batch*l->outputs); + l->output_gpu = cuda_make_array(l->output, total_batch*l->outputs); if (l->batch_normalize) { cuda_free(l->x_gpu); cuda_free(l->x_norm_gpu); - l->x_gpu = cuda_make_array(l->output, l->batch*l->outputs); - l->x_norm_gpu = cuda_make_array(l->output, l->batch*l->outputs); + l->x_gpu = cuda_make_array(l->output, total_batch*l->outputs); + l->x_norm_gpu = cuda_make_array(l->output, total_batch*l->outputs); } if (l->xnor) { From 52312f5f8f56fba12736540c39c06b9bd5df77c1 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 28 Feb 2019 21:04:58 +0100 Subject: [PATCH 0366/1365] [appveyor/cuda] extract all files from CUDA archive --- appveyor.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/appveyor.yml b/appveyor.yml index 665fcfa0570..af204a895b2 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -34,7 +34,7 @@ install: - cd %WORKSPACE%\ - if [%USE_CUDA%]==[yes] curl -L https://developer.nvidia.com/compute/cuda/10.0/Prod/local_installers/cuda_10.0.130_411.31_windows -o cuda_10.0.130_411.31_windows.exe - if [%USE_CUDA%]==[yes] if NOT EXIST C:\CUDA mkdir C:\CUDA - - if [%USE_CUDA%]==[yes] 7z x cuda_10.0.130_411.31_windows.exe -oC:\CUDA nvcc/* nvrtc*/* + - if [%USE_CUDA%]==[yes] 7z x cuda_10.0.130_411.31_windows.exe -oC:\CUDA - mkdir cygwin-downloads - ps: if($env:COMPILER -eq "cygwin") { Invoke-WebRequest https://cygwin.com/setup-x86_64.exe -OutFile $env:WORKSPACE\cygwin-setup.exe } - if [%COMPILER%]==[cygwin] %WORKSPACE%\cygwin-setup.exe --quiet-mode --no-shortcuts --no-startmenu --no-desktop --upgrade-also --root C:\cygwin64 --local-package-dir %WORKSPACE%\cygwin-downloads --packages gcc-g++,cmake,libopencv-devel From 6dbfeee159bfd34a24a87aa9481dd4717c53a45f Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 1 Mar 2019 09:15:34 +0100 Subject: [PATCH 0367/1365] [appveyor] fix CUDA paths again --- appveyor.yml | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/appveyor.yml b/appveyor.yml index af204a895b2..861934a0e4e 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -13,8 +13,8 @@ environment: VCPKG_ROOT: C:\projects\vcpkg VCPKG_DEFAULT_TRIPLET: x64-windows USE_CUDA: yes - CUDACXX: C:\CUDA\nvcc\bin\nvcc.exe - CUDA_PATH: C:\CUDA\nvcc + CUDACXX: C:\CUDA\bin\nvcc.exe + CUDA_PATH: C:\CUDA\ - platform: Win64 COMPILER: vs VCPKG_ROOT: C:\projects\vcpkg @@ -33,8 +33,12 @@ install: - git submodule -q update --init --recursive - cd %WORKSPACE%\ - if [%USE_CUDA%]==[yes] curl -L https://developer.nvidia.com/compute/cuda/10.0/Prod/local_installers/cuda_10.0.130_411.31_windows -o cuda_10.0.130_411.31_windows.exe - - if [%USE_CUDA%]==[yes] if NOT EXIST C:\CUDA mkdir C:\CUDA - - if [%USE_CUDA%]==[yes] 7z x cuda_10.0.130_411.31_windows.exe -oC:\CUDA + - if [%USE_CUDA%]==[yes] mkdir C:\CUDATEMP + - if [%USE_CUDA%]==[yes] mkdir C:\CUDA + - if [%USE_CUDA%]==[yes] 7z x cuda_10.0.130_411.31_windows.exe -oC:\CUDATEMP + - if [%USE_CUDA%]==[yes] cd C:\CUDATEMP + - if [%USE_CUDA%]==[yes] FOR /D %%G in ("*") DO xcopy C:\CUDATEMP\%%G\* C:\CUDA\ /s /y + - cd %WORKSPACE%\ - mkdir cygwin-downloads - ps: if($env:COMPILER -eq "cygwin") { Invoke-WebRequest https://cygwin.com/setup-x86_64.exe -OutFile $env:WORKSPACE\cygwin-setup.exe } - if [%COMPILER%]==[cygwin] %WORKSPACE%\cygwin-setup.exe --quiet-mode --no-shortcuts --no-startmenu --no-desktop --upgrade-also --root C:\cygwin64 --local-package-dir %WORKSPACE%\cygwin-downloads --packages gcc-g++,cmake,libopencv-devel From 278df5e89e639bbe16dc8f000a4832973046d3d0 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 1 Mar 2019 11:11:25 +0100 Subject: [PATCH 0368/1365] [appveyor] disable building opencv[cuda], it's too slow --- appveyor.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/appveyor.yml b/appveyor.yml index 861934a0e4e..e0abb0b5c0a 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -46,8 +46,7 @@ install: - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] cd %VCPKG_ROOT% - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] bootstrap-vcpkg.bat - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg integrate install - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] if NOT [%USE_CUDA%]==[yes] vcpkg install opencv pthreads - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] if [%USE_CUDA%]==[yes] vcpkg install opencv[cuda] pthreads + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg install opencv pthreads ## opencv[cuda] is too big to build, timing out (>1h). We use plain openCV also for CUDA builds (toolchain can manage this strange situation anyway) - cd %WORKSPACE%\darknet\ - mkdir build_debug && cd build_debug - if [%COMPILER%]==[cygwin] %CYGSH% 'cmake .. -G "Unix Makefiles" -DCMAKE_BUILD_TYPE="Debug"' From 6c7540420b25faa547ab946f909baff68e1ff54d Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 1 Mar 2019 11:23:31 +0100 Subject: [PATCH 0369/1365] [opencv-cuda] handle properly cuda enabled but opencv built without cuda support --- CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 4fce9c307e9..9732035b080 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -103,7 +103,7 @@ else() endif() if(OpenCV_FOUND) - if(NOT OpenCV_CUDA_VERSION) + if(ENABLE_CUDA AND NOT OpenCV_CUDA_VERSION) set(SKIP_USELIB_TRACK "TRUE" CACHE BOOL "Skip building uselib_track" FORCE) message(WARNING "The build of OpenCV does not have CUDA support, so some tools have been disabled! Please rebuild OpenCV with CUDA support to build them!") endif() From 2c0d857110c30c9fb24bea6d26448a6ef3c78ecd Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 1 Mar 2019 12:03:41 +0100 Subject: [PATCH 0370/1365] [appveyor-cuda] add another env variable --- appveyor.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/appveyor.yml b/appveyor.yml index e0abb0b5c0a..2012a3107e3 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -15,6 +15,7 @@ environment: USE_CUDA: yes CUDACXX: C:\CUDA\bin\nvcc.exe CUDA_PATH: C:\CUDA\ + CUDA_PATH_V10_0: C:\CUDA\ - platform: Win64 COMPILER: vs VCPKG_ROOT: C:\projects\vcpkg @@ -46,6 +47,7 @@ install: - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] cd %VCPKG_ROOT% - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] bootstrap-vcpkg.bat - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg integrate install + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] if [%USE_CUDA%]==[yes] vcpkg install cuda - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg install opencv pthreads ## opencv[cuda] is too big to build, timing out (>1h). We use plain openCV also for CUDA builds (toolchain can manage this strange situation anyway) - cd %WORKSPACE%\darknet\ - mkdir build_debug && cd build_debug From cfb80e39cd0c69811f696b469aadc6b181453b0b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 1 Mar 2019 15:49:23 +0300 Subject: [PATCH 0371/1365] Fixed http_stream.cpp --- src/http_stream.cpp | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/http_stream.cpp b/src/http_stream.cpp index dbaca92da4c..6840002a027 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -196,8 +196,9 @@ class MJPG_sender std::vector params; params.push_back(IMWRITE_JPEG_QUALITY); params.push_back(quality); - //cv::imencode(".jpg", frame, outbuf, params); //REMOVED FOR COMPATIBILITY - std::cerr << "cv::imencode call disabled!" << std::endl; + cv::imencode(".jpg", frame, outbuf, params); //REMOVED FOR COMPATIBILITY + // https://docs.opencv.org/3.4/d4/da8/group__imgcodecs.html#ga292d81be8d76901bff7988d18d2b42ac + //std::cerr << "cv::imencode call disabled!" << std::endl; size_t outlen = outbuf.size(); #ifdef _WIN32 From e0596a10465718d9f39008c8f4b5072314929057 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 1 Mar 2019 16:56:00 +0300 Subject: [PATCH 0372/1365] Added cmd/sh scripts for downloading ILSVRC2012 train & valid for Windows --- scripts/datasets.txt | 36 +++++++++++++++++++ scripts/windows/win_cifar.cmd | 19 ++++++++++ .../win_get_imagenet_train_48hours.cmd | 25 +++++++++++++ scripts/windows/win_get_imagenet_valid.cmd | 36 +++++++++++++++++++ scripts/windows/win_install_cygwin.cmd | 12 +++++++ scripts/windows/windows_imagenet_label.sh | 16 +++++++++ scripts/windows/windows_imagenet_train.sh | 17 +++++++++ 7 files changed, 161 insertions(+) create mode 100644 scripts/datasets.txt create mode 100644 scripts/windows/win_cifar.cmd create mode 100644 scripts/windows/win_get_imagenet_train_48hours.cmd create mode 100644 scripts/windows/win_get_imagenet_valid.cmd create mode 100644 scripts/windows/win_install_cygwin.cmd create mode 100644 scripts/windows/windows_imagenet_label.sh create mode 100644 scripts/windows/windows_imagenet_train.sh diff --git a/scripts/datasets.txt b/scripts/datasets.txt new file mode 100644 index 00000000000..8151c0831ce --- /dev/null +++ b/scripts/datasets.txt @@ -0,0 +1,36 @@ + + +Datasets: + +Pascal VOC: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html + +MS COCO: http://cocodataset.org/#download + +ImageNet: http://imagenet.stanford.edu/download.php + +ImageNet (ILSVRC2012): http://www.image-net.org/challenges/LSVRC/2012/nonpub-downloads + +Open Images: https://storage.googleapis.com/openimages/web/download.html + +Cityscapes: https://www.cityscapes-dataset.com/ + +Object Tracking Benchmark: http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html + +MARS: http://www.liangzheng.com.cn/Project/project_mars.html + +Market-1501: http://www.liangzheng.org/Project/project_reid.html + +German Traffic Sign Recognition Benchmark: http://benchmark.ini.rub.de/ + +Labeled Faces in the Wild: http://vis-www.cs.umass.edu/lfw/ + +Core50: https://vlomonaco.github.io/core50/ + +Visual Question Answering: https://visualqa.org/download.html + +Large Movie Review Dataset: http://ai.stanford.edu/~amaas/data/sentiment/ + + +Other datasets (Music, Natural Images, Artificial Datasets, Faces, Text, Speech, Recommendation Systems, Misc): http://deeplearning.net/datasets/ + +25 datasets: https://www.analyticsvidhya.com/blog/2018/03/comprehensive-collection-deep-learning-datasets/ \ No newline at end of file diff --git a/scripts/windows/win_cifar.cmd b/scripts/windows/win_cifar.cmd new file mode 100644 index 00000000000..a53f95af3fc --- /dev/null +++ b/scripts/windows/win_cifar.cmd @@ -0,0 +1,19 @@ +echo Run install_cygwin.cmd before: + + +c:\cygwin64\bin\wget https://pjreddie.com/media/files/cifar.tgz + +c:\cygwin64\bin\gzip -d "%CD:\=/%/cifar.tgz" + +c:\cygwin64\bin\tar --force-local -xvf "%CD:\=/%/cifar.tar" + +c:\cygwin64\bin\cat "%CD:\=/%/labels.txt" + + +c:\cygwin64\bin\find "%CD:\=/%/cifar/train" -name \*.png > "%CD:\=/%/cifar/train.list" + +c:\cygwin64\bin\find "%CD:\=/%/cifar/test" -name \*.png > "%CD:\=/%/cifar/test.list" + + + +pause \ No newline at end of file diff --git a/scripts/windows/win_get_imagenet_train_48hours.cmd b/scripts/windows/win_get_imagenet_train_48hours.cmd new file mode 100644 index 00000000000..2c560608ee8 --- /dev/null +++ b/scripts/windows/win_get_imagenet_train_48hours.cmd @@ -0,0 +1,25 @@ +echo Run install_cygwin.cmd before: + +rem http://www.image-net.org/challenges/LSVRC/2012/nonpub-downloads +rem https://github.com/amd/OpenCL-caffe/wiki/Instructions-to-create-ImageNet-2012-data + + +c:\cygwin64\bin\bash -l -c "cd %CD:\=/%/; echo $PWD" + +echo Wait several hours... + +c:\cygwin64\bin\wget http://www.image-net.org/challenges/LSVRC/2012/nnoupb/ILSVRC2012_img_train.tar + +c:\cygwin64\bin\mkdir -p "%CD:\=/%/ILSVRC2012_img_train" + +c:\cygwin64\bin\tar --force-local -xf "%CD:\=/%/ILSVRC2012_img_train.tar" -C "%CD:\=/%/ILSVRC2012_img_train" + + + +c:\cygwin64\bin\bash -l -c "cd %CD:\=/%/; %CD:\=/%/windows_imagenet_train.sh" + +c:\cygwin64\bin\find "%CD:\=/%/ILSVRC2012_img_train" -name \*.JPEG > imagenet1k.train.list + + + +pause \ No newline at end of file diff --git a/scripts/windows/win_get_imagenet_valid.cmd b/scripts/windows/win_get_imagenet_valid.cmd new file mode 100644 index 00000000000..1eefdf07ab3 --- /dev/null +++ b/scripts/windows/win_get_imagenet_valid.cmd @@ -0,0 +1,36 @@ +echo Run install_cygwin.cmd before: + +rem http://www.image-net.org/challenges/LSVRC/2012/nonpub-downloads +rem https://github.com/amd/OpenCL-caffe/wiki/Instructions-to-create-ImageNet-2012-data + + +c:\cygwin64\bin\bash -l -c "cd %CD:\=/%/; echo $PWD" + + +c:\cygwin64\bin\wget http://www.image-net.org/challenges/LSVRC/2012/nnoupb/ILSVRC2012_bbox_val_v3.tgz + +c:\cygwin64\bin\gzip -d "%CD:\=/%/ILSVRC2012_bbox_val_v3.tgz" + +c:\cygwin64\bin\tar --force-local -xvf "%CD:\=/%/ILSVRC2012_bbox_val_v3.tar" + + +c:\cygwin64\bin\wget http://www.image-net.org/challenges/LSVRC/2012/nnoupb/ILSVRC2012_img_val.tar + +c:\cygwin64\bin\mkdir -p "%CD:\=/%/imgs" + +c:\cygwin64\bin\tar --force-local -xf "%CD:\=/%/ILSVRC2012_img_val.tar" -C "%CD:\=/%/imgs" + + +echo Wait a few hours... + +rem c:\cygwin64\bin\wget https://pjreddie.com/media/files/imagenet_label.sh + +c:\cygwin64\bin\dos2unix "%CD:\=/%/windows_imagenet_label.sh" + +c:\cygwin64\bin\bash -l -c "cd %CD:\=/%/; %CD:\=/%/windows_imagenet_label.sh" + +c:\cygwin64\bin\find "%CD:\=/%/labelled" -name \*.JPEG > inet.val.list + + + +pause \ No newline at end of file diff --git a/scripts/windows/win_install_cygwin.cmd b/scripts/windows/win_install_cygwin.cmd new file mode 100644 index 00000000000..6a2bda4a812 --- /dev/null +++ b/scripts/windows/win_install_cygwin.cmd @@ -0,0 +1,12 @@ +echo Download file: https://www.cygwin.com/setup-x86_64.exe + + +setup-x86_64.exe -q -P dos2unix,wget,tar,untar,gzip,unzip,qawk,bzip2,git,vim,gcc-g++,make,grep,sed,find + + +# wget rawgit.com/transcode-open/apt-cyg/master/apt-cyg +# install apt-cyg /bin + +echo Finished + +pause \ No newline at end of file diff --git a/scripts/windows/windows_imagenet_label.sh b/scripts/windows/windows_imagenet_label.sh new file mode 100644 index 00000000000..a371875daf5 --- /dev/null +++ b/scripts/windows/windows_imagenet_label.sh @@ -0,0 +1,16 @@ +#!/bin/bash + +mkdir -p labelled +wd=`pwd` + +for f in val/*.xml; +do +label=`grep -m1 "" $f | grep -oP '\K[^<]*'` +im=`echo $f | sed 's/val/imgs/; s/xml/JPEG/'` +out=`echo $im | sed 's/JPEG/'${label}'.JPEG/; s/imgs/labelled/'` +mv ${wd}/$im ${wd}/$out +#ln -s ${wd}/$im ${wd}/$out +done + +#find ${wd}/labelled -name \*.JPEG > inet.val.list + diff --git a/scripts/windows/windows_imagenet_train.sh b/scripts/windows/windows_imagenet_train.sh new file mode 100644 index 00000000000..11e6430b943 --- /dev/null +++ b/scripts/windows/windows_imagenet_train.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +#wget http://www.image-net.org/challenges/LSVRC/2012/nnoupb/ILSVRC2012_img_train.tar +#mkdir -p ILSVRC2012_img_train +#tar --force-local -xf ILSVRC2012_img_train.tar -C ILSVRC2012_img_train + +wd=`pwd` + +for f in ILSVRC2012_img_train/*.tar; +do +name=$(echo "$f" | cut -f 1 -d '.') +mkdir "${wd}/${name}" +tar --force-local -xf "${wd}/${f}" -C "${wd}/${name}" +done + +#find "${wd}/ILSVRC2012_img_train" -name \*.JPEG > imagenet1k.train.list + From 50956447f88279f81571a2827d003cd0d70ac243 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 1 Mar 2019 19:01:14 +0300 Subject: [PATCH 0373/1365] Fixed issue with killed mjpeg_stream --- src/demo.c | 8 ++++---- src/http_stream.cpp | 46 ++++++++++++++++++++++----------------------- src/image.c | 2 +- 3 files changed, 28 insertions(+), 28 deletions(-) diff --git a/src/demo.c b/src/demo.c index 80f925ac8ca..8cf5f2858b9 100644 --- a/src/demo.c +++ b/src/demo.c @@ -129,7 +129,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int demo_ext_output = ext_output; demo_json_port = json_port; printf("Demo\n"); - net = parse_network_cfg_custom(cfgfile, 1, 0); // set batch=1 + net = parse_network_cfg_custom(cfgfile, 1, 1); // set batch=1 if(weightfile){ load_weights(&net, weightfile); } @@ -237,7 +237,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int ++frame_id; if (demo_json_port > 0) { - int timeout = 200; + int timeout = 400000; send_json(local_dets, local_nboxes, l.classes, demo_names, frame_id, demo_json_port, timeout); } @@ -268,8 +268,8 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int // if you run it with param -mjpeg_port 8090 then open URL in your web-browser: http://localhost:8090 if (mjpeg_port > 0 && show_img) { int port = mjpeg_port; - int timeout = 200; - int jpeg_quality = 30; // 1 - 100 + int timeout = 400000; + int jpeg_quality = 40; // 1 - 100 send_mjpeg(show_img, port, timeout, jpeg_quality); } diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 6840002a027..b911f22f2a3 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -115,7 +115,7 @@ class MJPG_sender public: - MJPG_sender(int port = 0, int _timeout = 200000, int _quality = 30) + MJPG_sender(int port = 0, int _timeout = 400000, int _quality = 30) : sock(INVALID_SOCKET) , timeout(_timeout) , quality(_quality) @@ -230,17 +230,17 @@ class MJPG_sender } maxfd = (maxfd>client ? maxfd : client); FD_SET(client, &master); - _write(client, "HTTP/1.0 200 OK\n", 0); + _write(client, "HTTP/1.0 200 OK\r\n", 0); _write(client, - "Server: Mozarella/2.2\n" - "Accept-Range: bytes\n" - "Connection: close\n" - "Max-Age: 0\n" - "Expires: 0\n" - "Cache-Control: no-cache, private\n" - "Pragma: no-cache\n" - "Content-Type: multipart/x-mixed-replace; boundary=mjpegstream\n" - "\n", 0); + "Server: Mozarella/2.2\r\n" + "Accept-Range: bytes\r\n" + "Connection: close\r\n" + "Max-Age: 0\r\n" + "Expires: 0\r\n" + "Cache-Control: no-cache, private\r\n" + "Pragma: no-cache\r\n" + "Content-Type: multipart/x-mixed-replace; boundary=mjpegstream\r\n" + "\r\n", 0); cerr << "MJPG_sender: new client " << client << endl; } else // existing client, just stream pix @@ -252,7 +252,7 @@ class MJPG_sender } char head[400]; - sprintf(head, "--mjpegstream\nContent-Type: image/jpeg\nContent-Length: %zu\n\n", outlen); + sprintf(head, "--mjpegstream\r\nContent-Type: image/jpeg\r\nContent-Length: %zu\r\n\r\n", outlen); _write(s, head, 0); int n = _write(s, (char*)(&outbuf[0]), outlen); //cerr << "known client " << s << " " << n << endl; @@ -303,7 +303,7 @@ class JSON_sender public: - JSON_sender(int port = 0, int _timeout = 200000) + JSON_sender(int port = 0, int _timeout = 400000) : sock(INVALID_SOCKET) , timeout(_timeout) { @@ -409,18 +409,18 @@ class JSON_sender } maxfd = (maxfd>client ? maxfd : client); FD_SET(client, &master); - _write(client, "HTTP/1.0 200 OK\n", 0); + _write(client, "HTTP/1.0 200 OK\r\n", 0); _write(client, - "Server: Mozarella/2.2\n" - "Accept-Range: bytes\n" - "Connection: close\n" - "Max-Age: 0\n" - "Expires: 0\n" - "Cache-Control: no-cache, private\n" - "Pragma: no-cache\n" - "Content-Type: application/json\n" + "Server: Mozarella/2.2\r\n" + "Accept-Range: bytes\r\n" + "Connection: close\r\n" + "Max-Age: 0\r\n" + "Expires: 0\r\n" + "Cache-Control: no-cache, private\r\n" + "Pragma: no-cache\r\n" + "Content-Type: application/json\r\n" //"Content-Type: multipart/x-mixed-replace; boundary=boundary\r\n" - "\n", 0); + "\r\n", 0); _write(client, "[\n", 0); // open JSON array int n = _write(client, outputbuf, outlen); cerr << "JSON_sender: new client " << client << endl; diff --git a/src/image.c b/src/image.c index ac869159589..dadaa1e5a21 100644 --- a/src/image.c +++ b/src/image.c @@ -829,7 +829,7 @@ void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_ else cvPutText(img, "- Saved", cvPoint(250, img_size - 10), &font, CV_RGB(255, 255, 255)); - if (mjpeg_port > 0) send_mjpeg(img, mjpeg_port, 200000, 80); + if (mjpeg_port > 0) send_mjpeg(img, mjpeg_port, 500000, 100); } #endif // OPENCV From 75f2a3e7cf7f9d54080e9f6327a765e8d25876dc Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 2 Mar 2019 03:32:24 +0300 Subject: [PATCH 0374/1365] Added object Detection & Tracking using conv-rnn layer on frames from video --- .../x64/cfg/yolov3-tiny_occlusion_track.cfg | 218 ++++++++++++++++++ cfg/yolov3-tiny_occlusion_track.cfg | 218 ++++++++++++++++++ include/darknet.h | 8 +- src/batchnorm_layer.c | 14 ++ src/convolutional_kernels.cu | 23 +- src/convolutional_layer.c | 1 + src/crnn_layer.c | 104 +++++++-- src/crnn_layer.h | 3 +- src/data.c | 171 +++++++++++--- src/data.h | 2 +- src/detector.c | 56 +++-- src/layer.c | 182 ++++++++------- src/network.c | 109 ++++++++- src/network.h | 1 + src/network_kernels.cu | 11 +- src/parser.c | 28 ++- 16 files changed, 962 insertions(+), 187 deletions(-) create mode 100644 build/darknet/x64/cfg/yolov3-tiny_occlusion_track.cfg create mode 100644 cfg/yolov3-tiny_occlusion_track.cfg diff --git a/build/darknet/x64/cfg/yolov3-tiny_occlusion_track.cfg b/build/darknet/x64/cfg/yolov3-tiny_occlusion_track.cfg new file mode 100644 index 00000000000..2ef25370eea --- /dev/null +++ b/build/darknet/x64/cfg/yolov3-tiny_occlusion_track.cfg @@ -0,0 +1,218 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=8 +subdivisions=4 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +track=1 +time_steps=20 +augment_speed=3 + +learning_rate=0.001 +burn_in=1000 +max_batches = 10000 +policy=steps +steps=9000,9500 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=16 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=1 + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +########### + + +[crnn] +batch_normalize=1 +size=3 +pad=1 +output=512 +hidden=256 +activation=leaky + +#[shortcut] +#from=-2 +#activation=linear + +########### + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=18 +activation=linear + + + +[yolo] +mask = 3,4,5 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=1 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=0 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 8 + +[crnn] +batch_normalize=1 +size=3 +pad=1 +output=256 +hidden=128 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + + +[convolutional] +size=1 +stride=1 +pad=1 +filters=18 +activation=linear + +[yolo] +mask = 0,1,2 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=1 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=0 diff --git a/cfg/yolov3-tiny_occlusion_track.cfg b/cfg/yolov3-tiny_occlusion_track.cfg new file mode 100644 index 00000000000..2ef25370eea --- /dev/null +++ b/cfg/yolov3-tiny_occlusion_track.cfg @@ -0,0 +1,218 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=8 +subdivisions=4 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +track=1 +time_steps=20 +augment_speed=3 + +learning_rate=0.001 +burn_in=1000 +max_batches = 10000 +policy=steps +steps=9000,9500 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=16 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=1 + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +########### + + +[crnn] +batch_normalize=1 +size=3 +pad=1 +output=512 +hidden=256 +activation=leaky + +#[shortcut] +#from=-2 +#activation=linear + +########### + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=18 +activation=linear + + + +[yolo] +mask = 3,4,5 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=1 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=0 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 8 + +[crnn] +batch_normalize=1 +size=3 +pad=1 +output=256 +hidden=128 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + + +[convolutional] +size=1 +stride=1 +pad=1 +filters=18 +activation=linear + +[yolo] +mask = 0,1,2 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=1 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=0 diff --git a/include/darknet.h b/include/darknet.h index 23be5b98ee6..8ca8b493798 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -570,7 +570,9 @@ typedef struct network { float saturation; float hue; int random; - int small_object; + int track; + int augment_speed; + int try_fix_nan; int gpu_index; tree *hierarchy; @@ -698,7 +700,9 @@ typedef struct load_args { int scale; int center; int coords; - int small_object; + int mini_batch; + int track; + int augment_speed; float jitter; int flip; float angle; diff --git a/src/batchnorm_layer.c b/src/batchnorm_layer.c index aed1a1ddce3..4f9536a99ce 100644 --- a/src/batchnorm_layer.c +++ b/src/batchnorm_layer.c @@ -205,6 +205,15 @@ void forward_batchnorm_layer_gpu(layer l, network_state state) .00001, l.mean_gpu, // output (should be FP32) l.variance_gpu); // output (should be FP32) + + if (state.net.try_fix_nan) { + fix_nan_and_inf(l.scales_gpu, l.n); + fix_nan_and_inf(l.biases_gpu, l.n); + fix_nan_and_inf(l.mean_gpu, l.n); + fix_nan_and_inf(l.variance_gpu, l.n); + fix_nan_and_inf(l.rolling_mean_gpu, l.n); + fix_nan_and_inf(l.rolling_variance_gpu, l.n); + } #else fast_mean_gpu(l.output_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.mean_gpu); fast_variance_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.variance_gpu); @@ -272,5 +281,10 @@ void backward_batchnorm_layer_gpu(layer l, network_state state) #endif if (l.type == BATCHNORM) simple_copy_ongpu(l.outputs*l.batch, l.delta_gpu, state.delta); //copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, state.delta, 1); + + if (state.net.try_fix_nan) { + fix_nan_and_inf(l.scale_updates_gpu, l.n); + fix_nan_and_inf(l.bias_updates_gpu, l.n); + } } #endif diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 05c847ce7df..9625ddc6f8d 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -519,6 +519,15 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) else { //#else + /* + int input_nan_inf = is_nan_or_inf(state.input, l.inputs * l.batch); + printf("\n is_nan_or_inf(state.input) = %d \n", input_nan_inf); + if (input_nan_inf) getchar(); + + int weights_nan_inf = is_nan_or_inf(l.weights_gpu, l.size * l.size * l.c * l.n); + printf("\n is_nan_or_inf(l.weights_gpu) = %d \n", weights_nan_inf); + if (weights_nan_inf) getchar(); + */ CHECK_CUDNN(cudnnConvolutionForward(cudnn_handle(), &alpha, //&one, @@ -581,6 +590,10 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //if(l.dot > 0) dot_error_gpu(l); if(l.binary || l.xnor) swap_binary(&l); //cudaDeviceSynchronize(); // for correct profiling of performance + + if (state.net.try_fix_nan) { + fix_nan_and_inf(l.output_gpu, l.outputs*l.batch); + } } void backward_convolutional_layer_gpu(convolutional_layer l, network_state state) @@ -609,7 +622,6 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in) && l.c % 8 == 0 && l.n % 8 == 0) { - const size_t input16_size = l.batch*l.c*l.w*l.h; const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h; @@ -762,6 +774,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state &one, l.dsrcTensorDesc, state.delta)); + if (l.binary || l.xnor) swap_binary(&l); if (l.xnor) gradient_array_ongpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, state.delta); } @@ -803,6 +816,14 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state } } #endif + if (state.net.try_fix_nan) { + if (state.delta) { + fix_nan_and_inf(state.delta, l.inputs * l.batch); + } + int size = l.size * l.size * l.c * l.n; + fix_nan_and_inf(l.weight_updates_gpu, size); + fix_nan_and_inf(l.weights_gpu, size); + } } void pull_convolutional_layer(convolutional_layer layer) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index f5f2cc9218a..756fe917da6 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -354,6 +354,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.pad = padding; l.batch_normalize = batch_normalize; l.learning_rate_scale = 1; + l.nweights = l.c*l.n*l.size*l.size; l.weights = (float*)calloc(c * n * size * size, sizeof(float)); l.weight_updates = (float*)calloc(c * n * size * size, sizeof(float)); diff --git a/src/crnn_layer.c b/src/crnn_layer.c index 08daa6ef0c8..40de100191c 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -26,7 +26,7 @@ static void increment_layer(layer *l, int steps) #endif } -layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize) +layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int xnor) { fprintf(stderr, "CRNN Layer: %d x %d x %d image, %d filters\n", h,w,c,output_filters); batch = batch / steps; @@ -34,33 +34,41 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.batch = batch; l.type = CRNN; l.steps = steps; + l.size = size; + l.stride = stride; + l.pad = pad; l.h = h; l.w = w; l.c = c; - l.out_h = h; - l.out_w = w; l.out_c = output_filters; - l.inputs = h*w*c; + l.inputs = h * w * c; l.hidden = h * w * hidden_filters; - l.outputs = l.out_h * l.out_w * l.out_c; + l.xnor = xnor; - l.state = (float*)calloc(l.hidden * batch * (steps + 1), sizeof(float)); + l.state = (float*)calloc(l.hidden * l.batch * (l.steps + 1), sizeof(float)); l.input_layer = (layer*)malloc(sizeof(layer)); - *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, size, stride, pad, activation, batch_normalize, 0, 0, 0, 0, 0); + *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.input_layer->batch = batch; if (l.workspace_size < l.input_layer->workspace_size) l.workspace_size = l.input_layer->workspace_size; l.self_layer = (layer*)malloc(sizeof(layer)); - *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, size, stride, pad, activation, batch_normalize, 0, 0, 0, 0, 0); + *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.self_layer->batch = batch; if (l.workspace_size < l.self_layer->workspace_size) l.workspace_size = l.self_layer->workspace_size; l.output_layer = (layer*)malloc(sizeof(layer)); - *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, size, stride, pad, activation, batch_normalize, 0, 0, 0, 0, 0); + *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.output_layer->batch = batch; if (l.workspace_size < l.output_layer->workspace_size) l.workspace_size = l.output_layer->workspace_size; + l.out_h = l.output_layer->out_h; + l.out_w = l.output_layer->out_w; + l.outputs = l.output_layer->outputs; + + assert(l.input_layer->outputs == l.self_layer->outputs); + assert(l.input_layer->outputs == l.output_layer->inputs); + l.output = l.output_layer->output; l.delta = l.output_layer->delta; @@ -72,7 +80,7 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.forward_gpu = forward_crnn_layer_gpu; l.backward_gpu = backward_crnn_layer_gpu; l.update_gpu = update_crnn_layer_gpu; - l.state_gpu = cuda_make_array(l.state, batch*l.hidden*(steps + 1)); + l.state_gpu = cuda_make_array(l.state, l.batch*l.hidden*(l.steps + 1)); l.output_gpu = l.output_layer->output_gpu; l.delta_gpu = l.output_layer->delta_gpu; #endif @@ -80,6 +88,46 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou return l; } +void resize_crnn_layer(layer *l, int w, int h) +{ + resize_convolutional_layer(l->input_layer, w, h); + if (l->workspace_size < l->input_layer->workspace_size) l->workspace_size = l->input_layer->workspace_size; + + resize_convolutional_layer(l->self_layer, w, h); + if (l->workspace_size < l->self_layer->workspace_size) l->workspace_size = l->self_layer->workspace_size; + + resize_convolutional_layer(l->output_layer, w, h); + if (l->workspace_size < l->output_layer->workspace_size) l->workspace_size = l->output_layer->workspace_size; + + l->output = l->output_layer->output; + l->delta = l->output_layer->delta; + + int hidden_filters = l->self_layer->c; + l->w = w; + l->h = h; + l->inputs = h * w * l->c; + l->hidden = h * w * hidden_filters; + + l->out_h = l->output_layer->out_h; + l->out_w = l->output_layer->out_w; + l->outputs = l->output_layer->outputs; + + assert(l->input_layer->inputs == l->inputs); + assert(l->self_layer->inputs == l->hidden); + assert(l->input_layer->outputs == l->self_layer->outputs); + assert(l->input_layer->outputs == l->output_layer->inputs); + + l->state = (float*)realloc(l->state, l->batch*l->hidden*(l->steps + 1)*sizeof(float)); + +#ifdef GPU + if (l->state_gpu) cudaFree(l->state_gpu); + l->state_gpu = cuda_make_array(l->state, l->batch*l->hidden*(l->steps + 1)); + + l->output_gpu = l->output_layer->output_gpu; + l->delta_gpu = l->output_layer->delta_gpu; +#endif +} + void update_crnn_layer(layer l, int batch, float learning_rate, float momentum, float decay) { update_convolutional_layer(*(l.input_layer), batch, learning_rate, momentum, decay); @@ -92,15 +140,19 @@ void forward_crnn_layer(layer l, network_state state) network_state s = {0}; s.train = state.train; s.workspace = state.workspace; + s.net = state.net; + //s.index = state.index; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); layer output_layer = *(l.output_layer); - fill_cpu(l.outputs * l.batch * l.steps, 0, output_layer.delta, 1); - fill_cpu(l.hidden * l.batch * l.steps, 0, self_layer.delta, 1); - fill_cpu(l.hidden * l.batch * l.steps, 0, input_layer.delta, 1); - if(state.train) fill_cpu(l.hidden * l.batch, 0, l.state, 1); + if (state.train) { + fill_cpu(l.outputs * l.batch * l.steps, 0, output_layer.delta, 1); + fill_cpu(l.hidden * l.batch * l.steps, 0, self_layer.delta, 1); + fill_cpu(l.hidden * l.batch * l.steps, 0, input_layer.delta, 1); + fill_cpu(l.hidden * l.batch, 0, l.state, 1); + } for (i = 0; i < l.steps; ++i) { s.input = state.input; @@ -134,6 +186,8 @@ void backward_crnn_layer(layer l, network_state state) network_state s = {0}; s.train = state.train; s.workspace = state.workspace; + s.net = state.net; + //s.index = state.index; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); @@ -208,6 +262,8 @@ void forward_crnn_layer_gpu(layer l, network_state state) network_state s = {0}; s.train = state.train; s.workspace = state.workspace; + s.net = state.net; + if(!state.train) s.index = state.index; // use TC only for detection int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); @@ -223,10 +279,12 @@ void forward_crnn_layer_gpu(layer l, network_state state) #endif //CUDNN_HALF */ - fill_ongpu(l.outputs * l.batch * l.steps, 0, output_layer.delta_gpu, 1); - fill_ongpu(l.hidden * l.batch * l.steps, 0, self_layer.delta_gpu, 1); - fill_ongpu(l.hidden * l.batch * l.steps, 0, input_layer.delta_gpu, 1); - if(state.train) fill_ongpu(l.hidden * l.batch, 0, l.state_gpu, 1); + if (state.train) { + fill_ongpu(l.outputs * l.batch * l.steps, 0, output_layer.delta_gpu, 1); + fill_ongpu(l.hidden * l.batch * l.steps, 0, self_layer.delta_gpu, 1); + fill_ongpu(l.hidden * l.batch * l.steps, 0, input_layer.delta_gpu, 1); + fill_ongpu(l.hidden * l.batch, 0, l.state_gpu, 1); + } for (i = 0; i < l.steps; ++i) { s.input = state.input; @@ -260,6 +318,8 @@ void backward_crnn_layer_gpu(layer l, network_state state) network_state s = {0}; s.train = state.train; s.workspace = state.workspace; + s.net = state.net; + //s.index = state.index; int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); @@ -267,6 +327,7 @@ void backward_crnn_layer_gpu(layer l, network_state state) increment_layer(&input_layer, l.steps - 1); increment_layer(&self_layer, l.steps - 1); increment_layer(&output_layer, l.steps - 1); + float *init_state_gpu = l.state_gpu; l.state_gpu += l.hidden*l.batch*l.steps; for (i = l.steps-1; i >= 0; --i) { //copy_ongpu(l.hidden * l.batch, input_layer.output_gpu, 1, l.state_gpu, 1); // commented in RNN @@ -291,9 +352,16 @@ void backward_crnn_layer_gpu(layer l, network_state state) else s.delta = 0; backward_convolutional_layer_gpu(input_layer, s); + if (state.net.try_fix_nan) { + fix_nan_and_inf(output_layer.delta_gpu, output_layer.inputs * output_layer.batch); + fix_nan_and_inf(self_layer.delta_gpu, self_layer.inputs * self_layer.batch); + fix_nan_and_inf(input_layer.delta_gpu, input_layer.inputs * input_layer.batch); + } + increment_layer(&input_layer, -1); increment_layer(&self_layer, -1); increment_layer(&output_layer, -1); } + fill_ongpu(l.hidden * l.batch, 0, init_state_gpu, 1); //clean l.state_gpu } #endif diff --git a/src/crnn_layer.h b/src/crnn_layer.h index cfbdd265bc4..33487020103 100644 --- a/src/crnn_layer.h +++ b/src/crnn_layer.h @@ -9,7 +9,8 @@ #ifdef __cplusplus extern "C" { #endif -layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize); +layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int xnor); +void resize_crnn_layer(layer *l, int w, int h); void forward_crnn_layer(layer l, network_state state); void backward_crnn_layer(layer l, network_state state); diff --git a/src/data.c b/src/data.c index 80c08f48d79..e6fa95c56b3 100644 --- a/src/data.c +++ b/src/data.c @@ -41,6 +41,38 @@ char **get_random_paths_indexes(char **paths, int n, int m, int *indexes) } */ +char **get_sequential_paths(char **paths, int n, int m, int mini_batch, int augment_speed) +{ + int speed = rand_int(1, augment_speed); + if (speed < 1) speed = 1; + char** sequentia_paths = (char**)calloc(n, sizeof(char*)); + int i; + pthread_mutex_lock(&mutex); + //printf("n = %d, mini_batch = %d \n", n, mini_batch); + unsigned int *start_time_indexes = (unsigned int *)calloc(mini_batch, sizeof(unsigned int)); + for (i = 0; i < mini_batch; ++i) { + start_time_indexes[i] = random_gen() % m; + //printf(" start_time_indexes[i] = %u, ", start_time_indexes[i]); + } + + for (i = 0; i < n; ++i) { + do { + int time_line_index = i % mini_batch; + unsigned int index = start_time_indexes[time_line_index] % m; + start_time_indexes[time_line_index] += speed; + + //int index = random_gen() % m; + sequentia_paths[i] = paths[index]; + //if(i == 0) printf("%s\n", paths[index]); + //printf(" index = %u - grp: %s \n", index, paths[index]); + if (strlen(sequentia_paths[i]) <= 4) printf(" Very small path to the image: %s \n", sequentia_paths[i]); + } while (strlen(sequentia_paths[i]) == 0); + } + //free(start_time_indexes); + pthread_mutex_unlock(&mutex); + return sequentia_paths; +} + char **get_random_paths(char **paths, int n, int m) { char** random_paths = (char**)calloc(n, sizeof(char*)); @@ -303,7 +335,7 @@ void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int } void fill_truth_detection(const char *path, int num_boxes, float *truth, int classes, int flip, float dx, float dy, float sx, float sy, - int small_object, int net_w, int net_h) + int net_w, int net_h) { char labelpath[4096]; replace_image_to_label(path, labelpath); @@ -313,12 +345,6 @@ void fill_truth_detection(const char *path, int num_boxes, float *truth, int cla box_label *boxes = read_boxes(labelpath, &count); float lowest_w = 1.F / net_w; float lowest_h = 1.F / net_h; - if (small_object == 1) { - for (i = 0; i < count; ++i) { - if (boxes[i].w < lowest_w) boxes[i].w = lowest_w; - if (boxes[i].h < lowest_h) boxes[i].h = lowest_h; - } - } randomize_boxes(boxes, count); correct_boxes(boxes, count, dx, dy, sx, sy, flip); if (count > num_boxes) count = num_boxes; @@ -729,6 +755,16 @@ data load_data_swag(char **paths, int n, int classes, float jitter) return d; } +static box float_to_box_stride(float *f, int stride) +{ + box b = { 0 }; + b.x = f[0]; + b.y = f[1 * stride]; + b.w = f[2 * stride]; + b.h = f[3 * stride]; + return b; +} + #ifdef OPENCV #include #include @@ -740,10 +776,12 @@ data load_data_swag(char **paths, int n, int classes, float jitter) #include "http_stream.h" -data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, float jitter, float hue, float saturation, float exposure, int small_object) +data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, float jitter, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed) { c = c ? c : 3; - char **random_paths = get_random_paths(paths, n, m); + char **random_paths; + if (track) random_paths = get_sequential_paths(paths, n, m, mini_batch, augment_speed); + else random_paths = get_random_paths(paths, n, m); int i; data d = {0}; d.shallow = 0; @@ -752,6 +790,10 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo d.X.vals = (float**)calloc(d.X.rows, sizeof(float*)); d.X.cols = h*w*c; + float r1, r2, r3, r4; + float dhue, dsat, dexp, flip; + int augmentation_calculated = 0; + d.y = make_matrix(n, 5*boxes); for(i = 0; i < n; ++i){ const char *filename = random_paths[i]; @@ -775,10 +817,25 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo int dw = (ow*jitter); int dh = (oh*jitter); - int pleft = rand_uniform_strong(-dw, dw); - int pright = rand_uniform_strong(-dw, dw); - int ptop = rand_uniform_strong(-dh, dh); - int pbot = rand_uniform_strong(-dh, dh); + if(!augmentation_calculated || !track) + { + augmentation_calculated = 1; + r1 = random_float(); + r2 = random_float(); + r3 = random_float(); + r4 = random_float(); + + dhue = rand_uniform_strong(-hue, hue); + dsat = rand_scale(saturation); + dexp = rand_scale(exposure); + + flip = use_flip ? random_gen() % 2 : 0; + } + + int pleft = rand_precalc_random(-dw, dw, r1); + int pright = rand_precalc_random(-dw, dw, r2); + int ptop = rand_precalc_random(-dh, dh, r3); + int pbot = rand_precalc_random(-dh, dh, r4); int swidth = ow - pleft - pright; int sheight = oh - ptop - pbot; @@ -786,22 +843,32 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo float sx = (float)swidth / ow; float sy = (float)sheight / oh; - int flip = use_flip ? random_gen()%2 : 0; - float dx = ((float)pleft/ow)/sx; float dy = ((float)ptop /oh)/sy; - float dhue = rand_uniform_strong(-hue, hue); - float dsat = rand_scale(saturation); - float dexp = rand_scale(exposure); - image ai = image_data_augmentation(src, w, h, pleft, ptop, swidth, sheight, flip, jitter, dhue, dsat, dexp); d.X.vals[i] = ai.data; - //show_image(ai, "aug"); - //cvWaitKey(0); + fill_truth_detection(filename, boxes, d.y.vals[i], classes, flip, dx, dy, 1./sx, 1./sy, w, h); - fill_truth_detection(filename, boxes, d.y.vals[i], classes, flip, dx, dy, 1./sx, 1./sy, small_object, w, h); + /* + { + char buff[10]; + sprintf(buff, "aug_%s_%d", random_paths[i], random_gen()); + int t; + for (t = 0; t < boxes; ++t) { + box b = float_to_box_stride(d.y.vals[i] + t*(4 + 1), 1); + if (!b.x) break; + int left = (b.x - b.w / 2.)*ai.w; + int right = (b.x + b.w / 2.)*ai.w; + int top = (b.y - b.h / 2.)*ai.h; + int bot = (b.y + b.h / 2.)*ai.h; + draw_box_width(ai, left, top, right, bot, 3, 150, 100, 50); // 3 channels RGB + } + + show_image(ai, buff); + cvWaitKey(0); + }*/ cvReleaseImage(&src); } @@ -809,10 +876,12 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo return d; } #else // OPENCV -data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, float jitter, float hue, float saturation, float exposure, int small_object) +data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, float jitter, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed) { c = c ? c : 3; - char **random_paths = get_random_paths(paths, n, m); + char **random_paths; + if(track) random_paths = get_sequential_paths(paths, n, m, mini_batch, augment_speed); + else random_paths = get_random_paths(paths, n, m); int i; data d = { 0 }; d.shallow = 0; @@ -821,6 +890,10 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo d.X.vals = (float**)calloc(d.X.rows, sizeof(float*)); d.X.cols = h*w*c; + float r1, r2, r3, r4; + float dhue, dsat, dexp, flip; + int augmentation_calculated = 0; + d.y = make_matrix(n, 5 * boxes); for (i = 0; i < n; ++i) { image orig = load_image(random_paths[i], 0, 0, c); @@ -831,10 +904,25 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo int dw = (ow*jitter); int dh = (oh*jitter); - int pleft = rand_uniform_strong(-dw, dw); - int pright = rand_uniform_strong(-dw, dw); - int ptop = rand_uniform_strong(-dh, dh); - int pbot = rand_uniform_strong(-dh, dh); + if (!augmentation_calculated || !track) + { + augmentation_calculated = 1; + r1 = random_float(); + r2 = random_float(); + r3 = random_float(); + r4 = random_float(); + + dhue = rand_uniform_strong(-hue, hue); + dsat = rand_scale(saturation); + dexp = rand_scale(exposure); + + flip = use_flip ? random_gen() % 2 : 0; + } + + int pleft = rand_precalc_random(-dw, dw, r1); + int pright = rand_precalc_random(-dw, dw, r2); + int ptop = rand_precalc_random(-dh, dh, r3); + int pbot = rand_precalc_random(-dh, dh, r4); int swidth = ow - pleft - pright; int sheight = oh - ptop - pbot; @@ -842,7 +930,6 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo float sx = (float)swidth / ow; float sy = (float)sheight / oh; - int flip = use_flip ? random_gen() % 2 : 0; image cropped = crop_image(orig, pleft, ptop, swidth, sheight); float dx = ((float)pleft / ow) / sx; @@ -850,10 +937,30 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo image sized = resize_image(cropped, w, h); if (flip) flip_image(sized); - random_distort_image(sized, hue, saturation, exposure); + distort_image(sized, dhue, dsat, dexp); + //random_distort_image(sized, hue, saturation, exposure); d.X.vals[i] = sized.data; - fill_truth_detection(random_paths[i], boxes, d.y.vals[i], classes, flip, dx, dy, 1. / sx, 1. / sy, small_object, w, h); + fill_truth_detection(random_paths[i], boxes, d.y.vals[i], classes, flip, dx, dy, 1. / sx, 1. / sy, w, h); + + /* + { + char buff[10]; + sprintf(buff, "aug_%s_%d", random_paths[i], random_gen()); + int t; + for (t = 0; t < boxes; ++t) { + box b = float_to_box_stride(d.y.vals[i] + t*(4 + 1), 1); + if (!b.x) break; + int left = (b.x - b.w / 2.)*sized.w; + int right = (b.x + b.w / 2.)*sized.w; + int top = (b.y - b.h / 2.)*sized.h; + int bot = (b.y + b.h / 2.)*sized.h; + draw_box_width(sized, left, top, right, bot, 3, 150, 100, 50); // 3 channels RGB + } + + show_image(sized, buff); + cvWaitKey(0); + }*/ free_image(orig); free_image(cropped); @@ -883,7 +990,7 @@ void *load_thread(void *ptr) } else if (a.type == REGION_DATA){ *a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure); } else if (a.type == DETECTION_DATA){ - *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.c, a.num_boxes, a.classes, a.flip, a.jitter, a.hue, a.saturation, a.exposure, a.small_object); + *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.c, a.num_boxes, a.classes, a.flip, a.jitter, a.hue, a.saturation, a.exposure, a.mini_batch, a.track, a.augment_speed); } else if (a.type == SWAG_DATA){ *a.d = load_data_swag(a.paths, a.n, a.classes, a.jitter); } else if (a.type == COMPARE_DATA){ diff --git a/src/data.h b/src/data.h index 58cd1178c48..b6ed5b802a6 100644 --- a/src/data.h +++ b/src/data.h @@ -86,7 +86,7 @@ void print_letters(float *pred, int n); data load_data_captcha(char **paths, int n, int m, int k, int w, int h); data load_data_captcha_encode(char **paths, int n, int m, int w, int h); data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h); -data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, float jitter, float hue, float saturation, float exposure, int small_object); +data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, float jitter, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed); data load_data_tag(char **paths, int n, int m, int k, int use_flip, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); data load_data_super(char **paths, int n, int m, int w, int h, int scale); diff --git a/src/detector.c b/src/detector.c index 96b730839ed..e82cc069829 100644 --- a/src/detector.c +++ b/src/detector.c @@ -76,11 +76,14 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i cuda_set_device(gpus[0]); printf(" Prepare additional network for mAP calculation...\n"); - net_map = parse_network_cfg_custom(cfgfile, 1, 0); + net_map = parse_network_cfg_custom(cfgfile, 1, 1); + + int k; // free memory unnecessary arrays for (k = 0; k < net_map.n; ++k) { - free_layer(net_map.layers[k]); + free_layer(net_map.layers[k]); } + /* #ifdef GPU cuda_free(net_map.workspace); cuda_free(net_map.input_state_gpu); @@ -89,6 +92,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i #else free(net_map.workspace); #endif + */ } srand(time(0)); @@ -156,7 +160,6 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i args.flip = net.flip; args.jitter = jitter; args.num_boxes = l.max_boxes; - args.small_object = net.small_object; args.d = &buffer; args.type = DETECTION_DATA; args.threads = 64; // 16 or 64 @@ -175,6 +178,14 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i int img_size = 1000; img = draw_train_chart(max_img_loss, net.max_batches, number_of_lines, img_size, dont_show); #endif //OPENCV + if (net.track) { + args.track = net.track; + args.augment_speed = net.augment_speed; + args.threads = net.subdivisions * ngpus; // 2 * ngpus; + args.mini_batch = net.batch / net.time_steps; + printf("\n Tracking! batch = %d, subdiv = %d, time_steps = %d, mini_batch = %d \n", net.batch, net.subdivisions, net.time_steps, args.mini_batch); + } + //printf(" imgs = %d \n", imgs); pthread_t load_thread = load_data(args); double time; @@ -183,14 +194,6 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i while (get_current_batch(net) < net.max_batches) { if (l.random && count++ % 10 == 0) { printf("Resizing\n"); - //int dim = (rand() % 12 + (init_w/32 - 5)) * 32; // +-160 - //int dim = (rand() % 4 + 16) * 32; - //if (get_current_batch(net)+100 > net.max_batches) dim = 544; - - //int random_val = rand() % 12; - //int dim_w = (random_val + (init_w / 32 - 5)) * 32; // +-160 - //int dim_h = (random_val + (init_h / 32 - 5)) * 32; // +-160 - float random_val = rand_scale(1.4); // *x or /x int dim_w = roundl(random_val*init_w / 32 + 1) * 32; int dim_h = roundl(random_val*init_h / 32 + 1) * 32; @@ -259,11 +262,13 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i i = get_current_batch(net); - int calc_map_for_each = iter_map + 4 * train_images_num / (net.batch * net.subdivisions); // calculate mAP for each 4 Epochs - calc_map_for_each = fmax(calc_map_for_each, net.burn_in); - calc_map_for_each = fmax(calc_map_for_each, 1000); + int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions); // calculate mAP for each 4 Epochs + calc_map_for_each = fmax(calc_map_for_each, 100); + int next_map_calc = iter_map + calc_map_for_each; + next_map_calc = fmax(next_map_calc, net.burn_in); + next_map_calc = fmax(next_map_calc, 1000); if (calc_map) { - printf("\n (next mAP calculation at %d iterations) ", calc_map_for_each); + printf("\n (next mAP calculation at %d iterations) ", next_map_calc); if (mean_average_precision > 0) printf("\n Last accuracy mAP@0.5 = %2.2f %% ", mean_average_precision * 100); } @@ -274,7 +279,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now() - time), i*imgs); int draw_precision = 0; - if (calc_map && (i >= calc_map_for_each || i == net.max_batches)) { + if (calc_map && (i >= next_map_calc || i == net.max_batches)) { if (l.random) { printf("Resizing to initial size: %d x %d \n", init_w, init_h); args.w = init_w; @@ -289,11 +294,13 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i net = nets[0]; } + copy_weights_net(net, &net_map); + // combine Training and Validation networks - network net_combined = combine_train_valid_networks(net, net_map); + //network net_combined = combine_train_valid_networks(net, net_map); iter_map = i; - mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net_combined); + mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net_map);// &net_combined); printf("\n mean_average_precision (mAP@0.5) = %f \n", mean_average_precision); draw_precision = 1; } @@ -351,6 +358,11 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i for (i = 0; i < ngpus; ++i) free_network(nets[i]); free(nets); //free_network(net); + + if (calc_map) { + net_map.n = 0; + free_network(net_map); + } } @@ -443,7 +455,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out int *map = 0; if (mapf) map = read_map(mapf); - network net = parse_network_cfg_custom(cfgfile, 1, 0); // set batch=1 + network net = parse_network_cfg_custom(cfgfile, 1, 1); // set batch=1 if (weightfile) { load_weights(&net, weightfile); } @@ -568,7 +580,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile) { - network net = parse_network_cfg_custom(cfgfile, 1, 0); // set batch=1 + network net = parse_network_cfg_custom(cfgfile, 1, 1); // set batch=1 if (weightfile) { load_weights(&net, weightfile); } @@ -682,7 +694,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa net = *existing_net; } else { - net = parse_network_cfg_custom(cfgfile, 1, 0); // set batch=1 + net = parse_network_cfg_custom(cfgfile, 1, 1); // set batch=1 if (weightfile) { load_weights(&net, weightfile); } @@ -1251,7 +1263,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam char **names = get_labels_custom(name_list, &names_size); //get_labels(name_list); image **alphabet = load_alphabet(); - network net = parse_network_cfg_custom(cfgfile, 1, 0); // set batch=1 + network net = parse_network_cfg_custom(cfgfile, 1, 1); // set batch=1 if (weightfile) { load_weights(&net, weightfile); } diff --git a/src/layer.c b/src/layer.c index 627b931db51..7db2621650e 100644 --- a/src/layer.c +++ b/src/layer.c @@ -4,35 +4,45 @@ void free_layer(layer l) { - if (l.type == DROPOUT) { - if (l.rand) free(l.rand); + // free layers: input_layer, self_layer, output_layer, ... + if (l.type == CRNN) { + if (l.input_layer) free_layer(*l.input_layer); + if (l.self_layer) free_layer(*l.self_layer); + if (l.output_layer) free_layer(*l.output_layer); + l.output = NULL; + l.delta = NULL; + l.output_gpu = NULL; + l.delta_gpu = NULL; + } + if (l.type == DROPOUT) { + if (l.rand) free(l.rand); #ifdef GPU - if (l.rand_gpu) cuda_free(l.rand_gpu); + if (l.rand_gpu) cuda_free(l.rand_gpu); #endif - return; - } - if (l.mask) free(l.mask); - if (l.cweights) free(l.cweights); - if (l.indexes) free(l.indexes); - if (l.input_layers) free(l.input_layers); - if (l.input_sizes) free(l.input_sizes); - if (l.map) free(l.map); - if (l.rand) free(l.rand); - if (l.cost) free(l.cost); - if (l.state) free(l.state); - if (l.prev_state) free(l.prev_state); - if (l.forgot_state) free(l.forgot_state); - if (l.forgot_delta) free(l.forgot_delta); - if (l.state_delta) free(l.state_delta); - if (l.concat) free(l.concat); - if (l.concat_delta) free(l.concat_delta); - if (l.binary_weights) free(l.binary_weights); - if (l.biases) free(l.biases); - if (l.bias_updates) free(l.bias_updates); - if (l.scales) free(l.scales); - if (l.scale_updates) free(l.scale_updates); - if (l.weights) free(l.weights); - if (l.weight_updates) free(l.weight_updates); + return; + } + if (l.mask) free(l.mask); + if (l.cweights) free(l.cweights); + if (l.indexes) free(l.indexes); + if (l.input_layers) free(l.input_layers); + if (l.input_sizes) free(l.input_sizes); + if (l.map) free(l.map); + if (l.rand) free(l.rand); + if (l.cost) free(l.cost); + if (l.state) free(l.state); + if (l.prev_state) free(l.prev_state); + if (l.forgot_state) free(l.forgot_state); + if (l.forgot_delta) free(l.forgot_delta); + if (l.state_delta) free(l.state_delta); + if (l.concat) free(l.concat); + if (l.concat_delta) free(l.concat_delta); + if (l.binary_weights) free(l.binary_weights); + if (l.biases) free(l.biases); + if (l.bias_updates) free(l.bias_updates); + if (l.scales) free(l.scales); + if (l.scale_updates) free(l.scale_updates); + if (l.weights) free(l.weights); + if (l.weight_updates) free(l.weight_updates); if (l.align_bit_weights) free(l.align_bit_weights); if (l.mean_arr) free(l.mean_arr); #ifdef GPU @@ -45,76 +55,76 @@ void free_layer(layer l) l.output = NULL; } #endif // GPU - if (l.delta) free(l.delta); - if (l.output) free(l.output); - if (l.squared) free(l.squared); - if (l.norms) free(l.norms); - if (l.spatial_mean) free(l.spatial_mean); - if (l.mean) free(l.mean); - if (l.variance) free(l.variance); - if (l.mean_delta) free(l.mean_delta); - if (l.variance_delta) free(l.variance_delta); - if (l.rolling_mean) free(l.rolling_mean); - if (l.rolling_variance) free(l.rolling_variance); - if (l.x) free(l.x); - if (l.x_norm) free(l.x_norm); - if (l.m) free(l.m); - if (l.v) free(l.v); - if (l.z_cpu) free(l.z_cpu); - if (l.r_cpu) free(l.r_cpu); - if (l.h_cpu) free(l.h_cpu); - if (l.binary_input) free(l.binary_input); + if (l.delta) free(l.delta); + if (l.output) free(l.output); + if (l.squared) free(l.squared); + if (l.norms) free(l.norms); + if (l.spatial_mean) free(l.spatial_mean); + if (l.mean) free(l.mean); + if (l.variance) free(l.variance); + if (l.mean_delta) free(l.mean_delta); + if (l.variance_delta) free(l.variance_delta); + if (l.rolling_mean) free(l.rolling_mean); + if (l.rolling_variance) free(l.rolling_variance); + if (l.x) free(l.x); + if (l.x_norm) free(l.x_norm); + if (l.m) free(l.m); + if (l.v) free(l.v); + if (l.z_cpu) free(l.z_cpu); + if (l.r_cpu) free(l.r_cpu); + if (l.h_cpu) free(l.h_cpu); + if (l.binary_input) free(l.binary_input); if (l.bin_re_packed_input) free(l.bin_re_packed_input); if (l.t_bit_input) free(l.t_bit_input); if (l.loss) free(l.loss); #ifdef GPU - if (l.indexes_gpu) cuda_free((float *)l.indexes_gpu); + if (l.indexes_gpu) cuda_free((float *)l.indexes_gpu); - if (l.z_gpu) cuda_free(l.z_gpu); - if (l.r_gpu) cuda_free(l.r_gpu); - if (l.h_gpu) cuda_free(l.h_gpu); - if (l.m_gpu) cuda_free(l.m_gpu); - if (l.v_gpu) cuda_free(l.v_gpu); - if (l.prev_state_gpu) cuda_free(l.prev_state_gpu); - if (l.forgot_state_gpu) cuda_free(l.forgot_state_gpu); - if (l.forgot_delta_gpu) cuda_free(l.forgot_delta_gpu); - if (l.state_gpu) cuda_free(l.state_gpu); - if (l.state_delta_gpu) cuda_free(l.state_delta_gpu); - if (l.gate_gpu) cuda_free(l.gate_gpu); - if (l.gate_delta_gpu) cuda_free(l.gate_delta_gpu); - if (l.save_gpu) cuda_free(l.save_gpu); - if (l.save_delta_gpu) cuda_free(l.save_delta_gpu); - if (l.concat_gpu) cuda_free(l.concat_gpu); - if (l.concat_delta_gpu) cuda_free(l.concat_delta_gpu); - if (l.binary_input_gpu) cuda_free(l.binary_input_gpu); - if (l.binary_weights_gpu) cuda_free(l.binary_weights_gpu); - if (l.mean_gpu) cuda_free(l.mean_gpu); - if (l.variance_gpu) cuda_free(l.variance_gpu); - if (l.rolling_mean_gpu) cuda_free(l.rolling_mean_gpu); - if (l.rolling_variance_gpu) cuda_free(l.rolling_variance_gpu); - if (l.variance_delta_gpu) cuda_free(l.variance_delta_gpu); - if (l.mean_delta_gpu) cuda_free(l.mean_delta_gpu); - if (l.x_gpu) cuda_free(l.x_gpu); - if (l.x_norm_gpu) cuda_free(l.x_norm_gpu); + if (l.z_gpu) cuda_free(l.z_gpu); + if (l.r_gpu) cuda_free(l.r_gpu); + if (l.h_gpu) cuda_free(l.h_gpu); + if (l.m_gpu) cuda_free(l.m_gpu); + if (l.v_gpu) cuda_free(l.v_gpu); + if (l.prev_state_gpu) cuda_free(l.prev_state_gpu); + if (l.forgot_state_gpu) cuda_free(l.forgot_state_gpu); + if (l.forgot_delta_gpu) cuda_free(l.forgot_delta_gpu); + if (l.state_gpu) cuda_free(l.state_gpu); + if (l.state_delta_gpu) cuda_free(l.state_delta_gpu); + if (l.gate_gpu) cuda_free(l.gate_gpu); + if (l.gate_delta_gpu) cuda_free(l.gate_delta_gpu); + if (l.save_gpu) cuda_free(l.save_gpu); + if (l.save_delta_gpu) cuda_free(l.save_delta_gpu); + if (l.concat_gpu) cuda_free(l.concat_gpu); + if (l.concat_delta_gpu) cuda_free(l.concat_delta_gpu); + if (l.binary_input_gpu) cuda_free(l.binary_input_gpu); + if (l.binary_weights_gpu) cuda_free(l.binary_weights_gpu); + if (l.mean_gpu) cuda_free(l.mean_gpu); + if (l.variance_gpu) cuda_free(l.variance_gpu); + if (l.rolling_mean_gpu) cuda_free(l.rolling_mean_gpu); + if (l.rolling_variance_gpu) cuda_free(l.rolling_variance_gpu); + if (l.variance_delta_gpu) cuda_free(l.variance_delta_gpu); + if (l.mean_delta_gpu) cuda_free(l.mean_delta_gpu); + if (l.x_gpu) cuda_free(l.x_gpu); // dont free + if (l.x_norm_gpu) cuda_free(l.x_norm_gpu); if (l.align_bit_weights_gpu) cuda_free((float *)l.align_bit_weights_gpu); if (l.mean_arr_gpu) cuda_free(l.mean_arr_gpu); if (l.align_workspace_gpu) cuda_free(l.align_workspace_gpu); if (l.transposed_align_workspace_gpu) cuda_free(l.transposed_align_workspace_gpu); - if (l.weights_gpu) cuda_free(l.weights_gpu); - if (l.weight_updates_gpu) cuda_free(l.weight_updates_gpu); - if (l.weights_gpu16) cuda_free(l.weights_gpu16); - if (l.weight_updates_gpu16) cuda_free(l.weight_updates_gpu16); - if (l.biases_gpu) cuda_free(l.biases_gpu); - if (l.bias_updates_gpu) cuda_free(l.bias_updates_gpu); - if (l.scales_gpu) cuda_free(l.scales_gpu); - if (l.scale_updates_gpu) cuda_free(l.scale_updates_gpu); - if (l.output_gpu) cuda_free(l.output_gpu); - if (l.delta_gpu) cuda_free(l.delta_gpu); - if (l.rand_gpu) cuda_free(l.rand_gpu); - if (l.squared_gpu) cuda_free(l.squared_gpu); - if (l.norms_gpu) cuda_free(l.norms_gpu); + if (l.weights_gpu) cuda_free(l.weights_gpu); + if (l.weight_updates_gpu) cuda_free(l.weight_updates_gpu); + if (l.weights_gpu16) cuda_free(l.weights_gpu16); + if (l.weight_updates_gpu16) cuda_free(l.weight_updates_gpu16); + if (l.biases_gpu) cuda_free(l.biases_gpu); + if (l.bias_updates_gpu) cuda_free(l.bias_updates_gpu); + if (l.scales_gpu) cuda_free(l.scales_gpu); + if (l.scale_updates_gpu) cuda_free(l.scale_updates_gpu); + if (l.output_gpu) cuda_free(l.output_gpu); + if (l.delta_gpu) cuda_free(l.delta_gpu); + if (l.rand_gpu) cuda_free(l.rand_gpu); + if (l.squared_gpu) cuda_free(l.squared_gpu); + if (l.norms_gpu) cuda_free(l.norms_gpu); #endif } diff --git a/src/network.c b/src/network.c index 134f7e9265d..b8d5e8a3496 100644 --- a/src/network.c +++ b/src/network.c @@ -452,6 +452,9 @@ int resize_network(network *net, int w, int h) //printf(" %d: layer = %d,", i, l.type); if(l.type == CONVOLUTIONAL){ resize_convolutional_layer(&l, w, h); + } + else if (l.type == CRNN) { + resize_crnn_layer(&l, w, h); }else if(l.type == CROP){ resize_crop_layer(&l, w, h); }else if(l.type == MAXPOOL){ @@ -1018,6 +1021,68 @@ void calculate_binary_weights(network net) } +void copy_cudnn_descriptors(layer src, layer *dst) +{ + dst->normTensorDesc = src.normTensorDesc; + dst->normDstTensorDesc = src.normDstTensorDesc; + dst->normDstTensorDescF16 = src.normDstTensorDescF16; + + dst->srcTensorDesc = src.srcTensorDesc; + dst->dstTensorDesc = src.dstTensorDesc; + + dst->srcTensorDesc16 = src.srcTensorDesc16; + dst->dstTensorDesc16 = src.dstTensorDesc16; + + //dst->batch = 1; + //dst->steps = 1; +} + +void copy_weights_pointers_gpu(layer src, layer *dst) +{ + dst->weights_gpu = src.weights_gpu; + dst->weights_gpu16 = src.weights_gpu16; + dst->biases_gpu = src.biases_gpu; + dst->scales_gpu = src.scales_gpu; + dst->rolling_mean_gpu = src.rolling_mean_gpu; + dst->rolling_variance_gpu = src.rolling_variance_gpu; + dst->mean_gpu = src.mean_gpu; + dst->variance_gpu = src.variance_gpu; + //dst->align_bit_weights_gpu = src.align_bit_weights_gpu; + dst->x_gpu = src.x_gpu; + + dst->output_gpu = src.output_gpu; +} + +void copy_weights_net(network net_train, network *net_map) +{ + int k; + for (k = 0; k < net_train.n; ++k) { + layer *l = &(net_train.layers[k]); + layer tmp_layer; + copy_cudnn_descriptors(net_map->layers[k], &tmp_layer); + net_map->layers[k] = net_train.layers[k]; + copy_cudnn_descriptors(tmp_layer, &net_map->layers[k]); + + if (l->type == CRNN) { + layer tmp_input_layer, tmp_self_layer, tmp_output_layer; + copy_cudnn_descriptors(*net_map->layers[k].input_layer, &tmp_input_layer); + copy_cudnn_descriptors(*net_map->layers[k].self_layer, &tmp_self_layer); + copy_cudnn_descriptors(*net_map->layers[k].output_layer, &tmp_output_layer); + net_map->layers[k].input_layer = net_train.layers[k].input_layer; + net_map->layers[k].self_layer = net_train.layers[k].self_layer; + net_map->layers[k].output_layer = net_train.layers[k].output_layer; + //net_map->layers[k].output_gpu = net_map->layers[k].output_layer->output_gpu; // already copied out of if() + + copy_cudnn_descriptors(tmp_input_layer, net_map->layers[k].input_layer); + copy_cudnn_descriptors(tmp_self_layer, net_map->layers[k].self_layer); + copy_cudnn_descriptors(tmp_output_layer, net_map->layers[k].output_layer); + } + net_map->layers[k].batch = 1; + net_map->layers[k].steps = 1; + } +} + + // combine Training and Validation networks network combine_train_valid_networks(network net_train, network net_map) { @@ -1026,26 +1091,48 @@ network combine_train_valid_networks(network net_train, network net_map) net_combined = net_train; net_combined.layers = old_layers; net_combined.batch = 1; + net_combined.time_steps = 1; int k; for (k = 0; k < net_train.n; ++k) { layer *l = &(net_train.layers[k]); - net_combined.layers[k] = net_train.layers[k]; - net_combined.layers[k].batch = 1; - if (l->type == CONVOLUTIONAL) { #ifdef CUDNN - net_combined.layers[k].normTensorDesc = net_map.layers[k].normTensorDesc; - net_combined.layers[k].normDstTensorDesc = net_map.layers[k].normDstTensorDesc; - net_combined.layers[k].normDstTensorDescF16 = net_map.layers[k].normDstTensorDescF16; + if (l->type == CONVOLUTIONAL) { + /* + net_combined.layers[k] = net_train.layers[k]; + net_combined.layers[k].batch = 1; + net_combined.layers[k].steps = 1; + copy_cudnn_descriptors(net_map.layers[k], &net_combined.layers[k]); + */ + net_combined.layers[k] = net_map.layers[k]; + //net_combined.layers[k] = net_train.layers[k]; + net_combined.layers[k].batch = 1; + net_combined.layers[k].steps = 1; - net_combined.layers[k].srcTensorDesc = net_map.layers[k].srcTensorDesc; - net_combined.layers[k].dstTensorDesc = net_map.layers[k].dstTensorDesc; + copy_weights_pointers_gpu(net_train.layers[k], &net_combined.layers[k]); - net_combined.layers[k].srcTensorDesc16 = net_map.layers[k].srcTensorDesc16; - net_combined.layers[k].dstTensorDesc16 = net_map.layers[k].dstTensorDesc16; -#endif // CUDNN + net_combined.layers[k].output_gpu = net_train.layers[k].output_gpu; + + } + else if (l->type == CRNN) { + net_combined.layers[k] = net_map.layers[k]; + net_combined.layers[k].batch = 1; + net_combined.layers[k].steps = 1; + // Don't use copy_cudnn_descriptors() here + + copy_weights_pointers_gpu(*net_train.layers[k].input_layer, net_combined.layers[k].input_layer); + copy_weights_pointers_gpu(*net_train.layers[k].self_layer, net_combined.layers[k].self_layer); + copy_weights_pointers_gpu(*net_train.layers[k].output_layer, net_combined.layers[k].output_layer); + + net_combined.layers[k].output_gpu = net_combined.layers[k].output_layer->output_gpu; } + else { + net_combined.layers[k] = net_train.layers[k]; + net_combined.layers[k].batch = 1; + net_combined.layers[k].steps = 1; + } +#endif // CUDNN } return net_combined; } diff --git a/src/network.h b/src/network.h index 002d596c68b..764bb7da603 100644 --- a/src/network.h +++ b/src/network.h @@ -161,6 +161,7 @@ int get_network_background(network net); //LIB_API void fuse_conv_batchnorm(network net); //LIB_API void calculate_binary_weights(network net); network combine_train_valid_networks(network net_train, network net_map); +void copy_weights_net(network net_train, network *net_map); #ifdef __cplusplus } diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 619b874219b..a40dae0a09d 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -163,9 +163,16 @@ void forward_backward_network_gpu(network net, float *x, float *y) int i; for (i = 0; i < net.n; ++i) { layer l = net.layers[i]; - if (l.weights_gpu && l.weights_gpu16) { + if (l.weights_gpu && l.weights_gpu16 && net.cudnn_half){ assert((l.c*l.n*l.size*l.size) > 0); - cuda_convert_f32_to_f16(l.weights_gpu, l.c*l.n*l.size*l.size, l.weights_gpu16); + if (l.type == CONVOLUTIONAL) { + cuda_convert_f32_to_f16(l.weights_gpu, l.c*l.n*l.size*l.size, l.weights_gpu16); + } + else if (l.type == CRNN) { + //cuda_convert_f32_to_f16(l.input_layer->weights_gpu, l.input_layer->nweights, l.input_layer->weights_gpu16); + //cuda_convert_f32_to_f16(l.self_layer->weights_gpu, l.self_layer->nweights, l.self_layer->weights_gpu16); + //cuda_convert_f32_to_f16(l.output_layer->weights_gpu, l.output_layer->nweights, l.output_layer->weights_gpu16); + } } } #endif diff --git a/src/parser.c b/src/parser.c index ff344f01fb4..11cc4bfcc51 100644 --- a/src/parser.c +++ b/src/parser.c @@ -184,15 +184,18 @@ layer parse_crnn(list *options, size_params params) { int size = option_find_int_quiet(options, "size", 3); int stride = option_find_int_quiet(options, "stride", 1); - int pad = option_find_int_quiet(options, "pad", 1); + int pad = option_find_int_quiet(options, "pad", 0); + int padding = option_find_int_quiet(options, "padding", 0); + if (pad) padding = size / 2; int output_filters = option_find_int(options, "output",1); int hidden_filters = option_find_int(options, "hidden",1); char *activation_s = option_find_str(options, "activation", "logistic"); ACTIVATION activation = get_activation(activation_s); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); + int xnor = option_find_int_quiet(options, "xnor", 0); - layer l = make_crnn_layer(params.batch, params.w, params.h, params.c, hidden_filters, output_filters, params.time_steps, size, stride, pad, activation, batch_normalize); + layer l = make_crnn_layer(params.batch, params.w, params.h, params.c, hidden_filters, output_filters, params.time_steps, size, stride, padding, activation, batch_normalize, xnor); l.shortcut = option_find_int_quiet(options, "shortcut", 0); @@ -638,6 +641,9 @@ void parse_net_options(list *options, network *net) net->decay = option_find_float(options, "decay", .0001); int subdivs = option_find_int(options, "subdivisions",1); net->time_steps = option_find_int_quiet(options, "time_steps",1); + net->track = option_find_int_quiet(options, "track", 0); + net->augment_speed = option_find_int_quiet(options, "augment_speed", 2); + net->try_fix_nan = option_find_int_quiet(options, "try_fix_nan", 0); net->batch /= subdivs; net->batch *= net->time_steps; net->subdivisions = subdivs; @@ -657,7 +663,6 @@ void parse_net_options(list *options, network *net) net->min_crop = option_find_int_quiet(options, "min_crop",net->w); net->flip = option_find_int_quiet(options, "flip", 1); - net->small_object = option_find_int_quiet(options, "small_object", 0); net->angle = option_find_float_quiet(options, "angle", 0); net->aspect = option_find_float_quiet(options, "aspect", 1); net->saturation = option_find_float_quiet(options, "saturation", 1); @@ -748,6 +753,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) params.inputs = net.inputs; if (batch > 0) net.batch = batch; if (time_steps > 0) net.time_steps = time_steps; + if (net.batch < net.time_steps) net.batch = net.time_steps; params.batch = net.batch; params.time_steps = net.time_steps; params.net = net; @@ -984,10 +990,10 @@ void save_convolutional_weights(layer l, FILE *fp) fwrite(l.rolling_variance, sizeof(float), l.n, fp); } fwrite(l.weights, sizeof(float), num, fp); - if(l.adam){ - fwrite(l.m, sizeof(float), num, fp); - fwrite(l.v, sizeof(float), num, fp); - } + //if(l.adam){ + // fwrite(l.m, sizeof(float), num, fp); + // fwrite(l.v, sizeof(float), num, fp); + //} } void save_batchnorm_weights(layer l, FILE *fp) @@ -1198,10 +1204,10 @@ void load_convolutional_weights(layer l, FILE *fp) } } fread(l.weights, sizeof(float), num, fp); - if(l.adam){ - fread(l.m, sizeof(float), num, fp); - fread(l.v, sizeof(float), num, fp); - } + //if(l.adam){ + // fread(l.m, sizeof(float), num, fp); + // fread(l.v, sizeof(float), num, fp); + //} //if(l.c == 3) scal_cpu(num, 1./256, l.weights, 1); if (l.flipped) { transpose_matrix(l.weights, l.c*l.size*l.size, l.n); From dd0fdfb684aea43932c3851c4ed4c6b480b4dfba Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 2 Mar 2019 03:37:55 +0300 Subject: [PATCH 0375/1365] Compile fix --- src/layer.c | 2 ++ src/network.c | 65 +++++++++++---------------------------------------- 2 files changed, 15 insertions(+), 52 deletions(-) diff --git a/src/layer.c b/src/layer.c index 7db2621650e..2361c1e08ac 100644 --- a/src/layer.c +++ b/src/layer.c @@ -11,8 +11,10 @@ void free_layer(layer l) if (l.output_layer) free_layer(*l.output_layer); l.output = NULL; l.delta = NULL; +#ifdef GPU l.output_gpu = NULL; l.delta_gpu = NULL; +#endif // GPU } if (l.type == DROPOUT) { if (l.rand) free(l.rand); diff --git a/src/network.c b/src/network.c index b8d5e8a3496..30f99c4df78 100644 --- a/src/network.c +++ b/src/network.c @@ -1023,6 +1023,7 @@ void calculate_binary_weights(network net) void copy_cudnn_descriptors(layer src, layer *dst) { +#ifdef CUDNN dst->normTensorDesc = src.normTensorDesc; dst->normDstTensorDesc = src.normDstTensorDesc; dst->normDstTensorDescF16 = src.normDstTensorDescF16; @@ -1032,25 +1033,7 @@ void copy_cudnn_descriptors(layer src, layer *dst) dst->srcTensorDesc16 = src.srcTensorDesc16; dst->dstTensorDesc16 = src.dstTensorDesc16; - - //dst->batch = 1; - //dst->steps = 1; -} - -void copy_weights_pointers_gpu(layer src, layer *dst) -{ - dst->weights_gpu = src.weights_gpu; - dst->weights_gpu16 = src.weights_gpu16; - dst->biases_gpu = src.biases_gpu; - dst->scales_gpu = src.scales_gpu; - dst->rolling_mean_gpu = src.rolling_mean_gpu; - dst->rolling_variance_gpu = src.rolling_variance_gpu; - dst->mean_gpu = src.mean_gpu; - dst->variance_gpu = src.variance_gpu; - //dst->align_bit_weights_gpu = src.align_bit_weights_gpu; - dst->x_gpu = src.x_gpu; - - dst->output_gpu = src.output_gpu; +#endif // CUDNN } void copy_weights_net(network net_train, network *net_map) @@ -1091,48 +1074,26 @@ network combine_train_valid_networks(network net_train, network net_map) net_combined = net_train; net_combined.layers = old_layers; net_combined.batch = 1; - net_combined.time_steps = 1; int k; for (k = 0; k < net_train.n; ++k) { layer *l = &(net_train.layers[k]); + net_combined.layers[k] = net_train.layers[k]; + net_combined.layers[k].batch = 1; -#ifdef CUDNN if (l->type == CONVOLUTIONAL) { - /* - net_combined.layers[k] = net_train.layers[k]; - net_combined.layers[k].batch = 1; - net_combined.layers[k].steps = 1; - copy_cudnn_descriptors(net_map.layers[k], &net_combined.layers[k]); - */ - net_combined.layers[k] = net_map.layers[k]; - //net_combined.layers[k] = net_train.layers[k]; - net_combined.layers[k].batch = 1; - net_combined.layers[k].steps = 1; - - copy_weights_pointers_gpu(net_train.layers[k], &net_combined.layers[k]); - - net_combined.layers[k].output_gpu = net_train.layers[k].output_gpu; - - } - else if (l->type == CRNN) { - net_combined.layers[k] = net_map.layers[k]; - net_combined.layers[k].batch = 1; - net_combined.layers[k].steps = 1; - // Don't use copy_cudnn_descriptors() here +#ifdef CUDNN + net_combined.layers[k].normTensorDesc = net_map.layers[k].normTensorDesc; + net_combined.layers[k].normDstTensorDesc = net_map.layers[k].normDstTensorDesc; + net_combined.layers[k].normDstTensorDescF16 = net_map.layers[k].normDstTensorDescF16; - copy_weights_pointers_gpu(*net_train.layers[k].input_layer, net_combined.layers[k].input_layer); - copy_weights_pointers_gpu(*net_train.layers[k].self_layer, net_combined.layers[k].self_layer); - copy_weights_pointers_gpu(*net_train.layers[k].output_layer, net_combined.layers[k].output_layer); + net_combined.layers[k].srcTensorDesc = net_map.layers[k].srcTensorDesc; + net_combined.layers[k].dstTensorDesc = net_map.layers[k].dstTensorDesc; - net_combined.layers[k].output_gpu = net_combined.layers[k].output_layer->output_gpu; - } - else { - net_combined.layers[k] = net_train.layers[k]; - net_combined.layers[k].batch = 1; - net_combined.layers[k].steps = 1; - } + net_combined.layers[k].srcTensorDesc16 = net_map.layers[k].srcTensorDesc16; + net_combined.layers[k].dstTensorDesc16 = net_map.layers[k].dstTensorDesc16; #endif // CUDNN + } } return net_combined; } From 42b8c0751ed85d8a71e7fc5705a3699a1375fd76 Mon Sep 17 00:00:00 2001 From: Alexey Date: Sun, 3 Mar 2019 13:18:32 +0300 Subject: [PATCH 0376/1365] Update Readme.md --- README.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index ee9eb8945b8..dd45cd40aad 100644 --- a/README.md +++ b/README.md @@ -14,14 +14,14 @@ * [Using vcpkg](#how-to-compile-on-windows-using-vcpkg) * [Legacy way](#how-to-compile-on-windows-legacy-way) 4. [How to train (Pascal VOC Data)](#how-to-train-pascal-voc-data) - * [How to train with multi-GPU:](#how-to-train-with-multi-gpu) -5. [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects) - * [How to train tiny-yolo (to detect your custom objects)](#how-to-train-tiny-yolo-to-detect-your-custom-objects) -6. [When should I stop training](#when-should-i-stop-training) -7. [How to calculate mAP on PascalVOC 2007](#how-to-calculate-map-on-pascalvoc-2007) -8. [How to improve object detection](#how-to-improve-object-detection) -9. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) -10. [How to use Yolo as DLL and SO libraries](#how-to-use-yolo-as-dll-and-so-libraries) +5. [How to train with multi-GPU:](#how-to-train-with-multi-gpu) +6. [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects) +7. [How to train tiny-yolo (to detect your custom objects)](#how-to-train-tiny-yolo-to-detect-your-custom-objects) +8. [When should I stop training](#when-should-i-stop-training) +9. [How to calculate mAP on PascalVOC 2007](#how-to-calculate-map-on-pascalvoc-2007) +10. [How to improve object detection](#how-to-improve-object-detection) +11. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) +12. [How to use Yolo as DLL and SO libraries](#how-to-use-yolo-as-dll-and-so-libraries) | ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) |   ![map_time](https://user-images.githubusercontent.com/4096485/52151356-e5d4a380-2683-11e9-9d7d-ac7bc192c477.jpg) mAP@0.5 (AP50) https://pjreddie.com/media/files/papers/YOLOv3.pdf | |---|---| @@ -423,7 +423,7 @@ Usually sufficient 2000 iterations for each class(object), but not less than 400 * **9002** - iteration number (number of batch) * **0.060730 avg** - average loss (error) - **the lower, the better** - When you see that average loss **0.xxxxxx avg** no longer decreases at many iterations then you should stop training. + When you see that average loss **0.xxxxxx avg** no longer decreases at many iterations then you should stop training. The final avgerage loss can be from `0.05` (for a small model and easy dataset) to `3.0` (for a big model and a difficult dataset). 2. Once training is stopped, you should take some of last `.weights`-files from `darknet\build\darknet\x64\backup` and choose the best of them: @@ -531,11 +531,11 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol then do this command: `./darknet partial cfg/yolov3.cfg yolov3.weights yolov3.conv.81 81` will be created file `yolov3.conv.81`, then train by using weights file `yolov3.conv.81` instead of `darknet53.conv.74` - * The more different objects you want to detect, the more complex network model should be used. But each: `model of object, side, illimination, scale, each 30 grad` of the turn and inclination angles - these are different objects from a neural network perspective. + * each: `model of object, side, illimination, scale, each 30 grad` of the turn and inclination angles - these are *different objects* from an internal perspective of the neural network. So the more *different objects* you want to detect, the more complex network model should be used. * recalculate anchors for your dataset for `width` and `height` from cfg-file: `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416` - then set the same 9 `anchors` in each of 3 `[yolo]`-layers in your cfg-file. But you should change indexes of anchors `masks=` for each [yolo]-layer, so that 1st-[yolo]-layer has anchors larger than 60x60, 2nd larger than 30x30, 3rd remaining. If many of the calculated anchors do not fit under the appropriate layers - then just try using all the default anchors. + then set the same 9 `anchors` in each of 3 `[yolo]`-layers in your cfg-file. But you should change indexes of anchors `masks=` for each [yolo]-layer, so that 1st-[yolo]-layer has anchors larger than 60x60, 2nd larger than 30x30, 3rd remaining. Also you should change the `filters=(classes + 5)*` before each [yolo]-layer. If many of the calculated anchors do not fit under the appropriate layers - then just try using all the default anchors. 2. After training - for detection: From 80d0582ee45766ddb5100aa6d6126428da165278 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 3 Mar 2019 13:07:49 +0100 Subject: [PATCH 0377/1365] fix cuda build on appveyor, remove old unsupported config on macOS --- .travis.yml | 12 ------------ appveyor.yml | 18 +++++++++--------- 2 files changed, 9 insertions(+), 21 deletions(-) diff --git a/.travis.yml b/.travis.yml index 82afaed9ce5..175033a9ca2 100644 --- a/.travis.yml +++ b/.travis.yml @@ -3,18 +3,6 @@ language: cpp matrix: include: - - os: osx - compiler: gcc - name: macOS - gcc@5 - opencv@2 - env: - - MATRIX_EVAL="brew install gcc@5 opencv@2 && CC=gcc-5 && CXX=g++-5 && OpenCV_DIR=/usr/local/opt/opencv@2" - - - os: osx - compiler: gcc - name: macOS - gcc@5 - opencv@3 - env: - - MATRIX_EVAL="brew install gcc@5 opencv@3 && CC=gcc-5 && CXX=g++-5 && OpenCV_DIR=/usr/local/opt/opencv@3" - - os: osx compiler: gcc name: macOS - native gcc (llvm backend) - opencv@2 diff --git a/appveyor.yml b/appveyor.yml index 2012a3107e3..005e72b304a 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -13,9 +13,6 @@ environment: VCPKG_ROOT: C:\projects\vcpkg VCPKG_DEFAULT_TRIPLET: x64-windows USE_CUDA: yes - CUDACXX: C:\CUDA\bin\nvcc.exe - CUDA_PATH: C:\CUDA\ - CUDA_PATH_V10_0: C:\CUDA\ - platform: Win64 COMPILER: vs VCPKG_ROOT: C:\projects\vcpkg @@ -33,12 +30,15 @@ install: - if [%COMPILER%]==[cygwin] SET PATH=%PATH:C:\Program Files\Git\usr\bin;=% - git submodule -q update --init --recursive - cd %WORKSPACE%\ - - if [%USE_CUDA%]==[yes] curl -L https://developer.nvidia.com/compute/cuda/10.0/Prod/local_installers/cuda_10.0.130_411.31_windows -o cuda_10.0.130_411.31_windows.exe - - if [%USE_CUDA%]==[yes] mkdir C:\CUDATEMP - - if [%USE_CUDA%]==[yes] mkdir C:\CUDA - - if [%USE_CUDA%]==[yes] 7z x cuda_10.0.130_411.31_windows.exe -oC:\CUDATEMP - - if [%USE_CUDA%]==[yes] cd C:\CUDATEMP - - if [%USE_CUDA%]==[yes] FOR /D %%G in ("*") DO xcopy C:\CUDATEMP\%%G\* C:\CUDA\ /s /y + - if [%USE_CUDA%]==[yes] curl -L https://developer.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda_10.1.105_418.96_win10.exe -o setup.exe + - if [%USE_CUDA%]==[yes] .\setup.exe -s nvcc_10.1 cuobjdump_10.1 nvprune_10.1 cupti_10.1 gpu_library_advisor_10.1 memcheck_10.1 nvdisasm_10.1 nvprof_10.1 visual_profiler_10.1 visual_studio_integration_10.1 cublas_10.1 cublas_dev_10.1 cudart_10.1 cufft_10.1 cufft_dev_10.1 curand_10.1 curand_dev_10.1 cusolver_10.1 cusolver_dev_10.1 cusparse_10.1 cusparse_dev_10.1 nvgraph_10.1 nvgraph_dev_10.1 npp_10.1 npp_dev_10.1 nvrtc_10.1 nvrtc_dev_10.1 nvml_dev_10.1 occupancy_calculator_10.1 fortran_examples_10.1 + - if [%USE_CUDA%]==[yes] set CUDACXX=%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin + - if [%USE_CUDA%]==[yes] set CMAKE_CUDA_COMPILER=%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin + - if [%USE_CUDA%]==[yes] set CUDA_TOOLKIT_ROOT_DIR=%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v10.1 + - if [%USE_CUDA%]==[yes] set CUDA_PATH=%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v10.1 + - if [%USE_CUDA%]==[yes] set CUDA_PATH_V10_1=%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v10.1 + - if [%USE_CUDA%]==[yes] set PATH=%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v10.1\nvvm\bin;%PATH% + - if [%USE_CUDA%]==[yes] set PATH=%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin;%PATH% - cd %WORKSPACE%\ - mkdir cygwin-downloads - ps: if($env:COMPILER -eq "cygwin") { Invoke-WebRequest https://cygwin.com/setup-x86_64.exe -OutFile $env:WORKSPACE\cygwin-setup.exe } From d5cb0b490c4206cc01777a28b40bef9f63897644 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 3 Mar 2019 15:09:41 +0300 Subject: [PATCH 0378/1365] scripts/README.md --- scripts/{datasets.txt => README.md} | 2 +- scripts/get_imagenet_train.sh | 17 +++++++++++++++++ 2 files changed, 18 insertions(+), 1 deletion(-) rename scripts/{datasets.txt => README.md} (98%) create mode 100644 scripts/get_imagenet_train.sh diff --git a/scripts/datasets.txt b/scripts/README.md similarity index 98% rename from scripts/datasets.txt rename to scripts/README.md index 8151c0831ce..0c8327f0cf1 100644 --- a/scripts/datasets.txt +++ b/scripts/README.md @@ -1,6 +1,6 @@ -Datasets: +### Datasets: Pascal VOC: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html diff --git a/scripts/get_imagenet_train.sh b/scripts/get_imagenet_train.sh new file mode 100644 index 00000000000..b3578423279 --- /dev/null +++ b/scripts/get_imagenet_train.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +wget http://www.image-net.org/challenges/LSVRC/2012/nnoupb/ILSVRC2012_img_train.tar +mkdir -p ILSVRC2012_img_train +tar --force-local -xf ILSVRC2012_img_train.tar -C ILSVRC2012_img_train + +wd=`pwd` + +for f in ILSVRC2012_img_train/*.tar; +do +name=$(echo "$f" | cut -f 1 -d '.') +mkdir "${wd}/${name}" +tar --force-local -xf "${wd}/${f}" -C "${wd}/${name}" +done + +find "${wd}/ILSVRC2012_img_train" -name \*.JPEG > imagenet1k.train.list + From 80c9fa76a469406c49033c6ab60d35a8017e44d4 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 3 Mar 2019 14:34:50 +0100 Subject: [PATCH 0379/1365] refresh build scripts --- build.ps1 | 68 +++++++++++++++++++++++++++++++++++++++---------------- build.sh | 7 ------ 2 files changed, 49 insertions(+), 26 deletions(-) diff --git a/build.ps1 b/build.ps1 index 708d9b654ef..e26570612f6 100755 --- a/build.ps1 +++ b/build.ps1 @@ -1,43 +1,73 @@ #!/usr/bin/env pwsh $number_of_build_workers=8 -$vstype="Community" +#$shared_lib="-DBUILD_SHARED_LIBS:BOOL=ON" -if ((Get-Command "cl.exe" -ErrorAction SilentlyContinue) -eq $null) -{ - pushd "C:\Program Files (x86)\Microsoft Visual Studio\2017\${vstype}\Common7\Tools" +if (Test-Path env:VCPKG_ROOT) { + $vcpkg_path = "$env:VCPKG_ROOT" + Write-Host "Found vcpkg in VCPKG_ROOT: $vcpkg_path" +} +elseif (Test-Path "${env:WORKSPACE}\vcpkg") { + $vcpkg_path = "${env:WORKSPACE}\vcpkg" + Write-Host "Found vcpkg in WORKSPACE\vcpkg: $vcpkg_path" +} +else { + Write-Host "Skipping vcpkg-enabled builds because the VCPKG_ROOT environment variable is not defined, using self-distributed libs`n" -ForegroundColor Yellow +} + +if ($null -eq $env:VCPKG_DEFAULT_TRIPLET) { + Write-Host "No default triplet has been set-up for vcpkg. Defaulting to x64-windows`n" -ForegroundColor Yellow + $vcpkg_triplet = "x64-windows" +} +else { + $vcpkg_triplet = $env:VCPKG_DEFAULT_TRIPLET +} + +if ($vcpkg_triplet -Match "x86") { + Throw "darknet is supported only in x64 builds!" +} + +if ($null -eq (Get-Command "cl.exe" -ErrorAction SilentlyContinue)) { + $vstype = "Professional" + if (Test-Path "C:\Program Files (x86)\Microsoft Visual Studio\2017\${vstype}\Common7\Tools") { + Write-Host "Found VS 2017 Professional" + } + else { + $vstype = "Community" + Write-Host "Found VS 2017 Community" + } + Push-Location "C:\Program Files (x86)\Microsoft Visual Studio\2017\${vstype}\Common7\Tools" cmd /c "VsDevCmd.bat -arch=x64 & set" | - foreach { + ForEach-Object { if ($_ -match "=") { $v = $_.split("="); set-item -force -path "ENV:\$($v[0])" -value "$($v[1])" } } - popd + Pop-Location Write-Host "Visual Studio 2017 ${vstype} Command Prompt variables set.`n" -ForegroundColor Yellow } -if (Test-Path env:VCPKG_ROOT) { +if (Test-Path $vcpkg_path) { # DEBUG New-Item -Path .\build_win_debug -ItemType directory -Force Set-Location build_win_debug - cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$env:VCPKG_ROOT\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$env:VCPKG_DEFAULT_TRIPLET" "-DCMAKE_BUILD_TYPE=Debug" .. - cmake --build . --config Debug --parallel ${number_of_build_workers} + cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Debug" $shared_lib .. + cmake --build . --config Debug --parallel ${number_of_build_workers} --target install Set-Location .. # RELEASE New-Item -Path .\build_win_release -ItemType directory -Force Set-Location build_win_release - cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$env:VCPKG_ROOT\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$env:VCPKG_DEFAULT_TRIPLET" "-DCMAKE_BUILD_TYPE=Release" .. - cmake --build . --config Release --parallel ${number_of_build_workers} + cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Release" $shared_lib .. + cmake --build . --config Release --parallel ${number_of_build_workers} --target install Set-Location .. } else { - Write-Host "Skipping vcpkg-enabled builds because the VCPKG_ROOT environment variable is not defined`n" -ForegroundColor Yellow + # USE LOCAL PTHREAD LIB, NO VCPKG, ONLY RELEASE + # if you want to manually force this case, remove VCPKG_ROOT env variable and remember to use "vcpkg integrate remove" in case you had enabled user-wide vcpkg integration + New-Item -Path .\build_win_release_novcpkg -ItemType directory -Force + Set-Location build_win_release_novcpkg + cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" $shared_lib .. + cmake --build . --config Release --parallel ${number_of_build_workers} --target install + Set-Location .. } - -# USE LOCAL PTHREAD LIB, NO VCPKG: remember to use "vcpkg.exe integrate remove" in case you had enable user-wide vcpkg integration -New-Item -Path .\build_win_release_cuda_int_libs -ItemType directory -Force -Set-Location build_win_release_cuda_int_libs -cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" .. -cmake --build . --config Release --parallel ${number_of_build_workers} -Set-Location .. diff --git a/build.sh b/build.sh index 444c5b2454f..aa6f9edaae7 100755 --- a/build.sh +++ b/build.sh @@ -2,13 +2,6 @@ number_of_build_workers=8 -if [[ "$OSTYPE" == "darwin"* && "$1" == "gcc" ]]; then - export CC="/usr/local/bin/gcc-8" - export CXX="/usr/local/bin/g++-8" -fi - -rm -f uselib darknet - # DEBUG mkdir -p build_debug cd build_debug From 40cdaaaa4379ab5c6433cf8a310c726436dbb6af Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 3 Mar 2019 19:57:40 +0100 Subject: [PATCH 0380/1365] add artifacts on appveyor --- appveyor.yml | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/appveyor.yml b/appveyor.yml index 005e72b304a..344c8324f85 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -61,7 +61,17 @@ install: - cd .. build_script: - - if [%COMPILER%]==[cygwin] cd build_debug && %CYGSH% 'cmake --build .' && cd .. - - if [%COMPILER%]==[cygwin] cd build_release && %CYGSH% 'cmake --build .' && cd .. - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] cd build_debug && cmake --build . --config Debug && cd .. - - if [%COMPILER%]==[vs] cd build_release && cmake --build . --config Release && cd .. + - if [%COMPILER%]==[cygwin] cd build_debug && %CYGSH% 'cmake --build . --target install' && cd .. + - if [%COMPILER%]==[cygwin] cd build_release && %CYGSH% 'cmake --build . --target install' && cd .. + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] cd build_debug && cmake --build . --config Debug --target install && cd .. + - if [%COMPILER%]==[vs] cd build_release && cmake --build . --config Release --target install && cd .. + +artifacts: + - path: './darklib.lib' + name: darklib.lib + - path: './darklib.dll' + name: darklib.dll + - path: './uselib.exe' + name: uselib.exe + - path: './darknet.exe' + name: darknet.exe From 8fd3e7ad25e62ffbe8daa5f1c29e53dcd68e7215 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 3 Mar 2019 23:35:38 +0100 Subject: [PATCH 0381/1365] switch debug/release build order, better futureproof for next cmakeTargets work --- build.ps1 | 14 +++++++------- build.sh | 16 ++++++++-------- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/build.ps1 b/build.ps1 index e26570612f6..c6590a9bf11 100755 --- a/build.ps1 +++ b/build.ps1 @@ -48,19 +48,19 @@ if ($null -eq (Get-Command "cl.exe" -ErrorAction SilentlyContinue)) { } if (Test-Path $vcpkg_path) { - # DEBUG - New-Item -Path .\build_win_debug -ItemType directory -Force - Set-Location build_win_debug - cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Debug" $shared_lib .. - cmake --build . --config Debug --parallel ${number_of_build_workers} --target install - Set-Location .. - # RELEASE New-Item -Path .\build_win_release -ItemType directory -Force Set-Location build_win_release cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Release" $shared_lib .. cmake --build . --config Release --parallel ${number_of_build_workers} --target install Set-Location .. + + # DEBUG + New-Item -Path .\build_win_debug -ItemType directory -Force + Set-Location build_win_debug + cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Debug" $shared_lib .. + cmake --build . --config Debug --parallel ${number_of_build_workers} --target install + Set-Location .. } else { # USE LOCAL PTHREAD LIB, NO VCPKG, ONLY RELEASE diff --git a/build.sh b/build.sh index aa6f9edaae7..6fc393025c1 100755 --- a/build.sh +++ b/build.sh @@ -2,14 +2,6 @@ number_of_build_workers=8 -# DEBUG -mkdir -p build_debug -cd build_debug -cmake .. -DCMAKE_BUILD_TYPE=Debug -cmake --build . --target install -- -j${number_of_build_workers} -#cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ -cd .. - # RELEASE mkdir -p build_release cd build_release @@ -17,3 +9,11 @@ cmake .. -DCMAKE_BUILD_TYPE=Release cmake --build . --target install -- -j${number_of_build_workers} #cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ cd .. + +# DEBUG +mkdir -p build_debug +cd build_debug +cmake .. -DCMAKE_BUILD_TYPE=Debug +cmake --build . --target install -- -j${number_of_build_workers} +#cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ +cd .. From 540beb52ec2731e80a68a1e8a590e2201e2b8279 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 4 Mar 2019 04:05:17 +0300 Subject: [PATCH 0382/1365] get & label OTB dataset --- scripts/windows/win_get_otb_human3.cmd | 22 ++++++++++++++++++ scripts/windows/windows_otb_get_labels.sh | 28 +++++++++++++++++++++++ 2 files changed, 50 insertions(+) create mode 100644 scripts/windows/win_get_otb_human3.cmd create mode 100644 scripts/windows/windows_otb_get_labels.sh diff --git a/scripts/windows/win_get_otb_human3.cmd b/scripts/windows/win_get_otb_human3.cmd new file mode 100644 index 00000000000..11fc5e30ffd --- /dev/null +++ b/scripts/windows/win_get_otb_human3.cmd @@ -0,0 +1,22 @@ +echo Run install_cygwin.cmd before: + +rem http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html + +c:\cygwin64\bin\bash -l -c "cd %CD:\=/%/; echo $PWD" + + +c:\cygwin64\bin\wget http://cvlab.hanyang.ac.kr/tracker_benchmark/seq/Human3.zip + +c:\cygwin64\bin\unzip -o "%CD:\=/%/Human3.zip" + + + +c:\cygwin64\bin\dos2unix "%CD:\=/%/windows_otb_get_labels.sh" + +c:\cygwin64\bin\bash -l -c "cd %CD:\=/%/; %CD:\=/%/windows_otb_get_labels.sh" + +rem c:\cygwin64\bin\find "%CD:\=/%/img" -name \*.jpg > otb_train.txt + + + +pause \ No newline at end of file diff --git a/scripts/windows/windows_otb_get_labels.sh b/scripts/windows/windows_otb_get_labels.sh new file mode 100644 index 00000000000..b707de09273 --- /dev/null +++ b/scripts/windows/windows_otb_get_labels.sh @@ -0,0 +1,28 @@ +#!/bin/bash + +export LC_NUMERIC="en_US.UTF-8" + +wd=`pwd` +IFS=',' + +dataset="Human3" +class_id=0 +w=480 +h=640 +num=1 + +while read -a line; do +filename=$(printf "$dataset/img/%04d.txt" $num) +#rm $filename.txt +echo "$class_id " > $filename +printf "%.5f " "$(($((line[0] + line[2]/2)) * 100000 / $w))e-5" >> $filename +printf "%.5f " "$(($((line[1] + line[3]/2)) * 100000 / $h))e-5" >> $filename +printf "%.5f " "$(($((line[2])) * 100000 / $w))e-5" >> $filename +printf "%.5f " "$(($((line[3])) * 100000 / $h))e-5" >> $filename +num=$((num + 1)) +done < $dataset/groundtruth_rect.txt + +echo "$dataset" > $dataset/otb.names + + +find $dataset/img -name \*.jpg > otb_train.txt \ No newline at end of file From fe9eaa9872b46e89ed8e233bd2416e9b423d03e0 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Mon, 4 Mar 2019 10:22:13 +0100 Subject: [PATCH 0383/1365] improve build script based on user feedback --- appveyor.yml | 9 +++------ build.ps1 | 16 ++++++++++++++++ 2 files changed, 19 insertions(+), 6 deletions(-) diff --git a/appveyor.yml b/appveyor.yml index 344c8324f85..d3713ae353f 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -32,13 +32,10 @@ install: - cd %WORKSPACE%\ - if [%USE_CUDA%]==[yes] curl -L https://developer.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda_10.1.105_418.96_win10.exe -o setup.exe - if [%USE_CUDA%]==[yes] .\setup.exe -s nvcc_10.1 cuobjdump_10.1 nvprune_10.1 cupti_10.1 gpu_library_advisor_10.1 memcheck_10.1 nvdisasm_10.1 nvprof_10.1 visual_profiler_10.1 visual_studio_integration_10.1 cublas_10.1 cublas_dev_10.1 cudart_10.1 cufft_10.1 cufft_dev_10.1 curand_10.1 curand_dev_10.1 cusolver_10.1 cusolver_dev_10.1 cusparse_10.1 cusparse_dev_10.1 nvgraph_10.1 nvgraph_dev_10.1 npp_10.1 npp_dev_10.1 nvrtc_10.1 nvrtc_dev_10.1 nvml_dev_10.1 occupancy_calculator_10.1 fortran_examples_10.1 - - if [%USE_CUDA%]==[yes] set CUDACXX=%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin - - if [%USE_CUDA%]==[yes] set CMAKE_CUDA_COMPILER=%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin - - if [%USE_CUDA%]==[yes] set CUDA_TOOLKIT_ROOT_DIR=%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v10.1 - if [%USE_CUDA%]==[yes] set CUDA_PATH=%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v10.1 - - if [%USE_CUDA%]==[yes] set CUDA_PATH_V10_1=%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v10.1 - - if [%USE_CUDA%]==[yes] set PATH=%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v10.1\nvvm\bin;%PATH% - - if [%USE_CUDA%]==[yes] set PATH=%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin;%PATH% + - if [%USE_CUDA%]==[yes] set CUDA_PATH_V10_1=%CUDA_PATH% + - if [%USE_CUDA%]==[yes] set CUDA_TOOLKIT_ROOT_DIR=%CUDA_PATH% + - if [%USE_CUDA%]==[yes] set PATH=%CUDA_PATH%\bin;%PATH% - cd %WORKSPACE%\ - mkdir cygwin-downloads - ps: if($env:COMPILER -eq "cygwin") { Invoke-WebRequest https://cygwin.com/setup-x86_64.exe -OutFile $env:WORKSPACE\cygwin-setup.exe } diff --git a/build.ps1 b/build.ps1 index c6590a9bf11..19985de38a3 100755 --- a/build.ps1 +++ b/build.ps1 @@ -47,6 +47,22 @@ if ($null -eq (Get-Command "cl.exe" -ErrorAction SilentlyContinue)) { Write-Host "Visual Studio 2017 ${vstype} Command Prompt variables set.`n" -ForegroundColor Yellow } +if ($null -eq (Get-Command "nvcc.exe" -ErrorAction SilentlyContinue)) { + if (Test-Path env:CUDA_PATH) { + $env:PATH += ";${env:CUDA_PATH}\bin" + } + else { + Write-Host "Unable to find CUDA, if necessary please install it or define a CUDA_PATH env variable pointing to the install folder`n" -ForegroundColor Yellow + } +} + +if (Test-Path env:CUDA_PATH) { + if (-Not(Test-Path env:CUDA_TOOLKIT_ROOT_DIR)) { + $env:CUDA_TOOLKIT_ROOT_DIR = "${env:CUDA_PATH}" + Write-Host "Added missing env variable CUDA_TOOLKIT_ROOT_DIR`n" -ForegroundColor Yellow + } +} + if (Test-Path $vcpkg_path) { # RELEASE New-Item -Path .\build_win_release -ItemType directory -Force From b751bac17505a742f149ada81d75689b5e692cde Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 5 Mar 2019 02:15:44 +0300 Subject: [PATCH 0384/1365] Fixed mAP calculation for CRNN layer during training --- README.md | 2 +- scripts/windows/otb_get_labels.sh | 50 +++++++++++++++++++++++ scripts/windows/win_get_otb_datasets.cmd | 18 ++++++++ scripts/windows/win_get_otb_human3.cmd | 22 ---------- scripts/windows/windows_otb_get_labels.sh | 28 ------------- src/crnn_layer.c | 2 +- 6 files changed, 70 insertions(+), 52 deletions(-) create mode 100644 scripts/windows/otb_get_labels.sh create mode 100644 scripts/windows/win_get_otb_datasets.cmd delete mode 100644 scripts/windows/win_get_otb_human3.cmd delete mode 100644 scripts/windows/windows_otb_get_labels.sh diff --git a/README.md b/README.md index dd45cd40aad..245a0f067ad 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ * [Explanations in issues](https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3AExplanations) 0. [Improvements in this repository](#improvements-in-this-repository) -1. [How to use](#how-to-use) +1. [How to use](#how-to-use-on-the-command-line) 2. [How to compile on Linux](#how-to-compile-on-linux) 3. How to compile on Windows * [Using vcpkg](#how-to-compile-on-windows-using-vcpkg) diff --git a/scripts/windows/otb_get_labels.sh b/scripts/windows/otb_get_labels.sh new file mode 100644 index 00000000000..43e53e4fab0 --- /dev/null +++ b/scripts/windows/otb_get_labels.sh @@ -0,0 +1,50 @@ +#!/bin/bash + +dataset=$1 +w=$2 +h=$3 + +# Parameters: Human3, CarScale, Human6, Biker +#w=480 +#h=640 +IFS=',' + +export LC_NUMERIC="en_US.UTF-8" + +wd=`pwd` +dataset_path="data/$dataset" + +class_id=0 +num=1 + +mkdir data +wget http://cvlab.hanyang.ac.kr/tracker_benchmark/seq/$dataset.zip + +unzip -o $dataset.zip -d data + +sed -i.bak 's/\o11/,/g' $dataset_path/groundtruth_rect.txt +sed -i.bak 's/\o11/,/g' $dataset_path/groundtruth_rect.txt +dos2unix $dataset_path/groundtruth_rect.txt + +while read -r left right width height; do +filename=$(printf "$dataset_path/img/%04d.txt" $num) +#rm $filename.txt +echo "$class_id " > $filename +printf "%.5f " "$(($((left + width/2)) * 100000 / $w))e-5" >> $filename +printf "%.5f " "$(($((right + height/2)) * 100000 / $h))e-5" >> $filename +printf "%.5f " "$(($((width)) * 100000 / $w))e-5" >> $filename +printf "%.5f " "$(($((height)) * 100000 / $h))e-5" >> $filename +num=$((num + 1)) +done < $dataset_path/groundtruth_rect.txt + +echo "$dataset" > $dataset_path/otb.names + + +find $dataset_path/img -name \*.jpg > data/$dataset/train.txt + +echo "classes = 1" > data/otb_$dataset.data +echo "train = data/$dataset/train.txt" >> data/otb_$dataset.data +echo "valid = data/$dataset/train.txt" >> data/otb_$dataset.data +echo "names = $dataset_path/otb.names" >> data/otb_$dataset.data +echo "backup = backup/" >> data/otb_$dataset.data +echo "results= results/" >> data/otb_$dataset.data \ No newline at end of file diff --git a/scripts/windows/win_get_otb_datasets.cmd b/scripts/windows/win_get_otb_datasets.cmd new file mode 100644 index 00000000000..8c12249b940 --- /dev/null +++ b/scripts/windows/win_get_otb_datasets.cmd @@ -0,0 +1,18 @@ +echo Run install_cygwin.cmd before: + +rem http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html + +c:\cygwin64\bin\bash -l -c "cd %CD:\=/%/; echo $PWD" + +c:\cygwin64\bin\dos2unix "%CD:\=/%/otb_get_labels.sh" + + +c:\cygwin64\bin\bash -l -c "cd %CD:\=/%/; %CD:\=/%/otb_get_labels.sh Suv 320 240" + +c:\cygwin64\bin\bash -l -c "cd %CD:\=/%/; %CD:\=/%/otb_get_labels.sh Liquor 640 480" + +c:\cygwin64\bin\bash -l -c "cd %CD:\=/%/; %CD:\=/%/otb_get_labels.sh Freeman4 360 240" + +c:\cygwin64\bin\bash -l -c "cd %CD:\=/%/; %CD:\=/%/otb_get_labels.sh Human3 480 640" + +pause \ No newline at end of file diff --git a/scripts/windows/win_get_otb_human3.cmd b/scripts/windows/win_get_otb_human3.cmd deleted file mode 100644 index 11fc5e30ffd..00000000000 --- a/scripts/windows/win_get_otb_human3.cmd +++ /dev/null @@ -1,22 +0,0 @@ -echo Run install_cygwin.cmd before: - -rem http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html - -c:\cygwin64\bin\bash -l -c "cd %CD:\=/%/; echo $PWD" - - -c:\cygwin64\bin\wget http://cvlab.hanyang.ac.kr/tracker_benchmark/seq/Human3.zip - -c:\cygwin64\bin\unzip -o "%CD:\=/%/Human3.zip" - - - -c:\cygwin64\bin\dos2unix "%CD:\=/%/windows_otb_get_labels.sh" - -c:\cygwin64\bin\bash -l -c "cd %CD:\=/%/; %CD:\=/%/windows_otb_get_labels.sh" - -rem c:\cygwin64\bin\find "%CD:\=/%/img" -name \*.jpg > otb_train.txt - - - -pause \ No newline at end of file diff --git a/scripts/windows/windows_otb_get_labels.sh b/scripts/windows/windows_otb_get_labels.sh deleted file mode 100644 index b707de09273..00000000000 --- a/scripts/windows/windows_otb_get_labels.sh +++ /dev/null @@ -1,28 +0,0 @@ -#!/bin/bash - -export LC_NUMERIC="en_US.UTF-8" - -wd=`pwd` -IFS=',' - -dataset="Human3" -class_id=0 -w=480 -h=640 -num=1 - -while read -a line; do -filename=$(printf "$dataset/img/%04d.txt" $num) -#rm $filename.txt -echo "$class_id " > $filename -printf "%.5f " "$(($((line[0] + line[2]/2)) * 100000 / $w))e-5" >> $filename -printf "%.5f " "$(($((line[1] + line[3]/2)) * 100000 / $h))e-5" >> $filename -printf "%.5f " "$(($((line[2])) * 100000 / $w))e-5" >> $filename -printf "%.5f " "$(($((line[3])) * 100000 / $h))e-5" >> $filename -num=$((num + 1)) -done < $dataset/groundtruth_rect.txt - -echo "$dataset" > $dataset/otb.names - - -find $dataset/img -name \*.jpg > otb_train.txt \ No newline at end of file diff --git a/src/crnn_layer.c b/src/crnn_layer.c index 40de100191c..0fc6da72750 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -263,7 +263,7 @@ void forward_crnn_layer_gpu(layer l, network_state state) s.train = state.train; s.workspace = state.workspace; s.net = state.net; - if(!state.train) s.index = state.index; // use TC only for detection + //if(!state.train) s.index = state.index; // don't use TC at all (especially without cuda_convert_f32_to_f16() ) int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); From 2c8ff8db0dab62eaa3dc2719e04140a9a0057c3d Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Mon, 4 Mar 2019 15:15:19 +0100 Subject: [PATCH 0385/1365] create cmake targets for proper lib export --- .gitignore | 8 ++ .travis.yml | 4 +- CMakeLists.txt | 146 ++++++++++++++++------- DarknetConfig.cmake.in | 33 +++++ appveyor.yml | 8 +- build.ps1 | 12 ++ build.sh | 22 +++- cmake/Modules/FindCUDNN.cmake | 36 +++++- cmake/Modules/FindDarknet.cmake | 43 +++++++ cmake/Modules/FindPThreads_windows.cmake | 70 ++++++----- src/darknet.c | 3 + 11 files changed, 298 insertions(+), 87 deletions(-) create mode 100644 DarknetConfig.cmake.in create mode 100644 cmake/Modules/FindDarknet.cmake diff --git a/.gitignore b/.gitignore index f2492274310..b415cea3d9e 100644 --- a/.gitignore +++ b/.gitignore @@ -4,6 +4,7 @@ *.out *.png *.so +*.exe mnist/ data/ caffe/ @@ -15,8 +16,10 @@ decaf/ submission/ cfg/ build/darknet/* +build_*/ !build/darknet/YoloWrapper.cs .fuse* +*.weights # OS Generated # .DS_Store* @@ -24,3 +27,8 @@ ehthumbs.db Icon? Thumbs.db *.swp + +lib/ +include/ +share/ + diff --git a/.travis.yml b/.travis.yml index 175033a9ca2..0ce769c6aa5 100644 --- a/.travis.yml +++ b/.travis.yml @@ -410,5 +410,5 @@ before_script: - cd .. script: - - cd build_debug && cmake --build . --target install && cd .. - - cd build_release && cmake --build . --target install && cd .. + - cd build_debug && cmake --build . --target install -- -j8 && cd .. + - cd build_release && cmake --build . --target install -- -j8 && cd .. diff --git a/CMakeLists.txt b/CMakeLists.txt index 9732035b080..e6e6323aba3 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,14 +1,17 @@ -# To use your libs, launch cmake with -DUSE_INTEGRATED_LIBS:BOOL=FALSE - cmake_minimum_required(VERSION 3.4) -#set(CMAKE_VERBOSE_MAKEFILE 1) +set(Darknet_MAJOR_VERSION 1) +set(Darknet_MINOR_VERSION 0) +set(Darknet_PATCH_VERSION 0) +set(Darknet_VERSION ${Darknet_MAJOR_VERSION}.${Darknet_MINOR_VERSION}.${Darknet_PATCH_VERSION}) + +set(CMAKE_VERBOSE_MAKEFILE "FALSE" CACHE BOOL "Create verbose makefile") if(DEFINED ENV{VCPKG_ROOT} AND NOT DEFINED CMAKE_TOOLCHAIN_FILE) set(CMAKE_TOOLCHAIN_FILE "${VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake" CACHE STRING "") endif() -project(darknet) +project(Darknet VERSION ${Darknet_VERSION}) if(WIN32 AND NOT DEFINED CMAKE_TOOLCHAIN_FILE) set(USE_INTEGRATED_LIBS "TRUE" CACHE BOOL "Use libs distributed with this repo") @@ -21,10 +24,20 @@ enable_language(CXX) set(CMAKE_CXX_STANDARD 11) set(CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/cmake/Modules/" ${CMAKE_MODULE_PATH}) -include_directories(${CMAKE_CURRENT_LIST_DIR}/include) -include_directories(${CMAKE_CURRENT_LIST_DIR}/src) -set(SKIP_USELIB_TRACK "FALSE" CACHE BOOL "Skip building uselib_track" FORCE) +set(CMAKE_INSTALL_PREFIX "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Install prefix" FORCE) +set(INSTALL_BIN_DIR "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Path where exe and dll will be installed" FORCE) +set(INSTALL_LIB_DIR "lib" CACHE PATH "Path where lib will be installed") +set(INSTALL_INCLUDE_DIR "include" CACHE PATH "Path where headers will be installed") +set(INSTALL_CMAKE_DIR "share/darknet" CACHE PATH "Path where cmake configs will be installed") +# Make relative paths absolute (needed later on) +foreach(p LIB BIN INCLUDE CMAKE) + set(var INSTALL_${p}_DIR) + if(NOT IS_ABSOLUTE "${${var}}") + set(${var} "${CMAKE_INSTALL_PREFIX}/${${var}}") + endif() +endforeach() + set(ENABLE_CUDA "TRUE" CACHE BOOL "Enable CUDA build") if(${CMAKE_VERSION} VERSION_LESS "3.8.0") set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA build" FORCE) @@ -61,17 +74,12 @@ endif() if(USE_INTEGRATED_LIBS) include_directories(${CMAKE_CURRENT_LIST_DIR}/3rdparty/include) - set(PTHREADS_INCLUDE_DIR ${CMAKE_CURRENT_LIST_DIR}/3rdparty/include) - set(PTHREADS_LIBRARY ${CMAKE_CURRENT_LIST_DIR}/3rdparty/lib/x64/pthreadVC2.lib) + set(PThreads_windows_INCLUDE_DIR ${CMAKE_CURRENT_LIST_DIR}/3rdparty/include) + set(PThreads_windows_LIBRARY ${CMAKE_CURRENT_LIST_DIR}/3rdparty/lib/x64/pthreadVC2.lib) add_definitions(-D_TIMESPEC_DEFINED) endif() -if(NOT APPLE) - set(CMAKE_SKIP_BUILD_RPATH FALSE) - set(CMAKE_BUILD_WITH_INSTALL_RPATH FALSE) - set(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE) -endif() -set(CMAKE_INSTALL_RPATH "${CMAKE_SOURCE_DIR}") +set(CMAKE_DEBUG_POSTFIX d) add_definitions(-DUSE_CMAKE_LIBS) @@ -96,19 +104,18 @@ if ("${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang") string(REGEX REPLACE "-O3" "-Ofast" CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) endif() -if(APPLE) - find_package(OpenCV PATHS "/usr/local/Cellar/opencv@3/3.4.5" NO_DEFAULT_PATH) -else() +set(ENABLE_OPENCV "TRUE" CACHE BOOL "Enable OpenCV integration") +if(ENABLE_OPENCV) find_package(OpenCV) endif() +set(SKIP_USELIB_TRACK "FALSE" CACHE BOOL "Skip building uselib_track" FORCE) if(OpenCV_FOUND) if(ENABLE_CUDA AND NOT OpenCV_CUDA_VERSION) set(SKIP_USELIB_TRACK "TRUE" CACHE BOOL "Skip building uselib_track" FORCE) message(WARNING "The build of OpenCV does not have CUDA support, so some tools have been disabled! Please rebuild OpenCV with CUDA support to build them!") endif() add_definitions(-DOPENCV) - include_directories(${OpenCV_INCLUDE_DIRS}) endif() if(ENABLE_CUDA) @@ -124,7 +131,6 @@ if(ENABLE_CUDA) find_package(CUDNN) if(CUDNN_FOUND) add_definitions(-DCUDNN) - include_directories(${CUDNN_INCLUDE_DIR}) if(CUDA_COMPUTE_MODEL VERSION_GREATER_EQUAL ${CUDA_COMPUTE_MODEL_70}) add_definitions(-DCUDNN_HALF) message(STATUS "Enabled CUDNN_HALF") @@ -165,7 +171,6 @@ set(CMAKE_THREAD_PREFER_PTHREAD ON) find_package(Threads REQUIRED) if(MSVC) find_package(PThreads_windows REQUIRED) - include_directories(${PTHREADS_INCLUDE_DIR}) endif() if(${CMAKE_VERSION} VERSION_LESS "3.9.0") @@ -180,7 +185,7 @@ endif() #look for all *.h files in src folder file(GLOB headers "${CMAKE_CURRENT_LIST_DIR}/src/*.h") #add also files in the include folder -list(APPEND sources +list(APPEND headers ${CMAKE_CURRENT_LIST_DIR}/include/darknet.h ) #remove windows only files, will be re-added if necessary later @@ -188,6 +193,7 @@ list(REMOVE_ITEM headers ${CMAKE_CURRENT_LIST_DIR}/src/gettimeofday.h ${CMAKE_CURRENT_LIST_DIR}/src/getopt.h ) +set(exported_headers ${headers}) #look for all *.c files in src folder file(GLOB sources "${CMAKE_CURRENT_LIST_DIR}/src/*.c") @@ -223,7 +229,7 @@ endif() set_source_files_properties(${sources} PROPERTIES LANGUAGE CXX) -add_library(darklib SHARED ${CMAKE_CURRENT_LIST_DIR}/include/yolo_v2_class.hpp ${CMAKE_CURRENT_LIST_DIR}/src/yolo_v2_class.cpp ${sources} ${headers} ${cuda_sources}) +add_library(darklib ${CMAKE_CURRENT_LIST_DIR}/include/yolo_v2_class.hpp ${CMAKE_CURRENT_LIST_DIR}/src/yolo_v2_class.cpp ${sources} ${headers} ${cuda_sources}) set_target_properties(darklib PROPERTIES POSITION_INDEPENDENT_CODE ON) if(ENABLE_CUDA) set_target_properties(darklib PROPERTIES CUDA_SEPARABLE_COMPILATION ON) @@ -235,6 +241,8 @@ if(OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRACK) add_executable(uselib_track ${CMAKE_CURRENT_LIST_DIR}/src/yolo_console_dll.cpp) target_compile_definitions(uselib_track PRIVATE TRACK_OPTFLOW=1) set_target_properties(uselib_track PROPERTIES LINKER_LANGUAGE CXX) + target_include_directories(uselib_track PUBLIC $ $ $) + target_link_libraries(uselib_track PRIVATE ${OpenCV_LIBS}) endif() add_executable(uselib ${CMAKE_CURRENT_LIST_DIR}/src/yolo_console_dll.cpp) @@ -244,45 +252,97 @@ add_executable(darknet ${CMAKE_CURRENT_LIST_DIR}/src/darknet.c ${sources} ${head set_source_files_properties(${CMAKE_CURRENT_LIST_DIR}/src/darknet.c PROPERTIES LANGUAGE CXX) set_target_properties(darknet PROPERTIES LINKER_LANGUAGE CXX) +target_include_directories(darknet PUBLIC $ $ $) +target_include_directories(darklib PUBLIC $ $ $) +target_include_directories(uselib PUBLIC $ $ $) + if(CUDNN_FOUND) - target_link_libraries(darknet ${CUDNN_LIBRARY}) - target_link_libraries(darklib ${CUDNN_LIBRARY}) + target_link_libraries(darknet PRIVATE CuDNN::CuDNN) + target_link_libraries(darklib PRIVATE CuDNN::CuDNN) endif() if(OpenCV_FOUND) - target_link_libraries(darknet ${OpenCV_LIBS}) - target_link_libraries(darklib ${OpenCV_LIBS}) + target_link_libraries(darknet PRIVATE ${OpenCV_LIBS}) + target_link_libraries(uselib PRIVATE ${OpenCV_LIBS}) + target_link_libraries(darklib PRIVATE ${OpenCV_LIBS}) endif() if(OPENMP_FOUND) - target_link_libraries(darknet OpenMP::OpenMP_CXX) - target_link_libraries(darklib OpenMP::OpenMP_CXX) + target_link_libraries(darknet PRIVATE OpenMP::OpenMP_CXX) + target_link_libraries(darklib PRIVATE OpenMP::OpenMP_CXX) endif() if(CMAKE_COMPILER_IS_GNUCC) - target_link_libraries(darknet m) - target_link_libraries(darklib m) + target_link_libraries(darknet PRIVATE m) + target_link_libraries(darklib PRIVATE m) endif() if(MSVC) - target_link_libraries(darknet ${PTHREADS_LIBRARY}) - target_link_libraries(darknet wsock32 ws2_32) - target_link_libraries(darklib ${PTHREADS_LIBRARY}) - target_link_libraries(darklib wsock32 ws2_32) + target_link_libraries(darknet PRIVATE PThreads_windows::PThreads_windows) + target_link_libraries(darknet PRIVATE wsock32 ws2_32) + target_link_libraries(darklib PRIVATE PThreads_windows::PThreads_windows) + target_link_libraries(darklib PRIVATE wsock32 ws2_32) endif() -target_link_libraries(darknet Threads::Threads) -target_link_libraries(darklib Threads::Threads) +target_link_libraries(darknet PRIVATE Threads::Threads) +target_link_libraries(darklib PRIVATE Threads::Threads) if(ENABLE_CUDA) - target_link_libraries(darknet curand cublas) - target_link_libraries(darklib curand cublas) + target_link_libraries(darknet PRIVATE curand cublas) + target_link_libraries(darklib PRIVATE curand cublas) + set_target_properties(darklib PROPERTIES CUDA_RESOLVE_DEVICE_SYMBOLS ON) endif() -target_link_libraries(uselib darklib) +target_link_libraries(uselib PRIVATE darklib) if(OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRACK) - target_link_libraries(uselib_track darklib) - install(TARGETS uselib_track DESTINATION ${CMAKE_SOURCE_DIR}/) + target_link_libraries(uselib_track PRIVATE darklib) endif() -install(TARGETS darklib uselib darknet DESTINATION ${CMAKE_SOURCE_DIR}/) +set_target_properties(darklib PROPERTIES PUBLIC_HEADER "${exported_headers};${CMAKE_CURRENT_LIST_DIR}/include/yolo_v2_class.hpp") + +install(TARGETS darklib uselib darknet EXPORT DarknetTargets + RUNTIME DESTINATION "${INSTALL_BIN_DIR}" + LIBRARY DESTINATION "${INSTALL_LIB_DIR}" + ARCHIVE DESTINATION "${INSTALL_LIB_DIR}" + PUBLIC_HEADER DESTINATION "${INSTALL_INCLUDE_DIR}" + COMPONENT dev +) +if(OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRACK) + install(TARGETS uselib_track + EXPORT DarknetTargets + RUNTIME DESTINATION "${INSTALL_BIN_DIR}" + LIBRARY DESTINATION "${INSTALL_LIB_DIR}" + ARCHIVE DESTINATION "${INSTALL_LIB_DIR}" + PUBLIC_HEADER DESTINATION "${INSTALL_INCLUDE_DIR}" + COMPONENT dev + ) +endif() + +install(EXPORT DarknetTargets + FILE DarknetTargets.cmake + NAMESPACE Darknet:: + DESTINATION "${INSTALL_CMAKE_DIR}" +) + +# Export the package for use from the build-tree (this registers the build-tree with a global CMake-registry) +export(PACKAGE Darknet) + +# Create the DarknetConfig.cmake +# First of all we compute the relative path between the cmake config file and the include path +file(RELATIVE_PATH REL_INCLUDE_DIR "${INSTALL_CMAKE_DIR}" "${INSTALL_INCLUDE_DIR}") +set(CONF_INCLUDE_DIRS "${PROJECT_SOURCE_DIR}" "${PROJECT_BINARY_DIR}") +configure_file(DarknetConfig.cmake.in "${PROJECT_BINARY_DIR}/DarknetConfig.cmake" @ONLY) +set(CONF_INCLUDE_DIRS "\${Darknet_CMAKE_DIR}/${REL_INCLUDE_DIR}") +configure_file(DarknetConfig.cmake.in "${PROJECT_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/DarknetConfig.cmake" @ONLY) + +# Create the DarknetConfigVersion.cmake +include(CMakePackageConfigHelpers) +write_basic_package_version_file("${PROJECT_BINARY_DIR}/DarknetConfigVersion.cmake" + COMPATIBILITY SameMajorVersion +) + +install(FILES + "${PROJECT_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/DarknetConfig.cmake" + "${PROJECT_BINARY_DIR}/DarknetConfigVersion.cmake" + DESTINATION "${INSTALL_CMAKE_DIR}" +) diff --git a/DarknetConfig.cmake.in b/DarknetConfig.cmake.in new file mode 100644 index 00000000000..0e8956f917e --- /dev/null +++ b/DarknetConfig.cmake.in @@ -0,0 +1,33 @@ +# Config file for the Darknet package +# It defines the following variables +# Darknet_INCLUDE_DIRS - include directories for Darknet +# Darknet_LIBRARIES - libraries to link against + +# Compute paths +get_filename_component(Darknet_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH) +set(Darknet_INCLUDE_DIRS "@CONF_INCLUDE_DIRS@") + +include(CMakeFindDependencyMacro) + +find_dependency(OpenCV REQUIRED) + +if(@ENABLE_CUDA@) + find_dependency(CUDNN) +endif() + +set(CMAKE_THREAD_PREFER_PTHREAD ON) +find_dependency(Threads) + +if(@MSVC@) + find_dependency(PThreads_windows) +endif() + +if("@CMAKE_VERSION@" VERSION_GREATER "3.9.0") + find_dependency(OpenMP) +endif() + +# Our library dependencies (contains definitions for IMPORTED targets) +include("${Darknet_CMAKE_DIR}/DarknetTargets.cmake") + +# These are IMPORTED targets created by DarknetTargets.cmake +set(Darknet_LIBRARIES darklib) diff --git a/appveyor.yml b/appveyor.yml index d3713ae353f..d1257e5d1e8 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -58,10 +58,10 @@ install: - cd .. build_script: - - if [%COMPILER%]==[cygwin] cd build_debug && %CYGSH% 'cmake --build . --target install' && cd .. - - if [%COMPILER%]==[cygwin] cd build_release && %CYGSH% 'cmake --build . --target install' && cd .. - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] cd build_debug && cmake --build . --config Debug --target install && cd .. - - if [%COMPILER%]==[vs] cd build_release && cmake --build . --config Release --target install && cd .. + - if [%COMPILER%]==[cygwin] cd build_debug && %CYGSH% 'cmake --build . --target install -- -j8' && cd .. + - if [%COMPILER%]==[cygwin] cd build_release && %CYGSH% 'cmake --build . --target install -- -j8' && cd .. + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] cd build_debug && cmake --build . --config Debug --parallel 8 --target install && cd .. + - if [%COMPILER%]==[vs] cd build_release && cmake --build . --config Release --parallel 8 --target install && cd .. artifacts: - path: './darklib.lib' diff --git a/build.ps1 b/build.ps1 index 19985de38a3..e5a1e29ba69 100755 --- a/build.ps1 +++ b/build.ps1 @@ -69,14 +69,22 @@ if (Test-Path $vcpkg_path) { Set-Location build_win_release cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Release" $shared_lib .. cmake --build . --config Release --parallel ${number_of_build_workers} --target install + Remove-Item DarknetConfig.cmake + Remove-Item DarknetConfigVersion.cmake Set-Location .. +# Remove-Item -Force DarknetConfig.cmake +# Remove-Item -Force DarknetConfigVersion.cmake # DEBUG New-Item -Path .\build_win_debug -ItemType directory -Force Set-Location build_win_debug cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Debug" $shared_lib .. cmake --build . --config Debug --parallel ${number_of_build_workers} --target install + Remove-Item DarknetConfig.cmake + Remove-Item DarknetConfigVersion.cmake Set-Location .. +# Remove-Item -Force DarknetConfig.cmake +# Remove-Item -Force DarknetConfigVersion.cmake } else { # USE LOCAL PTHREAD LIB, NO VCPKG, ONLY RELEASE @@ -85,5 +93,9 @@ else { Set-Location build_win_release_novcpkg cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" $shared_lib .. cmake --build . --config Release --parallel ${number_of_build_workers} --target install + Remove-Item DarknetConfig.cmake + Remove-Item DarknetConfigVersion.cmake Set-Location .. +# Remove-Item -Force DarknetConfig.cmake +# Remove-Item -Force DarknetConfigVersion.cmake } diff --git a/build.sh b/build.sh index 6fc393025c1..b41eddfec70 100755 --- a/build.sh +++ b/build.sh @@ -2,18 +2,36 @@ number_of_build_workers=8 +if [[ "$OSTYPE" == "darwin"* ]]; then + OpenCV_DIR="/usr/local/Cellar/opencv@3/3.4.5" + additional_defines="-DOpenCV_DIR=${OpenCV_DIR}" + if [[ "$1" == "gcc" ]]; then + export CC="/usr/local/bin/gcc-8" + export CXX="/usr/local/bin/g++-8" + fi +fi + + # RELEASE mkdir -p build_release cd build_release -cmake .. -DCMAKE_BUILD_TYPE=Release +cmake .. -DCMAKE_BUILD_TYPE=Release ${additional_defines} cmake --build . --target install -- -j${number_of_build_workers} #cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ +rm DarknetConfig.cmake +rm DarknetConfigVersion.cmake cd .. +rm DarknetConfig.cmake +rm DarknetConfigVersion.cmake # DEBUG mkdir -p build_debug cd build_debug -cmake .. -DCMAKE_BUILD_TYPE=Debug +cmake .. -DCMAKE_BUILD_TYPE=Debug ${additional_defines} cmake --build . --target install -- -j${number_of_build_workers} #cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ +rm DarknetConfig.cmake +rm DarknetConfigVersion.cmake cd .. +rm DarknetConfig.cmake +rm DarknetConfigVersion.cmake diff --git a/cmake/Modules/FindCUDNN.cmake b/cmake/Modules/FindCUDNN.cmake index ef78833e9c1..04c5eed2fae 100644 --- a/cmake/Modules/FindCUDNN.cmake +++ b/cmake/Modules/FindCUDNN.cmake @@ -56,9 +56,37 @@ if(CUDNN_FOUND) else() set(CUDNN_VERSION "${CUDNN_VERSION_MAJOR}.${CUDNN_VERSION_MINOR}.${CUDNN_VERSION_PATCH}") endif() +endif() + +set(CUDNN_INCLUDE_DIRS ${CUDNN_INCLUDE_DIR}) +set(CUDNN_LIBRARIES ${CUDNN_LIBRARY}) +message(STATUS "Found cuDNN: v${CUDNN_VERSION} (include: ${CUDNN_INCLUDE_DIR}, library: ${CUDNN_LIBRARY})") +mark_as_advanced(CUDNN_ROOT_DIR CUDNN_LIBRARY CUDNN_INCLUDE_DIR) - set(CUDNN_INCLUDE_DIRS ${CUDNN_INCLUDE_DIR}) - set(CUDNN_LIBRARIES ${CUDNN_LIBRARY}) - message(STATUS "Found cuDNN: v${CUDNN_VERSION} (include: ${CUDNN_INCLUDE_DIR}, library: ${CUDNN_LIBRARY})") - mark_as_advanced(CUDNN_ROOT_DIR CUDNN_LIBRARY CUDNN_INCLUDE_DIR) +# Register imported libraries: +# 1. If we can find a Windows .dll file (or if we can find both Debug and +# Release libraries), we will set appropriate target properties for these. +# 2. However, for most systems, we will only register the import location and +# include directory. + +# Look for dlls, for Release and Debug libraries. +if(WIN32) + string( REPLACE ".lib" ".dll" CUDNN_LIBRARY_DLL "${CUDNN_LIBRARY}" ) +endif() + +if( CUDNN_FOUND AND NOT TARGET CuDNN::CuDNN ) + if( EXISTS "${CUDNN_LIBRARY_RELEASE_DLL}" ) + add_library( CuDNN::CuDNN SHARED IMPORTED ) + set_target_properties( CuDNN::CuDNN PROPERTIES + IMPORTED_LOCATION "${CUDNN_LIBRARY_DLL}" + IMPORTED_IMPLIB "${CUDNN_LIBRARY}" + INTERFACE_INCLUDE_DIRECTORIES "${CUDNN_INCLUDE_DIR}" + IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) + else() + add_library( CuDNN::CuDNN UNKNOWN IMPORTED ) + set_target_properties( CuDNN::CuDNN PROPERTIES + IMPORTED_LOCATION "${CUDNN_LIBRARY}" + INTERFACE_INCLUDE_DIRECTORIES "${CUDNN_INCLUDE_DIR}" + IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) + endif() endif() diff --git a/cmake/Modules/FindDarknet.cmake b/cmake/Modules/FindDarknet.cmake new file mode 100644 index 00000000000..8861e1a728c --- /dev/null +++ b/cmake/Modules/FindDarknet.cmake @@ -0,0 +1,43 @@ +# Distributed under the OSI-approved BSD 3-Clause License. +# Copyright Stefano Sinigardi + +#.rst: +# FindDarknet +# ------------ +# +# Find the Darknet includes and library. +# +# Result Variables +# ^^^^^^^^^^^^^^^^ +# +# This module defines the following variables: +# +# ``Darknet_FOUND`` +# True if Darknet library found +# +# ``Darknet_INCLUDE_DIR`` +# Location of Darknet headers +# +# ``Darknet_LIBRARY`` +# List of libraries to link with when using Darknet +# + +include(${CMAKE_ROOT}/Modules/FindPackageHandleStandardArgs.cmake) +include(${CMAKE_ROOT}/Modules/SelectLibraryConfigurations.cmake) + +if(NOT Darknet_INCLUDE_DIR) + find_path(Darknet_INCLUDE_DIR NAMES darknet.h PATHS darknet darknet/include) +endif() + +# Allow libraries to be set manually +if(NOT Darknet_LIBRARY) + find_library(Darknet_LIBRARY_RELEASE NAMES darklib PATHS darknet darknet/lib) + find_library(Darknet_LIBRARY_DEBUG NAMES darklibd PATHS darknet darknet/lib) + select_library_configurations(Darknet) +endif() + +set(Darknet_INCLUDE_DIRS "${Darknet_INCLUDE_DIR}") +set(Darknet_LIBRARIES "${Darknet_LIBRARY}") + +find_package_handle_standard_args(Darknet DEFAULT_MSG Darknet_LIBRARY Darknet_INCLUDE_DIR) +mark_as_advanced(Darknet_INCLUDE_DIR Darknet_INCLUDE_DIRS Darknet_LIBRARY Darknet_LIBRARIES) diff --git a/cmake/Modules/FindPThreads_windows.cmake b/cmake/Modules/FindPThreads_windows.cmake index 2b7d76a4c8f..90edc75cdcf 100644 --- a/cmake/Modules/FindPThreads_windows.cmake +++ b/cmake/Modules/FindPThreads_windows.cmake @@ -12,32 +12,32 @@ # # This module defines the following variables: # -# ``PTHREADS_FOUND`` -# True if PThreads library found +# ``PThreads_windows_FOUND`` +# True if PThreads_windows library found # -# ``PTHREADS_INCLUDE_DIR`` -# Location of PThreads headers +# ``PThreads_windows_INCLUDE_DIR`` +# Location of PThreads_windows headers # -# ``PTHREADS_LIBRARY`` -# List of libraries to link with when using PThreads +# ``PThreads_windows_LIBRARY`` +# List of libraries to link with when using PThreads_windows # include(${CMAKE_ROOT}/Modules/FindPackageHandleStandardArgs.cmake) include(${CMAKE_ROOT}/Modules/SelectLibraryConfigurations.cmake) -if(NOT PTHREADS_INCLUDE_DIR) - find_path(PTHREADS_INCLUDE_DIR NAMES pthread.h) +if(NOT PThreads_windows_INCLUDE_DIR) + find_path(PThreads_windows_INCLUDE_DIR NAMES pthread.h) endif() # Allow libraries to be set manually -if(NOT PTHREADS_LIBRARY) - find_library(PTHREADS_LIBRARY_RELEASE NAMES pthreadsVC2) - find_library(PTHREADS_LIBRARY_DEBUG NAMES pthreadsVC2d) - select_library_configurations(PTHREADS) +if(NOT PThreads_windows_LIBRARY) + find_library(PThreads_windows_LIBRARY_RELEASE NAMES pthreadsVC2) + find_library(PThreads_windows_LIBRARY_DEBUG NAMES pthreadsVC2d) + select_library_configurations(PThreads_windows) endif() -find_package_handle_standard_args(PTHREADS DEFAULT_MSG PTHREADS_LIBRARY PTHREADS_INCLUDE_DIR) -mark_as_advanced(PTHREADS_INCLUDE_DIR PTHREADS_LIBRARY) +find_package_handle_standard_args(PThreads_windows DEFAULT_MSG PThreads_windows_LIBRARY PThreads_windows_INCLUDE_DIR) +mark_as_advanced(PThreads_windows_INCLUDE_DIR PThreads_windows_LIBRARY) # Register imported libraries: @@ -48,30 +48,36 @@ mark_as_advanced(PTHREADS_INCLUDE_DIR PTHREADS_LIBRARY) # Look for dlls, for Release and Debug libraries. if(WIN32) - string( REPLACE ".lib" ".dll" PTHREADS_LIBRARY_RELEASE_DLL "${PTHREADS_LIBRARY_RELEASE}" ) - string( REPLACE ".lib" ".dll" PTHREADS_LIBRARY_DEBUG_DLL "${PTHREADS_LIBRARY_DEBUG}" ) + string( REPLACE ".lib" ".dll" PThreads_windows_LIBRARY_RELEASE_DLL "${PThreads_windows_LIBRARY_RELEASE}" ) + string( REPLACE ".lib" ".dll" PThreads_windows_LIBRARY_DEBUG_DLL "${PThreads_windows_LIBRARY_DEBUG}" ) endif() -if( PTHREADS_FOUND AND NOT TARGET PThreads::PThreads ) - if( EXISTS "${PTHREADS_LIBRARY_RELEASE_DLL}" ) - add_library( PThreads::PThreads SHARED IMPORTED ) - set_target_properties( PThreads::PThreads PROPERTIES - IMPORTED_LOCATION_RELEASE "${PTHREADS_LIBRARY_RELEASE_DLL}" - IMPORTED_IMPLIB "${PTHREADS_LIBRARY_RELEASE}" - INTERFACE_INCLUDE_DIRECTORIES "${PTHREADS_INCLUDE_DIR}" +if( PThreads_windows_FOUND AND NOT TARGET PThreads_windows::PThreads_windows ) + if( EXISTS "${PThreads_windows_LIBRARY_RELEASE_DLL}" ) + add_library( PThreads_windows::PThreads_windows SHARED IMPORTED ) + set_target_properties( PThreads_windows::PThreads_windows PROPERTIES + IMPORTED_LOCATION_RELEASE "${PThreads_windows_LIBRARY_RELEASE_DLL}" + IMPORTED_IMPLIB "${PThreads_windows_LIBRARY_RELEASE}" + INTERFACE_INCLUDE_DIRECTORIES "${PThreads_windows_INCLUDE_DIR}" IMPORTED_CONFIGURATIONS Release IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) - if( EXISTS "${PTHREADS_LIBRARY_DEBUG_DLL}" ) - set_property( TARGET PThreads::PThreads APPEND PROPERTY IMPORTED_CONFIGURATIONS Debug ) - set_target_properties( FFTW::fftw3 PROPERTIES - IMPORTED_LOCATION_DEBUG "${PTHREADS_LIBRARY_DEBUG_DLL}" - IMPORTED_IMPLIB_DEBUG "${PTHREADS_LIBRARY_DEBUG}" ) + if( EXISTS "${PThreads_windows_LIBRARY_DEBUG_DLL}" ) + set_property( TARGET PThreads_windows::PThreads_windows APPEND PROPERTY IMPORTED_CONFIGURATIONS Debug ) + set_target_properties( PThreads_windows::PThreads_windows PROPERTIES + IMPORTED_LOCATION_DEBUG "${PThreads_windows_LIBRARY_DEBUG_DLL}" + IMPORTED_IMPLIB_DEBUG "${PThreads_windows_LIBRARY_DEBUG}" ) endif() else() - add_library( PThreads::PThreads UNKNOWN IMPORTED ) - set_target_properties( PThreads::PThreads PROPERTIES - IMPORTED_LOCATION "${PTHREADS_LIBRARY}" - INTERFACE_INCLUDE_DIRECTORIES "${PTHREADS_INCLUDE_DIR}" + add_library( PThreads_windows::PThreads_windows UNKNOWN IMPORTED ) + set_target_properties( PThreads_windows::PThreads_windows PROPERTIES + IMPORTED_LOCATION_RELEASE "${PThreads_windows_LIBRARY}" + INTERFACE_INCLUDE_DIRECTORIES "${PThreads_windows_INCLUDE_DIR}" + IMPORTED_CONFIGURATIONS Release IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) + if( EXISTS "${PThreads_windows_LIBRARY_DEBUG}" ) + set_property( TARGET PThreads_windows::PThreads_windows APPEND PROPERTY IMPORTED_CONFIGURATIONS Debug ) + set_target_properties( PThreads_windows::PThreads_windows PROPERTIES + IMPORTED_LOCATION_DEBUG "${PThreads_windows_LIBRARY_DEBUG}" ) + endif() endif() endif() diff --git a/src/darknet.c b/src/darknet.c index 957614630bc..bc7a7b7d781 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -1,6 +1,9 @@ #include #include #include +#if defined(_MSC_VER) && defined(_DEBUG) +#include +#endif #include "darknet.h" #include "parser.h" From 8804479dbd1b63c2a4b2066194ac1207057d5d2d Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 5 Mar 2019 18:33:53 +0100 Subject: [PATCH 0386/1365] [appveyor] explode matrix to workaround time bomb --- appveyor.yml | 37 +++++++++++++++++++++++-------------- 1 file changed, 23 insertions(+), 14 deletions(-) diff --git a/appveyor.yml b/appveyor.yml index d1257e5d1e8..469cd9daf6d 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -10,16 +10,31 @@ environment: CYGSH: C:\cygwin64\bin\bash -c - platform: Win64 COMPILER: vs + configuration: Release VCPKG_ROOT: C:\projects\vcpkg VCPKG_DEFAULT_TRIPLET: x64-windows USE_CUDA: yes - platform: Win64 COMPILER: vs + configuration: Debug + VCPKG_ROOT: C:\projects\vcpkg + VCPKG_DEFAULT_TRIPLET: x64-windows + USE_CUDA: yes + - platform: Win64 + COMPILER: vs + configuration: Release + VCPKG_ROOT: C:\projects\vcpkg + VCPKG_DEFAULT_TRIPLET: x64-windows + USE_CUDA: no + - platform: Win64 + COMPILER: vs + configuration: Debug VCPKG_ROOT: C:\projects\vcpkg VCPKG_DEFAULT_TRIPLET: x64-windows USE_CUDA: no - platform: Win64 COMPILER: vs + configuration: Release USE_INTEGRATED_LIBS: yes install: @@ -49,26 +64,20 @@ install: - cd %WORKSPACE%\darknet\ - mkdir build_debug && cd build_debug - if [%COMPILER%]==[cygwin] %CYGSH% 'cmake .. -G "Unix Makefiles" -DCMAKE_BUILD_TYPE="Debug"' - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=%VCPKG_ROOT%\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=%VCPKG_DEFAULT_TRIPLET%" -DCMAKE_BUILD_TYPE="Debug" .. + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] if [%configuration%]==[Debug] cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=%VCPKG_ROOT%\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=%VCPKG_DEFAULT_TRIPLET%" -DCMAKE_BUILD_TYPE="Debug" .. - cd .. - mkdir build_release && cd build_release - if [%COMPILER%]==[cygwin] %CYGSH% 'cmake .. -G "Unix Makefiles" -DCMAKE_BUILD_TYPE="Release"' - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=%VCPKG_ROOT%\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=%VCPKG_DEFAULT_TRIPLET%" -DCMAKE_BUILD_TYPE="Release" .. + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] if [%configuration%]==[Release] cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=%VCPKG_ROOT%\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=%VCPKG_DEFAULT_TRIPLET%" -DCMAKE_BUILD_TYPE="Release" .. - if [%COMPILER%]==[vs] if [%USE_INTEGRATED_LIBS%]==[yes] cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" -DCMAKE_BUILD_TYPE="Release" .. - cd .. build_script: - - if [%COMPILER%]==[cygwin] cd build_debug && %CYGSH% 'cmake --build . --target install -- -j8' && cd .. - - if [%COMPILER%]==[cygwin] cd build_release && %CYGSH% 'cmake --build . --target install -- -j8' && cd .. - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] cd build_debug && cmake --build . --config Debug --parallel 8 --target install && cd .. - - if [%COMPILER%]==[vs] cd build_release && cmake --build . --config Release --parallel 8 --target install && cd .. + - if [%COMPILER%]==[cygwin] cd build_debug && %CYGSH% 'cmake --build . --target install -- -j8' && cd .. + - if [%COMPILER%]==[cygwin] cd build_release && %CYGSH% 'cmake --build . --target install -- -j8' && cd .. + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] if [%configuration%]==[Debug] cd build_debug && cmake --build . --config Debug --parallel 8 --target install && cd .. + - if [%COMPILER%]==[vs] if [%configuration%]==[Release] cd build_release && cmake --build . --config Release --parallel 8 --target install && cd .. artifacts: - - path: './darklib.lib' - name: darklib.lib - - path: './darklib.dll' - name: darklib.dll - - path: './uselib.exe' - name: uselib.exe - - path: './darknet.exe' - name: darknet.exe + - path: lib + - path: '*.exe' From 17fed7c4e254708699c905e958c76ed0cf2ed9d9 Mon Sep 17 00:00:00 2001 From: Steve Zaretti Date: Wed, 6 Mar 2019 10:19:13 +0100 Subject: [PATCH 0387/1365] Segfault if height > width ./darknet classifier train data/sort.data cfg/alexnet.cfg -dont_show alexnet 1 compute_capability = 750, cudnn_half = 1 layer filters size input output 0 conv 96 11 x11 / 4 128 x 256 x 3 -> 30 x 62 x 96 0.130 BF 1 max 3 x 3 / 2 30 x 62 x 96 -> 14 x 30 x 96 0.000 BF 2 conv 256 5 x 5 / 1 14 x 30 x 96 -> 14 x 30 x 256 0.516 BF 3 max 3 x 3 / 2 14 x 30 x 256 -> 6 x 14 x 256 0.000 BF 4 conv 384 3 x 3 / 1 6 x 14 x 256 -> 6 x 14 x 384 0.149 BF 5 conv 384 3 x 3 / 1 6 x 14 x 384 -> 6 x 14 x 384 0.223 BF 6 conv 256 3 x 3 / 1 6 x 14 x 384 -> 6 x 14 x 256 0.149 BF 7 max 3 x 3 / 2 6 x 14 x 256 -> 2 x 6 x 256 0.000 BF 8 connected 3072 -> 4096 9 dropout p = 0.50 4096 -> 4096 10 connected 4096 -> 4096 11 dropout p = 0.50 4096 -> 4096 12 connected 4096 -> 1261 13 softmax 1261 14 cost 1261 Total BFLOPS 1.167 Allocate additional workspace_size = 1940.23 MB Learning Rate: 0.01, Momentum: 0.9, Decay: 0.0005 893253 Loaded: 0.205056 seconds Segmentation fault (core dumped) This PR fix this issue: ./darknet classifier train data/sort.data cfg/alexnet.cfg -dont_show alexnet 1 compute_capability = 750, cudnn_half = 1 layer filters size input output 0 conv 96 11 x11 / 4 128 x 256 x 3 -> 30 x 62 x 96 0.130 BF 1 max 3 x 3 / 2 30 x 62 x 96 -> 14 x 30 x 96 0.000 BF 2 conv 256 5 x 5 / 1 14 x 30 x 96 -> 14 x 30 x 256 0.516 BF 3 max 3 x 3 / 2 14 x 30 x 256 -> 6 x 14 x 256 0.000 BF 4 conv 384 3 x 3 / 1 6 x 14 x 256 -> 6 x 14 x 384 0.149 BF 5 conv 384 3 x 3 / 1 6 x 14 x 384 -> 6 x 14 x 384 0.223 BF 6 conv 256 3 x 3 / 1 6 x 14 x 384 -> 6 x 14 x 256 0.149 BF 7 max 3 x 3 / 2 6 x 14 x 256 -> 2 x 6 x 256 0.000 BF 8 connected 3072 -> 4096 9 dropout p = 0.50 4096 -> 4096 10 connected 4096 -> 4096 11 dropout p = 0.50 4096 -> 4096 12 connected 4096 -> 1261 13 softmax 1261 14 cost 1261 Total BFLOPS 1.167 Allocate additional workspace_size = 1940.23 MB Learning Rate: 0.01, Momentum: 0.9, Decay: 0.0005 893253 Loaded: 0.509795 seconds 1, 0.000: 4.070450, 4.070450 avg, 0.010000 rate, 0.310202 seconds, 128 images Loaded: 0.416345 seconds 2, 0.000: 4.069521, 4.070357 avg, 0.010000 rate, 0.055622 seconds, 256 images Loaded: 0.451087 seconds I'll tell you later if training is converging. --- src/classifier.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/classifier.c b/src/classifier.c index 6207385d65e..b5c78d65a7d 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -100,7 +100,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, args.exposure = net.exposure; args.saturation = net.saturation; args.hue = net.hue; - args.size = net.w; + args.size = net.w > net.h ? net.w : net.h; args.paths = paths; args.classes = classes; From 4fc5c6e7b0e44022f9611b93e5bd5a0ccbca8c76 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 8 Mar 2019 21:48:23 +0100 Subject: [PATCH 0388/1365] improve compatibility with latest pthreadVC renaming on vcpkg --- cmake/Modules/FindPThreads_windows.cmake | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cmake/Modules/FindPThreads_windows.cmake b/cmake/Modules/FindPThreads_windows.cmake index 90edc75cdcf..2012cd1d463 100644 --- a/cmake/Modules/FindPThreads_windows.cmake +++ b/cmake/Modules/FindPThreads_windows.cmake @@ -31,8 +31,8 @@ endif() # Allow libraries to be set manually if(NOT PThreads_windows_LIBRARY) - find_library(PThreads_windows_LIBRARY_RELEASE NAMES pthreadsVC2) - find_library(PThreads_windows_LIBRARY_DEBUG NAMES pthreadsVC2d) + find_library(PThreads_windows_LIBRARY_RELEASE NAMES pthreadsVC2 pthreadVC2) + find_library(PThreads_windows_LIBRARY_DEBUG NAMES pthreadsVC2d pthreadVC2d) select_library_configurations(PThreads_windows) endif() From 5be12ce0589d822b9fb836f8db45ccedce832b85 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 13 Mar 2019 22:56:44 +0100 Subject: [PATCH 0389/1365] move Stb lib to 3rdparty folder --- .../{ => pthreads}/dll/x64/pthreadGC2.dll | Bin .../{ => pthreads}/dll/x64/pthreadVC2.dll | Bin .../{ => pthreads}/dll/x86/pthreadGC2.dll | Bin .../{ => pthreads}/dll/x86/pthreadGCE2.dll | Bin .../{ => pthreads}/dll/x86/pthreadVC2.dll | Bin .../{ => pthreads}/dll/x86/pthreadVCE2.dll | Bin .../{ => pthreads}/dll/x86/pthreadVSE2.dll | Bin 3rdparty/{ => pthreads}/include/pthread.h | 0 3rdparty/{ => pthreads}/include/sched.h | 0 3rdparty/{ => pthreads}/include/semaphore.h | 0 .../{ => pthreads}/lib/x64/libpthreadGC2.a | Bin .../{ => pthreads}/lib/x64/pthreadVC2.lib | Bin .../{ => pthreads}/lib/x86/libpthreadGC2.a | Bin .../{ => pthreads}/lib/x86/libpthreadGCE2.a | Bin .../{ => pthreads}/lib/x86/pthreadVC2.lib | Bin .../{ => pthreads}/lib/x86/pthreadVCE2.lib | Bin .../{ => pthreads}/lib/x86/pthreadVSE2.lib | Bin {src => 3rdparty/stb/include}/stb_image.h | 0 .../stb/include}/stb_image_write.h | 0 CMakeLists.txt | 15 ++++----- DarknetConfig.cmake.in | 11 +++++-- Makefile | 27 ++++++++-------- build.ps1 | 8 +---- build.sh | 14 ++++---- cmake/Modules/FindStb.cmake | 30 ++++++++++++++++++ image_yolov2.sh | 0 image_yolov3.sh | 0 json_mjpeg_streams.sh | 0 net_cam_v3.sh | 0 video_v2.sh | 0 video_yolov3.sh | 0 31 files changed, 65 insertions(+), 40 deletions(-) rename 3rdparty/{ => pthreads}/dll/x64/pthreadGC2.dll (100%) rename 3rdparty/{ => pthreads}/dll/x64/pthreadVC2.dll (100%) rename 3rdparty/{ => pthreads}/dll/x86/pthreadGC2.dll (100%) rename 3rdparty/{ => pthreads}/dll/x86/pthreadGCE2.dll (100%) rename 3rdparty/{ => pthreads}/dll/x86/pthreadVC2.dll (100%) rename 3rdparty/{ => pthreads}/dll/x86/pthreadVCE2.dll (100%) rename 3rdparty/{ => pthreads}/dll/x86/pthreadVSE2.dll (100%) rename 3rdparty/{ => pthreads}/include/pthread.h (100%) rename 3rdparty/{ => pthreads}/include/sched.h (100%) rename 3rdparty/{ => pthreads}/include/semaphore.h (100%) rename 3rdparty/{ => pthreads}/lib/x64/libpthreadGC2.a (100%) rename 3rdparty/{ => pthreads}/lib/x64/pthreadVC2.lib (100%) rename 3rdparty/{ => pthreads}/lib/x86/libpthreadGC2.a (100%) rename 3rdparty/{ => pthreads}/lib/x86/libpthreadGCE2.a (100%) rename 3rdparty/{ => pthreads}/lib/x86/pthreadVC2.lib (100%) rename 3rdparty/{ => pthreads}/lib/x86/pthreadVCE2.lib (100%) rename 3rdparty/{ => pthreads}/lib/x86/pthreadVSE2.lib (100%) rename {src => 3rdparty/stb/include}/stb_image.h (100%) rename {src => 3rdparty/stb/include}/stb_image_write.h (100%) create mode 100755 cmake/Modules/FindStb.cmake mode change 100644 => 100755 image_yolov2.sh mode change 100644 => 100755 image_yolov3.sh mode change 100644 => 100755 json_mjpeg_streams.sh mode change 100644 => 100755 net_cam_v3.sh mode change 100644 => 100755 video_v2.sh mode change 100644 => 100755 video_yolov3.sh diff --git a/3rdparty/dll/x64/pthreadGC2.dll b/3rdparty/pthreads/dll/x64/pthreadGC2.dll similarity index 100% rename from 3rdparty/dll/x64/pthreadGC2.dll rename to 3rdparty/pthreads/dll/x64/pthreadGC2.dll diff --git a/3rdparty/dll/x64/pthreadVC2.dll b/3rdparty/pthreads/dll/x64/pthreadVC2.dll similarity index 100% rename from 3rdparty/dll/x64/pthreadVC2.dll rename to 3rdparty/pthreads/dll/x64/pthreadVC2.dll diff --git a/3rdparty/dll/x86/pthreadGC2.dll b/3rdparty/pthreads/dll/x86/pthreadGC2.dll similarity index 100% rename from 3rdparty/dll/x86/pthreadGC2.dll rename to 3rdparty/pthreads/dll/x86/pthreadGC2.dll diff --git a/3rdparty/dll/x86/pthreadGCE2.dll b/3rdparty/pthreads/dll/x86/pthreadGCE2.dll similarity index 100% rename from 3rdparty/dll/x86/pthreadGCE2.dll rename to 3rdparty/pthreads/dll/x86/pthreadGCE2.dll diff --git a/3rdparty/dll/x86/pthreadVC2.dll b/3rdparty/pthreads/dll/x86/pthreadVC2.dll similarity index 100% rename from 3rdparty/dll/x86/pthreadVC2.dll rename to 3rdparty/pthreads/dll/x86/pthreadVC2.dll diff --git a/3rdparty/dll/x86/pthreadVCE2.dll b/3rdparty/pthreads/dll/x86/pthreadVCE2.dll similarity index 100% rename from 3rdparty/dll/x86/pthreadVCE2.dll rename to 3rdparty/pthreads/dll/x86/pthreadVCE2.dll diff --git a/3rdparty/dll/x86/pthreadVSE2.dll b/3rdparty/pthreads/dll/x86/pthreadVSE2.dll similarity index 100% rename from 3rdparty/dll/x86/pthreadVSE2.dll rename to 3rdparty/pthreads/dll/x86/pthreadVSE2.dll diff --git a/3rdparty/include/pthread.h b/3rdparty/pthreads/include/pthread.h similarity index 100% rename from 3rdparty/include/pthread.h rename to 3rdparty/pthreads/include/pthread.h diff --git a/3rdparty/include/sched.h b/3rdparty/pthreads/include/sched.h similarity index 100% rename from 3rdparty/include/sched.h rename to 3rdparty/pthreads/include/sched.h diff --git a/3rdparty/include/semaphore.h b/3rdparty/pthreads/include/semaphore.h similarity index 100% rename from 3rdparty/include/semaphore.h rename to 3rdparty/pthreads/include/semaphore.h diff --git a/3rdparty/lib/x64/libpthreadGC2.a b/3rdparty/pthreads/lib/x64/libpthreadGC2.a similarity index 100% rename from 3rdparty/lib/x64/libpthreadGC2.a rename to 3rdparty/pthreads/lib/x64/libpthreadGC2.a diff --git a/3rdparty/lib/x64/pthreadVC2.lib b/3rdparty/pthreads/lib/x64/pthreadVC2.lib similarity index 100% rename from 3rdparty/lib/x64/pthreadVC2.lib rename to 3rdparty/pthreads/lib/x64/pthreadVC2.lib diff --git a/3rdparty/lib/x86/libpthreadGC2.a b/3rdparty/pthreads/lib/x86/libpthreadGC2.a similarity index 100% rename from 3rdparty/lib/x86/libpthreadGC2.a rename to 3rdparty/pthreads/lib/x86/libpthreadGC2.a diff --git a/3rdparty/lib/x86/libpthreadGCE2.a b/3rdparty/pthreads/lib/x86/libpthreadGCE2.a similarity index 100% rename from 3rdparty/lib/x86/libpthreadGCE2.a rename to 3rdparty/pthreads/lib/x86/libpthreadGCE2.a diff --git a/3rdparty/lib/x86/pthreadVC2.lib b/3rdparty/pthreads/lib/x86/pthreadVC2.lib similarity index 100% rename from 3rdparty/lib/x86/pthreadVC2.lib rename to 3rdparty/pthreads/lib/x86/pthreadVC2.lib diff --git a/3rdparty/lib/x86/pthreadVCE2.lib b/3rdparty/pthreads/lib/x86/pthreadVCE2.lib similarity index 100% rename from 3rdparty/lib/x86/pthreadVCE2.lib rename to 3rdparty/pthreads/lib/x86/pthreadVCE2.lib diff --git a/3rdparty/lib/x86/pthreadVSE2.lib b/3rdparty/pthreads/lib/x86/pthreadVSE2.lib similarity index 100% rename from 3rdparty/lib/x86/pthreadVSE2.lib rename to 3rdparty/pthreads/lib/x86/pthreadVSE2.lib diff --git a/src/stb_image.h b/3rdparty/stb/include/stb_image.h similarity index 100% rename from src/stb_image.h rename to 3rdparty/stb/include/stb_image.h diff --git a/src/stb_image_write.h b/3rdparty/stb/include/stb_image_write.h similarity index 100% rename from src/stb_image_write.h rename to 3rdparty/stb/include/stb_image_write.h diff --git a/CMakeLists.txt b/CMakeLists.txt index e6e6323aba3..8c6c38a612a 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -73,14 +73,12 @@ if(ENABLE_CUDA AND NOT CUDA_COMPUTE_MODEL) endif() if(USE_INTEGRATED_LIBS) - include_directories(${CMAKE_CURRENT_LIST_DIR}/3rdparty/include) - set(PThreads_windows_INCLUDE_DIR ${CMAKE_CURRENT_LIST_DIR}/3rdparty/include) - set(PThreads_windows_LIBRARY ${CMAKE_CURRENT_LIST_DIR}/3rdparty/lib/x64/pthreadVC2.lib) + set(PThreads_windows_DIR ${CMAKE_CURRENT_LIST_DIR}/3rdparty/pthreads CACHE PATH "Path where pthreads for windows can be located") add_definitions(-D_TIMESPEC_DEFINED) endif() +set(Stb_DIR ${CMAKE_CURRENT_LIST_DIR}/3rdparty/stb/include CACHE PATH "Path where Stb image library can be located") set(CMAKE_DEBUG_POSTFIX d) - add_definitions(-DUSE_CMAKE_LIBS) if(MSVC) @@ -108,6 +106,7 @@ set(ENABLE_OPENCV "TRUE" CACHE BOOL "Enable OpenCV integration") if(ENABLE_OPENCV) find_package(OpenCV) endif() +find_package(Stb REQUIRED) set(SKIP_USELIB_TRACK "FALSE" CACHE BOOL "Skip building uselib_track" FORCE) if(OpenCV_FOUND) @@ -241,7 +240,7 @@ if(OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRACK) add_executable(uselib_track ${CMAKE_CURRENT_LIST_DIR}/src/yolo_console_dll.cpp) target_compile_definitions(uselib_track PRIVATE TRACK_OPTFLOW=1) set_target_properties(uselib_track PROPERTIES LINKER_LANGUAGE CXX) - target_include_directories(uselib_track PUBLIC $ $ $) + target_include_directories(uselib_track PUBLIC $ $ $ $) target_link_libraries(uselib_track PRIVATE ${OpenCV_LIBS}) endif() @@ -252,9 +251,9 @@ add_executable(darknet ${CMAKE_CURRENT_LIST_DIR}/src/darknet.c ${sources} ${head set_source_files_properties(${CMAKE_CURRENT_LIST_DIR}/src/darknet.c PROPERTIES LANGUAGE CXX) set_target_properties(darknet PROPERTIES LINKER_LANGUAGE CXX) -target_include_directories(darknet PUBLIC $ $ $) -target_include_directories(darklib PUBLIC $ $ $) -target_include_directories(uselib PUBLIC $ $ $) +target_include_directories(darknet PUBLIC $ $ $ $) +target_include_directories(darklib PUBLIC $ $ $ $) +target_include_directories(uselib PUBLIC $ $ $ $) if(CUDNN_FOUND) target_link_libraries(darknet PRIVATE CuDNN::CuDNN) diff --git a/DarknetConfig.cmake.in b/DarknetConfig.cmake.in index 0e8956f917e..606503f2e1f 100644 --- a/DarknetConfig.cmake.in +++ b/DarknetConfig.cmake.in @@ -6,10 +6,15 @@ # Compute paths get_filename_component(Darknet_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH) set(Darknet_INCLUDE_DIRS "@CONF_INCLUDE_DIRS@") +list(APPEND CMAKE_MODULE_PATH "${Darknet_CMAKE_DIR}") include(CMakeFindDependencyMacro) -find_dependency(OpenCV REQUIRED) +if(@ENABLE_OPENCV@) + find_dependency(OpenCV REQUIRED) +endif() + +find_dependency(Stb REQUIRED HINTS "@Stb_DIR@") if(@ENABLE_CUDA@) find_dependency(CUDNN) @@ -18,11 +23,11 @@ endif() set(CMAKE_THREAD_PREFER_PTHREAD ON) find_dependency(Threads) -if(@MSVC@) +if(MSVC) find_dependency(PThreads_windows) endif() -if("@CMAKE_VERSION@" VERSION_GREATER "3.9.0") +if("${CMAKE_VERSION}" VERSION_GREATER "3.9.0") find_dependency(OpenMP) endif() diff --git a/Makefile b/Makefile index d06e6ace4b8..dd6e6e2e35d 100644 --- a/Makefile +++ b/Makefile @@ -32,7 +32,7 @@ OS := $(shell uname) # GTX 1080, GTX 1070, GTX 1060, GTX 1050, GTX 1030, Titan Xp, Tesla P40, Tesla P4 # ARCH= -gencode arch=compute_61,code=sm_61 -gencode arch=compute_61,code=compute_61 -# GP100/Tesla P100 – DGX-1 +# GP100/Tesla P100 - DGX-1 # ARCH= -gencode arch=compute_60,code=sm_60 # For Jetson TX1, Tegra X1, DRIVE CX, DRIVE PX - uncomment: @@ -53,30 +53,30 @@ endif CC=gcc CPP=g++ -NVCC=nvcc +NVCC=nvcc OPTS=-Ofast -LDFLAGS= -lm -pthread -COMMON= -Iinclude/ +LDFLAGS= -lm -pthread +COMMON= -Iinclude/ -I3rdparty/stb/include CFLAGS=-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -ifeq ($(DEBUG), 1) +ifeq ($(DEBUG), 1) #OPTS= -O0 -g #OPTS= -Og -g COMMON+= -DDEBUG CFLAGS+= -DDEBUG else -ifeq ($(AVX), 1) +ifeq ($(AVX), 1) CFLAGS+= -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a endif endif CFLAGS+=$(OPTS) -ifeq ($(OPENCV), 1) +ifeq ($(OPENCV), 1) COMMON+= -DOPENCV CFLAGS+= -DOPENCV -LDFLAGS+= `pkg-config --libs opencv` -COMMON+= `pkg-config --cflags opencv` +LDFLAGS+= `pkg-config --libs opencv` +COMMON+= `pkg-config --cflags opencv` endif ifeq ($(OPENMP), 1) @@ -112,8 +112,8 @@ ARCH+= -gencode arch=compute_70,code=[sm_70,compute_70] endif OBJ=http_stream.o gemm.o utils.o cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o upsample_layer.o lstm_layer.o -ifeq ($(GPU), 1) -LDFLAGS+= -lstdc++ +ifeq ($(GPU), 1) +LDFLAGS+= -lstdc++ OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o endif @@ -122,12 +122,12 @@ DEPS = $(wildcard src/*.h) Makefile include/darknet.h all: obj backup results setchmod $(EXEC) $(LIBNAMESO) $(APPNAMESO) -ifeq ($(LIBSO), 1) +ifeq ($(LIBSO), 1) CFLAGS+= -fPIC $(LIBNAMESO): $(OBJS) include/yolo_v2_class.hpp src/yolo_v2_class.cpp $(CPP) -shared -std=c++11 -fvisibility=hidden -DLIB_EXPORTS $(COMMON) $(CFLAGS) $(OBJS) src/yolo_v2_class.cpp -o $@ $(LDFLAGS) - + $(APPNAMESO): $(LIBNAMESO) include/yolo_v2_class.hpp src/yolo_console_dll.cpp $(CPP) -std=c++11 $(COMMON) $(CFLAGS) -o $@ src/yolo_console_dll.cpp $(LDFLAGS) -L ./ -l:$(LIBNAMESO) endif @@ -157,4 +157,3 @@ setchmod: clean: rm -rf $(OBJS) $(EXEC) $(LIBNAMESO) $(APPNAMESO) - diff --git a/build.ps1 b/build.ps1 index e5a1e29ba69..8282a8a26a5 100755 --- a/build.ps1 +++ b/build.ps1 @@ -63,7 +63,7 @@ if (Test-Path env:CUDA_PATH) { } } -if (Test-Path $vcpkg_path) { +if ($vcpkg_path) { # RELEASE New-Item -Path .\build_win_release -ItemType directory -Force Set-Location build_win_release @@ -72,8 +72,6 @@ if (Test-Path $vcpkg_path) { Remove-Item DarknetConfig.cmake Remove-Item DarknetConfigVersion.cmake Set-Location .. -# Remove-Item -Force DarknetConfig.cmake -# Remove-Item -Force DarknetConfigVersion.cmake # DEBUG New-Item -Path .\build_win_debug -ItemType directory -Force @@ -83,8 +81,6 @@ if (Test-Path $vcpkg_path) { Remove-Item DarknetConfig.cmake Remove-Item DarknetConfigVersion.cmake Set-Location .. -# Remove-Item -Force DarknetConfig.cmake -# Remove-Item -Force DarknetConfigVersion.cmake } else { # USE LOCAL PTHREAD LIB, NO VCPKG, ONLY RELEASE @@ -96,6 +92,4 @@ else { Remove-Item DarknetConfig.cmake Remove-Item DarknetConfigVersion.cmake Set-Location .. -# Remove-Item -Force DarknetConfig.cmake -# Remove-Item -Force DarknetConfigVersion.cmake } diff --git a/build.sh b/build.sh index b41eddfec70..7ff54d91dcf 100755 --- a/build.sh +++ b/build.sh @@ -18,11 +18,10 @@ cd build_release cmake .. -DCMAKE_BUILD_TYPE=Release ${additional_defines} cmake --build . --target install -- -j${number_of_build_workers} #cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ -rm DarknetConfig.cmake -rm DarknetConfigVersion.cmake +rm -f DarknetConfig.cmake +rm -f DarknetConfigVersion.cmake cd .. -rm DarknetConfig.cmake -rm DarknetConfigVersion.cmake +cp cmake/Modules/*.cmake share/darknet # DEBUG mkdir -p build_debug @@ -30,8 +29,7 @@ cd build_debug cmake .. -DCMAKE_BUILD_TYPE=Debug ${additional_defines} cmake --build . --target install -- -j${number_of_build_workers} #cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ -rm DarknetConfig.cmake -rm DarknetConfigVersion.cmake +rm -f DarknetConfig.cmake +rm -f DarknetConfigVersion.cmake cd .. -rm DarknetConfig.cmake -rm DarknetConfigVersion.cmake +cp cmake/Modules/*.cmake share/darknet diff --git a/cmake/Modules/FindStb.cmake b/cmake/Modules/FindStb.cmake new file mode 100755 index 00000000000..33ec81da632 --- /dev/null +++ b/cmake/Modules/FindStb.cmake @@ -0,0 +1,30 @@ +# Distributed under the OSI-approved BSD 3-Clause License. +# Copyright Stefano Sinigardi + +#.rst: +# FindStb +# ------------ +# +# Find the Stb include headers. +# +# Result Variables +# ^^^^^^^^^^^^^^^^ +# +# This module defines the following variables: +# +# ``Stb_FOUND`` +# True if Stb library found +# +# ``Stb_INCLUDE_DIR`` +# Location of Stb headers +# + +include(${CMAKE_ROOT}/Modules/FindPackageHandleStandardArgs.cmake) +include(${CMAKE_ROOT}/Modules/SelectLibraryConfigurations.cmake) + +if(NOT Stb_INCLUDE_DIR) + find_path(Stb_INCLUDE_DIR NAMES stb_image.h PATHS ${Stb_DIR}) +endif() + +find_package_handle_standard_args(Stb DEFAULT_MSG Stb_INCLUDE_DIR) +mark_as_advanced(Stb_INCLUDE_DIR) diff --git a/image_yolov2.sh b/image_yolov2.sh old mode 100644 new mode 100755 diff --git a/image_yolov3.sh b/image_yolov3.sh old mode 100644 new mode 100755 diff --git a/json_mjpeg_streams.sh b/json_mjpeg_streams.sh old mode 100644 new mode 100755 diff --git a/net_cam_v3.sh b/net_cam_v3.sh old mode 100644 new mode 100755 diff --git a/video_v2.sh b/video_v2.sh old mode 100644 new mode 100755 diff --git a/video_yolov3.sh b/video_yolov3.sh old mode 100644 new mode 100755 From d6294761be89ce0eeb62d08f7ed93adff5d5d2c2 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 13 Mar 2019 23:36:13 +0100 Subject: [PATCH 0390/1365] x64 only --- .../pthreads/{dll/x64 => bin}/pthreadGC2.dll | Bin .../pthreads/{dll/x64 => bin}/pthreadVC2.dll | Bin 3rdparty/pthreads/dll/x86/pthreadGC2.dll | Bin 119888 -> 0 bytes 3rdparty/pthreads/dll/x86/pthreadGCE2.dll | Bin 121953 -> 0 bytes 3rdparty/pthreads/dll/x86/pthreadVC2.dll | Bin 55808 -> 0 bytes 3rdparty/pthreads/dll/x86/pthreadVCE2.dll | Bin 61952 -> 0 bytes 3rdparty/pthreads/dll/x86/pthreadVSE2.dll | Bin 57344 -> 0 bytes .../pthreads/lib/{x64 => }/libpthreadGC2.a | Bin 3rdparty/pthreads/lib/{x64 => }/pthreadVC2.lib | Bin 3rdparty/pthreads/lib/x86/libpthreadGC2.a | Bin 93480 -> 0 bytes 3rdparty/pthreads/lib/x86/libpthreadGCE2.a | Bin 93486 -> 0 bytes 3rdparty/pthreads/lib/x86/pthreadVC2.lib | Bin 30334 -> 0 bytes 3rdparty/pthreads/lib/x86/pthreadVCE2.lib | Bin 30460 -> 0 bytes 3rdparty/pthreads/lib/x86/pthreadVSE2.lib | Bin 30460 -> 0 bytes CMakeLists.txt | 2 +- cmake/Modules/FindPThreads_windows.cmake | 6 +++--- cmake/Modules/FindStb.cmake | 2 +- 17 files changed, 5 insertions(+), 5 deletions(-) rename 3rdparty/pthreads/{dll/x64 => bin}/pthreadGC2.dll (100%) rename 3rdparty/pthreads/{dll/x64 => bin}/pthreadVC2.dll (100%) delete mode 100644 3rdparty/pthreads/dll/x86/pthreadGC2.dll delete mode 100644 3rdparty/pthreads/dll/x86/pthreadGCE2.dll delete mode 100644 3rdparty/pthreads/dll/x86/pthreadVC2.dll delete mode 100644 3rdparty/pthreads/dll/x86/pthreadVCE2.dll delete mode 100644 3rdparty/pthreads/dll/x86/pthreadVSE2.dll rename 3rdparty/pthreads/lib/{x64 => }/libpthreadGC2.a (100%) rename 3rdparty/pthreads/lib/{x64 => }/pthreadVC2.lib (100%) delete mode 100644 3rdparty/pthreads/lib/x86/libpthreadGC2.a delete mode 100644 3rdparty/pthreads/lib/x86/libpthreadGCE2.a delete mode 100644 3rdparty/pthreads/lib/x86/pthreadVC2.lib delete mode 100644 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vU5)9#5?B~7G_HFIUi{^7-`0(IB7s$~ZdRkkpC-FtPP#8YxvjC-OOF2sJweCv diff --git a/CMakeLists.txt b/CMakeLists.txt index 8c6c38a612a..d902e82fee4 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -76,7 +76,7 @@ if(USE_INTEGRATED_LIBS) set(PThreads_windows_DIR ${CMAKE_CURRENT_LIST_DIR}/3rdparty/pthreads CACHE PATH "Path where pthreads for windows can be located") add_definitions(-D_TIMESPEC_DEFINED) endif() -set(Stb_DIR ${CMAKE_CURRENT_LIST_DIR}/3rdparty/stb/include CACHE PATH "Path where Stb image library can be located") +set(Stb_DIR ${CMAKE_CURRENT_LIST_DIR}/3rdparty/stb CACHE PATH "Path where Stb image library can be located") set(CMAKE_DEBUG_POSTFIX d) add_definitions(-DUSE_CMAKE_LIBS) diff --git a/cmake/Modules/FindPThreads_windows.cmake b/cmake/Modules/FindPThreads_windows.cmake index 2012cd1d463..29c10941fca 100644 --- a/cmake/Modules/FindPThreads_windows.cmake +++ b/cmake/Modules/FindPThreads_windows.cmake @@ -26,13 +26,13 @@ include(${CMAKE_ROOT}/Modules/FindPackageHandleStandardArgs.cmake) include(${CMAKE_ROOT}/Modules/SelectLibraryConfigurations.cmake) if(NOT PThreads_windows_INCLUDE_DIR) - find_path(PThreads_windows_INCLUDE_DIR NAMES pthread.h) + find_path(PThreads_windows_INCLUDE_DIR NAMES pthread.h PATHS ${PThreads_windows_DIR} PATH_SUFFIXES include) endif() # Allow libraries to be set manually if(NOT PThreads_windows_LIBRARY) - find_library(PThreads_windows_LIBRARY_RELEASE NAMES pthreadsVC2 pthreadVC2) - find_library(PThreads_windows_LIBRARY_DEBUG NAMES pthreadsVC2d pthreadVC2d) + find_library(PThreads_windows_LIBRARY_RELEASE NAMES pthreadsVC2 pthreadVC2 PATHS ${PThreads_windows_DIR} PATH_SUFFIXES lib) + find_library(PThreads_windows_LIBRARY_DEBUG NAMES pthreadsVC2d pthreadVC2d PATHS ${PThreads_windows_DIR} PATH_SUFFIXES lib) select_library_configurations(PThreads_windows) endif() diff --git a/cmake/Modules/FindStb.cmake b/cmake/Modules/FindStb.cmake index 33ec81da632..1db213a6578 100755 --- a/cmake/Modules/FindStb.cmake +++ b/cmake/Modules/FindStb.cmake @@ -23,7 +23,7 @@ include(${CMAKE_ROOT}/Modules/FindPackageHandleStandardArgs.cmake) include(${CMAKE_ROOT}/Modules/SelectLibraryConfigurations.cmake) if(NOT Stb_INCLUDE_DIR) - find_path(Stb_INCLUDE_DIR NAMES stb_image.h PATHS ${Stb_DIR}) + find_path(Stb_INCLUDE_DIR NAMES stb_image.h PATHS ${Stb_DIR} PATH_SUFFIXES include) endif() find_package_handle_standard_args(Stb DEFAULT_MSG Stb_INCLUDE_DIR) From a3a836f56a2eb1984ffbc7e9abf5a08351180403 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 13 Mar 2019 23:40:44 +0100 Subject: [PATCH 0391/1365] add a switch to force build with local libs --- build.ps1 | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/build.ps1 b/build.ps1 index 8282a8a26a5..1bed249c56b 100755 --- a/build.ps1 +++ b/build.ps1 @@ -2,12 +2,13 @@ $number_of_build_workers=8 #$shared_lib="-DBUILD_SHARED_LIBS:BOOL=ON" +$force_using_include_libs=$false -if (Test-Path env:VCPKG_ROOT) { +if ((Test-Path env:VCPKG_ROOT) -and -not $force_using_include_libs) { $vcpkg_path = "$env:VCPKG_ROOT" Write-Host "Found vcpkg in VCPKG_ROOT: $vcpkg_path" } -elseif (Test-Path "${env:WORKSPACE}\vcpkg") { +elseif ((Test-Path "${env:WORKSPACE}\vcpkg") -and -not $force_using_include_libs) { $vcpkg_path = "${env:WORKSPACE}\vcpkg" Write-Host "Found vcpkg in WORKSPACE\vcpkg: $vcpkg_path" } From f9085d8c2a2c2319ca6270b3a032195829f049a1 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 13 Mar 2019 23:45:01 +0100 Subject: [PATCH 0392/1365] remove unused cmake module --- cmake/Modules/FindDarknet.cmake | 43 --------------------------------- 1 file changed, 43 deletions(-) delete mode 100644 cmake/Modules/FindDarknet.cmake diff --git a/cmake/Modules/FindDarknet.cmake b/cmake/Modules/FindDarknet.cmake deleted file mode 100644 index 8861e1a728c..00000000000 --- a/cmake/Modules/FindDarknet.cmake +++ /dev/null @@ -1,43 +0,0 @@ -# Distributed under the OSI-approved BSD 3-Clause License. -# Copyright Stefano Sinigardi - -#.rst: -# FindDarknet -# ------------ -# -# Find the Darknet includes and library. -# -# Result Variables -# ^^^^^^^^^^^^^^^^ -# -# This module defines the following variables: -# -# ``Darknet_FOUND`` -# True if Darknet library found -# -# ``Darknet_INCLUDE_DIR`` -# Location of Darknet headers -# -# ``Darknet_LIBRARY`` -# List of libraries to link with when using Darknet -# - -include(${CMAKE_ROOT}/Modules/FindPackageHandleStandardArgs.cmake) -include(${CMAKE_ROOT}/Modules/SelectLibraryConfigurations.cmake) - -if(NOT Darknet_INCLUDE_DIR) - find_path(Darknet_INCLUDE_DIR NAMES darknet.h PATHS darknet darknet/include) -endif() - -# Allow libraries to be set manually -if(NOT Darknet_LIBRARY) - find_library(Darknet_LIBRARY_RELEASE NAMES darklib PATHS darknet darknet/lib) - find_library(Darknet_LIBRARY_DEBUG NAMES darklibd PATHS darknet darknet/lib) - select_library_configurations(Darknet) -endif() - -set(Darknet_INCLUDE_DIRS "${Darknet_INCLUDE_DIR}") -set(Darknet_LIBRARIES "${Darknet_LIBRARY}") - -find_package_handle_standard_args(Darknet DEFAULT_MSG Darknet_LIBRARY Darknet_INCLUDE_DIR) -mark_as_advanced(Darknet_INCLUDE_DIR Darknet_INCLUDE_DIRS Darknet_LIBRARY Darknet_LIBRARIES) From fc2996968ecaa8ce36508f4036bea082684850a1 Mon Sep 17 00:00:00 2001 From: Josh Veitch-Michaelis Date: Fri, 15 Mar 2019 21:58:53 +0000 Subject: [PATCH 0393/1365] find annotation files for tiff images --- src/utils.c | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/utils.c b/src/utils.c index b4d17c23ecf..904aa2e0727 100644 --- a/src/utils.c +++ b/src/utils.c @@ -252,6 +252,8 @@ void replace_image_to_label(const char* input_path, char* output_path) find_replace_extension(output_path, ".BMP", ".txt", output_path); find_replace_extension(output_path, ".ppm", ".txt", output_path); find_replace_extension(output_path, ".PPM", ".txt", output_path); + find_replace_extension(output_path, ".tiff", ".txt", output_path); + find_replace_extension(output_path, ".TIFF", ".txt", output_path); } float sec(clock_t clocks) From 1a3971f5f72db513f388dfa0617aa07afcb3022b Mon Sep 17 00:00:00 2001 From: Josh Veitch-Michaelis Date: Fri, 15 Mar 2019 22:01:04 +0000 Subject: [PATCH 0394/1365] alert user if annotation file doesn't exist Currently darknet will continue silently if an annotation file isn't found. This can happen when the user provides an unsupported image format and the extension is not replaced. Probably this function should be modified to change any extension to .txt, since OpenCV (for example) supports many image extensions. --- src/utils.c | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/src/utils.c b/src/utils.c index 904aa2e0727..e11b4f3b5d7 100644 --- a/src/utils.c +++ b/src/utils.c @@ -254,6 +254,13 @@ void replace_image_to_label(const char* input_path, char* output_path) find_replace_extension(output_path, ".PPM", ".txt", output_path); find_replace_extension(output_path, ".tiff", ".txt", output_path); find_replace_extension(output_path, ".TIFF", ".txt", output_path); + + // Check file ends with txt and exists: + char output_path_ext[3]; + memcpy( output_path_ext, &output_path[strlen(output_path)-3], 3); + if( strcmp("txt", output_path_ext) != 0){ + fprintf(stderr, "Failed to find valid annotation file: %s \n", output_path_ext); + } } float sec(clock_t clocks) From f73ace38fafa605d21d5c8e86f16c30f72dfb8ac Mon Sep 17 00:00:00 2001 From: Josh Veitch-Michaelis Date: Fri, 15 Mar 2019 22:10:38 +0000 Subject: [PATCH 0395/1365] fix output message --- src/utils.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/utils.c b/src/utils.c index e11b4f3b5d7..2ab20accea6 100644 --- a/src/utils.c +++ b/src/utils.c @@ -255,11 +255,11 @@ void replace_image_to_label(const char* input_path, char* output_path) find_replace_extension(output_path, ".tiff", ".txt", output_path); find_replace_extension(output_path, ".TIFF", ".txt", output_path); - // Check file ends with txt and exists: + // Check file ends with txt: char output_path_ext[3]; memcpy( output_path_ext, &output_path[strlen(output_path)-3], 3); if( strcmp("txt", output_path_ext) != 0){ - fprintf(stderr, "Failed to find valid annotation file: %s \n", output_path_ext); + fprintf(stderr, "Failed to infer label file name (check image extension is supported): %s \n", output_path); } } From 328cce0bf53c22ed2ea829a13614060bf41b47d2 Mon Sep 17 00:00:00 2001 From: Josh Veitch-Michaelis Date: Fri, 15 Mar 2019 22:51:15 +0000 Subject: [PATCH 0396/1365] cleaner check for extension --- src/utils.c | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/src/utils.c b/src/utils.c index 2ab20accea6..a2a6178ac16 100644 --- a/src/utils.c +++ b/src/utils.c @@ -256,10 +256,13 @@ void replace_image_to_label(const char* input_path, char* output_path) find_replace_extension(output_path, ".TIFF", ".txt", output_path); // Check file ends with txt: - char output_path_ext[3]; - memcpy( output_path_ext, &output_path[strlen(output_path)-3], 3); - if( strcmp("txt", output_path_ext) != 0){ - fprintf(stderr, "Failed to infer label file name (check image extension is supported): %s \n", output_path); + if(strlen(output_path) > 4) { + char *output_path_ext = output_path + strlen(output_path) - 4; + if( strcmp(".txt", output_path_ext) != 0){ + fprintf(stderr, "Failed to infer label file name (check image extension is supported): %s \n", output_path); + } + }else{ + fprintf(stderr, "Label file name is too short: %s \n", output_path); } } From 93ff87f74bcfa3bde7cc0868e57eeddb0a510c81 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sat, 16 Mar 2019 22:24:36 +0100 Subject: [PATCH 0397/1365] [win-pthreads] correctly handle static libs --- cmake/Modules/FindPThreads_windows.cmake | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cmake/Modules/FindPThreads_windows.cmake b/cmake/Modules/FindPThreads_windows.cmake index 29c10941fca..e1c4cfd25f3 100644 --- a/cmake/Modules/FindPThreads_windows.cmake +++ b/cmake/Modules/FindPThreads_windows.cmake @@ -70,7 +70,7 @@ if( PThreads_windows_FOUND AND NOT TARGET PThreads_windows::PThreads_windows ) else() add_library( PThreads_windows::PThreads_windows UNKNOWN IMPORTED ) set_target_properties( PThreads_windows::PThreads_windows PROPERTIES - IMPORTED_LOCATION_RELEASE "${PThreads_windows_LIBRARY}" + IMPORTED_LOCATION_RELEASE "${PThreads_windows_LIBRARY_RELEASE}" INTERFACE_INCLUDE_DIRECTORIES "${PThreads_windows_INCLUDE_DIR}" IMPORTED_CONFIGURATIONS Release IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) From b6e15f16561c401c993092d180198555c1360cf7 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 18 Mar 2019 02:48:52 +0300 Subject: [PATCH 0398/1365] ZED 3D Camera support added to ./uselib (yolo_console_cpp.exe) example --- Makefile | 2 +- README.md | 10 +- build/darknet/darknet.vcxproj | 6 +- build/darknet/x64/cfg/crnn.train.cfg | 10 +- build/darknet/yolo_console_dll.vcxproj | 13 +- build/darknet/yolo_cpp_dll.vcxproj | 11 +- cfg/crnn.train.cfg | 10 +- include/darknet.h | 5 +- include/yolo_v2_class.hpp | 447 ++++++++++++-- scripts/README.md | 23 +- src/activation_kernels.cu | 2 +- src/activation_layer.c | 2 +- src/activations.h | 2 +- src/avgpool_layer.c | 2 +- src/avgpool_layer.h | 2 +- src/avgpool_layer_kernels.cu | 2 +- src/blas.h | 2 +- src/blas_kernels.cu | 2 +- src/classifier.c | 2 +- src/col2im_kernels.cu | 2 +- src/connected_layer.c | 2 +- src/convolutional_kernels.cu | 9 +- src/convolutional_layer.c | 6 - src/convolutional_layer.h | 2 +- src/cost_layer.c | 2 +- src/crnn_layer.c | 22 +- src/crop_layer.c | 2 +- src/crop_layer_kernels.cu | 2 +- src/{cuda.c => dark_cuda.c} | 17 + src/{cuda.h => dark_cuda.h} | 5 +- src/darknet.c | 2 +- src/data.c | 2 +- src/deconvolutional_kernels.cu | 2 +- src/deconvolutional_layer.h | 2 +- src/detection_layer.c | 2 +- src/detector.c | 13 +- src/dropout_layer.c | 2 +- src/dropout_layer_kernels.cu | 2 +- src/gemm.c | 2 +- src/gru_layer.c | 2 +- src/http_stream.cpp | 224 ++++---- src/im2col_kernels.cu | 3 +- src/image.c | 2 +- src/layer.c | 2 +- src/local_layer.h | 2 +- src/lstm_layer.c | 2 +- src/maxpool_layer.c | 6 +- src/maxpool_layer.h | 2 +- src/maxpool_layer_kernels.cu | 2 +- src/network_kernels.cu | 5 +- src/region_layer.c | 2 +- src/reorg_layer.c | 2 +- src/reorg_layer.h | 2 +- src/reorg_old_layer.c | 2 +- src/reorg_old_layer.h | 2 +- src/rnn_layer.c | 2 +- src/route_layer.c | 2 +- src/shortcut_layer.c | 2 +- src/softmax_layer.c | 2 +- src/upsample_layer.c | 2 +- src/upsample_layer.h | 2 +- src/utils.c | 2 + src/yolo_console_dll.cpp | 767 +++++++++++++++---------- src/yolo_layer.c | 2 +- src/yolo_v2_class.cpp | 72 ++- 65 files changed, 1217 insertions(+), 555 deletions(-) rename src/{cuda.c => dark_cuda.c} (96%) rename src/{cuda.h => dark_cuda.h} (96%) diff --git a/Makefile b/Makefile index d06e6ace4b8..c59088afc1f 100644 --- a/Makefile +++ b/Makefile @@ -111,7 +111,7 @@ CFLAGS+= -DCUDNN_HALF ARCH+= -gencode arch=compute_70,code=[sm_70,compute_70] endif -OBJ=http_stream.o gemm.o utils.o cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o upsample_layer.o lstm_layer.o +OBJ=http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o upsample_layer.o lstm_layer.o ifeq ($(GPU), 1) LDFLAGS+= -lstdc++ OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o diff --git a/README.md b/README.md index 245a0f067ad..c605ce1f5e7 100644 --- a/README.md +++ b/README.md @@ -76,9 +76,9 @@ You can get cfg-files by path: `darknet/cfg/` ##### Examples of results -[![Everything Is AWESOME](http://img.youtube.com/vi/VOC3huqHrss/0.jpg)](https://www.youtube.com/watch?v=VOC3huqHrss "Everything Is AWESOME") +[![Yolo v3](http://img.youtube.com/vi/VOC3huqHrss/0.jpg)](https://www.youtube.com/watch?v=MPU2HistivI "Yolo v3") -Others: https://www.youtube.com/channel/UC7ev3hNVkx4DzZ3LO19oebg +Others: https://www.youtube.com/user/pjreddie/videos ### Improvements in this repository @@ -344,11 +344,13 @@ Training Yolo v3: 5. You should label each object on images from your dataset. Use this visual GUI-software for marking bounded boxes of objects and generating annotation files for Yolo v2 & v3: https://github.com/AlexeyAB/Yolo_mark -It will create `.txt`-file for each `.jpg`-image-file - in the same directory and with the same name, but with `.txt`-extension, and put to file: object number and object coordinates on this image, for each object in new line: ` ` +It will create `.txt`-file for each `.jpg`-image-file - in the same directory and with the same name, but with `.txt`-extension, and put to file: object number and object coordinates on this image, for each object in new line: + +` ` Where: * `` - integer object number from `0` to `(classes-1)` - * ` ` - float values relative to width and height of image, it can be equal from (0.0 to 1.0] + * ` ` - float values **relative** to width and height of image, it can be equal from `(0.0 to 1.0]` * for example: ` = / ` or ` = / ` * atention: ` ` - are center of rectangle (are not top-left corner) diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj index 1858f063ff2..d629cac5b52 100644 --- a/build/darknet/darknet.vcxproj +++ b/build/darknet/darknet.vcxproj @@ -133,7 +133,7 @@ true true C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include;$(cudnn)\include - CUDNN_HALF;OPENCV;CUDNN;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + OPENCV;CUDNN_HALF;CUDNN;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y CompileAsCpp @@ -188,8 +188,8 @@ - + @@ -251,7 +251,7 @@ - + diff --git a/build/darknet/x64/cfg/crnn.train.cfg b/build/darknet/x64/cfg/crnn.train.cfg index dcc12d748df..e0e0b54c4c9 100644 --- a/build/darknet/x64/cfg/crnn.train.cfg +++ b/build/darknet/x64/cfg/crnn.train.cfg @@ -11,8 +11,16 @@ policy=steps steps=1000,1500 scales=.1,.1 -[rnn] +try_fix_nan=1 + +[connected] +output=256 +activation=leaky + +[crnn] batch_normalize=1 +size=1 +pad=0 output = 1024 hidden=1024 activation=leaky diff --git a/build/darknet/yolo_console_dll.vcxproj b/build/darknet/yolo_console_dll.vcxproj index a965fcaabe2..c93844b71bc 100644 --- a/build/darknet/yolo_console_dll.vcxproj +++ b/build/darknet/yolo_console_dll.vcxproj @@ -51,8 +51,7 @@ MultiByte - - + @@ -93,7 +92,7 @@ _CRT_SECURE_NO_WARNINGS;_MBCS;%(PreprocessorDefinitions) - C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib + C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;$(CUDA_PATH)\lib\x64\;$(ZED_SDK_ROOT_DIR)\lib @@ -117,7 +116,7 @@ true true true - ..\..\include;C:\opencv_source\opencv\bin\install\include + ..\..\include;C:\opencv_source\opencv\bin\install\include;$(CUDA_PATH)\include;$(ZED_SDK_ROOT_DIR)\include OPENCV;_CRT_SECURE_NO_WARNINGS;_MBCS;%(PreprocessorDefinitions) Async true @@ -125,7 +124,8 @@ true true - C:\opencv_source\opencv\bin\install\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib + C:\opencv_source\opencv\bin\install\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;$(CUDA_PATH)\lib\x64\;$(ZED_SDK_ROOT_DIR)\lib + kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) @@ -138,6 +138,5 @@ - - + \ No newline at end of file diff --git a/build/darknet/yolo_cpp_dll.vcxproj b/build/darknet/yolo_cpp_dll.vcxproj index 97ff65c666c..c03bbea30f5 100644 --- a/build/darknet/yolo_cpp_dll.vcxproj +++ b/build/darknet/yolo_cpp_dll.vcxproj @@ -102,7 +102,7 @@ true $(CUDA_PATH)lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) $(OutDir)\$(TargetName)$(TargetExt) - ..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;%(AdditionalDependencies) + ..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;cuda.lib;%(AdditionalDependencies) true @@ -142,8 +142,7 @@ c++1y CompileAsCpp Default - - + OPENCV; true @@ -151,7 +150,7 @@ true true C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) - ..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;%(AdditionalDependencies) + ..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;cuda.lib;%(AdditionalDependencies) $(OutDir)\$(TargetName)$(TargetExt) @@ -192,8 +191,8 @@ - + @@ -257,7 +256,7 @@ - + diff --git a/cfg/crnn.train.cfg b/cfg/crnn.train.cfg index dcc12d748df..e0e0b54c4c9 100644 --- a/cfg/crnn.train.cfg +++ b/cfg/crnn.train.cfg @@ -11,8 +11,16 @@ policy=steps steps=1000,1500 scales=.1,.1 -[rnn] +try_fix_nan=1 + +[connected] +output=256 +activation=leaky + +[crnn] batch_normalize=1 +size=1 +pad=0 output = 1024 hidden=1024 activation=leaky diff --git a/include/darknet.h b/include/darknet.h index 8ca8b493798..0a1451e375a 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -792,10 +792,11 @@ LIB_API void free_data(data d); LIB_API pthread_t load_data(load_args args); LIB_API pthread_t load_data_in_thread(load_args args); -// cuda.h +// dark_cuda.h LIB_API void cuda_pull_array(float *x_gpu, float *x, size_t n); LIB_API void cuda_pull_array_async(float *x_gpu, float *x, size_t n); LIB_API void cuda_set_device(int n); +LIB_API void *cuda_get_context(); // utils.h LIB_API void free_ptrs(void **ptrs, int n); @@ -809,6 +810,8 @@ LIB_API metadata get_metadata(char *file); // http_stream.h +LIB_API void delete_json_sender(); +LIB_API void send_json_custom(char const* send_buf, int port, int timeout); LIB_API double get_time_point(); void start_timer(); void stop_timer(); diff --git a/include/yolo_v2_class.hpp b/include/yolo_v2_class.hpp index efe2b5f334b..f92787214ef 100644 --- a/include/yolo_v2_class.hpp +++ b/include/yolo_v2_class.hpp @@ -25,6 +25,7 @@ struct bbox_t { unsigned int obj_id; // class of object - from range [0, classes-1] unsigned int track_id; // tracking id for video (0 - untracked, 1 - inf - tracked object) unsigned int frames_counter; // counter of frames on which the object was detected + float x_3d, y_3d, z_3d; // center of object (in Meters) if ZED 3D Camera is used }; struct image_t { @@ -60,8 +61,8 @@ extern "C" LIB_API int get_device_name(int gpu, char* deviceName); class Detector { std::shared_ptr detector_gpu_ptr; std::deque> prev_bbox_vec_deque; - const int cur_gpu_id; public: + const int cur_gpu_id; float nms = .4; bool wait_stream; @@ -79,6 +80,11 @@ class Detector { LIB_API std::vector tracking_id(std::vector cur_bbox_vec, bool const change_history = true, int const frames_story = 5, int const max_dist = 40); + LIB_API void *get_cuda_context(); + + LIB_API bool send_json_http(std::vector cur_bbox_vec, std::vector obj_names, int frame_id, + std::string filename = "", int timeout = 400000, int port = 8070); + std::vector detect_resized(image_t img, int init_w, int init_h, float thresh = 0.2, bool use_mean = false) { if (img.data == NULL) @@ -115,7 +121,10 @@ class Detector { static std::shared_ptr mat_to_image(cv::Mat img_src) { cv::Mat img; - cv::cvtColor(img_src, img, cv::COLOR_RGB2BGR); + if (img_src.channels() == 4) cv::cvtColor(img_src, img, cv::COLOR_RGBA2BGR); + else if (img_src.channels() == 3) cv::cvtColor(img_src, img, cv::COLOR_RGB2BGR); + else if (img_src.channels() == 1) cv::cvtColor(img_src, img, cv::COLOR_GRAY2BGR); + else std::cerr << " Warning: img_src.channels() is not 1, 3 or 4. It is = " << img_src.channels() << std::endl; std::shared_ptr image_ptr(new image_t, [](image_t *img) { free_image(*img); delete img; }); std::shared_ptr ipl_small = std::make_shared(img); *image_ptr = ipl_to_image(ipl_small.get()); @@ -166,7 +175,7 @@ class Detector { #endif // OPENCV }; - +// -------------------------------------------------------------------------------- #if defined(TRACK_OPTFLOW) && defined(OPENCV) && defined(GPU) @@ -183,7 +192,7 @@ class Tracker_optflow { const int flow_error; - Tracker_optflow(int _gpu_id = 0, int win_size = 9, int max_level = 3, int iterations = 8000, int _flow_error = -1) : + Tracker_optflow(int _gpu_id = 0, int win_size = 15, int max_level = 3, int iterations = 8000, int _flow_error = -1) : gpu_count(cv::cuda::getCudaEnabledDeviceCount()), gpu_id(std::min(_gpu_id, gpu_count-1)), flow_error((_flow_error > 0)? _flow_error:(win_size*4)) { @@ -249,18 +258,32 @@ class Tracker_optflow { if (old_gpu_id != gpu_id) cv::cuda::setDevice(gpu_id); - if (src_mat.channels() == 3) { + if (src_mat.channels() == 1 || src_mat.channels() == 3 || src_mat.channels() == 4) { if (src_mat_gpu.cols == 0) { src_mat_gpu = cv::cuda::GpuMat(src_mat.size(), src_mat.type()); src_grey_gpu = cv::cuda::GpuMat(src_mat.size(), CV_8UC1); } - update_cur_bbox_vec(_cur_bbox_vec); + if (src_mat.channels() == 1) { + src_mat_gpu.upload(src_mat, stream); + src_mat_gpu.copyTo(src_grey_gpu); + } + else if (src_mat.channels() == 3) { + src_mat_gpu.upload(src_mat, stream); + cv::cuda::cvtColor(src_mat_gpu, src_grey_gpu, CV_BGR2GRAY, 1, stream); + } + else if (src_mat.channels() == 4) { + src_mat_gpu.upload(src_mat, stream); + cv::cuda::cvtColor(src_mat_gpu, src_grey_gpu, CV_BGRA2GRAY, 1, stream); + } + else { + std::cerr << " Warning: src_mat.channels() is not: 1, 3 or 4. It is = " << src_mat.channels() << " \n"; + return; + } - //src_grey_gpu.upload(src_mat, stream); // use BGR - src_mat_gpu.upload(src_mat, stream); - cv::cuda::cvtColor(src_mat_gpu, src_grey_gpu, CV_BGR2GRAY, 1, stream); } + update_cur_bbox_vec(_cur_bbox_vec); + if (old_gpu_id != gpu_id) cv::cuda::setDevice(old_gpu_id); } @@ -355,7 +378,7 @@ class Tracker_optflow { const int flow_error; - Tracker_optflow(int win_size = 9, int max_level = 3, int iterations = 8000, int _flow_error = -1) : + Tracker_optflow(int win_size = 15, int max_level = 3, int iterations = 8000, int _flow_error = -1) : flow_error((_flow_error > 0)? _flow_error:(win_size*4)) { sync_PyrLKOpticalFlow = cv::SparsePyrLKOpticalFlow::create(); @@ -396,12 +419,20 @@ class Tracker_optflow { void update_tracking_flow(cv::Mat new_src_mat, std::vector _cur_bbox_vec) { - if (new_src_mat.channels() == 3) { - - update_cur_bbox_vec(_cur_bbox_vec); - + if (new_src_mat.channels() == 1) { + src_grey = new_src_mat.clone(); + } + else if (new_src_mat.channels() == 3) { cv::cvtColor(new_src_mat, src_grey, CV_BGR2GRAY, 1); } + else if (new_src_mat.channels() == 4) { + cv::cvtColor(new_src_mat, src_grey, CV_BGRA2GRAY, 1); + } + else { + std::cerr << " Warning: new_src_mat.channels() is not: 1, 3 or 4. It is = " << new_src_mat.channels() << " \n"; + return; + } + update_cur_bbox_vec(_cur_bbox_vec); } @@ -416,6 +447,7 @@ class Tracker_optflow { if (src_grey.rows != dst_grey.rows || src_grey.cols != dst_grey.cols) { src_grey = dst_grey.clone(); + //std::cerr << " Warning: src_grey.rows != dst_grey.rows || src_grey.cols != dst_grey.cols \n"; return cur_bbox_vec; } @@ -611,56 +643,361 @@ class preview_boxes_t { } } }; -#endif // OPENCV -//extern "C" { -#endif // __cplusplus -/* - // C - wrappers - LIB_API void create_detector(char const* cfg_filename, char const* weight_filename, int gpu_id); - LIB_API void delete_detector(); - LIB_API bbox_t* detect_custom(image_t img, float thresh, bool use_mean, int *result_size); - LIB_API bbox_t* detect_resized(image_t img, int init_w, int init_h, float thresh, bool use_mean, int *result_size); - LIB_API bbox_t* detect(image_t img, int *result_size); - LIB_API image_t load_img(char *image_filename); - LIB_API void free_img(image_t m); +class track_kalman_t +{ + int track_id_counter; + std::chrono::steady_clock::time_point global_last_time; + float dT; -#ifdef __cplusplus -} // extern "C" +public: + int max_objects; // max objects for tracking + int min_frames; // min frames to consider an object as detected + const float max_dist; // max distance (in px) to track with the same ID + cv::Size img_size; // max value of x,y,w,h + + struct tst_t { + int track_id; + int state_id; + std::chrono::steady_clock::time_point last_time; + int detection_count; + tst_t() : track_id(-1), state_id(-1) {} + }; + std::vector track_id_state_id_time; + std::vector result_vec_pred; -static std::shared_ptr c_detector_ptr; -static std::vector c_result_vec; + struct one_kalman_t; + std::vector kalman_vec; -void create_detector(char const* cfg_filename, char const* weight_filename, int gpu_id) { - c_detector_ptr = std::make_shared(cfg_filename, weight_filename, gpu_id); -} + struct one_kalman_t + { + cv::KalmanFilter kf; + cv::Mat state; + cv::Mat meas; + int measSize, stateSize, contrSize; + + void set_delta_time(float dT) { + kf.transitionMatrix.at(2) = dT; + kf.transitionMatrix.at(9) = dT; + } -void delete_detector() { c_detector_ptr.reset(); } + void set(bbox_t box) + { + initialize_kalman(); + + kf.errorCovPre.at(0) = 1; // px + kf.errorCovPre.at(7) = 1; // px + kf.errorCovPre.at(14) = 1; + kf.errorCovPre.at(21) = 1; + kf.errorCovPre.at(28) = 1; // px + kf.errorCovPre.at(35) = 1; // px + + state.at(0) = box.x; + state.at(1) = box.y; + state.at(2) = 0; + state.at(3) = 0; + state.at(4) = box.w; + state.at(5) = box.h; + // <<<< Initialization + + kf.statePost = state; + } -bbox_t* detect_custom(image_t img, float thresh, bool use_mean, int *result_size) { - c_result_vec = static_cast(c_detector_ptr.get())->detect(img, thresh, use_mean); - *result_size = c_result_vec.size(); - return c_result_vec.data(); -} + // Kalman.correct() calculates: statePost = statePre + gain * (z(k)-measurementMatrix*statePre); + // corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k)) + void correct(bbox_t box) { + meas.at(0) = box.x; + meas.at(1) = box.y; + meas.at(2) = box.w; + meas.at(3) = box.h; + + kf.correct(meas); + + bbox_t new_box = predict(); + if (new_box.w == 0 || new_box.h == 0) { + set(box); + //std::cerr << " force set(): track_id = " << box.track_id << + // ", x = " << box.x << ", y = " << box.y << ", w = " << box.w << ", h = " << box.h << std::endl; + } + } -bbox_t* detect_resized(image_t img, int init_w, int init_h, float thresh, bool use_mean, int *result_size) { - c_result_vec = static_cast(c_detector_ptr.get())->detect_resized(img, init_w, init_h, thresh, use_mean); - *result_size = c_result_vec.size(); - return c_result_vec.data(); -} + // Kalman.predict() calculates: statePre = TransitionMatrix * statePost; + // predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k) + bbox_t predict() { + bbox_t box; + state = kf.predict(); + + box.x = state.at(0); + box.y = state.at(1); + box.w = state.at(4); + box.h = state.at(5); + return box; + } -bbox_t* detect(image_t img, int *result_size) { - return detect_custom(img, 0.24, true, result_size); -} + void initialize_kalman() + { + kf = cv::KalmanFilter(stateSize, measSize, contrSize, CV_32F); + + // Transition State Matrix A + // Note: set dT at each processing step! + // [ 1 0 dT 0 0 0 ] + // [ 0 1 0 dT 0 0 ] + // [ 0 0 1 0 0 0 ] + // [ 0 0 0 1 0 0 ] + // [ 0 0 0 0 1 0 ] + // [ 0 0 0 0 0 1 ] + cv::setIdentity(kf.transitionMatrix); + + // Measure Matrix H + // [ 1 0 0 0 0 0 ] + // [ 0 1 0 0 0 0 ] + // [ 0 0 0 0 1 0 ] + // [ 0 0 0 0 0 1 ] + kf.measurementMatrix = cv::Mat::zeros(measSize, stateSize, CV_32F); + kf.measurementMatrix.at(0) = 1.0f; + kf.measurementMatrix.at(7) = 1.0f; + kf.measurementMatrix.at(16) = 1.0f; + kf.measurementMatrix.at(23) = 1.0f; + + // Process Noise Covariance Matrix Q - result smoother with lower values (1e-2) + // [ Ex 0 0 0 0 0 ] + // [ 0 Ey 0 0 0 0 ] + // [ 0 0 Ev_x 0 0 0 ] + // [ 0 0 0 Ev_y 0 0 ] + // [ 0 0 0 0 Ew 0 ] + // [ 0 0 0 0 0 Eh ] + //cv::setIdentity(kf.processNoiseCov, cv::Scalar(1e-3)); + kf.processNoiseCov.at(0) = 1e-2; + kf.processNoiseCov.at(7) = 1e-2; + kf.processNoiseCov.at(14) = 1e-2;// 5.0f; + kf.processNoiseCov.at(21) = 1e-2;// 5.0f; + kf.processNoiseCov.at(28) = 1e-2; + kf.processNoiseCov.at(35) = 1e-2; + + // Measures Noise Covariance Matrix R - result smoother with higher values (1e-1) + cv::setIdentity(kf.measurementNoiseCov, cv::Scalar(1e-1)); + + //cv::setIdentity(kf.errorCovPost, cv::Scalar::all(1e-2)); + // <<<< Kalman Filter + + set_delta_time(0); + } -image_t load_img(char *image_filename) { - return static_cast(c_detector_ptr.get())->load_image(image_filename); -} -void free_img(image_t m) { - static_cast(c_detector_ptr.get())->free_image(m); -} + + one_kalman_t(int _stateSize = 6, int _measSize = 4, int _contrSize = 0) : + kf(_stateSize, _measSize, _contrSize, CV_32F), measSize(_measSize), stateSize(_stateSize), contrSize(_contrSize) + { + state = cv::Mat(stateSize, 1, CV_32F); // [x,y,v_x,v_y,w,h] + meas = cv::Mat(measSize, 1, CV_32F); // [z_x,z_y,z_w,z_h] + //cv::Mat procNoise(stateSize, 1, type) + // [E_x,E_y,E_v_x,E_v_y,E_w,E_h] + + initialize_kalman(); + } + }; + // ------------------------------------------ + + + + track_kalman_t(int _max_objects = 1000, int _min_frames = 3, float _max_dist = 40, cv::Size _img_size = cv::Size(10000, 10000)) : + max_objects(_max_objects), min_frames(_min_frames), max_dist(_max_dist), img_size(_img_size), + track_id_counter(0) + { + kalman_vec.resize(max_objects); + track_id_state_id_time.resize(max_objects); + result_vec_pred.resize(max_objects); + } + + float calc_dt() { + dT = std::chrono::duration(std::chrono::steady_clock::now() - global_last_time).count(); + return dT; + } + + static float get_distance(float src_x, float src_y, float dst_x, float dst_y) { + return sqrtf((src_x - dst_x)*(src_x - dst_x) + (src_y - dst_y)*(src_y - dst_y)); + } + + void clear_old_states() { + // clear old bboxes + for (size_t state_id = 0; state_id < track_id_state_id_time.size(); ++state_id) + { + float time_sec = std::chrono::duration(std::chrono::steady_clock::now() - track_id_state_id_time[state_id].last_time).count(); + float time_wait = 0.5; // 0.5 second + if (track_id_state_id_time[state_id].track_id > -1) + { + if ((result_vec_pred[state_id].x > img_size.width) || + (result_vec_pred[state_id].y > img_size.height)) + { + track_id_state_id_time[state_id].track_id = -1; + } + + if (time_sec >= time_wait || track_id_state_id_time[state_id].detection_count < 0) { + //std::cerr << " remove track_id = " << track_id_state_id_time[state_id].track_id << ", state_id = " << state_id << std::endl; + track_id_state_id_time[state_id].track_id = -1; // remove bbox + } + } + } + } + + tst_t get_state_id(bbox_t find_box, std::vector &busy_vec) + { + tst_t tst; + tst.state_id = -1; + + float min_dist = std::numeric_limits::max(); + + for (size_t i = 0; i < max_objects; ++i) + { + if (track_id_state_id_time[i].track_id > -1 && result_vec_pred[i].obj_id == find_box.obj_id && busy_vec[i] == false) + { + bbox_t pred_box = result_vec_pred[i]; + + float dist = get_distance(pred_box.x, pred_box.y, find_box.x, find_box.y); + + float movement_dist = std::max(max_dist, static_cast(std::max(pred_box.w, pred_box.h)) ); + + if ((dist < movement_dist) && (dist < min_dist)) { + min_dist = dist; + tst.state_id = i; + } + } + } + + if (tst.state_id > -1) { + track_id_state_id_time[tst.state_id].last_time = std::chrono::steady_clock::now(); + track_id_state_id_time[tst.state_id].detection_count = std::max(track_id_state_id_time[tst.state_id].detection_count + 2, 10); + tst = track_id_state_id_time[tst.state_id]; + busy_vec[tst.state_id] = true; + } + else { + //std::cerr << " Didn't find: obj_id = " << find_box.obj_id << ", x = " << find_box.x << ", y = " << find_box.y << + // ", track_id_counter = " << track_id_counter << std::endl; + } + + return tst; + } + + tst_t new_state_id(std::vector &busy_vec) + { + tst_t tst; + // find empty cell to add new track_id + auto it = std::find_if(track_id_state_id_time.begin(), track_id_state_id_time.end(), [&](tst_t &v) { return v.track_id == -1; }); + if (it != track_id_state_id_time.end()) { + it->state_id = it - track_id_state_id_time.begin(); + //it->track_id = track_id_counter++; + it->track_id = 0; + it->last_time = std::chrono::steady_clock::now(); + it->detection_count = 1; + tst = *it; + busy_vec[it->state_id] = true; + } + + return tst; + } + + std::vector find_state_ids(std::vector result_vec) + { + std::vector tst_vec(result_vec.size()); + + std::vector busy_vec(max_objects, false); + + for (size_t i = 0; i < result_vec.size(); ++i) + { + tst_t tst = get_state_id(result_vec[i], busy_vec); + int state_id = tst.state_id; + int track_id = tst.track_id; + + // if new state_id + if (state_id < 0) { + tst = new_state_id(busy_vec); + state_id = tst.state_id; + track_id = tst.track_id; + if (state_id > -1) { + kalman_vec[state_id].set(result_vec[i]); + //std::cerr << " post: "; + } + } + + //std::cerr << " track_id = " << track_id << ", state_id = " << state_id << + // ", x = " << result_vec[i].x << ", det_count = " << tst.detection_count << std::endl; + + if (state_id > -1) { + tst_vec[i] = tst; + result_vec_pred[state_id] = result_vec[i]; + result_vec_pred[state_id].track_id = track_id; + } + } + + return tst_vec; + } + + std::vector predict() + { + clear_old_states(); + std::vector result_vec; + + for (size_t i = 0; i < max_objects; ++i) + { + tst_t tst = track_id_state_id_time[i]; + if (tst.track_id > -1) { + bbox_t box = kalman_vec[i].predict(); + + result_vec_pred[i].x = box.x; + result_vec_pred[i].y = box.y; + result_vec_pred[i].w = box.w; + result_vec_pred[i].h = box.h; + + if (tst.detection_count >= min_frames) + { + if (track_id_state_id_time[i].track_id == 0) { + track_id_state_id_time[i].track_id = ++track_id_counter; + result_vec_pred[i].track_id = track_id_counter; + } + + result_vec.push_back(result_vec_pred[i]); + } + } + } + //std::cerr << " result_vec.size() = " << result_vec.size() << std::endl; + + //global_last_time = std::chrono::steady_clock::now(); + + return result_vec; + } + + + std::vector correct(std::vector result_vec) + { + calc_dt(); + clear_old_states(); + + for (size_t i = 0; i < max_objects; ++i) + track_id_state_id_time[i].detection_count--; + + std::vector tst_vec = find_state_ids(result_vec); + + for (size_t i = 0; i < tst_vec.size(); ++i) { + tst_t tst = tst_vec[i]; + int state_id = tst.state_id; + if (state_id > -1) + { + kalman_vec[state_id].set_delta_time(dT); + kalman_vec[state_id].correct(result_vec_pred[state_id]); + } + } + + result_vec = predict(); + + global_last_time = std::chrono::steady_clock::now(); + + return result_vec; + } + +}; +// ---------------------------------------------- +#endif // OPENCV #endif // __cplusplus -*/ -#endif + +#endif // YOLO_V2_CLASS_HPP diff --git a/scripts/README.md b/scripts/README.md index 0c8327f0cf1..36985f81cba 100644 --- a/scripts/README.md +++ b/scripts/README.md @@ -16,6 +16,12 @@ Cityscapes: https://www.cityscapes-dataset.com/ Object Tracking Benchmark: http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html +MOT (Multiple object tracking benchmark): https://motchallenge.net/ + +VOT (Visual object tracking): http://www.votchallenge.net/challenges.html + +FREE FLIR Thermal Dataset (infrared): https://www.flir.eu/oem/adas/adas-dataset-form/ + MARS: http://www.liangzheng.com.cn/Project/project_mars.html Market-1501: http://www.liangzheng.org/Project/project_reid.html @@ -30,7 +36,22 @@ Visual Question Answering: https://visualqa.org/download.html Large Movie Review Dataset: http://ai.stanford.edu/~amaas/data/sentiment/ +KITTI (for autonomous driving): http://www.cvlibs.net/datasets/kitti/ + +nuScenes (for autonomous driving): https://www.nuscenes.org/overview + +---- + +Wikipedia's List of datasets: https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research Other datasets (Music, Natural Images, Artificial Datasets, Faces, Text, Speech, Recommendation Systems, Misc): http://deeplearning.net/datasets/ -25 datasets: https://www.analyticsvidhya.com/blog/2018/03/comprehensive-collection-deep-learning-datasets/ \ No newline at end of file +25 datasets: https://www.analyticsvidhya.com/blog/2018/03/comprehensive-collection-deep-learning-datasets/ + +List of datasets: https://riemenschneider.hayko.at/vision/dataset/index.php + +Another list of datasets: http://homepages.inf.ed.ac.uk/rbf/CVonline/Imagedbase.htm + +Pedestrian DATASETs for Vision based Detection and Tracking: https://hemprasad.wordpress.com/2014/11/08/pedestrian-datasets-for-vision-based-detection-and-tracking/ + +TrackingNet: https://tracking-net.org/ \ No newline at end of file diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index 0144ca51345..6c9445a6e7c 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -4,7 +4,7 @@ #include "cublas_v2.h" #include "activations.h" -#include "cuda.h" +#include "dark_cuda.h" __device__ float lhtan_activate_kernel(float x) diff --git a/src/activation_layer.c b/src/activation_layer.c index 2c323b8d083..b8b5d0236c3 100644 --- a/src/activation_layer.c +++ b/src/activation_layer.c @@ -1,6 +1,6 @@ #include "activation_layer.h" #include "utils.h" -#include "cuda.h" +#include "dark_cuda.h" #include "blas.h" #include "gemm.h" diff --git a/src/activations.h b/src/activations.h index 4ecf97d711d..4a382b2df83 100644 --- a/src/activations.h +++ b/src/activations.h @@ -1,7 +1,7 @@ #ifndef ACTIVATIONS_H #define ACTIVATIONS_H #include "darknet.h" -#include "cuda.h" +#include "dark_cuda.h" #include "math.h" //typedef enum{ diff --git a/src/avgpool_layer.c b/src/avgpool_layer.c index bae5ff4df7c..20838bbddd2 100644 --- a/src/avgpool_layer.c +++ b/src/avgpool_layer.c @@ -1,5 +1,5 @@ #include "avgpool_layer.h" -#include "cuda.h" +#include "dark_cuda.h" #include avgpool_layer make_avgpool_layer(int batch, int w, int h, int c) diff --git a/src/avgpool_layer.h b/src/avgpool_layer.h index f7679aa9329..2277ec6d060 100644 --- a/src/avgpool_layer.h +++ b/src/avgpool_layer.h @@ -2,7 +2,7 @@ #define AVGPOOL_LAYER_H #include "image.h" -#include "cuda.h" +#include "dark_cuda.h" #include "layer.h" #include "network.h" diff --git a/src/avgpool_layer_kernels.cu b/src/avgpool_layer_kernels.cu index b38ba45094a..676faa69809 100644 --- a/src/avgpool_layer_kernels.cu +++ b/src/avgpool_layer_kernels.cu @@ -3,7 +3,7 @@ #include "cublas_v2.h" #include "avgpool_layer.h" -#include "cuda.h" +#include "dark_cuda.h" __global__ void forward_avgpool_layer_kernel(int n, int w, int h, int c, float *input, float *output) { diff --git a/src/blas.h b/src/blas.h index 12d4b9a3b4d..8e91fff2f76 100644 --- a/src/blas.h +++ b/src/blas.h @@ -1,7 +1,7 @@ #ifndef BLAS_H #define BLAS_H #ifdef GPU -#include "cuda.h" +#include "dark_cuda.h" #include "tree.h" #endif #ifdef __cplusplus diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 98592c81668..2070bc1f029 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -4,7 +4,7 @@ #include #include "blas.h" -#include "cuda.h" +#include "dark_cuda.h" #include "utils.h" #include "tree.h" diff --git a/src/classifier.c b/src/classifier.c index b5c78d65a7d..5471957ae04 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -5,7 +5,7 @@ #include "blas.h" #include "assert.h" #include "classifier.h" -#include "cuda.h" +#include "dark_cuda.h" #ifdef WIN32 #include #include "gettimeofday.h" diff --git a/src/col2im_kernels.cu b/src/col2im_kernels.cu index e3b1d233969..2f18a0fd3c4 100644 --- a/src/col2im_kernels.cu +++ b/src/col2im_kernels.cu @@ -3,7 +3,7 @@ #include "cublas_v2.h" #include "col2im.h" -#include "cuda.h" +#include "dark_cuda.h" // src: https://github.com/BVLC/caffe/blob/master/src/caffe/util/im2col.cu // You may also want to read: https://github.com/BVLC/caffe/blob/master/LICENSE diff --git a/src/connected_layer.c b/src/connected_layer.c index 3520e9140e8..242ab8fb106 100644 --- a/src/connected_layer.c +++ b/src/connected_layer.c @@ -2,7 +2,7 @@ #include "batchnorm_layer.h" #include "convolutional_layer.h" #include "utils.h" -#include "cuda.h" +#include "dark_cuda.h" #include "blas.h" #include "gemm.h" diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 9625ddc6f8d..8ae99710a6b 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -2,12 +2,6 @@ #include "curand.h" #include "cublas_v2.h" -#ifdef CUDNN -#ifndef USE_CMAKE_LIBS -#pragma comment(lib, "cudnn.lib") -#endif -#endif - #include "convolutional_layer.h" #include "batchnorm_layer.h" #include "gemm.h" @@ -15,7 +9,7 @@ #include "im2col.h" #include "col2im.h" #include "utils.h" -#include "cuda.h" +#include "dark_cuda.h" __global__ void binarize_kernel(float *x, int n, float *binary) @@ -598,6 +592,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) void backward_convolutional_layer_gpu(convolutional_layer l, network_state state) { + if(state.net.try_fix_nan) constrain_ongpu(l.outputs*l.batch, 1, l.delta_gpu, 1); gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); if (!l.batch_normalize) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 756fe917da6..e42e86a7663 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -8,12 +8,6 @@ #include #include -#ifdef CUDNN -#ifndef USE_CMAKE_LIBS -#pragma comment(lib, "cudnn.lib") -#endif -#endif - #ifdef AI2 #include "xnor_layer.h" #endif diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h index bad3b84e2a4..bfc1229f988 100644 --- a/src/convolutional_layer.h +++ b/src/convolutional_layer.h @@ -1,7 +1,7 @@ #ifndef CONVOLUTIONAL_LAYER_H #define CONVOLUTIONAL_LAYER_H -#include "cuda.h" +#include "dark_cuda.h" #include "image.h" #include "activations.h" #include "layer.h" diff --git a/src/cost_layer.c b/src/cost_layer.c index 33fd8572bbf..09188106dc3 100644 --- a/src/cost_layer.c +++ b/src/cost_layer.c @@ -1,6 +1,6 @@ #include "cost_layer.h" #include "utils.h" -#include "cuda.h" +#include "dark_cuda.h" #include "blas.h" #include #include diff --git a/src/crnn_layer.c b/src/crnn_layer.c index 0fc6da72750..59cac509d15 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -1,7 +1,7 @@ #include "crnn_layer.h" #include "convolutional_layer.h" #include "utils.h" -#include "cuda.h" +#include "dark_cuda.h" #include "blas.h" #include "gemm.h" @@ -268,16 +268,18 @@ void forward_crnn_layer_gpu(layer l, network_state state) layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); layer output_layer = *(l.output_layer); - /* -#ifdef CUDNN_HALF -// slow and bad - s.index = state.index; - s.net = state.net; - cuda_convert_f32_to_f16(input_layer.weights_gpu, input_layer.c*input_layer.n*input_layer.size*input_layer.size, input_layer.weights_gpu16); - cuda_convert_f32_to_f16(self_layer.weights_gpu, self_layer.c*self_layer.n*self_layer.size*self_layer.size, self_layer.weights_gpu16); - cuda_convert_f32_to_f16(output_layer.weights_gpu, output_layer.c*output_layer.n*output_layer.size*output_layer.size, output_layer.weights_gpu16); + +/* +#ifdef CUDNN_HALF // slow and bad for training + if (!state.train && state.net.cudnn_half) { + s.index = state.index; + cuda_convert_f32_to_f16(input_layer.weights_gpu, input_layer.c*input_layer.n*input_layer.size*input_layer.size, input_layer.weights_gpu16); + cuda_convert_f32_to_f16(self_layer.weights_gpu, self_layer.c*self_layer.n*self_layer.size*self_layer.size, self_layer.weights_gpu16); + cuda_convert_f32_to_f16(output_layer.weights_gpu, output_layer.c*output_layer.n*output_layer.size*output_layer.size, output_layer.weights_gpu16); + } #endif //CUDNN_HALF - */ +*/ + if (state.train) { fill_ongpu(l.outputs * l.batch * l.steps, 0, output_layer.delta_gpu, 1); diff --git a/src/crop_layer.c b/src/crop_layer.c index 258030be30c..092237f403d 100644 --- a/src/crop_layer.c +++ b/src/crop_layer.c @@ -1,5 +1,5 @@ #include "crop_layer.h" -#include "cuda.h" +#include "dark_cuda.h" #include image get_crop_image(crop_layer l) diff --git a/src/crop_layer_kernels.cu b/src/crop_layer_kernels.cu index 5b084fe5ec8..46afca6c593 100644 --- a/src/crop_layer_kernels.cu +++ b/src/crop_layer_kernels.cu @@ -4,7 +4,7 @@ #include "crop_layer.h" #include "utils.h" -#include "cuda.h" +#include "dark_cuda.h" #include "image.h" __device__ float get_pixel_kernel(float *image, int w, int h, int x, int y, int c) diff --git a/src/cuda.c b/src/dark_cuda.c similarity index 96% rename from src/cuda.c rename to src/dark_cuda.c index 9e7745e2496..42aa6fc8e83 100644 --- a/src/cuda.c +++ b/src/dark_cuda.c @@ -14,6 +14,15 @@ int gpu_index = 0; #include "assert.h" #include #include +#include + +#pragma comment(lib, "cuda.lib") + +#ifdef CUDNN +#ifndef USE_CMAKE_LIBS +#pragma comment(lib, "cudnn.lib") +#endif // USE_CMAKE_LIBS +#endif // CUDNN void cuda_set_device(int n) { @@ -30,6 +39,14 @@ int cuda_get_device() return n; } +void *cuda_get_context() +{ + CUcontext pctx; + CUresult status = cuCtxGetCurrent(&pctx); + if(status != CUDA_SUCCESS) fprintf(stderr, " Error: cuCtxGetCurrent() is failed \n"); + return (void *)pctx; +} + void check_error(cudaError_t status) { cudaError_t status2 = cudaGetLastError(); diff --git a/src/cuda.h b/src/dark_cuda.h similarity index 96% rename from src/cuda.h rename to src/dark_cuda.h index fe33a258b2e..34614dc16c0 100644 --- a/src/cuda.h +++ b/src/dark_cuda.h @@ -18,11 +18,12 @@ extern int gpu_index; #define WARP_SIZE 32 #define BLOCK_TRANSPOSE32 256 +#include #include #include #include #include -#include +//#include #ifdef CUDNN #include @@ -87,4 +88,4 @@ void cudnn_check_error_extended(cudnnStatus_t status, const char *file, int line #else // GPU //LIB_API void cuda_set_device(int n); #endif // GPU -#endif // CUDA_H +#endif // DARKCUDA_H diff --git a/src/darknet.c b/src/darknet.c index bc7a7b7d781..c10a7b2fef9 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -8,7 +8,7 @@ #include "darknet.h" #include "parser.h" #include "utils.h" -#include "cuda.h" +#include "dark_cuda.h" #include "blas.h" #include "connected_layer.h" diff --git a/src/data.c b/src/data.c index e6fa95c56b3..e364ed76741 100644 --- a/src/data.c +++ b/src/data.c @@ -1,7 +1,7 @@ #include "data.h" #include "utils.h" #include "image.h" -#include "cuda.h" +#include "dark_cuda.h" #include #include diff --git a/src/deconvolutional_kernels.cu b/src/deconvolutional_kernels.cu index b0ba1a8656c..472b2247977 100644 --- a/src/deconvolutional_kernels.cu +++ b/src/deconvolutional_kernels.cu @@ -9,7 +9,7 @@ #include "im2col.h" #include "col2im.h" #include "utils.h" -#include "cuda.h" +#include "dark_cuda.h" extern "C" void forward_deconvolutional_layer_gpu(deconvolutional_layer layer, network_state state) { diff --git a/src/deconvolutional_layer.h b/src/deconvolutional_layer.h index 2e1c4527e1e..bb15a4296f7 100644 --- a/src/deconvolutional_layer.h +++ b/src/deconvolutional_layer.h @@ -1,7 +1,7 @@ #ifndef DECONVOLUTIONAL_LAYER_H #define DECONVOLUTIONAL_LAYER_H -#include "cuda.h" +#include "dark_cuda.h" #include "image.h" #include "activations.h" #include "layer.h" diff --git a/src/detection_layer.c b/src/detection_layer.c index 60fdc90dd85..64d133f964b 100644 --- a/src/detection_layer.c +++ b/src/detection_layer.c @@ -3,7 +3,7 @@ #include "softmax_layer.h" #include "blas.h" #include "box.h" -#include "cuda.h" +#include "dark_cuda.h" #include "utils.h" #include #include diff --git a/src/detector.c b/src/detector.c index e82cc069829..96222379176 100644 --- a/src/detector.c +++ b/src/detector.c @@ -923,6 +923,11 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa printf("\n detections_count = %d, unique_truth_count = %d \n", detections_count, unique_truth_count); + int* detection_per_class_count = (int*)calloc(classes, sizeof(int)); + for (j = 0; j < detections_count; ++j) { + detection_per_class_count[detections[j].class_id]++; + } + int* truth_flags = (int*)calloc(unique_truth_count, sizeof(int)); int rank; @@ -945,7 +950,8 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa { truth_flags[d.unique_truth_index] = 1; pr[d.class_id][rank].tp++; // true-positive - } + } else + pr[d.class_id][rank].fp++; } else { pr[d.class_id][rank].fp++; // false-positive @@ -963,6 +969,10 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa if ((tp + fn) > 0) pr[i][rank].recall = (double)tp / (double)(tp + fn); else pr[i][rank].recall = 0; + + if (rank == (detections_count - 1) && detection_per_class_count[i] != (tp + fp)) { // check for last rank + printf(" class_id: %d - detections = %d, tp+fp = %d, tp = %d, fp = %d \n", i, detection_per_class_count[i], tp+fp, tp, fp); + } } } @@ -1014,6 +1024,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa free(pr); free(detections); free(truth_classes_count); + free(detection_per_class_count); fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); if (reinforcement_fd != NULL) fclose(reinforcement_fd); diff --git a/src/dropout_layer.c b/src/dropout_layer.c index 9eb22982a4b..3c2abed4a01 100644 --- a/src/dropout_layer.c +++ b/src/dropout_layer.c @@ -1,6 +1,6 @@ #include "dropout_layer.h" #include "utils.h" -#include "cuda.h" +#include "dark_cuda.h" #include #include diff --git a/src/dropout_layer_kernels.cu b/src/dropout_layer_kernels.cu index f6a93c91405..ceba0fa38bc 100644 --- a/src/dropout_layer_kernels.cu +++ b/src/dropout_layer_kernels.cu @@ -3,7 +3,7 @@ #include "cublas_v2.h" #include "dropout_layer.h" -#include "cuda.h" +#include "dark_cuda.h" #include "utils.h" __global__ void yoloswag420blazeit360noscope(float *input, int size, float *rand, float prob, float scale) diff --git a/src/gemm.c b/src/gemm.c index b110da619e2..fec1e16aaea 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -1,7 +1,7 @@ #include "gemm.h" #include "utils.h" #include "im2col.h" -#include "cuda.h" +#include "dark_cuda.h" #include #include #include diff --git a/src/gru_layer.c b/src/gru_layer.c index eac751a5739..29acdaa2027 100644 --- a/src/gru_layer.c +++ b/src/gru_layer.c @@ -1,7 +1,7 @@ #include "gru_layer.h" #include "connected_layer.h" #include "utils.h" -#include "cuda.h" +#include "dark_cuda.h" #include "blas.h" #include "gemm.h" diff --git a/src/http_stream.cpp b/src/http_stream.cpp index b911f22f2a3..e61e506db80 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -1,7 +1,8 @@ +#define _XOPEN_SOURCE #include "image.h" #include "http_stream.h" -#ifdef OPENCV + // // a single-threaded, multi client(using select), debug webserver - streaming out mjpg. // on win, _WIN32 has to be defined, must link against ws2_32.lib (socks on linux are for free) @@ -11,6 +12,8 @@ #include #include #include +#include +#include using std::cerr; using std::endl; @@ -87,24 +90,12 @@ static int close_socket(SOCKET s) { #endif // _WIN32 -#include -#include -#include -#include -#ifndef CV_VERSION_EPOCH -#include -#endif -using namespace cv; - - - -class MJPG_sender +class JSON_sender { SOCKET sock; SOCKET maxfd; fd_set master; int timeout; // master sock timeout, shutdown after timeout usec. - int quality; // jpeg compression [1..100] int close_all_sockets; int _write(int sock, char const*const s, int len) @@ -115,10 +106,9 @@ class MJPG_sender public: - MJPG_sender(int port = 0, int _timeout = 400000, int _quality = 30) + JSON_sender(int port = 0, int _timeout = 400000) : sock(INVALID_SOCKET) , timeout(_timeout) - , quality(_quality) { close_all_sockets = 0; FD_ZERO(&master); @@ -126,7 +116,7 @@ class MJPG_sender open(port); } - ~MJPG_sender() + ~JSON_sender() { close_all(); release(); @@ -143,8 +133,7 @@ class MJPG_sender void close_all() { close_all_sockets = 1; - cv::Mat tmp(cv::Size(10, 10), CV_8UC3); - write(tmp); + write("\n]"); // close JSON array } bool open(int port) @@ -165,12 +154,12 @@ class MJPG_sender #endif if (::bind(sock, (SOCKADDR*)&address, sizeof(SOCKADDR_IN)) == SOCKET_ERROR) { - cerr << "error MJPG_sender: couldn't bind sock " << sock << " to port " << port << "!" << endl; + cerr << "error JSON_sender: couldn't bind sock " << sock << " to port " << port << "!" << endl; return release(); } if (::listen(sock, 10) == SOCKET_ERROR) { - cerr << "error MJPG_sender: couldn't listen on sock " << sock << " on port " << port << " !" << endl; + cerr << "error JSON_sender: couldn't listen on sock " << sock << " on port " << port << " !" << endl; return release(); } FD_ZERO(&master); @@ -184,7 +173,7 @@ class MJPG_sender return sock != INVALID_SOCKET; } - bool write(const Mat & frame) + bool write(char const* outputbuf) { fd_set rread = master; struct timeval select_timeout = { 0, 0 }; @@ -192,14 +181,7 @@ class MJPG_sender if (::select(maxfd + 1, &rread, NULL, NULL, &select_timeout) <= 0) return true; // nothing broken, there's just noone listening - std::vector outbuf; - std::vector params; - params.push_back(IMWRITE_JPEG_QUALITY); - params.push_back(quality); - cv::imencode(".jpg", frame, outbuf, params); //REMOVED FOR COMPATIBILITY - // https://docs.opencv.org/3.4/d4/da8/group__imgcodecs.html#ga292d81be8d76901bff7988d18d2b42ac - //std::cerr << "cv::imencode call disabled!" << std::endl; - size_t outlen = outbuf.size(); + size_t outlen = strlen(outputbuf); #ifdef _WIN32 for (unsigned i = 0; iclient ? maxfd : client); FD_SET(client, &master); @@ -239,60 +221,106 @@ class MJPG_sender "Expires: 0\r\n" "Cache-Control: no-cache, private\r\n" "Pragma: no-cache\r\n" - "Content-Type: multipart/x-mixed-replace; boundary=mjpegstream\r\n" + "Content-Type: application/json\r\n" + //"Content-Type: multipart/x-mixed-replace; boundary=boundary\r\n" "\r\n", 0); - cerr << "MJPG_sender: new client " << client << endl; + _write(client, "[\n", 0); // open JSON array + int n = _write(client, outputbuf, outlen); + cerr << "JSON_sender: new client " << client << endl; } else // existing client, just stream pix { - if (close_all_sockets) { - int result = close_socket(s); - cerr << "MJPG_sender: close clinet: " << result << " \n"; - continue; - } - - char head[400]; - sprintf(head, "--mjpegstream\r\nContent-Type: image/jpeg\r\nContent-Length: %zu\r\n\r\n", outlen); - _write(s, head, 0); - int n = _write(s, (char*)(&outbuf[0]), outlen); - //cerr << "known client " << s << " " << n << endl; + //char head[400]; + // application/x-resource+json or application/x-collection+json - when you are representing REST resources and collections + // application/json or text/json or text/javascript or text/plain. + // https://stackoverflow.com/questions/477816/what-is-the-correct-json-content-type + //sprintf(head, "\r\nContent-Length: %zu\r\n\r\n", outlen); + //sprintf(head, "--boundary\r\nContent-Type: application/json\r\nContent-Length: %zu\r\n\r\n", outlen); + //_write(s, head, 0); + if (!close_all_sockets) _write(s, ", \n", 0); + int n = _write(s, outputbuf, outlen); if (n < outlen) { - cerr << "MJPG_sender: kill client " << s << endl; + cerr << "JSON_sender: kill client " << s << endl; ::shutdown(s, 2); FD_CLR(s, &master); } + + if (close_all_sockets) { + int result = close_socket(s); + cerr << "JSON_sender: close clinet: " << result << " \n"; + continue; + } } } if (close_all_sockets) { int result = close_socket(sock); - cerr << "MJPG_sender: close acceptor: " << result << " \n\n"; + cerr << "JSON_sender: close acceptor: " << result << " \n\n"; } return true; - } + } }; // ---------------------------------------- -void send_mjpeg(IplImage* ipl, int port, int timeout, int quality) +static std::unique_ptr js_ptr; +static std::mutex mtx; + +void delete_json_sender() +{ + std::lock_guard lock(mtx); + js_ptr.release(); +} + +void send_json_custom(char const* send_buf, int port, int timeout) { try { - static MJPG_sender wri(port, timeout, quality); - cv::Mat mat = cv::cvarrToMat(ipl); - wri.write(mat); - std::cout << " MJPEG-stream sent. \n"; + std::lock_guard lock(mtx); + if(!js_ptr) js_ptr.reset(new JSON_sender(port, timeout)); + + js_ptr->write(send_buf); } catch (...) { - cerr << " Error in send_mjpeg() function \n"; + cerr << " Error in send_json_custom() function \n"; + } +} + +void send_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id, int port, int timeout) +{ + try { + char *send_buf = detection_to_json(dets, nboxes, classes, names, frame_id, NULL); + + send_json_custom(send_buf, port, timeout); + std::cout << " JSON-stream sent. \n"; + + free(send_buf); + } + catch (...) { + cerr << " Error in send_json() function \n"; } } // ---------------------------------------- -class JSON_sender + +#ifdef OPENCV + +#include +#include +#include +#include +#ifndef CV_VERSION_EPOCH +#include +#endif +using namespace cv; + + + +class MJPG_sender { SOCKET sock; SOCKET maxfd; fd_set master; int timeout; // master sock timeout, shutdown after timeout usec. + int quality; // jpeg compression [1..100] int close_all_sockets; int _write(int sock, char const*const s, int len) @@ -303,9 +331,10 @@ class JSON_sender public: - JSON_sender(int port = 0, int _timeout = 400000) + MJPG_sender(int port = 0, int _timeout = 400000, int _quality = 30) : sock(INVALID_SOCKET) , timeout(_timeout) + , quality(_quality) { close_all_sockets = 0; FD_ZERO(&master); @@ -313,7 +342,7 @@ class JSON_sender open(port); } - ~JSON_sender() + ~MJPG_sender() { close_all(); release(); @@ -330,7 +359,8 @@ class JSON_sender void close_all() { close_all_sockets = 1; - write("\n]"); // close JSON array + cv::Mat tmp(cv::Size(10, 10), CV_8UC3); + write(tmp); } bool open(int port) @@ -351,12 +381,12 @@ class JSON_sender #endif if (::bind(sock, (SOCKADDR*)&address, sizeof(SOCKADDR_IN)) == SOCKET_ERROR) { - cerr << "error JSON_sender: couldn't bind sock " << sock << " to port " << port << "!" << endl; + cerr << "error MJPG_sender: couldn't bind sock " << sock << " to port " << port << "!" << endl; return release(); } if (::listen(sock, 10) == SOCKET_ERROR) { - cerr << "error JSON_sender: couldn't listen on sock " << sock << " on port " << port << " !" << endl; + cerr << "error MJPG_sender: couldn't listen on sock " << sock << " on port " << port << " !" << endl; return release(); } FD_ZERO(&master); @@ -370,7 +400,7 @@ class JSON_sender return sock != INVALID_SOCKET; } - bool write(char *outputbuf) + bool write(const Mat & frame) { fd_set rread = master; struct timeval select_timeout = { 0, 0 }; @@ -378,7 +408,14 @@ class JSON_sender if (::select(maxfd + 1, &rread, NULL, NULL, &select_timeout) <= 0) return true; // nothing broken, there's just noone listening - size_t outlen = strlen(outputbuf); + std::vector outbuf; + std::vector params; + params.push_back(IMWRITE_JPEG_QUALITY); + params.push_back(quality); + cv::imencode(".jpg", frame, outbuf, params); //REMOVED FOR COMPATIBILITY + // https://docs.opencv.org/3.4/d4/da8/group__imgcodecs.html#ga292d81be8d76901bff7988d18d2b42ac + //std::cerr << "cv::imencode call disabled!" << std::endl; + size_t outlen = outbuf.size(); #ifdef _WIN32 for (unsigned i = 0; iclient ? maxfd : client); FD_SET(client, &master); @@ -418,62 +455,52 @@ class JSON_sender "Expires: 0\r\n" "Cache-Control: no-cache, private\r\n" "Pragma: no-cache\r\n" - "Content-Type: application/json\r\n" - //"Content-Type: multipart/x-mixed-replace; boundary=boundary\r\n" + "Content-Type: multipart/x-mixed-replace; boundary=mjpegstream\r\n" "\r\n", 0); - _write(client, "[\n", 0); // open JSON array - int n = _write(client, outputbuf, outlen); - cerr << "JSON_sender: new client " << client << endl; + cerr << "MJPG_sender: new client " << client << endl; } else // existing client, just stream pix { - //char head[400]; - // application/x-resource+json or application/x-collection+json - when you are representing REST resources and collections - // application/json or text/json or text/javascript or text/plain. - // https://stackoverflow.com/questions/477816/what-is-the-correct-json-content-type - //sprintf(head, "\r\nContent-Length: %zu\r\n\r\n", outlen); - //sprintf(head, "--boundary\r\nContent-Type: application/json\r\nContent-Length: %zu\r\n\r\n", outlen); - //_write(s, head, 0); - if(!close_all_sockets) _write(s, ", \n", 0); - int n = _write(s, outputbuf, outlen); + if (close_all_sockets) { + int result = close_socket(s); + cerr << "MJPG_sender: close clinet: " << result << " \n"; + continue; + } + + char head[400]; + sprintf(head, "--mjpegstream\r\nContent-Type: image/jpeg\r\nContent-Length: %zu\r\n\r\n", outlen); + _write(s, head, 0); + int n = _write(s, (char*)(&outbuf[0]), outlen); + //cerr << "known client " << s << " " << n << endl; if (n < outlen) { - cerr << "JSON_sender: kill client " << s << endl; + cerr << "MJPG_sender: kill client " << s << endl; ::shutdown(s, 2); FD_CLR(s, &master); } - - if (close_all_sockets) { - int result = close_socket(s); - cerr << "JSON_sender: close clinet: " << result << " \n"; - continue; - } } } if (close_all_sockets) { int result = close_socket(sock); - cerr << "JSON_sender: close acceptor: " << result << " \n\n"; + cerr << "MJPG_sender: close acceptor: " << result << " \n\n"; } return true; } }; // ---------------------------------------- -void send_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id, int port, int timeout) +void send_mjpeg(IplImage* ipl, int port, int timeout, int quality) { try { - static JSON_sender js(port, timeout); - char *send_buf = detection_to_json(dets, nboxes, classes, names, frame_id, NULL); - - js.write(send_buf); - std::cout << " JSON-stream sent. \n"; - free(send_buf); + static MJPG_sender wri(port, timeout, quality); + cv::Mat mat = cv::cvarrToMat(ipl); + wri.write(mat); + std::cout << " MJPEG-stream sent. \n"; } catch (...) { - cerr << " Error in send_json() function \n"; + cerr << " Error in send_mjpeg() function \n"; } } - // ---------------------------------------- CvCapture* get_capture_video_stream(const char *path) { @@ -641,8 +668,7 @@ image load_image_resize(char *filename, int w, int h, int c, image *im) return out; } - -#endif // OPENCV +#endif // OPENCV // ----------------------------------------------------- diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index da952df5d5a..539824a9f6a 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -4,11 +4,10 @@ #include #include "im2col.h" -#include "cuda.h" +#include "dark_cuda.h" #include #include -//#include template diff --git a/src/image.c b/src/image.c index dadaa1e5a21..72b4bdfbcce 100644 --- a/src/image.c +++ b/src/image.c @@ -1,7 +1,7 @@ #include "image.h" #include "utils.h" #include "blas.h" -#include "cuda.h" +#include "dark_cuda.h" #include #ifndef _USE_MATH_DEFINES #define _USE_MATH_DEFINES diff --git a/src/layer.c b/src/layer.c index 2361c1e08ac..ae87065f4d5 100644 --- a/src/layer.c +++ b/src/layer.c @@ -1,5 +1,5 @@ #include "layer.h" -#include "cuda.h" +#include "dark_cuda.h" #include void free_layer(layer l) diff --git a/src/local_layer.h b/src/local_layer.h index 198ca362f93..8ef0dbad393 100644 --- a/src/local_layer.h +++ b/src/local_layer.h @@ -1,7 +1,7 @@ #ifndef LOCAL_LAYER_H #define LOCAL_LAYER_H -#include "cuda.h" +#include "dark_cuda.h" #include "image.h" #include "activations.h" #include "layer.h" diff --git a/src/lstm_layer.c b/src/lstm_layer.c index 2894d3d3140..bf1e303b4c5 100644 --- a/src/lstm_layer.c +++ b/src/lstm_layer.c @@ -1,7 +1,7 @@ #include "lstm_layer.h" #include "connected_layer.h" #include "utils.h" -#include "cuda.h" +#include "dark_cuda.h" #include "blas.h" #include "gemm.h" diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index cf99e619fdf..2e217459e8f 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -1,5 +1,5 @@ #include "maxpool_layer.h" -#include "cuda.h" +#include "dark_cuda.h" #include "gemm.h" #include @@ -32,8 +32,8 @@ void cudnn_maxpool_setup(layer *l) CUDNN_NOT_PROPAGATE_NAN, // CUDNN_PROPAGATE_NAN, CUDNN_NOT_PROPAGATE_NAN l->size, l->size, - 0, //l.pad, - 0, //l.pad, + l->pad/2, //0, //l.pad, + l->pad/2, //0, //l.pad, l->stride, l->stride); diff --git a/src/maxpool_layer.h b/src/maxpool_layer.h index ba6a20209f1..0a90c37699f 100644 --- a/src/maxpool_layer.h +++ b/src/maxpool_layer.h @@ -2,7 +2,7 @@ #define MAXPOOL_LAYER_H #include "image.h" -#include "cuda.h" +#include "dark_cuda.h" #include "layer.h" #include "network.h" diff --git a/src/maxpool_layer_kernels.cu b/src/maxpool_layer_kernels.cu index 29aa257cf25..36fccf322a3 100644 --- a/src/maxpool_layer_kernels.cu +++ b/src/maxpool_layer_kernels.cu @@ -3,7 +3,7 @@ #include "cublas_v2.h" #include "maxpool_layer.h" -#include "cuda.h" +#include "dark_cuda.h" __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride, int size, int pad, float *input, float *output, int *indexes) { diff --git a/src/network_kernels.cu b/src/network_kernels.cu index a40dae0a09d..298372df21b 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -1,7 +1,4 @@ -//#include "cuda_runtime.h" -//#include "curand.h" -//#include "cublas_v2.h" -#include "cuda.h" +#include "dark_cuda.h" #include #include diff --git a/src/region_layer.c b/src/region_layer.c index 3221e77ed6a..74c10e13f3e 100644 --- a/src/region_layer.c +++ b/src/region_layer.c @@ -2,7 +2,7 @@ #include "activations.h" #include "blas.h" #include "box.h" -#include "cuda.h" +#include "dark_cuda.h" #include "utils.h" #include #include diff --git a/src/reorg_layer.c b/src/reorg_layer.c index 47c5efa4046..72d0577309e 100644 --- a/src/reorg_layer.c +++ b/src/reorg_layer.c @@ -1,5 +1,5 @@ #include "reorg_layer.h" -#include "cuda.h" +#include "dark_cuda.h" #include "blas.h" #include diff --git a/src/reorg_layer.h b/src/reorg_layer.h index b6ed379cd58..f2b909307dc 100644 --- a/src/reorg_layer.h +++ b/src/reorg_layer.h @@ -2,7 +2,7 @@ #define REORG_LAYER_H #include "image.h" -#include "cuda.h" +#include "dark_cuda.h" #include "layer.h" #include "network.h" diff --git a/src/reorg_old_layer.c b/src/reorg_old_layer.c index defc034d88f..530da202094 100644 --- a/src/reorg_old_layer.c +++ b/src/reorg_old_layer.c @@ -1,5 +1,5 @@ #include "reorg_old_layer.h" -#include "cuda.h" +#include "dark_cuda.h" #include "blas.h" #include diff --git a/src/reorg_old_layer.h b/src/reorg_old_layer.h index b66769b5a65..15c61f8dc6f 100644 --- a/src/reorg_old_layer.h +++ b/src/reorg_old_layer.h @@ -2,7 +2,7 @@ #define REORG_OLD_LAYER_H #include "image.h" -#include "cuda.h" +#include "dark_cuda.h" #include "layer.h" #include "network.h" diff --git a/src/rnn_layer.c b/src/rnn_layer.c index 22aade094e0..28163d754de 100644 --- a/src/rnn_layer.c +++ b/src/rnn_layer.c @@ -1,7 +1,7 @@ #include "rnn_layer.h" #include "connected_layer.h" #include "utils.h" -#include "cuda.h" +#include "dark_cuda.h" #include "blas.h" #include "gemm.h" diff --git a/src/route_layer.c b/src/route_layer.c index 8bd6817aafa..b502fbe72b0 100644 --- a/src/route_layer.c +++ b/src/route_layer.c @@ -1,5 +1,5 @@ #include "route_layer.h" -#include "cuda.h" +#include "dark_cuda.h" #include "blas.h" #include diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index b84b72f22b2..06cd6056033 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -1,5 +1,5 @@ #include "shortcut_layer.h" -#include "cuda.h" +#include "dark_cuda.h" #include "blas.h" #include #include diff --git a/src/softmax_layer.c b/src/softmax_layer.c index 3cd607beb8b..9bbff9a028b 100644 --- a/src/softmax_layer.c +++ b/src/softmax_layer.c @@ -1,6 +1,6 @@ #include "softmax_layer.h" #include "blas.h" -#include "cuda.h" +#include "dark_cuda.h" #include "utils.h" #include "blas.h" diff --git a/src/upsample_layer.c b/src/upsample_layer.c index d31dd49e9ea..1a2783c2ddb 100644 --- a/src/upsample_layer.c +++ b/src/upsample_layer.c @@ -1,5 +1,5 @@ #include "upsample_layer.h" -#include "cuda.h" +#include "dark_cuda.h" #include "blas.h" #include diff --git a/src/upsample_layer.h b/src/upsample_layer.h index 68aff32978b..4461cb15d79 100644 --- a/src/upsample_layer.h +++ b/src/upsample_layer.h @@ -1,6 +1,6 @@ #ifndef UPSAMPLE_LAYER_H #define UPSAMPLE_LAYER_H -#include "cuda.h" +#include "dark_cuda.h" #include "layer.h" #include "network.h" diff --git a/src/utils.c b/src/utils.c index b4d17c23ecf..904aa2e0727 100644 --- a/src/utils.c +++ b/src/utils.c @@ -252,6 +252,8 @@ void replace_image_to_label(const char* input_path, char* output_path) find_replace_extension(output_path, ".BMP", ".txt", output_path); find_replace_extension(output_path, ".ppm", ".txt", output_path); find_replace_extension(output_path, ".PPM", ".txt", output_path); + find_replace_extension(output_path, ".tiff", ".txt", output_path); + find_replace_extension(output_path, ".TIFF", ".txt", output_path); } float sec(clock_t clocks) diff --git a/src/yolo_console_dll.cpp b/src/yolo_console_dll.cpp index be84f4d580a..12ab208c064 100644 --- a/src/yolo_console_dll.cpp +++ b/src/yolo_console_dll.cpp @@ -5,21 +5,146 @@ #include #include #include +#include #include -#include // std::mutex, std::unique_lock -#include // std::condition_variable +#include // std::mutex, std::unique_lock +#include // std::unordered_map -// To use tracking - uncomment the following line. Tracking is supported only by OpenCV 3.x +// It makes sense only for video-Camera (not for video-File) +// To use - uncomment the following line. Optical-flow is supported only by OpenCV 3.x - 4.x //#define TRACK_OPTFLOW +//#define GPU + +// To use 3D-stereo camera ZED - uncomment the following line. ZED_SDK should be installed. +//#define ZED_STEREO -//#include "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1\include\cuda_runtime.h" -//#pragma comment(lib, "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v9.1/lib/x64/cudart.lib") -//static std::shared_ptr device_ptr(NULL, [](void *img) { cudaDeviceReset(); }); #include "yolo_v2_class.hpp" // imported functions from DLL #ifdef OPENCV +#ifdef ZED_STEREO +#include +#pragma comment(lib, "sl_core64.lib") +#pragma comment(lib, "sl_input64.lib") +#pragma comment(lib, "sl_zed64.lib") + +float getMedian(std::vector &v) { + size_t n = v.size() / 2; + std::nth_element(v.begin(), v.begin() + n, v.end()); + return v[n]; +} + +std::vector get_3d_coordinates(std::vector bbox_vect, cv::Mat xyzrgba) +{ + bool valid_measure; + int i, j; + const int R_max = 4; + + std::vector bbox3d_vect; + + for (auto &cur_box : bbox_vect) { + + int center_i = cur_box.x + cur_box.w * 0.5f, center_j = cur_box.y + cur_box.h * 0.5f; + + std::vector x_vect, y_vect, z_vect; + for (int R = 0; R < R_max; R++) { + for (int y = -R; y <= R; y++) { + for (int x = -R; x <= R; x++) { + i = center_i + x; + j = center_j + y; + sl::float4 out(NAN, NAN, NAN, NAN); + if (i >= 0 && i < xyzrgba.cols && j >= 0 && j < xyzrgba.rows) { + cv::Vec4f &elem = xyzrgba.at(j, i); // x,y,z,w + out.x = elem[0]; + out.y = elem[1]; + out.z = elem[2]; + out.w = elem[3]; + } + valid_measure = std::isfinite(out.z); + if (valid_measure) + { + x_vect.push_back(out.x); + y_vect.push_back(out.y); + z_vect.push_back(out.z); + } + } + } + } + + if (x_vect.size() * y_vect.size() * z_vect.size() > 0) + { + cur_box.x_3d = getMedian(x_vect); + cur_box.y_3d = getMedian(y_vect); + cur_box.z_3d = getMedian(z_vect); + } + else { + cur_box.x_3d = NAN; + cur_box.y_3d = NAN; + cur_box.z_3d = NAN; + } + + bbox3d_vect.emplace_back(cur_box); + } + + return bbox3d_vect; +} + +cv::Mat slMat2cvMat(sl::Mat &input) { + // Mapping between MAT_TYPE and CV_TYPE + int cv_type = -1; + switch (input.getDataType()) { + case sl::MAT_TYPE_32F_C1: + cv_type = CV_32FC1; + break; + case sl::MAT_TYPE_32F_C2: + cv_type = CV_32FC2; + break; + case sl::MAT_TYPE_32F_C3: + cv_type = CV_32FC3; + break; + case sl::MAT_TYPE_32F_C4: + cv_type = CV_32FC4; + break; + case sl::MAT_TYPE_8U_C1: + cv_type = CV_8UC1; + break; + case sl::MAT_TYPE_8U_C2: + cv_type = CV_8UC2; + break; + case sl::MAT_TYPE_8U_C3: + cv_type = CV_8UC3; + break; + case sl::MAT_TYPE_8U_C4: + cv_type = CV_8UC4; + break; + default: + break; + } + return cv::Mat(input.getHeight(), input.getWidth(), cv_type, input.getPtr(sl::MEM_CPU)); +} + +cv::Mat zed_capture_rgb(sl::Camera &zed) { + sl::Mat left; + zed.retrieveImage(left); + return slMat2cvMat(left).clone(); +} + +cv::Mat zed_capture_3d(sl::Camera &zed) { + sl::Mat cur_cloud; + zed.retrieveMeasure(cur_cloud, sl::MEASURE_XYZ); + return slMat2cvMat(cur_cloud).clone(); +} + +static sl::Camera zed; // ZED-camera + +#else // ZED_STEREO +std::vector get_3d_coordinates(std::vector bbox_vect, cv::Mat xyzrgba) { + return bbox_vect; +} +#endif // ZED_STEREO + + #include // C++ #include #ifndef CV_VERSION_EPOCH @@ -44,139 +169,6 @@ #endif // USE_CMAKE_LIBS #endif // CV_VERSION_EPOCH -class track_kalman { -public: - cv::KalmanFilter kf; - int state_size, meas_size, contr_size; - - - track_kalman(int _state_size = 10, int _meas_size = 10, int _contr_size = 0) - : state_size(_state_size), meas_size(_meas_size), contr_size(_contr_size) - { - kf.init(state_size, meas_size, contr_size, CV_32F); - - cv::setIdentity(kf.measurementMatrix); - cv::setIdentity(kf.measurementNoiseCov, cv::Scalar::all(1e-1)); - cv::setIdentity(kf.processNoiseCov, cv::Scalar::all(1e-5)); - cv::setIdentity(kf.errorCovPost, cv::Scalar::all(1e-2)); - cv::setIdentity(kf.transitionMatrix); - } - - void set(std::vector result_vec) { - for (size_t i = 0; i < result_vec.size() && i < state_size*2; ++i) { - kf.statePost.at(i * 2 + 0) = result_vec[i].x; - kf.statePost.at(i * 2 + 1) = result_vec[i].y; - } - } - - // Kalman.correct() calculates: statePost = statePre + gain * (z(k)-measurementMatrix*statePre); - // corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k)) - std::vector correct(std::vector result_vec) { - cv::Mat measurement(meas_size, 1, CV_32F); - for (size_t i = 0; i < result_vec.size() && i < meas_size * 2; ++i) { - measurement.at(i * 2 + 0) = result_vec[i].x; - measurement.at(i * 2 + 1) = result_vec[i].y; - } - cv::Mat estimated = kf.correct(measurement); - for (size_t i = 0; i < result_vec.size() && i < meas_size * 2; ++i) { - result_vec[i].x = estimated.at(i * 2 + 0); - result_vec[i].y = estimated.at(i * 2 + 1); - } - return result_vec; - } - - // Kalman.predict() calculates: statePre = TransitionMatrix * statePost; - // predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k) - std::vector predict() { - std::vector result_vec; - cv::Mat control; - cv::Mat prediction = kf.predict(control); - for (size_t i = 0; i < prediction.rows && i < state_size * 2; ++i) { - result_vec[i].x = prediction.at(i * 2 + 0); - result_vec[i].y = prediction.at(i * 2 + 1); - } - return result_vec; - } - -}; - - - - -class extrapolate_coords_t { -public: - std::vector old_result_vec; - std::vector dx_vec, dy_vec, time_vec; - std::vector old_dx_vec, old_dy_vec; - - void new_result(std::vector new_result_vec, float new_time) { - old_dx_vec = dx_vec; - old_dy_vec = dy_vec; - if (old_dx_vec.size() != old_result_vec.size()) std::cout << "old_dx != old_res \n"; - dx_vec = std::vector(new_result_vec.size(), 0); - dy_vec = std::vector(new_result_vec.size(), 0); - update_result(new_result_vec, new_time, false); - old_result_vec = new_result_vec; - time_vec = std::vector(new_result_vec.size(), new_time); - } - - void update_result(std::vector new_result_vec, float new_time, bool update = true) { - for (size_t i = 0; i < new_result_vec.size(); ++i) { - for (size_t k = 0; k < old_result_vec.size(); ++k) { - if (old_result_vec[k].track_id == new_result_vec[i].track_id && old_result_vec[k].obj_id == new_result_vec[i].obj_id) { - float const delta_time = new_time - time_vec[k]; - if (abs(delta_time) < 1) break; - size_t index = (update) ? k : i; - float dx = ((float)new_result_vec[i].x - (float)old_result_vec[k].x) / delta_time; - float dy = ((float)new_result_vec[i].y - (float)old_result_vec[k].y) / delta_time; - float old_dx = dx, old_dy = dy; - - // if it's shaking - if (update) { - if (dx * dx_vec[i] < 0) dx = dx / 2; - if (dy * dy_vec[i] < 0) dy = dy / 2; - } else { - if (dx * old_dx_vec[k] < 0) dx = dx / 2; - if (dy * old_dy_vec[k] < 0) dy = dy / 2; - } - dx_vec[index] = dx; - dy_vec[index] = dy; - - //if (old_dx == dx && old_dy == dy) std::cout << "not shakin \n"; - //else std::cout << "shakin \n"; - - if (dx_vec[index] > 1000 || dy_vec[index] > 1000) { - //std::cout << "!!! bad dx or dy, dx = " << dx_vec[index] << ", dy = " << dy_vec[index] << - // ", delta_time = " << delta_time << ", update = " << update << std::endl; - dx_vec[index] = 0; - dy_vec[index] = 0; - } - old_result_vec[k].x = new_result_vec[i].x; - old_result_vec[k].y = new_result_vec[i].y; - time_vec[k] = new_time; - break; - } - } - } - } - - std::vector predict(float cur_time) { - std::vector result_vec = old_result_vec; - for (size_t i = 0; i < old_result_vec.size(); ++i) { - float const delta_time = cur_time - time_vec[i]; - auto &bbox = result_vec[i]; - float new_x = (float) bbox.x + dx_vec[i] * delta_time; - float new_y = (float) bbox.y + dy_vec[i] * delta_time; - if (new_x > 0) bbox.x = new_x; - else bbox.x = 0; - if (new_y > 0) bbox.y = new_y; - else bbox.y = 0; - } - return result_vec; - } - -}; - void draw_boxes(cv::Mat mat_img, std::vector result_vec, std::vector obj_names, int current_det_fps = -1, int current_cap_fps = -1) @@ -190,11 +182,22 @@ void draw_boxes(cv::Mat mat_img, std::vector result_vec, std::vector 0) obj_name += " - " + std::to_string(i.track_id); cv::Size const text_size = getTextSize(obj_name, cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, 2, 0); - int const max_width = (text_size.width > i.w + 2) ? text_size.width : (i.w + 2); - cv::rectangle(mat_img, cv::Point2f(std::max((int)i.x - 1, 0), std::max((int)i.y - 30, 0)), - cv::Point2f(std::min((int)i.x + max_width, mat_img.cols-1), std::min((int)i.y, mat_img.rows-1)), + int max_width = (text_size.width > i.w + 2) ? text_size.width : (i.w + 2); + std::string coords_3d; + if (!isnan(i.z_3d)) { + std::stringstream ss; + ss << std::fixed << std::setprecision(2) << "x:" << i.x_3d << "m y:" << i.y_3d << "m z:" << i.z_3d << "m "; + coords_3d = ss.str(); + cv::Size const text_size_3d = getTextSize(ss.str(), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, 1, 0); + int const max_width_3d = (text_size_3d.width > i.w + 2) ? text_size_3d.width : (i.w + 2); + if (max_width_3d > max_width) max_width = max_width_3d; + } + + cv::rectangle(mat_img, cv::Point2f(std::max((int)i.x - 1, 0), std::max((int)i.y - 35, 0)), + cv::Point2f(std::min((int)i.x + max_width, mat_img.cols - 1), std::min((int)i.y, mat_img.rows - 1)), color, CV_FILLED, 8, 0); - putText(mat_img, obj_name, cv::Point2f(i.x, i.y - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, cv::Scalar(0, 0, 0), 2); + putText(mat_img, obj_name, cv::Point2f(i.x, i.y - 16), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, cv::Scalar(0, 0, 0), 2); + if(!coords_3d.empty()) putText(mat_img, coords_3d, cv::Point2f(i.x, i.y-1), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cv::Scalar(0, 0, 0), 1); } } if (current_det_fps >= 0 && current_cap_fps >= 0) { @@ -205,7 +208,8 @@ void draw_boxes(cv::Mat mat_img, std::vector result_vec, std::vector const result_vec, std::vector const obj_names) { +void show_console_result(std::vector const result_vec, std::vector const obj_names, int frame_id = -1) { + if (frame_id >= 0) std::cout << " Frame: " << frame_id << std::endl; for (auto &i : result_vec) { if (obj_names.size() > i.obj_id) std::cout << obj_names[i.obj_id] << " - "; std::cout << "obj_id = " << i.obj_id << ", x = " << i.x << ", y = " << i.y @@ -223,6 +227,38 @@ std::vector objects_names_from_file(std::string const filename) { return file_lines; } +template +class send_one_replaceable_object_t { + const bool sync; + std::atomic a_ptr; +public: + + void send(T const& _obj) { + T *new_ptr = new T; + *new_ptr = _obj; + if (sync) { + while (a_ptr.load()) std::this_thread::sleep_for(std::chrono::milliseconds(3)); + } + std::unique_ptr old_ptr(a_ptr.exchange(new_ptr)); + } + + T receive() { + std::unique_ptr ptr; + do { + while(!a_ptr.load()) std::this_thread::sleep_for(std::chrono::milliseconds(3)); + ptr.reset(a_ptr.exchange(NULL)); + } while (!ptr); + T obj = *ptr; + return obj; + } + + bool is_object_present() { + return (a_ptr.load() != NULL); + } + + send_one_replaceable_object_t(bool _sync) : sync(_sync), a_ptr(NULL) + {} +}; int main(int argc, char *argv[]) { @@ -239,17 +275,23 @@ int main(int argc, char *argv[]) } else if (argc > 1) filename = argv[1]; - float const thresh = (argc > 5) ? std::stof(argv[5]) : 0.20; + float const thresh = (argc > 5) ? std::stof(argv[5]) : 0.2; Detector detector(cfg_file, weights_file); auto obj_names = objects_names_from_file(names_file); std::string out_videofile = "result.avi"; - bool const save_output_videofile = true; -#ifdef TRACK_OPTFLOW + bool const save_output_videofile = false; // true - for history + bool const send_network = false; // true - for remote detection + bool const use_kalman_filter = false; // true - for stationary camera + + bool detection_sync = true; // true - for video-file +#ifdef TRACK_OPTFLOW // for slow GPU + detection_sync = false; Tracker_optflow tracker_flow; - detector.wait_stream = true; -#endif + //detector.wait_stream = true; +#endif // TRACK_OPTFLOW + while (true) { @@ -259,187 +301,318 @@ int main(int argc, char *argv[]) try { #ifdef OPENCV - extrapolate_coords_t extrapolate_coords; - bool extrapolate_flag = false; - float cur_time_extrapolate = 0, old_time_extrapolate = 0; preview_boxes_t large_preview(100, 150, false), small_preview(50, 50, true); bool show_small_boxes = false; std::string const file_ext = filename.substr(filename.find_last_of(".") + 1); std::string const protocol = filename.substr(0, 7); if (file_ext == "avi" || file_ext == "mp4" || file_ext == "mjpg" || file_ext == "mov" || // video file - protocol == "rtmp://" || protocol == "rtsp://" || protocol == "http://" || protocol == "https:/") // video network stream + protocol == "rtmp://" || protocol == "rtsp://" || protocol == "http://" || protocol == "https:/" || // video network stream + filename == "zed_camera" || file_ext == "svo" || filename == "web_camera") // ZED stereo camera + { - cv::Mat cap_frame, cur_frame, det_frame, write_frame; - std::queue track_optflow_queue; - int passed_flow_frames = 0; - std::shared_ptr det_image; - std::vector result_vec, thread_result_vec; - detector.nms = 0.02; // comment it - if track_id is not required - std::atomic consumed, videowrite_ready; - bool exit_flag = false; - consumed = true; - videowrite_ready = true; - std::atomic fps_det_counter, fps_cap_counter; - fps_det_counter = 0; - fps_cap_counter = 0; - int current_det_fps = 0, current_cap_fps = 0; - std::thread t_detect, t_cap, t_videowrite; - std::mutex mtx; - std::condition_variable cv_detected, cv_pre_tracked; + if (protocol == "rtsp://" || protocol == "http://" || protocol == "https:/" || filename == "zed_camera" || filename == "web_camera") + detection_sync = false; + + cv::Mat cur_frame; + std::atomic fps_cap_counter(0), fps_det_counter(0); + std::atomic current_fps_cap(0), current_fps_det(0); + std::atomic exit_flag(false); std::chrono::steady_clock::time_point steady_start, steady_end; - cv::VideoCapture cap(filename); cap >> cur_frame; - int const video_fps = cap.get(CV_CAP_PROP_FPS); + int video_fps = 25; + bool use_zed_camera = false; + + track_kalman_t track_kalman; + +#ifdef ZED_STEREO + sl::InitParameters init_params; + init_params.camera_resolution = sl::RESOLUTION_HD720; + init_params.coordinate_units = sl::UNIT_METER; + //init_params.sdk_cuda_ctx = (CUcontext)detector.get_cuda_context(); + init_params.sdk_gpu_id = detector.cur_gpu_id; + init_params.camera_buffer_count_linux = 2; + if (file_ext == "svo") init_params.svo_input_filename.set(filename.c_str()); + if (filename == "zed_camera" || file_ext == "svo") { + std::cout << "ZED 3D Camera " << zed.open(init_params) << std::endl; + cur_frame = zed_capture_rgb(zed); + use_zed_camera = true; + } +#endif // ZED_STEREO + + cv::VideoCapture cap; + if (filename == "web_camera") { + cap.open(0); + video_fps = cap.get(CV_CAP_PROP_FPS); + cap >> cur_frame; + } else if (!use_zed_camera) { + cap.open(filename); + video_fps = cap.get(CV_CAP_PROP_FPS); + cap >> cur_frame; + } cv::Size const frame_size = cur_frame.size(); + //cv::Size const frame_size(cap.get(CV_CAP_PROP_FRAME_WIDTH), cap.get(CV_CAP_PROP_FRAME_HEIGHT)); + std::cout << "\n Video size: " << frame_size << std::endl; + cv::VideoWriter output_video; if (save_output_videofile) output_video.open(out_videofile, CV_FOURCC('D', 'I', 'V', 'X'), std::max(35, video_fps), frame_size, true); - while (!cur_frame.empty()) + struct detection_data_t { + cv::Mat cap_frame; + std::shared_ptr det_image; + std::vector result_vec; + cv::Mat draw_frame; + bool new_detection; + uint64_t frame_id; + bool exit_flag; + cv::Mat zed_cloud; + std::queue track_optflow_queue; + detection_data_t() : exit_flag(false), new_detection(false) {} + }; + + const bool sync = detection_sync; // sync data exchange + send_one_replaceable_object_t cap2prepare(sync), cap2draw(sync), + prepare2detect(sync), detect2draw(sync), draw2show(sync), draw2write(sync), draw2net(sync); + + std::thread t_cap, t_prepare, t_detect, t_post, t_draw, t_write, t_network; + + // capture new video-frame + if (t_cap.joinable()) t_cap.join(); + t_cap = std::thread([&]() { - // always sync - if (t_cap.joinable()) { - t_cap.join(); - ++fps_cap_counter; - cur_frame = cap_frame.clone(); - } - t_cap = std::thread([&]() { cap >> cap_frame; }); - ++cur_time_extrapolate; + uint64_t frame_id = 0; + detection_data_t detection_data; + do { + detection_data = detection_data_t(); +#ifdef ZED_STEREO + if (use_zed_camera) { + while (zed.grab() != sl::SUCCESS) std::this_thread::sleep_for(std::chrono::milliseconds(2)); + detection_data.cap_frame = zed_capture_rgb(zed); + detection_data.zed_cloud = zed_capture_3d(zed); + } + else +#endif // ZED_STEREO + { + cap >> detection_data.cap_frame; + } + fps_cap_counter++; + detection_data.frame_id = frame_id++; + if (detection_data.cap_frame.empty() || exit_flag) { + std::cout << " exit_flag: detection_data.cap_frame.size = " << detection_data.cap_frame.size() << std::endl; + detection_data.exit_flag = true; + detection_data.cap_frame = cv::Mat(frame_size, CV_8UC3); + } - // swap result bouned-boxes and input-frame - if(consumed) - { - std::unique_lock lock(mtx); - det_image = detector.mat_to_image_resize(cur_frame); - auto old_result_vec = detector.tracking_id(result_vec); - auto detected_result_vec = thread_result_vec; - result_vec = detected_result_vec; -#ifdef TRACK_OPTFLOW - // track optical flow - if (track_optflow_queue.size() > 0) { - //std::cout << "\n !!!! all = " << track_optflow_queue.size() << ", cur = " << passed_flow_frames << std::endl; - cv::Mat first_frame = track_optflow_queue.front(); - tracker_flow.update_tracking_flow(track_optflow_queue.front(), result_vec); + if (!detection_sync) { + cap2draw.send(detection_data); // skip detection + } + cap2prepare.send(detection_data); + } while (!detection_data.exit_flag); + std::cout << " t_cap exit \n"; + }); - while (track_optflow_queue.size() > 1) { - track_optflow_queue.pop(); - result_vec = tracker_flow.tracking_flow(track_optflow_queue.front(), true); - } - track_optflow_queue.pop(); - passed_flow_frames = 0; - result_vec = detector.tracking_id(result_vec); - auto tmp_result_vec = detector.tracking_id(detected_result_vec, false); - small_preview.set(first_frame, tmp_result_vec); + // pre-processing video frame (resize, convertion) + t_prepare = std::thread([&]() + { + std::shared_ptr det_image; + detection_data_t detection_data; + do { + detection_data = cap2prepare.receive(); + + det_image = detector.mat_to_image_resize(detection_data.cap_frame); + detection_data.det_image = det_image; + prepare2detect.send(detection_data); // detection + + } while (!detection_data.exit_flag); + std::cout << " t_prepare exit \n"; + }); - extrapolate_coords.new_result(tmp_result_vec, old_time_extrapolate); - old_time_extrapolate = cur_time_extrapolate; - extrapolate_coords.update_result(result_vec, cur_time_extrapolate - 1); + + // detection by Yolo + if (t_detect.joinable()) t_detect.join(); + t_detect = std::thread([&]() + { + std::shared_ptr det_image; + detection_data_t detection_data; + do { + detection_data = prepare2detect.receive(); + det_image = detection_data.det_image; + std::vector result_vec; + + if(det_image) + result_vec = detector.detect_resized(*det_image, frame_size.width, frame_size.height, thresh, true); // true + fps_det_counter++; + //std::this_thread::sleep_for(std::chrono::milliseconds(150)); + + detection_data.new_detection = true; + detection_data.result_vec = result_vec; + detect2draw.send(detection_data); + } while (!detection_data.exit_flag); + std::cout << " t_detect exit \n"; + }); + + // draw rectangles (and track objects) + t_draw = std::thread([&]() + { + std::queue track_optflow_queue; + detection_data_t detection_data; + do { + + // for Video-file + if (detection_sync) { + detection_data = detect2draw.receive(); } -#else - result_vec = detector.tracking_id(result_vec); // comment it - if track_id is not required - extrapolate_coords.new_result(result_vec, cur_time_extrapolate - 1); -#endif - // add old tracked objects - for (auto &i : old_result_vec) { - auto it = std::find_if(result_vec.begin(), result_vec.end(), - [&i](bbox_t const& b) { return b.track_id == i.track_id && b.obj_id == i.obj_id; }); - bool track_id_absent = (it == result_vec.end()); - if (track_id_absent) { - if (i.frames_counter-- > 1) - result_vec.push_back(i); + // for Video-camera + else + { + // get new Detection result if present + if (detect2draw.is_object_present()) { + cv::Mat old_cap_frame = detection_data.cap_frame; // use old captured frame + detection_data = detect2draw.receive(); + if (!old_cap_frame.empty()) detection_data.cap_frame = old_cap_frame; } + // get new Captured frame else { - it->frames_counter = std::min((unsigned)3, i.frames_counter + 1); + std::vector old_result_vec = detection_data.result_vec; // use old detections + detection_data = cap2draw.receive(); + detection_data.result_vec = old_result_vec; } } + + cv::Mat cap_frame = detection_data.cap_frame; + cv::Mat draw_frame = detection_data.cap_frame.clone(); + std::vector result_vec = detection_data.result_vec; + #ifdef TRACK_OPTFLOW - tracker_flow.update_cur_bbox_vec(result_vec); - result_vec = tracker_flow.tracking_flow(cur_frame, true); // track optical flow -#endif - consumed = false; - cv_pre_tracked.notify_all(); - } - // launch thread once - Detection - if (!t_detect.joinable()) { - t_detect = std::thread([&]() { - auto current_image = det_image; - consumed = true; - while (current_image.use_count() > 0 && !exit_flag) { - auto result = detector.detect_resized(*current_image, frame_size.width, frame_size.height, - thresh, false); // true - ++fps_det_counter; - std::unique_lock lock(mtx); - thread_result_vec = result; - consumed = true; - cv_detected.notify_all(); - if (detector.wait_stream) { - while (consumed && !exit_flag) cv_pre_tracked.wait(lock); - } - current_image = det_image; + if (detection_data.new_detection) { + tracker_flow.update_tracking_flow(detection_data.cap_frame, detection_data.result_vec); + while (track_optflow_queue.size() > 0) { + draw_frame = track_optflow_queue.back(); + result_vec = tracker_flow.tracking_flow(track_optflow_queue.front(), false); + track_optflow_queue.pop(); } - }); - } - //while (!consumed); // sync detection - - if (!cur_frame.empty()) { - steady_end = std::chrono::steady_clock::now(); - if (std::chrono::duration(steady_end - steady_start).count() >= 1) { - current_det_fps = fps_det_counter; - current_cap_fps = fps_cap_counter; - steady_start = steady_end; - fps_det_counter = 0; - fps_cap_counter = 0; } + else { + track_optflow_queue.push(cap_frame); + result_vec = tracker_flow.tracking_flow(cap_frame, false); + } + detection_data.new_detection = true; // to correct kalman filter +#endif //TRACK_OPTFLOW - large_preview.set(cur_frame, result_vec); -#ifdef TRACK_OPTFLOW - ++passed_flow_frames; - track_optflow_queue.push(cur_frame.clone()); - result_vec = tracker_flow.tracking_flow(cur_frame); // track optical flow - extrapolate_coords.update_result(result_vec, cur_time_extrapolate); - small_preview.draw(cur_frame, show_small_boxes); -#endif - auto result_vec_draw = result_vec; - if (extrapolate_flag) { - result_vec_draw = extrapolate_coords.predict(cur_time_extrapolate); - cv::putText(cur_frame, "extrapolate", cv::Point2f(10, 40), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.0, cv::Scalar(50, 50, 0), 2); + // track ID by using kalman filter + if (use_kalman_filter) { + if (detection_data.new_detection) { + result_vec = track_kalman.correct(result_vec); + } + else { + result_vec = track_kalman.predict(); + } } - draw_boxes(cur_frame, result_vec_draw, obj_names, current_det_fps, current_cap_fps); - //show_console_result(result_vec, obj_names); - large_preview.draw(cur_frame); - - cv::imshow("window name", cur_frame); - int key = cv::waitKey(3); // 3 or 16ms - if (key == 'f') show_small_boxes = !show_small_boxes; - if (key == 'p') while (true) if(cv::waitKey(100) == 'p') break; - if (key == 'e') extrapolate_flag = !extrapolate_flag; - if (key == 27) { exit_flag = true; break; } - - if (output_video.isOpened() && videowrite_ready) { - if (t_videowrite.joinable()) t_videowrite.join(); - write_frame = cur_frame.clone(); - videowrite_ready = false; - t_videowrite = std::thread([&]() { - output_video << write_frame; videowrite_ready = true; - }); + // track ID by using custom function + else { + int frame_story = std::max(5, current_fps_cap.load()); + result_vec = detector.tracking_id(result_vec, true, frame_story, 40); } + + if (use_zed_camera && !detection_data.zed_cloud.empty()) { + result_vec = get_3d_coordinates(result_vec, detection_data.zed_cloud); + } + + //small_preview.set(draw_frame, result_vec); + //large_preview.set(draw_frame, result_vec); + draw_boxes(draw_frame, result_vec, obj_names, current_fps_det, current_fps_cap); + //show_console_result(result_vec, obj_names, detection_data.frame_id); + //large_preview.draw(draw_frame); + //small_preview.draw(draw_frame, true); + + detection_data.result_vec = result_vec; + detection_data.draw_frame = draw_frame; + draw2show.send(detection_data); + if (send_network) draw2net.send(detection_data); + if (output_video.isOpened()) draw2write.send(detection_data); + } while (!detection_data.exit_flag); + std::cout << " t_draw exit \n"; + }); + + + // write frame to videofile + t_write = std::thread([&]() + { + if (output_video.isOpened()) { + detection_data_t detection_data; + cv::Mat output_frame; + do { + detection_data = draw2write.receive(); + if(detection_data.draw_frame.channels() == 4) cv::cvtColor(detection_data.draw_frame, output_frame, CV_RGBA2RGB); + else output_frame = detection_data.draw_frame; + output_video << output_frame; + } while (!detection_data.exit_flag); + output_video.release(); } + std::cout << " t_write exit \n"; + }); + + // send detection to the network + t_network = std::thread([&]() + { + if (send_network) { + detection_data_t detection_data; + do { + detection_data = draw2net.receive(); + + detector.send_json_http(detection_data.result_vec, obj_names, detection_data.frame_id, filename); -#ifndef TRACK_OPTFLOW - // wait detection result for video-file only (not for net-cam) - if (protocol != "rtsp://" && protocol != "http://" && protocol != "https:/") { - std::unique_lock lock(mtx); - while (!consumed) cv_detected.wait(lock); + } while (!detection_data.exit_flag); } -#endif - } - exit_flag = true; + std::cout << " t_network exit \n"; + }); + + + // show detection + detection_data_t detection_data; + do { + + steady_end = std::chrono::steady_clock::now(); + float time_sec = std::chrono::duration(steady_end - steady_start).count(); + if (time_sec >= 1) { + current_fps_det = fps_det_counter.load() / time_sec; + current_fps_cap = fps_cap_counter.load() / time_sec; + steady_start = steady_end; + fps_det_counter = 0; + fps_cap_counter = 0; + } + + detection_data = draw2show.receive(); + cv::Mat draw_frame = detection_data.draw_frame; + + //if (extrapolate_flag) { + // cv::putText(draw_frame, "extrapolate", cv::Point2f(10, 40), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.0, cv::Scalar(50, 50, 0), 2); + //} + + cv::imshow("window name", draw_frame); + int key = cv::waitKey(3); // 3 or 16ms + if (key == 'f') show_small_boxes = !show_small_boxes; + if (key == 'p') while (true) if (cv::waitKey(100) == 'p') break; + //if (key == 'e') extrapolate_flag = !extrapolate_flag; + if (key == 27) { exit_flag = true;} + + //std::cout << " current_fps_det = " << current_fps_det << ", current_fps_cap = " << current_fps_cap << std::endl; + } while (!detection_data.exit_flag); + std::cout << " show detection exit \n"; + + cv::destroyWindow("window name"); + // wait for all threads if (t_cap.joinable()) t_cap.join(); + if (t_prepare.joinable()) t_prepare.join(); if (t_detect.joinable()) t_detect.join(); - if (t_videowrite.joinable()) t_videowrite.join(); - std::cout << "Video ended \n"; + if (t_post.joinable()) t_post.join(); + if (t_draw.joinable()) t_draw.join(); + if (t_write.joinable()) t_write.join(); + if (t_network.joinable()) t_network.join(); + break; + } else if (file_ext == "txt") { // list of image files std::ifstream file(filename); @@ -470,14 +643,14 @@ int main(int argc, char *argv[]) show_console_result(result_vec, obj_names); cv::waitKey(0); } -#else +#else // OPENCV //std::vector result_vec = detector.detect(filename); auto img = detector.load_image(filename); std::vector result_vec = detector.detect(img); detector.free_image(img); show_console_result(result_vec, obj_names); -#endif +#endif // OPENCV } catch (std::exception &e) { std::cerr << "exception: " << e.what() << "\n"; getchar(); } catch (...) { std::cerr << "unknown exception \n"; getchar(); } diff --git a/src/yolo_layer.c b/src/yolo_layer.c index a9309d1dd59..d303b5aa20b 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -2,7 +2,7 @@ #include "activations.h" #include "blas.h" #include "box.h" -#include "cuda.h" +#include "dark_cuda.h" #include "utils.h" #include diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index 1b07b43bfb3..a7418c166c0 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -250,8 +250,6 @@ LIB_API std::vector Detector::detect(image_t img, float thresh, bool use #endif //std::cout << "net.gpu_index = " << net.gpu_index << std::endl; - //float nms = .4; - image im; im.c = img.c; im.data = img.data; @@ -305,6 +303,9 @@ LIB_API std::vector Detector::detect(image_t img, float thresh, bool use bbox.obj_id = obj_id; bbox.prob = prob; bbox.track_id = 0; + bbox.x_3d = NAN; + bbox.y_3d = NAN; + bbox.z_3d = NAN; bbox_vec.push_back(bbox); } @@ -379,3 +380,70 @@ LIB_API std::vector Detector::tracking_id(std::vector cur_bbox_v return cur_bbox_vec; } + + +LIB_API bool Detector::send_json_http(std::vector cur_bbox_vec, std::vector obj_names, int frame_id, std::string filename, int timeout, int port) +{ + //int timeout = 400000; + //int port = 8070; + //send_json(local_dets, local_nboxes, l.classes, demo_names, frame_id, demo_json_port, timeout); + + std::string send_str; + + char *tmp_buf = (char *)calloc(1024, sizeof(char)); + if (!filename.empty()) { + sprintf(tmp_buf, "{\n \"frame_id\":%d, \n \"filename\":\"%s\", \n \"objects\": [ \n", frame_id, filename); + } + else { + sprintf(tmp_buf, "{\n \"frame_id\":%d, \n \"objects\": [ \n", frame_id); + } + send_str = tmp_buf; + free(tmp_buf); + + for (auto & i : cur_bbox_vec) { + char *buf = (char *)calloc(2048, sizeof(char)); + + sprintf(buf, " {\"class_id\":%d, \"name\":\"%s\", \"absolute_coordinates\":{\"center_x\":%d, \"center_y\":%d, \"width\":%d, \"height\":%d}, \"confidence\":%f", + i.obj_id, obj_names[i.obj_id], i.x, i.y, i.w, i.h, i.prob); + + //sprintf(buf, " {\"class_id\":%d, \"name\":\"%s\", \"relative_coordinates\":{\"center_x\":%f, \"center_y\":%f, \"width\":%f, \"height\":%f}, \"confidence\":%f", + // i.obj_id, obj_names[i.obj_id], i.x, i.y, i.w, i.h, i.prob); + + send_str += buf; + + if (!isnan(i.z_3d)) { + sprintf(buf, "\n , \"coordinates_in_meters\":{\"x_3d\":%.2f, \"y_3d\":%.2f, \"z_3d\":%.2f}", + i.x_3d*100, i.y_3d, i.z_3d); + send_str += buf; + } + + send_str += "}\n"; + + free(buf); + } + + //send_str += "\n ] \n}, \n"; + send_str += "\n ] \n}"; + + send_json_custom(send_str.c_str(), port, timeout); + return true; +} + +void *Detector::get_cuda_context() +{ +#ifdef GPU + int old_gpu_index; + cudaGetDevice(&old_gpu_index); + if (cur_gpu_id != old_gpu_index) + cudaSetDevice(cur_gpu_id); + + void *cuda_context = cuda_get_context(); + + if (cur_gpu_id != old_gpu_index) + cudaSetDevice(old_gpu_index); + + return cuda_context; +#else // GPU + return NULL; +#endif // GPU +} \ No newline at end of file From d762df531f14aaf19ef370ed39dfd53368c6c0e4 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 18 Mar 2019 02:53:48 +0300 Subject: [PATCH 0399/1365] Compile fix --- include/yolo_v2_class.hpp | 1 + 1 file changed, 1 insertion(+) diff --git a/include/yolo_v2_class.hpp b/include/yolo_v2_class.hpp index f92787214ef..6d86195acbb 100644 --- a/include/yolo_v2_class.hpp +++ b/include/yolo_v2_class.hpp @@ -44,6 +44,7 @@ struct bbox_t_container { #include #include #include +#include #ifdef OPENCV #include // C++ From 24dc5b785343a97e6af30cf3e633fed201ba3c93 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 18 Mar 2019 02:56:32 +0300 Subject: [PATCH 0400/1365] Another compile fix --- src/yolo_console_dll.cpp | 4 ++-- src/yolo_v2_class.cpp | 3 ++- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/src/yolo_console_dll.cpp b/src/yolo_console_dll.cpp index 12ab208c064..42c3632211d 100644 --- a/src/yolo_console_dll.cpp +++ b/src/yolo_console_dll.cpp @@ -8,7 +8,7 @@ #include #include #include // std::mutex, std::unique_lock -#include // std::unordered_map +#include // It makes sense only for video-Camera (not for video-File) @@ -184,7 +184,7 @@ void draw_boxes(cv::Mat mat_img, std::vector result_vec, std::vector i.w + 2) ? text_size.width : (i.w + 2); std::string coords_3d; - if (!isnan(i.z_3d)) { + if (!std::isnan(i.z_3d)) { std::stringstream ss; ss << std::fixed << std::setprecision(2) << "x:" << i.x_3d << "m y:" << i.y_3d << "m z:" << i.z_3d << "m "; coords_3d = ss.str(); diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index a7418c166c0..86c7acbd4b5 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -20,6 +20,7 @@ extern "C" { #include #include #include +#include //static Detector* detector = NULL; @@ -411,7 +412,7 @@ LIB_API bool Detector::send_json_http(std::vector cur_bbox_vec, std::vec send_str += buf; - if (!isnan(i.z_3d)) { + if (!std::isnan(i.z_3d)) { sprintf(buf, "\n , \"coordinates_in_meters\":{\"x_3d\":%.2f, \"y_3d\":%.2f, \"z_3d\":%.2f}", i.x_3d*100, i.y_3d, i.z_3d); send_str += buf; From 44c881233a5904adbb9fe93b39a93aa417ab8140 Mon Sep 17 00:00:00 2001 From: none Date: Mon, 18 Mar 2019 09:17:17 +0900 Subject: [PATCH 0401/1365] add Visual studio 2017 "Enterprise" edition to edition check. --- build.ps1 | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/build.ps1 b/build.ps1 index e5a1e29ba69..d937b427b5d 100755 --- a/build.ps1 +++ b/build.ps1 @@ -30,12 +30,16 @@ if ($vcpkg_triplet -Match "x86") { if ($null -eq (Get-Command "cl.exe" -ErrorAction SilentlyContinue)) { $vstype = "Professional" if (Test-Path "C:\Program Files (x86)\Microsoft Visual Studio\2017\${vstype}\Common7\Tools") { - Write-Host "Found VS 2017 Professional" } else { - $vstype = "Community" - Write-Host "Found VS 2017 Community" + $vstype = "Enterprise" + if (Test-Path "C:\Program Files (x86)\Microsoft Visual Studio\2017\${vstype}\Common7\Tools") { + } + else { + $vstype = "Community" + } } + Write-Host "Found VS 2017 ${vstype}" Push-Location "C:\Program Files (x86)\Microsoft Visual Studio\2017\${vstype}\Common7\Tools" cmd /c "VsDevCmd.bat -arch=x64 & set" | ForEach-Object { From 9c3237acc810cce4216297bc6167713345064858 Mon Sep 17 00:00:00 2001 From: none Date: Mon, 18 Mar 2019 09:44:43 +0900 Subject: [PATCH 0402/1365] suppress C4819 warnings to the cuda file in CP932 environment. --- CMakeLists.txt | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index e6e6323aba3..145d286d96d 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -84,7 +84,7 @@ set(CMAKE_DEBUG_POSTFIX d) add_definitions(-DUSE_CMAKE_LIBS) if(MSVC) - set(CMAKE_CXX_FLAGS "/wd4013 /wd4018 /wd4028 /wd4047 /wd4068 /wd4090 /wd4101 /wd4113 /wd4133 /wd4190 /wd4244 /wd4267 /wd4305 /wd4477 /wd4996 /fp:fast ${CMAKE_CXX_FLAGS}") + set(CMAKE_CXX_FLAGS "/wd4013 /wd4018 /wd4028 /wd4047 /wd4068 /wd4090 /wd4101 /wd4113 /wd4133 /wd4190 /wd4244 /wd4267 /wd4305 /wd4477 /wd4996 /wd4819 /fp:fast ${CMAKE_CXX_FLAGS}") string(REGEX REPLACE "/O2" "/Ox" CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) add_definitions(-D_CRT_RAND_S) add_definitions(-DNOMINMAX) @@ -139,15 +139,15 @@ if(ENABLE_CUDA) if (MSVC) if(CUDNN_FOUND) if(OpenCV_FOUND) - set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/DGPU,/DCUDNN,/DOPENCV\" ${CMAKE_CUDA_FLAGS}") + set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/wd4819,/DGPU,/DCUDNN,/DOPENCV\" ${CMAKE_CUDA_FLAGS}") else() - set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/DGPU,/DCUDNN\" ${CMAKE_CUDA_FLAGS}") + set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/wd4819,/DGPU,/DCUDNN\" ${CMAKE_CUDA_FLAGS}") endif() else() if(OpenCV_FOUND) - set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/DGPU,/DOPENCV\" ${CMAKE_CUDA_FLAGS}") + set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/wd4819,/DGPU,/DOPENCV\" ${CMAKE_CUDA_FLAGS}") else() - set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/DGPU\" ${CMAKE_CUDA_FLAGS}") + set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/wd4819,/DGPU\" ${CMAKE_CUDA_FLAGS}") endif() endif() else() From 2f0592d540cdf67396a15f1514075dd615cfba88 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 18 Mar 2019 15:22:38 +0300 Subject: [PATCH 0403/1365] Compile fixes --- CMakeLists.txt | 4 ++-- src/dark_cuda.c | 3 ++- src/yolo_v2_class.cpp | 6 +++--- 3 files changed, 7 insertions(+), 6 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index e6e6323aba3..05acdad3643 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -288,8 +288,8 @@ target_link_libraries(darknet PRIVATE Threads::Threads) target_link_libraries(darklib PRIVATE Threads::Threads) if(ENABLE_CUDA) - target_link_libraries(darknet PRIVATE curand cublas) - target_link_libraries(darklib PRIVATE curand cublas) + target_link_libraries(darknet PRIVATE curand cublas cuda) + target_link_libraries(darklib PRIVATE curand cublas cuda) set_target_properties(darklib PROPERTIES CUDA_RESOLVE_DEVICE_SYMBOLS ON) endif() diff --git a/src/dark_cuda.c b/src/dark_cuda.c index 42aa6fc8e83..5b57ea1d3dd 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -8,13 +8,14 @@ int gpu_index = 0; #ifdef GPU -#include "cuda.h" +#include "dark_cuda.h" #include "utils.h" #include "blas.h" #include "assert.h" #include #include #include +#include #pragma comment(lib, "cuda.lib") diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index 86c7acbd4b5..3ebc80248a0 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -393,7 +393,7 @@ LIB_API bool Detector::send_json_http(std::vector cur_bbox_vec, std::vec char *tmp_buf = (char *)calloc(1024, sizeof(char)); if (!filename.empty()) { - sprintf(tmp_buf, "{\n \"frame_id\":%d, \n \"filename\":\"%s\", \n \"objects\": [ \n", frame_id, filename); + sprintf(tmp_buf, "{\n \"frame_id\":%d, \n \"filename\":\"%s\", \n \"objects\": [ \n", frame_id, filename.c_str()); } else { sprintf(tmp_buf, "{\n \"frame_id\":%d, \n \"objects\": [ \n", frame_id); @@ -405,7 +405,7 @@ LIB_API bool Detector::send_json_http(std::vector cur_bbox_vec, std::vec char *buf = (char *)calloc(2048, sizeof(char)); sprintf(buf, " {\"class_id\":%d, \"name\":\"%s\", \"absolute_coordinates\":{\"center_x\":%d, \"center_y\":%d, \"width\":%d, \"height\":%d}, \"confidence\":%f", - i.obj_id, obj_names[i.obj_id], i.x, i.y, i.w, i.h, i.prob); + i.obj_id, obj_names[i.obj_id].c_str(), i.x, i.y, i.w, i.h, i.prob); //sprintf(buf, " {\"class_id\":%d, \"name\":\"%s\", \"relative_coordinates\":{\"center_x\":%f, \"center_y\":%f, \"width\":%f, \"height\":%f}, \"confidence\":%f", // i.obj_id, obj_names[i.obj_id], i.x, i.y, i.w, i.h, i.prob); @@ -414,7 +414,7 @@ LIB_API bool Detector::send_json_http(std::vector cur_bbox_vec, std::vec if (!std::isnan(i.z_3d)) { sprintf(buf, "\n , \"coordinates_in_meters\":{\"x_3d\":%.2f, \"y_3d\":%.2f, \"z_3d\":%.2f}", - i.x_3d*100, i.y_3d, i.z_3d); + i.x_3d, i.y_3d, i.z_3d); send_str += buf; } From 4cd10ee547f59fcf45df1a23a49da9a9769b633b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 18 Mar 2019 16:05:42 +0300 Subject: [PATCH 0404/1365] Another one fix --- include/yolo_v2_class.hpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/include/yolo_v2_class.hpp b/include/yolo_v2_class.hpp index 6d86195acbb..de13e489448 100644 --- a/include/yolo_v2_class.hpp +++ b/include/yolo_v2_class.hpp @@ -84,7 +84,7 @@ class Detector { LIB_API void *get_cuda_context(); LIB_API bool send_json_http(std::vector cur_bbox_vec, std::vector obj_names, int frame_id, - std::string filename = "", int timeout = 400000, int port = 8070); + std::string filename = std::string(), int timeout = 400000, int port = 8070); std::vector detect_resized(image_t img, int init_w, int init_h, float thresh = 0.2, bool use_mean = false) { From 84be2e8316d643a6d1b63800d527fd20320db782 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 18 Mar 2019 18:11:39 +0300 Subject: [PATCH 0405/1365] Minor fix --- include/yolo_v2_class.hpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/include/yolo_v2_class.hpp b/include/yolo_v2_class.hpp index de13e489448..2d42576dc48 100644 --- a/include/yolo_v2_class.hpp +++ b/include/yolo_v2_class.hpp @@ -45,6 +45,9 @@ struct bbox_t_container { #include #include #include +#include +#include +#include #ifdef OPENCV #include // C++ From b3254ed523a1160c8c97d3da8a4841bbb08e689f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 18 Mar 2019 19:25:48 +0300 Subject: [PATCH 0406/1365] Fixed many warnings --- src/captcha.c | 2 +- src/cifar.c | 4 ++-- src/classifier.c | 6 +++--- src/compare.c | 2 +- src/convolutional_layer.c | 25 +++++++++++++------------ src/darknet.c | 2 +- src/darkunistd.h | 2 +- src/data.c | 8 ++++---- src/detector.c | 21 ++++++++++----------- src/dice.c | 2 +- src/gemm.c | 28 ++++++++++++++-------------- src/go.c | 2 +- src/http_stream.cpp | 2 +- src/matrix.c | 3 +-- src/network.c | 6 +++--- src/rnn.c | 2 +- src/tag.c | 2 +- src/utils.c | 17 ++++++++--------- src/writing.c | 2 +- src/yolo_v2_class.cpp | 10 +++++----- 20 files changed, 73 insertions(+), 75 deletions(-) diff --git a/src/captcha.c b/src/captcha.c index 0bb15b847fd..0cc15915257 100644 --- a/src/captcha.c +++ b/src/captcha.c @@ -85,7 +85,7 @@ void train_captcha(char *cfgfile, char *weightfile) float loss = train_network(net, train); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), *net.seen); + printf("%d: %f, %f avg, %lf seconds, %ld images\n", i, loss, avg_loss, sec(clock()-time), *net.seen); free_data(train); if(i%100==0){ char buff[256]; diff --git a/src/cifar.c b/src/cifar.c index 04dec15590a..24a13a57a24 100644 --- a/src/cifar.c +++ b/src/cifar.c @@ -33,7 +33,7 @@ void train_cifar(char *cfgfile, char *weightfile) float loss = train_network_sgd(net, train, 1); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.95 + loss*.05; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); + printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); if(*net.seen/N > epoch){ epoch = *net.seen/N; char buff[256]; @@ -89,7 +89,7 @@ void train_cifar_distill(char *cfgfile, char *weightfile) float loss = train_network_sgd(net, train, 1); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.95 + loss*.05; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); + printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); if(*net.seen/N > epoch){ epoch = *net.seen/N; char buff[256]; diff --git a/src/classifier.c b/src/classifier.c index 5471957ae04..961e4cbbd8c 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -177,7 +177,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, draw_precision = 1; } - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); + printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); #ifdef OPENCV draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, "top5", dont_show, mjpeg_port); #endif // OPENCV @@ -198,7 +198,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, if (ngpus != 1) sync_nets(nets, ngpus, 0); #endif char buff[256]; - sprintf(buff, "%s/%s_last.weights", backup_directory, base, i); + sprintf(buff, "%s/%s_last.weights", backup_directory, base); save_weights(net, buff); } free_data(train); @@ -791,7 +791,7 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi int* indexes = (int*)calloc(top, sizeof(int)); char buff[256]; char *input = buff; - int size = net.w; + //int size = net.w; while(1){ if(filename){ strncpy(input, filename, 256); diff --git a/src/compare.c b/src/compare.c index 5c1e0451e53..bb8422611b9 100644 --- a/src/compare.c +++ b/src/compare.c @@ -54,7 +54,7 @@ void train_compare(char *cfgfile, char *weightfile) float loss = train_network(net, train); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%.3f: %f, %f avg, %lf seconds, %d images\n", (float)*net.seen/N, loss, avg_loss, sec(clock()-time), *net.seen); + printf("%.3f: %f, %f avg, %lf seconds, %ld images\n", (float)*net.seen/N, loss, avg_loss, sec(clock()-time), *net.seen); free_data(train); if(i%100 == 0){ char buff[256]; diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index e42e86a7663..84d36d93ef0 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -679,7 +679,7 @@ void float_to_bit(float *src, unsigned char *dst, size_t size) { void bit_to_float(unsigned char *src, float *dst, size_t size, size_t filters, float *mean_arr) { memset(dst, 0, size *sizeof(float)); - size_t i, src_i, src_shift; + size_t i; for (i = 0; i < size; ++i) { float mean_val = 1; @@ -726,7 +726,7 @@ void binary_align_weights(convolutional_layer *l) const int items_per_channel = l->size*l->size; for (i = 0; i < items_per_channel; ++i) { - uint32_t val = 0; + //uint32_t val = 0; int c_pack; for (c_pack = 0; c_pack < 32; ++c_pack) { float src = l->binary_weights[fil*items_per_filter + (chan + c_pack)*items_per_channel + i]; @@ -749,8 +749,8 @@ void binary_align_weights(convolutional_layer *l) //if (l->n >= 32) if(gpu_index >= 0) { - int M = l->n; - int N = l->out_w*l->out_h; + //int M = l->n; + //int N = l->out_w*l->out_h; //printf("\n M = %d, N = %d, M %% 8 = %d, N %% 8 = %d - weights \n", M, N, M % 8, N % 8); //printf("\n l.w = %d, l.c = %d, l.n = %d \n", l->w, l->c, l->n); for (i = 0; i < align_weights_size / 8; ++i) l->align_bit_weights[i] = ~(l->align_bit_weights[i]); @@ -806,7 +806,7 @@ size_t binary_transpose_align_input(int k, int n, float *b, char **t_bit_input, size_t t_bit_input_size = t_intput_size / 8;// +1; memset(*t_bit_input, 0, t_bit_input_size * sizeof(char)); - int src_size = k * bit_align; + //int src_size = k * bit_align; // b - [bit_align, k] - [l.bit_align, l.size*l.size*l.c] = src_size // t_input - [bit_align, k] - [n', k] @@ -862,8 +862,8 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) int ldb_align = l.lda_align; size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; - size_t t_intput_size = new_ldb * l.bit_align;// n; - size_t t_bit_input_size = t_intput_size / 8;// +1; + //size_t t_intput_size = new_ldb * l.bit_align;// n; + //size_t t_bit_input_size = t_intput_size / 8;// +1; int re_packed_input_size = l.c * l.w * l.h; memset(state.workspace, 0, re_packed_input_size * sizeof(float)); @@ -928,17 +928,18 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //im2col_cpu_custom_align(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align); im2col_cpu_custom_bin(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace, l.bit_align); - size_t output_size = l.outputs; + //size_t output_size = l.outputs; //float *count_output = calloc(output_size, sizeof(float)); //size_t bit_output_size = output_size / 8 + 1; //char *bit_output = calloc(bit_output_size, sizeof(char)); - size_t intput_size = n * k; // (out_h*out_w) X (l.size*l.size*l.c) : after im2col() - size_t bit_input_size = intput_size / 8 + 1; + //size_t intput_size = n * k; // (out_h*out_w) X (l.size*l.size*l.c) : after im2col() + //size_t bit_input_size = intput_size / 8 + 1; //char *bit_input = calloc(bit_input_size, sizeof(char)); - size_t weights_size = k * m; //l.size*l.size*l.c*l.n; - size_t bit_weights_size = weights_size / 8 + 1; + //size_t weights_size = k * m; //l.size*l.size*l.c*l.n; + //size_t bit_weights_size = weights_size / 8 + 1; + //char *bit_weights = calloc(bit_weights_size, sizeof(char)); //float *mean_arr = calloc(l.n, sizeof(float)); diff --git a/src/darknet.c b/src/darknet.c index c10a7b2fef9..67c7d9a4331 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -479,7 +479,7 @@ int main(int argc, char **argv) float thresh = find_float_arg(argc, argv, "-thresh", .24); int ext_output = find_arg(argc, argv, "-ext_output"); char *filename = (argc > 4) ? argv[4]: 0; - test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, 0.5, 0, 1, 0, NULL); + test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, 0.5, 0, ext_output, 0, NULL); } else if (0 == strcmp(argv[1], "cifar")){ run_cifar(argc, argv); } else if (0 == strcmp(argv[1], "go")){ diff --git a/src/darkunistd.h b/src/darkunistd.h index 1ba3ef2e076..d2d555c313a 100644 --- a/src/darkunistd.h +++ b/src/darkunistd.h @@ -21,7 +21,7 @@ These may be OR'd together. */ #define R_OK 4 /* Test for read permission. */ #define W_OK 2 /* Test for write permission. */ -#define X_OK R_OK /* execute permission - unsupported in Windows, \ +#define X_OK R_OK /* execute permission - unsupported in Windows, */ #define F_OK 0 /* Test for existence. */ #define access _access diff --git a/src/data.c b/src/data.c index e364ed76741..a36125196d3 100644 --- a/src/data.c +++ b/src/data.c @@ -790,8 +790,8 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo d.X.vals = (float**)calloc(d.X.rows, sizeof(float*)); d.X.cols = h*w*c; - float r1, r2, r3, r4; - float dhue, dsat, dexp, flip; + float r1 = 0, r2 = 0, r3 = 0, r4 = 0; + float dhue = 0, dsat = 0, dexp = 0, flip = 0; int augmentation_calculated = 0; d.y = make_matrix(n, 5*boxes); @@ -890,8 +890,8 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo d.X.vals = (float**)calloc(d.X.rows, sizeof(float*)); d.X.cols = h*w*c; - float r1, r2, r3, r4; - float dhue, dsat, dexp, flip; + float r1 = 0, r2 = 0, r3 = 0, r4 = 0; + float dhue = 0, dsat = 0, dexp = 0, flip = 0; int augmentation_calculated = 0; d.y = make_matrix(n, 5 * boxes); diff --git a/src/detector.c b/src/detector.c index 96222379176..173bcf785d9 100644 --- a/src/detector.c +++ b/src/detector.c @@ -326,7 +326,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i if (ngpus != 1) sync_nets(nets, ngpus, 0); #endif char buff[256]; - sprintf(buff, "%s/%s_last.weights", backup_directory, base, i); + sprintf(buff, "%s/%s_last.weights", backup_directory, base); save_weights(net, buff); } free_data(train); @@ -594,7 +594,7 @@ void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile) list *plist = get_paths(valid_images); char **paths = (char **)list_to_array(plist); - layer l = net.layers[net.n - 1]; + //layer l = net.layers[net.n - 1]; int j, k; @@ -681,16 +681,16 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa char *difficult_valid_images = option_find_str(options, "difficult", NULL); char *name_list = option_find_str(options, "names", "data/names.list"); char **names = get_labels(name_list); - char *mapf = option_find_str(options, "map", 0); - int *map = 0; - if (mapf) map = read_map(mapf); + //char *mapf = option_find_str(options, "map", 0); + //int *map = 0; + //if (mapf) map = read_map(mapf); FILE* reinforcement_fd = NULL; network net; - int initial_batch; + //int initial_batch; if (existing_net) { char *train_images = option_find_str(options, "train", "data/train.txt"); - char *valid_images = option_find_str(options, "valid", train_images); + valid_images = option_find_str(options, "valid", train_images); net = *existing_net; } else { @@ -1131,8 +1131,8 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int printf("\n"); for (i = 0; i < number_of_boxes; ++i) { - float w = boxes_data.vals[i][0] = rel_width_height_array[i * 2]; - float h = boxes_data.vals[i][1] = rel_width_height_array[i * 2 + 1]; + boxes_data.vals[i][0] = rel_width_height_array[i * 2]; + boxes_data.vals[i][1] = rel_width_height_array[i * 2 + 1]; //if (w > 410 || h > 410) printf("i:%d, w = %f, h = %f \n", i, w, h); } @@ -1170,7 +1170,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int float anchor_w = anchors_data.centers.vals[cluster_idx][0]; //centers->data.fl[cluster_idx * 2]; float anchor_h = anchors_data.centers.vals[cluster_idx][1]; //centers->data.fl[cluster_idx * 2 + 1]; if (best_iou > 1 || best_iou < 0) { // || box_w > width || box_h > height) { - printf(" Wrong label: i = %d, box_w = %d, box_h = %d, anchor_w = %d, anchor_h = %d, iou = %f \n", + printf(" Wrong label: i = %d, box_w = %f, box_h = %f, anchor_w = %f, anchor_h = %f, iou = %f \n", i, box_w, box_h, anchor_w, anchor_h, best_iou); } else avg_iou += best_iou; @@ -1287,7 +1287,6 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam if (net.layers[net.n - 1].classes > names_size) getchar(); } srand(2222222); - double time; char buff[256]; char *input = buff; char *json_buf = NULL; diff --git a/src/dice.c b/src/dice.c index 94155271e6a..8a0393a8bdf 100644 --- a/src/dice.c +++ b/src/dice.c @@ -33,7 +33,7 @@ void train_dice(char *cfgfile, char *weightfile) float loss = train_network(net, train); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), *net.seen); + printf("%d: %f, %f avg, %lf seconds, %ld images\n", i, loss, avg_loss, sec(clock()-time), *net.seen); free_data(train); if((i % 100) == 0) net.learning_rate *= .1; if(i%100==0){ diff --git a/src/gemm.c b/src/gemm.c index fec1e16aaea..861a190669e 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -321,7 +321,7 @@ void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, // is not used void transpose_32x32_bits_my(uint32_t *A, uint32_t *B, int lda, int ldb) { - unsigned x, y, t; + unsigned int x, y; for (y = 0; y < 32; ++y) { for (x = 0; x < 32; ++x) { if (A[y * lda] & (1 << x)) B[x * ldb] |= (uint32_t)1 << y; @@ -400,7 +400,7 @@ void transpose_32x32_bits_reversed_diagonale(uint32_t *A, uint32_t *B, int m, in void transpose_8x8_bits_my(unsigned char *A, unsigned char *B, int lda, int ldb) { - unsigned x, y, t; + unsigned x, y; for (y = 0; y < 8; ++y) { for (x = 0; x < 8; ++x) { if (A[y * lda] & (1 << x)) B[x * ldb] |= 1 << y; @@ -755,7 +755,7 @@ void gemm_nn_fast(int M, int N, int K, float ALPHA, for (i = 0; i < (M / TILE_M)*TILE_M; i += TILE_M) { int j, k; - int i_d, j_d, k_d; + int i_d, k_d; for (k = 0; k < (K / TILE_K)*TILE_K; k += TILE_K) { @@ -768,8 +768,8 @@ void gemm_nn_fast(int M, int N, int K, float ALPHA, __m256 result256; __m256 a256_0, b256_0; // AVX __m256 a256_1, b256_1; // AVX - __m256 a256_2, b256_2; // AVX - __m256 a256_3, b256_3; // AVX + __m256 a256_2;// , b256_2; // AVX + __m256 a256_3;// , b256_3; // AVX __m256 c256_0, c256_1, c256_2, c256_3; __m256 c256_4, c256_5, c256_6, c256_7; @@ -943,8 +943,8 @@ void gemm_nn_bin_32bit_packed(int M, int N, int K, float ALPHA, void convolution_2d_old(int w, int h, int ksize, int n, int c, int pad, int stride, float *weights, float *input, float *output) { - const int out_h = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1 - const int out_w = (w + 2 * pad - ksize) / stride + 1; // output_width=input_width for stride=1 and pad=1 + //const int out_h = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1 + //const int out_w = (w + 2 * pad - ksize) / stride + 1; // output_width=input_width for stride=1 and pad=1 int fil; // filter index @@ -991,8 +991,8 @@ void convolution_2d_old(int w, int h, int ksize, int n, int c, int pad, int stri void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride, float *weights, float *input, float *output, float *mean) { - const int out_h = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1 - const int out_w = (w + 2 * pad - ksize) / stride + 1; // output_width=input_width for stride=1 and pad=1 + //const int out_h = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1 + //const int out_w = (w + 2 * pad - ksize) / stride + 1; // output_width=input_width for stride=1 and pad=1 int i; #if defined(_OPENMP) @@ -1203,7 +1203,7 @@ void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, float mean_val_0 = mean_arr[i + 0]; float mean_val_1 = mean_arr[i + 1]; int j, k; - __m256i all_1 = _mm256_set1_epi8(255); + //__m256i all_1 = _mm256_set1_epi8(255); //for (j = 0; j < N; ++j) for (j = 0; j < (N/2)*2; j += 2) @@ -1770,7 +1770,7 @@ void float_to_bit(float *src, unsigned char *dst, size_t size) memset(dst, 0, dst_size); size_t i; - __m256i all256_sing1 = _mm256_set_epi32(0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000); + //__m256i all256_sing1 = _mm256_set_epi32(0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000); __m256 float_zero256 = _mm256_set1_ps(0.0); for (i = 0; i < size; i+=8) @@ -1881,8 +1881,8 @@ void forward_maxpool_layer_avx(float *src, float *dst, int *indexes, int size, i else if (size == 2 && stride == 2 && is_avx() == 1) { for (j = 0; j < out_w - 4; j += 4) { int out_index = j + out_w*(i + out_h*(k + c*b)); - float max = -FLT_MAX; - int max_i = -1; + //float max = -FLT_MAX; + //int max_i = -1; __m128 max128 = _mm_set1_ps(-FLT_MAX); for (n = 0; n < size; ++n) { @@ -2513,7 +2513,7 @@ void convolution_repacked(uint32_t *packed_input, uint32_t *packed_weights, floa #pragma omp parallel for for (fil = 0; fil < n; ++fil) { float mean_val = mean_arr[fil]; - int chan, c_pack, y, x, f_y, f_x; + int chan, y, x, f_y, f_x; // c_pack // channel index for (chan = 0; chan < c / 32; ++chan) //for (chan = 0; chan < l.c; chan += 32) diff --git a/src/go.c b/src/go.c index 54a739d6a18..79529432c28 100644 --- a/src/go.c +++ b/src/go.c @@ -144,7 +144,7 @@ void train_go(char *cfgfile, char *weightfile) float loss = train_network_datum(net, board, move) / net.batch; if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.95 + loss*.05; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); + printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); if(*net.seen/N > epoch){ epoch = *net.seen/N; char buff[256]; diff --git a/src/http_stream.cpp b/src/http_stream.cpp index e61e506db80..77096d2b60c 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -181,7 +181,7 @@ class JSON_sender if (::select(maxfd + 1, &rread, NULL, NULL, &select_timeout) <= 0) return true; // nothing broken, there's just noone listening - size_t outlen = strlen(outputbuf); + int outlen = static_cast(strlen(outputbuf)); #ifdef _WIN32 for (unsigned i = 0; ibatch; #ifdef GPU + const int size = get_network_input_size(*net) * net->batch; if(gpu_index >= 0){ printf(" try to allocate additional workspace_size = %1.2f MB \n", (float)workspace_size / 1000000); net->workspace = cuda_make_array(0, workspace_size/sizeof(float) + 1); @@ -728,10 +728,10 @@ char *detection_to_json(detection *dets, int nboxes, int classes, char **names, char *send_buf = (char *)calloc(1024, sizeof(char)); if (filename) { - sprintf(send_buf, "{\n \"frame_id\":%d, \n \"filename\":\"%s\", \n \"objects\": [ \n", frame_id, filename); + sprintf(send_buf, "{\n \"frame_id\":%lld, \n \"filename\":\"%s\", \n \"objects\": [ \n", frame_id, filename); } else { - sprintf(send_buf, "{\n \"frame_id\":%d, \n \"objects\": [ \n", frame_id); + sprintf(send_buf, "{\n \"frame_id\":%lld, \n \"objects\": [ \n", frame_id); } int i, j; diff --git a/src/rnn.c b/src/rnn.c index 93107585c2e..5aee0863261 100644 --- a/src/rnn.c +++ b/src/rnn.c @@ -163,7 +163,7 @@ void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear, int i = (*net.seen)/net.batch; int streams = batch/steps; - printf("\n batch = %d, steps = %d, streams = %d, subdivisions = %d, text_size = %d \n", batch, steps, streams, net.subdivisions, size); + printf("\n batch = %d, steps = %d, streams = %d, subdivisions = %d, text_size = %ld \n", batch, steps, streams, net.subdivisions, size); printf(" global_batch = %d \n", batch*net.subdivisions); size_t* offsets = (size_t*)calloc(streams, sizeof(size_t)); int j; diff --git a/src/tag.c b/src/tag.c index 4033216ee59..0942d50324c 100644 --- a/src/tag.c +++ b/src/tag.c @@ -64,7 +64,7 @@ void train_tag(char *cfgfile, char *weightfile, int clear) float loss = train_network(net, train); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); + printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); free_data(train); if(*net.seen/N > epoch){ epoch = *net.seen/N; diff --git a/src/utils.c b/src/utils.c index a2a6178ac16..6afc187c213 100644 --- a/src/utils.c +++ b/src/utils.c @@ -254,7 +254,7 @@ void replace_image_to_label(const char* input_path, char* output_path) find_replace_extension(output_path, ".PPM", ".txt", output_path); find_replace_extension(output_path, ".tiff", ".txt", output_path); find_replace_extension(output_path, ".TIFF", ".txt", output_path); - + // Check file ends with txt: if(strlen(output_path) > 4) { char *output_path_ext = output_path + strlen(output_path) - 4; @@ -768,13 +768,12 @@ float rand_uniform(float min, float max) max = swap; } - if (RAND_MAX < 65536) { +#if (RAND_MAX < 65536) int rnd = rand()*(RAND_MAX + 1) + rand(); return ((float)rnd / (RAND_MAX*RAND_MAX) * (max - min)) + min; - } - else { +#else return ((float)rand() / RAND_MAX * (max - min)) + min; - } +#endif //return (random_float() * (max - min)) + min; } @@ -802,12 +801,12 @@ unsigned int random_gen() unsigned int rnd = 0; #ifdef WIN32 rand_s(&rnd); -#else +#else // WIN32 rnd = rand(); - if (RAND_MAX < 65536) { +#if (RAND_MAX < 65536) rnd = rand()*(RAND_MAX + 1) + rnd; - } -#endif +#endif //(RAND_MAX < 65536) +#endif // WIN32 return rnd; } diff --git a/src/writing.c b/src/writing.c index 4acda60a78c..e0d8019e301 100644 --- a/src/writing.c +++ b/src/writing.c @@ -69,7 +69,7 @@ void train_writing(char *cfgfile, char *weightfile) if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); + printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); free_data(train); if(get_current_batch(net)%100 == 0){ char buff[256]; diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index 3ebc80248a0..93cccf44f53 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -104,8 +104,8 @@ struct detector_gpu_t { LIB_API Detector::Detector(std::string cfg_filename, std::string weight_filename, int gpu_id) : cur_gpu_id(gpu_id) { wait_stream = 0; - int old_gpu_index; #ifdef GPU + int old_gpu_index; check_cuda( cudaGetDevice(&old_gpu_index) ); #endif @@ -151,7 +151,7 @@ LIB_API Detector::Detector(std::string cfg_filename, std::string weight_filename LIB_API Detector::~Detector() { detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); - layer l = detector_gpu.net.layers[detector_gpu.net.n - 1]; + //layer l = detector_gpu.net.layers[detector_gpu.net.n - 1]; free(detector_gpu.track_id); @@ -159,8 +159,8 @@ LIB_API Detector::~Detector() for (int j = 0; j < NFRAMES; ++j) free(detector_gpu.predictions[j]); for (int j = 0; j < NFRAMES; ++j) if (detector_gpu.images[j].data) free(detector_gpu.images[j].data); - int old_gpu_index; #ifdef GPU + int old_gpu_index; cudaGetDevice(&old_gpu_index); cuda_set_device(detector_gpu.net.gpu_index); #endif @@ -241,8 +241,8 @@ LIB_API std::vector Detector::detect(image_t img, float thresh, bool use { detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); network &net = detector_gpu.net; - int old_gpu_index; #ifdef GPU + int old_gpu_index; cudaGetDevice(&old_gpu_index); if(cur_gpu_id != old_gpu_index) cudaSetDevice(net.gpu_index); @@ -289,7 +289,7 @@ LIB_API std::vector Detector::detect(image_t img, float thresh, bool use std::vector bbox_vec; - for (size_t i = 0; i < nboxes; ++i) { + for (int i = 0; i < nboxes; ++i) { box b = dets[i].bbox; int const obj_id = max_index(dets[i].prob, l.classes); float const prob = dets[i].prob[obj_id]; From 8bcba6c105a631b7e6fb4215edc7c7cb48cf8ec3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 19 Mar 2019 00:17:09 +0300 Subject: [PATCH 0407/1365] Minor fixes. Added ZED_CAMERA to the Makefile. --- Makefile | 7 +++++++ build/darknet/darknet.vcxproj | 4 ++-- build/darknet/yolo_cpp_dll.vcxproj | 4 ++-- src/yolo_console_dll.cpp | 5 +++++ src/yolo_v2_class.cpp | 1 + 5 files changed, 17 insertions(+), 4 deletions(-) diff --git a/Makefile b/Makefile index c59088afc1f..aa7eec39ad5 100644 --- a/Makefile +++ b/Makefile @@ -5,6 +5,7 @@ OPENCV=0 AVX=0 OPENMP=0 LIBSO=0 +ZED_CAMERA=0 # set GPU=1 and CUDNN=1 to speedup on GPU # set CUDNN_HALF=1 to further speedup 3 x times (Mixed-precision on Tensor Cores) GPU: Volta, Xavier, Turing and higher @@ -111,6 +112,12 @@ CFLAGS+= -DCUDNN_HALF ARCH+= -gencode arch=compute_70,code=[sm_70,compute_70] endif +ifeq ($(ZED_CAMERA), 1) +CFLAGS+= -DZED_CAMERA -I/usr/local/zed/include +LDFLAGS+= -L/usr/local/zed/lib -lsl_core -lsl_input -lsl_zed +#-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0 +endif + OBJ=http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o upsample_layer.o lstm_layer.o ifeq ($(GPU), 1) LDFLAGS+= -lstdc++ diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj index d629cac5b52..f46efa15f81 100644 --- a/build/darknet/darknet.vcxproj +++ b/build/darknet/darknet.vcxproj @@ -96,7 +96,7 @@ true - C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;C:\opencv_2.4.13\opencv\build\x64\vc14\lib;$(CUDA_PATH)lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) + C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;C:\opencv_2.4.13\opencv\build\x64\vc14\lib;$(CUDA_PATH)\lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) $(OutDir)\$(TargetName)$(TargetExt) ..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;%(AdditionalDependencies) true @@ -146,7 +146,7 @@ true true true - C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;$(CUDA_PATH)lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) + C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;$(CUDA_PATH)\lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) ..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;%(AdditionalDependencies) $(OutDir)\$(TargetName)$(TargetExt) diff --git a/build/darknet/yolo_cpp_dll.vcxproj b/build/darknet/yolo_cpp_dll.vcxproj index c03bbea30f5..16050e9e21e 100644 --- a/build/darknet/yolo_cpp_dll.vcxproj +++ b/build/darknet/yolo_cpp_dll.vcxproj @@ -100,7 +100,7 @@ true - $(CUDA_PATH)lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) + $(CUDA_PATH)\lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) $(OutDir)\$(TargetName)$(TargetExt) ..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;cuda.lib;%(AdditionalDependencies) true @@ -149,7 +149,7 @@ true true true - C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) + C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)\lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) ..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;cuda.lib;%(AdditionalDependencies) $(OutDir)\$(TargetName)$(TargetExt) diff --git a/src/yolo_console_dll.cpp b/src/yolo_console_dll.cpp index 42c3632211d..2d7330fa4aa 100644 --- a/src/yolo_console_dll.cpp +++ b/src/yolo_console_dll.cpp @@ -334,6 +334,11 @@ int main(int argc, char *argv[]) if (file_ext == "svo") init_params.svo_input_filename.set(filename.c_str()); if (filename == "zed_camera" || file_ext == "svo") { std::cout << "ZED 3D Camera " << zed.open(init_params) << std::endl; + if (!zed.isOpened()) { + std::cout << " Error: ZED Camera should be connected to USB 3.0. And ZED_SDK should be installed. \n"; + getchar(); + return 0; + } cur_frame = zed_capture_rgb(zed); use_zed_camera = true; } diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index 93cccf44f53..ab582bad52c 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -304,6 +304,7 @@ LIB_API std::vector Detector::detect(image_t img, float thresh, bool use bbox.obj_id = obj_id; bbox.prob = prob; bbox.track_id = 0; + bbox.frames_counter = 0; bbox.x_3d = NAN; bbox.y_3d = NAN; bbox.z_3d = NAN; From d64693eb7733975d113d6353706328aaf6c6f05a Mon Sep 17 00:00:00 2001 From: Josh Veitch-Michaelis Date: Mon, 18 Mar 2019 23:26:04 +0000 Subject: [PATCH 0408/1365] add SGDR policy --- include/darknet.h | 5 ++++- src/network.c | 6 ++++++ src/parser.c | 4 ++++ 3 files changed, 14 insertions(+), 1 deletion(-) diff --git a/include/darknet.h b/include/darknet.h index 0a1451e375a..fb62fc2a032 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -518,7 +518,7 @@ struct layer { // network.h typedef enum { - CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM + CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM, SGDR } learning_rate_policy; // network.h @@ -534,6 +534,9 @@ typedef struct network { learning_rate_policy policy; float learning_rate; + float learning_rate_min; + float learning_rate_max; + int batches_per_cycle; float momentum; float decay; float gamma; diff --git a/src/network.c b/src/network.c index cfc747cb483..32e5e96eeb5 100644 --- a/src/network.c +++ b/src/network.c @@ -117,6 +117,12 @@ float get_current_rate(network net) return net.learning_rate * pow(rand_uniform(0,1), net.power); case SIG: return net.learning_rate * (1./(1.+exp(net.gamma*(batch_num - net.step)))); + case SGDR: + rate = net.learning_rate_min + + 0.5*(net.learning_rate_max-net.learning_rate_min) + * (1. + cos( (float) (batch_num % net.batches_per_cycle)*3.14159265 / net.batches_per_cycle)); + + return rate; default: fprintf(stderr, "Policy is weird!\n"); return net.learning_rate; diff --git a/src/parser.c b/src/parser.c index 11cc4bfcc51..8e3af25f223 100644 --- a/src/parser.c +++ b/src/parser.c @@ -629,6 +629,7 @@ learning_rate_policy get_policy(char *s) if (strcmp(s, "exp")==0) return EXP; if (strcmp(s, "sigmoid")==0) return SIG; if (strcmp(s, "steps")==0) return STEPS; + if (strcmp(s, "sgdr")==0) return SGDR; fprintf(stderr, "Couldn't find policy %s, going with constant\n", s); return CONSTANT; } @@ -637,6 +638,9 @@ void parse_net_options(list *options, network *net) { net->batch = option_find_int(options, "batch",1); net->learning_rate = option_find_float(options, "learning_rate", .001); + net->learning_rate_min = option_find_float_quiet(options, "learning_rate_min", .00001); + net->learning_rate_max = option_find_float_quiet(options, "learning_rate_max", .001); + net->batches_per_cycle = option_find_int(options, "sgdr_cycle", 500); net->momentum = option_find_float(options, "momentum", .9); net->decay = option_find_float(options, "decay", .0001); int subdivs = option_find_int(options, "subdivisions",1); From 53081900e5c740c75a1bba50068aa48123082e05 Mon Sep 17 00:00:00 2001 From: Josh Veitch-Michaelis Date: Mon, 18 Mar 2019 23:37:37 +0000 Subject: [PATCH 0409/1365] make option quiet --- src/parser.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/parser.c b/src/parser.c index 8e3af25f223..77cac7148e0 100644 --- a/src/parser.c +++ b/src/parser.c @@ -640,7 +640,7 @@ void parse_net_options(list *options, network *net) net->learning_rate = option_find_float(options, "learning_rate", .001); net->learning_rate_min = option_find_float_quiet(options, "learning_rate_min", .00001); net->learning_rate_max = option_find_float_quiet(options, "learning_rate_max", .001); - net->batches_per_cycle = option_find_int(options, "sgdr_cycle", 500); + net->batches_per_cycle = option_find_int_quiet(options, "sgdr_cycle", 500); net->momentum = option_find_float(options, "momentum", .9); net->decay = option_find_float(options, "decay", .0001); int subdivs = option_find_int(options, "subdivisions",1); From 6cbf08321a5c2f81da6120d70b15167d9fc7f7e3 Mon Sep 17 00:00:00 2001 From: Josh Veitch-Michaelis Date: Tue, 19 Mar 2019 00:02:01 +0000 Subject: [PATCH 0410/1365] get rid of learning_rate_max --- src/network.c | 2 +- src/parser.c | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/src/network.c b/src/network.c index 32e5e96eeb5..fd7cddfbd8d 100644 --- a/src/network.c +++ b/src/network.c @@ -119,7 +119,7 @@ float get_current_rate(network net) return net.learning_rate * (1./(1.+exp(net.gamma*(batch_num - net.step)))); case SGDR: rate = net.learning_rate_min + - 0.5*(net.learning_rate_max-net.learning_rate_min) + 0.5*(net.learning_rate-net.learning_rate_min) * (1. + cos( (float) (batch_num % net.batches_per_cycle)*3.14159265 / net.batches_per_cycle)); return rate; diff --git a/src/parser.c b/src/parser.c index 77cac7148e0..557e07ba1d7 100644 --- a/src/parser.c +++ b/src/parser.c @@ -639,7 +639,6 @@ void parse_net_options(list *options, network *net) net->batch = option_find_int(options, "batch",1); net->learning_rate = option_find_float(options, "learning_rate", .001); net->learning_rate_min = option_find_float_quiet(options, "learning_rate_min", .00001); - net->learning_rate_max = option_find_float_quiet(options, "learning_rate_max", .001); net->batches_per_cycle = option_find_int_quiet(options, "sgdr_cycle", 500); net->momentum = option_find_float(options, "momentum", .9); net->decay = option_find_float(options, "decay", .0001); From 0557f4314108c7efba4ee16e792e58ac5e347865 Mon Sep 17 00:00:00 2001 From: Aymeric Dujardin Date: Tue, 19 Mar 2019 12:41:47 +0100 Subject: [PATCH 0411/1365] ZED Camera support CMakeLists + Fix Makefile --- CMakeLists.txt | 24 ++++++++++++++++++++++++ Makefile | 4 ++-- 2 files changed, 26 insertions(+), 2 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index d007115d79b..f7f5fd6bfb7 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -167,6 +167,25 @@ if(ENABLE_CUDA) endif() endif() +set(ENABLE_ZED_CAMERA "TRUE" CACHE BOOL "Enable ZED Camera support") +if(ENABLE_CUDA) + if(ENABLE_ZED_CAMERA) + find_package(ZED 2 REQUIRED) + if(ZED_FOUND) + include_directories(${ZED_INCLUDE_DIRS}) + link_directories(${ZED_LIBRARY_DIR}) + add_definitions(-DZED_STEREO) + message("ZED Camera support enabled") + else() + message(WARNING "ZED SDK not found !") + set(ENABLE_ZED_CAMERA "FALSE" CACHE BOOL "Enable ZED Camera support" FORCE) + endif() + endif() +else() + message(WARNING "ZED SDK requires CUDA !") + set(ENABLE_ZED_CAMERA "FALSE" CACHE BOOL "Enable ZED Camera support" FORCE) +endif() + set(CMAKE_THREAD_PREFER_PTHREAD ON) find_package(Threads REQUIRED) if(MSVC) @@ -287,6 +306,11 @@ endif() target_link_libraries(darknet PRIVATE Threads::Threads) target_link_libraries(darklib PRIVATE Threads::Threads) +if(ENABLE_ZED_CAMERA) + target_link_libraries(darknet PRIVATE ${ZED_LIBRARIES}) + target_link_libraries(uselib PRIVATE ${ZED_LIBRARIES}) +endif() + if(ENABLE_CUDA) target_link_libraries(darknet PRIVATE curand cublas cuda) target_link_libraries(darklib PRIVATE curand cublas cuda) diff --git a/Makefile b/Makefile index aa7eec39ad5..b9bed33bff6 100644 --- a/Makefile +++ b/Makefile @@ -33,7 +33,7 @@ OS := $(shell uname) # GTX 1080, GTX 1070, GTX 1060, GTX 1050, GTX 1030, Titan Xp, Tesla P40, Tesla P4 # ARCH= -gencode arch=compute_61,code=sm_61 -gencode arch=compute_61,code=compute_61 -# GP100/Tesla P100 – DGX-1 +# GP100/Tesla P100 DGX-1 # ARCH= -gencode arch=compute_60,code=sm_60 # For Jetson TX1, Tegra X1, DRIVE CX, DRIVE PX - uncomment: @@ -113,7 +113,7 @@ ARCH+= -gencode arch=compute_70,code=[sm_70,compute_70] endif ifeq ($(ZED_CAMERA), 1) -CFLAGS+= -DZED_CAMERA -I/usr/local/zed/include +CFLAGS+= -DZED_STEREO -I/usr/local/zed/include LDFLAGS+= -L/usr/local/zed/lib -lsl_core -lsl_input -lsl_zed #-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0 endif From f1d47703b4c03cf591d8190c63c59fd2e469fdf1 Mon Sep 17 00:00:00 2001 From: Aymeric Dujardin Date: Tue, 19 Mar 2019 14:23:28 +0100 Subject: [PATCH 0412/1365] ZED CMake fail silently if not found --- CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index f7f5fd6bfb7..16febf088d7 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -170,7 +170,7 @@ endif() set(ENABLE_ZED_CAMERA "TRUE" CACHE BOOL "Enable ZED Camera support") if(ENABLE_CUDA) if(ENABLE_ZED_CAMERA) - find_package(ZED 2 REQUIRED) + find_package(ZED 2 QUIET) if(ZED_FOUND) include_directories(${ZED_INCLUDE_DIRS}) link_directories(${ZED_LIBRARY_DIR}) From d2abf0fa57d1f13c85b31929950ba65bc5ea9fde Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 19 Mar 2019 19:06:24 +0300 Subject: [PATCH 0413/1365] Minor fix --- src/detector.c | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/src/detector.c b/src/detector.c index 173bcf785d9..62a8a078edc 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1044,7 +1044,6 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa return mean_average_precision; } -//#ifdef OPENCV typedef struct { float w, h; } anchors_t; @@ -1246,6 +1245,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int cvCircle(img, pt, 1, CV_RGB(red_id, green_id, blue_id), CV_FILLED, 8, 0); //if(pt.x > img_size || pt.y > img_size) printf("\n pt.x = %d, pt.y = %d \n", pt.x, pt.y); } + save_cv_png(img, "cloud.png"); cvShowImage("clusters", img); cvWaitKey(0); cvReleaseImage(&img); @@ -1259,11 +1259,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int getchar(); } -//#else -//void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int show) { -// printf(" k-means++ can't be used without OpenCV, because there is used cvKMeans2 implementation \n"); -//} -//#endif // OPENCV + void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile) From 9dddf82ab6ae6a0ed666515fb5a93574f2fc510e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 19 Mar 2019 20:21:34 +0300 Subject: [PATCH 0414/1365] Compile fix. And minor fixes --- include/yolo_v2_class.hpp | 4 ++-- src/image.h | 1 + src/yolo_console_dll.cpp | 8 +++++++- 3 files changed, 10 insertions(+), 3 deletions(-) diff --git a/include/yolo_v2_class.hpp b/include/yolo_v2_class.hpp index 2d42576dc48..bf3b28aa5a0 100644 --- a/include/yolo_v2_class.hpp +++ b/include/yolo_v2_class.hpp @@ -776,8 +776,8 @@ class track_kalman_t kf.processNoiseCov.at(7) = 1e-2; kf.processNoiseCov.at(14) = 1e-2;// 5.0f; kf.processNoiseCov.at(21) = 1e-2;// 5.0f; - kf.processNoiseCov.at(28) = 1e-2; - kf.processNoiseCov.at(35) = 1e-2; + kf.processNoiseCov.at(28) = 5e-3; + kf.processNoiseCov.at(35) = 5e-3; // Measures Noise Covariance Matrix R - result smoother with higher values (1e-1) cv::setIdentity(kf.measurementNoiseCov, cv::Scalar(1e-1)); diff --git a/src/image.h b/src/image.h index be947d34176..91c6fde7d3c 100644 --- a/src/image.h +++ b/src/image.h @@ -96,6 +96,7 @@ image **load_alphabet(); image get_image_from_stream(CvCapture* cap); image get_image_from_stream_cpp(CvCapture* cap); image ipl_to_image(IplImage* src); +void save_cv_png(IplImage *img, const char *name); #endif //float get_pixel(image m, int x, int y, int c); //float get_pixel_extend(image m, int x, int y, int c); diff --git a/src/yolo_console_dll.cpp b/src/yolo_console_dll.cpp index 2d7330fa4aa..b0e25e2d9f9 100644 --- a/src/yolo_console_dll.cpp +++ b/src/yolo_console_dll.cpp @@ -39,12 +39,14 @@ std::vector get_3d_coordinates(std::vector bbox_vect, cv::Mat xy { bool valid_measure; int i, j; - const int R_max = 4; + const unsigned int R_max_global = 10; std::vector bbox3d_vect; for (auto &cur_box : bbox_vect) { + const unsigned int obj_size = std::min(cur_box.w, cur_box.h); + const unsigned int R_max = std::min(R_max_global, obj_size / 2); int center_i = cur_box.x + cur_box.w * 0.5f, center_j = cur_box.y + cur_box.h * 0.5f; std::vector x_vect, y_vect, z_vect; @@ -183,6 +185,8 @@ void draw_boxes(cv::Mat mat_img, std::vector result_vec, std::vector 0) obj_name += " - " + std::to_string(i.track_id); cv::Size const text_size = getTextSize(obj_name, cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, 2, 0); int max_width = (text_size.width > i.w + 2) ? text_size.width : (i.w + 2); + max_width = std::max(max_width, (int)i.w + 2); + //max_width = std::max(max_width, 283); std::string coords_3d; if (!std::isnan(i.z_3d)) { std::stringstream ss; @@ -326,6 +330,8 @@ int main(int argc, char *argv[]) #ifdef ZED_STEREO sl::InitParameters init_params; + init_params.depth_minimum_distance = 0.5; + init_params.depth_mode = sl::DEPTH_MODE_ULTRA; init_params.camera_resolution = sl::RESOLUTION_HD720; init_params.coordinate_units = sl::UNIT_METER; //init_params.sdk_cuda_ctx = (CUcontext)detector.get_cuda_context(); From fefc7ef80331d3ef860c2fb6a24e33d80390945d Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 19 Mar 2019 20:07:02 +0100 Subject: [PATCH 0415/1365] improve CUDA setup instructions and templates in scripts --- CMakeSettings.json | 160 +++++++++++++++++++++++++++++++++++++++++++-- README.md | 4 +- build.ps1 | 42 ++++++++---- 3 files changed, 187 insertions(+), 19 deletions(-) diff --git a/CMakeSettings.json b/CMakeSettings.json index 968e68bae45..4df04b61ac4 100644 --- a/CMakeSettings.json +++ b/CMakeSettings.json @@ -20,9 +20,28 @@ "cmakeCommandArgs": "", "configurationType": "Debug", "generator": "Visual Studio 15 2017 Win64", - "name": "x64-Debug", - "variables": [ + "name": "x64-Debug static", + "variables": [{ + "name": "CMAKE_TOOLCHAIN_FILE", + "value": "${env.VCPKG_ROOT}\\scripts\\buildsystems\\vcpkg.cmake" + }, + { + "name": "USE_INTEGRATED_LIBS", + "value": "FALSE" + }, { + "name": "VCPKG_TARGET_TRIPLET", + "value": "${env.VCPKG_DEFAULT_TRIPLET}" + } + ] + }, { + "buildCommandArgs": "-m -v:minimal", + "buildRoot": "${env.USERPROFILE}\\CMakeBuilds\\${workspaceHash}\\build\\${name}", + "cmakeCommandArgs": "", + "configurationType": "Release", + "generator": "Visual Studio 15 2017 Win64", + "name": "x64-Release static", + "variables": [{ "name": "CMAKE_TOOLCHAIN_FILE", "value": "${env.VCPKG_ROOT}\\scripts\\buildsystems\\vcpkg.cmake" }, @@ -35,16 +54,139 @@ "value": "${env.VCPKG_DEFAULT_TRIPLET}" } ] - }, + }, { + "buildCommandArgs": "-m -v:minimal", + "buildRoot": "${env.USERPROFILE}\\CMakeBuilds\\${workspaceHash}\\build\\${name}", + "cmakeCommandArgs": "", + "configurationType": "Debug", + "generator": "Visual Studio 15 2017 Win64", + "name": "x64-Debug static w/ CUDA", + "variables": [{ + "name": "CMAKE_TOOLCHAIN_FILE", + "value": "${env.VCPKG_ROOT}\\scripts\\buildsystems\\vcpkg.cmake" + }, + { + "name": "USE_INTEGRATED_LIBS", + "value": "FALSE" + }, + { + "name": "CUDA_COMPUTE_MODEL", + "value": "30" + }, + { + "name": "VCPKG_TARGET_TRIPLET", + "value": "${env.VCPKG_DEFAULT_TRIPLET}" + } + ] + }, { + "buildCommandArgs": "-m -v:minimal", + "buildRoot": "${env.USERPROFILE}\\CMakeBuilds\\${workspaceHash}\\build\\${name}", + "cmakeCommandArgs": "", + "configurationType": "Release", + "generator": "Visual Studio 15 2017 Win64", + "name": "x64-Release static w/ CUDA", + "variables": [{ + "name": "CMAKE_TOOLCHAIN_FILE", + "value": "${env.VCPKG_ROOT}\\scripts\\buildsystems\\vcpkg.cmake" + }, + { + "name": "USE_INTEGRATED_LIBS", + "value": "FALSE" + }, + { + "name": "CUDA_COMPUTE_MODEL", + "value": "30" + }, + { + "name": "VCPKG_TARGET_TRIPLET", + "value": "${env.VCPKG_DEFAULT_TRIPLET}" + } + ] + } { + "buildCommandArgs": "-m -v:minimal", + "buildRoot": "${env.USERPROFILE}\\CMakeBuilds\\${workspaceHash}\\build\\${name}", + "cmakeCommandArgs": "", + "configurationType": "Debug", + "generator": "Visual Studio 15 2017 Win64", + "name": "x64-Debug dynamic", + "variables": [{ + "name": "CMAKE_TOOLCHAIN_FILE", + "value": "${env.VCPKG_ROOT}\\scripts\\buildsystems\\vcpkg.cmake" + }, + { + "name": "USE_INTEGRATED_LIBS", + "value": "FALSE" + }, + { + "name": "BUILD_SHARED_LIBS", + "value": "TRUE" + }, + { + "name": "VCPKG_TARGET_TRIPLET", + "value": "${env.VCPKG_DEFAULT_TRIPLET}" + } + ] + }, { "buildCommandArgs": "-m -v:minimal", "buildRoot": "${env.USERPROFILE}\\CMakeBuilds\\${workspaceHash}\\build\\${name}", "cmakeCommandArgs": "", "configurationType": "Release", "generator": "Visual Studio 15 2017 Win64", - "name": "x64-Release", - "variables": [ + "name": "x64-Release dynamic", + "variables": [{ + "name": "CMAKE_TOOLCHAIN_FILE", + "value": "${env.VCPKG_ROOT}\\scripts\\buildsystems\\vcpkg.cmake" + }, + { + "name": "USE_INTEGRATED_LIBS", + "value": "FALSE" + }, + { + "name": "BUILD_SHARED_LIBS", + "value": "TRUE" + }, + { + "name": "VCPKG_TARGET_TRIPLET", + "value": "${env.VCPKG_DEFAULT_TRIPLET}" + } + ] + }, { + "buildCommandArgs": "-m -v:minimal", + "buildRoot": "${env.USERPROFILE}\\CMakeBuilds\\${workspaceHash}\\build\\${name}", + "cmakeCommandArgs": "", + "configurationType": "Debug", + "generator": "Visual Studio 15 2017 Win64", + "name": "x64-Debug dynamic w/ CUDA", + "variables": [{ + "name": "CMAKE_TOOLCHAIN_FILE", + "value": "${env.VCPKG_ROOT}\\scripts\\buildsystems\\vcpkg.cmake" + }, { + "name": "USE_INTEGRATED_LIBS", + "value": "FALSE" + }, + { + "name": "CUDA_COMPUTE_MODEL", + "value": "30" + }, + { + "name": "BUILD_SHARED_LIBS", + "value": "TRUE" + }, + { + "name": "VCPKG_TARGET_TRIPLET", + "value": "${env.VCPKG_DEFAULT_TRIPLET}" + } + ] + }, { + "buildCommandArgs": "-m -v:minimal", + "buildRoot": "${env.USERPROFILE}\\CMakeBuilds\\${workspaceHash}\\build\\${name}", + "cmakeCommandArgs": "", + "configurationType": "Release", + "generator": "Visual Studio 15 2017 Win64", + "name": "x64-Release dynamic w/ CUDA", + "variables": [{ "name": "CMAKE_TOOLCHAIN_FILE", "value": "${env.VCPKG_ROOT}\\scripts\\buildsystems\\vcpkg.cmake" }, @@ -52,6 +194,14 @@ "name": "USE_INTEGRATED_LIBS", "value": "FALSE" }, + { + "name": "CUDA_COMPUTE_MODEL", + "value": "30" + }, + { + "name": "BUILD_SHARED_LIBS", + "value": "TRUE" + }, { "name": "VCPKG_TARGET_TRIPLET", "value": "${env.VCPKG_DEFAULT_TRIPLET}" diff --git a/README.md b/README.md index 9b740ae207f..e04ffe979fe 100644 --- a/README.md +++ b/README.md @@ -183,9 +183,9 @@ PS \> cd $env:VCPKG_ROOT PS Code\vcpkg> .\vcpkg install pthreads opencv #replace with opencv[cuda] in case you want to use cuda-accelerated openCV ``` -8. [necessary only with CUDA] Customize the CMakeLists.txt with the preferred compute capability +8. [necessary only with CUDA] Customize the `build.ps1` script enabling the appropriate `my_cuda_compute_model` line. If not manually defined, CMake toolchain will automatically use the very low 3.0 CUDA compute model -9. Build with the Powershell script `build.ps1` or use the "Open Folder" functionality of Visual Studio 2017. In the first option, if you want to use Visual Studio, you will find a custom solution created for you by CMake after the build containing all the appropriate config flags for your system. +9. Build with the Powershell script `build.ps1`. If you want to use Visual Studio, you will find a custom solution created for you by CMake after the build containing all the appropriate config flags for your system. ### How to compile on Windows (legacy way) diff --git a/build.ps1 b/build.ps1 index 1dfcc4fd48f..7063098c5a5 100755 --- a/build.ps1 +++ b/build.ps1 @@ -4,6 +4,18 @@ $number_of_build_workers=8 #$shared_lib="-DBUILD_SHARED_LIBS:BOOL=ON" $force_using_include_libs=$false +#$my_cuda_compute_model=75 #Compute capability for Tesla T4, RTX 2080 +#$my_cuda_compute_model=72 #Compute capability for Jetson Xavier +#$my_cuda_compute_model=70 #Compute capability for Tesla V100 +#$my_cuda_compute_model=62 #Compute capability for Jetson TX2 +#$my_cuda_compute_model=61 #Compute capability for Tesla P40 +#$my_cuda_compute_model=60 #Compute capability for Tesla P100 +#$my_cuda_compute_model=53 #Compute capability for Jetson TX1 +#$my_cuda_compute_model=52 #Compute capability for Tesla M40/M60 +#$my_cuda_compute_model=37 #Compute capability for Tesla K80 +#$my_cuda_compute_model=35 #Compute capability for Tesla K20/K40 +#$my_cuda_compute_model=30 #Compute capability for Tesla K10, Quadro K4000 + if ((Test-Path env:VCPKG_ROOT) -and -not $force_using_include_libs) { $vcpkg_path = "$env:VCPKG_ROOT" Write-Host "Found vcpkg in VCPKG_ROOT: $vcpkg_path" @@ -68,33 +80,39 @@ if (Test-Path env:CUDA_PATH) { } } -if ($vcpkg_path) { - # RELEASE - New-Item -Path .\build_win_release -ItemType directory -Force - Set-Location build_win_release - cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Release" $shared_lib .. - cmake --build . --config Release --parallel ${number_of_build_workers} --target install - Remove-Item DarknetConfig.cmake - Remove-Item DarknetConfigVersion.cmake - Set-Location .. +if($my_cuda_compute_model) { + $additional_build_setup = "-DCUDA_COMPUTE_MODEL=${my_cuda_compute_model}" +} +if ($vcpkg_path) { # DEBUG New-Item -Path .\build_win_debug -ItemType directory -Force Set-Location build_win_debug - cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Debug" $shared_lib .. + cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Debug" $shared_lib $additional_build_setup .. cmake --build . --config Debug --parallel ${number_of_build_workers} --target install Remove-Item DarknetConfig.cmake Remove-Item DarknetConfigVersion.cmake Set-Location .. + + # RELEASE + New-Item -Path .\build_win_release -ItemType directory -Force + Set-Location build_win_release + cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Release" $shared_lib $additional_build_setup .. + cmake --build . --config Release --parallel ${number_of_build_workers} --target install + Remove-Item DarknetConfig.cmake + Remove-Item DarknetConfigVersion.cmake + Copy-Item *.dll .. + Set-Location .. } else { - # USE LOCAL PTHREAD LIB, NO VCPKG, ONLY RELEASE + # USE LOCAL PTHREAD LIB AND LOCAL STB HEADER, NO VCPKG, ONLY RELEASE MODE SUPPORTED # if you want to manually force this case, remove VCPKG_ROOT env variable and remember to use "vcpkg integrate remove" in case you had enabled user-wide vcpkg integration New-Item -Path .\build_win_release_novcpkg -ItemType directory -Force Set-Location build_win_release_novcpkg - cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" $shared_lib .. + cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" $shared_lib $additional_build_setup .. cmake --build . --config Release --parallel ${number_of_build_workers} --target install Remove-Item DarknetConfig.cmake Remove-Item DarknetConfigVersion.cmake + Copy-Item ..\3rdparty\pthreads\bin\pthreadVC2.dll .. Set-Location .. } From 790de0543560bcd846a1a5e62137c59e97122f5a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 19 Mar 2019 23:12:40 +0300 Subject: [PATCH 0416/1365] Fix bug with demo.c --- src/demo.c | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/demo.c b/src/demo.c index 8cf5f2858b9..5950091b845 100644 --- a/src/demo.c +++ b/src/demo.c @@ -75,7 +75,8 @@ void *fetch_in_thread(void *ptr) //error("Stream closed."); printf("Stream closed.\n"); flag_exit = 1; - exit(EXIT_FAILURE); + //exit(EXIT_FAILURE); + return 0; } //in_s = resize_image(in, net.w, net.h); From 685fae5feddd500514a513025d8886c07b6db5b8 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 20 Mar 2019 17:00:23 +0300 Subject: [PATCH 0417/1365] Moved 3rd party libraries: pthreads and stb --- build/darknet/darknet.vcxproj | 12 ++++++------ build/darknet/darknet_no_gpu.vcxproj | 16 ++++++++-------- build/darknet/yolo_console_dll.vcxproj | 8 ++++---- build/darknet/yolo_cpp_dll.vcxproj | 12 ++++++------ build/darknet/yolo_cpp_dll_no_gpu.vcxproj | 16 ++++++++-------- pthreadVC2.dll | Bin 0 -> 82944 bytes 6 files changed, 32 insertions(+), 32 deletions(-) create mode 100644 pthreadVC2.dll diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj index f46efa15f81..044bee19285 100644 --- a/build/darknet/darknet.vcxproj +++ b/build/darknet/darknet.vcxproj @@ -88,7 +88,7 @@ Level3 Disabled true - C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include;$(cudnn)\include + $(OpenCV_ROOT_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include;$(cudnn)\include CUDNN_HALF;CUDNN;_CRTDBG_MAP_ALLOC;_MBCS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;DEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) OPENCV; true @@ -96,9 +96,9 @@ true - C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;C:\opencv_2.4.13\opencv\build\x64\vc14\lib;$(CUDA_PATH)\lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) + $(OpenCV_ROOT_DIR)\x64\vc15\lib;$(OpenCV_ROOT_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)\lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) $(OutDir)\$(TargetName)$(TargetExt) - ..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;%(AdditionalDependencies) + pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;%(AdditionalDependencies) true @@ -132,7 +132,7 @@ true true true - C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include;$(cudnn)\include + $(OpenCV_ROOT_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include;$(cudnn)\include OPENCV;CUDNN_HALF;CUDNN;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y @@ -146,8 +146,8 @@ true true true - C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;$(CUDA_PATH)\lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) - ..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;%(AdditionalDependencies) + $(OpenCV_ROOT_DIR)\x64\vc15\lib;$(OpenCV_ROOT_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)\lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) + pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;%(AdditionalDependencies) $(OutDir)\$(TargetName)$(TargetExt) diff --git a/build/darknet/darknet_no_gpu.vcxproj b/build/darknet/darknet_no_gpu.vcxproj index 40145d20b38..c587b70309a 100644 --- a/build/darknet/darknet_no_gpu.vcxproj +++ b/build/darknet/darknet_no_gpu.vcxproj @@ -94,7 +94,7 @@ Level3 Disabled true - C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories) + $(OpenCV_ROOT_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories) _MBCS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) OPENCV;CUDNN true @@ -102,9 +102,9 @@ true - C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;%(AdditionalLibraryDirectories) + $(OpenCV_ROOT_DIR)\x64\vc15\lib;$(OpenCV_ROOT_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) $(OutDir)\$(TargetName)$(TargetExt) - ..\..\3rdparty\lib\x64\pthreadVC2.lib;%(AdditionalDependencies) + pthreadVC2.lib;%(AdditionalDependencies) compute_30,sm_30;compute_52,sm_52 @@ -138,7 +138,7 @@ true true true - C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories) + $(OpenCV_ROOT_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories) OPENCV;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y @@ -151,8 +151,8 @@ true true true - C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;%(AdditionalLibraryDirectories) - ..\..\3rdparty\lib\x64\pthreadVC2.lib;%(AdditionalDependencies) + $(OpenCV_ROOT_DIR)\x64\vc15\lib;$(OpenCV_ROOT_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) + pthreadVC2.lib;%(AdditionalDependencies) $(OutDir)\$(TargetName)$(TargetExt) @@ -193,8 +193,8 @@ - + @@ -256,7 +256,7 @@ - + diff --git a/build/darknet/yolo_console_dll.vcxproj b/build/darknet/yolo_console_dll.vcxproj index c93844b71bc..124b55feb0b 100644 --- a/build/darknet/yolo_console_dll.vcxproj +++ b/build/darknet/yolo_console_dll.vcxproj @@ -87,12 +87,12 @@ Level3 Disabled true - ..\..\include;C:\opencv_3.0\opencv\build\include + ..\..\include;$(OpenCV_ROOT_DIR)\include;C:\opencv_3.0\opencv\build\include MultiThreadedDLL _CRT_SECURE_NO_WARNINGS;_MBCS;%(PreprocessorDefinitions) - C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;$(CUDA_PATH)\lib\x64\;$(ZED_SDK_ROOT_DIR)\lib + $(OpenCV_ROOT_DIR)\x64\vc15\lib;$(OpenCV_ROOT_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;$(CUDA_PATH)\lib\x64\;$(ZED_SDK_ROOT_DIR)\lib @@ -116,7 +116,7 @@ true true true - ..\..\include;C:\opencv_source\opencv\bin\install\include;$(CUDA_PATH)\include;$(ZED_SDK_ROOT_DIR)\include + ..\..\include;$(OpenCV_ROOT_DIR)\include;C:\opencv_source\opencv\bin\install\include;$(CUDA_PATH)\include;$(ZED_SDK_ROOT_DIR)\include OPENCV;_CRT_SECURE_NO_WARNINGS;_MBCS;%(PreprocessorDefinitions) Async true @@ -124,7 +124,7 @@ true true - C:\opencv_source\opencv\bin\install\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;$(CUDA_PATH)\lib\x64\;$(ZED_SDK_ROOT_DIR)\lib + $(OpenCV_ROOT_DIR)\x64\vc15\lib;$(OpenCV_ROOT_DIR)\x64\vc14\lib;C:\opencv_source\opencv\bin\install\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;$(CUDA_PATH)\lib\x64\;$(ZED_SDK_ROOT_DIR)\lib kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) diff --git a/build/darknet/yolo_cpp_dll.vcxproj b/build/darknet/yolo_cpp_dll.vcxproj index 16050e9e21e..b29690a385f 100644 --- a/build/darknet/yolo_cpp_dll.vcxproj +++ b/build/darknet/yolo_cpp_dll.vcxproj @@ -92,7 +92,7 @@ Level3 Disabled true - ..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include;$(cudnn)\include + $(OpenCV_ROOT_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include;$(cudnn)\include _MBCS;LIB_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;DEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) CUDNN_HALF;CUDNN true @@ -100,9 +100,9 @@ true - $(CUDA_PATH)\lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) + $(OpenCV_ROOT_DIR)\x64\vc15\lib;$(OpenCV_ROOT_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)\lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) $(OutDir)\$(TargetName)$(TargetExt) - ..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;cuda.lib;%(AdditionalDependencies) + pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;cuda.lib;%(AdditionalDependencies) true @@ -136,7 +136,7 @@ true true true - C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include;$(cudnn)\include + $(OpenCV_ROOT_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include;$(cudnn)\include CUDNN_HALF;CUDNN;GPU;LIB_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y @@ -149,8 +149,8 @@ true true true - C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)\lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;%(AdditionalLibraryDirectories) - ..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;cuda.lib;%(AdditionalDependencies) + $(OpenCV_ROOT_DIR)\x64\vc15\lib;$(OpenCV_ROOT_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)\lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) + pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;cuda.lib;%(AdditionalDependencies) $(OutDir)\$(TargetName)$(TargetExt) diff --git a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj index 01f00256d68..5ea3aa1ba74 100644 --- a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj +++ b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj @@ -89,7 +89,7 @@ Level3 Disabled true - ..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include + $(OpenCV_ROOT_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories) _MBCS;LIB_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) CUDNN true @@ -97,9 +97,9 @@ true - %(AdditionalLibraryDirectories) + $(OpenCV_ROOT_DIR)\x64\vc15\lib;$(OpenCV_ROOT_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) $(OutDir)\$(TargetName)$(TargetExt) - ..\..\3rdparty\lib\x64\pthreadVC2.lib;%(AdditionalDependencies) + pthreadVC2.lib;%(AdditionalDependencies) true @@ -133,7 +133,7 @@ true true true - C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories) + $(OpenCV_ROOT_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories) LIB_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y @@ -148,8 +148,8 @@ true true true - C:\opencv_3.0\opencv\build\x64\vc14\lib;%(AdditionalLibraryDirectories) - ..\..\3rdparty\lib\x64\pthreadVC2.lib;%(AdditionalDependencies) + $(OpenCV_ROOT_DIR)\x64\vc15\lib;$(OpenCV_ROOT_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) + pthreadVC2.lib;%(AdditionalDependencies) $(OutDir)\$(TargetName)$(TargetExt) @@ -177,8 +177,8 @@ - + @@ -242,7 +242,7 @@ - + diff --git a/pthreadVC2.dll b/pthreadVC2.dll new file mode 100644 index 0000000000000000000000000000000000000000..165b4d26ec41c4e563782f116b4a2e96936b0fef GIT binary patch literal 82944 zcmeF43t&{m)%a&gHd#W#24pe37Y!O;7ztJ)QnQf24QxtKP!y0LpdhFbb`^^V7*brX zsnkcc*3Z&vtG2d9Ygdyn#M!zkYuLbMM@F z&pBt#oH=u5w(P<+Dn%)kinDE{*7KIXBK@C|O zp1bU+3nR1mUNUV;q(X;pjSSJ>=SMEq?~C2|*^&AB`y4m?T=zX$zt5RJd!Cfpwg=@U zO3nFNx;pNWjT61NCUs1>TbB%_>Iu2R3C(|+ceeJg@X1U3U6k_6gFo-B)(|VeATwxeEKg;>xM3xGb=Vrsj+7g)P$49{s!0LuZUI;b&{#q 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HcmV?d00001 From 1cd332e4cac001ffcc12a24c72640fe02b69a8a0 Mon Sep 17 00:00:00 2001 From: Alexey Date: Wed, 20 Mar 2019 17:23:40 +0300 Subject: [PATCH 0418/1365] Update Readme.md --- README.md | 38 +++++++++++++++----------------------- 1 file changed, 15 insertions(+), 23 deletions(-) diff --git a/README.md b/README.md index e04ffe979fe..2b0b010e6a0 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,16 @@ # Yolo-v3 and Yolo-v2 for Windows and Linux ### (neural network for object detection) - Tensor Cores can be used on [Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) and [Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows-using-vcpkg) +Contributors: https://github.com/AlexeyAB/darknet/graphs/contributors +More details: http://pjreddie.com/darknet/yolo/ + + [![CircleCI](https://circleci.com/gh/AlexeyAB/darknet.svg?style=svg)](https://circleci.com/gh/AlexeyAB/darknet) +[![TravisCI](https://travis-ci.org/AlexeyAB/darknet.svg?branch=master)](https://travis-ci.org/AlexeyAB/darknet) +[![AppveyorCI](https://ci.appveyor.com/api/projects/status/594bwb5uoc1fxwiu/branch/master?svg=true)](https://ci.appveyor.com/project/AlexeyAB/darknet/branch/master) -* [Requirements](#requirements) + +* [Requirements (and how to install dependecies)](#requirements) * [Pre-trained models](#pre-trained-models) * [Explanations in issues](https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3AExplanations) @@ -31,28 +38,13 @@ * Yolo v2 on Pascal VOC 2007: https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg * Yolo v2 on Pascal VOC 2012 (comp4): https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg -## "You Only Look Once: Unified, Real-Time Object Detection (versions 2 & 3)" - -A Yolo cross-platform Windows and Linux version (for object detection). Contributors: https://github.com/AlexeyAB/darknet/graphs/contributors - -This repository is forked from Linux-version: https://github.com/pjreddie/darknet - -More details: http://pjreddie.com/darknet/yolo/ - -This repository supports: - -* both Windows and Linux -* both OpenCV 2.x.x and OpenCV <= 4.0 -* both cuDNN >= v7 -* CUDA >= 7.5 -* also create SO-library on Linux and DLL-library on Windows - ### Requirements +* Windows or Linux * **CMake >= 3.8** for modern CUDA support: https://cmake.org/download/ * **CUDA 10.0**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) -* **OpenCV < 4.0**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or [OpenCV Releases](https://opencv.org/releases.html) (on Windows set system variable `OPENCV_DIR` = `C:\opencv\build` where are `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png)) -* **cuDNN >= 7.0 for CUDA 10.0** https://developer.nvidia.com/rdp/cudnn-archive (set system variable `CUDNN` = `C:\cudnn` where did you unpack cuDNN. On Linux in `.bashrc`-file, on Windows see the [image](https://user-images.githubusercontent.com/4096485/53249764-019ef880-36ca-11e9-8ffe-d9cf47e7e462.jpg) ) +* **OpenCV < 4.0**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or download from [OpenCV official site](https://opencv.org/releases.html) (on Windows set system variable `OPENCV_DIR` = `C:\opencv\build` - where are the `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png)) +* **cuDNN >= 7.0 for CUDA 10.0** https://developer.nvidia.com/rdp/cudnn-archive (on **Linux** copy `cudnn.h`,`libcudnn.so`... as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on **Windows** copy `cudnn.h`,`cudnn64_7.dll`, `cudnn64_7.lib` as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows ) * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported * on Linux **GCC or Clang**, on Windows **MSVS 2017 (v15)** https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community&rel=15# @@ -111,15 +103,15 @@ On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector On Linux find executable file `./darknet` in the root directory, while on Windows find it in the directory `\build\darknet\x64` -* Yolo v3 COCO - **image**: `darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` +* Yolo v3 COCO - **image**: `darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights -thresh 0.25` * **Output coordinates** of objects: `darknet.exe detector test cfg/coco.data yolov3.cfg yolov3.weights -ext_output dog.jpg` * Yolo v3 COCO - **video**: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights -ext_output test.mp4` * Yolo v3 COCO - **WebCam 0**: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights -c 0` * Yolo v3 COCO for **net-videocam** - Smart WebCam: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg` -* Yolo v3 - **save result videofile res.avi**: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights -thresh 0.25 test.mp4 -out_filename res.avi` +* Yolo v3 - **save result videofile res.avi**: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights test.mp4 -out_filename res.avi` * Yolo v3 **Tiny** COCO - video: `darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4` * **JSON and MJPEG server** that allows multiple connections from your soft or Web-browser `ip-address:8070` and 8090: `./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output` -* Yolo v3 Tiny **on GPU #0**: `darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 0 test.mp4` +* Yolo v3 Tiny **on GPU #1**: `darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 1 test.mp4` * Alternative method Yolo v3 COCO - image: `darknet.exe detect cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` * Train on **Amazon EC2**, to see mAP & Loss-chart using URL like: `http://ec2-35-160-228-91.us-west-2.compute.amazonaws.com:8090` in the Chrome/Firefox: `./darknet detector train cfg/coco.data yolov3.cfg darknet53.conv.74 -dont_show -mjpeg_port 8090 -map` @@ -501,7 +493,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * increase network resolution in your `.cfg`-file (`height=608`, `width=608` or any value multiple of 32) - it will increase precision - * check that each object are mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: https://github.com/AlexeyAB/Yolo_mark + * check that each object is mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: https://github.com/AlexeyAB/Yolo_mark * for each object which you want to detect - there must be at least 1 similar object in the Training dataset with about the same: shape, side of object, relative size, angle of rotation, tilt, illumination. So desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 different images for each class or more, and you should train `2000*classes` iterations or more From 198d169d222b6f2da845ccc9c381d6922c3fdaaf Mon Sep 17 00:00:00 2001 From: agirbau Date: Fri, 22 Mar 2019 12:09:50 +0100 Subject: [PATCH 0419/1365] Compute Precision and recall per class Doing ./darknet detector map ... now returns precision and recall per class instead of a global precision and recall --- src/detector.c | 37 +++++++++++++++++++++++++++++++++---- 1 file changed, 33 insertions(+), 4 deletions(-) diff --git a/src/detector.c b/src/detector.c index 62a8a078edc..2a1cc1512fb 100644 --- a/src/detector.c +++ b/src/detector.c @@ -751,7 +751,18 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa int unique_truth_count = 0; int* truth_classes_count = (int*)calloc(classes, sizeof(int)); - + + // For multi-class precision and recall computation + float avg_iou_per_class[classes]; + int tp_for_thresh_per_class[classes]; + int fp_for_thresh_per_class[classes]; + int unique_truth_count_per_class[classes]; + + memset(avg_iou_per_class, 0.0, classes * sizeof(float)); + memset(tp_for_thresh_per_class, 0, classes * sizeof(int)); + memset(fp_for_thresh_per_class, 0, classes * sizeof(int)); + memset(unique_truth_count_per_class, 0, classes * sizeof(int)); + for (t = 0; t < nthreads; ++t) { args.path = paths[i + t]; args.im = &buf[t]; @@ -800,6 +811,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa int i, j; for (j = 0; j < num_labels; ++j) { truth_classes_count[truth[j].id]++; + unique_truth_count_per_class[truth[j].id]++; } // difficult @@ -876,9 +888,13 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa if (truth_index > -1 && found == 0) { avg_iou += max_iou; ++tp_for_thresh; + avg_iou_per_class[class_id] += max_iou; + tp_for_thresh_per_class[class_id]++; } - else + else{ fp_for_thresh++; + fp_for_thresh_per_class[class_id]++; + } } } } @@ -905,7 +921,11 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa if ((tp_for_thresh + fp_for_thresh) > 0) avg_iou = avg_iou / (tp_for_thresh + fp_for_thresh); - + for(int class_id = 0; class_id < classes; class_id++){ + if ((tp_for_thresh_per_class[class_id] + fp_for_thresh_per_class[class_id]) > 0) + avg_iou_per_class[class_id] = avg_iou_per_class[class_id] / (tp_for_thresh_per_class[class_id] + fp_for_thresh_per_class[class_id]); + } + // SORT(detections) qsort(detections, detections_count, sizeof(box_prob), detections_comparator); @@ -983,6 +1003,8 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa for (i = 0; i < classes; ++i) { double avg_precision = 0; + float class_precision = 0.0; + float class_recall = 0.0; int point; for (point = 0; point < 11; ++point) { double cur_recall = point * 0.1; @@ -1000,10 +1022,17 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa avg_precision += cur_precision; } avg_precision = avg_precision / 11; - printf("class_id = %d, name = %s, \t ap = %2.2f %% \n", i, names[i], avg_precision * 100); + + printf("\nTP = %d, FP = %d \n", tp_for_thresh_per_class[i], fp_for_thresh_per_class[i]); + class_precision = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)fp_for_thresh_per_class[i]); + class_recall = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)(unique_truth_count_per_class[i] - tp_for_thresh_per_class[i])); + + printf("class_id = %d, name = %s, \t P = %1.2f, \t R = %1.2f, \t avg IOU = %2.2f %%, \t ap = %2.2f %% \n", i, names[i], class_precision, class_recall, avg_iou_per_class[i], avg_precision * 100); mean_average_precision += avg_precision; } + printf("TP = %1.2f, FP = %1.2f \n", (float)tp_for_thresh, (float)fp_for_thresh); + const float cur_precision = (float)tp_for_thresh / ((float)tp_for_thresh + (float)fp_for_thresh); const float cur_recall = (float)tp_for_thresh / ((float)tp_for_thresh + (float)(unique_truth_count - tp_for_thresh)); const float f1_score = 2.F * cur_precision * cur_recall / (cur_precision + cur_recall); From cad99fd75d944fda1d26f7e57e0cf5fb9d4fdf8f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 22 Mar 2019 15:37:35 +0300 Subject: [PATCH 0420/1365] Fixed memory leak for Training with -map flag and random=1 --- src/classifier.c | 8 ++++++++ src/detector.c | 1 + 2 files changed, 9 insertions(+) diff --git a/src/classifier.c b/src/classifier.c index 961e4cbbd8c..22471d619d9 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -777,6 +777,9 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi set_batch_network(&net, 1); srand(2222222); + fuse_conv_batchnorm(net); + calculate_binary_weights(net); + list *options = read_data_cfg(datacfg); char *name_list = option_find_str(options, "names", 0); @@ -873,6 +876,8 @@ void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_ load_weights(&net, weightfile); } srand(time(0)); + fuse_conv_batchnorm(net); + calculate_binary_weights(net); list *options = read_data_cfg(datacfg); @@ -1175,6 +1180,9 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind set_batch_network(&net, 1); list *options = read_data_cfg(datacfg); + fuse_conv_batchnorm(net); + calculate_binary_weights(net); + srand(2222222); CvCapture * cap; diff --git a/src/detector.c b/src/detector.c index 62a8a078edc..82a09a8e054 100644 --- a/src/detector.c +++ b/src/detector.c @@ -285,6 +285,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i args.w = init_w; args.h = init_h; pthread_join(load_thread, 0); + free_data(train); train = buffer; load_thread = load_data(args); int k; From 9815012a01c13f70806381e0c5d556599117c76b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 22 Mar 2019 15:40:17 +0300 Subject: [PATCH 0421/1365] Fixed path in vcxproj-files from $(OpenCV_ROOT_DIR) to $(OPENCV_DIR) --- build/darknet/darknet.vcxproj | 8 ++++---- build/darknet/darknet_no_gpu.vcxproj | 8 ++++---- build/darknet/yolo_console_dll.vcxproj | 8 ++++---- build/darknet/yolo_cpp_dll.vcxproj | 8 ++++---- build/darknet/yolo_cpp_dll_no_gpu.vcxproj | 8 ++++---- 5 files changed, 20 insertions(+), 20 deletions(-) diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj index 044bee19285..2f93f454e6b 100644 --- a/build/darknet/darknet.vcxproj +++ b/build/darknet/darknet.vcxproj @@ -88,7 +88,7 @@ Level3 Disabled true - $(OpenCV_ROOT_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include;$(cudnn)\include + $(OPENCV_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include;$(cudnn)\include CUDNN_HALF;CUDNN;_CRTDBG_MAP_ALLOC;_MBCS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;DEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) OPENCV; true @@ -96,7 +96,7 @@ true - $(OpenCV_ROOT_DIR)\x64\vc15\lib;$(OpenCV_ROOT_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)\lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) + $(OPENCV_DIR)\x64\vc15\lib;$(OPENCV_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)\lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) $(OutDir)\$(TargetName)$(TargetExt) pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;%(AdditionalDependencies) true @@ -132,7 +132,7 @@ true true true - $(OpenCV_ROOT_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include;$(cudnn)\include + $(OPENCV_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include;$(cudnn)\include OPENCV;CUDNN_HALF;CUDNN;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y @@ -146,7 +146,7 @@ true true true - $(OpenCV_ROOT_DIR)\x64\vc15\lib;$(OpenCV_ROOT_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)\lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) + $(OPENCV_DIR)\x64\vc15\lib;$(OPENCV_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)\lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;%(AdditionalDependencies) $(OutDir)\$(TargetName)$(TargetExt) diff --git a/build/darknet/darknet_no_gpu.vcxproj b/build/darknet/darknet_no_gpu.vcxproj index c587b70309a..e2fd6fa6370 100644 --- a/build/darknet/darknet_no_gpu.vcxproj +++ b/build/darknet/darknet_no_gpu.vcxproj @@ -94,7 +94,7 @@ Level3 Disabled true - $(OpenCV_ROOT_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories) + $(OPENCV_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories) _MBCS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) OPENCV;CUDNN true @@ -102,7 +102,7 @@ true - $(OpenCV_ROOT_DIR)\x64\vc15\lib;$(OpenCV_ROOT_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) + $(OPENCV_DIR)\x64\vc15\lib;$(OPENCV_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) $(OutDir)\$(TargetName)$(TargetExt) pthreadVC2.lib;%(AdditionalDependencies) @@ -138,7 +138,7 @@ true true true - $(OpenCV_ROOT_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories) + $(OPENCV_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories) OPENCV;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y @@ -151,7 +151,7 @@ true true true - $(OpenCV_ROOT_DIR)\x64\vc15\lib;$(OpenCV_ROOT_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) + $(OPENCV_DIR)\x64\vc15\lib;$(OPENCV_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) pthreadVC2.lib;%(AdditionalDependencies) $(OutDir)\$(TargetName)$(TargetExt) diff --git a/build/darknet/yolo_console_dll.vcxproj b/build/darknet/yolo_console_dll.vcxproj index 124b55feb0b..20eb14acd20 100644 --- a/build/darknet/yolo_console_dll.vcxproj +++ b/build/darknet/yolo_console_dll.vcxproj @@ -87,12 +87,12 @@ Level3 Disabled true - ..\..\include;$(OpenCV_ROOT_DIR)\include;C:\opencv_3.0\opencv\build\include + ..\..\include;$(OPENCV_DIR)\include;C:\opencv_3.0\opencv\build\include MultiThreadedDLL _CRT_SECURE_NO_WARNINGS;_MBCS;%(PreprocessorDefinitions) - $(OpenCV_ROOT_DIR)\x64\vc15\lib;$(OpenCV_ROOT_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;$(CUDA_PATH)\lib\x64\;$(ZED_SDK_ROOT_DIR)\lib + $(OPENCV_DIR)\x64\vc15\lib;$(OPENCV_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;$(CUDA_PATH)\lib\x64\;$(ZED_SDK_ROOT_DIR)\lib @@ -116,7 +116,7 @@ true true true - ..\..\include;$(OpenCV_ROOT_DIR)\include;C:\opencv_source\opencv\bin\install\include;$(CUDA_PATH)\include;$(ZED_SDK_ROOT_DIR)\include + ..\..\include;$(OPENCV_DIR)\include;C:\opencv_source\opencv\bin\install\include;$(CUDA_PATH)\include;$(ZED_SDK_ROOT_DIR)\include OPENCV;_CRT_SECURE_NO_WARNINGS;_MBCS;%(PreprocessorDefinitions) Async true @@ -124,7 +124,7 @@ true true - $(OpenCV_ROOT_DIR)\x64\vc15\lib;$(OpenCV_ROOT_DIR)\x64\vc14\lib;C:\opencv_source\opencv\bin\install\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;$(CUDA_PATH)\lib\x64\;$(ZED_SDK_ROOT_DIR)\lib + $(OPENCV_DIR)\x64\vc15\lib;$(OPENCV_DIR)\x64\vc14\lib;C:\opencv_source\opencv\bin\install\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;C:\opencv_2.4.13\opencv\build\x64\vc12\lib;$(CUDA_PATH)\lib\x64\;$(ZED_SDK_ROOT_DIR)\lib kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies) diff --git a/build/darknet/yolo_cpp_dll.vcxproj b/build/darknet/yolo_cpp_dll.vcxproj index b29690a385f..499d186cc38 100644 --- a/build/darknet/yolo_cpp_dll.vcxproj +++ b/build/darknet/yolo_cpp_dll.vcxproj @@ -92,7 +92,7 @@ Level3 Disabled true - $(OpenCV_ROOT_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include;$(cudnn)\include + $(OPENCV_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include;$(cudnn)\include _MBCS;LIB_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;DEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) CUDNN_HALF;CUDNN true @@ -100,7 +100,7 @@ true - $(OpenCV_ROOT_DIR)\x64\vc15\lib;$(OpenCV_ROOT_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)\lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) + $(OPENCV_DIR)\x64\vc15\lib;$(OPENCV_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)\lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) $(OutDir)\$(TargetName)$(TargetExt) pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;cuda.lib;%(AdditionalDependencies) true @@ -136,7 +136,7 @@ true true true - $(OpenCV_ROOT_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include;$(cudnn)\include + $(OPENCV_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include;$(cudnn)\include CUDNN_HALF;CUDNN;GPU;LIB_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y @@ -149,7 +149,7 @@ true true true - $(OpenCV_ROOT_DIR)\x64\vc15\lib;$(OpenCV_ROOT_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)\lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) + $(OPENCV_DIR)\x64\vc15\lib;$(OPENCV_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)\lib\$(PlatformName);$(CUDNN)\lib\x64;$(cudnn)\lib\x64;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;cuda.lib;%(AdditionalDependencies) $(OutDir)\$(TargetName)$(TargetExt) diff --git a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj index 5ea3aa1ba74..3b3c70a434a 100644 --- a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj +++ b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj @@ -89,7 +89,7 @@ Level3 Disabled true - $(OpenCV_ROOT_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories) + $(OPENCV_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories) _MBCS;LIB_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) CUDNN true @@ -97,7 +97,7 @@ true - $(OpenCV_ROOT_DIR)\x64\vc15\lib;$(OpenCV_ROOT_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) + $(OPENCV_DIR)\x64\vc15\lib;$(OPENCV_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) $(OutDir)\$(TargetName)$(TargetExt) pthreadVC2.lib;%(AdditionalDependencies) true @@ -133,7 +133,7 @@ true true true - $(OpenCV_ROOT_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories) + $(OPENCV_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories) LIB_EXPORTS;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y @@ -148,7 +148,7 @@ true true true - $(OpenCV_ROOT_DIR)\x64\vc15\lib;$(OpenCV_ROOT_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) + $(OPENCV_DIR)\x64\vc15\lib;$(OPENCV_DIR)\x64\vc14\lib;C:\opencv_3.0\opencv\build\x64\vc14\lib;..\..\3rdparty\pthreads\lib;%(AdditionalLibraryDirectories) pthreadVC2.lib;%(AdditionalDependencies) $(OutDir)\$(TargetName)$(TargetExt) From ab9e891f60927c0be52fa883fcab1a1737ada3e7 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 26 Mar 2019 18:32:36 +0300 Subject: [PATCH 0422/1365] Minor fixes --- CMakeLists.txt | 2 +- README.md | 19 ++++++++++++++----- build/darknet/YoloWrapper.cs | 3 ++- src/yolo_console_dll.cpp | 2 +- 4 files changed, 18 insertions(+), 8 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 6a5076ad737..54609b19adc 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -247,7 +247,7 @@ endif() set_source_files_properties(${sources} PROPERTIES LANGUAGE CXX) -add_library(darklib ${CMAKE_CURRENT_LIST_DIR}/include/yolo_v2_class.hpp ${CMAKE_CURRENT_LIST_DIR}/src/yolo_v2_class.cpp ${sources} ${headers} ${cuda_sources}) +add_library(darklib SHARED ${CMAKE_CURRENT_LIST_DIR}/include/yolo_v2_class.hpp ${CMAKE_CURRENT_LIST_DIR}/src/yolo_v2_class.cpp ${sources} ${headers} ${cuda_sources}) set_target_properties(darklib PROPERTIES POSITION_INDEPENDENT_CODE ON) if(ENABLE_CUDA) set_target_properties(darklib PROPERTIES CUDA_SEPARABLE_COMPILATION ON) diff --git a/README.md b/README.md index 2b0b010e6a0..62795108f2d 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# Yolo-v3 and Yolo-v2 for Windows and Linux +# Yolo-v3 and Yolo-v2 for Windows and Linux ### (neural network for object detection) - Tensor Cores can be used on [Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) and [Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows-using-vcpkg) Contributors: https://github.com/AlexeyAB/darknet/graphs/contributors @@ -13,6 +13,7 @@ More details: http://pjreddie.com/darknet/yolo/ * [Requirements (and how to install dependecies)](#requirements) * [Pre-trained models](#pre-trained-models) * [Explanations in issues](https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3AExplanations) +* [Yolo v3 in other frameworks (TensorFlow, OpenVINO, OpenCV-dnn, ...)](#yolo-v3-in-other-frameworks) 0. [Improvements in this repository](#improvements-in-this-repository) 1. [How to use](#how-to-use-on-the-command-line) @@ -66,6 +67,12 @@ Put it near compiled: darknet.exe You can get cfg-files by path: `darknet/cfg/` +#### Yolo v3 in other frameworks + +* Convert `yolov3.weights`/`cfg` model to **TensorFlow**: by using [mystic123](https://github.com/mystic123/tensorflow-yolo-v3) or [jinyu121](https://github.com/jinyu121/DW2TF) projects +* To use Yolo v3 model in **Intel OpenVINO** (Myriad X / USB Neural Compute Stick / Arria FPGA): read this [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) +* **OpenCV-dnn** is very fast DNN implementation on CPU (x86/ARM-Android), use `yolov3.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221), [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) + ##### Examples of results [![Yolo v3](http://img.youtube.com/vi/VOC3huqHrss/0.jpg)](https://www.youtube.com/watch?v=MPU2HistivI "Yolo v3") @@ -113,14 +120,16 @@ On Linux find executable file `./darknet` in the root directory, while on Window * **JSON and MJPEG server** that allows multiple connections from your soft or Web-browser `ip-address:8070` and 8090: `./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output` * Yolo v3 Tiny **on GPU #1**: `darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 1 test.mp4` * Alternative method Yolo v3 COCO - image: `darknet.exe detect cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` -* Train on **Amazon EC2**, to see mAP & Loss-chart using URL like: `http://ec2-35-160-228-91.us-west-2.compute.amazonaws.com:8090` in the Chrome/Firefox: +* Train on **Amazon EC2**, to see mAP & Loss-chart using URL like: `http://ec2-35-160-228-91.us-west-2.compute.amazonaws.com:8090` in the Chrome/Firefox (**Darknet should be compiled with OpenCV**): `./darknet detector train cfg/coco.data yolov3.cfg darknet53.conv.74 -dont_show -mjpeg_port 8090 -map` -* 186 MB Yolo9000 - image: `darknet.exe detector test cfg/combine9k.data yolo9000.cfg yolo9000.weights` +* 186 MB Yolo9000 - image: `darknet.exe detector test cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights` * Remeber to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app +* To process a list of images `data/train.txt` and save results of detection to `result.json` file use: + `darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights -ext_output -dont_show -out result.json < data/train.txt` * To process a list of images `data/train.txt` and save results of detection to `result.txt` use: - `darknet.exe detector test cfg/coco.data yolov3.cfg yolov3.weights -dont_show -ext_output < data/train.txt > result.txt` + `darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights -dont_show -ext_output < data/train.txt > result.txt` * Pseudo-lableing - to process a list of images `data/new_train.txt` and save results of detection in Yolo training format for each image as label `.txt` (in this way you can increase the amount of training data) use: - `darknet.exe detector test cfg/coco.data yolov3.cfg yolov3.weights -thresh 0.25 -dont_show -save_labels < data/new_train.txt` + `darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights -thresh 0.25 -dont_show -save_labels < data/new_train.txt` * To calculate anchors: `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416` * To check accuracy mAP@IoU=50: `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights` * To check accuracy mAP@IoU=75: `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights -iou_thresh 0.75` diff --git a/build/darknet/YoloWrapper.cs b/build/darknet/YoloWrapper.cs index f0c1b0c270f..52c12adb80f 100644 --- a/build/darknet/YoloWrapper.cs +++ b/build/darknet/YoloWrapper.cs @@ -24,10 +24,11 @@ public class YoloWrapper : IDisposable public struct bbox_t { public UInt32 x, y, w, h; // (x,y) - top-left corner, (w, h) - width & height of bounded box - public float prob; // confidence - probability that the object was found correctly + public float prob; // confidence - probability that the object was found correctly public UInt32 obj_id; // class of object - from range [0, classes-1] public UInt32 track_id; // tracking id for video (0 - untracked, 1 - inf - tracked object) public UInt32 frames_counter; + public float x_3d, y_3d, z_3d; // 3-D coordinates, if there is used 3D-stereo camera }; [StructLayout(LayoutKind.Sequential)] diff --git a/src/yolo_console_dll.cpp b/src/yolo_console_dll.cpp index b0e25e2d9f9..d0707ec0956 100644 --- a/src/yolo_console_dll.cpp +++ b/src/yolo_console_dll.cpp @@ -332,7 +332,7 @@ int main(int argc, char *argv[]) sl::InitParameters init_params; init_params.depth_minimum_distance = 0.5; init_params.depth_mode = sl::DEPTH_MODE_ULTRA; - init_params.camera_resolution = sl::RESOLUTION_HD720; + init_params.camera_resolution = sl::RESOLUTION_HD720;// sl::RESOLUTION_HD1080, sl::RESOLUTION_HD720 init_params.coordinate_units = sl::UNIT_METER; //init_params.sdk_cuda_ctx = (CUcontext)detector.get_cuda_context(); init_params.sdk_gpu_id = detector.cur_gpu_id; From fbf7a5fa4cd35432d7d22a478a8a32d96a9e5ab6 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 27 Mar 2019 00:11:13 +0300 Subject: [PATCH 0423/1365] Minor fix --- build/darknet/x64/calc_mAP.cmd | 5 ++--- include/darknet.h | 2 +- src/detector.c | 7 ++++--- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/build/darknet/x64/calc_mAP.cmd b/build/darknet/x64/calc_mAP.cmd index 614a422aab0..9b4fafb3bb1 100644 --- a/build/darknet/x64/calc_mAP.cmd +++ b/build/darknet/x64/calc_mAP.cmd @@ -1,11 +1,10 @@ rem # How to calculate mAP (mean average precision) -rem darknet.exe detector map data/voc.data cfg/yolov2-tiny-voc.cfg yolov2-tiny-voc.weights +rem darknet.exe detector map data/voc.data cfg/yolov2-tiny-voc.cfg yolov2-tiny-voc.weights -11points -darknet.exe detector map data/voc.data cfg/yolov2-voc.cfg yolo-voc.weights - +darknet.exe detector map data/voc.data cfg/yolov2-voc.cfg yolo-voc.weights -11points diff --git a/include/darknet.h b/include/darknet.h index 0a1451e375a..fc56a4f17aa 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -767,7 +767,7 @@ LIB_API layer* get_network_layer(network* net, int i); LIB_API detection *make_network_boxes(network *net, float thresh, int *num); LIB_API void reset_rnn(network *net); LIB_API float *network_predict_image(network *net, image im); -LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, network *existing_net); +LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, const int map_11_points, network *existing_net); LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port); LIB_API void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile); diff --git a/src/detector.c b/src/detector.c index 82a09a8e054..4f421593a92 100644 --- a/src/detector.c +++ b/src/detector.c @@ -301,7 +301,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i //network net_combined = combine_train_valid_networks(net, net_map); iter_map = i; - mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, &net_map);// &net_combined); + mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, 0, &net_map);// &net_combined); printf("\n mean_average_precision (mAP@0.5) = %f \n", mean_average_precision); draw_precision = 1; } @@ -674,7 +674,7 @@ int detections_comparator(const void *pa, const void *pb) return 0; } -float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, network *existing_net) +float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, const int map_11_points, network *existing_net) { int j; list *options = read_data_cfg(datacfg); @@ -1420,6 +1420,7 @@ void run_detector(int argc, char **argv) int dont_show = find_arg(argc, argv, "-dont_show"); int show = find_arg(argc, argv, "-show"); int calc_map = find_arg(argc, argv, "-map"); + int map_11_points = find_arg(argc, argv, "-11points"); check_mistakes = find_arg(argc, argv, "-check_mistakes"); int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1); int json_port = find_int_arg(argc, argv, "-json_port", -1); @@ -1479,7 +1480,7 @@ void run_detector(int argc, char **argv) else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map, mjpeg_port); else if (0 == strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile); else if (0 == strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights); - else if (0 == strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh, iou_thresh, NULL); + else if (0 == strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh, iou_thresh, map_11_points, NULL); else if (0 == strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, height, show); else if (0 == strcmp(argv[2], "demo")) { list *options = read_data_cfg(datacfg); From f4825906916130ac1c32eb69c9fb812aacde1838 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 27 Mar 2019 01:34:06 +0300 Subject: [PATCH 0424/1365] Added -map_points flag for mAP calculation for MSCOCO, PascalVOC2007 / 2010-2012, ImageNet --- build/darknet/x64/calc_mAP.cmd | 4 +- build/darknet/x64/calc_mAP_coco.cmd | 16 ++++ include/darknet.h | 2 +- src/detector.c | 114 ++++++++++++++++++---------- 4 files changed, 91 insertions(+), 45 deletions(-) create mode 100644 build/darknet/x64/calc_mAP_coco.cmd diff --git a/build/darknet/x64/calc_mAP.cmd b/build/darknet/x64/calc_mAP.cmd index 9b4fafb3bb1..4622c52511b 100644 --- a/build/darknet/x64/calc_mAP.cmd +++ b/build/darknet/x64/calc_mAP.cmd @@ -1,10 +1,10 @@ rem # How to calculate mAP (mean average precision) -rem darknet.exe detector map data/voc.data cfg/yolov2-tiny-voc.cfg yolov2-tiny-voc.weights -11points +rem darknet.exe detector map data/voc.data cfg/yolov2-tiny-voc.cfg yolov2-tiny-voc.weights -points 11 -darknet.exe detector map data/voc.data cfg/yolov2-voc.cfg yolo-voc.weights -11points +darknet.exe detector map data/voc.data cfg/yolov2-voc.cfg yolo-voc.weights -points 11 diff --git a/build/darknet/x64/calc_mAP_coco.cmd b/build/darknet/x64/calc_mAP_coco.cmd new file mode 100644 index 00000000000..e4bd32dec02 --- /dev/null +++ b/build/darknet/x64/calc_mAP_coco.cmd @@ -0,0 +1,16 @@ +rem # How to calculate Yolo v3 mAP on MS COCO + +rem darknet.exe detector map cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -points 101 + + +darknet.exe detector map cfg/coco.data cfg/yolov3-spp.cfg yolov3-spp.weights -points 101 + + +rem darknet.exe detector map cfg/coco.data cfg/yolov3.cfg yolov3.weights -points 101 + + +rem darknet.exe detector map cfg/coco.data cfg/yolov3.cfg yolov3.weights -iou_thresh 0.75 -points 101 + + + +pause diff --git a/include/darknet.h b/include/darknet.h index fc56a4f17aa..e04796418a4 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -767,7 +767,7 @@ LIB_API layer* get_network_layer(network* net, int i); LIB_API detection *make_network_boxes(network *net, float thresh, int *num); LIB_API void reset_rnn(network *net); LIB_API float *network_predict_image(network *net, image im); -LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, const int map_11_points, network *existing_net); +LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, const int map_points, network *existing_net); LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port); LIB_API void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile); diff --git a/src/detector.c b/src/detector.c index b28783ff1a8..d189d6b8f2c 100644 --- a/src/detector.c +++ b/src/detector.c @@ -674,7 +674,7 @@ int detections_comparator(const void *pa, const void *pb) return 0; } -float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, const int map_11_points, network *existing_net) +float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, const int map_points, network *existing_net) { int j; list *options = read_data_cfg(datacfg); @@ -752,18 +752,12 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa int unique_truth_count = 0; int* truth_classes_count = (int*)calloc(classes, sizeof(int)); - + // For multi-class precision and recall computation - float avg_iou_per_class[classes]; - int tp_for_thresh_per_class[classes]; - int fp_for_thresh_per_class[classes]; - int unique_truth_count_per_class[classes]; - - memset(avg_iou_per_class, 0.0, classes * sizeof(float)); - memset(tp_for_thresh_per_class, 0, classes * sizeof(int)); - memset(fp_for_thresh_per_class, 0, classes * sizeof(int)); - memset(unique_truth_count_per_class, 0, classes * sizeof(int)); - + float *avg_iou_per_class = (int*)calloc(classes, sizeof(int)); + int *tp_for_thresh_per_class = (int*)calloc(classes, sizeof(int)); + int *fp_for_thresh_per_class = (int*)calloc(classes, sizeof(int)); + for (t = 0; t < nthreads; ++t) { args.path = paths[i + t]; args.im = &buf[t]; @@ -812,7 +806,6 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa int i, j; for (j = 0; j < num_labels; ++j) { truth_classes_count[truth[j].id]++; - unique_truth_count_per_class[truth[j].id]++; } // difficult @@ -881,10 +874,11 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa // calc avg IoU, true-positives, false-positives for required Threshold if (prob > thresh_calc_avg_iou) { int z, found = 0; - for (z = checkpoint_detections_count; z < detections_count - 1; ++z) + for (z = checkpoint_detections_count; z < detections_count - 1; ++z) { if (detections[z].unique_truth_index == truth_index) { found = 1; break; } + } if (truth_index > -1 && found == 0) { avg_iou += max_iou; @@ -922,11 +916,12 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa if ((tp_for_thresh + fp_for_thresh) > 0) avg_iou = avg_iou / (tp_for_thresh + fp_for_thresh); - for(int class_id = 0; class_id < classes; class_id++){ + int class_id; + for(class_id = 0; class_id < classes; class_id++){ if ((tp_for_thresh_per_class[class_id] + fp_for_thresh_per_class[class_id]) > 0) avg_iou_per_class[class_id] = avg_iou_per_class[class_id] / (tp_for_thresh_per_class[class_id] + fp_for_thresh_per_class[class_id]); } - + // SORT(detections) qsort(detections, detections_count, sizeof(box_prob), detections_comparator); @@ -1004,47 +999,74 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa for (i = 0; i < classes; ++i) { double avg_precision = 0; - float class_precision = 0.0; - float class_recall = 0.0; - int point; - for (point = 0; point < 11; ++point) { - double cur_recall = point * 0.1; - double cur_precision = 0; - for (rank = 0; rank < detections_count; ++rank) + + // MS COCO - uses 101-Recall-points on PR-chart. + // PascalVOC2007 - uses 11-Recall-points on PR-chart. + // PascalVOC2010–2012 - uses Area-Under-Curve on PR-chart. + // ImageNet - uses Area-Under-Curve on PR-chart. + + // correct mAP calculation: ImageNet, PascalVOC 2010-2012 + if (map_points == 0) + { + double last_recall = pr[i][detections_count - 1].recall; + double last_precision = pr[i][detections_count - 1].precision; + for (rank = detections_count - 2; rank >= 0; --rank) { - if (pr[i][rank].recall >= cur_recall) { // > or >= - if (pr[i][rank].precision > cur_precision) { - cur_precision = pr[i][rank].precision; - } + double delta_recall = last_recall - pr[i][rank].recall; + last_recall = pr[i][rank].recall; + + if (pr[i][rank].precision > last_precision) { + last_precision = pr[i][rank].precision; } + + avg_precision += delta_recall * last_precision; } - //printf("class_id = %d, point = %d, cur_recall = %.4f, cur_precision = %.4f \n", i, point, cur_recall, cur_precision); + } + // MSCOCO - 101 Recall-points, PascalVOC - 11 Recall-points + else + { + int point; + for (point = 0; point < map_points; ++point) { + double cur_recall = point * 1.0 / (map_points-1); + double cur_precision = 0; + for (rank = 0; rank < detections_count; ++rank) + { + if (pr[i][rank].recall >= cur_recall) { // > or >= + if (pr[i][rank].precision > cur_precision) { + cur_precision = pr[i][rank].precision; + } + } + } + //printf("class_id = %d, point = %d, cur_recall = %.4f, cur_precision = %.4f \n", i, point, cur_recall, cur_precision); - avg_precision += cur_precision; + avg_precision += cur_precision; + } + avg_precision = avg_precision / map_points; } - avg_precision = avg_precision / 11; - - printf("\nTP = %d, FP = %d \n", tp_for_thresh_per_class[i], fp_for_thresh_per_class[i]); - class_precision = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)fp_for_thresh_per_class[i]); - class_recall = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)(unique_truth_count_per_class[i] - tp_for_thresh_per_class[i])); - - printf("class_id = %d, name = %s, \t P = %1.2f, \t R = %1.2f, \t avg IOU = %2.2f %%, \t ap = %2.2f %% \n", i, names[i], class_precision, class_recall, avg_iou_per_class[i], avg_precision * 100); + + printf("class_id = %d, name = %s, ap = %2.2f%% \t (TP = %d, FP = %d) \n", + i, names[i], avg_precision * 100, tp_for_thresh_per_class[i], fp_for_thresh_per_class[i]); + + float class_precision = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)fp_for_thresh_per_class[i]); + float class_recall = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)(truth_classes_count[i] - tp_for_thresh_per_class[i])); + //printf("Precision = %1.2f, Recall = %1.2f, avg IOU = %2.2f%% \n\n", class_precision, class_recall, avg_iou_per_class[i]); + mean_average_precision += avg_precision; } - printf("TP = %1.2f, FP = %1.2f \n", (float)tp_for_thresh, (float)fp_for_thresh); - const float cur_precision = (float)tp_for_thresh / ((float)tp_for_thresh + (float)fp_for_thresh); const float cur_recall = (float)tp_for_thresh / ((float)tp_for_thresh + (float)(unique_truth_count - tp_for_thresh)); const float f1_score = 2.F * cur_precision * cur_recall / (cur_precision + cur_recall); - printf(" for thresh = %1.2f, precision = %1.2f, recall = %1.2f, F1-score = %1.2f \n", + printf("\n for thresh = %1.2f, precision = %1.2f, recall = %1.2f, F1-score = %1.2f \n", thresh_calc_avg_iou, cur_precision, cur_recall, f1_score); printf(" for thresh = %0.2f, TP = %d, FP = %d, FN = %d, average IoU = %2.2f %% \n", thresh_calc_avg_iou, tp_for_thresh, fp_for_thresh, unique_truth_count - tp_for_thresh, avg_iou * 100); mean_average_precision = mean_average_precision / classes; - printf("\n IoU threshold = %2.0f %% \n", iou_thresh * 100); + printf("\n IoU threshold = %2.0f %%, ", iou_thresh * 100); + if (map_points) printf("used %d Recall-points \n", map_points); + else printf("used Area-Under-Curve for each unique Recall \n"); printf(" mean average precision (mAP@%0.2f) = %f, or %2.2f %% \n", iou_thresh, mean_average_precision, mean_average_precision * 100); @@ -1056,7 +1078,15 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa free(truth_classes_count); free(detection_per_class_count); + free(avg_iou_per_class); + free(tp_for_thresh_per_class); + free(fp_for_thresh_per_class); + fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); + printf("\nSet -map_points flag:\n"); + printf(" `-map_points 101` for MS COCO \n"); + printf(" `-map_points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n"); + printf(" `-map_points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n"); if (reinforcement_fd != NULL) fclose(reinforcement_fd); // free memory @@ -1449,7 +1479,7 @@ void run_detector(int argc, char **argv) int dont_show = find_arg(argc, argv, "-dont_show"); int show = find_arg(argc, argv, "-show"); int calc_map = find_arg(argc, argv, "-map"); - int map_11_points = find_arg(argc, argv, "-11points"); + int map_points = find_int_arg(argc, argv, "-points", 0); check_mistakes = find_arg(argc, argv, "-check_mistakes"); int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1); int json_port = find_int_arg(argc, argv, "-json_port", -1); @@ -1509,7 +1539,7 @@ void run_detector(int argc, char **argv) else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map, mjpeg_port); else if (0 == strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile); else if (0 == strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights); - else if (0 == strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh, iou_thresh, map_11_points, NULL); + else if (0 == strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh, iou_thresh, map_points, NULL); else if (0 == strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, height, show); else if (0 == strcmp(argv[2], "demo")) { list *options = read_data_cfg(datacfg); From 7b1ef39607047b4bcd01c450b0ead002c5148bb1 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 27 Mar 2019 13:52:28 +0300 Subject: [PATCH 0425/1365] Compile fix --- .travis.yml | 26 +++++++++++++------------- src/detector.c | 10 +++++----- 2 files changed, 18 insertions(+), 18 deletions(-) diff --git a/.travis.yml b/.travis.yml index 0ce769c6aa5..fe18df55aee 100644 --- a/.travis.yml +++ b/.travis.yml @@ -212,7 +212,7 @@ matrix: - libopencv-dev env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: clang @@ -225,7 +225,7 @@ matrix: - clang-3.6 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="CC=clang-3.6 && CXX=clang++-3.6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="CC=clang-3.6 && CXX=clang++-3.6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: clang @@ -240,7 +240,7 @@ matrix: - clang-3.8 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="CC=clang-3.8 && CXX=clang++-3.8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="CC=clang-3.8 && CXX=clang++-3.8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: clang @@ -255,7 +255,7 @@ matrix: - clang-3.9 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="CC=clang-3.9 && CXX=clang++-3.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="CC=clang-3.9 && CXX=clang++-3.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: clang @@ -270,7 +270,7 @@ matrix: - clang-4.0 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="CC=clang-4.0 && CXX=clang++-4.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="CC=clang-4.0 && CXX=clang++-4.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: clang @@ -285,7 +285,7 @@ matrix: - clang-5.0 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="CC=clang-5.0 && CXX=clang++-5.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="CC=clang-5.0 && CXX=clang++-5.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: clang @@ -301,7 +301,7 @@ matrix: - clang-6.0 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="CC=clang-6.0 && CXX=clang++-6.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="CC=clang-6.0 && CXX=clang++-6.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: gcc @@ -313,7 +313,7 @@ matrix: - libopencv-dev env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: gcc @@ -328,7 +328,7 @@ matrix: - g++-4.9 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="CC=gcc-4.9 && CXX=g++-4.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="CC=gcc-4.9 && CXX=g++-4.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: gcc @@ -343,7 +343,7 @@ matrix: - g++-5 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="CC=gcc-5 && CXX=g++-5 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="CC=gcc-5 && CXX=g++-5 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: gcc @@ -358,7 +358,7 @@ matrix: - libopencv-dev env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="CC=gcc-6 && CXX=g++-6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="CC=gcc-6 && CXX=g++-6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: gcc @@ -373,7 +373,7 @@ matrix: - g++-7 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="CC=gcc-7 && CXX=g++-7 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="CC=gcc-7 && CXX=g++-7 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: gcc @@ -388,7 +388,7 @@ matrix: - g++-8 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" before_install: - travis_retry eval "${MATRIX_EVAL}" diff --git a/src/detector.c b/src/detector.c index d189d6b8f2c..46d19615e3f 100644 --- a/src/detector.c +++ b/src/detector.c @@ -754,7 +754,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa int* truth_classes_count = (int*)calloc(classes, sizeof(int)); // For multi-class precision and recall computation - float *avg_iou_per_class = (int*)calloc(classes, sizeof(int)); + float *avg_iou_per_class = (float*)calloc(classes, sizeof(float)); int *tp_for_thresh_per_class = (int*)calloc(classes, sizeof(int)); int *fp_for_thresh_per_class = (int*)calloc(classes, sizeof(int)); @@ -1083,10 +1083,10 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa free(fp_for_thresh_per_class); fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); - printf("\nSet -map_points flag:\n"); - printf(" `-map_points 101` for MS COCO \n"); - printf(" `-map_points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n"); - printf(" `-map_points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n"); + printf("\nSet -points flag:\n"); + printf(" `-points 101` for MS COCO \n"); + printf(" `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n"); + printf(" `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n"); if (reinforcement_fd != NULL) fclose(reinforcement_fd); // free memory From f5fd086407ef3caacc98672c3b8ad8c0e2e0491c Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 28 Mar 2019 00:11:16 +0300 Subject: [PATCH 0426/1365] Minor fix --- .travis.yml | 26 +++++++++++++------------- CMakeLists.txt | 2 +- README.md | 12 ++++++++++-- src/crnn_layer.c | 3 +-- src/dark_cuda.c | 4 ++++ src/network_kernels.cu | 2 +- 6 files changed, 30 insertions(+), 19 deletions(-) diff --git a/.travis.yml b/.travis.yml index fe18df55aee..f63463aebb7 100644 --- a/.travis.yml +++ b/.travis.yml @@ -212,7 +212,7 @@ matrix: - libopencv-dev env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: clang @@ -225,7 +225,7 @@ matrix: - clang-3.6 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="CC=clang-3.6 && CXX=clang++-3.6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="CC=clang-3.6 && CXX=clang++-3.6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: clang @@ -240,7 +240,7 @@ matrix: - clang-3.8 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="CC=clang-3.8 && CXX=clang++-3.8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="CC=clang-3.8 && CXX=clang++-3.8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: clang @@ -255,7 +255,7 @@ matrix: - clang-3.9 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="CC=clang-3.9 && CXX=clang++-3.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="CC=clang-3.9 && CXX=clang++-3.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: clang @@ -270,7 +270,7 @@ matrix: - clang-4.0 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="CC=clang-4.0 && CXX=clang++-4.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="CC=clang-4.0 && CXX=clang++-4.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: clang @@ -285,7 +285,7 @@ matrix: - clang-5.0 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="CC=clang-5.0 && CXX=clang++-5.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="CC=clang-5.0 && CXX=clang++-5.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: clang @@ -301,7 +301,7 @@ matrix: - clang-6.0 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="CC=clang-6.0 && CXX=clang++-6.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="CC=clang-6.0 && CXX=clang++-6.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: gcc @@ -313,7 +313,7 @@ matrix: - libopencv-dev env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: gcc @@ -328,7 +328,7 @@ matrix: - g++-4.9 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="CC=gcc-4.9 && CXX=g++-4.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="CC=gcc-4.9 && CXX=g++-4.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: gcc @@ -343,7 +343,7 @@ matrix: - g++-5 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="CC=gcc-5 && CXX=g++-5 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="CC=gcc-5 && CXX=g++-5 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: gcc @@ -358,7 +358,7 @@ matrix: - libopencv-dev env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="CC=gcc-6 && CXX=g++-6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="CC=gcc-6 && CXX=g++-6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: gcc @@ -373,7 +373,7 @@ matrix: - g++-7 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="CC=gcc-7 && CXX=g++-7 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="CC=gcc-7 && CXX=g++-7 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux compiler: gcc @@ -388,7 +388,7 @@ matrix: - g++-8 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" before_install: - travis_retry eval "${MATRIX_EVAL}" diff --git a/CMakeLists.txt b/CMakeLists.txt index 54609b19adc..5668b934cc3 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -176,7 +176,7 @@ if(ENABLE_CUDA) add_definitions(-DZED_STEREO) message("ZED Camera support enabled") else() - message(WARNING "ZED SDK not found !") + message("ZED SDK not found !") set(ENABLE_ZED_CAMERA "FALSE" CACHE BOOL "Enable ZED Camera support" FORCE) endif() endif() diff --git a/README.md b/README.md index 62795108f2d..05cddf3853a 100644 --- a/README.md +++ b/README.md @@ -49,6 +49,10 @@ More details: http://pjreddie.com/darknet/yolo/ * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported * on Linux **GCC or Clang**, on Windows **MSVS 2017 (v15)** https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community&rel=15# +Compiling on **Windows** by using `Cmake-GUI` as on this [**IMAGE**](https://user-images.githubusercontent.com/4096485/55107892-6becf380-50e3-11e9-9a0a-556a943c429a.png): Configure -> Optional platform for generator (Set: x64) -> Finish -> Generate -> Open Project -> x64 & Release -> Build -> Build solution + +Compiling on **Linux** by using command `make` (or alternative way by using command: `cmake . && make` ) + #### Pre-trained models There are weights-file for different cfg-files (smaller size -> faster speed & lower accuracy: @@ -69,7 +73,7 @@ You can get cfg-files by path: `darknet/cfg/` #### Yolo v3 in other frameworks -* Convert `yolov3.weights`/`cfg` model to **TensorFlow**: by using [mystic123](https://github.com/mystic123/tensorflow-yolo-v3) or [jinyu121](https://github.com/jinyu121/DW2TF) projects +* Convert `yolov3.weights`/`cfg` model to **TensorFlow**: by using [mystic123](https://github.com/mystic123/tensorflow-yolo-v3) or [jinyu121](https://github.com/jinyu121/DW2TF) projects, and [TensorFlow-lite](https://www.tensorflow.org/lite/guide/get_started#2_convert_the_model_format) * To use Yolo v3 model in **Intel OpenVINO** (Myriad X / USB Neural Compute Stick / Arria FPGA): read this [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) * **OpenCV-dnn** is very fast DNN implementation on CPU (x86/ARM-Android), use `yolov3.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221), [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) @@ -165,6 +169,10 @@ To run Darknet on Linux use examples from this article, just use `./darknet` ins ### How to compile on Windows (using `vcpkg`) +If you have installed: MSVS 2015/2017, CUDA 10, cuDNN 7.x, OpenCV 3.x. Then compile Darknet by using `C:\Program Files\CMake\bin\cmake-gui.exe` as on this [**IMAGE**](https://user-images.githubusercontent.com/4096485/55107892-6becf380-50e3-11e9-9a0a-556a943c429a.png): Configure -> Optional platform for generator (Set: x64) -> Finish -> Generate -> Open Project -> x64 & Release -> Build -> Build solution + +Otherwise: + 1. Install or update Visual Studio to at least version 2017, making sure to have it fully patched (run again the installer if not sure to automatically update to latest version). If you need to install from scratch, download VS from here: [Visual Studio 2017 Community](http://visualstudio.com) 2. Install CUDA and cuDNN @@ -387,7 +395,7 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an 9. After training is complete - get result `yolo-obj_final.weights` from path `build\darknet\x64\backup\` - * After each 100 iterations you can stop and later start training from this point. For example, after 2000 iterations you can stop training, and later just copy `yolo-obj_2000.weights` from `build\darknet\x64\backup\` to `build\darknet\x64\` and start training using: `darknet.exe detector train data/obj.data yolo-obj.cfg yolo-obj_2000.weights` + * After each 100 iterations you can stop and later start training from this point. For example, after 2000 iterations you can stop training, and later just start training using: `darknet.exe detector train data/obj.data yolo-obj.cfg backup\yolo-obj_2000.weights` (in the original repository https://github.com/pjreddie/darknet the weights-file is saved only once every 10 000 iterations `if(iterations > 1000)`) diff --git a/src/crnn_layer.c b/src/crnn_layer.c index 59cac509d15..a4016dfb948 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -269,7 +269,7 @@ void forward_crnn_layer_gpu(layer l, network_state state) layer self_layer = *(l.self_layer); layer output_layer = *(l.output_layer); -/* + #ifdef CUDNN_HALF // slow and bad for training if (!state.train && state.net.cudnn_half) { s.index = state.index; @@ -278,7 +278,6 @@ void forward_crnn_layer_gpu(layer l, network_state state) cuda_convert_f32_to_f16(output_layer.weights_gpu, output_layer.c*output_layer.n*output_layer.size*output_layer.size, output_layer.weights_gpu16); } #endif //CUDNN_HALF -*/ if (state.train) { diff --git a/src/dark_cuda.c b/src/dark_cuda.c index 5b57ea1d3dd..165dad5f284 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -25,6 +25,10 @@ int gpu_index = 0; #endif // USE_CMAKE_LIBS #endif // CUDNN +#if defined(CUDNN_HALF) && !defined(CUDNN) +#error "If you set CUDNN_HALF=1 then you must set CUDNN=1" +#endif + void cuda_set_device(int n) { gpu_index = n; diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 298372df21b..7aabc149dd6 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -156,7 +156,7 @@ void forward_backward_network_gpu(network net, float *x, float *y) state.delta = 0; state.truth = *net.truth_gpu; state.train = 1; -#ifdef CUDNN_HALF +#if defined(CUDNN_HALF) && defined(CUDNN) int i; for (i = 0; i < net.n; ++i) { layer l = net.layers[i]; From da74882fe1f3fff3d204482c5332590f95eb409a Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 28 Mar 2019 17:54:27 +0100 Subject: [PATCH 0427/1365] cleanup in preparation to opencv-4 work --- .gitignore | 7 +++- CMakeLists.txt | 22 ++++++------- build.ps1 | 20 ++++++++---- build.sh | 20 ++++++++++-- cmake/Modules/FindStb.cmake | 0 pthreadVC2.dll | Bin 82944 -> 0 bytes src/convolutional_kernels.cu | 18 +++++------ src/gemm.c | 4 +-- src/im2col_kernels.cu | 60 +++++++++++++++++------------------ 9 files changed, 88 insertions(+), 63 deletions(-) mode change 100755 => 100644 cmake/Modules/FindStb.cmake delete mode 100644 pthreadVC2.dll diff --git a/.gitignore b/.gitignore index b415cea3d9e..68a7a13bf17 100644 --- a/.gitignore +++ b/.gitignore @@ -1,10 +1,12 @@ *.o +*.a *.dSYM *.csv *.out *.png *.so *.exe +*.dll mnist/ data/ caffe/ @@ -28,7 +30,10 @@ Icon? Thumbs.db *.swp +# IDE generated # +.vs/ +.vscode/ + lib/ include/ share/ - diff --git a/CMakeLists.txt b/CMakeLists.txt index 5668b934cc3..f22ba83a5b0 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,4 +1,4 @@ -cmake_minimum_required(VERSION 3.4) +cmake_minimum_required(VERSION 3.8) set(Darknet_MAJOR_VERSION 1) set(Darknet_MINOR_VERSION 0) @@ -174,14 +174,14 @@ if(ENABLE_CUDA) include_directories(${ZED_INCLUDE_DIRS}) link_directories(${ZED_LIBRARY_DIR}) add_definitions(-DZED_STEREO) - message("ZED Camera support enabled") + message(STATUS "ZED SDK enabled") else() - message("ZED SDK not found !") + message(STATUS "ZED SDK not found") set(ENABLE_ZED_CAMERA "FALSE" CACHE BOOL "Enable ZED Camera support" FORCE) endif() endif() else() - message(WARNING "ZED SDK requires CUDA !") + message(STATUS "ZED SDK not enabled, since it requires CUDA") set(ENABLE_ZED_CAMERA "FALSE" CACHE BOOL "Enable ZED Camera support" FORCE) endif() @@ -247,7 +247,7 @@ endif() set_source_files_properties(${sources} PROPERTIES LANGUAGE CXX) -add_library(darklib SHARED ${CMAKE_CURRENT_LIST_DIR}/include/yolo_v2_class.hpp ${CMAKE_CURRENT_LIST_DIR}/src/yolo_v2_class.cpp ${sources} ${headers} ${cuda_sources}) +add_library(darklib ${CMAKE_CURRENT_LIST_DIR}/include/yolo_v2_class.hpp ${CMAKE_CURRENT_LIST_DIR}/src/yolo_v2_class.cpp ${sources} ${headers} ${cuda_sources}) set_target_properties(darklib PROPERTIES POSITION_INDEPENDENT_CODE ON) if(ENABLE_CUDA) set_target_properties(darklib PROPERTIES CUDA_SEPARABLE_COMPILATION ON) @@ -323,21 +323,19 @@ endif() set_target_properties(darklib PROPERTIES PUBLIC_HEADER "${exported_headers};${CMAKE_CURRENT_LIST_DIR}/include/yolo_v2_class.hpp") -install(TARGETS darklib uselib darknet EXPORT DarknetTargets +install(TARGETS darklib EXPORT DarknetTargets RUNTIME DESTINATION "${INSTALL_BIN_DIR}" LIBRARY DESTINATION "${INSTALL_LIB_DIR}" ARCHIVE DESTINATION "${INSTALL_LIB_DIR}" PUBLIC_HEADER DESTINATION "${INSTALL_INCLUDE_DIR}" COMPONENT dev ) +install(TARGETS uselib darknet + DESTINATION "${INSTALL_BIN_DIR}" +) if(OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRACK) install(TARGETS uselib_track - EXPORT DarknetTargets - RUNTIME DESTINATION "${INSTALL_BIN_DIR}" - LIBRARY DESTINATION "${INSTALL_LIB_DIR}" - ARCHIVE DESTINATION "${INSTALL_LIB_DIR}" - PUBLIC_HEADER DESTINATION "${INSTALL_INCLUDE_DIR}" - COMPONENT dev + DESTINATION "${INSTALL_BIN_DIR}" ) endif() diff --git a/build.ps1 b/build.ps1 index 7063098c5a5..0fd60a93606 100755 --- a/build.ps1 +++ b/build.ps1 @@ -1,7 +1,7 @@ #!/usr/bin/env pwsh $number_of_build_workers=8 -#$shared_lib="-DBUILD_SHARED_LIBS:BOOL=ON" +$create_shared_lib="-DBUILD_SHARED_LIBS:BOOL=ON" $force_using_include_libs=$false #$my_cuda_compute_model=75 #Compute capability for Tesla T4, RTX 2080 @@ -88,31 +88,37 @@ if ($vcpkg_path) { # DEBUG New-Item -Path .\build_win_debug -ItemType directory -Force Set-Location build_win_debug - cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Debug" $shared_lib $additional_build_setup .. - cmake --build . --config Debug --parallel ${number_of_build_workers} --target install + cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Debug" $create_shared_lib $additional_build_setup .. + cmake --build . --config Debug --target install + #cmake --build . --config Debug --parallel ${number_of_build_workers} --target install #valid only for CMake 3.12+ Remove-Item DarknetConfig.cmake Remove-Item DarknetConfigVersion.cmake Set-Location .. + Copy-Item cmake\Modules\*.cmake share\darknet\ # RELEASE New-Item -Path .\build_win_release -ItemType directory -Force Set-Location build_win_release - cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Release" $shared_lib $additional_build_setup .. - cmake --build . --config Release --parallel ${number_of_build_workers} --target install + cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Release" $create_shared_lib $additional_build_setup .. + cmake --build . --config Release --target install + #cmake --build . --config Release --parallel ${number_of_build_workers} --target install #valid only for CMake 3.12+ Remove-Item DarknetConfig.cmake Remove-Item DarknetConfigVersion.cmake Copy-Item *.dll .. Set-Location .. + Copy-Item cmake\Modules\*.cmake share\darknet\ } else { # USE LOCAL PTHREAD LIB AND LOCAL STB HEADER, NO VCPKG, ONLY RELEASE MODE SUPPORTED # if you want to manually force this case, remove VCPKG_ROOT env variable and remember to use "vcpkg integrate remove" in case you had enabled user-wide vcpkg integration New-Item -Path .\build_win_release_novcpkg -ItemType directory -Force Set-Location build_win_release_novcpkg - cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" $shared_lib $additional_build_setup .. - cmake --build . --config Release --parallel ${number_of_build_workers} --target install + cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" $create_shared_lib $additional_build_setup .. + cmake --build . --config Release --target install + #cmake --build . --config Release --parallel ${number_of_build_workers} --target install #valid only for CMake 3.12+ Remove-Item DarknetConfig.cmake Remove-Item DarknetConfigVersion.cmake Copy-Item ..\3rdparty\pthreads\bin\pthreadVC2.dll .. Set-Location .. + Copy-Item cmake\Modules\*.cmake share\darknet\ } diff --git a/build.sh b/build.sh index 7ff54d91dcf..f74351d1346 100755 --- a/build.sh +++ b/build.sh @@ -1,6 +1,19 @@ #!/usr/bin/env bash number_of_build_workers=8 +create_shared_lib="-DBUILD_SHARED_LIBS:BOOL=ON" + +#my_cuda_compute_model=75 #Compute capability for Tesla T4, RTX 2080 +#my_cuda_compute_model=72 #Compute capability for Jetson Xavier +#my_cuda_compute_model=70 #Compute capability for Tesla V100 +#my_cuda_compute_model=62 #Compute capability for Jetson TX2 +#my_cuda_compute_model=61 #Compute capability for Tesla P40 +#my_cuda_compute_model=60 #Compute capability for Tesla P100 +#my_cuda_compute_model=53 #Compute capability for Jetson TX1 +#my_cuda_compute_model=52 #Compute capability for Tesla M40/M60 +#my_cuda_compute_model=37 #Compute capability for Tesla K80 +#my_cuda_compute_model=35 #Compute capability for Tesla K20/K40 +#my_cuda_compute_model=30 #Compute capability for Tesla K10, Quadro K4000 if [[ "$OSTYPE" == "darwin"* ]]; then OpenCV_DIR="/usr/local/Cellar/opencv@3/3.4.5" @@ -11,11 +24,14 @@ if [[ "$OSTYPE" == "darwin"* ]]; then fi fi +if [[ ! -z "$my_cuda_compute_model" ]]; then + additional_build_setup="-DCUDA_COMPUTE_MODEL=${my_cuda_compute_model}" +fi # RELEASE mkdir -p build_release cd build_release -cmake .. -DCMAKE_BUILD_TYPE=Release ${additional_defines} +cmake .. -DCMAKE_BUILD_TYPE=Release ${additional_defines} ${create_shared_lib} ${additional_build_setup} cmake --build . --target install -- -j${number_of_build_workers} #cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ rm -f DarknetConfig.cmake @@ -26,7 +42,7 @@ cp cmake/Modules/*.cmake share/darknet # DEBUG mkdir -p build_debug cd build_debug -cmake .. -DCMAKE_BUILD_TYPE=Debug ${additional_defines} +cmake .. -DCMAKE_BUILD_TYPE=Debug ${additional_defines} ${create_shared_lib} ${additional_build_setup} cmake --build . --target install -- -j${number_of_build_workers} 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zA!>P^!(FQ_5{tV9ezzm$Bm#SgHNriDCNX2IPrK@YhG@T)w4GAgEiI{xla#dOExw~q zz1f1H)``b*9CBsv+V<7Qx3&2`UA1o$Mi6Nre!85xyfG>kjhyAB)fFWjwbXlZeDRy$ zLd#>TyR{)2OJ|d~QsfUqez_qUZI6Z~pU_;xod$`!?z*^}>#|c+tGbAAB&0l-^q?Slj+$&LW8PSG;Ow@^`vAEDL%M-t)I=fkXAL71SPHs%b8OJ~P`;AdQdP`GO zN0QCGR<84WG5O(fZWmk1a^Gpl-O{u0qjqJyUfW5gtTX!8Tk)K_M78@~LchpTrCw`Z zlgd)XTHm+ws=QkkzME|BO<9(!RyGpq32bUh2L@!+3M*sTA%0VCAf?@RO@-gN z>b24_cQE6z$`2-%&8Mxf)X?uoQm%F6blv-MhZEx<)v(lV| zu6mzvPFHI4P%x|X{nVyzjJ|qoINteETW zl7$|=f3bNDr-SV5Gqh#t1+E^Fe$1HM*t8=QqMXSVGq+F z`7BZ~qVis9_TaPpqb7Ja?8mz)wM=#U^dZB%dGyfI?*?e&FlA(}BE+SRG5Qtj-Ni|CcJp7OGCf}_dUN%?+`nWH|L>j>i%?4{!w zh&sI3HyI7d&({e5XzUkZf8BBX%NqR(dvmN`TawiB@yoJXZ`dF0g&gNN>goUuoM|!& z;pmsn2A=KsXVMx{Wo)y|ag0`!*2aF#6LPB6U-c3AY>vRR_^)^_b?4MiUzg{#Z4K<1 Pg~k_;L-p5Zd<6apgZj`N diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 8ae99710a6b..cfa4fe7e8fc 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -184,18 +184,18 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) if (l.align_bit_weights_gpu && !state.train && l.c >= 32) { //return; - cudaError_t status = cudaSuccess; - int input_size = l.c*l.h*l.w*l.batch; + //cudaError_t status = cudaSuccess; + //int input_size = l.c*l.h*l.w*l.batch; int m = l.n; int k = l.size*l.size*l.c; int n = l.out_w*l.out_h; - float * a = l.weights_gpu; + //float * a = l.weights_gpu; int ldb_align = l.lda_align; size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; - size_t t_intput_size = new_ldb * n; - size_t t_bit_input_size = t_intput_size / 8;// +1; + //size_t t_intput_size = new_ldb * n; + //size_t t_bit_input_size = t_intput_size / 8;// +1; if (l.c % 32 == 0) { @@ -208,8 +208,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) int ldb_align = l.lda_align; size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; - size_t t_intput_size = new_ldb * l.bit_align;// n; - size_t t_bit_input_size = t_intput_size / 8;// +1; + //size_t t_intput_size = new_ldb * l.bit_align;// n; + //size_t t_bit_input_size = t_intput_size / 8;// +1; const int new_c = l.c / 32; @@ -408,7 +408,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); #ifdef CUDNN - float one = 1; // alpha[0], beta[0] is float for HALF and FLOAT + //float one = 1; // alpha[0], beta[0] is float for HALF and FLOAT float alpha = 1, beta = 0; //#ifdef CUDNN_HALF @@ -609,7 +609,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state if(l.xnor) state.input = l.binary_input_gpu; #ifdef CUDNN - float one = 1; + float one = 1.f; float alpha = 1, beta = 0; //#ifdef CUDNN_HALF diff --git a/src/gemm.c b/src/gemm.c index 861a190669e..336f3bdd645 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -900,7 +900,7 @@ void gemm_nn_bin_32bit_packed(int M, int N, int K, float ALPHA, { __m256i b256 = *((__m256i*)&B[s*ldb + j]); __m256i xor256 = _mm256_xor_si256(a256, b256); // xnor = xor(a,b) - __m256i all_1 = _mm256_set1_epi8(255); + __m256i all_1 = _mm256_set1_epi8((char)255); __m256i xnor256 = _mm256_andnot_si256(xor256, all_1); // xnor = not(xor(a,b)) // waiting for - CPUID Flags: AVX512VPOPCNTDQ: __m512i _mm512_popcnt_epi32(__m512i a) @@ -1162,7 +1162,7 @@ static inline int popcnt256_custom(__m256i n) { } static inline void xnor_avx2_popcnt(__m256i a_bit256, __m256i b_bit256, __m256i *count_sum) { - __m256i c_bit256 = _mm256_set1_epi8(255); + __m256i c_bit256 = _mm256_set1_epi8((char)255); __m256i xor256 = _mm256_xor_si256(a_bit256, b_bit256); // xnor = not(xor(a,b)) c_bit256 = _mm256_andnot_si256(xor256, c_bit256); // can be optimized - we can do other NOT for wegihts once and do not do this NOT diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 539824a9f6a..954a4694492 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -228,8 +228,8 @@ __global__ void im2col_align_bin_gpu_kernel(const int n, const float* data_im, const int height_col, const int width_col, float *data_col, const int bit_align) { - __shared__ float tmp_s[1]; - __shared__ ulonglong4 tmp256_s[1]; + //__shared__ float tmp_s[1]; + //__shared__ ulonglong4 tmp256_s[1]; //#define SHRED_VALS ((BLOCK / 169) * ) @@ -414,7 +414,7 @@ __global__ void float_to_bit_gpu_kernel(float *src, unsigned char *dst, size_t s if ((index + i * 1024) < size) src_val = src[index + i*1024]; else src_val = 0; //unsigned int bit_mask = __ballot_sync(0xffffffff, src_val > 0); - const int num_of_warps = blockDim.x / WARP_SIZE; + //const int num_of_warps = blockDim.x / WARP_SIZE; const int warp_id = threadIdx.x / WARP_SIZE; const int lane_id = threadIdx.x % WARP_SIZE; @@ -436,7 +436,7 @@ void float_to_bit_gpu(float *src, unsigned char *dst, size_t size) } // -------------------------------- - +/* __device__ __host__ static inline void remove_bit(unsigned char *const dst, size_t index) { size_t dst_i = index / 8; int dst_shift = index % 8; @@ -449,6 +449,7 @@ __device__ __host__ static inline void set_bit(unsigned char *const dst, size_t dst[dst_i] |= 1 << dst_shift; //dst[dst_i] |= 1 << (8 - dst_shift); } +*/ __device__ __host__ static inline unsigned char get_bit(unsigned char const*const src, size_t index) { size_t src_i = index / 8; @@ -643,9 +644,9 @@ __global__ void transpose_bin_gpu_kernel_32(uint32_t *A, uint32_t *B, const int void transpose_bin_gpu(unsigned char *A, unsigned char *B, const int n, const int m, const int lda, const int ldb, const int block_size) { - int size = n*m/ (8*8) + 1; + //int size = n*m/ (8*8) + 1; int size32 = n*m / (32*32) + 1; - const int num_blocks = size / BLOCK + 1; + //const int num_blocks = size / BLOCK + 1; const int num_blocks32 = size32 / BLOCK_TRANSPOSE32 + 1; transpose_bin_gpu_kernel_32 << > >((uint32_t *)A, (uint32_t *)B, n, m, lda, ldb, block_size); //transpose_bin_gpu_kernel << > >(A, B, n, m, lda, ldb, block_size); @@ -692,10 +693,10 @@ __global__ void transpose_uint32_kernel_2(uint32_t *src, uint32_t *dst, int src_ //new_ldb - aligned (k) by 256 const int src_w_align = src_w + (32 - src_w % 32); - const int src_h_align = src_h + (32 - src_h % 32); + //const int src_h_align = src_h + (32 - src_h % 32); const int warps_in_width = src_w_align / 32; - const int warps_in_height = src_h_align / 32; + //const int warps_in_height = src_h_align / 32; @@ -789,7 +790,7 @@ void repack_input_gpu(float *input, float *re_packed_input, int w, int h, int c) // 256 channels -> 8 channels (with 32 floats) __global__ void repack_input_kernel_2(float *input, float *re_packed_input, int w, int h, int c) { - __shared__ uint32_t tmp[33 * 32]; // 33x32 is misaligned 32 x 32 to avoid bank conflicts + //__shared__ uint32_t tmp[33 * 32]; // 33x32 is misaligned 32 x 32 to avoid bank conflicts int index = blockIdx.x*blockDim.x + threadIdx.x; @@ -943,15 +944,15 @@ void fill_int8_gpu(unsigned char *src, unsigned char val, size_t size) { //typedef unsigned int uint32_t; //typedef unsigned char uint8_t; //typedef char int8_t; - +/* __device__ __host__ static inline uint64_t broadcast_bit_1_to_64(uint8_t src) { return (src > 0) ? 0xFFFFFFFFFFFFFFFF : 0; } - +*/ __device__ __host__ static inline uint8_t xnor_bit1(uint8_t a, uint8_t b) { return ~(a^b) & 0b1; } - +/* __device__ __host__ static inline uint32_t xnor_int32(uint32_t a, uint32_t b) { return ~(a^b); } @@ -977,13 +978,13 @@ __device__ __host__ static inline ulonglong4 xnor_int256(ulonglong4 a, ulonglong res.z = ~(a.z^b.z); return res; } - +*/ //------- - +/* __device__ __host__ static inline uint8_t xor_bit1(uint8_t a, uint8_t b) { return (a^b) & 0b1; } - +*/ __device__ __host__ static inline uint32_t xor_int32(uint32_t a, uint32_t b) { return (a^b); } @@ -991,7 +992,7 @@ __device__ __host__ static inline uint32_t xor_int32(uint32_t a, uint32_t b) { __device__ __host__ static inline uint64_t xor_int64(uint64_t a, uint64_t b) { return (a^b); } - +/* __device__ __host__ static inline uint4 xor_int128(uint4 a, uint4 b) { uint4 res; res.w = (a.w^b.w); @@ -1000,7 +1001,7 @@ __device__ __host__ static inline uint4 xor_int128(uint4 a, uint4 b) { res.z = (a.z^b.z); return res; } - +*/ __device__ __host__ static inline ulonglong4 xor_int256(ulonglong4 a, ulonglong4 b) { ulonglong4 res; res.w = (a.w^b.w); @@ -1010,12 +1011,11 @@ __device__ __host__ static inline ulonglong4 xor_int256(ulonglong4 a, ulonglong4 return res; } - +/* __device__ static inline int popcnt_256(ulonglong4 a) { return __popcll(a.w) + __popcll(a.x) + __popcll(a.y) + __popcll(a.z); } -/* __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int K, unsigned char *A, int lda, unsigned char *B, int ldb, @@ -1269,7 +1269,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel(int M, int N, i */ - int i, j, k, h; + int i, j, k;//, h; // 47% = 29 + 10 + 8 j = global_warp_id % (N_aligned / WMMA_Nx2); j = j * WMMA_Nx2; @@ -1277,7 +1277,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel(int M, int N, i i = global_warp_id / (N_aligned / WMMA_Nx2); i = i * WMMA_M; - int count = 0; + //int count = 0; k = 0; if (i < M) //if (i < M) // l.n - filters [16 - 55 - 1024] @@ -1323,7 +1323,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel(int M, int N, i // Custom XOR-GEMM int k_d = lane_id % 4; int i_d = lane_id / 4; - int j_d = lane_id / 4; + //int j_d = lane_id / 4; int32_t accum_c_val[8*2]; // wmma::fill_fragment(c_frag, 0); for (int local_j = 0; local_j < 8*2; ++local_j) { @@ -1333,9 +1333,9 @@ __global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel(int M, int N, i // 8 x 8 x 4 (uint32_t, 4 * 32 = 128 bit) for (; k < K; k += 128) // l.size*l.size*l.c - one filter size [27 - 144 - 9216] { - int64_t A_cur_index = (i*lda + k) / 8; + //int64_t A_cur_index = (i*lda + k) / 8; //int64_t A_cur_index = (local_i*lda + k) / 8; - int64_t B_cur_index = (j*ldb + k) / 8; + //int64_t B_cur_index = (j*ldb + k) / 8; // lda, ldb - are in bits // 8*4 = 32 @@ -1593,7 +1593,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int } __syncthreads(); - int i, j, k, h; + int i, j, k; //, h; // 47% = 29 + 10 + 8 j = index % N; { // out_h*out_w - one channel output size [169 - 173056] @@ -2115,7 +2115,7 @@ __global__ void convolve_bin_gpu_kernel(float *input, float *weights, float *out for (chan = 0; chan < in_c; ++chan) { //int const weights_pre_index = fil*in_c*size*size + chan*size*size; - int const weights_pre_index = fil*new_lda + chan*size*size; + //int const weights_pre_index = fil*new_lda + chan*size*size; int const input_pre_index = chan*in_w*in_h; __shared__ uint32_t input_shared[416*416/32 + 1]; // 21.2 KB bytes (for input size 832x832) @@ -2139,8 +2139,8 @@ __global__ void convolve_bin_gpu_kernel(float *input, float *weights, float *out } __syncthreads(); */ - int src_index = -1; - uint32_t input_byte; + //int src_index = -1; + //uint32_t input_byte; if (fil < n) // (1-6 for one BLOCK) { @@ -2154,8 +2154,8 @@ __global__ void convolve_bin_gpu_kernel(float *input, float *weights, float *out int input_x = x + f_x - pad; if (input_y < 0 || input_x < 0 || input_y >= in_h || input_x >= in_w) continue; - int input_index = input_pre_index + input_y*in_w + input_x; - int weights_index = weights_pre_index + f_y*size + f_x; + //int input_index = input_pre_index + input_y*in_w + input_x; + //int weights_index = weights_pre_index + f_y*size + f_x; //int weights_index = fil*in_c*size*size + chan*size*size + f_y*size + f_x; //int weights_index = fil*new_lda + chan*size*size + f_y*size + f_x; From 05f027cc7268eeeb5049bddcd43c335443376dec Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 29 Mar 2019 09:15:55 +0100 Subject: [PATCH 0428/1365] shared libs are really always default now, static are opt-in only --- CMakeLists.txt | 1 + build.ps1 | 7 +++---- build.sh | 10 +++++----- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index f22ba83a5b0..3a154582e51 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -6,6 +6,7 @@ set(Darknet_PATCH_VERSION 0) set(Darknet_VERSION ${Darknet_MAJOR_VERSION}.${Darknet_MINOR_VERSION}.${Darknet_PATCH_VERSION}) set(CMAKE_VERBOSE_MAKEFILE "FALSE" CACHE BOOL "Create verbose makefile") +set(BUILD_SHARED_LIBS "TRUE" CACHE BOOL "Create darklib as a shared library") if(DEFINED ENV{VCPKG_ROOT} AND NOT DEFINED CMAKE_TOOLCHAIN_FILE) set(CMAKE_TOOLCHAIN_FILE "${VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake" CACHE STRING "") diff --git a/build.ps1 b/build.ps1 index 0fd60a93606..478d803ed5c 100755 --- a/build.ps1 +++ b/build.ps1 @@ -1,7 +1,6 @@ #!/usr/bin/env pwsh $number_of_build_workers=8 -$create_shared_lib="-DBUILD_SHARED_LIBS:BOOL=ON" $force_using_include_libs=$false #$my_cuda_compute_model=75 #Compute capability for Tesla T4, RTX 2080 @@ -88,7 +87,7 @@ if ($vcpkg_path) { # DEBUG New-Item -Path .\build_win_debug -ItemType directory -Force Set-Location build_win_debug - cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Debug" $create_shared_lib $additional_build_setup .. + cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Debug" $additional_build_setup .. cmake --build . --config Debug --target install #cmake --build . --config Debug --parallel ${number_of_build_workers} --target install #valid only for CMake 3.12+ Remove-Item DarknetConfig.cmake @@ -99,7 +98,7 @@ if ($vcpkg_path) { # RELEASE New-Item -Path .\build_win_release -ItemType directory -Force Set-Location build_win_release - cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Release" $create_shared_lib $additional_build_setup .. + cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Release" $additional_build_setup .. cmake --build . --config Release --target install #cmake --build . --config Release --parallel ${number_of_build_workers} --target install #valid only for CMake 3.12+ Remove-Item DarknetConfig.cmake @@ -113,7 +112,7 @@ else { # if you want to manually force this case, remove VCPKG_ROOT env variable and remember to use "vcpkg integrate remove" in case you had enabled user-wide vcpkg integration New-Item -Path .\build_win_release_novcpkg -ItemType directory -Force Set-Location build_win_release_novcpkg - cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" $create_shared_lib $additional_build_setup .. + cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" $additional_build_setup .. cmake --build . --config Release --target install #cmake --build . --config Release --parallel ${number_of_build_workers} --target install #valid only for CMake 3.12+ Remove-Item DarknetConfig.cmake diff --git a/build.sh b/build.sh index f74351d1346..7c0a41ef0af 100755 --- a/build.sh +++ b/build.sh @@ -1,8 +1,6 @@ #!/usr/bin/env bash -number_of_build_workers=8 -create_shared_lib="-DBUILD_SHARED_LIBS:BOOL=ON" - +## by default darknet is built with CC 3.0 if cuda is found. Uncomment another CC (only one!) if you prefer a different choice #my_cuda_compute_model=75 #Compute capability for Tesla T4, RTX 2080 #my_cuda_compute_model=72 #Compute capability for Jetson Xavier #my_cuda_compute_model=70 #Compute capability for Tesla V100 @@ -15,6 +13,8 @@ create_shared_lib="-DBUILD_SHARED_LIBS:BOOL=ON" #my_cuda_compute_model=35 #Compute capability for Tesla K20/K40 #my_cuda_compute_model=30 #Compute capability for Tesla K10, Quadro K4000 +number_of_build_workers=8 + if [[ "$OSTYPE" == "darwin"* ]]; then OpenCV_DIR="/usr/local/Cellar/opencv@3/3.4.5" additional_defines="-DOpenCV_DIR=${OpenCV_DIR}" @@ -31,7 +31,7 @@ fi # RELEASE mkdir -p build_release cd build_release -cmake .. -DCMAKE_BUILD_TYPE=Release ${additional_defines} ${create_shared_lib} ${additional_build_setup} +cmake .. -DCMAKE_BUILD_TYPE=Release ${additional_defines} ${additional_build_setup} cmake --build . --target install -- -j${number_of_build_workers} #cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ rm -f DarknetConfig.cmake @@ -42,7 +42,7 @@ cp cmake/Modules/*.cmake share/darknet # DEBUG mkdir -p build_debug cd build_debug -cmake .. -DCMAKE_BUILD_TYPE=Debug ${additional_defines} ${create_shared_lib} ${additional_build_setup} +cmake .. -DCMAKE_BUILD_TYPE=Debug ${additional_defines} ${additional_build_setup} cmake --build . --target install -- -j${number_of_build_workers} #cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ rm -f DarknetConfig.cmake From 05b23f5d6ecf1e72d21368fcfa906561edac362b Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 29 Mar 2019 10:16:35 +0100 Subject: [PATCH 0429/1365] add some more messages to vcpkg auto-integration --- CMakeLists.txt | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 3a154582e51..3cc384cce3e 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -9,7 +9,9 @@ set(CMAKE_VERBOSE_MAKEFILE "FALSE" CACHE BOOL "Create verbose makefile") set(BUILD_SHARED_LIBS "TRUE" CACHE BOOL "Create darklib as a shared library") if(DEFINED ENV{VCPKG_ROOT} AND NOT DEFINED CMAKE_TOOLCHAIN_FILE) - set(CMAKE_TOOLCHAIN_FILE "${VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake" CACHE STRING "") + set(CMAKE_TOOLCHAIN_FILE "$ENV{VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake" CACHE STRING "") + message(STATUS "VCPKG found: $ENV{VCPKG_ROOT}") + message(STATUS "Using VCPKG integration") endif() project(Darknet VERSION ${Darknet_VERSION}) From dcd41f37cc377bf8eddfd2eee904e1319a68165b Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 29 Mar 2019 11:04:19 +0100 Subject: [PATCH 0430/1365] add ld_library_path to travis to fix [maybe] linking problems --- .travis.yml | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/.travis.yml b/.travis.yml index f63463aebb7..5e2bbcefcb3 100644 --- a/.travis.yml +++ b/.travis.yml @@ -212,6 +212,7 @@ matrix: - libopencv-dev env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -225,6 +226,7 @@ matrix: - clang-3.6 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=clang-3.6 && CXX=clang++-3.6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -240,6 +242,7 @@ matrix: - clang-3.8 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=clang-3.8 && CXX=clang++-3.8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -255,6 +258,7 @@ matrix: - clang-3.9 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=clang-3.9 && CXX=clang++-3.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -270,6 +274,7 @@ matrix: - clang-4.0 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=clang-4.0 && CXX=clang++-4.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -285,6 +290,7 @@ matrix: - clang-5.0 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=clang-5.0 && CXX=clang++-5.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -301,6 +307,7 @@ matrix: - clang-6.0 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=clang-6.0 && CXX=clang++-6.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -313,6 +320,7 @@ matrix: - libopencv-dev env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -328,6 +336,7 @@ matrix: - g++-4.9 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=gcc-4.9 && CXX=g++-4.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -343,6 +352,7 @@ matrix: - g++-5 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=gcc-5 && CXX=g++-5 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -358,6 +368,7 @@ matrix: - libopencv-dev env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=gcc-6 && CXX=g++-6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -373,6 +384,7 @@ matrix: - g++-7 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=gcc-7 && CXX=g++-7 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -388,6 +400,7 @@ matrix: - g++-8 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" before_install: From a97f41322f5e30fc1d23f83251fb844a9aeb3ef9 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 29 Mar 2019 11:08:12 +0100 Subject: [PATCH 0431/1365] removed cygwin ci because it has a cmake too old, leave a note for a todo build from source --- appveyor.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/appveyor.yml b/appveyor.yml index 469cd9daf6d..4d61c6a090f 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -4,10 +4,10 @@ clone_folder: c:\projects\darknet environment: WORKSPACE: C:\projects matrix: - - platform: Cygwin64 - COMPILER: cygwin - CYGWIN_NOWINPATH: yes - CYGSH: C:\cygwin64\bin\bash -c + #- platform: Cygwin64 # removed because it is missing CMake 3.8 (minimum version required) ## todo: download and build from source! + # COMPILER: cygwin + # CYGWIN_NOWINPATH: yes + # CYGSH: C:\cygwin64\bin\bash -c - platform: Win64 COMPILER: vs configuration: Release From bff37eb40640b9b163b6b70795b3b6a254826ef2 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 29 Mar 2019 14:06:23 +0100 Subject: [PATCH 0432/1365] add reference to opencv[ffmpeg], fix spell errors, fix LD_LIBRARY_PATH for cuda on travis-ci, create a script to update cmake on cygwin-appveyor-ci --- .travis.yml | 26 +++++++++++++------------- README.md | 6 +++--- appveyor.yml | 17 ++++++++++++----- 3 files changed, 28 insertions(+), 21 deletions(-) diff --git a/.travis.yml b/.travis.yml index 5e2bbcefcb3..31c620bb9e6 100644 --- a/.travis.yml +++ b/.travis.yml @@ -212,7 +212,7 @@ matrix: - libopencv-dev env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -226,7 +226,7 @@ matrix: - clang-3.6 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=clang-3.6 && CXX=clang++-3.6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -242,7 +242,7 @@ matrix: - clang-3.8 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=clang-3.8 && CXX=clang++-3.8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -258,7 +258,7 @@ matrix: - clang-3.9 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=clang-3.9 && CXX=clang++-3.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -274,7 +274,7 @@ matrix: - clang-4.0 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=clang-4.0 && CXX=clang++-4.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -290,7 +290,7 @@ matrix: - clang-5.0 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=clang-5.0 && CXX=clang++-5.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -307,7 +307,7 @@ matrix: - clang-6.0 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=clang-6.0 && CXX=clang++-6.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -320,7 +320,7 @@ matrix: - libopencv-dev env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -336,7 +336,7 @@ matrix: - g++-4.9 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=gcc-4.9 && CXX=g++-4.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -352,7 +352,7 @@ matrix: - g++-5 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=gcc-5 && CXX=g++-5 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -368,7 +368,7 @@ matrix: - libopencv-dev env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=gcc-6 && CXX=g++-6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -384,7 +384,7 @@ matrix: - g++-7 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=gcc-7 && CXX=g++-7 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" - os: linux @@ -400,7 +400,7 @@ matrix: - g++-8 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib:${LD_LIBRARY_PATH}" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" before_install: diff --git a/README.md b/README.md index 05cddf3853a..0a0f43a0a85 100644 --- a/README.md +++ b/README.md @@ -189,7 +189,7 @@ Otherwise: ```PowerShell PS \> cd $env:VCPKG_ROOT -PS Code\vcpkg> .\vcpkg install pthreads opencv #replace with opencv[cuda] in case you want to use cuda-accelerated openCV +PS Code\vcpkg> .\vcpkg install pthreads opencv[ffmpeg] #replace with opencv[cuda,ffmpeg] in case you want to use cuda-accelerated openCV ``` 8. [necessary only with CUDA] Customize the `build.ps1` script enabling the appropriate `my_cuda_compute_model` line. If not manually defined, CMake toolchain will automatically use the very low 3.0 CUDA compute model @@ -218,7 +218,7 @@ PS Code\vcpkg> .\vcpkg install pthreads opencv #replace with opencv[cuda 3. If you **don't have GPU**, but have **MSVS 2015 and OpenCV 3.0** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet_no_gpu.sln`, set **x64** and **Release**, and do the: Build -> Build darknet_no_gpu -4. If you have **OpenCV 2.4.13** instead of 3.0 then you should change pathes after `\darknet.sln` is opened +4. If you have **OpenCV 2.4.13** instead of 3.0 then you should change paths after `\darknet.sln` is opened 4.1 (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories: `C:\opencv_2.4.13\opencv\build\include` @@ -288,7 +288,7 @@ Then add to your created project: (**Note:** To disable Loss-Window use flag `-dont_show`. If you are using CPU, try `darknet_no_gpu.exe` instead of `darknet.exe`.) -If required change pathes in the file `build\darknet\cfg\voc.data` +If required change paths in the file `build\darknet\cfg\voc.data` More information about training by the link: http://pjreddie.com/darknet/yolo/#train-voc diff --git a/appveyor.yml b/appveyor.yml index 4d61c6a090f..3c69f3784e6 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -4,10 +4,10 @@ clone_folder: c:\projects\darknet environment: WORKSPACE: C:\projects matrix: - #- platform: Cygwin64 # removed because it is missing CMake 3.8 (minimum version required) ## todo: download and build from source! - # COMPILER: cygwin - # CYGWIN_NOWINPATH: yes - # CYGSH: C:\cygwin64\bin\bash -c + - platform: Cygwin64 + COMPILER: cygwin + CYGWIN_NOWINPATH: yes + CYGSH: C:\cygwin64\bin\bash -c - platform: Win64 COMPILER: vs configuration: Release @@ -41,7 +41,7 @@ install: - if [%COMPILER%]==[vs] cinst cmake ninja - if [%COMPILER%]==[vs] SET "PATH=C:\Program Files\CMake\bin;%PATH%" - if [%COMPILER%]==[vs] call "C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\VC\Auxiliary\Build\vcvarsall.bat" x64 - - if [%COMPILER%]==[cygwin] SET "PATH=C:\cygwin64\bin;C:\cygwin64\usr\bin;%PATH%" + - if [%COMPILER%]==[cygwin] SET "PATH=C:\cygwin64\usr\local\bin;C:\cygwin64\bin;C:\cygwin64\usr\bin;%PATH%" - if [%COMPILER%]==[cygwin] SET PATH=%PATH:C:\Program Files\Git\usr\bin;=% - git submodule -q update --init --recursive - cd %WORKSPACE%\ @@ -55,6 +55,13 @@ install: - mkdir cygwin-downloads - ps: if($env:COMPILER -eq "cygwin") { Invoke-WebRequest https://cygwin.com/setup-x86_64.exe -OutFile $env:WORKSPACE\cygwin-setup.exe } - if [%COMPILER%]==[cygwin] %WORKSPACE%\cygwin-setup.exe --quiet-mode --no-shortcuts --no-startmenu --no-desktop --upgrade-also --root C:\cygwin64 --local-package-dir %WORKSPACE%\cygwin-downloads --packages gcc-g++,cmake,libopencv-devel + - ps: if($env:COMPILER -eq "cygwin") { Invoke-WebRequest https://github.com/Kitware/CMake/releases/download/v3.14.0/cmake-3.14.0.tar.gz -OutFile $env:WORKSPACE\cmake-3.14.0.tar.gz } + - if [%COMPILER%]==[cygwin] %CYGSH% 'tar zxvf cmake-3.14.0.tar.gz' + - if [%COMPILER%]==[cygwin] cd %WORKSPACE%\cmake-3.14.0 + - if [%COMPILER%]==[cygwin] %CYGSH% 'cmake .' + - if [%COMPILER%]==[cygwin] %CYGSH% 'make -j8' + - if [%COMPILER%]==[cygwin] %CYGSH% 'make install' + - if [%COMPILER%]==[cygwin] cd %WORKSPACE% - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] git clone https://github.com/Microsoft/vcpkg - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] cd %VCPKG_ROOT% - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] bootstrap-vcpkg.bat From 751d7509522c5621f179ffc373dd16e44692bf05 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 29 Mar 2019 15:37:27 +0100 Subject: [PATCH 0433/1365] other fixes for travis-ci --- .travis.yml | 52 ++++++++++++++++++++++++++-------------------------- 1 file changed, 26 insertions(+), 26 deletions(-) diff --git a/.travis.yml b/.travis.yml index 31c620bb9e6..cf5087f7549 100644 --- a/.travis.yml +++ b/.travis.yml @@ -212,8 +212,8 @@ matrix: - libopencv-dev env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" + - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: clang @@ -226,8 +226,8 @@ matrix: - clang-3.6 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="CC=clang-3.6 && CXX=clang++-3.6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" + - MATRIX_EVAL="CC=clang-3.6 && CXX=clang++-3.6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: clang @@ -242,8 +242,8 @@ matrix: - clang-3.8 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="CC=clang-3.8 && CXX=clang++-3.8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" + - MATRIX_EVAL="CC=clang-3.8 && CXX=clang++-3.8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: clang @@ -258,8 +258,8 @@ matrix: - clang-3.9 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="CC=clang-3.9 && CXX=clang++-3.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" + - MATRIX_EVAL="CC=clang-3.9 && CXX=clang++-3.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: clang @@ -274,8 +274,8 @@ matrix: - clang-4.0 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="CC=clang-4.0 && CXX=clang++-4.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" + - MATRIX_EVAL="CC=clang-4.0 && CXX=clang++-4.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: clang @@ -290,8 +290,8 @@ matrix: - clang-5.0 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="CC=clang-5.0 && CXX=clang++-5.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" + - MATRIX_EVAL="CC=clang-5.0 && CXX=clang++-5.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: clang @@ -307,8 +307,8 @@ matrix: - clang-6.0 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="CC=clang-6.0 && CXX=clang++-6.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" + - MATRIX_EVAL="CC=clang-6.0 && CXX=clang++-6.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: gcc @@ -320,8 +320,8 @@ matrix: - libopencv-dev env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" + - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: gcc @@ -336,8 +336,8 @@ matrix: - g++-4.9 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="CC=gcc-4.9 && CXX=g++-4.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" + - MATRIX_EVAL="CC=gcc-4.9 && CXX=g++-4.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: gcc @@ -352,8 +352,8 @@ matrix: - g++-5 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="CC=gcc-5 && CXX=g++-5 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" + - MATRIX_EVAL="CC=gcc-5 && CXX=g++-5 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: gcc @@ -368,8 +368,8 @@ matrix: - libopencv-dev env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="CC=gcc-6 && CXX=g++-6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" + - MATRIX_EVAL="CC=gcc-6 && CXX=g++-6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: gcc @@ -384,8 +384,8 @@ matrix: - g++-7 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="CC=gcc-7 && CXX=g++-7 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" + - MATRIX_EVAL="CC=gcc-7 && CXX=g++-7 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: gcc @@ -400,8 +400,8 @@ matrix: - g++-8 env: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev" + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" + - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" before_install: - travis_retry eval "${MATRIX_EVAL}" From c35c94798a3fc760cede18efb7c51231f2e3355b Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 29 Mar 2019 16:09:04 +0100 Subject: [PATCH 0434/1365] [clang] force -pthread flag on linux to avoid some cmake bugs --- CMakeLists.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/CMakeLists.txt b/CMakeLists.txt index 3cc384cce3e..e41574648df 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -100,6 +100,9 @@ if(CMAKE_COMPILER_IS_GNUCC) endif() if ("${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang") + if (UNIX AND NOT APPLE) + set(CMAKE_CXX_FLAGS "-pthread ${CMAKE_CXX_FLAGS}") #force pthread to avoid bugs in some cmake setups + endif() set(CMAKE_CXX_FLAGS "-Wno-deprecated -Wno-writable-strings ${CMAKE_CXX_FLAGS}") string(REGEX REPLACE "-O0" "-Og" CMAKE_CXX_FLAGS_DEBUG ${CMAKE_CXX_FLAGS_DEBUG}) string(REGEX REPLACE "-O3" "-Ofast" CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) From 7a3600f221aed2b47a0e0d30780e391d63012e62 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 29 Mar 2019 16:11:42 +0100 Subject: [PATCH 0435/1365] [cygwin] add libncurses dependency --- appveyor.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/appveyor.yml b/appveyor.yml index 3c69f3784e6..76abe844ec3 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -54,7 +54,7 @@ install: - cd %WORKSPACE%\ - mkdir cygwin-downloads - ps: if($env:COMPILER -eq "cygwin") { Invoke-WebRequest https://cygwin.com/setup-x86_64.exe -OutFile $env:WORKSPACE\cygwin-setup.exe } - - if [%COMPILER%]==[cygwin] %WORKSPACE%\cygwin-setup.exe --quiet-mode --no-shortcuts --no-startmenu --no-desktop --upgrade-also --root C:\cygwin64 --local-package-dir %WORKSPACE%\cygwin-downloads --packages gcc-g++,cmake,libopencv-devel + - if [%COMPILER%]==[cygwin] %WORKSPACE%\cygwin-setup.exe --quiet-mode --no-shortcuts --no-startmenu --no-desktop --upgrade-also --root C:\cygwin64 --local-package-dir %WORKSPACE%\cygwin-downloads --packages gcc-g++,cmake,libopencv-devel,libncurses-devel - ps: if($env:COMPILER -eq "cygwin") { Invoke-WebRequest https://github.com/Kitware/CMake/releases/download/v3.14.0/cmake-3.14.0.tar.gz -OutFile $env:WORKSPACE\cmake-3.14.0.tar.gz } - if [%COMPILER%]==[cygwin] %CYGSH% 'tar zxvf cmake-3.14.0.tar.gz' - if [%COMPILER%]==[cygwin] cd %WORKSPACE%\cmake-3.14.0 From ba75f06333d557e27c06c3e95022e9401a5b470e Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 29 Mar 2019 17:11:03 +0100 Subject: [PATCH 0436/1365] [appveyor] add opencv[ffmpeg] also in ci --- appveyor.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/appveyor.yml b/appveyor.yml index 76abe844ec3..0860ce29588 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -67,7 +67,7 @@ install: - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] bootstrap-vcpkg.bat - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg integrate install - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] if [%USE_CUDA%]==[yes] vcpkg install cuda - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg install opencv pthreads ## opencv[cuda] is too big to build, timing out (>1h). We use plain openCV also for CUDA builds (toolchain can manage this strange situation anyway) + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg install opencv[ffmpeg] pthreads ## opencv[ffmpeg,cuda] is too big to build, timing out (>1h). We use plain openCV also for CUDA builds (toolchain can manage this strange situation anyway) - cd %WORKSPACE%\darknet\ - mkdir build_debug && cd build_debug - if [%COMPILER%]==[cygwin] %CYGSH% 'cmake .. -G "Unix Makefiles" -DCMAKE_BUILD_TYPE="Debug"' From f19d1440cb85331f4d5ce40b3dccd4582c690687 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 4 Apr 2019 22:57:05 +0300 Subject: [PATCH 0437/1365] Moved all OpenCV functions to the image_opencv.cpp/h --- .circleci/config.yml | 2 + CMakeLists.txt | 1 + Makefile | 2 +- build/darknet/darknet.vcxproj | 2 + build/darknet/darknet_no_gpu.vcxproj | 2 + build/darknet/yolo_cpp_dll.vcxproj | 2 + build/darknet/yolo_cpp_dll_no_gpu.vcxproj | 2 + include/yolo_v2_class.hpp | 51 +- scripts/README.md | 2 + src/art.c | 19 +- src/cifar.c | 4 - src/classifier.c | 37 +- src/coco.c | 12 +- src/crnn_layer.c | 6 +- src/darknet.c | 5 +- src/data.c | 27 +- src/demo.c | 88 +- src/detector.c | 111 +- src/go.c | 3 - src/http_stream.cpp | 168 +-- src/http_stream.h | 21 +- src/image.c | 601 +---------- src/image.h | 19 +- src/image_opencv.cpp | 1165 +++++++++++++++++++++ src/image_opencv.h | 105 ++ src/network.c | 10 +- src/network.h | 1 + src/network_kernels.cu | 18 +- src/nightmare.c | 8 +- src/rnn.c | 4 - src/rnn_vid.c | 23 +- src/super.c | 3 - src/swag.c | 4 - src/tag.c | 4 - src/voxel.c | 13 +- src/writing.c | 11 +- src/yolo.c | 17 +- src/yolo_v2_class.cpp | 47 - 38 files changed, 1463 insertions(+), 1157 deletions(-) create mode 100644 src/image_opencv.cpp create mode 100644 src/image_opencv.h diff --git a/.circleci/config.yml b/.circleci/config.yml index 8b7e27f3bcd..dc004b3f908 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -13,6 +13,8 @@ jobs: - run: make clean - run: make LIBSO=1 GPU=0 CUDNN=0 OPENCV=1 -j 8 - run: make clean + - run: make LIBSO=1 GPU=1 CUDNN=0 OPENCV=1 -j 8 + - run: make clean - run: make LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 -j 8 - run: make clean - run: make LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 CUDNN_HALF=1 -j 8 diff --git a/CMakeLists.txt b/CMakeLists.txt index e41574648df..323c1972583 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -224,6 +224,7 @@ file(GLOB sources "${CMAKE_CURRENT_LIST_DIR}/src/*.c") #add also .cpp files list(APPEND sources ${CMAKE_CURRENT_LIST_DIR}/src/http_stream.cpp + ${CMAKE_CURRENT_LIST_DIR}/src/image_opencv.cpp ) #remove darknet.c file which is necessary only for the executable, not for the lib list(REMOVE_ITEM sources diff --git a/Makefile b/Makefile index d184ca17a20..85e1a3e8c0d 100644 --- a/Makefile +++ b/Makefile @@ -118,7 +118,7 @@ LDFLAGS+= -L/usr/local/zed/lib -lsl_core -lsl_input -lsl_zed #-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0 endif -OBJ=http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o upsample_layer.o lstm_layer.o +OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o upsample_layer.o lstm_layer.o ifeq ($(GPU), 1) LDFLAGS+= -lstdc++ OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj index 2f93f454e6b..29d69fe393c 100644 --- a/build/darknet/darknet.vcxproj +++ b/build/darknet/darknet.vcxproj @@ -205,6 +205,7 @@ + @@ -264,6 +265,7 @@ + diff --git a/build/darknet/darknet_no_gpu.vcxproj b/build/darknet/darknet_no_gpu.vcxproj index e2fd6fa6370..ec4cea997ff 100644 --- a/build/darknet/darknet_no_gpu.vcxproj +++ b/build/darknet/darknet_no_gpu.vcxproj @@ -210,6 +210,7 @@ + @@ -269,6 +270,7 @@ + diff --git a/build/darknet/yolo_cpp_dll.vcxproj b/build/darknet/yolo_cpp_dll.vcxproj index 499d186cc38..f2c22b03862 100644 --- a/build/darknet/yolo_cpp_dll.vcxproj +++ b/build/darknet/yolo_cpp_dll.vcxproj @@ -208,6 +208,7 @@ + @@ -269,6 +270,7 @@ + diff --git a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj index 3b3c70a434a..5300e40b771 100644 --- a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj +++ b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj @@ -194,6 +194,7 @@ + @@ -255,6 +256,7 @@ + diff --git a/include/yolo_v2_class.hpp b/include/yolo_v2_class.hpp index bf3b28aa5a0..fcaddcf0a51 100644 --- a/include/yolo_v2_class.hpp +++ b/include/yolo_v2_class.hpp @@ -48,6 +48,7 @@ struct bbox_t_container { #include #include #include +#include #ifdef OPENCV #include // C++ @@ -61,6 +62,7 @@ extern "C" LIB_API int detect_mat(const uint8_t* data, const size_t data_length, extern "C" LIB_API int dispose(); extern "C" LIB_API int get_device_count(); extern "C" LIB_API int get_device_name(int gpu, char* deviceName); +extern "C" LIB_API void send_json_custom(char const* send_buf, int port, int timeout); class Detector { std::shared_ptr detector_gpu_ptr; @@ -86,8 +88,8 @@ class Detector { LIB_API void *get_cuda_context(); - LIB_API bool send_json_http(std::vector cur_bbox_vec, std::vector obj_names, int frame_id, - std::string filename = std::string(), int timeout = 400000, int port = 8070); + //LIB_API bool send_json_http(std::vector cur_bbox_vec, std::vector obj_names, int frame_id, + // std::string filename = std::string(), int timeout = 400000, int port = 8070); std::vector detect_resized(image_t img, int init_w, int init_h, float thresh = 0.2, bool use_mean = false) { @@ -178,6 +180,51 @@ class Detector { #endif // OPENCV +public: + + bool send_json_http(std::vector cur_bbox_vec, std::vector obj_names, int frame_id, + std::string filename = std::string(), int timeout = 400000, int port = 8070) + { + std::string send_str; + + char *tmp_buf = (char *)calloc(1024, sizeof(char)); + if (!filename.empty()) { + sprintf(tmp_buf, "{\n \"frame_id\":%d, \n \"filename\":\"%s\", \n \"objects\": [ \n", frame_id, filename.c_str()); + } + else { + sprintf(tmp_buf, "{\n \"frame_id\":%d, \n \"objects\": [ \n", frame_id); + } + send_str = tmp_buf; + free(tmp_buf); + + for (auto & i : cur_bbox_vec) { + char *buf = (char *)calloc(2048, sizeof(char)); + + sprintf(buf, " {\"class_id\":%d, \"name\":\"%s\", \"absolute_coordinates\":{\"center_x\":%d, \"center_y\":%d, \"width\":%d, \"height\":%d}, \"confidence\":%f", + i.obj_id, obj_names[i.obj_id].c_str(), i.x, i.y, i.w, i.h, i.prob); + + //sprintf(buf, " {\"class_id\":%d, \"name\":\"%s\", \"relative_coordinates\":{\"center_x\":%f, \"center_y\":%f, \"width\":%f, \"height\":%f}, \"confidence\":%f", + // i.obj_id, obj_names[i.obj_id], i.x, i.y, i.w, i.h, i.prob); + + send_str += buf; + + if (!std::isnan(i.z_3d)) { + sprintf(buf, "\n , \"coordinates_in_meters\":{\"x_3d\":%.2f, \"y_3d\":%.2f, \"z_3d\":%.2f}", + i.x_3d, i.y_3d, i.z_3d); + send_str += buf; + } + + send_str += "}\n"; + + free(buf); + } + + //send_str += "\n ] \n}, \n"; + send_str += "\n ] \n}"; + + send_json_custom(send_str.c_str(), port, timeout); + return true; + } }; // -------------------------------------------------------------------------------- diff --git a/scripts/README.md b/scripts/README.md index 36985f81cba..5debe7f97be 100644 --- a/scripts/README.md +++ b/scripts/README.md @@ -10,6 +10,8 @@ ImageNet: http://imagenet.stanford.edu/download.php ImageNet (ILSVRC2012): http://www.image-net.org/challenges/LSVRC/2012/nonpub-downloads +ImageNet (ILSVRC2015): http://image-net.org/small/download.php + Open Images: https://storage.googleapis.com/openimages/web/download.html Cityscapes: https://www.cityscapes-dataset.com/ diff --git a/src/art.c b/src/art.c index e447176781b..91fc94d63c7 100644 --- a/src/art.c +++ b/src/art.c @@ -12,16 +12,6 @@ #endif -#ifdef OPENCV -#include -#include -#ifndef CV_VERSION_EPOCH -#include -#endif -image get_image_from_stream(CvCapture *cap); -#endif - - void demo_art(char *cfgfile, char *weightfile, int cam_index) { #ifdef OPENCV @@ -32,14 +22,13 @@ void demo_art(char *cfgfile, char *weightfile, int cam_index) set_batch_network(&net, 1); srand(2222222); - CvCapture * cap; + cap_cv * cap; - cap = cvCaptureFromCAM(cam_index); + cap = get_capture_webcam(cam_index); char *window = "ArtJudgementBot9000!!!"; if(!cap) error("Couldn't connect to webcam.\n"); - cvNamedWindow(window, CV_WINDOW_NORMAL); - cvResizeWindow(window, 512, 512); + create_window_cv(window, 0, 512, 512); int i; int idx[] = {37, 401, 434}; int n = sizeof(idx)/sizeof(idx[0]); @@ -71,7 +60,7 @@ void demo_art(char *cfgfile, char *weightfile, int cam_index) free_image(in_s); free_image(in); - cvWaitKey(1); + wait_key_cv(1); } #endif } diff --git a/src/cifar.c b/src/cifar.c index 24a13a57a24..1b87cb5f016 100644 --- a/src/cifar.c +++ b/src/cifar.c @@ -4,10 +4,6 @@ #include "option_list.h" #include "blas.h" -#ifdef OPENCV -#include -#endif - void train_cifar(char *cfgfile, char *weightfile) { srand(time(0)); diff --git a/src/classifier.c b/src/classifier.c index 22471d619d9..ca196c66f3c 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -13,21 +13,6 @@ #include #endif -#ifdef OPENCV -#include -#include -#ifndef CV_VERSION_EPOCH -#include -#endif -#include "image.h" -#include "http_stream.h" -IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show); - -void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, - float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port); - -#endif - float validate_classifier_single(char *datacfg, char *filename, char *weightfile, network *existing_net, int topk_custom); float *get_regression_values(char **labels, int n) @@ -111,7 +96,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, #ifdef OPENCV //args.threads = 3; - IplImage* img = NULL; + mat_cv* img = NULL; float max_img_loss = 10; int number_of_lines = 100; int img_size = 1000; @@ -211,8 +196,8 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, save_weights(net, buff); #ifdef OPENCV - cvReleaseImage(&img); - cvDestroyAllWindows(); + release_ipl(&img); + destroy_all_windows_cv(); #endif free_network(net); @@ -958,7 +943,7 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i list *options = read_data_cfg(datacfg); srand(2222222); - CvCapture * cap; + cap_cv * cap; if (filename) { //cap = cvCaptureFromFile(filename); @@ -979,8 +964,7 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i int* indexes = (int*)calloc(top, sizeof(int)); if(!cap) error("Couldn't connect to webcam.\n"); - //cvNamedWindow("Threat", CV_WINDOW_NORMAL); - //cvResizeWindow("Threat", 512, 512); + create_window_cv("Threat", 0, 512, 512); float fps = 0; int i; @@ -1070,7 +1054,7 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i if(1){ show_image(out, "Threat"); - cvWaitKey(10); + wait_key_cv(10); } free_image(in_s); free_image(in); @@ -1086,7 +1070,7 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename) { -#ifdef OPENCV +#ifdef OPENCV_DISABLE int bad_cats[] = {218, 539, 540, 1213, 1501, 1742, 1911, 2415, 4348, 19223, 368, 369, 370, 1133, 1200, 1306, 2122, 2301, 2537, 2823, 3179, 3596, 3639, 4489, 5107, 5140, 5289, 6240, 6631, 6762, 7048, 7171, 7969, 7984, 7989, 8824, 8927, 9915, 10270, 10448, 13401, 15205, 18358, 18894, 18895, 19249, 19697}; printf("Classifier Demo\n"); @@ -1184,7 +1168,7 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind calculate_binary_weights(net); srand(2222222); - CvCapture * cap; + cap_cv * cap; if(filename){ //cap = cvCaptureFromFile(filename); @@ -1204,8 +1188,7 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind int* indexes = (int*)calloc(top, sizeof(int)); if(!cap) error("Couldn't connect to webcam.\n"); - cvNamedWindow("Classifier", CV_WINDOW_NORMAL); - cvResizeWindow("Classifier", 512, 512); + create_window_cv("Classifier", 0, 512, 512); float fps = 0; int i; @@ -1236,7 +1219,7 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind free_image(in_s); free_image(in); - cvWaitKey(10); + wait_key_cv(10);// cvWaitKey(10); gettimeofday(&tval_after, NULL); timersub(&tval_after, &tval_before, &tval_result); diff --git a/src/coco.c b/src/coco.c index e3cfccea009..931c406c7a8 100644 --- a/src/coco.c +++ b/src/coco.c @@ -8,10 +8,6 @@ #include "box.h" #include "demo.h" -#ifdef OPENCV -#include -#endif - char *coco_classes[] = {"person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic light","fire hydrant","stop sign","parking meter","bench","bird","cat","dog","horse","sheep","cow","elephant","bear","zebra","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite","baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife","spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza","donut","cake","chair","couch","potted plant","bed","dining table","toilet","tv","laptop","mouse","remote","keyboard","cell phone","microwave","oven","toaster","sink","refrigerator","book","clock","vase","scissors","teddy bear","hair drier","toothbrush"}; int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90}; @@ -354,10 +350,10 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh) show_image(im, "predictions"); free_image(im); free_image(sized); -#ifdef OPENCV - cvWaitKey(0); - cvDestroyAllWindows(); -#endif + + wait_until_press_key_cv(); + destroy_all_windows_cv(); + if (filename) break; } } diff --git a/src/crnn_layer.c b/src/crnn_layer.c index a4016dfb948..c591276adf9 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -263,13 +263,13 @@ void forward_crnn_layer_gpu(layer l, network_state state) s.train = state.train; s.workspace = state.workspace; s.net = state.net; - //if(!state.train) s.index = state.index; // don't use TC at all (especially without cuda_convert_f32_to_f16() ) + if(!state.train) s.index = state.index; // don't use TC for training (especially without cuda_convert_f32_to_f16() ) int i; layer input_layer = *(l.input_layer); layer self_layer = *(l.self_layer); layer output_layer = *(l.output_layer); - +/* #ifdef CUDNN_HALF // slow and bad for training if (!state.train && state.net.cudnn_half) { s.index = state.index; @@ -278,7 +278,7 @@ void forward_crnn_layer_gpu(layer l, network_state state) cuda_convert_f32_to_f16(output_layer.weights_gpu, output_layer.c*output_layer.n*output_layer.size*output_layer.size, output_layer.weights_gpu16); } #endif //CUDNN_HALF - +*/ if (state.train) { fill_ongpu(l.outputs * l.batch * l.steps, 0, output_layer.delta_gpu, 1); diff --git a/src/darknet.c b/src/darknet.c index 67c7d9a4331..1c5c08b623a 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -12,9 +12,6 @@ #include "blas.h" #include "connected_layer.h" -#ifdef OPENCV -#include -#endif extern void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top); extern void run_voxel(int argc, char **argv); @@ -426,7 +423,7 @@ void visualize(char *cfgfile, char *weightfile) } visualize_network(net); #ifdef OPENCV - cvWaitKey(0); + wait_until_press_key_cv(); #endif } diff --git a/src/data.c b/src/data.c index a36125196d3..221a6211c90 100644 --- a/src/data.c +++ b/src/data.c @@ -766,13 +766,6 @@ static box float_to_box_stride(float *f, int stride) } #ifdef OPENCV -#include -#include -#include -#ifndef CV_VERSION_EPOCH -#include -#include -#endif #include "http_stream.h" @@ -799,8 +792,8 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo const char *filename = random_paths[i]; int flag = (c >= 3); - IplImage *src; - if ((src = cvLoadImage(filename, flag)) == 0) + mat_cv *src; + if ((src = load_image_mat_cv(filename, flag)) == 0) { fprintf(stderr, "Cannot load image \"%s\"\n", filename); char buff[256]; @@ -811,8 +804,8 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo //exit(0); } - int oh = src->height; - int ow = src->width; + int oh = get_height_cv(src); + int ow = get_width_cv(src); int dw = (ow*jitter); int dh = (oh*jitter); @@ -851,7 +844,8 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo fill_truth_detection(filename, boxes, d.y.vals[i], classes, flip, dx, dy, 1./sx, 1./sy, w, h); - /* + const int show_augmented_images = 0; + if(show_augmented_images) { char buff[10]; sprintf(buff, "aug_%s_%d", random_paths[i], random_gen()); @@ -867,10 +861,11 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo } show_image(ai, buff); - cvWaitKey(0); - }*/ + wait_until_press_key_cv(); + } + - cvReleaseImage(&src); + release_ipl(&src); } free(random_paths); return d; @@ -959,7 +954,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo } show_image(sized, buff); - cvWaitKey(0); + wait_until_press_key_cv(); }*/ free_image(orig); diff --git a/src/demo.c b/src/demo.c index 5950091b845..61e2380fe93 100644 --- a/src/demo.c +++ b/src/demo.c @@ -16,14 +16,8 @@ #ifdef OPENCV -#include -#include -#include -#ifndef CV_VERSION_EPOCH -#include -#endif + #include "http_stream.h" -image get_image_from_stream(CvCapture *cap); static char **demo_names; static image **demo_alphabet; @@ -35,7 +29,8 @@ static detection *dets = NULL; static network net; static image in_s ; static image det_s; -static CvCapture * cap; + +static cap_cv *cap; static int cpp_video_capture = 0; static float fps = 0; static float demo_thresh = 0; @@ -46,33 +41,24 @@ static int demo_json_port = -1; static float* predictions[NFRAMES]; static int demo_index = 0; static image images[NFRAMES]; -static IplImage* ipl_images[NFRAMES]; +static mat_cv* cv_images[NFRAMES]; static float *avg; -void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output); -void show_image_cv_ipl(IplImage *disp, const char *name); -void save_cv_png(IplImage *img, const char *name); -void save_cv_jpg(IplImage *img, const char *name); -image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close); -image get_image_from_stream_letterbox(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close); -int get_stream_fps(CvCapture *cap, int cpp_video_capture); -IplImage* in_img; -IplImage* det_img; -IplImage* show_img; +mat_cv* in_img; +mat_cv* det_img; +mat_cv* show_img; -static int flag_exit; -static int letter_box = 0; +static volatile int flag_exit; +static volatile int letter_box = 0; void *fetch_in_thread(void *ptr) { - //in = get_image_from_stream(cap); int dont_close_stream = 0; // set 1 if your IP-camera periodically turns off and turns on video-stream if(letter_box) in_s = get_image_from_stream_letterbox(cap, net.w, net.h, net.c, &in_img, cpp_video_capture, dont_close_stream); else in_s = get_image_from_stream_resize(cap, net.w, net.h, net.c, &in_img, cpp_video_capture, dont_close_stream); if(!in_s.data){ - //error("Stream closed."); printf("Stream closed.\n"); flag_exit = 1; //exit(EXIT_FAILURE); @@ -95,12 +81,12 @@ void *detect_in_thread(void *ptr) free_image(det_s); - ipl_images[demo_index] = det_img; - det_img = ipl_images[(demo_index + NFRAMES / 2 + 1) % NFRAMES]; + cv_images[demo_index] = det_img; + det_img = cv_images[(demo_index + NFRAMES / 2 + 1) % NFRAMES]; demo_index = (demo_index + 1) % NFRAMES; if (letter_box) - dets = get_network_boxes(&net, in_img->width, in_img->height, demo_thresh, demo_thresh, 0, 1, &nboxes, 1); // letter box + dets = get_network_boxes(&net, get_width_cv(in_img), get_height_cv(in_img), demo_thresh, demo_thresh, 0, 1, &nboxes, 1); // letter box else dets = get_network_boxes(&net, net.w, net.h, demo_thresh, demo_thresh, 0, 1, &nboxes, 0); // resized @@ -134,7 +120,6 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int if(weightfile){ load_weights(&net, weightfile); } - //set_batch_network(&net, 1); fuse_conv_batchnorm(net); calculate_binary_weights(net); srand(2222222); @@ -193,27 +178,26 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int int count = 0; if(!prefix && !dont_show){ - cvNamedWindow("Demo", CV_WINDOW_NORMAL); - cvMoveWindow("Demo", 0, 0); - cvResizeWindow("Demo", 1352, 1013); + int full_screen = 0; + create_window_cv("Demo", full_screen, 1352, 1013); } - CvVideoWriter* output_video_writer = NULL; // cv::VideoWriter output_video; + + write_cv* output_video_writer = NULL; if (out_filename && !flag_exit) { - CvSize size; - size.width = det_img->width, size.height = det_img->height; int src_fps = 25; src_fps = get_stream_fps(cap, cpp_video_capture); - - //const char* output_name = "test_dnn_out.avi"; - //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('H', '2', '6', '4'), src_fps, size, 1); - output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('D', 'I', 'V', 'X'), src_fps, size, 1); - //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'J', 'P', 'G'), src_fps, size, 1); - //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'P', '4', 'V'), src_fps, size, 1); - //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('M', 'P', '4', '2'), src_fps, size, 1); - //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('X', 'V', 'I', 'D'), src_fps, size, 1); - //output_video_writer = cvCreateVideoWriter(out_filename, CV_FOURCC('W', 'M', 'V', '2'), src_fps, size, 1); + output_video_writer = + create_video_writer(out_filename, 'D', 'I', 'V', 'X', src_fps, get_width_cv(det_img), get_height_cv(det_img), 1); + + //'H', '2', '6', '4' + //'D', 'I', 'V', 'X' + //'M', 'J', 'P', 'G' + //'M', 'P', '4', 'V' + //'M', 'P', '4', '2' + //'X', 'V', 'I', 'D' + //'W', 'M', 'V', '2' } double before = get_wall_time(); @@ -231,9 +215,9 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int //if (nms) do_nms_obj(local_dets, local_nboxes, l.classes, nms); // bad results if (nms) do_nms_sort(local_dets, local_nboxes, l.classes, nms); - printf("\033[2J"); - printf("\033[1;1H"); - printf("\nFPS:%.1f\n", fps); + //printf("\033[2J"); + //printf("\033[1;1H"); + //printf("\nFPS:%.1f\n", fps); printf("Objects:\n\n"); ++frame_id; @@ -245,10 +229,12 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int draw_detections_cv_v3(show_img, local_dets, local_nboxes, demo_thresh, demo_names, demo_alphabet, demo_classes, demo_ext_output); free_detections(local_dets, local_nboxes); + printf("\nFPS:%.1f\n", fps); + if(!prefix){ if (!dont_show) { show_image_cv_ipl(show_img, "Demo"); - int c = cvWaitKey(1); + int c = wait_key_cv(1); if (c == 10) { if (frame_skip == 0) frame_skip = 60; else if (frame_skip == 4) frame_skip = 0; @@ -276,11 +262,11 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int // save video file if (output_video_writer && show_img) { - cvWriteFrame(output_video_writer, show_img); + write_frame_cv(output_video_writer, show_img); printf("\n cvWriteFrame \n"); } - cvReleaseImage(&show_img); + release_ipl(&show_img); pthread_join(fetch_thread, 0); pthread_join(detect_thread, 0); @@ -307,13 +293,13 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int } printf("input video stream closed. \n"); if (output_video_writer) { - cvReleaseVideoWriter(&output_video_writer); + release_video_writer(&output_video_writer); printf("output_video_writer closed. \n"); } // free memory - cvReleaseImage(&show_img); - cvReleaseImage(&in_img); + release_ipl(&show_img); + release_ipl(&in_img); free_image(in_s); free(avg); diff --git a/src/detector.c b/src/detector.c index 46d19615e3f..179498a8688 100644 --- a/src/detector.c +++ b/src/detector.c @@ -16,37 +16,6 @@ typedef __compar_fn_t comparison_fn_t; #endif #endif -#ifdef OPENCV -#include -#include -//#include "opencv2/core/core.hpp" -#include -#include - -#ifndef CV_VERSION_EPOCH -#include -#define OPENCV_VERSION CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR)"" CVAUX_STR(CV_VERSION_REVISION) -#ifndef USE_CMAKE_LIBS -#pragma comment(lib, "opencv_world" OPENCV_VERSION ".lib") -#endif // USE_CMAKE_LIBS -#else -#define OPENCV_VERSION CVAUX_STR(CV_VERSION_EPOCH)"" CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR) -#ifndef USE_CMAKE_LIBS -#pragma comment(lib, "opencv_core" OPENCV_VERSION ".lib") -#pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib") -#pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib") -#endif // USE_CMAKE_LIBS -#endif -IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show); - -void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, - float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port); -#endif // OPENCV - -#ifndef CV_RGB -#define CV_RGB(r, g, b) cvScalar( (b), (g), (r), 0 ) -#endif // OPENCV - #include "http_stream.h" int check_mistakes; @@ -83,16 +52,6 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i for (k = 0; k < net_map.n; ++k) { free_layer(net_map.layers[k]); } - /* -#ifdef GPU - cuda_free(net_map.workspace); - cuda_free(net_map.input_state_gpu); - if (*net_map.input16_gpu) cuda_free(*net_map.input16_gpu); - if (*net_map.output16_gpu) cuda_free(*net_map.output16_gpu); -#else - free(net_map.workspace); -#endif - */ } srand(time(0)); @@ -172,7 +131,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i #ifdef OPENCV args.threads = 3 * ngpus; // Amazon EC2 Tesla V100: p3.2xlarge (8 logical cores) - p3.16xlarge //args.threads = 12 * ngpus; // Ryzen 7 2700X (16 logical cores) - IplImage* img = NULL; + mat_cv* img = NULL; float max_img_loss = 5; int number_of_lines = 100; int img_size = 1000; @@ -249,7 +208,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i float loss = 0; #ifdef GPU if (ngpus == 1) { - loss = train_network(net, train); + loss = train_network_waitkey(net, train, 1); } else { loss = train_networks(nets, ngpus, train, 4); @@ -340,8 +299,8 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i save_weights(net, buff); #ifdef OPENCV - cvReleaseImage(&img); - cvDestroyAllWindows(); + release_ipl(&img); + destroy_all_windows_cv(); #endif // free memory @@ -1263,56 +1222,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int if (show) { #ifdef OPENCV - CvMat* labels = cvCreateMat(number_of_boxes, 1, CV_32SC1); - CvMat* points = cvCreateMat(number_of_boxes, 2, CV_32FC1); - CvMat* centers = cvCreateMat(num_of_clusters, 2, CV_32FC1); - - for (i = 0; i < number_of_boxes; ++i) { - points->data.fl[i * 2] = rel_width_height_array[i * 2]; - points->data.fl[i * 2 + 1] = rel_width_height_array[i * 2 + 1]; - //cvSet1D(points, i * 2, cvScalar(rel_width_height_array[i * 2], 0, 0, 0)); - //cvSet1D(points, i * 2 + 1, cvScalar(rel_width_height_array[i * 2 + 1], 0, 0, 0)); - } - - for (i = 0; i < num_of_clusters; ++i) { - centers->data.fl[i * 2] = anchors_data.centers.vals[i][0]; - centers->data.fl[i * 2 + 1] = anchors_data.centers.vals[i][1]; - } - - for (i = 0; i < number_of_boxes; ++i) { - labels->data.i[i] = anchors_data.assignments[i]; - } - - size_t img_size = 700; - IplImage* img = cvCreateImage(cvSize(img_size, img_size), 8, 3); - cvZero(img); - for (j = 0; j < num_of_clusters; ++j) { - CvPoint pt1, pt2; - pt1.x = pt1.y = 0; - pt2.x = centers->data.fl[j * 2] * img_size / width; - pt2.y = centers->data.fl[j * 2 + 1] * img_size / height; - cvRectangle(img, pt1, pt2, CV_RGB(255, 255, 255), 1, 8, 0); - } - - for (i = 0; i < number_of_boxes; ++i) { - CvPoint pt; - pt.x = points->data.fl[i * 2] * img_size / width; - pt.y = points->data.fl[i * 2 + 1] * img_size / height; - int cluster_idx = labels->data.i[i]; - int red_id = (cluster_idx * (uint64_t)123 + 55) % 255; - int green_id = (cluster_idx * (uint64_t)321 + 33) % 255; - int blue_id = (cluster_idx * (uint64_t)11 + 99) % 255; - cvCircle(img, pt, 1, CV_RGB(red_id, green_id, blue_id), CV_FILLED, 8, 0); - //if(pt.x > img_size || pt.y > img_size) printf("\n pt.x = %d, pt.y = %d \n", pt.x, pt.y); - } - save_cv_png(img, "cloud.png"); - cvShowImage("clusters", img); - cvWaitKey(0); - cvReleaseImage(&img); - cvDestroyAllWindows(); - cvReleaseMat(&labels); - cvReleaseMat(&points); - cvReleaseMat(¢ers); + show_acnhors(number_of_boxes, num_of_clusters, rel_width_height_array, anchors_data, width, height); #endif // OPENCV } free(rel_width_height_array); @@ -1334,7 +1244,6 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam if (weightfile) { load_weights(&net, weightfile); } - //set_batch_network(&net, 1); fuse_conv_batchnorm(net); calculate_binary_weights(net); if (net.layers[net.n - 1].classes != names_size) { @@ -1439,14 +1348,12 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam free_detections(dets, nboxes); free_image(im); free_image(sized); - //free(boxes); - //free_ptrs((void **)probs, l.w*l.h*l.n); -#ifdef OPENCV + if (!dont_show) { - cvWaitKey(0); - cvDestroyAllWindows(); + wait_until_press_key_cv(); + destroy_all_windows_cv(); } -#endif + if (filename) break; } diff --git a/src/go.c b/src/go.c index 79529432c28..5d507768e50 100644 --- a/src/go.c +++ b/src/go.c @@ -4,9 +4,6 @@ #include "option_list.h" #include "blas.h" -#ifdef OPENCV -#include -#endif int inverted = 1; int noi = 1; diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 77096d2b60c..ec9d115d9ab 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -489,7 +489,9 @@ class MJPG_sender }; // ---------------------------------------- -void send_mjpeg(IplImage* ipl, int port, int timeout, int quality) +struct mat_cv :IplImage { int a[0]; }; + +void send_mjpeg(mat_cv* ipl, int port, int timeout, int quality) { try { static MJPG_sender wri(port, timeout, quality); @@ -503,170 +505,6 @@ void send_mjpeg(IplImage* ipl, int port, int timeout, int quality) } // ---------------------------------------- -CvCapture* get_capture_video_stream(const char *path) { - CvCapture* cap = NULL; - try { - cap = (CvCapture*)new cv::VideoCapture(path); - } - catch (...) { - cerr << " Error: video-stream " << path << " can't be opened! \n"; - } - return cap; -} -// ---------------------------------------- - -CvCapture* get_capture_webcam(int index) { - CvCapture* cap = NULL; - try { - cap = (CvCapture*)new cv::VideoCapture(index); - //((cv::VideoCapture*)cap)->set(CV_CAP_PROP_FRAME_WIDTH, 1280); - //((cv::VideoCapture*)cap)->set(CV_CAP_PROP_FRAME_HEIGHT, 960); - } - catch (...) { - cerr << " Error: Web-camera " << index << " can't be opened! \n"; - } - return cap; -} -// ---------------------------------------- - -IplImage* get_webcam_frame(CvCapture *cap) { - IplImage* src = NULL; - try { - cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; - cv::Mat frame; - if (cpp_cap.isOpened()) - { - cpp_cap >> frame; - IplImage tmp = frame; - src = cvCloneImage(&tmp); - } - else { - std::cout << " Video-stream stopped! \n"; - } - } - catch (...) { - std::cout << " Video-stream stoped! \n"; - } - return src; -} - -int get_stream_fps_cpp(CvCapture *cap) { - int fps = 25; - try { - cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; -#ifndef CV_VERSION_EPOCH // OpenCV 3.x - fps = cpp_cap.get(CAP_PROP_FPS); -#else // OpenCV 2.x - fps = cpp_cap.get(CV_CAP_PROP_FPS); -#endif - } - catch (...) { - cerr << " Can't get FPS of source videofile. For output video FPS = 25 by default. \n"; - } - return fps; -} -// ---------------------------------------- - -image image_data_augmentation(IplImage* ipl, int w, int h, - int pleft, int ptop, int swidth, int sheight, int flip, - float jitter, float dhue, float dsat, float dexp) -{ - image out; - try { - cv::Mat img = cv::cvarrToMat(ipl); - - // crop - cv::Rect src_rect(pleft, ptop, swidth, sheight); - cv::Rect img_rect(cv::Point2i(0, 0), img.size()); - cv::Rect new_src_rect = src_rect & img_rect; - - cv::Rect dst_rect(cv::Point2i(std::max(0, -pleft), std::max(0, -ptop)), new_src_rect.size()); - - cv::Mat cropped(cv::Size(src_rect.width, src_rect.height), img.type()); - cropped.setTo(cv::Scalar::all(0)); - - img(new_src_rect).copyTo(cropped(dst_rect)); - - // resize - cv::Mat sized; - cv::resize(cropped, sized, cv::Size(w, h), 0, 0, INTER_LINEAR); - - // flip - if (flip) { - cv::flip(sized, cropped, 1); // 0 - x-axis, 1 - y-axis, -1 - both axes (x & y) - sized = cropped.clone(); - } - - // HSV augmentation - // CV_BGR2HSV, CV_RGB2HSV, CV_HSV2BGR, CV_HSV2RGB - if (ipl->nChannels >= 3) - { - cv::Mat hsv_src; - cvtColor(sized, hsv_src, CV_BGR2HSV); // also BGR -> RGB - - std::vector hsv; - cv::split(hsv_src, hsv); - - hsv[1] *= dsat; - hsv[2] *= dexp; - hsv[0] += 179 * dhue; - - cv::merge(hsv, hsv_src); - - cvtColor(hsv_src, sized, CV_HSV2RGB); // now RGB instead of BGR - } - else - { - sized *= dexp; - } - - //std::stringstream window_name; - //window_name << "augmentation - " << ipl; - //cv::imshow(window_name.str(), sized); - //cv::waitKey(0); - - // Mat -> IplImage -> image - IplImage src = sized; - out = ipl_to_image(&src); - } - catch (...) { - cerr << "OpenCV can't augment image: " << w << " x " << h << " \n"; - out = ipl_to_image(ipl); - } - return out; -} - - -image load_image_resize(char *filename, int w, int h, int c, image *im) -{ - image out; - cv::Mat img(h, w, CV_8UC3); - try { - int flag = -1; - if (c == 0) flag = 1; - else if (c == 1) { flag = 0; img = cv::Mat(h, w, CV_8UC1); } - else if (c == 3) { flag = 1; img = cv::Mat(h, w, CV_8UC3); } - else { - cerr << "OpenCV can't force load with " << c << " channels\n"; - } - //throw std::runtime_error("runtime_error"); - cv::Mat loaded_image = cv::imread(filename, flag); - cv::cvtColor(loaded_image, loaded_image, cv::COLOR_RGB2BGR); - IplImage tmp1 = loaded_image; - *im = ipl_to_image(&tmp1); - - cv::resize(loaded_image, img, cv::Size(w, h), 0, 0, CV_INTER_LINEAR); - - IplImage tmp2 = img; - out = ipl_to_image(&tmp2); - } - catch (...) { - cerr << "OpenCV can't load image %s " << filename << " \n"; - out = make_image(w, h, c); - *im = make_image(w, h, c); - } - return out; -} #endif // OPENCV diff --git a/src/http_stream.h b/src/http_stream.h index 1e9a3cf1be3..03b065109b0 100644 --- a/src/http_stream.h +++ b/src/http_stream.h @@ -2,34 +2,17 @@ #define HTTP_STREAM_H #include "darknet.h" -#ifdef OPENCV -#include -#include -#include -#ifndef CV_VERSION_EPOCH -#include -#endif -#endif - #ifdef __cplusplus extern "C" { #endif #include "image.h" #include -#ifdef OPENCV void send_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id, int port, int timeout); -void send_mjpeg(IplImage* ipl, int port, int timeout, int quality); -CvCapture* get_capture_webcam(int index); -CvCapture* get_capture_video_stream(const char *path); -IplImage* get_webcam_frame(CvCapture *cap); -int get_stream_fps_cpp(CvCapture *cap); -image image_data_augmentation(IplImage* ipl, int w, int h, - int pleft, int ptop, int swidth, int sheight, int flip, - float jitter, float dhue, float dsat, float dexp); +#ifdef OPENCV +void send_mjpeg(mat_cv* ipl, int port, int timeout, int quality); -image load_image_resize(char *filename, int w, int h, int c, image *im); #endif // OPENCV #ifdef __cplusplus diff --git a/src/image.c b/src/image.c index 72b4bdfbcce..2f085801dbe 100644 --- a/src/image.c +++ b/src/image.c @@ -17,25 +17,8 @@ #include "stb_image_write.h" #endif -#ifdef OPENCV -#include -#include -#include -#include -#ifndef CV_VERSION_EPOCH -#include -#include -#include "http_stream.h" -#endif -#include "http_stream.h" - -#ifndef CV_RGB -#define CV_RGB(r, g, b) cvScalar( (b), (g), (r), 0 ) -#endif -#endif - extern int check_mistakes; -int windows = 0; +//int windows = 0; float colors[6][3] = { {1,0,1}, {0,0,1},{0,1,1},{0,1,0},{1,1,0},{1,0,0} }; @@ -506,333 +489,6 @@ void draw_detections(image im, int num, float thresh, box *boxes, float **probs, } } -#ifdef OPENCV - -void save_cv_png(IplImage *img, const char *name) -{ - IplImage* img_rgb = cvCreateImage(cvSize(img->width, img->height), 8, 3); - cvCvtColor(img, img_rgb, CV_RGB2BGR); - stbi_write_png(name, img_rgb->width, img_rgb->height, 3, (char *)img_rgb->imageData, 0); - cvRelease((void**)&img_rgb); -} - -void save_cv_jpg(IplImage *img, const char *name) -{ - IplImage* img_rgb = cvCreateImage(cvSize(img->width, img->height), 8, 3); - cvCvtColor(img, img_rgb, CV_RGB2BGR); - stbi_write_jpg(name, img_rgb->width, img_rgb->height, 3, (char *)img_rgb->imageData, 80); - cvRelease((void**)&img_rgb); -} - -void draw_detections_cv_v3(IplImage* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output) -{ - int i, j; - if (!show_img) return; - static int frame_id = 0; - frame_id++; - - for (i = 0; i < num; ++i) { - char labelstr[4096] = { 0 }; - int class_id = -1; - for (j = 0; j < classes; ++j) { - int show = strncmp(names[j], "dont_show", 9); - if (dets[i].prob[j] > thresh && show) { - if (class_id < 0) { - strcat(labelstr, names[j]); - class_id = j; - } - else { - strcat(labelstr, ", "); - strcat(labelstr, names[j]); - } - printf("%s: %.0f%% ", names[j], dets[i].prob[j] * 100); - } - } - if (class_id >= 0) { - int width = show_img->height * .006; - - //if(0){ - //width = pow(prob, 1./2.)*10+1; - //alphabet = 0; - //} - - //printf("%d %s: %.0f%%\n", i, names[class_id], prob*100); - int offset = class_id * 123457 % classes; - float red = get_color(2, offset, classes); - float green = get_color(1, offset, classes); - float blue = get_color(0, offset, classes); - float rgb[3]; - - //width = prob*20+2; - - rgb[0] = red; - rgb[1] = green; - rgb[2] = blue; - box b = dets[i].bbox; - b.w = (b.w < 1) ? b.w : 1; - b.h = (b.h < 1) ? b.h : 1; - b.x = (b.x < 1) ? b.x : 1; - b.y = (b.y < 1) ? b.y : 1; - //printf("%f %f %f %f\n", b.x, b.y, b.w, b.h); - - int left = (b.x - b.w / 2.)*show_img->width; - int right = (b.x + b.w / 2.)*show_img->width; - int top = (b.y - b.h / 2.)*show_img->height; - int bot = (b.y + b.h / 2.)*show_img->height; - - if (left < 0) left = 0; - if (right > show_img->width - 1) right = show_img->width - 1; - if (top < 0) top = 0; - if (bot > show_img->height - 1) bot = show_img->height - 1; - - //int b_x_center = (left + right) / 2; - //int b_y_center = (top + bot) / 2; - //int b_width = right - left; - //int b_height = bot - top; - //sprintf(labelstr, "%d x %d - w: %d, h: %d", b_x_center, b_y_center, b_width, b_height); - - float const font_size = show_img->height / 1000.F; - CvPoint pt1, pt2, pt_text, pt_text_bg1, pt_text_bg2; - pt1.x = left; - pt1.y = top; - pt2.x = right; - pt2.y = bot; - pt_text.x = left; - pt_text.y = top - 12; - pt_text_bg1.x = left; - pt_text_bg1.y = top - (10 + 25 * font_size); - pt_text_bg2.x = right; - pt_text_bg2.y = top; - CvScalar color; - color.val[0] = red * 256; - color.val[1] = green * 256; - color.val[2] = blue * 256; - - // you should create directory: result_img - //static int copied_frame_id = -1; - //static IplImage* copy_img = NULL; - //if (copied_frame_id != frame_id) { - // copied_frame_id = frame_id; - // if(copy_img == NULL) copy_img = cvCreateImage(cvSize(show_img->width, show_img->height), show_img->depth, show_img->nChannels); - // cvCopy(show_img, copy_img, 0); - //} - //static int img_id = 0; - //img_id++; - //char image_name[1024]; - //sprintf(image_name, "result_img/img_%d_%d_%d_%s.jpg", frame_id, img_id, class_id, names[class_id]); - //CvRect rect = cvRect(pt1.x, pt1.y, pt2.x - pt1.x, pt2.y - pt1.y); - //cvSetImageROI(copy_img, rect); - //cvSaveImage(image_name, copy_img, 0); - //cvResetImageROI(copy_img); - - cvRectangle(show_img, pt1, pt2, color, width, 8, 0); - if (ext_output) - printf("\t(left_x: %4.0f top_y: %4.0f width: %4.0f height: %4.0f)\n", - (float)left, (float)top, b.w*show_img->width, b.h*show_img->height); - else - printf("\n"); - - cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0); - cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0); // filled - CvScalar black_color; - black_color.val[0] = 0; - CvFont font; - cvInitFont(&font, CV_FONT_HERSHEY_SIMPLEX, font_size, font_size, 0, font_size * 3, 8); - cvPutText(show_img, labelstr, pt_text, &font, black_color); - } - } - if (ext_output) { - fflush(stdout); - } -} - -void draw_detections_cv(IplImage* show_img, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes) -{ - int i; - - for (i = 0; i < num; ++i) { - int class_id = max_index(probs[i], classes); - float prob = probs[i][class_id]; - if (prob > thresh) { - - int width = show_img->height * .012; - - if (0) { - width = pow(prob, 1. / 2.) * 10 + 1; - alphabet = 0; - } - - printf("%s: %.0f%%\n", names[class_id], prob * 100); - int offset = class_id * 123457 % classes; - float red = get_color(2, offset, classes); - float green = get_color(1, offset, classes); - float blue = get_color(0, offset, classes); - float rgb[3]; - - //width = prob*20+2; - - rgb[0] = red; - rgb[1] = green; - rgb[2] = blue; - box b = boxes[i]; - - int left = (b.x - b.w / 2.)*show_img->width; - int right = (b.x + b.w / 2.)*show_img->width; - int top = (b.y - b.h / 2.)*show_img->height; - int bot = (b.y + b.h / 2.)*show_img->height; - - if (left < 0) left = 0; - if (right > show_img->width - 1) right = show_img->width - 1; - if (top < 0) top = 0; - if (bot > show_img->height - 1) bot = show_img->height - 1; - - float const font_size = show_img->height / 1000.F; - CvPoint pt1, pt2, pt_text, pt_text_bg1, pt_text_bg2; - pt1.x = left; - pt1.y = top; - pt2.x = right; - pt2.y = bot; - pt_text.x = left; - pt_text.y = top - 12; - pt_text_bg1.x = left; - pt_text_bg1.y = top - (10+25*font_size); - pt_text_bg2.x = right; - pt_text_bg2.y = top; - CvScalar color; - color.val[0] = red * 256; - color.val[1] = green * 256; - color.val[2] = blue * 256; - - cvRectangle(show_img, pt1, pt2, color, width, 8, 0); - //printf("left=%d, right=%d, top=%d, bottom=%d, obj_id=%d, obj=%s \n", left, right, top, bot, class_id, names[class_id]); - cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0); - cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0); // filled - CvScalar black_color; - black_color.val[0] = 0; - CvFont font; - cvInitFont(&font, CV_FONT_HERSHEY_SIMPLEX, font_size, font_size, 0, font_size * 3, 8); - cvPutText(show_img, names[class_id], pt_text, &font, black_color); - } - } -} - -IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show) -{ - int img_offset = 50; - int draw_size = img_size - img_offset; - IplImage* img = cvCreateImage(cvSize(img_size, img_size), 8, 3); - cvSet(img, CV_RGB(255, 255, 255), 0); - CvPoint pt1, pt2, pt_text; - CvFont font; - cvInitFont(&font, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA); - char char_buff[100]; - int i; - // vertical lines - pt1.x = img_offset; pt2.x = img_size, pt_text.x = 10; - for (i = 1; i <= number_of_lines; ++i) { - pt1.y = pt2.y = (float)i * draw_size / number_of_lines; - cvLine(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0); - if (i % 10 == 0) { - sprintf(char_buff, "%2.1f", max_img_loss*(number_of_lines - i) / number_of_lines); - pt_text.y = pt1.y + 5; - cvPutText(img, char_buff, pt_text, &font, CV_RGB(0, 0, 0)); - cvLine(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0); - } - } - // horizontal lines - pt1.y = draw_size; pt2.y = 0, pt_text.y = draw_size + 15; - for (i = 0; i <= number_of_lines; ++i) { - pt1.x = pt2.x = img_offset + (float)i * draw_size / number_of_lines; - cvLine(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0); - if (i % 10 == 0) { - sprintf(char_buff, "%d", max_batches * i / number_of_lines); - pt_text.x = pt1.x - 20; - cvPutText(img, char_buff, pt_text, &font, CV_RGB(0, 0, 0)); - cvLine(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0); - } - } - - cvPutText(img, "Loss", cvPoint(0, 35), &font, CV_RGB(0, 0, 255)); - cvPutText(img, "Iteration number", cvPoint(draw_size / 2, img_size - 10), &font, CV_RGB(0, 0, 0)); - char max_batches_buff[100]; - sprintf(max_batches_buff, "in cfg max_batches=%d", max_batches); - cvPutText(img, max_batches_buff, cvPoint(draw_size - 195, img_size - 10), &font, CV_RGB(0, 0, 0)); - cvPutText(img, "Press 's' to save: chart.png", cvPoint(5, img_size - 10), &font, CV_RGB(0, 0, 0)); - if (!dont_show) { - printf(" If error occurs - run training with flag: -dont_show \n"); - cvNamedWindow("average loss", CV_WINDOW_NORMAL); - cvMoveWindow("average loss", 0, 0); - cvResizeWindow("average loss", img_size, img_size); - cvShowImage("average loss", img); - cvWaitKey(20); - } - return img; -} - -void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, - float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port) -{ - int img_offset = 50; - int draw_size = img_size - img_offset; - CvFont font; - cvInitFont(&font, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA); - char char_buff[100]; - CvPoint pt1, pt2; - pt1.x = img_offset + draw_size * (float)current_batch / max_batches; - pt1.y = draw_size * (1 - avg_loss / max_img_loss); - if (pt1.y < 0) pt1.y = 1; - cvCircle(img, pt1, 1, CV_RGB(0, 0, 255), CV_FILLED, 8, 0); - - // precision - if (draw_precision) { - static float old_precision = 0; - static int iteration_old = 0; - static int text_iteration_old = 0; - if(iteration_old == 0) cvPutText(img, accuracy_name, cvPoint(0, 12), &font, CV_RGB(255, 0, 0)); - - cvLine(img, - cvPoint(img_offset + draw_size * (float)iteration_old / max_batches, draw_size * (1 - old_precision)), - cvPoint(img_offset + draw_size * (float)current_batch / max_batches, draw_size * (1 - precision)), - CV_RGB(255, 0, 0), 1, 8, 0); - - if (((int)(old_precision*10) != (int)(precision*10)) || (current_batch - text_iteration_old) >= max_batches/10) { - text_iteration_old = current_batch; - sprintf(char_buff, "%2.0f%% ", precision * 100); - CvFont font3; - cvInitFont(&font3, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 5, CV_AA); - cvPutText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), &font3, CV_RGB(255, 255, 255)); - - CvFont font2; - cvInitFont(&font2, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA); - cvPutText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), &font2, CV_RGB(200, 0, 0)); - } - old_precision = precision; - iteration_old = current_batch; - } - - sprintf(char_buff, "current avg loss = %2.4f iteration = %d", avg_loss, current_batch); - pt1.x = 55, pt1.y = 10; - pt2.x = pt1.x + 460, pt2.y = pt1.y + 20; - cvRectangle(img, pt1, pt2, CV_RGB(255, 255, 255), CV_FILLED, 8, 0); - pt1.y += 15; - cvPutText(img, char_buff, pt1, &font, CV_RGB(0, 0, 0)); - - int k = 0; - if (!dont_show) { - cvShowImage("average loss", img); - k = cvWaitKey(20); - } - if (k == 's' || current_batch == (max_batches - 1) || current_batch % 100 == 0) { - save_cv_png(img, "chart.png"); - cvPutText(img, "- Saved", cvPoint(250, img_size - 10), &font, CV_RGB(255, 0, 0)); - } - else - cvPutText(img, "- Saved", cvPoint(250, img_size - 10), &font, CV_RGB(255, 255, 255)); - - if (mjpeg_port > 0) send_mjpeg(img, mjpeg_port, 500000, 100); -} -#endif // OPENCV - void transpose_image(image im) { assert(im.w == im.h); @@ -1005,64 +661,6 @@ void rgbgr_image(image im) } } -#ifdef OPENCV -void show_image_cv(image p, const char *name) -{ - int x,y,k; - image copy = copy_image(p); - constrain_image(copy); - if(p.c == 3) rgbgr_image(copy); - //normalize_image(copy); - - char buff[256]; - //sprintf(buff, "%s (%d)", name, windows); - sprintf(buff, "%s", name); - - IplImage *disp = cvCreateImage(cvSize(p.w,p.h), IPL_DEPTH_8U, p.c); - int step = disp->widthStep; - cvNamedWindow(buff, CV_WINDOW_NORMAL); - //cvMoveWindow(buff, 100*(windows%10) + 200*(windows/10), 100*(windows%10)); - ++windows; - for(y = 0; y < p.h; ++y){ - for(x = 0; x < p.w; ++x){ - for(k= 0; k < p.c; ++k){ - disp->imageData[y*step + x*p.c + k] = (unsigned char)(get_pixel(copy,x,y,k)*255); - } - } - } - free_image(copy); - if(0){ - int w = 448; - int h = w*p.h/p.w; - if(h > 1000){ - h = 1000; - w = h*p.w/p.h; - } - IplImage *buffer = disp; - disp = cvCreateImage(cvSize(w, h), buffer->depth, buffer->nChannels); - cvResize(buffer, disp, CV_INTER_LINEAR); - cvReleaseImage(&buffer); - } - cvShowImage(buff, disp); - - cvReleaseImage(&disp); -} - - -void show_image_cv_ipl(IplImage *disp, const char *name) -{ - if (disp == NULL) return; - char buff[256]; - //sprintf(buff, "%s (%d)", name, windows); - sprintf(buff, "%s", name); - cvNamedWindow(buff, CV_WINDOW_NORMAL); - //cvMoveWindow(buff, 100*(windows%10) + 200*(windows/10), 100*(windows%10)); - ++windows; - cvShowImage(buff, disp); - //cvReleaseImage(&disp); -} -#endif // OPENCV - void show_image(image p, const char *name) { #ifdef OPENCV @@ -1070,190 +668,8 @@ void show_image(image p, const char *name) #else fprintf(stderr, "Not compiled with OpenCV, saving to %s.png instead\n", name); save_image(p, name); -#endif -} - -#ifdef OPENCV - -image ipl_to_image(IplImage* src) -{ - unsigned char *data = (unsigned char *)src->imageData; - int h = src->height; - int w = src->width; - int c = src->nChannels; - int step = src->widthStep; - image out = make_image(w, h, c); - int i, j, k, count=0;; - - for(k= 0; k < c; ++k){ - for(i = 0; i < h; ++i){ - for(j = 0; j < w; ++j){ - out.data[count++] = data[i*step + j*c + k]/255.; - } - } - } - return out; -} - -image load_image_cv(char *filename, int channels) -{ - IplImage* src = 0; - int flag = -1; - if (channels == 0) flag = 1; - else if (channels == 1) flag = 0; - else if (channels == 3) flag = 1; - else { - fprintf(stderr, "OpenCV can't force load with %d channels\n", channels); - } - - if( (src = cvLoadImage(filename, flag)) == 0 ) - { - char shrinked_filename[1024]; - if (strlen(filename) >= 1024) sprintf(shrinked_filename, "name is too long"); - else sprintf(shrinked_filename, "%s", filename); - fprintf(stderr, "Cannot load image \"%s\"\n", shrinked_filename); - FILE* fw = fopen("bad.list", "a"); - fwrite(shrinked_filename, sizeof(char), strlen(shrinked_filename), fw); - char *new_line = "\n"; - fwrite(new_line, sizeof(char), strlen(new_line), fw); - fclose(fw); - if (check_mistakes) getchar(); - return make_image(10,10,3); - //exit(EXIT_FAILURE); - } - image out = ipl_to_image(src); - cvReleaseImage(&src); - if (out.c > 1) - rgbgr_image(out); - return out; -} - -image get_image_from_stream(CvCapture *cap) -{ - IplImage* src = cvQueryFrame(cap); - if (!src) return make_empty_image(0,0,0); - image im = ipl_to_image(src); - rgbgr_image(im); - return im; -} - -image get_image_from_stream_cpp(CvCapture *cap) -{ - //IplImage* src = cvQueryFrame(cap); - IplImage* src; - static int once = 1; - if (once) { - once = 0; - do { - src = get_webcam_frame(cap); - if (!src) return make_empty_image(0, 0, 0); - } while (src->width < 1 || src->height < 1 || src->nChannels < 1); - printf("Video stream: %d x %d \n", src->width, src->height); - } - else - src = get_webcam_frame(cap); - - if (!src) return make_empty_image(0, 0, 0); - image im = ipl_to_image(src); - rgbgr_image(im); - return im; -} - -int wait_for_stream(CvCapture *cap, IplImage* src, int dont_close) { - if (!src) { - if (dont_close) src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3); - else return 0; - } - if (src->width < 1 || src->height < 1 || src->nChannels < 1) { - if (dont_close) { - cvReleaseImage(&src); - int z = 0; - for (z = 0; z < 20; ++z) { - get_webcam_frame(cap); - cvReleaseImage(&src); - } - src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3); - } - else return 0; - } - return 1; -} - -image get_image_from_stream_resize(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close) -{ - c = c ? c : 3; - IplImage* src; - if (cpp_video_capture) { - static int once = 1; - if (once) { - once = 0; - do { - src = get_webcam_frame(cap); - if (!src) return make_empty_image(0, 0, 0); - } while (src->width < 1 || src->height < 1 || src->nChannels < 1); - printf("Video stream: %d x %d \n", src->width, src->height); - } else - src = get_webcam_frame(cap); - } - else src = cvQueryFrame(cap); - - if (cpp_video_capture) - if(!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0); - IplImage* new_img = cvCreateImage(cvSize(w, h), IPL_DEPTH_8U, c); - *in_img = cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c); - cvResize(src, *in_img, CV_INTER_LINEAR); - cvResize(src, new_img, CV_INTER_LINEAR); - image im = ipl_to_image(new_img); - cvReleaseImage(&new_img); - if (cpp_video_capture) cvReleaseImage(&src); - if (c>1) - rgbgr_image(im); - return im; -} - -image get_image_from_stream_letterbox(CvCapture *cap, int w, int h, int c, IplImage** in_img, int cpp_video_capture, int dont_close) -{ - c = c ? c : 3; - IplImage* src; - if (cpp_video_capture) { - static int once = 1; - if (once) { - once = 0; - do { - src = get_webcam_frame(cap); - if (!src) return make_empty_image(0, 0, 0); - } while (src->width < 1 || src->height < 1 || src->nChannels < 1); - printf("Video stream: %d x %d \n", src->width, src->height); - } - else - src = get_webcam_frame(cap); - } - else src = cvQueryFrame(cap); - - if (cpp_video_capture) - if (!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0); - *in_img = cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c); - cvResize(src, *in_img, CV_INTER_LINEAR); - image tmp = ipl_to_image(src); - image im = letterbox_image(tmp, w, h); - free_image(tmp); - if (cpp_video_capture) cvReleaseImage(&src); - if (c>1) rgbgr_image(im); - return im; -} - -int get_stream_fps(CvCapture *cap, int cpp_video_capture) -{ - int fps = 25; - if (cpp_video_capture) { - fps = get_stream_fps_cpp(cap); - } - else { - fps = cvGetCaptureProperty(cap, CV_CAP_PROP_FPS); - } - return fps; -} #endif // OPENCV +} void save_image_png(image im, const char *name) { @@ -1925,6 +1341,7 @@ void test_resize(char *filename) show_image(c2, "C2"); show_image(c3, "C3"); show_image(c4, "C4"); + #ifdef OPENCV while(1){ image aug = random_augment_image(im, 0, .75, 320, 448, 320); @@ -1946,7 +1363,7 @@ void test_resize(char *filename) show_image(c, "rand"); printf("%f %f %f\n", dhue, dsat, dexp); free_image(c); - cvWaitKey(0); + wait_until_press_key_cv(); } #endif } @@ -1989,17 +1406,11 @@ image load_image_stb(char *filename, int channels) image load_image(char *filename, int w, int h, int c) { #ifdef OPENCV - -#ifndef CV_VERSION_EPOCH - //image out = load_image_stb(filename, c); // OpenCV 3.x + //image out = load_image_stb(filename, c); image out = load_image_cv(filename, c); -#else - image out = load_image_cv(filename, c); // OpenCV 2.4.x -#endif - #else image out = load_image_stb(filename, c); // without OpenCV -#endif +#endif // OPENCV if((h && w) && (h != out.h || w != out.w)){ image resized = resize_image(out, w, h); diff --git a/src/image.h b/src/image.h index 91c6fde7d3c..3a1c5b9a73c 100644 --- a/src/image.h +++ b/src/image.h @@ -7,16 +7,9 @@ #include #include #include -#ifdef OPENCV -#include -#include -#include -#include -#ifndef CV_VERSION_EPOCH -#include -#include -#endif -#endif + +#include "image_opencv.h" + #include "box.h" #ifdef __cplusplus extern "C" { @@ -92,12 +85,6 @@ image load_image(char *filename, int w, int h, int c); //LIB_API image load_image_color(char *filename, int w, int h); image **load_alphabet(); -#ifdef OPENCV -image get_image_from_stream(CvCapture* cap); -image get_image_from_stream_cpp(CvCapture* cap); -image ipl_to_image(IplImage* src); -void save_cv_png(IplImage *img, const char *name); -#endif //float get_pixel(image m, int x, int y, int c); //float get_pixel_extend(image m, int x, int y, int c); //void set_pixel(image m, int x, int y, int c, float val); diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp new file mode 100644 index 00000000000..d09e3018e2e --- /dev/null +++ b/src/image_opencv.cpp @@ -0,0 +1,1165 @@ +#ifdef OPENCV + +#include "image_opencv.h" +#include "utils.h" + +#include +#include +#include + +#include "opencv2/opencv.hpp" + +using namespace cv; + +using std::cerr; +using std::endl; + +// OpenCV libraries +#ifndef CV_VERSION_EPOCH +#define OPENCV_VERSION CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR)"" CVAUX_STR(CV_VERSION_REVISION) +#ifndef USE_CMAKE_LIBS +#pragma comment(lib, "opencv_world" OPENCV_VERSION ".lib") +#endif // USE_CMAKE_LIBS +#else // CV_VERSION_EPOCH +#define OPENCV_VERSION CVAUX_STR(CV_VERSION_EPOCH)"" CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR) +#ifndef USE_CMAKE_LIBS +#pragma comment(lib, "opencv_core" OPENCV_VERSION ".lib") +#pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib") +#pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib") +#endif // USE_CMAKE_LIBS +#endif // CV_VERSION_EPOCH + +// OpenCV includes +#include +#include +#include +#include +#ifndef CV_VERSION_EPOCH +#include +#include +#include "http_stream.h" +#endif +#include "http_stream.h" + +#ifndef CV_RGB +#define CV_RGB(r, g, b) cvScalar( (b), (g), (r), 0 ) +#endif + +extern "C" { + + struct mat_cv :IplImage { int a[0]; }; + //struct cap_cv : CvCapture { int a[0]; }; + //struct write_cv : CvVideoWriter { int a[0]; }; + +// ==================================================================== +// cv::Mat / IplImage +// ==================================================================== +image ipl_to_image(IplImage* src); + +image load_image_cv(char *filename, int channels) +{ + IplImage* src = 0; + int flag = -1; + if (channels == 0) flag = 1; + else if (channels == 1) flag = 0; + else if (channels == 3) flag = 1; + else { + fprintf(stderr, "OpenCV can't force load with %d channels\n", channels); + } + + if ((src = cvLoadImage(filename, flag)) == 0) + { + char shrinked_filename[1024]; + if (strlen(filename) >= 1024) sprintf(shrinked_filename, "name is too long"); + else sprintf(shrinked_filename, "%s", filename); + fprintf(stderr, "Cannot load image \"%s\"\n", shrinked_filename); + FILE* fw = fopen("bad.list", "a"); + fwrite(shrinked_filename, sizeof(char), strlen(shrinked_filename), fw); + char *new_line = "\n"; + fwrite(new_line, sizeof(char), strlen(new_line), fw); + fclose(fw); + //if (check_mistakes) getchar(); + return make_image(10, 10, 3); + //exit(EXIT_FAILURE); + } + image out = ipl_to_image(src); + cvReleaseImage(&src); + if (out.c > 1) + rgbgr_image(out); + return out; +} +// ---------------------------------------- + +mat_cv *load_image_mat_cv(const char *filename, int flag) +{ + return (mat_cv *)cvLoadImage(filename, flag); +} +// ---------------------------------------- + + +image load_image_resize(char *filename, int w, int h, int c, image *im) +{ + image out; + cv::Mat img(h, w, CV_8UC3); + try { + int flag = -1; + if (c == 0) flag = 1; + else if (c == 1) { flag = 0; img = cv::Mat(h, w, CV_8UC1); } + else if (c == 3) { flag = 1; img = cv::Mat(h, w, CV_8UC3); } + else { + cerr << "OpenCV can't force load with " << c << " channels\n"; + } + //throw std::runtime_error("runtime_error"); + cv::Mat loaded_image = cv::imread(filename, flag); + cv::cvtColor(loaded_image, loaded_image, cv::COLOR_RGB2BGR); + IplImage tmp1 = loaded_image; + *im = ipl_to_image(&tmp1); + + cv::resize(loaded_image, img, cv::Size(w, h), 0, 0, CV_INTER_LINEAR); + + IplImage tmp2 = img; + out = ipl_to_image(&tmp2); + } + catch (...) { + cerr << "OpenCV can't load image %s " << filename << " \n"; + out = make_image(w, h, c); + *im = make_image(w, h, c); + } + return out; +} +// ---------------------------------------- + +int get_width_cv(mat_cv *ipl_src) +{ + IplImage *ipl = (IplImage *)ipl_src; + return ipl->width; +} +// ---------------------------------------- + +int get_height_cv(mat_cv *ipl) +{ + //IplImage *ipl = (IplImage *)ipl_src; + return ipl->height; +} +// ---------------------------------------- + +void release_ipl(mat_cv **ipl) +{ + IplImage **ipl_img = (IplImage **)ipl; + if (*ipl_img) cvReleaseImage(ipl_img); + *ipl_img = NULL; +} + +// ==================================================================== +// image-to-ipl, ipl-to-image +// ==================================================================== +mat_cv *image_to_ipl(image im) +{ + int x, y, c; + IplImage *disp = cvCreateImage(cvSize(im.w, im.h), IPL_DEPTH_8U, im.c); + int step = disp->widthStep; + for (y = 0; y < im.h; ++y) { + for (x = 0; x < im.w; ++x) { + for (c = 0; c < im.c; ++c) { + float val = im.data[c*im.h*im.w + y*im.w + x]; + disp->imageData[y*step + x*im.c + c] = (unsigned char)(val * 255); + } + } + } + return (mat_cv *)disp; +} +// ---------------------------------------- + +image ipl_to_image(IplImage* src) +{ + int h = src->height; + int w = src->width; + int c = src->nChannels; + image im = make_image(w, h, c); + unsigned char *data = (unsigned char *)src->imageData; + int step = src->widthStep; + int i, j, k; + + for (i = 0; i < h; ++i) { + for (k = 0; k < c; ++k) { + for (j = 0; j < w; ++j) { + im.data[k*w*h + i*w + j] = data[i*step + j*c + k] / 255.; + } + } + } + return im; +} +// ---------------------------------------- + +image ipl_to_image_custom(mat_cv* src) +{ + return ipl_to_image(src); +} +// ---------------------------------------- + +Mat image_to_mat(image im) +{ + image copy = copy_image(im); + constrain_image(copy); + if (im.c == 3) rgbgr_image(copy); + + IplImage *ipl = image_to_ipl(copy); + Mat m = cvarrToMat(ipl, true); + cvReleaseImage(&ipl); + free_image(copy); + return m; +} +// ---------------------------------------- + +image mat_to_image(Mat m) +{ + IplImage ipl = m; + image im = ipl_to_image((mat_cv *)&ipl); + rgbgr_image(im); + return im; +} + +// ==================================================================== +// Window +// ==================================================================== +void create_window_cv(char const* window_name, int full_screen, int width, int height) +{ + int window_type = CV_WINDOW_NORMAL; + if (full_screen) window_type = CV_WINDOW_FULLSCREEN; + + cvNamedWindow(window_name, window_type); + cvMoveWindow(window_name, 0, 0); + cvResizeWindow(window_name, width, height); +} +// ---------------------------------------- + +void destroy_all_windows_cv() +{ + cvDestroyAllWindows(); +} +// ---------------------------------------- + +int wait_key_cv(int delay) +{ + return cvWaitKey(delay); +} +// ---------------------------------------- + +int wait_until_press_key_cv() +{ + return wait_key_cv(0); +} +// ---------------------------------------- + +void make_window(char *name, int w, int h, int fullscreen) +{ + namedWindow(name, WINDOW_NORMAL); + if (fullscreen) { + setWindowProperty(name, CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN); + } + else { + resizeWindow(name, w, h); + if (strcmp(name, "Demo") == 0) moveWindow(name, 0, 0); + } +} +// ---------------------------------------- + +static float get_pixel(image m, int x, int y, int c) +{ + assert(x < m.w && y < m.h && c < m.c); + return m.data[c*m.h*m.w + y*m.w + x]; +} +// ---------------------------------------- + +void show_image_cv(image p, const char *name) +{ + int x, y, k; + image copy = copy_image(p); + constrain_image(copy); + if (p.c == 3) rgbgr_image(copy); + //normalize_image(copy); + + char buff[256]; + //sprintf(buff, "%s (%d)", name, windows); + sprintf(buff, "%s", name); + + IplImage *disp = cvCreateImage(cvSize(p.w, p.h), IPL_DEPTH_8U, p.c); + int step = disp->widthStep; + cvNamedWindow(buff, CV_WINDOW_NORMAL); + //cvMoveWindow(buff, 100*(windows%10) + 200*(windows/10), 100*(windows%10)); + //++windows; + for (y = 0; y < p.h; ++y) { + for (x = 0; x < p.w; ++x) { + for (k = 0; k < p.c; ++k) { + disp->imageData[y*step + x*p.c + k] = (unsigned char)(get_pixel(copy, x, y, k) * 255); + } + } + } + free_image(copy); + if (0) { + int w = 448; + int h = w*p.h / p.w; + if (h > 1000) { + h = 1000; + w = h*p.w / p.h; + } + IplImage *buffer = disp; + disp = cvCreateImage(cvSize(w, h), buffer->depth, buffer->nChannels); + cvResize(buffer, disp, CV_INTER_LINEAR); + cvReleaseImage(&buffer); + } + cvShowImage(buff, disp); + + cvReleaseImage(&disp); +} +// ---------------------------------------- + +void show_image_cv_ipl(mat_cv *disp, const char *name) +{ + if (disp == NULL) return; + char buff[256]; + //sprintf(buff, "%s (%d)", name, windows); + sprintf(buff, "%s", name); + cvNamedWindow(buff, CV_WINDOW_NORMAL); + //cvMoveWindow(buff, 100*(windows%10) + 200*(windows/10), 100*(windows%10)); + //++windows; + cvShowImage(buff, disp); + //cvReleaseImage(&disp); +} + + +// ==================================================================== +// Video Writer +// ==================================================================== +write_cv *create_video_writer(char *out_filename, char c1, char c2, char c3, char c4, int fps, int width, int height, int is_color) +{ + CvVideoWriter * output_video_writer = + cvCreateVideoWriter(out_filename, CV_FOURCC(c1, c2, c3, c4), fps, cvSize(width, height), is_color); + + return (write_cv *)output_video_writer; +} + +int write_frame_cv(write_cv *output_video_writer, mat_cv *show_img) +{ + return cvWriteFrame((CvVideoWriter *)output_video_writer, show_img); +} + +void release_video_writer(write_cv **output_video_writer) +{ + cvReleaseVideoWriter((CvVideoWriter **)output_video_writer); +} + +/* +void *open_video_stream(const char *f, int c, int w, int h, int fps) +{ + VideoCapture *cap; + if(f) cap = new VideoCapture(f); + else cap = new VideoCapture(c); + if(!cap->isOpened()) return 0; + if(w) cap->set(CV_CAP_PROP_FRAME_WIDTH, w); + if(h) cap->set(CV_CAP_PROP_FRAME_HEIGHT, w); + if(fps) cap->set(CV_CAP_PROP_FPS, w); + return (void *) cap; +} + + +image get_image_from_stream(void *p) +{ + VideoCapture *cap = (VideoCapture *)p; + Mat m; + *cap >> m; + if(m.empty()) return make_empty_image(0,0,0); + return mat_to_image(m); +} + + + +int show_image_cv(image im, const char* name, int ms) +{ + Mat m = image_to_mat(im); + imshow(name, m); + int c = waitKey(ms); + if (c != -1) c = c%256; + return c; +} +*/ + + +// ==================================================================== +// Video Capture +// ==================================================================== + +cap_cv* get_capture_video_stream(const char *path) { + CvCapture* cap = NULL; + try { + cap = (CvCapture*)new cv::VideoCapture(path); + } + catch (...) { + cerr << " Error: video-stream " << path << " can't be opened! \n"; + } + return (cap_cv*)cap; +} +// ---------------------------------------- + +cap_cv* get_capture_webcam(int index) +{ + CvCapture* cap = NULL; + try { + cap = (CvCapture*)new cv::VideoCapture(index); + //((cv::VideoCapture*)cap)->set(CV_CAP_PROP_FRAME_WIDTH, 1280); + //((cv::VideoCapture*)cap)->set(CV_CAP_PROP_FRAME_HEIGHT, 960); + } + catch (...) { + cerr << " Error: Web-camera " << index << " can't be opened! \n"; + } + return (cap_cv*)cap; +} +// ---------------------------------------- + +void release_capture(cap_cv* cap) +{ + try { + cv::VideoCapture *cpp_cap = (cv::VideoCapture *)cap; + delete cpp_cap; + } + catch (...) { + cerr << " Error: cv::VideoCapture " << cap << " can't be released! \n"; + } +} +// ---------------------------------------- + +mat_cv* get_capture_frame_cv(cap_cv *cap) { + IplImage* src = NULL; + try { + cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; + cv::Mat frame; + if (cpp_cap.isOpened()) + { + cpp_cap >> frame; + IplImage tmp = frame; + src = cvCloneImage(&tmp); + } + else { + std::cout << " Video-stream stopped! \n"; + } + } + catch (...) { + std::cout << " Video-stream stoped! \n"; + } + return (mat_cv *)src; +} +// ---------------------------------------- + +int get_stream_fps_cpp_cv(cap_cv *cap) +{ + int fps = 25; + try { + cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; +#ifndef CV_VERSION_EPOCH // OpenCV 3.x + fps = cpp_cap.get(CAP_PROP_FPS); +#else // OpenCV 2.x + fps = cpp_cap.get(CV_CAP_PROP_FPS); +#endif + } + catch (...) { + cerr << " Can't get FPS of source videofile. For output video FPS = 25 by default. \n"; + } + return fps; +} +// ---------------------------------------- + +double get_capture_property_cv(cap_cv *cap, int property_id) +{ + try { + cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; + return cpp_cap.get(property_id); + } + catch (...) { + cerr << " Can't get property of source video-stream. \n"; + } + return 0; +} +// ---------------------------------------- + +double get_capture_frame_count_cv(cap_cv *cap) +{ + try { + cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; +#ifndef CV_VERSION_EPOCH // OpenCV 3.x + return cpp_cap.get(CAP_PROP_FRAME_COUNT); +#else // OpenCV 2.x + return cpp_cap.get(CV_CAP_PROP_FRAME_COUNT); +#endif + } + catch (...) { + cerr << " Can't get CAP_PROP_FRAME_COUNT of source videofile. \n"; + } + return 0; +} +// ---------------------------------------- + +int set_capture_property_cv(cap_cv *cap, int property_id, double value) +{ + try { + cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; + return cpp_cap.set(property_id, value); + } + catch (...) { + cerr << " Can't set property of source video-stream. \n"; + } + return false; +} +// ---------------------------------------- + +int set_capture_position_frame_cv(cap_cv *cap, int index) +{ + try { + cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; +#ifndef CV_VERSION_EPOCH // OpenCV 3.x + return cpp_cap.set(CAP_PROP_POS_FRAMES, index); +#else // OpenCV 2.x + return cpp_cap.set(CV_CAP_PROP_POS_FRAMES, index); +#endif + } + catch (...) { + cerr << " Can't set CAP_PROP_POS_FRAMES of source videofile. \n"; + } + return false; +} +// ---------------------------------------- + +// only for using in image_opencv.cpp +mat_cv* get_capture_frame(CvCapture *cap) +{ + return get_capture_frame_cv((cap_cv *)cap); +} +// ---------------------------------------- + +// only for using in image_opencv.cpp +int get_stream_fps_cpp(CvCapture *cap) +{ + return get_stream_fps_cpp_cv((cap_cv *)cap); +} + + +// ==================================================================== +// ... Video Capture +// ==================================================================== +image get_image_from_stream(cap_cv *cap_src) +{ + CvCapture *cap = (CvCapture *)cap_src; + IplImage* src = cvQueryFrame(cap); + if (!src) return make_empty_image(0, 0, 0); + image im = ipl_to_image(src); + rgbgr_image(im); + return im; +} +// ---------------------------------------- + +image get_image_from_stream_cpp(cap_cv *cap_src) +{ + CvCapture *cap = (CvCapture *)cap_src; + //IplImage* src = cvQueryFrame(cap); + IplImage* src; + static int once = 1; + if (once) { + once = 0; + do { + src = get_capture_frame(cap); + if (!src) return make_empty_image(0, 0, 0); + } while (src->width < 1 || src->height < 1 || src->nChannels < 1); + printf("Video stream: %d x %d \n", src->width, src->height); + } + else + src = get_capture_frame(cap); + + if (!src) return make_empty_image(0, 0, 0); + image im = ipl_to_image(src); + rgbgr_image(im); + return im; +} +// ---------------------------------------- + +int wait_for_stream(CvCapture *cap, IplImage* src, int dont_close) +{ + if (!src) { + if (dont_close) src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3); + else return 0; + } + if (src->width < 1 || src->height < 1 || src->nChannels < 1) { + if (dont_close) { + cvReleaseImage(&src); + int z = 0; + for (z = 0; z < 20; ++z) { + get_capture_frame(cap); + cvReleaseImage(&src); + } + src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3); + } + else return 0; + } + return 1; +} +// ---------------------------------------- + +image get_image_from_stream_resize(cap_cv *cap_src, int w, int h, int c, mat_cv** in_img, int cpp_video_capture, int dont_close) +{ + CvCapture *cap = (CvCapture *)cap_src; + c = c ? c : 3; + IplImage* src; + if (cpp_video_capture) { + static int once = 1; + if (once) { + once = 0; + do { + src = get_capture_frame(cap); + if (!src) return make_empty_image(0, 0, 0); + } while (src->width < 1 || src->height < 1 || src->nChannels < 1); + printf("Video stream: %d x %d \n", src->width, src->height); + } + else + src = get_capture_frame(cap); + } + else src = cvQueryFrame(cap); + + if (cpp_video_capture) + if (!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0); + IplImage* new_img = cvCreateImage(cvSize(w, h), IPL_DEPTH_8U, c); + *in_img = (mat_cv *)cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c); + cvResize(src, *in_img, CV_INTER_LINEAR); + cvResize(src, new_img, CV_INTER_LINEAR); + image im = ipl_to_image(new_img); + cvReleaseImage(&new_img); + if (cpp_video_capture) cvReleaseImage(&src); + if (c>1) + rgbgr_image(im); + return im; +} +// ---------------------------------------- + +image get_image_from_stream_letterbox(cap_cv *cap_src, int w, int h, int c, mat_cv** in_img, int cpp_video_capture, int dont_close) +{ + CvCapture *cap = (CvCapture *)cap_src; + c = c ? c : 3; + IplImage* src; + if (cpp_video_capture) { + static int once = 1; + if (once) { + once = 0; + do { + src = get_capture_frame(cap); + if (!src) return make_empty_image(0, 0, 0); + } while (src->width < 1 || src->height < 1 || src->nChannels < 1); + printf("Video stream: %d x %d \n", src->width, src->height); + } + else + src = get_capture_frame(cap); + } + else src = cvQueryFrame(cap); + + if (cpp_video_capture) + if (!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0); + *in_img = (mat_cv *)cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c); + cvResize(src, *in_img, CV_INTER_LINEAR); + image tmp = ipl_to_image(src); + image im = letterbox_image(tmp, w, h); + free_image(tmp); + if (cpp_video_capture) cvReleaseImage(&src); + if (c>1) rgbgr_image(im); + return im; +} +// ---------------------------------------- + +int get_stream_fps(cap_cv *cap_src, int cpp_video_capture) +{ + CvCapture *cap = (CvCapture *)cap_src; + int fps = 25; + if (cpp_video_capture) { + fps = get_stream_fps_cpp(cap); + } + else { + fps = cvGetCaptureProperty(cap, CV_CAP_PROP_FPS); + } + return fps; +} +// ---------------------------------------- + + +// ==================================================================== +// Image Saving +// ==================================================================== +extern int stbi_write_png(char const *filename, int w, int h, int comp, const void *data, int stride_in_bytes); +extern int stbi_write_jpg(char const *filename, int x, int y, int comp, const void *data, int quality); + +void save_cv_png(mat_cv *img_src, const char *name) +{ + IplImage* img = (IplImage* )img_src; + IplImage* img_rgb = cvCreateImage(cvSize(img->width, img->height), 8, 3); + cvCvtColor(img, img_rgb, CV_RGB2BGR); + stbi_write_png(name, img_rgb->width, img_rgb->height, 3, (char *)img_rgb->imageData, 0); + cvRelease((void**)&img_rgb); +} +// ---------------------------------------- + +void save_cv_jpg(mat_cv *img_src, const char *name) +{ + IplImage* img = (IplImage*)img_src; + IplImage* img_rgb = cvCreateImage(cvSize(img->width, img->height), 8, 3); + cvCvtColor(img, img_rgb, CV_RGB2BGR); + stbi_write_jpg(name, img_rgb->width, img_rgb->height, 3, (char *)img_rgb->imageData, 80); + cvRelease((void**)&img_rgb); +} +// ---------------------------------------- + +// ==================================================================== +// Draw Detection +// ==================================================================== +void draw_detections_cv_v3(mat_cv* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output) +{ + int i, j; + if (!show_img) return; + static int frame_id = 0; + frame_id++; + + for (i = 0; i < num; ++i) { + char labelstr[4096] = { 0 }; + int class_id = -1; + for (j = 0; j < classes; ++j) { + int show = strncmp(names[j], "dont_show", 9); + if (dets[i].prob[j] > thresh && show) { + if (class_id < 0) { + strcat(labelstr, names[j]); + class_id = j; + } + else { + strcat(labelstr, ", "); + strcat(labelstr, names[j]); + } + printf("%s: %.0f%% ", names[j], dets[i].prob[j] * 100); + } + } + if (class_id >= 0) { + int width = show_img->height * .006; + + //if(0){ + //width = pow(prob, 1./2.)*10+1; + //alphabet = 0; + //} + + //printf("%d %s: %.0f%%\n", i, names[class_id], prob*100); + int offset = class_id * 123457 % classes; + float red = get_color(2, offset, classes); + float green = get_color(1, offset, classes); + float blue = get_color(0, offset, classes); + float rgb[3]; + + //width = prob*20+2; + + rgb[0] = red; + rgb[1] = green; + rgb[2] = blue; + box b = dets[i].bbox; + b.w = (b.w < 1) ? b.w : 1; + b.h = (b.h < 1) ? b.h : 1; + b.x = (b.x < 1) ? b.x : 1; + b.y = (b.y < 1) ? b.y : 1; + //printf("%f %f %f %f\n", b.x, b.y, b.w, b.h); + + int left = (b.x - b.w / 2.)*show_img->width; + int right = (b.x + b.w / 2.)*show_img->width; + int top = (b.y - b.h / 2.)*show_img->height; + int bot = (b.y + b.h / 2.)*show_img->height; + + if (left < 0) left = 0; + if (right > show_img->width - 1) right = show_img->width - 1; + if (top < 0) top = 0; + if (bot > show_img->height - 1) bot = show_img->height - 1; + + //int b_x_center = (left + right) / 2; + //int b_y_center = (top + bot) / 2; + //int b_width = right - left; + //int b_height = bot - top; + //sprintf(labelstr, "%d x %d - w: %d, h: %d", b_x_center, b_y_center, b_width, b_height); + + float const font_size = show_img->height / 1000.F; + CvPoint pt1, pt2, pt_text, pt_text_bg1, pt_text_bg2; + pt1.x = left; + pt1.y = top; + pt2.x = right; + pt2.y = bot; + pt_text.x = left; + pt_text.y = top - 12; + pt_text_bg1.x = left; + pt_text_bg1.y = top - (10 + 25 * font_size); + pt_text_bg2.x = right; + pt_text_bg2.y = top; + CvScalar color; + color.val[0] = red * 256; + color.val[1] = green * 256; + color.val[2] = blue * 256; + + // you should create directory: result_img + //static int copied_frame_id = -1; + //static IplImage* copy_img = NULL; + //if (copied_frame_id != frame_id) { + // copied_frame_id = frame_id; + // if(copy_img == NULL) copy_img = cvCreateImage(cvSize(show_img->width, show_img->height), show_img->depth, show_img->nChannels); + // cvCopy(show_img, copy_img, 0); + //} + //static int img_id = 0; + //img_id++; + //char image_name[1024]; + //sprintf(image_name, "result_img/img_%d_%d_%d_%s.jpg", frame_id, img_id, class_id, names[class_id]); + //CvRect rect = cvRect(pt1.x, pt1.y, pt2.x - pt1.x, pt2.y - pt1.y); + //cvSetImageROI(copy_img, rect); + //cvSaveImage(image_name, copy_img, 0); + //cvResetImageROI(copy_img); + + cvRectangle(show_img, pt1, pt2, color, width, 8, 0); + if (ext_output) + printf("\t(left_x: %4.0f top_y: %4.0f width: %4.0f height: %4.0f)\n", + (float)left, (float)top, b.w*show_img->width, b.h*show_img->height); + else + printf("\n"); + + cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0); + cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0); // filled + CvScalar black_color; + black_color.val[0] = 0; + CvFont font; + cvInitFont(&font, CV_FONT_HERSHEY_SIMPLEX, font_size, font_size, 0, font_size * 3, 8); + cvPutText(show_img, labelstr, pt_text, &font, black_color); + } + } + if (ext_output) { + fflush(stdout); + } +} +// ---------------------------------------- + +void draw_detections_cv(mat_cv* show_img, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes) +{ + int i; + + for (i = 0; i < num; ++i) { + int class_id = max_index(probs[i], classes); + float prob = probs[i][class_id]; + if (prob > thresh) { + + int width = show_img->height * .012; + + if (0) { + width = pow(prob, 1. / 2.) * 10 + 1; + alphabet = 0; + } + + printf("%s: %.0f%%\n", names[class_id], prob * 100); + int offset = class_id * 123457 % classes; + float red = get_color(2, offset, classes); + float green = get_color(1, offset, classes); + float blue = get_color(0, offset, classes); + float rgb[3]; + + //width = prob*20+2; + + rgb[0] = red; + rgb[1] = green; + rgb[2] = blue; + box b = boxes[i]; + + int left = (b.x - b.w / 2.)*show_img->width; + int right = (b.x + b.w / 2.)*show_img->width; + int top = (b.y - b.h / 2.)*show_img->height; + int bot = (b.y + b.h / 2.)*show_img->height; + + if (left < 0) left = 0; + if (right > show_img->width - 1) right = show_img->width - 1; + if (top < 0) top = 0; + if (bot > show_img->height - 1) bot = show_img->height - 1; + + float const font_size = show_img->height / 1000.F; + CvPoint pt1, pt2, pt_text, pt_text_bg1, pt_text_bg2; + pt1.x = left; + pt1.y = top; + pt2.x = right; + pt2.y = bot; + pt_text.x = left; + pt_text.y = top - 12; + pt_text_bg1.x = left; + pt_text_bg1.y = top - (10 + 25 * font_size); + pt_text_bg2.x = right; + pt_text_bg2.y = top; + CvScalar color; + color.val[0] = red * 256; + color.val[1] = green * 256; + color.val[2] = blue * 256; + + cvRectangle(show_img, pt1, pt2, color, width, 8, 0); + //printf("left=%d, right=%d, top=%d, bottom=%d, obj_id=%d, obj=%s \n", left, right, top, bot, class_id, names[class_id]); + cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0); + cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0); // filled + CvScalar black_color; + black_color.val[0] = 0; + CvFont font; + cvInitFont(&font, CV_FONT_HERSHEY_SIMPLEX, font_size, font_size, 0, font_size * 3, 8); + cvPutText(show_img, names[class_id], pt_text, &font, black_color); + } + } +} + +// ==================================================================== +// Draw Loss & Accuracy chart +// ==================================================================== +mat_cv* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show) +{ + int img_offset = 50; + int draw_size = img_size - img_offset; + IplImage* img = cvCreateImage(cvSize(img_size, img_size), 8, 3); + cvSet(img, CV_RGB(255, 255, 255), 0); + CvPoint pt1, pt2, pt_text; + CvFont font; + cvInitFont(&font, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA); + char char_buff[100]; + int i; + // vertical lines + pt1.x = img_offset; pt2.x = img_size, pt_text.x = 10; + for (i = 1; i <= number_of_lines; ++i) { + pt1.y = pt2.y = (float)i * draw_size / number_of_lines; + cvLine(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0); + if (i % 10 == 0) { + sprintf(char_buff, "%2.1f", max_img_loss*(number_of_lines - i) / number_of_lines); + pt_text.y = pt1.y + 5; + cvPutText(img, char_buff, pt_text, &font, CV_RGB(0, 0, 0)); + cvLine(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0); + } + } + // horizontal lines + pt1.y = draw_size; pt2.y = 0, pt_text.y = draw_size + 15; + for (i = 0; i <= number_of_lines; ++i) { + pt1.x = pt2.x = img_offset + (float)i * draw_size / number_of_lines; + cvLine(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0); + if (i % 10 == 0) { + sprintf(char_buff, "%d", max_batches * i / number_of_lines); + pt_text.x = pt1.x - 20; + cvPutText(img, char_buff, pt_text, &font, CV_RGB(0, 0, 0)); + cvLine(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0); + } + } + + cvPutText(img, "Loss", cvPoint(0, 35), &font, CV_RGB(0, 0, 255)); + cvPutText(img, "Iteration number", cvPoint(draw_size / 2, img_size - 10), &font, CV_RGB(0, 0, 0)); + char max_batches_buff[100]; + sprintf(max_batches_buff, "in cfg max_batches=%d", max_batches); + cvPutText(img, max_batches_buff, cvPoint(draw_size - 195, img_size - 10), &font, CV_RGB(0, 0, 0)); + cvPutText(img, "Press 's' to save: chart.png", cvPoint(5, img_size - 10), &font, CV_RGB(0, 0, 0)); + if (!dont_show) { + printf(" If error occurs - run training with flag: -dont_show \n"); + cvNamedWindow("average loss", CV_WINDOW_NORMAL); + cvMoveWindow("average loss", 0, 0); + cvResizeWindow("average loss", img_size, img_size); + cvShowImage("average loss", img); + cvWaitKey(20); + } + return (mat_cv*)img; +} +// ---------------------------------------- + +void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, + float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port) +{ + IplImage* img = (IplImage*)img_src; + int img_offset = 50; + int draw_size = img_size - img_offset; + CvFont font; + cvInitFont(&font, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA); + char char_buff[100]; + CvPoint pt1, pt2; + pt1.x = img_offset + draw_size * (float)current_batch / max_batches; + pt1.y = draw_size * (1 - avg_loss / max_img_loss); + if (pt1.y < 0) pt1.y = 1; + cvCircle(img, pt1, 1, CV_RGB(0, 0, 255), CV_FILLED, 8, 0); + + // precision + if (draw_precision) { + static float old_precision = 0; + static int iteration_old = 0; + static int text_iteration_old = 0; + if (iteration_old == 0) cvPutText(img, accuracy_name, cvPoint(0, 12), &font, CV_RGB(255, 0, 0)); + + cvLine(img, + cvPoint(img_offset + draw_size * (float)iteration_old / max_batches, draw_size * (1 - old_precision)), + cvPoint(img_offset + draw_size * (float)current_batch / max_batches, draw_size * (1 - precision)), + CV_RGB(255, 0, 0), 1, 8, 0); + + if (((int)(old_precision * 10) != (int)(precision * 10)) || (current_batch - text_iteration_old) >= max_batches / 10) { + text_iteration_old = current_batch; + sprintf(char_buff, "%2.0f%% ", precision * 100); + CvFont font3; + cvInitFont(&font3, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 5, CV_AA); + cvPutText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), &font3, CV_RGB(255, 255, 255)); + + CvFont font2; + cvInitFont(&font2, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA); + cvPutText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), &font2, CV_RGB(200, 0, 0)); + } + old_precision = precision; + iteration_old = current_batch; + } + + sprintf(char_buff, "current avg loss = %2.4f iteration = %d", avg_loss, current_batch); + pt1.x = 55, pt1.y = 10; + pt2.x = pt1.x + 460, pt2.y = pt1.y + 20; + cvRectangle(img, pt1, pt2, CV_RGB(255, 255, 255), CV_FILLED, 8, 0); + pt1.y += 15; + cvPutText(img, char_buff, pt1, &font, CV_RGB(0, 0, 0)); + + int k = 0; + if (!dont_show) { + cvShowImage("average loss", img); + k = cvWaitKey(20); + } + if (k == 's' || current_batch == (max_batches - 1) || current_batch % 100 == 0) { + save_cv_png((mat_cv *)img, "chart.png"); + cvPutText(img, "- Saved", cvPoint(250, img_size - 10), &font, CV_RGB(255, 0, 0)); + } + else + cvPutText(img, "- Saved", cvPoint(250, img_size - 10), &font, CV_RGB(255, 255, 255)); + + if (mjpeg_port > 0) send_mjpeg((mat_cv *)img, mjpeg_port, 500000, 100); +} +// ---------------------------------------- + + +// ==================================================================== +// Data augmentation +// ==================================================================== +image image_data_augmentation(mat_cv* ipl, int w, int h, + int pleft, int ptop, int swidth, int sheight, int flip, + float jitter, float dhue, float dsat, float dexp) +{ + image out; + try { + cv::Mat img = cv::cvarrToMat(ipl); + + // crop + cv::Rect src_rect(pleft, ptop, swidth, sheight); + cv::Rect img_rect(cv::Point2i(0, 0), img.size()); + cv::Rect new_src_rect = src_rect & img_rect; + + cv::Rect dst_rect(cv::Point2i(std::max(0, -pleft), std::max(0, -ptop)), new_src_rect.size()); + + cv::Mat cropped(cv::Size(src_rect.width, src_rect.height), img.type()); + cropped.setTo(cv::Scalar::all(0)); + + img(new_src_rect).copyTo(cropped(dst_rect)); + + // resize + cv::Mat sized; + cv::resize(cropped, sized, cv::Size(w, h), 0, 0, INTER_LINEAR); + + // flip + if (flip) { + cv::flip(sized, cropped, 1); // 0 - x-axis, 1 - y-axis, -1 - both axes (x & y) + sized = cropped.clone(); + } + + // HSV augmentation + // CV_BGR2HSV, CV_RGB2HSV, CV_HSV2BGR, CV_HSV2RGB + if (ipl->nChannels >= 3) + { + cv::Mat hsv_src; + cvtColor(sized, hsv_src, CV_BGR2HSV); // also BGR -> RGB + + std::vector hsv; + cv::split(hsv_src, hsv); + + hsv[1] *= dsat; + hsv[2] *= dexp; + hsv[0] += 179 * dhue; + + cv::merge(hsv, hsv_src); + + cvtColor(hsv_src, sized, CV_HSV2RGB); // now RGB instead of BGR + } + else + { + sized *= dexp; + } + + //std::stringstream window_name; + //window_name << "augmentation - " << ipl; + //cv::imshow(window_name.str(), sized); + //cv::waitKey(0); + + // Mat -> IplImage -> image + IplImage src = sized; + out = ipl_to_image(&src); + } + catch (...) { + cerr << "OpenCV can't augment image: " << w << " x " << h << " \n"; + out = ipl_to_image(ipl); + } + return out; +} + +// ==================================================================== +// Show Anchors +// ==================================================================== +void show_acnhors(int number_of_boxes, int num_of_clusters, float *rel_width_height_array, model anchors_data, int width, int height) +{ + CvMat* labels = cvCreateMat(number_of_boxes, 1, CV_32SC1); + CvMat* points = cvCreateMat(number_of_boxes, 2, CV_32FC1); + CvMat* centers = cvCreateMat(num_of_clusters, 2, CV_32FC1); + + int i, j; + for (i = 0; i < number_of_boxes; ++i) { + points->data.fl[i * 2] = rel_width_height_array[i * 2]; + points->data.fl[i * 2 + 1] = rel_width_height_array[i * 2 + 1]; + //cvSet1D(points, i * 2, cvScalar(rel_width_height_array[i * 2], 0, 0, 0)); + //cvSet1D(points, i * 2 + 1, cvScalar(rel_width_height_array[i * 2 + 1], 0, 0, 0)); + } + + for (i = 0; i < num_of_clusters; ++i) { + centers->data.fl[i * 2] = anchors_data.centers.vals[i][0]; + centers->data.fl[i * 2 + 1] = anchors_data.centers.vals[i][1]; + } + + for (i = 0; i < number_of_boxes; ++i) { + labels->data.i[i] = anchors_data.assignments[i]; + } + + size_t img_size = 700; + IplImage* img = cvCreateImage(cvSize(img_size, img_size), 8, 3); + cvZero(img); + for (i = 0; i < number_of_boxes; ++i) { + CvPoint pt; + pt.x = points->data.fl[i * 2] * img_size / width; + pt.y = points->data.fl[i * 2 + 1] * img_size / height; + int cluster_idx = labels->data.i[i]; + int red_id = (cluster_idx * (uint64_t)123 + 55) % 255; + int green_id = (cluster_idx * (uint64_t)321 + 33) % 255; + int blue_id = (cluster_idx * (uint64_t)11 + 99) % 255; + cvCircle(img, pt, 1, CV_RGB(red_id, green_id, blue_id), CV_FILLED, 8, 0); + //if(pt.x > img_size || pt.y > img_size) printf("\n pt.x = %d, pt.y = %d \n", pt.x, pt.y); + } + + for (j = 0; j < num_of_clusters; ++j) { + CvPoint pt1, pt2; + pt1.x = pt1.y = 0; + pt2.x = centers->data.fl[j * 2] * img_size / width; + pt2.y = centers->data.fl[j * 2 + 1] * img_size / height; + cvRectangle(img, pt1, pt2, CV_RGB(255, 255, 255), 1, 8, 0); + } + save_cv_png((mat_cv *)img, "cloud.png"); + cvShowImage("clusters", img); + cvWaitKey(0); + cvReleaseImage(&img); + cvDestroyAllWindows(); + cvReleaseMat(&labels); + cvReleaseMat(&points); + cvReleaseMat(¢ers); +} + +} // extern "C" + +#endif // OPENCV diff --git a/src/image_opencv.h b/src/image_opencv.h new file mode 100644 index 00000000000..a1fb421420b --- /dev/null +++ b/src/image_opencv.h @@ -0,0 +1,105 @@ +#ifndef IMAGE_OPENCV_H +#define IMAGE_OPENCV_H + +#include "image.h" +#include "matrix.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#ifdef OPENCV + +// declaration +typedef struct mat_cv mat_cv; +typedef struct cap_cv cap_cv; +typedef struct write_cv write_cv; + +// cv::Mat / IplImage +image load_image_cv(char *filename, int channels); +mat_cv *load_image_mat_cv(const char *filename, int flag); +image load_image_resize(char *filename, int w, int h, int c, image *im); +int get_width_cv(mat_cv *ipl); +int get_height_cv(mat_cv *ipl); +void release_ipl(mat_cv **ipl); + +// image-to-ipl, ipl-to-image +mat_cv *image_to_ipl(image im); +image ipl_to_image_custom(mat_cv* src); +// Mat image_to_mat(image im); +// image mat_to_image(Mat m) + +// Window +void create_window_cv(char const* window_name, int full_screen, int width, int height); +void destroy_all_windows_cv(); +int wait_key_cv(int delay); +int wait_until_press_key_cv(); +void make_window(char *name, int w, int h, int fullscreen); +void show_image_cv(image p, const char *name); +void show_image_cv_ipl(mat_cv *disp, const char *name); + +// Video Writer +write_cv *create_video_writer(char *out_filename, char c1, char c2, char c3, char c4, int fps, int width, int height, int is_color); +int write_frame_cv(write_cv *output_video_writer, mat_cv *show_img); +void release_video_writer(write_cv **output_video_writer); + + +//void *open_video_stream(const char *f, int c, int w, int h, int fps); +//image get_image_from_stream(void *p); +//image load_image_cv(char *filename, int channels); +//int show_image_cv(image im, const char* name, int ms); + +// Video Capture +cap_cv* get_capture_video_stream(const char *path); +cap_cv* get_capture_webcam(int index); +void release_capture(cap_cv* cap); + +mat_cv* get_capture_frame_cv(cap_cv *cap); +int get_stream_fps_cpp_cv(cap_cv *cap); +double get_capture_property_cv(cap_cv *cap, int property_id); +double get_capture_frame_count_cv(cap_cv *cap); +int set_capture_property_cv(cap_cv *cap, int property_id, double value); +int set_capture_position_frame_cv(cap_cv *cap, int index); + +// ... Video Capture +image get_image_from_stream(cap_cv *cap); +image get_image_from_stream_cpp(cap_cv *cap); +image get_image_from_stream_resize(cap_cv *cap, int w, int h, int c, mat_cv** in_img, int cpp_video_capture, int dont_close); +image get_image_from_stream_letterbox(cap_cv *cap, int w, int h, int c, mat_cv** in_img, int cpp_video_capture, int dont_close); +int get_stream_fps(cap_cv *cap, int cpp_video_capture); + + +// Image Saving +void save_cv_png(mat_cv *img, const char *name); +void save_cv_jpg(mat_cv *img, const char *name); + +// Draw Detection +void draw_detections_cv_v3(mat_cv* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output); +void draw_detections_cv(mat_cv* show_img, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes); + +// Draw Loss & Accuracy chart +mat_cv* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show); +void draw_train_loss(mat_cv* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, + float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port); + +// Data augmentation +image image_data_augmentation(mat_cv* ipl, int w, int h, + int pleft, int ptop, int swidth, int sheight, int flip, + float jitter, float dhue, float dsat, float dexp); + +// Show Anchors +void show_acnhors(int number_of_boxes, int num_of_clusters, float *rel_width_height_array, model anchors_data, int width, int height); + +#else + +static int wait_key_cv(int delay) { return 0; } +static int wait_until_press_key_cv() { return 0; } +static void destroy_all_windows_cv() {} + +#endif // OPENCV + +#ifdef __cplusplus +} +#endif + +#endif // IMAGE_OPENCV_H \ No newline at end of file diff --git a/src/network.c b/src/network.c index fd7cddfbd8d..df2dea96d5e 100644 --- a/src/network.c +++ b/src/network.c @@ -118,10 +118,10 @@ float get_current_rate(network net) case SIG: return net.learning_rate * (1./(1.+exp(net.gamma*(batch_num - net.step)))); case SGDR: - rate = net.learning_rate_min + + rate = net.learning_rate_min + 0.5*(net.learning_rate-net.learning_rate_min) * (1. + cos( (float) (batch_num % net.batches_per_cycle)*3.14159265 / net.batches_per_cycle)); - + return rate; default: fprintf(stderr, "Policy is weird!\n"); @@ -322,6 +322,11 @@ float train_network_sgd(network net, data d, int n) } float train_network(network net, data d) +{ + return train_network_waitkey(net, d, 0); +} + +float train_network_waitkey(network net, data d, int is_main_thread) { assert(d.X.rows % net.batch == 0); int batch = net.batch; @@ -335,6 +340,7 @@ float train_network(network net, data d) get_next_batch(d, batch, i*batch, X, y); float err = train_network_datum(net, X, y); sum += err; + wait_key_cv(5); } free(X); free(y); diff --git a/src/network.h b/src/network.h index 764bb7da603..222aca39211 100644 --- a/src/network.h +++ b/src/network.h @@ -116,6 +116,7 @@ void backward_network(network net, network_state state); void update_network(network net); float train_network(network net, data d); +float train_network_waitkey(network net, data d, int is_main_thread); float train_network_batch(network net, data d, int n); float train_network_sgd(network net, data d, int n); float train_network_datum(network net, float *x, float *y); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 7aabc149dd6..b5f0f7db346 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -32,9 +32,9 @@ #include "shortcut_layer.h" #include "blas.h" -#ifdef OPENCV -#include -#endif +//#ifdef OPENCV +//#include +//#endif #include "http_stream.h" @@ -160,15 +160,15 @@ void forward_backward_network_gpu(network net, float *x, float *y) int i; for (i = 0; i < net.n; ++i) { layer l = net.layers[i]; - if (l.weights_gpu && l.weights_gpu16 && net.cudnn_half){ + if (net.cudnn_half){ assert((l.c*l.n*l.size*l.size) > 0); - if (l.type == CONVOLUTIONAL) { + if (l.type == CONVOLUTIONAL && l.weights_gpu && l.weights_gpu16) { cuda_convert_f32_to_f16(l.weights_gpu, l.c*l.n*l.size*l.size, l.weights_gpu16); } - else if (l.type == CRNN) { - //cuda_convert_f32_to_f16(l.input_layer->weights_gpu, l.input_layer->nweights, l.input_layer->weights_gpu16); - //cuda_convert_f32_to_f16(l.self_layer->weights_gpu, l.self_layer->nweights, l.self_layer->weights_gpu16); - //cuda_convert_f32_to_f16(l.output_layer->weights_gpu, l.output_layer->nweights, l.output_layer->weights_gpu16); + else if (l.type == CRNN && l.input_layer->weights_gpu && l.input_layer->weights_gpu16) { + cuda_convert_f32_to_f16(l.input_layer->weights_gpu, l.input_layer->nweights, l.input_layer->weights_gpu16); + cuda_convert_f32_to_f16(l.self_layer->weights_gpu, l.self_layer->nweights, l.self_layer->weights_gpu16); + cuda_convert_f32_to_f16(l.output_layer->weights_gpu, l.output_layer->nweights, l.output_layer->weights_gpu16); } } } diff --git a/src/nightmare.c b/src/nightmare.c index bd8ccc3b504..5c1ca04ae18 100644 --- a/src/nightmare.c +++ b/src/nightmare.c @@ -4,10 +4,6 @@ #include "blas.h" #include "utils.h" -#ifdef OPENCV -#include -#endif - // ./darknet nightmare cfg/extractor.recon.cfg ~/trained/yolo-coco.conv frame6.png -reconstruct -iters 500 -i 3 -lambda .1 -rate .01 -smooth 2 float abs_mean(float *x, int n) @@ -268,7 +264,7 @@ void run_nightmare(int argc, char **argv) //if ((n+1)%30 == 0) rate *= .5; show_image(im, "reconstruction"); #ifdef OPENCV - cvWaitKey(10); + wait_key_cv(10); #endif }else{ int layer = max_layer + rand()%range - range/2; @@ -291,7 +287,7 @@ void run_nightmare(int argc, char **argv) printf("%d %s\n", e, buff); save_image(im, buff); //show_image(im, buff); - //cvWaitKey(0); + //wait_key_cv(0); if(rotate){ image rot = rotate_image(im, rotate); diff --git a/src/rnn.c b/src/rnn.c index 5aee0863261..6e1ed721e83 100644 --- a/src/rnn.c +++ b/src/rnn.c @@ -4,10 +4,6 @@ #include "blas.h" #include "parser.h" -#ifdef OPENCV -#include -#endif - typedef struct { float *x; float *y; diff --git a/src/rnn_vid.c b/src/rnn_vid.c index 7613ab25bf0..ea3d64fce1e 100644 --- a/src/rnn_vid.c +++ b/src/rnn_vid.c @@ -5,14 +5,6 @@ #include "blas.h" #ifdef OPENCV -#include -#include -#ifndef CV_VERSION_EPOCH -#include -#endif -image get_image_from_stream(CvCapture *cap); -image ipl_to_image(IplImage* src); - void reconstruct_picture(network net, float *features, image recon, image update, float rate, float momentum, float lambda, int smooth_size, int iters); @@ -33,8 +25,8 @@ float_pair get_rnn_vid_data(network net, char **files, int n, int batch, int ste int input_size = net.w*net.h*net.c; float* input = (float*)calloc(input_size * net.batch, sizeof(float)); char *filename = files[rand()%n]; - CvCapture *cap = cvCaptureFromFile(filename); - int frames = cvGetCaptureProperty(cap, CV_CAP_PROP_FRAME_COUNT); + cap_cv *cap = get_capture_video_stream(filename); + int frames = get_capture_frame_count_cv(cap); int index = rand() % (frames - steps - 2); if (frames < (steps + 4)){ --b; @@ -43,12 +35,12 @@ float_pair get_rnn_vid_data(network net, char **files, int n, int batch, int ste } printf("frames: %d, index: %d\n", frames, index); - cvSetCaptureProperty(cap, CV_CAP_PROP_POS_FRAMES, index); + set_capture_position_frame_cv(cap, index); int i; for(i = 0; i < net.batch; ++i){ - IplImage* src = cvQueryFrame(cap); - image im = ipl_to_image(src); + mat_cv *src = get_capture_frame_cv(cap); + image im = ipl_to_image_custom(src); rgbgr_image(im); image re = resize_image(im, net.w, net.h); //show_image(re, "loaded"); @@ -65,7 +57,7 @@ float_pair get_rnn_vid_data(network net, char **files, int n, int batch, int ste memcpy(feats + (b + i*batch)*output_size, output + i*output_size, output_size*sizeof(float)); } - cvReleaseCapture(&cap); + release_capture(cap); //cvReleaseCapture(&cap); } //printf("%d %d %d\n", out_im.w, out_im.h, out_im.c); @@ -164,7 +156,8 @@ void generate_vid_rnn(char *cfgfile, char *weightfile) set_batch_network(&net, 1); int i; - CvCapture* cap = cvCaptureFromFile("extra/vid/ILSVRC2015/Data/VID/snippets/val/ILSVRC2015_val_00007030.mp4"); + cap_cv *cap = get_capture_video_stream("extra/vid/ILSVRC2015/Data/VID/snippets/val/ILSVRC2015_val_00007030.mp4"); + //CvCapture* cap = cvCaptureFromFile("extra/vid/ILSVRC2015/Data/VID/snippets/val/ILSVRC2015_val_00007030.mp4"); float *feat; float *next; next = NULL; diff --git a/src/super.c b/src/super.c index 0f96fddb937..35e7f6cf806 100644 --- a/src/super.c +++ b/src/super.c @@ -3,9 +3,6 @@ #include "utils.h" #include "parser.h" -#ifdef OPENCV -#include -#endif void train_super(char *cfgfile, char *weightfile) { diff --git a/src/swag.c b/src/swag.c index d50200072c1..210f03f4003 100644 --- a/src/swag.c +++ b/src/swag.c @@ -5,10 +5,6 @@ #include "parser.h" #include "box.h" -#ifdef OPENCV -#include -#endif - void train_swag(char *cfgfile, char *weightfile) { char *train_images = "data/voc.0712.trainval"; diff --git a/src/tag.c b/src/tag.c index 0942d50324c..ec9327af5a4 100644 --- a/src/tag.c +++ b/src/tag.c @@ -2,10 +2,6 @@ #include "utils.h" #include "parser.h" -#ifdef OPENCV -#include -#endif - void train_tag(char *cfgfile, char *weightfile, int clear) { srand(time(0)); diff --git a/src/voxel.c b/src/voxel.c index 57fb17b6dd4..ea7442b98e0 100644 --- a/src/voxel.c +++ b/src/voxel.c @@ -3,15 +3,6 @@ #include "utils.h" #include "parser.h" -#ifdef OPENCV -#include -#include -#ifndef CV_VERSION_EPOCH -#include -#endif -image get_image_from_stream(CvCapture *cap); -#endif - void extract_voxel(char *lfile, char *rfile, char *prefix) { #ifdef OPENCV @@ -19,8 +10,8 @@ void extract_voxel(char *lfile, char *rfile, char *prefix) int h = 1080; int shift = 0; int count = 0; - CvCapture *lcap = cvCaptureFromFile(lfile); - CvCapture *rcap = cvCaptureFromFile(rfile); + cap_cv *lcap = get_capture_video_stream(lfile); + cap_cv *rcap = get_capture_video_stream(rfile); while(1){ image l = get_image_from_stream(lcap); image r = get_image_from_stream(rcap); diff --git a/src/writing.c b/src/writing.c index e0d8019e301..29785b7b759 100644 --- a/src/writing.c +++ b/src/writing.c @@ -2,10 +2,6 @@ #include "utils.h" #include "parser.h" -#ifdef OPENCV -#include -#endif - void train_writing(char *cfgfile, char *weightfile) { char* backup_directory = "backup/"; @@ -122,10 +118,9 @@ void test_writing(char *cfgfile, char *weightfile, char *filename) show_image(pred, "prediction"); show_image(im, "orig"); -#ifdef OPENCV - cvWaitKey(0); - cvDestroyAllWindows(); -#endif + + wait_until_press_key_cv(); + destroy_all_windows_cv(); free_image(upsampled); free_image(thresh); diff --git a/src/yolo.c b/src/yolo.c index fc7d8a6edd3..07a2092c0b1 100644 --- a/src/yolo.c +++ b/src/yolo.c @@ -6,15 +6,6 @@ #include "box.h" #include "demo.h" -#ifdef OPENCV -#include -#include -#include -#ifndef CV_VERSION_EPOCH -#include -#endif -#endif - char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"}; void train_yolo(char *cfgfile, char *weightfile) @@ -327,10 +318,10 @@ void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh) free_image(im); free_image(sized); -#ifdef OPENCV - cvWaitKey(0); - cvDestroyAllWindows(); -#endif + + wait_until_press_key_cv(); + destroy_all_windows_cv(); + if (filename) break; } } diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index ab582bad52c..6eaa38b7291 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -384,53 +384,6 @@ LIB_API std::vector Detector::tracking_id(std::vector cur_bbox_v } -LIB_API bool Detector::send_json_http(std::vector cur_bbox_vec, std::vector obj_names, int frame_id, std::string filename, int timeout, int port) -{ - //int timeout = 400000; - //int port = 8070; - //send_json(local_dets, local_nboxes, l.classes, demo_names, frame_id, demo_json_port, timeout); - - std::string send_str; - - char *tmp_buf = (char *)calloc(1024, sizeof(char)); - if (!filename.empty()) { - sprintf(tmp_buf, "{\n \"frame_id\":%d, \n \"filename\":\"%s\", \n \"objects\": [ \n", frame_id, filename.c_str()); - } - else { - sprintf(tmp_buf, "{\n \"frame_id\":%d, \n \"objects\": [ \n", frame_id); - } - send_str = tmp_buf; - free(tmp_buf); - - for (auto & i : cur_bbox_vec) { - char *buf = (char *)calloc(2048, sizeof(char)); - - sprintf(buf, " {\"class_id\":%d, \"name\":\"%s\", \"absolute_coordinates\":{\"center_x\":%d, \"center_y\":%d, \"width\":%d, \"height\":%d}, \"confidence\":%f", - i.obj_id, obj_names[i.obj_id].c_str(), i.x, i.y, i.w, i.h, i.prob); - - //sprintf(buf, " {\"class_id\":%d, \"name\":\"%s\", \"relative_coordinates\":{\"center_x\":%f, \"center_y\":%f, \"width\":%f, \"height\":%f}, \"confidence\":%f", - // i.obj_id, obj_names[i.obj_id], i.x, i.y, i.w, i.h, i.prob); - - send_str += buf; - - if (!std::isnan(i.z_3d)) { - sprintf(buf, "\n , \"coordinates_in_meters\":{\"x_3d\":%.2f, \"y_3d\":%.2f, \"z_3d\":%.2f}", - i.x_3d, i.y_3d, i.z_3d); - send_str += buf; - } - - send_str += "}\n"; - - free(buf); - } - - //send_str += "\n ] \n}, \n"; - send_str += "\n ] \n}"; - - send_json_custom(send_str.c_str(), port, timeout); - return true; -} - void *Detector::get_cuda_context() { #ifdef GPU From d7a95aefb2209275af9145f8849962c46a00b39b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 5 Apr 2019 16:04:26 +0300 Subject: [PATCH 0438/1365] Added flag -show_imgs for Training, to show augmented images with bboxes --- include/darknet.h | 3 ++- src/data.c | 28 +++++++++++++++++----------- src/data.h | 3 ++- src/detector.c | 6 ++++-- 4 files changed, 25 insertions(+), 15 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index d601deb4212..0e404f475e9 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -706,6 +706,7 @@ typedef struct load_args { int mini_batch; int track; int augment_speed; + int show_imgs; float jitter; int flip; float angle; @@ -771,7 +772,7 @@ LIB_API detection *make_network_boxes(network *net, float thresh, int *num); LIB_API void reset_rnn(network *net); LIB_API float *network_predict_image(network *net, image im); LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, const int map_points, network *existing_net); -LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port); +LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port, int show_imgs); LIB_API void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile); LIB_API int network_width(network *net); diff --git a/src/data.c b/src/data.c index 221a6211c90..e46395663c5 100644 --- a/src/data.c +++ b/src/data.c @@ -769,7 +769,8 @@ static box float_to_box_stride(float *f, int stride) #include "http_stream.h" -data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, float jitter, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed) +data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, float jitter, + float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int show_imgs) { c = c ? c : 3; char **random_paths; @@ -844,10 +845,9 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo fill_truth_detection(filename, boxes, d.y.vals[i], classes, flip, dx, dy, 1./sx, 1./sy, w, h); - const int show_augmented_images = 0; - if(show_augmented_images) + if(show_imgs) { - char buff[10]; + char buff[1000]; sprintf(buff, "aug_%s_%d", random_paths[i], random_gen()); int t; for (t = 0; t < boxes; ++t) { @@ -860,18 +860,20 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo draw_box_width(ai, left, top, right, bot, 3, 150, 100, 50); // 3 channels RGB } + save_image(ai, buff); show_image(ai, buff); wait_until_press_key_cv(); + printf("\nYou use flag -show_imgs, so will be saved aug_...jpg images. Click on window and press ESC button \n"); } - release_ipl(&src); } free(random_paths); return d; } #else // OPENCV -data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, float jitter, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed) +data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, float jitter, + float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int show_imgs) { c = c ? c : 3; char **random_paths; @@ -938,10 +940,10 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo fill_truth_detection(random_paths[i], boxes, d.y.vals[i], classes, flip, dx, dy, 1. / sx, 1. / sy, w, h); - /* + if(show_imgs) { - char buff[10]; - sprintf(buff, "aug_%s_%d", random_paths[i], random_gen()); + char buff[1000]; + sprintf(buff, "aug_%s_%d", basecfg(random_paths[i]), random_gen()); int t; for (t = 0; t < boxes; ++t) { box b = float_to_box_stride(d.y.vals[i] + t*(4 + 1), 1); @@ -954,8 +956,11 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo } show_image(sized, buff); + save_image(sized, buff); wait_until_press_key_cv(); - }*/ + printf("\nYou use flag -show_imgs, so will be saved aug_...jpg images. Press Enter: \n"); + getchar(); + } free_image(orig); free_image(cropped); @@ -985,7 +990,8 @@ void *load_thread(void *ptr) } else if (a.type == REGION_DATA){ *a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure); } else if (a.type == DETECTION_DATA){ - *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.c, a.num_boxes, a.classes, a.flip, a.jitter, a.hue, a.saturation, a.exposure, a.mini_batch, a.track, a.augment_speed); + *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.c, a.num_boxes, a.classes, a.flip, a.jitter, + a.hue, a.saturation, a.exposure, a.mini_batch, a.track, a.augment_speed, a.show_imgs); } else if (a.type == SWAG_DATA){ *a.d = load_data_swag(a.paths, a.n, a.classes, a.jitter); } else if (a.type == COMPARE_DATA){ diff --git a/src/data.h b/src/data.h index b6ed5b802a6..ea2367eec9d 100644 --- a/src/data.h +++ b/src/data.h @@ -86,7 +86,8 @@ void print_letters(float *pred, int n); data load_data_captcha(char **paths, int n, int m, int k, int w, int h); data load_data_captcha_encode(char **paths, int n, int m, int w, int h); data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h); -data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, float jitter, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed); +data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, float jitter, + float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int show_imgs); data load_data_tag(char **paths, int n, int m, int k, int use_flip, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); data load_data_super(char **paths, int n, int m, int w, int h, int scale); diff --git a/src/detector.c b/src/detector.c index 179498a8688..b52910f4f22 100644 --- a/src/detector.c +++ b/src/detector.c @@ -22,7 +22,7 @@ int check_mistakes; static int coco_ids[] = { 1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90 }; -void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port) +void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port, int show_imgs) { list *options = read_data_cfg(datacfg); char *train_images = option_find_str(options, "train", "data/train.txt"); @@ -127,6 +127,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i args.exposure = net.exposure; args.saturation = net.saturation; args.hue = net.hue; + args.show_imgs = show_imgs; #ifdef OPENCV args.threads = 3 * ngpus; // Amazon EC2 Tesla V100: p3.2xlarge (8 logical cores) - p3.16xlarge @@ -1388,6 +1389,7 @@ void run_detector(int argc, char **argv) int calc_map = find_arg(argc, argv, "-map"); int map_points = find_int_arg(argc, argv, "-points", 0); check_mistakes = find_arg(argc, argv, "-check_mistakes"); + int show_imgs = find_arg(argc, argv, "-show_imgs"); int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1); int json_port = find_int_arg(argc, argv, "-json_port", -1); char *out_filename = find_char_arg(argc, argv, "-out_filename", 0); @@ -1443,7 +1445,7 @@ void run_detector(int argc, char **argv) if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0; char *filename = (argc > 6) ? argv[6] : 0; if (0 == strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels, outfile); - else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map, mjpeg_port); + else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map, mjpeg_port, show_imgs); else if (0 == strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile); else if (0 == strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights); else if (0 == strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh, iou_thresh, map_points, NULL); From 79c669707cc79878fbc581f98bab39caed7ee226 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 5 Apr 2019 18:56:08 +0300 Subject: [PATCH 0439/1365] Some C OpenCV functions replaced by CPP functions --- src/art.c | 2 +- src/classifier.c | 2 - src/demo.c | 9 +- src/image_opencv.cpp | 319 +++++++++++++++++-------------------------- src/image_opencv.h | 17 +-- src/rnn_vid.c | 2 +- src/voxel.c | 4 +- 7 files changed, 138 insertions(+), 217 deletions(-) diff --git a/src/art.c b/src/art.c index 91fc94d63c7..22e8ab86f3b 100644 --- a/src/art.c +++ b/src/art.c @@ -34,7 +34,7 @@ void demo_art(char *cfgfile, char *weightfile, int cam_index) int n = sizeof(idx)/sizeof(idx[0]); while(1){ - image in = get_image_from_stream(cap); + image in = get_image_from_stream_cpp(cap); image in_s = resize_image(in, net.w, net.h); show_image(in, window); diff --git a/src/classifier.c b/src/classifier.c index ca196c66f3c..9773e2713fe 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -1171,10 +1171,8 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind cap_cv * cap; if(filename){ - //cap = cvCaptureFromFile(filename); cap = get_capture_video_stream(filename); }else{ - //cap = cvCaptureFromCAM(cam_index); cap = get_capture_webcam(cam_index); } diff --git a/src/demo.c b/src/demo.c index 61e2380fe93..5c19769ee44 100644 --- a/src/demo.c +++ b/src/demo.c @@ -31,7 +31,6 @@ static image in_s ; static image det_s; static cap_cv *cap; -static int cpp_video_capture = 0; static float fps = 0; static float demo_thresh = 0; static int demo_ext_output = 0; @@ -55,9 +54,9 @@ void *fetch_in_thread(void *ptr) { int dont_close_stream = 0; // set 1 if your IP-camera periodically turns off and turns on video-stream if(letter_box) - in_s = get_image_from_stream_letterbox(cap, net.w, net.h, net.c, &in_img, cpp_video_capture, dont_close_stream); + in_s = get_image_from_stream_letterbox(cap, net.w, net.h, net.c, &in_img, dont_close_stream); else - in_s = get_image_from_stream_resize(cap, net.w, net.h, net.c, &in_img, cpp_video_capture, dont_close_stream); + in_s = get_image_from_stream_resize(cap, net.w, net.h, net.c, &in_img, dont_close_stream); if(!in_s.data){ printf("Stream closed.\n"); flag_exit = 1; @@ -126,11 +125,9 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int if(filename){ printf("video file: %s\n", filename); - cpp_video_capture = 1; cap = get_capture_video_stream(filename); }else{ printf("Webcam index: %d\n", cam_index); - cpp_video_capture = 1; cap = get_capture_webcam(cam_index); } @@ -187,7 +184,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int if (out_filename && !flag_exit) { int src_fps = 25; - src_fps = get_stream_fps(cap, cpp_video_capture); + src_fps = get_stream_fps_cpp_cv(cap); output_video_writer = create_video_writer(out_filename, 'D', 'I', 'V', 'X', src_fps, get_width_cv(det_img), get_height_cv(det_img), 1); diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index d09e3018e2e..e67e25426c3 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1,13 +1,21 @@ -#ifdef OPENCV - #include "image_opencv.h" + +#ifdef OPENCV #include "utils.h" #include #include #include -#include "opencv2/opencv.hpp" +#include +#include +#include +#include + +#include +#include +#include +#include using namespace cv; @@ -37,8 +45,8 @@ using std::endl; #ifndef CV_VERSION_EPOCH #include #include -#include "http_stream.h" #endif + #include "http_stream.h" #ifndef CV_RGB @@ -48,8 +56,8 @@ using std::endl; extern "C" { struct mat_cv :IplImage { int a[0]; }; - //struct cap_cv : CvCapture { int a[0]; }; - //struct write_cv : CvVideoWriter { int a[0]; }; + struct cap_cv : cv::VideoCapture { int a[0]; }; + struct write_cv : cv::VideoWriter { int a[0]; }; // ==================================================================== // cv::Mat / IplImage @@ -197,6 +205,12 @@ image ipl_to_image_custom(mat_cv* src) } // ---------------------------------------- +cv::Mat ipl_to_mat(IplImage *ipl) +{ + Mat m = cvarrToMat(ipl, true); + return m; +} + Mat image_to_mat(image im) { image copy = copy_image(im); @@ -224,24 +238,24 @@ image mat_to_image(Mat m) // ==================================================================== void create_window_cv(char const* window_name, int full_screen, int width, int height) { - int window_type = CV_WINDOW_NORMAL; - if (full_screen) window_type = CV_WINDOW_FULLSCREEN; + int window_type = WINDOW_NORMAL; + if (full_screen) window_type = WINDOW_FULLSCREEN; - cvNamedWindow(window_name, window_type); - cvMoveWindow(window_name, 0, 0); - cvResizeWindow(window_name, width, height); + cv::namedWindow(window_name, window_type); + cv::moveWindow(window_name, 0, 0); + cv::resizeWindow(window_name, width, height); } // ---------------------------------------- void destroy_all_windows_cv() { - cvDestroyAllWindows(); + cv::destroyAllWindows(); } // ---------------------------------------- int wait_key_cv(int delay) { - return cvWaitKey(delay); + return cv::waitKey(delay); } // ---------------------------------------- @@ -253,13 +267,13 @@ int wait_until_press_key_cv() void make_window(char *name, int w, int h, int fullscreen) { - namedWindow(name, WINDOW_NORMAL); + cv::namedWindow(name, WINDOW_NORMAL); if (fullscreen) { - setWindowProperty(name, CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN); + cv::setWindowProperty(name, WND_PROP_FULLSCREEN, WINDOW_FULLSCREEN); } else { - resizeWindow(name, w, h); - if (strcmp(name, "Demo") == 0) moveWindow(name, 0, 0); + cv::resizeWindow(name, w, h); + if (strcmp(name, "Demo") == 0) cv::moveWindow(name, 0, 0); } } // ---------------------------------------- @@ -320,7 +334,7 @@ void show_image_cv_ipl(mat_cv *disp, const char *name) char buff[256]; //sprintf(buff, "%s (%d)", name, windows); sprintf(buff, "%s", name); - cvNamedWindow(buff, CV_WINDOW_NORMAL); + cv::namedWindow(buff, WINDOW_NORMAL); //cvMoveWindow(buff, 100*(windows%10) + 200*(windows/10), 100*(windows%10)); //++windows; cvShowImage(buff, disp); @@ -333,20 +347,47 @@ void show_image_cv_ipl(mat_cv *disp, const char *name) // ==================================================================== write_cv *create_video_writer(char *out_filename, char c1, char c2, char c3, char c4, int fps, int width, int height, int is_color) { - CvVideoWriter * output_video_writer = - cvCreateVideoWriter(out_filename, CV_FOURCC(c1, c2, c3, c4), fps, cvSize(width, height), is_color); + try { + cv::VideoWriter * output_video_writer = + new cv::VideoWriter(out_filename, CV_FOURCC(c1, c2, c3, c4), fps, cv::Size(width, height), is_color); return (write_cv *)output_video_writer; + } + catch (...) { + cerr << "OpenCV exception: create_video_writer \n"; + } + return NULL; } -int write_frame_cv(write_cv *output_video_writer, mat_cv *show_img) +void write_frame_cv(write_cv *output_video_writer, mat_cv *show_img) { - return cvWriteFrame((CvVideoWriter *)output_video_writer, show_img); + try { + cv::VideoWriter *out = (cv::VideoWriter *)output_video_writer; + out->write(ipl_to_mat(show_img)); + } + catch (...) { + cerr << "OpenCV exception: write_frame_cv \n"; + } } void release_video_writer(write_cv **output_video_writer) { - cvReleaseVideoWriter((CvVideoWriter **)output_video_writer); + try { + if (output_video_writer) { + std::cout << " closing..."; + cv::VideoWriter *out = *(cv::VideoWriter **)output_video_writer; + out->release(); + delete out; + output_video_writer = NULL; + std::cout << " closed!"; + } + else { + cerr << "OpenCV exception: output_video_writer isn't created \n"; + } + } + catch (...) { + cerr << "OpenCV exception: release_video_writer \n"; + } } /* @@ -390,12 +431,12 @@ int show_image_cv(image im, const char* name, int ms) // ==================================================================== cap_cv* get_capture_video_stream(const char *path) { - CvCapture* cap = NULL; + cv::VideoCapture* cap = NULL; try { - cap = (CvCapture*)new cv::VideoCapture(path); + cap = new cv::VideoCapture(path); } catch (...) { - cerr << " Error: video-stream " << path << " can't be opened! \n"; + cerr << " OpenCV exception: video-stream " << path << " can't be opened! \n"; } return (cap_cv*)cap; } @@ -403,14 +444,14 @@ cap_cv* get_capture_video_stream(const char *path) { cap_cv* get_capture_webcam(int index) { - CvCapture* cap = NULL; + cv::VideoCapture* cap = NULL; try { - cap = (CvCapture*)new cv::VideoCapture(index); - //((cv::VideoCapture*)cap)->set(CV_CAP_PROP_FRAME_WIDTH, 1280); - //((cv::VideoCapture*)cap)->set(CV_CAP_PROP_FRAME_HEIGHT, 960); + cap = new cv::VideoCapture(index); + //cap->set(CV_CAP_PROP_FRAME_WIDTH, 1280); + //cap->set(CV_CAP_PROP_FRAME_HEIGHT, 960); } catch (...) { - cerr << " Error: Web-camera " << index << " can't be opened! \n"; + cerr << " OpenCV exception: Web-camera " << index << " can't be opened! \n"; } return (cap_cv*)cap; } @@ -423,7 +464,7 @@ void release_capture(cap_cv* cap) delete cpp_cap; } catch (...) { - cerr << " Error: cv::VideoCapture " << cap << " can't be released! \n"; + cerr << " OpenCV exception: cv::VideoCapture " << cap << " can't be released! \n"; } } // ---------------------------------------- @@ -431,20 +472,21 @@ void release_capture(cap_cv* cap) mat_cv* get_capture_frame_cv(cap_cv *cap) { IplImage* src = NULL; try { - cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; - cv::Mat frame; - if (cpp_cap.isOpened()) - { - cpp_cap >> frame; - IplImage tmp = frame; - src = cvCloneImage(&tmp); - } - else { - std::cout << " Video-stream stopped! \n"; + if (cap) { + cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; + cv::Mat frame; + if (cpp_cap.isOpened()) + { + cpp_cap >> frame; + IplImage tmp = frame; + src = cvCloneImage(&tmp); + } + else std::cout << " Video-stream stopped! \n"; } + else cerr << " cv::VideoCapture isn't created \n"; } catch (...) { - std::cout << " Video-stream stoped! \n"; + std::cout << " OpenCV exception: Video-stream stoped! \n"; } return (mat_cv *)src; } @@ -475,7 +517,7 @@ double get_capture_property_cv(cap_cv *cap, int property_id) return cpp_cap.get(property_id); } catch (...) { - cerr << " Can't get property of source video-stream. \n"; + cerr << " OpenCV exception: Can't get property of source video-stream. \n"; } return 0; } @@ -492,7 +534,7 @@ double get_capture_frame_count_cv(cap_cv *cap) #endif } catch (...) { - cerr << " Can't get CAP_PROP_FRAME_COUNT of source videofile. \n"; + cerr << " OpenCV exception: Can't get CAP_PROP_FRAME_COUNT of source videofile. \n"; } return 0; } @@ -528,50 +570,26 @@ int set_capture_position_frame_cv(cap_cv *cap, int index) } // ---------------------------------------- -// only for using in image_opencv.cpp -mat_cv* get_capture_frame(CvCapture *cap) -{ - return get_capture_frame_cv((cap_cv *)cap); -} -// ---------------------------------------- - -// only for using in image_opencv.cpp -int get_stream_fps_cpp(CvCapture *cap) -{ - return get_stream_fps_cpp_cv((cap_cv *)cap); -} // ==================================================================== // ... Video Capture // ==================================================================== -image get_image_from_stream(cap_cv *cap_src) -{ - CvCapture *cap = (CvCapture *)cap_src; - IplImage* src = cvQueryFrame(cap); - if (!src) return make_empty_image(0, 0, 0); - image im = ipl_to_image(src); - rgbgr_image(im); - return im; -} -// ---------------------------------------- -image get_image_from_stream_cpp(cap_cv *cap_src) +image get_image_from_stream_cpp(cap_cv *cap) { - CvCapture *cap = (CvCapture *)cap_src; - //IplImage* src = cvQueryFrame(cap); IplImage* src; static int once = 1; if (once) { once = 0; do { - src = get_capture_frame(cap); + src = get_capture_frame_cv(cap); if (!src) return make_empty_image(0, 0, 0); } while (src->width < 1 || src->height < 1 || src->nChannels < 1); printf("Video stream: %d x %d \n", src->width, src->height); } else - src = get_capture_frame(cap); + src = get_capture_frame_cv(cap); if (!src) return make_empty_image(0, 0, 0); image im = ipl_to_image(src); @@ -580,7 +598,7 @@ image get_image_from_stream_cpp(cap_cv *cap_src) } // ---------------------------------------- -int wait_for_stream(CvCapture *cap, IplImage* src, int dont_close) +int wait_for_stream(cap_cv *cap, IplImage* src, int dont_close) { if (!src) { if (dont_close) src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3); @@ -591,7 +609,7 @@ int wait_for_stream(CvCapture *cap, IplImage* src, int dont_close) cvReleaseImage(&src); int z = 0; for (z = 0; z < 20; ++z) { - get_capture_frame(cap); + get_capture_frame_cv(cap); cvReleaseImage(&src); } src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3); @@ -602,89 +620,65 @@ int wait_for_stream(CvCapture *cap, IplImage* src, int dont_close) } // ---------------------------------------- -image get_image_from_stream_resize(cap_cv *cap_src, int w, int h, int c, mat_cv** in_img, int cpp_video_capture, int dont_close) +image get_image_from_stream_resize(cap_cv *cap, int w, int h, int c, mat_cv** in_img, int dont_close) { - CvCapture *cap = (CvCapture *)cap_src; c = c ? c : 3; IplImage* src; - if (cpp_video_capture) { - static int once = 1; - if (once) { - once = 0; - do { - src = get_capture_frame(cap); - if (!src) return make_empty_image(0, 0, 0); - } while (src->width < 1 || src->height < 1 || src->nChannels < 1); - printf("Video stream: %d x %d \n", src->width, src->height); - } - else - src = get_capture_frame(cap); + + static int once = 1; + if (once) { + once = 0; + do { + src = get_capture_frame_cv(cap); + if (!src) return make_empty_image(0, 0, 0); + } while (src->width < 1 || src->height < 1 || src->nChannels < 1); + printf("Video stream: %d x %d \n", src->width, src->height); } - else src = cvQueryFrame(cap); + else + src = get_capture_frame_cv(cap); - if (cpp_video_capture) - if (!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0); + if (!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0); IplImage* new_img = cvCreateImage(cvSize(w, h), IPL_DEPTH_8U, c); *in_img = (mat_cv *)cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c); cvResize(src, *in_img, CV_INTER_LINEAR); cvResize(src, new_img, CV_INTER_LINEAR); image im = ipl_to_image(new_img); cvReleaseImage(&new_img); - if (cpp_video_capture) cvReleaseImage(&src); + cvReleaseImage(&src); if (c>1) rgbgr_image(im); return im; } // ---------------------------------------- -image get_image_from_stream_letterbox(cap_cv *cap_src, int w, int h, int c, mat_cv** in_img, int cpp_video_capture, int dont_close) +image get_image_from_stream_letterbox(cap_cv *cap, int w, int h, int c, mat_cv** in_img, int dont_close) { - CvCapture *cap = (CvCapture *)cap_src; c = c ? c : 3; IplImage* src; - if (cpp_video_capture) { - static int once = 1; - if (once) { - once = 0; - do { - src = get_capture_frame(cap); - if (!src) return make_empty_image(0, 0, 0); - } while (src->width < 1 || src->height < 1 || src->nChannels < 1); - printf("Video stream: %d x %d \n", src->width, src->height); - } - else - src = get_capture_frame(cap); + static int once = 1; + if (once) { + once = 0; + do { + src = get_capture_frame_cv(cap); + if (!src) return make_empty_image(0, 0, 0); + } while (src->width < 1 || src->height < 1 || src->nChannels < 1); + printf("Video stream: %d x %d \n", src->width, src->height); } - else src = cvQueryFrame(cap); + else + src = get_capture_frame_cv(cap); - if (cpp_video_capture) - if (!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0); + if (!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0); *in_img = (mat_cv *)cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c); cvResize(src, *in_img, CV_INTER_LINEAR); image tmp = ipl_to_image(src); image im = letterbox_image(tmp, w, h); free_image(tmp); - if (cpp_video_capture) cvReleaseImage(&src); + cvReleaseImage(&src); if (c>1) rgbgr_image(im); return im; } // ---------------------------------------- -int get_stream_fps(cap_cv *cap_src, int cpp_video_capture) -{ - CvCapture *cap = (CvCapture *)cap_src; - int fps = 25; - if (cpp_video_capture) { - fps = get_stream_fps_cpp(cap); - } - else { - fps = cvGetCaptureProperty(cap, CV_CAP_PROP_FPS); - } - return fps; -} -// ---------------------------------------- - - // ==================================================================== // Image Saving // ==================================================================== @@ -837,76 +831,6 @@ void draw_detections_cv_v3(mat_cv* show_img, detection *dets, int num, float thr } // ---------------------------------------- -void draw_detections_cv(mat_cv* show_img, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes) -{ - int i; - - for (i = 0; i < num; ++i) { - int class_id = max_index(probs[i], classes); - float prob = probs[i][class_id]; - if (prob > thresh) { - - int width = show_img->height * .012; - - if (0) { - width = pow(prob, 1. / 2.) * 10 + 1; - alphabet = 0; - } - - printf("%s: %.0f%%\n", names[class_id], prob * 100); - int offset = class_id * 123457 % classes; - float red = get_color(2, offset, classes); - float green = get_color(1, offset, classes); - float blue = get_color(0, offset, classes); - float rgb[3]; - - //width = prob*20+2; - - rgb[0] = red; - rgb[1] = green; - rgb[2] = blue; - box b = boxes[i]; - - int left = (b.x - b.w / 2.)*show_img->width; - int right = (b.x + b.w / 2.)*show_img->width; - int top = (b.y - b.h / 2.)*show_img->height; - int bot = (b.y + b.h / 2.)*show_img->height; - - if (left < 0) left = 0; - if (right > show_img->width - 1) right = show_img->width - 1; - if (top < 0) top = 0; - if (bot > show_img->height - 1) bot = show_img->height - 1; - - float const font_size = show_img->height / 1000.F; - CvPoint pt1, pt2, pt_text, pt_text_bg1, pt_text_bg2; - pt1.x = left; - pt1.y = top; - pt2.x = right; - pt2.y = bot; - pt_text.x = left; - pt_text.y = top - 12; - pt_text_bg1.x = left; - pt_text_bg1.y = top - (10 + 25 * font_size); - pt_text_bg2.x = right; - pt_text_bg2.y = top; - CvScalar color; - color.val[0] = red * 256; - color.val[1] = green * 256; - color.val[2] = blue * 256; - - cvRectangle(show_img, pt1, pt2, color, width, 8, 0); - //printf("left=%d, right=%d, top=%d, bottom=%d, obj_id=%d, obj=%s \n", left, right, top, bot, class_id, names[class_id]); - cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0); - cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0); // filled - CvScalar black_color; - black_color.val[0] = 0; - CvFont font; - cvInitFont(&font, CV_FONT_HERSHEY_SIMPLEX, font_size, font_size, 0, font_size * 3, 8); - cvPutText(show_img, names[class_id], pt_text, &font, black_color); - } - } -} - // ==================================================================== // Draw Loss & Accuracy chart // ==================================================================== @@ -1162,4 +1086,9 @@ void show_acnhors(int number_of_boxes, int num_of_clusters, float *rel_width_hei } // extern "C" + +#else // OPENCV +int wait_key_cv(int delay) { return 0; } +int wait_until_press_key_cv() { return 0; } +void destroy_all_windows_cv() {} #endif // OPENCV diff --git a/src/image_opencv.h b/src/image_opencv.h index a1fb421420b..bdfe8546f3a 100644 --- a/src/image_opencv.h +++ b/src/image_opencv.h @@ -40,7 +40,7 @@ void show_image_cv_ipl(mat_cv *disp, const char *name); // Video Writer write_cv *create_video_writer(char *out_filename, char c1, char c2, char c3, char c4, int fps, int width, int height, int is_color); -int write_frame_cv(write_cv *output_video_writer, mat_cv *show_img); +void write_frame_cv(write_cv *output_video_writer, mat_cv *show_img); void release_video_writer(write_cv **output_video_writer); @@ -62,11 +62,9 @@ int set_capture_property_cv(cap_cv *cap, int property_id, double value); int set_capture_position_frame_cv(cap_cv *cap, int index); // ... Video Capture -image get_image_from_stream(cap_cv *cap); image get_image_from_stream_cpp(cap_cv *cap); -image get_image_from_stream_resize(cap_cv *cap, int w, int h, int c, mat_cv** in_img, int cpp_video_capture, int dont_close); -image get_image_from_stream_letterbox(cap_cv *cap, int w, int h, int c, mat_cv** in_img, int cpp_video_capture, int dont_close); -int get_stream_fps(cap_cv *cap, int cpp_video_capture); +image get_image_from_stream_resize(cap_cv *cap, int w, int h, int c, mat_cv** in_img, int dont_close); +image get_image_from_stream_letterbox(cap_cv *cap, int w, int h, int c, mat_cv** in_img, int dont_close); // Image Saving @@ -75,7 +73,6 @@ void save_cv_jpg(mat_cv *img, const char *name); // Draw Detection void draw_detections_cv_v3(mat_cv* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output); -void draw_detections_cv(mat_cv* show_img, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes); // Draw Loss & Accuracy chart mat_cv* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show); @@ -90,11 +87,11 @@ image image_data_augmentation(mat_cv* ipl, int w, int h, // Show Anchors void show_acnhors(int number_of_boxes, int num_of_clusters, float *rel_width_height_array, model anchors_data, int width, int height); -#else +#else // OPENCV -static int wait_key_cv(int delay) { return 0; } -static int wait_until_press_key_cv() { return 0; } -static void destroy_all_windows_cv() {} +int wait_key_cv(int delay); +int wait_until_press_key_cv(); +void destroy_all_windows_cv(); #endif // OPENCV diff --git a/src/rnn_vid.c b/src/rnn_vid.c index ea3d64fce1e..1e9f6f12f57 100644 --- a/src/rnn_vid.c +++ b/src/rnn_vid.c @@ -163,7 +163,7 @@ void generate_vid_rnn(char *cfgfile, char *weightfile) next = NULL; image last; for(i = 0; i < 25; ++i){ - image im = get_image_from_stream(cap); + image im = get_image_from_stream_cpp(cap); image re = resize_image(im, extractor.w, extractor.h); feat = network_predict(extractor, re.data); if(i > 0){ diff --git a/src/voxel.c b/src/voxel.c index ea7442b98e0..9f50112be9a 100644 --- a/src/voxel.c +++ b/src/voxel.c @@ -13,8 +13,8 @@ void extract_voxel(char *lfile, char *rfile, char *prefix) cap_cv *lcap = get_capture_video_stream(lfile); cap_cv *rcap = get_capture_video_stream(rfile); while(1){ - image l = get_image_from_stream(lcap); - image r = get_image_from_stream(rcap); + image l = get_image_from_stream_cpp(lcap); + image r = get_image_from_stream_cpp(rcap); if(!l.w || !r.w) break; if(count%100 == 0) { shift = best_3d_shift_r(l, r, -l.h/100, l.h/100); From 4d6bba5e6901383c2763648ae34092c7f27ef49f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 5 Apr 2019 19:05:19 +0300 Subject: [PATCH 0440/1365] Compile fix --- src/image_opencv.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index e67e25426c3..cd5e929de8d 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -7,7 +7,7 @@ #include #include -#include +//#include #include #include #include From 33b854a63109a629a81d78d2ad6cfc471392658d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 5 Apr 2019 19:28:26 +0300 Subject: [PATCH 0441/1365] Another compile fix --- src/image_opencv.cpp | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index cd5e929de8d..3df8f5a35fc 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -7,15 +7,18 @@ #include #include -//#include #include +#include #include #include -#include -#include -#include -#include +#include +#include +#ifndef CV_VERSION_EPOCH +#include +#include +#include +#endif using namespace cv; @@ -239,7 +242,7 @@ image mat_to_image(Mat m) void create_window_cv(char const* window_name, int full_screen, int width, int height) { int window_type = WINDOW_NORMAL; - if (full_screen) window_type = WINDOW_FULLSCREEN; + if (full_screen) window_type = CV_WINDOW_FULLSCREEN; cv::namedWindow(window_name, window_type); cv::moveWindow(window_name, 0, 0); @@ -269,7 +272,7 @@ void make_window(char *name, int w, int h, int fullscreen) { cv::namedWindow(name, WINDOW_NORMAL); if (fullscreen) { - cv::setWindowProperty(name, WND_PROP_FULLSCREEN, WINDOW_FULLSCREEN); + cv::setWindowProperty(name, WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN); } else { cv::resizeWindow(name, w, h); From 4e47429d8257bc043f114c816b33de0d49793356 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 5 Apr 2019 21:21:13 +0300 Subject: [PATCH 0442/1365] Another part of OpenCV C-functions replaced by CPP-functions --- src/detector.c | 3 +- src/image_opencv.cpp | 164 +++++++++++++++++++++++-------------------- src/network.c | 4 +- src/network.h | 2 +- 4 files changed, 91 insertions(+), 82 deletions(-) diff --git a/src/detector.c b/src/detector.c index b52910f4f22..3cfe98800ae 100644 --- a/src/detector.c +++ b/src/detector.c @@ -209,7 +209,8 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i float loss = 0; #ifdef GPU if (ngpus == 1) { - loss = train_network_waitkey(net, train, 1); + int wait_key = (dont_show) ? 0 : 1; + loss = train_network_waitkey(net, train, wait_key); } else { loss = train_networks(nets, ngpus, train, 4); diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 3df8f5a35fc..079a88b3f29 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -214,6 +214,13 @@ cv::Mat ipl_to_mat(IplImage *ipl) return m; } +IplImage *mat_to_ipl(cv::Mat mat) +{ + IplImage *ipl = new IplImage; + *ipl = mat; + return ipl; +} + Mat image_to_mat(image im) { image copy = copy_image(im); @@ -335,13 +342,9 @@ void show_image_cv_ipl(mat_cv *disp, const char *name) { if (disp == NULL) return; char buff[256]; - //sprintf(buff, "%s (%d)", name, windows); sprintf(buff, "%s", name); cv::namedWindow(buff, WINDOW_NORMAL); - //cvMoveWindow(buff, 100*(windows%10) + 200*(windows/10), 100*(windows%10)); - //++windows; cvShowImage(buff, disp); - //cvReleaseImage(&disp); } @@ -688,6 +691,23 @@ image get_image_from_stream_letterbox(cap_cv *cap, int w, int h, int c, mat_cv** extern int stbi_write_png(char const *filename, int w, int h, int comp, const void *data, int stride_in_bytes); extern int stbi_write_jpg(char const *filename, int x, int y, int comp, const void *data, int quality); +void save_mat_png(cv::Mat img_src, const char *name) +{ + cv::Mat img_rgb; + cv::cvtColor(img_src, img_rgb, CV_RGB2BGR); + stbi_write_png(name, img_rgb.cols, img_rgb.rows, 3, (char *)img_rgb.data, 0); +} +// ---------------------------------------- + +void save_mat_jpg(cv::Mat img_src, const char *name) +{ + cv::Mat img_rgb; + cv::cvtColor(img_src, img_rgb, CV_RGB2BGR); + stbi_write_jpg(name, img_rgb.cols, img_rgb.rows, 3, (char *)img_rgb.data, 80); +} +// ---------------------------------------- + + void save_cv_png(mat_cv *img_src, const char *name) { IplImage* img = (IplImage* )img_src; @@ -841,79 +861,78 @@ mat_cv* draw_train_chart(float max_img_loss, int max_batches, int number_of_line { int img_offset = 50; int draw_size = img_size - img_offset; - IplImage* img = cvCreateImage(cvSize(img_size, img_size), 8, 3); - cvSet(img, CV_RGB(255, 255, 255), 0); - CvPoint pt1, pt2, pt_text; - CvFont font; - cvInitFont(&font, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA); + cv::Mat *img_ptr = new cv::Mat(img_size, img_size, CV_8UC3, CV_RGB(255, 255, 255)); + cv::Mat &img = *img_ptr; + cv::Point pt1, pt2, pt_text; + char char_buff[100]; int i; // vertical lines pt1.x = img_offset; pt2.x = img_size, pt_text.x = 10; for (i = 1; i <= number_of_lines; ++i) { pt1.y = pt2.y = (float)i * draw_size / number_of_lines; - cvLine(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0); + cv::line(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0); if (i % 10 == 0) { sprintf(char_buff, "%2.1f", max_img_loss*(number_of_lines - i) / number_of_lines); pt_text.y = pt1.y + 5; - cvPutText(img, char_buff, pt_text, &font, CV_RGB(0, 0, 0)); - cvLine(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0); + + cv::putText(img, char_buff, pt_text, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + cv::line(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0); } } // horizontal lines pt1.y = draw_size; pt2.y = 0, pt_text.y = draw_size + 15; for (i = 0; i <= number_of_lines; ++i) { pt1.x = pt2.x = img_offset + (float)i * draw_size / number_of_lines; - cvLine(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0); + cv::line(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0); if (i % 10 == 0) { sprintf(char_buff, "%d", max_batches * i / number_of_lines); pt_text.x = pt1.x - 20; - cvPutText(img, char_buff, pt_text, &font, CV_RGB(0, 0, 0)); - cvLine(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0); + cv::putText(img, char_buff, pt_text, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + cv::line(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0); } } - cvPutText(img, "Loss", cvPoint(0, 35), &font, CV_RGB(0, 0, 255)); - cvPutText(img, "Iteration number", cvPoint(draw_size / 2, img_size - 10), &font, CV_RGB(0, 0, 0)); + cv::putText(img, "Loss", cvPoint(0, 35), CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + cv::putText(img, "Iteration number", cvPoint(draw_size / 2, img_size - 10), CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); char max_batches_buff[100]; sprintf(max_batches_buff, "in cfg max_batches=%d", max_batches); - cvPutText(img, max_batches_buff, cvPoint(draw_size - 195, img_size - 10), &font, CV_RGB(0, 0, 0)); - cvPutText(img, "Press 's' to save: chart.png", cvPoint(5, img_size - 10), &font, CV_RGB(0, 0, 0)); + cv::putText(img, max_batches_buff, cvPoint(draw_size - 195, img_size - 10), CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + cv::putText(img, "Press 's' to save : chart.png", cvPoint(5, img_size - 10), CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); if (!dont_show) { printf(" If error occurs - run training with flag: -dont_show \n"); - cvNamedWindow("average loss", CV_WINDOW_NORMAL); - cvMoveWindow("average loss", 0, 0); - cvResizeWindow("average loss", img_size, img_size); - cvShowImage("average loss", img); - cvWaitKey(20); + cv::namedWindow("average loss", CV_WINDOW_NORMAL); + cv::moveWindow("average loss", 0, 0); + cv::resizeWindow("average loss", img_size, img_size); + cv::imshow("average loss", img); + cv::waitKey(20); } - return (mat_cv*)img; + return (mat_cv*)img_ptr; } // ---------------------------------------- void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port) { - IplImage* img = (IplImage*)img_src; + cv::Mat &img = *(cv::Mat*)img_src; int img_offset = 50; int draw_size = img_size - img_offset; - CvFont font; - cvInitFont(&font, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA); char char_buff[100]; CvPoint pt1, pt2; pt1.x = img_offset + draw_size * (float)current_batch / max_batches; pt1.y = draw_size * (1 - avg_loss / max_img_loss); if (pt1.y < 0) pt1.y = 1; - cvCircle(img, pt1, 1, CV_RGB(0, 0, 255), CV_FILLED, 8, 0); + cv::circle(img, pt1, 1, CV_RGB(0, 0, 255), CV_FILLED, 8, 0); // precision if (draw_precision) { static float old_precision = 0; static int iteration_old = 0; static int text_iteration_old = 0; - if (iteration_old == 0) cvPutText(img, accuracy_name, cvPoint(0, 12), &font, CV_RGB(255, 0, 0)); + if (iteration_old == 0) + cv::putText(img, accuracy_name, cvPoint(0, 12), CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 0, 0), 1, CV_AA); - cvLine(img, + cv::line(img, cvPoint(img_offset + draw_size * (float)iteration_old / max_batches, draw_size * (1 - old_precision)), cvPoint(img_offset + draw_size * (float)current_batch / max_batches, draw_size * (1 - precision)), CV_RGB(255, 0, 0), 1, 8, 0); @@ -921,13 +940,9 @@ void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_im if (((int)(old_precision * 10) != (int)(precision * 10)) || (current_batch - text_iteration_old) >= max_batches / 10) { text_iteration_old = current_batch; sprintf(char_buff, "%2.0f%% ", precision * 100); - CvFont font3; - cvInitFont(&font3, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 5, CV_AA); - cvPutText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), &font3, CV_RGB(255, 255, 255)); + cv::putText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 5, CV_AA); - CvFont font2; - cvInitFont(&font2, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, 0.7, 0, 1, CV_AA); - cvPutText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), &font2, CV_RGB(200, 0, 0)); + cv::putText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(200, 0, 0), 1, CV_AA); } old_precision = precision; iteration_old = current_batch; @@ -936,23 +951,23 @@ void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_im sprintf(char_buff, "current avg loss = %2.4f iteration = %d", avg_loss, current_batch); pt1.x = 55, pt1.y = 10; pt2.x = pt1.x + 460, pt2.y = pt1.y + 20; - cvRectangle(img, pt1, pt2, CV_RGB(255, 255, 255), CV_FILLED, 8, 0); + cv::rectangle(img, pt1, pt2, CV_RGB(255, 255, 255), CV_FILLED, 8, 0); pt1.y += 15; - cvPutText(img, char_buff, pt1, &font, CV_RGB(0, 0, 0)); + cv::putText(img, char_buff, pt1, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); int k = 0; if (!dont_show) { - cvShowImage("average loss", img); - k = cvWaitKey(20); + cv::imshow("average loss", img); + k = cv::waitKey(20); } if (k == 's' || current_batch == (max_batches - 1) || current_batch % 100 == 0) { - save_cv_png((mat_cv *)img, "chart.png"); - cvPutText(img, "- Saved", cvPoint(250, img_size - 10), &font, CV_RGB(255, 0, 0)); + save_mat_png(img, "chart.png"); + cv::putText(img, "- Saved", cvPoint(260, img_size - 10), CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 0, 0), 1, CV_AA); } else - cvPutText(img, "- Saved", cvPoint(250, img_size - 10), &font, CV_RGB(255, 255, 255)); + cv::putText(img, "- Saved", cvPoint(260, img_size - 10), CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 1, CV_AA); - if (mjpeg_port > 0) send_mjpeg((mat_cv *)img, mjpeg_port, 500000, 100); + if (mjpeg_port > 0) send_mjpeg((mat_cv *)mat_to_ipl(img), mjpeg_port, 500000, 100); } // ---------------------------------------- @@ -1034,57 +1049,50 @@ image image_data_augmentation(mat_cv* ipl, int w, int h, // ==================================================================== void show_acnhors(int number_of_boxes, int num_of_clusters, float *rel_width_height_array, model anchors_data, int width, int height) { - CvMat* labels = cvCreateMat(number_of_boxes, 1, CV_32SC1); - CvMat* points = cvCreateMat(number_of_boxes, 2, CV_32FC1); - CvMat* centers = cvCreateMat(num_of_clusters, 2, CV_32FC1); + cv::Mat labels = cv::Mat(number_of_boxes, 1, CV_32SC1); + cv::Mat points = cv::Mat(number_of_boxes, 2, CV_32FC1); + cv::Mat centers = cv::Mat(num_of_clusters, 2, CV_32FC1); - int i, j; - for (i = 0; i < number_of_boxes; ++i) { - points->data.fl[i * 2] = rel_width_height_array[i * 2]; - points->data.fl[i * 2 + 1] = rel_width_height_array[i * 2 + 1]; - //cvSet1D(points, i * 2, cvScalar(rel_width_height_array[i * 2], 0, 0, 0)); - //cvSet1D(points, i * 2 + 1, cvScalar(rel_width_height_array[i * 2 + 1], 0, 0, 0)); + for (int i = 0; i < number_of_boxes; ++i) { + points.at(i, 0) = rel_width_height_array[i * 2]; + points.at(i, 1) = rel_width_height_array[i * 2 + 1]; } - for (i = 0; i < num_of_clusters; ++i) { - centers->data.fl[i * 2] = anchors_data.centers.vals[i][0]; - centers->data.fl[i * 2 + 1] = anchors_data.centers.vals[i][1]; + for (int i = 0; i < num_of_clusters; ++i) { + centers.at(i, 0) = anchors_data.centers.vals[i][0]; + centers.at(i, 1) = anchors_data.centers.vals[i][1]; } - for (i = 0; i < number_of_boxes; ++i) { - labels->data.i[i] = anchors_data.assignments[i]; + for (int i = 0; i < number_of_boxes; ++i) { + labels.at(i, 0) = anchors_data.assignments[i]; } size_t img_size = 700; - IplImage* img = cvCreateImage(cvSize(img_size, img_size), 8, 3); - cvZero(img); - for (i = 0; i < number_of_boxes; ++i) { + cv::Mat img = cv::Mat(img_size, img_size, CV_8UC3); + + for (int i = 0; i < number_of_boxes; ++i) { CvPoint pt; - pt.x = points->data.fl[i * 2] * img_size / width; - pt.y = points->data.fl[i * 2 + 1] * img_size / height; - int cluster_idx = labels->data.i[i]; + pt.x = points.at(i, 0) * img_size / width; + pt.y = points.at(i, 1) * img_size / height; + int cluster_idx = labels.at(i, 0); int red_id = (cluster_idx * (uint64_t)123 + 55) % 255; int green_id = (cluster_idx * (uint64_t)321 + 33) % 255; int blue_id = (cluster_idx * (uint64_t)11 + 99) % 255; - cvCircle(img, pt, 1, CV_RGB(red_id, green_id, blue_id), CV_FILLED, 8, 0); + cv::circle(img, pt, 1, CV_RGB(red_id, green_id, blue_id), CV_FILLED, 8, 0); //if(pt.x > img_size || pt.y > img_size) printf("\n pt.x = %d, pt.y = %d \n", pt.x, pt.y); } - for (j = 0; j < num_of_clusters; ++j) { + for (int j = 0; j < num_of_clusters; ++j) { CvPoint pt1, pt2; pt1.x = pt1.y = 0; - pt2.x = centers->data.fl[j * 2] * img_size / width; - pt2.y = centers->data.fl[j * 2 + 1] * img_size / height; - cvRectangle(img, pt1, pt2, CV_RGB(255, 255, 255), 1, 8, 0); + pt2.x = centers.at(j, 0) * img_size / width; + pt2.y = centers.at(j, 1) * img_size / height; + cv::rectangle(img, pt1, pt2, CV_RGB(255, 255, 255), 1, 8, 0); } - save_cv_png((mat_cv *)img, "cloud.png"); - cvShowImage("clusters", img); - cvWaitKey(0); - cvReleaseImage(&img); - cvDestroyAllWindows(); - cvReleaseMat(&labels); - cvReleaseMat(&points); - cvReleaseMat(¢ers); + save_mat_png(img, "cloud.png"); + cv::imshow("clusters", img); + cv::waitKey(0); + cv::destroyAllWindows(); } } // extern "C" diff --git a/src/network.c b/src/network.c index df2dea96d5e..89d97b450c4 100644 --- a/src/network.c +++ b/src/network.c @@ -326,7 +326,7 @@ float train_network(network net, data d) return train_network_waitkey(net, d, 0); } -float train_network_waitkey(network net, data d, int is_main_thread) +float train_network_waitkey(network net, data d, int wait_key) { assert(d.X.rows % net.batch == 0); int batch = net.batch; @@ -340,7 +340,7 @@ float train_network_waitkey(network net, data d, int is_main_thread) get_next_batch(d, batch, i*batch, X, y); float err = train_network_datum(net, X, y); sum += err; - wait_key_cv(5); + if(wait_key) wait_key_cv(5); } free(X); free(y); diff --git a/src/network.h b/src/network.h index 222aca39211..c4138c74c45 100644 --- a/src/network.h +++ b/src/network.h @@ -116,7 +116,7 @@ void backward_network(network net, network_state state); void update_network(network net); float train_network(network net, data d); -float train_network_waitkey(network net, data d, int is_main_thread); +float train_network_waitkey(network net, data d, int wait_key); float train_network_batch(network net, data d, int n); float train_network_sgd(network net, data d, int n); float train_network_datum(network net, float *x, float *y); From 811f772b45e9db4db25ee4a1915076020233852a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 6 Apr 2019 01:36:06 +0300 Subject: [PATCH 0443/1365] 3rd part of OpenCV C-functions replaced by CPP-functions --- src/data.c | 17 +-- src/image_opencv.cpp | 353 +++++++++++++++++++++++++++---------------- src/image_opencv.h | 24 ++- src/rnn_vid.c | 2 +- 4 files changed, 243 insertions(+), 153 deletions(-) diff --git a/src/data.c b/src/data.c index e46395663c5..29c65d72843 100644 --- a/src/data.c +++ b/src/data.c @@ -794,19 +794,14 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo int flag = (c >= 3); mat_cv *src; - if ((src = load_image_mat_cv(filename, flag)) == 0) - { - fprintf(stderr, "Cannot load image \"%s\"\n", filename); - char buff[256]; - sprintf(buff, "echo %s >> bad.list", filename); - system(buff); + src = load_image_mat_cv(filename, flag); + if (src == NULL) { if (check_mistakes) getchar(); continue; - //exit(0); } - int oh = get_height_cv(src); - int ow = get_width_cv(src); + int oh = get_height_mat(src); + int ow = get_width_mat(src); int dw = (ow*jitter); int dh = (oh*jitter); @@ -848,7 +843,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo if(show_imgs) { char buff[1000]; - sprintf(buff, "aug_%s_%d", random_paths[i], random_gen()); + sprintf(buff, "aug_%s_%d", basecfg(random_paths[i]), random_gen()); int t; for (t = 0; t < boxes; ++t) { box b = float_to_box_stride(d.y.vals[i] + t*(4 + 1), 1); @@ -866,7 +861,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo printf("\nYou use flag -show_imgs, so will be saved aug_...jpg images. Click on window and press ESC button \n"); } - release_ipl(&src); + release_mat(&src); } free(random_paths); return d; diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 079a88b3f29..70dd3387c3d 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -3,9 +3,13 @@ #ifdef OPENCV #include "utils.h" -#include -#include -#include +#include +#include +#include +#include +#include +#include +#include #include #include @@ -63,76 +67,96 @@ extern "C" { struct write_cv : cv::VideoWriter { int a[0]; }; // ==================================================================== -// cv::Mat / IplImage +// cv::Mat // ==================================================================== -image ipl_to_image(IplImage* src); + image mat_to_image(cv::Mat mat); + cv::Mat image_to_mat(image img); + image ipl_to_image(mat_cv* src); + mat_cv *image_to_ipl(image img); + cv::Mat ipl_to_mat(IplImage *ipl); + IplImage *mat_to_ipl(cv::Mat mat); -image load_image_cv(char *filename, int channels) + +mat_cv *load_image_mat_cv(const char *filename, int flag) { - IplImage* src = 0; - int flag = -1; - if (channels == 0) flag = 1; - else if (channels == 1) flag = 0; - else if (channels == 3) flag = 1; + try { + cv::Mat *mat_ptr = new cv::Mat(); + cv::Mat &mat = *mat_ptr; + mat = cv::imread(filename, flag); + if (mat.empty()) + { + delete mat_ptr; + std::string shrinked_filename = filename; + if (shrinked_filename.length() > 1024) { + shrinked_filename += "name is too long: "; + shrinked_filename.resize(1024); + } + cerr << "Cannot load image " << shrinked_filename << std::endl; + std::ofstream bad_list("bad.list", std::ios::out | std::ios::app); + bad_list << shrinked_filename << std::endl; + //if (check_mistakes) getchar(); + delete mat_ptr; + return NULL; + } + cv::cvtColor(mat, mat, CV_RGB2BGR); + + return (mat_cv *)mat_ptr; + } + catch (...) { + cerr << "OpenCV exception: load_image_mat_cv \n"; + } + return NULL; +} +// ---------------------------------------- + +cv::Mat load_image_mat(char *filename, int channels) +{ + int flag = IMREAD_UNCHANGED; + if (channels == 0) flag = IMREAD_COLOR; + else if (channels == 1) flag = IMREAD_GRAYSCALE; + else if (channels == 3) flag = IMREAD_COLOR; else { fprintf(stderr, "OpenCV can't force load with %d channels\n", channels); } + //flag |= IMREAD_IGNORE_ORIENTATION; // un-comment it if you want - if ((src = cvLoadImage(filename, flag)) == 0) - { - char shrinked_filename[1024]; - if (strlen(filename) >= 1024) sprintf(shrinked_filename, "name is too long"); - else sprintf(shrinked_filename, "%s", filename); - fprintf(stderr, "Cannot load image \"%s\"\n", shrinked_filename); - FILE* fw = fopen("bad.list", "a"); - fwrite(shrinked_filename, sizeof(char), strlen(shrinked_filename), fw); - char *new_line = "\n"; - fwrite(new_line, sizeof(char), strlen(new_line), fw); - fclose(fw); - //if (check_mistakes) getchar(); - return make_image(10, 10, 3); - //exit(EXIT_FAILURE); + cv::Mat *mat_ptr = (cv::Mat *)load_image_mat_cv(filename, flag); + + if (mat_ptr == NULL) { + return cv::Mat(); } - image out = ipl_to_image(src); - cvReleaseImage(&src); - if (out.c > 1) - rgbgr_image(out); - return out; + cv::Mat mat = *mat_ptr; + delete mat_ptr; + + return mat; } // ---------------------------------------- -mat_cv *load_image_mat_cv(const char *filename, int flag) +image load_image_cv(char *filename, int channels) { - return (mat_cv *)cvLoadImage(filename, flag); + cv::Mat mat = load_image_mat(filename, channels); + + if (mat.empty()) { + return make_image(10, 10, 3); + } + return mat_to_image(mat); } // ---------------------------------------- - image load_image_resize(char *filename, int w, int h, int c, image *im) { image out; - cv::Mat img(h, w, CV_8UC3); try { - int flag = -1; - if (c == 0) flag = 1; - else if (c == 1) { flag = 0; img = cv::Mat(h, w, CV_8UC1); } - else if (c == 3) { flag = 1; img = cv::Mat(h, w, CV_8UC3); } - else { - cerr << "OpenCV can't force load with " << c << " channels\n"; - } - //throw std::runtime_error("runtime_error"); - cv::Mat loaded_image = cv::imread(filename, flag); - cv::cvtColor(loaded_image, loaded_image, cv::COLOR_RGB2BGR); - IplImage tmp1 = loaded_image; - *im = ipl_to_image(&tmp1); + cv::Mat loaded_image = load_image_mat(filename, c); - cv::resize(loaded_image, img, cv::Size(w, h), 0, 0, CV_INTER_LINEAR); + *im = mat_to_image(loaded_image); - IplImage tmp2 = img; - out = ipl_to_image(&tmp2); + cv::Mat resized(h, w, CV_8UC3); + cv::resize(loaded_image, resized, cv::Size(w, h), 0, 0, CV_INTER_LINEAR); + out = mat_to_image(resized); } catch (...) { - cerr << "OpenCV can't load image %s " << filename << " \n"; + cerr << " OpenCV exception: load_image_resize() can't load image %s " << filename << " \n"; out = make_image(w, h, c); *im = make_image(w, h, c); } @@ -140,6 +164,42 @@ image load_image_resize(char *filename, int w, int h, int c, image *im) } // ---------------------------------------- +int get_width_mat(mat_cv *mat) +{ + if (mat == NULL) { + cerr << " Pointer is NULL in get_width_mat() \n"; + return 0; + } + return ((cv::Mat *)mat)->cols; +} +// ---------------------------------------- + +int get_height_mat(mat_cv *mat) +{ + if (mat == NULL) { + cerr << " Pointer is NULL in get_height_mat() \n"; + return 0; + } + return ((cv::Mat *)mat)->rows; +} +// ---------------------------------------- + +void release_mat(mat_cv **mat) +{ + try { + cv::Mat **mat_ptr = (cv::Mat **)mat; + if (*mat_ptr) delete *mat_ptr; + *mat_ptr = NULL; + } + catch (...) { + cerr << "OpenCV exception: release_mat \n"; + } +} + +// ==================================================================== +// IplImage +// ==================================================================== + int get_width_cv(mat_cv *ipl_src) { IplImage *ipl = (IplImage *)ipl_src; @@ -149,7 +209,6 @@ int get_width_cv(mat_cv *ipl_src) int get_height_cv(mat_cv *ipl) { - //IplImage *ipl = (IplImage *)ipl_src; return ipl->height; } // ---------------------------------------- @@ -160,9 +219,10 @@ void release_ipl(mat_cv **ipl) if (*ipl_img) cvReleaseImage(ipl_img); *ipl_img = NULL; } +// ---------------------------------------- // ==================================================================== -// image-to-ipl, ipl-to-image +// image-to-ipl, ipl-to-image, image_to_mat, mat_to_image // ==================================================================== mat_cv *image_to_ipl(image im) { @@ -181,8 +241,9 @@ mat_cv *image_to_ipl(image im) } // ---------------------------------------- -image ipl_to_image(IplImage* src) +image ipl_to_image(mat_cv* src_ptr) { + IplImage* src = (IplImage*)src_ptr; int h = src->height; int w = src->width; int c = src->nChannels; @@ -202,17 +263,12 @@ image ipl_to_image(IplImage* src) } // ---------------------------------------- -image ipl_to_image_custom(mat_cv* src) -{ - return ipl_to_image(src); -} -// ---------------------------------------- - cv::Mat ipl_to_mat(IplImage *ipl) { Mat m = cvarrToMat(ipl, true); return m; } +// ---------------------------------------- IplImage *mat_to_ipl(cv::Mat mat) { @@ -220,26 +276,47 @@ IplImage *mat_to_ipl(cv::Mat mat) *ipl = mat; return ipl; } +// ---------------------------------------- -Mat image_to_mat(image im) +Mat image_to_mat(image img) { - image copy = copy_image(im); - constrain_image(copy); - if (im.c == 3) rgbgr_image(copy); - - IplImage *ipl = image_to_ipl(copy); - Mat m = cvarrToMat(ipl, true); - cvReleaseImage(&ipl); - free_image(copy); - return m; + int channels = img.c; + int width = img.w; + int height = img.h; + cv::Mat mat = cv::Mat(height, width, CV_8UC(channels)); + int step = mat.step; + + for (int y = 0; y < img.h; ++y) { + for (int x = 0; x < img.w; ++x) { + for (int c = 0; c < img.c; ++c) { + float val = img.data[c*img.h*img.w + y*img.w + x]; + mat.data[y*step + x*img.c + c] = (unsigned char)(val * 255); + } + } + } + return mat; } // ---------------------------------------- -image mat_to_image(Mat m) +image mat_to_image(cv::Mat mat) { - IplImage ipl = m; - image im = ipl_to_image((mat_cv *)&ipl); - rgbgr_image(im); + int w = mat.cols; + int h = mat.rows; + int c = mat.channels(); + image im = make_image(w, h, c); + unsigned char *data = (unsigned char *)mat.data; + int step = mat.step; + for (int y = 0; y < h; ++y) { + for (int k = 0; k < c; ++k) { + for (int x = 0; x < w; ++x) { + //uint8_t val = mat.ptr(y)[c * x + k]; + //uint8_t val = mat.at(y, x).val[k]; + //im.data[k*w*h + y*w + x] = val / 255.0f; + + im.data[k*w*h + y*w + x] = data[y*step + x*c + k] / 255.0f; + } + } + } return im; } @@ -248,24 +325,40 @@ image mat_to_image(Mat m) // ==================================================================== void create_window_cv(char const* window_name, int full_screen, int width, int height) { - int window_type = WINDOW_NORMAL; - if (full_screen) window_type = CV_WINDOW_FULLSCREEN; + try { + int window_type = WINDOW_NORMAL; + if (full_screen) window_type = CV_WINDOW_FULLSCREEN; - cv::namedWindow(window_name, window_type); - cv::moveWindow(window_name, 0, 0); - cv::resizeWindow(window_name, width, height); + cv::namedWindow(window_name, window_type); + cv::moveWindow(window_name, 0, 0); + cv::resizeWindow(window_name, width, height); + } + catch (...) { + cerr << "OpenCV exception: create_window_cv \n"; + } } // ---------------------------------------- void destroy_all_windows_cv() { - cv::destroyAllWindows(); + try { + cv::destroyAllWindows(); + } + catch (...) { + cerr << "OpenCV exception: destroy_all_windows_cv \n"; + } } // ---------------------------------------- int wait_key_cv(int delay) { - return cv::waitKey(delay); + try { + return cv::waitKey(delay); + } + catch (...) { + cerr << "OpenCV exception: wait_key_cv \n"; + } + return -1; } // ---------------------------------------- @@ -277,13 +370,18 @@ int wait_until_press_key_cv() void make_window(char *name, int w, int h, int fullscreen) { - cv::namedWindow(name, WINDOW_NORMAL); - if (fullscreen) { - cv::setWindowProperty(name, WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN); + try { + cv::namedWindow(name, WINDOW_NORMAL); + if (fullscreen) { + cv::setWindowProperty(name, WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN); + } + else { + cv::resizeWindow(name, w, h); + if (strcmp(name, "Demo") == 0) cv::moveWindow(name, 0, 0); + } } - else { - cv::resizeWindow(name, w, h); - if (strcmp(name, "Demo") == 0) cv::moveWindow(name, 0, 0); + catch (...) { + cerr << "OpenCV exception: make_window \n"; } } // ---------------------------------------- @@ -297,44 +395,19 @@ static float get_pixel(image m, int x, int y, int c) void show_image_cv(image p, const char *name) { - int x, y, k; - image copy = copy_image(p); - constrain_image(copy); - if (p.c == 3) rgbgr_image(copy); - //normalize_image(copy); - - char buff[256]; - //sprintf(buff, "%s (%d)", name, windows); - sprintf(buff, "%s", name); - - IplImage *disp = cvCreateImage(cvSize(p.w, p.h), IPL_DEPTH_8U, p.c); - int step = disp->widthStep; - cvNamedWindow(buff, CV_WINDOW_NORMAL); - //cvMoveWindow(buff, 100*(windows%10) + 200*(windows/10), 100*(windows%10)); - //++windows; - for (y = 0; y < p.h; ++y) { - for (x = 0; x < p.w; ++x) { - for (k = 0; k < p.c; ++k) { - disp->imageData[y*step + x*p.c + k] = (unsigned char)(get_pixel(copy, x, y, k) * 255); - } - } + try { + image copy = copy_image(p); + constrain_image(copy); + + cv::Mat mat = image_to_mat(copy); + cv::cvtColor(mat, mat, CV_RGB2BGR); + cv::namedWindow(name, WINDOW_NORMAL); + cv::imshow(name, mat); + free_image(copy); } - free_image(copy); - if (0) { - int w = 448; - int h = w*p.h / p.w; - if (h > 1000) { - h = 1000; - w = h*p.w / p.h; - } - IplImage *buffer = disp; - disp = cvCreateImage(cvSize(w, h), buffer->depth, buffer->nChannels); - cvResize(buffer, disp, CV_INTER_LINEAR); - cvReleaseImage(&buffer); + catch (...) { + cerr << "OpenCV exception: show_image_cv \n"; } - cvShowImage(buff, disp); - - cvReleaseImage(&disp); } // ---------------------------------------- @@ -346,7 +419,20 @@ void show_image_cv_ipl(mat_cv *disp, const char *name) cv::namedWindow(buff, WINDOW_NORMAL); cvShowImage(buff, disp); } +// ---------------------------------------- +void show_image_mat(mat_cv *mat_ptr, const char *name) +{ + try { + if (mat_ptr == NULL) return; + cv::Mat &mat = *(cv::Mat *)mat_ptr; + cv::namedWindow(name, WINDOW_NORMAL); + cv::imshow(name, mat); + } + catch (...) { + cerr << "OpenCV exception: show_image_mat \n"; + } +} // ==================================================================== // Video Writer @@ -598,7 +684,7 @@ image get_image_from_stream_cpp(cap_cv *cap) src = get_capture_frame_cv(cap); if (!src) return make_empty_image(0, 0, 0); - image im = ipl_to_image(src); + image im = ipl_to_image((mat_cv*)src); rgbgr_image(im); return im; } @@ -648,7 +734,7 @@ image get_image_from_stream_resize(cap_cv *cap, int w, int h, int c, mat_cv** in *in_img = (mat_cv *)cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c); cvResize(src, *in_img, CV_INTER_LINEAR); cvResize(src, new_img, CV_INTER_LINEAR); - image im = ipl_to_image(new_img); + image im = ipl_to_image((mat_cv*)new_img); cvReleaseImage(&new_img); cvReleaseImage(&src); if (c>1) @@ -676,7 +762,7 @@ image get_image_from_stream_letterbox(cap_cv *cap, int w, int h, int c, mat_cv** if (!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0); *in_img = (mat_cv *)cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c); cvResize(src, *in_img, CV_INTER_LINEAR); - image tmp = ipl_to_image(src); + image tmp = ipl_to_image((mat_cv*)src); image im = letterbox_image(tmp, w, h); free_image(tmp); cvReleaseImage(&src); @@ -975,13 +1061,13 @@ void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_im // ==================================================================== // Data augmentation // ==================================================================== -image image_data_augmentation(mat_cv* ipl, int w, int h, +image image_data_augmentation(mat_cv* mat, int w, int h, int pleft, int ptop, int swidth, int sheight, int flip, float jitter, float dhue, float dsat, float dexp) { image out; try { - cv::Mat img = cv::cvarrToMat(ipl); + cv::Mat img = *(cv::Mat *)mat; // crop cv::Rect src_rect(pleft, ptop, swidth, sheight); @@ -1007,10 +1093,10 @@ image image_data_augmentation(mat_cv* ipl, int w, int h, // HSV augmentation // CV_BGR2HSV, CV_RGB2HSV, CV_HSV2BGR, CV_HSV2RGB - if (ipl->nChannels >= 3) + if(img.channels() >= 3) { cv::Mat hsv_src; - cvtColor(sized, hsv_src, CV_BGR2HSV); // also BGR -> RGB + cvtColor(sized, hsv_src, CV_RGB2HSV); // RGB to HSV std::vector hsv; cv::split(hsv_src, hsv); @@ -1021,7 +1107,7 @@ image image_data_augmentation(mat_cv* ipl, int w, int h, cv::merge(hsv, hsv_src); - cvtColor(hsv_src, sized, CV_HSV2RGB); // now RGB instead of BGR + cvtColor(hsv_src, sized, CV_HSV2RGB); // HSV to RGB (the same as previous) } else { @@ -1033,13 +1119,12 @@ image image_data_augmentation(mat_cv* ipl, int w, int h, //cv::imshow(window_name.str(), sized); //cv::waitKey(0); - // Mat -> IplImage -> image - IplImage src = sized; - out = ipl_to_image(&src); + // Mat -> image + out = mat_to_image(sized); } catch (...) { cerr << "OpenCV can't augment image: " << w << " x " << h << " \n"; - out = ipl_to_image(ipl); + out = mat_to_image(*(cv::Mat*)mat); } return out; } diff --git a/src/image_opencv.h b/src/image_opencv.h index bdfe8546f3a..e38773d5dd9 100644 --- a/src/image_opencv.h +++ b/src/image_opencv.h @@ -15,19 +15,28 @@ typedef struct mat_cv mat_cv; typedef struct cap_cv cap_cv; typedef struct write_cv write_cv; -// cv::Mat / IplImage -image load_image_cv(char *filename, int channels); +// cv::Mat mat_cv *load_image_mat_cv(const char *filename, int flag); +image load_image_cv(char *filename, int channels); image load_image_resize(char *filename, int w, int h, int c, image *im); +int get_width_mat(mat_cv *mat); +int get_height_mat(mat_cv *mat); +void release_mat(mat_cv **mat); + +// IplImage int get_width_cv(mat_cv *ipl); int get_height_cv(mat_cv *ipl); void release_ipl(mat_cv **ipl); -// image-to-ipl, ipl-to-image +// image-to-ipl, ipl-to-image, image_to_mat, mat_to_image mat_cv *image_to_ipl(image im); -image ipl_to_image_custom(mat_cv* src); -// Mat image_to_mat(image im); -// image mat_to_image(Mat m) +image ipl_to_image(mat_cv* src_ptr); +// mat_cv *image_to_ipl(image im) +// image ipl_to_image(mat_cv* src_ptr) +// cv::Mat ipl_to_mat(IplImage *ipl) +// IplImage *mat_to_ipl(cv::Mat mat) +// Mat image_to_mat(image img) +// image mat_to_image(cv::Mat mat) // Window void create_window_cv(char const* window_name, int full_screen, int width, int height); @@ -37,6 +46,7 @@ int wait_until_press_key_cv(); void make_window(char *name, int w, int h, int fullscreen); void show_image_cv(image p, const char *name); void show_image_cv_ipl(mat_cv *disp, const char *name); +void show_image_mat(mat_cv *mat_ptr, const char *name); // Video Writer write_cv *create_video_writer(char *out_filename, char c1, char c2, char c3, char c4, int fps, int width, int height, int is_color); @@ -80,7 +90,7 @@ void draw_train_loss(mat_cv* img, int img_size, float avg_loss, float max_img_lo float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port); // Data augmentation -image image_data_augmentation(mat_cv* ipl, int w, int h, +image image_data_augmentation(mat_cv* mat, int w, int h, int pleft, int ptop, int swidth, int sheight, int flip, float jitter, float dhue, float dsat, float dexp); diff --git a/src/rnn_vid.c b/src/rnn_vid.c index 1e9f6f12f57..798a393fd7b 100644 --- a/src/rnn_vid.c +++ b/src/rnn_vid.c @@ -40,7 +40,7 @@ float_pair get_rnn_vid_data(network net, char **files, int n, int batch, int ste int i; for(i = 0; i < net.batch; ++i){ mat_cv *src = get_capture_frame_cv(cap); - image im = ipl_to_image_custom(src); + image im = ipl_to_image(src); rgbgr_image(im); image re = resize_image(im, net.w, net.h); //show_image(re, "loaded"); From 97679224ba9ccaa6b181d4933b2c7c3fbf4a8f04 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 6 Apr 2019 04:05:31 +0300 Subject: [PATCH 0444/1365] All OpenCV C-functions replaced by CPP-functions --- src/classifier.c | 2 +- src/demo.c | 14 ++-- src/detector.c | 2 +- src/http_stream.cpp | 8 +-- src/http_stream.h | 2 +- src/image_opencv.cpp | 156 ++++++++++++++++++++++++------------------- src/image_opencv.h | 17 +++-- src/rnn_vid.c | 2 +- 8 files changed, 114 insertions(+), 89 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index 9773e2713fe..f9708e44c70 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -196,7 +196,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, save_weights(net, buff); #ifdef OPENCV - release_ipl(&img); + release_mat(&img); destroy_all_windows_cv(); #endif diff --git a/src/demo.c b/src/demo.c index 5c19769ee44..8219de53e3c 100644 --- a/src/demo.c +++ b/src/demo.c @@ -48,7 +48,7 @@ mat_cv* det_img; mat_cv* show_img; static volatile int flag_exit; -static volatile int letter_box = 0; +static const int letter_box = 0; void *fetch_in_thread(void *ptr) { @@ -85,7 +85,7 @@ void *detect_in_thread(void *ptr) demo_index = (demo_index + 1) % NFRAMES; if (letter_box) - dets = get_network_boxes(&net, get_width_cv(in_img), get_height_cv(in_img), demo_thresh, demo_thresh, 0, 1, &nboxes, 1); // letter box + dets = get_network_boxes(&net, get_width_mat(in_img), get_height_mat(in_img), demo_thresh, demo_thresh, 0, 1, &nboxes, 1); // letter box else dets = get_network_boxes(&net, net.w, net.h, demo_thresh, demo_thresh, 0, 1, &nboxes, 0); // resized @@ -186,7 +186,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int int src_fps = 25; src_fps = get_stream_fps_cpp_cv(cap); output_video_writer = - create_video_writer(out_filename, 'D', 'I', 'V', 'X', src_fps, get_width_cv(det_img), get_height_cv(det_img), 1); + create_video_writer(out_filename, 'D', 'I', 'V', 'X', src_fps, get_width_mat(det_img), get_height_mat(det_img), 1); //'H', '2', '6', '4' //'D', 'I', 'V', 'X' @@ -230,7 +230,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int if(!prefix){ if (!dont_show) { - show_image_cv_ipl(show_img, "Demo"); + show_image_mat(show_img, "Demo"); int c = wait_key_cv(1); if (c == 10) { if (frame_skip == 0) frame_skip = 60; @@ -263,7 +263,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int printf("\n cvWriteFrame \n"); } - release_ipl(&show_img); + release_mat(&show_img); pthread_join(fetch_thread, 0); pthread_join(detect_thread, 0); @@ -295,8 +295,8 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int } // free memory - release_ipl(&show_img); - release_ipl(&in_img); + release_mat(&show_img); + release_mat(&in_img); free_image(in_s); free(avg); diff --git a/src/detector.c b/src/detector.c index 3cfe98800ae..3f980adddac 100644 --- a/src/detector.c +++ b/src/detector.c @@ -301,7 +301,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i save_weights(net, buff); #ifdef OPENCV - release_ipl(&img); + release_mat(&img); destroy_all_windows_cv(); #endif diff --git a/src/http_stream.cpp b/src/http_stream.cpp index ec9d115d9ab..550beb783f0 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -489,14 +489,14 @@ class MJPG_sender }; // ---------------------------------------- -struct mat_cv :IplImage { int a[0]; }; +struct mat_cv : cv::Mat { int a[0]; }; -void send_mjpeg(mat_cv* ipl, int port, int timeout, int quality) +void send_mjpeg(mat_cv* mat, int port, int timeout, int quality) { try { static MJPG_sender wri(port, timeout, quality); - cv::Mat mat = cv::cvarrToMat(ipl); - wri.write(mat); + //cv::Mat mat = cv::cvarrToMat(ipl); + wri.write(*mat); std::cout << " MJPEG-stream sent. \n"; } catch (...) { diff --git a/src/http_stream.h b/src/http_stream.h index 03b065109b0..04945c6ba5e 100644 --- a/src/http_stream.h +++ b/src/http_stream.h @@ -11,7 +11,7 @@ extern "C" { void send_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id, int port, int timeout); #ifdef OPENCV -void send_mjpeg(mat_cv* ipl, int port, int timeout, int quality); +void send_mjpeg(mat_cv* mat, int port, int timeout, int quality); #endif // OPENCV diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 70dd3387c3d..9a893ba52dc 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -62,7 +62,7 @@ using std::endl; extern "C" { - struct mat_cv :IplImage { int a[0]; }; + struct mat_cv : cv::Mat { int a[0]; }; struct cap_cv : cv::VideoCapture { int a[0]; }; struct write_cv : cv::VideoWriter { int a[0]; }; @@ -199,7 +199,7 @@ void release_mat(mat_cv **mat) // ==================================================================== // IplImage // ==================================================================== - +/* int get_width_cv(mat_cv *ipl_src) { IplImage *ipl = (IplImage *)ipl_src; @@ -207,8 +207,9 @@ int get_width_cv(mat_cv *ipl_src) } // ---------------------------------------- -int get_height_cv(mat_cv *ipl) +int get_height_cv(mat_cv *ipl_src) { + IplImage *ipl = (IplImage *)ipl_src; return ipl->height; } // ---------------------------------------- @@ -220,10 +221,11 @@ void release_ipl(mat_cv **ipl) *ipl_img = NULL; } // ---------------------------------------- - +*/ // ==================================================================== // image-to-ipl, ipl-to-image, image_to_mat, mat_to_image // ==================================================================== +/* mat_cv *image_to_ipl(image im) { int x, y, c; @@ -262,7 +264,7 @@ image ipl_to_image(mat_cv* src_ptr) return im; } // ---------------------------------------- - +*/ cv::Mat ipl_to_mat(IplImage *ipl) { Mat m = cvarrToMat(ipl, true); @@ -320,6 +322,11 @@ image mat_to_image(cv::Mat mat) return im; } +image mat_to_image_cv(mat_cv *mat) +{ + return mat_to_image(*mat); +} + // ==================================================================== // Window // ==================================================================== @@ -451,11 +458,13 @@ write_cv *create_video_writer(char *out_filename, char c1, char c2, char c3, cha return NULL; } -void write_frame_cv(write_cv *output_video_writer, mat_cv *show_img) +void write_frame_cv(write_cv *output_video_writer, mat_cv *mat) { try { cv::VideoWriter *out = (cv::VideoWriter *)output_video_writer; - out->write(ipl_to_mat(show_img)); + //out->write(ipl_to_mat(show_img)); + out->write(*mat); + show_image_mat(mat, "mat"); } catch (...) { cerr << "OpenCV exception: write_frame_cv \n"; @@ -505,8 +514,6 @@ image get_image_from_stream(void *p) return mat_to_image(m); } - - int show_image_cv(image im, const char* name, int ms) { Mat m = image_to_mat(im); @@ -562,16 +569,18 @@ void release_capture(cap_cv* cap) // ---------------------------------------- mat_cv* get_capture_frame_cv(cap_cv *cap) { - IplImage* src = NULL; + //IplImage* src = NULL; + cv::Mat *mat = new cv::Mat(); try { if (cap) { cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; - cv::Mat frame; + //cv::Mat frame; if (cpp_cap.isOpened()) { - cpp_cap >> frame; - IplImage tmp = frame; - src = cvCloneImage(&tmp); + cpp_cap >> *mat; + //cpp_cap >> frame; + //IplImage tmp = frame; + //src = cvCloneImage(&tmp); } else std::cout << " Video-stream stopped! \n"; } @@ -580,7 +589,7 @@ mat_cv* get_capture_frame_cv(cap_cv *cap) { catch (...) { std::cout << " OpenCV exception: Video-stream stoped! \n"; } - return (mat_cv *)src; + return (mat_cv *)mat; } // ---------------------------------------- @@ -670,41 +679,42 @@ int set_capture_position_frame_cv(cap_cv *cap, int index) image get_image_from_stream_cpp(cap_cv *cap) { - IplImage* src; + //IplImage* src = NULL; + cv::Mat *src = new cv::Mat(); static int once = 1; if (once) { once = 0; do { src = get_capture_frame_cv(cap); if (!src) return make_empty_image(0, 0, 0); - } while (src->width < 1 || src->height < 1 || src->nChannels < 1); - printf("Video stream: %d x %d \n", src->width, src->height); + } while (src->cols < 1 || src->rows < 1 || src->channels() < 1); + printf("Video stream: %d x %d \n", src->cols, src->rows); } else src = get_capture_frame_cv(cap); if (!src) return make_empty_image(0, 0, 0); - image im = ipl_to_image((mat_cv*)src); + image im = mat_to_image(*src);// ipl_to_image((mat_cv*)src); rgbgr_image(im); return im; } // ---------------------------------------- -int wait_for_stream(cap_cv *cap, IplImage* src, int dont_close) +int wait_for_stream(cap_cv *cap, cv::Mat* src, int dont_close) { if (!src) { - if (dont_close) src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3); + if (dont_close) src = new cv::Mat(416, 416, CV_8UC(3)); // cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3); else return 0; } - if (src->width < 1 || src->height < 1 || src->nChannels < 1) { + if (src->cols < 1 || src->rows < 1 || src->channels() < 1) { if (dont_close) { - cvReleaseImage(&src); + delete src;// cvReleaseImage(&src); int z = 0; for (z = 0; z < 20; ++z) { get_capture_frame_cv(cap); - cvReleaseImage(&src); + delete src;// cvReleaseImage(&src); } - src = cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3); + src = new cv::Mat(416, 416, CV_8UC(3)); // cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3); } else return 0; } @@ -715,7 +725,7 @@ int wait_for_stream(cap_cv *cap, IplImage* src, int dont_close) image get_image_from_stream_resize(cap_cv *cap, int w, int h, int c, mat_cv** in_img, int dont_close) { c = c ? c : 3; - IplImage* src; + cv::Mat *src = NULL; static int once = 1; if (once) { @@ -723,22 +733,23 @@ image get_image_from_stream_resize(cap_cv *cap, int w, int h, int c, mat_cv** in do { src = get_capture_frame_cv(cap); if (!src) return make_empty_image(0, 0, 0); - } while (src->width < 1 || src->height < 1 || src->nChannels < 1); - printf("Video stream: %d x %d \n", src->width, src->height); + } while (src->cols < 1 || src->rows < 1 || src->channels() < 1); + printf("Video stream: %d x %d \n", src->cols, src->rows); } else src = get_capture_frame_cv(cap); if (!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0); - IplImage* new_img = cvCreateImage(cvSize(w, h), IPL_DEPTH_8U, c); - *in_img = (mat_cv *)cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c); - cvResize(src, *in_img, CV_INTER_LINEAR); - cvResize(src, new_img, CV_INTER_LINEAR); - image im = ipl_to_image((mat_cv*)new_img); - cvReleaseImage(&new_img); - cvReleaseImage(&src); - if (c>1) - rgbgr_image(im); + + *(cv::Mat **)in_img = src; + + cv::Mat new_img = cv::Mat(h, w, CV_8UC(c)); + cv::resize(*src, new_img, new_img.size(), 0, 0, CV_INTER_LINEAR); + if (c>1) cv::cvtColor(new_img, new_img, CV_RGB2BGR); + image im = mat_to_image(new_img); + + //show_image_cv(im, "im"); + //show_image_mat(*in_img, "in_img"); return im; } // ---------------------------------------- @@ -746,27 +757,33 @@ image get_image_from_stream_resize(cap_cv *cap, int w, int h, int c, mat_cv** in image get_image_from_stream_letterbox(cap_cv *cap, int w, int h, int c, mat_cv** in_img, int dont_close) { c = c ? c : 3; - IplImage* src; + //IplImage* src; + cv::Mat *src = NULL; static int once = 1; if (once) { once = 0; do { src = get_capture_frame_cv(cap); if (!src) return make_empty_image(0, 0, 0); - } while (src->width < 1 || src->height < 1 || src->nChannels < 1); - printf("Video stream: %d x %d \n", src->width, src->height); + } while (src->cols < 1 || src->rows < 1 || src->channels() < 1); + printf("Video stream: %d x %d \n", src->cols, src->rows); } else src = get_capture_frame_cv(cap); - if (!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0); - *in_img = (mat_cv *)cvCreateImage(cvSize(src->width, src->height), IPL_DEPTH_8U, c); - cvResize(src, *in_img, CV_INTER_LINEAR); - image tmp = ipl_to_image((mat_cv*)src); + if (!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0); // passes (cv::Mat *)src while should be (cv::Mat **)src + + *in_img = (mat_cv *)new cv::Mat(src->rows, src->cols, CV_8UC(c)); + cv::resize(*src, **in_img, (*in_img)->size(), 0, 0, CV_INTER_LINEAR); + + if (c>1) cv::cvtColor(*src, *src, CV_RGB2BGR); + image tmp = mat_to_image(*src); image im = letterbox_image(tmp, w, h); free_image(tmp); - cvReleaseImage(&src); - if (c>1) rgbgr_image(im); + release_mat((mat_cv **)&src); + + //show_image_cv(im, "im"); + //show_image_mat(*in_img, "in_img"); return im; } // ---------------------------------------- @@ -817,8 +834,9 @@ void save_cv_jpg(mat_cv *img_src, const char *name) // ==================================================================== // Draw Detection // ==================================================================== -void draw_detections_cv_v3(mat_cv* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output) +void draw_detections_cv_v3(mat_cv* mat, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output) { + cv::Mat *show_img = mat; int i, j; if (!show_img) return; static int frame_id = 0; @@ -833,6 +851,9 @@ void draw_detections_cv_v3(mat_cv* show_img, detection *dets, int num, float thr if (class_id < 0) { strcat(labelstr, names[j]); class_id = j; + char buff[10]; + sprintf(buff, " (%2.0f%%)", dets[i].prob[j]*100); + strcat(labelstr, buff); } else { strcat(labelstr, ", "); @@ -842,7 +863,7 @@ void draw_detections_cv_v3(mat_cv* show_img, detection *dets, int num, float thr } } if (class_id >= 0) { - int width = show_img->height * .006; + int width = show_img->rows * .006; //if(0){ //width = pow(prob, 1./2.)*10+1; @@ -862,21 +883,25 @@ void draw_detections_cv_v3(mat_cv* show_img, detection *dets, int num, float thr rgb[1] = green; rgb[2] = blue; box b = dets[i].bbox; + if (std::isnan(b.w) || std::isinf(b.w)) b.w = 0.5; + if (std::isnan(b.h) || std::isinf(b.h)) b.h = 0.5; + if (std::isnan(b.x) || std::isinf(b.x)) b.x = 0.5; + if (std::isnan(b.y) || std::isinf(b.y)) b.y = 0.5; b.w = (b.w < 1) ? b.w : 1; b.h = (b.h < 1) ? b.h : 1; b.x = (b.x < 1) ? b.x : 1; b.y = (b.y < 1) ? b.y : 1; //printf("%f %f %f %f\n", b.x, b.y, b.w, b.h); - int left = (b.x - b.w / 2.)*show_img->width; - int right = (b.x + b.w / 2.)*show_img->width; - int top = (b.y - b.h / 2.)*show_img->height; - int bot = (b.y + b.h / 2.)*show_img->height; + int left = (b.x - b.w / 2.)*show_img->cols; + int right = (b.x + b.w / 2.)*show_img->cols; + int top = (b.y - b.h / 2.)*show_img->rows; + int bot = (b.y + b.h / 2.)*show_img->rows; if (left < 0) left = 0; - if (right > show_img->width - 1) right = show_img->width - 1; + if (right > show_img->cols - 1) right = show_img->cols - 1; if (top < 0) top = 0; - if (bot > show_img->height - 1) bot = show_img->height - 1; + if (bot > show_img->rows - 1) bot = show_img->rows - 1; //int b_x_center = (left + right) / 2; //int b_y_center = (top + bot) / 2; @@ -884,16 +909,16 @@ void draw_detections_cv_v3(mat_cv* show_img, detection *dets, int num, float thr //int b_height = bot - top; //sprintf(labelstr, "%d x %d - w: %d, h: %d", b_x_center, b_y_center, b_width, b_height); - float const font_size = show_img->height / 1000.F; + float const font_size = show_img->rows / 1200.F; CvPoint pt1, pt2, pt_text, pt_text_bg1, pt_text_bg2; pt1.x = left; pt1.y = top; pt2.x = right; pt2.y = bot; pt_text.x = left; - pt_text.y = top - 12; + pt_text.y = top - 4;// 12; pt_text_bg1.x = left; - pt_text_bg1.y = top - (10 + 25 * font_size); + pt_text_bg1.y = top - (3 + 25 * font_size); pt_text_bg2.x = right; pt_text_bg2.y = top; CvScalar color; @@ -918,20 +943,17 @@ void draw_detections_cv_v3(mat_cv* show_img, detection *dets, int num, float thr //cvSaveImage(image_name, copy_img, 0); //cvResetImageROI(copy_img); - cvRectangle(show_img, pt1, pt2, color, width, 8, 0); + cv::rectangle(*show_img, pt1, pt2, color, width, 8, 0); if (ext_output) printf("\t(left_x: %4.0f top_y: %4.0f width: %4.0f height: %4.0f)\n", - (float)left, (float)top, b.w*show_img->width, b.h*show_img->height); + (float)left, (float)top, b.w*show_img->cols, b.h*show_img->rows); else printf("\n"); - cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0); - cvRectangle(show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0); // filled - CvScalar black_color; - black_color.val[0] = 0; - CvFont font; - cvInitFont(&font, CV_FONT_HERSHEY_SIMPLEX, font_size, font_size, 0, font_size * 3, 8); - cvPutText(show_img, labelstr, pt_text, &font, black_color); + cv::rectangle(*show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0); + cv::rectangle(*show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0); // filled + cv::Scalar black_color = CV_RGB(0,0,0); + cv::putText(*show_img, labelstr, pt_text, CV_FONT_HERSHEY_SIMPLEX, font_size, black_color, font_size * 3, CV_AA); } } if (ext_output) { @@ -1053,7 +1075,7 @@ void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_im else cv::putText(img, "- Saved", cvPoint(260, img_size - 10), CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 1, CV_AA); - if (mjpeg_port > 0) send_mjpeg((mat_cv *)mat_to_ipl(img), mjpeg_port, 500000, 100); + if (mjpeg_port > 0) send_mjpeg((mat_cv *)&img, mjpeg_port, 500000, 100); } // ---------------------------------------- diff --git a/src/image_opencv.h b/src/image_opencv.h index e38773d5dd9..9a4d3ed0256 100644 --- a/src/image_opencv.h +++ b/src/image_opencv.h @@ -23,20 +23,23 @@ int get_width_mat(mat_cv *mat); int get_height_mat(mat_cv *mat); void release_mat(mat_cv **mat); -// IplImage -int get_width_cv(mat_cv *ipl); -int get_height_cv(mat_cv *ipl); -void release_ipl(mat_cv **ipl); +// IplImage - to delete +//int get_width_cv(mat_cv *ipl); +//int get_height_cv(mat_cv *ipl); +//void release_ipl(mat_cv **ipl); // image-to-ipl, ipl-to-image, image_to_mat, mat_to_image -mat_cv *image_to_ipl(image im); -image ipl_to_image(mat_cv* src_ptr); +//mat_cv *image_to_ipl(image im); // to delete +//image ipl_to_image(mat_cv* src_ptr); // to delete + + // mat_cv *image_to_ipl(image im) // image ipl_to_image(mat_cv* src_ptr) // cv::Mat ipl_to_mat(IplImage *ipl) // IplImage *mat_to_ipl(cv::Mat mat) // Mat image_to_mat(image img) // image mat_to_image(cv::Mat mat) +image mat_to_image_cv(mat_cv *mat); // Window void create_window_cv(char const* window_name, int full_screen, int width, int height); @@ -50,7 +53,7 @@ void show_image_mat(mat_cv *mat_ptr, const char *name); // Video Writer write_cv *create_video_writer(char *out_filename, char c1, char c2, char c3, char c4, int fps, int width, int height, int is_color); -void write_frame_cv(write_cv *output_video_writer, mat_cv *show_img); +void write_frame_cv(write_cv *output_video_writer, mat_cv *mat); void release_video_writer(write_cv **output_video_writer); diff --git a/src/rnn_vid.c b/src/rnn_vid.c index 798a393fd7b..a0fb0a3c790 100644 --- a/src/rnn_vid.c +++ b/src/rnn_vid.c @@ -40,7 +40,7 @@ float_pair get_rnn_vid_data(network net, char **files, int n, int batch, int ste int i; for(i = 0; i < net.batch; ++i){ mat_cv *src = get_capture_frame_cv(cap); - image im = ipl_to_image(src); + image im = mat_to_image_cv(src); rgbgr_image(im); image re = resize_image(im, net.w, net.h); //show_image(re, "loaded"); From 71d20264d17366ca3179078c42b731bf94a8e71b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 6 Apr 2019 15:13:47 +0300 Subject: [PATCH 0445/1365] Use -dont_show -show_imgs for Training - saves augmented images without showing --- src/data.c | 12 ++++++++---- src/detector.c | 1 + 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/src/data.c b/src/data.c index 29c65d72843..6d885b1085b 100644 --- a/src/data.c +++ b/src/data.c @@ -856,8 +856,10 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo } save_image(ai, buff); - show_image(ai, buff); - wait_until_press_key_cv(); + if (show_imgs == 1) { + show_image(ai, buff); + wait_until_press_key_cv(); + } printf("\nYou use flag -show_imgs, so will be saved aug_...jpg images. Click on window and press ESC button \n"); } @@ -951,8 +953,10 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo } show_image(sized, buff); - save_image(sized, buff); - wait_until_press_key_cv(); + if (show_imgs == 1) { + save_image(sized, buff); + wait_until_press_key_cv(); + } printf("\nYou use flag -show_imgs, so will be saved aug_...jpg images. Press Enter: \n"); getchar(); } diff --git a/src/detector.c b/src/detector.c index 3f980adddac..a1995ab3be6 100644 --- a/src/detector.c +++ b/src/detector.c @@ -127,6 +127,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i args.exposure = net.exposure; args.saturation = net.saturation; args.hue = net.hue; + if (dont_show && show_imgs) show_imgs = 2; args.show_imgs = show_imgs; #ifdef OPENCV From d6b9512a00dbf8e92ef3db9e3e80d0092ac10485 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 6 Apr 2019 16:26:17 +0300 Subject: [PATCH 0446/1365] Fixed bug when image is missing --- src/image_opencv.cpp | 1 - 1 file changed, 1 deletion(-) diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 9a893ba52dc..ead2ee72b0b 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -95,7 +95,6 @@ mat_cv *load_image_mat_cv(const char *filename, int flag) std::ofstream bad_list("bad.list", std::ios::out | std::ios::app); bad_list << shrinked_filename << std::endl; //if (check_mistakes) getchar(); - delete mat_ptr; return NULL; } cv::cvtColor(mat, mat, CV_RGB2BGR); From ca4b5eb03d922cb479ff07811965dcd49e97364b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 7 Apr 2019 00:40:19 +0300 Subject: [PATCH 0447/1365] Bug fix with assert() during training --- build/darknet/darknet.vcxproj | 5 ++--- src/network_kernels.cu | 3 ++- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj index 29d69fe393c..787602d56c0 100644 --- a/build/darknet/darknet.vcxproj +++ b/build/darknet/darknet.vcxproj @@ -133,13 +133,12 @@ true true $(OPENCV_DIR)\include;C:\opencv_3.0\opencv\build\include;..\..\include;..\..\3rdparty\stb\include;..\..\3rdparty\pthreads\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include;$(cudnn)\include - OPENCV;CUDNN_HALF;CUDNN;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) + OPENCV;CUDNN_HALF;CUDNN;_TIMESPEC_DEFINED;_SCL_SECURE_NO_WARNINGS;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;GPU;WIN32;_CONSOLE;_LIB;%(PreprocessorDefinitions) c11 c++1y CompileAsCpp Default - - + NDEBUG true diff --git a/src/network_kernels.cu b/src/network_kernels.cu index b5f0f7db346..9162c8b2b36 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -161,11 +161,12 @@ void forward_backward_network_gpu(network net, float *x, float *y) for (i = 0; i < net.n; ++i) { layer l = net.layers[i]; if (net.cudnn_half){ - assert((l.c*l.n*l.size*l.size) > 0); if (l.type == CONVOLUTIONAL && l.weights_gpu && l.weights_gpu16) { + assert((l.c*l.n*l.size*l.size) > 0); cuda_convert_f32_to_f16(l.weights_gpu, l.c*l.n*l.size*l.size, l.weights_gpu16); } else if (l.type == CRNN && l.input_layer->weights_gpu && l.input_layer->weights_gpu16) { + assert((l.input_layer->c*l.input_layer->n*l.input_layer->size*l.input_layer->size) > 0); cuda_convert_f32_to_f16(l.input_layer->weights_gpu, l.input_layer->nweights, l.input_layer->weights_gpu16); cuda_convert_f32_to_f16(l.self_layer->weights_gpu, l.self_layer->nweights, l.self_layer->weights_gpu16); cuda_convert_f32_to_f16(l.output_layer->weights_gpu, l.output_layer->nweights, l.output_layer->weights_gpu16); From b6a824df39d1a79f15916d2c11133ce27bc0ab06 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 8 Apr 2019 02:42:44 +0300 Subject: [PATCH 0448/1365] Minor fix --- README.md | 4 ++++ src/blas.c | 2 +- src/http_stream.cpp | 3 +++ src/image_opencv.cpp | 11 +++++++---- 4 files changed, 15 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 0a0f43a0a85..e5a51ed9c3d 100644 --- a/README.md +++ b/README.md @@ -313,6 +313,8 @@ Training Yolo v3: * change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L3) * change line subdivisions to [`subdivisions=8`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4) + * change line max_batches to (`classes*2000`), f.e. [`max_batches=6000`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L20) if you train for 3 classes + * change line steps to 80% and 90% of max_batches, f.e. [`steps=4800,5400`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L22) * change line `classes=80` to your number of objects in each of 3 `[yolo]`-layers: * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L610 * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L696 @@ -467,6 +469,8 @@ Or just train with `-map` flag: So you will see mAP-chart (red-line) in the Loss-chart Window. mAP will be calculated for each 4 Epochs using `valid=valid.txt` file that is specified in `obj.data` file (`1 Epoch = images_in_train_txt / batch` iterations) +(to change the max x-axis value - change [`max_batches=`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L20) parameter to `2000*classes`, f.e. `max_batches=6000` for 3 classes) + ![loss_chart_map_chart](https://hsto.org/webt/yd/vl/ag/ydvlagutof2zcnjodstgroen8ac.jpeg) Example of custom object detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights` diff --git a/src/blas.c b/src/blas.c index 09033e254ea..c68b64c3d5e 100644 --- a/src/blas.c +++ b/src/blas.c @@ -234,7 +234,7 @@ void smooth_l1_cpu(int n, float *pred, float *truth, float *delta, float *error) } else { error[i] = 2*abs_val - 1; - delta[i] = (diff < 0) ? 1 : -1; + delta[i] = (diff > 0) ? 1 : -1; } } } diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 550beb783f0..3ce2a212bca 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -489,11 +489,14 @@ class MJPG_sender }; // ---------------------------------------- +static std::mutex mtx_mjpeg; + struct mat_cv : cv::Mat { int a[0]; }; void send_mjpeg(mat_cv* mat, int port, int timeout, int quality) { try { + std::lock_guard lock(mtx_mjpeg); static MJPG_sender wri(port, timeout, quality); //cv::Mat mat = cv::cvarrToMat(ipl); wri.write(*mat); diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index ead2ee72b0b..b3c5ae431fe 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -862,7 +862,7 @@ void draw_detections_cv_v3(mat_cv* mat, detection *dets, int num, float thresh, } } if (class_id >= 0) { - int width = show_img->rows * .006; + int width = std::max(1.0f, show_img->rows * .002f); //if(0){ //width = pow(prob, 1./2.)*10+1; @@ -908,7 +908,8 @@ void draw_detections_cv_v3(mat_cv* mat, detection *dets, int num, float thresh, //int b_height = bot - top; //sprintf(labelstr, "%d x %d - w: %d, h: %d", b_x_center, b_y_center, b_width, b_height); - float const font_size = show_img->rows / 1200.F; + float const font_size = show_img->rows / 1000.F; + cv::Size const text_size = cv::getTextSize(labelstr, cv::FONT_HERSHEY_COMPLEX_SMALL, font_size, 1, 0); CvPoint pt1, pt2, pt_text, pt_text_bg1, pt_text_bg2; pt1.x = left; pt1.y = top; @@ -917,8 +918,9 @@ void draw_detections_cv_v3(mat_cv* mat, detection *dets, int num, float thresh, pt_text.x = left; pt_text.y = top - 4;// 12; pt_text_bg1.x = left; - pt_text_bg1.y = top - (3 + 25 * font_size); + pt_text_bg1.y = top - (1 + 18 * font_size); pt_text_bg2.x = right; + if ((right - left) < text_size.width) pt_text_bg2.x = left + text_size.width; pt_text_bg2.y = top; CvScalar color; color.val[0] = red * 256; @@ -952,7 +954,8 @@ void draw_detections_cv_v3(mat_cv* mat, detection *dets, int num, float thresh, cv::rectangle(*show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0); cv::rectangle(*show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0); // filled cv::Scalar black_color = CV_RGB(0,0,0); - cv::putText(*show_img, labelstr, pt_text, CV_FONT_HERSHEY_SIMPLEX, font_size, black_color, font_size * 3, CV_AA); + cv::putText(*show_img, labelstr, pt_text, CV_FONT_HERSHEY_COMPLEX_SMALL, font_size, black_color, 2*font_size, CV_AA); + // CV_FONT_HERSHEY_COMPLEX_SMALL, CV_FONT_HERSHEY_SIMPLEX } } if (ext_output) { From 199b88624e5cb4d79235cbb9bf4215349600e4a5 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 8 Apr 2019 19:21:39 +0300 Subject: [PATCH 0449/1365] Removed some old OpenCV C API --- src/image_opencv.cpp | 66 ++++++++++++++++++++++---------------------- 1 file changed, 33 insertions(+), 33 deletions(-) diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index b3c5ae431fe..66c06a70397 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -50,8 +50,8 @@ using std::endl; #include #include #ifndef CV_VERSION_EPOCH -#include -#include +//#include +//#include #endif #include "http_stream.h" @@ -97,7 +97,7 @@ mat_cv *load_image_mat_cv(const char *filename, int flag) //if (check_mistakes) getchar(); return NULL; } - cv::cvtColor(mat, mat, CV_RGB2BGR); + cv::cvtColor(mat, mat, cv::COLOR_RGB2BGR); return (mat_cv *)mat_ptr; } @@ -151,7 +151,7 @@ image load_image_resize(char *filename, int w, int h, int c, image *im) *im = mat_to_image(loaded_image); cv::Mat resized(h, w, CV_8UC3); - cv::resize(loaded_image, resized, cv::Size(w, h), 0, 0, CV_INTER_LINEAR); + cv::resize(loaded_image, resized, cv::Size(w, h), 0, 0, cv::INTER_LINEAR); out = mat_to_image(resized); } catch (...) { @@ -332,7 +332,7 @@ image mat_to_image_cv(mat_cv *mat) void create_window_cv(char const* window_name, int full_screen, int width, int height) { try { - int window_type = WINDOW_NORMAL; + int window_type = cv::WINDOW_NORMAL; if (full_screen) window_type = CV_WINDOW_FULLSCREEN; cv::namedWindow(window_name, window_type); @@ -406,7 +406,7 @@ void show_image_cv(image p, const char *name) constrain_image(copy); cv::Mat mat = image_to_mat(copy); - cv::cvtColor(mat, mat, CV_RGB2BGR); + cv::cvtColor(mat, mat, cv::COLOR_RGB2BGR); cv::namedWindow(name, WINDOW_NORMAL); cv::imshow(name, mat); free_image(copy); @@ -743,8 +743,8 @@ image get_image_from_stream_resize(cap_cv *cap, int w, int h, int c, mat_cv** in *(cv::Mat **)in_img = src; cv::Mat new_img = cv::Mat(h, w, CV_8UC(c)); - cv::resize(*src, new_img, new_img.size(), 0, 0, CV_INTER_LINEAR); - if (c>1) cv::cvtColor(new_img, new_img, CV_RGB2BGR); + cv::resize(*src, new_img, new_img.size(), 0, 0, cv::INTER_LINEAR); + if (c>1) cv::cvtColor(new_img, new_img, cv::COLOR_RGB2BGR); image im = mat_to_image(new_img); //show_image_cv(im, "im"); @@ -773,9 +773,9 @@ image get_image_from_stream_letterbox(cap_cv *cap, int w, int h, int c, mat_cv** if (!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0); // passes (cv::Mat *)src while should be (cv::Mat **)src *in_img = (mat_cv *)new cv::Mat(src->rows, src->cols, CV_8UC(c)); - cv::resize(*src, **in_img, (*in_img)->size(), 0, 0, CV_INTER_LINEAR); + cv::resize(*src, **in_img, (*in_img)->size(), 0, 0, cv::INTER_LINEAR); - if (c>1) cv::cvtColor(*src, *src, CV_RGB2BGR); + if (c>1) cv::cvtColor(*src, *src, cv::COLOR_RGB2BGR); image tmp = mat_to_image(*src); image im = letterbox_image(tmp, w, h); free_image(tmp); @@ -796,7 +796,7 @@ extern int stbi_write_jpg(char const *filename, int x, int y, int comp, const vo void save_mat_png(cv::Mat img_src, const char *name) { cv::Mat img_rgb; - cv::cvtColor(img_src, img_rgb, CV_RGB2BGR); + cv::cvtColor(img_src, img_rgb, cv::COLOR_RGB2BGR); stbi_write_png(name, img_rgb.cols, img_rgb.rows, 3, (char *)img_rgb.data, 0); } // ---------------------------------------- @@ -804,7 +804,7 @@ void save_mat_png(cv::Mat img_src, const char *name) void save_mat_jpg(cv::Mat img_src, const char *name) { cv::Mat img_rgb; - cv::cvtColor(img_src, img_rgb, CV_RGB2BGR); + cv::cvtColor(img_src, img_rgb, cv::COLOR_RGB2BGR); stbi_write_jpg(name, img_rgb.cols, img_rgb.rows, 3, (char *)img_rgb.data, 80); } // ---------------------------------------- @@ -814,7 +814,7 @@ void save_cv_png(mat_cv *img_src, const char *name) { IplImage* img = (IplImage* )img_src; IplImage* img_rgb = cvCreateImage(cvSize(img->width, img->height), 8, 3); - cvCvtColor(img, img_rgb, CV_RGB2BGR); + cvCvtColor(img, img_rgb, cv::COLOR_RGB2BGR); stbi_write_png(name, img_rgb->width, img_rgb->height, 3, (char *)img_rgb->imageData, 0); cvRelease((void**)&img_rgb); } @@ -824,7 +824,7 @@ void save_cv_jpg(mat_cv *img_src, const char *name) { IplImage* img = (IplImage*)img_src; IplImage* img_rgb = cvCreateImage(cvSize(img->width, img->height), 8, 3); - cvCvtColor(img, img_rgb, CV_RGB2BGR); + cvCvtColor(img, img_rgb, cv::COLOR_RGB2BGR); stbi_write_jpg(name, img_rgb->width, img_rgb->height, 3, (char *)img_rgb->imageData, 80); cvRelease((void**)&img_rgb); } @@ -954,8 +954,8 @@ void draw_detections_cv_v3(mat_cv* mat, detection *dets, int num, float thresh, cv::rectangle(*show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0); cv::rectangle(*show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0); // filled cv::Scalar black_color = CV_RGB(0,0,0); - cv::putText(*show_img, labelstr, pt_text, CV_FONT_HERSHEY_COMPLEX_SMALL, font_size, black_color, 2*font_size, CV_AA); - // CV_FONT_HERSHEY_COMPLEX_SMALL, CV_FONT_HERSHEY_SIMPLEX + cv::putText(*show_img, labelstr, pt_text, cv::FONT_HERSHEY_COMPLEX_SMALL, font_size, black_color, 2*font_size, CV_AA); + // cv::FONT_HERSHEY_COMPLEX_SMALL, cv::FONT_HERSHEY_SIMPLEX } } if (ext_output) { @@ -986,7 +986,7 @@ mat_cv* draw_train_chart(float max_img_loss, int max_batches, int number_of_line sprintf(char_buff, "%2.1f", max_img_loss*(number_of_lines - i) / number_of_lines); pt_text.y = pt1.y + 5; - cv::putText(img, char_buff, pt_text, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + cv::putText(img, char_buff, pt_text, cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); cv::line(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0); } } @@ -998,20 +998,20 @@ mat_cv* draw_train_chart(float max_img_loss, int max_batches, int number_of_line if (i % 10 == 0) { sprintf(char_buff, "%d", max_batches * i / number_of_lines); pt_text.x = pt1.x - 20; - cv::putText(img, char_buff, pt_text, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + cv::putText(img, char_buff, pt_text, cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); cv::line(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0); } } - cv::putText(img, "Loss", cvPoint(0, 35), CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); - cv::putText(img, "Iteration number", cvPoint(draw_size / 2, img_size - 10), CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + cv::putText(img, "Loss", cvPoint(0, 35), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + cv::putText(img, "Iteration number", cvPoint(draw_size / 2, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); char max_batches_buff[100]; sprintf(max_batches_buff, "in cfg max_batches=%d", max_batches); - cv::putText(img, max_batches_buff, cvPoint(draw_size - 195, img_size - 10), CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); - cv::putText(img, "Press 's' to save : chart.png", cvPoint(5, img_size - 10), CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + cv::putText(img, max_batches_buff, cvPoint(draw_size - 195, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + cv::putText(img, "Press 's' to save : chart.png", cvPoint(5, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); if (!dont_show) { printf(" If error occurs - run training with flag: -dont_show \n"); - cv::namedWindow("average loss", CV_WINDOW_NORMAL); + cv::namedWindow("average loss", cv::WINDOW_NORMAL); cv::moveWindow("average loss", 0, 0); cv::resizeWindow("average loss", img_size, img_size); cv::imshow("average loss", img); @@ -1040,7 +1040,7 @@ void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_im static int iteration_old = 0; static int text_iteration_old = 0; if (iteration_old == 0) - cv::putText(img, accuracy_name, cvPoint(0, 12), CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 0, 0), 1, CV_AA); + cv::putText(img, accuracy_name, cvPoint(0, 12), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 0, 0), 1, CV_AA); cv::line(img, cvPoint(img_offset + draw_size * (float)iteration_old / max_batches, draw_size * (1 - old_precision)), @@ -1050,9 +1050,9 @@ void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_im if (((int)(old_precision * 10) != (int)(precision * 10)) || (current_batch - text_iteration_old) >= max_batches / 10) { text_iteration_old = current_batch; sprintf(char_buff, "%2.0f%% ", precision * 100); - cv::putText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 5, CV_AA); + cv::putText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 5, CV_AA); - cv::putText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(200, 0, 0), 1, CV_AA); + cv::putText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(200, 0, 0), 1, CV_AA); } old_precision = precision; iteration_old = current_batch; @@ -1063,7 +1063,7 @@ void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_im pt2.x = pt1.x + 460, pt2.y = pt1.y + 20; cv::rectangle(img, pt1, pt2, CV_RGB(255, 255, 255), CV_FILLED, 8, 0); pt1.y += 15; - cv::putText(img, char_buff, pt1, CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + cv::putText(img, char_buff, pt1, cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); int k = 0; if (!dont_show) { @@ -1072,10 +1072,10 @@ void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_im } if (k == 's' || current_batch == (max_batches - 1) || current_batch % 100 == 0) { save_mat_png(img, "chart.png"); - cv::putText(img, "- Saved", cvPoint(260, img_size - 10), CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 0, 0), 1, CV_AA); + cv::putText(img, "- Saved", cvPoint(260, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 0, 0), 1, CV_AA); } else - cv::putText(img, "- Saved", cvPoint(260, img_size - 10), CV_FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 1, CV_AA); + cv::putText(img, "- Saved", cvPoint(260, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 1, CV_AA); if (mjpeg_port > 0) send_mjpeg((mat_cv *)&img, mjpeg_port, 500000, 100); } @@ -1107,7 +1107,7 @@ image image_data_augmentation(mat_cv* mat, int w, int h, // resize cv::Mat sized; - cv::resize(cropped, sized, cv::Size(w, h), 0, 0, INTER_LINEAR); + cv::resize(cropped, sized, cv::Size(w, h), 0, 0, cv::INTER_LINEAR); // flip if (flip) { @@ -1116,11 +1116,11 @@ image image_data_augmentation(mat_cv* mat, int w, int h, } // HSV augmentation - // CV_BGR2HSV, CV_RGB2HSV, CV_HSV2BGR, CV_HSV2RGB + // cv::COLOR_BGR2HSV, cv::COLOR_RGB2HSV, cv::COLOR_HSV2BGR, cv::COLOR_HSV2RGB if(img.channels() >= 3) { cv::Mat hsv_src; - cvtColor(sized, hsv_src, CV_RGB2HSV); // RGB to HSV + cvtColor(sized, hsv_src, cv::COLOR_RGB2HSV); // RGB to HSV std::vector hsv; cv::split(hsv_src, hsv); @@ -1131,7 +1131,7 @@ image image_data_augmentation(mat_cv* mat, int w, int h, cv::merge(hsv, hsv_src); - cvtColor(hsv_src, sized, CV_HSV2RGB); // HSV to RGB (the same as previous) + cvtColor(hsv_src, sized, cv::COLOR_HSV2RGB); // HSV to RGB (the same as previous) } else { From 5a094da200f836f79483d705530c7425a659a7c1 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 8 Apr 2019 19:32:27 +0300 Subject: [PATCH 0450/1365] Another fix for old and new OpenCV --- src/image_opencv.cpp | 24 +++++++++++++++--------- 1 file changed, 15 insertions(+), 9 deletions(-) diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 66c06a70397..e653e03aea8 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -18,12 +18,22 @@ #include #include + +// includes for OpenCV >= 3.x #ifndef CV_VERSION_EPOCH #include #include #include #endif +// OpenCV includes for OpenCV 2.x +#ifdef CV_VERSION_EPOCH +#include +#include +#include +#include +#endif + using namespace cv; using std::cerr; @@ -44,15 +54,7 @@ using std::endl; #endif // USE_CMAKE_LIBS #endif // CV_VERSION_EPOCH -// OpenCV includes -#include -#include -#include -#include -#ifndef CV_VERSION_EPOCH -//#include -//#include -#endif + #include "http_stream.h" @@ -447,7 +449,11 @@ write_cv *create_video_writer(char *out_filename, char c1, char c2, char c3, cha { try { cv::VideoWriter * output_video_writer = +#ifdef CV_VERSION_EPOCH new cv::VideoWriter(out_filename, CV_FOURCC(c1, c2, c3, c4), fps, cv::Size(width, height), is_color); +#else + new cv::VideoWriter(out_filename, cv::VideoWriter::fourcc(c1, c2, c3, c4), fps, cv::Size(width, height), is_color); +#endif return (write_cv *)output_video_writer; } From 42a4081e94111452af3b772d40aa01428d379d86 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 8 Apr 2019 20:49:42 +0300 Subject: [PATCH 0451/1365] OpenCV 2.x, 3.x, 4.x are supported --- src/image_opencv.cpp | 88 +++++++++++++++++++++++--------------------- src/image_opencv.h | 2 +- 2 files changed, 47 insertions(+), 43 deletions(-) diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index e653e03aea8..29fc2453718 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -54,14 +54,20 @@ using std::endl; #endif // USE_CMAKE_LIBS #endif // CV_VERSION_EPOCH - - #include "http_stream.h" #ifndef CV_RGB #define CV_RGB(r, g, b) cvScalar( (b), (g), (r), 0 ) #endif +#ifndef CV_FILLED +#define CV_FILLED cv::FILLED +#endif + +#ifndef CV_AA +#define CV_AA cv::LINE_AA +#endif + extern "C" { struct mat_cv : cv::Mat { int a[0]; }; @@ -75,8 +81,8 @@ extern "C" { cv::Mat image_to_mat(image img); image ipl_to_image(mat_cv* src); mat_cv *image_to_ipl(image img); - cv::Mat ipl_to_mat(IplImage *ipl); - IplImage *mat_to_ipl(cv::Mat mat); +// cv::Mat ipl_to_mat(IplImage *ipl); +// IplImage *mat_to_ipl(cv::Mat mat); mat_cv *load_image_mat_cv(const char *filename, int flag) @@ -222,11 +228,11 @@ void release_ipl(mat_cv **ipl) *ipl_img = NULL; } // ---------------------------------------- -*/ + // ==================================================================== // image-to-ipl, ipl-to-image, image_to_mat, mat_to_image // ==================================================================== -/* + mat_cv *image_to_ipl(image im) { int x, y, c; @@ -265,7 +271,7 @@ image ipl_to_image(mat_cv* src_ptr) return im; } // ---------------------------------------- -*/ + cv::Mat ipl_to_mat(IplImage *ipl) { Mat m = cvarrToMat(ipl, true); @@ -280,6 +286,7 @@ IplImage *mat_to_ipl(cv::Mat mat) return ipl; } // ---------------------------------------- +*/ Mat image_to_mat(image img) { @@ -335,8 +342,11 @@ void create_window_cv(char const* window_name, int full_screen, int width, int h { try { int window_type = cv::WINDOW_NORMAL; +#ifdef CV_VERSION_EPOCH // OpenCV 2.x if (full_screen) window_type = CV_WINDOW_FULLSCREEN; - +#else + if (full_screen) window_type = cv::WINDOW_FULLSCREEN; +#endif cv::namedWindow(window_name, window_type); cv::moveWindow(window_name, 0, 0); cv::resizeWindow(window_name, width, height); @@ -381,7 +391,11 @@ void make_window(char *name, int w, int h, int fullscreen) try { cv::namedWindow(name, WINDOW_NORMAL); if (fullscreen) { +#ifdef CV_VERSION_EPOCH // OpenCV 2.x cv::setWindowProperty(name, WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN); +#else + cv::setWindowProperty(name, WND_PROP_FULLSCREEN, cv::WINDOW_FULLSCREEN); +#endif } else { cv::resizeWindow(name, w, h); @@ -419,6 +433,7 @@ void show_image_cv(image p, const char *name) } // ---------------------------------------- +/* void show_image_cv_ipl(mat_cv *disp, const char *name) { if (disp == NULL) return; @@ -428,6 +443,7 @@ void show_image_cv_ipl(mat_cv *disp, const char *name) cvShowImage(buff, disp); } // ---------------------------------------- +*/ void show_image_mat(mat_cv *mat_ptr, const char *name) { @@ -574,18 +590,13 @@ void release_capture(cap_cv* cap) // ---------------------------------------- mat_cv* get_capture_frame_cv(cap_cv *cap) { - //IplImage* src = NULL; cv::Mat *mat = new cv::Mat(); try { if (cap) { cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; - //cv::Mat frame; if (cpp_cap.isOpened()) { cpp_cap >> *mat; - //cpp_cap >> frame; - //IplImage tmp = frame; - //src = cvCloneImage(&tmp); } else std::cout << " Video-stream stopped! \n"; } @@ -684,7 +695,6 @@ int set_capture_position_frame_cv(cap_cv *cap, int index) image get_image_from_stream_cpp(cap_cv *cap) { - //IplImage* src = NULL; cv::Mat *src = new cv::Mat(); static int once = 1; if (once) { @@ -699,7 +709,7 @@ image get_image_from_stream_cpp(cap_cv *cap) src = get_capture_frame_cv(cap); if (!src) return make_empty_image(0, 0, 0); - image im = mat_to_image(*src);// ipl_to_image((mat_cv*)src); + image im = mat_to_image(*src); rgbgr_image(im); return im; } @@ -762,7 +772,6 @@ image get_image_from_stream_resize(cap_cv *cap, int w, int h, int c, mat_cv** in image get_image_from_stream_letterbox(cap_cv *cap, int w, int h, int c, mat_cv** in_img, int dont_close) { c = c ? c : 3; - //IplImage* src; cv::Mat *src = NULL; static int once = 1; if (once) { @@ -818,24 +827,19 @@ void save_mat_jpg(cv::Mat img_src, const char *name) void save_cv_png(mat_cv *img_src, const char *name) { - IplImage* img = (IplImage* )img_src; - IplImage* img_rgb = cvCreateImage(cvSize(img->width, img->height), 8, 3); - cvCvtColor(img, img_rgb, cv::COLOR_RGB2BGR); - stbi_write_png(name, img_rgb->width, img_rgb->height, 3, (char *)img_rgb->imageData, 0); - cvRelease((void**)&img_rgb); + cv::Mat* img = (cv::Mat* )img_src; + save_mat_png(*img, name); } // ---------------------------------------- void save_cv_jpg(mat_cv *img_src, const char *name) { - IplImage* img = (IplImage*)img_src; - IplImage* img_rgb = cvCreateImage(cvSize(img->width, img->height), 8, 3); - cvCvtColor(img, img_rgb, cv::COLOR_RGB2BGR); - stbi_write_jpg(name, img_rgb->width, img_rgb->height, 3, (char *)img_rgb->imageData, 80); - cvRelease((void**)&img_rgb); + cv::Mat* img = (cv::Mat*)img_src; + save_mat_jpg(*img, name); } // ---------------------------------------- + // ==================================================================== // Draw Detection // ==================================================================== @@ -916,7 +920,7 @@ void draw_detections_cv_v3(mat_cv* mat, detection *dets, int num, float thresh, float const font_size = show_img->rows / 1000.F; cv::Size const text_size = cv::getTextSize(labelstr, cv::FONT_HERSHEY_COMPLEX_SMALL, font_size, 1, 0); - CvPoint pt1, pt2, pt_text, pt_text_bg1, pt_text_bg2; + cv::Point pt1, pt2, pt_text, pt_text_bg1, pt_text_bg2; pt1.x = left; pt1.y = top; pt2.x = right; @@ -928,7 +932,7 @@ void draw_detections_cv_v3(mat_cv* mat, detection *dets, int num, float thresh, pt_text_bg2.x = right; if ((right - left) < text_size.width) pt_text_bg2.x = left + text_size.width; pt_text_bg2.y = top; - CvScalar color; + cv::Scalar color; color.val[0] = red * 256; color.val[1] = green * 256; color.val[2] = blue * 256; @@ -1009,12 +1013,12 @@ mat_cv* draw_train_chart(float max_img_loss, int max_batches, int number_of_line } } - cv::putText(img, "Loss", cvPoint(0, 35), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); - cv::putText(img, "Iteration number", cvPoint(draw_size / 2, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + cv::putText(img, "Loss", cv::Point(0, 35), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + cv::putText(img, "Iteration number", cv::Point(draw_size / 2, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); char max_batches_buff[100]; sprintf(max_batches_buff, "in cfg max_batches=%d", max_batches); - cv::putText(img, max_batches_buff, cvPoint(draw_size - 195, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); - cv::putText(img, "Press 's' to save : chart.png", cvPoint(5, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + cv::putText(img, max_batches_buff, cv::Point(draw_size - 195, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + cv::putText(img, "Press 's' to save : chart.png", cv::Point(5, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); if (!dont_show) { printf(" If error occurs - run training with flag: -dont_show \n"); cv::namedWindow("average loss", cv::WINDOW_NORMAL); @@ -1034,7 +1038,7 @@ void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_im int img_offset = 50; int draw_size = img_size - img_offset; char char_buff[100]; - CvPoint pt1, pt2; + cv::Point pt1, pt2; pt1.x = img_offset + draw_size * (float)current_batch / max_batches; pt1.y = draw_size * (1 - avg_loss / max_img_loss); if (pt1.y < 0) pt1.y = 1; @@ -1046,19 +1050,19 @@ void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_im static int iteration_old = 0; static int text_iteration_old = 0; if (iteration_old == 0) - cv::putText(img, accuracy_name, cvPoint(0, 12), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 0, 0), 1, CV_AA); + cv::putText(img, accuracy_name, cv::Point(0, 12), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 0, 0), 1, CV_AA); cv::line(img, - cvPoint(img_offset + draw_size * (float)iteration_old / max_batches, draw_size * (1 - old_precision)), - cvPoint(img_offset + draw_size * (float)current_batch / max_batches, draw_size * (1 - precision)), + cv::Point(img_offset + draw_size * (float)iteration_old / max_batches, draw_size * (1 - old_precision)), + cv::Point(img_offset + draw_size * (float)current_batch / max_batches, draw_size * (1 - precision)), CV_RGB(255, 0, 0), 1, 8, 0); if (((int)(old_precision * 10) != (int)(precision * 10)) || (current_batch - text_iteration_old) >= max_batches / 10) { text_iteration_old = current_batch; sprintf(char_buff, "%2.0f%% ", precision * 100); - cv::putText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 5, CV_AA); + cv::putText(img, char_buff, cv::Point(pt1.x - 30, draw_size * (1 - precision) + 15), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 5, CV_AA); - cv::putText(img, char_buff, cvPoint(pt1.x - 30, draw_size * (1 - precision) + 15), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(200, 0, 0), 1, CV_AA); + cv::putText(img, char_buff, cv::Point(pt1.x - 30, draw_size * (1 - precision) + 15), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(200, 0, 0), 1, CV_AA); } old_precision = precision; iteration_old = current_batch; @@ -1078,10 +1082,10 @@ void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_im } if (k == 's' || current_batch == (max_batches - 1) || current_batch % 100 == 0) { save_mat_png(img, "chart.png"); - cv::putText(img, "- Saved", cvPoint(260, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 0, 0), 1, CV_AA); + cv::putText(img, "- Saved", cv::Point(260, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 0, 0), 1, CV_AA); } else - cv::putText(img, "- Saved", cvPoint(260, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 1, CV_AA); + cv::putText(img, "- Saved", cv::Point(260, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 1, CV_AA); if (mjpeg_port > 0) send_mjpeg((mat_cv *)&img, mjpeg_port, 500000, 100); } @@ -1186,7 +1190,7 @@ void show_acnhors(int number_of_boxes, int num_of_clusters, float *rel_width_hei cv::Mat img = cv::Mat(img_size, img_size, CV_8UC3); for (int i = 0; i < number_of_boxes; ++i) { - CvPoint pt; + cv::Point pt; pt.x = points.at(i, 0) * img_size / width; pt.y = points.at(i, 1) * img_size / height; int cluster_idx = labels.at(i, 0); @@ -1198,7 +1202,7 @@ void show_acnhors(int number_of_boxes, int num_of_clusters, float *rel_width_hei } for (int j = 0; j < num_of_clusters; ++j) { - CvPoint pt1, pt2; + cv::Point pt1, pt2; pt1.x = pt1.y = 0; pt2.x = centers.at(j, 0) * img_size / width; pt2.y = centers.at(j, 1) * img_size / height; diff --git a/src/image_opencv.h b/src/image_opencv.h index 9a4d3ed0256..13e67210cd8 100644 --- a/src/image_opencv.h +++ b/src/image_opencv.h @@ -48,7 +48,7 @@ int wait_key_cv(int delay); int wait_until_press_key_cv(); void make_window(char *name, int w, int h, int fullscreen); void show_image_cv(image p, const char *name); -void show_image_cv_ipl(mat_cv *disp, const char *name); +//void show_image_cv_ipl(mat_cv *disp, const char *name); void show_image_mat(mat_cv *mat_ptr, const char *name); // Video Writer From 1cad888641804b1ddddf03ad316aa3a6e9e679ad Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 8 Apr 2019 21:23:48 +0300 Subject: [PATCH 0452/1365] Fix for uselib and OpenCV 2.x, 3.x, 4.x --- src/yolo_console_dll.cpp | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/src/yolo_console_dll.cpp b/src/yolo_console_dll.cpp index d0707ec0956..4f2b2b7432e 100644 --- a/src/yolo_console_dll.cpp +++ b/src/yolo_console_dll.cpp @@ -149,7 +149,7 @@ std::vector get_3d_coordinates(std::vector bbox_vect, cv::Mat xy #include // C++ #include -#ifndef CV_VERSION_EPOCH +#ifndef CV_VERSION_EPOCH // OpenCV 3.x and 4.x #include #define OPENCV_VERSION CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR)"" CVAUX_STR(CV_VERSION_REVISION) #ifndef USE_CMAKE_LIBS @@ -162,12 +162,13 @@ std::vector get_3d_coordinates(std::vector bbox_vect, cv::Mat xy #pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib") #endif // TRACK_OPTFLOW #endif // USE_CMAKE_LIBS -#else +#else // OpenCV 2.x #define OPENCV_VERSION CVAUX_STR(CV_VERSION_EPOCH)"" CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR) #ifndef USE_CMAKE_LIBS #pragma comment(lib, "opencv_core" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib") +#pragma comment(lib, "opencv_video" OPENCV_VERSION ".lib") #endif // USE_CMAKE_LIBS #endif // CV_VERSION_EPOCH @@ -353,20 +354,27 @@ int main(int argc, char *argv[]) cv::VideoCapture cap; if (filename == "web_camera") { cap.open(0); - video_fps = cap.get(CV_CAP_PROP_FPS); cap >> cur_frame; } else if (!use_zed_camera) { cap.open(filename); - video_fps = cap.get(CV_CAP_PROP_FPS); cap >> cur_frame; } +#ifdef CV_VERSION_EPOCH // OpenCV 2.x + video_fps = cap.get(CV_CAP_PROP_FPS); +#else + video_fps = cap.get(cv::CAP_PROP_FPS); +#endif cv::Size const frame_size = cur_frame.size(); //cv::Size const frame_size(cap.get(CV_CAP_PROP_FRAME_WIDTH), cap.get(CV_CAP_PROP_FRAME_HEIGHT)); std::cout << "\n Video size: " << frame_size << std::endl; cv::VideoWriter output_video; if (save_output_videofile) +#ifdef CV_VERSION_EPOCH // OpenCV 2.x output_video.open(out_videofile, CV_FOURCC('D', 'I', 'V', 'X'), std::max(35, video_fps), frame_size, true); +#else + output_video.open(out_videofile, cv::VideoWriter::fourcc('D', 'I', 'V', 'X'), std::max(35, video_fps), frame_size, true); +#endif struct detection_data_t { cv::Mat cap_frame; From f6d45f1408cb9b1bf93bfab0e9a4e4225483eff0 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 9 Apr 2019 09:06:58 +0200 Subject: [PATCH 0453/1365] [travis] improve CI configurations, add no-opencv-ones, add opencv4 on macOS [build.sh] improve handling of vcpkg [readme] update instructions for newer visual studio versions [build.ps1] add VS2015 and VS2019 using vswhere --- .travis.yml | 298 ++++++++++++++++++++++++++++++++++++++++++++----- CMakeLists.txt | 6 +- README.md | 24 ++-- build.ps1 | 102 +++++++++++++---- build.sh | 27 ++++- 5 files changed, 386 insertions(+), 71 deletions(-) diff --git a/.travis.yml b/.travis.yml index cf5087f7549..b01e6d36438 100644 --- a/.travis.yml +++ b/.travis.yml @@ -3,44 +3,254 @@ language: cpp matrix: include: + - os: osx + compiler: gcc + name: macOS - native gcc (llvm backend) + env: + - additional_defines="" + - MATRIX_EVAL="" + - os: osx compiler: gcc name: macOS - native gcc (llvm backend) - opencv@2 env: - - MATRIX_EVAL="brew install opencv@2 && OpenCV_DIR=/usr/local/opt/opencv@2" + - OpenCV_DIR="/usr/local/Cellar/opencv@2/2.4.13.7_2/" + - additional_defines="-DOpenCV_DIR=${OpenCV_DIR}" + - MATRIX_EVAL="brew install opencv@2" - os: osx compiler: gcc name: macOS - native gcc (llvm backend) - opencv@3 env: - - MATRIX_EVAL="brew install opencv@3 && OpenCV_DIR=/usr/local/opt/opencv@3" + - OpenCV_DIR="/usr/local/Cellar/opencv@3/3.4.5/" + - additional_defines="-DOpenCV_DIR=${OpenCV_DIR}" + - MATRIX_EVAL="brew install opencv@3" + + - os: osx + compiler: gcc + name: macOS - native gcc (llvm backend) - opencv(latest) + env: + - additional_defines="" + - MATRIX_EVAL="brew install opencv" + + - os: osx + compiler: clang + name: macOS - native clang + env: + - additional_defines="" + - MATRIX_EVAL="" - os: osx compiler: clang name: macOS - native clang - opencv@2 env: - - MATRIX_EVAL="brew install opencv@2 && OpenCV_DIR=/usr/local/opt/opencv@2" + - OpenCV_DIR="/usr/local/Cellar/opencv@2/2.4.13.7_2/" + - additional_defines="-DOpenCV_DIR=${OpenCV_DIR}" + - MATRIX_EVAL="brew install opencv@2" - os: osx compiler: clang name: macOS - native clang - opencv@3 env: - - MATRIX_EVAL="brew install opencv@3 && OpenCV_DIR=/usr/local/opt/opencv@3" + - OpenCV_DIR="/usr/local/Cellar/opencv@3/3.4.5/" + - additional_defines="-DOpenCV_DIR=${OpenCV_DIR}" + - MATRIX_EVAL="brew install opencv@3" + + - os: osx + compiler: clang + name: macOS - native clang - opencv(latest) + env: + - additional_defines="" + - MATRIX_EVAL="brew install opencv" - os: linux compiler: clang name: ubuntu 14.04 - native clang + addons: + apt: + update: true + env: + - additional_defines="" + - MATRIX_EVAL="" + + - os: linux + compiler: clang + name: ubuntu 14.04 - clang@3.6 + addons: + apt: + update: true + packages: + - clang-3.6 + env: + - additional_defines="" + - MATRIX_EVAL="CC=clang-3.6 && CXX=clang++-3.6" + + - os: linux + compiler: clang + name: ubuntu 14.04 - clang@3.8 + addons: + apt: + update: true + sources: + - llvm-toolchain-precise-3.8 + packages: + - clang-3.8 + env: + - additional_defines="" + - MATRIX_EVAL="CC=clang-3.8 && CXX=clang++-3.8" + + - os: linux + compiler: clang + name: ubuntu 14.04 - clang@3.9 + addons: + apt: + update: true + sources: + - llvm-toolchain-trusty-3.9 + packages: + - clang-3.9 + env: + - additional_defines="" + - MATRIX_EVAL="CC=clang-3.9 && CXX=clang++-3.9" + + - os: linux + compiler: clang + name: ubuntu 14.04 - clang@4.0 + addons: + apt: + update: true + sources: + - llvm-toolchain-trusty-4.0 + packages: + - clang-4.0 + env: + - additional_defines="" + - MATRIX_EVAL="CC=clang-4.0 && CXX=clang++-4.0" + + - os: linux + compiler: clang + name: ubuntu 14.04 - clang@5.0 + addons: + apt: + update: true + sources: + - llvm-toolchain-trusty-5.0 + packages: + - clang-5.0 + env: + - additional_defines="" + - MATRIX_EVAL="CC=clang-5.0 && CXX=clang++-5.0" + + - os: linux + compiler: clang + name: ubuntu 14.04 - clang@6.0 + addons: + apt: + update: true + sources: + - llvm-toolchain-trusty-6.0 + - ubuntu-toolchain-r-test + packages: + - clang-6.0 + env: + - additional_defines="" + - MATRIX_EVAL="CC=clang-6.0 && CXX=clang++-6.0" + + - os: linux + compiler: gcc + name: ubuntu 14.04 - native gcc + addons: + apt: + update: true + env: + - additional_defines="" + - MATRIX_EVAL="" + + - os: linux + compiler: gcc + name: ubuntu 14.04 - gcc@4.9 + addons: + apt: + update: true + sources: + - ubuntu-toolchain-r-test + packages: + - g++-4.9 + env: + - additional_defines="" + - MATRIX_EVAL="CC=gcc-4.9 && CXX=g++-4.9" + + - os: linux + compiler: gcc + name: ubuntu 14.04 - gcc@5.0 + addons: + apt: + update: true + sources: + - ubuntu-toolchain-r-test + packages: + - g++-5 + env: + - additional_defines="" + - MATRIX_EVAL="CC=gcc-5 && CXX=g++-5" + + - os: linux + compiler: gcc + name: ubuntu 14.04 - gcc@6.0 + addons: + apt: + update: true + sources: + - ubuntu-toolchain-r-test + packages: + - g++-6 + env: + - additional_defines="" + - MATRIX_EVAL="CC=gcc-6 && CXX=g++-6" + + - os: linux + compiler: gcc + name: ubuntu 14.04 - gcc@7.0 + addons: + apt: + update: true + sources: + - ubuntu-toolchain-r-test + packages: + - g++-7 + env: + - additional_defines="" + - MATRIX_EVAL="CC=gcc-7 && CXX=g++-7" + + - os: linux + compiler: gcc + name: ubuntu 14.04 - gcc@8.0 + addons: + apt: + update: true + sources: + - ubuntu-toolchain-r-test + packages: + - g++-8 + env: + - additional_defines="" + - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8" + + - os: linux + compiler: clang + name: ubuntu 14.04 - native clang - opencv@2 addons: apt: update: true packages: - libopencv-dev env: + - additional_defines="" - MATRIX_EVAL="" - os: linux compiler: clang - name: ubuntu 14.04 - clang@3.6 + name: ubuntu 14.04 - clang@3.6 - opencv@2 addons: apt: update: true @@ -48,11 +258,12 @@ matrix: - libopencv-dev - clang-3.6 env: + - additional_defines="" - MATRIX_EVAL="CC=clang-3.6 && CXX=clang++-3.6" - os: linux compiler: clang - name: ubuntu 14.04 - clang@3.8 + name: ubuntu 14.04 - clang@3.8 - opencv@2 addons: apt: update: true @@ -62,11 +273,12 @@ matrix: - libopencv-dev - clang-3.8 env: + - additional_defines="" - MATRIX_EVAL="CC=clang-3.8 && CXX=clang++-3.8" - os: linux compiler: clang - name: ubuntu 14.04 - clang@3.9 + name: ubuntu 14.04 - clang@3.9 - opencv@2 addons: apt: update: true @@ -76,11 +288,12 @@ matrix: - libopencv-dev - clang-3.9 env: + - additional_defines="" - MATRIX_EVAL="CC=clang-3.9 && CXX=clang++-3.9" - os: linux compiler: clang - name: ubuntu 14.04 - clang@4.0 + name: ubuntu 14.04 - clang@4.0 - opencv@2 addons: apt: update: true @@ -90,11 +303,12 @@ matrix: - libopencv-dev - clang-4.0 env: + - additional_defines="" - MATRIX_EVAL="CC=clang-4.0 && CXX=clang++-4.0" - os: linux compiler: clang - name: ubuntu 14.04 - clang@5.0 + name: ubuntu 14.04 - clang@5.0 - opencv@2 addons: apt: update: true @@ -104,11 +318,12 @@ matrix: - libopencv-dev - clang-5.0 env: + - additional_defines="" - MATRIX_EVAL="CC=clang-5.0 && CXX=clang++-5.0" - os: linux compiler: clang - name: ubuntu 14.04 - clang@6.0 + name: ubuntu 14.04 - clang@6.0 - opencv@2 addons: apt: update: true @@ -119,22 +334,24 @@ matrix: - libopencv-dev - clang-6.0 env: + - additional_defines="" - MATRIX_EVAL="CC=clang-6.0 && CXX=clang++-6.0" - os: linux compiler: gcc - name: ubuntu 14.04 - native gcc + name: ubuntu 14.04 - native gcc - opencv@2 addons: apt: update: true packages: - libopencv-dev env: + - additional_defines="" - MATRIX_EVAL="" - os: linux compiler: gcc - name: ubuntu 14.04 - gcc@4.9 + name: ubuntu 14.04 - gcc@4.9 - opencv@2 addons: apt: update: true @@ -144,11 +361,12 @@ matrix: - libopencv-dev - g++-4.9 env: + - additional_defines="" - MATRIX_EVAL="CC=gcc-4.9 && CXX=g++-4.9" - os: linux compiler: gcc - name: ubuntu 14.04 - gcc@5.0 + name: ubuntu 14.04 - gcc@5.0 - opencv@2 addons: apt: update: true @@ -158,11 +376,12 @@ matrix: - libopencv-dev - g++-5 env: + - additional_defines="" - MATRIX_EVAL="CC=gcc-5 && CXX=g++-5" - os: linux compiler: gcc - name: ubuntu 14.04 - gcc@6.0 + name: ubuntu 14.04 - gcc@6.0 - opencv@2 addons: apt: update: true @@ -172,11 +391,12 @@ matrix: - g++-6 - libopencv-dev env: + - additional_defines="" - MATRIX_EVAL="CC=gcc-6 && CXX=g++-6" - os: linux compiler: gcc - name: ubuntu 14.04 - gcc@7.0 + name: ubuntu 14.04 - gcc@7.0 - opencv@2 addons: apt: update: true @@ -186,11 +406,12 @@ matrix: - libopencv-dev - g++-7 env: + - additional_defines="" - MATRIX_EVAL="CC=gcc-7 && CXX=g++-7" - os: linux compiler: gcc - name: ubuntu 14.04 - gcc@8.0 + name: ubuntu 14.04 - gcc@8.0 - opencv@2 addons: apt: update: true @@ -200,24 +421,26 @@ matrix: - libopencv-dev - g++-8 env: + - additional_defines="" - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8" - os: linux compiler: clang - name: ubuntu 14.04 - native clang - CUDA 10 + name: ubuntu 14.04 - native clang - CUDA 10 - opencv@2 addons: apt: update: true packages: - libopencv-dev env: + - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: clang - name: ubuntu 14.04 - clang@3.6 - CUDA 10 + name: ubuntu 14.04 - clang@3.6 - CUDA 10 - opencv@2 addons: apt: update: true @@ -225,13 +448,14 @@ matrix: - libopencv-dev - clang-3.6 env: + - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=clang-3.6 && CXX=clang++-3.6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: clang - name: ubuntu 14.04 - clang@3.8 - CUDA 10 + name: ubuntu 14.04 - clang@3.8 - CUDA 10 - opencv@2 addons: apt: update: true @@ -241,13 +465,14 @@ matrix: - libopencv-dev - clang-3.8 env: + - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=clang-3.8 && CXX=clang++-3.8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: clang - name: ubuntu 14.04 - clang@3.9 - CUDA 10 + name: ubuntu 14.04 - clang@3.9 - CUDA 10 - opencv@2 addons: apt: update: true @@ -257,13 +482,14 @@ matrix: - libopencv-dev - clang-3.9 env: + - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=clang-3.9 && CXX=clang++-3.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: clang - name: ubuntu 14.04 - clang@4.0 - CUDA 10 + name: ubuntu 14.04 - clang@4.0 - CUDA 10 - opencv@2 addons: apt: update: true @@ -273,13 +499,14 @@ matrix: - libopencv-dev - clang-4.0 env: + - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=clang-4.0 && CXX=clang++-4.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: clang - name: ubuntu 14.04 - clang@5.0 - CUDA 10 + name: ubuntu 14.04 - clang@5.0 - CUDA 10 - opencv@2 addons: apt: update: true @@ -289,13 +516,14 @@ matrix: - libopencv-dev - clang-5.0 env: + - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=clang-5.0 && CXX=clang++-5.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: clang - name: ubuntu 14.04 - clang@6.0 - CUDA 10 + name: ubuntu 14.04 - clang@6.0 - CUDA 10 - opencv@2 addons: apt: update: true @@ -306,26 +534,28 @@ matrix: - libopencv-dev - clang-6.0 env: + - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=clang-6.0 && CXX=clang++-6.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: gcc - name: ubuntu 14.04 - native gcc - CUDA 10 + name: ubuntu 14.04 - native gcc - CUDA 10 - opencv@2 addons: apt: update: true packages: - libopencv-dev env: + - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: gcc - name: ubuntu 14.04 - gcc@4.9 - CUDA 10 + name: ubuntu 14.04 - gcc@4.9 - CUDA 10 - opencv@2 addons: apt: update: true @@ -335,13 +565,14 @@ matrix: - libopencv-dev - g++-4.9 env: + - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=gcc-4.9 && CXX=g++-4.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: gcc - name: ubuntu 14.04 - gcc@5.0 - CUDA 10 + name: ubuntu 14.04 - gcc@5.0 - CUDA 10 - opencv@2 addons: apt: update: true @@ -351,13 +582,14 @@ matrix: - libopencv-dev - g++-5 env: + - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=gcc-5 && CXX=g++-5 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: gcc - name: ubuntu 14.04 - gcc@6.0 - CUDA 10 + name: ubuntu 14.04 - gcc@6.0 - CUDA 10 - opencv@2 addons: apt: update: true @@ -367,13 +599,14 @@ matrix: - g++-6 - libopencv-dev env: + - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=gcc-6 && CXX=g++-6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: gcc - name: ubuntu 14.04 - gcc@7.0 - CUDA 10 + name: ubuntu 14.04 - gcc@7.0 - CUDA 10 - opencv@2 addons: apt: update: true @@ -383,13 +616,14 @@ matrix: - libopencv-dev - g++-7 env: + - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=gcc-7 && CXX=g++-7 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: gcc - name: ubuntu 14.04 - gcc@8.0 - CUDA 10 + name: ubuntu 14.04 - gcc@8.0 - CUDA 10 - opencv@2 addons: apt: update: true @@ -399,6 +633,7 @@ matrix: - libopencv-dev - g++-8 env: + - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" @@ -413,13 +648,14 @@ install: - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then export PATH=$PWD/cmake-3.13.4-Linux-x86_64/bin:$PATH ;fi before_script: + - echo ${additional_defines} - mkdir build_debug - cd build_debug - - cmake .. -DCMAKE_BUILD_TYPE="Debug" + - cmake .. -DCMAKE_BUILD_TYPE="Debug" ${additional_defines} - cd .. - mkdir build_release - cd build_release - - cmake .. -DCMAKE_BUILD_TYPE="Release" + - cmake .. -DCMAKE_BUILD_TYPE="Release" ${additional_defines} - cd .. script: diff --git a/CMakeLists.txt b/CMakeLists.txt index 323c1972583..acd6ca07012 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -262,7 +262,7 @@ endif() target_compile_definitions(darklib PRIVATE LIB_EXPORTS=1) set_target_properties(darklib PROPERTIES LINKER_LANGUAGE CXX) -if(OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRACK) +if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRACK) add_executable(uselib_track ${CMAKE_CURRENT_LIST_DIR}/src/yolo_console_dll.cpp) target_compile_definitions(uselib_track PRIVATE TRACK_OPTFLOW=1) set_target_properties(uselib_track PROPERTIES LINKER_LANGUAGE CXX) @@ -324,7 +324,7 @@ if(ENABLE_CUDA) endif() target_link_libraries(uselib PRIVATE darklib) -if(OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRACK) +if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRACK) target_link_libraries(uselib_track PRIVATE darklib) endif() @@ -340,7 +340,7 @@ install(TARGETS darklib EXPORT DarknetTargets install(TARGETS uselib darknet DESTINATION "${INSTALL_BIN_DIR}" ) -if(OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRACK) +if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRACK) install(TARGETS uselib_track DESTINATION "${INSTALL_BIN_DIR}" ) diff --git a/README.md b/README.md index e5a51ed9c3d..ce12232eb2b 100644 --- a/README.md +++ b/README.md @@ -44,10 +44,10 @@ More details: http://pjreddie.com/darknet/yolo/ * Windows or Linux * **CMake >= 3.8** for modern CUDA support: https://cmake.org/download/ * **CUDA 10.0**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) -* **OpenCV < 4.0**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or download from [OpenCV official site](https://opencv.org/releases.html) (on Windows set system variable `OPENCV_DIR` = `C:\opencv\build` - where are the `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png)) +* **OpenCV < 4.0**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or download from [OpenCV official site](https://opencv.org/releases.html) (on Windows set system variable `OpenCV_DIR` = `C:\opencv\build` - where are the `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png)) * **cuDNN >= 7.0 for CUDA 10.0** https://developer.nvidia.com/rdp/cudnn-archive (on **Linux** copy `cudnn.h`,`libcudnn.so`... as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on **Windows** copy `cudnn.h`,`cudnn64_7.dll`, `cudnn64_7.lib` as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows ) * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported -* on Linux **GCC or Clang**, on Windows **MSVS 2017 (v15)** https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community&rel=15# +* on Linux **GCC or Clang**, on Windows **MSVC 2015/2017/2019** https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community Compiling on **Windows** by using `Cmake-GUI` as on this [**IMAGE**](https://user-images.githubusercontent.com/4096485/55107892-6becf380-50e3-11e9-9a0a-556a943c429a.png): Configure -> Optional platform for generator (Set: x64) -> Finish -> Generate -> Open Project -> x64 & Release -> Build -> Build solution @@ -169,11 +169,11 @@ To run Darknet on Linux use examples from this article, just use `./darknet` ins ### How to compile on Windows (using `vcpkg`) -If you have installed: MSVS 2015/2017, CUDA 10, cuDNN 7.x, OpenCV 3.x. Then compile Darknet by using `C:\Program Files\CMake\bin\cmake-gui.exe` as on this [**IMAGE**](https://user-images.githubusercontent.com/4096485/55107892-6becf380-50e3-11e9-9a0a-556a943c429a.png): Configure -> Optional platform for generator (Set: x64) -> Finish -> Generate -> Open Project -> x64 & Release -> Build -> Build solution +If you have already installed Visual Studio 2015/2017/2019, CUDA 10, cuDNN 7.x, OpenCV 3.x, then compile Darknet by using `C:\Program Files\CMake\bin\cmake-gui.exe` as on this [**IMAGE**](https://user-images.githubusercontent.com/4096485/55107892-6becf380-50e3-11e9-9a0a-556a943c429a.png): Configure -> Optional platform for generator (Set: x64) -> Finish -> Generate -> Open Project -> x64 & Release -> Build -> Build solution -Otherwise: +Otherwise, follow these steps: -1. Install or update Visual Studio to at least version 2017, making sure to have it fully patched (run again the installer if not sure to automatically update to latest version). If you need to install from scratch, download VS from here: [Visual Studio 2017 Community](http://visualstudio.com) +1. Install or update Visual Studio to at least version 2017, making sure to have it fully patched (run again the installer if not sure to automatically update to latest version). If you need to install from scratch, download VS from here: [Visual Studio Community](http://visualstudio.com) 2. Install CUDA and cuDNN @@ -185,7 +185,7 @@ Otherwise: 6. Define another environment variable, with name `VCPKG_DEFAULT_TRIPLET` and value `x64-windows` -7. Open a Powershell (as a standard user) and type (the last command requires a confirmation and is used to clean up unnecessary files) +7. Open Powershell and type these commands: ```PowerShell PS \> cd $env:VCPKG_ROOT @@ -194,11 +194,11 @@ PS Code\vcpkg> .\vcpkg install pthreads opencv[ffmpeg] #replace with ope 8. [necessary only with CUDA] Customize the `build.ps1` script enabling the appropriate `my_cuda_compute_model` line. If not manually defined, CMake toolchain will automatically use the very low 3.0 CUDA compute model -9. Build with the Powershell script `build.ps1`. If you want to use Visual Studio, you will find a custom solution created for you by CMake after the build containing all the appropriate config flags for your system. +9. Open Powershell, go to the `darknet` folder and build with the command `.\build.ps1`. If you want to use Visual Studio, you will find two custom solutions created for you by CMake after the build, one in `build_win_debug` and the other in `build_win_release`, containing all the appropriate config flags for your system. ### How to compile on Windows (legacy way) -1. If you have **MSVS 2015, CUDA 10.0, cuDNN 7.4 and OpenCV 3.x** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release** https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg and do the: Build -> Build darknet. Also add Windows system variable `CUDNN` with path to CUDNN: https://user-images.githubusercontent.com/4096485/53249764-019ef880-36ca-11e9-8ffe-d9cf47e7e462.jpg **NOTE:** If installing OpenCV, use OpenCV 3.4.0 or earlier. This is a bug in OpenCV 3.4.1 in the C API (see [#500](https://github.com/AlexeyAB/darknet/issues/500)). +1. If you have **CUDA 10.0, cuDNN 7.4 and OpenCV 3.x** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then open `build\darknet\darknet.sln`, set **x64** and **Release** https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg and do the: Build -> Build darknet. Also add Windows system variable `CUDNN` with path to CUDNN: https://user-images.githubusercontent.com/4096485/53249764-019ef880-36ca-11e9-8ffe-d9cf47e7e462.jpg **NOTE:** If installing OpenCV, use OpenCV 3.4.0 or earlier. This is a bug in OpenCV 3.4.1 in the C API (see [#500](https://github.com/AlexeyAB/darknet/issues/500)). 1.1. Find files `opencv_world320.dll` and `opencv_ffmpeg320_64.dll` (or `opencv_world340.dll` and `opencv_ffmpeg340_64.dll`) in `C:\opencv_3.0\opencv\build\x64\vc14\bin` and put it near with `darknet.exe` @@ -216,7 +216,7 @@ PS Code\vcpkg> .\vcpkg install pthreads opencv[ffmpeg] #replace with ope 2. If you have other version of **CUDA (not 10.0)** then open `build\darknet\darknet.vcxproj` by using Notepad, find 2 places with "CUDA 10.0" and change it to your CUDA-version. Then open `\darknet.sln` -> (right click on project) -> properties -> CUDA C/C++ -> Device and remove there `;compute_75,sm_75`. Then do step 1 -3. If you **don't have GPU**, but have **MSVS 2015 and OpenCV 3.0** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet_no_gpu.sln`, set **x64** and **Release**, and do the: Build -> Build darknet_no_gpu +3. If you **don't have GPU**, but have **OpenCV 3.0** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then open `build\darknet\darknet_no_gpu.sln`, set **x64** and **Release**, and do the: Build -> Build darknet_no_gpu 4. If you have **OpenCV 2.4.13** instead of 3.0 then you should change paths after `\darknet.sln` is opened @@ -227,7 +227,7 @@ PS Code\vcpkg> .\vcpkg install pthreads opencv[ffmpeg] #replace with ope 5. If you have GPU with Tensor Cores (nVidia Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x: `\darknet.sln` -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add here: `CUDNN_HALF;` - **Note:** CUDA must be installed only after that MSVS2015 had been installed. + **Note:** CUDA must be installed only after Visual Studio has been installed. ### How to compile (custom): @@ -605,11 +605,11 @@ There are 2 APIs: ---- -1. To compile Yolo as C++ DLL-file `yolo_cpp_dll.dll` - open in MSVS2015 file `build\darknet\yolo_cpp_dll.sln`, set **x64** and **Release**, and do the: Build -> Build yolo_cpp_dll +1. To compile Yolo as C++ DLL-file `yolo_cpp_dll.dll` - open the solution `build\darknet\yolo_cpp_dll.sln`, set **x64** and **Release**, and do the: Build -> Build yolo_cpp_dll * You should have installed **CUDA 10.0** * To use cuDNN do: (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add at the beginning of line: `CUDNN;` -2. To use Yolo as DLL-file in your C++ console application - open in MSVS2015 file `build\darknet\yolo_console_dll.sln`, set **x64** and **Release**, and do the: Build -> Build yolo_console_dll +2. To use Yolo as DLL-file in your C++ console application - open the solution `build\darknet\yolo_console_dll.sln`, set **x64** and **Release**, and do the: Build -> Build yolo_console_dll * you can run your console application from Windows Explorer `build\darknet\x64\yolo_console_dll.exe` **use this command**: `yolo_console_dll.exe data/coco.names yolov3.cfg yolov3.weights test.mp4` diff --git a/build.ps1 b/build.ps1 index 478d803ed5c..c78b847e20d 100755 --- a/build.ps1 +++ b/build.ps1 @@ -15,6 +15,55 @@ $force_using_include_libs=$false #$my_cuda_compute_model=35 #Compute capability for Tesla K20/K40 #$my_cuda_compute_model=30 #Compute capability for Tesla K10, Quadro K4000 +function getProgramFiles32bit() { + $out = ${env:PROGRAMFILES(X86)} + if ($null -eq $out) { + $out = ${env:PROGRAMFILES} + } + + if ($null -eq $out) { + throw "Could not find [Program Files 32-bit]" + } + + return $out +} + +function getLatestVisualStudioWithDesktopWorkloadPath() { + $programFiles = getProgramFiles32bit + $vswhereExe = "$programFiles\Microsoft Visual Studio\Installer\vswhere.exe" + if (Test-Path $vswhereExe) { + $output = & $vswhereExe -latest -requires Microsoft.VisualStudio.Workload.NativeDesktop -format xml + [xml]$asXml = $output + foreach ($instance in $asXml.instances.instance) + { + $installationPath = $instance.InstallationPath -replace "\\$" # Remove potential trailing backslash + } + } + else { + Write-Verbose "Could not locate vswhere at $vswhereExe" + } + return $installationPath +} + + +function getLatestVisualStudioWithDesktopWorkloadVersion() { + $programFiles = getProgramFiles32bit + $vswhereExe = "$programFiles\Microsoft Visual Studio\Installer\vswhere.exe" + if (Test-Path $vswhereExe) { + $output = & $vswhereExe -latest -requires Microsoft.VisualStudio.Workload.NativeDesktop -format xml + [xml]$asXml = $output + foreach ($instance in $asXml.instances.instance) + { + $installationVersion = $instance.InstallationVersion + } + } + else { + Write-Verbose "Could not locate vswhere at $vswhereExe" + } + return $installationVersion +} + + if ((Test-Path env:VCPKG_ROOT) -and -not $force_using_include_libs) { $vcpkg_path = "$env:VCPKG_ROOT" Write-Host "Found vcpkg in VCPKG_ROOT: $vcpkg_path" @@ -40,19 +89,9 @@ if ($vcpkg_triplet -Match "x86") { } if ($null -eq (Get-Command "cl.exe" -ErrorAction SilentlyContinue)) { - $vstype = "Professional" - if (Test-Path "C:\Program Files (x86)\Microsoft Visual Studio\2017\${vstype}\Common7\Tools") { - } - else { - $vstype = "Enterprise" - if (Test-Path "C:\Program Files (x86)\Microsoft Visual Studio\2017\${vstype}\Common7\Tools") { - } - else { - $vstype = "Community" - } - } - Write-Host "Found VS 2017 ${vstype}" - Push-Location "C:\Program Files (x86)\Microsoft Visual Studio\2017\${vstype}\Common7\Tools" + $vsfound=getLatestVisualStudioWithDesktopWorkloadPath + Write-Host "Found VS in ${vsfound}" + Push-Location "${vsfound}\Common7\Tools" cmd /c "VsDevCmd.bat -arch=x64 & set" | ForEach-Object { if ($_ -match "=") { @@ -60,12 +99,29 @@ if ($null -eq (Get-Command "cl.exe" -ErrorAction SilentlyContinue)) { } } Pop-Location - Write-Host "Visual Studio 2017 ${vstype} Command Prompt variables set.`n" -ForegroundColor Yellow + Write-Host "Visual Studio Command Prompt variables set`n" -ForegroundColor Yellow } +$tokens = getLatestVisualStudioWithDesktopWorkloadVersion +$tokens = $tokens.split('.') +if ($tokens[0] -eq "14") { + $generator = "Visual Studio 14 2015" +} +elseif ($tokens[0] -eq "15") { + $generator = "Visual Studio 15 2017" +} +elseif ($tokens[0] -eq "16") { + $generator = "Visual Studio 16 2019" +} +else { + throw "Unknown Visual Studio version, unsupported configuration" +} +Write-Host "Setting up environment to use CMake generator: $generator`n" -ForegroundColor Yellow + if ($null -eq (Get-Command "nvcc.exe" -ErrorAction SilentlyContinue)) { if (Test-Path env:CUDA_PATH) { $env:PATH += ";${env:CUDA_PATH}\bin" + Write-Host "Found cuda in ${env:CUDA_PATH}`n" -ForegroundColor Yellow } else { Write-Host "Unable to find CUDA, if necessary please install it or define a CUDA_PATH env variable pointing to the install folder`n" -ForegroundColor Yellow @@ -77,17 +133,21 @@ if (Test-Path env:CUDA_PATH) { $env:CUDA_TOOLKIT_ROOT_DIR = "${env:CUDA_PATH}" Write-Host "Added missing env variable CUDA_TOOLKIT_ROOT_DIR`n" -ForegroundColor Yellow } + if ($my_cuda_compute_model) { + $additional_build_setup = "-DCUDA_COMPUTE_MODEL=${my_cuda_compute_model}" + Write-Host "Using compute capability ${my_cuda_compute_model}`n" -ForegroundColor Yellow + } + else { + Write-Host "Using default compute capability`n" -ForegroundColor Yellow + } } -if($my_cuda_compute_model) { - $additional_build_setup = "-DCUDA_COMPUTE_MODEL=${my_cuda_compute_model}" -} if ($vcpkg_path) { # DEBUG New-Item -Path .\build_win_debug -ItemType directory -Force Set-Location build_win_debug - cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Debug" $additional_build_setup .. + cmake -G "$generator" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Debug" $additional_build_setup .. cmake --build . --config Debug --target install #cmake --build . --config Debug --parallel ${number_of_build_workers} --target install #valid only for CMake 3.12+ Remove-Item DarknetConfig.cmake @@ -98,7 +158,7 @@ if ($vcpkg_path) { # RELEASE New-Item -Path .\build_win_release -ItemType directory -Force Set-Location build_win_release - cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Release" $additional_build_setup .. + cmake -G "$generator" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Release" $additional_build_setup .. cmake --build . --config Release --target install #cmake --build . --config Release --parallel ${number_of_build_workers} --target install #valid only for CMake 3.12+ Remove-Item DarknetConfig.cmake @@ -112,12 +172,12 @@ else { # if you want to manually force this case, remove VCPKG_ROOT env variable and remember to use "vcpkg integrate remove" in case you had enabled user-wide vcpkg integration New-Item -Path .\build_win_release_novcpkg -ItemType directory -Force Set-Location build_win_release_novcpkg - cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" $additional_build_setup .. + cmake -G "$generator" -T "host=x64" -A "x64" $additional_build_setup .. cmake --build . --config Release --target install #cmake --build . --config Release --parallel ${number_of_build_workers} --target install #valid only for CMake 3.12+ Remove-Item DarknetConfig.cmake Remove-Item DarknetConfigVersion.cmake - Copy-Item ..\3rdparty\pthreads\bin\pthreadVC2.dll .. + Copy-Item ..\3rdparty\pthreads\bin\*.dll .. Set-Location .. Copy-Item cmake\Modules\*.cmake share\darknet\ } diff --git a/build.sh b/build.sh index 7c0a41ef0af..786524439e5 100755 --- a/build.sh +++ b/build.sh @@ -14,24 +14,43 @@ #my_cuda_compute_model=30 #Compute capability for Tesla K10, Quadro K4000 number_of_build_workers=8 +bypass_vcpkg=true if [[ "$OSTYPE" == "darwin"* ]]; then - OpenCV_DIR="/usr/local/Cellar/opencv@3/3.4.5" - additional_defines="-DOpenCV_DIR=${OpenCV_DIR}" if [[ "$1" == "gcc" ]]; then export CC="/usr/local/bin/gcc-8" export CXX="/usr/local/bin/g++-8" fi + vcpkg_triplet="x64-darwin" +else + vcpkg_triplet="x64-linux" fi if [[ ! -z "$my_cuda_compute_model" ]]; then additional_build_setup="-DCUDA_COMPUTE_MODEL=${my_cuda_compute_model}" fi +if [[ -d ${VCPKG_ROOT} ]] && [ ! "$bypass_vcpkg" = true ] +then + vcpkg_path="${VCPKG_ROOT}" + vcpkg_define="-DCMAKE_TOOLCHAIN_FILE=${vcpkg_path}/scripts/buildsystems/vcpkg.cmake" + vcpkg_triplet_define="-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" + echo "Found vcpkg in VCPKG_ROOT: ${vcpkg_path}" +elif [ -d ${WORKSPACE}/vcpkg${vcpkg_fork} ] && [ ! "$bypass_vcpkg" = true ] +then + vcpkg_path="${WORKSPACE}/vcpkg" + vcpkg_define="-DCMAKE_TOOLCHAIN_FILE=${vcpkg_path}/scripts/buildsystems/vcpkg.cmake" + vcpkg_triplet_define="-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" + echo "Found vcpkg in WORKSPACE/vcpkg: ${vcpkg_path}" +elif [ ! "$bypass_vcpkg" = true ] +then + (>&2 echo "darknet is unsupported without vcpkg, use at your own risk!") +fi + # RELEASE mkdir -p build_release cd build_release -cmake .. -DCMAKE_BUILD_TYPE=Release ${additional_defines} ${additional_build_setup} +cmake .. -DCMAKE_BUILD_TYPE=Release ${vcpkg_define} ${vcpkg_triplet_define} ${additional_defines} ${additional_build_setup} cmake --build . --target install -- -j${number_of_build_workers} #cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ rm -f DarknetConfig.cmake @@ -42,7 +61,7 @@ cp cmake/Modules/*.cmake share/darknet # DEBUG mkdir -p build_debug cd build_debug -cmake .. -DCMAKE_BUILD_TYPE=Debug ${additional_defines} ${additional_build_setup} +cmake .. -DCMAKE_BUILD_TYPE=Debug ${vcpkg_define} ${vcpkg_triplet_define} ${additional_defines} ${additional_build_setup} cmake --build . --target install -- -j${number_of_build_workers} #cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ rm -f DarknetConfig.cmake From 5d089859c2b5300512788887f394401d9f7fae6d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 9 Apr 2019 23:53:27 +0300 Subject: [PATCH 0454/1365] Minor fix --- build/darknet/x64/calc_mAP.cmd | 4 +-- build/darknet/x64/calc_mAP_voc_py.cmd | 4 +-- build/darknet/x64/cfg/voc.data | 7 +++-- build/darknet/x64/darknet_coco.cmd | 2 +- build/darknet/x64/darknet_demo_coco.cmd | 2 +- build/darknet/x64/darknet_demo_store.cmd | 2 +- build/darknet/x64/darknet_demo_voc.cmd | 2 +- build/darknet/x64/darknet_demo_voc_param.cmd | 2 +- build/darknet/x64/darknet_demo_voc_tiny.cmd | 2 +- build/darknet/x64/darknet_net_cam_coco.cmd | 2 +- build/darknet/x64/darknet_net_cam_voc.cmd | 2 +- build/darknet/x64/darknet_tiny_v2.cmd | 2 +- build/darknet/x64/darknet_voc.cmd | 4 +-- build/darknet/x64/darknet_voc_tiny_v2.cmd | 2 +- build/darknet/x64/darknet_web_cam_voc.cmd | 4 +-- build/darknet/x64/darknet_yolo_v3.cmd | 2 +- .../x64/darknet_yolo_v3_openimages.cmd | 4 +-- build/darknet/x64/darknet_yolo_v3_video.cmd | 2 +- build/darknet/x64/train_voc.cmd | 2 +- src/utils.c | 30 +++++++++++++++++++ src/utils.h | 2 ++ 21 files changed, 59 insertions(+), 26 deletions(-) diff --git a/build/darknet/x64/calc_mAP.cmd b/build/darknet/x64/calc_mAP.cmd index 4622c52511b..a1bf2e57e7d 100644 --- a/build/darknet/x64/calc_mAP.cmd +++ b/build/darknet/x64/calc_mAP.cmd @@ -1,10 +1,10 @@ rem # How to calculate mAP (mean average precision) -rem darknet.exe detector map data/voc.data cfg/yolov2-tiny-voc.cfg yolov2-tiny-voc.weights -points 11 +rem darknet.exe detector map cfg/voc.data cfg/yolov2-tiny-voc.cfg yolov2-tiny-voc.weights -points 11 -darknet.exe detector map data/voc.data cfg/yolov2-voc.cfg yolo-voc.weights -points 11 +darknet.exe detector map cfg/voc.data cfg/yolov2-voc.cfg yolo-voc.weights -points 11 diff --git a/build/darknet/x64/calc_mAP_voc_py.cmd b/build/darknet/x64/calc_mAP_voc_py.cmd index d69b0ee3f52..cf73ffc0174 100644 --- a/build/darknet/x64/calc_mAP_voc_py.cmd +++ b/build/darknet/x64/calc_mAP_voc_py.cmd @@ -3,9 +3,9 @@ rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\Scripts\pip install cPi rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\Scripts\pip install _pickle -rem darknet.exe detector valid data/voc.data cfg/yolov2-tiny-voc.cfg yolov2-tiny-voc.weights +rem darknet.exe detector valid cfg/voc.data cfg/yolov2-tiny-voc.cfg yolov2-tiny-voc.weights -darknet.exe detector valid data/voc.data cfg/yolov2-voc.cfg yolo-voc.weights +darknet.exe detector valid cfg/voc.data cfg/yolov2-voc.cfg yolo-voc.weights reval_voc_py3.py --year 2007 --classes data\voc.names --image_set test --voc_dir E:\VOC2007_2012\VOCtrainval_11-May-2012\VOCdevkit results diff --git a/build/darknet/x64/cfg/voc.data b/build/darknet/x64/cfg/voc.data index eb6af25caaa..d6775870f65 100644 --- a/build/darknet/x64/cfg/voc.data +++ b/build/darknet/x64/cfg/voc.data @@ -1,6 +1,7 @@ classes= 20 -train = /home/pjreddie/data/voc/train.txt -valid = /home/pjreddie/data/voc/2007_test.txt +train = data/train_voc.txt +valid = data/2007_test.txt +#difficult = data/difficult_2007_test.txt names = data/voc.names -backup = /home/pjreddie/backup/ +backup = backup/ diff --git a/build/darknet/x64/darknet_coco.cmd b/build/darknet/x64/darknet_coco.cmd index 4500fc3d3c4..01cd1aafebe 100644 --- a/build/darknet/x64/darknet_coco.cmd +++ b/build/darknet/x64/darknet_coco.cmd @@ -1,6 +1,6 @@ -darknet.exe detector test data/coco.data cfg/yolov2.cfg yolov2.weights -i 0 -thresh 0.25 +darknet.exe detector test cfg/coco.data cfg/yolov2.cfg yolov2.weights -i 0 -thresh 0.25 pause \ No newline at end of file diff --git a/build/darknet/x64/darknet_demo_coco.cmd b/build/darknet/x64/darknet_demo_coco.cmd index 6a9b2bb1729..5223cac337e 100644 --- a/build/darknet/x64/darknet_demo_coco.cmd +++ b/build/darknet/x64/darknet_demo_coco.cmd @@ -1,6 +1,6 @@ -darknet.exe detector demo data/coco.data cfg/yolov2.cfg yolov2.weights test.mp4 -i 0 -out_filename res.avi +darknet.exe detector demo cfg/coco.data cfg/yolov2.cfg yolov2.weights test.mp4 -i 0 -out_filename res.avi pause \ No newline at end of file diff --git a/build/darknet/x64/darknet_demo_store.cmd b/build/darknet/x64/darknet_demo_store.cmd index 8c3cd5bc219..655c9810770 100644 --- a/build/darknet/x64/darknet_demo_store.cmd +++ b/build/darknet/x64/darknet_demo_store.cmd @@ -1,4 +1,4 @@ -darknet.exe detector demo data/voc.data cfg/yolov2-voc.cfg yolo-voc.weights test.mp4 -i 0 -out_filename res.avi +darknet.exe detector demo cfg/voc.data cfg/yolov2-voc.cfg yolo-voc.weights test.mp4 -i 0 -out_filename res.avi pause \ No newline at end of file diff --git a/build/darknet/x64/darknet_demo_voc.cmd b/build/darknet/x64/darknet_demo_voc.cmd index 7a9b56bf430..ad8a42b9eb5 100644 --- a/build/darknet/x64/darknet_demo_voc.cmd +++ b/build/darknet/x64/darknet_demo_voc.cmd @@ -1,4 +1,4 @@ -darknet.exe detector demo data/voc.data cfg/yolov2-voc.cfg yolo-voc.weights test.mp4 -i 0 +darknet.exe detector demo cfg/voc.data cfg/yolov2-voc.cfg yolo-voc.weights test.mp4 -i 0 pause \ No newline at end of file diff --git a/build/darknet/x64/darknet_demo_voc_param.cmd b/build/darknet/x64/darknet_demo_voc_param.cmd index 7f684ce26d6..5142e9ae395 100644 --- a/build/darknet/x64/darknet_demo_voc_param.cmd +++ b/build/darknet/x64/darknet_demo_voc_param.cmd @@ -1,6 +1,6 @@ -darknet.exe detector demo data/voc.data cfg/yolov2-voc.cfg yolo-voc.weights %1 -i 0 +darknet.exe detector demo cfg/voc.data cfg/yolov2-voc.cfg yolo-voc.weights %1 -i 0 pause \ No newline at end of file diff --git a/build/darknet/x64/darknet_demo_voc_tiny.cmd b/build/darknet/x64/darknet_demo_voc_tiny.cmd index 19bbe0c7004..b2b2bcb49f2 100644 --- a/build/darknet/x64/darknet_demo_voc_tiny.cmd +++ b/build/darknet/x64/darknet_demo_voc_tiny.cmd @@ -1,6 +1,6 @@ -darknet.exe detector demo data/voc.data cfg/yolov2-tiny-voc.cfg yolov2-tiny-voc.weights test.mp4 -i 0 +darknet.exe detector demo cfg/voc.data cfg/yolov2-tiny-voc.cfg yolov2-tiny-voc.weights test.mp4 -i 0 pause \ No newline at end of file diff --git a/build/darknet/x64/darknet_net_cam_coco.cmd b/build/darknet/x64/darknet_net_cam_coco.cmd index 414bff067ac..fcab6aeabe4 100644 --- a/build/darknet/x64/darknet_net_cam_coco.cmd +++ b/build/darknet/x64/darknet_net_cam_coco.cmd @@ -5,7 +5,7 @@ # # Replace the address below, on shown in the phone application -darknet.exe detector demo data/coco.data cfg/yolov2.cfg yolov2.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0 +darknet.exe detector demo cfg/coco.data cfg/yolov2.cfg yolov2.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0 pause \ No newline at end of file diff --git a/build/darknet/x64/darknet_net_cam_voc.cmd b/build/darknet/x64/darknet_net_cam_voc.cmd index 2a0a3ae4bee..82e4786f536 100644 --- a/build/darknet/x64/darknet_net_cam_voc.cmd +++ b/build/darknet/x64/darknet_net_cam_voc.cmd @@ -5,7 +5,7 @@ # # Replace the address below, on shown in the phone application -darknet.exe detector demo data/voc.data cfg/yolov2-voc.cfg yolo-voc.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0 +darknet.exe detector demo cfg/voc.data cfg/yolov2-voc.cfg yolo-voc.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0 pause \ No newline at end of file diff --git a/build/darknet/x64/darknet_tiny_v2.cmd b/build/darknet/x64/darknet_tiny_v2.cmd index 621ebf639ad..777da269259 100644 --- a/build/darknet/x64/darknet_tiny_v2.cmd +++ b/build/darknet/x64/darknet_tiny_v2.cmd @@ -1,5 +1,5 @@ -darknet.exe detector test data/coco.data cfg/yolov2-tiny.cfg yolov2-tiny.weights -i 0 dog.jpg -thresh 0.51 +darknet.exe detector test cfg/coco.data cfg/yolov2-tiny.cfg yolov2-tiny.weights -i 0 dog.jpg -thresh 0.51 diff --git a/build/darknet/x64/darknet_voc.cmd b/build/darknet/x64/darknet_voc.cmd index abc5002200d..c7a644786a9 100644 --- a/build/darknet/x64/darknet_voc.cmd +++ b/build/darknet/x64/darknet_voc.cmd @@ -1,8 +1,8 @@ -rem darknet.exe detector test data/voc.data cfg/yolov2-voc.cfg yolo-voc.weights 009460.jpg +rem darknet.exe detector test cfg/voc.data cfg/yolov2-voc.cfg yolo-voc.weights 009460.jpg -darknet.exe detector test data/voc.data cfg/yolov2-voc.cfg yolo-voc.weights -i 0 -thresh 0.24 dog.jpg +darknet.exe detector test cfg/voc.data cfg/yolov2-voc.cfg yolo-voc.weights -i 0 -thresh 0.24 dog.jpg pause \ No newline at end of file diff --git a/build/darknet/x64/darknet_voc_tiny_v2.cmd b/build/darknet/x64/darknet_voc_tiny_v2.cmd index 490fd9ff3bf..51988196649 100644 --- a/build/darknet/x64/darknet_voc_tiny_v2.cmd +++ b/build/darknet/x64/darknet_voc_tiny_v2.cmd @@ -1,5 +1,5 @@ -darknet.exe detector test data/voc.data cfg/yolov2-tiny-voc.cfg yolov2-tiny-voc.weights -i 0 dog.jpg +darknet.exe detector test cfg/voc.data cfg/yolov2-tiny-voc.cfg yolov2-tiny-voc.weights -i 0 dog.jpg diff --git a/build/darknet/x64/darknet_web_cam_voc.cmd b/build/darknet/x64/darknet_web_cam_voc.cmd index 1b509c12984..42472bc0526 100644 --- a/build/darknet/x64/darknet_web_cam_voc.cmd +++ b/build/darknet/x64/darknet_web_cam_voc.cmd @@ -1,9 +1,9 @@ # play video from Web-Camera number 0 -rem darknet.exe detector demo data/voc.data cfg/yolov2-voc.cfg yolo-voc.weights -c 0 +rem darknet.exe detector demo cfg/voc.data cfg/yolov2-voc.cfg yolo-voc.weights -c 0 -darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -c 0 +darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights -c 0 pause \ No newline at end of file diff --git a/build/darknet/x64/darknet_yolo_v3.cmd b/build/darknet/x64/darknet_yolo_v3.cmd index 2d05bdc13d3..d8fde4c5351 100644 --- a/build/darknet/x64/darknet_yolo_v3.cmd +++ b/build/darknet/x64/darknet_yolo_v3.cmd @@ -1,5 +1,5 @@ -darknet.exe detector test data/coco.data yolov3.cfg yolov3.weights -i 0 -thresh 0.25 dog.jpg -ext_output +darknet.exe detector test cfg/coco.data yolov3.cfg yolov3.weights -i 0 -thresh 0.25 dog.jpg -ext_output pause \ No newline at end of file diff --git a/build/darknet/x64/darknet_yolo_v3_openimages.cmd b/build/darknet/x64/darknet_yolo_v3_openimages.cmd index 0305e9cb3c5..06d10fe52bc 100644 --- a/build/darknet/x64/darknet_yolo_v3_openimages.cmd +++ b/build/darknet/x64/darknet_yolo_v3_openimages.cmd @@ -1,8 +1,8 @@ -darknet.exe detector test data/openimages.data cfg/yolov3-openimages.cfg yolov3-openimages.weights -i 0 -thresh 0.25 dog.jpg -ext_output +darknet.exe detector test cfg/openimages.data cfg/yolov3-openimages.cfg yolov3-openimages.weights -i 0 -thresh 0.25 dog.jpg -ext_output -darknet.exe detector demo data/openimages.data cfg/yolov3-openimages.cfg yolov3-openimages.weights -i 0 -thresh 0.25 street1k.mp4 -ext_output +darknet.exe detector demo cfg/openimages.data cfg/yolov3-openimages.cfg yolov3-openimages.weights -i 0 -thresh 0.25 street1k.mp4 -ext_output pause \ No newline at end of file diff --git a/build/darknet/x64/darknet_yolo_v3_video.cmd b/build/darknet/x64/darknet_yolo_v3_video.cmd index 26efcfdc048..88c5a69f80b 100644 --- a/build/darknet/x64/darknet_yolo_v3_video.cmd +++ b/build/darknet/x64/darknet_yolo_v3_video.cmd @@ -1,5 +1,5 @@ -darknet.exe detector demo data/coco.data yolov3.cfg yolov3.weights -i 0 -thresh 0.25 -ext_output test.mp4 +darknet.exe detector demo cfg/coco.data yolov3.cfg yolov3.weights -i 0 -thresh 0.25 -ext_output test.mp4 pause \ No newline at end of file diff --git a/build/darknet/x64/train_voc.cmd b/build/darknet/x64/train_voc.cmd index 72714346c57..c74d9ca1a5b 100644 --- a/build/darknet/x64/train_voc.cmd +++ b/build/darknet/x64/train_voc.cmd @@ -1 +1 @@ -darknet.exe detector train data/voc.data yolo-voc.cfg darknet19_448.conv.23 \ No newline at end of file +darknet.exe detector train cfg/voc.data yolo-voc.cfg darknet19_448.conv.23 \ No newline at end of file diff --git a/src/utils.c b/src/utils.c index 6afc187c213..debe5785547 100644 --- a/src/utils.c +++ b/src/utils.c @@ -872,3 +872,33 @@ int check_array_is_inf(float *arr, int size) } return 0; } + +int *random_index_order(int min, int max) +{ + int *inds = calloc(max - min, sizeof(int)); + int i; + for (i = min; i < max; ++i) { + inds[i - min] = i; + } + for (i = min; i < max - 1; ++i) { + int swap = inds[i - min]; + int index = i + rand() % (max - i); + inds[i - min] = inds[index - min]; + inds[index - min] = swap; + } + return inds; +} + +int max_int_index(int *a, int n) +{ + if (n <= 0) return -1; + int i, max_i = 0; + int max = a[0]; + for (i = 1; i < n; ++i) { + if (a[i] > max) { + max = a[i]; + max_i = i; + } + } + return max_i; +} \ No newline at end of file diff --git a/src/utils.h b/src/utils.h index bae6adff1b6..0db4f9198ed 100644 --- a/src/utils.h +++ b/src/utils.h @@ -76,6 +76,8 @@ unsigned int uint_rand(unsigned int less_than); int check_array_is_nan(float *arr, int size); int check_array_is_inf(float *arr, int size); int int_index(int *a, int val, int n); +int *random_index_order(int min, int max); +int max_int_index(int *a, int n); #ifdef __cplusplus } From 486562b2f23b0d6bd540c4a73e0f985f8a6a0109 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 10 Apr 2019 00:56:50 +0300 Subject: [PATCH 0455/1365] Compile fix --- src/utils.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/utils.c b/src/utils.c index debe5785547..cdaf1e617b7 100644 --- a/src/utils.c +++ b/src/utils.c @@ -875,7 +875,7 @@ int check_array_is_inf(float *arr, int size) int *random_index_order(int min, int max) { - int *inds = calloc(max - min, sizeof(int)); + int *inds = (int *)calloc(max - min, sizeof(int)); int i; for (i = min; i < max; ++i) { inds[i - min] = i; From e782d9618017e7fb6ee5c2b5fafc9e90d3d36bec Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 10 Apr 2019 10:51:54 +0200 Subject: [PATCH 0456/1365] [VS] add support for BuildTools (only compilers, partial installation of Visual Studio) --- CMakeLists.txt | 2 +- DarknetConfig.cmake.in | 2 +- build.ps1 | 62 ++++++++++++++++++++++++++++-------------- build.sh | 4 +-- 4 files changed, 45 insertions(+), 25 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index acd6ca07012..5a8868b59d9 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -29,7 +29,7 @@ set(CMAKE_CXX_STANDARD 11) set(CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/cmake/Modules/" ${CMAKE_MODULE_PATH}) set(CMAKE_INSTALL_PREFIX "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Install prefix" FORCE) -set(INSTALL_BIN_DIR "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Path where exe and dll will be installed" FORCE) +set(INSTALL_BIN_DIR "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Path where exe and dll will be installed") set(INSTALL_LIB_DIR "lib" CACHE PATH "Path where lib will be installed") set(INSTALL_INCLUDE_DIR "include" CACHE PATH "Path where headers will be installed") set(INSTALL_CMAKE_DIR "share/darknet" CACHE PATH "Path where cmake configs will be installed") diff --git a/DarknetConfig.cmake.in b/DarknetConfig.cmake.in index 606503f2e1f..8b8bd4c73cd 100644 --- a/DarknetConfig.cmake.in +++ b/DarknetConfig.cmake.in @@ -14,7 +14,7 @@ if(@ENABLE_OPENCV@) find_dependency(OpenCV REQUIRED) endif() -find_dependency(Stb REQUIRED HINTS "@Stb_DIR@") +find_dependency(Stb REQUIRED) if(@ENABLE_CUDA@) find_dependency(CUDNN) diff --git a/build.ps1 b/build.ps1 index c78b847e20d..8017ce002f3 100755 --- a/build.ps1 +++ b/build.ps1 @@ -32,15 +32,25 @@ function getLatestVisualStudioWithDesktopWorkloadPath() { $programFiles = getProgramFiles32bit $vswhereExe = "$programFiles\Microsoft Visual Studio\Installer\vswhere.exe" if (Test-Path $vswhereExe) { - $output = & $vswhereExe -latest -requires Microsoft.VisualStudio.Workload.NativeDesktop -format xml + $output = & $vswhereExe -products * -latest -requires Microsoft.VisualStudio.Workload.NativeDesktop -format xml [xml]$asXml = $output - foreach ($instance in $asXml.instances.instance) - { + foreach ($instance in $asXml.instances.instance) { + $installationPath = $instance.InstallationPath -replace "\\$" # Remove potential trailing backslash + } + if (!$installationPath) { + Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also partial installations" -ForegroundColor Yellow + $output = & $vswhereExe -products * -latest -format xml + [xml]$asXml = $output + foreach ($instance in $asXml.instances.instance) { $installationPath = $instance.InstallationPath -replace "\\$" # Remove potential trailing backslash + } + } + if (!$installationPath) { + Throw "Could not locate any installation of Visual Studio" } } else { - Write-Verbose "Could not locate vswhere at $vswhereExe" + Throw "Could not locate vswhere at $vswhereExe" } return $installationPath } @@ -50,15 +60,25 @@ function getLatestVisualStudioWithDesktopWorkloadVersion() { $programFiles = getProgramFiles32bit $vswhereExe = "$programFiles\Microsoft Visual Studio\Installer\vswhere.exe" if (Test-Path $vswhereExe) { - $output = & $vswhereExe -latest -requires Microsoft.VisualStudio.Workload.NativeDesktop -format xml + $output = & $vswhereExe -products * -latest -requires Microsoft.VisualStudio.Workload.NativeDesktop -format xml [xml]$asXml = $output - foreach ($instance in $asXml.instances.instance) - { - $installationVersion = $instance.InstallationVersion + foreach ($instance in $asXml.instances.instance) { + $installationVersion = $instance.InstallationVersion + } + if (!$installationVersion) { + Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also partial installations" -ForegroundColor Yellow + $output = & $vswhereExe -products * -latest -format xml + [xml]$asXml = $output + foreach ($instance in $asXml.instances.instance) { + $installationVersion = $instance.installationVersion + } + } + if (!$installationVersion) { + Throw "Could not locate any installation of Visual Studio" } } else { - Write-Verbose "Could not locate vswhere at $vswhereExe" + Throw "Could not locate vswhere at $vswhereExe" } return $installationVersion } @@ -77,7 +97,7 @@ else { } if ($null -eq $env:VCPKG_DEFAULT_TRIPLET) { - Write-Host "No default triplet has been set-up for vcpkg. Defaulting to x64-windows`n" -ForegroundColor Yellow + Write-Host "No default triplet has been set-up for vcpkg. Defaulting to x64-windows" -ForegroundColor Yellow $vcpkg_triplet = "x64-windows" } else { @@ -89,17 +109,17 @@ if ($vcpkg_triplet -Match "x86") { } if ($null -eq (Get-Command "cl.exe" -ErrorAction SilentlyContinue)) { - $vsfound=getLatestVisualStudioWithDesktopWorkloadPath + $vsfound = getLatestVisualStudioWithDesktopWorkloadPath Write-Host "Found VS in ${vsfound}" Push-Location "${vsfound}\Common7\Tools" - cmd /c "VsDevCmd.bat -arch=x64 & set" | - ForEach-Object { + cmd.exe /c "VsDevCmd.bat -arch=x64 & set" | + ForEach-Object { if ($_ -match "=") { - $v = $_.split("="); set-item -force -path "ENV:\$($v[0])" -value "$($v[1])" + $v = $_.split("="); Set-Item -force -path "ENV:\$($v[0])" -value "$($v[1])" } } Pop-Location - Write-Host "Visual Studio Command Prompt variables set`n" -ForegroundColor Yellow + Write-Host "Visual Studio Command Prompt variables set" -ForegroundColor Yellow } $tokens = getLatestVisualStudioWithDesktopWorkloadVersion @@ -116,29 +136,29 @@ elseif ($tokens[0] -eq "16") { else { throw "Unknown Visual Studio version, unsupported configuration" } -Write-Host "Setting up environment to use CMake generator: $generator`n" -ForegroundColor Yellow +Write-Host "Setting up environment to use CMake generator: $generator" -ForegroundColor Yellow if ($null -eq (Get-Command "nvcc.exe" -ErrorAction SilentlyContinue)) { if (Test-Path env:CUDA_PATH) { $env:PATH += ";${env:CUDA_PATH}\bin" - Write-Host "Found cuda in ${env:CUDA_PATH}`n" -ForegroundColor Yellow + Write-Host "Found cuda in ${env:CUDA_PATH}" -ForegroundColor Yellow } else { - Write-Host "Unable to find CUDA, if necessary please install it or define a CUDA_PATH env variable pointing to the install folder`n" -ForegroundColor Yellow + Write-Host "Unable to find CUDA, if necessary please install it or define a CUDA_PATH env variable pointing to the install folder" -ForegroundColor Yellow } } if (Test-Path env:CUDA_PATH) { if (-Not(Test-Path env:CUDA_TOOLKIT_ROOT_DIR)) { $env:CUDA_TOOLKIT_ROOT_DIR = "${env:CUDA_PATH}" - Write-Host "Added missing env variable CUDA_TOOLKIT_ROOT_DIR`n" -ForegroundColor Yellow + Write-Host "Added missing env variable CUDA_TOOLKIT_ROOT_DIR" -ForegroundColor Yellow } if ($my_cuda_compute_model) { $additional_build_setup = "-DCUDA_COMPUTE_MODEL=${my_cuda_compute_model}" - Write-Host "Using compute capability ${my_cuda_compute_model}`n" -ForegroundColor Yellow + Write-Host "Using compute capability ${my_cuda_compute_model}" -ForegroundColor Yellow } else { - Write-Host "Using default compute capability`n" -ForegroundColor Yellow + Write-Host "Using default compute capability" -ForegroundColor Yellow } } diff --git a/build.sh b/build.sh index 786524439e5..d72acab086f 100755 --- a/build.sh +++ b/build.sh @@ -56,7 +56,7 @@ cmake --build . --target install -- -j${number_of_build_workers} rm -f DarknetConfig.cmake rm -f DarknetConfigVersion.cmake cd .. -cp cmake/Modules/*.cmake share/darknet +cp cmake/Modules/*.cmake share/darknet/ # DEBUG mkdir -p build_debug @@ -67,4 +67,4 @@ cmake --build . --target install -- -j${number_of_build_workers} rm -f DarknetConfig.cmake rm -f DarknetConfigVersion.cmake cd .. -cp cmake/Modules/*.cmake share/darknet +cp cmake/Modules/*.cmake share/darknet/ From e647a59f480296813c4c53d1fa46a2c8d5513029 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 10 Apr 2019 13:52:53 +0200 Subject: [PATCH 0457/1365] [README] include OpenCV 4 into supported OpenCV versions --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index ce12232eb2b..8d87a98eb29 100644 --- a/README.md +++ b/README.md @@ -44,7 +44,7 @@ More details: http://pjreddie.com/darknet/yolo/ * Windows or Linux * **CMake >= 3.8** for modern CUDA support: https://cmake.org/download/ * **CUDA 10.0**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) -* **OpenCV < 4.0**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or download from [OpenCV official site](https://opencv.org/releases.html) (on Windows set system variable `OpenCV_DIR` = `C:\opencv\build` - where are the `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png)) +* **OpenCV > 2.4**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or download from [OpenCV official site](https://opencv.org/releases.html) (on Windows set system variable `OpenCV_DIR` = `C:\opencv\build` - where are the `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png)) * **cuDNN >= 7.0 for CUDA 10.0** https://developer.nvidia.com/rdp/cudnn-archive (on **Linux** copy `cudnn.h`,`libcudnn.so`... as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on **Windows** copy `cudnn.h`,`cudnn64_7.dll`, `cudnn64_7.lib` as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows ) * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported * on Linux **GCC or Clang**, on Windows **MSVC 2015/2017/2019** https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community @@ -100,7 +100,7 @@ Others: https://www.youtube.com/user/pjreddie/videos * run `./darknet detector demo ... -json_port 8070 -mjpeg_port 8090` as JSON and MJPEG server to get results online over the network by using your soft or Web-browser * added calculation of anchors for training * added example of Detection and Tracking objects: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp -* fixed code for use Web-cam on OpenCV 3.x +* fixed code for use Web-cam on OpenCV > 3.x * run-time tips and warnings if you use incorrect cfg-file or dataset * many other fixes of code... @@ -159,7 +159,7 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c * `GPU=1` to build with CUDA to accelerate by using GPU (CUDA should be in `/usr/local/cuda`) * `CUDNN=1` to build with cuDNN v5-v7 to accelerate training by using GPU (cuDNN should be in `/usr/local/cudnn`) * `CUDNN_HALF=1` to build for Tensor Cores (on Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x -* `OPENCV=1` to build with OpenCV 3.x/2.4.x - allows to detect on video files and video streams from network cameras or web-cams +* `OPENCV=1` to build with OpenCV 4.x/3.x/2.4.x - allows to detect on video files and video streams from network cameras or web-cams * `DEBUG=1` to bould debug version of Yolo * `OPENMP=1` to build with OpenMP support to accelerate Yolo by using multi-core CPU * `LIBSO=1` to build a library `darknet.so` and binary runable file `uselib` that uses this library. Or you can try to run so `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib test.mp4` How to use this SO-library from your own code - you can look at C++ example: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp @@ -169,7 +169,7 @@ To run Darknet on Linux use examples from this article, just use `./darknet` ins ### How to compile on Windows (using `vcpkg`) -If you have already installed Visual Studio 2015/2017/2019, CUDA 10, cuDNN 7.x, OpenCV 3.x, then compile Darknet by using `C:\Program Files\CMake\bin\cmake-gui.exe` as on this [**IMAGE**](https://user-images.githubusercontent.com/4096485/55107892-6becf380-50e3-11e9-9a0a-556a943c429a.png): Configure -> Optional platform for generator (Set: x64) -> Finish -> Generate -> Open Project -> x64 & Release -> Build -> Build solution +If you have already installed Visual Studio 2015/2017/2019, CUDA > 10.0, cuDNN > 7.0, OpenCV > 2.4, then compile Darknet by using `C:\Program Files\CMake\bin\cmake-gui.exe` as on this [**IMAGE**](https://user-images.githubusercontent.com/4096485/55107892-6becf380-50e3-11e9-9a0a-556a943c429a.png): Configure -> Optional platform for generator (Set: x64) -> Finish -> Generate -> Open Project -> x64 & Release -> Build -> Build solution Otherwise, follow these steps: @@ -198,7 +198,7 @@ PS Code\vcpkg> .\vcpkg install pthreads opencv[ffmpeg] #replace with ope ### How to compile on Windows (legacy way) -1. If you have **CUDA 10.0, cuDNN 7.4 and OpenCV 3.x** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then open `build\darknet\darknet.sln`, set **x64** and **Release** https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg and do the: Build -> Build darknet. Also add Windows system variable `CUDNN` with path to CUDNN: https://user-images.githubusercontent.com/4096485/53249764-019ef880-36ca-11e9-8ffe-d9cf47e7e462.jpg **NOTE:** If installing OpenCV, use OpenCV 3.4.0 or earlier. This is a bug in OpenCV 3.4.1 in the C API (see [#500](https://github.com/AlexeyAB/darknet/issues/500)). +1. If you have **CUDA 10.0, cuDNN 7.4 and OpenCV 3.x** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then open `build\darknet\darknet.sln`, set **x64** and **Release** https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg and do the: Build -> Build darknet. Also add Windows system variable `CUDNN` with path to CUDNN: https://user-images.githubusercontent.com/4096485/53249764-019ef880-36ca-11e9-8ffe-d9cf47e7e462.jpg 1.1. Find files `opencv_world320.dll` and `opencv_ffmpeg320_64.dll` (or `opencv_world340.dll` and `opencv_ffmpeg340_64.dll`) in `C:\opencv_3.0\opencv\build\x64\vc14\bin` and put it near with `darknet.exe` From 1cbdd293f305607a487e184c2875de6a10c133cb Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 10 Apr 2019 14:02:19 +0200 Subject: [PATCH 0458/1365] sync cmake version with hardcoded version --- CMakeLists.txt | 11 ++++++++--- src/parser.c | 9 +++++---- src/version.h | 3 +++ src/version.h.in | 3 +++ 4 files changed, 19 insertions(+), 7 deletions(-) create mode 100644 src/version.h create mode 100644 src/version.h.in diff --git a/CMakeLists.txt b/CMakeLists.txt index 5a8868b59d9..4f22a0377bd 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,8 +1,8 @@ cmake_minimum_required(VERSION 3.8) -set(Darknet_MAJOR_VERSION 1) -set(Darknet_MINOR_VERSION 0) -set(Darknet_PATCH_VERSION 0) +set(Darknet_MAJOR_VERSION 0) +set(Darknet_MINOR_VERSION 2) +set(Darknet_PATCH_VERSION 5) set(Darknet_VERSION ${Darknet_MAJOR_VERSION}.${Darknet_MINOR_VERSION}.${Darknet_PATCH_VERSION}) set(CMAKE_VERBOSE_MAKEFILE "FALSE" CACHE BOOL "Create verbose makefile") @@ -41,6 +41,11 @@ foreach(p LIB BIN INCLUDE CMAKE) endif() endforeach() +configure_file( + "${CMAKE_CURRENT_LIST_DIR}/src/version.h.in" + "${CMAKE_CURRENT_LIST_DIR}/src/version.h" +) + set(ENABLE_CUDA "TRUE" CACHE BOOL "Enable CUDA build") if(${CMAKE_VERSION} VERSION_LESS "3.8.0") set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA build" FORCE) diff --git a/src/parser.c b/src/parser.c index 557e07ba1d7..782b5820c0f 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1,6 +1,7 @@ #include #include #include +#include #include "activation_layer.h" #include "activations.h" @@ -32,8 +33,8 @@ #include "softmax_layer.h" #include "utils.h" #include "upsample_layer.h" +#include "version.h" #include "yolo_layer.h" -#include typedef struct{ char *type; @@ -1038,9 +1039,9 @@ void save_weights_upto(network net, char *filename, int cutoff) FILE *fp = fopen(filename, "wb"); if(!fp) file_error(filename); - int major = 0; - int minor = 2; - int revision = 5; + int major = MAJOR_VERSION; + int minor = MINOR_VERSION; + int revision = PATCH_VERSION; fwrite(&major, sizeof(int), 1, fp); fwrite(&minor, sizeof(int), 1, fp); fwrite(&revision, sizeof(int), 1, fp); diff --git a/src/version.h b/src/version.h new file mode 100644 index 00000000000..7b103078dfd --- /dev/null +++ b/src/version.h @@ -0,0 +1,3 @@ +#define MAJOR_VERSION 0 +#define MINOR_VERSION 2 +#define PATCH_VERSION 5 diff --git a/src/version.h.in b/src/version.h.in new file mode 100644 index 00000000000..e9081190741 --- /dev/null +++ b/src/version.h.in @@ -0,0 +1,3 @@ +#define MAJOR_VERSION @Darknet_MAJOR_VERSION@ +#define MINOR_VERSION @Darknet_MINOR_VERSION@ +#define PATCH_VERSION @Darknet_PATCH_VERSION@ From ac0dc5e696f7cb22cdcff1b75e54e5764156ff22 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 10 Apr 2019 16:29:58 +0200 Subject: [PATCH 0459/1365] [CI] add opencv@3 on ubuntu, add caching mechanism for vcpkg on appveyor --- .travis.yml | 204 +++++++++++++++++++++++++++++++++++++++++++++++++++ appveyor.yml | 14 ++-- 2 files changed, 211 insertions(+), 7 deletions(-) diff --git a/.travis.yml b/.travis.yml index b01e6d36438..b34ca94bdc1 100644 --- a/.travis.yml +++ b/.travis.yml @@ -424,6 +424,210 @@ matrix: - additional_defines="" - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8" + - os: linux + compiler: clang + name: ubuntu 14.04 - native clang - opencv@3 + addons: + apt: + update: true + sources: + - sourceline: 'ppa:timsc/opencv-3.4' + packages: + - libopencv-dev + env: + - additional_defines="" + - MATRIX_EVAL="" + + - os: linux + compiler: clang + name: ubuntu 14.04 - clang@3.6 - opencv@3 + addons: + apt: + update: true + sources: + - sourceline: 'ppa:timsc/opencv-3.4' + packages: + - libopencv-dev + - clang-3.6 + env: + - additional_defines="" + - MATRIX_EVAL="CC=clang-3.6 && CXX=clang++-3.6" + + - os: linux + compiler: clang + name: ubuntu 14.04 - clang@3.8 - opencv@3 + addons: + apt: + update: true + sources: + - llvm-toolchain-precise-3.8 + - sourceline: 'ppa:timsc/opencv-3.4' + packages: + - libopencv-dev + - clang-3.8 + env: + - additional_defines="" + - MATRIX_EVAL="CC=clang-3.8 && CXX=clang++-3.8" + + - os: linux + compiler: clang + name: ubuntu 14.04 - clang@3.9 - opencv@3 + addons: + apt: + update: true + sources: + - llvm-toolchain-trusty-3.9 + - sourceline: 'ppa:timsc/opencv-3.4' + packages: + - libopencv-dev + - clang-3.9 + env: + - additional_defines="" + - MATRIX_EVAL="CC=clang-3.9 && CXX=clang++-3.9" + + - os: linux + compiler: clang + name: ubuntu 14.04 - clang@4.0 - opencv@3 + addons: + apt: + update: true + sources: + - llvm-toolchain-trusty-4.0 + - sourceline: 'ppa:timsc/opencv-3.4' + packages: + - libopencv-dev + - clang-4.0 + env: + - additional_defines="" + - MATRIX_EVAL="CC=clang-4.0 && CXX=clang++-4.0" + + - os: linux + compiler: clang + name: ubuntu 14.04 - clang@5.0 - opencv@3 + addons: + apt: + update: true + sources: + - llvm-toolchain-trusty-5.0 + - sourceline: 'ppa:timsc/opencv-3.4' + packages: + - libopencv-dev + - clang-5.0 + env: + - additional_defines="" + - MATRIX_EVAL="CC=clang-5.0 && CXX=clang++-5.0" + + - os: linux + compiler: clang + name: ubuntu 14.04 - clang@6.0 - opencv@3 + addons: + apt: + update: true + sources: + - llvm-toolchain-trusty-6.0 + - ubuntu-toolchain-r-test + - sourceline: 'ppa:timsc/opencv-3.4' + packages: + - libopencv-dev + - clang-6.0 + env: + - additional_defines="" + - MATRIX_EVAL="CC=clang-6.0 && CXX=clang++-6.0" + + - os: linux + compiler: gcc + name: ubuntu 14.04 - native gcc - opencv@3 + addons: + apt: + update: true + sources: + - sourceline: 'ppa:timsc/opencv-3.4' + packages: + - libopencv-dev + env: + - additional_defines="" + - MATRIX_EVAL="" + + - os: linux + compiler: gcc + name: ubuntu 14.04 - gcc@4.9 - opencv@3 + addons: + apt: + update: true + sources: + - ubuntu-toolchain-r-test + - sourceline: 'ppa:timsc/opencv-3.4' + packages: + - libopencv-dev + - g++-4.9 + env: + - additional_defines="" + - MATRIX_EVAL="CC=gcc-4.9 && CXX=g++-4.9" + + - os: linux + compiler: gcc + name: ubuntu 14.04 - gcc@5.0 - opencv@3 + addons: + apt: + update: true + sources: + - ubuntu-toolchain-r-test + - sourceline: 'ppa:timsc/opencv-3.4' + packages: + - libopencv-dev + - g++-5 + env: + - additional_defines="" + - MATRIX_EVAL="CC=gcc-5 && CXX=g++-5" + + - os: linux + compiler: gcc + name: ubuntu 14.04 - gcc@6.0 - opencv@3 + addons: + apt: + update: true + sources: + - ubuntu-toolchain-r-test + - sourceline: 'ppa:timsc/opencv-3.4' + packages: + - g++-6 + - libopencv-dev + env: + - additional_defines="" + - MATRIX_EVAL="CC=gcc-6 && CXX=g++-6" + + - os: linux + compiler: gcc + name: ubuntu 14.04 - gcc@7.0 - opencv@3 + addons: + apt: + update: true + sources: + - ubuntu-toolchain-r-test + - sourceline: 'ppa:timsc/opencv-3.4' + packages: + - libopencv-dev + - g++-7 + env: + - additional_defines="" + - MATRIX_EVAL="CC=gcc-7 && CXX=g++-7" + + - os: linux + compiler: gcc + name: ubuntu 14.04 - gcc@8.0 - opencv@3 + addons: + apt: + update: true + sources: + - ubuntu-toolchain-r-test + - sourceline: 'ppa:timsc/opencv-3.4' + packages: + - libopencv-dev + - g++-8 + env: + - additional_defines="" + - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8" + - os: linux compiler: clang name: ubuntu 14.04 - native clang - CUDA 10 - opencv@2 diff --git a/appveyor.yml b/appveyor.yml index 0860ce29588..911ed3d1c22 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -1,5 +1,6 @@ image: Visual Studio 2017 clone_folder: c:\projects\darknet +cache: C:\Tools\vcpkg\installed\ environment: WORKSPACE: C:\projects @@ -11,25 +12,25 @@ environment: - platform: Win64 COMPILER: vs configuration: Release - VCPKG_ROOT: C:\projects\vcpkg + VCPKG_ROOT: C:\Tools\vcpkg VCPKG_DEFAULT_TRIPLET: x64-windows USE_CUDA: yes - platform: Win64 COMPILER: vs configuration: Debug - VCPKG_ROOT: C:\projects\vcpkg + VCPKG_ROOT: C:\Tools\vcpkg VCPKG_DEFAULT_TRIPLET: x64-windows USE_CUDA: yes - platform: Win64 COMPILER: vs configuration: Release - VCPKG_ROOT: C:\projects\vcpkg + VCPKG_ROOT: C:\Tools\vcpkg VCPKG_DEFAULT_TRIPLET: x64-windows USE_CUDA: no - platform: Win64 COMPILER: vs configuration: Debug - VCPKG_ROOT: C:\projects\vcpkg + VCPKG_ROOT: C:\Tools\vcpkg VCPKG_DEFAULT_TRIPLET: x64-windows USE_CUDA: no - platform: Win64 @@ -62,10 +63,9 @@ install: - if [%COMPILER%]==[cygwin] %CYGSH% 'make -j8' - if [%COMPILER%]==[cygwin] %CYGSH% 'make install' - if [%COMPILER%]==[cygwin] cd %WORKSPACE% - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] git clone https://github.com/Microsoft/vcpkg - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] cd %VCPKG_ROOT% - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] bootstrap-vcpkg.bat - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg integrate install + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] git pull + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] .\bootstrap-vcpkg.bat - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] if [%USE_CUDA%]==[yes] vcpkg install cuda - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg install opencv[ffmpeg] pthreads ## opencv[ffmpeg,cuda] is too big to build, timing out (>1h). We use plain openCV also for CUDA builds (toolchain can manage this strange situation anyway) - cd %WORKSPACE%\darknet\ From 5b8929becf23496666788d86450d2a47d6745ef0 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 10 Apr 2019 18:20:55 +0200 Subject: [PATCH 0460/1365] fixes for downstream projects using darklib, disable debug build --- CMakeLists.txt | 7 ++++++- DarknetConfig.cmake.in | 9 +++++++++ build.ps1 | 20 ++++++++++---------- build.sh | 22 +++++++++++----------- 4 files changed, 36 insertions(+), 22 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 4f22a0377bd..585eb0ed42d 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -80,6 +80,10 @@ if(ENABLE_CUDA AND NOT CUDA_COMPUTE_MODEL) set_property(CACHE CUDA_COMPUTE_MODEL PROPERTY STRINGS ${CUDA_COMPUTE_MODEL_75} ${CUDA_COMPUTE_MODEL_72} ${CUDA_COMPUTE_MODEL_70} ${CUDA_COMPUTE_MODEL_62} ${CUDA_COMPUTE_MODEL_61} ${CUDA_COMPUTE_MODEL_60} ${CUDA_COMPUTE_MODEL_53} ${CUDA_COMPUTE_MODEL_52} ${CUDA_COMPUTE_MODEL_37} ${CUDA_COMPUTE_MODEL_35} ${CUDA_COMPUTE_MODEL_30}) endif() +if(NOT ENABLE_CUDA) + set(CUDA_COMPUTE_MODEL "0" CACHE STRING "CUDA Compute Model set to 0 because CUDA is missing" FORCE) +endif() + if(USE_INTEGRATED_LIBS) set(PThreads_windows_DIR ${CMAKE_CURRENT_LIST_DIR}/3rdparty/pthreads CACHE PATH "Path where pthreads for windows can be located") add_definitions(-D_TIMESPEC_DEFINED) @@ -123,7 +127,8 @@ set(SKIP_USELIB_TRACK "FALSE" CACHE BOOL "Skip building uselib_track" FORCE) if(OpenCV_FOUND) if(ENABLE_CUDA AND NOT OpenCV_CUDA_VERSION) set(SKIP_USELIB_TRACK "TRUE" CACHE BOOL "Skip building uselib_track" FORCE) - message(WARNING "The build of OpenCV does not have CUDA support, so some tools have been disabled! Please rebuild OpenCV with CUDA support to build them!") + message(STATUS " -> darknet is fine for now, but uselib_track has been disabled!") + message(STATUS " -> Please rebuild OpenCV from sources with CUDA support to enable it") endif() add_definitions(-DOPENCV) endif() diff --git a/DarknetConfig.cmake.in b/DarknetConfig.cmake.in index 8b8bd4c73cd..2c137516f90 100644 --- a/DarknetConfig.cmake.in +++ b/DarknetConfig.cmake.in @@ -12,12 +12,21 @@ include(CMakeFindDependencyMacro) if(@ENABLE_OPENCV@) find_dependency(OpenCV REQUIRED) + add_definitions(-DOPENCV) endif() find_dependency(Stb REQUIRED) if(@ENABLE_CUDA@) find_dependency(CUDNN) + add_definitions(-DGPU) + if(CUDNN_FOUND) + add_definitions(-DCUDNN) + if(@CUDA_COMPUTE_MODEL@ VERSION_GREATER_EQUAL @CUDA_COMPUTE_MODEL_70@) + add_definitions(-DCUDNN_HALF) + message(STATUS "Enabled CUDNN_HALF") + endif() + endif() endif() set(CMAKE_THREAD_PREFER_PTHREAD ON) diff --git a/build.ps1 b/build.ps1 index 8017ce002f3..e18ee4fa7d2 100755 --- a/build.ps1 +++ b/build.ps1 @@ -164,16 +164,16 @@ if (Test-Path env:CUDA_PATH) { if ($vcpkg_path) { - # DEBUG - New-Item -Path .\build_win_debug -ItemType directory -Force - Set-Location build_win_debug - cmake -G "$generator" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Debug" $additional_build_setup .. - cmake --build . --config Debug --target install - #cmake --build . --config Debug --parallel ${number_of_build_workers} --target install #valid only for CMake 3.12+ - Remove-Item DarknetConfig.cmake - Remove-Item DarknetConfigVersion.cmake - Set-Location .. - Copy-Item cmake\Modules\*.cmake share\darknet\ + ## DEBUG + #New-Item -Path .\build_win_debug -ItemType directory -Force + #Set-Location build_win_debug + #cmake -G "$generator" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" #"-DCMAKE_BUILD_TYPE=Debug" $additional_build_setup .. + #cmake --build . --config Debug --target install + ##cmake --build . --config Debug --parallel ${number_of_build_workers} --target install #valid only for CMake 3.12+ + #Remove-Item DarknetConfig.cmake + #Remove-Item DarknetConfigVersion.cmake + #Set-Location .. + #Copy-Item cmake\Modules\*.cmake share\darknet\ # RELEASE New-Item -Path .\build_win_release -ItemType directory -Force diff --git a/build.sh b/build.sh index d72acab086f..a14db12e6a8 100755 --- a/build.sh +++ b/build.sh @@ -47,6 +47,17 @@ then (>&2 echo "darknet is unsupported without vcpkg, use at your own risk!") fi +## DEBUG +#mkdir -p build_debug +#cd build_debug +#cmake .. -DCMAKE_BUILD_TYPE=Debug ${vcpkg_define} ${vcpkg_triplet_define} ${additional_defines} ${additional_build_setup} +#cmake --build . --target install -- -j${number_of_build_workers} +##cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ +#rm -f DarknetConfig.cmake +#rm -f DarknetConfigVersion.cmake +#cd .. +#cp cmake/Modules/*.cmake share/darknet/ + # RELEASE mkdir -p build_release cd build_release @@ -57,14 +68,3 @@ rm -f DarknetConfig.cmake rm -f DarknetConfigVersion.cmake cd .. cp cmake/Modules/*.cmake share/darknet/ - -# DEBUG -mkdir -p build_debug -cd build_debug -cmake .. -DCMAKE_BUILD_TYPE=Debug ${vcpkg_define} ${vcpkg_triplet_define} ${additional_defines} ${additional_build_setup} -cmake --build . --target install -- -j${number_of_build_workers} -#cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ -rm -f DarknetConfig.cmake -rm -f DarknetConfigVersion.cmake -cd .. -cp cmake/Modules/*.cmake share/darknet/ From 8c970498a296ed129ffef7d872ccc25d42d1afda Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 11 Apr 2019 02:47:19 +0300 Subject: [PATCH 0461/1365] Minor fix --- include/darknet.h | 4 ++++ scripts/README.md | 4 +++- src/darknet.c | 2 +- src/data.c | 2 +- src/detector.c | 11 +++-------- src/image_opencv.cpp | 41 +++++++++++++++++++++++------------------ src/layer.c | 15 ++++++++++++--- src/network.c | 4 +++- src/option_list.h | 2 +- src/utils.h | 5 +++-- 10 files changed, 54 insertions(+), 36 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 0e404f475e9..82f033e3bea 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -5,6 +5,10 @@ #define inline __inline #endif +#if defined(DEBUG) && !defined(_CRTDBG_MAP_ALLOC) +#define _CRTDBG_MAP_ALLOC +#endif + #include #include #include diff --git a/scripts/README.md b/scripts/README.md index 5debe7f97be..273ed2a7dcd 100644 --- a/scripts/README.md +++ b/scripts/README.md @@ -56,4 +56,6 @@ Another list of datasets: http://homepages.inf.ed.ac.uk/rbf/CVonline/Imagedbase. Pedestrian DATASETs for Vision based Detection and Tracking: https://hemprasad.wordpress.com/2014/11/08/pedestrian-datasets-for-vision-based-detection-and-tracking/ -TrackingNet: https://tracking-net.org/ \ No newline at end of file +TrackingNet: https://tracking-net.org/ + +RGB, RGBD, Texture-mapped 3D mesh models: http://www.ycbbenchmarks.com/ \ No newline at end of file diff --git a/src/darknet.c b/src/darknet.c index 1c5c08b623a..06092d48192 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -1,3 +1,4 @@ +#include "darknet.h" #include #include #include @@ -5,7 +6,6 @@ #include #endif -#include "darknet.h" #include "parser.h" #include "utils.h" #include "dark_cuda.h" diff --git a/src/data.c b/src/data.c index 6d885b1085b..5de9e92e16a 100644 --- a/src/data.c +++ b/src/data.c @@ -68,7 +68,7 @@ char **get_sequential_paths(char **paths, int n, int m, int mini_batch, int augm if (strlen(sequentia_paths[i]) <= 4) printf(" Very small path to the image: %s \n", sequentia_paths[i]); } while (strlen(sequentia_paths[i]) == 0); } - //free(start_time_indexes); + free(start_time_indexes); pthread_mutex_unlock(&mutex); return sequentia_paths; } diff --git a/src/detector.c b/src/detector.c index a1995ab3be6..b1a612cb54b 100644 --- a/src/detector.c +++ b/src/detector.c @@ -29,7 +29,6 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i char *valid_images = option_find_str(options, "valid", train_images); char *backup_directory = option_find_str(options, "backup", "/backup/"); - int train_images_num = 0; network net_map; if (calc_map) { FILE* valid_file = fopen(valid_images, "r"); @@ -39,18 +38,14 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i exit(-1); } else fclose(valid_file); - list *plist = get_paths(train_images); - train_images_num = plist->size; - free_list(plist); cuda_set_device(gpus[0]); printf(" Prepare additional network for mAP calculation...\n"); net_map = parse_network_cfg_custom(cfgfile, 1, 1); - int k; // free memory unnecessary arrays for (k = 0; k < net_map.n; ++k) { - free_layer(net_map.layers[k]); + free_layer(net_map.layers[k]); } } @@ -97,7 +92,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i float jitter = l.jitter; list *plist = get_paths(train_images); - //int N = plist->size; + int train_images_num = plist->size; char **paths = (char **)list_to_array(plist); int init_w = net.w; @@ -1225,7 +1220,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int if (show) { #ifdef OPENCV - show_acnhors(number_of_boxes, num_of_clusters, rel_width_height_array, anchors_data, width, height); + //show_acnhors(number_of_boxes, num_of_clusters, rel_width_height_array, anchors_data, width, height); #endif // OPENCV } free(rel_width_height_array); diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 29fc2453718..a227db103b7 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -34,19 +34,26 @@ #include #endif -using namespace cv; +//using namespace cv; using std::cerr; using std::endl; +#ifdef DEBUG +#define OCV_D "d" +#else +#define OCV_D +#endif//DEBUG + + // OpenCV libraries #ifndef CV_VERSION_EPOCH -#define OPENCV_VERSION CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR)"" CVAUX_STR(CV_VERSION_REVISION) +#define OPENCV_VERSION CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR)"" CVAUX_STR(CV_VERSION_REVISION) OCV_D #ifndef USE_CMAKE_LIBS #pragma comment(lib, "opencv_world" OPENCV_VERSION ".lib") #endif // USE_CMAKE_LIBS #else // CV_VERSION_EPOCH -#define OPENCV_VERSION CVAUX_STR(CV_VERSION_EPOCH)"" CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR) +#define OPENCV_VERSION CVAUX_STR(CV_VERSION_EPOCH)"" CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR) OCV_D #ifndef USE_CMAKE_LIBS #pragma comment(lib, "opencv_core" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib") @@ -118,10 +125,10 @@ mat_cv *load_image_mat_cv(const char *filename, int flag) cv::Mat load_image_mat(char *filename, int channels) { - int flag = IMREAD_UNCHANGED; - if (channels == 0) flag = IMREAD_COLOR; - else if (channels == 1) flag = IMREAD_GRAYSCALE; - else if (channels == 3) flag = IMREAD_COLOR; + int flag = cv::IMREAD_UNCHANGED; + if (channels == 0) flag = cv::IMREAD_COLOR; + else if (channels == 1) flag = cv::IMREAD_GRAYSCALE; + else if (channels == 3) flag = cv::IMREAD_COLOR; else { fprintf(stderr, "OpenCV can't force load with %d channels\n", channels); } @@ -288,7 +295,7 @@ IplImage *mat_to_ipl(cv::Mat mat) // ---------------------------------------- */ -Mat image_to_mat(image img) +cv::Mat image_to_mat(image img) { int channels = img.c; int width = img.w; @@ -389,12 +396,12 @@ int wait_until_press_key_cv() void make_window(char *name, int w, int h, int fullscreen) { try { - cv::namedWindow(name, WINDOW_NORMAL); + cv::namedWindow(name, cv::WINDOW_NORMAL); if (fullscreen) { #ifdef CV_VERSION_EPOCH // OpenCV 2.x - cv::setWindowProperty(name, WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN); + cv::setWindowProperty(name, cv::WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN); #else - cv::setWindowProperty(name, WND_PROP_FULLSCREEN, cv::WINDOW_FULLSCREEN); + cv::setWindowProperty(name, cv::WND_PROP_FULLSCREEN, cv::WINDOW_FULLSCREEN); #endif } else { @@ -423,7 +430,7 @@ void show_image_cv(image p, const char *name) cv::Mat mat = image_to_mat(copy); cv::cvtColor(mat, mat, cv::COLOR_RGB2BGR); - cv::namedWindow(name, WINDOW_NORMAL); + cv::namedWindow(name, cv::WINDOW_NORMAL); cv::imshow(name, mat); free_image(copy); } @@ -450,7 +457,7 @@ void show_image_mat(mat_cv *mat_ptr, const char *name) try { if (mat_ptr == NULL) return; cv::Mat &mat = *(cv::Mat *)mat_ptr; - cv::namedWindow(name, WINDOW_NORMAL); + cv::namedWindow(name, cv::WINDOW_NORMAL); cv::imshow(name, mat); } catch (...) { @@ -483,9 +490,7 @@ void write_frame_cv(write_cv *output_video_writer, mat_cv *mat) { try { cv::VideoWriter *out = (cv::VideoWriter *)output_video_writer; - //out->write(ipl_to_mat(show_img)); out->write(*mat); - show_image_mat(mat, "mat"); } catch (...) { cerr << "OpenCV exception: write_frame_cv \n"; @@ -615,7 +620,7 @@ int get_stream_fps_cpp_cv(cap_cv *cap) try { cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; #ifndef CV_VERSION_EPOCH // OpenCV 3.x - fps = cpp_cap.get(CAP_PROP_FPS); + fps = cpp_cap.get(cv::CAP_PROP_FPS); #else // OpenCV 2.x fps = cpp_cap.get(CV_CAP_PROP_FPS); #endif @@ -645,7 +650,7 @@ double get_capture_frame_count_cv(cap_cv *cap) try { cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; #ifndef CV_VERSION_EPOCH // OpenCV 3.x - return cpp_cap.get(CAP_PROP_FRAME_COUNT); + return cpp_cap.get(cv::CAP_PROP_FRAME_COUNT); #else // OpenCV 2.x return cpp_cap.get(CV_CAP_PROP_FRAME_COUNT); #endif @@ -675,7 +680,7 @@ int set_capture_position_frame_cv(cap_cv *cap, int index) try { cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; #ifndef CV_VERSION_EPOCH // OpenCV 3.x - return cpp_cap.set(CAP_PROP_POS_FRAMES, index); + return cpp_cap.set(cv::CAP_PROP_POS_FRAMES, index); #else // OpenCV 2.x return cpp_cap.set(CV_CAP_PROP_POS_FRAMES, index); #endif diff --git a/src/layer.c b/src/layer.c index ae87065f4d5..a60978486a4 100644 --- a/src/layer.c +++ b/src/layer.c @@ -6,9 +6,18 @@ void free_layer(layer l) { // free layers: input_layer, self_layer, output_layer, ... if (l.type == CRNN) { - if (l.input_layer) free_layer(*l.input_layer); - if (l.self_layer) free_layer(*l.self_layer); - if (l.output_layer) free_layer(*l.output_layer); + if (l.input_layer) { + free_layer(*l.input_layer); + free(l.input_layer); + } + if (l.self_layer) { + free_layer(*l.self_layer); + free(l.self_layer); + } + if (l.output_layer) { + free_layer(*l.output_layer); + free(l.output_layer); + } l.output = NULL; l.delta = NULL; #ifdef GPU diff --git a/src/network.c b/src/network.c index 89d97b450c4..248b93ff0c9 100644 --- a/src/network.c +++ b/src/network.c @@ -1,7 +1,9 @@ +#include "darknet.h" + #include #include #include -#include "darknet.h" + #include "network.h" #include "image.h" #include "data.h" diff --git a/src/option_list.h b/src/option_list.h index a9e3e0b7e30..9efa274aa67 100644 --- a/src/option_list.h +++ b/src/option_list.h @@ -1,7 +1,7 @@ #ifndef OPTION_LIST_H #define OPTION_LIST_H -#include "list.h" #include "darknet.h" +#include "list.h" typedef struct{ char *key; diff --git a/src/utils.h b/src/utils.h index 0db4f9198ed..183bb641cc3 100644 --- a/src/utils.h +++ b/src/utils.h @@ -1,10 +1,11 @@ #ifndef UTILS_H #define UTILS_H +#include "darknet.h" +#include "list.h" + #include #include -#include "list.h" -#include "darknet.h" #ifdef __cplusplus extern "C" { #endif From 918fb0706ece2ccf9042936f2f1fff56a105e78f Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 12 Apr 2019 10:31:39 +0200 Subject: [PATCH 0462/1365] add setup prerequisite script scaffolding --- .gitignore | 4 ++++ DarknetConfig.cmake.in | 2 ++ setup.ps1 | 43 ++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 49 insertions(+) create mode 100755 setup.ps1 diff --git a/.gitignore b/.gitignore index 68a7a13bf17..f7f3beff4f4 100644 --- a/.gitignore +++ b/.gitignore @@ -34,6 +34,10 @@ Thumbs.db .vs/ .vscode/ +# Managed by CMake +src/version.h + +# Build artifacts lib/ include/ share/ diff --git a/DarknetConfig.cmake.in b/DarknetConfig.cmake.in index 2c137516f90..67f2abc936f 100644 --- a/DarknetConfig.cmake.in +++ b/DarknetConfig.cmake.in @@ -34,6 +34,8 @@ find_dependency(Threads) if(MSVC) find_dependency(PThreads_windows) + add_definitions(-D_CRT_SECURE_NO_WARNINGS) + set(CMAKE_CXX_FLAGS "/wd4018 /wd4244 /wd4267 /wd4305 ${CMAKE_CXX_FLAGS}") endif() if("${CMAKE_VERSION}" VERSION_GREATER "3.9.0") diff --git a/setup.ps1 b/setup.ps1 new file mode 100755 index 00000000000..a47697b2bc4 --- /dev/null +++ b/setup.ps1 @@ -0,0 +1,43 @@ +## enable or disable installed components + +$install_cuda=$false +$vcpkg_folder=".\" +$temp_folder=".\temp" + +########################### + +New-Item -Path . -Name $temp_folder -ItemType "directory" +Set-Location $temp_folder + +# Download and install Chocolatey +Set-ExecutionPolicy unrestricted +Invoke-Expression ((New-Object System.Net.WebClient).DownloadString('https://chocolatey.org/install.ps1')) +choco.exe install -y cmake ninja powershell git + +# Download and install VS Build Tools +Invoke-WebRequest https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=BuildTools&rel=16 -OutFile .\vs_buildtools.exe +.\vs_buildtools.exe –quiet –add Microsoft.VisualStudio.Workload.VCTools –includeRecommended + +if ($install_cuda) { + # Download and install CUDA + Invoke-WebRequest https://developer.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda_10.1.105_418.96_win10.exe -OutFile .\cuda_setup.exe + .\cuda_setup.exe -s nvcc_10.1 cuobjdump_10.1 nvprune_10.1 cupti_10.1 gpu_library_advisor_10.1 memcheck_10.1 nvdisasm_10.1 nvprof_10.1 visual_profiler_10.1 visual_studio_integration_10.1 cublas_10.1 cublas_dev_10.1 cudart_10.1 cufft_10.1 cufft_dev_10.1 curand_10.1 curand_dev_10.1 cusolver_10.1 cusolver_dev_10.1 cusparse_10.1 cusparse_dev_10.1 nvgraph_10.1 nvgraph_dev_10.1 npp_10.1 npp_dev_10.1 nvrtc_10.1 nvrtc_dev_10.1 nvml_dev_10.1 occupancy_calculator_10.1 fortran_examples_10.1 + + $env:CUDA_PATH = "${env:ProgramFiles}\NVIDIA GPU Computing Toolkit\CUDA\v10.1" + $env:CUDA_PATH_V10_1 = $env:CUDA_PATH + $env:CUDA_TOOLKIT_ROOT_DIR = $env:CUDA_PATH + $env:PATH += ";${env:CUDA_PATH}\bin;" + + $feature = "opencv-cuda" +} +else { + $feature = "opencv" +} + +Remove-Item -r $temp_folder +Set-Location .. +Set-Location $vcpkg_folder\ +git.exe clone https://github.com/Microsoft/vcpkg +Set-Location vcpkg +.\bootstrap-vcpkg.bat +.\vcpkg.exe install darknet[$feature] From 4fe2c997d15f0f1363e445bb0cffdec422700933 Mon Sep 17 00:00:00 2001 From: wbattel4607 Date: Sat, 13 Apr 2019 12:48:58 -0500 Subject: [PATCH 0463/1365] Save weights with best mAP --- src/detector.c | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/src/detector.c b/src/detector.c index b1a612cb54b..d7a24801d6f 100644 --- a/src/detector.c +++ b/src/detector.c @@ -102,6 +102,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i iter_save_last = get_current_batch(net); iter_map = get_current_batch(net); float mean_average_precision = -1; + float best_map = mean_average_precision; load_args args = { 0 }; args.w = net.w; @@ -226,7 +227,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i next_map_calc = fmax(next_map_calc, 1000); if (calc_map) { printf("\n (next mAP calculation at %d iterations) ", next_map_calc); - if (mean_average_precision > 0) printf("\n Last accuracy mAP@0.5 = %2.2f %% ", mean_average_precision * 100); + if (mean_average_precision > 0) printf("\n Last accuracy mAP@0.5 = %2.2f %%, best = %2.2f %% ", mean_average_precision * 100, best_map * 100); } if (net.cudnn_half) { @@ -260,6 +261,14 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i iter_map = i; mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, 0, &net_map);// &net_combined); printf("\n mean_average_precision (mAP@0.5) = %f \n", mean_average_precision); + if (mean_average_precision > best_map) { + best_map = mean_average_precision; + printf("New best mAP!\n"); + char buff[256]; + sprintf(buff, "%s/%s_best.weights", backup_directory, base); + save_weights(net, buff); + } + draw_precision = 1; } #ifdef OPENCV From 099b71d1de6b992ce8f9d7ff585c84efd0d4bf94 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 16 Apr 2019 16:35:54 +0300 Subject: [PATCH 0464/1365] Removed IplImage completely --- README.md | 2 +- include/yolo_v2_class.hpp | 31 ++- src/image_opencv.cpp | 433 ++++++++++++++++++++------------------ 3 files changed, 239 insertions(+), 227 deletions(-) diff --git a/README.md b/README.md index 8d87a98eb29..15e4d8f49b6 100644 --- a/README.md +++ b/README.md @@ -44,7 +44,7 @@ More details: http://pjreddie.com/darknet/yolo/ * Windows or Linux * **CMake >= 3.8** for modern CUDA support: https://cmake.org/download/ * **CUDA 10.0**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) -* **OpenCV > 2.4**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or download from [OpenCV official site](https://opencv.org/releases.html) (on Windows set system variable `OpenCV_DIR` = `C:\opencv\build` - where are the `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png)) +* **OpenCV >= 2.4**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or download from [OpenCV official site](https://opencv.org/releases.html) (on Windows set system variable `OpenCV_DIR` = `C:\opencv\build` - where are the `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png)) * **cuDNN >= 7.0 for CUDA 10.0** https://developer.nvidia.com/rdp/cudnn-archive (on **Linux** copy `cudnn.h`,`libcudnn.so`... as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on **Windows** copy `cudnn.h`,`cudnn64_7.dll`, `cudnn64_7.lib` as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows ) * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported * on Linux **GCC or Clang**, on Windows **MSVC 2015/2017/2019** https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community diff --git a/include/yolo_v2_class.hpp b/include/yolo_v2_class.hpp index fcaddcf0a51..323f7a1a66f 100644 --- a/include/yolo_v2_class.hpp +++ b/include/yolo_v2_class.hpp @@ -132,33 +132,28 @@ class Detector { else if (img_src.channels() == 1) cv::cvtColor(img_src, img, cv::COLOR_GRAY2BGR); else std::cerr << " Warning: img_src.channels() is not 1, 3 or 4. It is = " << img_src.channels() << std::endl; std::shared_ptr image_ptr(new image_t, [](image_t *img) { free_image(*img); delete img; }); - std::shared_ptr ipl_small = std::make_shared(img); - *image_ptr = ipl_to_image(ipl_small.get()); + *image_ptr = mat_to_image_custom(img); return image_ptr; } private: - static image_t ipl_to_image(IplImage* src) + static image_t mat_to_image_custom(cv::Mat mat) { - unsigned char *data = (unsigned char *)src->imageData; - int h = src->height; - int w = src->width; - int c = src->nChannels; - int step = src->widthStep; - image_t out = make_image_custom(w, h, c); - int count = 0; - - for (int k = 0; k < c; ++k) { - for (int i = 0; i < h; ++i) { - int i_step = i*step; - for (int j = 0; j < w; ++j) { - out.data[count++] = data[i_step + j*c + k] / 255.; + int w = mat.cols; + int h = mat.rows; + int c = mat.channels(); + image_t im = make_image_custom(w, h, c); + unsigned char *data = (unsigned char *)mat.data; + int step = mat.step; + for (int y = 0; y < h; ++y) { + for (int k = 0; k < c; ++k) { + for (int x = 0; x < w; ++x) { + im.data[k*w*h + y*w + x] = data[y*step + x*c + k] / 255.0f; } } } - - return out; + return im; } static image_t make_empty_image(int w, int h, int c) diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index a227db103b7..ff6a073ecb0 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -86,8 +86,8 @@ extern "C" { // ==================================================================== image mat_to_image(cv::Mat mat); cv::Mat image_to_mat(image img); - image ipl_to_image(mat_cv* src); - mat_cv *image_to_ipl(image img); +// image ipl_to_image(mat_cv* src); +// mat_cv *image_to_ipl(image img); // cv::Mat ipl_to_mat(IplImage *ipl); // IplImage *mat_to_ipl(cv::Mat mat); @@ -850,131 +850,136 @@ void save_cv_jpg(mat_cv *img_src, const char *name) // ==================================================================== void draw_detections_cv_v3(mat_cv* mat, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output) { - cv::Mat *show_img = mat; - int i, j; - if (!show_img) return; - static int frame_id = 0; - frame_id++; - - for (i = 0; i < num; ++i) { - char labelstr[4096] = { 0 }; - int class_id = -1; - for (j = 0; j < classes; ++j) { - int show = strncmp(names[j], "dont_show", 9); - if (dets[i].prob[j] > thresh && show) { - if (class_id < 0) { - strcat(labelstr, names[j]); - class_id = j; - char buff[10]; - sprintf(buff, " (%2.0f%%)", dets[i].prob[j]*100); - strcat(labelstr, buff); - } - else { - strcat(labelstr, ", "); - strcat(labelstr, names[j]); + try { + cv::Mat *show_img = mat; + int i, j; + if (!show_img) return; + static int frame_id = 0; + frame_id++; + + for (i = 0; i < num; ++i) { + char labelstr[4096] = { 0 }; + int class_id = -1; + for (j = 0; j < classes; ++j) { + int show = strncmp(names[j], "dont_show", 9); + if (dets[i].prob[j] > thresh && show) { + if (class_id < 0) { + strcat(labelstr, names[j]); + class_id = j; + char buff[10]; + sprintf(buff, " (%2.0f%%)", dets[i].prob[j] * 100); + strcat(labelstr, buff); + } + else { + strcat(labelstr, ", "); + strcat(labelstr, names[j]); + } + printf("%s: %.0f%% ", names[j], dets[i].prob[j] * 100); } - printf("%s: %.0f%% ", names[j], dets[i].prob[j] * 100); + } + if (class_id >= 0) { + int width = std::max(1.0f, show_img->rows * .002f); + + //if(0){ + //width = pow(prob, 1./2.)*10+1; + //alphabet = 0; + //} + + //printf("%d %s: %.0f%%\n", i, names[class_id], prob*100); + int offset = class_id * 123457 % classes; + float red = get_color(2, offset, classes); + float green = get_color(1, offset, classes); + float blue = get_color(0, offset, classes); + float rgb[3]; + + //width = prob*20+2; + + rgb[0] = red; + rgb[1] = green; + rgb[2] = blue; + box b = dets[i].bbox; + if (std::isnan(b.w) || std::isinf(b.w)) b.w = 0.5; + if (std::isnan(b.h) || std::isinf(b.h)) b.h = 0.5; + if (std::isnan(b.x) || std::isinf(b.x)) b.x = 0.5; + if (std::isnan(b.y) || std::isinf(b.y)) b.y = 0.5; + b.w = (b.w < 1) ? b.w : 1; + b.h = (b.h < 1) ? b.h : 1; + b.x = (b.x < 1) ? b.x : 1; + b.y = (b.y < 1) ? b.y : 1; + //printf("%f %f %f %f\n", b.x, b.y, b.w, b.h); + + int left = (b.x - b.w / 2.)*show_img->cols; + int right = (b.x + b.w / 2.)*show_img->cols; + int top = (b.y - b.h / 2.)*show_img->rows; + int bot = (b.y + b.h / 2.)*show_img->rows; + + if (left < 0) left = 0; + if (right > show_img->cols - 1) right = show_img->cols - 1; + if (top < 0) top = 0; + if (bot > show_img->rows - 1) bot = show_img->rows - 1; + + //int b_x_center = (left + right) / 2; + //int b_y_center = (top + bot) / 2; + //int b_width = right - left; + //int b_height = bot - top; + //sprintf(labelstr, "%d x %d - w: %d, h: %d", b_x_center, b_y_center, b_width, b_height); + + float const font_size = show_img->rows / 1000.F; + cv::Size const text_size = cv::getTextSize(labelstr, cv::FONT_HERSHEY_COMPLEX_SMALL, font_size, 1, 0); + cv::Point pt1, pt2, pt_text, pt_text_bg1, pt_text_bg2; + pt1.x = left; + pt1.y = top; + pt2.x = right; + pt2.y = bot; + pt_text.x = left; + pt_text.y = top - 4;// 12; + pt_text_bg1.x = left; + pt_text_bg1.y = top - (3 + 18 * font_size); + pt_text_bg2.x = right; + if ((right - left) < text_size.width) pt_text_bg2.x = left + text_size.width; + pt_text_bg2.y = top; + cv::Scalar color; + color.val[0] = red * 256; + color.val[1] = green * 256; + color.val[2] = blue * 256; + + // you should create directory: result_img + //static int copied_frame_id = -1; + //static IplImage* copy_img = NULL; + //if (copied_frame_id != frame_id) { + // copied_frame_id = frame_id; + // if(copy_img == NULL) copy_img = cvCreateImage(cvSize(show_img->width, show_img->height), show_img->depth, show_img->nChannels); + // cvCopy(show_img, copy_img, 0); + //} + //static int img_id = 0; + //img_id++; + //char image_name[1024]; + //sprintf(image_name, "result_img/img_%d_%d_%d_%s.jpg", frame_id, img_id, class_id, names[class_id]); + //CvRect rect = cvRect(pt1.x, pt1.y, pt2.x - pt1.x, pt2.y - pt1.y); + //cvSetImageROI(copy_img, rect); + //cvSaveImage(image_name, copy_img, 0); + //cvResetImageROI(copy_img); + + cv::rectangle(*show_img, pt1, pt2, color, width, 8, 0); + if (ext_output) + printf("\t(left_x: %4.0f top_y: %4.0f width: %4.0f height: %4.0f)\n", + (float)left, (float)top, b.w*show_img->cols, b.h*show_img->rows); + else + printf("\n"); + + cv::rectangle(*show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0); + cv::rectangle(*show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0); // filled + cv::Scalar black_color = CV_RGB(0, 0, 0); + cv::putText(*show_img, labelstr, pt_text, cv::FONT_HERSHEY_COMPLEX_SMALL, font_size, black_color, 2 * font_size, CV_AA); + // cv::FONT_HERSHEY_COMPLEX_SMALL, cv::FONT_HERSHEY_SIMPLEX } } - if (class_id >= 0) { - int width = std::max(1.0f, show_img->rows * .002f); - - //if(0){ - //width = pow(prob, 1./2.)*10+1; - //alphabet = 0; - //} - - //printf("%d %s: %.0f%%\n", i, names[class_id], prob*100); - int offset = class_id * 123457 % classes; - float red = get_color(2, offset, classes); - float green = get_color(1, offset, classes); - float blue = get_color(0, offset, classes); - float rgb[3]; - - //width = prob*20+2; - - rgb[0] = red; - rgb[1] = green; - rgb[2] = blue; - box b = dets[i].bbox; - if (std::isnan(b.w) || std::isinf(b.w)) b.w = 0.5; - if (std::isnan(b.h) || std::isinf(b.h)) b.h = 0.5; - if (std::isnan(b.x) || std::isinf(b.x)) b.x = 0.5; - if (std::isnan(b.y) || std::isinf(b.y)) b.y = 0.5; - b.w = (b.w < 1) ? b.w : 1; - b.h = (b.h < 1) ? b.h : 1; - b.x = (b.x < 1) ? b.x : 1; - b.y = (b.y < 1) ? b.y : 1; - //printf("%f %f %f %f\n", b.x, b.y, b.w, b.h); - - int left = (b.x - b.w / 2.)*show_img->cols; - int right = (b.x + b.w / 2.)*show_img->cols; - int top = (b.y - b.h / 2.)*show_img->rows; - int bot = (b.y + b.h / 2.)*show_img->rows; - - if (left < 0) left = 0; - if (right > show_img->cols - 1) right = show_img->cols - 1; - if (top < 0) top = 0; - if (bot > show_img->rows - 1) bot = show_img->rows - 1; - - //int b_x_center = (left + right) / 2; - //int b_y_center = (top + bot) / 2; - //int b_width = right - left; - //int b_height = bot - top; - //sprintf(labelstr, "%d x %d - w: %d, h: %d", b_x_center, b_y_center, b_width, b_height); - - float const font_size = show_img->rows / 1000.F; - cv::Size const text_size = cv::getTextSize(labelstr, cv::FONT_HERSHEY_COMPLEX_SMALL, font_size, 1, 0); - cv::Point pt1, pt2, pt_text, pt_text_bg1, pt_text_bg2; - pt1.x = left; - pt1.y = top; - pt2.x = right; - pt2.y = bot; - pt_text.x = left; - pt_text.y = top - 4;// 12; - pt_text_bg1.x = left; - pt_text_bg1.y = top - (1 + 18 * font_size); - pt_text_bg2.x = right; - if ((right - left) < text_size.width) pt_text_bg2.x = left + text_size.width; - pt_text_bg2.y = top; - cv::Scalar color; - color.val[0] = red * 256; - color.val[1] = green * 256; - color.val[2] = blue * 256; - - // you should create directory: result_img - //static int copied_frame_id = -1; - //static IplImage* copy_img = NULL; - //if (copied_frame_id != frame_id) { - // copied_frame_id = frame_id; - // if(copy_img == NULL) copy_img = cvCreateImage(cvSize(show_img->width, show_img->height), show_img->depth, show_img->nChannels); - // cvCopy(show_img, copy_img, 0); - //} - //static int img_id = 0; - //img_id++; - //char image_name[1024]; - //sprintf(image_name, "result_img/img_%d_%d_%d_%s.jpg", frame_id, img_id, class_id, names[class_id]); - //CvRect rect = cvRect(pt1.x, pt1.y, pt2.x - pt1.x, pt2.y - pt1.y); - //cvSetImageROI(copy_img, rect); - //cvSaveImage(image_name, copy_img, 0); - //cvResetImageROI(copy_img); - - cv::rectangle(*show_img, pt1, pt2, color, width, 8, 0); - if (ext_output) - printf("\t(left_x: %4.0f top_y: %4.0f width: %4.0f height: %4.0f)\n", - (float)left, (float)top, b.w*show_img->cols, b.h*show_img->rows); - else - printf("\n"); - - cv::rectangle(*show_img, pt_text_bg1, pt_text_bg2, color, width, 8, 0); - cv::rectangle(*show_img, pt_text_bg1, pt_text_bg2, color, CV_FILLED, 8, 0); // filled - cv::Scalar black_color = CV_RGB(0,0,0); - cv::putText(*show_img, labelstr, pt_text, cv::FONT_HERSHEY_COMPLEX_SMALL, font_size, black_color, 2*font_size, CV_AA); - // cv::FONT_HERSHEY_COMPLEX_SMALL, cv::FONT_HERSHEY_SIMPLEX + if (ext_output) { + fflush(stdout); } } - if (ext_output) { - fflush(stdout); + catch (...) { + cerr << "OpenCV exception: draw_detections_cv_v3() \n"; } } // ---------------------------------------- @@ -984,53 +989,58 @@ void draw_detections_cv_v3(mat_cv* mat, detection *dets, int num, float thresh, // ==================================================================== mat_cv* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show) { - int img_offset = 50; + int img_offset = 60; int draw_size = img_size - img_offset; cv::Mat *img_ptr = new cv::Mat(img_size, img_size, CV_8UC3, CV_RGB(255, 255, 255)); cv::Mat &img = *img_ptr; cv::Point pt1, pt2, pt_text; - char char_buff[100]; - int i; - // vertical lines - pt1.x = img_offset; pt2.x = img_size, pt_text.x = 10; - for (i = 1; i <= number_of_lines; ++i) { - pt1.y = pt2.y = (float)i * draw_size / number_of_lines; - cv::line(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0); - if (i % 10 == 0) { - sprintf(char_buff, "%2.1f", max_img_loss*(number_of_lines - i) / number_of_lines); - pt_text.y = pt1.y + 5; - - cv::putText(img, char_buff, pt_text, cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); - cv::line(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0); + try { + char char_buff[100]; + int i; + // vertical lines + pt1.x = img_offset; pt2.x = img_size, pt_text.x = 30; + for (i = 1; i <= number_of_lines; ++i) { + pt1.y = pt2.y = (float)i * draw_size / number_of_lines; + cv::line(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0); + if (i % 10 == 0) { + sprintf(char_buff, "%2.1f", max_img_loss*(number_of_lines - i) / number_of_lines); + pt_text.y = pt1.y + 3; + + cv::putText(img, char_buff, pt_text, cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + cv::line(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0); + } } - } - // horizontal lines - pt1.y = draw_size; pt2.y = 0, pt_text.y = draw_size + 15; - for (i = 0; i <= number_of_lines; ++i) { - pt1.x = pt2.x = img_offset + (float)i * draw_size / number_of_lines; - cv::line(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0); - if (i % 10 == 0) { - sprintf(char_buff, "%d", max_batches * i / number_of_lines); - pt_text.x = pt1.x - 20; - cv::putText(img, char_buff, pt_text, cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); - cv::line(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0); + // horizontal lines + pt1.y = draw_size; pt2.y = 0, pt_text.y = draw_size + 15; + for (i = 0; i <= number_of_lines; ++i) { + pt1.x = pt2.x = img_offset + (float)i * draw_size / number_of_lines; + cv::line(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0); + if (i % 10 == 0) { + sprintf(char_buff, "%d", max_batches * i / number_of_lines); + pt_text.x = pt1.x - 20; + cv::putText(img, char_buff, pt_text, cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + cv::line(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0); + } } - } - cv::putText(img, "Loss", cv::Point(0, 35), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); - cv::putText(img, "Iteration number", cv::Point(draw_size / 2, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); - char max_batches_buff[100]; - sprintf(max_batches_buff, "in cfg max_batches=%d", max_batches); - cv::putText(img, max_batches_buff, cv::Point(draw_size - 195, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); - cv::putText(img, "Press 's' to save : chart.png", cv::Point(5, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); - if (!dont_show) { - printf(" If error occurs - run training with flag: -dont_show \n"); - cv::namedWindow("average loss", cv::WINDOW_NORMAL); - cv::moveWindow("average loss", 0, 0); - cv::resizeWindow("average loss", img_size, img_size); - cv::imshow("average loss", img); - cv::waitKey(20); + cv::putText(img, "Loss", cv::Point(10, 55), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 255), 1, CV_AA); + cv::putText(img, "Iteration number", cv::Point(draw_size / 2, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + char max_batches_buff[100]; + sprintf(max_batches_buff, "in cfg max_batches=%d", max_batches); + cv::putText(img, max_batches_buff, cv::Point(draw_size - 195, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + cv::putText(img, "Press 's' to save : chart.png", cv::Point(5, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + if (!dont_show) { + printf(" If error occurs - run training with flag: -dont_show \n"); + cv::namedWindow("average loss", cv::WINDOW_NORMAL); + cv::moveWindow("average loss", 0, 0); + cv::resizeWindow("average loss", img_size, img_size); + cv::imshow("average loss", img); + cv::waitKey(20); + } + } + catch (...) { + cerr << "OpenCV exception: draw_train_chart() \n"; } return (mat_cv*)img_ptr; } @@ -1039,60 +1049,67 @@ mat_cv* draw_train_chart(float max_img_loss, int max_batches, int number_of_line void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port) { - cv::Mat &img = *(cv::Mat*)img_src; - int img_offset = 50; - int draw_size = img_size - img_offset; - char char_buff[100]; - cv::Point pt1, pt2; - pt1.x = img_offset + draw_size * (float)current_batch / max_batches; - pt1.y = draw_size * (1 - avg_loss / max_img_loss); - if (pt1.y < 0) pt1.y = 1; - cv::circle(img, pt1, 1, CV_RGB(0, 0, 255), CV_FILLED, 8, 0); - - // precision - if (draw_precision) { - static float old_precision = 0; - static int iteration_old = 0; - static int text_iteration_old = 0; - if (iteration_old == 0) - cv::putText(img, accuracy_name, cv::Point(0, 12), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 0, 0), 1, CV_AA); - - cv::line(img, - cv::Point(img_offset + draw_size * (float)iteration_old / max_batches, draw_size * (1 - old_precision)), - cv::Point(img_offset + draw_size * (float)current_batch / max_batches, draw_size * (1 - precision)), - CV_RGB(255, 0, 0), 1, 8, 0); - - if (((int)(old_precision * 10) != (int)(precision * 10)) || (current_batch - text_iteration_old) >= max_batches / 10) { - text_iteration_old = current_batch; + try { + cv::Mat &img = *(cv::Mat*)img_src; + int img_offset = 60; + int draw_size = img_size - img_offset; + char char_buff[100]; + cv::Point pt1, pt2; + pt1.x = img_offset + draw_size * (float)current_batch / max_batches; + pt1.y = draw_size * (1 - avg_loss / max_img_loss); + if (pt1.y < 0) pt1.y = 1; + cv::circle(img, pt1, 1, CV_RGB(0, 0, 255), CV_FILLED, 8, 0); + + // precision + if (draw_precision) { + static float old_precision = 0; + static int iteration_old = 0; + static int text_iteration_old = 0; + if (iteration_old == 0) + cv::putText(img, accuracy_name, cv::Point(10, 12), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 0, 0), 1, CV_AA); + + cv::line(img, + cv::Point(img_offset + draw_size * (float)iteration_old / max_batches, draw_size * (1 - old_precision)), + cv::Point(img_offset + draw_size * (float)current_batch / max_batches, draw_size * (1 - precision)), + CV_RGB(255, 0, 0), 1, 8, 0); + sprintf(char_buff, "%2.0f%% ", precision * 100); - cv::putText(img, char_buff, cv::Point(pt1.x - 30, draw_size * (1 - precision) + 15), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 5, CV_AA); + cv::putText(img, char_buff, cv::Point(10, 28), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 5, CV_AA); + cv::putText(img, char_buff, cv::Point(10, 28), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(200, 0, 0), 1, CV_AA); - cv::putText(img, char_buff, cv::Point(pt1.x - 30, draw_size * (1 - precision) + 15), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(200, 0, 0), 1, CV_AA); + if (((int)(old_precision * 10) != (int)(precision * 10)) || (current_batch - text_iteration_old) >= max_batches / 10) { + text_iteration_old = current_batch; + cv::putText(img, char_buff, cv::Point(pt1.x - 30, draw_size * (1 - precision) + 15), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 5, CV_AA); + cv::putText(img, char_buff, cv::Point(pt1.x - 30, draw_size * (1 - precision) + 15), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(200, 0, 0), 1, CV_AA); + } + old_precision = precision; + iteration_old = current_batch; } - old_precision = precision; - iteration_old = current_batch; - } - sprintf(char_buff, "current avg loss = %2.4f iteration = %d", avg_loss, current_batch); - pt1.x = 55, pt1.y = 10; - pt2.x = pt1.x + 460, pt2.y = pt1.y + 20; - cv::rectangle(img, pt1, pt2, CV_RGB(255, 255, 255), CV_FILLED, 8, 0); - pt1.y += 15; - cv::putText(img, char_buff, pt1, cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + sprintf(char_buff, "current avg loss = %2.4f iteration = %d", avg_loss, current_batch); + pt1.x = 15, pt1.y = draw_size + 18; + pt2.x = pt1.x + 460, pt2.y = pt1.y + 20; + cv::rectangle(img, pt1, pt2, CV_RGB(255, 255, 255), CV_FILLED, 8, 0); + pt1.y += 15; + cv::putText(img, char_buff, pt1, cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 100), 1, CV_AA); + + int k = 0; + if (!dont_show) { + cv::imshow("average loss", img); + k = cv::waitKey(20); + } + if (k == 's' || current_batch == (max_batches - 1) || current_batch % 100 == 0) { + save_mat_png(img, "chart.png"); + cv::putText(img, "- Saved", cv::Point(260, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 0, 0), 1, CV_AA); + } + else + cv::putText(img, "- Saved", cv::Point(260, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 1, CV_AA); - int k = 0; - if (!dont_show) { - cv::imshow("average loss", img); - k = cv::waitKey(20); + if (mjpeg_port > 0) send_mjpeg((mat_cv *)&img, mjpeg_port, 500000, 100); } - if (k == 's' || current_batch == (max_batches - 1) || current_batch % 100 == 0) { - save_mat_png(img, "chart.png"); - cv::putText(img, "- Saved", cv::Point(260, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 0, 0), 1, CV_AA); + catch (...) { + cerr << "OpenCV exception: draw_train_loss() \n"; } - else - cv::putText(img, "- Saved", cv::Point(260, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 1, CV_AA); - - if (mjpeg_port > 0) send_mjpeg((mat_cv *)&img, mjpeg_port, 500000, 100); } // ---------------------------------------- From 77917d02fb8ffbdcb3a27a81db3578e45cdd8ad7 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 17 Apr 2019 11:31:09 +0200 Subject: [PATCH 0465/1365] [headers] move .h files to our own subfolder to avoid clashes with other libraries --- .gitignore | 2 +- CMakeLists.txt | 15 ++++++++++----- DarknetConfig.cmake.in | 19 ++++++------------- 3 files changed, 17 insertions(+), 19 deletions(-) diff --git a/.gitignore b/.gitignore index 68a7a13bf17..fa45a2df250 100644 --- a/.gitignore +++ b/.gitignore @@ -35,5 +35,5 @@ Thumbs.db .vscode/ lib/ -include/ +include/darknet/ share/ diff --git a/CMakeLists.txt b/CMakeLists.txt index 585eb0ed42d..75861f39a3c 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -47,6 +47,8 @@ configure_file( ) set(ENABLE_CUDA "TRUE" CACHE BOOL "Enable CUDA build") +set(ENABLE_CUDNN "FALSE" CACHE BOOL "Enable CUDNN") +set(ENABLE_OPENMP "TRUE" CACHE BOOL "Enable OpenMP") if(${CMAKE_VERSION} VERSION_LESS "3.8.0") set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA build" FORCE) message(WARNING "To build with CUDA support you need CMake 3.8.0+") @@ -146,6 +148,7 @@ if(ENABLE_CUDA) find_package(CUDNN) if(CUDNN_FOUND) add_definitions(-DCUDNN) + set(ENABLE_CUDNN "TRUE" CACHE BOOL "Enable CUDNN" FORCE) if(CUDA_COMPUTE_MODEL VERSION_GREATER_EQUAL ${CUDA_COMPUTE_MODEL_70}) add_definitions(-DCUDNN_HALF) message(STATUS "Enabled CUDNN_HALF") @@ -213,6 +216,8 @@ else() find_package(OpenMP) if(OPENMP_FOUND) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") + else() + set(ENABLE_OPENMP "FALSE" CACHE BOOL "Enable OpenMP" FORCE) endif() endif() @@ -276,7 +281,7 @@ if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRA add_executable(uselib_track ${CMAKE_CURRENT_LIST_DIR}/src/yolo_console_dll.cpp) target_compile_definitions(uselib_track PRIVATE TRACK_OPTFLOW=1) set_target_properties(uselib_track PROPERTIES LINKER_LANGUAGE CXX) - target_include_directories(uselib_track PUBLIC $ $ $ $) + target_include_directories(uselib_track PUBLIC $ $ $ $) target_link_libraries(uselib_track PRIVATE ${OpenCV_LIBS}) endif() @@ -287,9 +292,9 @@ add_executable(darknet ${CMAKE_CURRENT_LIST_DIR}/src/darknet.c ${sources} ${head set_source_files_properties(${CMAKE_CURRENT_LIST_DIR}/src/darknet.c PROPERTIES LANGUAGE CXX) set_target_properties(darknet PROPERTIES LINKER_LANGUAGE CXX) -target_include_directories(darknet PUBLIC $ $ $ $) -target_include_directories(darklib PUBLIC $ $ $ $) -target_include_directories(uselib PUBLIC $ $ $ $) +target_include_directories(darknet PUBLIC $ $ $ $) +target_include_directories(darklib PUBLIC $ $ $ $) +target_include_directories(uselib PUBLIC $ $ $ $) if(CUDNN_FOUND) target_link_libraries(darknet PRIVATE CuDNN::CuDNN) @@ -344,7 +349,7 @@ install(TARGETS darklib EXPORT DarknetTargets RUNTIME DESTINATION "${INSTALL_BIN_DIR}" LIBRARY DESTINATION "${INSTALL_LIB_DIR}" ARCHIVE DESTINATION "${INSTALL_LIB_DIR}" - PUBLIC_HEADER DESTINATION "${INSTALL_INCLUDE_DIR}" + PUBLIC_HEADER DESTINATION "${INSTALL_INCLUDE_DIR}/darknet" COMPONENT dev ) install(TARGETS uselib darknet diff --git a/DarknetConfig.cmake.in b/DarknetConfig.cmake.in index 2c137516f90..2719ef5c3a8 100644 --- a/DarknetConfig.cmake.in +++ b/DarknetConfig.cmake.in @@ -1,26 +1,21 @@ # Config file for the Darknet package -# It defines the following variables -# Darknet_INCLUDE_DIRS - include directories for Darknet -# Darknet_LIBRARIES - libraries to link against -# Compute paths get_filename_component(Darknet_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH) -set(Darknet_INCLUDE_DIRS "@CONF_INCLUDE_DIRS@") list(APPEND CMAKE_MODULE_PATH "${Darknet_CMAKE_DIR}") include(CMakeFindDependencyMacro) if(@ENABLE_OPENCV@) - find_dependency(OpenCV REQUIRED) + find_dependency(OpenCV) add_definitions(-DOPENCV) endif() -find_dependency(Stb REQUIRED) +find_dependency(Stb) if(@ENABLE_CUDA@) - find_dependency(CUDNN) add_definitions(-DGPU) - if(CUDNN_FOUND) + if(@ENABLE_CUDNN@) + find_dependency(CUDNN) add_definitions(-DCUDNN) if(@CUDA_COMPUTE_MODEL@ VERSION_GREATER_EQUAL @CUDA_COMPUTE_MODEL_70@) add_definitions(-DCUDNN_HALF) @@ -36,12 +31,10 @@ if(MSVC) find_dependency(PThreads_windows) endif() -if("${CMAKE_VERSION}" VERSION_GREATER "3.9.0") +if(@ENABLE_OPENMP@) find_dependency(OpenMP) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") endif() # Our library dependencies (contains definitions for IMPORTED targets) include("${Darknet_CMAKE_DIR}/DarknetTargets.cmake") - -# These are IMPORTED targets created by DarknetTargets.cmake -set(Darknet_LIBRARIES darklib) From ff7d920a5db29f00e1a2a3ac5de353e37e35a401 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 17 Apr 2019 14:19:58 +0200 Subject: [PATCH 0466/1365] [build.sh] use correct osx triplet name --- build.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/build.sh b/build.sh index a14db12e6a8..b69ebe6b3a6 100755 --- a/build.sh +++ b/build.sh @@ -21,7 +21,7 @@ if [[ "$OSTYPE" == "darwin"* ]]; then export CC="/usr/local/bin/gcc-8" export CXX="/usr/local/bin/g++-8" fi - vcpkg_triplet="x64-darwin" + vcpkg_triplet="x64-osx" else vcpkg_triplet="x64-linux" fi From eebe735f8455c278ea9aa34831d00061c954bbdf Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 18 Apr 2019 09:17:31 +0200 Subject: [PATCH 0467/1365] [FindCUDNN] fix unnecessary message and dll symbol --- cmake/Modules/FindCUDNN.cmake | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/cmake/Modules/FindCUDNN.cmake b/cmake/Modules/FindCUDNN.cmake index 04c5eed2fae..3a6bc0d6895 100644 --- a/cmake/Modules/FindCUDNN.cmake +++ b/cmake/Modules/FindCUDNN.cmake @@ -60,8 +60,10 @@ endif() set(CUDNN_INCLUDE_DIRS ${CUDNN_INCLUDE_DIR}) set(CUDNN_LIBRARIES ${CUDNN_LIBRARY}) -message(STATUS "Found cuDNN: v${CUDNN_VERSION} (include: ${CUDNN_INCLUDE_DIR}, library: ${CUDNN_LIBRARY})") -mark_as_advanced(CUDNN_ROOT_DIR CUDNN_LIBRARY CUDNN_INCLUDE_DIR) +if(CUDNN_FOUND) + message(STATUS "Found cuDNN: v${CUDNN_VERSION} (include: ${CUDNN_INCLUDE_DIR}, library: ${CUDNN_LIBRARY})") +endif() +mark_as_advanced(CUDNN_LIBRARY CUDNN_INCLUDE_DIR) # Register imported libraries: # 1. If we can find a Windows .dll file (or if we can find both Debug and @@ -75,7 +77,7 @@ if(WIN32) endif() if( CUDNN_FOUND AND NOT TARGET CuDNN::CuDNN ) - if( EXISTS "${CUDNN_LIBRARY_RELEASE_DLL}" ) + if( EXISTS "${CUDNN_LIBRARY_DLL}" ) add_library( CuDNN::CuDNN SHARED IMPORTED ) set_target_properties( CuDNN::CuDNN PROPERTIES IMPORTED_LOCATION "${CUDNN_LIBRARY_DLL}" From df0d33de29f1cbb54d174d5205aa4be02daad158 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 18 Apr 2019 09:40:57 +0200 Subject: [PATCH 0468/1365] [build.sh] fix problems with empty env variables --- build.sh | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/build.sh b/build.sh index b69ebe6b3a6..481bbaf56a7 100755 --- a/build.sh +++ b/build.sh @@ -30,13 +30,13 @@ if [[ ! -z "$my_cuda_compute_model" ]]; then additional_build_setup="-DCUDA_COMPUTE_MODEL=${my_cuda_compute_model}" fi -if [[ -d ${VCPKG_ROOT} ]] && [ ! "$bypass_vcpkg" = true ] +if [[ ! -z "${VCPKG_ROOT}" ]] && [ -d ${VCPKG_ROOT} ] && [ ! "$bypass_vcpkg" = true ] then vcpkg_path="${VCPKG_ROOT}" vcpkg_define="-DCMAKE_TOOLCHAIN_FILE=${vcpkg_path}/scripts/buildsystems/vcpkg.cmake" vcpkg_triplet_define="-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" echo "Found vcpkg in VCPKG_ROOT: ${vcpkg_path}" -elif [ -d ${WORKSPACE}/vcpkg${vcpkg_fork} ] && [ ! "$bypass_vcpkg" = true ] +elif [[ ! -z "${WORKSPACE}" ]] && [ -d ${WORKSPACE}/vcpkg ] && [ ! "$bypass_vcpkg" = true ] then vcpkg_path="${WORKSPACE}/vcpkg" vcpkg_define="-DCMAKE_TOOLCHAIN_FILE=${vcpkg_path}/scripts/buildsystems/vcpkg.cmake" From 6497ba1bfd1a0dbf8699d5b826c00c97c355a74e Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 18 Apr 2019 16:16:55 +0200 Subject: [PATCH 0469/1365] [dark.lib] set correct function visibility when building shared libs also for non-msvc compilers --- .gitignore | 1 - CMakeLists.txt | 55 +++++++++++++++++++++++++++----------------------- 2 files changed, 30 insertions(+), 26 deletions(-) diff --git a/.gitignore b/.gitignore index fa45a2df250..8ca9c8f715b 100644 --- a/.gitignore +++ b/.gitignore @@ -35,5 +35,4 @@ Thumbs.db .vscode/ lib/ -include/darknet/ share/ diff --git a/CMakeLists.txt b/CMakeLists.txt index 75861f39a3c..5be4f733862 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -6,7 +6,7 @@ set(Darknet_PATCH_VERSION 5) set(Darknet_VERSION ${Darknet_MAJOR_VERSION}.${Darknet_MINOR_VERSION}.${Darknet_PATCH_VERSION}) set(CMAKE_VERBOSE_MAKEFILE "FALSE" CACHE BOOL "Create verbose makefile") -set(BUILD_SHARED_LIBS "TRUE" CACHE BOOL "Create darklib as a shared library") +set(BUILD_SHARED_LIBS "TRUE" CACHE BOOL "Create dark as a shared library") if(DEFINED ENV{VCPKG_ROOT} AND NOT DEFINED CMAKE_TOOLCHAIN_FILE) set(CMAKE_TOOLCHAIN_FILE "$ENV{VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake" CACHE STRING "") @@ -232,7 +232,7 @@ list(REMOVE_ITEM headers ${CMAKE_CURRENT_LIST_DIR}/src/gettimeofday.h ${CMAKE_CURRENT_LIST_DIR}/src/getopt.h ) -set(exported_headers ${headers}) +#set(exported_headers ${headers}) #look for all *.c files in src folder file(GLOB sources "${CMAKE_CURRENT_LIST_DIR}/src/*.c") @@ -269,19 +269,21 @@ endif() set_source_files_properties(${sources} PROPERTIES LANGUAGE CXX) -add_library(darklib ${CMAKE_CURRENT_LIST_DIR}/include/yolo_v2_class.hpp ${CMAKE_CURRENT_LIST_DIR}/src/yolo_v2_class.cpp ${sources} ${headers} ${cuda_sources}) -set_target_properties(darklib PROPERTIES POSITION_INDEPENDENT_CODE ON) +add_library(dark ${CMAKE_CURRENT_LIST_DIR}/include/yolo_v2_class.hpp ${CMAKE_CURRENT_LIST_DIR}/src/yolo_v2_class.cpp ${sources} ${headers} ${cuda_sources}) +set_target_properties(dark PROPERTIES POSITION_INDEPENDENT_CODE ON) if(ENABLE_CUDA) - set_target_properties(darklib PROPERTIES CUDA_SEPARABLE_COMPILATION ON) + set_target_properties(dark PROPERTIES CUDA_SEPARABLE_COMPILATION ON) endif() -target_compile_definitions(darklib PRIVATE LIB_EXPORTS=1) -set_target_properties(darklib PROPERTIES LINKER_LANGUAGE CXX) +if(BUILD_SHARED_LIBS) + target_compile_definitions(dark PRIVATE LIB_EXPORTS=1) +endif() +set_target_properties(dark PROPERTIES LINKER_LANGUAGE CXX) if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRACK) add_executable(uselib_track ${CMAKE_CURRENT_LIST_DIR}/src/yolo_console_dll.cpp) target_compile_definitions(uselib_track PRIVATE TRACK_OPTFLOW=1) set_target_properties(uselib_track PROPERTIES LINKER_LANGUAGE CXX) - target_include_directories(uselib_track PUBLIC $ $ $ $) + target_include_directories(uselib_track PUBLIC $ $ $ $) target_link_libraries(uselib_track PRIVATE ${OpenCV_LIBS}) endif() @@ -292,40 +294,40 @@ add_executable(darknet ${CMAKE_CURRENT_LIST_DIR}/src/darknet.c ${sources} ${head set_source_files_properties(${CMAKE_CURRENT_LIST_DIR}/src/darknet.c PROPERTIES LANGUAGE CXX) set_target_properties(darknet PROPERTIES LINKER_LANGUAGE CXX) -target_include_directories(darknet PUBLIC $ $ $ $) -target_include_directories(darklib PUBLIC $ $ $ $) -target_include_directories(uselib PUBLIC $ $ $ $) +target_include_directories(darknet PUBLIC $ $ $ $) +target_include_directories(dark PUBLIC $ $ $ $) +target_include_directories(uselib PUBLIC $ $ $ $) if(CUDNN_FOUND) target_link_libraries(darknet PRIVATE CuDNN::CuDNN) - target_link_libraries(darklib PRIVATE CuDNN::CuDNN) + target_link_libraries(dark PRIVATE CuDNN::CuDNN) endif() if(OpenCV_FOUND) target_link_libraries(darknet PRIVATE ${OpenCV_LIBS}) target_link_libraries(uselib PRIVATE ${OpenCV_LIBS}) - target_link_libraries(darklib PRIVATE ${OpenCV_LIBS}) + target_link_libraries(dark PRIVATE ${OpenCV_LIBS}) endif() if(OPENMP_FOUND) target_link_libraries(darknet PRIVATE OpenMP::OpenMP_CXX) - target_link_libraries(darklib PRIVATE OpenMP::OpenMP_CXX) + target_link_libraries(dark PRIVATE OpenMP::OpenMP_CXX) endif() if(CMAKE_COMPILER_IS_GNUCC) target_link_libraries(darknet PRIVATE m) - target_link_libraries(darklib PRIVATE m) + target_link_libraries(dark PRIVATE m) endif() if(MSVC) target_link_libraries(darknet PRIVATE PThreads_windows::PThreads_windows) target_link_libraries(darknet PRIVATE wsock32 ws2_32) - target_link_libraries(darklib PRIVATE PThreads_windows::PThreads_windows) - target_link_libraries(darklib PRIVATE wsock32 ws2_32) + target_link_libraries(dark PRIVATE PThreads_windows::PThreads_windows) + target_link_libraries(dark PRIVATE wsock32 ws2_32) endif() target_link_libraries(darknet PRIVATE Threads::Threads) -target_link_libraries(darklib PRIVATE Threads::Threads) +target_link_libraries(dark PRIVATE Threads::Threads) if(ENABLE_ZED_CAMERA) target_link_libraries(darknet PRIVATE ${ZED_LIBRARIES}) @@ -334,22 +336,25 @@ endif() if(ENABLE_CUDA) target_link_libraries(darknet PRIVATE curand cublas cuda) - target_link_libraries(darklib PRIVATE curand cublas cuda) - set_target_properties(darklib PROPERTIES CUDA_RESOLVE_DEVICE_SYMBOLS ON) + target_link_libraries(dark PRIVATE curand cublas cuda) + set_target_properties(dark PROPERTIES CUDA_RESOLVE_DEVICE_SYMBOLS ON) endif() -target_link_libraries(uselib PRIVATE darklib) +target_link_libraries(uselib PRIVATE dark) if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRACK) - target_link_libraries(uselib_track PRIVATE darklib) + target_link_libraries(uselib_track PRIVATE dark) endif() -set_target_properties(darklib PROPERTIES PUBLIC_HEADER "${exported_headers};${CMAKE_CURRENT_LIST_DIR}/include/yolo_v2_class.hpp") +#set_target_properties(dark PROPERTIES PUBLIC_HEADER "${exported_headers};${CMAKE_CURRENT_LIST_DIR}/include/yolo_v2_class.hpp") +set_target_properties(dark PROPERTIES PUBLIC_HEADER "${CMAKE_CURRENT_LIST_DIR}/include/darknet.h;${CMAKE_CURRENT_LIST_DIR}/include/yolo_v2_class.hpp") + +set_target_properties(dark PROPERTIES CXX_VISIBILITY_PRESET hidden) -install(TARGETS darklib EXPORT DarknetTargets +install(TARGETS dark EXPORT DarknetTargets RUNTIME DESTINATION "${INSTALL_BIN_DIR}" LIBRARY DESTINATION "${INSTALL_LIB_DIR}" ARCHIVE DESTINATION "${INSTALL_LIB_DIR}" - PUBLIC_HEADER DESTINATION "${INSTALL_INCLUDE_DIR}/darknet" + PUBLIC_HEADER DESTINATION "${INSTALL_INCLUDE_DIR}" COMPONENT dev ) install(TARGETS uselib darknet From f0a158e0cd3d4481135c0648b7a8515365612f68 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 18 Apr 2019 18:03:35 +0200 Subject: [PATCH 0470/1365] [ci] add tests with vcpkg on linux --- .travis.yml | 57 ++++++++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 52 insertions(+), 5 deletions(-) diff --git a/.travis.yml b/.travis.yml index b34ca94bdc1..79875e7e38f 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,4 +1,8 @@ language: cpp +cache: + timeout: 1000 + directories: + - $HOME/vcpkg matrix: include: @@ -236,6 +240,22 @@ matrix: - additional_defines="" - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8" + - os: linux + compiler: gcc + name: ubuntu 14.04 - gcc@8.0 - vcpkg + addons: + apt: + update: true + sources: + - ubuntu-toolchain-r-test + packages: + - g++-8 + env: + - additional_defines="" + - USE_VCPKG=true + - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" + - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8" + - os: linux compiler: clang name: ubuntu 14.04 - native clang - opencv@2 @@ -842,24 +862,51 @@ matrix: - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" + - os: linux + compiler: gcc + name: ubuntu 14.04 - gcc@8.0 - CUDA 10 - vcpkg + addons: + apt: + update: true + sources: + - ubuntu-toolchain-r-test + packages: + - g++-8 + env: + - additional_defines="" + - USE_VCPKG=true + - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" + - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" + - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" + before_install: - travis_retry eval "${MATRIX_EVAL}" install: # CMake upgrade on Linux - - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then wget --no-check-certificate https://github.com/Kitware/CMake/releases/download/v3.13.4/cmake-3.13.4-Linux-x86_64.tar.gz ;fi - - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then tar -xzf cmake-3.13.4-Linux-x86_64.tar.gz ;fi - - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then export PATH=$PWD/cmake-3.13.4-Linux-x86_64/bin:$PATH ;fi + - if [[ "$TRAVIS_OS_NAME" == "linux" ]] ; then wget --no-check-certificate https://github.com/Kitware/CMake/releases/download/v3.13.4/cmake-3.13.4-Linux-x86_64.tar.gz ; fi + - if [[ "$TRAVIS_OS_NAME" == "linux" ]] ; then tar -xzf cmake-3.13.4-Linux-x86_64.tar.gz ; fi + - if [[ "$TRAVIS_OS_NAME" == "linux" ]] ; then export PATH=$PWD/cmake-3.13.4-Linux-x86_64/bin:$PATH ; fi + - pushd $HOME + - if [ -d "$HOME/vcpkg/.git" ] ; then echo vcpkg cached ; else rm -rf vcpkg ; git clone https://github.com/Microsoft/vcpkg ; fi + - cd vcpkg + - git pull + - ./bootstrap-vcpkg.sh + - if [[ USE_VCPKG = true ]] ; then travis_wait ./vcpkg install stb pthreads ; fi + - if [[ USE_VCPKG = true ]] && [[ ! -z "${CUDACXX}" ]] ; then travis_wait ./vcpkg install opencv[ffmpeg,cuda] ; fi + - if [[ USE_VCPKG = true ]] && [[ -z "${CUDACXX}" ]] ; then travis_wait ./vcpkg install opencv[ffmpeg] ; fi + - popd before_script: - echo ${additional_defines} - mkdir build_debug - cd build_debug - - cmake .. -DCMAKE_BUILD_TYPE="Debug" ${additional_defines} + - cmake .. -DCMAKE_BUILD_TYPE="Debug" ${VCPKG_DEFINES} ${additional_defines} - cd .. - mkdir build_release - cd build_release - - cmake .. -DCMAKE_BUILD_TYPE="Release" ${additional_defines} + - cmake .. -DCMAKE_BUILD_TYPE="Release" ${VCPKG_DEFINES} ${additional_defines} - cd .. script: From b8962b22ebc653b844e3a8915c5d6ca2f885fc23 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 18 Apr 2019 18:23:12 +0200 Subject: [PATCH 0471/1365] disable vcpkg bootstrap when not required --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 79875e7e38f..edf74c550cb 100644 --- a/.travis.yml +++ b/.travis.yml @@ -892,7 +892,7 @@ install: - if [ -d "$HOME/vcpkg/.git" ] ; then echo vcpkg cached ; else rm -rf vcpkg ; git clone https://github.com/Microsoft/vcpkg ; fi - cd vcpkg - git pull - - ./bootstrap-vcpkg.sh + - if [[ USE_VCPKG = true ]] ; then ./bootstrap-vcpkg.sh ; fi - if [[ USE_VCPKG = true ]] ; then travis_wait ./vcpkg install stb pthreads ; fi - if [[ USE_VCPKG = true ]] && [[ ! -z "${CUDACXX}" ]] ; then travis_wait ./vcpkg install opencv[ffmpeg,cuda] ; fi - if [[ USE_VCPKG = true ]] && [[ -z "${CUDACXX}" ]] ; then travis_wait ./vcpkg install opencv[ffmpeg] ; fi From 73cc42c96dc64cb478d5b661b99b9bc7cb56c9e4 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 23 Apr 2019 15:29:53 +0200 Subject: [PATCH 0472/1365] improve CI compatibility with updated opencv@2 and opencv@3 on mac, fix variable not expanded for vcpkg --- .gitignore | 5 +++++ .travis.yml | 16 ++++++++-------- DarknetConfig.cmake.in | 2 ++ 3 files changed, 15 insertions(+), 8 deletions(-) diff --git a/.gitignore b/.gitignore index 8ca9c8f715b..142b185d2d4 100644 --- a/.gitignore +++ b/.gitignore @@ -34,5 +34,10 @@ Thumbs.db .vs/ .vscode/ +# Managed by CMake +src/version.h + +# Build artifacts lib/ share/ +include/darknet/ diff --git a/.travis.yml b/.travis.yml index edf74c550cb..cd999df270d 100644 --- a/.travis.yml +++ b/.travis.yml @@ -18,7 +18,7 @@ matrix: compiler: gcc name: macOS - native gcc (llvm backend) - opencv@2 env: - - OpenCV_DIR="/usr/local/Cellar/opencv@2/2.4.13.7_2/" + - OpenCV_DIR="/usr/local/opt/opencv@2/" - additional_defines="-DOpenCV_DIR=${OpenCV_DIR}" - MATRIX_EVAL="brew install opencv@2" @@ -26,7 +26,7 @@ matrix: compiler: gcc name: macOS - native gcc (llvm backend) - opencv@3 env: - - OpenCV_DIR="/usr/local/Cellar/opencv@3/3.4.5/" + - OpenCV_DIR="/usr/local/opt/opencv@3/" - additional_defines="-DOpenCV_DIR=${OpenCV_DIR}" - MATRIX_EVAL="brew install opencv@3" @@ -48,7 +48,7 @@ matrix: compiler: clang name: macOS - native clang - opencv@2 env: - - OpenCV_DIR="/usr/local/Cellar/opencv@2/2.4.13.7_2/" + - OpenCV_DIR="/usr/local/opt/opencv@2/" - additional_defines="-DOpenCV_DIR=${OpenCV_DIR}" - MATRIX_EVAL="brew install opencv@2" @@ -56,7 +56,7 @@ matrix: compiler: clang name: macOS - native clang - opencv@3 env: - - OpenCV_DIR="/usr/local/Cellar/opencv@3/3.4.5/" + - OpenCV_DIR="/usr/local/opt/opencv@3/" - additional_defines="-DOpenCV_DIR=${OpenCV_DIR}" - MATRIX_EVAL="brew install opencv@3" @@ -892,10 +892,10 @@ install: - if [ -d "$HOME/vcpkg/.git" ] ; then echo vcpkg cached ; else rm -rf vcpkg ; git clone https://github.com/Microsoft/vcpkg ; fi - cd vcpkg - git pull - - if [[ USE_VCPKG = true ]] ; then ./bootstrap-vcpkg.sh ; fi - - if [[ USE_VCPKG = true ]] ; then travis_wait ./vcpkg install stb pthreads ; fi - - if [[ USE_VCPKG = true ]] && [[ ! -z "${CUDACXX}" ]] ; then travis_wait ./vcpkg install opencv[ffmpeg,cuda] ; fi - - if [[ USE_VCPKG = true ]] && [[ -z "${CUDACXX}" ]] ; then travis_wait ./vcpkg install opencv[ffmpeg] ; fi + - if [ "${USE_VCPKG}" = true ] ; then ./bootstrap-vcpkg.sh ; fi + - if [ "${USE_VCPKG}" = true ] ; then travis_wait ./vcpkg install stb pthreads ; fi + - if [ "${USE_VCPKG}" = true ] && [[ ! -z "${CUDACXX}" ]] ; then travis_wait ./vcpkg install opencv[ffmpeg,cuda] ; fi + - if [ "${USE_VCPKG}" = true ] && [[ -z "${CUDACXX}" ]] ; then travis_wait ./vcpkg install opencv[ffmpeg] ; fi - popd before_script: diff --git a/DarknetConfig.cmake.in b/DarknetConfig.cmake.in index 2719ef5c3a8..6f43f729c98 100644 --- a/DarknetConfig.cmake.in +++ b/DarknetConfig.cmake.in @@ -29,6 +29,8 @@ find_dependency(Threads) if(MSVC) find_dependency(PThreads_windows) + #add_definitions(-D_CRT_SECURE_NO_WARNINGS) + #set(CMAKE_CXX_FLAGS "/wd4018 /wd4244 /wd4267 /wd4305 ${CMAKE_CXX_FLAGS}") endif() if(@ENABLE_OPENMP@) From b805bdebbd7f979ec71c22cca636fde7d8ae3a5a Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 23 Apr 2019 17:25:48 +0200 Subject: [PATCH 0473/1365] [travis,cuda] add missing symbol --- .travis.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.travis.yml b/.travis.yml index cd999df270d..9ac9581c1ab 100644 --- a/.travis.yml +++ b/.travis.yml @@ -877,6 +877,7 @@ matrix: - USE_VCPKG=true - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - CUDA_PATH=/usr/local/cuda-10.0 - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" From 49986d5a20087ffaebb0a7397a7ffaf6218f47c7 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 23 Apr 2019 17:34:34 +0200 Subject: [PATCH 0474/1365] install header files to darknet/ subfolder --- CMakeLists.txt | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 5be4f733862..79a84e5fafc 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -31,7 +31,7 @@ set(CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/cmake/Modules/" ${CMAKE_MODULE_ set(CMAKE_INSTALL_PREFIX "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Install prefix" FORCE) set(INSTALL_BIN_DIR "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Path where exe and dll will be installed") set(INSTALL_LIB_DIR "lib" CACHE PATH "Path where lib will be installed") -set(INSTALL_INCLUDE_DIR "include" CACHE PATH "Path where headers will be installed") +set(INSTALL_INCLUDE_DIR "include/darknet" CACHE PATH "Path where headers will be installed") set(INSTALL_CMAKE_DIR "share/darknet" CACHE PATH "Path where cmake configs will be installed") # Make relative paths absolute (needed later on) foreach(p LIB BIN INCLUDE CMAKE) @@ -283,7 +283,7 @@ if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRA add_executable(uselib_track ${CMAKE_CURRENT_LIST_DIR}/src/yolo_console_dll.cpp) target_compile_definitions(uselib_track PRIVATE TRACK_OPTFLOW=1) set_target_properties(uselib_track PROPERTIES LINKER_LANGUAGE CXX) - target_include_directories(uselib_track PUBLIC $ $ $ $) + target_include_directories(uselib_track PUBLIC $ $ $ $) target_link_libraries(uselib_track PRIVATE ${OpenCV_LIBS}) endif() @@ -294,9 +294,9 @@ add_executable(darknet ${CMAKE_CURRENT_LIST_DIR}/src/darknet.c ${sources} ${head set_source_files_properties(${CMAKE_CURRENT_LIST_DIR}/src/darknet.c PROPERTIES LANGUAGE CXX) set_target_properties(darknet PROPERTIES LINKER_LANGUAGE CXX) -target_include_directories(darknet PUBLIC $ $ $ $) -target_include_directories(dark PUBLIC $ $ $ $) -target_include_directories(uselib PUBLIC $ $ $ $) +target_include_directories(darknet PUBLIC $ $ $ $) +target_include_directories(dark PUBLIC $ $ $ $) +target_include_directories(uselib PUBLIC $ $ $ $) if(CUDNN_FOUND) target_link_libraries(darknet PRIVATE CuDNN::CuDNN) From c2071db3ae2673ada4bc90efa0f7c32ee18a267b Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 23 Apr 2019 18:49:51 +0200 Subject: [PATCH 0475/1365] fully recicle install location in target definition --- CMakeLists.txt | 34 ++++++++++++++++++---------------- 1 file changed, 18 insertions(+), 16 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 79a84e5fafc..0fa6adc7a88 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -33,18 +33,6 @@ set(INSTALL_BIN_DIR "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Path where exe set(INSTALL_LIB_DIR "lib" CACHE PATH "Path where lib will be installed") set(INSTALL_INCLUDE_DIR "include/darknet" CACHE PATH "Path where headers will be installed") set(INSTALL_CMAKE_DIR "share/darknet" CACHE PATH "Path where cmake configs will be installed") -# Make relative paths absolute (needed later on) -foreach(p LIB BIN INCLUDE CMAKE) - set(var INSTALL_${p}_DIR) - if(NOT IS_ABSOLUTE "${${var}}") - set(${var} "${CMAKE_INSTALL_PREFIX}/${${var}}") - endif() -endforeach() - -configure_file( - "${CMAKE_CURRENT_LIST_DIR}/src/version.h.in" - "${CMAKE_CURRENT_LIST_DIR}/src/version.h" -) set(ENABLE_CUDA "TRUE" CACHE BOOL "Enable CUDA build") set(ENABLE_CUDNN "FALSE" CACHE BOOL "Enable CUDNN") @@ -221,6 +209,20 @@ else() endif() endif() +set(DARKNET_INSTALL_INCLUDE_DIR ${INSTALL_INCLUDE_DIR}) +# Make relative paths absolute (needed later on) +foreach(p LIB BIN INCLUDE CMAKE) + set(var INSTALL_${p}_DIR) + if(NOT IS_ABSOLUTE "${${var}}") + set(${var} "${CMAKE_INSTALL_PREFIX}/${${var}}") + endif() +endforeach() + +configure_file( + "${CMAKE_CURRENT_LIST_DIR}/src/version.h.in" + "${CMAKE_CURRENT_LIST_DIR}/src/version.h" +) + #look for all *.h files in src folder file(GLOB headers "${CMAKE_CURRENT_LIST_DIR}/src/*.h") #add also files in the include folder @@ -283,7 +285,7 @@ if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRA add_executable(uselib_track ${CMAKE_CURRENT_LIST_DIR}/src/yolo_console_dll.cpp) target_compile_definitions(uselib_track PRIVATE TRACK_OPTFLOW=1) set_target_properties(uselib_track PROPERTIES LINKER_LANGUAGE CXX) - target_include_directories(uselib_track PUBLIC $ $ $ $) + target_include_directories(uselib_track PUBLIC $ $ $ $) target_link_libraries(uselib_track PRIVATE ${OpenCV_LIBS}) endif() @@ -294,9 +296,9 @@ add_executable(darknet ${CMAKE_CURRENT_LIST_DIR}/src/darknet.c ${sources} ${head set_source_files_properties(${CMAKE_CURRENT_LIST_DIR}/src/darknet.c PROPERTIES LANGUAGE CXX) set_target_properties(darknet PROPERTIES LINKER_LANGUAGE CXX) -target_include_directories(darknet PUBLIC $ $ $ $) -target_include_directories(dark PUBLIC $ $ $ $) -target_include_directories(uselib PUBLIC $ $ $ $) +target_include_directories(darknet PUBLIC $ $ $ $) +target_include_directories(dark PUBLIC $ $ $ $) +target_include_directories(uselib PUBLIC $ $ $ $) if(CUDNN_FOUND) target_link_libraries(darknet PRIVATE CuDNN::CuDNN) From 8cd5a9364abc2afd753229b6291604132e625cc3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 23 Apr 2019 20:46:10 +0300 Subject: [PATCH 0476/1365] OpenCV fix for channel=1. README.md fix --- README.md | 10 +++++++--- src/image_opencv.cpp | 25 +++++++++++++++++-------- 2 files changed, 24 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 15e4d8f49b6..ae4c7974f4d 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,14 @@ # Yolo-v3 and Yolo-v2 for Windows and Linux ### (neural network for object detection) - Tensor Cores can be used on [Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) and [Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows-using-vcpkg) -Contributors: https://github.com/AlexeyAB/darknet/graphs/contributors More details: http://pjreddie.com/darknet/yolo/ [![CircleCI](https://circleci.com/gh/AlexeyAB/darknet.svg?style=svg)](https://circleci.com/gh/AlexeyAB/darknet) [![TravisCI](https://travis-ci.org/AlexeyAB/darknet.svg?branch=master)](https://travis-ci.org/AlexeyAB/darknet) [![AppveyorCI](https://ci.appveyor.com/api/projects/status/594bwb5uoc1fxwiu/branch/master?svg=true)](https://ci.appveyor.com/project/AlexeyAB/darknet/branch/master) +[![Contributors](https://img.shields.io/github/contributors/AlexeyAB/Darknet.svg)](https://github.com/AlexeyAB/darknet/graphs/contributors) +[![License: Unlicense](https://img.shields.io/badge/license-Unlicense-blue.svg)](https://github.com/AlexeyAB/darknet/blob/master/LICENSE) * [Requirements (and how to install dependecies)](#requirements) @@ -73,9 +74,10 @@ You can get cfg-files by path: `darknet/cfg/` #### Yolo v3 in other frameworks -* Convert `yolov3.weights`/`cfg` model to **TensorFlow**: by using [mystic123](https://github.com/mystic123/tensorflow-yolo-v3) or [jinyu121](https://github.com/jinyu121/DW2TF) projects, and [TensorFlow-lite](https://www.tensorflow.org/lite/guide/get_started#2_convert_the_model_format) -* To use Yolo v3 model in **Intel OpenVINO** (Myriad X / USB Neural Compute Stick / Arria FPGA): read this [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) +* **TensorFlow:** convert `yolov3.weights`/`cfg` files to `yolov3.ckpt`/`pb/meta`: by using [mystic123](https://github.com/mystic123/tensorflow-yolo-v3) or [jinyu121](https://github.com/jinyu121/DW2TF) projects, and [TensorFlow-lite](https://www.tensorflow.org/lite/guide/get_started#2_convert_the_model_format) +* **Intel OpenVINO:** (Myriad X / USB Neural Compute Stick / Arria FPGA): read this [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) * **OpenCV-dnn** is very fast DNN implementation on CPU (x86/ARM-Android), use `yolov3.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221), [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) +* **PyTorch > ONNX > CoreML > iOS** how to convert cfg/weights-files to pt-file: [ultralytics/yolov3](https://github.com/ultralytics/yolov3#darknet-conversion) ##### Examples of results @@ -164,6 +166,8 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c * `OPENMP=1` to build with OpenMP support to accelerate Yolo by using multi-core CPU * `LIBSO=1` to build a library `darknet.so` and binary runable file `uselib` that uses this library. Or you can try to run so `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib test.mp4` How to use this SO-library from your own code - you can look at C++ example: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp or use in such a way: `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov3.cfg yolov3.weights test.mp4` +* `ZED_CAMERA=1` to build a library with ZED-3D-camera support (should be ZED SDK installed), then run + `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov3.cfg yolov3.weights zed_camera` To run Darknet on Linux use examples from this article, just use `./darknet` instead of `darknet.exe`, i.e. use this command: `./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights` diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index ff6a073ecb0..8ed15b95d76 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -10,6 +10,7 @@ #include #include #include +#include #include #include @@ -112,7 +113,8 @@ mat_cv *load_image_mat_cv(const char *filename, int flag) //if (check_mistakes) getchar(); return NULL; } - cv::cvtColor(mat, mat, cv::COLOR_RGB2BGR); + if (mat.channels() == 3) cv::cvtColor(mat, mat, cv::COLOR_RGB2BGR); + else if (mat.channels() == 4) cv::cvtColor(mat, mat, cv::COLOR_RGBA2BGRA); return (mat_cv *)mat_ptr; } @@ -429,7 +431,8 @@ void show_image_cv(image p, const char *name) constrain_image(copy); cv::Mat mat = image_to_mat(copy); - cv::cvtColor(mat, mat, cv::COLOR_RGB2BGR); + if (mat.channels() == 3) cv::cvtColor(mat, mat, cv::COLOR_RGB2BGR); + else if (mat.channels() == 4) cv::cvtColor(mat, mat, cv::COLOR_RGBA2BGR); cv::namedWindow(name, cv::WINDOW_NORMAL); cv::imshow(name, mat); free_image(copy); @@ -816,7 +819,7 @@ extern int stbi_write_jpg(char const *filename, int x, int y, int comp, const vo void save_mat_png(cv::Mat img_src, const char *name) { cv::Mat img_rgb; - cv::cvtColor(img_src, img_rgb, cv::COLOR_RGB2BGR); + if (img_src.channels() >= 3) cv::cvtColor(img_src, img_rgb, cv::COLOR_RGB2BGR); stbi_write_png(name, img_rgb.cols, img_rgb.rows, 3, (char *)img_rgb.data, 0); } // ---------------------------------------- @@ -824,7 +827,7 @@ void save_mat_png(cv::Mat img_src, const char *name) void save_mat_jpg(cv::Mat img_src, const char *name) { cv::Mat img_rgb; - cv::cvtColor(img_src, img_rgb, cv::COLOR_RGB2BGR); + if (img_src.channels() >= 3) cv::cvtColor(img_src, img_rgb, cv::COLOR_RGB2BGR); stbi_write_jpg(name, img_rgb.cols, img_rgb.rows, 3, (char *)img_rgb.data, 80); } // ---------------------------------------- @@ -1063,6 +1066,7 @@ void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_im // precision if (draw_precision) { static float old_precision = 0; + static float max_precision = 0; static int iteration_old = 0; static int text_iteration_old = 0; if (iteration_old == 0) @@ -1073,12 +1077,14 @@ void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_im cv::Point(img_offset + draw_size * (float)current_batch / max_batches, draw_size * (1 - precision)), CV_RGB(255, 0, 0), 1, 8, 0); - sprintf(char_buff, "%2.0f%% ", precision * 100); + sprintf(char_buff, "%2.1f%% ", precision * 100); cv::putText(img, char_buff, cv::Point(10, 28), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 5, CV_AA); cv::putText(img, char_buff, cv::Point(10, 28), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(200, 0, 0), 1, CV_AA); - if (((int)(old_precision * 10) != (int)(precision * 10)) || (current_batch - text_iteration_old) >= max_batches / 10) { + if (((int)(old_precision * 10) != (int)(precision * 10)) || (max_precision < precision) || (current_batch - text_iteration_old) >= max_batches / 10) { text_iteration_old = current_batch; + max_precision = std::max(max_precision, precision); + sprintf(char_buff, "%2.0f%% ", precision * 100); cv::putText(img, char_buff, cv::Point(pt1.x - 30, draw_size * (1 - precision) + 15), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 5, CV_AA); cv::putText(img, char_buff, cv::Point(pt1.x - 30, draw_size * (1 - precision) + 15), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(200, 0, 0), 1, CV_AA); } @@ -1098,7 +1104,9 @@ void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_im cv::imshow("average loss", img); k = cv::waitKey(20); } - if (k == 's' || current_batch == (max_batches - 1) || current_batch % 100 == 0) { + static int old_batch = 0; + if (k == 's' || current_batch == (max_batches - 1) || (current_batch / 100 > old_batch / 100)) { + old_batch = current_batch; save_mat_png(img, "chart.png"); cv::putText(img, "- Saved", cv::Point(260, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 0, 0), 1, CV_AA); } @@ -1133,7 +1141,8 @@ image image_data_augmentation(mat_cv* mat, int w, int h, cv::Rect dst_rect(cv::Point2i(std::max(0, -pleft), std::max(0, -ptop)), new_src_rect.size()); cv::Mat cropped(cv::Size(src_rect.width, src_rect.height), img.type()); - cropped.setTo(cv::Scalar::all(0)); + //cropped.setTo(cv::Scalar::all(0)); + cropped.setTo(cv::mean(img)); img(new_src_rect).copyTo(cropped(dst_rect)); From bc95ce3c250233c380633c4ef6ee98cfe74eeecd Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 24 Apr 2019 02:45:48 +0300 Subject: [PATCH 0477/1365] Optimization when data augmentation is disabled (for OpenCV) --- src/image_opencv.cpp | 52 +++++++++++++++++++++++++------------------- 1 file changed, 30 insertions(+), 22 deletions(-) diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 8ed15b95d76..48064672c40 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1139,44 +1139,52 @@ image image_data_augmentation(mat_cv* mat, int w, int h, cv::Rect new_src_rect = src_rect & img_rect; cv::Rect dst_rect(cv::Point2i(std::max(0, -pleft), std::max(0, -ptop)), new_src_rect.size()); + cv::Mat sized; - cv::Mat cropped(cv::Size(src_rect.width, src_rect.height), img.type()); - //cropped.setTo(cv::Scalar::all(0)); - cropped.setTo(cv::mean(img)); + if (src_rect.x == 0 && src_rect.y == 0 && src_rect.size() == img.size()) { + cv::resize(img, sized, cv::Size(w, h), 0, 0, cv::INTER_LINEAR); + } + else { + cv::Mat cropped(src_rect.size(), img.type()); + //cropped.setTo(cv::Scalar::all(0)); + cropped.setTo(cv::mean(img)); - img(new_src_rect).copyTo(cropped(dst_rect)); + img(new_src_rect).copyTo(cropped(dst_rect)); - // resize - cv::Mat sized; - cv::resize(cropped, sized, cv::Size(w, h), 0, 0, cv::INTER_LINEAR); + // resize + cv::resize(cropped, sized, cv::Size(w, h), 0, 0, cv::INTER_LINEAR); + } // flip if (flip) { + cv::Mat cropped; cv::flip(sized, cropped, 1); // 0 - x-axis, 1 - y-axis, -1 - both axes (x & y) sized = cropped.clone(); } // HSV augmentation // cv::COLOR_BGR2HSV, cv::COLOR_RGB2HSV, cv::COLOR_HSV2BGR, cv::COLOR_HSV2RGB - if(img.channels() >= 3) - { - cv::Mat hsv_src; - cvtColor(sized, hsv_src, cv::COLOR_RGB2HSV); // RGB to HSV + if (dsat != 1 || dexp != 1 || dhue != 0) { + if (img.channels() >= 3) + { + cv::Mat hsv_src; + cvtColor(sized, hsv_src, cv::COLOR_RGB2HSV); // RGB to HSV - std::vector hsv; - cv::split(hsv_src, hsv); + std::vector hsv; + cv::split(hsv_src, hsv); - hsv[1] *= dsat; - hsv[2] *= dexp; - hsv[0] += 179 * dhue; + hsv[1] *= dsat; + hsv[2] *= dexp; + hsv[0] += 179 * dhue; - cv::merge(hsv, hsv_src); + cv::merge(hsv, hsv_src); - cvtColor(hsv_src, sized, cv::COLOR_HSV2RGB); // HSV to RGB (the same as previous) - } - else - { - sized *= dexp; + cvtColor(hsv_src, sized, cv::COLOR_HSV2RGB); // HSV to RGB (the same as previous) + } + else + { + sized *= dexp; + } } //std::stringstream window_name; From 42d08fd820335584365d393da3967853676a8c35 Mon Sep 17 00:00:00 2001 From: Aymeric DUJARDIN Date: Wed, 24 Apr 2019 15:18:41 +0200 Subject: [PATCH 0478/1365] Fix cv exception when using both TRACK_OPTFLOW and ZED_STEREO --- src/yolo_console_dll.cpp | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/yolo_console_dll.cpp b/src/yolo_console_dll.cpp index 4f2b2b7432e..60da53f6516 100644 --- a/src/yolo_console_dll.cpp +++ b/src/yolo_console_dll.cpp @@ -129,7 +129,9 @@ cv::Mat slMat2cvMat(sl::Mat &input) { cv::Mat zed_capture_rgb(sl::Camera &zed) { sl::Mat left; zed.retrieveImage(left); - return slMat2cvMat(left).clone(); + cv::Mat left_rgb; + cv::cvtColor(slMat2cvMat(left), left_rgb, CV_RGBA2RGB); + return left_rgb; } cv::Mat zed_capture_3d(sl::Camera &zed) { From 21e6372a5b2f9202210a89597d5c6786a88c18d5 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 30 Apr 2019 11:10:52 +0200 Subject: [PATCH 0479/1365] [ci] allow vcpkg failures, export CC and CXX symbols, use stb from vcpkg when not testing integrated libs --- .travis.yml | 110 +++++++++++++++++++++++++++++---------------------- appveyor.yml | 3 +- 2 files changed, 64 insertions(+), 49 deletions(-) diff --git a/.travis.yml b/.travis.yml index 9ac9581c1ab..8d1b2988ff9 100644 --- a/.travis.yml +++ b/.travis.yml @@ -67,6 +67,15 @@ matrix: - additional_defines="" - MATRIX_EVAL="brew install opencv" + - os: osx + name: macOS - vcpkg + osx_image: xcode10.1 + env: + - additional_defines="" + - MATRIX_EVAL="brew install gcc && unset CC && unset CXX" + - USE_VCPKG=true + - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" + - os: linux compiler: clang name: ubuntu 14.04 - native clang @@ -87,7 +96,7 @@ matrix: - clang-3.6 env: - additional_defines="" - - MATRIX_EVAL="CC=clang-3.6 && CXX=clang++-3.6" + - MATRIX_EVAL="export CC=clang-3.6 && export CXX=clang++-3.6" - os: linux compiler: clang @@ -101,7 +110,7 @@ matrix: - clang-3.8 env: - additional_defines="" - - MATRIX_EVAL="CC=clang-3.8 && CXX=clang++-3.8" + - MATRIX_EVAL="export CC=clang-3.8 && export CXX=clang++-3.8" - os: linux compiler: clang @@ -115,7 +124,7 @@ matrix: - clang-3.9 env: - additional_defines="" - - MATRIX_EVAL="CC=clang-3.9 && CXX=clang++-3.9" + - MATRIX_EVAL="export CC=clang-3.9 && export CXX=clang++-3.9" - os: linux compiler: clang @@ -129,7 +138,7 @@ matrix: - clang-4.0 env: - additional_defines="" - - MATRIX_EVAL="CC=clang-4.0 && CXX=clang++-4.0" + - MATRIX_EVAL="export CC=clang-4.0 && export CXX=clang++-4.0" - os: linux compiler: clang @@ -143,7 +152,7 @@ matrix: - clang-5.0 env: - additional_defines="" - - MATRIX_EVAL="CC=clang-5.0 && CXX=clang++-5.0" + - MATRIX_EVAL="export CC=clang-5.0 && export CXX=clang++-5.0" - os: linux compiler: clang @@ -158,7 +167,7 @@ matrix: - clang-6.0 env: - additional_defines="" - - MATRIX_EVAL="CC=clang-6.0 && CXX=clang++-6.0" + - MATRIX_EVAL="export CC=clang-6.0 && export CXX=clang++-6.0" - os: linux compiler: gcc @@ -182,7 +191,7 @@ matrix: - g++-4.9 env: - additional_defines="" - - MATRIX_EVAL="CC=gcc-4.9 && CXX=g++-4.9" + - MATRIX_EVAL="export CC=gcc-4.9 && export CXX=g++-4.9" - os: linux compiler: gcc @@ -196,7 +205,7 @@ matrix: - g++-5 env: - additional_defines="" - - MATRIX_EVAL="CC=gcc-5 && CXX=g++-5" + - MATRIX_EVAL="export CC=gcc-5 && export CXX=g++-5" - os: linux compiler: gcc @@ -210,7 +219,7 @@ matrix: - g++-6 env: - additional_defines="" - - MATRIX_EVAL="CC=gcc-6 && CXX=g++-6" + - MATRIX_EVAL="export CC=gcc-6 && export CXX=g++-6" - os: linux compiler: gcc @@ -224,7 +233,7 @@ matrix: - g++-7 env: - additional_defines="" - - MATRIX_EVAL="CC=gcc-7 && CXX=g++-7" + - MATRIX_EVAL="export CC=gcc-7 && export CXX=g++-7" - os: linux compiler: gcc @@ -238,7 +247,7 @@ matrix: - g++-8 env: - additional_defines="" - - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8" + - MATRIX_EVAL="export CC=gcc-8 && export CXX=g++-8" - os: linux compiler: gcc @@ -254,7 +263,7 @@ matrix: - additional_defines="" - USE_VCPKG=true - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" - - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8" + - MATRIX_EVAL="export CC=gcc-8 && export CXX=g++-8" - os: linux compiler: clang @@ -279,7 +288,7 @@ matrix: - clang-3.6 env: - additional_defines="" - - MATRIX_EVAL="CC=clang-3.6 && CXX=clang++-3.6" + - MATRIX_EVAL="export CC=clang-3.6 && export CXX=clang++-3.6" - os: linux compiler: clang @@ -294,7 +303,7 @@ matrix: - clang-3.8 env: - additional_defines="" - - MATRIX_EVAL="CC=clang-3.8 && CXX=clang++-3.8" + - MATRIX_EVAL="export CC=clang-3.8 && export CXX=clang++-3.8" - os: linux compiler: clang @@ -309,7 +318,7 @@ matrix: - clang-3.9 env: - additional_defines="" - - MATRIX_EVAL="CC=clang-3.9 && CXX=clang++-3.9" + - MATRIX_EVAL="export CC=clang-3.9 && export CXX=clang++-3.9" - os: linux compiler: clang @@ -324,7 +333,7 @@ matrix: - clang-4.0 env: - additional_defines="" - - MATRIX_EVAL="CC=clang-4.0 && CXX=clang++-4.0" + - MATRIX_EVAL="export CC=clang-4.0 && export CXX=clang++-4.0" - os: linux compiler: clang @@ -339,7 +348,7 @@ matrix: - clang-5.0 env: - additional_defines="" - - MATRIX_EVAL="CC=clang-5.0 && CXX=clang++-5.0" + - MATRIX_EVAL="export CC=clang-5.0 && export CXX=clang++-5.0" - os: linux compiler: clang @@ -355,7 +364,7 @@ matrix: - clang-6.0 env: - additional_defines="" - - MATRIX_EVAL="CC=clang-6.0 && CXX=clang++-6.0" + - MATRIX_EVAL="export CC=clang-6.0 && export CXX=clang++-6.0" - os: linux compiler: gcc @@ -382,7 +391,7 @@ matrix: - g++-4.9 env: - additional_defines="" - - MATRIX_EVAL="CC=gcc-4.9 && CXX=g++-4.9" + - MATRIX_EVAL="export CC=gcc-4.9 && export CXX=g++-4.9" - os: linux compiler: gcc @@ -397,7 +406,7 @@ matrix: - g++-5 env: - additional_defines="" - - MATRIX_EVAL="CC=gcc-5 && CXX=g++-5" + - MATRIX_EVAL="export CC=gcc-5 && export CXX=g++-5" - os: linux compiler: gcc @@ -412,7 +421,7 @@ matrix: - libopencv-dev env: - additional_defines="" - - MATRIX_EVAL="CC=gcc-6 && CXX=g++-6" + - MATRIX_EVAL="export CC=gcc-6 && export CXX=g++-6" - os: linux compiler: gcc @@ -427,7 +436,7 @@ matrix: - g++-7 env: - additional_defines="" - - MATRIX_EVAL="CC=gcc-7 && CXX=g++-7" + - MATRIX_EVAL="export CC=gcc-7 && export CXX=g++-7" - os: linux compiler: gcc @@ -442,7 +451,7 @@ matrix: - g++-8 env: - additional_defines="" - - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8" + - MATRIX_EVAL="export CC=gcc-8 && export CXX=g++-8" - os: linux compiler: clang @@ -471,7 +480,7 @@ matrix: - clang-3.6 env: - additional_defines="" - - MATRIX_EVAL="CC=clang-3.6 && CXX=clang++-3.6" + - MATRIX_EVAL="export CC=clang-3.6 && export CXX=clang++-3.6" - os: linux compiler: clang @@ -487,7 +496,7 @@ matrix: - clang-3.8 env: - additional_defines="" - - MATRIX_EVAL="CC=clang-3.8 && CXX=clang++-3.8" + - MATRIX_EVAL="export CC=clang-3.8 && export CXX=clang++-3.8" - os: linux compiler: clang @@ -503,7 +512,7 @@ matrix: - clang-3.9 env: - additional_defines="" - - MATRIX_EVAL="CC=clang-3.9 && CXX=clang++-3.9" + - MATRIX_EVAL="export CC=clang-3.9 && export CXX=clang++-3.9" - os: linux compiler: clang @@ -519,7 +528,7 @@ matrix: - clang-4.0 env: - additional_defines="" - - MATRIX_EVAL="CC=clang-4.0 && CXX=clang++-4.0" + - MATRIX_EVAL="export CC=clang-4.0 && export CXX=clang++-4.0" - os: linux compiler: clang @@ -535,7 +544,7 @@ matrix: - clang-5.0 env: - additional_defines="" - - MATRIX_EVAL="CC=clang-5.0 && CXX=clang++-5.0" + - MATRIX_EVAL="export CC=clang-5.0 && export CXX=clang++-5.0" - os: linux compiler: clang @@ -552,7 +561,7 @@ matrix: - clang-6.0 env: - additional_defines="" - - MATRIX_EVAL="CC=clang-6.0 && CXX=clang++-6.0" + - MATRIX_EVAL="export CC=clang-6.0 && export CXX=clang++-6.0" - os: linux compiler: gcc @@ -582,7 +591,7 @@ matrix: - g++-4.9 env: - additional_defines="" - - MATRIX_EVAL="CC=gcc-4.9 && CXX=g++-4.9" + - MATRIX_EVAL="export CC=gcc-4.9 && export CXX=g++-4.9" - os: linux compiler: gcc @@ -598,7 +607,7 @@ matrix: - g++-5 env: - additional_defines="" - - MATRIX_EVAL="CC=gcc-5 && CXX=g++-5" + - MATRIX_EVAL="export CC=gcc-5 && export CXX=g++-5" - os: linux compiler: gcc @@ -614,7 +623,7 @@ matrix: - libopencv-dev env: - additional_defines="" - - MATRIX_EVAL="CC=gcc-6 && CXX=g++-6" + - MATRIX_EVAL="export CC=gcc-6 && export CXX=g++-6" - os: linux compiler: gcc @@ -630,7 +639,7 @@ matrix: - g++-7 env: - additional_defines="" - - MATRIX_EVAL="CC=gcc-7 && CXX=g++-7" + - MATRIX_EVAL="export CC=gcc-7 && export CXX=g++-7" - os: linux compiler: gcc @@ -646,7 +655,7 @@ matrix: - g++-8 env: - additional_defines="" - - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8" + - MATRIX_EVAL="export CC=gcc-8 && export CXX=g++-8" - os: linux compiler: clang @@ -675,7 +684,7 @@ matrix: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="CC=clang-3.6 && CXX=clang++-3.6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" + - MATRIX_EVAL="export CC=clang-3.6 && export CXX=clang++-3.6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: clang @@ -692,7 +701,7 @@ matrix: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="CC=clang-3.8 && CXX=clang++-3.8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" + - MATRIX_EVAL="export CC=clang-3.8 && export CXX=clang++-3.8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: clang @@ -709,7 +718,7 @@ matrix: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="CC=clang-3.9 && CXX=clang++-3.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" + - MATRIX_EVAL="export CC=clang-3.9 && export CXX=clang++-3.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: clang @@ -726,7 +735,7 @@ matrix: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="CC=clang-4.0 && CXX=clang++-4.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" + - MATRIX_EVAL="export CC=clang-4.0 && export CXX=clang++-4.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: clang @@ -743,7 +752,7 @@ matrix: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="CC=clang-5.0 && CXX=clang++-5.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" + - MATRIX_EVAL="export CC=clang-5.0 && export CXX=clang++-5.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: clang @@ -761,7 +770,7 @@ matrix: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="CC=clang-6.0 && CXX=clang++-6.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" + - MATRIX_EVAL="export CC=clang-6.0 && export CXX=clang++-6.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: gcc @@ -792,7 +801,7 @@ matrix: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="CC=gcc-4.9 && CXX=g++-4.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" + - MATRIX_EVAL="export CC=gcc-4.9 && export CXX=g++-4.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: gcc @@ -809,7 +818,7 @@ matrix: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="CC=gcc-5 && CXX=g++-5 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" + - MATRIX_EVAL="export CC=gcc-5 && export CXX=g++-5 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: gcc @@ -826,7 +835,7 @@ matrix: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="CC=gcc-6 && CXX=g++-6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" + - MATRIX_EVAL="export CC=gcc-6 && export CXX=g++-6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: gcc @@ -843,7 +852,7 @@ matrix: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="CC=gcc-7 && CXX=g++-7 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" + - MATRIX_EVAL="export CC=gcc-7 && export CXX=g++-7 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: gcc @@ -860,7 +869,7 @@ matrix: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" + - MATRIX_EVAL="export CC=gcc-8 && export CXX=g++-8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: gcc @@ -879,7 +888,11 @@ matrix: - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - CUDA_PATH=/usr/local/cuda-10.0 - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="CC=gcc-8 && CXX=g++-8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" + - MATRIX_EVAL="export CC=gcc-8 && export CXX=g++-8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" + + #do not consider known failing builds for the whole build status + allow_failures: + - name: macOS - vcpkg before_install: - travis_retry eval "${MATRIX_EVAL}" @@ -895,8 +908,9 @@ install: - git pull - if [ "${USE_VCPKG}" = true ] ; then ./bootstrap-vcpkg.sh ; fi - if [ "${USE_VCPKG}" = true ] ; then travis_wait ./vcpkg install stb pthreads ; fi - - if [ "${USE_VCPKG}" = true ] && [[ ! -z "${CUDACXX}" ]] ; then travis_wait ./vcpkg install opencv[ffmpeg,cuda] ; fi - - if [ "${USE_VCPKG}" = true ] && [[ -z "${CUDACXX}" ]] ; then travis_wait ./vcpkg install opencv[ffmpeg] ; fi + #- if [ "${USE_VCPKG}" = true ] && [[ ! -z "${CUDACXX}" ]] ; then travis_wait ./vcpkg install opencv[ffmpeg,cuda] ; fi + #- if [ "${USE_VCPKG}" = true ] && [[ -z "${CUDACXX}" ]] ; then travis_wait ./vcpkg install opencv[ffmpeg] ; fi + - if [ "${USE_VCPKG}" = true ] ; then travis_wait ./vcpkg install opencv[ffmpeg] ; fi - popd before_script: diff --git a/appveyor.yml b/appveyor.yml index 911ed3d1c22..08ce819bf58 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -67,7 +67,8 @@ install: - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] git pull - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] .\bootstrap-vcpkg.bat - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] if [%USE_CUDA%]==[yes] vcpkg install cuda - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg install opencv[ffmpeg] pthreads ## opencv[ffmpeg,cuda] is too big to build, timing out (>1h). We use plain openCV also for CUDA builds (toolchain can manage this strange situation anyway) + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg install stb pthreads + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg install opencv[ffmpeg] ## opencv[ffmpeg,cuda] is too big to build, timing out (>1h). We use plain openCV also for CUDA builds (toolchain can manage this strange situation anyway) - cd %WORKSPACE%\darknet\ - mkdir build_debug && cd build_debug - if [%COMPILER%]==[cygwin] %CYGSH% 'cmake .. -G "Unix Makefiles" -DCMAKE_BUILD_TYPE="Debug"' From 3a20020264842679629c46efed391518fcfb5096 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 30 Apr 2019 16:15:57 +0200 Subject: [PATCH 0480/1365] [ci] extend timeout for vcpkg, add all vcpkg configs to allowed failures --- .travis.yml | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/.travis.yml b/.travis.yml index 8d1b2988ff9..4a0b50fadee 100644 --- a/.travis.yml +++ b/.travis.yml @@ -893,6 +893,8 @@ matrix: #do not consider known failing builds for the whole build status allow_failures: - name: macOS - vcpkg + - name: ubuntu 14.04 - gcc@8.0 - vcpkg + - name: ubuntu 14.04 - gcc@8.0 - CUDA 10 - vcpkg before_install: - travis_retry eval "${MATRIX_EVAL}" @@ -908,9 +910,8 @@ install: - git pull - if [ "${USE_VCPKG}" = true ] ; then ./bootstrap-vcpkg.sh ; fi - if [ "${USE_VCPKG}" = true ] ; then travis_wait ./vcpkg install stb pthreads ; fi - #- if [ "${USE_VCPKG}" = true ] && [[ ! -z "${CUDACXX}" ]] ; then travis_wait ./vcpkg install opencv[ffmpeg,cuda] ; fi - #- if [ "${USE_VCPKG}" = true ] && [[ -z "${CUDACXX}" ]] ; then travis_wait ./vcpkg install opencv[ffmpeg] ; fi - - if [ "${USE_VCPKG}" = true ] ; then travis_wait ./vcpkg install opencv[ffmpeg] ; fi + - if [ "${USE_VCPKG}" = true ] && [[ ! -z "${CUDACXX}" ]] ; then travis_wait 45 ./vcpkg install opencv[ffmpeg,cuda] ; fi + - if [ "${USE_VCPKG}" = true ] && [[ -z "${CUDACXX}" ]] ; then travis_wait 45 ./vcpkg install opencv[ffmpeg] ; fi - popd before_script: From 84ee1f42c6f9c2ffabaf3d3d5b1da1d185c4a972 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 30 Apr 2019 19:25:47 +0200 Subject: [PATCH 0481/1365] [travis] disable vcpkg's built opencv[cuda] also on cuda-enabled configs, since it causes internal compiler errors. Waiting for updates from vcpkg --- .travis.yml | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/.travis.yml b/.travis.yml index 4a0b50fadee..8834e2af54e 100644 --- a/.travis.yml +++ b/.travis.yml @@ -890,11 +890,10 @@ matrix: - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="export CC=gcc-8 && export CXX=g++-8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - #do not consider known failing builds for the whole build status - allow_failures: - - name: macOS - vcpkg - - name: ubuntu 14.04 - gcc@8.0 - vcpkg - - name: ubuntu 14.04 - gcc@8.0 - CUDA 10 - vcpkg +# allow_failures: +# - name: macOS - vcpkg +# - name: ubuntu 14.04 - gcc@8.0 - vcpkg +# - name: ubuntu 14.04 - gcc@8.0 - CUDA 10 - vcpkg before_install: - travis_retry eval "${MATRIX_EVAL}" @@ -910,8 +909,9 @@ install: - git pull - if [ "${USE_VCPKG}" = true ] ; then ./bootstrap-vcpkg.sh ; fi - if [ "${USE_VCPKG}" = true ] ; then travis_wait ./vcpkg install stb pthreads ; fi - - if [ "${USE_VCPKG}" = true ] && [[ ! -z "${CUDACXX}" ]] ; then travis_wait 45 ./vcpkg install opencv[ffmpeg,cuda] ; fi - - if [ "${USE_VCPKG}" = true ] && [[ -z "${CUDACXX}" ]] ; then travis_wait 45 ./vcpkg install opencv[ffmpeg] ; fi + #- if [ "${USE_VCPKG}" = true ] && [[ ! -z "${CUDACXX}" ]] ; then travis_wait 45 ./vcpkg install opencv[ffmpeg,cuda] --recurse; fi + #- if [ "${USE_VCPKG}" = true ] && [[ -z "${CUDACXX}" ]] ; then travis_wait 45 ./vcpkg install opencv[ffmpeg] --recurse; fi + - if [ "${USE_VCPKG}" = true ] ; then travis_wait 45 ./vcpkg install opencv[ffmpeg] ; fi - popd before_script: From 31dc6c86808dc7c0e79c7721907b4108f2d92fd7 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 6 May 2019 00:01:56 +0300 Subject: [PATCH 0482/1365] Added LSTM sequence detector, and blur data augmentation (for OpenCV only) --- Makefile | 2 +- README.md | 8 +-- build/darknet/darknet.vcxproj | 4 ++ build/darknet/darknet_no_gpu.vcxproj | 2 + build/darknet/x64/partial.cmd | 3 + build/darknet/yolo_cpp_dll.vcxproj | 2 + build/darknet/yolo_cpp_dll_no_gpu.vcxproj | 2 + include/darknet.h | 18 ++++- src/convolutional_layer.c | 4 +- src/crnn_layer.c | 12 ++++ src/crnn_layer.h | 1 + src/data.c | 28 +++++--- src/data.h | 2 +- src/demo.c | 2 + src/detector.c | 29 ++++++-- src/image_opencv.cpp | 38 ++++++++++- src/image_opencv.h | 3 +- src/layer.c | 83 +++++++++++++++++++---- src/lstm_layer.c | 1 + src/lstm_layer.h | 1 + src/network.c | 31 +++++++++ src/network.h | 3 + src/network_kernels.cu | 16 +++++ src/parser.c | 58 ++++++++++++++++ src/yolo_v2_class.cpp | 1 + 25 files changed, 315 insertions(+), 39 deletions(-) diff --git a/Makefile b/Makefile index 85e1a3e8c0d..7f0547d3360 100644 --- a/Makefile +++ b/Makefile @@ -118,7 +118,7 @@ LDFLAGS+= -L/usr/local/zed/lib -lsl_core -lsl_input -lsl_zed #-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0 endif -OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o upsample_layer.o lstm_layer.o +OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o upsample_layer.o lstm_layer.o conv_lstm_layer.o ifeq ($(GPU), 1) LDFLAGS+= -lstdc++ OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o diff --git a/README.md b/README.md index ae4c7974f4d..92cfe1a5518 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ More details: http://pjreddie.com/darknet/yolo/ * [Requirements (and how to install dependecies)](#requirements) * [Pre-trained models](#pre-trained-models) * [Explanations in issues](https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3AExplanations) -* [Yolo v3 in other frameworks (TensorFlow, OpenVINO, OpenCV-dnn, ...)](#yolo-v3-in-other-frameworks) +* [Yolo v3 in other frameworks (TensorFlow, PyTorch, OpenVINO, OpenCV-dnn,...)](#yolo-v3-in-other-frameworks) 0. [Improvements in this repository](#improvements-in-this-repository) 1. [How to use](#how-to-use-on-the-command-line) @@ -75,9 +75,9 @@ You can get cfg-files by path: `darknet/cfg/` #### Yolo v3 in other frameworks * **TensorFlow:** convert `yolov3.weights`/`cfg` files to `yolov3.ckpt`/`pb/meta`: by using [mystic123](https://github.com/mystic123/tensorflow-yolo-v3) or [jinyu121](https://github.com/jinyu121/DW2TF) projects, and [TensorFlow-lite](https://www.tensorflow.org/lite/guide/get_started#2_convert_the_model_format) -* **Intel OpenVINO:** (Myriad X / USB Neural Compute Stick / Arria FPGA): read this [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) -* **OpenCV-dnn** is very fast DNN implementation on CPU (x86/ARM-Android), use `yolov3.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221), [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) -* **PyTorch > ONNX > CoreML > iOS** how to convert cfg/weights-files to pt-file: [ultralytics/yolov3](https://github.com/ultralytics/yolov3#darknet-conversion) +* **Intel OpenVINO 2019 R1:** (Myriad X / USB Neural Compute Stick / Arria FPGA): read this [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) +* **OpenCV-dnn** is a very fast DNN implementation on CPU (x86/ARM-Android), use `yolov3.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221), [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) +* **PyTorch > ONNX > CoreML > iOS** how to convert cfg/weights-files to pt-file: [ultralytics/yolov3](https://github.com/ultralytics/yolov3#darknet-conversion) and [iOS App](https://itunes.apple.com/app/id1452689527) ##### Examples of results diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj index 787602d56c0..5a78ac602ec 100644 --- a/build/darknet/darknet.vcxproj +++ b/build/darknet/darknet.vcxproj @@ -140,6 +140,8 @@ Default NDEBUG true + + true @@ -183,6 +185,7 @@ + @@ -248,6 +251,7 @@ + diff --git a/build/darknet/darknet_no_gpu.vcxproj b/build/darknet/darknet_no_gpu.vcxproj index ec4cea997ff..5b0fe209248 100644 --- a/build/darknet/darknet_no_gpu.vcxproj +++ b/build/darknet/darknet_no_gpu.vcxproj @@ -189,6 +189,7 @@ + @@ -254,6 +255,7 @@ + diff --git a/build/darknet/x64/partial.cmd b/build/darknet/x64/partial.cmd index f0c2b9e8198..03759e8afeb 100644 --- a/build/darknet/x64/partial.cmd +++ b/build/darknet/x64/partial.cmd @@ -33,6 +33,9 @@ darknet.exe partial cfg/yolov3-spp.cfg yolov3-spp.weights yolov3-spp.conv.85 85 darknet.exe partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15 +darknet.exe partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.14 14 + + darknet.exe partial cfg/yolo9000.cfg yolo9000.weights yolo9000.conv.22 22 diff --git a/build/darknet/yolo_cpp_dll.vcxproj b/build/darknet/yolo_cpp_dll.vcxproj index f2c22b03862..b14766998a2 100644 --- a/build/darknet/yolo_cpp_dll.vcxproj +++ b/build/darknet/yolo_cpp_dll.vcxproj @@ -187,6 +187,7 @@ + @@ -254,6 +255,7 @@ + diff --git a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj index 5300e40b771..c5c17e84d66 100644 --- a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj +++ b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj @@ -173,6 +173,7 @@ + @@ -240,6 +241,7 @@ + diff --git a/include/darknet.h b/include/darknet.h index 82f033e3bea..29a629248bf 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -32,7 +32,6 @@ #endif #endif -#define NFRAMES 3 #define SECRET_NUM -1234 #ifdef GPU @@ -136,6 +135,7 @@ typedef enum { RNN, GRU, LSTM, + CONV_LSTM, CRNN, BATCHNORM, NETWORK, @@ -208,6 +208,7 @@ struct layer { int index; int binary; int xnor; + int peephole; int use_bin_output; int steps; int hidden; @@ -354,6 +355,7 @@ struct layer { float *z_cpu; float *r_cpu; float *h_cpu; + float *stored_h_cpu; float * prev_state_cpu; float *temp_cpu; @@ -369,6 +371,7 @@ struct layer { float *g_cpu; float *o_cpu; float *c_cpu; + float *stored_c_cpu; float *dc_cpu; float *binary_input; @@ -407,10 +410,13 @@ struct layer { struct layer *uh; struct layer *uo; struct layer *wo; + struct layer *vo; struct layer *uf; struct layer *wf; + struct layer *vf; struct layer *ui; struct layer *wi; + struct layer *vi; struct layer *ug; struct layer *wg; @@ -424,6 +430,7 @@ struct layer { float *z_gpu; float *r_gpu; float *h_gpu; + float *stored_h_gpu; float *temp_gpu; float *temp2_gpu; @@ -432,12 +439,16 @@ struct layer { float *dh_gpu; float *hh_gpu; float *prev_cell_gpu; + float *prev_state_gpu; + float *last_prev_state_gpu; + float *last_prev_cell_gpu; float *cell_gpu; float *f_gpu; float *i_gpu; float *g_gpu; float *o_gpu; float *c_gpu; + float *stored_c_gpu; float *dc_gpu; // adam @@ -451,7 +462,6 @@ struct layer { float * combine_gpu; float * combine_delta_gpu; - float * prev_state_gpu; float * forgot_state_gpu; float * forgot_delta_gpu; float * state_gpu; @@ -571,6 +581,7 @@ typedef struct network { float min_ratio; int center; int flip; // horizontal flip 50% probability augmentaiont for classifier training (default = 1) + int blur; float angle; float aspect; float exposure; @@ -579,6 +590,8 @@ typedef struct network { int random; int track; int augment_speed; + int sequential_subdivisions; + int current_subdivision; int try_fix_nan; int gpu_index; @@ -713,6 +726,7 @@ typedef struct load_args { int show_imgs; float jitter; int flip; + int blur; float angle; float aspect; float saturation; diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 84d36d93ef0..16cb6b9325c 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -446,8 +446,8 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.weights_gpu = cuda_make_array(l.weights, c*n*size*size); l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size); #ifdef CUDNN_HALF - l.weights_gpu16 = cuda_make_array(NULL, c*n*size*size / 2); //cuda_make_array(l.weights, c*n*size*size / 2); - l.weight_updates_gpu16 = cuda_make_array(NULL, c*n*size*size / 2); //cuda_make_array(l.weight_updates, c*n*size*size / 2); + l.weights_gpu16 = cuda_make_array(NULL, c*n*size*size / 2 + 1); //cuda_make_array(l.weights, c*n*size*size / 2); + l.weight_updates_gpu16 = cuda_make_array(NULL, c*n*size*size / 2 + 1); //cuda_make_array(l.weight_updates, c*n*size*size / 2); #endif l.biases_gpu = cuda_make_array(l.biases, n); diff --git a/src/crnn_layer.c b/src/crnn_layer.c index c591276adf9..80a0c7f8494 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -85,6 +85,8 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.delta_gpu = l.output_layer->delta_gpu; #endif + l.bflops = l.input_layer->bflops + l.self_layer->bflops + l.output_layer->bflops; + return l; } @@ -128,6 +130,16 @@ void resize_crnn_layer(layer *l, int w, int h) #endif } +void free_state_crnn(layer l) +{ + int i; + for (i = 0; i < l.outputs * l.batch; ++i) l.self_layer->output[i] = rand_uniform(-1, 1); + +#ifdef GPU + cuda_push_array(l.self_layer->output_gpu, l.self_layer->output, l.outputs * l.batch); +#endif // GPU +} + void update_crnn_layer(layer l, int batch, float learning_rate, float momentum, float decay) { update_convolutional_layer(*(l.input_layer), batch, learning_rate, momentum, decay); diff --git a/src/crnn_layer.h b/src/crnn_layer.h index 33487020103..58944182b01 100644 --- a/src/crnn_layer.h +++ b/src/crnn_layer.h @@ -11,6 +11,7 @@ extern "C" { #endif layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int xnor); void resize_crnn_layer(layer *l, int w, int h); +void free_state_crnn(layer l); void forward_crnn_layer(layer l, network_state state); void backward_crnn_layer(layer l, network_state state); diff --git a/src/data.c b/src/data.c index 5de9e92e16a..9b93f1a8193 100644 --- a/src/data.c +++ b/src/data.c @@ -231,6 +231,15 @@ void correct_boxes(box_label *boxes, int n, float dx, float dy, float sx, float boxes[i].h = 999999; continue; } + if ((boxes[i].x + boxes[i].w / 2) < 0 || (boxes[i].y + boxes[i].h / 2) < 0 || + (boxes[i].x - boxes[i].w / 2) > 1 || (boxes[i].y - boxes[i].h / 2) > 1) + { + boxes[i].x = 999999; + boxes[i].y = 999999; + boxes[i].w = 999999; + boxes[i].h = 999999; + continue; + } boxes[i].left = boxes[i].left * sx - dx; boxes[i].right = boxes[i].right * sx - dx; boxes[i].top = boxes[i].top * sy - dy; @@ -378,7 +387,7 @@ void fill_truth_detection(const char *path, int num_boxes, float *truth, int cla continue; } if (x == 999999 || y == 999999) { - printf("\n Wrong annotation: x = 0, y = 0 \n"); + printf("\n Wrong annotation: x = 0, y = 0, < 0 or > 1 \n"); sprintf(buff, "echo %s \"Wrong annotation: x = 0 or y = 0\" >> bad_label.list", labelpath); system(buff); ++sub; @@ -769,7 +778,7 @@ static box float_to_box_stride(float *f, int stride) #include "http_stream.h" -data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, float jitter, +data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int use_blur, float jitter, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int show_imgs) { c = c ? c : 3; @@ -785,7 +794,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo d.X.cols = h*w*c; float r1 = 0, r2 = 0, r3 = 0, r4 = 0; - float dhue = 0, dsat = 0, dexp = 0, flip = 0; + float dhue = 0, dsat = 0, dexp = 0, flip = 0, blur = 0; int augmentation_calculated = 0; d.y = make_matrix(n, 5*boxes); @@ -819,6 +828,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo dexp = rand_scale(exposure); flip = use_flip ? random_gen() % 2 : 0; + blur = rand_int(0, 1) ? (use_blur) : 0; } int pleft = rand_precalc_random(-dw, dw, r1); @@ -835,10 +845,12 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo float dx = ((float)pleft/ow)/sx; float dy = ((float)ptop /oh)/sy; - image ai = image_data_augmentation(src, w, h, pleft, ptop, swidth, sheight, flip, jitter, dhue, dsat, dexp); - d.X.vals[i] = ai.data; + fill_truth_detection(filename, boxes, d.y.vals[i], classes, flip, dx, dy, 1. / sx, 1. / sy, w, h); - fill_truth_detection(filename, boxes, d.y.vals[i], classes, flip, dx, dy, 1./sx, 1./sy, w, h); + image ai = image_data_augmentation(src, w, h, pleft, ptop, swidth, sheight, flip, jitter, dhue, dsat, dexp, + blur, boxes, d.y.vals[i]); + + d.X.vals[i] = ai.data; if(show_imgs) { @@ -869,7 +881,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo return d; } #else // OPENCV -data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, float jitter, +data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int use_blur, float jitter, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int show_imgs) { c = c ? c : 3; @@ -989,7 +1001,7 @@ void *load_thread(void *ptr) } else if (a.type == REGION_DATA){ *a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure); } else if (a.type == DETECTION_DATA){ - *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.c, a.num_boxes, a.classes, a.flip, a.jitter, + *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.c, a.num_boxes, a.classes, a.flip, a.blur, a.jitter, a.hue, a.saturation, a.exposure, a.mini_batch, a.track, a.augment_speed, a.show_imgs); } else if (a.type == SWAG_DATA){ *a.d = load_data_swag(a.paths, a.n, a.classes, a.jitter); diff --git a/src/data.h b/src/data.h index ea2367eec9d..3305db50ca6 100644 --- a/src/data.h +++ b/src/data.h @@ -86,7 +86,7 @@ void print_letters(float *pred, int n); data load_data_captcha(char **paths, int n, int m, int k, int w, int h); data load_data_captcha_encode(char **paths, int n, int m, int w, int h); data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h); -data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, float jitter, +data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int use_blur, float jitter, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int show_imgs); data load_data_tag(char **paths, int n, int m, int k, int use_flip, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); diff --git a/src/demo.c b/src/demo.c index 8219de53e3c..4262b03f021 100644 --- a/src/demo.c +++ b/src/demo.c @@ -37,6 +37,8 @@ static int demo_ext_output = 0; static long long int frame_id = 0; static int demo_json_port = -1; +#define NFRAMES 3 + static float* predictions[NFRAMES]; static int demo_index = 0; static image images[NFRAMES]; diff --git a/src/detector.c b/src/detector.c index b1a612cb54b..f535a032138 100644 --- a/src/detector.c +++ b/src/detector.c @@ -47,6 +47,15 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i for (k = 0; k < net_map.n; ++k) { free_layer(net_map.layers[k]); } + + char *name_list = option_find_str(options, "names", "data/names.list"); + int names_size = 0; + char **names = get_labels_custom(name_list, &names_size); + if (net_map.layers[net_map.n - 1].classes != names_size) { + printf(" Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", + name_list, names_size, net_map.layers[net_map.n - 1].classes, cfgfile); + if (net_map.layers[net_map.n - 1].classes > names_size) getchar(); + } } srand(time(0)); @@ -119,6 +128,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i args.threads = 64; // 16 or 64 args.angle = net.angle; + args.blur = net.blur; args.exposure = net.exposure; args.saturation = net.saturation; args.hue = net.hue; @@ -137,7 +147,8 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i if (net.track) { args.track = net.track; args.augment_speed = net.augment_speed; - args.threads = net.subdivisions * ngpus; // 2 * ngpus; + if (net.sequential_subdivisions) args.threads = net.sequential_subdivisions * ngpus; + else args.threads = net.subdivisions * ngpus; // 2 * ngpus; args.mini_batch = net.batch / net.time_steps; printf("\n Tracking! batch = %d, subdiv = %d, time_steps = %d, mini_batch = %d \n", net.batch, net.subdivisions, net.time_steps, args.mini_batch); } @@ -223,7 +234,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i calc_map_for_each = fmax(calc_map_for_each, 100); int next_map_calc = iter_map + calc_map_for_each; next_map_calc = fmax(next_map_calc, net.burn_in); - next_map_calc = fmax(next_map_calc, 1000); + next_map_calc = fmax(next_map_calc, 400); if (calc_map) { printf("\n (next mAP calculation at %d iterations) ", next_map_calc); if (mean_average_precision > 0) printf("\n Last accuracy mAP@0.5 = %2.2f %% ", mean_average_precision * 100); @@ -638,7 +649,8 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa char *valid_images = option_find_str(options, "valid", "data/train.txt"); char *difficult_valid_images = option_find_str(options, "difficult", NULL); char *name_list = option_find_str(options, "names", "data/names.list"); - char **names = get_labels(name_list); + int names_size = 0; + char **names = get_labels_custom(name_list, &names_size); //get_labels(name_list); //char *mapf = option_find_str(options, "map", 0); //int *map = 0; //if (mapf) map = read_map(mapf); @@ -650,6 +662,8 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa char *train_images = option_find_str(options, "train", "data/train.txt"); valid_images = option_find_str(options, "valid", train_images); net = *existing_net; + remember_network_recurrent_state(*existing_net); + free_network_recurrent_state(*existing_net); } else { net = parse_network_cfg_custom(cfgfile, 1, 1); // set batch=1 @@ -660,6 +674,11 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa fuse_conv_batchnorm(net); calculate_binary_weights(net); } + if (net.layers[net.n - 1].classes != names_size) { + printf(" Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", + name_list, names_size, net.layers[net.n - 1].classes, cfgfile); + getchar(); + } srand(time(0)); printf("\n calculation mAP (mean average precision)...\n"); @@ -1053,6 +1072,8 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa if (existing_net) { //set_batch_network(&net, initial_batch); + //free_network_recurrent_state(*existing_net); + restore_network_recurrent_state(*existing_net); } else { free_network(net); @@ -1220,7 +1241,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int if (show) { #ifdef OPENCV - //show_acnhors(number_of_boxes, num_of_clusters, rel_width_height_array, anchors_data, width, height); + show_acnhors(number_of_boxes, num_of_clusters, rel_width_height_array, anchors_data, width, height); #endif // OPENCV } free(rel_width_height_array); diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 48064672c40..1387b633045 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1125,9 +1125,20 @@ void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_im // ==================================================================== // Data augmentation // ==================================================================== +static box float_to_box_stride(float *f, int stride) +{ + box b = { 0 }; + b.x = f[0]; + b.y = f[1 * stride]; + b.w = f[2 * stride]; + b.h = f[3 * stride]; + return b; +} + image image_data_augmentation(mat_cv* mat, int w, int h, int pleft, int ptop, int swidth, int sheight, int flip, - float jitter, float dhue, float dsat, float dexp) + float jitter, float dhue, float dsat, float dexp, + int blur, int num_boxes, float *truth) { image out; try { @@ -1192,6 +1203,31 @@ image image_data_augmentation(mat_cv* mat, int w, int h, //cv::imshow(window_name.str(), sized); //cv::waitKey(0); + if (blur) { + cv::Mat dst(sized.size(), sized.type()); + if(blur == 1) cv::GaussianBlur(sized, dst, cv::Size(31, 31), 0); + else cv::GaussianBlur(sized, dst, cv::Size((blur / 2) * 2 + 1, (blur / 2) * 2 + 1), 0); + cv::Rect img_rect(0, 0, sized.cols, sized.rows); + //std::cout << " blur num_boxes = " << num_boxes << std::endl; + + if (blur == 1) { + int t; + for (t = 0; t < num_boxes; ++t) { + box b = float_to_box_stride(truth + t*(4 + 1), 1); + if (!b.x) break; + int left = (b.x - b.w / 2.)*sized.cols; + int width = b.w*sized.cols; + int top = (b.y - b.h / 2.)*sized.rows; + int height = b.h*sized.rows; + cv::Rect roi(left, top, width, height); + roi = roi & img_rect; + + sized(roi).copyTo(dst(roi)); + } + } + dst.copyTo(sized); + } + // Mat -> image out = mat_to_image(sized); } diff --git a/src/image_opencv.h b/src/image_opencv.h index 13e67210cd8..21ee6939fae 100644 --- a/src/image_opencv.h +++ b/src/image_opencv.h @@ -95,7 +95,8 @@ void draw_train_loss(mat_cv* img, int img_size, float avg_loss, float max_img_lo // Data augmentation image image_data_augmentation(mat_cv* mat, int w, int h, int pleft, int ptop, int swidth, int sheight, int flip, - float jitter, float dhue, float dsat, float dexp); + float jitter, float dhue, float dsat, float dexp, + int blur, int num_boxes, float *truth); // Show Anchors void show_acnhors(int number_of_boxes, int num_of_clusters, float *rel_width_height_array, model anchors_data, int width, int height); diff --git a/src/layer.c b/src/layer.c index a60978486a4..6409bd892e7 100644 --- a/src/layer.c +++ b/src/layer.c @@ -2,22 +2,40 @@ #include "dark_cuda.h" #include +void free_sublayer(layer *l) +{ + if (l) { + free_layer(*l); + free(l); + } +} + void free_layer(layer l) { - // free layers: input_layer, self_layer, output_layer, ... - if (l.type == CRNN) { - if (l.input_layer) { - free_layer(*l.input_layer); - free(l.input_layer); - } - if (l.self_layer) { - free_layer(*l.self_layer); - free(l.self_layer); + if (l.type == CONV_LSTM) { + if (l.peephole) { + free_sublayer(l.vf); + free_sublayer(l.vi); + free_sublayer(l.vo); } - if (l.output_layer) { - free_layer(*l.output_layer); - free(l.output_layer); + else { + free(l.vf); + free(l.vi); + free(l.vo); } + free_sublayer(l.wf); + free_sublayer(l.wi); + free_sublayer(l.wg); + free_sublayer(l.wo); + free_sublayer(l.uf); + free_sublayer(l.ui); + free_sublayer(l.ug); + free_sublayer(l.uo); + } + if (l.type == CRNN) { + free_sublayer(l.input_layer); + free_sublayer(l.self_layer); + free_sublayer(l.output_layer); l.output = NULL; l.delta = NULL; #ifdef GPU @@ -83,21 +101,36 @@ void free_layer(layer l) if (l.v) free(l.v); if (l.z_cpu) free(l.z_cpu); if (l.r_cpu) free(l.r_cpu); - if (l.h_cpu) free(l.h_cpu); if (l.binary_input) free(l.binary_input); if (l.bin_re_packed_input) free(l.bin_re_packed_input); if (l.t_bit_input) free(l.t_bit_input); if (l.loss) free(l.loss); + // CONV-LSTM + if (l.f_cpu) free(l.f_cpu); + if (l.i_cpu) free(l.i_cpu); + if (l.g_cpu) free(l.g_cpu); + if (l.o_cpu) free(l.o_cpu); + if (l.c_cpu) free(l.c_cpu); + if (l.h_cpu) free(l.h_cpu); + if (l.temp_cpu) free(l.temp_cpu); + if (l.temp2_cpu) free(l.temp2_cpu); + if (l.temp3_cpu) free(l.temp3_cpu); + if (l.dc_cpu) free(l.dc_cpu); + if (l.dh_cpu) free(l.dh_cpu); + if (l.prev_state_cpu) free(l.prev_state_cpu); + if (l.prev_cell_cpu) free(l.prev_cell_cpu); + if (l.stored_c_cpu) free(l.stored_c_cpu); + if (l.stored_h_cpu) free(l.stored_h_cpu); + if (l.cell_cpu) free(l.cell_cpu); + #ifdef GPU if (l.indexes_gpu) cuda_free((float *)l.indexes_gpu); if (l.z_gpu) cuda_free(l.z_gpu); if (l.r_gpu) cuda_free(l.r_gpu); - if (l.h_gpu) cuda_free(l.h_gpu); if (l.m_gpu) cuda_free(l.m_gpu); if (l.v_gpu) cuda_free(l.v_gpu); - if (l.prev_state_gpu) cuda_free(l.prev_state_gpu); if (l.forgot_state_gpu) cuda_free(l.forgot_state_gpu); if (l.forgot_delta_gpu) cuda_free(l.forgot_delta_gpu); if (l.state_gpu) cuda_free(l.state_gpu); @@ -137,5 +170,25 @@ void free_layer(layer l) if (l.rand_gpu) cuda_free(l.rand_gpu); if (l.squared_gpu) cuda_free(l.squared_gpu); if (l.norms_gpu) cuda_free(l.norms_gpu); + + // CONV-LSTM + if (l.f_gpu) cuda_free(l.f_gpu); + if (l.i_gpu) cuda_free(l.i_gpu); + if (l.g_gpu) cuda_free(l.g_gpu); + if (l.o_gpu) cuda_free(l.o_gpu); + if (l.c_gpu) cuda_free(l.c_gpu); + if (l.h_gpu) cuda_free(l.h_gpu); + if (l.temp_gpu) cuda_free(l.temp_gpu); + if (l.temp2_gpu) cuda_free(l.temp2_gpu); + if (l.temp3_gpu) cuda_free(l.temp3_gpu); + if (l.dc_gpu) cuda_free(l.dc_gpu); + if (l.dh_gpu) cuda_free(l.dh_gpu); + if (l.prev_state_gpu) cuda_free(l.prev_state_gpu); + if (l.prev_cell_gpu) cuda_free(l.prev_cell_gpu); + if (l.stored_c_gpu) cuda_free(l.stored_c_gpu); + if (l.stored_h_gpu) cuda_free(l.stored_h_gpu); + if (l.last_prev_state_gpu) cuda_free(l.last_prev_state_gpu); + if (l.last_prev_cell_gpu) cuda_free(l.last_prev_cell_gpu); + if (l.cell_gpu) cuda_free(l.cell_gpu); #endif } diff --git a/src/lstm_layer.c b/src/lstm_layer.c index bf1e303b4c5..f7b79477104 100644 --- a/src/lstm_layer.c +++ b/src/lstm_layer.c @@ -95,6 +95,7 @@ layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_n l.forward = forward_lstm_layer; l.update = update_lstm_layer; + l.backward = backward_lstm_layer; l.prev_state_cpu = (float*)calloc(batch*outputs, sizeof(float)); l.prev_cell_cpu = (float*)calloc(batch*outputs, sizeof(float)); diff --git a/src/lstm_layer.h b/src/lstm_layer.h index f60a0cabafe..dc8eb37a38d 100644 --- a/src/lstm_layer.h +++ b/src/lstm_layer.h @@ -12,6 +12,7 @@ extern "C" { layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_normalize); void forward_lstm_layer(layer l, network_state state); +void backward_lstm_layer(layer l, network_state state); void update_lstm_layer(layer l, int batch, float learning_rate, float momentum, float decay); #ifdef GPU diff --git a/src/network.c b/src/network.c index 248b93ff0c9..98d1446bdfd 100644 --- a/src/network.c +++ b/src/network.c @@ -15,6 +15,7 @@ #include "gru_layer.h" #include "rnn_layer.h" #include "crnn_layer.h" +#include "conv_lstm_layer.h" #include "local_layer.h" #include "convolutional_layer.h" #include "activation_layer.h" @@ -315,6 +316,7 @@ float train_network_sgd(network net, data d, int n) float sum = 0; for(i = 0; i < n; ++i){ get_random_batch(d, batch, X, y); + net.current_subdivision = i; float err = train_network_datum(net, X, y); sum += err; } @@ -340,6 +342,7 @@ float train_network_waitkey(network net, data d, int wait_key) float sum = 0; for(i = 0; i < n; ++i){ get_next_batch(d, batch, i*batch, X, y); + net.current_subdivision = i; float err = train_network_datum(net, X, y); sum += err; if(wait_key) wait_key_cv(5); @@ -1111,3 +1114,31 @@ network combine_train_valid_networks(network net_train, network net_map) } return net_combined; } + +void free_network_recurrent_state(network net) +{ + int k; + for (k = 0; k < net.n; ++k) { + if (net.layers[k].type == CONV_LSTM) free_state_conv_lstm(net.layers[k]); + if (net.layers[k].type == CRNN) free_state_crnn(net.layers[k]); + } +} + + +void remember_network_recurrent_state(network net) +{ + int k; + for (k = 0; k < net.n; ++k) { + if (net.layers[k].type == CONV_LSTM) remember_state_conv_lstm(net.layers[k]); + //if (net.layers[k].type == CRNN) free_state_crnn(net.layers[k]); + } +} + +void restore_network_recurrent_state(network net) +{ + int k; + for (k = 0; k < net.n; ++k) { + if (net.layers[k].type == CONV_LSTM) restore_state_conv_lstm(net.layers[k]); + if (net.layers[k].type == CRNN) free_state_crnn(net.layers[k]); + } +} \ No newline at end of file diff --git a/src/network.h b/src/network.h index c4138c74c45..1b272ac19ae 100644 --- a/src/network.h +++ b/src/network.h @@ -163,6 +163,9 @@ int get_network_background(network net); //LIB_API void calculate_binary_weights(network net); network combine_train_valid_networks(network net_train, network net_map); void copy_weights_net(network net_train, network *net_map); +void free_network_recurrent_state(network net); +void remember_network_recurrent_state(network net); +void restore_network_recurrent_state(network net); #ifdef __cplusplus } diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 9162c8b2b36..b66f4959666 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -171,6 +171,22 @@ void forward_backward_network_gpu(network net, float *x, float *y) cuda_convert_f32_to_f16(l.self_layer->weights_gpu, l.self_layer->nweights, l.self_layer->weights_gpu16); cuda_convert_f32_to_f16(l.output_layer->weights_gpu, l.output_layer->nweights, l.output_layer->weights_gpu16); } + else if (l.type == CONV_LSTM && l.wf->weights_gpu && l.wf->weights_gpu16) { + assert((l.wf->c * l.wf->n * l.wf->size * l.wf->size) > 0); + if (l.peephole) { + cuda_convert_f32_to_f16(l.vf->weights_gpu, l.vf->nweights, l.vf->weights_gpu16); + cuda_convert_f32_to_f16(l.vi->weights_gpu, l.vi->nweights, l.vi->weights_gpu16); + cuda_convert_f32_to_f16(l.vo->weights_gpu, l.vo->nweights, l.vo->weights_gpu16); + } + cuda_convert_f32_to_f16(l.wf->weights_gpu, l.wf->nweights, l.wf->weights_gpu16); + cuda_convert_f32_to_f16(l.wi->weights_gpu, l.wi->nweights, l.wi->weights_gpu16); + cuda_convert_f32_to_f16(l.wg->weights_gpu, l.wg->nweights, l.wg->weights_gpu16); + cuda_convert_f32_to_f16(l.wo->weights_gpu, l.wo->nweights, l.wo->weights_gpu16); + cuda_convert_f32_to_f16(l.uf->weights_gpu, l.uf->nweights, l.uf->weights_gpu16); + cuda_convert_f32_to_f16(l.ui->weights_gpu, l.ui->nweights, l.ui->weights_gpu16); + cuda_convert_f32_to_f16(l.ug->weights_gpu, l.ug->nweights, l.ug->weights_gpu16); + cuda_convert_f32_to_f16(l.uo->weights_gpu, l.uo->nweights, l.uo->weights_gpu16); + } } } #endif diff --git a/src/parser.c b/src/parser.c index 782b5820c0f..64dbac2dd61 100644 --- a/src/parser.c +++ b/src/parser.c @@ -20,6 +20,7 @@ #include "list.h" #include "local_layer.h" #include "lstm_layer.h" +#include "conv_lstm_layer.h" #include "maxpool_layer.h" #include "normalization_layer.h" #include "option_list.h" @@ -61,6 +62,7 @@ LAYER_TYPE string_to_layer_type(char * type) if (strcmp(type, "[crnn]")==0) return CRNN; if (strcmp(type, "[gru]")==0) return GRU; if (strcmp(type, "[lstm]")==0) return LSTM; + if (strcmp(type, "[conv_lstm]") == 0) return CONV_LSTM; if (strcmp(type, "[rnn]")==0) return RNN; if (strcmp(type, "[conn]")==0 || strcmp(type, "[connected]")==0) return CONNECTED; @@ -239,6 +241,29 @@ layer parse_lstm(list *options, size_params params) return l; } +layer parse_conv_lstm(list *options, size_params params) +{ + // a ConvLSTM with a larger transitional kernel should be able to capture faster motions + int size = option_find_int_quiet(options, "size", 3); + int stride = option_find_int_quiet(options, "stride", 1); + int pad = option_find_int_quiet(options, "pad", 0); + int padding = option_find_int_quiet(options, "padding", 0); + if (pad) padding = size / 2; + + int output_filters = option_find_int(options, "output", 1); + char *activation_s = option_find_str(options, "activation", "LINEAR"); + ACTIVATION activation = get_activation(activation_s); + int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); + int xnor = option_find_int_quiet(options, "xnor", 0); + int peephole = option_find_int_quiet(options, "peephole", 1); + + layer l = make_conv_lstm_layer(params.batch, params.w, params.h, params.c, output_filters, params.time_steps, size, stride, padding, activation, batch_normalize, peephole, xnor); + + l.shortcut = option_find_int_quiet(options, "shortcut", 0); + + return l; +} + connected_layer parse_connected(list *options, size_params params) { int output = option_find_int(options, "output",1); @@ -647,6 +672,7 @@ void parse_net_options(list *options, network *net) net->time_steps = option_find_int_quiet(options, "time_steps",1); net->track = option_find_int_quiet(options, "track", 0); net->augment_speed = option_find_int_quiet(options, "augment_speed", 2); + net->sequential_subdivisions = option_find_int_quiet(options, "sequential_subdivisions", 0); net->try_fix_nan = option_find_int_quiet(options, "try_fix_nan", 0); net->batch /= subdivs; net->batch *= net->time_steps; @@ -666,6 +692,7 @@ void parse_net_options(list *options, network *net) net->max_crop = option_find_int_quiet(options, "max_crop",net->w*2); net->min_crop = option_find_int_quiet(options, "min_crop",net->w); net->flip = option_find_int_quiet(options, "flip", 1); + net->blur = option_find_int_quiet(options, "blur", 0); net->angle = option_find_float_quiet(options, "angle", 0); net->aspect = option_find_float_quiet(options, "aspect", 1); @@ -789,6 +816,8 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) l = parse_gru(options, params); }else if(lt == LSTM){ l = parse_lstm(options, params); + }else if (lt == CONV_LSTM) { + l = parse_conv_lstm(options, params); }else if(lt == CRNN){ l = parse_crnn(options, params); }else if(lt == CONNECTED){ @@ -1076,6 +1105,20 @@ void save_weights_upto(network net, char *filename, int cutoff) save_connected_weights(*(l.ui), fp); save_connected_weights(*(l.ug), fp); save_connected_weights(*(l.uo), fp); + } if (l.type == CONV_LSTM) { + if (l.peephole) { + save_convolutional_weights(*(l.vf), fp); + save_convolutional_weights(*(l.vi), fp); + save_convolutional_weights(*(l.vo), fp); + } + save_convolutional_weights(*(l.wf), fp); + save_convolutional_weights(*(l.wi), fp); + save_convolutional_weights(*(l.wg), fp); + save_convolutional_weights(*(l.wo), fp); + save_convolutional_weights(*(l.uf), fp); + save_convolutional_weights(*(l.ui), fp); + save_convolutional_weights(*(l.ug), fp); + save_convolutional_weights(*(l.uo), fp); } if(l.type == CRNN){ save_convolutional_weights(*(l.input_layer), fp); save_convolutional_weights(*(l.self_layer), fp); @@ -1298,6 +1341,21 @@ void load_weights_upto(network *net, char *filename, int cutoff) load_connected_weights(*(l.ug), fp, transpose); load_connected_weights(*(l.uo), fp, transpose); } + if (l.type == CONV_LSTM) { + if (l.peephole) { + load_convolutional_weights(*(l.vf), fp); + load_convolutional_weights(*(l.vi), fp); + load_convolutional_weights(*(l.vo), fp); + } + load_convolutional_weights(*(l.wf), fp); + load_convolutional_weights(*(l.wi), fp); + load_convolutional_weights(*(l.wg), fp); + load_convolutional_weights(*(l.wo), fp); + load_convolutional_weights(*(l.uf), fp); + load_convolutional_weights(*(l.ui), fp); + load_convolutional_weights(*(l.ug), fp); + load_convolutional_weights(*(l.uo), fp); + } if(l.type == LOCAL){ int locations = l.out_w*l.out_h; int size = l.size*l.size*l.c*l.n*locations; diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index 6eaa38b7291..cffbd625718 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -22,6 +22,7 @@ extern "C" { #include #include +#define NFRAMES 3 //static Detector* detector = NULL; static std::unique_ptr detector; From a7e5976c1b8e0d826d064bebd58fce2a046f718c Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 9 May 2019 22:47:23 +0300 Subject: [PATCH 0483/1365] Added sequential subdivisions --- include/darknet.h | 2 + src/conv_lstm_layer.c | 1162 ++++++++++++++++++++++++++++++++++++++++ src/conv_lstm_layer.h | 33 ++ src/detector.c | 10 +- src/network.c | 34 ++ src/network.h | 3 + src/network_kernels.cu | 6 +- src/parser.c | 12 +- 8 files changed, 1257 insertions(+), 5 deletions(-) create mode 100644 src/conv_lstm_layer.c create mode 100644 src/conv_lstm_layer.h diff --git a/include/darknet.h b/include/darknet.h index 29a629248bf..fbd7e27d8a8 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -559,6 +559,7 @@ typedef struct network { int time_steps; int step; int max_batches; + float *seq_scales; float *scales; int *steps; int num_steps; @@ -591,6 +592,7 @@ typedef struct network { int track; int augment_speed; int sequential_subdivisions; + int init_sequential_subdivisions; int current_subdivision; int try_fix_nan; diff --git a/src/conv_lstm_layer.c b/src/conv_lstm_layer.c new file mode 100644 index 00000000000..7d395ad0c58 --- /dev/null +++ b/src/conv_lstm_layer.c @@ -0,0 +1,1162 @@ +// Page 4: https://arxiv.org/abs/1506.04214v2 +// Page 3: https://arxiv.org/pdf/1705.06368v3.pdf +// https://wikimedia.org/api/rest_v1/media/math/render/svg/1edbece2559479959fe829e9c6657efb380debe7 + +#include "conv_lstm_layer.h" +#include "connected_layer.h" +#include "convolutional_layer.h" +#include "utils.h" +#include "dark_cuda.h" +#include "blas.h" +#include "gemm.h" + +#include +#include +#include +#include + +static void increment_layer(layer *l, int steps) +{ + int num = l->outputs*l->batch*steps; + l->output += num; + l->delta += num; + l->x += num; + l->x_norm += num; + +#ifdef GPU + l->output_gpu += num; + l->delta_gpu += num; + l->x_gpu += num; + l->x_norm_gpu += num; +#endif +} + + +layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int peephole, int xnor) +{ + fprintf(stderr, "CONV_LSTM Layer: %d x %d x %d image, %d filters\n", h, w, c, output_filters); + /* + batch = batch / steps; + layer l = { (LAYER_TYPE)0 }; + l.batch = batch; + l.type = LSTM; + l.steps = steps; + l.inputs = inputs; + l.out_w = 1; + l.out_h = 1; + l.out_c = outputs; + */ + batch = batch / steps; + layer l = { (LAYER_TYPE)0 }; + l.batch = batch; + l.type = CONV_LSTM; + l.steps = steps; + l.size = size; + l.stride = stride; + l.pad = pad; + l.h = h; + l.w = w; + l.c = c; + l.out_c = output_filters; + l.inputs = h * w * c; + l.xnor = xnor; + l.peephole = peephole; + + // U + l.uf = (layer*)malloc(sizeof(layer)); + *(l.uf) = make_convolutional_layer(batch, steps, h, w, c, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + l.uf->batch = batch; + if (l.workspace_size < l.uf->workspace_size) l.workspace_size = l.uf->workspace_size; + + l.ui = (layer*)malloc(sizeof(layer)); + *(l.ui) = make_convolutional_layer(batch, steps, h, w, c, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + l.ui->batch = batch; + if (l.workspace_size < l.ui->workspace_size) l.workspace_size = l.ui->workspace_size; + + l.ug = (layer*)malloc(sizeof(layer)); + *(l.ug) = make_convolutional_layer(batch, steps, h, w, c, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + l.ug->batch = batch; + if (l.workspace_size < l.ug->workspace_size) l.workspace_size = l.ug->workspace_size; + + l.uo = (layer*)malloc(sizeof(layer)); + *(l.uo) = make_convolutional_layer(batch, steps, h, w, c, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + l.uo->batch = batch; + if (l.workspace_size < l.uo->workspace_size) l.workspace_size = l.uo->workspace_size; + + + // W + l.wf = (layer*)malloc(sizeof(layer)); + *(l.wf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + l.wf->batch = batch; + if (l.workspace_size < l.wf->workspace_size) l.workspace_size = l.wf->workspace_size; + + l.wi = (layer*)malloc(sizeof(layer)); + *(l.wi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + l.wi->batch = batch; + if (l.workspace_size < l.wi->workspace_size) l.workspace_size = l.wi->workspace_size; + + l.wg = (layer*)malloc(sizeof(layer)); + *(l.wg) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + l.wg->batch = batch; + if (l.workspace_size < l.wg->workspace_size) l.workspace_size = l.wg->workspace_size; + + l.wo = (layer*)malloc(sizeof(layer)); + *(l.wo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + l.wo->batch = batch; + if (l.workspace_size < l.wo->workspace_size) l.workspace_size = l.wo->workspace_size; + + + // V + l.vf = (layer*)malloc(sizeof(layer)); + if (l.peephole) { + *(l.vf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + l.vf->batch = batch; + if (l.workspace_size < l.vf->workspace_size) l.workspace_size = l.vf->workspace_size; + } + + l.vi = (layer*)malloc(sizeof(layer)); + if (l.peephole) { + *(l.vi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + l.vi->batch = batch; + if (l.workspace_size < l.vi->workspace_size) l.workspace_size = l.vi->workspace_size; + } + + l.vo = (layer*)malloc(sizeof(layer)); + if (l.peephole) { + *(l.vo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + l.vo->batch = batch; + if (l.workspace_size < l.vo->workspace_size) l.workspace_size = l.vo->workspace_size; + } + + + l.batch_normalize = batch_normalize; + + l.out_h = l.wo->out_h; + l.out_w = l.wo->out_w; + l.outputs = l.wo->outputs; + int outputs = l.outputs; + l.inputs = w*h*c; + + assert(l.wo->outputs == l.uo->outputs); + + l.output = (float*)calloc(outputs * batch * steps, sizeof(float)); + //l.state = (float*)calloc(outputs * batch, sizeof(float)); + + l.forward = forward_conv_lstm_layer; + l.update = update_conv_lstm_layer; + l.backward = backward_conv_lstm_layer; + + l.prev_state_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.prev_cell_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.cell_cpu = (float*)calloc(batch*outputs*steps, sizeof(float)); + + l.f_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.i_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.g_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.o_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.c_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.stored_c_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.h_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.stored_h_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.temp_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.temp2_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.temp3_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.dc_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.dh_cpu = (float*)calloc(batch*outputs, sizeof(float)); + +#ifdef GPU + l.forward_gpu = forward_conv_lstm_layer_gpu; + l.backward_gpu = backward_conv_lstm_layer_gpu; + l.update_gpu = update_conv_lstm_layer_gpu; + + //l.state_gpu = cuda_make_array(l.state, batch*l.outputs); + + l.output_gpu = cuda_make_array(0, batch*outputs*steps); + l.delta_gpu = cuda_make_array(0, batch*l.outputs*steps); + + l.prev_state_gpu = cuda_make_array(0, batch*outputs); + l.prev_cell_gpu = cuda_make_array(0, batch*outputs); + l.cell_gpu = cuda_make_array(0, batch*outputs*steps); + + l.f_gpu = cuda_make_array(0, batch*outputs); + l.i_gpu = cuda_make_array(0, batch*outputs); + l.g_gpu = cuda_make_array(0, batch*outputs); + l.o_gpu = cuda_make_array(0, batch*outputs); + l.c_gpu = cuda_make_array(0, batch*outputs); + l.h_gpu = cuda_make_array(0, batch*outputs); + l.stored_c_gpu = cuda_make_array(0, batch*outputs); + l.stored_h_gpu = cuda_make_array(0, batch*outputs); + l.temp_gpu = cuda_make_array(0, batch*outputs); + l.temp2_gpu = cuda_make_array(0, batch*outputs); + l.temp3_gpu = cuda_make_array(0, batch*outputs); + l.dc_gpu = cuda_make_array(0, batch*outputs); + l.dh_gpu = cuda_make_array(0, batch*outputs); + l.last_prev_state_gpu = cuda_make_array(0, l.batch*l.outputs); + l.last_prev_cell_gpu = cuda_make_array(0, l.batch*l.outputs); + +#endif + + l.bflops = l.uf->bflops + l.ui->bflops + l.ug->bflops + l.uo->bflops + + l.wf->bflops + l.wi->bflops + l.wg->bflops + l.wo->bflops + + l.vf->bflops + l.vi->bflops + l.vo->bflops; + + return l; +} + +void update_conv_lstm_layer(layer l, int batch, float learning_rate, float momentum, float decay) +{ + if (l.peephole) { + update_convolutional_layer(*(l.vf), batch, learning_rate, momentum, decay); + update_convolutional_layer(*(l.vi), batch, learning_rate, momentum, decay); + update_convolutional_layer(*(l.vo), batch, learning_rate, momentum, decay); + } + update_convolutional_layer(*(l.wf), batch, learning_rate, momentum, decay); + update_convolutional_layer(*(l.wi), batch, learning_rate, momentum, decay); + update_convolutional_layer(*(l.wg), batch, learning_rate, momentum, decay); + update_convolutional_layer(*(l.wo), batch, learning_rate, momentum, decay); + update_convolutional_layer(*(l.uf), batch, learning_rate, momentum, decay); + update_convolutional_layer(*(l.ui), batch, learning_rate, momentum, decay); + update_convolutional_layer(*(l.ug), batch, learning_rate, momentum, decay); + update_convolutional_layer(*(l.uo), batch, learning_rate, momentum, decay); +} + +void resize_conv_lstm_layer(layer *l, int w, int h) +{ + if (l->peephole) { + resize_convolutional_layer(l->vf, w, h); + if (l->workspace_size < l->vf->workspace_size) l->workspace_size = l->vf->workspace_size; + + resize_convolutional_layer(l->vi, w, h); + if (l->workspace_size < l->vi->workspace_size) l->workspace_size = l->vi->workspace_size; + + resize_convolutional_layer(l->vo, w, h); + if (l->workspace_size < l->vo->workspace_size) l->workspace_size = l->vo->workspace_size; + } + + resize_convolutional_layer(l->wf, w, h); + if (l->workspace_size < l->wf->workspace_size) l->workspace_size = l->wf->workspace_size; + + resize_convolutional_layer(l->wi, w, h); + if (l->workspace_size < l->wi->workspace_size) l->workspace_size = l->wi->workspace_size; + + resize_convolutional_layer(l->wg, w, h); + if (l->workspace_size < l->wg->workspace_size) l->workspace_size = l->wg->workspace_size; + + resize_convolutional_layer(l->wo, w, h); + if (l->workspace_size < l->wo->workspace_size) l->workspace_size = l->wo->workspace_size; + + + resize_convolutional_layer(l->uf, w, h); + if (l->workspace_size < l->uf->workspace_size) l->workspace_size = l->uf->workspace_size; + + resize_convolutional_layer(l->ui, w, h); + if (l->workspace_size < l->ui->workspace_size) l->workspace_size = l->ui->workspace_size; + + resize_convolutional_layer(l->ug, w, h); + if (l->workspace_size < l->ug->workspace_size) l->workspace_size = l->ug->workspace_size; + + resize_convolutional_layer(l->uo, w, h); + if (l->workspace_size < l->uo->workspace_size) l->workspace_size = l->uo->workspace_size; + + l->w = w; + l->h = h; + l->out_h = l->wo->out_h; + l->out_w = l->wo->out_w; + l->outputs = l->wo->outputs; + int outputs = l->outputs; + l->inputs = w*h*l->c; + int steps = l->steps; + int batch = l->batch; + + assert(l->wo->outputs == l->uo->outputs); + + l->output = (float*)realloc(l->output, outputs * batch * steps * sizeof(float)); + //l->state = (float*)realloc(l->state, outputs * batch * sizeof(float)); + + l->prev_state_cpu = (float*)realloc(l->prev_state_cpu, batch*outputs * sizeof(float)); + l->prev_cell_cpu = (float*)realloc(l->prev_cell_cpu, batch*outputs * sizeof(float)); + l->cell_cpu = (float*)realloc(l->cell_cpu, batch*outputs*steps * sizeof(float)); + + l->f_cpu = (float*)realloc(l->f_cpu, batch*outputs * sizeof(float)); + l->i_cpu = (float*)realloc(l->i_cpu, batch*outputs * sizeof(float)); + l->g_cpu = (float*)realloc(l->g_cpu, batch*outputs * sizeof(float)); + l->o_cpu = (float*)realloc(l->o_cpu, batch*outputs * sizeof(float)); + l->c_cpu = (float*)realloc(l->c_cpu, batch*outputs * sizeof(float)); + l->h_cpu = (float*)realloc(l->h_cpu, batch*outputs * sizeof(float)); + l->temp_cpu = (float*)realloc(l->temp_cpu, batch*outputs * sizeof(float)); + l->temp2_cpu = (float*)realloc(l->temp2_cpu, batch*outputs * sizeof(float)); + l->temp3_cpu = (float*)realloc(l->temp3_cpu, batch*outputs * sizeof(float)); + l->dc_cpu = (float*)realloc(l->dc_cpu, batch*outputs * sizeof(float)); + l->dh_cpu = (float*)realloc(l->dh_cpu, batch*outputs * sizeof(float)); + l->stored_c_cpu = (float*)realloc(l->stored_c_cpu, batch*outputs * sizeof(float)); + l->stored_h_cpu = (float*)realloc(l->stored_h_cpu, batch*outputs * sizeof(float)); + +#ifdef GPU + //if (l->state_gpu) cudaFree(l->state_gpu); + //l->state_gpu = cuda_make_array(l->state, batch*l->outputs); + + if (l->output_gpu) cudaFree(l->output_gpu); + l->output_gpu = cuda_make_array(0, batch*outputs*steps); + + if (l->delta_gpu) cudaFree(l->delta_gpu); + l->delta_gpu = cuda_make_array(0, batch*outputs*steps); + + if (l->prev_state_gpu) cudaFree(l->prev_state_gpu); + l->prev_state_gpu = cuda_make_array(0, batch*outputs); + + if (l->prev_cell_gpu) cudaFree(l->prev_cell_gpu); + l->prev_cell_gpu = cuda_make_array(0, batch*outputs); + + if (l->cell_gpu) cudaFree(l->cell_gpu); + l->cell_gpu = cuda_make_array(0, batch*outputs*steps); + + if (l->f_gpu) cudaFree(l->f_gpu); + l->f_gpu = cuda_make_array(0, batch*outputs); + + if (l->i_gpu) cudaFree(l->i_gpu); + l->i_gpu = cuda_make_array(0, batch*outputs); + + if (l->g_gpu) cudaFree(l->g_gpu); + l->g_gpu = cuda_make_array(0, batch*outputs); + + if (l->o_gpu) cudaFree(l->o_gpu); + l->o_gpu = cuda_make_array(0, batch*outputs); + + if (l->c_gpu) cudaFree(l->c_gpu); + l->c_gpu = cuda_make_array(0, batch*outputs); + + if (l->h_gpu) cudaFree(l->h_gpu); + l->h_gpu = cuda_make_array(0, batch*outputs); + + if (l->temp_gpu) cudaFree(l->temp_gpu); + l->temp_gpu = cuda_make_array(0, batch*outputs); + + if (l->temp2_gpu) cudaFree(l->temp2_gpu); + l->temp2_gpu = cuda_make_array(0, batch*outputs); + + if (l->temp3_gpu) cudaFree(l->temp3_gpu); + l->temp3_gpu = cuda_make_array(0, batch*outputs); + + if (l->dc_gpu) cudaFree(l->dc_gpu); + l->dc_gpu = cuda_make_array(0, batch*outputs); + + if (l->dh_gpu) cudaFree(l->dh_gpu); + l->dh_gpu = cuda_make_array(0, batch*outputs); + + if (l->stored_c_gpu) cudaFree(l->stored_c_gpu); + l->stored_c_gpu = cuda_make_array(0, batch*outputs); + + if (l->stored_h_gpu) cudaFree(l->stored_h_gpu); + l->stored_h_gpu = cuda_make_array(0, batch*outputs); + + if (l->last_prev_state_gpu) cudaFree(l->last_prev_state_gpu); + l->last_prev_state_gpu = cuda_make_array(0, batch*outputs); + + if (l->last_prev_cell_gpu) cudaFree(l->last_prev_cell_gpu); + l->last_prev_cell_gpu = cuda_make_array(0, batch*outputs); +#endif +} + +void free_state_conv_lstm(layer l) +{ + int i; + for (i = 0; i < l.outputs * l.batch; ++i) l.h_cpu[i] = 0; + for (i = 0; i < l.outputs * l.batch; ++i) l.c_cpu[i] = 0; + +#ifdef GPU + cuda_push_array(l.h_gpu, l.h_cpu, l.outputs * l.batch); + cuda_push_array(l.c_gpu, l.c_cpu, l.outputs * l.batch); + + //fill_ongpu(l.outputs * l.batch, 0, l.dc_gpu, 1); // dont use + //fill_ongpu(l.outputs * l.batch, 0, l.dh_gpu, 1); // dont use +#endif // GPU +} + +void randomize_state_conv_lstm(layer l) +{ + int i; + for (i = 0; i < l.outputs * l.batch; ++i) l.h_cpu[i] = rand_uniform(-1, 1); + for (i = 0; i < l.outputs * l.batch; ++i) l.c_cpu[i] = rand_uniform(-1, 1); + +#ifdef GPU + cuda_push_array(l.h_gpu, l.h_cpu, l.outputs * l.batch); + cuda_push_array(l.c_gpu, l.c_cpu, l.outputs * l.batch); +#endif // GPU +} + + +void remember_state_conv_lstm(layer l) +{ + memcpy(l.stored_c_cpu, l.c_cpu, l.outputs * l.batch * sizeof(float)); + memcpy(l.stored_h_cpu, l.h_cpu, l.outputs * l.batch * sizeof(float)); + +#ifdef GPU + copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.stored_c_gpu, 1); + copy_ongpu(l.outputs*l.batch, l.h_gpu, 1, l.stored_h_gpu, 1); +#endif // GPU +} + +void restore_state_conv_lstm(layer l) +{ + memcpy(l.c_cpu, l.stored_c_cpu, l.outputs * l.batch * sizeof(float)); + memcpy(l.h_cpu, l.stored_h_cpu, l.outputs * l.batch * sizeof(float)); + +#ifdef GPU + copy_ongpu(l.outputs*l.batch, l.stored_c_gpu, 1, l.c_gpu, 1); + copy_ongpu(l.outputs*l.batch, l.stored_h_gpu, 1, l.h_gpu, 1); +#endif // GPU +} + +void forward_conv_lstm_layer(layer l, network_state state) +{ + network_state s = { 0 }; + s.train = state.train; + s.workspace = state.workspace; + s.net = state.net; + int i; + layer vf = *(l.vf); + layer vi = *(l.vi); + layer vo = *(l.vo); + + layer wf = *(l.wf); + layer wi = *(l.wi); + layer wg = *(l.wg); + layer wo = *(l.wo); + + layer uf = *(l.uf); + layer ui = *(l.ui); + layer ug = *(l.ug); + layer uo = *(l.uo); + + if (l.peephole) { + fill_cpu(l.outputs * l.batch * l.steps, 0, vf.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, vi.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, vo.delta, 1); + } + + fill_cpu(l.outputs * l.batch * l.steps, 0, wf.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wi.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wg.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wo.delta, 1); + + fill_cpu(l.outputs * l.batch * l.steps, 0, uf.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, ui.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, ug.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, uo.delta, 1); + if (state.train) { + fill_cpu(l.outputs * l.batch * l.steps, 0, l.delta, 1); + } + + for (i = 0; i < l.steps; ++i) + { + if (l.peephole) { + assert(l.outputs == vf.out_w * vf.out_h * vf.out_c); + s.input = l.c_cpu; + forward_convolutional_layer(vf, s); + forward_convolutional_layer(vi, s); + // vo below + } + + assert(l.outputs == wf.out_w * wf.out_h * wf.out_c); + assert(wf.c == l.out_c && wi.c == l.out_c && wg.c == l.out_c && wo.c == l.out_c); + + s.input = l.h_cpu; + forward_convolutional_layer(wf, s); + forward_convolutional_layer(wi, s); + forward_convolutional_layer(wg, s); + forward_convolutional_layer(wo, s); + + s.input = state.input; + forward_convolutional_layer(uf, s); + forward_convolutional_layer(ui, s); + forward_convolutional_layer(ug, s); + forward_convolutional_layer(uo, s); + + // f = wf + uf + vf + copy_cpu(l.outputs*l.batch, wf.output, 1, l.f_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, uf.output, 1, l.f_cpu, 1); + if (l.peephole) axpy_cpu(l.outputs*l.batch, 1, vf.output, 1, l.f_cpu, 1); + + // i = wi + ui + vi + copy_cpu(l.outputs*l.batch, wi.output, 1, l.i_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ui.output, 1, l.i_cpu, 1); + if (l.peephole) axpy_cpu(l.outputs*l.batch, 1, vi.output, 1, l.i_cpu, 1); + + // g = wg + ug + copy_cpu(l.outputs*l.batch, wg.output, 1, l.g_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ug.output, 1, l.g_cpu, 1); + + activate_array(l.f_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.i_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.g_cpu, l.outputs*l.batch, TANH); + + // c = f*c + i*g + copy_cpu(l.outputs*l.batch, l.i_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.g_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.f_cpu, 1, l.c_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, l.temp_cpu, 1, l.c_cpu, 1); + + // o = wo + uo + vo(c_new) + s.input = l.c_cpu; + if (l.peephole) forward_convolutional_layer(vo, s); + copy_cpu(l.outputs*l.batch, wo.output, 1, l.o_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, uo.output, 1, l.o_cpu, 1); + if (l.peephole) axpy_cpu(l.outputs*l.batch, 1, vo.output, 1, l.o_cpu, 1); + activate_array(l.o_cpu, l.outputs*l.batch, LOGISTIC); + + // h = o * tanh(c) + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.h_cpu, 1); + activate_array(l.h_cpu, l.outputs*l.batch, TANH); + mul_cpu(l.outputs*l.batch, l.o_cpu, 1, l.h_cpu, 1); + + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.cell_cpu, 1); + copy_cpu(l.outputs*l.batch, l.h_cpu, 1, l.output, 1); + + state.input += l.inputs*l.batch; + l.output += l.outputs*l.batch; + l.cell_cpu += l.outputs*l.batch; + + if (l.peephole) { + increment_layer(&vf, 1); + increment_layer(&vi, 1); + increment_layer(&vo, 1); + } + + increment_layer(&wf, 1); + increment_layer(&wi, 1); + increment_layer(&wg, 1); + increment_layer(&wo, 1); + + increment_layer(&uf, 1); + increment_layer(&ui, 1); + increment_layer(&ug, 1); + increment_layer(&uo, 1); + } +} + +void backward_conv_lstm_layer(layer l, network_state state) +{ + network_state s = { 0 }; + s.train = state.train; + s.workspace = state.workspace; + int i; + layer vf = *(l.vf); + layer vi = *(l.vi); + layer vo = *(l.vo); + + layer wf = *(l.wf); + layer wi = *(l.wi); + layer wg = *(l.wg); + layer wo = *(l.wo); + + layer uf = *(l.uf); + layer ui = *(l.ui); + layer ug = *(l.ug); + layer uo = *(l.uo); + + if (l.peephole) { + increment_layer(&vf, l.steps - 1); + increment_layer(&vi, l.steps - 1); + increment_layer(&vo, l.steps - 1); + } + + increment_layer(&wf, l.steps - 1); + increment_layer(&wi, l.steps - 1); + increment_layer(&wg, l.steps - 1); + increment_layer(&wo, l.steps - 1); + + increment_layer(&uf, l.steps - 1); + increment_layer(&ui, l.steps - 1); + increment_layer(&ug, l.steps - 1); + increment_layer(&uo, l.steps - 1); + + state.input += l.inputs*l.batch*(l.steps - 1); + if (state.delta) state.delta += l.inputs*l.batch*(l.steps - 1); + + l.output += l.outputs*l.batch*(l.steps - 1); + l.cell_cpu += l.outputs*l.batch*(l.steps - 1); + l.delta += l.outputs*l.batch*(l.steps - 1); + + for (i = l.steps - 1; i >= 0; --i) { + if (i != 0) copy_cpu(l.outputs*l.batch, l.cell_cpu - l.outputs*l.batch, 1, l.prev_cell_cpu, 1); + copy_cpu(l.outputs*l.batch, l.cell_cpu, 1, l.c_cpu, 1); + if (i != 0) copy_cpu(l.outputs*l.batch, l.output - l.outputs*l.batch, 1, l.prev_state_cpu, 1); + copy_cpu(l.outputs*l.batch, l.output, 1, l.h_cpu, 1); + + l.dh_cpu = (i == 0) ? 0 : l.delta - l.outputs*l.batch; + + // f = wf + uf + vf + copy_cpu(l.outputs*l.batch, wf.output, 1, l.f_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, uf.output, 1, l.f_cpu, 1); + if (l.peephole) axpy_cpu(l.outputs*l.batch, 1, vf.output, 1, l.f_cpu, 1); + + // i = wi + ui + vi + copy_cpu(l.outputs*l.batch, wi.output, 1, l.i_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ui.output, 1, l.i_cpu, 1); + if (l.peephole) axpy_cpu(l.outputs*l.batch, 1, vi.output, 1, l.i_cpu, 1); + + // g = wg + ug + copy_cpu(l.outputs*l.batch, wg.output, 1, l.g_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ug.output, 1, l.g_cpu, 1); + + // o = wo + uo + vo + copy_cpu(l.outputs*l.batch, wo.output, 1, l.o_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, uo.output, 1, l.o_cpu, 1); + if (l.peephole) axpy_cpu(l.outputs*l.batch, 1, vo.output, 1, l.o_cpu, 1); + + activate_array(l.f_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.i_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.g_cpu, l.outputs*l.batch, TANH); + activate_array(l.o_cpu, l.outputs*l.batch, LOGISTIC); + + copy_cpu(l.outputs*l.batch, l.delta, 1, l.temp3_cpu, 1); + + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.temp_cpu, 1); + activate_array(l.temp_cpu, l.outputs*l.batch, TANH); + + copy_cpu(l.outputs*l.batch, l.temp3_cpu, 1, l.temp2_cpu, 1); + mul_cpu(l.outputs*l.batch, l.o_cpu, 1, l.temp2_cpu, 1); + + gradient_array(l.temp_cpu, l.outputs*l.batch, TANH, l.temp2_cpu); + axpy_cpu(l.outputs*l.batch, 1, l.dc_cpu, 1, l.temp2_cpu, 1); + // temp = tanh(c) + // temp2 = delta * o * grad_tanh(tahc(c)) + // temp3 = delta + + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.temp_cpu, 1); + activate_array(l.temp_cpu, l.outputs*l.batch, TANH); + mul_cpu(l.outputs*l.batch, l.temp3_cpu, 1, l.temp_cpu, 1); + gradient_array(l.o_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu); + // delta for o(w,u,v): temp = delta * tanh(c) * grad_logistic(o) + // delta for c,f,i,g(w,u,v): temp2 = delta * o * grad_tanh(tahc(c)) + delta_c(???) + // delta for output: temp3 = delta + + // o + // delta for O(w,u,v): temp = delta * tanh(c) * grad_logistic(o) + if (l.peephole) { + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, vo.delta, 1); + s.input = l.cell_cpu; + //s.delta = l.dc_cpu; + backward_convolutional_layer(vo, s); + } + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wo.delta, 1); + s.input = l.prev_state_cpu; + //s.delta = l.dh_cpu; + backward_convolutional_layer(wo, s); + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, uo.delta, 1); + s.input = state.input; + s.delta = state.delta; + backward_convolutional_layer(uo, s); + + // g + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.i_cpu, 1, l.temp_cpu, 1); + gradient_array(l.g_cpu, l.outputs*l.batch, TANH, l.temp_cpu); + // delta for c,f,i,g(w,u,v): temp2 = (delta * o * grad_tanh(tahc(c)) + delta_c(???)) * g * grad_logistic(i) + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wg.delta, 1); + s.input = l.prev_state_cpu; + //s.delta = l.dh_cpu; + backward_convolutional_layer(wg, s); + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, ug.delta, 1); + s.input = state.input; + s.delta = state.delta; + backward_convolutional_layer(ug, s); + + // i + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.g_cpu, 1, l.temp_cpu, 1); + gradient_array(l.i_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu); + // delta for c,f,i,g(w,u,v): temp2 = (delta * o * grad_tanh(tahc(c)) + delta_c(???)) * g * grad_logistic(i) + + if (l.peephole) { + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, vi.delta, 1); + s.input = l.prev_cell_cpu; + //s.delta = l.dc_cpu; + backward_convolutional_layer(vi, s); + } + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wi.delta, 1); + s.input = l.prev_state_cpu; + //s.delta = l.dh_cpu; + backward_convolutional_layer(wi, s); + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, ui.delta, 1); + s.input = state.input; + s.delta = state.delta; + backward_convolutional_layer(ui, s); + + // f + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.prev_cell_cpu, 1, l.temp_cpu, 1); + gradient_array(l.f_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu); + // delta for c,f,i,g(w,u,v): temp2 = (delta * o * grad_tanh(tahc(c)) + delta_c(???)) * c * grad_logistic(f) + + if (l.peephole) { + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, vf.delta, 1); + s.input = l.prev_cell_cpu; + //s.delta = l.dc_cpu; + backward_convolutional_layer(vf, s); + } + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wf.delta, 1); + s.input = l.prev_state_cpu; + //s.delta = l.dh_cpu; + backward_convolutional_layer(wf, s); + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, uf.delta, 1); + s.input = state.input; + s.delta = state.delta; + backward_convolutional_layer(uf, s); + + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.f_cpu, 1, l.temp_cpu, 1); + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, l.dc_cpu, 1); + + state.input -= l.inputs*l.batch; + if (state.delta) state.delta -= l.inputs*l.batch; + l.output -= l.outputs*l.batch; + l.cell_cpu -= l.outputs*l.batch; + l.delta -= l.outputs*l.batch; + + if (l.peephole) { + increment_layer(&vf, -1); + increment_layer(&vi, -1); + increment_layer(&vo, -1); + } + + increment_layer(&wf, -1); + increment_layer(&wi, -1); + increment_layer(&wg, -1); + increment_layer(&wo, -1); + + increment_layer(&uf, -1); + increment_layer(&ui, -1); + increment_layer(&ug, -1); + increment_layer(&uo, -1); + } +} + +#ifdef GPU +void pull_conv_lstm_layer(layer l) +{ + if (l.peephole) { + pull_convolutional_layer(*(l.vf)); + pull_convolutional_layer(*(l.vi)); + pull_convolutional_layer(*(l.vo)); + } + pull_convolutional_layer(*(l.wf)); + pull_convolutional_layer(*(l.wi)); + pull_convolutional_layer(*(l.wg)); + pull_convolutional_layer(*(l.wo)); + pull_convolutional_layer(*(l.uf)); + pull_convolutional_layer(*(l.ui)); + pull_convolutional_layer(*(l.ug)); + pull_convolutional_layer(*(l.uo)); +} + +void push_conv_lstm_layer(layer l) +{ + if (l.peephole) { + push_convolutional_layer(*(l.vf)); + push_convolutional_layer(*(l.vi)); + push_convolutional_layer(*(l.vo)); + } + push_convolutional_layer(*(l.wf)); + push_convolutional_layer(*(l.wi)); + push_convolutional_layer(*(l.wg)); + push_convolutional_layer(*(l.wo)); + push_convolutional_layer(*(l.uf)); + push_convolutional_layer(*(l.ui)); + push_convolutional_layer(*(l.ug)); + push_convolutional_layer(*(l.uo)); +} + +void update_conv_lstm_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay) +{ + if (l.peephole) { + update_convolutional_layer_gpu(*(l.vf), batch, learning_rate, momentum, decay); + update_convolutional_layer_gpu(*(l.vi), batch, learning_rate, momentum, decay); + update_convolutional_layer_gpu(*(l.vo), batch, learning_rate, momentum, decay); + } + update_convolutional_layer_gpu(*(l.wf), batch, learning_rate, momentum, decay); + update_convolutional_layer_gpu(*(l.wi), batch, learning_rate, momentum, decay); + update_convolutional_layer_gpu(*(l.wg), batch, learning_rate, momentum, decay); + update_convolutional_layer_gpu(*(l.wo), batch, learning_rate, momentum, decay); + update_convolutional_layer_gpu(*(l.uf), batch, learning_rate, momentum, decay); + update_convolutional_layer_gpu(*(l.ui), batch, learning_rate, momentum, decay); + update_convolutional_layer_gpu(*(l.ug), batch, learning_rate, momentum, decay); + update_convolutional_layer_gpu(*(l.uo), batch, learning_rate, momentum, decay); +} + +void forward_conv_lstm_layer_gpu(layer l, network_state state) +{ + network_state s = { 0 }; + s.train = state.train; + s.workspace = state.workspace; + s.net = state.net; + //if (!state.train) s.index = state.index; // don't use TC for training (especially without cuda_convert_f32_to_f16() ) + int i; + layer vf = *(l.vf); + layer vi = *(l.vi); + layer vo = *(l.vo); + + layer wf = *(l.wf); + layer wi = *(l.wi); + layer wg = *(l.wg); + layer wo = *(l.wo); + + layer uf = *(l.uf); + layer ui = *(l.ui); + layer ug = *(l.ug); + layer uo = *(l.uo); + + if (state.train) { + if (l.peephole) { + fill_ongpu(l.outputs * l.batch * l.steps, 0, vf.delta_gpu, 1); + fill_ongpu(l.outputs * l.batch * l.steps, 0, vi.delta_gpu, 1); + fill_ongpu(l.outputs * l.batch * l.steps, 0, vo.delta_gpu, 1); + } + + fill_ongpu(l.outputs * l.batch * l.steps, 0, wf.delta_gpu, 1); + fill_ongpu(l.outputs * l.batch * l.steps, 0, wi.delta_gpu, 1); + fill_ongpu(l.outputs * l.batch * l.steps, 0, wg.delta_gpu, 1); + fill_ongpu(l.outputs * l.batch * l.steps, 0, wo.delta_gpu, 1); + + fill_ongpu(l.outputs * l.batch * l.steps, 0, uf.delta_gpu, 1); + fill_ongpu(l.outputs * l.batch * l.steps, 0, ui.delta_gpu, 1); + fill_ongpu(l.outputs * l.batch * l.steps, 0, ug.delta_gpu, 1); + fill_ongpu(l.outputs * l.batch * l.steps, 0, uo.delta_gpu, 1); + + fill_ongpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1); + } + + for (i = 0; i < l.steps; ++i) + { + if (l.peephole) { + assert(l.outputs == vf.out_w * vf.out_h * vf.out_c); + s.input = l.c_gpu; + forward_convolutional_layer_gpu(vf, s); + forward_convolutional_layer_gpu(vi, s); + // vo below + } + + assert(l.outputs == wf.out_w * wf.out_h * wf.out_c); + assert(wf.c == l.out_c && wi.c == l.out_c && wg.c == l.out_c && wo.c == l.out_c); + + s.input = l.h_gpu; + forward_convolutional_layer_gpu(wf, s); + forward_convolutional_layer_gpu(wi, s); + forward_convolutional_layer_gpu(wg, s); + forward_convolutional_layer_gpu(wo, s); + + assert(l.inputs == uf.w * uf.h * uf.c); + assert(uf.c == l.c && ui.c == l.c && ug.c == l.c && uo.c == l.c); + + s.input = state.input; + forward_convolutional_layer_gpu(uf, s); + forward_convolutional_layer_gpu(ui, s); + forward_convolutional_layer_gpu(ug, s); + forward_convolutional_layer_gpu(uo, s); + + // f = wf + uf + vf + copy_ongpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1); + if (l.peephole) axpy_ongpu(l.outputs*l.batch, 1, vf.output_gpu, 1, l.f_gpu, 1); + + // i = wi + ui + vi + copy_ongpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1); + if (l.peephole) axpy_ongpu(l.outputs*l.batch, 1, vi.output_gpu, 1, l.i_gpu, 1); + + // g = wg + ug + copy_ongpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1); + + activate_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH); + + // c = f*c + i*g + copy_ongpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1); + mul_ongpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1); + mul_ongpu(l.outputs*l.batch, l.f_gpu, 1, l.c_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, l.temp_gpu, 1, l.c_gpu, 1); + + // o = wo + uo + vo(c_new) + if (l.peephole) { + s.input = l.c_gpu; + forward_convolutional_layer_gpu(vo, s); + } + copy_ongpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1); + if (l.peephole) axpy_ongpu(l.outputs*l.batch, 1, vo.output_gpu, 1, l.o_gpu, 1); + activate_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC); + + // h = o * tanh(c) + copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.h_gpu, 1); + activate_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH); + mul_ongpu(l.outputs*l.batch, l.o_gpu, 1, l.h_gpu, 1); + + //constrain_ongpu(l.outputs*l.batch, 1, l.c_gpu, 1); + //constrain_ongpu(l.outputs*l.batch, 1, l.h_gpu, 1); + fix_nan_and_inf(l.c_gpu, l.outputs*l.batch); + fix_nan_and_inf(l.h_gpu, l.outputs*l.batch); + + copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.cell_gpu, 1); + copy_ongpu(l.outputs*l.batch, l.h_gpu, 1, l.output_gpu, 1); + + state.input += l.inputs*l.batch; + l.output_gpu += l.outputs*l.batch; + l.cell_gpu += l.outputs*l.batch; + + if (l.peephole) { + increment_layer(&vf, 1); + increment_layer(&vi, 1); + increment_layer(&vo, 1); + } + + increment_layer(&wf, 1); + increment_layer(&wi, 1); + increment_layer(&wg, 1); + increment_layer(&wo, 1); + + increment_layer(&uf, 1); + increment_layer(&ui, 1); + increment_layer(&ug, 1); + increment_layer(&uo, 1); + } +} + +void backward_conv_lstm_layer_gpu(layer l, network_state state) +{ + float *last_output = l.output_gpu + l.outputs*l.batch*(l.steps - 1); + float *last_cell = l.cell_gpu + l.outputs*l.batch*(l.steps - 1); + + network_state s = { 0 }; + s.train = state.train; + s.workspace = state.workspace; + s.net = state.net; + int i; + layer vf = *(l.vf); + layer vi = *(l.vi); + layer vo = *(l.vo); + + layer wf = *(l.wf); + layer wi = *(l.wi); + layer wg = *(l.wg); + layer wo = *(l.wo); + + layer uf = *(l.uf); + layer ui = *(l.ui); + layer ug = *(l.ug); + layer uo = *(l.uo); + + if (l.peephole) { + increment_layer(&vf, l.steps - 1); + increment_layer(&vi, l.steps - 1); + increment_layer(&vo, l.steps - 1); + } + + increment_layer(&wf, l.steps - 1); + increment_layer(&wi, l.steps - 1); + increment_layer(&wg, l.steps - 1); + increment_layer(&wo, l.steps - 1); + + increment_layer(&uf, l.steps - 1); + increment_layer(&ui, l.steps - 1); + increment_layer(&ug, l.steps - 1); + increment_layer(&uo, l.steps - 1); + + state.input += l.inputs*l.batch*(l.steps - 1); + if (state.delta) state.delta += l.inputs*l.batch*(l.steps - 1); + + l.output_gpu += l.outputs*l.batch*(l.steps - 1); + l.cell_gpu += l.outputs*l.batch*(l.steps - 1); + l.delta_gpu += l.outputs*l.batch*(l.steps - 1); + + fix_nan_and_inf(l.dc_gpu, l.outputs*l.batch); + //fill_ongpu(l.outputs * l.batch, 0, l.dc_gpu, 1); // dont use + const int sequence = get_sequence_value(state.net); + + for (i = l.steps - 1; i >= 0; --i) { + if (i != 0) copy_ongpu(l.outputs*l.batch, l.cell_gpu - l.outputs*l.batch, 1, l.prev_cell_gpu, 1); + //else fill_ongpu(l.outputs * l.batch, 0, l.prev_cell_gpu, 1); // dont use + else if (state.net.current_subdivision % sequence != 0) copy_ongpu(l.outputs*l.batch, l.last_prev_cell_gpu, 1, l.prev_cell_gpu, 1); + + copy_ongpu(l.outputs*l.batch, l.cell_gpu, 1, l.c_gpu, 1); + + if (i != 0) copy_ongpu(l.outputs*l.batch, l.output_gpu - l.outputs*l.batch, 1, l.prev_state_gpu, 1); + //else fill_ongpu(l.outputs * l.batch, 0, l.prev_state_gpu, 1); // dont use + else if(state.net.current_subdivision % sequence != 0) copy_ongpu(l.outputs*l.batch, l.last_prev_state_gpu, 1, l.prev_state_gpu, 1); + + copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.h_gpu, 1); + + l.dh_gpu = (i == 0) ? 0 : l.delta_gpu - l.outputs*l.batch; + + // f = wf + uf + vf + copy_ongpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1); + if (l.peephole) axpy_ongpu(l.outputs*l.batch, 1, vf.output_gpu, 1, l.f_gpu, 1); + + // i = wi + ui + vi + copy_ongpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1); + if (l.peephole) axpy_ongpu(l.outputs*l.batch, 1, vi.output_gpu, 1, l.i_gpu, 1); + + // g = wg + ug + copy_ongpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1); + + // o = wo + uo + vo + copy_ongpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1); + axpy_ongpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1); + if (l.peephole) axpy_ongpu(l.outputs*l.batch, 1, vo.output_gpu, 1, l.o_gpu, 1); + + activate_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH); + activate_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC); + + + copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, l.temp3_gpu, 1); // temp3 = delta + + copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1); + activate_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH); // temp = tanh(c) + + copy_ongpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp2_gpu, 1); + mul_ongpu(l.outputs*l.batch, l.o_gpu, 1, l.temp2_gpu, 1); // temp2 = delta * o + + gradient_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH, l.temp2_gpu); // temp2 = delta * o * grad_tanh(tahc(c)) + //??? + axpy_ongpu(l.outputs*l.batch, 1, l.dc_gpu, 1, l.temp2_gpu, 1); // temp2 = delta * o * grad_tanh(tahc(c)) + delta_c(???) + // temp = tanh(c) + // temp2 = delta * o * grad_tanh(tahc(c)) + delta_c(???) + // temp3 = delta + + copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1); + activate_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH); // temp = tanh(c) + + mul_ongpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp_gpu, 1); // temp = delta * tanh(c) + gradient_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu); // temp = delta * tanh(c) * grad_logistic(o) + // delta for o(w,u,v): temp = delta * tanh(c) * grad_logistic(o) + // delta for c,f,i,g(w,u,v): temp2 = delta * o * grad_tanh(tahc(c)) + delta_c(???) + // delta for output: temp3 = delta + + // o + // delta for O(w,u,v): temp = delta * tanh(c) * grad_logistic(o) + if (l.peephole) { + copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, vo.delta_gpu, 1); + s.input = l.cell_gpu; + //s.delta = l.dc_gpu; + backward_convolutional_layer_gpu(vo, s); + } + + copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wo.delta_gpu, 1); + s.input = l.prev_state_gpu; + //s.delta = l.dh_gpu; + backward_convolutional_layer_gpu(wo, s); + + copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, uo.delta_gpu, 1); + s.input = state.input; + s.delta = state.delta; + backward_convolutional_layer_gpu(uo, s); + + // g + copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + mul_ongpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1); + gradient_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH, l.temp_gpu); + // delta for c,f,i,g(w,u,v): temp = (delta * o * grad_tanh(tahc(c)) + delta_c(???)) * i * grad_tanh(g) + + copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wg.delta_gpu, 1); + s.input = l.prev_state_gpu; + //s.delta = l.dh_gpu; + backward_convolutional_layer_gpu(wg, s); + + copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, ug.delta_gpu, 1); + s.input = state.input; + s.delta = state.delta; + backward_convolutional_layer_gpu(ug, s); + + // i + copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + mul_ongpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1); + gradient_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu); + // delta for c,f,i,g(w,u,v): temp = (delta * o * grad_tanh(tahc(c)) + delta_c(???)) * g * grad_logistic(i) + + if (l.peephole) { + copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, vi.delta_gpu, 1); + s.input = l.prev_cell_gpu; + //s.delta = l.dc_gpu; + backward_convolutional_layer_gpu(vi, s); + } + + copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wi.delta_gpu, 1); + s.input = l.prev_state_gpu; + //s.delta = l.dh_gpu; + backward_convolutional_layer_gpu(wi, s); + + copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, ui.delta_gpu, 1); + s.input = state.input; + s.delta = state.delta; + backward_convolutional_layer_gpu(ui, s); + + // f + copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + mul_ongpu(l.outputs*l.batch, l.prev_cell_gpu, 1, l.temp_gpu, 1); + gradient_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu); + // delta for c,f,i,g(w,u,v): temp = (delta * o * grad_tanh(tahc(c)) + delta_c(???)) * c * grad_logistic(f) + + if (l.peephole) { + copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, vf.delta_gpu, 1); + s.input = l.prev_cell_gpu; + //s.delta = l.dc_gpu; + backward_convolutional_layer_gpu(vf, s); + } + + copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wf.delta_gpu, 1); + s.input = l.prev_state_gpu; + //s.delta = l.dh_gpu; + backward_convolutional_layer_gpu(wf, s); + + copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, uf.delta_gpu, 1); + s.input = state.input; + s.delta = state.delta; + backward_convolutional_layer_gpu(uf, s); + + // c + copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + mul_ongpu(l.outputs*l.batch, l.f_gpu, 1, l.temp_gpu, 1); + copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, l.dc_gpu, 1); + // delta for c,f,i,g(w,u,v): delta_c = temp = (delta * o * grad_tanh(tahc(c)) + delta_c(???)) * f // (grad_linear(c)==1) + + state.input -= l.inputs*l.batch; + if (state.delta) state.delta -= l.inputs*l.batch; // new delta: state.delta = prev_layer.delta_gpu; + l.output_gpu -= l.outputs*l.batch; + l.cell_gpu -= l.outputs*l.batch; + l.delta_gpu -= l.outputs*l.batch; + + if (l.peephole) { + increment_layer(&vf, -1); + increment_layer(&vi, -1); + increment_layer(&vo, -1); + } + + increment_layer(&wf, -1); + increment_layer(&wi, -1); + increment_layer(&wg, -1); + increment_layer(&wo, -1); + + increment_layer(&uf, -1); + increment_layer(&ui, -1); + increment_layer(&ug, -1); + increment_layer(&uo, -1); + } + + copy_ongpu(l.outputs*l.batch, last_output, 1, l.last_prev_state_gpu, 1); + copy_ongpu(l.outputs*l.batch, last_cell, 1, l.last_prev_cell_gpu, 1); +} +#endif diff --git a/src/conv_lstm_layer.h b/src/conv_lstm_layer.h new file mode 100644 index 00000000000..3bd9b4efa63 --- /dev/null +++ b/src/conv_lstm_layer.h @@ -0,0 +1,33 @@ +#ifndef CONV_LSTM_LAYER_H +#define CONV_LSTM_LAYER_H + +#include "activations.h" +#include "layer.h" +#include "network.h" +#define USET + +#ifdef __cplusplus +extern "C" { +#endif +layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int peephole, int xnor); +void resize_conv_lstm_layer(layer *l, int w, int h); +void free_state_conv_lstm(layer l); +void randomize_state_conv_lstm(layer l); +void remember_state_conv_lstm(layer l); +void restore_state_conv_lstm(layer l); + +void forward_conv_lstm_layer(layer l, network_state state); +void backward_conv_lstm_layer(layer l, network_state state); +void update_conv_lstm_layer(layer l, int batch, float learning_rate, float momentum, float decay); + +#ifdef GPU +void forward_conv_lstm_layer_gpu(layer l, network_state state); +void backward_conv_lstm_layer_gpu(layer l, network_state state); +void update_conv_lstm_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay); +#endif + +#ifdef __cplusplus +} +#endif + +#endif // CONV_LSTM_LAYER_H diff --git a/src/detector.c b/src/detector.c index f535a032138..a34354f16c5 100644 --- a/src/detector.c +++ b/src/detector.c @@ -191,6 +191,11 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i time = what_time_is_it_now(); pthread_join(load_thread, 0); train = buffer; + if (net.track) { + net.sequential_subdivisions = get_current_seq_subdivisions(net); + args.threads = net.sequential_subdivisions * ngpus; + printf(" sequential_subdivisions = %d \n", net.sequential_subdivisions); + } load_thread = load_data(args); /* @@ -662,7 +667,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa char *train_images = option_find_str(options, "train", "data/train.txt"); valid_images = option_find_str(options, "valid", train_images); net = *existing_net; - remember_network_recurrent_state(*existing_net); + //remember_network_recurrent_state(*existing_net); free_network_recurrent_state(*existing_net); } else { @@ -1073,7 +1078,8 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa if (existing_net) { //set_batch_network(&net, initial_batch); //free_network_recurrent_state(*existing_net); - restore_network_recurrent_state(*existing_net); + //restore_network_recurrent_state(*existing_net); + randomize_network_recurrent_state(*existing_net); } else { free_network(net); diff --git a/src/network.c b/src/network.c index 98d1446bdfd..b25aa6395b7 100644 --- a/src/network.c +++ b/src/network.c @@ -91,6 +91,30 @@ void reset_rnn(network *net) reset_network_state(net, 0); } +float get_current_seq_subdivisions(network net) +{ + int sequence_subdivisions = net.init_sequential_subdivisions; + + if (net.policy) + { + int batch_num = get_current_batch(net); + int i; + for (i = 0; i < net.num_steps; ++i) { + if (net.steps[i] > batch_num) break; + sequence_subdivisions *= net.seq_scales[i]; + } + } + return sequence_subdivisions; +} + +int get_sequence_value(network net) +{ + int sequence = 1; + if (net.sequential_subdivisions != 0) sequence = net.subdivisions / net.sequential_subdivisions; + if (sequence < 1) sequence = 1; + return sequence; +} + float get_current_rate(network net) { int batch_num = get_current_batch(net); @@ -928,6 +952,7 @@ void free_network(network net) } free(net.layers); + free(net.seq_scales); free(net.scales); free(net.steps); free(net.seen); @@ -1124,6 +1149,15 @@ void free_network_recurrent_state(network net) } } +void randomize_network_recurrent_state(network net) +{ + int k; + for (k = 0; k < net.n; ++k) { + if (net.layers[k].type == CONV_LSTM) randomize_state_conv_lstm(net.layers[k]); + if (net.layers[k].type == CRNN) free_state_crnn(net.layers[k]); + } +} + void remember_network_recurrent_state(network net) { diff --git a/src/network.h b/src/network.h index 1b272ac19ae..186fe3d8bbb 100644 --- a/src/network.h +++ b/src/network.h @@ -104,6 +104,8 @@ void backward_network_gpu(network net, network_state state); void update_network_gpu(network net); #endif +float get_current_seq_subdivisions(network net); +int get_sequence_value(network net); float get_current_rate(network net); int get_current_batch(network net); void free_network(network net); @@ -164,6 +166,7 @@ int get_network_background(network net); network combine_train_valid_networks(network net_train, network net_map); void copy_weights_net(network net_train, network *net_map); void free_network_recurrent_state(network net); +void randomize_network_recurrent_state(network net); void remember_network_recurrent_state(network net); void restore_network_recurrent_state(network net); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index b66f4959666..6e50df0bff6 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -126,7 +126,7 @@ void update_network_gpu(network net) { cuda_set_device(net.gpu_index); int i; - int update_batch = net.batch*net.subdivisions; + int update_batch = net.batch*net.subdivisions * get_sequence_value(net); float rate = get_current_rate(net); for(i = 0; i < net.n; ++i){ layer l = net.layers[i]; @@ -200,7 +200,9 @@ float train_network_datum_gpu(network net, float *x, float *y) *net.seen += net.batch; forward_backward_network_gpu(net, x, y); float error = get_network_cost(net); - if (((*net.seen) / net.batch) % net.subdivisions == 0) update_network_gpu(net); + //if (((*net.seen) / net.batch) % net.subdivisions == 0) update_network_gpu(net); + const int sequence = get_sequence_value(net); + if (((*net.seen) / net.batch) % (net.subdivisions*sequence) == 0) update_network_gpu(net); return error; } diff --git a/src/parser.c b/src/parser.c index 64dbac2dd61..9ca3d5e2662 100644 --- a/src/parser.c +++ b/src/parser.c @@ -672,7 +672,7 @@ void parse_net_options(list *options, network *net) net->time_steps = option_find_int_quiet(options, "time_steps",1); net->track = option_find_int_quiet(options, "track", 0); net->augment_speed = option_find_int_quiet(options, "augment_speed", 2); - net->sequential_subdivisions = option_find_int_quiet(options, "sequential_subdivisions", 0); + net->init_sequential_subdivisions = net->sequential_subdivisions = option_find_int_quiet(options, "sequential_subdivisions", subdivs); net->try_fix_nan = option_find_int_quiet(options, "try_fix_nan", 0); net->batch /= subdivs; net->batch *= net->time_steps; @@ -721,6 +721,7 @@ void parse_net_options(list *options, network *net) } else if (net->policy == STEPS){ char *l = option_find(options, "steps"); char *p = option_find(options, "scales"); + char *s = option_find(options, "seq_scales"); if(!l || !p) error("STEPS policy must have steps and scales in cfg file"); int len = strlen(l); @@ -731,6 +732,14 @@ void parse_net_options(list *options, network *net) } int* steps = (int*)calloc(n, sizeof(int)); float* scales = (float*)calloc(n, sizeof(float)); + float* seq_scales = (float*)calloc(n, sizeof(float)); + for (i = 0; i < n; ++i) { + if (s) { + seq_scales[i] = atof(s); + s = strchr(s, ',') + 1; + } else + seq_scales[i] = 1; + } for(i = 0; i < n; ++i){ int step = atoi(l); float scale = atof(p); @@ -741,6 +750,7 @@ void parse_net_options(list *options, network *net) } net->scales = scales; net->steps = steps; + net->seq_scales = seq_scales; net->num_steps = n; } else if (net->policy == EXP){ net->gamma = option_find_float(options, "gamma", 1); From 4f72fcc015a7825cf0c3d3eee143c13853b1a2c3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 10 May 2019 16:46:48 +0300 Subject: [PATCH 0484/1365] Added grouped convolutional (depth-wise convolutional) --- src/conv_lstm_layer.c | 25 +-- src/conv_lstm_layer.h | 2 +- src/convolutional_kernels.cu | 150 ++++++++------- src/convolutional_layer.c | 359 +++++++++++++++++++---------------- src/convolutional_layer.h | 2 +- src/crnn_layer.c | 9 +- src/crnn_layer.h | 2 +- src/detector.c | 11 +- src/network.c | 2 +- src/network_kernels.cu | 18 +- src/parser.c | 23 ++- src/yolo_layer.c | 4 + 12 files changed, 325 insertions(+), 282 deletions(-) diff --git a/src/conv_lstm_layer.c b/src/conv_lstm_layer.c index 7d395ad0c58..1764cb90f03 100644 --- a/src/conv_lstm_layer.c +++ b/src/conv_lstm_layer.c @@ -32,7 +32,7 @@ static void increment_layer(layer *l, int steps) } -layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int peephole, int xnor) +layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, int groups, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int peephole, int xnor) { fprintf(stderr, "CONV_LSTM Layer: %d x %d x %d image, %d filters\n", h, w, c, output_filters); /* @@ -57,6 +57,7 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i l.h = h; l.w = w; l.c = c; + l.groups = groups; l.out_c = output_filters; l.inputs = h * w * c; l.xnor = xnor; @@ -64,44 +65,44 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i // U l.uf = (layer*)malloc(sizeof(layer)); - *(l.uf) = make_convolutional_layer(batch, steps, h, w, c, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.uf) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.uf->batch = batch; if (l.workspace_size < l.uf->workspace_size) l.workspace_size = l.uf->workspace_size; l.ui = (layer*)malloc(sizeof(layer)); - *(l.ui) = make_convolutional_layer(batch, steps, h, w, c, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.ui) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.ui->batch = batch; if (l.workspace_size < l.ui->workspace_size) l.workspace_size = l.ui->workspace_size; l.ug = (layer*)malloc(sizeof(layer)); - *(l.ug) = make_convolutional_layer(batch, steps, h, w, c, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.ug) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.ug->batch = batch; if (l.workspace_size < l.ug->workspace_size) l.workspace_size = l.ug->workspace_size; l.uo = (layer*)malloc(sizeof(layer)); - *(l.uo) = make_convolutional_layer(batch, steps, h, w, c, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.uo) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.uo->batch = batch; if (l.workspace_size < l.uo->workspace_size) l.workspace_size = l.uo->workspace_size; // W l.wf = (layer*)malloc(sizeof(layer)); - *(l.wf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.wf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.wf->batch = batch; if (l.workspace_size < l.wf->workspace_size) l.workspace_size = l.wf->workspace_size; l.wi = (layer*)malloc(sizeof(layer)); - *(l.wi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.wi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.wi->batch = batch; if (l.workspace_size < l.wi->workspace_size) l.workspace_size = l.wi->workspace_size; l.wg = (layer*)malloc(sizeof(layer)); - *(l.wg) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.wg) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.wg->batch = batch; if (l.workspace_size < l.wg->workspace_size) l.workspace_size = l.wg->workspace_size; l.wo = (layer*)malloc(sizeof(layer)); - *(l.wo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.wo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.wo->batch = batch; if (l.workspace_size < l.wo->workspace_size) l.workspace_size = l.wo->workspace_size; @@ -109,21 +110,21 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i // V l.vf = (layer*)malloc(sizeof(layer)); if (l.peephole) { - *(l.vf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.vf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.vf->batch = batch; if (l.workspace_size < l.vf->workspace_size) l.workspace_size = l.vf->workspace_size; } l.vi = (layer*)malloc(sizeof(layer)); if (l.peephole) { - *(l.vi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.vi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.vi->batch = batch; if (l.workspace_size < l.vi->workspace_size) l.workspace_size = l.vi->workspace_size; } l.vo = (layer*)malloc(sizeof(layer)); if (l.peephole) { - *(l.vo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.vo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.vo->batch = batch; if (l.workspace_size < l.vo->workspace_size) l.workspace_size = l.vo->workspace_size; } diff --git a/src/conv_lstm_layer.h b/src/conv_lstm_layer.h index 3bd9b4efa63..56a57298243 100644 --- a/src/conv_lstm_layer.h +++ b/src/conv_lstm_layer.h @@ -9,7 +9,7 @@ #ifdef __cplusplus extern "C" { #endif -layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int peephole, int xnor); +layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, int groups, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int peephole, int xnor); void resize_conv_lstm_layer(layer *l, int w, int h); void free_state_conv_lstm(layer l); void randomize_state_conv_lstm(layer l); diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index cfa4fe7e8fc..9185279f261 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -166,20 +166,16 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) { //fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); if(l.binary){ - binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu); + binarize_weights_gpu(l.weights_gpu, l.n, (l.c / l.groups)*l.size*l.size, l.binary_weights_gpu); swap_binary(&l); } if(l.xnor){ if (!l.align_bit_weights_gpu || state.train) { - //binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu); + //binarize_weights_gpu(l.weights_gpu, l.n, (l.c / l.groups)*l.size*l.size, l.binary_weights_gpu); - fast_binarize_weights_gpu(l.weights_gpu, l.n, l.c*l.size*l.size, l.binary_weights_gpu, l.mean_arr_gpu); + fast_binarize_weights_gpu(l.weights_gpu, l.n, (l.c / l.groups)*l.size*l.size, l.binary_weights_gpu, l.mean_arr_gpu); } - //swap_binary(&l); - //binarize_gpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input_gpu); - //state.input = l.binary_input_gpu; - //cudaDeviceSynchronize(); if (l.align_bit_weights_gpu && !state.train && l.c >= 32) { @@ -187,11 +183,15 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //cudaError_t status = cudaSuccess; //int input_size = l.c*l.h*l.w*l.batch; - int m = l.n; - int k = l.size*l.size*l.c; + int m = l.n / l.groups; + int k = l.size*l.size*l.c / l.groups; int n = l.out_w*l.out_h; //float * a = l.weights_gpu; + // int i, j; + // for(i = 0; i < l.batch; ++i){ + // for (j = 0; j < l.groups; ++j) { + int ldb_align = l.lda_align; size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; //size_t t_intput_size = new_ldb * n; @@ -551,22 +551,25 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) #else fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); - int i; - int m = l.n; - int k = l.size*l.size*l.c; + int i, j; + int m = l.n / l.groups; + int k = l.size*l.size*l.c / l.groups; int n = l.out_w*l.out_h; for(i = 0; i < l.batch; ++i){ - float *im = state.input + i*l.c*l.h*l.w; - float * a = l.weights_gpu; - float * b = state.workspace; - float * c = l.output_gpu; - if (l.size == 1) { - b = im; - } - else { - im2col_ongpu(im, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace); + for (j = 0; j < l.groups; ++j) { + //float *im = state.input + i*l.c*l.h*l.w; + float *im = state.input + (i*l.groups + j)*l.c / l.groups*l.h*l.w; + float *a = l.weights_gpu + j*l.nweights / l.groups; + float *b = state.workspace; + float *c = l.output_gpu + (i*l.groups + j)*n*m; + if (l.size == 1) { + b = im; + } + else { + im2col_ongpu(im, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, state.workspace); + } + gemm_ongpu(0, 0, m, n, k, 1., a, k, b, n, 1., c + i*m*n, n); } - gemm_ongpu(0,0,m,n,k,1.,a,k,b,n,1.,c+i*m*n,n); } if (l.batch_normalize) { @@ -782,32 +785,38 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state backward_batchnorm_layer_gpu(l, state); } - int m = l.n; - int n = l.size*l.size*l.c; + int m = l.n / l.groups; + int n = l.size*l.size*l.c / l.groups; int k = l.out_w*l.out_h; - int i; + int i, j; for(i = 0; i < l.batch; ++i){ - float * a = l.delta_gpu; - float * b = state.workspace; - float * c = l.weight_updates_gpu; + for (j = 0; j < l.groups; ++j) { + float * a = l.delta_gpu + (i*l.groups + j)*m*k; + float * b = state.workspace; + float * c = l.weight_updates_gpu + j*l.nweights / l.groups; + + float *im = state.input + (i*l.groups + j)*l.c / l.groups*l.h*l.w; - im2col_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace); - gemm_ongpu(0,1,m,n,k,1,a + i*m*k,k,b,k,1,c,n); + im2col_ongpu(im, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, state.workspace); + gemm_ongpu(0, 1, m, n, k, 1, a + i*m*k, k, b, k, 1, c, n); - if(state.delta){ - if(l.binary || l.xnor) swap_binary(&l); - float * a = l.weights_gpu; - float * b = l.delta_gpu; - float * c = state.workspace; + if (state.delta) { + if (l.binary || l.xnor) swap_binary(&l); + float * a = l.weights_gpu + j*l.nweights / l.groups; + float * b = l.delta_gpu + (i*l.groups + j)*m*k; + float * c = state.workspace; - gemm_ongpu(1,0,n,k,m,1,a,n,b + i*k*m,k,0,c,k); + gemm_ongpu(1, 0, n, k, m, 1, a, n, b + i*k*m, k, 0, c, k); - col2im_ongpu(state.workspace, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta + i*l.c*l.h*l.w); - if(l.binary || l.xnor) { - swap_binary(&l); + float *delta = state.delta + (i*l.groups + j)*l.c / l.groups*l.h*l.w; + + col2im_ongpu(state.workspace, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, delta); + if (l.binary || l.xnor) { + swap_binary(&l); + } + if (l.xnor) gradient_array_ongpu(original_input + i*l.c*l.h*l.w, l.c*l.h*l.w, HARDTAN, state.delta + i*l.c*l.h*l.w); } - if(l.xnor) gradient_array_ongpu(original_input + i*l.c*l.h*l.w, l.c*l.h*l.w, HARDTAN, state.delta + i*l.c*l.h*l.w); } } #endif @@ -821,43 +830,43 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state } } -void pull_convolutional_layer(convolutional_layer layer) +void pull_convolutional_layer(convolutional_layer l) { - cuda_pull_array_async(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size); - cuda_pull_array_async(layer.biases_gpu, layer.biases, layer.n); - cuda_pull_array_async(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size); - cuda_pull_array_async(layer.bias_updates_gpu, layer.bias_updates, layer.n); - if (layer.batch_normalize){ - cuda_pull_array_async(layer.scales_gpu, layer.scales, layer.n); - cuda_pull_array_async(layer.rolling_mean_gpu, layer.rolling_mean, layer.n); - cuda_pull_array_async(layer.rolling_variance_gpu, layer.rolling_variance, layer.n); + cuda_pull_array_async(l.weights_gpu, l.weights, l.nweights); + cuda_pull_array_async(l.biases_gpu, l.biases, l.n); + cuda_pull_array_async(l.weight_updates_gpu, l.weight_updates, l.nweights); + cuda_pull_array_async(l.bias_updates_gpu, l.bias_updates, l.n); + if (l.batch_normalize){ + cuda_pull_array_async(l.scales_gpu, l.scales, l.n); + cuda_pull_array_async(l.rolling_mean_gpu, l.rolling_mean, l.n); + cuda_pull_array_async(l.rolling_variance_gpu, l.rolling_variance, l.n); } - if (layer.adam){ - cuda_pull_array_async(layer.m_gpu, layer.m, layer.c*layer.n*layer.size*layer.size); - cuda_pull_array_async(layer.v_gpu, layer.v, layer.c*layer.n*layer.size*layer.size); + if (l.adam){ + cuda_pull_array_async(l.m_gpu, l.m, l.nweights); + cuda_pull_array_async(l.v_gpu, l.v, l.nweights); } CHECK_CUDA(cudaPeekAtLastError()); cudaStreamSynchronize(get_cuda_stream()); } -void push_convolutional_layer(convolutional_layer layer) +void push_convolutional_layer(convolutional_layer l) { - cuda_push_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size); + cuda_push_array(l.weights_gpu, l.weights, l.nweights); #ifdef CUDNN_HALF - assert((layer.c*layer.n*layer.size*layer.size) > 0); - cuda_convert_f32_to_f16(layer.weights_gpu, layer.c*layer.n*layer.size*layer.size, layer.weights_gpu16); + assert(l.nweights > 0); + cuda_convert_f32_to_f16(l.weights_gpu, l.nweights, l.weights_gpu16); #endif - cuda_push_array(layer.biases_gpu, layer.biases, layer.n); - cuda_push_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size); - cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n); - if (layer.batch_normalize){ - cuda_push_array(layer.scales_gpu, layer.scales, layer.n); - cuda_push_array(layer.rolling_mean_gpu, layer.rolling_mean, layer.n); - cuda_push_array(layer.rolling_variance_gpu, layer.rolling_variance, layer.n); + cuda_push_array(l.biases_gpu, l.biases, l.n); + cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.nweights); + cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n); + if (l.batch_normalize){ + cuda_push_array(l.scales_gpu, l.scales, l.n); + cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.n); + cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.n); } - if (layer.adam){ - cuda_push_array(layer.m_gpu, layer.m, layer.c*layer.n*layer.size*layer.size); - cuda_push_array(layer.v_gpu, layer.v, layer.c*layer.n*layer.size*layer.size); + if (l.adam){ + cuda_push_array(l.m_gpu, l.m, l.nweights); + cuda_push_array(l.v_gpu, l.v, l.nweights); } CHECK_CUDA(cudaPeekAtLastError()); } @@ -868,11 +877,10 @@ void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init //float momentum = a.momentum; //float decay = a.decay; //int batch = a.batch; - int size = l.size*l.size*l.c*l.n; // old if (l.adam) { //adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.nweights, batch, a.t); - adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, size, batch, l.t); + adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.nweights, batch, l.t); adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.n, batch, l.t); if (l.scales_gpu) { @@ -883,9 +891,9 @@ void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init //axpy_ongpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); //axpy_ongpu(l.nweights, learning_rate / batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); //scal_ongpu(l.nweights, momentum, l.weight_updates_gpu, 1); - axpy_ongpu(size, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); - axpy_ongpu(size, learning_rate / batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); - scal_ongpu(size, momentum, l.weight_updates_gpu, 1); + axpy_ongpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); + axpy_ongpu(l.nweights, learning_rate / batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); + scal_ongpu(l.nweights, momentum, l.weight_updates_gpu, 1); axpy_ongpu(l.n, learning_rate / batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); scal_ongpu(l.n, momentum, l.bias_updates_gpu, 1); diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 16cb6b9325c..f9c0ee01116 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -140,7 +140,7 @@ size_t get_workspace_size32(layer l){ if (workspace_size < re_packed_input_size) workspace_size = re_packed_input_size; return workspace_size; } - return (size_t)l.out_h*l.out_w*l.size*l.size*l.c*sizeof(float); + return (size_t)l.out_h*l.out_w*l.size*l.size*(l.c / l.groups)*sizeof(float); } size_t get_workspace_size16(layer l) { @@ -231,9 +231,14 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) // 3. FP32 Master Copy of Weights // More: http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#tensor_ops CHECK_CUDNN(cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH)); + CHECK_CUDNN(cudnnSetConvolutionGroupCount(l->convDesc, l->groups)); #if((CUDNN_MAJOR*10 + CUDNN_MINOR) >= 72) // cuDNN >= 7.2 CHECK_CUDNN(cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION)); #endif +#else //if(CUDNN_MAJOR >= 7) + if (l->groups > 1) { + error("CUDNN < 7 doesn't support groups, please upgrade!"); + } #endif // INT8_CONFIG, INT8_EXT_CONFIG, INT8x4_CONFIG and INT8x4_EXT_CONFIG are only supported @@ -243,23 +248,23 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) // backward delta CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->dsrcTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->c, l->h, l->w)); CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->ddstTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->out_c, l->out_h, l->out_w)); - CHECK_CUDNN(cudnnSetFilter4dDescriptor(l->dweightDesc, data_type, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size)); + CHECK_CUDNN(cudnnSetFilter4dDescriptor(l->dweightDesc, data_type, CUDNN_TENSOR_NCHW, l->n, l->c / l->groups, l->size, l->size)); // forward CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->c, l->h, l->w)); CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->out_c, l->out_h, l->out_w)); - CHECK_CUDNN(cudnnSetFilter4dDescriptor(l->weightDesc, data_type, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size)); + CHECK_CUDNN(cudnnSetFilter4dDescriptor(l->weightDesc, data_type, CUDNN_TENSOR_NCHW, l->n, l->c / l->groups, l->size, l->size)); //#ifdef CUDNN_HALF // backward delta CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->dsrcTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->c, l->h, l->w)); CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->ddstTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w)); - CHECK_CUDNN(cudnnSetFilter4dDescriptor(l->dweightDesc16, CUDNN_DATA_HALF, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size)); + CHECK_CUDNN(cudnnSetFilter4dDescriptor(l->dweightDesc16, CUDNN_DATA_HALF, CUDNN_TENSOR_NCHW, l->n, l->c / l->groups, l->size, l->size)); // forward CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->srcTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->c, l->h, l->w)); CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->dstTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w)); - CHECK_CUDNN(cudnnSetFilter4dDescriptor(l->weightDesc16, CUDNN_DATA_HALF, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size)); + CHECK_CUDNN(cudnnSetFilter4dDescriptor(l->weightDesc16, CUDNN_DATA_HALF, CUDNN_TENSOR_NCHW, l->n, l->c / l->groups, l->size, l->size)); // batch norm CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->normDstTensorDescF16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w)); @@ -326,17 +331,21 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) #endif #endif -convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index) +convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index) { int total_batch = batch*steps; int i; convolutional_layer l = { (LAYER_TYPE)0 }; l.type = CONVOLUTIONAL; + if (xnor) groups = 1; // disable groups for XNOR-net + if (groups < 1) groups = 1; + l.index = index; l.h = h; l.w = w; l.c = c; + l.groups = groups; l.n = n; l.binary = binary; l.xnor = xnor; @@ -348,17 +357,17 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.pad = padding; l.batch_normalize = batch_normalize; l.learning_rate_scale = 1; - l.nweights = l.c*l.n*l.size*l.size; + l.nweights = (c / groups) * n * size * size; - l.weights = (float*)calloc(c * n * size * size, sizeof(float)); - l.weight_updates = (float*)calloc(c * n * size * size, sizeof(float)); + l.weights = (float*)calloc(l.nweights, sizeof(float)); + l.weight_updates = (float*)calloc(l.nweights, sizeof(float)); l.biases = (float*)calloc(n, sizeof(float)); l.bias_updates = (float*)calloc(n, sizeof(float)); // float scale = 1./sqrt(size*size*c); - float scale = sqrt(2./(size*size*c)); - for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_uniform(-1, 1); + float scale = sqrt(2./(size*size*c/groups)); + for(i = 0; i < l.nweights; ++i) l.weights[i] = scale*rand_uniform(-1, 1); // rand_normal(); int out_h = convolutional_out_height(l); int out_w = convolutional_out_width(l); l.out_h = out_h; @@ -375,12 +384,12 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.backward = backward_convolutional_layer; l.update = update_convolutional_layer; if(binary){ - l.binary_weights = (float*)calloc(c * n * size * size, sizeof(float)); - l.cweights = (char*)calloc(c * n * size * size, sizeof(char)); + l.binary_weights = (float*)calloc(l.nweights, sizeof(float)); + l.cweights = (char*)calloc(l.nweights, sizeof(char)); l.scales = (float*)calloc(n, sizeof(float)); } if(xnor){ - l.binary_weights = (float*)calloc(c * n * size * size, sizeof(float)); + l.binary_weights = (float*)calloc(l.nweights, sizeof(float)); l.binary_input = (float*)calloc(l.inputs * l.batch, sizeof(float)); int align = 32;// 8; @@ -420,8 +429,8 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, } if(adam){ l.adam = 1; - l.m = (float*)calloc(c * n * size * size, sizeof(float)); - l.v = (float*)calloc(c * n * size * size, sizeof(float)); + l.m = (float*)calloc(l.nweights, sizeof(float)); + l.v = (float*)calloc(l.nweights, sizeof(float)); l.bias_m = (float*)calloc(n, sizeof(float)); l.scale_m = (float*)calloc(n, sizeof(float)); l.bias_v = (float*)calloc(n, sizeof(float)); @@ -435,19 +444,19 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, if(gpu_index >= 0){ if (adam) { - l.m_gpu = cuda_make_array(l.m, c*n*size*size); - l.v_gpu = cuda_make_array(l.v, c*n*size*size); + l.m_gpu = cuda_make_array(l.m, l.nweights); + l.v_gpu = cuda_make_array(l.v, l.nweights); l.bias_m_gpu = cuda_make_array(l.bias_m, n); l.bias_v_gpu = cuda_make_array(l.bias_v, n); l.scale_m_gpu = cuda_make_array(l.scale_m, n); l.scale_v_gpu = cuda_make_array(l.scale_v, n); } - l.weights_gpu = cuda_make_array(l.weights, c*n*size*size); - l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size); + l.weights_gpu = cuda_make_array(l.weights, l.nweights); + l.weight_updates_gpu = cuda_make_array(l.weight_updates, l.nweights); #ifdef CUDNN_HALF - l.weights_gpu16 = cuda_make_array(NULL, c*n*size*size / 2 + 1); //cuda_make_array(l.weights, c*n*size*size / 2); - l.weight_updates_gpu16 = cuda_make_array(NULL, c*n*size*size / 2 + 1); //cuda_make_array(l.weight_updates, c*n*size*size / 2); + l.weights_gpu16 = cuda_make_array(NULL, l.nweights / 2 + 1); + l.weight_updates_gpu16 = cuda_make_array(NULL, l.nweights / 2 + 1); #endif l.biases_gpu = cuda_make_array(l.biases, n); @@ -457,10 +466,10 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.delta_gpu = cuda_make_array(l.delta, total_batch*out_h*out_w*n); if(binary){ - l.binary_weights_gpu = cuda_make_array(l.weights, c*n*size*size); + l.binary_weights_gpu = cuda_make_array(l.weights, l.nweights); } if(xnor){ - l.binary_weights_gpu = cuda_make_array(l.weights, c*n*size*size); + l.binary_weights_gpu = cuda_make_array(l.weights, l.nweights); l.mean_arr_gpu = cuda_make_array(0, l.n); l.binary_input_gpu = cuda_make_array(0, l.inputs*l.batch); } @@ -490,7 +499,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.workspace_size = get_convolutional_workspace_size(l); //fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c); - l.bflops = (2.0 * l.n * l.size*l.size*l.c * l.out_h*l.out_w) / 1000000000.; + l.bflops = (2.0 * l.nweights * l.out_h*l.out_w) / 1000000000.; if (l.xnor && l.use_bin_output) fprintf(stderr, "convXB"); else if (l.xnor) fprintf(stderr, "convX "); else fprintf(stderr, "conv "); @@ -504,8 +513,8 @@ void denormalize_convolutional_layer(convolutional_layer l) int i, j; for(i = 0; i < l.n; ++i){ float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .00001); - for(j = 0; j < l.c*l.size*l.size; ++j){ - l.weights[i*l.c*l.size*l.size + j] *= scale; + for(j = 0; j < l.nweights; ++j){ + l.weights[i*l.nweights + j] *= scale; } l.biases[i] -= l.rolling_mean[i] * scale; l.scales[i] = 1; @@ -516,7 +525,7 @@ void denormalize_convolutional_layer(convolutional_layer l) void test_convolutional_layer() { - convolutional_layer l = make_convolutional_layer(1, 1, 5, 5, 3, 2, 5, 2, 1, LEAKY, 1, 0, 0, 0, 0, 0); + convolutional_layer l = make_convolutional_layer(1, 1, 5, 5, 3, 2, 1, 5, 2, 1, LEAKY, 1, 0, 0, 0, 0, 0); l.batch_normalize = 1; float data[] = {1,1,1,1,1, 1,1,1,1,1, @@ -691,8 +700,8 @@ void bit_to_float(unsigned char *src, float *dst, size_t size, size_t filters, f void binary_align_weights(convolutional_layer *l) { - int m = l->n; - int k = l->size*l->size*l->c; + int m = l->n; // (l->n / l->groups) + int k = l->size*l->size*l->c; // ->size*l->size*(l->c / l->groups) size_t new_lda = k + (l->lda_align - k % l->lda_align); // (k / 8 + 1) * 8; l->new_lda = new_lda; @@ -823,13 +832,13 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) { int out_h = convolutional_out_height(l); int out_w = convolutional_out_width(l); - int i; + int i, j; fill_cpu(l.outputs*l.batch, 0, l.output, 1); if (l.xnor && (!l.align_bit_weights || state.train)) { if (!l.align_bit_weights || state.train) { - binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights); + binarize_weights(l.weights, l.n, l.nweights, l.binary_weights); //printf("\n binarize_weights l.align_bit_weights = %p \n", l.align_bit_weights); } swap_binary(&l); @@ -837,147 +846,150 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) state.input = l.binary_input; } - int m = l.n; - int k = l.size*l.size*l.c; + int m = l.n / l.groups; + int k = l.size*l.size*l.c / l.groups; int n = out_h*out_w; - float *a = l.weights; - float *b = state.workspace; - float *c = l.output; - static int u = 0; u++; for(i = 0; i < l.batch; ++i){ + for (j = 0; j < l.groups; ++j) { - //gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); - //gemm_nn_custom(m, n, k, 1, a, k, b, n, c, n); - if (l.xnor && l.align_bit_weights && !state.train) - { - memset(b, 0, l.bit_align*l.size*l.size*l.c * sizeof(float)); + float *a = l.weights + j*l.nweights / l.groups; + float *b = state.workspace; + float *c = l.output + (i*l.groups + j)*n*m; - if(l.c % 32 == 0) + //gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); + //gemm_nn_custom(m, n, k, 1, a, k, b, n, c, n); + if (l.xnor && l.align_bit_weights && !state.train) { - //printf(" l.index = %d - new XNOR \n", l.index); + memset(b, 0, l.bit_align*l.size*l.size*l.c * sizeof(float)); - int ldb_align = l.lda_align; - size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; - //size_t t_intput_size = new_ldb * l.bit_align;// n; - //size_t t_bit_input_size = t_intput_size / 8;// +1; + if (l.c % 32 == 0) + { + //printf(" l.index = %d - new XNOR \n", l.index); - int re_packed_input_size = l.c * l.w * l.h; - memset(state.workspace, 0, re_packed_input_size * sizeof(float)); + int ldb_align = l.lda_align; + size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8; + //size_t t_intput_size = new_ldb * l.bit_align;// n; + //size_t t_bit_input_size = t_intput_size / 8;// +1; - const size_t new_c = l.c / 32; - size_t in_re_packed_input_size = new_c * l.w * l.h + 1; - memset(l.bin_re_packed_input, 0, in_re_packed_input_size * sizeof(uint32_t)); + int re_packed_input_size = l.c * l.w * l.h; + memset(state.workspace, 0, re_packed_input_size * sizeof(float)); - //float *re_packed_input = calloc(l.c * l.w * l.h, sizeof(float)); - //uint32_t *bin_re_packed_input = calloc(new_c * l.w * l.h + 1, sizeof(uint32_t)); + const size_t new_c = l.c / 32; + size_t in_re_packed_input_size = new_c * l.w * l.h + 1; + memset(l.bin_re_packed_input, 0, in_re_packed_input_size * sizeof(uint32_t)); - // float32x4 by channel (as in cuDNN) - repack_input(state.input, state.workspace, l.w, l.h, l.c); + //float *re_packed_input = calloc(l.c * l.w * l.h, sizeof(float)); + //uint32_t *bin_re_packed_input = calloc(new_c * l.w * l.h + 1, sizeof(uint32_t)); - // 32 x floats -> 1 x uint32_t - float_to_bit(state.workspace, (unsigned char *)l.bin_re_packed_input, l.c * l.w * l.h); + // float32x4 by channel (as in cuDNN) + repack_input(state.input, state.workspace, l.w, l.h, l.c); - //free(re_packed_input); + // 32 x floats -> 1 x uint32_t + float_to_bit(state.workspace, (unsigned char *)l.bin_re_packed_input, l.c * l.w * l.h); - // slow - convolution the packed inputs and weights: float x 32 by channel (as in cuDNN) - //convolution_repacked((uint32_t *)bin_re_packed_input, (uint32_t *)l.align_bit_weights, l.output, - // l.w, l.h, l.c, l.n, l.size, l.pad, l.new_lda, l.mean_arr); + //free(re_packed_input); - // // then exit from if() + // slow - convolution the packed inputs and weights: float x 32 by channel (as in cuDNN) + //convolution_repacked((uint32_t *)bin_re_packed_input, (uint32_t *)l.align_bit_weights, l.output, + // l.w, l.h, l.c, l.n, l.size, l.pad, l.new_lda, l.mean_arr); + // // then exit from if() - im2col_cpu_custom((float *)l.bin_re_packed_input, new_c, l.h, l.w, l.size, l.stride, l.pad, state.workspace); - //im2col_cpu((float *)bin_re_packed_input, new_c, l.h, l.w, l.size, l.stride, l.pad, b); - //free(bin_re_packed_input); + im2col_cpu_custom((float *)l.bin_re_packed_input, new_c, l.h, l.w, l.size, l.stride, l.pad, state.workspace); + //im2col_cpu((float *)bin_re_packed_input, new_c, l.h, l.w, l.size, l.stride, l.pad, b); - int new_k = l.size*l.size*l.c / 32; + //free(bin_re_packed_input); - // good for (l.c == 64) - //gemm_nn_bin_32bit_packed(m, n, new_k, 1, - // l.align_bit_weights, l.new_lda/32, - // b, n, - // c, n, l.mean_arr); + int new_k = l.size*l.size*l.c / 32; -// // then exit from if() + // good for (l.c == 64) + //gemm_nn_bin_32bit_packed(m, n, new_k, 1, + // l.align_bit_weights, l.new_lda/32, + // b, n, + // c, n, l.mean_arr); - transpose_uint32((uint32_t *)state.workspace, (uint32_t*)l.t_bit_input, new_k, n, n, new_ldb); + // // then exit from if() - // the main GEMM function - gemm_nn_custom_bin_mean_transposed(m, n, k, 1, (unsigned char*)l.align_bit_weights, new_ldb, (unsigned char*)l.t_bit_input, new_ldb, c, n, l.mean_arr); + transpose_uint32((uint32_t *)state.workspace, (uint32_t*)l.t_bit_input, new_k, n, n, new_ldb); - // // alternative GEMM - //gemm_nn_bin_transposed_32bit_packed(m, n, new_k, 1, - // l.align_bit_weights, l.new_lda/32, - // t_bit_input, new_ldb / 32, - // c, n, l.mean_arr); + // the main GEMM function + gemm_nn_custom_bin_mean_transposed(m, n, k, 1, (unsigned char*)l.align_bit_weights, new_ldb, (unsigned char*)l.t_bit_input, new_ldb, c, n, l.mean_arr); - //free(t_bit_input); + // // alternative GEMM + //gemm_nn_bin_transposed_32bit_packed(m, n, new_k, 1, + // l.align_bit_weights, l.new_lda/32, + // t_bit_input, new_ldb / 32, + // c, n, l.mean_arr); - } - else - { // else (l.c % 32 != 0) + //free(t_bit_input); - //-------------------------------------------------------- - //printf(" l.index = %d - old XNOR \n", l.index); + } + else + { // else (l.c % 32 != 0) - //im2col_cpu_custom_align(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align); - im2col_cpu_custom_bin(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace, l.bit_align); + //-------------------------------------------------------- + //printf(" l.index = %d - old XNOR \n", l.index); - //size_t output_size = l.outputs; - //float *count_output = calloc(output_size, sizeof(float)); - //size_t bit_output_size = output_size / 8 + 1; - //char *bit_output = calloc(bit_output_size, sizeof(char)); + //im2col_cpu_custom_align(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align); + im2col_cpu_custom_bin(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, state.workspace, l.bit_align); - //size_t intput_size = n * k; // (out_h*out_w) X (l.size*l.size*l.c) : after im2col() - //size_t bit_input_size = intput_size / 8 + 1; - //char *bit_input = calloc(bit_input_size, sizeof(char)); + //size_t output_size = l.outputs; + //float *count_output = calloc(output_size, sizeof(float)); + //size_t bit_output_size = output_size / 8 + 1; + //char *bit_output = calloc(bit_output_size, sizeof(char)); - //size_t weights_size = k * m; //l.size*l.size*l.c*l.n; - //size_t bit_weights_size = weights_size / 8 + 1; + //size_t intput_size = n * k; // (out_h*out_w) X (l.size*l.size*l.c) : after im2col() + //size_t bit_input_size = intput_size / 8 + 1; + //char *bit_input = calloc(bit_input_size, sizeof(char)); - //char *bit_weights = calloc(bit_weights_size, sizeof(char)); - //float *mean_arr = calloc(l.n, sizeof(float)); + //size_t weights_size = k * m; //l.size*l.size*l.c*l.n; + //size_t bit_weights_size = weights_size / 8 + 1; - // transpose B from NxK to KxN (x-axis (ldb = l.size*l.size*l.c) - should be multiple of 8 bits) - { - //size_t ldb_align = 256; // 256 bit for AVX2 - int ldb_align = l.lda_align; - size_t new_ldb = k + (ldb_align - k%ldb_align); - size_t t_intput_size = binary_transpose_align_input(k, n, state.workspace, &l.t_bit_input, ldb_align, l.bit_align); + //char *bit_weights = calloc(bit_weights_size, sizeof(char)); + //float *mean_arr = calloc(l.n, sizeof(float)); - // 5x times faster than gemm()-float32 - gemm_nn_custom_bin_mean_transposed(m, n, k, 1, (unsigned char*)l.align_bit_weights, new_ldb, (unsigned char*)l.t_bit_input, new_ldb, c, n, l.mean_arr); + // transpose B from NxK to KxN (x-axis (ldb = l.size*l.size*l.c) - should be multiple of 8 bits) + { + //size_t ldb_align = 256; // 256 bit for AVX2 + int ldb_align = l.lda_align; + size_t new_ldb = k + (ldb_align - k%ldb_align); + size_t t_intput_size = binary_transpose_align_input(k, n, state.workspace, &l.t_bit_input, ldb_align, l.bit_align); - //gemm_nn_custom_bin_mean_transposed(m, n, k, 1, bit_weights, k, t_bit_input, new_ldb, c, n, mean_arr); + // 5x times faster than gemm()-float32 + gemm_nn_custom_bin_mean_transposed(m, n, k, 1, (unsigned char*)l.align_bit_weights, new_ldb, (unsigned char*)l.t_bit_input, new_ldb, c, n, l.mean_arr); - //free(t_input); - //free(t_bit_input); - //} - } + //gemm_nn_custom_bin_mean_transposed(m, n, k, 1, bit_weights, k, t_bit_input, new_ldb, c, n, mean_arr); - } + //free(t_input); + //free(t_bit_input); + //} + } - add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w); + } - //activate_array(l.output, m*n*l.batch, l.activation); - activate_array_cpu_custom(l.output, m*n*l.batch, l.activation); - return; + add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w); - } - else { - //printf(" l.index = %d - FP32 \n", l.index); - im2col_cpu_custom(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b); + //activate_array(l.output, m*n*l.batch, l.activation); + activate_array_cpu_custom(l.output, m*n*l.batch, l.activation); + return; - gemm(0, 0, m, n, k, 1, a, k, b, n, 1, c, n); - // bit-count to float + } + else { + //printf(" l.index = %d - FP32 \n", l.index); + im2col_cpu(state.input + (i*l.groups + j)*l.c / l.groups*l.h*l.w, + l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, b); + + gemm(0, 0, m, n, k, 1, a, k, b, n, 1, c, n); + // bit-count to float + } + c += n*m; + state.input += l.c*l.h*l.w; } - c += n*m; - state.input += l.c*l.h*l.w; } if(l.batch_normalize){ @@ -986,63 +998,72 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w); //activate_array(l.output, m*n*l.batch, l.activation); - activate_array_cpu_custom(l.output, m*n*l.batch, l.activation); + activate_array_cpu_custom(l.output, l.outputs*l.batch, l.activation); if(l.binary || l.xnor) swap_binary(&l); } + void backward_convolutional_layer(convolutional_layer l, network_state state) { - int i; - int m = l.n; - int n = l.size*l.size*l.c; - int k = convolutional_out_height(l)* - convolutional_out_width(l); + int i, j; + int m = l.n / l.groups; + int n = l.size*l.size*l.c / l.groups; + int k = l.out_w*l.out_h; - gradient_array(l.output, m*k*l.batch, l.activation, l.delta); - backward_bias(l.bias_updates, l.delta, l.batch, l.n, k); + gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); - if(l.batch_normalize){ + if (l.batch_normalize) { backward_batchnorm_layer(l, state); } + else { + backward_bias(l.bias_updates, l.delta, l.batch, l.n, k); + } - for(i = 0; i < l.batch; ++i){ - float *a = l.delta + i*m*k; - float *b = state.workspace; - float *c = l.weight_updates; + for (i = 0; i < l.batch; ++i) { + for (j = 0; j < l.groups; ++j) { + float *a = l.delta + (i*l.groups + j)*m*k; + float *b = state.workspace; + float *c = l.weight_updates + j*l.nweights / l.groups; - float *im = state.input+i*l.c*l.h*l.w; + float *im = state.input + (i*l.groups + j)*l.c / l.groups*l.h*l.w; - im2col_cpu(im, l.c, l.h, l.w, + im2col_cpu(im, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, b); - gemm(0,1,m,n,k,1,a,k,b,k,1,c,n); + gemm(0, 1, m, n, k, 1, a, k, b, k, 1, c, n); - if(state.delta){ - a = l.weights; - b = l.delta + i*m*k; - c = state.workspace; + if (state.delta) { + a = l.weights + j*l.nweights / l.groups; + b = l.delta + (i*l.groups + j)*m*k; + c = state.workspace; - gemm(1,0,n,k,m,1,a,n,b,k,0,c,k); + gemm(1, 0, n, k, m, 1, a, n, b, k, 0, c, k); - col2im_cpu(state.workspace, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta+i*l.c*l.h*l.w); + col2im_cpu(state.workspace, l.c / l.groups, l.h, l.w, l.size, l.stride, + l.pad, state.delta + (i*l.groups + j)*l.c / l.groups*l.h*l.w); + } } } } -void update_convolutional_layer(convolutional_layer l, int batch, float learning_rate, float momentum, float decay) +void update_convolutional_layer(convolutional_layer l, update_args a) { - int size = l.size*l.size*l.c*l.n; - axpy_cpu(l.n, learning_rate/batch, l.bias_updates, 1, l.biases, 1); + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; + + axpy_cpu(l.n, learning_rate / batch, l.bias_updates, 1, l.biases, 1); scal_cpu(l.n, momentum, l.bias_updates, 1); - if(l.scales){ - axpy_cpu(l.n, learning_rate/batch, l.scale_updates, 1, l.scales, 1); + if (l.scales) { + axpy_cpu(l.n, learning_rate / batch, l.scale_updates, 1, l.scales, 1); scal_cpu(l.n, momentum, l.scale_updates, 1); } - axpy_cpu(size, -decay*batch, l.weights, 1, l.weight_updates, 1); - axpy_cpu(size, learning_rate/batch, l.weight_updates, 1, l.weights, 1); - scal_cpu(size, momentum, l.weight_updates, 1); + axpy_cpu(l.nweights, -decay*batch, l.weights, 1, l.weight_updates, 1); + axpy_cpu(l.nweights, learning_rate / batch, l.weight_updates, 1, l.weights, 1); + scal_cpu(l.nweights, momentum, l.weight_updates, 1); } @@ -1050,14 +1071,14 @@ image get_convolutional_weight(convolutional_layer l, int i) { int h = l.size; int w = l.size; - int c = l.c; - return float_to_image(w,h,c,l.weights+i*h*w*c); + int c = l.c / l.groups; + return float_to_image(w, h, c, l.weights + i*h*w*c); } void rgbgr_weights(convolutional_layer l) { int i; - for(i = 0; i < l.n; ++i){ + for (i = 0; i < l.n; ++i) { image im = get_convolutional_weight(l, i); if (im.c == 3) { rgbgr_image(im); @@ -1068,7 +1089,7 @@ void rgbgr_weights(convolutional_layer l) void rescale_weights(convolutional_layer l, float scale, float trans) { int i; - for(i = 0; i < l.n; ++i){ + for (i = 0; i < l.n; ++i) { image im = get_convolutional_weight(l, i); if (im.c == 3) { scale_image(im, scale); @@ -1080,12 +1101,18 @@ void rescale_weights(convolutional_layer l, float scale, float trans) image *get_weights(convolutional_layer l) { - image* weights = (image*)calloc(l.n, sizeof(image)); + image *weights = calloc(l.n, sizeof(image)); int i; - for(i = 0; i < l.n; ++i){ + for (i = 0; i < l.n; ++i) { weights[i] = copy_image(get_convolutional_weight(l, i)); - //normalize_image(weights[i]); + normalize_image(weights[i]); + /* + char buff[256]; + sprintf(buff, "filter%d", i); + save_image(weights[i], buff); + */ } + //error("hey"); return weights; } @@ -1102,4 +1129,4 @@ image *visualize_convolutional_layer(convolutional_layer l, char *window, image //save_image(dc, buff); free_image(dc); return single_weights; -} +} \ No newline at end of file diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h index da7b8feb383..dc00dabf265 100644 --- a/src/convolutional_layer.h +++ b/src/convolutional_layer.h @@ -30,7 +30,7 @@ void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16); #endif size_t get_convolutional_workspace_size(layer l); -convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index); +convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index); void denormalize_convolutional_layer(convolutional_layer l); void resize_convolutional_layer(convolutional_layer *layer, int w, int h); void forward_convolutional_layer(const convolutional_layer layer, network_state state); diff --git a/src/crnn_layer.c b/src/crnn_layer.c index 80a0c7f8494..f466508ed81 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -26,7 +26,7 @@ static void increment_layer(layer *l, int steps) #endif } -layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int xnor) +layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int groups, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int xnor) { fprintf(stderr, "CRNN Layer: %d x %d x %d image, %d filters\n", h,w,c,output_filters); batch = batch / steps; @@ -40,6 +40,7 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.h = h; l.w = w; l.c = c; + l.groups = groups; l.out_c = output_filters; l.inputs = h * w * c; l.hidden = h * w * hidden_filters; @@ -48,17 +49,17 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.state = (float*)calloc(l.hidden * l.batch * (l.steps + 1), sizeof(float)); l.input_layer = (layer*)malloc(sizeof(layer)); - *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.input_layer->batch = batch; if (l.workspace_size < l.input_layer->workspace_size) l.workspace_size = l.input_layer->workspace_size; l.self_layer = (layer*)malloc(sizeof(layer)); - *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.self_layer->batch = batch; if (l.workspace_size < l.self_layer->workspace_size) l.workspace_size = l.self_layer->workspace_size; l.output_layer = (layer*)malloc(sizeof(layer)); - *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.output_layer->batch = batch; if (l.workspace_size < l.output_layer->workspace_size) l.workspace_size = l.output_layer->workspace_size; diff --git a/src/crnn_layer.h b/src/crnn_layer.h index 58944182b01..55feb599bb5 100644 --- a/src/crnn_layer.h +++ b/src/crnn_layer.h @@ -9,7 +9,7 @@ #ifdef __cplusplus extern "C" { #endif -layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int xnor); +layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int groups, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int xnor); void resize_crnn_layer(layer *l, int w, int h); void free_state_crnn(layer l); diff --git a/src/detector.c b/src/detector.c index a34354f16c5..4fc7458544c 100644 --- a/src/detector.c +++ b/src/detector.c @@ -42,19 +42,18 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i cuda_set_device(gpus[0]); printf(" Prepare additional network for mAP calculation...\n"); net_map = parse_network_cfg_custom(cfgfile, 1, 1); + const int net_classes = net_map.layers[net_map.n - 1].classes; int k; // free memory unnecessary arrays - for (k = 0; k < net_map.n; ++k) { - free_layer(net_map.layers[k]); - } + for (k = 0; k < net_map.n - 1; ++k) free_layer(net_map.layers[k]); char *name_list = option_find_str(options, "names", "data/names.list"); int names_size = 0; char **names = get_labels_custom(name_list, &names_size); - if (net_map.layers[net_map.n - 1].classes != names_size) { + if (net_classes != names_size) { printf(" Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", - name_list, names_size, net_map.layers[net_map.n - 1].classes, cfgfile); - if (net_map.layers[net_map.n - 1].classes > names_size) getchar(); + name_list, names_size, net_classes, cfgfile); + if (net_classes > names_size) getchar(); } } diff --git a/src/network.c b/src/network.c index b25aa6395b7..32f2b9e2b93 100644 --- a/src/network.c +++ b/src/network.c @@ -997,7 +997,7 @@ void fuse_conv_batchnorm(network net) { l->biases[f] = l->biases[f] - (double)l->scales[f] * l->rolling_mean[f] / (sqrt((double)l->rolling_variance[f]) + .000001f); - const size_t filter_size = l->size*l->size*l->c; + const size_t filter_size = l->size*l->size*l->c / l->groups; int i; for (i = 0; i < filter_size; ++i) { int w_index = f*filter_size + i; diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 6e50df0bff6..40f71eb0ba5 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -237,7 +237,7 @@ void pull_updates(layer l) { if(l.type == CONVOLUTIONAL){ cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.n); - cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.n*l.size*l.size*l.c); + cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.nweights); if(l.scale_updates) cuda_pull_array(l.scale_updates_gpu, l.scale_updates, l.n); } else if(l.type == CONNECTED){ cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.outputs); @@ -249,7 +249,7 @@ void push_updates(layer l) { if(l.type == CONVOLUTIONAL){ cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n); - cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.n*l.size*l.size*l.c); + cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.nweights); if(l.scale_updates) cuda_push_array(l.scale_updates_gpu, l.scale_updates, l.n); } else if(l.type == CONNECTED){ cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.outputs); @@ -271,7 +271,7 @@ void merge_weights(layer l, layer base) { if (l.type == CONVOLUTIONAL) { axpy_cpu(l.n, 1, l.biases, 1, base.biases, 1); - axpy_cpu(l.n*l.size*l.size*l.c, 1, l.weights, 1, base.weights, 1); + axpy_cpu(l.nweights, 1, l.weights, 1, base.weights, 1); if (l.scales) { axpy_cpu(l.n, 1, l.scales, 1, base.scales, 1); } @@ -285,7 +285,7 @@ void scale_weights(layer l, float s) { if (l.type == CONVOLUTIONAL) { scal_cpu(l.n, s, l.biases, 1); - scal_cpu(l.n*l.size*l.size*l.c, s, l.weights, 1); + scal_cpu(l.nweights, s, l.weights, 1); if (l.scales) { scal_cpu(l.n, s, l.scales, 1); } @@ -300,7 +300,7 @@ void pull_weights(layer l) { if(l.type == CONVOLUTIONAL){ cuda_pull_array(l.biases_gpu, l.biases, l.n); - cuda_pull_array(l.weights_gpu, l.weights, l.n*l.size*l.size*l.c); + cuda_pull_array(l.weights_gpu, l.weights, l.nweights); if(l.scales) cuda_pull_array(l.scales_gpu, l.scales, l.n); } else if(l.type == CONNECTED){ cuda_pull_array(l.biases_gpu, l.biases, l.outputs); @@ -312,7 +312,7 @@ void push_weights(layer l) { if(l.type == CONVOLUTIONAL){ cuda_push_array(l.biases_gpu, l.biases, l.n); - cuda_push_array(l.weights_gpu, l.weights, l.n*l.size*l.size*l.c); + cuda_push_array(l.weights_gpu, l.weights, l.nweights); if(l.scales) cuda_push_array(l.scales_gpu, l.scales, l.n); } else if(l.type == CONNECTED){ cuda_push_array(l.biases_gpu, l.biases, l.outputs); @@ -324,7 +324,7 @@ void distribute_weights(layer l, layer base) { if(l.type == CONVOLUTIONAL){ cuda_push_array(l.biases_gpu, base.biases, l.n); - cuda_push_array(l.weights_gpu, base.weights, l.n*l.size*l.size*l.c); + cuda_push_array(l.weights_gpu, base.weights, l.nweights); if(base.scales) cuda_push_array(l.scales_gpu, base.scales, l.n); } else if(l.type == CONNECTED){ cuda_push_array(l.biases_gpu, base.biases, l.outputs); @@ -337,7 +337,7 @@ void merge_updates(layer l, layer base) { if (l.type == CONVOLUTIONAL) { axpy_cpu(l.n, 1, l.bias_updates, 1, base.bias_updates, 1); - axpy_cpu(l.n*l.size*l.size*l.c, 1, l.weight_updates, 1, base.weight_updates, 1); + axpy_cpu(l.nweights, 1, l.weight_updates, 1, base.weight_updates, 1); if (l.scale_updates) { axpy_cpu(l.n, 1, l.scale_updates, 1, base.scale_updates, 1); } @@ -351,7 +351,7 @@ void distribute_updates(layer l, layer base) { if(l.type == CONVOLUTIONAL){ cuda_push_array(l.bias_updates_gpu, base.bias_updates, l.n); - cuda_push_array(l.weight_updates_gpu, base.weight_updates, l.n*l.size*l.size*l.c); + cuda_push_array(l.weight_updates_gpu, base.weight_updates, l.nweights); if(base.scale_updates) cuda_push_array(l.scale_updates_gpu, base.scale_updates, l.n); } else if(l.type == CONNECTED){ cuda_push_array(l.bias_updates_gpu, base.bias_updates, l.outputs); diff --git a/src/parser.c b/src/parser.c index 9ca3d5e2662..cca3641bfbc 100644 --- a/src/parser.c +++ b/src/parser.c @@ -150,6 +150,7 @@ local_layer parse_local(list *options, size_params params) convolutional_layer parse_convolutional(list *options, size_params params) { int n = option_find_int(options, "filters",1); + int groups = option_find_int_quiet(options, "groups", 1); int size = option_find_int(options, "size",1); int stride = option_find_int(options, "stride",1); int pad = option_find_int_quiet(options, "pad",0); @@ -170,7 +171,7 @@ convolutional_layer parse_convolutional(list *options, size_params params) int xnor = option_find_int_quiet(options, "xnor", 0); int use_bin_output = option_find_int_quiet(options, "bin_output", 0); - convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,size,stride,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index); + convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index); layer.flipped = option_find_int_quiet(options, "flipped", 0); layer.dot = option_find_float_quiet(options, "dot", 0); @@ -193,12 +194,13 @@ layer parse_crnn(list *options, size_params params) int output_filters = option_find_int(options, "output",1); int hidden_filters = option_find_int(options, "hidden",1); + int groups = option_find_int_quiet(options, "groups", 1); char *activation_s = option_find_str(options, "activation", "logistic"); ACTIVATION activation = get_activation(activation_s); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); int xnor = option_find_int_quiet(options, "xnor", 0); - layer l = make_crnn_layer(params.batch, params.w, params.h, params.c, hidden_filters, output_filters, params.time_steps, size, stride, padding, activation, batch_normalize, xnor); + layer l = make_crnn_layer(params.batch, params.w, params.h, params.c, hidden_filters, output_filters, groups, params.time_steps, size, stride, padding, activation, batch_normalize, xnor); l.shortcut = option_find_int_quiet(options, "shortcut", 0); @@ -251,13 +253,14 @@ layer parse_conv_lstm(list *options, size_params params) if (pad) padding = size / 2; int output_filters = option_find_int(options, "output", 1); + int groups = option_find_int_quiet(options, "groups", 1); char *activation_s = option_find_str(options, "activation", "LINEAR"); ACTIVATION activation = get_activation(activation_s); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); int xnor = option_find_int_quiet(options, "xnor", 0); int peephole = option_find_int_quiet(options, "peephole", 1); - layer l = make_conv_lstm_layer(params.batch, params.w, params.h, params.c, output_filters, params.time_steps, size, stride, padding, activation, batch_normalize, peephole, xnor); + layer l = make_conv_lstm_layer(params.batch, params.w, params.h, params.c, output_filters, groups, params.time_steps, size, stride, padding, activation, batch_normalize, peephole, xnor); l.shortcut = option_find_int_quiet(options, "shortcut", 0); @@ -989,8 +992,8 @@ void save_convolutional_weights_binary(layer l, FILE *fp) pull_convolutional_layer(l); } #endif - binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights); - int size = l.c*l.size*l.size; + int size = (l.c/l.groups)*l.size*l.size; + binarize_weights(l.weights, l.n, size, l.binary_weights); int i, j, k; fwrite(l.biases, sizeof(float), l.n, fp); if (l.batch_normalize){ @@ -1025,7 +1028,7 @@ void save_convolutional_weights(layer l, FILE *fp) pull_convolutional_layer(l); } #endif - int num = l.n*l.c*l.size*l.size; + int num = l.nweights; fwrite(l.biases, sizeof(float), l.n, fp); if (l.batch_normalize){ fwrite(l.scales, sizeof(float), l.n, fp); @@ -1209,7 +1212,7 @@ void load_convolutional_weights_binary(layer l, FILE *fp) fread(l.rolling_mean, sizeof(float), l.n, fp); fread(l.rolling_variance, sizeof(float), l.n, fp); } - int size = l.c*l.size*l.size; + int size = (l.c / l.groups)*l.size*l.size; int i, j, k; for(i = 0; i < l.n; ++i){ float mean = 0; @@ -1237,7 +1240,7 @@ void load_convolutional_weights(layer l, FILE *fp) //load_convolutional_weights_binary(l, fp); //return; } - int num = l.n*l.c*l.size*l.size; + int num = l.nweights; fread(l.biases, sizeof(float), l.n, fp); //fread(l.weights, sizeof(float), num, fp); // as in connected layer if (l.batch_normalize && (!l.dontloadscales)){ @@ -1267,9 +1270,9 @@ void load_convolutional_weights(layer l, FILE *fp) //} //if(l.c == 3) scal_cpu(num, 1./256, l.weights, 1); if (l.flipped) { - transpose_matrix(l.weights, l.c*l.size*l.size, l.n); + transpose_matrix(l.weights, (l.c/l.groups)*l.size*l.size, l.n); } - //if (l.binary) binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.weights); + //if (l.binary) binarize_weights(l.weights, l.n, (l.c/l.groups)*l.size*l.size, l.weights); #ifdef GPU if(gpu_index >= 0){ push_convolutional_layer(l); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index d303b5aa20b..ae48ef7ae07 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -240,6 +240,7 @@ void forward_yolo_layer(const layer l, network_state state) int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; if (class_id >= l.classes) { printf(" Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1); + printf(" truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f, class_id = %d \n", truth.x, truth.y, truth.w, truth.h, class_id); getchar(); continue; // if label contains class_id more than number of classes in the cfg-file } @@ -271,6 +272,9 @@ void forward_yolo_layer(const layer l, network_state state) } for(t = 0; t < l.max_boxes; ++t){ box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); + if (truth.x < 0 || truth.y < 0 || truth.x > 1 || truth.y > 1 || truth.w < 0 || truth.h < 0) { + printf(" Wrong label: truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f \n", truth.x, truth.y, truth.w, truth.h); + } int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; if (class_id >= l.classes) continue; // if label contains class_id more than number of classes in the cfg-file From f27f7cc5d09af1b17bea655dee425b2bcd3f61aa Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Mon, 13 May 2019 00:05:27 +0200 Subject: [PATCH 0485/1365] move towards using only modern cmake --- CMakeLists.txt | 32 +++++++++++++++++++------------- DarknetConfig.cmake.in | 1 - 2 files changed, 19 insertions(+), 14 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 0fa6adc7a88..05b56e89b8d 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -36,6 +36,7 @@ set(INSTALL_CMAKE_DIR "share/darknet" CACHE PATH "Path where cmak set(ENABLE_CUDA "TRUE" CACHE BOOL "Enable CUDA build") set(ENABLE_CUDNN "FALSE" CACHE BOOL "Enable CUDNN") +set(ENABLE_CUDNN_HALF "FALSE" CACHE BOOL "Enable CUDNN Half precision") set(ENABLE_OPENMP "TRUE" CACHE BOOL "Enable OpenMP") if(${CMAKE_VERSION} VERSION_LESS "3.8.0") set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA build" FORCE) @@ -120,11 +121,9 @@ if(OpenCV_FOUND) message(STATUS " -> darknet is fine for now, but uselib_track has been disabled!") message(STATUS " -> Please rebuild OpenCV from sources with CUDA support to enable it") endif() - add_definitions(-DOPENCV) endif() if(ENABLE_CUDA) - add_definitions(-DGPU) if(NOT WIN32) include_directories("${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES}") link_directories("${CMAKE_CUDA_IMPLICIT_LINK_DIRECTORIES}") @@ -135,10 +134,9 @@ if(ENABLE_CUDA) endif() find_package(CUDNN) if(CUDNN_FOUND) - add_definitions(-DCUDNN) set(ENABLE_CUDNN "TRUE" CACHE BOOL "Enable CUDNN" FORCE) if(CUDA_COMPUTE_MODEL VERSION_GREATER_EQUAL ${CUDA_COMPUTE_MODEL_70}) - add_definitions(-DCUDNN_HALF) + set(ENABLE_CUDNN_HALF "TRUE" CACHE BOOL "Enable CUDNN Half precision" FORCE) message(STATUS "Enabled CUDNN_HALF") endif() endif() @@ -202,9 +200,7 @@ if(${CMAKE_VERSION} VERSION_LESS "3.9.0") message(WARNING "To build with OpenMP support you need CMake 3.9.0+") else() find_package(OpenMP) - if(OPENMP_FOUND) - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") - else() + if(NOT OPENMP_FOUND) set(ENABLE_OPENMP "FALSE" CACHE BOOL "Enable OpenMP" FORCE) endif() endif() @@ -283,10 +279,6 @@ set_target_properties(dark PROPERTIES LINKER_LANGUAGE CXX) if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRACK) add_executable(uselib_track ${CMAKE_CURRENT_LIST_DIR}/src/yolo_console_dll.cpp) - target_compile_definitions(uselib_track PRIVATE TRACK_OPTFLOW=1) - set_target_properties(uselib_track PROPERTIES LINKER_LANGUAGE CXX) - target_include_directories(uselib_track PUBLIC $ $ $ $) - target_link_libraries(uselib_track PRIVATE ${OpenCV_LIBS}) endif() add_executable(uselib ${CMAKE_CURRENT_LIST_DIR}/src/yolo_console_dll.cpp) @@ -303,17 +295,25 @@ target_include_directories(uselib PUBLIC $ Date: Mon, 13 May 2019 17:23:02 +0200 Subject: [PATCH 0486/1365] move towards using only modern cmake - part 2 --- .gitignore | 1 + CMakeLists.txt | 186 ++++++++++++++++++++++++++----------------------- 2 files changed, 101 insertions(+), 86 deletions(-) diff --git a/.gitignore b/.gitignore index 142b185d2d4..f5d9a42a83a 100644 --- a/.gitignore +++ b/.gitignore @@ -7,6 +7,7 @@ *.so *.exe *.dll +*.lib mnist/ data/ caffe/ diff --git a/CMakeLists.txt b/CMakeLists.txt index 05b56e89b8d..3334bcfe333 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -30,24 +30,28 @@ set(CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/cmake/Modules/" ${CMAKE_MODULE_ set(CMAKE_INSTALL_PREFIX "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Install prefix" FORCE) set(INSTALL_BIN_DIR "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Path where exe and dll will be installed") -set(INSTALL_LIB_DIR "lib" CACHE PATH "Path where lib will be installed") +set(INSTALL_LIB_DIR "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Path where lib will be installed") set(INSTALL_INCLUDE_DIR "include/darknet" CACHE PATH "Path where headers will be installed") set(INSTALL_CMAKE_DIR "share/darknet" CACHE PATH "Path where cmake configs will be installed") -set(ENABLE_CUDA "TRUE" CACHE BOOL "Enable CUDA build") +set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA support") set(ENABLE_CUDNN "FALSE" CACHE BOOL "Enable CUDNN") set(ENABLE_CUDNN_HALF "FALSE" CACHE BOOL "Enable CUDNN Half precision") -set(ENABLE_OPENMP "TRUE" CACHE BOOL "Enable OpenMP") + if(${CMAKE_VERSION} VERSION_LESS "3.8.0") - set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA build" FORCE) + set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA support" FORCE) message(WARNING "To build with CUDA support you need CMake 3.8.0+") else() include(CheckLanguage) check_language(CUDA) if(CMAKE_CUDA_COMPILER) enable_language(CUDA) - else() - set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA build" FORCE) + find_package(CUDA REQUIRED) + if(CUDA_VERSION VERSION_LESS "9.0") + message(STATUS "Unsupported CUDA version, please upgrade to CUDA 9+. Disabling CUDA support") + else() + set(ENABLE_CUDA "TRUE" CACHE BOOL "Enable CUDA support" FORCE) + endif() endif() endif() @@ -77,42 +81,53 @@ endif() if(USE_INTEGRATED_LIBS) set(PThreads_windows_DIR ${CMAKE_CURRENT_LIST_DIR}/3rdparty/pthreads CACHE PATH "Path where pthreads for windows can be located") - add_definitions(-D_TIMESPEC_DEFINED) endif() set(Stb_DIR ${CMAKE_CURRENT_LIST_DIR}/3rdparty/stb CACHE PATH "Path where Stb image library can be located") set(CMAKE_DEBUG_POSTFIX d) -add_definitions(-DUSE_CMAKE_LIBS) - +set(CMAKE_THREAD_PREFER_PTHREAD ON) +find_package(Threads REQUIRED) if(MSVC) - set(CMAKE_CXX_FLAGS "/wd4013 /wd4018 /wd4028 /wd4047 /wd4068 /wd4090 /wd4101 /wd4113 /wd4133 /wd4190 /wd4244 /wd4267 /wd4305 /wd4477 /wd4996 /wd4819 /fp:fast ${CMAKE_CXX_FLAGS}") - string(REGEX REPLACE "/O2" "/Ox" CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) - add_definitions(-D_CRT_RAND_S) - add_definitions(-DNOMINMAX) - add_definitions(-D_USE_MATH_DEFINES) + find_package(PThreads_windows REQUIRED) +endif() +find_package(OpenCV) +find_package(Stb REQUIRED) +if(${CMAKE_VERSION} VERSION_LESS "3.9.0") + message(WARNING "To build with OpenMP support you need CMake 3.9.0+") +else() + find_package(OpenMP) endif() -if(CMAKE_COMPILER_IS_GNUCC) - set(CMAKE_CXX_FLAGS "-Wno-unused-result -Wno-write-strings ${CMAKE_CXX_FLAGS}") - string(REGEX REPLACE "-O0" "-Og" CMAKE_CXX_FLAGS_DEBUG ${CMAKE_CXX_FLAGS_DEBUG}) - string(REGEX REPLACE "-O3" "-Ofast" CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) - set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a") +set(ADDITIONAL_CXX_FLAGS "-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -Wno-write-strings") +set(ADDITIONAL_C_FLAGS "-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -Wno-write-strings") + +if(MSVC) + set(ADDITIONAL_CXX_FLAGS "/wd4013 /wd4018 /wd4028 /wd4047 /wd4068 /wd4090 /wd4101 /wd4113 /wd4133 /wd4190 /wd4244 /wd4267 /wd4305 /wd4477 /wd4996 /wd4819 /fp:fast") + set(ADDITIONAL_C_FLAGS "/wd4013 /wd4018 /wd4028 /wd4047 /wd4068 /wd4090 /wd4101 /wd4113 /wd4133 /wd4190 /wd4244 /wd4267 /wd4305 /wd4477 /wd4996 /wd4819 /fp:fast") + set(CMAKE_CXX_FLAGS "${ADDITIONAL_CXX_FLAGS} ${CMAKE_CXX_FLAGS}") + set(CMAKE_C_FLAGS "${ADDITIONAL_C_FLAGS} ${CMAKE_C_FLAGS}") + string(REGEX REPLACE "/O2" "/Ox" CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) + string(REGEX REPLACE "/O2" "/Ox" CMAKE_C_FLAGS_RELEASE ${CMAKE_C_FLAGS_RELEASE}) endif() -if ("${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang") - if (UNIX AND NOT APPLE) - set(CMAKE_CXX_FLAGS "-pthread ${CMAKE_CXX_FLAGS}") #force pthread to avoid bugs in some cmake setups +if(CMAKE_COMPILER_IS_GNUCC OR "${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang") + if ("${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang") + if (UNIX AND NOT APPLE) + set(CMAKE_CXX_FLAGS "-pthread ${CMAKE_CXX_FLAGS}") #force pthread to avoid bugs in some cmake setups + set(CMAKE_C_FLAGS "-pthread ${CMAKE_C_FLAGS}") + endif() endif() - set(CMAKE_CXX_FLAGS "-Wno-deprecated -Wno-writable-strings ${CMAKE_CXX_FLAGS}") + set(CMAKE_CXX_FLAGS "${ADDITIONAL_CXX_FLAGS} ${CMAKE_CXX_FLAGS}") + set(CMAKE_C_FLAGS "${ADDITIONAL_C_FLAGS} ${CMAKE_C_FLAGS}") string(REGEX REPLACE "-O0" "-Og" CMAKE_CXX_FLAGS_DEBUG ${CMAKE_CXX_FLAGS_DEBUG}) string(REGEX REPLACE "-O3" "-Ofast" CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) + string(REGEX REPLACE "-O0" "-Og" CMAKE_C_FLAGS_DEBUG ${CMAKE_C_FLAGS_DEBUG}) + string(REGEX REPLACE "-O3" "-Ofast" CMAKE_C_FLAGS_RELEASE ${CMAKE_C_FLAGS_RELEASE}) + set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a") + set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a") endif() -set(ENABLE_OPENCV "TRUE" CACHE BOOL "Enable OpenCV integration") -if(ENABLE_OPENCV) - find_package(OpenCV) -endif() -find_package(Stb REQUIRED) +string(REPLACE " " "," ADDITIONAL_CXX_FLAGS_COMMA_SEPARATED "${ADDITIONAL_CXX_FLAGS}") set(SKIP_USELIB_TRACK "FALSE" CACHE BOOL "Skip building uselib_track" FORCE) if(OpenCV_FOUND) @@ -124,13 +139,9 @@ if(OpenCV_FOUND) endif() if(ENABLE_CUDA) - if(NOT WIN32) - include_directories("${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES}") - link_directories("${CMAKE_CUDA_IMPLICIT_LINK_DIRECTORIES}") - endif() - if(CUDA_COMPUTE_MODEL VERSION_GREATER_EQUAL ${CUDA_COMPUTE_MODEL_75} AND CMAKE_CUDA_COMPILER_VERSION VERSION_LESS_EQUAL 9) - message(STATUS "CUDA 10 is required for Compute Model 7.5 - Lowering settings to 7.0") - set(CUDA_COMPUTE_MODEL ${CUDA_COMPUTE_MODEL_70}) + if(CUDA_COMPUTE_MODEL VERSION_GREATER_EQUAL ${CUDA_COMPUTE_MODEL_75} AND CUDA_VERSION VERSION_LESS_EQUAL "9.2") + message(STATUS "CUDA 10 is required for Compute Model 7.5 - Lowering settings to 7.2") + set(CUDA_COMPUTE_MODEL ${CUDA_COMPUTE_MODEL_72}) endif() find_package(CUDNN) if(CUDNN_FOUND) @@ -140,32 +151,33 @@ if(ENABLE_CUDA) message(STATUS "Enabled CUDNN_HALF") endif() endif() + set(CUDA_COMPUTECAPABILITY_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}]") if (MSVC) if(CUDNN_FOUND) if(OpenCV_FOUND) - set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/wd4819,/DGPU,/DCUDNN,/DOPENCV\" ${CMAKE_CUDA_FLAGS}") + set(CMAKE_CUDA_FLAGS "${CUDA_COMPUTECAPABILITY_FLAGS} -Wno-deprecated-declarations -Xcompiler=\"${ADDITIONAL_CXX_FLAGS_COMMA_SEPARATED},/DGPU,/DCUDNN,/DOPENCV\" ${CMAKE_CUDA_FLAGS}") else() - set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/wd4819,/DGPU,/DCUDNN\" ${CMAKE_CUDA_FLAGS}") + set(CMAKE_CUDA_FLAGS "${CUDA_COMPUTECAPABILITY_FLAGS} -Wno-deprecated-declarations -Xcompiler=\"${ADDITIONAL_CXX_FLAGS_COMMA_SEPARATED},/DGPU,/DCUDNN\" ${CMAKE_CUDA_FLAGS}") endif() else() if(OpenCV_FOUND) - set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/wd4819,/DGPU,/DOPENCV\" ${CMAKE_CUDA_FLAGS}") + set(CMAKE_CUDA_FLAGS "${CUDA_COMPUTECAPABILITY_FLAGS} -Wno-deprecated-declarations -Xcompiler=\"${ADDITIONAL_CXX_FLAGS_COMMA_SEPARATED},/DGPU,/DOPENCV\" ${CMAKE_CUDA_FLAGS}") else() - set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] -Wno-deprecated-declarations -Xcompiler=\"/wd4028,/wd4190,/wd4244,/wd4267,/wd4305,/wd4477,/wd4996,/wd4819,/DGPU\" ${CMAKE_CUDA_FLAGS}") + set(CMAKE_CUDA_FLAGS "${CUDA_COMPUTECAPABILITY_FLAGS} -Wno-deprecated-declarations -Xcompiler=\"${ADDITIONAL_CXX_FLAGS_COMMA_SEPARATED},/DGPU\" ${CMAKE_CUDA_FLAGS}") endif() endif() else() if(CUDNN_FOUND) if(OpenCV_FOUND) - set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] --compiler-options \"-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -fPIC -fopenmp -Ofast -DOPENCV -DGPU -DCUDNN \" ${CMAKE_CUDA_FLAGS}") + set(CMAKE_CUDA_FLAGS "${CUDA_COMPUTECAPABILITY_FLAGS} --compiler-options \"${ADDITIONAL_CXX_FLAGS} -fPIC -fopenmp -Ofast -DOPENCV -DGPU -DCUDNN \" ${CMAKE_CUDA_FLAGS}") else() - set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] --compiler-options \"-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -fPIC -fopenmp -Ofast -DGPU -DCUDNN \" ${CMAKE_CUDA_FLAGS}") + set(CMAKE_CUDA_FLAGS "${CUDA_COMPUTECAPABILITY_FLAGS} --compiler-options \"${ADDITIONAL_CXX_FLAGS} -fPIC -fopenmp -Ofast -DGPU -DCUDNN \" ${CMAKE_CUDA_FLAGS}") endif() else() if(OpenCV_FOUND) - set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] --compiler-options \"-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -fPIC -fopenmp -Ofast -DOPENCV -DGPU \" ${CMAKE_CUDA_FLAGS}") + set(CMAKE_CUDA_FLAGS "${CUDA_COMPUTECAPABILITY_FLAGS} --compiler-options \"${ADDITIONAL_CXX_FLAGS} -fPIC -fopenmp -Ofast -DOPENCV -DGPU \" ${CMAKE_CUDA_FLAGS}") else() - set(CMAKE_CUDA_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}] --compiler-options \"-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -fPIC -fopenmp -Ofast -DGPU \" ${CMAKE_CUDA_FLAGS}") + set(CMAKE_CUDA_FLAGS "${CUDA_COMPUTECAPABILITY_FLAGS} --compiler-options \"${ADDITIONAL_CXX_FLAGS} -fPIC -fopenmp -Ofast -DGPU \" ${CMAKE_CUDA_FLAGS}") endif() endif() endif() @@ -178,7 +190,6 @@ if(ENABLE_CUDA) if(ZED_FOUND) include_directories(${ZED_INCLUDE_DIRS}) link_directories(${ZED_LIBRARY_DIR}) - add_definitions(-DZED_STEREO) message(STATUS "ZED SDK enabled") else() message(STATUS "ZED SDK not found") @@ -190,21 +201,6 @@ else() set(ENABLE_ZED_CAMERA "FALSE" CACHE BOOL "Enable ZED Camera support" FORCE) endif() -set(CMAKE_THREAD_PREFER_PTHREAD ON) -find_package(Threads REQUIRED) -if(MSVC) - find_package(PThreads_windows REQUIRED) -endif() - -if(${CMAKE_VERSION} VERSION_LESS "3.9.0") - message(WARNING "To build with OpenMP support you need CMake 3.9.0+") -else() - find_package(OpenMP) - if(NOT OPENMP_FOUND) - set(ENABLE_OPENMP "FALSE" CACHE BOOL "Enable OpenMP" FORCE) - endif() -endif() - set(DARKNET_INSTALL_INCLUDE_DIR ${INSTALL_INCLUDE_DIR}) # Make relative paths absolute (needed later on) foreach(p LIB BIN INCLUDE CMAKE) @@ -225,11 +221,13 @@ file(GLOB headers "${CMAKE_CURRENT_LIST_DIR}/src/*.h") list(APPEND headers ${CMAKE_CURRENT_LIST_DIR}/include/darknet.h ) -#remove windows only files, will be re-added if necessary later -list(REMOVE_ITEM headers - ${CMAKE_CURRENT_LIST_DIR}/src/gettimeofday.h - ${CMAKE_CURRENT_LIST_DIR}/src/getopt.h -) +#remove windows only files +if(NOT WIN32) + list(REMOVE_ITEM headers + ${CMAKE_CURRENT_LIST_DIR}/src/gettimeofday.h + ${CMAKE_CURRENT_LIST_DIR}/src/getopt.h + ) +endif() #set(exported_headers ${headers}) #look for all *.c files in src folder @@ -243,29 +241,19 @@ list(APPEND sources list(REMOVE_ITEM sources ${CMAKE_CURRENT_LIST_DIR}/src/darknet.c ) -#remove windows only files, will be re-added if necessary later -list(REMOVE_ITEM sources - ${CMAKE_CURRENT_LIST_DIR}/src/gettimeofday.c - ${CMAKE_CURRENT_LIST_DIR}/src/getopt.c -) - -if(ENABLE_CUDA) - file(GLOB cuda_sources "${CMAKE_CURRENT_LIST_DIR}/src/*.cu") -endif() - -if(WIN32) - list(APPEND sources +#remove windows only files +if(NOT WIN32) + list(REMOVE_ITEM sources ${CMAKE_CURRENT_LIST_DIR}/src/gettimeofday.c ${CMAKE_CURRENT_LIST_DIR}/src/getopt.c ) +endif() - list(APPEND headers - ${CMAKE_CURRENT_LIST_DIR}/src/gettimeofday.h - ${CMAKE_CURRENT_LIST_DIR}/src/getopt.h - ) +if(ENABLE_CUDA) + file(GLOB cuda_sources "${CMAKE_CURRENT_LIST_DIR}/src/*.cu") endif() -set_source_files_properties(${sources} PROPERTIES LANGUAGE CXX) +#set_source_files_properties(${sources} PROPERTIES LANGUAGE CXX) add_library(dark ${CMAKE_CURRENT_LIST_DIR}/include/yolo_v2_class.hpp ${CMAKE_CURRENT_LIST_DIR}/src/yolo_v2_class.cpp ${sources} ${headers} ${cuda_sources}) set_target_properties(dark PROPERTIES POSITION_INDEPENDENT_CODE ON) @@ -275,23 +263,27 @@ endif() if(BUILD_SHARED_LIBS) target_compile_definitions(dark PRIVATE LIB_EXPORTS=1) endif() -set_target_properties(dark PROPERTIES LINKER_LANGUAGE CXX) +#set_target_properties(dark PROPERTIES LINKER_LANGUAGE CXX) if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRACK) add_executable(uselib_track ${CMAKE_CURRENT_LIST_DIR}/src/yolo_console_dll.cpp) endif() add_executable(uselib ${CMAKE_CURRENT_LIST_DIR}/src/yolo_console_dll.cpp) -set_target_properties(uselib PROPERTIES LINKER_LANGUAGE CXX) +#set_target_properties(uselib PROPERTIES LINKER_LANGUAGE CXX) add_executable(darknet ${CMAKE_CURRENT_LIST_DIR}/src/darknet.c ${sources} ${headers} ${cuda_sources}) -set_source_files_properties(${CMAKE_CURRENT_LIST_DIR}/src/darknet.c PROPERTIES LANGUAGE CXX) -set_target_properties(darknet PROPERTIES LINKER_LANGUAGE CXX) +#set_source_files_properties(${CMAKE_CURRENT_LIST_DIR}/src/darknet.c PROPERTIES LANGUAGE CXX) +#set_target_properties(darknet PROPERTIES LINKER_LANGUAGE CXX) target_include_directories(darknet PUBLIC $ $ $ $) target_include_directories(dark PUBLIC $ $ $ $) target_include_directories(uselib PUBLIC $ $ $ $) +target_compile_definitions(darknet PRIVATE -DUSE_CMAKE_LIBS) +target_compile_definitions(dark PRIVATE -DUSE_CMAKE_LIBS) +target_compile_definitions(uselib PRIVATE -DUSE_CMAKE_LIBS) + if(CUDNN_FOUND) target_link_libraries(darknet PRIVATE CuDNN::CuDNN) target_link_libraries(dark PRIVATE CuDNN::CuDNN) @@ -313,7 +305,9 @@ endif() if(OPENMP_FOUND) target_link_libraries(darknet PUBLIC OpenMP::OpenMP_CXX) + target_link_libraries(darknet PUBLIC OpenMP::OpenMP_C) target_link_libraries(dark PUBLIC OpenMP::OpenMP_CXX) + target_link_libraries(dark PUBLIC OpenMP::OpenMP_C) endif() if(CMAKE_COMPILER_IS_GNUCC) @@ -326,6 +320,9 @@ if(MSVC) target_link_libraries(darknet PRIVATE wsock32 ws2_32) target_link_libraries(dark PRIVATE PThreads_windows::PThreads_windows) target_link_libraries(dark PRIVATE wsock32 ws2_32) + target_compile_definitions(darknet PRIVATE -D_CRT_RAND_S -DNOMINMAX -D_USE_MATH_DEFINES) + target_compile_definitions(dark PRIVATE -D_CRT_RAND_S -DNOMINMAX -D_USE_MATH_DEFINES) + target_compile_definitions(uselib PRIVATE -D_CRT_RAND_S -DNOMINMAX -D_USE_MATH_DEFINES) endif() target_link_libraries(darknet PRIVATE Threads::Threads) @@ -333,7 +330,11 @@ target_link_libraries(dark PRIVATE Threads::Threads) if(ENABLE_ZED_CAMERA) target_link_libraries(darknet PRIVATE ${ZED_LIBRARIES}) + target_link_libraries(dark PRIVATE ${ZED_LIBRARIES}) target_link_libraries(uselib PRIVATE ${ZED_LIBRARIES}) + target_compile_definitions(darknet PRIVATE -DZED_STEREO) + target_compile_definitions(uselib PRIVATE -DZED_STEREO) + target_compile_definitions(dark PRIVATE -DZED_STEREO) endif() if(ENABLE_CUDA) @@ -344,13 +345,26 @@ if(ENABLE_CUDA) target_compile_definitions(dark PUBLIC -DGPU) endif() +if(USE_INTEGRATED_LIBS) + target_compile_definitions(darknet PRIVATE -D_TIMESPEC_DEFINED) + target_compile_definitions(dark PRIVATE -D_TIMESPEC_DEFINED) +endif() + target_link_libraries(uselib PRIVATE dark) if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRACK) target_link_libraries(uselib_track PRIVATE dark) target_compile_definitions(uselib_track PRIVATE TRACK_OPTFLOW=1) - set_target_properties(uselib_track PROPERTIES LINKER_LANGUAGE CXX) + target_compile_definitions(uselib_track PRIVATE -DUSE_CMAKE_LIBS) + #set_target_properties(uselib_track PROPERTIES LINKER_LANGUAGE CXX) target_include_directories(uselib_track PRIVATE ${CMAKE_CURRENT_LIST_DIR}/include) target_link_libraries(uselib_track PRIVATE ${OpenCV_LIBS}) + if(ENABLE_ZED_CAMERA) + target_link_libraries(uselib_track PRIVATE ${ZED_LIBRARIES}) + target_compile_definitions(uselib_track PRIVATE -DZED_STEREO) + endif() + if(MSVC) + target_compile_definitions(uselib_track PRIVATE -D_CRT_RAND_S -DNOMINMAX -D_USE_MATH_DEFINES) + endif() endif() #set_target_properties(dark PROPERTIES PUBLIC_HEADER "${exported_headers};${CMAKE_CURRENT_LIST_DIR}/include/yolo_v2_class.hpp") From d21dc00c6eef8fff2f048d153b8bad4e15e8005d Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Mon, 13 May 2019 18:51:01 +0200 Subject: [PATCH 0487/1365] move towards using only modern cmake - part 3 --- CMakeLists.txt | 83 +++++++++++++----------------------------- DarknetConfig.cmake.in | 11 +----- 2 files changed, 27 insertions(+), 67 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 3334bcfe333..8d0384b3cd4 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -51,34 +51,16 @@ else() message(STATUS "Unsupported CUDA version, please upgrade to CUDA 9+. Disabling CUDA support") else() set(ENABLE_CUDA "TRUE" CACHE BOOL "Enable CUDA support" FORCE) + cuda_select_nvcc_arch_flags(CUDA_ARCH_FLAGS "Auto") + message(STATUS "Building with CUDA flags: " "${CUDA_ARCH_FLAGS}") + if ("arch=compute_75,code=sm_75" IN_LIST CUDA_ARCH_FLAGS) + set(ENABLE_CUDNN_HALF "TRUE" CACHE BOOL "Enable CUDNN Half precision" FORCE) + message(STATUS "Your setup supports half precision!") + endif() endif() endif() endif() -set(CUDA_COMPUTE_MODEL_75 "75") #Compute capability for Tesla T4, RTX 2080 -set(CUDA_COMPUTE_MODEL_72 "72") #Compute capability for Jetson Xavier -set(CUDA_COMPUTE_MODEL_70 "70") #Compute capability for Tesla V100 -set(CUDA_COMPUTE_MODEL_62 "62") #Compute capability for Jetson TX2 -set(CUDA_COMPUTE_MODEL_61 "61") #Compute capability for Tesla P40 -set(CUDA_COMPUTE_MODEL_60 "60") #Compute capability for Tesla P100 -set(CUDA_COMPUTE_MODEL_53 "53") #Compute capability for Jetson TX1 -set(CUDA_COMPUTE_MODEL_52 "52") #Compute capability for Tesla M40/M60 -set(CUDA_COMPUTE_MODEL_37 "37") #Compute capability for Tesla K80 -set(CUDA_COMPUTE_MODEL_35 "35") #Compute capability for Tesla K20/K40 -set(CUDA_COMPUTE_MODEL_30 "30") #Compute capability for Tesla K10, Quadro K4000 - -set(default_cuda_compute_model ${CUDA_COMPUTE_MODEL_30}) -if(ENABLE_CUDA AND NOT CUDA_COMPUTE_MODEL) - message(STATUS "Setting the compute model to ${default_cuda_compute_model} as none was specified.") - set(CUDA_COMPUTE_MODEL "${default_cuda_compute_model}" CACHE - STRING "Choose the best compute model supported by your GPU" FORCE) - set_property(CACHE CUDA_COMPUTE_MODEL PROPERTY STRINGS ${CUDA_COMPUTE_MODEL_75} ${CUDA_COMPUTE_MODEL_72} ${CUDA_COMPUTE_MODEL_70} ${CUDA_COMPUTE_MODEL_62} ${CUDA_COMPUTE_MODEL_61} ${CUDA_COMPUTE_MODEL_60} ${CUDA_COMPUTE_MODEL_53} ${CUDA_COMPUTE_MODEL_52} ${CUDA_COMPUTE_MODEL_37} ${CUDA_COMPUTE_MODEL_35} ${CUDA_COMPUTE_MODEL_30}) -endif() - -if(NOT ENABLE_CUDA) - set(CUDA_COMPUTE_MODEL "0" CACHE STRING "CUDA Compute Model set to 0 because CUDA is missing" FORCE) -endif() - if(USE_INTEGRATED_LIBS) set(PThreads_windows_DIR ${CMAKE_CURRENT_LIST_DIR}/3rdparty/pthreads CACHE PATH "Path where pthreads for windows can be located") endif() @@ -127,8 +109,6 @@ if(CMAKE_COMPILER_IS_GNUCC OR "${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang") set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a") endif() -string(REPLACE " " "," ADDITIONAL_CXX_FLAGS_COMMA_SEPARATED "${ADDITIONAL_CXX_FLAGS}") - set(SKIP_USELIB_TRACK "FALSE" CACHE BOOL "Skip building uselib_track" FORCE) if(OpenCV_FOUND) if(ENABLE_CUDA AND NOT OpenCV_CUDA_VERSION) @@ -139,48 +119,37 @@ if(OpenCV_FOUND) endif() if(ENABLE_CUDA) - if(CUDA_COMPUTE_MODEL VERSION_GREATER_EQUAL ${CUDA_COMPUTE_MODEL_75} AND CUDA_VERSION VERSION_LESS_EQUAL "9.2") - message(STATUS "CUDA 10 is required for Compute Model 7.5 - Lowering settings to 7.2") - set(CUDA_COMPUTE_MODEL ${CUDA_COMPUTE_MODEL_72}) - endif() find_package(CUDNN) if(CUDNN_FOUND) set(ENABLE_CUDNN "TRUE" CACHE BOOL "Enable CUDNN" FORCE) - if(CUDA_COMPUTE_MODEL VERSION_GREATER_EQUAL ${CUDA_COMPUTE_MODEL_70}) - set(ENABLE_CUDNN_HALF "TRUE" CACHE BOOL "Enable CUDNN Half precision" FORCE) - message(STATUS "Enabled CUDNN_HALF") - endif() endif() - set(CUDA_COMPUTECAPABILITY_FLAGS "-gencode arch=compute_${CUDA_COMPUTE_MODEL},code=[sm_${CUDA_COMPUTE_MODEL},compute_${CUDA_COMPUTE_MODEL}]") +endif() + +if(ENABLE_CUDA) if (MSVC) + set(ADDITIONAL_CXX_FLAGS "${ADDITIONAL_CXX_FLAGS} /DGPU") if(CUDNN_FOUND) - if(OpenCV_FOUND) - set(CMAKE_CUDA_FLAGS "${CUDA_COMPUTECAPABILITY_FLAGS} -Wno-deprecated-declarations -Xcompiler=\"${ADDITIONAL_CXX_FLAGS_COMMA_SEPARATED},/DGPU,/DCUDNN,/DOPENCV\" ${CMAKE_CUDA_FLAGS}") - else() - set(CMAKE_CUDA_FLAGS "${CUDA_COMPUTECAPABILITY_FLAGS} -Wno-deprecated-declarations -Xcompiler=\"${ADDITIONAL_CXX_FLAGS_COMMA_SEPARATED},/DGPU,/DCUDNN\" ${CMAKE_CUDA_FLAGS}") - endif() - else() - if(OpenCV_FOUND) - set(CMAKE_CUDA_FLAGS "${CUDA_COMPUTECAPABILITY_FLAGS} -Wno-deprecated-declarations -Xcompiler=\"${ADDITIONAL_CXX_FLAGS_COMMA_SEPARATED},/DGPU,/DOPENCV\" ${CMAKE_CUDA_FLAGS}") - else() - set(CMAKE_CUDA_FLAGS "${CUDA_COMPUTECAPABILITY_FLAGS} -Wno-deprecated-declarations -Xcompiler=\"${ADDITIONAL_CXX_FLAGS_COMMA_SEPARATED},/DGPU\" ${CMAKE_CUDA_FLAGS}") - endif() + set(ADDITIONAL_CXX_FLAGS "${ADDITIONAL_CXX_FLAGS} /DCUDNN") + endif() + if(OpenCV_FOUND) + set(ADDITIONAL_CXX_FLAGS "${ADDITIONAL_CXX_FLAGS} /DOPENCV") endif() + string(REPLACE " " "," ADDITIONAL_CXX_FLAGS_COMMA_SEPARATED "${ADDITIONAL_CXX_FLAGS}") + set(CUDA_HOST_COMPILER_FLAGS "-Wno-deprecated-declarations -Xcompiler=\"${ADDITIONAL_CXX_FLAGS_COMMA_SEPARATED}\"") else() + set(ADDITIONAL_CXX_FLAGS "${ADDITIONAL_CXX_FLAGS} -DGPU") if(CUDNN_FOUND) - if(OpenCV_FOUND) - set(CMAKE_CUDA_FLAGS "${CUDA_COMPUTECAPABILITY_FLAGS} --compiler-options \"${ADDITIONAL_CXX_FLAGS} -fPIC -fopenmp -Ofast -DOPENCV -DGPU -DCUDNN \" ${CMAKE_CUDA_FLAGS}") - else() - set(CMAKE_CUDA_FLAGS "${CUDA_COMPUTECAPABILITY_FLAGS} --compiler-options \"${ADDITIONAL_CXX_FLAGS} -fPIC -fopenmp -Ofast -DGPU -DCUDNN \" ${CMAKE_CUDA_FLAGS}") - endif() - else() - if(OpenCV_FOUND) - set(CMAKE_CUDA_FLAGS "${CUDA_COMPUTECAPABILITY_FLAGS} --compiler-options \"${ADDITIONAL_CXX_FLAGS} -fPIC -fopenmp -Ofast -DOPENCV -DGPU \" ${CMAKE_CUDA_FLAGS}") - else() - set(CMAKE_CUDA_FLAGS "${CUDA_COMPUTECAPABILITY_FLAGS} --compiler-options \"${ADDITIONAL_CXX_FLAGS} -fPIC -fopenmp -Ofast -DGPU \" ${CMAKE_CUDA_FLAGS}") - endif() + set(ADDITIONAL_CXX_FLAGS "${ADDITIONAL_CXX_FLAGS} -DCUDNN") endif() + if(OpenCV_FOUND) + set(ADDITIONAL_CXX_FLAGS "${ADDITIONAL_CXX_FLAGS} -DOPENCV") + endif() + set(CUDA_HOST_COMPILER_FLAGS "--compiler-options \" ${ADDITIONAL_CXX_FLAGS} -fPIC -fopenmp -Ofast \"") endif() + + string (REPLACE ";" " " CUDA_ARCH_FLAGS_SPACE_SEPARATED "${CUDA_ARCH_FLAGS}") + set(CMAKE_CUDA_FLAGS "${CUDA_ARCH_FLAGS_SPACE_SEPARATED} ${CUDA_HOST_COMPILER_FLAGS} ${CMAKE_CUDA_FLAGS}") + message(STATUS "CMAKE_CUDA_FLAGS: ${CMAKE_CUDA_FLAGS}") endif() set(ENABLE_ZED_CAMERA "TRUE" CACHE BOOL "Enable ZED Camera support") diff --git a/DarknetConfig.cmake.in b/DarknetConfig.cmake.in index dbd22112c2c..d0f471320ff 100644 --- a/DarknetConfig.cmake.in +++ b/DarknetConfig.cmake.in @@ -7,30 +7,21 @@ include(CMakeFindDependencyMacro) if(@ENABLE_OPENCV@) find_dependency(OpenCV) - add_definitions(-DOPENCV) endif() find_dependency(Stb) if(@ENABLE_CUDA@) - add_definitions(-DGPU) if(@ENABLE_CUDNN@) find_dependency(CUDNN) - add_definitions(-DCUDNN) - if(@CUDA_COMPUTE_MODEL@ VERSION_GREATER_EQUAL @CUDA_COMPUTE_MODEL_70@) - add_definitions(-DCUDNN_HALF) - message(STATUS "Enabled CUDNN_HALF") - endif() endif() endif() set(CMAKE_THREAD_PREFER_PTHREAD ON) find_dependency(Threads) -if(MSVC) +if(@MSVC@) find_dependency(PThreads_windows) - #add_definitions(-D_CRT_SECURE_NO_WARNINGS) - #set(CMAKE_CXX_FLAGS "/wd4018 /wd4244 /wd4267 /wd4305 ${CMAKE_CXX_FLAGS}") endif() if(@ENABLE_OPENMP@) From 920b29f15feb0ac533982495a074da718dc2dfe7 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Mon, 13 May 2019 22:56:03 +0200 Subject: [PATCH 0488/1365] fixes for ci and included files --- .travis.yml | 27 +++++++++++++++++++++++++++ CMakeLists.txt | 2 ++ build.ps1 | 19 ------------------- build.sh | 17 ----------------- include/darknet.h | 8 ++++---- src/activation_kernels.cu | 6 +++--- src/avgpool_layer_kernels.cu | 6 +++--- src/blas_kernels.cu | 8 ++++---- src/col2im_kernels.cu | 6 +++--- src/convolutional_kernels.cu | 6 +++--- src/crop_layer_kernels.cu | 6 +++--- src/deconvolutional_kernels.cu | 6 +++--- src/dropout_layer_kernels.cu | 6 +++--- src/maxpool_layer_kernels.cu | 6 +++--- 14 files changed, 61 insertions(+), 68 deletions(-) diff --git a/.travis.yml b/.travis.yml index 8834e2af54e..93a4f6bdbe8 100644 --- a/.travis.yml +++ b/.travis.yml @@ -668,6 +668,8 @@ matrix: env: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - CUDA_PATH=/usr/local/cuda-10.0 + - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" @@ -683,6 +685,8 @@ matrix: env: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - CUDA_PATH=/usr/local/cuda-10.0 + - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="export CC=clang-3.6 && export CXX=clang++-3.6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" @@ -700,6 +704,8 @@ matrix: env: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - CUDA_PATH=/usr/local/cuda-10.0 + - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="export CC=clang-3.8 && export CXX=clang++-3.8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" @@ -717,6 +723,8 @@ matrix: env: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - CUDA_PATH=/usr/local/cuda-10.0 + - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="export CC=clang-3.9 && export CXX=clang++-3.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" @@ -734,6 +742,8 @@ matrix: env: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - CUDA_PATH=/usr/local/cuda-10.0 + - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="export CC=clang-4.0 && export CXX=clang++-4.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" @@ -751,6 +761,8 @@ matrix: env: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - CUDA_PATH=/usr/local/cuda-10.0 + - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="export CC=clang-5.0 && export CXX=clang++-5.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" @@ -769,6 +781,8 @@ matrix: env: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - CUDA_PATH=/usr/local/cuda-10.0 + - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="export CC=clang-6.0 && export CXX=clang++-6.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" @@ -783,6 +797,8 @@ matrix: env: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - CUDA_PATH=/usr/local/cuda-10.0 + - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" @@ -800,6 +816,8 @@ matrix: env: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - CUDA_PATH=/usr/local/cuda-10.0 + - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="export CC=gcc-4.9 && export CXX=g++-4.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" @@ -817,6 +835,8 @@ matrix: env: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - CUDA_PATH=/usr/local/cuda-10.0 + - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="export CC=gcc-5 && export CXX=g++-5 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" @@ -834,6 +854,8 @@ matrix: env: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - CUDA_PATH=/usr/local/cuda-10.0 + - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="export CC=gcc-6 && export CXX=g++-6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" @@ -851,6 +873,8 @@ matrix: env: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - CUDA_PATH=/usr/local/cuda-10.0 + - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="export CC=gcc-7 && export CXX=g++-7 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" @@ -868,6 +892,8 @@ matrix: env: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc + - CUDA_PATH=/usr/local/cuda-10.0 + - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="export CC=gcc-8 && export CXX=g++-8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" @@ -887,6 +913,7 @@ matrix: - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - CUDA_PATH=/usr/local/cuda-10.0 + - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="export CC=gcc-8 && export CXX=g++-8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" diff --git a/CMakeLists.txt b/CMakeLists.txt index 8d0384b3cd4..9ebd7a361fb 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -307,6 +307,8 @@ if(ENABLE_ZED_CAMERA) endif() if(ENABLE_CUDA) + target_include_directories(darknet PRIVATE $ +#include +#include #ifdef CUDNN -#include "cudnn.h" +#include #endif #endif diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index 6c9445a6e7c..6bd86dcae37 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -1,7 +1,7 @@ #include "darknet.h" -#include "cuda_runtime.h" -#include "curand.h" -#include "cublas_v2.h" +#include +#include +#include #include "activations.h" #include "dark_cuda.h" diff --git a/src/avgpool_layer_kernels.cu b/src/avgpool_layer_kernels.cu index 676faa69809..b8cdd603d63 100644 --- a/src/avgpool_layer_kernels.cu +++ b/src/avgpool_layer_kernels.cu @@ -1,6 +1,6 @@ -#include "cuda_runtime.h" -#include "curand.h" -#include "cublas_v2.h" +#include +#include +#include #include "avgpool_layer.h" #include "dark_cuda.h" diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 2070bc1f029..4115669316c 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -1,6 +1,6 @@ -#include "cuda_runtime.h" -#include "curand.h" -#include "cublas_v2.h" +#include +#include +#include #include #include "blas.h" @@ -1021,4 +1021,4 @@ extern "C" int is_nan_or_inf(float *input, size_t size) CHECK_CUDA(cudaFreeHost(pinned_return)); return ret_val; -} \ No newline at end of file +} diff --git a/src/col2im_kernels.cu b/src/col2im_kernels.cu index 2f18a0fd3c4..a6e985f1132 100644 --- a/src/col2im_kernels.cu +++ b/src/col2im_kernels.cu @@ -1,6 +1,6 @@ -#include "cuda_runtime.h" -#include "curand.h" -#include "cublas_v2.h" +#include +#include +#include #include "col2im.h" #include "dark_cuda.h" diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index cfa4fe7e8fc..892ccc93443 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -1,6 +1,6 @@ -#include "cuda_runtime.h" -#include "curand.h" -#include "cublas_v2.h" +#include +#include +#include #include "convolutional_layer.h" #include "batchnorm_layer.h" diff --git a/src/crop_layer_kernels.cu b/src/crop_layer_kernels.cu index 46afca6c593..85783bcc39f 100644 --- a/src/crop_layer_kernels.cu +++ b/src/crop_layer_kernels.cu @@ -1,6 +1,6 @@ -#include "cuda_runtime.h" -#include "curand.h" -#include "cublas_v2.h" +#include +#include +#include #include "crop_layer.h" #include "utils.h" diff --git a/src/deconvolutional_kernels.cu b/src/deconvolutional_kernels.cu index 472b2247977..6af65ebbd46 100644 --- a/src/deconvolutional_kernels.cu +++ b/src/deconvolutional_kernels.cu @@ -1,6 +1,6 @@ -#include "cuda_runtime.h" -#include "curand.h" -#include "cublas_v2.h" +#include +#include +#include #include "convolutional_layer.h" #include "deconvolutional_layer.h" diff --git a/src/dropout_layer_kernels.cu b/src/dropout_layer_kernels.cu index ceba0fa38bc..d3af0ea614f 100644 --- a/src/dropout_layer_kernels.cu +++ b/src/dropout_layer_kernels.cu @@ -1,6 +1,6 @@ -#include "cuda_runtime.h" -#include "curand.h" -#include "cublas_v2.h" +#include +#include +#include #include "dropout_layer.h" #include "dark_cuda.h" diff --git a/src/maxpool_layer_kernels.cu b/src/maxpool_layer_kernels.cu index 36fccf322a3..341a5b8b81c 100644 --- a/src/maxpool_layer_kernels.cu +++ b/src/maxpool_layer_kernels.cu @@ -1,6 +1,6 @@ -#include "cuda_runtime.h" -#include "curand.h" -#include "cublas_v2.h" +#include +#include +#include #include "maxpool_layer.h" #include "dark_cuda.h" From d853658fcfe079d298b6dcef744082398cfd5f30 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 14 May 2019 09:46:18 +0200 Subject: [PATCH 0489/1365] fix symbols usage --- DarknetConfig.cmake.in | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/DarknetConfig.cmake.in b/DarknetConfig.cmake.in index d0f471320ff..169cfa9ba77 100644 --- a/DarknetConfig.cmake.in +++ b/DarknetConfig.cmake.in @@ -5,14 +5,14 @@ list(APPEND CMAKE_MODULE_PATH "${Darknet_CMAKE_DIR}") include(CMakeFindDependencyMacro) -if(@ENABLE_OPENCV@) +if(@OpenCV_FOUND@) find_dependency(OpenCV) endif() find_dependency(Stb) if(@ENABLE_CUDA@) - if(@ENABLE_CUDNN@) + if(@CUDNN_FOUND@) find_dependency(CUDNN) endif() endif() @@ -24,7 +24,7 @@ if(@MSVC@) find_dependency(PThreads_windows) endif() -if(@ENABLE_OPENMP@) +if(@OPENMP_FOUND@) find_dependency(OpenMP) endif() From 5e242dfe4d7cda92a236e65850923ee8ed027d82 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 14 May 2019 11:41:25 +0200 Subject: [PATCH 0490/1365] remove deprecated json file --- CMakeLists.txt | 4 +- CMakeSettings.json | 212 --------------------------------------------- 2 files changed, 2 insertions(+), 214 deletions(-) delete mode 100644 CMakeSettings.json diff --git a/CMakeLists.txt b/CMakeLists.txt index 9ebd7a361fb..319d9fe9388 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -307,8 +307,8 @@ if(ENABLE_ZED_CAMERA) endif() if(ENABLE_CUDA) - target_include_directories(darknet PRIVATE $ Date: Tue, 14 May 2019 14:35:22 +0300 Subject: [PATCH 0491/1365] Some conv-lstm, sgdr and other fixes --- README.md | 4 +-- include/darknet.h | 2 ++ src/activation_kernels.cu | 47 +++++++++++++++++++++++++- src/blas_kernels.cu | 2 +- src/conv_lstm_layer.c | 6 +++- src/convolutional_layer.c | 11 +++--- src/detector.c | 6 ++-- src/image_opencv.cpp | 2 +- src/network.c | 15 +++++++-- src/parser.c | 70 +++++++++++++++++++++------------------ 10 files changed, 114 insertions(+), 51 deletions(-) diff --git a/README.md b/README.md index 92cfe1a5518..47ec2752dd0 100644 --- a/README.md +++ b/README.md @@ -518,9 +518,9 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * increase network resolution in your `.cfg`-file (`height=608`, `width=608` or any value multiple of 32) - it will increase precision - * check that each object is mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: https://github.com/AlexeyAB/Yolo_mark + * check that each object that you want to detect is mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: https://github.com/AlexeyAB/Yolo_mark - * for each object which you want to detect - there must be at least 1 similar object in the Training dataset with about the same: shape, side of object, relative size, angle of rotation, tilt, illumination. So desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 different images for each class or more, and you should train `2000*classes` iterations or more + * for each object which you want to detect - there must be at least 1 similar object in the Training dataset with about the same: shape, side of object, relative size, angle of rotation, tilt, illumination. So desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 different images for each class or more, and you should train `2000*classes` iterations or more * desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty `.txt` files) - use as many images of negative samples as there are images with objects diff --git a/include/darknet.h b/include/darknet.h index fbd7e27d8a8..68ab1fc74b5 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -211,6 +211,7 @@ struct layer { int peephole; int use_bin_output; int steps; + int state_constrain; int hidden; int truth; float smooth; @@ -551,6 +552,7 @@ typedef struct network { float learning_rate_min; float learning_rate_max; int batches_per_cycle; + int batches_cycle_mult; float momentum; float decay; float gamma; diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index 6c9445a6e7c..4e5fa9f8c78 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -210,6 +210,30 @@ __global__ void activate_array_logistic_kernel(float *x, int n) } } +__global__ void activate_array_tanh_kernel(float *x, int n) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < n) { + x[index] = tanh_activate_kernel(x[index]); + } +} + +__global__ void activate_array_hardtan_kernel(float *x, int n) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < n) { + x[index] = hardtan_activate_kernel(x[index]); + } +} + +__global__ void activate_array_relu_kernel(float *x, int n) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < n) { + x[index] = relu_activate_kernel(x[index]); + } +} + __global__ void gradient_array_kernel(float *x, int n, ACTIVATION a, float *delta) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -240,6 +264,14 @@ __global__ void gradient_array_logistic_kernel(float *x, int n, float *delta) } } +__global__ void gradient_array_tanh_kernel(float *x, int n, float *delta) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < n) { + delta[index] *= tanh_gradient_kernel(x[index]); + } +} + __global__ void gradient_array_hardtan_kernel(float *x, int n, float *delta) { int index = blockIdx.x*blockDim.x + threadIdx.x; @@ -248,12 +280,23 @@ __global__ void gradient_array_hardtan_kernel(float *x, int n, float *delta) } } +__global__ void gradient_array_relu_kernel(float *x, int n, float *delta) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < n) { + delta[index] *= relu_gradient_kernel(x[index]); + } +} + extern "C" void activate_array_ongpu(float *x, int n, ACTIVATION a) { const int num_blocks = get_number_of_blocks(n, BLOCK); if (a == LINEAR) return; else if(a == LEAKY) activate_array_leaky_kernel << > >(x, n); else if (a == LOGISTIC) activate_array_logistic_kernel << > >(x, n); + else if (a == TANH) activate_array_tanh_kernel << > >(x, n); + else if (a == HARDTAN) activate_array_hardtan_kernel << > >(x, n); + else if (a == RELU) activate_array_relu_kernel << > >(x, n); else if (a == SELU) activate_array_selu_kernel << > >(x, n); else activate_array_kernel<<>>(x, n, a); @@ -266,8 +309,10 @@ extern "C" void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta if (a == LINEAR) return; else if (a == LEAKY) gradient_array_leaky_kernel << > >(x, n, delta); else if (a == LOGISTIC) gradient_array_logistic_kernel << > >(x, n, delta); - else if (a == SELU) gradient_array_selu_kernel << > >(x, n, delta); + else if (a == TANH) gradient_array_tanh_kernel << > >(x, n, delta); else if (a == HARDTAN) gradient_array_hardtan_kernel << > >(x, n, delta); + else if (a == RELU) gradient_array_relu_kernel << > >(x, n, delta); + else if (a == SELU) gradient_array_selu_kernel << > >(x, n, delta); else gradient_array_kernel << > > (x, n, a, delta); CHECK_CUDA(cudaPeekAtLastError()); diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 2070bc1f029..66905127e56 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -983,7 +983,7 @@ __global__ void fix_nan_and_inf_kernel(float *input, size_t size) if (index < size) { float val = input[index]; if (isnan(val) || isinf(val)) - input[index] = index; // pseudo random value + input[index] = 1.0f / index; // pseudo random value } } diff --git a/src/conv_lstm_layer.c b/src/conv_lstm_layer.c index 1764cb90f03..b51a3aed06c 100644 --- a/src/conv_lstm_layer.c +++ b/src/conv_lstm_layer.c @@ -902,13 +902,14 @@ void forward_conv_lstm_layer_gpu(layer l, network_state state) activate_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH); mul_ongpu(l.outputs*l.batch, l.o_gpu, 1, l.h_gpu, 1); + if(l.state_constrain) constrain_ongpu(l.outputs*l.batch, l.state_constrain, l.c_gpu, 1); //constrain_ongpu(l.outputs*l.batch, 1, l.c_gpu, 1); //constrain_ongpu(l.outputs*l.batch, 1, l.h_gpu, 1); fix_nan_and_inf(l.c_gpu, l.outputs*l.batch); fix_nan_and_inf(l.h_gpu, l.outputs*l.batch); copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.cell_gpu, 1); - copy_ongpu(l.outputs*l.batch, l.h_gpu, 1, l.output_gpu, 1); + copy_ongpu(l.outputs*l.batch, l.h_gpu, 1, l.output_gpu, 1); // required for both Detection and Training state.input += l.inputs*l.batch; l.output_gpu += l.outputs*l.batch; @@ -1159,5 +1160,8 @@ void backward_conv_lstm_layer_gpu(layer l, network_state state) copy_ongpu(l.outputs*l.batch, last_output, 1, l.last_prev_state_gpu, 1); copy_ongpu(l.outputs*l.batch, last_cell, 1, l.last_prev_cell_gpu, 1); + + // free state after each 100 iterations + //if (get_current_batch(state.net) % 100) free_state_conv_lstm(l); // dont use } #endif diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index f9c0ee01116..d983ab61725 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -1046,13 +1046,9 @@ void backward_convolutional_layer(convolutional_layer l, network_state state) } } -void update_convolutional_layer(convolutional_layer l, update_args a) +void update_convolutional_layer(convolutional_layer l, int batch, float learning_rate, float momentum, float decay) { - float learning_rate = a.learning_rate*l.learning_rate_scale; - float momentum = a.momentum; - float decay = a.decay; - int batch = a.batch; - + //int size = l.size*l.size*l.c*l.n; axpy_cpu(l.n, learning_rate / batch, l.bias_updates, 1, l.biases, 1); scal_cpu(l.n, momentum, l.bias_updates, 1); @@ -1067,6 +1063,7 @@ void update_convolutional_layer(convolutional_layer l, update_args a) } + image get_convolutional_weight(convolutional_layer l, int i) { int h = l.size; @@ -1101,7 +1098,7 @@ void rescale_weights(convolutional_layer l, float scale, float trans) image *get_weights(convolutional_layer l) { - image *weights = calloc(l.n, sizeof(image)); + image *weights = (image *)calloc(l.n, sizeof(image)); int i; for (i = 0; i < l.n; ++i) { weights[i] = copy_image(get_convolutional_weight(l, i)); diff --git a/src/detector.c b/src/detector.c index 4fc7458544c..14ad3618bdf 100644 --- a/src/detector.c +++ b/src/detector.c @@ -666,7 +666,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa char *train_images = option_find_str(options, "train", "data/train.txt"); valid_images = option_find_str(options, "valid", train_images); net = *existing_net; - //remember_network_recurrent_state(*existing_net); + remember_network_recurrent_state(*existing_net); free_network_recurrent_state(*existing_net); } else { @@ -1077,8 +1077,8 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa if (existing_net) { //set_batch_network(&net, initial_batch); //free_network_recurrent_state(*existing_net); - //restore_network_recurrent_state(*existing_net); - randomize_network_recurrent_state(*existing_net); + restore_network_recurrent_state(*existing_net); + //randomize_network_recurrent_state(*existing_net); } else { free_network(net); diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 1387b633045..d7e9a50b0d7 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1081,7 +1081,7 @@ void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_im cv::putText(img, char_buff, cv::Point(10, 28), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 5, CV_AA); cv::putText(img, char_buff, cv::Point(10, 28), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(200, 0, 0), 1, CV_AA); - if (((int)(old_precision * 10) != (int)(precision * 10)) || (max_precision < precision) || (current_batch - text_iteration_old) >= max_batches / 10) { + if ((std::fabs(old_precision - precision) > 0.1) || (max_precision < precision) || (current_batch - text_iteration_old) >= max_batches / 10) { text_iteration_old = current_batch; max_precision = std::max(max_precision, precision); sprintf(char_buff, "%2.0f%% ", precision * 100); diff --git a/src/network.c b/src/network.c index 32f2b9e2b93..a46d15a013d 100644 --- a/src/network.c +++ b/src/network.c @@ -95,7 +95,7 @@ float get_current_seq_subdivisions(network net) { int sequence_subdivisions = net.init_sequential_subdivisions; - if (net.policy) + if (net.num_steps > 0) { int batch_num = get_current_batch(net); int i; @@ -145,11 +145,20 @@ float get_current_rate(network net) case SIG: return net.learning_rate * (1./(1.+exp(net.gamma*(batch_num - net.step)))); case SGDR: + { + int last_iteration_start = 0; + int cycle_size = net.batches_per_cycle; + while ((last_iteration_start + cycle_size) < batch_num) + { + last_iteration_start += cycle_size; + cycle_size *= net.batches_cycle_mult; + } rate = net.learning_rate_min + - 0.5*(net.learning_rate-net.learning_rate_min) - * (1. + cos( (float) (batch_num % net.batches_per_cycle)*3.14159265 / net.batches_per_cycle)); + 0.5*(net.learning_rate - net.learning_rate_min) + * (1. + cos((float)(batch_num - last_iteration_start)*3.14159265 / cycle_size)); return rate; + } default: fprintf(stderr, "Policy is weird!\n"); return net.learning_rate; diff --git a/src/parser.c b/src/parser.c index cca3641bfbc..95c92250ebe 100644 --- a/src/parser.c +++ b/src/parser.c @@ -200,7 +200,7 @@ layer parse_crnn(list *options, size_params params) int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); int xnor = option_find_int_quiet(options, "xnor", 0); - layer l = make_crnn_layer(params.batch, params.w, params.h, params.c, hidden_filters, output_filters, groups, params.time_steps, size, stride, padding, activation, batch_normalize, xnor); + layer l = make_crnn_layer(params.batch, params.h, params.w, params.c, hidden_filters, output_filters, groups, params.time_steps, size, stride, padding, activation, batch_normalize, xnor); l.shortcut = option_find_int_quiet(options, "shortcut", 0); @@ -260,8 +260,9 @@ layer parse_conv_lstm(list *options, size_params params) int xnor = option_find_int_quiet(options, "xnor", 0); int peephole = option_find_int_quiet(options, "peephole", 1); - layer l = make_conv_lstm_layer(params.batch, params.w, params.h, params.c, output_filters, groups, params.time_steps, size, stride, padding, activation, batch_normalize, peephole, xnor); + layer l = make_conv_lstm_layer(params.batch, params.h, params.w, params.c, output_filters, groups, params.time_steps, size, stride, padding, activation, batch_normalize, peephole, xnor); + l.state_constrain = option_find_int_quiet(options, "state_constrain", params.time_steps * 32); l.shortcut = option_find_int_quiet(options, "shortcut", 0); return l; @@ -668,7 +669,8 @@ void parse_net_options(list *options, network *net) net->batch = option_find_int(options, "batch",1); net->learning_rate = option_find_float(options, "learning_rate", .001); net->learning_rate_min = option_find_float_quiet(options, "learning_rate_min", .00001); - net->batches_per_cycle = option_find_int_quiet(options, "sgdr_cycle", 500); + net->batches_per_cycle = option_find_int_quiet(options, "sgdr_cycle", 1000); + net->batches_cycle_mult = option_find_int_quiet(options, "sgdr_mult", 2); net->momentum = option_find_float(options, "momentum", .9); net->decay = option_find_float(options, "decay", .0001); int subdivs = option_find_int(options, "subdivisions",1); @@ -721,40 +723,44 @@ void parse_net_options(list *options, network *net) if(net->policy == STEP){ net->step = option_find_int(options, "step", 1); net->scale = option_find_float(options, "scale", 1); - } else if (net->policy == STEPS){ + } else if (net->policy == STEPS || net->policy == SGDR){ char *l = option_find(options, "steps"); char *p = option_find(options, "scales"); char *s = option_find(options, "seq_scales"); - if(!l || !p) error("STEPS policy must have steps and scales in cfg file"); + if(net->policy == STEPS && (!l || !p)) error("STEPS policy must have steps and scales in cfg file"); - int len = strlen(l); - int n = 1; - int i; - for(i = 0; i < len; ++i){ - if (l[i] == ',') ++n; - } - int* steps = (int*)calloc(n, sizeof(int)); - float* scales = (float*)calloc(n, sizeof(float)); - float* seq_scales = (float*)calloc(n, sizeof(float)); - for (i = 0; i < n; ++i) { - if (s) { - seq_scales[i] = atof(s); - s = strchr(s, ',') + 1; - } else - seq_scales[i] = 1; - } - for(i = 0; i < n; ++i){ - int step = atoi(l); - float scale = atof(p); - l = strchr(l, ',')+1; - p = strchr(p, ',')+1; - steps[i] = step; - scales[i] = scale; + if (l) { + int len = strlen(l); + int n = 1; + int i; + for (i = 0; i < len; ++i) { + if (l[i] == ',') ++n; + } + int* steps = (int*)calloc(n, sizeof(int)); + float* scales = (float*)calloc(n, sizeof(float)); + float* seq_scales = (float*)calloc(n, sizeof(float)); + for (i = 0; i < n; ++i) { + float scale = 1.0; + if (p) { + scale = atof(p); + p = strchr(p, ',') + 1; + } + float sequence_scale = 1.0; + if (s) { + sequence_scale = atof(s); + s = strchr(s, ',') + 1; + } + int step = atoi(l); + l = strchr(l, ',') + 1; + steps[i] = step; + scales[i] = scale; + seq_scales[i] = sequence_scale; + } + net->scales = scales; + net->steps = steps; + net->seq_scales = seq_scales; + net->num_steps = n; } - net->scales = scales; - net->steps = steps; - net->seq_scales = seq_scales; - net->num_steps = n; } else if (net->policy == EXP){ net->gamma = option_find_float(options, "gamma", 1); } else if (net->policy == SIG){ From 335ae915685edff13655ad8c493012044461bd3d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 14 May 2019 17:29:18 +0300 Subject: [PATCH 0492/1365] Minor fix --- src/conv_lstm_layer.c | 24 ++++++++++++------------ src/crnn_layer.c | 6 +++--- src/lstm_layer.c | 16 ++++++++-------- src/parser.c | 2 +- src/rnn_layer.c | 6 +++--- 5 files changed, 27 insertions(+), 27 deletions(-) diff --git a/src/conv_lstm_layer.c b/src/conv_lstm_layer.c index b51a3aed06c..12426ab671e 100644 --- a/src/conv_lstm_layer.c +++ b/src/conv_lstm_layer.c @@ -64,65 +64,65 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i l.peephole = peephole; // U - l.uf = (layer*)malloc(sizeof(layer)); + l.uf = (layer*)calloc(1, sizeof(layer)); *(l.uf) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.uf->batch = batch; if (l.workspace_size < l.uf->workspace_size) l.workspace_size = l.uf->workspace_size; - l.ui = (layer*)malloc(sizeof(layer)); + l.ui = (layer*)calloc(1, sizeof(layer)); *(l.ui) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.ui->batch = batch; if (l.workspace_size < l.ui->workspace_size) l.workspace_size = l.ui->workspace_size; - l.ug = (layer*)malloc(sizeof(layer)); + l.ug = (layer*)calloc(1, sizeof(layer)); *(l.ug) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.ug->batch = batch; if (l.workspace_size < l.ug->workspace_size) l.workspace_size = l.ug->workspace_size; - l.uo = (layer*)malloc(sizeof(layer)); + l.uo = (layer*)calloc(1, sizeof(layer)); *(l.uo) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.uo->batch = batch; if (l.workspace_size < l.uo->workspace_size) l.workspace_size = l.uo->workspace_size; // W - l.wf = (layer*)malloc(sizeof(layer)); + l.wf = (layer*)calloc(1, sizeof(layer)); *(l.wf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.wf->batch = batch; if (l.workspace_size < l.wf->workspace_size) l.workspace_size = l.wf->workspace_size; - l.wi = (layer*)malloc(sizeof(layer)); + l.wi = (layer*)calloc(1, sizeof(layer)); *(l.wi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.wi->batch = batch; if (l.workspace_size < l.wi->workspace_size) l.workspace_size = l.wi->workspace_size; - l.wg = (layer*)malloc(sizeof(layer)); + l.wg = (layer*)calloc(1, sizeof(layer)); *(l.wg) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.wg->batch = batch; if (l.workspace_size < l.wg->workspace_size) l.workspace_size = l.wg->workspace_size; - l.wo = (layer*)malloc(sizeof(layer)); + l.wo = (layer*)calloc(1, sizeof(layer)); *(l.wo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.wo->batch = batch; if (l.workspace_size < l.wo->workspace_size) l.workspace_size = l.wo->workspace_size; // V - l.vf = (layer*)malloc(sizeof(layer)); + l.vf = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { *(l.vf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.vf->batch = batch; if (l.workspace_size < l.vf->workspace_size) l.workspace_size = l.vf->workspace_size; } - l.vi = (layer*)malloc(sizeof(layer)); + l.vi = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { *(l.vi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.vi->batch = batch; if (l.workspace_size < l.vi->workspace_size) l.workspace_size = l.vi->workspace_size; } - l.vo = (layer*)malloc(sizeof(layer)); + l.vo = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { *(l.vo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.vo->batch = batch; @@ -909,7 +909,7 @@ void forward_conv_lstm_layer_gpu(layer l, network_state state) fix_nan_and_inf(l.h_gpu, l.outputs*l.batch); copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.cell_gpu, 1); - copy_ongpu(l.outputs*l.batch, l.h_gpu, 1, l.output_gpu, 1); // required for both Detection and Training + copy_ongpu(l.outputs*l.batch, l.h_gpu, 1, l.output_gpu, 1); // is required for both Detection and Training state.input += l.inputs*l.batch; l.output_gpu += l.outputs*l.batch; diff --git a/src/crnn_layer.c b/src/crnn_layer.c index f466508ed81..8534c69a7f1 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -48,17 +48,17 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.state = (float*)calloc(l.hidden * l.batch * (l.steps + 1), sizeof(float)); - l.input_layer = (layer*)malloc(sizeof(layer)); + l.input_layer = (layer*)calloc(1, sizeof(layer)); *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.input_layer->batch = batch; if (l.workspace_size < l.input_layer->workspace_size) l.workspace_size = l.input_layer->workspace_size; - l.self_layer = (layer*)malloc(sizeof(layer)); + l.self_layer = (layer*)calloc(1, sizeof(layer)); *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.self_layer->batch = batch; if (l.workspace_size < l.self_layer->workspace_size) l.workspace_size = l.self_layer->workspace_size; - l.output_layer = (layer*)malloc(sizeof(layer)); + l.output_layer = (layer*)calloc(1, sizeof(layer)); *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.output_layer->batch = batch; if (l.workspace_size < l.output_layer->workspace_size) l.workspace_size = l.output_layer->workspace_size; diff --git a/src/lstm_layer.c b/src/lstm_layer.c index f7b79477104..94664ce3aa1 100644 --- a/src/lstm_layer.c +++ b/src/lstm_layer.c @@ -39,49 +39,49 @@ layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_n l.out_h = 1; l.out_c = outputs; - l.uf = (layer*)malloc(sizeof(layer)); + l.uf = (layer*)calloc(1, sizeof(layer)); fprintf(stderr, "\t\t"); *(l.uf) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.uf->batch = batch; if (l.workspace_size < l.uf->workspace_size) l.workspace_size = l.uf->workspace_size; - l.ui = (layer*)malloc(sizeof(layer)); + l.ui = (layer*)calloc(1, sizeof(layer)); fprintf(stderr, "\t\t"); *(l.ui) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.ui->batch = batch; if (l.workspace_size < l.ui->workspace_size) l.workspace_size = l.ui->workspace_size; - l.ug = (layer*)malloc(sizeof(layer)); + l.ug = (layer*)calloc(1, sizeof(layer)); fprintf(stderr, "\t\t"); *(l.ug) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.ug->batch = batch; if (l.workspace_size < l.ug->workspace_size) l.workspace_size = l.ug->workspace_size; - l.uo = (layer*)malloc(sizeof(layer)); + l.uo = (layer*)calloc(1, sizeof(layer)); fprintf(stderr, "\t\t"); *(l.uo) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.uo->batch = batch; if (l.workspace_size < l.uo->workspace_size) l.workspace_size = l.uo->workspace_size; - l.wf = (layer*)malloc(sizeof(layer)); + l.wf = (layer*)calloc(1, sizeof(layer)); fprintf(stderr, "\t\t"); *(l.wf) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.wf->batch = batch; if (l.workspace_size < l.wf->workspace_size) l.workspace_size = l.wf->workspace_size; - l.wi = (layer*)malloc(sizeof(layer)); + l.wi = (layer*)calloc(1, sizeof(layer)); fprintf(stderr, "\t\t"); *(l.wi) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.wi->batch = batch; if (l.workspace_size < l.wi->workspace_size) l.workspace_size = l.wi->workspace_size; - l.wg = (layer*)malloc(sizeof(layer)); + l.wg = (layer*)calloc(1, sizeof(layer)); fprintf(stderr, "\t\t"); *(l.wg) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.wg->batch = batch; if (l.workspace_size < l.wg->workspace_size) l.workspace_size = l.wg->workspace_size; - l.wo = (layer*)malloc(sizeof(layer)); + l.wo = (layer*)calloc(1, sizeof(layer)); fprintf(stderr, "\t\t"); *(l.wo) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.wo->batch = batch; diff --git a/src/parser.c b/src/parser.c index 95c92250ebe..63a223190bb 100644 --- a/src/parser.c +++ b/src/parser.c @@ -258,7 +258,7 @@ layer parse_conv_lstm(list *options, size_params params) ACTIVATION activation = get_activation(activation_s); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); int xnor = option_find_int_quiet(options, "xnor", 0); - int peephole = option_find_int_quiet(options, "peephole", 1); + int peephole = option_find_int_quiet(options, "peephole", 0); layer l = make_conv_lstm_layer(params.batch, params.h, params.w, params.c, output_filters, groups, params.time_steps, size, stride, padding, activation, batch_normalize, peephole, xnor); diff --git a/src/rnn_layer.c b/src/rnn_layer.c index 28163d754de..4b5b9c2c64c 100644 --- a/src/rnn_layer.c +++ b/src/rnn_layer.c @@ -42,19 +42,19 @@ layer make_rnn_layer(int batch, int inputs, int hidden, int outputs, int steps, l.state = (float*)calloc(batch * hidden * (steps + 1), sizeof(float)); - l.input_layer = (layer*)malloc(sizeof(layer)); + l.input_layer = (layer*)calloc(1, sizeof(layer)); fprintf(stderr, "\t\t"); *(l.input_layer) = make_connected_layer(batch, steps, inputs, hidden, activation, batch_normalize); l.input_layer->batch = batch; if (l.workspace_size < l.input_layer->workspace_size) l.workspace_size = l.input_layer->workspace_size; - l.self_layer = (layer*)malloc(sizeof(layer)); + l.self_layer = (layer*)calloc(1, sizeof(layer)); fprintf(stderr, "\t\t"); *(l.self_layer) = make_connected_layer(batch, steps, hidden, hidden, (log==2)?LOGGY:(log==1?LOGISTIC:activation), batch_normalize); l.self_layer->batch = batch; if (l.workspace_size < l.self_layer->workspace_size) l.workspace_size = l.self_layer->workspace_size; - l.output_layer = (layer*)malloc(sizeof(layer)); + l.output_layer = (layer*)calloc(1, sizeof(layer)); fprintf(stderr, "\t\t"); *(l.output_layer) = make_connected_layer(batch, steps, hidden, outputs, activation, batch_normalize); l.output_layer->batch = batch; From 4b1ecfb419c34db5cff4c5dc4e5bb3be482fd9b0 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 17 May 2019 02:17:55 +0300 Subject: [PATCH 0493/1365] Minor fix --- README.md | 3 ++- scripts/README.md | 2 ++ 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 47ec2752dd0..52df11995a8 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ More details: http://pjreddie.com/darknet/yolo/ * [Requirements (and how to install dependecies)](#requirements) * [Pre-trained models](#pre-trained-models) * [Explanations in issues](https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3AExplanations) -* [Yolo v3 in other frameworks (TensorFlow, PyTorch, OpenVINO, OpenCV-dnn,...)](#yolo-v3-in-other-frameworks) +* [Yolo v3 in other frameworks (TensorRT, TensorFlow, PyTorch, OpenVINO, OpenCV-dnn,...)](#yolo-v3-in-other-frameworks) 0. [Improvements in this repository](#improvements-in-this-repository) 1. [How to use](#how-to-use-on-the-command-line) @@ -78,6 +78,7 @@ You can get cfg-files by path: `darknet/cfg/` * **Intel OpenVINO 2019 R1:** (Myriad X / USB Neural Compute Stick / Arria FPGA): read this [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) * **OpenCV-dnn** is a very fast DNN implementation on CPU (x86/ARM-Android), use `yolov3.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221), [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) * **PyTorch > ONNX > CoreML > iOS** how to convert cfg/weights-files to pt-file: [ultralytics/yolov3](https://github.com/ultralytics/yolov3#darknet-conversion) and [iOS App](https://itunes.apple.com/app/id1452689527) +* **TensorRT** for YOLOv3 (-70% faster inference): [TensorRT & DeepStream](https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps) ##### Examples of results diff --git a/scripts/README.md b/scripts/README.md index 273ed2a7dcd..a38327ecb1b 100644 --- a/scripts/README.md +++ b/scripts/README.md @@ -12,6 +12,8 @@ ImageNet (ILSVRC2012): http://www.image-net.org/challenges/LSVRC/2012/nonpub-dow ImageNet (ILSVRC2015): http://image-net.org/small/download.php +ImageNet VID: http://bvisionweb1.cs.unc.edu/ilsvrc2015/download-videos-3j16.php + Open Images: https://storage.googleapis.com/openimages/web/download.html Cityscapes: https://www.cityscapes-dataset.com/ From 0ae5191580d49fdf4392e56152d4386b89224500 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 17 May 2019 02:31:26 +0300 Subject: [PATCH 0494/1365] Use Tensor Cores for forward-inference in conv-LSTM --- src/conv_lstm_layer.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/conv_lstm_layer.c b/src/conv_lstm_layer.c index 12426ab671e..26e068018a3 100644 --- a/src/conv_lstm_layer.c +++ b/src/conv_lstm_layer.c @@ -799,7 +799,7 @@ void forward_conv_lstm_layer_gpu(layer l, network_state state) s.train = state.train; s.workspace = state.workspace; s.net = state.net; - //if (!state.train) s.index = state.index; // don't use TC for training (especially without cuda_convert_f32_to_f16() ) + if (!state.train) s.index = state.index; // don't use TC for training (especially without cuda_convert_f32_to_f16() ) int i; layer vf = *(l.vf); layer vi = *(l.vi); From 578d1c0fba16413b5eea2c59d5cd6c44597490a6 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Mon, 20 May 2019 15:17:14 +0200 Subject: [PATCH 0495/1365] [cmake+cuda] improve friendliness of architecture selection --- CMakeLists.txt | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 319d9fe9388..e41a5f77632 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -38,20 +38,22 @@ set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA support") set(ENABLE_CUDNN "FALSE" CACHE BOOL "Enable CUDNN") set(ENABLE_CUDNN_HALF "FALSE" CACHE BOOL "Enable CUDNN Half precision") -if(${CMAKE_VERSION} VERSION_LESS "3.8.0") +if(${CMAKE_VERSION} VERSION_LESS "3.9.0") set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA support" FORCE) - message(WARNING "To build with CUDA support you need CMake 3.8.0+") + message(WARNING "To build with CUDA support you need CMake 3.9.0+") else() include(CheckLanguage) check_language(CUDA) if(CMAKE_CUDA_COMPILER) + set(CUDA_ARCHITECTURES "Auto" CACHE STRING "\"Auto\" detects local machine GPU compute arch at runtime, \"Common\" and \"All\" cover common and entire subsets of architectures, \"Names\" is a list of architectures to enable by name, \"Numbers\" is a list of compute capabilities (version number) to enable") + set_property(CACHE CUDA_ARCHITECTURES PROPERTY STRINGS "Auto" "Common" "All" "Kepler Maxwell Kepler+Tegra Maxwell+Tegra Pascal" "3.0 7.5") enable_language(CUDA) find_package(CUDA REQUIRED) if(CUDA_VERSION VERSION_LESS "9.0") message(STATUS "Unsupported CUDA version, please upgrade to CUDA 9+. Disabling CUDA support") else() set(ENABLE_CUDA "TRUE" CACHE BOOL "Enable CUDA support" FORCE) - cuda_select_nvcc_arch_flags(CUDA_ARCH_FLAGS "Auto") + cuda_select_nvcc_arch_flags(CUDA_ARCH_FLAGS ${CUDA_ARCHITECTURES}) message(STATUS "Building with CUDA flags: " "${CUDA_ARCH_FLAGS}") if ("arch=compute_75,code=sm_75" IN_LIST CUDA_ARCH_FLAGS) set(ENABLE_CUDNN_HALF "TRUE" CACHE BOOL "Enable CUDNN Half precision" FORCE) From ae3ce1b1815803fb07a7f2fe75d8b0bd78c5c19b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 20 May 2019 22:37:42 +0300 Subject: [PATCH 0496/1365] Minor fixes --- src/convolutional_kernels.cu | 18 +++++++++--------- src/data.c | 3 ++- src/detector.c | 4 ++-- src/network.c | 8 ++++++-- src/parser.c | 1 + 5 files changed, 20 insertions(+), 14 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 9185279f261..2e8432babcb 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -484,14 +484,14 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) l.normDstTensorDescF16, output16, // output l.normTensorDesc, - l.scales_gpu, - l.biases_gpu, + l.scales_gpu, // input + l.biases_gpu, // input .01, - l.rolling_mean_gpu, // output (should be FP32) - l.rolling_variance_gpu, // output (should be FP32) + l.rolling_mean_gpu, // input/output (should be FP32) + l.rolling_variance_gpu, // input/output (should be FP32) .00001, - l.mean_gpu, // output (should be FP32) - l.variance_gpu)); // output (should be FP32) + l.mean_gpu, // output (should be FP32) - optional cache to speedup cudnnBatchNormalizationBackward() + l.variance_gpu)); // output (should be FP32) - optional cache to speedup cudnnBatchNormalizationBackward() cuda_convert_f16_to_f32(output16, output16_size, l.output_gpu); //forward_batchnorm_layer_gpu(l, state); @@ -658,13 +658,13 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state &one, &one, l.normDstTensorDescF16, - l.x_gpu, // input + l.x_gpu, // input (input in BN-forward-inference) l.normDstTensorDescF16, delta16, // input l.normDstTensorDescF16, - l.x_norm_gpu, // output + l.x_norm_gpu, // output (new delta) l.normTensorDesc, - l.scales_gpu, // output (should be FP32) + l.scales_gpu, // input (should be FP32) l.scale_updates_gpu, // output (should be FP32) l.bias_updates_gpu, // output (should be FP32) .00001, diff --git a/src/data.c b/src/data.c index 9b93f1a8193..53959b4c00c 100644 --- a/src/data.c +++ b/src/data.c @@ -781,6 +781,7 @@ static box float_to_box_stride(float *f, int stride) data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int use_blur, float jitter, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int show_imgs) { + const int random_index = random_gen(); c = c ? c : 3; char **random_paths; if (track) random_paths = get_sequential_paths(paths, n, m, mini_batch, augment_speed); @@ -855,7 +856,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo if(show_imgs) { char buff[1000]; - sprintf(buff, "aug_%s_%d", basecfg(random_paths[i]), random_gen()); + sprintf(buff, "aug_%d_%d_%s_%d", random_index, i, basecfg(random_paths[i]), random_gen()); int t; for (t = 0; t < boxes; ++t) { box b = float_to_box_stride(d.y.vals[i] + t*(4 + 1), 1); diff --git a/src/detector.c b/src/detector.c index 14ad3618bdf..9199673cb1a 100644 --- a/src/detector.c +++ b/src/detector.c @@ -147,7 +147,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i args.track = net.track; args.augment_speed = net.augment_speed; if (net.sequential_subdivisions) args.threads = net.sequential_subdivisions * ngpus; - else args.threads = net.subdivisions * ngpus; // 2 * ngpus; + else args.threads = net.subdivisions * ngpus; args.mini_batch = net.batch / net.time_steps; printf("\n Tracking! batch = %d, subdiv = %d, time_steps = %d, mini_batch = %d \n", net.batch, net.subdivisions, net.time_steps, args.mini_batch); } @@ -193,7 +193,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i if (net.track) { net.sequential_subdivisions = get_current_seq_subdivisions(net); args.threads = net.sequential_subdivisions * ngpus; - printf(" sequential_subdivisions = %d \n", net.sequential_subdivisions); + printf(" sequential_subdivisions = %d, sequence = %d \n", net.sequential_subdivisions, get_sequence_value(net)); } load_thread = load_data(args); diff --git a/src/network.c b/src/network.c index a46d15a013d..86c248c4a1f 100644 --- a/src/network.c +++ b/src/network.c @@ -104,6 +104,8 @@ float get_current_seq_subdivisions(network net) sequence_subdivisions *= net.seq_scales[i]; } } + if (sequence_subdivisions < 1) sequence_subdivisions = 1; + if (sequence_subdivisions > net.subdivisions) sequence_subdivisions = net.subdivisions; return sequence_subdivisions; } @@ -1004,14 +1006,16 @@ void fuse_conv_batchnorm(network net) int f; for (f = 0; f < l->n; ++f) { - l->biases[f] = l->biases[f] - (double)l->scales[f] * l->rolling_mean[f] / (sqrt((double)l->rolling_variance[f]) + .000001f); + //l->biases[f] = l->biases[f] - (double)l->scales[f] * l->rolling_mean[f] / (sqrt((double)l->rolling_variance[f]) + .000001f); + l->biases[f] = l->biases[f] - (double)l->scales[f] * l->rolling_mean[f] / (sqrt((double)l->rolling_variance[f] + .000001)); const size_t filter_size = l->size*l->size*l->c / l->groups; int i; for (i = 0; i < filter_size; ++i) { int w_index = f*filter_size + i; - l->weights[w_index] = (double)l->weights[w_index] * l->scales[f] / (sqrt((double)l->rolling_variance[f]) + .000001f); + //l->weights[w_index] = (double)l->weights[w_index] * l->scales[f] / (sqrt((double)l->rolling_variance[f]) + .000001f); + l->weights[w_index] = (double)l->weights[w_index] * l->scales[f] / (sqrt((double)l->rolling_variance[f] + .000001)); } } diff --git a/src/parser.c b/src/parser.c index 63a223190bb..ac7fffb7d46 100644 --- a/src/parser.c +++ b/src/parser.c @@ -678,6 +678,7 @@ void parse_net_options(list *options, network *net) net->track = option_find_int_quiet(options, "track", 0); net->augment_speed = option_find_int_quiet(options, "augment_speed", 2); net->init_sequential_subdivisions = net->sequential_subdivisions = option_find_int_quiet(options, "sequential_subdivisions", subdivs); + if (net->sequential_subdivisions > subdivs) net->init_sequential_subdivisions = net->sequential_subdivisions = subdivs; net->try_fix_nan = option_find_int_quiet(options, "try_fix_nan", 0); net->batch /= subdivs; net->batch *= net->time_steps; From b9ea49af250a3eab3b8775efa53db0f0ff063357 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 20 May 2019 23:58:55 +0300 Subject: [PATCH 0497/1365] Fixed reorg layer is [reorg3d], old reorg layer is [reorg] --- src/parser.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/parser.c b/src/parser.c index ac7fffb7d46..56a0bcb78f9 100644 --- a/src/parser.c +++ b/src/parser.c @@ -68,8 +68,8 @@ LAYER_TYPE string_to_layer_type(char * type) || strcmp(type, "[connected]")==0) return CONNECTED; if (strcmp(type, "[max]")==0 || strcmp(type, "[maxpool]")==0) return MAXPOOL; - if (strcmp(type, "[reorg]")==0) return REORG; - if (strcmp(type, "[reorg_old]") == 0) return REORG_OLD; + if (strcmp(type, "[reorg3d]")==0) return REORG; + if (strcmp(type, "[reorg]") == 0) return REORG_OLD; if (strcmp(type, "[avg]")==0 || strcmp(type, "[avgpool]")==0) return AVGPOOL; if (strcmp(type, "[dropout]")==0) return DROPOUT; From 0109a8dda92380845a2c5232a00ca166f377050c Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 21 May 2019 18:01:08 +0300 Subject: [PATCH 0498/1365] Some conv-lstm improvements (inference speedup) --- src/blas.c | 19 ++++++++ src/blas.h | 6 +++ src/blas_kernels.cu | 64 ++++++++++++++++++++++++++ src/conv_lstm_layer.c | 102 ++++++++++++++++++++++++------------------ 4 files changed, 148 insertions(+), 43 deletions(-) diff --git a/src/blas.c b/src/blas.c index c68b64c3d5e..a3ff84b6515 100644 --- a/src/blas.c +++ b/src/blas.c @@ -334,3 +334,22 @@ void upsample_cpu(float *in, int w, int h, int c, int batch, int stride, int for } } } + + +void constrain_cpu(int size, float ALPHA, float *X) +{ + int i; + for (i = 0; i < size; ++i) { + X[i] = fminf(ALPHA, fmaxf(-ALPHA, X[i])); + } +} + +void fix_nan_and_inf_cpu(float *input, size_t size) +{ + int i; + for (i = 0; i < size; ++i) { + float val = input[i]; + if (isnan(val) || isinf(val)) + input[i] = 1.0f / i; // pseudo random value + } +} \ No newline at end of file diff --git a/src/blas.h b/src/blas.h index 8e91fff2f76..faec12362e7 100644 --- a/src/blas.h +++ b/src/blas.h @@ -46,6 +46,8 @@ void softmax(float *input, int n, float temp, float *output, int stride); void upsample_cpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out); void softmax_cpu(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output); void softmax_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *error); +void constrain_cpu(int size, float ALPHA, float *X); +void fix_nan_and_inf_cpu(float *input, size_t size); #ifdef GPU @@ -105,6 +107,10 @@ void softmax_tree_gpu(float *input, int spatial, int batch, int stride, float te void fix_nan_and_inf(float *input, size_t size); int is_nan_or_inf(float *input, size_t size); +void add_3_arrays_activate(float *a1, float *a2, float *a3, size_t size, ACTIVATION a, float *dst); +void sum_of_mults(float *a1, float *a2, float *b1, float *b2, size_t size, float *dst); +void activate_and_mult(float *a1, float *a2, size_t size, ACTIVATION a, float *dst); + #endif #ifdef __cplusplus } diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 66905127e56..cf6bed275fa 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -1021,4 +1021,68 @@ extern "C" int is_nan_or_inf(float *input, size_t size) CHECK_CUDA(cudaFreeHost(pinned_return)); return ret_val; +} + + +__global__ void add_3_arrays_activate_kernel(float *a1, float *a2, float *a3, size_t size, ACTIVATION a, float *dst) +{ + const int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < size) { + float val = 0; + val += a1[index]; + val += a2[index]; + if (a3) val += a3[index]; + if (a == LOGISTIC) val = 1.f / (1.f + expf(-val)); + else if(a == TANH) val = (2 / (1 + expf(-2 * val)) - 1); + dst[index] = val; + } +} + +extern "C" void add_3_arrays_activate(float *a1, float *a2, float *a3, size_t size, ACTIVATION a, float *dst) +{ + const int block_size = BLOCK; + const int num_blocks = get_number_of_blocks(size, block_size); + if (a != LOGISTIC && a != TANH) { + printf(" add_3_arrays_activate() doesn't support activation %d, it supports only LOGISTIC and TANH \n", a); + exit(EXIT_FAILURE); + } + add_3_arrays_activate_kernel << > >(a1, a2, a3, size, a, dst); +} + + +__global__ void sum_of_mults_kernel(float *a1, float *a2, float *b1, float *b2, size_t size, float *dst) +{ + const int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < size) { + dst[index] = a1[index] * a2[index] + b1[index] * b2[index]; + } +} + +extern "C" void sum_of_mults(float *a1, float *a2, float *b1, float *b2, size_t size, float *dst) +{ + const int block_size = BLOCK; + const int num_blocks = get_number_of_blocks(size, block_size); + sum_of_mults_kernel << > >(a1, a2, b1, b2, size, dst); +} + + +__global__ void activate_and_mult_kernel(float *a1, float *a2, size_t size, ACTIVATION a, float *dst) +{ + const int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < size) { + float val = a1[index]; + if (a == TANH) val = (2 / (1 + expf(-2 * val)) - 1); + dst[index] = val * a2[index]; + } +} + +extern "C" void activate_and_mult(float *a1, float *a2, size_t size, ACTIVATION a, float *dst) +{ + const int block_size = BLOCK; + const int num_blocks = get_number_of_blocks(size, block_size); + if (a != TANH) { + printf(" activat_and_mult() doesn't support activation %d, it supports only TANH \n", a); + exit(EXIT_FAILURE); + } + activate_and_mult_kernel << > >(a1, a2, size, a, dst); } \ No newline at end of file diff --git a/src/conv_lstm_layer.c b/src/conv_lstm_layer.c index 26e068018a3..4dbc14d8e67 100644 --- a/src/conv_lstm_layer.c +++ b/src/conv_lstm_layer.c @@ -201,6 +201,9 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i l.wf->bflops + l.wi->bflops + l.wg->bflops + l.wo->bflops + l.vf->bflops + l.vi->bflops + l.vo->bflops; + if(l.peephole) l.bflops += 12 * l.outputs*l.batch / 1000000000.; + else l.bflops += 9 * l.outputs*l.batch / 1000000000.; + return l; } @@ -429,22 +432,23 @@ void forward_conv_lstm_layer(layer l, network_state state) layer ug = *(l.ug); layer uo = *(l.uo); - if (l.peephole) { - fill_cpu(l.outputs * l.batch * l.steps, 0, vf.delta, 1); - fill_cpu(l.outputs * l.batch * l.steps, 0, vi.delta, 1); - fill_cpu(l.outputs * l.batch * l.steps, 0, vo.delta, 1); - } + if (state.train) { + if (l.peephole) { + fill_cpu(l.outputs * l.batch * l.steps, 0, vf.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, vi.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, vo.delta, 1); + } - fill_cpu(l.outputs * l.batch * l.steps, 0, wf.delta, 1); - fill_cpu(l.outputs * l.batch * l.steps, 0, wi.delta, 1); - fill_cpu(l.outputs * l.batch * l.steps, 0, wg.delta, 1); - fill_cpu(l.outputs * l.batch * l.steps, 0, wo.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wf.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wi.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wg.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wo.delta, 1); + + fill_cpu(l.outputs * l.batch * l.steps, 0, uf.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, ui.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, ug.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, uo.delta, 1); - fill_cpu(l.outputs * l.batch * l.steps, 0, uf.delta, 1); - fill_cpu(l.outputs * l.batch * l.steps, 0, ui.delta, 1); - fill_cpu(l.outputs * l.batch * l.steps, 0, ug.delta, 1); - fill_cpu(l.outputs * l.batch * l.steps, 0, uo.delta, 1); - if (state.train) { fill_cpu(l.outputs * l.batch * l.steps, 0, l.delta, 1); } @@ -467,6 +471,9 @@ void forward_conv_lstm_layer(layer l, network_state state) forward_convolutional_layer(wg, s); forward_convolutional_layer(wo, s); + assert(l.inputs == uf.w * uf.h * uf.c); + assert(uf.c == l.c && ui.c == l.c && ug.c == l.c && uo.c == l.c); + s.input = state.input; forward_convolutional_layer(uf, s); forward_convolutional_layer(ui, s); @@ -498,8 +505,10 @@ void forward_conv_lstm_layer(layer l, network_state state) axpy_cpu(l.outputs*l.batch, 1, l.temp_cpu, 1, l.c_cpu, 1); // o = wo + uo + vo(c_new) - s.input = l.c_cpu; - if (l.peephole) forward_convolutional_layer(vo, s); + if (l.peephole) { + s.input = l.c_cpu; + forward_convolutional_layer(vo, s); + } copy_cpu(l.outputs*l.batch, wo.output, 1, l.o_cpu, 1); axpy_cpu(l.outputs*l.batch, 1, uo.output, 1, l.o_cpu, 1); if (l.peephole) axpy_cpu(l.outputs*l.batch, 1, vo.output, 1, l.o_cpu, 1); @@ -510,6 +519,10 @@ void forward_conv_lstm_layer(layer l, network_state state) activate_array(l.h_cpu, l.outputs*l.batch, TANH); mul_cpu(l.outputs*l.batch, l.o_cpu, 1, l.h_cpu, 1); + if (l.state_constrain) constrain_cpu(l.outputs*l.batch, l.state_constrain, l.c_cpu); + fix_nan_and_inf_cpu(l.c_cpu, l.outputs*l.batch); + fix_nan_and_inf_cpu(l.h_cpu, l.outputs*l.batch); + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.cell_cpu, 1); copy_cpu(l.outputs*l.batch, l.h_cpu, 1, l.output, 1); @@ -864,52 +877,55 @@ void forward_conv_lstm_layer_gpu(layer l, network_state state) forward_convolutional_layer_gpu(uo, s); // f = wf + uf + vf - copy_ongpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1); - if (l.peephole) axpy_ongpu(l.outputs*l.batch, 1, vf.output_gpu, 1, l.f_gpu, 1); + add_3_arrays_activate(wf.output_gpu, uf.output_gpu, (l.peephole)?vf.output_gpu:NULL, l.outputs*l.batch, LOGISTIC, l.f_gpu); + //copy_ongpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1); + //axpy_ongpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1); + //if (l.peephole) axpy_ongpu(l.outputs*l.batch, 1, vf.output_gpu, 1, l.f_gpu, 1); + //activate_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC); // i = wi + ui + vi - copy_ongpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1); - if (l.peephole) axpy_ongpu(l.outputs*l.batch, 1, vi.output_gpu, 1, l.i_gpu, 1); + add_3_arrays_activate(wi.output_gpu, ui.output_gpu, (l.peephole) ? vi.output_gpu : NULL, l.outputs*l.batch, LOGISTIC, l.i_gpu); + //copy_ongpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1); + //axpy_ongpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1); + //if (l.peephole) axpy_ongpu(l.outputs*l.batch, 1, vi.output_gpu, 1, l.i_gpu, 1); + //activate_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC); // g = wg + ug - copy_ongpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1); - - activate_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC); - activate_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC); - activate_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH); + add_3_arrays_activate(wg.output_gpu, ug.output_gpu, NULL, l.outputs*l.batch, TANH, l.g_gpu); + //copy_ongpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1); + //axpy_ongpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1); + //activate_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH); // c = f*c + i*g - copy_ongpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1); - mul_ongpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1); - mul_ongpu(l.outputs*l.batch, l.f_gpu, 1, l.c_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, l.temp_gpu, 1, l.c_gpu, 1); + sum_of_mults(l.f_gpu, l.c_gpu, l.i_gpu, l.g_gpu, l.outputs*l.batch, l.c_gpu); // decreases mAP??? + //copy_ongpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1); + //mul_ongpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1); + //mul_ongpu(l.outputs*l.batch, l.f_gpu, 1, l.c_gpu, 1); + //axpy_ongpu(l.outputs*l.batch, 1, l.temp_gpu, 1, l.c_gpu, 1); // o = wo + uo + vo(c_new) if (l.peephole) { s.input = l.c_gpu; forward_convolutional_layer_gpu(vo, s); } - copy_ongpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1); - if (l.peephole) axpy_ongpu(l.outputs*l.batch, 1, vo.output_gpu, 1, l.o_gpu, 1); - activate_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC); + add_3_arrays_activate(wo.output_gpu, uo.output_gpu, (l.peephole) ? vo.output_gpu : NULL, l.outputs*l.batch, LOGISTIC, l.o_gpu); + //copy_ongpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1); + //axpy_ongpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1); + //if (l.peephole) axpy_ongpu(l.outputs*l.batch, 1, vo.output_gpu, 1, l.o_gpu, 1); + //activate_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC); // h = o * tanh(c) - copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.h_gpu, 1); - activate_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH); - mul_ongpu(l.outputs*l.batch, l.o_gpu, 1, l.h_gpu, 1); + activate_and_mult(l.c_gpu, l.o_gpu, l.outputs*l.batch, TANH, l.h_gpu); + //simple_copy_ongpu(l.outputs*l.batch, l.c_gpu, l.h_gpu); + //activate_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH); + //mul_ongpu(l.outputs*l.batch, l.o_gpu, 1, l.h_gpu, 1); if(l.state_constrain) constrain_ongpu(l.outputs*l.batch, l.state_constrain, l.c_gpu, 1); - //constrain_ongpu(l.outputs*l.batch, 1, l.c_gpu, 1); - //constrain_ongpu(l.outputs*l.batch, 1, l.h_gpu, 1); fix_nan_and_inf(l.c_gpu, l.outputs*l.batch); fix_nan_and_inf(l.h_gpu, l.outputs*l.batch); - copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.cell_gpu, 1); - copy_ongpu(l.outputs*l.batch, l.h_gpu, 1, l.output_gpu, 1); // is required for both Detection and Training + if(state.train) simple_copy_ongpu(l.outputs*l.batch, l.c_gpu, l.cell_gpu); + simple_copy_ongpu(l.outputs*l.batch, l.h_gpu, l.output_gpu); // is required for both Detection and Training state.input += l.inputs*l.batch; l.output_gpu += l.outputs*l.batch; From 034243d1ffd38c026f53104c2ccd9bca50ad9354 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 21 May 2019 18:06:43 +0300 Subject: [PATCH 0499/1365] Compile fix --- src/blas.h | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/blas.h b/src/blas.h index faec12362e7..09b9c9a70e5 100644 --- a/src/blas.h +++ b/src/blas.h @@ -1,9 +1,13 @@ #ifndef BLAS_H #define BLAS_H +#include +#include "darknet.h" + #ifdef GPU #include "dark_cuda.h" #include "tree.h" #endif + #ifdef __cplusplus extern "C" { #endif From 9c9344a1ff4541499c8f69ea997be5145b4e1de3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 21 May 2019 23:47:47 +0300 Subject: [PATCH 0500/1365] Added flag -letter_box for Detection --- include/darknet.h | 2 +- src/coco.c | 2 +- src/darknet.c | 2 +- src/demo.c | 7 ++++--- src/demo.h | 2 +- src/detector.c | 15 ++++++++------- src/yolo.c | 2 +- 7 files changed, 17 insertions(+), 15 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 68ab1fc74b5..0eb52b37b03 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -796,7 +796,7 @@ LIB_API float *network_predict_image(network *net, image im); LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, const int map_points, network *existing_net); LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port, int show_imgs); LIB_API void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, - float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile); + float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile, int letter_box); LIB_API int network_width(network *net); LIB_API int network_height(network *net); LIB_API void optimize_picture(network *net, image orig, int max_layer, float scale, float rate, float thresh, int norm); diff --git a/src/coco.c b/src/coco.c index 931c406c7a8..c1535a35f19 100644 --- a/src/coco.c +++ b/src/coco.c @@ -384,5 +384,5 @@ void run_coco(int argc, char **argv) else if(0==strcmp(argv[2], "valid")) validate_coco(cfg, weights); else if(0==strcmp(argv[2], "recall")) validate_coco_recall(cfg, weights); else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, hier_thresh, cam_index, filename, coco_classes, 80, frame_skip, - prefix, out_filename, mjpeg_port, json_port, dont_show, ext_output); + prefix, out_filename, mjpeg_port, json_port, dont_show, ext_output, 0); } diff --git a/src/darknet.c b/src/darknet.c index 06092d48192..f7ac6593cd5 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -476,7 +476,7 @@ int main(int argc, char **argv) float thresh = find_float_arg(argc, argv, "-thresh", .24); int ext_output = find_arg(argc, argv, "-ext_output"); char *filename = (argc > 4) ? argv[4]: 0; - test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, 0.5, 0, ext_output, 0, NULL); + test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, 0.5, 0, ext_output, 0, NULL, 0); } else if (0 == strcmp(argv[1], "cifar")){ run_cifar(argc, argv); } else if (0 == strcmp(argv[1], "go")){ diff --git a/src/demo.c b/src/demo.c index 4262b03f021..6c7f5d39848 100644 --- a/src/demo.c +++ b/src/demo.c @@ -50,7 +50,7 @@ mat_cv* det_img; mat_cv* show_img; static volatile int flag_exit; -static const int letter_box = 0; +static int letter_box = 0; void *fetch_in_thread(void *ptr) { @@ -104,8 +104,9 @@ double get_wall_time() } void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, - int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output) + int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output, int letter_box_in) { + letter_box = letter_box_in; in_img = det_img = show_img = NULL; //skip = frame_skip; image **alphabet = load_alphabet(); @@ -321,7 +322,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int } #else void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, - int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output) + int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output, int letter_box_in) { fprintf(stderr, "Demo needs OpenCV for webcam images.\n"); } diff --git a/src/demo.h b/src/demo.h index b26b9592f1b..1f749b899e5 100644 --- a/src/demo.h +++ b/src/demo.h @@ -6,7 +6,7 @@ extern "C" { #endif void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, - int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output); + int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output, int letter_box_in); #ifdef __cplusplus } #endif diff --git a/src/detector.c b/src/detector.c index 9199673cb1a..d7ed7af65ea 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1256,7 +1256,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, - float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile) + float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile, int letter_box) { list *options = read_data_cfg(datacfg); char *name_list = option_find_str(options, "names", "data/names.list"); @@ -1304,9 +1304,9 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam //image im; //image sized = load_image_resize(input, net.w, net.h, net.c, &im); image im = load_image(input, 0, 0, net.c); - image sized = resize_image(im, net.w, net.h); - int letterbox = 0; - //image sized = letterbox_image(im, net.w, net.h); letterbox = 1; + image sized; + if(letter_box) sized = letterbox_image(im, net.w, net.h); + else sized = resize_image(im, net.w, net.h); layer l = net.layers[net.n - 1]; //box *boxes = calloc(l.w*l.h*l.n, sizeof(box)); @@ -1323,7 +1323,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam //printf("%s: Predicted in %f seconds.\n", input, (what_time_is_it_now()-time)); int nboxes = 0; - detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); + detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letter_box); if (nms) do_nms_sort(dets, nboxes, l.classes, nms); draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, ext_output); save_image(im, "predictions"); @@ -1409,6 +1409,7 @@ void run_detector(int argc, char **argv) { int dont_show = find_arg(argc, argv, "-dont_show"); int show = find_arg(argc, argv, "-show"); + int letter_box = find_arg(argc, argv, "-letter_box"); int calc_map = find_arg(argc, argv, "-map"); int map_points = find_int_arg(argc, argv, "-points", 0); check_mistakes = find_arg(argc, argv, "-check_mistakes"); @@ -1467,7 +1468,7 @@ void run_detector(int argc, char **argv) if (strlen(weights) > 0) if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0; char *filename = (argc > 6) ? argv[6] : 0; - if (0 == strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels, outfile); + if (0 == strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels, outfile, letter_box); else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map, mjpeg_port, show_imgs); else if (0 == strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile); else if (0 == strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights); @@ -1482,7 +1483,7 @@ void run_detector(int argc, char **argv) if (strlen(filename) > 0) if (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0; demo(cfg, weights, thresh, hier_thresh, cam_index, filename, names, classes, frame_skip, prefix, out_filename, - mjpeg_port, json_port, dont_show, ext_output); + mjpeg_port, json_port, dont_show, ext_output, letter_box); free_list_contents_kvp(options); free_list(options); diff --git a/src/yolo.c b/src/yolo.c index 07a2092c0b1..711470eade2 100644 --- a/src/yolo.c +++ b/src/yolo.c @@ -351,5 +351,5 @@ void run_yolo(int argc, char **argv) else if(0==strcmp(argv[2], "valid")) validate_yolo(cfg, weights); else if(0==strcmp(argv[2], "recall")) validate_yolo_recall(cfg, weights); else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, hier_thresh, cam_index, filename, voc_names, 20, frame_skip, - prefix, out_filename, mjpeg_port, json_port, dont_show, ext_output); + prefix, out_filename, mjpeg_port, json_port, dont_show, ext_output, 0); } From 3aca0b71666bac0dd5760833aea036e7bd897c8a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 24 May 2019 18:48:11 +0300 Subject: [PATCH 0501/1365] conv-LSTM training speedup --- src/conv_lstm_layer.c | 121 ++++++++++++++++++++++-------------------- src/image_opencv.cpp | 2 +- 2 files changed, 63 insertions(+), 60 deletions(-) diff --git a/src/conv_lstm_layer.c b/src/conv_lstm_layer.c index 4dbc14d8e67..f3041cedc65 100644 --- a/src/conv_lstm_layer.c +++ b/src/conv_lstm_layer.c @@ -634,7 +634,7 @@ void backward_conv_lstm_layer(layer l, network_state state) gradient_array(l.temp_cpu, l.outputs*l.batch, TANH, l.temp2_cpu); axpy_cpu(l.outputs*l.batch, 1, l.dc_cpu, 1, l.temp2_cpu, 1); // temp = tanh(c) - // temp2 = delta * o * grad_tanh(tahc(c)) + // temp2 = delta * o * grad_tanh(tanh(c)) // temp3 = delta copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.temp_cpu, 1); @@ -642,7 +642,7 @@ void backward_conv_lstm_layer(layer l, network_state state) mul_cpu(l.outputs*l.batch, l.temp3_cpu, 1, l.temp_cpu, 1); gradient_array(l.o_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu); // delta for o(w,u,v): temp = delta * tanh(c) * grad_logistic(o) - // delta for c,f,i,g(w,u,v): temp2 = delta * o * grad_tanh(tahc(c)) + delta_c(???) + // delta for c,f,i,g(w,u,v): temp2 = delta * o * grad_tanh(tanh(c)) + delta_c(???) // delta for output: temp3 = delta // o @@ -668,7 +668,7 @@ void backward_conv_lstm_layer(layer l, network_state state) copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); mul_cpu(l.outputs*l.batch, l.i_cpu, 1, l.temp_cpu, 1); gradient_array(l.g_cpu, l.outputs*l.batch, TANH, l.temp_cpu); - // delta for c,f,i,g(w,u,v): temp2 = (delta * o * grad_tanh(tahc(c)) + delta_c(???)) * g * grad_logistic(i) + // delta for c,f,i,g(w,u,v): temp2 = (delta * o * grad_tanh(tanh(c)) + delta_c(???)) * g * grad_logistic(i) copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wg.delta, 1); s.input = l.prev_state_cpu; @@ -684,7 +684,7 @@ void backward_conv_lstm_layer(layer l, network_state state) copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); mul_cpu(l.outputs*l.batch, l.g_cpu, 1, l.temp_cpu, 1); gradient_array(l.i_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu); - // delta for c,f,i,g(w,u,v): temp2 = (delta * o * grad_tanh(tahc(c)) + delta_c(???)) * g * grad_logistic(i) + // delta for c,f,i,g(w,u,v): temp2 = (delta * o * grad_tanh(tanh(c)) + delta_c(???)) * g * grad_logistic(i) if (l.peephole) { copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, vi.delta, 1); @@ -707,7 +707,7 @@ void backward_conv_lstm_layer(layer l, network_state state) copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); mul_cpu(l.outputs*l.batch, l.prev_cell_cpu, 1, l.temp_cpu, 1); gradient_array(l.f_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu); - // delta for c,f,i,g(w,u,v): temp2 = (delta * o * grad_tanh(tahc(c)) + delta_c(???)) * c * grad_logistic(f) + // delta for c,f,i,g(w,u,v): temp2 = (delta * o * grad_tanh(tanh(c)) + delta_c(???)) * c * grad_logistic(f) if (l.peephole) { copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, vf.delta, 1); @@ -920,9 +920,9 @@ void forward_conv_lstm_layer_gpu(layer l, network_state state) //activate_array_ongpu(l.h_gpu, l.outputs*l.batch, TANH); //mul_ongpu(l.outputs*l.batch, l.o_gpu, 1, l.h_gpu, 1); - if(l.state_constrain) constrain_ongpu(l.outputs*l.batch, l.state_constrain, l.c_gpu, 1); fix_nan_and_inf(l.c_gpu, l.outputs*l.batch); fix_nan_and_inf(l.h_gpu, l.outputs*l.batch); + if (l.state_constrain) constrain_ongpu(l.outputs*l.batch, l.state_constrain, l.c_gpu, 1); if(state.train) simple_copy_ongpu(l.outputs*l.batch, l.c_gpu, l.cell_gpu); simple_copy_ongpu(l.outputs*l.batch, l.h_gpu, l.output_gpu); // is required for both Detection and Training @@ -996,160 +996,163 @@ void backward_conv_lstm_layer_gpu(layer l, network_state state) l.cell_gpu += l.outputs*l.batch*(l.steps - 1); l.delta_gpu += l.outputs*l.batch*(l.steps - 1); - fix_nan_and_inf(l.dc_gpu, l.outputs*l.batch); //fill_ongpu(l.outputs * l.batch, 0, l.dc_gpu, 1); // dont use const int sequence = get_sequence_value(state.net); for (i = l.steps - 1; i >= 0; --i) { - if (i != 0) copy_ongpu(l.outputs*l.batch, l.cell_gpu - l.outputs*l.batch, 1, l.prev_cell_gpu, 1); + if (i != 0) simple_copy_ongpu(l.outputs*l.batch, l.cell_gpu - l.outputs*l.batch, l.prev_cell_gpu); //else fill_ongpu(l.outputs * l.batch, 0, l.prev_cell_gpu, 1); // dont use - else if (state.net.current_subdivision % sequence != 0) copy_ongpu(l.outputs*l.batch, l.last_prev_cell_gpu, 1, l.prev_cell_gpu, 1); + else if (state.net.current_subdivision % sequence != 0) simple_copy_ongpu(l.outputs*l.batch, l.last_prev_cell_gpu, l.prev_cell_gpu); - copy_ongpu(l.outputs*l.batch, l.cell_gpu, 1, l.c_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch, l.cell_gpu, l.c_gpu); - if (i != 0) copy_ongpu(l.outputs*l.batch, l.output_gpu - l.outputs*l.batch, 1, l.prev_state_gpu, 1); + if (i != 0) simple_copy_ongpu(l.outputs*l.batch, l.output_gpu - l.outputs*l.batch, l.prev_state_gpu); //else fill_ongpu(l.outputs * l.batch, 0, l.prev_state_gpu, 1); // dont use - else if(state.net.current_subdivision % sequence != 0) copy_ongpu(l.outputs*l.batch, l.last_prev_state_gpu, 1, l.prev_state_gpu, 1); + else if(state.net.current_subdivision % sequence != 0) simple_copy_ongpu(l.outputs*l.batch, l.last_prev_state_gpu, l.prev_state_gpu); - copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.h_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch, l.output_gpu, l.h_gpu); l.dh_gpu = (i == 0) ? 0 : l.delta_gpu - l.outputs*l.batch; // f = wf + uf + vf - copy_ongpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1); - if (l.peephole) axpy_ongpu(l.outputs*l.batch, 1, vf.output_gpu, 1, l.f_gpu, 1); + add_3_arrays_activate(wf.output_gpu, uf.output_gpu, (l.peephole) ? vf.output_gpu : NULL, l.outputs*l.batch, LOGISTIC, l.f_gpu); + //copy_ongpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1); + //axpy_ongpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1); + //if (l.peephole) axpy_ongpu(l.outputs*l.batch, 1, vf.output_gpu, 1, l.f_gpu, 1); + //activate_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC); // i = wi + ui + vi - copy_ongpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1); - if (l.peephole) axpy_ongpu(l.outputs*l.batch, 1, vi.output_gpu, 1, l.i_gpu, 1); + add_3_arrays_activate(wi.output_gpu, ui.output_gpu, (l.peephole) ? vi.output_gpu : NULL, l.outputs*l.batch, LOGISTIC, l.i_gpu); + //copy_ongpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1); + //axpy_ongpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1); + //if (l.peephole) axpy_ongpu(l.outputs*l.batch, 1, vi.output_gpu, 1, l.i_gpu, 1); + //activate_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC); // g = wg + ug - copy_ongpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1); + add_3_arrays_activate(wg.output_gpu, ug.output_gpu, NULL, l.outputs*l.batch, TANH, l.g_gpu); + //copy_ongpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1); + //axpy_ongpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1); + //activate_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH); // o = wo + uo + vo - copy_ongpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1); - axpy_ongpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1); - if (l.peephole) axpy_ongpu(l.outputs*l.batch, 1, vo.output_gpu, 1, l.o_gpu, 1); - - activate_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC); - activate_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC); - activate_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH); - activate_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC); + add_3_arrays_activate(wo.output_gpu, uo.output_gpu, (l.peephole) ? vo.output_gpu : NULL, l.outputs*l.batch, LOGISTIC, l.o_gpu); + //copy_ongpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1); + //axpy_ongpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1); + //if (l.peephole) axpy_ongpu(l.outputs*l.batch, 1, vo.output_gpu, 1, l.o_gpu, 1); + //activate_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC); - copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, l.temp3_gpu, 1); // temp3 = delta + simple_copy_ongpu(l.outputs*l.batch, l.delta_gpu, l.temp3_gpu); // temp3 = delta - copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch, l.c_gpu, l.temp_gpu); activate_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH); // temp = tanh(c) - copy_ongpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp2_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch, l.temp3_gpu, l.temp2_gpu); mul_ongpu(l.outputs*l.batch, l.o_gpu, 1, l.temp2_gpu, 1); // temp2 = delta * o - gradient_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH, l.temp2_gpu); // temp2 = delta * o * grad_tanh(tahc(c)) + gradient_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH, l.temp2_gpu); // temp2 = delta * o * grad_tanh(tanh(c)) //??? - axpy_ongpu(l.outputs*l.batch, 1, l.dc_gpu, 1, l.temp2_gpu, 1); // temp2 = delta * o * grad_tanh(tahc(c)) + delta_c(???) + axpy_ongpu(l.outputs*l.batch, 1, l.dc_gpu, 1, l.temp2_gpu, 1); // temp2 = delta * o * grad_tanh(tanh(c)) + delta_c(???) // temp = tanh(c) - // temp2 = delta * o * grad_tanh(tahc(c)) + delta_c(???) + // temp2 = delta * o * grad_tanh(tanh(c)) + delta_c(???) // temp3 = delta - copy_ongpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch, l.c_gpu, l.temp_gpu); activate_array_ongpu(l.temp_gpu, l.outputs*l.batch, TANH); // temp = tanh(c) mul_ongpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp_gpu, 1); // temp = delta * tanh(c) gradient_array_ongpu(l.o_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu); // temp = delta * tanh(c) * grad_logistic(o) // delta for o(w,u,v): temp = delta * tanh(c) * grad_logistic(o) - // delta for c,f,i,g(w,u,v): temp2 = delta * o * grad_tanh(tahc(c)) + delta_c(???) + // delta for c,f,i,g(w,u,v): temp2 = delta * o * grad_tanh(tanh(c)) + delta_c(???) // delta for output: temp3 = delta // o // delta for O(w,u,v): temp = delta * tanh(c) * grad_logistic(o) if (l.peephole) { - copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, vo.delta_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch, l.temp_gpu, vo.delta_gpu); s.input = l.cell_gpu; //s.delta = l.dc_gpu; backward_convolutional_layer_gpu(vo, s); } - copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wo.delta_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch, l.temp_gpu, wo.delta_gpu); s.input = l.prev_state_gpu; //s.delta = l.dh_gpu; backward_convolutional_layer_gpu(wo, s); - copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, uo.delta_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch, l.temp_gpu, uo.delta_gpu); s.input = state.input; s.delta = state.delta; backward_convolutional_layer_gpu(uo, s); // g - copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch, l.temp2_gpu, l.temp_gpu); mul_ongpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1); gradient_array_ongpu(l.g_gpu, l.outputs*l.batch, TANH, l.temp_gpu); - // delta for c,f,i,g(w,u,v): temp = (delta * o * grad_tanh(tahc(c)) + delta_c(???)) * i * grad_tanh(g) + // delta for c,f,i,g(w,u,v): temp = (delta * o * grad_tanh(tanh(c)) + delta_c(???)) * i * grad_tanh(g) - copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wg.delta_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch, l.temp_gpu, wg.delta_gpu); s.input = l.prev_state_gpu; //s.delta = l.dh_gpu; backward_convolutional_layer_gpu(wg, s); - copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, ug.delta_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch, l.temp_gpu, ug.delta_gpu); s.input = state.input; s.delta = state.delta; backward_convolutional_layer_gpu(ug, s); // i - copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch, l.temp2_gpu, l.temp_gpu); mul_ongpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1); gradient_array_ongpu(l.i_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu); - // delta for c,f,i,g(w,u,v): temp = (delta * o * grad_tanh(tahc(c)) + delta_c(???)) * g * grad_logistic(i) + // delta for c,f,i,g(w,u,v): temp = (delta * o * grad_tanh(tanh(c)) + delta_c(???)) * g * grad_logistic(i) if (l.peephole) { - copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, vi.delta_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch, l.temp_gpu, vi.delta_gpu); s.input = l.prev_cell_gpu; //s.delta = l.dc_gpu; backward_convolutional_layer_gpu(vi, s); } - copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wi.delta_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch, l.temp_gpu, wi.delta_gpu); s.input = l.prev_state_gpu; //s.delta = l.dh_gpu; backward_convolutional_layer_gpu(wi, s); - copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, ui.delta_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch, l.temp_gpu, ui.delta_gpu); s.input = state.input; s.delta = state.delta; backward_convolutional_layer_gpu(ui, s); // f - copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch, l.temp2_gpu, l.temp_gpu); mul_ongpu(l.outputs*l.batch, l.prev_cell_gpu, 1, l.temp_gpu, 1); gradient_array_ongpu(l.f_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu); - // delta for c,f,i,g(w,u,v): temp = (delta * o * grad_tanh(tahc(c)) + delta_c(???)) * c * grad_logistic(f) + // delta for c,f,i,g(w,u,v): temp = (delta * o * grad_tanh(tanh(c)) + delta_c(???)) * c * grad_logistic(f) if (l.peephole) { - copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, vf.delta_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch, l.temp_gpu, vf.delta_gpu); s.input = l.prev_cell_gpu; //s.delta = l.dc_gpu; backward_convolutional_layer_gpu(vf, s); } - copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, wf.delta_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch, l.temp_gpu, wf.delta_gpu); s.input = l.prev_state_gpu; //s.delta = l.dh_gpu; backward_convolutional_layer_gpu(wf, s); - copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, uf.delta_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch, l.temp_gpu, uf.delta_gpu); s.input = state.input; s.delta = state.delta; backward_convolutional_layer_gpu(uf, s); // c - copy_ongpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch, l.temp2_gpu, l.temp_gpu); mul_ongpu(l.outputs*l.batch, l.f_gpu, 1, l.temp_gpu, 1); - copy_ongpu(l.outputs*l.batch, l.temp_gpu, 1, l.dc_gpu, 1); - // delta for c,f,i,g(w,u,v): delta_c = temp = (delta * o * grad_tanh(tahc(c)) + delta_c(???)) * f // (grad_linear(c)==1) + simple_copy_ongpu(l.outputs*l.batch, l.temp_gpu, l.dc_gpu); + fix_nan_and_inf(l.dc_gpu, l.outputs*l.batch); + // delta for c,f,i,g(w,u,v): delta_c = temp = (delta * o * grad_tanh(tanh(c)) + delta_c(???)) * f // (grad_linear(c)==1) state.input -= l.inputs*l.batch; if (state.delta) state.delta -= l.inputs*l.batch; // new delta: state.delta = prev_layer.delta_gpu; @@ -1174,8 +1177,8 @@ void backward_conv_lstm_layer_gpu(layer l, network_state state) increment_layer(&uo, -1); } - copy_ongpu(l.outputs*l.batch, last_output, 1, l.last_prev_state_gpu, 1); - copy_ongpu(l.outputs*l.batch, last_cell, 1, l.last_prev_cell_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch, last_output, l.last_prev_state_gpu); + simple_copy_ongpu(l.outputs*l.batch, last_cell, l.last_prev_cell_gpu); // free state after each 100 iterations //if (get_current_batch(state.net) % 100) free_state_conv_lstm(l); // dont use diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index d7e9a50b0d7..d6f726c3c15 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -153,7 +153,7 @@ image load_image_cv(char *filename, int channels) cv::Mat mat = load_image_mat(filename, channels); if (mat.empty()) { - return make_image(10, 10, 3); + return make_image(10, 10, channels); } return mat_to_image(mat); } From 24f7d94ab4214dd43805270056effd95bfd9a5d9 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 27 May 2019 20:55:42 +0300 Subject: [PATCH 0502/1365] Fixed resizing REORG_OLD layer --- src/detector.c | 2 +- src/network.c | 2 ++ 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/src/detector.c b/src/detector.c index c750ff7af58..bf8d1181fa6 100644 --- a/src/detector.c +++ b/src/detector.c @@ -86,7 +86,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i printf("\n Error: You set incorrect value batch=1 for Training! You should set batch=64 subdivision=64 \n"); getchar(); } - else if (actual_batch_size < 64) { + else if (actual_batch_size < 8) { printf("\n Warning: You set batch=%d lower than 64! It is recommended to set batch=64 subdivision=64 \n", actual_batch_size); } diff --git a/src/network.c b/src/network.c index 86c248c4a1f..d54727b39aa 100644 --- a/src/network.c +++ b/src/network.c @@ -523,6 +523,8 @@ int resize_network(network *net, int w, int h) resize_upsample_layer(&l, w, h); }else if(l.type == REORG){ resize_reorg_layer(&l, w, h); + } else if (l.type == REORG_OLD) { + resize_reorg_old_layer(&l, w, h); }else if(l.type == AVGPOOL){ resize_avgpool_layer(&l, w, h); }else if(l.type == NORMALIZATION){ From 55dcd1bcb8d83f27c9118a9a4684ad73190e2ca3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 27 May 2019 22:10:07 +0300 Subject: [PATCH 0503/1365] Compile fix --- src/network.c | 1 + 1 file changed, 1 insertion(+) diff --git a/src/network.c b/src/network.c index d54727b39aa..be02957fdf9 100644 --- a/src/network.c +++ b/src/network.c @@ -25,6 +25,7 @@ #include "batchnorm_layer.h" #include "maxpool_layer.h" #include "reorg_layer.h" +#include "reorg_old_layer.h" #include "avgpool_layer.h" #include "cost_layer.h" #include "softmax_layer.h" From 6e13527f06480505556455dd9aad34d627a74501 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 1 Jun 2019 01:27:18 +0300 Subject: [PATCH 0504/1365] Added GIoU --- .../x64/cfg/yolov3-voc.yolov3-giou-40.cfg | 808 ++++++++++++++++++ build/darknet/x64/cfg/yolov3.coco-giou-12.cfg | 806 +++++++++++++++++ cfg/yolov3-voc.yolov3-giou-40.cfg | 808 ++++++++++++++++++ cfg/yolov3.coco-giou-12.cfg | 806 +++++++++++++++++ include/darknet.h | 27 + src/box.c | 132 ++- src/box.h | 3 + src/option_list.c | 7 + src/option_list.h | 1 + src/parser.c | 10 +- src/utils.c | 13 + src/utils.h | 1 + src/yolo_layer.c | 160 +++- 13 files changed, 3551 insertions(+), 31 deletions(-) create mode 100644 build/darknet/x64/cfg/yolov3-voc.yolov3-giou-40.cfg create mode 100644 build/darknet/x64/cfg/yolov3.coco-giou-12.cfg create mode 100644 cfg/yolov3-voc.yolov3-giou-40.cfg create mode 100644 cfg/yolov3.coco-giou-12.cfg diff --git a/build/darknet/x64/cfg/yolov3-voc.yolov3-giou-40.cfg b/build/darknet/x64/cfg/yolov3-voc.yolov3-giou-40.cfg new file mode 100644 index 00000000000..b56f8a5d698 --- /dev/null +++ b/build/darknet/x64/cfg/yolov3-voc.yolov3-giou-40.cfg @@ -0,0 +1,808 @@ +[net] +# Testing +# batch=1 +# subdivisions=1 +# Training +batch=64 +subdivisions=16 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +## single gpu +learning_rate=0.001 +burn_in=1000 +max_batches = 100400 + +## 2x +#learning_rate=0.0005 +#burn_in=2000 +#max_batches = 100400 +#max_batches = 200800 + +## 4x +#learning_rate=0.00025 +#burn_in=4000 +#max_batches = 50200 +##max_batches = 200800 + +policy=steps +steps=40000,45000 +scales=.1,.1 + + + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=75 +activation=linear + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=20 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +random=1 +iou_normalizer=0.25 +cls_normalizer=1.0 +iou_loss=giou + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=75 +activation=linear + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=20 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +random=1 +iou_normalizer=0.25 +cls_normalizer=1.0 +iou_loss=giou + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=75 +activation=linear + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=20 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +random=1 +iou_normalizer=0.25 +cls_normalizer=1.0 +iou_loss=giou + diff --git a/build/darknet/x64/cfg/yolov3.coco-giou-12.cfg b/build/darknet/x64/cfg/yolov3.coco-giou-12.cfg new file mode 100644 index 00000000000..f3fd72db0b1 --- /dev/null +++ b/build/darknet/x64/cfg/yolov3.coco-giou-12.cfg @@ -0,0 +1,806 @@ +[net] +# Testing +# batch=1 +# subdivisions=1 +# Training +batch=64 +subdivisions=16 +width=608 +height=608 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +## single gpu +learning_rate=0.001 +burn_in=1000 +max_batches = 550400 + +## 2 gpu +#learning_rate=0.0005 +#burn_in=2000 +#max_batches = 500200 + +## 4 gpu +#learning_rate=0.00025 +#burn_in=4000 +#max_batches = 500200 +###max_batches = 2000800 + +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 +iou_normalizer=0.5 +iou_loss=giou + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 +iou_normalizer=0.5 +iou_loss=giou + + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 +iou_normalizer=0.5 +iou_loss=giou diff --git a/cfg/yolov3-voc.yolov3-giou-40.cfg b/cfg/yolov3-voc.yolov3-giou-40.cfg new file mode 100644 index 00000000000..b56f8a5d698 --- /dev/null +++ b/cfg/yolov3-voc.yolov3-giou-40.cfg @@ -0,0 +1,808 @@ +[net] +# Testing +# batch=1 +# subdivisions=1 +# Training +batch=64 +subdivisions=16 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +## single gpu +learning_rate=0.001 +burn_in=1000 +max_batches = 100400 + +## 2x +#learning_rate=0.0005 +#burn_in=2000 +#max_batches = 100400 +#max_batches = 200800 + +## 4x +#learning_rate=0.00025 +#burn_in=4000 +#max_batches = 50200 +##max_batches = 200800 + +policy=steps +steps=40000,45000 +scales=.1,.1 + + + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=75 +activation=linear + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=20 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +random=1 +iou_normalizer=0.25 +cls_normalizer=1.0 +iou_loss=giou + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=75 +activation=linear + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=20 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +random=1 +iou_normalizer=0.25 +cls_normalizer=1.0 +iou_loss=giou + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=75 +activation=linear + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=20 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +random=1 +iou_normalizer=0.25 +cls_normalizer=1.0 +iou_loss=giou + diff --git a/cfg/yolov3.coco-giou-12.cfg b/cfg/yolov3.coco-giou-12.cfg new file mode 100644 index 00000000000..f3fd72db0b1 --- /dev/null +++ b/cfg/yolov3.coco-giou-12.cfg @@ -0,0 +1,806 @@ +[net] +# Testing +# batch=1 +# subdivisions=1 +# Training +batch=64 +subdivisions=16 +width=608 +height=608 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +## single gpu +learning_rate=0.001 +burn_in=1000 +max_batches = 550400 + +## 2 gpu +#learning_rate=0.0005 +#burn_in=2000 +#max_batches = 500200 + +## 4 gpu +#learning_rate=0.00025 +#burn_in=4000 +#max_batches = 500200 +###max_batches = 2000800 + +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 +iou_normalizer=0.5 +iou_loss=giou + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 +iou_normalizer=0.5 +iou_loss=giou + + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 +iou_normalizer=0.5 +iou_loss=giou diff --git a/include/darknet.h b/include/darknet.h index 0eb52b37b03..2af9f2534e6 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -105,6 +105,11 @@ typedef enum { LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU }ACTIVATION; +// parser.h +typedef enum { + IOU, GIOU, MSE +} IOU_LOSS; + // image.h typedef enum{ PNG, BMP, TGA, JPG @@ -309,6 +314,10 @@ struct layer { float *weights; float *weight_updates; + float iou_normalizer; + float cls_normalizer; + int iou_loss; + char *align_bit_weights_gpu; float *mean_arr_gpu; float *align_workspace_gpu; @@ -671,6 +680,24 @@ typedef struct box { float x, y, w, h; } box; +// box.h +typedef struct boxabs { + float left, right, top, bot; +} boxabs; + +// box.h +typedef struct dxrep { + float dt, db, dl, dr; +} dxrep; + +// box.h +typedef struct ious { + float iou, giou; + dxrep dx_iou; + dxrep dx_giou; +} ious; + + // box.h typedef struct detection{ box bbox; diff --git a/src/box.c b/src/box.c index 28371362482..640f54a299e 100644 --- a/src/box.c +++ b/src/box.c @@ -64,6 +64,30 @@ dbox derivative(box a, box b) return d; } +// where c is the smallest box that fully encompases a and b +boxabs box_c(box a, box b) { + boxabs ba = { 0 }; + ba.top = fmin(a.y - a.h / 2, b.y - b.h / 2); + ba.bot = fmax(a.y + a.h / 2, b.y + b.h / 2); + ba.left = fmin(a.x - a.w / 2, b.x - b.w / 2); + ba.right = fmax(a.x + a.w / 2, b.x + b.w / 2); + return ba; +} + +// representation from x, y, w, h to top, left, bottom, right +boxabs to_tblr(box a) { + boxabs tblr = { 0 }; + float t = a.y - (a.h / 2); + float b = a.y + (a.h / 2); + float l = a.x - (a.w / 2); + float r = a.x + (a.w / 2); + tblr.top = t; + tblr.bot = b; + tblr.left = l; + tblr.right = r; + return tblr; +} + float overlap(float x1, float w1, float x2, float w2) { float l1 = x1 - w1/2; @@ -93,7 +117,113 @@ float box_union(box a, box b) float box_iou(box a, box b) { - return box_intersection(a, b)/box_union(a, b); + //return box_intersection(a, b)/box_union(a, b); + + float I = box_intersection(a, b); + float U = box_union(a, b); + if (I == 0 || U == 0) { + return 0; + } + return I / U; +} + +float box_giou(box a, box b) +{ + boxabs ba = box_c(a, b); + float w = ba.right - ba.left; + float h = ba.bot - ba.top; + float c = w*h; + float iou = box_iou(a, b); + if (c == 0) { + return iou; + } + float u = box_union(a, b); + float giou_term = (c - u) / c; +#ifdef DEBUG_PRINTS + printf(" c: %f, u: %f, giou_term: %f\n", c, u, giou_term); +#endif + return iou - giou_term; +} + +dxrep dx_box_iou(box pred, box truth, IOU_LOSS iou_loss) { + boxabs pred_tblr = to_tblr(pred); + float pred_t = fmin(pred_tblr.top, pred_tblr.bot); + float pred_b = fmax(pred_tblr.top, pred_tblr.bot); + float pred_l = fmin(pred_tblr.left, pred_tblr.right); + float pred_r = fmax(pred_tblr.left, pred_tblr.right); + + boxabs truth_tblr = to_tblr(truth); +#ifdef DEBUG_PRINTS + printf("\niou: %f, giou: %f\n", box_iou(pred, truth), box_giou(pred, truth)); + printf("pred: x,y,w,h: (%f, %f, %f, %f) -> t,b,l,r: (%f, %f, %f, %f)\n", pred.x, pred.y, pred.w, pred.h, pred_tblr.top, pred_tblr.bot, pred_tblr.left, pred_tblr.right); + printf("truth: x,y,w,h: (%f, %f, %f, %f) -> t,b,l,r: (%f, %f, %f, %f)\n", truth.x, truth.y, truth.w, truth.h, truth_tblr.top, truth_tblr.bot, truth_tblr.left, truth_tblr.right); +#endif + //printf("pred (t,b,l,r): (%f, %f, %f, %f)\n", pred_t, pred_b, pred_l, pred_r); + //printf("trut (t,b,l,r): (%f, %f, %f, %f)\n", truth_tblr.top, truth_tblr.bot, truth_tblr.left, truth_tblr.right); + dxrep dx = { 0 }; + float X = (pred_b - pred_t) * (pred_r - pred_l); + float Xhat = (truth_tblr.bot - truth_tblr.top) * (truth_tblr.right - truth_tblr.left); + float Ih = fmin(pred_b, truth_tblr.bot) - fmax(pred_t, truth_tblr.top); + float Iw = fmin(pred_r, truth_tblr.right) - fmax(pred_l, truth_tblr.left); + float I = Iw * Ih; + float U = X + Xhat - I; + + float Cw = fmax(pred_r, truth_tblr.right) - fmin(pred_l, truth_tblr.left); + float Ch = fmax(pred_b, truth_tblr.bot) - fmin(pred_t, truth_tblr.top); + float C = Cw * Ch; + + // float IoU = I / U; + // Partial Derivatives, derivatives + float dX_wrt_t = -1 * (pred_r - pred_l); + float dX_wrt_b = pred_r - pred_l; + float dX_wrt_l = -1 * (pred_b - pred_t); + float dX_wrt_r = pred_b - pred_t; + + // gradient of I min/max in IoU calc (prediction) + float dI_wrt_t = pred_t > truth_tblr.top ? (-1 * Iw) : 0; + float dI_wrt_b = pred_b < truth_tblr.bot ? Iw : 0; + float dI_wrt_l = pred_l > truth_tblr.left ? (-1 * Ih) : 0; + float dI_wrt_r = pred_r < truth_tblr.right ? Ih : 0; + // derivative of U with regard to x + float dU_wrt_t = dX_wrt_t - dI_wrt_t; + float dU_wrt_b = dX_wrt_b - dI_wrt_b; + float dU_wrt_l = dX_wrt_l - dI_wrt_l; + float dU_wrt_r = dX_wrt_r - dI_wrt_r; + // gradient of C min/max in IoU calc (prediction) + float dC_wrt_t = pred_t < truth_tblr.top ? (-1 * Cw) : 0; + float dC_wrt_b = pred_b > truth_tblr.bot ? Cw : 0; + float dC_wrt_l = pred_l < truth_tblr.left ? (-1 * Ch) : 0; + float dC_wrt_r = pred_r > truth_tblr.right ? Ch : 0; + + // Final IOU loss (prediction) (negative of IOU gradient, we want the negative loss) + float p_dt = 0; + float p_db = 0; + float p_dl = 0; + float p_dr = 0; + if (U > 0) { + p_dt = ((U * dI_wrt_t) - (I * dU_wrt_t)) / (U * U); + p_db = ((U * dI_wrt_b) - (I * dU_wrt_b)) / (U * U); + p_dl = ((U * dI_wrt_l) - (I * dU_wrt_l)) / (U * U); + p_dr = ((U * dI_wrt_r) - (I * dU_wrt_r)) / (U * U); + } + + if (iou_loss == GIOU) { + if (C > 0) { + // apply "C" term from gIOU + p_dt += ((C * dU_wrt_t) - (U * dC_wrt_t)) / (C * C); + p_db += ((C * dU_wrt_b) - (U * dC_wrt_b)) / (C * C); + p_dl += ((C * dU_wrt_l) - (U * dC_wrt_l)) / (C * C); + p_dr += ((C * dU_wrt_r) - (U * dC_wrt_r)) / (C * C); + } + } + + // apply grad from prediction min/max for correct corner selection + dx.dt = pred_tblr.top < pred_tblr.bot ? p_dt : p_db; + dx.db = pred_tblr.top < pred_tblr.bot ? p_db : p_dt; + dx.dl = pred_tblr.left < pred_tblr.right ? p_dl : p_dr; + dx.dr = pred_tblr.left < pred_tblr.right ? p_dr : p_dl; + + return dx; } float box_rmse(box a, box b) diff --git a/src/box.h b/src/box.h index 9d6aa4f3469..2392fedd20c 100644 --- a/src/box.h +++ b/src/box.h @@ -33,7 +33,10 @@ extern "C" { box float_to_box(float *f); float box_iou(box a, box b); float box_rmse(box a, box b); +dxrep dx_box_iou(box a, box b, IOU_LOSS iou_loss); +float box_giou(box a, box b); dbox diou(box a, box b); +boxabs to_tblr(box a); void do_nms(box *boxes, float **probs, int total, int classes, float thresh); void do_nms_sort_v2(box *boxes, float **probs, int total, int classes, float thresh); //LIB_API void do_nms_sort(detection *dets, int total, int classes, float thresh); diff --git a/src/option_list.c b/src/option_list.c index 22bc8c1bef6..e4fa7fe4552 100644 --- a/src/option_list.c +++ b/src/option_list.c @@ -112,6 +112,13 @@ char *option_find_str(list *l, char *key, char *def) return def; } +char *option_find_str_quiet(list *l, char *key, char *def) +{ + char *v = option_find(l, key); + if (v) return v; + return def; +} + int option_find_int(list *l, char *key, int def) { char *v = option_find(l, key); diff --git a/src/option_list.h b/src/option_list.h index 9efa274aa67..726b559aa4e 100644 --- a/src/option_list.h +++ b/src/option_list.h @@ -18,6 +18,7 @@ int read_option(char *s, list *options); void option_insert(list *l, char *key, char *val); char *option_find(list *l, char *key); char *option_find_str(list *l, char *key, char *def); +char *option_find_str_quiet(list *l, char *key, char *def); int option_find_int(list *l, char *key, int def); int option_find_int_quiet(list *l, char *key, int def); float option_find_float(list *l, char *key, float def); diff --git a/src/parser.c b/src/parser.c index 56a0bcb78f9..2e444b9d135 100644 --- a/src/parser.c +++ b/src/parser.c @@ -333,7 +333,15 @@ layer parse_yolo(list *options, size_params params) } //assert(l.outputs == params.inputs); - //l.max_boxes = option_find_int_quiet(options, "max", 90); + l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75); + l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1); + char *iou_loss = option_find_str_quiet(options, "iou_loss", "mse"); // "iou"); + + if (strcmp(iou_loss, "mse") == 0) l.iou_loss = MSE; + else if (strcmp(iou_loss, "giou") == 0) l.iou_loss = GIOU; + else l.iou_loss = IOU; + fprintf(stderr, "Yolo layer params: iou loss: %s, iou_normalizer: %f, cls_normalizer: %f\n", (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.cls_normalizer); + l.jitter = option_find_float(options, "jitter", .2); l.focal_loss = option_find_int_quiet(options, "focal_loss", 0); diff --git a/src/utils.c b/src/utils.c index cdaf1e617b7..bee427ed743 100644 --- a/src/utils.c +++ b/src/utils.c @@ -621,6 +621,19 @@ float mag_array(float *a, int n) return sqrt(sum); } +// indicies to skip is a bit array +float mag_array_skip(float *a, int n, int * indices_to_skip) +{ + int i; + float sum = 0; + for (i = 0; i < n; ++i) { + if (indices_to_skip[i] != 1) { + sum += a[i] * a[i]; + } + } + return sqrt(sum); +} + void scale_array(float *a, int n, float s) { int i; diff --git a/src/utils.h b/src/utils.h index 183bb641cc3..fe4efe04ef1 100644 --- a/src/utils.h +++ b/src/utils.h @@ -58,6 +58,7 @@ float mean_array(float *a, int n); void mean_arrays(float **a, int n, int els, float *avg); float variance_array(float *a, int n); float mag_array(float *a, int n); +float mag_array_skip(float *a, int n, int * indices_to_skip); float dist_array(float *a, float *b, int n, int sub); float **one_hot_encode(float *a, int n, int k); float sec(clock_t clocks); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index ae48ef7ae07..18fdf040207 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -127,7 +127,7 @@ box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw b.h = exp(x[index + 3*stride]) * biases[2*n+1] / h; return b; } - +/* float delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride) { box pred = get_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride); @@ -144,7 +144,57 @@ float delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i delta[index + 3*stride] = scale * (th - x[index + 3*stride]); return iou; } +*/ + +ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss) +{ + ious all_ious = { 0 }; + // i - step in layer width + // j - step in layer height + // Returns a box in absolute coordinates + box pred = get_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride); + all_ious.iou = box_iou(pred, truth); + all_ious.giou = box_giou(pred, truth); + // avoid nan in dx_box_iou + if (pred.w == 0) { pred.w = 1.0; } + if (pred.h == 0) { pred.h = 1.0; } + if (iou_loss == MSE) // old loss + { + float tx = (truth.x*lw - i); + float ty = (truth.y*lh - j); + float tw = log(truth.w*w / biases[2 * n]); + float th = log(truth.h*h / biases[2 * n + 1]); + + delta[index + 0 * stride] = scale * (tx - x[index + 0 * stride]); + delta[index + 1 * stride] = scale * (ty - x[index + 1 * stride]); + delta[index + 2 * stride] = scale * (tw - x[index + 2 * stride]); + delta[index + 3 * stride] = scale * (th - x[index + 3 * stride]); + } + else { + // https://github.com/generalized-iou/g-darknet + // https://arxiv.org/abs/1902.09630v2 + // https://giou.stanford.edu/ + all_ious.dx_iou = dx_box_iou(pred, truth, iou_loss); + + // jacobian^t (transpose) + delta[index + 0 * stride] = (all_ious.dx_iou.dl + all_ious.dx_iou.dr); + delta[index + 1 * stride] = (all_ious.dx_iou.dt + all_ious.dx_iou.db); + delta[index + 2 * stride] = ((-0.5 * all_ious.dx_iou.dl) + (0.5 * all_ious.dx_iou.dr)); + delta[index + 3 * stride] = ((-0.5 * all_ious.dx_iou.dt) + (0.5 * all_ious.dx_iou.db)); + + // predict exponential, apply gradient of e^delta_t ONLY for w,h + delta[index + 2 * stride] *= exp(x[index + 2 * stride]); + delta[index + 3 * stride] *= exp(x[index + 3 * stride]); + + // normalize iou weight + delta[index + 0 * stride] *= iou_normalizer; + delta[index + 1 * stride] *= iou_normalizer; + delta[index + 2 * stride] *= iou_normalizer; + delta[index + 3 * stride] *= iou_normalizer; + } + return all_ious; +} void delta_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, int focal_loss) { @@ -202,23 +252,27 @@ static box float_to_box_stride(float *f, int stride) void forward_yolo_layer(const layer l, network_state state) { - int i,j,b,t,n; - memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float)); + int i, j, b, t, n; + memcpy(l.output, state.input, l.outputs*l.batch * sizeof(float)); #ifndef GPU - for (b = 0; b < l.batch; ++b){ - for(n = 0; n < l.n; ++n){ + for (b = 0; b < l.batch; ++b) { + for (n = 0; n < l.n; ++n) { int index = entry_index(l, b, n*l.w*l.h, 0); - activate_array(l.output + index, 2*l.w*l.h, LOGISTIC); + activate_array(l.output + index, 2 * l.w*l.h, LOGISTIC); index = entry_index(l, b, n*l.w*l.h, 4); - activate_array(l.output + index, (1+l.classes)*l.w*l.h, LOGISTIC); + activate_array(l.output + index, (1 + l.classes)*l.w*l.h, LOGISTIC); } } #endif memset(l.delta, 0, l.outputs * l.batch * sizeof(float)); - if(!state.train) return; - float avg_iou = 0; + if (!state.train) return; + //float avg_iou = 0; + float tot_iou = 0; + float tot_giou = 0; + float tot_iou_loss = 0; + float tot_giou_loss = 0; float recall = 0; float recall75 = 0; float avg_cat = 0; @@ -235,7 +289,7 @@ void forward_yolo_layer(const layer l, network_state state) box pred = get_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w*l.h); float best_iou = 0; int best_t = 0; - for(t = 0; t < l.max_boxes; ++t){ + for (t = 0; t < l.max_boxes; ++t) { box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; if (class_id >= l.classes) { @@ -244,7 +298,7 @@ void forward_yolo_layer(const layer l, network_state state) getchar(); continue; // if label contains class_id more than number of classes in the cfg-file } - if(!truth.x) break; // continue; + if (!truth.x) break; // continue; float iou = box_iou(pred, truth); if (iou > best_iou) { best_iou = iou; @@ -253,24 +307,24 @@ void forward_yolo_layer(const layer l, network_state state) } int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4); avg_anyobj += l.output[obj_index]; - l.delta[obj_index] = 0 - l.output[obj_index]; + l.delta[obj_index] = l.cls_normalizer * (0 - l.output[obj_index]); if (best_iou > l.ignore_thresh) { l.delta[obj_index] = 0; } if (best_iou > l.truth_thresh) { - l.delta[obj_index] = 1 - l.output[obj_index]; + l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); int class_id = state.truth[best_t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss); box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); - delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h); + delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss); } } } } - for(t = 0; t < l.max_boxes; ++t){ + for (t = 0; t < l.max_boxes; ++t) { box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); if (truth.x < 0 || truth.y < 0 || truth.x > 1 || truth.y > 1 || truth.w < 0 || truth.h < 0) { printf(" Wrong label: truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f \n", truth.x, truth.y, truth.w, truth.h); @@ -278,32 +332,40 @@ void forward_yolo_layer(const layer l, network_state state) int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; if (class_id >= l.classes) continue; // if label contains class_id more than number of classes in the cfg-file - if(!truth.x) break; // continue; + if (!truth.x) break; // continue; float best_iou = 0; int best_n = 0; i = (truth.x * l.w); j = (truth.y * l.h); box truth_shift = truth; truth_shift.x = truth_shift.y = 0; - for(n = 0; n < l.total; ++n){ - box pred = {0}; - pred.w = l.biases[2*n]/ state.net.w; - pred.h = l.biases[2*n+1]/ state.net.h; + for (n = 0; n < l.total; ++n) { + box pred = { 0 }; + pred.w = l.biases[2 * n] / state.net.w; + pred.h = l.biases[2 * n + 1] / state.net.h; float iou = box_iou(pred, truth_shift); - if (iou > best_iou){ + if (iou > best_iou) { best_iou = iou; best_n = n; } } int mask_n = int_index(l.mask, best_n, l.n); - if(mask_n >= 0){ + if (mask_n >= 0) { int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); - float iou = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h); + //float iou = + ious all_ious = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss); + + // range is 0 <= 1 + tot_iou += all_ious.iou; + tot_iou_loss += 1 - all_ious.iou; + // range is -1 <= giou <= 1 + tot_giou += all_ious.giou; + tot_giou_loss += 1 - all_ious.giou; int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4); avg_obj += l.output[obj_index]; - l.delta[obj_index] = 1 - l.output[obj_index]; + l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; @@ -312,14 +374,54 @@ void forward_yolo_layer(const layer l, network_state state) ++count; ++class_count; - if(iou > .5) recall += 1; - if(iou > .75) recall75 += 1; - avg_iou += iou; + //if(iou > .5) recall += 1; + //if(iou > .75) recall75 += 1; + //avg_iou += iou; + if (all_ious.iou > .5) recall += 1; + if (all_ious.iou > .75) recall75 += 1; } } } - *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); - printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d\n", state.index, avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, recall75/count, count); + //*(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); + //printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d\n", state.index, avg_iou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count); + + float avg_iou_loss = 0; + // gIOU loss + MSE (objectness) loss + if (l.iou_loss == MSE) { + *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); + } + else { + // Always compute classification loss both for iou + cls loss and for logging with mse loss + // TODO: remove IOU loss fields before computing MSE on class + // probably split into two arrays + int stride = l.w*l.h; + float* no_iou_loss_delta = calloc(l.batch * l.outputs, sizeof(float)); + memcpy(no_iou_loss_delta, l.delta, l.batch * l.outputs * sizeof(float)); + for (b = 0; b < l.batch; ++b) { + for (j = 0; j < l.h; ++j) { + for (i = 0; i < l.w; ++i) { + for (n = 0; n < l.n; ++n) { + int index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); + no_iou_loss_delta[index + 0 * stride] = 0; + no_iou_loss_delta[index + 1 * stride] = 0; + no_iou_loss_delta[index + 2 * stride] = 0; + no_iou_loss_delta[index + 3 * stride] = 0; + } + } + } + } + float classification_loss = l.cls_normalizer * pow(mag_array(no_iou_loss_delta, l.outputs * l.batch), 2); + free(no_iou_loss_delta); + + if (l.iou_loss == GIOU) { + avg_iou_loss = count > 0 ? l.iou_normalizer * (tot_giou_loss / count) : 0; + } + else { + avg_iou_loss = count > 0 ? l.iou_normalizer * (tot_iou_loss / count) : 0; + } + *(l.cost) = avg_iou_loss + classification_loss; + } + printf("v3 (%s loss, Normalizer: (iou: %f, cls: %f) Region %d Avg (IOU: %f, GIOU: %f), Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d\n", (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.cls_normalizer, state.index, tot_iou / count, tot_giou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count); } void backward_yolo_layer(const layer l, network_state state) From eac26226a7fc0a9da2b684a564f8f086eaf38390 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 4 Jun 2019 21:20:59 +0300 Subject: [PATCH 0505/1365] Added scale_x_y param to [yolo]-layer (for sigmoid). In previous commit is used GIoU(generalized-iou) from https://github.com/generalized-iou/g-darknet --- include/darknet.h | 1 + scripts/README.md | 2 ++ src/blas.c | 6 ++++++ src/blas.h | 2 ++ src/blas_kernels.cu | 12 ++++++++++++ src/detector.c | 4 ++-- src/parser.c | 3 ++- src/yolo_layer.c | 4 +++- 8 files changed, 30 insertions(+), 4 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 2af9f2534e6..7ab05d5ad43 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -314,6 +314,7 @@ struct layer { float *weights; float *weight_updates; + float scale_x_y; float iou_normalizer; float cls_normalizer; int iou_loss; diff --git a/scripts/README.md b/scripts/README.md index a38327ecb1b..c38626eca81 100644 --- a/scripts/README.md +++ b/scripts/README.md @@ -2,6 +2,8 @@ ### Datasets: +BDD100K - Diverse Driving Video: https://bair.berkeley.edu/blog/2018/05/30/bdd/ + Pascal VOC: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html MS COCO: http://cocodataset.org/#download diff --git a/src/blas.c b/src/blas.c index a3ff84b6515..d00cb89ddf0 100644 --- a/src/blas.c +++ b/src/blas.c @@ -169,6 +169,12 @@ void scal_cpu(int N, float ALPHA, float *X, int INCX) for(i = 0; i < N; ++i) X[i*INCX] *= ALPHA; } +void scal_add_cpu(int N, float ALPHA, float BETA, float *X, int INCX) +{ + int i; + for (i = 0; i < N; ++i) X[i*INCX] = X[i*INCX] * ALPHA + BETA; +} + void fill_cpu(int N, float ALPHA, float *X, int INCX) { int i; diff --git a/src/blas.h b/src/blas.h index 09b9c9a70e5..ca9cd588c3b 100644 --- a/src/blas.h +++ b/src/blas.h @@ -27,6 +27,7 @@ void mul_cpu(int N, float *X, int INCX, float *Y, int INCY); void axpy_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY); void copy_cpu(int N, float *X, int INCX, float *Y, int INCY); void scal_cpu(int N, float ALPHA, float *X, int INCX); +void scal_add_cpu(int N, float ALPHA, float BETA, float *X, int INCX); void fill_cpu(int N, float ALPHA, float * X, int INCX); float dot_cpu(int N, float *X, int INCX, float *Y, int INCY); void test_gpu_blas(); @@ -61,6 +62,7 @@ void simple_copy_ongpu(int size, float *src, float *dst); void copy_ongpu(int N, float * X, int INCX, float * Y, int INCY); void copy_ongpu_offset(int N, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY); void scal_ongpu(int N, float ALPHA, float * X, int INCX); +void scal_add_ongpu(int N, float ALPHA, float BETA, float * X, int INCX); void supp_ongpu(int N, float ALPHA, float * X, int INCX); void mask_gpu_new_api(int N, float * X, float mask_num, float * mask, float val); void mask_ongpu(int N, float * X, float mask_num, float * mask); diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index cf6bed275fa..8474ebdec76 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -414,6 +414,12 @@ __global__ void scal_kernel(int N, float ALPHA, float *X, int INCX) if(i < N) X[i*INCX] *= ALPHA; } +__global__ void scal_add_kernel(int N, float ALPHA, float BETA, float *X, int INCX) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (i < N) X[i*INCX] = X[i*INCX] * ALPHA + BETA; +} + __global__ void fill_kernel(int N, float ALPHA, float *X, int INCX) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -644,6 +650,12 @@ extern "C" void scal_ongpu(int N, float ALPHA, float * X, int INCX) CHECK_CUDA(cudaPeekAtLastError()); } +extern "C" void scal_add_ongpu(int N, float ALPHA, float BETA, float * X, int INCX) +{ + scal_add_kernel << > >(N, ALPHA, BETA, X, INCX); + CHECK_CUDA(cudaPeekAtLastError()); +} + extern "C" void supp_ongpu(int N, float ALPHA, float * X, int INCX) { supp_kernel<<>>(N, ALPHA, X, INCX); diff --git a/src/detector.c b/src/detector.c index bf8d1181fa6..c9386914ced 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1046,10 +1046,10 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa const float cur_precision = (float)tp_for_thresh / ((float)tp_for_thresh + (float)fp_for_thresh); const float cur_recall = (float)tp_for_thresh / ((float)tp_for_thresh + (float)(unique_truth_count - tp_for_thresh)); const float f1_score = 2.F * cur_precision * cur_recall / (cur_precision + cur_recall); - printf("\n for thresh = %1.2f, precision = %1.2f, recall = %1.2f, F1-score = %1.2f \n", + printf("\n for conf_thresh = %1.2f, precision = %1.2f, recall = %1.2f, F1-score = %1.2f \n", thresh_calc_avg_iou, cur_precision, cur_recall, f1_score); - printf(" for thresh = %0.2f, TP = %d, FP = %d, FN = %d, average IoU = %2.2f %% \n", + printf(" for conf_thresh = %0.2f, TP = %d, FP = %d, FN = %d, average IoU = %2.2f %% \n", thresh_calc_avg_iou, tp_for_thresh, fp_for_thresh, unique_truth_count - tp_for_thresh, avg_iou * 100); mean_average_precision = mean_average_precision / classes; diff --git a/src/parser.c b/src/parser.c index 2e444b9d135..e285448c035 100644 --- a/src/parser.c +++ b/src/parser.c @@ -333,6 +333,7 @@ layer parse_yolo(list *options, size_params params) } //assert(l.outputs == params.inputs); + l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1); l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75); l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1); char *iou_loss = option_find_str_quiet(options, "iou_loss", "mse"); // "iou"); @@ -340,7 +341,7 @@ layer parse_yolo(list *options, size_params params) if (strcmp(iou_loss, "mse") == 0) l.iou_loss = MSE; else if (strcmp(iou_loss, "giou") == 0) l.iou_loss = GIOU; else l.iou_loss = IOU; - fprintf(stderr, "Yolo layer params: iou loss: %s, iou_normalizer: %f, cls_normalizer: %f\n", (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.cls_normalizer); + fprintf(stderr, "[yolo] params: iou loss: %s, iou_norm: %2.2f, cls_norm: %2.2f, scale_x_y: %2.2f\n", (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.cls_normalizer, l.scale_x_y); l.jitter = option_find_float(options, "jitter", .2); l.focal_loss = option_find_int_quiet(options, "focal_loss", 0); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 18fdf040207..d9d7d905e56 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -259,7 +259,8 @@ void forward_yolo_layer(const layer l, network_state state) for (b = 0; b < l.batch; ++b) { for (n = 0; n < l.n; ++n) { int index = entry_index(l, b, n*l.w*l.h, 0); - activate_array(l.output + index, 2 * l.w*l.h, LOGISTIC); + activate_array(l.output + index, 2 * l.w*l.h, LOGISTIC); // x,y, + scal_add_cpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale x,y index = entry_index(l, b, n*l.w*l.h, 4); activate_array(l.output + index, (1 + l.classes)*l.w*l.h, LOGISTIC); } @@ -553,6 +554,7 @@ void forward_yolo_layer_gpu(const layer l, network_state state) // if(y->1) x -> inf // if(y->0) x -> -inf activate_array_ongpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC); // x,y + scal_add_ongpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output_gpu + index, 1); // scale x,y index = entry_index(l, b, n*l.w*l.h, 4); activate_array_ongpu(l.output_gpu + index, (1+l.classes)*l.w*l.h, LOGISTIC); // classes and objectness } From 1f6f2e1162511acd342e9fc7921c0d5bef4e2daa Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 4 Jun 2019 21:46:11 +0300 Subject: [PATCH 0506/1365] Compile fix --- include/darknet.h | 2 +- src/yolo_layer.c | 21 +-------------------- 2 files changed, 2 insertions(+), 21 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 7ab05d5ad43..494bed2fb87 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -317,7 +317,7 @@ struct layer { float scale_x_y; float iou_normalizer; float cls_normalizer; - int iou_loss; + IOU_LOSS iou_loss; char *align_bit_weights_gpu; float *mean_arr_gpu; diff --git a/src/yolo_layer.c b/src/yolo_layer.c index d9d7d905e56..a76b5efb082 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -127,24 +127,6 @@ box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw b.h = exp(x[index + 3*stride]) * biases[2*n+1] / h; return b; } -/* -float delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride) -{ - box pred = get_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride); - float iou = box_iou(pred, truth); - - float tx = (truth.x*lw - i); - float ty = (truth.y*lh - j); - float tw = log(truth.w*w / biases[2*n]); - float th = log(truth.h*h / biases[2*n + 1]); - - delta[index + 0*stride] = scale * (tx - x[index + 0*stride]); - delta[index + 1*stride] = scale * (ty - x[index + 1*stride]); - delta[index + 2*stride] = scale * (tw - x[index + 2*stride]); - delta[index + 3*stride] = scale * (th - x[index + 3*stride]); - return iou; -} -*/ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss) { @@ -354,7 +336,6 @@ void forward_yolo_layer(const layer l, network_state state) int mask_n = int_index(l.mask, best_n, l.n); if (mask_n >= 0) { int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); - //float iou = ious all_ious = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss); // range is 0 <= 1 @@ -396,7 +377,7 @@ void forward_yolo_layer(const layer l, network_state state) // TODO: remove IOU loss fields before computing MSE on class // probably split into two arrays int stride = l.w*l.h; - float* no_iou_loss_delta = calloc(l.batch * l.outputs, sizeof(float)); + float* no_iou_loss_delta = (float *)calloc(l.batch * l.outputs, sizeof(float)); memcpy(no_iou_loss_delta, l.delta, l.batch * l.outputs * sizeof(float)); for (b = 0; b < l.batch; ++b) { for (j = 0; j < l.h; ++j) { From abba310982212089d8111f3da661688e9b2f9728 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 5 Jun 2019 01:04:16 +0300 Subject: [PATCH 0507/1365] Minor fix in cost_layer --- src/cost_layer.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/cost_layer.c b/src/cost_layer.c index c967c29d440..3038c3dafcd 100644 --- a/src/cost_layer.c +++ b/src/cost_layer.c @@ -51,8 +51,8 @@ cost_layer make_cost_layer(int batch, int inputs, COST_TYPE cost_type, float sca l.forward_gpu = forward_cost_layer_gpu; l.backward_gpu = backward_cost_layer_gpu; - l.delta_gpu = cuda_make_array(l.output, inputs*batch); - l.output_gpu = cuda_make_array(l.delta, inputs*batch); + l.delta_gpu = cuda_make_array(l.delta, inputs*batch); + l.output_gpu = cuda_make_array(l.output, inputs*batch); #endif return l; } From ff0dbcbb7dab7905ba108c223e485543cc18ce2f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 5 Jun 2019 15:51:03 +0300 Subject: [PATCH 0508/1365] Fix network resizing for resize_conv_lstm_layer() --- src/network.c | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/network.c b/src/network.c index be02957fdf9..d6b8f5c7a08 100644 --- a/src/network.c +++ b/src/network.c @@ -508,6 +508,8 @@ int resize_network(network *net, int w, int h) } else if (l.type == CRNN) { resize_crnn_layer(&l, w, h); + }else if (l.type == CONV_LSTM) { + resize_conv_lstm_layer(&l, w, h); }else if(l.type == CROP){ resize_crop_layer(&l, w, h); }else if(l.type == MAXPOOL){ From 57baf1a487d147a16a44c04f45ade6c0fb425b80 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 6 Jun 2019 02:55:11 +0300 Subject: [PATCH 0509/1365] Added Mixup data augmentation if is used mixup=1 --- include/darknet.h | 2 + src/data.c | 347 +++++++++++++++++++++++++++---------------- src/data.h | 2 +- src/detector.c | 1 + src/image_opencv.cpp | 8 + src/image_opencv.h | 3 + src/parser.c | 1 + 7 files changed, 237 insertions(+), 127 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 7fe2d0b25eb..1ab2f339ff6 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -595,6 +595,7 @@ typedef struct network { int center; int flip; // horizontal flip 50% probability augmentaiont for classifier training (default = 1) int blur; + int mixup; float angle; float aspect; float exposure; @@ -759,6 +760,7 @@ typedef struct load_args { float jitter; int flip; int blur; + int mixup; float angle; float aspect; float saturation; diff --git a/src/data.c b/src/data.c index 53959b4c00c..005c59aa491 100644 --- a/src/data.c +++ b/src/data.c @@ -774,18 +774,52 @@ static box float_to_box_stride(float *f, int stride) return b; } +void blend_truth(float *new_truth, int boxes, float *old_truth) +{ + const int t_size = 4 + 1; + int count_new_truth = 0; + int t; + for (t = 0; t < boxes; ++t) { + float x = new_truth[t*(4 + 1)]; + if (!x) break; + count_new_truth++; + + } + for (t = count_new_truth; t < boxes; ++t) { + float *new_truth_ptr = new_truth + t*t_size; + float *old_truth_ptr = old_truth + (t - count_new_truth)*t_size; + float x = old_truth_ptr[0]; + if (!x) break; + + new_truth_ptr[0] = old_truth_ptr[0]; + new_truth_ptr[1] = old_truth_ptr[1]; + new_truth_ptr[2] = old_truth_ptr[2]; + new_truth_ptr[3] = old_truth_ptr[3]; + new_truth_ptr[4] = old_truth_ptr[4]; + } + //printf("\n was %d bboxes, now %d bboxes \n", count_new_truth, t); +} + #ifdef OPENCV #include "http_stream.h" -data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int use_blur, float jitter, +data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int use_blur, int use_mixup, float jitter, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int show_imgs) { const int random_index = random_gen(); c = c ? c : 3; char **random_paths; + char **mixup_random_paths = NULL; if (track) random_paths = get_sequential_paths(paths, n, m, mini_batch, augment_speed); else random_paths = get_random_paths(paths, n, m); + + int mixup = use_mixup ? random_gen() % 2 : 0; + //printf("\n mixup = %d \n", mixup); + if (mixup) { + if (track) mixup_random_paths = get_sequential_paths(paths, n, m, mini_batch, augment_speed); + else mixup_random_paths = get_random_paths(paths, n, m); + } int i; data d = {0}; d.shallow = 0; @@ -799,96 +833,135 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo int augmentation_calculated = 0; d.y = make_matrix(n, 5*boxes); - for(i = 0; i < n; ++i){ - const char *filename = random_paths[i]; + int i_mixup = 0; + for (i_mixup = 0; i_mixup <= mixup; i_mixup++) { + if (i_mixup) augmentation_calculated = 0; + for (i = 0; i < n; ++i) { + float *truth = (float*)calloc(5 * boxes, sizeof(float)); + const char *filename = (i_mixup) ? mixup_random_paths[i] : random_paths[i]; + + int flag = (c >= 3); + mat_cv *src; + src = load_image_mat_cv(filename, flag); + if (src == NULL) { + if (check_mistakes) getchar(); + continue; + } - int flag = (c >= 3); - mat_cv *src; - src = load_image_mat_cv(filename, flag); - if (src == NULL) { - if (check_mistakes) getchar(); - continue; - } + int oh = get_height_mat(src); + int ow = get_width_mat(src); - int oh = get_height_mat(src); - int ow = get_width_mat(src); + int dw = (ow*jitter); + int dh = (oh*jitter); - int dw = (ow*jitter); - int dh = (oh*jitter); + if (!augmentation_calculated || !track) + { + augmentation_calculated = 1; + r1 = random_float(); + r2 = random_float(); + r3 = random_float(); + r4 = random_float(); - if(!augmentation_calculated || !track) - { - augmentation_calculated = 1; - r1 = random_float(); - r2 = random_float(); - r3 = random_float(); - r4 = random_float(); - - dhue = rand_uniform_strong(-hue, hue); - dsat = rand_scale(saturation); - dexp = rand_scale(exposure); - - flip = use_flip ? random_gen() % 2 : 0; - blur = rand_int(0, 1) ? (use_blur) : 0; - } + dhue = rand_uniform_strong(-hue, hue); + dsat = rand_scale(saturation); + dexp = rand_scale(exposure); - int pleft = rand_precalc_random(-dw, dw, r1); - int pright = rand_precalc_random(-dw, dw, r2); - int ptop = rand_precalc_random(-dh, dh, r3); - int pbot = rand_precalc_random(-dh, dh, r4); + flip = use_flip ? random_gen() % 2 : 0; + blur = rand_int(0, 1) ? (use_blur) : 0; + } - int swidth = ow - pleft - pright; - int sheight = oh - ptop - pbot; + int pleft = rand_precalc_random(-dw, dw, r1); + int pright = rand_precalc_random(-dw, dw, r2); + int ptop = rand_precalc_random(-dh, dh, r3); + int pbot = rand_precalc_random(-dh, dh, r4); - float sx = (float)swidth / ow; - float sy = (float)sheight / oh; + int swidth = ow - pleft - pright; + int sheight = oh - ptop - pbot; - float dx = ((float)pleft/ow)/sx; - float dy = ((float)ptop /oh)/sy; + float sx = (float)swidth / ow; + float sy = (float)sheight / oh; - fill_truth_detection(filename, boxes, d.y.vals[i], classes, flip, dx, dy, 1. / sx, 1. / sy, w, h); + float dx = ((float)pleft / ow) / sx; + float dy = ((float)ptop / oh) / sy; - image ai = image_data_augmentation(src, w, h, pleft, ptop, swidth, sheight, flip, jitter, dhue, dsat, dexp, - blur, boxes, d.y.vals[i]); + fill_truth_detection(filename, boxes, truth, classes, flip, dx, dy, 1. / sx, 1. / sy, w, h); - d.X.vals[i] = ai.data; + image ai = image_data_augmentation(src, w, h, pleft, ptop, swidth, sheight, flip, jitter, dhue, dsat, dexp, + blur, boxes, d.y.vals[i]); - if(show_imgs) - { - char buff[1000]; - sprintf(buff, "aug_%d_%d_%s_%d", random_index, i, basecfg(random_paths[i]), random_gen()); - int t; - for (t = 0; t < boxes; ++t) { - box b = float_to_box_stride(d.y.vals[i] + t*(4 + 1), 1); - if (!b.x) break; - int left = (b.x - b.w / 2.)*ai.w; - int right = (b.x + b.w / 2.)*ai.w; - int top = (b.y - b.h / 2.)*ai.h; - int bot = (b.y + b.h / 2.)*ai.h; - draw_box_width(ai, left, top, right, bot, 3, 150, 100, 50); // 3 channels RGB + if (i_mixup) { + image old_img = ai; + old_img.data = d.X.vals[i]; + //show_image(ai, "new"); + //show_image(old_img, "old"); + //wait_until_press_key_cv(); + blend_images_cv(ai, 0.5, old_img, 0.5); + blend_truth(truth, boxes, d.y.vals[i]); } - save_image(ai, buff); - if (show_imgs == 1) { - show_image(ai, buff); - wait_until_press_key_cv(); + d.X.vals[i] = ai.data; + memcpy(d.y.vals[i], truth, 5*boxes * sizeof(float)); + + if (show_imgs)// && i_mixup) // delete i_mixup + { + image tmp_ai = copy_image(ai); + char buff[1000]; + sprintf(buff, "aug_%d_%d_%s_%d", random_index, i, basecfg(filename), random_gen()); + int t; + for (t = 0; t < boxes; ++t) { + box b = float_to_box_stride(d.y.vals[i] + t*(4 + 1), 1); + if (!b.x) break; + int left = (b.x - b.w / 2.)*ai.w; + int right = (b.x + b.w / 2.)*ai.w; + int top = (b.y - b.h / 2.)*ai.h; + int bot = (b.y + b.h / 2.)*ai.h; + draw_box_width(tmp_ai, left, top, right, bot, 1, 150, 100, 50); // 3 channels RGB + } + + save_image(tmp_ai, buff); + if (show_imgs == 1) { + show_image(tmp_ai, buff); + wait_until_press_key_cv(); + } + printf("\nYou use flag -show_imgs, so will be saved aug_...jpg images. Click on window and press ESC button \n"); + free_image(tmp_ai); } - printf("\nYou use flag -show_imgs, so will be saved aug_...jpg images. Click on window and press ESC button \n"); - } - release_mat(&src); + release_mat(&src); + free(truth); + } } free(random_paths); + if(mixup_random_paths) free(mixup_random_paths); return d; } #else // OPENCV -data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int use_blur, float jitter, +void blend_images(image new_img, float alpha, image old_img, float beta) +{ + int i; + int data_size = new_img.w * new_img.h * new_img.c; + #pragma omp parallel for + for (i = 0; i < data_size; ++i) + new_img.data[i] = new_img.data[i] * alpha + old_img.data[i] * beta; +} + +data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int use_blur, int use_mixup, float jitter, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int show_imgs) { + const int random_index = random_gen(); c = c ? c : 3; char **random_paths; + char **mixup_random_paths = NULL; if(track) random_paths = get_sequential_paths(paths, n, m, mini_batch, augment_speed); else random_paths = get_random_paths(paths, n, m); + + int mixup = use_mixup ? random_gen() % 2 : 0; + //printf("\n mixup = %d \n", mixup); + if (mixup) { + if (track) mixup_random_paths = get_sequential_paths(paths, n, m, mini_batch, augment_speed); + else mixup_random_paths = get_random_paths(paths, n, m); + } + int i; data d = { 0 }; d.shallow = 0; @@ -902,82 +975,104 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo int augmentation_calculated = 0; d.y = make_matrix(n, 5 * boxes); - for (i = 0; i < n; ++i) { - image orig = load_image(random_paths[i], 0, 0, c); - - int oh = orig.h; - int ow = orig.w; - - int dw = (ow*jitter); - int dh = (oh*jitter); - - if (!augmentation_calculated || !track) - { - augmentation_calculated = 1; - r1 = random_float(); - r2 = random_float(); - r3 = random_float(); - r4 = random_float(); - - dhue = rand_uniform_strong(-hue, hue); - dsat = rand_scale(saturation); - dexp = rand_scale(exposure); - - flip = use_flip ? random_gen() % 2 : 0; - } + int i_mixup = 0; + for (i_mixup = 0; i_mixup <= mixup; i_mixup++) { + if (i_mixup) augmentation_calculated = 0; + for (i = 0; i < n; ++i) { + float *truth = (float*)calloc(5 * boxes, sizeof(float)); + char *filename = (i_mixup) ? mixup_random_paths[i] : random_paths[i]; + + image orig = load_image(filename, 0, 0, c); + + int oh = orig.h; + int ow = orig.w; + + int dw = (ow*jitter); + int dh = (oh*jitter); + + if (!augmentation_calculated || !track) + { + augmentation_calculated = 1; + r1 = random_float(); + r2 = random_float(); + r3 = random_float(); + r4 = random_float(); + + dhue = rand_uniform_strong(-hue, hue); + dsat = rand_scale(saturation); + dexp = rand_scale(exposure); + + flip = use_flip ? random_gen() % 2 : 0; + } - int pleft = rand_precalc_random(-dw, dw, r1); - int pright = rand_precalc_random(-dw, dw, r2); - int ptop = rand_precalc_random(-dh, dh, r3); - int pbot = rand_precalc_random(-dh, dh, r4); + int pleft = rand_precalc_random(-dw, dw, r1); + int pright = rand_precalc_random(-dw, dw, r2); + int ptop = rand_precalc_random(-dh, dh, r3); + int pbot = rand_precalc_random(-dh, dh, r4); - int swidth = ow - pleft - pright; - int sheight = oh - ptop - pbot; + int swidth = ow - pleft - pright; + int sheight = oh - ptop - pbot; - float sx = (float)swidth / ow; - float sy = (float)sheight / oh; + float sx = (float)swidth / ow; + float sy = (float)sheight / oh; - image cropped = crop_image(orig, pleft, ptop, swidth, sheight); + image cropped = crop_image(orig, pleft, ptop, swidth, sheight); - float dx = ((float)pleft / ow) / sx; - float dy = ((float)ptop / oh) / sy; + float dx = ((float)pleft / ow) / sx; + float dy = ((float)ptop / oh) / sy; - image sized = resize_image(cropped, w, h); - if (flip) flip_image(sized); - distort_image(sized, dhue, dsat, dexp); - //random_distort_image(sized, hue, saturation, exposure); - d.X.vals[i] = sized.data; + image sized = resize_image(cropped, w, h); + if (flip) flip_image(sized); + distort_image(sized, dhue, dsat, dexp); + //random_distort_image(sized, hue, saturation, exposure); - fill_truth_detection(random_paths[i], boxes, d.y.vals[i], classes, flip, dx, dy, 1. / sx, 1. / sy, w, h); + fill_truth_detection(filename, boxes, truth, classes, flip, dx, dy, 1. / sx, 1. / sy, w, h); - if(show_imgs) - { - char buff[1000]; - sprintf(buff, "aug_%s_%d", basecfg(random_paths[i]), random_gen()); - int t; - for (t = 0; t < boxes; ++t) { - box b = float_to_box_stride(d.y.vals[i] + t*(4 + 1), 1); - if (!b.x) break; - int left = (b.x - b.w / 2.)*sized.w; - int right = (b.x + b.w / 2.)*sized.w; - int top = (b.y - b.h / 2.)*sized.h; - int bot = (b.y + b.h / 2.)*sized.h; - draw_box_width(sized, left, top, right, bot, 3, 150, 100, 50); // 3 channels RGB + if (i_mixup) { + image old_img = sized; + old_img.data = d.X.vals[i]; + //show_image(sized, "new"); + //show_image(old_img, "old"); + //wait_until_press_key_cv(); + blend_images(sized, 0.5, old_img, 0.5); + blend_truth(truth, boxes, d.y.vals[i]); } - show_image(sized, buff); - if (show_imgs == 1) { + d.X.vals[i] = sized.data; + memcpy(d.y.vals[i], truth, 5 * boxes * sizeof(float)); + + if (show_imgs)// && i_mixup) + { + char buff[1000]; + sprintf(buff, "aug_%d_%d_%s_%d", random_index, i, basecfg(filename), random_gen()); + + int t; + for (t = 0; t < boxes; ++t) { + box b = float_to_box_stride(d.y.vals[i] + t*(4 + 1), 1); + if (!b.x) break; + int left = (b.x - b.w / 2.)*sized.w; + int right = (b.x + b.w / 2.)*sized.w; + int top = (b.y - b.h / 2.)*sized.h; + int bot = (b.y + b.h / 2.)*sized.h; + draw_box_width(sized, left, top, right, bot, 1, 150, 100, 50); // 3 channels RGB + } + save_image(sized, buff); - wait_until_press_key_cv(); + if (show_imgs == 1) { + show_image(sized, buff); + wait_until_press_key_cv(); + } + printf("\nYou use flag -show_imgs, so will be saved aug_...jpg images. Press Enter: \n"); + //getchar(); } - printf("\nYou use flag -show_imgs, so will be saved aug_...jpg images. Press Enter: \n"); - getchar(); - } - free_image(orig); - free_image(cropped); + free_image(orig); + free_image(cropped); + free(truth); + } } free(random_paths); + if (mixup_random_paths) free(mixup_random_paths); return d; } #endif // OPENCV @@ -1002,7 +1097,7 @@ void *load_thread(void *ptr) } else if (a.type == REGION_DATA){ *a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure); } else if (a.type == DETECTION_DATA){ - *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.c, a.num_boxes, a.classes, a.flip, a.blur, a.jitter, + *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.c, a.num_boxes, a.classes, a.flip, a.blur, a.mixup, a.jitter, a.hue, a.saturation, a.exposure, a.mini_batch, a.track, a.augment_speed, a.show_imgs); } else if (a.type == SWAG_DATA){ *a.d = load_data_swag(a.paths, a.n, a.classes, a.jitter); diff --git a/src/data.h b/src/data.h index 3305db50ca6..b99b4e9aa13 100644 --- a/src/data.h +++ b/src/data.h @@ -86,7 +86,7 @@ void print_letters(float *pred, int n); data load_data_captcha(char **paths, int n, int m, int k, int w, int h); data load_data_captcha_encode(char **paths, int n, int m, int w, int h); data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h); -data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int use_blur, float jitter, +data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int use_blur, int use_mixup, float jitter, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int show_imgs); data load_data_tag(char **paths, int n, int m, int k, int use_flip, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); diff --git a/src/detector.c b/src/detector.c index c9386914ced..c77a5889f58 100644 --- a/src/detector.c +++ b/src/detector.c @@ -129,6 +129,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i args.angle = net.angle; args.blur = net.blur; + args.mixup = net.mixup; args.exposure = net.exposure; args.saturation = net.saturation; args.hue = net.hue; diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index d6f726c3c15..b60f03cb7ef 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1238,6 +1238,14 @@ image image_data_augmentation(mat_cv* mat, int w, int h, return out; } +// blend two images with (alpha and beta) +void blend_images_cv(image new_img, float alpha, image old_img, float beta) +{ + cv::Mat new_mat(cv::Size(new_img.w, new_img.h), CV_32FC(new_img.c), new_img.data);// , size_t step = AUTO_STEP) + cv::Mat old_mat(cv::Size(old_img.w, old_img.h), CV_32FC(old_img.c), old_img.data); + cv::addWeighted(new_mat, alpha, old_mat, beta, 0.0, new_mat); +} + // ==================================================================== // Show Anchors // ==================================================================== diff --git a/src/image_opencv.h b/src/image_opencv.h index 21ee6939fae..2af5f24e777 100644 --- a/src/image_opencv.h +++ b/src/image_opencv.h @@ -98,6 +98,9 @@ image image_data_augmentation(mat_cv* mat, int w, int h, float jitter, float dhue, float dsat, float dexp, int blur, int num_boxes, float *truth); +// blend two images with (alpha and beta) +void blend_images_cv(image new_img, float alpha, image old_img, float beta); + // Show Anchors void show_acnhors(int number_of_boxes, int num_of_clusters, float *rel_width_height_array, model anchors_data, int width, int height); diff --git a/src/parser.c b/src/parser.c index e285448c035..b37b2873311 100644 --- a/src/parser.c +++ b/src/parser.c @@ -708,6 +708,7 @@ void parse_net_options(list *options, network *net) net->min_crop = option_find_int_quiet(options, "min_crop",net->w); net->flip = option_find_int_quiet(options, "flip", 1); net->blur = option_find_int_quiet(options, "blur", 0); + net->mixup = option_find_int_quiet(options, "mixup", 0); net->angle = option_find_float_quiet(options, "angle", 0); net->aspect = option_find_float_quiet(options, "aspect", 1); From 3ff27b1cdd4580e6e9346bd7a2c63bde3498a2e3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 7 Jun 2019 14:17:43 +0300 Subject: [PATCH 0510/1365] Mixup bug fix --- src/data.c | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/data.c b/src/data.c index 005c59aa491..3858d427abb 100644 --- a/src/data.c +++ b/src/data.c @@ -897,6 +897,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo //wait_until_press_key_cv(); blend_images_cv(ai, 0.5, old_img, 0.5); blend_truth(truth, boxes, d.y.vals[i]); + free_image(old_img); } d.X.vals[i] = ai.data; @@ -1036,6 +1037,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo //wait_until_press_key_cv(); blend_images(sized, 0.5, old_img, 0.5); blend_truth(truth, boxes, d.y.vals[i]); + free_image(old_img); } d.X.vals[i] = sized.data; From 12db38ccbf515e45dfdcd342a312a02b2dcd0e38 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 8 Jun 2019 17:42:53 +0300 Subject: [PATCH 0511/1365] Fixed input_shortcut_gpu() for layers with different numbers of channels --- src/blas_kernels.cu | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 55d5e5d4d17..ed418c107c4 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -749,7 +749,9 @@ extern "C" void input_shortcut_gpu(float *in, int batch, int w1, int h1, int c1, if (sample < 1) sample = 1; int size = batch * minw * minh * minc; - input_shortcut_kernel << > >(in, size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, out); + //input_shortcut_kernel << > >(in, size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, out); + simple_copy_ongpu(w2 * h2 * c2 * batch, in, out); + shortcut_kernel << > >(size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, out); CHECK_CUDA(cudaPeekAtLastError()); } From 88ce9dcca60f1f2967b3908c42a4547622df2fe8 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 8 Jun 2019 19:41:44 +0300 Subject: [PATCH 0512/1365] Added dilation parameter for convolutional, conv_lstm and crnn layers --- include/darknet.h | 1 + src/col2im.c | 53 ++++++++++++++++++++++++ src/col2im.h | 14 +++++++ src/col2im_kernels.cu | 79 ++++++++++++++++++++++++++++++++++++ src/conv_lstm_layer.c | 25 ++++++------ src/conv_lstm_layer.h | 2 +- src/convolutional_kernels.cu | 34 ++++++++++++++-- src/convolutional_layer.c | 49 +++++++++++++++++----- src/convolutional_layer.h | 2 +- src/crnn_layer.c | 9 ++-- src/crnn_layer.h | 2 +- src/im2col.c | 53 ++++++++++++++++++++++++ src/im2col.h | 14 +++++++ src/im2col_kernels.cu | 72 ++++++++++++++++++++++++++++++++ src/parser.c | 9 ++-- 15 files changed, 383 insertions(+), 35 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 1ab2f339ff6..4154d664b8c 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -204,6 +204,7 @@ struct layer { int size; int side; int stride; + int dilation; int reverse; int flatten; int spatial; diff --git a/src/col2im.c b/src/col2im.c index 925d054c8bf..660b0e5823c 100644 --- a/src/col2im.c +++ b/src/col2im.c @@ -37,3 +37,56 @@ void col2im_cpu(float* data_col, } } } +// ---------------------------------------- +void caffe_set(const int N, const float alpha, float* Y) { + if (alpha == 0) { + memset(Y, 0, sizeof(float) * N); // NOLINT(caffe/alt_fn) + return; + } + for (int i = 0; i < N; ++i) { + Y[i] = alpha; + } +} + +inline int is_a_ge_zero_and_a_lt_b(int a, int b) { + return (unsigned)(a) < (unsigned)(b); +} + +// https://github.com/BVLC/caffe/blob/master/src/caffe/util/im2col.cpp +void col2im_cpu_ext(const float* data_col, const int channels, + const int height, const int width, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + float* data_im) +{ + caffe_set(height * width * channels, 0.0F, data_im); + const int output_h = (height + 2 * pad_h - + (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int output_w = (width + 2 * pad_w - + (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + const int channel_size = height * width; + for (int channel = channels; channel--; data_im += channel_size) { + for (int kernel_row = 0; kernel_row < kernel_h; kernel_row++) { + for (int kernel_col = 0; kernel_col < kernel_w; kernel_col++) { + int input_row = -pad_h + kernel_row * dilation_h; + for (int output_rows = output_h; output_rows; output_rows--) { + if (!is_a_ge_zero_and_a_lt_b(input_row, height)) { + data_col += output_w; + } + else { + int input_col = -pad_w + kernel_col * dilation_w; + for (int output_col = output_w; output_col; output_col--) { + if (is_a_ge_zero_and_a_lt_b(input_col, width)) { + data_im[input_row * width + input_col] += *data_col; + } + data_col++; + input_col += stride_w; + } + } + input_row += stride_h; + } + } + } + } +} \ No newline at end of file diff --git a/src/col2im.h b/src/col2im.h index a8493e382b4..984f7c4b787 100644 --- a/src/col2im.h +++ b/src/col2im.h @@ -8,10 +8,24 @@ void col2im_cpu(float* data_col, int channels, int height, int width, int ksize, int stride, int pad, float* data_im); +void col2im_cpu_ext(const float* data_col, const int channels, + const int height, const int width, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + float* data_im); + #ifdef GPU void col2im_ongpu(float *data_col, int channels, int height, int width, int ksize, int stride, int pad, float *data_im); + + +void col2im_gpu_ext(const float* data_col, const int channels, + const int height, const int width, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, + const int stride_w, const int dilation_h, const int dilation_w, + float* data_im); #endif #ifdef __cplusplus } diff --git a/src/col2im_kernels.cu b/src/col2im_kernels.cu index a6e985f1132..0e07bc37ae2 100644 --- a/src/col2im_kernels.cu +++ b/src/col2im_kernels.cu @@ -55,3 +55,82 @@ void col2im_ongpu(float *data_col, CHECK_CUDA(cudaPeekAtLastError()); } +// ----------------------------------------- + +// CUDA: use 512 threads per block +const int CAFFE_CUDA_NUM_THREADS = 512; + +// CUDA: number of blocks for threads. +inline int CAFFE_GET_BLOCKS(const int N) { + return (N + CAFFE_CUDA_NUM_THREADS - 1) / CAFFE_CUDA_NUM_THREADS; +} + +// CUDA: grid stride looping +#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ + i < (n); \ + i += blockDim.x * gridDim.x) + +// https://github.com/BVLC/caffe/blob/master/src/caffe/util/im2col.cu +__global__ void col2im_gpu_kernel_ext(const int n, const float* data_col, + const int height, const int width, const int channels, + const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int height_col, const int width_col, + float* data_im) { + CUDA_KERNEL_LOOP(index, n) { + float val = 0; + const int w_im = index % width + pad_w; + const int h_im = (index / width) % height + pad_h; + const int c_im = index / (width * height); + int kernel_extent_w = (kernel_w - 1) * dilation_w + 1; + int kernel_extent_h = (kernel_h - 1) * dilation_h + 1; + // compute the start and end of the output + const int w_col_start = + (w_im < kernel_extent_w) ? 0 : (w_im - kernel_extent_w) / stride_w + 1; + const int w_col_end = min(w_im / stride_w + 1, width_col); + const int h_col_start = + (h_im < kernel_extent_h) ? 0 : (h_im - kernel_extent_h) / stride_h + 1; + const int h_col_end = min(h_im / stride_h + 1, height_col); + // TODO: use LCM of stride and dilation to avoid unnecessary loops + for (int h_col = h_col_start; h_col < h_col_end; h_col += 1) { + for (int w_col = w_col_start; w_col < w_col_end; w_col += 1) { + int h_k = (h_im - h_col * stride_h); + int w_k = (w_im - w_col * stride_w); + if (h_k % dilation_h == 0 && w_k % dilation_w == 0) { + h_k /= dilation_h; + w_k /= dilation_w; + int data_col_index = (((c_im * kernel_h + h_k) * kernel_w + w_k) * + height_col + h_col) * width_col + w_col; + val += data_col[data_col_index]; + } + } + } + data_im[index] = val; + } +} + +void col2im_gpu_ext(const float* data_col, const int channels, + const int height, const int width, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, + const int stride_w, const int dilation_h, const int dilation_w, + float* data_im) +{ + int height_col = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / + stride_h + 1; + int width_col = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / + stride_w + 1; + int num_kernels = channels * height * width; + // To avoid involving atomic operations, we will launch one kernel per + // bottom dimension, and then in the kernel add up the top dimensions. + // NOLINT_NEXT_LINE(whitespace/operators) + col2im_gpu_kernel_ext<< > >( + num_kernels, data_col, height, width, channels, kernel_h, kernel_w, + pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, + height_col, width_col, data_im); + + CHECK_CUDA(cudaPeekAtLastError()); +} \ No newline at end of file diff --git a/src/conv_lstm_layer.c b/src/conv_lstm_layer.c index f3041cedc65..5caa3754e55 100644 --- a/src/conv_lstm_layer.c +++ b/src/conv_lstm_layer.c @@ -32,7 +32,7 @@ static void increment_layer(layer *l, int steps) } -layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, int groups, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int peephole, int xnor) +layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, int groups, int steps, int size, int stride, int dilation, int pad, ACTIVATION activation, int batch_normalize, int peephole, int xnor) { fprintf(stderr, "CONV_LSTM Layer: %d x %d x %d image, %d filters\n", h, w, c, output_filters); /* @@ -53,6 +53,7 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i l.steps = steps; l.size = size; l.stride = stride; + l.dilation = dilation; l.pad = pad; l.h = h; l.w = w; @@ -65,44 +66,44 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i // U l.uf = (layer*)calloc(1, sizeof(layer)); - *(l.uf) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.uf) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.uf->batch = batch; if (l.workspace_size < l.uf->workspace_size) l.workspace_size = l.uf->workspace_size; l.ui = (layer*)calloc(1, sizeof(layer)); - *(l.ui) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.ui) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.ui->batch = batch; if (l.workspace_size < l.ui->workspace_size) l.workspace_size = l.ui->workspace_size; l.ug = (layer*)calloc(1, sizeof(layer)); - *(l.ug) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.ug) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.ug->batch = batch; if (l.workspace_size < l.ug->workspace_size) l.workspace_size = l.ug->workspace_size; l.uo = (layer*)calloc(1, sizeof(layer)); - *(l.uo) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.uo) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.uo->batch = batch; if (l.workspace_size < l.uo->workspace_size) l.workspace_size = l.uo->workspace_size; // W l.wf = (layer*)calloc(1, sizeof(layer)); - *(l.wf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.wf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.wf->batch = batch; if (l.workspace_size < l.wf->workspace_size) l.workspace_size = l.wf->workspace_size; l.wi = (layer*)calloc(1, sizeof(layer)); - *(l.wi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.wi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.wi->batch = batch; if (l.workspace_size < l.wi->workspace_size) l.workspace_size = l.wi->workspace_size; l.wg = (layer*)calloc(1, sizeof(layer)); - *(l.wg) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.wg) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.wg->batch = batch; if (l.workspace_size < l.wg->workspace_size) l.workspace_size = l.wg->workspace_size; l.wo = (layer*)calloc(1, sizeof(layer)); - *(l.wo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.wo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.wo->batch = batch; if (l.workspace_size < l.wo->workspace_size) l.workspace_size = l.wo->workspace_size; @@ -110,21 +111,21 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i // V l.vf = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { - *(l.vf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.vf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.vf->batch = batch; if (l.workspace_size < l.vf->workspace_size) l.workspace_size = l.vf->workspace_size; } l.vi = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { - *(l.vi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.vi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.vi->batch = batch; if (l.workspace_size < l.vi->workspace_size) l.workspace_size = l.vi->workspace_size; } l.vo = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { - *(l.vo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.vo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.vo->batch = batch; if (l.workspace_size < l.vo->workspace_size) l.workspace_size = l.vo->workspace_size; } diff --git a/src/conv_lstm_layer.h b/src/conv_lstm_layer.h index 56a57298243..17e4fdc3a66 100644 --- a/src/conv_lstm_layer.h +++ b/src/conv_lstm_layer.h @@ -9,7 +9,7 @@ #ifdef __cplusplus extern "C" { #endif -layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, int groups, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int peephole, int xnor); +layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, int groups, int steps, int size, int stride, int dilation, int pad, ACTIVATION activation, int batch_normalize, int peephole, int xnor); void resize_conv_lstm_layer(layer *l, int w, int h); void free_state_conv_lstm(layer l); void randomize_state_conv_lstm(layer l); diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index a26b95eaf0d..dd6625f1e14 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -566,7 +566,17 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) b = im; } else { - im2col_ongpu(im, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, state.workspace); + //im2col_ongpu(im, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, state.workspace); + + im2col_gpu_ext(im, // input + l.c / l.groups, // input channels + l.h, l.w, // input size (h, w) + l.size, l.size, // kernel size (h, w) + l.pad, l.pad, // padding (h, w) + l.stride, l.stride, // stride (h, w) + l.dilation, l.dilation, // dilation (h, w) + state.workspace); // output + } gemm_ongpu(0, 0, m, n, k, 1., a, k, b, n, 1., c + i*m*n, n); } @@ -798,7 +808,15 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state float *im = state.input + (i*l.groups + j)*l.c / l.groups*l.h*l.w; - im2col_ongpu(im, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, state.workspace); + //im2col_ongpu(im, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, state.workspace); + im2col_gpu_ext(im, // input + l.c / l.groups, // input channels + l.h, l.w, // input size (h, w) + l.size, l.size, // kernel size (h, w) + l.pad, l.pad, // padding (h, w) + l.stride, l.stride, // stride (h, w) + l.dilation, l.dilation, // dilation (h, w) + state.workspace); // output gemm_ongpu(0, 1, m, n, k, 1, a + i*m*k, k, b, k, 1, c, n); if (state.delta) { @@ -811,7 +829,17 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state float *delta = state.delta + (i*l.groups + j)*l.c / l.groups*l.h*l.w; - col2im_ongpu(state.workspace, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, delta); + //col2im_ongpu(state.workspace, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, delta); + col2im_gpu_ext( + state.workspace, // input + l.c / l.groups, // input channels + l.h, l.w, // input size (h, w) + l.size, l.size, // kernel size (h, w) + l.pad, l.pad, // padding size (h, w) + l.stride, l.stride, // stride size (h, w) + l.dilation, l.dilation, // dilation size (h, w) + delta); // output (delta) + if (l.binary || l.xnor) { swap_binary(&l); } diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index d983ab61725..84bc75fb75a 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -275,9 +275,9 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->normDstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w)); #if(CUDNN_MAJOR >= 6) - CHECK_CUDNN(cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); // cudnn >= 6.0 + CHECK_CUDNN(cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, l->dilation, l->dilation, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); // cudnn >= 6.0 #else - CHECK_CUDNN(cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION)); // cudnn 5.1 + CHECK_CUDNN(cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, l->dilation, l->dilation, CUDNN_CROSS_CORRELATION)); // cudnn 5.1 #endif int forward_algo = CUDNN_CONVOLUTION_FWD_PREFER_FASTEST; int backward_algo = CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST; @@ -331,7 +331,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) #endif #endif -convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index) +convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index) { int total_batch = batch*steps; int i; @@ -353,6 +353,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.batch = batch; l.steps = steps; l.stride = stride; + l.dilation = dilation; l.size = size; l.pad = padding; l.batch_normalize = batch_normalize; @@ -525,7 +526,7 @@ void denormalize_convolutional_layer(convolutional_layer l) void test_convolutional_layer() { - convolutional_layer l = make_convolutional_layer(1, 1, 5, 5, 3, 2, 1, 5, 2, 1, LEAKY, 1, 0, 0, 0, 0, 0); + convolutional_layer l = make_convolutional_layer(1, 1, 5, 5, 3, 2, 1, 5, 2, 1, 1, LEAKY, 1, 0, 0, 0, 0, 0); l.batch_normalize = 1; float data[] = {1,1,1,1,1, 1,1,1,1,1, @@ -981,8 +982,17 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) } else { //printf(" l.index = %d - FP32 \n", l.index); - im2col_cpu(state.input + (i*l.groups + j)*l.c / l.groups*l.h*l.w, - l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, b); + float *im = state.input + (i*l.groups + j)*l.c / l.groups*l.h*l.w; + //im2col_cpu(im, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, b); + + im2col_cpu_ext(im, // input + l.c / l.groups, // input channels + l.h, l.w, // input size (h, w) + l.size, l.size, // kernel size (h, w) + l.pad, l.pad, // padding (h, w) + l.stride, l.stride, // stride (h, w) + l.dilation, l.dilation, // dilation (h, w) + b); // output gemm(0, 0, m, n, k, 1, a, k, b, n, 1, c, n); // bit-count to float @@ -1028,8 +1038,17 @@ void backward_convolutional_layer(convolutional_layer l, network_state state) float *im = state.input + (i*l.groups + j)*l.c / l.groups*l.h*l.w; - im2col_cpu(im, l.c / l.groups, l.h, l.w, - l.size, l.stride, l.pad, b); + //im2col_cpu(im, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, b); + im2col_cpu_ext( + im, // input + l.c / l.groups, // input channels + l.h, l.w, // input size (h, w) + l.size, l.size, // kernel size (h, w) + l.pad, l.pad, // padding (h, w) + l.stride, l.stride, // stride (h, w) + l.dilation, l.dilation, // dilation (h, w) + b); // output + gemm(0, 1, m, n, k, 1, a, k, b, k, 1, c, n); if (state.delta) { @@ -1039,8 +1058,18 @@ void backward_convolutional_layer(convolutional_layer l, network_state state) gemm(1, 0, n, k, m, 1, a, n, b, k, 0, c, k); - col2im_cpu(state.workspace, l.c / l.groups, l.h, l.w, l.size, l.stride, - l.pad, state.delta + (i*l.groups + j)*l.c / l.groups*l.h*l.w); + //col2im_cpu(state.workspace, l.c / l.groups, l.h, l.w, l.size, l.stride, + // l.pad, state.delta + (i*l.groups + j)*l.c / l.groups*l.h*l.w); + + col2im_cpu_ext( + state.workspace, // input + l.c / l.groups, // input channels (h, w) + l.h, l.w, // input size (h, w) + l.size, l.size, // kernel size (h, w) + l.pad, l.pad, // padding (h, w) + l.stride, l.stride, // stride (h, w) + l.dilation, l.dilation, // dilation (h, w) + state.delta + (i*l.groups + j)*l.c / l.groups*l.h*l.w); // output (delta) } } } diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h index dc00dabf265..586bd2cd182 100644 --- a/src/convolutional_layer.h +++ b/src/convolutional_layer.h @@ -30,7 +30,7 @@ void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16); #endif size_t get_convolutional_workspace_size(layer l); -convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index); +convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index); void denormalize_convolutional_layer(convolutional_layer l); void resize_convolutional_layer(convolutional_layer *layer, int w, int h); void forward_convolutional_layer(const convolutional_layer layer, network_state state); diff --git a/src/crnn_layer.c b/src/crnn_layer.c index 8534c69a7f1..d7c75b50fe9 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -26,7 +26,7 @@ static void increment_layer(layer *l, int steps) #endif } -layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int groups, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int xnor) +layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int groups, int steps, int size, int stride, int dilation, int pad, ACTIVATION activation, int batch_normalize, int xnor) { fprintf(stderr, "CRNN Layer: %d x %d x %d image, %d filters\n", h,w,c,output_filters); batch = batch / steps; @@ -36,6 +36,7 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.steps = steps; l.size = size; l.stride = stride; + l.dilation = dilation; l.pad = pad; l.h = h; l.w = w; @@ -49,17 +50,17 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.state = (float*)calloc(l.hidden * l.batch * (l.steps + 1), sizeof(float)); l.input_layer = (layer*)calloc(1, sizeof(layer)); - *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.input_layer->batch = batch; if (l.workspace_size < l.input_layer->workspace_size) l.workspace_size = l.input_layer->workspace_size; l.self_layer = (layer*)calloc(1, sizeof(layer)); - *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.self_layer->batch = batch; if (l.workspace_size < l.self_layer->workspace_size) l.workspace_size = l.self_layer->workspace_size; l.output_layer = (layer*)calloc(1, sizeof(layer)); - *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, groups, size, stride, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); l.output_layer->batch = batch; if (l.workspace_size < l.output_layer->workspace_size) l.workspace_size = l.output_layer->workspace_size; diff --git a/src/crnn_layer.h b/src/crnn_layer.h index 55feb599bb5..33560aae462 100644 --- a/src/crnn_layer.h +++ b/src/crnn_layer.h @@ -9,7 +9,7 @@ #ifdef __cplusplus extern "C" { #endif -layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int groups, int steps, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int xnor); +layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int groups, int steps, int size, int stride, int dilation, int pad, ACTIVATION activation, int batch_normalize, int xnor); void resize_crnn_layer(layer *l, int w, int h); void free_state_crnn(layer l); diff --git a/src/im2col.c b/src/im2col.c index 40b5251db02..ee08405ad3e 100644 --- a/src/im2col.c +++ b/src/im2col.c @@ -37,3 +37,56 @@ void im2col_cpu(float* data_im, } } } + + +// Function uses casting from int to unsigned to compare if value of +// parameter a is greater or equal to zero and lower than value of +// parameter b. The b parameter is of type signed and is always positive, +// therefore its value is always lower than 0x800... where casting +// negative value of a parameter converts it to value higher than 0x800... +// The casting allows to use one condition instead of two. +inline int is_a_ge_zero_and_a_lt_b(int a, int b) { + return (unsigned)(a) < (unsigned)(b); +} + +// https://github.com/BVLC/caffe/blob/master/src/caffe/util/im2col.cpp +void im2col_cpu_ext(const float* data_im, const int channels, + const int height, const int width, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + float* data_col) +{ + const int output_h = (height + 2 * pad_h - + (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int output_w = (width + 2 * pad_w - + (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + const int channel_size = height * width; + for (int channel = channels; channel--; data_im += channel_size) { + for (int kernel_row = 0; kernel_row < kernel_h; kernel_row++) { + for (int kernel_col = 0; kernel_col < kernel_w; kernel_col++) { + int input_row = -pad_h + kernel_row * dilation_h; + for (int output_rows = output_h; output_rows; output_rows--) { + if (!is_a_ge_zero_and_a_lt_b(input_row, height)) { + for (int output_cols = output_w; output_cols; output_cols--) { + *(data_col++) = 0; + } + } + else { + int input_col = -pad_w + kernel_col * dilation_w; + for (int output_col = output_w; output_col; output_col--) { + if (is_a_ge_zero_and_a_lt_b(input_col, width)) { + *(data_col++) = data_im[input_row * width + input_col]; + } + else { + *(data_col++) = 0; + } + input_col += stride_w; + } + } + input_row += stride_h; + } + } + } + } +} diff --git a/src/im2col.h b/src/im2col.h index 35f0039ac3d..65dd6ec8a44 100644 --- a/src/im2col.h +++ b/src/im2col.h @@ -14,12 +14,26 @@ void im2col_cpu(float* data_im, float im2col_get_pixel(float* im, int height, int width, int channels, int row, int col, int channel, int pad); +void im2col_cpu_ext(const float* data_im, const int channels, + const int height, const int width, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + float* data_col); + #ifdef GPU void im2col_ongpu(float *im, int channels, int height, int width, int ksize, int stride, int pad,float *data_col); +void im2col_gpu_ext(const float* data_im, const int channels, + const int height, const int width, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + float* data_col); + void im2col_align_ongpu(float *im, int channels, int height, int width, int ksize, int stride, int pad, float *data_col, int bit_align); diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 954a4694492..54961d3a2d1 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -2214,3 +2214,75 @@ void convolve_bin_gpu(float *input, float *weights, float *output, int in_w, int } // -------------------------------- + +// CUDA: use 512 threads per block +const int CAFFE_CUDA_NUM_THREADS = 512; + +// CUDA: number of blocks for threads. +inline int CAFFE_GET_BLOCKS(const int N) { + return (N + CAFFE_CUDA_NUM_THREADS - 1) / CAFFE_CUDA_NUM_THREADS; +} + +// CUDA: grid stride looping +#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ + i < (n); \ + i += blockDim.x * gridDim.x) + +// https://github.com/BVLC/caffe/blob/master/src/caffe/util/im2col.cu +__global__ void im2col_gpu_kernel_ext(const int n, const float* data_im, + const int height, const int width, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int height_col, const int width_col, + float* data_col) { + CUDA_KERNEL_LOOP(index, n) { + const int h_index = index / width_col; + const int h_col = h_index % height_col; + const int w_col = index % width_col; + const int c_im = h_index / height_col; + const int c_col = c_im * kernel_h * kernel_w; + const int h_offset = h_col * stride_h - pad_h; + const int w_offset = w_col * stride_w - pad_w; + float* data_col_ptr = data_col; + data_col_ptr += (c_col * height_col + h_col) * width_col + w_col; + const float* data_im_ptr = data_im; + data_im_ptr += (c_im * height + h_offset) * width + w_offset; + for (int i = 0; i < kernel_h; ++i) { + for (int j = 0; j < kernel_w; ++j) { + int h_im = h_offset + i * dilation_h; + int w_im = w_offset + j * dilation_w; + *data_col_ptr = + (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) ? + data_im_ptr[i * dilation_h * width + j * dilation_w] : 0; + data_col_ptr += height_col * width_col; + } + } + } +} + + +void im2col_gpu_ext(const float* data_im, const int channels, + const int height, const int width, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + float* data_col) +{ + // We are going to launch channels * height_col * width_col kernels, each + // kernel responsible for copying a single-channel grid. + int height_col = (height + 2 * pad_h - + (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + int width_col = (width + 2 * pad_w - + (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + int num_kernels = channels * height_col * width_col; + // NOLINT_NEXT_LINE(whitespace/operators) + im2col_gpu_kernel_ext << > >( + num_kernels, data_im, height, width, kernel_h, kernel_w, pad_h, + pad_w, stride_h, stride_w, dilation_h, dilation_w, height_col, + width_col, data_col); + + CHECK_CUDA(cudaPeekAtLastError()); +} \ No newline at end of file diff --git a/src/parser.c b/src/parser.c index b37b2873311..0cb906a5d77 100644 --- a/src/parser.c +++ b/src/parser.c @@ -153,6 +153,7 @@ convolutional_layer parse_convolutional(list *options, size_params params) int groups = option_find_int_quiet(options, "groups", 1); int size = option_find_int(options, "size",1); int stride = option_find_int(options, "stride",1); + int dilation = option_find_int_quiet(options, "dilation", 1); int pad = option_find_int_quiet(options, "pad",0); int padding = option_find_int_quiet(options, "padding",0); if(pad) padding = size/2; @@ -171,7 +172,7 @@ convolutional_layer parse_convolutional(list *options, size_params params) int xnor = option_find_int_quiet(options, "xnor", 0); int use_bin_output = option_find_int_quiet(options, "bin_output", 0); - convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index); + convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride,dilation,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index); layer.flipped = option_find_int_quiet(options, "flipped", 0); layer.dot = option_find_float_quiet(options, "dot", 0); @@ -188,6 +189,7 @@ layer parse_crnn(list *options, size_params params) { int size = option_find_int_quiet(options, "size", 3); int stride = option_find_int_quiet(options, "stride", 1); + int dilation = option_find_int_quiet(options, "dilation", 1); int pad = option_find_int_quiet(options, "pad", 0); int padding = option_find_int_quiet(options, "padding", 0); if (pad) padding = size / 2; @@ -200,7 +202,7 @@ layer parse_crnn(list *options, size_params params) int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); int xnor = option_find_int_quiet(options, "xnor", 0); - layer l = make_crnn_layer(params.batch, params.h, params.w, params.c, hidden_filters, output_filters, groups, params.time_steps, size, stride, padding, activation, batch_normalize, xnor); + layer l = make_crnn_layer(params.batch, params.h, params.w, params.c, hidden_filters, output_filters, groups, params.time_steps, size, stride, dilation, padding, activation, batch_normalize, xnor); l.shortcut = option_find_int_quiet(options, "shortcut", 0); @@ -248,6 +250,7 @@ layer parse_conv_lstm(list *options, size_params params) // a ConvLSTM with a larger transitional kernel should be able to capture faster motions int size = option_find_int_quiet(options, "size", 3); int stride = option_find_int_quiet(options, "stride", 1); + int dilation = option_find_int_quiet(options, "dilation", 1); int pad = option_find_int_quiet(options, "pad", 0); int padding = option_find_int_quiet(options, "padding", 0); if (pad) padding = size / 2; @@ -260,7 +263,7 @@ layer parse_conv_lstm(list *options, size_params params) int xnor = option_find_int_quiet(options, "xnor", 0); int peephole = option_find_int_quiet(options, "peephole", 0); - layer l = make_conv_lstm_layer(params.batch, params.h, params.w, params.c, output_filters, groups, params.time_steps, size, stride, padding, activation, batch_normalize, peephole, xnor); + layer l = make_conv_lstm_layer(params.batch, params.h, params.w, params.c, output_filters, groups, params.time_steps, size, stride, dilation, padding, activation, batch_normalize, peephole, xnor); l.state_constrain = option_find_int_quiet(options, "state_constrain", params.time_steps * 32); l.shortcut = option_find_int_quiet(options, "shortcut", 0); From d1be8ddb33964a8b0cfcbf3568d8cf24064456c4 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 9 Jun 2019 00:19:44 +0300 Subject: [PATCH 0513/1365] compile fix --- src/col2im.c | 2 +- src/im2col.c | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/col2im.c b/src/col2im.c index 660b0e5823c..3f18070b465 100644 --- a/src/col2im.c +++ b/src/col2im.c @@ -48,7 +48,7 @@ void caffe_set(const int N, const float alpha, float* Y) { } } -inline int is_a_ge_zero_and_a_lt_b(int a, int b) { +inline static int is_a_ge_zero_and_a_lt_b(int a, int b) { return (unsigned)(a) < (unsigned)(b); } diff --git a/src/im2col.c b/src/im2col.c index ee08405ad3e..45613db6dc5 100644 --- a/src/im2col.c +++ b/src/im2col.c @@ -45,7 +45,7 @@ void im2col_cpu(float* data_im, // therefore its value is always lower than 0x800... where casting // negative value of a parameter converts it to value higher than 0x800... // The casting allows to use one condition instead of two. -inline int is_a_ge_zero_and_a_lt_b(int a, int b) { +inline static int is_a_ge_zero_and_a_lt_b(int a, int b) { return (unsigned)(a) < (unsigned)(b); } From 85b0722af7293d1600a5dd1b19838c640401b295 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 9 Jun 2019 01:15:24 +0300 Subject: [PATCH 0514/1365] Another compile fix --- src/col2im.c | 11 ++++++----- src/im2col.c | 13 +++++++------ 2 files changed, 13 insertions(+), 11 deletions(-) diff --git a/src/col2im.c b/src/col2im.c index 3f18070b465..dea72f24f99 100644 --- a/src/col2im.c +++ b/src/col2im.c @@ -66,17 +66,18 @@ void col2im_cpu_ext(const float* data_col, const int channels, const int output_w = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; const int channel_size = height * width; - for (int channel = channels; channel--; data_im += channel_size) { - for (int kernel_row = 0; kernel_row < kernel_h; kernel_row++) { - for (int kernel_col = 0; kernel_col < kernel_w; kernel_col++) { + int channel, kernel_row, kernel_col, output_rows, output_col; + for (channel = channels; channel--; data_im += channel_size) { + for (kernel_row = 0; kernel_row < kernel_h; kernel_row++) { + for (kernel_col = 0; kernel_col < kernel_w; kernel_col++) { int input_row = -pad_h + kernel_row * dilation_h; - for (int output_rows = output_h; output_rows; output_rows--) { + for (output_rows = output_h; output_rows; output_rows--) { if (!is_a_ge_zero_and_a_lt_b(input_row, height)) { data_col += output_w; } else { int input_col = -pad_w + kernel_col * dilation_w; - for (int output_col = output_w; output_col; output_col--) { + for (output_col = output_w; output_col; output_col--) { if (is_a_ge_zero_and_a_lt_b(input_col, width)) { data_im[input_row * width + input_col] += *data_col; } diff --git a/src/im2col.c b/src/im2col.c index 45613db6dc5..4951f8f9008 100644 --- a/src/im2col.c +++ b/src/im2col.c @@ -62,19 +62,20 @@ void im2col_cpu_ext(const float* data_im, const int channels, const int output_w = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; const int channel_size = height * width; - for (int channel = channels; channel--; data_im += channel_size) { - for (int kernel_row = 0; kernel_row < kernel_h; kernel_row++) { - for (int kernel_col = 0; kernel_col < kernel_w; kernel_col++) { + int channel, kernel_row, kernel_col, output_rows, output_col; + for (channel = channels; channel--; data_im += channel_size) { + for (kernel_row = 0; kernel_row < kernel_h; kernel_row++) { + for (kernel_col = 0; kernel_col < kernel_w; kernel_col++) { int input_row = -pad_h + kernel_row * dilation_h; - for (int output_rows = output_h; output_rows; output_rows--) { + for (output_rows = output_h; output_rows; output_rows--) { if (!is_a_ge_zero_and_a_lt_b(input_row, height)) { - for (int output_cols = output_w; output_cols; output_cols--) { + for (output_col = output_w; output_col; output_col--) { *(data_col++) = 0; } } else { int input_col = -pad_w + kernel_col * dilation_w; - for (int output_col = output_w; output_col; output_col--) { + for (output_col = output_w; output_col; output_col--) { if (is_a_ge_zero_and_a_lt_b(input_col, width)) { *(data_col++) = data_im[input_row * width + input_col]; } From 94c806ffadc4b052bfaabe1904b79cabc6c10140 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 9 Jun 2019 03:07:04 +0300 Subject: [PATCH 0515/1365] final fix --- src/col2im.c | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/col2im.c b/src/col2im.c index dea72f24f99..c31c868ec11 100644 --- a/src/col2im.c +++ b/src/col2im.c @@ -30,7 +30,7 @@ void col2im_cpu(float* data_col, int im_row = h_offset + h * stride; int im_col = w_offset + w * stride; int col_index = (c * height_col + h) * width_col + w; - double val = data_col[col_index]; + float val = data_col[col_index]; col2im_add_pixel(data_im, height, width, channels, im_row, im_col, c_im, pad, val); } @@ -43,7 +43,8 @@ void caffe_set(const int N, const float alpha, float* Y) { memset(Y, 0, sizeof(float) * N); // NOLINT(caffe/alt_fn) return; } - for (int i = 0; i < N; ++i) { + int i; + for (i = 0; i < N; ++i) { Y[i] = alpha; } } From 378d49e1c33cc8c064cb27c99815de0698b2ad93 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 9 Jun 2019 18:46:07 +0300 Subject: [PATCH 0516/1365] Bug fix for MS COCO json output --- src/detector.c | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/detector.c b/src/detector.c index c77a5889f58..9501d105e57 100644 --- a/src/detector.c +++ b/src/detector.c @@ -554,7 +554,11 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out if (fps) fclose(fps[j]); } if (coco) { +#ifdef WIN32 + fseek(fp, -3, SEEK_CUR); +#else fseek(fp, -2, SEEK_CUR); +#endif fprintf(fp, "\n]\n"); fclose(fp); } From f1b6a6e008f67117a4c035833781defa41fc724a Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Mon, 10 Jun 2019 10:33:20 +0200 Subject: [PATCH 0517/1365] fixes for clang/mac --- CMakeLists.txt | 9 +++++++-- src/detector.c | 4 ++-- 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index e41a5f77632..df97373803b 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -28,7 +28,10 @@ enable_language(CXX) set(CMAKE_CXX_STANDARD 11) set(CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/cmake/Modules/" ${CMAKE_MODULE_PATH}) -set(CMAKE_INSTALL_PREFIX "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Install prefix" FORCE) +if (CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT) + set(CMAKE_INSTALL_PREFIX "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Install prefix" FORCE) +endif() + set(INSTALL_BIN_DIR "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Path where exe and dll will be installed") set(INSTALL_LIB_DIR "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Path where lib will be installed") set(INSTALL_INCLUDE_DIR "include/darknet" CACHE PATH "Path where headers will be installed") @@ -74,7 +77,9 @@ find_package(Threads REQUIRED) if(MSVC) find_package(PThreads_windows REQUIRED) endif() -find_package(OpenCV) +if(ENABLE_OPENCV) + find_package(OpenCV) +endif() find_package(Stb REQUIRED) if(${CMAKE_VERSION} VERSION_LESS "3.9.0") message(WARNING "To build with OpenMP support you need CMake 3.9.0+") diff --git a/src/detector.c b/src/detector.c index 9501d105e57..074365fbb7c 100644 --- a/src/detector.c +++ b/src/detector.c @@ -18,7 +18,7 @@ typedef __compar_fn_t comparison_fn_t; #include "http_stream.h" -int check_mistakes; +int check_mistakes = 0; static int coco_ids[] = { 1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90 }; @@ -996,7 +996,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa // MS COCO - uses 101-Recall-points on PR-chart. // PascalVOC2007 - uses 11-Recall-points on PR-chart. - // PascalVOC2010–2012 - uses Area-Under-Curve on PR-chart. + // PascalVOC2010-2012 - uses Area-Under-Curve on PR-chart. // ImageNet - uses Area-Under-Curve on PR-chart. // correct mAP calculation: ImageNet, PascalVOC 2010-2012 From f14c6e2fd9bbab16c34c0fab8bfc56dc4acd2814 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Mon, 10 Jun 2019 10:56:23 +0200 Subject: [PATCH 0518/1365] fully restore opencv option which got lost somehow --- CMakeLists.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/CMakeLists.txt b/CMakeLists.txt index df97373803b..6825ba01e35 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -37,6 +37,7 @@ set(INSTALL_LIB_DIR "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Path where lib set(INSTALL_INCLUDE_DIR "include/darknet" CACHE PATH "Path where headers will be installed") set(INSTALL_CMAKE_DIR "share/darknet" CACHE PATH "Path where cmake configs will be installed") +set(ENABLE_OPENCV "TRUE" CACHE BOOL "Enable OpenCV integration") set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA support") set(ENABLE_CUDNN "FALSE" CACHE BOOL "Enable CUDNN") set(ENABLE_CUDNN_HALF "FALSE" CACHE BOOL "Enable CUDNN Half precision") From 69601cd8b0860156a5fca0a61580bf10fe5aa367 Mon Sep 17 00:00:00 2001 From: acxz Date: Mon, 10 Jun 2019 12:44:18 -0400 Subject: [PATCH 0519/1365] added cmake build instructions and reorganized some install instructions --- README.md | 48 ++++++++++++++++++++++++++++++++++++++++-------- 1 file changed, 40 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 52df11995a8..97b0bae824c 100644 --- a/README.md +++ b/README.md @@ -18,9 +18,12 @@ More details: http://pjreddie.com/darknet/yolo/ 0. [Improvements in this repository](#improvements-in-this-repository) 1. [How to use](#how-to-use-on-the-command-line) -2. [How to compile on Linux](#how-to-compile-on-linux) +2. How to compile on Linux + * [Using cmake](#how-to-compile-on-linux-using-cmake) + * [Using make](#how-to-compile-on-linux-using-make) 3. How to compile on Windows * [Using vcpkg](#how-to-compile-on-windows-using-vcpkg) + * [Using Cmake-GUI](#how-to-compile-on-windows-using-Cmake-GUI) * [Legacy way](#how-to-compile-on-windows-legacy-way) 4. [How to train (Pascal VOC Data)](#how-to-train-pascal-voc-data) 5. [How to train with multi-GPU:](#how-to-train-with-multi-gpu) @@ -50,10 +53,6 @@ More details: http://pjreddie.com/darknet/yolo/ * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported * on Linux **GCC or Clang**, on Windows **MSVC 2015/2017/2019** https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community -Compiling on **Windows** by using `Cmake-GUI` as on this [**IMAGE**](https://user-images.githubusercontent.com/4096485/55107892-6becf380-50e3-11e9-9a0a-556a943c429a.png): Configure -> Optional platform for generator (Set: x64) -> Finish -> Generate -> Open Project -> x64 & Release -> Build -> Build solution - -Compiling on **Linux** by using command `make` (or alternative way by using command: `cmake . && make` ) - #### Pre-trained models There are weights-file for different cfg-files (smaller size -> faster speed & lower accuracy: @@ -154,7 +153,23 @@ On Linux find executable file `./darknet` in the root directory, while on Window * Yolo v3 COCO-model: `darknet.exe detector demo data/coco.data yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` -### How to compile on Linux +### How to compile on Linux (using `cmake`) + +The `CMakeLists.txt` will attempt to find installed optional dependencies like +CUDA, cudnn, ZED and build against those. It will also create a shared object +library file to use `darknet` for code development. + +Inside the cloned repository: + +``` +mkdir build-release +cd build-release +cmake .. +make +make install +``` + +### How to compile on Linux (using `make`) Just do `make` in the darknet directory. Before make, you can set such options in the `Makefile`: [link](https://github.com/AlexeyAB/darknet/blob/9c1b9a2cf6363546c152251be578a21f3c3caec6/Makefile#L1) @@ -201,6 +216,19 @@ PS Code\vcpkg> .\vcpkg install pthreads opencv[ffmpeg] #replace with ope 9. Open Powershell, go to the `darknet` folder and build with the command `.\build.ps1`. If you want to use Visual Studio, you will find two custom solutions created for you by CMake after the build, one in `build_win_debug` and the other in `build_win_release`, containing all the appropriate config flags for your system. +### How to compile on Windows (using `Cmake-GUI`) + +Using `Cmake-GUI` as shown here on this [**IMAGE**](https://user-images.githubusercontent.com/4096485/55107892-6becf380-50e3-11e9-9a0a-556a943c429a.png): + +1. Configure +2. Optional platform for generator (Set: x64) +3. Finish +4. Generate +5. Open Project +6. x64 & Release +7. Build +8. Build solution + ### How to compile on Windows (legacy way) 1. If you have **CUDA 10.0, cuDNN 7.4 and OpenCV 3.x** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then open `build\darknet\darknet.sln`, set **x64** and **Release** https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg and do the: Build -> Build darknet. Also add Windows system variable `CUDNN` with path to CUDNN: https://user-images.githubusercontent.com/4096485/53249764-019ef880-36ca-11e9-8ffe-d9cf47e7e462.jpg @@ -596,8 +624,12 @@ With example of: `train.txt`, `obj.names`, `obj.data`, `yolo-obj.cfg`, `air`1-6` ## How to use Yolo as DLL and SO libraries -* on Linux - set `LIBSO=1` in the `Makefile` and do `make` -* on Windows - compile `build\darknet\yolo_cpp_dll.sln` or `build\darknet\yolo_cpp_dll_no_gpu.sln` solution +* on Linux + * build `darknet` using `cmake` or + * set `LIBSO=1` in the `Makefile` and do `make` +* on Windows + * compile `build\darknet\yolo_cpp_dll.sln` solution or + * compile `build\darknet\yolo_cpp_dll_no_gpu.sln` solution There are 2 APIs: * C API: https://github.com/AlexeyAB/darknet/blob/master/include/darknet.h From fead96a0022eb5b160f87324478d41c60f5166a9 Mon Sep 17 00:00:00 2001 From: acxz Date: Mon, 10 Jun 2019 13:00:05 -0400 Subject: [PATCH 0520/1365] fixed link to cmake-gui section --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 97b0bae824c..521c2ab59f4 100644 --- a/README.md +++ b/README.md @@ -23,7 +23,7 @@ More details: http://pjreddie.com/darknet/yolo/ * [Using make](#how-to-compile-on-linux-using-make) 3. How to compile on Windows * [Using vcpkg](#how-to-compile-on-windows-using-vcpkg) - * [Using Cmake-GUI](#how-to-compile-on-windows-using-Cmake-GUI) + * [Using Cmake-GUI](#how-to-compile-on-windows-using-cmake-gui) * [Legacy way](#how-to-compile-on-windows-legacy-way) 4. [How to train (Pascal VOC Data)](#how-to-train-pascal-voc-data) 5. [How to train with multi-GPU:](#how-to-train-with-multi-gpu) From f1f45b295c40f3f4c96629691cf9d18355e81fca Mon Sep 17 00:00:00 2001 From: Aymeric DUJARDIN Date: Wed, 12 Jun 2019 09:23:57 +0200 Subject: [PATCH 0521/1365] Fix Ubuntu cmake compilation (nvcc fatal fopenmp + pthread link uselib) --- CMakeLists.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index e41a5f77632..16c43d7689d 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -274,7 +274,7 @@ if(OpenCV_FOUND) target_compile_definitions(dark PUBLIC -DOPENCV) endif() -if(OPENMP_FOUND) +if(WIN32 AND OPENMP_FOUND) target_link_libraries(darknet PUBLIC OpenMP::OpenMP_CXX) target_link_libraries(darknet PUBLIC OpenMP::OpenMP_C) target_link_libraries(dark PUBLIC OpenMP::OpenMP_CXX) @@ -298,6 +298,7 @@ endif() target_link_libraries(darknet PRIVATE Threads::Threads) target_link_libraries(dark PRIVATE Threads::Threads) +target_link_libraries(uselib PRIVATE Threads::Threads) if(ENABLE_ZED_CAMERA) target_link_libraries(darknet PRIVATE ${ZED_LIBRARIES}) From 1b7a0dca0930c756730cee98ae91bf045f12091e Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 12 Jun 2019 16:45:48 +0200 Subject: [PATCH 0522/1365] revert #3389 --- CMakeLists.txt | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 040a921d230..a4ce165dffe 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -82,8 +82,8 @@ if(ENABLE_OPENCV) find_package(OpenCV) endif() find_package(Stb REQUIRED) -if(${CMAKE_VERSION} VERSION_LESS "3.9.0") - message(WARNING "To build with OpenMP support you need CMake 3.9.0+") +if(${CMAKE_VERSION} VERSION_LESS "3.11.0") + message(WARNING "To build with OpenMP support you need CMake 3.11.0+") else() find_package(OpenMP) endif() @@ -280,7 +280,7 @@ if(OpenCV_FOUND) target_compile_definitions(dark PUBLIC -DOPENCV) endif() -if(WIN32 AND OPENMP_FOUND) +if(OPENMP_FOUND) target_link_libraries(darknet PUBLIC OpenMP::OpenMP_CXX) target_link_libraries(darknet PUBLIC OpenMP::OpenMP_C) target_link_libraries(dark PUBLIC OpenMP::OpenMP_CXX) @@ -304,7 +304,6 @@ endif() target_link_libraries(darknet PRIVATE Threads::Threads) target_link_libraries(dark PRIVATE Threads::Threads) -target_link_libraries(uselib PRIVATE Threads::Threads) if(ENABLE_ZED_CAMERA) target_link_libraries(darknet PRIVATE ${ZED_LIBRARIES}) From b78db932cde64271d05f65a829623e073566454a Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 12 Jun 2019 17:17:40 +0200 Subject: [PATCH 0523/1365] fix missing pthread link for downstream users --- CMakeLists.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index a4ce165dffe..3024a7778fa 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -302,8 +302,8 @@ if(MSVC) target_compile_definitions(uselib PRIVATE -D_CRT_RAND_S -DNOMINMAX -D_USE_MATH_DEFINES) endif() -target_link_libraries(darknet PRIVATE Threads::Threads) -target_link_libraries(dark PRIVATE Threads::Threads) +target_link_libraries(darknet PUBLIC Threads::Threads) +target_link_libraries(dark PUBLIC Threads::Threads) if(ENABLE_ZED_CAMERA) target_link_libraries(darknet PRIVATE ${ZED_LIBRARIES}) From de07ab6924913c868e8d4bc1e24a5167e8e20a66 Mon Sep 17 00:00:00 2001 From: acxz Date: Wed, 12 Jun 2019 12:14:25 -0400 Subject: [PATCH 0524/1365] added more ways to create so an dll files --- README.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 521c2ab59f4..2f85a17c15e 100644 --- a/README.md +++ b/README.md @@ -625,11 +625,13 @@ With example of: `train.txt`, `obj.names`, `obj.data`, `yolo-obj.cfg`, `air`1-6` ## How to use Yolo as DLL and SO libraries * on Linux + * using `build.sh` or * build `darknet` using `cmake` or * set `LIBSO=1` in the `Makefile` and do `make` * on Windows - * compile `build\darknet\yolo_cpp_dll.sln` solution or - * compile `build\darknet\yolo_cpp_dll_no_gpu.sln` solution + * using `build.ps1` or + * build `darknet` using `cmake` or + * compile `build\darknet\yolo_cpp_dll.sln` solution or `build\darknet\yolo_cpp_dll_no_gpu.sln` solution There are 2 APIs: * C API: https://github.com/AlexeyAB/darknet/blob/master/include/darknet.h From c0a9c741c34e0ad84e6cbbd4d6617b7fa99b089b Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 13 Jun 2019 06:54:54 +0200 Subject: [PATCH 0525/1365] clean up of README about deprecated option, invert logic of vcpkg flag in build.ps1 for improved clarity --- README.md | 4 +--- build.ps1 | 19 ++++++++++--------- 2 files changed, 11 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index 52df11995a8..5a5c493cd6c 100644 --- a/README.md +++ b/README.md @@ -197,9 +197,7 @@ PS \> cd $env:VCPKG_ROOT PS Code\vcpkg> .\vcpkg install pthreads opencv[ffmpeg] #replace with opencv[cuda,ffmpeg] in case you want to use cuda-accelerated openCV ``` -8. [necessary only with CUDA] Customize the `build.ps1` script enabling the appropriate `my_cuda_compute_model` line. If not manually defined, CMake toolchain will automatically use the very low 3.0 CUDA compute model - -9. Open Powershell, go to the `darknet` folder and build with the command `.\build.ps1`. If you want to use Visual Studio, you will find two custom solutions created for you by CMake after the build, one in `build_win_debug` and the other in `build_win_release`, containing all the appropriate config flags for your system. +8. Open Powershell, go to the `darknet` folder and build with the command `.\build.ps1`. If you want to use Visual Studio, you will find two custom solutions created for you by CMake after the build, one in `build_win_debug` and the other in `build_win_release`, containing all the appropriate config flags for your system. ### How to compile on Windows (legacy way) diff --git a/build.ps1 b/build.ps1 index b36d305404d..8030ca99b8c 100755 --- a/build.ps1 +++ b/build.ps1 @@ -1,7 +1,7 @@ #!/usr/bin/env pwsh $number_of_build_workers=8 -$force_using_include_libs=$false +$use_vcpkg=$true function getProgramFiles32bit() { $out = ${env:PROGRAMFILES(X86)} @@ -72,27 +72,27 @@ function getLatestVisualStudioWithDesktopWorkloadVersion() { } -if ((Test-Path env:VCPKG_ROOT) -and -not $force_using_include_libs) { +if ((Test-Path env:VCPKG_ROOT) -and $use_vcpkg) { $vcpkg_path = "$env:VCPKG_ROOT" Write-Host "Found vcpkg in VCPKG_ROOT: $vcpkg_path" } -elseif ((Test-Path "${env:WORKSPACE}\vcpkg") -and -not $force_using_include_libs) { +elseif ((Test-Path "${env:WORKSPACE}\vcpkg") -and $use_vcpkg) { $vcpkg_path = "${env:WORKSPACE}\vcpkg" Write-Host "Found vcpkg in WORKSPACE\vcpkg: $vcpkg_path" } else { - Write-Host "Skipping vcpkg-enabled builds because the VCPKG_ROOT environment variable is not defined, using self-distributed libs`n" -ForegroundColor Yellow + Write-Host "Skipping vcpkg-enabled builds because the VCPKG_ROOT environment variable is not defined or you requested to avoid VCPKG, using self-distributed libs`n" -ForegroundColor Yellow } -if ($null -eq $env:VCPKG_DEFAULT_TRIPLET) { +if ($null -eq $env:VCPKG_DEFAULT_TRIPLET -and $use_vcpkg) { Write-Host "No default triplet has been set-up for vcpkg. Defaulting to x64-windows" -ForegroundColor Yellow $vcpkg_triplet = "x64-windows" } -else { +elseif ($use_vcpkg) { $vcpkg_triplet = $env:VCPKG_DEFAULT_TRIPLET } -if ($vcpkg_triplet -Match "x86") { +if ($vcpkg_triplet -Match "x86" -and $use_vcpkg) { Throw "darknet is supported only in x64 builds!" } @@ -144,7 +144,7 @@ if (Test-Path env:CUDA_PATH) { } -if ($vcpkg_path) { +if ($use_vcpkg) { ## DEBUG #New-Item -Path .\build_win_debug -ItemType directory -Force #Set-Location build_win_debug @@ -153,6 +153,7 @@ if ($vcpkg_path) { ##cmake --build . --config Debug --parallel ${number_of_build_workers} --target install #valid only for CMake 3.12+ #Remove-Item DarknetConfig.cmake #Remove-Item DarknetConfigVersion.cmake + #Copy-Item Debug\*.dll .. #Set-Location .. #Copy-Item cmake\Modules\*.cmake share\darknet\ @@ -164,7 +165,7 @@ if ($vcpkg_path) { #cmake --build . --config Release --parallel ${number_of_build_workers} --target install #valid only for CMake 3.12+ Remove-Item DarknetConfig.cmake Remove-Item DarknetConfigVersion.cmake - Copy-Item *.dll .. + Copy-Item Release\*.dll .. Set-Location .. Copy-Item cmake\Modules\*.cmake share\darknet\ } From bd689f91ac13c9f5fcfb74b9409e972f011b3a65 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 13 Jun 2019 14:43:23 +0300 Subject: [PATCH 0526/1365] Added TridentNet (state-of-art on MS COCO) --- build/darknet/x64/cfg/resnet152_trident.cfg | 2177 +++++++++++++++++++ build/darknet/x64/partial.cmd | 2 + cfg/resnet152_trident.cfg | 2177 +++++++++++++++++++ include/darknet.h | 1 + src/coco.c | 4 + src/conv_lstm_layer.c | 22 +- src/convolutional_layer.c | 115 +- src/convolutional_layer.h | 2 +- src/crnn_layer.c | 6 +- src/layer.c | 52 +- src/parser.c | 15 +- 11 files changed, 4491 insertions(+), 82 deletions(-) create mode 100644 build/darknet/x64/cfg/resnet152_trident.cfg create mode 100644 cfg/resnet152_trident.cfg diff --git a/build/darknet/x64/cfg/resnet152_trident.cfg b/build/darknet/x64/cfg/resnet152_trident.cfg new file mode 100644 index 00000000000..c7e0e255700 --- /dev/null +++ b/build/darknet/x64/cfg/resnet152_trident.cfg @@ -0,0 +1,2177 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=64 +width=608 +height=608 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + + +learning_rate=0.001 +burn_in=1000 +max_batches = 10000 + +policy=sgdr +sgdr_cycle=1000 +sgdr_mult=2 +steps=4000,6000,8000,9000 +#scales=1, 1, 0.1, 0.1 + + +[convolutional] +batch_normalize=1 +filters=64 +size=7 +stride=2 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +# Conv 4 +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + + +### TridentNet - large objects - Start + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +dilation=3 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +dilation=3 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +dilation=3 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +dilation=3 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +dilation=3 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +dilation=3 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +dilation=3 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +## Conv 5 +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +dilation=3 +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +dilation=3 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +dilation=3 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=2048 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=24 +activation=linear + +[yolo] +mask = 8,9,10,11 +anchors = 8,8, 10,13, 16,30, 33,23, 32,32, 30,61, 62,45, 59,119, 80,80, 116,90, 156,198, 373,326 +classes=1 +num=12 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=0 + +### TridentNet - large objects - End + + + + + + + +### TridentNet - medium objects - Start + +[route] +layers = 165 + +[convolutional] +share_index=166 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=167 +dilation=2 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=168 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=170 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=171 +dilation=2 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=172 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=174 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=175 +dilation=2 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=176 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=178 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=179 +dilation=2 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=180 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=182 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=183 +dilation=2 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=184 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=186 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=187 +dilation=2 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=188 +dilation=2 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +## Conv 5 +[convolutional] +share_index=190 +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=191 +dilation=2 +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +share_index=192 +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=194 +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=195 +dilation=2 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=196 +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=198 +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=199 +dilation=2 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=200 +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 49 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=24 +activation=linear + +[yolo] +mask = 4,5,6,7 +anchors = 8,8, 10,13, 16,30, 33,23, 32,32, 30,61, 62,45, 64,64, 59,119, 116,90, 156,198, 373,326 +classes=1 +num=12 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=0 + +### TridentNet - medium objects - End + + + + + + + + + + + +### TridentNet - small objects - Start + +[route] +layers = 165 + +[convolutional] +share_index=166 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=167 +dilation=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=168 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=170 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=171 +dilation=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=172 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=174 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=175 +dilation=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=176 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=178 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=179 +dilation=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=180 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=182 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=183 +dilation=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=184 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=186 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=187 +dilation=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=188 +dilation=1 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +## Conv 5 +[convolutional] +share_index=190 +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=191 +dilation=1 +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +share_index=192 +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=194 +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=195 +dilation=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=196 +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=198 +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=199 +dilation=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=200 +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=512 +activation=leaky + +[upsample] +stride=4 + +[route] +layers = -1, 17 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=24 +activation=linear + +[yolo] +mask = 0,1,2,3 +anchors = 8,8, 10,13, 16,30, 33,23, 32,32, 30,61, 62,45, 64,64, 59,119, 116,90, 156,198, 373,326 +classes=1 +num=12 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=0 + +### TridentNet - small objects - End + diff --git a/build/darknet/x64/partial.cmd b/build/darknet/x64/partial.cmd index 03759e8afeb..5aa972f36fa 100644 --- a/build/darknet/x64/partial.cmd +++ b/build/darknet/x64/partial.cmd @@ -35,6 +35,8 @@ darknet.exe partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 darknet.exe partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.14 14 +darknet.exe partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.13 13 + darknet.exe partial cfg/yolo9000.cfg yolo9000.weights yolo9000.conv.22 22 diff --git a/cfg/resnet152_trident.cfg b/cfg/resnet152_trident.cfg new file mode 100644 index 00000000000..c7e0e255700 --- /dev/null +++ b/cfg/resnet152_trident.cfg @@ -0,0 +1,2177 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=64 +width=608 +height=608 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + + +learning_rate=0.001 +burn_in=1000 +max_batches = 10000 + +policy=sgdr +sgdr_cycle=1000 +sgdr_mult=2 +steps=4000,6000,8000,9000 +#scales=1, 1, 0.1, 0.1 + + +[convolutional] +batch_normalize=1 +filters=64 +size=7 +stride=2 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +# Conv 4 +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + + +### TridentNet - large objects - Start + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +dilation=3 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +dilation=3 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +dilation=3 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +dilation=3 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +dilation=3 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +dilation=3 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +dilation=3 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +## Conv 5 +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +dilation=3 +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +dilation=3 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +dilation=3 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=2048 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=24 +activation=linear + +[yolo] +mask = 8,9,10,11 +anchors = 8,8, 10,13, 16,30, 33,23, 32,32, 30,61, 62,45, 59,119, 80,80, 116,90, 156,198, 373,326 +classes=1 +num=12 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=0 + +### TridentNet - large objects - End + + + + + + + +### TridentNet - medium objects - Start + +[route] +layers = 165 + +[convolutional] +share_index=166 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=167 +dilation=2 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=168 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=170 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=171 +dilation=2 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=172 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=174 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=175 +dilation=2 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=176 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=178 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=179 +dilation=2 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=180 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=182 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=183 +dilation=2 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=184 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=186 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=187 +dilation=2 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=188 +dilation=2 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +## Conv 5 +[convolutional] +share_index=190 +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=191 +dilation=2 +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +share_index=192 +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=194 +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=195 +dilation=2 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=196 +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=198 +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=199 +dilation=2 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=200 +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 49 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=24 +activation=linear + +[yolo] +mask = 4,5,6,7 +anchors = 8,8, 10,13, 16,30, 33,23, 32,32, 30,61, 62,45, 64,64, 59,119, 116,90, 156,198, 373,326 +classes=1 +num=12 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=0 + +### TridentNet - medium objects - End + + + + + + + + + + + +### TridentNet - small objects - Start + +[route] +layers = 165 + +[convolutional] +share_index=166 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=167 +dilation=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=168 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=170 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=171 +dilation=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=172 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=174 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=175 +dilation=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=176 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=178 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=179 +dilation=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=180 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=182 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=183 +dilation=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=184 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=186 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=187 +dilation=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=188 +dilation=1 +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +## Conv 5 +[convolutional] +share_index=190 +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=191 +dilation=1 +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +share_index=192 +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=194 +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=195 +dilation=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=196 +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +share_index=198 +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=199 +dilation=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +share_index=200 +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=512 +activation=leaky + +[upsample] +stride=4 + +[route] +layers = -1, 17 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=24 +activation=linear + +[yolo] +mask = 0,1,2,3 +anchors = 8,8, 10,13, 16,30, 33,23, 32,32, 30,61, 62,45, 64,64, 59,119, 116,90, 156,198, 373,326 +classes=1 +num=12 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=0 + +### TridentNet - small objects - End + diff --git a/include/darknet.h b/include/darknet.h index 4154d664b8c..e807ff9bc68 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -185,6 +185,7 @@ struct layer { void(*forward_gpu) (struct layer, struct network_state); void(*backward_gpu) (struct layer, struct network_state); void(*update_gpu) (struct layer, int, float, float, float); + layer *share_layer; int batch_normalize; int shortcut; int batch; diff --git a/src/coco.c b/src/coco.c index c1535a35f19..cdfd3dff391 100644 --- a/src/coco.c +++ b/src/coco.c @@ -218,7 +218,11 @@ void validate_coco(char *cfgfile, char *weightfile) free_image(val_resized[t]); } } +#ifdef WIN32 + fseek(fp, -3, SEEK_CUR); +#else fseek(fp, -2, SEEK_CUR); +#endif fprintf(fp, "\n]\n"); fclose(fp); diff --git a/src/conv_lstm_layer.c b/src/conv_lstm_layer.c index 5caa3754e55..6cbaf1c3911 100644 --- a/src/conv_lstm_layer.c +++ b/src/conv_lstm_layer.c @@ -66,44 +66,44 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i // U l.uf = (layer*)calloc(1, sizeof(layer)); - *(l.uf) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.uf) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.uf->batch = batch; if (l.workspace_size < l.uf->workspace_size) l.workspace_size = l.uf->workspace_size; l.ui = (layer*)calloc(1, sizeof(layer)); - *(l.ui) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.ui) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.ui->batch = batch; if (l.workspace_size < l.ui->workspace_size) l.workspace_size = l.ui->workspace_size; l.ug = (layer*)calloc(1, sizeof(layer)); - *(l.ug) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.ug) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.ug->batch = batch; if (l.workspace_size < l.ug->workspace_size) l.workspace_size = l.ug->workspace_size; l.uo = (layer*)calloc(1, sizeof(layer)); - *(l.uo) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.uo) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.uo->batch = batch; if (l.workspace_size < l.uo->workspace_size) l.workspace_size = l.uo->workspace_size; // W l.wf = (layer*)calloc(1, sizeof(layer)); - *(l.wf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.wf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.wf->batch = batch; if (l.workspace_size < l.wf->workspace_size) l.workspace_size = l.wf->workspace_size; l.wi = (layer*)calloc(1, sizeof(layer)); - *(l.wi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.wi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.wi->batch = batch; if (l.workspace_size < l.wi->workspace_size) l.workspace_size = l.wi->workspace_size; l.wg = (layer*)calloc(1, sizeof(layer)); - *(l.wg) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.wg) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.wg->batch = batch; if (l.workspace_size < l.wg->workspace_size) l.workspace_size = l.wg->workspace_size; l.wo = (layer*)calloc(1, sizeof(layer)); - *(l.wo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.wo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.wo->batch = batch; if (l.workspace_size < l.wo->workspace_size) l.workspace_size = l.wo->workspace_size; @@ -111,21 +111,21 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i // V l.vf = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { - *(l.vf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.vf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.vf->batch = batch; if (l.workspace_size < l.vf->workspace_size) l.workspace_size = l.vf->workspace_size; } l.vi = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { - *(l.vi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.vi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.vi->batch = batch; if (l.workspace_size < l.vi->workspace_size) l.workspace_size = l.vi->workspace_size; } l.vo = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { - *(l.vo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.vo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.vo->batch = batch; if (l.workspace_size < l.vo->workspace_size) l.workspace_size = l.vo->workspace_size; } diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 84bc75fb75a..de77a5850fe 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -274,10 +274,11 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1)); CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->normDstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w)); + //printf("\n l->dilation = %d, l->pad = %d, l->size = %d \n", l->dilation, l->pad, l->size); #if(CUDNN_MAJOR >= 6) - CHECK_CUDNN(cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, l->dilation, l->dilation, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); // cudnn >= 6.0 + CHECK_CUDNN(cudnnSetConvolution2dDescriptor(l->convDesc, l->pad * l->dilation, l->pad* l->dilation, l->stride, l->stride, l->dilation, l->dilation, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); // cudnn >= 6.0 #else - CHECK_CUDNN(cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, l->dilation, l->dilation, CUDNN_CROSS_CORRELATION)); // cudnn 5.1 + CHECK_CUDNN(cudnnSetConvolution2dDescriptor(l->convDesc, l->pad * l->dilation, l->pad * l->dilation, l->stride, l->stride, l->dilation, l->dilation, CUDNN_CROSS_CORRELATION)); // cudnn 5.1 #endif int forward_algo = CUDNN_CONVOLUTION_FWD_PREFER_FASTEST; int backward_algo = CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST; @@ -331,7 +332,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) #endif #endif -convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index) +convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, convolutional_layer *share_layer) { int total_batch = batch*steps; int i; @@ -341,6 +342,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, if (xnor) groups = 1; // disable groups for XNOR-net if (groups < 1) groups = 1; + l.share_layer = share_layer; l.index = index; l.h = h; l.w = w; @@ -360,11 +362,20 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.learning_rate_scale = 1; l.nweights = (c / groups) * n * size * size; - l.weights = (float*)calloc(l.nweights, sizeof(float)); - l.weight_updates = (float*)calloc(l.nweights, sizeof(float)); + if (l.share_layer) { + l.weights = l.share_layer->weights; + l.weight_updates = l.share_layer->weight_updates; - l.biases = (float*)calloc(n, sizeof(float)); - l.bias_updates = (float*)calloc(n, sizeof(float)); + l.biases = l.share_layer->biases; + l.bias_updates = l.share_layer->bias_updates; + } + else { + l.weights = (float*)calloc(l.nweights, sizeof(float)); + l.weight_updates = (float*)calloc(l.nweights, sizeof(float)); + + l.biases = (float*)calloc(n, sizeof(float)); + l.bias_updates = (float*)calloc(n, sizeof(float)); + } // float scale = 1./sqrt(size*size*c); float scale = sqrt(2./(size*size*c/groups)); @@ -411,20 +422,33 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, } if(batch_normalize){ - l.scales = (float*)calloc(n, sizeof(float)); - l.scale_updates = (float*)calloc(n, sizeof(float)); - for(i = 0; i < n; ++i){ - l.scales[i] = 1; + if (l.share_layer) { + l.scales = l.share_layer->scales; + l.scale_updates = l.share_layer->scale_updates; + l.mean = l.share_layer->mean; + l.variance = l.share_layer->variance; + l.mean_delta = l.share_layer->mean_delta; + l.variance_delta = l.share_layer->variance_delta; + l.rolling_mean = l.share_layer->rolling_mean; + l.rolling_variance = l.share_layer->rolling_variance; } + else { + l.scales = (float*)calloc(n, sizeof(float)); + l.scale_updates = (float*)calloc(n, sizeof(float)); + for (i = 0; i < n; ++i) { + l.scales[i] = 1; + } - l.mean = (float*)calloc(n, sizeof(float)); - l.variance = (float*)calloc(n, sizeof(float)); + l.mean = (float*)calloc(n, sizeof(float)); + l.variance = (float*)calloc(n, sizeof(float)); - l.mean_delta = (float*)calloc(n, sizeof(float)); - l.variance_delta = (float*)calloc(n, sizeof(float)); + l.mean_delta = (float*)calloc(n, sizeof(float)); + l.variance_delta = (float*)calloc(n, sizeof(float)); + + l.rolling_mean = (float*)calloc(n, sizeof(float)); + l.rolling_variance = (float*)calloc(n, sizeof(float)); + } - l.rolling_mean = (float*)calloc(n, sizeof(float)); - l.rolling_variance = (float*)calloc(n, sizeof(float)); l.x = (float*)calloc(total_batch * l.outputs, sizeof(float)); l.x_norm = (float*)calloc(total_batch * l.outputs, sizeof(float)); } @@ -452,16 +476,24 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.scale_m_gpu = cuda_make_array(l.scale_m, n); l.scale_v_gpu = cuda_make_array(l.scale_v, n); } - - l.weights_gpu = cuda_make_array(l.weights, l.nweights); - l.weight_updates_gpu = cuda_make_array(l.weight_updates, l.nweights); + if (l.share_layer) { + l.weights_gpu = l.share_layer->weights_gpu; + l.weight_updates_gpu = l.share_layer->weight_updates_gpu; + l.weights_gpu16 = l.share_layer->weights_gpu16; + l.weight_updates_gpu16 = l.share_layer->weight_updates_gpu16; + l.biases_gpu = l.share_layer->biases_gpu; + l.bias_updates_gpu = l.share_layer->bias_updates_gpu; + } + else { + l.weights_gpu = cuda_make_array(l.weights, l.nweights); + l.weight_updates_gpu = cuda_make_array(l.weight_updates, l.nweights); #ifdef CUDNN_HALF - l.weights_gpu16 = cuda_make_array(NULL, l.nweights / 2 + 1); - l.weight_updates_gpu16 = cuda_make_array(NULL, l.nweights / 2 + 1); + l.weights_gpu16 = cuda_make_array(NULL, l.nweights / 2 + 1); + l.weight_updates_gpu16 = cuda_make_array(NULL, l.nweights / 2 + 1); #endif - - l.biases_gpu = cuda_make_array(l.biases, n); - l.bias_updates_gpu = cuda_make_array(l.bias_updates, n); + l.biases_gpu = cuda_make_array(l.biases, n); + l.bias_updates_gpu = cuda_make_array(l.bias_updates, n); + } l.output_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n); l.delta_gpu = cuda_make_array(l.delta, total_batch*out_h*out_w*n); @@ -476,18 +508,29 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, } if(batch_normalize){ - l.scales_gpu = cuda_make_array(l.scales, n); - l.scale_updates_gpu = cuda_make_array(l.scale_updates, n); - - l.mean_gpu = cuda_make_array(l.mean, n); - l.variance_gpu = cuda_make_array(l.variance, n); + if (l.share_layer) { + l.scales_gpu = l.share_layer->scales_gpu; + l.scale_updates_gpu = l.share_layer->scale_updates_gpu; + l.mean_gpu = l.share_layer->mean_gpu; + l.variance_gpu = l.share_layer->variance_gpu; + l.rolling_mean_gpu = l.share_layer->rolling_mean_gpu; + l.rolling_variance_gpu = l.share_layer->rolling_variance_gpu; + l.mean_delta_gpu = l.share_layer->mean_delta_gpu; + l.variance_delta_gpu = l.share_layer->variance_delta_gpu; + } + else { + l.scales_gpu = cuda_make_array(l.scales, n); + l.scale_updates_gpu = cuda_make_array(l.scale_updates, n); - l.rolling_mean_gpu = cuda_make_array(l.mean, n); - l.rolling_variance_gpu = cuda_make_array(l.variance, n); + l.mean_gpu = cuda_make_array(l.mean, n); + l.variance_gpu = cuda_make_array(l.variance, n); - l.mean_delta_gpu = cuda_make_array(l.mean, n); - l.variance_delta_gpu = cuda_make_array(l.variance, n); + l.rolling_mean_gpu = cuda_make_array(l.mean, n); + l.rolling_variance_gpu = cuda_make_array(l.variance, n); + l.mean_delta_gpu = cuda_make_array(l.mean, n); + l.variance_delta_gpu = cuda_make_array(l.variance, n); + } l.x_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n); l.x_norm_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n); } @@ -504,7 +547,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, if (l.xnor && l.use_bin_output) fprintf(stderr, "convXB"); else if (l.xnor) fprintf(stderr, "convX "); else fprintf(stderr, "conv "); - fprintf(stderr, "%5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); + fprintf(stderr, "%5d %2d x%2d /%2d(%d)%4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", n, size, size, stride, dilation, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); return l; } @@ -526,7 +569,7 @@ void denormalize_convolutional_layer(convolutional_layer l) void test_convolutional_layer() { - convolutional_layer l = make_convolutional_layer(1, 1, 5, 5, 3, 2, 1, 5, 2, 1, 1, LEAKY, 1, 0, 0, 0, 0, 0); + convolutional_layer l = make_convolutional_layer(1, 1, 5, 5, 3, 2, 1, 5, 2, 1, 1, LEAKY, 1, 0, 0, 0, 0, 0, NULL); l.batch_normalize = 1; float data[] = {1,1,1,1,1, 1,1,1,1,1, diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h index 586bd2cd182..e937b4c5bd6 100644 --- a/src/convolutional_layer.h +++ b/src/convolutional_layer.h @@ -30,7 +30,7 @@ void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16); #endif size_t get_convolutional_workspace_size(layer l); -convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index); +convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, convolutional_layer *share_layer); void denormalize_convolutional_layer(convolutional_layer l); void resize_convolutional_layer(convolutional_layer *layer, int w, int h); void forward_convolutional_layer(const convolutional_layer layer, network_state state); diff --git a/src/crnn_layer.c b/src/crnn_layer.c index d7c75b50fe9..7609003b4f2 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -50,17 +50,17 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.state = (float*)calloc(l.hidden * l.batch * (l.steps + 1), sizeof(float)); l.input_layer = (layer*)calloc(1, sizeof(layer)); - *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.input_layer->batch = batch; if (l.workspace_size < l.input_layer->workspace_size) l.workspace_size = l.input_layer->workspace_size; l.self_layer = (layer*)calloc(1, sizeof(layer)); - *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.self_layer->batch = batch; if (l.workspace_size < l.self_layer->workspace_size) l.workspace_size = l.self_layer->workspace_size; l.output_layer = (layer*)calloc(1, sizeof(layer)); - *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0); + *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.output_layer->batch = batch; if (l.workspace_size < l.output_layer->workspace_size) l.workspace_size = l.output_layer->workspace_size; diff --git a/src/layer.c b/src/layer.c index 6409bd892e7..ade58f4ecef 100644 --- a/src/layer.c +++ b/src/layer.c @@ -66,12 +66,12 @@ void free_layer(layer l) if (l.concat) free(l.concat); if (l.concat_delta) free(l.concat_delta); if (l.binary_weights) free(l.binary_weights); - if (l.biases) free(l.biases); - if (l.bias_updates) free(l.bias_updates); - if (l.scales) free(l.scales); - if (l.scale_updates) free(l.scale_updates); - if (l.weights) free(l.weights); - if (l.weight_updates) free(l.weight_updates); + if (l.biases) free(l.biases), l.biases = NULL; + if (l.bias_updates) free(l.bias_updates), l.bias_updates = NULL; + if (l.scales) free(l.scales), l.scales = NULL; + if (l.scale_updates) free(l.scale_updates), l.scale_updates = NULL; + if (l.weights) free(l.weights), l.weights = NULL; + if (l.weight_updates) free(l.weight_updates), l.weight_updates = NULL; if (l.align_bit_weights) free(l.align_bit_weights); if (l.mean_arr) free(l.mean_arr); #ifdef GPU @@ -89,12 +89,12 @@ void free_layer(layer l) if (l.squared) free(l.squared); if (l.norms) free(l.norms); if (l.spatial_mean) free(l.spatial_mean); - if (l.mean) free(l.mean); - if (l.variance) free(l.variance); - if (l.mean_delta) free(l.mean_delta); - if (l.variance_delta) free(l.variance_delta); - if (l.rolling_mean) free(l.rolling_mean); - if (l.rolling_variance) free(l.rolling_variance); + if (l.mean) free(l.mean), l.mean = NULL; + if (l.variance) free(l.variance), l.variance = NULL; + if (l.mean_delta) free(l.mean_delta), l.mean_delta = NULL; + if (l.variance_delta) free(l.variance_delta), l.variance_delta = NULL; + if (l.rolling_mean) free(l.rolling_mean), l.rolling_mean = NULL; + if (l.rolling_variance) free(l.rolling_variance), l.rolling_variance = NULL; if (l.x) free(l.x); if (l.x_norm) free(l.x_norm); if (l.m) free(l.m); @@ -143,12 +143,12 @@ void free_layer(layer l) if (l.concat_delta_gpu) cuda_free(l.concat_delta_gpu); if (l.binary_input_gpu) cuda_free(l.binary_input_gpu); if (l.binary_weights_gpu) cuda_free(l.binary_weights_gpu); - if (l.mean_gpu) cuda_free(l.mean_gpu); - if (l.variance_gpu) cuda_free(l.variance_gpu); - if (l.rolling_mean_gpu) cuda_free(l.rolling_mean_gpu); - if (l.rolling_variance_gpu) cuda_free(l.rolling_variance_gpu); - if (l.variance_delta_gpu) cuda_free(l.variance_delta_gpu); - if (l.mean_delta_gpu) cuda_free(l.mean_delta_gpu); + if (l.mean_gpu) cuda_free(l.mean_gpu), l.mean_gpu = NULL; + if (l.variance_gpu) cuda_free(l.variance_gpu), l.variance_gpu = NULL; + if (l.rolling_mean_gpu) cuda_free(l.rolling_mean_gpu), l.rolling_mean_gpu = NULL; + if (l.rolling_variance_gpu) cuda_free(l.rolling_variance_gpu), l.rolling_variance_gpu = NULL; + if (l.variance_delta_gpu) cuda_free(l.variance_delta_gpu), l.variance_delta_gpu = NULL; + if (l.mean_delta_gpu) cuda_free(l.mean_delta_gpu), l.mean_delta_gpu = NULL; if (l.x_gpu) cuda_free(l.x_gpu); // dont free if (l.x_norm_gpu) cuda_free(l.x_norm_gpu); @@ -157,14 +157,14 @@ void free_layer(layer l) if (l.align_workspace_gpu) cuda_free(l.align_workspace_gpu); if (l.transposed_align_workspace_gpu) cuda_free(l.transposed_align_workspace_gpu); - if (l.weights_gpu) cuda_free(l.weights_gpu); - if (l.weight_updates_gpu) cuda_free(l.weight_updates_gpu); - if (l.weights_gpu16) cuda_free(l.weights_gpu16); - if (l.weight_updates_gpu16) cuda_free(l.weight_updates_gpu16); - if (l.biases_gpu) cuda_free(l.biases_gpu); - if (l.bias_updates_gpu) cuda_free(l.bias_updates_gpu); - if (l.scales_gpu) cuda_free(l.scales_gpu); - if (l.scale_updates_gpu) cuda_free(l.scale_updates_gpu); + if (l.weights_gpu) cuda_free(l.weights_gpu), l.weights_gpu = NULL; + if (l.weight_updates_gpu) cuda_free(l.weight_updates_gpu), l.weight_updates_gpu = NULL; + if (l.weights_gpu16) cuda_free(l.weights_gpu16), l.weights_gpu16 = NULL; + if (l.weight_updates_gpu16) cuda_free(l.weight_updates_gpu16), l.weight_updates_gpu16 = NULL; + if (l.biases_gpu) cuda_free(l.biases_gpu), l.biases_gpu = NULL; + if (l.bias_updates_gpu) cuda_free(l.bias_updates_gpu), l.bias_updates_gpu = NULL; + if (l.scales_gpu) cuda_free(l.scales_gpu), l.scales_gpu = NULL; + if (l.scale_updates_gpu) cuda_free(l.scale_updates_gpu), l.scale_updates_gpu = NULL; if (l.output_gpu) cuda_free(l.output_gpu); if (l.delta_gpu) cuda_free(l.delta_gpu); if (l.rand_gpu) cuda_free(l.rand_gpu); diff --git a/src/parser.c b/src/parser.c index 0cb906a5d77..2c0951327b5 100644 --- a/src/parser.c +++ b/src/parser.c @@ -147,13 +147,14 @@ local_layer parse_local(list *options, size_params params) return layer; } -convolutional_layer parse_convolutional(list *options, size_params params) +convolutional_layer parse_convolutional(list *options, size_params params, network net) { int n = option_find_int(options, "filters",1); int groups = option_find_int_quiet(options, "groups", 1); int size = option_find_int(options, "size",1); int stride = option_find_int(options, "stride",1); int dilation = option_find_int_quiet(options, "dilation", 1); + if (size == 1) dilation = 1; int pad = option_find_int_quiet(options, "pad",0); int padding = option_find_int_quiet(options, "padding",0); if(pad) padding = size/2; @@ -161,6 +162,10 @@ convolutional_layer parse_convolutional(list *options, size_params params) char *activation_s = option_find_str(options, "activation", "logistic"); ACTIVATION activation = get_activation(activation_s); + int share_index = option_find_int_quiet(options, "share_index", -1); + convolutional_layer *share_layer = NULL; + if(share_layer > -1) share_layer = &net.layers[share_index]; + int batch,h,w,c; h = params.h; w = params.w; @@ -172,7 +177,7 @@ convolutional_layer parse_convolutional(list *options, size_params params) int xnor = option_find_int_quiet(options, "xnor", 0); int use_bin_output = option_find_int_quiet(options, "bin_output", 0); - convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride,dilation,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index); + convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride,dilation,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index, share_layer); layer.flipped = option_find_int_quiet(options, "flipped", 0); layer.dot = option_find_float_quiet(options, "dot", 0); @@ -838,7 +843,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) layer l = { (LAYER_TYPE)0 }; LAYER_TYPE lt = string_to_layer_type(s->type); if(lt == CONVOLUTIONAL){ - l = parse_convolutional(options, params); + l = parse_convolutional(options, params, net); }else if(lt == LOCAL){ l = parse_local(options, params); }else if(lt == ACTIVE){ @@ -1112,7 +1117,7 @@ void save_weights_upto(network net, char *filename, int cutoff) int i; for(i = 0; i < net.n && i < cutoff; ++i){ layer l = net.layers[i]; - if(l.type == CONVOLUTIONAL){ + if(l.type == CONVOLUTIONAL && l.share_layer == NULL){ save_convolutional_weights(l, fp); } if(l.type == CONNECTED){ save_connected_weights(l, fp); @@ -1337,7 +1342,7 @@ void load_weights_upto(network *net, char *filename, int cutoff) for(i = 0; i < net->n && i < cutoff; ++i){ layer l = net->layers[i]; if (l.dontload) continue; - if(l.type == CONVOLUTIONAL){ + if(l.type == CONVOLUTIONAL && l.share_layer == NULL){ load_convolutional_weights(l, fp); } if(l.type == CONNECTED){ From f0582446f26ada84144eeea2721a0aafd7950b73 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 15 Jun 2019 02:32:57 +0300 Subject: [PATCH 0527/1365] Fixed Depth-wise (grouped) convolution for CPU and GPU (CUDNN=0/1) --- src/avgpool_layer.c | 2 +- src/convolutional_kernels.cu | 10 +++++-- src/convolutional_layer.c | 58 +++++++++++++++++++++++------------- src/maxpool_layer.c | 2 +- src/parser.c | 2 +- 5 files changed, 47 insertions(+), 27 deletions(-) diff --git a/src/avgpool_layer.c b/src/avgpool_layer.c index 20838bbddd2..3ae022e802e 100644 --- a/src/avgpool_layer.c +++ b/src/avgpool_layer.c @@ -4,7 +4,7 @@ avgpool_layer make_avgpool_layer(int batch, int w, int h, int c) { - fprintf(stderr, "avg %4d x%4d x%4d -> %4d\n", w, h, c, c); + fprintf(stderr, "avg %4d x%4d x%4d -> %4d\n", w, h, c, c); avgpool_layer l = { (LAYER_TYPE)0 }; l.type = AVGPOOL; l.batch = batch; diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index dd6625f1e14..ffc9583d78a 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -578,7 +578,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) state.workspace); // output } - gemm_ongpu(0, 0, m, n, k, 1., a, k, b, n, 1., c + i*m*n, n); + //gemm_ongpu(0, 0, m, n, k, 1., a, k, b, n, 1., c + i*m*n, n); + gemm_ongpu(0, 0, m, n, k, 1, a, k, b, n, 1, c, n); } } @@ -817,7 +818,8 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state l.stride, l.stride, // stride (h, w) l.dilation, l.dilation, // dilation (h, w) state.workspace); // output - gemm_ongpu(0, 1, m, n, k, 1, a + i*m*k, k, b, k, 1, c, n); + //gemm_ongpu(0, 1, m, n, k, 1, a + i*m*k, k, b, k, 1, c, n); + gemm_ongpu(0, 1, m, n, k, 1, a, k, b, k, 1, c, n); if (state.delta) { if (l.binary || l.xnor) swap_binary(&l); @@ -825,7 +827,9 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state float * b = l.delta_gpu + (i*l.groups + j)*m*k; float * c = state.workspace; - gemm_ongpu(1, 0, n, k, m, 1, a, n, b + i*k*m, k, 0, c, k); + //gemm_ongpu(1, 0, n, k, m, 1, a, n, b + i*k*m, k, 0, c, k); + gemm_ongpu(1, 0, n, k, m, 1, a, n, b, k, 0, c, k); + float *delta = state.delta + (i*l.groups + j)*l.c / l.groups*l.h*l.w; diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index de77a5850fe..c7f51a660b2 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -547,7 +547,16 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, if (l.xnor && l.use_bin_output) fprintf(stderr, "convXB"); else if (l.xnor) fprintf(stderr, "convX "); else fprintf(stderr, "conv "); - fprintf(stderr, "%5d %2d x%2d /%2d(%d)%4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", n, size, size, stride, dilation, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); + + if(groups > 1) fprintf(stderr, "%5d/%3d ", n, groups); + else fprintf(stderr, "%5d ", n); + + if(dilation > 1) fprintf(stderr, "%2d x%2d/%2d(%1d)", size, size, stride, dilation); + else fprintf(stderr, "%2d x%2d/%2d ", size, size, stride); + + fprintf(stderr, "%4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); + + //fprintf(stderr, "%5d/%2d %2d x%2d /%2d(%d)%4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", n, groups, size, size, stride, dilation, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); return l; } @@ -897,12 +906,13 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) static int u = 0; u++; - for(i = 0; i < l.batch; ++i){ - for (j = 0; j < l.groups; ++j) { - - float *a = l.weights + j*l.nweights / l.groups; + for(i = 0; i < l.batch; ++i) + { + for (j = 0; j < l.groups; ++j) + { + float *a = l.weights +j*l.nweights / l.groups; float *b = state.workspace; - float *c = l.output + (i*l.groups + j)*n*m; + float *c = l.output +(i*l.groups + j)*n*m; //gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); //gemm_nn_custom(m, n, k, 1, a, k, b, n, c, n); @@ -1025,23 +1035,29 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) } else { //printf(" l.index = %d - FP32 \n", l.index); - float *im = state.input + (i*l.groups + j)*l.c / l.groups*l.h*l.w; - //im2col_cpu(im, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, b); - - im2col_cpu_ext(im, // input - l.c / l.groups, // input channels - l.h, l.w, // input size (h, w) - l.size, l.size, // kernel size (h, w) - l.pad, l.pad, // padding (h, w) - l.stride, l.stride, // stride (h, w) - l.dilation, l.dilation, // dilation (h, w) - b); // output + float *im = state.input + (i*l.groups + j)*(l.c / l.groups)*l.h*l.w; + if (l.size == 1) { + b = im; + } + else { + //im2col_cpu(im, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, b); + + im2col_cpu_ext(im, // input + l.c / l.groups, // input channels + l.h, l.w, // input size (h, w) + l.size, l.size, // kernel size (h, w) + l.pad, l.pad, // padding (h, w) + l.stride, l.stride, // stride (h, w) + l.dilation, l.dilation, // dilation (h, w) + b); // output + + } gemm(0, 0, m, n, k, 1, a, k, b, n, 1, c, n); // bit-count to float } - c += n*m; - state.input += l.c*l.h*l.w; + //c += n*m; + //state.input += l.c*l.h*l.w; } } @@ -1079,7 +1095,7 @@ void backward_convolutional_layer(convolutional_layer l, network_state state) float *b = state.workspace; float *c = l.weight_updates + j*l.nweights / l.groups; - float *im = state.input + (i*l.groups + j)*l.c / l.groups*l.h*l.w; + float *im = state.input + (i*l.groups + j)* (l.c / l.groups)*l.h*l.w; //im2col_cpu(im, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, b); im2col_cpu_ext( @@ -1112,7 +1128,7 @@ void backward_convolutional_layer(convolutional_layer l, network_state state) l.pad, l.pad, // padding (h, w) l.stride, l.stride, // stride (h, w) l.dilation, l.dilation, // dilation (h, w) - state.delta + (i*l.groups + j)*l.c / l.groups*l.h*l.w); // output (delta) + state.delta + (i*l.groups + j)* (l.c / l.groups)*l.h*l.w); // output (delta) } } } diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index 2e217459e8f..68f381990fb 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -78,7 +78,7 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s #endif // GPU l.bflops = (l.size*l.size*l.c * l.out_h*l.out_w) / 1000000000.; - fprintf(stderr, "max %d x %d / %d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); + fprintf(stderr, "max %d x %d/%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); return l; } diff --git a/src/parser.c b/src/parser.c index 2c0951327b5..f3c02198369 100644 --- a/src/parser.c +++ b/src/parser.c @@ -834,7 +834,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) n = n->next; int count = 0; free_section(s); - fprintf(stderr, "layer filters size input output\n"); + fprintf(stderr, " layer filters size/strd(dil) input output\n"); while(n){ params.index = count; fprintf(stderr, "%4d ", count); From 8c80ba6b53429347bd6249a16cd576c971a0776b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 15 Jun 2019 16:41:22 +0300 Subject: [PATCH 0528/1365] Fixed weights-size for Depthwise grouped convolutional --- src/convolutional_kernels.cu | 10 +++++----- src/convolutional_layer.c | 4 ++-- src/network_kernels.cu | 4 ++-- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index ffc9583d78a..ab8d7fa8593 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -518,7 +518,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) printf("\n is_nan_or_inf(state.input) = %d \n", input_nan_inf); if (input_nan_inf) getchar(); - int weights_nan_inf = is_nan_or_inf(l.weights_gpu, l.size * l.size * l.c * l.n); + int weights_nan_inf = is_nan_or_inf(l.weights_gpu, l.nweights); printf("\n is_nan_or_inf(l.weights_gpu) = %d \n", weights_nan_inf); if (weights_nan_inf) getchar(); */ @@ -697,8 +697,8 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state // calculate conv weight updates // Already: l.weight_updates_gpu = (l.weight_updates_gpu - l.weight*decay*batch*subdivision)*momentum // so we should copy f32 to f16, or compute: f16=(w_up - w*d*b*s)*m - assert((l.c*l.n*l.size*l.size) > 0); - cuda_convert_f32_to_f16(l.weight_updates_gpu, l.c*l.n*l.size*l.size, l.weight_updates_gpu16); + assert((l.nweights) > 0); + cuda_convert_f32_to_f16(l.weight_updates_gpu, l.nweights, l.weight_updates_gpu16); CHECK_CUDNN(cudnnConvolutionBackwardFilter(cudnn_handle(), &one, @@ -714,7 +714,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state l.dweightDesc16, l.weight_updates_gpu16)); // l.weight_updates_gpu); - cuda_convert_f16_to_f32(l.weight_updates_gpu16, l.c*l.n*l.size*l.size, l.weight_updates_gpu); + cuda_convert_f16_to_f32(l.weight_updates_gpu16, l.nweights, l.weight_updates_gpu); if (state.delta) { if (l.binary || l.xnor) swap_binary(&l); @@ -856,7 +856,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state if (state.delta) { fix_nan_and_inf(state.delta, l.inputs * l.batch); } - int size = l.size * l.size * l.c * l.n; + int size = l.nweights; fix_nan_and_inf(l.weight_updates_gpu, size); fix_nan_and_inf(l.weights_gpu, size); } diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index c7f51a660b2..bc27672daac 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -1001,7 +1001,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //size_t bit_input_size = intput_size / 8 + 1; //char *bit_input = calloc(bit_input_size, sizeof(char)); - //size_t weights_size = k * m; //l.size*l.size*l.c*l.n; + //size_t weights_size = k * m; //l.size*l.size*l.c*l.n; // l.nweights //size_t bit_weights_size = weights_size / 8 + 1; //char *bit_weights = calloc(bit_weights_size, sizeof(char)); @@ -1136,7 +1136,7 @@ void backward_convolutional_layer(convolutional_layer l, network_state state) void update_convolutional_layer(convolutional_layer l, int batch, float learning_rate, float momentum, float decay) { - //int size = l.size*l.size*l.c*l.n; + //int size = l.nweights; axpy_cpu(l.n, learning_rate / batch, l.bias_updates, 1, l.biases, 1); scal_cpu(l.n, momentum, l.bias_updates, 1); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 40f71eb0ba5..edaefc340d6 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -162,8 +162,8 @@ void forward_backward_network_gpu(network net, float *x, float *y) layer l = net.layers[i]; if (net.cudnn_half){ if (l.type == CONVOLUTIONAL && l.weights_gpu && l.weights_gpu16) { - assert((l.c*l.n*l.size*l.size) > 0); - cuda_convert_f32_to_f16(l.weights_gpu, l.c*l.n*l.size*l.size, l.weights_gpu16); + assert((l.nweights) > 0); + cuda_convert_f32_to_f16(l.weights_gpu, l.nweights, l.weights_gpu16); } else if (l.type == CRNN && l.input_layer->weights_gpu && l.input_layer->weights_gpu16) { assert((l.input_layer->c*l.input_layer->n*l.input_layer->size*l.input_layer->size) > 0); From cc41339805d8b48edef67d0dcf4925b45c212277 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 19 Jun 2019 23:36:02 +0300 Subject: [PATCH 0529/1365] Added [scale_channels] layer for squeeze-and-excitation blocks --- Makefile | 2 +- build/darknet/darknet.vcxproj | 2 + include/darknet.h | 2 + src/blas.h | 5 ++ src/blas_kernels.cu | 46 +++++++++++++ src/classifier.c | 4 +- src/convolutional_kernels.cu | 7 +- src/dropout_layer_kernels.cu | 7 ++ src/network.c | 1 + src/network_kernels.cu | 1 + src/parser.c | 38 +++++++++++ src/scale_channels_layer.c | 118 ++++++++++++++++++++++++++++++++++ src/scale_channels_layer.h | 23 +++++++ src/shortcut_layer.c | 2 + 14 files changed, 253 insertions(+), 5 deletions(-) create mode 100644 src/scale_channels_layer.c create mode 100644 src/scale_channels_layer.h diff --git a/Makefile b/Makefile index 7f0547d3360..dc880be9fc1 100644 --- a/Makefile +++ b/Makefile @@ -118,7 +118,7 @@ LDFLAGS+= -L/usr/local/zed/lib -lsl_core -lsl_input -lsl_zed #-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0 endif -OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o upsample_layer.o lstm_layer.o conv_lstm_layer.o +OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o upsample_layer.o lstm_layer.o conv_lstm_layer.o scale_channels_layer.o ifeq ($(GPU), 1) LDFLAGS+= -lstdc++ OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj index 5a78ac602ec..019e02c617d 100644 --- a/build/darknet/darknet.vcxproj +++ b/build/darknet/darknet.vcxproj @@ -226,6 +226,7 @@ + @@ -284,6 +285,7 @@ + diff --git a/include/darknet.h b/include/darknet.h index e807ff9bc68..25e702b2b0d 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -136,6 +136,7 @@ typedef enum { AVGPOOL, LOCAL, SHORTCUT, + SCALE_CHANNELS, ACTIVE, RNN, GRU, @@ -153,6 +154,7 @@ typedef enum { UPSAMPLE, LOGXENT, L2NORM, + EMPTY, BLANK } LAYER_TYPE; diff --git a/src/blas.h b/src/blas.h index ca9cd588c3b..2b46eba31f5 100644 --- a/src/blas.h +++ b/src/blas.h @@ -117,6 +117,11 @@ void add_3_arrays_activate(float *a1, float *a2, float *a3, size_t size, ACTIVAT void sum_of_mults(float *a1, float *a2, float *b1, float *b2, size_t size, float *dst); void activate_and_mult(float *a1, float *a2, size_t size, ACTIVATION a, float *dst); +void scale_channels_gpu(float *in_w_h_c, int size, int channel_size, float *scales_c, float *out); +void backward_scale_channels_gpu(float *in_w_h_c_delta, int size, int channel_size, + float *in_scales_c, float *out_from_delta, + float *in_from_output, float *out_state_delta); + #endif #ifdef __cplusplus } diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index ed418c107c4..35f9a1ad130 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -1099,3 +1099,49 @@ extern "C" void activate_and_mult(float *a1, float *a2, size_t size, ACTIVATION } activate_and_mult_kernel << > >(a1, a2, size, a, dst); } + + + +__global__ void scale_channels_kernel(float *in_w_h_c, int size, int channel_size, float *scales_c, float *out) +{ + const int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < size) { + out[index] = in_w_h_c[index] * scales_c[index / channel_size]; + } +} + +extern "C" void scale_channels_gpu(float *in_w_h_c, int size, int channel_size, float *scales_c, float *out) +{ + const int block_size = BLOCK; + const int num_blocks = get_number_of_blocks(size, block_size); + scale_channels_kernel << > >(in_w_h_c, size, channel_size, scales_c, out); + CHECK_CUDA(cudaPeekAtLastError()); +} + + +__global__ void backward_scale_channels_kernel(float *in_w_h_c_delta, int size, int channel_size, + float *in_scales_c, float *out_from_delta, + float *in_from_output, float *out_state_delta) +{ + const int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < size) { + out_state_delta[index / channel_size] += in_w_h_c_delta[index] * in_from_output[index]; // l.delta * from (should be divided by channel_size?) + out_from_delta[index] += in_scales_c[index / channel_size] * in_w_h_c_delta[index]; // input * l.delta + + //out_state_delta[index / channel_size] += in_w_h_c_delta[index] / channel_size; + //out_from_delta[index] = in_w_h_c_delta[index]; + } +} + +extern "C" void backward_scale_channels_gpu(float *in_w_h_c_delta, int size, int channel_size, + float *in_scales_c, float *out_from_delta, + float *in_from_output, float *out_state_delta) +{ + const int block_size = BLOCK; + const int num_blocks = get_number_of_blocks(size, block_size); + backward_scale_channels_kernel << > > (in_w_h_c_delta, size, channel_size, + in_scales_c, out_from_delta, + in_from_output, out_state_delta); + + CHECK_CUDA(cudaPeekAtLastError()); +} diff --git a/src/classifier.c b/src/classifier.c index f9708e44c70..f5fbfaebf63 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -134,14 +134,14 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, #else loss = train_network(net, train); #endif - if(avg_loss == -1) avg_loss = loss; + if(avg_loss == -1 || isnan(avg_loss) || isinf(avg_loss)) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; i = get_current_batch(net); int calc_topk_for_each = iter_topk + 2 * train_images_num / (net.batch * net.subdivisions); // calculate TOPk for each 2 Epochs calc_topk_for_each = fmax(calc_topk_for_each, net.burn_in); - calc_topk_for_each = fmax(calc_topk_for_each, 1000); + calc_topk_for_each = fmax(calc_topk_for_each, 100); if (i % 10 == 0) { if (calc_topk) { fprintf(stderr, "\n (next TOP5 calculation at %d iterations) ", calc_topk_for_each); diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index ab8d7fa8593..eaa6e8030fc 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -415,7 +415,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //if (state.use_mixed_precision) { int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in) && - l.c % 8 == 0 && l.n % 8 == 0) + (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train) { //printf("\n CUDNN_HALF!!! state.index = %d \n", state.index); @@ -629,7 +629,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state //#ifdef CUDNN_HALF int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in) && - l.c % 8 == 0 && l.n % 8 == 0) + (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train) { const size_t input16_size = l.batch*l.c*l.w*l.h; const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h; @@ -910,6 +910,9 @@ void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init //float decay = a.decay; //int batch = a.batch; + fix_nan_and_inf(l.weight_updates_gpu, l.nweights); + fix_nan_and_inf(l.weights_gpu, l.nweights); + if (l.adam) { //adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.nweights, batch, a.t); adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.nweights, batch, l.t); diff --git a/src/dropout_layer_kernels.cu b/src/dropout_layer_kernels.cu index d3af0ea614f..dadaa9fe4a5 100644 --- a/src/dropout_layer_kernels.cu +++ b/src/dropout_layer_kernels.cu @@ -15,6 +15,10 @@ __global__ void yoloswag420blazeit360noscope(float *input, int size, float *rand void forward_dropout_layer_gpu(dropout_layer layer, network_state state) { if (!state.train) return; + int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); + //if (iteration_num < state.net.burn_in) return; + + int size = layer.inputs*layer.batch; cuda_random(layer.rand_gpu, size); /* @@ -32,6 +36,9 @@ void forward_dropout_layer_gpu(dropout_layer layer, network_state state) void backward_dropout_layer_gpu(dropout_layer layer, network_state state) { if(!state.delta) return; + int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); + //if (iteration_num < state.net.burn_in) return; + int size = layer.inputs*layer.batch; yoloswag420blazeit360noscope<<>>(state.delta, size, layer.rand_gpu, layer.probability, layer.scale); diff --git a/src/network.c b/src/network.c index d6b8f5c7a08..0e655c78d3c 100644 --- a/src/network.c +++ b/src/network.c @@ -318,6 +318,7 @@ void backward_network(network net, network_state state) } layer l = net.layers[i]; if (l.stopbackward) break; + if (l.onlyforward) continue; l.backward(l, state); } } diff --git a/src/network_kernels.cu b/src/network_kernels.cu index edaefc340d6..3ebf2900e95 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -106,6 +106,7 @@ void backward_network_gpu(network net, network_state state) state.input = prev.output_gpu; state.delta = prev.delta_gpu; } + if (l.onlyforward) continue; l.backward_gpu(l, state); /* diff --git a/src/parser.c b/src/parser.c index f3c02198369..d45f625ca00 100644 --- a/src/parser.c +++ b/src/parser.c @@ -31,6 +31,7 @@ #include "rnn_layer.h" #include "route_layer.h" #include "shortcut_layer.h" +#include "scale_channels_layer.h" #include "softmax_layer.h" #include "utils.h" #include "upsample_layer.h" @@ -48,6 +49,7 @@ LAYER_TYPE string_to_layer_type(char * type) { if (strcmp(type, "[shortcut]")==0) return SHORTCUT; + if (strcmp(type, "[scale_channels]") == 0) return SCALE_CHANNELS; if (strcmp(type, "[crop]")==0) return CROP; if (strcmp(type, "[cost]")==0) return COST; if (strcmp(type, "[detection]")==0) return DETECTION; @@ -80,6 +82,7 @@ LAYER_TYPE string_to_layer_type(char * type) || strcmp(type, "[softmax]")==0) return SOFTMAX; if (strcmp(type, "[route]")==0) return ROUTE; if (strcmp(type, "[upsample]") == 0) return UPSAMPLE; + if (strcmp(type, "[empty]") == 0) return EMPTY; return BLANK; } @@ -600,6 +603,24 @@ layer parse_shortcut(list *options, size_params params, network net) } +layer parse_scale_channels(list *options, size_params params, network net) +{ + char *l = option_find(options, "from"); + int index = atoi(l); + if (index < 0) index = params.index + index; + + int batch = params.batch; + layer from = net.layers[index]; + + layer s = make_scale_channels_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c); + + char *activation_s = option_find_str_quiet(options, "activation", "linear"); + ACTIVATION activation = get_activation(activation_s); + s.activation = activation; + return s; +} + + layer parse_activation(list *options, size_params params) { char *activation_s = option_find_str(options, "activation", "linear"); @@ -895,6 +916,10 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) l = parse_shortcut(options, params, net); net.layers[count - 1].use_bin_output = 0; net.layers[l.index].use_bin_output = 0; + }else if (lt == SCALE_CHANNELS) { + l = parse_scale_channels(options, params, net); + net.layers[count - 1].use_bin_output = 0; + net.layers[l.index].use_bin_output = 0; }else if(lt == DROPOUT){ l = parse_dropout(options, params); l.output = net.layers[count-1].output; @@ -902,6 +927,19 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) #ifdef GPU l.output_gpu = net.layers[count-1].output_gpu; l.delta_gpu = net.layers[count-1].delta_gpu; +#endif + } + else if (lt == EMPTY) { + layer empty_layer; + empty_layer.out_w = params.w; + empty_layer.out_h = params.h; + empty_layer.out_c = params.c; + l = empty_layer; + l.output = net.layers[count - 1].output; + l.delta = net.layers[count - 1].delta; +#ifdef GPU + l.output_gpu = net.layers[count - 1].output_gpu; + l.delta_gpu = net.layers[count - 1].delta_gpu; #endif }else{ fprintf(stderr, "Type not recognized: %s\n", s->type); diff --git a/src/scale_channels_layer.c b/src/scale_channels_layer.c new file mode 100644 index 00000000000..32d11014048 --- /dev/null +++ b/src/scale_channels_layer.c @@ -0,0 +1,118 @@ +#include "scale_channels_layer.h" +#include "dark_cuda.h" +#include "blas.h" +#include +#include + +layer make_scale_channels_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2) +{ + fprintf(stderr,"scale Layer: %d\n", index); + layer l = { (LAYER_TYPE)0 }; + l.type = SCALE_CHANNELS; + l.batch = batch; + l.w = w; + l.h = h; + l.c = c; + assert(w == 1 & h == 1); + + l.out_w = w2; + l.out_h = h2; + l.out_c = c2; + assert(l.out_c == l.c); + + l.outputs = l.out_w*l.out_h*l.out_c; + l.inputs = l.outputs; + l.index = index; + + l.delta = (float*)calloc(l.outputs * batch, sizeof(float)); + l.output = (float*)calloc(l.outputs * batch, sizeof(float)); + + l.forward = forward_scale_channels_layer; + l.backward = backward_scale_channels_layer; +#ifdef GPU + l.forward_gpu = forward_scale_channels_layer_gpu; + l.backward_gpu = backward_scale_channels_layer_gpu; + + l.delta_gpu = cuda_make_array(l.delta, l.outputs*batch); + l.output_gpu = cuda_make_array(l.output, l.outputs*batch); +#endif + return l; +} + +void resize_scale_channels_layer(layer *l, int w, int h) +{ + l->out_w = w; + l->out_h = h; + l->outputs = l->out_w*l->out_h*l->out_c; + l->inputs = l->outputs; + l->delta = (float*)realloc(l->delta, l->outputs * l->batch * sizeof(float)); + l->output = (float*)realloc(l->output, l->outputs * l->batch * sizeof(float)); + +#ifdef GPU + cuda_free(l->output_gpu); + cuda_free(l->delta_gpu); + l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch); + l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch); +#endif + +} + +void forward_scale_channels_layer(const layer l, network_state state) +{ + int size = l.batch * l.out_c * l.out_w * l.out_h; + int channel_size = l.out_w * l.out_h; + float *from_output = state.net.layers[l.index].output; + + int i; + #pragma omp parallel for + for (i = 0; i < size; ++i) { + l.output[i] = state.input[i / channel_size] * from_output[i]; + } + + activate_array(l.output, l.outputs*l.batch, l.activation); +} + +void backward_scale_channels_layer(const layer l, network_state state) +{ + gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); + //axpy_cpu(l.outputs*l.batch, 1, l.delta, 1, state.delta, 1); + //scale_cpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta, l.w, l.h, l.c, state.net.layers[l.index].delta); + + int size = l.batch * l.out_c * l.out_w * l.out_h; + int channel_size = l.out_w * l.out_h; + float *from_output = state.net.layers[l.index].output; + float *from_delta = state.net.layers[l.index].delta; + + int i; + #pragma omp parallel for + for (i = 0; i < size; ++i) { + state.delta[i / channel_size] += l.delta[i] * from_output[i]; // l.delta * from (should be divided by channel_size?) + + from_delta[i] = state.input[i / channel_size] * l.delta[i]; // input * l.delta + } +} + +#ifdef GPU +void forward_scale_channels_layer_gpu(const layer l, network_state state) +{ + int size = l.batch * l.out_c * l.out_w * l.out_h; + int channel_size = l.out_w * l.out_h; + + scale_channels_gpu(state.net.layers[l.index].output_gpu, size, channel_size, state.input, l.output_gpu); + + activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); +} + +void backward_scale_channels_layer_gpu(const layer l, network_state state) +{ + gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); + + int size = l.batch * l.out_c * l.out_w * l.out_h; + int channel_size = l.out_w * l.out_h; + float *from_output = state.net.layers[l.index].output_gpu; + float *from_delta = state.net.layers[l.index].delta_gpu; + + + backward_scale_channels_gpu(l.delta_gpu, size, channel_size, state.input, from_delta, from_output, state.delta); +} +#endif diff --git a/src/scale_channels_layer.h b/src/scale_channels_layer.h new file mode 100644 index 00000000000..a20c070397b --- /dev/null +++ b/src/scale_channels_layer.h @@ -0,0 +1,23 @@ +#ifndef SCALE_CHANNELS_LAYER_H +#define SCALE_CHANNELS_LAYER_H + +#include "layer.h" +#include "network.h" + +#ifdef __cplusplus +extern "C" { +#endif +layer make_scale_channels_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2); +void forward_scale_channels_layer(const layer l, network_state state); +void backward_scale_channels_layer(const layer l, network_state state); +void resize_scale_channels_layer(layer *l, int w, int h); + +#ifdef GPU +void forward_scale_channels_layer_gpu(const layer l, network_state state); +void backward_scale_channels_layer_gpu(const layer l, network_state state); +#endif + +#ifdef __cplusplus +} +#endif +#endif // SCALE_CHANNELS_LAYER_H diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index 06cd6056033..070ae5d95dc 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -19,6 +19,8 @@ layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int l.outputs = w*h*c; l.inputs = l.outputs; + if(w != w2 || h != h2 || c != c) fprintf(stderr, " w = %d, w2 = %d, h = %d, h2 = %d, c = %d, c2 = %d \n", w, w2, h, h2, c, c2); + l.index = index; l.delta = (float*)calloc(l.outputs * batch, sizeof(float)); From 9e9b2c493630a8ebd05d8732f0eb6b12a078bb88 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 20 Jun 2019 20:52:26 +0300 Subject: [PATCH 0530/1365] Update Readme.md --- README.md | 11 +++++++++++ ...abeling.cmd => darknet_yolov3_pseudo_labeling.cmd} | 0 2 files changed, 11 insertions(+) rename build/darknet/x64/{darknet_yolov3_pseuso_labeling.cmd => darknet_yolov3_pseudo_labeling.cmd} (100%) diff --git a/README.md b/README.md index 5a5c493cd6c..5ee3d804086 100644 --- a/README.md +++ b/README.md @@ -15,6 +15,7 @@ More details: http://pjreddie.com/darknet/yolo/ * [Pre-trained models](#pre-trained-models) * [Explanations in issues](https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3AExplanations) * [Yolo v3 in other frameworks (TensorRT, TensorFlow, PyTorch, OpenVINO, OpenCV-dnn,...)](#yolo-v3-in-other-frameworks) +* [Datasets](#datasets) 0. [Improvements in this repository](#improvements-in-this-repository) 1. [How to use](#how-to-use-on-the-command-line) @@ -79,6 +80,16 @@ You can get cfg-files by path: `darknet/cfg/` * **OpenCV-dnn** is a very fast DNN implementation on CPU (x86/ARM-Android), use `yolov3.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221), [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) * **PyTorch > ONNX > CoreML > iOS** how to convert cfg/weights-files to pt-file: [ultralytics/yolov3](https://github.com/ultralytics/yolov3#darknet-conversion) and [iOS App](https://itunes.apple.com/app/id1452689527) * **TensorRT** for YOLOv3 (-70% faster inference): [TensorRT & DeepStream](https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps) +* **TVM** - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backends (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about + +#### Datasets + +* MS COCO: use `./scripts/get_coco_dataset.sh` to get labeled MS COCO detection dataset +* OpenImages: use `python ./scripts/get_openimages_dataset.py` for labeling train detection dataset +* Pascal VOC: use `python ./scripts/voc_label.py` for labeling Train/Test/Val detection datasets +* ILSVRC2012 (ImageNet classification): use `./scripts/get_imagenet_train.sh` (also `imagenet_label.sh` for labeling valid set) +* German/Belgium/Russian/LISA/MASTIF Traffic Sign Datasets for Detection - use this parsers: https://github.com/angeligareta/Datasets2Darknet#detection-task +* List of other datasets: https://github.com/AlexeyAB/darknet/tree/master/scripts#datasets ##### Examples of results diff --git a/build/darknet/x64/darknet_yolov3_pseuso_labeling.cmd b/build/darknet/x64/darknet_yolov3_pseudo_labeling.cmd similarity index 100% rename from build/darknet/x64/darknet_yolov3_pseuso_labeling.cmd rename to build/darknet/x64/darknet_yolov3_pseudo_labeling.cmd From aeb9da6918eaa5330a27803f8176a6314c716a7f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 22 Jun 2019 01:37:33 +0300 Subject: [PATCH 0531/1365] Fixed darknet_no_gpu.vcxproj and yolo_cpp_dll_no_gpu.vcxproj (added scale_channels_layer) --- build/darknet/darknet_no_gpu.vcxproj | 2 ++ build/darknet/yolo_cpp_dll_no_gpu.vcxproj | 2 ++ 2 files changed, 4 insertions(+) diff --git a/build/darknet/darknet_no_gpu.vcxproj b/build/darknet/darknet_no_gpu.vcxproj index 5b0fe209248..ee55f63e3a7 100644 --- a/build/darknet/darknet_no_gpu.vcxproj +++ b/build/darknet/darknet_no_gpu.vcxproj @@ -230,6 +230,7 @@ + @@ -288,6 +289,7 @@ + diff --git a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj index c5c17e84d66..cbb0f84fbeb 100644 --- a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj +++ b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj @@ -214,6 +214,7 @@ + @@ -274,6 +275,7 @@ + From 1ed71f4b2987a3681ffa43825d3753ea927bf574 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 22 Jun 2019 17:03:06 +0300 Subject: [PATCH 0532/1365] Fixed CUDA-implementation of back-propagation for [scale_channels]-layer --- src/blas_kernels.cu | 36 ++++++++++++++++++++++++++++++++---- src/scale_channels_layer.c | 5 ++--- 2 files changed, 34 insertions(+), 7 deletions(-) diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 35f9a1ad130..641c584c6d8 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -1119,17 +1119,45 @@ extern "C" void scale_channels_gpu(float *in_w_h_c, int size, int channel_size, } +__inline__ __device__ +float warpAllReduceSum(float val) { + for (int mask = WARP_SIZE / 2; mask > 0; mask /= 2) +#if CUDART_VERSION >= 9000 + val += __shfl_xor_sync(0xffffffff, val, mask); +#else + val += __shfl_xor(val, mask); +#endif + return val; +} + __global__ void backward_scale_channels_kernel(float *in_w_h_c_delta, int size, int channel_size, float *in_scales_c, float *out_from_delta, float *in_from_output, float *out_state_delta) { const int index = blockIdx.x*blockDim.x + threadIdx.x; + int osd_index = index / channel_size; + if (index < size) { - out_state_delta[index / channel_size] += in_w_h_c_delta[index] * in_from_output[index]; // l.delta * from (should be divided by channel_size?) - out_from_delta[index] += in_scales_c[index / channel_size] * in_w_h_c_delta[index]; // input * l.delta + //out_state_delta[osd_index] += in_w_h_c_delta[index] * in_from_output[index]; // l.delta * from (should be divided by channel_size?) + + int warp_id = index / 32; + int index_warp_start = warp_id * 32; + int osd_index_warp_start = index_warp_start / channel_size; + int osd_index_warp_end = (index_warp_start + 31) / channel_size; + + if (osd_index_warp_start == osd_index_warp_end) // all thread in warp process the same channel + { + float sum = warpAllReduceSum(in_w_h_c_delta[index] * in_from_output[index]); // l.delta * from + if (threadIdx.x % 32 == 0) { + atomicAdd(&out_state_delta[osd_index], sum); + //out_state_delta[osd_index] += sum; + } + } + else { + atomicAdd(&out_state_delta[osd_index], in_w_h_c_delta[index] * in_from_output[index]); // l.delta * from + } - //out_state_delta[index / channel_size] += in_w_h_c_delta[index] / channel_size; - //out_from_delta[index] = in_w_h_c_delta[index]; + out_from_delta[index] += in_scales_c[osd_index] * in_w_h_c_delta[index]; // input * l.delta // atomic isn't required here } } diff --git a/src/scale_channels_layer.c b/src/scale_channels_layer.c index 32d11014048..7322570116e 100644 --- a/src/scale_channels_layer.c +++ b/src/scale_channels_layer.c @@ -86,9 +86,9 @@ void backward_scale_channels_layer(const layer l, network_state state) int i; #pragma omp parallel for for (i = 0; i < size; ++i) { - state.delta[i / channel_size] += l.delta[i] * from_output[i]; // l.delta * from (should be divided by channel_size?) + state.delta[i / channel_size] += l.delta[i] * from_output[i] / channel_size; // l.delta * from (should be divided by channel_size?) - from_delta[i] = state.input[i / channel_size] * l.delta[i]; // input * l.delta + from_delta[i] += state.input[i / channel_size] * l.delta[i]; // input * l.delta } } @@ -112,7 +112,6 @@ void backward_scale_channels_layer_gpu(const layer l, network_state state) float *from_output = state.net.layers[l.index].output_gpu; float *from_delta = state.net.layers[l.index].delta_gpu; - backward_scale_channels_gpu(l.delta_gpu, size, channel_size, state.input, from_delta, from_output, state.delta); } #endif From c9129c207823a96f0a1b3a840883a6c510073347 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 22 Jun 2019 17:04:00 +0300 Subject: [PATCH 0533/1365] Added letter_box=1 param in [net] section (cfg-file) for keeping aspect ratio during training --- include/darknet.h | 4 ++- src/data.c | 72 +++++++++++++++++++++++++++++++++++++++----- src/data.h | 4 +-- src/detector.c | 19 ++++++------ src/image_opencv.cpp | 2 +- src/image_opencv.h | 2 +- src/network.h | 2 +- src/parser.c | 1 + 8 files changed, 83 insertions(+), 23 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 25e702b2b0d..552cad7859e 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -600,6 +600,7 @@ typedef struct network { int flip; // horizontal flip 50% probability augmentaiont for classifier training (default = 1) int blur; int mixup; + int letter_box; float angle; float aspect; float exposure; @@ -760,6 +761,7 @@ typedef struct load_args { int mini_batch; int track; int augment_speed; + int letter_box; int show_imgs; float jitter; int flip; @@ -827,7 +829,7 @@ LIB_API layer* get_network_layer(network* net, int i); LIB_API detection *make_network_boxes(network *net, float thresh, int *num); LIB_API void reset_rnn(network *net); LIB_API float *network_predict_image(network *net, image im); -LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, const int map_points, network *existing_net); +LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, const int map_points, int letter_box, network *existing_net); LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port, int show_imgs); LIB_API void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile, int letter_box); diff --git a/src/data.c b/src/data.c index 3858d427abb..571bfd019d5 100644 --- a/src/data.c +++ b/src/data.c @@ -804,8 +804,8 @@ void blend_truth(float *new_truth, int boxes, float *old_truth) #include "http_stream.h" -data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int use_blur, int use_mixup, float jitter, - float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int show_imgs) +data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int use_blur, int use_mixup, + float jitter, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int show_imgs) { const int random_index = random_gen(); c = c ? c : 3; @@ -828,7 +828,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo d.X.vals = (float**)calloc(d.X.rows, sizeof(float*)); d.X.cols = h*w*c; - float r1 = 0, r2 = 0, r3 = 0, r4 = 0; + float r1 = 0, r2 = 0, r3 = 0, r4 = 0, r_scale = 0; float dhue = 0, dsat = 0, dexp = 0, flip = 0, blur = 0; int augmentation_calculated = 0; @@ -862,6 +862,8 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo r3 = random_float(); r4 = random_float(); + r_scale = random_float(); + dhue = rand_uniform_strong(-hue, hue); dsat = rand_scale(saturation); dexp = rand_scale(exposure); @@ -874,6 +876,33 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo int pright = rand_precalc_random(-dw, dw, r2); int ptop = rand_precalc_random(-dh, dh, r3); int pbot = rand_precalc_random(-dh, dh, r4); + //printf("\n pleft = %d, pright = %d, ptop = %d, pbot = %d, ow = %d, oh = %d \n", pleft, pright, ptop, pbot, ow, oh); + + float scale = rand_precalc_random(.25, 2, r_scale); // unused currently + + if (letter_box) + { + float img_ar = (float)ow / (float)oh; + float net_ar = (float)w / (float)h; + float result_ar = img_ar / net_ar; + //printf(" ow = %d, oh = %d, w = %d, h = %d, img_ar = %f, net_ar = %f, result_ar = %f \n", ow, oh, w, h, img_ar, net_ar, result_ar); + if (result_ar > 1) // sheight - should be increased + { + float oh_tmp = ow / net_ar; + float delta_h = (oh_tmp - oh)/2; + ptop = ptop - delta_h; + pbot = pbot - delta_h; + //printf(" result_ar = %f, oh_tmp = %f, delta_h = %d, ptop = %f, pbot = %f \n", result_ar, oh_tmp, delta_h, ptop, pbot); + } + else // swidth - should be increased + { + float ow_tmp = oh * net_ar; + float delta_w = (ow_tmp - ow)/2; + pleft = pleft - delta_w; + pright = pright - delta_w; + //printf(" result_ar = %f, ow_tmp = %f, delta_w = %d, pleft = %f, pright = %f \n", result_ar, ow_tmp, delta_w, pleft, pright); + } + } int swidth = ow - pleft - pright; int sheight = oh - ptop - pbot; @@ -884,9 +913,10 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo float dx = ((float)pleft / ow) / sx; float dy = ((float)ptop / oh) / sy; + fill_truth_detection(filename, boxes, truth, classes, flip, dx, dy, 1. / sx, 1. / sy, w, h); - image ai = image_data_augmentation(src, w, h, pleft, ptop, swidth, sheight, flip, jitter, dhue, dsat, dexp, + image ai = image_data_augmentation(src, w, h, pleft, ptop, swidth, sheight, flip, dhue, dsat, dexp, blur, boxes, d.y.vals[i]); if (i_mixup) { @@ -947,7 +977,7 @@ void blend_images(image new_img, float alpha, image old_img, float beta) } data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int use_blur, int use_mixup, float jitter, - float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int show_imgs) + float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int show_imgs) { const int random_index = random_gen(); c = c ? c : 3; @@ -971,7 +1001,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo d.X.vals = (float**)calloc(d.X.rows, sizeof(float*)); d.X.cols = h*w*c; - float r1 = 0, r2 = 0, r3 = 0, r4 = 0; + float r1 = 0, r2 = 0, r3 = 0, r4 = 0, r_scale; float dhue = 0, dsat = 0, dexp = 0, flip = 0; int augmentation_calculated = 0; @@ -999,6 +1029,8 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo r3 = random_float(); r4 = random_float(); + r_scale = random_float(); + dhue = rand_uniform_strong(-hue, hue); dsat = rand_scale(saturation); dexp = rand_scale(exposure); @@ -1011,6 +1043,32 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo int ptop = rand_precalc_random(-dh, dh, r3); int pbot = rand_precalc_random(-dh, dh, r4); + float scale = rand_precalc_random(.25, 2, r_scale); // unused currently + + if (letter_box) + { + float img_ar = (float)ow / (float)oh; + float net_ar = (float)w / (float)h; + float result_ar = img_ar / net_ar; + //printf(" ow = %d, oh = %d, w = %d, h = %d, img_ar = %f, net_ar = %f, result_ar = %f \n", ow, oh, w, h, img_ar, net_ar, result_ar); + if (result_ar > 1) // sheight - should be increased + { + float oh_tmp = ow / net_ar; + float delta_h = (oh_tmp - oh) / 2; + ptop = ptop - delta_h; + pbot = pbot - delta_h; + //printf(" result_ar = %f, oh_tmp = %f, delta_h = %d, ptop = %f, pbot = %f \n", result_ar, oh_tmp, delta_h, ptop, pbot); + } + else // swidth - should be increased + { + float ow_tmp = oh * net_ar; + float delta_w = (ow_tmp - ow) / 2; + pleft = pleft - delta_w; + pright = pright - delta_w; + //printf(" result_ar = %f, ow_tmp = %f, delta_w = %d, pleft = %f, pright = %f \n", result_ar, ow_tmp, delta_w, pleft, pright); + } + } + int swidth = ow - pleft - pright; int sheight = oh - ptop - pbot; @@ -1100,7 +1158,7 @@ void *load_thread(void *ptr) *a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure); } else if (a.type == DETECTION_DATA){ *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.c, a.num_boxes, a.classes, a.flip, a.blur, a.mixup, a.jitter, - a.hue, a.saturation, a.exposure, a.mini_batch, a.track, a.augment_speed, a.show_imgs); + a.hue, a.saturation, a.exposure, a.mini_batch, a.track, a.augment_speed, a.letter_box, a.show_imgs); } else if (a.type == SWAG_DATA){ *a.d = load_data_swag(a.paths, a.n, a.classes, a.jitter); } else if (a.type == COMPARE_DATA){ diff --git a/src/data.h b/src/data.h index b99b4e9aa13..17edc5cf468 100644 --- a/src/data.h +++ b/src/data.h @@ -86,8 +86,8 @@ void print_letters(float *pred, int n); data load_data_captcha(char **paths, int n, int m, int k, int w, int h); data load_data_captcha_encode(char **paths, int n, int m, int w, int h); data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h); -data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int use_blur, int use_mixup, float jitter, - float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int show_imgs); +data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int use_blur, int use_mixup, + float jitter, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int show_imgs); data load_data_tag(char **paths, int n, int m, int k, int use_flip, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); data load_data_super(char **paths, int n, int m, int w, int h, int scale); diff --git a/src/detector.c b/src/detector.c index 074365fbb7c..9f77ea5afb1 100644 --- a/src/detector.c +++ b/src/detector.c @@ -133,6 +133,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i args.exposure = net.exposure; args.saturation = net.saturation; args.hue = net.hue; + args.letter_box = net.letter_box; if (dont_show && show_imgs) show_imgs = 2; args.show_imgs = show_imgs; @@ -275,7 +276,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i //network net_combined = combine_train_valid_networks(net, net_map); iter_map = i; - mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, 0, &net_map);// &net_combined); + mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, 0, net.letter_box, &net_map);// &net_combined); printf("\n mean_average_precision (mAP@0.5) = %f \n", mean_average_precision); if (mean_average_precision > best_map) { best_map = mean_average_precision; @@ -660,7 +661,7 @@ int detections_comparator(const void *pa, const void *pb) return 0; } -float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, const int map_points, network *existing_net) +float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, const int map_points, int letter_box, network *existing_net) { int j; list *options = read_data_cfg(datacfg); @@ -733,8 +734,8 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa args.w = net.w; args.h = net.h; args.c = net.c; - args.type = IMAGE_DATA; - //args.type = LETTERBOX_DATA; + if (letter_box) args.type = LETTERBOX_DATA; + else args.type = IMAGE_DATA; //const float thresh_calc_avg_iou = 0.24; float avg_iou = 0; @@ -783,14 +784,12 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa float hier_thresh = 0; detection *dets; if (args.type == LETTERBOX_DATA) { - int letterbox = 1; - dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); + dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letter_box); } else { - int letterbox = 0; - dets = get_network_boxes(&net, 1, 1, thresh, hier_thresh, 0, 0, &nboxes, letterbox); + dets = get_network_boxes(&net, 1, 1, thresh, hier_thresh, 0, 0, &nboxes, letter_box); } - //detection *dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); // for letterbox=1 + //detection *dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letter_box); // for letter_box=1 if (nms) do_nms_sort(dets, nboxes, l.classes, nms); char labelpath[4096]; @@ -1486,7 +1485,7 @@ void run_detector(int argc, char **argv) else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map, mjpeg_port, show_imgs); else if (0 == strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile); else if (0 == strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights); - else if (0 == strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh, iou_thresh, map_points, NULL); + else if (0 == strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh, iou_thresh, map_points, letter_box, NULL); else if (0 == strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, height, show); else if (0 == strcmp(argv[2], "demo")) { list *options = read_data_cfg(datacfg); diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index b60f03cb7ef..91d07bd7a8b 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1137,7 +1137,7 @@ static box float_to_box_stride(float *f, int stride) image image_data_augmentation(mat_cv* mat, int w, int h, int pleft, int ptop, int swidth, int sheight, int flip, - float jitter, float dhue, float dsat, float dexp, + float dhue, float dsat, float dexp, int blur, int num_boxes, float *truth) { image out; diff --git a/src/image_opencv.h b/src/image_opencv.h index 2af5f24e777..860f27e85a3 100644 --- a/src/image_opencv.h +++ b/src/image_opencv.h @@ -95,7 +95,7 @@ void draw_train_loss(mat_cv* img, int img_size, float avg_loss, float max_img_lo // Data augmentation image image_data_augmentation(mat_cv* mat, int w, int h, int pleft, int ptop, int swidth, int sheight, int flip, - float jitter, float dhue, float dsat, float dexp, + float dhue, float dsat, float dexp, int blur, int num_boxes, float *truth); // blend two images with (alpha and beta) diff --git a/src/network.h b/src/network.h index 186fe3d8bbb..2bee2cb2901 100644 --- a/src/network.h +++ b/src/network.h @@ -153,7 +153,7 @@ float get_network_cost(network net); //LIB_API network *load_network_custom(char *cfg, char *weights, int clear, int batch); //LIB_API network *load_network(char *cfg, char *weights, int clear); //LIB_API float *network_predict_image(network *net, image im); -//LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, network *existing_net); +//LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, int map_points, int letter_box, network *existing_net); //LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port); //LIB_API int network_width(network *net); //LIB_API int network_height(network *net); diff --git a/src/parser.c b/src/parser.c index d45f625ca00..5f6658e6be7 100644 --- a/src/parser.c +++ b/src/parser.c @@ -738,6 +738,7 @@ void parse_net_options(list *options, network *net) net->flip = option_find_int_quiet(options, "flip", 1); net->blur = option_find_int_quiet(options, "blur", 0); net->mixup = option_find_int_quiet(options, "mixup", 0); + net->letter_box = option_find_int_quiet(options, "letter_box", 0); net->angle = option_find_float_quiet(options, "angle", 0); net->aspect = option_find_float_quiet(options, "aspect", 1); From 5ec35922d5215e11466a9bb1602f81d1746ccbe5 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 22 Jun 2019 19:17:41 +0300 Subject: [PATCH 0534/1365] Fixed yolo_cpp_dll.vcxproj --- build/darknet/x64/cfg/darknet53_448_xnor.cfg | 619 +++++++++++++++++++ build/darknet/yolo_cpp_dll.vcxproj | 2 + cfg/darknet53_448_xnor.cfg | 619 +++++++++++++++++++ 3 files changed, 1240 insertions(+) create mode 100644 build/darknet/x64/cfg/darknet53_448_xnor.cfg create mode 100644 cfg/darknet53_448_xnor.cfg diff --git a/build/darknet/x64/cfg/darknet53_448_xnor.cfg b/build/darknet/x64/cfg/darknet53_448_xnor.cfg new file mode 100644 index 00000000000..a48ef698a47 --- /dev/null +++ b/build/darknet/x64/cfg/darknet53_448_xnor.cfg @@ -0,0 +1,619 @@ +[net] +# Training - start training with darknet53.weights +batch=120 +subdivisions=20 + +# Testing +#batch=1 +#subdivisions=1 + +height=448 +width=448 +channels=3 +min_crop=448 +max_crop=512 + +burn_in=1000 +learning_rate=0.1 +policy=poly +power=4 +max_batches=100000 +momentum=0.9 +decay=0.0005 + + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +xnor=1 +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +xnor=1 +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[avgpool] + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=1 +activation=linear + +[softmax] +groups=1 + diff --git a/build/darknet/yolo_cpp_dll.vcxproj b/build/darknet/yolo_cpp_dll.vcxproj index b14766998a2..b4d197657cd 100644 --- a/build/darknet/yolo_cpp_dll.vcxproj +++ b/build/darknet/yolo_cpp_dll.vcxproj @@ -228,6 +228,7 @@ + @@ -288,6 +289,7 @@ + diff --git a/cfg/darknet53_448_xnor.cfg b/cfg/darknet53_448_xnor.cfg new file mode 100644 index 00000000000..a48ef698a47 --- /dev/null +++ b/cfg/darknet53_448_xnor.cfg @@ -0,0 +1,619 @@ +[net] +# Training - start training with darknet53.weights +batch=120 +subdivisions=20 + +# Testing +#batch=1 +#subdivisions=1 + +height=448 +width=448 +channels=3 +min_crop=448 +max_crop=512 + +burn_in=1000 +learning_rate=0.1 +policy=poly +power=4 +max_batches=100000 +momentum=0.9 +decay=0.0005 + + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +xnor=1 +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +xnor=1 +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +xnor=1 +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +xnor=1 +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[avgpool] + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=1 +activation=linear + +[softmax] +groups=1 + From 88cccfcad4f9591a429c1e71c88a42e0e81a5e80 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 26 Jun 2019 15:48:22 +0300 Subject: [PATCH 0535/1365] Added Swish-activation --- include/darknet.h | 4 +++- src/activation_kernels.cu | 38 ++++++++++++++++++++++++++++++++++++ src/activations.c | 25 ++++++++++++++++++++++++ src/activations.h | 4 ++++ src/convolutional_kernels.cu | 10 +++++++--- src/convolutional_layer.c | 13 +++++++++--- src/layer.c | 10 ++++++---- 7 files changed, 93 insertions(+), 11 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 552cad7859e..eefe4df4d80 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -102,7 +102,7 @@ typedef struct tree { // activations.h typedef enum { - LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU + LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, SWISH }ACTIVATION; // parser.h @@ -339,6 +339,7 @@ struct layer { float *col_image; float * delta; float * output; + float * output_sigmoid; int delta_pinned; int output_pinned; float * loss; @@ -522,6 +523,7 @@ struct layer { float * scale_change_gpu; float * output_gpu; + float * output_sigmoid_gpu; float * loss_gpu; float * delta_gpu; float * rand_gpu; diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index 6fd23c2ed27..24563c69d6e 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -186,6 +186,19 @@ __global__ void activate_array_kernel(float *x, int n, ACTIVATION a) if(i < n) x[i] = activate_kernel(x[i], a); } + + +__global__ void activate_array_swish_kernel(float *x, int n, float *output_sigmoid_gpu, float *output_gpu) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (i < n) { + float x_val = x[i]; + float sigmoid = logistic_activate_kernel(x_val); + output_sigmoid_gpu[i] = sigmoid; + output_gpu[i] = x_val * sigmoid; + } +} + __global__ void activate_array_leaky_kernel(float *x, int n) { int index = blockIdx.x*blockDim.x + threadIdx.x; @@ -240,6 +253,16 @@ __global__ void gradient_array_kernel(float *x, int n, ACTIVATION a, float *delt if(i < n) delta[i] *= gradient_kernel(x[i], a); } +// https://github.com/BVLC/caffe/blob/04ab089db018a292ae48d51732dd6c66766b36b6/src/caffe/layers/swish_layer.cu#L28-L30 +__global__ void gradient_array_swish_kernel(float *x, int n, float *sigmoid_gpu, float *delta) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (i < n) { + float swish = x[i]; + delta[i] *= swish + sigmoid_gpu[i] * (1 - swish); // gradient_kernel(x[i], a); + } +} + __global__ void gradient_array_leaky_kernel(float *x, int n, float *delta) { int index = blockIdx.x*blockDim.x + threadIdx.x; @@ -303,6 +326,13 @@ extern "C" void activate_array_ongpu(float *x, int n, ACTIVATION a) CHECK_CUDA(cudaPeekAtLastError()); } +extern "C" void activate_array_swish_ongpu(float *x, int n, float *output_sigmoid_gpu, float *output_gpu) +{ + const int num_blocks = get_number_of_blocks(n, BLOCK); + activate_array_swish_kernel << > >(x, n, output_sigmoid_gpu, output_gpu); + CHECK_CUDA(cudaPeekAtLastError()); +} + extern "C" void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta) { const int num_blocks = get_number_of_blocks(n, BLOCK); @@ -317,3 +347,11 @@ extern "C" void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta gradient_array_kernel << > > (x, n, a, delta); CHECK_CUDA(cudaPeekAtLastError()); } + + +extern "C" void gradient_array_swish_ongpu(float *x, int n, float *sigmoid_gpu, float *delta) +{ + const int num_blocks = get_number_of_blocks(n, BLOCK); + gradient_array_swish_kernel << > > (x, n, sigmoid_gpu, delta); + CHECK_CUDA(cudaPeekAtLastError()); +} \ No newline at end of file diff --git a/src/activations.c b/src/activations.c index 245fffa9a52..c3ea4818aeb 100644 --- a/src/activations.c +++ b/src/activations.c @@ -45,6 +45,7 @@ char *get_activation_string(ACTIVATION a) ACTIVATION get_activation(char *s) { if (strcmp(s, "logistic")==0) return LOGISTIC; + if (strcmp(s, "swish") == 0) return SWISH; if (strcmp(s, "loggy")==0) return LOGGY; if (strcmp(s, "relu")==0) return RELU; if (strcmp(s, "elu")==0) return ELU; @@ -120,6 +121,18 @@ void activate_array(float *x, const int n, const ACTIVATION a) } } +void activate_array_swish(float *x, const int n, float * output_sigmoid, float * output) +{ + int i; + #pragma omp parallel for + for (i = 0; i < n; ++i) { + float x_val = x[i]; + float sigmoid = logistic_activate(x_val); + output_sigmoid[i] = sigmoid; + output[i] = x_val * sigmoid; + } +} + float gradient(float x, ACTIVATION a) { switch(a){ @@ -158,7 +171,19 @@ float gradient(float x, ACTIVATION a) void gradient_array(const float *x, const int n, const ACTIVATION a, float *delta) { int i; + #pragma omp parallel for for(i = 0; i < n; ++i){ delta[i] *= gradient(x[i], a); } } + +// https://github.com/BVLC/caffe/blob/04ab089db018a292ae48d51732dd6c66766b36b6/src/caffe/layers/swish_layer.cpp#L54-L56 +void gradient_array_swish(const float *x, const int n, const float * sigmoid, float * delta) +{ + int i; + #pragma omp parallel for + for (i = 0; i < n; ++i) { + float swish = x[i]; + delta[i] *= swish + sigmoid[i]*(1 - swish); + } +} diff --git a/src/activations.h b/src/activations.h index 4a382b2df83..19f3822c8e3 100644 --- a/src/activations.h +++ b/src/activations.h @@ -17,10 +17,14 @@ char *get_activation_string(ACTIVATION a); float activate(float x, ACTIVATION a); float gradient(float x, ACTIVATION a); void gradient_array(const float *x, const int n, const ACTIVATION a, float *delta); +void gradient_array_swish(const float *x, const int n, const float * sigmoid, float * delta); void activate_array(float *x, const int n, const ACTIVATION a); +void activate_array_swish(float *x, const int n, float * output_sigmoid, float * output); #ifdef GPU void activate_array_ongpu(float *x, int n, ACTIVATION a); +void activate_array_swish_ongpu(float *x, int n, float *output_sigmoid_gpu, float *output_gpu); void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta); +void gradient_array_swish_ongpu(float *x, int n, float *sigmoid_gpu, float *delta); #endif static inline float stair_activate(float x) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index eaa6e8030fc..e404ecabd85 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -391,7 +391,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) */ //add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); - if (l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); + if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.output_sigmoid_gpu, l.output_gpu); + else if (l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if(l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if (l.binary || l.xnor) swap_binary(&l); //cudaDeviceSynchronize(); @@ -594,7 +595,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //#ifndef CUDNN_HALF //#endif // no CUDNN_HALF - if (l.activation != LINEAR) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); + if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.output_sigmoid_gpu, l.output_gpu); + else if (l.activation != LINEAR) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if(l.dot > 0) dot_error_gpu(l); if(l.binary || l.xnor) swap_binary(&l); //cudaDeviceSynchronize(); // for correct profiling of performance @@ -607,7 +609,9 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) void backward_convolutional_layer_gpu(convolutional_layer l, network_state state) { if(state.net.try_fix_nan) constrain_ongpu(l.outputs*l.batch, 1, l.delta_gpu, 1); - gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); + + if (l.activation == SWISH) gradient_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.output_sigmoid_gpu, l.delta_gpu); + else gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); if (!l.batch_normalize) backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index bc27672daac..7d74d2a0857 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -462,7 +462,11 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.scale_v = (float*)calloc(n, sizeof(float)); } + if(l.activation == SWISH) l.output_sigmoid = (float*)calloc(total_batch*l.outputs, sizeof(float)); + #ifdef GPU + if (l.activation == SWISH) l.output_sigmoid_gpu = cuda_make_array(l.output_sigmoid, total_batch*out_h*out_w*n); + l.forward_gpu = forward_convolutional_layer_gpu; l.backward_gpu = backward_convolutional_layer_gpu; l.update_gpu = update_convolutional_layer_gpu; @@ -1029,7 +1033,8 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w); //activate_array(l.output, m*n*l.batch, l.activation); - activate_array_cpu_custom(l.output, m*n*l.batch, l.activation); + if (l.activation == SWISH) activate_array_swish(l.output, l.outputs*l.batch, l.output_sigmoid, l.output); + else activate_array_cpu_custom(l.output, m*n*l.batch, l.activation); return; } @@ -1067,7 +1072,8 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w); //activate_array(l.output, m*n*l.batch, l.activation); - activate_array_cpu_custom(l.output, l.outputs*l.batch, l.activation); + if (l.activation == SWISH) activate_array_swish(l.output, l.outputs*l.batch, l.output_sigmoid, l.output); + else activate_array_cpu_custom(l.output, l.outputs*l.batch, l.activation); if(l.binary || l.xnor) swap_binary(&l); } @@ -1080,7 +1086,8 @@ void backward_convolutional_layer(convolutional_layer l, network_state state) int n = l.size*l.size*l.c / l.groups; int k = l.out_w*l.out_h; - gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); + if (l.activation == SWISH) gradient_array_swish(l.output, l.outputs*l.batch, l.output_sigmoid, l.delta); + else gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); if (l.batch_normalize) { backward_batchnorm_layer(l, state); diff --git a/src/layer.c b/src/layer.c index ade58f4ecef..c0b48afc2b8 100644 --- a/src/layer.c +++ b/src/layer.c @@ -84,8 +84,9 @@ void free_layer(layer l) l.output = NULL; } #endif // GPU - if (l.delta) free(l.delta); - if (l.output) free(l.output); + if (l.delta) free(l.delta), l.delta = NULL; + if (l.output) free(l.output), l.output = NULL; + if (l.output_sigmoid) free(l.output_sigmoid), l.output_sigmoid = NULL; if (l.squared) free(l.squared); if (l.norms) free(l.norms); if (l.spatial_mean) free(l.spatial_mean); @@ -165,8 +166,9 @@ void free_layer(layer l) if (l.bias_updates_gpu) cuda_free(l.bias_updates_gpu), l.bias_updates_gpu = NULL; if (l.scales_gpu) cuda_free(l.scales_gpu), l.scales_gpu = NULL; if (l.scale_updates_gpu) cuda_free(l.scale_updates_gpu), l.scale_updates_gpu = NULL; - if (l.output_gpu) cuda_free(l.output_gpu); - if (l.delta_gpu) cuda_free(l.delta_gpu); + if (l.output_gpu) cuda_free(l.output_gpu), l.output_gpu = NULL; + if (l.output_sigmoid_gpu) cuda_free(l.output_sigmoid_gpu), l.output_sigmoid_gpu = NULL; + if (l.delta_gpu) cuda_free(l.delta_gpu), l.delta_gpu = NULL; if (l.rand_gpu) cuda_free(l.rand_gpu); if (l.squared_gpu) cuda_free(l.squared_gpu); if (l.norms_gpu) cuda_free(l.norms_gpu); From 993af0fb5b5ec8fe3303d73cbbbcee2938b3051f Mon Sep 17 00:00:00 2001 From: shooorf Date: Wed, 26 Jun 2019 20:47:09 +0300 Subject: [PATCH 0536/1365] Check if image does not require resizing --- src/image.c | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/image.c b/src/image.c index 2f085801dbe..995fa849ddd 100644 --- a/src/image.c +++ b/src/image.c @@ -1275,6 +1275,8 @@ float bilinear_interpolate(image im, float x, float y, int c) image resize_image(image im, int w, int h) { + if (im.w == w && im.h == h) return copy_image(im); + image resized = make_image(w, h, im.c); image part = make_image(w, im.h, im.c); int r, c, k; From 7d4143332d001f03b9edc895cfc163a3cff76a12 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 29 Jun 2019 16:52:49 +0300 Subject: [PATCH 0537/1365] Minor output fixes --- scripts/imagenet_label.sh | 4 ++++ src/avgpool_layer.c | 2 +- src/convolutional_layer.c | 6 +++--- src/dropout_layer.c | 2 +- src/maxpool_layer.c | 2 +- src/reorg_layer.c | 2 +- src/upsample_layer.c | 4 ++-- 7 files changed, 13 insertions(+), 9 deletions(-) diff --git a/scripts/imagenet_label.sh b/scripts/imagenet_label.sh index 01e4306ee3c..1335c72107d 100644 --- a/scripts/imagenet_label.sh +++ b/scripts/imagenet_label.sh @@ -1,5 +1,9 @@ #!/bin/bash +#wget http://www.image-net.org/challenges/LSVRC/2012/nnoupb/ILSVRC2012_bbox_val_v3.tgz +#other downloads: http://www.image-net.org/challenges/LSVRC/2012/nonpub-downloads +#read: https://pjreddie.com/darknet/imagenet/ + mkdir -p labelled wd=`pwd` diff --git a/src/avgpool_layer.c b/src/avgpool_layer.c index 3ae022e802e..08081bd8041 100644 --- a/src/avgpool_layer.c +++ b/src/avgpool_layer.c @@ -4,7 +4,7 @@ avgpool_layer make_avgpool_layer(int batch, int w, int h, int c) { - fprintf(stderr, "avg %4d x%4d x%4d -> %4d\n", w, h, c, c); + fprintf(stderr, "avg %4d x%4d x%4d -> %4d\n", w, h, c, c); avgpool_layer l = { (LAYER_TYPE)0 }; l.type = AVGPOOL; l.batch = batch; diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 7d74d2a0857..b8972f45966 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -552,13 +552,13 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, else if (l.xnor) fprintf(stderr, "convX "); else fprintf(stderr, "conv "); - if(groups > 1) fprintf(stderr, "%5d/%3d ", n, groups); - else fprintf(stderr, "%5d ", n); + if(groups > 1) fprintf(stderr, "%5d/%4d ", n, groups); + else fprintf(stderr, "%5d ", n); if(dilation > 1) fprintf(stderr, "%2d x%2d/%2d(%1d)", size, size, stride, dilation); else fprintf(stderr, "%2d x%2d/%2d ", size, size, stride); - fprintf(stderr, "%4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); + fprintf(stderr, "%4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); //fprintf(stderr, "%5d/%2d %2d x%2d /%2d(%d)%4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", n, groups, size, size, stride, dilation, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); diff --git a/src/dropout_layer.c b/src/dropout_layer.c index 3c2abed4a01..0d34ed24b2c 100644 --- a/src/dropout_layer.c +++ b/src/dropout_layer.c @@ -21,7 +21,7 @@ dropout_layer make_dropout_layer(int batch, int inputs, float probability) l.backward_gpu = backward_dropout_layer_gpu; l.rand_gpu = cuda_make_array(l.rand, inputs*batch); #endif - fprintf(stderr, "dropout p = %.2f %4d -> %4d\n", probability, inputs, inputs); + fprintf(stderr, "dropout p = %.2f %4d -> %4d\n", probability, inputs, inputs); return l; } diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index 68f381990fb..00cb0473913 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -78,7 +78,7 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s #endif // GPU l.bflops = (l.size*l.size*l.c * l.out_h*l.out_w) / 1000000000.; - fprintf(stderr, "max %d x %d/%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); + fprintf(stderr, "max %d x %d/%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); return l; } diff --git a/src/reorg_layer.c b/src/reorg_layer.c index 72d0577309e..8094a066328 100644 --- a/src/reorg_layer.c +++ b/src/reorg_layer.c @@ -23,7 +23,7 @@ layer make_reorg_layer(int batch, int w, int h, int c, int stride, int reverse) l.out_c = c*(stride*stride); } l.reverse = reverse; - fprintf(stderr, "reorg /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c); + fprintf(stderr, "reorg /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c); l.outputs = l.out_h * l.out_w * l.out_c; l.inputs = h*w*c; int output_size = l.out_h * l.out_w * l.out_c * batch; diff --git a/src/upsample_layer.c b/src/upsample_layer.c index 1a2783c2ddb..70d8de4d963 100644 --- a/src/upsample_layer.c +++ b/src/upsample_layer.c @@ -36,8 +36,8 @@ layer make_upsample_layer(int batch, int w, int h, int c, int stride) l.delta_gpu = cuda_make_array(l.delta, l.outputs*batch); l.output_gpu = cuda_make_array(l.output, l.outputs*batch); #endif - if(l.reverse) fprintf(stderr, "downsample %2dx %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c); - else fprintf(stderr, "upsample %2dx %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c); + if(l.reverse) fprintf(stderr, "downsample %2dx %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c); + else fprintf(stderr, "upsample %2dx %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c); return l; } From d4402d29c2845a9aef97f9394dc619986ffbdf0c Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 30 Jun 2019 13:53:11 +0300 Subject: [PATCH 0538/1365] Added maxpool_depth= and out_channels= params to [maxpool] --- include/darknet.h | 2 + src/maxpool_layer.c | 53 ++++++++++++++++++++++++--- src/maxpool_layer.h | 2 +- src/maxpool_layer_kernels.cu | 71 +++++++++++++++++++++++++++++++++++- src/parser.c | 4 +- 5 files changed, 123 insertions(+), 9 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index eefe4df4d80..1584a362d43 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -208,6 +208,8 @@ struct layer { int side; int stride; int dilation; + int maxpool_depth; + int out_channels; int reverse; int flatten; int spatial; diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index 00cb0473913..4d2ee49f57f 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -45,7 +45,7 @@ void cudnn_maxpool_setup(layer *l) } -maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride, int padding) +maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride, int padding, int maxpool_depth, int out_channels) { maxpool_layer l = { (LAYER_TYPE)0 }; l.type = MAXPOOL; @@ -54,9 +54,18 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s l.w = w; l.c = c; l.pad = padding; - l.out_w = (w + padding - size) / stride + 1; - l.out_h = (h + padding - size) / stride + 1; - l.out_c = c; + l.maxpool_depth = maxpool_depth; + l.out_channels = out_channels; + if (maxpool_depth) { + l.out_c = out_channels; + l.out_w = l.w; + l.out_h = l.h; + } + else { + l.out_w = (w + padding - size) / stride + 1; + l.out_h = (h + padding - size) / stride + 1; + l.out_c = c; + } l.outputs = l.out_h * l.out_w * l.out_c; l.inputs = h*w*c; l.size = size; @@ -90,7 +99,7 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h) l->out_w = (w + l->pad - l->size) / l->stride + 1; l->out_h = (h + l->pad - l->size) / l->stride + 1; - l->outputs = l->out_w * l->out_h * l->c; + l->outputs = l->out_w * l->out_h * l->out_c; int output_size = l->outputs * l->batch; l->indexes = (int*)realloc(l->indexes, output_size * sizeof(int)); @@ -111,6 +120,37 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h) void forward_maxpool_layer(const maxpool_layer l, network_state state) { + if (l.maxpool_depth) + { + int b, i, j, k, g; + for (b = 0; b < l.batch; ++b) { + #pragma omp parallel for + for (i = 0; i < l.h; ++i) { + for (j = 0; j < l.w; ++j) { + for (g = 0; g < l.out_c; ++g) + { + int out_index = j + l.w*(i + l.h*(g + l.out_c*b)); + float max = -FLT_MAX; + int max_i = -1; + + for (k = g; k < l.c; k += l.out_c) + { + int in_index = j + l.w*(i + l.h*(k + l.c*b)); + float val = state.input[in_index]; + + max_i = (val > max) ? in_index : max_i; + max = (val > max) ? val : max; + } + l.output[out_index] = max; + l.indexes[out_index] = max_i; + } + } + } + } + return; + } + + if (!state.train) { forward_maxpool_layer_avx(state.input, l.output, l.indexes, l.size, l.w, l.h, l.out_w, l.out_h, l.c, l.pad, l.stride, l.batch); return; @@ -156,7 +196,8 @@ void backward_maxpool_layer(const maxpool_layer l, network_state state) int i; int h = l.out_h; int w = l.out_w; - int c = l.c; + int c = l.out_c; + #pragma omp parallel for for(i = 0; i < h*w*c*l.batch; ++i){ int index = l.indexes[i]; state.delta[index] += l.delta[i]; diff --git a/src/maxpool_layer.h b/src/maxpool_layer.h index 0a90c37699f..0c1f6148946 100644 --- a/src/maxpool_layer.h +++ b/src/maxpool_layer.h @@ -12,7 +12,7 @@ typedef layer maxpool_layer; extern "C" { #endif image get_maxpool_image(maxpool_layer l); -maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride, int padding); +maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride, int padding, int maxpool_depth, int out_channels); void resize_maxpool_layer(maxpool_layer *l, int w, int h); void forward_maxpool_layer(const maxpool_layer l, network_state state); void backward_maxpool_layer(const maxpool_layer l, network_state state); diff --git a/src/maxpool_layer_kernels.cu b/src/maxpool_layer_kernels.cu index 341a5b8b81c..82d631b358a 100644 --- a/src/maxpool_layer_kernels.cu +++ b/src/maxpool_layer_kernels.cu @@ -5,6 +5,50 @@ #include "maxpool_layer.h" #include "dark_cuda.h" +__global__ void forward_maxpool_depth_layer_kernel(int n, int w, int h, int c, int out_c, int batch, float *input, float *output, int *indexes) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= n) return; + + int j = id % w; + id = id / w; + int i = id % h; + id = id / h; + //int g = id % out_c; + //id = id / out_c; + int b = id % batch; + + int k; + for (int g = 0; g < out_c; ++g) + { + int out_index = j + w*(i + h*(g + out_c*b)); + float max = -FLT_MAX; + int max_i = -1; + + for (k = g; k < c; k += out_c) + { + int in_index = j + w*(i + h*(k + c*b)); + float val = input[in_index]; + + max_i = (val > max) ? in_index : max_i; + max = (val > max) ? val : max; + } + output[out_index] = max; + indexes[out_index] = max_i; + } +} + + +__global__ void backward_maxpool_depth_layer_kernel(int n, int w, int h, int c, int batch, float *delta, float *prev_delta, int *indexes) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= n) return; + + int index = indexes[id]; + prev_delta[index] += delta[id]; +} + + __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride, int size, int pad, float *input, float *output, int *indexes) { int h = (in_h + pad - size) / stride + 1; @@ -84,6 +128,19 @@ __global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_ extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network_state state) { + if (layer.maxpool_depth) { + int h = layer.out_h; + int w = layer.out_w; + int c = 1;// layer.out_c; + + size_t n = h*w*c*layer.batch; + + forward_maxpool_depth_layer_kernel << > >( + n, layer.w, layer.h, layer.c, layer.out_c, layer.batch, state.input, layer.output_gpu, layer.indexes_gpu); + CHECK_CUDA(cudaPeekAtLastError()); + + return; + } #ifdef CUDNN_DISABLED if (!state.train && layer.stride == layer.size) { @@ -111,7 +168,7 @@ extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network_state sta int h = layer.out_h; int w = layer.out_w; - int c = layer.c; + int c = layer.out_c; size_t n = h*w*c*layer.batch; @@ -121,6 +178,18 @@ extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network_state sta extern "C" void backward_maxpool_layer_gpu(maxpool_layer layer, network_state state) { + if (layer.maxpool_depth) { + int h = layer.out_h; + int w = layer.out_w; + int c = layer.out_c; + + size_t n = h * w * c * layer.batch; + + backward_maxpool_depth_layer_kernel << > >(n, layer.w, layer.h, layer.c, layer.batch, layer.delta_gpu, state.delta, layer.indexes_gpu); + CHECK_CUDA(cudaPeekAtLastError()); + return; + } + size_t n = layer.h*layer.w*layer.c*layer.batch; backward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.pad, layer.delta_gpu, state.delta, layer.indexes_gpu); diff --git a/src/parser.c b/src/parser.c index 5f6658e6be7..30510132546 100644 --- a/src/parser.c +++ b/src/parser.c @@ -534,6 +534,8 @@ maxpool_layer parse_maxpool(list *options, size_params params) int stride = option_find_int(options, "stride",1); int size = option_find_int(options, "size",stride); int padding = option_find_int_quiet(options, "padding", size-1); + int maxpool_depth = option_find_int_quiet(options, "maxpool_depth", 0); + int out_channels = option_find_int_quiet(options, "out_channels", 1); int batch,h,w,c; h = params.h; @@ -542,7 +544,7 @@ maxpool_layer parse_maxpool(list *options, size_params params) batch=params.batch; if(!(h && w && c)) error("Layer before maxpool layer must output image."); - maxpool_layer layer = make_maxpool_layer(batch,h,w,c,size,stride,padding); + maxpool_layer layer = make_maxpool_layer(batch, h, w, c, size, stride, padding, maxpool_depth, out_channels); return layer; } From 54e2d0b0e8909bc1da8a2d15113b4f2669ce2f4e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 1 Jul 2019 19:07:13 +0300 Subject: [PATCH 0539/1365] Added ability to use letter_box resizing in darknet.py sample --- build/darknet/x64/darknet.py | 11 +++++++++-- build/darknet/x64/darknet_python.cmd | 4 ++-- darknet.py | 11 +++++++++-- include/darknet.h | 1 + src/network.c | 18 ++++++++++++++++++ 5 files changed, 39 insertions(+), 6 deletions(-) diff --git a/build/darknet/x64/darknet.py b/build/darknet/x64/darknet.py index 9408aabc635..10c9a456ebd 100644 --- a/build/darknet/x64/darknet.py +++ b/build/darknet/x64/darknet.py @@ -202,6 +202,10 @@ def network_height(net): predict_image.argtypes = [c_void_p, IMAGE] predict_image.restype = POINTER(c_float) +predict_image_letterbox = lib.network_predict_image_letterbox +predict_image_letterbox.argtypes = [c_void_p, IMAGE] +predict_image_letterbox.restype = POINTER(c_float) + def array_to_image(arr): import numpy as np # need to return old values to avoid python freeing memory @@ -251,9 +255,12 @@ def detect_image(net, meta, im, thresh=.5, hier_thresh=.5, nms=.45, debug= False pnum = pointer(num) if debug: print("Assigned pnum") predict_image(net, im) + letter_box = 0 + #predict_image_letterbox(net, im) + #letter_box = 1 if debug: print("did prediction") - #dets = get_network_boxes(net, custom_image_bgr.shape[1], custom_image_bgr.shape[0], thresh, hier_thresh, None, 0, pnum, 0) # OpenCV - dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum, 0) + #dets = get_network_boxes(net, custom_image_bgr.shape[1], custom_image_bgr.shape[0], thresh, hier_thresh, None, 0, pnum, letter_box) # OpenCV + dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum, letter_box) if debug: print("Got dets") num = pnum[0] if debug: print("got zeroth index of pnum") diff --git a/build/darknet/x64/darknet_python.cmd b/build/darknet/x64/darknet_python.cmd index d64fe3649a6..b2df11d8207 100644 --- a/build/darknet/x64/darknet_python.cmd +++ b/build/darknet/x64/darknet_python.cmd @@ -4,7 +4,7 @@ rem C:\Python27\Scripts\pip install scikit-image rem C:\Python27\Scripts\pip install scipy rem C:\Python27\Scripts\pip install opencv-python -C:\Python27\python.exe darknet.py +rem C:\Python27\python.exe darknet.py @@ -14,6 +14,6 @@ rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\Scripts\pip install sci rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\Scripts\pip install scipy rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\Scripts\pip install opencv-python -rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\python.exe darknet.py +C:\Users\Alex\AppData\Local\Programs\Python\Python36\python.exe darknet.py pause \ No newline at end of file diff --git a/darknet.py b/darknet.py index 9408aabc635..10c9a456ebd 100644 --- a/darknet.py +++ b/darknet.py @@ -202,6 +202,10 @@ def network_height(net): predict_image.argtypes = [c_void_p, IMAGE] predict_image.restype = POINTER(c_float) +predict_image_letterbox = lib.network_predict_image_letterbox +predict_image_letterbox.argtypes = [c_void_p, IMAGE] +predict_image_letterbox.restype = POINTER(c_float) + def array_to_image(arr): import numpy as np # need to return old values to avoid python freeing memory @@ -251,9 +255,12 @@ def detect_image(net, meta, im, thresh=.5, hier_thresh=.5, nms=.45, debug= False pnum = pointer(num) if debug: print("Assigned pnum") predict_image(net, im) + letter_box = 0 + #predict_image_letterbox(net, im) + #letter_box = 1 if debug: print("did prediction") - #dets = get_network_boxes(net, custom_image_bgr.shape[1], custom_image_bgr.shape[0], thresh, hier_thresh, None, 0, pnum, 0) # OpenCV - dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum, 0) + #dets = get_network_boxes(net, custom_image_bgr.shape[1], custom_image_bgr.shape[0], thresh, hier_thresh, None, 0, pnum, letter_box) # OpenCV + dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum, letter_box) if debug: print("Got dets") num = pnum[0] if debug: print("got zeroth index of pnum") diff --git a/include/darknet.h b/include/darknet.h index 1584a362d43..232d0732ff1 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -833,6 +833,7 @@ LIB_API layer* get_network_layer(network* net, int i); LIB_API detection *make_network_boxes(network *net, float thresh, int *num); LIB_API void reset_rnn(network *net); LIB_API float *network_predict_image(network *net, image im); +LIB_API float *network_predict_image_letterbox(network *net, image im); LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, const int map_points, int letter_box, network *existing_net); LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port, int show_imgs); LIB_API void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, diff --git a/src/network.c b/src/network.c index 0e655c78d3c..e0be39952ff 100644 --- a/src/network.c +++ b/src/network.c @@ -842,6 +842,24 @@ float *network_predict_image(network *net, image im) return p; } +float *network_predict_image_letterbox(network *net, image im) +{ + //image imr = letterbox_image(im, net->w, net->h); + float *p; + if (net->batch != 1) set_batch_network(net, 1); + if (im.w == net->w && im.h == net->h) { + // Input image is the same size as our net, predict on that image + p = network_predict(*net, im.data); + } + else { + // Need to resize image to the desired size for the net + image imr = letterbox_image(im, net->w, net->h); + p = network_predict(*net, imr.data); + free_image(imr); + } + return p; +} + int network_width(network *net) { return net->w; } int network_height(network *net) { return net->h; } From 5a6afe96d3aa8aed19405577db7dba0ff173c848 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 3 Jul 2019 13:54:13 +0300 Subject: [PATCH 0540/1365] Fixed scale_channels_layer resize --- src/network.c | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/network.c b/src/network.c index e0be39952ff..faf3db983e0 100644 --- a/src/network.c +++ b/src/network.c @@ -32,6 +32,7 @@ #include "dropout_layer.h" #include "route_layer.h" #include "shortcut_layer.h" +#include "scale_channels_layer.h" #include "yolo_layer.h" #include "upsample_layer.h" #include "parser.h" @@ -523,6 +524,8 @@ int resize_network(network *net, int w, int h) resize_route_layer(&l, net); }else if (l.type == SHORTCUT) { resize_shortcut_layer(&l, w, h); + }else if (l.type == SCALE_CHANNELS) { + resize_scale_channels_layer(&l, w, h); }else if (l.type == UPSAMPLE) { resize_upsample_layer(&l, w, h); }else if(l.type == REORG){ From e299925c5c51829e7704ed883b6ec6d4b18984ae Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 9 Jul 2019 10:39:48 +0200 Subject: [PATCH 0541/1365] re-enable cuda choice --- CMakeLists.txt | 9 ++++++--- DarknetConfig.cmake.in | 3 +++ 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 3024a7778fa..e4a6d624664 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -38,13 +38,13 @@ set(INSTALL_INCLUDE_DIR "include/darknet" CACHE PATH "Path where head set(INSTALL_CMAKE_DIR "share/darknet" CACHE PATH "Path where cmake configs will be installed") set(ENABLE_OPENCV "TRUE" CACHE BOOL "Enable OpenCV integration") -set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA support") +set(ENABLE_CUDA "TRUE" CACHE BOOL "Enable CUDA support") set(ENABLE_CUDNN "FALSE" CACHE BOOL "Enable CUDNN") set(ENABLE_CUDNN_HALF "FALSE" CACHE BOOL "Enable CUDNN Half precision") if(${CMAKE_VERSION} VERSION_LESS "3.9.0") - set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA support" FORCE) message(WARNING "To build with CUDA support you need CMake 3.9.0+") + set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA support" FORCE) else() include(CheckLanguage) check_language(CUDA) @@ -55,8 +55,8 @@ else() find_package(CUDA REQUIRED) if(CUDA_VERSION VERSION_LESS "9.0") message(STATUS "Unsupported CUDA version, please upgrade to CUDA 9+. Disabling CUDA support") + set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA support" FORCE) else() - set(ENABLE_CUDA "TRUE" CACHE BOOL "Enable CUDA support" FORCE) cuda_select_nvcc_arch_flags(CUDA_ARCH_FLAGS ${CUDA_ARCHITECTURES}) message(STATUS "Building with CUDA flags: " "${CUDA_ARCH_FLAGS}") if ("arch=compute_75,code=sm_75" IN_LIST CUDA_ARCH_FLAGS) @@ -64,6 +64,9 @@ else() message(STATUS "Your setup supports half precision!") endif() endif() + else() + message(STATUS "Unable to find a CUDA compiler. Disabling CUDA support") + set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA support" FORCE) endif() endif() diff --git a/DarknetConfig.cmake.in b/DarknetConfig.cmake.in index 169cfa9ba77..1f0e2864707 100644 --- a/DarknetConfig.cmake.in +++ b/DarknetConfig.cmake.in @@ -12,6 +12,9 @@ endif() find_dependency(Stb) if(@ENABLE_CUDA@) + enable_language(CUDA) + find_dependency(CUDA) + cuda_select_nvcc_arch_flags(CUDA_ARCH_FLAGS ${CUDA_ARCHITECTURES}) if(@CUDNN_FOUND@) find_dependency(CUDNN) endif() From 15a19c4e83dc21d97ba613b22498c16ee57dc26e Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 11 Jul 2019 18:55:24 +0200 Subject: [PATCH 0542/1365] avoid 'command line is too long' error on windows --- CMakeLists.txt | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index e4a6d624664..39c4f39a2a6 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -48,7 +48,7 @@ if(${CMAKE_VERSION} VERSION_LESS "3.9.0") else() include(CheckLanguage) check_language(CUDA) - if(CMAKE_CUDA_COMPILER) + if(CMAKE_CUDA_COMPILER AND ENABLE_CUDA) set(CUDA_ARCHITECTURES "Auto" CACHE STRING "\"Auto\" detects local machine GPU compute arch at runtime, \"Common\" and \"All\" cover common and entire subsets of architectures, \"Names\" is a list of architectures to enable by name, \"Numbers\" is a list of compute capabilities (version number) to enable") set_property(CACHE CUDA_ARCHITECTURES PROPERTY STRINGS "Auto" "Common" "All" "Kepler Maxwell Kepler+Tegra Maxwell+Tegra Pascal" "3.0 7.5") enable_language(CUDA) @@ -65,7 +65,6 @@ else() endif() endif() else() - message(STATUS "Unable to find a CUDA compiler. Disabling CUDA support") set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA support" FORCE) endif() endif() @@ -83,6 +82,9 @@ if(MSVC) endif() if(ENABLE_OPENCV) find_package(OpenCV) + if(OpenCV_FOUND) + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_video") + endif() endif() find_package(Stb REQUIRED) if(${CMAKE_VERSION} VERSION_LESS "3.11.0") @@ -126,6 +128,8 @@ if(OpenCV_FOUND) set(SKIP_USELIB_TRACK "TRUE" CACHE BOOL "Skip building uselib_track" FORCE) message(STATUS " -> darknet is fine for now, but uselib_track has been disabled!") message(STATUS " -> Please rebuild OpenCV from sources with CUDA support to enable it") + else() + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_cudaoptflow") endif() endif() @@ -276,9 +280,9 @@ if(CUDNN_FOUND) endif() if(OpenCV_FOUND) - target_link_libraries(darknet PRIVATE ${OpenCV_LIBS}) - target_link_libraries(uselib PRIVATE ${OpenCV_LIBS}) - target_link_libraries(dark PRIVATE ${OpenCV_LIBS}) + target_link_libraries(darknet PRIVATE ${OpenCV_LINKED_COMPONENTS}) + target_link_libraries(uselib PRIVATE ${OpenCV_LINKED_COMPONENTS}) + target_link_libraries(dark PRIVATE ${OpenCV_LINKED_COMPONENTS}) target_compile_definitions(darknet PRIVATE -DOPENCV) target_compile_definitions(dark PUBLIC -DOPENCV) endif() @@ -339,7 +343,7 @@ if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRA target_compile_definitions(uselib_track PRIVATE -DUSE_CMAKE_LIBS) #set_target_properties(uselib_track PROPERTIES LINKER_LANGUAGE CXX) target_include_directories(uselib_track PRIVATE ${CMAKE_CURRENT_LIST_DIR}/include) - target_link_libraries(uselib_track PRIVATE ${OpenCV_LIBS}) + target_link_libraries(uselib_track PRIVATE ${OpenCV_LINKED_COMPONENTS}) if(ENABLE_ZED_CAMERA) target_link_libraries(uselib_track PRIVATE ${ZED_LIBRARIES}) target_compile_definitions(uselib_track PRIVATE -DZED_STEREO) From 2c4c96f50c563a14fe87aa9499e7c33d4599bf50 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 11 Jul 2019 22:35:08 +0200 Subject: [PATCH 0543/1365] fix regression --- CMakeLists.txt | 34 ++++++++++++++++++++-- DarknetConfig.cmake.in | 2 -- build.ps1 | 65 ++++++++++++++++++++++++++++++++---------- 3 files changed, 81 insertions(+), 20 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 39c4f39a2a6..2c874141cce 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -83,7 +83,30 @@ endif() if(ENABLE_OPENCV) find_package(OpenCV) if(OpenCV_FOUND) - list(APPEND OpenCV_LINKED_COMPONENTS "opencv_video") + if(TARGET opencv_world) + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_world") + else() + if(TARGET opencv_video) + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_video") + endif() + if(OpenCV_VERSION VERSION_LESS "4.0") + if(TARGET opencv_videoio) + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_videoio") + endif() + if(TARGET opencv_highgui) + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_highgui") + endif() + if(TARGET opencv_imgproc) + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_imgproc") + endif() + if(TARGET opencv_imgcodecs) + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_imgcodecs") + endif() + if(TARGET opencv_core) + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_core") + endif() + endif() + endif() endif() endif() find_package(Stb REQUIRED) @@ -128,8 +151,13 @@ if(OpenCV_FOUND) set(SKIP_USELIB_TRACK "TRUE" CACHE BOOL "Skip building uselib_track" FORCE) message(STATUS " -> darknet is fine for now, but uselib_track has been disabled!") message(STATUS " -> Please rebuild OpenCV from sources with CUDA support to enable it") - else() - list(APPEND OpenCV_LINKED_COMPONENTS "opencv_cudaoptflow") + elseif(ENABLE_CUDA AND OpenCV_CUDA_VERSION) + if(TARGET opencv_cudaoptflow) + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_cudaoptflow") + endif() + if(TARGET opencv_cudaimgproc) + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_cudaimgproc") + endif() endif() endif() diff --git a/DarknetConfig.cmake.in b/DarknetConfig.cmake.in index 1f0e2864707..140227a1d2f 100644 --- a/DarknetConfig.cmake.in +++ b/DarknetConfig.cmake.in @@ -9,8 +9,6 @@ if(@OpenCV_FOUND@) find_dependency(OpenCV) endif() -find_dependency(Stb) - if(@ENABLE_CUDA@) enable_language(CUDA) find_dependency(CUDA) diff --git a/build.ps1 b/build.ps1 index 8030ca99b8c..770be9e1a67 100755 --- a/build.ps1 +++ b/build.ps1 @@ -2,6 +2,7 @@ $number_of_build_workers=8 $use_vcpkg=$true +$force_ninja=$true function getProgramFiles32bit() { $out = ${env:PROGRAMFILES(X86)} @@ -112,17 +113,22 @@ if ($null -eq (Get-Command "cl.exe" -ErrorAction SilentlyContinue)) { $tokens = getLatestVisualStudioWithDesktopWorkloadVersion $tokens = $tokens.split('.') -if ($tokens[0] -eq "14") { - $generator = "Visual Studio 14 2015" -} -elseif ($tokens[0] -eq "15") { - $generator = "Visual Studio 15 2017" -} -elseif ($tokens[0] -eq "16") { - $generator = "Visual Studio 16 2019" +if($force_ninja) { + $generator = "Ninja" } else { - throw "Unknown Visual Studio version, unsupported configuration" + if ($tokens[0] -eq "14") { + $generator = "Visual Studio 14 2015" + } + elseif ($tokens[0] -eq "15") { + $generator = "Visual Studio 15 2017" + } + elseif ($tokens[0] -eq "16") { + $generator = "Visual Studio 16 2019" + } + else { + throw "Unknown Visual Studio version, unsupported configuration" + } } Write-Host "Setting up environment to use CMake generator: $generator" -ForegroundColor Yellow @@ -148,24 +154,44 @@ if ($use_vcpkg) { ## DEBUG #New-Item -Path .\build_win_debug -ItemType directory -Force #Set-Location build_win_debug - #cmake -G "$generator" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" #"-DCMAKE_BUILD_TYPE=Debug" $additional_build_setup .. + #if ($force_ninja) { + #cmake -G "$generator" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" #"-DCMAKE_BUILD_TYPE=Debug" $additional_build_setup .. + #$dllfolder = "." + #} + #else { + #cmake -G "$generator" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Debug" $additional_build_setup .. + #$dllfolder = "Debug" + #} #cmake --build . --config Debug --target install ##cmake --build . --config Debug --parallel ${number_of_build_workers} --target install #valid only for CMake 3.12+ #Remove-Item DarknetConfig.cmake #Remove-Item DarknetConfigVersion.cmake - #Copy-Item Debug\*.dll .. + #$dllfiles = Get-ChildItem ${dllfolder}\*.dll + #if ($dllfiles) { + # Copy-Item $dllfiles .. + #} #Set-Location .. #Copy-Item cmake\Modules\*.cmake share\darknet\ # RELEASE New-Item -Path .\build_win_release -ItemType directory -Force Set-Location build_win_release - cmake -G "$generator" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Release" $additional_build_setup .. + if($force_ninja) { + cmake -G "$generator" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Release" $additional_build_setup .. + $dllfolder = "." + } + else { + cmake -G "$generator" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Release" $additional_build_setup .. + $dllfolder = "Release" + } cmake --build . --config Release --target install #cmake --build . --config Release --parallel ${number_of_build_workers} --target install #valid only for CMake 3.12+ Remove-Item DarknetConfig.cmake Remove-Item DarknetConfigVersion.cmake - Copy-Item Release\*.dll .. + $dllfiles = Get-ChildItem ${dllfolder}\*.dll + if ($dllfiles) { + Copy-Item $dllfiles .. + } Set-Location .. Copy-Item cmake\Modules\*.cmake share\darknet\ } @@ -174,12 +200,21 @@ else { # if you want to manually force this case, remove VCPKG_ROOT env variable and remember to use "vcpkg integrate remove" in case you had enabled user-wide vcpkg integration New-Item -Path .\build_win_release_novcpkg -ItemType directory -Force Set-Location build_win_release_novcpkg - cmake -G "$generator" -T "host=x64" -A "x64" $additional_build_setup .. + if($force_ninja) { + cmake -G "$generator" $additional_build_setup .. + } + else { + cmake -G "$generator" -T "host=x64" -A "x64" $additional_build_setup .. + } cmake --build . --config Release --target install #cmake --build . --config Release --parallel ${number_of_build_workers} --target install #valid only for CMake 3.12+ Remove-Item DarknetConfig.cmake Remove-Item DarknetConfigVersion.cmake - Copy-Item ..\3rdparty\pthreads\bin\*.dll .. + $dllfolder = "..\3rdparty\pthreads\bin" + $dllfiles = Get-ChildItem ${dllfolder}\*.dll + if ($dllfiles) { + Copy-Item $dllfiles .. + } Set-Location .. Copy-Item cmake\Modules\*.cmake share\darknet\ } From ebb9a3cf53e31e3ccf0249ea2ea94ce49019456c Mon Sep 17 00:00:00 2001 From: vinjn Date: Wed, 17 Jul 2019 20:38:13 +0800 Subject: [PATCH 0544/1365] Make "darknet.exe classifier predict" behavior same as "darknet.exe classifer validate" --- src/classifier.c | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/classifier.c b/src/classifier.c index f5fbfaebf63..11fa92df913 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -791,7 +791,8 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi strtok(input, "\n"); } image im = load_image_color(input, 0, 0); - image r = letterbox_image(im, net.w, net.h); + image resized = resize_min(im, net.w); + image r = crop_image(resized, (resized.w - net.w)/2, (resized.h - net.h)/2, net.w, net.h); //image r = resize_min(im, size); //resize_network(&net, r.w, r.h); printf("%d %d\n", r.w, r.h); From b9e7cd3d354377994036e07203dc0ab3d228d3a4 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 18 Jul 2019 10:48:19 +0200 Subject: [PATCH 0545/1365] introduce functions to detect build features --- .gitignore | 6 ++++++ include/yolo_v2_class.hpp | 13 ++++++++----- src/yolo_v2_class.cpp | 25 +++++++++++++++++++++++++ 3 files changed, 39 insertions(+), 5 deletions(-) diff --git a/.gitignore b/.gitignore index f5d9a42a83a..251fac31c3c 100644 --- a/.gitignore +++ b/.gitignore @@ -23,6 +23,12 @@ build_*/ !build/darknet/YoloWrapper.cs .fuse* *.weights +build/*.cmake +build/*.ninja +build/*.txt +build/*.json +build/CMakeFiles/ +build/detect_cuda_compute_capabilities.cu # OS Generated # .DS_Store* diff --git a/include/yolo_v2_class.hpp b/include/yolo_v2_class.hpp index 323f7a1a66f..f25610d0424 100644 --- a/include/yolo_v2_class.hpp +++ b/include/yolo_v2_class.hpp @@ -62,6 +62,9 @@ extern "C" LIB_API int detect_mat(const uint8_t* data, const size_t data_length, extern "C" LIB_API int dispose(); extern "C" LIB_API int get_device_count(); extern "C" LIB_API int get_device_name(int gpu, char* deviceName); +extern "C" LIB_API bool built_with_cuda(); +extern "C" LIB_API bool built_with_cudnn(); +extern "C" LIB_API bool built_with_opencv(); extern "C" LIB_API void send_json_custom(char const* send_buf, int port, int timeout); class Detector { @@ -88,7 +91,7 @@ class Detector { LIB_API void *get_cuda_context(); - //LIB_API bool send_json_http(std::vector cur_bbox_vec, std::vector obj_names, int frame_id, + //LIB_API bool send_json_http(std::vector cur_bbox_vec, std::vector obj_names, int frame_id, // std::string filename = std::string(), int timeout = 400000, int port = 8070); std::vector detect_resized(image_t img, int init_w, int init_h, float thresh = 0.2, bool use_mean = false) @@ -477,7 +480,7 @@ class Tracker_optflow { else { std::cerr << " Warning: new_src_mat.channels() is not: 1, 3 or 4. It is = " << new_src_mat.channels() << " \n"; return; - } + } update_cur_bbox_vec(_cur_bbox_vec); } @@ -691,7 +694,7 @@ class preview_boxes_t { }; -class track_kalman_t +class track_kalman_t { int track_id_counter; std::chrono::steady_clock::time_point global_last_time; @@ -847,7 +850,7 @@ class track_kalman_t track_kalman_t(int _max_objects = 1000, int _min_frames = 3, float _max_dist = 40, cv::Size _img_size = cv::Size(10000, 10000)) : - max_objects(_max_objects), min_frames(_min_frames), max_dist(_max_dist), img_size(_img_size), + max_objects(_max_objects), min_frames(_min_frames), max_dist(_max_dist), img_size(_img_size), track_id_counter(0) { kalman_vec.resize(max_objects); @@ -917,7 +920,7 @@ class track_kalman_t busy_vec[tst.state_id] = true; } else { - //std::cerr << " Didn't find: obj_id = " << find_box.obj_id << ", x = " << find_box.x << ", y = " << find_box.y << + //std::cerr << " Didn't find: obj_id = " << find_box.obj_id << ", x = " << find_box.x << ", y = " << find_box.y << // ", track_id_counter = " << track_id_counter << std::endl; } diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index cffbd625718..4667794a839 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -72,6 +72,31 @@ int get_device_count() { #endif // GPU } +bool built_with_cuda(){ +#ifdef GPU + return true; +#else + return false; +#endif +} + +bool built_with_cudnn(){ +#ifdef CUDNN + return true; +#else + return false; +#endif +} + +bool built_with_opencv(){ +#ifdef OPENCV + return true; +#else + return false; +#endif +} + + int get_device_name(int gpu, char* deviceName) { #ifdef GPU cudaDeviceProp prop; From 6daa15b84f4b80611700634ee110dbfd3aafc15f Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 18 Jul 2019 13:14:09 +0200 Subject: [PATCH 0546/1365] add a message when package is found --- .gitignore | 2 ++ DarknetConfig.cmake.in | 10 +++++++++- build.ps1 | 10 +++++----- 3 files changed, 16 insertions(+), 6 deletions(-) diff --git a/.gitignore b/.gitignore index 251fac31c3c..146fa0dd251 100644 --- a/.gitignore +++ b/.gitignore @@ -29,6 +29,8 @@ build/*.txt build/*.json build/CMakeFiles/ build/detect_cuda_compute_capabilities.cu +build/.ninja_deps +build/.ninja_log # OS Generated # .DS_Store* diff --git a/DarknetConfig.cmake.in b/DarknetConfig.cmake.in index 140227a1d2f..cbd7f066b90 100644 --- a/DarknetConfig.cmake.in +++ b/DarknetConfig.cmake.in @@ -21,8 +21,9 @@ endif() set(CMAKE_THREAD_PREFER_PTHREAD ON) find_dependency(Threads) -if(@MSVC@) +if(MSVC) find_dependency(PThreads_windows) + set(CMAKE_CXX_FLAGS "/wd4018 /wd4244 /wd4267 /wd4305 ${CMAKE_CXX_FLAGS}") endif() if(@OPENMP_FOUND@) @@ -31,3 +32,10 @@ endif() # Our library dependencies (contains definitions for IMPORTED targets) include("${Darknet_CMAKE_DIR}/DarknetTargets.cmake") +include("${Darknet_CMAKE_DIR}/DarknetConfigVersion.cmake") + +get_target_property(FULL_DARKNET_INCLUDE_DIRS Darknet::dark INTERFACE_INCLUDE_DIRECTORIES) +list(GET FULL_DARKNET_INCLUDE_DIRS 0 Darknet_INCLUDE_DIR) +get_filename_component(Darknet_INCLUDE_DIR "${Darknet_INCLUDE_DIR}" REALPATH) + +find_package_handle_standard_args(Darknet REQUIRED_VARS Darknet_INCLUDE_DIR VERSION_VAR PACKAGE_VERSION) diff --git a/build.ps1 b/build.ps1 index 770be9e1a67..81fb8ef4fcb 100755 --- a/build.ps1 +++ b/build.ps1 @@ -2,7 +2,7 @@ $number_of_build_workers=8 $use_vcpkg=$true -$force_ninja=$true +$use_ninja=$false function getProgramFiles32bit() { $out = ${env:PROGRAMFILES(X86)} @@ -113,7 +113,7 @@ if ($null -eq (Get-Command "cl.exe" -ErrorAction SilentlyContinue)) { $tokens = getLatestVisualStudioWithDesktopWorkloadVersion $tokens = $tokens.split('.') -if($force_ninja) { +if($use_ninja) { $generator = "Ninja" } else { @@ -154,7 +154,7 @@ if ($use_vcpkg) { ## DEBUG #New-Item -Path .\build_win_debug -ItemType directory -Force #Set-Location build_win_debug - #if ($force_ninja) { + #if ($use_ninja) { #cmake -G "$generator" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" #"-DCMAKE_BUILD_TYPE=Debug" $additional_build_setup .. #$dllfolder = "." #} @@ -176,7 +176,7 @@ if ($use_vcpkg) { # RELEASE New-Item -Path .\build_win_release -ItemType directory -Force Set-Location build_win_release - if($force_ninja) { + if($use_ninja) { cmake -G "$generator" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Release" $additional_build_setup .. $dllfolder = "." } @@ -200,7 +200,7 @@ else { # if you want to manually force this case, remove VCPKG_ROOT env variable and remember to use "vcpkg integrate remove" in case you had enabled user-wide vcpkg integration New-Item -Path .\build_win_release_novcpkg -ItemType directory -Force Set-Location build_win_release_novcpkg - if($force_ninja) { + if($use_ninja) { cmake -G "$generator" $additional_build_setup .. } else { From ae0306e851e14c4727b8b78588aeda8f8b8ca14f Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 23 Jul 2019 15:59:02 +0200 Subject: [PATCH 0547/1365] option to build project using cpp compiler if c not working/available --- .gitignore | 2 ++ .travis.yml | 18 +++++++++++++++++- CMakeLists.txt | 23 +++++++++++++++++------ appveyor.yml | 8 +++++++- build.ps1 | 4 ++++ build.sh | 10 ++++++---- src/col2im.c | 1 + src/data.c | 2 +- src/gemm.c | 4 +++- src/parser.c | 2 +- 10 files changed, 59 insertions(+), 15 deletions(-) diff --git a/.gitignore b/.gitignore index 146fa0dd251..79dee2345c9 100644 --- a/.gitignore +++ b/.gitignore @@ -8,6 +8,7 @@ *.exe *.dll *.lib +*.dylib mnist/ data/ caffe/ @@ -31,6 +32,7 @@ build/CMakeFiles/ build/detect_cuda_compute_capabilities.cu build/.ninja_deps build/.ninja_log +build/Makefile # OS Generated # .DS_Store* diff --git a/.travis.yml b/.travis.yml index 93a4f6bdbe8..19335fd608d 100644 --- a/.travis.yml +++ b/.travis.yml @@ -44,6 +44,13 @@ matrix: - additional_defines="" - MATRIX_EVAL="" + - os: osx + compiler: clang + name: macOS - native clang - C++ mode + env: + - additional_defines="-DBUILD_AS_CPP:BOOL=TRUE" + - MATRIX_EVAL="" + - os: osx compiler: clang name: macOS - native clang - opencv@2 @@ -179,6 +186,16 @@ matrix: - additional_defines="" - MATRIX_EVAL="" + - os: linux + compiler: gcc + name: ubuntu 14.04 - native gcc - C++ mode + addons: + apt: + update: true + env: + - additional_defines="-DBUILD_AS_CPP:BOOL=TRUE" + - MATRIX_EVAL="" + - os: linux compiler: gcc name: ubuntu 14.04 - gcc@4.9 @@ -942,7 +959,6 @@ install: - popd before_script: - - echo ${additional_defines} - mkdir build_debug - cd build_debug - cmake .. -DCMAKE_BUILD_TYPE="Debug" ${VCPKG_DEFINES} ${additional_defines} diff --git a/CMakeLists.txt b/CMakeLists.txt index 2c874141cce..bbc53bd1a6b 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -7,6 +7,7 @@ set(Darknet_VERSION ${Darknet_MAJOR_VERSION}.${Darknet_MINOR_VERSION}.${Darknet_ set(CMAKE_VERBOSE_MAKEFILE "FALSE" CACHE BOOL "Create verbose makefile") set(BUILD_SHARED_LIBS "TRUE" CACHE BOOL "Create dark as a shared library") +set(BUILD_AS_CPP "FALSE" CACHE BOOL "Build Darknet using C++ compiler also for C files") if(DEFINED ENV{VCPKG_ROOT} AND NOT DEFINED CMAKE_TOOLCHAIN_FILE) set(CMAKE_TOOLCHAIN_FILE "$ENV{VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake" CACHE STRING "") @@ -265,7 +266,9 @@ if(ENABLE_CUDA) file(GLOB cuda_sources "${CMAKE_CURRENT_LIST_DIR}/src/*.cu") endif() -#set_source_files_properties(${sources} PROPERTIES LANGUAGE CXX) +if(BUILD_AS_CPP) + set_source_files_properties(${sources} PROPERTIES LANGUAGE CXX) +endif() add_library(dark ${CMAKE_CURRENT_LIST_DIR}/include/yolo_v2_class.hpp ${CMAKE_CURRENT_LIST_DIR}/src/yolo_v2_class.cpp ${sources} ${headers} ${cuda_sources}) set_target_properties(dark PROPERTIES POSITION_INDEPENDENT_CODE ON) @@ -275,18 +278,24 @@ endif() if(BUILD_SHARED_LIBS) target_compile_definitions(dark PRIVATE LIB_EXPORTS=1) endif() -#set_target_properties(dark PROPERTIES LINKER_LANGUAGE CXX) +if(BUILD_AS_CPP) + set_target_properties(dark PROPERTIES LINKER_LANGUAGE CXX) +endif() if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRACK) add_executable(uselib_track ${CMAKE_CURRENT_LIST_DIR}/src/yolo_console_dll.cpp) endif() add_executable(uselib ${CMAKE_CURRENT_LIST_DIR}/src/yolo_console_dll.cpp) -#set_target_properties(uselib PROPERTIES LINKER_LANGUAGE CXX) +if(BUILD_AS_CPP) + set_target_properties(uselib PROPERTIES LINKER_LANGUAGE CXX) +endif() add_executable(darknet ${CMAKE_CURRENT_LIST_DIR}/src/darknet.c ${sources} ${headers} ${cuda_sources}) -#set_source_files_properties(${CMAKE_CURRENT_LIST_DIR}/src/darknet.c PROPERTIES LANGUAGE CXX) -#set_target_properties(darknet PROPERTIES LINKER_LANGUAGE CXX) +if(BUILD_AS_CPP) + set_source_files_properties(${CMAKE_CURRENT_LIST_DIR}/src/darknet.c PROPERTIES LANGUAGE CXX) + set_target_properties(darknet PROPERTIES LINKER_LANGUAGE CXX) +endif() target_include_directories(darknet PUBLIC $ $ $ $) target_include_directories(dark PUBLIC $ $ $ $) @@ -369,7 +378,9 @@ if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRA target_link_libraries(uselib_track PRIVATE dark) target_compile_definitions(uselib_track PRIVATE TRACK_OPTFLOW=1) target_compile_definitions(uselib_track PRIVATE -DUSE_CMAKE_LIBS) - #set_target_properties(uselib_track PROPERTIES LINKER_LANGUAGE CXX) + if(BUILD_AS_CPP) + set_target_properties(uselib_track PROPERTIES LINKER_LANGUAGE CXX) + endif() target_include_directories(uselib_track PRIVATE ${CMAKE_CURRENT_LIST_DIR}/include) target_link_libraries(uselib_track PRIVATE ${OpenCV_LINKED_COMPONENTS}) if(ENABLE_ZED_CAMERA) diff --git a/appveyor.yml b/appveyor.yml index 08ce819bf58..26b1add487b 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -37,6 +37,11 @@ environment: COMPILER: vs configuration: Release USE_INTEGRATED_LIBS: yes + - platform: Win64 + COMPILER: vs + configuration: Release + USE_INTEGRATED_LIBS: yes + FORCE_CPP: yes install: - if [%COMPILER%]==[vs] cinst cmake ninja @@ -77,7 +82,8 @@ install: - mkdir build_release && cd build_release - if [%COMPILER%]==[cygwin] %CYGSH% 'cmake .. -G "Unix Makefiles" -DCMAKE_BUILD_TYPE="Release"' - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] if [%configuration%]==[Release] cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=%VCPKG_ROOT%\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=%VCPKG_DEFAULT_TRIPLET%" -DCMAKE_BUILD_TYPE="Release" .. - - if [%COMPILER%]==[vs] if [%USE_INTEGRATED_LIBS%]==[yes] cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" -DCMAKE_BUILD_TYPE="Release" .. + - if [%COMPILER%]==[vs] if [%USE_INTEGRATED_LIBS%]==[yes] if [%FORCE_CPP%]==[yes] cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" -DCMAKE_BUILD_TYPE="Release" "-DBUILD_AS_CPP:BOOL=TRUE" .. + - if [%COMPILER%]==[vs] if [%USE_INTEGRATED_LIBS%]==[yes] cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" -DCMAKE_BUILD_TYPE="Release" .. - cd .. build_script: diff --git a/build.ps1 b/build.ps1 index 81fb8ef4fcb..ec83168f6dd 100755 --- a/build.ps1 +++ b/build.ps1 @@ -3,6 +3,7 @@ $number_of_build_workers=8 $use_vcpkg=$true $use_ninja=$false +$force_cpp_build=$false function getProgramFiles32bit() { $out = ${env:PROGRAMFILES(X86)} @@ -149,6 +150,9 @@ if (Test-Path env:CUDA_PATH) { } } +if($force_cpp_build) { + $additional_build_setup="-DBUILD_AS_CPP:BOOL=TRUE" +} if ($use_vcpkg) { ## DEBUG diff --git a/build.sh b/build.sh index 8bbabf2f2fc..6c8df676685 100755 --- a/build.sh +++ b/build.sh @@ -2,12 +2,9 @@ number_of_build_workers=8 bypass_vcpkg=true +force_cpp_build=false if [[ "$OSTYPE" == "darwin"* ]]; then - if [[ "$1" == "gcc" ]]; then - export CC="/usr/local/bin/gcc-8" - export CXX="/usr/local/bin/g++-8" - fi vcpkg_triplet="x64-osx" else vcpkg_triplet="x64-linux" @@ -30,6 +27,11 @@ then (>&2 echo "darknet is unsupported without vcpkg, use at your own risk!") fi +if [ "$force_cpp_build" = true ] +then + additional_build_setup="-DBUILD_AS_CPP:BOOL=TRUE" +fi + ## DEBUG #mkdir -p build_debug #cd build_debug diff --git a/src/col2im.c b/src/col2im.c index c31c868ec11..4b8cb592e6f 100644 --- a/src/col2im.c +++ b/src/col2im.c @@ -1,5 +1,6 @@ #include #include +#include #include "col2im.h" void col2im_add_pixel(float *im, int height, int width, int channels, int row, int col, int channel, int pad, float val) diff --git a/src/data.c b/src/data.c index 571bfd019d5..6dd3274ae7b 100644 --- a/src/data.c +++ b/src/data.c @@ -937,7 +937,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo { image tmp_ai = copy_image(ai); char buff[1000]; - sprintf(buff, "aug_%d_%d_%s_%d", random_index, i, basecfg(filename), random_gen()); + sprintf(buff, "aug_%d_%d_%s_%d", random_index, i, basecfg((char*)filename), random_gen()); int t; for (t = 0; t < boxes; ++t) { box b = float_to_box_stride(d.y.vals[i] + t*(4 + 1), 1); diff --git a/src/gemm.c b/src/gemm.c index 336f3bdd645..0e1bd5ea047 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -1120,8 +1120,10 @@ void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride, static inline int popcnt128(__m128i n) { const __m128i n_hi = _mm_unpackhi_epi64(n, n); -#ifdef _MSC_VER +#if defined(_MSC_VER) return __popcnt64(_mm_cvtsi128_si64(n)) + __popcnt64(_mm_cvtsi128_si64(n_hi)); +#elif defined(__APPLE__) && defined(__clang__) + return _mm_popcnt_u64(_mm_cvtsi128_si64(n)) + _mm_popcnt_u64(_mm_cvtsi128_si64(n_hi)); #else return __popcntq(_mm_cvtsi128_si64(n)) + __popcntq(_mm_cvtsi128_si64(n_hi)); #endif diff --git a/src/parser.c b/src/parser.c index 30510132546..8c7567623a8 100644 --- a/src/parser.c +++ b/src/parser.c @@ -167,7 +167,7 @@ convolutional_layer parse_convolutional(list *options, size_params params, netwo int share_index = option_find_int_quiet(options, "share_index", -1); convolutional_layer *share_layer = NULL; - if(share_layer > -1) share_layer = &net.layers[share_index]; + if((long long int)share_layer > -1) share_layer = &net.layers[share_index]; int batch,h,w,c; h = params.h; From 61bab85a54a94e1fab0d56c1fa6ce05c445b0b25 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 26 Jul 2019 11:16:14 +0200 Subject: [PATCH 0548/1365] add compatibility with opencv 4.1.1 --- CMakeLists.txt | 30 ++++++++++++++---------------- 1 file changed, 14 insertions(+), 16 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index bbc53bd1a6b..d50278e04a6 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -90,22 +90,20 @@ if(ENABLE_OPENCV) if(TARGET opencv_video) list(APPEND OpenCV_LINKED_COMPONENTS "opencv_video") endif() - if(OpenCV_VERSION VERSION_LESS "4.0") - if(TARGET opencv_videoio) - list(APPEND OpenCV_LINKED_COMPONENTS "opencv_videoio") - endif() - if(TARGET opencv_highgui) - list(APPEND OpenCV_LINKED_COMPONENTS "opencv_highgui") - endif() - if(TARGET opencv_imgproc) - list(APPEND OpenCV_LINKED_COMPONENTS "opencv_imgproc") - endif() - if(TARGET opencv_imgcodecs) - list(APPEND OpenCV_LINKED_COMPONENTS "opencv_imgcodecs") - endif() - if(TARGET opencv_core) - list(APPEND OpenCV_LINKED_COMPONENTS "opencv_core") - endif() + if(TARGET opencv_videoio) + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_videoio") + endif() + if(TARGET opencv_highgui) + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_highgui") + endif() + if(TARGET opencv_imgproc) + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_imgproc") + endif() + if(TARGET opencv_imgcodecs) + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_imgcodecs") + endif() + if(TARGET opencv_core) + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_core") endif() endif() endif() From 6761e832cc01776f9471f52367405151522bbd3f Mon Sep 17 00:00:00 2001 From: Alexey Date: Sun, 28 Jul 2019 00:03:41 +0300 Subject: [PATCH 0549/1365] Bug fix for TridentNet Bug fix for TridentNet - share_index --- src/parser.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/parser.c b/src/parser.c index 8c7567623a8..e1afd766b2c 100644 --- a/src/parser.c +++ b/src/parser.c @@ -167,7 +167,7 @@ convolutional_layer parse_convolutional(list *options, size_params params, netwo int share_index = option_find_int_quiet(options, "share_index", -1); convolutional_layer *share_layer = NULL; - if((long long int)share_layer > -1) share_layer = &net.layers[share_index]; + if(share_index > -1) share_layer = &net.layers[share_index]; int batch,h,w,c; h = params.h; From b60c2304fa6d5d6de3ddce7937d770ae460955b5 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Mon, 29 Jul 2019 17:17:18 +0200 Subject: [PATCH 0550/1365] enable darknet as a library also when only MSVC build tools are installed --- DarknetConfig.cmake.in | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/DarknetConfig.cmake.in b/DarknetConfig.cmake.in index cbd7f066b90..c8f4787db55 100644 --- a/DarknetConfig.cmake.in +++ b/DarknetConfig.cmake.in @@ -10,7 +10,11 @@ if(@OpenCV_FOUND@) endif() if(@ENABLE_CUDA@) - enable_language(CUDA) + include(CheckLanguage) + check_language(CUDA) + if(NOT CMAKE_CUDA_COMPILER) + message(STATUS " --> WARNING: Unable to find native CUDA integration!") + endif() find_dependency(CUDA) cuda_select_nvcc_arch_flags(CUDA_ARCH_FLAGS ${CUDA_ARCHITECTURES}) if(@CUDNN_FOUND@) From 76b42fc7764ae5d501278fe0a31ebef459198d7a Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 30 Jul 2019 11:05:50 +0200 Subject: [PATCH 0551/1365] export missing defines and includes, hide unnecessary public symbols --- CMakeLists.txt | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index d50278e04a6..4d5f21a60be 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -318,15 +318,16 @@ if(OpenCV_FOUND) target_link_libraries(darknet PRIVATE ${OpenCV_LINKED_COMPONENTS}) target_link_libraries(uselib PRIVATE ${OpenCV_LINKED_COMPONENTS}) target_link_libraries(dark PRIVATE ${OpenCV_LINKED_COMPONENTS}) + target_include_directories(dark PUBLIC ${OpenCV_INCLUDE_DIRS}) target_compile_definitions(darknet PRIVATE -DOPENCV) target_compile_definitions(dark PUBLIC -DOPENCV) endif() if(OPENMP_FOUND) - target_link_libraries(darknet PUBLIC OpenMP::OpenMP_CXX) - target_link_libraries(darknet PUBLIC OpenMP::OpenMP_C) - target_link_libraries(dark PUBLIC OpenMP::OpenMP_CXX) - target_link_libraries(dark PUBLIC OpenMP::OpenMP_C) + target_link_libraries(darknet PRIVATE OpenMP::OpenMP_CXX) + target_link_libraries(darknet PRIVATE OpenMP::OpenMP_C) + target_link_libraries(dark PRIVATE OpenMP::OpenMP_CXX) + target_link_libraries(dark PRIVATE OpenMP::OpenMP_C) endif() if(CMAKE_COMPILER_IS_GNUCC) @@ -341,11 +342,12 @@ if(MSVC) target_link_libraries(dark PRIVATE wsock32 ws2_32) target_compile_definitions(darknet PRIVATE -D_CRT_RAND_S -DNOMINMAX -D_USE_MATH_DEFINES) target_compile_definitions(dark PRIVATE -D_CRT_RAND_S -DNOMINMAX -D_USE_MATH_DEFINES) + target_compile_definitions(dark PUBLIC -D_CRT_SECURE_NO_WARNINGS) target_compile_definitions(uselib PRIVATE -D_CRT_RAND_S -DNOMINMAX -D_USE_MATH_DEFINES) endif() -target_link_libraries(darknet PUBLIC Threads::Threads) -target_link_libraries(dark PUBLIC Threads::Threads) +target_link_libraries(darknet PRIVATE Threads::Threads) +target_link_libraries(dark PRIVATE Threads::Threads) if(ENABLE_ZED_CAMERA) target_link_libraries(darknet PRIVATE ${ZED_LIBRARIES}) @@ -357,7 +359,7 @@ if(ENABLE_ZED_CAMERA) endif() if(ENABLE_CUDA) - target_include_directories(darknet PUBLIC ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES}) + target_include_directories(darknet PRIVATE ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES}) target_include_directories(dark PUBLIC ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES}) target_link_libraries(darknet PRIVATE curand cublas cuda) target_link_libraries(dark PRIVATE curand cublas cuda) From c32588f433cf36f75760649668ef1650a882c7c8 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 30 Jul 2019 13:38:21 +0200 Subject: [PATCH 0552/1365] add missing links for cpp targets --- CMakeLists.txt | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/CMakeLists.txt b/CMakeLists.txt index 4d5f21a60be..11f06fce859 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -340,6 +340,7 @@ if(MSVC) target_link_libraries(darknet PRIVATE wsock32 ws2_32) target_link_libraries(dark PRIVATE PThreads_windows::PThreads_windows) target_link_libraries(dark PRIVATE wsock32 ws2_32) + target_link_libraries(uselib PRIVATE PThreads_windows::PThreads_windows) target_compile_definitions(darknet PRIVATE -D_CRT_RAND_S -DNOMINMAX -D_USE_MATH_DEFINES) target_compile_definitions(dark PRIVATE -D_CRT_RAND_S -DNOMINMAX -D_USE_MATH_DEFINES) target_compile_definitions(dark PUBLIC -D_CRT_SECURE_NO_WARNINGS) @@ -348,6 +349,7 @@ endif() target_link_libraries(darknet PRIVATE Threads::Threads) target_link_libraries(dark PRIVATE Threads::Threads) +target_link_libraries(uselib PRIVATE Threads::Threads) if(ENABLE_ZED_CAMERA) target_link_libraries(darknet PRIVATE ${ZED_LIBRARIES}) @@ -388,8 +390,10 @@ if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRA target_compile_definitions(uselib_track PRIVATE -DZED_STEREO) endif() if(MSVC) + target_link_libraries(uselib_track PRIVATE PThreads_windows::PThreads_windows) target_compile_definitions(uselib_track PRIVATE -D_CRT_RAND_S -DNOMINMAX -D_USE_MATH_DEFINES) endif() + target_link_libraries(uselib_track PRIVATE Threads::Threads) endif() #set_target_properties(dark PROPERTIES PUBLIC_HEADER "${exported_headers};${CMAKE_CURRENT_LIST_DIR}/include/yolo_v2_class.hpp") From 651a8dd7a6e1fa669c3650291078dd1cad2c2612 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 30 Jul 2019 14:56:44 +0200 Subject: [PATCH 0553/1365] restore OMP symbols as public --- CMakeLists.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 11f06fce859..939501df81f 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -326,8 +326,8 @@ endif() if(OPENMP_FOUND) target_link_libraries(darknet PRIVATE OpenMP::OpenMP_CXX) target_link_libraries(darknet PRIVATE OpenMP::OpenMP_C) - target_link_libraries(dark PRIVATE OpenMP::OpenMP_CXX) - target_link_libraries(dark PRIVATE OpenMP::OpenMP_C) + target_link_libraries(dark PUBLIC OpenMP::OpenMP_CXX) + target_link_libraries(dark PUBLIC OpenMP::OpenMP_C) endif() if(CMAKE_COMPILER_IS_GNUCC) From 6e7c3a53c723f1abcc9d8e463b55214f49b615b3 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 30 Jul 2019 16:01:12 +0200 Subject: [PATCH 0554/1365] move travis to ubuntu18.04 and simplify test matrix --- .travis.yml | 819 +++------------------------------------------------- 1 file changed, 38 insertions(+), 781 deletions(-) diff --git a/.travis.yml b/.travis.yml index 19335fd608d..060f36cae97 100644 --- a/.travis.yml +++ b/.travis.yml @@ -9,14 +9,14 @@ matrix: - os: osx compiler: gcc - name: macOS - native gcc (llvm backend) + name: macOS - gcc (llvm backend) env: - additional_defines="" - MATRIX_EVAL="" - os: osx compiler: gcc - name: macOS - native gcc (llvm backend) - opencv@2 + name: macOS - gcc (llvm backend) - opencv@2 env: - OpenCV_DIR="/usr/local/opt/opencv@2/" - additional_defines="-DOpenCV_DIR=${OpenCV_DIR}" @@ -24,7 +24,7 @@ matrix: - os: osx compiler: gcc - name: macOS - native gcc (llvm backend) - opencv@3 + name: macOS - gcc (llvm backend) - opencv@3 env: - OpenCV_DIR="/usr/local/opt/opencv@3/" - additional_defines="-DOpenCV_DIR=${OpenCV_DIR}" @@ -32,28 +32,28 @@ matrix: - os: osx compiler: gcc - name: macOS - native gcc (llvm backend) - opencv(latest) + name: macOS - gcc (llvm backend) - opencv(latest) env: - additional_defines="" - MATRIX_EVAL="brew install opencv" - os: osx compiler: clang - name: macOS - native clang + name: macOS - clang env: - additional_defines="" - MATRIX_EVAL="" - os: osx compiler: clang - name: macOS - native clang - C++ mode + name: macOS - clang - C++ mode env: - additional_defines="-DBUILD_AS_CPP:BOOL=TRUE" - MATRIX_EVAL="" - os: osx compiler: clang - name: macOS - native clang - opencv@2 + name: macOS - clang - opencv@2 env: - OpenCV_DIR="/usr/local/opt/opencv@2/" - additional_defines="-DOpenCV_DIR=${OpenCV_DIR}" @@ -61,7 +61,7 @@ matrix: - os: osx compiler: clang - name: macOS - native clang - opencv@3 + name: macOS - clang - opencv@3 env: - OpenCV_DIR="/usr/local/opt/opencv@3/" - additional_defines="-DOpenCV_DIR=${OpenCV_DIR}" @@ -69,13 +69,13 @@ matrix: - os: osx compiler: clang - name: macOS - native clang - opencv(latest) + name: macOS - clang - opencv(latest) env: - additional_defines="" - MATRIX_EVAL="brew install opencv" - os: osx - name: macOS - vcpkg + name: macOS - clang - vcpkg osx_image: xcode10.1 env: - additional_defines="" @@ -85,727 +85,68 @@ matrix: - os: linux compiler: clang - name: ubuntu 14.04 - native clang - addons: - apt: - update: true - env: - - additional_defines="" - - MATRIX_EVAL="" - - - os: linux - compiler: clang - name: ubuntu 14.04 - clang@3.6 - addons: - apt: - update: true - packages: - - clang-3.6 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=clang-3.6 && export CXX=clang++-3.6" - - - os: linux - compiler: clang - name: ubuntu 14.04 - clang@3.8 - addons: - apt: - update: true - sources: - - llvm-toolchain-precise-3.8 - packages: - - clang-3.8 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=clang-3.8 && export CXX=clang++-3.8" - - - os: linux - compiler: clang - name: ubuntu 14.04 - clang@3.9 - addons: - apt: - update: true - sources: - - llvm-toolchain-trusty-3.9 - packages: - - clang-3.9 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=clang-3.9 && export CXX=clang++-3.9" - - - os: linux - compiler: clang - name: ubuntu 14.04 - clang@4.0 - addons: - apt: - update: true - sources: - - llvm-toolchain-trusty-4.0 - packages: - - clang-4.0 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=clang-4.0 && export CXX=clang++-4.0" - - - os: linux - compiler: clang - name: ubuntu 14.04 - clang@5.0 - addons: - apt: - update: true - sources: - - llvm-toolchain-trusty-5.0 - packages: - - clang-5.0 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=clang-5.0 && export CXX=clang++-5.0" - - - os: linux - compiler: clang - name: ubuntu 14.04 - clang@6.0 - addons: - apt: - update: true - sources: - - llvm-toolchain-trusty-6.0 - - ubuntu-toolchain-r-test - packages: - - clang-6.0 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=clang-6.0 && export CXX=clang++-6.0" - - - os: linux - compiler: gcc - name: ubuntu 14.04 - native gcc - addons: - apt: - update: true - env: - - additional_defines="" - - MATRIX_EVAL="" - - - os: linux - compiler: gcc - name: ubuntu 14.04 - native gcc - C++ mode - addons: - apt: - update: true - env: - - additional_defines="-DBUILD_AS_CPP:BOOL=TRUE" - - MATRIX_EVAL="" - - - os: linux - compiler: gcc - name: ubuntu 14.04 - gcc@4.9 - addons: - apt: - update: true - sources: - - ubuntu-toolchain-r-test - packages: - - g++-4.9 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=gcc-4.9 && export CXX=g++-4.9" - - - os: linux - compiler: gcc - name: ubuntu 14.04 - gcc@5.0 - addons: - apt: - update: true - sources: - - ubuntu-toolchain-r-test - packages: - - g++-5 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=gcc-5 && export CXX=g++-5" - - - os: linux - compiler: gcc - name: ubuntu 14.04 - gcc@6.0 - addons: - apt: - update: true - sources: - - ubuntu-toolchain-r-test - packages: - - g++-6 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=gcc-6 && export CXX=g++-6" - - - os: linux - compiler: gcc - name: ubuntu 14.04 - gcc@7.0 - addons: - apt: - update: true - sources: - - ubuntu-toolchain-r-test - packages: - - g++-7 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=gcc-7 && export CXX=g++-7" - - - os: linux - compiler: gcc - name: ubuntu 14.04 - gcc@8.0 - addons: - apt: - update: true - sources: - - ubuntu-toolchain-r-test - packages: - - g++-8 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=gcc-8 && export CXX=g++-8" - - - os: linux - compiler: gcc - name: ubuntu 14.04 - gcc@8.0 - vcpkg - addons: - apt: - update: true - sources: - - ubuntu-toolchain-r-test - packages: - - g++-8 - env: - - additional_defines="" - - USE_VCPKG=true - - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" - - MATRIX_EVAL="export CC=gcc-8 && export CXX=g++-8" - - - os: linux - compiler: clang - name: ubuntu 14.04 - native clang - opencv@2 - addons: - apt: - update: true - packages: - - libopencv-dev - env: - - additional_defines="" - - MATRIX_EVAL="" - - - os: linux - compiler: clang - name: ubuntu 14.04 - clang@3.6 - opencv@2 - addons: - apt: - update: true - packages: - - libopencv-dev - - clang-3.6 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=clang-3.6 && export CXX=clang++-3.6" - - - os: linux - compiler: clang - name: ubuntu 14.04 - clang@3.8 - opencv@2 - addons: - apt: - update: true - sources: - - llvm-toolchain-precise-3.8 - packages: - - libopencv-dev - - clang-3.8 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=clang-3.8 && export CXX=clang++-3.8" - - - os: linux - compiler: clang - name: ubuntu 14.04 - clang@3.9 - opencv@2 - addons: - apt: - update: true - sources: - - llvm-toolchain-trusty-3.9 - packages: - - libopencv-dev - - clang-3.9 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=clang-3.9 && export CXX=clang++-3.9" - - - os: linux - compiler: clang - name: ubuntu 14.04 - clang@4.0 - opencv@2 - addons: - apt: - update: true - sources: - - llvm-toolchain-trusty-4.0 - packages: - - libopencv-dev - - clang-4.0 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=clang-4.0 && export CXX=clang++-4.0" - - - os: linux - compiler: clang - name: ubuntu 14.04 - clang@5.0 - opencv@2 - addons: - apt: - update: true - sources: - - llvm-toolchain-trusty-5.0 - packages: - - libopencv-dev - - clang-5.0 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=clang-5.0 && export CXX=clang++-5.0" - - - os: linux - compiler: clang - name: ubuntu 14.04 - clang@6.0 - opencv@2 - addons: - apt: - update: true - sources: - - llvm-toolchain-trusty-6.0 - - ubuntu-toolchain-r-test - packages: - - libopencv-dev - - clang-6.0 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=clang-6.0 && export CXX=clang++-6.0" - - - os: linux - compiler: gcc - name: ubuntu 14.04 - native gcc - opencv@2 - addons: - apt: - update: true - packages: - - libopencv-dev - env: - - additional_defines="" - - MATRIX_EVAL="" - - - os: linux - compiler: gcc - name: ubuntu 14.04 - gcc@4.9 - opencv@2 - addons: - apt: - update: true - sources: - - ubuntu-toolchain-r-test - packages: - - libopencv-dev - - g++-4.9 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=gcc-4.9 && export CXX=g++-4.9" - - - os: linux - compiler: gcc - name: ubuntu 14.04 - gcc@5.0 - opencv@2 - addons: - apt: - update: true - sources: - - ubuntu-toolchain-r-test - packages: - - libopencv-dev - - g++-5 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=gcc-5 && export CXX=g++-5" - - - os: linux - compiler: gcc - name: ubuntu 14.04 - gcc@6.0 - opencv@2 - addons: - apt: - update: true - sources: - - ubuntu-toolchain-r-test - packages: - - g++-6 - - libopencv-dev - env: - - additional_defines="" - - MATRIX_EVAL="export CC=gcc-6 && export CXX=g++-6" - - - os: linux - compiler: gcc - name: ubuntu 14.04 - gcc@7.0 - opencv@2 - addons: - apt: - update: true - sources: - - ubuntu-toolchain-r-test - packages: - - libopencv-dev - - g++-7 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=gcc-7 && export CXX=g++-7" - - - os: linux - compiler: gcc - name: ubuntu 14.04 - gcc@8.0 - opencv@2 - addons: - apt: - update: true - sources: - - ubuntu-toolchain-r-test - packages: - - libopencv-dev - - g++-8 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=gcc-8 && export CXX=g++-8" - - - os: linux - compiler: clang - name: ubuntu 14.04 - native clang - opencv@3 - addons: - apt: - update: true - sources: - - sourceline: 'ppa:timsc/opencv-3.4' - packages: - - libopencv-dev + dist: bionic + name: ubuntu 18.04 - clang env: - additional_defines="" - MATRIX_EVAL="" - - os: linux - compiler: clang - name: ubuntu 14.04 - clang@3.6 - opencv@3 - addons: - apt: - update: true - sources: - - sourceline: 'ppa:timsc/opencv-3.4' - packages: - - libopencv-dev - - clang-3.6 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=clang-3.6 && export CXX=clang++-3.6" - - - os: linux - compiler: clang - name: ubuntu 14.04 - clang@3.8 - opencv@3 - addons: - apt: - update: true - sources: - - llvm-toolchain-precise-3.8 - - sourceline: 'ppa:timsc/opencv-3.4' - packages: - - libopencv-dev - - clang-3.8 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=clang-3.8 && export CXX=clang++-3.8" - - - os: linux - compiler: clang - name: ubuntu 14.04 - clang@3.9 - opencv@3 - addons: - apt: - update: true - sources: - - llvm-toolchain-trusty-3.9 - - sourceline: 'ppa:timsc/opencv-3.4' - packages: - - libopencv-dev - - clang-3.9 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=clang-3.9 && export CXX=clang++-3.9" - - - os: linux - compiler: clang - name: ubuntu 14.04 - clang@4.0 - opencv@3 - addons: - apt: - update: true - sources: - - llvm-toolchain-trusty-4.0 - - sourceline: 'ppa:timsc/opencv-3.4' - packages: - - libopencv-dev - - clang-4.0 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=clang-4.0 && export CXX=clang++-4.0" - - - os: linux - compiler: clang - name: ubuntu 14.04 - clang@5.0 - opencv@3 - addons: - apt: - update: true - sources: - - llvm-toolchain-trusty-5.0 - - sourceline: 'ppa:timsc/opencv-3.4' - packages: - - libopencv-dev - - clang-5.0 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=clang-5.0 && export CXX=clang++-5.0" - - - os: linux - compiler: clang - name: ubuntu 14.04 - clang@6.0 - opencv@3 - addons: - apt: - update: true - sources: - - llvm-toolchain-trusty-6.0 - - ubuntu-toolchain-r-test - - sourceline: 'ppa:timsc/opencv-3.4' - packages: - - libopencv-dev - - clang-6.0 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=clang-6.0 && export CXX=clang++-6.0" - - os: linux compiler: gcc - name: ubuntu 14.04 - native gcc - opencv@3 - addons: - apt: - update: true - sources: - - sourceline: 'ppa:timsc/opencv-3.4' - packages: - - libopencv-dev + dist: bionic + name: ubuntu 18.04 - gcc env: - additional_defines="" - MATRIX_EVAL="" - os: linux compiler: gcc - name: ubuntu 14.04 - gcc@4.9 - opencv@3 - addons: - apt: - update: true - sources: - - ubuntu-toolchain-r-test - - sourceline: 'ppa:timsc/opencv-3.4' - packages: - - libopencv-dev - - g++-4.9 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=gcc-4.9 && export CXX=g++-4.9" - - - os: linux - compiler: gcc - name: ubuntu 14.04 - gcc@5.0 - opencv@3 - addons: - apt: - update: true - sources: - - ubuntu-toolchain-r-test - - sourceline: 'ppa:timsc/opencv-3.4' - packages: - - libopencv-dev - - g++-5 - env: - - additional_defines="" - - MATRIX_EVAL="export CC=gcc-5 && export CXX=g++-5" - - - os: linux - compiler: gcc - name: ubuntu 14.04 - gcc@6.0 - opencv@3 - addons: - apt: - update: true - sources: - - ubuntu-toolchain-r-test - - sourceline: 'ppa:timsc/opencv-3.4' - packages: - - g++-6 - - libopencv-dev - env: - - additional_defines="" - - MATRIX_EVAL="export CC=gcc-6 && export CXX=g++-6" - - - os: linux - compiler: gcc - name: ubuntu 14.04 - gcc@7.0 - opencv@3 - addons: - apt: - update: true - sources: - - ubuntu-toolchain-r-test - - sourceline: 'ppa:timsc/opencv-3.4' - packages: - - libopencv-dev - - g++-7 + dist: bionic + name: ubuntu 18.04 - g++ env: - - additional_defines="" - - MATRIX_EVAL="export CC=gcc-7 && export CXX=g++-7" + - additional_defines="-DBUILD_AS_CPP:BOOL=TRUE" + - MATRIX_EVAL="" - os: linux compiler: gcc - name: ubuntu 14.04 - gcc@8.0 - opencv@3 - addons: - apt: - update: true - sources: - - ubuntu-toolchain-r-test - - sourceline: 'ppa:timsc/opencv-3.4' - packages: - - libopencv-dev - - g++-8 + dist: bionic + name: ubuntu 18.04 - gcc - vcpkg env: - additional_defines="" + - USE_VCPKG=true + - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" - MATRIX_EVAL="export CC=gcc-8 && export CXX=g++-8" - os: linux compiler: clang - name: ubuntu 14.04 - native clang - CUDA 10 - opencv@2 - addons: - apt: - update: true - packages: - - libopencv-dev - env: - - additional_defines="" - - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - CUDA_PATH=/usr/local/cuda-10.0 - - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - - - os: linux - compiler: clang - name: ubuntu 14.04 - clang@3.6 - CUDA 10 - opencv@2 - addons: - apt: - update: true - packages: - - libopencv-dev - - clang-3.6 - env: - - additional_defines="" - - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - CUDA_PATH=/usr/local/cuda-10.0 - - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="export CC=clang-3.6 && export CXX=clang++-3.6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - - - os: linux - compiler: clang - name: ubuntu 14.04 - clang@3.8 - CUDA 10 - opencv@2 - addons: - apt: - update: true - sources: - - llvm-toolchain-precise-3.8 - packages: - - libopencv-dev - - clang-3.8 - env: - - additional_defines="" - - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - CUDA_PATH=/usr/local/cuda-10.0 - - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="export CC=clang-3.8 && export CXX=clang++-3.8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - - - os: linux - compiler: clang - name: ubuntu 14.04 - clang@3.9 - CUDA 10 - opencv@2 - addons: - apt: - update: true - sources: - - llvm-toolchain-trusty-3.9 - packages: - - libopencv-dev - - clang-3.9 - env: - - additional_defines="" - - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - CUDA_PATH=/usr/local/cuda-10.0 - - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="export CC=clang-3.9 && export CXX=clang++-3.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - - - os: linux - compiler: clang - name: ubuntu 14.04 - clang@4.0 - CUDA 10 - opencv@2 + dist: bionic + name: ubuntu 18.04 - clang - opencv@3 addons: apt: update: true - sources: - - llvm-toolchain-trusty-4.0 packages: - libopencv-dev - - clang-4.0 env: - additional_defines="" - - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - CUDA_PATH=/usr/local/cuda-10.0 - - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="export CC=clang-4.0 && export CXX=clang++-4.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" + - MATRIX_EVAL="" - os: linux - compiler: clang - name: ubuntu 14.04 - clang@5.0 - CUDA 10 - opencv@2 + compiler: gcc + dist: bionic + name: ubuntu 18.04 - gcc - opencv@3 addons: apt: update: true - sources: - - llvm-toolchain-trusty-5.0 packages: - libopencv-dev - - clang-5.0 env: - additional_defines="" - - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - CUDA_PATH=/usr/local/cuda-10.0 - - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="export CC=clang-5.0 && export CXX=clang++-5.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" + - MATRIX_EVAL="" - os: linux compiler: clang - name: ubuntu 14.04 - clang@6.0 - CUDA 10 - opencv@2 - addons: - apt: - update: true - sources: - - llvm-toolchain-trusty-6.0 - - ubuntu-toolchain-r-test - packages: - - libopencv-dev - - clang-6.0 - env: - - additional_defines="" - - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - CUDA_PATH=/usr/local/cuda-10.0 - - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="export CC=clang-6.0 && export CXX=clang++-6.0 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - - - os: linux - compiler: gcc - name: ubuntu 14.04 - native gcc - CUDA 10 - opencv@2 + dist: bionic + name: ubuntu 18.04 - clang - CUDA 10 - opencv@3 addons: apt: update: true @@ -821,109 +162,25 @@ matrix: - os: linux compiler: gcc - name: ubuntu 14.04 - gcc@4.9 - CUDA 10 - opencv@2 - addons: - apt: - update: true - sources: - - ubuntu-toolchain-r-test - packages: - - libopencv-dev - - g++-4.9 - env: - - additional_defines="" - - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - CUDA_PATH=/usr/local/cuda-10.0 - - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="export CC=gcc-4.9 && export CXX=g++-4.9 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - - - os: linux - compiler: gcc - name: ubuntu 14.04 - gcc@5.0 - CUDA 10 - opencv@2 - addons: - apt: - update: true - sources: - - ubuntu-toolchain-r-test - packages: - - libopencv-dev - - g++-5 - env: - - additional_defines="" - - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - CUDA_PATH=/usr/local/cuda-10.0 - - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="export CC=gcc-5 && export CXX=g++-5 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - - - os: linux - compiler: gcc - name: ubuntu 14.04 - gcc@6.0 - CUDA 10 - opencv@2 - addons: - apt: - update: true - sources: - - ubuntu-toolchain-r-test - packages: - - g++-6 - - libopencv-dev - env: - - additional_defines="" - - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - CUDA_PATH=/usr/local/cuda-10.0 - - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="export CC=gcc-6 && export CXX=g++-6 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - - - os: linux - compiler: gcc - name: ubuntu 14.04 - gcc@7.0 - CUDA 10 - opencv@2 - addons: - apt: - update: true - sources: - - ubuntu-toolchain-r-test - packages: - - libopencv-dev - - g++-7 - env: - - additional_defines="" - - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - CUDA_PATH=/usr/local/cuda-10.0 - - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="export CC=gcc-7 && export CXX=g++-7 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - - - os: linux - compiler: gcc - name: ubuntu 14.04 - gcc@8.0 - CUDA 10 - opencv@2 + dist: bionic + name: ubuntu 18.04 - gcc - CUDA 10 - opencv@3 addons: apt: update: true - sources: - - ubuntu-toolchain-r-test packages: - libopencv-dev - - g++-8 env: - additional_defines="" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - CUDA_PATH=/usr/local/cuda-10.0 - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="export CC=gcc-8 && export CXX=g++-8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" + - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" - os: linux compiler: gcc - name: ubuntu 14.04 - gcc@8.0 - CUDA 10 - vcpkg - addons: - apt: - update: true - sources: - - ubuntu-toolchain-r-test - packages: - - g++-8 + dist: bionic + name: ubuntu 18.04 - gcc - CUDA 10 - vcpkg env: - additional_defines="" - USE_VCPKG=true @@ -936,8 +193,8 @@ matrix: # allow_failures: # - name: macOS - vcpkg -# - name: ubuntu 14.04 - gcc@8.0 - vcpkg -# - name: ubuntu 14.04 - gcc@8.0 - CUDA 10 - vcpkg +# - name: ubuntu 18.04 - gcc - vcpkg +# - name: ubuntu 18.04 - gcc - CUDA 10 - vcpkg before_install: - travis_retry eval "${MATRIX_EVAL}" From 769f76ef6292b5aebf6299c4474c5105eb33bf90 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 30 Jul 2019 17:39:18 +0200 Subject: [PATCH 0555/1365] fixes for modern clang on linux --- .travis.yml | 4 ++-- src/gemm.c | 21 ++++++++++++++++++++- 2 files changed, 22 insertions(+), 3 deletions(-) diff --git a/.travis.yml b/.travis.yml index 060f36cae97..63c7b2c4d50 100644 --- a/.travis.yml +++ b/.travis.yml @@ -115,7 +115,7 @@ matrix: - additional_defines="" - USE_VCPKG=true - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" - - MATRIX_EVAL="export CC=gcc-8 && export CXX=g++-8" + - MATRIX_EVAL="" - os: linux compiler: clang @@ -189,7 +189,7 @@ matrix: - CUDA_PATH=/usr/local/cuda-10.0 - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="export CC=gcc-8 && export CXX=g++-8 && wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" + - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" # allow_failures: # - name: macOS - vcpkg diff --git a/src/gemm.c b/src/gemm.c index 0e1bd5ea047..151388284b1 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -550,7 +550,26 @@ static inline float _castu32_f32(uint32_t a) { } static inline float _mm256_extract_float32(__m256 a, const int index) { - return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), index)); + switch(index) { + case 0: + return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 0)); + case 1: + return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 1)); + case 2: + return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 2)); + case 3: + return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 3)); + case 4: + return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 4)); + case 5: + return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 5)); + case 6: + return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 6)); + case 7: + return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 7)); + default: + return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 0)); + } } void asm_cpuid(uint32_t* abcd, uint32_t eax) From 992ee6b4b6a946020526537dc0915248eceba3e8 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 31 Jul 2019 06:51:27 +0200 Subject: [PATCH 0556/1365] everything is publicly linked for libdark --- CMakeLists.txt | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 939501df81f..841884b0bf0 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -305,7 +305,7 @@ target_compile_definitions(uselib PRIVATE -DUSE_CMAKE_LIBS) if(CUDNN_FOUND) target_link_libraries(darknet PRIVATE CuDNN::CuDNN) - target_link_libraries(dark PRIVATE CuDNN::CuDNN) + target_link_libraries(dark PUBLIC CuDNN::CuDNN) target_compile_definitions(darknet PRIVATE -DCUDNN) target_compile_definitions(dark PUBLIC -DCUDNN) if(ENABLE_CUDNN_HALF) @@ -317,7 +317,7 @@ endif() if(OpenCV_FOUND) target_link_libraries(darknet PRIVATE ${OpenCV_LINKED_COMPONENTS}) target_link_libraries(uselib PRIVATE ${OpenCV_LINKED_COMPONENTS}) - target_link_libraries(dark PRIVATE ${OpenCV_LINKED_COMPONENTS}) + target_link_libraries(dark PUBLIC ${OpenCV_LINKED_COMPONENTS}) target_include_directories(dark PUBLIC ${OpenCV_INCLUDE_DIRS}) target_compile_definitions(darknet PRIVATE -DOPENCV) target_compile_definitions(dark PUBLIC -DOPENCV) @@ -332,14 +332,14 @@ endif() if(CMAKE_COMPILER_IS_GNUCC) target_link_libraries(darknet PRIVATE m) - target_link_libraries(dark PRIVATE m) + target_link_libraries(dark PUBLIC m) endif() if(MSVC) target_link_libraries(darknet PRIVATE PThreads_windows::PThreads_windows) target_link_libraries(darknet PRIVATE wsock32 ws2_32) - target_link_libraries(dark PRIVATE PThreads_windows::PThreads_windows) - target_link_libraries(dark PRIVATE wsock32 ws2_32) + target_link_libraries(dark PUBLIC PThreads_windows::PThreads_windows) + target_link_libraries(dark PUBLIC wsock32 ws2_32) target_link_libraries(uselib PRIVATE PThreads_windows::PThreads_windows) target_compile_definitions(darknet PRIVATE -D_CRT_RAND_S -DNOMINMAX -D_USE_MATH_DEFINES) target_compile_definitions(dark PRIVATE -D_CRT_RAND_S -DNOMINMAX -D_USE_MATH_DEFINES) @@ -348,23 +348,23 @@ if(MSVC) endif() target_link_libraries(darknet PRIVATE Threads::Threads) -target_link_libraries(dark PRIVATE Threads::Threads) +target_link_libraries(dark PUBLIC Threads::Threads) target_link_libraries(uselib PRIVATE Threads::Threads) if(ENABLE_ZED_CAMERA) target_link_libraries(darknet PRIVATE ${ZED_LIBRARIES}) - target_link_libraries(dark PRIVATE ${ZED_LIBRARIES}) + target_link_libraries(dark PUBLIC ${ZED_LIBRARIES}) target_link_libraries(uselib PRIVATE ${ZED_LIBRARIES}) target_compile_definitions(darknet PRIVATE -DZED_STEREO) target_compile_definitions(uselib PRIVATE -DZED_STEREO) - target_compile_definitions(dark PRIVATE -DZED_STEREO) + target_compile_definitions(dark PUBLIC -DZED_STEREO) endif() if(ENABLE_CUDA) target_include_directories(darknet PRIVATE ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES}) target_include_directories(dark PUBLIC ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES}) target_link_libraries(darknet PRIVATE curand cublas cuda) - target_link_libraries(dark PRIVATE curand cublas cuda) + target_link_libraries(dark PUBLIC curand cublas cuda) set_target_properties(dark PROPERTIES CUDA_RESOLVE_DEVICE_SYMBOLS ON) target_compile_definitions(darknet PRIVATE -DGPU) target_compile_definitions(dark PUBLIC -DGPU) From 98a2a7a7b76e3e904bb498bec79d1e5b5b245e57 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 31 Jul 2019 10:15:28 +0200 Subject: [PATCH 0557/1365] re-enable full opencv linking as an option --- CMakeLists.txt | 52 +++++++++++++++++++++++++++++--------------------- 1 file changed, 30 insertions(+), 22 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 841884b0bf0..5523fc48f88 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -39,6 +39,7 @@ set(INSTALL_INCLUDE_DIR "include/darknet" CACHE PATH "Path where head set(INSTALL_CMAKE_DIR "share/darknet" CACHE PATH "Path where cmake configs will be installed") set(ENABLE_OPENCV "TRUE" CACHE BOOL "Enable OpenCV integration") +set(SELECT_OPENCV_MODULES "TRUE" CACHE BOOL "Use only few selected OpenCV modules to circumvent some Ninja+CMake bugs on Windows") set(ENABLE_CUDA "TRUE" CACHE BOOL "Enable CUDA support") set(ENABLE_CUDNN "FALSE" CACHE BOOL "Enable CUDNN") set(ENABLE_CUDNN_HALF "FALSE" CACHE BOOL "Enable CUDNN Half precision") @@ -84,27 +85,34 @@ endif() if(ENABLE_OPENCV) find_package(OpenCV) if(OpenCV_FOUND) - if(TARGET opencv_world) - list(APPEND OpenCV_LINKED_COMPONENTS "opencv_world") - else() - if(TARGET opencv_video) - list(APPEND OpenCV_LINKED_COMPONENTS "opencv_video") - endif() - if(TARGET opencv_videoio) - list(APPEND OpenCV_LINKED_COMPONENTS "opencv_videoio") - endif() - if(TARGET opencv_highgui) - list(APPEND OpenCV_LINKED_COMPONENTS "opencv_highgui") - endif() - if(TARGET opencv_imgproc) - list(APPEND OpenCV_LINKED_COMPONENTS "opencv_imgproc") - endif() - if(TARGET opencv_imgcodecs) - list(APPEND OpenCV_LINKED_COMPONENTS "opencv_imgcodecs") - endif() - if(TARGET opencv_core) - list(APPEND OpenCV_LINKED_COMPONENTS "opencv_core") + if(SELECT_OPENCV_MODULES) + if(TARGET opencv_world) + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_world") + else() + if(TARGET opencv_core) + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_core") + endif() + if(TARGET opencv_highgui) + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_highgui") + endif() + if(TARGET opencv_imgproc) + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_imgproc") + endif() + if(TARGET opencv_video) + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_video") + endif() + if(TARGET opencv_videoio) + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_videoio") + endif() + if(TARGET opencv_imgcodecs) + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_imgcodecs") + endif() + if(TARGET opencv_text) + list(APPEND OpenCV_LINKED_COMPONENTS "opencv_text") + endif() endif() + else() + list(APPEND OpenCV_LINKED_COMPONENTS ${OpenCV_LIBS}) endif() endif() endif() @@ -305,7 +313,7 @@ target_compile_definitions(uselib PRIVATE -DUSE_CMAKE_LIBS) if(CUDNN_FOUND) target_link_libraries(darknet PRIVATE CuDNN::CuDNN) - target_link_libraries(dark PUBLIC CuDNN::CuDNN) + target_link_libraries(dark PRIVATE CuDNN::CuDNN) target_compile_definitions(darknet PRIVATE -DCUDNN) target_compile_definitions(dark PUBLIC -DCUDNN) if(ENABLE_CUDNN_HALF) @@ -364,7 +372,7 @@ if(ENABLE_CUDA) target_include_directories(darknet PRIVATE ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES}) target_include_directories(dark PUBLIC ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES}) target_link_libraries(darknet PRIVATE curand cublas cuda) - target_link_libraries(dark PUBLIC curand cublas cuda) + target_link_libraries(dark PRIVATE curand cublas cuda) set_target_properties(dark PROPERTIES CUDA_RESOLVE_DEVICE_SYMBOLS ON) target_compile_definitions(darknet PRIVATE -DGPU) target_compile_definitions(dark PUBLIC -DGPU) From 53699c189bbc389b5e5926154314273596a69ede Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 31 Jul 2019 13:30:20 +0200 Subject: [PATCH 0558/1365] use selected OpenCV modules only on Windows --- CMakeLists.txt | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 5523fc48f88..acdb8f67aa4 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -6,7 +6,12 @@ set(Darknet_PATCH_VERSION 5) set(Darknet_VERSION ${Darknet_MAJOR_VERSION}.${Darknet_MINOR_VERSION}.${Darknet_PATCH_VERSION}) set(CMAKE_VERBOSE_MAKEFILE "FALSE" CACHE BOOL "Create verbose makefile") -set(BUILD_SHARED_LIBS "TRUE" CACHE BOOL "Create dark as a shared library") +set(BUILD_SHARED_LIBS "FALSE" CACHE BOOL "Create dark as a shared library") +if (WIN32) + set(SELECT_OPENCV_MODULES "TRUE" CACHE BOOL "Use only few selected OpenCV modules to circumvent some Ninja+CMake bugs on Windows") +else() + set(SELECT_OPENCV_MODULES "FALSE" CACHE BOOL "Use only few selected OpenCV modules to circumvent some Ninja+CMake bugs on Windows") +endif() set(BUILD_AS_CPP "FALSE" CACHE BOOL "Build Darknet using C++ compiler also for C files") if(DEFINED ENV{VCPKG_ROOT} AND NOT DEFINED CMAKE_TOOLCHAIN_FILE) @@ -39,7 +44,6 @@ set(INSTALL_INCLUDE_DIR "include/darknet" CACHE PATH "Path where head set(INSTALL_CMAKE_DIR "share/darknet" CACHE PATH "Path where cmake configs will be installed") set(ENABLE_OPENCV "TRUE" CACHE BOOL "Enable OpenCV integration") -set(SELECT_OPENCV_MODULES "TRUE" CACHE BOOL "Use only few selected OpenCV modules to circumvent some Ninja+CMake bugs on Windows") set(ENABLE_CUDA "TRUE" CACHE BOOL "Enable CUDA support") set(ENABLE_CUDNN "FALSE" CACHE BOOL "Enable CUDNN") set(ENABLE_CUDNN_HALF "FALSE" CACHE BOOL "Enable CUDNN Half precision") From 9baf9861d73d8b0994dfe9c1afd726b60be1f5f6 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 1 Aug 2019 07:47:38 +0200 Subject: [PATCH 0559/1365] restore shared lib as default --- .gitignore | 3 +++ CMakeLists.txt | 2 +- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 79dee2345c9..2a1cf1b2143 100644 --- a/.gitignore +++ b/.gitignore @@ -52,3 +52,6 @@ src/version.h lib/ share/ include/darknet/ +uselib +uselib_track +darknet diff --git a/CMakeLists.txt b/CMakeLists.txt index acdb8f67aa4..d5d329d5301 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -6,7 +6,7 @@ set(Darknet_PATCH_VERSION 5) set(Darknet_VERSION ${Darknet_MAJOR_VERSION}.${Darknet_MINOR_VERSION}.${Darknet_PATCH_VERSION}) set(CMAKE_VERBOSE_MAKEFILE "FALSE" CACHE BOOL "Create verbose makefile") -set(BUILD_SHARED_LIBS "FALSE" CACHE BOOL "Create dark as a shared library") +set(BUILD_SHARED_LIBS "TRUE" CACHE BOOL "Create dark as a shared library") if (WIN32) set(SELECT_OPENCV_MODULES "TRUE" CACHE BOOL "Use only few selected OpenCV modules to circumvent some Ninja+CMake bugs on Windows") else() From 4d135b5992504115a5c7e1cea98da5d0ea0fccd3 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 1 Aug 2019 11:22:30 +0200 Subject: [PATCH 0560/1365] [pthread cmake module] fix shared target handling --- cmake/Modules/FindPThreads_windows.cmake | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/cmake/Modules/FindPThreads_windows.cmake b/cmake/Modules/FindPThreads_windows.cmake index e1c4cfd25f3..02d8a72ded7 100644 --- a/cmake/Modules/FindPThreads_windows.cmake +++ b/cmake/Modules/FindPThreads_windows.cmake @@ -39,6 +39,12 @@ endif() find_package_handle_standard_args(PThreads_windows DEFAULT_MSG PThreads_windows_LIBRARY PThreads_windows_INCLUDE_DIR) mark_as_advanced(PThreads_windows_INCLUDE_DIR PThreads_windows_LIBRARY) +set(PThreads_windows_DLL_DIR ${PThreads_windows_INCLUDE_DIR}) +list(TRANSFORM PThreads_windows_DLL_DIR APPEND "/../bin") +message(STATUS "PThreads_windows_DLL_DIR: ${PThreads_windows_DLL_DIR}") + +find_file(PThreads_windows_LIBRARY_RELEASE_DLL NAMES pthreadVC2.dll PATHS ${PThreads_windows_DLL_DIR}) +find_file(PThreads_windows_LIBRARY_DEBUG_DLL NAMES pthreadVC2d.dll PATHS ${PThreads_windows_DLL_DIR}) # Register imported libraries: # 1. If we can find a Windows .dll file (or if we can find both Debug and @@ -46,12 +52,6 @@ mark_as_advanced(PThreads_windows_INCLUDE_DIR PThreads_windows_LIBRARY) # 2. However, for most systems, we will only register the import location and # include directory. -# Look for dlls, for Release and Debug libraries. -if(WIN32) - string( REPLACE ".lib" ".dll" PThreads_windows_LIBRARY_RELEASE_DLL "${PThreads_windows_LIBRARY_RELEASE}" ) - string( REPLACE ".lib" ".dll" PThreads_windows_LIBRARY_DEBUG_DLL "${PThreads_windows_LIBRARY_DEBUG}" ) -endif() - if( PThreads_windows_FOUND AND NOT TARGET PThreads_windows::PThreads_windows ) if( EXISTS "${PThreads_windows_LIBRARY_RELEASE_DLL}" ) add_library( PThreads_windows::PThreads_windows SHARED IMPORTED ) From a57a16998dce6ee576779569a49328bd950abe19 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 1 Aug 2019 13:19:19 +0200 Subject: [PATCH 0561/1365] move bool from set cache to option --- CMakeLists.txt | 50 ++++++++++++++++++++++++++++---------------------- 1 file changed, 28 insertions(+), 22 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index d5d329d5301..1d0567c6d4b 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -5,16 +5,25 @@ set(Darknet_MINOR_VERSION 2) set(Darknet_PATCH_VERSION 5) set(Darknet_VERSION ${Darknet_MAJOR_VERSION}.${Darknet_MINOR_VERSION}.${Darknet_PATCH_VERSION}) -set(CMAKE_VERBOSE_MAKEFILE "FALSE" CACHE BOOL "Create verbose makefile") -set(BUILD_SHARED_LIBS "TRUE" CACHE BOOL "Create dark as a shared library") +option(CMAKE_VERBOSE_MAKEFILE "Create verbose makefile" OFF) +option(CUDA_VERBOSE_BUILD "Create verbose CUDA build" OFF) +option(BUILD_SHARED_LIBS "Create dark as a shared library" ON) +option(BUILD_AS_CPP "Build Darknet using C++ compiler also for C files" OFF) +option(BUILD_USELIB_TRACK "Build uselib_track" ON) +option(MANUALLY_EXPORT_TRACK_OPTFLOW "Manually export the TRACK_OPTFLOW=1 define" OFF) +option(ENABLE_OPENCV "Enable OpenCV integration" ON) +option(ENABLE_CUDA "Enable CUDA support" ON) +option(ENABLE_CUDNN "Enable CUDNN" ON) +option(ENABLE_CUDNN_HALF "Enable CUDNN Half precision" ON) +option(ENABLE_ZED_CAMERA "Enable ZED Camera support" ON) +option(ENABLE_VCPKG_INTEGRATION "Enable VCPKG integration" ON) if (WIN32) - set(SELECT_OPENCV_MODULES "TRUE" CACHE BOOL "Use only few selected OpenCV modules to circumvent some Ninja+CMake bugs on Windows") + option(SELECT_OPENCV_MODULES "Use only few selected OpenCV modules to circumvent some Ninja+CMake bugs on Windows" ON) else() - set(SELECT_OPENCV_MODULES "FALSE" CACHE BOOL "Use only few selected OpenCV modules to circumvent some Ninja+CMake bugs on Windows") + option(SELECT_OPENCV_MODULES "Use only few selected OpenCV modules to circumvent some Ninja+CMake bugs on Windows" OFF) endif() -set(BUILD_AS_CPP "FALSE" CACHE BOOL "Build Darknet using C++ compiler also for C files") -if(DEFINED ENV{VCPKG_ROOT} AND NOT DEFINED CMAKE_TOOLCHAIN_FILE) +if(ENABLE_VCPKG_INTEGRATION AND DEFINED ENV{VCPKG_ROOT} AND NOT DEFINED CMAKE_TOOLCHAIN_FILE) set(CMAKE_TOOLCHAIN_FILE "$ENV{VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake" CACHE STRING "") message(STATUS "VCPKG found: $ENV{VCPKG_ROOT}") message(STATUS "Using VCPKG integration") @@ -43,11 +52,6 @@ set(INSTALL_LIB_DIR "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Path where lib set(INSTALL_INCLUDE_DIR "include/darknet" CACHE PATH "Path where headers will be installed") set(INSTALL_CMAKE_DIR "share/darknet" CACHE PATH "Path where cmake configs will be installed") -set(ENABLE_OPENCV "TRUE" CACHE BOOL "Enable OpenCV integration") -set(ENABLE_CUDA "TRUE" CACHE BOOL "Enable CUDA support") -set(ENABLE_CUDNN "FALSE" CACHE BOOL "Enable CUDNN") -set(ENABLE_CUDNN_HALF "FALSE" CACHE BOOL "Enable CUDNN Half precision") - if(${CMAKE_VERSION} VERSION_LESS "3.9.0") message(WARNING "To build with CUDA support you need CMake 3.9.0+") set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA support" FORCE) @@ -65,9 +69,9 @@ else() else() cuda_select_nvcc_arch_flags(CUDA_ARCH_FLAGS ${CUDA_ARCHITECTURES}) message(STATUS "Building with CUDA flags: " "${CUDA_ARCH_FLAGS}") - if ("arch=compute_75,code=sm_75" IN_LIST CUDA_ARCH_FLAGS) - set(ENABLE_CUDNN_HALF "TRUE" CACHE BOOL "Enable CUDNN Half precision" FORCE) - message(STATUS "Your setup supports half precision!") + if (NOT "arch=compute_75,code=sm_75" IN_LIST CUDA_ARCH_FLAGS) + set(ENABLE_CUDNN_HALF "FALSE" CACHE BOOL "Enable CUDNN Half precision" FORCE) + message(STATUS "Your setup does not supports half precision (it requires CC >= 7.5)") endif() endif() else() @@ -156,10 +160,9 @@ if(CMAKE_COMPILER_IS_GNUCC OR "${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang") set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a") endif() -set(SKIP_USELIB_TRACK "FALSE" CACHE BOOL "Skip building uselib_track" FORCE) if(OpenCV_FOUND) if(ENABLE_CUDA AND NOT OpenCV_CUDA_VERSION) - set(SKIP_USELIB_TRACK "TRUE" CACHE BOOL "Skip building uselib_track" FORCE) + set(BUILD_USELIB_TRACK "FALSE" CACHE BOOL "Skip building uselib_track" FORCE) message(STATUS " -> darknet is fine for now, but uselib_track has been disabled!") message(STATUS " -> Please rebuild OpenCV from sources with CUDA support to enable it") elseif(ENABLE_CUDA AND OpenCV_CUDA_VERSION) @@ -174,8 +177,8 @@ endif() if(ENABLE_CUDA) find_package(CUDNN) - if(CUDNN_FOUND) - set(ENABLE_CUDNN "TRUE" CACHE BOOL "Enable CUDNN" FORCE) + if(NOT CUDNN_FOUND) + set(ENABLE_CUDNN "FALSE" CACHE BOOL "Enable CUDNN" FORCE) endif() endif() @@ -206,7 +209,6 @@ if(ENABLE_CUDA) message(STATUS "CMAKE_CUDA_FLAGS: ${CMAKE_CUDA_FLAGS}") endif() -set(ENABLE_ZED_CAMERA "TRUE" CACHE BOOL "Enable ZED Camera support") if(ENABLE_CUDA) if(ENABLE_ZED_CAMERA) find_package(ZED 2 QUIET) @@ -292,7 +294,7 @@ if(BUILD_AS_CPP) set_target_properties(dark PROPERTIES LINKER_LANGUAGE CXX) endif() -if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRACK) +if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND BUILD_USELIB_TRACK) add_executable(uselib_track ${CMAKE_CURRENT_LIST_DIR}/src/yolo_console_dll.cpp) endif() @@ -315,6 +317,10 @@ target_compile_definitions(darknet PRIVATE -DUSE_CMAKE_LIBS) target_compile_definitions(dark PRIVATE -DUSE_CMAKE_LIBS) target_compile_definitions(uselib PRIVATE -DUSE_CMAKE_LIBS) +if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND BUILD_USELIB_TRACK AND NOT MANUALLY_EXPORT_TRACK_OPTFLOW) + target_compile_definitions(dark PUBLIC TRACK_OPTFLOW=1) +endif() + if(CUDNN_FOUND) target_link_libraries(darknet PRIVATE CuDNN::CuDNN) target_link_libraries(dark PRIVATE CuDNN::CuDNN) @@ -388,7 +394,7 @@ if(USE_INTEGRATED_LIBS) endif() target_link_libraries(uselib PRIVATE dark) -if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRACK) +if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND BUILD_USELIB_TRACK) target_link_libraries(uselib_track PRIVATE dark) target_compile_definitions(uselib_track PRIVATE TRACK_OPTFLOW=1) target_compile_definitions(uselib_track PRIVATE -DUSE_CMAKE_LIBS) @@ -423,7 +429,7 @@ install(TARGETS dark EXPORT DarknetTargets install(TARGETS uselib darknet DESTINATION "${INSTALL_BIN_DIR}" ) -if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND NOT SKIP_USELIB_TRACK) +if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND BUILD_USELIB_TRACK) install(TARGETS uselib_track DESTINATION "${INSTALL_BIN_DIR}" ) From 73baf8531e403da3ce46e42982ebe44eef686b57 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 1 Aug 2019 13:23:04 +0200 Subject: [PATCH 0562/1365] bump tweak version --- CMakeLists.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 1d0567c6d4b..1a613fcd5e6 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -3,7 +3,8 @@ cmake_minimum_required(VERSION 3.8) set(Darknet_MAJOR_VERSION 0) set(Darknet_MINOR_VERSION 2) set(Darknet_PATCH_VERSION 5) -set(Darknet_VERSION ${Darknet_MAJOR_VERSION}.${Darknet_MINOR_VERSION}.${Darknet_PATCH_VERSION}) +set(Darknet_TWEAK_VERSION 1) +set(Darknet_VERSION ${Darknet_MAJOR_VERSION}.${Darknet_MINOR_VERSION}.${Darknet_PATCH_VERSION}.${Darknet_TWEAK_VERSION}) option(CMAKE_VERBOSE_MAKEFILE "Create verbose makefile" OFF) option(CUDA_VERBOSE_BUILD "Create verbose CUDA build" OFF) From 09c996f8fb74ade00e50ef03fc6ac06e9235c19c Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 1 Aug 2019 13:39:45 +0200 Subject: [PATCH 0563/1365] upgrade FindCUDNN module to better deal with dlls and targets --- cmake/Modules/FindCUDNN.cmake | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/cmake/Modules/FindCUDNN.cmake b/cmake/Modules/FindCUDNN.cmake index 3a6bc0d6895..3aa2aa81c85 100644 --- a/cmake/Modules/FindCUDNN.cmake +++ b/cmake/Modules/FindCUDNN.cmake @@ -70,10 +70,11 @@ mark_as_advanced(CUDNN_LIBRARY CUDNN_INCLUDE_DIR) # Release libraries), we will set appropriate target properties for these. # 2. However, for most systems, we will only register the import location and # include directory. - -# Look for dlls, for Release and Debug libraries. if(WIN32) - string( REPLACE ".lib" ".dll" CUDNN_LIBRARY_DLL "${CUDNN_LIBRARY}" ) + set(CUDNN_DLL_DIR ${CUDNN_INCLUDE_DIR}) + list(TRANSFORM CUDNN_DLL_DIR APPEND "/../bin") + message(STATUS "CUDNN_DLL_DIR: ${CUDNN_DLL_DIR}") + find_file(CUDNN_LIBRARY_DLL NAMES cudnn64_7.dll PATHS ${CUDNN_DLL_DIR}) endif() if( CUDNN_FOUND AND NOT TARGET CuDNN::CuDNN ) From 7377f661f6d00fe085b34d74e8364a48de3938e8 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 2 Aug 2019 09:01:58 +0200 Subject: [PATCH 0564/1365] fix label option --- CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 1a613fcd5e6..cbaf616d018 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -163,7 +163,7 @@ endif() if(OpenCV_FOUND) if(ENABLE_CUDA AND NOT OpenCV_CUDA_VERSION) - set(BUILD_USELIB_TRACK "FALSE" CACHE BOOL "Skip building uselib_track" FORCE) + set(BUILD_USELIB_TRACK "FALSE" CACHE BOOL "Build uselib_track" FORCE) message(STATUS " -> darknet is fine for now, but uselib_track has been disabled!") message(STATUS " -> Please rebuild OpenCV from sources with CUDA support to enable it") elseif(ENABLE_CUDA AND OpenCV_CUDA_VERSION) From 3869d7ec40988cb0d7bf9b4790221402438fa3f0 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 2 Aug 2019 10:12:36 +0200 Subject: [PATCH 0565/1365] restrict the opencv module selection to the only problematic configuration --- CMakeLists.txt | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index cbaf616d018..a624b592679 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -18,11 +18,6 @@ option(ENABLE_CUDNN "Enable CUDNN" ON) option(ENABLE_CUDNN_HALF "Enable CUDNN Half precision" ON) option(ENABLE_ZED_CAMERA "Enable ZED Camera support" ON) option(ENABLE_VCPKG_INTEGRATION "Enable VCPKG integration" ON) -if (WIN32) - option(SELECT_OPENCV_MODULES "Use only few selected OpenCV modules to circumvent some Ninja+CMake bugs on Windows" ON) -else() - option(SELECT_OPENCV_MODULES "Use only few selected OpenCV modules to circumvent some Ninja+CMake bugs on Windows" OFF) -endif() if(ENABLE_VCPKG_INTEGRATION AND DEFINED ENV{VCPKG_ROOT} AND NOT DEFINED CMAKE_TOOLCHAIN_FILE) set(CMAKE_TOOLCHAIN_FILE "$ENV{VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake" CACHE STRING "") @@ -80,6 +75,12 @@ else() endif() endif() +if (WIN32 AND ENABLE_CUDA AND CMAKE_MAKE_PROGRAM MATCHES "ninja") + option(SELECT_OPENCV_MODULES "Use only few selected OpenCV modules to circumvent 8192 char limit when using Ninja on Windows" ON) +else() + option(SELECT_OPENCV_MODULES "Use only few selected OpenCV modules to circumvent 8192 char limit when using Ninja on Windows" OFF) +endif() + if(USE_INTEGRATED_LIBS) set(PThreads_windows_DIR ${CMAKE_CURRENT_LIST_DIR}/3rdparty/pthreads CACHE PATH "Path where pthreads for windows can be located") endif() From 44d2fce4dd0a3d7c44a6c62b91c7cc4a501b86ef Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 2 Aug 2019 14:59:23 +0200 Subject: [PATCH 0566/1365] remove unreadable char --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 5ee3d804086..a25d585f489 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# Yolo-v3 and Yolo-v2 for Windows and Linux +# Yolo-v3 and Yolo-v2 for Windows and Linux ### (neural network for object detection) - Tensor Cores can be used on [Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) and [Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows-using-vcpkg) More details: http://pjreddie.com/darknet/yolo/ From 2fa9792c56c99f1e042be1e5594c8e5307af7b4e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 2 Aug 2019 19:29:05 +0300 Subject: [PATCH 0567/1365] Added [sam] layer --- Makefile | 2 +- build/darknet/darknet.vcxproj | 2 + build/darknet/darknet_no_gpu.vcxproj | 2 + build/darknet/yolo_cpp_dll.vcxproj | 2 + build/darknet/yolo_cpp_dll_no_gpu.vcxproj | 2 + include/darknet.h | 1 + src/network.c | 8 +- src/parser.c | 24 +++++ src/sam_layer.c | 118 ++++++++++++++++++++++ src/sam_layer.h | 23 +++++ 10 files changed, 181 insertions(+), 3 deletions(-) create mode 100644 src/sam_layer.c create mode 100644 src/sam_layer.h diff --git a/Makefile b/Makefile index dc880be9fc1..8172301a15a 100644 --- a/Makefile +++ b/Makefile @@ -118,7 +118,7 @@ LDFLAGS+= -L/usr/local/zed/lib -lsl_core -lsl_input -lsl_zed #-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0 endif -OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o upsample_layer.o lstm_layer.o conv_lstm_layer.o scale_channels_layer.o +OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o upsample_layer.o lstm_layer.o conv_lstm_layer.o scale_channels_layer.o sam.o ifeq ($(GPU), 1) LDFLAGS+= -lstdc++ OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj index 019e02c617d..d7dc91590fa 100644 --- a/build/darknet/darknet.vcxproj +++ b/build/darknet/darknet.vcxproj @@ -226,6 +226,7 @@ + @@ -285,6 +286,7 @@ + diff --git a/build/darknet/darknet_no_gpu.vcxproj b/build/darknet/darknet_no_gpu.vcxproj index ee55f63e3a7..dcfce05d057 100644 --- a/build/darknet/darknet_no_gpu.vcxproj +++ b/build/darknet/darknet_no_gpu.vcxproj @@ -230,6 +230,7 @@ + @@ -289,6 +290,7 @@ + diff --git a/build/darknet/yolo_cpp_dll.vcxproj b/build/darknet/yolo_cpp_dll.vcxproj index b4d197657cd..d3f60a0260f 100644 --- a/build/darknet/yolo_cpp_dll.vcxproj +++ b/build/darknet/yolo_cpp_dll.vcxproj @@ -228,6 +228,7 @@ + @@ -289,6 +290,7 @@ + diff --git a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj index cbb0f84fbeb..f719d3f28b7 100644 --- a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj +++ b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj @@ -214,6 +214,7 @@ + @@ -275,6 +276,7 @@ + diff --git a/include/darknet.h b/include/darknet.h index 232d0732ff1..72466ab174c 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -137,6 +137,7 @@ typedef enum { LOCAL, SHORTCUT, SCALE_CHANNELS, + SAM, ACTIVE, RNN, GRU, diff --git a/src/network.c b/src/network.c index faf3db983e0..6e64a8ceb56 100644 --- a/src/network.c +++ b/src/network.c @@ -212,6 +212,10 @@ char *get_layer_string(LAYER_TYPE a) return "route"; case SHORTCUT: return "shortcut"; + case SCALE_CHANNELS: + return "scale_channels"; + case SAM: + return "sam"; case NORMALIZATION: return "normalization"; case BATCHNORM: @@ -524,8 +528,8 @@ int resize_network(network *net, int w, int h) resize_route_layer(&l, net); }else if (l.type == SHORTCUT) { resize_shortcut_layer(&l, w, h); - }else if (l.type == SCALE_CHANNELS) { - resize_scale_channels_layer(&l, w, h); + //}else if (l.type == SCALE_CHANNELS) { + // resize_scale_channels_layer(&l, w, h); }else if (l.type == UPSAMPLE) { resize_upsample_layer(&l, w, h); }else if(l.type == REORG){ diff --git a/src/parser.c b/src/parser.c index e1afd766b2c..5145ba58c9e 100644 --- a/src/parser.c +++ b/src/parser.c @@ -32,6 +32,7 @@ #include "route_layer.h" #include "shortcut_layer.h" #include "scale_channels_layer.h" +#include "sam_layer.h" #include "softmax_layer.h" #include "utils.h" #include "upsample_layer.h" @@ -50,6 +51,7 @@ LAYER_TYPE string_to_layer_type(char * type) if (strcmp(type, "[shortcut]")==0) return SHORTCUT; if (strcmp(type, "[scale_channels]") == 0) return SCALE_CHANNELS; + if (strcmp(type, "[sam]") == 0) return SAM; if (strcmp(type, "[crop]")==0) return CROP; if (strcmp(type, "[cost]")==0) return COST; if (strcmp(type, "[detection]")==0) return DETECTION; @@ -622,6 +624,23 @@ layer parse_scale_channels(list *options, size_params params, network net) return s; } +layer parse_sam(list *options, size_params params, network net) +{ + char *l = option_find(options, "from"); + int index = atoi(l); + if (index < 0) index = params.index + index; + + int batch = params.batch; + layer from = net.layers[index]; + + layer s = make_scale_channels_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c); + + char *activation_s = option_find_str_quiet(options, "activation", "linear"); + ACTIVATION activation = get_activation(activation_s); + s.activation = activation; + return s; +} + layer parse_activation(list *options, size_params params) { @@ -923,6 +942,11 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) l = parse_scale_channels(options, params, net); net.layers[count - 1].use_bin_output = 0; net.layers[l.index].use_bin_output = 0; + } + else if (lt == SAM) { + l = parse_sam(options, params, net); + net.layers[count - 1].use_bin_output = 0; + net.layers[l.index].use_bin_output = 0; }else if(lt == DROPOUT){ l = parse_dropout(options, params); l.output = net.layers[count-1].output; diff --git a/src/sam_layer.c b/src/sam_layer.c new file mode 100644 index 00000000000..7e922ce1389 --- /dev/null +++ b/src/sam_layer.c @@ -0,0 +1,118 @@ +#include "sam_layer.h" +#include "dark_cuda.h" +#include "blas.h" +#include +#include + +layer make_sam_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2) +{ + fprintf(stderr,"scale Layer: %d\n", index); + layer l = { (LAYER_TYPE)0 }; + l.type = SAM; + l.batch = batch; + l.w = w; + l.h = h; + l.c = c; + + l.out_w = w2; + l.out_h = h2; + l.out_c = c2; + assert(l.out_c == l.c); + assert(l.w == l.out_w & l.h == l.out_h); + + l.outputs = l.out_w*l.out_h*l.out_c; + l.inputs = l.outputs; + l.index = index; + + l.delta = (float*)calloc(l.outputs * batch, sizeof(float)); + l.output = (float*)calloc(l.outputs * batch, sizeof(float)); + + l.forward = forward_sam_layer; + l.backward = backward_sam_layer; +#ifdef GPU + l.forward_gpu = forward_sam_layer_gpu; + l.backward_gpu = backward_sam_layer_gpu; + + l.delta_gpu = cuda_make_array(l.delta, l.outputs*batch); + l.output_gpu = cuda_make_array(l.output, l.outputs*batch); +#endif + return l; +} + +void resize_sam_layer(layer *l, int w, int h) +{ + l->out_w = w; + l->out_h = h; + l->outputs = l->out_w*l->out_h*l->out_c; + l->inputs = l->outputs; + l->delta = (float*)realloc(l->delta, l->outputs * l->batch * sizeof(float)); + l->output = (float*)realloc(l->output, l->outputs * l->batch * sizeof(float)); + +#ifdef GPU + cuda_free(l->output_gpu); + cuda_free(l->delta_gpu); + l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch); + l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch); +#endif + +} + +void forward_sam_layer(const layer l, network_state state) +{ + int size = l.batch * l.out_c * l.out_w * l.out_h; + int channel_size = 1; + float *from_output = state.net.layers[l.index].output; + + int i; + #pragma omp parallel for + for (i = 0; i < size; ++i) { + l.output[i] = state.input[i] * from_output[i]; + } + + activate_array(l.output, l.outputs*l.batch, l.activation); +} + +void backward_sam_layer(const layer l, network_state state) +{ + gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); + //axpy_cpu(l.outputs*l.batch, 1, l.delta, 1, state.delta, 1); + //scale_cpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta, l.w, l.h, l.c, state.net.layers[l.index].delta); + + int size = l.batch * l.out_c * l.out_w * l.out_h; + int channel_size = 1; + float *from_output = state.net.layers[l.index].output; + float *from_delta = state.net.layers[l.index].delta; + + int i; + #pragma omp parallel for + for (i = 0; i < size; ++i) { + state.delta[i] += l.delta[i] * from_output[i]; // l.delta * from (should be divided by channel_size?) + + from_delta[i] = state.input[i] * l.delta[i]; // input * l.delta + } +} + +#ifdef GPU +void forward_sam_layer_gpu(const layer l, network_state state) +{ + int size = l.batch * l.out_c * l.out_w * l.out_h; + int channel_size = 1; + + scale_channels_gpu(state.net.layers[l.index].output_gpu, size, channel_size, state.input, l.output_gpu); + + activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); +} + +void backward_sam_layer_gpu(const layer l, network_state state) +{ + gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); + + int size = l.batch * l.out_c * l.out_w * l.out_h; + int channel_size = 1; + float *from_output = state.net.layers[l.index].output_gpu; + float *from_delta = state.net.layers[l.index].delta_gpu; + + + backward_scale_channels_gpu(l.delta_gpu, size, channel_size, state.input, from_delta, from_output, state.delta); +} +#endif diff --git a/src/sam_layer.h b/src/sam_layer.h new file mode 100644 index 00000000000..0fa66fa26fd --- /dev/null +++ b/src/sam_layer.h @@ -0,0 +1,23 @@ +#ifndef SAM_CHANNELS_LAYER_H +#define SAM_CHANNELS_LAYER_H + +#include "layer.h" +#include "network.h" + +#ifdef __cplusplus +extern "C" { +#endif +layer make_sam_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2); +void forward_sam_layer(const layer l, network_state state); +void backward_sam_layer(const layer l, network_state state); +void resize_sam_layer(layer *l, int w, int h); + +#ifdef GPU +void forward_sam_layer_gpu(const layer l, network_state state); +void backward_sam_layer_gpu(const layer l, network_state state); +#endif + +#ifdef __cplusplus +} +#endif +#endif // SAM_CHANNELS_LAYER_H From 644621791d455015fcdead8cee7f582ff68f3e9f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 2 Aug 2019 19:36:38 +0300 Subject: [PATCH 0568/1365] Compile fix --- Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 8172301a15a..25a85f81855 100644 --- a/Makefile +++ b/Makefile @@ -118,7 +118,7 @@ LDFLAGS+= -L/usr/local/zed/lib -lsl_core -lsl_input -lsl_zed #-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0 endif -OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o upsample_layer.o lstm_layer.o conv_lstm_layer.o scale_channels_layer.o sam.o +OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o upsample_layer.o lstm_layer.o conv_lstm_layer.o scale_channels_layer.o sam_layer.o ifeq ($(GPU), 1) LDFLAGS+= -lstdc++ OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o From 4bf9b223a233b6a8ec9d64db9fcf64bf441f0172 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 5 Aug 2019 16:06:57 +0300 Subject: [PATCH 0569/1365] Fix sam layer --- src/blas.h | 7 +++++++ src/blas_kernels.cu | 45 ++++++++++++++++++++++++++++++++++++++++++++ src/parser.c | 2 +- src/sam_layer.c | 4 ++-- src/shortcut_layer.c | 2 +- 5 files changed, 56 insertions(+), 4 deletions(-) diff --git a/src/blas.h b/src/blas.h index 2b46eba31f5..1225235c3e5 100644 --- a/src/blas.h +++ b/src/blas.h @@ -122,6 +122,13 @@ void backward_scale_channels_gpu(float *in_w_h_c_delta, int size, int channel_si float *in_scales_c, float *out_from_delta, float *in_from_output, float *out_state_delta); + +void backward_sam_gpu(float *in_w_h_c_delta, int size, int channel_size, + float *in_scales_c, float *out_from_delta, + float *in_from_output, float *out_state_delta); + +void sam_gpu(float *in_w_h_c, int size, int channel_size, float *scales_c, float *out); + #endif #ifdef __cplusplus } diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 641c584c6d8..fc6dc29f9e4 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -1173,3 +1173,48 @@ extern "C" void backward_scale_channels_gpu(float *in_w_h_c_delta, int size, int CHECK_CUDA(cudaPeekAtLastError()); } + + +__global__ void sam_kernel(float *in_w_h_c, int size, int channel_size, float *scales_c, float *out) +{ + const int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < size) { + out[index] = in_w_h_c[index] * scales_c[index]; + } +} + +extern "C" void sam_gpu(float *in_w_h_c, int size, int channel_size, float *scales_c, float *out) +{ + const int block_size = BLOCK; + const int num_blocks = get_number_of_blocks(size, block_size); + sam_kernel << > >(in_w_h_c, size, channel_size, scales_c, out); + CHECK_CUDA(cudaPeekAtLastError()); +} + + +__global__ void backward_sam_kernel(float *in_w_h_c_delta, int size, int channel_size, + float *in_scales_c, float *out_from_delta, + float *in_from_output, float *out_state_delta) +{ + const int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < size) { + out_state_delta[index] += in_w_h_c_delta[index] * in_from_output[index]; // l.delta * from (should be divided by channel_size?) + out_from_delta[index] += in_scales_c[index] * in_w_h_c_delta[index]; // input * l.delta + + //out_state_delta[index] += in_w_h_c_delta[index]; + //out_from_delta[index] = in_w_h_c_delta[index]; + } +} + +extern "C" void backward_sam_gpu(float *in_w_h_c_delta, int size, int channel_size, + float *in_scales_c, float *out_from_delta, + float *in_from_output, float *out_state_delta) +{ + const int block_size = BLOCK; + const int num_blocks = get_number_of_blocks(size, block_size); + backward_sam_kernel << > > (in_w_h_c_delta, size, channel_size, + in_scales_c, out_from_delta, + in_from_output, out_state_delta); + + CHECK_CUDA(cudaPeekAtLastError()); +} \ No newline at end of file diff --git a/src/parser.c b/src/parser.c index 5145ba58c9e..1b79efe80ef 100644 --- a/src/parser.c +++ b/src/parser.c @@ -633,7 +633,7 @@ layer parse_sam(list *options, size_params params, network net) int batch = params.batch; layer from = net.layers[index]; - layer s = make_scale_channels_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c); + layer s = make_sam_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c); char *activation_s = option_find_str_quiet(options, "activation", "linear"); ACTIVATION activation = get_activation(activation_s); diff --git a/src/sam_layer.c b/src/sam_layer.c index 7e922ce1389..da28e319775 100644 --- a/src/sam_layer.c +++ b/src/sam_layer.c @@ -98,7 +98,7 @@ void forward_sam_layer_gpu(const layer l, network_state state) int size = l.batch * l.out_c * l.out_w * l.out_h; int channel_size = 1; - scale_channels_gpu(state.net.layers[l.index].output_gpu, size, channel_size, state.input, l.output_gpu); + sam_gpu(state.net.layers[l.index].output_gpu, size, channel_size, state.input, l.output_gpu); activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); } @@ -113,6 +113,6 @@ void backward_sam_layer_gpu(const layer l, network_state state) float *from_delta = state.net.layers[l.index].delta_gpu; - backward_scale_channels_gpu(l.delta_gpu, size, channel_size, state.input, from_delta, from_output, state.delta); + backward_sam_gpu(l.delta_gpu, size, channel_size, state.input, from_delta, from_output, state.delta); } #endif diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index 070ae5d95dc..1f7c6d35e90 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -19,7 +19,7 @@ layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int l.outputs = w*h*c; l.inputs = l.outputs; - if(w != w2 || h != h2 || c != c) fprintf(stderr, " w = %d, w2 = %d, h = %d, h2 = %d, c = %d, c2 = %d \n", w, w2, h, h2, c, c2); + if(w != w2 || h != h2 || c != c2) fprintf(stderr, " w = %d, w2 = %d, h = %d, h2 = %d, c = %d, c2 = %d \n", w, w2, h, h2, c, c2); l.index = index; From c1f184249e996d7248e40cffee138a11cdd89ae0 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 8 Aug 2019 00:29:06 +0300 Subject: [PATCH 0570/1365] Added assisted_excitation_forward for training on CPU --- include/darknet.h | 3 + src/convolutional_layer.c | 118 +++++++++++++++++++++++++++++++++++++- src/convolutional_layer.h | 1 + src/detector.c | 2 + src/parser.c | 1 + 5 files changed, 124 insertions(+), 1 deletion(-) diff --git a/include/darknet.h b/include/darknet.h index 72466ab174c..5d87a8323a4 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -278,6 +278,7 @@ struct layer { float focus; int classfix; int absolute; + int assisted_excitation; int onlyforward; int stopbackward; @@ -580,6 +581,8 @@ typedef struct network { int time_steps; int step; int max_batches; + int num_boxes; + int train_images_num; float *seq_scales; float *scales; int *steps; diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index b8972f45966..20fc45c6836 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -1076,6 +1076,121 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) else activate_array_cpu_custom(l.output, l.outputs*l.batch, l.activation); if(l.binary || l.xnor) swap_binary(&l); + + //visualize_convolutional_layer(l, "conv_visual", NULL); + //wait_until_press_key_cv(); + + if(l.assisted_excitation && state.train) assisted_excitation_forward(l, state); +} + +static box float_to_box_stride(float *f, int stride) +{ + box b = { 0 }; + b.x = f[0]; + b.y = f[1 * stride]; + b.w = f[2 * stride]; + b.h = f[3 * stride]; + return b; +} + +void assisted_excitation_forward(convolutional_layer l, network_state state) +{ + const int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); + + // epoch + const float epoch = (float)(*state.net.seen) / state.net.train_images_num; + + // calculate alpha + //const float alpha = (1 + cos(3.141592 * iteration_num)) / (2 * state.net.max_batches); + //const float alpha = (1 + cos(3.141592 * epoch)) / (2 * state.net.max_batches); + const float alpha = (1 + cos(3.141592 * iteration_num / state.net.max_batches)) / 2; + + //printf("\n epoch = %f, alpha = %f, seen = %d, max_batches = %d, train_images_num = %d \n", + // epoch, alpha, (*state.net.seen), state.net.max_batches, state.net.train_images_num); + + float *a_avg = (float *)calloc(l.out_w * l.out_h * l.batch, sizeof(float)); + float *g = (float *)calloc(l.out_w * l.out_h * l.batch, sizeof(float)); + + int b; + int w, h, c; + + l.max_boxes = state.net.num_boxes; + l.truths = l.max_boxes*(4 + 1); + + for (b = 0; b < l.batch; ++b) + { + // calculate G + int t; + for (t = 0; t < state.net.num_boxes; ++t) { + box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); + if (!truth.x) break; // continue; + + int left = floor((truth.x - truth.w / 2) * l.out_w); + int right = ceil((truth.x + truth.w / 2) * l.out_w); + int top = floor((truth.y - truth.h / 2) * l.out_h); + int bottom = ceil((truth.y + truth.h / 2) * l.out_h); + + for (w = left; w <= right; w++) { + for (h = top; h < bottom; h++) { + g[w + l.out_w * h + l.out_w*l.out_h*b] = 1; + } + } + } + } + + for (b = 0; b < l.batch; ++b) + { + // calculate average A + for (w = 0; w < l.out_w; w++) { + for (h = 0; h < l.out_h; h++) { + for (c = 0; c < l.out_c; c++) { + a_avg[w + l.out_w*(h + l.out_h*b)] += l.output[w + l.out_w*(h + l.out_h*(c + l.out_c*b))]; + } + a_avg[w + l.out_w*(h + l.out_h*b)] /= l.out_c; // a_avg / d + } + } + } + + // change activation + for (b = 0; b < l.batch; ++b) + { + for (w = 0; w < l.out_w; w++) { + for (h = 0; h < l.out_h; h++) { + for (c = 0; c < l.out_c; c++) + { + // a = a + alpha(t) + e(c,i,j) = a + alpha(t) + g(i,j) * avg_a(i,j) / channels + l.output[w + l.out_w*(h + l.out_h*(c + l.out_c*b))] += + alpha * + g[w + l.out_w*(h + l.out_h*b)] * + a_avg[w + l.out_w*(h + l.out_h*b)]; + + //l.output[w + l.out_w*(h + l.out_h*(c + l.out_c*b))] = + // alpha * g[w + l.out_w*(h + l.out_h*b)] * a_avg[w + l.out_w*(h + l.out_h*b)]; + } + } + } + } + + if(0) // visualize ground truth + { + for (b = 0; b < l.batch; ++b) + { + image img = float_to_image(l.out_w, l.out_h, 1, &g[l.out_w*l.out_h*b]); + char buff[100]; + sprintf(buff, "a_excitation_%d", b); + show_image_cv(img, buff); + + image img2 = float_to_image(l.out_w, l.out_h, 1, &l.output[l.out_w*l.out_h*l.out_c*b]); + char buff2[100]; + sprintf(buff2, "a_excitation_act_%d", b); + show_image_cv(img2, buff2); + wait_key_cv(5); + } + wait_until_press_key_cv(); + } + + free(g); + free(a_avg); } @@ -1221,4 +1336,5 @@ image *visualize_convolutional_layer(convolutional_layer l, char *window, image //save_image(dc, buff); free_image(dc); return single_weights; -} \ No newline at end of file +} + diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h index e937b4c5bd6..e62b155c45f 100644 --- a/src/convolutional_layer.h +++ b/src/convolutional_layer.h @@ -56,6 +56,7 @@ int convolutional_out_height(convolutional_layer layer); int convolutional_out_width(convolutional_layer layer); void rescale_weights(convolutional_layer l, float scale, float trans); void rgbgr_weights(convolutional_layer l); +void assisted_excitation_forward(convolutional_layer l, network_state state); #ifdef __cplusplus } diff --git a/src/detector.c b/src/detector.c index 9f77ea5afb1..689ace6da83 100644 --- a/src/detector.c +++ b/src/detector.c @@ -123,6 +123,8 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i args.flip = net.flip; args.jitter = jitter; args.num_boxes = l.max_boxes; + net.num_boxes = args.num_boxes; + net.train_images_num = train_images_num; args.d = &buffer; args.type = DETECTION_DATA; args.threads = 64; // 16 or 64 diff --git a/src/parser.c b/src/parser.c index 1b79efe80ef..09e79d2df75 100644 --- a/src/parser.c +++ b/src/parser.c @@ -185,6 +185,7 @@ convolutional_layer parse_convolutional(list *options, size_params params, netwo convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride,dilation,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index, share_layer); layer.flipped = option_find_int_quiet(options, "flipped", 0); layer.dot = option_find_float_quiet(options, "dot", 0); + layer.assisted_excitation = option_find_float_quiet(options, "assisted_excitation", 0); if(params.net.adam){ layer.B1 = params.net.B1; From 480b3eccb19ac65508e49fb3116ccda65551bb6e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 8 Aug 2019 01:58:24 +0300 Subject: [PATCH 0571/1365] Compile fix --- src/convolutional_layer.c | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 20fc45c6836..6ff5b8b3d4f 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -1173,6 +1173,7 @@ void assisted_excitation_forward(convolutional_layer l, network_state state) if(0) // visualize ground truth { +#ifdef OPENCV for (b = 0; b < l.batch; ++b) { image img = float_to_image(l.out_w, l.out_h, 1, &g[l.out_w*l.out_h*b]); @@ -1187,6 +1188,7 @@ void assisted_excitation_forward(convolutional_layer l, network_state state) wait_key_cv(5); } wait_until_press_key_cv(); +#endif // OPENCV } free(g); From 4c315ea26b56c2bf20ebc240d94386c6e3cc83db Mon Sep 17 00:00:00 2001 From: Alexey Date: Fri, 9 Aug 2019 12:42:39 +0300 Subject: [PATCH 0572/1365] Update Readme.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a25d585f489..03492ade7e8 100644 --- a/README.md +++ b/README.md @@ -79,7 +79,7 @@ You can get cfg-files by path: `darknet/cfg/` * **Intel OpenVINO 2019 R1:** (Myriad X / USB Neural Compute Stick / Arria FPGA): read this [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) * **OpenCV-dnn** is a very fast DNN implementation on CPU (x86/ARM-Android), use `yolov3.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221), [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) * **PyTorch > ONNX > CoreML > iOS** how to convert cfg/weights-files to pt-file: [ultralytics/yolov3](https://github.com/ultralytics/yolov3#darknet-conversion) and [iOS App](https://itunes.apple.com/app/id1452689527) -* **TensorRT** for YOLOv3 (-70% faster inference): [TensorRT & DeepStream](https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps) +* **TensorRT** for YOLOv3 (-70% faster inference): [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) * **TVM** - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backends (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about #### Datasets From b02b124a0ebd94302bf17f6be8fb7a5598a6d196 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 22 Aug 2019 08:36:01 +0200 Subject: [PATCH 0573/1365] add bash setup script --- setup.ps1 | 13 +++++-------- setup.sh | 48 ++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 53 insertions(+), 8 deletions(-) create mode 100755 setup.sh diff --git a/setup.ps1 b/setup.ps1 index a47697b2bc4..f12ea33953c 100755 --- a/setup.ps1 +++ b/setup.ps1 @@ -12,11 +12,8 @@ Set-Location $temp_folder # Download and install Chocolatey Set-ExecutionPolicy unrestricted Invoke-Expression ((New-Object System.Net.WebClient).DownloadString('https://chocolatey.org/install.ps1')) -choco.exe install -y cmake ninja powershell git - -# Download and install VS Build Tools -Invoke-WebRequest https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=BuildTools&rel=16 -OutFile .\vs_buildtools.exe -.\vs_buildtools.exe –quiet –add Microsoft.VisualStudio.Workload.VCTools –includeRecommended +choco.exe install -y cmake ninja powershell git vscode +choco-exe install -y visualstudio2019buildtools --package-parameters "--add Microsoft.VisualStudio.Component.VC.CoreBuildTools --includeRecommended --includeOptional --passive --locale en-US --lang en-US" if ($install_cuda) { # Download and install CUDA @@ -28,10 +25,10 @@ if ($install_cuda) { $env:CUDA_TOOLKIT_ROOT_DIR = $env:CUDA_PATH $env:PATH += ";${env:CUDA_PATH}\bin;" - $feature = "opencv-cuda" + $features = "full" } else { - $feature = "opencv" + $features = "opencv-base,weights,weights-train" } Remove-Item -r $temp_folder @@ -40,4 +37,4 @@ Set-Location $vcpkg_folder\ git.exe clone https://github.com/Microsoft/vcpkg Set-Location vcpkg .\bootstrap-vcpkg.bat -.\vcpkg.exe install darknet[$feature] +.\vcpkg.exe install darknet[${features}]:x64-windows diff --git a/setup.sh b/setup.sh new file mode 100755 index 00000000000..41e0f366b9a --- /dev/null +++ b/setup.sh @@ -0,0 +1,48 @@ +#!/usr/bin/env bash + +## enable or disable installed components + +install_cuda=false +vcpkg_folder="." +temp_folder="./temp" + +########################### + +mkdir $temp_folder +cd $temp_folder + +sudo apt-get install cmake git ninja-build build-essentials g++ + +if [ "$install_cuda" = true ] ; then + if [[ "$OSTYPE" == "darwin"* ]]; then + echo "Unable to provide CUDA on macOS" + else + # Download and install CUDA + wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb + sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub + sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb + wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb + sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb + sudo apt-get -y update + sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 + sudo apt-get install -y --no-install-recommends libcudnn7-dev + sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1 + + export CUDACXX=/usr/local/cuda-10.0/bin/nvcc + export CUDA_PATH=/usr/local/cuda-10.0 + export CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 + export LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" + + features = "full" + fi +else + features = "opencv-base,weights,weights-train" +fi + +rm -rf $temp_folder +cd .. +cd $vcpkg_folder +git clone https://github.com/Microsoft/vcpkg +cd vcpkg +./bootstrap-vcpkg.sh +./vcpkg install darknet[${features}] From 35346d2ef80dc46b9eb4fba57da1737a49f4743e Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sat, 24 Aug 2019 13:46:45 +0200 Subject: [PATCH 0574/1365] [cudnn] remove hardcoded version in cmake module (#3738) collateral fixes: * handle opencv4 upgrade on vcpkg * add yasm (ffmpeg prerequisite) on travis * install ffmpeg separately from opencv to circumvent time limits * build only release to halve ci time * do not build as shared libs when using vcpkg, since default triplets are static * remove debug configs from appveyor matrix --- .travis.yml | 30 +++++++++++++++++++----------- appveyor.yml | 30 +++++++++--------------------- build.sh | 2 ++ cmake/Modules/FindCUDNN.cmake | 21 +++++++-------------- 4 files changed, 37 insertions(+), 46 deletions(-) diff --git a/.travis.yml b/.travis.yml index 63c7b2c4d50..7ae323e6dd1 100644 --- a/.travis.yml +++ b/.travis.yml @@ -78,8 +78,8 @@ matrix: name: macOS - clang - vcpkg osx_image: xcode10.1 env: - - additional_defines="" - - MATRIX_EVAL="brew install gcc && unset CC && unset CXX" + - additional_defines="-DBUILD_SHARED_LIBS=OFF" + - MATRIX_EVAL="brew install gcc && brew install yasm && unset CC && unset CXX" - USE_VCPKG=true - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" @@ -111,8 +111,13 @@ matrix: compiler: gcc dist: bionic name: ubuntu 18.04 - gcc - vcpkg + addons: + apt: + update: true + packages: + - yasm env: - - additional_defines="" + - additional_defines="-DBUILD_SHARED_LIBS=OFF" - USE_VCPKG=true - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" - MATRIX_EVAL="" @@ -181,8 +186,13 @@ matrix: compiler: gcc dist: bionic name: ubuntu 18.04 - gcc - CUDA 10 - vcpkg + addons: + apt: + update: true + packages: + - yasm env: - - additional_defines="" + - additional_defines="-DBUILD_SHARED_LIBS=OFF" - USE_VCPKG=true - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" - CUDACXX=/usr/local/cuda-10.0/bin/nvcc @@ -209,22 +219,20 @@ install: - cd vcpkg - git pull - if [ "${USE_VCPKG}" = true ] ; then ./bootstrap-vcpkg.sh ; fi - - if [ "${USE_VCPKG}" = true ] ; then travis_wait ./vcpkg install stb pthreads ; fi + - if [ "${USE_VCPKG}" = true ] ; then echo "set(VCPKG_BUILD_TYPE release)" >> triplets/x64-osx.cmake ; fi + - if [ "${USE_VCPKG}" = true ] ; then echo "set(VCPKG_BUILD_TYPE release)" >> triplets/x64-linux.cmake ; fi + - if [ "${USE_VCPKG}" = true ] ; then travis_wait ./vcpkg install stb pthreads --recurse; fi + - if [ "${USE_VCPKG}" = true ] ; then travis_wait 45 ./vcpkg install ffmpeg --recurse; fi #- if [ "${USE_VCPKG}" = true ] && [[ ! -z "${CUDACXX}" ]] ; then travis_wait 45 ./vcpkg install opencv[ffmpeg,cuda] --recurse; fi #- if [ "${USE_VCPKG}" = true ] && [[ -z "${CUDACXX}" ]] ; then travis_wait 45 ./vcpkg install opencv[ffmpeg] --recurse; fi - - if [ "${USE_VCPKG}" = true ] ; then travis_wait 45 ./vcpkg install opencv[ffmpeg] ; fi + - if [ "${USE_VCPKG}" = true ] ; then travis_wait 45 ./vcpkg install opencv[ffmpeg] --recurse; fi - popd before_script: - - mkdir build_debug - - cd build_debug - - cmake .. -DCMAKE_BUILD_TYPE="Debug" ${VCPKG_DEFINES} ${additional_defines} - - cd .. - mkdir build_release - cd build_release - cmake .. -DCMAKE_BUILD_TYPE="Release" ${VCPKG_DEFINES} ${additional_defines} - cd .. script: - - cd build_debug && cmake --build . --target install -- -j8 && cd .. - cd build_release && cmake --build . --target install -- -j8 && cd .. diff --git a/appveyor.yml b/appveyor.yml index 26b1add487b..380feba4a55 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -10,29 +10,17 @@ environment: CYGWIN_NOWINPATH: yes CYGSH: C:\cygwin64\bin\bash -c - platform: Win64 - COMPILER: vs - configuration: Release - VCPKG_ROOT: C:\Tools\vcpkg - VCPKG_DEFAULT_TRIPLET: x64-windows USE_CUDA: yes - - platform: Win64 - COMPILER: vs - configuration: Debug - VCPKG_ROOT: C:\Tools\vcpkg - VCPKG_DEFAULT_TRIPLET: x64-windows - USE_CUDA: yes - - platform: Win64 COMPILER: vs configuration: Release VCPKG_ROOT: C:\Tools\vcpkg VCPKG_DEFAULT_TRIPLET: x64-windows - USE_CUDA: no - platform: Win64 + USE_CUDA: no COMPILER: vs - configuration: Debug + configuration: Release VCPKG_ROOT: C:\Tools\vcpkg VCPKG_DEFAULT_TRIPLET: x64-windows - USE_CUDA: no - platform: Win64 COMPILER: vs configuration: Release @@ -71,14 +59,15 @@ install: - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] cd %VCPKG_ROOT% - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] git pull - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] .\bootstrap-vcpkg.bat - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] if [%USE_CUDA%]==[yes] vcpkg install cuda - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg install stb pthreads - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg install opencv[ffmpeg] ## opencv[ffmpeg,cuda] is too big to build, timing out (>1h). We use plain openCV also for CUDA builds (toolchain can manage this strange situation anyway) + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] echo set(VCPKG_BUILD_TYPE release) >> triplets\%VCPKG_DEFAULT_TRIPLET%.cmake + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] if [%USE_CUDA%]==[yes] vcpkg install cuda --recurse + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg install stb pthreads --recurse + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg install ffmpeg --recurse + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg install opencv[ffmpeg] --recurse ## opencv[ffmpeg,cuda] is too big to build, timing out (>1h). We use plain openCV also for CUDA builds (toolchain can manage this strange situation anyway) - cd %WORKSPACE%\darknet\ - - mkdir build_debug && cd build_debug + - if [%COMPILER%]==[cygwin] mkdir build_debug && cd build_debug - if [%COMPILER%]==[cygwin] %CYGSH% 'cmake .. -G "Unix Makefiles" -DCMAKE_BUILD_TYPE="Debug"' - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] if [%configuration%]==[Debug] cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=%VCPKG_ROOT%\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=%VCPKG_DEFAULT_TRIPLET%" -DCMAKE_BUILD_TYPE="Debug" .. - - cd .. + - if [%COMPILER%]==[cygwin] cd .. - mkdir build_release && cd build_release - if [%COMPILER%]==[cygwin] %CYGSH% 'cmake .. -G "Unix Makefiles" -DCMAKE_BUILD_TYPE="Release"' - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] if [%configuration%]==[Release] cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=%VCPKG_ROOT%\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=%VCPKG_DEFAULT_TRIPLET%" -DCMAKE_BUILD_TYPE="Release" .. @@ -89,7 +78,6 @@ install: build_script: - if [%COMPILER%]==[cygwin] cd build_debug && %CYGSH% 'cmake --build . --target install -- -j8' && cd .. - if [%COMPILER%]==[cygwin] cd build_release && %CYGSH% 'cmake --build . --target install -- -j8' && cd .. - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] if [%configuration%]==[Debug] cd build_debug && cmake --build . --config Debug --parallel 8 --target install && cd .. - if [%COMPILER%]==[vs] if [%configuration%]==[Release] cd build_release && cmake --build . --config Release --parallel 8 --target install && cd .. artifacts: diff --git a/build.sh b/build.sh index 6c8df676685..dfd5446b47d 100755 --- a/build.sh +++ b/build.sh @@ -16,12 +16,14 @@ then vcpkg_define="-DCMAKE_TOOLCHAIN_FILE=${vcpkg_path}/scripts/buildsystems/vcpkg.cmake" vcpkg_triplet_define="-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" echo "Found vcpkg in VCPKG_ROOT: ${vcpkg_path}" + additional_defines="-DBUILD_SHARED_LIBS=OFF" elif [[ ! -z "${WORKSPACE}" ]] && [ -d ${WORKSPACE}/vcpkg ] && [ ! "$bypass_vcpkg" = true ] then vcpkg_path="${WORKSPACE}/vcpkg" vcpkg_define="-DCMAKE_TOOLCHAIN_FILE=${vcpkg_path}/scripts/buildsystems/vcpkg.cmake" vcpkg_triplet_define="-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" echo "Found vcpkg in WORKSPACE/vcpkg: ${vcpkg_path}" + additional_defines="-DBUILD_SHARED_LIBS=OFF" elif [ ! "$bypass_vcpkg" = true ] then (>&2 echo "darknet is unsupported without vcpkg, use at your own risk!") diff --git a/cmake/Modules/FindCUDNN.cmake b/cmake/Modules/FindCUDNN.cmake index 3aa2aa81c85..871295faaee 100644 --- a/cmake/Modules/FindCUDNN.cmake +++ b/cmake/Modules/FindCUDNN.cmake @@ -34,10 +34,7 @@ if(NOT CUDNN_LIBRARY) PATH_SUFFIXES lib lib64 cuda/lib cuda/lib64 lib/x64) endif() -find_package_handle_standard_args( - CUDNN DEFAULT_MSG CUDNN_INCLUDE_DIR CUDNN_LIBRARY) - -if(CUDNN_FOUND) +if(EXISTS "${CUDNN_INCLUDE_DIR}/cudnn.h") file(READ ${CUDNN_INCLUDE_DIR}/cudnn.h CUDNN_HEADER_CONTENTS) string(REGEX MATCH "define CUDNN_MAJOR * +([0-9]+)" CUDNN_VERSION_MAJOR "${CUDNN_HEADER_CONTENTS}") @@ -60,21 +57,17 @@ endif() set(CUDNN_INCLUDE_DIRS ${CUDNN_INCLUDE_DIR}) set(CUDNN_LIBRARIES ${CUDNN_LIBRARY}) -if(CUDNN_FOUND) - message(STATUS "Found cuDNN: v${CUDNN_VERSION} (include: ${CUDNN_INCLUDE_DIR}, library: ${CUDNN_LIBRARY})") -endif() mark_as_advanced(CUDNN_LIBRARY CUDNN_INCLUDE_DIR) -# Register imported libraries: -# 1. If we can find a Windows .dll file (or if we can find both Debug and -# Release libraries), we will set appropriate target properties for these. -# 2. However, for most systems, we will only register the import location and -# include directory. +find_package_handle_standard_args(CUDNN + REQUIRED_VARS CUDNN_INCLUDE_DIR CUDNN_LIBRARY + VERSION_VAR CUDNN_VERSION +) + if(WIN32) set(CUDNN_DLL_DIR ${CUDNN_INCLUDE_DIR}) list(TRANSFORM CUDNN_DLL_DIR APPEND "/../bin") - message(STATUS "CUDNN_DLL_DIR: ${CUDNN_DLL_DIR}") - find_file(CUDNN_LIBRARY_DLL NAMES cudnn64_7.dll PATHS ${CUDNN_DLL_DIR}) + find_file(CUDNN_LIBRARY_DLL NAMES cudnn64_${CUDNN_VERSION_MAJOR}.dll PATHS ${CUDNN_DLL_DIR}) endif() if( CUDNN_FOUND AND NOT TARGET CuDNN::CuDNN ) From 8d80a65288df8aac8a7080e17bcdea6136bd186d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 27 Aug 2019 14:04:39 +0300 Subject: [PATCH 0575/1365] Max pool layer can use stride=2 or stride_x=2 stride_y=4 (isn't tested well) --- include/darknet.h | 2 ++ src/box.c | 2 ++ src/maxpool_layer.c | 30 ++++++++++++++++++------------ src/maxpool_layer.h | 2 +- src/maxpool_layer_kernels.cu | 31 ++++++++++++++++--------------- src/parser.c | 14 ++++++++------ 6 files changed, 47 insertions(+), 34 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 5d87a8323a4..5cfd274db52 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -208,6 +208,8 @@ struct layer { int size; int side; int stride; + int stride_x; + int stride_y; int dilation; int maxpool_depth; int out_channels; diff --git a/src/box.c b/src/box.c index 640f54a299e..1b5c4998a6b 100644 --- a/src/box.c +++ b/src/box.c @@ -207,6 +207,8 @@ dxrep dx_box_iou(box pred, box truth, IOU_LOSS iou_loss) { p_dr = ((U * dI_wrt_r) - (I * dU_wrt_r)) / (U * U); } + // GIoU = I/U - (C-U)/C + // C is the smallest convex hull that encloses both Detection and Truth if (iou_loss == GIOU) { if (C > 0) { // apply "C" term from gIOU diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index 4d2ee49f57f..000efe90663 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -34,8 +34,8 @@ void cudnn_maxpool_setup(layer *l) l->size, l->pad/2, //0, //l.pad, l->pad/2, //0, //l.pad, - l->stride, - l->stride); + l->stride_x, + l->stride_y); cudnnCreateTensorDescriptor(&l->srcTensorDesc); cudnnCreateTensorDescriptor(&l->dstTensorDesc); @@ -45,7 +45,7 @@ void cudnn_maxpool_setup(layer *l) } -maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride, int padding, int maxpool_depth, int out_channels) +maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride_x, int stride_y, int padding, int maxpool_depth, int out_channels) { maxpool_layer l = { (LAYER_TYPE)0 }; l.type = MAXPOOL; @@ -62,14 +62,16 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s l.out_h = l.h; } else { - l.out_w = (w + padding - size) / stride + 1; - l.out_h = (h + padding - size) / stride + 1; + l.out_w = (w + padding - size) / stride_x + 1; + l.out_h = (h + padding - size) / stride_y + 1; l.out_c = c; } l.outputs = l.out_h * l.out_w * l.out_c; l.inputs = h*w*c; l.size = size; - l.stride = stride; + l.stride = stride_x; + l.stride_x = stride_x; + l.stride_y = stride_y; int output_size = l.out_h * l.out_w * l.out_c * batch; l.indexes = (int*)calloc(output_size, sizeof(int)); l.output = (float*)calloc(output_size, sizeof(float)); @@ -87,7 +89,11 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s #endif // GPU l.bflops = (l.size*l.size*l.c * l.out_h*l.out_w) / 1000000000.; - fprintf(stderr, "max %d x %d/%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); + if(stride_x == stride_y) + fprintf(stderr, "max %d x %d/%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride_x, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); + else + fprintf(stderr, "max %d x %d/%2dx%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride_x, stride_y, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); + return l; } @@ -97,8 +103,8 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h) l->w = w; l->inputs = h*w*l->c; - l->out_w = (w + l->pad - l->size) / l->stride + 1; - l->out_h = (h + l->pad - l->size) / l->stride + 1; + l->out_w = (w + l->pad - l->size) / l->stride_x + 1; + l->out_h = (h + l->pad - l->size) / l->stride_y + 1; l->outputs = l->out_w * l->out_h * l->out_c; int output_size = l->outputs * l->batch; @@ -151,7 +157,7 @@ void forward_maxpool_layer(const maxpool_layer l, network_state state) } - if (!state.train) { + if (!state.train && l.stride_x == l.stride_y) { forward_maxpool_layer_avx(state.input, l.output, l.indexes, l.size, l.w, l.h, l.out_w, l.out_h, l.c, l.pad, l.stride, l.batch); return; } @@ -173,8 +179,8 @@ void forward_maxpool_layer(const maxpool_layer l, network_state state) int max_i = -1; for(n = 0; n < l.size; ++n){ for(m = 0; m < l.size; ++m){ - int cur_h = h_offset + i*l.stride + n; - int cur_w = w_offset + j*l.stride + m; + int cur_h = h_offset + i*l.stride_y + n; + int cur_w = w_offset + j*l.stride_x + m; int index = cur_w + l.w*(cur_h + l.h*(k + b*l.c)); int valid = (cur_h >= 0 && cur_h < l.h && cur_w >= 0 && cur_w < l.w); diff --git a/src/maxpool_layer.h b/src/maxpool_layer.h index 0c1f6148946..4994d45700d 100644 --- a/src/maxpool_layer.h +++ b/src/maxpool_layer.h @@ -12,7 +12,7 @@ typedef layer maxpool_layer; extern "C" { #endif image get_maxpool_image(maxpool_layer l); -maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride, int padding, int maxpool_depth, int out_channels); +maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride_x, int stride_y, int padding, int maxpool_depth, int out_channels); void resize_maxpool_layer(maxpool_layer *l, int w, int h); void forward_maxpool_layer(const maxpool_layer l, network_state state); void backward_maxpool_layer(const maxpool_layer l, network_state state); diff --git a/src/maxpool_layer_kernels.cu b/src/maxpool_layer_kernels.cu index 82d631b358a..8e8511003e5 100644 --- a/src/maxpool_layer_kernels.cu +++ b/src/maxpool_layer_kernels.cu @@ -49,10 +49,10 @@ __global__ void backward_maxpool_depth_layer_kernel(int n, int w, int h, int c, } -__global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride, int size, int pad, float *input, float *output, int *indexes) +__global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride_x, int stride_y, int size, int pad, float *input, float *output, int *indexes) { - int h = (in_h + pad - size) / stride + 1; - int w = (in_w + pad - size) / stride + 1; + int h = (in_h + pad - size) / stride_y + 1; + int w = (in_w + pad - size) / stride_x + 1; int c = in_c; int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -75,8 +75,8 @@ __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c int l, m; for(l = 0; l < size; ++l){ for(m = 0; m < size; ++m){ - int cur_h = h_offset + i*stride + l; - int cur_w = w_offset + j*stride + m; + int cur_h = h_offset + i*stride_y + l; + int cur_w = w_offset + j*stride_x + m; int index = cur_w + in_w*(cur_h + in_h*(k + b*in_c)); int valid = (cur_h >= 0 && cur_h < in_h && cur_w >= 0 && cur_w < in_w); @@ -89,12 +89,13 @@ __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c indexes[out_index] = max_i; } -__global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride, int size, int pad, float *delta, float *prev_delta, int *indexes) +__global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride_x, int stride_y, int size, int pad, float *delta, float *prev_delta, int *indexes) { - int h = (in_h + pad - size) / stride + 1; - int w = (in_w + pad - size) / stride + 1; + int h = (in_h + pad - size) / stride_y + 1; + int w = (in_w + pad - size) / stride_x + 1; int c = in_c; - int area = (size-1)/stride; + int area_x = (size - 1) / stride_x; + int area_y = (size - 1) / stride_y; int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(id >= n) return; @@ -113,10 +114,10 @@ __global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_ float d = 0; int l, m; - for(l = -area; l < area+1; ++l){ - for(m = -area; m < area+1; ++m){ - int out_w = (j-w_offset)/stride + m; - int out_h = (i-h_offset)/stride + l; + for(l = -area_y; l < area_y+1; ++l){ + for(m = -area_x; m < area_x+1; ++m){ + int out_w = (j-w_offset)/stride_x + m; + int out_h = (i-h_offset)/stride_y + l; int out_index = out_w + w*(out_h + h*(k + c*b)); int valid = (out_w >= 0 && out_w < w && out_h >= 0 && out_h < h); @@ -172,7 +173,7 @@ extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network_state sta size_t n = h*w*c*layer.batch; - forward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.pad, state.input, layer.output_gpu, layer.indexes_gpu); + forward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride_x, layer.stride_y, layer.size, layer.pad, state.input, layer.output_gpu, layer.indexes_gpu); CHECK_CUDA(cudaPeekAtLastError()); } @@ -192,6 +193,6 @@ extern "C" void backward_maxpool_layer_gpu(maxpool_layer layer, network_state st size_t n = layer.h*layer.w*layer.c*layer.batch; - backward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.pad, layer.delta_gpu, state.delta, layer.indexes_gpu); + backward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride_x, layer.stride_y, layer.size, layer.pad, layer.delta_gpu, state.delta, layer.indexes_gpu); CHECK_CUDA(cudaPeekAtLastError()); } diff --git a/src/parser.c b/src/parser.c index 09e79d2df75..ac8f9613fae 100644 --- a/src/parser.c +++ b/src/parser.c @@ -535,6 +535,8 @@ layer parse_reorg_old(list *options, size_params params) maxpool_layer parse_maxpool(list *options, size_params params) { int stride = option_find_int(options, "stride",1); + int stride_x = option_find_int_quiet(options, "stride_x", stride); + int stride_y = option_find_int_quiet(options, "stride_y", stride); int size = option_find_int(options, "size",stride); int padding = option_find_int_quiet(options, "padding", size-1); int maxpool_depth = option_find_int_quiet(options, "maxpool_depth", 0); @@ -547,7 +549,7 @@ maxpool_layer parse_maxpool(list *options, size_params params) batch=params.batch; if(!(h && w && c)) error("Layer before maxpool layer must output image."); - maxpool_layer layer = make_maxpool_layer(batch, h, w, c, size, stride, padding, maxpool_depth, out_channels); + maxpool_layer layer = make_maxpool_layer(batch, h, w, c, size, stride_x, stride_y, padding, maxpool_depth, out_channels); return layer; } @@ -1332,12 +1334,12 @@ void load_convolutional_weights(layer l, FILE *fp) //return; } int num = l.nweights; - fread(l.biases, sizeof(float), l.n, fp); + if (fread(l.biases, sizeof(float), l.n, fp) < l.n) printf("\n Warning: Unexpected end of wights-file! \n"); //fread(l.weights, sizeof(float), num, fp); // as in connected layer if (l.batch_normalize && (!l.dontloadscales)){ - fread(l.scales, sizeof(float), l.n, fp); - fread(l.rolling_mean, sizeof(float), l.n, fp); - fread(l.rolling_variance, sizeof(float), l.n, fp); + if(fread(l.scales, sizeof(float), l.n, fp) < l.n) printf("\n Warning: Unexpected end of wights-file! \n"); + if(fread(l.rolling_mean, sizeof(float), l.n, fp) < l.n) printf("\n Warning: Unexpected end of wights-file! \n"); + if(fread(l.rolling_variance, sizeof(float), l.n, fp) < l.n) printf("\n Warning: Unexpected end of wights-file! \n"); if(0){ int i; for(i = 0; i < l.n; ++i){ @@ -1354,7 +1356,7 @@ void load_convolutional_weights(layer l, FILE *fp) fill_cpu(l.n, 0, l.rolling_variance, 1); } } - fread(l.weights, sizeof(float), num, fp); + if(fread(l.weights, sizeof(float), num, fp) < num) printf("\n Warning: Unexpected end of wights-file! \n"); //if(l.adam){ // fread(l.m, sizeof(float), num, fp); // fread(l.v, sizeof(float), num, fp); From 4acf924aaf7e61b07b0d8387347674d782e32cce Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 29 Aug 2019 18:30:33 +0300 Subject: [PATCH 0576/1365] minor fix --- src/parser.c | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/src/parser.c b/src/parser.c index ac8f9613fae..3883d041d4b 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1334,12 +1334,12 @@ void load_convolutional_weights(layer l, FILE *fp) //return; } int num = l.nweights; - if (fread(l.biases, sizeof(float), l.n, fp) < l.n) printf("\n Warning: Unexpected end of wights-file! \n"); + if (fread(l.biases, sizeof(float), l.n, fp) < l.n) printf("\n Warning: Unexpected end of wights-file! l.biase - l.index = %d \n", l.index); //fread(l.weights, sizeof(float), num, fp); // as in connected layer if (l.batch_normalize && (!l.dontloadscales)){ - if(fread(l.scales, sizeof(float), l.n, fp) < l.n) printf("\n Warning: Unexpected end of wights-file! \n"); - if(fread(l.rolling_mean, sizeof(float), l.n, fp) < l.n) printf("\n Warning: Unexpected end of wights-file! \n"); - if(fread(l.rolling_variance, sizeof(float), l.n, fp) < l.n) printf("\n Warning: Unexpected end of wights-file! \n"); + if(fread(l.scales, sizeof(float), l.n, fp) < l.n) printf("\n Warning: Unexpected end of wights-file! l.scales - l.index = %d \n", l.index); + if(fread(l.rolling_mean, sizeof(float), l.n, fp) < l.n) printf("\n Warning: Unexpected end of wights-file! l.rolling_mean - l.index = %d \n", l.index); + if(fread(l.rolling_variance, sizeof(float), l.n, fp) < l.n) printf("\n Warning: Unexpected end of wights-file! l.rolling_variance - l.index = %d \n", l.index); if(0){ int i; for(i = 0; i < l.n; ++i){ @@ -1356,7 +1356,7 @@ void load_convolutional_weights(layer l, FILE *fp) fill_cpu(l.n, 0, l.rolling_variance, 1); } } - if(fread(l.weights, sizeof(float), num, fp) < num) printf("\n Warning: Unexpected end of wights-file! \n"); + if(fread(l.weights, sizeof(float), num, fp) < num) printf("\n Warning: Unexpected end of wights-file! l.weights - l.index = %d \n", l.index); //if(l.adam){ // fread(l.m, sizeof(float), num, fp); // fread(l.v, sizeof(float), num, fp); From 58906ef812323444181386c7dd4e6dda8a377a48 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 29 Aug 2019 23:34:04 +0300 Subject: [PATCH 0577/1365] minor fix: Unexpected end of wights-file! --- src/parser.c | 25 +++++++++++++++++++------ 1 file changed, 19 insertions(+), 6 deletions(-) diff --git a/src/parser.c b/src/parser.c index 3883d041d4b..6cc4790bf32 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1327,6 +1327,12 @@ void load_convolutional_weights_binary(layer l, FILE *fp) #endif } +void check_read_size(size_t read_bytes, int required_bytes) +{ + if (read_bytes > 0 && read_bytes < required_bytes) return 0; + return 1; +} + void load_convolutional_weights(layer l, FILE *fp) { if(l.binary){ @@ -1334,12 +1340,17 @@ void load_convolutional_weights(layer l, FILE *fp) //return; } int num = l.nweights; - if (fread(l.biases, sizeof(float), l.n, fp) < l.n) printf("\n Warning: Unexpected end of wights-file! l.biase - l.index = %d \n", l.index); + int read_bytes; + read_bytes = fread(l.biases, sizeof(float), l.n, fp); + if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.biases - l.index = %d \n", l.index); //fread(l.weights, sizeof(float), num, fp); // as in connected layer if (l.batch_normalize && (!l.dontloadscales)){ - if(fread(l.scales, sizeof(float), l.n, fp) < l.n) printf("\n Warning: Unexpected end of wights-file! l.scales - l.index = %d \n", l.index); - if(fread(l.rolling_mean, sizeof(float), l.n, fp) < l.n) printf("\n Warning: Unexpected end of wights-file! l.rolling_mean - l.index = %d \n", l.index); - if(fread(l.rolling_variance, sizeof(float), l.n, fp) < l.n) printf("\n Warning: Unexpected end of wights-file! l.rolling_variance - l.index = %d \n", l.index); + read_bytes = fread(l.scales, sizeof(float), l.n, fp); + if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.scales - l.index = %d \n", l.index); + read_bytes = fread(l.rolling_mean, sizeof(float), l.n, fp); + if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.rolling_mean - l.index = %d \n", l.index); + read_bytes = fread(l.rolling_variance, sizeof(float), l.n, fp); + if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.rolling_variance - l.index = %d \n", l.index); if(0){ int i; for(i = 0; i < l.n; ++i){ @@ -1356,7 +1367,8 @@ void load_convolutional_weights(layer l, FILE *fp) fill_cpu(l.n, 0, l.rolling_variance, 1); } } - if(fread(l.weights, sizeof(float), num, fp) < num) printf("\n Warning: Unexpected end of wights-file! l.weights - l.index = %d \n", l.index); + read_bytes = fread(l.weights, sizeof(float), num, fp); + if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.weights - l.index = %d \n", l.index); //if(l.adam){ // fread(l.m, sizeof(float), num, fp); // fread(l.v, sizeof(float), num, fp); @@ -1473,8 +1485,9 @@ void load_weights_upto(network *net, char *filename, int cutoff) } #endif } + if (feof(fp)) break; } - fprintf(stderr, "Done!\n"); + fprintf(stderr, "Done! Loaded %d layers from weights-file \n", i); fclose(fp); } From 660a9b225cb30ad06397b5c39e9612d9eff59ea9 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 30 Aug 2019 00:29:26 +0300 Subject: [PATCH 0578/1365] compile fix --- src/parser.c | 6 ------ 1 file changed, 6 deletions(-) diff --git a/src/parser.c b/src/parser.c index 6cc4790bf32..48bd42bd3ce 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1327,12 +1327,6 @@ void load_convolutional_weights_binary(layer l, FILE *fp) #endif } -void check_read_size(size_t read_bytes, int required_bytes) -{ - if (read_bytes > 0 && read_bytes < required_bytes) return 0; - return 1; -} - void load_convolutional_weights(layer l, FILE *fp) { if(l.binary){ From 2a382e5a4bb55a6cca636f3d7025be2cfbdfc68a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 30 Aug 2019 20:28:56 +0300 Subject: [PATCH 0579/1365] Fix training TridentNet --- src/layer.c | 1 + 1 file changed, 1 insertion(+) diff --git a/src/layer.c b/src/layer.c index c0b48afc2b8..68d1b35b68a 100644 --- a/src/layer.c +++ b/src/layer.c @@ -12,6 +12,7 @@ void free_sublayer(layer *l) void free_layer(layer l) { + if (l.share_layer != NULL) return; // don't free shared layers if (l.type == CONV_LSTM) { if (l.peephole) { free_sublayer(l.vf); From 102ab710a9ee13c8a160923301d15ae8fcff6188 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 1 Sep 2019 14:11:17 +0300 Subject: [PATCH 0580/1365] Fixed fuse_conv_batchnorm() for TridentNet --- src/convolutional_layer.c | 1 + src/network.c | 4 ++++ 2 files changed, 5 insertions(+) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 6ff5b8b3d4f..93ac79a0711 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -550,6 +550,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.bflops = (2.0 * l.nweights * l.out_h*l.out_w) / 1000000000.; if (l.xnor && l.use_bin_output) fprintf(stderr, "convXB"); else if (l.xnor) fprintf(stderr, "convX "); + else if(l.share_layer) fprintf(stderr, "convS "); else fprintf(stderr, "conv "); if(groups > 1) fprintf(stderr, "%5d/%4d ", n, groups); diff --git a/src/network.c b/src/network.c index 6e64a8ceb56..9bdab8adce1 100644 --- a/src/network.c +++ b/src/network.c @@ -1033,6 +1033,10 @@ void fuse_conv_batchnorm(network net) if (l->type == CONVOLUTIONAL) { //printf(" Merges Convolutional-%d and batch_norm \n", j); + if (l->share_layer != NULL) { + l->batch_normalize = 0; + } + if (l->batch_normalize) { int f; for (f = 0; f < l->n; ++f) From a4a06c337a082bba02da2077bfb687dadf3bf580 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 1 Sep 2019 16:07:33 +0300 Subject: [PATCH 0581/1365] Addex stride_x= and stride_y= for [convolutional] layer for MatrixNet --- src/conv_lstm_layer.c | 22 +++++++++++----------- src/convolutional_kernels.cu | 8 ++++---- src/convolutional_layer.c | 33 +++++++++++++++++++-------------- src/convolutional_layer.h | 2 +- src/crnn_layer.c | 6 +++--- src/parser.c | 9 ++++++--- 6 files changed, 44 insertions(+), 36 deletions(-) diff --git a/src/conv_lstm_layer.c b/src/conv_lstm_layer.c index 6cbaf1c3911..5da2bab39e0 100644 --- a/src/conv_lstm_layer.c +++ b/src/conv_lstm_layer.c @@ -66,44 +66,44 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i // U l.uf = (layer*)calloc(1, sizeof(layer)); - *(l.uf) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.uf) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.uf->batch = batch; if (l.workspace_size < l.uf->workspace_size) l.workspace_size = l.uf->workspace_size; l.ui = (layer*)calloc(1, sizeof(layer)); - *(l.ui) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.ui) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.ui->batch = batch; if (l.workspace_size < l.ui->workspace_size) l.workspace_size = l.ui->workspace_size; l.ug = (layer*)calloc(1, sizeof(layer)); - *(l.ug) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.ug) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.ug->batch = batch; if (l.workspace_size < l.ug->workspace_size) l.workspace_size = l.ug->workspace_size; l.uo = (layer*)calloc(1, sizeof(layer)); - *(l.uo) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.uo) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.uo->batch = batch; if (l.workspace_size < l.uo->workspace_size) l.workspace_size = l.uo->workspace_size; // W l.wf = (layer*)calloc(1, sizeof(layer)); - *(l.wf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.wf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.wf->batch = batch; if (l.workspace_size < l.wf->workspace_size) l.workspace_size = l.wf->workspace_size; l.wi = (layer*)calloc(1, sizeof(layer)); - *(l.wi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.wi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.wi->batch = batch; if (l.workspace_size < l.wi->workspace_size) l.workspace_size = l.wi->workspace_size; l.wg = (layer*)calloc(1, sizeof(layer)); - *(l.wg) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.wg) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.wg->batch = batch; if (l.workspace_size < l.wg->workspace_size) l.workspace_size = l.wg->workspace_size; l.wo = (layer*)calloc(1, sizeof(layer)); - *(l.wo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.wo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.wo->batch = batch; if (l.workspace_size < l.wo->workspace_size) l.workspace_size = l.wo->workspace_size; @@ -111,21 +111,21 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i // V l.vf = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { - *(l.vf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.vf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.vf->batch = batch; if (l.workspace_size < l.vf->workspace_size) l.workspace_size = l.vf->workspace_size; } l.vi = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { - *(l.vi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.vi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.vi->batch = batch; if (l.workspace_size < l.vi->workspace_size) l.workspace_size = l.vi->workspace_size; } l.vo = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { - *(l.vo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.vo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.vo->batch = batch; if (l.workspace_size < l.vo->workspace_size) l.workspace_size = l.vo->workspace_size; } diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index e404ecabd85..07a0a0d7121 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -177,7 +177,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) fast_binarize_weights_gpu(l.weights_gpu, l.n, (l.c / l.groups)*l.size*l.size, l.binary_weights_gpu, l.mean_arr_gpu); } - if (l.align_bit_weights_gpu && !state.train && l.c >= 32) + if (l.align_bit_weights_gpu && !state.train && l.c >= 32 && l.stride_x == l.stride_y) { //return; //cudaError_t status = cudaSuccess; @@ -574,7 +574,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) l.h, l.w, // input size (h, w) l.size, l.size, // kernel size (h, w) l.pad, l.pad, // padding (h, w) - l.stride, l.stride, // stride (h, w) + l.stride_y, l.stride_x, // stride (h, w) l.dilation, l.dilation, // dilation (h, w) state.workspace); // output @@ -819,7 +819,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state l.h, l.w, // input size (h, w) l.size, l.size, // kernel size (h, w) l.pad, l.pad, // padding (h, w) - l.stride, l.stride, // stride (h, w) + l.stride_y, l.stride_x, // stride (h, w) l.dilation, l.dilation, // dilation (h, w) state.workspace); // output //gemm_ongpu(0, 1, m, n, k, 1, a + i*m*k, k, b, k, 1, c, n); @@ -844,7 +844,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state l.h, l.w, // input size (h, w) l.size, l.size, // kernel size (h, w) l.pad, l.pad, // padding size (h, w) - l.stride, l.stride, // stride size (h, w) + l.stride_y, l.stride_x, // stride size (h, w) l.dilation, l.dilation, // dilation size (h, w) delta); // output (delta) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 93ac79a0711..207e3f27cf6 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -76,12 +76,12 @@ void binarize_input(float *input, int n, int size, float *binary) int convolutional_out_height(convolutional_layer l) { - return (l.h + 2*l.pad - l.size) / l.stride + 1; + return (l.h + 2*l.pad - l.size) / l.stride_y + 1; } int convolutional_out_width(convolutional_layer l) { - return (l.w + 2*l.pad - l.size) / l.stride + 1; + return (l.w + 2*l.pad - l.size) / l.stride_x + 1; } image get_convolutional_image(convolutional_layer l) @@ -276,9 +276,9 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) //printf("\n l->dilation = %d, l->pad = %d, l->size = %d \n", l->dilation, l->pad, l->size); #if(CUDNN_MAJOR >= 6) - CHECK_CUDNN(cudnnSetConvolution2dDescriptor(l->convDesc, l->pad * l->dilation, l->pad* l->dilation, l->stride, l->stride, l->dilation, l->dilation, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); // cudnn >= 6.0 + CHECK_CUDNN(cudnnSetConvolution2dDescriptor(l->convDesc, l->pad * l->dilation, l->pad* l->dilation, l->stride_y, l->stride_x, l->dilation, l->dilation, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); // cudnn >= 6.0 #else - CHECK_CUDNN(cudnnSetConvolution2dDescriptor(l->convDesc, l->pad * l->dilation, l->pad * l->dilation, l->stride, l->stride, l->dilation, l->dilation, CUDNN_CROSS_CORRELATION)); // cudnn 5.1 + CHECK_CUDNN(cudnnSetConvolution2dDescriptor(l->convDesc, l->pad * l->dilation, l->pad * l->dilation, l->stride_y, l->stride_x, l->dilation, l->dilation, CUDNN_CROSS_CORRELATION)); // cudnn 5.1 #endif int forward_algo = CUDNN_CONVOLUTION_FWD_PREFER_FASTEST; int backward_algo = CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST; @@ -332,7 +332,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) #endif #endif -convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, convolutional_layer *share_layer) +convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, convolutional_layer *share_layer) { int total_batch = batch*steps; int i; @@ -354,7 +354,9 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.use_bin_output = use_bin_output; l.batch = batch; l.steps = steps; - l.stride = stride; + l.stride = stride_x; + l.stride_y = stride_x; + l.stride_x = stride_y; l.dilation = dilation; l.size = size; l.pad = padding; @@ -553,11 +555,14 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, else if(l.share_layer) fprintf(stderr, "convS "); else fprintf(stderr, "conv "); - if(groups > 1) fprintf(stderr, "%5d/%4d ", n, groups); + if (groups > 1) fprintf(stderr, "%5d/%4d ", n, groups); else fprintf(stderr, "%5d ", n); - if(dilation > 1) fprintf(stderr, "%2d x%2d/%2d(%1d)", size, size, stride, dilation); - else fprintf(stderr, "%2d x%2d/%2d ", size, size, stride); + if (stride_x != stride_y) fprintf(stderr, "%2d x%2d/%2dx%2d ", size, size, stride_x, stride_y); + else { + if (dilation > 1) fprintf(stderr, "%2d x%2d/%2d(%1d)", size, size, stride_x, dilation); + else fprintf(stderr, "%2d x%2d/%2d ", size, size, stride_x); + } fprintf(stderr, "%4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); @@ -583,7 +588,7 @@ void denormalize_convolutional_layer(convolutional_layer l) void test_convolutional_layer() { - convolutional_layer l = make_convolutional_layer(1, 1, 5, 5, 3, 2, 1, 5, 2, 1, 1, LEAKY, 1, 0, 0, 0, 0, 0, NULL); + convolutional_layer l = make_convolutional_layer(1, 1, 5, 5, 3, 2, 1, 5, 2, 2, 1, 1, LEAKY, 1, 0, 0, 0, 0, 0, NULL); l.batch_normalize = 1; float data[] = {1,1,1,1,1, 1,1,1,1,1, @@ -921,7 +926,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); //gemm_nn_custom(m, n, k, 1, a, k, b, n, c, n); - if (l.xnor && l.align_bit_weights && !state.train) + if (l.xnor && l.align_bit_weights && !state.train && l.stride_x == l.stride_y) { memset(b, 0, l.bit_align*l.size*l.size*l.c * sizeof(float)); @@ -1053,7 +1058,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) l.h, l.w, // input size (h, w) l.size, l.size, // kernel size (h, w) l.pad, l.pad, // padding (h, w) - l.stride, l.stride, // stride (h, w) + l.stride_y, l.stride_x, // stride (h, w) l.dilation, l.dilation, // dilation (h, w) b); // output @@ -1229,7 +1234,7 @@ void backward_convolutional_layer(convolutional_layer l, network_state state) l.h, l.w, // input size (h, w) l.size, l.size, // kernel size (h, w) l.pad, l.pad, // padding (h, w) - l.stride, l.stride, // stride (h, w) + l.stride_y, l.stride_x, // stride (h, w) l.dilation, l.dilation, // dilation (h, w) b); // output @@ -1251,7 +1256,7 @@ void backward_convolutional_layer(convolutional_layer l, network_state state) l.h, l.w, // input size (h, w) l.size, l.size, // kernel size (h, w) l.pad, l.pad, // padding (h, w) - l.stride, l.stride, // stride (h, w) + l.stride_y, l.stride_x, // stride (h, w) l.dilation, l.dilation, // dilation (h, w) state.delta + (i*l.groups + j)* (l.c / l.groups)*l.h*l.w); // output (delta) } diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h index e62b155c45f..1167175ccf4 100644 --- a/src/convolutional_layer.h +++ b/src/convolutional_layer.h @@ -30,7 +30,7 @@ void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16); #endif size_t get_convolutional_workspace_size(layer l); -convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, convolutional_layer *share_layer); +convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, convolutional_layer *share_layer); void denormalize_convolutional_layer(convolutional_layer l); void resize_convolutional_layer(convolutional_layer *layer, int w, int h); void forward_convolutional_layer(const convolutional_layer layer, network_state state); diff --git a/src/crnn_layer.c b/src/crnn_layer.c index 7609003b4f2..eaded279099 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -50,17 +50,17 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.state = (float*)calloc(l.hidden * l.batch * (l.steps + 1), sizeof(float)); l.input_layer = (layer*)calloc(1, sizeof(layer)); - *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.input_layer->batch = batch; if (l.workspace_size < l.input_layer->workspace_size) l.workspace_size = l.input_layer->workspace_size; l.self_layer = (layer*)calloc(1, sizeof(layer)); - *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.self_layer->batch = batch; if (l.workspace_size < l.self_layer->workspace_size) l.workspace_size = l.self_layer->workspace_size; l.output_layer = (layer*)calloc(1, sizeof(layer)); - *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.output_layer->batch = batch; if (l.workspace_size < l.output_layer->workspace_size) l.workspace_size = l.output_layer->workspace_size; diff --git a/src/parser.c b/src/parser.c index 48bd42bd3ce..8283f7ed0f0 100644 --- a/src/parser.c +++ b/src/parser.c @@ -158,6 +158,8 @@ convolutional_layer parse_convolutional(list *options, size_params params, netwo int groups = option_find_int_quiet(options, "groups", 1); int size = option_find_int(options, "size",1); int stride = option_find_int(options, "stride",1); + int stride_x = option_find_int_quiet(options, "stride_x", stride); + int stride_y = option_find_int_quiet(options, "stride_y", stride); int dilation = option_find_int_quiet(options, "dilation", 1); if (size == 1) dilation = 1; int pad = option_find_int_quiet(options, "pad",0); @@ -167,9 +169,10 @@ convolutional_layer parse_convolutional(list *options, size_params params, netwo char *activation_s = option_find_str(options, "activation", "logistic"); ACTIVATION activation = get_activation(activation_s); - int share_index = option_find_int_quiet(options, "share_index", -1); + int share_index = option_find_int_quiet(options, "share_index", -1000000000); convolutional_layer *share_layer = NULL; - if(share_index > -1) share_layer = &net.layers[share_index]; + if(share_index >= 0) share_layer = &net.layers[share_index]; + else if(share_index != -1000000000) share_layer = &net.layers[params.index + share_index]; int batch,h,w,c; h = params.h; @@ -182,7 +185,7 @@ convolutional_layer parse_convolutional(list *options, size_params params, netwo int xnor = option_find_int_quiet(options, "xnor", 0); int use_bin_output = option_find_int_quiet(options, "bin_output", 0); - convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride,dilation,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index, share_layer); + convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride_x,stride_y,dilation,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index, share_layer); layer.flipped = option_find_int_quiet(options, "flipped", 0); layer.dot = option_find_float_quiet(options, "dot", 0); layer.assisted_excitation = option_find_float_quiet(options, "assisted_excitation", 0); From 11b8b2bddf32190a83dbf7868881520b1e564377 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 2 Sep 2019 02:13:11 +0300 Subject: [PATCH 0582/1365] Added antialiasing=1 param for [convolutional]-layer on GPU --- include/darknet.h | 2 ++ src/conv_lstm_layer.c | 22 +++++++-------- src/convolutional_kernels.cu | 24 +++++++++++++++++ src/convolutional_layer.c | 52 ++++++++++++++++++++++++++++++++++-- src/convolutional_layer.h | 2 +- src/crnn_layer.c | 6 ++--- src/layer.c | 4 +++ src/parser.c | 19 +++++++++---- 8 files changed, 109 insertions(+), 22 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 5cfd274db52..a7a62b47bd7 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -211,6 +211,7 @@ struct layer { int stride_x; int stride_y; int dilation; + int antialiasing; int maxpool_depth; int out_channels; int reverse; @@ -528,6 +529,7 @@ struct layer { float * scale_updates_gpu; float * scale_change_gpu; + float * input_antialiasing_gpu; float * output_gpu; float * output_sigmoid_gpu; float * loss_gpu; diff --git a/src/conv_lstm_layer.c b/src/conv_lstm_layer.c index 5da2bab39e0..a6da3bf0c2c 100644 --- a/src/conv_lstm_layer.c +++ b/src/conv_lstm_layer.c @@ -66,44 +66,44 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i // U l.uf = (layer*)calloc(1, sizeof(layer)); - *(l.uf) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.uf) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); l.uf->batch = batch; if (l.workspace_size < l.uf->workspace_size) l.workspace_size = l.uf->workspace_size; l.ui = (layer*)calloc(1, sizeof(layer)); - *(l.ui) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.ui) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); l.ui->batch = batch; if (l.workspace_size < l.ui->workspace_size) l.workspace_size = l.ui->workspace_size; l.ug = (layer*)calloc(1, sizeof(layer)); - *(l.ug) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.ug) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); l.ug->batch = batch; if (l.workspace_size < l.ug->workspace_size) l.workspace_size = l.ug->workspace_size; l.uo = (layer*)calloc(1, sizeof(layer)); - *(l.uo) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.uo) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); l.uo->batch = batch; if (l.workspace_size < l.uo->workspace_size) l.workspace_size = l.uo->workspace_size; // W l.wf = (layer*)calloc(1, sizeof(layer)); - *(l.wf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.wf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); l.wf->batch = batch; if (l.workspace_size < l.wf->workspace_size) l.workspace_size = l.wf->workspace_size; l.wi = (layer*)calloc(1, sizeof(layer)); - *(l.wi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.wi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); l.wi->batch = batch; if (l.workspace_size < l.wi->workspace_size) l.workspace_size = l.wi->workspace_size; l.wg = (layer*)calloc(1, sizeof(layer)); - *(l.wg) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.wg) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); l.wg->batch = batch; if (l.workspace_size < l.wg->workspace_size) l.workspace_size = l.wg->workspace_size; l.wo = (layer*)calloc(1, sizeof(layer)); - *(l.wo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.wo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); l.wo->batch = batch; if (l.workspace_size < l.wo->workspace_size) l.workspace_size = l.wo->workspace_size; @@ -111,21 +111,21 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i // V l.vf = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { - *(l.vf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.vf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); l.vf->batch = batch; if (l.workspace_size < l.vf->workspace_size) l.workspace_size = l.vf->workspace_size; } l.vi = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { - *(l.vi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.vi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); l.vi->batch = batch; if (l.workspace_size < l.vi->workspace_size) l.workspace_size = l.vi->workspace_size; } l.vo = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { - *(l.vo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.vo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); l.vo->batch = batch; if (l.workspace_size < l.vo->workspace_size) l.workspace_size = l.vo->workspace_size; } diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 07a0a0d7121..b476ac76e3d 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -604,10 +604,34 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) if (state.net.try_fix_nan) { fix_nan_and_inf(l.output_gpu, l.outputs*l.batch); } + + if (l.antialiasing) { + network_state s = { 0 }; + s.train = state.train; + s.workspace = state.workspace; + s.net = state.net; + if (!state.train) s.index = state.index; // don't use TC for training (especially without cuda_convert_f32_to_f16() ) + s.input = l.output_gpu; + forward_convolutional_layer_gpu(*(l.input_layer), s); + simple_copy_ongpu(l.outputs*l.batch, l.output_gpu, l.input_antialiasing_gpu); + simple_copy_ongpu(l.input_layer->outputs*l.input_layer->batch, l.input_layer->output_gpu, l.output_gpu); + } } void backward_convolutional_layer_gpu(convolutional_layer l, network_state state) { + if (l.antialiasing) { + network_state s = { 0 }; + s.train = state.train; + s.workspace = state.workspace; + s.net = state.net; + s.delta = l.delta_gpu; + s.input = l.input_antialiasing_gpu; + //if (!state.train) s.index = state.index; // don't use TC for training (especially without cuda_convert_f32_to_f16() ) + simple_copy_ongpu(l.input_layer->outputs*l.input_layer->batch, l.delta_gpu, l.input_layer->delta_gpu); + backward_convolutional_layer_gpu(*(l.input_layer), s); + } + if(state.net.try_fix_nan) constrain_ongpu(l.outputs*l.batch, 1, l.delta_gpu, 1); if (l.activation == SWISH) gradient_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.output_sigmoid_gpu, l.delta_gpu); diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 207e3f27cf6..11402721545 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -332,7 +332,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) #endif #endif -convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, convolutional_layer *share_layer) +convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, int antialiasing, convolutional_layer *share_layer) { int total_batch = batch*steps; int i; @@ -342,6 +342,13 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, if (xnor) groups = 1; // disable groups for XNOR-net if (groups < 1) groups = 1; + const int blur_stride_x = stride_x; + const int blur_stride_y = stride_y; + l.antialiasing = antialiasing; + if (antialiasing) { + stride_x = stride_y = l.stride = l.stride_x = l.stride_y = 1; // use stride=1 in host-layer + } + l.share_layer = share_layer; l.index = index; l.h = h; @@ -568,6 +575,47 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, //fprintf(stderr, "%5d/%2d %2d x%2d /%2d(%d)%4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", n, groups, size, size, stride, dilation, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); + if (l.antialiasing) { + printf("AA: "); + l.input_layer = (layer*)calloc(1, sizeof(layer)); + const int blur_size = 3; + *(l.input_layer) = make_convolutional_layer(batch, steps, out_h, out_w, n, n, n, blur_size, blur_stride_x, blur_stride_y, 1, blur_size / 2, LINEAR, 0, 0, 0, 0, 0, index, 0, NULL); + const int blur_nweights = n * blur_size * blur_size; // (n / n) * n * blur_size * blur_size; + int i; + for (i = 0; i < blur_nweights; i += (blur_size*blur_size)) { + /* + l.input_layer->weights[i + 0] = 0; + l.input_layer->weights[i + 1] = 0; + l.input_layer->weights[i + 2] = 0; + + l.input_layer->weights[i + 3] = 0; + l.input_layer->weights[i + 4] = 1; + l.input_layer->weights[i + 5] = 0; + + l.input_layer->weights[i + 6] = 0; + l.input_layer->weights[i + 7] = 0; + l.input_layer->weights[i + 8] = 0; + */ + l.input_layer->weights[i + 0] = 1 / 16.f; + l.input_layer->weights[i + 1] = 2 / 16.f; + l.input_layer->weights[i + 2] = 1 / 16.f; + + l.input_layer->weights[i + 3] = 2 / 16.f; + l.input_layer->weights[i + 4] = 4 / 16.f; + l.input_layer->weights[i + 5] = 2 / 16.f; + + l.input_layer->weights[i + 6] = 1 / 16.f; + l.input_layer->weights[i + 7] = 2 / 16.f; + l.input_layer->weights[i + 8] = 1 / 16.f; + + } + for (i = 0; i < n; ++i) l.input_layer->biases[i] = 0; +#ifdef GPU + l.input_antialiasing_gpu = cuda_make_array(NULL, l.batch*l.outputs); + push_convolutional_layer(*(l.input_layer)); +#endif // GPU + } + return l; } @@ -588,7 +636,7 @@ void denormalize_convolutional_layer(convolutional_layer l) void test_convolutional_layer() { - convolutional_layer l = make_convolutional_layer(1, 1, 5, 5, 3, 2, 1, 5, 2, 2, 1, 1, LEAKY, 1, 0, 0, 0, 0, 0, NULL); + convolutional_layer l = make_convolutional_layer(1, 1, 5, 5, 3, 2, 1, 5, 2, 2, 1, 1, LEAKY, 1, 0, 0, 0, 0, 0, 0, NULL); l.batch_normalize = 1; float data[] = {1,1,1,1,1, 1,1,1,1,1, diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h index 1167175ccf4..1012663a5b3 100644 --- a/src/convolutional_layer.h +++ b/src/convolutional_layer.h @@ -30,7 +30,7 @@ void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16); #endif size_t get_convolutional_workspace_size(layer l); -convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, convolutional_layer *share_layer); +convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, int antialiasing, convolutional_layer *share_layer); void denormalize_convolutional_layer(convolutional_layer l); void resize_convolutional_layer(convolutional_layer *layer, int w, int h); void forward_convolutional_layer(const convolutional_layer layer, network_state state); diff --git a/src/crnn_layer.c b/src/crnn_layer.c index eaded279099..e3114fc9497 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -50,17 +50,17 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.state = (float*)calloc(l.hidden * l.batch * (l.steps + 1), sizeof(float)); l.input_layer = (layer*)calloc(1, sizeof(layer)); - *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); l.input_layer->batch = batch; if (l.workspace_size < l.input_layer->workspace_size) l.workspace_size = l.input_layer->workspace_size; l.self_layer = (layer*)calloc(1, sizeof(layer)); - *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); l.self_layer->batch = batch; if (l.workspace_size < l.self_layer->workspace_size) l.workspace_size = l.self_layer->workspace_size; l.output_layer = (layer*)calloc(1, sizeof(layer)); - *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); + *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); l.output_layer->batch = batch; if (l.workspace_size < l.output_layer->workspace_size) l.workspace_size = l.output_layer->workspace_size; diff --git a/src/layer.c b/src/layer.c index 68d1b35b68a..b6ae95dba12 100644 --- a/src/layer.c +++ b/src/layer.c @@ -13,6 +13,9 @@ void free_sublayer(layer *l) void free_layer(layer l) { if (l.share_layer != NULL) return; // don't free shared layers + if (l.antialiasing) { + free_sublayer(l.input_layer); + } if (l.type == CONV_LSTM) { if (l.peephole) { free_sublayer(l.vf); @@ -167,6 +170,7 @@ void free_layer(layer l) if (l.bias_updates_gpu) cuda_free(l.bias_updates_gpu), l.bias_updates_gpu = NULL; if (l.scales_gpu) cuda_free(l.scales_gpu), l.scales_gpu = NULL; if (l.scale_updates_gpu) cuda_free(l.scale_updates_gpu), l.scale_updates_gpu = NULL; + if (l.input_antialiasing_gpu) cuda_free(l.input_antialiasing_gpu), l.input_antialiasing_gpu = NULL; if (l.output_gpu) cuda_free(l.output_gpu), l.output_gpu = NULL; if (l.output_sigmoid_gpu) cuda_free(l.output_sigmoid_gpu), l.output_sigmoid_gpu = NULL; if (l.delta_gpu) cuda_free(l.delta_gpu), l.delta_gpu = NULL; diff --git a/src/parser.c b/src/parser.c index 8283f7ed0f0..fda2bacc041 100644 --- a/src/parser.c +++ b/src/parser.c @@ -161,6 +161,7 @@ convolutional_layer parse_convolutional(list *options, size_params params, netwo int stride_x = option_find_int_quiet(options, "stride_x", stride); int stride_y = option_find_int_quiet(options, "stride_y", stride); int dilation = option_find_int_quiet(options, "dilation", 1); + int antialiasing = option_find_int_quiet(options, "antialiasing", 0); if (size == 1) dilation = 1; int pad = option_find_int_quiet(options, "pad",0); int padding = option_find_int_quiet(options, "padding",0); @@ -185,7 +186,7 @@ convolutional_layer parse_convolutional(list *options, size_params params, netwo int xnor = option_find_int_quiet(options, "xnor", 0); int use_bin_output = option_find_int_quiet(options, "bin_output", 0); - convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride_x,stride_y,dilation,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index, share_layer); + convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride_x,stride_y,dilation,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index, antialiasing, share_layer); layer.flipped = option_find_int_quiet(options, "flipped", 0); layer.dot = option_find_float_quiet(options, "dot", 0); layer.assisted_excitation = option_find_float_quiet(options, "assisted_excitation", 0); @@ -991,10 +992,18 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) n = n->next; ++count; if(n){ - params.h = l.out_h; - params.w = l.out_w; - params.c = l.out_c; - params.inputs = l.outputs; + if (l.antialiasing) { + params.h = l.input_layer->out_h; + params.w = l.input_layer->out_w; + params.c = l.input_layer->out_c; + params.inputs = l.input_layer->outputs; + } + else { + params.h = l.out_h; + params.w = l.out_w; + params.c = l.out_c; + params.inputs = l.outputs; + } } if (l.bflops > 0) bflops += l.bflops; } From 80ceee4fca9fd44082e587ecb70c45cbc8e26dca Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 2 Sep 2019 14:24:54 +0300 Subject: [PATCH 0583/1365] Added antialiasing=1 param for [convolutional]-layer on CPU (only forward inference) --- src/convolutional_layer.c | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 11402721545..10c1f324188 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -1135,6 +1135,18 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //wait_until_press_key_cv(); if(l.assisted_excitation && state.train) assisted_excitation_forward(l, state); + + if (l.antialiasing) { + network_state s = { 0 }; + s.train = state.train; + s.workspace = state.workspace; + s.net = state.net; + if (!state.train) s.index = state.index; // don't use TC for training (especially without cuda_convert_f32_to_f16() ) + s.input = l.output; + forward_convolutional_layer(*(l.input_layer), s); + //simple_copy_ongpu(l.outputs*l.batch, l.output, l.input_antialiasing); + memcpy(l.output, l.input_layer->output, l.input_layer->outputs * l.input_layer->batch * sizeof(float)); + } } static box float_to_box_stride(float *f, int stride) From 9e26472b1ac245a87289b1a90af3007cd937d94b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 2 Sep 2019 15:25:42 +0300 Subject: [PATCH 0584/1365] Added antialiasing=1 param for [maxpool]-layer on GPU and CPU --- src/convolutional_layer.c | 3 +- src/maxpool_layer.c | 115 +++++++++++++++++++++++++++-------- src/maxpool_layer.h | 2 +- src/maxpool_layer_kernels.cu | 41 ++++++++++--- src/parser.c | 3 +- 5 files changed, 126 insertions(+), 38 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 10c1f324188..c5c59576349 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -576,7 +576,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, //fprintf(stderr, "%5d/%2d %2d x%2d /%2d(%d)%4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", n, groups, size, size, stride, dilation, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); if (l.antialiasing) { - printf("AA: "); + printf("AA: "); l.input_layer = (layer*)calloc(1, sizeof(layer)); const int blur_size = 3; *(l.input_layer) = make_convolutional_layer(batch, steps, out_h, out_w, n, n, n, blur_size, blur_stride_x, blur_stride_y, 1, blur_size / 2, LINEAR, 0, 0, 0, 0, 0, index, 0, NULL); @@ -1141,7 +1141,6 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) s.train = state.train; s.workspace = state.workspace; s.net = state.net; - if (!state.train) s.index = state.index; // don't use TC for training (especially without cuda_convert_f32_to_f16() ) s.input = l.output; forward_convolutional_layer(*(l.input_layer), s); //simple_copy_ongpu(l.outputs*l.batch, l.output, l.input_antialiasing); diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index 000efe90663..1239262197d 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -1,4 +1,5 @@ #include "maxpool_layer.h" +#include "convolutional_layer.h" #include "dark_cuda.h" #include "gemm.h" #include @@ -45,10 +46,18 @@ void cudnn_maxpool_setup(layer *l) } -maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride_x, int stride_y, int padding, int maxpool_depth, int out_channels) +maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride_x, int stride_y, int padding, int maxpool_depth, int out_channels, int antialiasing) { maxpool_layer l = { (LAYER_TYPE)0 }; l.type = MAXPOOL; + + const int blur_stride_x = stride_x; + const int blur_stride_y = stride_y; + l.antialiasing = antialiasing; + if (antialiasing) { + stride_x = stride_y = l.stride = l.stride_x = l.stride_y = 1; // use stride=1 in host-layer + } + l.batch = batch; l.h = h; l.w = w; @@ -94,6 +103,46 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s else fprintf(stderr, "max %d x %d/%2dx%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride_x, stride_y, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); + if (l.antialiasing) { + printf("AA: "); + l.input_layer = (layer*)calloc(1, sizeof(layer)); + const int blur_size = 3; + *(l.input_layer) = make_convolutional_layer(batch, 1, l.out_h, l.out_w, l.out_c, l.out_c, l.out_c, blur_size, blur_stride_x, blur_stride_y, 1, blur_size / 2, LINEAR, 0, 0, 0, 0, 0, 1, 0, NULL); + const int blur_nweights = l.out_c * blur_size * blur_size; // (n / n) * n * blur_size * blur_size; + int i; + for (i = 0; i < blur_nweights; i += (blur_size*blur_size)) { + /* + l.input_layer->weights[i + 0] = 0; + l.input_layer->weights[i + 1] = 0; + l.input_layer->weights[i + 2] = 0; + + l.input_layer->weights[i + 3] = 0; + l.input_layer->weights[i + 4] = 1; + l.input_layer->weights[i + 5] = 0; + + l.input_layer->weights[i + 6] = 0; + l.input_layer->weights[i + 7] = 0; + l.input_layer->weights[i + 8] = 0; + */ + l.input_layer->weights[i + 0] = 1 / 16.f; + l.input_layer->weights[i + 1] = 2 / 16.f; + l.input_layer->weights[i + 2] = 1 / 16.f; + + l.input_layer->weights[i + 3] = 2 / 16.f; + l.input_layer->weights[i + 4] = 4 / 16.f; + l.input_layer->weights[i + 5] = 2 / 16.f; + + l.input_layer->weights[i + 6] = 1 / 16.f; + l.input_layer->weights[i + 7] = 2 / 16.f; + l.input_layer->weights[i + 8] = 1 / 16.f; + } + for (i = 0; i < l.out_c; ++i) l.input_layer->biases[i] = 0; +#ifdef GPU + l.input_antialiasing_gpu = cuda_make_array(NULL, l.batch*l.outputs); + push_convolutional_layer(*(l.input_layer)); +#endif // GPU + } + return l; } @@ -159,42 +208,54 @@ void forward_maxpool_layer(const maxpool_layer l, network_state state) if (!state.train && l.stride_x == l.stride_y) { forward_maxpool_layer_avx(state.input, l.output, l.indexes, l.size, l.w, l.h, l.out_w, l.out_h, l.c, l.pad, l.stride, l.batch); - return; } + else { - int b,i,j,k,m,n; - int w_offset = -l.pad / 2; - int h_offset = -l.pad / 2; + int b, i, j, k, m, n; + int w_offset = -l.pad / 2; + int h_offset = -l.pad / 2; - int h = l.out_h; - int w = l.out_w; - int c = l.c; + int h = l.out_h; + int w = l.out_w; + int c = l.c; - for(b = 0; b < l.batch; ++b){ - for(k = 0; k < c; ++k){ - for(i = 0; i < h; ++i){ - for(j = 0; j < w; ++j){ - int out_index = j + w*(i + h*(k + c*b)); - float max = -FLT_MAX; - int max_i = -1; - for(n = 0; n < l.size; ++n){ - for(m = 0; m < l.size; ++m){ - int cur_h = h_offset + i*l.stride_y + n; - int cur_w = w_offset + j*l.stride_x + m; - int index = cur_w + l.w*(cur_h + l.h*(k + b*l.c)); - int valid = (cur_h >= 0 && cur_h < l.h && - cur_w >= 0 && cur_w < l.w); - float val = (valid != 0) ? state.input[index] : -FLT_MAX; - max_i = (val > max) ? index : max_i; - max = (val > max) ? val : max; + for (b = 0; b < l.batch; ++b) { + for (k = 0; k < c; ++k) { + for (i = 0; i < h; ++i) { + for (j = 0; j < w; ++j) { + int out_index = j + w*(i + h*(k + c*b)); + float max = -FLT_MAX; + int max_i = -1; + for (n = 0; n < l.size; ++n) { + for (m = 0; m < l.size; ++m) { + int cur_h = h_offset + i*l.stride_y + n; + int cur_w = w_offset + j*l.stride_x + m; + int index = cur_w + l.w*(cur_h + l.h*(k + b*l.c)); + int valid = (cur_h >= 0 && cur_h < l.h && + cur_w >= 0 && cur_w < l.w); + float val = (valid != 0) ? state.input[index] : -FLT_MAX; + max_i = (val > max) ? index : max_i; + max = (val > max) ? val : max; + } } + l.output[out_index] = max; + l.indexes[out_index] = max_i; } - l.output[out_index] = max; - l.indexes[out_index] = max_i; } } } } + + if (l.antialiasing) { + network_state s = { 0 }; + s.train = state.train; + s.workspace = state.workspace; + s.net = state.net; + s.input = l.output; + forward_convolutional_layer(*(l.input_layer), s); + //simple_copy_ongpu(l.outputs*l.batch, l.output, l.input_antialiasing); + memcpy(l.output, l.input_layer->output, l.input_layer->outputs * l.input_layer->batch * sizeof(float)); + } } void backward_maxpool_layer(const maxpool_layer l, network_state state) diff --git a/src/maxpool_layer.h b/src/maxpool_layer.h index 4994d45700d..cfedf9d9ee6 100644 --- a/src/maxpool_layer.h +++ b/src/maxpool_layer.h @@ -12,7 +12,7 @@ typedef layer maxpool_layer; extern "C" { #endif image get_maxpool_image(maxpool_layer l); -maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride_x, int stride_y, int padding, int maxpool_depth, int out_channels); +maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride_x, int stride_y, int padding, int maxpool_depth, int out_channels, int antialiasing); void resize_maxpool_layer(maxpool_layer *l, int w, int h); void forward_maxpool_layer(const maxpool_layer l, network_state state); void backward_maxpool_layer(const maxpool_layer l, network_state state); diff --git a/src/maxpool_layer_kernels.cu b/src/maxpool_layer_kernels.cu index 8e8511003e5..cc546a0b50c 100644 --- a/src/maxpool_layer_kernels.cu +++ b/src/maxpool_layer_kernels.cu @@ -3,6 +3,8 @@ #include #include "maxpool_layer.h" +#include "convolutional_layer.h" +#include "blas.h" #include "dark_cuda.h" __global__ void forward_maxpool_depth_layer_kernel(int n, int w, int h, int c, int out_c, int batch, float *input, float *output, int *indexes) @@ -163,22 +165,47 @@ extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network_state sta //cudnnDestroyTensorDescriptor(layer.srcTensorDesc); //cudnnDestroyTensorDescriptor(layer.dstTensorDesc); - return; } + else #endif + { + int h = layer.out_h; + int w = layer.out_w; + int c = layer.out_c; - int h = layer.out_h; - int w = layer.out_w; - int c = layer.out_c; + size_t n = h*w*c*layer.batch; - size_t n = h*w*c*layer.batch; + forward_maxpool_layer_kernel << > > (n, layer.h, layer.w, layer.c, layer.stride_x, layer.stride_y, layer.size, layer.pad, state.input, layer.output_gpu, layer.indexes_gpu); + CHECK_CUDA(cudaPeekAtLastError()); + } - forward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride_x, layer.stride_y, layer.size, layer.pad, state.input, layer.output_gpu, layer.indexes_gpu); - CHECK_CUDA(cudaPeekAtLastError()); + if (layer.antialiasing) { + network_state s = { 0 }; + s.train = state.train; + s.workspace = state.workspace; + s.net = state.net; + if (!state.train) s.index = state.index; // don't use TC for training (especially without cuda_convert_f32_to_f16() ) + s.input = layer.output_gpu; + forward_convolutional_layer_gpu(*(layer.input_layer), s); + simple_copy_ongpu(layer.outputs*layer.batch, layer.output_gpu, layer.input_antialiasing_gpu); + simple_copy_ongpu(layer.input_layer->outputs*layer.input_layer->batch, layer.input_layer->output_gpu, layer.output_gpu); + } } extern "C" void backward_maxpool_layer_gpu(maxpool_layer layer, network_state state) { + if (layer.antialiasing) { + network_state s = { 0 }; + s.train = state.train; + s.workspace = state.workspace; + s.net = state.net; + s.delta = layer.delta_gpu; + s.input = layer.input_antialiasing_gpu; + //if (!state.train) s.index = state.index; // don't use TC for training (especially without cuda_convert_f32_to_f16() ) + simple_copy_ongpu(layer.input_layer->outputs*layer.input_layer->batch, layer.delta_gpu, layer.input_layer->delta_gpu); + backward_convolutional_layer_gpu(*(layer.input_layer), s); + } + if (layer.maxpool_depth) { int h = layer.out_h; int w = layer.out_w; diff --git a/src/parser.c b/src/parser.c index fda2bacc041..b89bf0acc7b 100644 --- a/src/parser.c +++ b/src/parser.c @@ -545,6 +545,7 @@ maxpool_layer parse_maxpool(list *options, size_params params) int padding = option_find_int_quiet(options, "padding", size-1); int maxpool_depth = option_find_int_quiet(options, "maxpool_depth", 0); int out_channels = option_find_int_quiet(options, "out_channels", 1); + int antialiasing = option_find_int_quiet(options, "antialiasing", 0); int batch,h,w,c; h = params.h; @@ -553,7 +554,7 @@ maxpool_layer parse_maxpool(list *options, size_params params) batch=params.batch; if(!(h && w && c)) error("Layer before maxpool layer must output image."); - maxpool_layer layer = make_maxpool_layer(batch, h, w, c, size, stride_x, stride_y, padding, maxpool_depth, out_channels); + maxpool_layer layer = make_maxpool_layer(batch, h, w, c, size, stride_x, stride_y, padding, maxpool_depth, out_channels, antialiasing); return layer; } From a63782ca8937f412e943b6c841a5671ce39c60fc Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 2 Sep 2019 15:55:05 +0300 Subject: [PATCH 0585/1365] Added: efficientnet_b0.cfg --- build/darknet/x64/cfg/efficientnet_b0.cfg | 1005 +++++++++++++++++++++ cfg/efficientnet_b0.cfg | 1005 +++++++++++++++++++++ 2 files changed, 2010 insertions(+) create mode 100644 build/darknet/x64/cfg/efficientnet_b0.cfg create mode 100644 cfg/efficientnet_b0.cfg diff --git a/build/darknet/x64/cfg/efficientnet_b0.cfg b/build/darknet/x64/cfg/efficientnet_b0.cfg new file mode 100644 index 00000000000..3bd3e895bc1 --- /dev/null +++ b/build/darknet/x64/cfg/efficientnet_b0.cfg @@ -0,0 +1,1005 @@ +[net] +# Training +batch=120 +subdivisions=4 +# Testing +#batch=1 +#subdivisions=1 +height=224 +width=224 +channels=3 +momentum=0.9 +decay=0.0005 +max_crop=256 + +burn_in=1000 +#burn_in=100 +learning_rate=0.256 +policy=poly +power=4 +max_batches=800000 +momentum=0.9 +decay=0.00005 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 + + +### CONV1 - 1 (1) +# conv1 +[convolutional] +filters=32 +size=3 +pad=1 +stride=2 +batch_normalize=1 +activation=swish + + +### CONV2 - MBConv1 - 1 (1) +# conv2_1_expand +[convolutional] +filters=32 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv2_1_dwise +[convolutional] +groups=32 +filters=32 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=4 (recommended r=16) +[convolutional] +filters=8 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=32 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv2_1_linear +[convolutional] +filters=16 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV3 - MBConv6 - 1 (2) +# conv2_2_expand +[convolutional] +filters=96 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv2_2_dwise +[convolutional] +groups=96 +filters=96 +size=3 +pad=1 +stride=2 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=8 (recommended r=16) +[convolutional] +filters=16 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=96 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv2_2_linear +[convolutional] +filters=24 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV3 - MBConv6 - 2 (2) +# conv3_1_expand +[convolutional] +filters=144 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv3_1_dwise +[convolutional] +groups=144 +filters=144 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=8 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=144 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv3_1_linear +[convolutional] +filters=24 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV4 - MBConv6 - 1 (2) +# dropout only before residual connection +[dropout] +probability=.2 + +# block_3_1 +[shortcut] +from=-9 +activation=linear + +# conv_3_2_expand +[convolutional] +filters=144 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_3_2_dwise +[convolutional] +groups=144 +filters=144 +size=5 +pad=1 +stride=2 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=8 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=144 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_3_2_linear +[convolutional] +filters=40 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV4 - MBConv6 - 2 (2) +# conv_4_1_expand +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_4_1_dwise +[convolutional] +groups=192 +filters=192 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=16 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=192 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_4_1_linear +[convolutional] +filters=40 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + + +### CONV5 - MBConv6 - 1 (3) +# dropout only before residual connection +[dropout] +probability=.2 + +# block_4_2 +[shortcut] +from=-9 +activation=linear + +# conv_4_3_expand +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_4_3_dwise +[convolutional] +groups=192 +filters=192 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=16 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=192 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_4_3_linear +[convolutional] +filters=80 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV5 - MBConv6 - 2 (3) +# conv_4_4_expand +[convolutional] +filters=384 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_4_4_dwise +[convolutional] +groups=384 +filters=384 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=24 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=384 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_4_4_linear +[convolutional] +filters=80 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV5 - MBConv6 - 3 (3) +# dropout only before residual connection +[dropout] +probability=.2 + +# block_4_4 +[shortcut] +from=-9 +activation=linear + +# conv_4_5_expand +[convolutional] +filters=384 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_4_5_dwise +[convolutional] +groups=384 +filters=384 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=24 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=384 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_4_5_linear +[convolutional] +filters=80 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV6 - MBConv6 - 1 (3) +# dropout only before residual connection +[dropout] +probability=.2 + +# block_4_6 +[shortcut] +from=-9 +activation=linear + +# conv_4_7_expand +[convolutional] +filters=384 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_4_7_dwise +[convolutional] +groups=384 +filters=384 +size=5 +pad=1 +stride=2 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=24 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=384 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_4_7_linear +[convolutional] +filters=112 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV6 - MBConv6 - 2 (3) +# conv_5_1_expand +[convolutional] +filters=576 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_5_1_dwise +[convolutional] +groups=576 +filters=576 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=32 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=576 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_5_1_linear +[convolutional] +filters=112 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV6 - MBConv6 - 3 (3) +# dropout only before residual connection +[dropout] +probability=.2 + +# block_5_1 +[shortcut] +from=-9 +activation=linear + +# conv_5_2_expand +[convolutional] +filters=576 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_5_2_dwise +[convolutional] +groups=576 +filters=576 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=32 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=576 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_5_2_linear +[convolutional] +filters=112 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 1 (4) +# dropout only before residual connection +[dropout] +probability=.2 + +# block_5_2 +[shortcut] +from=-9 +activation=linear + +# conv_5_3_expand +[convolutional] +filters=576 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_5_3_dwise +[convolutional] +groups=576 +filters=576 +size=5 +pad=1 +stride=2 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=32 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=576 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_5_3_linear +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 2 (4) +# conv_6_1_expand +[convolutional] +filters=960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_6_1_dwise +[convolutional] +groups=960 +filters=960 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=64 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=960 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_6_1_linear +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 3 (4) +# dropout only before residual connection +[dropout] +probability=.2 + +# block_6_1 +[shortcut] +from=-9 +activation=linear + +# conv_6_2_expand +[convolutional] +filters=960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_6_2_dwise +[convolutional] +groups=960 +filters=960 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=64 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=960 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_6_2_linear +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 4 (4) +# dropout only before residual connection +[dropout] +probability=.2 + +# block_6_1 +[shortcut] +from=-9 +activation=linear + +# conv_6_2_expand +[convolutional] +filters=960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_6_2_dwise +[convolutional] +groups=960 +filters=960 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=64 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=960 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_6_2_linear +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV8 - MBConv6 - 1 (1) +# dropout only before residual connection +[dropout] +probability=.2 + +# block_6_2 +[shortcut] +from=-9 +activation=linear + +# conv_6_3_expand +[convolutional] +filters=960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_6_3_dwise +[convolutional] +groups=960 +filters=960 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=64 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=960 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_6_3_linear +[convolutional] +filters=320 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV9 - Conv2d 1x1 +# conv_6_4 +[convolutional] +filters=1280 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + + +[avgpool] + +[dropout] +probability=.2 + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=0 +activation=linear + +[softmax] +groups=1 + +#[cost] +#type=sse + diff --git a/cfg/efficientnet_b0.cfg b/cfg/efficientnet_b0.cfg new file mode 100644 index 00000000000..3bd3e895bc1 --- /dev/null +++ b/cfg/efficientnet_b0.cfg @@ -0,0 +1,1005 @@ +[net] +# Training +batch=120 +subdivisions=4 +# Testing +#batch=1 +#subdivisions=1 +height=224 +width=224 +channels=3 +momentum=0.9 +decay=0.0005 +max_crop=256 + +burn_in=1000 +#burn_in=100 +learning_rate=0.256 +policy=poly +power=4 +max_batches=800000 +momentum=0.9 +decay=0.00005 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 + + +### CONV1 - 1 (1) +# conv1 +[convolutional] +filters=32 +size=3 +pad=1 +stride=2 +batch_normalize=1 +activation=swish + + +### CONV2 - MBConv1 - 1 (1) +# conv2_1_expand +[convolutional] +filters=32 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv2_1_dwise +[convolutional] +groups=32 +filters=32 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=4 (recommended r=16) +[convolutional] +filters=8 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=32 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv2_1_linear +[convolutional] +filters=16 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV3 - MBConv6 - 1 (2) +# conv2_2_expand +[convolutional] +filters=96 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv2_2_dwise +[convolutional] +groups=96 +filters=96 +size=3 +pad=1 +stride=2 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=8 (recommended r=16) +[convolutional] +filters=16 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=96 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv2_2_linear +[convolutional] +filters=24 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV3 - MBConv6 - 2 (2) +# conv3_1_expand +[convolutional] +filters=144 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv3_1_dwise +[convolutional] +groups=144 +filters=144 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=8 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=144 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv3_1_linear +[convolutional] +filters=24 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV4 - MBConv6 - 1 (2) +# dropout only before residual connection +[dropout] +probability=.2 + +# block_3_1 +[shortcut] +from=-9 +activation=linear + +# conv_3_2_expand +[convolutional] +filters=144 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_3_2_dwise +[convolutional] +groups=144 +filters=144 +size=5 +pad=1 +stride=2 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=8 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=144 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_3_2_linear +[convolutional] +filters=40 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV4 - MBConv6 - 2 (2) +# conv_4_1_expand +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_4_1_dwise +[convolutional] +groups=192 +filters=192 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=16 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=192 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_4_1_linear +[convolutional] +filters=40 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + + +### CONV5 - MBConv6 - 1 (3) +# dropout only before residual connection +[dropout] +probability=.2 + +# block_4_2 +[shortcut] +from=-9 +activation=linear + +# conv_4_3_expand +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_4_3_dwise +[convolutional] +groups=192 +filters=192 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=16 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=192 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_4_3_linear +[convolutional] +filters=80 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV5 - MBConv6 - 2 (3) +# conv_4_4_expand +[convolutional] +filters=384 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_4_4_dwise +[convolutional] +groups=384 +filters=384 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=24 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=384 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_4_4_linear +[convolutional] +filters=80 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV5 - MBConv6 - 3 (3) +# dropout only before residual connection +[dropout] +probability=.2 + +# block_4_4 +[shortcut] +from=-9 +activation=linear + +# conv_4_5_expand +[convolutional] +filters=384 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_4_5_dwise +[convolutional] +groups=384 +filters=384 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=24 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=384 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_4_5_linear +[convolutional] +filters=80 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV6 - MBConv6 - 1 (3) +# dropout only before residual connection +[dropout] +probability=.2 + +# block_4_6 +[shortcut] +from=-9 +activation=linear + +# conv_4_7_expand +[convolutional] +filters=384 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_4_7_dwise +[convolutional] +groups=384 +filters=384 +size=5 +pad=1 +stride=2 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=24 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=384 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_4_7_linear +[convolutional] +filters=112 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV6 - MBConv6 - 2 (3) +# conv_5_1_expand +[convolutional] +filters=576 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_5_1_dwise +[convolutional] +groups=576 +filters=576 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=32 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=576 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_5_1_linear +[convolutional] +filters=112 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV6 - MBConv6 - 3 (3) +# dropout only before residual connection +[dropout] +probability=.2 + +# block_5_1 +[shortcut] +from=-9 +activation=linear + +# conv_5_2_expand +[convolutional] +filters=576 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_5_2_dwise +[convolutional] +groups=576 +filters=576 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=32 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=576 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_5_2_linear +[convolutional] +filters=112 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 1 (4) +# dropout only before residual connection +[dropout] +probability=.2 + +# block_5_2 +[shortcut] +from=-9 +activation=linear + +# conv_5_3_expand +[convolutional] +filters=576 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_5_3_dwise +[convolutional] +groups=576 +filters=576 +size=5 +pad=1 +stride=2 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=32 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=576 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_5_3_linear +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 2 (4) +# conv_6_1_expand +[convolutional] +filters=960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_6_1_dwise +[convolutional] +groups=960 +filters=960 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=64 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=960 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_6_1_linear +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 3 (4) +# dropout only before residual connection +[dropout] +probability=.2 + +# block_6_1 +[shortcut] +from=-9 +activation=linear + +# conv_6_2_expand +[convolutional] +filters=960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_6_2_dwise +[convolutional] +groups=960 +filters=960 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=64 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=960 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_6_2_linear +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 4 (4) +# dropout only before residual connection +[dropout] +probability=.2 + +# block_6_1 +[shortcut] +from=-9 +activation=linear + +# conv_6_2_expand +[convolutional] +filters=960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_6_2_dwise +[convolutional] +groups=960 +filters=960 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=64 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=960 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_6_2_linear +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV8 - MBConv6 - 1 (1) +# dropout only before residual connection +[dropout] +probability=.2 + +# block_6_2 +[shortcut] +from=-9 +activation=linear + +# conv_6_3_expand +[convolutional] +filters=960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_6_3_dwise +[convolutional] +groups=960 +filters=960 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=64 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=960 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_6_3_linear +[convolutional] +filters=320 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV9 - Conv2d 1x1 +# conv_6_4 +[convolutional] +filters=1280 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + + +[avgpool] + +[dropout] +probability=.2 + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=0 +activation=linear + +[softmax] +groups=1 + +#[cost] +#type=sse + From be5d0d66933e50585688bc86bb42786de55893ab Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 3 Sep 2019 01:35:05 +0300 Subject: [PATCH 0586/1365] Added assisted_excitation=1 for [convolutional] layer on GPU --- include/darknet.h | 3 + src/conv_lstm_layer.c | 22 ++-- src/convolutional_kernels.cu | 191 +++++++++++++++++++++++++++++++++++ src/convolutional_layer.c | 25 +++-- src/convolutional_layer.h | 3 +- src/crnn_layer.c | 6 +- src/maxpool_layer.c | 2 +- src/parser.c | 6 +- 8 files changed, 232 insertions(+), 26 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index a7a62b47bd7..e78abe6a5c9 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -537,6 +537,9 @@ struct layer { float * rand_gpu; float * squared_gpu; float * norms_gpu; + + float *gt_gpu; + float *a_avg_gpu; #ifdef CUDNN cudnnTensorDescriptor_t srcTensorDesc, dstTensorDesc; cudnnTensorDescriptor_t srcTensorDesc16, dstTensorDesc16; diff --git a/src/conv_lstm_layer.c b/src/conv_lstm_layer.c index a6da3bf0c2c..4ae67b44a83 100644 --- a/src/conv_lstm_layer.c +++ b/src/conv_lstm_layer.c @@ -66,44 +66,44 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i // U l.uf = (layer*)calloc(1, sizeof(layer)); - *(l.uf) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); + *(l.uf) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); l.uf->batch = batch; if (l.workspace_size < l.uf->workspace_size) l.workspace_size = l.uf->workspace_size; l.ui = (layer*)calloc(1, sizeof(layer)); - *(l.ui) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); + *(l.ui) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); l.ui->batch = batch; if (l.workspace_size < l.ui->workspace_size) l.workspace_size = l.ui->workspace_size; l.ug = (layer*)calloc(1, sizeof(layer)); - *(l.ug) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); + *(l.ug) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); l.ug->batch = batch; if (l.workspace_size < l.ug->workspace_size) l.workspace_size = l.ug->workspace_size; l.uo = (layer*)calloc(1, sizeof(layer)); - *(l.uo) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); + *(l.uo) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); l.uo->batch = batch; if (l.workspace_size < l.uo->workspace_size) l.workspace_size = l.uo->workspace_size; // W l.wf = (layer*)calloc(1, sizeof(layer)); - *(l.wf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); + *(l.wf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); l.wf->batch = batch; if (l.workspace_size < l.wf->workspace_size) l.workspace_size = l.wf->workspace_size; l.wi = (layer*)calloc(1, sizeof(layer)); - *(l.wi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); + *(l.wi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); l.wi->batch = batch; if (l.workspace_size < l.wi->workspace_size) l.workspace_size = l.wi->workspace_size; l.wg = (layer*)calloc(1, sizeof(layer)); - *(l.wg) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); + *(l.wg) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); l.wg->batch = batch; if (l.workspace_size < l.wg->workspace_size) l.workspace_size = l.wg->workspace_size; l.wo = (layer*)calloc(1, sizeof(layer)); - *(l.wo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); + *(l.wo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); l.wo->batch = batch; if (l.workspace_size < l.wo->workspace_size) l.workspace_size = l.wo->workspace_size; @@ -111,21 +111,21 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i // V l.vf = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { - *(l.vf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); + *(l.vf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); l.vf->batch = batch; if (l.workspace_size < l.vf->workspace_size) l.workspace_size = l.vf->workspace_size; } l.vi = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { - *(l.vi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); + *(l.vi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); l.vi->batch = batch; if (l.workspace_size < l.vi->workspace_size) l.workspace_size = l.vi->workspace_size; } l.vo = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { - *(l.vo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); + *(l.vo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); l.vo->batch = batch; if (l.workspace_size < l.vo->workspace_size) l.workspace_size = l.vo->workspace_size; } diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index b476ac76e3d..566fb893335 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -605,6 +605,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) fix_nan_and_inf(l.output_gpu, l.outputs*l.batch); } + if(l.assisted_excitation && state.train) assisted_excitation_forward_gpu(l, state); + if (l.antialiasing) { network_state s = { 0 }; s.train = state.train; @@ -890,6 +892,195 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state } } +static box float_to_box_stride(float *f, int stride) +{ + box b = { 0 }; + b.x = f[0]; + b.y = f[1 * stride]; + b.w = f[2 * stride]; + b.h = f[3 * stride]; + return b; +} + +__global__ void calc_avg_activation_kernel(float *src, float *dst, int size, int channels, int batches) +{ + int i = blockIdx.x * blockDim.x + threadIdx.x; + int xy = i % size; + int b = i / size; + + if (i < size*batches) { + dst[i] = 0; + for (int c = 0; c < channels; ++c) { + dst[i] += src[xy + size*(c + channels*b)]; + } + dst[i] = dst[i] / channels; + } +} + +#include + +void calc_avg_activation_gpu(float *src, float *dst, int size, int channels, int batches) +{ + const int num_blocks = get_number_of_blocks(size*batches, BLOCK); + + std::cout << " size = " << size << ", channels = " << channels << ", batches = " << batches << std::endl; + calc_avg_activation_kernel << > > (src, dst, size, channels, batches); +} + + +__global__ void assisted_activation_kernel(float alpha, float *output, float *gt_gpu, float *a_avg_gpu, int size, int channels, int batches) +{ + int i = blockIdx.x * blockDim.x + threadIdx.x; + int xy = i % size; + int b = i / size; + + if (b < batches) { + for (int c = 0; c < channels; ++c) { + output[xy + size*(c + channels*b)] += alpha * gt_gpu[i] * a_avg_gpu[i]; + } + } +} + +void assisted_activation_gpu(float alpha, float *output, float *gt_gpu, float *a_avg_gpu, int size, int channels, int batches) +{ + const int num_blocks = get_number_of_blocks(size*batches, BLOCK); + + assisted_activation_kernel << > > (alpha, output, gt_gpu, a_avg_gpu, size, channels, batches); +} + +void assisted_excitation_forward_gpu(convolutional_layer l, network_state state) +{ + const int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); + + // epoch + const float epoch = (float)(*state.net.seen) / state.net.train_images_num; + + // calculate alpha + //const float alpha = (1 + cos(3.141592 * iteration_num)) / (2 * state.net.max_batches); + //const float alpha = (1 + cos(3.141592 * epoch)) / (2 * state.net.max_batches); + const float alpha = (1 + cos(3.141592 * iteration_num / state.net.max_batches)) / 2; + + //printf("\n epoch = %f, alpha = %f, seen = %d, max_batches = %d, train_images_num = %d \n", + // epoch, alpha, (*state.net.seen), state.net.max_batches, state.net.train_images_num); + + //const int size = l.outputs * l.batch; + + float *a_avg = (float *)calloc(l.out_w * l.out_h * l.batch, sizeof(float)); + float *gt = (float *)calloc(l.out_w * l.out_h * l.batch, sizeof(float)); + + int b; + int w, h, c; + + l.max_boxes = state.net.num_boxes; + l.truths = l.max_boxes*(4 + 1); + + int num_truth = l.batch*l.truths; + float *truth_cpu = (float *)calloc(num_truth, sizeof(float)); + cuda_pull_array(state.truth, truth_cpu, num_truth); + //cudaStreamSynchronize(get_cuda_stream()); + //CHECK_CUDA(cudaPeekAtLastError()); + + for (b = 0; b < l.batch; ++b) + { + // calculate G + int t; + for (t = 0; t < state.net.num_boxes; ++t) { + box truth = float_to_box_stride(truth_cpu + t*(4 + 1) + b*l.truths, 1); + if (!truth.x) break; // continue; + + int left = floor((truth.x - truth.w / 2) * l.out_w); + int right = ceil((truth.x + truth.w / 2) * l.out_w); + int top = floor((truth.y - truth.h / 2) * l.out_h); + int bottom = ceil((truth.y + truth.h / 2) * l.out_h); + + for (w = left; w <= right; w++) { + for (h = top; h < bottom; h++) { + gt[w + l.out_w * h + l.out_w*l.out_h*b] = 1; + } + } + } + } + + cuda_push_array(l.gt_gpu, gt, l.out_w * l.out_h * l.batch); + //cudaStreamSynchronize(get_cuda_stream()); + //CHECK_CUDA(cudaPeekAtLastError()); + + // calc avg_output on GPU - for whole batch + calc_avg_activation_gpu(l.output_gpu, l.a_avg_gpu, l.out_w * l.out_h, l.out_c, l.batch); + //cudaStreamSynchronize(get_cuda_stream()); + //CHECK_CUDA(cudaPeekAtLastError()); + + // calc new output + assisted_activation_gpu(alpha, l.output_gpu, l.gt_gpu, l.a_avg_gpu, l.out_w * l.out_h, l.out_c, l.batch); + //cudaStreamSynchronize(get_cuda_stream()); + //CHECK_CUDA(cudaPeekAtLastError()); + + + + /* + for (b = 0; b < l.batch; ++b) + { + // calculate average A + for (w = 0; w < l.out_w; w++) { + for (h = 0; h < l.out_h; h++) { + for (c = 0; c < l.out_c; c++) { + a_avg[w + l.out_w*(h + l.out_h*b)] += l.output[w + l.out_w*(h + l.out_h*(c + l.out_c*b))]; + } + a_avg[w + l.out_w*(h + l.out_h*b)] /= l.out_c; // a_avg / d + } + } + } + + // change activation + for (b = 0; b < l.batch; ++b) + { + for (w = 0; w < l.out_w; w++) { + for (h = 0; h < l.out_h; h++) { + for (c = 0; c < l.out_c; c++) + { + // a = a + alpha(t) + e(c,i,j) = a + alpha(t) + g(i,j) * avg_a(i,j) / channels + l.output[w + l.out_w*(h + l.out_h*(c + l.out_c*b))] += + alpha * + g[w + l.out_w*(h + l.out_h*b)] * + a_avg[w + l.out_w*(h + l.out_h*b)]; + + //l.output[w + l.out_w*(h + l.out_h*(c + l.out_c*b))] = + // alpha * g[w + l.out_w*(h + l.out_h*b)] * a_avg[w + l.out_w*(h + l.out_h*b)]; + } + } + } + } + */ + + if (0) // visualize ground truth + { +#ifdef OPENCV + cuda_pull_array(l.output_gpu, l.output, l.outputs * l.batch); + cudaStreamSynchronize(get_cuda_stream()); + CHECK_CUDA(cudaPeekAtLastError()); + + for (b = 0; b < l.batch; ++b) + { + image img = float_to_image(l.out_w, l.out_h, 1, >[l.out_w*l.out_h*b]); + char buff[100]; + sprintf(buff, "a_excitation_%d", b); + show_image_cv(img, buff); + + image img2 = float_to_image(l.out_w, l.out_h, 1, &l.output[l.out_w*l.out_h*l.out_c*b]); + char buff2[100]; + sprintf(buff2, "a_excitation_act_%d", b); + show_image_cv(img2, buff2); + wait_key_cv(5); + } + wait_until_press_key_cv(); +#endif // OPENCV + } + + free(truth_cpu); + free(gt); + free(a_avg); +} + void pull_convolutional_layer(convolutional_layer l) { cuda_pull_array_async(l.weights_gpu, l.weights, l.nweights); diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index c5c59576349..157058eb91c 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -332,7 +332,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) #endif #endif -convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, int antialiasing, convolutional_layer *share_layer) +convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, int antialiasing, convolutional_layer *share_layer, int assisted_excitation) { int total_batch = batch*steps; int i; @@ -349,6 +349,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, stride_x = stride_y = l.stride = l.stride_x = l.stride_y = 1; // use stride=1 in host-layer } + l.assisted_excitation = assisted_excitation; l.share_layer = share_layer; l.index = index; l.h = h; @@ -503,7 +504,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, #ifdef CUDNN_HALF l.weights_gpu16 = cuda_make_array(NULL, l.nweights / 2 + 1); l.weight_updates_gpu16 = cuda_make_array(NULL, l.nweights / 2 + 1); -#endif +#endif // CUDNN_HALF l.biases_gpu = cuda_make_array(l.biases, n); l.bias_updates_gpu = cuda_make_array(l.bias_updates, n); } @@ -547,19 +548,27 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.x_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n); l.x_norm_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n); } + + if (l.assisted_excitation) + { + const int size = l.out_w * l.out_h * l.batch; + l.gt_gpu = cuda_make_array(NULL, size); + l.a_avg_gpu = cuda_make_array(NULL, size); + } #ifdef CUDNN create_convolutional_cudnn_tensors(&l); cudnn_convolutional_setup(&l, cudnn_fastest); -#endif +#endif // CUDNN } -#endif +#endif // GPU l.workspace_size = get_convolutional_workspace_size(l); //fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c); l.bflops = (2.0 * l.nweights * l.out_h*l.out_w) / 1000000000.; if (l.xnor && l.use_bin_output) fprintf(stderr, "convXB"); else if (l.xnor) fprintf(stderr, "convX "); - else if(l.share_layer) fprintf(stderr, "convS "); + else if (l.share_layer) fprintf(stderr, "convS "); + else if (l.assisted_excitation) fprintf(stderr, "convAE"); else fprintf(stderr, "conv "); if (groups > 1) fprintf(stderr, "%5d/%4d ", n, groups); @@ -579,7 +588,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, printf("AA: "); l.input_layer = (layer*)calloc(1, sizeof(layer)); const int blur_size = 3; - *(l.input_layer) = make_convolutional_layer(batch, steps, out_h, out_w, n, n, n, blur_size, blur_stride_x, blur_stride_y, 1, blur_size / 2, LINEAR, 0, 0, 0, 0, 0, index, 0, NULL); + *(l.input_layer) = make_convolutional_layer(batch, steps, out_h, out_w, n, n, n, blur_size, blur_stride_x, blur_stride_y, 1, blur_size / 2, LINEAR, 0, 0, 0, 0, 0, index, 0, NULL, 0); const int blur_nweights = n * blur_size * blur_size; // (n / n) * n * blur_size * blur_size; int i; for (i = 0; i < blur_nweights; i += (blur_size*blur_size)) { @@ -636,7 +645,7 @@ void denormalize_convolutional_layer(convolutional_layer l) void test_convolutional_layer() { - convolutional_layer l = make_convolutional_layer(1, 1, 5, 5, 3, 2, 1, 5, 2, 2, 1, 1, LEAKY, 1, 0, 0, 0, 0, 0, 0, NULL); + convolutional_layer l = make_convolutional_layer(1, 1, 5, 5, 3, 2, 1, 5, 2, 2, 1, 1, LEAKY, 1, 0, 0, 0, 0, 0, 0, NULL, 0); l.batch_normalize = 1; float data[] = {1,1,1,1,1, 1,1,1,1,1, @@ -1236,7 +1245,7 @@ void assisted_excitation_forward(convolutional_layer l, network_state state) } } - if(0) // visualize ground truth + if(1) // visualize ground truth { #ifdef OPENCV for (b = 0; b < l.batch; ++b) diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h index 1012663a5b3..0072ce549c3 100644 --- a/src/convolutional_layer.h +++ b/src/convolutional_layer.h @@ -30,7 +30,7 @@ void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16); #endif size_t get_convolutional_workspace_size(layer l); -convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, int antialiasing, convolutional_layer *share_layer); +convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, int antialiasing, convolutional_layer *share_layer, int assisted_excitation); void denormalize_convolutional_layer(convolutional_layer l); void resize_convolutional_layer(convolutional_layer *layer, int w, int h); void forward_convolutional_layer(const convolutional_layer layer, network_state state); @@ -57,6 +57,7 @@ int convolutional_out_width(convolutional_layer layer); void rescale_weights(convolutional_layer l, float scale, float trans); void rgbgr_weights(convolutional_layer l); void assisted_excitation_forward(convolutional_layer l, network_state state); +void assisted_excitation_forward_gpu(convolutional_layer l, network_state state); #ifdef __cplusplus } diff --git a/src/crnn_layer.c b/src/crnn_layer.c index e3114fc9497..588db7411a0 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -50,17 +50,17 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.state = (float*)calloc(l.hidden * l.batch * (l.steps + 1), sizeof(float)); l.input_layer = (layer*)calloc(1, sizeof(layer)); - *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); + *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); l.input_layer->batch = batch; if (l.workspace_size < l.input_layer->workspace_size) l.workspace_size = l.input_layer->workspace_size; l.self_layer = (layer*)calloc(1, sizeof(layer)); - *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); + *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); l.self_layer->batch = batch; if (l.workspace_size < l.self_layer->workspace_size) l.workspace_size = l.self_layer->workspace_size; l.output_layer = (layer*)calloc(1, sizeof(layer)); - *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL); + *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); l.output_layer->batch = batch; if (l.workspace_size < l.output_layer->workspace_size) l.workspace_size = l.output_layer->workspace_size; diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index 1239262197d..27d338603d1 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -107,7 +107,7 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s printf("AA: "); l.input_layer = (layer*)calloc(1, sizeof(layer)); const int blur_size = 3; - *(l.input_layer) = make_convolutional_layer(batch, 1, l.out_h, l.out_w, l.out_c, l.out_c, l.out_c, blur_size, blur_stride_x, blur_stride_y, 1, blur_size / 2, LINEAR, 0, 0, 0, 0, 0, 1, 0, NULL); + *(l.input_layer) = make_convolutional_layer(batch, 1, l.out_h, l.out_w, l.out_c, l.out_c, l.out_c, blur_size, blur_stride_x, blur_stride_y, 1, blur_size / 2, LINEAR, 0, 0, 0, 0, 0, 1, 0, NULL, 0); const int blur_nweights = l.out_c * blur_size * blur_size; // (n / n) * n * blur_size * blur_size; int i; for (i = 0; i < blur_nweights; i += (blur_size*blur_size)) { diff --git a/src/parser.c b/src/parser.c index b89bf0acc7b..97d6aef9c96 100644 --- a/src/parser.c +++ b/src/parser.c @@ -170,6 +170,8 @@ convolutional_layer parse_convolutional(list *options, size_params params, netwo char *activation_s = option_find_str(options, "activation", "logistic"); ACTIVATION activation = get_activation(activation_s); + int assisted_excitation = option_find_float_quiet(options, "assisted_excitation", 0); + int share_index = option_find_int_quiet(options, "share_index", -1000000000); convolutional_layer *share_layer = NULL; if(share_index >= 0) share_layer = &net.layers[share_index]; @@ -186,10 +188,10 @@ convolutional_layer parse_convolutional(list *options, size_params params, netwo int xnor = option_find_int_quiet(options, "xnor", 0); int use_bin_output = option_find_int_quiet(options, "bin_output", 0); - convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride_x,stride_y,dilation,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index, antialiasing, share_layer); + convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride_x,stride_y,dilation,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index, antialiasing, share_layer, assisted_excitation); layer.flipped = option_find_int_quiet(options, "flipped", 0); layer.dot = option_find_float_quiet(options, "dot", 0); - layer.assisted_excitation = option_find_float_quiet(options, "assisted_excitation", 0); + if(params.net.adam){ layer.B1 = params.net.B1; From 9c02df864e32259292e3189a0879b361165eadfb Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 4 Sep 2019 18:50:56 +0300 Subject: [PATCH 0587/1365] Fixed assisted_excitation and added also for [shortcut] layer --- src/convolutional_kernels.cu | 33 ++++++++++++++++++++++++++------- src/convolutional_layer.c | 9 +++++++-- src/image.c | 14 ++++++++++++++ src/image.h | 1 + src/layer.c | 4 ++++ src/parser.c | 3 ++- src/shortcut_layer.c | 21 +++++++++++++++++---- src/shortcut_layer.h | 2 +- 8 files changed, 72 insertions(+), 15 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 566fb893335..d766c9cf7cb 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -917,13 +917,10 @@ __global__ void calc_avg_activation_kernel(float *src, float *dst, int size, int } } -#include - void calc_avg_activation_gpu(float *src, float *dst, int size, int channels, int batches) { const int num_blocks = get_number_of_blocks(size*batches, BLOCK); - std::cout << " size = " << size << ", channels = " << channels << ", batches = " << batches << std::endl; calc_avg_activation_kernel << > > (src, dst, size, channels, batches); } @@ -937,6 +934,9 @@ __global__ void assisted_activation_kernel(float alpha, float *output, float *gt if (b < batches) { for (int c = 0; c < channels; ++c) { output[xy + size*(c + channels*b)] += alpha * gt_gpu[i] * a_avg_gpu[i]; + //output[xy + size*(c + channels*b)] += gt_gpu[i] * a_avg_gpu[i]; + //output[xy + size*(c + channels*b)] += gt_gpu[i] * output[xy + size*(c + channels*b)]; + //output[xy + size*(c + channels*b)] = a_avg_gpu[i]; } } } @@ -953,12 +953,18 @@ void assisted_excitation_forward_gpu(convolutional_layer l, network_state state) const int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); // epoch - const float epoch = (float)(*state.net.seen) / state.net.train_images_num; + //const float epoch = (float)(*state.net.seen) / state.net.train_images_num; // calculate alpha //const float alpha = (1 + cos(3.141592 * iteration_num)) / (2 * state.net.max_batches); //const float alpha = (1 + cos(3.141592 * epoch)) / (2 * state.net.max_batches); - const float alpha = (1 + cos(3.141592 * iteration_num / state.net.max_batches)) / 2; + //const float alpha = (1 + cos(3.141592 * iteration_num / state.net.max_batches)) / 2; + float alpha = (1 + cos(3.141592 * iteration_num / state.net.max_batches)); + + if (l.assisted_excitation > 1) { + if (iteration_num > l.assisted_excitation) alpha = 0; + else alpha = (1 + cos(3.141592 * iteration_num / l.assisted_excitation)); + } //printf("\n epoch = %f, alpha = %f, seen = %d, max_batches = %d, train_images_num = %d \n", // epoch, alpha, (*state.net.seen), state.net.max_batches, state.net.train_images_num); @@ -969,7 +975,7 @@ void assisted_excitation_forward_gpu(convolutional_layer l, network_state state) float *gt = (float *)calloc(l.out_w * l.out_h * l.batch, sizeof(float)); int b; - int w, h, c; + int w, h; l.max_boxes = state.net.num_boxes; l.truths = l.max_boxes*(4 + 1); @@ -1061,15 +1067,28 @@ void assisted_excitation_forward_gpu(convolutional_layer l, network_state state) for (b = 0; b < l.batch; ++b) { + printf(" Assisted Excitation alpha = %f \n", alpha); image img = float_to_image(l.out_w, l.out_h, 1, >[l.out_w*l.out_h*b]); char buff[100]; sprintf(buff, "a_excitation_%d", b); show_image_cv(img, buff); - image img2 = float_to_image(l.out_w, l.out_h, 1, &l.output[l.out_w*l.out_h*l.out_c*b]); + //image img2 = float_to_image(l.out_w, l.out_h, 1, &l.output[l.out_w*l.out_h*l.out_c*b]); + image img2 = float_to_image_scaled(l.out_w, l.out_h, 1, &l.output[l.out_w*l.out_h*l.out_c*b]); char buff2[100]; sprintf(buff2, "a_excitation_act_%d", b); show_image_cv(img2, buff2); + + /* + int c = l.out_c; + if (c > 4) c = 4; + image img3 = float_to_image(l.out_w, l.out_h, c, &l.output[l.out_w*l.out_h*l.out_c*b]); + image dc = collapse_image_layers(img3, 1); + char buff3[100]; + sprintf(buff3, "a_excitation_act_collapsed_%d", b); + show_image_cv(dc, buff3); + */ + wait_key_cv(5); } wait_until_press_key_cv(); diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 157058eb91c..72bb602589e 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -1172,12 +1172,17 @@ void assisted_excitation_forward(convolutional_layer l, network_state state) const int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); // epoch - const float epoch = (float)(*state.net.seen) / state.net.train_images_num; + //const float epoch = (float)(*state.net.seen) / state.net.train_images_num; // calculate alpha //const float alpha = (1 + cos(3.141592 * iteration_num)) / (2 * state.net.max_batches); //const float alpha = (1 + cos(3.141592 * epoch)) / (2 * state.net.max_batches); - const float alpha = (1 + cos(3.141592 * iteration_num / state.net.max_batches)) / 2; + float alpha = (1 + cos(3.141592 * iteration_num / state.net.max_batches)); + + if (l.assisted_excitation > 1) { + if (iteration_num > l.assisted_excitation) alpha = 0; + else alpha = (1 + cos(3.141592 * iteration_num / l.assisted_excitation)); + } //printf("\n epoch = %f, alpha = %f, seen = %d, max_batches = %d, train_images_num = %d \n", // epoch, alpha, (*state.net.seen), state.net.max_batches, state.net.train_images_num); diff --git a/src/image.c b/src/image.c index 2f085801dbe..8befaa2b8c6 100644 --- a/src/image.c +++ b/src/image.c @@ -770,6 +770,20 @@ image make_random_image(int w, int h, int c) return out; } +image float_to_image_scaled(int w, int h, int c, float *data) +{ + image out = make_image(w, h, c); + int abs_max = 0; + int i = 0; + for (i = 0; i < w*h*c; ++i) { + if (fabs(data[i]) > abs_max) abs_max = fabs(data[i]); + } + for (i = 0; i < w*h*c; ++i) { + out.data[i] = data[i] / abs_max; + } + return out; +} + image float_to_image(int w, int h, int c, float *data) { image out = make_empty_image(w,h,c); diff --git a/src/image.h b/src/image.h index 3a1c5b9a73c..14792c9b9ff 100644 --- a/src/image.h +++ b/src/image.h @@ -79,6 +79,7 @@ void print_image(image m); //LIB_API image make_image(int w, int h, int c); image make_random_image(int w, int h, int c); image make_empty_image(int w, int h, int c); +image float_to_image_scaled(int w, int h, int c, float *data); image float_to_image(int w, int h, int c, float *data); image copy_image(image p); image load_image(char *filename, int w, int h, int c); diff --git a/src/layer.c b/src/layer.c index b6ae95dba12..e9ae67b5ff5 100644 --- a/src/layer.c +++ b/src/layer.c @@ -157,6 +157,10 @@ void free_layer(layer l) if (l.x_gpu) cuda_free(l.x_gpu); // dont free if (l.x_norm_gpu) cuda_free(l.x_norm_gpu); + // assisted excitation + if (l.gt_gpu) cuda_free(l.gt_gpu); + if (l.a_avg_gpu) cuda_free(l.a_avg_gpu); + if (l.align_bit_weights_gpu) cuda_free((float *)l.align_bit_weights_gpu); if (l.mean_arr_gpu) cuda_free(l.mean_arr_gpu); if (l.align_workspace_gpu) cuda_free(l.align_workspace_gpu); diff --git a/src/parser.c b/src/parser.c index 97d6aef9c96..4b56dfc4c10 100644 --- a/src/parser.c +++ b/src/parser.c @@ -601,6 +601,7 @@ layer parse_batchnorm(list *options, size_params params) layer parse_shortcut(list *options, size_params params, network net) { + int assisted_excitation = option_find_float_quiet(options, "assisted_excitation", 0); char *l = option_find(options, "from"); int index = atoi(l); if(index < 0) index = params.index + index; @@ -608,7 +609,7 @@ layer parse_shortcut(list *options, size_params params, network net) int batch = params.batch; layer from = net.layers[index]; - layer s = make_shortcut_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c); + layer s = make_shortcut_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c, assisted_excitation); char *activation_s = option_find_str(options, "activation", "linear"); ACTIVATION activation = get_activation(activation_s); diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index 1f7c6d35e90..d056a6a0a20 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -4,9 +4,10 @@ #include #include -layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2) +layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2, int assisted_excitation) { - fprintf(stderr,"Shortcut Layer: %d\n", index); + if(assisted_excitation) fprintf(stderr, "Shortcut Layer - AE: %d\n", index); + else fprintf(stderr,"Shortcut Layer: %d\n", index); layer l = { (LAYER_TYPE)0 }; l.type = SHORTCUT; l.batch = batch; @@ -19,6 +20,8 @@ layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int l.outputs = w*h*c; l.inputs = l.outputs; + l.assisted_excitation = assisted_excitation; + if(w != w2 || h != h2 || c != c2) fprintf(stderr, " w = %d, w2 = %d, h = %d, h2 = %d, c = %d, c2 = %d \n", w, w2, h, h2, c, c2); l.index = index; @@ -28,13 +31,19 @@ layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int l.forward = forward_shortcut_layer; l.backward = backward_shortcut_layer; - #ifdef GPU +#ifdef GPU l.forward_gpu = forward_shortcut_layer_gpu; l.backward_gpu = backward_shortcut_layer_gpu; l.delta_gpu = cuda_make_array(l.delta, l.outputs*batch); l.output_gpu = cuda_make_array(l.output, l.outputs*batch); - #endif + if (l.assisted_excitation) + { + const int size = l.out_w * l.out_h * l.batch; + l.gt_gpu = cuda_make_array(NULL, size); + l.a_avg_gpu = cuda_make_array(NULL, size); + } +#endif // GPU return l; } @@ -72,6 +81,8 @@ void forward_shortcut_layer(const layer l, network_state state) shortcut_cpu(l.batch, l.w, l.h, l.c, state.net.layers[l.index].output, l.out_w, l.out_h, l.out_c, l.output); } activate_array(l.output, l.outputs*l.batch, l.activation); + + if (l.assisted_excitation && state.train) assisted_excitation_forward(l, state); } void backward_shortcut_layer(const layer l, network_state state) @@ -89,6 +100,8 @@ void forward_shortcut_layer_gpu(const layer l, network_state state) //shortcut_gpu(l.batch, l.w, l.h, l.c, state.net.layers[l.index].output_gpu, l.out_w, l.out_h, l.out_c, l.output_gpu); input_shortcut_gpu(state.input, l.batch, l.w, l.h, l.c, state.net.layers[l.index].output_gpu, l.out_w, l.out_h, l.out_c, l.output_gpu); activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); + + if (l.assisted_excitation && state.train) assisted_excitation_forward_gpu(l, state); } void backward_shortcut_layer_gpu(const layer l, network_state state) diff --git a/src/shortcut_layer.h b/src/shortcut_layer.h index b24aa3e6682..ad8d45f3e28 100644 --- a/src/shortcut_layer.h +++ b/src/shortcut_layer.h @@ -7,7 +7,7 @@ #ifdef __cplusplus extern "C" { #endif -layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2); +layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2, int assisted_excitation); void forward_shortcut_layer(const layer l, network_state state); void backward_shortcut_layer(const layer l, network_state state); void resize_shortcut_layer(layer *l, int w, int h); From 1e0b50c29e3c589ddbcccafc84b1ccd7c538c16b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 4 Sep 2019 18:51:40 +0300 Subject: [PATCH 0588/1365] Added yolov3-tiny-prn.cfg and enet-coco.cfg (EfficientNetb0-Yolo- 45.5% mAP@0.5 - 3.7 BFlops) https://github.com/WongKinYiu/PartialResidualNetworks --- README.md | 6 + build/darknet/x64/cfg/enet-coco.cfg | 1072 +++++++++++++++++++++ build/darknet/x64/cfg/yolov3-tiny-prn.cfg | 199 ++++ cfg/enet-coco.cfg | 1072 +++++++++++++++++++++ cfg/yolov3-tiny-prn.cfg | 199 ++++ 5 files changed, 2548 insertions(+) create mode 100644 build/darknet/x64/cfg/enet-coco.cfg create mode 100644 build/darknet/x64/cfg/yolov3-tiny-prn.cfg create mode 100644 cfg/enet-coco.cfg create mode 100644 cfg/yolov3-tiny-prn.cfg diff --git a/README.md b/README.md index 03492ade7e8..b79d9786e1c 100644 --- a/README.md +++ b/README.md @@ -63,12 +63,18 @@ There are weights-file for different cfg-files (smaller size -> faster speed & l * `yolov3-spp.cfg` (240 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-spp.weights * `yolov3.cfg` (236 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3.weights * `yolov3-tiny.cfg` (34 MB COCO **Yolo v3 tiny**) - requires 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-tiny.weights +* `enet-coco.cfg` (EfficientNetb0-Yolo- 45.5% mAP@0.5 - 3.7 BFlops) [enetb0-coco_final.weights](https://drive.google.com/file/d/1FlHeQjWEQVJt0ay1PVsiuuMzmtNyv36m/view) and `yolov3-tiny-prn.cfg` (33.1% mAP@0.5 - 3.5 BFlops - [more](https://github.com/WongKinYiu/PartialResidualNetworks)) + +

CLICK ME - Yolo v2 models + * `yolov2.cfg` (194 MB COCO Yolo v2) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov2.weights * `yolo-voc.cfg` (194 MB VOC Yolo v2) - requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo-voc.weights * `yolov2-tiny.cfg` (43 MB COCO Yolo v2) - requires 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov2-tiny.weights * `yolov2-tiny-voc.cfg` (60 MB VOC Yolo v2) - requires 1 GB GPU-RAM: http://pjreddie.com/media/files/yolov2-tiny-voc.weights * `yolo9000.cfg` (186 MB Yolo9000-model) - requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo9000.weights +
+ Put it near compiled: darknet.exe You can get cfg-files by path: `darknet/cfg/` diff --git a/build/darknet/x64/cfg/enet-coco.cfg b/build/darknet/x64/cfg/enet-coco.cfg new file mode 100644 index 00000000000..b530ed360b3 --- /dev/null +++ b/build/darknet/x64/cfg/enet-coco.cfg @@ -0,0 +1,1072 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +### CONV1 - 1 (1) +# conv1 +[convolutional] +filters=32 +size=3 +pad=1 +stride=2 +batch_normalize=1 +activation=swish + + +### CONV2 - MBConv1 - 1 (1) +# conv2_1_expand +[convolutional] +filters=32 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv2_1_dwise +[convolutional] +groups=32 +filters=32 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=4 (recommended r=16) +[convolutional] +filters=8 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=32 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv2_1_linear +[convolutional] +filters=16 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV3 - MBConv6 - 1 (2) +# conv2_2_expand +[convolutional] +filters=96 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv2_2_dwise +[convolutional] +groups=96 +filters=96 +size=3 +pad=1 +stride=2 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=8 (recommended r=16) +[convolutional] +filters=16 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=96 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv2_2_linear +[convolutional] +filters=24 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV3 - MBConv6 - 2 (2) +# conv3_1_expand +[convolutional] +filters=144 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv3_1_dwise +[convolutional] +groups=144 +filters=144 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=8 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=144 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv3_1_linear +[convolutional] +filters=24 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV4 - MBConv6 - 1 (2) +# dropout only before residual connection +[dropout] +probability=.0 + +# block_3_1 +[shortcut] +from=-9 +activation=linear + +# conv_3_2_expand +[convolutional] +filters=144 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_3_2_dwise +[convolutional] +groups=144 +filters=144 +size=5 +pad=1 +stride=2 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=8 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=144 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_3_2_linear +[convolutional] +filters=40 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV4 - MBConv6 - 2 (2) +# conv_4_1_expand +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_4_1_dwise +[convolutional] +groups=192 +filters=192 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=16 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=192 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_4_1_linear +[convolutional] +filters=40 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + + +### CONV5 - MBConv6 - 1 (3) +# dropout only before residual connection +[dropout] +probability=.0 + +# block_4_2 +[shortcut] +from=-9 +activation=linear + +# conv_4_3_expand +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_4_3_dwise +[convolutional] +groups=192 +filters=192 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=16 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=192 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_4_3_linear +[convolutional] +filters=80 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV5 - MBConv6 - 2 (3) +# conv_4_4_expand +[convolutional] +filters=384 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_4_4_dwise +[convolutional] +groups=384 +filters=384 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=24 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=384 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_4_4_linear +[convolutional] +filters=80 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV5 - MBConv6 - 3 (3) +# dropout only before residual connection +[dropout] +probability=.0 + +# block_4_4 +[shortcut] +from=-9 +activation=linear + +# conv_4_5_expand +[convolutional] +filters=384 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_4_5_dwise +[convolutional] +groups=384 +filters=384 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=24 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=384 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_4_5_linear +[convolutional] +filters=80 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV6 - MBConv6 - 1 (3) +# dropout only before residual connection +[dropout] +probability=.0 + +# block_4_6 +[shortcut] +from=-9 +activation=linear + +# conv_4_7_expand +[convolutional] +filters=384 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_4_7_dwise +[convolutional] +groups=384 +filters=384 +size=5 +pad=1 +stride=2 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=24 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=384 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_4_7_linear +[convolutional] +filters=112 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV6 - MBConv6 - 2 (3) +# conv_5_1_expand +[convolutional] +filters=576 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_5_1_dwise +[convolutional] +groups=576 +filters=576 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=32 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=576 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_5_1_linear +[convolutional] +filters=112 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV6 - MBConv6 - 3 (3) +# dropout only before residual connection +[dropout] +probability=.0 + +# block_5_1 +[shortcut] +from=-9 +activation=linear + +# conv_5_2_expand +[convolutional] +filters=576 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_5_2_dwise +[convolutional] +groups=576 +filters=576 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=32 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=576 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_5_2_linear +[convolutional] +filters=112 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 1 (4) +# dropout only before residual connection +[dropout] +probability=.0 + +# block_5_2 +[shortcut] +from=-9 +activation=linear + +# conv_5_3_expand +[convolutional] +filters=576 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_5_3_dwise +[convolutional] +groups=576 +filters=576 +size=5 +pad=1 +stride=2 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=32 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=576 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_5_3_linear +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 2 (4) +# conv_6_1_expand +[convolutional] +filters=960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_6_1_dwise +[convolutional] +groups=960 +filters=960 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=64 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=960 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_6_1_linear +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 3 (4) +# dropout only before residual connection +[dropout] +probability=.0 + +# block_6_1 +[shortcut] +from=-9 +activation=linear + +# conv_6_2_expand +[convolutional] +filters=960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_6_2_dwise +[convolutional] +groups=960 +filters=960 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=64 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=960 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_6_2_linear +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 4 (4) +# dropout only before residual connection +[dropout] +probability=.0 + +# block_6_1 +[shortcut] +from=-9 +activation=linear + +# conv_6_2_expand +[convolutional] +filters=960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_6_2_dwise +[convolutional] +groups=960 +filters=960 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=64 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=960 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_6_2_linear +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV8 - MBConv6 - 1 (1) +# dropout only before residual connection +[dropout] +probability=.0 + +# block_6_2 +[shortcut] +from=-9 +activation=linear + +# conv_6_3_expand +[convolutional] +filters=960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_6_3_dwise +[convolutional] +groups=960 +filters=960 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=64 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=960 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_6_3_linear +[convolutional] +filters=320 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV9 - Conv2d 1x1 +# conv_6_4 +[convolutional] +filters=1280 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +########################## + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +activation=leaky +from=-2 + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + + +[yolo] +mask = 3,4,5 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=0 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[shortcut] +activation=leaky +from=90 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +activation=leaky +from=-3 + +[shortcut] +activation=leaky +from=90 + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + +[yolo] +mask = 1,2,3 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=0 + diff --git a/build/darknet/x64/cfg/yolov3-tiny-prn.cfg b/build/darknet/x64/cfg/yolov3-tiny-prn.cfg new file mode 100644 index 00000000000..109c969cb2a --- /dev/null +++ b/build/darknet/x64/cfg/yolov3-tiny-prn.cfg @@ -0,0 +1,199 @@ +[net] +# Testing +batch=1 +subdivisions=1 +# Training +#batch=64 +#subdivisions=8 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=16 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=1 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +activation=leaky +from=-3 + +########### + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +activation=leaky +from=-2 + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + + +[yolo] +mask = 3,4,5 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[shortcut] +activation=leaky +from=8 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +activation=leaky +from=-3 + +[shortcut] +activation=leaky +from=8 + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + +[yolo] +mask = 1,2,3 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 diff --git a/cfg/enet-coco.cfg b/cfg/enet-coco.cfg new file mode 100644 index 00000000000..b530ed360b3 --- /dev/null +++ b/cfg/enet-coco.cfg @@ -0,0 +1,1072 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +### CONV1 - 1 (1) +# conv1 +[convolutional] +filters=32 +size=3 +pad=1 +stride=2 +batch_normalize=1 +activation=swish + + +### CONV2 - MBConv1 - 1 (1) +# conv2_1_expand +[convolutional] +filters=32 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv2_1_dwise +[convolutional] +groups=32 +filters=32 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=4 (recommended r=16) +[convolutional] +filters=8 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=32 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv2_1_linear +[convolutional] +filters=16 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV3 - MBConv6 - 1 (2) +# conv2_2_expand +[convolutional] +filters=96 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv2_2_dwise +[convolutional] +groups=96 +filters=96 +size=3 +pad=1 +stride=2 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=8 (recommended r=16) +[convolutional] +filters=16 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=96 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv2_2_linear +[convolutional] +filters=24 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV3 - MBConv6 - 2 (2) +# conv3_1_expand +[convolutional] +filters=144 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv3_1_dwise +[convolutional] +groups=144 +filters=144 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=8 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=144 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv3_1_linear +[convolutional] +filters=24 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV4 - MBConv6 - 1 (2) +# dropout only before residual connection +[dropout] +probability=.0 + +# block_3_1 +[shortcut] +from=-9 +activation=linear + +# conv_3_2_expand +[convolutional] +filters=144 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_3_2_dwise +[convolutional] +groups=144 +filters=144 +size=5 +pad=1 +stride=2 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=8 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=144 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_3_2_linear +[convolutional] +filters=40 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV4 - MBConv6 - 2 (2) +# conv_4_1_expand +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_4_1_dwise +[convolutional] +groups=192 +filters=192 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=16 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=192 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_4_1_linear +[convolutional] +filters=40 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + + +### CONV5 - MBConv6 - 1 (3) +# dropout only before residual connection +[dropout] +probability=.0 + +# block_4_2 +[shortcut] +from=-9 +activation=linear + +# conv_4_3_expand +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_4_3_dwise +[convolutional] +groups=192 +filters=192 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=16 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=192 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_4_3_linear +[convolutional] +filters=80 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV5 - MBConv6 - 2 (3) +# conv_4_4_expand +[convolutional] +filters=384 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_4_4_dwise +[convolutional] +groups=384 +filters=384 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=24 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=384 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_4_4_linear +[convolutional] +filters=80 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV5 - MBConv6 - 3 (3) +# dropout only before residual connection +[dropout] +probability=.0 + +# block_4_4 +[shortcut] +from=-9 +activation=linear + +# conv_4_5_expand +[convolutional] +filters=384 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_4_5_dwise +[convolutional] +groups=384 +filters=384 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=24 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=384 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_4_5_linear +[convolutional] +filters=80 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV6 - MBConv6 - 1 (3) +# dropout only before residual connection +[dropout] +probability=.0 + +# block_4_6 +[shortcut] +from=-9 +activation=linear + +# conv_4_7_expand +[convolutional] +filters=384 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_4_7_dwise +[convolutional] +groups=384 +filters=384 +size=5 +pad=1 +stride=2 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=24 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=384 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_4_7_linear +[convolutional] +filters=112 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV6 - MBConv6 - 2 (3) +# conv_5_1_expand +[convolutional] +filters=576 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_5_1_dwise +[convolutional] +groups=576 +filters=576 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=32 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=576 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_5_1_linear +[convolutional] +filters=112 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV6 - MBConv6 - 3 (3) +# dropout only before residual connection +[dropout] +probability=.0 + +# block_5_1 +[shortcut] +from=-9 +activation=linear + +# conv_5_2_expand +[convolutional] +filters=576 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_5_2_dwise +[convolutional] +groups=576 +filters=576 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=32 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=576 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_5_2_linear +[convolutional] +filters=112 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 1 (4) +# dropout only before residual connection +[dropout] +probability=.0 + +# block_5_2 +[shortcut] +from=-9 +activation=linear + +# conv_5_3_expand +[convolutional] +filters=576 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_5_3_dwise +[convolutional] +groups=576 +filters=576 +size=5 +pad=1 +stride=2 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=32 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=576 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_5_3_linear +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 2 (4) +# conv_6_1_expand +[convolutional] +filters=960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_6_1_dwise +[convolutional] +groups=960 +filters=960 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=64 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=960 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_6_1_linear +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 3 (4) +# dropout only before residual connection +[dropout] +probability=.0 + +# block_6_1 +[shortcut] +from=-9 +activation=linear + +# conv_6_2_expand +[convolutional] +filters=960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_6_2_dwise +[convolutional] +groups=960 +filters=960 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=64 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=960 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_6_2_linear +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 4 (4) +# dropout only before residual connection +[dropout] +probability=.0 + +# block_6_1 +[shortcut] +from=-9 +activation=linear + +# conv_6_2_expand +[convolutional] +filters=960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_6_2_dwise +[convolutional] +groups=960 +filters=960 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=64 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=960 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_6_2_linear +[convolutional] +filters=192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV8 - MBConv6 - 1 (1) +# dropout only before residual connection +[dropout] +probability=.0 + +# block_6_2 +[shortcut] +from=-9 +activation=linear + +# conv_6_3_expand +[convolutional] +filters=960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +# conv_6_3_dwise +[convolutional] +groups=960 +filters=960 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=swish + + +#squeeze-n-excitation +[avgpool] + +# squeeze ratio r=16 (recommended r=16) +[convolutional] +filters=64 +size=1 +stride=1 +activation=swish + +# excitation +[convolutional] +filters=960 +size=1 +stride=1 +activation=logistic + +# multiply channels +[scale_channels] +from=-4 + + +# conv_6_3_linear +[convolutional] +filters=320 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV9 - Conv2d 1x1 +# conv_6_4 +[convolutional] +filters=1280 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=swish + +########################## + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +activation=leaky +from=-2 + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + + +[yolo] +mask = 3,4,5 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=0 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[shortcut] +activation=leaky +from=90 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +activation=leaky +from=-3 + +[shortcut] +activation=leaky +from=90 + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + +[yolo] +mask = 1,2,3 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=0 + diff --git a/cfg/yolov3-tiny-prn.cfg b/cfg/yolov3-tiny-prn.cfg new file mode 100644 index 00000000000..109c969cb2a --- /dev/null +++ b/cfg/yolov3-tiny-prn.cfg @@ -0,0 +1,199 @@ +[net] +# Testing +batch=1 +subdivisions=1 +# Training +#batch=64 +#subdivisions=8 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=16 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=1 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +activation=leaky +from=-3 + +########### + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +activation=leaky +from=-2 + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + + +[yolo] +mask = 3,4,5 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[shortcut] +activation=leaky +from=8 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +activation=leaky +from=-3 + +[shortcut] +activation=leaky +from=8 + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + +[yolo] +mask = 1,2,3 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 From f6fa4a56d938f4f8c69774d3622e768e7411507d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 4 Sep 2019 19:58:36 +0300 Subject: [PATCH 0589/1365] compile fix --- build/darknet/x64/cfg/yolov3-tiny-prn.cfg | 8 ++++---- cfg/yolov3-tiny-prn.cfg | 8 ++++---- src/shortcut_layer.c | 1 + 3 files changed, 9 insertions(+), 8 deletions(-) diff --git a/build/darknet/x64/cfg/yolov3-tiny-prn.cfg b/build/darknet/x64/cfg/yolov3-tiny-prn.cfg index 109c969cb2a..215162e973b 100644 --- a/build/darknet/x64/cfg/yolov3-tiny-prn.cfg +++ b/build/darknet/x64/cfg/yolov3-tiny-prn.cfg @@ -1,10 +1,10 @@ [net] # Testing -batch=1 -subdivisions=1 +#batch=1 +#subdivisions=1 # Training -#batch=64 -#subdivisions=8 +batch=64 +subdivisions=8 width=416 height=416 channels=3 diff --git a/cfg/yolov3-tiny-prn.cfg b/cfg/yolov3-tiny-prn.cfg index 109c969cb2a..215162e973b 100644 --- a/cfg/yolov3-tiny-prn.cfg +++ b/cfg/yolov3-tiny-prn.cfg @@ -1,10 +1,10 @@ [net] # Testing -batch=1 -subdivisions=1 +#batch=1 +#subdivisions=1 # Training -#batch=64 -#subdivisions=8 +batch=64 +subdivisions=8 width=416 height=416 channels=3 diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index d056a6a0a20..8345858e47d 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -1,4 +1,5 @@ #include "shortcut_layer.h" +#include "convolutional_layer.h" #include "dark_cuda.h" #include "blas.h" #include From 2012f1c3aa07fe19763bdb90f8eb2b8c6cd04624 Mon Sep 17 00:00:00 2001 From: cyy Date: Thu, 5 Sep 2019 18:48:04 +0800 Subject: [PATCH 0590/1365] fix some memory leaks and other linter warnings --- src/classifier.c | 2 ++ src/data.c | 12 +++++---- src/detector.c | 66 ++++++++++++++++++++++++++++++++++-------------- src/matrix.c | 1 + 4 files changed, 57 insertions(+), 24 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index 11fa92df913..4cd9e769d53 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -204,6 +204,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, free_ptrs((void**)labels, classes); free_ptrs((void**)paths, plist->size); free_list(plist); + free(nets); free(base); } @@ -458,6 +459,7 @@ void validate_classifier_10(char *datacfg, char *filename, char *weightfile) printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1)); } + free(indexes); } void validate_classifier_full(char *datacfg, char *filename, char *weightfile) diff --git a/src/data.c b/src/data.c index 6dd3274ae7b..790248f1b6f 100644 --- a/src/data.c +++ b/src/data.c @@ -193,6 +193,9 @@ box_label *read_boxes(char *filename, int *n) int count = 0; while(fscanf(file, "%d %f %f %f %f", &id, &x, &y, &w, &h) == 5){ boxes = (box_label*)realloc(boxes, (count + 1) * sizeof(box_label)); + if(!boxes) { + error("realloc failed"); + } boxes[count].id = id; boxes[count].x = x; boxes[count].y = y; @@ -878,7 +881,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo int pbot = rand_precalc_random(-dh, dh, r4); //printf("\n pleft = %d, pright = %d, ptop = %d, pbot = %d, ow = %d, oh = %d \n", pleft, pright, ptop, pbot, ow, oh); - float scale = rand_precalc_random(.25, 2, r_scale); // unused currently + //float scale = rand_precalc_random(.25, 2, r_scale); // unused currently if (letter_box) { @@ -969,10 +972,9 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo #else // OPENCV void blend_images(image new_img, float alpha, image old_img, float beta) { - int i; int data_size = new_img.w * new_img.h * new_img.c; #pragma omp parallel for - for (i = 0; i < data_size; ++i) + for (int i = 0; i < data_size; ++i) new_img.data[i] = new_img.data[i] * alpha + old_img.data[i] * beta; } @@ -1594,8 +1596,8 @@ data *split_data(data d, int part, int total) int i; int start = part*d.X.rows/total; int end = (part+1)*d.X.rows/total; - data train; - data test; + data train ={0}; + data test ={0}; train.shallow = test.shallow = 1; test.X.rows = test.y.rows = end-start; diff --git a/src/detector.c b/src/detector.c index 689ace6da83..8c0ec47be4e 100644 --- a/src/detector.c +++ b/src/detector.c @@ -49,7 +49,6 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i char *name_list = option_find_str(options, "names", "data/names.list"); int names_size = 0; - char **names = get_labels_custom(name_list, &names_size); if (net_classes != names_size) { printf(" Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", name_list, names_size, net_classes, cfgfile); @@ -159,13 +158,12 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i //printf(" imgs = %d \n", imgs); pthread_t load_thread = load_data(args); - double time; int count = 0; //while(i*imgs < N*120){ while (get_current_batch(net) < net.max_batches) { if (l.random && count++ % 10 == 0) { printf("Resizing\n"); - float random_val = rand_scale(1.4); // *x or /x + float random_val = rand_scale(1.4f); // *x or /x int dim_w = roundl(random_val*init_w / 32 + 1) * 32; int dim_h = roundl(random_val*init_h / 32 + 1) * 32; @@ -192,7 +190,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i } net = nets[0]; } - time = what_time_is_it_now(); + double time = what_time_is_it_now(); pthread_join(load_thread, 0); train = buffer; if (net.track) { @@ -381,7 +379,7 @@ static void print_cocos(FILE *fp, char *image_path, detection *dets, int num_box float bh = ymax - ymin; for (j = 0; j < classes; ++j) { - if (dets[i].prob[j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, dets[i].prob[j]); + if (dets[i].prob[j] > 0) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, dets[i].prob[j]); } } } @@ -423,7 +421,7 @@ void print_imagenet_detections(FILE *fp, int id, detection *dets, int total, int for (j = 0; j < classes; ++j) { int myclass = j; - if (dets[i].prob[myclass]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j + 1, dets[i].prob[myclass], + if (dets[i].prob[myclass] > 0) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j + 1, dets[i].prob[myclass], xmin, ymin, xmax, ymax); } } @@ -537,7 +535,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out int nboxes = 0; int letterbox = (args.type == LETTERBOX_DATA); detection *dets = get_network_boxes(&net, w, h, thresh, .5, map, 0, &nboxes, letterbox); - if (nms) do_nms_sort(dets, nboxes, classes, nms); + if (nms > 0) do_nms_sort(dets, nboxes, classes, nms); if (coco) { print_cocos(fp, path, dets, nboxes, classes, w, h); } @@ -553,9 +551,20 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out free_image(val_resized[t]); } } + free(buf); + free(buf_resized); + free(val); + free(val_resized); for (j = 0; j < classes; ++j) { if (fps) fclose(fps[j]); } + if(fps) { + free(fps); + } + for (t = 0; t < nthreads; ++t) { + pthread_join(thr[t], 0); + } + free(thr); if (coco) { #ifdef WIN32 fseek(fp, -3, SEEK_CUR); @@ -565,7 +574,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out fprintf(fp, "\n]\n"); fclose(fp); } - fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)time(0) - start); + fprintf(stderr, "Total Detection Time: %ld Seconds\n", time(0) - start); } void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile) @@ -655,8 +664,8 @@ typedef struct { int detections_comparator(const void *pa, const void *pb) { - box_prob a = *(box_prob *)pa; - box_prob b = *(box_prob *)pb; + box_prob a = *(const box_prob *)pa; + box_prob b = *(const box_prob *)pb; float diff = a.p - b.p; if (diff < 0) return 1; else if (diff > 0) return -1; @@ -826,6 +835,9 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa if (prob > 0) { detections_count++; detections = (box_prob*)realloc(detections, detections_count * sizeof(box_prob)); + if (!detections) { + error("realloc failed"); + } detections[detections_count - 1].b = dets[i].bbox; detections[detections_count - 1].p = prob; detections[detections_count - 1].image_index = image_index; @@ -907,6 +919,12 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa free_image(val_resized[t]); } } + free(val); + free(val_resized); + for (t = 0; t < nthreads; ++t) { + pthread_join(thr[t], 0); + } + free(thr); if ((tp_for_thresh + fp_for_thresh) > 0) avg_iou = avg_iou / (tp_for_thresh + fp_for_thresh); @@ -1077,7 +1095,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa free(tp_for_thresh_per_class); free(fp_for_thresh_per_class); - fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); + fprintf(stderr, "Total Detection Time: %ld Seconds\n", time(0) - start); printf("\nSet -points flag:\n"); printf(" `-points 101` for MS COCO \n"); printf(" `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n"); @@ -1108,8 +1126,8 @@ typedef struct { int anchors_comparator(const void *pa, const void *pb) { - anchors_t a = *(anchors_t *)pa; - anchors_t b = *(anchors_t *)pb; + anchors_t a = *(const anchors_t *)pa; + anchors_t b = *(const anchors_t *)pb; float diff = b.w*b.h - a.w*a.h; if (diff < 0) return 1; else if (diff > 0) return -1; @@ -1174,6 +1192,9 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int } number_of_boxes++; rel_width_height_array = (float*)realloc(rel_width_height_array, 2 * number_of_boxes * sizeof(float)); + if (!rel_width_height_array) { + error("realloc failed"); + } rel_width_height_array[number_of_boxes * 2 - 2] = truth[j].w * width; rel_width_height_array[number_of_boxes * 2 - 1] = truth[j].h * height; printf("\r loaded \t image: %d \t box: %d", i + 1, number_of_boxes); @@ -1221,7 +1242,11 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int float box_union = box_w*box_h + anchor_w*anchor_h - box_intersect; float iou = box_intersect / box_union; float distance = 1 - iou; - if (distance < min_dist) min_dist = distance, cluster_idx = j, best_iou = iou; + if (distance < min_dist) { + min_dist = distance; + cluster_idx = j; + best_iou = iou; + } } float anchor_w = anchors_data.centers.vals[cluster_idx][0]; //centers->data.fl[cluster_idx * 2]; @@ -1298,6 +1323,9 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam FILE* json_file = NULL; if (outfile) { json_file = fopen(outfile, "wb"); + if(!json_file) { + error("fopen failed"); + } char *tmp = "[\n"; fwrite(tmp, sizeof(char), strlen(tmp), json_file); } @@ -1339,14 +1367,14 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam int nboxes = 0; detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letter_box); - if (nms) do_nms_sort(dets, nboxes, l.classes, nms); + if (nms > 0) do_nms_sort(dets, nboxes, l.classes, nms); draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, ext_output); save_image(im, "predictions"); if (!dont_show) { show_image(im, "predictions"); } - if (outfile) { + if (json_file) { if (json_buf) { char *tmp = ", \n"; fwrite(tmp, sizeof(char), strlen(tmp), json_file); @@ -1396,7 +1424,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam if (filename) break; } - if (outfile) { + if (json_file) { char *tmp = "\n]"; fwrite(tmp, sizeof(char), strlen(tmp), json_file); fclose(json_file); @@ -1456,9 +1484,9 @@ void run_detector(int argc, char **argv) int ngpus = 0; if (gpu_list) { printf("%s\n", gpu_list); - int len = strlen(gpu_list); + size_t len = strlen(gpu_list); ngpus = 1; - int i; + size_t i; for (i = 0; i < len; ++i) { if (gpu_list[i] == ',') ++ngpus; } diff --git a/src/matrix.c b/src/matrix.c index edacb07b5f2..aceca6e64ac 100644 --- a/src/matrix.c +++ b/src/matrix.c @@ -251,6 +251,7 @@ void kmeans_maximization(matrix data, int *assignments, matrix centers) if(centers.vals[i][j] == 0) centers.vals[i][j] = old_centers.vals[i][j]; } } + free(counts); free_matrix(old_centers); } From f7aae7385928135951092db91f014a8d1c7e1c4c Mon Sep 17 00:00:00 2001 From: cyy Date: Thu, 5 Sep 2019 18:48:56 +0800 Subject: [PATCH 0591/1365] check cuda function failure --- src/dark_cuda.c | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/src/dark_cuda.c b/src/dark_cuda.c index 165dad5f284..50ac96e0fd7 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -117,7 +117,6 @@ cudaStream_t get_cuda_stream() { if (status != cudaSuccess) { printf(" cudaStreamCreate error: %d \n", status); const char *s = cudaGetErrorString(status); - char buffer[256]; printf("CUDA Error: %s\n", s); status = cudaStreamCreateWithFlags(&streamsArray[i], cudaStreamDefault); CHECK_CUDA(status); @@ -138,7 +137,6 @@ cudaStream_t get_cuda_memcpy_stream() { if (status != cudaSuccess) { printf(" cudaStreamCreate-Memcpy error: %d \n", status); const char *s = cudaGetErrorString(status); - char buffer[256]; printf("CUDA Error: %s\n", s); status = cudaStreamCreateWithFlags(&streamsArray2[i], cudaStreamDefault); CHECK_CUDA(status); @@ -159,6 +157,7 @@ cudnnHandle_t cudnn_handle() cudnnCreate(&handle[i]); init[i] = 1; cudnnStatus_t status = cudnnSetStream(handle[i], get_cuda_stream()); + CHECK_CUDA(status); } return handle[i]; } @@ -287,7 +286,7 @@ int *cuda_make_int_array_new_api(int *x, size_t n) CHECK_CUDA(status); if (x) { //status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice, get_cuda_stream()); - cudaError_t status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyHostToDevice, get_cuda_stream()); + status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyHostToDevice, get_cuda_stream()); CHECK_CUDA(status); } if (!x_gpu) error("Cuda malloc failed\n"); From 1bd68010f43f28c5362d4c2536fdaf04d63b05cb Mon Sep 17 00:00:00 2001 From: cyy Date: Thu, 5 Sep 2019 18:51:46 +0800 Subject: [PATCH 0592/1365] comment out unused code --- src/gemm.c | 2 ++ src/network.c | 15 +++++++++------ 2 files changed, 11 insertions(+), 6 deletions(-) diff --git a/src/gemm.c b/src/gemm.c index 151388284b1..abeae31cd71 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -319,6 +319,7 @@ void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, //---------------------------- // is not used +/* void transpose_32x32_bits_my(uint32_t *A, uint32_t *B, int lda, int ldb) { unsigned int x, y; @@ -328,6 +329,7 @@ void transpose_32x32_bits_my(uint32_t *A, uint32_t *B, int lda, int ldb) } } } +*/ #ifndef GPU uint8_t reverse_8_bit(uint8_t a) { diff --git a/src/network.c b/src/network.c index 6e64a8ceb56..fddfeb8a5ed 100644 --- a/src/network.c +++ b/src/network.c @@ -61,6 +61,7 @@ int get_current_batch(network net) return batch_num; } +/* void reset_momentum(network net) { if (net.momentum == 0) return; @@ -71,6 +72,7 @@ void reset_momentum(network net) //if(net.gpu_index >= 0) update_network_gpu(net); #endif } +*/ void reset_network_state(network *net, int b) { @@ -333,7 +335,7 @@ float train_network_datum(network net, float *x, float *y) #ifdef GPU if(gpu_index >= 0) return train_network_datum_gpu(net, x, y); #endif - network_state state; + network_state state={0}; *net.seen += net.batch; state.index = 0; state.net = net; @@ -398,7 +400,7 @@ float train_network_waitkey(network net, data d, int wait_key) float train_network_batch(network net, data d, int n) { int i,j; - network_state state; + network_state state={0}; state.index = 0; state.net = net; state.train = 1; @@ -666,7 +668,7 @@ float *network_predict(network net, float *input) if(gpu_index >= 0) return network_predict_gpu(net, input); #endif - network_state state; + network_state state = {0}; state.net = net; state.index = 0; state.input = input; @@ -819,13 +821,14 @@ char *detection_to_json(detection *dets, int nboxes, int classes, char **names, int buf_len = strlen(buf); int total_len = send_buf_len + buf_len + 100; send_buf = (char *)realloc(send_buf, total_len * sizeof(char)); - if (!send_buf) return 0;// exit(-1); + if (!send_buf) { + error("realloc failed"); + } strcat(send_buf, buf); free(buf); } } } - //strcat(send_buf, "\n ] \n}, \n"); strcat(send_buf, "\n ] \n}"); return send_buf; } @@ -1219,4 +1222,4 @@ void restore_network_recurrent_state(network net) if (net.layers[k].type == CONV_LSTM) restore_state_conv_lstm(net.layers[k]); if (net.layers[k].type == CRNN) free_state_crnn(net.layers[k]); } -} \ No newline at end of file +} From b672ac988d77821dea427e78cd64c31775abf778 Mon Sep 17 00:00:00 2001 From: cyy Date: Thu, 5 Sep 2019 19:01:28 +0800 Subject: [PATCH 0593/1365] fix memory leaks and other pointer issues --- src/parser.c | 10 +++++---- src/rnn.c | 15 ++++++++++++++ src/utils.c | 54 +++++++++++++++++++++++++++--------------------- src/yolo.c | 10 ++++----- src/yolo_layer.c | 2 +- 5 files changed, 57 insertions(+), 34 deletions(-) diff --git a/src/parser.c b/src/parser.c index 09e79d2df75..26e561c5446 100644 --- a/src/parser.c +++ b/src/parser.c @@ -672,8 +672,8 @@ layer parse_upsample(list *options, size_params params, network net) route_layer parse_route(list *options, size_params params, network net) { char *l = option_find(options, "layers"); - int len = strlen(l); if(!l) error("Route Layer must specify input layers"); + int len = strlen(l); int n = 1; int i; for(i = 0; i < len; ++i){ @@ -958,7 +958,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) #endif } else if (lt == EMPTY) { - layer empty_layer; + layer empty_layer={0}; empty_layer.out_w = params.w; empty_layer.out_h = params.h; empty_layer.out_c = params.c; @@ -1484,10 +1484,11 @@ void load_weights(network *net, char *filename) // load network & force - set batch size network *load_network_custom(char *cfg, char *weights, int clear, int batch) { - printf(" Try to load cfg: %s, weights: %s, clear = %d \n", cfg, weights, clear); + printf(" Try to load cfg: %s, clear = %d \n", cfg, clear); network* net = (network*)calloc(1, sizeof(network)); *net = parse_network_cfg_custom(cfg, batch, 0); if (weights && weights[0] != 0) { + printf(" Try to load weights: %s \n", weights); load_weights(net, weights); } if (clear) (*net->seen) = 0; @@ -1497,10 +1498,11 @@ network *load_network_custom(char *cfg, char *weights, int clear, int batch) // load network & get batch size from cfg-file network *load_network(char *cfg, char *weights, int clear) { - printf(" Try to load cfg: %s, weights: %s, clear = %d \n", cfg, weights, clear); + printf(" Try to load cfg: %s, clear = %d \n", cfg, clear); network* net = (network*)calloc(1, sizeof(network)); *net = parse_network_cfg(cfg); if (weights && weights[0] != 0) { + printf(" Try to load weights: %s \n", weights); load_weights(net, weights); } if (clear) (*net->seen) = 0; diff --git a/src/rnn.c b/src/rnn.c index 6e1ed721e83..49cadd342d5 100644 --- a/src/rnn.c +++ b/src/rnn.c @@ -15,6 +15,9 @@ int *read_tokenized_data(char *filename, size_t *read) size_t count = 0; FILE *fp = fopen(filename, "r"); int* d = (int*)calloc(size, sizeof(int)); + if(!d) { + error("calloc failed"); + } int n, one; one = fscanf(fp, "%d", &n); while(one == 1){ @@ -22,12 +25,18 @@ int *read_tokenized_data(char *filename, size_t *read) if(count > size){ size = size*2; d = (int*)realloc(d, size * sizeof(int)); + if(!d) { + error("realloc failed"); + } } d[count-1] = n; one = fscanf(fp, "%d", &n); } fclose(fp); d = (int*)realloc(d, count * sizeof(int)); + if(!d) { + error("realloc failed"); + } *read = count; return d; } @@ -44,11 +53,17 @@ char **read_tokens(char *filename, size_t *read) if(count > size){ size = size*2; d = (char**)realloc(d, size * sizeof(char*)); + if(!d) { + error("realloc failed"); + } } d[count-1] = line; } fclose(fp); d = (char**)realloc(d, count * sizeof(char*)); + if(!d) { + error("realloc failed"); + } *read = count; return d; } diff --git a/src/utils.c b/src/utils.c index bee427ed743..b580159ea88 100644 --- a/src/utils.c +++ b/src/utils.c @@ -1,3 +1,6 @@ +#ifndef _GNU_SOURCE +#define _GNU_SOURCE +#endif #include "utils.h" #include #include @@ -39,8 +42,13 @@ int *read_map(char *filename) while((str=fgetl(file))){ ++n; map = (int*)realloc(map, n * sizeof(int)); + if(!map) { + error("realloc failed"); + } map[n-1] = atoi(str); + free(str); } + fclose(file); return map; } @@ -173,13 +181,15 @@ void pm(int M, int N, float *A) void find_replace(const char* str, char* orig, char* rep, char* output) { - char* buffer = (char*)calloc(8192, sizeof(char)); + char* buffer = strdup(str); + if(!buffer) { + error("strdup failed"); + } char *p; - - sprintf(buffer, "%s", str); if(!(p = strstr(buffer, orig))){ // Is 'orig' even in 'str'? - sprintf(output, "%s", str); - free(buffer); + if(str!=output) { + strcpy(output,str); + } return; } @@ -191,8 +201,10 @@ void find_replace(const char* str, char* orig, char* rep, char* output) void trim(char *str) { - char* buffer = (char*)calloc(8192, sizeof(char)); - sprintf(buffer, "%s", str); + char* buffer = strdup(str); + if(!buffer) { + error("strdup failed"); + } char *p = buffer; while (*p == ' ' || *p == '\t') ++p; @@ -202,27 +214,23 @@ void trim(char *str) *end = '\0'; --end; } - sprintf(str, "%s", p); - + strcpy(str,p); free(buffer); } void find_replace_extension(char *str, char *orig, char *rep, char *output) { - char* buffer = (char*)calloc(8192, sizeof(char)); - - sprintf(buffer, "%s", str); + char* buffer = strdup(str); + if(!buffer) { + error("strdup failed"); + } char *p = strstr(buffer, orig); - int offset = (p - buffer); - int chars_from_end = strlen(buffer) - offset; - if (!p || chars_from_end != strlen(orig)) { // Is 'orig' even in 'str' AND is 'orig' found at the end of 'str'? - sprintf(output, "%s", str); - free(buffer); - return; + if (p && *(p+strlen(orig)) == '\0') { + *p = '\0'; + sprintf(output, "%s%s%s", buffer, rep, p + strlen(orig)); + } else if(str!=output) { + strcpy(output,str); } - - *p = '\0'; - sprintf(output, "%s%s%s", buffer, rep, p + strlen(orig)); free(buffer); } @@ -685,9 +693,9 @@ int max_index(float *a, int n) int top_max_index(float *a, int n, int k) { + if (n <= 0) return -1; float *values = (float*)calloc(k, sizeof(float)); int *indexes = (int*)calloc(k, sizeof(int)); - if (n <= 0) return -1; int i, j; for (i = 0; i < n; ++i) { for (j = 0; j < k; ++j) { @@ -914,4 +922,4 @@ int max_int_index(int *a, int n) } } return max_i; -} \ No newline at end of file +} diff --git a/src/yolo.c b/src/yolo.c index 711470eade2..06e4facb9c2 100644 --- a/src/yolo.c +++ b/src/yolo.c @@ -190,6 +190,10 @@ void validate_yolo(char *cfgfile, char *weightfile) } } fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); + for(j = 0; j < classes; ++j){ + fclose(fps[j]); + } + free(fps); } void validate_yolo_recall(char *cfgfile, char *weightfile) @@ -211,12 +215,6 @@ void validate_yolo_recall(char *cfgfile, char *weightfile) int side = l.side; int j, k; - FILE** fps = (FILE**)calloc(classes, sizeof(FILE*)); - for(j = 0; j < classes; ++j){ - char buff[1024]; - snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]); - fps[j] = fopen(buff, "w"); - } box* boxes = (box*)calloc(side * side * l.n, sizeof(box)); float** probs = (float**)calloc(side * side * l.n, sizeof(float*)); for(j = 0; j < side*side*l.n; ++j) probs[j] = (float*)calloc(classes, sizeof(float)); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index a76b5efb082..1619d8afa3a 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -203,7 +203,7 @@ void delta_yolo_class(float *output, float *delta, int index, int class_id, int delta[index + stride*n] *= alpha*grad; - if (n == class_id) *avg_cat += output[index + stride*n]; + if (n == class_id && avg_cat) *avg_cat += output[index + stride*n]; } } else { From d6e443a965aa47605ce4fceb9a8d8b27f404cbde Mon Sep 17 00:00:00 2001 From: cyy Date: Thu, 5 Sep 2019 19:01:42 +0800 Subject: [PATCH 0594/1365] remove duplicated code --- src/darknet.c | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/darknet.c b/src/darknet.c index f7ac6593cd5..77a89899263 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -529,8 +529,6 @@ int main(int argc, char **argv) oneoff(argv[2], argv[3], argv[4]); } else if (0 == strcmp(argv[1], "partial")){ partial(argv[2], argv[3], argv[4], atoi(argv[5])); - } else if (0 == strcmp(argv[1], "average")){ - average(argc, argv); } else if (0 == strcmp(argv[1], "visualize")){ visualize(argv[2], (argc > 3) ? argv[3] : 0); } else if (0 == strcmp(argv[1], "imtest")){ From c3b06d096a83bca6b603e86162a0f847561a370c Mon Sep 17 00:00:00 2001 From: cyy Date: Thu, 5 Sep 2019 19:02:11 +0800 Subject: [PATCH 0595/1365] add macro for some GNU externsion --- src/image.c | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/image.c b/src/image.c index 2f085801dbe..2abd9f10815 100644 --- a/src/image.c +++ b/src/image.c @@ -1,3 +1,6 @@ +#ifndef _GNU_SOURCE +#define _GNU_SOURCE +#endif #include "image.h" #include "utils.h" #include "blas.h" From 994f95534870d8597f10326b2608706b96259283 Mon Sep 17 00:00:00 2001 From: cyy Date: Thu, 5 Sep 2019 19:02:45 +0800 Subject: [PATCH 0596/1365] fix prototype --- src/cost_layer.h | 2 +- src/normalization_layer.h | 2 +- src/region_layer.h | 2 +- src/reorg_layer.h | 2 +- src/reorg_old_layer.h | 2 +- 5 files changed, 5 insertions(+), 5 deletions(-) diff --git a/src/cost_layer.h b/src/cost_layer.h index 92666aa3268..b350003e017 100644 --- a/src/cost_layer.h +++ b/src/cost_layer.h @@ -10,7 +10,7 @@ extern "C" { #endif COST_TYPE get_cost_type(char *s); char *get_cost_string(COST_TYPE a); -cost_layer make_cost_layer(int batch, int inputs, COST_TYPE type, float scale); +cost_layer make_cost_layer(int batch, int inputs, COST_TYPE cost_type, float scale); void forward_cost_layer(const cost_layer l, network_state state); void backward_cost_layer(const cost_layer l, network_state state); void resize_cost_layer(cost_layer *l, int inputs); diff --git a/src/normalization_layer.h b/src/normalization_layer.h index b7b3d81a864..2ac9b0f6904 100644 --- a/src/normalization_layer.h +++ b/src/normalization_layer.h @@ -9,7 +9,7 @@ extern "C" { #endif layer make_normalization_layer(int batch, int w, int h, int c, int size, float alpha, float beta, float kappa); -void resize_normalization_layer(layer *layer, int h, int w); +void resize_normalization_layer(layer *layer, int w, int h); void forward_normalization_layer(const layer layer, network_state state); void backward_normalization_layer(const layer layer, network_state state); void visualize_normalization_layer(layer layer, char *window); diff --git a/src/region_layer.h b/src/region_layer.h index 2b40d80ff5c..e616624e2fb 100644 --- a/src/region_layer.h +++ b/src/region_layer.h @@ -9,7 +9,7 @@ typedef layer region_layer; #ifdef __cplusplus extern "C" { #endif -region_layer make_region_layer(int batch, int h, int w, int n, int classes, int coords, int max_boxes); +region_layer make_region_layer(int batch, int w, int h, int n, int classes, int coords, int max_boxes); void forward_region_layer(const region_layer l, network_state state); void backward_region_layer(const region_layer l, network_state state); void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness, int *map); diff --git a/src/reorg_layer.h b/src/reorg_layer.h index f2b909307dc..63185686383 100644 --- a/src/reorg_layer.h +++ b/src/reorg_layer.h @@ -9,7 +9,7 @@ #ifdef __cplusplus extern "C" { #endif -layer make_reorg_layer(int batch, int h, int w, int c, int stride, int reverse); +layer make_reorg_layer(int batch, int w, int h, int c, int stride, int reverse); void resize_reorg_layer(layer *l, int w, int h); void forward_reorg_layer(const layer l, network_state state); void backward_reorg_layer(const layer l, network_state state); diff --git a/src/reorg_old_layer.h b/src/reorg_old_layer.h index 15c61f8dc6f..caa8c91858e 100644 --- a/src/reorg_old_layer.h +++ b/src/reorg_old_layer.h @@ -9,7 +9,7 @@ #ifdef __cplusplus extern "C" { #endif -layer make_reorg_old_layer(int batch, int h, int w, int c, int stride, int reverse); +layer make_reorg_old_layer(int batch, int w, int h, int c, int stride, int reverse); void resize_reorg_old_layer(layer *l, int w, int h); void forward_reorg_old_layer(const layer l, network_state state); void backward_reorg_old_layer(const layer l, network_state state); From e0e709f423787dccd37edb41d96cf4cf30557a00 Mon Sep 17 00:00:00 2001 From: cyy Date: Thu, 5 Sep 2019 19:02:59 +0800 Subject: [PATCH 0597/1365] error should exit -1 --- scripts/kmeansiou.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/kmeansiou.c b/scripts/kmeansiou.c index 02f9e9a5db4..783d09a0b4a 100644 --- a/scripts/kmeansiou.c +++ b/scripts/kmeansiou.c @@ -223,7 +223,7 @@ void copy(double *x, double *y, int n) void error(char *s){ fprintf(stderr, "Error: %s\n", s); - exit(0); + exit(-1); } char *fgetl(FILE *fp) From e33019e669cbfe3dfc9037664335bfcb6ae8b988 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 5 Sep 2019 14:47:26 +0300 Subject: [PATCH 0598/1365] Fixed stride_x and stride_y. Fixed AntiAliasing. Minor fixes. --- src/convolutional_layer.c | 4 ++-- src/data.c | 12 ++++++++++-- src/image_opencv.cpp | 13 +++++++++++-- src/network.c | 7 +++++++ src/sam_layer.c | 4 ++-- 5 files changed, 32 insertions(+), 8 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 72bb602589e..00b27f6b88f 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -363,8 +363,8 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.batch = batch; l.steps = steps; l.stride = stride_x; - l.stride_y = stride_x; - l.stride_x = stride_y; + l.stride_x = stride_x; + l.stride_y = stride_y; l.dilation = dilation; l.size = size; l.pad = padding; diff --git a/src/data.c b/src/data.c index 6dd3274ae7b..4206871533f 100644 --- a/src/data.c +++ b/src/data.c @@ -343,7 +343,7 @@ void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int free(boxes); } -void fill_truth_detection(const char *path, int num_boxes, float *truth, int classes, int flip, float dx, float dy, float sx, float sy, +int fill_truth_detection(const char *path, int num_boxes, float *truth, int classes, int flip, float dx, float dy, float sx, float sy, int net_w, int net_h) { char labelpath[4096]; @@ -352,6 +352,7 @@ void fill_truth_detection(const char *path, int num_boxes, float *truth, int cla int count = 0; int i; box_label *boxes = read_boxes(labelpath, &count); + int min_w_h = 0; float lowest_w = 1.F / net_w; float lowest_h = 1.F / net_h; randomize_boxes(boxes, count); @@ -424,8 +425,13 @@ void fill_truth_detection(const char *path, int num_boxes, float *truth, int cla truth[(i-sub)*5+2] = w; truth[(i-sub)*5+3] = h; truth[(i-sub)*5+4] = id; + + if (min_w_h == 0) min_w_h = w*net_w; + if (min_w_h > w*net_w) min_w_h = w*net_w; + if (min_w_h > h*net_h) min_w_h = h*net_h; } free(boxes); + return min_w_h; } @@ -914,7 +920,9 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo float dy = ((float)ptop / oh) / sy; - fill_truth_detection(filename, boxes, truth, classes, flip, dx, dy, 1. / sx, 1. / sy, w, h); + int min_w_h = fill_truth_detection(filename, boxes, truth, classes, flip, dx, dy, 1. / sx, 1. / sy, w, h); + + if (min_w_h < blur*4) blur = 0; // disable blur if one of the objects is too small image ai = image_data_augmentation(src, w, h, pleft, ptop, swidth, sheight, flip, dhue, dsat, dexp, blur, boxes, d.y.vals[i]); diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 91d07bd7a8b..b6cb65f9f2a 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1206,11 +1206,20 @@ image image_data_augmentation(mat_cv* mat, int w, int h, if (blur) { cv::Mat dst(sized.size(), sized.type()); if(blur == 1) cv::GaussianBlur(sized, dst, cv::Size(31, 31), 0); - else cv::GaussianBlur(sized, dst, cv::Size((blur / 2) * 2 + 1, (blur / 2) * 2 + 1), 0); - cv::Rect img_rect(0, 0, sized.cols, sized.rows); + else { + cv::Size kernel_size = cv::Size((blur / 2) * 2 + 1, (blur / 2) * 2 + 1); + cv::GaussianBlur(sized, dst, kernel_size, 0); + + // sharpen + //cv::Mat img_tmp; + //cv::GaussianBlur(dst, img_tmp, cv::Size(), 3); + //cv::addWeighted(dst, 1.5, img_tmp, -0.5, 0, img_tmp); + //dst = img_tmp; + } //std::cout << " blur num_boxes = " << num_boxes << std::endl; if (blur == 1) { + cv::Rect img_rect(0, 0, sized.cols, sized.rows); int t; for (t = 0; t < num_boxes; ++t) { box b = float_to_box_stride(truth + t*(4 + 1), 1); diff --git a/src/network.c b/src/network.c index 9bdab8adce1..82dc4d53978 100644 --- a/src/network.c +++ b/src/network.c @@ -1150,6 +1150,13 @@ void copy_weights_net(network net_train, network *net_map) copy_cudnn_descriptors(tmp_self_layer, net_map->layers[k].self_layer); copy_cudnn_descriptors(tmp_output_layer, net_map->layers[k].output_layer); } + else if(l->input_layer) // for AntiAliasing + { + layer tmp_input_layer; + copy_cudnn_descriptors(*net_map->layers[k].input_layer, &tmp_input_layer); + net_map->layers[k].input_layer = net_train.layers[k].input_layer; + copy_cudnn_descriptors(tmp_input_layer, net_map->layers[k].input_layer); + } net_map->layers[k].batch = 1; net_map->layers[k].steps = 1; } diff --git a/src/sam_layer.c b/src/sam_layer.c index da28e319775..70e55052883 100644 --- a/src/sam_layer.c +++ b/src/sam_layer.c @@ -60,7 +60,7 @@ void resize_sam_layer(layer *l, int w, int h) void forward_sam_layer(const layer l, network_state state) { int size = l.batch * l.out_c * l.out_w * l.out_h; - int channel_size = 1; + //int channel_size = 1; float *from_output = state.net.layers[l.index].output; int i; @@ -79,7 +79,7 @@ void backward_sam_layer(const layer l, network_state state) //scale_cpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta, l.w, l.h, l.c, state.net.layers[l.index].delta); int size = l.batch * l.out_c * l.out_w * l.out_h; - int channel_size = 1; + //int channel_size = 1; float *from_output = state.net.layers[l.index].output; float *from_delta = state.net.layers[l.index].delta; From 927238070f7d13aa2d536a1b684394142142ba4e Mon Sep 17 00:00:00 2001 From: cyy Date: Thu, 5 Sep 2019 20:33:29 +0800 Subject: [PATCH 0599/1365] fix memory leak --- src/classifier.c | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/src/classifier.c b/src/classifier.c index 4cd9e769d53..92caf78f7e0 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -521,6 +521,7 @@ void validate_classifier_full(char *datacfg, char *filename, char *weightfile) printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1)); } + free(indexes); } @@ -600,6 +601,7 @@ float validate_classifier_single(char *datacfg, char *filename, char *weightfile else printf("\n"); printf("%d: top 1: %f, top %d: %f", i, avg_acc/(i+1), topk, avg_topk/(i+1)); } + free(indexes); if (existing_net) { set_batch_network(&net, old_batch); } @@ -670,6 +672,7 @@ void validate_classifier_multi(char *datacfg, char *filename, char *weightfile) printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1)); } + free(indexes); } void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int layer_num) @@ -689,7 +692,6 @@ void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filena int top = option_find_int(options, "top", 1); if (top > classes) top = classes; - int i = 0; char **names = get_labels(name_list); clock_t time; int* indexes = (int*)calloc(top, sizeof(int)); @@ -753,6 +755,7 @@ void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filena free_image(im); if (filename) break; } + free(indexes); } void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top) @@ -814,6 +817,7 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi free_image(im); if (filename) break; } + free(indexes); } From cd296d1beeb1a4f083d34ba8d49d5a88e50e8962 Mon Sep 17 00:00:00 2001 From: cyy Date: Thu, 5 Sep 2019 20:33:41 +0800 Subject: [PATCH 0600/1365] fix cpp compiling error --- src/parser.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/parser.c b/src/parser.c index 26e561c5446..427d8d38706 100644 --- a/src/parser.c +++ b/src/parser.c @@ -958,7 +958,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) #endif } else if (lt == EMPTY) { - layer empty_layer={0}; + layer empty_layer = {(LAYER_TYPE)0}; empty_layer.out_w = params.w; empty_layer.out_h = params.h; empty_layer.out_c = params.c; From b0b1584a069c12a214791c339bbff9dc3904283e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 6 Sep 2019 01:53:36 +0300 Subject: [PATCH 0601/1365] Minor fix --- src/convolutional_layer.c | 2 +- src/maxpool_layer.c | 8 +++++--- src/sam_layer.c | 2 +- src/scale_channels_layer.c | 2 +- 4 files changed, 8 insertions(+), 6 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 00b27f6b88f..10a45bab5d5 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -574,7 +574,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, if (groups > 1) fprintf(stderr, "%5d/%4d ", n, groups); else fprintf(stderr, "%5d ", n); - if (stride_x != stride_y) fprintf(stderr, "%2d x%2d/%2dx%2d ", size, size, stride_x, stride_y); + if (stride_x != stride_y) fprintf(stderr, "%2dx%2d/%2dx%2d ", size, size, stride_x, stride_y); else { if (dilation > 1) fprintf(stderr, "%2d x%2d/%2d(%1d)", size, size, stride_x, dilation); else fprintf(stderr, "%2d x%2d/%2d ", size, size, stride_x); diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index 27d338603d1..2f290497dcb 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -98,10 +98,12 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s #endif // GPU l.bflops = (l.size*l.size*l.c * l.out_h*l.out_w) / 1000000000.; - if(stride_x == stride_y) - fprintf(stderr, "max %d x %d/%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride_x, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); + if (maxpool_depth) + fprintf(stderr, "max-depth %2dx%2d/%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride_x, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); + else if(stride_x == stride_y) + fprintf(stderr, "max %2dx%2d/%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride_x, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); else - fprintf(stderr, "max %d x %d/%2dx%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride_x, stride_y, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); + fprintf(stderr, "max %2dx%2d/%2dx%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride_x, stride_y, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); if (l.antialiasing) { printf("AA: "); diff --git a/src/sam_layer.c b/src/sam_layer.c index 70e55052883..e95290d7867 100644 --- a/src/sam_layer.c +++ b/src/sam_layer.c @@ -18,7 +18,7 @@ layer make_sam_layer(int batch, int index, int w, int h, int c, int w2, int h2, l.out_h = h2; l.out_c = c2; assert(l.out_c == l.c); - assert(l.w == l.out_w & l.h == l.out_h); + assert(l.w == l.out_w && l.h == l.out_h); l.outputs = l.out_w*l.out_h*l.out_c; l.inputs = l.outputs; diff --git a/src/scale_channels_layer.c b/src/scale_channels_layer.c index 7322570116e..80be5361126 100644 --- a/src/scale_channels_layer.c +++ b/src/scale_channels_layer.c @@ -13,7 +13,7 @@ layer make_scale_channels_layer(int batch, int index, int w, int h, int c, int w l.w = w; l.h = h; l.c = c; - assert(w == 1 & h == 1); + assert(w == 1 && h == 1); l.out_w = w2; l.out_h = h2; From 3c4ddd65c5ee4fafabc1f0d75a50de1ab5841ca9 Mon Sep 17 00:00:00 2001 From: cyy Date: Fri, 6 Sep 2019 10:18:28 +0800 Subject: [PATCH 0602/1365] fix memory leak --- src/classifier.c | 15 +++++++++------ src/coco.c | 45 +++++++++++++++++++++++++++++++++++++++++---- src/go.c | 28 ++++++++++++++++++++++++++++ 3 files changed, 78 insertions(+), 10 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index 92caf78f7e0..fba06a6eae3 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -704,7 +704,7 @@ void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filena printf("Enter Image Path: "); fflush(stdout); input = fgets(input, 256, stdin); - if(!input) return; + if(!input) break; strtok(input, "\n"); } image orig = load_image_color(input, 0, 0); @@ -724,13 +724,13 @@ void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filena float *predictions = network_predict(net, X); layer l = net.layers[layer_num]; - for(i = 0; i < l.c; ++i){ + for(int i = 0; i < l.c; ++i){ if(l.rolling_mean) printf("%f %f %f\n", l.rolling_mean[i], l.rolling_variance[i], l.scales[i]); } #ifdef GPU cuda_pull_array(l.output_gpu, l.output, l.outputs); #endif - for(i = 0; i < l.outputs; ++i){ + for(int i = 0; i < l.outputs; ++i){ printf("%f\n", l.output[i]); } /* @@ -748,7 +748,7 @@ void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filena top_predictions(net, top, indexes); printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); - for(i = 0; i < top; ++i){ + for(int i = 0; i < top; ++i){ int index = indexes[i]; printf("%s: %f\n", names[index], predictions[index]); } @@ -782,6 +782,9 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi char **names = get_labels(name_list); clock_t time; int* indexes = (int*)calloc(top, sizeof(int)); + if(!indexes) { + error("calloc failed"); + } char buff[256]; char *input = buff; //int size = net.w; @@ -792,7 +795,7 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi printf("Enter Image Path: "); fflush(stdout); input = fgets(input, 256, stdin); - if(!input) return; + if(!input) break; strtok(input, "\n"); } image im = load_image_color(input, 0, 0); @@ -1278,7 +1281,7 @@ void run_classifier(int argc, char **argv) char *layer_s = (argc > 7) ? argv[7]: 0; int layer = layer_s ? atoi(layer_s) : -1; if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename, top); - else if(0==strcmp(argv[2], "try")) try_classifier(data, cfg, weights, filename, atoi(layer_s)); + else if(0==strcmp(argv[2], "try")) try_classifier(data, cfg, weights, filename, layer); else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear, dont_show, mjpeg_port, calc_topk); else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename); else if(0==strcmp(argv[2], "gun")) gun_classifier(data, cfg, weights, cam_index, filename); diff --git a/src/coco.c b/src/coco.c index cdfd3dff391..83089d764a6 100644 --- a/src/coco.c +++ b/src/coco.c @@ -248,15 +248,31 @@ void validate_coco_recall(char *cfgfile, char *weightfile) int side = l.side; int j, k; + /* unused code,why? FILE** fps = (FILE**)calloc(classes, sizeof(FILE*)); + if(!fps) { + error("calloc failed"); + } for(j = 0; j < classes; ++j){ char buff[1024]; snprintf(buff, 1024, "%s%s.txt", base, coco_classes[j]); fps[j] = fopen(buff, "w"); } + */ box* boxes = (box*)calloc(side * side * l.n, sizeof(box)); + if(!boxes) { + error("calloc failed"); + } float** probs = (float**)calloc(side * side * l.n, sizeof(float*)); - for(j = 0; j < side*side*l.n; ++j) probs[j] = (float*)calloc(classes, sizeof(float)); + if(!probs) { + error("calloc failed"); + } + for(j = 0; j < side*side*l.n; ++j) { + probs[j] = (float*)calloc(classes, sizeof(float)); + if(!probs[j]) { + error("calloc failed"); + } + } int m = plist->size; int i=0; @@ -278,7 +294,7 @@ void validate_coco_recall(char *cfgfile, char *weightfile) char *id = basecfg(path); network_predict(net, sized.data); get_detection_boxes(l, 1, 1, thresh, probs, boxes, 1); - if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms_thresh); + // nms is always 0 if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms_thresh); char labelpath[4096]; replace_image_to_label(path, labelpath); @@ -311,6 +327,11 @@ void validate_coco_recall(char *cfgfile, char *weightfile) free_image(orig); free_image(sized); } + free(boxes); + for(j = 0; j < side*side*l.n; ++j) { + free(probs[j]); + } + free(probs); } void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh) @@ -329,8 +350,19 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh) char *input = buff; int j; box* boxes = (box*)calloc(l.side * l.side * l.n, sizeof(box)); + if(!boxes) { + error("calloc failed"); + } float** probs = (float**)calloc(l.side * l.side * l.n, sizeof(float*)); - for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = (float*)calloc(l.classes, sizeof(float)); + if(!probs) { + error("calloc failed"); + } + for(j = 0; j < l.side*l.side*l.n; ++j) { + probs[j] = (float*)calloc(l.classes, sizeof(float)); + if(!probs[j]) { + error("calloc failed"); + } + } while(1){ if(filename){ strncpy(input, filename, 256); @@ -338,7 +370,7 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh) printf("Enter Image Path: "); fflush(stdout); input = fgets(input, 256, stdin); - if(!input) return; + if(!input) break; strtok(input, "\n"); } image im = load_image_color(input,0,0); @@ -360,6 +392,11 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh) if (filename) break; } + free(boxes); + for(j = 0; j < l.side*l.side*l.n; ++j) { + free(probs[j]); + } + free(probs); } void run_coco(int argc, char **argv) diff --git a/src/go.c b/src/go.c index 5d507768e50..faa48d10d45 100644 --- a/src/go.c +++ b/src/go.c @@ -128,7 +128,13 @@ void train_go(char *cfgfile, char *weightfile) char buff[256]; float* board = (float*)calloc(19 * 19 * net.batch, sizeof(float)); + if(!board) { + error("calloc failed"); + } float* move = (float*)calloc(19 * 19 * net.batch, sizeof(float)); + if(!move) { + error("calloc failed"); + } moves m = load_go_moves("backup/go.train"); //moves m = load_go_moves("games.txt"); @@ -165,6 +171,8 @@ void train_go(char *cfgfile, char *weightfile) free_network(net); free(base); + free(board); + free(move); } void propagate_liberty(float *board, int *lib, int *visited, int row, int col, int side) @@ -409,7 +417,13 @@ void valid_go(char *cfgfile, char *weightfile, int multi) printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); float* board = (float*)calloc(19 * 19, sizeof(float)); + if(!board) { + error("calloc failed"); + } float* move = (float*)calloc(19 * 19, sizeof(float)); + if(!move) { + error("calloc failed"); + } moves m = load_go_moves("backup/go.test"); int N = m.n; @@ -426,6 +440,8 @@ void valid_go(char *cfgfile, char *weightfile, int multi) if(index == truth) ++correct; printf("%d Accuracy %f\n", i, (float) correct/(i+1)); } + free(board); + free(move); } void engine_go(char *filename, char *weightfile, int multi) @@ -762,8 +778,17 @@ void self_go(char *filename, char *weightfile, char *f2, char *w2, int multi) set_batch_network(&net, 1); set_batch_network(&net2, 1); float* board = (float*)calloc(19 * 19, sizeof(float)); + if(!board) { + error("calloc failed"); + } char* one = (char*)calloc(91, sizeof(char)); + if(!one) { + error("calloc failed"); + } char* two = (char*)calloc(91, sizeof(char)); + if(!two) { + error("calloc failed"); + } int done = 0; int player = 1; int p1 = 0; @@ -818,6 +843,9 @@ void self_go(char *filename, char *weightfile, char *f2, char *w2, int multi) player = -player; } + free(board); + free(one); + free(two); } void run_go(int argc, char **argv) From b5e583b8d33bdb08e216944175cf7040c98fd3c0 Mon Sep 17 00:00:00 2001 From: cyy Date: Fri, 6 Sep 2019 16:11:50 +0800 Subject: [PATCH 0603/1365] fix windows error --- src/data.c | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/data.c b/src/data.c index 790248f1b6f..24755b875fe 100644 --- a/src/data.c +++ b/src/data.c @@ -973,8 +973,9 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo void blend_images(image new_img, float alpha, image old_img, float beta) { int data_size = new_img.w * new_img.h * new_img.c; + int i; #pragma omp parallel for - for (int i = 0; i < data_size; ++i) + for (i = 0; i < data_size; ++i) new_img.data[i] = new_img.data[i] * alpha + old_img.data[i] * beta; } From 1c71f001531a5df0637903117c6568725d7a66b3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 7 Sep 2019 15:15:19 +0300 Subject: [PATCH 0604/1365] Fixed shortcut_layer for antialiasing --- src/data.c | 3 +++ src/image_opencv.cpp | 7 +++++-- src/parser.c | 1 + 3 files changed, 9 insertions(+), 2 deletions(-) diff --git a/src/data.c b/src/data.c index 4206871533f..d8f0c9e81c0 100644 --- a/src/data.c +++ b/src/data.c @@ -959,6 +959,9 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo save_image(tmp_ai, buff); if (show_imgs == 1) { + //char buff_src[1000]; + //sprintf(buff_src, "src_%d_%d_%s_%d", random_index, i, basecfg((char*)filename), random_gen()); + //show_image_mat(src, buff_src); show_image(tmp_ai, buff); wait_until_press_key_cv(); } diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index b6cb65f9f2a..cc50c0719a1 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1207,8 +1207,11 @@ image image_data_augmentation(mat_cv* mat, int w, int h, cv::Mat dst(sized.size(), sized.type()); if(blur == 1) cv::GaussianBlur(sized, dst, cv::Size(31, 31), 0); else { - cv::Size kernel_size = cv::Size((blur / 2) * 2 + 1, (blur / 2) * 2 + 1); - cv::GaussianBlur(sized, dst, kernel_size, 0); + int ksize = (blur / 2) * 2 + 1; + cv::Size kernel_size = cv::Size(ksize, ksize); + //cv::GaussianBlur(sized, dst, kernel_size, 0); + //cv::medianBlur(sized, dst, ksize); + cv::bilateralFilter(sized, dst, ksize, 75, 75); // sharpen //cv::Mat img_tmp; diff --git a/src/parser.c b/src/parser.c index 4b56dfc4c10..829134d1131 100644 --- a/src/parser.c +++ b/src/parser.c @@ -608,6 +608,7 @@ layer parse_shortcut(list *options, size_params params, network net) int batch = params.batch; layer from = net.layers[index]; + if (from.antialiasing) from = *from.input_layer; layer s = make_shortcut_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c, assisted_excitation); From 81ab7adc28cf560fbb3309285588aa684c60d4f9 Mon Sep 17 00:00:00 2001 From: cyy Date: Tue, 10 Sep 2019 10:33:01 +0800 Subject: [PATCH 0605/1365] fix warnings --- src/convolutional_layer.c | 4 ++-- src/detector.c | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 6ff5b8b3d4f..859efeea5cf 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -607,8 +607,6 @@ void test_convolutional_layer() void resize_convolutional_layer(convolutional_layer *l, int w, int h) { int total_batch = l->batch*l->steps; - int old_w = l->w; - int old_h = l->h; l->w = w; l->h = h; int out_w = convolutional_out_width(*l); @@ -632,6 +630,8 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) } #ifdef GPU + int old_w = l->w; + int old_h = l->h; if (old_w < w || old_h < h) { cuda_free(l->delta_gpu); cuda_free(l->output_gpu); diff --git a/src/detector.c b/src/detector.c index 8c0ec47be4e..7a147e24939 100644 --- a/src/detector.c +++ b/src/detector.c @@ -18,7 +18,7 @@ typedef __compar_fn_t comparison_fn_t; #include "http_stream.h" -int check_mistakes = 0; +extern int check_mistakes = 0; static int coco_ids[] = { 1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90 }; @@ -1177,7 +1177,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int int num_labels = 0; box_label *truth = read_boxes(labelpath, &num_labels); //printf(" new path: %s \n", labelpath); - char buff[1024]; + char buff[6144]; for (j = 0; j < num_labels; ++j) { if (truth[j].x > 1 || truth[j].x <= 0 || truth[j].y > 1 || truth[j].y <= 0 || From d3b3e8382f9ae94d62570116c4834d570d42dbf9 Mon Sep 17 00:00:00 2001 From: cyy Date: Tue, 10 Sep 2019 10:33:57 +0800 Subject: [PATCH 0606/1365] add xcalloc to fix calloc failure --- src/utils.c | 27 +++++++++++++++++++++++++++ src/utils.h | 3 +++ src/yolo.c | 43 +++++++++++++++++++++++++------------------ 3 files changed, 55 insertions(+), 18 deletions(-) diff --git a/src/utils.c b/src/utils.c index b580159ea88..2c3eb83f7c2 100644 --- a/src/utils.c +++ b/src/utils.c @@ -23,6 +23,21 @@ #pragma warning(disable: 4996) #endif +void *xcalloc(size_t nmemb, size_t size) { + void *ptr=calloc(nmemb,size); + if(!ptr) { + calloc_error(); + } + return ptr; +} +void *xrealloc(void *ptr, size_t size) { + ptr=realloc(ptr,size); + if(!ptr) { + realloc_error(); + } + return ptr; +} + double what_time_is_it_now() { struct timeval time; @@ -308,6 +323,18 @@ void malloc_error() exit(EXIT_FAILURE); } +void calloc_error() +{ + fprintf(stderr, "Calloc error\n"); + exit(EXIT_FAILURE); +} + +void realloc_error() +{ + fprintf(stderr, "Realloc error\n"); + exit(EXIT_FAILURE); +} + void file_error(char *s) { fprintf(stderr, "Couldn't open file: %s\n", s); diff --git a/src/utils.h b/src/utils.h index fe4efe04ef1..70d729b7617 100644 --- a/src/utils.h +++ b/src/utils.h @@ -13,6 +13,7 @@ extern "C" { LIB_API void free_ptrs(void **ptrs, int n); LIB_API void top_k(float *a, int n, int k, int *index); +void *xcalloc(size_t nmemb, size_t size); double what_time_is_it_now(); int *read_map(char *filename); void shuffle(void *arr, size_t n, size_t size); @@ -30,6 +31,8 @@ LIB_API void find_replace(const char* str, char* orig, char* rep, char* output); void replace_image_to_label(const char* input_path, char* output_path); void error(const char *s); void malloc_error(); +void calloc_error(); +void realloc_error(); void file_error(char *s); void strip(char *s); void strip_args(char *s); diff --git a/src/yolo.c b/src/yolo.c index 06e4facb9c2..265839a4f72 100644 --- a/src/yolo.c +++ b/src/yolo.c @@ -124,15 +124,15 @@ void validate_yolo(char *cfgfile, char *weightfile) int classes = l.classes; int j; - FILE** fps = (FILE**)calloc(classes, sizeof(FILE*)); + FILE** fps = (FILE**)xcalloc(classes, sizeof(FILE*)); for(j = 0; j < classes; ++j){ char buff[1024]; snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]); fps[j] = fopen(buff, "w"); } - box* boxes = (box*)calloc(l.side * l.side * l.n, sizeof(box)); - float** probs = (float**)calloc(l.side * l.side * l.n, sizeof(float*)); - for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = (float*)calloc(classes, sizeof(float)); + box* boxes = (box*)xcalloc(l.side * l.side * l.n, sizeof(box)); + float** probs = (float**)xcalloc(l.side * l.side * l.n, sizeof(float*)); + for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = (float*)xcalloc(classes, sizeof(float)); int m = plist->size; int i=0; @@ -143,11 +143,11 @@ void validate_yolo(char *cfgfile, char *weightfile) float iou_thresh = .5; int nthreads = 8; - image* val = (image*)calloc(nthreads, sizeof(image)); - image* val_resized = (image*)calloc(nthreads, sizeof(image)); - image* buf = (image*)calloc(nthreads, sizeof(image)); - image* buf_resized = (image*)calloc(nthreads, sizeof(image)); - pthread_t* thr = (pthread_t*)calloc(nthreads, sizeof(pthread_t)); + image* val = (image*)xcalloc(nthreads, sizeof(image)); + image* val_resized = (image*)xcalloc(nthreads, sizeof(image)); + image* buf = (image*)xcalloc(nthreads, sizeof(image)); + image* buf_resized = (image*)xcalloc(nthreads, sizeof(image)); + pthread_t* thr = (pthread_t*)xcalloc(nthreads, sizeof(pthread_t)); load_args args = {0}; args.w = net.w; @@ -206,7 +206,6 @@ void validate_yolo_recall(char *cfgfile, char *weightfile) fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); srand(time(0)); - char *base = "results/comp4_det_test_"; list *plist = get_paths("data/voc.2007.test"); char **paths = (char **)list_to_array(plist); @@ -215,9 +214,11 @@ void validate_yolo_recall(char *cfgfile, char *weightfile) int side = l.side; int j, k; - box* boxes = (box*)calloc(side * side * l.n, sizeof(box)); - float** probs = (float**)calloc(side * side * l.n, sizeof(float*)); - for(j = 0; j < side*side*l.n; ++j) probs[j] = (float*)calloc(classes, sizeof(float)); + box* boxes = (box*)xcalloc(side * side * l.n, sizeof(box)); + float** probs = (float**)xcalloc(side * side * l.n, sizeof(float*)); + for(j = 0; j < side*side*l.n; ++j) { + probs[j] = (float*)xcalloc(classes, sizeof(float)); + } int m = plist->size; int i=0; @@ -283,14 +284,15 @@ void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh) detection_layer l = net.layers[net.n-1]; set_batch_network(&net, 1); srand(2222222); - clock_t time; char buff[256]; char *input = buff; int j; float nms=.4; - box* boxes = (box*)calloc(l.side * l.side * l.n, sizeof(box)); - float** probs = (float**)calloc(l.side * l.side * l.n, sizeof(float*)); - for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = (float*)calloc(l.classes, sizeof(float)); + box* boxes = (box*)xcalloc(l.side * l.side * l.n, sizeof(box)); + float** probs = (float**)xcalloc(l.side * l.side * l.n, sizeof(float*)); + for(j = 0; j < l.side*l.side*l.n; ++j) { + probs[j] = (float*)xcalloc(l.classes, sizeof(float)); + } while(1){ if(filename){ strncpy(input, filename, 256); @@ -304,7 +306,7 @@ void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh) image im = load_image_color(input,0,0); image sized = resize_image(im, net.w, net.h); float *X = sized.data; - time=clock(); + clock_t time=clock(); network_predict(net, X); printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); get_detection_boxes(l, 1, 1, thresh, probs, boxes, 0); @@ -322,6 +324,11 @@ void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh) if (filename) break; } + free(boxes); + for(j = 0; j < l.side*l.side*l.n; ++j) { + free(probs[j]); + } + free(probs); } void run_yolo(int argc, char **argv) From c71701b3c3a01616be0c0417195731cbc4498a4e Mon Sep 17 00:00:00 2001 From: cyy Date: Wed, 11 Sep 2019 23:44:58 +0800 Subject: [PATCH 0607/1365] revert change --- src/convolutional_layer.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 859efeea5cf..6ff5b8b3d4f 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -607,6 +607,8 @@ void test_convolutional_layer() void resize_convolutional_layer(convolutional_layer *l, int w, int h) { int total_batch = l->batch*l->steps; + int old_w = l->w; + int old_h = l->h; l->w = w; l->h = h; int out_w = convolutional_out_width(*l); @@ -630,8 +632,6 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) } #ifdef GPU - int old_w = l->w; - int old_h = l->h; if (old_w < w || old_h < h) { cuda_free(l->delta_gpu); cuda_free(l->output_gpu); From 011eed632d70c4e907ad512e2ac1e04d90b1072c Mon Sep 17 00:00:00 2001 From: cyy Date: Fri, 13 Sep 2019 09:34:41 +0800 Subject: [PATCH 0608/1365] fix calloc realloc failure --- src/activation_layer.c | 4 +- src/avgpool_layer.c | 4 +- src/batchnorm_layer.c | 16 +++--- src/blas.c | 2 +- src/box.c | 3 +- src/classifier.c | 28 +++++----- src/coco.c | 51 ++++++------------ src/compare.c | 10 ++-- src/connected_layer.c | 32 ++++++------ src/conv_lstm_layer.c | 100 ++++++++++++++++++------------------ src/convolutional_layer.c | 80 ++++++++++++++--------------- src/cost_layer.c | 10 ++-- src/crnn_layer.c | 10 ++-- src/crop_layer.c | 5 +- src/dark_cuda.c | 2 +- src/darknet.c | 6 +-- src/data.c | 62 +++++++++++----------- src/deconvolutional_layer.c | 20 ++++---- src/detection_layer.c | 12 ++--- src/detector.c | 52 +++++++++---------- src/dropout_layer.c | 4 +- src/gemm.c | 4 +- src/go.c | 32 ++++++------ src/gru_layer.c | 20 ++++---- src/image.c | 20 ++++---- src/list.c | 2 +- src/local_layer.c | 14 ++--- src/lstm_layer.c | 50 +++++++++--------- src/matrix.c | 28 +++++----- src/maxpool_layer.c | 12 ++--- src/network.c | 58 ++++++++++----------- src/normalization_layer.c | 16 +++--- src/parser.c | 24 ++++----- src/region_layer.c | 18 +++---- src/reorg_layer.c | 8 +-- src/reorg_old_layer.c | 8 +-- src/rnn.c | 34 ++++++------ src/rnn_layer.c | 8 +-- src/rnn_vid.c | 4 +- src/route_layer.c | 8 +-- src/sam_layer.c | 8 +-- src/scale_channels_layer.c | 8 +-- src/shortcut_layer.c | 8 +-- src/softmax_layer.c | 8 +-- src/tree.c | 20 ++++---- src/upsample_layer.c | 8 +-- src/utils.c | 21 ++++---- src/utils.h | 1 + src/yolo_layer.c | 30 +++++------ 49 files changed, 489 insertions(+), 504 deletions(-) diff --git a/src/activation_layer.c b/src/activation_layer.c index b8b5d0236c3..4383d7e1b3d 100644 --- a/src/activation_layer.c +++ b/src/activation_layer.c @@ -18,8 +18,8 @@ layer make_activation_layer(int batch, int inputs, ACTIVATION activation) l.outputs = inputs; l.batch=batch; - l.output = (float*)calloc(batch * inputs, sizeof(float)); - l.delta = (float*)calloc(batch * inputs, sizeof(float)); + l.output = (float*)xcalloc(batch * inputs, sizeof(float)); + l.delta = (float*)xcalloc(batch * inputs, sizeof(float)); l.forward = forward_activation_layer; l.backward = backward_activation_layer; diff --git a/src/avgpool_layer.c b/src/avgpool_layer.c index 08081bd8041..1306fd38636 100644 --- a/src/avgpool_layer.c +++ b/src/avgpool_layer.c @@ -17,8 +17,8 @@ avgpool_layer make_avgpool_layer(int batch, int w, int h, int c) l.outputs = l.out_c; l.inputs = h*w*c; int output_size = l.outputs * batch; - l.output = (float*)calloc(output_size, sizeof(float)); - l.delta = (float*)calloc(output_size, sizeof(float)); + l.output = (float*)xcalloc(output_size, sizeof(float)); + l.delta = (float*)xcalloc(output_size, sizeof(float)); l.forward = forward_avgpool_layer; l.backward = backward_avgpool_layer; #ifdef GPU diff --git a/src/batchnorm_layer.c b/src/batchnorm_layer.c index 4f9536a99ce..018294df6e5 100644 --- a/src/batchnorm_layer.c +++ b/src/batchnorm_layer.c @@ -11,23 +11,23 @@ layer make_batchnorm_layer(int batch, int w, int h, int c) layer.h = layer.out_h = h; layer.w = layer.out_w = w; layer.c = layer.out_c = c; - layer.output = (float*)calloc(h * w * c * batch, sizeof(float)); - layer.delta = (float*)calloc(h * w * c * batch, sizeof(float)); + layer.output = (float*)xcalloc(h * w * c * batch, sizeof(float)); + layer.delta = (float*)xcalloc(h * w * c * batch, sizeof(float)); layer.inputs = w*h*c; layer.outputs = layer.inputs; - layer.scales = (float*)calloc(c, sizeof(float)); - layer.scale_updates = (float*)calloc(c, sizeof(float)); + layer.scales = (float*)xcalloc(c, sizeof(float)); + layer.scale_updates = (float*)xcalloc(c, sizeof(float)); int i; for(i = 0; i < c; ++i){ layer.scales[i] = 1; } - layer.mean = (float*)calloc(c, sizeof(float)); - layer.variance = (float*)calloc(c, sizeof(float)); + layer.mean = (float*)xcalloc(c, sizeof(float)); + layer.variance = (float*)xcalloc(c, sizeof(float)); - layer.rolling_mean = (float*)calloc(c, sizeof(float)); - layer.rolling_variance = (float*)calloc(c, sizeof(float)); + layer.rolling_mean = (float*)xcalloc(c, sizeof(float)); + layer.rolling_variance = (float*)xcalloc(c, sizeof(float)); layer.forward = forward_batchnorm_layer; layer.backward = backward_batchnorm_layer; diff --git a/src/blas.c b/src/blas.c index d00cb89ddf0..96fc067f963 100644 --- a/src/blas.c +++ b/src/blas.c @@ -34,7 +34,7 @@ void reorg_cpu(float *x, int out_w, int out_h, int out_c, int batch, int stride, void flatten(float *x, int size, int layers, int batch, int forward) { - float* swap = (float*)calloc(size * layers * batch, sizeof(float)); + float* swap = (float*)xcalloc(size * layers * batch, sizeof(float)); int i,c,b; for(b = 0; b < batch; ++b){ for(c = 0; c < layers; ++c){ diff --git a/src/box.c b/src/box.c index 640f54a299e..a4180f29066 100644 --- a/src/box.c +++ b/src/box.c @@ -1,4 +1,5 @@ #include "box.h" +#include "utils.h" #include #include #include @@ -379,7 +380,7 @@ int nms_comparator(const void *pa, const void *pb) void do_nms_sort_v2(box *boxes, float **probs, int total, int classes, float thresh) { int i, j, k; - sortable_bbox* s = (sortable_bbox*)calloc(total, sizeof(sortable_bbox)); + sortable_bbox* s = (sortable_bbox*)xcalloc(total, sizeof(sortable_bbox)); for(i = 0; i < total; ++i){ s[i].index = i; diff --git a/src/classifier.c b/src/classifier.c index fba06a6eae3..d75b320e595 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -17,7 +17,7 @@ float validate_classifier_single(char *datacfg, char *filename, char *weightfile float *get_regression_values(char **labels, int n) { - float* v = (float*)calloc(n, sizeof(float)); + float* v = (float*)xcalloc(n, sizeof(float)); int i; for(i = 0; i < n; ++i){ char *p = strchr(labels[i], ' '); @@ -35,7 +35,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, char *base = basecfg(cfgfile); printf("%s\n", base); printf("%d\n", ngpus); - network* nets = (network*)calloc(ngpus, sizeof(network)); + network* nets = (network*)xcalloc(ngpus, sizeof(network)); srand(time(0)); int seed = rand(); @@ -415,7 +415,7 @@ void validate_classifier_10(char *datacfg, char *filename, char *weightfile) float avg_acc = 0; float avg_topk = 0; - int* indexes = (int*)calloc(topk, sizeof(int)); + int* indexes = (int*)xcalloc(topk, sizeof(int)); for(i = 0; i < m; ++i){ int class_id = -1; @@ -442,7 +442,7 @@ void validate_classifier_10(char *datacfg, char *filename, char *weightfile) images[7] = crop_image(im, 0, 0, w, h); images[8] = crop_image(im, -shift, shift, w, h); images[9] = crop_image(im, shift, shift, w, h); - float* pred = (float*)calloc(classes, sizeof(float)); + float* pred = (float*)xcalloc(classes, sizeof(float)); for(j = 0; j < 10; ++j){ float *p = network_predict(net, images[j].data); if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy, 1); @@ -489,7 +489,7 @@ void validate_classifier_full(char *datacfg, char *filename, char *weightfile) float avg_acc = 0; float avg_topk = 0; - int* indexes = (int*)calloc(topk, sizeof(int)); + int* indexes = (int*)xcalloc(topk, sizeof(int)); int size = net.w; for(i = 0; i < m; ++i){ @@ -567,7 +567,7 @@ float validate_classifier_single(char *datacfg, char *filename, char *weightfile float avg_acc = 0; float avg_topk = 0; - int* indexes = (int*)calloc(topk, sizeof(int)); + int* indexes = (int*)xcalloc(topk, sizeof(int)); for(i = 0; i < m; ++i){ int class_id = -1; @@ -638,7 +638,7 @@ void validate_classifier_multi(char *datacfg, char *filename, char *weightfile) float avg_acc = 0; float avg_topk = 0; - int* indexes = (int*)calloc(topk, sizeof(int)); + int* indexes = (int*)xcalloc(topk, sizeof(int)); for(i = 0; i < m; ++i){ int class_id = -1; @@ -649,7 +649,7 @@ void validate_classifier_multi(char *datacfg, char *filename, char *weightfile) break; } } - float* pred = (float*)calloc(classes, sizeof(float)); + float* pred = (float*)xcalloc(classes, sizeof(float)); image im = load_image_color(paths[i], 0, 0); for(j = 0; j < nscales; ++j){ image r = resize_min(im, scales[j]); @@ -694,7 +694,7 @@ void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filena char **names = get_labels(name_list); clock_t time; - int* indexes = (int*)calloc(top, sizeof(int)); + int* indexes = (int*)xcalloc(top, sizeof(int)); char buff[256]; char *input = buff; while(1){ @@ -781,7 +781,7 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi int i = 0; char **names = get_labels(name_list); clock_t time; - int* indexes = (int*)calloc(top, sizeof(int)); + int* indexes = (int*)xcalloc(top, sizeof(int)); if(!indexes) { error("calloc failed"); } @@ -971,7 +971,7 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i char *name_list = option_find_str(options, "names", 0); char **names = get_labels(name_list); - int* indexes = (int*)calloc(top, sizeof(int)); + int* indexes = (int*)xcalloc(top, sizeof(int)); if(!cap) error("Couldn't connect to webcam.\n"); create_window_cv("Threat", 0, 512, 512); @@ -1110,7 +1110,7 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde char *name_list = option_find_str(options, "names", 0); char **names = get_labels(name_list); - int* indexes = (int*)calloc(top, sizeof(int)); + int* indexes = (int*)xcalloc(top, sizeof(int)); if(!cap) error("Couldn't connect to webcam.\n"); cvNamedWindow("Threat Detection", CV_WINDOW_NORMAL); @@ -1193,7 +1193,7 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind char *name_list = option_find_str(options, "names", 0); char **names = get_labels(name_list); - int* indexes = (int*)calloc(top, sizeof(int)); + int* indexes = (int*)xcalloc(top, sizeof(int)); if(!cap) error("Couldn't connect to webcam.\n"); create_window_cv("Classifier", 0, 512, 512); @@ -1258,7 +1258,7 @@ void run_classifier(int argc, char **argv) for(i = 0; i < len; ++i){ if (gpu_list[i] == ',') ++ngpus; } - gpus = (int*)calloc(ngpus, sizeof(int)); + gpus = (int*)xcalloc(ngpus, sizeof(int)); for(i = 0; i < ngpus; ++i){ gpus[i] = atoi(gpu_list); gpu_list = strchr(gpu_list, ',')+1; diff --git a/src/coco.c b/src/coco.c index 83089d764a6..20febecece1 100644 --- a/src/coco.c +++ b/src/coco.c @@ -160,9 +160,9 @@ void validate_coco(char *cfgfile, char *weightfile) FILE *fp = fopen(buff, "w"); fprintf(fp, "[\n"); - box* boxes = (box*)calloc(side * side * l.n, sizeof(box)); - float** probs = (float**)calloc(side * side * l.n, sizeof(float*)); - for(j = 0; j < side*side*l.n; ++j) probs[j] = (float*)calloc(classes, sizeof(float)); + box* boxes = (box*)xcalloc(side * side * l.n, sizeof(box)); + float** probs = (float**)xcalloc(side * side * l.n, sizeof(float*)); + for(j = 0; j < side*side*l.n; ++j) probs[j] = (float*)xcalloc(classes, sizeof(float)); int m = plist->size; int i=0; @@ -173,11 +173,11 @@ void validate_coco(char *cfgfile, char *weightfile) float iou_thresh = .5; int nthreads = 8; - image* val = (image*)calloc(nthreads, sizeof(image)); - image* val_resized = (image*)calloc(nthreads, sizeof(image)); - image* buf = (image*)calloc(nthreads, sizeof(image)); - image* buf_resized = (image*)calloc(nthreads, sizeof(image)); - pthread_t* thr = (pthread_t*)calloc(nthreads, sizeof(pthread_t)); + image* val = (image*)xcalloc(nthreads, sizeof(image)); + image* val_resized = (image*)xcalloc(nthreads, sizeof(image)); + image* buf = (image*)xcalloc(nthreads, sizeof(image)); + image* buf_resized = (image*)xcalloc(nthreads, sizeof(image)); + pthread_t* thr = (pthread_t*)xcalloc(nthreads, sizeof(pthread_t)); load_args args = {0}; args.w = net.w; @@ -249,29 +249,17 @@ void validate_coco_recall(char *cfgfile, char *weightfile) int j, k; /* unused code,why? - FILE** fps = (FILE**)calloc(classes, sizeof(FILE*)); - if(!fps) { - error("calloc failed"); - } + FILE** fps = (FILE**)xcalloc(classes, sizeof(FILE*)); for(j = 0; j < classes; ++j){ char buff[1024]; snprintf(buff, 1024, "%s%s.txt", base, coco_classes[j]); fps[j] = fopen(buff, "w"); } */ - box* boxes = (box*)calloc(side * side * l.n, sizeof(box)); - if(!boxes) { - error("calloc failed"); - } - float** probs = (float**)calloc(side * side * l.n, sizeof(float*)); - if(!probs) { - error("calloc failed"); - } + box* boxes = (box*)xcalloc(side * side * l.n, sizeof(box)); + float** probs = (float**)xcalloc(side * side * l.n, sizeof(float*)); for(j = 0; j < side*side*l.n; ++j) { - probs[j] = (float*)calloc(classes, sizeof(float)); - if(!probs[j]) { - error("calloc failed"); - } + probs[j] = (float*)xcalloc(classes, sizeof(float)); } int m = plist->size; @@ -349,19 +337,10 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh) char buff[256]; char *input = buff; int j; - box* boxes = (box*)calloc(l.side * l.side * l.n, sizeof(box)); - if(!boxes) { - error("calloc failed"); - } - float** probs = (float**)calloc(l.side * l.side * l.n, sizeof(float*)); - if(!probs) { - error("calloc failed"); - } + box* boxes = (box*)xcalloc(l.side * l.side * l.n, sizeof(box)); + float** probs = (float**)xcalloc(l.side * l.side * l.n, sizeof(float*)); for(j = 0; j < l.side*l.side*l.n; ++j) { - probs[j] = (float*)calloc(l.classes, sizeof(float)); - if(!probs[j]) { - error("calloc failed"); - } + probs[j] = (float*)xcalloc(l.classes, sizeof(float)); } while(1){ if(filename){ diff --git a/src/compare.c b/src/compare.c index bb8422611b9..62edabe965c 100644 --- a/src/compare.c +++ b/src/compare.c @@ -176,7 +176,7 @@ int bbox_comparator(const void *a, const void *b) image im1 = load_image_color(box1.filename, net.w, net.h); image im2 = load_image_color(box2.filename, net.w, net.h); - float* X = (float*)calloc(net.w * net.h * net.c, sizeof(float)); + float* X = (float*)xcalloc(net.w * net.h * net.c, sizeof(float)); memcpy(X, im1.data, im1.w*im1.h*im1.c*sizeof(float)); memcpy(X+im1.w*im1.h*im1.c, im2.data, im2.w*im2.h*im2.c*sizeof(float)); float *predictions = network_predict(net, X); @@ -205,7 +205,7 @@ void bbox_fight(network net, sortable_bbox *a, sortable_bbox *b, int classes, in { image im1 = load_image_color(a->filename, net.w, net.h); image im2 = load_image_color(b->filename, net.w, net.h); - float* X = (float*)calloc(net.w * net.h * net.c, sizeof(float)); + float* X = (float*)xcalloc(net.w * net.h * net.c, sizeof(float)); memcpy(X, im1.data, im1.w*im1.h*im1.c*sizeof(float)); memcpy(X+im1.w*im1.h*im1.c, im2.data, im2.w*im2.h*im2.c*sizeof(float)); float *predictions = network_predict(net, X); @@ -239,7 +239,7 @@ void SortMaster3000(char *filename, char *weightfile) char **paths = (char **)list_to_array(plist); int N = plist->size; free_list(plist); - sortable_bbox* boxes = (sortable_bbox*)calloc(N, sizeof(sortable_bbox)); + sortable_bbox* boxes = (sortable_bbox*)xcalloc(N, sizeof(sortable_bbox)); printf("Sorting %d boxes...\n", N); for(i = 0; i < N; ++i){ boxes[i].filename = paths[i]; @@ -274,13 +274,13 @@ void BattleRoyaleWithCheese(char *filename, char *weightfile) int N = plist->size; int total = N; free_list(plist); - sortable_bbox* boxes = (sortable_bbox*)calloc(N, sizeof(sortable_bbox)); + sortable_bbox* boxes = (sortable_bbox*)xcalloc(N, sizeof(sortable_bbox)); printf("Battling %d boxes...\n", N); for(i = 0; i < N; ++i){ boxes[i].filename = paths[i]; boxes[i].net = net; boxes[i].classes = classes; - boxes[i].elos = (float*)calloc(classes, sizeof(float)); + boxes[i].elos = (float*)xcalloc(classes, sizeof(float)); for(j = 0; j < classes; ++j){ boxes[i].elos[j] = 1500; } diff --git a/src/connected_layer.c b/src/connected_layer.c index 242ab8fb106..1b1218e5f2a 100644 --- a/src/connected_layer.c +++ b/src/connected_layer.c @@ -74,14 +74,14 @@ connected_layer make_connected_layer(int batch, int steps, int inputs, int outpu l.activation = activation; l.learning_rate_scale = 1; - l.output = (float*)calloc(total_batch * outputs, sizeof(float)); - l.delta = (float*)calloc(total_batch * outputs, sizeof(float)); + l.output = (float*)xcalloc(total_batch * outputs, sizeof(float)); + l.delta = (float*)xcalloc(total_batch * outputs, sizeof(float)); - l.weight_updates = (float*)calloc(inputs * outputs, sizeof(float)); - l.bias_updates = (float*)calloc(outputs, sizeof(float)); + l.weight_updates = (float*)xcalloc(inputs * outputs, sizeof(float)); + l.bias_updates = (float*)xcalloc(outputs, sizeof(float)); - l.weights = (float*)calloc(outputs * inputs, sizeof(float)); - l.biases = (float*)calloc(outputs, sizeof(float)); + l.weights = (float*)xcalloc(outputs * inputs, sizeof(float)); + l.biases = (float*)xcalloc(outputs, sizeof(float)); l.forward = forward_connected_layer; l.backward = backward_connected_layer; @@ -98,22 +98,22 @@ connected_layer make_connected_layer(int batch, int steps, int inputs, int outpu } if(batch_normalize){ - l.scales = (float*)calloc(outputs, sizeof(float)); - l.scale_updates = (float*)calloc(outputs, sizeof(float)); + l.scales = (float*)xcalloc(outputs, sizeof(float)); + l.scale_updates = (float*)xcalloc(outputs, sizeof(float)); for(i = 0; i < outputs; ++i){ l.scales[i] = 1; } - l.mean = (float*)calloc(outputs, sizeof(float)); - l.mean_delta = (float*)calloc(outputs, sizeof(float)); - l.variance = (float*)calloc(outputs, sizeof(float)); - l.variance_delta = (float*)calloc(outputs, sizeof(float)); + l.mean = (float*)xcalloc(outputs, sizeof(float)); + l.mean_delta = (float*)xcalloc(outputs, sizeof(float)); + l.variance = (float*)xcalloc(outputs, sizeof(float)); + l.variance_delta = (float*)xcalloc(outputs, sizeof(float)); - l.rolling_mean = (float*)calloc(outputs, sizeof(float)); - l.rolling_variance = (float*)calloc(outputs, sizeof(float)); + l.rolling_mean = (float*)xcalloc(outputs, sizeof(float)); + l.rolling_variance = (float*)xcalloc(outputs, sizeof(float)); - l.x = (float*)calloc(total_batch * outputs, sizeof(float)); - l.x_norm = (float*)calloc(total_batch * outputs, sizeof(float)); + l.x = (float*)xcalloc(total_batch * outputs, sizeof(float)); + l.x_norm = (float*)xcalloc(total_batch * outputs, sizeof(float)); } #ifdef GPU diff --git a/src/conv_lstm_layer.c b/src/conv_lstm_layer.c index 6cbaf1c3911..2fc2164f686 100644 --- a/src/conv_lstm_layer.c +++ b/src/conv_lstm_layer.c @@ -65,65 +65,65 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i l.peephole = peephole; // U - l.uf = (layer*)calloc(1, sizeof(layer)); + l.uf = (layer*)xcalloc(1, sizeof(layer)); *(l.uf) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.uf->batch = batch; if (l.workspace_size < l.uf->workspace_size) l.workspace_size = l.uf->workspace_size; - l.ui = (layer*)calloc(1, sizeof(layer)); + l.ui = (layer*)xcalloc(1, sizeof(layer)); *(l.ui) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.ui->batch = batch; if (l.workspace_size < l.ui->workspace_size) l.workspace_size = l.ui->workspace_size; - l.ug = (layer*)calloc(1, sizeof(layer)); + l.ug = (layer*)xcalloc(1, sizeof(layer)); *(l.ug) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.ug->batch = batch; if (l.workspace_size < l.ug->workspace_size) l.workspace_size = l.ug->workspace_size; - l.uo = (layer*)calloc(1, sizeof(layer)); + l.uo = (layer*)xcalloc(1, sizeof(layer)); *(l.uo) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.uo->batch = batch; if (l.workspace_size < l.uo->workspace_size) l.workspace_size = l.uo->workspace_size; // W - l.wf = (layer*)calloc(1, sizeof(layer)); + l.wf = (layer*)xcalloc(1, sizeof(layer)); *(l.wf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.wf->batch = batch; if (l.workspace_size < l.wf->workspace_size) l.workspace_size = l.wf->workspace_size; - l.wi = (layer*)calloc(1, sizeof(layer)); + l.wi = (layer*)xcalloc(1, sizeof(layer)); *(l.wi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.wi->batch = batch; if (l.workspace_size < l.wi->workspace_size) l.workspace_size = l.wi->workspace_size; - l.wg = (layer*)calloc(1, sizeof(layer)); + l.wg = (layer*)xcalloc(1, sizeof(layer)); *(l.wg) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.wg->batch = batch; if (l.workspace_size < l.wg->workspace_size) l.workspace_size = l.wg->workspace_size; - l.wo = (layer*)calloc(1, sizeof(layer)); + l.wo = (layer*)xcalloc(1, sizeof(layer)); *(l.wo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.wo->batch = batch; if (l.workspace_size < l.wo->workspace_size) l.workspace_size = l.wo->workspace_size; // V - l.vf = (layer*)calloc(1, sizeof(layer)); + l.vf = (layer*)xcalloc(1, sizeof(layer)); if (l.peephole) { *(l.vf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.vf->batch = batch; if (l.workspace_size < l.vf->workspace_size) l.workspace_size = l.vf->workspace_size; } - l.vi = (layer*)calloc(1, sizeof(layer)); + l.vi = (layer*)xcalloc(1, sizeof(layer)); if (l.peephole) { *(l.vi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.vi->batch = batch; if (l.workspace_size < l.vi->workspace_size) l.workspace_size = l.vi->workspace_size; } - l.vo = (layer*)calloc(1, sizeof(layer)); + l.vo = (layer*)xcalloc(1, sizeof(layer)); if (l.peephole) { *(l.vo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.vo->batch = batch; @@ -141,30 +141,30 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i assert(l.wo->outputs == l.uo->outputs); - l.output = (float*)calloc(outputs * batch * steps, sizeof(float)); - //l.state = (float*)calloc(outputs * batch, sizeof(float)); + l.output = (float*)xcalloc(outputs * batch * steps, sizeof(float)); + //l.state = (float*)xcalloc(outputs * batch, sizeof(float)); l.forward = forward_conv_lstm_layer; l.update = update_conv_lstm_layer; l.backward = backward_conv_lstm_layer; - l.prev_state_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.prev_cell_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.cell_cpu = (float*)calloc(batch*outputs*steps, sizeof(float)); - - l.f_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.i_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.g_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.o_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.c_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.stored_c_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.h_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.stored_h_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.temp_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.temp2_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.temp3_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.dc_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.dh_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.prev_state_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.prev_cell_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.cell_cpu = (float*)xcalloc(batch*outputs*steps, sizeof(float)); + + l.f_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.i_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.g_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.o_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.c_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.stored_c_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.h_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.stored_h_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.temp_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.temp2_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.temp3_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.dc_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.dh_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); #ifdef GPU l.forward_gpu = forward_conv_lstm_layer_gpu; @@ -275,26 +275,26 @@ void resize_conv_lstm_layer(layer *l, int w, int h) assert(l->wo->outputs == l->uo->outputs); - l->output = (float*)realloc(l->output, outputs * batch * steps * sizeof(float)); - //l->state = (float*)realloc(l->state, outputs * batch * sizeof(float)); - - l->prev_state_cpu = (float*)realloc(l->prev_state_cpu, batch*outputs * sizeof(float)); - l->prev_cell_cpu = (float*)realloc(l->prev_cell_cpu, batch*outputs * sizeof(float)); - l->cell_cpu = (float*)realloc(l->cell_cpu, batch*outputs*steps * sizeof(float)); - - l->f_cpu = (float*)realloc(l->f_cpu, batch*outputs * sizeof(float)); - l->i_cpu = (float*)realloc(l->i_cpu, batch*outputs * sizeof(float)); - l->g_cpu = (float*)realloc(l->g_cpu, batch*outputs * sizeof(float)); - l->o_cpu = (float*)realloc(l->o_cpu, batch*outputs * sizeof(float)); - l->c_cpu = (float*)realloc(l->c_cpu, batch*outputs * sizeof(float)); - l->h_cpu = (float*)realloc(l->h_cpu, batch*outputs * sizeof(float)); - l->temp_cpu = (float*)realloc(l->temp_cpu, batch*outputs * sizeof(float)); - l->temp2_cpu = (float*)realloc(l->temp2_cpu, batch*outputs * sizeof(float)); - l->temp3_cpu = (float*)realloc(l->temp3_cpu, batch*outputs * sizeof(float)); - l->dc_cpu = (float*)realloc(l->dc_cpu, batch*outputs * sizeof(float)); - l->dh_cpu = (float*)realloc(l->dh_cpu, batch*outputs * sizeof(float)); - l->stored_c_cpu = (float*)realloc(l->stored_c_cpu, batch*outputs * sizeof(float)); - l->stored_h_cpu = (float*)realloc(l->stored_h_cpu, batch*outputs * sizeof(float)); + l->output = (float*)xrealloc(l->output, outputs * batch * steps * sizeof(float)); + //l->state = (float*)xrealloc(l->state, outputs * batch * sizeof(float)); + + l->prev_state_cpu = (float*)xrealloc(l->prev_state_cpu, batch*outputs * sizeof(float)); + l->prev_cell_cpu = (float*)xrealloc(l->prev_cell_cpu, batch*outputs * sizeof(float)); + l->cell_cpu = (float*)xrealloc(l->cell_cpu, batch*outputs*steps * sizeof(float)); + + l->f_cpu = (float*)xrealloc(l->f_cpu, batch*outputs * sizeof(float)); + l->i_cpu = (float*)xrealloc(l->i_cpu, batch*outputs * sizeof(float)); + l->g_cpu = (float*)xrealloc(l->g_cpu, batch*outputs * sizeof(float)); + l->o_cpu = (float*)xrealloc(l->o_cpu, batch*outputs * sizeof(float)); + l->c_cpu = (float*)xrealloc(l->c_cpu, batch*outputs * sizeof(float)); + l->h_cpu = (float*)xrealloc(l->h_cpu, batch*outputs * sizeof(float)); + l->temp_cpu = (float*)xrealloc(l->temp_cpu, batch*outputs * sizeof(float)); + l->temp2_cpu = (float*)xrealloc(l->temp2_cpu, batch*outputs * sizeof(float)); + l->temp3_cpu = (float*)xrealloc(l->temp3_cpu, batch*outputs * sizeof(float)); + l->dc_cpu = (float*)xrealloc(l->dc_cpu, batch*outputs * sizeof(float)); + l->dh_cpu = (float*)xrealloc(l->dh_cpu, batch*outputs * sizeof(float)); + l->stored_c_cpu = (float*)xrealloc(l->stored_c_cpu, batch*outputs * sizeof(float)); + l->stored_h_cpu = (float*)xrealloc(l->stored_h_cpu, batch*outputs * sizeof(float)); #ifdef GPU //if (l->state_gpu) cudaFree(l->state_gpu); diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 6ff5b8b3d4f..c5cf39f9908 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -370,11 +370,11 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.bias_updates = l.share_layer->bias_updates; } else { - l.weights = (float*)calloc(l.nweights, sizeof(float)); - l.weight_updates = (float*)calloc(l.nweights, sizeof(float)); + l.weights = (float*)xcalloc(l.nweights, sizeof(float)); + l.weight_updates = (float*)xcalloc(l.nweights, sizeof(float)); - l.biases = (float*)calloc(n, sizeof(float)); - l.bias_updates = (float*)calloc(n, sizeof(float)); + l.biases = (float*)xcalloc(n, sizeof(float)); + l.bias_updates = (float*)xcalloc(n, sizeof(float)); } // float scale = 1./sqrt(size*size*c); @@ -389,36 +389,36 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.inputs = l.w * l.h * l.c; l.activation = activation; - l.output = (float*)calloc(total_batch*l.outputs, sizeof(float)); - l.delta = (float*)calloc(total_batch*l.outputs, sizeof(float)); + l.output = (float*)xcalloc(total_batch*l.outputs, sizeof(float)); + l.delta = (float*)xcalloc(total_batch*l.outputs, sizeof(float)); l.forward = forward_convolutional_layer; l.backward = backward_convolutional_layer; l.update = update_convolutional_layer; if(binary){ - l.binary_weights = (float*)calloc(l.nweights, sizeof(float)); - l.cweights = (char*)calloc(l.nweights, sizeof(char)); - l.scales = (float*)calloc(n, sizeof(float)); + l.binary_weights = (float*)xcalloc(l.nweights, sizeof(float)); + l.cweights = (char*)xcalloc(l.nweights, sizeof(char)); + l.scales = (float*)xcalloc(n, sizeof(float)); } if(xnor){ - l.binary_weights = (float*)calloc(l.nweights, sizeof(float)); - l.binary_input = (float*)calloc(l.inputs * l.batch, sizeof(float)); + l.binary_weights = (float*)xcalloc(l.nweights, sizeof(float)); + l.binary_input = (float*)xcalloc(l.inputs * l.batch, sizeof(float)); int align = 32;// 8; int src_align = l.out_h*l.out_w; l.bit_align = src_align + (align - src_align % align); - l.mean_arr = (float*)calloc(l.n, sizeof(float)); + l.mean_arr = (float*)xcalloc(l.n, sizeof(float)); const size_t new_c = l.c / 32; size_t in_re_packed_input_size = new_c * l.w * l.h + 1; - l.bin_re_packed_input = (uint32_t*)calloc(in_re_packed_input_size, sizeof(uint32_t)); + l.bin_re_packed_input = (uint32_t*)xcalloc(in_re_packed_input_size, sizeof(uint32_t)); l.lda_align = 256; // AVX2 int k = l.size*l.size*l.c; size_t k_aligned = k + (l.lda_align - k%l.lda_align); size_t t_bit_input_size = k_aligned * l.bit_align / 8; - l.t_bit_input = (char*)calloc(t_bit_input_size, sizeof(char)); + l.t_bit_input = (char*)xcalloc(t_bit_input_size, sizeof(char)); } if(batch_normalize){ @@ -433,36 +433,36 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.rolling_variance = l.share_layer->rolling_variance; } else { - l.scales = (float*)calloc(n, sizeof(float)); - l.scale_updates = (float*)calloc(n, sizeof(float)); + l.scales = (float*)xcalloc(n, sizeof(float)); + l.scale_updates = (float*)xcalloc(n, sizeof(float)); for (i = 0; i < n; ++i) { l.scales[i] = 1; } - l.mean = (float*)calloc(n, sizeof(float)); - l.variance = (float*)calloc(n, sizeof(float)); + l.mean = (float*)xcalloc(n, sizeof(float)); + l.variance = (float*)xcalloc(n, sizeof(float)); - l.mean_delta = (float*)calloc(n, sizeof(float)); - l.variance_delta = (float*)calloc(n, sizeof(float)); + l.mean_delta = (float*)xcalloc(n, sizeof(float)); + l.variance_delta = (float*)xcalloc(n, sizeof(float)); - l.rolling_mean = (float*)calloc(n, sizeof(float)); - l.rolling_variance = (float*)calloc(n, sizeof(float)); + l.rolling_mean = (float*)xcalloc(n, sizeof(float)); + l.rolling_variance = (float*)xcalloc(n, sizeof(float)); } - l.x = (float*)calloc(total_batch * l.outputs, sizeof(float)); - l.x_norm = (float*)calloc(total_batch * l.outputs, sizeof(float)); + l.x = (float*)xcalloc(total_batch * l.outputs, sizeof(float)); + l.x_norm = (float*)xcalloc(total_batch * l.outputs, sizeof(float)); } if(adam){ l.adam = 1; - l.m = (float*)calloc(l.nweights, sizeof(float)); - l.v = (float*)calloc(l.nweights, sizeof(float)); - l.bias_m = (float*)calloc(n, sizeof(float)); - l.scale_m = (float*)calloc(n, sizeof(float)); - l.bias_v = (float*)calloc(n, sizeof(float)); - l.scale_v = (float*)calloc(n, sizeof(float)); + l.m = (float*)xcalloc(l.nweights, sizeof(float)); + l.v = (float*)xcalloc(l.nweights, sizeof(float)); + l.bias_m = (float*)xcalloc(n, sizeof(float)); + l.scale_m = (float*)xcalloc(n, sizeof(float)); + l.bias_v = (float*)xcalloc(n, sizeof(float)); + l.scale_v = (float*)xcalloc(n, sizeof(float)); } - if(l.activation == SWISH) l.output_sigmoid = (float*)calloc(total_batch*l.outputs, sizeof(float)); + if(l.activation == SWISH) l.output_sigmoid = (float*)xcalloc(total_batch*l.outputs, sizeof(float)); #ifdef GPU if (l.activation == SWISH) l.output_sigmoid_gpu = cuda_make_array(l.output_sigmoid, total_batch*out_h*out_w*n); @@ -620,11 +620,11 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) l->outputs = l->out_h * l->out_w * l->out_c; l->inputs = l->w * l->h * l->c; - l->output = (float*)realloc(l->output, total_batch * l->outputs * sizeof(float)); - l->delta = (float*)realloc(l->delta, total_batch * l->outputs * sizeof(float)); + l->output = (float*)xrealloc(l->output, total_batch * l->outputs * sizeof(float)); + l->delta = (float*)xrealloc(l->delta, total_batch * l->outputs * sizeof(float)); if(l->batch_normalize){ - l->x = (float*)realloc(l->x, total_batch * l->outputs * sizeof(float)); - l->x_norm = (float*)realloc(l->x_norm, total_batch * l->outputs * sizeof(float)); + l->x = (float*)xrealloc(l->x, total_batch * l->outputs * sizeof(float)); + l->x_norm = (float*)xrealloc(l->x_norm, total_batch * l->outputs * sizeof(float)); } if (l->xnor) { @@ -766,8 +766,8 @@ void binary_align_weights(convolutional_layer *l) size_t align_weights_size = new_lda * m; l->align_bit_weights_size = align_weights_size / 8 + 1; - float* align_weights = (float*)calloc(align_weights_size, sizeof(float)); - l->align_bit_weights = (char*)calloc(l->align_bit_weights_size, sizeof(char)); + float* align_weights = (float*)xcalloc(align_weights_size, sizeof(float)); + l->align_bit_weights = (char*)xcalloc(l->align_bit_weights_size, sizeof(char)); size_t i, j; // align A without transpose @@ -1108,8 +1108,8 @@ void assisted_excitation_forward(convolutional_layer l, network_state state) //printf("\n epoch = %f, alpha = %f, seen = %d, max_batches = %d, train_images_num = %d \n", // epoch, alpha, (*state.net.seen), state.net.max_batches, state.net.train_images_num); - float *a_avg = (float *)calloc(l.out_w * l.out_h * l.batch, sizeof(float)); - float *g = (float *)calloc(l.out_w * l.out_h * l.batch, sizeof(float)); + float *a_avg = (float *)xcalloc(l.out_w * l.out_h * l.batch, sizeof(float)); + float *g = (float *)xcalloc(l.out_w * l.out_h * l.batch, sizeof(float)); int b; int w, h, c; @@ -1310,7 +1310,7 @@ void rescale_weights(convolutional_layer l, float scale, float trans) image *get_weights(convolutional_layer l) { - image *weights = (image *)calloc(l.n, sizeof(image)); + image *weights = (image *)xcalloc(l.n, sizeof(image)); int i; for (i = 0; i < l.n; ++i) { weights[i] = copy_image(get_convolutional_weight(l, i)); diff --git a/src/cost_layer.c b/src/cost_layer.c index 3038c3dafcd..ed1cc134479 100644 --- a/src/cost_layer.c +++ b/src/cost_layer.c @@ -41,9 +41,9 @@ cost_layer make_cost_layer(int batch, int inputs, COST_TYPE cost_type, float sca l.inputs = inputs; l.outputs = inputs; l.cost_type = cost_type; - l.delta = (float*)calloc(inputs * batch, sizeof(float)); - l.output = (float*)calloc(inputs * batch, sizeof(float)); - l.cost = (float*)calloc(1, sizeof(float)); + l.delta = (float*)xcalloc(inputs * batch, sizeof(float)); + l.output = (float*)xcalloc(inputs * batch, sizeof(float)); + l.cost = (float*)xcalloc(1, sizeof(float)); l.forward = forward_cost_layer; l.backward = backward_cost_layer; @@ -61,8 +61,8 @@ void resize_cost_layer(cost_layer *l, int inputs) { l->inputs = inputs; l->outputs = inputs; - l->delta = (float*)realloc(l->delta, inputs * l->batch * sizeof(float)); - l->output = (float*)realloc(l->output, inputs * l->batch * sizeof(float)); + l->delta = (float*)xrealloc(l->delta, inputs * l->batch * sizeof(float)); + l->output = (float*)xrealloc(l->output, inputs * l->batch * sizeof(float)); #ifdef GPU cuda_free(l->delta_gpu); cuda_free(l->output_gpu); diff --git a/src/crnn_layer.c b/src/crnn_layer.c index 7609003b4f2..e9f11fde2b7 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -47,19 +47,19 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.hidden = h * w * hidden_filters; l.xnor = xnor; - l.state = (float*)calloc(l.hidden * l.batch * (l.steps + 1), sizeof(float)); + l.state = (float*)xcalloc(l.hidden * l.batch * (l.steps + 1), sizeof(float)); - l.input_layer = (layer*)calloc(1, sizeof(layer)); + l.input_layer = (layer*)xcalloc(1, sizeof(layer)); *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.input_layer->batch = batch; if (l.workspace_size < l.input_layer->workspace_size) l.workspace_size = l.input_layer->workspace_size; - l.self_layer = (layer*)calloc(1, sizeof(layer)); + l.self_layer = (layer*)xcalloc(1, sizeof(layer)); *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.self_layer->batch = batch; if (l.workspace_size < l.self_layer->workspace_size) l.workspace_size = l.self_layer->workspace_size; - l.output_layer = (layer*)calloc(1, sizeof(layer)); + l.output_layer = (layer*)xcalloc(1, sizeof(layer)); *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, groups, size, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, NULL); l.output_layer->batch = batch; if (l.workspace_size < l.output_layer->workspace_size) l.workspace_size = l.output_layer->workspace_size; @@ -121,7 +121,7 @@ void resize_crnn_layer(layer *l, int w, int h) assert(l->input_layer->outputs == l->self_layer->outputs); assert(l->input_layer->outputs == l->output_layer->inputs); - l->state = (float*)realloc(l->state, l->batch*l->hidden*(l->steps + 1)*sizeof(float)); + l->state = (float*)xrealloc(l->state, l->batch*l->hidden*(l->steps + 1)*sizeof(float)); #ifdef GPU if (l->state_gpu) cudaFree(l->state_gpu); diff --git a/src/crop_layer.c b/src/crop_layer.c index 092237f403d..2d1fafc22aa 100644 --- a/src/crop_layer.c +++ b/src/crop_layer.c @@ -1,3 +1,4 @@ +#include "utils.h" #include "crop_layer.h" #include "dark_cuda.h" #include @@ -32,7 +33,7 @@ crop_layer make_crop_layer(int batch, int h, int w, int c, int crop_height, int l.out_c = c; l.inputs = l.w * l.h * l.c; l.outputs = l.out_w * l.out_h * l.out_c; - l.output = (float*)calloc(l.outputs * batch, sizeof(float)); + l.output = (float*)xcalloc(l.outputs * batch, sizeof(float)); l.forward = forward_crop_layer; l.backward = backward_crop_layer; @@ -56,7 +57,7 @@ void resize_crop_layer(layer *l, int w, int h) l->inputs = l->w * l->h * l->c; l->outputs = l->out_h * l->out_w * l->out_c; - l->output = (float*)realloc(l->output, l->batch * l->outputs * sizeof(float)); + l->output = (float*)xrealloc(l->output, l->batch * l->outputs * sizeof(float)); #ifdef GPU cuda_free(l->output_gpu); l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch); diff --git a/src/dark_cuda.c b/src/dark_cuda.c index 50ac96e0fd7..3a74e3a5e85 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -257,7 +257,7 @@ void cuda_random(float *x_gpu, size_t n) float cuda_compare(float *x_gpu, float *x, size_t n, char *s) { - float* tmp = (float*)calloc(n, sizeof(float)); + float* tmp = (float*)xcalloc(n, sizeof(float)); cuda_pull_array(x_gpu, tmp, n); //int i; //for(i = 0; i < n; ++i) printf("%f %f\n", tmp[i], x[i]); diff --git a/src/darknet.c b/src/darknet.c index 77a89899263..8bb53cbbb4f 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -258,12 +258,12 @@ layer normalize_layer(layer l, int n) { int j; l.batch_normalize=1; - l.scales = (float*)calloc(n, sizeof(float)); + l.scales = (float*)xcalloc(n, sizeof(float)); for(j = 0; j < n; ++j){ l.scales[j] = 1; } - l.rolling_mean = (float*)calloc(n, sizeof(float)); - l.rolling_variance = (float*)calloc(n, sizeof(float)); + l.rolling_mean = (float*)xcalloc(n, sizeof(float)); + l.rolling_variance = (float*)xcalloc(n, sizeof(float)); return l; } diff --git a/src/data.c b/src/data.c index 24755b875fe..4e265e3e05c 100644 --- a/src/data.c +++ b/src/data.c @@ -45,11 +45,11 @@ char **get_sequential_paths(char **paths, int n, int m, int mini_batch, int augm { int speed = rand_int(1, augment_speed); if (speed < 1) speed = 1; - char** sequentia_paths = (char**)calloc(n, sizeof(char*)); + char** sequentia_paths = (char**)xcalloc(n, sizeof(char*)); int i; pthread_mutex_lock(&mutex); //printf("n = %d, mini_batch = %d \n", n, mini_batch); - unsigned int *start_time_indexes = (unsigned int *)calloc(mini_batch, sizeof(unsigned int)); + unsigned int *start_time_indexes = (unsigned int *)xcalloc(mini_batch, sizeof(unsigned int)); for (i = 0; i < mini_batch; ++i) { start_time_indexes[i] = random_gen() % m; //printf(" start_time_indexes[i] = %u, ", start_time_indexes[i]); @@ -75,7 +75,7 @@ char **get_sequential_paths(char **paths, int n, int m, int mini_batch, int augm char **get_random_paths(char **paths, int n, int m) { - char** random_paths = (char**)calloc(n, sizeof(char*)); + char** random_paths = (char**)xcalloc(n, sizeof(char*)); int i; pthread_mutex_lock(&mutex); //printf("n = %d \n", n); @@ -94,7 +94,7 @@ char **get_random_paths(char **paths, int n, int m) char **find_replace_paths(char **paths, int n, char *find, char *replace) { - char** replace_paths = (char**)calloc(n, sizeof(char*)); + char** replace_paths = (char**)xcalloc(n, sizeof(char*)); int i; for(i = 0; i < n; ++i){ char replaced[4096]; @@ -109,7 +109,7 @@ matrix load_image_paths_gray(char **paths, int n, int w, int h) int i; matrix X; X.rows = n; - X.vals = (float**)calloc(X.rows, sizeof(float*)); + X.vals = (float**)xcalloc(X.rows, sizeof(float*)); X.cols = 0; for(i = 0; i < n; ++i){ @@ -130,7 +130,7 @@ matrix load_image_paths(char **paths, int n, int w, int h) int i; matrix X; X.rows = n; - X.vals = (float**)calloc(X.rows, sizeof(float*)); + X.vals = (float**)xcalloc(X.rows, sizeof(float*)); X.cols = 0; for(i = 0; i < n; ++i){ @@ -146,7 +146,7 @@ matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int int i; matrix X; X.rows = n; - X.vals = (float**)calloc(X.rows, sizeof(float*)); + X.vals = (float**)xcalloc(X.rows, sizeof(float*)); X.cols = 0; for(i = 0; i < n; ++i){ @@ -173,7 +173,7 @@ extern int check_mistakes; box_label *read_boxes(char *filename, int *n) { - box_label* boxes = (box_label*)calloc(1, sizeof(box_label)); + box_label* boxes = (box_label*)xcalloc(1, sizeof(box_label)); FILE *file = fopen(filename, "r"); if (!file) { printf("Can't open label file. (This can be normal only if you use MSCOCO): %s \n", filename); @@ -192,7 +192,7 @@ box_label *read_boxes(char *filename, int *n) int id; int count = 0; while(fscanf(file, "%d %f %f %f %f", &id, &x, &y, &w, &h) == 5){ - boxes = (box_label*)realloc(boxes, (count + 1) * sizeof(box_label)); + boxes = (box_label*)xrealloc(boxes, (count + 1) * sizeof(box_label)); if(!boxes) { error("realloc failed"); } @@ -604,7 +604,7 @@ data load_data_region(int n, char **paths, int m, int w, int h, int size, int cl d.shallow = 0; d.X.rows = n; - d.X.vals = (float**)calloc(d.X.rows, sizeof(float*)); + d.X.vals = (float**)xcalloc(d.X.rows, sizeof(float*)); d.X.cols = h*w*3; @@ -658,7 +658,7 @@ data load_data_compare(int n, char **paths, int m, int classes, int w, int h) d.shallow = 0; d.X.rows = n; - d.X.vals = (float**)calloc(d.X.rows, sizeof(float*)); + d.X.vals = (float**)xcalloc(d.X.rows, sizeof(float*)); d.X.cols = h*w*6; int k = 2*(classes); @@ -667,7 +667,7 @@ data load_data_compare(int n, char **paths, int m, int classes, int w, int h) image im1 = load_image_color(paths[i*2], w, h); image im2 = load_image_color(paths[i*2+1], w, h); - d.X.vals[i] = (float*)calloc(d.X.cols, sizeof(float)); + d.X.vals[i] = (float*)xcalloc(d.X.cols, sizeof(float)); memcpy(d.X.vals[i], im1.data, h*w*3*sizeof(float)); memcpy(d.X.vals[i] + h*w*3, im2.data, h*w*3*sizeof(float)); @@ -729,7 +729,7 @@ data load_data_swag(char **paths, int n, int classes, float jitter) d.h = h; d.X.rows = 1; - d.X.vals = (float**)calloc(d.X.rows, sizeof(float*)); + d.X.vals = (float**)xcalloc(d.X.rows, sizeof(float*)); d.X.cols = h*w*3; int k = (4+classes)*30; @@ -828,7 +828,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo d.shallow = 0; d.X.rows = n; - d.X.vals = (float**)calloc(d.X.rows, sizeof(float*)); + d.X.vals = (float**)xcalloc(d.X.rows, sizeof(float*)); d.X.cols = h*w*c; float r1 = 0, r2 = 0, r3 = 0, r4 = 0, r_scale = 0; @@ -840,7 +840,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo for (i_mixup = 0; i_mixup <= mixup; i_mixup++) { if (i_mixup) augmentation_calculated = 0; for (i = 0; i < n; ++i) { - float *truth = (float*)calloc(5 * boxes, sizeof(float)); + float *truth = (float*)xcalloc(5 * boxes, sizeof(float)); const char *filename = (i_mixup) ? mixup_random_paths[i] : random_paths[i]; int flag = (c >= 3); @@ -1001,7 +1001,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo d.shallow = 0; d.X.rows = n; - d.X.vals = (float**)calloc(d.X.rows, sizeof(float*)); + d.X.vals = (float**)xcalloc(d.X.rows, sizeof(float*)); d.X.cols = h*w*c; float r1 = 0, r2 = 0, r3 = 0, r4 = 0, r_scale; @@ -1013,7 +1013,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo for (i_mixup = 0; i_mixup <= mixup; i_mixup++) { if (i_mixup) augmentation_calculated = 0; for (i = 0; i < n; ++i) { - float *truth = (float*)calloc(5 * boxes, sizeof(float)); + float *truth = (float*)xcalloc(5 * boxes, sizeof(float)); char *filename = (i_mixup) ? mixup_random_paths[i] : random_paths[i]; image orig = load_image(filename, 0, 0, c); @@ -1182,7 +1182,7 @@ void *load_thread(void *ptr) pthread_t load_data_in_thread(load_args args) { pthread_t thread; - struct load_args* ptr = (load_args*)calloc(1, sizeof(struct load_args)); + struct load_args* ptr = (load_args*)xcalloc(1, sizeof(struct load_args)); *ptr = args; if(pthread_create(&thread, 0, load_thread, ptr)) error("Thread creation failed"); return thread; @@ -1197,8 +1197,8 @@ void *load_threads(void *ptr) data *out = args.d; int total = args.n; free(ptr); - data* buffers = (data*)calloc(args.threads, sizeof(data)); - pthread_t* threads = (pthread_t*)calloc(args.threads, sizeof(pthread_t)); + data* buffers = (data*)xcalloc(args.threads, sizeof(data)); + pthread_t* threads = (pthread_t*)xcalloc(args.threads, sizeof(pthread_t)); for(i = 0; i < args.threads; ++i){ args.d = buffers + i; args.n = (i+1) * total/args.threads - i * total/args.threads; @@ -1221,7 +1221,7 @@ void *load_threads(void *ptr) pthread_t load_data(load_args args) { pthread_t thread; - struct load_args* ptr = (load_args*)calloc(1, sizeof(struct load_args)); + struct load_args* ptr = (load_args*)xcalloc(1, sizeof(struct load_args)); *ptr = args; if(pthread_create(&thread, 0, load_threads, ptr)) error("Thread creation failed"); return thread; @@ -1275,11 +1275,11 @@ data load_data_super(char **paths, int n, int m, int w, int h, int scale) int i; d.X.rows = n; - d.X.vals = (float**)calloc(n, sizeof(float*)); + d.X.vals = (float**)xcalloc(n, sizeof(float*)); d.X.cols = w*h*3; d.y.rows = n; - d.y.vals = (float**)calloc(n, sizeof(float*)); + d.y.vals = (float**)xcalloc(n, sizeof(float*)); d.y.cols = w*scale * h*scale * 3; for(i = 0; i < n; ++i){ @@ -1327,7 +1327,7 @@ matrix concat_matrix(matrix m1, matrix m2) matrix m; m.cols = m1.cols; m.rows = m1.rows+m2.rows; - m.vals = (float**)calloc(m1.rows + m2.rows, sizeof(float*)); + m.vals = (float**)xcalloc(m1.rows + m2.rows, sizeof(float*)); for(i = 0; i < m1.rows; ++i){ m.vals[count++] = m1.vals[i]; } @@ -1579,8 +1579,8 @@ data get_random_data(data d, int num) r.X.cols = d.X.cols; r.y.cols = d.y.cols; - r.X.vals = (float**)calloc(num, sizeof(float*)); - r.y.vals = (float**)calloc(num, sizeof(float*)); + r.X.vals = (float**)xcalloc(num, sizeof(float*)); + r.y.vals = (float**)xcalloc(num, sizeof(float*)); int i; for(i = 0; i < num; ++i){ @@ -1593,7 +1593,7 @@ data get_random_data(data d, int num) data *split_data(data d, int part, int total) { - data* split = (data*)calloc(2, sizeof(data)); + data* split = (data*)xcalloc(2, sizeof(data)); int i; int start = part*d.X.rows/total; int end = (part+1)*d.X.rows/total; @@ -1606,10 +1606,10 @@ data *split_data(data d, int part, int total) train.X.cols = test.X.cols = d.X.cols; train.y.cols = test.y.cols = d.y.cols; - train.X.vals = (float**)calloc(train.X.rows, sizeof(float*)); - test.X.vals = (float**)calloc(test.X.rows, sizeof(float*)); - train.y.vals = (float**)calloc(train.y.rows, sizeof(float*)); - test.y.vals = (float**)calloc(test.y.rows, sizeof(float*)); + train.X.vals = (float**)xcalloc(train.X.rows, sizeof(float*)); + test.X.vals = (float**)xcalloc(test.X.rows, sizeof(float*)); + train.y.vals = (float**)xcalloc(train.y.rows, sizeof(float*)); + test.y.vals = (float**)xcalloc(test.y.rows, sizeof(float*)); for(i = 0; i < start; ++i){ train.X.vals[i] = d.X.vals[i]; diff --git a/src/deconvolutional_layer.c b/src/deconvolutional_layer.c index a138fb5325f..4f4e4cc22d9 100644 --- a/src/deconvolutional_layer.c +++ b/src/deconvolutional_layer.c @@ -57,11 +57,11 @@ deconvolutional_layer make_deconvolutional_layer(int batch, int h, int w, int c, l.stride = stride; l.size = size; - l.weights = (float*)calloc(c * n * size * size, sizeof(float)); - l.weight_updates = (float*)calloc(c * n * size * size, sizeof(float)); + l.weights = (float*)xcalloc(c * n * size * size, sizeof(float)); + l.weight_updates = (float*)xcalloc(c * n * size * size, sizeof(float)); - l.biases = (float*)calloc(n, sizeof(float)); - l.bias_updates = (float*)calloc(n, sizeof(float)); + l.biases = (float*)xcalloc(n, sizeof(float)); + l.bias_updates = (float*)xcalloc(n, sizeof(float)); float scale = 1./sqrt(size*size*c); for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_normal(); for(i = 0; i < n; ++i){ @@ -76,9 +76,9 @@ deconvolutional_layer make_deconvolutional_layer(int batch, int h, int w, int c, l.outputs = l.out_w * l.out_h * l.out_c; l.inputs = l.w * l.h * l.c; - l.col_image = (float*)calloc(h * w * size * size * n, sizeof(float)); - l.output = (float*)calloc(l.batch * out_h * out_w * n, sizeof(float)); - l.delta = (float*)calloc(l.batch * out_h * out_w * n, sizeof(float)); + l.col_image = (float*)xcalloc(h * w * size * size * n, sizeof(float)); + l.output = (float*)xcalloc(l.batch * out_h * out_w * n, sizeof(float)); + l.delta = (float*)xcalloc(l.batch * out_h * out_w * n, sizeof(float)); l.forward = forward_deconvolutional_layer; l.backward = backward_deconvolutional_layer; @@ -110,11 +110,11 @@ void resize_deconvolutional_layer(deconvolutional_layer *l, int h, int w) int out_h = deconvolutional_out_height(*l); int out_w = deconvolutional_out_width(*l); - l->col_image = (float*)realloc(l->col_image, + l->col_image = (float*)xrealloc(l->col_image, out_h*out_w*l->size*l->size*l->c*sizeof(float)); - l->output = (float*)realloc(l->output, + l->output = (float*)xrealloc(l->output, l->batch*out_h * out_w * l->n*sizeof(float)); - l->delta = (float*)realloc(l->delta, + l->delta = (float*)xrealloc(l->delta, l->batch*out_h * out_w * l->n*sizeof(float)); #ifdef GPU cuda_free(l->col_image_gpu); diff --git a/src/detection_layer.c b/src/detection_layer.c index 64d133f964b..3c6528a9b77 100644 --- a/src/detection_layer.c +++ b/src/detection_layer.c @@ -25,11 +25,11 @@ detection_layer make_detection_layer(int batch, int inputs, int n, int side, int l.w = side; l.h = side; assert(side*side*((1 + l.coords)*l.n + l.classes) == inputs); - l.cost = (float*)calloc(1, sizeof(float)); + l.cost = (float*)xcalloc(1, sizeof(float)); l.outputs = l.inputs; l.truths = l.side*l.side*(1+l.coords+l.classes); - l.output = (float*)calloc(batch * l.outputs, sizeof(float)); - l.delta = (float*)calloc(batch * l.outputs, sizeof(float)); + l.output = (float*)xcalloc(batch * l.outputs, sizeof(float)); + l.delta = (float*)xcalloc(batch * l.outputs, sizeof(float)); l.forward = forward_detection_layer; l.backward = backward_detection_layer; @@ -182,7 +182,7 @@ void forward_detection_layer(const detection_layer l, network_state state) } if(0){ - float* costs = (float*)calloc(l.batch * locations * l.n, sizeof(float)); + float* costs = (float*)xcalloc(l.batch * locations * l.n, sizeof(float)); for (b = 0; b < l.batch; ++b) { int index = b*l.inputs; for (i = 0; i < locations; ++i) { @@ -259,11 +259,11 @@ void forward_detection_layer_gpu(const detection_layer l, network_state state) return; } - float* in_cpu = (float*)calloc(l.batch * l.inputs, sizeof(float)); + float* in_cpu = (float*)xcalloc(l.batch * l.inputs, sizeof(float)); float *truth_cpu = 0; if(state.truth){ int num_truth = l.batch*l.side*l.side*(1+l.coords+l.classes); - truth_cpu = (float*)calloc(num_truth, sizeof(float)); + truth_cpu = (float*)xcalloc(num_truth, sizeof(float)); cuda_pull_array(state.truth, truth_cpu, num_truth); } cuda_pull_array(state.input, in_cpu, l.batch*l.inputs); diff --git a/src/detector.c b/src/detector.c index 7a147e24939..c48b6449554 100644 --- a/src/detector.c +++ b/src/detector.c @@ -60,7 +60,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i char *base = basecfg(cfgfile); printf("%s\n", base); float avg_loss = -1; - network* nets = (network*)calloc(ngpus, sizeof(network)); + network* nets = (network*)xcalloc(ngpus, sizeof(network)); srand(time(0)); int seed = rand(); @@ -475,7 +475,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out } else { if (!outfile) outfile = "comp4_det_test_"; - fps = (FILE**)calloc(classes, sizeof(FILE*)); + fps = (FILE**)xcalloc(classes, sizeof(FILE*)); for (j = 0; j < classes; ++j) { snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]); fps[j] = fopen(buff, "w"); @@ -492,11 +492,11 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out int nthreads = 4; if (m < 4) nthreads = m; - image* val = (image*)calloc(nthreads, sizeof(image)); - image* val_resized = (image*)calloc(nthreads, sizeof(image)); - image* buf = (image*)calloc(nthreads, sizeof(image)); - image* buf_resized = (image*)calloc(nthreads, sizeof(image)); - pthread_t* thr = (pthread_t*)calloc(nthreads, sizeof(pthread_t)); + image* val = (image*)xcalloc(nthreads, sizeof(image)); + image* val_resized = (image*)xcalloc(nthreads, sizeof(image)); + image* buf = (image*)xcalloc(nthreads, sizeof(image)); + image* buf_resized = (image*)xcalloc(nthreads, sizeof(image)); + pthread_t* thr = (pthread_t*)xcalloc(nthreads, sizeof(pthread_t)); load_args args = { 0 }; args.w = net.w; @@ -735,11 +735,11 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa int nthreads = 4; if (m < 4) nthreads = m; - image* val = (image*)calloc(nthreads, sizeof(image)); - image* val_resized = (image*)calloc(nthreads, sizeof(image)); - image* buf = (image*)calloc(nthreads, sizeof(image)); - image* buf_resized = (image*)calloc(nthreads, sizeof(image)); - pthread_t* thr = (pthread_t*)calloc(nthreads, sizeof(pthread_t)); + image* val = (image*)xcalloc(nthreads, sizeof(image)); + image* val_resized = (image*)xcalloc(nthreads, sizeof(image)); + image* buf = (image*)xcalloc(nthreads, sizeof(image)); + image* buf_resized = (image*)xcalloc(nthreads, sizeof(image)); + pthread_t* thr = (pthread_t*)xcalloc(nthreads, sizeof(pthread_t)); load_args args = { 0 }; args.w = net.w; @@ -753,16 +753,16 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa int tp_for_thresh = 0; int fp_for_thresh = 0; - box_prob* detections = (box_prob*)calloc(1, sizeof(box_prob)); + box_prob* detections = (box_prob*)xcalloc(1, sizeof(box_prob)); int detections_count = 0; int unique_truth_count = 0; - int* truth_classes_count = (int*)calloc(classes, sizeof(int)); + int* truth_classes_count = (int*)xcalloc(classes, sizeof(int)); // For multi-class precision and recall computation - float *avg_iou_per_class = (float*)calloc(classes, sizeof(float)); - int *tp_for_thresh_per_class = (int*)calloc(classes, sizeof(int)); - int *fp_for_thresh_per_class = (int*)calloc(classes, sizeof(int)); + float *avg_iou_per_class = (float*)xcalloc(classes, sizeof(float)); + int *tp_for_thresh_per_class = (int*)xcalloc(classes, sizeof(int)); + int *fp_for_thresh_per_class = (int*)xcalloc(classes, sizeof(int)); for (t = 0; t < nthreads; ++t) { args.path = paths[i + t]; @@ -834,7 +834,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa float prob = dets[i].prob[class_id]; if (prob > 0) { detections_count++; - detections = (box_prob*)realloc(detections, detections_count * sizeof(box_prob)); + detections = (box_prob*)xrealloc(detections, detections_count * sizeof(box_prob)); if (!detections) { error("realloc failed"); } @@ -945,19 +945,19 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa } pr_t; // for PR-curve - pr_t** pr = (pr_t**)calloc(classes, sizeof(pr_t*)); + pr_t** pr = (pr_t**)xcalloc(classes, sizeof(pr_t*)); for (i = 0; i < classes; ++i) { - pr[i] = (pr_t*)calloc(detections_count, sizeof(pr_t)); + pr[i] = (pr_t*)xcalloc(detections_count, sizeof(pr_t)); } printf("\n detections_count = %d, unique_truth_count = %d \n", detections_count, unique_truth_count); - int* detection_per_class_count = (int*)calloc(classes, sizeof(int)); + int* detection_per_class_count = (int*)xcalloc(classes, sizeof(int)); for (j = 0; j < detections_count; ++j) { detection_per_class_count[detections[j].class_id]++; } - int* truth_flags = (int*)calloc(unique_truth_count, sizeof(int)); + int* truth_flags = (int*)xcalloc(unique_truth_count, sizeof(int)); int rank; for (rank = 0; rank < detections_count; ++rank) { @@ -1155,7 +1155,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int } //float pointsdata[] = { 1,1, 2,2, 6,6, 5,5, 10,10 }; - float* rel_width_height_array = (float*)calloc(1000, sizeof(float)); + float* rel_width_height_array = (float*)xcalloc(1000, sizeof(float)); list *options = read_data_cfg(datacfg); @@ -1191,7 +1191,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int if (check_mistakes) getchar(); } number_of_boxes++; - rel_width_height_array = (float*)realloc(rel_width_height_array, 2 * number_of_boxes * sizeof(float)); + rel_width_height_array = (float*)xrealloc(rel_width_height_array, 2 * number_of_boxes * sizeof(float)); if (!rel_width_height_array) { error("realloc failed"); } @@ -1354,7 +1354,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam //box *boxes = calloc(l.w*l.h*l.n, sizeof(box)); //float **probs = calloc(l.w*l.h*l.n, sizeof(float*)); - //for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float*)calloc(l.classes, sizeof(float)); + //for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float*)xcalloc(l.classes, sizeof(float)); float *X = sized.data; @@ -1490,7 +1490,7 @@ void run_detector(int argc, char **argv) for (i = 0; i < len; ++i) { if (gpu_list[i] == ',') ++ngpus; } - gpus = (int*)calloc(ngpus, sizeof(int)); + gpus = (int*)xcalloc(ngpus, sizeof(int)); for (i = 0; i < ngpus; ++i) { gpus[i] = atoi(gpu_list); gpu_list = strchr(gpu_list, ',') + 1; diff --git a/src/dropout_layer.c b/src/dropout_layer.c index 0d34ed24b2c..54e489fcc3c 100644 --- a/src/dropout_layer.c +++ b/src/dropout_layer.c @@ -12,7 +12,7 @@ dropout_layer make_dropout_layer(int batch, int inputs, float probability) l.inputs = inputs; l.outputs = inputs; l.batch = batch; - l.rand = (float*)calloc(inputs * batch, sizeof(float)); + l.rand = (float*)xcalloc(inputs * batch, sizeof(float)); l.scale = 1./(1.-probability); l.forward = forward_dropout_layer; l.backward = backward_dropout_layer; @@ -27,7 +27,7 @@ dropout_layer make_dropout_layer(int batch, int inputs, float probability) void resize_dropout_layer(dropout_layer *l, int inputs) { - l->rand = (float*)realloc(l->rand, l->inputs * l->batch * sizeof(float)); + l->rand = (float*)xrealloc(l->rand, l->inputs * l->batch * sizeof(float)); #ifdef GPU cuda_free(l->rand_gpu); diff --git a/src/gemm.c b/src/gemm.c index abeae31cd71..1fec9322905 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -49,7 +49,7 @@ void gemm_bin(int M, int N, int K, float ALPHA, float *random_matrix(int rows, int cols) { int i; - float* m = (float*)calloc(rows * cols, sizeof(float)); + float* m = (float*)xcalloc(rows * cols, sizeof(float)); for(i = 0; i < rows*cols; ++i){ m[i] = (float)rand()/RAND_MAX; } @@ -2370,7 +2370,7 @@ void float_to_bit(float *src, unsigned char *dst, size_t size) memset(dst, 0, dst_size); size_t i; - char* byte_arr = (char*)calloc(size, sizeof(char)); + char* byte_arr = (char*)xcalloc(size, sizeof(char)); for (i = 0; i < size; ++i) { if (src[i] > 0) byte_arr[i] = 1; } diff --git a/src/go.c b/src/go.c index faa48d10d45..92f2f0618cc 100644 --- a/src/go.c +++ b/src/go.c @@ -32,21 +32,21 @@ moves load_go_moves(char *filename) { moves m; m.n = 128; - m.data = (char**)calloc(128, sizeof(char*)); + m.data = (char**)xcalloc(128, sizeof(char*)); FILE *fp = fopen(filename, "rb"); int count = 0; char *line = 0; while((line = fgetgo(fp))){ if(count >= m.n){ m.n *= 2; - m.data = (char**)realloc(m.data, m.n * sizeof(char*)); + m.data = (char**)xrealloc(m.data, m.n * sizeof(char*)); } m.data[count] = line; ++count; } printf("%d\n", count); m.n = count; - m.data = (char**)realloc(m.data, count * sizeof(char*)); + m.data = (char**)xrealloc(m.data, count * sizeof(char*)); return m; } @@ -127,11 +127,11 @@ void train_go(char *cfgfile, char *weightfile) char* backup_directory = "backup/"; char buff[256]; - float* board = (float*)calloc(19 * 19 * net.batch, sizeof(float)); + float* board = (float*)xcalloc(19 * 19 * net.batch, sizeof(float)); if(!board) { error("calloc failed"); } - float* move = (float*)calloc(19 * 19 * net.batch, sizeof(float)); + float* move = (float*)xcalloc(19 * 19 * net.batch, sizeof(float)); if(!move) { error("calloc failed"); } @@ -192,7 +192,7 @@ void propagate_liberty(float *board, int *lib, int *visited, int row, int col, i int *calculate_liberties(float *board) { - int* lib = (int*)calloc(19 * 19, sizeof(int)); + int* lib = (int*)xcalloc(19 * 19, sizeof(int)); int visited[361]; int i, j; for(j = 0; j < 19; ++j){ @@ -416,11 +416,11 @@ void valid_go(char *cfgfile, char *weightfile, int multi) set_batch_network(&net, 1); printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); - float* board = (float*)calloc(19 * 19, sizeof(float)); + float* board = (float*)xcalloc(19 * 19, sizeof(float)); if(!board) { error("calloc failed"); } - float* move = (float*)calloc(19 * 19, sizeof(float)); + float* move = (float*)xcalloc(19 * 19, sizeof(float)); if(!move) { error("calloc failed"); } @@ -452,9 +452,9 @@ void engine_go(char *filename, char *weightfile, int multi) } srand(time(0)); set_batch_network(&net, 1); - float* board = (float*)calloc(19 * 19, sizeof(float)); - char* one = (char*)calloc(91, sizeof(char)); - char* two = (char*)calloc(91, sizeof(char)); + float* board = (float*)xcalloc(19 * 19, sizeof(float)); + char* one = (char*)xcalloc(91, sizeof(char)); + char* two = (char*)xcalloc(91, sizeof(char)); int passed = 0; while(1){ char buff[256]; @@ -625,8 +625,8 @@ void test_go(char *cfg, char *weights, int multi) } srand(time(0)); set_batch_network(&net, 1); - float* board = (float*)calloc(19 * 19, sizeof(float)); - float* move = (float*)calloc(19 * 19, sizeof(float)); + float* board = (float*)xcalloc(19 * 19, sizeof(float)); + float* move = (float*)xcalloc(19 * 19, sizeof(float)); int color = 1; while(1){ float *output = network_predict(net, board); @@ -777,15 +777,15 @@ void self_go(char *filename, char *weightfile, char *f2, char *w2, int multi) int count = 0; set_batch_network(&net, 1); set_batch_network(&net2, 1); - float* board = (float*)calloc(19 * 19, sizeof(float)); + float* board = (float*)xcalloc(19 * 19, sizeof(float)); if(!board) { error("calloc failed"); } - char* one = (char*)calloc(91, sizeof(char)); + char* one = (char*)xcalloc(91, sizeof(char)); if(!one) { error("calloc failed"); } - char* two = (char*)calloc(91, sizeof(char)); + char* two = (char*)xcalloc(91, sizeof(char)); if(!two) { error("calloc failed"); } diff --git a/src/gru_layer.c b/src/gru_layer.c index 29acdaa2027..b24bee94ac4 100644 --- a/src/gru_layer.c +++ b/src/gru_layer.c @@ -74,16 +74,16 @@ layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_no l.outputs = outputs; - l.output = (float*)calloc(outputs * batch * steps, sizeof(float)); - l.delta = (float*)calloc(outputs * batch * steps, sizeof(float)); - l.state = (float*)calloc(outputs * batch, sizeof(float)); - l.prev_state = (float*)calloc(outputs * batch, sizeof(float)); - l.forgot_state = (float*)calloc(outputs * batch, sizeof(float)); - l.forgot_delta = (float*)calloc(outputs * batch, sizeof(float)); - - l.r_cpu = (float*)calloc(outputs * batch, sizeof(float)); - l.z_cpu = (float*)calloc(outputs * batch, sizeof(float)); - l.h_cpu = (float*)calloc(outputs * batch, sizeof(float)); + l.output = (float*)xcalloc(outputs * batch * steps, sizeof(float)); + l.delta = (float*)xcalloc(outputs * batch * steps, sizeof(float)); + l.state = (float*)xcalloc(outputs * batch, sizeof(float)); + l.prev_state = (float*)xcalloc(outputs * batch, sizeof(float)); + l.forgot_state = (float*)xcalloc(outputs * batch, sizeof(float)); + l.forgot_delta = (float*)xcalloc(outputs * batch, sizeof(float)); + + l.r_cpu = (float*)xcalloc(outputs * batch, sizeof(float)); + l.z_cpu = (float*)xcalloc(outputs * batch, sizeof(float)); + l.h_cpu = (float*)xcalloc(outputs * batch, sizeof(float)); l.forward = forward_gru_layer; l.backward = backward_gru_layer; diff --git a/src/image.c b/src/image.c index 2abd9f10815..57e26fe7f1e 100644 --- a/src/image.c +++ b/src/image.c @@ -249,9 +249,9 @@ image **load_alphabet() { int i, j; const int nsize = 8; - image** alphabets = (image**)calloc(nsize, sizeof(image*)); + image** alphabets = (image**)xcalloc(nsize, sizeof(image*)); for(j = 0; j < nsize; ++j){ - alphabets[j] = (image*)calloc(128, sizeof(image)); + alphabets[j] = (image*)xcalloc(128, sizeof(image)); for(i = 32; i < 127; ++i){ char buff[256]; sprintf(buff, "data/labels/%d_%d.png", i, j); @@ -267,7 +267,7 @@ image **load_alphabet() detection_with_class* get_actual_detections(detection *dets, int dets_num, float thresh, int* selected_detections_num, char **names) { int selected_num = 0; - detection_with_class* result_arr = (detection_with_class*)calloc(dets_num, sizeof(detection_with_class)); + detection_with_class* result_arr = (detection_with_class*)xcalloc(dets_num, sizeof(detection_with_class)); int i; for (i = 0; i < dets_num; ++i) { int best_class = -1; @@ -619,8 +619,8 @@ void normalize_image(image p) void normalize_image2(image p) { - float* min = (float*)calloc(p.c, sizeof(float)); - float* max = (float*)calloc(p.c, sizeof(float)); + float* min = (float*)xcalloc(p.c, sizeof(float)); + float* max = (float*)xcalloc(p.c, sizeof(float)); int i,j; for(i = 0; i < p.c; ++i) min[i] = max[i] = p.data[i*p.h*p.w]; @@ -649,7 +649,7 @@ void normalize_image2(image p) image copy_image(image p) { image copy = p; - copy.data = (float*)calloc(p.h * p.w * p.c, sizeof(float)); + copy.data = (float*)xcalloc(p.h * p.w * p.c, sizeof(float)); memcpy(copy.data, p.data, p.h*p.w*p.c*sizeof(float)); return copy; } @@ -679,7 +679,7 @@ void save_image_png(image im, const char *name) char buff[256]; //sprintf(buff, "%s (%d)", name, windows); sprintf(buff, "%s.png", name); - unsigned char* data = (unsigned char*)calloc(im.w * im.h * im.c, sizeof(unsigned char)); + unsigned char* data = (unsigned char*)xcalloc(im.w * im.h * im.c, sizeof(unsigned char)); int i,k; for(k = 0; k < im.c; ++k){ for(i = 0; i < im.w*im.h; ++i){ @@ -700,7 +700,7 @@ void save_image_options(image im, const char *name, IMTYPE f, int quality) else if (f == TGA) sprintf(buff, "%s.tga", name); else if (f == JPG) sprintf(buff, "%s.jpg", name); else sprintf(buff, "%s.png", name); - unsigned char* data = (unsigned char*)calloc(im.w * im.h * im.c, sizeof(unsigned char)); + unsigned char* data = (unsigned char*)xcalloc(im.w * im.h * im.c, sizeof(unsigned char)); int i, k; for (k = 0; k < im.c; ++k) { for (i = 0; i < im.w*im.h; ++i) { @@ -758,14 +758,14 @@ image make_empty_image(int w, int h, int c) image make_image(int w, int h, int c) { image out = make_empty_image(w,h,c); - out.data = (float*)calloc(h * w * c, sizeof(float)); + out.data = (float*)xcalloc(h * w * c, sizeof(float)); return out; } image make_random_image(int w, int h, int c) { image out = make_empty_image(w,h,c); - out.data = (float*)calloc(h * w * c, sizeof(float)); + out.data = (float*)xcalloc(h * w * c, sizeof(float)); int i; for(i = 0; i < w*h*c; ++i){ out.data[i] = (rand_normal() * .25) + .5; diff --git a/src/list.c b/src/list.c index 6ba093022f6..580826d65ac 100644 --- a/src/list.c +++ b/src/list.c @@ -93,7 +93,7 @@ void free_list_contents_kvp(list *l) void **list_to_array(list *l) { - void** a = (void**)calloc(l->size, sizeof(void*)); + void** a = (void**)xcalloc(l->size, sizeof(void*)); int count = 0; node *n = l->front; while(n){ diff --git a/src/local_layer.c b/src/local_layer.c index 9c68e9d897c..83b43c9a1b2 100644 --- a/src/local_layer.c +++ b/src/local_layer.c @@ -47,19 +47,19 @@ local_layer make_local_layer(int batch, int h, int w, int c, int n, int size, in l.outputs = l.out_h * l.out_w * l.out_c; l.inputs = l.w * l.h * l.c; - l.weights = (float*)calloc(c * n * size * size * locations, sizeof(float)); - l.weight_updates = (float*)calloc(c * n * size * size * locations, sizeof(float)); + l.weights = (float*)xcalloc(c * n * size * size * locations, sizeof(float)); + l.weight_updates = (float*)xcalloc(c * n * size * size * locations, sizeof(float)); - l.biases = (float*)calloc(l.outputs, sizeof(float)); - l.bias_updates = (float*)calloc(l.outputs, sizeof(float)); + l.biases = (float*)xcalloc(l.outputs, sizeof(float)); + l.bias_updates = (float*)xcalloc(l.outputs, sizeof(float)); // float scale = 1./sqrt(size*size*c); float scale = sqrt(2./(size*size*c)); for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_uniform(-1,1); - l.col_image = (float*)calloc(out_h * out_w * size * size * c, sizeof(float)); - l.output = (float*)calloc(l.batch * out_h * out_w * n, sizeof(float)); - l.delta = (float*)calloc(l.batch * out_h * out_w * n, sizeof(float)); + l.col_image = (float*)xcalloc(out_h * out_w * size * size * c, sizeof(float)); + l.output = (float*)xcalloc(l.batch * out_h * out_w * n, sizeof(float)); + l.delta = (float*)xcalloc(l.batch * out_h * out_w * n, sizeof(float)); l.forward = forward_local_layer; l.backward = backward_local_layer; diff --git a/src/lstm_layer.c b/src/lstm_layer.c index 94664ce3aa1..e0cf77972b5 100644 --- a/src/lstm_layer.c +++ b/src/lstm_layer.c @@ -39,49 +39,49 @@ layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_n l.out_h = 1; l.out_c = outputs; - l.uf = (layer*)calloc(1, sizeof(layer)); + l.uf = (layer*)xcalloc(1, sizeof(layer)); fprintf(stderr, "\t\t"); *(l.uf) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.uf->batch = batch; if (l.workspace_size < l.uf->workspace_size) l.workspace_size = l.uf->workspace_size; - l.ui = (layer*)calloc(1, sizeof(layer)); + l.ui = (layer*)xcalloc(1, sizeof(layer)); fprintf(stderr, "\t\t"); *(l.ui) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.ui->batch = batch; if (l.workspace_size < l.ui->workspace_size) l.workspace_size = l.ui->workspace_size; - l.ug = (layer*)calloc(1, sizeof(layer)); + l.ug = (layer*)xcalloc(1, sizeof(layer)); fprintf(stderr, "\t\t"); *(l.ug) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.ug->batch = batch; if (l.workspace_size < l.ug->workspace_size) l.workspace_size = l.ug->workspace_size; - l.uo = (layer*)calloc(1, sizeof(layer)); + l.uo = (layer*)xcalloc(1, sizeof(layer)); fprintf(stderr, "\t\t"); *(l.uo) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.uo->batch = batch; if (l.workspace_size < l.uo->workspace_size) l.workspace_size = l.uo->workspace_size; - l.wf = (layer*)calloc(1, sizeof(layer)); + l.wf = (layer*)xcalloc(1, sizeof(layer)); fprintf(stderr, "\t\t"); *(l.wf) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.wf->batch = batch; if (l.workspace_size < l.wf->workspace_size) l.workspace_size = l.wf->workspace_size; - l.wi = (layer*)calloc(1, sizeof(layer)); + l.wi = (layer*)xcalloc(1, sizeof(layer)); fprintf(stderr, "\t\t"); *(l.wi) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.wi->batch = batch; if (l.workspace_size < l.wi->workspace_size) l.workspace_size = l.wi->workspace_size; - l.wg = (layer*)calloc(1, sizeof(layer)); + l.wg = (layer*)xcalloc(1, sizeof(layer)); fprintf(stderr, "\t\t"); *(l.wg) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.wg->batch = batch; if (l.workspace_size < l.wg->workspace_size) l.workspace_size = l.wg->workspace_size; - l.wo = (layer*)calloc(1, sizeof(layer)); + l.wo = (layer*)xcalloc(1, sizeof(layer)); fprintf(stderr, "\t\t"); *(l.wo) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.wo->batch = batch; @@ -90,28 +90,28 @@ layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_n l.batch_normalize = batch_normalize; l.outputs = outputs; - l.output = (float*)calloc(outputs * batch * steps, sizeof(float)); - l.state = (float*)calloc(outputs * batch, sizeof(float)); + l.output = (float*)xcalloc(outputs * batch * steps, sizeof(float)); + l.state = (float*)xcalloc(outputs * batch, sizeof(float)); l.forward = forward_lstm_layer; l.update = update_lstm_layer; l.backward = backward_lstm_layer; - l.prev_state_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.prev_cell_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.cell_cpu = (float*)calloc(batch*outputs*steps, sizeof(float)); - - l.f_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.i_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.g_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.o_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.c_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.h_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.temp_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.temp2_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.temp3_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.dc_cpu = (float*)calloc(batch*outputs, sizeof(float)); - l.dh_cpu = (float*)calloc(batch*outputs, sizeof(float)); + l.prev_state_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.prev_cell_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.cell_cpu = (float*)xcalloc(batch*outputs*steps, sizeof(float)); + + l.f_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.i_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.g_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.o_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.c_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.h_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.temp_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.temp2_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.temp3_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.dc_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); + l.dh_cpu = (float*)xcalloc(batch*outputs, sizeof(float)); #ifdef GPU l.forward_gpu = forward_lstm_layer_gpu; diff --git a/src/matrix.c b/src/matrix.c index aceca6e64ac..715ee80c9dd 100644 --- a/src/matrix.c +++ b/src/matrix.c @@ -15,7 +15,7 @@ void free_matrix(matrix m) float matrix_topk_accuracy(matrix truth, matrix guess, int k) { - int* indexes = (int*)calloc(k, sizeof(int)); + int* indexes = (int*)xcalloc(k, sizeof(int)); int n = truth.cols; int i,j; int correct = 0; @@ -48,15 +48,15 @@ matrix resize_matrix(matrix m, int size) int i; if (m.rows == size) return m; if (m.rows < size) { - m.vals = (float**)realloc(m.vals, size * sizeof(float*)); + m.vals = (float**)xrealloc(m.vals, size * sizeof(float*)); for (i = m.rows; i < size; ++i) { - m.vals[i] = (float*)calloc(m.cols, sizeof(float)); + m.vals[i] = (float*)xcalloc(m.cols, sizeof(float)); } } else if (m.rows > size) { for (i = size; i < m.rows; ++i) { free(m.vals[i]); } - m.vals = (float**)realloc(m.vals, size * sizeof(float*)); + m.vals = (float**)xrealloc(m.vals, size * sizeof(float*)); } m.rows = size; return m; @@ -79,9 +79,9 @@ matrix make_matrix(int rows, int cols) matrix m; m.rows = rows; m.cols = cols; - m.vals = (float**)calloc(m.rows, sizeof(float*)); + m.vals = (float**)xcalloc(m.rows, sizeof(float*)); for(i = 0; i < m.rows; ++i){ - m.vals[i] = (float*)calloc(m.cols, sizeof(float)); + m.vals[i] = (float*)xcalloc(m.cols, sizeof(float)); } return m; } @@ -92,7 +92,7 @@ matrix hold_out_matrix(matrix *m, int n) matrix h; h.rows = n; h.cols = m->cols; - h.vals = (float**)calloc(h.rows, sizeof(float*)); + h.vals = (float**)xcalloc(h.rows, sizeof(float*)); for(i = 0; i < n; ++i){ int index = rand()%m->rows; h.vals[i] = m->vals[index]; @@ -103,7 +103,7 @@ matrix hold_out_matrix(matrix *m, int n) float *pop_column(matrix *m, int c) { - float* col = (float*)calloc(m->rows, sizeof(float)); + float* col = (float*)xcalloc(m->rows, sizeof(float)); int i, j; for(i = 0; i < m->rows; ++i){ col[i] = m->vals[i][c]; @@ -127,18 +127,18 @@ matrix csv_to_matrix(char *filename) int n = 0; int size = 1024; - m.vals = (float**)calloc(size, sizeof(float*)); + m.vals = (float**)xcalloc(size, sizeof(float*)); while((line = fgetl(fp))){ if(m.cols == -1) m.cols = count_fields(line); if(n == size){ size *= 2; - m.vals = (float**)realloc(m.vals, size * sizeof(float*)); + m.vals = (float**)xrealloc(m.vals, size * sizeof(float*)); } m.vals[n] = parse_fields(line, m.cols); free(line); ++n; } - m.vals = (float**)realloc(m.vals, n * sizeof(float*)); + m.vals = (float**)xrealloc(m.vals, n * sizeof(float*)); m.rows = n; return m; } @@ -225,7 +225,7 @@ void kmeans_maximization(matrix data, int *assignments, matrix centers) matrix old_centers = make_matrix(centers.rows, centers.cols); int i, j; - int *counts = (int*)calloc(centers.rows, sizeof(int)); + int *counts = (int*)xcalloc(centers.rows, sizeof(int)); for (i = 0; i < centers.rows; ++i) { for (j = 0; j < centers.cols; ++j) { old_centers.vals[i][j] = centers.vals[i][j]; @@ -269,7 +269,7 @@ void random_centers(matrix data, matrix centers) { int *sample(int n) { int i; - int* s = (int*)calloc(n, sizeof(int)); + int* s = (int*)xcalloc(n, sizeof(int)); for (i = 0; i < n; ++i) s[i] = i; for (i = n - 1; i >= 0; --i) { int swap = s[i]; @@ -301,7 +301,7 @@ void copy(float *x, float *y, int n) model do_kmeans(matrix data, int k) { matrix centers = make_matrix(k, data.cols); - int* assignments = (int*)calloc(data.rows, sizeof(int)); + int* assignments = (int*)xcalloc(data.rows, sizeof(int)); //smart_centers(data, centers); random_centers(data, centers); // IoU = 67.31% after kmeans diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index 4d2ee49f57f..797123f664a 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -71,9 +71,9 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s l.size = size; l.stride = stride; int output_size = l.out_h * l.out_w * l.out_c * batch; - l.indexes = (int*)calloc(output_size, sizeof(int)); - l.output = (float*)calloc(output_size, sizeof(float)); - l.delta = (float*)calloc(output_size, sizeof(float)); + l.indexes = (int*)xcalloc(output_size, sizeof(int)); + l.output = (float*)xcalloc(output_size, sizeof(float)); + l.delta = (float*)xcalloc(output_size, sizeof(float)); l.forward = forward_maxpool_layer; l.backward = backward_maxpool_layer; #ifdef GPU @@ -102,9 +102,9 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h) l->outputs = l->out_w * l->out_h * l->out_c; int output_size = l->outputs * l->batch; - l->indexes = (int*)realloc(l->indexes, output_size * sizeof(int)); - l->output = (float*)realloc(l->output, output_size * sizeof(float)); - l->delta = (float*)realloc(l->delta, output_size * sizeof(float)); + l->indexes = (int*)xrealloc(l->indexes, output_size * sizeof(int)); + l->output = (float*)xrealloc(l->output, output_size * sizeof(float)); + l->delta = (float*)xrealloc(l->delta, output_size * sizeof(float)); #ifdef GPU CHECK_CUDA(cudaFree((float *)l->indexes_gpu)); diff --git a/src/network.c b/src/network.c index fddfeb8a5ed..403c1ca0d08 100644 --- a/src/network.c +++ b/src/network.c @@ -232,16 +232,16 @@ network make_network(int n) { network net = {0}; net.n = n; - net.layers = (layer*)calloc(net.n, sizeof(layer)); - net.seen = (uint64_t*)calloc(1, sizeof(uint64_t)); + net.layers = (layer*)xcalloc(net.n, sizeof(layer)); + net.seen = (uint64_t*)xcalloc(1, sizeof(uint64_t)); #ifdef GPU - net.input_gpu = (float**)calloc(1, sizeof(float*)); - net.truth_gpu = (float**)calloc(1, sizeof(float*)); + net.input_gpu = (float**)xcalloc(1, sizeof(float*)); + net.truth_gpu = (float**)xcalloc(1, sizeof(float*)); - net.input16_gpu = (float**)calloc(1, sizeof(float*)); - net.output16_gpu = (float**)calloc(1, sizeof(float*)); - net.max_input16_size = (size_t*)calloc(1, sizeof(size_t)); - net.max_output16_size = (size_t*)calloc(1, sizeof(size_t)); + net.input16_gpu = (float**)xcalloc(1, sizeof(float*)); + net.output16_gpu = (float**)xcalloc(1, sizeof(float*)); + net.max_input16_size = (size_t*)xcalloc(1, sizeof(size_t)); + net.max_output16_size = (size_t*)xcalloc(1, sizeof(size_t)); #endif return net; } @@ -353,8 +353,8 @@ float train_network_datum(network net, float *x, float *y) float train_network_sgd(network net, data d, int n) { int batch = net.batch; - float* X = (float*)calloc(batch * d.X.cols, sizeof(float)); - float* y = (float*)calloc(batch * d.y.cols, sizeof(float)); + float* X = (float*)xcalloc(batch * d.X.cols, sizeof(float)); + float* y = (float*)xcalloc(batch * d.y.cols, sizeof(float)); int i; float sum = 0; @@ -379,8 +379,8 @@ float train_network_waitkey(network net, data d, int wait_key) assert(d.X.rows % net.batch == 0); int batch = net.batch; int n = d.X.rows / batch; - float* X = (float*)calloc(batch * d.X.cols, sizeof(float)); - float* y = (float*)calloc(batch * d.y.cols, sizeof(float)); + float* X = (float*)xcalloc(batch * d.X.cols, sizeof(float)); + float* y = (float*)xcalloc(batch * d.y.cols, sizeof(float)); int i; float sum = 0; @@ -450,11 +450,11 @@ int recalculate_workspace_size(network *net) } else { free(net->workspace); - net->workspace = (float*)calloc(1, workspace_size); + net->workspace = (float*)xcalloc(1, workspace_size); } #else free(net->workspace); - net->workspace = (float*)calloc(1, workspace_size); + net->workspace = (float*)xcalloc(1, workspace_size); #endif //fprintf(stderr, " Done!\n"); return 0; @@ -565,19 +565,19 @@ int resize_network(network *net, int w, int h) net->input_pinned_cpu_flag = 1; else { cudaGetLastError(); // reset CUDA-error - net->input_pinned_cpu = (float*)calloc(size, sizeof(float)); + net->input_pinned_cpu = (float*)xcalloc(size, sizeof(float)); net->input_pinned_cpu_flag = 0; } printf(" CUDA allocate done! \n"); }else { free(net->workspace); - net->workspace = (float*)calloc(1, workspace_size); + net->workspace = (float*)xcalloc(1, workspace_size); if(!net->input_pinned_cpu_flag) - net->input_pinned_cpu = (float*)realloc(net->input_pinned_cpu, size * sizeof(float)); + net->input_pinned_cpu = (float*)xrealloc(net->input_pinned_cpu, size * sizeof(float)); } #else free(net->workspace); - net->workspace = (float*)calloc(1, workspace_size); + net->workspace = (float*)xcalloc(1, workspace_size); #endif //fprintf(stderr, " Done!\n"); return 0; @@ -702,11 +702,11 @@ detection *make_network_boxes(network *net, float thresh, int *num) int i; int nboxes = num_detections(net, thresh); if (num) *num = nboxes; - detection* dets = (detection*)calloc(nboxes, sizeof(detection)); + detection* dets = (detection*)xcalloc(nboxes, sizeof(detection)); for (i = 0; i < nboxes; ++i) { - dets[i].prob = (float*)calloc(l.classes, sizeof(float)); + dets[i].prob = (float*)xcalloc(l.classes, sizeof(float)); if (l.coords > 4) { - dets[i].mask = (float*)calloc(l.coords - 4, sizeof(float)); + dets[i].mask = (float*)xcalloc(l.coords - 4, sizeof(float)); } } return dets; @@ -715,10 +715,10 @@ detection *make_network_boxes(network *net, float thresh, int *num) void custom_get_region_detections(layer l, int w, int h, int net_w, int net_h, float thresh, int *map, float hier, int relative, detection *dets, int letter) { - box* boxes = (box*)calloc(l.w * l.h * l.n, sizeof(box)); - float** probs = (float**)calloc(l.w * l.h * l.n, sizeof(float*)); + box* boxes = (box*)xcalloc(l.w * l.h * l.n, sizeof(box)); + float** probs = (float**)xcalloc(l.w * l.h * l.n, sizeof(float*)); int i, j; - for (j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float*)calloc(l.classes, sizeof(float)); + for (j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float*)xcalloc(l.classes, sizeof(float)); get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, map); for (j = 0; j < l.w*l.h*l.n; ++j) { dets[j].classes = l.classes; @@ -793,7 +793,7 @@ char *detection_to_json(detection *dets, int nboxes, int classes, char **names, { const float thresh = 0.005; // function get_network_boxes() has already filtred dets by actual threshold - char *send_buf = (char *)calloc(1024, sizeof(char)); + char *send_buf = (char *)xcalloc(1024, sizeof(char)); if (filename) { sprintf(send_buf, "{\n \"frame_id\":%lld, \n \"filename\":\"%s\", \n \"objects\": [ \n", frame_id, filename); } @@ -810,7 +810,7 @@ char *detection_to_json(detection *dets, int nboxes, int classes, char **names, { if (class_id != -1) strcat(send_buf, ", \n"); class_id = j; - char *buf = (char *)calloc(2048, sizeof(char)); + char *buf = (char *)xcalloc(2048, sizeof(char)); //sprintf(buf, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f}", // image_id, j, dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h, dets[i].prob[j]); @@ -820,7 +820,7 @@ char *detection_to_json(detection *dets, int nboxes, int classes, char **names, int send_buf_len = strlen(send_buf); int buf_len = strlen(buf); int total_len = send_buf_len + buf_len + 100; - send_buf = (char *)realloc(send_buf, total_len * sizeof(char)); + send_buf = (char *)xrealloc(send_buf, total_len * sizeof(char)); if (!send_buf) { error("realloc failed"); } @@ -878,7 +878,7 @@ matrix network_predict_data_multi(network net, data test, int n) int i,j,b,m; int k = get_network_output_size(net); matrix pred = make_matrix(test.X.rows, k); - float* X = (float*)calloc(net.batch * test.X.rows, sizeof(float)); + float* X = (float*)xcalloc(net.batch * test.X.rows, sizeof(float)); for(i = 0; i < test.X.rows; i += net.batch){ for(b = 0; b < net.batch; ++b){ if(i+b == test.X.rows) break; @@ -903,7 +903,7 @@ matrix network_predict_data(network net, data test) int i,j,b; int k = get_network_output_size(net); matrix pred = make_matrix(test.X.rows, k); - float* X = (float*)calloc(net.batch * test.X.cols, sizeof(float)); + float* X = (float*)xcalloc(net.batch * test.X.cols, sizeof(float)); for(i = 0; i < test.X.rows; i += net.batch){ for(b = 0; b < net.batch; ++b){ if(i+b == test.X.rows) break; diff --git a/src/normalization_layer.c b/src/normalization_layer.c index 9d4afcbb83d..d8a2ac588bf 100644 --- a/src/normalization_layer.c +++ b/src/normalization_layer.c @@ -15,10 +15,10 @@ layer make_normalization_layer(int batch, int w, int h, int c, int size, float a layer.size = size; layer.alpha = alpha; layer.beta = beta; - layer.output = (float*)calloc(h * w * c * batch, sizeof(float)); - layer.delta = (float*)calloc(h * w * c * batch, sizeof(float)); - layer.squared = (float*)calloc(h * w * c * batch, sizeof(float)); - layer.norms = (float*)calloc(h * w * c * batch, sizeof(float)); + layer.output = (float*)xcalloc(h * w * c * batch, sizeof(float)); + layer.delta = (float*)xcalloc(h * w * c * batch, sizeof(float)); + layer.squared = (float*)xcalloc(h * w * c * batch, sizeof(float)); + layer.norms = (float*)xcalloc(h * w * c * batch, sizeof(float)); layer.inputs = w*h*c; layer.outputs = layer.inputs; @@ -46,10 +46,10 @@ void resize_normalization_layer(layer *layer, int w, int h) layer->out_w = w; layer->inputs = w*h*c; layer->outputs = layer->inputs; - layer->output = (float*)realloc(layer->output, h * w * c * batch * sizeof(float)); - layer->delta = (float*)realloc(layer->delta, h * w * c * batch * sizeof(float)); - layer->squared = (float*)realloc(layer->squared, h * w * c * batch * sizeof(float)); - layer->norms = (float*)realloc(layer->norms, h * w * c * batch * sizeof(float)); + layer->output = (float*)xrealloc(layer->output, h * w * c * batch * sizeof(float)); + layer->delta = (float*)xrealloc(layer->delta, h * w * c * batch * sizeof(float)); + layer->squared = (float*)xrealloc(layer->squared, h * w * c * batch * sizeof(float)); + layer->norms = (float*)xrealloc(layer->norms, h * w * c * batch * sizeof(float)); #ifdef GPU cuda_free(layer->output_gpu); cuda_free(layer->delta_gpu); diff --git a/src/parser.c b/src/parser.c index 427d8d38706..3d3c3752dec 100644 --- a/src/parser.c +++ b/src/parser.c @@ -319,7 +319,7 @@ int *parse_yolo_mask(char *a, int *num) for (i = 0; i < len; ++i) { if (a[i] == ',') ++n; } - mask = (int*)calloc(n, sizeof(int)); + mask = (int*)xcalloc(n, sizeof(int)); for (i = 0; i < n; ++i) { int val = atoi(a); mask[i] = val; @@ -680,8 +680,8 @@ route_layer parse_route(list *options, size_params params, network net) if (l[i] == ',') ++n; } - int* layers = (int*)calloc(n, sizeof(int)); - int* sizes = (int*)calloc(n, sizeof(int)); + int* layers = (int*)xcalloc(n, sizeof(int)); + int* sizes = (int*)xcalloc(n, sizeof(int)); for(i = 0; i < n; ++i){ int index = atoi(l); l = strchr(l, ',')+1; @@ -799,9 +799,9 @@ void parse_net_options(list *options, network *net) for (i = 0; i < len; ++i) { if (l[i] == ',') ++n; } - int* steps = (int*)calloc(n, sizeof(int)); - float* scales = (float*)calloc(n, sizeof(float)); - float* seq_scales = (float*)calloc(n, sizeof(float)); + int* steps = (int*)xcalloc(n, sizeof(int)); + float* scales = (float*)xcalloc(n, sizeof(float)); + float* seq_scales = (float*)xcalloc(n, sizeof(float)); for (i = 0; i < n; ++i) { float scale = 1.0; if (p) { @@ -1007,7 +1007,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) if (cudaSuccess == cudaHostAlloc(&net.input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped)) net.input_pinned_cpu_flag = 1; else { cudaGetLastError(); // reset CUDA-error - net.input_pinned_cpu = (float*)calloc(size, sizeof(float)); + net.input_pinned_cpu = (float*)xcalloc(size, sizeof(float)); } // pre-allocate memory for inference on Tensor Cores (fp16) @@ -1022,12 +1022,12 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) net.workspace = cuda_make_array(0, workspace_size / sizeof(float) + 1); } else { - net.workspace = (float*)calloc(1, workspace_size); + net.workspace = (float*)xcalloc(1, workspace_size); } } #else if (workspace_size) { - net.workspace = (float*)calloc(1, workspace_size); + net.workspace = (float*)xcalloc(1, workspace_size); } #endif @@ -1248,7 +1248,7 @@ void save_weights(network net, char *filename) void transpose_matrix(float *a, int rows, int cols) { - float* transpose = (float*)calloc(rows * cols, sizeof(float)); + float* transpose = (float*)xcalloc(rows * cols, sizeof(float)); int x, y; for(x = 0; x < rows; ++x){ for(y = 0; y < cols; ++y){ @@ -1485,7 +1485,7 @@ void load_weights(network *net, char *filename) network *load_network_custom(char *cfg, char *weights, int clear, int batch) { printf(" Try to load cfg: %s, clear = %d \n", cfg, clear); - network* net = (network*)calloc(1, sizeof(network)); + network* net = (network*)xcalloc(1, sizeof(network)); *net = parse_network_cfg_custom(cfg, batch, 0); if (weights && weights[0] != 0) { printf(" Try to load weights: %s \n", weights); @@ -1499,7 +1499,7 @@ network *load_network_custom(char *cfg, char *weights, int clear, int batch) network *load_network(char *cfg, char *weights, int clear) { printf(" Try to load cfg: %s, clear = %d \n", cfg, clear); - network* net = (network*)calloc(1, sizeof(network)); + network* net = (network*)xcalloc(1, sizeof(network)); *net = parse_network_cfg(cfg); if (weights && weights[0] != 0) { printf(" Try to load weights: %s \n", weights); diff --git a/src/region_layer.c b/src/region_layer.c index 4610d63737d..f062edabe2a 100644 --- a/src/region_layer.c +++ b/src/region_layer.c @@ -22,15 +22,15 @@ region_layer make_region_layer(int batch, int w, int h, int n, int classes, int l.w = w; l.classes = classes; l.coords = coords; - l.cost = (float*)calloc(1, sizeof(float)); - l.biases = (float*)calloc(n * 2, sizeof(float)); - l.bias_updates = (float*)calloc(n * 2, sizeof(float)); + l.cost = (float*)xcalloc(1, sizeof(float)); + l.biases = (float*)xcalloc(n * 2, sizeof(float)); + l.bias_updates = (float*)xcalloc(n * 2, sizeof(float)); l.outputs = h*w*n*(classes + coords + 1); l.inputs = l.outputs; l.max_boxes = max_boxes; l.truths = max_boxes*(5); - l.delta = (float*)calloc(batch * l.outputs, sizeof(float)); - l.output = (float*)calloc(batch * l.outputs, sizeof(float)); + l.delta = (float*)xcalloc(batch * l.outputs, sizeof(float)); + l.output = (float*)xcalloc(batch * l.outputs, sizeof(float)); int i; for(i = 0; i < n*2; ++i){ l.biases[i] = .5; @@ -63,8 +63,8 @@ void resize_region_layer(layer *l, int w, int h) l->outputs = h*w*l->n*(l->classes + l->coords + 1); l->inputs = l->outputs; - l->output = (float*)realloc(l->output, l->batch * l->outputs * sizeof(float)); - l->delta = (float*)realloc(l->delta, l->batch * l->outputs * sizeof(float)); + l->output = (float*)xrealloc(l->output, l->batch * l->outputs * sizeof(float)); + l->delta = (float*)xrealloc(l->delta, l->batch * l->outputs * sizeof(float)); #ifdef GPU if (old_w < w || old_h < h) { @@ -446,11 +446,11 @@ void forward_region_layer_gpu(const region_layer l, network_state state) softmax_gpu(l.output_gpu+5, l.classes, l.classes + 5, l.w*l.h*l.n*l.batch, 1, l.output_gpu + 5); } - float* in_cpu = (float*)calloc(l.batch * l.inputs, sizeof(float)); + float* in_cpu = (float*)xcalloc(l.batch * l.inputs, sizeof(float)); float *truth_cpu = 0; if(state.truth){ int num_truth = l.batch*l.truths; - truth_cpu = (float*)calloc(num_truth, sizeof(float)); + truth_cpu = (float*)xcalloc(num_truth, sizeof(float)); cuda_pull_array(state.truth, truth_cpu, num_truth); } cuda_pull_array(l.output_gpu, in_cpu, l.batch*l.inputs); diff --git a/src/reorg_layer.c b/src/reorg_layer.c index 8094a066328..10c11199a0e 100644 --- a/src/reorg_layer.c +++ b/src/reorg_layer.c @@ -27,8 +27,8 @@ layer make_reorg_layer(int batch, int w, int h, int c, int stride, int reverse) l.outputs = l.out_h * l.out_w * l.out_c; l.inputs = h*w*c; int output_size = l.out_h * l.out_w * l.out_c * batch; - l.output = (float*)calloc(output_size, sizeof(float)); - l.delta = (float*)calloc(output_size, sizeof(float)); + l.output = (float*)xcalloc(output_size, sizeof(float)); + l.delta = (float*)xcalloc(output_size, sizeof(float)); l.forward = forward_reorg_layer; l.backward = backward_reorg_layer; @@ -64,8 +64,8 @@ void resize_reorg_layer(layer *l, int w, int h) l->inputs = l->outputs; int output_size = l->outputs * l->batch; - l->output = (float*)realloc(l->output, output_size * sizeof(float)); - l->delta = (float*)realloc(l->delta, output_size * sizeof(float)); + l->output = (float*)xrealloc(l->output, output_size * sizeof(float)); + l->delta = (float*)xrealloc(l->delta, output_size * sizeof(float)); #ifdef GPU cuda_free(l->output_gpu); diff --git a/src/reorg_old_layer.c b/src/reorg_old_layer.c index 530da202094..f6ad2a8b251 100644 --- a/src/reorg_old_layer.c +++ b/src/reorg_old_layer.c @@ -27,8 +27,8 @@ layer make_reorg_old_layer(int batch, int w, int h, int c, int stride, int rever l.outputs = l.out_h * l.out_w * l.out_c; l.inputs = h*w*c; int output_size = l.out_h * l.out_w * l.out_c * batch; - l.output = (float*)calloc(output_size, sizeof(float)); - l.delta = (float*)calloc(output_size, sizeof(float)); + l.output = (float*)xcalloc(output_size, sizeof(float)); + l.delta = (float*)xcalloc(output_size, sizeof(float)); l.forward = forward_reorg_old_layer; l.backward = backward_reorg_old_layer; @@ -64,8 +64,8 @@ void resize_reorg_old_layer(layer *l, int w, int h) l->inputs = l->outputs; int output_size = l->outputs * l->batch; - l->output = (float*)realloc(l->output, output_size * sizeof(float)); - l->delta = (float*)realloc(l->delta, output_size * sizeof(float)); + l->output = (float*)xrealloc(l->output, output_size * sizeof(float)); + l->delta = (float*)xrealloc(l->delta, output_size * sizeof(float)); #ifdef GPU cuda_free(l->output_gpu); diff --git a/src/rnn.c b/src/rnn.c index 49cadd342d5..bfc57b709df 100644 --- a/src/rnn.c +++ b/src/rnn.c @@ -14,7 +14,7 @@ int *read_tokenized_data(char *filename, size_t *read) size_t size = 512; size_t count = 0; FILE *fp = fopen(filename, "r"); - int* d = (int*)calloc(size, sizeof(int)); + int* d = (int*)xcalloc(size, sizeof(int)); if(!d) { error("calloc failed"); } @@ -24,7 +24,7 @@ int *read_tokenized_data(char *filename, size_t *read) ++count; if(count > size){ size = size*2; - d = (int*)realloc(d, size * sizeof(int)); + d = (int*)xrealloc(d, size * sizeof(int)); if(!d) { error("realloc failed"); } @@ -33,7 +33,7 @@ int *read_tokenized_data(char *filename, size_t *read) one = fscanf(fp, "%d", &n); } fclose(fp); - d = (int*)realloc(d, count * sizeof(int)); + d = (int*)xrealloc(d, count * sizeof(int)); if(!d) { error("realloc failed"); } @@ -46,13 +46,13 @@ char **read_tokens(char *filename, size_t *read) size_t size = 512; size_t count = 0; FILE *fp = fopen(filename, "r"); - char** d = (char**)calloc(size, sizeof(char*)); + char** d = (char**)xcalloc(size, sizeof(char*)); char *line; while((line=fgetl(fp)) != 0){ ++count; if(count > size){ size = size*2; - d = (char**)realloc(d, size * sizeof(char*)); + d = (char**)xrealloc(d, size * sizeof(char*)); if(!d) { error("realloc failed"); } @@ -60,7 +60,7 @@ char **read_tokens(char *filename, size_t *read) d[count-1] = line; } fclose(fp); - d = (char**)realloc(d, count * sizeof(char*)); + d = (char**)xrealloc(d, count * sizeof(char*)); if(!d) { error("realloc failed"); } @@ -70,8 +70,8 @@ char **read_tokens(char *filename, size_t *read) float_pair get_rnn_token_data(int *tokens, size_t *offsets, int characters, size_t len, int batch, int steps) { - float* x = (float*)calloc(batch * steps * characters, sizeof(float)); - float* y = (float*)calloc(batch * steps * characters, sizeof(float)); + float* x = (float*)xcalloc(batch * steps * characters, sizeof(float)); + float* y = (float*)xcalloc(batch * steps * characters, sizeof(float)); int i,j; for(i = 0; i < batch; ++i){ for(j = 0; j < steps; ++j){ @@ -96,8 +96,8 @@ float_pair get_rnn_token_data(int *tokens, size_t *offsets, int characters, size float_pair get_rnn_data(unsigned char *text, size_t *offsets, int characters, size_t len, int batch, int steps) { - float* x = (float*)calloc(batch * steps * characters, sizeof(float)); - float* y = (float*)calloc(batch * steps * characters, sizeof(float)); + float* x = (float*)xcalloc(batch * steps * characters, sizeof(float)); + float* y = (float*)xcalloc(batch * steps * characters, sizeof(float)); int i,j; for(i = 0; i < batch; ++i){ for(j = 0; j < steps; ++j){ @@ -152,7 +152,7 @@ void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear, size = ftell(fp); fseek(fp, 0, SEEK_SET); - text = (unsigned char *)calloc(size + 1, sizeof(char)); + text = (unsigned char *)xcalloc(size + 1, sizeof(char)); fread(text, 1, size, fp); fclose(fp); } @@ -176,7 +176,7 @@ void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear, int streams = batch/steps; printf("\n batch = %d, steps = %d, streams = %d, subdivisions = %d, text_size = %ld \n", batch, steps, streams, net.subdivisions, size); printf(" global_batch = %d \n", batch*net.subdivisions); - size_t* offsets = (size_t*)calloc(streams, sizeof(size_t)); + size_t* offsets = (size_t*)xcalloc(streams, sizeof(size_t)); int j; for(j = 0; j < streams; ++j){ offsets[j] = rand_size_t()%size; @@ -259,7 +259,7 @@ void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float t for(i = 0; i < net.n; ++i) net.layers[i].temperature = temp; int c = 0; int len = strlen(seed); - float* input = (float*)calloc(inputs, sizeof(float)); + float* input = (float*)xcalloc(inputs, sizeof(float)); /* fill_cpu(inputs, 0, input, 1); @@ -318,7 +318,7 @@ void test_tactic_rnn(char *cfgfile, char *weightfile, int num, float temp, int r int i, j; for(i = 0; i < net.n; ++i) net.layers[i].temperature = temp; int c = 0; - float* input = (float*)calloc(inputs, sizeof(float)); + float* input = (float*)xcalloc(inputs, sizeof(float)); float *out = 0; while((c = getc(stdin)) != EOF){ @@ -357,7 +357,7 @@ void valid_tactic_rnn(char *cfgfile, char *weightfile, char *seed) int words = 1; int c; int len = strlen(seed); - float* input = (float*)calloc(inputs, sizeof(float)); + float* input = (float*)xcalloc(inputs, sizeof(float)); int i; for(i = 0; i < len; ++i){ c = seed[i]; @@ -409,7 +409,7 @@ void valid_char_rnn(char *cfgfile, char *weightfile, char *seed) int words = 1; int c; int len = strlen(seed); - float* input = (float*)calloc(inputs, sizeof(float)); + float* input = (float*)xcalloc(inputs, sizeof(float)); int i; for(i = 0; i < len; ++i){ c = seed[i]; @@ -448,7 +448,7 @@ void vec_char_rnn(char *cfgfile, char *weightfile, char *seed) int c; int seed_len = strlen(seed); - float* input = (float*)calloc(inputs, sizeof(float)); + float* input = (float*)xcalloc(inputs, sizeof(float)); int i; char *line; while((line=fgetl(stdin)) != 0){ diff --git a/src/rnn_layer.c b/src/rnn_layer.c index 4b5b9c2c64c..328bcbf07d9 100644 --- a/src/rnn_layer.c +++ b/src/rnn_layer.c @@ -40,21 +40,21 @@ layer make_rnn_layer(int batch, int inputs, int hidden, int outputs, int steps, l.out_h = 1; l.out_c = outputs; - l.state = (float*)calloc(batch * hidden * (steps + 1), sizeof(float)); + l.state = (float*)xcalloc(batch * hidden * (steps + 1), sizeof(float)); - l.input_layer = (layer*)calloc(1, sizeof(layer)); + l.input_layer = (layer*)xcalloc(1, sizeof(layer)); fprintf(stderr, "\t\t"); *(l.input_layer) = make_connected_layer(batch, steps, inputs, hidden, activation, batch_normalize); l.input_layer->batch = batch; if (l.workspace_size < l.input_layer->workspace_size) l.workspace_size = l.input_layer->workspace_size; - l.self_layer = (layer*)calloc(1, sizeof(layer)); + l.self_layer = (layer*)xcalloc(1, sizeof(layer)); fprintf(stderr, "\t\t"); *(l.self_layer) = make_connected_layer(batch, steps, hidden, hidden, (log==2)?LOGGY:(log==1?LOGISTIC:activation), batch_normalize); l.self_layer->batch = batch; if (l.workspace_size < l.self_layer->workspace_size) l.workspace_size = l.self_layer->workspace_size; - l.output_layer = (layer*)calloc(1, sizeof(layer)); + l.output_layer = (layer*)xcalloc(1, sizeof(layer)); fprintf(stderr, "\t\t"); *(l.output_layer) = make_connected_layer(batch, steps, hidden, outputs, activation, batch_normalize); l.output_layer->batch = batch; diff --git a/src/rnn_vid.c b/src/rnn_vid.c index a0fb0a3c790..a5ff5278435 100644 --- a/src/rnn_vid.c +++ b/src/rnn_vid.c @@ -20,10 +20,10 @@ float_pair get_rnn_vid_data(network net, char **files, int n, int batch, int ste image out_im = get_network_image(net); int output_size = out_im.w*out_im.h*out_im.c; printf("%d %d %d\n", out_im.w, out_im.h, out_im.c); - float* feats = (float*)calloc(net.batch * batch * output_size, sizeof(float)); + float* feats = (float*)xcalloc(net.batch * batch * output_size, sizeof(float)); for(b = 0; b < batch; ++b){ int input_size = net.w*net.h*net.c; - float* input = (float*)calloc(input_size * net.batch, sizeof(float)); + float* input = (float*)xcalloc(input_size * net.batch, sizeof(float)); char *filename = files[rand()%n]; cap_cv *cap = get_capture_video_stream(filename); int frames = get_capture_frame_count_cv(cap); diff --git a/src/route_layer.c b/src/route_layer.c index b502fbe72b0..a9d35fc3582 100644 --- a/src/route_layer.c +++ b/src/route_layer.c @@ -21,8 +21,8 @@ route_layer make_route_layer(int batch, int n, int *input_layers, int *input_siz fprintf(stderr, "\n"); l.outputs = outputs; l.inputs = outputs; - l.delta = (float*)calloc(outputs * batch, sizeof(float)); - l.output = (float*)calloc(outputs * batch, sizeof(float)); + l.delta = (float*)xcalloc(outputs * batch, sizeof(float)); + l.output = (float*)xcalloc(outputs * batch, sizeof(float)); l.forward = forward_route_layer; l.backward = backward_route_layer; @@ -58,8 +58,8 @@ void resize_route_layer(route_layer *l, network *net) } } l->inputs = l->outputs; - l->delta = (float*)realloc(l->delta, l->outputs * l->batch * sizeof(float)); - l->output = (float*)realloc(l->output, l->outputs * l->batch * sizeof(float)); + l->delta = (float*)xrealloc(l->delta, l->outputs * l->batch * sizeof(float)); + l->output = (float*)xrealloc(l->output, l->outputs * l->batch * sizeof(float)); #ifdef GPU cuda_free(l->output_gpu); diff --git a/src/sam_layer.c b/src/sam_layer.c index da28e319775..fbb1aadb4c4 100644 --- a/src/sam_layer.c +++ b/src/sam_layer.c @@ -24,8 +24,8 @@ layer make_sam_layer(int batch, int index, int w, int h, int c, int w2, int h2, l.inputs = l.outputs; l.index = index; - l.delta = (float*)calloc(l.outputs * batch, sizeof(float)); - l.output = (float*)calloc(l.outputs * batch, sizeof(float)); + l.delta = (float*)xcalloc(l.outputs * batch, sizeof(float)); + l.output = (float*)xcalloc(l.outputs * batch, sizeof(float)); l.forward = forward_sam_layer; l.backward = backward_sam_layer; @@ -45,8 +45,8 @@ void resize_sam_layer(layer *l, int w, int h) l->out_h = h; l->outputs = l->out_w*l->out_h*l->out_c; l->inputs = l->outputs; - l->delta = (float*)realloc(l->delta, l->outputs * l->batch * sizeof(float)); - l->output = (float*)realloc(l->output, l->outputs * l->batch * sizeof(float)); + l->delta = (float*)xrealloc(l->delta, l->outputs * l->batch * sizeof(float)); + l->output = (float*)xrealloc(l->output, l->outputs * l->batch * sizeof(float)); #ifdef GPU cuda_free(l->output_gpu); diff --git a/src/scale_channels_layer.c b/src/scale_channels_layer.c index 7322570116e..847eeda417c 100644 --- a/src/scale_channels_layer.c +++ b/src/scale_channels_layer.c @@ -24,8 +24,8 @@ layer make_scale_channels_layer(int batch, int index, int w, int h, int c, int w l.inputs = l.outputs; l.index = index; - l.delta = (float*)calloc(l.outputs * batch, sizeof(float)); - l.output = (float*)calloc(l.outputs * batch, sizeof(float)); + l.delta = (float*)xcalloc(l.outputs * batch, sizeof(float)); + l.output = (float*)xcalloc(l.outputs * batch, sizeof(float)); l.forward = forward_scale_channels_layer; l.backward = backward_scale_channels_layer; @@ -45,8 +45,8 @@ void resize_scale_channels_layer(layer *l, int w, int h) l->out_h = h; l->outputs = l->out_w*l->out_h*l->out_c; l->inputs = l->outputs; - l->delta = (float*)realloc(l->delta, l->outputs * l->batch * sizeof(float)); - l->output = (float*)realloc(l->output, l->outputs * l->batch * sizeof(float)); + l->delta = (float*)xrealloc(l->delta, l->outputs * l->batch * sizeof(float)); + l->output = (float*)xrealloc(l->output, l->outputs * l->batch * sizeof(float)); #ifdef GPU cuda_free(l->output_gpu); diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index 1f7c6d35e90..fdc70aeda9e 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -23,8 +23,8 @@ layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int l.index = index; - l.delta = (float*)calloc(l.outputs * batch, sizeof(float)); - l.output = (float*)calloc(l.outputs * batch, sizeof(float)); + l.delta = (float*)xcalloc(l.outputs * batch, sizeof(float)); + l.output = (float*)xcalloc(l.outputs * batch, sizeof(float)); l.forward = forward_shortcut_layer; l.backward = backward_shortcut_layer; @@ -46,8 +46,8 @@ void resize_shortcut_layer(layer *l, int w, int h) l->h = l->out_h = h; l->outputs = w*h*l->out_c; l->inputs = l->outputs; - l->delta = (float*)realloc(l->delta, l->outputs * l->batch * sizeof(float)); - l->output = (float*)realloc(l->output, l->outputs * l->batch * sizeof(float)); + l->delta = (float*)xrealloc(l->delta, l->outputs * l->batch * sizeof(float)); + l->output = (float*)xrealloc(l->output, l->outputs * l->batch * sizeof(float)); #ifdef GPU cuda_free(l->output_gpu); diff --git a/src/softmax_layer.c b/src/softmax_layer.c index 9bbff9a028b..4977c2d6442 100644 --- a/src/softmax_layer.c +++ b/src/softmax_layer.c @@ -36,10 +36,10 @@ softmax_layer make_softmax_layer(int batch, int inputs, int groups) l.groups = groups; l.inputs = inputs; l.outputs = inputs; - l.loss = (float*)calloc(inputs * batch, sizeof(float)); - l.output = (float*)calloc(inputs * batch, sizeof(float)); - l.delta = (float*)calloc(inputs * batch, sizeof(float)); - l.cost = (float*)calloc(1, sizeof(float)); + l.loss = (float*)xcalloc(inputs * batch, sizeof(float)); + l.output = (float*)xcalloc(inputs * batch, sizeof(float)); + l.delta = (float*)xcalloc(inputs * batch, sizeof(float)); + l.cost = (float*)xcalloc(1, sizeof(float)); l.forward = forward_softmax_layer; l.backward = backward_softmax_layer; diff --git a/src/tree.c b/src/tree.c index 4383d693e13..8a2c23169b2 100644 --- a/src/tree.c +++ b/src/tree.c @@ -93,42 +93,42 @@ tree *read_tree(char *filename) int groups = 0; int n = 0; while((line=fgetl(fp)) != 0){ - char* id = (char*)calloc(256, sizeof(char)); + char* id = (char*)xcalloc(256, sizeof(char)); int parent = -1; sscanf(line, "%s %d", id, &parent); - t.parent = (int*)realloc(t.parent, (n + 1) * sizeof(int)); + t.parent = (int*)xrealloc(t.parent, (n + 1) * sizeof(int)); t.parent[n] = parent; - t.name = (char**)realloc(t.name, (n + 1) * sizeof(char*)); + t.name = (char**)xrealloc(t.name, (n + 1) * sizeof(char*)); t.name[n] = id; if(parent != last_parent){ ++groups; - t.group_offset = (int*)realloc(t.group_offset, groups * sizeof(int)); + t.group_offset = (int*)xrealloc(t.group_offset, groups * sizeof(int)); t.group_offset[groups - 1] = n - group_size; - t.group_size = (int*)realloc(t.group_size, groups * sizeof(int)); + t.group_size = (int*)xrealloc(t.group_size, groups * sizeof(int)); t.group_size[groups - 1] = group_size; group_size = 0; last_parent = parent; } - t.group = (int*)realloc(t.group, (n + 1) * sizeof(int)); + t.group = (int*)xrealloc(t.group, (n + 1) * sizeof(int)); t.group[n] = groups; ++n; ++group_size; } ++groups; - t.group_offset = (int*)realloc(t.group_offset, groups * sizeof(int)); + t.group_offset = (int*)xrealloc(t.group_offset, groups * sizeof(int)); t.group_offset[groups - 1] = n - group_size; - t.group_size = (int*)realloc(t.group_size, groups * sizeof(int)); + t.group_size = (int*)xrealloc(t.group_size, groups * sizeof(int)); t.group_size[groups - 1] = group_size; t.n = n; t.groups = groups; - t.leaf = (int*)calloc(n, sizeof(int)); + t.leaf = (int*)xcalloc(n, sizeof(int)); int i; for(i = 0; i < n; ++i) t.leaf[i] = 1; for(i = 0; i < n; ++i) if(t.parent[i] >= 0) t.leaf[t.parent[i]] = 0; fclose(fp); - tree* tree_ptr = (tree*)calloc(1, sizeof(tree)); + tree* tree_ptr = (tree*)xcalloc(1, sizeof(tree)); *tree_ptr = t; //error(0); return tree_ptr; diff --git a/src/upsample_layer.c b/src/upsample_layer.c index 70d8de4d963..7e756782994 100644 --- a/src/upsample_layer.c +++ b/src/upsample_layer.c @@ -24,8 +24,8 @@ layer make_upsample_layer(int batch, int w, int h, int c, int stride) l.stride = stride; l.outputs = l.out_w*l.out_h*l.out_c; l.inputs = l.w*l.h*l.c; - l.delta = (float*)calloc(l.outputs * batch, sizeof(float)); - l.output = (float*)calloc(l.outputs * batch, sizeof(float)); + l.delta = (float*)xcalloc(l.outputs * batch, sizeof(float)); + l.output = (float*)xcalloc(l.outputs * batch, sizeof(float)); l.forward = forward_upsample_layer; l.backward = backward_upsample_layer; @@ -53,8 +53,8 @@ void resize_upsample_layer(layer *l, int w, int h) } l->outputs = l->out_w*l->out_h*l->out_c; l->inputs = l->h*l->w*l->c; - l->delta = (float*)realloc(l->delta, l->outputs * l->batch * sizeof(float)); - l->output = (float*)realloc(l->output, l->outputs * l->batch * sizeof(float)); + l->delta = (float*)xrealloc(l->delta, l->outputs * l->batch * sizeof(float)); + l->output = (float*)xrealloc(l->output, l->outputs * l->batch * sizeof(float)); #ifdef GPU cuda_free(l->output_gpu); diff --git a/src/utils.c b/src/utils.c index 2c3eb83f7c2..60c2f5a614c 100644 --- a/src/utils.c +++ b/src/utils.c @@ -56,7 +56,7 @@ int *read_map(char *filename) if(!file) file_error(filename); while((str=fgetl(file))){ ++n; - map = (int*)realloc(map, n * sizeof(int)); + map = (int*)xrealloc(map, n * sizeof(int)); if(!map) { error("realloc failed"); } @@ -81,7 +81,7 @@ void sorta_shuffle(void *arr, size_t n, size_t size, size_t sections) void shuffle(void *arr, size_t n, size_t size) { size_t i; - void* swp = (void*)calloc(1, size); + void* swp = (void*)xcalloc(1, size); for(i = 0; i < n-1; ++i){ size_t j = i + random_gen()/(RAND_MAX / (n-i)+1); memcpy(swp, (char*)arr+(j*size), size); @@ -418,7 +418,7 @@ char *fgetl(FILE *fp) while((line[curr-1] != '\n') && !feof(fp)){ if(curr == size-1){ size *= 2; - line = (char*)realloc(line, size * sizeof(char)); + line = (char*)xrealloc(line, size * sizeof(char)); if(!line) { printf("%ld\n", size); malloc_error(); @@ -497,6 +497,9 @@ void write_all(int fd, char *buffer, size_t bytes) char *copy_string(char *s) { + if(!s) { + return NULL; + } char* copy = (char*)malloc(strlen(s) + 1); strncpy(copy, s, strlen(s)+1); return copy; @@ -533,7 +536,7 @@ int count_fields(char *line) float *parse_fields(char *line, int n) { - float* field = (float*)calloc(n, sizeof(float)); + float* field = (float*)xcalloc(n, sizeof(float)); char *c, *p, *end; int count = 0; int done = 0; @@ -721,8 +724,8 @@ int max_index(float *a, int n) int top_max_index(float *a, int n, int k) { if (n <= 0) return -1; - float *values = (float*)calloc(k, sizeof(float)); - int *indexes = (int*)calloc(k, sizeof(int)); + float *values = (float*)xcalloc(k, sizeof(float)); + int *indexes = (int*)xcalloc(k, sizeof(int)); int i, j; for (i = 0; i < n; ++i) { for (j = 0; j < k; ++j) { @@ -835,9 +838,9 @@ float rand_scale(float s) float **one_hot_encode(float *a, int n, int k) { int i; - float** t = (float**)calloc(n, sizeof(float*)); + float** t = (float**)xcalloc(n, sizeof(float*)); for(i = 0; i < n; ++i){ - t[i] = (float*)calloc(k, sizeof(float)); + t[i] = (float*)xcalloc(k, sizeof(float)); int index = (int)a[i]; t[i][index] = 1; } @@ -923,7 +926,7 @@ int check_array_is_inf(float *arr, int size) int *random_index_order(int min, int max) { - int *inds = (int *)calloc(max - min, sizeof(int)); + int *inds = (int *)xcalloc(max - min, sizeof(int)); int i; for (i = min; i < max; ++i) { inds[i - min] = i; diff --git a/src/utils.h b/src/utils.h index 70d729b7617..05b6da21600 100644 --- a/src/utils.h +++ b/src/utils.h @@ -14,6 +14,7 @@ LIB_API void free_ptrs(void **ptrs, int n); LIB_API void top_k(float *a, int n, int k, int *index); void *xcalloc(size_t nmemb, size_t size); +void *xrealloc(void *ptr, size_t size); double what_time_is_it_now(); int *read_map(char *filename); void shuffle(void *arr, size_t n, size_t size); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 1619d8afa3a..fe2e85ebeba 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -26,22 +26,22 @@ layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int l.out_h = l.h; l.out_c = l.c; l.classes = classes; - l.cost = (float*)calloc(1, sizeof(float)); - l.biases = (float*)calloc(total * 2, sizeof(float)); + l.cost = (float*)xcalloc(1, sizeof(float)); + l.biases = (float*)xcalloc(total * 2, sizeof(float)); if(mask) l.mask = mask; else{ - l.mask = (int*)calloc(n, sizeof(int)); + l.mask = (int*)xcalloc(n, sizeof(int)); for(i = 0; i < n; ++i){ l.mask[i] = i; } } - l.bias_updates = (float*)calloc(n * 2, sizeof(float)); + l.bias_updates = (float*)xcalloc(n * 2, sizeof(float)); l.outputs = h*w*n*(classes + 4 + 1); l.inputs = l.outputs; l.max_boxes = max_boxes; l.truths = l.max_boxes*(4 + 1); // 90*(4 + 1); - l.delta = (float*)calloc(batch * l.outputs, sizeof(float)); - l.output = (float*)calloc(batch * l.outputs, sizeof(float)); + l.delta = (float*)xcalloc(batch * l.outputs, sizeof(float)); + l.output = (float*)xcalloc(batch * l.outputs, sizeof(float)); for(i = 0; i < total*2; ++i){ l.biases[i] = .5; } @@ -58,14 +58,14 @@ layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int if (cudaSuccess == cudaHostAlloc(&l.output, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1; else { cudaGetLastError(); // reset CUDA-error - l.output = (float*)calloc(batch * l.outputs, sizeof(float)); + l.output = (float*)xcalloc(batch * l.outputs, sizeof(float)); } free(l.delta); if (cudaSuccess == cudaHostAlloc(&l.delta, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1; else { cudaGetLastError(); // reset CUDA-error - l.delta = (float*)calloc(batch * l.outputs, sizeof(float)); + l.delta = (float*)xcalloc(batch * l.outputs, sizeof(float)); } #endif @@ -83,15 +83,15 @@ void resize_yolo_layer(layer *l, int w, int h) l->outputs = h*w*l->n*(l->classes + 4 + 1); l->inputs = l->outputs; - if (!l->output_pinned) l->output = (float*)realloc(l->output, l->batch*l->outputs * sizeof(float)); - if (!l->delta_pinned) l->delta = (float*)realloc(l->delta, l->batch*l->outputs*sizeof(float)); + if (!l->output_pinned) l->output = (float*)xrealloc(l->output, l->batch*l->outputs * sizeof(float)); + if (!l->delta_pinned) l->delta = (float*)xrealloc(l->delta, l->batch*l->outputs*sizeof(float)); #ifdef GPU if (l->output_pinned) { cudaFreeHost(l->output); if (cudaSuccess != cudaHostAlloc(&l->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) { cudaGetLastError(); // reset CUDA-error - l->output = (float*)realloc(l->output, l->batch * l->outputs * sizeof(float)); + l->output = (float*)xrealloc(l->output, l->batch * l->outputs * sizeof(float)); l->output_pinned = 0; } } @@ -100,7 +100,7 @@ void resize_yolo_layer(layer *l, int w, int h) cudaFreeHost(l->delta); if (cudaSuccess != cudaHostAlloc(&l->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) { cudaGetLastError(); // reset CUDA-error - l->delta = (float*)realloc(l->delta, l->batch * l->outputs * sizeof(float)); + l->delta = (float*)xrealloc(l->delta, l->batch * l->outputs * sizeof(float)); l->delta_pinned = 0; } } @@ -377,7 +377,7 @@ void forward_yolo_layer(const layer l, network_state state) // TODO: remove IOU loss fields before computing MSE on class // probably split into two arrays int stride = l.w*l.h; - float* no_iou_loss_delta = (float *)calloc(l.batch * l.outputs, sizeof(float)); + float* no_iou_loss_delta = (float *)xcalloc(l.batch * l.outputs, sizeof(float)); memcpy(no_iou_loss_delta, l.delta, l.batch * l.outputs * sizeof(float)); for (b = 0; b < l.batch; ++b) { for (j = 0; j < l.h; ++j) { @@ -547,13 +547,13 @@ void forward_yolo_layer_gpu(const layer l, network_state state) return; } - float *in_cpu = (float *)calloc(l.batch*l.inputs, sizeof(float)); + float *in_cpu = (float *)xcalloc(l.batch*l.inputs, sizeof(float)); cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); memcpy(in_cpu, l.output, l.batch*l.outputs*sizeof(float)); float *truth_cpu = 0; if (state.truth) { int num_truth = l.batch*l.truths; - truth_cpu = (float *)calloc(num_truth, sizeof(float)); + truth_cpu = (float *)xcalloc(num_truth, sizeof(float)); cuda_pull_array(state.truth, truth_cpu, num_truth); } network_state cpu_state = state; From f10c84c218eef8eeea334dce35db61027b121700 Mon Sep 17 00:00:00 2001 From: cyy Date: Fri, 13 Sep 2019 09:40:40 +0800 Subject: [PATCH 0609/1365] fix calloc realloc failure --- src/avgpool_layer.c | 1 + src/batchnorm_layer.c | 1 + src/blas.c | 3 ++- src/gru_layer.c | 12 ++++++------ 4 files changed, 10 insertions(+), 7 deletions(-) diff --git a/src/avgpool_layer.c b/src/avgpool_layer.c index 1306fd38636..2b595aacf5b 100644 --- a/src/avgpool_layer.c +++ b/src/avgpool_layer.c @@ -1,5 +1,6 @@ #include "avgpool_layer.h" #include "dark_cuda.h" +#include "utils.h" #include avgpool_layer make_avgpool_layer(int batch, int w, int h, int c) diff --git a/src/batchnorm_layer.c b/src/batchnorm_layer.c index 018294df6e5..253ce288406 100644 --- a/src/batchnorm_layer.c +++ b/src/batchnorm_layer.c @@ -1,5 +1,6 @@ #include "batchnorm_layer.h" #include "blas.h" +#include "utils.h" #include layer make_batchnorm_layer(int batch, int w, int h, int c) diff --git a/src/blas.c b/src/blas.c index 96fc067f963..212fccdbd87 100644 --- a/src/blas.c +++ b/src/blas.c @@ -1,4 +1,5 @@ #include "blas.h" +#include "utils.h" #include #include @@ -358,4 +359,4 @@ void fix_nan_and_inf_cpu(float *input, size_t size) if (isnan(val) || isinf(val)) input[i] = 1.0f / i; // pseudo random value } -} \ No newline at end of file +} diff --git a/src/gru_layer.c b/src/gru_layer.c index b24bee94ac4..787f46ea65a 100644 --- a/src/gru_layer.c +++ b/src/gru_layer.c @@ -36,36 +36,36 @@ layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_no l.steps = steps; l.inputs = inputs; - l.input_z_layer = (layer*)malloc(sizeof(layer)); + l.input_z_layer = (layer*)xcalloc(1,sizeof(layer)); fprintf(stderr, "\t\t"); *(l.input_z_layer) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.input_z_layer->batch = batch; - l.state_z_layer = (layer*)malloc(sizeof(layer)); + l.state_z_layer = (layer*)xcalloc(1,sizeof(layer)); fprintf(stderr, "\t\t"); *(l.state_z_layer) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.state_z_layer->batch = batch; - l.input_r_layer = (layer*)malloc(sizeof(layer)); + l.input_r_layer = (layer*)xcalloc(1,sizeof(layer)); fprintf(stderr, "\t\t"); *(l.input_r_layer) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.input_r_layer->batch = batch; - l.state_r_layer = (layer*)malloc(sizeof(layer)); + l.state_r_layer = (layer*)xcalloc(1,sizeof(layer)); fprintf(stderr, "\t\t"); *(l.state_r_layer) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.state_r_layer->batch = batch; - l.input_h_layer = (layer*)malloc(sizeof(layer)); + l.input_h_layer = (layer*)xcalloc(1,sizeof(layer)); fprintf(stderr, "\t\t"); *(l.input_h_layer) = make_connected_layer(batch, steps, inputs, outputs, LINEAR, batch_normalize); l.input_h_layer->batch = batch; - l.state_h_layer = (layer*)malloc(sizeof(layer)); + l.state_h_layer = (layer*)xcalloc(1,sizeof(layer)); fprintf(stderr, "\t\t"); *(l.state_h_layer) = make_connected_layer(batch, steps, outputs, outputs, LINEAR, batch_normalize); l.state_h_layer->batch = batch; From fa74f691cb624d916503af309de13300638d097f Mon Sep 17 00:00:00 2001 From: "gilberto.plaza" Date: Wed, 18 Sep 2019 13:49:54 +0200 Subject: [PATCH 0610/1365] Removed useless snippet that only breaks if batch processing is enabled --- src/yolo_layer.c | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/yolo_layer.c b/src/yolo_layer.c index a76b5efb082..20ee8e34391 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -492,7 +492,9 @@ int get_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, //printf("\n l.batch = %d, l.w = %d, l.h = %d, l.n = %d \n", l.batch, l.w, l.h, l.n); int i,j,n; float *predictions = l.output; - if (l.batch == 2) avg_flipped_yolo(l); + // This snippet below is not necessary + // Need to comment it in order to batch processing >= 2 images + //if (l.batch == 2) avg_flipped_yolo(l); int count = 0; for (i = 0; i < l.w*l.h; ++i){ int row = i / l.w; From 41144ba358cce93d20efcde0c25ad05acf681cb5 Mon Sep 17 00:00:00 2001 From: cyy Date: Fri, 20 Sep 2019 10:32:00 +0800 Subject: [PATCH 0611/1365] fix calloc realloc failure --- src/classifier.c | 3 --- src/data.c | 3 --- src/detector.c | 3 --- src/gemm.c | 6 +++--- src/go.c | 23 +---------------------- src/list.c | 5 +++-- src/maxpool_layer.c | 1 + src/network.c | 3 --- src/normalization_layer.c | 1 + src/option_list.c | 6 ++++-- src/parser.c | 2 +- src/reorg_layer.c | 1 + src/reorg_old_layer.c | 1 + src/rnn.c | 15 --------------- src/route_layer.c | 1 + src/sam_layer.c | 1 + src/scale_channels_layer.c | 1 + src/shortcut_layer.c | 1 + src/upsample_layer.c | 1 + src/utils.c | 17 ++++++++++------- src/utils.h | 1 + 21 files changed, 32 insertions(+), 64 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index d75b320e595..e0cc0b344e1 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -782,9 +782,6 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi char **names = get_labels(name_list); clock_t time; int* indexes = (int*)xcalloc(top, sizeof(int)); - if(!indexes) { - error("calloc failed"); - } char buff[256]; char *input = buff; //int size = net.w; diff --git a/src/data.c b/src/data.c index 4e265e3e05c..abe1d98eede 100644 --- a/src/data.c +++ b/src/data.c @@ -193,9 +193,6 @@ box_label *read_boxes(char *filename, int *n) int count = 0; while(fscanf(file, "%d %f %f %f %f", &id, &x, &y, &w, &h) == 5){ boxes = (box_label*)xrealloc(boxes, (count + 1) * sizeof(box_label)); - if(!boxes) { - error("realloc failed"); - } boxes[count].id = id; boxes[count].x = x; boxes[count].y = y; diff --git a/src/detector.c b/src/detector.c index c48b6449554..7d96ab852a3 100644 --- a/src/detector.c +++ b/src/detector.c @@ -835,9 +835,6 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa if (prob > 0) { detections_count++; detections = (box_prob*)xrealloc(detections, detections_count * sizeof(box_prob)); - if (!detections) { - error("realloc failed"); - } detections[detections_count - 1].b = dets[i].bbox; detections[detections_count - 1].p = prob; detections[detections_count - 1].image_index = image_index; diff --git a/src/gemm.c b/src/gemm.c index 1fec9322905..e0295cb23b7 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -154,7 +154,7 @@ void gemm_nn_custom_bin_mean(int M, int N, int K, float ALPHA_UNUSED, unsigned char *B, int ldb, float *C, int ldc, float *mean_arr) { - int *count_arr = calloc(M*N, sizeof(int)); + int *count_arr = xcalloc(M*N, sizeof(int)); int i, j, k; for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] @@ -184,7 +184,7 @@ void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, unsigned char *B, int ldb, float *C, int ldc, float *mean_arr) { - int *count_arr = calloc(M*N, sizeof(int)); + int *count_arr = xcalloc(M*N, sizeof(int)); int i, j, k; for (i = 0; i < M; ++i) { // l.n - filters [16 - 55 - 1024] @@ -213,7 +213,7 @@ void gemm_nn_custom_bin_mean(int M, int N, int K, float ALPHA_UNUSED, unsigned char *B, int ldb, float *C, int ldc, float *mean_arr) { - int *count_arr = calloc(M*N, sizeof(int)); + int *count_arr = xcalloc(M*N, sizeof(int)); int i; diff --git a/src/go.c b/src/go.c index 92f2f0618cc..4b01045e118 100644 --- a/src/go.c +++ b/src/go.c @@ -19,7 +19,7 @@ char *fgetgo(FILE *fp) { if(feof(fp)) return 0; size_t size = 94; - char* line = (char*)malloc(size * sizeof(char)); + char* line = (char*)xmalloc(size * sizeof(char)); if(size != fread(line, sizeof(char), size, fp)){ free(line); return 0; @@ -128,13 +128,7 @@ void train_go(char *cfgfile, char *weightfile) char buff[256]; float* board = (float*)xcalloc(19 * 19 * net.batch, sizeof(float)); - if(!board) { - error("calloc failed"); - } float* move = (float*)xcalloc(19 * 19 * net.batch, sizeof(float)); - if(!move) { - error("calloc failed"); - } moves m = load_go_moves("backup/go.train"); //moves m = load_go_moves("games.txt"); @@ -417,13 +411,7 @@ void valid_go(char *cfgfile, char *weightfile, int multi) printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); float* board = (float*)xcalloc(19 * 19, sizeof(float)); - if(!board) { - error("calloc failed"); - } float* move = (float*)xcalloc(19 * 19, sizeof(float)); - if(!move) { - error("calloc failed"); - } moves m = load_go_moves("backup/go.test"); int N = m.n; @@ -778,17 +766,8 @@ void self_go(char *filename, char *weightfile, char *f2, char *w2, int multi) set_batch_network(&net, 1); set_batch_network(&net2, 1); float* board = (float*)xcalloc(19 * 19, sizeof(float)); - if(!board) { - error("calloc failed"); - } char* one = (char*)xcalloc(91, sizeof(char)); - if(!one) { - error("calloc failed"); - } char* two = (char*)xcalloc(91, sizeof(char)); - if(!two) { - error("calloc failed"); - } int done = 0; int player = 1; int p1 = 0; diff --git a/src/list.c b/src/list.c index 580826d65ac..4c24e477773 100644 --- a/src/list.c +++ b/src/list.c @@ -1,11 +1,12 @@ #include #include #include "list.h" +#include "utils.h" #include "option_list.h" list *make_list() { - list* l = (list*)malloc(sizeof(list)); + list* l = (list*)xmalloc(sizeof(list)); l->size = 0; l->front = 0; l->back = 0; @@ -40,7 +41,7 @@ void *list_pop(list *l){ void list_insert(list *l, void *val) { - node* newnode = (node*)malloc(sizeof(node)); + node* newnode = (node*)xmalloc(sizeof(node)); newnode->val = val; newnode->next = 0; diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index 797123f664a..b0ea1957b40 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -1,5 +1,6 @@ #include "maxpool_layer.h" #include "dark_cuda.h" +#include "utils.h" #include "gemm.h" #include diff --git a/src/network.c b/src/network.c index 403c1ca0d08..886dd11847f 100644 --- a/src/network.c +++ b/src/network.c @@ -821,9 +821,6 @@ char *detection_to_json(detection *dets, int nboxes, int classes, char **names, int buf_len = strlen(buf); int total_len = send_buf_len + buf_len + 100; send_buf = (char *)xrealloc(send_buf, total_len * sizeof(char)); - if (!send_buf) { - error("realloc failed"); - } strcat(send_buf, buf); free(buf); } diff --git a/src/normalization_layer.c b/src/normalization_layer.c index d8a2ac588bf..d6af6212b10 100644 --- a/src/normalization_layer.c +++ b/src/normalization_layer.c @@ -1,5 +1,6 @@ #include "normalization_layer.h" #include "blas.h" +#include "utils.h" #include layer make_normalization_layer(int batch, int w, int h, int c, int size, float alpha, float beta, float kappa) diff --git a/src/option_list.c b/src/option_list.c index e4fa7fe4552..306f0e37628 100644 --- a/src/option_list.c +++ b/src/option_list.c @@ -48,7 +48,9 @@ metadata get_metadata(char *file) } m.classes = option_find_int(options, "classes", 2); free_list(options); - printf("Loaded - names_list: %s, classes = %d \n", name_list, m.classes); + if(name_list) { + printf("Loaded - names_list: %s, classes = %d \n", name_list, m.classes); + } return m; } @@ -72,7 +74,7 @@ int read_option(char *s, list *options) void option_insert(list *l, char *key, char *val) { - kvp* p = (kvp*)malloc(sizeof(kvp)); + kvp* p = (kvp*)xmalloc(sizeof(kvp)); p->key = key; p->val = val; p->used = 0; diff --git a/src/parser.c b/src/parser.c index 3d3c3752dec..836a129db59 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1054,7 +1054,7 @@ list *read_cfg(char *filename) strip(line); switch(line[0]){ case '[': - current = (section*)malloc(sizeof(section)); + current = (section*)xmalloc(sizeof(section)); list_insert(sections, current); current->options = make_list(); current->type = line; diff --git a/src/reorg_layer.c b/src/reorg_layer.c index 10c11199a0e..7a4c0aecca2 100644 --- a/src/reorg_layer.c +++ b/src/reorg_layer.c @@ -1,6 +1,7 @@ #include "reorg_layer.h" #include "dark_cuda.h" #include "blas.h" +#include "utils.h" #include diff --git a/src/reorg_old_layer.c b/src/reorg_old_layer.c index f6ad2a8b251..cb715e6e81d 100644 --- a/src/reorg_old_layer.c +++ b/src/reorg_old_layer.c @@ -1,4 +1,5 @@ #include "reorg_old_layer.h" +#include "utils.h" #include "dark_cuda.h" #include "blas.h" #include diff --git a/src/rnn.c b/src/rnn.c index bfc57b709df..6c9b38c878c 100644 --- a/src/rnn.c +++ b/src/rnn.c @@ -15,9 +15,6 @@ int *read_tokenized_data(char *filename, size_t *read) size_t count = 0; FILE *fp = fopen(filename, "r"); int* d = (int*)xcalloc(size, sizeof(int)); - if(!d) { - error("calloc failed"); - } int n, one; one = fscanf(fp, "%d", &n); while(one == 1){ @@ -25,18 +22,12 @@ int *read_tokenized_data(char *filename, size_t *read) if(count > size){ size = size*2; d = (int*)xrealloc(d, size * sizeof(int)); - if(!d) { - error("realloc failed"); - } } d[count-1] = n; one = fscanf(fp, "%d", &n); } fclose(fp); d = (int*)xrealloc(d, count * sizeof(int)); - if(!d) { - error("realloc failed"); - } *read = count; return d; } @@ -53,17 +44,11 @@ char **read_tokens(char *filename, size_t *read) if(count > size){ size = size*2; d = (char**)xrealloc(d, size * sizeof(char*)); - if(!d) { - error("realloc failed"); - } } d[count-1] = line; } fclose(fp); d = (char**)xrealloc(d, count * sizeof(char*)); - if(!d) { - error("realloc failed"); - } *read = count; return d; } diff --git a/src/route_layer.c b/src/route_layer.c index a9d35fc3582..9217c30ec00 100644 --- a/src/route_layer.c +++ b/src/route_layer.c @@ -1,4 +1,5 @@ #include "route_layer.h" +#include "utils.h" #include "dark_cuda.h" #include "blas.h" #include diff --git a/src/sam_layer.c b/src/sam_layer.c index fbb1aadb4c4..a2ba5486ea2 100644 --- a/src/sam_layer.c +++ b/src/sam_layer.c @@ -1,4 +1,5 @@ #include "sam_layer.h" +#include "utils.h" #include "dark_cuda.h" #include "blas.h" #include diff --git a/src/scale_channels_layer.c b/src/scale_channels_layer.c index 847eeda417c..c1f6af28044 100644 --- a/src/scale_channels_layer.c +++ b/src/scale_channels_layer.c @@ -1,4 +1,5 @@ #include "scale_channels_layer.h" +#include "utils.h" #include "dark_cuda.h" #include "blas.h" #include diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index fdc70aeda9e..37f1bcc46a7 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -1,6 +1,7 @@ #include "shortcut_layer.h" #include "dark_cuda.h" #include "blas.h" +#include "utils.h" #include #include diff --git a/src/upsample_layer.c b/src/upsample_layer.c index 7e756782994..778f5b4d5dc 100644 --- a/src/upsample_layer.c +++ b/src/upsample_layer.c @@ -1,5 +1,6 @@ #include "upsample_layer.h" #include "dark_cuda.h" +#include "utils.h" #include "blas.h" #include diff --git a/src/utils.c b/src/utils.c index 60c2f5a614c..416682e3700 100644 --- a/src/utils.c +++ b/src/utils.c @@ -23,6 +23,13 @@ #pragma warning(disable: 4996) #endif +void *xmalloc(size_t size) { + void *ptr=malloc(size); + if(!ptr) { + malloc_error(); + } + return ptr; +} void *xcalloc(size_t nmemb, size_t size) { void *ptr=calloc(nmemb,size); if(!ptr) { @@ -319,7 +326,7 @@ void error(const char *s) void malloc_error() { - fprintf(stderr, "Malloc error\n"); + fprintf(stderr, "xMalloc error\n"); exit(EXIT_FAILURE); } @@ -407,7 +414,7 @@ char *fgetl(FILE *fp) { if(feof(fp)) return 0; size_t size = 512; - char* line = (char*)malloc(size * sizeof(char)); + char* line = (char*)xmalloc(size * sizeof(char)); if(!fgets(line, size, fp)){ free(line); return 0; @@ -419,10 +426,6 @@ char *fgetl(FILE *fp) if(curr == size-1){ size *= 2; line = (char*)xrealloc(line, size * sizeof(char)); - if(!line) { - printf("%ld\n", size); - malloc_error(); - } } size_t readsize = size-curr; if(readsize > INT_MAX) readsize = INT_MAX-1; @@ -500,7 +503,7 @@ char *copy_string(char *s) if(!s) { return NULL; } - char* copy = (char*)malloc(strlen(s) + 1); + char* copy = (char*)xmalloc(strlen(s) + 1); strncpy(copy, s, strlen(s)+1); return copy; } diff --git a/src/utils.h b/src/utils.h index 05b6da21600..a20b17d803d 100644 --- a/src/utils.h +++ b/src/utils.h @@ -13,6 +13,7 @@ extern "C" { LIB_API void free_ptrs(void **ptrs, int n); LIB_API void top_k(float *a, int n, int k, int *index); +void *xmalloc(size_t size); void *xcalloc(size_t nmemb, size_t size); void *xrealloc(void *ptr, size_t size); double what_time_is_it_now(); From 36d47b99be620786e403682cc288ea07576d90d3 Mon Sep 17 00:00:00 2001 From: cyy Date: Fri, 20 Sep 2019 10:36:54 +0800 Subject: [PATCH 0612/1365] fix memory leak --- src/utils.c | 1 + 1 file changed, 1 insertion(+) diff --git a/src/utils.c b/src/utils.c index 416682e3700..7686ab40584 100644 --- a/src/utils.c +++ b/src/utils.c @@ -212,6 +212,7 @@ void find_replace(const char* str, char* orig, char* rep, char* output) if(str!=output) { strcpy(output,str); } + free(buffer); return; } From 6f4d93bb9f59a805e6884789c97623e98806450d Mon Sep 17 00:00:00 2001 From: Alexey Date: Sun, 22 Sep 2019 17:38:42 +0300 Subject: [PATCH 0613/1365] Update readme.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index b79d9786e1c..36019663e86 100644 --- a/README.md +++ b/README.md @@ -569,7 +569,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * each: `model of object, side, illimination, scale, each 30 grad` of the turn and inclination angles - these are *different objects* from an internal perspective of the neural network. So the more *different objects* you want to detect, the more complex network model should be used. - * recalculate anchors for your dataset for `width` and `height` from cfg-file: + * Only if you are an **expert** in neural detection networks - recalculate anchors for your dataset for `width` and `height` from cfg-file: `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416` then set the same 9 `anchors` in each of 3 `[yolo]`-layers in your cfg-file. But you should change indexes of anchors `masks=` for each [yolo]-layer, so that 1st-[yolo]-layer has anchors larger than 60x60, 2nd larger than 30x30, 3rd remaining. Also you should change the `filters=(classes + 5)*` before each [yolo]-layer. If many of the calculated anchors do not fit under the appropriate layers - then just try using all the default anchors. From 05545b260784cb5ce9b27f85462c4a31137b684b Mon Sep 17 00:00:00 2001 From: Alexey Date: Tue, 24 Sep 2019 15:31:28 +0300 Subject: [PATCH 0614/1365] Create FUNDING.yml --- .github/FUNDING.yml | 12 ++++++++++++ 1 file changed, 12 insertions(+) create mode 100644 .github/FUNDING.yml diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml new file mode 100644 index 00000000000..0c5ae2e2b8f --- /dev/null +++ b/.github/FUNDING.yml @@ -0,0 +1,12 @@ +# These are supported funding model platforms + +github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2] +patreon: # Replace with a single Patreon username +open_collective: # Replace with a single Open Collective username +ko_fi: # Replace with a single Ko-fi username +tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel +community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry +liberapay: # Replace with a single Liberapay username +issuehunt: # Replace with a single IssueHunt username +otechie: # Replace with a single Otechie username +custom: ['https://paypal.me/alexeyab84', 'https://blockchain.coinmarketcap.com/address/bitcoin/36La9T7DoLVMrUQzm6rBDGsxutyvDzbHnp', 'https://etherscan.io/address/0x193d56BE3C65e3Fb8f48c291B17C0702e211A588#', 'https://explorer.zcha.in/accounts/t1PzwJ28Prb7Nk8fgfT3RXCr6Xtw54tgjoy'] # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2'] From 2fa539779f4e12e264b9e1b2fc463ac7edec165c Mon Sep 17 00:00:00 2001 From: Alexey Date: Fri, 27 Sep 2019 22:35:56 +0300 Subject: [PATCH 0615/1365] Readme.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 36019663e86..ca3c2d9f37d 100644 --- a/README.md +++ b/README.md @@ -540,6 +540,8 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty `.txt` files) - use as many images of negative samples as there are images with objects + * What is the best way to mark objects: label only the visible part of the object, or label the visible and overlapped part of the object, or label a little more than the entire object (with a little gap)? Mark as you like - how would you like it to be detected. + * for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last `[yolo]`-layer or `[region]`-layer in your cfg-file (the global maximum number of objects that can be detected by YoloV3 is `0,0615234375*(width*height)` where are width and height are parameters from `[net]` section in cfg-file) * for training for small objects (smaller than 16x16 after the image is resized to 416x416) - set `layers = -1, 11` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L720 From ff0b739e4357afba477c25f37fba08410ec86b04 Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 3 Oct 2019 12:12:00 +0300 Subject: [PATCH 0616/1365] Update Readme.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index ca3c2d9f37d..29e5174451e 100644 --- a/README.md +++ b/README.md @@ -451,11 +451,11 @@ Usually sufficient 2000 iterations for each class(object), but not less than 400 > Region Avg IOU: 0.798363, Class: 0.893232, Obj: 0.700808, No Obj: 0.004567, Avg Recall: 1.000000, count: 8 > Region Avg IOU: 0.800677, Class: 0.892181, Obj: 0.701590, No Obj: 0.004574, Avg Recall: 1.000000, count: 8 > - > **9002**: 0.211667, **0.060730 avg**, 0.001000 rate, 3.868000 seconds, 576128 images + > **9002**: 0.211667, **0.60730 avg**, 0.001000 rate, 3.868000 seconds, 576128 images > Loaded: 0.000000 seconds * **9002** - iteration number (number of batch) - * **0.060730 avg** - average loss (error) - **the lower, the better** + * **0.60730 avg** - average loss (error) - **the lower, the better** When you see that average loss **0.xxxxxx avg** no longer decreases at many iterations then you should stop training. The final avgerage loss can be from `0.05` (for a small model and easy dataset) to `3.0` (for a big model and a difficult dataset). From 7181c7435f6ccc99b9c9340eccb5bfd16826804e Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 3 Oct 2019 16:40:08 +0300 Subject: [PATCH 0617/1365] Update readme.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 29e5174451e..fa44665be17 100644 --- a/README.md +++ b/README.md @@ -333,7 +333,7 @@ Training Yolo v3: * change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L3) * change line subdivisions to [`subdivisions=8`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4) - * change line max_batches to (`classes*2000`), f.e. [`max_batches=6000`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L20) if you train for 3 classes + * change line max_batches to (`classes*2000` but not less than `4000`), f.e. [`max_batches=6000`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L20) if you train for 3 classes * change line steps to 80% and 90% of max_batches, f.e. [`steps=4800,5400`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L22) * change line `classes=80` to your number of objects in each of 3 `[yolo]`-layers: * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L610 From e24c96dc8bfe151c9c39684c34345f7981b3a08e Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 3 Oct 2019 17:28:06 +0300 Subject: [PATCH 0618/1365] Update Readme.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index fa44665be17..8027726284f 100644 --- a/README.md +++ b/README.md @@ -536,6 +536,8 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * check that each object that you want to detect is mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: https://github.com/AlexeyAB/Yolo_mark + * my Loss is very high and mAP is very low, is training wrong? Run training with ` -show_imgs` flag at the end of training command, do you see correct bounded boxes of objects (in windows or in files `aug_...jpg`)? If no - your training dataset is wrong. + * for each object which you want to detect - there must be at least 1 similar object in the Training dataset with about the same: shape, side of object, relative size, angle of rotation, tilt, illumination. So desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 different images for each class or more, and you should train `2000*classes` iterations or more * desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty `.txt` files) - use as many images of negative samples as there are images with objects From 1108eafa95e9a6e03d5fab5bcf2b67c540be4bc4 Mon Sep 17 00:00:00 2001 From: enes Date: Wed, 16 Oct 2019 10:00:57 +0300 Subject: [PATCH 0619/1365] Batch inference added --- darknet.py | 81 +++++++++++++++++++++++++++++++++++++++++++++++ include/darknet.h | 6 ++++ src/network.c | 81 +++++++++++++++++++++++++++++++++++++++++++++++ src/yolo_layer.c | 48 ++++++++++++++++++++++++++++ src/yolo_layer.h | 2 ++ 5 files changed, 218 insertions(+) diff --git a/darknet.py b/darknet.py index 10c9a456ebd..35987465da1 100644 --- a/darknet.py +++ b/darknet.py @@ -61,6 +61,9 @@ class DETECTION(Structure): ("objectness", c_float), ("sort_class", c_int)] +class DETNUMPAIR(Structure): + _fields_ = [("num", c_int), + ("dets", POINTER(DETECTION))] class IMAGE(Structure): _fields_ = [("w", c_int), @@ -157,6 +160,9 @@ def network_height(net): free_detections = lib.free_detections free_detections.argtypes = [POINTER(DETECTION), c_int] +free_batch_detections = lib.free_batch_detections +free_batch_detections.argtypes = [POINTER(DETNUMPAIR), c_int] + free_ptrs = lib.free_ptrs free_ptrs.argtypes = [POINTER(c_void_p), c_int] @@ -206,6 +212,11 @@ def network_height(net): predict_image_letterbox.argtypes = [c_void_p, IMAGE] predict_image_letterbox.restype = POINTER(c_float) +network_predict_custom = lib.network_predict_custom +network_predict_custom.argtypes = [c_void_p, IMAGE, c_int, c_int, c_int, + c_float, c_float, POINTER(c_int), c_int, c_int] +network_predict_custom.restype = POINTER(DETNUMPAIR) + def array_to_image(arr): import numpy as np # need to return old values to avoid python freeing memory @@ -441,5 +452,75 @@ def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yo print("Unable to show image: "+str(e)) return detections +def performBatchDetect(thresh= 0.25, configPath = "./cfg/yolov3.cfg", weightPath = "yolov3.weights", metaPath= "./cfg/coco.data", hier_thresh=.5, nms=.45, batch_size=3): + import cv2 + import numpy as np + # NB! Image sizes should be the same + # You can change the images, yet, be sure that they have the same width and height + img_samples = ['data/person.jpg', 'data/person.jpg', 'data/person.jpg'] + image_list = [cv2.imread(k) for k in img_samples] + + if len(image_list) > batch_size: + raise ValueError( + "Please check if batch size is equal to the number of images passed to the function") + net = load_net_custom(configPath.encode('utf-8'), weightPath.encode('utf-8'), 0, batch_size) + meta = load_meta(metaPath.encode('utf-8')) + pred_height, pred_width, c = image_list[0].shape + net_width, net_height = (network_width(net), network_height(net)) + img_list = [] + for custom_image_bgr in image_list: + custom_image = cv2.cvtColor(custom_image_bgr, cv2.COLOR_BGR2RGB) + custom_image = cv2.resize( + custom_image, (net_width, net_height), interpolation=cv2.INTER_NEAREST) + custom_image = custom_image.transpose(2, 0, 1) + img_list.append(custom_image) + + arr = np.concatenate(img_list, axis=0) + arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0 + data = arr.ctypes.data_as(POINTER(c_float)) + im = IMAGE(net_width, net_height, c, data) + + batch_dets = network_predict_custom(net, im, batch_size, pred_width, + pred_height, thresh, hier_thresh, None, 0, 0) + batch_boxes = [] + batch_scores = [] + batch_classes = [] + for b in range(batch_size): + num = batch_dets[b].num + dets = batch_dets[b].dets + if nms: + do_nms_obj(dets, num, meta.classes, nms) + boxes = [] + scores = [] + classes = [] + for i in range(num): + det = dets[i] + score = -1 + label = None + for c in range(det.classes): + p = det.prob[c] + if p > score: + score = p + label = c + if score > thresh: + box = det.bbox + left, top, right, bottom = map(int,(box.x - box.w / 2, box.y - box.h / 2, + box.x + box.w / 2, box.y + box.h / 2)) + boxes.append((top, left, bottom, right)) + scores.append(score) + classes.append(label) + boxColor = (int(255 * (1 - (score ** 2))), int(255 * (score ** 2)), 0) + cv2.rectangle(image_list[b], (left, top), + (right, bottom), boxColor, 2) + cv2.imwrite(os.path.basename(img_samples[b]),image_list[b]) + + batch_boxes.append(boxes) + batch_scores.append(scores) + batch_classes.append(classes) + free_batch_detections(batch_dets, batch_size) + return batch_boxes, batch_scores, batch_classes + if __name__ == "__main__": print(performDetect()) + # Uncomment the following line to see batch inference working + #print(performBatchDetect()) \ No newline at end of file diff --git a/include/darknet.h b/include/darknet.h index e78abe6a5c9..f0648066b7e 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -730,6 +730,12 @@ typedef struct detection{ int sort_class; } detection; +// network.c -batch inference +typedef struct detNumPair { + int num; + detection *dets; +} detNumPair, *pdetNumPair; + // matrix.h typedef struct matrix { int rows, cols; diff --git a/src/network.c b/src/network.c index 82dc4d53978..c1de0a4636c 100644 --- a/src/network.c +++ b/src/network.c @@ -694,6 +694,22 @@ int num_detections(network *net, float thresh) return s; } +int num_detections_custom(network *net, float thresh, int b) +{ + int i; + int s = 0; + for (i = 0; i < net->n; ++i) { + layer l = net->layers[i]; + if (l.type == YOLO) { + s += yolo_num_detections_custom(l, thresh, b); + } + if (l.type == DETECTION || l.type == REGION) { + s += l.w*l.h*l.n; + } + } + return s; +} + detection *make_network_boxes(network *net, float thresh, int *num) { layer l = net->layers[net->n - 1]; @@ -710,6 +726,21 @@ detection *make_network_boxes(network *net, float thresh, int *num) return dets; } +detection *make_network_boxes_custom(network *net, float thresh, int *num, int batch) +{ + int i; + layer l = net->layers[net->n - 1]; + int nboxes = num_detections_custom(net, thresh, batch); + if (num) *num = nboxes; + detection* dets = (detection*)calloc(nboxes, sizeof(detection)); + for (i = 0; i < nboxes; ++i) { + dets[i].prob = (float*)calloc(l.classes, sizeof(float)); + if (l.coords > 4) { + dets[i].mask = (float*)calloc(l.coords - 4, sizeof(float)); + } + } + return dets; +} void custom_get_region_detections(layer l, int w, int h, int net_w, int net_h, float thresh, int *map, float hier, int relative, detection *dets, int letter) { @@ -761,6 +792,33 @@ void fill_network_boxes(network *net, int w, int h, float thresh, float hier, in } } +void fill_network_boxes_custom(network *net, int w, int h, float thresh, float hier, int *map, int relative, detection *dets, int letter, int batch) +{ + int prev_classes = -1; + int j; + for (j = 0; j < net->n; ++j) { + layer l = net->layers[j]; + if (l.type == YOLO) { + int count = get_yolo_detections_custom(l, w, h, net->w, net->h, thresh, map, relative, dets, letter, batch); + dets += count; + if (prev_classes < 0) prev_classes = l.classes; + else if (prev_classes != l.classes) { + printf(" Error: Different [yolo] layers have different number of classes = %d and %d - check your cfg-file! \n", + prev_classes, l.classes); + } + } + if (l.type == REGION) { + custom_get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets, letter); + //get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets); + dets += l.w*l.h*l.n; + } + if (l.type == DETECTION) { + get_detection_detections(l, w, h, thresh, dets); + dets += l.w*l.h*l.n; + } + } +} + detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter) { detection *dets = make_network_boxes(net, thresh, num); @@ -778,6 +836,14 @@ void free_detections(detection *dets, int n) free(dets); } +void free_batch_detections(detNumPair *detNumPairs, int n) +{ + int i; + for(i=0; iw, net->h); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 20ee8e34391..6b589d71e38 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -461,6 +461,21 @@ int yolo_num_detections(layer l, float thresh) return count; } +int yolo_num_detections_custom(layer l, float thresh, int batch) +{ + int i, n; + int count = 0; + for (i = 0; i < l.w*l.h; ++i){ + for(n = 0; n < l.n; ++n){ + int obj_index = entry_index(l, batch, n*l.w*l.h + i, 4); + if(l.output[obj_index] > thresh){ + ++count; + } + } + } + return count; +} + void avg_flipped_yolo(layer l) { int i,j,n,z; @@ -522,6 +537,39 @@ int get_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, return count; } +int get_yolo_detections_custom(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets, int letter,int batch) +{ + //printf("\n l.batch = %d, l.w = %d, l.h = %d, l.n = %d \n", l.batch, l.w, l.h, l.n); + int i,j,n; + float *predictions = l.output; + //if (l.batch == 2) avg_flipped_yolo(l); + int count = 0; + for (i = 0; i < l.w*l.h; ++i){ + int row = i / l.w; + int col = i % l.w; + for(n = 0; n < l.n; ++n){ + int obj_index = entry_index(l, batch, n*l.w*l.h + i, 4); + float objectness = predictions[obj_index]; + //if(objectness <= thresh) continue; // incorrect behavior for Nan values + if (objectness > thresh) { + //printf("\n objectness = %f, thresh = %f, i = %d, n = %d \n", objectness, thresh, i, n); + int box_index = entry_index(l, batch, n*l.w*l.h + i, 0); + dets[count].bbox = get_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h); + dets[count].objectness = objectness; + dets[count].classes = l.classes; + for (j = 0; j < l.classes; ++j) { + int class_index = entry_index(l, batch, n*l.w*l.h + i, 4 + 1 + j); + float prob = objectness*predictions[class_index]; + dets[count].prob[j] = (prob > thresh) ? prob : 0; + } + ++count; + } + } + } + correct_yolo_boxes(dets, count, w, h, netw, neth, relative, letter); + return count; +} + #ifdef GPU void forward_yolo_layer_gpu(const layer l, network_state state) diff --git a/src/yolo_layer.h b/src/yolo_layer.h index d67482fe2fb..83607452851 100644 --- a/src/yolo_layer.h +++ b/src/yolo_layer.h @@ -13,7 +13,9 @@ void forward_yolo_layer(const layer l, network_state state); void backward_yolo_layer(const layer l, network_state state); void resize_yolo_layer(layer *l, int w, int h); int yolo_num_detections(layer l, float thresh); +int yolo_num_detections_custom(layer l, float thresh, int batch); int get_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets, int letter); +int get_yolo_detections_custom(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets, int letter, int batch); void correct_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative, int letter); #ifdef GPU From c435fca73874d69dd6bdeb988fd45a0e8fb9a9c1 Mon Sep 17 00:00:00 2001 From: enes Date: Wed, 16 Oct 2019 15:08:37 +0300 Subject: [PATCH 0620/1365] explicit casting for c++ --- src/network.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/network.c b/src/network.c index c1de0a4636c..ab7533b13bf 100644 --- a/src/network.c +++ b/src/network.c @@ -919,7 +919,7 @@ detNumPair* network_predict_custom(network *net, image im, int batch, int w, int { set_batch_network(net, batch); network_predict(*net, im.data); - detNumPair *pdets = malloc(batch*sizeof(detNumPair)); + detNumPair *pdets = ( struct detNumPair * )malloc(batch*sizeof(detNumPair)); int num; for(int b=0;b Date: Fri, 18 Oct 2019 19:24:49 -0400 Subject: [PATCH 0621/1365] Add readability changes Make CMake-GUI install more visible than vcpkg install --- README.md | 38 ++++++++++++++++++++++---------------- 1 file changed, 22 insertions(+), 16 deletions(-) diff --git a/README.md b/README.md index 2f85a17c15e..8f14d167ba9 100644 --- a/README.md +++ b/README.md @@ -22,8 +22,8 @@ More details: http://pjreddie.com/darknet/yolo/ * [Using cmake](#how-to-compile-on-linux-using-cmake) * [Using make](#how-to-compile-on-linux-using-make) 3. How to compile on Windows + * [Using CMake-GUI](#how-to-compile-on-windows-using-cmake-gui) * [Using vcpkg](#how-to-compile-on-windows-using-vcpkg) - * [Using Cmake-GUI](#how-to-compile-on-windows-using-cmake-gui) * [Legacy way](#how-to-compile-on-windows-legacy-way) 4. [How to train (Pascal VOC Data)](#how-to-train-pascal-voc-data) 5. [How to train with multi-GPU:](#how-to-train-with-multi-gpu) @@ -159,7 +159,7 @@ The `CMakeLists.txt` will attempt to find installed optional dependencies like CUDA, cudnn, ZED and build against those. It will also create a shared object library file to use `darknet` for code development. -Inside the cloned repository: +Do inside the cloned repository: ``` mkdir build-release @@ -187,9 +187,28 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c To run Darknet on Linux use examples from this article, just use `./darknet` instead of `darknet.exe`, i.e. use this command: `./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights` +### How to compile on Windows (using `CMake-GUI`) + +This is the recommended approach to build Darknet on Windows if you have already +installed Visual Studio 2015/2017/2019, CUDA > 10.0, cuDNN > 7.0, and +OpenCV > 2.4. + +Use `CMake-GUI` as shown here on this [**IMAGE**](https://user-images.githubusercontent.com/4096485/55107892-6becf380-50e3-11e9-9a0a-556a943c429a.png): + +1. Configure +2. Optional platform for generator (Set: x64) +3. Finish +4. Generate +5. Open Project +6. Set: x64 & Release +7. Build +8. Build solution + ### How to compile on Windows (using `vcpkg`) -If you have already installed Visual Studio 2015/2017/2019, CUDA > 10.0, cuDNN > 7.0, OpenCV > 2.4, then compile Darknet by using `C:\Program Files\CMake\bin\cmake-gui.exe` as on this [**IMAGE**](https://user-images.githubusercontent.com/4096485/55107892-6becf380-50e3-11e9-9a0a-556a943c429a.png): Configure -> Optional platform for generator (Set: x64) -> Finish -> Generate -> Open Project -> x64 & Release -> Build -> Build solution +If you have already installed Visual Studio 2015/2017/2019, CUDA > 10.0, +cuDNN > 7.0, OpenCV > 2.4, then to compile Darknet it is recommended to use +[CMake-GUI](#how-to-compile-on-windows-using-cmake-gui). Otherwise, follow these steps: @@ -216,19 +235,6 @@ PS Code\vcpkg> .\vcpkg install pthreads opencv[ffmpeg] #replace with ope 9. Open Powershell, go to the `darknet` folder and build with the command `.\build.ps1`. If you want to use Visual Studio, you will find two custom solutions created for you by CMake after the build, one in `build_win_debug` and the other in `build_win_release`, containing all the appropriate config flags for your system. -### How to compile on Windows (using `Cmake-GUI`) - -Using `Cmake-GUI` as shown here on this [**IMAGE**](https://user-images.githubusercontent.com/4096485/55107892-6becf380-50e3-11e9-9a0a-556a943c429a.png): - -1. Configure -2. Optional platform for generator (Set: x64) -3. Finish -4. Generate -5. Open Project -6. x64 & Release -7. Build -8. Build solution - ### How to compile on Windows (legacy way) 1. If you have **CUDA 10.0, cuDNN 7.4 and OpenCV 3.x** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then open `build\darknet\darknet.sln`, set **x64** and **Release** https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg and do the: Build -> Build darknet. Also add Windows system variable `CUDNN` with path to CUDNN: https://user-images.githubusercontent.com/4096485/53249764-019ef880-36ca-11e9-8ffe-d9cf47e7e462.jpg From 2eb68d5177d43d6927753631a0d4fad5446cab17 Mon Sep 17 00:00:00 2001 From: Alexey Date: Sat, 19 Oct 2019 15:09:15 +0300 Subject: [PATCH 0622/1365] Update Readme.md --- README.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/README.md b/README.md index 2aed1180a43..9042a6f99e7 100644 --- a/README.md +++ b/README.md @@ -625,6 +625,13 @@ Here you can find repository with GUI-software for marking bounded boxes of obje With example of: `train.txt`, `obj.names`, `obj.data`, `yolo-obj.cfg`, `air`1-6`.txt`, `bird`1-4`.txt` for 2 classes of objects (air, bird) and `train_obj.cmd` with example how to train this image-set with Yolo v2 & v3 +Different tools for marking objects in images: +1. in C++: https://github.com/AlexeyAB/Yolo_mark +2. in Python: https://github.com/tzutalin/labelImg +3. in Python: https://github.com/Cartucho/OpenLabeling +4. in C++: https://www.ccoderun.ca/darkmark/ + + ## Using Yolo9000 Simultaneous detection and classification of 9000 objects: `darknet.exe detector test cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights data/dog.jpg` From b392621e2eba3a9c830b71bee50f10b15b5f4c33 Mon Sep 17 00:00:00 2001 From: enes Date: Sat, 19 Oct 2019 16:18:44 +0300 Subject: [PATCH 0623/1365] batch inference refactoring --- darknet.py | 17 +++++++---------- src/network.c | 32 ++++++++++++++++---------------- src/yolo_layer.c | 5 ++--- src/yolo_layer.h | 4 ++-- 4 files changed, 27 insertions(+), 31 deletions(-) diff --git a/darknet.py b/darknet.py index 35987465da1..1dde00fbab1 100644 --- a/darknet.py +++ b/darknet.py @@ -212,10 +212,10 @@ def network_height(net): predict_image_letterbox.argtypes = [c_void_p, IMAGE] predict_image_letterbox.restype = POINTER(c_float) -network_predict_custom = lib.network_predict_custom -network_predict_custom.argtypes = [c_void_p, IMAGE, c_int, c_int, c_int, +network_predict_batch = lib.network_predict_batch +network_predict_batch.argtypes = [c_void_p, IMAGE, c_int, c_int, c_int, c_float, c_float, POINTER(c_int), c_int, c_int] -network_predict_custom.restype = POINTER(DETNUMPAIR) +network_predict_batch.restype = POINTER(DETNUMPAIR) def array_to_image(arr): import numpy as np @@ -460,9 +460,6 @@ def performBatchDetect(thresh= 0.25, configPath = "./cfg/yolov3.cfg", weightPath img_samples = ['data/person.jpg', 'data/person.jpg', 'data/person.jpg'] image_list = [cv2.imread(k) for k in img_samples] - if len(image_list) > batch_size: - raise ValueError( - "Please check if batch size is equal to the number of images passed to the function") net = load_net_custom(configPath.encode('utf-8'), weightPath.encode('utf-8'), 0, batch_size) meta = load_meta(metaPath.encode('utf-8')) pred_height, pred_width, c = image_list[0].shape @@ -480,7 +477,7 @@ def performBatchDetect(thresh= 0.25, configPath = "./cfg/yolov3.cfg", weightPath data = arr.ctypes.data_as(POINTER(c_float)) im = IMAGE(net_width, net_height, c, data) - batch_dets = network_predict_custom(net, im, batch_size, pred_width, + batch_dets = network_predict_batch(net, im, batch_size, pred_width, pred_height, thresh, hier_thresh, None, 0, 0) batch_boxes = [] batch_scores = [] @@ -521,6 +518,6 @@ def performBatchDetect(thresh= 0.25, configPath = "./cfg/yolov3.cfg", weightPath return batch_boxes, batch_scores, batch_classes if __name__ == "__main__": - print(performDetect()) - # Uncomment the following line to see batch inference working - #print(performBatchDetect()) \ No newline at end of file + #print(performDetect()) + #Uncomment the following line to see batch inference working + print(performBatchDetect()) \ No newline at end of file diff --git a/src/network.c b/src/network.c index ab7533b13bf..c3a195a70e5 100644 --- a/src/network.c +++ b/src/network.c @@ -694,14 +694,14 @@ int num_detections(network *net, float thresh) return s; } -int num_detections_custom(network *net, float thresh, int b) +int num_detections_batch(network *net, float thresh, int batch) { int i; int s = 0; for (i = 0; i < net->n; ++i) { layer l = net->layers[i]; if (l.type == YOLO) { - s += yolo_num_detections_custom(l, thresh, b); + s += yolo_num_detections_batch(l, thresh, batch); } if (l.type == DETECTION || l.type == REGION) { s += l.w*l.h*l.n; @@ -726,12 +726,13 @@ detection *make_network_boxes(network *net, float thresh, int *num) return dets; } -detection *make_network_boxes_custom(network *net, float thresh, int *num, int batch) +detection *make_network_boxes_batch(network *net, float thresh, int *num, int batch) { int i; layer l = net->layers[net->n - 1]; - int nboxes = num_detections_custom(net, thresh, batch); - if (num) *num = nboxes; + int nboxes = num_detections_batch(net, thresh, batch); + assert(num != NULL); + *num = nboxes; detection* dets = (detection*)calloc(nboxes, sizeof(detection)); for (i = 0; i < nboxes; ++i) { dets[i].prob = (float*)calloc(l.classes, sizeof(float)); @@ -792,14 +793,14 @@ void fill_network_boxes(network *net, int w, int h, float thresh, float hier, in } } -void fill_network_boxes_custom(network *net, int w, int h, float thresh, float hier, int *map, int relative, detection *dets, int letter, int batch) +void fill_network_boxes_batch(network *net, int w, int h, float thresh, float hier, int *map, int relative, detection *dets, int letter, int batch) { int prev_classes = -1; int j; for (j = 0; j < net->n; ++j) { layer l = net->layers[j]; if (l.type == YOLO) { - int count = get_yolo_detections_custom(l, w, h, net->w, net->h, thresh, map, relative, dets, letter, batch); + int count = get_yolo_detections_batch(l, w, h, net->w, net->h, thresh, map, relative, dets, letter, batch); dets += count; if (prev_classes < 0) prev_classes = l.classes; else if (prev_classes != l.classes) { @@ -840,7 +841,7 @@ void free_batch_detections(detNumPair *detNumPairs, int n) { int i; for(i=0; i Date: Mon, 21 Oct 2019 15:33:01 +0300 Subject: [PATCH 0624/1365] Use ignore_thresh only if class_id matched. Temporary changed Assisted_Excitation (reduces background activations rather than enhancing objects activations). Added antialiasiong=2 for 2x2. --- src/convolutional_kernels.cu | 40 +++++++++++++++++---- src/convolutional_layer.c | 70 +++++++++++++++++++++--------------- src/detector.c | 3 +- src/http_stream.cpp | 2 +- src/maxpool_layer.c | 69 +++++++++++++++++++++-------------- src/yolo_layer.c | 32 ++++++++++++++++- 6 files changed, 151 insertions(+), 65 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index d766c9cf7cb..edfb03b81c7 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -948,6 +948,30 @@ void assisted_activation_gpu(float alpha, float *output, float *gt_gpu, float *a assisted_activation_kernel << > > (alpha, output, gt_gpu, a_avg_gpu, size, channels, batches); } + +__global__ void assisted_activation2_kernel(float alpha, float *output, float *gt_gpu, float *a_avg_gpu, int size, int channels, int batches) +{ + int i = blockIdx.x * blockDim.x + threadIdx.x; + int xy = i % size; + int b = i / size; + float beta = 1 - alpha; + + if (b < batches) { + for (int c = 0; c < channels; ++c) { + if(gt_gpu[i] == 0) + output[xy + size*(c + channels*b)] *= beta; + + } + } +} + +void assisted_activation2_gpu(float alpha, float *output, float *gt_gpu, float *a_avg_gpu, int size, int channels, int batches) +{ + const int num_blocks = get_number_of_blocks(size*batches, BLOCK); + + assisted_activation2_kernel << > > (alpha, output, gt_gpu, a_avg_gpu, size, channels, batches); +} + void assisted_excitation_forward_gpu(convolutional_layer l, network_state state) { const int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); @@ -958,12 +982,13 @@ void assisted_excitation_forward_gpu(convolutional_layer l, network_state state) // calculate alpha //const float alpha = (1 + cos(3.141592 * iteration_num)) / (2 * state.net.max_batches); //const float alpha = (1 + cos(3.141592 * epoch)) / (2 * state.net.max_batches); - //const float alpha = (1 + cos(3.141592 * iteration_num / state.net.max_batches)) / 2; - float alpha = (1 + cos(3.141592 * iteration_num / state.net.max_batches)); + float alpha = (1 + cos(3.141592 * iteration_num / state.net.max_batches)) / 2; + //float alpha = (1 + cos(3.141592 * iteration_num / state.net.max_batches)); if (l.assisted_excitation > 1) { - if (iteration_num > l.assisted_excitation) alpha = 0; - else alpha = (1 + cos(3.141592 * iteration_num / l.assisted_excitation)); + if (iteration_num < state.net.burn_in) alpha = 0; + else if (iteration_num > l.assisted_excitation) alpha = 0; + else alpha = (1 + cos(3.141592 * iteration_num / l.assisted_excitation)) / 2; } //printf("\n epoch = %f, alpha = %f, seen = %d, max_batches = %d, train_images_num = %d \n", @@ -1017,7 +1042,8 @@ void assisted_excitation_forward_gpu(convolutional_layer l, network_state state) //CHECK_CUDA(cudaPeekAtLastError()); // calc new output - assisted_activation_gpu(alpha, l.output_gpu, l.gt_gpu, l.a_avg_gpu, l.out_w * l.out_h, l.out_c, l.batch); + assisted_activation2_gpu(alpha, l.output_gpu, l.gt_gpu, l.a_avg_gpu, l.out_w * l.out_h, l.out_c, l.batch); + //assisted_activation_gpu(alpha, l.output_gpu, l.gt_gpu, l.a_avg_gpu, l.out_w * l.out_h, l.out_c, l.batch); //cudaStreamSynchronize(get_cuda_stream()); //CHECK_CUDA(cudaPeekAtLastError()); @@ -1070,13 +1096,13 @@ void assisted_excitation_forward_gpu(convolutional_layer l, network_state state) printf(" Assisted Excitation alpha = %f \n", alpha); image img = float_to_image(l.out_w, l.out_h, 1, >[l.out_w*l.out_h*b]); char buff[100]; - sprintf(buff, "a_excitation_%d", b); + sprintf(buff, "a_excitation_gt_%d", b); show_image_cv(img, buff); //image img2 = float_to_image(l.out_w, l.out_h, 1, &l.output[l.out_w*l.out_h*l.out_c*b]); image img2 = float_to_image_scaled(l.out_w, l.out_h, 1, &l.output[l.out_w*l.out_h*l.out_c*b]); char buff2[100]; - sprintf(buff2, "a_excitation_act_%d", b); + sprintf(buff2, "a_excitation_output_%d", b); show_image_cv(img2, buff2); /* diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 10a45bab5d5..8bce5aa67d9 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -587,36 +587,50 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, if (l.antialiasing) { printf("AA: "); l.input_layer = (layer*)calloc(1, sizeof(layer)); - const int blur_size = 3; - *(l.input_layer) = make_convolutional_layer(batch, steps, out_h, out_w, n, n, n, blur_size, blur_stride_x, blur_stride_y, 1, blur_size / 2, LINEAR, 0, 0, 0, 0, 0, index, 0, NULL, 0); + int blur_size = 3; + int blur_pad = blur_size / 2; + if (l.antialiasing == 2) { + blur_size = 2; + blur_pad = 0; + } + *(l.input_layer) = make_convolutional_layer(batch, steps, out_h, out_w, n, n, n, blur_size, blur_stride_x, blur_stride_y, 1, blur_pad, LINEAR, 0, 0, 0, 0, 0, index, 0, NULL, 0); const int blur_nweights = n * blur_size * blur_size; // (n / n) * n * blur_size * blur_size; int i; - for (i = 0; i < blur_nweights; i += (blur_size*blur_size)) { - /* - l.input_layer->weights[i + 0] = 0; - l.input_layer->weights[i + 1] = 0; - l.input_layer->weights[i + 2] = 0; - - l.input_layer->weights[i + 3] = 0; - l.input_layer->weights[i + 4] = 1; - l.input_layer->weights[i + 5] = 0; - - l.input_layer->weights[i + 6] = 0; - l.input_layer->weights[i + 7] = 0; - l.input_layer->weights[i + 8] = 0; - */ - l.input_layer->weights[i + 0] = 1 / 16.f; - l.input_layer->weights[i + 1] = 2 / 16.f; - l.input_layer->weights[i + 2] = 1 / 16.f; - - l.input_layer->weights[i + 3] = 2 / 16.f; - l.input_layer->weights[i + 4] = 4 / 16.f; - l.input_layer->weights[i + 5] = 2 / 16.f; - - l.input_layer->weights[i + 6] = 1 / 16.f; - l.input_layer->weights[i + 7] = 2 / 16.f; - l.input_layer->weights[i + 8] = 1 / 16.f; - + if (blur_size == 2) { + for (i = 0; i < blur_nweights; i += (blur_size*blur_size)) { + l.input_layer->weights[i + 0] = 1 / 4.f; + l.input_layer->weights[i + 1] = 1 / 4.f; + l.input_layer->weights[i + 2] = 1 / 4.f; + l.input_layer->weights[i + 3] = 1 / 4.f; + } + } + else { + for (i = 0; i < blur_nweights; i += (blur_size*blur_size)) { + /* + l.input_layer->weights[i + 0] = 0; + l.input_layer->weights[i + 1] = 0; + l.input_layer->weights[i + 2] = 0; + + l.input_layer->weights[i + 3] = 0; + l.input_layer->weights[i + 4] = 1; + l.input_layer->weights[i + 5] = 0; + + l.input_layer->weights[i + 6] = 0; + l.input_layer->weights[i + 7] = 0; + l.input_layer->weights[i + 8] = 0; + */ + l.input_layer->weights[i + 0] = 1 / 16.f; + l.input_layer->weights[i + 1] = 2 / 16.f; + l.input_layer->weights[i + 2] = 1 / 16.f; + + l.input_layer->weights[i + 3] = 2 / 16.f; + l.input_layer->weights[i + 4] = 4 / 16.f; + l.input_layer->weights[i + 5] = 2 / 16.f; + + l.input_layer->weights[i + 6] = 1 / 16.f; + l.input_layer->weights[i + 7] = 2 / 16.f; + l.input_layer->weights[i + 8] = 1 / 16.f; + } } for (i = 0; i < n; ++i) l.input_layer->biases[i] = 0; #ifdef GPU diff --git a/src/detector.c b/src/detector.c index 689ace6da83..efe5571aa8d 100644 --- a/src/detector.c +++ b/src/detector.c @@ -798,7 +798,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa replace_image_to_label(path, labelpath); int num_labels = 0; box_label *truth = read_boxes(labelpath, &num_labels); - int i, j; + int j; for (j = 0; j < num_labels; ++j) { truth_classes_count[truth[j].id]++; } @@ -818,6 +818,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa const int checkpoint_detections_count = detections_count; + int i; for (i = 0; i < nboxes; ++i) { int class_id; diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 3ce2a212bca..af2e6730c74 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -48,7 +48,7 @@ static int close_socket(SOCKET s) { cerr << "Close socket: out = " << close_output << ", in = " << close_input << " \n"; return result; } -#else // nix +#else // _WIN32 - else: nix #include "darkunistd.h" #include #include diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index 2f290497dcb..dca9c1b15f7 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -108,35 +108,50 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s if (l.antialiasing) { printf("AA: "); l.input_layer = (layer*)calloc(1, sizeof(layer)); - const int blur_size = 3; - *(l.input_layer) = make_convolutional_layer(batch, 1, l.out_h, l.out_w, l.out_c, l.out_c, l.out_c, blur_size, blur_stride_x, blur_stride_y, 1, blur_size / 2, LINEAR, 0, 0, 0, 0, 0, 1, 0, NULL, 0); + int blur_size = 3; + int blur_pad = blur_size / 2; + if (l.antialiasing == 2) { + blur_size = 2; + blur_pad = 0; + } + *(l.input_layer) = make_convolutional_layer(batch, 1, l.out_h, l.out_w, l.out_c, l.out_c, l.out_c, blur_size, blur_stride_x, blur_stride_y, 1, blur_pad, LINEAR, 0, 0, 0, 0, 0, 1, 0, NULL, 0); const int blur_nweights = l.out_c * blur_size * blur_size; // (n / n) * n * blur_size * blur_size; int i; - for (i = 0; i < blur_nweights; i += (blur_size*blur_size)) { - /* - l.input_layer->weights[i + 0] = 0; - l.input_layer->weights[i + 1] = 0; - l.input_layer->weights[i + 2] = 0; - - l.input_layer->weights[i + 3] = 0; - l.input_layer->weights[i + 4] = 1; - l.input_layer->weights[i + 5] = 0; - - l.input_layer->weights[i + 6] = 0; - l.input_layer->weights[i + 7] = 0; - l.input_layer->weights[i + 8] = 0; - */ - l.input_layer->weights[i + 0] = 1 / 16.f; - l.input_layer->weights[i + 1] = 2 / 16.f; - l.input_layer->weights[i + 2] = 1 / 16.f; - - l.input_layer->weights[i + 3] = 2 / 16.f; - l.input_layer->weights[i + 4] = 4 / 16.f; - l.input_layer->weights[i + 5] = 2 / 16.f; - - l.input_layer->weights[i + 6] = 1 / 16.f; - l.input_layer->weights[i + 7] = 2 / 16.f; - l.input_layer->weights[i + 8] = 1 / 16.f; + if (blur_size == 2) { + for (i = 0; i < blur_nweights; i += (blur_size*blur_size)) { + l.input_layer->weights[i + 0] = 1 / 4.f; + l.input_layer->weights[i + 1] = 1 / 4.f; + l.input_layer->weights[i + 2] = 1 / 4.f; + l.input_layer->weights[i + 3] = 1 / 4.f; + } + } + else { + for (i = 0; i < blur_nweights; i += (blur_size*blur_size)) { + /* + l.input_layer->weights[i + 0] = 0; + l.input_layer->weights[i + 1] = 0; + l.input_layer->weights[i + 2] = 0; + + l.input_layer->weights[i + 3] = 0; + l.input_layer->weights[i + 4] = 1; + l.input_layer->weights[i + 5] = 0; + + l.input_layer->weights[i + 6] = 0; + l.input_layer->weights[i + 7] = 0; + l.input_layer->weights[i + 8] = 0; + */ + l.input_layer->weights[i + 0] = 1 / 16.f; + l.input_layer->weights[i + 1] = 2 / 16.f; + l.input_layer->weights[i + 2] = 1 / 16.f; + + l.input_layer->weights[i + 3] = 2 / 16.f; + l.input_layer->weights[i + 4] = 4 / 16.f; + l.input_layer->weights[i + 5] = 2 / 16.f; + + l.input_layer->weights[i + 6] = 1 / 16.f; + l.input_layer->weights[i + 7] = 2 / 16.f; + l.input_layer->weights[i + 8] = 1 / 16.f; + } } for (i = 0; i < l.out_c; ++i) l.input_layer->biases[i] = 0; #ifdef GPU diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 20ee8e34391..2006f4b8047 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -128,6 +128,26 @@ box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw return b; } + +int get_yolo_class(float *output, int classes, int class_index, int stride, float objectness) +{ + int class_id = 0; + float max_prob = FLT_MIN; + + int j; + for (j = 0; j < classes; ++j) { + float prob = objectness * output[class_index + stride*j]; + if (prob > max_prob) { + max_prob = prob; + class_id = j; + } + //int class_index = entry_index(l, 0, n*l.w*l.h + i, 4 + 1 + j); + //float prob = objectness*predictions[class_index]; + //dets[count].prob[j] = (prob > thresh) ? prob : 0; + } + return class_id; +} + ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss) { ious all_ious = { 0 }; @@ -272,6 +292,7 @@ void forward_yolo_layer(const layer l, network_state state) box pred = get_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w*l.h); float best_iou = 0; int best_t = 0; + int class_id_match = 0; for (t = 0; t < l.max_boxes; ++t) { box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; @@ -282,8 +303,17 @@ void forward_yolo_layer(const layer l, network_state state) continue; // if label contains class_id more than number of classes in the cfg-file } if (!truth.x) break; // continue; + + int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); + int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4); + float objectness = l.output[obj_index]; + int pred_class_id = get_yolo_class(l.output, l.classes, class_index, l.w*l.h, objectness); + if (class_id == pred_class_id) class_id_match = 1; + else class_id_match = 0; + float iou = box_iou(pred, truth); - if (iou > best_iou) { + //if (iou > best_iou) { + if (iou > best_iou && class_id_match == 1) { best_iou = iou; best_t = t; } From bb7d69941cbce4f67f10406395d685ea92be9478 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 21 Oct 2019 15:49:46 +0300 Subject: [PATCH 0625/1365] Added debugging info for Training Classifier for case: Too many or too few labels --- src/data.c | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/src/data.c b/src/data.c index d8f0c9e81c0..7cb7bf0a998 100644 --- a/src/data.c +++ b/src/data.c @@ -498,7 +498,16 @@ void fill_truth(char *path, char **labels, int k, float *truth) ++count; } } - if(count != 1) printf("Too many or too few labels: %d, %s\n", count, path); + if (count != 1) { + printf("Too many or too few labels: %d, %s\n", count, path); + count = 0; + for (i = 0; i < k; ++i) { + if (strstr(path, labels[i])) { + printf("\t label %d: %s \n", count, labels[i]); + count++; + } + } + } } void fill_hierarchy(float *truth, int k, tree *hierarchy) From b3a24952985352f81d759095aa3b38c4e761c342 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 25 Oct 2019 20:47:17 +0300 Subject: [PATCH 0626/1365] Added Gaussian YOLOv3 layer [Gaussian_yolo] --- Makefile | 2 +- build/darknet/darknet.vcxproj | 2 + include/darknet.h | 2 + src/box.c | 10 + src/box.h | 1 + src/convolutional_kernels.cu | 11 +- src/convolutional_layer.c | 11 +- src/data.c | 11 +- src/gaussian_yolo_layer.c | 445 ++++++++++++++++++++++++++++++++++ src/gaussian_yolo_layer.h | 20 ++ src/network.c | 16 ++ src/parser.c | 65 +++++ src/yolo_layer.c | 10 - 13 files changed, 565 insertions(+), 41 deletions(-) create mode 100644 src/gaussian_yolo_layer.c create mode 100644 src/gaussian_yolo_layer.h diff --git a/Makefile b/Makefile index 25a85f81855..41e5fc8d737 100644 --- a/Makefile +++ b/Makefile @@ -118,7 +118,7 @@ LDFLAGS+= -L/usr/local/zed/lib -lsl_core -lsl_input -lsl_zed #-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0 endif -OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o upsample_layer.o lstm_layer.o conv_lstm_layer.o scale_channels_layer.o sam_layer.o +OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o gaussian_yolo_layer.o upsample_layer.o lstm_layer.o conv_lstm_layer.o scale_channels_layer.o sam_layer.o ifeq ($(GPU), 1) LDFLAGS+= -lstdc++ OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj index d7dc91590fa..b685bebd50b 100644 --- a/build/darknet/darknet.vcxproj +++ b/build/darknet/darknet.vcxproj @@ -199,6 +199,7 @@ + @@ -263,6 +264,7 @@ + diff --git a/include/darknet.h b/include/darknet.h index e78abe6a5c9..00b49921f52 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -149,6 +149,7 @@ typedef enum { XNOR, REGION, YOLO, + GAUSSIAN_YOLO, ISEG, REORG, REORG_OLD, @@ -728,6 +729,7 @@ typedef struct detection{ float *mask; float objectness; int sort_class; + float *uc; // Gaussian_YOLOv3 - tx,ty,tw,th uncertainty } detection; // matrix.h diff --git a/src/box.c b/src/box.c index 1b5c4998a6b..c6a27ed587f 100644 --- a/src/box.c +++ b/src/box.c @@ -13,6 +13,16 @@ box float_to_box(float *f) return b; } +box float_to_box_stride(float *f, int stride) +{ + box b = { 0 }; + b.x = f[0]; + b.y = f[1 * stride]; + b.w = f[2 * stride]; + b.h = f[3 * stride]; + return b; +} + dbox derivative(box a, box b) { dbox d; diff --git a/src/box.h b/src/box.h index 2392fedd20c..172c135293c 100644 --- a/src/box.h +++ b/src/box.h @@ -31,6 +31,7 @@ typedef struct detection_with_class { extern "C" { #endif box float_to_box(float *f); +box float_to_box_stride(float *f, int stride); float box_iou(box a, box b); float box_rmse(box a, box b); dxrep dx_box_iou(box a, box b, IOU_LOSS iou_loss); diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index edfb03b81c7..23005ccb91e 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -10,6 +10,7 @@ #include "col2im.h" #include "utils.h" #include "dark_cuda.h" +#include "box.h" __global__ void binarize_kernel(float *x, int n, float *binary) @@ -892,16 +893,6 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state } } -static box float_to_box_stride(float *f, int stride) -{ - box b = { 0 }; - b.x = f[0]; - b.y = f[1 * stride]; - b.w = f[2 * stride]; - b.h = f[3 * stride]; - return b; -} - __global__ void calc_avg_activation_kernel(float *src, float *dst, int size, int channels, int batches) { int i = blockIdx.x * blockDim.x + threadIdx.x; diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 8bce5aa67d9..6818b603529 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -5,6 +5,7 @@ #include "col2im.h" #include "blas.h" #include "gemm.h" +#include "box.h" #include #include @@ -1171,16 +1172,6 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) } } -static box float_to_box_stride(float *f, int stride) -{ - box b = { 0 }; - b.x = f[0]; - b.y = f[1 * stride]; - b.w = f[2 * stride]; - b.h = f[3 * stride]; - return b; -} - void assisted_excitation_forward(convolutional_layer l, network_state state) { const int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); diff --git a/src/data.c b/src/data.c index 7cb7bf0a998..c0af1ab857d 100644 --- a/src/data.c +++ b/src/data.c @@ -2,6 +2,7 @@ #include "utils.h" #include "image.h" #include "dark_cuda.h" +#include "box.h" #include #include @@ -779,16 +780,6 @@ data load_data_swag(char **paths, int n, int classes, float jitter) return d; } -static box float_to_box_stride(float *f, int stride) -{ - box b = { 0 }; - b.x = f[0]; - b.y = f[1 * stride]; - b.w = f[2 * stride]; - b.h = f[3 * stride]; - return b; -} - void blend_truth(float *new_truth, int boxes, float *old_truth) { const int t_size = 4 + 1; diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c new file mode 100644 index 00000000000..320834018d9 --- /dev/null +++ b/src/gaussian_yolo_layer.c @@ -0,0 +1,445 @@ +// Gaussian YOLOv3 implementation +// Author: Jiwoong Choi +// ICCV 2019 Paper: http://openaccess.thecvf.com/content_ICCV_2019/html/Choi_Gaussian_YOLOv3_An_Accurate_and_Fast_Object_Detector_Using_Localization_ICCV_2019_paper.html +// arxiv.org: https://arxiv.org/abs/1904.04620v2 +// source code: https://github.com/jwchoi384/Gaussian_YOLOv3 + +#include "gaussian_yolo_layer.h" +#include "activations.h" +#include "blas.h" +#include "box.h" +#include "dark_cuda.h" +#include "utils.h" + +#include +#include +#include +#include + +#ifndef M_PI +#define M_PI 3.141592 +#endif + +layer make_gaussian_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes) +{ + int i; + layer l = {0}; + l.type = GAUSSIAN_YOLO; + + l.n = n; + l.total = total; + l.batch = batch; + l.h = h; + l.w = w; + l.c = n*(classes + 8 + 1); + l.out_w = l.w; + l.out_h = l.h; + l.out_c = l.c; + l.classes = classes; + l.cost = calloc(1, sizeof(float)); + l.biases = calloc(total*2, sizeof(float)); + if(mask) l.mask = mask; + else{ + l.mask = calloc(n, sizeof(int)); + for(i = 0; i < n; ++i){ + l.mask[i] = i; + } + } + l.bias_updates = calloc(n*2, sizeof(float)); + l.outputs = h*w*n*(classes + 8 + 1); + l.inputs = l.outputs; + l.truths = 90*(4 + 1); + l.delta = calloc(batch*l.outputs, sizeof(float)); + l.output = calloc(batch*l.outputs, sizeof(float)); + for(i = 0; i < total*2; ++i){ + l.biases[i] = .5; + } + + l.forward = forward_gaussian_yolo_layer; + l.backward = backward_gaussian_yolo_layer; +#ifdef GPU + l.forward_gpu = forward_gaussian_yolo_layer_gpu; + l.backward_gpu = backward_gaussian_yolo_layer_gpu; + l.output_gpu = cuda_make_array(l.output, batch*l.outputs); + l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs); +#endif + + fprintf(stderr, "Gaussian_yolo\n"); + srand(0); + + return l; +} + +void resize_gaussian_yolo_layer(layer *l, int w, int h) +{ + l->w = w; + l->h = h; + + l->outputs = h*w*l->n*(l->classes + 8 + 1); + l->inputs = l->outputs; + + l->output = realloc(l->output, l->batch*l->outputs*sizeof(float)); + l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float)); + +#ifdef GPU + cuda_free(l->delta_gpu); + cuda_free(l->output_gpu); + + l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs); + l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs); +#endif +} + +box get_gaussian_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, int stride) +{ + box b; + b.x = (i + x[index + 0*stride]) / lw; + b.y = (j + x[index + 2*stride]) / lh; + b.w = exp(x[index + 4*stride]) * biases[2*n] / w; + b.h = exp(x[index + 6*stride]) * biases[2*n+1] / h; + return b; +} + +float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride) +{ + box pred = get_gaussian_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride); + float iou = box_iou(pred, truth); + + float tx = (truth.x*lw - i); + float ty = (truth.y*lh - j); + float tw = log(truth.w*w / biases[2*n]); + float th = log(truth.h*h / biases[2*n + 1]); + + float sigma_const = 0.3; + float epsi = pow(10,-9); + + float in_exp_x = (tx - x[index + 0*stride])/x[index+1*stride]; + float in_exp_x_2 = pow(in_exp_x, 2); + float normal_dist_x = exp(in_exp_x_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+1*stride]+sigma_const)); + + float in_exp_y = (ty - x[index + 2*stride])/x[index+3*stride]; + float in_exp_y_2 = pow(in_exp_y, 2); + float normal_dist_y = exp(in_exp_y_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+3*stride]+sigma_const)); + + float in_exp_w = (tw - x[index + 4*stride])/x[index+5*stride]; + float in_exp_w_2 = pow(in_exp_w, 2); + float normal_dist_w = exp(in_exp_w_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+5*stride]+sigma_const)); + + float in_exp_h = (th - x[index + 6*stride])/x[index+7*stride]; + float in_exp_h_2 = pow(in_exp_h, 2); + float normal_dist_h = exp(in_exp_h_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+7*stride]+sigma_const)); + + float temp_x = (1./2.) * 1./(normal_dist_x+epsi) * normal_dist_x * scale; + float temp_y = (1./2.) * 1./(normal_dist_y+epsi) * normal_dist_y * scale; + float temp_w = (1./2.) * 1./(normal_dist_w+epsi) * normal_dist_w * scale; + float temp_h = (1./2.) * 1./(normal_dist_h+epsi) * normal_dist_h * scale; + + delta[index + 0*stride] = temp_x * in_exp_x * (1./x[index+1*stride]); + delta[index + 2*stride] = temp_y * in_exp_y * (1./x[index+3*stride]); + delta[index + 4*stride] = temp_w * in_exp_w * (1./x[index+5*stride]); + delta[index + 6*stride] = temp_h * in_exp_h * (1./x[index+7*stride]); + + delta[index + 1*stride] = temp_x * (in_exp_x_2/x[index+1*stride] - 1./(x[index+1*stride]+sigma_const)); + delta[index + 3*stride] = temp_y * (in_exp_y_2/x[index+3*stride] - 1./(x[index+3*stride]+sigma_const)); + delta[index + 5*stride] = temp_w * (in_exp_w_2/x[index+5*stride] - 1./(x[index+5*stride]+sigma_const)); + delta[index + 7*stride] = temp_h * (in_exp_h_2/x[index+7*stride] - 1./(x[index+7*stride]+sigma_const)); + return iou; +} + + +void delta_gaussian_yolo_class(float *output, float *delta, int index, int class, int classes, int stride, float *avg_cat) +{ + int n; + if (delta[index]){ + delta[index + stride*class] = 1 - output[index + stride*class]; + if(avg_cat) *avg_cat += output[index + stride*class]; + return; + } + for(n = 0; n < classes; ++n){ + delta[index + stride*n] = ((n == class)?1 : 0) - output[index + stride*n]; + if(n == class && avg_cat) *avg_cat += output[index + stride*n]; + } +} + +static int entry_gaussian_index(layer l, int batch, int location, int entry) +{ + int n = location / (l.w*l.h); + int loc = location % (l.w*l.h); + return batch*l.outputs + n*l.w*l.h*(8+l.classes+1) + entry*l.w*l.h + loc; +} + +void forward_gaussian_yolo_layer(const layer l, network net) +{ + int i,j,b,t,n; + memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float)); + +#ifndef GPU + for (b = 0; b < l.batch; ++b){ + for(n = 0; n < l.n; ++n){ + // x : mu, sigma + int index = entry_gaussian_index(l, b, n*l.w*l.h, 0); + activate_array(l.output + index, 2*l.w*l.h, LOGISTIC); + // y : mu, sigma + index = entry_gaussian_index(l, b, n*l.w*l.h, 2); + activate_array(l.output + index, 2*l.w*l.h, LOGISTIC); + // w : sigma + index = entry_gaussian_index(l, b, n*l.w*l.h, 5); + activate_array(l.output + index, l.w*l.h, LOGISTIC); + // h : sigma + index = entry_gaussian_index(l, b, n*l.w*l.h, 7); + activate_array(l.output + index, l.w*l.h, LOGISTIC); + // objectness & class + index = entry_gaussian_index(l, b, n*l.w*l.h, 8); + activate_array(l.output + index, (1+l.classes)*l.w*l.h, LOGISTIC); + } + } +#endif + + memset(l.delta, 0, l.outputs * l.batch * sizeof(float)); + if(!net.train) return; + float avg_iou = 0; + float recall = 0; + float recall75 = 0; + float avg_cat = 0; + float avg_obj = 0; + float avg_anyobj = 0; + int count = 0; + int class_count = 0; + *(l.cost) = 0; + for (b = 0; b < l.batch; ++b) { + for (j = 0; j < l.h; ++j) { + for (i = 0; i < l.w; ++i) { + for (n = 0; n < l.n; ++n) { + int box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0); + box pred = get_gaussian_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, net.w, net.h, l.w*l.h); + float best_iou = 0; + int best_t = 0; + for(t = 0; t < l.max_boxes; ++t){ + box truth = float_to_box_stride(net.truth + t*(4 + 1) + b*l.truths, 1); + if(!truth.x) break; + float iou = box_iou(pred, truth); + if (iou > best_iou) { + best_iou = iou; + best_t = t; + } + } + int obj_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 8); + avg_anyobj += l.output[obj_index]; + l.delta[obj_index] = 0 - l.output[obj_index]; + if (best_iou > l.ignore_thresh) { + l.delta[obj_index] = 0; + } + if (best_iou > l.truth_thresh) { + l.delta[obj_index] = 1 - l.output[obj_index]; + + int class = net.truth[best_t*(4 + 1) + b*l.truths + 4]; + if (l.map) class = l.map[class]; + int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9); + delta_gaussian_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, 0); + box truth = float_to_box_stride(net.truth + best_t*(4 + 1) + b*l.truths, 1); + delta_gaussian_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, net.w, net.h, l.delta, (2-truth.w*truth.h), l.w*l.h); + } + } + } + } + for(t = 0; t < l.max_boxes; ++t){ + box truth = float_to_box_stride(net.truth + t*(4 + 1) + b*l.truths, 1); + + if(!truth.x) break; + float best_iou = 0; + int best_n = 0; + i = (truth.x * l.w); + j = (truth.y * l.h); + box truth_shift = truth; + truth_shift.x = truth_shift.y = 0; + for(n = 0; n < l.total; ++n){ + box pred = {0}; + pred.w = l.biases[2*n]/net.w; + pred.h = l.biases[2*n+1]/net.h; + float iou = box_iou(pred, truth_shift); + if (iou > best_iou){ + best_iou = iou; + best_n = n; + } + } + + int mask_n = int_index(l.mask, best_n, l.n); + if(mask_n >= 0){ + int box_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); + float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, net.w, net.h, l.delta, (2-truth.w*truth.h), l.w*l.h); + + int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8); + avg_obj += l.output[obj_index]; + l.delta[obj_index] = 1 - l.output[obj_index]; + + int class = net.truth[t*(4 + 1) + b*l.truths + 4]; + if (l.map) class = l.map[class]; + int class_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 9); + delta_gaussian_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, &avg_cat); + + ++count; + ++class_count; + if(iou > .5) recall += 1; + if(iou > .75) recall75 += 1; + avg_iou += iou; + } + } + } + *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); + printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d\n", net.index, avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, recall75/count, count); +} + +void backward_gaussian_yolo_layer(const layer l, network net) +{ + axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, net.delta, 1); +} + +void correct_gaussian_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative) +{ + int i; + int new_w=0; + int new_h=0; + if (((float)netw/w) < ((float)neth/h)) { + new_w = netw; + new_h = (h * netw)/w; + } else { + new_h = neth; + new_w = (w * neth)/h; + } + for (i = 0; i < n; ++i){ + box b = dets[i].bbox; + b.x = (b.x - (netw - new_w)/2./netw) / ((float)new_w/netw); + b.y = (b.y - (neth - new_h)/2./neth) / ((float)new_h/neth); + b.w *= (float)netw/new_w; + b.h *= (float)neth/new_h; + if(!relative){ + b.x *= w; + b.w *= w; + b.y *= h; + b.h *= h; + } + dets[i].bbox = b; + } +} + +int gaussian_yolo_num_detections(layer l, float thresh) +{ + int i, n; + int count = 0; + for (i = 0; i < l.w*l.h; ++i){ + for(n = 0; n < l.n; ++n){ + int obj_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 8); + if(l.output[obj_index] > thresh){ + ++count; + } + } + } + return count; +} + +/* +void avg_flipped_gaussian_yolo(layer l) +{ + int i,j,n,z; + float *flip = l.output + l.outputs; + for (j = 0; j < l.h; ++j) { + for (i = 0; i < l.w/2; ++i) { + for (n = 0; n < l.n; ++n) { + for(z = 0; z < l.classes + 8 + 1; ++z){ + int i1 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + i; + int i2 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + (l.w - i - 1); + float swap = flip[i1]; + flip[i1] = flip[i2]; + flip[i2] = swap; + if(z == 0){ + flip[i1] = -flip[i1]; + flip[i2] = -flip[i2]; + } + } + } + } + } + for(i = 0; i < l.outputs; ++i){ + l.output[i] = (l.output[i] + flip[i])/2.; + } +} +*/ + +int get_gaussian_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets) +{ + int i,j,n; + float *predictions = l.output; + //if (l.batch == 2) avg_flipped_gaussian_yolo(l); + int count = 0; + for (i = 0; i < l.w*l.h; ++i){ + int row = i / l.w; + int col = i % l.w; + for(n = 0; n < l.n; ++n){ + int obj_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 8); + float objectness = predictions[obj_index]; + if(objectness <= thresh) continue; + int box_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 0); + dets[count].bbox = get_gaussian_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h); + dets[count].objectness = objectness; + dets[count].classes = l.classes; + + dets[count].uc[0] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 1)]; // tx uncertainty + dets[count].uc[1] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 3)]; // ty uncertainty + dets[count].uc[2] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 5)]; // tw uncertainty + dets[count].uc[3] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 7)]; // th uncertainty + + for(j = 0; j < l.classes; ++j){ + int class_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 9 + j); + float uc_aver = (dets[count].uc[0] + dets[count].uc[1] + dets[count].uc[2] + dets[count].uc[3])/4.0; + float prob = objectness*predictions[class_index]*(1.0-uc_aver); + dets[count].prob[j] = (prob > thresh) ? prob : 0; + } + ++count; + } + } + correct_gaussian_yolo_boxes(dets, count, w, h, netw, neth, relative); + return count; +} + +#ifdef GPU + +void forward_gaussian_yolo_layer_gpu(const layer l, network net) +{ + copy_ongpu(l.batch*l.inputs, net.input_gpu, 1, l.output_gpu, 1); + int b, n; + for (b = 0; b < l.batch; ++b) + { + for(n = 0; n < l.n; ++n) + { + // x : mu, sigma + int index = entry_gaussian_index(l, b, n*l.w*l.h, 0); + activate_array_ongpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC); + // y : mu, sigma + index = entry_gaussian_index(l, b, n*l.w*l.h, 2); + activate_array_ongpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC); + // w : sigma + index = entry_gaussian_index(l, b, n*l.w*l.h, 5); + activate_array_ongpu(l.output_gpu + index, l.w*l.h, LOGISTIC); + // h : sigma + index = entry_gaussian_index(l, b, n*l.w*l.h, 7); + activate_array_ongpu(l.output_gpu + index, l.w*l.h, LOGISTIC); + // objectness & class + index = entry_gaussian_index(l, b, n*l.w*l.h, 8); + activate_array_ongpu(l.output_gpu + index, (1+l.classes)*l.w*l.h, LOGISTIC); + } + } + if(!net.train || l.onlyforward){ + cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); + return; + } + + cuda_pull_array(l.output_gpu, net.input, l.batch*l.inputs); + forward_gaussian_yolo_layer(l, net); + cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs); +} + +void backward_gaussian_yolo_layer_gpu(const layer l, network net) +{ + axpy_ongpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1); +} +#endif diff --git a/src/gaussian_yolo_layer.h b/src/gaussian_yolo_layer.h new file mode 100644 index 00000000000..96cb2a8f4a6 --- /dev/null +++ b/src/gaussian_yolo_layer.h @@ -0,0 +1,20 @@ +//Gaussian YOLOv3 implementation +#ifndef GAUSSIAN_YOLO_LAYER_H +#define GAUSSIAN_YOLO_LAYER_H + +#include "darknet.h" +#include "layer.h" +#include "network.h" + +layer make_gaussian_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes); +void forward_gaussian_yolo_layer(const layer l, network net); +void backward_gaussian_yolo_layer(const layer l, network net); +void resize_gaussian_yolo_layer(layer *l, int w, int h); +int gaussian_yolo_num_detections(layer l, float thresh); + +#ifdef GPU +void forward_gaussian_yolo_layer_gpu(const layer l, network net); +void backward_gaussian_yolo_layer_gpu(layer l, network net); +#endif + +#endif diff --git a/src/network.c b/src/network.c index 82dc4d53978..0658788044c 100644 --- a/src/network.c +++ b/src/network.c @@ -34,6 +34,7 @@ #include "shortcut_layer.h" #include "scale_channels_layer.h" #include "yolo_layer.h" +#include "gaussian_yolo_layer.h" #include "upsample_layer.h" #include "parser.h" @@ -202,6 +203,10 @@ char *get_layer_string(LAYER_TYPE a) return "detection"; case REGION: return "region"; + case YOLO: + return "yolo"; + case GAUSSIAN_YOLO: + return "Gaussian_yolo"; case DROPOUT: return "dropout"; case CROP: @@ -524,6 +529,8 @@ int resize_network(network *net, int w, int h) resize_region_layer(&l, w, h); }else if (l.type == YOLO) { resize_yolo_layer(&l, w, h); + }else if (l.type == GAUSSIAN_YOLO) { + resize_gaussian_yolo_layer(&l, w, h); }else if(l.type == ROUTE){ resize_route_layer(&l, net); }else if (l.type == SHORTCUT) { @@ -687,6 +694,9 @@ int num_detections(network *net, float thresh) if (l.type == YOLO) { s += yolo_num_detections(l, thresh); } + if (l.type == GAUSSIAN_YOLO) { + s += gaussian_yolo_num_detections(l, thresh); + } if (l.type == DETECTION || l.type == REGION) { s += l.w*l.h*l.n; } @@ -703,6 +713,8 @@ detection *make_network_boxes(network *net, float thresh, int *num) detection* dets = (detection*)calloc(nboxes, sizeof(detection)); for (i = 0; i < nboxes; ++i) { dets[i].prob = (float*)calloc(l.classes, sizeof(float)); + // tx,ty,tw,th uncertainty + dets[i].uc = calloc(4, sizeof(float)); // Gaussian_YOLOv3 if (l.coords > 4) { dets[i].mask = (float*)calloc(l.coords - 4, sizeof(float)); } @@ -749,6 +761,10 @@ void fill_network_boxes(network *net, int w, int h, float thresh, float hier, in prev_classes, l.classes); } } + if (l.type == GAUSSIAN_YOLO) { + int count = get_gaussian_yolo_detections(l, w, h, net->w, net->h, thresh, map, relative, dets); + dets += count; + } if (l.type == REGION) { custom_get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets, letter); //get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets); diff --git a/src/parser.c b/src/parser.c index 829134d1131..b31c7673102 100644 --- a/src/parser.c +++ b/src/parser.c @@ -38,6 +38,7 @@ #include "upsample_layer.h" #include "version.h" #include "yolo_layer.h" +#include "gaussian_yolo_layer.h" typedef struct{ char *type; @@ -57,6 +58,7 @@ LAYER_TYPE string_to_layer_type(char * type) if (strcmp(type, "[detection]")==0) return DETECTION; if (strcmp(type, "[region]")==0) return REGION; if (strcmp(type, "[yolo]") == 0) return YOLO; + if (strcmp(type, "[Gaussian_yolo]") == 0) return GAUSSIAN_YOLO; if (strcmp(type, "[local]")==0) return LOCAL; if (strcmp(type, "[conv]")==0 || strcmp(type, "[convolutional]")==0) return CONVOLUTIONAL; @@ -390,6 +392,67 @@ layer parse_yolo(list *options, size_params params) return l; } + +int *parse_gaussian_yolo_mask(char *a, int *num) // Gaussian_YOLOv3 +{ + int *mask = 0; + if (a) { + int len = strlen(a); + int n = 1; + int i; + for (i = 0; i < len; ++i) { + if (a[i] == ',') ++n; + } + mask = calloc(n, sizeof(int)); + for (i = 0; i < n; ++i) { + int val = atoi(a); + mask[i] = val; + a = strchr(a, ',') + 1; + } + *num = n; + } + return mask; +} + + +layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3 +{ + int classes = option_find_int(options, "classes", 20); + int total = option_find_int(options, "num", 1); + int num = total; + + char *a = option_find_str(options, "mask", 0); + int *mask = parse_gaussian_yolo_mask(a, &num); + layer l = make_gaussian_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes); + assert(l.outputs == params.inputs); + + l.max_boxes = option_find_int_quiet(options, "max", 90); + l.jitter = option_find_float(options, "jitter", .2); + + l.ignore_thresh = option_find_float(options, "ignore_thresh", .5); + l.truth_thresh = option_find_float(options, "truth_thresh", 1); + l.random = option_find_int_quiet(options, "random", 0); + + char *map_file = option_find_str(options, "map", 0); + if (map_file) l.map = read_map(map_file); + + a = option_find_str(options, "anchors", 0); + if (a) { + int len = strlen(a); + int n = 1; + int i; + for (i = 0; i < len; ++i) { + if (a[i] == ',') ++n; + } + for (i = 0; i < n; ++i) { + float bias = atof(a); + l.biases[i] = bias; + a = strchr(a, ',') + 1; + } + } + return l; +} + layer parse_region(list *options, size_params params) { int coords = option_find_int(options, "coords", 4); @@ -923,6 +986,8 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) l = parse_region(options, params); }else if (lt == YOLO) { l = parse_yolo(options, params); + }else if (lt == GAUSSIAN_YOLO) { + l = parse_gaussian_yolo(options, params); }else if(lt == DETECTION){ l = parse_detection(options, params); }else if(lt == SOFTMAX){ diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 2006f4b8047..424811df6e6 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -242,16 +242,6 @@ static int entry_index(layer l, int batch, int location, int entry) return batch*l.outputs + n*l.w*l.h*(4+l.classes+1) + entry*l.w*l.h + loc; } -static box float_to_box_stride(float *f, int stride) -{ - box b = { 0 }; - b.x = f[0]; - b.y = f[1 * stride]; - b.w = f[2 * stride]; - b.h = f[3 * stride]; - return b; -} - void forward_yolo_layer(const layer l, network_state state) { int i, j, b, t, n; From 24788b806175a9cfbbeb0e2057f0673cd0d3e657 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 25 Oct 2019 20:56:58 +0300 Subject: [PATCH 0627/1365] Compile fix --- src/image_opencv.cpp | 9 --------- 1 file changed, 9 deletions(-) diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index cc50c0719a1..d4a69e6f197 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1125,15 +1125,6 @@ void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_im // ==================================================================== // Data augmentation // ==================================================================== -static box float_to_box_stride(float *f, int stride) -{ - box b = { 0 }; - b.x = f[0]; - b.y = f[1 * stride]; - b.w = f[2 * stride]; - b.h = f[3 * stride]; - return b; -} image image_data_augmentation(mat_cv* mat, int w, int h, int pleft, int ptop, int swidth, int sheight, int flip, From 72f6de30b2a75fd67436cff4638b8fa36e3fb205 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 25 Oct 2019 22:14:27 +0300 Subject: [PATCH 0628/1365] another compile fix --- src/gaussian_yolo_layer.c | 2 +- src/parser.c | 12 +++++++++--- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index 320834018d9..74604e52346 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -23,7 +23,7 @@ layer make_gaussian_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes) { int i; - layer l = {0}; + layer l = { (LAYER_TYPE)0 }; l.type = GAUSSIAN_YOLO; l.n = n; diff --git a/src/parser.c b/src/parser.c index b31c7673102..445771ca4aa 100644 --- a/src/parser.c +++ b/src/parser.c @@ -159,9 +159,15 @@ convolutional_layer parse_convolutional(list *options, size_params params, netwo int n = option_find_int(options, "filters",1); int groups = option_find_int_quiet(options, "groups", 1); int size = option_find_int(options, "size",1); - int stride = option_find_int(options, "stride",1); - int stride_x = option_find_int_quiet(options, "stride_x", stride); - int stride_y = option_find_int_quiet(options, "stride_y", stride); + int stride = -1; + //int stride = option_find_int(options, "stride",1); + int stride_x = option_find_int_quiet(options, "stride_x", -1); + int stride_y = option_find_int_quiet(options, "stride_y", -1); + if (stride_x < 1 || stride_y < 1) { + stride = option_find_int(options, "stride", 1); + if (stride_x < 1) stride_x = stride; + if (stride_y < 1) stride_y = stride; + } int dilation = option_find_int_quiet(options, "dilation", 1); int antialiasing = option_find_int_quiet(options, "antialiasing", 0); if (size == 1) dilation = 1; From f18338de2667a402cd78e61f50423f4953cd32cc Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 26 Oct 2019 01:29:41 +0300 Subject: [PATCH 0629/1365] Fixed [Gaussian_yolo] layer (tested for training and detection) --- src/gaussian_yolo_layer.c | 154 ++++++++++++++++++++++++++++---------- src/gaussian_yolo_layer.h | 12 +-- src/network.c | 5 +- src/parser.c | 3 +- 4 files changed, 127 insertions(+), 47 deletions(-) diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index 74604e52346..ddb43939ce9 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -20,7 +20,7 @@ #define M_PI 3.141592 #endif -layer make_gaussian_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes) +layer make_gaussian_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes, int max_boxes) { int i; layer l = { (LAYER_TYPE)0 }; @@ -36,21 +36,22 @@ layer make_gaussian_yolo_layer(int batch, int w, int h, int n, int total, int *m l.out_h = l.h; l.out_c = l.c; l.classes = classes; - l.cost = calloc(1, sizeof(float)); - l.biases = calloc(total*2, sizeof(float)); + l.cost = (float*)calloc(1, sizeof(float)); + l.biases = (float*)calloc(total*2, sizeof(float)); if(mask) l.mask = mask; else{ - l.mask = calloc(n, sizeof(int)); + l.mask = (int*)calloc(n, sizeof(int)); for(i = 0; i < n; ++i){ l.mask[i] = i; } } - l.bias_updates = calloc(n*2, sizeof(float)); + l.bias_updates = (float*)calloc(n*2, sizeof(float)); l.outputs = h*w*n*(classes + 8 + 1); l.inputs = l.outputs; - l.truths = 90*(4 + 1); - l.delta = calloc(batch*l.outputs, sizeof(float)); - l.output = calloc(batch*l.outputs, sizeof(float)); + l.max_boxes = max_boxes; + l.truths = l.max_boxes*(4 + 1); + l.delta = (float*)calloc(batch*l.outputs, sizeof(float)); + l.output = (float*)calloc(batch*l.outputs, sizeof(float)); for(i = 0; i < total*2; ++i){ l.biases[i] = .5; } @@ -62,10 +63,26 @@ layer make_gaussian_yolo_layer(int batch, int w, int h, int n, int total, int *m l.backward_gpu = backward_gaussian_yolo_layer_gpu; l.output_gpu = cuda_make_array(l.output, batch*l.outputs); l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs); + + /* + free(l.output); + if (cudaSuccess == cudaHostAlloc(&l.output, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1; + else { + cudaGetLastError(); // reset CUDA-error + l.output = (float*)calloc(batch * l.outputs, sizeof(float)); + } + + free(l.delta); + if (cudaSuccess == cudaHostAlloc(&l.delta, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1; + else { + cudaGetLastError(); // reset CUDA-error + l.delta = (float*)calloc(batch * l.outputs, sizeof(float)); + } + */ #endif fprintf(stderr, "Gaussian_yolo\n"); - srand(0); + srand(time(0)); return l; } @@ -78,10 +95,33 @@ void resize_gaussian_yolo_layer(layer *l, int w, int h) l->outputs = h*w*l->n*(l->classes + 8 + 1); l->inputs = l->outputs; - l->output = realloc(l->output, l->batch*l->outputs*sizeof(float)); - l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float)); + l->output = realloc(l->output, l->batch*l->outputs * sizeof(float)); + l->delta = realloc(l->delta, l->batch*l->outputs * sizeof(float)); + + //if (!l->output_pinned) l->output = (float*)realloc(l->output, l->batch*l->outputs * sizeof(float)); + //if (!l->delta_pinned) l->delta = (float*)realloc(l->delta, l->batch*l->outputs * sizeof(float)); #ifdef GPU + /* + if (l->output_pinned) { + cudaFreeHost(l->output); + if (cudaSuccess != cudaHostAlloc(&l->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) { + cudaGetLastError(); // reset CUDA-error + l->output = (float*)realloc(l->output, l->batch * l->outputs * sizeof(float)); + l->output_pinned = 0; + } + } + + if (l->delta_pinned) { + cudaFreeHost(l->delta); + if (cudaSuccess != cudaHostAlloc(&l->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) { + cudaGetLastError(); // reset CUDA-error + l->delta = (float*)realloc(l->delta, l->batch * l->outputs * sizeof(float)); + l->delta_pinned = 0; + } + } + */ + cuda_free(l->delta_gpu); cuda_free(l->output_gpu); @@ -168,10 +208,10 @@ static int entry_gaussian_index(layer l, int batch, int location, int entry) return batch*l.outputs + n*l.w*l.h*(8+l.classes+1) + entry*l.w*l.h + loc; } -void forward_gaussian_yolo_layer(const layer l, network net) +void forward_gaussian_yolo_layer(const layer l, network_state state) { int i,j,b,t,n; - memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float)); + memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float)); #ifndef GPU for (b = 0; b < l.batch; ++b){ @@ -196,7 +236,7 @@ void forward_gaussian_yolo_layer(const layer l, network net) #endif memset(l.delta, 0, l.outputs * l.batch * sizeof(float)); - if(!net.train) return; + if (!state.train) return; float avg_iou = 0; float recall = 0; float recall75 = 0; @@ -211,11 +251,11 @@ void forward_gaussian_yolo_layer(const layer l, network net) for (i = 0; i < l.w; ++i) { for (n = 0; n < l.n; ++n) { int box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0); - box pred = get_gaussian_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, net.w, net.h, l.w*l.h); + box pred = get_gaussian_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w*l.h); float best_iou = 0; int best_t = 0; for(t = 0; t < l.max_boxes; ++t){ - box truth = float_to_box_stride(net.truth + t*(4 + 1) + b*l.truths, 1); + box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); if(!truth.x) break; float iou = box_iou(pred, truth); if (iou > best_iou) { @@ -232,18 +272,18 @@ void forward_gaussian_yolo_layer(const layer l, network net) if (best_iou > l.truth_thresh) { l.delta[obj_index] = 1 - l.output[obj_index]; - int class = net.truth[best_t*(4 + 1) + b*l.truths + 4]; + int class = state.truth[best_t*(4 + 1) + b*l.truths + 4]; if (l.map) class = l.map[class]; int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9); delta_gaussian_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, 0); - box truth = float_to_box_stride(net.truth + best_t*(4 + 1) + b*l.truths, 1); - delta_gaussian_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, net.w, net.h, l.delta, (2-truth.w*truth.h), l.w*l.h); + box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); + delta_gaussian_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h); } } } } for(t = 0; t < l.max_boxes; ++t){ - box truth = float_to_box_stride(net.truth + t*(4 + 1) + b*l.truths, 1); + box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); if(!truth.x) break; float best_iou = 0; @@ -254,8 +294,8 @@ void forward_gaussian_yolo_layer(const layer l, network net) truth_shift.x = truth_shift.y = 0; for(n = 0; n < l.total; ++n){ box pred = {0}; - pred.w = l.biases[2*n]/net.w; - pred.h = l.biases[2*n+1]/net.h; + pred.w = l.biases[2*n]/ state.net.w; + pred.h = l.biases[2*n+1]/ state.net.h; float iou = box_iou(pred, truth_shift); if (iou > best_iou){ best_iou = iou; @@ -266,13 +306,13 @@ void forward_gaussian_yolo_layer(const layer l, network net) int mask_n = int_index(l.mask, best_n, l.n); if(mask_n >= 0){ int box_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); - float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, net.w, net.h, l.delta, (2-truth.w*truth.h), l.w*l.h); + float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h); int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8); avg_obj += l.output[obj_index]; l.delta[obj_index] = 1 - l.output[obj_index]; - int class = net.truth[t*(4 + 1) + b*l.truths + 4]; + int class = state.truth[t*(4 + 1) + b*l.truths + 4]; if (l.map) class = l.map[class]; int class_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 9); delta_gaussian_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, &avg_cat); @@ -286,19 +326,34 @@ void forward_gaussian_yolo_layer(const layer l, network net) } } *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); - printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d\n", net.index, avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, recall75/count, count); + printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d\n", state.index, avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, recall75/count, count); } -void backward_gaussian_yolo_layer(const layer l, network net) +void backward_gaussian_yolo_layer(const layer l, network_state state) { - axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, net.delta, 1); + axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1); } -void correct_gaussian_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative) +void correct_gaussian_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative, int letter) { int i; int new_w=0; int new_h=0; + if (letter) { + if (((float)netw / w) < ((float)neth / h)) { + new_w = netw; + new_h = (h * netw) / w; + } + else { + new_h = neth; + new_w = (w * neth) / h; + } + } + else { + new_w = netw; + new_h = neth; + } + /* if (((float)netw/w) < ((float)neth/h)) { new_w = netw; new_h = (h * netw)/w; @@ -306,6 +361,7 @@ void correct_gaussian_yolo_boxes(detection *dets, int n, int w, int h, int netw, new_h = neth; new_w = (w * neth)/h; } + */ for (i = 0; i < n; ++i){ box b = dets[i].bbox; b.x = (b.x - (netw - new_w)/2./netw) / ((float)new_w/netw); @@ -365,7 +421,7 @@ void avg_flipped_gaussian_yolo(layer l) } */ -int get_gaussian_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets) +int get_gaussian_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets, int letter) { int i,j,n; float *predictions = l.output; @@ -390,22 +446,22 @@ int get_gaussian_yolo_detections(layer l, int w, int h, int netw, int neth, floa for(j = 0; j < l.classes; ++j){ int class_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 9 + j); - float uc_aver = (dets[count].uc[0] + dets[count].uc[1] + dets[count].uc[2] + dets[count].uc[3])/4.0; + float uc_aver = (dets[count].uc[0] + dets[count].uc[1] + dets[count].uc[2] + dets[count].uc[3]) / 4.0; float prob = objectness*predictions[class_index]*(1.0-uc_aver); dets[count].prob[j] = (prob > thresh) ? prob : 0; } ++count; } } - correct_gaussian_yolo_boxes(dets, count, w, h, netw, neth, relative); + correct_gaussian_yolo_boxes(dets, count, w, h, netw, neth, relative, letter); return count; } #ifdef GPU -void forward_gaussian_yolo_layer_gpu(const layer l, network net) +void forward_gaussian_yolo_layer_gpu(const layer l, network_state state) { - copy_ongpu(l.batch*l.inputs, net.input_gpu, 1, l.output_gpu, 1); + copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1); int b, n; for (b = 0; b < l.batch; ++b) { @@ -428,18 +484,38 @@ void forward_gaussian_yolo_layer_gpu(const layer l, network net) activate_array_ongpu(l.output_gpu + index, (1+l.classes)*l.w*l.h, LOGISTIC); } } - if(!net.train || l.onlyforward){ - cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); + + if (!state.train || l.onlyforward) { + //cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); + cuda_pull_array_async(l.output_gpu, l.output, l.batch*l.outputs); + CHECK_CUDA(cudaPeekAtLastError()); return; } - cuda_pull_array(l.output_gpu, net.input, l.batch*l.inputs); - forward_gaussian_yolo_layer(l, net); + float *in_cpu = (float *)calloc(l.batch*l.inputs, sizeof(float)); + cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); + memcpy(in_cpu, l.output, l.batch*l.outputs * sizeof(float)); + float *truth_cpu = 0; + if (state.truth) { + int num_truth = l.batch*l.truths; + truth_cpu = (float *)calloc(num_truth, sizeof(float)); + cuda_pull_array(state.truth, truth_cpu, num_truth); + } + network_state cpu_state = state; + cpu_state.net = state.net; + cpu_state.index = state.index; + cpu_state.train = state.train; + cpu_state.truth = truth_cpu; + cpu_state.input = in_cpu; + forward_gaussian_yolo_layer(l, cpu_state); + //forward_yolo_layer(l, state); cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs); + free(in_cpu); + if (cpu_state.truth) free(cpu_state.truth); } -void backward_gaussian_yolo_layer_gpu(const layer l, network net) +void backward_gaussian_yolo_layer_gpu(const layer l, network_state state) { - axpy_ongpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1); + axpy_ongpu(l.batch*l.inputs, 1, l.delta_gpu, 1, state.delta, 1); } #endif diff --git a/src/gaussian_yolo_layer.h b/src/gaussian_yolo_layer.h index 96cb2a8f4a6..9080881dc68 100644 --- a/src/gaussian_yolo_layer.h +++ b/src/gaussian_yolo_layer.h @@ -6,15 +6,17 @@ #include "layer.h" #include "network.h" -layer make_gaussian_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes); -void forward_gaussian_yolo_layer(const layer l, network net); -void backward_gaussian_yolo_layer(const layer l, network net); +layer make_gaussian_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes, int max_boxes); +void forward_gaussian_yolo_layer(const layer l, network_state state); +void backward_gaussian_yolo_layer(const layer l, network_state state); void resize_gaussian_yolo_layer(layer *l, int w, int h); int gaussian_yolo_num_detections(layer l, float thresh); +int get_gaussian_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets, int letter); +void correct_gaussian_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative, int letter); #ifdef GPU -void forward_gaussian_yolo_layer_gpu(const layer l, network net); -void backward_gaussian_yolo_layer_gpu(layer l, network net); +void forward_gaussian_yolo_layer_gpu(const layer l, network_state state); +void backward_gaussian_yolo_layer_gpu(layer l, network_state state); #endif #endif diff --git a/src/network.c b/src/network.c index 0658788044c..cfe994343e8 100644 --- a/src/network.c +++ b/src/network.c @@ -714,7 +714,7 @@ detection *make_network_boxes(network *net, float thresh, int *num) for (i = 0; i < nboxes; ++i) { dets[i].prob = (float*)calloc(l.classes, sizeof(float)); // tx,ty,tw,th uncertainty - dets[i].uc = calloc(4, sizeof(float)); // Gaussian_YOLOv3 + dets[i].uc = (float*)calloc(4, sizeof(float)); // Gaussian_YOLOv3 if (l.coords > 4) { dets[i].mask = (float*)calloc(l.coords - 4, sizeof(float)); } @@ -762,7 +762,7 @@ void fill_network_boxes(network *net, int w, int h, float thresh, float hier, in } } if (l.type == GAUSSIAN_YOLO) { - int count = get_gaussian_yolo_detections(l, w, h, net->w, net->h, thresh, map, relative, dets); + int count = get_gaussian_yolo_detections(l, w, h, net->w, net->h, thresh, map, relative, dets, letter); dets += count; } if (l.type == REGION) { @@ -789,6 +789,7 @@ void free_detections(detection *dets, int n) int i; for (i = 0; i < n; ++i) { free(dets[i].prob); + if (dets[i].uc) free(dets[i].uc); if (dets[i].mask) free(dets[i].mask); } free(dets); diff --git a/src/parser.c b/src/parser.c index 445771ca4aa..1367d35b06a 100644 --- a/src/parser.c +++ b/src/parser.c @@ -424,12 +424,13 @@ int *parse_gaussian_yolo_mask(char *a, int *num) // Gaussian_YOLOv3 layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3 { int classes = option_find_int(options, "classes", 20); + int max_boxes = option_find_int_quiet(options, "max", 90); int total = option_find_int(options, "num", 1); int num = total; char *a = option_find_str(options, "mask", 0); int *mask = parse_gaussian_yolo_mask(a, &num); - layer l = make_gaussian_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes); + layer l = make_gaussian_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes, max_boxes); assert(l.outputs == params.inputs); l.max_boxes = option_find_int_quiet(options, "max", 90); From 29c71a190acb82aa4beda8762e087b658f4b0347 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 26 Oct 2019 01:30:11 +0300 Subject: [PATCH 0630/1365] Fixed antialiasing=1 for [convolutional] layer --- src/convolutional_kernels.cu | 4 +++- src/maxpool_layer_kernels.cu | 4 +++- src/yolo_layer.c | 3 ++- 3 files changed, 8 insertions(+), 3 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 23005ccb91e..cb5a1b99bae 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -628,11 +628,13 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state s.train = state.train; s.workspace = state.workspace; s.net = state.net; - s.delta = l.delta_gpu; + s.delta = l.delta_gpu; // s.delta will be returned to l.delta_gpu s.input = l.input_antialiasing_gpu; //if (!state.train) s.index = state.index; // don't use TC for training (especially without cuda_convert_f32_to_f16() ) simple_copy_ongpu(l.input_layer->outputs*l.input_layer->batch, l.delta_gpu, l.input_layer->delta_gpu); backward_convolutional_layer_gpu(*(l.input_layer), s); + + simple_copy_ongpu(l.outputs*l.batch, l.input_antialiasing_gpu, l.output_gpu); } if(state.net.try_fix_nan) constrain_ongpu(l.outputs*l.batch, 1, l.delta_gpu, 1); diff --git a/src/maxpool_layer_kernels.cu b/src/maxpool_layer_kernels.cu index cc546a0b50c..8677b4d713d 100644 --- a/src/maxpool_layer_kernels.cu +++ b/src/maxpool_layer_kernels.cu @@ -199,11 +199,13 @@ extern "C" void backward_maxpool_layer_gpu(maxpool_layer layer, network_state st s.train = state.train; s.workspace = state.workspace; s.net = state.net; - s.delta = layer.delta_gpu; + s.delta = layer.delta_gpu; // s.delta will be returned to l.delta_gpu s.input = layer.input_antialiasing_gpu; //if (!state.train) s.index = state.index; // don't use TC for training (especially without cuda_convert_f32_to_f16() ) simple_copy_ongpu(layer.input_layer->outputs*layer.input_layer->batch, layer.delta_gpu, layer.input_layer->delta_gpu); backward_convolutional_layer_gpu(*(layer.input_layer), s); + + //simple_copy_ongpu(layer.outputs*layer.batch, layer.input_antialiasing_gpu, layer.output_gpu); } if (layer.maxpool_depth) { diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 424811df6e6..906ed427f01 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -423,7 +423,8 @@ void forward_yolo_layer(const layer l, network_state state) } *(l.cost) = avg_iou_loss + classification_loss; } - printf("v3 (%s loss, Normalizer: (iou: %f, cls: %f) Region %d Avg (IOU: %f, GIOU: %f), Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d\n", (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.cls_normalizer, state.index, tot_iou / count, tot_giou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count); + printf("v3 (%s loss, Normalizer: (iou: %f, cls: %f) Region %d Avg (IOU: %f, GIOU: %f), Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d\n", + (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.cls_normalizer, state.index, tot_iou / count, tot_giou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count); } void backward_yolo_layer(const layer l, network_state state) From fa7687e6b5936532ce35a607509fa5736e8f3c0d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 26 Oct 2019 01:40:59 +0300 Subject: [PATCH 0631/1365] cfg-param blur > 1 is fixed (bilateral filter removes textures) --- src/data.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/data.c b/src/data.c index c0af1ab857d..6c9e565db89 100644 --- a/src/data.c +++ b/src/data.c @@ -922,7 +922,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo int min_w_h = fill_truth_detection(filename, boxes, truth, classes, flip, dx, dy, 1. / sx, 1. / sy, w, h); - if (min_w_h < blur*4) blur = 0; // disable blur if one of the objects is too small + if (min_w_h/4 < blur) blur = min_w_h / 4; // disable blur if one of the objects is too small image ai = image_data_augmentation(src, w, h, pleft, ptop, swidth, sheight, flip, dhue, dsat, dexp, blur, boxes, d.y.vals[i]); From f8c72acd42d73d0ee200d92b3d605f50e0928f5a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 26 Oct 2019 14:47:00 +0300 Subject: [PATCH 0632/1365] Added scale_x_y to [Gaussian_yolo]. Fixed blur=10; --- src/data.c | 8 +++-- src/gaussian_yolo_layer.c | 67 +++++++++++++++++++++------------------ src/image_opencv.cpp | 5 ++- src/parser.c | 1 + 4 files changed, 48 insertions(+), 33 deletions(-) diff --git a/src/data.c b/src/data.c index 6c9e565db89..1b9117d93cf 100644 --- a/src/data.c +++ b/src/data.c @@ -875,7 +875,11 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo dexp = rand_scale(exposure); flip = use_flip ? random_gen() % 2 : 0; - blur = rand_int(0, 1) ? (use_blur) : 0; + + //blur = rand_int(0, 1) ? (use_blur) : 0; + int tmp_blur = rand_int(0, 2); // 0 - disable, 1 - blur background, 2 - blur the whole image + if (tmp_blur == 2) blur = use_blur; + else blur = tmp_blur; } int pleft = rand_precalc_random(-dw, dw, r1); @@ -922,7 +926,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo int min_w_h = fill_truth_detection(filename, boxes, truth, classes, flip, dx, dy, 1. / sx, 1. / sy, w, h); - if (min_w_h/4 < blur) blur = min_w_h / 4; // disable blur if one of the objects is too small + if (min_w_h/4 < blur && blur > 1) blur = min_w_h / 4; // disable blur if one of the objects is too small image ai = image_data_augmentation(src, w, h, pleft, ptop, swidth, sheight, flip, dhue, dsat, dexp, blur, boxes, d.y.vals[i]); diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index ddb43939ce9..3b58cc5a404 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -95,8 +95,8 @@ void resize_gaussian_yolo_layer(layer *l, int w, int h) l->outputs = h*w*l->n*(l->classes + 8 + 1); l->inputs = l->outputs; - l->output = realloc(l->output, l->batch*l->outputs * sizeof(float)); - l->delta = realloc(l->delta, l->batch*l->outputs * sizeof(float)); + l->output = (float *)realloc(l->output, l->batch*l->outputs * sizeof(float)); + l->delta = (float *)realloc(l->delta, l->batch*l->outputs * sizeof(float)); //if (!l->output_pinned) l->output = (float*)realloc(l->output, l->batch*l->outputs * sizeof(float)); //if (!l->delta_pinned) l->delta = (float*)realloc(l->delta, l->batch*l->outputs * sizeof(float)); @@ -187,17 +187,17 @@ float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int ind } -void delta_gaussian_yolo_class(float *output, float *delta, int index, int class, int classes, int stride, float *avg_cat) +void delta_gaussian_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat) { int n; if (delta[index]){ - delta[index + stride*class] = 1 - output[index + stride*class]; - if(avg_cat) *avg_cat += output[index + stride*class]; + delta[index + stride*class_id] = 1 - output[index + stride*class_id]; + if(avg_cat) *avg_cat += output[index + stride*class_id]; return; } for(n = 0; n < classes; ++n){ - delta[index + stride*n] = ((n == class)?1 : 0) - output[index + stride*n]; - if(n == class && avg_cat) *avg_cat += output[index + stride*n]; + delta[index + stride*n] = ((n == class_id)?1 : 0) - output[index + stride*n]; + if(n == class_id && avg_cat) *avg_cat += output[index + stride*n]; } } @@ -219,9 +219,11 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) // x : mu, sigma int index = entry_gaussian_index(l, b, n*l.w*l.h, 0); activate_array(l.output + index, 2*l.w*l.h, LOGISTIC); + scal_add_cpu(l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale x // y : mu, sigma index = entry_gaussian_index(l, b, n*l.w*l.h, 2); activate_array(l.output + index, 2*l.w*l.h, LOGISTIC); + scal_add_cpu(l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale y // w : sigma index = entry_gaussian_index(l, b, n*l.w*l.h, 5); activate_array(l.output + index, l.w*l.h, LOGISTIC); @@ -272,10 +274,10 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) if (best_iou > l.truth_thresh) { l.delta[obj_index] = 1 - l.output[obj_index]; - int class = state.truth[best_t*(4 + 1) + b*l.truths + 4]; - if (l.map) class = l.map[class]; + int class_id = state.truth[best_t*(4 + 1) + b*l.truths + 4]; + if (l.map) class_id = l.map[class_id]; int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9); - delta_gaussian_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, 0); + delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0); box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); delta_gaussian_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h); } @@ -312,10 +314,10 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) avg_obj += l.output[obj_index]; l.delta[obj_index] = 1 - l.output[obj_index]; - int class = state.truth[t*(4 + 1) + b*l.truths + 4]; - if (l.map) class = l.map[class]; + int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; + if (l.map) class_id = l.map[class_id]; int class_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 9); - delta_gaussian_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, &avg_cat); + delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat); ++count; ++class_count; @@ -433,24 +435,27 @@ int get_gaussian_yolo_detections(layer l, int w, int h, int netw, int neth, floa for(n = 0; n < l.n; ++n){ int obj_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 8); float objectness = predictions[obj_index]; - if(objectness <= thresh) continue; - int box_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 0); - dets[count].bbox = get_gaussian_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h); - dets[count].objectness = objectness; - dets[count].classes = l.classes; - - dets[count].uc[0] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 1)]; // tx uncertainty - dets[count].uc[1] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 3)]; // ty uncertainty - dets[count].uc[2] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 5)]; // tw uncertainty - dets[count].uc[3] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 7)]; // th uncertainty - - for(j = 0; j < l.classes; ++j){ - int class_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 9 + j); - float uc_aver = (dets[count].uc[0] + dets[count].uc[1] + dets[count].uc[2] + dets[count].uc[3]) / 4.0; - float prob = objectness*predictions[class_index]*(1.0-uc_aver); - dets[count].prob[j] = (prob > thresh) ? prob : 0; + if (objectness <= thresh) continue; // incorrect behavior for Nan values + + if (objectness > thresh) { + int box_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 0); + dets[count].bbox = get_gaussian_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h); + dets[count].objectness = objectness; + dets[count].classes = l.classes; + + dets[count].uc[0] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 1)]; // tx uncertainty + dets[count].uc[1] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 3)]; // ty uncertainty + dets[count].uc[2] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 5)]; // tw uncertainty + dets[count].uc[3] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 7)]; // th uncertainty + + for (j = 0; j < l.classes; ++j) { + int class_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 9 + j); + float uc_aver = (dets[count].uc[0] + dets[count].uc[1] + dets[count].uc[2] + dets[count].uc[3]) / 4.0; + float prob = objectness*predictions[class_index] * (1.0 - uc_aver); + dets[count].prob[j] = (prob > thresh) ? prob : 0; + } + ++count; } - ++count; } } correct_gaussian_yolo_boxes(dets, count, w, h, netw, neth, relative, letter); @@ -470,9 +475,11 @@ void forward_gaussian_yolo_layer_gpu(const layer l, network_state state) // x : mu, sigma int index = entry_gaussian_index(l, b, n*l.w*l.h, 0); activate_array_ongpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC); + scal_add_ongpu(l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output_gpu + index, 1); // scale x // y : mu, sigma index = entry_gaussian_index(l, b, n*l.w*l.h, 2); activate_array_ongpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC); + scal_add_ongpu(l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output_gpu + index, 1); // scale y // w : sigma index = entry_gaussian_index(l, b, n*l.w*l.h, 5); activate_array_ongpu(l.output_gpu + index, l.w*l.h, LOGISTIC); diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index d4a69e6f197..a9d3b560cd8 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1196,7 +1196,10 @@ image image_data_augmentation(mat_cv* mat, int w, int h, if (blur) { cv::Mat dst(sized.size(), sized.type()); - if(blur == 1) cv::GaussianBlur(sized, dst, cv::Size(31, 31), 0); + if (blur == 1) { + //cv::GaussianBlur(sized, dst, cv::Size(31, 31), 0); + cv::bilateralFilter(sized, dst, 31, 75, 75); + } else { int ksize = (blur / 2) * 2 + 1; cv::Size kernel_size = cv::Size(ksize, ksize); diff --git a/src/parser.c b/src/parser.c index 1367d35b06a..e0962f02edc 100644 --- a/src/parser.c +++ b/src/parser.c @@ -433,6 +433,7 @@ layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3 layer l = make_gaussian_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes, max_boxes); assert(l.outputs == params.inputs); + l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1); l.max_boxes = option_find_int_quiet(options, "max", 90); l.jitter = option_find_float(options, "jitter", .2); From 69b7a19f5bfce760debf2057b71b0882c535f350 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 27 Oct 2019 02:45:31 +0300 Subject: [PATCH 0633/1365] minor fix --- src/data.c | 3 ++- src/parser.c | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/src/data.c b/src/data.c index 1b9117d93cf..2780a82c85b 100644 --- a/src/data.c +++ b/src/data.c @@ -841,7 +841,8 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo d.y = make_matrix(n, 5*boxes); int i_mixup = 0; for (i_mixup = 0; i_mixup <= mixup; i_mixup++) { - if (i_mixup) augmentation_calculated = 0; + if (i_mixup) augmentation_calculated = 0; // recalculate augmentation for the 2nd sequence if(track==1) + for (i = 0; i < n; ++i) { float *truth = (float*)calloc(5 * boxes, sizeof(float)); const char *filename = (i_mixup) ? mixup_random_paths[i] : random_paths[i]; diff --git a/src/parser.c b/src/parser.c index e0962f02edc..f425560f04a 100644 --- a/src/parser.c +++ b/src/parser.c @@ -409,7 +409,7 @@ int *parse_gaussian_yolo_mask(char *a, int *num) // Gaussian_YOLOv3 for (i = 0; i < len; ++i) { if (a[i] == ',') ++n; } - mask = calloc(n, sizeof(int)); + mask = (int *)calloc(n, sizeof(int)); for (i = 0; i < n; ++i) { int val = atoi(a); mask[i] = val; From 6e736339259142c87bd86fd7362eac4e6043a8cc Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 29 Oct 2019 14:53:57 +0300 Subject: [PATCH 0634/1365] Fixed Blur and Try to use new Assisted Excitation. --- src/convolutional_kernels.cu | 33 +++++++++++++++++++++++---------- src/data.c | 2 +- src/image_opencv.cpp | 2 +- 3 files changed, 25 insertions(+), 12 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index cb5a1b99bae..0b94dd29db4 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -978,10 +978,14 @@ void assisted_excitation_forward_gpu(convolutional_layer l, network_state state) float alpha = (1 + cos(3.141592 * iteration_num / state.net.max_batches)) / 2; //float alpha = (1 + cos(3.141592 * iteration_num / state.net.max_batches)); - if (l.assisted_excitation > 1) { - if (iteration_num < state.net.burn_in) alpha = 0; - else if (iteration_num > l.assisted_excitation) alpha = 0; - else alpha = (1 + cos(3.141592 * iteration_num / l.assisted_excitation)) / 2; + if (l.assisted_excitation == 1) { + if (iteration_num > state.net.max_batches / 2) return; + } + else { + if (iteration_num < state.net.burn_in) return; + else if (iteration_num > l.assisted_excitation) return; + else + alpha = (1 + cos(3.141592 * iteration_num / (state.net.burn_in + l.assisted_excitation))) / 2; // from 1 to 0 } //printf("\n epoch = %f, alpha = %f, seen = %d, max_batches = %d, train_images_num = %d \n", @@ -1011,11 +1015,19 @@ void assisted_excitation_forward_gpu(convolutional_layer l, network_state state) for (t = 0; t < state.net.num_boxes; ++t) { box truth = float_to_box_stride(truth_cpu + t*(4 + 1) + b*l.truths, 1); if (!truth.x) break; // continue; - - int left = floor((truth.x - truth.w / 2) * l.out_w); - int right = ceil((truth.x + truth.w / 2) * l.out_w); - int top = floor((truth.y - truth.h / 2) * l.out_h); - int bottom = ceil((truth.y + truth.h / 2) * l.out_h); + float beta = 1 - alpha; // from 0 to 1 + float dw = (1 - truth.w) * beta; + float dh = (1 - truth.h) * beta; + //printf(" alpha = %f, beta = %f, truth.w = %f, dw = %f, tw+dw = %f, l.out_w = %d \n", alpha, beta, truth.w, dw, truth.w+dw, l.out_w); + + int left = floor((truth.x - (dw + truth.w) / 2) * l.out_w); + int right = ceil((truth.x + (dw + truth.w) / 2) * l.out_w); + int top = floor((truth.y - (dh + truth.h) / 2) * l.out_h); + int bottom = ceil((truth.y + (dh + truth.h) / 2) * l.out_h); + if (left < 0) left = 0; + if (top < 0) top = 0; + if (right > l.out_w) right = l.out_w; + if (bottom > l.out_h) bottom = l.out_h; for (w = left; w <= right; w++) { for (h = top; h < bottom; h++) { @@ -1035,7 +1047,8 @@ void assisted_excitation_forward_gpu(convolutional_layer l, network_state state) //CHECK_CUDA(cudaPeekAtLastError()); // calc new output - assisted_activation2_gpu(alpha, l.output_gpu, l.gt_gpu, l.a_avg_gpu, l.out_w * l.out_h, l.out_c, l.batch); + assisted_activation2_gpu(1, l.output_gpu, l.gt_gpu, l.a_avg_gpu, l.out_w * l.out_h, l.out_c, l.batch); // AE3: gt increases (beta = 1 - alpha = 0) + //assisted_activation2_gpu(alpha, l.output_gpu, l.gt_gpu, l.a_avg_gpu, l.out_w * l.out_h, l.out_c, l.batch); //assisted_activation_gpu(alpha, l.output_gpu, l.gt_gpu, l.a_avg_gpu, l.out_w * l.out_h, l.out_c, l.batch); //cudaStreamSynchronize(get_cuda_stream()); //CHECK_CUDA(cudaPeekAtLastError()); diff --git a/src/data.c b/src/data.c index 2780a82c85b..622e401ef75 100644 --- a/src/data.c +++ b/src/data.c @@ -927,7 +927,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo int min_w_h = fill_truth_detection(filename, boxes, truth, classes, flip, dx, dy, 1. / sx, 1. / sy, w, h); - if (min_w_h/4 < blur && blur > 1) blur = min_w_h / 4; // disable blur if one of the objects is too small + if (min_w_h / 8 < blur && blur > 1) blur = min_w_h / 8; // disable blur if one of the objects is too small image ai = image_data_augmentation(src, w, h, pleft, ptop, swidth, sheight, flip, dhue, dsat, dexp, blur, boxes, d.y.vals[i]); diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index a9d3b560cd8..912a0b1b6d2 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1198,7 +1198,7 @@ image image_data_augmentation(mat_cv* mat, int w, int h, cv::Mat dst(sized.size(), sized.type()); if (blur == 1) { //cv::GaussianBlur(sized, dst, cv::Size(31, 31), 0); - cv::bilateralFilter(sized, dst, 31, 75, 75); + cv::bilateralFilter(sized, dst, 17, 75, 75); } else { int ksize = (blur / 2) * 2 + 1; From 4d9addedd719cfb5d68cb3b39d5c7f9193071c0d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 29 Oct 2019 14:55:12 +0300 Subject: [PATCH 0635/1365] Fixed legacy compilation using MSVS (gaussian_yolo_layer) --- build/darknet/darknet_no_gpu.vcxproj | 2 ++ build/darknet/yolo_cpp_dll.vcxproj | 2 ++ build/darknet/yolo_cpp_dll_no_gpu.vcxproj | 2 ++ 3 files changed, 6 insertions(+) diff --git a/build/darknet/darknet_no_gpu.vcxproj b/build/darknet/darknet_no_gpu.vcxproj index dcfce05d057..72d23af3d09 100644 --- a/build/darknet/darknet_no_gpu.vcxproj +++ b/build/darknet/darknet_no_gpu.vcxproj @@ -203,6 +203,7 @@ + @@ -267,6 +268,7 @@ + diff --git a/build/darknet/yolo_cpp_dll.vcxproj b/build/darknet/yolo_cpp_dll.vcxproj index d3f60a0260f..813cae31e9d 100644 --- a/build/darknet/yolo_cpp_dll.vcxproj +++ b/build/darknet/yolo_cpp_dll.vcxproj @@ -201,6 +201,7 @@ + @@ -267,6 +268,7 @@ + diff --git a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj index f719d3f28b7..c9f3ddfa29c 100644 --- a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj +++ b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj @@ -187,6 +187,7 @@ + @@ -253,6 +254,7 @@ + From 52e3bb252fddadf73957000303fa1a2ca247e306 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 4 Nov 2019 14:39:20 +0300 Subject: [PATCH 0636/1365] Use non-blocking sockets for JSON_server and MJPEG_server --- include/yolo_v2_class.hpp | 1 + src/http_stream.cpp | 28 ++++++++++++++++++++++++++++ src/parser.c | 3 +++ src/yolo_v2_class.cpp | 9 ++++++--- 4 files changed, 38 insertions(+), 3 deletions(-) diff --git a/include/yolo_v2_class.hpp b/include/yolo_v2_class.hpp index f25610d0424..5543df7f8fc 100644 --- a/include/yolo_v2_class.hpp +++ b/include/yolo_v2_class.hpp @@ -70,6 +70,7 @@ extern "C" LIB_API void send_json_custom(char const* send_buf, int port, int tim class Detector { std::shared_ptr detector_gpu_ptr; std::deque> prev_bbox_vec_deque; + std::string _cfg_filename, _weight_filename; public: const int cur_gpu_id; float nms = .4; diff --git a/src/http_stream.cpp b/src/http_stream.cpp index af2e6730c74..927a65bcf35 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -148,6 +148,20 @@ class JSON_sender if (setsockopt(sock, SOL_SOCKET, SO_REUSEADDR, (const char*)&reuse, sizeof(reuse)) < 0) cerr << "setsockopt(SO_REUSEADDR) failed" << endl; + // Non-blocking sockets + // Windows: ioctlsocket() and FIONBIO + // Linux: fcntl() and O_NONBLOCK +#ifdef WIN32 + unsigned long i_mode = 1; + int result = ioctlsocket(sock, FIONBIO, &i_mode); + if (result != NO_ERROR) { + std::cerr << "ioctlsocket(FIONBIO) failed with error: " << result << std::endl; + } +#else // WIN32 + int flags = fcntl(sock, F_GETFL, 0); + fcntl(sock, F_SETFL, flags | O_NONBLOCK); +#endif // WIN32 + #ifdef SO_REUSEPORT if (setsockopt(sock, SOL_SOCKET, SO_REUSEPORT, (const char*)&reuse, sizeof(reuse)) < 0) cerr << "setsockopt(SO_REUSEPORT) failed" << endl; @@ -375,6 +389,20 @@ class MJPG_sender if (setsockopt(sock, SOL_SOCKET, SO_REUSEADDR, (const char*)&reuse, sizeof(reuse)) < 0) cerr << "setsockopt(SO_REUSEADDR) failed" << endl; + // Non-blocking sockets + // Windows: ioctlsocket() and FIONBIO + // Linux: fcntl() and O_NONBLOCK +#ifdef WIN32 + unsigned long i_mode = 1; + int result = ioctlsocket(sock, FIONBIO, &i_mode); + if (result != NO_ERROR) { + std::cerr << "ioctlsocket(FIONBIO) failed with error: " << result << std::endl; + } +#else // WIN32 + int flags = fcntl(sock, F_GETFL, 0); + fcntl(sock, F_SETFL, flags | O_NONBLOCK); +#endif // WIN32 + #ifdef SO_REUSEPORT if (setsockopt(sock, SOL_SOCKET, SO_REUSEPORT, (const char*)&reuse, sizeof(reuse)) < 0) cerr << "setsockopt(SO_REUSEPORT) failed" << endl; diff --git a/src/parser.c b/src/parser.c index f425560f04a..2cf295f070e 100644 --- a/src/parser.c +++ b/src/parser.c @@ -168,6 +168,9 @@ convolutional_layer parse_convolutional(list *options, size_params params, netwo if (stride_x < 1) stride_x = stride; if (stride_y < 1) stride_y = stride; } + else { + stride = option_find_int_quiet(options, "stride", 1); + } int dilation = option_find_int_quiet(options, "dilation", 1); int antialiasing = option_find_int_quiet(options, "antialiasing", 0); if (size == 1) dilation = 1; diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index 4667794a839..e01dadbb306 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -147,8 +147,11 @@ LIB_API Detector::Detector(std::string cfg_filename, std::string weight_filename net.gpu_index = cur_gpu_id; //gpu_index = i; - char *cfgfile = const_cast(cfg_filename.data()); - char *weightfile = const_cast(weight_filename.data()); + _cfg_filename = cfg_filename; + _weight_filename = weight_filename; + + char *cfgfile = const_cast(_cfg_filename.c_str()); + char *weightfile = const_cast(_weight_filename.c_str()); net = parse_network_cfg_custom(cfgfile, 1, 0); if (weightfile) { @@ -243,7 +246,7 @@ static image load_image_stb(char *filename, int channels) LIB_API image_t Detector::load_image(std::string image_filename) { - char *input = const_cast(image_filename.data()); + char *input = const_cast(image_filename.c_str()); image im = load_image_stb(input, 3); image_t img; From 7018be435f06a9ac26c5b7494ac96068dbdf497a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 4 Nov 2019 14:45:34 +0300 Subject: [PATCH 0637/1365] Compile fix --- src/http_stream.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 927a65bcf35..781139b3dff 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -50,6 +50,7 @@ static int close_socket(SOCKET s) { } #else // _WIN32 - else: nix #include "darkunistd.h" +#include #include #include #include From e345b8793c4dd8c28e0854145ac15b912b771bc6 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 5 Nov 2019 16:06:24 +0300 Subject: [PATCH 0638/1365] Fixed ./darknet partial for Anti-aliasing --- src/convolutional_layer.c | 19 ++++--------------- src/maxpool_layer.c | 19 ++++--------------- src/parser.c | 2 ++ 3 files changed, 10 insertions(+), 30 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 6818b603529..bf5beac7972 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -607,19 +607,6 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, } else { for (i = 0; i < blur_nweights; i += (blur_size*blur_size)) { - /* - l.input_layer->weights[i + 0] = 0; - l.input_layer->weights[i + 1] = 0; - l.input_layer->weights[i + 2] = 0; - - l.input_layer->weights[i + 3] = 0; - l.input_layer->weights[i + 4] = 1; - l.input_layer->weights[i + 5] = 0; - - l.input_layer->weights[i + 6] = 0; - l.input_layer->weights[i + 7] = 0; - l.input_layer->weights[i + 8] = 0; - */ l.input_layer->weights[i + 0] = 1 / 16.f; l.input_layer->weights[i + 1] = 2 / 16.f; l.input_layer->weights[i + 2] = 1 / 16.f; @@ -635,8 +622,10 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, } for (i = 0; i < n; ++i) l.input_layer->biases[i] = 0; #ifdef GPU - l.input_antialiasing_gpu = cuda_make_array(NULL, l.batch*l.outputs); - push_convolutional_layer(*(l.input_layer)); + if (gpu_index >= 0) { + l.input_antialiasing_gpu = cuda_make_array(NULL, l.batch*l.outputs); + push_convolutional_layer(*(l.input_layer)); + } #endif // GPU } diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index dca9c1b15f7..a05074ecc74 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -127,19 +127,6 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s } else { for (i = 0; i < blur_nweights; i += (blur_size*blur_size)) { - /* - l.input_layer->weights[i + 0] = 0; - l.input_layer->weights[i + 1] = 0; - l.input_layer->weights[i + 2] = 0; - - l.input_layer->weights[i + 3] = 0; - l.input_layer->weights[i + 4] = 1; - l.input_layer->weights[i + 5] = 0; - - l.input_layer->weights[i + 6] = 0; - l.input_layer->weights[i + 7] = 0; - l.input_layer->weights[i + 8] = 0; - */ l.input_layer->weights[i + 0] = 1 / 16.f; l.input_layer->weights[i + 1] = 2 / 16.f; l.input_layer->weights[i + 2] = 1 / 16.f; @@ -155,8 +142,10 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s } for (i = 0; i < l.out_c; ++i) l.input_layer->biases[i] = 0; #ifdef GPU - l.input_antialiasing_gpu = cuda_make_array(NULL, l.batch*l.outputs); - push_convolutional_layer(*(l.input_layer)); + if (gpu_index >= 0) { + l.input_antialiasing_gpu = cuda_make_array(NULL, l.batch*l.outputs); + push_convolutional_layer(*(l.input_layer)); + } #endif // GPU } diff --git a/src/parser.c b/src/parser.c index 2cf295f070e..edda54d1d92 100644 --- a/src/parser.c +++ b/src/parser.c @@ -951,6 +951,8 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) params.inputs = net.inputs; if (batch > 0) net.batch = batch; if (time_steps > 0) net.time_steps = time_steps; + if (net.batch < 1) net.batch = 1; + if (net.time_steps < 1) net.time_steps = 1; if (net.batch < net.time_steps) net.batch = net.time_steps; params.batch = net.batch; params.time_steps = net.time_steps; From d628e8eab7371f136cc05ae090b904eede7f9c55 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 5 Nov 2019 16:57:03 +0300 Subject: [PATCH 0639/1365] Fixed darknet.py for Uncertainty (gaussian_yolo_layer) --- build/darknet/x64/darknet.py | 3 ++- darknet.py | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/build/darknet/x64/darknet.py b/build/darknet/x64/darknet.py index 10c9a456ebd..5cfb26ba16e 100644 --- a/build/darknet/x64/darknet.py +++ b/build/darknet/x64/darknet.py @@ -59,7 +59,8 @@ class DETECTION(Structure): ("prob", POINTER(c_float)), ("mask", POINTER(c_float)), ("objectness", c_float), - ("sort_class", c_int)] + ("sort_class", c_int), + ("uc", POINTER(c_float))] class IMAGE(Structure): diff --git a/darknet.py b/darknet.py index 10c9a456ebd..5cfb26ba16e 100644 --- a/darknet.py +++ b/darknet.py @@ -59,7 +59,8 @@ class DETECTION(Structure): ("prob", POINTER(c_float)), ("mask", POINTER(c_float)), ("objectness", c_float), - ("sort_class", c_int)] + ("sort_class", c_int), + ("uc", POINTER(c_float))] class IMAGE(Structure): From bf8ea4183dc265ac17f7c9d939dc815269f0a213 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 7 Nov 2019 01:15:58 +0300 Subject: [PATCH 0640/1365] Added MISH activation, use activation=mish in [convolutional] layers --- include/darknet.h | 6 +++--- src/activation_kernels.cu | 36 ++++++++++++++++++++++++++++++++++++ src/activations.c | 25 +++++++++++++++++++++++++ src/activations.h | 6 +++++- src/convolutional_kernels.cu | 9 ++++++--- src/convolutional_layer.c | 13 ++++++++----- src/layer.c | 4 ++-- 7 files changed, 85 insertions(+), 14 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 00b49921f52..55f94ac5224 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -102,7 +102,7 @@ typedef struct tree { // activations.h typedef enum { - LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, SWISH + LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, SWISH, MISH }ACTIVATION; // parser.h @@ -347,7 +347,7 @@ struct layer { float *col_image; float * delta; float * output; - float * output_sigmoid; + float * activation_input; int delta_pinned; int output_pinned; float * loss; @@ -532,7 +532,7 @@ struct layer { float * input_antialiasing_gpu; float * output_gpu; - float * output_sigmoid_gpu; + float * activation_input_gpu; float * loss_gpu; float * delta_gpu; float * rand_gpu; diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index 24563c69d6e..846c586fada 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -199,6 +199,16 @@ __global__ void activate_array_swish_kernel(float *x, int n, float *output_sigmo } } +__global__ void activate_array_mish_kernel(float *x, int n, float *activation_input, float *output_gpu) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (i < n) { + float x_val = x[i]; + activation_input[i] = x_val; // store value before activation + output_gpu[i] = x_val * tanh_activate_kernel(log(1 + expf(x_val))); + } +} + __global__ void activate_array_leaky_kernel(float *x, int n) { int index = blockIdx.x*blockDim.x + threadIdx.x; @@ -263,6 +273,18 @@ __global__ void gradient_array_swish_kernel(float *x, int n, float *sigmoid_gpu, } } +__global__ void gradient_array_mish_kernel(int n, float *activation_input, float *delta) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (i < n) { + float x = activation_input[i]; + float d = 2 * expf(x) + expf(2 * x) + 2; + float w = 4 * (x + 1) + 4 * expf(2 * x) + expf(3 * x) + expf(x)*(4 * x + 6); + float derivative = expf(x) * w / (d * d); + delta[i] *= derivative; + } +} + __global__ void gradient_array_leaky_kernel(float *x, int n, float *delta) { int index = blockIdx.x*blockDim.x + threadIdx.x; @@ -333,6 +355,13 @@ extern "C" void activate_array_swish_ongpu(float *x, int n, float *output_sigmoi CHECK_CUDA(cudaPeekAtLastError()); } +extern "C" void activate_array_mish_ongpu(float *x, int n, float *activation_input_gpu, float *output_gpu) +{ + const int num_blocks = get_number_of_blocks(n, BLOCK); + activate_array_mish_kernel << > >(x, n, activation_input_gpu, output_gpu); + CHECK_CUDA(cudaPeekAtLastError()); +} + extern "C" void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta) { const int num_blocks = get_number_of_blocks(n, BLOCK); @@ -354,4 +383,11 @@ extern "C" void gradient_array_swish_ongpu(float *x, int n, float *sigmoid_gpu, const int num_blocks = get_number_of_blocks(n, BLOCK); gradient_array_swish_kernel << > > (x, n, sigmoid_gpu, delta); CHECK_CUDA(cudaPeekAtLastError()); +} + +extern "C" void gradient_array_mish_ongpu(int n, float *activation_input_gpu, float *delta) +{ + const int num_blocks = get_number_of_blocks(n, BLOCK); + gradient_array_mish_kernel << > > (n, activation_input_gpu, delta); + CHECK_CUDA(cudaPeekAtLastError()); } \ No newline at end of file diff --git a/src/activations.c b/src/activations.c index c3ea4818aeb..5311fb97a77 100644 --- a/src/activations.c +++ b/src/activations.c @@ -46,6 +46,7 @@ ACTIVATION get_activation(char *s) { if (strcmp(s, "logistic")==0) return LOGISTIC; if (strcmp(s, "swish") == 0) return SWISH; + if (strcmp(s, "mish") == 0) return MISH; if (strcmp(s, "loggy")==0) return LOGGY; if (strcmp(s, "relu")==0) return RELU; if (strcmp(s, "elu")==0) return ELU; @@ -133,6 +134,17 @@ void activate_array_swish(float *x, const int n, float * output_sigmoid, float * } } +void activate_array_mish(float *x, const int n, float * activation_input, float * output) +{ + int i; + #pragma omp parallel for + for (i = 0; i < n; ++i) { + float x_val = x[i]; + activation_input[i] = x_val; // store value before activation + output[i] = x_val * tanh_activate(log(1 + expf(x_val))); + } +} + float gradient(float x, ACTIVATION a) { switch(a){ @@ -187,3 +199,16 @@ void gradient_array_swish(const float *x, const int n, const float * sigmoid, fl delta[i] *= swish + sigmoid[i]*(1 - swish); } } + +void gradient_array_mish(const int n, const float * activation_input, float * delta) +{ + int i; + #pragma omp parallel for + for (i = 0; i < n; ++i) { + float x = activation_input[i]; + float d = 2 * expf(x) + expf(2 * x) + 2; + float w = 4 * (x + 1) + 4 * expf(2 * x) + expf(3 * x) + expf(x)*(4 * x + 6); + float derivative = expf(x) * w / (d * d); + delta[i] *= derivative; + } +} diff --git a/src/activations.h b/src/activations.h index 19f3822c8e3..bba5ca8d10a 100644 --- a/src/activations.h +++ b/src/activations.h @@ -5,7 +5,7 @@ #include "math.h" //typedef enum{ -// LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU +// LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, SWISH, MISH //}ACTIVATION; #ifdef __cplusplus @@ -18,13 +18,17 @@ float activate(float x, ACTIVATION a); float gradient(float x, ACTIVATION a); void gradient_array(const float *x, const int n, const ACTIVATION a, float *delta); void gradient_array_swish(const float *x, const int n, const float * sigmoid, float * delta); +void gradient_array_mish(const int n, const float * activation_input, float * delta); void activate_array(float *x, const int n, const ACTIVATION a); void activate_array_swish(float *x, const int n, float * output_sigmoid, float * output); +void activate_array_mish(float *x, const int n, float * activation_input, float * output); #ifdef GPU void activate_array_ongpu(float *x, int n, ACTIVATION a); void activate_array_swish_ongpu(float *x, int n, float *output_sigmoid_gpu, float *output_gpu); +void activate_array_mish_ongpu(float *x, int n, float *activation_input_gpu, float *output_gpu); void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta); void gradient_array_swish_ongpu(float *x, int n, float *sigmoid_gpu, float *delta); +void gradient_array_mish_ongpu(int n, float *activation_input_gpu, float *delta); #endif static inline float stair_activate(float x) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 0b94dd29db4..a73f277ee92 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -392,7 +392,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) */ //add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); - if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.output_sigmoid_gpu, l.output_gpu); + if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); + else if (l.activation == MISH) activate_array_mish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if(l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if (l.binary || l.xnor) swap_binary(&l); @@ -596,7 +597,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //#ifndef CUDNN_HALF //#endif // no CUDNN_HALF - if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.output_sigmoid_gpu, l.output_gpu); + if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); + else if (l.activation == MISH) activate_array_mish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation != LINEAR) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if(l.dot > 0) dot_error_gpu(l); if(l.binary || l.xnor) swap_binary(&l); @@ -639,7 +641,8 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state if(state.net.try_fix_nan) constrain_ongpu(l.outputs*l.batch, 1, l.delta_gpu, 1); - if (l.activation == SWISH) gradient_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.output_sigmoid_gpu, l.delta_gpu); + if (l.activation == SWISH) gradient_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.delta_gpu); + else if (l.activation == MISH) gradient_array_mish_ongpu(l.outputs*l.batch, l.activation_input_gpu, l.delta_gpu); else gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); if (!l.batch_normalize) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index bf5beac7972..b76d7ee735f 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -473,10 +473,10 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.scale_v = (float*)calloc(n, sizeof(float)); } - if(l.activation == SWISH) l.output_sigmoid = (float*)calloc(total_batch*l.outputs, sizeof(float)); + if (l.activation == SWISH || l.activation == MISH) l.activation_input = (float*)calloc(total_batch*l.outputs, sizeof(float)); #ifdef GPU - if (l.activation == SWISH) l.output_sigmoid_gpu = cuda_make_array(l.output_sigmoid, total_batch*out_h*out_w*n); + if (l.activation == SWISH || l.activation == MISH) l.activation_input_gpu = cuda_make_array(l.activation_input, total_batch*out_h*out_w*n); l.forward_gpu = forward_convolutional_layer_gpu; l.backward_gpu = backward_convolutional_layer_gpu; @@ -1100,7 +1100,8 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w); //activate_array(l.output, m*n*l.batch, l.activation); - if (l.activation == SWISH) activate_array_swish(l.output, l.outputs*l.batch, l.output_sigmoid, l.output); + if (l.activation == SWISH) activate_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.output); + else if (l.activation == MISH) activate_array_mish(l.output, l.outputs*l.batch, l.activation_input, l.output); else activate_array_cpu_custom(l.output, m*n*l.batch, l.activation); return; @@ -1139,7 +1140,8 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w); //activate_array(l.output, m*n*l.batch, l.activation); - if (l.activation == SWISH) activate_array_swish(l.output, l.outputs*l.batch, l.output_sigmoid, l.output); + if (l.activation == SWISH) activate_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.output); + else if (l.activation == MISH) activate_array_mish(l.output, l.outputs*l.batch, l.activation_input, l.output); else activate_array_cpu_custom(l.output, l.outputs*l.batch, l.activation); if(l.binary || l.xnor) swap_binary(&l); @@ -1276,7 +1278,8 @@ void backward_convolutional_layer(convolutional_layer l, network_state state) int n = l.size*l.size*l.c / l.groups; int k = l.out_w*l.out_h; - if (l.activation == SWISH) gradient_array_swish(l.output, l.outputs*l.batch, l.output_sigmoid, l.delta); + if (l.activation == SWISH) gradient_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.delta); + else if (l.activation == MISH) gradient_array_mish(l.outputs*l.batch, l.activation_input, l.delta); else gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); if (l.batch_normalize) { diff --git a/src/layer.c b/src/layer.c index e9ae67b5ff5..9fe4a439364 100644 --- a/src/layer.c +++ b/src/layer.c @@ -90,7 +90,7 @@ void free_layer(layer l) #endif // GPU if (l.delta) free(l.delta), l.delta = NULL; if (l.output) free(l.output), l.output = NULL; - if (l.output_sigmoid) free(l.output_sigmoid), l.output_sigmoid = NULL; + if (l.activation_input) free(l.activation_input), l.activation_input = NULL; if (l.squared) free(l.squared); if (l.norms) free(l.norms); if (l.spatial_mean) free(l.spatial_mean); @@ -176,7 +176,7 @@ void free_layer(layer l) if (l.scale_updates_gpu) cuda_free(l.scale_updates_gpu), l.scale_updates_gpu = NULL; if (l.input_antialiasing_gpu) cuda_free(l.input_antialiasing_gpu), l.input_antialiasing_gpu = NULL; if (l.output_gpu) cuda_free(l.output_gpu), l.output_gpu = NULL; - if (l.output_sigmoid_gpu) cuda_free(l.output_sigmoid_gpu), l.output_sigmoid_gpu = NULL; + if (l.activation_input_gpu) cuda_free(l.activation_input_gpu), l.activation_input_gpu = NULL; if (l.delta_gpu) cuda_free(l.delta_gpu), l.delta_gpu = NULL; if (l.rand_gpu) cuda_free(l.rand_gpu); if (l.squared_gpu) cuda_free(l.squared_gpu); From c7c7078de7952fbdcfe7b4a7d60303e56e84846b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 7 Nov 2019 01:18:47 +0300 Subject: [PATCH 0641/1365] Added URL to the description of activation MISH on GitHub --- src/activation_kernels.cu | 2 ++ src/activations.c | 2 ++ 2 files changed, 4 insertions(+) diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index 846c586fada..d8ff25f42e3 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -199,6 +199,7 @@ __global__ void activate_array_swish_kernel(float *x, int n, float *output_sigmo } } +// https://github.com/digantamisra98/Mish __global__ void activate_array_mish_kernel(float *x, int n, float *activation_input, float *output_gpu) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -273,6 +274,7 @@ __global__ void gradient_array_swish_kernel(float *x, int n, float *sigmoid_gpu, } } +// https://github.com/digantamisra98/Mish __global__ void gradient_array_mish_kernel(int n, float *activation_input, float *delta) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; diff --git a/src/activations.c b/src/activations.c index 5311fb97a77..da92af0af38 100644 --- a/src/activations.c +++ b/src/activations.c @@ -134,6 +134,7 @@ void activate_array_swish(float *x, const int n, float * output_sigmoid, float * } } +// https://github.com/digantamisra98/Mish void activate_array_mish(float *x, const int n, float * activation_input, float * output) { int i; @@ -200,6 +201,7 @@ void gradient_array_swish(const float *x, const int n, const float * sigmoid, fl } } +// https://github.com/digantamisra98/Mish void gradient_array_mish(const int n, const float * activation_input, float * delta) { int i; From 704bd1138e967bedf30ba4292369b3057aa5abeb Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 7 Nov 2019 23:49:10 +0300 Subject: [PATCH 0642/1365] Fixed params.net in parse_convolutional() and parse_route() --- src/parser.c | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/src/parser.c b/src/parser.c index edda54d1d92..52f3edceead 100644 --- a/src/parser.c +++ b/src/parser.c @@ -154,7 +154,7 @@ local_layer parse_local(list *options, size_params params) return layer; } -convolutional_layer parse_convolutional(list *options, size_params params, network net) +convolutional_layer parse_convolutional(list *options, size_params params) { int n = option_find_int(options, "filters",1); int groups = option_find_int_quiet(options, "groups", 1); @@ -185,8 +185,8 @@ convolutional_layer parse_convolutional(list *options, size_params params, netwo int share_index = option_find_int_quiet(options, "share_index", -1000000000); convolutional_layer *share_layer = NULL; - if(share_index >= 0) share_layer = &net.layers[share_index]; - else if(share_index != -1000000000) share_layer = &net.layers[params.index + share_index]; + if(share_index >= 0) share_layer = ¶ms.net.layers[share_index]; + else if(share_index != -1000000000) share_layer = ¶ms.net.layers[params.index + share_index]; int batch,h,w,c; h = params.h; @@ -754,7 +754,7 @@ layer parse_upsample(list *options, size_params params, network net) return l; } -route_layer parse_route(list *options, size_params params, network net) +route_layer parse_route(list *options, size_params params) { char *l = option_find(options, "layers"); int len = strlen(l); @@ -772,19 +772,19 @@ route_layer parse_route(list *options, size_params params, network net) l = strchr(l, ',')+1; if(index < 0) index = params.index + index; layers[i] = index; - sizes[i] = net.layers[index].outputs; + sizes[i] = params.net.layers[index].outputs; } int batch = params.batch; route_layer layer = make_route_layer(batch, n, layers, sizes); - convolutional_layer first = net.layers[layers[0]]; + convolutional_layer first = params.net.layers[layers[0]]; layer.out_w = first.out_w; layer.out_h = first.out_h; layer.out_c = first.out_c; for(i = 1; i < n; ++i){ int index = layers[i]; - convolutional_layer next = net.layers[index]; + convolutional_layer next = params.net.layers[index]; if(next.out_w == first.out_w && next.out_h == first.out_h){ layer.out_c += next.out_c; }else{ @@ -974,7 +974,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) layer l = { (LAYER_TYPE)0 }; LAYER_TYPE lt = string_to_layer_type(s->type); if(lt == CONVOLUTIONAL){ - l = parse_convolutional(options, params, net); + l = parse_convolutional(options, params); }else if(lt == LOCAL){ l = parse_local(options, params); }else if(lt == ACTIVE){ @@ -1019,7 +1019,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) }else if(lt == AVGPOOL){ l = parse_avgpool(options, params); }else if(lt == ROUTE){ - l = parse_route(options, params, net); + l = parse_route(options, params); int k; for (k = 0; k < l.n; ++k) net.layers[l.input_layers[k]].use_bin_output = 0; }else if (lt == UPSAMPLE) { From 0fa9c8f10588cd5db54b742bc2bcbefcddd87000 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 8 Nov 2019 01:06:49 +0300 Subject: [PATCH 0643/1365] Added groups= and groupd_id= params to the [route] layer --- include/darknet.h | 1 + src/parser.c | 6 +++++- src/route_layer.c | 35 ++++++++++++++++++++++++++--------- src/route_layer.h | 2 +- 4 files changed, 33 insertions(+), 11 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 55f94ac5224..f29cb15689a 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -206,6 +206,7 @@ struct layer { int n; int max_boxes; int groups; + int group_id; int size; int side; int stride; diff --git a/src/parser.c b/src/parser.c index 52f3edceead..e0b65f3bf3b 100644 --- a/src/parser.c +++ b/src/parser.c @@ -776,7 +776,10 @@ route_layer parse_route(list *options, size_params params) } int batch = params.batch; - route_layer layer = make_route_layer(batch, n, layers, sizes); + int groups = option_find_int_quiet(options, "groups", 1); + int group_id = option_find_int_quiet(options, "group_id", 0); + + route_layer layer = make_route_layer(batch, n, layers, sizes, groups, group_id); convolutional_layer first = params.net.layers[layers[0]]; layer.out_w = first.out_w; @@ -791,6 +794,7 @@ route_layer parse_route(list *options, size_params params) layer.out_h = layer.out_w = layer.out_c = 0; } } + layer.out_c = layer.out_c / layer.groups; return layer; } diff --git a/src/route_layer.c b/src/route_layer.c index b502fbe72b0..b636d4824e8 100644 --- a/src/route_layer.c +++ b/src/route_layer.c @@ -3,7 +3,7 @@ #include "blas.h" #include -route_layer make_route_layer(int batch, int n, int *input_layers, int *input_sizes) +route_layer make_route_layer(int batch, int n, int *input_layers, int *input_sizes, int groups, int group_id) { fprintf(stderr,"route "); route_layer l = { (LAYER_TYPE)0 }; @@ -12,6 +12,8 @@ route_layer make_route_layer(int batch, int n, int *input_layers, int *input_siz l.n = n; l.input_layers = input_layers; l.input_sizes = input_sizes; + l.groups = groups; + l.group_id = group_id; int i; int outputs = 0; for(i = 0; i < n; ++i){ @@ -19,6 +21,7 @@ route_layer make_route_layer(int batch, int n, int *input_layers, int *input_siz outputs += input_sizes[i]; } fprintf(stderr, "\n"); + outputs = outputs / groups; l.outputs = outputs; l.inputs = outputs; l.delta = (float*)calloc(outputs * batch, sizeof(float)); @@ -57,6 +60,8 @@ void resize_route_layer(route_layer *l, network *net) l->out_h = l->out_w = l->out_c = 0; } } + l->out_c = l->out_c / l->groups; + l->outputs = l->outputs / l->groups; l->inputs = l->outputs; l->delta = (float*)realloc(l->delta, l->outputs * l->batch * sizeof(float)); l->output = (float*)realloc(l->output, l->outputs * l->batch * sizeof(float)); @@ -78,10 +83,13 @@ void forward_route_layer(const route_layer l, network_state state) int index = l.input_layers[i]; float *input = state.net.layers[index].output; int input_size = l.input_sizes[i]; + int part_input_size = input_size / l.groups; for(j = 0; j < l.batch; ++j){ - copy_cpu(input_size, input + j*input_size, 1, l.output + offset + j*l.outputs, 1); + //copy_cpu(input_size, input + j*input_size, 1, l.output + offset + j*l.outputs, 1); + copy_cpu(part_input_size, input + j*input_size + part_input_size*l.group_id, 1, l.output + offset + j*l.outputs, 1); } - offset += input_size; + //offset += input_size; + offset += part_input_size; } } @@ -93,10 +101,13 @@ void backward_route_layer(const route_layer l, network_state state) int index = l.input_layers[i]; float *delta = state.net.layers[index].delta; int input_size = l.input_sizes[i]; + int part_input_size = input_size / l.groups; for(j = 0; j < l.batch; ++j){ - axpy_cpu(input_size, 1, l.delta + offset + j*l.outputs, 1, delta + j*input_size, 1); + //axpy_cpu(input_size, 1, l.delta + offset + j*l.outputs, 1, delta + j*input_size, 1); + axpy_cpu(part_input_size, 1, l.delta + offset + j*l.outputs, 1, delta + j*input_size + part_input_size*l.group_id, 1); } - offset += input_size; + //offset += input_size; + offset += part_input_size; } } @@ -109,11 +120,14 @@ void forward_route_layer_gpu(const route_layer l, network_state state) int index = l.input_layers[i]; float *input = state.net.layers[index].output_gpu; int input_size = l.input_sizes[i]; + int part_input_size = input_size / l.groups; for(j = 0; j < l.batch; ++j){ //copy_ongpu(input_size, input + j*input_size, 1, l.output_gpu + offset + j*l.outputs, 1); - simple_copy_ongpu(input_size, input + j*input_size, l.output_gpu + offset + j*l.outputs); + //simple_copy_ongpu(input_size, input + j*input_size, l.output_gpu + offset + j*l.outputs); + simple_copy_ongpu(part_input_size, input + j*input_size + part_input_size*l.group_id, l.output_gpu + offset + j*l.outputs); } - offset += input_size; + //offset += input_size; + offset += part_input_size; } } @@ -125,10 +139,13 @@ void backward_route_layer_gpu(const route_layer l, network_state state) int index = l.input_layers[i]; float *delta = state.net.layers[index].delta_gpu; int input_size = l.input_sizes[i]; + int part_input_size = input_size / l.groups; for(j = 0; j < l.batch; ++j){ - axpy_ongpu(input_size, 1, l.delta_gpu + offset + j*l.outputs, 1, delta + j*input_size, 1); + //axpy_ongpu(input_size, 1, l.delta_gpu + offset + j*l.outputs, 1, delta + j*input_size, 1); + axpy_ongpu(part_input_size, 1, l.delta_gpu + offset + j*l.outputs, 1, delta + j*input_size + part_input_size*l.group_id, 1); } - offset += input_size; + //offset += input_size; + offset += part_input_size; } } #endif diff --git a/src/route_layer.h b/src/route_layer.h index 3ee9019385f..2ebe396039c 100644 --- a/src/route_layer.h +++ b/src/route_layer.h @@ -8,7 +8,7 @@ typedef layer route_layer; #ifdef __cplusplus extern "C" { #endif -route_layer make_route_layer(int batch, int n, int *input_layers, int *input_size); +route_layer make_route_layer(int batch, int n, int *input_layers, int *input_size, int groups, int group_id); void forward_route_layer(const route_layer l, network_state state); void backward_route_layer(const route_layer l, network_state state); void resize_route_layer(route_layer *l, network *net); From 0eee8404bf271256f3455491fe3c55830f14f31d Mon Sep 17 00:00:00 2001 From: Alexey Date: Sat, 9 Nov 2019 14:03:29 +0300 Subject: [PATCH 0644/1365] Update Readme.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 9042a6f99e7..5c0efdeff1a 100644 --- a/README.md +++ b/README.md @@ -607,6 +607,8 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * each: `model of object, side, illimination, scale, each 30 grad` of the turn and inclination angles - these are *different objects* from an internal perspective of the neural network. So the more *different objects* you want to detect, the more complex network model should be used. + * to make the detected bounded boxes more accurate, you can add 3 parameters `ignore_thresh = .9 iou_normalizer=0.5 iou_loss=giou` to each `[yolo]` layer and train, it will increase mAP@0.9, but decrease mAP@0.5. + * Only if you are an **expert** in neural detection networks - recalculate anchors for your dataset for `width` and `height` from cfg-file: `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416` then set the same 9 `anchors` in each of 3 `[yolo]`-layers in your cfg-file. But you should change indexes of anchors `masks=` for each [yolo]-layer, so that 1st-[yolo]-layer has anchors larger than 60x60, 2nd larger than 30x30, 3rd remaining. Also you should change the `filters=(classes + 5)*` before each [yolo]-layer. If many of the calculated anchors do not fit under the appropriate layers - then just try using all the default anchors. From d91d59a22fea9c266f06c2ae5edb23d38fd83c20 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 9 Nov 2019 22:48:18 +0300 Subject: [PATCH 0645/1365] Optimized memory allocation for Detection (inference only), without allocation memory for training --- include/darknet.h | 1 + src/conv_lstm_layer.c | 25 +++---- src/conv_lstm_layer.h | 2 +- src/convolutional_kernels.cu | 10 ++- src/convolutional_layer.c | 122 ++++++++++++++++++++++++----------- src/convolutional_layer.h | 3 +- src/crnn_layer.c | 9 +-- src/crnn_layer.h | 2 +- src/gemm.c | 4 +- src/maxpool_layer.c | 44 ++++++++----- src/maxpool_layer.h | 2 +- src/maxpool_layer_kernels.cu | 4 +- src/network.c | 1 + src/parser.c | 14 ++-- src/shortcut_layer.c | 16 +++-- src/shortcut_layer.h | 2 +- src/yolo_layer.c | 2 +- 17 files changed, 169 insertions(+), 94 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index f29cb15689a..8be704061b1 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -190,6 +190,7 @@ struct layer { void(*backward_gpu) (struct layer, struct network_state); void(*update_gpu) (struct layer, int, float, float, float); layer *share_layer; + int train; int batch_normalize; int shortcut; int batch; diff --git a/src/conv_lstm_layer.c b/src/conv_lstm_layer.c index 4ae67b44a83..b4059ed1ec8 100644 --- a/src/conv_lstm_layer.c +++ b/src/conv_lstm_layer.c @@ -32,7 +32,7 @@ static void increment_layer(layer *l, int steps) } -layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, int groups, int steps, int size, int stride, int dilation, int pad, ACTIVATION activation, int batch_normalize, int peephole, int xnor) +layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, int groups, int steps, int size, int stride, int dilation, int pad, ACTIVATION activation, int batch_normalize, int peephole, int xnor, int train) { fprintf(stderr, "CONV_LSTM Layer: %d x %d x %d image, %d filters\n", h, w, c, output_filters); /* @@ -48,6 +48,7 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i */ batch = batch / steps; layer l = { (LAYER_TYPE)0 }; + l.train = train; l.batch = batch; l.type = CONV_LSTM; l.steps = steps; @@ -66,44 +67,44 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i // U l.uf = (layer*)calloc(1, sizeof(layer)); - *(l.uf) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); + *(l.uf) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); l.uf->batch = batch; if (l.workspace_size < l.uf->workspace_size) l.workspace_size = l.uf->workspace_size; l.ui = (layer*)calloc(1, sizeof(layer)); - *(l.ui) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); + *(l.ui) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); l.ui->batch = batch; if (l.workspace_size < l.ui->workspace_size) l.workspace_size = l.ui->workspace_size; l.ug = (layer*)calloc(1, sizeof(layer)); - *(l.ug) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); + *(l.ug) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); l.ug->batch = batch; if (l.workspace_size < l.ug->workspace_size) l.workspace_size = l.ug->workspace_size; l.uo = (layer*)calloc(1, sizeof(layer)); - *(l.uo) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); + *(l.uo) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); l.uo->batch = batch; if (l.workspace_size < l.uo->workspace_size) l.workspace_size = l.uo->workspace_size; // W l.wf = (layer*)calloc(1, sizeof(layer)); - *(l.wf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); + *(l.wf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); l.wf->batch = batch; if (l.workspace_size < l.wf->workspace_size) l.workspace_size = l.wf->workspace_size; l.wi = (layer*)calloc(1, sizeof(layer)); - *(l.wi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); + *(l.wi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); l.wi->batch = batch; if (l.workspace_size < l.wi->workspace_size) l.workspace_size = l.wi->workspace_size; l.wg = (layer*)calloc(1, sizeof(layer)); - *(l.wg) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); + *(l.wg) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); l.wg->batch = batch; if (l.workspace_size < l.wg->workspace_size) l.workspace_size = l.wg->workspace_size; l.wo = (layer*)calloc(1, sizeof(layer)); - *(l.wo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); + *(l.wo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); l.wo->batch = batch; if (l.workspace_size < l.wo->workspace_size) l.workspace_size = l.wo->workspace_size; @@ -111,21 +112,21 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i // V l.vf = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { - *(l.vf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); + *(l.vf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); l.vf->batch = batch; if (l.workspace_size < l.vf->workspace_size) l.workspace_size = l.vf->workspace_size; } l.vi = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { - *(l.vi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); + *(l.vi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); l.vi->batch = batch; if (l.workspace_size < l.vi->workspace_size) l.workspace_size = l.vi->workspace_size; } l.vo = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { - *(l.vo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); + *(l.vo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); l.vo->batch = batch; if (l.workspace_size < l.vo->workspace_size) l.workspace_size = l.vo->workspace_size; } diff --git a/src/conv_lstm_layer.h b/src/conv_lstm_layer.h index 17e4fdc3a66..0a0438b04f5 100644 --- a/src/conv_lstm_layer.h +++ b/src/conv_lstm_layer.h @@ -9,7 +9,7 @@ #ifdef __cplusplus extern "C" { #endif -layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, int groups, int steps, int size, int stride, int dilation, int pad, ACTIVATION activation, int batch_normalize, int peephole, int xnor); +layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, int groups, int steps, int size, int stride, int dilation, int pad, ACTIVATION activation, int batch_normalize, int peephole, int xnor, int train); void resize_conv_lstm_layer(layer *l, int w, int h); void free_state_conv_lstm(layer l); void randomize_state_conv_lstm(layer l); diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index a73f277ee92..1a6b5f8affb 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -986,7 +986,8 @@ void assisted_excitation_forward_gpu(convolutional_layer l, network_state state) } else { if (iteration_num < state.net.burn_in) return; - else if (iteration_num > l.assisted_excitation) return; + else + if (iteration_num > l.assisted_excitation) return; else alpha = (1 + cos(3.141592 * iteration_num / (state.net.burn_in + l.assisted_excitation))) / 2; // from 1 to 0 } @@ -1018,6 +1019,7 @@ void assisted_excitation_forward_gpu(convolutional_layer l, network_state state) for (t = 0; t < state.net.num_boxes; ++t) { box truth = float_to_box_stride(truth_cpu + t*(4 + 1) + b*l.truths, 1); if (!truth.x) break; // continue; + //float beta = 0; float beta = 1 - alpha; // from 0 to 1 float dw = (1 - truth.w) * beta; float dh = (1 - truth.h) * beta; @@ -1162,8 +1164,10 @@ void push_convolutional_layer(convolutional_layer l) cuda_convert_f32_to_f16(l.weights_gpu, l.nweights, l.weights_gpu16); #endif cuda_push_array(l.biases_gpu, l.biases, l.n); - cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.nweights); - cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n); + if (l.train) { + cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.nweights); + cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n); + } if (l.batch_normalize){ cuda_push_array(l.scales_gpu, l.scales, l.n); cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.n); diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index b76d7ee735f..7f2bac9690b 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -123,7 +123,7 @@ size_t get_workspace_size32(layer l){ l.dweightDesc, l.bf_algo, &s)); - if (s > most) most = s; + if (s > most && l.train) most = s; CHECK_CUDNN(cudnnGetConvolutionBackwardDataWorkspaceSize(cudnn_handle(), l.weightDesc, l.ddstTensorDesc, @@ -131,7 +131,7 @@ size_t get_workspace_size32(layer l){ l.dsrcTensorDesc, l.bd_algo, &s)); - if (s > most) most = s; + if (s > most && l.train) most = s; return most; } #endif @@ -164,7 +164,7 @@ size_t get_workspace_size16(layer l) { l.dweightDesc16, l.bf_algo16, &s)); - if (s > most) most = s; + if (s > most && l.train) most = s; CHECK_CUDNN(cudnnGetConvolutionBackwardDataWorkspaceSize(cudnn_handle(), l.weightDesc16, l.ddstTensorDesc16, @@ -172,7 +172,7 @@ size_t get_workspace_size16(layer l) { l.dsrcTensorDesc16, l.bd_algo16, &s)); - if (s > most) most = s; + if (s > most && l.train) most = s; return most; } #endif @@ -333,12 +333,43 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) #endif #endif -convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, int antialiasing, convolutional_layer *share_layer, int assisted_excitation) + +void free_convolutional_batchnorm(convolutional_layer *l) +{ + if (!l->share_layer) { + free(l->scales); + free(l->scale_updates); + free(l->mean); + free(l->variance); + free(l->mean_delta); + free(l->variance_delta); + free(l->rolling_mean); + free(l->rolling_variance); + free(l->x); + free(l->x_norm); + +#ifdef GPU + cuda_free(l->scales_gpu); + cuda_free(l->scale_updates_gpu); + cuda_free(l->mean_gpu); + cuda_free(l->variance_gpu); + cuda_free(l->mean_delta_gpu); + cuda_free(l->variance_delta_gpu); + cuda_free(l->rolling_mean_gpu); + cuda_free(l->rolling_variance_gpu); + cuda_free(l->x_gpu); + cuda_free(l->x_norm_gpu); +#endif + } +} + +convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, int antialiasing, convolutional_layer *share_layer, int assisted_excitation, int train) { int total_batch = batch*steps; int i; convolutional_layer l = { (LAYER_TYPE)0 }; l.type = CONVOLUTIONAL; + l.train = train; if (xnor) groups = 1; // disable groups for XNOR-net if (groups < 1) groups = 1; @@ -382,10 +413,12 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, } else { l.weights = (float*)calloc(l.nweights, sizeof(float)); - l.weight_updates = (float*)calloc(l.nweights, sizeof(float)); - l.biases = (float*)calloc(n, sizeof(float)); - l.bias_updates = (float*)calloc(n, sizeof(float)); + + if (train) { + l.weight_updates = (float*)calloc(l.nweights, sizeof(float)); + l.bias_updates = (float*)calloc(n, sizeof(float)); + } } // float scale = 1./sqrt(size*size*c); @@ -401,7 +434,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.activation = activation; l.output = (float*)calloc(total_batch*l.outputs, sizeof(float)); - l.delta = (float*)calloc(total_batch*l.outputs, sizeof(float)); + if (train) l.delta = (float*)calloc(total_batch*l.outputs, sizeof(float)); l.forward = forward_convolutional_layer; l.backward = backward_convolutional_layer; @@ -445,23 +478,27 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, } else { l.scales = (float*)calloc(n, sizeof(float)); - l.scale_updates = (float*)calloc(n, sizeof(float)); for (i = 0; i < n; ++i) { l.scales[i] = 1; } + if (train) { + l.scale_updates = (float*)calloc(n, sizeof(float)); - l.mean = (float*)calloc(n, sizeof(float)); - l.variance = (float*)calloc(n, sizeof(float)); + l.mean = (float*)calloc(n, sizeof(float)); + l.variance = (float*)calloc(n, sizeof(float)); - l.mean_delta = (float*)calloc(n, sizeof(float)); - l.variance_delta = (float*)calloc(n, sizeof(float)); + l.mean_delta = (float*)calloc(n, sizeof(float)); + l.variance_delta = (float*)calloc(n, sizeof(float)); + } l.rolling_mean = (float*)calloc(n, sizeof(float)); l.rolling_variance = (float*)calloc(n, sizeof(float)); } - l.x = (float*)calloc(total_batch * l.outputs, sizeof(float)); - l.x_norm = (float*)calloc(total_batch * l.outputs, sizeof(float)); + if (train) { + l.x = (float*)calloc(total_batch * l.outputs, sizeof(float)); + l.x_norm = (float*)calloc(total_batch * l.outputs, sizeof(float)); + } } if(adam){ l.adam = 1; @@ -501,17 +538,17 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, } else { l.weights_gpu = cuda_make_array(l.weights, l.nweights); - l.weight_updates_gpu = cuda_make_array(l.weight_updates, l.nweights); + if (train) l.weight_updates_gpu = cuda_make_array(l.weight_updates, l.nweights); #ifdef CUDNN_HALF l.weights_gpu16 = cuda_make_array(NULL, l.nweights / 2 + 1); - l.weight_updates_gpu16 = cuda_make_array(NULL, l.nweights / 2 + 1); + if (train) l.weight_updates_gpu16 = cuda_make_array(NULL, l.nweights / 2 + 1); #endif // CUDNN_HALF l.biases_gpu = cuda_make_array(l.biases, n); - l.bias_updates_gpu = cuda_make_array(l.bias_updates, n); + if (train) l.bias_updates_gpu = cuda_make_array(l.bias_updates, n); } l.output_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n); - l.delta_gpu = cuda_make_array(l.delta, total_batch*out_h*out_w*n); + if (train) l.delta_gpu = cuda_make_array(l.delta, total_batch*out_h*out_w*n); if(binary){ l.binary_weights_gpu = cuda_make_array(l.weights, l.nweights); @@ -535,19 +572,25 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, } else { l.scales_gpu = cuda_make_array(l.scales, n); - l.scale_updates_gpu = cuda_make_array(l.scale_updates, n); - l.mean_gpu = cuda_make_array(l.mean, n); - l.variance_gpu = cuda_make_array(l.variance, n); + if (train) { + l.scale_updates_gpu = cuda_make_array(l.scale_updates, n); + + l.mean_gpu = cuda_make_array(l.mean, n); + l.variance_gpu = cuda_make_array(l.variance, n); + + l.mean_delta_gpu = cuda_make_array(l.mean, n); + l.variance_delta_gpu = cuda_make_array(l.variance, n); + } l.rolling_mean_gpu = cuda_make_array(l.mean, n); l.rolling_variance_gpu = cuda_make_array(l.variance, n); + } - l.mean_delta_gpu = cuda_make_array(l.mean, n); - l.variance_delta_gpu = cuda_make_array(l.variance, n); + if (train) { + l.x_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n); + l.x_norm_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n); } - l.x_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n); - l.x_norm_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n); } if (l.assisted_excitation) @@ -594,7 +637,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, blur_size = 2; blur_pad = 0; } - *(l.input_layer) = make_convolutional_layer(batch, steps, out_h, out_w, n, n, n, blur_size, blur_stride_x, blur_stride_y, 1, blur_pad, LINEAR, 0, 0, 0, 0, 0, index, 0, NULL, 0); + *(l.input_layer) = make_convolutional_layer(batch, steps, out_h, out_w, n, n, n, blur_size, blur_stride_x, blur_stride_y, 1, blur_pad, LINEAR, 0, 0, 0, 0, 0, index, 0, NULL, 0, train); const int blur_nweights = n * blur_size * blur_size; // (n / n) * n * blur_size * blur_size; int i; if (blur_size == 2) { @@ -649,7 +692,7 @@ void denormalize_convolutional_layer(convolutional_layer l) void test_convolutional_layer() { - convolutional_layer l = make_convolutional_layer(1, 1, 5, 5, 3, 2, 1, 5, 2, 2, 1, 1, LEAKY, 1, 0, 0, 0, 0, 0, 0, NULL, 0); + convolutional_layer l = make_convolutional_layer(1, 1, 5, 5, 3, 2, 1, 5, 2, 2, 1, 1, LEAKY, 1, 0, 0, 0, 0, 0, 0, NULL, 0, 0); l.batch_normalize = 1; float data[] = {1,1,1,1,1, 1,1,1,1,1, @@ -688,10 +731,13 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) l->inputs = l->w * l->h * l->c; l->output = (float*)realloc(l->output, total_batch * l->outputs * sizeof(float)); - l->delta = (float*)realloc(l->delta, total_batch * l->outputs * sizeof(float)); - if(l->batch_normalize){ - l->x = (float*)realloc(l->x, total_batch * l->outputs * sizeof(float)); - l->x_norm = (float*)realloc(l->x_norm, total_batch * l->outputs * sizeof(float)); + if (l->train) { + l->delta = (float*)realloc(l->delta, total_batch * l->outputs * sizeof(float)); + + if (l->batch_normalize) { + l->x = (float*)realloc(l->x, total_batch * l->outputs * sizeof(float)); + l->x_norm = (float*)realloc(l->x_norm, total_batch * l->outputs * sizeof(float)); + } } if (l->xnor) { @@ -700,10 +746,12 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) #ifdef GPU if (old_w < w || old_h < h) { - cuda_free(l->delta_gpu); - cuda_free(l->output_gpu); + if (l->train) { + cuda_free(l->delta_gpu); + l->delta_gpu = cuda_make_array(l->delta, total_batch*l->outputs); + } - l->delta_gpu = cuda_make_array(l->delta, total_batch*l->outputs); + cuda_free(l->output_gpu); l->output_gpu = cuda_make_array(l->output, total_batch*l->outputs); if (l->batch_normalize) { @@ -1246,7 +1294,7 @@ void assisted_excitation_forward(convolutional_layer l, network_state state) } } - if(1) // visualize ground truth + if(0) // visualize ground truth { #ifdef OPENCV for (b = 0; b < l.batch; ++b) diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h index 0072ce549c3..817f900b783 100644 --- a/src/convolutional_layer.h +++ b/src/convolutional_layer.h @@ -28,9 +28,10 @@ void create_convolutional_cudnn_tensors(layer *l); void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16); #endif #endif +void free_convolutional_batchnorm(convolutional_layer *l); size_t get_convolutional_workspace_size(layer l); -convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, int antialiasing, convolutional_layer *share_layer, int assisted_excitation); +convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, int antialiasing, convolutional_layer *share_layer, int assisted_excitation, int train); void denormalize_convolutional_layer(convolutional_layer l); void resize_convolutional_layer(convolutional_layer *layer, int w, int h); void forward_convolutional_layer(const convolutional_layer layer, network_state state); diff --git a/src/crnn_layer.c b/src/crnn_layer.c index 588db7411a0..cbeaa5087bc 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -26,11 +26,12 @@ static void increment_layer(layer *l, int steps) #endif } -layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int groups, int steps, int size, int stride, int dilation, int pad, ACTIVATION activation, int batch_normalize, int xnor) +layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int groups, int steps, int size, int stride, int dilation, int pad, ACTIVATION activation, int batch_normalize, int xnor, int train) { fprintf(stderr, "CRNN Layer: %d x %d x %d image, %d filters\n", h,w,c,output_filters); batch = batch / steps; layer l = { (LAYER_TYPE)0 }; + l.train = train; l.batch = batch; l.type = CRNN; l.steps = steps; @@ -50,17 +51,17 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.state = (float*)calloc(l.hidden * l.batch * (l.steps + 1), sizeof(float)); l.input_layer = (layer*)calloc(1, sizeof(layer)); - *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); + *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); l.input_layer->batch = batch; if (l.workspace_size < l.input_layer->workspace_size) l.workspace_size = l.input_layer->workspace_size; l.self_layer = (layer*)calloc(1, sizeof(layer)); - *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); + *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); l.self_layer->batch = batch; if (l.workspace_size < l.self_layer->workspace_size) l.workspace_size = l.self_layer->workspace_size; l.output_layer = (layer*)calloc(1, sizeof(layer)); - *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0); + *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); l.output_layer->batch = batch; if (l.workspace_size < l.output_layer->workspace_size) l.workspace_size = l.output_layer->workspace_size; diff --git a/src/crnn_layer.h b/src/crnn_layer.h index 33560aae462..c2dc7a25258 100644 --- a/src/crnn_layer.h +++ b/src/crnn_layer.h @@ -9,7 +9,7 @@ #ifdef __cplusplus extern "C" { #endif -layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int groups, int steps, int size, int stride, int dilation, int pad, ACTIVATION activation, int batch_normalize, int xnor); +layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int groups, int steps, int size, int stride, int dilation, int pad, ACTIVATION activation, int batch_normalize, int xnor, int train); void resize_crnn_layer(layer *l, int w, int h); void free_state_crnn(layer l); diff --git a/src/gemm.c b/src/gemm.c index 151388284b1..9f5cb882c61 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -1949,7 +1949,7 @@ void forward_maxpool_layer_avx(float *src, float *dst, int *indexes, int size, i } } dst[out_index] = max; - indexes[out_index] = max_i; + if (indexes) indexes[out_index] = max_i; } } } @@ -2452,7 +2452,7 @@ void forward_maxpool_layer_avx(float *src, float *dst, int *indexes, int size, i } } dst[out_index] = max; - indexes[out_index] = max_i; + if (indexes) indexes[out_index] = max_i; } } } diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index a05074ecc74..54aa9c0ed91 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -46,10 +46,11 @@ void cudnn_maxpool_setup(layer *l) } -maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride_x, int stride_y, int padding, int maxpool_depth, int out_channels, int antialiasing) +maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride_x, int stride_y, int padding, int maxpool_depth, int out_channels, int antialiasing, int train) { maxpool_layer l = { (LAYER_TYPE)0 }; l.type = MAXPOOL; + l.train = train; const int blur_stride_x = stride_x; const int blur_stride_y = stride_y; @@ -82,21 +83,25 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s l.stride_x = stride_x; l.stride_y = stride_y; int output_size = l.out_h * l.out_w * l.out_c * batch; - l.indexes = (int*)calloc(output_size, sizeof(int)); + + if (train) { + l.indexes = (int*)calloc(output_size, sizeof(int)); + l.delta = (float*)calloc(output_size, sizeof(float)); + } l.output = (float*)calloc(output_size, sizeof(float)); - l.delta = (float*)calloc(output_size, sizeof(float)); l.forward = forward_maxpool_layer; l.backward = backward_maxpool_layer; - #ifdef GPU +#ifdef GPU l.forward_gpu = forward_maxpool_layer_gpu; l.backward_gpu = backward_maxpool_layer_gpu; - l.indexes_gpu = cuda_make_int_array(output_size); + if (train) { + l.indexes_gpu = cuda_make_int_array(output_size); + l.delta_gpu = cuda_make_array(l.delta, output_size); + } l.output_gpu = cuda_make_array(l.output, output_size); - l.delta_gpu = cuda_make_array(l.delta, output_size); - cudnn_maxpool_setup(&l); - #endif // GPU +#endif // GPU l.bflops = (l.size*l.size*l.c * l.out_h*l.out_w) / 1000000000.; if (maxpool_depth) fprintf(stderr, "max-depth %2dx%2d/%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride_x, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); @@ -114,7 +119,7 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s blur_size = 2; blur_pad = 0; } - *(l.input_layer) = make_convolutional_layer(batch, 1, l.out_h, l.out_w, l.out_c, l.out_c, l.out_c, blur_size, blur_stride_x, blur_stride_y, 1, blur_pad, LINEAR, 0, 0, 0, 0, 0, 1, 0, NULL, 0); + *(l.input_layer) = make_convolutional_layer(batch, 1, l.out_h, l.out_w, l.out_c, l.out_c, l.out_c, blur_size, blur_stride_x, blur_stride_y, 1, blur_pad, LINEAR, 0, 0, 0, 0, 0, 1, 0, NULL, 0, train); const int blur_nweights = l.out_c * blur_size * blur_size; // (n / n) * n * blur_size * blur_size; int i; if (blur_size == 2) { @@ -163,17 +168,22 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h) l->outputs = l->out_w * l->out_h * l->out_c; int output_size = l->outputs * l->batch; - l->indexes = (int*)realloc(l->indexes, output_size * sizeof(int)); + if (l->train) { + l->indexes = (int*)realloc(l->indexes, output_size * sizeof(int)); + l->delta = (float*)realloc(l->delta, output_size * sizeof(float)); + } l->output = (float*)realloc(l->output, output_size * sizeof(float)); - l->delta = (float*)realloc(l->delta, output_size * sizeof(float)); #ifdef GPU - CHECK_CUDA(cudaFree((float *)l->indexes_gpu)); CHECK_CUDA(cudaFree(l->output_gpu)); - CHECK_CUDA(cudaFree(l->delta_gpu)); - l->indexes_gpu = cuda_make_int_array(output_size); l->output_gpu = cuda_make_array(l->output, output_size); - l->delta_gpu = cuda_make_array(l->delta, output_size); + + if (l->train) { + CHECK_CUDA(cudaFree((float *)l->indexes_gpu)); + CHECK_CUDA(cudaFree(l->delta_gpu)); + l->indexes_gpu = cuda_make_int_array(output_size); + l->delta_gpu = cuda_make_array(l->delta, output_size); + } cudnn_maxpool_setup(l); #endif @@ -203,7 +213,7 @@ void forward_maxpool_layer(const maxpool_layer l, network_state state) max = (val > max) ? val : max; } l.output[out_index] = max; - l.indexes[out_index] = max_i; + if (l.indexes) l.indexes[out_index] = max_i; } } } @@ -245,7 +255,7 @@ void forward_maxpool_layer(const maxpool_layer l, network_state state) } } l.output[out_index] = max; - l.indexes[out_index] = max_i; + if (l.indexes) l.indexes[out_index] = max_i; } } } diff --git a/src/maxpool_layer.h b/src/maxpool_layer.h index cfedf9d9ee6..3986a4c6360 100644 --- a/src/maxpool_layer.h +++ b/src/maxpool_layer.h @@ -12,7 +12,7 @@ typedef layer maxpool_layer; extern "C" { #endif image get_maxpool_image(maxpool_layer l); -maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride_x, int stride_y, int padding, int maxpool_depth, int out_channels, int antialiasing); +maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride_x, int stride_y, int padding, int maxpool_depth, int out_channels, int antialiasing, int train); void resize_maxpool_layer(maxpool_layer *l, int w, int h); void forward_maxpool_layer(const maxpool_layer l, network_state state); void backward_maxpool_layer(const maxpool_layer l, network_state state); diff --git a/src/maxpool_layer_kernels.cu b/src/maxpool_layer_kernels.cu index 8677b4d713d..c15143c556b 100644 --- a/src/maxpool_layer_kernels.cu +++ b/src/maxpool_layer_kernels.cu @@ -36,7 +36,7 @@ __global__ void forward_maxpool_depth_layer_kernel(int n, int w, int h, int c, i max = (val > max) ? val : max; } output[out_index] = max; - indexes[out_index] = max_i; + if (indexes) indexes[out_index] = max_i; } } @@ -88,7 +88,7 @@ __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c } } output[out_index] = max; - indexes[out_index] = max_i; + if (indexes) indexes[out_index] = max_i; } __global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride_x, int stride_y, int size, int pad, float *delta, float *prev_delta, int *indexes) diff --git a/src/network.c b/src/network.c index cfe994343e8..c2249a54df6 100644 --- a/src/network.c +++ b/src/network.c @@ -1071,6 +1071,7 @@ void fuse_conv_batchnorm(network net) } } + free_convolutional_batchnorm(l); l->batch_normalize = 0; #ifdef GPU if (gpu_index >= 0) { diff --git a/src/parser.c b/src/parser.c index e0b65f3bf3b..1e5ad78c781 100644 --- a/src/parser.c +++ b/src/parser.c @@ -130,6 +130,7 @@ typedef struct size_params{ int c; int index; int time_steps; + int train; network net; } size_params; @@ -199,7 +200,7 @@ convolutional_layer parse_convolutional(list *options, size_params params) int xnor = option_find_int_quiet(options, "xnor", 0); int use_bin_output = option_find_int_quiet(options, "bin_output", 0); - convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride_x,stride_y,dilation,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index, antialiasing, share_layer, assisted_excitation); + convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride_x,stride_y,dilation,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index, antialiasing, share_layer, assisted_excitation, params.train); layer.flipped = option_find_int_quiet(options, "flipped", 0); layer.dot = option_find_float_quiet(options, "dot", 0); @@ -230,7 +231,7 @@ layer parse_crnn(list *options, size_params params) int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); int xnor = option_find_int_quiet(options, "xnor", 0); - layer l = make_crnn_layer(params.batch, params.h, params.w, params.c, hidden_filters, output_filters, groups, params.time_steps, size, stride, dilation, padding, activation, batch_normalize, xnor); + layer l = make_crnn_layer(params.batch, params.h, params.w, params.c, hidden_filters, output_filters, groups, params.time_steps, size, stride, dilation, padding, activation, batch_normalize, xnor, params.train); l.shortcut = option_find_int_quiet(options, "shortcut", 0); @@ -291,7 +292,7 @@ layer parse_conv_lstm(list *options, size_params params) int xnor = option_find_int_quiet(options, "xnor", 0); int peephole = option_find_int_quiet(options, "peephole", 0); - layer l = make_conv_lstm_layer(params.batch, params.h, params.w, params.c, output_filters, groups, params.time_steps, size, stride, dilation, padding, activation, batch_normalize, peephole, xnor); + layer l = make_conv_lstm_layer(params.batch, params.h, params.w, params.c, output_filters, groups, params.time_steps, size, stride, dilation, padding, activation, batch_normalize, peephole, xnor, params.train); l.state_constrain = option_find_int_quiet(options, "state_constrain", params.time_steps * 32); l.shortcut = option_find_int_quiet(options, "shortcut", 0); @@ -630,7 +631,7 @@ maxpool_layer parse_maxpool(list *options, size_params params) batch=params.batch; if(!(h && w && c)) error("Layer before maxpool layer must output image."); - maxpool_layer layer = make_maxpool_layer(batch, h, w, c, size, stride_x, stride_y, padding, maxpool_depth, out_channels, antialiasing); + maxpool_layer layer = make_maxpool_layer(batch, h, w, c, size, stride_x, stride_y, padding, maxpool_depth, out_channels, antialiasing, params.train); return layer; } @@ -684,7 +685,7 @@ layer parse_shortcut(list *options, size_params params, network net) layer from = net.layers[index]; if (from.antialiasing) from = *from.input_layer; - layer s = make_shortcut_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c, assisted_excitation); + layer s = make_shortcut_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c, assisted_excitation, params.train); char *activation_s = option_find_str(options, "activation", "linear"); ACTIVATION activation = get_activation(activation_s); @@ -944,6 +945,9 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) net.gpu_index = gpu_index; size_params params; + if (batch > 0) params.train = 0; // allocates memory for Detection only + else params.train = 1; // allocates memory for Detection & Training + section *s = (section *)n->val; list *options = s->options; if(!is_network(s)) error("First section must be [net] or [network]"); diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index 8345858e47d..615a5ea384b 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -5,11 +5,12 @@ #include #include -layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2, int assisted_excitation) +layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2, int assisted_excitation, int train) { if(assisted_excitation) fprintf(stderr, "Shortcut Layer - AE: %d\n", index); else fprintf(stderr,"Shortcut Layer: %d\n", index); layer l = { (LAYER_TYPE)0 }; + l.train = train; l.type = SHORTCUT; l.batch = batch; l.w = w2; @@ -27,7 +28,7 @@ layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int l.index = index; - l.delta = (float*)calloc(l.outputs * batch, sizeof(float)); + if (train) l.delta = (float*)calloc(l.outputs * batch, sizeof(float)); l.output = (float*)calloc(l.outputs * batch, sizeof(float)); l.forward = forward_shortcut_layer; @@ -36,7 +37,7 @@ layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int l.forward_gpu = forward_shortcut_layer_gpu; l.backward_gpu = backward_shortcut_layer_gpu; - l.delta_gpu = cuda_make_array(l.delta, l.outputs*batch); + if (train) l.delta_gpu = cuda_make_array(l.delta, l.outputs*batch); l.output_gpu = cuda_make_array(l.output, l.outputs*batch); if (l.assisted_excitation) { @@ -56,14 +57,17 @@ void resize_shortcut_layer(layer *l, int w, int h) l->h = l->out_h = h; l->outputs = w*h*l->out_c; l->inputs = l->outputs; - l->delta = (float*)realloc(l->delta, l->outputs * l->batch * sizeof(float)); + if (l->train) l->delta = (float*)realloc(l->delta, l->outputs * l->batch * sizeof(float)); l->output = (float*)realloc(l->output, l->outputs * l->batch * sizeof(float)); #ifdef GPU cuda_free(l->output_gpu); - cuda_free(l->delta_gpu); l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch); - l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch); + + if (l->train) { + cuda_free(l->delta_gpu); + l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch); + } #endif } diff --git a/src/shortcut_layer.h b/src/shortcut_layer.h index ad8d45f3e28..89f22ceb3be 100644 --- a/src/shortcut_layer.h +++ b/src/shortcut_layer.h @@ -7,7 +7,7 @@ #ifdef __cplusplus extern "C" { #endif -layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2, int assisted_excitation); +layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2, int assisted_excitation, int train); void forward_shortcut_layer(const layer l, network_state state); void backward_shortcut_layer(const layer l, network_state state); void resize_shortcut_layer(layer *l, int w, int h); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 906ed427f01..06d2b513624 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -282,7 +282,6 @@ void forward_yolo_layer(const layer l, network_state state) box pred = get_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w*l.h); float best_iou = 0; int best_t = 0; - int class_id_match = 0; for (t = 0; t < l.max_boxes; ++t) { box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; @@ -298,6 +297,7 @@ void forward_yolo_layer(const layer l, network_state state) int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4); float objectness = l.output[obj_index]; int pred_class_id = get_yolo_class(l.output, l.classes, class_index, l.w*l.h, objectness); + int class_id_match = 0; if (class_id == pred_class_id) class_id_match = 1; else class_id_match = 0; From 20659fc1a8ec858d24ad2b70f55add810dc5caff Mon Sep 17 00:00:00 2001 From: 7FM <41307817+7FM@users.noreply.github.com> Date: Mon, 11 Nov 2019 22:08:06 +0100 Subject: [PATCH 0646/1365] Fix undefined behavior caused by delete Avoid multiple deletes of the same address and memory leaks --- src/image_opencv.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 912a0b1b6d2..6951fb9a80e 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -734,7 +734,7 @@ int wait_for_stream(cap_cv *cap, cv::Mat* src, int dont_close) delete src;// cvReleaseImage(&src); int z = 0; for (z = 0; z < 20; ++z) { - get_capture_frame_cv(cap); + src = get_capture_frame_cv(cap); delete src;// cvReleaseImage(&src); } src = new cv::Mat(416, 416, CV_8UC(3)); // cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3); From 9fe201807e244398e8aeaeed43c8823491fd421b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 12 Nov 2019 15:05:34 +0300 Subject: [PATCH 0647/1365] Revert to old Assisted Excitation version. --- src/convolutional_kernels.cu | 8 ++++---- src/parser.c | 10 ++++++++++ src/route_layer.c | 2 +- 3 files changed, 15 insertions(+), 5 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 1a6b5f8affb..e5d57eabde9 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -1019,8 +1019,8 @@ void assisted_excitation_forward_gpu(convolutional_layer l, network_state state) for (t = 0; t < state.net.num_boxes; ++t) { box truth = float_to_box_stride(truth_cpu + t*(4 + 1) + b*l.truths, 1); if (!truth.x) break; // continue; - //float beta = 0; - float beta = 1 - alpha; // from 0 to 1 + float beta = 0; + //float beta = 1 - alpha; // from 0 to 1 float dw = (1 - truth.w) * beta; float dh = (1 - truth.h) * beta; //printf(" alpha = %f, beta = %f, truth.w = %f, dw = %f, tw+dw = %f, l.out_w = %d \n", alpha, beta, truth.w, dw, truth.w+dw, l.out_w); @@ -1052,9 +1052,9 @@ void assisted_excitation_forward_gpu(convolutional_layer l, network_state state) //CHECK_CUDA(cudaPeekAtLastError()); // calc new output - assisted_activation2_gpu(1, l.output_gpu, l.gt_gpu, l.a_avg_gpu, l.out_w * l.out_h, l.out_c, l.batch); // AE3: gt increases (beta = 1 - alpha = 0) + //assisted_activation2_gpu(1, l.output_gpu, l.gt_gpu, l.a_avg_gpu, l.out_w * l.out_h, l.out_c, l.batch); // AE3: gt increases (beta = 1 - alpha = 0) //assisted_activation2_gpu(alpha, l.output_gpu, l.gt_gpu, l.a_avg_gpu, l.out_w * l.out_h, l.out_c, l.batch); - //assisted_activation_gpu(alpha, l.output_gpu, l.gt_gpu, l.a_avg_gpu, l.out_w * l.out_h, l.out_c, l.batch); + assisted_activation_gpu(alpha, l.output_gpu, l.gt_gpu, l.a_avg_gpu, l.out_w * l.out_h, l.out_c, l.batch); //cudaStreamSynchronize(get_cuda_stream()); //CHECK_CUDA(cudaPeekAtLastError()); diff --git a/src/parser.c b/src/parser.c index 1e5ad78c781..4d259f43a96 100644 --- a/src/parser.c +++ b/src/parser.c @@ -797,6 +797,16 @@ route_layer parse_route(list *options, size_params params) } layer.out_c = layer.out_c / layer.groups; + layer.w = first.w; + layer.h = first.h; + layer.c = layer.out_c; + + if (n > 3) fprintf(stderr, " \t "); + else if (n > 1) fprintf(stderr, " \t "); + else fprintf(stderr, " \t\t "); + + fprintf(stderr, " -> %4d x%4d x%4d \n", layer.w, layer.h, layer.c, layer.out_w, layer.out_h, layer.out_c); + return layer; } diff --git a/src/route_layer.c b/src/route_layer.c index b636d4824e8..8e3f15f8a9d 100644 --- a/src/route_layer.c +++ b/src/route_layer.c @@ -20,10 +20,10 @@ route_layer make_route_layer(int batch, int n, int *input_layers, int *input_siz fprintf(stderr," %d", input_layers[i]); outputs += input_sizes[i]; } - fprintf(stderr, "\n"); outputs = outputs / groups; l.outputs = outputs; l.inputs = outputs; + //fprintf(stderr, " inputs = %d \t outputs = %d, groups = %d, group_id = %d \n", l.inputs, l.outputs, l.groups, l.group_id); l.delta = (float*)calloc(outputs * batch, sizeof(float)); l.output = (float*)calloc(outputs * batch, sizeof(float)); From ded620ac6b23443b102bd58897877b0ce9100fa4 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 12 Nov 2019 15:11:33 +0300 Subject: [PATCH 0648/1365] fixed memory deallocation --- include/darknet.h | 1 + src/convolutional_layer.c | 40 +++++++++++++++++++-------------------- 2 files changed, 21 insertions(+), 20 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 8be704061b1..0acac8a60b7 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -873,6 +873,7 @@ LIB_API void free_layer(layer); LIB_API void free_data(data d); LIB_API pthread_t load_data(load_args args); LIB_API pthread_t load_data_in_thread(load_args args); +LIB_API void *load_thread(void *ptr); // dark_cuda.h LIB_API void cuda_pull_array(float *x_gpu, float *x, size_t n); diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 7f2bac9690b..4dd2cf96f9b 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -337,28 +337,28 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) void free_convolutional_batchnorm(convolutional_layer *l) { if (!l->share_layer) { - free(l->scales); - free(l->scale_updates); - free(l->mean); - free(l->variance); - free(l->mean_delta); - free(l->variance_delta); - free(l->rolling_mean); - free(l->rolling_variance); - free(l->x); - free(l->x_norm); + free(l->scales); l->scales = NULL; + free(l->scale_updates); l->scale_updates = NULL; + free(l->mean); l->mean = NULL; + free(l->variance); l->variance = NULL; + free(l->mean_delta); l->mean_delta = NULL; + free(l->variance_delta); l->variance_delta = NULL; + free(l->rolling_mean); l->rolling_mean = NULL; + free(l->rolling_variance); l->rolling_variance = NULL; + free(l->x); l->x = NULL; + free(l->x_norm); l->x_norm = NULL; #ifdef GPU - cuda_free(l->scales_gpu); - cuda_free(l->scale_updates_gpu); - cuda_free(l->mean_gpu); - cuda_free(l->variance_gpu); - cuda_free(l->mean_delta_gpu); - cuda_free(l->variance_delta_gpu); - cuda_free(l->rolling_mean_gpu); - cuda_free(l->rolling_variance_gpu); - cuda_free(l->x_gpu); - cuda_free(l->x_norm_gpu); + cuda_free(l->scales_gpu); l->scales_gpu = NULL; + cuda_free(l->scale_updates_gpu); l->scale_updates_gpu = NULL; + cuda_free(l->mean_gpu); l->mean_gpu = NULL; + cuda_free(l->variance_gpu); l->variance_gpu = NULL; + cuda_free(l->mean_delta_gpu); l->mean_delta_gpu = NULL; + cuda_free(l->variance_delta_gpu); l->variance_delta_gpu = NULL; + cuda_free(l->rolling_mean_gpu); l->rolling_mean_gpu = NULL; + cuda_free(l->rolling_variance_gpu); l->rolling_variance_gpu = NULL; + cuda_free(l->x_gpu); l->x_gpu = NULL; + cuda_free(l->x_norm_gpu); l->x_norm_gpu = NULL; #endif } } From 70bf88ce01950e93f8bd54b53c585b98bae1146a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 12 Nov 2019 15:21:21 +0300 Subject: [PATCH 0649/1365] Fixed swish-activation for [shortcut_layer] --- src/convolutional_layer.c | 2 +- src/shortcut_layer.c | 26 ++++++++++++++++++++++---- 2 files changed, 23 insertions(+), 5 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 4dd2cf96f9b..92e72732c1c 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -513,7 +513,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, if (l.activation == SWISH || l.activation == MISH) l.activation_input = (float*)calloc(total_batch*l.outputs, sizeof(float)); #ifdef GPU - if (l.activation == SWISH || l.activation == MISH) l.activation_input_gpu = cuda_make_array(l.activation_input, total_batch*out_h*out_w*n); + if (l.activation == SWISH || l.activation == MISH) l.activation_input_gpu = cuda_make_array(l.activation_input, total_batch*l.outputs); l.forward_gpu = forward_convolutional_layer_gpu; l.backward_gpu = backward_convolutional_layer_gpu; diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index 615a5ea384b..a1dd3724e47 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -2,6 +2,7 @@ #include "convolutional_layer.h" #include "dark_cuda.h" #include "blas.h" +#include "gemm.h" #include #include @@ -33,7 +34,11 @@ layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int l.forward = forward_shortcut_layer; l.backward = backward_shortcut_layer; + + if (l.activation == SWISH || l.activation == MISH) l.activation_input = (float*)calloc(l.batch*l.outputs, sizeof(float)); #ifdef GPU + if (l.activation == SWISH || l.activation == MISH) l.activation_input_gpu = cuda_make_array(l.activation_input, l.batch*l.outputs); + l.forward_gpu = forward_shortcut_layer_gpu; l.backward_gpu = backward_shortcut_layer_gpu; @@ -85,14 +90,21 @@ void forward_shortcut_layer(const layer l, network_state state) copy_cpu(l.outputs*l.batch, state.input, 1, l.output, 1); shortcut_cpu(l.batch, l.w, l.h, l.c, state.net.layers[l.index].output, l.out_w, l.out_h, l.out_c, l.output); } - activate_array(l.output, l.outputs*l.batch, l.activation); + + //activate_array(l.output, l.outputs*l.batch, l.activation); + if (l.activation == SWISH) activate_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.output); + else if (l.activation == MISH) activate_array_mish(l.output, l.outputs*l.batch, l.activation_input, l.output); + else activate_array_cpu_custom(l.output, l.outputs*l.batch, l.activation); if (l.assisted_excitation && state.train) assisted_excitation_forward(l, state); } void backward_shortcut_layer(const layer l, network_state state) { - gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); + if (l.activation == SWISH) gradient_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.delta); + else if (l.activation == MISH) gradient_array_mish(l.outputs*l.batch, l.activation_input, l.delta); + else gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); + axpy_cpu(l.outputs*l.batch, 1, l.delta, 1, state.delta, 1); shortcut_cpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta, l.w, l.h, l.c, state.net.layers[l.index].delta); } @@ -104,14 +116,20 @@ void forward_shortcut_layer_gpu(const layer l, network_state state) //simple_copy_ongpu(l.outputs*l.batch, state.input, l.output_gpu); //shortcut_gpu(l.batch, l.w, l.h, l.c, state.net.layers[l.index].output_gpu, l.out_w, l.out_h, l.out_c, l.output_gpu); input_shortcut_gpu(state.input, l.batch, l.w, l.h, l.c, state.net.layers[l.index].output_gpu, l.out_w, l.out_h, l.out_c, l.output_gpu); - activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); + + if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); + else if (l.activation == MISH) activate_array_mish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); + else activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); if (l.assisted_excitation && state.train) assisted_excitation_forward_gpu(l, state); } void backward_shortcut_layer_gpu(const layer l, network_state state) { - gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); + if (l.activation == SWISH) gradient_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.delta_gpu); + else if (l.activation == MISH) gradient_array_mish_ongpu(l.outputs*l.batch, l.activation_input_gpu, l.delta_gpu); + else gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); + axpy_ongpu(l.outputs*l.batch, 1, l.delta_gpu, 1, state.delta, 1); shortcut_gpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta_gpu, l.w, l.h, l.c, state.net.layers[l.index].delta_gpu); } From 3652d7d3745a29ab161cf3a77492f1ac8b04b090 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 12 Nov 2019 15:31:07 +0300 Subject: [PATCH 0650/1365] Fixed MISH instability --- src/activation_kernels.cu | 24 ++++++++++++++++++------ src/activations.c | 23 ++++++++++++++++++----- 2 files changed, 36 insertions(+), 11 deletions(-) diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index d8ff25f42e3..67504e71611 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -275,15 +275,27 @@ __global__ void gradient_array_swish_kernel(float *x, int n, float *sigmoid_gpu, } // https://github.com/digantamisra98/Mish -__global__ void gradient_array_mish_kernel(int n, float *activation_input, float *delta) +__global__ void gradient_array_mish_kernel(int n, float *activation_input_gpu, float *delta) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (i < n) { - float x = activation_input[i]; - float d = 2 * expf(x) + expf(2 * x) + 2; - float w = 4 * (x + 1) + 4 * expf(2 * x) + expf(3 * x) + expf(x)*(4 * x + 6); - float derivative = expf(x) * w / (d * d); - delta[i] *= derivative; + const float THRESHOLD = 20.0f; + + // implementation from TensorFlow: https://github.com/tensorflow/addons/commit/093cdfa85d334cbe19a37624c33198f3140109ed + // implementation from Pytorch: https://github.com/thomasbrandon/mish-cuda/blob/master/csrc/mish.h#L26-L31 + float inp = activation_input_gpu[i]; + const float sp = (inp < THRESHOLD) ? log1p(exp(inp)) : inp; + const float grad_sp = 1 - exp(-sp); + const float tsp = tanh(sp); + const float grad_tsp = (1 - tsp*tsp) * grad_sp; + const float grad = inp * grad_tsp + tsp; + delta[i] *= grad; + + //float x = activation_input[i]; + //float d = 2 * expf(x) + expf(2 * x) + 2; + //float w = 4 * (x + 1) + 4 * expf(2 * x) + expf(3 * x) + expf(x)*(4 * x + 6); + //float derivative = expf(x) * w / (d * d); + //delta[i] *= derivative; } } diff --git a/src/activations.c b/src/activations.c index da92af0af38..55b060bd94c 100644 --- a/src/activations.c +++ b/src/activations.c @@ -207,10 +207,23 @@ void gradient_array_mish(const int n, const float * activation_input, float * de int i; #pragma omp parallel for for (i = 0; i < n; ++i) { - float x = activation_input[i]; - float d = 2 * expf(x) + expf(2 * x) + 2; - float w = 4 * (x + 1) + 4 * expf(2 * x) + expf(3 * x) + expf(x)*(4 * x + 6); - float derivative = expf(x) * w / (d * d); - delta[i] *= derivative; + const float THRESHOLD = 20.0f; + + // implementation from TensorFlow: https://github.com/tensorflow/addons/commit/093cdfa85d334cbe19a37624c33198f3140109ed + // implementation from Pytorch: https://github.com/thomasbrandon/mish-cuda/blob/master/csrc/mish.h#L26-L31 + float inp = activation_input[i]; + const float sp = (inp < THRESHOLD) ? log1p(exp(inp)) : inp; + const float grad_sp = 1 - exp(-sp); + const float tsp = tanh(sp); + const float grad_tsp = (1 - tsp*tsp) * grad_sp; + const float grad = inp * grad_tsp + tsp; + delta[i] *= grad; + + + //float x = activation_input[i]; + //float d = 2 * expf(x) + expf(2 * x) + 2; + //float w = 4 * (x + 1) + 4 * expf(2 * x) + expf(3 * x) + expf(x)*(4 * x + 6); + //float derivative = expf(x) * w / (d * d); + //delta[i] *= derivative; } } From c516b6cb0a08f82023067a649d10238ff18cf1e1 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 12 Nov 2019 20:20:28 +0300 Subject: [PATCH 0651/1365] Take TopK from obj.data file for Classifier --- src/classifier.c | 9 ++++++--- src/parser.c | 13 ++++++++----- 2 files changed, 14 insertions(+), 8 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index 11fa92df913..86549ed8f40 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -63,6 +63,9 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, char *label_list = option_find_str(options, "labels", "data/labels.list"); char *train_list = option_find_str(options, "train", "data/train.list"); int classes = option_find_int(options, "classes", 2); + int topk_data = option_find_int(options, "top", 5); + char topk_buff[10]; + sprintf(topk_buff, "top%d", topk_data); char **labels = get_labels(label_list); list *plist = get_paths(train_list); @@ -157,14 +160,14 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int draw_precision = 0; if (calc_topk && (i >= calc_topk_for_each || i == net.max_batches)) { iter_topk = i; - topk = validate_classifier_single(datacfg, cfgfile, weightfile, &net, 5); // calc TOP5 - printf("\n accuracy TOP5 = %f \n", topk); + topk = validate_classifier_single(datacfg, cfgfile, weightfile, &net, topk_data); // calc TOP5 + printf("\n accuracy %s = %f \n", topk_buff, topk); draw_precision = 1; } printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); #ifdef OPENCV - draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, "top5", dont_show, mjpeg_port); + draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, topk_buff, dont_show, mjpeg_port); #endif // OPENCV if (i >= (iter_save + 1000)) { diff --git a/src/parser.c b/src/parser.c index 4d259f43a96..e47c2097526 100644 --- a/src/parser.c +++ b/src/parser.c @@ -801,11 +801,14 @@ route_layer parse_route(list *options, size_params params) layer.h = first.h; layer.c = layer.out_c; - if (n > 3) fprintf(stderr, " \t "); - else if (n > 1) fprintf(stderr, " \t "); - else fprintf(stderr, " \t\t "); - - fprintf(stderr, " -> %4d x%4d x%4d \n", layer.w, layer.h, layer.c, layer.out_w, layer.out_h, layer.out_c); + if (n > 3) fprintf(stderr, " \t "); + else if (n > 1) fprintf(stderr, " \t "); + else fprintf(stderr, " \t\t "); + + fprintf(stderr, " "); + if (layer.groups > 1) fprintf(stderr, "%d/%d", layer.group_id, layer.groups); + else fprintf(stderr, " "); + fprintf(stderr, " -> %4d x%4d x%4d \n", layer.out_w, layer.out_h, layer.out_c); return layer; } From 11142d00bedbafb015991fb20a05a5eb048200d6 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 13 Nov 2019 20:58:53 +0300 Subject: [PATCH 0652/1365] Fixed non-square network for Training Classifier --- src/classifier.c | 1 + src/convolutional_layer.c | 1 + src/data.c | 36 ++++++++++++++++++++---------------- src/data.h | 6 +++--- 4 files changed, 25 insertions(+), 19 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index 86549ed8f40..c5f8e2f18ca 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -77,6 +77,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, load_args args = {0}; args.w = net.w; args.h = net.h; + args.c = net.c; args.threads = 32; args.hierarchy = net.hierarchy; diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 92e72732c1c..f9d66ebf3b6 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -609,6 +609,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, //fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c); l.bflops = (2.0 * l.nweights * l.out_h*l.out_w) / 1000000000.; + if (l.xnor) l.bflops = l.bflops / 32; if (l.xnor && l.use_bin_output) fprintf(stderr, "convXB"); else if (l.xnor) fprintf(stderr, "convX "); else if (l.share_layer) fprintf(stderr, "convS "); diff --git a/src/data.c b/src/data.c index 622e401ef75..f4b64b998e3 100644 --- a/src/data.c +++ b/src/data.c @@ -142,7 +142,7 @@ matrix load_image_paths(char **paths, int n, int w, int h) return X; } -matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure) +matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure) { int i; matrix X; @@ -151,6 +151,7 @@ matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int X.cols = 0; for(i = 0; i < n; ++i){ + int size = w > h ? w : h; image im = load_image_color(paths[i], 0, 0); image crop = random_augment_image(im, angle, aspect, min, max, size); int flip = use_flip ? random_gen() % 2 : 0; @@ -158,14 +159,17 @@ matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int flip_image(crop); random_distort_image(crop, hue, saturation, exposure); - /* - show_image(im, "orig"); - show_image(crop, "crop"); - cvWaitKey(0); - */ + image sized = resize_image(crop, w, h); + + //show_image(im, "orig"); + //show_image(sized, "sized"); + //show_image(sized, paths[i]); + //wait_until_press_key_cv(); + //printf("w = %d, h = %d \n", sized.w, sized.h); + free_image(im); - X.vals[i] = crop.data; - X.cols = crop.h*crop.w*crop.c; + X.vals[i] = sized.data; + X.cols = sized.h*sized.w*sized.c; } return X; } @@ -1165,7 +1169,7 @@ void *load_thread(void *ptr) if (a.type == OLD_CLASSIFICATION_DATA){ *a.d = load_data_old(a.paths, a.n, a.m, a.labels, a.classes, a.w, a.h); } else if (a.type == CLASSIFICATION_DATA){ - *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.flip, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure); + *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.flip, a.min, a.max, a.w, a.h, a.angle, a.aspect, a.hue, a.saturation, a.exposure); } else if (a.type == SUPER_DATA){ *a.d = load_data_super(a.paths, a.n, a.m, a.w, a.h, a.scale); } else if (a.type == WRITING_DATA){ @@ -1186,7 +1190,7 @@ void *load_thread(void *ptr) *(a.im) = load_image(a.path, 0, 0, a.c); *(a.resized) = letterbox_image(*(a.im), a.w, a.h); } else if (a.type == TAG_DATA){ - *a.d = load_data_tag(a.paths, a.n, a.m, a.classes, a.flip, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure); + *a.d = load_data_tag(a.paths, a.n, a.m, a.classes, a.flip, a.min, a.max, a.w, a.h, a.angle, a.aspect, a.hue, a.saturation, a.exposure); } free(ptr); return 0; @@ -1310,25 +1314,25 @@ data load_data_super(char **paths, int n, int m, int w, int h, int scale) return d; } -data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure) +data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure) { if(m) paths = get_random_paths(paths, n, m); data d = {0}; d.shallow = 0; - d.X = load_image_augment_paths(paths, n, use_flip, min, max, size, angle, aspect, hue, saturation, exposure); + d.X = load_image_augment_paths(paths, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure); d.y = load_labels_paths(paths, n, labels, k, hierarchy); if(m) free(paths); return d; } -data load_data_tag(char **paths, int n, int m, int k, int use_flip, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure) +data load_data_tag(char **paths, int n, int m, int k, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure) { if(m) paths = get_random_paths(paths, n, m); data d = {0}; - d.w = size; - d.h = size; + d.w = w; + d.h = h; d.shallow = 0; - d.X = load_image_augment_paths(paths, n, use_flip, min, max, size, angle, aspect, hue, saturation, exposure); + d.X = load_image_augment_paths(paths, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure); d.y = load_tags_paths(paths, n, k); if(m) free(paths); return d; diff --git a/src/data.h b/src/data.h index 17edc5cf468..250e655a9b5 100644 --- a/src/data.h +++ b/src/data.h @@ -88,10 +88,10 @@ data load_data_captcha_encode(char **paths, int n, int m, int w, int h); data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h); data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int use_blur, int use_mixup, float jitter, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int show_imgs); -data load_data_tag(char **paths, int n, int m, int k, int use_flip, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); -matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); +data load_data_tag(char **paths, int n, int m, int k, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure); +matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure); data load_data_super(char **paths, int n, int m, int w, int h, int scale); -data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); +data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure); data load_go(char *filename); box_label *read_boxes(char *filename, int *n); From a4012895c16aacf82416b3383b567fc143532c51 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 13 Nov 2019 21:12:30 +0300 Subject: [PATCH 0653/1365] accelerated the c++ example of usage DLL/SO library for images if compiled with OpenCV --- src/yolo_console_dll.cpp | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/yolo_console_dll.cpp b/src/yolo_console_dll.cpp index 60da53f6516..f942fe13e3d 100644 --- a/src/yolo_console_dll.cpp +++ b/src/yolo_console_dll.cpp @@ -650,10 +650,12 @@ int main(int argc, char *argv[]) } else { // image file + // to achive high performance for multiple images do these 2 lines in another thread cv::Mat mat_img = cv::imread(filename); + auto det_image = detector.mat_to_image_resize(mat_img); auto start = std::chrono::steady_clock::now(); - std::vector result_vec = detector.detect(mat_img); + std::vector result_vec = detector.detect_resized(*det_image, mat_img.size().width, mat_img.size().height); auto end = std::chrono::steady_clock::now(); std::chrono::duration spent = end - start; std::cout << " Time: " << spent.count() << " sec \n"; From f42923350b9e9eeba372ec9792a2d162d31b0016 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 14 Nov 2019 03:08:35 +0300 Subject: [PATCH 0654/1365] Minor fix in batchnorm_layer (side effect in python samples) --- src/batchnorm_layer.c | 4 ++-- src/parser.c | 1 + 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/src/batchnorm_layer.c b/src/batchnorm_layer.c index 4f9536a99ce..cbcc14d85e4 100644 --- a/src/batchnorm_layer.c +++ b/src/batchnorm_layer.c @@ -182,9 +182,9 @@ void forward_batchnorm_layer_gpu(layer l, network_state state) if (l.type == BATCHNORM) simple_copy_ongpu(l.outputs*l.batch, state.input, l.output_gpu); //copy_ongpu(l.outputs*l.batch, state.input, 1, l.output_gpu, 1); - simple_copy_ongpu(l.outputs*l.batch, l.output_gpu, l.x_gpu); - //copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1); if (state.train) { + simple_copy_ongpu(l.outputs*l.batch, l.output_gpu, l.x_gpu); + //copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1); #ifdef CUDNN float one = 1; float zero = 0; diff --git a/src/parser.c b/src/parser.c index e47c2097526..b05a293f786 100644 --- a/src/parser.c +++ b/src/parser.c @@ -978,6 +978,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) params.batch = net.batch; params.time_steps = net.time_steps; params.net = net; + printf("batch = %d, time_steps = %d, train = %d \n", net.batch, net.time_steps, params.train); float bflops = 0; size_t workspace_size = 0; From 509ba13acf9b6738d9838eb1553a2f40e36c0c2e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 14 Nov 2019 14:14:54 +0300 Subject: [PATCH 0655/1365] Fixed recent memory leak for Classifier --- src/data.c | 1 + 1 file changed, 1 insertion(+) diff --git a/src/data.c b/src/data.c index f4b64b998e3..d15aca09b8c 100644 --- a/src/data.c +++ b/src/data.c @@ -168,6 +168,7 @@ matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int //printf("w = %d, h = %d \n", sized.w, sized.h); free_image(im); + free_image(crop); X.vals[i] = sized.data; X.cols = sized.h*sized.w*sized.c; } From ee370e765d3de505df5657f33dde605625214d0b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 14 Nov 2019 23:18:21 +0300 Subject: [PATCH 0656/1365] Fixed ignore_thresh --- src/yolo_layer.c | 34 ++++++++++++++-------------------- 1 file changed, 14 insertions(+), 20 deletions(-) diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 06d2b513624..42e595c1fcf 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -129,23 +129,18 @@ box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw } -int get_yolo_class(float *output, int classes, int class_index, int stride, float objectness) +int compare_yolo_class(float *output, int classes, int class_index, int stride, float objectness, int class_id) { - int class_id = 0; - float max_prob = FLT_MIN; + const float conf_thresh = 0.25; int j; for (j = 0; j < classes; ++j) { float prob = objectness * output[class_index + stride*j]; - if (prob > max_prob) { - max_prob = prob; - class_id = j; + if (prob > conf_thresh) { + return 1; } - //int class_index = entry_index(l, 0, n*l.w*l.h + i, 4 + 1 + j); - //float prob = objectness*predictions[class_index]; - //dets[count].prob[j] = (prob > thresh) ? prob : 0; } - return class_id; + return 0; } ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss) @@ -280,6 +275,8 @@ void forward_yolo_layer(const layer l, network_state state) for (n = 0; n < l.n; ++n) { int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); box pred = get_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w*l.h); + float best_match_iou = 0; + int best_match_t = 0; float best_iou = 0; int best_t = 0; for (t = 0; t < l.max_boxes; ++t) { @@ -296,14 +293,14 @@ void forward_yolo_layer(const layer l, network_state state) int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4); float objectness = l.output[obj_index]; - int pred_class_id = get_yolo_class(l.output, l.classes, class_index, l.w*l.h, objectness); - int class_id_match = 0; - if (class_id == pred_class_id) class_id_match = 1; - else class_id_match = 0; + int class_id_match = compare_yolo_class(l.output, l.classes, class_index, l.w*l.h, objectness, class_id); float iou = box_iou(pred, truth); - //if (iou > best_iou) { - if (iou > best_iou && class_id_match == 1) { + if (iou > best_match_iou && class_id_match == 1) { + best_match_iou = iou; + best_match_t = t; + } + if (iou > best_iou) { best_iou = iou; best_t = t; } @@ -311,7 +308,7 @@ void forward_yolo_layer(const layer l, network_state state) int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4); avg_anyobj += l.output[obj_index]; l.delta[obj_index] = l.cls_normalizer * (0 - l.output[obj_index]); - if (best_iou > l.ignore_thresh) { + if (best_match_iou > l.ignore_thresh) { l.delta[obj_index] = 0; } if (best_iou > l.truth_thresh) { @@ -376,9 +373,6 @@ void forward_yolo_layer(const layer l, network_state state) ++count; ++class_count; - //if(iou > .5) recall += 1; - //if(iou > .75) recall75 += 1; - //avg_iou += iou; if (all_ious.iou > .5) recall += 1; if (all_ious.iou > .75) recall75 += 1; } From dd34fe156a1a03709b3444bd84d6ccf55f03c835 Mon Sep 17 00:00:00 2001 From: dccho Date: Thu, 14 Nov 2019 15:36:22 -0500 Subject: [PATCH 0657/1365] enable random resize training for efficient net --- src/convolutional_layer.c | 4 ++++ src/dropout_layer.c | 3 ++- src/network.c | 21 ++++++++++++++++----- src/scale_channels_layer.c | 7 ++++--- src/scale_channels_layer.h | 2 +- 5 files changed, 27 insertions(+), 10 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index f9d66ebf3b6..e784f5e4b89 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -745,6 +745,7 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) //l->binary_input = realloc(l->inputs*l->batch, sizeof(float)); } + if (l->activation == SWISH || l->activation == MISH) l->activation_input = (float*)realloc(l->activation_input, total_batch*l->outputs * sizeof(float)); #ifdef GPU if (old_w < w || old_h < h) { if (l->train) { @@ -767,6 +768,9 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) cuda_free(l->binary_input_gpu); l->binary_input_gpu = cuda_make_array(0, l->inputs*l->batch); } + + cuda_free(l->activation_input_gpu); + if (l->activation == SWISH || l->activation == MISH) l->activation_input_gpu = cuda_make_array(l->activation_input, total_batch*l->outputs); } #ifdef CUDNN cudnn_convolutional_setup(l, cudnn_fastest); diff --git a/src/dropout_layer.c b/src/dropout_layer.c index 0d34ed24b2c..c32c5c616bc 100644 --- a/src/dropout_layer.c +++ b/src/dropout_layer.c @@ -27,11 +27,12 @@ dropout_layer make_dropout_layer(int batch, int inputs, float probability) void resize_dropout_layer(dropout_layer *l, int inputs) { + l->inputs = l->outputs = inputs; l->rand = (float*)realloc(l->rand, l->inputs * l->batch * sizeof(float)); #ifdef GPU cuda_free(l->rand_gpu); - l->rand_gpu = cuda_make_array(l->rand, inputs*l->batch); + l->rand_gpu = cuda_make_array(l->rand, l->inputs*l->batch); #endif } diff --git a/src/network.c b/src/network.c index c2249a54df6..96c935d9ce2 100644 --- a/src/network.c +++ b/src/network.c @@ -535,8 +535,16 @@ int resize_network(network *net, int w, int h) resize_route_layer(&l, net); }else if (l.type == SHORTCUT) { resize_shortcut_layer(&l, w, h); - //}else if (l.type == SCALE_CHANNELS) { - // resize_scale_channels_layer(&l, w, h); + }else if (l.type == SCALE_CHANNELS) { + resize_scale_channels_layer(&l, net); + }else if (l.type == DROPOUT) { + resize_dropout_layer(&l, inputs); + l.output = net->layers[i - 1].output; + l.delta = net->layers[i - 1].delta; +#ifdef GPU + l.output_gpu = net->layers[i-1].output_gpu; + l.delta_gpu = net->layers[i-1].delta_gpu; +#endif }else if (l.type == UPSAMPLE) { resize_upsample_layer(&l, w, h); }else if(l.type == REORG){ @@ -556,9 +564,12 @@ int resize_network(network *net, int w, int h) if(l.workspace_size > workspace_size) workspace_size = l.workspace_size; inputs = l.outputs; net->layers[i] = l; - w = l.out_w; - h = l.out_h; - if(l.type == AVGPOOL) break; + if(l.type != DROPOUT) + { + w = l.out_w; + h = l.out_h; + } + //if(l.type == AVGPOOL) break; } #ifdef GPU const int size = get_network_input_size(*net) * net->batch; diff --git a/src/scale_channels_layer.c b/src/scale_channels_layer.c index 80be5361126..bcb54c1b497 100644 --- a/src/scale_channels_layer.c +++ b/src/scale_channels_layer.c @@ -39,10 +39,11 @@ layer make_scale_channels_layer(int batch, int index, int w, int h, int c, int w return l; } -void resize_scale_channels_layer(layer *l, int w, int h) +void resize_scale_channels_layer(layer *l, network *net) { - l->out_w = w; - l->out_h = h; + layer first = net->layers[l->index]; + l->out_w = first.out_w; + l->out_h = first.out_h; l->outputs = l->out_w*l->out_h*l->out_c; l->inputs = l->outputs; l->delta = (float*)realloc(l->delta, l->outputs * l->batch * sizeof(float)); diff --git a/src/scale_channels_layer.h b/src/scale_channels_layer.h index a20c070397b..fdaa4b924fe 100644 --- a/src/scale_channels_layer.h +++ b/src/scale_channels_layer.h @@ -10,7 +10,7 @@ extern "C" { layer make_scale_channels_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2); void forward_scale_channels_layer(const layer l, network_state state); void backward_scale_channels_layer(const layer l, network_state state); -void resize_scale_channels_layer(layer *l, int w, int h); +void resize_scale_channels_layer(layer *l, network *net); #ifdef GPU void forward_scale_channels_layer_gpu(const layer l, network_state state); From e7e85b358cb0531f7154fdd68306c4c4dc96b5d2 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 15 Nov 2019 01:14:55 +0300 Subject: [PATCH 0658/1365] Added delta_yolo_box_accumulate(). Added iou_thresh=0.213 parameter to [yolo] layer IoU(anchor,truth). --- include/darknet.h | 1 + src/parser.c | 1 + src/yolo_layer.c | 100 +++++++++++++++++++++++++++++++++++++++++++++- 3 files changed, 100 insertions(+), 2 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 0acac8a60b7..2bd70d1cde3 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -280,6 +280,7 @@ struct layer { int random; float ignore_thresh; float truth_thresh; + float iou_thresh; float thresh; float focus; int classfix; diff --git a/src/parser.c b/src/parser.c index b05a293f786..472b179d20d 100644 --- a/src/parser.c +++ b/src/parser.c @@ -380,6 +380,7 @@ layer parse_yolo(list *options, size_params params) l.ignore_thresh = option_find_float(options, "ignore_thresh", .5); l.truth_thresh = option_find_float(options, "truth_thresh", 1); + l.iou_thresh = option_find_float_quiet(options, "iou_thresh", 1); // recommended to use iou_thresh=0.213 in [yolo] l.random = option_find_int_quiet(options, "random", 0); char *map_file = option_find_str(options, "map", 0); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 42e595c1fcf..6c02310617a 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -193,6 +193,63 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, return all_ious; } +ious delta_yolo_box_accumulate(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss) +{ + ious all_ious = { 0 }; + // i - step in layer width + // j - step in layer height + // Returns a box in absolute coordinates + box pred = get_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride); + all_ious.iou = box_iou(pred, truth); + all_ious.giou = box_giou(pred, truth); + // avoid nan in dx_box_iou + if (pred.w == 0) { pred.w = 1.0; } + if (pred.h == 0) { pred.h = 1.0; } + if (iou_loss == MSE) // old loss + { + float tx = (truth.x*lw - i); + float ty = (truth.y*lh - j); + float tw = log(truth.w*w / biases[2 * n]); + float th = log(truth.h*h / biases[2 * n + 1]); + + // accumulate delta + delta[index + 0 * stride] += scale * (tx - x[index + 0 * stride]); + delta[index + 1 * stride] += scale * (ty - x[index + 1 * stride]); + delta[index + 2 * stride] += scale * (tw - x[index + 2 * stride]); + delta[index + 3 * stride] += scale * (th - x[index + 3 * stride]); + } + else { + // https://github.com/generalized-iou/g-darknet + // https://arxiv.org/abs/1902.09630v2 + // https://giou.stanford.edu/ + all_ious.dx_iou = dx_box_iou(pred, truth, iou_loss); + + // jacobian^t (transpose) + float dx = (all_ious.dx_iou.dl + all_ious.dx_iou.dr); + float dy = (all_ious.dx_iou.dt + all_ious.dx_iou.db); + float dw = ((-0.5 * all_ious.dx_iou.dl) + (0.5 * all_ious.dx_iou.dr)); + float dh = ((-0.5 * all_ious.dx_iou.dt) + (0.5 * all_ious.dx_iou.db)); + + // predict exponential, apply gradient of e^delta_t ONLY for w,h + dw *= exp(x[index + 2 * stride]); + dh *= exp(x[index + 3 * stride]); + + // normalize iou weight + dx *= iou_normalizer; + dy *= iou_normalizer; + dw *= iou_normalizer; + dh *= iou_normalizer; + + // accumulate delta + delta[index + 0 * stride] += dx; + delta[index + 1 * stride] += dy; + delta[index + 2 * stride] += dw; + delta[index + 3 * stride] += dh; + } + + return all_ious; +} + void delta_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, int focal_loss) { int n; @@ -254,6 +311,7 @@ void forward_yolo_layer(const layer l, network_state state) } #endif + // delta is zeroed memset(l.delta, 0, l.outputs * l.batch * sizeof(float)); if (!state.train) return; //float avg_iou = 0; @@ -319,7 +377,7 @@ void forward_yolo_layer(const layer l, network_state state) int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss); box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); - delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss); + delta_yolo_box_accumulate(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss); } } } @@ -353,7 +411,7 @@ void forward_yolo_layer(const layer l, network_state state) int mask_n = int_index(l.mask, best_n, l.n); if (mask_n >= 0) { int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); - ious all_ious = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss); + ious all_ious = delta_yolo_box_accumulate(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss); // range is 0 <= 1 tot_iou += all_ious.iou; @@ -376,6 +434,44 @@ void forward_yolo_layer(const layer l, network_state state) if (all_ious.iou > .5) recall += 1; if (all_ious.iou > .75) recall75 += 1; } + + // iou_thresh + for (n = 0; n < l.total; ++n) { + int mask_n = int_index(l.mask, n, l.n); + if (mask_n >= 0 && n != best_n) { + box pred = { 0 }; + pred.w = l.biases[2 * n] / state.net.w; + pred.h = l.biases[2 * n + 1] / state.net.h; + float iou = box_iou(pred, truth_shift); + // iou, n + + if (iou > l.iou_thresh) { + int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); + ious all_ious = delta_yolo_box_accumulate(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss); + + // range is 0 <= 1 + tot_iou += all_ious.iou; + tot_iou_loss += 1 - all_ious.iou; + // range is -1 <= giou <= 1 + tot_giou += all_ious.giou; + tot_giou_loss += 1 - all_ious.giou; + + int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4); + avg_obj += l.output[obj_index]; + l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); + + int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; + if (l.map) class_id = l.map[class_id]; + int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); + delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss); + + ++count; + ++class_count; + if (all_ious.iou > .5) recall += 1; + if (all_ious.iou > .75) recall75 += 1; + } + } + } } } //*(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); From 3555beb91401cf746fd357b4029231143d00f58f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 15 Nov 2019 22:51:06 +0300 Subject: [PATCH 0659/1365] Fixed some memory leaks in secondary functions --- src/classifier.c | 2 ++ src/coco.c | 10 ++++++- src/detector.c | 17 ++++++++++- src/gemm.c | 68 ++++++++++++++++++++++---------------------- src/go.c | 1 + src/image_opencv.cpp | 4 ++- src/network.c | 7 ++++- src/utils.c | 4 ++- src/yolo.c | 8 ++++++ 9 files changed, 82 insertions(+), 39 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index c5f8e2f18ca..c077f61ae65 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -1288,4 +1288,6 @@ void run_classifier(int argc, char **argv) else if(0==strcmp(argv[2], "valid10")) validate_classifier_10(data, cfg, weights); else if(0==strcmp(argv[2], "validcrop")) validate_classifier_crop(data, cfg, weights); else if(0==strcmp(argv[2], "validfull")) validate_classifier_full(data, cfg, weights); + + if (gpus && gpu_list && ngpus > 1) free(gpus); } diff --git a/src/coco.c b/src/coco.c index cdfd3dff391..03dd3a61415 100644 --- a/src/coco.c +++ b/src/coco.c @@ -226,6 +226,12 @@ void validate_coco(char *cfgfile, char *weightfile) fprintf(fp, "\n]\n"); fclose(fp); + if (val) free(val); + if (val_resized) free(val_resized); + if (buf) free(buf); + if (buf_resized) free(buf_resized); + if (thr) free(thr); + fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); } @@ -307,7 +313,9 @@ void validate_coco_recall(char *cfgfile, char *weightfile) } fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total); - free(id); + + if (fps) free(fps); + if (id) free(id); free_image(orig); free_image(sized); } diff --git a/src/detector.c b/src/detector.c index efe5571aa8d..7f32c4fa7a6 100644 --- a/src/detector.c +++ b/src/detector.c @@ -556,6 +556,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out for (j = 0; j < classes; ++j) { if (fps) fclose(fps[j]); } + if (fps) free(fps); if (coco) { #ifdef WIN32 fseek(fp, -3, SEEK_CUR); @@ -563,8 +564,15 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out fseek(fp, -2, SEEK_CUR); #endif fprintf(fp, "\n]\n"); - fclose(fp); } + if (fp) fclose(fp); + + if (val) free(val); + if (val_resized) free(val_resized); + if (thr) free(thr); + if (buf) free(buf); + if (buf_resized) free(buf_resized); + fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)time(0) - start); } @@ -1099,6 +1107,11 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa else { free_network(net); } + if (val) free(val); + if (val_resized) free(val_resized); + if (thr) free(thr); + if (buf) free(buf); + if (buf_resized) free(buf_resized); return mean_average_precision; } @@ -1505,4 +1518,6 @@ void run_detector(int argc, char **argv) free_list(options); } else printf(" There isn't such command: %s", argv[2]); + + if (gpus && gpu_list && ngpus > 1) free(gpus); } diff --git a/src/gemm.c b/src/gemm.c index 9f5cb882c61..51f77cac7a9 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -324,7 +324,7 @@ void transpose_32x32_bits_my(uint32_t *A, uint32_t *B, int lda, int ldb) unsigned int x, y; for (y = 0; y < 32; ++y) { for (x = 0; x < 32; ++x) { - if (A[y * lda] & (1 << x)) B[x * ldb] |= (uint32_t)1 << y; + if (A[y * lda] & ((uint32_t)1 << x)) B[x * ldb] |= (uint32_t)1 << y; } } } @@ -636,48 +636,48 @@ void check_cpu_features(void) { // Detect Features if (nIds >= 0x00000001) { cpuid(info, 0x00000001); - HW_MMX = (info[3] & ((int)1 << 23)) != 0; - HW_SSE = (info[3] & ((int)1 << 25)) != 0; - HW_SSE2 = (info[3] & ((int)1 << 26)) != 0; - HW_SSE3 = (info[2] & ((int)1 << 0)) != 0; + HW_MMX = (info[3] & ((uint32_t)1 << 23)) != 0; + HW_SSE = (info[3] & ((uint32_t)1 << 25)) != 0; + HW_SSE2 = (info[3] & ((uint32_t)1 << 26)) != 0; + HW_SSE3 = (info[2] & ((uint32_t)1 << 0)) != 0; - HW_SSSE3 = (info[2] & ((int)1 << 9)) != 0; - HW_SSE41 = (info[2] & ((int)1 << 19)) != 0; - HW_SSE42 = (info[2] & ((int)1 << 20)) != 0; - HW_AES = (info[2] & ((int)1 << 25)) != 0; + HW_SSSE3 = (info[2] & ((uint32_t)1 << 9)) != 0; + HW_SSE41 = (info[2] & ((uint32_t)1 << 19)) != 0; + HW_SSE42 = (info[2] & ((uint32_t)1 << 20)) != 0; + HW_AES = (info[2] & ((uint32_t)1 << 25)) != 0; - HW_AVX = (info[2] & ((int)1 << 28)) != 0; - HW_FMA3 = (info[2] & ((int)1 << 12)) != 0; + HW_AVX = (info[2] & ((uint32_t)1 << 28)) != 0; + HW_FMA3 = (info[2] & ((uint32_t)1 << 12)) != 0; - HW_RDRAND = (info[2] & ((int)1 << 30)) != 0; + HW_RDRAND = (info[2] & ((uint32_t)1 << 30)) != 0; } if (nIds >= 0x00000007) { cpuid(info, 0x00000007); - HW_AVX2 = (info[1] & ((int)1 << 5)) != 0; - - HW_BMI1 = (info[1] & ((int)1 << 3)) != 0; - HW_BMI2 = (info[1] & ((int)1 << 8)) != 0; - HW_ADX = (info[1] & ((int)1 << 19)) != 0; - HW_SHA = (info[1] & ((int)1 << 29)) != 0; - HW_PREFETCHWT1 = (info[2] & ((int)1 << 0)) != 0; - - HW_AVX512F = (info[1] & ((int)1 << 16)) != 0; - HW_AVX512CD = (info[1] & ((int)1 << 28)) != 0; - HW_AVX512PF = (info[1] & ((int)1 << 26)) != 0; - HW_AVX512ER = (info[1] & ((int)1 << 27)) != 0; - HW_AVX512VL = (info[1] & ((int)1 << 31)) != 0; - HW_AVX512BW = (info[1] & ((int)1 << 30)) != 0; - HW_AVX512DQ = (info[1] & ((int)1 << 17)) != 0; - HW_AVX512IFMA = (info[1] & ((int)1 << 21)) != 0; - HW_AVX512VBMI = (info[2] & ((int)1 << 1)) != 0; + HW_AVX2 = (info[1] & ((uint32_t)1 << 5)) != 0; + + HW_BMI1 = (info[1] & ((uint32_t)1 << 3)) != 0; + HW_BMI2 = (info[1] & ((uint32_t)1 << 8)) != 0; + HW_ADX = (info[1] & ((uint32_t)1 << 19)) != 0; + HW_SHA = (info[1] & ((uint32_t)1 << 29)) != 0; + HW_PREFETCHWT1 = (info[2] & ((uint32_t)1 << 0)) != 0; + + HW_AVX512F = (info[1] & ((uint32_t)1 << 16)) != 0; + HW_AVX512CD = (info[1] & ((uint32_t)1 << 28)) != 0; + HW_AVX512PF = (info[1] & ((uint32_t)1 << 26)) != 0; + HW_AVX512ER = (info[1] & ((uint32_t)1 << 27)) != 0; + HW_AVX512VL = (info[1] & ((uint32_t)1 << 31)) != 0; + HW_AVX512BW = (info[1] & ((uint32_t)1 << 30)) != 0; + HW_AVX512DQ = (info[1] & ((uint32_t)1 << 17)) != 0; + HW_AVX512IFMA = (info[1] & ((uint32_t)1 << 21)) != 0; + HW_AVX512VBMI = (info[2] & ((uint32_t)1 << 1)) != 0; } if (nExIds >= 0x80000001) { cpuid(info, 0x80000001); - HW_x64 = (info[3] & ((int)1 << 29)) != 0; - HW_ABM = (info[2] & ((int)1 << 5)) != 0; - HW_SSE4a = (info[2] & ((int)1 << 6)) != 0; - HW_FMA4 = (info[2] & ((int)1 << 16)) != 0; - HW_XOP = (info[2] & ((int)1 << 11)) != 0; + HW_x64 = (info[3] & ((uint32_t)1 << 29)) != 0; + HW_ABM = (info[2] & ((uint32_t)1 << 5)) != 0; + HW_SSE4a = (info[2] & ((uint32_t)1 << 6)) != 0; + HW_FMA4 = (info[2] & ((uint32_t)1 << 16)) != 0; + HW_XOP = (info[2] & ((uint32_t)1 << 11)) != 0; } } diff --git a/src/go.c b/src/go.c index 5d507768e50..88da6c0d14d 100644 --- a/src/go.c +++ b/src/go.c @@ -47,6 +47,7 @@ moves load_go_moves(char *filename) printf("%d\n", count); m.n = count; m.data = (char**)realloc(m.data, count * sizeof(char*)); + fclose(fp); return m; } diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 6951fb9a80e..0a8ccd9841e 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -703,11 +703,12 @@ int set_capture_position_frame_cv(cap_cv *cap, int index) image get_image_from_stream_cpp(cap_cv *cap) { - cv::Mat *src = new cv::Mat(); + cv::Mat *src = NULL; static int once = 1; if (once) { once = 0; do { + if (src) delete src; src = get_capture_frame_cv(cap); if (!src) return make_empty_image(0, 0, 0); } while (src->cols < 1 || src->rows < 1 || src->channels() < 1); @@ -719,6 +720,7 @@ image get_image_from_stream_cpp(cap_cv *cap) if (!src) return make_empty_image(0, 0, 0); image im = mat_to_image(*src); rgbgr_image(im); + if (src) delete src; return im; } // ---------------------------------------- diff --git a/src/network.c b/src/network.c index c2249a54df6..15c9a9bca80 100644 --- a/src/network.c +++ b/src/network.c @@ -809,6 +809,7 @@ char *detection_to_json(detection *dets, int nboxes, int classes, char **names, const float thresh = 0.005; // function get_network_boxes() has already filtred dets by actual threshold char *send_buf = (char *)calloc(1024, sizeof(char)); + if (!send_buf) return 0; if (filename) { sprintf(send_buf, "{\n \"frame_id\":%lld, \n \"filename\":\"%s\", \n \"objects\": [ \n", frame_id, filename); } @@ -826,6 +827,7 @@ char *detection_to_json(detection *dets, int nboxes, int classes, char **names, if (class_id != -1) strcat(send_buf, ", \n"); class_id = j; char *buf = (char *)calloc(2048, sizeof(char)); + if (!buf) return 0; //sprintf(buf, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f}", // image_id, j, dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h, dets[i].prob[j]); @@ -836,7 +838,10 @@ char *detection_to_json(detection *dets, int nboxes, int classes, char **names, int buf_len = strlen(buf); int total_len = send_buf_len + buf_len + 100; send_buf = (char *)realloc(send_buf, total_len * sizeof(char)); - if (!send_buf) return 0;// exit(-1); + if (!send_buf) { + if (buf) free(buf); + return 0;// exit(-1); + } strcat(send_buf, buf); free(buf); } diff --git a/src/utils.c b/src/utils.c index bee427ed743..f18769ce76b 100644 --- a/src/utils.c +++ b/src/utils.c @@ -41,6 +41,7 @@ int *read_map(char *filename) map = (int*)realloc(map, n * sizeof(int)); map[n-1] = atoi(str); } + if (file) fclose(file); return map; } @@ -65,6 +66,7 @@ void shuffle(void *arr, size_t n, size_t size) memcpy((char*)arr+(j*size), (char*)arr+(i*size), size); memcpy((char*)arr+(i*size), swp, size); } + free(swp); } void del_arg(int argc, char **argv, int index) @@ -685,9 +687,9 @@ int max_index(float *a, int n) int top_max_index(float *a, int n, int k) { + if (n <= 0) return -1; float *values = (float*)calloc(k, sizeof(float)); int *indexes = (int*)calloc(k, sizeof(int)); - if (n <= 0) return -1; int i, j; for (i = 0; i < n; ++i) { for (j = 0; j < k; ++j) { diff --git a/src/yolo.c b/src/yolo.c index 711470eade2..339d49cd5ce 100644 --- a/src/yolo.c +++ b/src/yolo.c @@ -189,6 +189,14 @@ void validate_yolo(char *cfgfile, char *weightfile) free_image(val_resized[t]); } } + + if (fps) free(fps); + if (val) free(val); + if (val_resized) free(val_resized); + if (buf) free(buf); + if (buf_resized) free(buf_resized); + if (thr) free(thr); + fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); } From 71e835458904f782a905a06d28b4558d9e9830b4 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 15 Nov 2019 23:33:16 +0300 Subject: [PATCH 0660/1365] Fixed source and destination overlap in sprintf() (i.e. in strcpy() inside) --- src/utils.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/utils.c b/src/utils.c index f18769ce76b..4651cc0a1e8 100644 --- a/src/utils.c +++ b/src/utils.c @@ -218,7 +218,7 @@ void find_replace_extension(char *str, char *orig, char *rep, char *output) int offset = (p - buffer); int chars_from_end = strlen(buffer) - offset; if (!p || chars_from_end != strlen(orig)) { // Is 'orig' even in 'str' AND is 'orig' found at the end of 'str'? - sprintf(output, "%s", str); + sprintf(output, "%s", buffer); free(buffer); return; } From 0cf4c16c9fca93502333fef383e1287b38ca38f8 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 16 Nov 2019 16:50:01 +0300 Subject: [PATCH 0661/1365] Added GIoU to [Gaussian_yolo]. Added iou_thresh=0.213 to [Gaussian_yolo]. --- src/gaussian_yolo_layer.c | 188 +++++++++++++++++++++++++++++++++----- src/parser.c | 18 +++- src/yolo_layer.c | 117 ++++++++++-------------- 3 files changed, 225 insertions(+), 98 deletions(-) diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index 3b58cc5a404..b834c0b13ce 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -81,7 +81,7 @@ layer make_gaussian_yolo_layer(int batch, int w, int h, int n, int total, int *m */ #endif - fprintf(stderr, "Gaussian_yolo\n"); + //fprintf(stderr, "Gaussian_yolo\n"); srand(time(0)); return l; @@ -140,32 +140,70 @@ box get_gaussian_yolo_box(float *x, float *biases, int n, int index, int i, int return b; } -float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride) +float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss, int accumulate) { box pred = get_gaussian_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride); - float iou = box_iou(pred, truth); - float tx = (truth.x*lw - i); - float ty = (truth.y*lh - j); - float tw = log(truth.w*w / biases[2*n]); - float th = log(truth.h*h / biases[2*n + 1]); + float iou; + ious all_ious = { 0 }; + all_ious.iou = box_iou(pred, truth); + all_ious.giou = box_giou(pred, truth); + if (pred.w == 0) { pred.w = 1.0; } + if (pred.h == 0) { pred.h = 1.0; } float sigma_const = 0.3; float epsi = pow(10,-9); - float in_exp_x = (tx - x[index + 0*stride])/x[index+1*stride]; + float dx, dy, dw, dh; + + if (iou_loss == MSE) { + iou = all_ious.iou; + + float tx = (truth.x*lw - i); + float ty = (truth.y*lh - j); + float tw = log(truth.w*w / biases[2 * n]); + float th = log(truth.h*h / biases[2 * n + 1]); + + dx = (tx - x[index + 0 * stride]); + dy = (ty - x[index + 2 * stride]); + dw = (tw - x[index + 4 * stride]); + dh = (th - x[index + 6 * stride]); + } + else + { + iou = all_ious.giou; + + // https://github.com/generalized-iou/g-darknet + // https://arxiv.org/abs/1902.09630v2 + // https://giou.stanford.edu/ + all_ious.dx_iou = dx_box_iou(pred, truth, iou_loss); + + // jacobian^t (transpose) + dx = (all_ious.dx_iou.dl + all_ious.dx_iou.dr); + dy = (all_ious.dx_iou.dt + all_ious.dx_iou.db); + dw = ((-0.5 * all_ious.dx_iou.dl) + (0.5 * all_ious.dx_iou.dr)); + dh = ((-0.5 * all_ious.dx_iou.dt) + (0.5 * all_ious.dx_iou.db)); + + // normalize iou weight + dx *= iou_normalizer; + dy *= iou_normalizer; + dw *= iou_normalizer; + dh *= iou_normalizer; + } + + float in_exp_x = dx / x[index+1*stride]; float in_exp_x_2 = pow(in_exp_x, 2); float normal_dist_x = exp(in_exp_x_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+1*stride]+sigma_const)); - float in_exp_y = (ty - x[index + 2*stride])/x[index+3*stride]; + float in_exp_y = dy / x[index+3*stride]; float in_exp_y_2 = pow(in_exp_y, 2); float normal_dist_y = exp(in_exp_y_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+3*stride]+sigma_const)); - float in_exp_w = (tw - x[index + 4*stride])/x[index+5*stride]; + float in_exp_w = dw / x[index+5*stride]; float in_exp_w_2 = pow(in_exp_w, 2); float normal_dist_w = exp(in_exp_w_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+5*stride]+sigma_const)); - float in_exp_h = (th - x[index + 6*stride])/x[index+7*stride]; + float in_exp_h = dh / x[index+7*stride]; float in_exp_h_2 = pow(in_exp_h, 2); float normal_dist_h = exp(in_exp_h_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+7*stride]+sigma_const)); @@ -174,15 +212,26 @@ float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int ind float temp_w = (1./2.) * 1./(normal_dist_w+epsi) * normal_dist_w * scale; float temp_h = (1./2.) * 1./(normal_dist_h+epsi) * normal_dist_h * scale; - delta[index + 0*stride] = temp_x * in_exp_x * (1./x[index+1*stride]); - delta[index + 2*stride] = temp_y * in_exp_y * (1./x[index+3*stride]); - delta[index + 4*stride] = temp_w * in_exp_w * (1./x[index+5*stride]); - delta[index + 6*stride] = temp_h * in_exp_h * (1./x[index+7*stride]); + if (!accumulate) { + delta[index + 0 * stride] = 0; + delta[index + 1 * stride] = 0; + delta[index + 2 * stride] = 0; + delta[index + 3 * stride] = 0; + delta[index + 4 * stride] = 0; + delta[index + 5 * stride] = 0; + delta[index + 6 * stride] = 0; + delta[index + 7 * stride] = 0; + } - delta[index + 1*stride] = temp_x * (in_exp_x_2/x[index+1*stride] - 1./(x[index+1*stride]+sigma_const)); - delta[index + 3*stride] = temp_y * (in_exp_y_2/x[index+3*stride] - 1./(x[index+3*stride]+sigma_const)); - delta[index + 5*stride] = temp_w * (in_exp_w_2/x[index+5*stride] - 1./(x[index+5*stride]+sigma_const)); - delta[index + 7*stride] = temp_h * (in_exp_h_2/x[index+7*stride] - 1./(x[index+7*stride]+sigma_const)); + delta[index + 0*stride] += temp_x * in_exp_x * (1./x[index+1*stride]); + delta[index + 2*stride] += temp_y * in_exp_y * (1./x[index+3*stride]); + delta[index + 4*stride] += temp_w * in_exp_w * (1./x[index+5*stride]); + delta[index + 6*stride] += temp_h * in_exp_h * (1./x[index+7*stride]); + + delta[index + 1*stride] += temp_x * (in_exp_x_2/x[index+1*stride] - 1./(x[index+1*stride]+sigma_const)); + delta[index + 3*stride] += temp_y * (in_exp_y_2/x[index+3*stride] - 1./(x[index+3*stride]+sigma_const)); + delta[index + 5*stride] += temp_w * (in_exp_w_2/x[index+5*stride] - 1./(x[index+5*stride]+sigma_const)); + delta[index + 7*stride] += temp_h * (in_exp_h_2/x[index+7*stride] - 1./(x[index+7*stride]+sigma_const)); return iou; } @@ -201,6 +250,18 @@ void delta_gaussian_yolo_class(float *output, float *delta, int index, int class } } +int compare_gaussian_yolo_class(float *output, int classes, int class_index, int stride, float objectness, int class_id, float conf_thresh) +{ + int j; + for (j = 0; j < classes; ++j) { + float prob = objectness * output[class_index + stride*j]; + if (prob > conf_thresh) { + return 1; + } + } + return 0; +} + static int entry_gaussian_index(layer l, int batch, int location, int entry) { int n = location / (l.w*l.h); @@ -254,12 +315,31 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) for (n = 0; n < l.n; ++n) { int box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0); box pred = get_gaussian_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w*l.h); + float best_match_iou = 0; + int best_match_t = 0; float best_iou = 0; int best_t = 0; for(t = 0; t < l.max_boxes; ++t){ box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); + int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; + if (class_id >= l.classes) { + printf(" Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1); + printf(" truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f, class_id = %d \n", truth.x, truth.y, truth.w, truth.h, class_id); + getchar(); + continue; // if label contains class_id more than number of classes in the cfg-file + } if(!truth.x) break; + + int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9); + int obj_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 8); + float objectness = l.output[obj_index]; + int class_id_match = compare_yolo_class(l.output, l.classes, class_index, l.w*l.h, objectness, class_id, 0.25f); + float iou = box_iou(pred, truth); + if (iou > best_match_iou && class_id_match == 1) { + best_match_iou = iou; + best_match_t = t; + } if (iou > best_iou) { best_iou = iou; best_t = t; @@ -267,19 +347,19 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) } int obj_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 8); avg_anyobj += l.output[obj_index]; - l.delta[obj_index] = 0 - l.output[obj_index]; - if (best_iou > l.ignore_thresh) { + l.delta[obj_index] = l.cls_normalizer * (0 - l.output[obj_index]); + if (best_match_iou > l.ignore_thresh) { l.delta[obj_index] = 0; } if (best_iou > l.truth_thresh) { - l.delta[obj_index] = 1 - l.output[obj_index]; + l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); int class_id = state.truth[best_t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9); delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0); box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); - delta_gaussian_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h); + delta_gaussian_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, 1); } } } @@ -308,11 +388,11 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int mask_n = int_index(l.mask, best_n, l.n); if(mask_n >= 0){ int box_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); - float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h); + float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, 1); int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8); avg_obj += l.output[obj_index]; - l.delta[obj_index] = 1 - l.output[obj_index]; + l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; @@ -325,6 +405,64 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) if(iou > .75) recall75 += 1; avg_iou += iou; } + + + // iou_thresh + for (n = 0; n < l.total; ++n) { + int mask_n = int_index(l.mask, n, l.n); + if (mask_n >= 0 && n != best_n && l.iou_thresh < 1.0f) { + box pred = { 0 }; + pred.w = l.biases[2 * n] / state.net.w; + pred.h = l.biases[2 * n + 1] / state.net.h; + float iou = box_iou(pred, truth_shift); + // iou, n + + if (iou > l.iou_thresh) { + int box_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); + float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, 1); + + int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8); + avg_obj += l.output[obj_index]; + l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); + + int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; + if (l.map) class_id = l.map[class_id]; + int class_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 9); + delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat); + + ++count; + ++class_count; + if (iou > .5) recall += 1; + if (iou > .75) recall75 += 1; + avg_iou += iou; + } + } + } + } + + // averages the deltas obtained by the function: delta_yolo_box()_accumulate + for (j = 0; j < l.h; ++j) { + for (i = 0; i < l.w; ++i) { + for (n = 0; n < l.n; ++n) { + int box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0); + int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9); + const int stride = l.w*l.h; + + int classes_in_one_box = 0; + for (n = 0; n < l.classes; ++n) { + if (l.delta[class_index + stride*n] > 0) classes_in_one_box++; + } + + l.delta[box_index + 0 * stride] /= classes_in_one_box; + l.delta[box_index + 1 * stride] /= classes_in_one_box; + l.delta[box_index + 2 * stride] /= classes_in_one_box; + l.delta[box_index + 3 * stride] /= classes_in_one_box; + l.delta[box_index + 4 * stride] /= classes_in_one_box; + l.delta[box_index + 5 * stride] /= classes_in_one_box; + l.delta[box_index + 6 * stride] /= classes_in_one_box; + l.delta[box_index + 7 * stride] /= classes_in_one_box; + } + } } } *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); diff --git a/src/parser.c b/src/parser.c index 472b179d20d..f0533f36e12 100644 --- a/src/parser.c +++ b/src/parser.c @@ -436,14 +436,28 @@ layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3 char *a = option_find_str(options, "mask", 0); int *mask = parse_gaussian_yolo_mask(a, &num); layer l = make_gaussian_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes, max_boxes); - assert(l.outputs == params.inputs); + if (l.outputs != params.inputs) { + printf("Error: l.outputs == params.inputs \n"); + printf("filters= in the [convolutional]-layer doesn't correspond to classes= or mask= in [Gaussian_yolo]-layer \n"); + exit(EXIT_FAILURE); + } + //assert(l.outputs == params.inputs); l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1); - l.max_boxes = option_find_int_quiet(options, "max", 90); + l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75); + l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1); + char *iou_loss = option_find_str_quiet(options, "iou_loss", "mse"); // "iou"); + + if (strcmp(iou_loss, "mse") == 0) l.iou_loss = MSE; + else if (strcmp(iou_loss, "giou") == 0) l.iou_loss = GIOU; + else l.iou_loss = IOU; + fprintf(stderr, "[Gaussian_yolo] iou loss: %s, iou_norm: %2.2f, cls_norm: %2.2f, scale: %2.2f\n", (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.cls_normalizer, l.scale_x_y); + l.jitter = option_find_float(options, "jitter", .2); l.ignore_thresh = option_find_float(options, "ignore_thresh", .5); l.truth_thresh = option_find_float(options, "truth_thresh", 1); + l.iou_thresh = option_find_float_quiet(options, "iou_thresh", 1); // recommended to use iou_thresh=0.213 in [yolo] l.random = option_find_int_quiet(options, "random", 0); char *map_file = option_find_str(options, "map", 0); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 6c02310617a..b663b73da74 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -128,72 +128,7 @@ box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw return b; } - -int compare_yolo_class(float *output, int classes, int class_index, int stride, float objectness, int class_id) -{ - const float conf_thresh = 0.25; - - int j; - for (j = 0; j < classes; ++j) { - float prob = objectness * output[class_index + stride*j]; - if (prob > conf_thresh) { - return 1; - } - } - return 0; -} - -ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss) -{ - ious all_ious = { 0 }; - // i - step in layer width - // j - step in layer height - // Returns a box in absolute coordinates - box pred = get_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride); - all_ious.iou = box_iou(pred, truth); - all_ious.giou = box_giou(pred, truth); - // avoid nan in dx_box_iou - if (pred.w == 0) { pred.w = 1.0; } - if (pred.h == 0) { pred.h = 1.0; } - if (iou_loss == MSE) // old loss - { - float tx = (truth.x*lw - i); - float ty = (truth.y*lh - j); - float tw = log(truth.w*w / biases[2 * n]); - float th = log(truth.h*h / biases[2 * n + 1]); - - delta[index + 0 * stride] = scale * (tx - x[index + 0 * stride]); - delta[index + 1 * stride] = scale * (ty - x[index + 1 * stride]); - delta[index + 2 * stride] = scale * (tw - x[index + 2 * stride]); - delta[index + 3 * stride] = scale * (th - x[index + 3 * stride]); - } - else { - // https://github.com/generalized-iou/g-darknet - // https://arxiv.org/abs/1902.09630v2 - // https://giou.stanford.edu/ - all_ious.dx_iou = dx_box_iou(pred, truth, iou_loss); - - // jacobian^t (transpose) - delta[index + 0 * stride] = (all_ious.dx_iou.dl + all_ious.dx_iou.dr); - delta[index + 1 * stride] = (all_ious.dx_iou.dt + all_ious.dx_iou.db); - delta[index + 2 * stride] = ((-0.5 * all_ious.dx_iou.dl) + (0.5 * all_ious.dx_iou.dr)); - delta[index + 3 * stride] = ((-0.5 * all_ious.dx_iou.dt) + (0.5 * all_ious.dx_iou.db)); - - // predict exponential, apply gradient of e^delta_t ONLY for w,h - delta[index + 2 * stride] *= exp(x[index + 2 * stride]); - delta[index + 3 * stride] *= exp(x[index + 3 * stride]); - - // normalize iou weight - delta[index + 0 * stride] *= iou_normalizer; - delta[index + 1 * stride] *= iou_normalizer; - delta[index + 2 * stride] *= iou_normalizer; - delta[index + 3 * stride] *= iou_normalizer; - } - - return all_ious; -} - -ious delta_yolo_box_accumulate(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss) +ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss, int accumulate) { ious all_ious = { 0 }; // i - step in layer width @@ -240,6 +175,13 @@ ious delta_yolo_box_accumulate(box truth, float *x, float *biases, int n, int in dw *= iou_normalizer; dh *= iou_normalizer; + if (!accumulate) { + delta[index + 0 * stride] = 0; + delta[index + 1 * stride] = 0; + delta[index + 2 * stride] = 0; + delta[index + 3 * stride] = 0; + } + // accumulate delta delta[index + 0 * stride] += dx; delta[index + 1 * stride] += dy; @@ -287,6 +229,18 @@ void delta_yolo_class(float *output, float *delta, int index, int class_id, int } } +int compare_yolo_class(float *output, int classes, int class_index, int stride, float objectness, int class_id, float conf_thresh) +{ + int j; + for (j = 0; j < classes; ++j) { + float prob = objectness * output[class_index + stride*j]; + if (prob > conf_thresh) { + return 1; + } + } + return 0; +} + static int entry_index(layer l, int batch, int location, int entry) { int n = location / (l.w*l.h); @@ -351,7 +305,7 @@ void forward_yolo_layer(const layer l, network_state state) int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4); float objectness = l.output[obj_index]; - int class_id_match = compare_yolo_class(l.output, l.classes, class_index, l.w*l.h, objectness, class_id); + int class_id_match = compare_yolo_class(l.output, l.classes, class_index, l.w*l.h, objectness, class_id, 0.25f); float iou = box_iou(pred, truth); if (iou > best_match_iou && class_id_match == 1) { @@ -377,7 +331,7 @@ void forward_yolo_layer(const layer l, network_state state) int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss); box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); - delta_yolo_box_accumulate(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss); + delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, 1); } } } @@ -411,7 +365,7 @@ void forward_yolo_layer(const layer l, network_state state) int mask_n = int_index(l.mask, best_n, l.n); if (mask_n >= 0) { int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); - ious all_ious = delta_yolo_box_accumulate(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss); + ious all_ious = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, 1); // range is 0 <= 1 tot_iou += all_ious.iou; @@ -438,7 +392,7 @@ void forward_yolo_layer(const layer l, network_state state) // iou_thresh for (n = 0; n < l.total; ++n) { int mask_n = int_index(l.mask, n, l.n); - if (mask_n >= 0 && n != best_n) { + if (mask_n >= 0 && n != best_n && l.iou_thresh < 1.0f) { box pred = { 0 }; pred.w = l.biases[2 * n] / state.net.w; pred.h = l.biases[2 * n + 1] / state.net.h; @@ -447,7 +401,7 @@ void forward_yolo_layer(const layer l, network_state state) if (iou > l.iou_thresh) { int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); - ious all_ious = delta_yolo_box_accumulate(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss); + ious all_ious = delta_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, 1); // range is 0 <= 1 tot_iou += all_ious.iou; @@ -473,6 +427,27 @@ void forward_yolo_layer(const layer l, network_state state) } } } + + // averages the deltas obtained by the function: delta_yolo_box()_accumulate + for (j = 0; j < l.h; ++j) { + for (i = 0; i < l.w; ++i) { + for (n = 0; n < l.n; ++n) { + int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); + int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); + const int stride = l.w*l.h; + + int classes_in_one_box = 0; + for (n = 0; n < l.classes; ++n) { + if (l.delta[class_index + stride*n] > 0) classes_in_one_box++; + } + + l.delta[box_index + 0 * stride] /= classes_in_one_box; + l.delta[box_index + 1 * stride] /= classes_in_one_box; + l.delta[box_index + 2 * stride] /= classes_in_one_box; + l.delta[box_index + 3 * stride] /= classes_in_one_box; + } + } + } } //*(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); //printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d\n", state.index, avg_iou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count); From 0d30db35fc2d3d8edf52ef2870c7f18b1a6eabe3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 16 Nov 2019 17:14:50 +0300 Subject: [PATCH 0662/1365] gaussian_yolo: added uc_normalizer and minor fix for iou_normalizer for GIoU. --- src/gaussian_yolo_layer.c | 59 ++++++++++++++++++++++++++------------- 1 file changed, 40 insertions(+), 19 deletions(-) diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index b834c0b13ce..7200fecd61b 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -140,7 +140,7 @@ box get_gaussian_yolo_box(float *x, float *biases, int n, int index, int i, int return b; } -float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss, int accumulate) +float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss, float uc_normalizer, int accumulate) { box pred = get_gaussian_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride); @@ -157,6 +157,7 @@ float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int ind float dx, dy, dw, dh; if (iou_loss == MSE) { + // MSE iou = all_ious.iou; float tx = (truth.x*lw - i); @@ -171,6 +172,7 @@ float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int ind } else { + // GIoU iou = all_ious.giou; // https://github.com/generalized-iou/g-darknet @@ -183,14 +185,9 @@ float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int ind dy = (all_ious.dx_iou.dt + all_ious.dx_iou.db); dw = ((-0.5 * all_ious.dx_iou.dl) + (0.5 * all_ious.dx_iou.dr)); dh = ((-0.5 * all_ious.dx_iou.dt) + (0.5 * all_ious.dx_iou.db)); - - // normalize iou weight - dx *= iou_normalizer; - dy *= iou_normalizer; - dw *= iou_normalizer; - dh *= iou_normalizer; } + // Gaussian float in_exp_x = dx / x[index+1*stride]; float in_exp_x_2 = pow(in_exp_x, 2); float normal_dist_x = exp(in_exp_x_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+1*stride]+sigma_const)); @@ -223,15 +220,39 @@ float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int ind delta[index + 7 * stride] = 0; } - delta[index + 0*stride] += temp_x * in_exp_x * (1./x[index+1*stride]); - delta[index + 2*stride] += temp_y * in_exp_y * (1./x[index+3*stride]); - delta[index + 4*stride] += temp_w * in_exp_w * (1./x[index+5*stride]); - delta[index + 6*stride] += temp_h * in_exp_h * (1./x[index+7*stride]); - - delta[index + 1*stride] += temp_x * (in_exp_x_2/x[index+1*stride] - 1./(x[index+1*stride]+sigma_const)); - delta[index + 3*stride] += temp_y * (in_exp_y_2/x[index+3*stride] - 1./(x[index+3*stride]+sigma_const)); - delta[index + 5*stride] += temp_w * (in_exp_w_2/x[index+5*stride] - 1./(x[index+5*stride]+sigma_const)); - delta[index + 7*stride] += temp_h * (in_exp_h_2/x[index+7*stride] - 1./(x[index+7*stride]+sigma_const)); + float delta_x = temp_x * in_exp_x * (1. / x[index + 1 * stride]); + float delta_y = temp_y * in_exp_y * (1. / x[index + 3 * stride]); + float delta_w = temp_w * in_exp_w * (1. / x[index + 5 * stride]); + float delta_h = temp_h * in_exp_h * (1. / x[index + 7 * stride]); + + float delta_ux = temp_x * (in_exp_x_2 / x[index + 1 * stride] - 1. / (x[index + 1 * stride] + sigma_const)); + float delta_uy = temp_y * (in_exp_y_2 / x[index + 3 * stride] - 1. / (x[index + 3 * stride] + sigma_const)); + float delta_uw = temp_w * (in_exp_w_2 / x[index + 5 * stride] - 1. / (x[index + 5 * stride] + sigma_const)); + float delta_uh = temp_h * (in_exp_h_2 / x[index + 7 * stride] - 1. / (x[index + 7 * stride] + sigma_const)); + + if (iou_loss != MSE) { + // normalize iou weight, for GIoU + delta_x *= iou_normalizer; + delta_y *= iou_normalizer; + delta_w *= iou_normalizer; + delta_h *= iou_normalizer; + } + // normalize Uncertainty weight + delta_ux *= uc_normalizer; + delta_uy *= uc_normalizer; + delta_uw *= uc_normalizer; + delta_uh *= uc_normalizer; + + + delta[index + 0 * stride] += delta_x; + delta[index + 2 * stride] += delta_y; + delta[index + 4 * stride] += delta_w; + delta[index + 6 * stride] += delta_h; + + delta[index + 1 * stride] += delta_ux; + delta[index + 3 * stride] += delta_uy; + delta[index + 5 * stride] += delta_uw; + delta[index + 7 * stride] += delta_uh; return iou; } @@ -359,7 +380,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9); delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0); box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); - delta_gaussian_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, 1); + delta_gaussian_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, l.uc_normalizer, 1); } } } @@ -388,7 +409,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int mask_n = int_index(l.mask, best_n, l.n); if(mask_n >= 0){ int box_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); - float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, 1); + float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, l.uc_normalizer, 1); int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8); avg_obj += l.output[obj_index]; @@ -419,7 +440,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) if (iou > l.iou_thresh) { int box_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); - float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, 1); + float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, l.uc_normalizer, 1); int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8); avg_obj += l.output[obj_index]; From 920de66bfa85a9e694d78b6138114c723c854d7c Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 16 Nov 2019 17:23:21 +0300 Subject: [PATCH 0663/1365] gaussian_yolo: added uc_normalizer and minor fix for iou_normalizer for GIoU. --- src/parser.c | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/parser.c b/src/parser.c index f0533f36e12..3728c4422f9 100644 --- a/src/parser.c +++ b/src/parser.c @@ -444,8 +444,9 @@ layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3 //assert(l.outputs == params.inputs); l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1); + l.uc_normalizer = option_find_float_quiet(options, "uc_normalizer", 1.0); l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75); - l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1); + l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1.0); char *iou_loss = option_find_str_quiet(options, "iou_loss", "mse"); // "iou"); if (strcmp(iou_loss, "mse") == 0) l.iou_loss = MSE; From 6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 16 Nov 2019 18:16:37 +0300 Subject: [PATCH 0664/1365] Bug fix --- build/darknet/x64/cfg/Gaussian_yolov3_BDD.cfg | 807 ++++++++++++++++++ cfg/Gaussian_yolov3_BDD.cfg | 807 ++++++++++++++++++ include/darknet.h | 1 + src/gaussian_yolo_layer.c | 94 +- src/yolo_layer.c | 28 +- 5 files changed, 1715 insertions(+), 22 deletions(-) create mode 100644 build/darknet/x64/cfg/Gaussian_yolov3_BDD.cfg create mode 100644 cfg/Gaussian_yolov3_BDD.cfg diff --git a/build/darknet/x64/cfg/Gaussian_yolov3_BDD.cfg b/build/darknet/x64/cfg/Gaussian_yolov3_BDD.cfg new file mode 100644 index 00000000000..2ca7ec600e3 --- /dev/null +++ b/build/darknet/x64/cfg/Gaussian_yolov3_BDD.cfg @@ -0,0 +1,807 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=16 +width=512 +height=512 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.0001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 +max_epochs = 300 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=57 +activation=linear + + +[Gaussian_yolo] +mask = 6,7,8 +anchors = 7,10, 14,24, 27,43, 32,97, 57,64, 92,109, 73,175, 141,178, 144,291 +classes=10 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +iou_thresh=0.213 +uc_normalizer=1.0 +cls_normalizer=1.0 +iou_normalizer=0.5 +iou_loss=giou +scale_x_y=1.0 +random=1 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=57 +activation=linear + + +[Gaussian_yolo] +mask = 3,4,5 +anchors = 7,10, 14,24, 27,43, 32,97, 57,64, 92,109, 73,175, 141,178, 144,291 +classes=10 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +iou_thresh=0.213 +uc_normalizer=1.0 +cls_normalizer=1.0 +iou_normalizer=0.5 +iou_loss=giou +scale_x_y=1.0 +random=1 + + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=57 +activation=linear + + +[Gaussian_yolo] +mask = 0,1,2 +anchors = 7,10, 14,24, 27,43, 32,97, 57,64, 92,109, 73,175, 141,178, 144,291 +classes=10 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +iou_thresh=0.213 +uc_normalizer=1.0 +cls_normalizer=1.0 +iou_normalizer=0.5 +iou_loss=giou +scale_x_y=1.0 +random=1 diff --git a/cfg/Gaussian_yolov3_BDD.cfg b/cfg/Gaussian_yolov3_BDD.cfg new file mode 100644 index 00000000000..2ca7ec600e3 --- /dev/null +++ b/cfg/Gaussian_yolov3_BDD.cfg @@ -0,0 +1,807 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=16 +width=512 +height=512 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.0001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 +max_epochs = 300 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=57 +activation=linear + + +[Gaussian_yolo] +mask = 6,7,8 +anchors = 7,10, 14,24, 27,43, 32,97, 57,64, 92,109, 73,175, 141,178, 144,291 +classes=10 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +iou_thresh=0.213 +uc_normalizer=1.0 +cls_normalizer=1.0 +iou_normalizer=0.5 +iou_loss=giou +scale_x_y=1.0 +random=1 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=57 +activation=linear + + +[Gaussian_yolo] +mask = 3,4,5 +anchors = 7,10, 14,24, 27,43, 32,97, 57,64, 92,109, 73,175, 141,178, 144,291 +classes=10 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +iou_thresh=0.213 +uc_normalizer=1.0 +cls_normalizer=1.0 +iou_normalizer=0.5 +iou_loss=giou +scale_x_y=1.0 +random=1 + + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=57 +activation=linear + + +[Gaussian_yolo] +mask = 0,1,2 +anchors = 7,10, 14,24, 27,43, 32,97, 57,64, 92,109, 73,175, 141,178, 144,291 +classes=10 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +iou_thresh=0.213 +uc_normalizer=1.0 +cls_normalizer=1.0 +iou_normalizer=0.5 +iou_loss=giou +scale_x_y=1.0 +random=1 diff --git a/include/darknet.h b/include/darknet.h index 2bd70d1cde3..7a906780c61 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -330,6 +330,7 @@ struct layer { float *weight_updates; float scale_x_y; + float uc_normalizer; float iou_normalizer; float cls_normalizer; IOU_LOSS iou_loss; diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index 7200fecd61b..16971437872 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -256,6 +256,26 @@ float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int ind return iou; } +void averages_gaussian_yolo_deltas(int class_index, int box_index, int stride, int classes, float *delta) +{ + + int classes_in_one_box = 0; + int c; + for (c = 0; c < classes; ++c) { + if (delta[class_index + stride*c] > 0) classes_in_one_box++; + } + + if (classes_in_one_box > 0) { + delta[box_index + 0 * stride] /= classes_in_one_box; + delta[box_index + 1 * stride] /= classes_in_one_box; + delta[box_index + 2 * stride] /= classes_in_one_box; + delta[box_index + 3 * stride] /= classes_in_one_box; + delta[box_index + 4 * stride] /= classes_in_one_box; + delta[box_index + 5 * stride] /= classes_in_one_box; + delta[box_index + 6 * stride] /= classes_in_one_box; + delta[box_index + 7 * stride] /= classes_in_one_box; + } +} void delta_gaussian_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat) { @@ -469,25 +489,73 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9); const int stride = l.w*l.h; - int classes_in_one_box = 0; - for (n = 0; n < l.classes; ++n) { - if (l.delta[class_index + stride*n] > 0) classes_in_one_box++; - } + averages_gaussian_yolo_deltas(class_index, box_index, stride, l.classes, l.delta); + } + } + } + } + + + // calculate: Classification-loss, IoU-loss and Uncertainty-loss + const int stride = l.w*l.h; + float* classification_lost = (float *)calloc(l.batch * l.outputs, sizeof(float)); + memcpy(classification_lost, l.delta, l.batch * l.outputs * sizeof(float)); + - l.delta[box_index + 0 * stride] /= classes_in_one_box; - l.delta[box_index + 1 * stride] /= classes_in_one_box; - l.delta[box_index + 2 * stride] /= classes_in_one_box; - l.delta[box_index + 3 * stride] /= classes_in_one_box; - l.delta[box_index + 4 * stride] /= classes_in_one_box; - l.delta[box_index + 5 * stride] /= classes_in_one_box; - l.delta[box_index + 6 * stride] /= classes_in_one_box; - l.delta[box_index + 7 * stride] /= classes_in_one_box; + for (b = 0; b < l.batch; ++b) { + for (j = 0; j < l.h; ++j) { + for (i = 0; i < l.w; ++i) { + for (n = 0; n < l.n; ++n) { + int box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0); + + classification_lost[box_index + 0 * stride] = 0; + classification_lost[box_index + 1 * stride] = 0; + classification_lost[box_index + 2 * stride] = 0; + classification_lost[box_index + 3 * stride] = 0; + classification_lost[box_index + 4 * stride] = 0; + classification_lost[box_index + 5 * stride] = 0; + classification_lost[box_index + 6 * stride] = 0; + classification_lost[box_index + 7 * stride] = 0; } } } } + float class_loss = pow(mag_array(classification_lost, l.outputs * l.batch), 2); + free(classification_lost); + + + float* except_uncertainty_lost = (float *)calloc(l.batch * l.outputs, sizeof(float)); + memcpy(except_uncertainty_lost, l.delta, l.batch * l.outputs * sizeof(float)); + for (b = 0; b < l.batch; ++b) { + for (j = 0; j < l.h; ++j) { + for (i = 0; i < l.w; ++i) { + for (n = 0; n < l.n; ++n) { + int box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0); + except_uncertainty_lost[box_index + 4 * stride] = 0; + except_uncertainty_lost[box_index + 5 * stride] = 0; + except_uncertainty_lost[box_index + 6 * stride] = 0; + except_uncertainty_lost[box_index + 7 * stride] = 0; + } + } + } + } + float except_uc_loss = pow(mag_array(except_uncertainty_lost, l.outputs * l.batch), 2); + free(except_uncertainty_lost); + *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); - printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d\n", state.index, avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, recall75/count, count); + + float loss = pow(mag_array(l.delta, l.outputs * l.batch), 2); + float uc_loss = loss - except_uc_loss; + float iou_loss = except_uc_loss - class_loss; + + loss /= l.batch; + class_loss /= l.batch; + uc_loss /= l.batch; + iou_loss /= l.batch; + + printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d, loss = %.2f, class_loss = %.2f, iou_loss = %.2f, uc_loss = %.2f \n", + state.index, avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, recall75/count, count, + loss, class_loss, iou_loss, uc_loss); } void backward_gaussian_yolo_layer(const layer l, network_state state) diff --git a/src/yolo_layer.c b/src/yolo_layer.c index b663b73da74..34185937459 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -192,6 +192,23 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, return all_ious; } +void averages_yolo_deltas(int class_index, int box_index, int stride, int classes, float *delta) +{ + + int classes_in_one_box = 0; + int c; + for (c = 0; c < classes; ++c) { + if (delta[class_index + stride*c] > 0) classes_in_one_box++; + } + + if (classes_in_one_box > 0) { + delta[box_index + 0 * stride] /= classes_in_one_box; + delta[box_index + 1 * stride] /= classes_in_one_box; + delta[box_index + 2 * stride] /= classes_in_one_box; + delta[box_index + 3 * stride] /= classes_in_one_box; + } +} + void delta_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, int focal_loss) { int n; @@ -436,19 +453,12 @@ void forward_yolo_layer(const layer l, network_state state) int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); const int stride = l.w*l.h; - int classes_in_one_box = 0; - for (n = 0; n < l.classes; ++n) { - if (l.delta[class_index + stride*n] > 0) classes_in_one_box++; - } - - l.delta[box_index + 0 * stride] /= classes_in_one_box; - l.delta[box_index + 1 * stride] /= classes_in_one_box; - l.delta[box_index + 2 * stride] /= classes_in_one_box; - l.delta[box_index + 3 * stride] /= classes_in_one_box; + averages_yolo_deltas(class_index, box_index, stride, l.classes, l.delta); } } } } + //*(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); //printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d\n", state.index, avg_iou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count); From 5d0aa6ec522afd5d859c068f40eded77d6704fa0 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 17 Nov 2019 01:18:28 +0300 Subject: [PATCH 0665/1365] Compile fix --- src/gaussian_yolo_layer.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index 16971437872..93569cf0eac 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -374,7 +374,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9); int obj_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 8); float objectness = l.output[obj_index]; - int class_id_match = compare_yolo_class(l.output, l.classes, class_index, l.w*l.h, objectness, class_id, 0.25f); + int class_id_match = compare_gaussian_yolo_class(l.output, l.classes, class_index, l.w*l.h, objectness, class_id, 0.25f); float iou = box_iou(pred, truth); if (iou > best_match_iou && class_id_match == 1) { From 77e60aecfe7f92de24fb1b96b5554fb16bc0ac06 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 17 Nov 2019 20:42:21 +0300 Subject: [PATCH 0666/1365] Minor fix --- src/box.c | 1 + src/detector.c | 1 + src/gaussian_yolo_layer.c | 70 +++++++++++++++++++++------------------ 3 files changed, 39 insertions(+), 33 deletions(-) diff --git a/src/box.c b/src/box.c index c6a27ed587f..22f85884009 100644 --- a/src/box.c +++ b/src/box.c @@ -425,6 +425,7 @@ int nms_comparator_v3(const void *pa, const void *pb) float diff = 0; if (b.sort_class >= 0) { diff = a.prob[b.sort_class] - b.prob[b.sort_class]; + //diff = a.objectness*a.prob[b.sort_class] - b.objectness*b.prob[b.sort_class]; } else { diff = a.objectness - b.objectness; diff --git a/src/detector.c b/src/detector.c index 7f32c4fa7a6..8177343ef3d 100644 --- a/src/detector.c +++ b/src/detector.c @@ -801,6 +801,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa } //detection *dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letter_box); // for letter_box=1 if (nms) do_nms_sort(dets, nboxes, l.classes, nms); + //if (nms) do_nms_obj(dets, nboxes, l.classes, nms); char labelpath[4096]; replace_image_to_label(path, labelpath); diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index 93569cf0eac..236e28e7d4e 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -156,36 +156,17 @@ float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int ind float dx, dy, dw, dh; - if (iou_loss == MSE) { - // MSE - iou = all_ious.iou; - - float tx = (truth.x*lw - i); - float ty = (truth.y*lh - j); - float tw = log(truth.w*w / biases[2 * n]); - float th = log(truth.h*h / biases[2 * n + 1]); - - dx = (tx - x[index + 0 * stride]); - dy = (ty - x[index + 2 * stride]); - dw = (tw - x[index + 4 * stride]); - dh = (th - x[index + 6 * stride]); - } - else - { - // GIoU - iou = all_ious.giou; + iou = all_ious.iou; - // https://github.com/generalized-iou/g-darknet - // https://arxiv.org/abs/1902.09630v2 - // https://giou.stanford.edu/ - all_ious.dx_iou = dx_box_iou(pred, truth, iou_loss); + float tx = (truth.x*lw - i); + float ty = (truth.y*lh - j); + float tw = log(truth.w*w / biases[2 * n]); + float th = log(truth.h*h / biases[2 * n + 1]); - // jacobian^t (transpose) - dx = (all_ious.dx_iou.dl + all_ious.dx_iou.dr); - dy = (all_ious.dx_iou.dt + all_ious.dx_iou.db); - dw = ((-0.5 * all_ious.dx_iou.dl) + (0.5 * all_ious.dx_iou.dr)); - dh = ((-0.5 * all_ious.dx_iou.dt) + (0.5 * all_ious.dx_iou.db)); - } + dx = (tx - x[index + 0 * stride]); + dy = (ty - x[index + 2 * stride]); + dw = (tw - x[index + 4 * stride]); + dh = (th - x[index + 6 * stride]); // Gaussian float in_exp_x = dx / x[index+1*stride]; @@ -231,19 +212,42 @@ float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int ind float delta_uh = temp_h * (in_exp_h_2 / x[index + 7 * stride] - 1. / (x[index + 7 * stride] + sigma_const)); if (iou_loss != MSE) { + // GIoU + iou = all_ious.giou; + + // https://github.com/generalized-iou/g-darknet + // https://arxiv.org/abs/1902.09630v2 + // https://giou.stanford.edu/ + all_ious.dx_iou = dx_box_iou(pred, truth, iou_loss); + + // jacobian^t (transpose) + float dx = (all_ious.dx_iou.dl + all_ious.dx_iou.dr); + float dy = (all_ious.dx_iou.dt + all_ious.dx_iou.db); + float dw = ((-0.5 * all_ious.dx_iou.dl) + (0.5 * all_ious.dx_iou.dr)); + float dh = ((-0.5 * all_ious.dx_iou.dt) + (0.5 * all_ious.dx_iou.db)); + + // predict exponential, apply gradient of e^delta_t ONLY for w,h + dw *= exp(x[index + 4 * stride]); + dh *= exp(x[index + 6 * stride]); + // normalize iou weight, for GIoU - delta_x *= iou_normalizer; - delta_y *= iou_normalizer; - delta_w *= iou_normalizer; - delta_h *= iou_normalizer; + dx *= iou_normalizer; + dy *= iou_normalizer; + dw *= iou_normalizer; + dh *= iou_normalizer; + + delta_x = (delta_x + dx) / 2; + delta_y = (delta_y + dy) / 2; + delta_w = (delta_w + dw) / 2; + delta_h = (delta_h + dh) / 2; } + // normalize Uncertainty weight delta_ux *= uc_normalizer; delta_uy *= uc_normalizer; delta_uw *= uc_normalizer; delta_uh *= uc_normalizer; - delta[index + 0 * stride] += delta_x; delta[index + 2 * stride] += delta_y; delta[index + 4 * stride] += delta_w; From 10c40551dcadec68050befa6a1cecc6f69049d0d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 18 Nov 2019 02:56:17 +0300 Subject: [PATCH 0667/1365] GIoU + Gaussian fix --- src/box.c | 3 +-- src/convolutional_layer.c | 5 +++++ src/gaussian_yolo_layer.c | 11 ++++++----- src/yolo_layer.c | 3 ++- 4 files changed, 14 insertions(+), 8 deletions(-) diff --git a/src/box.c b/src/box.c index 22f85884009..cb28ce8e4d7 100644 --- a/src/box.c +++ b/src/box.c @@ -424,8 +424,7 @@ int nms_comparator_v3(const void *pa, const void *pb) detection b = *(detection *)pb; float diff = 0; if (b.sort_class >= 0) { - diff = a.prob[b.sort_class] - b.prob[b.sort_class]; - //diff = a.objectness*a.prob[b.sort_class] - b.objectness*b.prob[b.sort_class]; + diff = a.prob[b.sort_class] - b.prob[b.sort_class]; // there is already: prob = objectness*prob } else { diff = a.objectness - b.objectness; diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index f9d66ebf3b6..39b65a70d4c 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -405,6 +405,11 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.nweights = (c / groups) * n * size * size; if (l.share_layer) { + if (l.size != l.share_layer->size || l.nweights != l.share_layer->nweights || l.c != l.share_layer->c || l.n != l.share_layer->n) { + printf("Layer size, nweights, channels or filters don't match for the share_layer"); + getchar(); + } + l.weights = l.share_layer->weights; l.weight_updates = l.share_layer->weight_updates; diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index 236e28e7d4e..109eb522c3a 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -236,10 +236,10 @@ float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int ind dw *= iou_normalizer; dh *= iou_normalizer; - delta_x = (delta_x + dx) / 2; - delta_y = (delta_y + dy) / 2; - delta_w = (delta_w + dw) / 2; - delta_h = (delta_h + dh) / 2; + delta_x = dx; + delta_y = dy; + delta_w = dw; + delta_h = dh; } // normalize Uncertainty weight @@ -299,7 +299,8 @@ int compare_gaussian_yolo_class(float *output, int classes, int class_index, int { int j; for (j = 0; j < classes; ++j) { - float prob = objectness * output[class_index + stride*j]; + //float prob = objectness * output[class_index + stride*j]; + float prob = output[class_index + stride*j]; if (prob > conf_thresh) { return 1; } diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 34185937459..08577db5ab4 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -250,7 +250,8 @@ int compare_yolo_class(float *output, int classes, int class_index, int stride, { int j; for (j = 0; j < classes; ++j) { - float prob = objectness * output[class_index + stride*j]; + //float prob = objectness * output[class_index + stride*j]; + float prob = output[class_index + stride*j]; if (prob > conf_thresh) { return 1; } From b4c0fbaec86e5088848094cdaf58d56f94138a13 Mon Sep 17 00:00:00 2001 From: dccho Date: Mon, 18 Nov 2019 09:52:22 -0500 Subject: [PATCH 0668/1365] fix memory free bug --- src/convolutional_layer.c | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index e784f5e4b89..805da22863d 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -769,8 +769,10 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) l->binary_input_gpu = cuda_make_array(0, l->inputs*l->batch); } - cuda_free(l->activation_input_gpu); - if (l->activation == SWISH || l->activation == MISH) l->activation_input_gpu = cuda_make_array(l->activation_input, total_batch*l->outputs); + if (l->activation == SWISH || l->activation == MISH) { + cuda_free(l->activation_input_gpu); + l->activation_input_gpu = cuda_make_array(l->activation_input, total_batch*l->outputs); + } } #ifdef CUDNN cudnn_convolutional_setup(l, cudnn_fastest); From 5a77940bd58ac29841a7ba36e18f73d32272c48a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mos=C3=A8=20Giordano?= Date: Thu, 21 Nov 2019 01:53:31 +0000 Subject: [PATCH 0669/1365] Add $(OBJDIR) as prerequisite to $(LIBNAMESO) The directory must exist before running this rule. When running `make` with several parallel jobs it can happen that $(LIBNAMESO) rule is run before creating the directory. --- Makefile | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/Makefile b/Makefile index 41e5fc8d737..c59991281ba 100644 --- a/Makefile +++ b/Makefile @@ -127,12 +127,12 @@ endif OBJS = $(addprefix $(OBJDIR), $(OBJ)) DEPS = $(wildcard src/*.h) Makefile include/darknet.h -all: obj backup results setchmod $(EXEC) $(LIBNAMESO) $(APPNAMESO) +all: $(OBJDIR) backup results setchmod $(EXEC) $(LIBNAMESO) $(APPNAMESO) ifeq ($(LIBSO), 1) CFLAGS+= -fPIC -$(LIBNAMESO): $(OBJS) include/yolo_v2_class.hpp src/yolo_v2_class.cpp +$(LIBNAMESO): $(OBJDIR) $(OBJS) include/yolo_v2_class.hpp src/yolo_v2_class.cpp $(CPP) -shared -std=c++11 -fvisibility=hidden -DLIB_EXPORTS $(COMMON) $(CFLAGS) $(OBJS) src/yolo_v2_class.cpp -o $@ $(LDFLAGS) $(APPNAMESO): $(LIBNAMESO) include/yolo_v2_class.hpp src/yolo_console_dll.cpp @@ -151,8 +151,8 @@ $(OBJDIR)%.o: %.cpp $(DEPS) $(OBJDIR)%.o: %.cu $(DEPS) $(NVCC) $(ARCH) $(COMMON) --compiler-options "$(CFLAGS)" -c $< -o $@ -obj: - mkdir -p obj +$(OBJDIR): + mkdir -p $(OBJDIR) backup: mkdir -p backup results: From f7a6f7b87cdfd49c9930d2c2a2d7fa5b52b30940 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 21 Nov 2019 14:11:52 +0300 Subject: [PATCH 0670/1365] Fixed MISH as in thomasbrandon/mish-cuda implementation with 1 Threshold --- src/activation_kernels.cu | 11 ++++++++--- src/activations.c | 9 ++++++--- 2 files changed, 14 insertions(+), 6 deletions(-) diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index 67504e71611..6ef165ce768 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -204,9 +204,14 @@ __global__ void activate_array_mish_kernel(float *x, int n, float *activation_in { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (i < n) { + const float MISH_THRESHOLD = 20; float x_val = x[i]; activation_input[i] = x_val; // store value before activation - output_gpu[i] = x_val * tanh_activate_kernel(log(1 + expf(x_val))); + //output_gpu[i] = x_val * tanh_activate_kernel(log(1 + expf(x_val))); + + // https://github.com/thomasbrandon/mish-cuda/blob/master/csrc/mish.h#L17-L20 + if (x_val < MISH_THRESHOLD) output_gpu[i] = x_val * tanh_activate_kernel(log(expf(x_val))); + else output_gpu[i] = x_val * tanh_activate_kernel(x_val); } } @@ -279,12 +284,12 @@ __global__ void gradient_array_mish_kernel(int n, float *activation_input_gpu, f { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (i < n) { - const float THRESHOLD = 20.0f; + const float MISH_THRESHOLD = 20.0f; // implementation from TensorFlow: https://github.com/tensorflow/addons/commit/093cdfa85d334cbe19a37624c33198f3140109ed // implementation from Pytorch: https://github.com/thomasbrandon/mish-cuda/blob/master/csrc/mish.h#L26-L31 float inp = activation_input_gpu[i]; - const float sp = (inp < THRESHOLD) ? log1p(exp(inp)) : inp; + const float sp = (inp < MISH_THRESHOLD) ? log1p(exp(inp)) : inp; const float grad_sp = 1 - exp(-sp); const float tsp = tanh(sp); const float grad_tsp = (1 - tsp*tsp) * grad_sp; diff --git a/src/activations.c b/src/activations.c index 55b060bd94c..83580cb24a5 100644 --- a/src/activations.c +++ b/src/activations.c @@ -137,12 +137,15 @@ void activate_array_swish(float *x, const int n, float * output_sigmoid, float * // https://github.com/digantamisra98/Mish void activate_array_mish(float *x, const int n, float * activation_input, float * output) { + const float MISH_THRESHOLD = 20; int i; #pragma omp parallel for for (i = 0; i < n; ++i) { float x_val = x[i]; activation_input[i] = x_val; // store value before activation - output[i] = x_val * tanh_activate(log(1 + expf(x_val))); + //output[i] = x_val * tanh_activate(log(1 + expf(x_val))); + if (x_val < MISH_THRESHOLD) output[i] = x_val * tanh_activate(log(expf(x_val))); + else output[i] = x_val * tanh_activate(x_val); } } @@ -207,12 +210,12 @@ void gradient_array_mish(const int n, const float * activation_input, float * de int i; #pragma omp parallel for for (i = 0; i < n; ++i) { - const float THRESHOLD = 20.0f; + const float MISH_THRESHOLD = 20.0f; // implementation from TensorFlow: https://github.com/tensorflow/addons/commit/093cdfa85d334cbe19a37624c33198f3140109ed // implementation from Pytorch: https://github.com/thomasbrandon/mish-cuda/blob/master/csrc/mish.h#L26-L31 float inp = activation_input[i]; - const float sp = (inp < THRESHOLD) ? log1p(exp(inp)) : inp; + const float sp = (inp < MISH_THRESHOLD) ? log1p(exp(inp)) : inp; const float grad_sp = 1 - exp(-sp); const float tsp = tanh(sp); const float grad_tsp = (1 - tsp*tsp) * grad_sp; From 7713a0209c0fc5d213db49243ab86306411fec5d Mon Sep 17 00:00:00 2001 From: Alexey Date: Fri, 22 Nov 2019 02:43:33 +0300 Subject: [PATCH 0671/1365] Update Readme.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 5c0efdeff1a..13a13167dd4 100644 --- a/README.md +++ b/README.md @@ -632,6 +632,7 @@ Different tools for marking objects in images: 2. in Python: https://github.com/tzutalin/labelImg 3. in Python: https://github.com/Cartucho/OpenLabeling 4. in C++: https://www.ccoderun.ca/darkmark/ +5. in JavaScript: https://github.com/opencv/cvat ## Using Yolo9000 From b9ca5ec781291f01174d6b496a9c3ebc59303c1f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 22 Nov 2019 14:20:53 +0300 Subject: [PATCH 0672/1365] Fixed MISH activation with 2 thresholds in Softplus --- src/activation_kernels.cu | 23 +++++++++++++++-------- src/activations.c | 6 ++---- src/activations.h | 5 +++++ 3 files changed, 22 insertions(+), 12 deletions(-) diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index 6ef165ce768..5b357d3fce6 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -35,6 +35,11 @@ __device__ float relie_activate_kernel(float x){return (x>0) ? x : .01f*x;} __device__ float ramp_activate_kernel(float x){return x*(x>0)+.1f*x;} __device__ float leaky_activate_kernel(float x){return (x>0) ? x : .1f*x;} __device__ float tanh_activate_kernel(float x){return (2/(1 + expf(-2*x)) - 1);} +__device__ float softplus_kernel(float x, float threshold = 20) { + if (x > threshold) return x; // too large + else if (x < -threshold) return expf(x); // too small + return logf(expf(x) + 1); +} __device__ float plse_activate_kernel(float x) { if(x < -4) return .01f * (x + 4); @@ -207,11 +212,12 @@ __global__ void activate_array_mish_kernel(float *x, int n, float *activation_in const float MISH_THRESHOLD = 20; float x_val = x[i]; activation_input[i] = x_val; // store value before activation - //output_gpu[i] = x_val * tanh_activate_kernel(log(1 + expf(x_val))); + //output_gpu[i] = x_val * tanh_activate_kernel(logf(1 + expf(x_val))); - // https://github.com/thomasbrandon/mish-cuda/blob/master/csrc/mish.h#L17-L20 - if (x_val < MISH_THRESHOLD) output_gpu[i] = x_val * tanh_activate_kernel(log(expf(x_val))); - else output_gpu[i] = x_val * tanh_activate_kernel(x_val); + // Pytorch: https://github.com/thomasbrandon/mish-cuda/blob/master/csrc/mish.h#L17-L20 + // TF: https://github.com/tensorflow/addons/blob/093cdfa85d334cbe19a37624c33198f3140109ed/tensorflow_addons/custom_ops/activations/cc/kernels/mish_op.h#L40-L49 + // log1p(x) == log(x + 1) + output_gpu[i] = x_val * tanh_activate_kernel( softplus_kernel(x_val, MISH_THRESHOLD) ); } } @@ -286,11 +292,12 @@ __global__ void gradient_array_mish_kernel(int n, float *activation_input_gpu, f if (i < n) { const float MISH_THRESHOLD = 20.0f; - // implementation from TensorFlow: https://github.com/tensorflow/addons/commit/093cdfa85d334cbe19a37624c33198f3140109ed + // implementation from TensorFlow: https://github.com/tensorflow/addons/blob/093cdfa85d334cbe19a37624c33198f3140109ed/tensorflow_addons/custom_ops/activations/cc/kernels/mish_op.h#L66-L80 // implementation from Pytorch: https://github.com/thomasbrandon/mish-cuda/blob/master/csrc/mish.h#L26-L31 - float inp = activation_input_gpu[i]; - const float sp = (inp < MISH_THRESHOLD) ? log1p(exp(inp)) : inp; - const float grad_sp = 1 - exp(-sp); + // log1p(x) == log(x + 1) + const float inp = activation_input_gpu[i]; + const float sp = softplus_kernel(inp, MISH_THRESHOLD); + const float grad_sp = 1 - expf(-sp); const float tsp = tanh(sp); const float grad_tsp = (1 - tsp*tsp) * grad_sp; const float grad = inp * grad_tsp + tsp; diff --git a/src/activations.c b/src/activations.c index 83580cb24a5..347a13ac47b 100644 --- a/src/activations.c +++ b/src/activations.c @@ -143,9 +143,7 @@ void activate_array_mish(float *x, const int n, float * activation_input, float for (i = 0; i < n; ++i) { float x_val = x[i]; activation_input[i] = x_val; // store value before activation - //output[i] = x_val * tanh_activate(log(1 + expf(x_val))); - if (x_val < MISH_THRESHOLD) output[i] = x_val * tanh_activate(log(expf(x_val))); - else output[i] = x_val * tanh_activate(x_val); + output[i] = x_val * tanh_activate( softplus_activate(x_val, MISH_THRESHOLD) ); } } @@ -215,7 +213,7 @@ void gradient_array_mish(const int n, const float * activation_input, float * de // implementation from TensorFlow: https://github.com/tensorflow/addons/commit/093cdfa85d334cbe19a37624c33198f3140109ed // implementation from Pytorch: https://github.com/thomasbrandon/mish-cuda/blob/master/csrc/mish.h#L26-L31 float inp = activation_input[i]; - const float sp = (inp < MISH_THRESHOLD) ? log1p(exp(inp)) : inp; + const float sp = softplus_activate(inp, MISH_THRESHOLD); const float grad_sp = 1 - exp(-sp); const float tsp = tanh(sp); const float grad_tsp = (1 - tsp*tsp) * grad_sp; diff --git a/src/activations.h b/src/activations.h index bba5ca8d10a..edd5b304ff8 100644 --- a/src/activations.h +++ b/src/activations.h @@ -53,6 +53,11 @@ static inline float relie_activate(float x){return (x>0) ? x : .01f*x;} static inline float ramp_activate(float x){return x*(x>0)+.1f*x;} static inline float leaky_activate(float x){return (x>0) ? x : .1f*x;} static inline float tanh_activate(float x){return (expf(2*x)-1)/(expf(2*x)+1);} +static inline float softplus_activate(float x, float threshold) { + if (x > threshold) return x; // too large + else if (x < -threshold) return expf(x); // too small + return logf(expf(x) + 1); +} static inline float plse_activate(float x) { if(x < -4) return .01f * (x + 4); From 4f70fc14e9bd33d24efd64dff16e959e2e391d39 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 23 Nov 2019 16:42:43 +0300 Subject: [PATCH 0673/1365] Added DIoU and CIoU to [yolo] and [Gaussian_yolo] --- include/darknet.h | 19 +- src/box.c | 443 ++++++++++++++++++++++++++++++++++---- src/box.h | 1 + src/demo.c | 5 +- src/detector.c | 17 +- src/gaussian_yolo_layer.c | 102 +++++++-- src/parser.c | 36 +++- src/utils.c | 19 ++ src/utils.h | 1 + src/yolo_layer.c | 73 +++++++ 10 files changed, 644 insertions(+), 72 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 7a906780c61..851b65421c8 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -107,9 +107,20 @@ typedef enum { // parser.h typedef enum { - IOU, GIOU, MSE + IOU, GIOU, MSE, DIOU, CIOU } IOU_LOSS; +// parser.h +typedef enum { + DEFAULT_NMS, GREEDY_NMS, DIOU_NMS +} NMS_KIND; + +// parser.h +typedef enum { + YOLO_CENTER, YOLO_LEFT_TOP, YOLO_RIGHT_BOTTOM +} YOLO_POINT; + + // image.h typedef enum{ PNG, BMP, TGA, JPG @@ -334,6 +345,9 @@ struct layer { float iou_normalizer; float cls_normalizer; IOU_LOSS iou_loss; + NMS_KIND nms_kind; + float beta_nms; + YOLO_POINT yolo_point; char *align_bit_weights_gpu; float *mean_arr_gpu; @@ -719,7 +733,7 @@ typedef struct dxrep { // box.h typedef struct ious { - float iou, giou; + float iou, giou, diou, ciou; dxrep dx_iou; dxrep dx_giou; } ious; @@ -835,6 +849,7 @@ LIB_API load_args get_base_args(network *net); // box.h LIB_API void do_nms_sort(detection *dets, int total, int classes, float thresh); LIB_API void do_nms_obj(detection *dets, int total, int classes, float thresh); +LIB_API void diounms_sort(detection *dets, int total, int classes, float thresh, NMS_KIND nms_kind, float beta1); // network.h LIB_API float *network_predict(network net, float *input); diff --git a/src/box.c b/src/box.c index cb28ce8e4d7..e700dc7140c 100644 --- a/src/box.c +++ b/src/box.c @@ -1,8 +1,12 @@ -#include "box.h" +#include "box.h" #include #include #include +#ifndef M_PI +#define M_PI 3.141592 +#endif + box float_to_box(float *f) { box b; @@ -23,6 +27,23 @@ box float_to_box_stride(float *f, int stride) return b; } + +dbox derivative(box a, box b) +{ + dbox d; + d.dx = 0; + d.dw = 0; + d.dy = 0; + d.dh = 0; + d.dx = a.x < b.x ? 1.0 : -1.0; + d.dy = a.y < b.y ? 1.0 : -1.0; + d.dw = a.w < b.w ? 1.0 : -1.0; + d.dh = a.h < b.h ? 1.0 : -1.0; + return d; +} + + +/* dbox derivative(box a, box b) { dbox d; @@ -73,6 +94,7 @@ dbox derivative(box a, box b) } return d; } +*/ // where c is the smallest box that fully encompases a and b boxabs box_c(box a, box b) { @@ -155,13 +177,75 @@ float box_giou(box a, box b) return iou - giou_term; } +float box_diou(box a, box b) +{ + boxabs ba = box_c(a, b); + float w = ba.right - ba.left; + float h = ba.bot - ba.top; + float c = w * w + h * h; + float iou = box_iou(a, b); + if (c == 0) { + return iou; + } + float d = (a.x - b.x) * (a.x - b.x) + (a.y - b.y) * (a.y - b.y); + float u = pow(d / c, 0.6); + float diou_term = u; +#ifdef DEBUG_PRINTS + printf(" c: %f, u: %f, riou_term: %f\n", c, u, diou_term); +#endif + return iou - diou_term; +} + +float box_diounms(box a, box b, float beta1) +{ + boxabs ba = box_c(a, b); + float w = ba.right - ba.left; + float h = ba.bot - ba.top; + float c = w * w + h * h; + float iou = box_iou(a, b); + if (c == 0) { + return iou; + } + float d = (a.x - b.x) * (a.x - b.x) + (a.y - b.y) * (a.y - b.y); + float u = pow(d / c, beta1); + float diou_term = u; +#ifdef DEBUG_PRINTS + printf(" c: %f, u: %f, riou_term: %f\n", c, u, diou_term); +#endif + return iou - diou_term; +} + +float box_ciou(box a, box b) +{ + boxabs ba = box_c(a, b); + float w = ba.right - ba.left; + float h = ba.bot - ba.top; + float c = w * w + h * h; + float iou = box_iou(a, b); + if (c == 0) { + return iou; + } + float u = (a.x - b.x) * (a.x - b.x) + (a.y - b.y) * (a.y - b.y); + float d = u / c; + float ar_gt = b.w / b.h; + float ar_pred = a.w / a.h; + float ar_loss = 4 / (M_PI * M_PI) * (atan(ar_gt) - atan(ar_pred)) * (atan(ar_gt) - atan(ar_pred)); + float alpha = ar_loss / (1 - iou + ar_loss + 0.000001); + float ciou_term = d + alpha * ar_loss; //ciou +#ifdef DEBUG_PRINTS + printf(" c: %f, u: %f, riou_term: %f\n", c, u, ciou_term); +#endif + return iou - ciou_term; +} + dxrep dx_box_iou(box pred, box truth, IOU_LOSS iou_loss) { - boxabs pred_tblr = to_tblr(pred); + boxabs pred_tblr = to_tblr(pred); float pred_t = fmin(pred_tblr.top, pred_tblr.bot); float pred_b = fmax(pred_tblr.top, pred_tblr.bot); float pred_l = fmin(pred_tblr.left, pred_tblr.right); float pred_r = fmax(pred_tblr.left, pred_tblr.right); - + //dbox dover = derivative(pred,truth); + //dbox diouu = diou(pred, truth); boxabs truth_tblr = to_tblr(truth); #ifdef DEBUG_PRINTS printf("\niou: %f, giou: %f\n", box_iou(pred, truth), box_giou(pred, truth)); @@ -170,24 +254,39 @@ dxrep dx_box_iou(box pred, box truth, IOU_LOSS iou_loss) { #endif //printf("pred (t,b,l,r): (%f, %f, %f, %f)\n", pred_t, pred_b, pred_l, pred_r); //printf("trut (t,b,l,r): (%f, %f, %f, %f)\n", truth_tblr.top, truth_tblr.bot, truth_tblr.left, truth_tblr.right); - dxrep dx = { 0 }; + dxrep ddx = {0}; float X = (pred_b - pred_t) * (pred_r - pred_l); float Xhat = (truth_tblr.bot - truth_tblr.top) * (truth_tblr.right - truth_tblr.left); float Ih = fmin(pred_b, truth_tblr.bot) - fmax(pred_t, truth_tblr.top); float Iw = fmin(pred_r, truth_tblr.right) - fmax(pred_l, truth_tblr.left); float I = Iw * Ih; float U = X + Xhat - I; - - float Cw = fmax(pred_r, truth_tblr.right) - fmin(pred_l, truth_tblr.left); - float Ch = fmax(pred_b, truth_tblr.bot) - fmin(pred_t, truth_tblr.top); - float C = Cw * Ch; - - // float IoU = I / U; - // Partial Derivatives, derivatives + float S = (pred.x-truth.x)*(pred.x-truth.x)+(pred.y-truth.y)*(pred.y-truth.y); + float giou_Cw = fmax(pred_r, truth_tblr.right) - fmin(pred_l, truth_tblr.left); + float giou_Ch = fmax(pred_b, truth_tblr.bot) - fmin(pred_t, truth_tblr.top); + float giou_C = giou_Cw * giou_Ch; + //float IoU = I / U; +//#ifdef DEBUG_PRINTS + //printf("X: %f", X); + //printf(", Xhat: %f", Xhat); + //printf(", Ih: %f", Ih); + //printf(", Iw: %f", Iw); + //printf(", I: %f", I); + //printf(", U: %f", U); + //printf(", IoU: %f\n", I / U); +//#endif + + //Partial Derivatives, derivatives float dX_wrt_t = -1 * (pred_r - pred_l); float dX_wrt_b = pred_r - pred_l; float dX_wrt_l = -1 * (pred_b - pred_t); float dX_wrt_r = pred_b - pred_t; + // UNUSED + //// Ground truth + //float dXhat_wrt_t = -1 * (truth_tblr.right - truth_tblr.left); + //float dXhat_wrt_b = truth_tblr.right - truth_tblr.left; + //float dXhat_wrt_l = -1 * (truth_tblr.bot - truth_tblr.top); + //float dXhat_wrt_r = truth_tblr.bot - truth_tblr.top; // gradient of I min/max in IoU calc (prediction) float dI_wrt_t = pred_t > truth_tblr.top ? (-1 * Iw) : 0; @@ -200,42 +299,262 @@ dxrep dx_box_iou(box pred, box truth, IOU_LOSS iou_loss) { float dU_wrt_l = dX_wrt_l - dI_wrt_l; float dU_wrt_r = dX_wrt_r - dI_wrt_r; // gradient of C min/max in IoU calc (prediction) - float dC_wrt_t = pred_t < truth_tblr.top ? (-1 * Cw) : 0; - float dC_wrt_b = pred_b > truth_tblr.bot ? Cw : 0; - float dC_wrt_l = pred_l < truth_tblr.left ? (-1 * Ch) : 0; - float dC_wrt_r = pred_r > truth_tblr.right ? Ch : 0; + float dC_wrt_t = pred_t < truth_tblr.top ? (-1 * giou_Cw) : 0; + float dC_wrt_b = pred_b > truth_tblr.bot ? giou_Cw : 0; + float dC_wrt_l = pred_l < truth_tblr.left ? (-1 * giou_Ch) : 0; + float dC_wrt_r = pred_r > truth_tblr.right ? giou_Ch : 0; - // Final IOU loss (prediction) (negative of IOU gradient, we want the negative loss) float p_dt = 0; float p_db = 0; float p_dl = 0; float p_dr = 0; - if (U > 0) { - p_dt = ((U * dI_wrt_t) - (I * dU_wrt_t)) / (U * U); - p_db = ((U * dI_wrt_b) - (I * dU_wrt_b)) / (U * U); - p_dl = ((U * dI_wrt_l) - (I * dU_wrt_l)) / (U * U); - p_dr = ((U * dI_wrt_r) - (I * dU_wrt_r)) / (U * U); + if (U > 0 ) { + p_dt = ((U * dI_wrt_t) - (I * dU_wrt_t)) / (U * U); + p_db = ((U * dI_wrt_b) - (I * dU_wrt_b)) / (U * U); + p_dl = ((U * dI_wrt_l) - (I * dU_wrt_l)) / (U * U); + p_dr = ((U * dI_wrt_r) - (I * dU_wrt_r)) / (U * U); } + // apply grad from prediction min/max for correct corner selection + p_dt = pred_tblr.top < pred_tblr.bot ? p_dt : p_db; + p_db = pred_tblr.top < pred_tblr.bot ? p_db : p_dt; + p_dl = pred_tblr.left < pred_tblr.right ? p_dl : p_dr; + p_dr = pred_tblr.left < pred_tblr.right ? p_dr : p_dl; - // GIoU = I/U - (C-U)/C - // C is the smallest convex hull that encloses both Detection and Truth if (iou_loss == GIOU) { + if (giou_C > 0) { + // apply "C" term from gIOU + p_dt += ((giou_C * dU_wrt_t) - (U * dC_wrt_t)) / (giou_C * giou_C); + p_db += ((giou_C * dU_wrt_b) - (U * dC_wrt_b)) / (giou_C * giou_C); + p_dl += ((giou_C * dU_wrt_l) - (U * dC_wrt_l)) / (giou_C * giou_C); + p_dr += ((giou_C * dU_wrt_r) - (U * dC_wrt_r)) / (giou_C * giou_C); + } + if (Iw<=0||Ih<=0) { + p_dt = ((giou_C * dU_wrt_t) - (U * dC_wrt_t)) / (giou_C * giou_C); + p_db = ((giou_C * dU_wrt_b) - (U * dC_wrt_b)) / (giou_C * giou_C); + p_dl = ((giou_C * dU_wrt_l) - (U * dC_wrt_l)) / (giou_C * giou_C); + p_dr = ((giou_C * dU_wrt_r) - (U * dC_wrt_r)) / (giou_C * giou_C); + } + } + + float Ct = fmin(pred.y - pred.h / 2,truth.y - truth.h / 2); + float Cb = fmax(pred.y + pred.h / 2,truth.y + truth.h / 2); + float Cl = fmin(pred.x - pred.w / 2,truth.x - truth.w / 2); + float Cr = fmax(pred.x + pred.w / 2,truth.x + truth.w / 2); + float Cw = Cr - Cl; + float Ch = Cb - Ct; + float C = Cw * Cw + Ch * Ch; + + float dCt_dx = 0; + float dCt_dy = pred_t < truth_tblr.top ? 1 : 0; + float dCt_dw = 0; + float dCt_dh = pred_t < truth_tblr.top ? -0.5 : 0; + + float dCb_dx = 0; + float dCb_dy = pred_b > truth_tblr.bot ? 1 : 0; + float dCb_dw = 0; + float dCb_dh = pred_b > truth_tblr.bot ? 0.5: 0; + + float dCl_dx = pred_l < truth_tblr.left ? 1 : 0; + float dCl_dy = 0; + float dCl_dw = pred_l < truth_tblr.left ? -0.5 : 0; + float dCl_dh = 0; + + float dCr_dx = pred_r > truth_tblr.right ? 1 : 0; + float dCr_dy = 0; + float dCr_dw = pred_r > truth_tblr.right ? 0.5 : 0; + float dCr_dh = 0; + + float dCw_dx = dCr_dx - dCl_dx; + float dCw_dy = dCr_dy - dCl_dy; + float dCw_dw = dCr_dw - dCl_dw; + float dCw_dh = dCr_dh - dCl_dh; + + float dCh_dx = dCb_dx - dCt_dx; + float dCh_dy = dCb_dy - dCt_dy; + float dCh_dw = dCb_dw - dCt_dw; + float dCh_dh = dCb_dh - dCt_dh; + + // UNUSED + //// ground truth + //float dI_wrt_xhat_t = pred_t < truth_tblr.top ? (-1 * Iw) : 0; + //float dI_wrt_xhat_b = pred_b > truth_tblr.bot ? Iw : 0; + //float dI_wrt_xhat_l = pred_l < truth_tblr.left ? (-1 * Ih) : 0; + //float dI_wrt_xhat_r = pred_r > truth_tblr.right ? Ih : 0; + + // Final IOU loss (prediction) (negative of IOU gradient, we want the negative loss) + float p_dx = 0; + float p_dy = 0; + float p_dw = 0; + float p_dh = 0; + + p_dx = p_dl + p_dr; //p_dx, p_dy, p_dw and p_dh are the gradient of IoU or GIoU. + p_dy = p_dt + p_db; + p_dw = (p_dr - p_dl); //For dw and dh, we do not divided by 2. + p_dh = (p_db - p_dt); + if (iou_loss == DIOU) { + if (C > 0) { + p_dx += (2*(truth.x-pred.x)*C-(2*Cw*dCw_dx+2*Ch*dCh_dx)*S) / (C * C); + p_dy += (2*(truth.y-pred.y)*C-(2*Cw*dCw_dy+2*Ch*dCh_dy)*S) / (C * C); + p_dw += (2*Cw*dCw_dw+2*Ch*dCh_dw)*S / (C * C); + p_dh += (2*Cw*dCw_dh+2*Ch*dCh_dh)*S / (C * C); + } + if (Iw<=0||Ih<=0){ + p_dx = (2*(truth.x-pred.x)*C-(2*Cw*dCw_dx+2*Ch*dCh_dx)*S) / (C * C); + p_dy = (2*(truth.y-pred.y)*C-(2*Cw*dCw_dy+2*Ch*dCh_dy)*S) / (C * C); + p_dw = (2*Cw*dCw_dw+2*Ch*dCh_dw)*S / (C * C); + p_dh = (2*Cw*dCw_dh+2*Ch*dCh_dh)*S / (C * C); + } + } + //The following codes are calculating the gradient of ciou. + + if (iou_loss == CIOU) { + float ar_gt = truth.w / truth.h; + float ar_pred = pred.w / pred.h; + float ar_loss = 4 / (M_PI * M_PI) * (atan(ar_gt) - atan(ar_pred)) * (atan(ar_gt) - atan(ar_pred)); + float alpha = ar_loss / (1 - I/U + ar_loss + 0.000001); + float ar_dw=8/(M_PI*M_PI)*(atan(ar_gt)-atan(ar_pred))*pred.h; + float ar_dh=-8/(M_PI*M_PI)*(atan(ar_gt)-atan(ar_pred))*pred.w; if (C > 0) { - // apply "C" term from gIOU - p_dt += ((C * dU_wrt_t) - (U * dC_wrt_t)) / (C * C); - p_db += ((C * dU_wrt_b) - (U * dC_wrt_b)) / (C * C); - p_dl += ((C * dU_wrt_l) - (U * dC_wrt_l)) / (C * C); - p_dr += ((C * dU_wrt_r) - (U * dC_wrt_r)) / (C * C); + // dar* + p_dx += (2*(truth.x-pred.x)*C-(2*Cw*dCw_dx+2*Ch*dCh_dx)*S) / (C * C); + p_dy += (2*(truth.y-pred.y)*C-(2*Cw*dCw_dy+2*Ch*dCh_dy)*S) / (C * C); + p_dw += (2*Cw*dCw_dw+2*Ch*dCh_dw)*S / (C * C) + alpha * ar_dw; + p_dh += (2*Cw*dCw_dh+2*Ch*dCh_dh)*S / (C * C) + alpha * ar_dh; + } + if (Iw<=0||Ih<=0){ + p_dx = (2*(truth.x-pred.x)*C-(2*Cw*dCw_dx+2*Ch*dCh_dx)*S) / (C * C); + p_dy = (2*(truth.y-pred.y)*C-(2*Cw*dCw_dy+2*Ch*dCh_dy)*S) / (C * C); + p_dw = (2*Cw*dCw_dw+2*Ch*dCh_dw)*S / (C * C) + alpha * ar_dw; + p_dh = (2*Cw*dCw_dh+2*Ch*dCh_dh)*S / (C * C) + alpha * ar_dh; } } + ddx.dt = p_dx; //We follow the original code released from GDarknet. So in yolo_layer.c, dt, db, dl, dr are already dx, dy, dw, dh. + ddx.db = p_dy; + ddx.dl = p_dw; + ddx.dr = p_dh; + + // UNUSED + //// ground truth + //float gt_dt = ((U * dI_wrt_xhat_t) - (I * (dXhat_wrt_t - dI_wrt_xhat_t))) / (U * U); + //float gt_db = ((U * dI_wrt_xhat_b) - (I * (dXhat_wrt_b - dI_wrt_xhat_b))) / (U * U); + //float gt_dl = ((U * dI_wrt_xhat_l) - (I * (dXhat_wrt_l - dI_wrt_xhat_l))) / (U * U); + //float gt_dr = ((U * dI_wrt_xhat_r) - (I * (dXhat_wrt_r - dI_wrt_xhat_r))) / (U * U); + + // no min/max grad applied + //dx.dt = dt; + //dx.db = db; + //dx.dl = dl; + //dx.dr = dr; + + //// sum in gt -- THIS DOESNT WORK + //dx.dt += gt_dt; + //dx.db += gt_db; + //dx.dl += gt_dl; + //dx.dr += gt_dr; + + //// instead, look at the change between pred and gt, and weight t/b/l/r appropriately... + //// need the real derivative here (I think?) + //float delta_t = fmax(truth_tblr.top, pred_t) - fmin(truth_tblr.top, pred_t); + //float delta_b = fmax(truth_tblr.bot, pred_b) - fmin(truth_tblr.bot, pred_b); + //float delta_l = fmax(truth_tblr.left, pred_l) - fmin(truth_tblr.left, pred_l); + //float delta_r = fmax(truth_tblr.right, pred_r) - fmin(truth_tblr.right, pred_r); + + //dx.dt *= delta_t / (delta_t + delta_b); + //dx.db *= delta_b / (delta_t + delta_b); + //dx.dl *= delta_l / (delta_l + delta_r); + //dx.dr *= delta_r / (delta_l + delta_r); + + // UNUSED + //// ground truth + //float gt_dt = ((U * dI_wrt_xhat_t) - (I * (dXhat_wrt_t - dI_wrt_xhat_t))) / (U * U); + //float gt_db = ((U * dI_wrt_xhat_b) - (I * (dXhat_wrt_b - dI_wrt_xhat_b))) / (U * U); + //float gt_dl = ((U * dI_wrt_xhat_l) - (I * (dXhat_wrt_l - dI_wrt_xhat_l))) / (U * U); + //float gt_dr = ((U * dI_wrt_xhat_r) - (I * (dXhat_wrt_r - dI_wrt_xhat_r))) / (U * U); + + // no min/max grad applied + //dx.dt = dt; + //dx.db = db; + //dx.dl = dl; + //dx.dr = dr; + // apply grad from prediction min/max for correct corner selection - dx.dt = pred_tblr.top < pred_tblr.bot ? p_dt : p_db; - dx.db = pred_tblr.top < pred_tblr.bot ? p_db : p_dt; - dx.dl = pred_tblr.left < pred_tblr.right ? p_dl : p_dr; - dx.dr = pred_tblr.left < pred_tblr.right ? p_dr : p_dl; + //dx.dt = pred_tblr.top < pred_tblr.bot ? p_dt : p_db; + //dx.db = pred_tblr.top < pred_tblr.bot ? p_db : p_dt; + //dx.dl = pred_tblr.left < pred_tblr.right ? p_dl : p_dr; + //dx.dr = pred_tblr.left < pred_tblr.right ? p_dr : p_dl; + + //// sum in gt -- THIS DOESNT WORK + //dx.dt += gt_dt; + //dx.db += gt_db; + //dx.dl += gt_dl; + //dx.dr += gt_dr; + + //// instead, look at the change between pred and gt, and weight t/b/l/r appropriately... + //// need the real derivative here (I think?) + //float delta_t = fmax(truth_tblr.top, pred_t) - fmin(truth_tblr.top, pred_t); + //float delta_b = fmax(truth_tblr.bot, pred_b) - fmin(truth_tblr.bot, pred_b); + //float delta_l = fmax(truth_tblr.left, pred_l) - fmin(truth_tblr.left, pred_l); + //float delta_r = fmax(truth_tblr.right, pred_r) - fmin(truth_tblr.right, pred_r); + + //dx.dt *= delta_t / (delta_t + delta_b); + //dx.db *= delta_b / (delta_t + delta_b); + //dx.dl *= delta_l / (delta_l + delta_r); + //dx.dr *= delta_r / (delta_l + delta_r); + +//#ifdef DEBUG_PRINTS + /*printf(" directions dt: "); + if ((pred_tblr.top < truth_tblr.top && dx.dt > 0) || (pred_tblr.top > truth_tblr.top && dx.dt < 0)) { + printf("✓"); + } else { + printf("ð’™"); + } + printf(", "); + if ((pred_tblr.bot < truth_tblr.bot && dx.db > 0) || (pred_tblr.bot > truth_tblr.bot && dx.db < 0)) { + printf("✓"); + } else { + printf("ð’™"); + } + printf(", "); + if ((pred_tblr.left < truth_tblr.left && dx.dl > 0) || (pred_tblr.left > truth_tblr.left && dx.dl < 0)) { + printf("✓"); + } else { + printf("ð’™"); + } + printf(", "); + if ((pred_tblr.right < truth_tblr.right && dx.dr > 0) || (pred_tblr.right > truth_tblr.right && dx.dr < 0)) { + printf("✓"); + } else { + printf("ð’™"); + } + printf("\n"); + + printf("dx dt:%f", dx.dt); + printf(", db: %f", dx.db); + printf(", dl: %f", dx.dl); + printf(", dr: %f | ", dx.dr); +#endif - return dx; +#ifdef DEBUG_NAN + if (isnan(dx.dt)) { printf("dt isnan\n"); } + if (isnan(dx.db)) { printf("db isnan\n"); } + if (isnan(dx.dl)) { printf("dl isnan\n"); } + if (isnan(dx.dr)) { printf("dr isnan\n"); } +#endif + +// // No update if 0 or nan +// if (dx.dt == 0 || isnan(dx.dt)) { dx.dt = 1; } +// if (dx.db == 0 || isnan(dx.db)) { dx.db = 1; } +// if (dx.dl == 0 || isnan(dx.dl)) { dx.dl = 1; } +// if (dx.dr == 0 || isnan(dx.dr)) { dx.dr = 1; } +// +//#ifdef DEBUG_PRINTS +// printf("dx dt:%f (t: %f, p: %f)", dx.dt, gt_dt, p_dt); +// printf(", db: %f (t: %f, p: %f)", dx.db, gt_db, p_db); +// printf(", dl: %f (t: %f, p: %f)", dx.dl, gt_dl, p_dl); +// printf(", dr: %f (t: %f, p: %f) | ", dx.dr, gt_dr, p_dr); +//#endif */ + return ddx; } float box_rmse(box a, box b) @@ -351,13 +670,13 @@ void test_box() dbox diou(box a, box b) { - float u = box_union(a,b); - float i = box_intersection(a,b); - dbox di = dintersect(a,b); - dbox du = dunion(a,b); - dbox dd = {0,0,0,0}; + float u = box_union(a, b); + float i = box_intersection(a, b); + dbox di = dintersect(a, b); + dbox du = dunion(a, b); + dbox dd = { 0,0,0,0 }; - if(i <= 0 || 1) { + if (i <= 0 || 1) { dd.dx = b.x - a.x; dd.dy = b.y - a.y; dd.dw = b.w - a.w; @@ -365,10 +684,10 @@ dbox diou(box a, box b) return dd; } - dd.dx = 2*pow((1-(i/u)),1)*(di.dx*u - du.dx*i)/(u*u); - dd.dy = 2*pow((1-(i/u)),1)*(di.dy*u - du.dy*i)/(u*u); - dd.dw = 2*pow((1-(i/u)),1)*(di.dw*u - du.dw*i)/(u*u); - dd.dh = 2*pow((1-(i/u)),1)*(di.dh*u - du.dh*i)/(u*u); + dd.dx = (di.dx*u - du.dx*i) / (u*u); + dd.dy = (di.dy*u - du.dy*i) / (u*u); + dd.dw = (di.dw*u - du.dw*i) / (u*u); + dd.dh = (di.dh*u - du.dh*i) / (u*u); return dd; } @@ -524,6 +843,44 @@ void do_nms(box *boxes, float **probs, int total, int classes, float thresh) } } +void diounms_sort(detection *dets, int total, int classes, float thresh, NMS_KIND nms_kind, float beta1) +{ + int i, j, k; + k = total - 1; + for (i = 0; i <= k; ++i) { + if (dets[i].objectness == 0) { + detection swap = dets[i]; + dets[i] = dets[k]; + dets[k] = swap; + --k; + --i; + } + } + total = k + 1; + + for (k = 0; k < classes; ++k) { + for (i = 0; i < total; ++i) { + dets[i].sort_class = k; + } + qsort(dets, total, sizeof(detection), nms_comparator); + for (i = 0; i < total; ++i) { + if (dets[i].prob[k] == 0) continue; + box a = dets[i].bbox; + for (j = i + 1; j < total; ++j) { + box b = dets[j].bbox; + if (box_iou(a, b) > thresh && nms_kind == GREEDY_NMS) { + dets[j].prob[k] = 0; + } + else { + if (box_diounms(a, b, beta1) > thresh && nms_kind == DIOU_NMS) { + dets[j].prob[k] = 0; + } + } + } + } + } +} + box encode_box(box b, box anchor) { box encode; diff --git a/src/box.h b/src/box.h index 172c135293c..c7f0fb4394a 100644 --- a/src/box.h +++ b/src/box.h @@ -42,6 +42,7 @@ void do_nms(box *boxes, float **probs, int total, int classes, float thresh); void do_nms_sort_v2(box *boxes, float **probs, int total, int classes, float thresh); //LIB_API void do_nms_sort(detection *dets, int total, int classes, float thresh); //LIB_API void do_nms_obj(detection *dets, int total, int classes, float thresh); +//LIB_API void diounms_sort(detection *dets, int total, int classes, float thresh, NMS_KIND nms_kind, float beta1); box decode_box(box b, box anchor); box encode_box(box b, box anchor); diff --git a/src/demo.c b/src/demo.c index 6c7f5d39848..63debde1a9d 100644 --- a/src/demo.c +++ b/src/demo.c @@ -213,7 +213,10 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int detection *local_dets = dets; //if (nms) do_nms_obj(local_dets, local_nboxes, l.classes, nms); // bad results - if (nms) do_nms_sort(local_dets, local_nboxes, l.classes, nms); + if (nms) { + if (l.nms_kind == DEFAULT_NMS) do_nms_sort(local_dets, local_nboxes, l.classes, nms); + else diounms_sort(local_dets, local_nboxes, l.classes, nms, l.nms_kind, l.beta_nms); + } //printf("\033[2J"); //printf("\033[1;1H"); diff --git a/src/detector.c b/src/detector.c index 8177343ef3d..feb86d5583f 100644 --- a/src/detector.c +++ b/src/detector.c @@ -243,7 +243,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i calc_map_for_each = fmax(calc_map_for_each, 100); int next_map_calc = iter_map + calc_map_for_each; next_map_calc = fmax(next_map_calc, net.burn_in); - next_map_calc = fmax(next_map_calc, 400); + //next_map_calc = fmax(next_map_calc, 400); if (calc_map) { printf("\n (next mAP calculation at %d iterations) ", next_map_calc); if (mean_average_precision > 0) printf("\n Last accuracy mAP@0.5 = %2.2f %%, best = %2.2f %% ", mean_average_precision * 100, best_map * 100); @@ -537,7 +537,10 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out int nboxes = 0; int letterbox = (args.type == LETTERBOX_DATA); detection *dets = get_network_boxes(&net, w, h, thresh, .5, map, 0, &nboxes, letterbox); - if (nms) do_nms_sort(dets, nboxes, classes, nms); + if (nms) { + if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms); + else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms); + } if (coco) { print_cocos(fp, path, dets, nboxes, classes, w, h); } @@ -800,7 +803,10 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa dets = get_network_boxes(&net, 1, 1, thresh, hier_thresh, 0, 0, &nboxes, letter_box); } //detection *dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letter_box); // for letter_box=1 - if (nms) do_nms_sort(dets, nboxes, l.classes, nms); + if (nms) { + if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms); + else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms); + } //if (nms) do_nms_obj(dets, nboxes, l.classes, nms); char labelpath[4096]; @@ -1354,7 +1360,10 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam int nboxes = 0; detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letter_box); - if (nms) do_nms_sort(dets, nboxes, l.classes, nms); + if (nms) { + if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms); + else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms); + } draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, ext_output); save_image(im, "predictions"); if (!dont_show) { diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index 109eb522c3a..ae3ee648140 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -130,24 +130,43 @@ void resize_gaussian_yolo_layer(layer *l, int w, int h) #endif } -box get_gaussian_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, int stride) +box get_gaussian_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, int stride, YOLO_POINT yolo_point) { box b; - b.x = (i + x[index + 0*stride]) / lw; - b.y = (j + x[index + 2*stride]) / lh; - b.w = exp(x[index + 4*stride]) * biases[2*n] / w; - b.h = exp(x[index + 6*stride]) * biases[2*n+1] / h; + + if (yolo_point == YOLO_CENTER) { + b.w = exp(x[index + 4 * stride]) * biases[2 * n] / w; + b.h = exp(x[index + 6 * stride]) * biases[2 * n + 1] / h; + b.x = (i + x[index + 0 * stride]) / lw; + b.y = (j + x[index + 2 * stride]) / lh; + } + else if (yolo_point == YOLO_LEFT_TOP) { + b.w = exp(x[index + 4 * stride]) * biases[2 * n] / w; + b.h = exp(x[index + 6 * stride]) * biases[2 * n + 1] / h; + b.x = (i + x[index + 0 * stride]) / lw + b.w/2; + b.y = (j + x[index + 2 * stride]) / lh + b.h/2; + } + else if (yolo_point == YOLO_RIGHT_BOTTOM) { + b.w = exp(x[index + 4 * stride]) * biases[2 * n] / w; + b.h = exp(x[index + 6 * stride]) * biases[2 * n + 1] / h; + b.x = (i + x[index + 0 * stride]) / lw - b.w / 2; + b.y = (j + x[index + 2 * stride]) / lh - b.h / 2; + } + return b; } -float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss, float uc_normalizer, int accumulate) +float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, + float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss, float uc_normalizer, int accumulate, YOLO_POINT yolo_point) { - box pred = get_gaussian_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride); + box pred = get_gaussian_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride, yolo_point); float iou; ious all_ious = { 0 }; all_ious.iou = box_iou(pred, truth); all_ious.giou = box_giou(pred, truth); + all_ious.diou = box_diou(pred, truth); + all_ious.ciou = box_ciou(pred, truth); if (pred.w == 0) { pred.w = 1.0; } if (pred.h == 0) { pred.h = 1.0; } @@ -158,10 +177,24 @@ float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int ind iou = all_ious.iou; - float tx = (truth.x*lw - i); - float ty = (truth.y*lh - j); - float tw = log(truth.w*w / biases[2 * n]); - float th = log(truth.h*h / biases[2 * n + 1]); + float tx, ty, tw, th; + + tw = log(truth.w*w / biases[2 * n]); + th = log(truth.h*h / biases[2 * n + 1]); + + if (yolo_point == YOLO_CENTER) { + tx = (truth.x*lw - i); + ty = (truth.y*lh - j); + + } + else if (yolo_point == YOLO_LEFT_TOP) { + tx = ((truth.x - truth.w / 2)*lw - i); + ty = ((truth.y - truth.h / 2)*lh - j); + } + else if (yolo_point == YOLO_RIGHT_BOTTOM) { + tx = ((truth.x + truth.w / 2)*lw - i); + ty = ((truth.y + truth.h / 2)*lh - j); + } dx = (tx - x[index + 0 * stride]); dy = (ty - x[index + 2 * stride]); @@ -220,9 +253,24 @@ float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int ind // https://giou.stanford.edu/ all_ious.dx_iou = dx_box_iou(pred, truth, iou_loss); + float dx, dy; + + if (yolo_point == YOLO_CENTER) { + dx = (all_ious.dx_iou.dl + all_ious.dx_iou.dr); + dy = (all_ious.dx_iou.dt + all_ious.dx_iou.db); + } + else if (yolo_point == YOLO_LEFT_TOP) { + dx = all_ious.dx_iou.dl; + dy = all_ious.dx_iou.dt; + } + else if (yolo_point == YOLO_RIGHT_BOTTOM) { + dx = all_ious.dx_iou.dr; + dy = all_ious.dx_iou.db; + } + // jacobian^t (transpose) - float dx = (all_ious.dx_iou.dl + all_ious.dx_iou.dr); - float dy = (all_ious.dx_iou.dt + all_ious.dx_iou.db); + //float dx = (all_ious.dx_iou.dl + all_ious.dx_iou.dr); + //float dy = (all_ious.dx_iou.dt + all_ious.dx_iou.db); float dw = ((-0.5 * all_ious.dx_iou.dl) + (0.5 * all_ious.dx_iou.dr)); float dh = ((-0.5 * all_ious.dx_iou.dt) + (0.5 * all_ious.dx_iou.db)); @@ -360,7 +408,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) for (i = 0; i < l.w; ++i) { for (n = 0; n < l.n; ++n) { int box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0); - box pred = get_gaussian_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w*l.h); + box pred = get_gaussian_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w*l.h, l.yolo_point); float best_match_iou = 0; int best_match_t = 0; float best_iou = 0; @@ -405,7 +453,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9); delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0); box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); - delta_gaussian_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, l.uc_normalizer, 1); + delta_gaussian_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, l.uc_normalizer, 1, l.yolo_point); } } } @@ -416,8 +464,22 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) if(!truth.x) break; float best_iou = 0; int best_n = 0; - i = (truth.x * l.w); - j = (truth.y * l.h); + //i = (truth.x * l.w); + //j = (truth.y * l.h); + + if (l.yolo_point == YOLO_CENTER) { + i = (truth.x * l.w); + j = (truth.y * l.h); + } + else if (l.yolo_point == YOLO_LEFT_TOP) { + i = ((truth.x - truth.w / 2) * l.w); + j = ((truth.y - truth.h / 2) * l.h); + } + else if (l.yolo_point == YOLO_RIGHT_BOTTOM) { + i = ((truth.x + truth.w / 2) * l.w); + j = ((truth.y + truth.h / 2) * l.h); + } + box truth_shift = truth; truth_shift.x = truth_shift.y = 0; for(n = 0; n < l.total; ++n){ @@ -434,7 +496,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int mask_n = int_index(l.mask, best_n, l.n); if(mask_n >= 0){ int box_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); - float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, l.uc_normalizer, 1); + float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, l.uc_normalizer, 1, l.yolo_point); int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8); avg_obj += l.output[obj_index]; @@ -465,7 +527,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) if (iou > l.iou_thresh) { int box_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); - float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, l.uc_normalizer, 1); + float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, l.uc_normalizer, 1, l.yolo_point); int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8); avg_obj += l.output[obj_index]; @@ -671,7 +733,7 @@ int get_gaussian_yolo_detections(layer l, int w, int h, int netw, int neth, floa if (objectness > thresh) { int box_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 0); - dets[count].bbox = get_gaussian_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h); + dets[count].bbox = get_gaussian_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h, l.yolo_point); dets[count].objectness = objectness; dets[count].classes = l.classes; diff --git a/src/parser.c b/src/parser.c index 3728c4422f9..e4e970695f0 100644 --- a/src/parser.c +++ b/src/parser.c @@ -372,8 +372,21 @@ layer parse_yolo(list *options, size_params params) if (strcmp(iou_loss, "mse") == 0) l.iou_loss = MSE; else if (strcmp(iou_loss, "giou") == 0) l.iou_loss = GIOU; + else if (strcmp(iou_loss, "diou") == 0) l.iou_loss = DIOU; + else if (strcmp(iou_loss, "ciou") == 0) l.iou_loss = CIOU; else l.iou_loss = IOU; - fprintf(stderr, "[yolo] params: iou loss: %s, iou_norm: %2.2f, cls_norm: %2.2f, scale_x_y: %2.2f\n", (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.cls_normalizer, l.scale_x_y); + fprintf(stderr, "[yolo] params: iou loss: %s (%d), iou_norm: %2.2f, cls_norm: %2.2f, scale_x_y: %2.2f\n", + iou_loss, l.iou_loss, l.iou_normalizer, l.cls_normalizer, l.scale_x_y); + + l.beta_nms = option_find_float_quiet(options, "beta_nms", 0.6); + char *nms_kind = option_find_str(options, "nms_kind", "default"); + if (strcmp(nms_kind, "default") == 0) l.nms_kind = DEFAULT_NMS; + else { + if (strcmp(nms_kind, "greedynms") == 0) l.nms_kind = GREEDY_NMS; + else if (strcmp(nms_kind, "diounms") == 0) l.nms_kind = DIOU_NMS; + else l.nms_kind = DEFAULT_NMS; + printf("nms_kind: %s (%d), beta = %f \n", nms_kind, l.nms_kind, l.beta_nms); + } l.jitter = option_find_float(options, "jitter", .2); l.focal_loss = option_find_int_quiet(options, "focal_loss", 0); @@ -451,8 +464,27 @@ layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3 if (strcmp(iou_loss, "mse") == 0) l.iou_loss = MSE; else if (strcmp(iou_loss, "giou") == 0) l.iou_loss = GIOU; + else if (strcmp(iou_loss, "diou") == 0) l.iou_loss = DIOU; + else if (strcmp(iou_loss, "ciou") == 0) l.iou_loss = CIOU; else l.iou_loss = IOU; - fprintf(stderr, "[Gaussian_yolo] iou loss: %s, iou_norm: %2.2f, cls_norm: %2.2f, scale: %2.2f\n", (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.cls_normalizer, l.scale_x_y); + + l.beta_nms = option_find_float_quiet(options, "beta_nms", 0.6); + char *nms_kind = option_find_str(options, "nms_kind", "default"); + if (strcmp(nms_kind, "default") == 0) l.nms_kind = DEFAULT_NMS; + else { + if (strcmp(nms_kind, "greedynms") == 0) l.nms_kind = GREEDY_NMS; + else if (strcmp(nms_kind, "diounms") == 0) l.nms_kind = DIOU_NMS; + else l.nms_kind = DEFAULT_NMS; + printf("nms_kind: %s (%d), beta = %f \n", nms_kind, l.nms_kind, l.beta_nms); + } + + char *yolo_point = option_find_str_quiet(options, "yolo_point", "center"); + if (strcmp(yolo_point, "left_top") == 0) l.yolo_point = YOLO_LEFT_TOP; + else if (strcmp(yolo_point, "right_bottom") == 0) l.yolo_point = YOLO_RIGHT_BOTTOM; + else l.yolo_point = YOLO_CENTER; + + fprintf(stderr, "[Gaussian_yolo] iou loss: %s (%d), iou_norm: %2.2f, cls_norm: %2.2f, scale: %2.2f, point: %d\n", + iou_loss, l.iou_loss, l.iou_normalizer, l.cls_normalizer, l.scale_x_y, l.yolo_point); l.jitter = option_find_float(options, "jitter", .2); diff --git a/src/utils.c b/src/utils.c index 4651cc0a1e8..af7cb9eaf01 100644 --- a/src/utils.c +++ b/src/utils.c @@ -916,4 +916,23 @@ int max_int_index(int *a, int n) } } return max_i; +} + +// Absolute box from relative coordinate bounding box and image size +boxabs box_to_boxabs(const box* b, const int img_w, const int img_h, const int bounds_check) +{ + boxabs ba; + ba.left = (b->x - b->w / 2.)*img_w; + ba.right = (b->x + b->w / 2.)*img_w; + ba.top = (b->y - b->h / 2.)*img_h; + ba.bot = (b->y + b->h / 2.)*img_h; + + if (bounds_check) { + if (ba.left < 0) ba.left = 0; + if (ba.right > img_w - 1) ba.right = img_w - 1; + if (ba.top < 0) ba.top = 0; + if (ba.bot > img_h - 1) ba.bot = img_h - 1; + } + + return ba; } \ No newline at end of file diff --git a/src/utils.h b/src/utils.h index fe4efe04ef1..998209067dd 100644 --- a/src/utils.h +++ b/src/utils.h @@ -80,6 +80,7 @@ int check_array_is_inf(float *arr, int size); int int_index(int *a, int val, int n); int *random_index_order(int min, int max); int max_int_index(int *a, int n); +boxabs box_to_boxabs(const box* b, const int img_w, const int img_h, const int bounds_check); #ifdef __cplusplus } diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 08577db5ab4..7a7ba6da56e 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -137,6 +137,8 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, box pred = get_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride); all_ious.iou = box_iou(pred, truth); all_ious.giou = box_giou(pred, truth); + all_ious.diou = box_diou(pred, truth); + all_ious.ciou = box_ciou(pred, truth); // avoid nan in dx_box_iou if (pred.w == 0) { pred.w = 1.0; } if (pred.h == 0) { pred.h = 1.0; } @@ -289,8 +291,12 @@ void forward_yolo_layer(const layer l, network_state state) //float avg_iou = 0; float tot_iou = 0; float tot_giou = 0; + float tot_diou = 0; + float tot_ciou = 0; float tot_iou_loss = 0; float tot_giou_loss = 0; + float tot_diou_loss = 0; + float tot_ciou_loss = 0; float recall = 0; float recall75 = 0; float avg_cat = 0; @@ -392,6 +398,12 @@ void forward_yolo_layer(const layer l, network_state state) tot_giou += all_ious.giou; tot_giou_loss += 1 - all_ious.giou; + tot_diou += all_ious.diou; + tot_diou_loss += 1 - all_ious.diou; + + tot_ciou += all_ious.ciou; + tot_ciou_loss += 1 - all_ious.ciou; + int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4); avg_obj += l.output[obj_index]; l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); @@ -428,6 +440,12 @@ void forward_yolo_layer(const layer l, network_state state) tot_giou += all_ious.giou; tot_giou_loss += 1 - all_ious.giou; + tot_diou += all_ious.diou; + tot_diou_loss += 1 - all_ious.diou; + + tot_ciou += all_ious.ciou; + tot_ciou_loss += 1 - all_ious.ciou; + int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4); avg_obj += l.output[obj_index]; l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); @@ -508,6 +526,60 @@ void backward_yolo_layer(const layer l, network_state state) axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1); } +// Converts output of the network to detection boxes +// w,h: image width,height +// netw,neth: network width,height +// relative: 1 (all callers seems to pass TRUE) +void correct_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative) +{ + int i; + // network height (or width) + int new_w = 0; + // network height (or width) + int new_h = 0; + // Compute scale given image w,h vs network w,h + // I think this "rotates" the image to match network to input image w/h ratio + // new_h and new_w are really just network width and height + if (((float)netw / w) < ((float)neth / h)) { + new_w = netw; + new_h = (h * netw) / w; + } + else { + new_h = neth; + new_w = (w * neth) / h; + } + // difference between network width and "rotated" width + float deltaw = netw - new_w; + // difference between network height and "rotated" height + float deltah = neth - new_h; + // ratio between rotated network width and network width + float ratiow = (float)new_w / netw; + // ratio between rotated network width and network width + float ratioh = (float)new_h / neth; + for (i = 0; i < n; ++i) { + + box b = dets[i].bbox; + // x = ( x - (deltaw/2)/netw ) / ratiow; + // x - [(1/2 the difference of the network width and rotated width) / (network width)] + b.x = (b.x - deltaw / 2. / netw) / ratiow; + b.y = (b.y - deltah / 2. / neth) / ratioh; + // scale to match rotation of incoming image + b.w *= 1 / ratiow; + b.h *= 1 / ratioh; + + // relative seems to always be == 1, I don't think we hit this condition, ever. + if (!relative) { + b.x *= w; + b.w *= w; + b.y *= h; + b.h *= h; + } + + dets[i].bbox = b; + } +} + +/* void correct_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative, int letter) { int i; @@ -542,6 +614,7 @@ void correct_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth dets[i].bbox = b; } } +*/ int yolo_num_detections(layer l, float thresh) { From d43e09cdf24708b61cbd159822860dedbf756f1f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 23 Nov 2019 16:44:45 +0300 Subject: [PATCH 0674/1365] Compile fix --- src/yolo_layer.c | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 7a7ba6da56e..81d2de709ae 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -530,7 +530,7 @@ void backward_yolo_layer(const layer l, network_state state) // w,h: image width,height // netw,neth: network width,height // relative: 1 (all callers seems to pass TRUE) -void correct_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative) +void correct_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative, int letter) { int i; // network height (or width) @@ -540,13 +540,19 @@ void correct_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth // Compute scale given image w,h vs network w,h // I think this "rotates" the image to match network to input image w/h ratio // new_h and new_w are really just network width and height - if (((float)netw / w) < ((float)neth / h)) { - new_w = netw; - new_h = (h * netw) / w; + if (letter) { + if (((float)netw / w) < ((float)neth / h)) { + new_w = netw; + new_h = (h * netw) / w; + } + else { + new_h = neth; + new_w = (w * neth) / h; + } } else { + new_w = netw; new_h = neth; - new_w = (w * neth) / h; } // difference between network width and "rotated" width float deltaw = netw - new_w; From 14212154d9340790634e0b5e664ae27802f45e38 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 23 Nov 2019 17:37:28 +0300 Subject: [PATCH 0675/1365] Minor compile fix and references to DIoU-darknet repo and paper --- src/box.c | 9 +++++++++ src/box.h | 2 ++ 2 files changed, 11 insertions(+) diff --git a/src/box.c b/src/box.c index e700dc7140c..0b3e9a92d14 100644 --- a/src/box.c +++ b/src/box.c @@ -177,6 +177,8 @@ float box_giou(box a, box b) return iou - giou_term; } +// https://github.com/Zzh-tju/DIoU-darknet +// https://arxiv.org/abs/1911.08287 float box_diou(box a, box b) { boxabs ba = box_c(a, b); @@ -215,6 +217,8 @@ float box_diounms(box a, box b, float beta1) return iou - diou_term; } +// https://github.com/Zzh-tju/DIoU-darknet +// https://arxiv.org/abs/1911.08287 float box_ciou(box a, box b) { boxabs ba = box_c(a, b); @@ -391,6 +395,9 @@ dxrep dx_box_iou(box pred, box truth, IOU_LOSS iou_loss) { p_dy = p_dt + p_db; p_dw = (p_dr - p_dl); //For dw and dh, we do not divided by 2. p_dh = (p_db - p_dt); + + // https://github.com/Zzh-tju/DIoU-darknet + // https://arxiv.org/abs/1911.08287 if (iou_loss == DIOU) { if (C > 0) { p_dx += (2*(truth.x-pred.x)*C-(2*Cw*dCw_dx+2*Ch*dCh_dx)*S) / (C * C); @@ -843,6 +850,8 @@ void do_nms(box *boxes, float **probs, int total, int classes, float thresh) } } +// https://github.com/Zzh-tju/DIoU-darknet +// https://arxiv.org/abs/1911.08287 void diounms_sort(detection *dets, int total, int classes, float thresh, NMS_KIND nms_kind, float beta1) { int i, j, k; diff --git a/src/box.h b/src/box.h index c7f0fb4394a..608a4c5869d 100644 --- a/src/box.h +++ b/src/box.h @@ -36,6 +36,8 @@ float box_iou(box a, box b); float box_rmse(box a, box b); dxrep dx_box_iou(box a, box b, IOU_LOSS iou_loss); float box_giou(box a, box b); +float box_diou(box a, box b); +float box_ciou(box a, box b); dbox diou(box a, box b); boxabs to_tblr(box a); void do_nms(box *boxes, float **probs, int total, int classes, float thresh); From 8cb3ee4e7956efbb4f858469256c9b139c511ab2 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 23 Nov 2019 18:59:29 +0300 Subject: [PATCH 0676/1365] diounms_sort() fixed --- include/darknet.h | 5 +++-- src/box.c | 32 +++++++++++++++++++++++++++++--- src/gaussian_yolo_layer.c | 3 +++ src/parser.c | 1 + 4 files changed, 36 insertions(+), 5 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 851b65421c8..20f87475b3d 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -112,12 +112,12 @@ typedef enum { // parser.h typedef enum { - DEFAULT_NMS, GREEDY_NMS, DIOU_NMS + DEFAULT_NMS, GREEDY_NMS, DIOU_NMS, CORNERS_NMS } NMS_KIND; // parser.h typedef enum { - YOLO_CENTER, YOLO_LEFT_TOP, YOLO_RIGHT_BOTTOM + YOLO_CENTER = 1 << 0, YOLO_LEFT_TOP = 1 << 1, YOLO_RIGHT_BOTTOM = 1 << 2 } YOLO_POINT; @@ -748,6 +748,7 @@ typedef struct detection{ float objectness; int sort_class; float *uc; // Gaussian_YOLOv3 - tx,ty,tw,th uncertainty + int points; // bit-0 - center, bit-1 - top-left-corner, bit-2 - bottom-right-corner } detection; // matrix.h diff --git a/src/box.c b/src/box.c index 0b3e9a92d14..5e39a2548fd 100644 --- a/src/box.c +++ b/src/box.c @@ -871,13 +871,36 @@ void diounms_sort(detection *dets, int total, int classes, float thresh, NMS_KIN for (i = 0; i < total; ++i) { dets[i].sort_class = k; } - qsort(dets, total, sizeof(detection), nms_comparator); - for (i = 0; i < total; ++i) { + qsort(dets, total, sizeof(detection), nms_comparator_v3); + for (i = 0; i < total; ++i) + { if (dets[i].prob[k] == 0) continue; box a = dets[i].bbox; for (j = i + 1; j < total; ++j) { box b = dets[j].bbox; - if (box_iou(a, b) > thresh && nms_kind == GREEDY_NMS) { + if (box_iou(a, b) > thresh && nms_kind == CORNERS_NMS) + { + float sum_prob = pow(dets[i].prob[k], 2) + pow(dets[j].prob[k], 2); + float alpha_prob = pow(dets[i].prob[k], 2) / sum_prob; + float beta_prob = pow(dets[j].prob[k], 2) / sum_prob; + //dets[i].bbox.x = (dets[i].bbox.x*alpha_prob + dets[j].bbox.x*beta_prob); + //dets[i].bbox.y = (dets[i].bbox.y*alpha_prob + dets[j].bbox.y*beta_prob); + //dets[i].bbox.w = (dets[i].bbox.w*alpha_prob + dets[j].bbox.w*beta_prob); + //dets[i].bbox.h = (dets[i].bbox.h*alpha_prob + dets[j].bbox.h*beta_prob); + /* + if (dets[j].points == YOLO_CENTER && (dets[i].points & dets[j].points) == 0) { + dets[i].bbox.x = (dets[i].bbox.x*alpha_prob + dets[j].bbox.x*beta_prob); + dets[i].bbox.y = (dets[i].bbox.y*alpha_prob + dets[j].bbox.y*beta_prob); + } + else if ((dets[i].points & dets[j].points) == 0) { + dets[i].bbox.w = (dets[i].bbox.w*alpha_prob + dets[j].bbox.w*beta_prob); + dets[i].bbox.h = (dets[i].bbox.h*alpha_prob + dets[j].bbox.h*beta_prob); + } + dets[i].points |= dets[j].points; + */ + dets[j].prob[k] = 0; + } + else if (box_iou(a, b) > thresh && nms_kind == GREEDY_NMS) { dets[j].prob[k] = 0; } else { @@ -886,6 +909,9 @@ void diounms_sort(detection *dets, int total, int classes, float thresh, NMS_KIN } } } + + //if ((nms_kind == CORNERS_NMS) && (dets[i].points != (YOLO_CENTER | YOLO_LEFT_TOP | YOLO_RIGHT_BOTTOM))) + // dets[i].prob[k] = 0; } } } diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index ae3ee648140..2e663651b0b 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -742,6 +742,9 @@ int get_gaussian_yolo_detections(layer l, int w, int h, int netw, int neth, floa dets[count].uc[2] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 5)]; // tw uncertainty dets[count].uc[3] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 7)]; // th uncertainty + dets[count].points = l.yolo_point; + //if (l.yolo_point != YOLO_CENTER) dets[count].objectness = objectness = 0; + for (j = 0; j < l.classes; ++j) { int class_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 9 + j); float uc_aver = (dets[count].uc[0] + dets[count].uc[1] + dets[count].uc[2] + dets[count].uc[3]) / 4.0; diff --git a/src/parser.c b/src/parser.c index e4e970695f0..f7086060483 100644 --- a/src/parser.c +++ b/src/parser.c @@ -474,6 +474,7 @@ layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3 else { if (strcmp(nms_kind, "greedynms") == 0) l.nms_kind = GREEDY_NMS; else if (strcmp(nms_kind, "diounms") == 0) l.nms_kind = DIOU_NMS; + else if (strcmp(nms_kind, "cornersnms") == 0) l.nms_kind = CORNERS_NMS; else l.nms_kind = DEFAULT_NMS; printf("nms_kind: %s (%d), beta = %f \n", nms_kind, l.nms_kind, l.beta_nms); } From 61f8f569b765f12caf1801745180024e0a0903ac Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 23 Nov 2019 19:45:14 +0300 Subject: [PATCH 0677/1365] CIOU and DIOU fix --- src/gaussian_yolo_layer.c | 37 +++++++++++++++++++------------------ src/yolo_layer.c | 14 ++++++++++---- 2 files changed, 29 insertions(+), 22 deletions(-) diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index 2e663651b0b..0fe8a5e16f5 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -134,21 +134,18 @@ box get_gaussian_yolo_box(float *x, float *biases, int n, int index, int i, int { box b; + b.w = exp(x[index + 4 * stride]) * biases[2 * n] / w; + b.h = exp(x[index + 6 * stride]) * biases[2 * n + 1] / h; + if (yolo_point == YOLO_CENTER) { - b.w = exp(x[index + 4 * stride]) * biases[2 * n] / w; - b.h = exp(x[index + 6 * stride]) * biases[2 * n + 1] / h; b.x = (i + x[index + 0 * stride]) / lw; b.y = (j + x[index + 2 * stride]) / lh; } else if (yolo_point == YOLO_LEFT_TOP) { - b.w = exp(x[index + 4 * stride]) * biases[2 * n] / w; - b.h = exp(x[index + 6 * stride]) * biases[2 * n + 1] / h; - b.x = (i + x[index + 0 * stride]) / lw + b.w/2; - b.y = (j + x[index + 2 * stride]) / lh + b.h/2; + b.x = (i + x[index + 0 * stride]) / lw + b.w / 2; + b.y = (j + x[index + 2 * stride]) / lh + b.h / 2; } else if (yolo_point == YOLO_RIGHT_BOTTOM) { - b.w = exp(x[index + 4 * stride]) * biases[2 * n] / w; - b.h = exp(x[index + 6 * stride]) * biases[2 * n + 1] / h; b.x = (i + x[index + 0 * stride]) / lw - b.w / 2; b.y = (j + x[index + 2 * stride]) / lh - b.h / 2; } @@ -185,7 +182,6 @@ float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int ind if (yolo_point == YOLO_CENTER) { tx = (truth.x*lw - i); ty = (truth.y*lh - j); - } else if (yolo_point == YOLO_LEFT_TOP) { tx = ((truth.x - truth.w / 2)*lw - i); @@ -251,28 +247,33 @@ float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int ind // https://github.com/generalized-iou/g-darknet // https://arxiv.org/abs/1902.09630v2 // https://giou.stanford.edu/ + // https://arxiv.org/abs/1911.08287v1 + // https://github.com/Zzh-tju/DIoU-darknet all_ious.dx_iou = dx_box_iou(pred, truth, iou_loss); - float dx, dy; + float dx, dy, dw, dh; + + dx = all_ious.dx_iou.dt; + dy = all_ious.dx_iou.db; + dw = all_ious.dx_iou.dl; + dh = all_ious.dx_iou.dr; if (yolo_point == YOLO_CENTER) { - dx = (all_ious.dx_iou.dl + all_ious.dx_iou.dr); - dy = (all_ious.dx_iou.dt + all_ious.dx_iou.db); } else if (yolo_point == YOLO_LEFT_TOP) { - dx = all_ious.dx_iou.dl; - dy = all_ious.dx_iou.dt; + dx = dx - dw/2; + dy = dy - dh/2; } else if (yolo_point == YOLO_RIGHT_BOTTOM) { - dx = all_ious.dx_iou.dr; - dy = all_ious.dx_iou.db; + dx = dx + dw / 2; + dy = dy + dh / 2; } // jacobian^t (transpose) //float dx = (all_ious.dx_iou.dl + all_ious.dx_iou.dr); //float dy = (all_ious.dx_iou.dt + all_ious.dx_iou.db); - float dw = ((-0.5 * all_ious.dx_iou.dl) + (0.5 * all_ious.dx_iou.dr)); - float dh = ((-0.5 * all_ious.dx_iou.dt) + (0.5 * all_ious.dx_iou.db)); + //float dw = ((-0.5 * all_ious.dx_iou.dl) + (0.5 * all_ious.dx_iou.dr)); + //float dh = ((-0.5 * all_ious.dx_iou.dt) + (0.5 * all_ious.dx_iou.db)); // predict exponential, apply gradient of e^delta_t ONLY for w,h dw *= exp(x[index + 4 * stride]); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 81d2de709ae..40e73878a75 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -162,10 +162,16 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, all_ious.dx_iou = dx_box_iou(pred, truth, iou_loss); // jacobian^t (transpose) - float dx = (all_ious.dx_iou.dl + all_ious.dx_iou.dr); - float dy = (all_ious.dx_iou.dt + all_ious.dx_iou.db); - float dw = ((-0.5 * all_ious.dx_iou.dl) + (0.5 * all_ious.dx_iou.dr)); - float dh = ((-0.5 * all_ious.dx_iou.dt) + (0.5 * all_ious.dx_iou.db)); + //float dx = (all_ious.dx_iou.dl + all_ious.dx_iou.dr); + //float dy = (all_ious.dx_iou.dt + all_ious.dx_iou.db); + //float dw = ((-0.5 * all_ious.dx_iou.dl) + (0.5 * all_ious.dx_iou.dr)); + //float dh = ((-0.5 * all_ious.dx_iou.dt) + (0.5 * all_ious.dx_iou.db)); + + // jacobian^t (transpose) + float dx = all_ious.dx_iou.dt; + float dy = all_ious.dx_iou.db; + float dw = all_ious.dx_iou.dl; + float dh = all_ious.dx_iou.dr; // predict exponential, apply gradient of e^delta_t ONLY for w,h dw *= exp(x[index + 2 * stride]); From 3abbd858084c9f3634a30307f36a0d23303796b6 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 23 Nov 2019 20:00:35 +0300 Subject: [PATCH 0678/1365] fixed consistency darknet and python code --- build/darknet/x64/darknet.py | 3 ++- darknet.py | 3 ++- src/gaussian_yolo_layer.c | 8 ++++---- 3 files changed, 8 insertions(+), 6 deletions(-) diff --git a/build/darknet/x64/darknet.py b/build/darknet/x64/darknet.py index 5cfb26ba16e..55afcb0ea42 100644 --- a/build/darknet/x64/darknet.py +++ b/build/darknet/x64/darknet.py @@ -60,7 +60,8 @@ class DETECTION(Structure): ("mask", POINTER(c_float)), ("objectness", c_float), ("sort_class", c_int), - ("uc", POINTER(c_float))] + ("uc", POINTER(c_float)), + ("points", c_int)] class IMAGE(Structure): diff --git a/darknet.py b/darknet.py index 5cfb26ba16e..55afcb0ea42 100644 --- a/darknet.py +++ b/darknet.py @@ -60,7 +60,8 @@ class DETECTION(Structure): ("mask", POINTER(c_float)), ("objectness", c_float), ("sort_class", c_int), - ("uc", POINTER(c_float))] + ("uc", POINTER(c_float)), + ("points", c_int)] class IMAGE(Structure): diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index 0fe8a5e16f5..d179ddd83d5 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -136,10 +136,10 @@ box get_gaussian_yolo_box(float *x, float *biases, int n, int index, int i, int b.w = exp(x[index + 4 * stride]) * biases[2 * n] / w; b.h = exp(x[index + 6 * stride]) * biases[2 * n + 1] / h; + b.x = (i + x[index + 0 * stride]) / lw; + b.y = (j + x[index + 2 * stride]) / lh; if (yolo_point == YOLO_CENTER) { - b.x = (i + x[index + 0 * stride]) / lw; - b.y = (j + x[index + 2 * stride]) / lh; } else if (yolo_point == YOLO_LEFT_TOP) { b.x = (i + x[index + 0 * stride]) / lw + b.w / 2; @@ -176,12 +176,12 @@ float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int ind float tx, ty, tw, th; + tx = (truth.x*lw - i); + ty = (truth.y*lh - j); tw = log(truth.w*w / biases[2 * n]); th = log(truth.h*h / biases[2 * n + 1]); if (yolo_point == YOLO_CENTER) { - tx = (truth.x*lw - i); - ty = (truth.y*lh - j); } else if (yolo_point == YOLO_LEFT_TOP) { tx = ((truth.x - truth.w / 2)*lw - i); From 55a6479ea24a91cf500ad611d51fa39483f54b7b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mos=C3=A8=20Giordano?= Date: Sun, 24 Nov 2019 00:17:32 +0000 Subject: [PATCH 0679/1365] Fix compilation for Windows with MinGW --- Makefile | 4 ++++ src/darkunistd.h | 2 +- src/gemm.c | 2 +- src/getopt.h | 6 +++--- src/gettimeofday.h | 6 +++--- 5 files changed, 12 insertions(+), 8 deletions(-) diff --git a/Makefile b/Makefile index c59991281ba..7db7f86999d 100644 --- a/Makefile +++ b/Makefile @@ -73,6 +73,10 @@ endif CFLAGS+=$(OPTS) +ifneq (,$(findstring MSYS_NT,$(OS))) +LDFLAGS+=-lws2_32 +endif + ifeq ($(OPENCV), 1) COMMON+= -DOPENCV CFLAGS+= -DOPENCV diff --git a/src/darkunistd.h b/src/darkunistd.h index d2d555c313a..6c06aef1c7e 100644 --- a/src/darkunistd.h +++ b/src/darkunistd.h @@ -7,7 +7,7 @@ * Please add functionality as needed */ -#include +#include #include /* for _getcwd() and _chdir() */ #include "getopt.h" #include diff --git a/src/gemm.c b/src/gemm.c index 51f77cac7a9..9d95e054573 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -512,7 +512,7 @@ static inline int popcnt_32(uint32_t val32) { //---------------------------- -#if (defined(__AVX__) && defined(__x86_64__)) || defined(_WIN64) +#if (defined(__AVX__) && defined(__x86_64__)) || (defined(_WIN64) && !defined(__MINGW32__)) #ifdef _WIN64 #include diff --git a/src/getopt.h b/src/getopt.h index 431ba864015..66ab5dfbe36 100644 --- a/src/getopt.h +++ b/src/getopt.h @@ -67,7 +67,7 @@ #include #include #define WIN32_LEAN_AND_MEAN -#include +#include #ifdef __cplusplus extern "C" { @@ -190,13 +190,13 @@ extern "C" { * Parse long options in argc/argv argument vector. * Returns -1 if short_too is set and the option does not match long_options. */ -static int parse_long_options(char* const* nargv, const char* options, const struct option* long_options, int* idx, int short_too); +/* static int parse_long_options(char* const* nargv, const char* options, const struct option* long_options, int* idx, int short_too); */ /* * getopt_internal -- * Parse argc/argv argument vector. Called by user level routines. */ -static int getopt_internal(int nargc, char* const* nargv, const char* options, const struct option* long_options, int* idx, int flags); +/* static int getopt_internal(int nargc, char* const* nargv, const char* options, const struct option* long_options, int* idx, int flags); */ /* * getopt_long -- diff --git a/src/gettimeofday.h b/src/gettimeofday.h index 4a9df6734fc..2e96dd2b852 100644 --- a/src/gettimeofday.h +++ b/src/gettimeofday.h @@ -1,9 +1,9 @@ #ifdef _WIN32 #define WIN32_LEAN_AND_MEAN -#include -#include +#include +#include #include -#include < time.h > +#include #include "darknet.h" #define CLOCK_REALTIME (1) From ed0b348573394e83863a7b16709e8ae1d27bbf14 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 24 Nov 2019 17:39:30 +0100 Subject: [PATCH 0680/1365] remove unnecessary gcc installation on travis --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 7ae323e6dd1..5354d01f90f 100644 --- a/.travis.yml +++ b/.travis.yml @@ -79,7 +79,7 @@ matrix: osx_image: xcode10.1 env: - additional_defines="-DBUILD_SHARED_LIBS=OFF" - - MATRIX_EVAL="brew install gcc && brew install yasm && unset CC && unset CXX" + - MATRIX_EVAL="brew install yasm && unset CC && unset CXX" - USE_VCPKG=true - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" From dea940c666153a510463d3e38cf27f4854b7b16e Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 24 Nov 2019 18:09:00 +0100 Subject: [PATCH 0681/1365] cleanup vcpkg cache --- .travis.yml | 2 ++ appveyor.yml | 2 ++ 2 files changed, 4 insertions(+) diff --git a/.travis.yml b/.travis.yml index 5354d01f90f..32095e578c1 100644 --- a/.travis.yml +++ b/.travis.yml @@ -217,6 +217,7 @@ install: - pushd $HOME - if [ -d "$HOME/vcpkg/.git" ] ; then echo vcpkg cached ; else rm -rf vcpkg ; git clone https://github.com/Microsoft/vcpkg ; fi - cd vcpkg + - git checkout . - git pull - if [ "${USE_VCPKG}" = true ] ; then ./bootstrap-vcpkg.sh ; fi - if [ "${USE_VCPKG}" = true ] ; then echo "set(VCPKG_BUILD_TYPE release)" >> triplets/x64-osx.cmake ; fi @@ -226,6 +227,7 @@ install: #- if [ "${USE_VCPKG}" = true ] && [[ ! -z "${CUDACXX}" ]] ; then travis_wait 45 ./vcpkg install opencv[ffmpeg,cuda] --recurse; fi #- if [ "${USE_VCPKG}" = true ] && [[ -z "${CUDACXX}" ]] ; then travis_wait 45 ./vcpkg install opencv[ffmpeg] --recurse; fi - if [ "${USE_VCPKG}" = true ] ; then travis_wait 45 ./vcpkg install opencv[ffmpeg] --recurse; fi + - if [ "${USE_VCPKG}" = true ] ; then rm -rf buildtrees; fi - popd before_script: diff --git a/appveyor.yml b/appveyor.yml index 380feba4a55..2abab7cd041 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -57,6 +57,7 @@ install: - if [%COMPILER%]==[cygwin] %CYGSH% 'make install' - if [%COMPILER%]==[cygwin] cd %WORKSPACE% - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] cd %VCPKG_ROOT% + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] git checkout . - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] git pull - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] .\bootstrap-vcpkg.bat - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] echo set(VCPKG_BUILD_TYPE release) >> triplets\%VCPKG_DEFAULT_TRIPLET%.cmake @@ -64,6 +65,7 @@ install: - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg install stb pthreads --recurse - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg install ffmpeg --recurse - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg install opencv[ffmpeg] --recurse ## opencv[ffmpeg,cuda] is too big to build, timing out (>1h). We use plain openCV also for CUDA builds (toolchain can manage this strange situation anyway) + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] rmdir "buildtrees" /S /Q - cd %WORKSPACE%\darknet\ - if [%COMPILER%]==[cygwin] mkdir build_debug && cd build_debug - if [%COMPILER%]==[cygwin] %CYGSH% 'cmake .. -G "Unix Makefiles" -DCMAKE_BUILD_TYPE="Debug"' From d723d2cfd0838fae2c34d3f19eb7cceda0a3fa44 Mon Sep 17 00:00:00 2001 From: HagegeR Date: Sun, 24 Nov 2019 19:49:03 +0200 Subject: [PATCH 0682/1365] added gaussian documentation I wasn't sure how to add the distinction without breaking the explanation flow, I hope I did ok... --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 13a13167dd4..23969c10e3c 100644 --- a/README.md +++ b/README.md @@ -377,7 +377,11 @@ Training Yolo v3: * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L603 * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L689 * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L776 - + * when using [[`gaussian_yolo`]](https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L608) layers, change [`filters=57`] filters=(classes + 9)x3 in the 3 `[convolutional]` before each `[gaussian_yolo]` layer + * https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L604 + * https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L696 + * https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L789 + So if `classes=1` then should be `filters=18`. If `classes=2` then write `filters=21`. **(Do not write in the cfg-file: filters=(classes + 5)x3)** From 8fba668a346685bbbfc2e3424ec323b06ac67842 Mon Sep 17 00:00:00 2001 From: HagegeR Date: Sun, 24 Nov 2019 22:41:09 +0200 Subject: [PATCH 0683/1365] Fix typo --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 23969c10e3c..b6cf620944c 100644 --- a/README.md +++ b/README.md @@ -377,7 +377,7 @@ Training Yolo v3: * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L603 * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L689 * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L776 - * when using [[`gaussian_yolo`]](https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L608) layers, change [`filters=57`] filters=(classes + 9)x3 in the 3 `[convolutional]` before each `[gaussian_yolo]` layer + * when using [[`Gaussian_yolo`]](https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L608) layers, change [`filters=57`] filters=(classes + 9)x3 in the 3 `[convolutional]` before each `[gaussian_yolo]` layer * https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L604 * https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L696 * https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L789 From b527b449e653d49924c6c8a62946d6013fb7cb84 Mon Sep 17 00:00:00 2001 From: Alexey Date: Sun, 24 Nov 2019 23:45:12 +0300 Subject: [PATCH 0684/1365] minor fix Readme.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index b6cf620944c..e5a0ecba036 100644 --- a/README.md +++ b/README.md @@ -377,7 +377,7 @@ Training Yolo v3: * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L603 * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L689 * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L776 - * when using [[`Gaussian_yolo`]](https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L608) layers, change [`filters=57`] filters=(classes + 9)x3 in the 3 `[convolutional]` before each `[gaussian_yolo]` layer + * when using `[Gaussian_yolo]` (https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L608) layers, change [`filters=57`] filters=(classes + 9)x3 in the 3 `[convolutional]` before each `[Gaussian_yolo]` layer * https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L604 * https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L696 * https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L789 From 55d84cd02a474287c1b7a28c6e0d6bb1f0d1aca8 Mon Sep 17 00:00:00 2001 From: Alexey Date: Sun, 24 Nov 2019 23:46:23 +0300 Subject: [PATCH 0685/1365] Update Readme.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e5a0ecba036..aedef40e400 100644 --- a/README.md +++ b/README.md @@ -377,7 +377,7 @@ Training Yolo v3: * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L603 * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L689 * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L776 - * when using `[Gaussian_yolo]` (https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L608) layers, change [`filters=57`] filters=(classes + 9)x3 in the 3 `[convolutional]` before each `[Gaussian_yolo]` layer + * when using [`[Gaussian_yolo]`](https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L608) layers, change [`filters=57`] filters=(classes + 9)x3 in the 3 `[convolutional]` before each `[Gaussian_yolo]` layer * https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L604 * https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L696 * https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L789 From 48914023da1656c5494deda77b0c683a64401ede Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 25 Nov 2019 18:10:39 +0300 Subject: [PATCH 0686/1365] Added init_cpu() function to init CPU just to speedup the 1st detection (if GPU=0) --- build/darknet/x64/darknet.py | 2 ++ darknet.py | 2 ++ include/darknet.h | 3 +++ src/gemm.c | 7 +++++++ src/parser.c | 4 ++-- 5 files changed, 16 insertions(+), 2 deletions(-) diff --git a/build/darknet/x64/darknet.py b/build/darknet/x64/darknet.py index 55afcb0ea42..da66e9c7fa1 100644 --- a/build/darknet/x64/darknet.py +++ b/build/darknet/x64/darknet.py @@ -144,6 +144,8 @@ def network_height(net): set_gpu = lib.cuda_set_device set_gpu.argtypes = [c_int] +init_cpu = lib.init_cpu + make_image = lib.make_image make_image.argtypes = [c_int, c_int, c_int] make_image.restype = IMAGE diff --git a/darknet.py b/darknet.py index 55afcb0ea42..da66e9c7fa1 100644 --- a/darknet.py +++ b/darknet.py @@ -144,6 +144,8 @@ def network_height(net): set_gpu = lib.cuda_set_device set_gpu.argtypes = [c_int] +init_cpu = lib.init_cpu + make_image = lib.make_image make_image.argtypes = [c_int, c_int, c_int] make_image.restype = IMAGE diff --git a/include/darknet.h b/include/darknet.h index 20f87475b3d..2456d6010e8 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -921,6 +921,9 @@ void stop_timer_and_show(); void stop_timer_and_show_name(char *name); void show_total_time(); +// gemm.h +LIB_API void init_cpu(); + #ifdef __cplusplus } #endif // __cplusplus diff --git a/src/gemm.c b/src/gemm.c index 9d95e054573..10e8ff66905 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -2855,3 +2855,10 @@ int test_gpu_blas() return 0; } #endif + + + +void init_cpu() { + is_avx(); + is_fma_avx2(); +} \ No newline at end of file diff --git a/src/parser.c b/src/parser.c index f7086060483..91104df32c9 100644 --- a/src/parser.c +++ b/src/parser.c @@ -379,7 +379,7 @@ layer parse_yolo(list *options, size_params params) iou_loss, l.iou_loss, l.iou_normalizer, l.cls_normalizer, l.scale_x_y); l.beta_nms = option_find_float_quiet(options, "beta_nms", 0.6); - char *nms_kind = option_find_str(options, "nms_kind", "default"); + char *nms_kind = option_find_str_quiet(options, "nms_kind", "default"); if (strcmp(nms_kind, "default") == 0) l.nms_kind = DEFAULT_NMS; else { if (strcmp(nms_kind, "greedynms") == 0) l.nms_kind = GREEDY_NMS; @@ -469,7 +469,7 @@ layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3 else l.iou_loss = IOU; l.beta_nms = option_find_float_quiet(options, "beta_nms", 0.6); - char *nms_kind = option_find_str(options, "nms_kind", "default"); + char *nms_kind = option_find_str_quiet(options, "nms_kind", "default"); if (strcmp(nms_kind, "default") == 0) l.nms_kind = DEFAULT_NMS; else { if (strcmp(nms_kind, "greedynms") == 0) l.nms_kind = GREEDY_NMS; From b832c727dc23e8daaf9f5a20dff6522f949b56b0 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 25 Nov 2019 18:40:09 +0300 Subject: [PATCH 0687/1365] Fixed random=1 for assisted_excitation_forward --- src/convolutional_layer.c | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 70556cea112..6749e30c6e5 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -778,6 +778,16 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) cuda_free(l->activation_input_gpu); l->activation_input_gpu = cuda_make_array(l->activation_input, total_batch*l->outputs); } + + if (l->assisted_excitation) + { + cuda_free(l->gt_gpu); + cuda_free(l->a_avg_gpu); + + const int size = l->out_w * l->out_h * l->batch; + l->gt_gpu = cuda_make_array(NULL, size); + l->a_avg_gpu = cuda_make_array(NULL, size); + } } #ifdef CUDNN cudnn_convolutional_setup(l, cudnn_fastest); From 0d98f20be1290d2a0e495577572893854f84607e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 26 Nov 2019 23:18:04 +0300 Subject: [PATCH 0688/1365] You can train with mini_batch=128 (batch=256 subdivisions=2) on GPU 8 GB VRAM + 128 GB CPU-RAM and more. Set in cfg-file: optimized_memory=3 and workspace_size_limit_MB=2000 or 4000 --- include/darknet.h | 8 +++ src/batchnorm_layer.c | 9 ++- src/convolutional_layer.c | 49 +++++++++++--- src/convolutional_layer.h | 1 + src/dark_cuda.c | 137 ++++++++++++++++++++++++++++++++++++-- src/dark_cuda.h | 7 +- src/detector.c | 2 +- src/layer.c | 10 +-- src/network.c | 3 +- src/network_kernels.cu | 9 +++ src/parser.c | 98 ++++++++++++++++++++++++++- 11 files changed, 305 insertions(+), 28 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 2456d6010e8..8aa7b8e94fe 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -239,6 +239,8 @@ struct layer { int xnor; int peephole; int use_bin_output; + int keep_delta_gpu; + int optimized_memory; int steps; int state_constrain; int hidden; @@ -677,7 +679,13 @@ typedef struct network { size_t *max_input16_size; size_t *max_output16_size; int wait_stream; + + float *global_delta_gpu; + float *state_delta_gpu; + size_t max_delta_gpu_size; #endif + int optimized_memory; + size_t workspace_size_limit; } network; // network.h diff --git a/src/batchnorm_layer.c b/src/batchnorm_layer.c index cbcc14d85e4..bb36351211d 100644 --- a/src/batchnorm_layer.c +++ b/src/batchnorm_layer.c @@ -184,7 +184,6 @@ void forward_batchnorm_layer_gpu(layer l, network_state state) if (state.train) { simple_copy_ongpu(l.outputs*l.batch, l.output_gpu, l.x_gpu); - //copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1); #ifdef CUDNN float one = 1; float zero = 0; @@ -259,16 +258,16 @@ void backward_batchnorm_layer_gpu(layer l, network_state state) l.normDstTensorDesc, l.delta_gpu, // input l.normDstTensorDesc, - l.x_norm_gpu, // output + l.output_gpu, //l.x_norm_gpu, // output l.normTensorDesc, - l.scales_gpu, // output (should be FP32) + l.scales_gpu, // input (should be FP32) l.scale_updates_gpu, // output (should be FP32) l.bias_updates_gpu, // output (should be FP32) .00001, l.mean_gpu, // input (should be FP32) l.variance_gpu); // input (should be FP32) - simple_copy_ongpu(l.outputs*l.batch, l.x_norm_gpu, l.delta_gpu); - //copy_ongpu(l.outputs*l.batch, l.x_norm_gpu, 1, l.delta_gpu, 1); + simple_copy_ongpu(l.outputs*l.batch, l.output_gpu, l.delta_gpu); + //simple_copy_ongpu(l.outputs*l.batch, l.x_norm_gpu, l.delta_gpu); #else backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h); backward_scale_gpu(l.x_norm_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.scale_updates_gpu); diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 6749e30c6e5..96c78998beb 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -212,7 +212,7 @@ void create_convolutional_cudnn_tensors(layer *l) CHECK_CUDNN(cudnnCreateConvolutionDescriptor(&l->convDesc)); } -void cudnn_convolutional_setup(layer *l, int cudnn_preference) +void cudnn_convolutional_setup(layer *l, int cudnn_preference, size_t workspace_size_specify) { // CUDNN_HALF @@ -291,6 +291,13 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) backward_filter = CUDNN_CONVOLUTION_BWD_FILTER_NO_WORKSPACE; printf(" CUDNN-slow "); } + if (cudnn_preference == cudnn_specify) + { + forward_algo = CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT; + backward_algo = CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT; + backward_filter = CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT; + //printf(" CUDNN-specified %zu ", workspace_size_specify); + } CHECK_CUDNN(cudnnGetConvolutionForwardAlgorithm(cudnn_handle(), l->srcTensorDesc, @@ -298,7 +305,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) l->convDesc, l->dstTensorDesc, (cudnnConvolutionFwdPreference_t)forward_algo, - 0, + workspace_size_specify, &l->fw_algo)); CHECK_CUDNN(cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(), l->weightDesc, @@ -306,7 +313,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) l->convDesc, l->dsrcTensorDesc, (cudnnConvolutionBwdDataPreference_t)backward_algo, - 0, + workspace_size_specify, &l->bd_algo)); CHECK_CUDNN(cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(), l->srcTensorDesc, @@ -314,7 +321,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference) l->convDesc, l->dweightDesc, (cudnnConvolutionBwdFilterPreference_t)backward_filter, - 0, + workspace_size_specify, &l->bf_algo)); //if (data_type == CUDNN_DATA_HALF) @@ -439,7 +446,9 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.activation = activation; l.output = (float*)calloc(total_batch*l.outputs, sizeof(float)); +#ifndef GPU if (train) l.delta = (float*)calloc(total_batch*l.outputs, sizeof(float)); +#endif // not GPU l.forward = forward_convolutional_layer; l.backward = backward_convolutional_layer; @@ -500,10 +509,14 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.rolling_variance = (float*)calloc(n, sizeof(float)); } +#ifndef GPU if (train) { l.x = (float*)calloc(total_batch * l.outputs, sizeof(float)); l.x_norm = (float*)calloc(total_batch * l.outputs, sizeof(float)); } + + if (l.activation == SWISH || l.activation == MISH) l.activation_input = (float*)calloc(total_batch*l.outputs, sizeof(float)); +#endif // not GPU } if(adam){ l.adam = 1; @@ -515,10 +528,11 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.scale_v = (float*)calloc(n, sizeof(float)); } - if (l.activation == SWISH || l.activation == MISH) l.activation_input = (float*)calloc(total_batch*l.outputs, sizeof(float)); #ifdef GPU - if (l.activation == SWISH || l.activation == MISH) l.activation_input_gpu = cuda_make_array(l.activation_input, total_batch*l.outputs); + if (l.activation == SWISH || l.activation == MISH) { + l.activation_input_gpu = cuda_make_array(l.activation_input, total_batch*l.outputs); + } l.forward_gpu = forward_convolutional_layer_gpu; l.backward_gpu = backward_convolutional_layer_gpu; @@ -583,9 +597,10 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.mean_gpu = cuda_make_array(l.mean, n); l.variance_gpu = cuda_make_array(l.variance, n); - +#ifndef CUDNN l.mean_delta_gpu = cuda_make_array(l.mean, n); l.variance_delta_gpu = cuda_make_array(l.variance, n); +#endif // CUDNN } l.rolling_mean_gpu = cuda_make_array(l.mean, n); @@ -594,7 +609,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, if (train) { l.x_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n); - l.x_norm_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n); + //l.x_norm_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n); } } @@ -606,7 +621,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, } #ifdef CUDNN create_convolutional_cudnn_tensors(&l); - cudnn_convolutional_setup(&l, cudnn_fastest); + cudnn_convolutional_setup(&l, cudnn_fastest, 0); #endif // CUDNN } #endif // GPU @@ -790,7 +805,7 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) } } #ifdef CUDNN - cudnn_convolutional_setup(l, cudnn_fastest); + cudnn_convolutional_setup(l, cudnn_fastest, 0); #endif #endif l->workspace_size = get_convolutional_workspace_size(*l); @@ -802,12 +817,24 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) CHECK_CUDA(cudaMemGetInfo(&free_byte, &total_byte)); if (l->workspace_size > free_byte || l->workspace_size >= total_byte / 2) { printf(" used slow CUDNN algo without Workspace! Need memory: %zu, available: %zu\n", l->workspace_size, (free_byte < total_byte/2) ? free_byte : total_byte/2); - cudnn_convolutional_setup(l, cudnn_smallest); + cudnn_convolutional_setup(l, cudnn_smallest, 0); l->workspace_size = get_convolutional_workspace_size(*l); } #endif } +void set_specified_workspace_limit(convolutional_layer *l, size_t workspace_size_limit) +{ +#ifdef CUDNN + size_t free_byte; + size_t total_byte; + CHECK_CUDA(cudaMemGetInfo(&free_byte, &total_byte)); + cudnn_convolutional_setup(l, cudnn_specify, workspace_size_limit); + l->workspace_size = get_convolutional_workspace_size(*l); + //printf("Set specified workspace limit for cuDNN: %zu, available: %zu, workspace = %zu \n", workspace_size_limit, free_byte, l->workspace_size); +#endif // CUDNN +} + void add_bias(float *output, float *biases, int batch, int n, int size) { int i,j,b; diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h index 817f900b783..c4da8702d1c 100644 --- a/src/convolutional_layer.h +++ b/src/convolutional_layer.h @@ -33,6 +33,7 @@ void free_convolutional_batchnorm(convolutional_layer *l); size_t get_convolutional_workspace_size(layer l); convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, int antialiasing, convolutional_layer *share_layer, int assisted_excitation, int train); void denormalize_convolutional_layer(convolutional_layer l); +void set_specified_workspace_limit(convolutional_layer *l, size_t workspace_size_limit); void resize_convolutional_layer(convolutional_layer *layer, int w, int h); void forward_convolutional_layer(const convolutional_layer layer, network_state state); void update_convolutional_layer(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay); diff --git a/src/dark_cuda.c b/src/dark_cuda.c index 165dad5f284..f9779eed3f6 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -226,16 +226,138 @@ cublasHandle_t blas_handle() return handle[i]; } +static float **pinned_ptr = NULL; +static size_t pinned_num_of_blocks = 0; +static size_t pinned_index = 0; +static size_t pinned_block_id = 0; +static const size_t pinned_block_size = (size_t)1024 * 1024 * 1024 * 1; // 1 GB block size +static pthread_mutex_t mutex_pinned = PTHREAD_MUTEX_INITIALIZER; + +// free CPU-pinned memory +void free_pinned_memory() +{ + if (pinned_ptr) { + int k; + for (k = 0; k < pinned_num_of_blocks; ++k) { + cuda_free_host(pinned_ptr[k]); + } + free(pinned_ptr); + pinned_ptr = NULL; + } +} + +// custom CPU-pinned memory allocation +void pre_allocate_pinned_memory(const size_t size) +{ + const size_t num_of_blocks = size / pinned_block_size + ((size % pinned_block_size) ? 1 : 0); + printf("pre_allocate... pinned_ptr = %p \n", pinned_ptr); + + pthread_mutex_lock(&mutex_pinned); + if (!pinned_ptr) { + pinned_ptr = (float *)calloc(num_of_blocks, sizeof(float *)); + if(!pinned_ptr) error("calloc failed in pre_allocate() \n"); + + printf("pre_allocate: size = %Iu MB, num_of_blocks = %Iu, block_size = %Iu MB \n", + size / (1024*1024), num_of_blocks, pinned_block_size / (1024 * 1024)); + + int k; + for (k = 0; k < num_of_blocks; ++k) { + cudaError_t status = cudaHostAlloc((void **)&pinned_ptr[k], pinned_block_size, cudaHostRegisterMapped); + if (status != cudaSuccess) fprintf(stderr, " Can't pre-allocate CUDA-pinned buffer on CPU-RAM \n"); + CHECK_CUDA(status); + if (!pinned_ptr[k]) error("cudaHostAlloc failed\n"); + else { + printf(" Allocated %d pinned block \n", pinned_block_size); + } + } + pinned_num_of_blocks = num_of_blocks; + } + pthread_mutex_unlock(&mutex_pinned); +} + +// simple - get pre-allocated pinned memory +float *cuda_make_array_pinned_preallocated(float *x, size_t n) +{ + pthread_mutex_lock(&mutex_pinned); + float *x_cpu = NULL; + const size_t memory_step = 4096; + const size_t size = sizeof(float)*n; + const size_t allocation_size = ((size / 4096) + 1) * 4096; + + if (pinned_ptr && pinned_block_id < pinned_num_of_blocks && (allocation_size < pinned_block_size/2)) + { + if ((allocation_size + pinned_index) > pinned_block_size) { + const float filled = (float)100 * pinned_index / pinned_block_size; + printf("\n Pinned block_id = %d, filled = %f %% \n", pinned_block_id, filled); + pinned_block_id++; + pinned_index = 0; + } + if ((allocation_size + pinned_index) < pinned_block_size && pinned_block_id < pinned_num_of_blocks) { + x_cpu = (float *)((char *)pinned_ptr[pinned_block_id] + pinned_index); + pinned_index += allocation_size; + } + else { + //printf("Pre-allocated pinned memory is over! \n"); + } + } + + if(!x_cpu) { + if (allocation_size > pinned_block_size / 2) { + printf("Try to allocate new pinned memory, size = %d MB \n", size / (1024 * 1024)); + cudaError_t status = cudaHostAlloc((void **)&x_cpu, size, cudaHostRegisterMapped); + if (status != cudaSuccess) fprintf(stderr, " Can't allocate CUDA-pinned memory on CPU-RAM (pre-allocated memory is over too) \n"); + CHECK_CUDA(status); + } + else { + printf("Try to allocate new pinned BLOCK, size = %d MB \n", size / (1024 * 1024)); + pinned_num_of_blocks++; + pinned_block_id = pinned_num_of_blocks - 1; + pinned_index = 0; + pinned_ptr = (float *)realloc(pinned_ptr, pinned_num_of_blocks * sizeof(float *)); + cudaError_t status = cudaHostAlloc((void **)&pinned_ptr[pinned_block_id], pinned_block_size, cudaHostRegisterMapped); + if (status != cudaSuccess) fprintf(stderr, " Can't pre-allocate CUDA-pinned buffer on CPU-RAM \n"); + CHECK_CUDA(status); + x_cpu = pinned_ptr[pinned_block_id]; + } + } + + if (x) { + cudaError_t status = cudaMemcpyAsync(x_cpu, x, size, cudaMemcpyDefault, get_cuda_stream()); + CHECK_CUDA(status); + } + + pthread_mutex_unlock(&mutex_pinned); + return x_cpu; +} + +float *cuda_make_array_pinned(float *x, size_t n) +{ + float *x_gpu; + size_t size = sizeof(float)*n; + //cudaError_t status = cudaMalloc((void **)&x_gpu, size); + cudaError_t status = cudaHostAlloc((void **)&x_gpu, size, cudaHostRegisterMapped); + if (status != cudaSuccess) fprintf(stderr, " Can't allocate CUDA-pinned memory on CPU-RAM \n"); + CHECK_CUDA(status); + if (x) { + status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyDefault, get_cuda_stream()); + CHECK_CUDA(status); + } + if (!x_gpu) error("cudaHostAlloc failed\n"); + return x_gpu; +} + float *cuda_make_array(float *x, size_t n) { float *x_gpu; size_t size = sizeof(float)*n; cudaError_t status = cudaMalloc((void **)&x_gpu, size); + //cudaError_t status = cudaMallocManaged((void **)&x_gpu, size, cudaMemAttachGlobal); + //status = cudaMemAdvise(x_gpu, size, cudaMemAdviseSetPreferredLocation, cudaCpuDeviceId); if (status != cudaSuccess) fprintf(stderr, " Try to set subdivisions=64 in your cfg-file. \n"); CHECK_CUDA(status); if(x){ //status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice); - status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyHostToDevice, get_cuda_stream()); + status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyDefault, get_cuda_stream()); CHECK_CUDA(status); } if(!x_gpu) error("Cuda malloc failed\n"); @@ -301,11 +423,18 @@ void cuda_free(float *x_gpu) CHECK_CUDA(status); } +void cuda_free_host(float *x_cpu) +{ + //cudaStreamSynchronize(get_cuda_stream()); + cudaError_t status = cudaFreeHost(x_cpu); + CHECK_CUDA(status); +} + void cuda_push_array(float *x_gpu, float *x, size_t n) { size_t size = sizeof(float)*n; //cudaError_t status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice); - cudaError_t status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyHostToDevice, get_cuda_stream()); + cudaError_t status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyDefault, get_cuda_stream()); CHECK_CUDA(status); } @@ -313,7 +442,7 @@ void cuda_pull_array(float *x_gpu, float *x, size_t n) { size_t size = sizeof(float)*n; //cudaError_t status = cudaMemcpy(x, x_gpu, size, cudaMemcpyDeviceToHost); - cudaError_t status = cudaMemcpyAsync(x, x_gpu, size, cudaMemcpyDeviceToHost, get_cuda_stream()); + cudaError_t status = cudaMemcpyAsync(x, x_gpu, size, cudaMemcpyDefault, get_cuda_stream()); CHECK_CUDA(status); cudaStreamSynchronize(get_cuda_stream()); } @@ -321,7 +450,7 @@ void cuda_pull_array(float *x_gpu, float *x, size_t n) void cuda_pull_array_async(float *x_gpu, float *x, size_t n) { size_t size = sizeof(float)*n; - cudaError_t status = cudaMemcpyAsync(x, x_gpu, size, cudaMemcpyDeviceToHost, get_cuda_stream()); + cudaError_t status = cudaMemcpyAsync(x, x_gpu, size, cudaMemcpyDefault, get_cuda_stream()); check_error(status); //cudaStreamSynchronize(get_cuda_stream()); } diff --git a/src/dark_cuda.h b/src/dark_cuda.h index 34614dc16c0..3c5aef9b870 100644 --- a/src/dark_cuda.h +++ b/src/dark_cuda.h @@ -57,6 +57,10 @@ extern "C" { #define CHECK_CUDA(X) check_error_extended(X, __FILE__ " : " __FUNCTION__, __LINE__, __DATE__ " - " __TIME__ ); cublasHandle_t blas_handle(); + void free_pinned_memory(); + void pre_allocate_pinned_memory(size_t size); + float *cuda_make_array_pinned_preallocated(float *x, size_t n); + float *cuda_make_array_pinned(float *x, size_t n); float *cuda_make_array(float *x, size_t n); int *cuda_make_int_array(size_t n); int *cuda_make_int_array_new_api(int *x, size_t n); @@ -64,6 +68,7 @@ extern "C" { //LIB_API void cuda_pull_array(float *x_gpu, float *x, size_t n); //LIB_API void cuda_set_device(int n); int cuda_get_device(); + void cuda_free_host(float *x_cpu); void cuda_free(float *x_gpu); void cuda_random(float *x_gpu, size_t n); float cuda_compare(float *x_gpu, float *x, size_t n, char *s); @@ -75,7 +80,7 @@ extern "C" { #ifdef CUDNN cudnnHandle_t cudnn_handle(); -enum {cudnn_fastest, cudnn_smallest}; +enum {cudnn_fastest, cudnn_smallest, cudnn_specify}; void cudnn_check_error_extended(cudnnStatus_t status, const char *file, int line, const char *date_time); #define CHECK_CUDNN(X) cudnn_check_error_extended(X, __FILE__ " : " __FUNCTION__, __LINE__, __DATE__ " - " __TIME__ ); diff --git a/src/detector.c b/src/detector.c index feb86d5583f..8e778e7aafc 100644 --- a/src/detector.c +++ b/src/detector.c @@ -278,7 +278,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i //network net_combined = combine_train_valid_networks(net, net_map); iter_map = i; - mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, 0, net.letter_box, &net_map);// &net_combined); + //mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, 0, net.letter_box, &net_map);// &net_combined); printf("\n mean_average_precision (mAP@0.5) = %f \n", mean_average_precision); if (mean_average_precision > best_map) { best_map = mean_average_precision; diff --git a/src/layer.c b/src/layer.c index 9fe4a439364..fcdb33facbc 100644 --- a/src/layer.c +++ b/src/layer.c @@ -154,7 +154,6 @@ void free_layer(layer l) if (l.rolling_variance_gpu) cuda_free(l.rolling_variance_gpu), l.rolling_variance_gpu = NULL; if (l.variance_delta_gpu) cuda_free(l.variance_delta_gpu), l.variance_delta_gpu = NULL; if (l.mean_delta_gpu) cuda_free(l.mean_delta_gpu), l.mean_delta_gpu = NULL; - if (l.x_gpu) cuda_free(l.x_gpu); // dont free if (l.x_norm_gpu) cuda_free(l.x_norm_gpu); // assisted excitation @@ -175,9 +174,12 @@ void free_layer(layer l) if (l.scales_gpu) cuda_free(l.scales_gpu), l.scales_gpu = NULL; if (l.scale_updates_gpu) cuda_free(l.scale_updates_gpu), l.scale_updates_gpu = NULL; if (l.input_antialiasing_gpu) cuda_free(l.input_antialiasing_gpu), l.input_antialiasing_gpu = NULL; - if (l.output_gpu) cuda_free(l.output_gpu), l.output_gpu = NULL; - if (l.activation_input_gpu) cuda_free(l.activation_input_gpu), l.activation_input_gpu = NULL; - if (l.delta_gpu) cuda_free(l.delta_gpu), l.delta_gpu = NULL; + if (l.optimized_memory < 2) { + if (l.x_gpu) cuda_free(l.x_gpu); l.x_gpu = NULL; + if (l.output_gpu) cuda_free(l.output_gpu), l.output_gpu = NULL; + if (l.activation_input_gpu) cuda_free(l.activation_input_gpu), l.activation_input_gpu = NULL; + } + if (l.delta_gpu && l.keep_delta_gpu && l.optimized_memory < 3) cuda_free(l.delta_gpu), l.delta_gpu = NULL; if (l.rand_gpu) cuda_free(l.rand_gpu); if (l.squared_gpu) cuda_free(l.squared_gpu); if (l.norms_gpu) cuda_free(l.norms_gpu); diff --git a/src/network.c b/src/network.c index 9137bc0a480..731b088344d 100644 --- a/src/network.c +++ b/src/network.c @@ -544,7 +544,7 @@ int resize_network(network *net, int w, int h) #ifdef GPU l.output_gpu = net->layers[i-1].output_gpu; l.delta_gpu = net->layers[i-1].delta_gpu; -#endif +#endif }else if (l.type == UPSAMPLE) { resize_upsample_layer(&l, w, h); }else if(l.type == REORG){ @@ -1035,6 +1035,7 @@ void free_network(network net) #ifdef GPU if (gpu_index >= 0) cuda_free(net.workspace); else free(net.workspace); + free_pinned_memory(); if (net.input_state_gpu) cuda_free(net.input_state_gpu); if (net.input_pinned_cpu) { // CPU if (net.input_pinned_cpu_flag) cudaFreeHost(net.input_pinned_cpu); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 3ebf2900e95..872a136da70 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -105,10 +105,19 @@ void backward_network_gpu(network net, network_state state) layer prev = net.layers[i-1]; state.input = prev.output_gpu; state.delta = prev.delta_gpu; + if (net.optimized_memory && !prev.keep_delta_gpu) { + state.delta = net.state_delta_gpu; + } } if (l.onlyforward) continue; l.backward_gpu(l, state); + layer prev = net.layers[i - 1]; + if (net.optimized_memory && state.delta && !prev.keep_delta_gpu) { + simple_copy_ongpu(prev.outputs*prev.batch, state.delta, prev.delta_gpu); + fill_ongpu(prev.outputs*prev.batch, 0, net.state_delta_gpu, 1); + } + /* if(i != 0) { diff --git a/src/parser.c b/src/parser.c index 91104df32c9..bc148a317c7 100644 --- a/src/parser.c +++ b/src/parser.c @@ -896,6 +896,9 @@ void parse_net_options(list *options, network *net) net->batch *= net->time_steps; net->subdivisions = subdivs; + net->optimized_memory = option_find_int_quiet(options, "optimized_memory", 0); + net->workspace_size_limit = (size_t)1024*1024 * option_find_float_quiet(options, "workspace_size_limit_MB", 1024); // 1024 MB by default + net->adam = option_find_int_quiet(options, "adam", 0); if(net->adam){ net->B1 = option_find_float(options, "B1", .9); @@ -1015,6 +1018,13 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) if(!is_network(s)) error("First section must be [net] or [network]"); parse_net_options(options, &net); +#ifdef GPU + printf("net.optimized_memory = %d \n", net.optimized_memory); + if (net.optimized_memory >= 2 && params.train) { + pre_allocate_pinned_memory((size_t)1024 * 1024 * 1024 * 8); // pre-allocate 8 GB CPU-RAM for pinned memory + } +#endif // GPU + params.h = net.h; params.w = net.w; params.c = net.c; @@ -1066,17 +1076,22 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) l = parse_crop(options, params); }else if(lt == COST){ l = parse_cost(options, params); + l.keep_delta_gpu = 1; }else if(lt == REGION){ l = parse_region(options, params); + l.keep_delta_gpu = 1; }else if (lt == YOLO) { l = parse_yolo(options, params); + l.keep_delta_gpu = 1; }else if (lt == GAUSSIAN_YOLO) { l = parse_gaussian_yolo(options, params); + l.keep_delta_gpu = 1; }else if(lt == DETECTION){ l = parse_detection(options, params); }else if(lt == SOFTMAX){ l = parse_softmax(options, params); net.hierarchy = l.softmax_tree; + l.keep_delta_gpu = 1; }else if(lt == NORMALIZATION){ l = parse_normalization(options, params); }else if(lt == BATCHNORM){ @@ -1092,22 +1107,28 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) }else if(lt == ROUTE){ l = parse_route(options, params); int k; - for (k = 0; k < l.n; ++k) net.layers[l.input_layers[k]].use_bin_output = 0; + for (k = 0; k < l.n; ++k) { + net.layers[l.input_layers[k]].use_bin_output = 0; + net.layers[l.input_layers[k]].keep_delta_gpu = 1; + } }else if (lt == UPSAMPLE) { l = parse_upsample(options, params, net); }else if(lt == SHORTCUT){ l = parse_shortcut(options, params, net); net.layers[count - 1].use_bin_output = 0; net.layers[l.index].use_bin_output = 0; + net.layers[l.index].keep_delta_gpu = 1; }else if (lt == SCALE_CHANNELS) { l = parse_scale_channels(options, params, net); net.layers[count - 1].use_bin_output = 0; net.layers[l.index].use_bin_output = 0; + net.layers[l.index].keep_delta_gpu = 1; } else if (lt == SAM) { l = parse_sam(options, params, net); net.layers[count - 1].use_bin_output = 0; net.layers[l.index].use_bin_output = 0; + net.layers[l.index].keep_delta_gpu = 1; }else if(lt == DROPOUT){ l = parse_dropout(options, params); l.output = net.layers[count-1].output; @@ -1132,6 +1153,42 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) }else{ fprintf(stderr, "Type not recognized: %s\n", s->type); } + +#ifdef GPU + // futher GPU-memory optimization: net.optimized_memory == 2 + if (net.optimized_memory >= 2 && params.train) + { + l.optimized_memory = net.optimized_memory; + if (l.output_gpu) { + cuda_free(l.output_gpu); + //l.output_gpu = cuda_make_array_pinned(l.output, l.batch*l.outputs); // l.steps + l.output_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps + } + if (l.activation_input_gpu) { + cuda_free(l.activation_input_gpu); + l.activation_input_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps + } + + if (l.x_gpu) { + cuda_free(l.x_gpu); + l.x_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps + } + + // maximum optimization + if (net.optimized_memory >= 3) { + if (l.delta_gpu) { + cuda_free(l.delta_gpu); + //l.delta_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps + //printf("\n\n PINNED DELTA GPU = %d \n", l.batch*l.outputs); + } + } + + if (l.type == CONVOLUTIONAL) { + set_specified_workspace_limit(&l, net.workspace_size_limit); // workspace size limit 1 GB + } + } +#endif // GPU + l.onlyforward = option_find_int_quiet(options, "onlyforward", 0); l.stopbackward = option_find_int_quiet(options, "stopbackward", 0); l.dontload = option_find_int_quiet(options, "dontload", 0); @@ -1162,6 +1219,45 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) if (l.bflops > 0) bflops += l.bflops; } free_list(sections); + +#ifdef GPU + if (net.optimized_memory && params.train) + { + int k; + for (k = 0; k < net.n; ++k) { + layer l = net.layers[k]; + // delta GPU-memory optimization: net.optimized_memory == 1 + if (!l.keep_delta_gpu) { + const size_t delta_size = l.outputs*l.batch; // l.steps + if (net.max_delta_gpu_size < delta_size) { + net.max_delta_gpu_size = delta_size; + if (net.global_delta_gpu) cuda_free(net.global_delta_gpu); + if (net.state_delta_gpu) cuda_free(net.state_delta_gpu); + assert(net.max_delta_gpu_size > 0); + net.global_delta_gpu = (float *)cuda_make_array(NULL, net.max_delta_gpu_size); + net.state_delta_gpu = (float *)cuda_make_array(NULL, net.max_delta_gpu_size); + } + if (l.delta_gpu) { + if (net.optimized_memory >= 3) {} + else cuda_free(l.delta_gpu); + } + l.delta_gpu = net.global_delta_gpu; + } + + // maximum optimization + if (net.optimized_memory >= 3) { + if (l.delta_gpu && l.keep_delta_gpu) { + //cuda_free(l.delta_gpu); // already called above + l.delta_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps + //printf("\n\n PINNED DELTA GPU = %d \n", l.batch*l.outputs); + } + } + + net.layers[k] = l; + } + } +#endif + net.outputs = get_network_output_size(net); net.output = get_network_output(net); fprintf(stderr, "Total BFLOPS %5.3f \n", bflops); From 94b8fc11939173881c28842ccadd69b14de7fdcb Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 26 Nov 2019 23:43:34 +0300 Subject: [PATCH 0689/1365] Compile fix --- src/connected_layer.c | 2 +- src/convolutional_layer.h | 2 +- src/network.c | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/src/connected_layer.c b/src/connected_layer.c index 242ab8fb106..6fff5c1cad2 100644 --- a/src/connected_layer.c +++ b/src/connected_layer.c @@ -147,7 +147,7 @@ connected_layer make_connected_layer(int batch, int steps, int inputs, int outpu } #ifdef CUDNN create_convolutional_cudnn_tensors(&l); - cudnn_convolutional_setup(&l, cudnn_fastest); // cudnn_fastest, cudnn_smallest + cudnn_convolutional_setup(&l, cudnn_fastest, 0); // cudnn_fastest, cudnn_smallest l.workspace_size = get_connected_workspace_size(l); #endif // CUDNN #endif // GPU diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h index c4da8702d1c..fe932963b6c 100644 --- a/src/convolutional_layer.h +++ b/src/convolutional_layer.h @@ -23,7 +23,7 @@ void pull_convolutional_layer(convolutional_layer layer); void add_bias_gpu(float *output, float *biases, int batch, int n, int size); void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size); #ifdef CUDNN -void cudnn_convolutional_setup(layer *l, int cudnn_preference); +void cudnn_convolutional_setup(layer *l, int cudnn_preference, size_t workspace_size_specify); void create_convolutional_cudnn_tensors(layer *l); void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16); #endif diff --git a/src/network.c b/src/network.c index 731b088344d..b63e91e29e9 100644 --- a/src/network.c +++ b/src/network.c @@ -472,7 +472,7 @@ void set_batch_network(network *net, int b) #ifdef CUDNN if(net->layers[i].type == CONVOLUTIONAL){ - cudnn_convolutional_setup(net->layers + i, cudnn_fastest); + cudnn_convolutional_setup(net->layers + i, cudnn_fastest, 0); } else if (net->layers[i].type == MAXPOOL) { cudnn_maxpool_setup(net->layers + i); From b4fcb8b2946a9fcb464926f62b6fd5949c972fd6 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 27 Nov 2019 03:51:58 +0300 Subject: [PATCH 0690/1365] Fixed: revert disabled mAP calculation during training. --- src/detector.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/detector.c b/src/detector.c index 8e778e7aafc..feb86d5583f 100644 --- a/src/detector.c +++ b/src/detector.c @@ -278,7 +278,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i //network net_combined = combine_train_valid_networks(net, net_map); iter_map = i; - //mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, 0, net.letter_box, &net_map);// &net_combined); + mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, 0, net.letter_box, &net_map);// &net_combined); printf("\n mean_average_precision (mAP@0.5) = %f \n", mean_average_precision); if (mean_average_precision > best_map) { best_map = mean_average_precision; From 31d483a2950a4d747b31f033e5725bb8de50ff1e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 27 Nov 2019 14:29:08 +0300 Subject: [PATCH 0691/1365] Minor fix --- src/dark_cuda.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/dark_cuda.c b/src/dark_cuda.c index f9779eed3f6..2eacf418970 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -254,7 +254,7 @@ void pre_allocate_pinned_memory(const size_t size) pthread_mutex_lock(&mutex_pinned); if (!pinned_ptr) { - pinned_ptr = (float *)calloc(num_of_blocks, sizeof(float *)); + pinned_ptr = (float **)calloc(num_of_blocks, sizeof(float *)); if(!pinned_ptr) error("calloc failed in pre_allocate() \n"); printf("pre_allocate: size = %Iu MB, num_of_blocks = %Iu, block_size = %Iu MB \n", @@ -313,7 +313,7 @@ float *cuda_make_array_pinned_preallocated(float *x, size_t n) pinned_num_of_blocks++; pinned_block_id = pinned_num_of_blocks - 1; pinned_index = 0; - pinned_ptr = (float *)realloc(pinned_ptr, pinned_num_of_blocks * sizeof(float *)); + pinned_ptr = (float **)realloc(pinned_ptr, pinned_num_of_blocks * sizeof(float *)); cudaError_t status = cudaHostAlloc((void **)&pinned_ptr[pinned_block_id], pinned_block_size, cudaHostRegisterMapped); if (status != cudaSuccess) fprintf(stderr, " Can't pre-allocate CUDA-pinned buffer on CPU-RAM \n"); CHECK_CUDA(status); From 68db8efb27170b411481e8765558d89b45635101 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 28 Nov 2019 20:34:01 +0300 Subject: [PATCH 0692/1365] Added Fusion features for ASFF and for BiFPN(EfficientDet) --- include/darknet.h | 3 +- src/activation_kernels.cu | 33 ++++++++++++++++++ src/activations.c | 25 ++++++++++++++ src/activations.h | 2 ++ src/blas.h | 4 +-- src/blas_kernels.cu | 66 +++++++++++++++++++++++------------- src/convolutional_kernels.cu | 1 + src/convolutional_layer.c | 2 ++ src/parser.c | 3 +- src/scale_channels_layer.c | 56 +++++++++++++++++++++++------- src/scale_channels_layer.h | 2 +- 11 files changed, 156 insertions(+), 41 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 8aa7b8e94fe..ebe4db36a08 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -102,7 +102,7 @@ typedef struct tree { // activations.h typedef enum { - LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, SWISH, MISH + LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, SWISH, MISH, NORM_CHAN }ACTIVATION; // parser.h @@ -235,6 +235,7 @@ struct layer { int sqrt; int flip; int index; + int scale_wh; int binary; int xnor; int peephole; diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index 5b357d3fce6..78a1140593e 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -416,4 +416,37 @@ extern "C" void gradient_array_mish_ongpu(int n, float *activation_input_gpu, fl const int num_blocks = get_number_of_blocks(n, BLOCK); gradient_array_mish_kernel << > > (n, activation_input_gpu, delta); CHECK_CUDA(cudaPeekAtLastError()); +} + + +__global__ void activate_array_normalize_channels_kernel(float *x, int size, int batch, int channels, int wh_step, float *output_gpu) +{ + int i = blockIdx.x * blockDim.x + threadIdx.x; + + int wh_i = i % wh_step; + int b = i / wh_step; + + const float eps = 0.0001; + if (i < size) { + float sum = eps; + int k; + for (k = 0; k < channels; ++k) { + sum += x[wh_i + k * wh_step + b*wh_step*channels]; + } + for (k = 0; k < channels; ++k) { + output_gpu[wh_i + k * wh_step + b*wh_step*channels] = x[wh_i + k * wh_step + b*wh_step*channels] / sum; + } + } +} + +extern "C" void activate_array_normalize_channels_ongpu(float *x, int n, int batch, int channels, int wh_step, float *output_gpu) +{ + // n = w*h*c*batch + // size = w*h*batch + int size = n / channels; + + const int num_blocks = get_number_of_blocks(size, BLOCK); + + activate_array_normalize_channels_kernel << > > (x, size, batch, channels, wh_step, output_gpu); + CHECK_CUDA(cudaPeekAtLastError()); } \ No newline at end of file diff --git a/src/activations.c b/src/activations.c index 347a13ac47b..0390274e916 100644 --- a/src/activations.c +++ b/src/activations.c @@ -47,6 +47,7 @@ ACTIVATION get_activation(char *s) if (strcmp(s, "logistic")==0) return LOGISTIC; if (strcmp(s, "swish") == 0) return SWISH; if (strcmp(s, "mish") == 0) return MISH; + if (strcmp(s, "normalize_channels") == 0) return NORM_CHAN; if (strcmp(s, "loggy")==0) return LOGGY; if (strcmp(s, "relu")==0) return RELU; if (strcmp(s, "elu")==0) return ELU; @@ -147,6 +148,30 @@ void activate_array_mish(float *x, const int n, float * activation_input, float } } +void activate_array_normalize_channels(float *x, const int n, int batch, int channels, int wh_step, float *output) +{ + int size = n / channels; + + int i; + #pragma omp parallel for + for (i = 0; i < size; ++i) { + int wh_i = i % wh_step; + int b = i / wh_step; + + const float eps = 0.0001; + if (i < size) { + float sum = eps; + int k; + for (k = 0; k < channels; ++k) { + sum += x[wh_i + k * wh_step + b*wh_step*channels]; + } + for (k = 0; k < channels; ++k) { + output[wh_i + k * wh_step + b*wh_step*channels] = x[wh_i + k * wh_step + b*wh_step*channels] / sum; + } + } + } +} + float gradient(float x, ACTIVATION a) { switch(a){ diff --git a/src/activations.h b/src/activations.h index edd5b304ff8..dff25820da7 100644 --- a/src/activations.h +++ b/src/activations.h @@ -22,6 +22,7 @@ void gradient_array_mish(const int n, const float * activation_input, float * de void activate_array(float *x, const int n, const ACTIVATION a); void activate_array_swish(float *x, const int n, float * output_sigmoid, float * output); void activate_array_mish(float *x, const int n, float * activation_input, float * output); +void activate_array_normalize_channels(float *x, const int n, int batch, int channels, int wh_step, float *output); #ifdef GPU void activate_array_ongpu(float *x, int n, ACTIVATION a); void activate_array_swish_ongpu(float *x, int n, float *output_sigmoid_gpu, float *output_gpu); @@ -29,6 +30,7 @@ void activate_array_mish_ongpu(float *x, int n, float *activation_input_gpu, flo void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta); void gradient_array_swish_ongpu(float *x, int n, float *sigmoid_gpu, float *delta); void gradient_array_mish_ongpu(int n, float *activation_input_gpu, float *delta); +void activate_array_normalize_channels_ongpu(float *x, int n, int batch, int channels, int wh_step, float *output_gpu); #endif static inline float stair_activate(float x) diff --git a/src/blas.h b/src/blas.h index 1225235c3e5..e186ff8661f 100644 --- a/src/blas.h +++ b/src/blas.h @@ -117,8 +117,8 @@ void add_3_arrays_activate(float *a1, float *a2, float *a3, size_t size, ACTIVAT void sum_of_mults(float *a1, float *a2, float *b1, float *b2, size_t size, float *dst); void activate_and_mult(float *a1, float *a2, size_t size, ACTIVATION a, float *dst); -void scale_channels_gpu(float *in_w_h_c, int size, int channel_size, float *scales_c, float *out); -void backward_scale_channels_gpu(float *in_w_h_c_delta, int size, int channel_size, +void scale_channels_gpu(float *in_w_h_c, int size, int channel_size, int batch_size, int scale_wh, float *scales_c, float *out); +void backward_scale_channels_gpu(float *in_w_h_c_delta, int size, int channel_size, int batch_size, int scale_wh, float *in_scales_c, float *out_from_delta, float *in_from_output, float *out_state_delta); diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index fc6dc29f9e4..e6e0739e539 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -1102,19 +1102,26 @@ extern "C" void activate_and_mult(float *a1, float *a2, size_t size, ACTIVATION -__global__ void scale_channels_kernel(float *in_w_h_c, int size, int channel_size, float *scales_c, float *out) +__global__ void scale_channels_kernel(float *in_w_h_c, int size, int channel_size, int batch_size, int scale_wh, float *scales_c, float *out) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { - out[index] = in_w_h_c[index] * scales_c[index / channel_size]; + if (scale_wh) { + int osd_index = index % channel_size + (index / batch_size)*channel_size; + + out[index] = in_w_h_c[index] * scales_c[osd_index]; + } + else { + out[index] = in_w_h_c[index] * scales_c[index / channel_size]; + } } } -extern "C" void scale_channels_gpu(float *in_w_h_c, int size, int channel_size, float *scales_c, float *out) +extern "C" void scale_channels_gpu(float *in_w_h_c, int size, int channel_size, int batch_size, int scale_wh, float *scales_c, float *out) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); - scale_channels_kernel << > >(in_w_h_c, size, channel_size, scales_c, out); + scale_channels_kernel << > >(in_w_h_c, size, channel_size, batch_size, scale_wh, scales_c, out); CHECK_CUDA(cudaPeekAtLastError()); } @@ -1130,44 +1137,57 @@ float warpAllReduceSum(float val) { return val; } -__global__ void backward_scale_channels_kernel(float *in_w_h_c_delta, int size, int channel_size, +__global__ void backward_scale_channels_kernel(float *in_w_h_c_delta, int size, int channel_size, int batch_size, int scale_wh, float *in_scales_c, float *out_from_delta, float *in_from_output, float *out_state_delta) { const int index = blockIdx.x*blockDim.x + threadIdx.x; - int osd_index = index / channel_size; if (index < size) { - //out_state_delta[osd_index] += in_w_h_c_delta[index] * in_from_output[index]; // l.delta * from (should be divided by channel_size?) - int warp_id = index / 32; - int index_warp_start = warp_id * 32; - int osd_index_warp_start = index_warp_start / channel_size; - int osd_index_warp_end = (index_warp_start + 31) / channel_size; - - if (osd_index_warp_start == osd_index_warp_end) // all thread in warp process the same channel + if (scale_wh) { - float sum = warpAllReduceSum(in_w_h_c_delta[index] * in_from_output[index]); // l.delta * from - if (threadIdx.x % 32 == 0) { - atomicAdd(&out_state_delta[osd_index], sum); - //out_state_delta[osd_index] += sum; - } - } - else { + int osd_index = index % channel_size + (index / batch_size)*channel_size; + + //out_state_delta[osd_index] += in_w_h_c_delta[index] * in_from_output[index]; // l.delta * from (should be divided by channel_size?) atomicAdd(&out_state_delta[osd_index], in_w_h_c_delta[index] * in_from_output[index]); // l.delta * from + + out_from_delta[index] += in_scales_c[osd_index] * in_w_h_c_delta[index]; // input * l.delta // atomic isn't required here + } + else { + int osd_index = index / channel_size; + //out_state_delta[osd_index] += in_w_h_c_delta[index] * in_from_output[index]; // l.delta * from (should be divided by channel_size?) + + int warp_id = index / 32; + int index_warp_start = warp_id * 32; + int osd_index_warp_start = index_warp_start / channel_size; + int osd_index_warp_end = (index_warp_start + 31) / channel_size; + + if (osd_index_warp_start == osd_index_warp_end) // all thread in warp process the same channel + { + float sum = warpAllReduceSum(in_w_h_c_delta[index] * in_from_output[index]); // l.delta * from + if (threadIdx.x % 32 == 0) { + atomicAdd(&out_state_delta[osd_index], sum); + //out_state_delta[osd_index] += sum; + } + } + else { + atomicAdd(&out_state_delta[osd_index], in_w_h_c_delta[index] * in_from_output[index]); // l.delta * from + } - out_from_delta[index] += in_scales_c[osd_index] * in_w_h_c_delta[index]; // input * l.delta // atomic isn't required here + out_from_delta[index] += in_scales_c[osd_index] * in_w_h_c_delta[index]; // input * l.delta // atomic isn't required here + } } } -extern "C" void backward_scale_channels_gpu(float *in_w_h_c_delta, int size, int channel_size, +extern "C" void backward_scale_channels_gpu(float *in_w_h_c_delta, int size, int channel_size, int batch_size, int scale_wh, float *in_scales_c, float *out_from_delta, float *in_from_output, float *out_state_delta) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); - backward_scale_channels_kernel << > > (in_w_h_c_delta, size, channel_size, + backward_scale_channels_kernel << > > (in_w_h_c_delta, size, channel_size, batch_size, scale_wh, in_scales_c, out_from_delta, in_from_output, out_state_delta); diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index e5d57eabde9..6b575b5f4f3 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -599,6 +599,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == MISH) activate_array_mish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); + else if (l.activation == NORM_CHAN) activate_array_normalize_channels_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu); else if (l.activation != LINEAR) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if(l.dot > 0) dot_error_gpu(l); if(l.binary || l.xnor) swap_binary(&l); diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 96c78998beb..960ff681aa3 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -1199,6 +1199,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //activate_array(l.output, m*n*l.batch, l.activation); if (l.activation == SWISH) activate_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.output); else if (l.activation == MISH) activate_array_mish(l.output, l.outputs*l.batch, l.activation_input, l.output); + else if (l.activation == NORM_CHAN) activate_array_normalize_channels(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output); else activate_array_cpu_custom(l.output, m*n*l.batch, l.activation); return; @@ -1239,6 +1240,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //activate_array(l.output, m*n*l.batch, l.activation); if (l.activation == SWISH) activate_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.output); else if (l.activation == MISH) activate_array_mish(l.output, l.outputs*l.batch, l.activation_input, l.output); + else if (l.activation == NORM_CHAN) activate_array_normalize_channels(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output); else activate_array_cpu_custom(l.output, l.outputs*l.batch, l.activation); if(l.binary || l.xnor) swap_binary(&l); diff --git a/src/parser.c b/src/parser.c index bc148a317c7..0824ceb14f7 100644 --- a/src/parser.c +++ b/src/parser.c @@ -748,11 +748,12 @@ layer parse_scale_channels(list *options, size_params params, network net) char *l = option_find(options, "from"); int index = atoi(l); if (index < 0) index = params.index + index; + int scale_wh = option_find_int_quiet(options, "scale_wh", 0); int batch = params.batch; layer from = net.layers[index]; - layer s = make_scale_channels_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c); + layer s = make_scale_channels_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c, scale_wh); char *activation_s = option_find_str_quiet(options, "activation", "linear"); ACTIVATION activation = get_activation(activation_s); diff --git a/src/scale_channels_layer.c b/src/scale_channels_layer.c index bcb54c1b497..9a5ad94f3a6 100644 --- a/src/scale_channels_layer.c +++ b/src/scale_channels_layer.c @@ -4,16 +4,18 @@ #include #include -layer make_scale_channels_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2) +layer make_scale_channels_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2, int scale_wh) { fprintf(stderr,"scale Layer: %d\n", index); layer l = { (LAYER_TYPE)0 }; l.type = SCALE_CHANNELS; l.batch = batch; + l.scale_wh = scale_wh; l.w = w; l.h = h; l.c = c; - assert(w == 1 && h == 1); + if (!l.scale_wh) assert(w == 1 && h == 1); + else assert(c == 1); l.out_w = w2; l.out_h = h2; @@ -62,12 +64,24 @@ void forward_scale_channels_layer(const layer l, network_state state) { int size = l.batch * l.out_c * l.out_w * l.out_h; int channel_size = l.out_w * l.out_h; + int batch_size = l.out_c * l.out_w * l.out_h; float *from_output = state.net.layers[l.index].output; - int i; - #pragma omp parallel for - for (i = 0; i < size; ++i) { - l.output[i] = state.input[i / channel_size] * from_output[i]; + if (l.scale_wh) { + int i; + #pragma omp parallel for + for (i = 0; i < size; ++i) { + int input_index = i % channel_size + (i / batch_size)*channel_size; + + l.output[i] = state.input[input_index] * from_output[i]; + } + } + else { + int i; + #pragma omp parallel for + for (i = 0; i < size; ++i) { + l.output[i] = state.input[i / channel_size] * from_output[i]; + } } activate_array(l.output, l.outputs*l.batch, l.activation); @@ -81,15 +95,29 @@ void backward_scale_channels_layer(const layer l, network_state state) int size = l.batch * l.out_c * l.out_w * l.out_h; int channel_size = l.out_w * l.out_h; + int batch_size = l.out_c * l.out_w * l.out_h; float *from_output = state.net.layers[l.index].output; float *from_delta = state.net.layers[l.index].delta; - int i; - #pragma omp parallel for - for (i = 0; i < size; ++i) { - state.delta[i / channel_size] += l.delta[i] * from_output[i] / channel_size; // l.delta * from (should be divided by channel_size?) + if (l.scale_wh) { + int i; + #pragma omp parallel for + for (i = 0; i < size; ++i) { + int input_index = i % channel_size + (i / batch_size)*channel_size; - from_delta[i] += state.input[i / channel_size] * l.delta[i]; // input * l.delta + state.delta[input_index] += l.delta[i] * from_output[i];// / l.out_c; // l.delta * from (should be divided by l.out_c?) + + from_delta[i] += state.input[input_index] * l.delta[i]; // input * l.delta + } + } + else { + int i; + #pragma omp parallel for + for (i = 0; i < size; ++i) { + state.delta[i / channel_size] += l.delta[i] * from_output[i];// / channel_size; // l.delta * from (should be divided by channel_size?) + + from_delta[i] += state.input[i / channel_size] * l.delta[i]; // input * l.delta + } } } @@ -98,8 +126,9 @@ void forward_scale_channels_layer_gpu(const layer l, network_state state) { int size = l.batch * l.out_c * l.out_w * l.out_h; int channel_size = l.out_w * l.out_h; + int batch_size = l.out_c * l.out_w * l.out_h; - scale_channels_gpu(state.net.layers[l.index].output_gpu, size, channel_size, state.input, l.output_gpu); + scale_channels_gpu(state.net.layers[l.index].output_gpu, size, channel_size, batch_size, l.scale_wh, state.input, l.output_gpu); activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); } @@ -110,9 +139,10 @@ void backward_scale_channels_layer_gpu(const layer l, network_state state) int size = l.batch * l.out_c * l.out_w * l.out_h; int channel_size = l.out_w * l.out_h; + int batch_size = l.out_c * l.out_w * l.out_h; float *from_output = state.net.layers[l.index].output_gpu; float *from_delta = state.net.layers[l.index].delta_gpu; - backward_scale_channels_gpu(l.delta_gpu, size, channel_size, state.input, from_delta, from_output, state.delta); + backward_scale_channels_gpu(l.delta_gpu, size, channel_size, batch_size, l.scale_wh, state.input, from_delta, from_output, state.delta); } #endif diff --git a/src/scale_channels_layer.h b/src/scale_channels_layer.h index fdaa4b924fe..c8d51ddac6d 100644 --- a/src/scale_channels_layer.h +++ b/src/scale_channels_layer.h @@ -7,7 +7,7 @@ #ifdef __cplusplus extern "C" { #endif -layer make_scale_channels_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2); +layer make_scale_channels_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2, int scale_wh); void forward_scale_channels_layer(const layer l, network_state state); void backward_scale_channels_layer(const layer l, network_state state); void resize_scale_channels_layer(layer *l, network *net); From eecc86116967b26dc9df86b0427e04e980cbdd83 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 28 Nov 2019 23:09:51 +0300 Subject: [PATCH 0693/1365] ReLU is integrated to activation=normalize_channels --- src/activation_kernels.cu | 11 +++++++++-- src/activations.c | 8 ++++++-- 2 files changed, 15 insertions(+), 4 deletions(-) diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index 78a1140593e..ae5c97a7729 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -126,6 +126,8 @@ __device__ float gradient_kernel(float x, ACTIVATION a) return loggy_gradient_kernel(x); case RELU: return relu_gradient_kernel(x); + case NORM_CHAN: + return relu_gradient_kernel(x); case ELU: return elu_gradient_kernel(x); case SELU: @@ -397,6 +399,7 @@ extern "C" void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta else if (a == TANH) gradient_array_tanh_kernel << > >(x, n, delta); else if (a == HARDTAN) gradient_array_hardtan_kernel << > >(x, n, delta); else if (a == RELU) gradient_array_relu_kernel << > >(x, n, delta); + else if (a == NORM_CHAN) gradient_array_relu_kernel << > >(x, n, delta); else if (a == SELU) gradient_array_selu_kernel << > >(x, n, delta); else gradient_array_kernel << > > (x, n, a, delta); @@ -431,10 +434,14 @@ __global__ void activate_array_normalize_channels_kernel(float *x, int size, int float sum = eps; int k; for (k = 0; k < channels; ++k) { - sum += x[wh_i + k * wh_step + b*wh_step*channels]; + float val = x[wh_i + k * wh_step + b*wh_step*channels]; + if (val > 0) sum += val; } for (k = 0; k < channels; ++k) { - output_gpu[wh_i + k * wh_step + b*wh_step*channels] = x[wh_i + k * wh_step + b*wh_step*channels] / sum; + float val = x[wh_i + k * wh_step + b*wh_step*channels]; + if (val > 0) val = val / sum; + else val = 0; + output_gpu[wh_i + k * wh_step + b*wh_step*channels] = val; } } } diff --git a/src/activations.c b/src/activations.c index 0390274e916..50466e75b58 100644 --- a/src/activations.c +++ b/src/activations.c @@ -163,10 +163,14 @@ void activate_array_normalize_channels(float *x, const int n, int batch, int cha float sum = eps; int k; for (k = 0; k < channels; ++k) { - sum += x[wh_i + k * wh_step + b*wh_step*channels]; + float val = x[wh_i + k * wh_step + b*wh_step*channels]; + if (val > 0) sum += val; } for (k = 0; k < channels; ++k) { - output[wh_i + k * wh_step + b*wh_step*channels] = x[wh_i + k * wh_step + b*wh_step*channels] / sum; + float val = x[wh_i + k * wh_step + b*wh_step*channels]; + if (val > 0) val = val / sum; + else val = 0; + output[wh_i + k * wh_step + b*wh_step*channels] = val; } } } From 9bb3c53698963f2a495be2dd9877d6ff523fe2ad Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 30 Nov 2019 00:50:11 +0300 Subject: [PATCH 0694/1365] Added resnet101.cfg --- build/darknet/x64/cfg/resnet101.cfg | 990 ++++++++++++++++++++++++++++ cfg/resnet101.cfg | 990 ++++++++++++++++++++++++++++ src/activations.c | 2 + 3 files changed, 1982 insertions(+) create mode 100644 build/darknet/x64/cfg/resnet101.cfg create mode 100644 cfg/resnet101.cfg diff --git a/build/darknet/x64/cfg/resnet101.cfg b/build/darknet/x64/cfg/resnet101.cfg new file mode 100644 index 00000000000..48dffbf2ec5 --- /dev/null +++ b/build/darknet/x64/cfg/resnet101.cfg @@ -0,0 +1,990 @@ +[net] +# Training +batch=128 +subdivisions=2 + +# Testing +#batch=1 +#subdivisions=1 + +height=256 +width=256 +channels=3 +min_crop=128 +max_crop=448 + +burn_in=1000 +learning_rate=0.1 +policy=poly +power=4 +max_batches=800000 +momentum=0.9 +decay=0.0005 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 + + + +[convolutional] +batch_normalize=1 +filters=64 +size=7 +stride=2 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +# Conv 4 +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +#Conv 5 +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + + + + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=1 +activation=linear + +[avgpool] + +[softmax] +groups=1 + +[cost] +type=sse + diff --git a/cfg/resnet101.cfg b/cfg/resnet101.cfg new file mode 100644 index 00000000000..48dffbf2ec5 --- /dev/null +++ b/cfg/resnet101.cfg @@ -0,0 +1,990 @@ +[net] +# Training +batch=128 +subdivisions=2 + +# Testing +#batch=1 +#subdivisions=1 + +height=256 +width=256 +channels=3 +min_crop=128 +max_crop=448 + +burn_in=1000 +learning_rate=0.1 +policy=poly +power=4 +max_batches=800000 +momentum=0.9 +decay=0.0005 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 + + + +[convolutional] +batch_normalize=1 +filters=64 +size=7 +stride=2 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + +# Conv 4 +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +#Conv 5 +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + + + + + + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=1 +activation=linear + +[avgpool] + +[softmax] +groups=1 + +[cost] +type=sse + diff --git a/src/activations.c b/src/activations.c index 50466e75b58..41b33665912 100644 --- a/src/activations.c +++ b/src/activations.c @@ -187,6 +187,8 @@ float gradient(float x, ACTIVATION a) return loggy_gradient(x); case RELU: return relu_gradient(x); + case NORM_CHAN: + return relu_gradient(x); case ELU: return elu_gradient(x); case SELU: From f1ffb09d8bffa82aa3cf0c2ca405763eaa591f59 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 30 Nov 2019 03:41:30 +0300 Subject: [PATCH 0695/1365] Fixed minor bugs and memory leaks --- src/detector.c | 1 + src/layer.c | 29 ++++++++++++++++++++++++++++- src/list.c | 11 +++++++++++ src/list.h | 1 + src/maxpool_layer.c | 22 +++++++++++++--------- src/utils.c | 2 +- 6 files changed, 55 insertions(+), 11 deletions(-) diff --git a/src/detector.c b/src/detector.c index feb86d5583f..86612b69ed9 100644 --- a/src/detector.c +++ b/src/detector.c @@ -55,6 +55,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i name_list, names_size, net_classes, cfgfile); if (net_classes > names_size) getchar(); } + free_ptrs((void**)names, net_map.layers[net_map.n - 1].classes); } srand(time(0)); diff --git a/src/layer.c b/src/layer.c index fcdb33facbc..4ef496fc88f 100644 --- a/src/layer.c +++ b/src/layer.c @@ -203,5 +203,32 @@ void free_layer(layer l) if (l.last_prev_state_gpu) cuda_free(l.last_prev_state_gpu); if (l.last_prev_cell_gpu) cuda_free(l.last_prev_cell_gpu); if (l.cell_gpu) cuda_free(l.cell_gpu); -#endif +#ifdef CUDNN_DISABLED // shouldn't be used for -map + if (l.srcTensorDesc) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.srcTensorDesc)); + if (l.dstTensorDesc) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.dstTensorDesc)); + if (l.srcTensorDesc16) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.srcTensorDesc16)); + if (l.dstTensorDesc16) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.dstTensorDesc16)); + if (l.dsrcTensorDesc) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.dsrcTensorDesc)); + if (l.ddstTensorDesc) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.ddstTensorDesc)); + if (l.dsrcTensorDesc16) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.dsrcTensorDesc16)); + if (l.ddstTensorDesc16) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.ddstTensorDesc16)); + if (l.normTensorDesc) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.normTensorDesc)); + if (l.normDstTensorDesc) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.normDstTensorDesc)); + if (l.normDstTensorDescF16) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.normDstTensorDescF16)); + + if (l.weightDesc) CHECK_CUDNN(cudnnDestroyFilterDescriptor(l.weightDesc)); + if (l.weightDesc16) CHECK_CUDNN(cudnnDestroyFilterDescriptor(l.weightDesc16)); + if (l.dweightDesc) CHECK_CUDNN(cudnnDestroyFilterDescriptor(l.dweightDesc)); + if (l.dweightDesc16) CHECK_CUDNN(cudnnDestroyFilterDescriptor(l.dweightDesc16)); + + if (l.convDesc) CHECK_CUDNN(cudnnDestroyConvolutionDescriptor(l.convDesc)); + + if (l.poolingDesc) CHECK_CUDNN(cudnnDestroyPoolingDescriptor(l.poolingDesc)); + + //cudnnConvolutionFwdAlgo_t fw_algo, fw_algo16; + //cudnnConvolutionBwdDataAlgo_t bd_algo, bd_algo16; + //cudnnConvolutionBwdFilterAlgo_t bf_algo, bf_algo16; +#endif // CUDNN + +#endif // GPU } diff --git a/src/list.c b/src/list.c index 6ba093022f6..624ecf657cc 100644 --- a/src/list.c +++ b/src/list.c @@ -65,6 +65,17 @@ void free_node(node *n) } } +void free_list_val(list *l) +{ + node *n = l->front; + node *next; + while (n) { + next = n->next; + free(n->val); + n = next; + } +} + void free_list(list *l) { free_node(l->front); diff --git a/src/list.h b/src/list.h index 2623174af1b..182648f7337 100644 --- a/src/list.h +++ b/src/list.h @@ -23,6 +23,7 @@ void list_insert(list *, void *); void **list_to_array(list *l); +void free_list_val(list *l); void free_list(list *l); void free_list_contents(list *l); void free_list_contents_kvp(list *l); diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index 54aa9c0ed91..6c483ce30ec 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -20,14 +20,19 @@ image get_maxpool_delta(maxpool_layer l) return float_to_image(w,h,c,l.delta); } +void create_maxpool_cudnn_tensors(layer *l) +{ +#ifdef CUDNN + CHECK_CUDNN(cudnnCreatePoolingDescriptor(&l->poolingDesc)); + CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->srcTensorDesc)); + CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->dstTensorDesc)); +#endif // CUDNN +} void cudnn_maxpool_setup(layer *l) { #ifdef CUDNN - cudnnStatus_t maxpool_status; - maxpool_status = cudnnCreatePoolingDescriptor(&l->poolingDesc); - - maxpool_status = cudnnSetPooling2dDescriptor( + CHECK_CUDNN(cudnnSetPooling2dDescriptor( l->poolingDesc, CUDNN_POOLING_MAX, CUDNN_NOT_PROPAGATE_NAN, // CUDNN_PROPAGATE_NAN, CUDNN_NOT_PROPAGATE_NAN @@ -36,12 +41,10 @@ void cudnn_maxpool_setup(layer *l) l->pad/2, //0, //l.pad, l->pad/2, //0, //l.pad, l->stride_x, - l->stride_y); + l->stride_y)); - cudnnCreateTensorDescriptor(&l->srcTensorDesc); - cudnnCreateTensorDescriptor(&l->dstTensorDesc); - cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w); - cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); + CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w)); + CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w)); #endif // CUDNN } @@ -99,6 +102,7 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s l.delta_gpu = cuda_make_array(l.delta, output_size); } l.output_gpu = cuda_make_array(l.output, output_size); + create_maxpool_cudnn_tensors(&l); cudnn_maxpool_setup(&l); #endif // GPU diff --git a/src/utils.c b/src/utils.c index af7cb9eaf01..b445e5ceef0 100644 --- a/src/utils.c +++ b/src/utils.c @@ -180,7 +180,7 @@ void find_replace(const char* str, char* orig, char* rep, char* output) sprintf(buffer, "%s", str); if(!(p = strstr(buffer, orig))){ // Is 'orig' even in 'str'? - sprintf(output, "%s", str); + sprintf(output, "%s", buffer); free(buffer); return; } From 9d96277a37177221caba0b971486798c17609a35 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 30 Nov 2019 16:07:32 +0300 Subject: [PATCH 0696/1365] Added mixup=1 for Classifier training --- build/darknet/x64/cfg/efficientnet_b0.cfg | 1 + src/classifier.c | 1 + src/data.c | 32 +++++++++++++++++++++-- src/data.h | 2 +- 4 files changed, 33 insertions(+), 3 deletions(-) diff --git a/build/darknet/x64/cfg/efficientnet_b0.cfg b/build/darknet/x64/cfg/efficientnet_b0.cfg index 3bd3e895bc1..01680fcf6bc 100644 --- a/build/darknet/x64/cfg/efficientnet_b0.cfg +++ b/build/darknet/x64/cfg/efficientnet_b0.cfg @@ -11,6 +11,7 @@ channels=3 momentum=0.9 decay=0.0005 max_crop=256 +mixup=1 burn_in=1000 #burn_in=100 diff --git a/src/classifier.c b/src/classifier.c index c077f61ae65..bdd4f053918 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -83,6 +83,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, args.min = net.min_crop; args.max = net.max_crop; + args.mixup = net.mixup; args.flip = net.flip; args.angle = net.angle; args.aspect = net.aspect; diff --git a/src/data.c b/src/data.c index d15aca09b8c..3faae6d19bc 100644 --- a/src/data.c +++ b/src/data.c @@ -1170,7 +1170,7 @@ void *load_thread(void *ptr) if (a.type == OLD_CLASSIFICATION_DATA){ *a.d = load_data_old(a.paths, a.n, a.m, a.labels, a.classes, a.w, a.h); } else if (a.type == CLASSIFICATION_DATA){ - *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.flip, a.min, a.max, a.w, a.h, a.angle, a.aspect, a.hue, a.saturation, a.exposure); + *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.flip, a.min, a.max, a.w, a.h, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.mixup); } else if (a.type == SUPER_DATA){ *a.d = load_data_super(a.paths, a.n, a.m, a.w, a.h, a.scale); } else if (a.type == WRITING_DATA){ @@ -1315,14 +1315,42 @@ data load_data_super(char **paths, int n, int m, int w, int h, int scale) return d; } -data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure) +data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int mixup) { + char **paths_stored = paths; if(m) paths = get_random_paths(paths, n, m); data d = {0}; d.shallow = 0; d.X = load_image_augment_paths(paths, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure); d.y = load_labels_paths(paths, n, labels, k, hierarchy); if(m) free(paths); + + if (mixup) { + char **paths_mix = get_random_paths(paths_stored, n, m); + data d2 = { 0 }; + d2.shallow = 0; + d2.X = load_image_augment_paths(paths_mix, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure); + d2.y = load_labels_paths(paths_mix, n, labels, k, hierarchy); + free(paths_mix); + + // mix + int i, j; + for (i = 0; i < d2.X.rows; ++i) { + + // mix images + for (j = 0; j < d2.X.cols; ++j) { + d.X.vals[i][j] = (d.X.vals[i][j] + d2.X.vals[i][j]) / 2.0f; + } + + // mix labels + for (j = 0; j < d2.y.cols; ++j) { + if (d2.y.vals[i][k] > 0) d.y.vals[i][k] = 1; + } + } + + free_data(d2); + } + return d; } diff --git a/src/data.h b/src/data.h index 250e655a9b5..bf29499c3f5 100644 --- a/src/data.h +++ b/src/data.h @@ -91,7 +91,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo data load_data_tag(char **paths, int n, int m, int k, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure); matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure); data load_data_super(char **paths, int n, int m, int w, int h, int scale); -data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure); +data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int mixup); data load_go(char *filename); box_label *read_boxes(char *filename, int *n); From 4fcb1b8e428edcf306407ae492250d617da13345 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 30 Nov 2019 17:49:28 +0300 Subject: [PATCH 0697/1365] Addex cutmix=1 and fixed mixup=1 for Classifier --- build/darknet/x64/cfg/efficientnet_b0.cfg | 3 +- cfg/efficientnet_b0.cfg | 2 + src/classifier.c | 10 ++- src/data.c | 90 ++++++++++++++++++++--- src/data.h | 2 +- src/parser.c | 3 + 6 files changed, 96 insertions(+), 14 deletions(-) diff --git a/build/darknet/x64/cfg/efficientnet_b0.cfg b/build/darknet/x64/cfg/efficientnet_b0.cfg index 01680fcf6bc..88ec3594561 100644 --- a/build/darknet/x64/cfg/efficientnet_b0.cfg +++ b/build/darknet/x64/cfg/efficientnet_b0.cfg @@ -11,7 +11,8 @@ channels=3 momentum=0.9 decay=0.0005 max_crop=256 -mixup=1 +#mixup=1 +cutmix=1 burn_in=1000 #burn_in=100 diff --git a/cfg/efficientnet_b0.cfg b/cfg/efficientnet_b0.cfg index 3bd3e895bc1..88ec3594561 100644 --- a/cfg/efficientnet_b0.cfg +++ b/cfg/efficientnet_b0.cfg @@ -11,6 +11,8 @@ channels=3 momentum=0.9 decay=0.0005 max_crop=256 +#mixup=1 +cutmix=1 burn_in=1000 #burn_in=100 diff --git a/src/classifier.c b/src/classifier.c index bdd4f053918..45b7c797dcd 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -27,7 +27,7 @@ float *get_regression_values(char **labels, int n) return v; } -void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int mjpeg_port, int calc_topk) +void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int mjpeg_port, int calc_topk, int show_imgs) { int i; @@ -83,7 +83,6 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, args.min = net.min_crop; args.max = net.max_crop; - args.mixup = net.mixup; args.flip = net.flip; args.angle = net.angle; args.aspect = net.aspect; @@ -92,6 +91,10 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, args.hue = net.hue; args.size = net.w > net.h ? net.w : net.h; + args.mixup = net.mixup; + if (dont_show && show_imgs) show_imgs = 2; + args.show_imgs = show_imgs; + args.paths = paths; args.classes = classes; args.n = imgs; @@ -1266,6 +1269,7 @@ void run_classifier(int argc, char **argv) } int dont_show = find_arg(argc, argv, "-dont_show"); + int show_imgs = find_arg(argc, argv, "-show_imgs"); int calc_topk = find_arg(argc, argv, "-topk"); int cam_index = find_int_arg(argc, argv, "-c", 0); int top = find_int_arg(argc, argv, "-t", 0); @@ -1278,7 +1282,7 @@ void run_classifier(int argc, char **argv) int layer = layer_s ? atoi(layer_s) : -1; if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename, top); else if(0==strcmp(argv[2], "try")) try_classifier(data, cfg, weights, filename, atoi(layer_s)); - else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear, dont_show, mjpeg_port, calc_topk); + else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear, dont_show, mjpeg_port, calc_topk, show_imgs); else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename); else if(0==strcmp(argv[2], "gun")) gun_classifier(data, cfg, weights, cam_index, filename); else if(0==strcmp(argv[2], "threat")) threat_classifier(data, cfg, weights, cam_index, filename); diff --git a/src/data.c b/src/data.c index 3faae6d19bc..29e2527e7d6 100644 --- a/src/data.c +++ b/src/data.c @@ -1170,7 +1170,7 @@ void *load_thread(void *ptr) if (a.type == OLD_CLASSIFICATION_DATA){ *a.d = load_data_old(a.paths, a.n, a.m, a.labels, a.classes, a.w, a.h); } else if (a.type == CLASSIFICATION_DATA){ - *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.flip, a.min, a.max, a.w, a.h, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.mixup); + *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.flip, a.min, a.max, a.w, a.h, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.mixup, a.show_imgs); } else if (a.type == SUPER_DATA){ *a.d = load_data_super(a.paths, a.n, a.m, a.w, a.h, a.scale); } else if (a.type == WRITING_DATA){ @@ -1315,7 +1315,7 @@ data load_data_super(char **paths, int n, int m, int w, int h, int scale) return d; } -data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int mixup) +data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int mixup, int show_imgs) { char **paths_stored = paths; if(m) paths = get_random_paths(paths, n, m); @@ -1331,24 +1331,96 @@ data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *h d2.shallow = 0; d2.X = load_image_augment_paths(paths_mix, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure); d2.y = load_labels_paths(paths_mix, n, labels, k, hierarchy); - free(paths_mix); // mix int i, j; for (i = 0; i < d2.X.rows; ++i) { - // mix images - for (j = 0; j < d2.X.cols; ++j) { - d.X.vals[i][j] = (d.X.vals[i][j] + d2.X.vals[i][j]) / 2.0f; + if (mixup == 3) mixup = rand_int(1, 2); // alternate MixUp and CutMix + + // MixUp + if (mixup == 1) { + // mix images + for (j = 0; j < d2.X.cols; ++j) { + d.X.vals[i][j] = (d.X.vals[i][j] + d2.X.vals[i][j]) / 2.0f; + } + + // mix labels + for (j = 0; j < d2.y.cols; ++j) { + d.y.vals[i][j] = (d.y.vals[i][j] + d2.y.vals[i][j]) / 2.0f; + } + } + // CutMix + else { + const float min_offset = 0.2; // 20% + const int cut_x = rand_int(w*min_offset, w*(1 - min_offset)); + const int cut_y = rand_int(h*min_offset, h*(1 - min_offset)); + int left, right, top, bot; + if (rand_int(0,1)) { + left = cut_x; + right = w; + } + else { + left = 0; + right = cut_x; + } + + if (rand_int(0, 1)) { + top = cut_y; + bot = h; + } + else { + top = 0; + bot = cut_y; + } + const float alpha = (float)(right - left)*(bot - top) / (float)(w*h); + const float beta = 1 - alpha; + + int c, x, y; + for (c = 0; c < 3; ++c) { + for (y = top; y < bot; ++y) { + for (x = left; x < right; ++x) { + int j = x + y*w + c*w*h; + d.X.vals[i][j] = d2.X.vals[i][j]; + } + } + } + + //printf("\n alpha = %f, beta = %f \n", alpha, beta); + // mix labels + for (j = 0; j < d.y.cols; ++j) { + d.y.vals[i][j] = d.y.vals[i][j] * beta + d2.y.vals[i][j] * alpha; + } } - // mix labels - for (j = 0; j < d2.y.cols; ++j) { - if (d2.y.vals[i][k] > 0) d.y.vals[i][k] = 1; + if (show_imgs) { + image im = make_empty_image(w, h, 3); + im.data = d.X.vals[i]; + char buff[1000]; + sprintf(buff, "aug_%d_%s_%d", i, basecfg((char*)paths_mix[i]), random_gen()); + save_image(im, buff); + + char buff_string[1000]; + sprintf(buff_string, "\n Classes: "); + for (j = 0; j < d.y.cols; ++j) { + if (d.y.vals[i][j] > 0) { + char buff_tmp[100]; + sprintf(buff_tmp, " %d (%f), ", j, d.y.vals[i][j]); + strcat(buff_string, buff_tmp); + } + } + printf("%s \n", buff_string); + + if (show_imgs == 1) { + show_image(im, buff); + wait_until_press_key_cv(); + } + printf("\nYou use flag -show_imgs, so will be saved aug_...jpg images. Click on window and press ESC button \n"); } } free_data(d2); + free(paths_mix); } return d; diff --git a/src/data.h b/src/data.h index bf29499c3f5..7170f8c256a 100644 --- a/src/data.h +++ b/src/data.h @@ -91,7 +91,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo data load_data_tag(char **paths, int n, int m, int k, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure); matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure); data load_data_super(char **paths, int n, int m, int w, int h, int scale); -data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int mixup); +data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int mixup, int show_imgs); data load_go(char *filename); box_label *read_boxes(char *filename, int *n); diff --git a/src/parser.c b/src/parser.c index 0824ceb14f7..d8cf47bdae2 100644 --- a/src/parser.c +++ b/src/parser.c @@ -916,6 +916,9 @@ void parse_net_options(list *options, network *net) net->flip = option_find_int_quiet(options, "flip", 1); net->blur = option_find_int_quiet(options, "blur", 0); net->mixup = option_find_int_quiet(options, "mixup", 0); + int cutmix = option_find_int_quiet(options, "cutmix", 0); + if (net->mixup && cutmix) net->mixup = 3; + else if (cutmix) net->mixup = 2; net->letter_box = option_find_int_quiet(options, "letter_box", 0); net->angle = option_find_float_quiet(options, "angle", 0); From 8a3a7ccac6d405af7d0384793f7cf3c44ed1031a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 30 Nov 2019 19:17:19 +0300 Subject: [PATCH 0698/1365] Added `./darknet detector demo ... -time_limit_sec 10` to limit detection time --- src/coco.c | 2 +- src/demo.c | 11 +++++++++-- src/demo.h | 2 +- src/detector.c | 3 ++- src/yolo.c | 2 +- 5 files changed, 14 insertions(+), 6 deletions(-) diff --git a/src/coco.c b/src/coco.c index 03dd3a61415..de8f0288784 100644 --- a/src/coco.c +++ b/src/coco.c @@ -396,5 +396,5 @@ void run_coco(int argc, char **argv) else if(0==strcmp(argv[2], "valid")) validate_coco(cfg, weights); else if(0==strcmp(argv[2], "recall")) validate_coco_recall(cfg, weights); else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, hier_thresh, cam_index, filename, coco_classes, 80, frame_skip, - prefix, out_filename, mjpeg_port, json_port, dont_show, ext_output, 0); + prefix, out_filename, mjpeg_port, json_port, dont_show, ext_output, 0, 0); } diff --git a/src/demo.c b/src/demo.c index 63debde1a9d..1b522be9465 100644 --- a/src/demo.c +++ b/src/demo.c @@ -7,6 +7,7 @@ #include "box.h" #include "image.h" #include "demo.h" +#include "darknet.h" #ifdef WIN32 #include #include "gettimeofday.h" @@ -104,7 +105,7 @@ double get_wall_time() } void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, - int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output, int letter_box_in) + int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output, int letter_box_in, int time_limit_sec) { letter_box = letter_box_in; in_img = det_img = show_img = NULL; @@ -200,6 +201,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int //'W', 'M', 'V', '2' } + const double start_time_lim = get_time_point(); double before = get_wall_time(); while(1){ @@ -274,6 +276,11 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int pthread_join(fetch_thread, 0); pthread_join(detect_thread, 0); + if (time_limit_sec > 0 && (get_time_point() - start_time_lim)/1000000 > time_limit_sec) { + printf(" start_time_lim = %f, get_time_point() = %f, time spent = %f \n", start_time_lim, get_time_point(), get_time_point() - start_time_lim); + break; + } + if (flag_exit == 1) break; if(delay == 0){ @@ -325,7 +332,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int } #else void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, - int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output, int letter_box_in) + int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output, int letter_box_in, int time_limit_sec) { fprintf(stderr, "Demo needs OpenCV for webcam images.\n"); } diff --git a/src/demo.h b/src/demo.h index 1f749b899e5..6e705d620dc 100644 --- a/src/demo.h +++ b/src/demo.h @@ -6,7 +6,7 @@ extern "C" { #endif void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, - int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output, int letter_box_in); + int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output, int letter_box_in, int time_limit_sec); #ifdef __cplusplus } #endif diff --git a/src/detector.c b/src/detector.c index 86612b69ed9..55f8248979a 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1456,6 +1456,7 @@ void run_detector(int argc, char **argv) int show_imgs = find_arg(argc, argv, "-show_imgs"); int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1); int json_port = find_int_arg(argc, argv, "-json_port", -1); + int time_limit_sec = find_int_arg(argc, argv, "-time_limit_sec", 0); char *out_filename = find_char_arg(argc, argv, "-out_filename", 0); char *outfile = find_char_arg(argc, argv, "-out", 0); char *prefix = find_char_arg(argc, argv, "-prefix", 0); @@ -1523,7 +1524,7 @@ void run_detector(int argc, char **argv) if (strlen(filename) > 0) if (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0; demo(cfg, weights, thresh, hier_thresh, cam_index, filename, names, classes, frame_skip, prefix, out_filename, - mjpeg_port, json_port, dont_show, ext_output, letter_box); + mjpeg_port, json_port, dont_show, ext_output, letter_box, time_limit_sec); free_list_contents_kvp(options); free_list(options); diff --git a/src/yolo.c b/src/yolo.c index 339d49cd5ce..b86c19ee369 100644 --- a/src/yolo.c +++ b/src/yolo.c @@ -359,5 +359,5 @@ void run_yolo(int argc, char **argv) else if(0==strcmp(argv[2], "valid")) validate_yolo(cfg, weights); else if(0==strcmp(argv[2], "recall")) validate_yolo_recall(cfg, weights); else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, hier_thresh, cam_index, filename, voc_names, 20, frame_skip, - prefix, out_filename, mjpeg_port, json_port, dont_show, ext_output, 0); + prefix, out_filename, mjpeg_port, json_port, dont_show, ext_output, 0, 0); } From e8dc28c3b92654894d824d178ad1d84a082e8b1c Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 30 Nov 2019 19:27:55 +0300 Subject: [PATCH 0699/1365] iou_normalizer= will affect on x,y,w,h for default [Gaussian_yolo] layer even without (C/D/GIoU) --- src/gaussian_yolo_layer.c | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index d179ddd83d5..eb64202f13f 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -279,18 +279,18 @@ float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int ind dw *= exp(x[index + 4 * stride]); dh *= exp(x[index + 6 * stride]); - // normalize iou weight, for GIoU - dx *= iou_normalizer; - dy *= iou_normalizer; - dw *= iou_normalizer; - dh *= iou_normalizer; - delta_x = dx; delta_y = dy; delta_w = dw; delta_h = dh; } + // normalize iou weight, for GIoU + delta_x *= iou_normalizer; + delta_y *= iou_normalizer; + delta_w *= iou_normalizer; + delta_h *= iou_normalizer; + // normalize Uncertainty weight delta_ux *= uc_normalizer; delta_uy *= uc_normalizer; From 7ae664f96db67d81a19d8a5e091e3661564f808d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 30 Nov 2019 19:43:14 +0300 Subject: [PATCH 0700/1365] improved accuracy of inference-time and FPS measurement for Classifier --- src/classifier.c | 22 +++++++++++++++------- 1 file changed, 15 insertions(+), 7 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index 45b7c797dcd..fc760d30cf1 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -806,11 +806,14 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi printf("%d %d\n", r.w, r.h); float *X = r.data; - time=clock(); + + double time = get_time_point(); float *predictions = network_predict(net, X); + printf("%s: Predicted in %lf milli-seconds.\n", input, ((double)get_time_point() - time) / 1000); + if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 0); top_k(predictions, net.outputs, top, indexes); - printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); + for(i = 0; i < top; ++i){ int index = indexes[i]; if(net.hierarchy) printf("%d, %s: %f, parent: %s \n",index, names[index], predictions[index], (net.hierarchy->parent[index] >= 0) ? names[net.hierarchy->parent[index]] : "Root"); @@ -1208,7 +1211,10 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind image in_s = resize_image(in, net.w, net.h); show_image(in, "Classifier"); + double time = get_time_point(); float *predictions = network_predict(net, in_s.data); + double frame_time_ms = (get_time_point() - time)/1000; + if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 1); top_predictions(net, top, indexes); @@ -1216,7 +1222,7 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind printf("\033[2J"); printf("\033[1;1H"); #endif - printf("\nFPS:%.0f\n",fps); + printf("\nFPS: %.2f \n", fps); for(i = 0; i < top; ++i){ int index = indexes[i]; @@ -1228,10 +1234,12 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind wait_key_cv(10);// cvWaitKey(10); - gettimeofday(&tval_after, NULL); - timersub(&tval_after, &tval_before, &tval_result); - float curr = 1000000.f/((long int)tval_result.tv_usec); - fps = .9*fps + .1*curr; + //gettimeofday(&tval_after, NULL); + //timersub(&tval_after, &tval_before, &tval_result); + //float curr = 1000000.f/((long int)tval_result.tv_usec); + float curr = 1000.f / frame_time_ms; + if (fps == 0) fps = curr; + else fps = .9*fps + .1*curr; } #endif } From fbdac785d284e638788af7535d2ce817f1c3c1a5 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 1 Dec 2019 00:09:05 +0300 Subject: [PATCH 0701/1365] Added HTTP POST sender with detected objects, use: ./darknet detector demo ... -http_post_host webhook.site/898bbd9b-0ddd-49cf-b81d-1f56be98d870 --- src/coco.c | 2 +- src/demo.c | 16 +- src/demo.h | 2 +- src/detector.c | 3 +- src/http_stream.cpp | 79 +- src/http_stream.h | 3 + src/httplib.h | 4036 +++++++++++++++++++++++++++++++++++++++++++ src/yolo.c | 2 +- 8 files changed, 4135 insertions(+), 8 deletions(-) create mode 100644 src/httplib.h diff --git a/src/coco.c b/src/coco.c index de8f0288784..ce698e65bf2 100644 --- a/src/coco.c +++ b/src/coco.c @@ -396,5 +396,5 @@ void run_coco(int argc, char **argv) else if(0==strcmp(argv[2], "valid")) validate_coco(cfg, weights); else if(0==strcmp(argv[2], "recall")) validate_coco_recall(cfg, weights); else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, hier_thresh, cam_index, filename, coco_classes, 80, frame_skip, - prefix, out_filename, mjpeg_port, json_port, dont_show, ext_output, 0, 0); + prefix, out_filename, mjpeg_port, json_port, dont_show, ext_output, 0, 0, 0); } diff --git a/src/demo.c b/src/demo.c index 1b522be9465..2fd54cf5f6b 100644 --- a/src/demo.c +++ b/src/demo.c @@ -105,7 +105,7 @@ double get_wall_time() } void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, - int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output, int letter_box_in, int time_limit_sec) + int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output, int letter_box_in, int time_limit_sec, char *http_post_host) { letter_box = letter_box_in; in_img = det_img = show_img = NULL; @@ -201,6 +201,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int //'W', 'M', 'V', '2' } + int send_http_post_once = 0; const double start_time_lim = get_time_point(); double before = get_wall_time(); @@ -231,6 +232,17 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int send_json(local_dets, local_nboxes, l.classes, demo_names, frame_id, demo_json_port, timeout); } + //char *http_post_server = "webhook.site/898bbd9b-0ddd-49cf-b81d-1f56be98d870"; + if (http_post_host && !send_http_post_once) { + int timeout = 3; // 3 seconds + int http_post_port = 80; // 443 https, 80 http + if (send_http_post_request(http_post_host, http_post_port, filename, + dets, nboxes, classes, names, frame_id, ext_output, timeout)) + { + if (time_limit_sec > 0) send_http_post_once = 1; + } + } + draw_detections_cv_v3(show_img, local_dets, local_nboxes, demo_thresh, demo_names, demo_alphabet, demo_classes, demo_ext_output); free_detections(local_dets, local_nboxes); @@ -332,7 +344,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int } #else void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, - int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output, int letter_box_in, int time_limit_sec) + int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output, int letter_box_in, int time_limit_sec, char *http_post_host) { fprintf(stderr, "Demo needs OpenCV for webcam images.\n"); } diff --git a/src/demo.h b/src/demo.h index 6e705d620dc..c49a9017472 100644 --- a/src/demo.h +++ b/src/demo.h @@ -6,7 +6,7 @@ extern "C" { #endif void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, - int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output, int letter_box_in, int time_limit_sec); + int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output, int letter_box_in, int time_limit_sec, char *http_post_host); #ifdef __cplusplus } #endif diff --git a/src/detector.c b/src/detector.c index 55f8248979a..c0fa0f39a48 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1456,6 +1456,7 @@ void run_detector(int argc, char **argv) int show_imgs = find_arg(argc, argv, "-show_imgs"); int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1); int json_port = find_int_arg(argc, argv, "-json_port", -1); + char *http_post_host = find_char_arg(argc, argv, "-http_post_host", 0); int time_limit_sec = find_int_arg(argc, argv, "-time_limit_sec", 0); char *out_filename = find_char_arg(argc, argv, "-out_filename", 0); char *outfile = find_char_arg(argc, argv, "-out", 0); @@ -1524,7 +1525,7 @@ void run_detector(int argc, char **argv) if (strlen(filename) > 0) if (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0; demo(cfg, weights, thresh, hier_thresh, cam_index, filename, names, classes, frame_skip, prefix, out_filename, - mjpeg_port, json_port, dont_show, ext_output, letter_box, time_limit_sec); + mjpeg_port, json_port, dont_show, ext_output, letter_box, time_limit_sec, http_post_host); free_list_contents_kvp(options); free_list(options); diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 781139b3dff..f05159733b5 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -2,7 +2,6 @@ #include "image.h" #include "http_stream.h" - // // a single-threaded, multi client(using select), debug webserver - streaming out mjpg. // on win, _WIN32 has to be defined, must link against ws2_32.lib (socks on linux are for free) @@ -24,8 +23,13 @@ using std::endl; #ifndef USE_CMAKE_LIBS #pragma comment(lib, "ws2_32.lib") #endif -#include "gettimeofday.h" +#define WIN32_LEAN_AND_MEAN +#define _WINSOCK_DEPRECATED_NO_WARNINGS +#include +#include +#include #include +#include "gettimeofday.h" #define PORT unsigned long #define ADDRPOINTER int* struct _INIT_W32DATA @@ -537,6 +541,77 @@ void send_mjpeg(mat_cv* mat, int port, int timeout, int quality) } // ---------------------------------------- +std::string get_system_frame_time_string() +{ + std::time_t t = std::chrono::system_clock::to_time_t(std::chrono::system_clock::now()); + struct tm tmp_buf; + localtime_s(&tmp_buf, &t); + char buff[256]; + std::strftime(buff, 256, "%A %F %T", &tmp_buf); + std::string system_frame_time = buff; + return system_frame_time; +} +// ---------------------------------------- + +//#define CPPHTTPLIB_OPENSSL_SUPPORT +#include "httplib.h" + +// https://webhook.site/ +// https://github.com/yhirose/cpp-httplib +// sent POST http request +int send_http_post_request(char *http_post_host, int server_port, char *videosource, + detection *dets, int nboxes, int classes, char **names, long long int frame_id, int ext_output, int timeout) +{ + const float thresh = 0.005; // function get_network_boxes() has already filtred dets by actual threshold + + std::string message; + + for (int i = 0; i < nboxes; ++i) { + char labelstr[4096] = { 0 }; + int class_id = -1; + for (int j = 0; j < classes; ++j) { + int show = strncmp(names[j], "dont_show", 9); + if (dets[i].prob[j] > thresh && show) { + if (class_id < 0) { + strcat(labelstr, names[j]); + class_id = j; + char buff[10]; + sprintf(buff, " (%2.0f%%)", dets[i].prob[j] * 100); + strcat(labelstr, buff); + } + else { + strcat(labelstr, ", "); + strcat(labelstr, names[j]); + } + printf("%s: %.0f%% ", names[j], dets[i].prob[j] * 100); + } + } + if (class_id >= 0) { + message += std::string(names[class_id]) + std::string(", id: ") + std::to_string(class_id) + "\n"; + } + } + + if (!message.empty()) + { + std::string time = get_system_frame_time_string(); + message += "\ntime:\n" + time + "\n"; + message += "videosource:\n" + std::string(videosource); + + std::string http_post_host_str = http_post_host; + int slash_index = http_post_host_str.find("/"); + + std::string http_path = http_post_host_str.substr(slash_index, http_post_host_str.length() - slash_index); + http_post_host_str = http_post_host_str.substr(0, slash_index); + + // send HTTP-Post request + httplib::Client cli(http_post_host_str.c_str(), server_port, timeout); + auto res = cli.Post(http_path.c_str(), message, "text/plain"); + + return 1; + } + + return 0; +} #endif // OPENCV diff --git a/src/http_stream.h b/src/http_stream.h index 04945c6ba5e..01800517e7a 100644 --- a/src/http_stream.h +++ b/src/http_stream.h @@ -13,6 +13,9 @@ void send_json(detection *dets, int nboxes, int classes, char **names, long long #ifdef OPENCV void send_mjpeg(mat_cv* mat, int port, int timeout, int quality); +int send_http_post_request(char *http_post_host, int server_port, char *videosource, + detection *dets, int nboxes, int classes, char **names, long long int frame_id, int ext_output, int timeout); + #endif // OPENCV #ifdef __cplusplus diff --git a/src/httplib.h b/src/httplib.h new file mode 100644 index 00000000000..41fbfb19419 --- /dev/null +++ b/src/httplib.h @@ -0,0 +1,4036 @@ +// +// httplib.h +// +// Copyright (c) 2019 Yuji Hirose. All rights reserved. +// MIT License +// + +#ifndef CPPHTTPLIB_HTTPLIB_H +#define CPPHTTPLIB_HTTPLIB_H + +/* + * Configuration + */ + +#ifndef CPPHTTPLIB_KEEPALIVE_TIMEOUT_SECOND +#define CPPHTTPLIB_KEEPALIVE_TIMEOUT_SECOND 5 +#endif + +#ifndef CPPHTTPLIB_KEEPALIVE_TIMEOUT_USECOND +#define CPPHTTPLIB_KEEPALIVE_TIMEOUT_USECOND 0 +#endif + +#ifndef CPPHTTPLIB_KEEPALIVE_MAX_COUNT +#define CPPHTTPLIB_KEEPALIVE_MAX_COUNT 5 +#endif + +#ifndef CPPHTTPLIB_READ_TIMEOUT_SECOND +#define CPPHTTPLIB_READ_TIMEOUT_SECOND 5 +#endif + +#ifndef CPPHTTPLIB_READ_TIMEOUT_USECOND +#define CPPHTTPLIB_READ_TIMEOUT_USECOND 0 +#endif + +#ifndef CPPHTTPLIB_REQUEST_URI_MAX_LENGTH +#define CPPHTTPLIB_REQUEST_URI_MAX_LENGTH 8192 +#endif + +#ifndef CPPHTTPLIB_REDIRECT_MAX_COUNT +#define CPPHTTPLIB_REDIRECT_MAX_COUNT 20 +#endif + +#ifndef CPPHTTPLIB_PAYLOAD_MAX_LENGTH +#define CPPHTTPLIB_PAYLOAD_MAX_LENGTH (std::numeric_limits::max)() +#endif + +#ifndef CPPHTTPLIB_RECV_BUFSIZ +#define CPPHTTPLIB_RECV_BUFSIZ size_t(4096u) +#endif + +#ifndef CPPHTTPLIB_THREAD_POOL_COUNT +#define CPPHTTPLIB_THREAD_POOL_COUNT 8 +#endif + +/* + * Headers + */ + +#ifdef _WIN32 +#ifndef _CRT_SECURE_NO_WARNINGS +#define _CRT_SECURE_NO_WARNINGS +#endif //_CRT_SECURE_NO_WARNINGS + +#ifndef _CRT_NONSTDC_NO_DEPRECATE +#define _CRT_NONSTDC_NO_DEPRECATE +#endif //_CRT_NONSTDC_NO_DEPRECATE + +#if defined(_MSC_VER) +#ifdef _WIN64 +using ssize_t = __int64; +#else +using ssize_t = int; +#endif + +#if _MSC_VER < 1900 +#define snprintf _snprintf_s +#endif +#endif // _MSC_VER + +#ifndef S_ISREG +#define S_ISREG(m) (((m)&S_IFREG) == S_IFREG) +#endif // S_ISREG + +#ifndef S_ISDIR +#define S_ISDIR(m) (((m)&S_IFDIR) == S_IFDIR) +#endif // S_ISDIR + +#ifndef NOMINMAX +#define NOMINMAX +#endif // NOMINMAX + +#include +#include +#include + +#ifndef WSA_FLAG_NO_HANDLE_INHERIT +#define WSA_FLAG_NO_HANDLE_INHERIT 0x80 +#endif + +#ifdef _MSC_VER +#pragma comment(lib, "ws2_32.lib") +#endif + +#ifndef strcasecmp +#define strcasecmp _stricmp +#endif // strcasecmp + +using socket_t = SOCKET; +#ifdef CPPHTTPLIB_USE_POLL +#define poll(fds, nfds, timeout) WSAPoll(fds, nfds, timeout) +#endif + +#else // not _WIN32 + +#include +#include +#include +#include +#ifdef CPPHTTPLIB_USE_POLL +#include +#endif +#include +#include +#include +#include +#include + +using socket_t = int; +#define INVALID_SOCKET (-1) +#endif //_WIN32 + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#ifdef CPPHTTPLIB_OPENSSL_SUPPORT +#include +#include +#include + +// #if OPENSSL_VERSION_NUMBER < 0x1010100fL +// #error Sorry, OpenSSL versions prior to 1.1.1 are not supported +// #endif + +#if OPENSSL_VERSION_NUMBER < 0x10100000L +#include +inline const unsigned char *ASN1_STRING_get0_data(const ASN1_STRING *asn1) { + return M_ASN1_STRING_data(asn1); +} +#endif +#endif + +#ifdef CPPHTTPLIB_ZLIB_SUPPORT +#include +#endif + +/* + * Declaration + */ +namespace httplib { + +namespace detail { + +struct ci { + bool operator()(const std::string &s1, const std::string &s2) const { + return std::lexicographical_compare( + s1.begin(), s1.end(), s2.begin(), s2.end(), + [](char c1, char c2) { return ::tolower(c1) < ::tolower(c2); }); + } +}; + +} // namespace detail + +enum class HttpVersion { v1_0 = 0, v1_1 }; + +using Headers = std::multimap; + +using Params = std::multimap; +using Match = std::smatch; + +using DataSink = std::function; + +using Done = std::function; + +using ContentProvider = std::function; + +using ContentProviderWithCloser = std::function; + +using ContentReceiver = std::function; + +using ContentReader = std::function; + +using Progress = std::function; + +struct Response; +using ResponseHandler = std::function; + +struct MultipartFile { + std::string filename; + std::string content_type; + size_t offset = 0; + size_t length = 0; +}; +using MultipartFiles = std::multimap; + +struct MultipartFormData { + std::string name; + std::string content; + std::string filename; + std::string content_type; +}; +using MultipartFormDataItems = std::vector; + +using Range = std::pair; +using Ranges = std::vector; + +struct Request { + std::string method; + std::string path; + Headers headers; + std::string body; + + // for server + std::string version; + std::string target; + Params params; + MultipartFiles files; + Ranges ranges; + Match matches; + + // for client + size_t redirect_count = CPPHTTPLIB_REDIRECT_MAX_COUNT; + ResponseHandler response_handler; + ContentReceiver content_receiver; + Progress progress; + +#ifdef CPPHTTPLIB_OPENSSL_SUPPORT + const SSL *ssl; +#endif + + bool has_header(const char *key) const; + std::string get_header_value(const char *key, size_t id = 0) const; + size_t get_header_value_count(const char *key) const; + void set_header(const char *key, const char *val); + void set_header(const char *key, const std::string &val); + + bool has_param(const char *key) const; + std::string get_param_value(const char *key, size_t id = 0) const; + size_t get_param_value_count(const char *key) const; + + bool has_file(const char *key) const; + MultipartFile get_file_value(const char *key) const; + + // private members... + size_t content_length; + ContentProvider content_provider; +}; + +struct Response { + std::string version; + int status; + Headers headers; + std::string body; + + bool has_header(const char *key) const; + std::string get_header_value(const char *key, size_t id = 0) const; + size_t get_header_value_count(const char *key) const; + void set_header(const char *key, const char *val); + void set_header(const char *key, const std::string &val); + + void set_redirect(const char *url); + void set_content(const char *s, size_t n, const char *content_type); + void set_content(const std::string &s, const char *content_type); + + void set_content_provider( + size_t length, + std::function provider, + std::function resource_releaser = [] {}); + + void set_chunked_content_provider( + std::function provider, + std::function resource_releaser = [] {}); + + Response() : status(-1), content_length(0) {} + + ~Response() { + if (content_provider_resource_releaser) { + content_provider_resource_releaser(); + } + } + + // private members... + size_t content_length; + ContentProviderWithCloser content_provider; + std::function content_provider_resource_releaser; +}; + +class Stream { +public: + virtual ~Stream() = default; + virtual int read(char *ptr, size_t size) = 0; + virtual int write(const char *ptr, size_t size1) = 0; + virtual int write(const char *ptr) = 0; + virtual int write(const std::string &s) = 0; + virtual std::string get_remote_addr() const = 0; + + template + int write_format(const char *fmt, const Args &... args); +}; + +class SocketStream : public Stream { +public: + SocketStream(socket_t sock, time_t read_timeout_sec, + time_t read_timeout_usec); + ~SocketStream() override; + + int read(char *ptr, size_t size) override; + int write(const char *ptr, size_t size) override; + int write(const char *ptr) override; + int write(const std::string &s) override; + std::string get_remote_addr() const override; + +private: + socket_t sock_; + time_t read_timeout_sec_; + time_t read_timeout_usec_; +}; + +class BufferStream : public Stream { +public: + BufferStream() = default; + ~BufferStream() override = default; + + int read(char *ptr, size_t size) override; + int write(const char *ptr, size_t size) override; + int write(const char *ptr) override; + int write(const std::string &s) override; + std::string get_remote_addr() const override; + + const std::string &get_buffer() const; + +private: + std::string buffer; +}; + +class TaskQueue { +public: + TaskQueue() = default; + virtual ~TaskQueue() = default; + virtual void enqueue(std::function fn) = 0; + virtual void shutdown() = 0; +}; + +#if CPPHTTPLIB_THREAD_POOL_COUNT > 0 +class ThreadPool : public TaskQueue { +public: + explicit ThreadPool(size_t n) : shutdown_(false) { + while (n) { + threads_.emplace_back(worker(*this)); + n--; + } + } + + ThreadPool(const ThreadPool &) = delete; + ~ThreadPool() override = default; + + void enqueue(std::function fn) override { + std::unique_lock lock(mutex_); + jobs_.push_back(fn); + cond_.notify_one(); + } + + void shutdown() override { + // Stop all worker threads... + { + std::unique_lock lock(mutex_); + shutdown_ = true; + } + + cond_.notify_all(); + + // Join... + for (auto& t : threads_) { + t.join(); + } + } + +private: + struct worker { + explicit worker(ThreadPool &pool) : pool_(pool) {} + + void operator()() { + for (;;) { + std::function fn; + { + std::unique_lock lock(pool_.mutex_); + + pool_.cond_.wait( + lock, [&] { return !pool_.jobs_.empty() || pool_.shutdown_; }); + + if (pool_.shutdown_ && pool_.jobs_.empty()) { break; } + + fn = pool_.jobs_.front(); + pool_.jobs_.pop_front(); + } + + assert(true == static_cast(fn)); + fn(); + } + } + + ThreadPool &pool_; + }; + friend struct worker; + + std::vector threads_; + std::list> jobs_; + + bool shutdown_; + + std::condition_variable cond_; + std::mutex mutex_; +}; +#elif CPPHTTPLIB_THREAD_POOL_COUNT == 0 +class Threads : public TaskQueue { +public: + Threads() : running_threads_(0) {} + virtual ~Threads() {} + + virtual void enqueue(std::function fn) override { + std::thread([=]() { + { + std::lock_guard guard(running_threads_mutex_); + running_threads_++; + } + + fn(); + + { + std::lock_guard guard(running_threads_mutex_); + running_threads_--; + } + }).detach(); + } + + virtual void shutdown() override { + for (;;) { + std::this_thread::sleep_for(std::chrono::milliseconds(10)); + std::lock_guard guard(running_threads_mutex_); + if (!running_threads_) { break; } + } + } + +private: + std::mutex running_threads_mutex_; + int running_threads_; +}; +#else +class NoThread : public TaskQueue { +public: + NoThread() {} + virtual ~NoThread() {} + + virtual void enqueue(std::function fn) override { + fn(); + } + + virtual void shutdown() override { + } +}; +#endif + +class Server { +public: + using Handler = std::function; + using HandlerWithContentReader = std::function; + using Logger = std::function; + + Server(); + + virtual ~Server(); + + virtual bool is_valid() const; + + Server &Get(const char *pattern, Handler handler); + Server &Post(const char *pattern, Handler handler); + Server &Post(const char *pattern, HandlerWithContentReader handler); + Server &Put(const char *pattern, Handler handler); + Server &Put(const char *pattern, HandlerWithContentReader handler); + Server &Patch(const char *pattern, Handler handler); + Server &Patch(const char *pattern, HandlerWithContentReader handler); + Server &Delete(const char *pattern, Handler handler); + Server &Options(const char *pattern, Handler handler); + + bool set_base_dir(const char *dir, const char *mount_point = nullptr); + void set_file_request_handler(Handler handler); + + void set_error_handler(Handler handler); + void set_logger(Logger logger); + + void set_keep_alive_max_count(size_t count); + void set_read_timeout(time_t sec, time_t usec); + void set_payload_max_length(size_t length); + + bool bind_to_port(const char *host, int port, int socket_flags = 0); + int bind_to_any_port(const char *host, int socket_flags = 0); + bool listen_after_bind(); + + bool listen(const char *host, int port, int socket_flags = 0); + + bool is_running() const; + void stop(); + + std::function new_task_queue; + +protected: + bool process_request(Stream &strm, bool last_connection, + bool &connection_close, + const std::function& setup_request); + + size_t keep_alive_max_count_; + time_t read_timeout_sec_; + time_t read_timeout_usec_; + size_t payload_max_length_; + +private: + using Handlers = std::vector>; + using HandersForContentReader = std::vector>; + + socket_t create_server_socket(const char *host, int port, + int socket_flags) const; + int bind_internal(const char *host, int port, int socket_flags); + bool listen_internal(); + + bool routing(Request &req, Response &res, Stream &strm, bool last_connection); + bool handle_file_request(Request &req, Response &res); + bool dispatch_request(Request &req, Response &res, Handlers &handlers); + bool dispatch_request_for_content_reader(Request &req, Response &res, + ContentReader content_reader, + HandersForContentReader &handlers); + + bool parse_request_line(const char *s, Request &req); + bool write_response(Stream &strm, bool last_connection, const Request &req, + Response &res); + bool write_content_with_provider(Stream &strm, const Request &req, + Response &res, const std::string &boundary, + const std::string &content_type); + bool read_content(Stream &strm, bool last_connection, Request &req, + Response &res); + bool read_content_with_content_receiver(Stream &strm, bool last_connection, + Request &req, Response &res, + ContentReceiver reveiver); + + virtual bool process_and_close_socket(socket_t sock); + + std::atomic is_running_; + std::atomic svr_sock_; + std::vector> base_dirs_; + Handler file_request_handler_; + Handlers get_handlers_; + Handlers post_handlers_; + HandersForContentReader post_handlers_for_content_reader; + Handlers put_handlers_; + HandersForContentReader put_handlers_for_content_reader; + Handlers patch_handlers_; + HandersForContentReader patch_handlers_for_content_reader; + Handlers delete_handlers_; + Handlers options_handlers_; + Handler error_handler_; + Logger logger_; +}; + +class Client { +public: + explicit Client(const char *host, int port = 80, time_t timeout_sec = 300); + + virtual ~Client(); + + virtual bool is_valid() const; + + std::shared_ptr Get(const char *path); + + std::shared_ptr Get(const char *path, const Headers &headers); + + std::shared_ptr Get(const char *path, Progress progress); + + std::shared_ptr Get(const char *path, const Headers &headers, + Progress progress); + + std::shared_ptr Get(const char *path, + ContentReceiver content_receiver); + + std::shared_ptr Get(const char *path, const Headers &headers, + ContentReceiver content_receiver); + + std::shared_ptr + Get(const char *path, ContentReceiver content_receiver, Progress progress); + + std::shared_ptr Get(const char *path, const Headers &headers, + ContentReceiver content_receiver, + Progress progress); + + std::shared_ptr Get(const char *path, const Headers &headers, + ResponseHandler response_handler, + ContentReceiver content_receiver); + + std::shared_ptr Get(const char *path, const Headers &headers, + ResponseHandler response_handler, + ContentReceiver content_receiver, + Progress progress); + + std::shared_ptr Head(const char *path); + + std::shared_ptr Head(const char *path, const Headers &headers); + + std::shared_ptr Post(const char *path, const std::string &body, + const char *content_type, + bool compress = false); + + std::shared_ptr Post(const char *path, const Headers &headers, + const std::string &body, + const char *content_type, + bool compress = false); + + std::shared_ptr Post(const char *path, size_t content_length, + ContentProvider content_provider, + const char *content_type, + bool compress = false); + + std::shared_ptr Post(const char *path, const Headers &headers, + size_t content_length, + ContentProvider content_provider, + const char *content_type, + bool compress = false); + + std::shared_ptr Post(const char *path, const Params ¶ms, + bool compress = false); + + std::shared_ptr Post(const char *path, const Headers &headers, + const Params ¶ms, bool compress = false); + + std::shared_ptr Post(const char *path, + const MultipartFormDataItems &items, + bool compress = false); + + std::shared_ptr Post(const char *path, const Headers &headers, + const MultipartFormDataItems &items, + bool compress = false); + + std::shared_ptr Put(const char *path, const std::string &body, + const char *content_type, + bool compress = false); + + std::shared_ptr Put(const char *path, const Headers &headers, + const std::string &body, + const char *content_type, + bool compress = false); + + std::shared_ptr Put(const char *path, size_t content_length, + ContentProvider content_provider, + const char *content_type, + bool compress = false); + + std::shared_ptr Put(const char *path, const Headers &headers, + size_t content_length, + ContentProvider content_provider, + const char *content_type, + bool compress = false); + + std::shared_ptr Patch(const char *path, const std::string &body, + const char *content_type, + bool compress = false); + + std::shared_ptr Patch(const char *path, const Headers &headers, + const std::string &body, + const char *content_type, + bool compress = false); + + std::shared_ptr Patch(const char *path, size_t content_length, + ContentProvider content_provider, + const char *content_type, + bool compress = false); + + std::shared_ptr Patch(const char *path, const Headers &headers, + size_t content_length, + ContentProvider content_provider, + const char *content_type, + bool compress = false); + + std::shared_ptr Delete(const char *path); + + std::shared_ptr Delete(const char *path, const std::string &body, + const char *content_type); + + std::shared_ptr Delete(const char *path, const Headers &headers); + + std::shared_ptr Delete(const char *path, const Headers &headers, + const std::string &body, + const char *content_type); + + std::shared_ptr Options(const char *path); + + std::shared_ptr Options(const char *path, const Headers &headers); + + bool send(const Request &req, Response &res); + + bool send(const std::vector &requests, + std::vector &responses); + + void set_keep_alive_max_count(size_t count); + void set_read_timeout(time_t sec, time_t usec); + + void follow_location(bool on); + +protected: + bool process_request(Stream &strm, const Request &req, Response &res, + bool last_connection, bool &connection_close); + + const std::string host_; + const int port_; + time_t timeout_sec_; + const std::string host_and_port_; + size_t keep_alive_max_count_; + time_t read_timeout_sec_; + time_t read_timeout_usec_; + size_t follow_location_; + +private: + socket_t create_client_socket() const; + bool read_response_line(Stream &strm, Response &res); + void write_request(Stream &strm, const Request &req, bool last_connection); + bool redirect(const Request &req, Response &res); + + std::shared_ptr + send_with_content_provider(const char *method, const char *path, + const Headers &headers, const std::string &body, + size_t content_length, + ContentProvider content_provider, + const char *content_type, bool compress); + + virtual bool process_and_close_socket( + socket_t sock, size_t request_count, + std::function + callback); + + virtual bool is_ssl() const; +}; + +inline void Get(std::vector &requests, const char *path, + const Headers &headers) { + Request req; + req.method = "GET"; + req.path = path; + req.headers = headers; + requests.emplace_back(std::move(req)); +} + +inline void Get(std::vector &requests, const char *path) { + Get(requests, path, Headers()); +} + +inline void Post(std::vector &requests, const char *path, + const Headers &headers, const std::string &body, + const char *content_type) { + Request req; + req.method = "POST"; + req.path = path; + req.headers = headers; + req.headers.emplace("Content-Type", content_type); + req.body = body; + requests.emplace_back(std::move(req)); +} + +inline void Post(std::vector &requests, const char *path, + const std::string &body, const char *content_type) { + Post(requests, path, Headers(), body, content_type); +} + +#ifdef CPPHTTPLIB_OPENSSL_SUPPORT +class SSLSocketStream : public Stream { +public: + SSLSocketStream(socket_t sock, SSL *ssl, time_t read_timeout_sec, + time_t read_timeout_usec); + virtual ~SSLSocketStream(); + + virtual int read(char *ptr, size_t size); + virtual int write(const char *ptr, size_t size); + virtual int write(const char *ptr); + virtual int write(const std::string &s); + virtual std::string get_remote_addr() const; + +private: + socket_t sock_; + SSL *ssl_; + time_t read_timeout_sec_; + time_t read_timeout_usec_; +}; + +class SSLServer : public Server { +public: + SSLServer(const char *cert_path, const char *private_key_path, + const char *client_ca_cert_file_path = nullptr, + const char *client_ca_cert_dir_path = nullptr); + + virtual ~SSLServer(); + + virtual bool is_valid() const; + +private: + virtual bool process_and_close_socket(socket_t sock); + + SSL_CTX *ctx_; + std::mutex ctx_mutex_; +}; + +class SSLClient : public Client { +public: + SSLClient(const char *host, int port = 443, time_t timeout_sec = 300, + const char *client_cert_path = nullptr, + const char *client_key_path = nullptr); + + virtual ~SSLClient(); + + virtual bool is_valid() const; + + void set_ca_cert_path(const char *ca_ceert_file_path, + const char *ca_cert_dir_path = nullptr); + void enable_server_certificate_verification(bool enabled); + + long get_openssl_verify_result() const; + + SSL_CTX *ssl_context() const noexcept; + +private: + virtual bool process_and_close_socket( + socket_t sock, size_t request_count, + std::function + callback); + virtual bool is_ssl() const; + + bool verify_host(X509 *server_cert) const; + bool verify_host_with_subject_alt_name(X509 *server_cert) const; + bool verify_host_with_common_name(X509 *server_cert) const; + bool check_host_name(const char *pattern, size_t pattern_len) const; + + SSL_CTX *ctx_; + std::mutex ctx_mutex_; + std::vector host_components_; + std::string ca_cert_file_path_; + std::string ca_cert_dir_path_; + bool server_certificate_verification_ = false; + long verify_result_ = 0; +}; +#endif + +/* + * Implementation + */ + +namespace detail { + +inline bool is_hex(char c, int &v) { + if (0x20 <= c && isdigit(c)) { + v = c - '0'; + return true; + } else if ('A' <= c && c <= 'F') { + v = c - 'A' + 10; + return true; + } else if ('a' <= c && c <= 'f') { + v = c - 'a' + 10; + return true; + } + return false; +} + +inline bool from_hex_to_i(const std::string &s, size_t i, size_t cnt, + int &val) { + if (i >= s.size()) { return false; } + + val = 0; + for (; cnt; i++, cnt--) { + if (!s[i]) { return false; } + int v = 0; + if (is_hex(s[i], v)) { + val = val * 16 + v; + } else { + return false; + } + } + return true; +} + +inline std::string from_i_to_hex(size_t n) { + const char *charset = "0123456789abcdef"; + std::string ret; + do { + ret = charset[n & 15] + ret; + n >>= 4; + } while (n > 0); + return ret; +} + +inline size_t to_utf8(int code, char *buff) { + if (code < 0x0080) { + buff[0] = (code & 0x7F); + return 1; + } else if (code < 0x0800) { + buff[0] = (0xC0 | ((code >> 6) & 0x1F)); + buff[1] = (0x80 | (code & 0x3F)); + return 2; + } else if (code < 0xD800) { + buff[0] = (0xE0 | ((code >> 12) & 0xF)); + buff[1] = (0x80 | ((code >> 6) & 0x3F)); + buff[2] = (0x80 | (code & 0x3F)); + return 3; + } else if (code < 0xE000) { // D800 - DFFF is invalid... + return 0; + } else if (code < 0x10000) { + buff[0] = (0xE0 | ((code >> 12) & 0xF)); + buff[1] = (0x80 | ((code >> 6) & 0x3F)); + buff[2] = (0x80 | (code & 0x3F)); + return 3; + } else if (code < 0x110000) { + buff[0] = (0xF0 | ((code >> 18) & 0x7)); + buff[1] = (0x80 | ((code >> 12) & 0x3F)); + buff[2] = (0x80 | ((code >> 6) & 0x3F)); + buff[3] = (0x80 | (code & 0x3F)); + return 4; + } + + // NOTREACHED + return 0; +} + +// NOTE: This code came up with the following stackoverflow post: +// https://stackoverflow.com/questions/180947/base64-decode-snippet-in-c +inline std::string base64_encode(const std::string &in) { + static const auto lookup = + "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"; + + std::string out; + out.reserve(in.size()); + + int val = 0; + int valb = -6; + + for (uint8_t c : in) { + val = (val << 8) + c; + valb += 8; + while (valb >= 0) { + out.push_back(lookup[(val >> valb) & 0x3F]); + valb -= 6; + } + } + + if (valb > -6) { out.push_back(lookup[((val << 8) >> (valb + 8)) & 0x3F]); } + + while (out.size() % 4) { + out.push_back('='); + } + + return out; +} + +inline bool is_file(const std::string &path) { + struct stat st; + return stat(path.c_str(), &st) >= 0 && S_ISREG(st.st_mode); +} + +inline bool is_dir(const std::string &path) { + struct stat st; + return stat(path.c_str(), &st) >= 0 && S_ISDIR(st.st_mode); +} + +inline bool is_valid_path(const std::string &path) { + size_t level = 0; + size_t i = 0; + + // Skip slash + while (i < path.size() && path[i] == '/') { + i++; + } + + while (i < path.size()) { + // Read component + auto beg = i; + while (i < path.size() && path[i] != '/') { + i++; + } + + auto len = i - beg; + assert(len > 0); + + if (!path.compare(beg, len, ".")) { + ; + } else if (!path.compare(beg, len, "..")) { + if (level == 0) { return false; } + level--; + } else { + level++; + } + + // Skip slash + while (i < path.size() && path[i] == '/') { + i++; + } + } + + return true; +} + +inline void read_file(const std::string &path, std::string &out) { + std::ifstream fs(path, std::ios_base::binary); + fs.seekg(0, std::ios_base::end); + auto size = fs.tellg(); + fs.seekg(0); + out.resize(static_cast(size)); + fs.read(&out[0], size); +} + +inline std::string file_extension(const std::string &path) { + std::smatch m; + auto re = std::regex("\\.([a-zA-Z0-9]+)$"); + if (std::regex_search(path, m, re)) { return m[1].str(); } + return std::string(); +} + +template void split(const char *b, const char *e, char d, Fn fn) { + int i = 0; + int beg = 0; + + while (e ? (b + i != e) : (b[i] != '\0')) { + if (b[i] == d) { + fn(&b[beg], &b[i]); + beg = i + 1; + } + i++; + } + + if (i) { fn(&b[beg], &b[i]); } +} + +// NOTE: until the read size reaches `fixed_buffer_size`, use `fixed_buffer` +// to store data. The call can set memory on stack for performance. +class stream_line_reader { +public: + stream_line_reader(Stream &strm, char *fixed_buffer, size_t fixed_buffer_size) + : strm_(strm), fixed_buffer_(fixed_buffer), + fixed_buffer_size_(fixed_buffer_size) {} + + const char *ptr() const { + if (glowable_buffer_.empty()) { + return fixed_buffer_; + } else { + return glowable_buffer_.data(); + } + } + + size_t size() const { + if (glowable_buffer_.empty()) { + return fixed_buffer_used_size_; + } else { + return glowable_buffer_.size(); + } + } + + bool getline() { + fixed_buffer_used_size_ = 0; + glowable_buffer_.clear(); + + for (size_t i = 0;; i++) { + char byte; + auto n = strm_.read(&byte, 1); + + if (n < 0) { + return false; + } else if (n == 0) { + if (i == 0) { + return false; + } else { + break; + } + } + + append(byte); + + if (byte == '\n') { break; } + } + + return true; + } + +private: + void append(char c) { + if (fixed_buffer_used_size_ < fixed_buffer_size_ - 1) { + fixed_buffer_[fixed_buffer_used_size_++] = c; + fixed_buffer_[fixed_buffer_used_size_] = '\0'; + } else { + if (glowable_buffer_.empty()) { + assert(fixed_buffer_[fixed_buffer_used_size_] == '\0'); + glowable_buffer_.assign(fixed_buffer_, fixed_buffer_used_size_); + } + glowable_buffer_ += c; + } + } + + Stream &strm_; + char *fixed_buffer_; + const size_t fixed_buffer_size_; + size_t fixed_buffer_used_size_ = 0; + std::string glowable_buffer_; +}; + +inline int close_socket(socket_t sock) { +#ifdef _WIN32 + return closesocket(sock); +#else + return close(sock); +#endif +} + +inline int select_read(socket_t sock, time_t sec, time_t usec) { +#ifdef CPPHTTPLIB_USE_POLL + struct pollfd pfd_read; + pfd_read.fd = sock; + pfd_read.events = POLLIN; + + auto timeout = static_cast(sec * 1000 + usec / 1000); + + return poll(&pfd_read, 1, timeout); +#else + fd_set fds; + FD_ZERO(&fds); + FD_SET(sock, &fds); + + timeval tv; + tv.tv_sec = static_cast(sec); + tv.tv_usec = static_cast(usec); + + return select(static_cast(sock + 1), &fds, nullptr, nullptr, &tv); +#endif +} + +inline bool wait_until_socket_is_ready(socket_t sock, time_t sec, time_t usec) { +#ifdef CPPHTTPLIB_USE_POLL + struct pollfd pfd_read; + pfd_read.fd = sock; + pfd_read.events = POLLIN | POLLOUT; + + auto timeout = static_cast(sec * 1000 + usec / 1000); + + if (poll(&pfd_read, 1, timeout) > 0 && + pfd_read.revents & (POLLIN | POLLOUT)) { + int error = 0; + socklen_t len = sizeof(error); + return getsockopt(sock, SOL_SOCKET, SO_ERROR, + reinterpret_cast(&error), &len) >= 0 && + !error; + } + return false; +#else + fd_set fdsr; + FD_ZERO(&fdsr); + FD_SET(sock, &fdsr); + + auto fdsw = fdsr; + auto fdse = fdsr; + + timeval tv; + tv.tv_sec = static_cast(sec); + tv.tv_usec = static_cast(usec); + + if (select(static_cast(sock + 1), &fdsr, &fdsw, &fdse, &tv) > 0 && + (FD_ISSET(sock, &fdsr) || FD_ISSET(sock, &fdsw))) { + int error = 0; + socklen_t len = sizeof(error); + return getsockopt(sock, SOL_SOCKET, SO_ERROR, reinterpret_cast(&error), &len) >= 0 && + !error; + } + return false; +#endif +} + +template +inline bool process_and_close_socket(bool is_client_request, socket_t sock, + size_t keep_alive_max_count, + time_t read_timeout_sec, + time_t read_timeout_usec, T callback) { + assert(keep_alive_max_count > 0); + + bool ret = false; + + if (keep_alive_max_count > 1) { + auto count = keep_alive_max_count; + while (count > 0 && + (is_client_request || + detail::select_read(sock, CPPHTTPLIB_KEEPALIVE_TIMEOUT_SECOND, + CPPHTTPLIB_KEEPALIVE_TIMEOUT_USECOND) > 0)) { + SocketStream strm(sock, read_timeout_sec, read_timeout_usec); + auto last_connection = count == 1; + auto connection_close = false; + + ret = callback(strm, last_connection, connection_close); + if (!ret || connection_close) { break; } + + count--; + } + } else { + SocketStream strm(sock, read_timeout_sec, read_timeout_usec); + auto dummy_connection_close = false; + ret = callback(strm, true, dummy_connection_close); + } + + close_socket(sock); + return ret; +} + +inline int shutdown_socket(socket_t sock) { +#ifdef _WIN32 + return shutdown(sock, SD_BOTH); +#else + return shutdown(sock, SHUT_RDWR); +#endif +} + +template +socket_t create_socket(const char *host, int port, Fn fn, + int socket_flags = 0) { +#ifdef _WIN32 +#define SO_SYNCHRONOUS_NONALERT 0x20 +#define SO_OPENTYPE 0x7008 + + int opt = SO_SYNCHRONOUS_NONALERT; + setsockopt(INVALID_SOCKET, SOL_SOCKET, SO_OPENTYPE, (char *)&opt, + sizeof(opt)); +#endif + + // Get address info + struct addrinfo hints; + struct addrinfo *result; + + memset(&hints, 0, sizeof(struct addrinfo)); + hints.ai_family = AF_UNSPEC; + hints.ai_socktype = SOCK_STREAM; + hints.ai_flags = socket_flags; + hints.ai_protocol = 0; + + auto service = std::to_string(port); + + if (getaddrinfo(host, service.c_str(), &hints, &result)) { + return INVALID_SOCKET; + } + + for (auto rp = result; rp; rp = rp->ai_next) { + // Create a socket +#ifdef _WIN32 + auto sock = WSASocketW(rp->ai_family, rp->ai_socktype, rp->ai_protocol, + nullptr, 0, WSA_FLAG_NO_HANDLE_INHERIT); +#else + auto sock = socket(rp->ai_family, rp->ai_socktype, rp->ai_protocol); +#endif + if (sock == INVALID_SOCKET) { continue; } + +#ifndef _WIN32 + if (fcntl(sock, F_SETFD, FD_CLOEXEC) == -1) { continue; } +#endif + + // Make 'reuse address' option available + int yes = 1; + setsockopt(sock, SOL_SOCKET, SO_REUSEADDR, reinterpret_cast(&yes), + sizeof(yes)); +#ifdef SO_REUSEPORT + setsockopt(sock, SOL_SOCKET, SO_REUSEPORT, reinterpret_cast(&yes), + sizeof(yes)); +#endif + + // bind or connect + if (fn(sock, *rp)) { + freeaddrinfo(result); + return sock; + } + + close_socket(sock); + } + + freeaddrinfo(result); + return INVALID_SOCKET; +} + +inline void set_nonblocking(socket_t sock, bool nonblocking) { +#ifdef _WIN32 + auto flags = nonblocking ? 1UL : 0UL; + ioctlsocket(sock, FIONBIO, &flags); +#else + auto flags = fcntl(sock, F_GETFL, 0); + fcntl(sock, F_SETFL, + nonblocking ? (flags | O_NONBLOCK) : (flags & (~O_NONBLOCK))); +#endif +} + +inline bool is_connection_error() { +#ifdef _WIN32 + return WSAGetLastError() != WSAEWOULDBLOCK; +#else + return errno != EINPROGRESS; +#endif +} + +inline std::string get_remote_addr(socket_t sock) { + struct sockaddr_storage addr; + socklen_t len = sizeof(addr); + + if (!getpeername(sock, reinterpret_cast(&addr), &len)) { + std::array ipstr{}; + + if (!getnameinfo(reinterpret_cast(&addr), len, ipstr.data(), ipstr.size(), + nullptr, 0, NI_NUMERICHOST)) { + return ipstr.data(); + } + } + + return std::string(); +} + +inline const char *find_content_type(const std::string &path) { + auto ext = file_extension(path); + if (ext == "txt") { + return "text/plain"; + } else if (ext == "html" || ext == "htm") { + return "text/html"; + } else if (ext == "css") { + return "text/css"; + } else if (ext == "jpeg" || ext == "jpg") { + return "image/jpg"; + } else if (ext == "png") { + return "image/png"; + } else if (ext == "gif") { + return "image/gif"; + } else if (ext == "svg") { + return "image/svg+xml"; + } else if (ext == "ico") { + return "image/x-icon"; + } else if (ext == "json") { + return "application/json"; + } else if (ext == "pdf") { + return "application/pdf"; + } else if (ext == "js") { + return "application/javascript"; + } else if (ext == "xml") { + return "application/xml"; + } else if (ext == "xhtml") { + return "application/xhtml+xml"; + } + return nullptr; +} + +inline const char *status_message(int status) { + switch (status) { + case 200: return "OK"; + case 206: return "Partial Content"; + case 301: return "Moved Permanently"; + case 302: return "Found"; + case 303: return "See Other"; + case 304: return "Not Modified"; + case 400: return "Bad Request"; + case 403: return "Forbidden"; + case 404: return "Not Found"; + case 413: return "Payload Too Large"; + case 414: return "Request-URI Too Long"; + case 415: return "Unsupported Media Type"; + case 416: return "Range Not Satisfiable"; + + default: + case 500: return "Internal Server Error"; + } +} + +#ifdef CPPHTTPLIB_ZLIB_SUPPORT +inline bool can_compress(const std::string &content_type) { + return !content_type.find("text/") || content_type == "image/svg+xml" || + content_type == "application/javascript" || + content_type == "application/json" || + content_type == "application/xml" || + content_type == "application/xhtml+xml"; +} + +inline bool compress(std::string &content) { + z_stream strm; + strm.zalloc = Z_NULL; + strm.zfree = Z_NULL; + strm.opaque = Z_NULL; + + auto ret = deflateInit2(&strm, Z_DEFAULT_COMPRESSION, Z_DEFLATED, 31, 8, + Z_DEFAULT_STRATEGY); + if (ret != Z_OK) { return false; } + + strm.avail_in = content.size(); + strm.next_in = + const_cast(reinterpret_cast(content.data())); + + std::string compressed; + + std::array buff{}; + do { + strm.avail_out = buff.size(); + strm.next_out = reinterpret_cast(buff.data()); + ret = deflate(&strm, Z_FINISH); + assert(ret != Z_STREAM_ERROR); + compressed.append(buff.data(), buff.size() - strm.avail_out); + } while (strm.avail_out == 0); + + assert(ret == Z_STREAM_END); + assert(strm.avail_in == 0); + + content.swap(compressed); + + deflateEnd(&strm); + return true; +} + +class decompressor { +public: + decompressor() { + strm.zalloc = Z_NULL; + strm.zfree = Z_NULL; + strm.opaque = Z_NULL; + + // 15 is the value of wbits, which should be at the maximum possible value + // to ensure that any gzip stream can be decoded. The offset of 16 specifies + // that the stream to decompress will be formatted with a gzip wrapper. + is_valid_ = inflateInit2(&strm, 16 + 15) == Z_OK; + } + + ~decompressor() { inflateEnd(&strm); } + + bool is_valid() const { return is_valid_; } + + template + bool decompress(const char *data, size_t data_length, T callback) { + int ret = Z_OK; + + strm.avail_in = data_length; + strm.next_in = const_cast(reinterpret_cast(data)); + + std::array buff{}; + do { + strm.avail_out = buff.size(); + strm.next_out = reinterpret_cast(buff.data()); + + ret = inflate(&strm, Z_NO_FLUSH); + assert(ret != Z_STREAM_ERROR); + switch (ret) { + case Z_NEED_DICT: + case Z_DATA_ERROR: + case Z_MEM_ERROR: inflateEnd(&strm); return false; + } + + if (!callback(buff.data(), buff.size() - strm.avail_out)) { return false; } + } while (strm.avail_out == 0); + + return ret == Z_OK || ret == Z_STREAM_END; + } + +private: + bool is_valid_; + z_stream strm; +}; +#endif + +inline bool has_header(const Headers &headers, const char *key) { + return headers.find(key) != headers.end(); +} + +inline const char *get_header_value(const Headers &headers, const char *key, + size_t id = 0, const char *def = nullptr) { + auto it = headers.find(key); + std::advance(it, id); + if (it != headers.end()) { return it->second.c_str(); } + return def; +} + +inline uint64_t get_header_value_uint64(const Headers &headers, const char *key, + int def = 0) { + auto it = headers.find(key); + if (it != headers.end()) { + return std::strtoull(it->second.data(), nullptr, 10); + } + return def; +} + +inline bool read_headers(Stream &strm, Headers &headers) { + static std::regex re(R"((.+?):\s*(.+?)\s*\r\n)"); + + const auto bufsiz = 2048; + char buf[bufsiz]; + + stream_line_reader line_reader(strm, buf, bufsiz); + + for (;;) { + if (!line_reader.getline()) { return false; } + if (!strcmp(line_reader.ptr(), "\r\n")) { break; } + std::cmatch m; + if (std::regex_match(line_reader.ptr(), m, re)) { + auto key = std::string(m[1]); + auto val = std::string(m[2]); + headers.emplace(key, val); + } + } + + return true; +} + +inline bool read_content_with_length(Stream &strm, uint64_t len, + Progress progress, ContentReceiver out) { + char buf[CPPHTTPLIB_RECV_BUFSIZ]; + + uint64_t r = 0; + while (r < len) { + auto read_len = static_cast(len - r); + auto n = strm.read(buf, std::min(read_len, CPPHTTPLIB_RECV_BUFSIZ)); + if (n <= 0) { return false; } + + if (!out(buf, n)) { return false; } + + r += n; + + if (progress) { + if (!progress(r, len)) { return false; } + } + } + + return true; +} + +inline void skip_content_with_length(Stream &strm, uint64_t len) { + char buf[CPPHTTPLIB_RECV_BUFSIZ]; + uint64_t r = 0; + while (r < len) { + auto read_len = static_cast(len - r); + auto n = strm.read(buf, std::min(read_len, CPPHTTPLIB_RECV_BUFSIZ)); + if (n <= 0) { return; } + r += n; + } +} + +inline bool read_content_without_length(Stream &strm, ContentReceiver out) { + char buf[CPPHTTPLIB_RECV_BUFSIZ]; + for (;;) { + auto n = strm.read(buf, CPPHTTPLIB_RECV_BUFSIZ); + if (n < 0) { + return false; + } else if (n == 0) { + return true; + } + if (!out(buf, n)) { return false; } + } + + return true; +} + +inline bool read_content_chunked(Stream &strm, ContentReceiver out) { + const auto bufsiz = 16; + char buf[bufsiz]; + + stream_line_reader line_reader(strm, buf, bufsiz); + + if (!line_reader.getline()) { return false; } + + auto chunk_len = std::stoi(line_reader.ptr(), 0, 16); + + while (chunk_len > 0) { + if (!read_content_with_length(strm, chunk_len, nullptr, out)) { + return false; + } + + if (!line_reader.getline()) { return false; } + + if (strcmp(line_reader.ptr(), "\r\n")) { break; } + + if (!line_reader.getline()) { return false; } + + chunk_len = std::stoi(line_reader.ptr(), 0, 16); + } + + if (chunk_len == 0) { + // Reader terminator after chunks + if (!line_reader.getline() || strcmp(line_reader.ptr(), "\r\n")) + return false; + } + + return true; +} + +inline bool is_chunked_transfer_encoding(const Headers &headers) { + return !strcasecmp(get_header_value(headers, "Transfer-Encoding", 0, ""), + "chunked"); +} + +template +bool read_content(Stream &strm, T &x, size_t payload_max_length, int &status, + Progress progress, ContentReceiver receiver) { + + ContentReceiver out = [&](const char *buf, size_t n) { + return receiver(buf, n); + }; + +#ifdef CPPHTTPLIB_ZLIB_SUPPORT + detail::decompressor decompressor; + + if (!decompressor.is_valid()) { + status = 500; + return false; + } + + if (x.get_header_value("Content-Encoding") == "gzip") { + out = [&](const char *buf, size_t n) { + return decompressor.decompress( + buf, n, [&](const char *buf, size_t n) { return receiver(buf, n); }); + }; + } +#else + if (x.get_header_value("Content-Encoding") == "gzip") { + status = 415; + return false; + } +#endif + + auto ret = true; + auto exceed_payload_max_length = false; + + if (is_chunked_transfer_encoding(x.headers)) { + ret = read_content_chunked(strm, out); + } else if (!has_header(x.headers, "Content-Length")) { + ret = read_content_without_length(strm, out); + } else { + auto len = get_header_value_uint64(x.headers, "Content-Length", 0); + if (len > payload_max_length) { + exceed_payload_max_length = true; + skip_content_with_length(strm, len); + ret = false; + } else if (len > 0) { + ret = read_content_with_length(strm, len, progress, out); + } + } + + if (!ret) { status = exceed_payload_max_length ? 413 : 400; } + + return ret; +} + +template +inline int write_headers(Stream &strm, const T &info, const Headers &headers) { + auto write_len = 0; + for (const auto &x : info.headers) { + auto len = + strm.write_format("%s: %s\r\n", x.first.c_str(), x.second.c_str()); + if (len < 0) { return len; } + write_len += len; + } + for (const auto &x : headers) { + auto len = + strm.write_format("%s: %s\r\n", x.first.c_str(), x.second.c_str()); + if (len < 0) { return len; } + write_len += len; + } + auto len = strm.write("\r\n"); + if (len < 0) { return len; } + write_len += len; + return write_len; +} + +inline ssize_t write_content(Stream &strm, + ContentProviderWithCloser content_provider, + size_t offset, size_t length) { + size_t begin_offset = offset; + size_t end_offset = offset + length; + while (offset < end_offset) { + ssize_t written_length = 0; + content_provider( + offset, end_offset - offset, + [&](const char *d, size_t l) { + offset += l; + written_length = strm.write(d, l); + }, + [&](void) { written_length = -1; }); + if (written_length < 0) { return written_length; } + } + return static_cast(offset - begin_offset); +} + +inline ssize_t +write_content_chunked(Stream &strm, + ContentProviderWithCloser content_provider) { + size_t offset = 0; + auto data_available = true; + ssize_t total_written_length = 0; + while (data_available) { + ssize_t written_length = 0; + content_provider( + offset, 0, + [&](const char *d, size_t l) { + data_available = l > 0; + offset += l; + + // Emit chunked response header and footer for each chunk + auto chunk = from_i_to_hex(l) + "\r\n" + std::string(d, l) + "\r\n"; + written_length = strm.write(chunk); + }, + [&](void) { + data_available = false; + written_length = strm.write("0\r\n\r\n"); + }); + + if (written_length < 0) { return written_length; } + total_written_length += written_length; + } + return total_written_length; +} + +template +inline bool redirect(T &cli, const Request &req, Response &res, + const std::string &path) { + Request new_req; + new_req.method = req.method; + new_req.path = path; + new_req.headers = req.headers; + new_req.body = req.body; + new_req.redirect_count = req.redirect_count - 1; + new_req.response_handler = req.response_handler; + new_req.content_receiver = req.content_receiver; + new_req.progress = req.progress; + + Response new_res; + auto ret = cli.send(new_req, new_res); + if (ret) { res = new_res; } + return ret; +} + +inline std::string encode_url(const std::string &s) { + std::string result; + + for (auto i = 0; s[i]; i++) { + switch (s[i]) { + case ' ': result += "%20"; break; + case '+': result += "%2B"; break; + case '\r': result += "%0D"; break; + case '\n': result += "%0A"; break; + case '\'': result += "%27"; break; + case ',': result += "%2C"; break; + case ':': result += "%3A"; break; + case ';': result += "%3B"; break; + default: + auto c = static_cast(s[i]); + if (c >= 0x80) { + result += '%'; + char hex[4]; + size_t len = snprintf(hex, sizeof(hex) - 1, "%02X", c); + assert(len == 2); + result.append(hex, len); + } else { + result += s[i]; + } + break; + } + } + + return result; +} + +inline std::string decode_url(const std::string &s) { + std::string result; + + for (size_t i = 0; i < s.size(); i++) { + if (s[i] == '%' && i + 1 < s.size()) { + if (s[i + 1] == 'u') { + int val = 0; + if (from_hex_to_i(s, i + 2, 4, val)) { + // 4 digits Unicode codes + char buff[4]; + size_t len = to_utf8(val, buff); + if (len > 0) { result.append(buff, len); } + i += 5; // 'u0000' + } else { + result += s[i]; + } + } else { + int val = 0; + if (from_hex_to_i(s, i + 1, 2, val)) { + // 2 digits hex codes + result += static_cast(val); + i += 2; // '00' + } else { + result += s[i]; + } + } + } else if (s[i] == '+') { + result += ' '; + } else { + result += s[i]; + } + } + + return result; +} + +inline void parse_query_text(const std::string &s, Params ¶ms) { + split(&s[0], &s[s.size()], '&', [&](const char *b, const char *e) { + std::string key; + std::string val; + split(b, e, '=', [&](const char *b, const char *e) { + if (key.empty()) { + key.assign(b, e); + } else { + val.assign(b, e); + } + }); + params.emplace(key, decode_url(val)); + }); +} + +inline bool parse_multipart_boundary(const std::string &content_type, + std::string &boundary) { + auto pos = content_type.find("boundary="); + if (pos == std::string::npos) { return false; } + + boundary = content_type.substr(pos + 9); + return true; +} + +inline bool parse_multipart_formdata(const std::string &boundary, + const std::string &body, + MultipartFiles &files) { + static std::string dash = "--"; + static std::string crlf = "\r\n"; + + static std::regex re_content_type("Content-Type: (.*?)$", + std::regex_constants::icase); + + static std::regex re_content_disposition( + "Content-Disposition: form-data; name=\"(.*?)\"(?:; filename=\"(.*?)\")?", + std::regex_constants::icase); + + auto dash_boundary = dash + boundary; + + auto pos = body.find(dash_boundary); + if (pos != 0) { return false; } + + pos += dash_boundary.size(); + + auto next_pos = body.find(crlf, pos); + if (next_pos == std::string::npos) { return false; } + + pos = next_pos + crlf.size(); + + while (pos < body.size()) { + next_pos = body.find(crlf, pos); + if (next_pos == std::string::npos) { return false; } + + std::string name; + MultipartFile file; + + auto header = body.substr(pos, (next_pos - pos)); + + while (pos != next_pos) { + std::smatch m; + if (std::regex_match(header, m, re_content_type)) { + file.content_type = m[1]; + } else if (std::regex_match(header, m, re_content_disposition)) { + name = m[1]; + file.filename = m[2]; + } + + pos = next_pos + crlf.size(); + + next_pos = body.find(crlf, pos); + if (next_pos == std::string::npos) { return false; } + + header = body.substr(pos, (next_pos - pos)); + } + + pos = next_pos + crlf.size(); + + next_pos = body.find(crlf + dash_boundary, pos); + + if (next_pos == std::string::npos) { return false; } + + file.offset = pos; + file.length = next_pos - pos; + + pos = next_pos + crlf.size() + dash_boundary.size(); + + next_pos = body.find(crlf, pos); + if (next_pos == std::string::npos) { return false; } + + files.emplace(name, file); + + pos = next_pos + crlf.size(); + } + + return true; +} + +inline bool parse_range_header(const std::string &s, Ranges &ranges) { + try { + static auto re_first_range = + std::regex(R"(bytes=(\d*-\d*(?:,\s*\d*-\d*)*))"); + std::smatch m; + if (std::regex_match(s, m, re_first_range)) { + auto pos = m.position(1); + auto len = m.length(1); + detail::split( + &s[pos], &s[pos + len], ',', [&](const char *b, const char *e) { + static auto re_another_range = std::regex(R"(\s*(\d*)-(\d*))"); + std::cmatch m; + if (std::regex_match(b, e, m, re_another_range)) { + ssize_t first = -1; + if (!m.str(1).empty()) { + first = static_cast(std::stoll(m.str(1))); + } + + ssize_t last = -1; + if (!m.str(2).empty()) { + last = static_cast(std::stoll(m.str(2))); + } + + if (first != -1 && last != -1 && first > last) { + throw std::runtime_error("invalid range error"); + } + ranges.emplace_back(std::make_pair(first, last)); + } + }); + return true; + } + return false; + } catch (...) { return false; } +} + +inline std::string to_lower(const char *beg, const char *end) { + std::string out; + auto it = beg; + while (it != end) { + out += static_cast(::tolower(*it)); + it++; + } + return out; +} + +inline std::string make_multipart_data_boundary() { + static const char data[] = + "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"; + + std::random_device seed_gen; + std::mt19937 engine(seed_gen()); + + std::string result = "--cpp-httplib-multipart-data-"; + + for (auto i = 0; i < 16; i++) { + result += data[engine() % (sizeof(data) - 1)]; + } + + return result; +} + +inline std::pair +get_range_offset_and_length(const Request &req, size_t content_length, + size_t index) { + auto r = req.ranges[index]; + + if (r.first == -1 && r.second == -1) { + return std::make_pair(0, content_length); + } + + if (r.first == -1) { + r.first = content_length - r.second; + r.second = content_length - 1; + } + + if (r.second == -1) { r.second = content_length - 1; } + + return std::make_pair(r.first, r.second - r.first + 1); +} + +inline std::string make_content_range_header_field(size_t offset, size_t length, + size_t content_length) { + std::string field = "bytes "; + field += std::to_string(offset); + field += "-"; + field += std::to_string(offset + length - 1); + field += "/"; + field += std::to_string(content_length); + return field; +} + +template +bool process_multipart_ranges_data(const Request &req, Response &res, + const std::string &boundary, + const std::string &content_type, + SToken stoken, CToken ctoken, + Content content) { + for (size_t i = 0; i < req.ranges.size(); i++) { + ctoken("--"); + stoken(boundary); + ctoken("\r\n"); + if (!content_type.empty()) { + ctoken("Content-Type: "); + stoken(content_type); + ctoken("\r\n"); + } + + auto offsets = detail::get_range_offset_and_length(req, res.body.size(), i); + auto offset = offsets.first; + auto length = offsets.second; + + ctoken("Content-Range: "); + stoken(make_content_range_header_field(offset, length, res.body.size())); + ctoken("\r\n"); + ctoken("\r\n"); + if (!content(offset, length)) { return false; } + ctoken("\r\n"); + } + + ctoken("--"); + stoken(boundary); + ctoken("--\r\n"); + + return true; +} + +inline std::string make_multipart_ranges_data(const Request &req, Response &res, + const std::string &boundary, + const std::string &content_type) { + std::string data; + + process_multipart_ranges_data( + req, res, boundary, content_type, + [&](const std::string &token) { data += token; }, + [&](const char *token) { data += token; }, + [&](size_t offset, size_t length) { + data += res.body.substr(offset, length); + return true; + }); + + return data; +} + +inline size_t +get_multipart_ranges_data_length(const Request &req, Response &res, + const std::string &boundary, + const std::string &content_type) { + size_t data_length = 0; + + process_multipart_ranges_data( + req, res, boundary, content_type, + [&](const std::string &token) { data_length += token.size(); }, + [&](const char *token) { data_length += strlen(token); }, + [&](size_t /*offset*/, size_t length) { + data_length += length; + return true; + }); + + return data_length; +} + +inline bool write_multipart_ranges_data(Stream &strm, const Request &req, + Response &res, + const std::string &boundary, + const std::string &content_type) { + return process_multipart_ranges_data( + req, res, boundary, content_type, + [&](const std::string &token) { strm.write(token); }, + [&](const char *token) { strm.write(token); }, + [&](size_t offset, size_t length) { + return detail::write_content(strm, res.content_provider, offset, + length) >= 0; + }); +} + +inline std::pair +get_range_offset_and_length(const Request &req, const Response &res, + size_t index) { + auto r = req.ranges[index]; + + if (r.second == -1) { r.second = res.content_length - 1; } + + return std::make_pair(r.first, r.second - r.first + 1); +} + +#ifdef _WIN32 +class WSInit { +public: + WSInit() { + WSADATA wsaData; + WSAStartup(0x0002, &wsaData); + } + + ~WSInit() { WSACleanup(); } +}; + +static WSInit wsinit_; +#endif + +} // namespace detail + +// Header utilities +inline std::pair make_range_header(Ranges ranges) { + std::string field = "bytes="; + auto i = 0; + for (auto r : ranges) { + if (i != 0) { field += ", "; } + if (r.first != -1) { field += std::to_string(r.first); } + field += '-'; + if (r.second != -1) { field += std::to_string(r.second); } + i++; + } + return std::make_pair("Range", field); +} + +inline std::pair +make_basic_authentication_header(const std::string &username, + const std::string &password) { + auto field = "Basic " + detail::base64_encode(username + ":" + password); + return std::make_pair("Authorization", field); +} + +// Request implementation +inline bool Request::has_header(const char *key) const { + return detail::has_header(headers, key); +} + +inline std::string Request::get_header_value(const char *key, size_t id) const { + return detail::get_header_value(headers, key, id, ""); +} + +inline size_t Request::get_header_value_count(const char *key) const { + auto r = headers.equal_range(key); + return std::distance(r.first, r.second); +} + +inline void Request::set_header(const char *key, const char *val) { + headers.emplace(key, val); +} + +inline void Request::set_header(const char *key, const std::string &val) { + headers.emplace(key, val); +} + +inline bool Request::has_param(const char *key) const { + return params.find(key) != params.end(); +} + +inline std::string Request::get_param_value(const char *key, size_t id) const { + auto it = params.find(key); + std::advance(it, id); + if (it != params.end()) { return it->second; } + return std::string(); +} + +inline size_t Request::get_param_value_count(const char *key) const { + auto r = params.equal_range(key); + return std::distance(r.first, r.second); +} + +inline bool Request::has_file(const char *key) const { + return files.find(key) != files.end(); +} + +inline MultipartFile Request::get_file_value(const char *key) const { + auto it = files.find(key); + if (it != files.end()) { return it->second; } + return MultipartFile(); +} + +// Response implementation +inline bool Response::has_header(const char *key) const { + return headers.find(key) != headers.end(); +} + +inline std::string Response::get_header_value(const char *key, + size_t id) const { + return detail::get_header_value(headers, key, id, ""); +} + +inline size_t Response::get_header_value_count(const char *key) const { + auto r = headers.equal_range(key); + return std::distance(r.first, r.second); +} + +inline void Response::set_header(const char *key, const char *val) { + headers.emplace(key, val); +} + +inline void Response::set_header(const char *key, const std::string &val) { + headers.emplace(key, val); +} + +inline void Response::set_redirect(const char *url) { + set_header("Location", url); + status = 302; +} + +inline void Response::set_content(const char *s, size_t n, + const char *content_type) { + body.assign(s, n); + set_header("Content-Type", content_type); +} + +inline void Response::set_content(const std::string &s, + const char *content_type) { + body = s; + set_header("Content-Type", content_type); +} + +inline void Response::set_content_provider( + size_t length, + std::function provider, + std::function resource_releaser) { + assert(length > 0); + content_length = length; + content_provider = [provider](size_t offset, size_t length, DataSink sink, + Done) { provider(offset, length, sink); }; + content_provider_resource_releaser = resource_releaser; +} + +inline void Response::set_chunked_content_provider( + std::function provider, + std::function resource_releaser) { + content_length = 0; + content_provider = [provider](size_t offset, size_t, DataSink sink, + Done done) { provider(offset, sink, done); }; + content_provider_resource_releaser = resource_releaser; +} + +// Rstream implementation +template +inline int Stream::write_format(const char *fmt, const Args &... args) { + std::array buf; + +#if defined(_MSC_VER) && _MSC_VER < 1900 + auto n = _snprintf_s(buf, bufsiz, buf.size() - 1, fmt, args...); +#else + auto n = snprintf(buf.data(), buf.size() - 1, fmt, args...); +#endif + if (n <= 0) { return n; } + + if (n >= static_cast(buf.size()) - 1) { + std::vector glowable_buf(buf.size()); + + while (n >= static_cast(glowable_buf.size() - 1)) { + glowable_buf.resize(glowable_buf.size() * 2); +#if defined(_MSC_VER) && _MSC_VER < 1900 + n = _snprintf_s(&glowable_buf[0], glowable_buf.size(), + glowable_buf.size() - 1, fmt, args...); +#else + n = snprintf(&glowable_buf[0], glowable_buf.size() - 1, fmt, args...); +#endif + } + return write(&glowable_buf[0], n); + } else { + return write(buf.data(), n); + } +} + +// Socket stream implementation +inline SocketStream::SocketStream(socket_t sock, time_t read_timeout_sec, + time_t read_timeout_usec) + : sock_(sock), read_timeout_sec_(read_timeout_sec), + read_timeout_usec_(read_timeout_usec) {} + +inline SocketStream::~SocketStream() {} + +inline int SocketStream::read(char *ptr, size_t size) { + if (detail::select_read(sock_, read_timeout_sec_, read_timeout_usec_) > 0) { + return recv(sock_, ptr, static_cast(size), 0); + } + return -1; +} + +inline int SocketStream::write(const char *ptr, size_t size) { + return send(sock_, ptr, static_cast(size), 0); +} + +inline int SocketStream::write(const char *ptr) { + return write(ptr, strlen(ptr)); +} + +inline int SocketStream::write(const std::string &s) { + return write(s.data(), s.size()); +} + +inline std::string SocketStream::get_remote_addr() const { + return detail::get_remote_addr(sock_); +} + +// Buffer stream implementation +inline int BufferStream::read(char *ptr, size_t size) { +#if defined(_MSC_VER) && _MSC_VER < 1900 + return static_cast(buffer._Copy_s(ptr, size, size)); +#else + return static_cast(buffer.copy(ptr, size)); +#endif +} + +inline int BufferStream::write(const char *ptr, size_t size) { + buffer.append(ptr, size); + return static_cast(size); +} + +inline int BufferStream::write(const char *ptr) { + return write(ptr, strlen(ptr)); +} + +inline int BufferStream::write(const std::string &s) { + return write(s.data(), s.size()); +} + +inline std::string BufferStream::get_remote_addr() const { return ""; } + +inline const std::string &BufferStream::get_buffer() const { return buffer; } + +// HTTP server implementation +inline Server::Server() + : keep_alive_max_count_(CPPHTTPLIB_KEEPALIVE_MAX_COUNT), + read_timeout_sec_(CPPHTTPLIB_READ_TIMEOUT_SECOND), + read_timeout_usec_(CPPHTTPLIB_READ_TIMEOUT_USECOND), + payload_max_length_(CPPHTTPLIB_PAYLOAD_MAX_LENGTH), is_running_(false), + svr_sock_(INVALID_SOCKET) { +#ifndef _WIN32 + signal(SIGPIPE, SIG_IGN); +#endif + new_task_queue = [] { +#if CPPHTTPLIB_THREAD_POOL_COUNT > 0 + return new ThreadPool(CPPHTTPLIB_THREAD_POOL_COUNT); +#elif CPPHTTPLIB_THREAD_POOL_COUNT == 0 + return new Threads(); +#else + return new NoThread(); +#endif + }; +} + +inline Server::~Server() {} + +inline Server &Server::Get(const char *pattern, Handler handler) { + get_handlers_.push_back(std::make_pair(std::regex(pattern), handler)); + return *this; +} + +inline Server &Server::Post(const char *pattern, Handler handler) { + post_handlers_.push_back(std::make_pair(std::regex(pattern), handler)); + return *this; +} + +inline Server &Server::Post(const char *pattern, + HandlerWithContentReader handler) { + post_handlers_for_content_reader.push_back( + std::make_pair(std::regex(pattern), handler)); + return *this; +} + +inline Server &Server::Put(const char *pattern, Handler handler) { + put_handlers_.push_back(std::make_pair(std::regex(pattern), handler)); + return *this; +} + +inline Server &Server::Put(const char *pattern, + HandlerWithContentReader handler) { + put_handlers_for_content_reader.push_back( + std::make_pair(std::regex(pattern), handler)); + return *this; +} + +inline Server &Server::Patch(const char *pattern, Handler handler) { + patch_handlers_.push_back(std::make_pair(std::regex(pattern), handler)); + return *this; +} + +inline Server &Server::Patch(const char *pattern, + HandlerWithContentReader handler) { + patch_handlers_for_content_reader.push_back( + std::make_pair(std::regex(pattern), handler)); + return *this; +} + +inline Server &Server::Delete(const char *pattern, Handler handler) { + delete_handlers_.push_back(std::make_pair(std::regex(pattern), handler)); + return *this; +} + +inline Server &Server::Options(const char *pattern, Handler handler) { + options_handlers_.push_back(std::make_pair(std::regex(pattern), handler)); + return *this; +} + +inline bool Server::set_base_dir(const char *dir, const char *mount_point) { + if (detail::is_dir(dir)) { + std::string mnt = mount_point ? mount_point : "/"; + if (!mnt.empty() && mnt[0] == '/') { + base_dirs_.emplace_back(mnt, dir); + return true; + } + } + return false; +} + +inline void Server::set_file_request_handler(Handler handler) { + file_request_handler_ = std::move(handler); +} + +inline void Server::set_error_handler(Handler handler) { + error_handler_ = std::move(handler); +} + +inline void Server::set_logger(Logger logger) { logger_ = std::move(logger); } + +inline void Server::set_keep_alive_max_count(size_t count) { + keep_alive_max_count_ = count; +} + +inline void Server::set_read_timeout(time_t sec, time_t usec) { + read_timeout_sec_ = sec; + read_timeout_usec_ = usec; +} + +inline void Server::set_payload_max_length(size_t length) { + payload_max_length_ = length; +} + +inline bool Server::bind_to_port(const char *host, int port, int socket_flags) { + if (bind_internal(host, port, socket_flags) < 0) return false; + return true; +} +inline int Server::bind_to_any_port(const char *host, int socket_flags) { + return bind_internal(host, 0, socket_flags); +} + +inline bool Server::listen_after_bind() { return listen_internal(); } + +inline bool Server::listen(const char *host, int port, int socket_flags) { + return bind_to_port(host, port, socket_flags) && listen_internal(); +} + +inline bool Server::is_running() const { return is_running_; } + +inline void Server::stop() { + if (is_running_) { + assert(svr_sock_ != INVALID_SOCKET); + std::atomic sock(svr_sock_.exchange(INVALID_SOCKET)); + detail::shutdown_socket(sock); + detail::close_socket(sock); + } +} + +inline bool Server::parse_request_line(const char *s, Request &req) { + static std::regex re( + "(GET|HEAD|POST|PUT|DELETE|CONNECT|OPTIONS|TRACE|PATCH|PRI) " + "(([^?]+)(?:\\?(.*?))?) (HTTP/1\\.[01])\r\n"); + + std::cmatch m; + if (std::regex_match(s, m, re)) { + req.version = std::string(m[5]); + req.method = std::string(m[1]); + req.target = std::string(m[2]); + req.path = detail::decode_url(m[3]); + + // Parse query text + auto len = std::distance(m[4].first, m[4].second); + if (len > 0) { detail::parse_query_text(m[4], req.params); } + + return true; + } + + return false; +} + +inline bool Server::write_response(Stream &strm, bool last_connection, + const Request &req, Response &res) { + assert(res.status != -1); + + if (400 <= res.status && error_handler_) { error_handler_(req, res); } + + // Response line + if (!strm.write_format("HTTP/1.1 %d %s\r\n", res.status, + detail::status_message(res.status))) { + return false; + } + + // Headers + if (last_connection || req.get_header_value("Connection") == "close") { + res.set_header("Connection", "close"); + } + + if (!last_connection && req.get_header_value("Connection") == "Keep-Alive") { + res.set_header("Connection", "Keep-Alive"); + } + + if (!res.has_header("Content-Type")) { + res.set_header("Content-Type", "text/plain"); + } + + if (!res.has_header("Accept-Ranges")) { + res.set_header("Accept-Ranges", "bytes"); + } + + std::string content_type; + std::string boundary; + + if (req.ranges.size() > 1) { + boundary = detail::make_multipart_data_boundary(); + + auto it = res.headers.find("Content-Type"); + if (it != res.headers.end()) { + content_type = it->second; + res.headers.erase(it); + } + + res.headers.emplace("Content-Type", + "multipart/byteranges; boundary=" + boundary); + } + + if (res.body.empty()) { + if (res.content_length > 0) { + size_t length = 0; + if (req.ranges.empty()) { + length = res.content_length; + } else if (req.ranges.size() == 1) { + auto offsets = + detail::get_range_offset_and_length(req, res.content_length, 0); + auto offset = offsets.first; + length = offsets.second; + auto content_range = detail::make_content_range_header_field( + offset, length, res.content_length); + res.set_header("Content-Range", content_range); + } else { + length = detail::get_multipart_ranges_data_length(req, res, boundary, + content_type); + } + res.set_header("Content-Length", std::to_string(length)); + } else { + if (res.content_provider) { + res.set_header("Transfer-Encoding", "chunked"); + } else { + res.set_header("Content-Length", "0"); + } + } + } else { + if (req.ranges.empty()) { + ; + } else if (req.ranges.size() == 1) { + auto offsets = + detail::get_range_offset_and_length(req, res.body.size(), 0); + auto offset = offsets.first; + auto length = offsets.second; + auto content_range = detail::make_content_range_header_field( + offset, length, res.body.size()); + res.set_header("Content-Range", content_range); + res.body = res.body.substr(offset, length); + } else { + res.body = + detail::make_multipart_ranges_data(req, res, boundary, content_type); + } + +#ifdef CPPHTTPLIB_ZLIB_SUPPORT + // TODO: 'Accpet-Encoding' has gzip, not gzip;q=0 + const auto &encodings = req.get_header_value("Accept-Encoding"); + if (encodings.find("gzip") != std::string::npos && + detail::can_compress(res.get_header_value("Content-Type"))) { + if (detail::compress(res.body)) { + res.set_header("Content-Encoding", "gzip"); + } + } +#endif + + auto length = std::to_string(res.body.size()); + res.set_header("Content-Length", length); + } + + if (!detail::write_headers(strm, res, Headers())) { return false; } + + // Body + if (req.method != "HEAD") { + if (!res.body.empty()) { + if (!strm.write(res.body)) { return false; } + } else if (res.content_provider) { + if (!write_content_with_provider(strm, req, res, boundary, + content_type)) { + return false; + } + } + } + + // Log + if (logger_) { logger_(req, res); } + + return true; +} + +inline bool +Server::write_content_with_provider(Stream &strm, const Request &req, + Response &res, const std::string &boundary, + const std::string &content_type) { + if (res.content_length) { + if (req.ranges.empty()) { + if (detail::write_content(strm, res.content_provider, 0, + res.content_length) < 0) { + return false; + } + } else if (req.ranges.size() == 1) { + auto offsets = + detail::get_range_offset_and_length(req, res.content_length, 0); + auto offset = offsets.first; + auto length = offsets.second; + if (detail::write_content(strm, res.content_provider, offset, length) < + 0) { + return false; + } + } else { + if (!detail::write_multipart_ranges_data(strm, req, res, boundary, + content_type)) { + return false; + } + } + } else { + if (detail::write_content_chunked(strm, res.content_provider) < 0) { + return false; + } + } + return true; +} + +inline bool Server::read_content(Stream &strm, bool last_connection, + Request &req, Response &res) { + if (!detail::read_content(strm, req, payload_max_length_, res.status, + Progress(), [&](const char *buf, size_t n) { + if (req.body.size() + n > req.body.max_size()) { + return false; + } + req.body.append(buf, n); + return true; + })) { + return write_response(strm, last_connection, req, res); + } + + const auto &content_type = req.get_header_value("Content-Type"); + + if (!content_type.find("application/x-www-form-urlencoded")) { + detail::parse_query_text(req.body, req.params); + } else if (!content_type.find("multipart/form-data")) { + std::string boundary; + if (!detail::parse_multipart_boundary(content_type, boundary) || + !detail::parse_multipart_formdata(boundary, req.body, req.files)) { + res.status = 400; + return write_response(strm, last_connection, req, res); + } + } + + return true; +} + +inline bool +Server::read_content_with_content_receiver(Stream &strm, bool last_connection, + Request &req, Response &res, + ContentReceiver receiver) { + if (!detail::read_content( + strm, req, payload_max_length_, res.status, Progress(), + [&](const char *buf, size_t n) { return receiver(buf, n); })) { + return write_response(strm, last_connection, req, res); + } + + return true; +} + +inline bool Server::handle_file_request(Request &req, Response &res) { + for (const auto& kv: base_dirs_) { + const auto& mount_point = kv.first; + const auto& base_dir = kv.second; + + // Prefix match + if (!req.path.find(mount_point)) { + std::string sub_path = "/" + req.path.substr(mount_point.size()); + if (detail::is_valid_path(sub_path)) { + auto path = base_dir + sub_path; + if (path.back() == '/') { path += "index.html"; } + + if (detail::is_file(path)) { + detail::read_file(path, res.body); + auto type = detail::find_content_type(path); + if (type) { res.set_header("Content-Type", type); } + res.status = 200; + if (file_request_handler_) { file_request_handler_(req, res); } + return true; + } + } + } + } + return false; +} + +inline socket_t Server::create_server_socket(const char *host, int port, + int socket_flags) const { + return detail::create_socket( + host, port, + [](socket_t sock, struct addrinfo &ai) -> bool { + if (::bind(sock, ai.ai_addr, static_cast(ai.ai_addrlen))) { + return false; + } + if (::listen(sock, 5)) { // Listen through 5 channels + return false; + } + return true; + }, + socket_flags); +} + +inline int Server::bind_internal(const char *host, int port, int socket_flags) { + if (!is_valid()) { return -1; } + + svr_sock_ = create_server_socket(host, port, socket_flags); + if (svr_sock_ == INVALID_SOCKET) { return -1; } + + if (port == 0) { + struct sockaddr_storage address; + socklen_t len = sizeof(address); + if (getsockname(svr_sock_, reinterpret_cast(&address), + &len) == -1) { + return -1; + } + if (address.ss_family == AF_INET) { + return ntohs(reinterpret_cast(&address)->sin_port); + } else if (address.ss_family == AF_INET6) { + return ntohs(reinterpret_cast(&address)->sin6_port); + } else { + return -1; + } + } else { + return port; + } +} + +inline bool Server::listen_internal() { + auto ret = true; + is_running_ = true; + + { + std::unique_ptr task_queue(new_task_queue()); + + for (;;) { + if (svr_sock_ == INVALID_SOCKET) { + // The server socket was closed by 'stop' method. + break; + } + + auto val = detail::select_read(svr_sock_, 0, 100000); + + if (val == 0) { // Timeout + continue; + } + + socket_t sock = accept(svr_sock_, nullptr, nullptr); + + if (sock == INVALID_SOCKET) { + if (errno == EMFILE) { + // The per-process limit of open file descriptors has been reached. + // Try to accept new connections after a short sleep. + std::this_thread::sleep_for(std::chrono::milliseconds(1)); + continue; + } + if (svr_sock_ != INVALID_SOCKET) { + detail::close_socket(svr_sock_); + ret = false; + } else { + ; // The server socket was closed by user. + } + break; + } + + task_queue->enqueue([=]() { process_and_close_socket(sock); }); + } + + task_queue->shutdown(); + } + + is_running_ = false; + return ret; +} + +inline bool Server::routing(Request &req, Response &res, Stream &strm, bool last_connection) { + // File handler + if (req.method == "GET" && handle_file_request(req, res)) { return true; } + + // Content reader handler + if (req.method == "POST" || req.method == "PUT" || req.method == "PATCH") { + ContentReader content_reader = [&](ContentReceiver receiver) { + return read_content_with_content_receiver(strm, last_connection, req, res, receiver); + }; + + if (req.method == "POST") { + if (dispatch_request_for_content_reader(req, res, content_reader, + post_handlers_for_content_reader)) { + return true; + } + } else if (req.method == "PUT") { + if (dispatch_request_for_content_reader(req, res, content_reader, + put_handlers_for_content_reader)) { + return true; + } + } else if (req.method == "PATCH") { + if (dispatch_request_for_content_reader( + req, res, content_reader, patch_handlers_for_content_reader)) { + return true; + } + } + } + + // Read content into `req.body` + if (req.method == "POST" || req.method == "PUT" || req.method == "PATCH" || req.method == "PRI") { + if (!read_content(strm, last_connection, req, res)) { + return false; + } + } + + // Regular handler + if (req.method == "GET" || req.method == "HEAD") { + return dispatch_request(req, res, get_handlers_); + } else if (req.method == "POST") { + return dispatch_request(req, res, post_handlers_); + } else if (req.method == "PUT") { + return dispatch_request(req, res, put_handlers_); + } else if (req.method == "DELETE") { + return dispatch_request(req, res, delete_handlers_); + } else if (req.method == "OPTIONS") { + return dispatch_request(req, res, options_handlers_); + } else if (req.method == "PATCH") { + return dispatch_request(req, res, patch_handlers_); + } + + res.status = 400; + return false; +} + +inline bool Server::dispatch_request(Request &req, Response &res, + Handlers &handlers) { + for (const auto &x : handlers) { + const auto &pattern = x.first; + const auto &handler = x.second; + + if (std::regex_match(req.path, req.matches, pattern)) { + handler(req, res); + return true; + } + } + return false; +} + +inline bool +Server::dispatch_request_for_content_reader(Request &req, Response &res, + ContentReader content_reader, + HandersForContentReader &handlers) { + for (const auto &x : handlers) { + const auto &pattern = x.first; + const auto &handler = x.second; + + if (std::regex_match(req.path, req.matches, pattern)) { + handler(req, res, content_reader); + return true; + } + } + return false; +} + +inline bool +Server::process_request(Stream &strm, bool last_connection, + bool &connection_close, + const std::function& setup_request) { + std::array buf{}; + + detail::stream_line_reader line_reader(strm, buf.data(), buf.size()); + + // Connection has been closed on client + if (!line_reader.getline()) { return false; } + + Request req; + Response res; + + res.version = "HTTP/1.1"; + + // Check if the request URI doesn't exceed the limit + if (line_reader.size() > CPPHTTPLIB_REQUEST_URI_MAX_LENGTH) { + Headers dummy; + detail::read_headers(strm, dummy); + res.status = 414; + return write_response(strm, last_connection, req, res); + } + + // Request line and headers + if (!parse_request_line(line_reader.ptr(), req) || + !detail::read_headers(strm, req.headers)) { + res.status = 400; + return write_response(strm, last_connection, req, res); + } + + if (req.get_header_value("Connection") == "close") { + connection_close = true; + } + + if (req.version == "HTTP/1.0" && + req.get_header_value("Connection") != "Keep-Alive") { + connection_close = true; + } + + req.set_header("REMOTE_ADDR", strm.get_remote_addr()); + + if (req.has_header("Range")) { + const auto &range_header_value = req.get_header_value("Range"); + if (!detail::parse_range_header(range_header_value, req.ranges)) { + // TODO: error + } + } + + if (setup_request) { setup_request(req); } + + // Rounting + if (routing(req, res, strm, last_connection)) { + if (res.status == -1) { res.status = req.ranges.empty() ? 200 : 206; } + } else { + if (res.status == -1) { res.status = 404; } + } + + return write_response(strm, last_connection, req, res); +} + +inline bool Server::is_valid() const { return true; } + +inline bool Server::process_and_close_socket(socket_t sock) { + return detail::process_and_close_socket( + false, sock, keep_alive_max_count_, read_timeout_sec_, read_timeout_usec_, + [this](Stream &strm, bool last_connection, bool &connection_close) { + return process_request(strm, last_connection, connection_close, + nullptr); + }); +} + +// HTTP client implementation +inline Client::Client(const char *host, int port, time_t timeout_sec) + : host_(host), port_(port), timeout_sec_(timeout_sec), + host_and_port_(host_ + ":" + std::to_string(port_)), + keep_alive_max_count_(CPPHTTPLIB_KEEPALIVE_MAX_COUNT), + read_timeout_sec_(CPPHTTPLIB_READ_TIMEOUT_SECOND), + read_timeout_usec_(CPPHTTPLIB_READ_TIMEOUT_USECOND), + follow_location_(false) {} + +inline Client::~Client() {} + +inline bool Client::is_valid() const { return true; } + +inline socket_t Client::create_client_socket() const { + return detail::create_socket( + host_.c_str(), port_, [=](socket_t sock, struct addrinfo &ai) -> bool { + detail::set_nonblocking(sock, true); + + auto ret = connect(sock, ai.ai_addr, static_cast(ai.ai_addrlen)); + if (ret < 0) { + if (detail::is_connection_error() || + !detail::wait_until_socket_is_ready(sock, timeout_sec_, 0)) { + detail::close_socket(sock); + return false; + } + } + + detail::set_nonblocking(sock, false); + return true; + }); +} + +inline bool Client::read_response_line(Stream &strm, Response &res) { + std::array buf; + + detail::stream_line_reader line_reader(strm, buf.data(), buf.size()); + + if (!line_reader.getline()) { return false; } + + const static std::regex re("(HTTP/1\\.[01]) (\\d+?) .*\r\n"); + + std::cmatch m; + if (std::regex_match(line_reader.ptr(), m, re)) { + res.version = std::string(m[1]); + res.status = std::stoi(std::string(m[2])); + } + + return true; +} + +inline bool Client::send(const Request &req, Response &res) { + if (req.path.empty()) { return false; } + + auto sock = create_client_socket(); + if (sock == INVALID_SOCKET) { return false; } + + auto ret = process_and_close_socket( + sock, 1, [&](Stream &strm, bool last_connection, bool &connection_close) { + return process_request(strm, req, res, last_connection, + connection_close); + }); + + if (ret && follow_location_ && (300 < res.status && res.status < 400)) { + ret = redirect(req, res); + } + + return ret; +} + +inline bool Client::send(const std::vector &requests, + std::vector &responses) { + size_t i = 0; + while (i < requests.size()) { + auto sock = create_client_socket(); + if (sock == INVALID_SOCKET) { return false; } + + if (!process_and_close_socket( + sock, requests.size() - i, + [&](Stream &strm, bool last_connection, + bool &connection_close) -> bool { + auto &req = requests[i]; + auto res = Response(); + i++; + + if (req.path.empty()) { return false; } + auto ret = process_request(strm, req, res, last_connection, + connection_close); + + if (ret && follow_location_ && + (300 < res.status && res.status < 400)) { + ret = redirect(req, res); + } + + if (ret) { responses.emplace_back(std::move(res)); } + + return ret; + })) { + return false; + } + } + + return true; +} + +inline bool Client::redirect(const Request &req, Response &res) { + if (req.redirect_count == 0) { return false; } + + auto location = res.get_header_value("location"); + if (location.empty()) { return false; } + + std::regex re( + R"(^(?:([^:/?#]+):)?(?://([^/?#]*))?([^?#]*(?:\?[^#]*)?)(?:#.*)?)"); + + auto scheme = is_ssl() ? "https" : "http"; + + std::smatch m; + if (regex_match(location, m, re)) { + auto next_scheme = m[1].str(); + auto next_host = m[2].str(); + auto next_path = m[3].str(); + if (next_host.empty()) { next_host = host_; } + if (next_path.empty()) { next_path = "/"; } + + if (next_scheme == scheme && next_host == host_) { + return detail::redirect(*this, req, res, next_path); + } else { + if (next_scheme == "https") { +#ifdef CPPHTTPLIB_OPENSSL_SUPPORT + SSLClient cli(next_host.c_str()); + cli.follow_location(true); + return detail::redirect(cli, req, res, next_path); +#else + return false; +#endif + } else { + Client cli(next_host.c_str()); + cli.follow_location(true); + return detail::redirect(cli, req, res, next_path); + } + } + } + return false; +} + +inline void Client::write_request(Stream &strm, const Request &req, + bool last_connection) { + BufferStream bstrm; + + // Request line + auto path = detail::encode_url(req.path); + + bstrm.write_format("%s %s HTTP/1.1\r\n", req.method.c_str(), path.c_str()); + + // Additonal headers + Headers headers; + if (last_connection) { headers.emplace("Connection", "close"); } + + if (!req.has_header("Host")) { + if (is_ssl()) { + if (port_ == 443) { + headers.emplace("Host", host_); + } else { + headers.emplace("Host", host_and_port_); + } + } else { + if (port_ == 80) { + headers.emplace("Host", host_); + } else { + headers.emplace("Host", host_and_port_); + } + } + } + + if (!req.has_header("Accept")) { headers.emplace("Accept", "*/*"); } + + if (!req.has_header("User-Agent")) { + headers.emplace("User-Agent", "cpp-httplib/0.2"); + } + + if (req.body.empty()) { + if (req.content_provider) { + auto length = std::to_string(req.content_length); + headers.emplace("Content-Length", length); + } else { + headers.emplace("Content-Length", "0"); + } + } else { + if (!req.has_header("Content-Type")) { + headers.emplace("Content-Type", "text/plain"); + } + + if (!req.has_header("Content-Length")) { + auto length = std::to_string(req.body.size()); + headers.emplace("Content-Length", length); + } + } + + detail::write_headers(bstrm, req, headers); + + // Flush buffer + auto &data = bstrm.get_buffer(); + strm.write(data.data(), data.size()); + + // Body + if (req.body.empty()) { + if (req.content_provider) { + size_t offset = 0; + size_t end_offset = req.content_length; + while (offset < end_offset) { + req.content_provider(offset, end_offset - offset, + [&](const char *d, size_t l) { + auto written_length = strm.write(d, l); + offset += written_length; + }); + } + } + } else { + strm.write(req.body); + } +} + +inline std::shared_ptr Client::send_with_content_provider( + const char *method, const char *path, const Headers &headers, + const std::string &body, size_t content_length, + ContentProvider content_provider, const char *content_type, bool compress) { +#ifndef CPPHTTPLIB_ZLIB_SUPPORT + (void)compress; +#endif + + Request req; + req.method = method; + req.headers = headers; + req.path = path; + + req.headers.emplace("Content-Type", content_type); + +#ifdef CPPHTTPLIB_ZLIB_SUPPORT + if (compress) { + if (content_provider) { + size_t offset = 0; + while (offset < content_length) { + content_provider(offset, content_length - offset, + [&](const char *data, size_t data_len) { + req.body.append(data, data_len); + offset += data_len; + }); + } + } else { + req.body = body; + } + + if (!detail::compress(req.body)) { return nullptr; } + req.headers.emplace("Content-Encoding", "gzip"); + } else +#endif + { + if (content_provider) { + req.content_length = content_length; + req.content_provider = content_provider; + } else { + req.body = body; + } + } + + auto res = std::make_shared(); + + return send(req, *res) ? res : nullptr; +} + +inline bool Client::process_request(Stream &strm, const Request &req, + Response &res, bool last_connection, + bool &connection_close) { + // Send request + write_request(strm, req, last_connection); + + // Receive response and headers + if (!read_response_line(strm, res) || + !detail::read_headers(strm, res.headers)) { + return false; + } + + if (res.get_header_value("Connection") == "close" || + res.version == "HTTP/1.0") { + connection_close = true; + } + + if (req.response_handler) { + if (!req.response_handler(res)) { return false; } + } + + // Body + if (req.method != "HEAD") { + ContentReceiver out = [&](const char *buf, size_t n) { + if (res.body.size() + n > res.body.max_size()) { return false; } + res.body.append(buf, n); + return true; + }; + + if (req.content_receiver) { + out = [&](const char *buf, size_t n) { + return req.content_receiver(buf, n); + }; + } + + int dummy_status; + if (!detail::read_content(strm, res, std::numeric_limits::max(), + dummy_status, req.progress, out)) { + return false; + } + } + + return true; +} + +inline bool Client::process_and_close_socket( + socket_t sock, size_t request_count, + std::function + callback) { + request_count = std::min(request_count, keep_alive_max_count_); + return detail::process_and_close_socket(true, sock, request_count, + read_timeout_sec_, read_timeout_usec_, + callback); +} + +inline bool Client::is_ssl() const { return false; } + +inline std::shared_ptr Client::Get(const char *path) { + Progress dummy; + return Get(path, Headers(), dummy); +} + +inline std::shared_ptr Client::Get(const char *path, + Progress progress) { + return Get(path, Headers(), std::move(progress)); +} + +inline std::shared_ptr Client::Get(const char *path, + const Headers &headers) { + Progress dummy; + return Get(path, headers, dummy); +} + +inline std::shared_ptr +Client::Get(const char *path, const Headers &headers, Progress progress) { + Request req; + req.method = "GET"; + req.path = path; + req.headers = headers; + req.progress = std::move(progress); + + auto res = std::make_shared(); + return send(req, *res) ? res : nullptr; +} + +inline std::shared_ptr Client::Get(const char *path, + ContentReceiver content_receiver) { + Progress dummy; + return Get(path, Headers(), nullptr, std::move(content_receiver), dummy); +} + +inline std::shared_ptr Client::Get(const char *path, + ContentReceiver content_receiver, + Progress progress) { + return Get(path, Headers(), nullptr, std::move(content_receiver), progress); +} + +inline std::shared_ptr Client::Get(const char *path, + const Headers &headers, + ContentReceiver content_receiver) { + Progress dummy; + return Get(path, headers, nullptr, std::move(content_receiver), dummy); +} + +inline std::shared_ptr Client::Get(const char *path, + const Headers &headers, + ContentReceiver content_receiver, + Progress progress) { + return Get(path, headers, nullptr, std::move(content_receiver), progress); +} + +inline std::shared_ptr Client::Get(const char *path, + const Headers &headers, + ResponseHandler response_handler, + ContentReceiver content_receiver) { + Progress dummy; + return Get(path, headers, std::move(response_handler), content_receiver, dummy); +} + +inline std::shared_ptr Client::Get(const char *path, + const Headers &headers, + ResponseHandler response_handler, + ContentReceiver content_receiver, + Progress progress) { + Request req; + req.method = "GET"; + req.path = path; + req.headers = headers; + req.response_handler = std::move(response_handler); + req.content_receiver = std::move(content_receiver); + req.progress = std::move(progress); + + auto res = std::make_shared(); + return send(req, *res) ? res : nullptr; +} + +inline std::shared_ptr Client::Head(const char *path) { + return Head(path, Headers()); +} + +inline std::shared_ptr Client::Head(const char *path, + const Headers &headers) { + Request req; + req.method = "HEAD"; + req.headers = headers; + req.path = path; + + auto res = std::make_shared(); + + return send(req, *res) ? res : nullptr; +} + +inline std::shared_ptr Client::Post(const char *path, + const std::string &body, + const char *content_type, + bool compress) { + return Post(path, Headers(), body, content_type, compress); +} + +inline std::shared_ptr +Client::Post(const char *path, const Headers &headers, const std::string &body, + const char *content_type, bool compress) { + return send_with_content_provider("POST", path, headers, body, 0, nullptr, + content_type, compress); +} + +inline std::shared_ptr +Client::Post(const char *path, const Params ¶ms, bool compress) { + return Post(path, Headers(), params, compress); +} + +inline std::shared_ptr Client::Post(const char *path, + size_t content_length, + ContentProvider content_provider, + const char *content_type, + bool compress) { + return Post(path, Headers(), content_length, content_provider, content_type, + compress); +} + +inline std::shared_ptr +Client::Post(const char *path, const Headers &headers, size_t content_length, + ContentProvider content_provider, const char *content_type, + bool compress) { + return send_with_content_provider("POST", path, headers, std::string(), + content_length, content_provider, + content_type, compress); +} + +inline std::shared_ptr Client::Post(const char *path, + const Headers &headers, + const Params ¶ms, + bool compress) { + std::string query; + for (auto it = params.begin(); it != params.end(); ++it) { + if (it != params.begin()) { query += "&"; } + query += it->first; + query += "="; + query += detail::encode_url(it->second); + } + + return Post(path, headers, query, "application/x-www-form-urlencoded", + compress); +} + +inline std::shared_ptr +Client::Post(const char *path, const MultipartFormDataItems &items, + bool compress) { + return Post(path, Headers(), items, compress); +} + +inline std::shared_ptr +Client::Post(const char *path, const Headers &headers, + const MultipartFormDataItems &items, bool compress) { + auto boundary = detail::make_multipart_data_boundary(); + + std::string body; + + for (const auto &item : items) { + body += "--" + boundary + "\r\n"; + body += "Content-Disposition: form-data; name=\"" + item.name + "\""; + if (!item.filename.empty()) { + body += "; filename=\"" + item.filename + "\""; + } + body += "\r\n"; + if (!item.content_type.empty()) { + body += "Content-Type: " + item.content_type + "\r\n"; + } + body += "\r\n"; + body += item.content + "\r\n"; + } + + body += "--" + boundary + "--\r\n"; + + std::string content_type = "multipart/form-data; boundary=" + boundary; + return Post(path, headers, body, content_type.c_str(), compress); +} + +inline std::shared_ptr Client::Put(const char *path, + const std::string &body, + const char *content_type, + bool compress) { + return Put(path, Headers(), body, content_type, compress); +} + +inline std::shared_ptr +Client::Put(const char *path, const Headers &headers, const std::string &body, + const char *content_type, bool compress) { + return send_with_content_provider("PUT", path, headers, body, 0, nullptr, + content_type, compress); +} + +inline std::shared_ptr Client::Put(const char *path, + size_t content_length, + ContentProvider content_provider, + const char *content_type, + bool compress) { + return Put(path, Headers(), content_length, content_provider, content_type, + compress); +} + +inline std::shared_ptr +Client::Put(const char *path, const Headers &headers, size_t content_length, + ContentProvider content_provider, const char *content_type, + bool compress) { + return send_with_content_provider("PUT", path, headers, std::string(), + content_length, content_provider, + content_type, compress); +} + +inline std::shared_ptr Client::Patch(const char *path, + const std::string &body, + const char *content_type, + bool compress) { + return Patch(path, Headers(), body, content_type, compress); +} + +inline std::shared_ptr +Client::Patch(const char *path, const Headers &headers, const std::string &body, + const char *content_type, bool compress) { + return send_with_content_provider("PATCH", path, headers, body, 0, nullptr, + content_type, compress); +} + +inline std::shared_ptr Client::Patch(const char *path, + size_t content_length, + ContentProvider content_provider, + const char *content_type, + bool compress) { + return Patch(path, Headers(), content_length, content_provider, content_type, + compress); +} + +inline std::shared_ptr +Client::Patch(const char *path, const Headers &headers, size_t content_length, + ContentProvider content_provider, const char *content_type, + bool compress) { + return send_with_content_provider("PATCH", path, headers, std::string(), + content_length, content_provider, + content_type, compress); +} + +inline std::shared_ptr Client::Delete(const char *path) { + return Delete(path, Headers(), std::string(), nullptr); +} + +inline std::shared_ptr Client::Delete(const char *path, + const std::string &body, + const char *content_type) { + return Delete(path, Headers(), body, content_type); +} + +inline std::shared_ptr Client::Delete(const char *path, + const Headers &headers) { + return Delete(path, headers, std::string(), nullptr); +} + +inline std::shared_ptr Client::Delete(const char *path, + const Headers &headers, + const std::string &body, + const char *content_type) { + Request req; + req.method = "DELETE"; + req.headers = headers; + req.path = path; + + if (content_type) { req.headers.emplace("Content-Type", content_type); } + req.body = body; + + auto res = std::make_shared(); + + return send(req, *res) ? res : nullptr; +} + +inline std::shared_ptr Client::Options(const char *path) { + return Options(path, Headers()); +} + +inline std::shared_ptr Client::Options(const char *path, + const Headers &headers) { + Request req; + req.method = "OPTIONS"; + req.path = path; + req.headers = headers; + + auto res = std::make_shared(); + + return send(req, *res) ? res : nullptr; +} + +inline void Client::set_keep_alive_max_count(size_t count) { + keep_alive_max_count_ = count; +} + +inline void Client::set_read_timeout(time_t sec, time_t usec) { + read_timeout_sec_ = sec; + read_timeout_usec_ = usec; +} + +inline void Client::follow_location(bool on) { follow_location_ = on; } + +/* + * SSL Implementation + */ +#ifdef CPPHTTPLIB_OPENSSL_SUPPORT +namespace detail { + +template +inline bool process_and_close_socket_ssl( + bool is_client_request, socket_t sock, size_t keep_alive_max_count, + time_t read_timeout_sec, time_t read_timeout_usec, SSL_CTX *ctx, + std::mutex &ctx_mutex, U SSL_connect_or_accept, V setup, T callback) { + assert(keep_alive_max_count > 0); + + SSL *ssl = nullptr; + { + std::lock_guard guard(ctx_mutex); + ssl = SSL_new(ctx); + } + + if (!ssl) { + close_socket(sock); + return false; + } + + auto bio = BIO_new_socket(static_cast(sock), BIO_NOCLOSE); + SSL_set_bio(ssl, bio, bio); + + if (!setup(ssl)) { + SSL_shutdown(ssl); + { + std::lock_guard guard(ctx_mutex); + SSL_free(ssl); + } + + close_socket(sock); + return false; + } + + bool ret = false; + + if (SSL_connect_or_accept(ssl) == 1) { + if (keep_alive_max_count > 1) { + auto count = keep_alive_max_count; + while (count > 0 && + (is_client_request || + detail::select_read(sock, CPPHTTPLIB_KEEPALIVE_TIMEOUT_SECOND, + CPPHTTPLIB_KEEPALIVE_TIMEOUT_USECOND) > 0)) { + SSLSocketStream strm(sock, ssl, read_timeout_sec, read_timeout_usec); + auto last_connection = count == 1; + auto connection_close = false; + + ret = callback(ssl, strm, last_connection, connection_close); + if (!ret || connection_close) { break; } + + count--; + } + } else { + SSLSocketStream strm(sock, ssl, read_timeout_sec, read_timeout_usec); + auto dummy_connection_close = false; + ret = callback(ssl, strm, true, dummy_connection_close); + } + } + + SSL_shutdown(ssl); + { + std::lock_guard guard(ctx_mutex); + SSL_free(ssl); + } + + close_socket(sock); + + return ret; +} + +#if OPENSSL_VERSION_NUMBER < 0x10100000L +static std::shared_ptr> openSSL_locks_; + +class SSLThreadLocks { +public: + SSLThreadLocks() { + openSSL_locks_ = + std::make_shared>(CRYPTO_num_locks()); + CRYPTO_set_locking_callback(locking_callback); + } + + ~SSLThreadLocks() { CRYPTO_set_locking_callback(nullptr); } + +private: + static void locking_callback(int mode, int type, const char * /*file*/, + int /*line*/) { + auto &locks = *openSSL_locks_; + if (mode & CRYPTO_LOCK) { + locks[type].lock(); + } else { + locks[type].unlock(); + } + } +}; + +#endif + +class SSLInit { +public: + SSLInit() { +#if OPENSSL_VERSION_NUMBER < 0x1010001fL + SSL_load_error_strings(); + SSL_library_init(); +#else + OPENSSL_init_ssl( + OPENSSL_INIT_LOAD_SSL_STRINGS | OPENSSL_INIT_LOAD_CRYPTO_STRINGS, NULL); +#endif + } + + ~SSLInit() { +#if OPENSSL_VERSION_NUMBER < 0x1010001fL + ERR_free_strings(); +#endif + } + +private: +#if OPENSSL_VERSION_NUMBER < 0x10100000L + SSLThreadLocks thread_init_; +#endif +}; + +static SSLInit sslinit_; + +} // namespace detail + +// SSL socket stream implementation +inline SSLSocketStream::SSLSocketStream(socket_t sock, SSL *ssl, + time_t read_timeout_sec, + time_t read_timeout_usec) + : sock_(sock), ssl_(ssl), read_timeout_sec_(read_timeout_sec), + read_timeout_usec_(read_timeout_usec) {} + +inline SSLSocketStream::~SSLSocketStream() {} + +inline int SSLSocketStream::read(char *ptr, size_t size) { + if (SSL_pending(ssl_) > 0 || + detail::select_read(sock_, read_timeout_sec_, read_timeout_usec_) > 0) { + return SSL_read(ssl_, ptr, static_cast(size)); + } + return -1; +} + +inline int SSLSocketStream::write(const char *ptr, size_t size) { + return SSL_write(ssl_, ptr, static_cast(size)); +} + +inline int SSLSocketStream::write(const char *ptr) { + return write(ptr, strlen(ptr)); +} + +inline int SSLSocketStream::write(const std::string &s) { + return write(s.data(), s.size()); +} + +inline std::string SSLSocketStream::get_remote_addr() const { + return detail::get_remote_addr(sock_); +} + +// SSL HTTP server implementation +inline SSLServer::SSLServer(const char *cert_path, const char *private_key_path, + const char *client_ca_cert_file_path, + const char *client_ca_cert_dir_path) { + ctx_ = SSL_CTX_new(SSLv23_server_method()); + + if (ctx_) { + SSL_CTX_set_options(ctx_, + SSL_OP_ALL | SSL_OP_NO_SSLv2 | SSL_OP_NO_SSLv3 | + SSL_OP_NO_COMPRESSION | + SSL_OP_NO_SESSION_RESUMPTION_ON_RENEGOTIATION); + + // auto ecdh = EC_KEY_new_by_curve_name(NID_X9_62_prime256v1); + // SSL_CTX_set_tmp_ecdh(ctx_, ecdh); + // EC_KEY_free(ecdh); + + if (SSL_CTX_use_certificate_chain_file(ctx_, cert_path) != 1 || + SSL_CTX_use_PrivateKey_file(ctx_, private_key_path, SSL_FILETYPE_PEM) != + 1) { + SSL_CTX_free(ctx_); + ctx_ = nullptr; + } else if (client_ca_cert_file_path || client_ca_cert_dir_path) { + // if (client_ca_cert_file_path) { + // auto list = SSL_load_client_CA_file(client_ca_cert_file_path); + // SSL_CTX_set_client_CA_list(ctx_, list); + // } + + SSL_CTX_load_verify_locations(ctx_, client_ca_cert_file_path, + client_ca_cert_dir_path); + + SSL_CTX_set_verify( + ctx_, + SSL_VERIFY_PEER | + SSL_VERIFY_FAIL_IF_NO_PEER_CERT, // SSL_VERIFY_CLIENT_ONCE, + nullptr); + } + } +} + +inline SSLServer::~SSLServer() { + if (ctx_) { SSL_CTX_free(ctx_); } +} + +inline bool SSLServer::is_valid() const { return ctx_; } + +inline bool SSLServer::process_and_close_socket(socket_t sock) { + return detail::process_and_close_socket_ssl( + false, sock, keep_alive_max_count_, read_timeout_sec_, read_timeout_usec_, + ctx_, ctx_mutex_, SSL_accept, [](SSL * /*ssl*/) { return true; }, + [this](SSL *ssl, Stream &strm, bool last_connection, + bool &connection_close) { + return process_request(strm, last_connection, connection_close, + [&](Request &req) { req.ssl = ssl; }); + }); +} + +// SSL HTTP client implementation +inline SSLClient::SSLClient(const char *host, int port, time_t timeout_sec, + const char *client_cert_path, + const char *client_key_path) + : Client(host, port, timeout_sec) { + ctx_ = SSL_CTX_new(SSLv23_client_method()); + + detail::split(&host_[0], &host_[host_.size()], '.', + [&](const char *b, const char *e) { + host_components_.emplace_back(std::string(b, e)); + }); + if (client_cert_path && client_key_path) { + if (SSL_CTX_use_certificate_file(ctx_, client_cert_path, + SSL_FILETYPE_PEM) != 1 || + SSL_CTX_use_PrivateKey_file(ctx_, client_key_path, SSL_FILETYPE_PEM) != + 1) { + SSL_CTX_free(ctx_); + ctx_ = nullptr; + } + } +} + +inline SSLClient::~SSLClient() { + if (ctx_) { SSL_CTX_free(ctx_); } +} + +inline bool SSLClient::is_valid() const { return ctx_; } + +inline void SSLClient::set_ca_cert_path(const char *ca_cert_file_path, + const char *ca_cert_dir_path) { + if (ca_cert_file_path) { ca_cert_file_path_ = ca_cert_file_path; } + if (ca_cert_dir_path) { ca_cert_dir_path_ = ca_cert_dir_path; } +} + +inline void SSLClient::enable_server_certificate_verification(bool enabled) { + server_certificate_verification_ = enabled; +} + +inline long SSLClient::get_openssl_verify_result() const { + return verify_result_; +} + +inline SSL_CTX *SSLClient::ssl_context() const noexcept { return ctx_; } + +inline bool SSLClient::process_and_close_socket( + socket_t sock, size_t request_count, + std::function + callback) { + + request_count = std::min(request_count, keep_alive_max_count_); + + return is_valid() && + detail::process_and_close_socket_ssl( + true, sock, request_count, read_timeout_sec_, read_timeout_usec_, + ctx_, ctx_mutex_, + [&](SSL *ssl) { + if (ca_cert_file_path_.empty()) { + SSL_CTX_set_verify(ctx_, SSL_VERIFY_NONE, nullptr); + } else { + if (!SSL_CTX_load_verify_locations( + ctx_, ca_cert_file_path_.c_str(), nullptr)) { + return false; + } + SSL_CTX_set_verify(ctx_, SSL_VERIFY_PEER, nullptr); + } + + if (SSL_connect(ssl) != 1) { return false; } + + if (server_certificate_verification_) { + verify_result_ = SSL_get_verify_result(ssl); + + if (verify_result_ != X509_V_OK) { return false; } + + auto server_cert = SSL_get_peer_certificate(ssl); + + if (server_cert == nullptr) { return false; } + + if (!verify_host(server_cert)) { + X509_free(server_cert); + return false; + } + X509_free(server_cert); + } + + return true; + }, + [&](SSL *ssl) { + SSL_set_tlsext_host_name(ssl, host_.c_str()); + return true; + }, + [&](SSL * /*ssl*/, Stream &strm, bool last_connection, + bool &connection_close) { + return callback(strm, last_connection, connection_close); + }); +} + +inline bool SSLClient::is_ssl() const { return true; } + +inline bool SSLClient::verify_host(X509 *server_cert) const { + /* Quote from RFC2818 section 3.1 "Server Identity" + + If a subjectAltName extension of type dNSName is present, that MUST + be used as the identity. Otherwise, the (most specific) Common Name + field in the Subject field of the certificate MUST be used. Although + the use of the Common Name is existing practice, it is deprecated and + Certification Authorities are encouraged to use the dNSName instead. + + Matching is performed using the matching rules specified by + [RFC2459]. If more than one identity of a given type is present in + the certificate (e.g., more than one dNSName name, a match in any one + of the set is considered acceptable.) Names may contain the wildcard + character * which is considered to match any single domain name + component or component fragment. E.g., *.a.com matches foo.a.com but + not bar.foo.a.com. f*.com matches foo.com but not bar.com. + + In some cases, the URI is specified as an IP address rather than a + hostname. In this case, the iPAddress subjectAltName must be present + in the certificate and must exactly match the IP in the URI. + + */ + return verify_host_with_subject_alt_name(server_cert) || + verify_host_with_common_name(server_cert); +} + +inline bool +SSLClient::verify_host_with_subject_alt_name(X509 *server_cert) const { + auto ret = false; + + auto type = GEN_DNS; + + struct in6_addr addr6; + struct in_addr addr; + size_t addr_len = 0; + +#ifndef __MINGW32__ + if (inet_pton(AF_INET6, host_.c_str(), &addr6)) { + type = GEN_IPADD; + addr_len = sizeof(struct in6_addr); + } else if (inet_pton(AF_INET, host_.c_str(), &addr)) { + type = GEN_IPADD; + addr_len = sizeof(struct in_addr); + } +#endif + + auto alt_names = static_cast( + X509_get_ext_d2i(server_cert, NID_subject_alt_name, nullptr, nullptr)); + + if (alt_names) { + auto dsn_matched = false; + auto ip_mached = false; + + auto count = sk_GENERAL_NAME_num(alt_names); + + for (auto i = 0; i < count && !dsn_matched; i++) { + auto val = sk_GENERAL_NAME_value(alt_names, i); + if (val->type == type) { + auto name = (const char *)ASN1_STRING_get0_data(val->d.ia5); + auto name_len = (size_t)ASN1_STRING_length(val->d.ia5); + + if (strlen(name) == name_len) { + switch (type) { + case GEN_DNS: dsn_matched = check_host_name(name, name_len); break; + + case GEN_IPADD: + if (!memcmp(&addr6, name, addr_len) || + !memcmp(&addr, name, addr_len)) { + ip_mached = true; + } + break; + } + } + } + } + + if (dsn_matched || ip_mached) { ret = true; } + } + + GENERAL_NAMES_free((STACK_OF(GENERAL_NAME) *)alt_names); + + return ret; +} + +inline bool SSLClient::verify_host_with_common_name(X509 *server_cert) const { + const auto subject_name = X509_get_subject_name(server_cert); + + if (subject_name != nullptr) { + char name[BUFSIZ]; + auto name_len = X509_NAME_get_text_by_NID(subject_name, NID_commonName, + name, sizeof(name)); + + if (name_len != -1) { return check_host_name(name, name_len); } + } + + return false; +} + +inline bool SSLClient::check_host_name(const char *pattern, + size_t pattern_len) const { + if (host_.size() == pattern_len && host_ == pattern) { return true; } + + // Wildcard match + // https://bugs.launchpad.net/ubuntu/+source/firefox-3.0/+bug/376484 + std::vector pattern_components; + detail::split(&pattern[0], &pattern[pattern_len], '.', + [&](const char *b, const char *e) { + pattern_components.emplace_back(std::string(b, e)); + }); + + if (host_components_.size() != pattern_components.size()) { return false; } + + auto itr = pattern_components.begin(); + for (const auto &h : host_components_) { + auto &p = *itr; + if (p != h && p != "*") { + auto partial_match = (p.size() > 0 && p[p.size() - 1] == '*' && + !p.compare(0, p.size() - 1, h)); + if (!partial_match) { return false; } + } + ++itr; + } + + return true; +} +#endif + +} // namespace httplib + +#endif // CPPHTTPLIB_HTTPLIB_H diff --git a/src/yolo.c b/src/yolo.c index b86c19ee369..5f918e50e49 100644 --- a/src/yolo.c +++ b/src/yolo.c @@ -359,5 +359,5 @@ void run_yolo(int argc, char **argv) else if(0==strcmp(argv[2], "valid")) validate_yolo(cfg, weights); else if(0==strcmp(argv[2], "recall")) validate_yolo_recall(cfg, weights); else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, hier_thresh, cam_index, filename, voc_names, 20, frame_skip, - prefix, out_filename, mjpeg_port, json_port, dont_show, ext_output, 0, 0); + prefix, out_filename, mjpeg_port, json_port, dont_show, ext_output, 0, 0, 0); } From a20e8b010a422a62e5ef11943b6146d54e21fdba Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 1 Dec 2019 00:13:03 +0300 Subject: [PATCH 0702/1365] Compile fix --- src/http_stream.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/http_stream.cpp b/src/http_stream.cpp index f05159733b5..94a865bb5b6 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -13,6 +13,7 @@ #include #include #include +#include using std::cerr; using std::endl; @@ -28,7 +29,6 @@ using std::endl; #include #include #include -#include #include "gettimeofday.h" #define PORT unsigned long #define ADDRPOINTER int* From 327103fb15280729b725cbcc82df1f2ae5fd5922 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 1 Dec 2019 00:17:02 +0300 Subject: [PATCH 0703/1365] another compile fix --- src/http_stream.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 94a865bb5b6..1233e044f43 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -13,6 +13,7 @@ #include #include #include +#define __STDC_WANT_LIB_EXT1__ 1 #include using std::cerr; using std::endl; From ef126a09137b7192600d87c83f95b758ffe642b1 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 1 Dec 2019 00:41:11 +0300 Subject: [PATCH 0704/1365] Compile fix --- src/http_stream.cpp | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 1233e044f43..0624a2eae2a 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -13,7 +13,6 @@ #include #include #include -#define __STDC_WANT_LIB_EXT1__ 1 #include using std::cerr; using std::endl; @@ -545,10 +544,9 @@ void send_mjpeg(mat_cv* mat, int port, int timeout, int quality) std::string get_system_frame_time_string() { std::time_t t = std::chrono::system_clock::to_time_t(std::chrono::system_clock::now()); - struct tm tmp_buf; - localtime_s(&tmp_buf, &t); + struct tm *tmp_buf = localtime(&t); char buff[256]; - std::strftime(buff, 256, "%A %F %T", &tmp_buf); + std::strftime(buff, 256, "%A %F %T", tmp_buf); std::string system_frame_time = buff; return system_frame_time; } From defcf7e3a584a14877c1f24e60715490c082d25a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 2 Dec 2019 00:23:32 +0300 Subject: [PATCH 0705/1365] iou_normalizer= now affects on default [yolo] with default mse-loss --- src/http_stream.cpp | 3 +++ src/yolo_layer.c | 57 ++++++++++++++++++++++++++------------------- 2 files changed, 36 insertions(+), 24 deletions(-) diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 0624a2eae2a..f9dc7790961 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -94,6 +94,9 @@ static int close_socket(SOCKET s) { } #endif // _WIN32 +#ifndef NI_MAXHOST +#define NI_MAXHOST 1025 +#endif class JSON_sender { diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 40e73878a75..36a8d5645b8 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -150,10 +150,10 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, float th = log(truth.h*h / biases[2 * n + 1]); // accumulate delta - delta[index + 0 * stride] += scale * (tx - x[index + 0 * stride]); - delta[index + 1 * stride] += scale * (ty - x[index + 1 * stride]); - delta[index + 2 * stride] += scale * (tw - x[index + 2 * stride]); - delta[index + 3 * stride] += scale * (th - x[index + 3 * stride]); + delta[index + 0 * stride] += scale * (tx - x[index + 0 * stride]) * iou_normalizer; + delta[index + 1 * stride] += scale * (ty - x[index + 1 * stride]) * iou_normalizer; + delta[index + 2 * stride] += scale * (tw - x[index + 2 * stride]) * iou_normalizer; + delta[index + 3 * stride] += scale * (th - x[index + 3 * stride]) * iou_normalizer; } else { // https://github.com/generalized-iou/g-darknet @@ -487,6 +487,27 @@ void forward_yolo_layer(const layer l, network_state state) //*(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); //printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d\n", state.index, avg_iou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count); + int stride = l.w*l.h; + float* no_iou_loss_delta = (float *)calloc(l.batch * l.outputs, sizeof(float)); + memcpy(no_iou_loss_delta, l.delta, l.batch * l.outputs * sizeof(float)); + for (b = 0; b < l.batch; ++b) { + for (j = 0; j < l.h; ++j) { + for (i = 0; i < l.w; ++i) { + for (n = 0; n < l.n; ++n) { + int index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); + no_iou_loss_delta[index + 0 * stride] = 0; + no_iou_loss_delta[index + 1 * stride] = 0; + no_iou_loss_delta[index + 2 * stride] = 0; + no_iou_loss_delta[index + 3 * stride] = 0; + } + } + } + } + float classification_loss = l.cls_normalizer * pow(mag_array(no_iou_loss_delta, l.outputs * l.batch), 2); + free(no_iou_loss_delta); + float loss = pow(mag_array(l.delta, l.outputs * l.batch), 2); + float iou_loss = loss - classification_loss; + float avg_iou_loss = 0; // gIOU loss + MSE (objectness) loss if (l.iou_loss == MSE) { @@ -496,24 +517,6 @@ void forward_yolo_layer(const layer l, network_state state) // Always compute classification loss both for iou + cls loss and for logging with mse loss // TODO: remove IOU loss fields before computing MSE on class // probably split into two arrays - int stride = l.w*l.h; - float* no_iou_loss_delta = (float *)calloc(l.batch * l.outputs, sizeof(float)); - memcpy(no_iou_loss_delta, l.delta, l.batch * l.outputs * sizeof(float)); - for (b = 0; b < l.batch; ++b) { - for (j = 0; j < l.h; ++j) { - for (i = 0; i < l.w; ++i) { - for (n = 0; n < l.n; ++n) { - int index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); - no_iou_loss_delta[index + 0 * stride] = 0; - no_iou_loss_delta[index + 1 * stride] = 0; - no_iou_loss_delta[index + 2 * stride] = 0; - no_iou_loss_delta[index + 3 * stride] = 0; - } - } - } - } - float classification_loss = l.cls_normalizer * pow(mag_array(no_iou_loss_delta, l.outputs * l.batch), 2); - free(no_iou_loss_delta); if (l.iou_loss == GIOU) { avg_iou_loss = count > 0 ? l.iou_normalizer * (tot_giou_loss / count) : 0; @@ -523,8 +526,14 @@ void forward_yolo_layer(const layer l, network_state state) } *(l.cost) = avg_iou_loss + classification_loss; } - printf("v3 (%s loss, Normalizer: (iou: %f, cls: %f) Region %d Avg (IOU: %f, GIOU: %f), Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d\n", - (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.cls_normalizer, state.index, tot_iou / count, tot_giou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count); + + loss /= l.batch; + classification_loss /= l.batch; + iou_loss /= l.batch; + + printf("v3 (%s loss, Normalizer: (iou: %f, cls: %f) Region %d Avg (IOU: %f, GIOU: %f), Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d, loss = %f, class_loss = %f, iou_loss = %f\n", + (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.cls_normalizer, state.index, tot_iou / count, tot_giou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count, + loss, classification_loss, iou_loss); } void backward_yolo_layer(const layer l, network_state state) From 55cfc272fbbb63c0897a81f821cebecdb2d13362 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 2 Dec 2019 01:42:14 +0300 Subject: [PATCH 0706/1365] Fixed bug with [shortcut] layer and SWISH / MISH activations --- src/parser.c | 14 ++++++++++---- src/shortcut_layer.c | 7 +++++-- src/shortcut_layer.h | 2 +- 3 files changed, 16 insertions(+), 7 deletions(-) diff --git a/src/parser.c b/src/parser.c index d8cf47bdae2..8b1fc0404fc 100644 --- a/src/parser.c +++ b/src/parser.c @@ -725,6 +725,9 @@ layer parse_batchnorm(list *options, size_params params) layer parse_shortcut(list *options, size_params params, network net) { + char *activation_s = option_find_str(options, "activation", "logistic"); + ACTIVATION activation = get_activation(activation_s); + int assisted_excitation = option_find_float_quiet(options, "assisted_excitation", 0); char *l = option_find(options, "from"); int index = atoi(l); @@ -734,11 +737,8 @@ layer parse_shortcut(list *options, size_params params, network net) layer from = net.layers[index]; if (from.antialiasing) from = *from.input_layer; - layer s = make_shortcut_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c, assisted_excitation, params.train); + layer s = make_shortcut_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c, assisted_excitation, activation, params.train); - char *activation_s = option_find_str(options, "activation", "linear"); - ACTIVATION activation = get_activation(activation_s); - s.activation = activation; return s; } @@ -758,6 +758,9 @@ layer parse_scale_channels(list *options, size_params params, network net) char *activation_s = option_find_str_quiet(options, "activation", "linear"); ACTIVATION activation = get_activation(activation_s); s.activation = activation; + if (activation == SWISH || activation == MISH) { + printf(" [scale_channels] layer doesn't support SWISH or MISH activations \n"); + } return s; } @@ -775,6 +778,9 @@ layer parse_sam(list *options, size_params params, network net) char *activation_s = option_find_str_quiet(options, "activation", "linear"); ACTIVATION activation = get_activation(activation_s); s.activation = activation; + if (activation == SWISH || activation == MISH) { + printf(" [sam] layer doesn't support SWISH or MISH activations \n"); + } return s; } diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index a1dd3724e47..f9b9209a667 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -6,7 +6,7 @@ #include #include -layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2, int assisted_excitation, int train) +layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2, int assisted_excitation, ACTIVATION activation, int train) { if(assisted_excitation) fprintf(stderr, "Shortcut Layer - AE: %d\n", index); else fprintf(stderr,"Shortcut Layer: %d\n", index); @@ -14,6 +14,7 @@ layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int l.train = train; l.type = SHORTCUT; l.batch = batch; + l.activation = activation; l.w = w2; l.h = h2; l.c = c2; @@ -34,8 +35,10 @@ layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int l.forward = forward_shortcut_layer; l.backward = backward_shortcut_layer; - +#ifndef GPU if (l.activation == SWISH || l.activation == MISH) l.activation_input = (float*)calloc(l.batch*l.outputs, sizeof(float)); +#endif // GPU + #ifdef GPU if (l.activation == SWISH || l.activation == MISH) l.activation_input_gpu = cuda_make_array(l.activation_input, l.batch*l.outputs); diff --git a/src/shortcut_layer.h b/src/shortcut_layer.h index 89f22ceb3be..b245868fdd8 100644 --- a/src/shortcut_layer.h +++ b/src/shortcut_layer.h @@ -7,7 +7,7 @@ #ifdef __cplusplus extern "C" { #endif -layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2, int assisted_excitation, int train); +layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2, int assisted_excitation, ACTIVATION activation, int train); void forward_shortcut_layer(const layer l, network_state state); void backward_shortcut_layer(const layer l, network_state state); void resize_shortcut_layer(layer *l, int w, int h); From 4c26117708469a1136cbb5dc44589f63f0d9c139 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 2 Dec 2019 02:13:17 +0300 Subject: [PATCH 0707/1365] Fixed CutMix - removed boundary-aware constraint --- src/data.c | 27 ++++++++------------------- 1 file changed, 8 insertions(+), 19 deletions(-) diff --git a/src/data.c b/src/data.c index 29e2527e7d6..6b7e039bf06 100644 --- a/src/data.c +++ b/src/data.c @@ -1353,26 +1353,15 @@ data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *h // CutMix else { const float min_offset = 0.2; // 20% - const int cut_x = rand_int(w*min_offset, w*(1 - min_offset)); - const int cut_y = rand_int(h*min_offset, h*(1 - min_offset)); - int left, right, top, bot; - if (rand_int(0,1)) { - left = cut_x; - right = w; - } - else { - left = 0; - right = cut_x; - } + const int cut_w = rand_int(w*min_offset, w*(1 - min_offset)); + const int cut_h = rand_int(h*min_offset, h*(1 - min_offset)); + const int cut_x = rand_int(0, w - cut_w - 1); + const int cut_y = rand_int(0, h - cut_h - 1); + const int left = cut_x; + const int right = cut_x + cut_w; + const int top = cut_y; + const int bot = cut_y + cut_h; - if (rand_int(0, 1)) { - top = cut_y; - bot = h; - } - else { - top = 0; - bot = cut_y; - } const float alpha = (float)(right - left)*(bot - top) / (float)(w*h); const float beta = 1 - alpha; From 9064755c3bd67ba775f46d859040c13b5fa6eff2 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 2 Dec 2019 03:11:22 +0300 Subject: [PATCH 0708/1365] Minor fix --- src/data.c | 58 ++++++++++++++++++++++++--------------------- src/http_stream.cpp | 12 +++++++--- 2 files changed, 40 insertions(+), 30 deletions(-) diff --git a/src/data.c b/src/data.c index 6b7e039bf06..4323b84d9ad 100644 --- a/src/data.c +++ b/src/data.c @@ -1325,7 +1325,7 @@ data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *h d.y = load_labels_paths(paths, n, labels, k, hierarchy); if(m) free(paths); - if (mixup) { + if (mixup && rand_int(0, 1)) { char **paths_mix = get_random_paths(paths_stored, n, m); data d2 = { 0 }; d2.shallow = 0; @@ -1352,9 +1352,10 @@ data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *h } // CutMix else { - const float min_offset = 0.2; // 20% - const int cut_w = rand_int(w*min_offset, w*(1 - min_offset)); - const int cut_h = rand_int(h*min_offset, h*(1 - min_offset)); + const float min = 0.3; // 0.3*0.3 = 9% + const float max = 0.8; // 0.8*0.8 = 64% + const int cut_w = rand_int(w*min, w*max); + const int cut_h = rand_int(h*min, h*max); const int cut_x = rand_int(0, w - cut_w - 1); const int cut_y = rand_int(0, h - cut_h - 1); const int left = cut_x; @@ -1381,35 +1382,38 @@ data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *h d.y.vals[i][j] = d.y.vals[i][j] * beta + d2.y.vals[i][j] * alpha; } } + } - if (show_imgs) { - image im = make_empty_image(w, h, 3); - im.data = d.X.vals[i]; - char buff[1000]; - sprintf(buff, "aug_%d_%s_%d", i, basecfg((char*)paths_mix[i]), random_gen()); - save_image(im, buff); + free_data(d2); + free(paths_mix); + } - char buff_string[1000]; - sprintf(buff_string, "\n Classes: "); - for (j = 0; j < d.y.cols; ++j) { - if (d.y.vals[i][j] > 0) { - char buff_tmp[100]; - sprintf(buff_tmp, " %d (%f), ", j, d.y.vals[i][j]); - strcat(buff_string, buff_tmp); - } + if (show_imgs) { + int i, j; + for (i = 0; i < d.X.rows; ++i) { + image im = make_empty_image(w, h, 3); + im.data = d.X.vals[i]; + char buff[1000]; + sprintf(buff, "aug_%d_%s_%d", i, basecfg((char*)paths[i]), random_gen()); + save_image(im, buff); + + char buff_string[1000]; + sprintf(buff_string, "\n Classes: "); + for (j = 0; j < d.y.cols; ++j) { + if (d.y.vals[i][j] > 0) { + char buff_tmp[100]; + sprintf(buff_tmp, " %d (%f), ", j, d.y.vals[i][j]); + strcat(buff_string, buff_tmp); } - printf("%s \n", buff_string); + } + printf("%s \n", buff_string); - if (show_imgs == 1) { - show_image(im, buff); - wait_until_press_key_cv(); - } - printf("\nYou use flag -show_imgs, so will be saved aug_...jpg images. Click on window and press ESC button \n"); + if (show_imgs == 1) { + show_image(im, buff); + wait_until_press_key_cv(); } } - - free_data(d2); - free(paths_mix); + printf("\nYou use flag -show_imgs, so will be saved aug_...jpg images. Click on window and press ESC button \n"); } return d; diff --git a/src/http_stream.cpp b/src/http_stream.cpp index f9dc7790961..45f629b2a08 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -94,9 +94,6 @@ static int close_socket(SOCKET s) { } #endif // _WIN32 -#ifndef NI_MAXHOST -#define NI_MAXHOST 1025 -#endif class JSON_sender { @@ -555,6 +552,15 @@ std::string get_system_frame_time_string() } // ---------------------------------------- + +#ifndef NI_MAXHOST +#define NI_MAXHOST 1025 +#endif + +#ifndef NI_NUMERICHOST +#define NI_NUMERICHOST 0x02 +#endif + //#define CPPHTTPLIB_OPENSSL_SUPPORT #include "httplib.h" From a95bc3f5b69cfec0c82ff4ba7ae182cedc77a9e9 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 2 Dec 2019 14:58:35 +0300 Subject: [PATCH 0709/1365] Fixed memory leak for ./darknet classifier predict ... < list.txt --- src/classifier.c | 6 ++++++ src/convolutional_kernels.cu | 1 + src/http_stream.cpp | 10 ++++++++++ 3 files changed, 17 insertions(+) diff --git a/src/classifier.c b/src/classifier.c index fc760d30cf1..15d789e1d0e 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -778,6 +778,7 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi char *name_list = option_find_str(options, "names", 0); if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list"); int classes = option_find_int(options, "classes", 2); + printf(" classes = %d, output in cfg = %d \n", classes, net.layers[net.n - 1].c); if (top == 0) top = option_find_int(options, "top", 1); if (top > classes) top = classes; @@ -821,8 +822,13 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi } if(r.data != im.data) free_image(r); free_image(im); + free_image(resized); if (filename) break; } + free(indexes); + free_network(net); + free_list_contents_kvp(options); + free_list(options); } diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 6b575b5f4f3..5b7c08192cf 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -394,6 +394,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == MISH) activate_array_mish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); + else if (l.activation == NORM_CHAN) activate_array_normalize_channels_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu); else if (l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if(l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if (l.binary || l.xnor) swap_binary(&l); diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 45f629b2a08..e68945629f5 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -553,6 +553,15 @@ std::string get_system_frame_time_string() // ---------------------------------------- +#ifdef __CYGWIN__ +int send_http_post_request(char *http_post_host, int server_port, char *videosource, + detection *dets, int nboxes, int classes, char **names, long long int frame_id, int ext_output, int timeout) +{ + std::cerr << " send_http_post_request() isn't implemented \n"; + return 0; +} +#else // __CYGWIN__ + #ifndef NI_MAXHOST #define NI_MAXHOST 1025 #endif @@ -620,6 +629,7 @@ int send_http_post_request(char *http_post_host, int server_port, char *videosou return 0; } +#endif // __CYGWIN__ #endif // OPENCV From acb578cb372a06c5e91110710e08bbb6e3810595 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 2 Dec 2019 16:46:13 +0300 Subject: [PATCH 0710/1365] Fixed training with GPU=1 CUDNN=0 --- .circleci/config.yml | 1 + src/convolutional_layer.c | 10 +++++++--- 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index dc004b3f908..85248d53e01 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -7,6 +7,7 @@ jobs: working_directory: ~/work steps: - checkout + - run: nvcc --version - run: make LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 -j 8 - run: make clean - run: make LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 AVX=1 -j 8 diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 960ff681aa3..834678a615c 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -609,7 +609,9 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, if (train) { l.x_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n); - //l.x_norm_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n); +#ifndef CUDNN + l.x_norm_gpu = cuda_make_array(l.output, total_batch*out_h*out_w*n); +#endif // CUDNN } } @@ -778,10 +780,12 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) if (l->batch_normalize) { cuda_free(l->x_gpu); - cuda_free(l->x_norm_gpu); - l->x_gpu = cuda_make_array(l->output, total_batch*l->outputs); + +#ifndef CUDNN + cuda_free(l->x_norm_gpu); l->x_norm_gpu = cuda_make_array(l->output, total_batch*l->outputs); +#endif // CUDNN } if (l->xnor) { From e5d464d3d007fad372bdd0b2152374e009998995 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 2 Dec 2019 17:41:45 +0300 Subject: [PATCH 0711/1365] Added mosaic=1 data augmentation for Classifier --- build/darknet/x64/cfg/efficientnet_b0.cfg | 3 +- cfg/efficientnet_b0.cfg | 3 +- src/data.c | 64 ++++++++++++++++++++--- src/parser.c | 4 +- 4 files changed, 65 insertions(+), 9 deletions(-) diff --git a/build/darknet/x64/cfg/efficientnet_b0.cfg b/build/darknet/x64/cfg/efficientnet_b0.cfg index 88ec3594561..c959a610455 100644 --- a/build/darknet/x64/cfg/efficientnet_b0.cfg +++ b/build/darknet/x64/cfg/efficientnet_b0.cfg @@ -11,8 +11,9 @@ channels=3 momentum=0.9 decay=0.0005 max_crop=256 -#mixup=1 +#mixup=4 cutmix=1 +mosaic=1 burn_in=1000 #burn_in=100 diff --git a/cfg/efficientnet_b0.cfg b/cfg/efficientnet_b0.cfg index 88ec3594561..c959a610455 100644 --- a/cfg/efficientnet_b0.cfg +++ b/cfg/efficientnet_b0.cfg @@ -11,8 +11,9 @@ channels=3 momentum=0.9 decay=0.0005 max_crop=256 -#mixup=1 +#mixup=4 cutmix=1 +mosaic=1 burn_in=1000 #burn_in=100 diff --git a/src/data.c b/src/data.c index 4323b84d9ad..90e0348c2d6 100644 --- a/src/data.c +++ b/src/data.c @@ -1323,7 +1323,6 @@ data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *h d.shallow = 0; d.X = load_image_augment_paths(paths, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure); d.y = load_labels_paths(paths, n, labels, k, hierarchy); - if(m) free(paths); if (mixup && rand_int(0, 1)) { char **paths_mix = get_random_paths(paths_stored, n, m); @@ -1331,14 +1330,32 @@ data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *h d2.shallow = 0; d2.X = load_image_augment_paths(paths_mix, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure); d2.y = load_labels_paths(paths_mix, n, labels, k, hierarchy); + free(paths_mix); + + data d3 = { 0 }; + d3.shallow = 0; + data d4 = { 0 }; + d4.shallow = 0; + if (mixup >= 3) { + char **paths_mix3 = get_random_paths(paths_stored, n, m); + d3.X = load_image_augment_paths(paths_mix3, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure); + d3.y = load_labels_paths(paths_mix3, n, labels, k, hierarchy); + free(paths_mix3); + + char **paths_mix4 = get_random_paths(paths_stored, n, m); + d4.X = load_image_augment_paths(paths_mix4, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure); + d4.y = load_labels_paths(paths_mix4, n, labels, k, hierarchy); + free(paths_mix4); + } + // mix int i, j; for (i = 0; i < d2.X.rows; ++i) { - if (mixup == 3) mixup = rand_int(1, 2); // alternate MixUp and CutMix + if (mixup == 4) mixup = rand_int(2, 3); // alternate MixUp and CutMix - // MixUp + // MixUp ----------------------------------- if (mixup == 1) { // mix images for (j = 0; j < d2.X.cols; ++j) { @@ -1350,8 +1367,8 @@ data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *h d.y.vals[i][j] = (d.y.vals[i][j] + d2.y.vals[i][j]) / 2.0f; } } - // CutMix - else { + // CutMix ----------------------------------- + else if (mixup == 2) { const float min = 0.3; // 0.3*0.3 = 9% const float max = 0.8; // 0.8*0.8 = 64% const int cut_w = rand_int(w*min, w*max); @@ -1382,10 +1399,43 @@ data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *h d.y.vals[i][j] = d.y.vals[i][j] * beta + d2.y.vals[i][j] * alpha; } } + // Mosaic ----------------------------------- + else if (mixup == 3) + { + const float min_offset = 0.2; // 20% + const int cut_x = rand_int(w*min_offset, w*(1 - min_offset)); + const int cut_y = rand_int(h*min_offset, h*(1 - min_offset)); + + float s1 = (float)(cut_x * cut_y) / (w*h); + float s2 = (float)((w - cut_x) * cut_y) / (w*h); + float s3 = (float)(cut_x * (h - cut_y)) / (w*h); + float s4 = (float)((w - cut_x) * (h - cut_y)) / (w*h); + + int c, x, y; + for (c = 0; c < 3; ++c) { + for (y = 0; y < h; ++y) { + for (x = 0; x < w; ++x) { + int j = x + y*w + c*w*h; + if (x < cut_x && y < cut_y) d.X.vals[i][j] = d.X.vals[i][j]; + if (x >= cut_x && y < cut_y) d.X.vals[i][j] = d2.X.vals[i][j]; + if (x < cut_x && y >= cut_y) d.X.vals[i][j] = d3.X.vals[i][j]; + if (x >= cut_x && y >= cut_y) d.X.vals[i][j] = d4.X.vals[i][j]; + } + } + } + + for (j = 0; j < d.y.cols; ++j) { + d.y.vals[i][j] = d.y.vals[i][j] * s1 + d2.y.vals[i][j] * s2 + d3.y.vals[i][j] * s3 + d4.y.vals[i][j] * s4; + } + } } free_data(d2); - free(paths_mix); + + if (mixup == 3) { + free_data(d3); + free_data(d4); + } } if (show_imgs) { @@ -1416,6 +1466,8 @@ data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *h printf("\nYou use flag -show_imgs, so will be saved aug_...jpg images. Click on window and press ESC button \n"); } + if (m) free(paths); + return d; } diff --git a/src/parser.c b/src/parser.c index 8b1fc0404fc..f1abe89d959 100644 --- a/src/parser.c +++ b/src/parser.c @@ -923,8 +923,10 @@ void parse_net_options(list *options, network *net) net->blur = option_find_int_quiet(options, "blur", 0); net->mixup = option_find_int_quiet(options, "mixup", 0); int cutmix = option_find_int_quiet(options, "cutmix", 0); - if (net->mixup && cutmix) net->mixup = 3; + int mosaic = option_find_int_quiet(options, "mosaic", 0); + if (mosaic && cutmix) net->mixup = 4; else if (cutmix) net->mixup = 2; + else if (mosaic) net->mixup = 3; net->letter_box = option_find_int_quiet(options, "letter_box", 0); net->angle = option_find_float_quiet(options, "angle", 0); From 99e0e57dc3085f5b92db9a57fea76d214853d12c Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 3 Dec 2019 07:22:00 +0100 Subject: [PATCH 0712/1365] move setup scripts to scripts subfolder --- setup.ps1 => scripts/setup.ps1 | 0 setup.sh => scripts/setup.sh | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename setup.ps1 => scripts/setup.ps1 (100%) rename setup.sh => scripts/setup.sh (100%) diff --git a/setup.ps1 b/scripts/setup.ps1 similarity index 100% rename from setup.ps1 rename to scripts/setup.ps1 diff --git a/setup.sh b/scripts/setup.sh similarity index 100% rename from setup.sh rename to scripts/setup.sh From 5d0352f961f4dc3db8ccad0570481c69305c0143 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 3 Dec 2019 18:35:57 +0300 Subject: [PATCH 0713/1365] Fixed CPU-RAM + GPU-processing bug for Dropout-layer --- src/network_kernels.cu | 10 ++++++---- src/parser.c | 7 ++++--- 2 files changed, 10 insertions(+), 7 deletions(-) diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 872a136da70..53e6a280105 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -112,10 +112,12 @@ void backward_network_gpu(network net, network_state state) if (l.onlyforward) continue; l.backward_gpu(l, state); - layer prev = net.layers[i - 1]; - if (net.optimized_memory && state.delta && !prev.keep_delta_gpu) { - simple_copy_ongpu(prev.outputs*prev.batch, state.delta, prev.delta_gpu); - fill_ongpu(prev.outputs*prev.batch, 0, net.state_delta_gpu, 1); + if (i != 0) { + layer prev = net.layers[i - 1]; + if (net.optimized_memory && state.delta && !prev.keep_delta_gpu && prev.delta_gpu != l.delta_gpu) { + simple_copy_ongpu(prev.outputs*prev.batch, state.delta, prev.delta_gpu); + fill_ongpu(prev.outputs*prev.batch, 0, net.state_delta_gpu, 1); + } } /* diff --git a/src/parser.c b/src/parser.c index f1abe89d959..0a86bf1cf1b 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1148,6 +1148,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) #ifdef GPU l.output_gpu = net.layers[count-1].output_gpu; l.delta_gpu = net.layers[count-1].delta_gpu; + l.keep_delta_gpu = 1; #endif } else if (lt == EMPTY) { @@ -1168,7 +1169,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) #ifdef GPU // futher GPU-memory optimization: net.optimized_memory == 2 - if (net.optimized_memory >= 2 && params.train) + if (net.optimized_memory >= 2 && params.train && l.type != DROPOUT) { l.optimized_memory = net.optimized_memory; if (l.output_gpu) { @@ -1187,7 +1188,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) } // maximum optimization - if (net.optimized_memory >= 3) { + if (net.optimized_memory >= 3 && l.type != DROPOUT) { if (l.delta_gpu) { cuda_free(l.delta_gpu); //l.delta_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps @@ -1257,7 +1258,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) } // maximum optimization - if (net.optimized_memory >= 3) { + if (net.optimized_memory >= 3 && l.type != DROPOUT) { if (l.delta_gpu && l.keep_delta_gpu) { //cuda_free(l.delta_gpu); // already called above l.delta_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps From 649abac372446e6c0114e8fbc9bbbb8b226318b9 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 4 Dec 2019 02:02:22 +0300 Subject: [PATCH 0714/1365] Fixed blur= parameter for train detector --- src/data.c | 8 ++++---- src/image_opencv.cpp | 4 ++++ 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/src/data.c b/src/data.c index 90e0348c2d6..62e5bd8f957 100644 --- a/src/data.c +++ b/src/data.c @@ -882,10 +882,10 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo flip = use_flip ? random_gen() % 2 : 0; - //blur = rand_int(0, 1) ? (use_blur) : 0; int tmp_blur = rand_int(0, 2); // 0 - disable, 1 - blur background, 2 - blur the whole image - if (tmp_blur == 2) blur = use_blur; - else blur = tmp_blur; + if (tmp_blur == 0) blur = 0; + else if (tmp_blur == 1) blur = 1; + else blur = use_blur; } int pleft = rand_precalc_random(-dw, dw, r1); @@ -935,7 +935,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo if (min_w_h / 8 < blur && blur > 1) blur = min_w_h / 8; // disable blur if one of the objects is too small image ai = image_data_augmentation(src, w, h, pleft, ptop, swidth, sheight, flip, dhue, dsat, dexp, - blur, boxes, d.y.vals[i]); + blur, boxes, truth); if (i_mixup) { image old_img = ai; diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 0a8ccd9841e..cd914d110ff 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1236,6 +1236,10 @@ image image_data_augmentation(mat_cv* mat, int w, int h, dst.copyTo(sized); } + //char txt[100]; + //sprintf(txt, "blur = %d", blur); + //cv::putText(sized, txt, cv::Point(100, 100), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.7, CV_RGB(255, 0, 0), 1, CV_AA); + // Mat -> image out = mat_to_image(sized); } From c6f0f7b22c0ad2ab50ff9adc6db1f90e7df1bcab Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 4 Dec 2019 15:36:07 +0300 Subject: [PATCH 0715/1365] reduced pinned memory step --- src/dark_cuda.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/dark_cuda.c b/src/dark_cuda.c index 2eacf418970..b930e9525ed 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -280,9 +280,9 @@ float *cuda_make_array_pinned_preallocated(float *x, size_t n) { pthread_mutex_lock(&mutex_pinned); float *x_cpu = NULL; - const size_t memory_step = 4096; + const size_t memory_step = 512;// 4096; const size_t size = sizeof(float)*n; - const size_t allocation_size = ((size / 4096) + 1) * 4096; + const size_t allocation_size = ((size / memory_step) + 1) * memory_step; if (pinned_ptr && pinned_block_id < pinned_num_of_blocks && (allocation_size < pinned_block_size/2)) { From 5ddf9c74a58ce61d2aa82b806b8d0912ab6cf8f3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 4 Dec 2019 17:42:32 +0300 Subject: [PATCH 0716/1365] Added mosaic=1 data augmentation for Detector. Fixed blur=. Fixed [scale_channels] for ASFF. --- src/data.c | 181 ++++++++++++++++++++++++++++++------- src/scale_channels_layer.c | 3 +- 2 files changed, 151 insertions(+), 33 deletions(-) diff --git a/src/data.c b/src/data.c index 62e5bd8f957..d73d8a1aecf 100644 --- a/src/data.c +++ b/src/data.c @@ -811,6 +811,73 @@ void blend_truth(float *new_truth, int boxes, float *old_truth) //printf("\n was %d bboxes, now %d bboxes \n", count_new_truth, t); } + +void blend_truth_mosaic(float *new_truth, int boxes, float *old_truth, int w, int h, int cut_x, int cut_y, int i_mixup) +{ + const int t_size = 4 + 1; + int count_new_truth = 0; + int t; + for (t = 0; t < boxes; ++t) { + float x = new_truth[t*(4 + 1)]; + if (!x) break; + count_new_truth++; + + } + int new_t = count_new_truth; + for (t = count_new_truth; t < boxes; ++t) { + float *new_truth_ptr = new_truth + new_t*t_size; + float *old_truth_ptr = old_truth + (t - count_new_truth)*t_size; + float x = old_truth_ptr[0]; + if (!x) break; + + float xb = old_truth_ptr[0]; + float yb = old_truth_ptr[1]; + float wb = old_truth_ptr[2]; + float hb = old_truth_ptr[3]; + + int left = (xb - wb / 2)*w; + int right = (xb + wb / 2)*w; + int top = (yb - hb / 2)*h; + int bot = (yb + hb / 2)*h; + + if ((i_mixup == 0 && left < cut_x && top < cut_y) || + (i_mixup == 1 && right > cut_x && top < cut_y) || + (i_mixup == 2 && left < cut_x && bot > cut_y) || + (i_mixup == 3 && right > cut_x && bot > cut_y)) + { + if ((i_mixup == 0 || i_mixup == 2) && right > cut_x) { + float diff_x = (float)(right - cut_x) / w; + xb = xb - diff_x / 2; + wb = wb - diff_x; + } + if ((i_mixup == 1 || i_mixup == 3) && left < cut_x) { + float diff_x = (float)(cut_x - left) / w; + xb = xb + diff_x / 2; + wb = wb - diff_x; + } + + if ((i_mixup == 0 || i_mixup == 1) && bot > cut_y) { + float diff_y = (float)(bot - cut_y) / h; + yb = yb - diff_y / 2; + hb = hb - diff_y; + } + if ((i_mixup == 2 || i_mixup == 3) && top < cut_y) { + float diff_y = (float)(cut_y - top) / h; + yb = yb + diff_y / 2; + hb = hb - diff_y; + } + + new_truth_ptr[0] = xb; + new_truth_ptr[1] = yb; + new_truth_ptr[2] = wb; + new_truth_ptr[3] = hb; + new_truth_ptr[4] = old_truth_ptr[4]; + new_t++; + } + } + //printf("\n was %d bboxes, now %d bboxes \n", count_new_truth, t); +} + #ifdef OPENCV #include "http_stream.h" @@ -820,18 +887,21 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo { const int random_index = random_gen(); c = c ? c : 3; - char **random_paths; - char **mixup_random_paths = NULL; - if (track) random_paths = get_sequential_paths(paths, n, m, mini_batch, augment_speed); - else random_paths = get_random_paths(paths, n, m); - int mixup = use_mixup ? random_gen() % 2 : 0; - //printf("\n mixup = %d \n", mixup); - if (mixup) { - if (track) mixup_random_paths = get_sequential_paths(paths, n, m, mini_batch, augment_speed); - else mixup_random_paths = get_random_paths(paths, n, m); - } + if (random_gen() % 2 == 0) use_mixup = 0; int i; + + int *cut_x = NULL, *cut_y = NULL; + if (use_mixup == 3) { + cut_x = (int*)calloc(n, sizeof(int)); + cut_y = (int*)calloc(n, sizeof(int)); + const float min_offset = 0.2; // 20% + for (i = 0; i < n; ++i) { + cut_x[i] = rand_int(w*min_offset, w*(1 - min_offset)); + cut_y[i] = rand_int(h*min_offset, h*(1 - min_offset)); + } + } + data d = {0}; d.shallow = 0; @@ -845,12 +915,16 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo d.y = make_matrix(n, 5*boxes); int i_mixup = 0; - for (i_mixup = 0; i_mixup <= mixup; i_mixup++) { + for (i_mixup = 0; i_mixup <= use_mixup; i_mixup++) { if (i_mixup) augmentation_calculated = 0; // recalculate augmentation for the 2nd sequence if(track==1) + char **random_paths; + if (track) random_paths = get_sequential_paths(paths, n, m, mini_batch, augment_speed); + else random_paths = get_random_paths(paths, n, m); + for (i = 0; i < n; ++i) { float *truth = (float*)calloc(5 * boxes, sizeof(float)); - const char *filename = (i_mixup) ? mixup_random_paths[i] : random_paths[i]; + const char *filename = random_paths[i]; int flag = (c >= 3); mat_cv *src; @@ -882,10 +956,12 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo flip = use_flip ? random_gen() % 2 : 0; - int tmp_blur = rand_int(0, 2); // 0 - disable, 1 - blur background, 2 - blur the whole image - if (tmp_blur == 0) blur = 0; - else if (tmp_blur == 1) blur = 1; - else blur = use_blur; + if (use_blur) { + int tmp_blur = rand_int(0, 2); // 0 - disable, 1 - blur background, 2 - blur the whole image + if (tmp_blur == 0) blur = 0; + else if (tmp_blur == 1) blur = 1; + else blur = use_blur; + } } int pleft = rand_precalc_random(-dw, dw, r1); @@ -935,27 +1011,67 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo if (min_w_h / 8 < blur && blur > 1) blur = min_w_h / 8; // disable blur if one of the objects is too small image ai = image_data_augmentation(src, w, h, pleft, ptop, swidth, sheight, flip, dhue, dsat, dexp, - blur, boxes, truth); + blur, boxes, d.y.vals[i]); - if (i_mixup) { - image old_img = ai; - old_img.data = d.X.vals[i]; - //show_image(ai, "new"); - //show_image(old_img, "old"); - //wait_until_press_key_cv(); - blend_images_cv(ai, 0.5, old_img, 0.5); - blend_truth(truth, boxes, d.y.vals[i]); - free_image(old_img); + if (use_mixup == 0) { + d.X.vals[i] = ai.data; + memcpy(d.y.vals[i], truth, 5 * boxes * sizeof(float)); } + else if (use_mixup == 1) { + if (i_mixup == 0) { + d.X.vals[i] = ai.data; + memcpy(d.y.vals[i], truth, 5 * boxes * sizeof(float)); + } + else if (i_mixup == 1) { + image old_img = make_empty_image(w, h, c); + old_img.data = d.X.vals[i]; + //show_image(ai, "new"); + //show_image(old_img, "old"); + //wait_until_press_key_cv(); + blend_images_cv(ai, 0.5, old_img, 0.5); + blend_truth(d.y.vals[i], boxes, truth); + free_image(old_img); + d.X.vals[i] = ai.data; + } + } + else if (use_mixup == 3) { + if (i_mixup == 0) { + image tmp_img = make_image(w, h, c); + d.X.vals[i] = tmp_img.data; + } - d.X.vals[i] = ai.data; - memcpy(d.y.vals[i], truth, 5*boxes * sizeof(float)); + int k, x, y; + for (k = 0; k < c; ++k) { + for (y = 0; y < h; ++y) { + int j = y*w + k*w*h; + if (i_mixup == 0 && y < cut_y[i]) { + memcpy(&d.X.vals[i][j + 0], &ai.data[j + 0], cut_x[i] * sizeof(float)); + } + if (i_mixup == 1 && y < cut_y[i]) { + memcpy(&d.X.vals[i][j + cut_x[i]], &ai.data[j + cut_x[i]], (w-cut_x[i]) * sizeof(float)); + } + if (i_mixup == 2 && y >= cut_y[i]) { + memcpy(&d.X.vals[i][j + 0], &ai.data[j + 0], cut_x[i] * sizeof(float)); + } + if (i_mixup == 3 && y >= cut_y[i]) { + memcpy(&d.X.vals[i][j + cut_x[i]], &ai.data[j + cut_x[i]], (w - cut_x[i]) * sizeof(float)); + } + } + } + + blend_truth_mosaic(d.y.vals[i], boxes, truth, w, h, cut_x[i], cut_y[i], i_mixup); + + free_image(ai); + ai.data = d.X.vals[i]; + } - if (show_imgs)// && i_mixup) // delete i_mixup + + if (show_imgs && i_mixup == use_mixup) // delete i_mixup { image tmp_ai = copy_image(ai); char buff[1000]; - sprintf(buff, "aug_%d_%d_%s_%d", random_index, i, basecfg((char*)filename), random_gen()); + //sprintf(buff, "aug_%d_%d_%s_%d", random_index, i, basecfg((char*)filename), random_gen()); + sprintf(buff, "aug_%d_%d_%d", random_index, i, random_gen()); int t; for (t = 0; t < boxes; ++t) { box b = float_to_box_stride(d.y.vals[i] + t*(4 + 1), 1); @@ -982,9 +1098,10 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo release_mat(&src); free(truth); } + if (random_paths) free(random_paths); } - free(random_paths); - if(mixup_random_paths) free(mixup_random_paths); + + return d; } #else // OPENCV diff --git a/src/scale_channels_layer.c b/src/scale_channels_layer.c index 9a5ad94f3a6..8cac40fed9a 100644 --- a/src/scale_channels_layer.c +++ b/src/scale_channels_layer.c @@ -20,7 +20,8 @@ layer make_scale_channels_layer(int batch, int index, int w, int h, int c, int w l.out_w = w2; l.out_h = h2; l.out_c = c2; - assert(l.out_c == l.c); + if (!l.scale_wh) assert(l.out_c == l.c); + else assert(l.out_w == l.w && l.out_h == l.h); l.outputs = l.out_w*l.out_h*l.out_c; l.inputs = l.outputs; From efc5478a23a3a3c66d6feefc6d6b485f13503bde Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 4 Dec 2019 17:46:40 +0300 Subject: [PATCH 0717/1365] Minor fix --- src/data.c | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/data.c b/src/data.c index d73d8a1aecf..9db08978e9b 100644 --- a/src/data.c +++ b/src/data.c @@ -888,6 +888,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo const int random_index = random_gen(); c = c ? c : 3; + assert(use_mixup != 2); if (random_gen() % 2 == 0) use_mixup = 0; int i; @@ -1124,6 +1125,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo if(track) random_paths = get_sequential_paths(paths, n, m, mini_batch, augment_speed); else random_paths = get_random_paths(paths, n, m); + assert(use_mixup < 2); int mixup = use_mixup ? random_gen() % 2 : 0; //printf("\n mixup = %d \n", mixup); if (mixup) { From 142fcdeb1e53ec78ec35d98503726075bd721a9b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 4 Dec 2019 23:37:06 +0300 Subject: [PATCH 0718/1365] Added blur=1 for Classifier training if OPENCV=1 (removes textures like in stylized-ImageNet) --- build/darknet/x64/cfg/efficientnet_b0.cfg | 1 + cfg/efficientnet_b0.cfg | 1 + src/classifier.c | 1 + src/data.c | 26 +++++++++++++++++++++-- src/data.h | 2 +- src/image_opencv.cpp | 10 +++++++++ src/image_opencv.h | 3 +++ src/network_kernels.cu | 4 ++-- 8 files changed, 43 insertions(+), 5 deletions(-) diff --git a/build/darknet/x64/cfg/efficientnet_b0.cfg b/build/darknet/x64/cfg/efficientnet_b0.cfg index c959a610455..70f780692a6 100644 --- a/build/darknet/x64/cfg/efficientnet_b0.cfg +++ b/build/darknet/x64/cfg/efficientnet_b0.cfg @@ -12,6 +12,7 @@ momentum=0.9 decay=0.0005 max_crop=256 #mixup=4 +blur=1 cutmix=1 mosaic=1 diff --git a/cfg/efficientnet_b0.cfg b/cfg/efficientnet_b0.cfg index c959a610455..70f780692a6 100644 --- a/cfg/efficientnet_b0.cfg +++ b/cfg/efficientnet_b0.cfg @@ -12,6 +12,7 @@ momentum=0.9 decay=0.0005 max_crop=256 #mixup=4 +blur=1 cutmix=1 mosaic=1 diff --git a/src/classifier.c b/src/classifier.c index 15d789e1d0e..33540e3dbc8 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -84,6 +84,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, args.min = net.min_crop; args.max = net.max_crop; args.flip = net.flip; + args.blur = net.blur; args.angle = net.angle; args.aspect = net.aspect; args.exposure = net.exposure; diff --git a/src/data.c b/src/data.c index 9db08978e9b..71d31d56edc 100644 --- a/src/data.c +++ b/src/data.c @@ -1289,7 +1289,7 @@ void *load_thread(void *ptr) if (a.type == OLD_CLASSIFICATION_DATA){ *a.d = load_data_old(a.paths, a.n, a.m, a.labels, a.classes, a.w, a.h); } else if (a.type == CLASSIFICATION_DATA){ - *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.flip, a.min, a.max, a.w, a.h, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.mixup, a.show_imgs); + *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.flip, a.min, a.max, a.w, a.h, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.mixup, a.blur, a.show_imgs); } else if (a.type == SUPER_DATA){ *a.d = load_data_super(a.paths, a.n, a.m, a.w, a.h, a.scale); } else if (a.type == WRITING_DATA){ @@ -1434,7 +1434,7 @@ data load_data_super(char **paths, int n, int m, int w, int h, int scale) return d; } -data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int mixup, int show_imgs) +data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int mixup, int use_blur, int show_imgs) { char **paths_stored = paths; if(m) paths = get_random_paths(paths, n, m); @@ -1557,6 +1557,28 @@ data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *h } } +#ifdef OPENCV + if (use_blur) { + int i; + for (i = 0; i < d.X.rows; ++i) { + if (random_gen() % 2) { + image im = make_empty_image(w, h, 3); + im.data = d.X.vals[i]; + int ksize = use_blur; + if (use_blur == 1) ksize = 17; + image blurred = blur_image(im, ksize); + free_image(im); + d.X.vals[i] = blurred.data; + //if (i == 0) { + // show_image(im, "Not blurred"); + // show_image(blurred, "blurred"); + // wait_until_press_key_cv(); + //} + } + } + } +#endif // OPENCV + if (show_imgs) { int i, j; for (i = 0; i < d.X.rows; ++i) { diff --git a/src/data.h b/src/data.h index 7170f8c256a..57ed3d9db92 100644 --- a/src/data.h +++ b/src/data.h @@ -91,7 +91,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo data load_data_tag(char **paths, int n, int m, int k, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure); matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure); data load_data_super(char **paths, int n, int m, int w, int h, int scale); -data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int mixup, int show_imgs); +data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int mixup, int use_blur, int show_imgs); data load_go(char *filename); box_label *read_boxes(char *filename, int *n); diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index cd914d110ff..6e887b822a8 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1258,6 +1258,16 @@ void blend_images_cv(image new_img, float alpha, image old_img, float beta) cv::addWeighted(new_mat, alpha, old_mat, beta, 0.0, new_mat); } +// bilateralFilter bluring +image blur_image(image src_img, int ksize) +{ + cv::Mat src = image_to_mat(src_img); + cv::Mat dst; + cv::bilateralFilter(src, dst, ksize, 75, 75); + image dst_img = mat_to_image(dst); + return dst_img; +} + // ==================================================================== // Show Anchors // ==================================================================== diff --git a/src/image_opencv.h b/src/image_opencv.h index 860f27e85a3..3bf44da6ed8 100644 --- a/src/image_opencv.h +++ b/src/image_opencv.h @@ -101,6 +101,9 @@ image image_data_augmentation(mat_cv* mat, int w, int h, // blend two images with (alpha and beta) void blend_images_cv(image new_img, float alpha, image old_img, float beta); +// bilateralFilter bluring +image blur_image(image src_img, int ksize); + // Show Anchors void show_acnhors(int number_of_boxes, int num_of_clusters, float *rel_width_height_array, model anchors_data, int width, int height); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 53e6a280105..0cce0507f0a 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -114,8 +114,8 @@ void backward_network_gpu(network net, network_state state) if (i != 0) { layer prev = net.layers[i - 1]; - if (net.optimized_memory && state.delta && !prev.keep_delta_gpu && prev.delta_gpu != l.delta_gpu) { - simple_copy_ongpu(prev.outputs*prev.batch, state.delta, prev.delta_gpu); + if (net.optimized_memory && state.delta && !prev.keep_delta_gpu) { + if (prev.delta_gpu != state.delta) simple_copy_ongpu(prev.outputs*prev.batch, state.delta, prev.delta_gpu); fill_ongpu(prev.outputs*prev.batch, 0, net.state_delta_gpu, 1); } } From 2207acd9c432669e3f4251107791a1df6c519d99 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 5 Dec 2019 15:26:44 +0300 Subject: [PATCH 0719/1365] Minor fix --- src/classifier.c | 3 +++ src/data.c | 13 ++++++++++++- 2 files changed, 15 insertions(+), 1 deletion(-) diff --git a/src/classifier.c b/src/classifier.c index 33540e3dbc8..b7976c45541 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -214,6 +214,9 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, free_ptrs((void**)paths, plist->size); free_list(plist); free(base); + + free_list_contents_kvp(options); + free_list(options); } diff --git a/src/data.c b/src/data.c index 71d31d56edc..7c964398dd4 100644 --- a/src/data.c +++ b/src/data.c @@ -1499,7 +1499,18 @@ data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *h const int top = cut_y; const int bot = cut_y + cut_h; - const float alpha = (float)(right - left)*(bot - top) / (float)(w*h); + assert(cut_x >= 0 && cut_x <= w); + assert(cut_y >= 0 && cut_y <= h); + assert(cut_w >= 0 && cut_w <= w); + assert(cut_h >= 0 && cut_h <= h); + + assert(right >= 0 && right <= w); + assert(bot >= 0 && bot <= h); + + assert(top <= bot); + assert(left <= right); + + const float alpha = (float)(cut_w*cut_h) / (float)(w*h); const float beta = 1 - alpha; int c, x, y; From 27da7f85c7512ae2bf4c8f62c10722876655dca2 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 5 Dec 2019 16:14:34 +0300 Subject: [PATCH 0720/1365] Minor fix --- src/image_opencv.cpp | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 6e887b822a8..ada4e8875ce 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -95,17 +95,15 @@ extern "C" { mat_cv *load_image_mat_cv(const char *filename, int flag) { + cv::Mat *mat_ptr = NULL; try { - cv::Mat *mat_ptr = new cv::Mat(); - cv::Mat &mat = *mat_ptr; - mat = cv::imread(filename, flag); + cv::Mat mat = cv::imread(filename, flag); if (mat.empty()) { - delete mat_ptr; std::string shrinked_filename = filename; if (shrinked_filename.length() > 1024) { - shrinked_filename += "name is too long: "; shrinked_filename.resize(1024); + shrinked_filename = std::string("name is too long: ") + shrinked_filename; } cerr << "Cannot load image " << shrinked_filename << std::endl; std::ofstream bad_list("bad.list", std::ios::out | std::ios::app); @@ -113,14 +111,19 @@ mat_cv *load_image_mat_cv(const char *filename, int flag) //if (check_mistakes) getchar(); return NULL; } - if (mat.channels() == 3) cv::cvtColor(mat, mat, cv::COLOR_RGB2BGR); - else if (mat.channels() == 4) cv::cvtColor(mat, mat, cv::COLOR_RGBA2BGRA); + cv::Mat dst; + if (mat.channels() == 3) cv::cvtColor(mat, dst, cv::COLOR_RGB2BGR); + else if (mat.channels() == 4) cv::cvtColor(mat, dst, cv::COLOR_RGBA2BGRA); + else dst = mat; + + mat_ptr = new cv::Mat(dst); return (mat_cv *)mat_ptr; } catch (...) { cerr << "OpenCV exception: load_image_mat_cv \n"; } + if (mat_ptr) delete mat_ptr; return NULL; } // ---------------------------------------- From aaba21bdd336c77561f6c79cb940ef9b52a2b11b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 5 Dec 2019 19:12:46 +0300 Subject: [PATCH 0721/1365] Added ./darknet detector valid for KITTI and BDD (were previously MSCOCO and ImageNet) --- src/detector.c | 139 ++++++++++++++++++++++++++++++++++++++++++++++++- src/utils.c | 11 ++++ src/utils.h | 1 + 3 files changed, 150 insertions(+), 1 deletion(-) diff --git a/src/detector.c b/src/detector.c index c0fa0f39a48..3cbc4488049 100644 --- a/src/detector.c +++ b/src/detector.c @@ -430,6 +430,104 @@ void print_imagenet_detections(FILE *fp, int id, detection *dets, int total, int } } +static void print_kitti_detections(FILE **fps, char *id, detection *dets, int total, int classes, int w, int h, char *outfile, char *prefix) +{ + char *kitti_ids[] = { "car", "pedestrian", "cyclist" }; + FILE *fpd = 0; + char buffd[1024]; + snprintf(buffd, 1024, "%s/%s/data/%s.txt", prefix, outfile, id); + + fpd = fopen(buffd, "w"); + int i, j; + for (i = 0; i < total; ++i) + { + float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.; + float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.; + float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.; + float ymax = dets[i].bbox.y + dets[i].bbox.h / 2.; + + if (xmin < 0) xmin = 0; + if (ymin < 0) ymin = 0; + if (xmax > w) xmax = w; + if (ymax > h) ymax = h; + + for (j = 0; j < classes; ++j) + { + //if (dets[i].prob[j]) fprintf(fpd, "%s 0 0 0 %f %f %f %f -1 -1 -1 -1 0 0 0 %f\n", kitti_ids[j], xmin, ymin, xmax, ymax, dets[i].prob[j]); + if (dets[i].prob[j]) fprintf(fpd, "%s -1 -1 -10 %f %f %f %f -1 -1 -1 -1000 -1000 -1000 -10 %f\n", kitti_ids[j], xmin, ymin, xmax, ymax, dets[i].prob[j]); + } + } + fclose(fpd); +} + +static void eliminate_bdd(char *buf, char *a) +{ + int n = 0; + int i, k; + for (i = 0; buf[i] != '\0'; i++) + { + if (buf[i] == a[n]) + { + k = i; + while (buf[i] == a[n]) + { + if (a[++n] == '\0') + { + for (k; buf[k + n] != '\0'; k++) + { + buf[k] = buf[k + n]; + } + buf[k] = '\0'; + break; + } + i++; + } + n = 0; i--; + } + } +} + +static void get_bdd_image_id(char *filename) +{ + char *p = strrchr(filename, '/'); + eliminate_bdd(p, ".jpg"); + eliminate_bdd(p, "/"); + strcpy(filename, p); +} + +static void print_bdd_detections(FILE *fp, char *image_path, detection *dets, int num_boxes, int classes, int w, int h) +{ + char *bdd_ids[] = { "bike" , "bus" , "car" , "motor" ,"person", "rider", "traffic light", "traffic sign", "train", "truck" }; + get_bdd_image_id(image_path); + int i, j; + + for (i = 0; i < num_boxes; ++i) + { + float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.; + float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.; + float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.; + float ymax = dets[i].bbox.y + dets[i].bbox.h / 2.; + + if (xmin < 0) xmin = 0; + if (ymin < 0) ymin = 0; + if (xmax > w) xmax = w; + if (ymax > h) ymax = h; + + float bx1 = xmin; + float by1 = ymin; + float bx2 = xmax; + float by2 = ymax; + + for (j = 0; j < classes; ++j) + { + if (dets[i].prob[j]) + { + fprintf(fp, "\t{\n\t\t\"name\":\"%s\",\n\t\t\"category\":\"%s\",\n\t\t\"bbox\":[%f, %f, %f, %f],\n\t\t\"score\":%f\n\t},\n", image_path, bdd_ids[j], bx1, by1, bx2, by2, dets[i].prob[j]); + } + } + } +} + void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *outfile) { int j; @@ -462,6 +560,9 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out FILE **fps = 0; int coco = 0; int imagenet = 0; + int bdd = 0; + int kitti = 0; + if (0 == strcmp(type, "coco")) { if (!outfile) outfile = "coco_results"; snprintf(buff, 1024, "%s/%s.json", prefix, outfile); @@ -469,6 +570,23 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out fprintf(fp, "[\n"); coco = 1; } + else if (0 == strcmp(type, "bdd")) { + if (!outfile) outfile = "bdd_results"; + snprintf(buff, 1024, "%s/%s.json", prefix, outfile); + fp = fopen(buff, "w"); + fprintf(fp, "[\n"); + bdd = 1; + } + else if (0 == strcmp(type, "kitti")) { + char buff2[1024]; + if (!outfile) outfile = "kitti_results"; + printf("%s\n", outfile); + snprintf(buff, 1024, "%s/%s", prefix, outfile); + int mkd = make_directory(buff, 0777); + snprintf(buff2, 1024, "%s/%s/data", prefix, outfile); + int mkd2 = make_directory(buff2, 0777); + kitti = 1; + } else if (0 == strcmp(type, "imagenet")) { if (!outfile) outfile = "imagenet-detection"; snprintf(buff, 1024, "%s/%s.txt", prefix, outfile); @@ -478,7 +596,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out } else { if (!outfile) outfile = "comp4_det_test_"; - fps = (FILE**)calloc(classes, sizeof(FILE*)); + fps = (FILE**) calloc(classes, sizeof(FILE *)); for (j = 0; j < classes; ++j) { snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]); fps[j] = fopen(buff, "w"); @@ -542,15 +660,23 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms); else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms); } + if (coco) { print_cocos(fp, path, dets, nboxes, classes, w, h); } else if (imagenet) { print_imagenet_detections(fp, i + t - nthreads + 1, dets, nboxes, classes, w, h); } + else if (bdd) { + print_bdd_detections(fp, path, dets, nboxes, classes, w, h); + } + else if (kitti) { + print_kitti_detections(fps, id, dets, nboxes, classes, w, h, outfile, prefix); + } else { print_detector_detections(fps, id, dets, nboxes, classes, w, h); } + free_detections(dets, nboxes); free(id); free_image(val[t]); @@ -569,6 +695,17 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out #endif fprintf(fp, "\n]\n"); } + + if (bdd) { +#ifdef WIN32 + fseek(fp, -3, SEEK_CUR); +#else + fseek(fp, -2, SEEK_CUR); +#endif + fprintf(fp, "\n]\n"); + fclose(fp); + } + if (fp) fclose(fp); if (val) free(val); diff --git a/src/utils.c b/src/utils.c index b445e5ceef0..4d8e439d476 100644 --- a/src/utils.c +++ b/src/utils.c @@ -14,8 +14,10 @@ #include "gettimeofday.h" #else #include +#include #endif + #ifndef USE_CMAKE_LIBS #pragma warning(disable: 4996) #endif @@ -935,4 +937,13 @@ boxabs box_to_boxabs(const box* b, const int img_w, const int img_h, const int b } return ba; +} + +int make_directory(char *path, int mode) +{ +#ifdef WIN32 + return _mkdir(path); +#else + return mkdir(path, mode); +#endif } \ No newline at end of file diff --git a/src/utils.h b/src/utils.h index 998209067dd..f7730d1c899 100644 --- a/src/utils.h +++ b/src/utils.h @@ -81,6 +81,7 @@ int int_index(int *a, int val, int n); int *random_index_order(int min, int max); int max_int_index(int *a, int n); boxabs box_to_boxabs(const box* b, const int img_w, const int img_h, const int bounds_check); +int make_directory(char *path, int mode); #ifdef __cplusplus } From ebf1d7a73b0d996378de17606425d77a6cea166b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 6 Dec 2019 03:46:34 +0300 Subject: [PATCH 0722/1365] Minor fixes --- src/classifier.c | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index b7976c45541..5545f96ea0a 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -209,14 +209,22 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, destroy_all_windows_cv(); #endif - free_network(net); - free_ptrs((void**)labels, classes); + pthread_join(load_thread, 0); + free_data(buffer); + + //free_network(net); + for (i = 0; i < ngpus; ++i) free_network(nets[i]); + free(nets); + + //free_ptrs((void**)labels, classes); + free(labels); free_ptrs((void**)paths, plist->size); free_list(plist); free(base); free_list_contents_kvp(options); free_list(options); + } From 5d13aad8879e1630145bb90208db518037d707a3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 6 Dec 2019 20:37:08 +0300 Subject: [PATCH 0723/1365] Fixed types in the image_opencv.cpp/h that could lead to a memory leak --- src/http_stream.cpp | 4 +- src/image_opencv.cpp | 140 ++++++++++++++++++++++--------------------- src/image_opencv.h | 10 +++- 3 files changed, 81 insertions(+), 73 deletions(-) diff --git a/src/http_stream.cpp b/src/http_stream.cpp index e68945629f5..4dcdfa5b917 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -524,7 +524,7 @@ class MJPG_sender static std::mutex mtx_mjpeg; -struct mat_cv : cv::Mat { int a[0]; }; +//struct mat_cv : cv::Mat { int a[0]; }; void send_mjpeg(mat_cv* mat, int port, int timeout, int quality) { @@ -532,7 +532,7 @@ void send_mjpeg(mat_cv* mat, int port, int timeout, int quality) std::lock_guard lock(mtx_mjpeg); static MJPG_sender wri(port, timeout, quality); //cv::Mat mat = cv::cvarrToMat(ipl); - wri.write(*mat); + wri.write(*(cv::Mat*)mat); std::cout << " MJPEG-stream sent. \n"; } catch (...) { diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index ada4e8875ce..3ce30e42381 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -78,9 +78,13 @@ using std::endl; extern "C" { - struct mat_cv : cv::Mat { int a[0]; }; - struct cap_cv : cv::VideoCapture { int a[0]; }; - struct write_cv : cv::VideoWriter { int a[0]; }; + //struct mat_cv : cv::Mat { }; + //struct cap_cv : cv::VideoCapture { }; + //struct write_cv : cv::VideoWriter { }; + + //struct mat_cv : cv::Mat { int a[0]; }; + //struct cap_cv : cv::VideoCapture { int a[0]; }; + //struct write_cv : cv::VideoWriter { int a[0]; }; // ==================================================================== // cv::Mat @@ -93,7 +97,7 @@ extern "C" { // IplImage *mat_to_ipl(cv::Mat mat); -mat_cv *load_image_mat_cv(const char *filename, int flag) +extern "C" mat_cv *load_image_mat_cv(const char *filename, int flag) { cv::Mat *mat_ptr = NULL; try { @@ -151,7 +155,7 @@ cv::Mat load_image_mat(char *filename, int channels) } // ---------------------------------------- -image load_image_cv(char *filename, int channels) +extern "C" image load_image_cv(char *filename, int channels) { cv::Mat mat = load_image_mat(filename, channels); @@ -162,7 +166,7 @@ image load_image_cv(char *filename, int channels) } // ---------------------------------------- -image load_image_resize(char *filename, int w, int h, int c, image *im) +extern "C" image load_image_resize(char *filename, int w, int h, int c, image *im) { image out; try { @@ -183,7 +187,7 @@ image load_image_resize(char *filename, int w, int h, int c, image *im) } // ---------------------------------------- -int get_width_mat(mat_cv *mat) +extern "C" int get_width_mat(mat_cv *mat) { if (mat == NULL) { cerr << " Pointer is NULL in get_width_mat() \n"; @@ -193,7 +197,7 @@ int get_width_mat(mat_cv *mat) } // ---------------------------------------- -int get_height_mat(mat_cv *mat) +extern "C" int get_height_mat(mat_cv *mat) { if (mat == NULL) { cerr << " Pointer is NULL in get_height_mat() \n"; @@ -203,7 +207,7 @@ int get_height_mat(mat_cv *mat) } // ---------------------------------------- -void release_mat(mat_cv **mat) +extern "C" void release_mat(mat_cv **mat) { try { cv::Mat **mat_ptr = (cv::Mat **)mat; @@ -219,21 +223,21 @@ void release_mat(mat_cv **mat) // IplImage // ==================================================================== /* -int get_width_cv(mat_cv *ipl_src) +extern "C" int get_width_cv(mat_cv *ipl_src) { IplImage *ipl = (IplImage *)ipl_src; return ipl->width; } // ---------------------------------------- -int get_height_cv(mat_cv *ipl_src) +extern "C" int get_height_cv(mat_cv *ipl_src) { IplImage *ipl = (IplImage *)ipl_src; return ipl->height; } // ---------------------------------------- -void release_ipl(mat_cv **ipl) +extern "C" void release_ipl(mat_cv **ipl) { IplImage **ipl_img = (IplImage **)ipl; if (*ipl_img) cvReleaseImage(ipl_img); @@ -245,7 +249,7 @@ void release_ipl(mat_cv **ipl) // image-to-ipl, ipl-to-image, image_to_mat, mat_to_image // ==================================================================== -mat_cv *image_to_ipl(image im) +extern "C" mat_cv *image_to_ipl(image im) { int x, y, c; IplImage *disp = cvCreateImage(cvSize(im.w, im.h), IPL_DEPTH_8U, im.c); @@ -262,7 +266,7 @@ mat_cv *image_to_ipl(image im) } // ---------------------------------------- -image ipl_to_image(mat_cv* src_ptr) +extern "C" image ipl_to_image(mat_cv* src_ptr) { IplImage* src = (IplImage*)src_ptr; int h = src->height; @@ -300,7 +304,7 @@ IplImage *mat_to_ipl(cv::Mat mat) // ---------------------------------------- */ -cv::Mat image_to_mat(image img) +extern "C" cv::Mat image_to_mat(image img) { int channels = img.c; int width = img.w; @@ -320,7 +324,7 @@ cv::Mat image_to_mat(image img) } // ---------------------------------------- -image mat_to_image(cv::Mat mat) +extern "C" image mat_to_image(cv::Mat mat) { int w = mat.cols; int h = mat.rows; @@ -344,13 +348,13 @@ image mat_to_image(cv::Mat mat) image mat_to_image_cv(mat_cv *mat) { - return mat_to_image(*mat); + return mat_to_image(*(cv::Mat*)mat); } // ==================================================================== // Window // ==================================================================== -void create_window_cv(char const* window_name, int full_screen, int width, int height) +extern "C" void create_window_cv(char const* window_name, int full_screen, int width, int height) { try { int window_type = cv::WINDOW_NORMAL; @@ -369,7 +373,7 @@ void create_window_cv(char const* window_name, int full_screen, int width, int h } // ---------------------------------------- -void destroy_all_windows_cv() +extern "C" void destroy_all_windows_cv() { try { cv::destroyAllWindows(); @@ -380,7 +384,7 @@ void destroy_all_windows_cv() } // ---------------------------------------- -int wait_key_cv(int delay) +extern "C" int wait_key_cv(int delay) { try { return cv::waitKey(delay); @@ -392,13 +396,13 @@ int wait_key_cv(int delay) } // ---------------------------------------- -int wait_until_press_key_cv() +extern "C" int wait_until_press_key_cv() { return wait_key_cv(0); } // ---------------------------------------- -void make_window(char *name, int w, int h, int fullscreen) +extern "C" void make_window(char *name, int w, int h, int fullscreen) { try { cv::namedWindow(name, cv::WINDOW_NORMAL); @@ -427,7 +431,7 @@ static float get_pixel(image m, int x, int y, int c) } // ---------------------------------------- -void show_image_cv(image p, const char *name) +extern "C" void show_image_cv(image p, const char *name) { try { image copy = copy_image(p); @@ -447,7 +451,7 @@ void show_image_cv(image p, const char *name) // ---------------------------------------- /* -void show_image_cv_ipl(mat_cv *disp, const char *name) +extern "C" void show_image_cv_ipl(mat_cv *disp, const char *name) { if (disp == NULL) return; char buff[256]; @@ -458,7 +462,7 @@ void show_image_cv_ipl(mat_cv *disp, const char *name) // ---------------------------------------- */ -void show_image_mat(mat_cv *mat_ptr, const char *name) +extern "C" void show_image_mat(mat_cv *mat_ptr, const char *name) { try { if (mat_ptr == NULL) return; @@ -474,7 +478,7 @@ void show_image_mat(mat_cv *mat_ptr, const char *name) // ==================================================================== // Video Writer // ==================================================================== -write_cv *create_video_writer(char *out_filename, char c1, char c2, char c3, char c4, int fps, int width, int height, int is_color) +extern "C" write_cv *create_video_writer(char *out_filename, char c1, char c2, char c3, char c4, int fps, int width, int height, int is_color) { try { cv::VideoWriter * output_video_writer = @@ -492,18 +496,18 @@ write_cv *create_video_writer(char *out_filename, char c1, char c2, char c3, cha return NULL; } -void write_frame_cv(write_cv *output_video_writer, mat_cv *mat) +extern "C" void write_frame_cv(write_cv *output_video_writer, mat_cv *mat) { try { cv::VideoWriter *out = (cv::VideoWriter *)output_video_writer; - out->write(*mat); + out->write(*(cv::Mat*)mat); } catch (...) { cerr << "OpenCV exception: write_frame_cv \n"; } } -void release_video_writer(write_cv **output_video_writer) +extern "C" void release_video_writer(write_cv **output_video_writer) { try { if (output_video_writer) { @@ -524,7 +528,7 @@ void release_video_writer(write_cv **output_video_writer) } /* -void *open_video_stream(const char *f, int c, int w, int h, int fps) +extern "C" void *open_video_stream(const char *f, int c, int w, int h, int fps) { VideoCapture *cap; if(f) cap = new VideoCapture(f); @@ -537,7 +541,7 @@ void *open_video_stream(const char *f, int c, int w, int h, int fps) } -image get_image_from_stream(void *p) +extern "C" image get_image_from_stream(void *p) { VideoCapture *cap = (VideoCapture *)p; Mat m; @@ -546,7 +550,7 @@ image get_image_from_stream(void *p) return mat_to_image(m); } -int show_image_cv(image im, const char* name, int ms) +extern "C" int show_image_cv(image im, const char* name, int ms) { Mat m = image_to_mat(im); imshow(name, m); @@ -561,7 +565,7 @@ int show_image_cv(image im, const char* name, int ms) // Video Capture // ==================================================================== -cap_cv* get_capture_video_stream(const char *path) { +extern "C" cap_cv* get_capture_video_stream(const char *path) { cv::VideoCapture* cap = NULL; try { cap = new cv::VideoCapture(path); @@ -573,7 +577,7 @@ cap_cv* get_capture_video_stream(const char *path) { } // ---------------------------------------- -cap_cv* get_capture_webcam(int index) +extern "C" cap_cv* get_capture_webcam(int index) { cv::VideoCapture* cap = NULL; try { @@ -588,7 +592,7 @@ cap_cv* get_capture_webcam(int index) } // ---------------------------------------- -void release_capture(cap_cv* cap) +extern "C" void release_capture(cap_cv* cap) { try { cv::VideoCapture *cpp_cap = (cv::VideoCapture *)cap; @@ -600,7 +604,7 @@ void release_capture(cap_cv* cap) } // ---------------------------------------- -mat_cv* get_capture_frame_cv(cap_cv *cap) { +extern "C" mat_cv* get_capture_frame_cv(cap_cv *cap) { cv::Mat *mat = new cv::Mat(); try { if (cap) { @@ -620,7 +624,7 @@ mat_cv* get_capture_frame_cv(cap_cv *cap) { } // ---------------------------------------- -int get_stream_fps_cpp_cv(cap_cv *cap) +extern "C" int get_stream_fps_cpp_cv(cap_cv *cap) { int fps = 25; try { @@ -638,7 +642,7 @@ int get_stream_fps_cpp_cv(cap_cv *cap) } // ---------------------------------------- -double get_capture_property_cv(cap_cv *cap, int property_id) +extern "C" double get_capture_property_cv(cap_cv *cap, int property_id) { try { cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; @@ -651,7 +655,7 @@ double get_capture_property_cv(cap_cv *cap, int property_id) } // ---------------------------------------- -double get_capture_frame_count_cv(cap_cv *cap) +extern "C" double get_capture_frame_count_cv(cap_cv *cap) { try { cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; @@ -668,7 +672,7 @@ double get_capture_frame_count_cv(cap_cv *cap) } // ---------------------------------------- -int set_capture_property_cv(cap_cv *cap, int property_id, double value) +extern "C" int set_capture_property_cv(cap_cv *cap, int property_id, double value) { try { cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; @@ -681,7 +685,7 @@ int set_capture_property_cv(cap_cv *cap, int property_id, double value) } // ---------------------------------------- -int set_capture_position_frame_cv(cap_cv *cap, int index) +extern "C" int set_capture_position_frame_cv(cap_cv *cap, int index) { try { cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; @@ -704,7 +708,7 @@ int set_capture_position_frame_cv(cap_cv *cap, int index) // ... Video Capture // ==================================================================== -image get_image_from_stream_cpp(cap_cv *cap) +extern "C" image get_image_from_stream_cpp(cap_cv *cap) { cv::Mat *src = NULL; static int once = 1; @@ -712,13 +716,13 @@ image get_image_from_stream_cpp(cap_cv *cap) once = 0; do { if (src) delete src; - src = get_capture_frame_cv(cap); + src = (cv::Mat*)get_capture_frame_cv(cap); if (!src) return make_empty_image(0, 0, 0); } while (src->cols < 1 || src->rows < 1 || src->channels() < 1); printf("Video stream: %d x %d \n", src->cols, src->rows); } else - src = get_capture_frame_cv(cap); + src = (cv::Mat*)get_capture_frame_cv(cap); if (!src) return make_empty_image(0, 0, 0); image im = mat_to_image(*src); @@ -728,7 +732,7 @@ image get_image_from_stream_cpp(cap_cv *cap) } // ---------------------------------------- -int wait_for_stream(cap_cv *cap, cv::Mat* src, int dont_close) +extern "C" int wait_for_stream(cap_cv *cap, cv::Mat* src, int dont_close) { if (!src) { if (dont_close) src = new cv::Mat(416, 416, CV_8UC(3)); // cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3); @@ -739,7 +743,7 @@ int wait_for_stream(cap_cv *cap, cv::Mat* src, int dont_close) delete src;// cvReleaseImage(&src); int z = 0; for (z = 0; z < 20; ++z) { - src = get_capture_frame_cv(cap); + src = (cv::Mat*)get_capture_frame_cv(cap); delete src;// cvReleaseImage(&src); } src = new cv::Mat(416, 416, CV_8UC(3)); // cvCreateImage(cvSize(416, 416), IPL_DEPTH_8U, 3); @@ -750,7 +754,7 @@ int wait_for_stream(cap_cv *cap, cv::Mat* src, int dont_close) } // ---------------------------------------- -image get_image_from_stream_resize(cap_cv *cap, int w, int h, int c, mat_cv** in_img, int dont_close) +extern "C" image get_image_from_stream_resize(cap_cv *cap, int w, int h, int c, mat_cv** in_img, int dont_close) { c = c ? c : 3; cv::Mat *src = NULL; @@ -759,13 +763,13 @@ image get_image_from_stream_resize(cap_cv *cap, int w, int h, int c, mat_cv** in if (once) { once = 0; do { - src = get_capture_frame_cv(cap); + src = (cv::Mat*)get_capture_frame_cv(cap); if (!src) return make_empty_image(0, 0, 0); } while (src->cols < 1 || src->rows < 1 || src->channels() < 1); printf("Video stream: %d x %d \n", src->cols, src->rows); } else - src = get_capture_frame_cv(cap); + src = (cv::Mat*)get_capture_frame_cv(cap); if (!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0); @@ -782,7 +786,7 @@ image get_image_from_stream_resize(cap_cv *cap, int w, int h, int c, mat_cv** in } // ---------------------------------------- -image get_image_from_stream_letterbox(cap_cv *cap, int w, int h, int c, mat_cv** in_img, int dont_close) +extern "C" image get_image_from_stream_letterbox(cap_cv *cap, int w, int h, int c, mat_cv** in_img, int dont_close) { c = c ? c : 3; cv::Mat *src = NULL; @@ -790,18 +794,18 @@ image get_image_from_stream_letterbox(cap_cv *cap, int w, int h, int c, mat_cv** if (once) { once = 0; do { - src = get_capture_frame_cv(cap); + src = (cv::Mat*)get_capture_frame_cv(cap); if (!src) return make_empty_image(0, 0, 0); } while (src->cols < 1 || src->rows < 1 || src->channels() < 1); printf("Video stream: %d x %d \n", src->cols, src->rows); } else - src = get_capture_frame_cv(cap); + src = (cv::Mat*)get_capture_frame_cv(cap); if (!wait_for_stream(cap, src, dont_close)) return make_empty_image(0, 0, 0); // passes (cv::Mat *)src while should be (cv::Mat **)src *in_img = (mat_cv *)new cv::Mat(src->rows, src->cols, CV_8UC(c)); - cv::resize(*src, **in_img, (*in_img)->size(), 0, 0, cv::INTER_LINEAR); + cv::resize(*src, **(cv::Mat**)in_img, (*(cv::Mat**)in_img)->size(), 0, 0, cv::INTER_LINEAR); if (c>1) cv::cvtColor(*src, *src, cv::COLOR_RGB2BGR); image tmp = mat_to_image(*src); @@ -821,7 +825,7 @@ image get_image_from_stream_letterbox(cap_cv *cap, int w, int h, int c, mat_cv** extern int stbi_write_png(char const *filename, int w, int h, int comp, const void *data, int stride_in_bytes); extern int stbi_write_jpg(char const *filename, int x, int y, int comp, const void *data, int quality); -void save_mat_png(cv::Mat img_src, const char *name) +extern "C" void save_mat_png(cv::Mat img_src, const char *name) { cv::Mat img_rgb; if (img_src.channels() >= 3) cv::cvtColor(img_src, img_rgb, cv::COLOR_RGB2BGR); @@ -829,7 +833,7 @@ void save_mat_png(cv::Mat img_src, const char *name) } // ---------------------------------------- -void save_mat_jpg(cv::Mat img_src, const char *name) +extern "C" void save_mat_jpg(cv::Mat img_src, const char *name) { cv::Mat img_rgb; if (img_src.channels() >= 3) cv::cvtColor(img_src, img_rgb, cv::COLOR_RGB2BGR); @@ -838,14 +842,14 @@ void save_mat_jpg(cv::Mat img_src, const char *name) // ---------------------------------------- -void save_cv_png(mat_cv *img_src, const char *name) +extern "C" void save_cv_png(mat_cv *img_src, const char *name) { cv::Mat* img = (cv::Mat* )img_src; save_mat_png(*img, name); } // ---------------------------------------- -void save_cv_jpg(mat_cv *img_src, const char *name) +extern "C" void save_cv_jpg(mat_cv *img_src, const char *name) { cv::Mat* img = (cv::Mat*)img_src; save_mat_jpg(*img, name); @@ -856,10 +860,10 @@ void save_cv_jpg(mat_cv *img_src, const char *name) // ==================================================================== // Draw Detection // ==================================================================== -void draw_detections_cv_v3(mat_cv* mat, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output) +extern "C" void draw_detections_cv_v3(mat_cv* mat, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output) { try { - cv::Mat *show_img = mat; + cv::Mat *show_img = (cv::Mat*)mat; int i, j; if (!show_img) return; static int frame_id = 0; @@ -995,7 +999,7 @@ void draw_detections_cv_v3(mat_cv* mat, detection *dets, int num, float thresh, // ==================================================================== // Draw Loss & Accuracy chart // ==================================================================== -mat_cv* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show) +extern "C" mat_cv* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show) { int img_offset = 60; int draw_size = img_size - img_offset; @@ -1054,7 +1058,7 @@ mat_cv* draw_train_chart(float max_img_loss, int max_batches, int number_of_line } // ---------------------------------------- -void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, +extern "C" void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port) { try { @@ -1131,7 +1135,7 @@ void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_im // Data augmentation // ==================================================================== -image image_data_augmentation(mat_cv* mat, int w, int h, +extern "C" image image_data_augmentation(mat_cv* mat, int w, int h, int pleft, int ptop, int swidth, int sheight, int flip, float dhue, float dsat, float dexp, int blur, int num_boxes, float *truth) @@ -1254,7 +1258,7 @@ image image_data_augmentation(mat_cv* mat, int w, int h, } // blend two images with (alpha and beta) -void blend_images_cv(image new_img, float alpha, image old_img, float beta) +extern "C" void blend_images_cv(image new_img, float alpha, image old_img, float beta) { cv::Mat new_mat(cv::Size(new_img.w, new_img.h), CV_32FC(new_img.c), new_img.data);// , size_t step = AUTO_STEP) cv::Mat old_mat(cv::Size(old_img.w, old_img.h), CV_32FC(old_img.c), old_img.data); @@ -1262,7 +1266,7 @@ void blend_images_cv(image new_img, float alpha, image old_img, float beta) } // bilateralFilter bluring -image blur_image(image src_img, int ksize) +extern "C" image blur_image(image src_img, int ksize) { cv::Mat src = image_to_mat(src_img); cv::Mat dst; @@ -1274,7 +1278,7 @@ image blur_image(image src_img, int ksize) // ==================================================================== // Show Anchors // ==================================================================== -void show_acnhors(int number_of_boxes, int num_of_clusters, float *rel_width_height_array, model anchors_data, int width, int height) +extern "C" void show_acnhors(int number_of_boxes, int num_of_clusters, float *rel_width_height_array, model anchors_data, int width, int height) { cv::Mat labels = cv::Mat(number_of_boxes, 1, CV_32SC1); cv::Mat points = cv::Mat(number_of_boxes, 2, CV_32FC1); @@ -1326,7 +1330,7 @@ void show_acnhors(int number_of_boxes, int num_of_clusters, float *rel_width_hei #else // OPENCV -int wait_key_cv(int delay) { return 0; } -int wait_until_press_key_cv() { return 0; } -void destroy_all_windows_cv() {} +extern "C" int wait_key_cv(int delay) { return 0; } +extern "C" int wait_until_press_key_cv() { return 0; } +extern "C" void destroy_all_windows_cv() {} #endif // OPENCV diff --git a/src/image_opencv.h b/src/image_opencv.h index 3bf44da6ed8..6fc07a0674d 100644 --- a/src/image_opencv.h +++ b/src/image_opencv.h @@ -11,9 +11,13 @@ extern "C" { #ifdef OPENCV // declaration -typedef struct mat_cv mat_cv; -typedef struct cap_cv cap_cv; -typedef struct write_cv write_cv; +typedef void* mat_cv; +typedef void* cap_cv; +typedef void* write_cv; + +//typedef struct mat_cv mat_cv; +//typedef struct cap_cv cap_cv; +//typedef struct write_cv write_cv; // cv::Mat mat_cv *load_image_mat_cv(const char *filename, int flag); From c9c745ccf1de97d01cc3c69f81e83011f6439f1a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 6 Dec 2019 23:25:12 +0300 Subject: [PATCH 0724/1365] Added [convolutional] activation=normalize_channels_softmax --- include/darknet.h | 2 +- src/activation_kernels.cu | 79 ++++++++++++++++++++++++++++++++++++ src/activations.c | 60 +++++++++++++++++++++++++++ src/activations.h | 4 ++ src/convolutional_kernels.cu | 3 ++ src/convolutional_layer.c | 3 ++ 6 files changed, 150 insertions(+), 1 deletion(-) diff --git a/include/darknet.h b/include/darknet.h index ebe4db36a08..857a652d80d 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -102,7 +102,7 @@ typedef struct tree { // activations.h typedef enum { - LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, SWISH, MISH, NORM_CHAN + LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, SWISH, MISH, NORM_CHAN, NORM_CHAN_SOFTMAX }ACTIVATION; // parser.h diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index ae5c97a7729..c5b114e3d2e 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -400,6 +400,10 @@ extern "C" void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta else if (a == HARDTAN) gradient_array_hardtan_kernel << > >(x, n, delta); else if (a == RELU) gradient_array_relu_kernel << > >(x, n, delta); else if (a == NORM_CHAN) gradient_array_relu_kernel << > >(x, n, delta); + else if (a == NORM_CHAN_SOFTMAX) { + printf(" Error: should be used custom NORM_CHAN_SOFTMAX-function for gradient \n"); + exit(0); + } else if (a == SELU) gradient_array_selu_kernel << > >(x, n, delta); else gradient_array_kernel << > > (x, n, a, delta); @@ -456,4 +460,79 @@ extern "C" void activate_array_normalize_channels_ongpu(float *x, int n, int bat activate_array_normalize_channels_kernel << > > (x, size, batch, channels, wh_step, output_gpu); CHECK_CUDA(cudaPeekAtLastError()); +} + + + +__global__ void activate_array_normalize_channels_softmax_kernel(float *x, int size, int batch, int channels, int wh_step, float *output_gpu) +{ + int i = blockIdx.x * blockDim.x + threadIdx.x; + + int wh_i = i % wh_step; + int b = i / wh_step; + + const float eps = 0.0001; + if (i < size) { + float sum = eps; + int k; + for (k = 0; k < channels; ++k) { + float val = x[wh_i + k * wh_step + b*wh_step*channels]; + sum += expf(val); + } + for (k = 0; k < channels; ++k) { + float val = x[wh_i + k * wh_step + b*wh_step*channels]; + val = expf(val) / sum; + output_gpu[wh_i + k * wh_step + b*wh_step*channels] = val; + } + } +} + +extern "C" void activate_array_normalize_channels_softmax_ongpu(float *x, int n, int batch, int channels, int wh_step, float *output_gpu) +{ + // n = w*h*c*batch + // size = w*h*batch + int size = n / channels; + + const int num_blocks = get_number_of_blocks(size, BLOCK); + + activate_array_normalize_channels_softmax_kernel << > > (x, size, batch, channels, wh_step, output_gpu); + CHECK_CUDA(cudaPeekAtLastError()); +} + + + +__global__ void gradient_array_normalize_channels_softmax_kernel(float *x, int size, int batch, int channels, int wh_step, float *delta_gpu) +{ + int i = blockIdx.x * blockDim.x + threadIdx.x; + + int wh_i = i % wh_step; + int b = i / wh_step; + + const float eps = 0.0001; + if (i < size) { + float grad = eps; + int k; + for (k = 0; k < channels; ++k) { + float out = x[wh_i + k * wh_step + b*wh_step*channels]; + float delta = delta_gpu[wh_i + k * wh_step + b*wh_step*channels]; + grad += out*delta; + } + for (k = 0; k < channels; ++k) { + float delta = delta_gpu[wh_i + k * wh_step + b*wh_step*channels]; + delta = delta * grad; + delta_gpu[wh_i + k * wh_step + b*wh_step*channels] = delta; + } + } +} + +extern "C" void gradient_array_normalize_channels_softmax_ongpu(float *output_gpu, int n, int batch, int channels, int wh_step, float *delta_gpu) +{ + // n = w*h*c*batch + // size = w*h*batch + int size = n / channels; + + const int num_blocks = get_number_of_blocks(size, BLOCK); + + gradient_array_normalize_channels_softmax_kernel << > > (output_gpu, size, batch, channels, wh_step, delta_gpu); + CHECK_CUDA(cudaPeekAtLastError()); } \ No newline at end of file diff --git a/src/activations.c b/src/activations.c index 41b33665912..b5743b6702a 100644 --- a/src/activations.c +++ b/src/activations.c @@ -48,6 +48,7 @@ ACTIVATION get_activation(char *s) if (strcmp(s, "swish") == 0) return SWISH; if (strcmp(s, "mish") == 0) return MISH; if (strcmp(s, "normalize_channels") == 0) return NORM_CHAN; + if (strcmp(s, "normalize_channels_softmax") == 0) return NORM_CHAN_SOFTMAX; if (strcmp(s, "loggy")==0) return LOGGY; if (strcmp(s, "relu")==0) return RELU; if (strcmp(s, "elu")==0) return ELU; @@ -176,6 +177,61 @@ void activate_array_normalize_channels(float *x, const int n, int batch, int cha } } +void activate_array_normalize_channels_softmax(float *x, const int n, int batch, int channels, int wh_step, float *output) +{ + int size = n / channels; + + int i; + #pragma omp parallel for + for (i = 0; i < size; ++i) { + int wh_i = i % wh_step; + int b = i / wh_step; + + const float eps = 0.0001; + if (i < size) { + float sum = eps; + int k; + for (k = 0; k < channels; ++k) { + float val = x[wh_i + k * wh_step + b*wh_step*channels]; + sum += expf(val); + } + for (k = 0; k < channels; ++k) { + float val = x[wh_i + k * wh_step + b*wh_step*channels]; + val = expf(val) / sum; + output[wh_i + k * wh_step + b*wh_step*channels] = val; + } + } + } +} + +void gradient_array_normalize_channels_softmax(float *x, const int n, int batch, int channels, int wh_step, float *delta) +{ + int size = n / channels; + + int i; + #pragma omp parallel for + for (i = 0; i < size; ++i) { + int wh_i = i % wh_step; + int b = i / wh_step; + + const float eps = 0.0001; + if (i < size) { + float grad = eps; + int k; + for (k = 0; k < channels; ++k) { + float out = x[wh_i + k * wh_step + b*wh_step*channels]; + float d = delta[wh_i + k * wh_step + b*wh_step*channels]; + grad += out*d; + } + for (k = 0; k < channels; ++k) { + float d = delta[wh_i + k * wh_step + b*wh_step*channels]; + d = d * grad; + delta[wh_i + k * wh_step + b*wh_step*channels] = d; + } + } + } +} + float gradient(float x, ACTIVATION a) { switch(a){ @@ -189,6 +245,10 @@ float gradient(float x, ACTIVATION a) return relu_gradient(x); case NORM_CHAN: return relu_gradient(x); + case NORM_CHAN_SOFTMAX: + printf(" Error: should be used custom NORM_CHAN_SOFTMAX-function for gradient \n"); + exit(0); + return 0; case ELU: return elu_gradient(x); case SELU: diff --git a/src/activations.h b/src/activations.h index dff25820da7..e2a32aa7abb 100644 --- a/src/activations.h +++ b/src/activations.h @@ -23,6 +23,7 @@ void activate_array(float *x, const int n, const ACTIVATION a); void activate_array_swish(float *x, const int n, float * output_sigmoid, float * output); void activate_array_mish(float *x, const int n, float * activation_input, float * output); void activate_array_normalize_channels(float *x, const int n, int batch, int channels, int wh_step, float *output); +void activate_array_normalize_channels_softmax(float *x, const int n, int batch, int channels, int wh_step, float *output); #ifdef GPU void activate_array_ongpu(float *x, int n, ACTIVATION a); void activate_array_swish_ongpu(float *x, int n, float *output_sigmoid_gpu, float *output_gpu); @@ -31,6 +32,9 @@ void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta); void gradient_array_swish_ongpu(float *x, int n, float *sigmoid_gpu, float *delta); void gradient_array_mish_ongpu(int n, float *activation_input_gpu, float *delta); void activate_array_normalize_channels_ongpu(float *x, int n, int batch, int channels, int wh_step, float *output_gpu); +void activate_array_normalize_channels_softmax_ongpu(float *x, int n, int batch, int channels, int wh_step, float *output_gpu); +void gradient_array_normalize_channels_softmax_ongpu(float *output_gpu, int n, int batch, int channels, int wh_step, float *delta_gpu); + #endif static inline float stair_activate(float x) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 5b7c08192cf..256e9e19c9a 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -395,6 +395,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == MISH) activate_array_mish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == NORM_CHAN) activate_array_normalize_channels_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu); + else if (l.activation == NORM_CHAN_SOFTMAX) activate_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu); else if (l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if(l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if (l.binary || l.xnor) swap_binary(&l); @@ -601,6 +602,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == MISH) activate_array_mish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == NORM_CHAN) activate_array_normalize_channels_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu); + else if (l.activation == NORM_CHAN_SOFTMAX) activate_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu); else if (l.activation != LINEAR) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if(l.dot > 0) dot_error_gpu(l); if(l.binary || l.xnor) swap_binary(&l); @@ -645,6 +647,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state if (l.activation == SWISH) gradient_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.delta_gpu); else if (l.activation == MISH) gradient_array_mish_ongpu(l.outputs*l.batch, l.activation_input_gpu, l.delta_gpu); + else if (l.activation == NORM_CHAN_SOFTMAX) gradient_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu); else gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); if (!l.batch_normalize) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 834678a615c..5c014399b2d 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -1204,6 +1204,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) if (l.activation == SWISH) activate_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.output); else if (l.activation == MISH) activate_array_mish(l.output, l.outputs*l.batch, l.activation_input, l.output); else if (l.activation == NORM_CHAN) activate_array_normalize_channels(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output); + else if (l.activation == NORM_CHAN_SOFTMAX) activate_array_normalize_channels_softmax(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output); else activate_array_cpu_custom(l.output, m*n*l.batch, l.activation); return; @@ -1245,6 +1246,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) if (l.activation == SWISH) activate_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.output); else if (l.activation == MISH) activate_array_mish(l.output, l.outputs*l.batch, l.activation_input, l.output); else if (l.activation == NORM_CHAN) activate_array_normalize_channels(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output); + else if (l.activation == NORM_CHAN_SOFTMAX) activate_array_normalize_channels_softmax(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output); else activate_array_cpu_custom(l.output, l.outputs*l.batch, l.activation); if(l.binary || l.xnor) swap_binary(&l); @@ -1383,6 +1385,7 @@ void backward_convolutional_layer(convolutional_layer l, network_state state) if (l.activation == SWISH) gradient_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.delta); else if (l.activation == MISH) gradient_array_mish(l.outputs*l.batch, l.activation_input, l.delta); + else if (l.activation == NORM_CHAN_SOFTMAX) gradient_array_normalize_channels_softmax(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.delta); else gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); if (l.batch_normalize) { From 4f52ba1a25ade35119cefc3840ef65a509851809 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 7 Dec 2019 00:22:06 +0300 Subject: [PATCH 0725/1365] Remove eps=0.0001 from gradient of normalize_channels_softmax --- src/activation_kernels.cu | 4 ++-- src/activations.c | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index c5b114e3d2e..8cf148a85ad 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -508,9 +508,9 @@ __global__ void gradient_array_normalize_channels_softmax_kernel(float *x, int s int wh_i = i % wh_step; int b = i / wh_step; - const float eps = 0.0001; + //const float eps = 0.0001; if (i < size) { - float grad = eps; + float grad = 0;// eps; int k; for (k = 0; k < channels; ++k) { float out = x[wh_i + k * wh_step + b*wh_step*channels]; diff --git a/src/activations.c b/src/activations.c index b5743b6702a..8a8ab862e1a 100644 --- a/src/activations.c +++ b/src/activations.c @@ -214,9 +214,9 @@ void gradient_array_normalize_channels_softmax(float *x, const int n, int batch, int wh_i = i % wh_step; int b = i / wh_step; - const float eps = 0.0001; + //const float eps = 0.0001; if (i < size) { - float grad = eps; + float grad = 0;// eps; int k; for (k = 0; k < channels; ++k) { float out = x[wh_i + k * wh_step + b*wh_step*channels]; From 87f36b79f54ac0b84512a3c1c4284d31d100f6f7 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 7 Dec 2019 16:09:26 +0300 Subject: [PATCH 0726/1365] Another version of mosaic=1 for Detector --- src/data.c | 92 ++++++++++++++++++++++++++++++++---------------- src/yolo_layer.c | 4 +++ 2 files changed, 65 insertions(+), 31 deletions(-) diff --git a/src/data.c b/src/data.c index 7c964398dd4..a253b5676c3 100644 --- a/src/data.c +++ b/src/data.c @@ -812,7 +812,7 @@ void blend_truth(float *new_truth, int boxes, float *old_truth) } -void blend_truth_mosaic(float *new_truth, int boxes, float *old_truth, int w, int h, int cut_x, int cut_y, int i_mixup) +void blend_truth_mosaic(float *new_truth, int boxes, float *old_truth, int w, int h, float cut_x, float cut_y, int i_mixup) { const int t_size = 4 + 1; int count_new_truth = 0; @@ -835,38 +835,64 @@ void blend_truth_mosaic(float *new_truth, int boxes, float *old_truth, int w, in float wb = old_truth_ptr[2]; float hb = old_truth_ptr[3]; + // shift 4 images + if (i_mixup == 0) { + xb = xb - (float)(w - cut_x) / w; + yb = yb - (float)(h - cut_y) / h; + } + if (i_mixup == 1) { + xb = xb + (float)cut_x / w; + yb = yb - (float)(h - cut_y) / h; + } + if (i_mixup == 2) { + xb = xb - (float)(w - cut_x) / w; + yb = yb + (float)cut_y / h; + } + if (i_mixup == 3) { + xb = xb + (float)cut_x / w; + yb = yb + (float)cut_y / h; + } + int left = (xb - wb / 2)*w; int right = (xb + wb / 2)*w; int top = (yb - hb / 2)*h; int bot = (yb + hb / 2)*h; - if ((i_mixup == 0 && left < cut_x && top < cut_y) || - (i_mixup == 1 && right > cut_x && top < cut_y) || - (i_mixup == 2 && left < cut_x && bot > cut_y) || - (i_mixup == 3 && right > cut_x && bot > cut_y)) - { - if ((i_mixup == 0 || i_mixup == 2) && right > cut_x) { - float diff_x = (float)(right - cut_x) / w; - xb = xb - diff_x / 2; - wb = wb - diff_x; - } - if ((i_mixup == 1 || i_mixup == 3) && left < cut_x) { - float diff_x = (float)(cut_x - left) / w; - xb = xb + diff_x / 2; - wb = wb - diff_x; - } + // fix out of bound + if (left < 0) { + float diff = (float)left / w; + xb = xb - diff / 2; + wb = wb + diff; + } - if ((i_mixup == 0 || i_mixup == 1) && bot > cut_y) { - float diff_y = (float)(bot - cut_y) / h; - yb = yb - diff_y / 2; - hb = hb - diff_y; - } - if ((i_mixup == 2 || i_mixup == 3) && top < cut_y) { - float diff_y = (float)(cut_y - top) / h; - yb = yb + diff_y / 2; - hb = hb - diff_y; - } + if (right > w) { + float diff = (float)(right - w) / w; + xb = xb - diff / 2; + wb = wb - diff; + } + + if (top < 0) { + float diff = (float)top / h; + yb = yb - diff / 2; + hb = hb + diff; + } + if (bot > h) { + float diff = (float)(bot - h) / h; + yb = yb - diff / 2; + hb = hb - diff; + } + + left = (xb - wb / 2)*w; + right = (xb + wb / 2)*w; + top = (yb - hb / 2)*h; + bot = (yb + hb / 2)*h; + + // leave only within the image + if(left >= 0 && right <= w && top >= 0 && bot <= h && + wb > 0 && wb < 1 && hb > 0 && hb < 1 && + xb > 0 && xb < 1 && yb > 0 && yb < 1) + { new_truth_ptr[0] = xb; new_truth_ptr[1] = yb; new_truth_ptr[2] = wb; @@ -889,7 +915,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo c = c ? c : 3; assert(use_mixup != 2); - if (random_gen() % 2 == 0) use_mixup = 0; + //if (random_gen() % 2 == 0) use_mixup = 0; int i; int *cut_x = NULL, *cut_y = NULL; @@ -1046,16 +1072,20 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo for (y = 0; y < h; ++y) { int j = y*w + k*w*h; if (i_mixup == 0 && y < cut_y[i]) { - memcpy(&d.X.vals[i][j + 0], &ai.data[j + 0], cut_x[i] * sizeof(float)); + int j_src = (w - cut_x[i]) + (y + h - cut_y[i])*w + k*w*h; + memcpy(&d.X.vals[i][j + 0], &ai.data[j_src], cut_x[i] * sizeof(float)); } if (i_mixup == 1 && y < cut_y[i]) { - memcpy(&d.X.vals[i][j + cut_x[i]], &ai.data[j + cut_x[i]], (w-cut_x[i]) * sizeof(float)); + int j_src = (y + h - cut_y[i])*w + k*w*h; + memcpy(&d.X.vals[i][j + cut_x[i]], &ai.data[j_src], (w-cut_x[i]) * sizeof(float)); } if (i_mixup == 2 && y >= cut_y[i]) { - memcpy(&d.X.vals[i][j + 0], &ai.data[j + 0], cut_x[i] * sizeof(float)); + int j_src = (w - cut_x[i]) + (y - cut_y[i])*w + k*w*h; + memcpy(&d.X.vals[i][j + 0], &ai.data[j_src], cut_x[i] * sizeof(float)); } if (i_mixup == 3 && y >= cut_y[i]) { - memcpy(&d.X.vals[i][j + cut_x[i]], &ai.data[j + cut_x[i]], (w - cut_x[i]) * sizeof(float)); + int j_src = (y - cut_y[i])*w + k*w*h; + memcpy(&d.X.vals[i][j + cut_x[i]], &ai.data[j_src], (w - cut_x[i]) * sizeof(float)); } } } diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 36a8d5645b8..6e45299f21c 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -369,7 +369,11 @@ void forward_yolo_layer(const layer l, network_state state) for (t = 0; t < l.max_boxes; ++t) { box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); if (truth.x < 0 || truth.y < 0 || truth.x > 1 || truth.y > 1 || truth.w < 0 || truth.h < 0) { + char buff[256]; printf(" Wrong label: truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f \n", truth.x, truth.y, truth.w, truth.h); + sprintf(buff, "echo \"Wrong label: truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f\" >> bad_label.list", + truth.x, truth.y, truth.w, truth.h); + system(buff); } int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; if (class_id >= l.classes) continue; // if label contains class_id more than number of classes in the cfg-file From 13f064f1be1ccf28fae002349ba53fbf61a122a5 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 7 Dec 2019 17:35:30 +0300 Subject: [PATCH 0727/1365] Improved mosaic=1 for Detector --- src/data.c | 48 ++++++++++++++++++++++++++++++++---------------- 1 file changed, 32 insertions(+), 16 deletions(-) diff --git a/src/data.c b/src/data.c index a253b5676c3..89d00e56367 100644 --- a/src/data.c +++ b/src/data.c @@ -812,7 +812,8 @@ void blend_truth(float *new_truth, int boxes, float *old_truth) } -void blend_truth_mosaic(float *new_truth, int boxes, float *old_truth, int w, int h, float cut_x, float cut_y, int i_mixup) +void blend_truth_mosaic(float *new_truth, int boxes, float *old_truth, int w, int h, float cut_x, float cut_y, int i_mixup, + int left_shift, int right_shift, int top_shift, int bot_shift) { const int t_size = 4 + 1; int count_new_truth = 0; @@ -835,22 +836,24 @@ void blend_truth_mosaic(float *new_truth, int boxes, float *old_truth, int w, in float wb = old_truth_ptr[2]; float hb = old_truth_ptr[3]; + + // shift 4 images if (i_mixup == 0) { - xb = xb - (float)(w - cut_x) / w; - yb = yb - (float)(h - cut_y) / h; + xb = xb - (float)(w - cut_x - right_shift) / w; + yb = yb - (float)(h - cut_y - bot_shift) / h; } if (i_mixup == 1) { - xb = xb + (float)cut_x / w; - yb = yb - (float)(h - cut_y) / h; + xb = xb + (float)(cut_x - left_shift) / w; + yb = yb - (float)(h - cut_y - bot_shift) / h; } if (i_mixup == 2) { - xb = xb - (float)(w - cut_x) / w; - yb = yb + (float)cut_y / h; + xb = xb - (float)(w - cut_x - right_shift) / w; + yb = yb + (float)(cut_y - top_shift) / h; } if (i_mixup == 3) { - xb = xb + (float)cut_x / w; - yb = yb + (float)cut_y / h; + xb = xb + (float)(cut_x - left_shift) / w; + yb = yb + (float)(cut_y - top_shift) / h; } int left = (xb - wb / 2)*w; @@ -915,7 +918,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo c = c ? c : 3; assert(use_mixup != 2); - //if (random_gen() % 2 == 0) use_mixup = 0; + if (random_gen() % 2 == 0) use_mixup = 0; int i; int *cut_x = NULL, *cut_y = NULL; @@ -1035,7 +1038,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo int min_w_h = fill_truth_detection(filename, boxes, truth, classes, flip, dx, dy, 1. / sx, 1. / sy, w, h); - if (min_w_h / 8 < blur && blur > 1) blur = min_w_h / 8; // disable blur if one of the objects is too small + if ((min_w_h / 8) < blur && blur > 1) blur = min_w_h / 8; // disable blur if one of the objects is too small image ai = image_data_augmentation(src, w, h, pleft, ptop, swidth, sheight, flip, dhue, dsat, dexp, blur, boxes, d.y.vals[i]); @@ -1067,30 +1070,43 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo d.X.vals[i] = tmp_img.data; } + if (flip) { + int tmp = pleft; + pleft = pright; + pright = tmp; + } + + const int left_shift = min(cut_x[i], max(0, (-pleft*w / ow))); + const int top_shift = min(cut_y[i], max(0, (-ptop*h / oh))); + + const int right_shift = min((w - cut_x[i]), max(0, (-pright*w / ow))); + const int bot_shift = min(h - cut_y[i], max(0, (-pbot*h / oh))); + + int k, x, y; for (k = 0; k < c; ++k) { for (y = 0; y < h; ++y) { int j = y*w + k*w*h; if (i_mixup == 0 && y < cut_y[i]) { - int j_src = (w - cut_x[i]) + (y + h - cut_y[i])*w + k*w*h; + int j_src = (w - cut_x[i] - right_shift) + (y + h - cut_y[i] - bot_shift)*w + k*w*h; memcpy(&d.X.vals[i][j + 0], &ai.data[j_src], cut_x[i] * sizeof(float)); } if (i_mixup == 1 && y < cut_y[i]) { - int j_src = (y + h - cut_y[i])*w + k*w*h; + int j_src = left_shift + (y + h - cut_y[i] - bot_shift)*w + k*w*h; memcpy(&d.X.vals[i][j + cut_x[i]], &ai.data[j_src], (w-cut_x[i]) * sizeof(float)); } if (i_mixup == 2 && y >= cut_y[i]) { - int j_src = (w - cut_x[i]) + (y - cut_y[i])*w + k*w*h; + int j_src = (w - cut_x[i] - right_shift) + (top_shift + y - cut_y[i])*w + k*w*h; memcpy(&d.X.vals[i][j + 0], &ai.data[j_src], cut_x[i] * sizeof(float)); } if (i_mixup == 3 && y >= cut_y[i]) { - int j_src = (y - cut_y[i])*w + k*w*h; + int j_src = left_shift + (top_shift + y - cut_y[i])*w + k*w*h; memcpy(&d.X.vals[i][j + cut_x[i]], &ai.data[j_src], (w - cut_x[i]) * sizeof(float)); } } } - blend_truth_mosaic(d.y.vals[i], boxes, truth, w, h, cut_x[i], cut_y[i], i_mixup); + blend_truth_mosaic(d.y.vals[i], boxes, truth, w, h, cut_x[i], cut_y[i], i_mixup, left_shift, right_shift, top_shift, bot_shift); free_image(ai); ai.data = d.X.vals[i]; From 3aa2e45ad369c72622f9458b6ebc7abb24226879 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 7 Dec 2019 17:42:01 +0300 Subject: [PATCH 0728/1365] Compile fix --- src/data.c | 8 ++++---- src/utils.h | 3 +++ 2 files changed, 7 insertions(+), 4 deletions(-) diff --git a/src/data.c b/src/data.c index 89d00e56367..d6f162fcff7 100644 --- a/src/data.c +++ b/src/data.c @@ -1076,11 +1076,11 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo pright = tmp; } - const int left_shift = min(cut_x[i], max(0, (-pleft*w / ow))); - const int top_shift = min(cut_y[i], max(0, (-ptop*h / oh))); + const int left_shift = min_val_cmp(cut_x[i], max_val_cmp(0, (-pleft*w / ow))); + const int top_shift = min_val_cmp(cut_y[i], max_val_cmp(0, (-ptop*h / oh))); - const int right_shift = min((w - cut_x[i]), max(0, (-pright*w / ow))); - const int bot_shift = min(h - cut_y[i], max(0, (-pbot*h / oh))); + const int right_shift = min_val_cmp((w - cut_x[i]), max_val_cmp(0, (-pright*w / ow))); + const int bot_shift = min_val_cmp(h - cut_y[i], max_val_cmp(0, (-pbot*h / oh))); int k, x, y; diff --git a/src/utils.h b/src/utils.h index f7730d1c899..9fa05fa7acc 100644 --- a/src/utils.h +++ b/src/utils.h @@ -83,6 +83,9 @@ int max_int_index(int *a, int n); boxabs box_to_boxabs(const box* b, const int img_w, const int img_h, const int bounds_check); int make_directory(char *path, int mode); +#define max_val_cmp(a,b) (((a) > (b)) ? (a) : (b)) +#define min_val_cmp(a,b) (((a) < (b)) ? (a) : (b)) + #ifdef __cplusplus } #endif From ffb4a1df67c7ec9ab7d23d35c03bc219fbf00344 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 8 Dec 2019 00:26:23 +0300 Subject: [PATCH 0729/1365] Compile fix --- src/activations.h | 1 + src/data.c | 3 ++- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/src/activations.h b/src/activations.h index e2a32aa7abb..127e372f9d0 100644 --- a/src/activations.h +++ b/src/activations.h @@ -24,6 +24,7 @@ void activate_array_swish(float *x, const int n, float * output_sigmoid, float * void activate_array_mish(float *x, const int n, float * activation_input, float * output); void activate_array_normalize_channels(float *x, const int n, int batch, int channels, int wh_step, float *output); void activate_array_normalize_channels_softmax(float *x, const int n, int batch, int channels, int wh_step, float *output); +void gradient_array_normalize_channels_softmax(float *x, const int n, int batch, int channels, int wh_step, float *delta); #ifdef GPU void activate_array_ongpu(float *x, int n, ACTIVATION a); void activate_array_swish_ongpu(float *x, int n, float *output_sigmoid_gpu, float *output_gpu); diff --git a/src/data.c b/src/data.c index d6f162fcff7..097dae04924 100644 --- a/src/data.c +++ b/src/data.c @@ -827,6 +827,7 @@ void blend_truth_mosaic(float *new_truth, int boxes, float *old_truth, int w, in int new_t = count_new_truth; for (t = count_new_truth; t < boxes; ++t) { float *new_truth_ptr = new_truth + new_t*t_size; + new_truth_ptr[0] = 0; float *old_truth_ptr = old_truth + (t - count_new_truth)*t_size; float x = old_truth_ptr[0]; if (!x) break; @@ -1041,7 +1042,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo if ((min_w_h / 8) < blur && blur > 1) blur = min_w_h / 8; // disable blur if one of the objects is too small image ai = image_data_augmentation(src, w, h, pleft, ptop, swidth, sheight, flip, dhue, dsat, dexp, - blur, boxes, d.y.vals[i]); + blur, boxes, truth); if (use_mixup == 0) { d.X.vals[i] = ai.data; From 67e94dc1ded99b6d53637fa56e76e03731088e58 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 8 Dec 2019 01:03:56 +0300 Subject: [PATCH 0730/1365] Minor fix --- src/utils.h | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/utils.h b/src/utils.h index 9fa05fa7acc..421a313dffc 100644 --- a/src/utils.h +++ b/src/utils.h @@ -6,6 +6,10 @@ #include #include +#ifndef M_PI +#define M_PI 3.14159265358979323846 // pi +#endif + #ifdef __cplusplus extern "C" { #endif From 318919e1cbb362aac6cb5c3d9388735f9ab594b6 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 8 Dec 2019 16:39:28 +0300 Subject: [PATCH 0731/1365] Added label_smooth_eps=0.1 for [yolo] and [Gaussian_yolo] layer for Label Smoothing --- include/darknet.h | 1 + src/detector.c | 2 +- src/gaussian_yolo_layer.c | 12 ++++++------ src/parser.c | 2 ++ src/yolo_layer.c | 12 ++++++------ 5 files changed, 16 insertions(+), 13 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 857a652d80d..eea68034a5e 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -257,6 +257,7 @@ struct layer { float learning_rate_scale; float clip; int focal_loss; + float label_smooth_eps; int noloss; int softmax; int classes; diff --git a/src/detector.c b/src/detector.c index 3cbc4488049..98c6fe8bb62 100644 --- a/src/detector.c +++ b/src/detector.c @@ -141,7 +141,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i args.show_imgs = show_imgs; #ifdef OPENCV - args.threads = 3 * ngpus; // Amazon EC2 Tesla V100: p3.2xlarge (8 logical cores) - p3.16xlarge + args.threads = 6 * ngpus; // 3 for - Amazon EC2 Tesla V100: p3.2xlarge (8 logical cores) - p3.16xlarge //args.threads = 12 * ngpus; // Ryzen 7 2700X (16 logical cores) mat_cv* img = NULL; float max_img_loss = 5; diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index eb64202f13f..e9e9312ed18 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -330,16 +330,16 @@ void averages_gaussian_yolo_deltas(int class_index, int box_index, int stride, i } } -void delta_gaussian_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat) +void delta_gaussian_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, float label_smooth_eps) { int n; if (delta[index]){ - delta[index + stride*class_id] = 1 - output[index + stride*class_id]; + delta[index + stride*class_id] = (1 - label_smooth_eps) - output[index + stride*class_id]; if(avg_cat) *avg_cat += output[index + stride*class_id]; return; } for(n = 0; n < classes; ++n){ - delta[index + stride*n] = ((n == class_id)?1 : 0) - output[index + stride*n]; + delta[index + stride*n] = ((n == class_id) ? (1 - label_smooth_eps) : (0 + label_smooth_eps/classes)) - output[index + stride*n]; if(n == class_id && avg_cat) *avg_cat += output[index + stride*n]; } } @@ -452,7 +452,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int class_id = state.truth[best_t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9); - delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0); + delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.label_smooth_eps); box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); delta_gaussian_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, l.uc_normalizer, 1, l.yolo_point); } @@ -506,7 +506,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; int class_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 9); - delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat); + delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.label_smooth_eps); ++count; ++class_count; @@ -537,7 +537,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; int class_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 9); - delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat); + delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.label_smooth_eps); ++count; ++class_count; diff --git a/src/parser.c b/src/parser.c index 0a86bf1cf1b..7c8d3438208 100644 --- a/src/parser.c +++ b/src/parser.c @@ -365,6 +365,7 @@ layer parse_yolo(list *options, size_params params) } //assert(l.outputs == params.inputs); + l.label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1); l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75); l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1); @@ -456,6 +457,7 @@ layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3 } //assert(l.outputs == params.inputs); + l.label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1); l.uc_normalizer = option_find_float_quiet(options, "uc_normalizer", 1.0); l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 6e45299f21c..47ce7257c1d 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -217,11 +217,11 @@ void averages_yolo_deltas(int class_index, int box_index, int stride, int classe } } -void delta_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, int focal_loss) +void delta_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, int focal_loss, float label_smooth_eps) { int n; if (delta[index + stride*class_id]){ - delta[index + stride*class_id] = 1 - output[index + stride*class_id]; + delta[index + stride*class_id] = (1 - label_smooth_eps) - output[index + stride*class_id]; if(avg_cat) *avg_cat += output[index + stride*class_id]; return; } @@ -248,7 +248,7 @@ void delta_yolo_class(float *output, float *delta, int index, int class_id, int else { // default for (n = 0; n < classes; ++n) { - delta[index + stride*n] = ((n == class_id) ? 1 : 0) - output[index + stride*n]; + delta[index + stride*n] = ((n == class_id) ? (1 - label_smooth_eps) : (0 + label_smooth_eps/classes)) - output[index + stride*n]; if (n == class_id && avg_cat) *avg_cat += output[index + stride*n]; } } @@ -359,7 +359,7 @@ void forward_yolo_layer(const layer l, network_state state) int class_id = state.truth[best_t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); - delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss); + delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss, l.label_smooth_eps); box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, 1); } @@ -421,7 +421,7 @@ void forward_yolo_layer(const layer l, network_state state) int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); - delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss); + delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps); ++count; ++class_count; @@ -463,7 +463,7 @@ void forward_yolo_layer(const layer l, network_state state) int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); - delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss); + delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps); ++count; ++class_count; From 2a873f34485c75d44a346f92ba7dcf2e2aa57a15 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 8 Dec 2019 17:25:01 +0300 Subject: [PATCH 0732/1365] Added label_smooth_eps=0.1 for [net] layer for Label Smoothing for Classifier --- include/darknet.h | 2 ++ src/classifier.c | 1 + src/data.c | 44 +++++++++++++++++++++++++++++++++++--------- src/data.h | 3 ++- src/parser.c | 1 + 5 files changed, 41 insertions(+), 10 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index eea68034a5e..823410255f1 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -638,6 +638,7 @@ typedef struct network { int flip; // horizontal flip 50% probability augmentaiont for classifier training (default = 1) int blur; int mixup; + float label_smooth_eps; int letter_box; float angle; float aspect; @@ -813,6 +814,7 @@ typedef struct load_args { int flip; int blur; int mixup; + float label_smooth_eps; float angle; float aspect; float saturation; diff --git a/src/classifier.c b/src/classifier.c index 5545f96ea0a..f5e2b1a92c0 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -92,6 +92,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, args.hue = net.hue; args.size = net.w > net.h ? net.w : net.h; + args.label_smooth_eps = net.label_smooth_eps; args.mixup = net.mixup; if (dont_show && show_imgs) show_imgs = 2; args.show_imgs = show_imgs; diff --git a/src/data.c b/src/data.c index 097dae04924..85fceae8368 100644 --- a/src/data.c +++ b/src/data.c @@ -516,6 +516,32 @@ void fill_truth(char *path, char **labels, int k, float *truth) } } +void fill_truth_smooth(char *path, char **labels, int k, float *truth, float label_smooth_eps) +{ + int i; + memset(truth, 0, k * sizeof(float)); + int count = 0; + for (i = 0; i < k; ++i) { + if (strstr(path, labels[i])) { + truth[i] = (1 - label_smooth_eps); + ++count; + } + else { + truth[i] = label_smooth_eps / (k - 1); + } + } + if (count != 1) { + printf("Too many or too few labels: %d, %s\n", count, path); + count = 0; + for (i = 0; i < k; ++i) { + if (strstr(path, labels[i])) { + printf("\t label %d: %s \n", count, labels[i]); + count++; + } + } + } +} + void fill_hierarchy(float *truth, int k, tree *hierarchy) { int j; @@ -548,12 +574,12 @@ void fill_hierarchy(float *truth, int k, tree *hierarchy) } } -matrix load_labels_paths(char **paths, int n, char **labels, int k, tree *hierarchy) +matrix load_labels_paths(char **paths, int n, char **labels, int k, tree *hierarchy, float label_smooth_eps) { matrix y = make_matrix(n, k); int i; for(i = 0; i < n && labels; ++i){ - fill_truth(paths[i], labels, k, y.vals[i]); + fill_truth_smooth(paths[i], labels, k, y.vals[i], label_smooth_eps); if(hierarchy){ fill_hierarchy(y.vals[i], k, hierarchy); } @@ -1336,7 +1362,7 @@ void *load_thread(void *ptr) if (a.type == OLD_CLASSIFICATION_DATA){ *a.d = load_data_old(a.paths, a.n, a.m, a.labels, a.classes, a.w, a.h); } else if (a.type == CLASSIFICATION_DATA){ - *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.flip, a.min, a.max, a.w, a.h, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.mixup, a.blur, a.show_imgs); + *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.flip, a.min, a.max, a.w, a.h, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.mixup, a.blur, a.show_imgs, a.label_smooth_eps); } else if (a.type == SUPER_DATA){ *a.d = load_data_super(a.paths, a.n, a.m, a.w, a.h, a.scale); } else if (a.type == WRITING_DATA){ @@ -1432,7 +1458,7 @@ data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int data d = {0}; d.shallow = 0; d.X = load_image_paths(paths, n, w, h); - d.y = load_labels_paths(paths, n, labels, k, 0); + d.y = load_labels_paths(paths, n, labels, k, 0, 0); if(m) free(paths); return d; } @@ -1481,21 +1507,21 @@ data load_data_super(char **paths, int n, int m, int w, int h, int scale) return d; } -data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int mixup, int use_blur, int show_imgs) +data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int mixup, int use_blur, int show_imgs, float label_smooth_eps) { char **paths_stored = paths; if(m) paths = get_random_paths(paths, n, m); data d = {0}; d.shallow = 0; d.X = load_image_augment_paths(paths, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure); - d.y = load_labels_paths(paths, n, labels, k, hierarchy); + d.y = load_labels_paths(paths, n, labels, k, hierarchy, label_smooth_eps); if (mixup && rand_int(0, 1)) { char **paths_mix = get_random_paths(paths_stored, n, m); data d2 = { 0 }; d2.shallow = 0; d2.X = load_image_augment_paths(paths_mix, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure); - d2.y = load_labels_paths(paths_mix, n, labels, k, hierarchy); + d2.y = load_labels_paths(paths_mix, n, labels, k, hierarchy, label_smooth_eps); free(paths_mix); data d3 = { 0 }; @@ -1505,12 +1531,12 @@ data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *h if (mixup >= 3) { char **paths_mix3 = get_random_paths(paths_stored, n, m); d3.X = load_image_augment_paths(paths_mix3, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure); - d3.y = load_labels_paths(paths_mix3, n, labels, k, hierarchy); + d3.y = load_labels_paths(paths_mix3, n, labels, k, hierarchy, label_smooth_eps); free(paths_mix3); char **paths_mix4 = get_random_paths(paths_stored, n, m); d4.X = load_image_augment_paths(paths_mix4, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure); - d4.y = load_labels_paths(paths_mix4, n, labels, k, hierarchy); + d4.y = load_labels_paths(paths_mix4, n, labels, k, hierarchy, label_smooth_eps); free(paths_mix4); } diff --git a/src/data.h b/src/data.h index 57ed3d9db92..dc18a928b52 100644 --- a/src/data.h +++ b/src/data.h @@ -91,7 +91,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo data load_data_tag(char **paths, int n, int m, int k, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure); matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure); data load_data_super(char **paths, int n, int m, int w, int h, int scale); -data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int mixup, int use_blur, int show_imgs); +data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int mixup, int use_blur, int show_imgs, float label_smooth_eps); data load_go(char *filename); box_label *read_boxes(char *filename, int *n); @@ -116,6 +116,7 @@ data *split_data(data d, int part, int total); data concat_data(data d1, data d2); data concat_datas(data *d, int n); void fill_truth(char *path, char **labels, int k, float *truth); +void fill_truth_smooth(char *path, char **labels, int k, float *truth, float label_smooth_eps); #ifdef __cplusplus } diff --git a/src/parser.c b/src/parser.c index 7c8d3438208..b4eb4f4b5b8 100644 --- a/src/parser.c +++ b/src/parser.c @@ -930,6 +930,7 @@ void parse_net_options(list *options, network *net) else if (cutmix) net->mixup = 2; else if (mosaic) net->mixup = 3; net->letter_box = option_find_int_quiet(options, "letter_box", 0); + net->label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); net->angle = option_find_float_quiet(options, "angle", 0); net->aspect = option_find_float_quiet(options, "aspect", 1); From c7e3ba3ed41e9fd114390263f9dd1657b71f676c Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 9 Dec 2019 15:21:22 +0300 Subject: [PATCH 0733/1365] Fixed memory leak in ./darknet detector demo ... --- src/demo.c | 10 +++++----- src/image_opencv.cpp | 2 ++ 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/src/demo.c b/src/demo.c index 2fd54cf5f6b..7066e6f5541 100644 --- a/src/demo.c +++ b/src/demo.c @@ -208,13 +208,13 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int while(1){ ++count; { - if(pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed"); - if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed"); - - float nms = .45; // 0.4F + const float nms = .45; // 0.4F int local_nboxes = nboxes; detection *local_dets = dets; + if(pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed"); + if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed"); + //if (nms) do_nms_obj(local_dets, local_nboxes, l.classes, nms); // bad results if (nms) { if (l.nms_kind == DEFAULT_NMS) do_nms_sort(local_dets, local_nboxes, l.classes, nms); @@ -237,7 +237,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int int timeout = 3; // 3 seconds int http_post_port = 80; // 443 https, 80 http if (send_http_post_request(http_post_host, http_post_port, filename, - dets, nboxes, classes, names, frame_id, ext_output, timeout)) + local_dets, nboxes, classes, names, frame_id, ext_output, timeout)) { if (time_limit_sec > 0) send_http_post_once = 1; } diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 3ce30e42381..39b9eb8d4bc 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -763,6 +763,7 @@ extern "C" image get_image_from_stream_resize(cap_cv *cap, int w, int h, int c, if (once) { once = 0; do { + if (src) delete src; src = (cv::Mat*)get_capture_frame_cv(cap); if (!src) return make_empty_image(0, 0, 0); } while (src->cols < 1 || src->rows < 1 || src->channels() < 1); @@ -794,6 +795,7 @@ extern "C" image get_image_from_stream_letterbox(cap_cv *cap, int w, int h, int if (once) { once = 0; do { + if (src) delete src; src = (cv::Mat*)get_capture_frame_cv(cap); if (!src) return make_empty_image(0, 0, 0); } while (src->cols < 1 || src->rows < 1 || src->channels() < 1); From fc173454b1da54cdd31100528dc44022b4869e29 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 9 Dec 2019 15:28:31 +0300 Subject: [PATCH 0734/1365] Fixed time_step=1 for darknet.so/dll --- src/yolo_v2_class.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index e01dadbb306..93812e7fabe 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -153,7 +153,7 @@ LIB_API Detector::Detector(std::string cfg_filename, std::string weight_filename char *cfgfile = const_cast(_cfg_filename.c_str()); char *weightfile = const_cast(_weight_filename.c_str()); - net = parse_network_cfg_custom(cfgfile, 1, 0); + net = parse_network_cfg_custom(cfgfile, 1, 1); if (weightfile) { load_weights(&net, weightfile); } From dbe34d78658746fcfc9548ebab759895ea05a70c Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 10 Dec 2019 01:37:35 +0300 Subject: [PATCH 0735/1365] Accelerated [Gaussian_yolo] layer --- src/gaussian_yolo_layer.c | 32 ++++++++++++++++---------------- src/yolo_layer.c | 8 ++++---- 2 files changed, 20 insertions(+), 20 deletions(-) diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index e9e9312ed18..e68a79b6494 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -64,7 +64,7 @@ layer make_gaussian_yolo_layer(int batch, int w, int h, int n, int total, int *m l.output_gpu = cuda_make_array(l.output, batch*l.outputs); l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs); - /* + free(l.output); if (cudaSuccess == cudaHostAlloc(&l.output, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1; else { @@ -78,7 +78,7 @@ layer make_gaussian_yolo_layer(int batch, int w, int h, int n, int total, int *m cudaGetLastError(); // reset CUDA-error l.delta = (float*)calloc(batch * l.outputs, sizeof(float)); } - */ + #endif //fprintf(stderr, "Gaussian_yolo\n"); @@ -95,32 +95,32 @@ void resize_gaussian_yolo_layer(layer *l, int w, int h) l->outputs = h*w*l->n*(l->classes + 8 + 1); l->inputs = l->outputs; - l->output = (float *)realloc(l->output, l->batch*l->outputs * sizeof(float)); - l->delta = (float *)realloc(l->delta, l->batch*l->outputs * sizeof(float)); + //l->output = (float *)realloc(l->output, l->batch*l->outputs * sizeof(float)); + //l->delta = (float *)realloc(l->delta, l->batch*l->outputs * sizeof(float)); - //if (!l->output_pinned) l->output = (float*)realloc(l->output, l->batch*l->outputs * sizeof(float)); - //if (!l->delta_pinned) l->delta = (float*)realloc(l->delta, l->batch*l->outputs * sizeof(float)); + if (!l->output_pinned) l->output = (float*)realloc(l->output, l->batch*l->outputs * sizeof(float)); + if (!l->delta_pinned) l->delta = (float*)realloc(l->delta, l->batch*l->outputs * sizeof(float)); #ifdef GPU - /* + if (l->output_pinned) { - cudaFreeHost(l->output); + CHECK_CUDA(cudaFreeHost(l->output)); if (cudaSuccess != cudaHostAlloc(&l->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) { cudaGetLastError(); // reset CUDA-error - l->output = (float*)realloc(l->output, l->batch * l->outputs * sizeof(float)); + l->output = (float*)calloc(l->batch * l->outputs, sizeof(float)); l->output_pinned = 0; } } if (l->delta_pinned) { - cudaFreeHost(l->delta); + CHECK_CUDA(cudaFreeHost(l->delta)); if (cudaSuccess != cudaHostAlloc(&l->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) { cudaGetLastError(); // reset CUDA-error - l->delta = (float*)realloc(l->delta, l->batch * l->outputs * sizeof(float)); + l->delta = (float*)calloc(l->batch * l->outputs, sizeof(float)); l->delta_pinned = 0; } } - */ + cuda_free(l->delta_gpu); cuda_free(l->output_gpu); @@ -473,12 +473,12 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) j = (truth.y * l.h); } else if (l.yolo_point == YOLO_LEFT_TOP) { - i = ((truth.x - truth.w / 2) * l.w); - j = ((truth.y - truth.h / 2) * l.h); + i = min_val_cmp(l.w-1, max_val_cmp(0, ((truth.x - truth.w / 2) * l.w))); + j = min_val_cmp(l.h-1, max_val_cmp(0, ((truth.y - truth.h / 2) * l.h))); } else if (l.yolo_point == YOLO_RIGHT_BOTTOM) { - i = ((truth.x + truth.w / 2) * l.w); - j = ((truth.y + truth.h / 2) * l.h); + i = min_val_cmp(l.w-1, max_val_cmp(0, ((truth.x + truth.w / 2) * l.w))); + j = min_val_cmp(l.h-1, max_val_cmp(0, ((truth.y + truth.h / 2) * l.h))); } box truth_shift = truth; diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 47ce7257c1d..458a3742ac6 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -88,19 +88,19 @@ void resize_yolo_layer(layer *l, int w, int h) #ifdef GPU if (l->output_pinned) { - cudaFreeHost(l->output); + CHECK_CUDA(cudaFreeHost(l->output)); if (cudaSuccess != cudaHostAlloc(&l->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) { cudaGetLastError(); // reset CUDA-error - l->output = (float*)realloc(l->output, l->batch * l->outputs * sizeof(float)); + l->output = (float*)calloc(l->batch * l->outputs, sizeof(float)); l->output_pinned = 0; } } if (l->delta_pinned) { - cudaFreeHost(l->delta); + CHECK_CUDA(cudaFreeHost(l->delta)); if (cudaSuccess != cudaHostAlloc(&l->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) { cudaGetLastError(); // reset CUDA-error - l->delta = (float*)realloc(l->delta, l->batch * l->outputs * sizeof(float)); + l->delta = (float*)calloc(l->batch * l->outputs, sizeof(float)); l->delta_pinned = 0; } } From a616fcd28ef4f0e6cb9d41cac7662dac8bdc90a5 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 10 Dec 2019 20:34:03 +0300 Subject: [PATCH 0736/1365] Minor fix --- src/data.c | 10 +++++----- src/image_opencv.cpp | 3 ++- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/src/data.c b/src/data.c index 85fceae8368..fce3ed996d5 100644 --- a/src/data.c +++ b/src/data.c @@ -380,7 +380,7 @@ int fill_truth_detection(const char *path, int num_boxes, float *truth, int clas // if truth (box for object) is smaller than 1x1 pix char buff[256]; if (id >= classes) { - printf("\n Wrong annotation: class_id = %d. But class_id should be [from 0 to %d] \n", id, (classes-1)); + printf("\n Wrong annotation: class_id = %d. But class_id should be [from 0 to %d], file: %s \n", id, (classes-1), labelpath); sprintf(buff, "echo %s \"Wrong annotation: class_id = %d. But class_id should be [from 0 to %d]\" >> bad_label.list", labelpath, id, (classes-1)); system(buff); getchar(); @@ -394,7 +394,7 @@ int fill_truth_detection(const char *path, int num_boxes, float *truth, int clas continue; } if (x == 999999 || y == 999999) { - printf("\n Wrong annotation: x = 0, y = 0, < 0 or > 1 \n"); + printf("\n Wrong annotation: x = 0, y = 0, < 0 or > 1, file: %s \n", labelpath); sprintf(buff, "echo %s \"Wrong annotation: x = 0 or y = 0\" >> bad_label.list", labelpath); system(buff); ++sub; @@ -402,7 +402,7 @@ int fill_truth_detection(const char *path, int num_boxes, float *truth, int clas continue; } if (x <= 0 || x > 1 || y <= 0 || y > 1) { - printf("\n Wrong annotation: x = %f, y = %f \n", x, y); + printf("\n Wrong annotation: x = %f, y = %f, file: %s \n", x, y, labelpath); sprintf(buff, "echo %s \"Wrong annotation: x = %f, y = %f\" >> bad_label.list", labelpath, x, y); system(buff); ++sub; @@ -410,14 +410,14 @@ int fill_truth_detection(const char *path, int num_boxes, float *truth, int clas continue; } if (w > 1) { - printf("\n Wrong annotation: w = %f \n", w); + printf("\n Wrong annotation: w = %f, file: %s \n", w, labelpath); sprintf(buff, "echo %s \"Wrong annotation: w = %f\" >> bad_label.list", labelpath, w); system(buff); w = 1; if (check_mistakes) getchar(); } if (h > 1) { - printf("\n Wrong annotation: h = %f \n", h); + printf("\n Wrong annotation: h = %f, file: %s \n", h, labelpath); sprintf(buff, "echo %s \"Wrong annotation: h = %f\" >> bad_label.list", labelpath, h); system(buff); h = 1; diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 39b9eb8d4bc..04a4a8f0d7d 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -605,8 +605,9 @@ extern "C" void release_capture(cap_cv* cap) // ---------------------------------------- extern "C" mat_cv* get_capture_frame_cv(cap_cv *cap) { - cv::Mat *mat = new cv::Mat(); + cv::Mat *mat = NULL; try { + mat = new cv::Mat(); if (cap) { cv::VideoCapture &cpp_cap = *(cv::VideoCapture *)cap; if (cpp_cap.isOpened()) From 1df3ddc7d6a3efe9401948d3f527f432f3001476 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 11 Dec 2019 13:16:41 +0300 Subject: [PATCH 0737/1365] Added DropBlock. Use [dropout] dropblock=1 dropblock_size=0.5 probability=0.1 --- include/darknet.h | 2 + src/dropout_layer.c | 19 +++++-- src/dropout_layer.h | 2 +- src/dropout_layer_kernels.cu | 102 +++++++++++++++++++++++++++++------ src/gaussian_yolo_layer.c | 6 +-- src/parser.c | 6 ++- src/yolo_layer.c | 2 +- 7 files changed, 112 insertions(+), 27 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 823410255f1..fd7112bda21 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -311,6 +311,8 @@ struct layer { float temperature; float probability; + float dropblock_size; + int dropblock; float scale; char * cweights; diff --git a/src/dropout_layer.c b/src/dropout_layer.c index c32c5c616bc..fdf6fa7e294 100644 --- a/src/dropout_layer.c +++ b/src/dropout_layer.c @@ -4,16 +4,28 @@ #include #include -dropout_layer make_dropout_layer(int batch, int inputs, float probability) +dropout_layer make_dropout_layer(int batch, int inputs, float probability, int dropblock, float dropblock_size, int w, int h, int c) { dropout_layer l = { (LAYER_TYPE)0 }; l.type = DROPOUT; l.probability = probability; + l.dropblock = dropblock; + l.dropblock_size = dropblock_size; + if (l.dropblock) { + l.out_w = l.w = w; + l.out_h = l.h = h; + l.out_c = l.c = c; + + if (l.w <= 0 || l.h <= 0 || l.c <= 0) { + printf(" Error: DropBlock - there must be positive values for: l.w=%d, l.h=%d, l.c=%d \n", l.w, l.h, l.c); + exit(0); + } + } l.inputs = inputs; l.outputs = inputs; l.batch = batch; l.rand = (float*)calloc(inputs * batch, sizeof(float)); - l.scale = 1./(1.-probability); + l.scale = 1./(1.0 - probability); l.forward = forward_dropout_layer; l.backward = backward_dropout_layer; #ifdef GPU @@ -21,7 +33,8 @@ dropout_layer make_dropout_layer(int batch, int inputs, float probability) l.backward_gpu = backward_dropout_layer_gpu; l.rand_gpu = cuda_make_array(l.rand, inputs*batch); #endif - fprintf(stderr, "dropout p = %.2f %4d -> %4d\n", probability, inputs, inputs); + if(l.dropblock) fprintf(stderr, "dropblock p = %.2f block_size = %.2f %4d -> %4d\n", probability, l.dropblock_size, inputs, inputs); + else fprintf(stderr, "dropout p = %.2f %4d -> %4d\n", probability, inputs, inputs); return l; } diff --git a/src/dropout_layer.h b/src/dropout_layer.h index 25bea3861dc..b0889e5ec7c 100644 --- a/src/dropout_layer.h +++ b/src/dropout_layer.h @@ -9,7 +9,7 @@ typedef layer dropout_layer; #ifdef __cplusplus extern "C" { #endif -dropout_layer make_dropout_layer(int batch, int inputs, float probability); +dropout_layer make_dropout_layer(int batch, int inputs, float probability, int dropblock, float dropblock_size, int w, int h, int c); void forward_dropout_layer(dropout_layer l, network_state state); void backward_dropout_layer(dropout_layer l, network_state state); diff --git a/src/dropout_layer_kernels.cu b/src/dropout_layer_kernels.cu index dadaa9fe4a5..b91e31bba95 100644 --- a/src/dropout_layer_kernels.cu +++ b/src/dropout_layer_kernels.cu @@ -1,6 +1,7 @@ #include #include #include +#include #include "dropout_layer.h" #include "dark_cuda.h" @@ -12,35 +13,104 @@ __global__ void yoloswag420blazeit360noscope(float *input, int size, float *rand if(id < size) input[id] = (rand[id] < prob) ? 0 : input[id]*scale; } -void forward_dropout_layer_gpu(dropout_layer layer, network_state state) +__global__ void drop_block_kernel(float *input, int size, float *mask, float scale) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id < size) input[id] = (mask[id]) ? 0 : (input[id] * scale); +} + +void forward_dropout_layer_gpu(dropout_layer l, network_state state) { if (!state.train) return; int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); //if (iteration_num < state.net.burn_in) return; + // dropblock + if (l.dropblock) { + //l.probability = 1 / keep_prob + const int max_blocks_per_channel = 3; + const float cur_prob = l.probability * (iteration_num / (float)state.net.max_batches); + + const int block_width = l.dropblock_size * l.w; + const int block_height = l.dropblock_size * l.h; + + const float prob_place_block = cur_prob / (l.dropblock_size * l.dropblock_size * max_blocks_per_channel); + + memset(l.rand, 0, l.batch * l.outputs * sizeof(float)); + + float count_ones = 0; + + int b, k, x, y, i; + for (b = 0; b < l.batch; b++) { + for (k = 0; k < l.c; k++) { + for (i = 0; i < max_blocks_per_channel; i++) { + float rnd = random_float(); + //printf(" rnd = %f \n", rnd); + if (rnd < prob_place_block) { + //count_ones += block_width *block_height; + const int pre_index = k*l.w*l.h + b*l.w*l.h*l.c; + const int x_block = rand_int(0, l.w - block_width - 1); + const int y_block = rand_int(0, l.h - block_height - 1); + for (y = y_block; y < (y_block + block_height); y++) { + for (x = x_block; x < (x_block + block_width); x++) { + const int index = x + y*l.w + pre_index; + l.rand[index] = 1; + } + } + } + } + } + } + + for (i = 0; i < (l.batch*l.outputs); ++i) if (l.rand[i]) count_ones++; + + cuda_push_array(l.rand_gpu, l.rand, l.batch*l.outputs); + + l.scale = (float)(l.batch*l.outputs) / (l.batch*l.outputs - count_ones); + + + //printf("\n l.scale = %f, cur_prob = %f, count_ones = %f, prob_place_block = %f, \n", + // l.scale, cur_prob, count_ones, prob_place_block); + + int size = l.inputs*l.batch; + + drop_block_kernel << > > (state.input, size, l.rand_gpu, l.scale); + CHECK_CUDA(cudaPeekAtLastError()); - int size = layer.inputs*layer.batch; - cuda_random(layer.rand_gpu, size); - /* - int i; - for(i = 0; i < size; ++i){ - layer.rand[i] = rand_uniform(); } - cuda_push_array(layer.rand_gpu, layer.rand, size); - */ + // dropout + else { + int size = l.inputs*l.batch; + cuda_random(l.rand_gpu, size); + /* + int i; + for(i = 0; i < size; ++i){ + layer.rand[i] = rand_uniform(); + } + cuda_push_array(layer.rand_gpu, layer.rand, size); + */ - yoloswag420blazeit360noscope<<>>(state.input, size, layer.rand_gpu, layer.probability, layer.scale); - CHECK_CUDA(cudaPeekAtLastError()); + yoloswag420blazeit360noscope << > > (state.input, size, l.rand_gpu, l.probability, l.scale); + CHECK_CUDA(cudaPeekAtLastError()); + } } -void backward_dropout_layer_gpu(dropout_layer layer, network_state state) +void backward_dropout_layer_gpu(dropout_layer l, network_state state) { if(!state.delta) return; - int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); + //int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); //if (iteration_num < state.net.burn_in) return; - int size = layer.inputs*layer.batch; + int size = l.inputs*l.batch; - yoloswag420blazeit360noscope<<>>(state.delta, size, layer.rand_gpu, layer.probability, layer.scale); - CHECK_CUDA(cudaPeekAtLastError()); + // dropblock + if (l.dropblock) { + drop_block_kernel << > > (state.delta, size, l.rand_gpu, l.scale); + CHECK_CUDA(cudaPeekAtLastError()); + } + // dropout + else { + yoloswag420blazeit360noscope << > > (state.delta, size, l.rand_gpu, l.probability, l.scale); + CHECK_CUDA(cudaPeekAtLastError()); + } } diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index e68a79b6494..c85a8b065e9 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -465,12 +465,10 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) if(!truth.x) break; float best_iou = 0; int best_n = 0; - //i = (truth.x * l.w); - //j = (truth.y * l.h); + i = (truth.x * l.w); + j = (truth.y * l.h); if (l.yolo_point == YOLO_CENTER) { - i = (truth.x * l.w); - j = (truth.y * l.h); } else if (l.yolo_point == YOLO_LEFT_TOP) { i = min_val_cmp(l.w-1, max_val_cmp(0, ((truth.x - truth.w / 2) * l.w))); diff --git a/src/parser.c b/src/parser.c index b4eb4f4b5b8..11c9358a320 100644 --- a/src/parser.c +++ b/src/parser.c @@ -701,8 +701,10 @@ avgpool_layer parse_avgpool(list *options, size_params params) dropout_layer parse_dropout(list *options, size_params params) { - float probability = option_find_float(options, "probability", .5); - dropout_layer layer = make_dropout_layer(params.batch, params.inputs, probability); + float probability = option_find_float(options, "probability", .2); + int dropblock = option_find_int_quiet(options, "dropblock", 0); + float dropblock_size = option_find_float_quiet(options, "dropblock_size", 0.5); + dropout_layer layer = make_dropout_layer(params.batch, params.inputs, probability, dropblock, dropblock_size, params.w, params.h, params.c); layer.out_w = params.w; layer.out_h = params.h; layer.out_c = params.c; diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 458a3742ac6..97c52023693 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -732,7 +732,7 @@ void forward_yolo_layer_gpu(const layer l, network_state state) // if(y->1) x -> inf // if(y->0) x -> -inf activate_array_ongpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC); // x,y - scal_add_ongpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output_gpu + index, 1); // scale x,y + if (l.scale_x_y != 1) scal_add_ongpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output_gpu + index, 1); // scale x,y index = entry_index(l, b, n*l.w*l.h, 4); activate_array_ongpu(l.output_gpu + index, (1+l.classes)*l.w*l.h, LOGISTIC); // classes and objectness } From d137d304c1410253894dbfb7abaadfe6f4f867e7 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 12 Dec 2019 14:52:51 +0300 Subject: [PATCH 0738/1365] Fixed backward_scale_channels_kernel() for ASFF --- src/blas_kernels.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index e6e0739e539..47c8db508ee 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -1150,7 +1150,7 @@ __global__ void backward_scale_channels_kernel(float *in_w_h_c_delta, int size, int osd_index = index % channel_size + (index / batch_size)*channel_size; //out_state_delta[osd_index] += in_w_h_c_delta[index] * in_from_output[index]; // l.delta * from (should be divided by channel_size?) - atomicAdd(&out_state_delta[osd_index], in_w_h_c_delta[index] * in_from_output[index]); // l.delta * from + atomicAdd(&out_state_delta[osd_index], in_w_h_c_delta[index] * in_from_output[index] / channel_size); // l.delta * from out_from_delta[index] += in_scales_c[osd_index] * in_w_h_c_delta[index]; // input * l.delta // atomic isn't required here From 642c065c0e7c681b90f10394edce9ce315aa60d8 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 13 Dec 2019 01:22:03 +0300 Subject: [PATCH 0739/1365] Fixed DropBlock [dropout] dropblock_size_abs=7 dropblock_size_rel=0.5 --- include/darknet.h | 3 ++- src/dropout_layer.c | 10 +++++++--- src/dropout_layer.h | 2 +- src/dropout_layer_kernels.cu | 28 +++++++++++++++++++++------- src/parser.c | 13 +++++++++++-- 5 files changed, 42 insertions(+), 14 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index fd7112bda21..383c3e1cde0 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -311,7 +311,8 @@ struct layer { float temperature; float probability; - float dropblock_size; + float dropblock_size_rel; + int dropblock_size_abs; int dropblock; float scale; diff --git a/src/dropout_layer.c b/src/dropout_layer.c index fdf6fa7e294..4ac7492cc54 100644 --- a/src/dropout_layer.c +++ b/src/dropout_layer.c @@ -4,13 +4,14 @@ #include #include -dropout_layer make_dropout_layer(int batch, int inputs, float probability, int dropblock, float dropblock_size, int w, int h, int c) +dropout_layer make_dropout_layer(int batch, int inputs, float probability, int dropblock, float dropblock_size_rel, int dropblock_size_abs, int w, int h, int c) { dropout_layer l = { (LAYER_TYPE)0 }; l.type = DROPOUT; l.probability = probability; l.dropblock = dropblock; - l.dropblock_size = dropblock_size; + l.dropblock_size_rel = dropblock_size_rel; + l.dropblock_size_abs = dropblock_size_abs; if (l.dropblock) { l.out_w = l.w = w; l.out_h = l.h = h; @@ -33,7 +34,10 @@ dropout_layer make_dropout_layer(int batch, int inputs, float probability, int d l.backward_gpu = backward_dropout_layer_gpu; l.rand_gpu = cuda_make_array(l.rand, inputs*batch); #endif - if(l.dropblock) fprintf(stderr, "dropblock p = %.2f block_size = %.2f %4d -> %4d\n", probability, l.dropblock_size, inputs, inputs); + if (l.dropblock) { + if(l.dropblock_size_abs) fprintf(stderr, "dropblock p = %.2f l.dropblock_size_abs = %d %4d -> %4d\n", probability, l.dropblock_size_abs, inputs, inputs); + else fprintf(stderr, "dropblock p = %.2f l.dropblock_size_rel = %.2f %4d -> %4d\n", probability, l.dropblock_size_rel, inputs, inputs); + } else fprintf(stderr, "dropout p = %.2f %4d -> %4d\n", probability, inputs, inputs); return l; } diff --git a/src/dropout_layer.h b/src/dropout_layer.h index b0889e5ec7c..fa02300a7d2 100644 --- a/src/dropout_layer.h +++ b/src/dropout_layer.h @@ -9,7 +9,7 @@ typedef layer dropout_layer; #ifdef __cplusplus extern "C" { #endif -dropout_layer make_dropout_layer(int batch, int inputs, float probability, int dropblock, float dropblock_size, int w, int h, int c); +dropout_layer make_dropout_layer(int batch, int inputs, float probability, int dropblock, float dropblock_size_rel, int dropblock_size_abs, int w, int h, int c); void forward_dropout_layer(dropout_layer l, network_state state); void backward_dropout_layer(dropout_layer l, network_state state); diff --git a/src/dropout_layer_kernels.cu b/src/dropout_layer_kernels.cu index b91e31bba95..0279f5b2936 100644 --- a/src/dropout_layer_kernels.cu +++ b/src/dropout_layer_kernels.cu @@ -25,16 +25,30 @@ void forward_dropout_layer_gpu(dropout_layer l, network_state state) int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); //if (iteration_num < state.net.burn_in) return; + // We gradually increase the block size and the probability of dropout - during the first half of the training + float multiplier = 1.0; + if(iteration_num < (state.net.max_batches / 2)) + multiplier = (iteration_num / (float)(state.net.max_batches / 2)); + // dropblock if (l.dropblock) { //l.probability = 1 / keep_prob - const int max_blocks_per_channel = 3; - const float cur_prob = l.probability * (iteration_num / (float)state.net.max_batches); + const int max_blocks_per_channel = 10; + const float cur_prob = l.probability * multiplier; + + int block_width = l.dropblock_size_abs * multiplier; + int block_height = l.dropblock_size_abs * multiplier; + + if (l.dropblock_size_rel) { + block_width = l.dropblock_size_rel * l.w * multiplier; + block_height = l.dropblock_size_rel * l.h * multiplier; + } - const int block_width = l.dropblock_size * l.w; - const int block_height = l.dropblock_size * l.h; + block_width = max_val_cmp(1, block_width); + block_height = max_val_cmp(1, block_height); - const float prob_place_block = cur_prob / (l.dropblock_size * l.dropblock_size * max_blocks_per_channel); + const float part_occupied_by_block = block_width * block_height / ((float)l.w * l.h); + const float prob_place_block = cur_prob / (part_occupied_by_block * max_blocks_per_channel); memset(l.rand, 0, l.batch * l.outputs * sizeof(float)); @@ -69,8 +83,8 @@ void forward_dropout_layer_gpu(dropout_layer l, network_state state) l.scale = (float)(l.batch*l.outputs) / (l.batch*l.outputs - count_ones); - //printf("\n l.scale = %f, cur_prob = %f, count_ones = %f, prob_place_block = %f, \n", - // l.scale, cur_prob, count_ones, prob_place_block); + //printf("\n l.scale = %f, cur_prob = %f, count_ones = %f, prob_place_block = %f, block_width = %d, block_height = %d \n", + // l.scale, cur_prob, count_ones, prob_place_block, block_width, block_height); int size = l.inputs*l.batch; diff --git a/src/parser.c b/src/parser.c index 11c9358a320..d5e67980200 100644 --- a/src/parser.c +++ b/src/parser.c @@ -703,8 +703,17 @@ dropout_layer parse_dropout(list *options, size_params params) { float probability = option_find_float(options, "probability", .2); int dropblock = option_find_int_quiet(options, "dropblock", 0); - float dropblock_size = option_find_float_quiet(options, "dropblock_size", 0.5); - dropout_layer layer = make_dropout_layer(params.batch, params.inputs, probability, dropblock, dropblock_size, params.w, params.h, params.c); + float dropblock_size_rel = option_find_float_quiet(options, "dropblock_size_rel", 0); + int dropblock_size_abs = option_find_float_quiet(options, "dropblock_size_abs", 0); + if (!dropblock_size_rel && !dropblock_size_abs) { + printf(" [dropout] - None of the parameters (dropblock_size_rel or dropblock_size_abs) are set, will be used: dropblock_size_abs = 7 \n"); + dropblock_size_abs = 7; + } + if (dropblock_size_rel && dropblock_size_abs) { + printf(" [dropout] - Both parameters are set, only the parameter will be used: dropblock_size_abs = %d \n", dropblock_size_abs); + dropblock_size_rel = 0; + } + dropout_layer layer = make_dropout_layer(params.batch, params.inputs, probability, dropblock, dropblock_size_rel, dropblock_size_abs, params.w, params.h, params.c); layer.out_w = params.w; layer.out_h = params.h; layer.out_c = params.c; From 764872a190d83b6f149220c6ec8aa0df1d2d5e49 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 13 Dec 2019 03:17:55 +0300 Subject: [PATCH 0740/1365] Minor fix --- src/dropout_layer_kernels.cu | 9 +++++---- src/parser.c | 9 +++++++-- 2 files changed, 12 insertions(+), 6 deletions(-) diff --git a/src/dropout_layer_kernels.cu b/src/dropout_layer_kernels.cu index 0279f5b2936..79ed37d96c7 100644 --- a/src/dropout_layer_kernels.cu +++ b/src/dropout_layer_kernels.cu @@ -66,10 +66,11 @@ void forward_dropout_layer_gpu(dropout_layer l, network_state state) const int x_block = rand_int(0, l.w - block_width - 1); const int y_block = rand_int(0, l.h - block_height - 1); for (y = y_block; y < (y_block + block_height); y++) { - for (x = x_block; x < (x_block + block_width); x++) { - const int index = x + y*l.w + pre_index; - l.rand[index] = 1; - } + memset(&l.rand[x_block + y*l.w + pre_index], 1, block_width * sizeof(float)); + //for (x = x_block; x < (x_block + block_width); x++) { + // const int index = x + y*l.w + pre_index; + // l.rand[index] = 1; + //} } } } diff --git a/src/parser.c b/src/parser.c index d5e67980200..411a8366e13 100644 --- a/src/parser.c +++ b/src/parser.c @@ -705,6 +705,10 @@ dropout_layer parse_dropout(list *options, size_params params) int dropblock = option_find_int_quiet(options, "dropblock", 0); float dropblock_size_rel = option_find_float_quiet(options, "dropblock_size_rel", 0); int dropblock_size_abs = option_find_float_quiet(options, "dropblock_size_abs", 0); + if (dropblock_size_abs > params.w || dropblock_size_abs > params.h) { + printf(" [dropout] - dropblock_size_abs = %d that is bigger than layer size %d x %d \n", dropblock_size_abs, params.w, params.h); + dropblock_size_abs = min_val_cmp(params.w, params.h); + } if (!dropblock_size_rel && !dropblock_size_abs) { printf(" [dropout] - None of the parameters (dropblock_size_rel or dropblock_size_abs) are set, will be used: dropblock_size_abs = 7 \n"); dropblock_size_abs = 7; @@ -898,10 +902,11 @@ learning_rate_policy get_policy(char *s) void parse_net_options(list *options, network *net) { + net->max_batches = option_find_int(options, "max_batches", 0); net->batch = option_find_int(options, "batch",1); net->learning_rate = option_find_float(options, "learning_rate", .001); net->learning_rate_min = option_find_float_quiet(options, "learning_rate_min", .00001); - net->batches_per_cycle = option_find_int_quiet(options, "sgdr_cycle", 1000); + net->batches_per_cycle = option_find_int_quiet(options, "sgdr_cycle", net->max_batches); net->batches_cycle_mult = option_find_int_quiet(options, "sgdr_mult", 2); net->momentum = option_find_float(options, "momentum", .9); net->decay = option_find_float(options, "decay", .0001); @@ -1013,7 +1018,7 @@ void parse_net_options(list *options, network *net) } else if (net->policy == POLY || net->policy == RANDOM){ //net->power = option_find_float(options, "power", 1); } - net->max_batches = option_find_int(options, "max_batches", 0); + } int is_network(section *s) From 7ae1ae5641b549ebaa5c816701c4b9ca73247a65 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 13 Dec 2019 18:52:54 +0300 Subject: [PATCH 0741/1365] Fixed activation=NORM_CHAN gradient --- src/activation_kernels.cu | 57 +++++++++++++++++++++++++++++++----- src/activations.c | 48 +++++++++++++++++++++++++----- src/activations.h | 2 ++ src/convolutional_kernels.cu | 1 + src/convolutional_layer.c | 1 + src/http_stream.cpp | 2 ++ 6 files changed, 95 insertions(+), 16 deletions(-) diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index 8cf148a85ad..46e957dd6d1 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -399,8 +399,8 @@ extern "C" void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta else if (a == TANH) gradient_array_tanh_kernel << > >(x, n, delta); else if (a == HARDTAN) gradient_array_hardtan_kernel << > >(x, n, delta); else if (a == RELU) gradient_array_relu_kernel << > >(x, n, delta); - else if (a == NORM_CHAN) gradient_array_relu_kernel << > >(x, n, delta); - else if (a == NORM_CHAN_SOFTMAX) { + //else if (a == NORM_CHAN) gradient_array_relu_kernel << > >(x, n, delta); + else if (a == NORM_CHAN_SOFTMAX || a == NORM_CHAN) { printf(" Error: should be used custom NORM_CHAN_SOFTMAX-function for gradient \n"); exit(0); } @@ -508,19 +508,20 @@ __global__ void gradient_array_normalize_channels_softmax_kernel(float *x, int s int wh_i = i % wh_step; int b = i / wh_step; - //const float eps = 0.0001; if (i < size) { - float grad = 0;// eps; + float grad = 0; int k; for (k = 0; k < channels; ++k) { - float out = x[wh_i + k * wh_step + b*wh_step*channels]; - float delta = delta_gpu[wh_i + k * wh_step + b*wh_step*channels]; + const int index = wh_i + k * wh_step + b*wh_step*channels; + float out = x[index]; + float delta = delta_gpu[index]; grad += out*delta; } for (k = 0; k < channels; ++k) { - float delta = delta_gpu[wh_i + k * wh_step + b*wh_step*channels]; + const int index = wh_i + k * wh_step + b*wh_step*channels; + float delta = delta_gpu[index]; delta = delta * grad; - delta_gpu[wh_i + k * wh_step + b*wh_step*channels] = delta; + delta_gpu[index] = delta; } } } @@ -535,4 +536,44 @@ extern "C" void gradient_array_normalize_channels_softmax_ongpu(float *output_gp gradient_array_normalize_channels_softmax_kernel << > > (output_gpu, size, batch, channels, wh_step, delta_gpu); CHECK_CUDA(cudaPeekAtLastError()); +} + + +__global__ void gradient_array_normalize_channels_kernel(float *x, int size, int batch, int channels, int wh_step, float *delta_gpu) +{ + int i = blockIdx.x * blockDim.x + threadIdx.x; + + int wh_i = i % wh_step; + int b = i / wh_step; + + if (i < size) { + float grad = 0; + int k; + for (k = 0; k < channels; ++k) { + const int index = wh_i + k * wh_step + b*wh_step*channels; + float out = x[index]; + float delta = delta_gpu[index]; + grad += out*delta; + } + for (k = 0; k < channels; ++k) { + const int index = wh_i + k * wh_step + b*wh_step*channels; + if (x[index] > 0) { + float delta = delta_gpu[index]; + delta = delta * grad; + delta_gpu[index] = delta; + } + } + } +} + +extern "C" void gradient_array_normalize_channels_ongpu(float *output_gpu, int n, int batch, int channels, int wh_step, float *delta_gpu) +{ + // n = w*h*c*batch + // size = w*h*batch + int size = n / channels; + + const int num_blocks = get_number_of_blocks(size, BLOCK); + + gradient_array_normalize_channels_kernel << > > (output_gpu, size, batch, channels, wh_step, delta_gpu); + CHECK_CUDA(cudaPeekAtLastError()); } \ No newline at end of file diff --git a/src/activations.c b/src/activations.c index 8a8ab862e1a..dc450b2f111 100644 --- a/src/activations.c +++ b/src/activations.c @@ -214,19 +214,51 @@ void gradient_array_normalize_channels_softmax(float *x, const int n, int batch, int wh_i = i % wh_step; int b = i / wh_step; - //const float eps = 0.0001; if (i < size) { - float grad = 0;// eps; + float grad = 0; int k; for (k = 0; k < channels; ++k) { - float out = x[wh_i + k * wh_step + b*wh_step*channels]; - float d = delta[wh_i + k * wh_step + b*wh_step*channels]; + const int index = wh_i + k * wh_step + b*wh_step*channels; + float out = x[index]; + float d = delta[index]; grad += out*d; } for (k = 0; k < channels; ++k) { - float d = delta[wh_i + k * wh_step + b*wh_step*channels]; + const int index = wh_i + k * wh_step + b*wh_step*channels; + float d = delta[index]; d = d * grad; - delta[wh_i + k * wh_step + b*wh_step*channels] = d; + delta[index] = d; + } + } + } +} + +void gradient_array_normalize_channels(float *x, const int n, int batch, int channels, int wh_step, float *delta) +{ + int size = n / channels; + + int i; + #pragma omp parallel for + for (i = 0; i < size; ++i) { + int wh_i = i % wh_step; + int b = i / wh_step; + + if (i < size) { + float grad = 0; + int k; + for (k = 0; k < channels; ++k) { + const int index = wh_i + k * wh_step + b*wh_step*channels; + float out = x[index]; + float d = delta[index]; + grad += out*d; + } + for (k = 0; k < channels; ++k) { + const int index = wh_i + k * wh_step + b*wh_step*channels; + if (x[index] > 0) { + float d = delta[index]; + d = d * grad; + delta[index] = d; + } } } } @@ -244,9 +276,9 @@ float gradient(float x, ACTIVATION a) case RELU: return relu_gradient(x); case NORM_CHAN: - return relu_gradient(x); + //return relu_gradient(x); case NORM_CHAN_SOFTMAX: - printf(" Error: should be used custom NORM_CHAN_SOFTMAX-function for gradient \n"); + printf(" Error: should be used custom NORM_CHAN or NORM_CHAN_SOFTMAX-function for gradient \n"); exit(0); return 0; case ELU: diff --git a/src/activations.h b/src/activations.h index 127e372f9d0..05631fe3ee2 100644 --- a/src/activations.h +++ b/src/activations.h @@ -23,6 +23,7 @@ void activate_array(float *x, const int n, const ACTIVATION a); void activate_array_swish(float *x, const int n, float * output_sigmoid, float * output); void activate_array_mish(float *x, const int n, float * activation_input, float * output); void activate_array_normalize_channels(float *x, const int n, int batch, int channels, int wh_step, float *output); +void gradient_array_normalize_channels(float *x, const int n, int batch, int channels, int wh_step, float *delta); void activate_array_normalize_channels_softmax(float *x, const int n, int batch, int channels, int wh_step, float *output); void gradient_array_normalize_channels_softmax(float *x, const int n, int batch, int channels, int wh_step, float *delta); #ifdef GPU @@ -33,6 +34,7 @@ void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta); void gradient_array_swish_ongpu(float *x, int n, float *sigmoid_gpu, float *delta); void gradient_array_mish_ongpu(int n, float *activation_input_gpu, float *delta); void activate_array_normalize_channels_ongpu(float *x, int n, int batch, int channels, int wh_step, float *output_gpu); +void gradient_array_normalize_channels_ongpu(float *output_gpu, int n, int batch, int channels, int wh_step, float *delta_gpu); void activate_array_normalize_channels_softmax_ongpu(float *x, int n, int batch, int channels, int wh_step, float *output_gpu); void gradient_array_normalize_channels_softmax_ongpu(float *output_gpu, int n, int batch, int channels, int wh_step, float *delta_gpu); diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 256e9e19c9a..08dc66dd2fd 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -648,6 +648,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state if (l.activation == SWISH) gradient_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.delta_gpu); else if (l.activation == MISH) gradient_array_mish_ongpu(l.outputs*l.batch, l.activation_input_gpu, l.delta_gpu); else if (l.activation == NORM_CHAN_SOFTMAX) gradient_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu); + else if (l.activation == NORM_CHAN) gradient_array_normalize_channels_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu); else gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); if (!l.batch_normalize) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 5c014399b2d..3fb80c51b6a 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -1386,6 +1386,7 @@ void backward_convolutional_layer(convolutional_layer l, network_state state) if (l.activation == SWISH) gradient_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.delta); else if (l.activation == MISH) gradient_array_mish(l.outputs*l.batch, l.activation_input, l.delta); else if (l.activation == NORM_CHAN_SOFTMAX) gradient_array_normalize_channels_softmax(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.delta); + else if (l.activation == NORM_CHAN) gradient_array_normalize_channels(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.delta); else gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); if (l.batch_normalize) { diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 4dcdfa5b917..350f1637b4f 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -544,6 +544,8 @@ void send_mjpeg(mat_cv* mat, int port, int timeout, int quality) std::string get_system_frame_time_string() { std::time_t t = std::chrono::system_clock::to_time_t(std::chrono::system_clock::now()); + static std::mutex mtx; + std::lock_guard lock(mtx); struct tm *tmp_buf = localtime(&t); char buff[256]; std::strftime(buff, 256, "%A %F %T", tmp_buf); From a08c8725641f203fd391a7d62c59fabf1a01587f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 14 Dec 2019 03:07:01 +0300 Subject: [PATCH 0742/1365] Sway conv-kernel --- include/darknet.h | 2 + src/blas.h | 5 + src/blas_kernels.cu | 205 +++++++++++++++++++++++++++++++++++ src/conv_lstm_layer.c | 22 ++-- src/convolutional_kernels.cu | 49 +++++++++ src/convolutional_layer.c | 20 ++-- src/convolutional_layer.h | 2 +- src/crnn_layer.c | 6 +- src/maxpool_layer.c | 2 +- src/parser.c | 5 +- 10 files changed, 293 insertions(+), 25 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 383c3e1cde0..6f5c78a9565 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -248,6 +248,7 @@ struct layer { int truth; float smooth; float dot; + int sway; float angle; float jitter; float saturation; @@ -542,6 +543,7 @@ struct layer { float * x_norm_gpu; float * weights_gpu; float * weight_updates_gpu; + float * weight_deform_gpu; float * weight_change_gpu; float * weights_gpu16; diff --git a/src/blas.h b/src/blas.h index e186ff8661f..35783a169c2 100644 --- a/src/blas.h +++ b/src/blas.h @@ -129,6 +129,11 @@ void backward_sam_gpu(float *in_w_h_c_delta, int size, int channel_size, void sam_gpu(float *in_w_h_c, int size, int channel_size, float *scales_c, float *out); +void rotate_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse); +void sway_and_flip_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse); +void reduce_and_expand_array_gpu(const float *src_gpu, float *dst_gpu, int size, int groups); +void expand_array_gpu(const float *src_gpu, float *dst_gpu, int size, int groups); + #endif #ifdef __cplusplus } diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 47c8db508ee..a7ce967a277 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -1236,5 +1236,210 @@ extern "C" void backward_sam_gpu(float *in_w_h_c_delta, int size, int channel_si in_scales_c, out_from_delta, in_from_output, out_state_delta); + CHECK_CUDA(cudaPeekAtLastError()); +} + + +__global__ void rotate_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, int angle, int reverse) +{ + const int index = blockIdx.x*blockDim.x + threadIdx.x; + const int kernel_area = kernel_size * kernel_size; + const int i = index * kernel_area; + + const int stage_step = (nweights / kernel_area) / 4; // 4 stages + const int stage_id = index / stage_step; + + // nweights = (c / groups) * n * size * size; + // kernel_area = size*size + + if (i < nweights) + { + // rotate left or right + if (reverse) angle = -angle; + + const float cos_a = cosf(angle * 3.14159265 / 180); + const float sin_a = sinf(angle * 3.14159265 / 180); + const int x_c = kernel_size / 2; + const int y_c = kernel_size / 2; + + for (int x = 0; x < kernel_size; ++x) { + for (int y = 0; y < kernel_size; ++y) { + // Xsource = x*cos(alpha) + y*sin(alpha) + // Ysource = -x*sin(alpha) + y*cos(alpha) + + float x_s = x_c + (x - x_c)*cos_a + (y - y_c)*sin_a; + float y_s = y_c - (x - x_c)*sin_a + (y - y_c)*cos_a; + + int x_0 = floor(x_s); // round down + int x_1 = ceil(x_s); // round up + if (x_0 == x_1) x_1 = x_0 + 1; + int y_0 = floor(y_s); + int y_1 = ceil(y_s); + if (y_0 == y_1) y_1 = y_0 + 1; + + float c_x_0 = x_1 - x_s; + float c_x_1 = x_s - x_0; + float c_y_0 = y_1 - y_s; + float c_y_1 = y_s - y_0; + + + float val = 0; + if (x_0 >= 0 && x_0 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_0 + y_0*kernel_size + i] * c_x_0 * c_y_0; + if (x_1 >= 0 && x_1 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_1 + y_0*kernel_size + i] * c_x_1 * c_y_0; + if (x_0 >= 0 && x_0 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_0 + y_1*kernel_size + i] * c_x_0 * c_y_1; + if (x_1 >= 0 && x_1 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_1 + y_1*kernel_size + i] * c_x_1 * c_y_1; + + weight_deform_gpu[x + y*kernel_size + i] = val; + } + } + } +} + + +extern "C" void rotate_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse) +{ + const int kernel_area = size*size; + const int block_size = BLOCK; + const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size); + rotate_weights_kernel << > > (src_weight_gpu, weight_deform_gpu, nweights, n, size, angle, reverse); + + CHECK_CUDA(cudaPeekAtLastError()); +} + + +__global__ void sway_and_flip_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, int angle, int reverse) +{ + const int index = blockIdx.x*blockDim.x + threadIdx.x; + const int kernel_area = kernel_size * kernel_size; + const int i = index * kernel_area; + + const int stage_step = (nweights / kernel_area) / 4; // 4 stages + const int stage_id = index / stage_step; + + // nweights = (c / groups) * n * size * size; + // kernel_area = size*size + + if (i < nweights) + { + + if (stage_id == 0) { + // simple copy + for (int x = 0; x < kernel_size; ++x) { + for (int y = 0; y < kernel_size; ++y) { + weight_deform_gpu[x + y*kernel_size + i] = src_weight_gpu[x + y*kernel_size + i]; + } + } + } + else if (stage_id == 1 || stage_id == 2) + { + // rotate left or right + if (stage_id == 2) angle = -angle; + if (reverse) angle = -angle; + + const float cos_a = cosf(angle * 3.14159265 / 180); + const float sin_a = sinf(angle * 3.14159265 / 180); + const int x_c = kernel_size / 2; + const int y_c = kernel_size / 2; + + for (int x = 0; x < kernel_size; ++x) { + for (int y = 0; y < kernel_size; ++y) { + // Xsource = x*cos(alpha) + y*sin(alpha) + // Ysource = -x*sin(alpha) + y*cos(alpha) + + float x_s = x_c + (x - x_c)*cos_a + (y - y_c)*sin_a; + float y_s = y_c - (x - x_c)*sin_a + (y - y_c)*cos_a; + + int x_0 = floor(x_s); // round down + int x_1 = ceil(x_s); // round up + if (x_0 == x_1) x_1 = x_0 + 1; + int y_0 = floor(y_s); + int y_1 = ceil(y_s); + if (y_0 == y_1) y_1 = y_0 + 1; + + float c_x_0 = x_1 - x_s; + float c_x_1 = x_s - x_0; + float c_y_0 = y_1 - y_s; + float c_y_1 = y_s - y_0; + + + float val = 0; + if (x_0 >= 0 && x_0 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_0 + y_0*kernel_size + i] * c_x_0 * c_y_0; + if (x_1 >= 0 && x_1 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_1 + y_0*kernel_size + i] * c_x_1 * c_y_0; + if (x_0 >= 0 && x_0 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_0 + y_1*kernel_size + i] * c_x_0 * c_y_1; + if (x_1 >= 0 && x_1 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_1 + y_1*kernel_size + i] * c_x_1 * c_y_1; + + weight_deform_gpu[x + y*kernel_size + i] = val; + } + } + } + else if (stage_id == 3) + { + // flip + for (int x = 0; x < kernel_size; ++x) { + for (int y = 0; y < kernel_size; ++y) { + weight_deform_gpu[(kernel_size - x - 1) + y*kernel_size + i] = src_weight_gpu[x + y*kernel_size + i]; + } + } + } + } +} + + +extern "C" void sway_and_flip_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse) +{ + const int kernel_area = size*size; + const int block_size = BLOCK; + const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size); + sway_and_flip_weights_kernel << > > (src_weight_gpu, weight_deform_gpu, nweights, n, size, angle, reverse); + + CHECK_CUDA(cudaPeekAtLastError()); +} + + +__global__ void reduce_and_expand_array_kernel(const float *src_gpu, float *dst_gpu, int current_size, int groups) +{ + const int index = blockIdx.x*blockDim.x + threadIdx.x; + + if (index < current_size) { + float val = 0; + for (int i = 0; i < groups; ++i) { + val += src_gpu[index + i*current_size]; + } + for (int i = 0; i < groups; ++i) { + dst_gpu[index + i*current_size] = val / groups; + } + } +} + +extern "C" void reduce_and_expand_array_gpu(const float *src_gpu, float *dst_gpu, int size, int groups) +{ + const int current_size = size / groups; + const int block_size = BLOCK; + const int num_blocks = get_number_of_blocks(current_size, block_size); + reduce_and_expand_array_kernel << > > (src_gpu, dst_gpu, current_size, groups); + + CHECK_CUDA(cudaPeekAtLastError()); +} + + + +__global__ void expand_array_kernel(const float *src_gpu, float *dst_gpu, int current_size, int groups) +{ + const int index = blockIdx.x*blockDim.x + threadIdx.x; + + if (index < current_size) { + for (int i = 0; i < groups; ++i) { + dst_gpu[index + i*current_size] = src_gpu[index]; + } + } +} + +extern "C" void expand_array_gpu(const float *src_gpu, float *dst_gpu, int size, int groups) +{ + const int current_size = size / groups; + const int block_size = BLOCK; + const int num_blocks = get_number_of_blocks(current_size, block_size); + expand_array_kernel << > > (src_gpu, dst_gpu, current_size, groups); + CHECK_CUDA(cudaPeekAtLastError()); } \ No newline at end of file diff --git a/src/conv_lstm_layer.c b/src/conv_lstm_layer.c index b4059ed1ec8..7d5a4989e48 100644 --- a/src/conv_lstm_layer.c +++ b/src/conv_lstm_layer.c @@ -67,44 +67,44 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i // U l.uf = (layer*)calloc(1, sizeof(layer)); - *(l.uf) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); + *(l.uf) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, 0, train); l.uf->batch = batch; if (l.workspace_size < l.uf->workspace_size) l.workspace_size = l.uf->workspace_size; l.ui = (layer*)calloc(1, sizeof(layer)); - *(l.ui) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); + *(l.ui) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, 0, train); l.ui->batch = batch; if (l.workspace_size < l.ui->workspace_size) l.workspace_size = l.ui->workspace_size; l.ug = (layer*)calloc(1, sizeof(layer)); - *(l.ug) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); + *(l.ug) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, 0, train); l.ug->batch = batch; if (l.workspace_size < l.ug->workspace_size) l.workspace_size = l.ug->workspace_size; l.uo = (layer*)calloc(1, sizeof(layer)); - *(l.uo) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); + *(l.uo) = make_convolutional_layer(batch, steps, h, w, c, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, 0, train); l.uo->batch = batch; if (l.workspace_size < l.uo->workspace_size) l.workspace_size = l.uo->workspace_size; // W l.wf = (layer*)calloc(1, sizeof(layer)); - *(l.wf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); + *(l.wf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, 0, train); l.wf->batch = batch; if (l.workspace_size < l.wf->workspace_size) l.workspace_size = l.wf->workspace_size; l.wi = (layer*)calloc(1, sizeof(layer)); - *(l.wi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); + *(l.wi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, 0, train); l.wi->batch = batch; if (l.workspace_size < l.wi->workspace_size) l.workspace_size = l.wi->workspace_size; l.wg = (layer*)calloc(1, sizeof(layer)); - *(l.wg) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); + *(l.wg) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, 0, train); l.wg->batch = batch; if (l.workspace_size < l.wg->workspace_size) l.workspace_size = l.wg->workspace_size; l.wo = (layer*)calloc(1, sizeof(layer)); - *(l.wo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); + *(l.wo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, 0, train); l.wo->batch = batch; if (l.workspace_size < l.wo->workspace_size) l.workspace_size = l.wo->workspace_size; @@ -112,21 +112,21 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i // V l.vf = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { - *(l.vf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); + *(l.vf) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, 0, train); l.vf->batch = batch; if (l.workspace_size < l.vf->workspace_size) l.workspace_size = l.vf->workspace_size; } l.vi = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { - *(l.vi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); + *(l.vi) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, 0, train); l.vi->batch = batch; if (l.workspace_size < l.vi->workspace_size) l.workspace_size = l.vi->workspace_size; } l.vo = (layer*)calloc(1, sizeof(layer)); if (l.peephole) { - *(l.vo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); + *(l.vo) = make_convolutional_layer(batch, steps, h, w, output_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, 0, train); l.vo->batch = batch; if (l.workspace_size < l.vo->workspace_size) l.workspace_size = l.vo->workspace_size; } diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 08dc66dd2fd..45f4947df1d 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -1188,6 +1188,38 @@ void push_convolutional_layer(convolutional_layer l) void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay) { + + /* + for (int angle = 0; angle < 360; angle++) { + printf(" angle = %d \n", angle); + sway_and_flip_weights_gpu(l.weights_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, angle, 0); + + cuda_pull_array(l.weight_deform_gpu, l.weights, l.nweights); + visualize_convolutional_layer(l, "weights", NULL); + wait_key_cv(10); + } + */ + + if (l.sway) { + + //for (l.angle = 0; l.angle < 360; l.angle++) + //{ + + sway_and_flip_weights_gpu(l.weight_updates_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, l.angle, 1); + + //simple_copy_ongpu(l.nweights, l.weight_updates_gpu, l.weight_deform_gpu); + + reduce_and_expand_array_gpu(l.weight_deform_gpu, l.weight_updates_gpu, l.nweights, 4); + + //printf(" angle = %f \n", l.angle); + //cuda_pull_array(l.weight_updates_gpu, l.weights, l.nweights); + //visualize_convolutional_layer(l, "weights", NULL); + //wait_key_cv(10); + //} + + } + + float learning_rate = learning_rate_init*l.learning_rate_scale; //float momentum = a.momentum; //float decay = a.decay; @@ -1221,6 +1253,23 @@ void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init scal_ongpu(l.n, momentum, l.scale_updates_gpu, 1); } } + + if (l.sway) { + //for (l.angle = 0; l.angle < 360; l.angle += 4) + //{ + expand_array_gpu(l.weights_gpu, l.weight_deform_gpu, l.nweights, 4); + + //simple_copy_ongpu(l.nweights, l.weight_deform_gpu, l.weights_gpu); + + sway_and_flip_weights_gpu(l.weight_deform_gpu, l.weights_gpu, l.nweights, l.n, l.size, l.angle, 0); + + //printf(" angle = %f \n", l.angle); + //cuda_pull_array(l.weights_gpu, l.weights, l.nweights); + //visualize_convolutional_layer(l, "weights", NULL); + //wait_key_cv(10); + //} + } + //if (l.clip) { // constrain_gpu(l.nweights, l.clip, l.weights_gpu, 1); //} diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 3fb80c51b6a..33f0c38635e 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -370,7 +370,7 @@ void free_convolutional_batchnorm(convolutional_layer *l) } } -convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, int antialiasing, convolutional_layer *share_layer, int assisted_excitation, int train) +convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, int antialiasing, convolutional_layer *share_layer, int assisted_excitation, int sway, int train) { int total_batch = batch*steps; int i; @@ -388,6 +388,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, stride_x = stride_y = l.stride = l.stride_x = l.stride_y = 1; // use stride=1 in host-layer } + l.sway = sway; l.assisted_excitation = assisted_excitation; l.share_layer = share_layer; l.index = index; @@ -530,15 +531,20 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, #ifdef GPU - if (l.activation == SWISH || l.activation == MISH) { - l.activation_input_gpu = cuda_make_array(l.activation_input, total_batch*l.outputs); - } + l.forward_gpu = forward_convolutional_layer_gpu; l.backward_gpu = backward_convolutional_layer_gpu; l.update_gpu = update_convolutional_layer_gpu; if(gpu_index >= 0){ + + if (l.activation == SWISH || l.activation == MISH) { + l.activation_input_gpu = cuda_make_array(l.activation_input, total_batch*l.outputs); + } + + if (l.sway) l.weight_deform_gpu = cuda_make_array(NULL, l.nweights); + if (adam) { l.m_gpu = cuda_make_array(l.m, l.nweights); l.v_gpu = cuda_make_array(l.v, l.nweights); @@ -660,7 +666,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, blur_size = 2; blur_pad = 0; } - *(l.input_layer) = make_convolutional_layer(batch, steps, out_h, out_w, n, n, n, blur_size, blur_stride_x, blur_stride_y, 1, blur_pad, LINEAR, 0, 0, 0, 0, 0, index, 0, NULL, 0, train); + *(l.input_layer) = make_convolutional_layer(batch, steps, out_h, out_w, n, n, n, blur_size, blur_stride_x, blur_stride_y, 1, blur_pad, LINEAR, 0, 0, 0, 0, 0, index, 0, NULL, 0, 0, train); const int blur_nweights = n * blur_size * blur_size; // (n / n) * n * blur_size * blur_size; int i; if (blur_size == 2) { @@ -715,7 +721,7 @@ void denormalize_convolutional_layer(convolutional_layer l) void test_convolutional_layer() { - convolutional_layer l = make_convolutional_layer(1, 1, 5, 5, 3, 2, 1, 5, 2, 2, 1, 1, LEAKY, 1, 0, 0, 0, 0, 0, 0, NULL, 0, 0); + convolutional_layer l = make_convolutional_layer(1, 1, 5, 5, 3, 2, 1, 5, 2, 2, 1, 1, LEAKY, 1, 0, 0, 0, 0, 0, 0, NULL, 0, 0, 0); l.batch_normalize = 1; float data[] = {1,1,1,1,1, 1,1,1,1,1, @@ -1517,7 +1523,7 @@ image *visualize_convolutional_layer(convolutional_layer l, char *window, image image dc = collapse_image_layers(delta, 1); char buff[256]; sprintf(buff, "%s: Output", window); - //show_image(dc, buff); + show_image(dc, buff); //save_image(dc, buff); free_image(dc); return single_weights; diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h index fe932963b6c..bca22b6526f 100644 --- a/src/convolutional_layer.h +++ b/src/convolutional_layer.h @@ -31,7 +31,7 @@ void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16); void free_convolutional_batchnorm(convolutional_layer *l); size_t get_convolutional_workspace_size(layer l); -convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, int antialiasing, convolutional_layer *share_layer, int assisted_excitation, int train); +convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, int antialiasing, convolutional_layer *share_layer, int assisted_excitation, int sway, int train); void denormalize_convolutional_layer(convolutional_layer l); void set_specified_workspace_limit(convolutional_layer *l, size_t workspace_size_limit); void resize_convolutional_layer(convolutional_layer *layer, int w, int h); diff --git a/src/crnn_layer.c b/src/crnn_layer.c index cbeaa5087bc..544b1564784 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -51,17 +51,17 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou l.state = (float*)calloc(l.hidden * l.batch * (l.steps + 1), sizeof(float)); l.input_layer = (layer*)calloc(1, sizeof(layer)); - *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); + *(l.input_layer) = make_convolutional_layer(batch, steps, h, w, c, hidden_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, 0, train); l.input_layer->batch = batch; if (l.workspace_size < l.input_layer->workspace_size) l.workspace_size = l.input_layer->workspace_size; l.self_layer = (layer*)calloc(1, sizeof(layer)); - *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); + *(l.self_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, hidden_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, 0, train); l.self_layer->batch = batch; if (l.workspace_size < l.self_layer->workspace_size) l.workspace_size = l.self_layer->workspace_size; l.output_layer = (layer*)calloc(1, sizeof(layer)); - *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, train); + *(l.output_layer) = make_convolutional_layer(batch, steps, h, w, hidden_filters, output_filters, groups, size, stride, stride, dilation, pad, activation, batch_normalize, 0, xnor, 0, 0, 0, 0, NULL, 0, 0, train); l.output_layer->batch = batch; if (l.workspace_size < l.output_layer->workspace_size) l.workspace_size = l.output_layer->workspace_size; diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index 6c483ce30ec..99a2b06ac83 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -123,7 +123,7 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s blur_size = 2; blur_pad = 0; } - *(l.input_layer) = make_convolutional_layer(batch, 1, l.out_h, l.out_w, l.out_c, l.out_c, l.out_c, blur_size, blur_stride_x, blur_stride_y, 1, blur_pad, LINEAR, 0, 0, 0, 0, 0, 1, 0, NULL, 0, train); + *(l.input_layer) = make_convolutional_layer(batch, 1, l.out_h, l.out_w, l.out_c, l.out_c, l.out_c, blur_size, blur_stride_x, blur_stride_y, 1, blur_pad, LINEAR, 0, 0, 0, 0, 0, 1, 0, NULL, 0, 0, train); const int blur_nweights = l.out_c * blur_size * blur_size; // (n / n) * n * blur_size * blur_size; int i; if (blur_size == 2) { diff --git a/src/parser.c b/src/parser.c index 411a8366e13..3d9dbd8e2d9 100644 --- a/src/parser.c +++ b/src/parser.c @@ -199,11 +199,12 @@ convolutional_layer parse_convolutional(list *options, size_params params) int binary = option_find_int_quiet(options, "binary", 0); int xnor = option_find_int_quiet(options, "xnor", 0); int use_bin_output = option_find_int_quiet(options, "bin_output", 0); + int sway = option_find_int_quiet(options, "sway", 0); - convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride_x,stride_y,dilation,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index, antialiasing, share_layer, assisted_excitation, params.train); + convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride_x,stride_y,dilation,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index, antialiasing, share_layer, assisted_excitation, sway, params.train); layer.flipped = option_find_int_quiet(options, "flipped", 0); layer.dot = option_find_float_quiet(options, "dot", 0); - + layer.angle = option_find_float_quiet(options, "angle", 15); if(params.net.adam){ layer.B1 = params.net.B1; From 5e035565ff6226aaea3377bce9bb8e2279f828ae Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 14 Dec 2019 18:11:09 +0300 Subject: [PATCH 0743/1365] Rotate conv-kernel --- include/darknet.h | 3 ++ src/blas.h | 3 +- src/blas_kernels.cu | 90 ++++++++++++++++++++++++++++++++++-- src/convolutional_kernels.cu | 16 ++++--- src/convolutional_layer.c | 6 +-- src/convolutional_layer.h | 2 +- src/parser.c | 12 ++++- 7 files changed, 116 insertions(+), 16 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 6f5c78a9565..d6ef4f8b4f2 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -248,7 +248,10 @@ struct layer { int truth; float smooth; float dot; + int deform; int sway; + int rotate; + int stretch; float angle; float jitter; float saturation; diff --git a/src/blas.h b/src/blas.h index 35783a169c2..a576b2187a0 100644 --- a/src/blas.h +++ b/src/blas.h @@ -129,8 +129,9 @@ void backward_sam_gpu(float *in_w_h_c_delta, int size, int channel_size, void sam_gpu(float *in_w_h_c, int size, int channel_size, float *scales_c, float *out); -void rotate_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse); +void smooth_rotate_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse); void sway_and_flip_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse); +void rotate_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int reverse); void reduce_and_expand_array_gpu(const float *src_gpu, float *dst_gpu, int size, int groups); void expand_array_gpu(const float *src_gpu, float *dst_gpu, int size, int groups); diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index a7ce967a277..ee9b409654b 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -1240,7 +1240,7 @@ extern "C" void backward_sam_gpu(float *in_w_h_c_delta, int size, int channel_si } -__global__ void rotate_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, int angle, int reverse) +__global__ void smooth_rotate_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, int angle, int reverse) { const int index = blockIdx.x*blockDim.x + threadIdx.x; const int kernel_area = kernel_size * kernel_size; @@ -1296,12 +1296,12 @@ __global__ void rotate_weights_kernel(const float *src_weight_gpu, float *weigh } -extern "C" void rotate_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse) +extern "C" void smooth_rotate_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse) { const int kernel_area = size*size; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size); - rotate_weights_kernel << > > (src_weight_gpu, weight_deform_gpu, nweights, n, size, angle, reverse); + smooth_rotate_weights_kernel << > > (src_weight_gpu, weight_deform_gpu, nweights, n, size, angle, reverse); CHECK_CUDA(cudaPeekAtLastError()); } @@ -1396,6 +1396,90 @@ extern "C" void sway_and_flip_weights_gpu(const float *src_weight_gpu, float *we } + + + + + +__global__ void rotate_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, int reverse) +{ + const int index = blockIdx.x*blockDim.x + threadIdx.x; + const int kernel_area = kernel_size * kernel_size; + const int i = index * kernel_area; + + const int stage_step = (nweights / kernel_area) / 4; // 4 stages + const int stage_id = index / stage_step; + + // nweights = (c / groups) * n * size * size; + // kernel_area = size*size + + if (i < nweights) + { + // if(reverse) + + if (stage_id == 0) { + // simple copy + for (int x = 0; x < kernel_size; ++x) { + for (int y = 0; y < kernel_size; ++y) { + const int src_i = x + y*kernel_size + i; + const int dst_i = x + y*kernel_size + i; + if (reverse) weight_deform_gpu[src_i] = src_weight_gpu[dst_i]; + else weight_deform_gpu[dst_i] = src_weight_gpu[src_i]; + } + } + } + else if (stage_id == 1) + { + // 90 degree clockwise rotation - 1 + for (int x = 0; x < kernel_size; ++x) { + for (int y = 0; y < kernel_size; ++y) { + const int src_i = x + y*kernel_size + i; + const int dst_i = (kernel_size - 1 - y) + x*kernel_size + i; + if (reverse) weight_deform_gpu[src_i] = src_weight_gpu[dst_i]; + else weight_deform_gpu[dst_i] = src_weight_gpu[src_i]; + } + } + } + else if (stage_id == 2) + { + // 180 degree clockwise rotation - 2 + for (int x = 0; x < kernel_size; ++x) { + for (int y = 0; y < kernel_size; ++y) { + const int src_i = x + y*kernel_size + i; + const int dst_i = (kernel_size - 1 - x) + (kernel_size - 1 - y)*kernel_size + i; + if (reverse) weight_deform_gpu[src_i] = src_weight_gpu[dst_i]; + else weight_deform_gpu[dst_i] = src_weight_gpu[src_i]; + } + } + } + else if (stage_id == 3) + { + // 270 degree clockwise rotation - 3 + for (int x = 0; x < kernel_size; ++x) { + for (int y = 0; y < kernel_size; ++y) { + const int src_i = x + y*kernel_size + i; + const int dst_i = y + (kernel_size - 1 - x)*kernel_size + i; + if (reverse) weight_deform_gpu[src_i] = src_weight_gpu[dst_i]; + else weight_deform_gpu[dst_i] = src_weight_gpu[src_i]; + } + } + } + } +} + + +extern "C" void rotate_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int reverse) +{ + const int kernel_area = size*size; + const int block_size = BLOCK; + const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size); + rotate_weights_kernel << > > (src_weight_gpu, weight_deform_gpu, nweights, n, size, reverse); + + CHECK_CUDA(cudaPeekAtLastError()); +} + + + __global__ void reduce_and_expand_array_kernel(const float *src_gpu, float *dst_gpu, int current_size, int groups) { const int index = blockIdx.x*blockDim.x + threadIdx.x; diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 45f4947df1d..d6cd1937e1c 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -1192,7 +1192,7 @@ void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init /* for (int angle = 0; angle < 360; angle++) { printf(" angle = %d \n", angle); - sway_and_flip_weights_gpu(l.weights_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, angle, 0); + smooth_rotate_weights_kernel(l.weights_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, angle, 0); cuda_pull_array(l.weight_deform_gpu, l.weights, l.nweights); visualize_convolutional_layer(l, "weights", NULL); @@ -1200,12 +1200,13 @@ void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init } */ - if (l.sway) { + if (l.deform) { //for (l.angle = 0; l.angle < 360; l.angle++) //{ - sway_and_flip_weights_gpu(l.weight_updates_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, l.angle, 1); + if (l.rotate) rotate_weights_gpu(l.weight_updates_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, 1); + else if (l.sway) sway_and_flip_weights_gpu(l.weight_updates_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, l.angle, 1); //simple_copy_ongpu(l.nweights, l.weight_updates_gpu, l.weight_deform_gpu); @@ -1254,16 +1255,17 @@ void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init } } - if (l.sway) { - //for (l.angle = 0; l.angle < 360; l.angle += 4) + if (l.deform) { + //for (l.angle = 0; l.angle < 50; l.angle += 0.1) //{ expand_array_gpu(l.weights_gpu, l.weight_deform_gpu, l.nweights, 4); //simple_copy_ongpu(l.nweights, l.weight_deform_gpu, l.weights_gpu); - sway_and_flip_weights_gpu(l.weight_deform_gpu, l.weights_gpu, l.nweights, l.n, l.size, l.angle, 0); + if (l.rotate) rotate_weights_gpu(l.weight_deform_gpu, l.weights_gpu, l.nweights, l.n, l.size, 0); + else if (l.sway) sway_and_flip_weights_gpu(l.weight_deform_gpu, l.weights_gpu, l.nweights, l.n, l.size, l.angle, 0); - //printf(" angle = %f \n", l.angle); + //printf(" angle = %f, reverse = %d \n", l.angle, 0); //cuda_pull_array(l.weights_gpu, l.weights, l.nweights); //visualize_convolutional_layer(l, "weights", NULL); //wait_key_cv(10); diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 33f0c38635e..8cecbc2f6d7 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -370,7 +370,7 @@ void free_convolutional_batchnorm(convolutional_layer *l) } } -convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, int antialiasing, convolutional_layer *share_layer, int assisted_excitation, int sway, int train) +convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, int antialiasing, convolutional_layer *share_layer, int assisted_excitation, int deform, int train) { int total_batch = batch*steps; int i; @@ -388,7 +388,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, stride_x = stride_y = l.stride = l.stride_x = l.stride_y = 1; // use stride=1 in host-layer } - l.sway = sway; + l.deform = deform; l.assisted_excitation = assisted_excitation; l.share_layer = share_layer; l.index = index; @@ -543,7 +543,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.activation_input_gpu = cuda_make_array(l.activation_input, total_batch*l.outputs); } - if (l.sway) l.weight_deform_gpu = cuda_make_array(NULL, l.nweights); + if (l.deform) l.weight_deform_gpu = cuda_make_array(NULL, l.nweights); if (adam) { l.m_gpu = cuda_make_array(l.m, l.nweights); diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h index bca22b6526f..18135937137 100644 --- a/src/convolutional_layer.h +++ b/src/convolutional_layer.h @@ -31,7 +31,7 @@ void cuda_convert_f32_to_f16(float* input_f32, size_t size, float *output_f16); void free_convolutional_batchnorm(convolutional_layer *l); size_t get_convolutional_workspace_size(layer l); -convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, int antialiasing, convolutional_layer *share_layer, int assisted_excitation, int sway, int train); +convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, int c, int n, int groups, int size, int stride_x, int stride_y, int dilation, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam, int use_bin_output, int index, int antialiasing, convolutional_layer *share_layer, int assisted_excitation, int deform, int train); void denormalize_convolutional_layer(convolutional_layer l); void set_specified_workspace_limit(convolutional_layer *l, size_t workspace_size_limit); void resize_convolutional_layer(convolutional_layer *layer, int w, int h); diff --git a/src/parser.c b/src/parser.c index 3d9dbd8e2d9..dfc66d3267d 100644 --- a/src/parser.c +++ b/src/parser.c @@ -200,10 +200,20 @@ convolutional_layer parse_convolutional(list *options, size_params params) int xnor = option_find_int_quiet(options, "xnor", 0); int use_bin_output = option_find_int_quiet(options, "bin_output", 0); int sway = option_find_int_quiet(options, "sway", 0); + int rotate = option_find_int_quiet(options, "rotate", 0); + int stretch = option_find_int_quiet(options, "stretch", 0); + if ((sway + rotate + stretch) > 1) { + printf(" Error: should be used only 1 param: sway=1, rotate=1 or stretch=1 in the [convolutional] layer \n"); + exit(0); + } + int deform = sway || rotate || stretch; - convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride_x,stride_y,dilation,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index, antialiasing, share_layer, assisted_excitation, sway, params.train); + convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride_x,stride_y,dilation,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index, antialiasing, share_layer, assisted_excitation, deform, params.train); layer.flipped = option_find_int_quiet(options, "flipped", 0); layer.dot = option_find_float_quiet(options, "dot", 0); + layer.sway = sway; + layer.rotate = rotate; + layer.stretch = stretch; layer.angle = option_find_float_quiet(options, "angle", 15); if(params.net.adam){ From b78aa3961ba70bf3a986b5f7a6dcf1846e1e9be6 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 14 Dec 2019 22:50:33 +0300 Subject: [PATCH 0744/1365] Dropped out items are has compensated --- src/blas_kernels.cu | 63 ++++++++++++++++++++++++++++++++++----------- src/parser.c | 4 +++ 2 files changed, 52 insertions(+), 15 deletions(-) diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index ee9b409654b..39521aa43a0 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -1262,8 +1262,10 @@ __global__ void smooth_rotate_weights_kernel(const float *src_weight_gpu, float const int x_c = kernel_size / 2; const int y_c = kernel_size / 2; - for (int x = 0; x < kernel_size; ++x) { - for (int y = 0; y < kernel_size; ++y) { + float dropout_sum = 0; + + for (int y = 0; y < kernel_size; ++y) { + for (int x = 0; x < kernel_size; ++x) { // Xsource = x*cos(alpha) + y*sin(alpha) // Ysource = -x*sin(alpha) + y*cos(alpha) @@ -1285,13 +1287,28 @@ __global__ void smooth_rotate_weights_kernel(const float *src_weight_gpu, float float val = 0; if (x_0 >= 0 && x_0 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_0 + y_0*kernel_size + i] * c_x_0 * c_y_0; + else dropout_sum += c_x_0 * c_y_0; + if (x_1 >= 0 && x_1 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_1 + y_0*kernel_size + i] * c_x_1 * c_y_0; + else dropout_sum += c_x_1 * c_y_0; + if (x_0 >= 0 && x_0 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_0 + y_1*kernel_size + i] * c_x_0 * c_y_1; + else dropout_sum += c_x_0 * c_y_1; + if (x_1 >= 0 && x_1 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_1 + y_1*kernel_size + i] * c_x_1 * c_y_1; + else dropout_sum += c_x_1 * c_y_1; weight_deform_gpu[x + y*kernel_size + i] = val; } } + + // compensate for dropped items + const float coef = (kernel_size*kernel_size) / (kernel_size*kernel_size - dropout_sum); + for (int y = 0; y < kernel_size; ++y) { + for (int x = 0; x < kernel_size; ++x) { + weight_deform_gpu[x + y*kernel_size + i] *= coef; + } + } } } @@ -1341,8 +1358,10 @@ __global__ void sway_and_flip_weights_kernel(const float *src_weight_gpu, float const int x_c = kernel_size / 2; const int y_c = kernel_size / 2; - for (int x = 0; x < kernel_size; ++x) { - for (int y = 0; y < kernel_size; ++y) { + float dropout_sum = 0; + + for (int y = 0; y < kernel_size; ++y) { + for (int x = 0; x < kernel_size; ++x) { // Xsource = x*cos(alpha) + y*sin(alpha) // Ysource = -x*sin(alpha) + y*cos(alpha) @@ -1361,22 +1380,36 @@ __global__ void sway_and_flip_weights_kernel(const float *src_weight_gpu, float float c_y_0 = y_1 - y_s; float c_y_1 = y_s - y_0; - float val = 0; if (x_0 >= 0 && x_0 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_0 + y_0*kernel_size + i] * c_x_0 * c_y_0; + else dropout_sum += c_x_0 * c_y_0; + if (x_1 >= 0 && x_1 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_1 + y_0*kernel_size + i] * c_x_1 * c_y_0; + else dropout_sum += c_x_1 * c_y_0; + if (x_0 >= 0 && x_0 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_0 + y_1*kernel_size + i] * c_x_0 * c_y_1; + else dropout_sum += c_x_0 * c_y_1; + if (x_1 >= 0 && x_1 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_1 + y_1*kernel_size + i] * c_x_1 * c_y_1; + else dropout_sum += c_x_1 * c_y_1; weight_deform_gpu[x + y*kernel_size + i] = val; } } + + // compensate for dropped items + const float coef = (kernel_size*kernel_size) / (kernel_size*kernel_size - dropout_sum); + for (int y = 0; y < kernel_size; ++y) { + for (int x = 0; x < kernel_size; ++x) { + weight_deform_gpu[x + y*kernel_size + i] *= coef; + } + } } else if (stage_id == 3) { // flip - for (int x = 0; x < kernel_size; ++x) { - for (int y = 0; y < kernel_size; ++y) { + for (int y = 0; y < kernel_size; ++y) { + for (int x = 0; x < kernel_size; ++x) { weight_deform_gpu[(kernel_size - x - 1) + y*kernel_size + i] = src_weight_gpu[x + y*kernel_size + i]; } } @@ -1419,8 +1452,8 @@ __global__ void rotate_weights_kernel(const float *src_weight_gpu, float *weigh if (stage_id == 0) { // simple copy - for (int x = 0; x < kernel_size; ++x) { - for (int y = 0; y < kernel_size; ++y) { + for (int y = 0; y < kernel_size; ++y) { + for (int x = 0; x < kernel_size; ++x) { const int src_i = x + y*kernel_size + i; const int dst_i = x + y*kernel_size + i; if (reverse) weight_deform_gpu[src_i] = src_weight_gpu[dst_i]; @@ -1431,8 +1464,8 @@ __global__ void rotate_weights_kernel(const float *src_weight_gpu, float *weigh else if (stage_id == 1) { // 90 degree clockwise rotation - 1 - for (int x = 0; x < kernel_size; ++x) { - for (int y = 0; y < kernel_size; ++y) { + for (int y = 0; y < kernel_size; ++y) { + for (int x = 0; x < kernel_size; ++x) { const int src_i = x + y*kernel_size + i; const int dst_i = (kernel_size - 1 - y) + x*kernel_size + i; if (reverse) weight_deform_gpu[src_i] = src_weight_gpu[dst_i]; @@ -1443,8 +1476,8 @@ __global__ void rotate_weights_kernel(const float *src_weight_gpu, float *weigh else if (stage_id == 2) { // 180 degree clockwise rotation - 2 - for (int x = 0; x < kernel_size; ++x) { - for (int y = 0; y < kernel_size; ++y) { + for (int y = 0; y < kernel_size; ++y) { + for (int x = 0; x < kernel_size; ++x) { const int src_i = x + y*kernel_size + i; const int dst_i = (kernel_size - 1 - x) + (kernel_size - 1 - y)*kernel_size + i; if (reverse) weight_deform_gpu[src_i] = src_weight_gpu[dst_i]; @@ -1455,8 +1488,8 @@ __global__ void rotate_weights_kernel(const float *src_weight_gpu, float *weigh else if (stage_id == 3) { // 270 degree clockwise rotation - 3 - for (int x = 0; x < kernel_size; ++x) { - for (int y = 0; y < kernel_size; ++y) { + for (int y = 0; y < kernel_size; ++y) { + for (int x = 0; x < kernel_size; ++x) { const int src_i = x + y*kernel_size + i; const int dst_i = y + (kernel_size - 1 - x)*kernel_size + i; if (reverse) weight_deform_gpu[src_i] = src_weight_gpu[dst_i]; diff --git a/src/parser.c b/src/parser.c index dfc66d3267d..55cda92938d 100644 --- a/src/parser.c +++ b/src/parser.c @@ -207,6 +207,10 @@ convolutional_layer parse_convolutional(list *options, size_params params) exit(0); } int deform = sway || rotate || stretch; + if (deform && size == 1) { + printf(" Error: params (sway=1, rotate=1 or stretch=1) should be used only with size >=3 in the [convolutional] layer \n"); + exit(0); + } convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride_x,stride_y,dilation,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index, antialiasing, share_layer, assisted_excitation, deform, params.train); layer.flipped = option_find_int_quiet(options, "flipped", 0); From f2fc239096d631b4ebbd14a35e76db6c65d7b5c5 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 15 Dec 2019 01:02:59 +0300 Subject: [PATCH 0745/1365] Stretch conv-kernel --- src/blas.h | 1 + src/blas_kernels.cu | 103 +++++++++++++++++++++++++++++++++++ src/convolutional_kernels.cu | 10 +++- 3 files changed, 111 insertions(+), 3 deletions(-) diff --git a/src/blas.h b/src/blas.h index a576b2187a0..8477bb4193f 100644 --- a/src/blas.h +++ b/src/blas.h @@ -130,6 +130,7 @@ void backward_sam_gpu(float *in_w_h_c_delta, int size, int channel_size, void sam_gpu(float *in_w_h_c, int size, int channel_size, float *scales_c, float *out); void smooth_rotate_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse); +void stretch_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, float scale, int reverse); void sway_and_flip_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse); void rotate_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int reverse); void reduce_and_expand_array_gpu(const float *src_gpu, float *dst_gpu, int size, int groups); diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 39521aa43a0..cdf8a3b3f7e 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -1324,6 +1324,109 @@ extern "C" void smooth_rotate_weights_gpu(const float *src_weight_gpu, float *we } + +__global__ void stretch_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, float scale, int reverse) +{ + const int index = blockIdx.x*blockDim.x + threadIdx.x; + const int kernel_area = kernel_size * kernel_size; + const int i = index * kernel_area; + + const int stage_step = (nweights / kernel_area) / 4; // 4 stages + const int stage_id = index / stage_step; + + // nweights = (c / groups) * n * size * size; + // kernel_area = size*size + + if (i < nweights) + { + + if (stage_id == 0) { + // simple copy + for (int x = 0; x < kernel_size; ++x) { + for (int y = 0; y < kernel_size; ++y) { + weight_deform_gpu[x + y*kernel_size + i] = src_weight_gpu[x + y*kernel_size + i]; + } + } + } + else if (stage_id > 0) + { + if (stage_id == 1) scale = 0.65; + else if (stage_id == 2) scale = 0.8; + else if (stage_id == 3) scale = 1.3; + + if (reverse) scale = 1 / scale; + + const int x_c = kernel_size / 2; + const int y_c = kernel_size / 2; + + float dropout_sum = 0; + + for (int y = 0; y < kernel_size; ++y) { + for (int x = 0; x < kernel_size; ++x) { + // Xsource = x_c + (x_d - x_c) / scale + // Ysource = y_c + (y_d - y_c) / scale + + float x_s = x_c + (x - x_c) / scale; + float y_s = y_c + (y - y_c) / scale; + + int x_0 = floor(x_s); // round down + int x_1 = ceil(x_s); // round up + if (x_0 == x_1) x_1 = x_0 + 1; + int y_0 = floor(y_s); + int y_1 = ceil(y_s); + if (y_0 == y_1) y_1 = y_0 + 1; + + float c_x_0 = x_1 - x_s; + float c_x_1 = x_s - x_0; + float c_y_0 = y_1 - y_s; + float c_y_1 = y_s - y_0; + + float val = 0; + if (x_0 >= 0 && x_0 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_0 + y_0*kernel_size + i] * c_x_0 * c_y_0; + else dropout_sum += c_x_0 * c_y_0; + + if (x_1 >= 0 && x_1 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_1 + y_0*kernel_size + i] * c_x_1 * c_y_0; + else dropout_sum += c_x_1 * c_y_0; + + if (x_0 >= 0 && x_0 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_0 + y_1*kernel_size + i] * c_x_0 * c_y_1; + else dropout_sum += c_x_0 * c_y_1; + + if (x_1 >= 0 && x_1 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_1 + y_1*kernel_size + i] * c_x_1 * c_y_1; + else dropout_sum += c_x_1 * c_y_1; + + weight_deform_gpu[x + y*kernel_size + i] = val; + } + } + + // compensate for dropped items + //const float coef = (kernel_size*kernel_size) / (kernel_size*kernel_size - dropout_sum); + for (int y = 0; y < kernel_size; ++y) { + for (int x = 0; x < kernel_size; ++x) { + weight_deform_gpu[x + y*kernel_size + i] /= scale;// *= coef; + } + } + } + } +} + + +extern "C" void stretch_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, float scale, int reverse) +{ + const int kernel_area = size*size; + const int block_size = BLOCK; + const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size); + stretch_weights_kernel << > > (src_weight_gpu, weight_deform_gpu, nweights, n, size, scale, reverse); + + CHECK_CUDA(cudaPeekAtLastError()); +} + + + + + + + + __global__ void sway_and_flip_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, int angle, int reverse) { const int index = blockIdx.x*blockDim.x + threadIdx.x; diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index d6cd1937e1c..8c821f48f96 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -1202,18 +1202,21 @@ void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init if (l.deform) { - //for (l.angle = 0; l.angle < 360; l.angle++) + //for (l.angle = 0; l.angle < 360; l.angle += 1) //{ + //stretch_weights_gpu(l.weight_updates_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, l.angle/180, 1); + //else simple_copy_ongpu(l.nweights, l.weight_updates_gpu, l.weight_deform_gpu); if (l.rotate) rotate_weights_gpu(l.weight_updates_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, 1); else if (l.sway) sway_and_flip_weights_gpu(l.weight_updates_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, l.angle, 1); + else if (l.stretch) stretch_weights_gpu(l.weight_updates_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, 0, 1); //simple_copy_ongpu(l.nweights, l.weight_updates_gpu, l.weight_deform_gpu); reduce_and_expand_array_gpu(l.weight_deform_gpu, l.weight_updates_gpu, l.nweights, 4); //printf(" angle = %f \n", l.angle); - //cuda_pull_array(l.weight_updates_gpu, l.weights, l.nweights); + //cuda_pull_array(l.weight_deform_gpu, l.weights, l.nweights); //visualize_convolutional_layer(l, "weights", NULL); //wait_key_cv(10); //} @@ -1256,7 +1259,7 @@ void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init } if (l.deform) { - //for (l.angle = 0; l.angle < 50; l.angle += 0.1) + //for (l.angle = 0; l.angle < 360; l.angle += 4) //{ expand_array_gpu(l.weights_gpu, l.weight_deform_gpu, l.nweights, 4); @@ -1264,6 +1267,7 @@ void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init if (l.rotate) rotate_weights_gpu(l.weight_deform_gpu, l.weights_gpu, l.nweights, l.n, l.size, 0); else if (l.sway) sway_and_flip_weights_gpu(l.weight_deform_gpu, l.weights_gpu, l.nweights, l.n, l.size, l.angle, 0); + else if (l.stretch) stretch_weights_gpu(l.weight_deform_gpu, l.weights_gpu, l.nweights, l.n, l.size, 0, 0); //printf(" angle = %f, reverse = %d \n", l.angle, 0); //cuda_pull_array(l.weights_gpu, l.weights, l.nweights); From f831835125d3181b03aa28134869099c82ca846e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 16 Dec 2019 16:17:44 +0300 Subject: [PATCH 0746/1365] Fixed DropBlock bug for Conv-networks. When are used: random=1 and [dropout] dropblock=1 --- build/darknet/x64/cfg/msr_152.cfg | 1598 ----------------------------- build/darknet/x64/cfg/msr_34.cfg | 366 ------- build/darknet/x64/cfg/msr_50.cfg | 558 ---------- cfg/msr_152.cfg | 1598 ----------------------------- cfg/msr_34.cfg | 366 ------- cfg/msr_50.cfg | 558 ---------- src/layer.c | 1 + src/network.c | 4 +- src/network_kernels.cu | 2 + 9 files changed, 6 insertions(+), 5045 deletions(-) delete mode 100644 build/darknet/x64/cfg/msr_152.cfg delete mode 100644 build/darknet/x64/cfg/msr_34.cfg delete mode 100644 build/darknet/x64/cfg/msr_50.cfg delete mode 100644 cfg/msr_152.cfg delete mode 100644 cfg/msr_34.cfg delete mode 100644 cfg/msr_50.cfg diff --git a/build/darknet/x64/cfg/msr_152.cfg b/build/darknet/x64/cfg/msr_152.cfg deleted file mode 100644 index b19c999dd1f..00000000000 --- a/build/darknet/x64/cfg/msr_152.cfg +++ /dev/null @@ -1,1598 +0,0 @@ -[net] -batch=128 -subdivisions=8 -height=256 -width=256 -channels=3 -momentum=0.9 -decay=0.0001 - -learning_rate=0.1 -policy=poly -power=4 -max_batches=500000 - -[crop] -crop_height=224 -crop_width=224 -flip=1 -saturation=1 -exposure=1 -angle=0 - -##### Conv 1 ##### - -[convolutional] -batch_normalize=1 -filters=64 -size=7 -stride=2 -pad=1 -activation=leaky - -[maxpool] -size=3 -stride=2 - - -##### Conv 2_x ##### - - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=linear - -[route] -layers=-4 - -[convolutional] -batch_normalize=1 -size=1 -stride=1 -pad=1 -activation=linear -filters=256 - -[shortcut] -from = -3 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -##### Conv 3_x ##### - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - - -[route] -layers=-4 - -[convolutional] -batch_normalize=1 -size=1 -stride=2 -pad=1 -activation=linear -filters=512 - -[shortcut] -from = -3 -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - - -##### Conv 4_x ##### - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - - -[route] -layers=-4 - -[convolutional] -batch_normalize=1 -size=1 -stride=2 -pad=1 -activation=linear -filters=1024 - -[shortcut] -from = -3 -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -##### Conv 5_x ##### - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=2048 -size=1 -stride=1 -pad=1 -activation=linear - - -[route] -layers=-4 - -[convolutional] -batch_normalize=1 -size=1 -stride=2 -pad=1 -activation=linear -filters=2048 - -[shortcut] -from = -3 -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=2048 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=2048 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - -[avgpool] - -[connected] -output=1000 -activation=leaky - -[softmax] -groups=1 - -[cost] -type=sse - diff --git a/build/darknet/x64/cfg/msr_34.cfg b/build/darknet/x64/cfg/msr_34.cfg deleted file mode 100644 index 5ae23cf587b..00000000000 --- a/build/darknet/x64/cfg/msr_34.cfg +++ /dev/null @@ -1,366 +0,0 @@ -[net] -batch=128 -subdivisions=1 -height=256 -width=256 -channels=3 -momentum=0.9 -decay=0.0005 - -learning_rate=0.1 -policy=poly -power=4 -max_batches=500000 - -[crop] -crop_height=224 -crop_width=224 -flip=1 -saturation=1 -exposure=1 -angle=0 - -[convolutional] -batch_normalize=1 -filters=64 -size=7 -stride=2 -pad=1 -activation=leaky - -[maxpool] -size=3 -stride=2 - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - - - - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - - - - - - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - - - - - - - - - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[avgpool] - -[connected] -output=1000 -activation=leaky - -[softmax] -groups=1 - -[cost] -type=sse - diff --git a/build/darknet/x64/cfg/msr_50.cfg b/build/darknet/x64/cfg/msr_50.cfg deleted file mode 100644 index 2edd21c1a66..00000000000 --- a/build/darknet/x64/cfg/msr_50.cfg +++ /dev/null @@ -1,558 +0,0 @@ -[net] -batch=128 -subdivisions=8 -height=256 -width=256 -channels=3 -momentum=0.9 -decay=0.0001 - -learning_rate=0.05 -policy=poly -power=4 -max_batches=500000 - - - -[crop] -crop_height=224 -crop_width=224 -flip=1 -saturation=1 -exposure=1 -angle=0 - -##### Conv 1 ##### - -[convolutional] -batch_normalize=1 -filters=64 -size=7 -stride=2 -pad=1 -activation=leaky - -[maxpool] -size=3 -stride=2 - - -##### Conv 2_x ##### - - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=linear - -[route] -layers=-4 - -[convolutional] -batch_normalize=1 -size=1 -stride=1 -pad=1 -activation=linear -filters=256 - -[shortcut] -from = -3 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - - -##### Conv 3_x ##### - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - -[route] -layers=-4 - -[convolutional] -batch_normalize=1 -size=1 -stride=2 -pad=1 -activation=linear -filters=512 - -[shortcut] -from = -3 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - - -##### Conv 4_x ##### - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[route] -layers=-4 - -[convolutional] -batch_normalize=1 -size=1 -stride=2 -pad=1 -activation=linear -filters=1024 - -[shortcut] -from = -3 -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - - -##### Conv 5_x ##### - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=2048 -size=1 -stride=1 -pad=1 -activation=linear - - -[route] -layers=-4 - -[convolutional] -batch_normalize=1 -size=1 -stride=2 -pad=1 -activation=linear -filters=2048 - -[shortcut] -from = -3 -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=2048 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=2048 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - -[avgpool] - -[connected] -output=1000 -activation=leaky - -[softmax] -groups=1 - -[cost] -type=sse - diff --git a/cfg/msr_152.cfg b/cfg/msr_152.cfg deleted file mode 100644 index b19c999dd1f..00000000000 --- a/cfg/msr_152.cfg +++ /dev/null @@ -1,1598 +0,0 @@ -[net] -batch=128 -subdivisions=8 -height=256 -width=256 -channels=3 -momentum=0.9 -decay=0.0001 - -learning_rate=0.1 -policy=poly -power=4 -max_batches=500000 - -[crop] -crop_height=224 -crop_width=224 -flip=1 -saturation=1 -exposure=1 -angle=0 - -##### Conv 1 ##### - -[convolutional] -batch_normalize=1 -filters=64 -size=7 -stride=2 -pad=1 -activation=leaky - -[maxpool] -size=3 -stride=2 - - -##### Conv 2_x ##### - - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=linear - -[route] -layers=-4 - -[convolutional] -batch_normalize=1 -size=1 -stride=1 -pad=1 -activation=linear -filters=256 - -[shortcut] -from = -3 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -##### Conv 3_x ##### - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - - -[route] -layers=-4 - -[convolutional] -batch_normalize=1 -size=1 -stride=2 -pad=1 -activation=linear -filters=512 - -[shortcut] -from = -3 -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - - -##### Conv 4_x ##### - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - - -[route] -layers=-4 - -[convolutional] -batch_normalize=1 -size=1 -stride=2 -pad=1 -activation=linear -filters=1024 - -[shortcut] -from = -3 -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - - -##### Conv 5_x ##### - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=2048 -size=1 -stride=1 -pad=1 -activation=linear - - -[route] -layers=-4 - -[convolutional] -batch_normalize=1 -size=1 -stride=2 -pad=1 -activation=linear -filters=2048 - -[shortcut] -from = -3 -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=2048 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=2048 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 - -activation=leaky - -[avgpool] - -[connected] -output=1000 -activation=leaky - -[softmax] -groups=1 - -[cost] -type=sse - diff --git a/cfg/msr_34.cfg b/cfg/msr_34.cfg deleted file mode 100644 index 5ae23cf587b..00000000000 --- a/cfg/msr_34.cfg +++ /dev/null @@ -1,366 +0,0 @@ -[net] -batch=128 -subdivisions=1 -height=256 -width=256 -channels=3 -momentum=0.9 -decay=0.0005 - -learning_rate=0.1 -policy=poly -power=4 -max_batches=500000 - -[crop] -crop_height=224 -crop_width=224 -flip=1 -saturation=1 -exposure=1 -angle=0 - -[convolutional] -batch_normalize=1 -filters=64 -size=7 -stride=2 -pad=1 -activation=leaky - -[maxpool] -size=3 -stride=2 - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - - - - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - - - - - - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - - - - - - - - - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from = -3 - -[avgpool] - -[connected] -output=1000 -activation=leaky - -[softmax] -groups=1 - -[cost] -type=sse - diff --git a/cfg/msr_50.cfg b/cfg/msr_50.cfg deleted file mode 100644 index 2edd21c1a66..00000000000 --- a/cfg/msr_50.cfg +++ /dev/null @@ -1,558 +0,0 @@ -[net] -batch=128 -subdivisions=8 -height=256 -width=256 -channels=3 -momentum=0.9 -decay=0.0001 - -learning_rate=0.05 -policy=poly -power=4 -max_batches=500000 - - - -[crop] -crop_height=224 -crop_width=224 -flip=1 -saturation=1 -exposure=1 -angle=0 - -##### Conv 1 ##### - -[convolutional] -batch_normalize=1 -filters=64 -size=7 -stride=2 -pad=1 -activation=leaky - -[maxpool] -size=3 -stride=2 - - -##### Conv 2_x ##### - - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=linear - -[route] -layers=-4 - -[convolutional] -batch_normalize=1 -size=1 -stride=1 -pad=1 -activation=linear -filters=256 - -[shortcut] -from = -3 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - - -##### Conv 3_x ##### - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - -[route] -layers=-4 - -[convolutional] -batch_normalize=1 -size=1 -stride=2 -pad=1 -activation=linear -filters=512 - -[shortcut] -from = -3 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - - -##### Conv 4_x ##### - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[route] -layers=-4 - -[convolutional] -batch_normalize=1 -size=1 -stride=2 -pad=1 -activation=linear -filters=1024 - -[shortcut] -from = -3 -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - - -##### Conv 5_x ##### - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=2048 -size=1 -stride=1 -pad=1 -activation=linear - - -[route] -layers=-4 - -[convolutional] -batch_normalize=1 -size=1 -stride=2 -pad=1 -activation=linear -filters=2048 - -[shortcut] -from = -3 -activation=leaky - - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=2048 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=2048 -size=1 -stride=1 -pad=1 -activation=linear - -[shortcut] -from = -4 -activation=leaky - -[avgpool] - -[connected] -output=1000 -activation=leaky - -[softmax] -groups=1 - -[cost] -type=sse - diff --git a/src/layer.c b/src/layer.c index 4ef496fc88f..3a09ae16c63 100644 --- a/src/layer.c +++ b/src/layer.c @@ -167,6 +167,7 @@ void free_layer(layer l) if (l.weights_gpu) cuda_free(l.weights_gpu), l.weights_gpu = NULL; if (l.weight_updates_gpu) cuda_free(l.weight_updates_gpu), l.weight_updates_gpu = NULL; + if (l.weight_deform_gpu) cuda_free(l.weight_deform_gpu), l.weight_deform_gpu = NULL; if (l.weights_gpu16) cuda_free(l.weights_gpu16), l.weights_gpu16 = NULL; if (l.weight_updates_gpu16) cuda_free(l.weight_updates_gpu16), l.weight_updates_gpu16 = NULL; if (l.biases_gpu) cuda_free(l.biases_gpu), l.biases_gpu = NULL; diff --git a/src/network.c b/src/network.c index b63e91e29e9..dd7e2c33b7a 100644 --- a/src/network.c +++ b/src/network.c @@ -539,6 +539,8 @@ int resize_network(network *net, int w, int h) resize_scale_channels_layer(&l, net); }else if (l.type == DROPOUT) { resize_dropout_layer(&l, inputs); + l.out_w = l.w = w; + l.out_h = l.h = h; l.output = net->layers[i - 1].output; l.delta = net->layers[i - 1].delta; #ifdef GPU @@ -564,7 +566,7 @@ int resize_network(network *net, int w, int h) if(l.workspace_size > workspace_size) workspace_size = l.workspace_size; inputs = l.outputs; net->layers[i] = l; - if(l.type != DROPOUT) + //if(l.type != DROPOUT) { w = l.out_w; h = l.out_h; diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 0cce0507f0a..0f1705bbe6e 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -137,12 +137,14 @@ void backward_network_gpu(network net, network_state state) void update_network_gpu(network net) { cuda_set_device(net.gpu_index); + const int iteration_num = (*net.seen) / (net.batch * net.subdivisions); int i; int update_batch = net.batch*net.subdivisions * get_sequence_value(net); float rate = get_current_rate(net); for(i = 0; i < net.n; ++i){ layer l = net.layers[i]; l.t = get_current_batch(net); + if (iteration_num > (net.max_batches * 2 / 3)) l.deform = 0; if(l.update_gpu){ l.update_gpu(l, update_batch, rate, net.momentum, net.decay); } From 1626f9d495908e669f3d4f8d6421c0d7e9aac489 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 16 Dec 2019 17:53:58 +0300 Subject: [PATCH 0747/1365] Some fixes --- include/darknet.h | 3 ++- src/classifier.c | 6 ++++-- src/demo.c | 13 ++++++------ src/detector.c | 8 +++++--- src/image_opencv.cpp | 14 ++++++------- src/image_opencv.h | 4 ++-- src/layer.c | 49 +++++++++++++++++++++++++------------------- 7 files changed, 55 insertions(+), 42 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index d6ef4f8b4f2..c2b91908f92 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -905,7 +905,8 @@ LIB_API image load_image_color(char *filename, int w, int h); LIB_API void free_image(image m); // layer.h -LIB_API void free_layer(layer); +LIB_API void free_layer_custom(layer l, int keep_cudnn_desc); +LIB_API void free_layer(layer l); // data.c LIB_API void free_data(data d); diff --git a/src/classifier.c b/src/classifier.c index f5e2b1a92c0..59ae3e94a65 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -110,7 +110,9 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, float max_img_loss = 10; int number_of_lines = 100; int img_size = 1000; - img = draw_train_chart(max_img_loss, net.max_batches, number_of_lines, img_size, dont_show); + char windows_name[100]; + sprintf(windows_name, "average loss (id:%d)", random_gen()); + img = draw_train_chart(windows_name, max_img_loss, net.max_batches, number_of_lines, img_size, dont_show); #endif //OPENCV data train; @@ -174,7 +176,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); #ifdef OPENCV - draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, topk_buff, dont_show, mjpeg_port); + draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, topk_buff, dont_show, mjpeg_port); #endif // OPENCV if (i >= (iter_save + 1000)) { diff --git a/src/demo.c b/src/demo.c index 7066e6f5541..bb482c29385 100644 --- a/src/demo.c +++ b/src/demo.c @@ -42,7 +42,6 @@ static int demo_json_port = -1; static float* predictions[NFRAMES]; static int demo_index = 0; -static image images[NFRAMES]; static mat_cv* cv_images[NFRAMES]; static float *avg; @@ -147,7 +146,6 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int avg = (float *) calloc(l.outputs, sizeof(float)); for(j = 0; j < NFRAMES; ++j) predictions[j] = (float *) calloc(l.outputs, sizeof(float)); - for(j = 0; j < NFRAMES; ++j) images[j] = make_image(1,1,3); if (l.classes != demo_classes) { printf("Parameters don't match: in cfg-file classes=%d, in data-file classes=%d \n", l.classes, demo_classes); @@ -171,6 +169,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int det_s = in_s; for (j = 0; j < NFRAMES / 2; ++j) { + free_detections(dets, nboxes); fetch_in_thread(0); detect_in_thread(0); det_img = in_img; @@ -283,7 +282,6 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int printf("\n cvWriteFrame \n"); } - release_mat(&show_img); pthread_join(fetch_thread, 0); pthread_join(detect_thread, 0); @@ -296,6 +294,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int if (flag_exit == 1) break; if(delay == 0){ + release_mat(&show_img); show_img = det_img; } det_img = in_img; @@ -320,13 +319,15 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int } // free memory - release_mat(&show_img); - release_mat(&in_img); free_image(in_s); + free_detections(dets, nboxes); free(avg); for (j = 0; j < NFRAMES; ++j) free(predictions[j]); - for (j = 0; j < NFRAMES; ++j) free_image(images[j]); + demo_index = (NFRAMES + demo_index - 1) % NFRAMES; + for (j = 0; j < NFRAMES; ++j) { + release_mat(&cv_images[j]); + } free_ptrs((void **)names, net.layers[net.n - 1].classes); diff --git a/src/detector.c b/src/detector.c index 98c6fe8bb62..aa9652d8af2 100644 --- a/src/detector.c +++ b/src/detector.c @@ -45,7 +45,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i const int net_classes = net_map.layers[net_map.n - 1].classes; int k; // free memory unnecessary arrays - for (k = 0; k < net_map.n - 1; ++k) free_layer(net_map.layers[k]); + for (k = 0; k < net_map.n - 1; ++k) free_layer_custom(net_map.layers[k], 1); char *name_list = option_find_str(options, "names", "data/names.list"); int names_size = 0; @@ -147,7 +147,9 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i float max_img_loss = 5; int number_of_lines = 100; int img_size = 1000; - img = draw_train_chart(max_img_loss, net.max_batches, number_of_lines, img_size, dont_show); + char windows_name[100]; + sprintf(windows_name, "average loss (id:%d)", random_gen()); + img = draw_train_chart(windows_name, max_img_loss, net.max_batches, number_of_lines, img_size, dont_show); #endif //OPENCV if (net.track) { args.track = net.track; @@ -292,7 +294,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i draw_precision = 1; } #ifdef OPENCV - draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, mean_average_precision, draw_precision, "mAP%", dont_show, mjpeg_port); + draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, mean_average_precision, draw_precision, "mAP%", dont_show, mjpeg_port); #endif // OPENCV //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) { diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 04a4a8f0d7d..b4df4762a28 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1002,7 +1002,7 @@ extern "C" void draw_detections_cv_v3(mat_cv* mat, detection *dets, int num, flo // ==================================================================== // Draw Loss & Accuracy chart // ==================================================================== -extern "C" mat_cv* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show) +extern "C" mat_cv* draw_train_chart(char *windows_name, float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show) { int img_offset = 60; int draw_size = img_size - img_offset; @@ -1047,10 +1047,10 @@ extern "C" mat_cv* draw_train_chart(float max_img_loss, int max_batches, int num cv::putText(img, "Press 's' to save : chart.png", cv::Point(5, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); if (!dont_show) { printf(" If error occurs - run training with flag: -dont_show \n"); - cv::namedWindow("average loss", cv::WINDOW_NORMAL); - cv::moveWindow("average loss", 0, 0); - cv::resizeWindow("average loss", img_size, img_size); - cv::imshow("average loss", img); + cv::namedWindow(windows_name, cv::WINDOW_NORMAL); + cv::moveWindow(windows_name, 0, 0); + cv::resizeWindow(windows_name, img_size, img_size); + cv::imshow(windows_name, img); cv::waitKey(20); } } @@ -1061,7 +1061,7 @@ extern "C" mat_cv* draw_train_chart(float max_img_loss, int max_batches, int num } // ---------------------------------------- -extern "C" void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, +extern "C" void draw_train_loss(char *windows_name, mat_cv* img_src, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port) { try { @@ -1113,7 +1113,7 @@ extern "C" void draw_train_loss(mat_cv* img_src, int img_size, float avg_loss, f int k = 0; if (!dont_show) { - cv::imshow("average loss", img); + cv::imshow(windows_name, img); k = cv::waitKey(20); } static int old_batch = 0; diff --git a/src/image_opencv.h b/src/image_opencv.h index 6fc07a0674d..90037640710 100644 --- a/src/image_opencv.h +++ b/src/image_opencv.h @@ -92,8 +92,8 @@ void save_cv_jpg(mat_cv *img, const char *name); void draw_detections_cv_v3(mat_cv* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output); // Draw Loss & Accuracy chart -mat_cv* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show); -void draw_train_loss(mat_cv* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, +mat_cv* draw_train_chart(char *windows_name, float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show); +void draw_train_loss(char *windows_name, mat_cv* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port); // Data augmentation diff --git a/src/layer.c b/src/layer.c index 3a09ae16c63..cab81ba2a35 100644 --- a/src/layer.c +++ b/src/layer.c @@ -11,6 +11,11 @@ void free_sublayer(layer *l) } void free_layer(layer l) +{ + free_layer_custom(l, 0); +} + +void free_layer_custom(layer l, int keep_cudnn_desc) { if (l.share_layer != NULL) return; // don't free shared layers if (l.antialiasing) { @@ -204,31 +209,33 @@ void free_layer(layer l) if (l.last_prev_state_gpu) cuda_free(l.last_prev_state_gpu); if (l.last_prev_cell_gpu) cuda_free(l.last_prev_cell_gpu); if (l.cell_gpu) cuda_free(l.cell_gpu); -#ifdef CUDNN_DISABLED // shouldn't be used for -map - if (l.srcTensorDesc) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.srcTensorDesc)); - if (l.dstTensorDesc) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.dstTensorDesc)); - if (l.srcTensorDesc16) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.srcTensorDesc16)); - if (l.dstTensorDesc16) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.dstTensorDesc16)); - if (l.dsrcTensorDesc) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.dsrcTensorDesc)); - if (l.ddstTensorDesc) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.ddstTensorDesc)); - if (l.dsrcTensorDesc16) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.dsrcTensorDesc16)); - if (l.ddstTensorDesc16) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.ddstTensorDesc16)); - if (l.normTensorDesc) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.normTensorDesc)); - if (l.normDstTensorDesc) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.normDstTensorDesc)); - if (l.normDstTensorDescF16) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.normDstTensorDescF16)); +#ifdef CUDNN // shouldn't be used for -map + if (!keep_cudnn_desc) { + if (l.srcTensorDesc) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.srcTensorDesc)); + if (l.dstTensorDesc) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.dstTensorDesc)); + if (l.srcTensorDesc16) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.srcTensorDesc16)); + if (l.dstTensorDesc16) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.dstTensorDesc16)); + if (l.dsrcTensorDesc) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.dsrcTensorDesc)); + if (l.ddstTensorDesc) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.ddstTensorDesc)); + if (l.dsrcTensorDesc16) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.dsrcTensorDesc16)); + if (l.ddstTensorDesc16) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.ddstTensorDesc16)); + if (l.normTensorDesc) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.normTensorDesc)); + if (l.normDstTensorDesc) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.normDstTensorDesc)); + if (l.normDstTensorDescF16) CHECK_CUDNN(cudnnDestroyTensorDescriptor(l.normDstTensorDescF16)); - if (l.weightDesc) CHECK_CUDNN(cudnnDestroyFilterDescriptor(l.weightDesc)); - if (l.weightDesc16) CHECK_CUDNN(cudnnDestroyFilterDescriptor(l.weightDesc16)); - if (l.dweightDesc) CHECK_CUDNN(cudnnDestroyFilterDescriptor(l.dweightDesc)); - if (l.dweightDesc16) CHECK_CUDNN(cudnnDestroyFilterDescriptor(l.dweightDesc16)); + if (l.weightDesc) CHECK_CUDNN(cudnnDestroyFilterDescriptor(l.weightDesc)); + if (l.weightDesc16) CHECK_CUDNN(cudnnDestroyFilterDescriptor(l.weightDesc16)); + if (l.dweightDesc) CHECK_CUDNN(cudnnDestroyFilterDescriptor(l.dweightDesc)); + if (l.dweightDesc16) CHECK_CUDNN(cudnnDestroyFilterDescriptor(l.dweightDesc16)); - if (l.convDesc) CHECK_CUDNN(cudnnDestroyConvolutionDescriptor(l.convDesc)); + if (l.convDesc) CHECK_CUDNN(cudnnDestroyConvolutionDescriptor(l.convDesc)); - if (l.poolingDesc) CHECK_CUDNN(cudnnDestroyPoolingDescriptor(l.poolingDesc)); + if (l.poolingDesc) CHECK_CUDNN(cudnnDestroyPoolingDescriptor(l.poolingDesc)); - //cudnnConvolutionFwdAlgo_t fw_algo, fw_algo16; - //cudnnConvolutionBwdDataAlgo_t bd_algo, bd_algo16; - //cudnnConvolutionBwdFilterAlgo_t bf_algo, bf_algo16; + //cudnnConvolutionFwdAlgo_t fw_algo, fw_algo16; + //cudnnConvolutionBwdDataAlgo_t bd_algo, bd_algo16; + //cudnnConvolutionBwdFilterAlgo_t bf_algo, bf_algo16; + } #endif // CUDNN #endif // GPU From f4b2612b04721ea72059a0d375681afeedeaf664 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 16 Dec 2019 18:26:50 +0300 Subject: [PATCH 0748/1365] Added the ability to compile in C++ (Makefile USE_CPP=1) --- .circleci/config.yml | 4 ++++ Makefile | 6 ++++++ 2 files changed, 10 insertions(+) diff --git a/.circleci/config.yml b/.circleci/config.yml index 85248d53e01..ad615c369e1 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -10,6 +10,8 @@ jobs: - run: nvcc --version - run: make LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 -j 8 - run: make clean + - run: make LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 DEBUG=1 -j 8 + - run: make clean - run: make LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 AVX=1 -j 8 - run: make clean - run: make LIBSO=1 GPU=0 CUDNN=0 OPENCV=1 -j 8 @@ -19,3 +21,5 @@ jobs: - run: make LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 -j 8 - run: make clean - run: make LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 CUDNN_HALF=1 -j 8 + - run: make clean + - run: make LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 CUDNN_HALF=1 USE_CPP=1 -j 8 \ No newline at end of file diff --git a/Makefile b/Makefile index 7db7f86999d..590db18c40b 100644 --- a/Makefile +++ b/Makefile @@ -11,6 +11,7 @@ ZED_CAMERA=0 # set CUDNN_HALF=1 to further speedup 3 x times (Mixed-precision on Tensor Cores) GPU: Volta, Xavier, Turing and higher # set AVX=1 and OPENMP=1 to speedup on CPU (if error occurs then set AVX=0) +USE_CPP=0 DEBUG=0 ARCH= -gencode arch=compute_30,code=sm_30 \ @@ -52,7 +53,12 @@ LIBNAMESO=libdarknet.so APPNAMESO=uselib endif +ifeq ($(USE_CPP), 1) +CC=g++ +else CC=gcc +endif + CPP=g++ NVCC=nvcc OPTS=-Ofast From 972608c8ef6e17fb8a44ce912d4e61f2023ad231 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 16 Dec 2019 18:32:20 +0300 Subject: [PATCH 0749/1365] Minor fix --- src/dark_cuda.c | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/src/dark_cuda.c b/src/dark_cuda.c index b930e9525ed..bbea167903c 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -101,7 +101,11 @@ dim3 cuda_gridsize(size_t n){ x = ceil(sqrt(k)); y = (n-1)/(x*BLOCK) + 1; } - dim3 d = { (unsigned int)x, (unsigned int)y, 1 }; + //dim3 d = { (unsigned int)x, (unsigned int)y, 1 }; + dim3 d; + d.x = x; + d.y = y; + d.z = 1; //printf("%ld %ld %ld %ld\n", n, x, y, x*y*BLOCK); return d; } From 63396082d7e77f4b460bdb2540469f5f1a3c7c48 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 16 Dec 2019 18:41:28 +0300 Subject: [PATCH 0750/1365] Another one minor fix --- .circleci/config.yml | 1 + src/http_stream.cpp | 4 ++-- src/http_stream.h | 2 +- 3 files changed, 4 insertions(+), 3 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index ad615c369e1..4360aa0cb60 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -8,6 +8,7 @@ jobs: steps: - checkout - run: nvcc --version + - run: gcc --version - run: make LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 -j 8 - run: make clean - run: make LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 DEBUG=1 -j 8 diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 350f1637b4f..1f3d138d189 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -556,7 +556,7 @@ std::string get_system_frame_time_string() #ifdef __CYGWIN__ -int send_http_post_request(char *http_post_host, int server_port, char *videosource, +int send_http_post_request(char *http_post_host, int server_port, const char *videosource, detection *dets, int nboxes, int classes, char **names, long long int frame_id, int ext_output, int timeout) { std::cerr << " send_http_post_request() isn't implemented \n"; @@ -578,7 +578,7 @@ int send_http_post_request(char *http_post_host, int server_port, char *videosou // https://webhook.site/ // https://github.com/yhirose/cpp-httplib // sent POST http request -int send_http_post_request(char *http_post_host, int server_port, char *videosource, +int send_http_post_request(char *http_post_host, int server_port, const char *videosource, detection *dets, int nboxes, int classes, char **names, long long int frame_id, int ext_output, int timeout) { const float thresh = 0.005; // function get_network_boxes() has already filtred dets by actual threshold diff --git a/src/http_stream.h b/src/http_stream.h index 01800517e7a..92765647645 100644 --- a/src/http_stream.h +++ b/src/http_stream.h @@ -13,7 +13,7 @@ void send_json(detection *dets, int nboxes, int classes, char **names, long long #ifdef OPENCV void send_mjpeg(mat_cv* mat, int port, int timeout, int quality); -int send_http_post_request(char *http_post_host, int server_port, char *videosource, +int send_http_post_request(char *http_post_host, int server_port, const char *videosource, detection *dets, int nboxes, int classes, char **names, long long int frame_id, int ext_output, int timeout); #endif // OPENCV From e33ecb785ee288fca1fe50326f5c7b039a9f5a11 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 17 Dec 2019 18:03:42 +0300 Subject: [PATCH 0751/1365] Added param counters_per_class=100,200,... to [yolo] and [Gaussian_yolo] to solve class-imbalance-problem --- include/darknet.h | 1 + src/gaussian_yolo_layer.c | 10 ++++++---- src/image_opencv.cpp | 2 +- src/layer.c | 1 + src/network_kernels.cu | 2 +- src/parser.c | 30 +++++++++++++++++++++++++++++- src/yolo_layer.c | 10 ++++++---- 7 files changed, 45 insertions(+), 11 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index c2b91908f92..72d5c22f8dc 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -261,6 +261,7 @@ struct layer { float learning_rate_scale; float clip; int focal_loss; + float *classes_multipliers; float label_smooth_eps; int noloss; int softmax; diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index c85a8b065e9..fe72c3c7f9a 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -330,16 +330,18 @@ void averages_gaussian_yolo_deltas(int class_index, int box_index, int stride, i } } -void delta_gaussian_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, float label_smooth_eps) +void delta_gaussian_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, float label_smooth_eps, float *classes_multipliers) { int n; if (delta[index]){ delta[index + stride*class_id] = (1 - label_smooth_eps) - output[index + stride*class_id]; + if (classes_multipliers) delta[index + stride*class_id] *= classes_multipliers[class_id]; if(avg_cat) *avg_cat += output[index + stride*class_id]; return; } for(n = 0; n < classes; ++n){ delta[index + stride*n] = ((n == class_id) ? (1 - label_smooth_eps) : (0 + label_smooth_eps/classes)) - output[index + stride*n]; + if (classes_multipliers) delta[index + stride*class_id] *= classes_multipliers[class_id]; if(n == class_id && avg_cat) *avg_cat += output[index + stride*n]; } } @@ -452,7 +454,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int class_id = state.truth[best_t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9); - delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.label_smooth_eps); + delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.label_smooth_eps, l.classes_multipliers); box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); delta_gaussian_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, l.uc_normalizer, 1, l.yolo_point); } @@ -504,7 +506,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; int class_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 9); - delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.label_smooth_eps); + delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.label_smooth_eps, l.classes_multipliers); ++count; ++class_count; @@ -535,7 +537,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; int class_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 9); - delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.label_smooth_eps); + delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.label_smooth_eps, l.classes_multipliers); ++count; ++class_count; diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index b4df4762a28..2bda69340a9 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1125,7 +1125,7 @@ extern "C" void draw_train_loss(char *windows_name, mat_cv* img_src, int img_siz else cv::putText(img, "- Saved", cv::Point(260, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 1, CV_AA); - if (mjpeg_port > 0) send_mjpeg((mat_cv *)&img, mjpeg_port, 500000, 100); + if (mjpeg_port > 0) send_mjpeg((mat_cv *)&img, mjpeg_port, 500000, 70); } catch (...) { cerr << "OpenCV exception: draw_train_loss() \n"; diff --git a/src/layer.c b/src/layer.c index cab81ba2a35..2ae781d0628 100644 --- a/src/layer.c +++ b/src/layer.c @@ -60,6 +60,7 @@ void free_layer_custom(layer l, int keep_cudnn_desc) return; } if (l.mask) free(l.mask); + if (l.classes_multipliers)free(l.classes_multipliers); if (l.cweights) free(l.cweights); if (l.indexes) free(l.indexes); if (l.input_layers) free(l.input_layers); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 0f1705bbe6e..133052bdeaa 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -144,7 +144,7 @@ void update_network_gpu(network net) for(i = 0; i < net.n; ++i){ layer l = net.layers[i]; l.t = get_current_batch(net); - if (iteration_num > (net.max_batches * 2 / 3)) l.deform = 0; + if (iteration_num > (net.max_batches * 1 / 2)) l.deform = 0; if(l.update_gpu){ l.update_gpu(l, update_batch, rate, net.momentum, net.decay); } diff --git a/src/parser.c b/src/parser.c index 55cda92938d..bc9f66a28d7 100644 --- a/src/parser.c +++ b/src/parser.c @@ -363,12 +363,34 @@ int *parse_yolo_mask(char *a, int *num) return mask; } +float *get_classes_multipliers(char *cpc, const int classes) +{ + float *classes_multipliers = NULL; + if (cpc) { + int classes_counters = classes; + int *counters_per_class = parse_yolo_mask(cpc, &classes_counters); + if (classes_counters != classes) { + printf(" number of values in counters_per_class = %d doesn't match with classes = %d \n", classes_counters, classes); + exit(0); + } + float max_counter = 0; + int i; + for (i = 0; i < classes_counters; ++i) if (max_counter < counters_per_class[i]) max_counter = counters_per_class[i]; + classes_multipliers = (float *)calloc(classes_counters, sizeof(float)); + for (i = 0; i < classes_counters; ++i) classes_multipliers[i] = max_counter / counters_per_class[i]; + free(counters_per_class); + printf(" classes_multipliers: "); + for (i = 0; i < classes_counters; ++i) printf("%.1f, ", classes_multipliers[i]); + printf("\n"); + } + return classes_multipliers; +} + layer parse_yolo(list *options, size_params params) { int classes = option_find_int(options, "classes", 20); int total = option_find_int(options, "num", 1); int num = total; - char *a = option_find_str(options, "mask", 0); int *mask = parse_yolo_mask(a, &num); int max_boxes = option_find_int_quiet(options, "max", 90); @@ -380,6 +402,9 @@ layer parse_yolo(list *options, size_params params) } //assert(l.outputs == params.inputs); + char *cpc = option_find_str(options, "counters_per_class", 0); + l.classes_multipliers = get_classes_multipliers(cpc, classes); + l.label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1); l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75); @@ -472,6 +497,9 @@ layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3 } //assert(l.outputs == params.inputs); + char *cpc = option_find_str(options, "counters_per_class", 0); + l.classes_multipliers = get_classes_multipliers(cpc, classes); + l.label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1); l.uc_normalizer = option_find_float_quiet(options, "uc_normalizer", 1.0); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 97c52023693..66a0ae4caba 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -217,11 +217,12 @@ void averages_yolo_deltas(int class_index, int box_index, int stride, int classe } } -void delta_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, int focal_loss, float label_smooth_eps) +void delta_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, int focal_loss, float label_smooth_eps, float *classes_multipliers) { int n; if (delta[index + stride*class_id]){ delta[index + stride*class_id] = (1 - label_smooth_eps) - output[index + stride*class_id]; + if (classes_multipliers) delta[index + stride*class_id] *= classes_multipliers[class_id]; if(avg_cat) *avg_cat += output[index + stride*class_id]; return; } @@ -249,6 +250,7 @@ void delta_yolo_class(float *output, float *delta, int index, int class_id, int // default for (n = 0; n < classes; ++n) { delta[index + stride*n] = ((n == class_id) ? (1 - label_smooth_eps) : (0 + label_smooth_eps/classes)) - output[index + stride*n]; + if (classes_multipliers) delta[index + stride*class_id] *= classes_multipliers[class_id]; if (n == class_id && avg_cat) *avg_cat += output[index + stride*n]; } } @@ -359,7 +361,7 @@ void forward_yolo_layer(const layer l, network_state state) int class_id = state.truth[best_t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); - delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss, l.label_smooth_eps); + delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, 1); } @@ -421,7 +423,7 @@ void forward_yolo_layer(const layer l, network_state state) int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); - delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps); + delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); ++count; ++class_count; @@ -463,7 +465,7 @@ void forward_yolo_layer(const layer l, network_state state) int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); - delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps); + delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); ++count; ++class_count; From e43a1c424d9a20b8425d8dd8f240867f2522df3f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 18 Dec 2019 03:53:40 +0300 Subject: [PATCH 0752/1365] Fixed a bug in counters_per_class= --- src/data.c | 2 +- src/gaussian_yolo_layer.c | 2 +- src/yolo_layer.c | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/src/data.c b/src/data.c index fce3ed996d5..b84ae7c231d 100644 --- a/src/data.c +++ b/src/data.c @@ -1545,7 +1545,7 @@ data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *h int i, j; for (i = 0; i < d2.X.rows; ++i) { - if (mixup == 4) mixup = rand_int(2, 3); // alternate MixUp and CutMix + if (mixup == 4) mixup = rand_int(2, 3); // alternate CutMix and Mosaic // MixUp ----------------------------------- if (mixup == 1) { diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index fe72c3c7f9a..82053e25305 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -341,7 +341,7 @@ void delta_gaussian_yolo_class(float *output, float *delta, int index, int class } for(n = 0; n < classes; ++n){ delta[index + stride*n] = ((n == class_id) ? (1 - label_smooth_eps) : (0 + label_smooth_eps/classes)) - output[index + stride*n]; - if (classes_multipliers) delta[index + stride*class_id] *= classes_multipliers[class_id]; + if (classes_multipliers && n == class_id) delta[index + stride*class_id] *= classes_multipliers[class_id]; if(n == class_id && avg_cat) *avg_cat += output[index + stride*n]; } } diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 66a0ae4caba..919c26f0730 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -250,7 +250,7 @@ void delta_yolo_class(float *output, float *delta, int index, int class_id, int // default for (n = 0; n < classes; ++n) { delta[index + stride*n] = ((n == class_id) ? (1 - label_smooth_eps) : (0 + label_smooth_eps/classes)) - output[index + stride*n]; - if (classes_multipliers) delta[index + stride*class_id] *= classes_multipliers[class_id]; + if (classes_multipliers && n == class_id) delta[index + stride*class_id] *= classes_multipliers[class_id]; if (n == class_id && avg_cat) *avg_cat += output[index + stride*n]; } } From 01ad1f54156f8e36ad2355a8cb942cbd34278057 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 18 Dec 2019 15:23:51 +0300 Subject: [PATCH 0753/1365] Added csresnext50-panet-spp.cfg --- .../darknet/x64/cfg/csresnext50-panet-spp.cfg | 1018 +++++++++++++++++ cfg/csresnext50-panet-spp.cfg | 1018 +++++++++++++++++ 2 files changed, 2036 insertions(+) create mode 100644 build/darknet/x64/cfg/csresnext50-panet-spp.cfg create mode 100644 cfg/csresnext50-panet-spp.cfg diff --git a/build/darknet/x64/cfg/csresnext50-panet-spp.cfg b/build/darknet/x64/cfg/csresnext50-panet-spp.cfg new file mode 100644 index 00000000000..ece11221e45 --- /dev/null +++ b/build/darknet/x64/cfg/csresnext50-panet-spp.cfg @@ -0,0 +1,1018 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=16 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +#19:104x104 38:52x52 65:26x26 80:13x13 for 416 + +[convolutional] +batch_normalize=1 +filters=64 +size=7 +stride=2 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +# 1-1 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 1-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 1-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 1-T + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +groups=32 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +# 2-1 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 2-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 2-3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 2-T + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +# 3-1 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 3-2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 3-3 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 3-4 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 3-5 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 3-T + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-24 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +groups=32 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +# 4-1 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 4-2 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 4-T + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-12 + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=leaky + +########################## + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 65 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 38 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +########################## + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=leaky + +[route] +layers = -1, -16 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=leaky + +[route] +layers = -1, -37 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 diff --git a/cfg/csresnext50-panet-spp.cfg b/cfg/csresnext50-panet-spp.cfg new file mode 100644 index 00000000000..ece11221e45 --- /dev/null +++ b/cfg/csresnext50-panet-spp.cfg @@ -0,0 +1,1018 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=16 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +#19:104x104 38:52x52 65:26x26 80:13x13 for 416 + +[convolutional] +batch_normalize=1 +filters=64 +size=7 +stride=2 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +# 1-1 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 1-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 1-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 1-T + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +groups=32 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +# 2-1 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 2-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 2-3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 2-T + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +# 3-1 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 3-2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 3-3 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 3-4 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 3-5 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 3-T + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-24 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +groups=32 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +# 4-1 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 4-2 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 4-T + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-12 + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=leaky + +########################## + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 65 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 38 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +########################## + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=leaky + +[route] +layers = -1, -16 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=leaky + +[route] +layers = -1, -37 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 From 7f25e5027962bcc8dfbef773ad5b732581dbc3f1 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 18 Dec 2019 15:34:02 +0300 Subject: [PATCH 0754/1365] Minor fix --- build/darknet/x64/cfg/csresnext50-panet-spp.cfg | 4 ++-- cfg/csresnext50-panet-spp.cfg | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/build/darknet/x64/cfg/csresnext50-panet-spp.cfg b/build/darknet/x64/cfg/csresnext50-panet-spp.cfg index ece11221e45..261d35b96d7 100644 --- a/build/darknet/x64/cfg/csresnext50-panet-spp.cfg +++ b/build/darknet/x64/cfg/csresnext50-panet-spp.cfg @@ -5,8 +5,8 @@ # Training batch=64 subdivisions=16 -width=416 -height=416 +width=512 +height=512 channels=3 momentum=0.9 decay=0.0005 diff --git a/cfg/csresnext50-panet-spp.cfg b/cfg/csresnext50-panet-spp.cfg index ece11221e45..261d35b96d7 100644 --- a/cfg/csresnext50-panet-spp.cfg +++ b/cfg/csresnext50-panet-spp.cfg @@ -5,8 +5,8 @@ # Training batch=64 subdivisions=16 -width=416 -height=416 +width=512 +height=512 channels=3 momentum=0.9 decay=0.0005 From 3004ee851c49e28a32fd60f2ae4a1ddf95b8b391 Mon Sep 17 00:00:00 2001 From: Alexey Date: Wed, 18 Dec 2019 15:59:01 +0300 Subject: [PATCH 0755/1365] Update Readme.md --- README.md | 28 ++++++++++++++++++++-------- 1 file changed, 20 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index aedef40e400..6a371bf29c8 100644 --- a/README.md +++ b/README.md @@ -36,11 +36,11 @@ More details: http://pjreddie.com/darknet/yolo/ 11. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) 12. [How to use Yolo as DLL and SO libraries](#how-to-use-yolo-as-dll-and-so-libraries) -| ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) |   ![map_time](https://user-images.githubusercontent.com/4096485/52151356-e5d4a380-2683-11e9-9d7d-ac7bc192c477.jpg) mAP@0.5 (AP50) https://pjreddie.com/media/files/papers/YOLOv3.pdf | +| ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) |   ![map_fps](https://user-images.githubusercontent.com/4096485/71088138-d5032280-21ae-11ea-87ff-4c8695c53a28.png) mAP@0.5 (AP50) https://arxiv.org/abs/1911.11929 https://github.com/WongKinYiu/CrossStagePartialNetworks - more models | |---|---| -* YOLOv3-spp better than YOLOv3 - mAP = 60.6%, FPS = 20: https://pjreddie.com/darknet/yolo/ -* Yolo v3 source chart for the RetinaNet on MS COCO got from Table 1 (e): https://arxiv.org/pdf/1708.02002.pdf +* Yolo v3 on MS COCO: [Speed / Accuracy (mAP@0.5) chart](https://user-images.githubusercontent.com/4096485/52151356-e5d4a380-2683-11e9-9d7d-ac7bc192c477.jpg) +* Yolo v3 on MS COCO (Yolo v3 vs RetinaNet) - Figure 3: https://arxiv.org/pdf/1804.02767v1.pdf * Yolo v2 on Pascal VOC 2007: https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg * Yolo v2 on Pascal VOC 2012 (comp4): https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg @@ -58,11 +58,23 @@ More details: http://pjreddie.com/darknet/yolo/ There are weights-file for different cfg-files (smaller size -> faster speed & lower accuracy: -* `yolov3-openimages.cfg` (247 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-openimages.weights -* `yolov3-spp.cfg` (240 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-spp.weights -* `yolov3.cfg` (236 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3.weights -* `yolov3-tiny.cfg` (34 MB COCO **Yolo v3 tiny**) - requires 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-tiny.weights -* `enet-coco.cfg` (EfficientNetb0-Yolo- 45.5% mAP@0.5 - 3.7 BFlops) [enetb0-coco_final.weights](https://drive.google.com/file/d/1FlHeQjWEQVJt0ay1PVsiuuMzmtNyv36m/view) and `yolov3-tiny-prn.cfg` (33.1% mAP@0.5 - 3.5 BFlops - [more](https://github.com/WongKinYiu/PartialResidualNetworks)) +* [csresnext50-panet-spp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/csresnext50-panet-spp.cfg) - **60.0% mAP@0.5 - 44 FPS** - 71.3 BFlops - 217 MB: [csresnext50-panet-spp_final.weights](https://drive.google.com/file/d/1aNXdM8qVy11nqTcd2oaVB3mf7ckr258-/view?usp=sharing) + +* [yolov3-spp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-spp.cfg) - **60.6% mAP@0.5 - 30 FPS** - 141.5 BFlops - 240 MB: [yolov3-spp.weights](https://pjreddie.com/media/files/yolov3-spp.weights) + +* [yolov3-tiny-prn.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny-prn.cfg) - **33.1% mAP@0.5 - 400 FPS** - 3.5 BFlops - 18.8 MB: [yolov3-tiny-prn.weights](https://drive.google.com/file/d/18yYZWyKbo4XSDVyztmsEcF9B_6bxrhUY/view?usp=sharing) + +* [enet-coco.cfg (EfficientNetB0-Yolov3)](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/enet-coco.cfg) - **45.5% mAP@0.5 - 60 FPS** - 3.7 BFlops - 18.3 MB: [enetb0-coco_final.weights](https://drive.google.com/file/d/1FlHeQjWEQVJt0ay1PVsiuuMzmtNyv36m/view) + +* [yolov3-openimages.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-openimages.cfg) - 247 MB: [yolov3-openimages.weights](https://pjreddie.com/media/files/yolov3-openimages.weights) + +
CLICK ME - Yolo v3 models + +* [yolov3.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3.cfg) - **55.3% mAP@0.5 - 46 FPS** - 65.9 BFlops - 236 MB: [yolov3.weights](https://pjreddie.com/media/files/yolov3.weights) + +* [yolov3-tiny.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny.cfg) - **33.1% mAP@0.5 - 330 FPS** - 5.6 BFlops - 33.7 MB: [yolov3-tiny.weights](https://pjreddie.com/media/files/yolov3-tiny.weights) + +
CLICK ME - Yolo v2 models From dc7277f152cc1ef84b408872b3d774730bdca581 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 20 Dec 2019 16:07:34 +0300 Subject: [PATCH 0756/1365] Increased speed on Tensor Cores for csresnext50-panet-spp.cfg model --- src/convolutional_kernels.cu | 4 ++-- src/dark_cuda.c | 1 + 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 8c821f48f96..45fff784808 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -420,7 +420,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //if (state.use_mixed_precision) { int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in) && - (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train) + (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups == 1) { //printf("\n CUDNN_HALF!!! state.index = %d \n", state.index); @@ -671,7 +671,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state //#ifdef CUDNN_HALF int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in) && - (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train) + (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups == 1) { const size_t input16_size = l.batch*l.c*l.w*l.h; const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h; diff --git a/src/dark_cuda.c b/src/dark_cuda.c index bbea167903c..7f94ac5c2ea 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -116,6 +116,7 @@ static int streamInit[16] = { 0 }; cudaStream_t get_cuda_stream() { int i = cuda_get_device(); if (!streamInit[i]) { + //printf("Create CUDA-stream \n"); cudaError_t status = cudaStreamCreate(&streamsArray[i]); //cudaError_t status = cudaStreamCreateWithFlags(&streamsArray[i], cudaStreamNonBlocking); if (status != cudaSuccess) { From e66d3b1bdbe9cefc2dd4ede5e341afe5fa6902b4 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 20 Dec 2019 16:48:31 +0300 Subject: [PATCH 0757/1365] Disabled CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION, and only if groups==1 use CUDNN_TENSOR_OP_MATH --- src/convolutional_layer.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 8cecbc2f6d7..1db47fc17d1 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -231,10 +231,10 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference, size_t workspace_ // 2. Loss Scaling - required only for: activation gradients. We do not use. // 3. FP32 Master Copy of Weights // More: http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#tensor_ops - CHECK_CUDNN(cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH)); CHECK_CUDNN(cudnnSetConvolutionGroupCount(l->convDesc, l->groups)); + if (l->groups == 1) CHECK_CUDNN(cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH)); #if((CUDNN_MAJOR*10 + CUDNN_MINOR) >= 72) // cuDNN >= 7.2 - CHECK_CUDNN(cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION)); + //CHECK_CUDNN(cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION)); // reduces the speed of regular and group convolution #endif #else //if(CUDNN_MAJOR >= 7) if (l->groups > 1) { From 005714a10c0d512e11d2648bc4d8751dbebcc07d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 20 Dec 2019 20:28:35 +0300 Subject: [PATCH 0758/1365] Added [local_avgpool] layer --- include/darknet.h | 2 + src/maxpool_layer.c | 163 ++++++++++++++++++++++++++++++----- src/maxpool_layer.h | 8 +- src/maxpool_layer_kernels.cu | 134 ++++++++++++++++++++++++++++ src/network.c | 2 + src/parser.c | 32 ++++++- 6 files changed, 317 insertions(+), 24 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 72d5c22f8dc..d65dd76d339 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -137,6 +137,7 @@ typedef enum { DECONVOLUTIONAL, CONNECTED, MAXPOOL, + LOCAL_AVGPOOL, SOFTMAX, DETECTION, DROPOUT, @@ -202,6 +203,7 @@ struct layer { void(*update_gpu) (struct layer, int, float, float, float); layer *share_layer; int train; + int avgpool; int batch_normalize; int shortcut; int batch; diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index 99a2b06ac83..029f19d74c9 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -49,10 +49,31 @@ void cudnn_maxpool_setup(layer *l) } -maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride_x, int stride_y, int padding, int maxpool_depth, int out_channels, int antialiasing, int train) +void cudnn_local_avgpool_setup(layer *l) +{ +#ifdef CUDNN + CHECK_CUDNN(cudnnSetPooling2dDescriptor( + l->poolingDesc, + CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING, + CUDNN_NOT_PROPAGATE_NAN, // CUDNN_PROPAGATE_NAN, CUDNN_NOT_PROPAGATE_NAN + l->size, + l->size, + l->pad / 2, //0, //l.pad, + l->pad / 2, //0, //l.pad, + l->stride_x, + l->stride_y)); + + CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w)); + CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w)); +#endif // CUDNN +} + +maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride_x, int stride_y, int padding, int maxpool_depth, int out_channels, int antialiasing, int avgpool, int train) { maxpool_layer l = { (LAYER_TYPE)0 }; - l.type = MAXPOOL; + l.avgpool = avgpool; + if (avgpool) l.type = LOCAL_AVGPOOL; + else l.type = MAXPOOL; l.train = train; const int blur_stride_x = stride_x; @@ -88,31 +109,53 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s int output_size = l.out_h * l.out_w * l.out_c * batch; if (train) { - l.indexes = (int*)calloc(output_size, sizeof(int)); + if (!avgpool) l.indexes = (int*)calloc(output_size, sizeof(int)); l.delta = (float*)calloc(output_size, sizeof(float)); } l.output = (float*)calloc(output_size, sizeof(float)); - l.forward = forward_maxpool_layer; - l.backward = backward_maxpool_layer; + if (avgpool) { + l.forward = forward_local_avgpool_layer; + l.backward = backward_local_avgpool_layer; + } + else { + l.forward = forward_maxpool_layer; + l.backward = backward_maxpool_layer; + } #ifdef GPU - l.forward_gpu = forward_maxpool_layer_gpu; - l.backward_gpu = backward_maxpool_layer_gpu; + if (avgpool) { + l.forward_gpu = forward_local_avgpool_layer_gpu; + l.backward_gpu = backward_local_avgpool_layer_gpu; + } + else { + l.forward_gpu = forward_maxpool_layer_gpu; + l.backward_gpu = backward_maxpool_layer_gpu; + } + if (train) { - l.indexes_gpu = cuda_make_int_array(output_size); + if (!avgpool) l.indexes_gpu = cuda_make_int_array(output_size); l.delta_gpu = cuda_make_array(l.delta, output_size); } l.output_gpu = cuda_make_array(l.output, output_size); create_maxpool_cudnn_tensors(&l); - cudnn_maxpool_setup(&l); + if (avgpool) cudnn_local_avgpool_setup(&l); + else cudnn_maxpool_setup(&l); #endif // GPU l.bflops = (l.size*l.size*l.c * l.out_h*l.out_w) / 1000000000.; - if (maxpool_depth) - fprintf(stderr, "max-depth %2dx%2d/%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride_x, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); - else if(stride_x == stride_y) - fprintf(stderr, "max %2dx%2d/%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride_x, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); - else - fprintf(stderr, "max %2dx%2d/%2dx%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride_x, stride_y, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); + if (avgpool) { + if (stride_x == stride_y) + fprintf(stderr, "avg %2dx%2d/%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride_x, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); + else + fprintf(stderr, "avg %2dx%2d/%2dx%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride_x, stride_y, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); + } + else { + if (maxpool_depth) + fprintf(stderr, "max-depth %2dx%2d/%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride_x, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); + else if (stride_x == stride_y) + fprintf(stderr, "max %2dx%2d/%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride_x, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); + else + fprintf(stderr, "max %2dx%2d/%2dx%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride_x, stride_y, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); + } if (l.antialiasing) { printf("AA: "); @@ -152,7 +195,7 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s for (i = 0; i < l.out_c; ++i) l.input_layer->biases[i] = 0; #ifdef GPU if (gpu_index >= 0) { - l.input_antialiasing_gpu = cuda_make_array(NULL, l.batch*l.outputs); + if (l.antialiasing) l.input_antialiasing_gpu = cuda_make_array(NULL, l.batch*l.outputs); push_convolutional_layer(*(l.input_layer)); } #endif // GPU @@ -173,7 +216,7 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h) int output_size = l->outputs * l->batch; if (l->train) { - l->indexes = (int*)realloc(l->indexes, output_size * sizeof(int)); + if (!l->avgpool) l->indexes = (int*)realloc(l->indexes, output_size * sizeof(int)); l->delta = (float*)realloc(l->delta, output_size * sizeof(float)); } l->output = (float*)realloc(l->output, output_size * sizeof(float)); @@ -183,13 +226,16 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h) l->output_gpu = cuda_make_array(l->output, output_size); if (l->train) { - CHECK_CUDA(cudaFree((float *)l->indexes_gpu)); + if (!l->avgpool) { + CHECK_CUDA(cudaFree((float *)l->indexes_gpu)); + l->indexes_gpu = cuda_make_int_array(output_size); + } CHECK_CUDA(cudaFree(l->delta_gpu)); - l->indexes_gpu = cuda_make_int_array(output_size); l->delta_gpu = cuda_make_array(l->delta, output_size); } - cudnn_maxpool_setup(l); + if(l->avgpool) cudnn_local_avgpool_setup(l); + else cudnn_maxpool_setup(l); #endif } @@ -229,7 +275,8 @@ void forward_maxpool_layer(const maxpool_layer l, network_state state) if (!state.train && l.stride_x == l.stride_y) { forward_maxpool_layer_avx(state.input, l.output, l.indexes, l.size, l.w, l.h, l.out_w, l.out_h, l.c, l.pad, l.stride, l.batch); } - else { + else + { int b, i, j, k, m, n; int w_offset = -l.pad / 2; @@ -290,3 +337,77 @@ void backward_maxpool_layer(const maxpool_layer l, network_state state) state.delta[index] += l.delta[i]; } } + + +void forward_local_avgpool_layer(const maxpool_layer l, network_state state) +{ + int b, i, j, k, m, n; + int w_offset = -l.pad / 2; + int h_offset = -l.pad / 2; + + int h = l.out_h; + int w = l.out_w; + int c = l.c; + + for (b = 0; b < l.batch; ++b) { + for (k = 0; k < c; ++k) { + for (i = 0; i < h; ++i) { + for (j = 0; j < w; ++j) { + int out_index = j + w*(i + h*(k + c*b)); + float avg = 0; + int counter = 0; + for (n = 0; n < l.size; ++n) { + for (m = 0; m < l.size; ++m) { + int cur_h = h_offset + i*l.stride_y + n; + int cur_w = w_offset + j*l.stride_x + m; + int index = cur_w + l.w*(cur_h + l.h*(k + b*l.c)); + int valid = (cur_h >= 0 && cur_h < l.h && + cur_w >= 0 && cur_w < l.w); + if (valid) { + counter++; + avg += state.input[index]; + } + + } + } + l.output[out_index] = avg / counter; + } + } + } + } +} + +void backward_local_avgpool_layer(const maxpool_layer l, network_state state) +{ + + int b, i, j, k, m, n; + int w_offset = -l.pad / 2; + int h_offset = -l.pad / 2; + + int h = l.out_h; + int w = l.out_w; + int c = l.c; + + for (b = 0; b < l.batch; ++b) { + for (k = 0; k < c; ++k) { + for (i = 0; i < h; ++i) { + for (j = 0; j < w; ++j) { + int out_index = j + w*(i + h*(k + c*b)); + for (n = 0; n < l.size; ++n) { + for (m = 0; m < l.size; ++m) { + int cur_h = h_offset + i*l.stride_y + n; + int cur_w = w_offset + j*l.stride_x + m; + int index = cur_w + l.w*(cur_h + l.h*(k + b*l.c)); + int valid = (cur_h >= 0 && cur_h < l.h && + cur_w >= 0 && cur_w < l.w); + + if (valid) state.delta[index] += l.delta[out_index] / (l.size*l.size); + } + } + + } + } + } + } + +} \ No newline at end of file diff --git a/src/maxpool_layer.h b/src/maxpool_layer.h index 3986a4c6360..3c00611785c 100644 --- a/src/maxpool_layer.h +++ b/src/maxpool_layer.h @@ -12,15 +12,21 @@ typedef layer maxpool_layer; extern "C" { #endif image get_maxpool_image(maxpool_layer l); -maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride_x, int stride_y, int padding, int maxpool_depth, int out_channels, int antialiasing, int train); +maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride_x, int stride_y, int padding, int maxpool_depth, int out_channels, int antialiasing, int avgpool, int train); void resize_maxpool_layer(maxpool_layer *l, int w, int h); void forward_maxpool_layer(const maxpool_layer l, network_state state); void backward_maxpool_layer(const maxpool_layer l, network_state state); +void forward_local_avgpool_layer(const maxpool_layer l, network_state state); +void backward_local_avgpool_layer(const maxpool_layer l, network_state state); + #ifdef GPU void forward_maxpool_layer_gpu(maxpool_layer l, network_state state); void backward_maxpool_layer_gpu(maxpool_layer l, network_state state); void cudnn_maxpool_setup(maxpool_layer *l); + +void forward_local_avgpool_layer_gpu(maxpool_layer layer, network_state state); +void backward_local_avgpool_layer_gpu(maxpool_layer layer, network_state state); #endif // GPU #ifdef __cplusplus diff --git a/src/maxpool_layer_kernels.cu b/src/maxpool_layer_kernels.cu index c15143c556b..114b8f981d4 100644 --- a/src/maxpool_layer_kernels.cu +++ b/src/maxpool_layer_kernels.cu @@ -129,6 +129,7 @@ __global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_ prev_delta[index] += d; } + extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network_state state) { if (layer.maxpool_depth) { @@ -225,3 +226,136 @@ extern "C" void backward_maxpool_layer_gpu(maxpool_layer layer, network_state st backward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride_x, layer.stride_y, layer.size, layer.pad, layer.delta_gpu, state.delta, layer.indexes_gpu); CHECK_CUDA(cudaPeekAtLastError()); } + + + + +__global__ void forward_local_avgpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride_x, int stride_y, int size, int pad, float *input, float *output) +{ + int h = (in_h + pad - size) / stride_y + 1; + int w = (in_w + pad - size) / stride_x + 1; + int c = in_c; + + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= n) return; + + int j = id % w; + id /= w; + int i = id % h; + id /= h; + int k = id % c; + id /= c; + int b = id; + + int w_offset = -pad / 2; + int h_offset = -pad / 2; + + int out_index = j + w*(i + h*(k + c*b)); + float avg = 0; + int counter = 0; + int l, m; + for (l = 0; l < size; ++l) { + for (m = 0; m < size; ++m) { + int cur_h = h_offset + i*stride_y + l; + int cur_w = w_offset + j*stride_x + m; + int index = cur_w + in_w*(cur_h + in_h*(k + b*in_c)); + int valid = (cur_h >= 0 && cur_h < in_h && + cur_w >= 0 && cur_w < in_w); + if (valid) { + counter++; + avg += input[index]; + } + } + } + output[out_index] = avg / counter; // as CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING +} + + +__global__ void backward_local_avgpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride_x, int stride_y, int size, int pad, float *delta, float *prev_delta) +{ + int h = (in_h + pad - size) / stride_y + 1; + int w = (in_w + pad - size) / stride_x + 1; + int c = in_c; + int area_x = (size - 1) / stride_x; + int area_y = (size - 1) / stride_y; + + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= n) return; + + int index = id; + int j = id % in_w; + id /= in_w; + int i = id % in_h; + id /= in_h; + int k = id % in_c; + id /= in_c; + int b = id; + + int w_offset = -pad / 2; + int h_offset = -pad / 2; + + int counter = 0; + float d = 0; + int l, m; + for (l = -area_y; l < area_y + 1; ++l) { + for (m = -area_x; m < area_x + 1; ++m) { + int out_w = (j - w_offset) / stride_x + m; + int out_h = (i - h_offset) / stride_y + l; + int out_index = out_w + w*(out_h + h*(k + c*b)); + int valid = (out_w >= 0 && out_w < w && out_h >= 0 && out_h < h); + if (valid) { + counter++; + d += delta[out_index]; + } + } + } + prev_delta[index] += d / counter; +} + + + +extern "C" void forward_local_avgpool_layer_gpu(maxpool_layer layer, network_state state) +{ + +#ifdef CUDNN_DISABLED + if (!state.train && layer.stride == layer.size) { + // cudnnPoolingBackward + cudnnStatus_t maxpool_status; + + float alpha = 1, beta = 0; + maxpool_status = cudnnPoolingForward( + cudnn_handle(), + layer.poolingDesc, + &alpha, + layer.srcTensorDesc, + state.input, + &beta, + layer.dstTensorDesc, + layer.output_gpu); + + //maxpool_status = cudnnDestroyPoolingDescriptor(poolingDesc); + //cudnnDestroyTensorDescriptor(layer.srcTensorDesc); + //cudnnDestroyTensorDescriptor(layer.dstTensorDesc); + + } + else +#endif + { + int h = layer.out_h; + int w = layer.out_w; + int c = layer.out_c; + + size_t n = h*w*c*layer.batch; + + forward_local_avgpool_layer_kernel << > > (n, layer.h, layer.w, layer.c, layer.stride_x, layer.stride_y, layer.size, layer.pad, state.input, layer.output_gpu); + CHECK_CUDA(cudaPeekAtLastError()); + } +} + +extern "C" void backward_local_avgpool_layer_gpu(maxpool_layer layer, network_state state) +{ + size_t n = layer.h*layer.w*layer.c*layer.batch; + + backward_local_avgpool_layer_kernel << > >(n, layer.h, layer.w, layer.c, layer.stride_x, layer.stride_y, layer.size, layer.pad, layer.delta_gpu, state.delta); + CHECK_CUDA(cudaPeekAtLastError()); +} diff --git a/src/network.c b/src/network.c index dd7e2c33b7a..4fae07d6726 100644 --- a/src/network.c +++ b/src/network.c @@ -525,6 +525,8 @@ int resize_network(network *net, int w, int h) resize_crop_layer(&l, w, h); }else if(l.type == MAXPOOL){ resize_maxpool_layer(&l, w, h); + }else if (l.type == LOCAL_AVGPOOL) { + resize_maxpool_layer(&l, w, h); }else if(l.type == REGION){ resize_region_layer(&l, w, h); }else if (l.type == YOLO) { diff --git a/src/parser.c b/src/parser.c index bc9f66a28d7..0b14d9d65e8 100644 --- a/src/parser.c +++ b/src/parser.c @@ -74,6 +74,8 @@ LAYER_TYPE string_to_layer_type(char * type) || strcmp(type, "[connected]")==0) return CONNECTED; if (strcmp(type, "[max]")==0 || strcmp(type, "[maxpool]")==0) return MAXPOOL; + if (strcmp(type, "[local_avg]") == 0 + || strcmp(type, "[local_avgpool]") == 0) return LOCAL_AVGPOOL; if (strcmp(type, "[reorg3d]")==0) return REORG; if (strcmp(type, "[reorg]") == 0) return REORG_OLD; if (strcmp(type, "[avg]")==0 @@ -707,6 +709,29 @@ layer parse_reorg_old(list *options, size_params params) return layer; } +maxpool_layer parse_local_avgpool(list *options, size_params params) +{ + int stride = option_find_int(options, "stride", 1); + int stride_x = option_find_int_quiet(options, "stride_x", stride); + int stride_y = option_find_int_quiet(options, "stride_y", stride); + int size = option_find_int(options, "size", stride); + int padding = option_find_int_quiet(options, "padding", size - 1); + int maxpool_depth = 0; + int out_channels = 1; + int antialiasing = 0; + const int avgpool = 1; + + int batch, h, w, c; + h = params.h; + w = params.w; + c = params.c; + batch = params.batch; + if (!(h && w && c)) error("Layer before [local_avgpool] layer must output image."); + + maxpool_layer layer = make_maxpool_layer(batch, h, w, c, size, stride_x, stride_y, padding, maxpool_depth, out_channels, antialiasing, avgpool, params.train); + return layer; +} + maxpool_layer parse_maxpool(list *options, size_params params) { int stride = option_find_int(options, "stride",1); @@ -717,15 +742,16 @@ maxpool_layer parse_maxpool(list *options, size_params params) int maxpool_depth = option_find_int_quiet(options, "maxpool_depth", 0); int out_channels = option_find_int_quiet(options, "out_channels", 1); int antialiasing = option_find_int_quiet(options, "antialiasing", 0); + const int avgpool = 0; int batch,h,w,c; h = params.h; w = params.w; c = params.c; batch=params.batch; - if(!(h && w && c)) error("Layer before maxpool layer must output image."); + if(!(h && w && c)) error("Layer before [maxpool] layer must output image."); - maxpool_layer layer = make_maxpool_layer(batch, h, w, c, size, stride_x, stride_y, padding, maxpool_depth, out_channels, antialiasing, params.train); + maxpool_layer layer = make_maxpool_layer(batch, h, w, c, size, stride_x, stride_y, padding, maxpool_depth, out_channels, antialiasing, avgpool, params.train); return layer; } @@ -1172,6 +1198,8 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) l = parse_batchnorm(options, params); }else if(lt == MAXPOOL){ l = parse_maxpool(options, params); + }else if (lt == LOCAL_AVGPOOL) { + l = parse_local_avgpool(options, params); }else if(lt == REORG){ l = parse_reorg(options, params); } else if (lt == REORG_OLD) { From d6eaa2e95f0dfe4285c0177714910a614af068c7 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 20 Dec 2019 21:37:28 +0300 Subject: [PATCH 0759/1365] Fixed [batchnorm] layer --- src/batchnorm_layer.c | 47 ++++++++++++++++++++++++++++++++++----- src/batchnorm_layer.h | 2 ++ src/convolutional_layer.c | 16 ++++++++----- 3 files changed, 54 insertions(+), 11 deletions(-) diff --git a/src/batchnorm_layer.c b/src/batchnorm_layer.c index bb36351211d..a025fc4a1fe 100644 --- a/src/batchnorm_layer.c +++ b/src/batchnorm_layer.c @@ -11,11 +11,15 @@ layer make_batchnorm_layer(int batch, int w, int h, int c) layer.h = layer.out_h = h; layer.w = layer.out_w = w; layer.c = layer.out_c = c; + layer.n = layer.c; layer.output = (float*)calloc(h * w * c * batch, sizeof(float)); layer.delta = (float*)calloc(h * w * c * batch, sizeof(float)); layer.inputs = w*h*c; layer.outputs = layer.inputs; + layer.biases = (float*)calloc(c, sizeof(float)); + layer.bias_updates = (float*)calloc(c, sizeof(float)); + layer.scales = (float*)calloc(c, sizeof(float)); layer.scale_updates = (float*)calloc(c, sizeof(float)); int i; @@ -31,13 +35,18 @@ layer make_batchnorm_layer(int batch, int w, int h, int c) layer.forward = forward_batchnorm_layer; layer.backward = backward_batchnorm_layer; + layer.update = update_batchnorm_layer; #ifdef GPU layer.forward_gpu = forward_batchnorm_layer_gpu; layer.backward_gpu = backward_batchnorm_layer_gpu; + layer.update_gpu = update_batchnorm_layer_gpu; layer.output_gpu = cuda_make_array(layer.output, h * w * c * batch); layer.delta_gpu = cuda_make_array(layer.delta, h * w * c * batch); + layer.biases_gpu = cuda_make_array(layer.biases, c); + layer.bias_updates_gpu = cuda_make_array(layer.bias_updates, c); + layer.scales_gpu = cuda_make_array(layer.scales, c); layer.scale_updates_gpu = cuda_make_array(layer.scale_updates, c); @@ -162,6 +171,19 @@ void backward_batchnorm_layer(const layer l, network_state state) if(l.type == BATCHNORM) copy_cpu(l.outputs*l.batch, l.delta, 1, state.delta, 1); } +void update_batchnorm_layer(layer l, int batch, float learning_rate, float momentum, float decay) +{ + //int size = l.nweights; + axpy_cpu(l.c, learning_rate / batch, l.bias_updates, 1, l.biases, 1); + scal_cpu(l.c, momentum, l.bias_updates, 1); + + axpy_cpu(l.c, learning_rate / batch, l.scale_updates, 1, l.scales, 1); + scal_cpu(l.c, momentum, l.scale_updates, 1); +} + + + + #ifdef GPU void pull_batchnorm_layer(layer l) @@ -213,7 +235,7 @@ void forward_batchnorm_layer_gpu(layer l, network_state state) fix_nan_and_inf(l.rolling_mean_gpu, l.n); fix_nan_and_inf(l.rolling_variance_gpu, l.n); } -#else +#else // CUDNN fast_mean_gpu(l.output_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.mean_gpu); fast_variance_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.variance_gpu); @@ -228,7 +250,7 @@ void forward_batchnorm_layer_gpu(layer l, network_state state) scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h); -#endif +#endif // CUDNN } else { normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); @@ -268,7 +290,7 @@ void backward_batchnorm_layer_gpu(layer l, network_state state) l.variance_gpu); // input (should be FP32) simple_copy_ongpu(l.outputs*l.batch, l.output_gpu, l.delta_gpu); //simple_copy_ongpu(l.outputs*l.batch, l.x_norm_gpu, l.delta_gpu); -#else +#else // CUDNN backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h); backward_scale_gpu(l.x_norm_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.scale_updates_gpu); @@ -277,7 +299,7 @@ void backward_batchnorm_layer_gpu(layer l, network_state state) fast_mean_delta_gpu(l.delta_gpu, l.variance_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.mean_delta_gpu); fast_variance_delta_gpu(l.x_gpu, l.delta_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.variance_delta_gpu); normalize_delta_gpu(l.x_gpu, l.mean_gpu, l.variance_gpu, l.mean_delta_gpu, l.variance_delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu); -#endif +#endif // CUDNN if (l.type == BATCHNORM) simple_copy_ongpu(l.outputs*l.batch, l.delta_gpu, state.delta); //copy_ongpu(l.outputs*l.batch, l.delta_gpu, 1, state.delta, 1); @@ -286,4 +308,19 @@ void backward_batchnorm_layer_gpu(layer l, network_state state) fix_nan_and_inf(l.bias_updates_gpu, l.n); } } -#endif + +void update_batchnorm_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay) +{ + float learning_rate = learning_rate_init*l.learning_rate_scale; + //float momentum = a.momentum; + //float decay = a.decay; + //int batch = a.batch; + + axpy_ongpu(l.c, learning_rate / batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); + scal_ongpu(l.c, momentum, l.bias_updates_gpu, 1); + + axpy_ongpu(l.c, learning_rate / batch, l.scale_updates_gpu, 1, l.scales_gpu, 1); + scal_ongpu(l.c, momentum, l.scale_updates_gpu, 1); +} + +#endif // GPU diff --git a/src/batchnorm_layer.h b/src/batchnorm_layer.h index 1ea38d72596..55c7d62c98a 100644 --- a/src/batchnorm_layer.h +++ b/src/batchnorm_layer.h @@ -11,10 +11,12 @@ extern "C" { layer make_batchnorm_layer(int batch, int w, int h, int c); void forward_batchnorm_layer(layer l, network_state state); void backward_batchnorm_layer(layer l, network_state state); +void update_batchnorm_layer(layer l, int batch, float learning_rate, float momentum, float decay); #ifdef GPU void forward_batchnorm_layer_gpu(layer l, network_state state); void backward_batchnorm_layer_gpu(layer l, network_state state); +void update_batchnorm_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay); void pull_batchnorm_layer(layer l); void push_batchnorm_layer(layer l); #endif diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 1db47fc17d1..cc05b3b08fc 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -1447,9 +1447,17 @@ void backward_convolutional_layer(convolutional_layer l, network_state state) } } -void update_convolutional_layer(convolutional_layer l, int batch, float learning_rate, float momentum, float decay) +void update_convolutional_layer(convolutional_layer l, int batch, float learning_rate_init, float momentum, float decay) { - //int size = l.nweights; + float learning_rate = learning_rate_init*l.learning_rate_scale; + //float momentum = a.momentum; + //float decay = a.decay; + //int batch = a.batch; + + axpy_cpu(l.nweights, -decay*batch, l.weights, 1, l.weight_updates, 1); + axpy_cpu(l.nweights, learning_rate / batch, l.weight_updates, 1, l.weights, 1); + scal_cpu(l.nweights, momentum, l.weight_updates, 1); + axpy_cpu(l.n, learning_rate / batch, l.bias_updates, 1, l.biases, 1); scal_cpu(l.n, momentum, l.bias_updates, 1); @@ -1457,10 +1465,6 @@ void update_convolutional_layer(convolutional_layer l, int batch, float learning axpy_cpu(l.n, learning_rate / batch, l.scale_updates, 1, l.scales, 1); scal_cpu(l.n, momentum, l.scale_updates, 1); } - - axpy_cpu(l.nweights, -decay*batch, l.weights, 1, l.weight_updates, 1); - axpy_cpu(l.nweights, learning_rate / batch, l.weight_updates, 1, l.weights, 1); - scal_cpu(l.nweights, momentum, l.weight_updates, 1); } From 5f6dced3ce5a4e96224a7974c802a7fde19cc166 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 20 Dec 2019 22:49:09 +0300 Subject: [PATCH 0760/1365] Another fix for [batchnorm] layer --- src/batchnorm_layer.c | 73 ++++++++++++++++++++++++++++++++++--------- src/batchnorm_layer.h | 4 ++- src/blas.h | 1 + src/network.c | 4 +-- src/parser.c | 4 ++- 5 files changed, 67 insertions(+), 19 deletions(-) diff --git a/src/batchnorm_layer.c b/src/batchnorm_layer.c index a025fc4a1fe..a2a850ec283 100644 --- a/src/batchnorm_layer.c +++ b/src/batchnorm_layer.c @@ -2,12 +2,13 @@ #include "blas.h" #include -layer make_batchnorm_layer(int batch, int w, int h, int c) +layer make_batchnorm_layer(int batch, int w, int h, int c, int train) { fprintf(stderr, "Batch Normalization Layer: %d x %d x %d image\n", w,h,c); layer layer = { (LAYER_TYPE)0 }; layer.type = BATCHNORM; layer.batch = batch; + layer.train = train; layer.h = layer.out_h = h; layer.w = layer.out_w = w; layer.c = layer.out_c = c; @@ -42,13 +43,19 @@ layer make_batchnorm_layer(int batch, int w, int h, int c) layer.update_gpu = update_batchnorm_layer_gpu; layer.output_gpu = cuda_make_array(layer.output, h * w * c * batch); - layer.delta_gpu = cuda_make_array(layer.delta, h * w * c * batch); layer.biases_gpu = cuda_make_array(layer.biases, c); - layer.bias_updates_gpu = cuda_make_array(layer.bias_updates, c); - layer.scales_gpu = cuda_make_array(layer.scales, c); - layer.scale_updates_gpu = cuda_make_array(layer.scale_updates, c); + + if (train) { + layer.delta_gpu = cuda_make_array(layer.delta, h * w * c * batch); + + layer.bias_updates_gpu = cuda_make_array(layer.bias_updates, c); + layer.scale_updates_gpu = cuda_make_array(layer.scale_updates, c); + + layer.mean_delta_gpu = cuda_make_array(layer.mean, c); + layer.variance_delta_gpu = cuda_make_array(layer.variance, c); + } layer.mean_gpu = cuda_make_array(layer.mean, c); layer.variance_gpu = cuda_make_array(layer.variance, c); @@ -56,16 +63,18 @@ layer make_batchnorm_layer(int batch, int w, int h, int c) layer.rolling_mean_gpu = cuda_make_array(layer.mean, c); layer.rolling_variance_gpu = cuda_make_array(layer.variance, c); - layer.mean_delta_gpu = cuda_make_array(layer.mean, c); - layer.variance_delta_gpu = cuda_make_array(layer.variance, c); + if (train) { + layer.x_gpu = cuda_make_array(layer.output, layer.batch*layer.outputs); +#ifndef CUDNN + layer.x_norm_gpu = cuda_make_array(layer.output, layer.batch*layer.outputs); +#endif // not CUDNN + } - layer.x_gpu = cuda_make_array(layer.output, layer.batch*layer.outputs); - layer.x_norm_gpu = cuda_make_array(layer.output, layer.batch*layer.outputs); #ifdef CUDNN - cudnnCreateTensorDescriptor(&layer.normTensorDesc); - cudnnCreateTensorDescriptor(&layer.normDstTensorDesc); - cudnnSetTensor4dDescriptor(layer.normDstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, layer.batch, layer.out_c, layer.out_h, layer.out_w); - cudnnSetTensor4dDescriptor(layer.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, layer.out_c, 1, 1); + CHECK_CUDNN(cudnnCreateTensorDescriptor(&layer.normTensorDesc)); + CHECK_CUDNN(cudnnCreateTensorDescriptor(&layer.normDstTensorDesc)); + CHECK_CUDNN(cudnnSetTensor4dDescriptor(layer.normDstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, layer.batch, layer.out_c, layer.out_h, layer.out_w)); + CHECK_CUDNN(cudnnSetTensor4dDescriptor(layer.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, layer.out_c, 1, 1)); #endif #endif return layer; @@ -129,9 +138,40 @@ void normalize_delta_cpu(float *x, float *mean, float *variance, float *mean_del } } -void resize_batchnorm_layer(layer *layer, int w, int h) +void resize_batchnorm_layer(layer *l, int w, int h) { - fprintf(stderr, "Not implemented\n"); + l->out_h = l->h = h; + l->out_w = l->w = w; + l->outputs = l->inputs = h*w*l->c; + + const int output_size = l->outputs * l->batch; + + l->output = (float*)realloc(l->output, output_size * sizeof(float)); + l->delta = (float*)realloc(l->delta, output_size * sizeof(float)); + +#ifdef GPU + cuda_free(l->output_gpu); + l->output_gpu = cuda_make_array(l->output, output_size); + + if (l->train) { + cuda_free(l->delta_gpu); + l->delta_gpu = cuda_make_array(l->delta, output_size); + + cuda_free(l->x_gpu); + l->x_gpu = cuda_make_array(l->output, output_size); +#ifndef CUDNN + cuda_free(l->x_norm_gpu); + l->x_norm_gpu = cuda_make_array(l->output, output_size); +#endif // not CUDNN + } + + +#ifdef CUDNN + CHECK_CUDNN(cudnnDestroyTensorDescriptor(l->normDstTensorDesc)); + CHECK_CUDNN(cudnnCreateTensorDescriptor(&l->normDstTensorDesc)); + CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->normDstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w)); +#endif // CUDNN +#endif // GPU } void forward_batchnorm_layer(layer l, network_state state) @@ -157,6 +197,7 @@ void forward_batchnorm_layer(layer l, network_state state) normalize_cpu(l.output, l.rolling_mean, l.rolling_variance, l.batch, l.out_c, l.out_h*l.out_w); } scale_bias(l.output, l.scales, l.batch, l.out_c, l.out_h*l.out_w); + add_bias(l.output, l.biases, l.batch, l.out_c, l.out_w*l.out_h); } void backward_batchnorm_layer(const layer l, network_state state) @@ -188,12 +229,14 @@ void update_batchnorm_layer(layer l, int batch, float learning_rate, float momen void pull_batchnorm_layer(layer l) { + cuda_pull_array(l.biases_gpu, l.biases, l.c); cuda_pull_array(l.scales_gpu, l.scales, l.c); cuda_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.c); cuda_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.c); } void push_batchnorm_layer(layer l) { + cuda_push_array(l.biases_gpu, l.biases, l.c); cuda_push_array(l.scales_gpu, l.scales, l.c); cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.c); cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.c); diff --git a/src/batchnorm_layer.h b/src/batchnorm_layer.h index 55c7d62c98a..f5137465313 100644 --- a/src/batchnorm_layer.h +++ b/src/batchnorm_layer.h @@ -8,11 +8,13 @@ #ifdef __cplusplus extern "C" { #endif -layer make_batchnorm_layer(int batch, int w, int h, int c); +layer make_batchnorm_layer(int batch, int w, int h, int c, int train); void forward_batchnorm_layer(layer l, network_state state); void backward_batchnorm_layer(layer l, network_state state); void update_batchnorm_layer(layer l, int batch, float learning_rate, float momentum, float decay); +void resize_batchnorm_layer(layer *l, int w, int h); + #ifdef GPU void forward_batchnorm_layer_gpu(layer l, network_state state); void backward_batchnorm_layer_gpu(layer l, network_state state); diff --git a/src/blas.h b/src/blas.h index 8477bb4193f..72c4ae9ebb7 100644 --- a/src/blas.h +++ b/src/blas.h @@ -37,6 +37,7 @@ void mean_cpu(float *x, int batch, int filters, int spatial, float *mean); void variance_cpu(float *x, float *mean, int batch, int filters, int spatial, float *variance); void normalize_cpu(float *x, float *mean, float *variance, int batch, int filters, int spatial); +void add_bias(float *output, float *biases, int batch, int n, int size); void scale_bias(float *output, float *scales, int batch, int n, int size); void backward_scale_cpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates); void mean_delta_cpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta); diff --git a/src/network.c b/src/network.c index 4fae07d6726..0a2117a88d4 100644 --- a/src/network.c +++ b/src/network.c @@ -527,6 +527,8 @@ int resize_network(network *net, int w, int h) resize_maxpool_layer(&l, w, h); }else if (l.type == LOCAL_AVGPOOL) { resize_maxpool_layer(&l, w, h); + }else if (l.type == BATCHNORM) { + resize_batchnorm_layer(&l, w, h); }else if(l.type == REGION){ resize_region_layer(&l, w, h); }else if (l.type == YOLO) { @@ -1079,7 +1081,6 @@ void fuse_conv_batchnorm(network net) int f; for (f = 0; f < l->n; ++f) { - //l->biases[f] = l->biases[f] - (double)l->scales[f] * l->rolling_mean[f] / (sqrt((double)l->rolling_variance[f]) + .000001f); l->biases[f] = l->biases[f] - (double)l->scales[f] * l->rolling_mean[f] / (sqrt((double)l->rolling_variance[f] + .000001)); const size_t filter_size = l->size*l->size*l->c / l->groups; @@ -1087,7 +1088,6 @@ void fuse_conv_batchnorm(network net) for (i = 0; i < filter_size; ++i) { int w_index = f*filter_size + i; - //l->weights[w_index] = (double)l->weights[w_index] * l->scales[f] / (sqrt((double)l->rolling_variance[f]) + .000001f); l->weights[w_index] = (double)l->weights[w_index] * l->scales[f] / (sqrt((double)l->rolling_variance[f] + .000001)); } } diff --git a/src/parser.c b/src/parser.c index 0b14d9d65e8..8da5189ea82 100644 --- a/src/parser.c +++ b/src/parser.c @@ -805,7 +805,7 @@ layer parse_normalization(list *options, size_params params) layer parse_batchnorm(list *options, size_params params) { - layer l = make_batchnorm_layer(params.batch, params.w, params.h, params.c); + layer l = make_batchnorm_layer(params.batch, params.w, params.h, params.c, params.train); return l; } @@ -1507,6 +1507,7 @@ void save_batchnorm_weights(layer l, FILE *fp) pull_batchnorm_layer(l); } #endif + fwrite(l.biases, sizeof(float), l.c, fp); fwrite(l.scales, sizeof(float), l.c, fp); fwrite(l.rolling_mean, sizeof(float), l.c, fp); fwrite(l.rolling_variance, sizeof(float), l.c, fp); @@ -1652,6 +1653,7 @@ void load_connected_weights(layer l, FILE *fp, int transpose) void load_batchnorm_weights(layer l, FILE *fp) { + fread(l.biases, sizeof(float), l.c, fp); fread(l.scales, sizeof(float), l.c, fp); fread(l.rolling_mean, sizeof(float), l.c, fp); fread(l.rolling_variance, sizeof(float), l.c, fp); From b2749c9e97c2485fd6b4a33d7f6de02226437674 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 21 Dec 2019 14:05:16 +0300 Subject: [PATCH 0761/1365] Update Readme.md --- README.md | 53 ++++++++++++++++++++++++++++++++--------------------- 1 file changed, 32 insertions(+), 21 deletions(-) diff --git a/README.md b/README.md index 6a371bf29c8..f8320b892a5 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ More details: http://pjreddie.com/darknet/yolo/ * [Requirements (and how to install dependecies)](#requirements) * [Pre-trained models](#pre-trained-models) * [Explanations in issues](https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3AExplanations) -* [Yolo v3 in other frameworks (TensorRT, TensorFlow, PyTorch, OpenVINO, OpenCV-dnn,...)](#yolo-v3-in-other-frameworks) +* [Yolo v3 in other frameworks (TensorRT, TensorFlow, PyTorch, OpenVINO, OpenCV-dnn, TVM,...)](#yolo-v3-in-other-frameworks) * [Datasets](#datasets) 0. [Improvements in this repository](#improvements-in-this-repository) @@ -44,19 +44,9 @@ More details: http://pjreddie.com/darknet/yolo/ * Yolo v2 on Pascal VOC 2007: https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg * Yolo v2 on Pascal VOC 2012 (comp4): https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg -### Requirements - -* Windows or Linux -* **CMake >= 3.8** for modern CUDA support: https://cmake.org/download/ -* **CUDA 10.0**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) -* **OpenCV >= 2.4**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or download from [OpenCV official site](https://opencv.org/releases.html) (on Windows set system variable `OpenCV_DIR` = `C:\opencv\build` - where are the `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png)) -* **cuDNN >= 7.0 for CUDA 10.0** https://developer.nvidia.com/rdp/cudnn-archive (on **Linux** copy `cudnn.h`,`libcudnn.so`... as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on **Windows** copy `cudnn.h`,`cudnn64_7.dll`, `cudnn64_7.lib` as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows ) -* **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported -* on Linux **GCC or Clang**, on Windows **MSVC 2015/2017/2019** https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community - #### Pre-trained models -There are weights-file for different cfg-files (smaller size -> faster speed & lower accuracy: +There are weights-file for different cfg-files (trained for MS COCO dataset): * [csresnext50-panet-spp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/csresnext50-panet-spp.cfg) - **60.0% mAP@0.5 - 44 FPS** - 71.3 BFlops - 217 MB: [csresnext50-panet-spp_final.weights](https://drive.google.com/file/d/1aNXdM8qVy11nqTcd2oaVB3mf7ckr258-/view?usp=sharing) @@ -66,7 +56,7 @@ There are weights-file for different cfg-files (smaller size -> faster speed & l * [enet-coco.cfg (EfficientNetB0-Yolov3)](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/enet-coco.cfg) - **45.5% mAP@0.5 - 60 FPS** - 3.7 BFlops - 18.3 MB: [enetb0-coco_final.weights](https://drive.google.com/file/d/1FlHeQjWEQVJt0ay1PVsiuuMzmtNyv36m/view) -* [yolov3-openimages.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-openimages.cfg) - 247 MB: [yolov3-openimages.weights](https://pjreddie.com/media/files/yolov3-openimages.weights) +* [yolov3-openimages.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-openimages.cfg) - 247 MB - OpenImages dataset: [yolov3-openimages.weights](https://pjreddie.com/media/files/yolov3-openimages.weights)
CLICK ME - Yolo v3 models @@ -90,14 +80,26 @@ Put it near compiled: darknet.exe You can get cfg-files by path: `darknet/cfg/` +### Requirements + +* Windows or Linux +* **CMake >= 3.8** for modern CUDA support: https://cmake.org/download/ +* **CUDA 10.0**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) +* **OpenCV >= 2.4**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or download from [OpenCV official site](https://opencv.org/releases.html) (on Windows set system variable `OpenCV_DIR` = `C:\opencv\build` - where are the `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png)) +* **cuDNN >= 7.0 for CUDA 10.0** https://developer.nvidia.com/rdp/cudnn-archive (on **Linux** copy `cudnn.h`,`libcudnn.so`... as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on **Windows** copy `cudnn.h`,`cudnn64_7.dll`, `cudnn64_7.lib` as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows ) +* **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported +* on Linux **GCC or Clang**, on Windows **MSVC 2015/2017/2019** https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community + + #### Yolo v3 in other frameworks * **TensorFlow:** convert `yolov3.weights`/`cfg` files to `yolov3.ckpt`/`pb/meta`: by using [mystic123](https://github.com/mystic123/tensorflow-yolo-v3) or [jinyu121](https://github.com/jinyu121/DW2TF) projects, and [TensorFlow-lite](https://www.tensorflow.org/lite/guide/get_started#2_convert_the_model_format) * **Intel OpenVINO 2019 R1:** (Myriad X / USB Neural Compute Stick / Arria FPGA): read this [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) -* **OpenCV-dnn** is a very fast DNN implementation on CPU (x86/ARM-Android), use `yolov3.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221), [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) +* **OpenCV-dnn** the fastest implementation for CPU (x86/ARM-Android), OpenCV can be compiled with [OpenVINO-backend](https://github.com/opencv/opencv/wiki/Intel's-Deep-Learning-Inference-Engine-backend) for running on (Myriad X / USB Neural Compute Stick / Arria FPGA), use `yolov3.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221) or [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) * **PyTorch > ONNX > CoreML > iOS** how to convert cfg/weights-files to pt-file: [ultralytics/yolov3](https://github.com/ultralytics/yolov3#darknet-conversion) and [iOS App](https://itunes.apple.com/app/id1452689527) * **TensorRT** for YOLOv3 (-70% faster inference): [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) * **TVM** - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backends (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about +* **Netron** - Visualizer for neural networks: https://github.com/lutzroeder/netron #### Datasets @@ -117,21 +119,24 @@ Others: https://www.youtube.com/user/pjreddie/videos ### Improvements in this repository * added support for Windows +* added State-of-Art models: CSP, PRN, EfficientNet +* added layers: [conv_lstm], [scale_channels] SE/ASFF/BiFPN, [local_avgpool], [sam], [Gaussian_yolo], [reorg3d] (fixed [reorg]), fixed [batchnorm] +* added the ability for training recurrent models (with layers conv-lstm`[conv_lstm]`/conv-rnn`[crnn]`) for accurate detection on video +* added data augmentation: `[net] mixup=1 cutmix=1 mosaic=1 blur=1`. Added activations: SWISH, MISH, NORM_CHAN, NORM_CHAN_SOFTMAX +* added the ability for training with GPU-processing using CPU-RAM to increase the mini_batch_size and increase accuracy (instead of batch-norm sync) * improved binary neural network performance **2x-4x times** for Detection on CPU and GPU if you trained your own weights by using this XNOR-net model (bit-1 inference) : https://github.com/AlexeyAB/darknet/blob/master/cfg/yolov3-tiny_xnor.cfg * improved neural network performance **~7%** by fusing 2 layers into 1: Convolutional + Batch-norm -* improved neural network performance Detection **3x times**, Training **2 x times** on GPU Volta (Tesla V100, Titan V, ...) using Tensor Cores if `CUDNN_HALF` defined in the `Makefile` or `darknet.sln` +* improved performance: Detection **2x times**, on GPU Volta/Turing (Tesla V100, GeForce RTX, ...) using Tensor Cores if `CUDNN_HALF` defined in the `Makefile` or `darknet.sln` * improved performance **~1.2x** times on FullHD, **~2x** times on 4K, for detection on the video (file/stream) using `darknet detector demo`... * improved performance **3.5 X times** of data augmentation for training (using OpenCV SSE/AVX functions instead of hand-written functions) - removes bottleneck for training on multi-GPU or GPU Volta -* improved performance of detection and training on Intel CPU with AVX (Yolo v3 **~85%**, Yolo v2 ~10%) -* fixed usage of `[reorg]`-layer +* improved performance of detection and training on Intel CPU with AVX (Yolo v3 **~85%**) * optimized memory allocation during network resizing when `random=1` -* optimized initialization GPU for detection - we use batch=1 initially instead of re-init with batch=1 +* optimized GPU initialization for detection - we use batch=1 initially instead of re-init with batch=1 * added correct calculation of **mAP, F1, IoU, Precision-Recall** using command `darknet detector map`... * added drawing of chart of average-Loss and accuracy-mAP (`-map` flag) during training * run `./darknet detector demo ... -json_port 8070 -mjpeg_port 8090` as JSON and MJPEG server to get results online over the network by using your soft or Web-browser * added calculation of anchors for training * added example of Detection and Tracking objects: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp -* fixed code for use Web-cam on OpenCV > 3.x * run-time tips and warnings if you use incorrect cfg-file or dataset * many other fixes of code... @@ -378,9 +383,10 @@ Training Yolo v3: 1. Create file `yolo-obj.cfg` with the same content as in `yolov3.cfg` (or copy `yolov3.cfg` to `yolo-obj.cfg)` and: * change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L3) - * change line subdivisions to [`subdivisions=8`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4) + * change line subdivisions to [`subdivisions=16`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4) * change line max_batches to (`classes*2000` but not less than `4000`), f.e. [`max_batches=6000`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L20) if you train for 3 classes * change line steps to 80% and 90% of max_batches, f.e. [`steps=4800,5400`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L22) + * set network size `width=416 height=416` or any value multiple of 32: https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L8-L9 * change line `classes=80` to your number of objects in each of 3 `[yolo]`-layers: * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L610 * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L696 @@ -452,7 +458,12 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an data/obj/img3.jpg ``` -7. Download pre-trained weights for the convolutional layers (154 MB): https://pjreddie.com/media/files/darknet53.conv.74 and put to the directory `build\darknet\x64` +7. Download pre-trained weights for the convolutional layers and put to the directory `build\darknet\x64` + * for `csresnext50-panet-spp.cfg` (133 MB): [csresnext50-panet-spp.conv.112](https://drive.google.com/file/d/16yMYCLQTY_oDlCIZPfn_sab6KD3zgzGq/view?usp=sharing) + * for `yolov3.cfg, yolov3-spp.cfg` (154 MB): [darknet53.conv.74](https://pjreddie.com/media/files/darknet53.conv.74) + * for `yolov3-tiny-prn.cfg , yolov3-tiny.cfg` (6 MB): [yolov3-tiny.conv.11](https://drive.google.com/file/d/18v36esoXCh-PsOKwyP2GWrpYDptDY8Zf/view?usp=sharing) + * for `enet-coco.cfg (EfficientNetB0-Yolov3)` (14 MB): [enetb0-coco.conv.132](https://drive.google.com/file/d/1uhh3D6RSn0ekgmsaTcl-ZW53WBaUDo6j/view?usp=sharing) + 8. Start training by using the command line: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74` From af54d951842a5e9fad047b8b54af5160adf2b082 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 22 Dec 2019 16:00:20 +0300 Subject: [PATCH 0762/1365] Added stretch_sway --- include/darknet.h | 1 + src/blas.h | 1 + src/blas_kernels.cu | 173 ++++++++++++++++++++++++++++++++++- src/convolutional_kernels.cu | 2 + src/convolutional_layer.c | 2 +- src/data.c | 4 +- src/parser.c | 8 +- 7 files changed, 181 insertions(+), 10 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index d65dd76d339..ef085960c92 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -254,6 +254,7 @@ struct layer { int sway; int rotate; int stretch; + int stretch_sway; float angle; float jitter; float saturation; diff --git a/src/blas.h b/src/blas.h index 72c4ae9ebb7..ebe7fb0e981 100644 --- a/src/blas.h +++ b/src/blas.h @@ -133,6 +133,7 @@ void sam_gpu(float *in_w_h_c, int size, int channel_size, float *scales_c, float void smooth_rotate_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse); void stretch_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, float scale, int reverse); void sway_and_flip_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse); +void stretch_sway_flip_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse); void rotate_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int reverse); void reduce_and_expand_array_gpu(const float *src_gpu, float *dst_gpu, int size, int groups); void expand_array_gpu(const float *src_gpu, float *dst_gpu, int size, int groups); diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index cdf8a3b3f7e..4e7eab2ee32 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -1402,6 +1402,7 @@ __global__ void stretch_weights_kernel(const float *src_weight_gpu, float *weig //const float coef = (kernel_size*kernel_size) / (kernel_size*kernel_size - dropout_sum); for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { + //if (scale < 1) weight_deform_gpu[x + y*kernel_size + i] /= scale;// *= coef; weight_deform_gpu[x + y*kernel_size + i] /= scale;// *= coef; } } @@ -1422,11 +1423,6 @@ extern "C" void stretch_weights_gpu(const float *src_weight_gpu, float *weight_d - - - - - __global__ void sway_and_flip_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, int angle, int reverse) { const int index = blockIdx.x*blockDim.x + threadIdx.x; @@ -1616,6 +1612,173 @@ extern "C" void rotate_weights_gpu(const float *src_weight_gpu, float *weight_de +__global__ void stretch_sway_flip_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, float angle, int reverse) +{ + const int index = blockIdx.x*blockDim.x + threadIdx.x; + const int kernel_area = kernel_size * kernel_size; + const int i = index * kernel_area; + + const int stage_step = (nweights / kernel_area) / 8; // 8 stages + const int stage_id = index / stage_step; + + // nweights = (c / groups) * n * size * size; + // kernel_area = size*size + + if (i < nweights) + { + + if (stage_id == 0) { + // simple copy + for (int x = 0; x < kernel_size; ++x) { + for (int y = 0; y < kernel_size; ++y) { + weight_deform_gpu[x + y*kernel_size + i] = src_weight_gpu[x + y*kernel_size + i]; + } + } + } + else if (stage_id == 1 || stage_id == 2 || stage_id == 3 || stage_id == 4) + { + float scale = 0.5; + if (stage_id == 1) scale = 0.65; + else if (stage_id == 2) scale = 0.8; + else if (stage_id == 3) scale = 1.2; + else if (stage_id == 4) scale = 1.4; + + if (reverse) scale = 1 / scale; + + const int x_c = kernel_size / 2; + const int y_c = kernel_size / 2; + + float dropout_sum = 0; + + for (int y = 0; y < kernel_size; ++y) { + for (int x = 0; x < kernel_size; ++x) { + // Xsource = x_c + (x_d - x_c) / scale + // Ysource = y_c + (y_d - y_c) / scale + + float x_s = x_c + (x - x_c) / scale; + float y_s = y_c + (y - y_c) / scale; + + int x_0 = floor(x_s); // round down + int x_1 = ceil(x_s); // round up + if (x_0 == x_1) x_1 = x_0 + 1; + int y_0 = floor(y_s); + int y_1 = ceil(y_s); + if (y_0 == y_1) y_1 = y_0 + 1; + + float c_x_0 = x_1 - x_s; + float c_x_1 = x_s - x_0; + float c_y_0 = y_1 - y_s; + float c_y_1 = y_s - y_0; + + float val = 0; + if (x_0 >= 0 && x_0 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_0 + y_0*kernel_size + i] * c_x_0 * c_y_0; + else dropout_sum += c_x_0 * c_y_0; + + if (x_1 >= 0 && x_1 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_1 + y_0*kernel_size + i] * c_x_1 * c_y_0; + else dropout_sum += c_x_1 * c_y_0; + + if (x_0 >= 0 && x_0 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_0 + y_1*kernel_size + i] * c_x_0 * c_y_1; + else dropout_sum += c_x_0 * c_y_1; + + if (x_1 >= 0 && x_1 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_1 + y_1*kernel_size + i] * c_x_1 * c_y_1; + else dropout_sum += c_x_1 * c_y_1; + + weight_deform_gpu[x + y*kernel_size + i] = val; + } + } + + // compensate for dropped items + //const float coef = (kernel_size*kernel_size) / (kernel_size*kernel_size - dropout_sum); + for (int y = 0; y < kernel_size; ++y) { + for (int x = 0; x < kernel_size; ++x) { + if(scale > 1) + weight_deform_gpu[x + y*kernel_size + i] *= scale;// *= coef; + } + } + } + else if (stage_id == 5 || stage_id == 6) + { + // rotate left or right + if (stage_id == 6) angle = -angle; + if (reverse) angle = -angle; + + const float cos_a = cosf(angle * 3.14159265 / 180); + const float sin_a = sinf(angle * 3.14159265 / 180); + const int x_c = kernel_size / 2; + const int y_c = kernel_size / 2; + + float dropout_sum = 0; + + for (int y = 0; y < kernel_size; ++y) { + for (int x = 0; x < kernel_size; ++x) { + // Xsource = x*cos(alpha) + y*sin(alpha) + // Ysource = -x*sin(alpha) + y*cos(alpha) + + float x_s = x_c + (x - x_c)*cos_a + (y - y_c)*sin_a; + float y_s = y_c - (x - x_c)*sin_a + (y - y_c)*cos_a; + + int x_0 = floor(x_s); // round down + int x_1 = ceil(x_s); // round up + if (x_0 == x_1) x_1 = x_0 + 1; + int y_0 = floor(y_s); + int y_1 = ceil(y_s); + if (y_0 == y_1) y_1 = y_0 + 1; + + float c_x_0 = x_1 - x_s; + float c_x_1 = x_s - x_0; + float c_y_0 = y_1 - y_s; + float c_y_1 = y_s - y_0; + + float val = 0; + if (x_0 >= 0 && x_0 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_0 + y_0*kernel_size + i] * c_x_0 * c_y_0; + else dropout_sum += c_x_0 * c_y_0; + + if (x_1 >= 0 && x_1 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_1 + y_0*kernel_size + i] * c_x_1 * c_y_0; + else dropout_sum += c_x_1 * c_y_0; + + if (x_0 >= 0 && x_0 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_0 + y_1*kernel_size + i] * c_x_0 * c_y_1; + else dropout_sum += c_x_0 * c_y_1; + + if (x_1 >= 0 && x_1 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_1 + y_1*kernel_size + i] * c_x_1 * c_y_1; + else dropout_sum += c_x_1 * c_y_1; + + weight_deform_gpu[x + y*kernel_size + i] = val; + } + } + + // compensate for dropped items + const float coef = (kernel_size*kernel_size) / (kernel_size*kernel_size - dropout_sum); + for (int y = 0; y < kernel_size; ++y) { + for (int x = 0; x < kernel_size; ++x) { + weight_deform_gpu[x + y*kernel_size + i] *= coef; + } + } + } + else if (stage_id == 7) + { + // flip + for (int y = 0; y < kernel_size; ++y) { + for (int x = 0; x < kernel_size; ++x) { + weight_deform_gpu[(kernel_size - x - 1) + y*kernel_size + i] = src_weight_gpu[x + y*kernel_size + i]; + } + } + } + } +} + + +extern "C" void stretch_sway_flip_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int angle, int reverse) +{ + const int kernel_area = size*size; + const int block_size = BLOCK; + const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size); + stretch_sway_flip_weights_kernel << > > (src_weight_gpu, weight_deform_gpu, nweights, n, size, angle, reverse); + + CHECK_CUDA(cudaPeekAtLastError()); +} + + + __global__ void reduce_and_expand_array_kernel(const float *src_gpu, float *dst_gpu, int current_size, int groups) { const int index = blockIdx.x*blockDim.x + threadIdx.x; diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 45fff784808..f31f84889ef 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -1210,6 +1210,7 @@ void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init if (l.rotate) rotate_weights_gpu(l.weight_updates_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, 1); else if (l.sway) sway_and_flip_weights_gpu(l.weight_updates_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, l.angle, 1); else if (l.stretch) stretch_weights_gpu(l.weight_updates_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, 0, 1); + else if (l.stretch_sway) stretch_sway_flip_weights_gpu(l.weight_updates_gpu, l.weight_deform_gpu, l.nweights, l.n, l.size, l.angle, 1); //simple_copy_ongpu(l.nweights, l.weight_updates_gpu, l.weight_deform_gpu); @@ -1268,6 +1269,7 @@ void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init if (l.rotate) rotate_weights_gpu(l.weight_deform_gpu, l.weights_gpu, l.nweights, l.n, l.size, 0); else if (l.sway) sway_and_flip_weights_gpu(l.weight_deform_gpu, l.weights_gpu, l.nweights, l.n, l.size, l.angle, 0); else if (l.stretch) stretch_weights_gpu(l.weight_deform_gpu, l.weights_gpu, l.nweights, l.n, l.size, 0, 0); + else if (l.stretch_sway) stretch_sway_flip_weights_gpu(l.weight_deform_gpu, l.weights_gpu, l.nweights, l.n, l.size, l.angle, 0); //printf(" angle = %f, reverse = %d \n", l.angle, 0); //cuda_pull_array(l.weights_gpu, l.weights, l.nweights); diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index cc05b3b08fc..d912e9500ee 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -232,7 +232,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference, size_t workspace_ // 3. FP32 Master Copy of Weights // More: http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#tensor_ops CHECK_CUDNN(cudnnSetConvolutionGroupCount(l->convDesc, l->groups)); - if (l->groups == 1) CHECK_CUDNN(cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH)); + //if (l->groups == 1) CHECK_CUDNN(cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH)); #if((CUDNN_MAJOR*10 + CUDNN_MINOR) >= 72) // cuDNN >= 7.2 //CHECK_CUDNN(cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION)); // reduces the speed of regular and group convolution #endif diff --git a/src/data.c b/src/data.c index b84ae7c231d..883585fcc0f 100644 --- a/src/data.c +++ b/src/data.c @@ -1628,7 +1628,9 @@ data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *h } for (j = 0; j < d.y.cols; ++j) { - d.y.vals[i][j] = d.y.vals[i][j] * s1 + d2.y.vals[i][j] * s2 + d3.y.vals[i][j] * s3 + d4.y.vals[i][j] * s4; + const float max_s = 1;// max_val_cmp(s1, max_val_cmp(s2, max_val_cmp(s3, s4))); + + d.y.vals[i][j] = d.y.vals[i][j] * s1 / max_s + d2.y.vals[i][j] * s2 / max_s + d3.y.vals[i][j] * s3 / max_s + d4.y.vals[i][j] * s4 / max_s; } } } diff --git a/src/parser.c b/src/parser.c index 8da5189ea82..94d23a30610 100644 --- a/src/parser.c +++ b/src/parser.c @@ -204,11 +204,12 @@ convolutional_layer parse_convolutional(list *options, size_params params) int sway = option_find_int_quiet(options, "sway", 0); int rotate = option_find_int_quiet(options, "rotate", 0); int stretch = option_find_int_quiet(options, "stretch", 0); - if ((sway + rotate + stretch) > 1) { + int stretch_sway = option_find_int_quiet(options, "stretch_sway", 0); + if ((sway + rotate + stretch + stretch_sway) > 1) { printf(" Error: should be used only 1 param: sway=1, rotate=1 or stretch=1 in the [convolutional] layer \n"); exit(0); } - int deform = sway || rotate || stretch; + int deform = sway || rotate || stretch || stretch_sway; if (deform && size == 1) { printf(" Error: params (sway=1, rotate=1 or stretch=1) should be used only with size >=3 in the [convolutional] layer \n"); exit(0); @@ -220,6 +221,7 @@ convolutional_layer parse_convolutional(list *options, size_params params) layer.sway = sway; layer.rotate = rotate; layer.stretch = stretch; + layer.stretch_sway = stretch_sway; layer.angle = option_find_float_quiet(options, "angle", 15); if(params.net.adam){ @@ -778,7 +780,7 @@ dropout_layer parse_dropout(list *options, size_params params) printf(" [dropout] - dropblock_size_abs = %d that is bigger than layer size %d x %d \n", dropblock_size_abs, params.w, params.h); dropblock_size_abs = min_val_cmp(params.w, params.h); } - if (!dropblock_size_rel && !dropblock_size_abs) { + if (dropblock && !dropblock_size_rel && !dropblock_size_abs) { printf(" [dropout] - None of the parameters (dropblock_size_rel or dropblock_size_abs) are set, will be used: dropblock_size_abs = 7 \n"); dropblock_size_abs = 7; } From 0a841f6a22f6a8fa64c9e4d1d38c168f619c9035 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 23 Dec 2019 02:04:23 +0300 Subject: [PATCH 0763/1365] Fixed strech_sway=1 param, local_avgpool for large size --- README.md | 2 +- src/blas_kernels.cu | 2 +- src/convolutional_kernels.cu | 4 ++-- src/maxpool_layer_kernels.cu | 2 +- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index f8320b892a5..4129fbb4952 100644 --- a/README.md +++ b/README.md @@ -36,7 +36,7 @@ More details: http://pjreddie.com/darknet/yolo/ 11. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) 12. [How to use Yolo as DLL and SO libraries](#how-to-use-yolo-as-dll-and-so-libraries) -| ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) |   ![map_fps](https://user-images.githubusercontent.com/4096485/71088138-d5032280-21ae-11ea-87ff-4c8695c53a28.png) mAP@0.5 (AP50) https://arxiv.org/abs/1911.11929 https://github.com/WongKinYiu/CrossStagePartialNetworks - more models | +| ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) |   ![map_fps](https://user-images.githubusercontent.com/4096485/71088138-d5032280-21ae-11ea-87ff-4c8695c53a28.png) mAP@0.5 (AP50) - FPS (GeForce 1080 Ti) https://arxiv.org/abs/1911.11929 https://github.com/WongKinYiu/CrossStagePartialNetworks - more models | |---|---| * Yolo v3 on MS COCO: [Speed / Accuracy (mAP@0.5) chart](https://user-images.githubusercontent.com/4096485/52151356-e5d4a380-2683-11e9-9d7d-ac7bc192c477.jpg) diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 4e7eab2ee32..7f84f33707c 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -1692,7 +1692,7 @@ __global__ void stretch_sway_flip_weights_kernel(const float *src_weight_gpu, f for (int y = 0; y < kernel_size; ++y) { for (int x = 0; x < kernel_size; ++x) { if(scale > 1) - weight_deform_gpu[x + y*kernel_size + i] *= scale;// *= coef; + weight_deform_gpu[x + y*kernel_size + i] /= scale;// *= coef; } } } diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index f31f84889ef..b3977a6deb6 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -420,7 +420,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //if (state.use_mixed_precision) { int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in) && - (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups == 1) + (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 2) { //printf("\n CUDNN_HALF!!! state.index = %d \n", state.index); @@ -671,7 +671,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state //#ifdef CUDNN_HALF int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in) && - (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups == 1) + (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 2) { const size_t input16_size = l.batch*l.c*l.w*l.h; const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h; diff --git a/src/maxpool_layer_kernels.cu b/src/maxpool_layer_kernels.cu index 114b8f981d4..7436a19e7cb 100644 --- a/src/maxpool_layer_kernels.cu +++ b/src/maxpool_layer_kernels.cu @@ -309,7 +309,7 @@ __global__ void backward_local_avgpool_layer_kernel(int n, int in_h, int in_w, i } } } - prev_delta[index] += d / counter; + if(counter > 0) prev_delta[index] += d / counter; } From f81033266e82b63d93e7f4de51800880ceea595b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 23 Dec 2019 20:08:16 +0300 Subject: [PATCH 0764/1365] Minor fix --- src/convolutional_kernels.cu | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index b3977a6deb6..eee5fe1a885 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -420,7 +420,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //if (state.use_mixed_precision) { int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in) && - (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 2) + (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 8) { //printf("\n CUDNN_HALF!!! state.index = %d \n", state.index); @@ -671,7 +671,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state //#ifdef CUDNN_HALF int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in) && - (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 2) + (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 8) { const size_t input16_size = l.batch*l.c*l.w*l.h; const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h; From 719180e7f1101d64f043c2604e248d2c20577df1 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 24 Dec 2019 04:30:22 +0300 Subject: [PATCH 0765/1365] Fixed swish/mish on CPU --- src/convolutional_layer.c | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index d912e9500ee..93e4ff3779c 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -515,10 +515,13 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.x = (float*)calloc(total_batch * l.outputs, sizeof(float)); l.x_norm = (float*)calloc(total_batch * l.outputs, sizeof(float)); } - - if (l.activation == SWISH || l.activation == MISH) l.activation_input = (float*)calloc(total_batch*l.outputs, sizeof(float)); #endif // not GPU } + +#ifndef GPU + if (l.activation == SWISH || l.activation == MISH) l.activation_input = (float*)calloc(total_batch*l.outputs, sizeof(float)); +#endif // not GPU + if(adam){ l.adam = 1; l.m = (float*)calloc(l.nweights, sizeof(float)); From 81c5bcea921215604554132511910011f405890e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 24 Dec 2019 22:03:59 +0300 Subject: [PATCH 0766/1365] Avg-loss will be saved to the 2 images: chart.png & chart_your_cfg_name.png --- src/detector.c | 2 +- src/image_opencv.cpp | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/src/detector.c b/src/detector.c index aa9652d8af2..6e425df6410 100644 --- a/src/detector.c +++ b/src/detector.c @@ -148,7 +148,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i int number_of_lines = 100; int img_size = 1000; char windows_name[100]; - sprintf(windows_name, "average loss (id:%d)", random_gen()); + sprintf(windows_name, "chart_%s.png", base); img = draw_train_chart(windows_name, max_img_loss, net.max_batches, number_of_lines, img_size, dont_show); #endif //OPENCV if (net.track) { diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 2bda69340a9..afcd86cecac 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1120,6 +1120,7 @@ extern "C" void draw_train_loss(char *windows_name, mat_cv* img_src, int img_siz if (k == 's' || current_batch == (max_batches - 1) || (current_batch / 100 > old_batch / 100)) { old_batch = current_batch; save_mat_png(img, "chart.png"); + save_mat_png(img, windows_name); cv::putText(img, "- Saved", cv::Point(260, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 0, 0), 1, CV_AA); } else From f64746107d057a958d96cc0b118ad3eae78a2a91 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 25 Dec 2019 00:17:01 +0300 Subject: [PATCH 0767/1365] Fixed add_bias vs batch_norm on CPU --- src/convolutional_layer.c | 4 +++- src/parser.c | 3 ++- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 93e4ff3779c..49dfab14d76 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -1249,7 +1249,9 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) if(l.batch_normalize){ forward_batchnorm_layer(l, state); } - add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w); + else { + add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w); + } //activate_array(l.output, m*n*l.batch, l.activation); if (l.activation == SWISH) activate_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.output); diff --git a/src/parser.c b/src/parser.c index 94d23a30610..ec9e30076bf 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1864,10 +1864,11 @@ network *load_network_custom(char *cfg, char *weights, int clear, int batch) { printf(" Try to load cfg: %s, weights: %s, clear = %d \n", cfg, weights, clear); network* net = (network*)calloc(1, sizeof(network)); - *net = parse_network_cfg_custom(cfg, batch, 0); + *net = parse_network_cfg_custom(cfg, batch, 1); if (weights && weights[0] != 0) { load_weights(net, weights); } + //fuse_conv_batchnorm(*net); if (clear) (*net->seen) = 0; return net; } From b8fe630119fea81200f6ca4641ce2514d893df04 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 25 Dec 2019 20:30:56 +0300 Subject: [PATCH 0768/1365] counters_per_class affects the bbox too --- src/gaussian_yolo_layer.c | 19 ++++++++++++------- src/yolo_layer.c | 19 ++++++++++++------- 2 files changed, 24 insertions(+), 14 deletions(-) diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index 82053e25305..999e7990398 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -456,7 +456,8 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9); delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.label_smooth_eps, l.classes_multipliers); box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); - delta_gaussian_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, l.uc_normalizer, 1, l.yolo_point); + const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; + delta_gaussian_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, l.uc_normalizer, 1, l.yolo_point); } } } @@ -496,15 +497,17 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int mask_n = int_index(l.mask, best_n, l.n); if(mask_n >= 0){ + int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; + if (l.map) class_id = l.map[class_id]; + int box_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); - float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, l.uc_normalizer, 1, l.yolo_point); + const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; + float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, l.uc_normalizer, 1, l.yolo_point); int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8); avg_obj += l.output[obj_index]; l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); - int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; - if (l.map) class_id = l.map[class_id]; int class_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 9); delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.label_smooth_eps, l.classes_multipliers); @@ -527,15 +530,17 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) // iou, n if (iou > l.iou_thresh) { + int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; + if (l.map) class_id = l.map[class_id]; + int box_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); - float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, l.uc_normalizer, 1, l.yolo_point); + const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; + float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, l.uc_normalizer, 1, l.yolo_point); int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8); avg_obj += l.output[obj_index]; l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); - int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; - if (l.map) class_id = l.map[class_id]; int class_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 9); delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.label_smooth_eps, l.classes_multipliers); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 919c26f0730..7c7e7144b81 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -363,7 +363,8 @@ void forward_yolo_layer(const layer l, network_state state) int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); - delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, 1); + const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; + delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1); } } } @@ -400,8 +401,12 @@ void forward_yolo_layer(const layer l, network_state state) int mask_n = int_index(l.mask, best_n, l.n); if (mask_n >= 0) { + int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; + if (l.map) class_id = l.map[class_id]; + int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); - ious all_ious = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, 1); + const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; + ious all_ious = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1); // range is 0 <= 1 tot_iou += all_ious.iou; @@ -420,8 +425,6 @@ void forward_yolo_layer(const layer l, network_state state) avg_obj += l.output[obj_index]; l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); - int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; - if (l.map) class_id = l.map[class_id]; int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); @@ -442,8 +445,12 @@ void forward_yolo_layer(const layer l, network_state state) // iou, n if (iou > l.iou_thresh) { + int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; + if (l.map) class_id = l.map[class_id]; + int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); - ious all_ious = delta_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer, l.iou_loss, 1); + const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; + ious all_ious = delta_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1); // range is 0 <= 1 tot_iou += all_ious.iou; @@ -462,8 +469,6 @@ void forward_yolo_layer(const layer l, network_state state) avg_obj += l.output[obj_index]; l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); - int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; - if (l.map) class_id = l.map[class_id]; int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); From 35a3870979e0d819208a3de7a24c39cc0539651d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 25 Dec 2019 20:34:24 +0300 Subject: [PATCH 0769/1365] counters_per_class affects the objectness too --- src/gaussian_yolo_layer.c | 4 ++-- src/yolo_layer.c | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index 999e7990398..477b0b841ca 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -506,7 +506,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8); avg_obj += l.output[obj_index]; - l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); + l.delta[obj_index] = class_multiplier * l.cls_normalizer * (1 - l.output[obj_index]); int class_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 9); delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.label_smooth_eps, l.classes_multipliers); @@ -539,7 +539,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8); avg_obj += l.output[obj_index]; - l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); + l.delta[obj_index] = class_multiplier * l.cls_normalizer * (1 - l.output[obj_index]); int class_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 9); delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.label_smooth_eps, l.classes_multipliers); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 7c7e7144b81..48562202eaa 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -423,7 +423,7 @@ void forward_yolo_layer(const layer l, network_state state) int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4); avg_obj += l.output[obj_index]; - l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); + l.delta[obj_index] = class_multiplier * l.cls_normalizer * (1 - l.output[obj_index]); int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); @@ -467,7 +467,7 @@ void forward_yolo_layer(const layer l, network_state state) int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4); avg_obj += l.output[obj_index]; - l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); + l.delta[obj_index] = class_multiplier * l.cls_normalizer * (1 - l.output[obj_index]); int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); From b8605bda1e1665eaa7649ac944efb57f38a2e2dd Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 27 Dec 2019 12:54:20 +0300 Subject: [PATCH 0770/1365] Fixed mosaic=1 cutmix=1 memory leak for Training Classifier --- src/data.c | 11 ++++++----- src/data.h | 2 +- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/src/data.c b/src/data.c index 883585fcc0f..2053030b888 100644 --- a/src/data.c +++ b/src/data.c @@ -1507,7 +1507,7 @@ data load_data_super(char **paths, int n, int m, int w, int h, int scale) return d; } -data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int mixup, int use_blur, int show_imgs, float label_smooth_eps) +data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int use_mixup, int use_blur, int show_imgs, float label_smooth_eps) { char **paths_stored = paths; if(m) paths = get_random_paths(paths, n, m); @@ -1516,7 +1516,7 @@ data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *h d.X = load_image_augment_paths(paths, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure); d.y = load_labels_paths(paths, n, labels, k, hierarchy, label_smooth_eps); - if (mixup && rand_int(0, 1)) { + if (use_mixup && rand_int(0, 1)) { char **paths_mix = get_random_paths(paths_stored, n, m); data d2 = { 0 }; d2.shallow = 0; @@ -1528,7 +1528,7 @@ data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *h d3.shallow = 0; data d4 = { 0 }; d4.shallow = 0; - if (mixup >= 3) { + if (use_mixup >= 3) { char **paths_mix3 = get_random_paths(paths_stored, n, m); d3.X = load_image_augment_paths(paths_mix3, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure); d3.y = load_labels_paths(paths_mix3, n, labels, k, hierarchy, label_smooth_eps); @@ -1545,7 +1545,8 @@ data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *h int i, j; for (i = 0; i < d2.X.rows; ++i) { - if (mixup == 4) mixup = rand_int(2, 3); // alternate CutMix and Mosaic + int mixup = use_mixup; + if (use_mixup == 4) mixup = rand_int(2, 3); // alternate CutMix and Mosaic // MixUp ----------------------------------- if (mixup == 1) { @@ -1637,7 +1638,7 @@ data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *h free_data(d2); - if (mixup == 3) { + if (use_mixup >= 3) { free_data(d3); free_data(d4); } diff --git a/src/data.h b/src/data.h index dc18a928b52..f675ae82351 100644 --- a/src/data.h +++ b/src/data.h @@ -91,7 +91,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo data load_data_tag(char **paths, int n, int m, int k, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure); matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure); data load_data_super(char **paths, int n, int m, int w, int h, int scale); -data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int mixup, int use_blur, int show_imgs, float label_smooth_eps); +data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int use_mixup, int use_blur, int show_imgs, float label_smooth_eps); data load_go(char *filename); box_label *read_boxes(char *filename, int *n); From 114a7f942b49eb906c9e03cc450d8df3c6691a33 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 28 Dec 2019 18:44:32 +0300 Subject: [PATCH 0771/1365] Added command line param -dontuse_opencv for training Classifier. Also use GaussianBlur instead of bilateralFilter for blur=1 for training Classifier and Detector. --- include/darknet.h | 1 + scripts/README.md | 2 ++ src/classifier.c | 10 ++++++---- src/data.c | 21 ++++++++++++--------- src/data.h | 4 ++-- src/image.c | 12 ++++++++++++ src/image.h | 1 + src/image_opencv.cpp | 12 +++++++----- 8 files changed, 43 insertions(+), 20 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index ef085960c92..940d980856c 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -822,6 +822,7 @@ typedef struct load_args { int augment_speed; int letter_box; int show_imgs; + int dontuse_opencv; float jitter; int flip; int blur; diff --git a/scripts/README.md b/scripts/README.md index c38626eca81..1cefec957e7 100644 --- a/scripts/README.md +++ b/scripts/README.md @@ -2,6 +2,8 @@ ### Datasets: +25 thousand datasets on Kaggle: https://www.kaggle.com/datasets + BDD100K - Diverse Driving Video: https://bair.berkeley.edu/blog/2018/05/30/bdd/ Pascal VOC: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html diff --git a/src/classifier.c b/src/classifier.c index 59ae3e94a65..e97e9cf4f4f 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -27,7 +27,7 @@ float *get_regression_values(char **labels, int n) return v; } -void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int mjpeg_port, int calc_topk, int show_imgs) +void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dontuse_opencv, int dont_show, int mjpeg_port, int calc_topk, int show_imgs) { int i; @@ -81,6 +81,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, args.threads = 32; args.hierarchy = net.hierarchy; + args.dontuse_opencv = dontuse_opencv; args.min = net.min_crop; args.max = net.max_crop; args.flip = net.flip; @@ -112,7 +113,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int img_size = 1000; char windows_name[100]; sprintf(windows_name, "average loss (id:%d)", random_gen()); - img = draw_train_chart(windows_name, max_img_loss, net.max_batches, number_of_lines, img_size, dont_show); + if (!dontuse_opencv) img = draw_train_chart(windows_name, max_img_loss, net.max_batches, number_of_lines, img_size, dont_show); #endif //OPENCV data train; @@ -176,7 +177,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); #ifdef OPENCV - draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, topk_buff, dont_show, mjpeg_port); + if (!dontuse_opencv) draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, topk_buff, dont_show, mjpeg_port); #endif // OPENCV if (i >= (iter_save + 1000)) { @@ -1298,6 +1299,7 @@ void run_classifier(int argc, char **argv) } int dont_show = find_arg(argc, argv, "-dont_show"); + int dontuse_opencv = find_arg(argc, argv, "-dontuse_opencv"); int show_imgs = find_arg(argc, argv, "-show_imgs"); int calc_topk = find_arg(argc, argv, "-topk"); int cam_index = find_int_arg(argc, argv, "-c", 0); @@ -1311,7 +1313,7 @@ void run_classifier(int argc, char **argv) int layer = layer_s ? atoi(layer_s) : -1; if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename, top); else if(0==strcmp(argv[2], "try")) try_classifier(data, cfg, weights, filename, atoi(layer_s)); - else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear, dont_show, mjpeg_port, calc_topk, show_imgs); + else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear, dontuse_opencv, dont_show, mjpeg_port, calc_topk, show_imgs); else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename); else if(0==strcmp(argv[2], "gun")) gun_classifier(data, cfg, weights, cam_index, filename); else if(0==strcmp(argv[2], "threat")) threat_classifier(data, cfg, weights, cam_index, filename); diff --git a/src/data.c b/src/data.c index 2053030b888..6fa37846804 100644 --- a/src/data.c +++ b/src/data.c @@ -142,7 +142,7 @@ matrix load_image_paths(char **paths, int n, int w, int h) return X; } -matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure) +matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int dontuse_opencv) { int i; matrix X; @@ -152,7 +152,10 @@ matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int for(i = 0; i < n; ++i){ int size = w > h ? w : h; - image im = load_image_color(paths[i], 0, 0); + image im; + if(dontuse_opencv) im = load_image_stb_resize(paths[i], 0, 0, 3); + else im = load_image_color(paths[i], 0, 0); + image crop = random_augment_image(im, angle, aspect, min, max, size); int flip = use_flip ? random_gen() % 2 : 0; if (flip) @@ -1362,7 +1365,7 @@ void *load_thread(void *ptr) if (a.type == OLD_CLASSIFICATION_DATA){ *a.d = load_data_old(a.paths, a.n, a.m, a.labels, a.classes, a.w, a.h); } else if (a.type == CLASSIFICATION_DATA){ - *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.flip, a.min, a.max, a.w, a.h, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.mixup, a.blur, a.show_imgs, a.label_smooth_eps); + *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.flip, a.min, a.max, a.w, a.h, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.mixup, a.blur, a.show_imgs, a.label_smooth_eps, a.dontuse_opencv); } else if (a.type == SUPER_DATA){ *a.d = load_data_super(a.paths, a.n, a.m, a.w, a.h, a.scale); } else if (a.type == WRITING_DATA){ @@ -1507,20 +1510,20 @@ data load_data_super(char **paths, int n, int m, int w, int h, int scale) return d; } -data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int use_mixup, int use_blur, int show_imgs, float label_smooth_eps) +data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int use_mixup, int use_blur, int show_imgs, float label_smooth_eps, int dontuse_opencv) { char **paths_stored = paths; if(m) paths = get_random_paths(paths, n, m); data d = {0}; d.shallow = 0; - d.X = load_image_augment_paths(paths, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure); + d.X = load_image_augment_paths(paths, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure, dontuse_opencv); d.y = load_labels_paths(paths, n, labels, k, hierarchy, label_smooth_eps); if (use_mixup && rand_int(0, 1)) { char **paths_mix = get_random_paths(paths_stored, n, m); data d2 = { 0 }; d2.shallow = 0; - d2.X = load_image_augment_paths(paths_mix, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure); + d2.X = load_image_augment_paths(paths_mix, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure, dontuse_opencv); d2.y = load_labels_paths(paths_mix, n, labels, k, hierarchy, label_smooth_eps); free(paths_mix); @@ -1530,12 +1533,12 @@ data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *h d4.shallow = 0; if (use_mixup >= 3) { char **paths_mix3 = get_random_paths(paths_stored, n, m); - d3.X = load_image_augment_paths(paths_mix3, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure); + d3.X = load_image_augment_paths(paths_mix3, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure, dontuse_opencv); d3.y = load_labels_paths(paths_mix3, n, labels, k, hierarchy, label_smooth_eps); free(paths_mix3); char **paths_mix4 = get_random_paths(paths_stored, n, m); - d4.X = load_image_augment_paths(paths_mix4, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure); + d4.X = load_image_augment_paths(paths_mix4, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure, dontuse_opencv); d4.y = load_labels_paths(paths_mix4, n, labels, k, hierarchy, label_smooth_eps); free(paths_mix4); } @@ -1706,7 +1709,7 @@ data load_data_tag(char **paths, int n, int m, int k, int use_flip, int min, int d.w = w; d.h = h; d.shallow = 0; - d.X = load_image_augment_paths(paths, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure); + d.X = load_image_augment_paths(paths, n, use_flip, min, max, w, h, angle, aspect, hue, saturation, exposure, 0); d.y = load_tags_paths(paths, n, k); if(m) free(paths); return d; diff --git a/src/data.h b/src/data.h index f675ae82351..e430363db01 100644 --- a/src/data.h +++ b/src/data.h @@ -89,9 +89,9 @@ data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int use_blur, int use_mixup, float jitter, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int show_imgs); data load_data_tag(char **paths, int n, int m, int k, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure); -matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure); +matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int dontuse_opencv); data load_data_super(char **paths, int n, int m, int w, int h, int scale); -data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int use_mixup, int use_blur, int show_imgs, float label_smooth_eps); +data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int use_mixup, int use_blur, int show_imgs, float label_smooth_eps, int dontuse_opencv); data load_go(char *filename); box_label *read_boxes(char *filename, int *n); diff --git a/src/image.c b/src/image.c index c24cb5dc615..d62dd2f66d9 100644 --- a/src/image.c +++ b/src/image.c @@ -1419,6 +1419,18 @@ image load_image_stb(char *filename, int channels) return im; } +image load_image_stb_resize(char *filename, int w, int h, int c) +{ + image out = load_image_stb(filename, c); // without OpenCV + + if ((h && w) && (h != out.h || w != out.w)) { + image resized = resize_image(out, w, h); + free_image(out); + out = resized; + } + return out; +} + image load_image(char *filename, int w, int h, int c) { #ifdef OPENCV diff --git a/src/image.h b/src/image.h index 14792c9b9ff..58897794772 100644 --- a/src/image.h +++ b/src/image.h @@ -83,6 +83,7 @@ image float_to_image_scaled(int w, int h, int c, float *data); image float_to_image(int w, int h, int c, float *data); image copy_image(image p); image load_image(char *filename, int w, int h, int c); +image load_image_stb_resize(char *filename, int w, int h, int c); //LIB_API image load_image_color(char *filename, int w, int h); image **load_alphabet(); diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index afcd86cecac..d678bd900a1 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1210,15 +1210,15 @@ extern "C" image image_data_augmentation(mat_cv* mat, int w, int h, if (blur) { cv::Mat dst(sized.size(), sized.type()); if (blur == 1) { - //cv::GaussianBlur(sized, dst, cv::Size(31, 31), 0); - cv::bilateralFilter(sized, dst, 17, 75, 75); + cv::GaussianBlur(sized, dst, cv::Size(17, 17), 0); + //cv::bilateralFilter(sized, dst, 17, 75, 75); } else { int ksize = (blur / 2) * 2 + 1; cv::Size kernel_size = cv::Size(ksize, ksize); - //cv::GaussianBlur(sized, dst, kernel_size, 0); + cv::GaussianBlur(sized, dst, kernel_size, 0); //cv::medianBlur(sized, dst, ksize); - cv::bilateralFilter(sized, dst, ksize, 75, 75); + //cv::bilateralFilter(sized, dst, ksize, 75, 75); // sharpen //cv::Mat img_tmp; @@ -1274,7 +1274,9 @@ extern "C" image blur_image(image src_img, int ksize) { cv::Mat src = image_to_mat(src_img); cv::Mat dst; - cv::bilateralFilter(src, dst, ksize, 75, 75); + cv::Size kernel_size = cv::Size(ksize, ksize); + cv::GaussianBlur(src, dst, kernel_size, 0); + //cv::bilateralFilter(src, dst, ksize, 75, 75); image dst_img = mat_to_image(dst); return dst_img; } From 00f0b05047374a11a3618a7b4c6ae1e14bb58178 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 28 Dec 2019 20:18:15 +0300 Subject: [PATCH 0772/1365] Added flag -benchmark for: ./darknet classifier demo ... -benchmark --- src/classifier.c | 54 +++++++++++++++++++++++++++++++++++------------- 1 file changed, 40 insertions(+), 14 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index e97e9cf4f4f..5d8e6c70833 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -1187,7 +1187,7 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde #endif } -void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename) +void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename, int benchmark) { #ifdef OPENCV printf("Classifier Demo\n"); @@ -1220,22 +1220,35 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind int* indexes = (int*)calloc(top, sizeof(int)); if(!cap) error("Couldn't connect to webcam.\n"); - create_window_cv("Classifier", 0, 512, 512); + if (!benchmark) create_window_cv("Classifier", 0, 512, 512); float fps = 0; int i; + double start_time = get_time_point(); + float avg_fps = 0; + int frame_counter = 0; + while(1){ struct timeval tval_before, tval_after, tval_result; gettimeofday(&tval_before, NULL); //image in = get_image_from_stream(cap); - image in = get_image_from_stream_cpp(cap); - image in_s = resize_image(in, net.w, net.h); - show_image(in, "Classifier"); + image in_s, in; + if (!benchmark) { + in = get_image_from_stream_cpp(cap); + in_s = resize_image(in, net.w, net.h); + show_image(in, "Classifier"); + } + else { + static image tmp; + if (!tmp.data) tmp = make_image(net.w, net.h, 3); + in_s = tmp; + } double time = get_time_point(); float *predictions = network_predict(net, in_s.data); double frame_time_ms = (get_time_point() - time)/1000; + frame_counter++; if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 1); top_predictions(net, top, indexes); @@ -1244,17 +1257,21 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind printf("\033[2J"); printf("\033[1;1H"); #endif - printf("\nFPS: %.2f \n", fps); + printf("\rFPS: %.2f \t AVG_FPS = %.2f ", fps, avg_fps); - for(i = 0; i < top; ++i){ - int index = indexes[i]; - printf("%.1f%%: %s\n", predictions[index]*100, names[index]); - } + if (!benchmark) { + printf("\n"); + for (i = 0; i < top; ++i) { + int index = indexes[i]; + printf("%.1f%%: %s\n", predictions[index] * 100, names[index]); + } - free_image(in_s); - free_image(in); + free_image(in_s); + free_image(in); - wait_key_cv(10);// cvWaitKey(10); + int c = wait_key_cv(10);// cvWaitKey(10); + if (c == 27 || c == 1048603) break; + } //gettimeofday(&tval_after, NULL); //timersub(&tval_after, &tval_before, &tval_result); @@ -1262,6 +1279,14 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind float curr = 1000.f / frame_time_ms; if (fps == 0) fps = curr; else fps = .9*fps + .1*curr; + + float spent_time = (get_time_point() - start_time) / 1000000; + if (spent_time >= 3.0f) { + //printf(" spent_time = %f \n", spent_time); + avg_fps = frame_counter / spent_time; + frame_counter = 0; + start_time = get_time_point(); + } } #endif } @@ -1299,6 +1324,7 @@ void run_classifier(int argc, char **argv) } int dont_show = find_arg(argc, argv, "-dont_show"); + int benchmark = find_arg(argc, argv, "-benchmark"); int dontuse_opencv = find_arg(argc, argv, "-dontuse_opencv"); int show_imgs = find_arg(argc, argv, "-show_imgs"); int calc_topk = find_arg(argc, argv, "-topk"); @@ -1314,7 +1340,7 @@ void run_classifier(int argc, char **argv) if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename, top); else if(0==strcmp(argv[2], "try")) try_classifier(data, cfg, weights, filename, atoi(layer_s)); else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear, dontuse_opencv, dont_show, mjpeg_port, calc_topk, show_imgs); - else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename); + else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename, benchmark); else if(0==strcmp(argv[2], "gun")) gun_classifier(data, cfg, weights, cam_index, filename); else if(0==strcmp(argv[2], "threat")) threat_classifier(data, cfg, weights, cam_index, filename); else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights, layer); From 987448cad8c81fb145824632b5188a4c7e44a0a6 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 28 Dec 2019 20:26:58 +0300 Subject: [PATCH 0773/1365] Minor fix --- src/classifier.c | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index 5d8e6c70833..a5f7e51e1e6 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -1257,14 +1257,15 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind printf("\033[2J"); printf("\033[1;1H"); #endif - printf("\rFPS: %.2f \t AVG_FPS = %.2f ", fps, avg_fps); + if (!benchmark) { - printf("\n"); + printf("\rFPS: %.2f (use -benchmark command line flag for correct measurement)\n", fps); for (i = 0; i < top; ++i) { int index = indexes[i]; printf("%.1f%%: %s\n", predictions[index] * 100, names[index]); } + printf("\n"); free_image(in_s); free_image(in); @@ -1272,6 +1273,9 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind int c = wait_key_cv(10);// cvWaitKey(10); if (c == 27 || c == 1048603) break; } + else { + printf("\rFPS: %.2f \t AVG_FPS = %.2f ", fps, avg_fps); + } //gettimeofday(&tval_after, NULL); //timersub(&tval_after, &tval_before, &tval_result); From 2c08a20b226b882eb47e06c62421d4f4fa785b41 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 28 Dec 2019 23:03:30 +0300 Subject: [PATCH 0774/1365] Added flag -benchmark_layers for: classifier demo, detector test, detector train --- include/darknet.h | 5 ++-- src/classifier.c | 7 +++-- src/darknet.c | 2 +- src/detector.c | 11 +++++--- src/network_kernels.cu | 63 ++++++++++++++++++++++++++++++++++++++---- 5 files changed, 73 insertions(+), 15 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 940d980856c..778123932cf 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -609,6 +609,7 @@ typedef struct network { layer *layers; float *output; learning_rate_policy policy; + int benchmark_layers; float learning_rate; float learning_rate_min; @@ -893,9 +894,9 @@ LIB_API void reset_rnn(network *net); LIB_API float *network_predict_image(network *net, image im); LIB_API float *network_predict_image_letterbox(network *net, image im); LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, const int map_points, int letter_box, network *existing_net); -LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port, int show_imgs); +LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port, int show_imgs, int benchmark_layers); LIB_API void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, - float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile, int letter_box); + float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile, int letter_box, int benchmark_layers); LIB_API int network_width(network *net); LIB_API int network_height(network *net); LIB_API void optimize_picture(network *net, image orig, int max_layer, float scale, float rate, float thresh, int norm); diff --git a/src/classifier.c b/src/classifier.c index a5f7e51e1e6..fdef8373a79 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -1187,7 +1187,7 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde #endif } -void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename, int benchmark) +void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename, int benchmark, int benchmark_layers) { #ifdef OPENCV printf("Classifier Demo\n"); @@ -1195,6 +1195,7 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind if(weightfile){ load_weights(&net, weightfile); } + net.benchmark_layers = benchmark_layers; set_batch_network(&net, 1); list *options = read_data_cfg(datacfg); @@ -1329,6 +1330,8 @@ void run_classifier(int argc, char **argv) int dont_show = find_arg(argc, argv, "-dont_show"); int benchmark = find_arg(argc, argv, "-benchmark"); + int benchmark_layers = find_arg(argc, argv, "-benchmark_layers"); + if (benchmark_layers) benchmark = 1; int dontuse_opencv = find_arg(argc, argv, "-dontuse_opencv"); int show_imgs = find_arg(argc, argv, "-show_imgs"); int calc_topk = find_arg(argc, argv, "-topk"); @@ -1344,7 +1347,7 @@ void run_classifier(int argc, char **argv) if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename, top); else if(0==strcmp(argv[2], "try")) try_classifier(data, cfg, weights, filename, atoi(layer_s)); else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear, dontuse_opencv, dont_show, mjpeg_port, calc_topk, show_imgs); - else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename, benchmark); + else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename, benchmark, benchmark_layers); else if(0==strcmp(argv[2], "gun")) gun_classifier(data, cfg, weights, cam_index, filename); else if(0==strcmp(argv[2], "threat")) threat_classifier(data, cfg, weights, cam_index, filename); else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights, layer); diff --git a/src/darknet.c b/src/darknet.c index f7ac6593cd5..0e42bf21a7a 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -476,7 +476,7 @@ int main(int argc, char **argv) float thresh = find_float_arg(argc, argv, "-thresh", .24); int ext_output = find_arg(argc, argv, "-ext_output"); char *filename = (argc > 4) ? argv[4]: 0; - test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, 0.5, 0, ext_output, 0, NULL, 0); + test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, 0.5, 0, ext_output, 0, NULL, 0, 0); } else if (0 == strcmp(argv[1], "cifar")){ run_cifar(argc, argv); } else if (0 == strcmp(argv[1], "go")){ diff --git a/src/detector.c b/src/detector.c index 6e425df6410..449b5756f8b 100644 --- a/src/detector.c +++ b/src/detector.c @@ -22,7 +22,7 @@ int check_mistakes = 0; static int coco_ids[] = { 1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90 }; -void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port, int show_imgs) +void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port, int show_imgs, int benchmark_layers) { list *options = read_data_cfg(datacfg); char *train_images = option_find_str(options, "train", "data/train.txt"); @@ -73,6 +73,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i cuda_set_device(gpus[i]); #endif nets[i] = parse_network_cfg(cfgfile); + nets[i].benchmark_layers = benchmark_layers; if (weightfile) { load_weights(&nets[i], weightfile); } @@ -1432,7 +1433,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, - float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile, int letter_box) + float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile, int letter_box, int benchmark_layers) { list *options = read_data_cfg(datacfg); char *name_list = option_find_str(options, "names", "data/names.list"); @@ -1444,6 +1445,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam if (weightfile) { load_weights(&net, weightfile); } + net.benchmark_layers = benchmark_layers; fuse_conv_batchnorm(net); calculate_binary_weights(net); if (net.layers[net.n - 1].classes != names_size) { @@ -1587,6 +1589,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam void run_detector(int argc, char **argv) { int dont_show = find_arg(argc, argv, "-dont_show"); + int benchmark_layers = find_arg(argc, argv, "-benchmark_layers"); int show = find_arg(argc, argv, "-show"); int letter_box = find_arg(argc, argv, "-letter_box"); int calc_map = find_arg(argc, argv, "-map"); @@ -1649,8 +1652,8 @@ void run_detector(int argc, char **argv) if (strlen(weights) > 0) if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0; char *filename = (argc > 6) ? argv[6] : 0; - if (0 == strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels, outfile, letter_box); - else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map, mjpeg_port, show_imgs); + if (0 == strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels, outfile, letter_box, benchmark_layers); + else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map, mjpeg_port, show_imgs, benchmark_layers); else if (0 == strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile); else if (0 == strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights); else if (0 == strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh, iou_thresh, map_points, letter_box, NULL); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 133052bdeaa..5386a42067e 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -42,9 +42,34 @@ float * get_network_output_gpu_layer(network net, int i); float * get_network_delta_gpu_layer(network net, int i); float * get_network_output_gpu(network net); +typedef struct time_benchmark_layers { + float time; + int layer_id, layer_type; +} time_benchmark_layers; + +int time_comparator(const void *pa, const void *pb) +{ + time_benchmark_layers a = *(time_benchmark_layers *)pa; + time_benchmark_layers b = *(time_benchmark_layers *)pb; + float diff = a.time - b.time; + if (diff < 0) return 1; + else if (diff > 0) return -1; + return 0; +} + void forward_network_gpu(network net, network_state state) { - //cudaDeviceSynchronize(); + static time_benchmark_layers *avg_time_per_layer = NULL; + static time_benchmark_layers *sorted_avg_time_per_layer = NULL; + double start_time, end_time; + if (net.benchmark_layers) { + if (!avg_time_per_layer) { + avg_time_per_layer = (time_benchmark_layers *)calloc(net.n, sizeof(time_benchmark_layers)); + sorted_avg_time_per_layer = (time_benchmark_layers *)calloc(net.n, sizeof(time_benchmark_layers)); + } + cudaDeviceSynchronize(); + } + //printf("\n"); state.workspace = net.workspace; int i; @@ -54,11 +79,28 @@ void forward_network_gpu(network net, network_state state) if(l.delta_gpu && state.train){ fill_ongpu(l.outputs * l.batch, 0, l.delta_gpu, 1); } - //printf("\n layer %d - type: %d - \n", i, l.type); - //start_timer(); + + if (net.benchmark_layers) { + start_time = get_time_point(); + } + l.forward_gpu(l, state); - //CHECK_CUDA(cudaDeviceSynchronize()); - //stop_timer_and_show(); + + if (net.benchmark_layers) { + CHECK_CUDA(cudaDeviceSynchronize()); + end_time = get_time_point(); + const double took_time = (end_time - start_time) / 1000; + const double alpha = 0.9; + if (avg_time_per_layer[i].time == 0) { + avg_time_per_layer[i].layer_id = i; + avg_time_per_layer[i].layer_type = l.type; + avg_time_per_layer[i].time = took_time; + } + else avg_time_per_layer[i].time = avg_time_per_layer[i].time * alpha + took_time * (1 - alpha); + + sorted_avg_time_per_layer[i] = avg_time_per_layer[i]; + printf("\n layer %d - type: %d - %lf ms - avg_time %lf ms \n", i, l.type, took_time, avg_time_per_layer[i].time); + } if(net.wait_stream) cudaStreamSynchronize(get_cuda_stream()); @@ -83,9 +125,18 @@ void forward_network_gpu(network net, network_state state) } */ } + + if (net.benchmark_layers) { + printf("\n\nSorted by time:\n"); + qsort(sorted_avg_time_per_layer, net.n, sizeof(time_benchmark_layers), time_comparator); + for (i = 0; i < net.n; ++i) { + //printf("\layer %d - type: %d - avg_time %lf ms \n", avg_time_per_layer[i].layer_id, avg_time_per_layer[i].layer_type, avg_time_per_layer[i].time); + printf("\%d - layer %d - type: %d - avg_time %lf ms \n", i, sorted_avg_time_per_layer[i].layer_id, sorted_avg_time_per_layer[i].layer_type, sorted_avg_time_per_layer[i].time); + } + } + //cudaStreamSynchronize(get_cuda_stream()); // sync CUDA-functions //cudaDeviceSynchronize(); - //show_total_time(); } void backward_network_gpu(network net, network_state state) From 7e7964388b17a3d1e5d6a4fd6a69948fbd3387f0 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 29 Dec 2019 14:55:12 +0300 Subject: [PATCH 0775/1365] Added output CUDA, cuDNN, OpenCV versions --- src/dark_cuda.c | 18 ++++++++++++++++++ src/dark_cuda.h | 1 + src/darknet.c | 6 ++++++ src/image_opencv.cpp | 14 ++++++++++++-- src/image_opencv.h | 3 +++ 5 files changed, 40 insertions(+), 2 deletions(-) diff --git a/src/dark_cuda.c b/src/dark_cuda.c index 7f94ac5c2ea..0da67a3c21f 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -475,6 +475,24 @@ int get_gpu_compute_capability(int i) return cc; } +void show_cuda_cudnn_info() +{ + int cuda_version = 0, cuda_driver_version = 0, device_count = 0; + CHECK_CUDA(cudaRuntimeGetVersion(&cuda_version)); + CHECK_CUDA(cudaDriverGetVersion(&cuda_driver_version)); + fprintf(stderr, " CUDA-version: %d (%d)", cuda_version, cuda_driver_version); + if(cuda_version < cuda_driver_version) printf(stderr, "\n Warning: CUDA-version is lower than Driver-version! \n"); +#ifdef CUDNN + fprintf(stderr, ", cuDNN: %d.%d.%d", CUDNN_MAJOR, CUDNN_MINOR, CUDNN_PATCHLEVEL); +#endif // CUDNN +#ifdef CUDNN_HALF + fprintf(stderr, ", CUDNN_HALF=1"); +#endif // CUDNN_HALF + CHECK_CUDA(cudaGetDeviceCount(&device_count)); + fprintf(stderr, ", GPU count: %d ", device_count); + fprintf(stderr, " \n"); +} + #else // GPU #include "darknet.h" void cuda_set_device(int n) {} diff --git a/src/dark_cuda.h b/src/dark_cuda.h index 3c5aef9b870..fe4176cf92e 100644 --- a/src/dark_cuda.h +++ b/src/dark_cuda.h @@ -77,6 +77,7 @@ extern "C" { cudaStream_t get_cuda_memcpy_stream(); int get_number_of_blocks(int array_size, int block_size); int get_gpu_compute_capability(int i); + void show_cuda_cudnn_info(); #ifdef CUDNN cudnnHandle_t cudnn_handle(); diff --git a/src/darknet.c b/src/darknet.c index 0e42bf21a7a..3782271b38e 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -433,6 +433,12 @@ int main(int argc, char **argv) _CrtSetDbgFlag(_CRTDBG_ALLOC_MEM_DF | _CRTDBG_LEAK_CHECK_DF); #endif +#ifdef GPU + show_cuda_cudnn_info(); +#endif// GPU + + show_opencv_info(); + int i; for (i = 0; i < argc; ++i) { if (!argv[i]) continue; diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index d678bd900a1..2853689ff77 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1,4 +1,5 @@ #include "image_opencv.h" +#include #ifdef OPENCV #include "utils.h" @@ -8,7 +9,6 @@ #include #include #include -#include #include #include @@ -1332,10 +1332,20 @@ extern "C" void show_acnhors(int number_of_boxes, int num_of_clusters, float *re cv::destroyAllWindows(); } -} // extern "C" +void show_opencv_info() +{ + std::cerr << " OpenCV version: " << CV_VERSION_MAJOR << "." << CV_VERSION_MINOR << "." << CVAUX_STR(CV_VERSION_REVISION) OCV_D + << std::endl; +} + +} // extern "C" #else // OPENCV +extern "C" void show_opencv_info() +{ + std::cerr << " OpenCV isn't used \n"; +} extern "C" int wait_key_cv(int delay) { return 0; } extern "C" int wait_until_press_key_cv() { return 0; } extern "C" void destroy_all_windows_cv() {} diff --git a/src/image_opencv.h b/src/image_opencv.h index 90037640710..136b7388faa 100644 --- a/src/image_opencv.h +++ b/src/image_opencv.h @@ -111,8 +111,11 @@ image blur_image(image src_img, int ksize); // Show Anchors void show_acnhors(int number_of_boxes, int num_of_clusters, float *rel_width_height_array, model anchors_data, int width, int height); +void show_opencv_info(); + #else // OPENCV +void show_opencv_info(); int wait_key_cv(int delay); int wait_until_press_key_cv(); void destroy_all_windows_cv(); From dcfeea30f195e0ca1210d580cac8b91b6beaf3f7 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 29 Dec 2019 15:12:22 +0300 Subject: [PATCH 0776/1365] Compile fix --- src/dark_cuda.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/dark_cuda.c b/src/dark_cuda.c index 0da67a3c21f..7606be3cd0a 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -481,7 +481,7 @@ void show_cuda_cudnn_info() CHECK_CUDA(cudaRuntimeGetVersion(&cuda_version)); CHECK_CUDA(cudaDriverGetVersion(&cuda_driver_version)); fprintf(stderr, " CUDA-version: %d (%d)", cuda_version, cuda_driver_version); - if(cuda_version < cuda_driver_version) printf(stderr, "\n Warning: CUDA-version is lower than Driver-version! \n"); + if(cuda_version < cuda_driver_version) fprintf(stderr, "\n Warning: CUDA-version is lower than Driver-version! \n"); #ifdef CUDNN fprintf(stderr, ", cuDNN: %d.%d.%d", CUDNN_MAJOR, CUDNN_MINOR, CUDNN_PATCHLEVEL); #endif // CUDNN From 489ea7fdce771144c3ef39d870926c71a1587af5 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 1 Jan 2020 21:38:06 +0300 Subject: [PATCH 0777/1365] Minor fix --- include/darknet.h | 1 + src/network.h | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/include/darknet.h b/include/darknet.h index 778123932cf..43bf23d8618 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -869,6 +869,7 @@ typedef struct box_label { LIB_API network *load_network(char *cfg, char *weights, int clear); LIB_API network *load_network_custom(char *cfg, char *weights, int clear, int batch); LIB_API network *load_network(char *cfg, char *weights, int clear); +LIB_API void free_network(network net); // network.c LIB_API load_args get_base_args(network *net); diff --git a/src/network.h b/src/network.h index 2bee2cb2901..7e51c37cc07 100644 --- a/src/network.h +++ b/src/network.h @@ -108,7 +108,7 @@ float get_current_seq_subdivisions(network net); int get_sequence_value(network net); float get_current_rate(network net); int get_current_batch(network net); -void free_network(network net); +//void free_network(network net); // darknet.h void compare_networks(network n1, network n2, data d); char *get_layer_string(LAYER_TYPE a); From 3ae534e2280bc37741723221844f3aab4bdac5c9 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 2 Jan 2020 23:53:06 +0300 Subject: [PATCH 0778/1365] csresnext50-panet-spp-original-optimal.cfg --- ...csresnext50-panet-spp-original-optimal.cfg | 1048 +++++++++++++++++ ...csresnext50-panet-spp-original-optimal.cfg | 1048 +++++++++++++++++ 2 files changed, 2096 insertions(+) create mode 100644 build/darknet/x64/cfg/csresnext50-panet-spp-original-optimal.cfg create mode 100644 cfg/csresnext50-panet-spp-original-optimal.cfg diff --git a/build/darknet/x64/cfg/csresnext50-panet-spp-original-optimal.cfg b/build/darknet/x64/cfg/csresnext50-panet-spp-original-optimal.cfg new file mode 100644 index 00000000000..02c538dd6c6 --- /dev/null +++ b/build/darknet/x64/cfg/csresnext50-panet-spp-original-optimal.cfg @@ -0,0 +1,1048 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=512 +height=512 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.00261 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +#cutmix=1 +mosaic=1 + +#19:104x104 38:52x52 65:26x26 80:13x13 for 416 + +[convolutional] +batch_normalize=1 +filters=64 +size=7 +stride=2 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +# 1-1 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 1-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 1-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 1-T + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +groups=32 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +# 2-1 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 2-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 2-3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 2-T + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +# 3-1 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 3-2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 3-3 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 3-4 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 3-5 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 3-T + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-24 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +groups=32 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +# 4-1 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 4-2 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 4-T + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-12 + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=leaky + +########################## + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 65 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 38 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +########################## + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 +scale_x_y = 1.2 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +uc_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 +beta1=0.6 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=leaky + +[route] +layers = -1, -16 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 +scale_x_y = 1.1 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +uc_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 +beta1=0.6 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=leaky + +[route] +layers = -1, -37 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 +scale_x_y = 1.05 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +uc_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 +beta1=0.6 diff --git a/cfg/csresnext50-panet-spp-original-optimal.cfg b/cfg/csresnext50-panet-spp-original-optimal.cfg new file mode 100644 index 00000000000..02c538dd6c6 --- /dev/null +++ b/cfg/csresnext50-panet-spp-original-optimal.cfg @@ -0,0 +1,1048 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=512 +height=512 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.00261 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +#cutmix=1 +mosaic=1 + +#19:104x104 38:52x52 65:26x26 80:13x13 for 416 + +[convolutional] +batch_normalize=1 +filters=64 +size=7 +stride=2 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +# 1-1 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 1-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 1-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 1-T + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +groups=32 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +# 2-1 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 2-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 2-3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 2-T + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +# 3-1 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 3-2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 3-3 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 3-4 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 3-5 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 3-T + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-24 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +groups=32 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +# 4-1 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 4-2 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +groups=32 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=linear + +[shortcut] +from=-4 +activation=leaky + +# 4-T + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-12 + +[convolutional] +batch_normalize=1 +filters=2048 +size=1 +stride=1 +pad=1 +activation=leaky + +########################## + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 65 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 38 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +########################## + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 +scale_x_y = 1.2 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +uc_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 +beta1=0.6 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=leaky + +[route] +layers = -1, -16 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 +scale_x_y = 1.1 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +uc_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 +beta1=0.6 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=leaky + +[route] +layers = -1, -37 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 +scale_x_y = 1.05 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +uc_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 +beta1=0.6 From 0be40dbc92541d6545328c693c112af71cababc2 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 3 Jan 2020 00:21:03 +0300 Subject: [PATCH 0779/1365] Update README.md --- README.md | 6 +++++- .../x64/cfg/csresnext50-panet-spp-original-optimal.cfg | 4 ++-- cfg/csresnext50-panet-spp-original-optimal.cfg | 4 ++-- 3 files changed, 9 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 4129fbb4952..7dde9dd5a0f 100644 --- a/README.md +++ b/README.md @@ -48,7 +48,9 @@ More details: http://pjreddie.com/darknet/yolo/ There are weights-file for different cfg-files (trained for MS COCO dataset): -* [csresnext50-panet-spp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/csresnext50-panet-spp.cfg) - **60.0% mAP@0.5 - 44 FPS** - 71.3 BFlops - 217 MB: [csresnext50-panet-spp_final.weights](https://drive.google.com/file/d/1aNXdM8qVy11nqTcd2oaVB3mf7ckr258-/view?usp=sharing) +FPS on GeForce 1080Ti: + +* [csresnext50-panet-spp-original-optimal.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/csresnext50-panet-spp-original-optimal.cfg) - **65.4% mAP@0.5 (43.2% AP@0.5..0.95) - 35 FPS** - 100.5 BFlops - 217 MB: [csresnext50-panet-spp-original-optimal_final.weights](https://drive.google.com/open?id=1_NnfVgj0EDtb_WLNoXV8Mo7WKgwdYZCc) * [yolov3-spp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-spp.cfg) - **60.6% mAP@0.5 - 30 FPS** - 141.5 BFlops - 240 MB: [yolov3-spp.weights](https://pjreddie.com/media/files/yolov3-spp.weights) @@ -60,6 +62,8 @@ There are weights-file for different cfg-files (trained for MS COCO dataset):
CLICK ME - Yolo v3 models +* [csresnext50-panet-spp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/csresnext50-panet-spp.cfg) - **60.0% mAP@0.5 - 44 FPS** - 71.3 BFlops - 217 MB: [csresnext50-panet-spp_final.weights](https://drive.google.com/file/d/1aNXdM8qVy11nqTcd2oaVB3mf7ckr258-/view?usp=sharing) + * [yolov3.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3.cfg) - **55.3% mAP@0.5 - 46 FPS** - 65.9 BFlops - 236 MB: [yolov3.weights](https://pjreddie.com/media/files/yolov3.weights) * [yolov3-tiny.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny.cfg) - **33.1% mAP@0.5 - 330 FPS** - 5.6 BFlops - 33.7 MB: [yolov3-tiny.weights](https://pjreddie.com/media/files/yolov3-tiny.weights) diff --git a/build/darknet/x64/cfg/csresnext50-panet-spp-original-optimal.cfg b/build/darknet/x64/cfg/csresnext50-panet-spp-original-optimal.cfg index 02c538dd6c6..5722cf69828 100644 --- a/build/darknet/x64/cfg/csresnext50-panet-spp-original-optimal.cfg +++ b/build/darknet/x64/cfg/csresnext50-panet-spp-original-optimal.cfg @@ -5,8 +5,8 @@ # Training batch=64 subdivisions=8 -width=512 -height=512 +width=608 +height=608 channels=3 momentum=0.949 decay=0.0005 diff --git a/cfg/csresnext50-panet-spp-original-optimal.cfg b/cfg/csresnext50-panet-spp-original-optimal.cfg index 02c538dd6c6..5722cf69828 100644 --- a/cfg/csresnext50-panet-spp-original-optimal.cfg +++ b/cfg/csresnext50-panet-spp-original-optimal.cfg @@ -5,8 +5,8 @@ # Training batch=64 subdivisions=8 -width=512 -height=512 +width=608 +height=608 channels=3 momentum=0.949 decay=0.0005 From cd1c7c32af3f40610aa8ed4c0a3d6ebfdd958460 Mon Sep 17 00:00:00 2001 From: Alexey Date: Fri, 3 Jan 2020 04:33:28 +0300 Subject: [PATCH 0780/1365] Update Readme.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7dde9dd5a0f..632f896272b 100644 --- a/README.md +++ b/README.md @@ -36,7 +36,7 @@ More details: http://pjreddie.com/darknet/yolo/ 11. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) 12. [How to use Yolo as DLL and SO libraries](#how-to-use-yolo-as-dll-and-so-libraries) -| ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) |   ![map_fps](https://user-images.githubusercontent.com/4096485/71088138-d5032280-21ae-11ea-87ff-4c8695c53a28.png) mAP@0.5 (AP50) - FPS (GeForce 1080 Ti) https://arxiv.org/abs/1911.11929 https://github.com/WongKinYiu/CrossStagePartialNetworks - more models | +| ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) |   ![map_fps](https://user-images.githubusercontent.com/4096485/71702416-6645dc00-2de0-11ea-8d65-de7d4b604021.png) mAP@0.5 (AP50) - FPS (GeForce 1080 Ti) https://arxiv.org/abs/1911.11929 https://github.com/WongKinYiu/CrossStagePartialNetworks - more models | |---|---| * Yolo v3 on MS COCO: [Speed / Accuracy (mAP@0.5) chart](https://user-images.githubusercontent.com/4096485/52151356-e5d4a380-2683-11e9-9d7d-ac7bc192c477.jpg) From b1c7331483cc85cd5e9ae236c2b53f522b68416f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 4 Jan 2020 15:43:55 +0300 Subject: [PATCH 0781/1365] Added fast random functions --- src/dropout_layer_kernels.cu | 8 +++--- src/utils.c | 51 +++++++++++++++++++++++++++++++++--- src/utils.h | 3 +++ 3 files changed, 55 insertions(+), 7 deletions(-) diff --git a/src/dropout_layer_kernels.cu b/src/dropout_layer_kernels.cu index 79ed37d96c7..09493bb068b 100644 --- a/src/dropout_layer_kernels.cu +++ b/src/dropout_layer_kernels.cu @@ -59,12 +59,14 @@ void forward_dropout_layer_gpu(dropout_layer l, network_state state) for (k = 0; k < l.c; k++) { for (i = 0; i < max_blocks_per_channel; i++) { float rnd = random_float(); - //printf(" rnd = %f \n", rnd); + printf(" rnd = %f \n", rnd); + //int rn = rand_int_fast(1, 7); + //printf(" rnd = %d \n", rn); if (rnd < prob_place_block) { //count_ones += block_width *block_height; const int pre_index = k*l.w*l.h + b*l.w*l.h*l.c; - const int x_block = rand_int(0, l.w - block_width - 1); - const int y_block = rand_int(0, l.h - block_height - 1); + const int x_block = rand_int_fast(0, l.w - block_width - 1); + const int y_block = rand_int_fast(0, l.h - block_height - 1); for (y = y_block; y < (y_block + block_height); y++) { memset(&l.rand[x_block + y*l.w + pre_index], 1, block_width * sizeof(float)); //for (x = x_block; x < (x_block + block_width); x++) { diff --git a/src/utils.c b/src/utils.c index 4d8e439d476..0e3a76fc19b 100644 --- a/src/utils.c +++ b/src/utils.c @@ -813,6 +813,40 @@ float **one_hot_encode(float *a, int n, int k) return t; } +static unsigned int x = 123456789, y = 362436069, z = 521288629; + +// Marsaglia's xorshf96 generator: period 2^96-1 +unsigned int random_gen_fast(void) +{ + unsigned int t; + x ^= x << 16; + x ^= x >> 5; + x ^= x << 1; + + t = x; + x = y; + y = z; + z = t ^ x ^ y; + + return z; +} + +float random_float_fast() +{ + return ((float)random_gen_fast() / (float)UINT_MAX); +} + +int rand_int_fast(int min, int max) +{ + if (max < min) { + int s = min; + min = max; + max = s; + } + int r = (random_gen_fast() % (max - min + 1)) + min; + return r; +} + unsigned int random_gen() { unsigned int rnd = 0; @@ -829,11 +863,20 @@ unsigned int random_gen() float random_float() { + unsigned int rnd = 0; #ifdef WIN32 - return ((float)random_gen() / (float)UINT_MAX); -#else - return ((float)random_gen() / (float)RAND_MAX); -#endif + rand_s(&rnd); + return ((float)rnd / (float)UINT_MAX); +#else // WIN32 + + rnd = rand(); +#if (RAND_MAX < 65536) + rnd = rand()*(RAND_MAX + 1) + rnd; + return((float)rnd / (float)(RAND_MAX*RAND_MAX)); +#endif //(RAND_MAX < 65536) + return ((float)rnd / (float)RAND_MAX); + +#endif // WIN32 } float rand_uniform_strong(float min, float max) diff --git a/src/utils.h b/src/utils.h index 421a313dffc..284e4556a01 100644 --- a/src/utils.h +++ b/src/utils.h @@ -73,6 +73,9 @@ char *find_char_arg(int argc, char **argv, char *arg, char *def); int sample_array(float *a, int n); int sample_array_custom(float *a, int n); void print_statistics(float *a, int n); +unsigned int random_gen_fast(void); +float random_float_fast(); +int rand_int_fast(int min, int max); unsigned int random_gen(); float random_float(); float rand_uniform_strong(float min, float max); From 3c134dc033ceaea9747b3f4ae673ee10c2c31c8e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 4 Jan 2020 15:47:15 +0300 Subject: [PATCH 0782/1365] Added fast random functions --- src/dropout_layer_kernels.cu | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/dropout_layer_kernels.cu b/src/dropout_layer_kernels.cu index 09493bb068b..a01bb9c6fe3 100644 --- a/src/dropout_layer_kernels.cu +++ b/src/dropout_layer_kernels.cu @@ -58,8 +58,8 @@ void forward_dropout_layer_gpu(dropout_layer l, network_state state) for (b = 0; b < l.batch; b++) { for (k = 0; k < l.c; k++) { for (i = 0; i < max_blocks_per_channel; i++) { - float rnd = random_float(); - printf(" rnd = %f \n", rnd); + float rnd = random_float_fast(); + //printf(" rnd = %f \n", rnd); //int rn = rand_int_fast(1, 7); //printf(" rnd = %d \n", rn); if (rnd < prob_place_block) { From 2652263727a7036dfb587fbda98bd30181d8a616 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 5 Jan 2020 00:53:03 +0300 Subject: [PATCH 0783/1365] Added flags -benchmark and -benchmark_layers for: ./darknet detector demo --- src/coco.c | 2 +- src/demo.c | 39 +++++++++++++++++++++++++++------------ src/demo.h | 2 +- src/detector.c | 5 ++++- src/yolo.c | 2 +- 5 files changed, 34 insertions(+), 16 deletions(-) diff --git a/src/coco.c b/src/coco.c index ce698e65bf2..52787d6de4a 100644 --- a/src/coco.c +++ b/src/coco.c @@ -396,5 +396,5 @@ void run_coco(int argc, char **argv) else if(0==strcmp(argv[2], "valid")) validate_coco(cfg, weights); else if(0==strcmp(argv[2], "recall")) validate_coco_recall(cfg, weights); else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, hier_thresh, cam_index, filename, coco_classes, 80, frame_skip, - prefix, out_filename, mjpeg_port, json_port, dont_show, ext_output, 0, 0, 0); + prefix, out_filename, mjpeg_port, json_port, dont_show, ext_output, 0, 0, 0, 0, 0); } diff --git a/src/demo.c b/src/demo.c index bb482c29385..6f196cdabe8 100644 --- a/src/demo.c +++ b/src/demo.c @@ -80,8 +80,6 @@ void *detect_in_thread(void *ptr) mean_arrays(predictions, NFRAMES, l.outputs, avg); l.output = avg; - free_image(det_s); - cv_images[demo_index] = det_img; det_img = cv_images[(demo_index + NFRAMES / 2 + 1) % NFRAMES]; demo_index = (demo_index + 1) % NFRAMES; @@ -104,7 +102,8 @@ double get_wall_time() } void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, - int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output, int letter_box_in, int time_limit_sec, char *http_post_host) + int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output, int letter_box_in, int time_limit_sec, char *http_post_host, + int benchmark, int benchmark_layers) { letter_box = letter_box_in; in_img = det_img = show_img = NULL; @@ -122,6 +121,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int if(weightfile){ load_weights(&net, weightfile); } + net.benchmark_layers = benchmark_layers; fuse_conv_batchnorm(net); calculate_binary_weights(net); srand(2222222); @@ -202,7 +202,10 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int int send_http_post_once = 0; const double start_time_lim = get_time_point(); - double before = get_wall_time(); + double before = get_time_point(); + double start_time = get_time_point(); + float avg_fps = 0; + int frame_counter = 0; while(1){ ++count; @@ -211,7 +214,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int int local_nboxes = nboxes; detection *local_dets = dets; - if(pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed"); + if (!benchmark) if (pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed"); if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed"); //if (nms) do_nms_obj(local_dets, local_nboxes, l.classes, nms); // bad results @@ -242,10 +245,10 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int } } - draw_detections_cv_v3(show_img, local_dets, local_nboxes, demo_thresh, demo_names, demo_alphabet, demo_classes, demo_ext_output); + if (!benchmark) draw_detections_cv_v3(show_img, local_dets, local_nboxes, demo_thresh, demo_names, demo_alphabet, demo_classes, demo_ext_output); free_detections(local_dets, local_nboxes); - printf("\nFPS:%.1f\n", fps); + printf("\nFPS:%.1f \t AVG_FPS:%.1f\n", fps, avg_fps); if(!prefix){ if (!dont_show) { @@ -282,9 +285,11 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int printf("\n cvWriteFrame \n"); } - - pthread_join(fetch_thread, 0); pthread_join(detect_thread, 0); + if (!benchmark) { + pthread_join(fetch_thread, 0); + free_image(det_s); + } if (time_limit_sec > 0 && (get_time_point() - start_time_lim)/1000000 > time_limit_sec) { printf(" start_time_lim = %f, get_time_point() = %f, time spent = %f \n", start_time_lim, get_time_point(), get_time_point() - start_time_lim); @@ -294,7 +299,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int if (flag_exit == 1) break; if(delay == 0){ - release_mat(&show_img); + if(!benchmark) release_mat(&show_img); show_img = det_img; } det_img = in_img; @@ -308,8 +313,17 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int //float curr = 1./(after - before); double after = get_time_point(); // more accurate time measurements float curr = 1000000. / (after - before); - fps = curr; + fps = fps*0.9 + curr*0.1; before = after; + + float spent_time = (get_time_point() - start_time) / 1000000; + frame_counter++; + if (spent_time >= 3.0f) { + //printf(" spent_time = %f \n", spent_time); + avg_fps = frame_counter / spent_time; + frame_counter = 0; + start_time = get_time_point(); + } } } printf("input video stream closed. \n"); @@ -345,7 +359,8 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int } #else void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, - int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output, int letter_box_in, int time_limit_sec, char *http_post_host) + int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output, int letter_box_in, int time_limit_sec, char *http_post_host, + int benchmark, int benchmark_layers) { fprintf(stderr, "Demo needs OpenCV for webcam images.\n"); } diff --git a/src/demo.h b/src/demo.h index c49a9017472..12781930243 100644 --- a/src/demo.h +++ b/src/demo.h @@ -6,7 +6,7 @@ extern "C" { #endif void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, - int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output, int letter_box_in, int time_limit_sec, char *http_post_host); + int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int json_port, int dont_show, int ext_output, int letter_box_in, int time_limit_sec, char *http_post_host, int benchmark, int benchmark_layers); #ifdef __cplusplus } #endif diff --git a/src/detector.c b/src/detector.c index 449b5756f8b..bb059c77a28 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1589,7 +1589,10 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam void run_detector(int argc, char **argv) { int dont_show = find_arg(argc, argv, "-dont_show"); + int benchmark = find_arg(argc, argv, "-benchmark"); int benchmark_layers = find_arg(argc, argv, "-benchmark_layers"); + if (benchmark_layers) benchmark = 1; + if (benchmark) dont_show = 1; int show = find_arg(argc, argv, "-show"); int letter_box = find_arg(argc, argv, "-letter_box"); int calc_map = find_arg(argc, argv, "-map"); @@ -1667,7 +1670,7 @@ void run_detector(int argc, char **argv) if (strlen(filename) > 0) if (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0; demo(cfg, weights, thresh, hier_thresh, cam_index, filename, names, classes, frame_skip, prefix, out_filename, - mjpeg_port, json_port, dont_show, ext_output, letter_box, time_limit_sec, http_post_host); + mjpeg_port, json_port, dont_show, ext_output, letter_box, time_limit_sec, http_post_host, benchmark, benchmark_layers); free_list_contents_kvp(options); free_list(options); diff --git a/src/yolo.c b/src/yolo.c index 5f918e50e49..c370eb0a2a0 100644 --- a/src/yolo.c +++ b/src/yolo.c @@ -359,5 +359,5 @@ void run_yolo(int argc, char **argv) else if(0==strcmp(argv[2], "valid")) validate_yolo(cfg, weights); else if(0==strcmp(argv[2], "recall")) validate_yolo_recall(cfg, weights); else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, hier_thresh, cam_index, filename, voc_names, 20, frame_skip, - prefix, out_filename, mjpeg_port, json_port, dont_show, ext_output, 0, 0, 0); + prefix, out_filename, mjpeg_port, json_port, dont_show, ext_output, 0, 0, 0, 0, 0); } From 8bd7dcbd581b666b61306012efc1de2f99050054 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 5 Jan 2020 14:04:11 +0300 Subject: [PATCH 0784/1365] Minor fix --- src/data.c | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/data.c b/src/data.c index 6fa37846804..c83c687709f 100644 --- a/src/data.c +++ b/src/data.c @@ -948,6 +948,10 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo c = c ? c : 3; assert(use_mixup != 2); + if (use_mixup == 3 && letter_box) { + printf("\n Combination: letter_box=1 & mosaic=1 - isn't supported, use only 1 of these parameters \n"); + exit(0); + } if (random_gen() % 2 == 0) use_mixup = 0; int i; From b267e34487231a401e79699c24294072096ae3f9 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 5 Jan 2020 14:55:21 +0300 Subject: [PATCH 0785/1365] multilayer [shortcut] in progress --- build/darknet/x64/partial.cmd | 4 ++ include/darknet.h | 6 +++ src/blas.c | 54 +++++++++++++++++++ src/blas.h | 5 ++ src/blas_kernels.cu | 69 ++++++++++++++++++++++++ src/dark_cuda.c | 15 ++++++ src/dark_cuda.h | 1 + src/layer.c | 5 ++ src/network.c | 2 +- src/network_kernels.cu | 4 +- src/parser.c | 51 +++++++++++++++--- src/shortcut_layer.c | 98 ++++++++++++++++++++++++----------- src/shortcut_layer.h | 5 +- 13 files changed, 277 insertions(+), 42 deletions(-) diff --git a/build/darknet/x64/partial.cmd b/build/darknet/x64/partial.cmd index 5aa972f36fa..79292b50cbd 100644 --- a/build/darknet/x64/partial.cmd +++ b/build/darknet/x64/partial.cmd @@ -6,6 +6,10 @@ rem Download Yolo9000: http://pjreddie.com/media/files/yolo9000.weights rem darknet.exe partial cfg/tiny-yolo-voc.cfg tiny-yolo-voc.weights tiny-yolo-voc.conv.13 13 + +darknet.exe partial cfg/csresnext50.cfg csresnext50.weights csresnext50.conv.75 75 + + darknet.exe partial cfg/darknet53_448.cfg darknet53_448.weights darknet53.conv.74 74 diff --git a/include/darknet.h b/include/darknet.h index 43bf23d8618..bd85e880594 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -328,6 +328,8 @@ struct layer { int * indexes; int * input_layers; int * input_sizes; + float **layers_output; + float **layers_delta; int * map; int * counts; float ** sums; @@ -575,6 +577,10 @@ struct layer { float *gt_gpu; float *a_avg_gpu; + + int *input_sizes_gpu; + float **layers_output_gpu; + float **layers_delta_gpu; #ifdef CUDNN cudnnTensorDescriptor_t srcTensorDesc, dstTensorDesc; cudnnTensorDescriptor_t srcTensorDesc16, dstTensorDesc16; diff --git a/src/blas.c b/src/blas.c index d00cb89ddf0..9533f74da39 100644 --- a/src/blas.c +++ b/src/blas.c @@ -68,6 +68,60 @@ void weighted_delta_cpu(float *a, float *b, float *s, float *da, float *db, floa } } +void shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *outputs_of_layers, float **layers_output, float *out, float *in) +{ + int id; + #pragma omp parallel for + for (id = 0; id < size; ++id) { + + int src_id = id; + int src_i = src_id % src_outputs; + src_id /= src_outputs; + int src_b = src_id; + + out[id] = in[id]; + + // layers + for (int i = 0; i < n; ++i) { + int add_outputs = outputs_of_layers[i]; + if (src_i < add_outputs) { + int add_index = add_outputs*src_b + src_i; + int out_index = id; + + float *add = layers_output[i]; + out[out_index] += add[add_index]; + } + } + } +} + +void backward_shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *outputs_of_layers, + float **layers_delta, float *delta_out, float *delta_in) +{ + int id; + #pragma omp parallel for + for (id = 0; id < size; ++id) { + int src_id = id; + int src_i = src_id % src_outputs; + src_id /= src_outputs; + int src_b = src_id; + + delta_out[id] += delta_in[id]; + + // layers + for (int i = 0; i < n; ++i) { + int add_outputs = outputs_of_layers[i]; + if (src_i < add_outputs) { + int add_index = add_outputs*src_b + src_i; + int out_index = id; + + float *layer_delta = layers_delta[i]; + layer_delta[add_index] += delta_in[id]; + } + } + } +} + void shortcut_cpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out) { int stride = w1/w2; diff --git a/src/blas.h b/src/blas.h index ebe7fb0e981..a2f7e5d4ff6 100644 --- a/src/blas.h +++ b/src/blas.h @@ -32,6 +32,9 @@ void fill_cpu(int N, float ALPHA, float * X, int INCX); float dot_cpu(int N, float *X, int INCX, float *Y, int INCY); void test_gpu_blas(); void shortcut_cpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out); +void shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *outputs_of_layers, float **layers_output, float *out, float *in); +void backward_shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *outputs_of_layers, + float **layers_delta, float *delta_out, float *delta_in); void mean_cpu(float *x, int batch, int filters, int spatial, float *mean); void variance_cpu(float *x, float *mean, int batch, int filters, int spatial, float *variance); @@ -84,6 +87,8 @@ void fast_variance_delta_gpu(float *x, float *delta, float *mean, float *varianc void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance); void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean); void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out); +void shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in); +void backward_shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_delta_gpu, float *delta_out, float *delta_in); void input_shortcut_gpu(float *in, int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out); void scale_bias_gpu(float *output, float *biases, int batch, int n, int size); void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates); diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 7f84f33707c..d1ee4b804ec 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -668,6 +668,75 @@ extern "C" void fill_ongpu(int N, float ALPHA, float * X, int INCX) CHECK_CUDA(cudaPeekAtLastError()); } +__global__ void shortcut_multilayer_kernel(int size, int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in) +{ + const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= size) return; + + int src_id = id; + int src_i = src_id % src_outputs; + src_id /= src_outputs; + int src_b = src_id; + + out[id] = in[id]; + + // layers + for (int i = 0; i < n; ++i) { + int add_outputs = outputs_of_layers_gpu[i]; + if (src_i < add_outputs) { + int add_index = add_outputs*src_b + src_i; + int out_index = id; + + float *add = layers_output_gpu[i]; + out[out_index] += add[add_index]; + } + } +} + +extern "C" void shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in) +{ + //printf(" src_outputs = %d, batch = %d, n = %d \n", src_outputs, batch, n); + int size = batch * src_outputs; + shortcut_multilayer_kernel << > > (size, src_outputs, batch, n, outputs_of_layers_gpu, layers_output_gpu, out, in); + CHECK_CUDA(cudaPeekAtLastError()); +} + + + +__global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, int batch, int n, int *outputs_of_layers_gpu, + float **layers_delta_gpu, float *delta_out, float *delta_in) +{ + const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= size) return; + + int src_id = id; + int src_i = src_id % src_outputs; + src_id /= src_outputs; + int src_b = src_id; + + delta_out[id] += delta_in[id]; + + // layers + for (int i = 0; i < n; ++i) { + int add_outputs = outputs_of_layers_gpu[i]; + if (src_i < add_outputs) { + int add_index = add_outputs*src_b + src_i; + int out_index = id; + + float *layer_delta = layers_delta_gpu[i]; + layer_delta[add_index] += delta_in[id]; + } + } +} + +extern "C" void backward_shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_delta_gpu, float *delta_out, float *delta_in) +{ + //printf(" src_outputs = %d, batch = %d, n = %d \n", src_outputs, batch, n); + int size = batch * src_outputs; + backward_shortcut_multilayer_kernel << > > (size, src_outputs, batch, n, outputs_of_layers_gpu, layers_delta_gpu, delta_out, delta_in); + CHECK_CUDA(cudaPeekAtLastError()); +} + __global__ void shortcut_kernel(int size, int minw, int minh, int minc, int stride, int sample, int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out) { int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; diff --git a/src/dark_cuda.c b/src/dark_cuda.c index 7606be3cd0a..e7b6e6afab0 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -369,6 +369,21 @@ float *cuda_make_array(float *x, size_t n) return x_gpu; } +void **cuda_make_array_pointers(void **x, size_t n) +{ + void **x_gpu; + size_t size = sizeof(void*) * n; + cudaError_t status = cudaMalloc((void **)&x_gpu, size); + if (status != cudaSuccess) fprintf(stderr, " Try to set subdivisions=64 in your cfg-file. \n"); + CHECK_CUDA(status); + if (x) { + status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyDefault, get_cuda_stream()); + CHECK_CUDA(status); + } + if (!x_gpu) error("Cuda malloc failed\n"); + return x_gpu; +} + void cuda_random(float *x_gpu, size_t n) { static curandGenerator_t gen[16]; diff --git a/src/dark_cuda.h b/src/dark_cuda.h index fe4176cf92e..4798d729c73 100644 --- a/src/dark_cuda.h +++ b/src/dark_cuda.h @@ -62,6 +62,7 @@ extern "C" { float *cuda_make_array_pinned_preallocated(float *x, size_t n); float *cuda_make_array_pinned(float *x, size_t n); float *cuda_make_array(float *x, size_t n); + void **cuda_make_array_pointers(void **x, size_t n); int *cuda_make_int_array(size_t n); int *cuda_make_int_array_new_api(int *x, size_t n); void cuda_push_array(float *x_gpu, float *x, size_t n); diff --git a/src/layer.c b/src/layer.c index 2ae781d0628..1acc4bf9b8f 100644 --- a/src/layer.c +++ b/src/layer.c @@ -65,6 +65,8 @@ void free_layer_custom(layer l, int keep_cudnn_desc) if (l.indexes) free(l.indexes); if (l.input_layers) free(l.input_layers); if (l.input_sizes) free(l.input_sizes); + if (l.layers_output) free(l.layers_output); + if (l.layers_delta) free(l.layers_delta); if (l.map) free(l.map); if (l.rand) free(l.rand); if (l.cost) free(l.cost); @@ -190,6 +192,9 @@ void free_layer_custom(layer l, int keep_cudnn_desc) if (l.rand_gpu) cuda_free(l.rand_gpu); if (l.squared_gpu) cuda_free(l.squared_gpu); if (l.norms_gpu) cuda_free(l.norms_gpu); + if (l.input_sizes_gpu) cuda_free(l.input_sizes_gpu); + if (l.layers_output_gpu) cuda_free(l.layers_output_gpu); + if (l.layers_delta_gpu) cuda_free(l.layers_delta_gpu); // CONV-LSTM if (l.f_gpu) cuda_free(l.f_gpu); diff --git a/src/network.c b/src/network.c index 0a2117a88d4..0de38420a4f 100644 --- a/src/network.c +++ b/src/network.c @@ -538,7 +538,7 @@ int resize_network(network *net, int w, int h) }else if(l.type == ROUTE){ resize_route_layer(&l, net); }else if (l.type == SHORTCUT) { - resize_shortcut_layer(&l, w, h); + resize_shortcut_layer(&l, w, h, net); }else if (l.type == SCALE_CHANNELS) { resize_scale_channels_layer(&l, net); }else if (l.type == DROPOUT) { diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 5386a42067e..2146f0ae878 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -130,8 +130,8 @@ void forward_network_gpu(network net, network_state state) printf("\n\nSorted by time:\n"); qsort(sorted_avg_time_per_layer, net.n, sizeof(time_benchmark_layers), time_comparator); for (i = 0; i < net.n; ++i) { - //printf("\layer %d - type: %d - avg_time %lf ms \n", avg_time_per_layer[i].layer_id, avg_time_per_layer[i].layer_type, avg_time_per_layer[i].time); - printf("\%d - layer %d - type: %d - avg_time %lf ms \n", i, sorted_avg_time_per_layer[i].layer_id, sorted_avg_time_per_layer[i].layer_type, sorted_avg_time_per_layer[i].time); + //printf("layer %d - type: %d - avg_time %lf ms \n", avg_time_per_layer[i].layer_id, avg_time_per_layer[i].layer_type, avg_time_per_layer[i].time); + printf("%d - layer %d - type: %d - avg_time %lf ms \n", i, sorted_avg_time_per_layer[i].layer_id, sorted_avg_time_per_layer[i].layer_type, sorted_avg_time_per_layer[i].time); } } diff --git a/src/parser.c b/src/parser.c index ec9e30076bf..d4b13ccb3ee 100644 --- a/src/parser.c +++ b/src/parser.c @@ -817,15 +817,54 @@ layer parse_shortcut(list *options, size_params params, network net) ACTIVATION activation = get_activation(activation_s); int assisted_excitation = option_find_float_quiet(options, "assisted_excitation", 0); + //char *l = option_find(options, "from"); + //int index = atoi(l); + //if(index < 0) index = params.index + index; + char *l = option_find(options, "from"); - int index = atoi(l); - if(index < 0) index = params.index + index; + int len = strlen(l); + if (!l) error("Route Layer must specify input layers: from = ..."); + int n = 1; + int i; + for (i = 0; i < len; ++i) { + if (l[i] == ',') ++n; + } - int batch = params.batch; - layer from = net.layers[index]; - if (from.antialiasing) from = *from.input_layer; + int* layers = (int*)calloc(n, sizeof(int)); + int* sizes = (int*)calloc(n, sizeof(int)); + float **layers_output = (float **)calloc(n, sizeof(float *)); + float **layers_delta = (float **)calloc(n, sizeof(float *)); + float **layers_output_gpu = (float **)calloc(n, sizeof(float *)); + float **layers_delta_gpu = (float **)calloc(n, sizeof(float *)); + + for (i = 0; i < n; ++i) { + int index = atoi(l); + l = strchr(l, ',') + 1; + if (index < 0) index = params.index + index; + layers[i] = index; + sizes[i] = params.net.layers[index].outputs; + layers_output[i] = params.net.layers[index].output; + layers_delta[i] = params.net.layers[index].delta; + + } + +#ifdef GPU + for (i = 0; i < n; ++i) { + layers_output_gpu[i] = params.net.layers[layers[i]].output_gpu; + layers_delta_gpu[i] = params.net.layers[layers[i]].delta_gpu; + } +#endif// GPU + + layer s = make_shortcut_layer(params.batch, n, layers, sizes, params.w, params.h, params.c, layers_output, layers_delta, layers_output_gpu, layers_delta_gpu, activation, params.train); - layer s = make_shortcut_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c, assisted_excitation, activation, params.train); + for (i = 0; i < n; ++i) { + int index = layers[i]; + assert(params.w == net.layers[index].out_w && params.h == net.layers[index].out_h); + + if (params.w != net.layers[index].out_w || params.h != net.layers[index].out_h || params.c != net.layers[index].out_c) + fprintf(stderr, " w = %d, w2 = %d, h = %d, h2 = %d, c = %d, c2 = %d \n", + params.w, net.layers[index].out_w, params.h, net.layers[index].out_h, params.c, params.net.layers[index].out_c); + } return s; } diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index f9b9209a667..f87d6b61d87 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -6,29 +6,36 @@ #include #include -layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2, int assisted_excitation, ACTIVATION activation, int train) +layer make_shortcut_layer(int batch, int n, int *input_layers, int* input_sizes, int w, int h, int c, + float **layers_output, float **layers_delta, float **layers_output_gpu, float **layers_delta_gpu, ACTIVATION activation, int train) { - if(assisted_excitation) fprintf(stderr, "Shortcut Layer - AE: %d\n", index); - else fprintf(stderr,"Shortcut Layer: %d\n", index); + fprintf(stderr, "Shortcut Layer: "); + int i; + for(i = 0; i < n; ++i) fprintf(stderr, "%d, ", input_layers[i]); + layer l = { (LAYER_TYPE)0 }; l.train = train; l.type = SHORTCUT; l.batch = batch; l.activation = activation; - l.w = w2; - l.h = h2; - l.c = c2; - l.out_w = w; - l.out_h = h; - l.out_c = c; + l.n = n; + l.input_layers = input_layers; + l.input_sizes = input_sizes; + l.layers_output = layers_output; + l.layers_delta = layers_delta; + + //l.w = w2; + //l.h = h2; + //l.c = c2; + l.w = l.out_w = w; + l.h = l.out_h = h; + l.c = l.out_c = c; l.outputs = w*h*c; l.inputs = l.outputs; - l.assisted_excitation = assisted_excitation; - - if(w != w2 || h != h2 || c != c2) fprintf(stderr, " w = %d, w2 = %d, h = %d, h2 = %d, c = %d, c2 = %d \n", w, w2, h, h2, c, c2); + //if(w != w2 || h != h2 || c != c2) fprintf(stderr, " w = %d, w2 = %d, h = %d, h2 = %d, c = %d, c2 = %d \n", w, w2, h, h2, c, c2); - l.index = index; + l.index = l.input_layers[0]; if (train) l.delta = (float*)calloc(l.outputs * batch, sizeof(float)); l.output = (float*)calloc(l.outputs * batch, sizeof(float)); @@ -47,17 +54,18 @@ layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int if (train) l.delta_gpu = cuda_make_array(l.delta, l.outputs*batch); l.output_gpu = cuda_make_array(l.output, l.outputs*batch); - if (l.assisted_excitation) - { - const int size = l.out_w * l.out_h * l.batch; - l.gt_gpu = cuda_make_array(NULL, size); - l.a_avg_gpu = cuda_make_array(NULL, size); - } + + l.input_sizes_gpu = cuda_make_array(input_sizes, l.n); + l.layers_output_gpu = cuda_make_array_pointers(layers_output_gpu, l.n); + l.layers_delta_gpu = cuda_make_array_pointers(layers_delta_gpu, l.n); #endif // GPU + + l.bflops = l.out_w * l.out_h * l.out_c * l.n / 1000000000.; + fprintf(stderr, " outputs:%4d x%4d x%4d %5.3f BF\n", l.out_w, l.out_h, l.out_c, l.bflops); return l; } -void resize_shortcut_layer(layer *l, int w, int h) +void resize_shortcut_layer(layer *l, int w, int h, network *net) { //assert(l->w == l->out_w); //assert(l->h == l->out_h); @@ -68,6 +76,13 @@ void resize_shortcut_layer(layer *l, int w, int h) if (l->train) l->delta = (float*)realloc(l->delta, l->outputs * l->batch * sizeof(float)); l->output = (float*)realloc(l->output, l->outputs * l->batch * sizeof(float)); + int i; + for (i = 0; i < l->n; ++i) { + int index = l->input_layers[i]; + l->input_sizes[i] = net->layers[index].outputs; + assert(l->w == net->layers[index].w && l->h == net->layers[index].h); + } + #ifdef GPU cuda_free(l->output_gpu); l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch); @@ -82,7 +97,11 @@ void resize_shortcut_layer(layer *l, int w, int h) void forward_shortcut_layer(const layer l, network_state state) { - if (l.w == l.out_w && l.h == l.out_h && l.c == l.out_c) { + int from_w = state.net.layers[l.index].w; + int from_h = state.net.layers[l.index].h; + int from_c = state.net.layers[l.index].c; + + if (l.n == 1 && from_w == l.w && from_h == l.h && from_c == l.c) { int size = l.batch * l.w * l.h * l.c; int i; #pragma omp parallel for @@ -90,16 +109,16 @@ void forward_shortcut_layer(const layer l, network_state state) l.output[i] = state.input[i] + state.net.layers[l.index].output[i]; } else { - copy_cpu(l.outputs*l.batch, state.input, 1, l.output, 1); - shortcut_cpu(l.batch, l.w, l.h, l.c, state.net.layers[l.index].output, l.out_w, l.out_h, l.out_c, l.output); + shortcut_multilayer_cpu(l.outputs * l.batch, l.outputs, l.batch, l.n, l.input_sizes, l.layers_output, l.output, state.input); } + //copy_cpu(l.outputs*l.batch, state.input, 1, l.output, 1); + //shortcut_cpu(l.batch, from_w, from_h, from_c, state.net.layers[l.index].output, l.out_w, l.out_h, l.out_c, l.output); + //activate_array(l.output, l.outputs*l.batch, l.activation); if (l.activation == SWISH) activate_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.output); else if (l.activation == MISH) activate_array_mish(l.output, l.outputs*l.batch, l.activation_input, l.output); else activate_array_cpu_custom(l.output, l.outputs*l.batch, l.activation); - - if (l.assisted_excitation && state.train) assisted_excitation_forward(l, state); } void backward_shortcut_layer(const layer l, network_state state) @@ -108,8 +127,11 @@ void backward_shortcut_layer(const layer l, network_state state) else if (l.activation == MISH) gradient_array_mish(l.outputs*l.batch, l.activation_input, l.delta); else gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); - axpy_cpu(l.outputs*l.batch, 1, l.delta, 1, state.delta, 1); - shortcut_cpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta, l.w, l.h, l.c, state.net.layers[l.index].delta); + backward_shortcut_multilayer_cpu(l.outputs * l.batch, l.outputs, l.batch, l.n, l.input_sizes, + l.layers_delta, state.delta, l.delta); + + //axpy_cpu(l.outputs*l.batch, 1, l.delta, 1, state.delta, 1); + //shortcut_cpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta, l.w, l.h, l.c, state.net.layers[l.index].delta); } #ifdef GPU @@ -118,13 +140,25 @@ void forward_shortcut_layer_gpu(const layer l, network_state state) //copy_ongpu(l.outputs*l.batch, state.input, 1, l.output_gpu, 1); //simple_copy_ongpu(l.outputs*l.batch, state.input, l.output_gpu); //shortcut_gpu(l.batch, l.w, l.h, l.c, state.net.layers[l.index].output_gpu, l.out_w, l.out_h, l.out_c, l.output_gpu); - input_shortcut_gpu(state.input, l.batch, l.w, l.h, l.c, state.net.layers[l.index].output_gpu, l.out_w, l.out_h, l.out_c, l.output_gpu); + + //input_shortcut_gpu(state.input, l.batch, l.w, l.h, l.c, state.net.layers[l.index].output_gpu, l.out_w, l.out_h, l.out_c, l.output_gpu); + + //----------- + //if (l.outputs == l.input_sizes[0]) + //if(l.n == 1) + //{ + // input_shortcut_gpu(state.input, l.batch, state.net.layers[l.index].w, state.net.layers[l.index].h, state.net.layers[l.index].c, + // state.net.layers[l.index].output_gpu, l.out_w, l.out_h, l.out_c, l.output_gpu); + //} + //else + { + shortcut_multilayer_gpu(l.outputs, l.batch, l.n, l.input_sizes_gpu, l.layers_output_gpu, l.output_gpu, state.input); + } if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == MISH) activate_array_mish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); - if (l.assisted_excitation && state.train) assisted_excitation_forward_gpu(l, state); } void backward_shortcut_layer_gpu(const layer l, network_state state) @@ -133,7 +167,9 @@ void backward_shortcut_layer_gpu(const layer l, network_state state) else if (l.activation == MISH) gradient_array_mish_ongpu(l.outputs*l.batch, l.activation_input_gpu, l.delta_gpu); else gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); - axpy_ongpu(l.outputs*l.batch, 1, l.delta_gpu, 1, state.delta, 1); - shortcut_gpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta_gpu, l.w, l.h, l.c, state.net.layers[l.index].delta_gpu); + backward_shortcut_multilayer_gpu(l.outputs, l.batch, l.n, l.input_sizes_gpu, l.layers_delta_gpu, state.delta, l.delta_gpu); + + //axpy_ongpu(l.outputs*l.batch, 1, l.delta_gpu, 1, state.delta, 1); + //shortcut_gpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta_gpu, l.w, l.h, l.c, state.net.layers[l.index].delta_gpu); } #endif diff --git a/src/shortcut_layer.h b/src/shortcut_layer.h index b245868fdd8..9341a4a3f26 100644 --- a/src/shortcut_layer.h +++ b/src/shortcut_layer.h @@ -7,10 +7,11 @@ #ifdef __cplusplus extern "C" { #endif -layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2, int assisted_excitation, ACTIVATION activation, int train); +layer make_shortcut_layer(int batch, int n, int *input_layers, int* input_sizes, int w, int h, int c, + float **layers_output, float **layers_delta, float **layers_output_gpu, float **layers_delta_gpu, ACTIVATION activation, int train); void forward_shortcut_layer(const layer l, network_state state); void backward_shortcut_layer(const layer l, network_state state); -void resize_shortcut_layer(layer *l, int w, int h); +void resize_shortcut_layer(layer *l, int w, int h, network *net); #ifdef GPU void forward_shortcut_layer_gpu(const layer l, network_state state); From c2ce28bbcc85446173fc8bf1808b72a963d8c1ae Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 6 Jan 2020 23:38:21 +0300 Subject: [PATCH 0786/1365] Compile fix --- src/layer.c | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/layer.c b/src/layer.c index 1acc4bf9b8f..ae72da6a863 100644 --- a/src/layer.c +++ b/src/layer.c @@ -192,9 +192,9 @@ void free_layer_custom(layer l, int keep_cudnn_desc) if (l.rand_gpu) cuda_free(l.rand_gpu); if (l.squared_gpu) cuda_free(l.squared_gpu); if (l.norms_gpu) cuda_free(l.norms_gpu); - if (l.input_sizes_gpu) cuda_free(l.input_sizes_gpu); - if (l.layers_output_gpu) cuda_free(l.layers_output_gpu); - if (l.layers_delta_gpu) cuda_free(l.layers_delta_gpu); + if (l.input_sizes_gpu) cuda_free((float*)l.input_sizes_gpu); + if (l.layers_output_gpu) cuda_free((float*)l.layers_output_gpu); + if (l.layers_delta_gpu) cuda_free((float*)l.layers_delta_gpu); // CONV-LSTM if (l.f_gpu) cuda_free(l.f_gpu); From c89482c39b5d0c7906b4385f65f3290dd13f22c9 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 7 Jan 2020 00:01:26 +0300 Subject: [PATCH 0787/1365] Fixed new [shortcut] layer --- src/blas.h | 1 + src/blas_kernels.cu | 6 ++++++ src/parser.c | 1 - src/shortcut_layer.c | 9 ++++++++- 4 files changed, 15 insertions(+), 2 deletions(-) diff --git a/src/blas.h b/src/blas.h index a2f7e5d4ff6..9e1d5a9a62a 100644 --- a/src/blas.h +++ b/src/blas.h @@ -63,6 +63,7 @@ void fix_nan_and_inf_cpu(float *input, size_t size); void axpy_ongpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY); void axpy_ongpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY); void simple_copy_ongpu(int size, float *src, float *dst); +void memcpy_ongpu(float *dst, float *src, int size_bytes); void copy_ongpu(int N, float * X, int INCX, float * Y, int INCY); void copy_ongpu_offset(int N, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY); void scal_ongpu(int N, float ALPHA, float * X, int INCX); diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index d1ee4b804ec..7eec0e10fdf 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -576,6 +576,12 @@ extern "C" void simple_copy_ongpu(int size, float *src, float *dst) CHECK_CUDA(cudaPeekAtLastError()); } +extern "C" void memcpy_ongpu(float *dst, float *src, int size_bytes) +{ + CHECK_CUDA(cudaMemcpyAsync(dst, src, size_bytes, cudaMemcpyDefault, get_cuda_stream())); + CHECK_CUDA(cudaPeekAtLastError()); +} + extern "C" void mul_ongpu(int N, float * X, int INCX, float * Y, int INCY) { mul_kernel<<>>(N, X, INCX, Y, INCY); diff --git a/src/parser.c b/src/parser.c index d4b13ccb3ee..478c620e725 100644 --- a/src/parser.c +++ b/src/parser.c @@ -845,7 +845,6 @@ layer parse_shortcut(list *options, size_params params, network net) sizes[i] = params.net.layers[index].outputs; layers_output[i] = params.net.layers[index].output; layers_delta[i] = params.net.layers[index].delta; - } #ifdef GPU diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index f87d6b61d87..50234c3286f 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -80,7 +80,10 @@ void resize_shortcut_layer(layer *l, int w, int h, network *net) for (i = 0; i < l->n; ++i) { int index = l->input_layers[i]; l->input_sizes[i] = net->layers[index].outputs; - assert(l->w == net->layers[index].w && l->h == net->layers[index].h); + l->layers_output[i] = net->layers[index].output; + l->layers_delta[i] = net->layers[index].delta; + + assert(l->w == net->layers[index].out_w && l->h == net->layers[index].out_h); } #ifdef GPU @@ -91,6 +94,10 @@ void resize_shortcut_layer(layer *l, int w, int h, network *net) cuda_free(l->delta_gpu); l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch); } + + memcpy_ongpu(l->input_sizes_gpu, l->input_sizes, l->n * sizeof(int)); + memcpy_ongpu(l->layers_output_gpu, l->layers_output, l->n * sizeof(float*)); + memcpy_ongpu(l->layers_delta_gpu, l->layers_delta, l->n * sizeof(float*)); #endif } From 25a3d79a7ab48e8f875d7b06847315285d7e177e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 7 Jan 2020 00:04:57 +0300 Subject: [PATCH 0788/1365] Compile fix --- src/dark_cuda.c | 2 +- src/shortcut_layer.c | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/dark_cuda.c b/src/dark_cuda.c index e7b6e6afab0..fde04f7b316 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -428,7 +428,7 @@ int *cuda_make_int_array_new_api(int *x, size_t n) cudaError_t status = cudaMalloc((void **)&x_gpu, size); CHECK_CUDA(status); if (x) { - //status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice, get_cuda_stream()); + //status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice); cudaError_t status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyHostToDevice, get_cuda_stream()); CHECK_CUDA(status); } diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index 50234c3286f..b7e9413afa0 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -55,7 +55,7 @@ layer make_shortcut_layer(int batch, int n, int *input_layers, int* input_sizes, if (train) l.delta_gpu = cuda_make_array(l.delta, l.outputs*batch); l.output_gpu = cuda_make_array(l.output, l.outputs*batch); - l.input_sizes_gpu = cuda_make_array(input_sizes, l.n); + l.input_sizes_gpu = cuda_make_int_array_new_api(input_sizes, l.n); l.layers_output_gpu = cuda_make_array_pointers(layers_output_gpu, l.n); l.layers_delta_gpu = cuda_make_array_pointers(layers_delta_gpu, l.n); #endif // GPU From e62506629eba5d68386b68f05c12bc745d0cfba3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 7 Jan 2020 01:17:28 +0300 Subject: [PATCH 0789/1365] Another one compile fix --- src/shortcut_layer.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index b7e9413afa0..931f394ea57 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -56,8 +56,8 @@ layer make_shortcut_layer(int batch, int n, int *input_layers, int* input_sizes, l.output_gpu = cuda_make_array(l.output, l.outputs*batch); l.input_sizes_gpu = cuda_make_int_array_new_api(input_sizes, l.n); - l.layers_output_gpu = cuda_make_array_pointers(layers_output_gpu, l.n); - l.layers_delta_gpu = cuda_make_array_pointers(layers_delta_gpu, l.n); + l.layers_output_gpu = cuda_make_array_pointers((void**)layers_output_gpu, l.n); + l.layers_delta_gpu = cuda_make_array_pointers((void**)layers_delta_gpu, l.n); #endif // GPU l.bflops = l.out_w * l.out_h * l.out_c * l.n / 1000000000.; From 9bd88d7fd7cef872998aa931f8280a1ba1578822 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 7 Jan 2020 17:11:57 +0300 Subject: [PATCH 0790/1365] weighted [shortcut] layer --- include/darknet.h | 6 +++ src/blas.c | 43 +++++++++++++++---- src/blas.h | 9 ++-- src/blas_kernels.cu | 53 ++++++++++++++++++------ src/parser.c | 49 +++++++++++++++++++--- src/shortcut_layer.c | 99 +++++++++++++++++++++++++++++++++++++++----- src/shortcut_layer.h | 6 ++- 7 files changed, 222 insertions(+), 43 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index bd85e880594..a6d2c4a9f90 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -120,6 +120,11 @@ typedef enum { YOLO_CENTER = 1 << 0, YOLO_LEFT_TOP = 1 << 1, YOLO_RIGHT_BOTTOM = 1 << 2 } YOLO_POINT; +// parser.h +typedef enum { + NO_WEIGHTS, PER_FEATURE, PER_CHANNEL +} WEIGHTS_TYPE_T; + // image.h typedef enum{ @@ -330,6 +335,7 @@ struct layer { int * input_sizes; float **layers_output; float **layers_delta; + WEIGHTS_TYPE_T weights_type; int * map; int * counts; float ** sums; diff --git a/src/blas.c b/src/blas.c index 9533f74da39..d10af5fffe1 100644 --- a/src/blas.c +++ b/src/blas.c @@ -68,36 +68,49 @@ void weighted_delta_cpu(float *a, float *b, float *s, float *da, float *db, floa } } -void shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *outputs_of_layers, float **layers_output, float *out, float *in) +void shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *outputs_of_layers, float **layers_output, float *out, float *in, float *weights, int nweights) { + // nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w) + const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) + const int step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 + int id; #pragma omp parallel for for (id = 0; id < size; ++id) { int src_id = id; - int src_i = src_id % src_outputs; + const int src_i = src_id % src_outputs; src_id /= src_outputs; int src_b = src_id; - out[id] = in[id]; + if(weights) out[id] = in[id] * weights[src_i / step]; // [0 or c or (c, h ,w)] + else out[id] = in[id]; // layers - for (int i = 0; i < n; ++i) { + int i; + for (i = 0; i < n; ++i) { int add_outputs = outputs_of_layers[i]; if (src_i < add_outputs) { int add_index = add_outputs*src_b + src_i; int out_index = id; float *add = layers_output[i]; - out[out_index] += add[add_index]; + const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)] + + if (weights) out[out_index] += add[add_index] * weights[weights_index]; // [0 or c or (c, h ,w)] + else out[out_index] += add[add_index]; } } } } void backward_shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *outputs_of_layers, - float **layers_delta, float *delta_out, float *delta_in) + float **layers_delta, float *delta_out, float *delta_in, float *weights, float *weight_updates, int nweights, float *in, float **layers_output) { + // nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w) + const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) + const int step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 + int id; #pragma omp parallel for for (id = 0; id < size; ++id) { @@ -106,17 +119,29 @@ void backward_shortcut_multilayer_cpu(int size, int src_outputs, int batch, int src_id /= src_outputs; int src_b = src_id; - delta_out[id] += delta_in[id]; + if (weights) { + delta_out[id] += delta_in[id] * weights[src_i / step]; // [0 or c or (c, h ,w)] + weight_updates[src_i / step] += delta_in[id] * in[id]; + } + else delta_out[id] += delta_in[id]; // layers - for (int i = 0; i < n; ++i) { + int i; + for (i = 0; i < n; ++i) { int add_outputs = outputs_of_layers[i]; if (src_i < add_outputs) { int add_index = add_outputs*src_b + src_i; int out_index = id; float *layer_delta = layers_delta[i]; - layer_delta[add_index] += delta_in[id]; + if (weights) { + float *add = layers_output[i]; + const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)] + + layer_delta[add_index] += delta_in[id] * weights[weights_index]; // [0 or c or (c, h ,w)] + weight_updates[weights_index] += delta_in[id] * add[add_index]; + } + else layer_delta[add_index] += delta_in[id]; } } } diff --git a/src/blas.h b/src/blas.h index 9e1d5a9a62a..ac441a4478f 100644 --- a/src/blas.h +++ b/src/blas.h @@ -32,9 +32,9 @@ void fill_cpu(int N, float ALPHA, float * X, int INCX); float dot_cpu(int N, float *X, int INCX, float *Y, int INCY); void test_gpu_blas(); void shortcut_cpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out); -void shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *outputs_of_layers, float **layers_output, float *out, float *in); +void shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *outputs_of_layers, float **layers_output, float *out, float *in, float *weights, int nweights); void backward_shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *outputs_of_layers, - float **layers_delta, float *delta_out, float *delta_in); + float **layers_delta, float *delta_out, float *delta_in, float *weights, float *weight_updates, int nweights, float *in, float **layers_output); void mean_cpu(float *x, int batch, int filters, int spatial, float *mean); void variance_cpu(float *x, float *mean, int batch, int filters, int spatial, float *variance); @@ -88,8 +88,9 @@ void fast_variance_delta_gpu(float *x, float *delta, float *mean, float *varianc void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance); void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean); void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out); -void shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in); -void backward_shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_delta_gpu, float *delta_out, float *delta_in); +void shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights); +void backward_shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_delta_gpu, float *delta_out, float *delta_in, + float *weights, float *weight_updates, int nweights, float *in, float **layers_output); void input_shortcut_gpu(float *in, int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out); void scale_bias_gpu(float *output, float *biases, int batch, int n, int size); void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates); diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 7eec0e10fdf..820f2e67316 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -674,17 +674,22 @@ extern "C" void fill_ongpu(int N, float ALPHA, float * X, int INCX) CHECK_CUDA(cudaPeekAtLastError()); } -__global__ void shortcut_multilayer_kernel(int size, int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in) +__global__ void shortcut_multilayer_kernel(int size, int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights) { const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (id >= size) return; + // nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w) + const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) + const int step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 + int src_id = id; - int src_i = src_id % src_outputs; + const int src_i = src_id % src_outputs; src_id /= src_outputs; int src_b = src_id; - out[id] = in[id]; + if (weights_gpu) out[id] = in[id] * weights_gpu[src_i / step]; // [0 or c or (c, h ,w)] + else out[id] = in[id]; // layers for (int i = 0; i < n; ++i) { @@ -694,33 +699,45 @@ __global__ void shortcut_multilayer_kernel(int size, int src_outputs, int batch, int out_index = id; float *add = layers_output_gpu[i]; - out[out_index] += add[add_index]; + const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)] + + if (weights_gpu) out[out_index] += add[add_index] * weights_gpu[weights_index]; // [0 or c or (c, h ,w)] + else out[out_index] += add[add_index]; } } } -extern "C" void shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in) +extern "C" void shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights) { //printf(" src_outputs = %d, batch = %d, n = %d \n", src_outputs, batch, n); int size = batch * src_outputs; - shortcut_multilayer_kernel << > > (size, src_outputs, batch, n, outputs_of_layers_gpu, layers_output_gpu, out, in); + shortcut_multilayer_kernel << > > (size, src_outputs, batch, n, outputs_of_layers_gpu, layers_output_gpu, out, in, weights_gpu, nweights); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, int batch, int n, int *outputs_of_layers_gpu, - float **layers_delta_gpu, float *delta_out, float *delta_in) + float **layers_delta_gpu, float *delta_out, float *delta_in, float *weights_gpu, float *weight_updates_gpu, int nweights, float *in, float **layers_output_gpu) { const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (id >= size) return; + // nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w) + const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) + const int step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 + //if(id == 0) printf(" layer_step = %d, step = %d \n", layer_step, step); + int src_id = id; - int src_i = src_id % src_outputs; + const int src_i = src_id % src_outputs; src_id /= src_outputs; int src_b = src_id; - delta_out[id] += delta_in[id]; + if (weights_gpu) { + delta_out[id] += delta_in[id] * weights_gpu[src_i / step]; // [0 or c or (c, h ,w)] + weight_updates_gpu[src_i / step] += delta_in[id] * in[id]; + } + else delta_out[id] += delta_in[id]; // layers for (int i = 0; i < n; ++i) { @@ -730,16 +747,28 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i int out_index = id; float *layer_delta = layers_delta_gpu[i]; - layer_delta[add_index] += delta_in[id]; + if (weights_gpu) { + float *add = layers_output_gpu[i]; + const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)] + layer_delta[add_index] += delta_in[id] * weights_gpu[weights_index]; + weight_updates_gpu[weights_index] += delta_in[id] * add[add_index]; + } + else layer_delta[add_index] += delta_in[id]; } } } -extern "C" void backward_shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_delta_gpu, float *delta_out, float *delta_in) +extern "C" void backward_shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, + float **layers_delta_gpu, float *delta_out, float *delta_in, float *weights_gpu, float *weight_updates_gpu, int nweights, float *in, float **layers_output_gpu) { + const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) + const int step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 + //printf(" nweights = %d, n = %d, layer_step = %d, step = %d \n", nweights, n, layer_step, step); + //printf(" src_outputs = %d, batch = %d, n = %d \n", src_outputs, batch, n); int size = batch * src_outputs; - backward_shortcut_multilayer_kernel << > > (size, src_outputs, batch, n, outputs_of_layers_gpu, layers_delta_gpu, delta_out, delta_in); + backward_shortcut_multilayer_kernel << > > (size, src_outputs, batch, n, outputs_of_layers_gpu, + layers_delta_gpu, delta_out, delta_in, weights_gpu, weight_updates_gpu, nweights, in, layers_output_gpu); CHECK_CUDA(cudaPeekAtLastError()); } diff --git a/src/parser.c b/src/parser.c index 478c620e725..c0239f80b85 100644 --- a/src/parser.c +++ b/src/parser.c @@ -816,10 +816,10 @@ layer parse_shortcut(list *options, size_params params, network net) char *activation_s = option_find_str(options, "activation", "logistic"); ACTIVATION activation = get_activation(activation_s); - int assisted_excitation = option_find_float_quiet(options, "assisted_excitation", 0); - //char *l = option_find(options, "from"); - //int index = atoi(l); - //if(index < 0) index = params.index + index; + char *weights_type_srt = option_find_str_quiet(options, "weights_type", "none"); + WEIGHTS_TYPE_T weights_type = NO_WEIGHTS; + if(strcmp(weights_type_srt, "per_feature") == 0) weights_type = PER_FEATURE; + else if (strcmp(weights_type_srt, "per_channel") == 0) weights_type = PER_CHANNEL; char *l = option_find(options, "from"); int len = strlen(l); @@ -854,7 +854,11 @@ layer parse_shortcut(list *options, size_params params, network net) } #endif// GPU - layer s = make_shortcut_layer(params.batch, n, layers, sizes, params.w, params.h, params.c, layers_output, layers_delta, layers_output_gpu, layers_delta_gpu, activation, params.train); + layer s = make_shortcut_layer(params.batch, n, layers, sizes, params.w, params.h, params.c, layers_output, layers_delta, + layers_output_gpu, layers_delta_gpu, weights_type, activation, params.train); + + free(layers_output_gpu); + free(layers_delta_gpu); for (i = 0; i < n; ++i) { int index = layers[i]; @@ -1515,6 +1519,18 @@ void save_convolutional_weights_binary(layer l, FILE *fp) } } +void save_shortcut_weights(layer l, FILE *fp) +{ +#ifdef GPU + if (gpu_index >= 0) { + pull_shortcut_layer(l); + } +#endif + int num = l.nweights; + fwrite(l.weights, sizeof(float), num, fp); + +} + void save_convolutional_weights(layer l, FILE *fp) { if(l.binary){ @@ -1591,8 +1607,10 @@ void save_weights_upto(network net, char *filename, int cutoff) int i; for(i = 0; i < net.n && i < cutoff; ++i){ layer l = net.layers[i]; - if(l.type == CONVOLUTIONAL && l.share_layer == NULL){ + if (l.type == CONVOLUTIONAL && l.share_layer == NULL) { save_convolutional_weights(l, fp); + } if (l.type == SHORTCUT && l.nweights > 0) { + save_shortcut_weights(l, fp); } if(l.type == CONNECTED){ save_connected_weights(l, fp); } if(l.type == BATCHNORM){ @@ -1786,6 +1804,22 @@ void load_convolutional_weights(layer l, FILE *fp) #endif } +void load_shortcut_weights(layer l, FILE *fp) +{ + if (l.binary) { + //load_convolutional_weights_binary(l, fp); + //return; + } + int num = l.nweights; + int read_bytes; + read_bytes = fread(l.weights, sizeof(float), num, fp); + if (read_bytes > 0 && read_bytes < num) printf("\n Warning: Unexpected end of wights-file! l.weights - l.index = %d \n", l.index); +#ifdef GPU + if (gpu_index >= 0) { + push_shortcut_layer(l); + } +#endif +} void load_weights_upto(network *net, char *filename, int cutoff) { @@ -1826,6 +1860,9 @@ void load_weights_upto(network *net, char *filename, int cutoff) if(l.type == CONVOLUTIONAL && l.share_layer == NULL){ load_convolutional_weights(l, fp); } + if (l.type == SHORTCUT && l.nweights > 0) { + load_shortcut_weights(l, fp); + } if(l.type == CONNECTED){ load_connected_weights(l, fp, transpose); } diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index 931f394ea57..15a50a98a10 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -7,7 +7,7 @@ #include layer make_shortcut_layer(int batch, int n, int *input_layers, int* input_sizes, int w, int h, int c, - float **layers_output, float **layers_delta, float **layers_output_gpu, float **layers_delta_gpu, ACTIVATION activation, int train) + float **layers_output, float **layers_delta, float **layers_output_gpu, float **layers_delta_gpu, WEIGHTS_TYPE_T weights_type, ACTIVATION activation, int train) { fprintf(stderr, "Shortcut Layer: "); int i; @@ -23,6 +23,7 @@ layer make_shortcut_layer(int batch, int n, int *input_layers, int* input_sizes, l.input_sizes = input_sizes; l.layers_output = layers_output; l.layers_delta = layers_delta; + l.weights_type = weights_type; //l.w = w2; //l.h = h2; @@ -40,6 +41,18 @@ layer make_shortcut_layer(int batch, int n, int *input_layers, int* input_sizes, if (train) l.delta = (float*)calloc(l.outputs * batch, sizeof(float)); l.output = (float*)calloc(l.outputs * batch, sizeof(float)); + if (l.weights_type == PER_FEATURE) l.nweights = (l.n + 1); + else if (l.weights_type == PER_CHANNEL) l.nweights = (l.n + 1) * l.c; + + if (l.nweights > 0) { + l.weights = (float*)calloc(l.nweights, sizeof(float)); + float scale = sqrt(2. / l.nweights); + for (i = 0; i < l.nweights; ++i) l.weights[i] = 1;// scale*rand_uniform(-1, 1); // rand_normal(); + + if (train) l.weight_updates = (float*)calloc(l.nweights, sizeof(float)); + l.update = update_shortcut_layer; + } + l.forward = forward_shortcut_layer; l.backward = backward_shortcut_layer; #ifndef GPU @@ -52,16 +65,23 @@ layer make_shortcut_layer(int batch, int n, int *input_layers, int* input_sizes, l.forward_gpu = forward_shortcut_layer_gpu; l.backward_gpu = backward_shortcut_layer_gpu; + if (l.nweights > 0) { + l.update_gpu = update_shortcut_layer_gpu; + l.weights_gpu = cuda_make_array(l.weights, l.nweights); + if (train) l.weight_updates_gpu = cuda_make_array(l.weight_updates, l.nweights); + } + if (train) l.delta_gpu = cuda_make_array(l.delta, l.outputs*batch); l.output_gpu = cuda_make_array(l.output, l.outputs*batch); l.input_sizes_gpu = cuda_make_int_array_new_api(input_sizes, l.n); - l.layers_output_gpu = cuda_make_array_pointers((void**)layers_output_gpu, l.n); - l.layers_delta_gpu = cuda_make_array_pointers((void**)layers_delta_gpu, l.n); + l.layers_output_gpu = (float**)cuda_make_array_pointers((void**)layers_output_gpu, l.n); + l.layers_delta_gpu = (float**)cuda_make_array_pointers((void**)layers_delta_gpu, l.n); #endif // GPU l.bflops = l.out_w * l.out_h * l.out_c * l.n / 1000000000.; - fprintf(stderr, " outputs:%4d x%4d x%4d %5.3f BF\n", l.out_w, l.out_h, l.out_c, l.bflops); + if (l.weights_type) l.bflops *= 2; + fprintf(stderr, " wt = %d, outputs:%4d x%4d x%4d %5.3f BF\n", l.weights_type, l.out_w, l.out_h, l.out_c, l.bflops); return l; } @@ -95,9 +115,21 @@ void resize_shortcut_layer(layer *l, int w, int h, network *net) l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch); } + float **layers_output_gpu = (float **)calloc(l->n, sizeof(float *)); + float **layers_delta_gpu = (float **)calloc(l->n, sizeof(float *)); + + for (i = 0; i < l->n; ++i) { + const int index = l->input_layers[i]; + layers_output_gpu[i] = net->layers[index].output_gpu; + layers_delta_gpu[i] = net->layers[index].delta_gpu; + } + memcpy_ongpu(l->input_sizes_gpu, l->input_sizes, l->n * sizeof(int)); - memcpy_ongpu(l->layers_output_gpu, l->layers_output, l->n * sizeof(float*)); - memcpy_ongpu(l->layers_delta_gpu, l->layers_delta, l->n * sizeof(float*)); + memcpy_ongpu(l->layers_output_gpu, layers_output_gpu, l->n * sizeof(float*)); + memcpy_ongpu(l->layers_delta_gpu, layers_delta_gpu, l->n * sizeof(float*)); + + free(layers_output_gpu); + free(layers_delta_gpu); #endif } @@ -108,7 +140,7 @@ void forward_shortcut_layer(const layer l, network_state state) int from_h = state.net.layers[l.index].h; int from_c = state.net.layers[l.index].c; - if (l.n == 1 && from_w == l.w && from_h == l.h && from_c == l.c) { + if (l.nweights == 0 && l.n == 1 && from_w == l.w && from_h == l.h && from_c == l.c) { int size = l.batch * l.w * l.h * l.c; int i; #pragma omp parallel for @@ -116,7 +148,7 @@ void forward_shortcut_layer(const layer l, network_state state) l.output[i] = state.input[i] + state.net.layers[l.index].output[i]; } else { - shortcut_multilayer_cpu(l.outputs * l.batch, l.outputs, l.batch, l.n, l.input_sizes, l.layers_output, l.output, state.input); + shortcut_multilayer_cpu(l.outputs * l.batch, l.outputs, l.batch, l.n, l.input_sizes, l.layers_output, l.output, state.input, l.weights, l.nweights); } //copy_cpu(l.outputs*l.batch, state.input, 1, l.output, 1); @@ -135,12 +167,24 @@ void backward_shortcut_layer(const layer l, network_state state) else gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); backward_shortcut_multilayer_cpu(l.outputs * l.batch, l.outputs, l.batch, l.n, l.input_sizes, - l.layers_delta, state.delta, l.delta); + l.layers_delta, state.delta, l.delta, l.weights, l.weight_updates, l.nweights, state.input, l.layers_output); //axpy_cpu(l.outputs*l.batch, 1, l.delta, 1, state.delta, 1); //shortcut_cpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta, l.w, l.h, l.c, state.net.layers[l.index].delta); } +void update_shortcut_layer(layer l, int batch, float learning_rate_init, float momentum, float decay) +{ + float learning_rate = learning_rate_init*l.learning_rate_scale; + //float momentum = a.momentum; + //float decay = a.decay; + //int batch = a.batch; + + axpy_cpu(l.nweights, -decay*batch, l.weights, 1, l.weight_updates, 1); + axpy_cpu(l.nweights, learning_rate / batch, l.weight_updates, 1, l.weights, 1); + scal_cpu(l.nweights, momentum, l.weight_updates, 1); +} + #ifdef GPU void forward_shortcut_layer_gpu(const layer l, network_state state) { @@ -159,7 +203,7 @@ void forward_shortcut_layer_gpu(const layer l, network_state state) //} //else { - shortcut_multilayer_gpu(l.outputs, l.batch, l.n, l.input_sizes_gpu, l.layers_output_gpu, l.output_gpu, state.input); + shortcut_multilayer_gpu(l.outputs, l.batch, l.n, l.input_sizes_gpu, l.layers_output_gpu, l.output_gpu, state.input, l.weights_gpu, l.nweights); } if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); @@ -174,9 +218,42 @@ void backward_shortcut_layer_gpu(const layer l, network_state state) else if (l.activation == MISH) gradient_array_mish_ongpu(l.outputs*l.batch, l.activation_input_gpu, l.delta_gpu); else gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); - backward_shortcut_multilayer_gpu(l.outputs, l.batch, l.n, l.input_sizes_gpu, l.layers_delta_gpu, state.delta, l.delta_gpu); + backward_shortcut_multilayer_gpu(l.outputs, l.batch, l.n, l.input_sizes_gpu, l.layers_delta_gpu, state.delta, l.delta_gpu, + l.weights_gpu, l.weight_updates_gpu, l.nweights, state.input, l.layers_output_gpu); //axpy_ongpu(l.outputs*l.batch, 1, l.delta_gpu, 1, state.delta, 1); //shortcut_gpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta_gpu, l.w, l.h, l.c, state.net.layers[l.index].delta_gpu); } + +void update_shortcut_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay) +{ + float learning_rate = learning_rate_init*l.learning_rate_scale; + //float momentum = a.momentum; + //float decay = a.decay; + //int batch = a.batch; + + fix_nan_and_inf(l.weight_updates_gpu, l.nweights); + fix_nan_and_inf(l.weights_gpu, l.nweights); + + axpy_ongpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); + axpy_ongpu(l.nweights, learning_rate / batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); + scal_ongpu(l.nweights, momentum, l.weight_updates_gpu, 1); + + //if (l.clip) { + // constrain_gpu(l.nweights, l.clip, l.weights_gpu, 1); + //} +} + +void pull_shortcut_layer(layer l) +{ + cuda_pull_array_async(l.weights_gpu, l.weights, l.nweights); + CHECK_CUDA(cudaPeekAtLastError()); + cudaStreamSynchronize(get_cuda_stream()); +} + +void push_shortcut_layer(layer l) +{ + cuda_push_array(l.weights_gpu, l.weights, l.nweights); + CHECK_CUDA(cudaPeekAtLastError()); +} #endif diff --git a/src/shortcut_layer.h b/src/shortcut_layer.h index 9341a4a3f26..e546693c04b 100644 --- a/src/shortcut_layer.h +++ b/src/shortcut_layer.h @@ -8,14 +8,18 @@ extern "C" { #endif layer make_shortcut_layer(int batch, int n, int *input_layers, int* input_sizes, int w, int h, int c, - float **layers_output, float **layers_delta, float **layers_output_gpu, float **layers_delta_gpu, ACTIVATION activation, int train); + float **layers_output, float **layers_delta, float **layers_output_gpu, float **layers_delta_gpu, WEIGHTS_TYPE_T weights_type, ACTIVATION activation, int train); void forward_shortcut_layer(const layer l, network_state state); void backward_shortcut_layer(const layer l, network_state state); +void update_shortcut_layer(layer l, int batch, float learning_rate_init, float momentum, float decay); void resize_shortcut_layer(layer *l, int w, int h, network *net); #ifdef GPU void forward_shortcut_layer_gpu(const layer l, network_state state); void backward_shortcut_layer_gpu(const layer l, network_state state); +void update_shortcut_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay); +void pull_shortcut_layer(layer l); +void push_shortcut_layer(layer l); #endif #ifdef __cplusplus From 40006b6e9b6d5ebf3a832b157b1ab168e80a84bb Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 8 Jan 2020 02:26:55 +0300 Subject: [PATCH 0791/1365] Compile fix --- src/blas.h | 2 +- src/blas_kernels.cu | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/blas.h b/src/blas.h index ac441a4478f..ccf8b35baa0 100644 --- a/src/blas.h +++ b/src/blas.h @@ -63,7 +63,7 @@ void fix_nan_and_inf_cpu(float *input, size_t size); void axpy_ongpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY); void axpy_ongpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY); void simple_copy_ongpu(int size, float *src, float *dst); -void memcpy_ongpu(float *dst, float *src, int size_bytes); +void memcpy_ongpu(void *dst, void *src, int size_bytes); void copy_ongpu(int N, float * X, int INCX, float * Y, int INCY); void copy_ongpu_offset(int N, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY); void scal_ongpu(int N, float ALPHA, float * X, int INCX); diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 820f2e67316..7ce533a7331 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -576,7 +576,7 @@ extern "C" void simple_copy_ongpu(int size, float *src, float *dst) CHECK_CUDA(cudaPeekAtLastError()); } -extern "C" void memcpy_ongpu(float *dst, float *src, int size_bytes) +extern "C" void memcpy_ongpu(void *dst, void *src, int size_bytes) { CHECK_CUDA(cudaMemcpyAsync(dst, src, size_bytes, cudaMemcpyDefault, get_cuda_stream())); CHECK_CUDA(cudaPeekAtLastError()); From 9d2e6f9a23d40fa464b27c18a91e9993d1d74cf1 Mon Sep 17 00:00:00 2001 From: cyy Date: Wed, 8 Jan 2020 10:56:10 +0800 Subject: [PATCH 0792/1365] fix compiling errors --- src/coco.c | 2 +- src/detector.c | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/src/coco.c b/src/coco.c index 48b168a47d7..c0f6448b6e9 100644 --- a/src/coco.c +++ b/src/coco.c @@ -318,7 +318,7 @@ void validate_coco_recall(char *cfgfile, char *weightfile) fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total); - if (fps) free(fps); + //if (fps) free(fps); if (id) free(id); free_image(orig); free_image(sized); diff --git a/src/detector.c b/src/detector.c index eda9dd4e2f5..32dcb7b3f0c 100644 --- a/src/detector.c +++ b/src/detector.c @@ -49,6 +49,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i char *name_list = option_find_str(options, "names", "data/names.list"); int names_size = 0; + char **names = get_labels_custom(name_list, &names_size); if (net_classes != names_size) { printf(" Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", name_list, names_size, net_classes, cfgfile); From cfac917b7568b5704743e49a46edb1392b10223e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 9 Jan 2020 01:30:57 +0300 Subject: [PATCH 0793/1365] Minor performance improvement --- src/convolutional_kernels.cu | 4 ++-- src/parser.c | 4 ++-- src/shortcut_layer.c | 12 ++++++------ 3 files changed, 10 insertions(+), 10 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index eee5fe1a885..1a00c1892de 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -420,7 +420,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //if (state.use_mixed_precision) { int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in) && - (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 8) + (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 8 && l.size > 1) { //printf("\n CUDNN_HALF!!! state.index = %d \n", state.index); @@ -671,7 +671,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state //#ifdef CUDNN_HALF int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in) && - (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 8) + (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 8 && l.size > 1) { const size_t input16_size = l.batch*l.c*l.w*l.h; const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h; diff --git a/src/parser.c b/src/parser.c index c0239f80b85..c2d9098b32e 100644 --- a/src/parser.c +++ b/src/parser.c @@ -865,8 +865,8 @@ layer parse_shortcut(list *options, size_params params, network net) assert(params.w == net.layers[index].out_w && params.h == net.layers[index].out_h); if (params.w != net.layers[index].out_w || params.h != net.layers[index].out_h || params.c != net.layers[index].out_c) - fprintf(stderr, " w = %d, w2 = %d, h = %d, h2 = %d, c = %d, c2 = %d \n", - params.w, net.layers[index].out_w, params.h, net.layers[index].out_h, params.c, params.net.layers[index].out_c); + fprintf(stderr, " (%4d x%4d x%4d) + (%4d x%4d x%4d) \n", + params.w, params.h, params.c, net.layers[index].out_w, net.layers[index].out_h, params.net.layers[index].out_c); } return s; diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index 15a50a98a10..a8da526b7cc 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -196,12 +196,12 @@ void forward_shortcut_layer_gpu(const layer l, network_state state) //----------- //if (l.outputs == l.input_sizes[0]) - //if(l.n == 1) - //{ - // input_shortcut_gpu(state.input, l.batch, state.net.layers[l.index].w, state.net.layers[l.index].h, state.net.layers[l.index].c, - // state.net.layers[l.index].output_gpu, l.out_w, l.out_h, l.out_c, l.output_gpu); - //} - //else + if(l.n == 1 && l.nweights == 0) + { + input_shortcut_gpu(state.input, l.batch, state.net.layers[l.index].w, state.net.layers[l.index].h, state.net.layers[l.index].c, + state.net.layers[l.index].output_gpu, l.out_w, l.out_h, l.out_c, l.output_gpu); + } + else { shortcut_multilayer_gpu(l.outputs, l.batch, l.n, l.input_sizes_gpu, l.layers_output_gpu, l.output_gpu, state.input, l.weights_gpu, l.nweights); } From 6878ecc2e2b26d383cf65811b9d9e17375ca14ed Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 9 Jan 2020 13:48:52 +0300 Subject: [PATCH 0794/1365] Minor fix --- src/shortcut_layer.c | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index a8da526b7cc..db5e3ad10c6 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -196,12 +196,12 @@ void forward_shortcut_layer_gpu(const layer l, network_state state) //----------- //if (l.outputs == l.input_sizes[0]) - if(l.n == 1 && l.nweights == 0) - { - input_shortcut_gpu(state.input, l.batch, state.net.layers[l.index].w, state.net.layers[l.index].h, state.net.layers[l.index].c, - state.net.layers[l.index].output_gpu, l.out_w, l.out_h, l.out_c, l.output_gpu); - } - else + //if(l.n == 1 && l.nweights == 0) + //{ + // input_shortcut_gpu(state.input, l.batch, state.net.layers[l.index].w, state.net.layers[l.index].h, state.net.layers[l.index].c, + // state.net.layers[l.index].output_gpu, l.out_w, l.out_h, l.out_c, l.output_gpu); + //} + //else { shortcut_multilayer_gpu(l.outputs, l.batch, l.n, l.input_sizes_gpu, l.layers_output_gpu, l.output_gpu, state.input, l.weights_gpu, l.nweights); } From a66bc8d6ff118b8e2b5e3e2fbca0484b496e8ef2 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 9 Jan 2020 19:25:02 +0300 Subject: [PATCH 0795/1365] Resizing Detector: random=1 (/1.4 - x1.4), random=3 (/3.0 - x3.0), random=0 (no resizing). Added param: resize_step=32 to the [net] section: use resize_step=128 for 7 subsampling layers. --- include/darknet.h | 3 ++- src/detector.c | 19 +++++++++++-------- src/parser.c | 9 +++++---- 3 files changed, 18 insertions(+), 13 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index a6d2c4a9f90..74cea7bea6c 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -305,7 +305,7 @@ struct layer { float mask_scale; float class_scale; int bias_match; - int random; + float random; float ignore_thresh; float truth_thresh; float iou_thresh; @@ -664,6 +664,7 @@ typedef struct network { int blur; int mixup; float label_smooth_eps; + int resize_step; int letter_box; float angle; float aspect; diff --git a/src/detector.c b/src/detector.c index bb059c77a28..3a74a431e79 100644 --- a/src/detector.c +++ b/src/detector.c @@ -168,19 +168,22 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i //while(i*imgs < N*120){ while (get_current_batch(net) < net.max_batches) { if (l.random && count++ % 10 == 0) { - printf("Resizing\n"); - float random_val = rand_scale(1.4); // *x or /x - int dim_w = roundl(random_val*init_w / 32 + 1) * 32; - int dim_h = roundl(random_val*init_h / 32 + 1) * 32; + float rand_coef = 1.4; + if (l.random != 1.0) rand_coef = l.random; + printf("Resizing, random_coef = %.2f \n", rand_coef); + float random_val = rand_scale(rand_coef); // *x or /x + int dim_w = roundl(random_val*init_w / net.resize_step + 1) * net.resize_step; + int dim_h = roundl(random_val*init_h / net.resize_step + 1) * net.resize_step; + if (random_val < 1 && (dim_w > init_w || dim_h > init_h)) dim_w = init_w, dim_h = init_h; // at the beginning if (avg_loss < 0) { - dim_w = roundl(1.4*init_w / 32 + 1) * 32; - dim_h = roundl(1.4*init_h / 32 + 1) * 32; + dim_w = roundl(rand_coef*init_w / net.resize_step + 1) * net.resize_step; + dim_h = roundl(rand_coef*init_h / net.resize_step + 1) * net.resize_step; } - if (dim_w < 32) dim_w = 32; - if (dim_h < 32) dim_h = 32; + if (dim_w < net.resize_step) dim_w = net.resize_step; + if (dim_h < net.resize_step) dim_h = net.resize_step; printf("%d x %d \n", dim_w, dim_h); args.w = dim_w; diff --git a/src/parser.c b/src/parser.c index c2d9098b32e..173a4d43a58 100644 --- a/src/parser.c +++ b/src/parser.c @@ -439,7 +439,7 @@ layer parse_yolo(list *options, size_params params) l.ignore_thresh = option_find_float(options, "ignore_thresh", .5); l.truth_thresh = option_find_float(options, "truth_thresh", 1); l.iou_thresh = option_find_float_quiet(options, "iou_thresh", 1); // recommended to use iou_thresh=0.213 in [yolo] - l.random = option_find_int_quiet(options, "random", 0); + l.random = option_find_float_quiet(options, "random", 0); char *map_file = option_find_str(options, "map", 0); if (map_file) l.map = read_map(map_file); @@ -541,7 +541,7 @@ layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3 l.ignore_thresh = option_find_float(options, "ignore_thresh", .5); l.truth_thresh = option_find_float(options, "truth_thresh", 1); l.iou_thresh = option_find_float_quiet(options, "iou_thresh", 1); // recommended to use iou_thresh=0.213 in [yolo] - l.random = option_find_int_quiet(options, "random", 0); + l.random = option_find_float_quiet(options, "random", 0); char *map_file = option_find_str(options, "map", 0); if (map_file) l.map = read_map(map_file); @@ -590,7 +590,7 @@ layer parse_region(list *options, size_params params) l.thresh = option_find_float(options, "thresh", .5); l.classfix = option_find_int_quiet(options, "classfix", 0); l.absolute = option_find_int_quiet(options, "absolute", 0); - l.random = option_find_int_quiet(options, "random", 0); + l.random = option_find_float_quiet(options, "random", 0); l.coord_scale = option_find_float(options, "coord_scale", 1); l.object_scale = option_find_float(options, "object_scale", 1); @@ -639,7 +639,7 @@ detection_layer parse_detection(list *options, size_params params) layer.noobject_scale = option_find_float(options, "noobject_scale", 1); layer.class_scale = option_find_float(options, "class_scale", 1); layer.jitter = option_find_float(options, "jitter", .2); - layer.random = option_find_int_quiet(options, "random", 0); + layer.random = option_find_float_quiet(options, "random", 0); layer.reorg = option_find_int_quiet(options, "reorg", 0); return layer; } @@ -1060,6 +1060,7 @@ void parse_net_options(list *options, network *net) else if (mosaic) net->mixup = 3; net->letter_box = option_find_int_quiet(options, "letter_box", 0); net->label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); + net->resize_step = option_find_float_quiet(options, "resize_step", 32); net->angle = option_find_float_quiet(options, "angle", 0); net->aspect = option_find_float_quiet(options, "aspect", 1); From 283333b52fc07a0e6db489cf56bc8f791bea9b00 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 9 Jan 2020 19:44:08 +0300 Subject: [PATCH 0796/1365] Added network resizing for SAM layer --- src/network.c | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/network.c b/src/network.c index 0de38420a4f..fd5eea844e5 100644 --- a/src/network.c +++ b/src/network.c @@ -541,6 +541,8 @@ int resize_network(network *net, int w, int h) resize_shortcut_layer(&l, w, h, net); }else if (l.type == SCALE_CHANNELS) { resize_scale_channels_layer(&l, net); + }else if (l.type == SAM) { + resize_sam_layer(&l, w, h); }else if (l.type == DROPOUT) { resize_dropout_layer(&l, inputs); l.out_w = l.w = w; From 0dbd0e1ef0b15bba63e7112bc5c7294d93e7bd2b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 9 Jan 2020 20:06:51 +0300 Subject: [PATCH 0797/1365] compile fix --- src/network.c | 1 + 1 file changed, 1 insertion(+) diff --git a/src/network.c b/src/network.c index fd5eea844e5..b5845025db2 100644 --- a/src/network.c +++ b/src/network.c @@ -33,6 +33,7 @@ #include "route_layer.h" #include "shortcut_layer.h" #include "scale_channels_layer.h" +#include "sam_layer.h" #include "yolo_layer.h" #include "gaussian_yolo_layer.h" #include "upsample_layer.h" From f1643cbf2e537a3893ca5f4cad9e6e40c7280f49 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 9 Jan 2020 20:54:09 +0300 Subject: [PATCH 0798/1365] Added: weights_normalizion = relu or softmax for [shortcut] layer --- include/darknet.h | 5 +++ src/blas.c | 77 ++++++++++++++++++++++++++++++++++------ src/blas.h | 8 ++--- src/blas_kernels.cu | 84 ++++++++++++++++++++++++++++++++++++-------- src/parser.c | 13 ++++--- src/route_layer.c | 3 +- src/shortcut_layer.c | 14 ++++---- src/shortcut_layer.h | 3 +- 8 files changed, 166 insertions(+), 41 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 74cea7bea6c..c83e76909b6 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -125,6 +125,10 @@ typedef enum { NO_WEIGHTS, PER_FEATURE, PER_CHANNEL } WEIGHTS_TYPE_T; +// parser.h +typedef enum { + NO_NORMALIZATION, RELU_NORMALIZATION, SOFTMAX_NORMALIZATION +} WEIGHTS_NORMALIZATION_T; // image.h typedef enum{ @@ -336,6 +340,7 @@ struct layer { float **layers_output; float **layers_delta; WEIGHTS_TYPE_T weights_type; + WEIGHTS_NORMALIZATION_T weights_normalizion; int * map; int * counts; float ** sums; diff --git a/src/blas.c b/src/blas.c index d10af5fffe1..511c673ee6a 100644 --- a/src/blas.c +++ b/src/blas.c @@ -68,7 +68,12 @@ void weighted_delta_cpu(float *a, float *b, float *s, float *da, float *db, floa } } -void shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *outputs_of_layers, float **layers_output, float *out, float *in, float *weights, int nweights) +static float relu(float src) { + if (src > 0) return src; + return 0; +} + +void shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *outputs_of_layers, float **layers_output, float *out, float *in, float *weights, int nweights, WEIGHTS_NORMALIZATION_T weights_normalizion) { // nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w) const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) @@ -83,11 +88,28 @@ void shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *o src_id /= src_outputs; int src_b = src_id; - if(weights) out[id] = in[id] * weights[src_i / step]; // [0 or c or (c, h ,w)] + float sum = 1; + int i; + if (weights_normalizion) { + const float eps = 0.0001; + sum = eps; + for (i = 0; i < (n + 1); ++i) { + const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] + if (weights_normalizion == RELU_NORMALIZATION) sum += relu(weights[weights_index]); + else if (weights_normalizion == SOFTMAX_NORMALIZATION) sum += expf(weights[weights_index]); + } + } + + if (weights) { + float w = weights[src_i / step]; + if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; + else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w) / sum; + + out[id] = in[id] * w; // [0 or c or (c, h ,w)] + } else out[id] = in[id]; // layers - int i; for (i = 0; i < n; ++i) { int add_outputs = outputs_of_layers[i]; if (src_i < add_outputs) { @@ -95,9 +117,15 @@ void shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *o int out_index = id; float *add = layers_output[i]; - const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)] - if (weights) out[out_index] += add[add_index] * weights[weights_index]; // [0 or c or (c, h ,w)] + if (weights) { + const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)] + float w = weights[weights_index]; + if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; + else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w) / sum; + + out[out_index] += add[add_index] * w; // [0 or c or (c, h ,w)] + } else out[out_index] += add[add_index]; } } @@ -105,7 +133,7 @@ void shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *o } void backward_shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *outputs_of_layers, - float **layers_delta, float *delta_out, float *delta_in, float *weights, float *weight_updates, int nweights, float *in, float **layers_output) + float **layers_delta, float *delta_out, float *delta_in, float *weights, float *weight_updates, int nweights, float *in, float **layers_output, WEIGHTS_NORMALIZATION_T weights_normalizion) { // nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w) const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) @@ -119,14 +147,37 @@ void backward_shortcut_multilayer_cpu(int size, int src_outputs, int batch, int src_id /= src_outputs; int src_b = src_id; + float grad = 1, sum = 1; + int i; + if (weights_normalizion) { + const float eps = 0.0001; + sum = eps; + for (i = 0; i < (n + 1); ++i) { + const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] + if (weights_normalizion == RELU_NORMALIZATION) sum += relu(weights[weights_index]); + else if (weights_normalizion == SOFTMAX_NORMALIZATION) sum += expf(weights[weights_index]); + } + + grad = 0; + for (i = 0; i < (n + 1); ++i) { + const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] + const float delta_w = delta_in[id] * in[id]; + if (weights_normalizion == RELU_NORMALIZATION) grad += delta_w * relu(weights[weights_index]) / sum; + else if (weights_normalizion == SOFTMAX_NORMALIZATION) grad += delta_w * expf(weights[weights_index]) / sum; + } + } + if (weights) { - delta_out[id] += delta_in[id] * weights[src_i / step]; // [0 or c or (c, h ,w)] - weight_updates[src_i / step] += delta_in[id] * in[id]; + float w = weights[src_i / step]; + if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; + else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w) / sum; + + delta_out[id] += delta_in[id] * w; // [0 or c or (c, h ,w)] + weight_updates[src_i / step] += delta_in[id] * in[id] * grad; } else delta_out[id] += delta_in[id]; // layers - int i; for (i = 0; i < n; ++i) { int add_outputs = outputs_of_layers[i]; if (src_i < add_outputs) { @@ -136,10 +187,14 @@ void backward_shortcut_multilayer_cpu(int size, int src_outputs, int batch, int float *layer_delta = layers_delta[i]; if (weights) { float *add = layers_output[i]; + const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)] + float w = weights[weights_index]; + if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; + else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w) / sum; - layer_delta[add_index] += delta_in[id] * weights[weights_index]; // [0 or c or (c, h ,w)] - weight_updates[weights_index] += delta_in[id] * add[add_index]; + layer_delta[add_index] += delta_in[id] * w; // [0 or c or (c, h ,w)] + weight_updates[weights_index] += delta_in[id] * add[add_index] * grad; } else layer_delta[add_index] += delta_in[id]; } diff --git a/src/blas.h b/src/blas.h index ccf8b35baa0..c030b86771e 100644 --- a/src/blas.h +++ b/src/blas.h @@ -32,9 +32,9 @@ void fill_cpu(int N, float ALPHA, float * X, int INCX); float dot_cpu(int N, float *X, int INCX, float *Y, int INCY); void test_gpu_blas(); void shortcut_cpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out); -void shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *outputs_of_layers, float **layers_output, float *out, float *in, float *weights, int nweights); +void shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *outputs_of_layers, float **layers_output, float *out, float *in, float *weights, int nweights, WEIGHTS_NORMALIZATION_T weights_normalizion); void backward_shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *outputs_of_layers, - float **layers_delta, float *delta_out, float *delta_in, float *weights, float *weight_updates, int nweights, float *in, float **layers_output); + float **layers_delta, float *delta_out, float *delta_in, float *weights, float *weight_updates, int nweights, float *in, float **layers_output, WEIGHTS_NORMALIZATION_T weights_normalizion); void mean_cpu(float *x, int batch, int filters, int spatial, float *mean); void variance_cpu(float *x, float *mean, int batch, int filters, int spatial, float *variance); @@ -88,9 +88,9 @@ void fast_variance_delta_gpu(float *x, float *delta, float *mean, float *varianc void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance); void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean); void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out); -void shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights); +void shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights, WEIGHTS_NORMALIZATION_T weights_normalizion); void backward_shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_delta_gpu, float *delta_out, float *delta_in, - float *weights, float *weight_updates, int nweights, float *in, float **layers_output); + float *weights, float *weight_updates, int nweights, float *in, float **layers_output, WEIGHTS_NORMALIZATION_T weights_normalizion); void input_shortcut_gpu(float *in, int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out); void scale_bias_gpu(float *output, float *biases, int batch, int n, int size); void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates); diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 7ce533a7331..7cf72dcf25e 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -674,7 +674,12 @@ extern "C" void fill_ongpu(int N, float ALPHA, float * X, int INCX) CHECK_CUDA(cudaPeekAtLastError()); } -__global__ void shortcut_multilayer_kernel(int size, int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights) +__device__ float relu(float src) { + if (src > 0) return src; + return 0; +} + +__global__ void shortcut_multilayer_kernel(int size, int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights, WEIGHTS_NORMALIZATION_T weights_normalizion) { const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (id >= size) return; @@ -688,7 +693,24 @@ __global__ void shortcut_multilayer_kernel(int size, int src_outputs, int batch, src_id /= src_outputs; int src_b = src_id; - if (weights_gpu) out[id] = in[id] * weights_gpu[src_i / step]; // [0 or c or (c, h ,w)] + float sum = 1; + if (weights_normalizion) { + const float eps = 0.0001; + sum = eps; + for (int i = 0; i < (n + 1); ++i) { + const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] + if (weights_normalizion == RELU_NORMALIZATION) sum += relu(weights_gpu[weights_index]); + else if (weights_normalizion == SOFTMAX_NORMALIZATION) sum += expf(weights_gpu[weights_index]); + } + } + + if (weights_gpu) { + float w = weights_gpu[src_i / step]; + if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; + else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w) / sum; + + out[id] = in[id] * w; // [0 or c or (c, h ,w)] + } else out[id] = in[id]; // layers @@ -699,26 +721,31 @@ __global__ void shortcut_multilayer_kernel(int size, int src_outputs, int batch, int out_index = id; float *add = layers_output_gpu[i]; - const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)] - if (weights_gpu) out[out_index] += add[add_index] * weights_gpu[weights_index]; // [0 or c or (c, h ,w)] + if (weights_gpu) { + const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)] + float w = weights_gpu[weights_index]; + if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; + else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w) / sum; + + out[out_index] += add[add_index] * w; // [0 or c or (c, h ,w)] + } else out[out_index] += add[add_index]; } } } -extern "C" void shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights) +extern "C" void shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights, WEIGHTS_NORMALIZATION_T weights_normalizion) { //printf(" src_outputs = %d, batch = %d, n = %d \n", src_outputs, batch, n); int size = batch * src_outputs; - shortcut_multilayer_kernel << > > (size, src_outputs, batch, n, outputs_of_layers_gpu, layers_output_gpu, out, in, weights_gpu, nweights); + shortcut_multilayer_kernel << > > (size, src_outputs, batch, n, outputs_of_layers_gpu, layers_output_gpu, out, in, weights_gpu, nweights, weights_normalizion); CHECK_CUDA(cudaPeekAtLastError()); } - __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, int batch, int n, int *outputs_of_layers_gpu, - float **layers_delta_gpu, float *delta_out, float *delta_in, float *weights_gpu, float *weight_updates_gpu, int nweights, float *in, float **layers_output_gpu) + float **layers_delta_gpu, float *delta_out, float *delta_in, float *weights_gpu, float *weight_updates_gpu, int nweights, float *in, float **layers_output_gpu, WEIGHTS_NORMALIZATION_T weights_normalizion) { const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (id >= size) return; @@ -733,9 +760,33 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i src_id /= src_outputs; int src_b = src_id; + float grad = 1, sum = 1; + int i; + if (weights_normalizion) { + const float eps = 0.0001; + sum = eps; + for (i = 0; i < (n + 1); ++i) { + const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] + if (weights_normalizion == RELU_NORMALIZATION) sum += relu(weights_gpu[weights_index]); + else if (weights_normalizion == SOFTMAX_NORMALIZATION) sum += expf(weights_gpu[weights_index]); + } + + grad = 0; + for (i = 0; i < (n + 1); ++i) { + const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] + const float delta_w = delta_in[id] * in[id]; + if (weights_normalizion == RELU_NORMALIZATION) grad += delta_w * relu(weights_gpu[weights_index]) / sum; + else if (weights_normalizion == SOFTMAX_NORMALIZATION) grad += delta_w * expf(weights_gpu[weights_index]) / sum; + } + } + if (weights_gpu) { - delta_out[id] += delta_in[id] * weights_gpu[src_i / step]; // [0 or c or (c, h ,w)] - weight_updates_gpu[src_i / step] += delta_in[id] * in[id]; + float w = weights_gpu[src_i / step]; + if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; + else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w) / sum; + + delta_out[id] += delta_in[id] * w; // [0 or c or (c, h ,w)] + weight_updates_gpu[src_i / step] += delta_in[id] * in[id] * grad; } else delta_out[id] += delta_in[id]; @@ -749,9 +800,14 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i float *layer_delta = layers_delta_gpu[i]; if (weights_gpu) { float *add = layers_output_gpu[i]; + const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)] - layer_delta[add_index] += delta_in[id] * weights_gpu[weights_index]; - weight_updates_gpu[weights_index] += delta_in[id] * add[add_index]; + float w = weights_gpu[weights_index]; + if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; + else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w) / sum; + + layer_delta[add_index] += delta_in[id] * w; + weight_updates_gpu[weights_index] += delta_in[id] * add[add_index] * grad; } else layer_delta[add_index] += delta_in[id]; } @@ -759,7 +815,7 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i } extern "C" void backward_shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, - float **layers_delta_gpu, float *delta_out, float *delta_in, float *weights_gpu, float *weight_updates_gpu, int nweights, float *in, float **layers_output_gpu) + float **layers_delta_gpu, float *delta_out, float *delta_in, float *weights_gpu, float *weight_updates_gpu, int nweights, float *in, float **layers_output_gpu, WEIGHTS_NORMALIZATION_T weights_normalizion) { const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) const int step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 @@ -768,7 +824,7 @@ extern "C" void backward_shortcut_multilayer_gpu(int src_outputs, int batch, int //printf(" src_outputs = %d, batch = %d, n = %d \n", src_outputs, batch, n); int size = batch * src_outputs; backward_shortcut_multilayer_kernel << > > (size, src_outputs, batch, n, outputs_of_layers_gpu, - layers_delta_gpu, delta_out, delta_in, weights_gpu, weight_updates_gpu, nweights, in, layers_output_gpu); + layers_delta_gpu, delta_out, delta_in, weights_gpu, weight_updates_gpu, nweights, in, layers_output_gpu, weights_normalizion); CHECK_CUDA(cudaPeekAtLastError()); } diff --git a/src/parser.c b/src/parser.c index 173a4d43a58..1797c3d5985 100644 --- a/src/parser.c +++ b/src/parser.c @@ -816,10 +816,15 @@ layer parse_shortcut(list *options, size_params params, network net) char *activation_s = option_find_str(options, "activation", "logistic"); ACTIVATION activation = get_activation(activation_s); - char *weights_type_srt = option_find_str_quiet(options, "weights_type", "none"); + char *weights_type_str = option_find_str_quiet(options, "weights_type", "none"); WEIGHTS_TYPE_T weights_type = NO_WEIGHTS; - if(strcmp(weights_type_srt, "per_feature") == 0) weights_type = PER_FEATURE; - else if (strcmp(weights_type_srt, "per_channel") == 0) weights_type = PER_CHANNEL; + if(strcmp(weights_type_str, "per_feature") == 0) weights_type = PER_FEATURE; + else if (strcmp(weights_type_str, "per_channel") == 0) weights_type = PER_CHANNEL; + + char *weights_normalizion_str = option_find_str_quiet(options, "weights_normalizion", "none"); + WEIGHTS_NORMALIZATION_T weights_normalizion = NO_NORMALIZATION; + if (strcmp(weights_normalizion_str, "relu") == 0) weights_normalizion = RELU_NORMALIZATION; + else if (strcmp(weights_normalizion_str, "softmax") == 0) weights_normalizion = SOFTMAX_NORMALIZATION; char *l = option_find(options, "from"); int len = strlen(l); @@ -855,7 +860,7 @@ layer parse_shortcut(list *options, size_params params, network net) #endif// GPU layer s = make_shortcut_layer(params.batch, n, layers, sizes, params.w, params.h, params.c, layers_output, layers_delta, - layers_output_gpu, layers_delta_gpu, weights_type, activation, params.train); + layers_output_gpu, layers_delta_gpu, weights_type, weights_normalizion, activation, params.train); free(layers_output_gpu); free(layers_delta_gpu); diff --git a/src/route_layer.c b/src/route_layer.c index 8e3f15f8a9d..1b9a2811775 100644 --- a/src/route_layer.c +++ b/src/route_layer.c @@ -56,8 +56,9 @@ void resize_route_layer(route_layer *l, network *net) if(next.out_w == first.out_w && next.out_h == first.out_h){ l->out_c += next.out_c; }else{ - printf("%d %d, %d %d\n", next.out_w, next.out_h, first.out_w, first.out_h); + printf("Error: Different size of input layers: %d x %d, %d x %d\n", next.out_w, next.out_h, first.out_w, first.out_h); l->out_h = l->out_w = l->out_c = 0; + exit(EXIT_FAILURE); } } l->out_c = l->out_c / l->groups; diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index db5e3ad10c6..281a6218f25 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -7,7 +7,8 @@ #include layer make_shortcut_layer(int batch, int n, int *input_layers, int* input_sizes, int w, int h, int c, - float **layers_output, float **layers_delta, float **layers_output_gpu, float **layers_delta_gpu, WEIGHTS_TYPE_T weights_type, ACTIVATION activation, int train) + float **layers_output, float **layers_delta, float **layers_output_gpu, float **layers_delta_gpu, WEIGHTS_TYPE_T weights_type, WEIGHTS_NORMALIZATION_T weights_normalizion, + ACTIVATION activation, int train) { fprintf(stderr, "Shortcut Layer: "); int i; @@ -24,6 +25,7 @@ layer make_shortcut_layer(int batch, int n, int *input_layers, int* input_sizes, l.layers_output = layers_output; l.layers_delta = layers_delta; l.weights_type = weights_type; + l.weights_normalizion = weights_normalizion; //l.w = w2; //l.h = h2; @@ -81,7 +83,7 @@ layer make_shortcut_layer(int batch, int n, int *input_layers, int* input_sizes, l.bflops = l.out_w * l.out_h * l.out_c * l.n / 1000000000.; if (l.weights_type) l.bflops *= 2; - fprintf(stderr, " wt = %d, outputs:%4d x%4d x%4d %5.3f BF\n", l.weights_type, l.out_w, l.out_h, l.out_c, l.bflops); + fprintf(stderr, " wt = %d, wn = %d, outputs:%4d x%4d x%4d %5.3f BF\n", l.weights_type, l.weights_normalizion, l.out_w, l.out_h, l.out_c, l.bflops); return l; } @@ -148,7 +150,7 @@ void forward_shortcut_layer(const layer l, network_state state) l.output[i] = state.input[i] + state.net.layers[l.index].output[i]; } else { - shortcut_multilayer_cpu(l.outputs * l.batch, l.outputs, l.batch, l.n, l.input_sizes, l.layers_output, l.output, state.input, l.weights, l.nweights); + shortcut_multilayer_cpu(l.outputs * l.batch, l.outputs, l.batch, l.n, l.input_sizes, l.layers_output, l.output, state.input, l.weights, l.nweights, l.weights_normalizion); } //copy_cpu(l.outputs*l.batch, state.input, 1, l.output, 1); @@ -167,7 +169,7 @@ void backward_shortcut_layer(const layer l, network_state state) else gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); backward_shortcut_multilayer_cpu(l.outputs * l.batch, l.outputs, l.batch, l.n, l.input_sizes, - l.layers_delta, state.delta, l.delta, l.weights, l.weight_updates, l.nweights, state.input, l.layers_output); + l.layers_delta, state.delta, l.delta, l.weights, l.weight_updates, l.nweights, state.input, l.layers_output, l.weights_normalizion); //axpy_cpu(l.outputs*l.batch, 1, l.delta, 1, state.delta, 1); //shortcut_cpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta, l.w, l.h, l.c, state.net.layers[l.index].delta); @@ -203,7 +205,7 @@ void forward_shortcut_layer_gpu(const layer l, network_state state) //} //else { - shortcut_multilayer_gpu(l.outputs, l.batch, l.n, l.input_sizes_gpu, l.layers_output_gpu, l.output_gpu, state.input, l.weights_gpu, l.nweights); + shortcut_multilayer_gpu(l.outputs, l.batch, l.n, l.input_sizes_gpu, l.layers_output_gpu, l.output_gpu, state.input, l.weights_gpu, l.nweights, l.weights_normalizion); } if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); @@ -219,7 +221,7 @@ void backward_shortcut_layer_gpu(const layer l, network_state state) else gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); backward_shortcut_multilayer_gpu(l.outputs, l.batch, l.n, l.input_sizes_gpu, l.layers_delta_gpu, state.delta, l.delta_gpu, - l.weights_gpu, l.weight_updates_gpu, l.nweights, state.input, l.layers_output_gpu); + l.weights_gpu, l.weight_updates_gpu, l.nweights, state.input, l.layers_output_gpu, l.weights_normalizion); //axpy_ongpu(l.outputs*l.batch, 1, l.delta_gpu, 1, state.delta, 1); //shortcut_gpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta_gpu, l.w, l.h, l.c, state.net.layers[l.index].delta_gpu); diff --git a/src/shortcut_layer.h b/src/shortcut_layer.h index e546693c04b..291916200aa 100644 --- a/src/shortcut_layer.h +++ b/src/shortcut_layer.h @@ -8,7 +8,8 @@ extern "C" { #endif layer make_shortcut_layer(int batch, int n, int *input_layers, int* input_sizes, int w, int h, int c, - float **layers_output, float **layers_delta, float **layers_output_gpu, float **layers_delta_gpu, WEIGHTS_TYPE_T weights_type, ACTIVATION activation, int train); + float **layers_output, float **layers_delta, float **layers_output_gpu, float **layers_delta_gpu, WEIGHTS_TYPE_T weights_type, WEIGHTS_NORMALIZATION_T weights_normalizion, + ACTIVATION activation, int train); void forward_shortcut_layer(const layer l, network_state state); void backward_shortcut_layer(const layer l, network_state state); void update_shortcut_layer(layer l, int batch, float learning_rate_init, float momentum, float decay); From 2116cba1ed123b38b432d7d8b9a2c235372fd553 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 10 Jan 2020 00:03:03 +0300 Subject: [PATCH 0799/1365] [shortcut] uses activation=linear by default --- src/parser.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/parser.c b/src/parser.c index 1797c3d5985..ac255fd7a51 100644 --- a/src/parser.c +++ b/src/parser.c @@ -813,7 +813,7 @@ layer parse_batchnorm(list *options, size_params params) layer parse_shortcut(list *options, size_params params, network net) { - char *activation_s = option_find_str(options, "activation", "logistic"); + char *activation_s = option_find_str(options, "activation", "linear"); ACTIVATION activation = get_activation(activation_s); char *weights_type_str = option_find_str_quiet(options, "weights_type", "none"); From 300406e26a155d8cf35f90985c478b9cea82d264 Mon Sep 17 00:00:00 2001 From: cyy Date: Fri, 10 Jan 2020 17:47:10 +0800 Subject: [PATCH 0800/1365] fix macro --- src/dark_cuda.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/dark_cuda.c b/src/dark_cuda.c index cfb6b825ab0..d16f9efc690 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -162,7 +162,7 @@ cudnnHandle_t cudnn_handle() cudnnCreate(&handle[i]); init[i] = 1; cudnnStatus_t status = cudnnSetStream(handle[i], get_cuda_stream()); - CHECK_CUDA(status); + CHECK_CUDNN(status); } return handle[i]; } From 619e39fc71a7c65f9d33aaca8ec05167796e88aa Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 10 Jan 2020 14:12:39 +0300 Subject: [PATCH 0801/1365] Fixed bug in [shortcut] --- src/blas.c | 10 ++++++---- src/blas_kernels.cu | 11 ++++++----- src/parser.c | 4 ++-- 3 files changed, 14 insertions(+), 11 deletions(-) diff --git a/src/blas.c b/src/blas.c index 511c673ee6a..02a8bf95cca 100644 --- a/src/blas.c +++ b/src/blas.c @@ -77,7 +77,8 @@ void shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *o { // nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w) const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) - const int step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 + int step = 0; + if (weights) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 int id; #pragma omp parallel for @@ -90,7 +91,7 @@ void shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *o float sum = 1; int i; - if (weights_normalizion) { + if (weights && weights_normalizion) { const float eps = 0.0001; sum = eps; for (i = 0; i < (n + 1); ++i) { @@ -137,7 +138,8 @@ void backward_shortcut_multilayer_cpu(int size, int src_outputs, int batch, int { // nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w) const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) - const int step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 + int step = 0; + if (weights) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 int id; #pragma omp parallel for @@ -149,7 +151,7 @@ void backward_shortcut_multilayer_cpu(int size, int src_outputs, int batch, int float grad = 1, sum = 1; int i; - if (weights_normalizion) { + if (weights && weights_normalizion) { const float eps = 0.0001; sum = eps; for (i = 0; i < (n + 1); ++i) { diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 7cf72dcf25e..2442cae1d3e 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -686,7 +686,8 @@ __global__ void shortcut_multilayer_kernel(int size, int src_outputs, int batch, // nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w) const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) - const int step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 + int step = 0; + if (weights_gpu) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 int src_id = id; const int src_i = src_id % src_outputs; @@ -694,7 +695,7 @@ __global__ void shortcut_multilayer_kernel(int size, int src_outputs, int batch, int src_b = src_id; float sum = 1; - if (weights_normalizion) { + if (weights_gpu && weights_normalizion) { const float eps = 0.0001; sum = eps; for (int i = 0; i < (n + 1); ++i) { @@ -752,8 +753,8 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i // nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w) const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) - const int step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 - //if(id == 0) printf(" layer_step = %d, step = %d \n", layer_step, step); + int step = 0; + if (weights_gpu) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 int src_id = id; const int src_i = src_id % src_outputs; @@ -762,7 +763,7 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i float grad = 1, sum = 1; int i; - if (weights_normalizion) { + if (weights_gpu && weights_normalizion) { const float eps = 0.0001; sum = eps; for (i = 0; i < (n + 1); ++i) { diff --git a/src/parser.c b/src/parser.c index ac255fd7a51..8823e1fa055 100644 --- a/src/parser.c +++ b/src/parser.c @@ -818,12 +818,12 @@ layer parse_shortcut(list *options, size_params params, network net) char *weights_type_str = option_find_str_quiet(options, "weights_type", "none"); WEIGHTS_TYPE_T weights_type = NO_WEIGHTS; - if(strcmp(weights_type_str, "per_feature") == 0) weights_type = PER_FEATURE; + if(strcmp(weights_type_str, "per_feature") == 0 || strcmp(weights_type_str, "per_layer") == 0) weights_type = PER_FEATURE; else if (strcmp(weights_type_str, "per_channel") == 0) weights_type = PER_CHANNEL; char *weights_normalizion_str = option_find_str_quiet(options, "weights_normalizion", "none"); WEIGHTS_NORMALIZATION_T weights_normalizion = NO_NORMALIZATION; - if (strcmp(weights_normalizion_str, "relu") == 0) weights_normalizion = RELU_NORMALIZATION; + if (strcmp(weights_normalizion_str, "relu") == 0 || strcmp(weights_normalizion_str, "avg_relu") == 0) weights_normalizion = RELU_NORMALIZATION; else if (strcmp(weights_normalizion_str, "softmax") == 0) weights_normalizion = SOFTMAX_NORMALIZATION; char *l = option_find(options, "from"); From 67c91e6bef3d0b90babbde2d892ef4b0183ca366 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 10 Jan 2020 14:44:48 +0300 Subject: [PATCH 0802/1365] Minor fix --- src/parser.c | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/src/parser.c b/src/parser.c index 8823e1fa055..ac8f689121f 100644 --- a/src/parser.c +++ b/src/parser.c @@ -820,11 +820,21 @@ layer parse_shortcut(list *options, size_params params, network net) WEIGHTS_TYPE_T weights_type = NO_WEIGHTS; if(strcmp(weights_type_str, "per_feature") == 0 || strcmp(weights_type_str, "per_layer") == 0) weights_type = PER_FEATURE; else if (strcmp(weights_type_str, "per_channel") == 0) weights_type = PER_CHANNEL; + else if (strcmp(weights_type_str, "none") != 0) { + printf("Error: Incorrect weights_type = %s \n Use one of: none, per_feature, per_channel \n", weights_type_str); + getchar(); + exit(0); + } char *weights_normalizion_str = option_find_str_quiet(options, "weights_normalizion", "none"); WEIGHTS_NORMALIZATION_T weights_normalizion = NO_NORMALIZATION; if (strcmp(weights_normalizion_str, "relu") == 0 || strcmp(weights_normalizion_str, "avg_relu") == 0) weights_normalizion = RELU_NORMALIZATION; else if (strcmp(weights_normalizion_str, "softmax") == 0) weights_normalizion = SOFTMAX_NORMALIZATION; + else if (strcmp(weights_type_str, "none") != 0) { + printf("Error: Incorrect weights_normalizion = %s \n Use one of: none, relu, softmax \n", weights_normalizion_str); + getchar(); + exit(0); + } char *l = option_find(options, "from"); int len = strlen(l); From fb8c944c77c52133a94ea44291ad972c295fa9b1 Mon Sep 17 00:00:00 2001 From: nog642 Date: Sat, 11 Jan 2020 17:21:43 -0800 Subject: [PATCH 0803/1365] replace ; with , --- src/layer.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/layer.c b/src/layer.c index ae72da6a863..93b968821d9 100644 --- a/src/layer.c +++ b/src/layer.c @@ -184,7 +184,7 @@ void free_layer_custom(layer l, int keep_cudnn_desc) if (l.scale_updates_gpu) cuda_free(l.scale_updates_gpu), l.scale_updates_gpu = NULL; if (l.input_antialiasing_gpu) cuda_free(l.input_antialiasing_gpu), l.input_antialiasing_gpu = NULL; if (l.optimized_memory < 2) { - if (l.x_gpu) cuda_free(l.x_gpu); l.x_gpu = NULL; + if (l.x_gpu) cuda_free(l.x_gpu), l.x_gpu = NULL; if (l.output_gpu) cuda_free(l.output_gpu), l.output_gpu = NULL; if (l.activation_input_gpu) cuda_free(l.activation_input_gpu), l.activation_input_gpu = NULL; } From 2247c5fcd7bc95e1bed6569a48ed231193c46dad Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 13 Jan 2020 22:12:44 +0300 Subject: [PATCH 0804/1365] minor fixes --- src/coco.c | 2 +- src/detector.c | 15 +++++++-------- 2 files changed, 8 insertions(+), 9 deletions(-) diff --git a/src/coco.c b/src/coco.c index c0f6448b6e9..7cac158879f 100644 --- a/src/coco.c +++ b/src/coco.c @@ -288,7 +288,7 @@ void validate_coco_recall(char *cfgfile, char *weightfile) char *id = basecfg(path); network_predict(net, sized.data); get_detection_boxes(l, 1, 1, thresh, probs, boxes, 1); - // nms is always 0 if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms_thresh); + if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms_thresh); char labelpath[4096]; replace_image_to_label(path, labelpath); diff --git a/src/detector.c b/src/detector.c index c67a43eb65c..94c10be6a06 100644 --- a/src/detector.c +++ b/src/detector.c @@ -18,7 +18,7 @@ typedef __compar_fn_t comparison_fn_t; #include "http_stream.h" -extern int check_mistakes = 0; +int check_mistakes = 0; static int coco_ids[] = { 1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90 }; @@ -1246,7 +1246,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa free(tp_for_thresh_per_class); free(fp_for_thresh_per_class); - fprintf(stderr, "Total Detection Time: %ld Seconds\n", time(0) - start); + fprintf(stderr, "Total Detection Time: %d Seconds\n", (int)(time(0) - start)); printf("\nSet -points flag:\n"); printf(" `-points 101` for MS COCO \n"); printf(" `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n"); @@ -1333,7 +1333,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int int num_labels = 0; box_label *truth = read_boxes(labelpath, &num_labels); //printf(" new path: %s \n", labelpath); - char buff[6144]; + char *buff = (char*)xcalloc(6144, sizeof(char)); for (j = 0; j < num_labels; ++j) { if (truth[j].x > 1 || truth[j].x <= 0 || truth[j].y > 1 || truth[j].y <= 0 || @@ -1348,13 +1348,12 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int } number_of_boxes++; rel_width_height_array = (float*)xrealloc(rel_width_height_array, 2 * number_of_boxes * sizeof(float)); - if (!rel_width_height_array) { - error("realloc failed"); - } + rel_width_height_array[number_of_boxes * 2 - 2] = truth[j].w * width; rel_width_height_array[number_of_boxes * 2 - 1] = truth[j].h * height; printf("\r loaded \t image: %d \t box: %d", i + 1, number_of_boxes); } + free(buff); } printf("\n all loaded. \n"); printf("\n calculating k-means++ ..."); @@ -1650,9 +1649,9 @@ void run_detector(int argc, char **argv) int ngpus = 0; if (gpu_list) { printf("%s\n", gpu_list); - size_t len = strlen(gpu_list); + int len = (int)strlen(gpu_list); ngpus = 1; - size_t i; + int i; for (i = 0; i < len; ++i) { if (gpu_list[i] == ',') ++ngpus; } From 04a050cf07d817a5c4381f75fb45354f31be7c4b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 13 Jan 2020 22:19:24 +0300 Subject: [PATCH 0805/1365] Fixed [shortcut] resize for swish/mish --- src/parser.c | 5 +++-- src/shortcut_layer.c | 7 +++++++ 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/src/parser.c b/src/parser.c index 3ef34bbaf99..291be389589 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1856,17 +1856,18 @@ void load_weights_upto(network *net, char *filename, int cutoff) fread(&minor, sizeof(int), 1, fp); fread(&revision, sizeof(int), 1, fp); if ((major * 10 + minor) >= 2) { - printf("\n seen 64 \n"); + printf("\n seen 64"); uint64_t iseen = 0; fread(&iseen, sizeof(uint64_t), 1, fp); *net->seen = iseen; } else { - printf("\n seen 32 \n"); + printf("\n seen 32"); uint32_t iseen = 0; fread(&iseen, sizeof(uint32_t), 1, fp); *net->seen = iseen; } + printf(", trained: %.0f K-images (%.0f Kilo-batches_64) \n", (float)(*net->seen / 1000), (float)(*net->seen / 64000)); int transpose = (major > 1000) || (minor > 1000); int i; diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index 0fecd2cedfc..3d0d0098474 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -110,6 +110,8 @@ void resize_shortcut_layer(layer *l, int w, int h, network *net) assert(l->w == net->layers[index].out_w && l->h == net->layers[index].out_h); } + if (l->activation == SWISH || l->activation == MISH) l->activation_input = (float*)realloc(l->activation_input, l->batch*l->outputs * sizeof(float)); + #ifdef GPU cuda_free(l->output_gpu); l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch); @@ -134,6 +136,11 @@ void resize_shortcut_layer(layer *l, int w, int h, network *net) free(layers_output_gpu); free(layers_delta_gpu); + + if (l->activation == SWISH || l->activation == MISH) { + cuda_free(l->activation_input_gpu); + l->activation_input_gpu = cuda_make_array(l->activation_input, l->batch*l->outputs); + } #endif } From 04f78ddf726fea1aa2ea8657632c3749ccf8e2bf Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 14 Jan 2020 00:18:35 +0300 Subject: [PATCH 0806/1365] Fixed bugs from PR --- src/blas_kernels.cu | 41 +++++++++++++++++++++++++++++------------ src/detector.c | 26 +++++++++++--------------- src/utils.c | 39 ++++++++++++++++++--------------------- 3 files changed, 58 insertions(+), 48 deletions(-) diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 2442cae1d3e..a2fab184422 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -694,21 +694,29 @@ __global__ void shortcut_multilayer_kernel(int size, int src_outputs, int batch, src_id /= src_outputs; int src_b = src_id; - float sum = 1; + float sum = 1, max_val = -INFINITY; if (weights_gpu && weights_normalizion) { + if (weights_normalizion == SOFTMAX_NORMALIZATION) { + for (int i = 0; i < (n + 1); ++i) { + const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] + const float w = weights_gpu[weights_index]; + if (max_val < w) max_val = w; + } + } const float eps = 0.0001; sum = eps; for (int i = 0; i < (n + 1); ++i) { const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] - if (weights_normalizion == RELU_NORMALIZATION) sum += relu(weights_gpu[weights_index]); - else if (weights_normalizion == SOFTMAX_NORMALIZATION) sum += expf(weights_gpu[weights_index]); + const float w = weights_gpu[weights_index]; + if (weights_normalizion == RELU_NORMALIZATION) sum += relu(w); + else if (weights_normalizion == SOFTMAX_NORMALIZATION) sum += expf(w - max_val); } } if (weights_gpu) { float w = weights_gpu[src_i / step]; if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; - else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w) / sum; + else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; out[id] = in[id] * w; // [0 or c or (c, h ,w)] } @@ -727,7 +735,7 @@ __global__ void shortcut_multilayer_kernel(int size, int src_outputs, int batch, const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)] float w = weights_gpu[weights_index]; if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; - else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w) / sum; + else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; out[out_index] += add[add_index] * w; // [0 or c or (c, h ,w)] } @@ -761,30 +769,39 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i src_id /= src_outputs; int src_b = src_id; - float grad = 1, sum = 1; + float grad = 1, sum = 1, max_val = -INFINITY; int i; if (weights_gpu && weights_normalizion) { + if (weights_normalizion == SOFTMAX_NORMALIZATION) { + for (int i = 0; i < (n + 1); ++i) { + const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] + float w = weights_gpu[weights_index]; + if (max_val < w) max_val = w; + } + } const float eps = 0.0001; sum = eps; for (i = 0; i < (n + 1); ++i) { const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] - if (weights_normalizion == RELU_NORMALIZATION) sum += relu(weights_gpu[weights_index]); - else if (weights_normalizion == SOFTMAX_NORMALIZATION) sum += expf(weights_gpu[weights_index]); + const float w = weights_gpu[weights_index]; + if (weights_normalizion == RELU_NORMALIZATION) sum += relu(w); + else if (weights_normalizion == SOFTMAX_NORMALIZATION) sum += expf(w - max_val); } grad = 0; for (i = 0; i < (n + 1); ++i) { const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] const float delta_w = delta_in[id] * in[id]; - if (weights_normalizion == RELU_NORMALIZATION) grad += delta_w * relu(weights_gpu[weights_index]) / sum; - else if (weights_normalizion == SOFTMAX_NORMALIZATION) grad += delta_w * expf(weights_gpu[weights_index]) / sum; + const float w = weights_gpu[weights_index]; + if (weights_normalizion == RELU_NORMALIZATION) grad += delta_w * relu(w) / sum; + else if (weights_normalizion == SOFTMAX_NORMALIZATION) grad += delta_w * expf(w - max_val) / sum; } } if (weights_gpu) { float w = weights_gpu[src_i / step]; if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; - else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w) / sum; + else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; delta_out[id] += delta_in[id] * w; // [0 or c or (c, h ,w)] weight_updates_gpu[src_i / step] += delta_in[id] * in[id] * grad; @@ -805,7 +822,7 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)] float w = weights_gpu[weights_index]; if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; - else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w) / sum; + else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; layer_delta[add_index] += delta_in[id] * w; weight_updates_gpu[weights_index] += delta_in[id] * add[add_index] * grad; diff --git a/src/detector.c b/src/detector.c index 94c10be6a06..e8cb0266b9c 100644 --- a/src/detector.c +++ b/src/detector.c @@ -688,14 +688,12 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out free_image(val_resized[t]); } } - free(buf); - free(buf_resized); - free(val); - free(val_resized); - for (j = 0; j < classes; ++j) { - if (fps) fclose(fps[j]); + if (fps) { + for (j = 0; j < classes; ++j) { + fclose(fps[j]); + } + free(fps); } - if (fps) free(fps); if (coco) { #ifdef WIN32 fseek(fp, -3, SEEK_CUR); @@ -922,12 +920,12 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa time_t start = time(0); for (i = nthreads; i < m + nthreads; i += nthreads) { fprintf(stderr, "\r%d", i); - for (t = 0; t < nthreads && i + t - nthreads < m; ++t) { + for (t = 0; t < nthreads && (i + t - nthreads) < m; ++t) { pthread_join(thr[t], 0); val[t] = buf[t]; val_resized[t] = buf_resized[t]; } - for (t = 0; t < nthreads && i + t < m; ++t) { + for (t = 0; t < nthreads && (i + t) < m; ++t) { args.path = paths[i + t]; args.im = &buf[t]; args.resized = &buf_resized[t]; @@ -1070,12 +1068,10 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa free_image(val_resized[t]); } } - free(val); - free(val_resized); - for (t = 0; t < nthreads; ++t) { - pthread_join(thr[t], 0); - } - free(thr); + + //for (t = 0; t < nthreads; ++t) { + // pthread_join(thr[t], 0); + //} if ((tp_for_thresh + fp_for_thresh) > 0) avg_iou = avg_iou / (tp_for_thresh + fp_for_thresh); diff --git a/src/utils.c b/src/utils.c index 4d62b30fa64..7448af891d7 100644 --- a/src/utils.c +++ b/src/utils.c @@ -32,6 +32,7 @@ void *xmalloc(size_t size) { } return ptr; } + void *xcalloc(size_t nmemb, size_t size) { void *ptr=calloc(nmemb,size); if(!ptr) { @@ -39,6 +40,7 @@ void *xcalloc(size_t nmemb, size_t size) { } return ptr; } + void *xrealloc(void *ptr, size_t size) { ptr=realloc(ptr,size); if(!ptr) { @@ -66,9 +68,6 @@ int *read_map(char *filename) while((str=fgetl(file))){ ++n; map = (int*)xrealloc(map, n * sizeof(int)); - if(!map) { - error("realloc failed"); - } map[n-1] = atoi(str); free(str); } @@ -206,31 +205,26 @@ void pm(int M, int N, float *A) void find_replace(const char* str, char* orig, char* rep, char* output) { - char* buffer = strdup(str); - if(!buffer) { - error("strdup failed"); - } + char* buffer = (char*)calloc(8192, sizeof(char)); char *p; - if(!(p = strstr(buffer, orig))){ // Is 'orig' even in 'str'? - if(str!=output) { - strcpy(output,str); - } + + sprintf(buffer, "%s", str); + if (!(p = strstr(buffer, orig))) { // Is 'orig' even in 'str'? + sprintf(output, "%s", buffer); free(buffer); return; } *p = '\0'; - sprintf(output, "%s%s%s", buffer, rep, p+strlen(orig)); + sprintf(output, "%s%s%s", buffer, rep, p + strlen(orig)); free(buffer); } void trim(char *str) { - char* buffer = strdup(str); - if(!buffer) { - error("strdup failed"); - } + char* buffer = (char*)xcalloc(8192, sizeof(char)); + sprintf(buffer, "%s", str); char *p = buffer; while (*p == ' ' || *p == '\t') ++p; @@ -240,16 +234,16 @@ void trim(char *str) *end = '\0'; --end; } - strcpy(str,p); + sprintf(str, "%s", p); + free(buffer); } void find_replace_extension(char *str, char *orig, char *rep, char *output) { - char* buffer = strdup(str); - if(!buffer) { - error("strdup failed"); - } + char* buffer = (char*)calloc(8192, sizeof(char)); + + sprintf(buffer, "%s", str); char *p = strstr(buffer, orig); int offset = (p - buffer); int chars_from_end = strlen(buffer) - offset; @@ -258,6 +252,9 @@ void find_replace_extension(char *str, char *orig, char *rep, char *output) free(buffer); return; } + + *p = '\0'; + sprintf(output, "%s%s%s", buffer, rep, p + strlen(orig)); free(buffer); } From 14172d42b68cf9c81ca1150020475f3e79c82fab Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 14 Jan 2020 00:21:39 +0300 Subject: [PATCH 0807/1365] Fixed weights_normalizion=softmax for [shortcut] layer --- src/blas.c | 41 +++++++++++++++++++++++++++++------------ 1 file changed, 29 insertions(+), 12 deletions(-) diff --git a/src/blas.c b/src/blas.c index 2df12e846e7..6d0c0cddb1e 100644 --- a/src/blas.c +++ b/src/blas.c @@ -90,22 +90,30 @@ void shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *o src_id /= src_outputs; int src_b = src_id; - float sum = 1; + float sum = 1, max_val = -INFINITY; int i; if (weights && weights_normalizion) { + if (weights_normalizion == SOFTMAX_NORMALIZATION) { + for (i = 0; i < (n + 1); ++i) { + const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] + float w = weights[weights_index]; + if (max_val < w) max_val = w; + } + } const float eps = 0.0001; sum = eps; for (i = 0; i < (n + 1); ++i) { const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] - if (weights_normalizion == RELU_NORMALIZATION) sum += relu(weights[weights_index]); - else if (weights_normalizion == SOFTMAX_NORMALIZATION) sum += expf(weights[weights_index]); + const float w = weights[weights_index]; + if (weights_normalizion == RELU_NORMALIZATION) sum += relu(w); + else if (weights_normalizion == SOFTMAX_NORMALIZATION) sum += expf(w - max_val); } } if (weights) { float w = weights[src_i / step]; if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; - else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w) / sum; + else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; out[id] = in[id] * w; // [0 or c or (c, h ,w)] } @@ -124,7 +132,7 @@ void shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *o const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)] float w = weights[weights_index]; if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; - else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w) / sum; + else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; out[out_index] += add[add_index] * w; // [0 or c or (c, h ,w)] } @@ -150,30 +158,39 @@ void backward_shortcut_multilayer_cpu(int size, int src_outputs, int batch, int src_id /= src_outputs; int src_b = src_id; - float grad = 1, sum = 1; + float grad = 1, sum = 1, max_val = -INFINITY; int i; if (weights && weights_normalizion) { + if (weights_normalizion == SOFTMAX_NORMALIZATION) { + for (i = 0; i < (n + 1); ++i) { + const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] + float w = weights[weights_index]; + if (max_val < w) max_val = w; + } + } const float eps = 0.0001; sum = eps; for (i = 0; i < (n + 1); ++i) { const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] - if (weights_normalizion == RELU_NORMALIZATION) sum += relu(weights[weights_index]); - else if (weights_normalizion == SOFTMAX_NORMALIZATION) sum += expf(weights[weights_index]); + const float w = weights[weights_index]; + if (weights_normalizion == RELU_NORMALIZATION) sum += relu(w); + else if (weights_normalizion == SOFTMAX_NORMALIZATION) sum += expf(w - max_val); } grad = 0; for (i = 0; i < (n + 1); ++i) { const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] const float delta_w = delta_in[id] * in[id]; - if (weights_normalizion == RELU_NORMALIZATION) grad += delta_w * relu(weights[weights_index]) / sum; - else if (weights_normalizion == SOFTMAX_NORMALIZATION) grad += delta_w * expf(weights[weights_index]) / sum; + const float w = weights[weights_index]; + if (weights_normalizion == RELU_NORMALIZATION) grad += delta_w * relu(w) / sum; + else if (weights_normalizion == SOFTMAX_NORMALIZATION) grad += delta_w * expf(w - max_val) / sum; } } if (weights) { float w = weights[src_i / step]; if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; - else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w) / sum; + else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; delta_out[id] += delta_in[id] * w; // [0 or c or (c, h ,w)] weight_updates[src_i / step] += delta_in[id] * in[id] * grad; @@ -194,7 +211,7 @@ void backward_shortcut_multilayer_cpu(int size, int src_outputs, int batch, int const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)] float w = weights[weights_index]; if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; - else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w) / sum; + else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; layer_delta[add_index] += delta_in[id] * w; // [0 or c or (c, h ,w)] weight_updates[weights_index] += delta_in[id] * add[add_index] * grad; From d88a9eb0d90b07ca2ea076210d0b83180c91667a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 15 Jan 2020 14:38:09 +0300 Subject: [PATCH 0808/1365] Tensor Cores fix --- src/convolutional_kernels.cu | 4 ++-- src/convolutional_layer.c | 2 +- src/darknet.c | 10 ++++------ 3 files changed, 7 insertions(+), 9 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 1a00c1892de..868bb59957d 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -420,7 +420,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //if (state.use_mixed_precision) { int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in) && - (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 8 && l.size > 1) + (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 1 && l.size > 1) { //printf("\n CUDNN_HALF!!! state.index = %d \n", state.index); @@ -671,7 +671,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state //#ifdef CUDNN_HALF int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in) && - (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 8 && l.size > 1) + (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 1 && l.size > 1) { const size_t input16_size = l.batch*l.c*l.w*l.h; const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h; diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 458a11a75b8..83e9f2ab11d 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -232,7 +232,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference, size_t workspace_ // 3. FP32 Master Copy of Weights // More: http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#tensor_ops CHECK_CUDNN(cudnnSetConvolutionGroupCount(l->convDesc, l->groups)); - //if (l->groups == 1) CHECK_CUDNN(cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH)); + CHECK_CUDNN(cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH)); #if((CUDNN_MAJOR*10 + CUDNN_MINOR) >= 72) // cuDNN >= 7.2 //CHECK_CUDNN(cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION)); // reduces the speed of regular and group convolution #endif diff --git a/src/darknet.c b/src/darknet.c index 44227bca3ab..ab5931daca9 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -433,12 +433,6 @@ int main(int argc, char **argv) _CrtSetDbgFlag(_CRTDBG_ALLOC_MEM_DF | _CRTDBG_LEAK_CHECK_DF); #endif -#ifdef GPU - show_cuda_cudnn_info(); -#endif// GPU - - show_opencv_info(); - int i; for (i = 0; i < argc; ++i) { if (!argv[i]) continue; @@ -466,8 +460,12 @@ int main(int argc, char **argv) cuda_set_device(gpu_index); CHECK_CUDA(cudaSetDeviceFlags(cudaDeviceScheduleBlockingSync)); } + + show_cuda_cudnn_info(); #endif + show_opencv_info(); + if (0 == strcmp(argv[1], "average")){ average(argc, argv); } else if (0 == strcmp(argv[1], "yolo")){ From 3902712947c91128503f2d153549d7c10bb19c37 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 16 Jan 2020 16:30:30 +0300 Subject: [PATCH 0809/1365] minor fix --- src/image.c | 10 +++++++++- src/image_opencv.cpp | 3 ++- 2 files changed, 11 insertions(+), 2 deletions(-) diff --git a/src/image.c b/src/image.c index 291b73acfb5..3d813ce87ff 100644 --- a/src/image.c +++ b/src/image.c @@ -331,7 +331,15 @@ void draw_detections_v3(image im, detection *dets, int num, float thresh, char * int j; for (j = 0; j < classes; ++j) { if (selected_detections[i].det.prob[j] > thresh && j != best_class) { - printf("%s: %.0f%%\n", names[j], selected_detections[i].det.prob[j] * 100); + printf("%s: %.0f%%", names[j], selected_detections[i].det.prob[j] * 100); + + if (ext_output) + printf("\t(left_x: %4.0f top_y: %4.0f width: %4.0f height: %4.0f)\n", + round((selected_detections[i].det.bbox.x - selected_detections[i].det.bbox.w / 2)*im.w), + round((selected_detections[i].det.bbox.y - selected_detections[i].det.bbox.h / 2)*im.h), + round(selected_detections[i].det.bbox.w*im.w), round(selected_detections[i].det.bbox.h*im.h)); + else + printf("\n"); } } } diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 2853689ff77..73808477476 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -884,12 +884,13 @@ extern "C" void draw_detections_cv_v3(mat_cv* mat, detection *dets, int num, flo char buff[10]; sprintf(buff, " (%2.0f%%)", dets[i].prob[j] * 100); strcat(labelstr, buff); + printf("%s: %.0f%% ", names[j], dets[i].prob[j] * 100); } else { strcat(labelstr, ", "); strcat(labelstr, names[j]); + printf(", %s: %.0f%% ", names[j], dets[i].prob[j] * 100); } - printf("%s: %.0f%% ", names[j], dets[i].prob[j] * 100); } } if (class_id >= 0) { From 2a9fe045f3fd385ec61a38c8225945482d0ad7c7 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 16 Jan 2020 20:32:15 +0300 Subject: [PATCH 0810/1365] Minor fix --- src/dark_cuda.c | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/dark_cuda.c b/src/dark_cuda.c index d16f9efc690..8c4574f5d87 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -85,7 +85,7 @@ void check_error_extended(cudaError_t status, const char *file, int line, const printf("CUDA status Error: file: %s() : line: %d : build time: %s \n", file, line, date_time); check_error(status); } -#ifdef DEBUG +#if defined(DEBUG) || defined(CUDA_DEBUG) status = cudaDeviceSynchronize(); if (status != cudaSuccess) printf("CUDA status = cudaDeviceSynchronize() Error: file: %s() : line: %d : build time: %s \n", file, line, date_time); @@ -170,7 +170,7 @@ cudnnHandle_t cudnn_handle() void cudnn_check_error(cudnnStatus_t status) { -#ifdef DEBUG +#if defined(DEBUG) || defined(CUDA_DEBUG) cudaDeviceSynchronize(); #endif cudnnStatus_t status2 = CUDNN_STATUS_SUCCESS; @@ -207,7 +207,7 @@ void cudnn_check_error_extended(cudnnStatus_t status, const char *file, int line printf("\n cuDNN status Error in: file: %s() : line: %d : build time: %s \n", file, line, date_time); cudnn_check_error(status); } -#ifdef DEBUG +#if defined(DEBUG) || defined(CUDA_DEBUG) status = cudaDeviceSynchronize(); if (status != CUDNN_STATUS_SUCCESS) printf("\n cuDNN status = cudaDeviceSynchronize() Error in: file: %s() : line: %d : build time: %s \n", file, line, date_time); From 678ba01335913efa809b1e8ddf1a01eeea61d578 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 22 Jan 2020 15:47:48 +0300 Subject: [PATCH 0811/1365] Minor fix for shortcut multilayer if it doesn't use weights --- src/blas.c | 4 ++-- src/blas_kernels.cu | 7 ++++--- src/darknet.c | 2 ++ src/parser.c | 5 ----- src/shortcut_layer.c | 46 ++++++++++++++++++++++++-------------------- 5 files changed, 33 insertions(+), 31 deletions(-) diff --git a/src/blas.c b/src/blas.c index 6d0c0cddb1e..9badc55fff6 100644 --- a/src/blas.c +++ b/src/blas.c @@ -79,7 +79,7 @@ void shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *o // nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w) const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) int step = 0; - if (weights) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 + if (nweights > 0) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 int id; #pragma omp parallel for @@ -148,7 +148,7 @@ void backward_shortcut_multilayer_cpu(int size, int src_outputs, int batch, int // nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w) const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) int step = 0; - if (weights) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 + if (nweights > 0) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 int id; #pragma omp parallel for diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index a2fab184422..18f2d95f068 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -687,7 +687,7 @@ __global__ void shortcut_multilayer_kernel(int size, int src_outputs, int batch, // nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w) const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) int step = 0; - if (weights_gpu) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 + if (nweights > 0) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 int src_id = id; const int src_i = src_id % src_outputs; @@ -762,7 +762,7 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i // nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w) const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) int step = 0; - if (weights_gpu) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 + if (nweights > 0) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 int src_id = id; const int src_i = src_id % src_outputs; @@ -836,7 +836,8 @@ extern "C" void backward_shortcut_multilayer_gpu(int src_outputs, int batch, int float **layers_delta_gpu, float *delta_out, float *delta_in, float *weights_gpu, float *weight_updates_gpu, int nweights, float *in, float **layers_output_gpu, WEIGHTS_NORMALIZATION_T weights_normalizion) { const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) - const int step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 + int step = 0; + if (nweights > 0) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 //printf(" nweights = %d, n = %d, layer_step = %d, step = %d \n", nweights, n, layer_step, step); //printf(" src_outputs = %d, batch = %d, n = %d \n", src_outputs, batch, n); diff --git a/src/darknet.c b/src/darknet.c index ab5931daca9..2b0d6a6d6dd 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -466,6 +466,8 @@ int main(int argc, char **argv) show_opencv_info(); + init_cpu(); + if (0 == strcmp(argv[1], "average")){ average(argc, argv); } else if (0 == strcmp(argv[1], "yolo")){ diff --git a/src/parser.c b/src/parser.c index 291be389589..0e530e489b5 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1544,7 +1544,6 @@ void save_shortcut_weights(layer l, FILE *fp) #endif int num = l.nweights; fwrite(l.weights, sizeof(float), num, fp); - } void save_convolutional_weights(layer l, FILE *fp) @@ -1822,10 +1821,6 @@ void load_convolutional_weights(layer l, FILE *fp) void load_shortcut_weights(layer l, FILE *fp) { - if (l.binary) { - //load_convolutional_weights_binary(l, fp); - //return; - } int num = l.nweights; int read_bytes; read_bytes = fread(l.weights, sizeof(float), num, fp); diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index 3d0d0098474..6ca14a60527 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -186,14 +186,16 @@ void backward_shortcut_layer(const layer l, network_state state) void update_shortcut_layer(layer l, int batch, float learning_rate_init, float momentum, float decay) { - float learning_rate = learning_rate_init*l.learning_rate_scale; - //float momentum = a.momentum; - //float decay = a.decay; - //int batch = a.batch; - - axpy_cpu(l.nweights, -decay*batch, l.weights, 1, l.weight_updates, 1); - axpy_cpu(l.nweights, learning_rate / batch, l.weight_updates, 1, l.weights, 1); - scal_cpu(l.nweights, momentum, l.weight_updates, 1); + if (l.nweights > 0) { + float learning_rate = learning_rate_init*l.learning_rate_scale; + //float momentum = a.momentum; + //float decay = a.decay; + //int batch = a.batch; + + axpy_cpu(l.nweights, -decay*batch, l.weights, 1, l.weight_updates, 1); + axpy_cpu(l.nweights, learning_rate / batch, l.weight_updates, 1, l.weights, 1); + scal_cpu(l.nweights, momentum, l.weight_updates, 1); + } } #ifdef GPU @@ -238,28 +240,30 @@ void backward_shortcut_layer_gpu(const layer l, network_state state) void update_shortcut_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay) { - float learning_rate = learning_rate_init*l.learning_rate_scale; - //float momentum = a.momentum; - //float decay = a.decay; - //int batch = a.batch; + if (l.nweights > 0) { + float learning_rate = learning_rate_init*l.learning_rate_scale; + //float momentum = a.momentum; + //float decay = a.decay; + //int batch = a.batch; - fix_nan_and_inf(l.weight_updates_gpu, l.nweights); - fix_nan_and_inf(l.weights_gpu, l.nweights); + fix_nan_and_inf(l.weight_updates_gpu, l.nweights); + fix_nan_and_inf(l.weights_gpu, l.nweights); - axpy_ongpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); - axpy_ongpu(l.nweights, learning_rate / batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); - scal_ongpu(l.nweights, momentum, l.weight_updates_gpu, 1); + axpy_ongpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); + axpy_ongpu(l.nweights, learning_rate / batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); + scal_ongpu(l.nweights, momentum, l.weight_updates_gpu, 1); - //if (l.clip) { - // constrain_gpu(l.nweights, l.clip, l.weights_gpu, 1); - //} + //if (l.clip) { + // constrain_gpu(l.nweights, l.clip, l.weights_gpu, 1); + //} + } } void pull_shortcut_layer(layer l) { cuda_pull_array_async(l.weights_gpu, l.weights, l.nweights); CHECK_CUDA(cudaPeekAtLastError()); - cudaStreamSynchronize(get_cuda_stream()); + CHECK_CUDA(cudaStreamSynchronize(get_cuda_stream())); } void push_shortcut_layer(layer l) From 57264d13721e72b4af107e7504b7f351fffd5033 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 22 Jan 2020 16:49:19 +0300 Subject: [PATCH 0812/1365] Fixed resnet152_trident.cfg --- build/darknet/x64/cfg/resnet152_trident.cfg | 10 +++++----- cfg/resnet152_trident.cfg | 10 +++++----- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/build/darknet/x64/cfg/resnet152_trident.cfg b/build/darknet/x64/cfg/resnet152_trident.cfg index c7e0e255700..1ac8cc73a78 100644 --- a/build/darknet/x64/cfg/resnet152_trident.cfg +++ b/build/darknet/x64/cfg/resnet152_trident.cfg @@ -148,7 +148,7 @@ pad=1 activation=linear [shortcut] -from=-4 +from=-2 activation=leaky [convolutional] @@ -374,7 +374,7 @@ pad=1 activation=linear [shortcut] -from=-4 +from=-2 activation=leaky [convolutional] @@ -1397,7 +1397,7 @@ pad=1 activation=linear [shortcut] -from=-4 +from=-2 activation=leaky [convolutional] @@ -1720,7 +1720,7 @@ pad=1 activation=linear [shortcut] -from=-4 +from=-2 activation=leaky [convolutional] @@ -2067,7 +2067,7 @@ pad=1 activation=linear [shortcut] -from=-4 +from=-2 activation=leaky [convolutional] diff --git a/cfg/resnet152_trident.cfg b/cfg/resnet152_trident.cfg index c7e0e255700..1ac8cc73a78 100644 --- a/cfg/resnet152_trident.cfg +++ b/cfg/resnet152_trident.cfg @@ -148,7 +148,7 @@ pad=1 activation=linear [shortcut] -from=-4 +from=-2 activation=leaky [convolutional] @@ -374,7 +374,7 @@ pad=1 activation=linear [shortcut] -from=-4 +from=-2 activation=leaky [convolutional] @@ -1397,7 +1397,7 @@ pad=1 activation=linear [shortcut] -from=-4 +from=-2 activation=leaky [convolutional] @@ -1720,7 +1720,7 @@ pad=1 activation=linear [shortcut] -from=-4 +from=-2 activation=leaky [convolutional] @@ -2067,7 +2067,7 @@ pad=1 activation=linear [shortcut] -from=-4 +from=-2 activation=leaky [convolutional] From 56cd545267cd7829bccd1d0d6cf95fcfbaf5cf4e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 22 Jan 2020 17:19:02 +0300 Subject: [PATCH 0813/1365] Improved inference FPS for [shortcut] layer --- src/blas_kernels.cu | 41 +++++++++++++++++++++++++++++++++++------ src/darknet.c | 3 +-- 2 files changed, 36 insertions(+), 8 deletions(-) diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 18f2d95f068..7d4667a2370 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -679,6 +679,28 @@ __device__ float relu(float src) { return 0; } +__global__ void shortcut_singlelayer_simple_kernel(int size, int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights, WEIGHTS_NORMALIZATION_T weights_normalizion) +{ + const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= size) return; + + int src_id = id; + const int src_i = src_id % src_outputs; + src_id /= src_outputs; + int src_b = src_id; + + float out_val = in[id]; + + int add_outputs = outputs_of_layers_gpu[0]; + if (src_i < add_outputs) { + int add_index = add_outputs*src_b + src_i; + + float *add = layers_output_gpu[0]; + out_val += add[add_index]; + } + out[id] = out_val; +} + __global__ void shortcut_multilayer_kernel(int size, int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights, WEIGHTS_NORMALIZATION_T weights_normalizion) { const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -713,21 +735,22 @@ __global__ void shortcut_multilayer_kernel(int size, int src_outputs, int batch, } } + float out_val = 0; + if (weights_gpu) { float w = weights_gpu[src_i / step]; if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; - out[id] = in[id] * w; // [0 or c or (c, h ,w)] + out_val = in[id] * w; // [0 or c or (c, h ,w)] } - else out[id] = in[id]; + else out_val = in[id]; // layers for (int i = 0; i < n; ++i) { int add_outputs = outputs_of_layers_gpu[i]; if (src_i < add_outputs) { int add_index = add_outputs*src_b + src_i; - int out_index = id; float *add = layers_output_gpu[i]; @@ -737,18 +760,24 @@ __global__ void shortcut_multilayer_kernel(int size, int src_outputs, int batch, if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; - out[out_index] += add[add_index] * w; // [0 or c or (c, h ,w)] + out_val += add[add_index] * w; // [0 or c or (c, h ,w)] } - else out[out_index] += add[add_index]; + else out_val += add[add_index]; } } + out[id] = out_val; } extern "C" void shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights, WEIGHTS_NORMALIZATION_T weights_normalizion) { //printf(" src_outputs = %d, batch = %d, n = %d \n", src_outputs, batch, n); int size = batch * src_outputs; - shortcut_multilayer_kernel << > > (size, src_outputs, batch, n, outputs_of_layers_gpu, layers_output_gpu, out, in, weights_gpu, nweights, weights_normalizion); + if (nweights == 0 && n == 1) { + shortcut_singlelayer_simple_kernel << > > (size, src_outputs, batch, n, outputs_of_layers_gpu, layers_output_gpu, out, in, weights_gpu, nweights, weights_normalizion); + } + else { + shortcut_multilayer_kernel << > > (size, src_outputs, batch, n, outputs_of_layers_gpu, layers_output_gpu, out, in, weights_gpu, nweights, weights_normalizion); + } CHECK_CUDA(cudaPeekAtLastError()); } diff --git a/src/darknet.c b/src/darknet.c index 2b0d6a6d6dd..ec47b05acdf 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -455,6 +455,7 @@ int main(int argc, char **argv) #ifndef GPU gpu_index = -1; + init_cpu(); #else if(gpu_index >= 0){ cuda_set_device(gpu_index); @@ -466,8 +467,6 @@ int main(int argc, char **argv) show_opencv_info(); - init_cpu(); - if (0 == strcmp(argv[1], "average")){ average(argc, argv); } else if (0 == strcmp(argv[1], "yolo")){ From ecf1aeb7e70092a1b3603c9cf2a09fc7d8277d69 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 23 Jan 2020 16:51:29 +0300 Subject: [PATCH 0814/1365] -benchmark_layers for backward too (for training) --- src/detector.c | 2 +- src/network_kernels.cu | 47 +++++++++++++++++++++++++++++++++++++++--- 2 files changed, 45 insertions(+), 4 deletions(-) diff --git a/src/detector.c b/src/detector.c index e8cb0266b9c..298f37f3628 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1608,7 +1608,7 @@ void run_detector(int argc, char **argv) int dont_show = find_arg(argc, argv, "-dont_show"); int benchmark = find_arg(argc, argv, "-benchmark"); int benchmark_layers = find_arg(argc, argv, "-benchmark_layers"); - if (benchmark_layers) benchmark = 1; + //if (benchmark_layers) benchmark = 1; if (benchmark) dont_show = 1; int show = find_arg(argc, argv, "-show"); int letter_box = find_arg(argc, argv, "-letter_box"); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 2146f0ae878..14ade8ed405 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -99,7 +99,7 @@ void forward_network_gpu(network net, network_state state) else avg_time_per_layer[i].time = avg_time_per_layer[i].time * alpha + took_time * (1 - alpha); sorted_avg_time_per_layer[i] = avg_time_per_layer[i]; - printf("\n layer %d - type: %d - %lf ms - avg_time %lf ms \n", i, l.type, took_time, avg_time_per_layer[i].time); + printf("\n fw-layer %d - type: %d - %lf ms - avg_time %lf ms \n", i, l.type, took_time, avg_time_per_layer[i].time); } if(net.wait_stream) @@ -127,11 +127,11 @@ void forward_network_gpu(network net, network_state state) } if (net.benchmark_layers) { - printf("\n\nSorted by time:\n"); + printf("\n\nSorted by time (forward):\n"); qsort(sorted_avg_time_per_layer, net.n, sizeof(time_benchmark_layers), time_comparator); for (i = 0; i < net.n; ++i) { //printf("layer %d - type: %d - avg_time %lf ms \n", avg_time_per_layer[i].layer_id, avg_time_per_layer[i].layer_type, avg_time_per_layer[i].time); - printf("%d - layer %d - type: %d - avg_time %lf ms \n", i, sorted_avg_time_per_layer[i].layer_id, sorted_avg_time_per_layer[i].layer_type, sorted_avg_time_per_layer[i].time); + printf("%d - fw-sort-layer %d - type: %d - avg_time %lf ms \n", i, sorted_avg_time_per_layer[i].layer_id, sorted_avg_time_per_layer[i].layer_type, sorted_avg_time_per_layer[i].time); } } @@ -141,6 +141,17 @@ void forward_network_gpu(network net, network_state state) void backward_network_gpu(network net, network_state state) { + static time_benchmark_layers *avg_time_per_layer = NULL; + static time_benchmark_layers *sorted_avg_time_per_layer = NULL; + double start_time, end_time; + if (net.benchmark_layers) { + if (!avg_time_per_layer) { + avg_time_per_layer = (time_benchmark_layers *)calloc(net.n, sizeof(time_benchmark_layers)); + sorted_avg_time_per_layer = (time_benchmark_layers *)calloc(net.n, sizeof(time_benchmark_layers)); + } + cudaDeviceSynchronize(); + } + state.workspace = net.workspace; int i; float * original_input = state.input; @@ -161,8 +172,29 @@ void backward_network_gpu(network net, network_state state) } } if (l.onlyforward) continue; + + if (net.benchmark_layers) { + start_time = get_time_point(); + } + l.backward_gpu(l, state); + if (net.benchmark_layers) { + CHECK_CUDA(cudaDeviceSynchronize()); + end_time = get_time_point(); + const double took_time = (end_time - start_time) / 1000; + const double alpha = 0.9; + if (avg_time_per_layer[i].time == 0) { + avg_time_per_layer[i].layer_id = i; + avg_time_per_layer[i].layer_type = l.type; + avg_time_per_layer[i].time = took_time; + } + else avg_time_per_layer[i].time = avg_time_per_layer[i].time * alpha + took_time * (1 - alpha); + + sorted_avg_time_per_layer[i] = avg_time_per_layer[i]; + printf("\n bw-layer %d - type: %d - %lf ms - avg_time %lf ms \n", i, l.type, took_time, avg_time_per_layer[i].time); + } + if (i != 0) { layer prev = net.layers[i - 1]; if (net.optimized_memory && state.delta && !prev.keep_delta_gpu) { @@ -183,6 +215,15 @@ void backward_network_gpu(network net, network_state state) } */ } + + if (net.benchmark_layers) { + printf("\n\nSorted by time (backward):\n"); + qsort(sorted_avg_time_per_layer, net.n, sizeof(time_benchmark_layers), time_comparator); + for (i = 0; i < net.n; ++i) { + //printf("layer %d - type: %d - avg_time %lf ms \n", avg_time_per_layer[i].layer_id, avg_time_per_layer[i].layer_type, avg_time_per_layer[i].time); + printf("%d - bw-sort-layer %d - type: %d - avg_time %lf ms \n", i, sorted_avg_time_per_layer[i].layer_id, sorted_avg_time_per_layer[i].layer_type, sorted_avg_time_per_layer[i].time); + } + } } void update_network_gpu(network net) From f0fd61b82931a2a41ddb16727394347cd751cdd6 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 23 Jan 2020 21:07:42 +0300 Subject: [PATCH 0815/1365] Minor fix --- src/parser.c | 2 ++ src/shortcut_layer.c | 1 + src/yolo_layer.c | 6 ++++++ 3 files changed, 9 insertions(+) diff --git a/src/parser.c b/src/parser.c index 0e530e489b5..bb99298525f 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1825,6 +1825,8 @@ void load_shortcut_weights(layer l, FILE *fp) int read_bytes; read_bytes = fread(l.weights, sizeof(float), num, fp); if (read_bytes > 0 && read_bytes < num) printf("\n Warning: Unexpected end of wights-file! l.weights - l.index = %d \n", l.index); + //for (int i = 0; i < l.nweights; ++i) printf(" %f, ", l.weights[i]); + //printf("\n\n"); #ifdef GPU if (gpu_index >= 0) { push_shortcut_layer(l); diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index 6ca14a60527..09d1d5dac58 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -27,6 +27,7 @@ layer make_shortcut_layer(int batch, int n, int *input_layers, int* input_sizes, l.layers_delta = layers_delta; l.weights_type = weights_type; l.weights_normalizion = weights_normalizion; + l.learning_rate_scale = 1; // not necessary //l.w = w2; //l.h = h2; diff --git a/src/yolo_layer.c b/src/yolo_layer.c index b20be742867..fb90c477ae6 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -5,6 +5,7 @@ #include "dark_cuda.h" #include "utils.h" +#include #include #include #include @@ -183,6 +184,11 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, dw *= iou_normalizer; dh *= iou_normalizer; + if (isnan(dx) || isinf(dx)) dx = 0; + if (isnan(dy) || isinf(dy)) dy = 0; + if (isnan(dw) || isinf(dw)) dw = 0; + if (isnan(dh) || isinf(dh)) dh = 0; + if (!accumulate) { delta[index + 0 * stride] = 0; delta[index + 1 * stride] = 0; From 4b59770886e4a3619eb90f4679623555d8b29281 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 23 Jan 2020 21:49:00 +0300 Subject: [PATCH 0816/1365] Added max_delta=10 or 1 to the [yolo] and [Gaussian_yolo] layers, to limit delta and reduce the probability of Nan. --- include/darknet.h | 1 + src/gaussian_yolo_layer.c | 41 +++++++++++++++++++++++++++++++++++---- src/parser.c | 2 ++ src/yolo_layer.c | 34 ++++++++++++++++++++++++-------- 4 files changed, 66 insertions(+), 12 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index c83e76909b6..322b8535e15 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -369,6 +369,7 @@ struct layer { float *weight_updates; float scale_x_y; + float max_delta; float uc_normalizer; float iou_normalizer; float cls_normalizer; diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index 477b0b841ca..2d2121d1c4c 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -153,8 +153,21 @@ box get_gaussian_yolo_box(float *x, float *biases, int n, int index, int i, int return b; } +static inline float fix_nan_inf(float val) +{ + if (isnan(val) || isinf(val)) val = 0; + return val; +} + +static inline float clip_value(float val, const float max_val) +{ + if (val > max_val) val = max_val; + else if (val < -max_val) val = -max_val; + return val; +} + float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, - float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss, float uc_normalizer, int accumulate, YOLO_POINT yolo_point) + float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss, float uc_normalizer, int accumulate, YOLO_POINT yolo_point, float max_delta) { box pred = get_gaussian_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride, yolo_point); @@ -297,6 +310,26 @@ float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int ind delta_uw *= uc_normalizer; delta_uh *= uc_normalizer; + delta_x = fix_nan_inf(delta_x); + delta_y = fix_nan_inf(delta_y); + delta_w = fix_nan_inf(delta_w); + delta_h = fix_nan_inf(delta_h); + + delta_ux = fix_nan_inf(delta_ux); + delta_uy = fix_nan_inf(delta_uy); + delta_uw = fix_nan_inf(delta_uw); + delta_uh = fix_nan_inf(delta_uh); + + delta_x = clip_value(delta_x, max_delta); + delta_y = clip_value(delta_y, max_delta); + delta_w = clip_value(delta_w, max_delta); + delta_h = clip_value(delta_h, max_delta); + + delta_ux = clip_value(delta_ux, max_delta); + delta_uy = clip_value(delta_uy, max_delta); + delta_uw = clip_value(delta_uw, max_delta); + delta_uh = clip_value(delta_uh, max_delta); + delta[index + 0 * stride] += delta_x; delta[index + 2 * stride] += delta_y; delta[index + 4 * stride] += delta_w; @@ -457,7 +490,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.label_smooth_eps, l.classes_multipliers); box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; - delta_gaussian_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, l.uc_normalizer, 1, l.yolo_point); + delta_gaussian_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, l.uc_normalizer, 1, l.yolo_point, l.max_delta); } } } @@ -502,7 +535,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int box_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; - float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, l.uc_normalizer, 1, l.yolo_point); + float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, l.uc_normalizer, 1, l.yolo_point, l.max_delta); int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8); avg_obj += l.output[obj_index]; @@ -535,7 +568,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int box_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; - float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, l.uc_normalizer, 1, l.yolo_point); + float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, l.uc_normalizer, 1, l.yolo_point, l.max_delta); int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8); avg_obj += l.output[obj_index]; diff --git a/src/parser.c b/src/parser.c index bb99298525f..216941abd69 100644 --- a/src/parser.c +++ b/src/parser.c @@ -411,6 +411,7 @@ layer parse_yolo(list *options, size_params params) l.label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1); + l.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10 l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75); l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1); char *iou_loss = option_find_str_quiet(options, "iou_loss", "mse"); // "iou"); @@ -506,6 +507,7 @@ layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3 l.label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1); + l.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10 l.uc_normalizer = option_find_float_quiet(options, "uc_normalizer", 1.0); l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75); l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1.0); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index fb90c477ae6..8f661a96abf 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -129,7 +129,20 @@ box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw return b; } -ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss, int accumulate) +static inline float fix_nan_inf(float val) +{ + if (isnan(val) || isinf(val)) val = 0; + return val; +} + +static inline float clip_value(float val, const float max_val) +{ + if (val > max_val) val = max_val; + else if (val < -max_val) val = -max_val; + return val; +} + +ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss, int accumulate, int max_delta) { ious all_ious = { 0 }; // i - step in layer width @@ -184,10 +197,15 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, dw *= iou_normalizer; dh *= iou_normalizer; - if (isnan(dx) || isinf(dx)) dx = 0; - if (isnan(dy) || isinf(dy)) dy = 0; - if (isnan(dw) || isinf(dw)) dw = 0; - if (isnan(dh) || isinf(dh)) dh = 0; + dx = fix_nan_inf(dx); + dy = fix_nan_inf(dy); + dw = fix_nan_inf(dw); + dh = fix_nan_inf(dh); + + dx = clip_value(dx, max_delta); + dy = clip_value(dy, max_delta); + dw = clip_value(dw, max_delta); + dh = clip_value(dh, max_delta); if (!accumulate) { delta[index + 0 * stride] = 0; @@ -370,7 +388,7 @@ void forward_yolo_layer(const layer l, network_state state) delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; - delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1); + delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta); } } } @@ -412,7 +430,7 @@ void forward_yolo_layer(const layer l, network_state state) int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; - ious all_ious = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1); + ious all_ious = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta); // range is 0 <= 1 tot_iou += all_ious.iou; @@ -456,7 +474,7 @@ void forward_yolo_layer(const layer l, network_state state) int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; - ious all_ious = delta_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1); + ious all_ious = delta_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta); // range is 0 <= 1 tot_iou += all_ious.iou; From a9f634dd18d51eac24969c47162aeed11d8b4a98 Mon Sep 17 00:00:00 2001 From: Aymeric DUJARDIN Date: Mon, 27 Jan 2020 19:12:08 +0100 Subject: [PATCH 0817/1365] Adding support for ZED SDK 3.0 (with 2.8 support maintained) --- Makefile | 8 ++++- src/yolo_console_dll.cpp | 70 +++++++++++++++++++++++++++++++++++++--- 2 files changed, 73 insertions(+), 5 deletions(-) diff --git a/Makefile b/Makefile index 590db18c40b..5daf5ad70be 100644 --- a/Makefile +++ b/Makefile @@ -5,7 +5,8 @@ OPENCV=0 AVX=0 OPENMP=0 LIBSO=0 -ZED_CAMERA=0 +ZED_CAMERA=0 # ZED SDK 3.0 and above +ZED_CAMERA_v2_8=0 # ZED SDK 2.X # set GPU=1 and CUDNN=1 to speedup on GPU # set CUDNN_HALF=1 to further speedup 3 x times (Mixed-precision on Tensor Cores) GPU: Volta, Xavier, Turing and higher @@ -124,8 +125,13 @@ endif ifeq ($(ZED_CAMERA), 1) CFLAGS+= -DZED_STEREO -I/usr/local/zed/include +ifeq ($(ZED_CAMERA_v2_8), 1) LDFLAGS+= -L/usr/local/zed/lib -lsl_core -lsl_input -lsl_zed #-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0 +else +LDFLAGS+= -L/usr/local/zed/lib -lsl_zed +#-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0 +endif endif OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o gaussian_yolo_layer.o upsample_layer.o lstm_layer.o conv_lstm_layer.o scale_channels_layer.o sam_layer.o diff --git a/src/yolo_console_dll.cpp b/src/yolo_console_dll.cpp index f942fe13e3d..1eee1723c79 100644 --- a/src/yolo_console_dll.cpp +++ b/src/yolo_console_dll.cpp @@ -24,9 +24,17 @@ #ifdef OPENCV #ifdef ZED_STEREO -#include +#include +#if ZED_SDK_MAJOR_VERSION == 2 +#define ZED_STEREO_2_COMPAT_MODE +#endif + +#undef GPU // avoid conflict with sl::MEM::GPU + +#ifdef ZED_STEREO_2_COMPAT_MODE #pragma comment(lib, "sl_core64.lib") #pragma comment(lib, "sl_input64.lib") +#endif #pragma comment(lib, "sl_zed64.lib") float getMedian(std::vector &v) { @@ -92,6 +100,7 @@ std::vector get_3d_coordinates(std::vector bbox_vect, cv::Mat xy return bbox3d_vect; } +#ifdef ZED_STEREO_2_COMPAT_MODE cv::Mat slMat2cvMat(sl::Mat &input) { // Mapping between MAT_TYPE and CV_TYPE int cv_type = -1; @@ -125,6 +134,41 @@ cv::Mat slMat2cvMat(sl::Mat &input) { } return cv::Mat(input.getHeight(), input.getWidth(), cv_type, input.getPtr(sl::MEM_CPU)); } +#else +cv::Mat slMat2cvMat(sl::Mat &input) { + // Mapping between MAT_TYPE and CV_TYPE + int cv_type = -1; + switch (input.getDataType()) { + case sl::MAT_TYPE::F32_C1: + cv_type = CV_32FC1; + break; + case sl::MAT_TYPE::F32_C2: + cv_type = CV_32FC2; + break; + case sl::MAT_TYPE::F32_C3: + cv_type = CV_32FC3; + break; + case sl::MAT_TYPE::F32_C4: + cv_type = CV_32FC4; + break; + case sl::MAT_TYPE::U8_C1: + cv_type = CV_8UC1; + break; + case sl::MAT_TYPE::U8_C2: + cv_type = CV_8UC2; + break; + case sl::MAT_TYPE::U8_C3: + cv_type = CV_8UC3; + break; + case sl::MAT_TYPE::U8_C4: + cv_type = CV_8UC4; + break; + default: + break; + } + return cv::Mat(input.getHeight(), input.getWidth(), cv_type, input.getPtr(sl::MEM::CPU)); +} +#endif cv::Mat zed_capture_rgb(sl::Camera &zed) { sl::Mat left; @@ -136,7 +180,11 @@ cv::Mat zed_capture_rgb(sl::Camera &zed) { cv::Mat zed_capture_3d(sl::Camera &zed) { sl::Mat cur_cloud; +#ifdef ZED_STEREO_2_COMPAT_MODE zed.retrieveMeasure(cur_cloud, sl::MEASURE_XYZ); +#else + zed.retrieveMeasure(cur_cloud, sl::MEASURE::XYZ); +#endif return slMat2cvMat(cur_cloud).clone(); } @@ -334,13 +382,21 @@ int main(int argc, char *argv[]) #ifdef ZED_STEREO sl::InitParameters init_params; init_params.depth_minimum_distance = 0.5; + #ifdef ZED_STEREO_2_COMPAT_MODE init_params.depth_mode = sl::DEPTH_MODE_ULTRA; init_params.camera_resolution = sl::RESOLUTION_HD720;// sl::RESOLUTION_HD1080, sl::RESOLUTION_HD720 init_params.coordinate_units = sl::UNIT_METER; - //init_params.sdk_cuda_ctx = (CUcontext)detector.get_cuda_context(); - init_params.sdk_gpu_id = detector.cur_gpu_id; init_params.camera_buffer_count_linux = 2; if (file_ext == "svo") init_params.svo_input_filename.set(filename.c_str()); + #else + init_params.depth_mode = sl::DEPTH_MODE::ULTRA; + init_params.camera_resolution = sl::RESOLUTION::HD720;// sl::RESOLUTION::HD1080, sl::RESOLUTION::HD720 + init_params.coordinate_units = sl::UNIT::METER; + if (file_ext == "svo") init_params.input.setFromSVOFile(filename.c_str()); + #endif + //init_params.sdk_cuda_ctx = (CUcontext)detector.get_cuda_context(); + init_params.sdk_gpu_id = detector.cur_gpu_id; + if (filename == "zed_camera" || file_ext == "svo") { std::cout << "ZED 3D Camera " << zed.open(init_params) << std::endl; if (!zed.isOpened()) { @@ -407,7 +463,13 @@ int main(int argc, char *argv[]) detection_data = detection_data_t(); #ifdef ZED_STEREO if (use_zed_camera) { - while (zed.grab() != sl::SUCCESS) std::this_thread::sleep_for(std::chrono::milliseconds(2)); + while (zed.grab() != + #ifdef ZED_STEREO_2_COMPAT_MODE + sl::SUCCESS + #else + sl::ERROR_CODE::SUCCESS + #endif + ) std::this_thread::sleep_for(std::chrono::milliseconds(2)); detection_data.cap_frame = zed_capture_rgb(zed); detection_data.zed_cloud = zed_capture_3d(zed); } From 298805cefebd0e71274bc12c068384703c9684b4 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 28 Jan 2020 03:44:24 +0300 Subject: [PATCH 0818/1365] Added new [convolutional] activation=normalize_channels_softmax_maxval for ASFF --- include/darknet.h | 2 +- src/activation_kernels.cu | 21 ++++++++++++++++----- src/activations.c | 20 +++++++++++++++++--- src/activations.h | 4 ++-- src/blas.c | 4 ++-- src/blas_kernels.cu | 5 +++-- src/convolutional_kernels.cu | 8 +++++--- src/convolutional_layer.c | 8 +++++--- 8 files changed, 51 insertions(+), 21 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 322b8535e15..0b65a79a431 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -102,7 +102,7 @@ typedef struct tree { // activations.h typedef enum { - LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, SWISH, MISH, NORM_CHAN, NORM_CHAN_SOFTMAX + LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, SWISH, MISH, NORM_CHAN, NORM_CHAN_SOFTMAX, NORM_CHAN_SOFTMAX_MAXVAL }ACTIVATION; // parser.h diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index 46e957dd6d1..08af147a698 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -2,6 +2,7 @@ #include #include #include +#include #include "activations.h" #include "dark_cuda.h" @@ -464,7 +465,7 @@ extern "C" void activate_array_normalize_channels_ongpu(float *x, int n, int bat -__global__ void activate_array_normalize_channels_softmax_kernel(float *x, int size, int batch, int channels, int wh_step, float *output_gpu) +__global__ void activate_array_normalize_channels_softmax_kernel(float *x, int size, int batch, int channels, int wh_step, float *output_gpu, int use_max_val) { int i = blockIdx.x * blockDim.x + threadIdx.x; @@ -474,20 +475,30 @@ __global__ void activate_array_normalize_channels_softmax_kernel(float *x, int s const float eps = 0.0001; if (i < size) { float sum = eps; + float max_val = -FLT_MAX; int k; + if (use_max_val) { + for (k = 0; k < channels; ++k) { + float val = x[wh_i + k * wh_step + b*wh_step*channels]; + if (val > max_val) max_val = val; + } + } + else + max_val = 0; + for (k = 0; k < channels; ++k) { float val = x[wh_i + k * wh_step + b*wh_step*channels]; - sum += expf(val); + sum += expf(val - max_val); } for (k = 0; k < channels; ++k) { float val = x[wh_i + k * wh_step + b*wh_step*channels]; - val = expf(val) / sum; + val = expf(val - max_val) / sum; output_gpu[wh_i + k * wh_step + b*wh_step*channels] = val; } } } -extern "C" void activate_array_normalize_channels_softmax_ongpu(float *x, int n, int batch, int channels, int wh_step, float *output_gpu) +extern "C" void activate_array_normalize_channels_softmax_ongpu(float *x, int n, int batch, int channels, int wh_step, float *output_gpu, int use_max_val) { // n = w*h*c*batch // size = w*h*batch @@ -495,7 +506,7 @@ extern "C" void activate_array_normalize_channels_softmax_ongpu(float *x, int n, const int num_blocks = get_number_of_blocks(size, BLOCK); - activate_array_normalize_channels_softmax_kernel << > > (x, size, batch, channels, wh_step, output_gpu); + activate_array_normalize_channels_softmax_kernel << > > (x, size, batch, channels, wh_step, output_gpu, use_max_val); CHECK_CUDA(cudaPeekAtLastError()); } diff --git a/src/activations.c b/src/activations.c index dc450b2f111..ba2601327b7 100644 --- a/src/activations.c +++ b/src/activations.c @@ -4,6 +4,7 @@ #include #include #include +#include char *get_activation_string(ACTIVATION a) { @@ -49,6 +50,7 @@ ACTIVATION get_activation(char *s) if (strcmp(s, "mish") == 0) return MISH; if (strcmp(s, "normalize_channels") == 0) return NORM_CHAN; if (strcmp(s, "normalize_channels_softmax") == 0) return NORM_CHAN_SOFTMAX; + if (strcmp(s, "normalize_channels_softmax_maxval") == 0) return NORM_CHAN_SOFTMAX_MAXVAL; if (strcmp(s, "loggy")==0) return LOGGY; if (strcmp(s, "relu")==0) return RELU; if (strcmp(s, "elu")==0) return ELU; @@ -177,7 +179,7 @@ void activate_array_normalize_channels(float *x, const int n, int batch, int cha } } -void activate_array_normalize_channels_softmax(float *x, const int n, int batch, int channels, int wh_step, float *output) +void activate_array_normalize_channels_softmax(float *x, const int n, int batch, int channels, int wh_step, float *output, int use_max_val) { int size = n / channels; @@ -190,14 +192,24 @@ void activate_array_normalize_channels_softmax(float *x, const int n, int batch, const float eps = 0.0001; if (i < size) { float sum = eps; + float max_val = -FLT_MAX; int k; + if (use_max_val) { + for (k = 0; k < channels; ++k) { + float val = x[wh_i + k * wh_step + b*wh_step*channels]; + if (val > max_val) max_val = val; + } + } + else + max_val = 0; + for (k = 0; k < channels; ++k) { float val = x[wh_i + k * wh_step + b*wh_step*channels]; - sum += expf(val); + sum += expf(val - max_val); } for (k = 0; k < channels; ++k) { float val = x[wh_i + k * wh_step + b*wh_step*channels]; - val = expf(val) / sum; + val = expf(val - max_val) / sum; output[wh_i + k * wh_step + b*wh_step*channels] = val; } } @@ -277,6 +289,8 @@ float gradient(float x, ACTIVATION a) return relu_gradient(x); case NORM_CHAN: //return relu_gradient(x); + case NORM_CHAN_SOFTMAX_MAXVAL: + //... case NORM_CHAN_SOFTMAX: printf(" Error: should be used custom NORM_CHAN or NORM_CHAN_SOFTMAX-function for gradient \n"); exit(0); diff --git a/src/activations.h b/src/activations.h index 05631fe3ee2..fdbc28eff0a 100644 --- a/src/activations.h +++ b/src/activations.h @@ -24,7 +24,7 @@ void activate_array_swish(float *x, const int n, float * output_sigmoid, float * void activate_array_mish(float *x, const int n, float * activation_input, float * output); void activate_array_normalize_channels(float *x, const int n, int batch, int channels, int wh_step, float *output); void gradient_array_normalize_channels(float *x, const int n, int batch, int channels, int wh_step, float *delta); -void activate_array_normalize_channels_softmax(float *x, const int n, int batch, int channels, int wh_step, float *output); +void activate_array_normalize_channels_softmax(float *x, const int n, int batch, int channels, int wh_step, float *output, int use_max_val); void gradient_array_normalize_channels_softmax(float *x, const int n, int batch, int channels, int wh_step, float *delta); #ifdef GPU void activate_array_ongpu(float *x, int n, ACTIVATION a); @@ -35,7 +35,7 @@ void gradient_array_swish_ongpu(float *x, int n, float *sigmoid_gpu, float *delt void gradient_array_mish_ongpu(int n, float *activation_input_gpu, float *delta); void activate_array_normalize_channels_ongpu(float *x, int n, int batch, int channels, int wh_step, float *output_gpu); void gradient_array_normalize_channels_ongpu(float *output_gpu, int n, int batch, int channels, int wh_step, float *delta_gpu); -void activate_array_normalize_channels_softmax_ongpu(float *x, int n, int batch, int channels, int wh_step, float *output_gpu); +void activate_array_normalize_channels_softmax_ongpu(float *x, int n, int batch, int channels, int wh_step, float *output_gpu, int use_max_val); void gradient_array_normalize_channels_softmax_ongpu(float *output_gpu, int n, int batch, int channels, int wh_step, float *delta_gpu); #endif diff --git a/src/blas.c b/src/blas.c index 9badc55fff6..d6f59367060 100644 --- a/src/blas.c +++ b/src/blas.c @@ -90,7 +90,7 @@ void shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *o src_id /= src_outputs; int src_b = src_id; - float sum = 1, max_val = -INFINITY; + float sum = 1, max_val = -FLT_MAX; int i; if (weights && weights_normalizion) { if (weights_normalizion == SOFTMAX_NORMALIZATION) { @@ -158,7 +158,7 @@ void backward_shortcut_multilayer_cpu(int size, int src_outputs, int batch, int src_id /= src_outputs; int src_b = src_id; - float grad = 1, sum = 1, max_val = -INFINITY; + float grad = 1, sum = 1, max_val = -FLT_MAX;; int i; if (weights && weights_normalizion) { if (weights_normalizion == SOFTMAX_NORMALIZATION) { diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 7d4667a2370..94538cd0f7f 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -2,6 +2,7 @@ #include #include #include +#include #include "blas.h" #include "dark_cuda.h" @@ -716,7 +717,7 @@ __global__ void shortcut_multilayer_kernel(int size, int src_outputs, int batch, src_id /= src_outputs; int src_b = src_id; - float sum = 1, max_val = -INFINITY; + float sum = 1, max_val = -FLT_MAX; if (weights_gpu && weights_normalizion) { if (weights_normalizion == SOFTMAX_NORMALIZATION) { for (int i = 0; i < (n + 1); ++i) { @@ -798,7 +799,7 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i src_id /= src_outputs; int src_b = src_id; - float grad = 1, sum = 1, max_val = -INFINITY; + float grad = 1, sum = 1, max_val = -FLT_MAX; int i; if (weights_gpu && weights_normalizion) { if (weights_normalizion == SOFTMAX_NORMALIZATION) { diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 868bb59957d..02f958813e8 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -395,7 +395,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == MISH) activate_array_mish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == NORM_CHAN) activate_array_normalize_channels_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu); - else if (l.activation == NORM_CHAN_SOFTMAX) activate_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu); + else if (l.activation == NORM_CHAN_SOFTMAX) activate_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu, 0); + else if (l.activation == NORM_CHAN_SOFTMAX_MAXVAL) activate_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu, 1); else if (l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if(l.activation != LINEAR && l.activation != LEAKY) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if (l.binary || l.xnor) swap_binary(&l); @@ -602,7 +603,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == MISH) activate_array_mish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == NORM_CHAN) activate_array_normalize_channels_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu); - else if (l.activation == NORM_CHAN_SOFTMAX) activate_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu); + else if (l.activation == NORM_CHAN_SOFTMAX) activate_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu, 0); + else if (l.activation == NORM_CHAN_SOFTMAX_MAXVAL) activate_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu, 1); else if (l.activation != LINEAR) activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); //if(l.dot > 0) dot_error_gpu(l); if(l.binary || l.xnor) swap_binary(&l); @@ -647,7 +649,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state if (l.activation == SWISH) gradient_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.delta_gpu); else if (l.activation == MISH) gradient_array_mish_ongpu(l.outputs*l.batch, l.activation_input_gpu, l.delta_gpu); - else if (l.activation == NORM_CHAN_SOFTMAX) gradient_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu); + else if (l.activation == NORM_CHAN_SOFTMAX || l.activation == NORM_CHAN_SOFTMAX_MAXVAL) gradient_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu); else if (l.activation == NORM_CHAN) gradient_array_normalize_channels_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu); else gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 83e9f2ab11d..d967b178e2f 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -1212,7 +1212,8 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) if (l.activation == SWISH) activate_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.output); else if (l.activation == MISH) activate_array_mish(l.output, l.outputs*l.batch, l.activation_input, l.output); else if (l.activation == NORM_CHAN) activate_array_normalize_channels(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output); - else if (l.activation == NORM_CHAN_SOFTMAX) activate_array_normalize_channels_softmax(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output); + else if (l.activation == NORM_CHAN_SOFTMAX) activate_array_normalize_channels_softmax(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output, 0); + else if (l.activation == NORM_CHAN_SOFTMAX_MAXVAL) activate_array_normalize_channels_softmax(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output, 1); else activate_array_cpu_custom(l.output, m*n*l.batch, l.activation); return; @@ -1256,7 +1257,8 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) if (l.activation == SWISH) activate_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.output); else if (l.activation == MISH) activate_array_mish(l.output, l.outputs*l.batch, l.activation_input, l.output); else if (l.activation == NORM_CHAN) activate_array_normalize_channels(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output); - else if (l.activation == NORM_CHAN_SOFTMAX) activate_array_normalize_channels_softmax(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output); + else if (l.activation == NORM_CHAN_SOFTMAX) activate_array_normalize_channels_softmax(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output, 0); + else if (l.activation == NORM_CHAN_SOFTMAX_MAXVAL) activate_array_normalize_channels_softmax(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output, 1); else activate_array_cpu_custom(l.output, l.outputs*l.batch, l.activation); if(l.binary || l.xnor) swap_binary(&l); @@ -1395,7 +1397,7 @@ void backward_convolutional_layer(convolutional_layer l, network_state state) if (l.activation == SWISH) gradient_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.delta); else if (l.activation == MISH) gradient_array_mish(l.outputs*l.batch, l.activation_input, l.delta); - else if (l.activation == NORM_CHAN_SOFTMAX) gradient_array_normalize_channels_softmax(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.delta); + else if (l.activation == NORM_CHAN_SOFTMAX || l.activation == NORM_CHAN_SOFTMAX_MAXVAL) gradient_array_normalize_channels_softmax(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.delta); else if (l.activation == NORM_CHAN) gradient_array_normalize_channels(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.delta); else gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); From 3be369d47b089631c9ad5374706f48ea37564d34 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 28 Jan 2020 18:30:48 +0300 Subject: [PATCH 0819/1365] Minor fix --- src/activation_kernels.cu | 2 +- src/activations.c | 2 +- src/convolutional_layer.c | 7 ++++++- 3 files changed, 8 insertions(+), 3 deletions(-) diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index 08af147a698..71372f564ef 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -480,7 +480,7 @@ __global__ void activate_array_normalize_channels_softmax_kernel(float *x, int s if (use_max_val) { for (k = 0; k < channels; ++k) { float val = x[wh_i + k * wh_step + b*wh_step*channels]; - if (val > max_val) max_val = val; + if (val > max_val || k == 0) max_val = val; } } else diff --git a/src/activations.c b/src/activations.c index ba2601327b7..fd586e2bb4c 100644 --- a/src/activations.c +++ b/src/activations.c @@ -197,7 +197,7 @@ void activate_array_normalize_channels_softmax(float *x, const int n, int batch, if (use_max_val) { for (k = 0; k < channels; ++k) { float val = x[wh_i + k * wh_step + b*wh_step*channels]; - if (val > max_val) max_val = val; + if (val > max_val || k == 0) max_val = val; } } else diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index d967b178e2f..5c24d94944f 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -436,7 +436,12 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, // float scale = 1./sqrt(size*size*c); float scale = sqrt(2./(size*size*c/groups)); - for(i = 0; i < l.nweights; ++i) l.weights[i] = scale*rand_uniform(-1, 1); // rand_normal(); + if (l.activation == NORM_CHAN_SOFTMAX_MAXVAL) { + for (i = 0; i < l.nweights; ++i) l.weights[i] = -10; // rand_normal(); + } + else { + for (i = 0; i < l.nweights; ++i) l.weights[i] = scale*rand_uniform(-1, 1); // rand_normal(); + } int out_h = convolutional_out_height(l); int out_w = convolutional_out_width(l); l.out_h = out_h; From 2862b28dc7327b2ec7085da0f06b665e48c71b50 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 29 Jan 2020 15:57:10 +0300 Subject: [PATCH 0820/1365] Training bug fixed. --- src/gaussian_yolo_layer.c | 20 +++++++++++--------- src/yolo_layer.c | 24 +++++++++++++++++------- 2 files changed, 28 insertions(+), 16 deletions(-) diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index 2d2121d1c4c..b249c101eff 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -320,15 +320,17 @@ float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int ind delta_uw = fix_nan_inf(delta_uw); delta_uh = fix_nan_inf(delta_uh); - delta_x = clip_value(delta_x, max_delta); - delta_y = clip_value(delta_y, max_delta); - delta_w = clip_value(delta_w, max_delta); - delta_h = clip_value(delta_h, max_delta); - - delta_ux = clip_value(delta_ux, max_delta); - delta_uy = clip_value(delta_uy, max_delta); - delta_uw = clip_value(delta_uw, max_delta); - delta_uh = clip_value(delta_uh, max_delta); + if (max_delta != FLT_MAX) { + delta_x = clip_value(delta_x, max_delta); + delta_y = clip_value(delta_y, max_delta); + delta_w = clip_value(delta_w, max_delta); + delta_h = clip_value(delta_h, max_delta); + + delta_ux = clip_value(delta_ux, max_delta); + delta_uy = clip_value(delta_uy, max_delta); + delta_uw = clip_value(delta_uw, max_delta); + delta_uh = clip_value(delta_uh, max_delta); + } delta[index + 0 * stride] += delta_x; delta[index + 2 * stride] += delta_y; diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 8f661a96abf..bf54f630e68 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -137,12 +137,18 @@ static inline float fix_nan_inf(float val) static inline float clip_value(float val, const float max_val) { - if (val > max_val) val = max_val; - else if (val < -max_val) val = -max_val; + if (val > max_val) { + //printf("\n val = %f > max_val = %f \n", val, max_val); + val = max_val; + } + else if (val < -max_val) { + //printf("\n val = %f < -max_val = %f \n", val, -max_val); + val = -max_val; + } return val; } -ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss, int accumulate, int max_delta) +ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss, int accumulate, float max_delta) { ious all_ious = { 0 }; // i - step in layer width @@ -197,15 +203,19 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, dw *= iou_normalizer; dh *= iou_normalizer; + dx = fix_nan_inf(dx); dy = fix_nan_inf(dy); dw = fix_nan_inf(dw); dh = fix_nan_inf(dh); - dx = clip_value(dx, max_delta); - dy = clip_value(dy, max_delta); - dw = clip_value(dw, max_delta); - dh = clip_value(dh, max_delta); + if (max_delta != FLT_MAX) { + dx = clip_value(dx, max_delta); + dy = clip_value(dy, max_delta); + dw = clip_value(dw, max_delta); + dh = clip_value(dh, max_delta); + } + if (!accumulate) { delta[index + 0 * stride] = 0; From 3000c3111f7caf9f658681f42f11346a00589aa6 Mon Sep 17 00:00:00 2001 From: vinjn Date: Wed, 29 Jan 2020 22:58:43 +0800 Subject: [PATCH 0821/1365] Expose crop_image and resize_min as LIB_API --- include/darknet.h | 2 ++ src/image.h | 4 ++-- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index c83e76909b6..50f108f7a1f 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -928,6 +928,8 @@ LIB_API void rgbgr_image(image im); LIB_API image make_image(int w, int h, int c); LIB_API image load_image_color(char *filename, int w, int h); LIB_API void free_image(image m); +LIB_API image crop_image(image im, int dx, int dy, int w, int h); +LIB_API image resize_min(image im, int min); // layer.h LIB_API void free_layer_custom(layer l, int keep_cudnn_desc); diff --git a/src/image.h b/src/image.h index 58897794772..904b87a8c98 100644 --- a/src/image.h +++ b/src/image.h @@ -33,7 +33,7 @@ void draw_detections(image im, int num, float thresh, box *boxes, float **probs, void draw_detections_v3(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output); image image_distance(image a, image b); void scale_image(image m, float s); -image crop_image(image im, int dx, int dy, int w, int h); +// image crop_image(image im, int dx, int dy, int w, int h); image random_crop_image(image im, int w, int h); image random_augment_image(image im, float angle, float aspect, int low, int high, int size); void random_distort_image(image im, float hue, float saturation, float exposure); @@ -42,7 +42,7 @@ void random_distort_image(image im, float hue, float saturation, float exposure) void fill_image(image m, float s); void letterbox_image_into(image im, int w, int h, image boxed); //LIB_API image letterbox_image(image im, int w, int h); -image resize_min(image im, int min); +// image resize_min(image im, int min); image resize_max(image im, int max); void translate_image(image m, float s); void normalize_image(image p); From 7bf83018e48ffaf3883f2020dedebb98929acf4f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 30 Jan 2020 17:00:34 +0300 Subject: [PATCH 0822/1365] Fixed Fully connected layers: [connected], [rnn], [gru], [lstm] --- src/connected_layer.c | 4 +++- src/convolutional_layer.c | 10 ++++++---- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/src/connected_layer.c b/src/connected_layer.c index 2c6f4beceb2..08a16aaa9f0 100644 --- a/src/connected_layer.c +++ b/src/connected_layer.c @@ -69,10 +69,12 @@ connected_layer make_connected_layer(int batch, int steps, int inputs, int outpu l.out_c = outputs; l.n = l.out_c; l.size = 1; - l.stride = 1; + l.stride = l.stride_x = l.stride_y = 1; l.pad = 0; l.activation = activation; l.learning_rate_scale = 1; + l.groups = 1; + l.dilation = 1; l.output = (float*)xcalloc(total_batch * outputs, sizeof(float)); l.delta = (float*)xcalloc(total_batch * outputs, sizeof(float)); diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 5c24d94944f..5ed316efc96 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -231,6 +231,9 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference, size_t workspace_ // 2. Loss Scaling - required only for: activation gradients. We do not use. // 3. FP32 Master Copy of Weights // More: http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#tensor_ops + if (l->groups < 1) l->groups = 1; + if (l->stride_x < 1) l->stride_x = 1; + if (l->stride_y < 1) l->stride_y = 1; CHECK_CUDNN(cudnnSetConvolutionGroupCount(l->convDesc, l->groups)); CHECK_CUDNN(cudnnSetConvolutionMathType(l->convDesc, CUDNN_TENSOR_OP_MATH)); #if((CUDNN_MAJOR*10 + CUDNN_MINOR) >= 72) // cuDNN >= 7.2 @@ -256,7 +259,6 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference, size_t workspace_ CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, data_type, l->batch, l->out_c, l->out_h, l->out_w)); CHECK_CUDNN(cudnnSetFilter4dDescriptor(l->weightDesc, data_type, CUDNN_TENSOR_NCHW, l->n, l->c / l->groups, l->size, l->size)); -//#ifdef CUDNN_HALF // backward delta CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->dsrcTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->c, l->h, l->w)); CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->ddstTensorDesc16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w)); @@ -269,15 +271,15 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference, size_t workspace_ // batch norm CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->normDstTensorDescF16, CUDNN_TENSOR_NCHW, CUDNN_DATA_HALF, l->batch, l->out_c, l->out_h, l->out_w)); -//#endif // batch norm CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1)); CHECK_CUDNN(cudnnSetTensor4dDescriptor(l->normDstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w)); - //printf("\n l->dilation = %d, l->pad = %d, l->size = %d \n", l->dilation, l->pad, l->size); + //printf("\n l->dilation = %d, l->pad = %d, l->size = %d, l->stride = %d, l->stride_x = %d, l->stride_y = %d, l->groups = %d, l->w = %d, l->h = %d, l->c = %d, l->n = %d, l->out_w = %d, l->out_h = %d, l->out_c = %d, l->batch = %d, data_type = %d \n", + // l->dilation, l->pad, l->size, l->stride, l->stride_x, l->stride_y, l->groups, l->w, l->h, l->c, l->n, l->out_w, l->out_h, l->out_c, l->batch, data_type); #if(CUDNN_MAJOR >= 6) - CHECK_CUDNN(cudnnSetConvolution2dDescriptor(l->convDesc, l->pad * l->dilation, l->pad* l->dilation, l->stride_y, l->stride_x, l->dilation, l->dilation, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); // cudnn >= 6.0 + CHECK_CUDNN(cudnnSetConvolution2dDescriptor(l->convDesc, l->pad * l->dilation, l->pad * l->dilation, l->stride_y, l->stride_x, l->dilation, l->dilation, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT)); // cudnn >= 6.0 #else CHECK_CUDNN(cudnnSetConvolution2dDescriptor(l->convDesc, l->pad * l->dilation, l->pad * l->dilation, l->stride_y, l->stride_x, l->dilation, l->dilation, CUDNN_CROSS_CORRELATION)); // cudnn 5.1 #endif From 2223a4b51a792dfeb9c69547fbe52c0fecb77eab Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 30 Jan 2020 19:26:13 +0300 Subject: [PATCH 0823/1365] minor fix --- src/parser.c | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/src/parser.c b/src/parser.c index 216941abd69..8320ef24422 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1201,6 +1201,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) params.net = net; printf("batch = %d, time_steps = %d, train = %d \n", net.batch, net.time_steps, params.train); + int avg_outputs = 0; float bflops = 0; size_t workspace_size = 0; size_t max_inputs = 0; @@ -1382,6 +1383,8 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) } } if (l.bflops > 0) bflops += l.bflops; + + avg_outputs += l.outputs; } free_list(sections); @@ -1425,7 +1428,9 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) net.outputs = get_network_output_size(net); net.output = get_network_output(net); + avg_outputs = avg_outputs / count; fprintf(stderr, "Total BFLOPS %5.3f \n", bflops); + fprintf(stderr, "avg_outputs = %d \n", avg_outputs); #ifdef GPU get_cuda_stream(); get_cuda_memcpy_stream(); From f441b3025aa7e4da75d7a88b65fc61bd28e048f2 Mon Sep 17 00:00:00 2001 From: Ksenya Ryabinova Date: Fri, 31 Jan 2020 16:04:34 +0300 Subject: [PATCH 0824/1365] Added -chart argument --- include/darknet.h | 2 +- src/classifier.c | 7 ++++--- src/detector.c | 7 ++++--- src/image_opencv.cpp | 17 ++++++++++++----- src/image_opencv.h | 4 ++-- 5 files changed, 23 insertions(+), 14 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 2be89f059fa..46217a39faf 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -914,7 +914,7 @@ LIB_API void reset_rnn(network *net); LIB_API float *network_predict_image(network *net, image im); LIB_API float *network_predict_image_letterbox(network *net, image im); LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, const int map_points, int letter_box, network *existing_net); -LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port, int show_imgs, int benchmark_layers); +LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port, int show_imgs, int benchmark_layers, char* chart_path); LIB_API void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile, int letter_box, int benchmark_layers); LIB_API int network_width(network *net); diff --git a/src/classifier.c b/src/classifier.c index b1ba936ad86..647b37a7fbc 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -27,7 +27,7 @@ float *get_regression_values(char **labels, int n) return v; } -void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dontuse_opencv, int dont_show, int mjpeg_port, int calc_topk, int show_imgs) +void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dontuse_opencv, int dont_show, int mjpeg_port, int calc_topk, int show_imgs, char* chart_path) { int i; @@ -113,7 +113,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int img_size = 1000; char windows_name[100]; sprintf(windows_name, "average loss (id:%d)", random_gen()); - if (!dontuse_opencv) img = draw_train_chart(windows_name, max_img_loss, net.max_batches, number_of_lines, img_size, dont_show); + if (!dontuse_opencv) img = draw_train_chart(windows_name, max_img_loss, net.max_batches, number_of_lines, img_size, dont_show, chart_path); #endif //OPENCV data train; @@ -1349,9 +1349,10 @@ void run_classifier(int argc, char **argv) char *filename = (argc > 6) ? argv[6]: 0; char *layer_s = (argc > 7) ? argv[7]: 0; int layer = layer_s ? atoi(layer_s) : -1; + char* chart_path = find_char_arg(argc, argv, "-chart", 0); if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename, top); else if(0==strcmp(argv[2], "try")) try_classifier(data, cfg, weights, filename, atoi(layer_s)); - else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear, dontuse_opencv, dont_show, mjpeg_port, calc_topk, show_imgs); + else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear, dontuse_opencv, dont_show, mjpeg_port, calc_topk, show_imgs, chart_path); else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename, benchmark, benchmark_layers); else if(0==strcmp(argv[2], "gun")) gun_classifier(data, cfg, weights, cam_index, filename); else if(0==strcmp(argv[2], "threat")) threat_classifier(data, cfg, weights, cam_index, filename); diff --git a/src/detector.c b/src/detector.c index 298f37f3628..e4a26b5809c 100644 --- a/src/detector.c +++ b/src/detector.c @@ -22,7 +22,7 @@ int check_mistakes = 0; static int coco_ids[] = { 1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90 }; -void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port, int show_imgs, int benchmark_layers) +void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port, int show_imgs, int benchmark_layers, char* chart_path) { list *options = read_data_cfg(datacfg); char *train_images = option_find_str(options, "train", "data/train.txt"); @@ -150,7 +150,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i int img_size = 1000; char windows_name[100]; sprintf(windows_name, "chart_%s.png", base); - img = draw_train_chart(windows_name, max_img_loss, net.max_batches, number_of_lines, img_size, dont_show); + img = draw_train_chart(windows_name, max_img_loss, net.max_batches, number_of_lines, img_size, dont_show, chart_path); #endif //OPENCV if (net.track) { args.track = net.track; @@ -1635,6 +1635,7 @@ void run_detector(int argc, char **argv) // and for recall mode (extended output table-like format with results for best_class fit) int ext_output = find_arg(argc, argv, "-ext_output"); int save_labels = find_arg(argc, argv, "-save_labels"); + char* chart_path = find_char_arg(argc, argv, "-chart", 0); if (argc < 4) { fprintf(stderr, "usage: %s %s [train/test/valid/demo/map] [data] [cfg] [weights (optional)]\n", argv[0], argv[1]); return; @@ -1673,7 +1674,7 @@ void run_detector(int argc, char **argv) if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0; char *filename = (argc > 6) ? argv[6] : 0; if (0 == strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels, outfile, letter_box, benchmark_layers); - else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map, mjpeg_port, show_imgs, benchmark_layers); + else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map, mjpeg_port, show_imgs, benchmark_layers, chart_path); else if (0 == strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile); else if (0 == strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights); else if (0 == strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh, iou_thresh, map_points, letter_box, NULL); diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 73808477476..8e3d17621cd 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1003,8 +1003,13 @@ extern "C" void draw_detections_cv_v3(mat_cv* mat, detection *dets, int num, flo // ==================================================================== // Draw Loss & Accuracy chart // ==================================================================== -extern "C" mat_cv* draw_train_chart(char *windows_name, float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show) +extern "C" mat_cv* draw_train_chart(char *windows_name, float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show, char* chart_path) { + if (chart_path != NULL && chart_path[0] != '\0'){ + cv::Mat* img_ptr = new cv::Mat(cv::imread(chart_path)); + return (mat_cv*)img_ptr; + } + int img_offset = 60; int draw_size = img_size - img_offset; cv::Mat *img_ptr = new cv::Mat(img_size, img_size, CV_8UC3, CV_RGB(255, 255, 255)); @@ -1085,10 +1090,12 @@ extern "C" void draw_train_loss(char *windows_name, mat_cv* img_src, int img_siz if (iteration_old == 0) cv::putText(img, accuracy_name, cv::Point(10, 12), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 0, 0), 1, CV_AA); - cv::line(img, - cv::Point(img_offset + draw_size * (float)iteration_old / max_batches, draw_size * (1 - old_precision)), - cv::Point(img_offset + draw_size * (float)current_batch / max_batches, draw_size * (1 - precision)), - CV_RGB(255, 0, 0), 1, 8, 0); + if (iteration_old != 0){ + cv::line(img, + cv::Point(img_offset + draw_size * (float)iteration_old / max_batches, draw_size * (1 - old_precision)), + cv::Point(img_offset + draw_size * (float)current_batch / max_batches, draw_size * (1 - precision)), + CV_RGB(255, 0, 0), 1, 8, 0); + } sprintf(char_buff, "%2.1f%% ", precision * 100); cv::putText(img, char_buff, cv::Point(10, 28), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 5, CV_AA); diff --git a/src/image_opencv.h b/src/image_opencv.h index 136b7388faa..39c45218f80 100644 --- a/src/image_opencv.h +++ b/src/image_opencv.h @@ -92,7 +92,7 @@ void save_cv_jpg(mat_cv *img, const char *name); void draw_detections_cv_v3(mat_cv* show_img, detection *dets, int num, float thresh, char **names, image **alphabet, int classes, int ext_output); // Draw Loss & Accuracy chart -mat_cv* draw_train_chart(char *windows_name, float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show); +mat_cv* draw_train_chart(char *windows_name, float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show, char* chart_path); void draw_train_loss(char *windows_name, mat_cv* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port); @@ -126,4 +126,4 @@ void destroy_all_windows_cv(); } #endif -#endif // IMAGE_OPENCV_H \ No newline at end of file +#endif // IMAGE_OPENCV_H From d51d89053afc4b7f50a30ace7b2fcf1b2ddd7598 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 2 Feb 2020 14:27:39 +0300 Subject: [PATCH 0825/1365] Added error message --- src/classifier.c | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/src/classifier.c b/src/classifier.c index b1ba936ad86..ec41dce1f1c 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -66,6 +66,11 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int topk_data = option_find_int(options, "top", 5); char topk_buff[10]; sprintf(topk_buff, "top%d", topk_data); + if (classes != net.layers[net.n - 1].inputs) { + printf(" Error: num of filters = %d in the last conv-layer in cfg-file doesn't match to classes = %d in data-file \n", + net.layers[net.n - 1].inputs, classes); + getchar(); + } char **labels = get_labels(label_list); list *plist = get_paths(train_list); @@ -800,6 +805,11 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list"); int classes = option_find_int(options, "classes", 2); printf(" classes = %d, output in cfg = %d \n", classes, net.layers[net.n - 1].c); + if (classes != net.layers[net.n - 1].inputs) { + printf(" Error: num of filters = %d in the last conv-layer in cfg-file doesn't match to classes = %d in data-file \n", + net.layers[net.n - 1].inputs, classes); + getchar(); + } if (top == 0) top = option_find_int(options, "top", 1); if (top > classes) top = classes; From ff672ca7c813b2ec0033775ce331d10f8849e070 Mon Sep 17 00:00:00 2001 From: Dev-Nash <56091931+Dev-Nash@users.noreply.github.com> Date: Thu, 6 Feb 2020 15:34:07 -0700 Subject: [PATCH 0826/1365] Update network.c Added method to free network from network* reference. Needed for C# integration. --- src/network.c | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/src/network.c b/src/network.c index d56bed5d429..791a3a19187 100644 --- a/src/network.c +++ b/src/network.c @@ -1029,6 +1029,11 @@ float network_accuracy_multi(network net, data d, int n) return acc; } +void free_network_ptr(network* net) +{ + free_network(*net); +} + void free_network(network net) { int i; From a25d4318366194d9d7cbb140e589e4ca88a2a621 Mon Sep 17 00:00:00 2001 From: Dev-Nash <56091931+Dev-Nash@users.noreply.github.com> Date: Thu, 6 Feb 2020 15:39:01 -0700 Subject: [PATCH 0827/1365] Update darknet.h Exposed new method with LIB_API. Needed for C# integration. --- include/darknet.h | 1 + 1 file changed, 1 insertion(+) diff --git a/include/darknet.h b/include/darknet.h index 2be89f059fa..89a9d55c3cb 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -889,6 +889,7 @@ LIB_API network *load_network(char *cfg, char *weights, int clear); LIB_API network *load_network_custom(char *cfg, char *weights, int clear, int batch); LIB_API network *load_network(char *cfg, char *weights, int clear); LIB_API void free_network(network net); +LIB_API void free_network_ptr(network* net); // network.c LIB_API load_args get_base_args(network *net); From 8dc4833e7f51b0a75dad47a00cc0c6c711279868 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 14 Feb 2020 18:28:10 +0300 Subject: [PATCH 0828/1365] Minor fix --- src/dark_cuda.c | 2 +- src/detector.c | 5 ++++- src/gaussian_yolo_layer.c | 4 ++-- src/parser.c | 2 +- src/yolo_layer.c | 4 ++-- 5 files changed, 10 insertions(+), 7 deletions(-) diff --git a/src/dark_cuda.c b/src/dark_cuda.c index 8c4574f5d87..2ab3b205441 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -495,7 +495,7 @@ void show_cuda_cudnn_info() CHECK_CUDA(cudaRuntimeGetVersion(&cuda_version)); CHECK_CUDA(cudaDriverGetVersion(&cuda_driver_version)); fprintf(stderr, " CUDA-version: %d (%d)", cuda_version, cuda_driver_version); - if(cuda_version < cuda_driver_version) fprintf(stderr, "\n Warning: CUDA-version is lower than Driver-version! \n"); + if(cuda_version > cuda_driver_version) fprintf(stderr, "\n Warning: CUDA-version is higher than Driver-version! \n"); #ifdef CUDNN fprintf(stderr, ", cuDNN: %d.%d.%d", CUDNN_MAJOR, CUDNN_MINOR, CUDNN_PATCHLEVEL); #endif // CUDNN diff --git a/src/detector.c b/src/detector.c index 298f37f3628..0aa6f3cca6d 100644 --- a/src/detector.c +++ b/src/detector.c @@ -225,7 +225,10 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i save_image(im, "truth11"); */ - printf("Loaded: %lf seconds\n", (what_time_is_it_now() - time)); + const double load_time = (what_time_is_it_now() - time); + printf("Loaded: %lf seconds", load_time); + if (load_time > 0.1 && avg_loss > 0) printf(" - performance bottleneck on CPU or Disk HDD/SSD"); + printf("\n"); time = what_time_is_it_now(); float loss = 0; diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index b249c101eff..b0a0c89a191 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -661,9 +661,9 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) uc_loss /= l.batch; iou_loss /= l.batch; - printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d, loss = %.2f, class_loss = %.2f, iou_loss = %.2f, uc_loss = %.2f \n", + printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d, class_loss = %.2f, iou_loss = %.2f, uc_loss = %.2f, total_loss = %.2f \n", state.index, avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, recall75/count, count, - loss, class_loss, iou_loss, uc_loss); + class_loss, iou_loss, uc_loss, loss); } void backward_gaussian_yolo_layer(const layer l, network_state state) diff --git a/src/parser.c b/src/parser.c index 8320ef24422..42443cfda82 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1965,7 +1965,7 @@ network *load_network_custom(char *cfg, char *weights, int clear, int batch) printf(" Try to load weights: %s \n", weights); load_weights(net, weights); } - //fuse_conv_batchnorm(*net); + fuse_conv_batchnorm(*net); if (clear) (*net->seen) = 0; return net; } diff --git a/src/yolo_layer.c b/src/yolo_layer.c index bf54f630e68..b1500abf62e 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -575,9 +575,9 @@ void forward_yolo_layer(const layer l, network_state state) classification_loss /= l.batch; iou_loss /= l.batch; - printf("v3 (%s loss, Normalizer: (iou: %f, cls: %f) Region %d Avg (IOU: %f, GIOU: %f), Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d, loss = %f, class_loss = %f, iou_loss = %f\n", + printf("v3 (%s loss, Normalizer: (iou: %.2f, cls: %.2f) Region %d Avg (IOU: %f, GIOU: %f), Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d, class_loss = %f, iou_loss = %f, total_loss = %f \n", (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.cls_normalizer, state.index, tot_iou / count, tot_giou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count, - loss, classification_loss, iou_loss); + classification_loss, iou_loss, loss); } void backward_yolo_layer(const layer l, network_state state) From e910f4a839df9cc2be27207530a29b6066c5e76f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 16 Feb 2020 18:23:49 +0300 Subject: [PATCH 0829/1365] NAN in batchnorm_layer is fixed --- src/blas.c | 2 +- src/blas_kernels.cu | 60 +++++++++++++++++++++++++-------------------- 2 files changed, 34 insertions(+), 28 deletions(-) diff --git a/src/blas.c b/src/blas.c index d6f59367060..877d2ce3e3e 100644 --- a/src/blas.c +++ b/src/blas.c @@ -287,7 +287,7 @@ void normalize_cpu(float *x, float *mean, float *variance, int batch, int filter for(f = 0; f < filters; ++f){ for(i = 0; i < spatial; ++i){ int index = b*filters*spatial + f*spatial + i; - x[index] = (x[index] - mean[f])/(sqrt(variance[f]) + .000001f); + x[index] = (x[index] - mean[f])/(sqrt(variance[f] + .000001f)); } } } diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 94538cd0f7f..3e5cb3c3d5e 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -9,24 +9,26 @@ #include "utils.h" #include "tree.h" -__global__ void scale_bias_kernel(float *output, float *biases, int n, int size) + +__global__ void scale_bias_kernel(float *output, float *scale, int batch, int filters, int spatial, int current_size) { - int offset = blockIdx.x * blockDim.x + threadIdx.x; - int filter = blockIdx.y; - int batch = blockIdx.z; + const int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index >= current_size) return; - if(offset < size) output[(batch*n+filter)*size + offset] *= biases[filter]; + int f = (index / spatial) % filters; + output[index] *= scale[f]; } -void scale_bias_gpu(float *output, float *biases, int batch, int n, int size) +void scale_bias_gpu(float *output, float *scale, int batch, int filters, int spatial) { - dim3 dimGrid((size-1)/BLOCK + 1, n, batch); - dim3 dimBlock(BLOCK, 1, 1); + const int current_size = batch * filters * spatial; + const int num_blocks = get_number_of_blocks(current_size, BLOCK); - scale_bias_kernel<<>>(output, biases, n, size); + scale_bias_kernel << > >(output, scale, batch, filters, spatial, current_size); CHECK_CUDA(cudaPeekAtLastError()); } + __global__ void backward_scale_kernel(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates) { __shared__ float part[BLOCK]; @@ -53,21 +55,21 @@ void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, CHECK_CUDA(cudaPeekAtLastError()); } -__global__ void add_bias_kernel(float *output, float *biases, int n, int size) +__global__ void add_bias_kernel(float *output, float *biases, int batch, int filters, int spatial, int current_size) { - int offset = blockIdx.x * blockDim.x + threadIdx.x; - int filter = blockIdx.y; - int batch = blockIdx.z; + const int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index >= current_size) return; - if(offset < size) output[(batch*n+filter)*size + offset] += biases[filter]; + int f = (index / spatial) % filters; + output[index] += biases[f]; } -void add_bias_gpu(float *output, float *biases, int batch, int n, int size) +void add_bias_gpu(float *output, float *biases, int batch, int filters, int spatial) { - dim3 dimGrid((size-1)/BLOCK + 1, n, batch); - dim3 dimBlock(BLOCK, 1, 1); + const int current_size = batch * filters * spatial; + const int num_blocks = get_number_of_blocks(current_size, BLOCK); - add_bias_kernel<<>>(output, biases, n, size); + add_bias_kernel << > >(output, biases, batch, filters, spatial, current_size); CHECK_CUDA(cudaPeekAtLastError()); } @@ -173,11 +175,20 @@ extern "C" void adam_update_gpu(float *w, float *d, float *m, float *v, float B1 __global__ void normalize_kernel(int N, float *x, float *mean, float *variance, int batch, int filters, int spatial) { - int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index >= N) return; - int f = (index/spatial)%filters; + int f = (index / spatial) % filters; - x[index] = (x[index] - mean[f])/(sqrtf(variance[f]) + .000001f); + x[index] = (x[index] - mean[f]) / (sqrtf(variance[f] + .000001f)); +} + +extern "C" void normalize_gpu(float *x, float *mean, float *variance, int batch, int filters, int spatial) +{ + const int current_size = batch * filters * spatial; + const int num_blocks = get_number_of_blocks(current_size, BLOCK); + + normalize_kernel << > >(current_size, x, mean, variance, batch, filters, spatial); + CHECK_CUDA(cudaPeekAtLastError()); } __global__ void normalize_delta_kernel(int N, float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta) @@ -459,12 +470,7 @@ __global__ void mul_kernel(int N, float *X, int INCX, float *Y, int INCY) } -extern "C" void normalize_gpu(float *x, float *mean, float *variance, int batch, int filters, int spatial) -{ - size_t N = batch*filters*spatial; - normalize_kernel<<>>(N, x, mean, variance, batch, filters, spatial); - CHECK_CUDA(cudaPeekAtLastError()); -} + __global__ void fast_mean_kernel(float *x, int batch, int filters, int spatial, float *mean) { From d2f26776bb46ca91c3132b07d8d5e48603482789 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 17 Feb 2020 04:12:27 +0300 Subject: [PATCH 0830/1365] Weights initialization is improved for [shortcut], and inference of [shortcut] is accelerated by normalizing during initialization. --- src/blas.h | 1 + src/blas_kernels.cu | 31 ++++++++++++++++++-- src/convolutional_kernels.cu | 6 ++-- src/convolutional_layer.c | 40 +++++++++++++------------- src/network.c | 55 ++++++++++++++++++++++++++++++++++++ src/shortcut_layer.c | 5 ++-- 6 files changed, 111 insertions(+), 27 deletions(-) diff --git a/src/blas.h b/src/blas.h index c030b86771e..aafff998fb7 100644 --- a/src/blas.h +++ b/src/blas.h @@ -119,6 +119,7 @@ void upsample_gpu(float *in, int w, int h, int c, int batch, int stride, int for void softmax_tree_gpu(float *input, int spatial, int batch, int stride, float temp, float *output, tree hier); void fix_nan_and_inf(float *input, size_t size); +void reset_nan_and_inf(float *input, size_t size); int is_nan_or_inf(float *input, size_t size); void add_3_arrays_activate(float *a1, float *a2, float *a3, size_t size, ACTIVATION a, float *dst); diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 3e5cb3c3d5e..eac136ca67d 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -840,7 +840,10 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; delta_out[id] += delta_in[id] * w; // [0 or c or (c, h ,w)] - weight_updates_gpu[src_i / step] += delta_in[id] * in[id] * grad; + float weights_update_tmp = delta_in[id] * in[id] * grad; + + if (!isnan(weights_update_tmp) && !isinf(weights_update_tmp)) + weight_updates_gpu[src_i / step] += weights_update_tmp; } else delta_out[id] += delta_in[id]; @@ -861,7 +864,10 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; layer_delta[add_index] += delta_in[id] * w; - weight_updates_gpu[weights_index] += delta_in[id] * add[add_index] * grad; + float weights_update_tmp = delta_in[id] * add[add_index] * grad; + + if (!isnan(weights_update_tmp) && !isinf(weights_update_tmp)) + weight_updates_gpu[weights_index] += weights_update_tmp; } else layer_delta[add_index] += delta_in[id]; } @@ -1226,6 +1232,27 @@ extern "C" void fix_nan_and_inf(float *input, size_t size) } +__global__ void reset_nan_and_inf_kernel(float *input, size_t size) +{ + const int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < size) { + float val = input[index]; + if (isnan(val) || isinf(val)) + input[index] = 0; + } +} + +extern "C" void reset_nan_and_inf(float *input, size_t size) +{ + const int block_size = BLOCK; + const int num_blocks = get_number_of_blocks(size, block_size); + reset_nan_and_inf_kernel << > >(input, size); + CHECK_CUDA(cudaPeekAtLastError()); + //CHECK_CUDA(cudaDeviceSynchronize()); +} + + + __global__ void is_nan_or_inf_kernel(float *input, size_t size, int *pinned_return) { const int index = blockIdx.x*blockDim.x + threadIdx.x; diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 02f958813e8..80c31026026 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -898,10 +898,10 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state #endif if (state.net.try_fix_nan) { if (state.delta) { - fix_nan_and_inf(state.delta, l.inputs * l.batch); + reset_nan_and_inf(state.delta, l.inputs * l.batch); } int size = l.nweights; - fix_nan_and_inf(l.weight_updates_gpu, size); + reset_nan_and_inf(l.weight_updates_gpu, size); fix_nan_and_inf(l.weights_gpu, size); } } @@ -1232,7 +1232,7 @@ void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init //float decay = a.decay; //int batch = a.batch; - fix_nan_and_inf(l.weight_updates_gpu, l.nweights); + reset_nan_and_inf(l.weight_updates_gpu, l.nweights); fix_nan_and_inf(l.weights_gpu, l.nweights); if (l.adam) { diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 5ed316efc96..ff7831d6795 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -346,28 +346,28 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference, size_t workspace_ void free_convolutional_batchnorm(convolutional_layer *l) { if (!l->share_layer) { - free(l->scales); l->scales = NULL; - free(l->scale_updates); l->scale_updates = NULL; - free(l->mean); l->mean = NULL; - free(l->variance); l->variance = NULL; - free(l->mean_delta); l->mean_delta = NULL; - free(l->variance_delta); l->variance_delta = NULL; - free(l->rolling_mean); l->rolling_mean = NULL; - free(l->rolling_variance); l->rolling_variance = NULL; - free(l->x); l->x = NULL; - free(l->x_norm); l->x_norm = NULL; + if (l->scales) free(l->scales), l->scales = NULL; + if (l->scale_updates) free(l->scale_updates), l->scale_updates = NULL; + if (l->mean) free(l->mean), l->mean = NULL; + if (l->variance) free(l->variance), l->variance = NULL; + if (l->mean_delta) free(l->mean_delta), l->mean_delta = NULL; + if (l->variance_delta) free(l->variance_delta), l->variance_delta = NULL; + if (l->rolling_mean) free(l->rolling_mean), l->rolling_mean = NULL; + if (l->rolling_variance) free(l->rolling_variance), l->rolling_variance = NULL; + if (l->x) free(l->x), l->x = NULL; + if (l->x_norm) free(l->x_norm), l->x_norm = NULL; #ifdef GPU - cuda_free(l->scales_gpu); l->scales_gpu = NULL; - cuda_free(l->scale_updates_gpu); l->scale_updates_gpu = NULL; - cuda_free(l->mean_gpu); l->mean_gpu = NULL; - cuda_free(l->variance_gpu); l->variance_gpu = NULL; - cuda_free(l->mean_delta_gpu); l->mean_delta_gpu = NULL; - cuda_free(l->variance_delta_gpu); l->variance_delta_gpu = NULL; - cuda_free(l->rolling_mean_gpu); l->rolling_mean_gpu = NULL; - cuda_free(l->rolling_variance_gpu); l->rolling_variance_gpu = NULL; - cuda_free(l->x_gpu); l->x_gpu = NULL; - cuda_free(l->x_norm_gpu); l->x_norm_gpu = NULL; + if (l->scales_gpu) cuda_free(l->scales_gpu), l->scales_gpu = NULL; + if (l->scale_updates_gpu) cuda_free(l->scale_updates_gpu), l->scale_updates_gpu = NULL; + if (l->mean_gpu) cuda_free(l->mean_gpu), l->mean_gpu = NULL; + if (l->variance_gpu) cuda_free(l->variance_gpu), l->variance_gpu = NULL; + if (l->mean_delta_gpu) cuda_free(l->mean_delta_gpu), l->mean_delta_gpu = NULL; + if (l->variance_delta_gpu) cuda_free(l->variance_delta_gpu), l->variance_delta_gpu = NULL; + if (l->rolling_mean_gpu) cuda_free(l->rolling_mean_gpu), l->rolling_mean_gpu = NULL; + if (l->rolling_variance_gpu) cuda_free(l->rolling_variance_gpu), l->rolling_variance_gpu = NULL; + if (l->x_gpu) cuda_free(l->x_gpu), l->x_gpu = NULL; + if (l->x_norm_gpu) cuda_free(l->x_norm_gpu), l->x_norm_gpu = NULL; #endif } } diff --git a/src/network.c b/src/network.c index d56bed5d429..2403f3bad11 100644 --- a/src/network.c +++ b/src/network.c @@ -1067,6 +1067,10 @@ void free_network(network net) #endif } +static float relu(float src) { + if (src > 0) return src; + return 0; +} void fuse_conv_batchnorm(network net) { @@ -1105,6 +1109,57 @@ void fuse_conv_batchnorm(network net) #endif } } + else if (l->type == SHORTCUT && l->weights && l->weights_normalizion) + { + if (l->nweights > 0) { + //cuda_pull_array(l.weights_gpu, l.weights, l.nweights); + for (int i = 0; i < l->nweights; ++i) printf(" w = %f,", l->weights[i]); + printf(" l->nweights = %d \n", l->nweights); + } + + // nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w) + const int layer_step = l->nweights / (l->n + 1); // 1 or l.c or (l.c * l.h * l.w) + + int chan, i; + for (chan = 0; chan < layer_step; ++chan) + { + float sum = 1, max_val = -FLT_MAX; + + if (l->weights_normalizion == SOFTMAX_NORMALIZATION) { + for (i = 0; i < (l->n + 1); ++i) { + int w_index = chan + i * layer_step; + float w = l->weights[w_index]; + if (max_val < w) max_val = w; + } + } + + const float eps = 0.0001; + sum = eps; + + for (i = 0; i < (l->n + 1); ++i) { + int w_index = chan + i * layer_step; + float w = l->weights[w_index]; + if (l->weights_normalizion == RELU_NORMALIZATION) sum += relu(w); + else if (l->weights_normalizion == SOFTMAX_NORMALIZATION) sum += expf(w - max_val); + } + + for (i = 0; i < (l->n + 1); ++i) { + int w_index = chan + i * layer_step; + float w = l->weights[w_index]; + if (l->weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; + else if (l->weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; + l->weights[w_index] = w; + } + } + + l->weights_normalizion = 0; + +#ifdef GPU + if (gpu_index >= 0) { + push_shortcut_layer(*l); + } +#endif + } else { //printf(" Fusion skip layer type: %d \n", l->type); } diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index 09d1d5dac58..0acb138a046 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -46,13 +46,14 @@ layer make_shortcut_layer(int batch, int n, int *input_layers, int* input_sizes, if (train) l.delta = (float*)xcalloc(l.outputs * batch, sizeof(float)); l.output = (float*)xcalloc(l.outputs * batch, sizeof(float)); + l.nweights = 0; if (l.weights_type == PER_FEATURE) l.nweights = (l.n + 1); else if (l.weights_type == PER_CHANNEL) l.nweights = (l.n + 1) * l.c; if (l.nweights > 0) { l.weights = (float*)calloc(l.nweights, sizeof(float)); float scale = sqrt(2. / l.nweights); - for (i = 0; i < l.nweights; ++i) l.weights[i] = 1;// scale*rand_uniform(-1, 1); // rand_normal(); + for (i = 0; i < l.nweights; ++i) l.weights[i] = 1 + 0.01*rand_uniform(-1, 1);// scale*rand_uniform(-1, 1); // rand_normal(); if (train) l.weight_updates = (float*)calloc(l.nweights, sizeof(float)); l.update = update_shortcut_layer; @@ -247,7 +248,7 @@ void update_shortcut_layer_gpu(layer l, int batch, float learning_rate_init, flo //float decay = a.decay; //int batch = a.batch; - fix_nan_and_inf(l.weight_updates_gpu, l.nweights); + reset_nan_and_inf(l.weight_updates_gpu, l.nweights); fix_nan_and_inf(l.weights_gpu, l.nweights); axpy_ongpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); From 653eceb1a72641cf978734dc15257f8273f1ac94 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 17 Feb 2020 04:21:20 +0300 Subject: [PATCH 0831/1365] Compile fix --- src/network.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/network.c b/src/network.c index 2403f3bad11..ee8004e3bca 100644 --- a/src/network.c +++ b/src/network.c @@ -1152,7 +1152,7 @@ void fuse_conv_batchnorm(network net) } } - l->weights_normalizion = 0; + l->weights_normalizion = NO_NORMALIZATION; #ifdef GPU if (gpu_index >= 0) { From 8d87df23887d139f13def25dd2fd72439207ca2d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 20 Feb 2020 19:03:54 +0300 Subject: [PATCH 0832/1365] Minor fix --- src/classifier.c | 4 ++-- src/dark_cuda.c | 8 ++++---- src/data.c | 10 ++++++++-- src/demo.c | 2 +- src/detector.c | 6 +++--- src/gaussian_yolo_layer.c | 4 ++-- src/image.c | 5 ++++- src/region_layer.c | 2 +- src/yolo_layer.c | 6 +++--- 9 files changed, 28 insertions(+), 19 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index ec41dce1f1c..27aafad233d 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -67,7 +67,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, char topk_buff[10]; sprintf(topk_buff, "top%d", topk_data); if (classes != net.layers[net.n - 1].inputs) { - printf(" Error: num of filters = %d in the last conv-layer in cfg-file doesn't match to classes = %d in data-file \n", + printf("\n Error: num of filters = %d in the last conv-layer in cfg-file doesn't match to classes = %d in data-file \n", net.layers[net.n - 1].inputs, classes); getchar(); } @@ -806,7 +806,7 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi int classes = option_find_int(options, "classes", 2); printf(" classes = %d, output in cfg = %d \n", classes, net.layers[net.n - 1].c); if (classes != net.layers[net.n - 1].inputs) { - printf(" Error: num of filters = %d in the last conv-layer in cfg-file doesn't match to classes = %d in data-file \n", + printf("\n Error: num of filters = %d in the last conv-layer in cfg-file doesn't match to classes = %d in data-file \n", net.layers[net.n - 1].inputs, classes); getchar(); } diff --git a/src/dark_cuda.c b/src/dark_cuda.c index 2ab3b205441..e51617af928 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -59,7 +59,7 @@ void check_error(cudaError_t status) { const char *s = cudaGetErrorString(status); char buffer[256]; - printf("CUDA Error: %s\n", s); + printf("\n CUDA Error: %s\n", s); snprintf(buffer, 256, "CUDA Error: %s", s); #ifdef WIN32 getchar(); @@ -70,7 +70,7 @@ void check_error(cudaError_t status) { const char *s = cudaGetErrorString(status2); char buffer[256]; - printf("CUDA Error Prev: %s\n", s); + printf("\n CUDA Error Prev: %s\n", s); snprintf(buffer, 256, "CUDA Error Prev: %s", s); #ifdef WIN32 getchar(); @@ -181,7 +181,7 @@ void cudnn_check_error(cudnnStatus_t status) { const char *s = cudnnGetErrorString(status); char buffer[256]; - printf("cuDNN Error: %s\n", s); + printf("\n cuDNN Error: %s\n", s); snprintf(buffer, 256, "cuDNN Error: %s", s); #ifdef WIN32 getchar(); @@ -192,7 +192,7 @@ void cudnn_check_error(cudnnStatus_t status) { const char *s = cudnnGetErrorString(status2); char buffer[256]; - printf("cuDNN Error Prev: %s\n", s); + printf("\n cuDNN Error Prev: %s\n", s); snprintf(buffer, 256, "cuDNN Error Prev: %s", s); #ifdef WIN32 getchar(); diff --git a/src/data.c b/src/data.c index 9ff2fa82c50..dc3a511bfff 100644 --- a/src/data.c +++ b/src/data.c @@ -192,7 +192,10 @@ box_label *read_boxes(char *filename, int *n) char *new_line = "\n"; fwrite(new_line, sizeof(char), strlen(new_line), fw); fclose(fw); - if (check_mistakes) getchar(); + if (check_mistakes) { + printf("\n Error in read_boxes() \n"); + getchar(); + } *n = 0; return boxes; @@ -994,7 +997,10 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo mat_cv *src; src = load_image_mat_cv(filename, flag); if (src == NULL) { - if (check_mistakes) getchar(); + if (check_mistakes) { + printf("\n Error in load_data_detection() - OpenCV \n"); + getchar(); + } continue; } diff --git a/src/demo.c b/src/demo.c index 6f196cdabe8..f261631dd51 100644 --- a/src/demo.c +++ b/src/demo.c @@ -148,7 +148,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int for(j = 0; j < NFRAMES; ++j) predictions[j] = (float *) calloc(l.outputs, sizeof(float)); if (l.classes != demo_classes) { - printf("Parameters don't match: in cfg-file classes=%d, in data-file classes=%d \n", l.classes, demo_classes); + printf("\n Parameters don't match: in cfg-file classes=%d, in data-file classes=%d \n", l.classes, demo_classes); getchar(); exit(0); } diff --git a/src/detector.c b/src/detector.c index 0aa6f3cca6d..2a1477240e7 100644 --- a/src/detector.c +++ b/src/detector.c @@ -51,7 +51,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i int names_size = 0; char **names = get_labels_custom(name_list, &names_size); if (net_classes != names_size) { - printf(" Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", + printf("\n Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", name_list, names_size, net_classes, cfgfile); if (net_classes > names_size) getchar(); } @@ -855,7 +855,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa calculate_binary_weights(net); } if (net.layers[net.n - 1].classes != names_size) { - printf(" Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", + printf("\n Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", name_list, names_size, net.layers[net.n - 1].classes, cfgfile); getchar(); } @@ -1466,7 +1466,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam fuse_conv_batchnorm(net); calculate_binary_weights(net); if (net.layers[net.n - 1].classes != names_size) { - printf(" Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", + printf("\n Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", name_list, names_size, net.layers[net.n - 1].classes, cfgfile); if (net.layers[net.n - 1].classes > names_size) getchar(); } diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index b0a0c89a191..6c1106451d3 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -455,7 +455,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; if (class_id >= l.classes) { - printf(" Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1); + printf("\n Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1); printf(" truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f, class_id = %d \n", truth.x, truth.y, truth.w, truth.h, class_id); getchar(); continue; // if label contains class_id more than number of classes in the cfg-file @@ -661,7 +661,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) uc_loss /= l.batch; iou_loss /= l.batch; - printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d, class_loss = %.2f, iou_loss = %.2f, uc_loss = %.2f, total_loss = %.2f \n", + fprintf(stderr, "Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d, class_loss = %.2f, iou_loss = %.2f, uc_loss = %.2f, total_loss = %.2f \n", state.index, avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, recall75/count, count, class_loss, iou_loss, uc_loss, loss); } diff --git a/src/image.c b/src/image.c index 3d813ce87ff..8c94b395fc9 100644 --- a/src/image.c +++ b/src/image.c @@ -1410,7 +1410,10 @@ image load_image_stb(char *filename, int channels) char *new_line = "\n"; fwrite(new_line, sizeof(char), strlen(new_line), fw); fclose(fw); - if (check_mistakes) getchar(); + if (check_mistakes) { + printf("\n Error in load_image_stb() \n"); + getchar(); + } return make_image(10, 10, 3); //exit(EXIT_FAILURE); } diff --git a/src/region_layer.c b/src/region_layer.c index f062edabe2a..091b42f9576 100644 --- a/src/region_layer.c +++ b/src/region_layer.c @@ -299,7 +299,7 @@ void forward_region_layer(const region_layer l, network_state state) box truth = float_to_box(state.truth + t*5 + b*l.truths); int class_id = state.truth[t * 5 + b*l.truths + 4]; if (class_id >= l.classes) { - printf(" Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes-1); + printf("\n Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes-1); getchar(); continue; // if label contains class_id more than number of classes in the cfg-file } diff --git a/src/yolo_layer.c b/src/yolo_layer.c index b1500abf62e..2c26fdedb83 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -361,8 +361,8 @@ void forward_yolo_layer(const layer l, network_state state) box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; if (class_id >= l.classes) { - printf(" Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1); - printf(" truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f, class_id = %d \n", truth.x, truth.y, truth.w, truth.h, class_id); + printf("\n Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1); + printf("\n truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f, class_id = %d \n", truth.x, truth.y, truth.w, truth.h, class_id); getchar(); continue; // if label contains class_id more than number of classes in the cfg-file } @@ -575,7 +575,7 @@ void forward_yolo_layer(const layer l, network_state state) classification_loss /= l.batch; iou_loss /= l.batch; - printf("v3 (%s loss, Normalizer: (iou: %.2f, cls: %.2f) Region %d Avg (IOU: %f, GIOU: %f), Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d, class_loss = %f, iou_loss = %f, total_loss = %f \n", + fprintf(stderr, "v3 (%s loss, Normalizer: (iou: %.2f, cls: %.2f) Region %d Avg (IOU: %f, GIOU: %f), Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d, class_loss = %f, iou_loss = %f, total_loss = %f \n", (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.cls_normalizer, state.index, tot_iou / count, tot_giou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count, classification_loss, iou_loss, loss); } From f106f07d0d8ce3b05280e1e415dfa768efc77760 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 20 Feb 2020 19:22:59 +0300 Subject: [PATCH 0833/1365] Minor fix --- src/data.c | 2 +- src/gaussian_yolo_layer.c | 4 +++- src/yolo_layer.c | 4 +++- 3 files changed, 7 insertions(+), 3 deletions(-) diff --git a/src/data.c b/src/data.c index dc3a511bfff..9e564e2cce8 100644 --- a/src/data.c +++ b/src/data.c @@ -389,7 +389,7 @@ int fill_truth_detection(const char *path, int num_boxes, float *truth, int clas printf("\n Wrong annotation: class_id = %d. But class_id should be [from 0 to %d], file: %s \n", id, (classes-1), labelpath); sprintf(buff, "echo %s \"Wrong annotation: class_id = %d. But class_id should be [from 0 to %d]\" >> bad_label.list", labelpath, id, (classes-1)); system(buff); - getchar(); + if (check_mistakes) getchar(); ++sub; continue; } diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index 6c1106451d3..7e9efb21b34 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -20,6 +20,8 @@ #define M_PI 3.141592 #endif +extern int check_mistakes; + layer make_gaussian_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes, int max_boxes) { int i; @@ -457,7 +459,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) if (class_id >= l.classes) { printf("\n Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1); printf(" truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f, class_id = %d \n", truth.x, truth.y, truth.w, truth.h, class_id); - getchar(); + if (check_mistakes) getchar(); continue; // if label contains class_id more than number of classes in the cfg-file } if(!truth.x) break; diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 2c26fdedb83..86a78895b52 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -11,6 +11,8 @@ #include #include +extern int check_mistakes; + layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes, int max_boxes) { int i; @@ -363,7 +365,7 @@ void forward_yolo_layer(const layer l, network_state state) if (class_id >= l.classes) { printf("\n Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1); printf("\n truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f, class_id = %d \n", truth.x, truth.y, truth.w, truth.h, class_id); - getchar(); + if (check_mistakes) getchar(); continue; // if label contains class_id more than number of classes in the cfg-file } if (!truth.x) break; // continue; From bc7209ab31d5ae5f72dd393634881541767fa322 Mon Sep 17 00:00:00 2001 From: Tiago Koji Castro Shibata Date: Fri, 28 Dec 2018 23:13:14 -0200 Subject: [PATCH 0834/1365] Fix compilation with latest OpenCV C APIs were removed and must be replaced with C++ calls --- Makefile | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Makefile b/Makefile index 590db18c40b..032c31a2c6d 100644 --- a/Makefile +++ b/Makefile @@ -86,8 +86,8 @@ endif ifeq ($(OPENCV), 1) COMMON+= -DOPENCV CFLAGS+= -DOPENCV -LDFLAGS+= `pkg-config --libs opencv` -COMMON+= `pkg-config --cflags opencv` +LDFLAGS+= `pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv` +COMMON+= `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` endif ifeq ($(OPENMP), 1) From 187918f56459caef582a622df88a32af8e065ac3 Mon Sep 17 00:00:00 2001 From: Tiago Koji Castro Shibata Date: Tue, 12 Mar 2019 18:54:28 -0300 Subject: [PATCH 0835/1365] Enable C++11 for OpenCV 4 --- Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 032c31a2c6d..9159a006272 100644 --- a/Makefile +++ b/Makefile @@ -59,7 +59,7 @@ else CC=gcc endif -CPP=g++ +CPP=g++ -std=c++11 NVCC=nvcc OPTS=-Ofast LDFLAGS= -lm -pthread From 64fb042c63637038671ae9d53c06165599b28912 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 20 Feb 2020 23:45:12 +0300 Subject: [PATCH 0836/1365] Averaging Mean/Variance for several mini_batches inside 1 batch. Path to CBN. --- include/darknet.h | 2 + src/batchnorm_layer.c | 106 +++++++++++++++++++----------- src/blas.h | 7 +- src/blas_kernels.cu | 122 +++++++++++++++++++++++++++++++---- src/convolutional_kernels.cu | 6 +- src/convolutional_layer.c | 10 +-- src/layer.c | 2 + src/network.c | 4 +- src/parser.c | 2 + 9 files changed, 199 insertions(+), 62 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 2be89f059fa..ac50e49ed3d 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -551,6 +551,8 @@ struct layer { float * mean_gpu; float * variance_gpu; + float * m_cbn_avg_gpu; + float * v_cbn_avg_gpu; float * rolling_mean_gpu; float * rolling_variance_gpu; diff --git a/src/batchnorm_layer.c b/src/batchnorm_layer.c index 333db97fc09..1b16dd5f7b6 100644 --- a/src/batchnorm_layer.c +++ b/src/batchnorm_layer.c @@ -251,51 +251,81 @@ void forward_batchnorm_layer_gpu(layer l, network_state state) if (state.train) { simple_copy_ongpu(l.outputs*l.batch, l.output_gpu, l.x_gpu); + + // cbn + if (l.batch_normalize == 2) { + + fast_mean_gpu(l.output_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.mean_gpu); + + //fast_v_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.v_cbn_gpu); + int minibatch_index = state.net.current_subdivision + 1; + float alpha = 0.01; + + int inverse_variance = 0; #ifdef CUDNN - float one = 1; - float zero = 0; - cudnnBatchNormalizationForwardTraining(cudnn_handle(), - CUDNN_BATCHNORM_SPATIAL, - &one, - &zero, - l.normDstTensorDesc, - l.x_gpu, // input - l.normDstTensorDesc, - l.output_gpu, // output - l.normTensorDesc, - l.scales_gpu, - l.biases_gpu, - .01, - l.rolling_mean_gpu, // output (should be FP32) - l.rolling_variance_gpu, // output (should be FP32) - .00001, - l.mean_gpu, // output (should be FP32) - l.variance_gpu); // output (should be FP32) - - if (state.net.try_fix_nan) { - fix_nan_and_inf(l.scales_gpu, l.n); - fix_nan_and_inf(l.biases_gpu, l.n); - fix_nan_and_inf(l.mean_gpu, l.n); - fix_nan_and_inf(l.variance_gpu, l.n); - fix_nan_and_inf(l.rolling_mean_gpu, l.n); - fix_nan_and_inf(l.rolling_variance_gpu, l.n); + inverse_variance = 1; +#endif // CUDNN + + fast_v_cbn_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, minibatch_index, l.m_cbn_avg_gpu, l.v_cbn_avg_gpu, l.variance_gpu, + alpha, l.rolling_mean_gpu, l.rolling_variance_gpu, inverse_variance, .00001); + + normalize_scale_bias_gpu(l.output_gpu, l.mean_gpu, l.variance_gpu, l.scales_gpu, l.biases_gpu, l.batch, l.out_c, l.out_h*l.out_w, .00001f); + +#ifndef CUDNN + simple_copy_ongpu(l.outputs*l.batch, l.output_gpu, l.x_norm_gpu); +#endif // CUDNN + + //printf("\n CBN \n"); } + else { +#ifdef CUDNN + float one = 1; + float zero = 0; + cudnnBatchNormalizationForwardTraining(cudnn_handle(), + CUDNN_BATCHNORM_SPATIAL, + &one, + &zero, + l.normDstTensorDesc, + l.x_gpu, // input + l.normDstTensorDesc, + l.output_gpu, // output + l.normTensorDesc, + l.scales_gpu, + l.biases_gpu, + .01, + l.rolling_mean_gpu, // output (should be FP32) + l.rolling_variance_gpu, // output (should be FP32) + .00001, + l.mean_gpu, // output (should be FP32) + l.variance_gpu); // output (should be FP32) + + if (state.net.try_fix_nan) { + fix_nan_and_inf(l.scales_gpu, l.n); + fix_nan_and_inf(l.biases_gpu, l.n); + fix_nan_and_inf(l.mean_gpu, l.n); + fix_nan_and_inf(l.variance_gpu, l.n); + fix_nan_and_inf(l.rolling_mean_gpu, l.n); + fix_nan_and_inf(l.rolling_variance_gpu, l.n); + } + + //simple_copy_ongpu(l.outputs*l.batch, l.output_gpu, l.x_norm_gpu); #else // CUDNN - fast_mean_gpu(l.output_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.mean_gpu); - fast_variance_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.variance_gpu); + fast_mean_gpu(l.output_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.mean_gpu); + fast_variance_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.variance_gpu); - scal_ongpu(l.out_c, .99, l.rolling_mean_gpu, 1); - axpy_ongpu(l.out_c, .01, l.mean_gpu, 1, l.rolling_mean_gpu, 1); - scal_ongpu(l.out_c, .99, l.rolling_variance_gpu, 1); - axpy_ongpu(l.out_c, .01, l.variance_gpu, 1, l.rolling_variance_gpu, 1); + scal_ongpu(l.out_c, .99, l.rolling_mean_gpu, 1); + axpy_ongpu(l.out_c, .01, l.mean_gpu, 1, l.rolling_mean_gpu, 1); + scal_ongpu(l.out_c, .99, l.rolling_variance_gpu, 1); + axpy_ongpu(l.out_c, .01, l.variance_gpu, 1, l.rolling_variance_gpu, 1); - copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1); - normalize_gpu(l.output_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); - copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_norm_gpu, 1); + copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1); + normalize_gpu(l.output_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); + copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_norm_gpu, 1); - scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); - add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h); + scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h); #endif // CUDNN + } } else { normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); diff --git a/src/blas.h b/src/blas.h index aafff998fb7..fd2cf0bbaac 100644 --- a/src/blas.h +++ b/src/blas.h @@ -85,14 +85,17 @@ void normalize_delta_gpu(float *x, float *mean, float *variance, float *mean_del void fast_mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta); void fast_variance_delta_gpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta); -void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance); void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean); +void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance); +void fast_v_cbn_gpu(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, float *m_avg, float *v_avg, float *variance, + const float alpha, float *rolling_mean_gpu, float *rolling_variance_gpu, int inverse_variance, float epsilon); +void normalize_scale_bias_gpu(float *x, float *mean, float *variance, float *scales, float *biases, int batch, int filters, int spatial, float epsilon); +void compare_2_arrays_gpu(float *one, float *two, int size); void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out); void shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights, WEIGHTS_NORMALIZATION_T weights_normalizion); void backward_shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_delta_gpu, float *delta_out, float *delta_in, float *weights, float *weight_updates, int nweights, float *in, float **layers_output, WEIGHTS_NORMALIZATION_T weights_normalizion); void input_shortcut_gpu(float *in, int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out); -void scale_bias_gpu(float *output, float *biases, int batch, int n, int size); void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates); void scale_bias_gpu(float *output, float *biases, int batch, int n, int size); void add_bias_gpu(float *output, float *biases, int batch, int n, int size); diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index eac136ca67d..7014e3faacf 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -10,6 +10,24 @@ #include "tree.h" +__global__ void compare_2_arrays_kernel(float *one, float *two, int size) +{ + const int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index >= size) return; + + if (one[index] != two[index]) printf(" i: %d - one = %f, two = %f \n", index, one[index], two[index]); +} + +void compare_2_arrays_gpu(float *one, float *two, int size) +{ + const int num_blocks = get_number_of_blocks(size, BLOCK); + + compare_2_arrays_kernel << > >(one, two, size); + CHECK_CUDA(cudaPeekAtLastError()); + CHECK_CUDA(cudaDeviceSynchronize()); +} + + __global__ void scale_bias_kernel(float *output, float *scale, int batch, int filters, int spatial, int current_size) { const int index = blockIdx.x*blockDim.x + threadIdx.x; @@ -179,7 +197,7 @@ __global__ void normalize_kernel(int N, float *x, float *mean, float *variance, if (index >= N) return; int f = (index / spatial) % filters; - x[index] = (x[index] - mean[f]) / (sqrtf(variance[f] + .000001f)); + x[index] = (x[index] - mean[f]) / (sqrtf(variance[f] + .00001f)); } extern "C" void normalize_gpu(float *x, float *mean, float *variance, int batch, int filters, int spatial) @@ -470,8 +488,6 @@ __global__ void mul_kernel(int N, float *X, int INCX, float *Y, int INCY) } - - __global__ void fast_mean_kernel(float *x, int batch, int filters, int spatial, float *mean) { const int threads = BLOCK; @@ -492,14 +508,21 @@ __global__ void fast_mean_kernel(float *x, int batch, int filters, int spatial, __syncthreads(); if(id == 0){ - mean[filter] = 0; + float mean_tmp = 0; for(i = 0; i < threads; ++i){ - mean[filter] += local[i]; + mean_tmp += local[i]; } - mean[filter] /= spatial * batch; + mean_tmp /= spatial * batch; + mean[filter] = mean_tmp; } } +extern "C" void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean) +{ + fast_mean_kernel << > >(x, batch, filters, spatial, mean); + CHECK_CUDA(cudaPeekAtLastError()); +} + __global__ void fast_variance_kernel(float *x, float *mean, int batch, int filters, int spatial, float *variance) { const int threads = BLOCK; @@ -521,27 +544,100 @@ __global__ void fast_variance_kernel(float *x, float *mean, int batch, int filt __syncthreads(); if(id == 0){ - variance[filter] = 0; + float variance_tmp = 0; for(i = 0; i < threads; ++i){ - variance[filter] += local[i]; + variance_tmp += local[i]; } - variance[filter] /= (spatial * batch - 1); + variance_tmp /= (spatial * batch);// -1); + variance[filter] = variance_tmp; } } -extern "C" void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean) +extern "C" void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance) { - fast_mean_kernel<<>>(x, batch, filters, spatial, mean); + fast_variance_kernel<<>>(x, mean, batch, filters, spatial, variance); CHECK_CUDA(cudaPeekAtLastError()); } -extern "C" void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance) + +__global__ void fast_v_cbn_kernel(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, float *m_avg, float *v_avg, float *variance, + const float alpha, float *rolling_mean_gpu, float *rolling_variance_gpu, int inverse_variance, float epsilon) { - fast_variance_kernel<<>>(x, mean, batch, filters, spatial, variance); + const int threads = BLOCK; + __shared__ float local[threads]; + + int id = threadIdx.x; + local[id] = 0; + + int filter = blockIdx.x; + + int i, j; + for (j = 0; j < batch; ++j) { + for (i = 0; i < spatial; i += threads) { + int index = j*spatial*filters + filter*spatial + i + id; + + local[id] += (i + id < spatial) ? powf(x[index], 2) : 0; + } + } + __syncthreads(); + + if (id == 0) { + float v_tmp = 0; + v_tmp = 0; + for (i = 0; i < threads; ++i) { + v_tmp += local[i]; + } + v_tmp /= (spatial * batch - 1); + + v_tmp = fmax(v_tmp, powf(mean[filter], 2)); + + + const float alpha_cbn = 1.0f / minibatch_index; + + m_avg[filter] = alpha_cbn * mean[filter] + (1 - alpha_cbn) * m_avg[filter]; + mean[filter] = m_avg[filter]; + + v_avg[filter] = alpha_cbn * v_tmp + (1 - alpha_cbn) * v_avg[filter]; + + float variance_tmp = fmax(0.0f, v_avg[filter] - powf(m_avg[filter], 2)); + if (inverse_variance) variance_tmp = 1.0f / sqrtf(variance_tmp + epsilon); + variance[filter] = variance_tmp; + + rolling_mean_gpu[filter] = alpha * mean[filter] + (1 - alpha) * rolling_mean_gpu[filter]; + + rolling_variance_gpu[filter] = alpha * variance[filter] + (1 - alpha) * rolling_variance_gpu[filter]; + } +} + +extern "C" void fast_v_cbn_gpu(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, float *m_avg, float *v_avg, float *variance, + const float alpha, float *rolling_mean_gpu, float *rolling_variance_gpu, int inverse_variance, float epsilon) +{ + fast_v_cbn_kernel << > >(x, mean, batch, filters, spatial, minibatch_index, m_avg, v_avg, variance, alpha, rolling_mean_gpu, rolling_variance_gpu, inverse_variance, epsilon); CHECK_CUDA(cudaPeekAtLastError()); } +__global__ void normalize_scale_bias_kernel(int N, float *x, float *mean, float *variance, float *scales, float *biases, int batch, int filters, int spatial, float epsilon) +{ + const int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index >= N) return; + int f = (index / spatial) % filters; + + float val = (x[index] - mean[f]) / (sqrtf(variance[f] + epsilon)) * scales[f] + biases[f]; + + if (!isnan(val) && !isinf(val)) + x[index] = val; +} + +extern "C" void normalize_scale_bias_gpu(float *x, float *mean, float *variance, float *scales, float *biases, int batch, int filters, int spatial, float epsilon) +{ + const int current_size = batch * filters * spatial; + const int num_blocks = get_number_of_blocks(current_size, BLOCK); + + normalize_scale_bias_kernel << > >(current_size, x, mean, variance, scales, biases, batch, filters, spatial, epsilon); + CHECK_CUDA(cudaPeekAtLastError()); +} + extern "C" void mean_gpu(float *x, int batch, int filters, int spatial, float *mean) { mean_kernel<<>>(x, batch, filters, spatial, mean); diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 80c31026026..00d3961c4ed 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -578,7 +578,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) l.c / l.groups, // input channels l.h, l.w, // input size (h, w) l.size, l.size, // kernel size (h, w) - l.pad, l.pad, // padding (h, w) + l.pad * l.dilation, l.pad * l.dilation, // padding (h, w) l.stride_y, l.stride_x, // stride (h, w) l.dilation, l.dilation, // dilation (h, w) state.workspace); // output @@ -858,7 +858,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state l.c / l.groups, // input channels l.h, l.w, // input size (h, w) l.size, l.size, // kernel size (h, w) - l.pad, l.pad, // padding (h, w) + l.pad * l.dilation, l.pad * l.dilation, // padding (h, w) l.stride_y, l.stride_x, // stride (h, w) l.dilation, l.dilation, // dilation (h, w) state.workspace); // output @@ -883,7 +883,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state l.c / l.groups, // input channels l.h, l.w, // input size (h, w) l.size, l.size, // kernel size (h, w) - l.pad, l.pad, // padding size (h, w) + l.pad * l.dilation, l.pad * l.dilation, // padding size (h, w) l.stride_y, l.stride_x, // stride size (h, w) l.dilation, l.dilation, // dilation size (h, w) delta); // output (delta) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index ff7831d6795..d92a7838de5 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -416,7 +416,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, if (l.share_layer) { if (l.size != l.share_layer->size || l.nweights != l.share_layer->nweights || l.c != l.share_layer->c || l.n != l.share_layer->n) { - printf("Layer size, nweights, channels or filters don't match for the share_layer"); + printf(" Layer size, nweights, channels or filters don't match for the share_layer"); getchar(); } @@ -611,6 +611,8 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.mean_gpu = cuda_make_array(l.mean, n); l.variance_gpu = cuda_make_array(l.variance, n); + l.m_cbn_avg_gpu = cuda_make_array(l.mean, n); + l.v_cbn_avg_gpu = cuda_make_array(l.variance, n); #ifndef CUDNN l.mean_delta_gpu = cuda_make_array(l.mean, n); l.variance_delta_gpu = cuda_make_array(l.variance, n); @@ -1238,7 +1240,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) l.c / l.groups, // input channels l.h, l.w, // input size (h, w) l.size, l.size, // kernel size (h, w) - l.pad, l.pad, // padding (h, w) + l.pad * l.dilation, l.pad * l.dilation, // padding (h, w) l.stride_y, l.stride_x, // stride (h, w) l.dilation, l.dilation, // dilation (h, w) b); // output @@ -1429,7 +1431,7 @@ void backward_convolutional_layer(convolutional_layer l, network_state state) l.c / l.groups, // input channels l.h, l.w, // input size (h, w) l.size, l.size, // kernel size (h, w) - l.pad, l.pad, // padding (h, w) + l.pad * l.dilation, l.pad * l.dilation, // padding (h, w) l.stride_y, l.stride_x, // stride (h, w) l.dilation, l.dilation, // dilation (h, w) b); // output @@ -1451,7 +1453,7 @@ void backward_convolutional_layer(convolutional_layer l, network_state state) l.c / l.groups, // input channels (h, w) l.h, l.w, // input size (h, w) l.size, l.size, // kernel size (h, w) - l.pad, l.pad, // padding (h, w) + l.pad * l.dilation, l.pad * l.dilation, // padding (h, w) l.stride_y, l.stride_x, // stride (h, w) l.dilation, l.dilation, // dilation (h, w) state.delta + (i*l.groups + j)* (l.c / l.groups)*l.h*l.w); // output (delta) diff --git a/src/layer.c b/src/layer.c index ae72da6a863..d00e2213433 100644 --- a/src/layer.c +++ b/src/layer.c @@ -158,6 +158,8 @@ void free_layer_custom(layer l, int keep_cudnn_desc) if (l.binary_weights_gpu) cuda_free(l.binary_weights_gpu); if (l.mean_gpu) cuda_free(l.mean_gpu), l.mean_gpu = NULL; if (l.variance_gpu) cuda_free(l.variance_gpu), l.variance_gpu = NULL; + if (l.m_cbn_avg_gpu) cuda_free(l.m_cbn_avg_gpu), l.m_cbn_avg_gpu = NULL; + if (l.v_cbn_avg_gpu) cuda_free(l.v_cbn_avg_gpu), l.v_cbn_avg_gpu = NULL; if (l.rolling_mean_gpu) cuda_free(l.rolling_mean_gpu), l.rolling_mean_gpu = NULL; if (l.rolling_variance_gpu) cuda_free(l.rolling_variance_gpu), l.rolling_variance_gpu = NULL; if (l.variance_delta_gpu) cuda_free(l.variance_delta_gpu), l.variance_delta_gpu = NULL; diff --git a/src/network.c b/src/network.c index ee8004e3bca..239b3d822ec 100644 --- a/src/network.c +++ b/src/network.c @@ -1089,14 +1089,14 @@ void fuse_conv_batchnorm(network net) int f; for (f = 0; f < l->n; ++f) { - l->biases[f] = l->biases[f] - (double)l->scales[f] * l->rolling_mean[f] / (sqrt((double)l->rolling_variance[f] + .000001)); + l->biases[f] = l->biases[f] - (double)l->scales[f] * l->rolling_mean[f] / (sqrt((double)l->rolling_variance[f] + .00001)); const size_t filter_size = l->size*l->size*l->c / l->groups; int i; for (i = 0; i < filter_size; ++i) { int w_index = f*filter_size + i; - l->weights[w_index] = (double)l->weights[w_index] * l->scales[f] / (sqrt((double)l->rolling_variance[f] + .000001)); + l->weights[w_index] = (double)l->weights[w_index] * l->scales[f] / (sqrt((double)l->rolling_variance[f] + .00001)); } } diff --git a/src/parser.c b/src/parser.c index 42443cfda82..f6f0fa0ea5e 100644 --- a/src/parser.c +++ b/src/parser.c @@ -198,6 +198,8 @@ convolutional_layer parse_convolutional(list *options, size_params params) batch=params.batch; if(!(h && w && c)) error("Layer before convolutional layer must output image."); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); + int cbn = option_find_int_quiet(options, "cbn", 0); + if (cbn) batch_normalize = 2; int binary = option_find_int_quiet(options, "binary", 0); int xnor = option_find_int_quiet(options, "xnor", 0); int use_bin_output = option_find_int_quiet(options, "bin_output", 0); From 0ee9c8668f787ddaa6ab51dc3b71bb87f521007f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 21 Feb 2020 19:22:30 +0300 Subject: [PATCH 0837/1365] Fixed CBN --- src/batchnorm_layer.c | 5 +++-- src/blas.h | 2 +- src/blas_kernels.cu | 22 ++++++++++++++-------- 3 files changed, 18 insertions(+), 11 deletions(-) diff --git a/src/batchnorm_layer.c b/src/batchnorm_layer.c index 1b16dd5f7b6..7d9b172d96a 100644 --- a/src/batchnorm_layer.c +++ b/src/batchnorm_layer.c @@ -269,13 +269,13 @@ void forward_batchnorm_layer_gpu(layer l, network_state state) fast_v_cbn_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, minibatch_index, l.m_cbn_avg_gpu, l.v_cbn_avg_gpu, l.variance_gpu, alpha, l.rolling_mean_gpu, l.rolling_variance_gpu, inverse_variance, .00001); - normalize_scale_bias_gpu(l.output_gpu, l.mean_gpu, l.variance_gpu, l.scales_gpu, l.biases_gpu, l.batch, l.out_c, l.out_h*l.out_w, .00001f); + normalize_scale_bias_gpu(l.output_gpu, l.mean_gpu, l.variance_gpu, l.scales_gpu, l.biases_gpu, l.batch, l.out_c, l.out_h*l.out_w, inverse_variance, .00001f); #ifndef CUDNN simple_copy_ongpu(l.outputs*l.batch, l.output_gpu, l.x_norm_gpu); #endif // CUDNN - //printf("\n CBN \n"); + //printf("\n CBN, minibatch_index = %d \n", minibatch_index); } else { #ifdef CUDNN @@ -341,6 +341,7 @@ void backward_batchnorm_layer_gpu(layer l, network_state state) l.mean_gpu = l.rolling_mean_gpu; l.variance_gpu = l.rolling_variance_gpu; } + #ifdef CUDNN float one = 1; float zero = 0; diff --git a/src/blas.h b/src/blas.h index fd2cf0bbaac..252497a821c 100644 --- a/src/blas.h +++ b/src/blas.h @@ -89,7 +89,7 @@ void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean); void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance); void fast_v_cbn_gpu(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, float *m_avg, float *v_avg, float *variance, const float alpha, float *rolling_mean_gpu, float *rolling_variance_gpu, int inverse_variance, float epsilon); -void normalize_scale_bias_gpu(float *x, float *mean, float *variance, float *scales, float *biases, int batch, int filters, int spatial, float epsilon); +void normalize_scale_bias_gpu(float *x, float *mean, float *variance, float *scales, float *biases, int batch, int filters, int spatial, int inverse_variance, float epsilon); void compare_2_arrays_gpu(float *one, float *two, int size); void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out); void shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights, WEIGHTS_NORMALIZATION_T weights_normalizion); diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 7014e3faacf..1e008028a93 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -15,7 +15,9 @@ __global__ void compare_2_arrays_kernel(float *one, float *two, int size) const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index >= size) return; - if (one[index] != two[index]) printf(" i: %d - one = %f, two = %f \n", index, one[index], two[index]); + const float diff = 100 * fabs(one[index] - two[index]) / fabs(one[index]); + + if (diff > 10) printf(" i: %d - one = %f, two = %f, diff = %f %% \n", index, one[index], two[index], diff); } void compare_2_arrays_gpu(float *one, float *two, int size) @@ -600,12 +602,12 @@ __global__ void fast_v_cbn_kernel(const float *x, float *mean, int batch, int f v_avg[filter] = alpha_cbn * v_tmp + (1 - alpha_cbn) * v_avg[filter]; float variance_tmp = fmax(0.0f, v_avg[filter] - powf(m_avg[filter], 2)); - if (inverse_variance) variance_tmp = 1.0f / sqrtf(variance_tmp + epsilon); - variance[filter] = variance_tmp; + if (inverse_variance) variance[filter] = 1.0f / sqrtf(variance_tmp + epsilon); + else variance[filter] = variance_tmp; rolling_mean_gpu[filter] = alpha * mean[filter] + (1 - alpha) * rolling_mean_gpu[filter]; - rolling_variance_gpu[filter] = alpha * variance[filter] + (1 - alpha) * rolling_variance_gpu[filter]; + rolling_variance_gpu[filter] = alpha * variance_tmp + (1 - alpha) * rolling_variance_gpu[filter]; } } @@ -617,24 +619,28 @@ extern "C" void fast_v_cbn_gpu(const float *x, float *mean, int batch, int filte } -__global__ void normalize_scale_bias_kernel(int N, float *x, float *mean, float *variance, float *scales, float *biases, int batch, int filters, int spatial, float epsilon) +__global__ void normalize_scale_bias_kernel(int N, float *x, float *mean, float *variance, float *scales, float *biases, int batch, int filters, int spatial, int inverse_variance, float epsilon) { const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index >= N) return; int f = (index / spatial) % filters; - float val = (x[index] - mean[f]) / (sqrtf(variance[f] + epsilon)) * scales[f] + biases[f]; + float val = 0; + if(inverse_variance) val = (x[index] - mean[f]) * variance[f]; + else val = (x[index] - mean[f]) / (sqrtf(variance[f] + epsilon)); + val *= scales[f]; + val += biases[f]; if (!isnan(val) && !isinf(val)) x[index] = val; } -extern "C" void normalize_scale_bias_gpu(float *x, float *mean, float *variance, float *scales, float *biases, int batch, int filters, int spatial, float epsilon) +extern "C" void normalize_scale_bias_gpu(float *x, float *mean, float *variance, float *scales, float *biases, int batch, int filters, int spatial, int inverse_variance, float epsilon) { const int current_size = batch * filters * spatial; const int num_blocks = get_number_of_blocks(current_size, BLOCK); - normalize_scale_bias_kernel << > >(current_size, x, mean, variance, scales, biases, batch, filters, spatial, epsilon); + normalize_scale_bias_kernel << > >(current_size, x, mean, variance, scales, biases, batch, filters, spatial, inverse_variance, epsilon); CHECK_CUDA(cudaPeekAtLastError()); } From 6fb817f68b471da21c04f8bd5ffef0d3c4d67c30 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 22 Feb 2020 01:04:55 +0300 Subject: [PATCH 0838/1365] Accelerated DropBlock on GPU --- include/darknet.h | 2 + src/dropout_layer.c | 6 +- src/dropout_layer_kernels.cu | 139 ++++++++++++++++++++++++++++++++++- src/layer.c | 4 +- 4 files changed, 144 insertions(+), 7 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index ac50e49ed3d..a13168da42d 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -586,6 +586,8 @@ struct layer { float * loss_gpu; float * delta_gpu; float * rand_gpu; + float * drop_blocks_scale; + float * drop_blocks_scale_gpu; float * squared_gpu; float * norms_gpu; diff --git a/src/dropout_layer.c b/src/dropout_layer.c index 395e6a35286..3778a7b9583 100644 --- a/src/dropout_layer.c +++ b/src/dropout_layer.c @@ -29,11 +29,13 @@ dropout_layer make_dropout_layer(int batch, int inputs, float probability, int d l.scale = 1./(1.0 - probability); l.forward = forward_dropout_layer; l.backward = backward_dropout_layer; - #ifdef GPU +#ifdef GPU l.forward_gpu = forward_dropout_layer_gpu; l.backward_gpu = backward_dropout_layer_gpu; l.rand_gpu = cuda_make_array(l.rand, inputs*batch); - #endif + l.drop_blocks_scale = cuda_make_array_pinned(l.rand, l.batch); + l.drop_blocks_scale_gpu = cuda_make_array(l.rand, l.batch); +#endif if (l.dropblock) { if(l.dropblock_size_abs) fprintf(stderr, "dropblock p = %.2f l.dropblock_size_abs = %d %4d -> %4d\n", probability, l.dropblock_size_abs, inputs, inputs); else fprintf(stderr, "dropblock p = %.2f l.dropblock_size_rel = %.2f %4d -> %4d\n", probability, l.dropblock_size_rel, inputs, inputs); diff --git a/src/dropout_layer_kernels.cu b/src/dropout_layer_kernels.cu index a01bb9c6fe3..1a753e3e0d5 100644 --- a/src/dropout_layer_kernels.cu +++ b/src/dropout_layer_kernels.cu @@ -6,6 +6,88 @@ #include "dropout_layer.h" #include "dark_cuda.h" #include "utils.h" +#include "blas.h" + + +__global__ void dropblock_fast_kernel(float *rand, float prob, int w, int h, int spatial, int filters, int block_size, float *drop_blocks_scale, float *output) +{ + const int threads = BLOCK; + __shared__ int index_block; + + const int id = threadIdx.x; + const int f = blockIdx.x % filters; + const int b = blockIdx.x / filters; + + if(id == 0) index_block = -1; + __syncthreads(); + + int i; + for (i = 0; i < spatial; i += threads) { + int index = b*spatial*f + f*spatial + i + id; + + if (i + id < spatial) { + if (rand[id] < prob) { + index_block = id; + } + } + } + + __syncthreads(); + if (index_block == -1) return; + + + int b_x = index_block % w; + int b_y = index_block / w; + + b_x = max(0, min(b_x, w - block_size)); + b_y = max(0, min(b_y, h - block_size)); + + int block_square_size = block_size * block_size; + + for (i = 0; i < block_square_size; i += threads) + { + int i_x = i % w; + int i_y = i / w; + + int x = b_x + i_x; + int y = b_y + i_y; + + int index = b*spatial*f + f*spatial + y*w + x; + + output[index] = 0; + } + + /* + if (id == 0) { + + for (int x = b_x; x < (b_x + block_size); ++x) + { + for (int y = b_y; y < (b_y + block_size); ++y) + { + int index = b*spatial*f + f*spatial + y*w + x; + + output[index] = 0; + } + } + } + */ + + if (id == 0 && drop_blocks_scale) { + atomicAdd(&drop_blocks_scale[b], 1); + } + +} + + +__global__ void scale_dropblock_kernel(float *output, int size, int outputs, float *drop_blocks_scale) +{ + const int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index >= size) return; + + const int b = size / outputs; + output[index] *= drop_blocks_scale[b]; +} + __global__ void yoloswag420blazeit360noscope(float *input, int size, float *rand, float prob, float scale) { @@ -27,8 +109,8 @@ void forward_dropout_layer_gpu(dropout_layer l, network_state state) // We gradually increase the block size and the probability of dropout - during the first half of the training float multiplier = 1.0; - if(iteration_num < (state.net.max_batches / 2)) - multiplier = (iteration_num / (float)(state.net.max_batches / 2)); + if(iteration_num < (state.net.max_batches)) + multiplier = (iteration_num / (float)(state.net.max_batches)); // dropblock if (l.dropblock) { @@ -47,6 +129,31 @@ void forward_dropout_layer_gpu(dropout_layer l, network_state state) block_width = max_val_cmp(1, block_width); block_height = max_val_cmp(1, block_height); + int size = l.inputs*l.batch; + cuda_random(l.rand_gpu, size); + + fill_ongpu(l.batch, 0, l.drop_blocks_scale_gpu, 1); + + int num_blocks = l.batch * l.c; + dropblock_fast_kernel << > > (l.rand_gpu, cur_prob, l.w, l.h, l.w*l.h, l.c, block_width, l.drop_blocks_scale_gpu, state.input); + CHECK_CUDA(cudaPeekAtLastError()); + + cuda_pull_array(l.drop_blocks_scale_gpu, l.drop_blocks_scale, l.batch); + + for (int b = 0; b < l.batch; ++b) { + const float prob = l.drop_blocks_scale[b] * block_width * block_width / (float)l.outputs; + const float scale = 1.0f / (1.0f - prob); + l.drop_blocks_scale[b] = scale; + //printf(" , %f , ", scale); + } + + cuda_push_array(l.drop_blocks_scale_gpu, l.drop_blocks_scale, l.batch); + + num_blocks = get_number_of_blocks(l.outputs * l.batch, BLOCK); + scale_dropblock_kernel << > > (state.input, l.outputs * l.batch, l.outputs, l.drop_blocks_scale_gpu); + CHECK_CUDA(cudaPeekAtLastError()); + + /* const float part_occupied_by_block = block_width * block_height / ((float)l.w * l.h); const float prob_place_block = cur_prob / (part_occupied_by_block * max_blocks_per_channel); @@ -93,7 +200,7 @@ void forward_dropout_layer_gpu(dropout_layer l, network_state state) drop_block_kernel << > > (state.input, size, l.rand_gpu, l.scale); CHECK_CUDA(cudaPeekAtLastError()); - + */ } // dropout else { @@ -122,8 +229,32 @@ void backward_dropout_layer_gpu(dropout_layer l, network_state state) // dropblock if (l.dropblock) { - drop_block_kernel << > > (state.delta, size, l.rand_gpu, l.scale); + int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); + float multiplier = 1.0; + if (iteration_num < (state.net.max_batches)) + multiplier = (iteration_num / (float)(state.net.max_batches)); + + int block_width = l.dropblock_size_abs * multiplier; + int block_height = l.dropblock_size_abs * multiplier; + + if (l.dropblock_size_rel) { + block_width = l.dropblock_size_rel * l.w * multiplier; + block_height = l.dropblock_size_rel * l.h * multiplier; + } + + block_width = max_val_cmp(1, block_width); + block_height = max_val_cmp(1, block_height); + + int num_blocks = l.batch * l.c; + dropblock_fast_kernel << > > (l.rand_gpu, l.probability, l.w, l.h, l.w*l.h, l.c, block_width, NULL, state.delta); + CHECK_CUDA(cudaPeekAtLastError()); + + num_blocks = get_number_of_blocks(l.outputs * l.batch, BLOCK); + scale_dropblock_kernel << > > (state.delta, l.outputs * l.batch, l.outputs, l.drop_blocks_scale_gpu); CHECK_CUDA(cudaPeekAtLastError()); + + //drop_block_kernel << > > (state.delta, size, l.rand_gpu, l.scale); + //CHECK_CUDA(cudaPeekAtLastError()); } // dropout else { diff --git a/src/layer.c b/src/layer.c index d00e2213433..4bc844416bb 100644 --- a/src/layer.c +++ b/src/layer.c @@ -55,7 +55,9 @@ void free_layer_custom(layer l, int keep_cudnn_desc) if (l.type == DROPOUT) { if (l.rand) free(l.rand); #ifdef GPU - if (l.rand_gpu) cuda_free(l.rand_gpu); + if (l.rand_gpu) cuda_free(l.rand_gpu); + if (l.drop_blocks_scale) cuda_free_host(l.drop_blocks_scale); + if (l.drop_blocks_scale_gpu) cuda_free(l.drop_blocks_scale_gpu); #endif return; } From 26366d65358198317fe1338e9b3ab851edf2dd6f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 23 Feb 2020 01:27:43 +0300 Subject: [PATCH 0839/1365] DropBlock is fixed --- src/dropout_layer_kernels.cu | 145 +++++++++++++---------------------- 1 file changed, 54 insertions(+), 91 deletions(-) diff --git a/src/dropout_layer_kernels.cu b/src/dropout_layer_kernels.cu index 1a753e3e0d5..f17cb66c4ff 100644 --- a/src/dropout_layer_kernels.cu +++ b/src/dropout_layer_kernels.cu @@ -12,26 +12,34 @@ __global__ void dropblock_fast_kernel(float *rand, float prob, int w, int h, int spatial, int filters, int block_size, float *drop_blocks_scale, float *output) { const int threads = BLOCK; - __shared__ int index_block; - const int id = threadIdx.x; const int f = blockIdx.x % filters; const int b = blockIdx.x / filters; - if(id == 0) index_block = -1; + __shared__ int prob_block; + __shared__ int index_block; + + if (id == 0) { + prob_block = 1.0 * 1000000; + index_block = -1; + } __syncthreads(); int i; - for (i = 0; i < spatial; i += threads) { - int index = b*spatial*f + f*spatial + i + id; - - if (i + id < spatial) { - if (rand[id] < prob) { - index_block = id; + for (i = id; i < spatial; i += threads) { + int index = b*spatial*f + f*spatial + i; + + if (rand[index] < prob) { + //Chose with the lowest rand[i] + int new_val = rand[index] * 1000000; + int old_val = atomicMin(&prob_block, new_val); + if (new_val < old_val) { + index_block = i; + //if (b == 0) printf("\n rand[i] = %f, prob = %f, b = %d, f = %d, i = %d, index_block = %d \n", rand[i], prob, b, f, i, index_block); } } - } + } __syncthreads(); if (index_block == -1) return; @@ -44,7 +52,7 @@ __global__ void dropblock_fast_kernel(float *rand, float prob, int w, int h, int int block_square_size = block_size * block_size; - for (i = 0; i < block_square_size; i += threads) + for (i = id; i < block_square_size; i += threads) { int i_x = i % w; int i_y = i / w; @@ -57,23 +65,9 @@ __global__ void dropblock_fast_kernel(float *rand, float prob, int w, int h, int output[index] = 0; } - /* - if (id == 0) { - - for (int x = b_x; x < (b_x + block_size); ++x) - { - for (int y = b_y; y < (b_y + block_size); ++y) - { - int index = b*spatial*f + f*spatial + y*w + x; - - output[index] = 0; - } - } - } - */ - if (id == 0 && drop_blocks_scale) { atomicAdd(&drop_blocks_scale[b], 1); + //if(b == 0) printf("\n index_block = %d \n", index_block); } } @@ -84,7 +78,7 @@ __global__ void scale_dropblock_kernel(float *output, int size, int outputs, flo const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index >= size) return; - const int b = size / outputs; + const int b = index / outputs; output[index] *= drop_blocks_scale[b]; } @@ -95,11 +89,6 @@ __global__ void yoloswag420blazeit360noscope(float *input, int size, float *rand if(id < size) input[id] = (rand[id] < prob) ? 0 : input[id]*scale; } -__global__ void drop_block_kernel(float *input, int size, float *mask, float scale) -{ - int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; - if (id < size) input[id] = (mask[id]) ? 0 : (input[id] * scale); -} void forward_dropout_layer_gpu(dropout_layer l, network_state state) { @@ -109,17 +98,18 @@ void forward_dropout_layer_gpu(dropout_layer l, network_state state) // We gradually increase the block size and the probability of dropout - during the first half of the training float multiplier = 1.0; - if(iteration_num < (state.net.max_batches)) - multiplier = (iteration_num / (float)(state.net.max_batches)); + if(iteration_num < (state.net.max_batches*0.85)) + multiplier = (iteration_num / (float)(state.net.max_batches*0.85)); // dropblock if (l.dropblock) { //l.probability = 1 / keep_prob - const int max_blocks_per_channel = 10; + //const int max_blocks_per_channel = 10; const float cur_prob = l.probability * multiplier; + const float cur_scale = 1.f / (1.f - cur_prob); - int block_width = l.dropblock_size_abs * multiplier; - int block_height = l.dropblock_size_abs * multiplier; + int block_width = l.dropblock_size_abs *multiplier; + int block_height = l.dropblock_size_abs *multiplier; if (l.dropblock_size_rel) { block_width = l.dropblock_size_rel * l.w * multiplier; @@ -129,78 +119,40 @@ void forward_dropout_layer_gpu(dropout_layer l, network_state state) block_width = max_val_cmp(1, block_width); block_height = max_val_cmp(1, block_height); + block_width = min_val_cmp(l.w, block_width); + block_height = min_val_cmp(l.h, block_height); + + const int block_size = min_val_cmp(block_width, block_height); + const float block_prob = cur_prob / (block_size*block_size); + int size = l.inputs*l.batch; cuda_random(l.rand_gpu, size); fill_ongpu(l.batch, 0, l.drop_blocks_scale_gpu, 1); int num_blocks = l.batch * l.c; - dropblock_fast_kernel << > > (l.rand_gpu, cur_prob, l.w, l.h, l.w*l.h, l.c, block_width, l.drop_blocks_scale_gpu, state.input); + dropblock_fast_kernel << > > (l.rand_gpu, block_prob, l.w, l.h, l.w*l.h, l.c, block_size, l.drop_blocks_scale_gpu, state.input); CHECK_CUDA(cudaPeekAtLastError()); cuda_pull_array(l.drop_blocks_scale_gpu, l.drop_blocks_scale, l.batch); for (int b = 0; b < l.batch; ++b) { - const float prob = l.drop_blocks_scale[b] * block_width * block_width / (float)l.outputs; + const float prob = l.drop_blocks_scale[b] * block_size * block_size / (float)l.outputs; const float scale = 1.0f / (1.0f - prob); + printf(" %d x %d - block_size = %d, block_size*block_size = %d , ", l.w, l.h, block_size, block_size*block_size); + printf(" , l.drop_blocks_scale[b] = %f, prob = %f, calc scale = %f \t cur_prob = %f, cur_scale = %f \n", + l.drop_blocks_scale[b], prob, scale, cur_prob, cur_scale); l.drop_blocks_scale[b] = scale; - //printf(" , %f , ", scale); } cuda_push_array(l.drop_blocks_scale_gpu, l.drop_blocks_scale, l.batch); num_blocks = get_number_of_blocks(l.outputs * l.batch, BLOCK); scale_dropblock_kernel << > > (state.input, l.outputs * l.batch, l.outputs, l.drop_blocks_scale_gpu); + //scal_ongpu(l.outputs * l.batch, cur_scale, state.input, 1); + //scal_ongpu(l.outputs * l.batch, l.drop_blocks_scale[0], state.input, 1); CHECK_CUDA(cudaPeekAtLastError()); - /* - const float part_occupied_by_block = block_width * block_height / ((float)l.w * l.h); - const float prob_place_block = cur_prob / (part_occupied_by_block * max_blocks_per_channel); - - memset(l.rand, 0, l.batch * l.outputs * sizeof(float)); - - float count_ones = 0; - - int b, k, x, y, i; - for (b = 0; b < l.batch; b++) { - for (k = 0; k < l.c; k++) { - for (i = 0; i < max_blocks_per_channel; i++) { - float rnd = random_float_fast(); - //printf(" rnd = %f \n", rnd); - //int rn = rand_int_fast(1, 7); - //printf(" rnd = %d \n", rn); - if (rnd < prob_place_block) { - //count_ones += block_width *block_height; - const int pre_index = k*l.w*l.h + b*l.w*l.h*l.c; - const int x_block = rand_int_fast(0, l.w - block_width - 1); - const int y_block = rand_int_fast(0, l.h - block_height - 1); - for (y = y_block; y < (y_block + block_height); y++) { - memset(&l.rand[x_block + y*l.w + pre_index], 1, block_width * sizeof(float)); - //for (x = x_block; x < (x_block + block_width); x++) { - // const int index = x + y*l.w + pre_index; - // l.rand[index] = 1; - //} - } - } - } - } - } - - for (i = 0; i < (l.batch*l.outputs); ++i) if (l.rand[i]) count_ones++; - - cuda_push_array(l.rand_gpu, l.rand, l.batch*l.outputs); - - l.scale = (float)(l.batch*l.outputs) / (l.batch*l.outputs - count_ones); - - - //printf("\n l.scale = %f, cur_prob = %f, count_ones = %f, prob_place_block = %f, block_width = %d, block_height = %d \n", - // l.scale, cur_prob, count_ones, prob_place_block, block_width, block_height); - - int size = l.inputs*l.batch; - - drop_block_kernel << > > (state.input, size, l.rand_gpu, l.scale); - CHECK_CUDA(cudaPeekAtLastError()); - */ } // dropout else { @@ -231,8 +183,11 @@ void backward_dropout_layer_gpu(dropout_layer l, network_state state) if (l.dropblock) { int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); float multiplier = 1.0; - if (iteration_num < (state.net.max_batches)) - multiplier = (iteration_num / (float)(state.net.max_batches)); + if (iteration_num < (state.net.max_batches*0.85)) + multiplier = (iteration_num / (float)(state.net.max_batches*0.85)); + + const float cur_prob = l.probability * multiplier; + const float cur_scale = 1.f / (1.f - cur_prob); int block_width = l.dropblock_size_abs * multiplier; int block_height = l.dropblock_size_abs * multiplier; @@ -245,12 +200,20 @@ void backward_dropout_layer_gpu(dropout_layer l, network_state state) block_width = max_val_cmp(1, block_width); block_height = max_val_cmp(1, block_height); + block_width = min_val_cmp(l.w, block_width); + block_height = min_val_cmp(l.h, block_height); + + const int block_size = min_val_cmp(block_width, block_height); + const float block_prob = cur_prob / (block_size*block_size); + int num_blocks = l.batch * l.c; - dropblock_fast_kernel << > > (l.rand_gpu, l.probability, l.w, l.h, l.w*l.h, l.c, block_width, NULL, state.delta); + dropblock_fast_kernel << > > (l.rand_gpu, block_prob, l.w, l.h, l.w*l.h, l.c, block_size, NULL, state.delta); CHECK_CUDA(cudaPeekAtLastError()); num_blocks = get_number_of_blocks(l.outputs * l.batch, BLOCK); scale_dropblock_kernel << > > (state.delta, l.outputs * l.batch, l.outputs, l.drop_blocks_scale_gpu); + //scal_ongpu(l.outputs * l.batch, cur_scale, state.input, 1); + //scal_ongpu(l.outputs * l.batch, l.drop_blocks_scale[0], state.input, 1); CHECK_CUDA(cudaPeekAtLastError()); //drop_block_kernel << > > (state.delta, size, l.rand_gpu, l.scale); From 876eafd3b14f6669d446f9782be335cba5319909 Mon Sep 17 00:00:00 2001 From: Aymeric DUJARDIN Date: Mon, 24 Feb 2020 10:21:57 +0100 Subject: [PATCH 0840/1365] Shorter sl::Mat cast function --- src/yolo_console_dll.cpp | 78 +++++++--------------------------------- 1 file changed, 12 insertions(+), 66 deletions(-) diff --git a/src/yolo_console_dll.cpp b/src/yolo_console_dll.cpp index 1eee1723c79..8f73d869696 100644 --- a/src/yolo_console_dll.cpp +++ b/src/yolo_console_dll.cpp @@ -100,75 +100,19 @@ std::vector get_3d_coordinates(std::vector bbox_vect, cv::Mat xy return bbox3d_vect; } -#ifdef ZED_STEREO_2_COMPAT_MODE cv::Mat slMat2cvMat(sl::Mat &input) { - // Mapping between MAT_TYPE and CV_TYPE - int cv_type = -1; - switch (input.getDataType()) { - case sl::MAT_TYPE_32F_C1: - cv_type = CV_32FC1; - break; - case sl::MAT_TYPE_32F_C2: - cv_type = CV_32FC2; - break; - case sl::MAT_TYPE_32F_C3: - cv_type = CV_32FC3; - break; - case sl::MAT_TYPE_32F_C4: - cv_type = CV_32FC4; - break; - case sl::MAT_TYPE_8U_C1: - cv_type = CV_8UC1; - break; - case sl::MAT_TYPE_8U_C2: - cv_type = CV_8UC2; - break; - case sl::MAT_TYPE_8U_C3: - cv_type = CV_8UC3; - break; - case sl::MAT_TYPE_8U_C4: - cv_type = CV_8UC4; - break; - default: - break; - } - return cv::Mat(input.getHeight(), input.getWidth(), cv_type, input.getPtr(sl::MEM_CPU)); -} + int cv_type = -1; // Mapping between MAT_TYPE and CV_TYPE + if(input.getDataType() == +#ifdef ZED_STEREO_2_COMPAT_MODE + sl::MAT_TYPE_32F_C4 #else -cv::Mat slMat2cvMat(sl::Mat &input) { - // Mapping between MAT_TYPE and CV_TYPE - int cv_type = -1; - switch (input.getDataType()) { - case sl::MAT_TYPE::F32_C1: - cv_type = CV_32FC1; - break; - case sl::MAT_TYPE::F32_C2: - cv_type = CV_32FC2; - break; - case sl::MAT_TYPE::F32_C3: - cv_type = CV_32FC3; - break; - case sl::MAT_TYPE::F32_C4: + sl::MAT_TYPE::F32_C4 +#endif + ) { cv_type = CV_32FC4; - break; - case sl::MAT_TYPE::U8_C1: - cv_type = CV_8UC1; - break; - case sl::MAT_TYPE::U8_C2: - cv_type = CV_8UC2; - break; - case sl::MAT_TYPE::U8_C3: - cv_type = CV_8UC3; - break; - case sl::MAT_TYPE::U8_C4: - cv_type = CV_8UC4; - break; - default: - break; - } + } else cv_type = CV_8UC4; // sl::Mat used are either RGBA images or XYZ (4C) point clouds return cv::Mat(input.getHeight(), input.getWidth(), cv_type, input.getPtr(sl::MEM::CPU)); } -#endif cv::Mat zed_capture_rgb(sl::Camera &zed) { sl::Mat left; @@ -180,11 +124,13 @@ cv::Mat zed_capture_rgb(sl::Camera &zed) { cv::Mat zed_capture_3d(sl::Camera &zed) { sl::Mat cur_cloud; + zed.retrieveMeasure(cur_cloud, #ifdef ZED_STEREO_2_COMPAT_MODE - zed.retrieveMeasure(cur_cloud, sl::MEASURE_XYZ); + sl::MEASURE_XYZ #else - zed.retrieveMeasure(cur_cloud, sl::MEASURE::XYZ); + sl::MEASURE::XYZ #endif + ); return slMat2cvMat(cur_cloud).clone(); } From c83c21459b3afeb84758dcc602fc612cf2139a69 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 24 Feb 2020 19:14:44 +0300 Subject: [PATCH 0841/1365] Fixed gradient for ASFF --- src/activation_kernels.cu | 14 ++++++++++---- src/convolutional_layer.c | 4 ++-- src/dropout_layer.c | 6 +++--- src/dropout_layer_kernels.cu | 6 ++++-- 4 files changed, 19 insertions(+), 11 deletions(-) diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index 71372f564ef..c7cd49f2a6d 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -520,17 +520,20 @@ __global__ void gradient_array_normalize_channels_softmax_kernel(float *x, int s int b = i / wh_step; if (i < size) { - float grad = 0; int k; + /* + float grad = 0; for (k = 0; k < channels; ++k) { const int index = wh_i + k * wh_step + b*wh_step*channels; float out = x[index]; float delta = delta_gpu[index]; - grad += out*delta; + grad += out*fabs(delta); } + */ for (k = 0; k < channels; ++k) { const int index = wh_i + k * wh_step + b*wh_step*channels; float delta = delta_gpu[index]; + float grad = x[index] * (1 - x[index]); delta = delta * grad; delta_gpu[index] = delta; } @@ -558,18 +561,21 @@ __global__ void gradient_array_normalize_channels_kernel(float *x, int size, int int b = i / wh_step; if (i < size) { - float grad = 0; int k; + /* + float grad = 0; for (k = 0; k < channels; ++k) { const int index = wh_i + k * wh_step + b*wh_step*channels; float out = x[index]; float delta = delta_gpu[index]; - grad += out*delta; + grad += out*fabs(delta); } + */ for (k = 0; k < channels; ++k) { const int index = wh_i + k * wh_step + b*wh_step*channels; if (x[index] > 0) { float delta = delta_gpu[index]; + float grad = x[index]; delta = delta * grad; delta_gpu[index] = delta; } diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index d92a7838de5..f6c4d3dca60 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -438,8 +438,8 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, // float scale = 1./sqrt(size*size*c); float scale = sqrt(2./(size*size*c/groups)); - if (l.activation == NORM_CHAN_SOFTMAX_MAXVAL) { - for (i = 0; i < l.nweights; ++i) l.weights[i] = -10; // rand_normal(); + if (l.activation == NORM_CHAN || l.activation == NORM_CHAN_SOFTMAX || l.activation == NORM_CHAN_SOFTMAX_MAXVAL) { + for (i = 0; i < l.nweights; ++i) l.weights[i] = 1; // rand_normal(); } else { for (i = 0; i < l.nweights; ++i) l.weights[i] = scale*rand_uniform(-1, 1); // rand_normal(); diff --git a/src/dropout_layer.c b/src/dropout_layer.c index 3778a7b9583..dccf7eeba50 100644 --- a/src/dropout_layer.c +++ b/src/dropout_layer.c @@ -37,10 +37,10 @@ dropout_layer make_dropout_layer(int batch, int inputs, float probability, int d l.drop_blocks_scale_gpu = cuda_make_array(l.rand, l.batch); #endif if (l.dropblock) { - if(l.dropblock_size_abs) fprintf(stderr, "dropblock p = %.2f l.dropblock_size_abs = %d %4d -> %4d\n", probability, l.dropblock_size_abs, inputs, inputs); - else fprintf(stderr, "dropblock p = %.2f l.dropblock_size_rel = %.2f %4d -> %4d\n", probability, l.dropblock_size_rel, inputs, inputs); + if(l.dropblock_size_abs) fprintf(stderr, "dropblock p = %.3f l.dropblock_size_abs = %d %4d -> %4d\n", probability, l.dropblock_size_abs, inputs, inputs); + else fprintf(stderr, "dropblock p = %.3f l.dropblock_size_rel = %.2f %4d -> %4d\n", probability, l.dropblock_size_rel, inputs, inputs); } - else fprintf(stderr, "dropout p = %.2f %4d -> %4d\n", probability, inputs, inputs); + else fprintf(stderr, "dropout p = %.3f %4d -> %4d\n", probability, inputs, inputs); return l; } diff --git a/src/dropout_layer_kernels.cu b/src/dropout_layer_kernels.cu index f17cb66c4ff..346de4bbb10 100644 --- a/src/dropout_layer_kernels.cu +++ b/src/dropout_layer_kernels.cu @@ -60,9 +60,11 @@ __global__ void dropblock_fast_kernel(float *rand, float prob, int w, int h, int int x = b_x + i_x; int y = b_y + i_y; - int index = b*spatial*f + f*spatial + y*w + x; + if (x < w && y < h) { + int index = b*spatial*f + f*spatial + y*w + x; - output[index] = 0; + output[index] = 0; + } } if (id == 0 && drop_blocks_scale) { From cfa40fe890d0fc7d100cfec2ee4976bf9838cb30 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 24 Feb 2020 19:49:20 +0300 Subject: [PATCH 0842/1365] Fixed gradient for BiFPN --- src/blas.c | 2 ++ src/blas_kernels.cu | 2 ++ 2 files changed, 4 insertions(+) diff --git a/src/blas.c b/src/blas.c index 877d2ce3e3e..b495a47e283 100644 --- a/src/blas.c +++ b/src/blas.c @@ -177,6 +177,7 @@ void backward_shortcut_multilayer_cpu(int size, int src_outputs, int batch, int else if (weights_normalizion == SOFTMAX_NORMALIZATION) sum += expf(w - max_val); } + /* grad = 0; for (i = 0; i < (n + 1); ++i) { const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] @@ -185,6 +186,7 @@ void backward_shortcut_multilayer_cpu(int size, int src_outputs, int batch, int if (weights_normalizion == RELU_NORMALIZATION) grad += delta_w * relu(w) / sum; else if (weights_normalizion == SOFTMAX_NORMALIZATION) grad += delta_w * expf(w - max_val) / sum; } + */ } if (weights) { diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 1e008028a93..f269f04e728 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -926,6 +926,7 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i else if (weights_normalizion == SOFTMAX_NORMALIZATION) sum += expf(w - max_val); } + /* grad = 0; for (i = 0; i < (n + 1); ++i) { const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] @@ -934,6 +935,7 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i if (weights_normalizion == RELU_NORMALIZATION) grad += delta_w * relu(w) / sum; else if (weights_normalizion == SOFTMAX_NORMALIZATION) grad += delta_w * expf(w - max_val) / sum; } + */ } if (weights_gpu) { From 3cb9125b95a860694cfafa5b3408aa6123749e1e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 24 Feb 2020 20:01:53 +0300 Subject: [PATCH 0843/1365] Another one fix of gradient for BiFPN --- src/blas_kernels.cu | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index f269f04e728..4dcc9facdeb 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -943,11 +943,15 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; + if (weights_normalizion == RELU_NORMALIZATION) grad = w; + else if (weights_normalizion == SOFTMAX_NORMALIZATION) grad = w*(1-w); + delta_out[id] += delta_in[id] * w; // [0 or c or (c, h ,w)] float weights_update_tmp = delta_in[id] * in[id] * grad; if (!isnan(weights_update_tmp) && !isinf(weights_update_tmp)) - weight_updates_gpu[src_i / step] += weights_update_tmp; + atomicAdd(&weight_updates_gpu[src_i / step], weights_update_tmp); + //weight_updates_gpu[src_i / step] += weights_update_tmp; } else delta_out[id] += delta_in[id]; @@ -967,11 +971,15 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; + if (weights_normalizion == RELU_NORMALIZATION) grad = w; + else if (weights_normalizion == SOFTMAX_NORMALIZATION) grad = w*(1 - w); + layer_delta[add_index] += delta_in[id] * w; float weights_update_tmp = delta_in[id] * add[add_index] * grad; if (!isnan(weights_update_tmp) && !isinf(weights_update_tmp)) - weight_updates_gpu[weights_index] += weights_update_tmp; + atomicAdd(&weight_updates_gpu[weights_index], weights_update_tmp); + //weight_updates_gpu[weights_index] += weights_update_tmp; } else layer_delta[add_index] += delta_in[id]; } From f6baa62c9b6151b9f615a1e56434d237553fd4af Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 24 Feb 2020 20:48:49 +0300 Subject: [PATCH 0844/1365] Accelerated BiFPN back-propagation --- src/blas_kernels.cu | 51 +++++++++++++++++++++++++++++++-------------- 1 file changed, 35 insertions(+), 16 deletions(-) diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 4dcc9facdeb..d7ad75ea384 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -9,6 +9,16 @@ #include "utils.h" #include "tree.h" +__inline__ __device__ +float warpAllReduceSum(float val) { + for (int mask = WARP_SIZE / 2; mask > 0; mask /= 2) +#if CUDART_VERSION >= 9000 + val += __shfl_xor_sync(0xffffffff, val, mask); +#else + val += __shfl_xor(val, mask); +#endif + return val; +} __global__ void compare_2_arrays_kernel(float *one, float *two, int size) { @@ -949,9 +959,18 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i delta_out[id] += delta_in[id] * w; // [0 or c or (c, h ,w)] float weights_update_tmp = delta_in[id] * in[id] * grad; - if (!isnan(weights_update_tmp) && !isinf(weights_update_tmp)) - atomicAdd(&weight_updates_gpu[src_i / step], weights_update_tmp); - //weight_updates_gpu[src_i / step] += weights_update_tmp; + if (layer_step == 1 && (size/32) > (id/32 + 1)) { + float wu = warpAllReduceSum(weights_update_tmp); + if (threadIdx.x % 32 == 0) { + if (!isnan(wu) && !isinf(wu)) + atomicAdd(&weight_updates_gpu[src_i / step], wu); + } + } + else { + if (!isnan(weights_update_tmp) && !isinf(weights_update_tmp)) + atomicAdd(&weight_updates_gpu[src_i / step], weights_update_tmp); + //weight_updates_gpu[src_i / step] += weights_update_tmp; + } } else delta_out[id] += delta_in[id]; @@ -977,9 +996,18 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i layer_delta[add_index] += delta_in[id] * w; float weights_update_tmp = delta_in[id] * add[add_index] * grad; - if (!isnan(weights_update_tmp) && !isinf(weights_update_tmp)) - atomicAdd(&weight_updates_gpu[weights_index], weights_update_tmp); - //weight_updates_gpu[weights_index] += weights_update_tmp; + if (layer_step == 1 && (size / 32) > (id / 32 + 1)) { + float wu = warpAllReduceSum(weights_update_tmp); + if (threadIdx.x % 32 == 0) { + if (!isnan(wu) && !isinf(wu)) + atomicAdd(&weight_updates_gpu[weights_index], wu); + } + } + else { + if (!isnan(weights_update_tmp) && !isinf(weights_update_tmp)) + atomicAdd(&weight_updates_gpu[weights_index], weights_update_tmp); + //weight_updates_gpu[weights_index] += weights_update_tmp; + } } else layer_delta[add_index] += delta_in[id]; } @@ -1480,16 +1508,7 @@ extern "C" void scale_channels_gpu(float *in_w_h_c, int size, int channel_size, } -__inline__ __device__ -float warpAllReduceSum(float val) { - for (int mask = WARP_SIZE / 2; mask > 0; mask /= 2) -#if CUDART_VERSION >= 9000 - val += __shfl_xor_sync(0xffffffff, val, mask); -#else - val += __shfl_xor(val, mask); -#endif - return val; -} + __global__ void backward_scale_channels_kernel(float *in_w_h_c_delta, int size, int channel_size, int batch_size, int scale_wh, float *in_scales_c, float *out_from_delta, From 10a586140c99a905a504e6813b7cc629ff50ea42 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 27 Feb 2020 18:03:58 +0300 Subject: [PATCH 0845/1365] Minor fix. Fixed forward MISH on CPU --- src/activations.h | 3 ++- src/network.c | 7 +++++++ src/network_kernels.cu | 10 ++++++++++ 3 files changed, 19 insertions(+), 1 deletion(-) diff --git a/src/activations.h b/src/activations.h index fdbc28eff0a..16285c0450a 100644 --- a/src/activations.h +++ b/src/activations.h @@ -61,7 +61,8 @@ static inline float selu_activate(float x) { return (x >= 0)*1.0507f*x + (x < 0) static inline float relie_activate(float x){return (x>0) ? x : .01f*x;} static inline float ramp_activate(float x){return x*(x>0)+.1f*x;} static inline float leaky_activate(float x){return (x>0) ? x : .1f*x;} -static inline float tanh_activate(float x){return (expf(2*x)-1)/(expf(2*x)+1);} +//static inline float tanh_activate(float x){return (expf(2*x)-1)/(expf(2*x)+1);} +static inline float tanh_activate(float x) { return (2 / (1 + expf(-2 * x)) - 1); } static inline float softplus_activate(float x, float threshold) { if (x > threshold) return x; // too large else if (x < -threshold) return expf(x); // too small diff --git a/src/network.c b/src/network.c index 239b3d822ec..771df85c733 100644 --- a/src/network.c +++ b/src/network.c @@ -266,6 +266,13 @@ void forward_network(network net, network_state state) l.forward(l, state); //printf("%d - Predicted in %lf milli-seconds.\n", i, ((double)get_time_point() - time) / 1000); state.input = l.output; + + /* + float avg_val = 0; + int k; + for (k = 0; k < l.outputs; ++k) avg_val += l.output[k]; + printf(" i: %d - avg_val = %f \n", i, avg_val / l.outputs); + */ } } diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 14ade8ed405..34d9ec6a879 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -106,6 +106,16 @@ void forward_network_gpu(network net, network_state state) cudaStreamSynchronize(get_cuda_stream()); state.input = l.output_gpu; //cudaDeviceSynchronize(); + + /* + cuda_pull_array(l.output_gpu, l.output, l.outputs); + cudaStreamSynchronize(get_cuda_stream()); + float avg_val = 0; + int k; + for (k = 0; k < l.outputs; ++k) avg_val += l.output[k]; + printf(" i: %d - avg_val = %f \n", i, avg_val / l.outputs); + */ + /* cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); if (l.out_w >= 0 && l.out_h >= 1 && l.c >= 3) { From ef979a1fd2d14f41c65aa4b15a5f1db390b7bb43 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 1 Mar 2020 14:11:08 +0300 Subject: [PATCH 0846/1365] For CPU and GPU structures in the darknet.h have the same size --- include/darknet.h | 27 +++++++++++++++++++++------ src/batchnorm_layer.c | 8 +++++--- src/blas.h | 2 +- src/blas_kernels.cu | 13 ++++++++----- src/darknet.c | 1 + 5 files changed, 36 insertions(+), 15 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index a13168da42d..429af213828 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -34,6 +34,8 @@ #define SECRET_NUM -1234 +typedef enum { UNUSED_DEF_VAL } UNUSED_ENUM_TYPE; + #ifdef GPU #include @@ -42,8 +44,8 @@ #ifdef CUDNN #include -#endif -#endif +#endif // CUDNN +#endif // GPU #ifdef __cplusplus extern "C" { @@ -495,7 +497,7 @@ struct layer { size_t workspace_size; -#ifdef GPU +//#ifdef GPU int *indexes_gpu; float *z_gpu; @@ -610,8 +612,21 @@ struct layer { cudnnConvolutionBwdDataAlgo_t bd_algo, bd_algo16; cudnnConvolutionBwdFilterAlgo_t bf_algo, bf_algo16; cudnnPoolingDescriptor_t poolingDesc; +#else // CUDNN + void* srcTensorDesc, *dstTensorDesc; + void* srcTensorDesc16, *dstTensorDesc16; + void* dsrcTensorDesc, *ddstTensorDesc; + void* dsrcTensorDesc16, *ddstTensorDesc16; + void* normTensorDesc, *normDstTensorDesc, *normDstTensorDescF16; + void* weightDesc, *weightDesc16; + void* dweightDesc, *dweightDesc16; + void* convDesc; + UNUSED_ENUM_TYPE fw_algo, fw_algo16; + UNUSED_ENUM_TYPE bd_algo, bd_algo16; + UNUSED_ENUM_TYPE bf_algo, bf_algo16; + void* poolingDesc; #endif // CUDNN -#endif // GPU +//#endif // GPU }; @@ -701,7 +716,7 @@ typedef struct network { float *cost; float clip; -#ifdef GPU +//#ifdef GPU //float *input_gpu; //float *truth_gpu; float *delta_gpu; @@ -722,7 +737,7 @@ typedef struct network { float *global_delta_gpu; float *state_delta_gpu; size_t max_delta_gpu_size; -#endif +//#endif // GPU int optimized_memory; size_t workspace_size_limit; } network; diff --git a/src/batchnorm_layer.c b/src/batchnorm_layer.c index 7d9b172d96a..8920d414036 100644 --- a/src/batchnorm_layer.c +++ b/src/batchnorm_layer.c @@ -258,15 +258,17 @@ void forward_batchnorm_layer_gpu(layer l, network_state state) fast_mean_gpu(l.output_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.mean_gpu); //fast_v_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.v_cbn_gpu); - int minibatch_index = state.net.current_subdivision + 1; - float alpha = 0.01; + const int minibatch_index = state.net.current_subdivision + 1; + const int max_minibatch_index = state.net.subdivisions; + //printf("\n minibatch_index = %d, max_minibatch_index = %d \n", minibatch_index, max_minibatch_index); + const float alpha = 0.01; int inverse_variance = 0; #ifdef CUDNN inverse_variance = 1; #endif // CUDNN - fast_v_cbn_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, minibatch_index, l.m_cbn_avg_gpu, l.v_cbn_avg_gpu, l.variance_gpu, + fast_v_cbn_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, minibatch_index, max_minibatch_index, l.m_cbn_avg_gpu, l.v_cbn_avg_gpu, l.variance_gpu, alpha, l.rolling_mean_gpu, l.rolling_variance_gpu, inverse_variance, .00001); normalize_scale_bias_gpu(l.output_gpu, l.mean_gpu, l.variance_gpu, l.scales_gpu, l.biases_gpu, l.batch, l.out_c, l.out_h*l.out_w, inverse_variance, .00001f); diff --git a/src/blas.h b/src/blas.h index 252497a821c..9cf665d83c3 100644 --- a/src/blas.h +++ b/src/blas.h @@ -87,7 +87,7 @@ void fast_variance_delta_gpu(float *x, float *delta, float *mean, float *varianc void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean); void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance); -void fast_v_cbn_gpu(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, float *m_avg, float *v_avg, float *variance, +void fast_v_cbn_gpu(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, int max_minibatch_index, float *m_avg, float *v_avg, float *variance, const float alpha, float *rolling_mean_gpu, float *rolling_variance_gpu, int inverse_variance, float epsilon); void normalize_scale_bias_gpu(float *x, float *mean, float *variance, float *scales, float *biases, int batch, int filters, int spatial, int inverse_variance, float epsilon); void compare_2_arrays_gpu(float *one, float *two, int size); diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index d7ad75ea384..331b53e7f95 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -572,7 +572,7 @@ extern "C" void fast_variance_gpu(float *x, float *mean, int batch, int filters, } -__global__ void fast_v_cbn_kernel(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, float *m_avg, float *v_avg, float *variance, +__global__ void fast_v_cbn_kernel(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, int max_minibatch_index, float *m_avg, float *v_avg, float *variance, const float alpha, float *rolling_mean_gpu, float *rolling_variance_gpu, int inverse_variance, float epsilon) { const int threads = BLOCK; @@ -615,16 +615,19 @@ __global__ void fast_v_cbn_kernel(const float *x, float *mean, int batch, int f if (inverse_variance) variance[filter] = 1.0f / sqrtf(variance_tmp + epsilon); else variance[filter] = variance_tmp; - rolling_mean_gpu[filter] = alpha * mean[filter] + (1 - alpha) * rolling_mean_gpu[filter]; + //if (max_minibatch_index == minibatch_index) + { + rolling_mean_gpu[filter] = alpha * mean[filter] + (1 - alpha) * rolling_mean_gpu[filter]; - rolling_variance_gpu[filter] = alpha * variance_tmp + (1 - alpha) * rolling_variance_gpu[filter]; + rolling_variance_gpu[filter] = alpha * variance_tmp + (1 - alpha) * rolling_variance_gpu[filter]; + } } } -extern "C" void fast_v_cbn_gpu(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, float *m_avg, float *v_avg, float *variance, +extern "C" void fast_v_cbn_gpu(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, int max_minibatch_index, float *m_avg, float *v_avg, float *variance, const float alpha, float *rolling_mean_gpu, float *rolling_variance_gpu, int inverse_variance, float epsilon) { - fast_v_cbn_kernel << > >(x, mean, batch, filters, spatial, minibatch_index, m_avg, v_avg, variance, alpha, rolling_mean_gpu, rolling_variance_gpu, inverse_variance, epsilon); + fast_v_cbn_kernel << > >(x, mean, batch, filters, spatial, minibatch_index, max_minibatch_index, m_avg, v_avg, variance, alpha, rolling_mean_gpu, rolling_variance_gpu, inverse_variance, epsilon); CHECK_CUDA(cudaPeekAtLastError()); } diff --git a/src/darknet.c b/src/darknet.c index ec47b05acdf..d4f88b0249a 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -455,6 +455,7 @@ int main(int argc, char **argv) #ifndef GPU gpu_index = -1; + printf(" GPU isn't used \n"); init_cpu(); #else if(gpu_index >= 0){ From df9e60281931844ea63a365c920c3b1a4dc8d8e1 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 1 Mar 2020 18:02:54 +0300 Subject: [PATCH 0847/1365] Fixed bug in MJPEG_PORT - clear socket after closing the connection --- src/http_stream.cpp | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 1f3d138d189..c71a7f479fa 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -258,10 +258,11 @@ class JSON_sender //_write(s, head, 0); if (!close_all_sockets) _write(s, ", \n", 0); int n = _write(s, outputbuf, outlen); - if (n < outlen) + if (n < (int)outlen) { cerr << "JSON_sender: kill client " << s << endl; - ::shutdown(s, 2); + close_socket(s); + //::shutdown(s, 2); FD_CLR(s, &master); } @@ -448,7 +449,7 @@ class MJPG_sender cv::imencode(".jpg", frame, outbuf, params); //REMOVED FOR COMPATIBILITY // https://docs.opencv.org/3.4/d4/da8/group__imgcodecs.html#ga292d81be8d76901bff7988d18d2b42ac //std::cerr << "cv::imencode call disabled!" << std::endl; - size_t outlen = outbuf.size(); + int outlen = static_cast(outbuf.size()); #ifdef _WIN32 for (unsigned i = 0; i Date: Mon, 2 Mar 2020 15:27:43 +0300 Subject: [PATCH 0848/1365] Added [net] dynamic_minibatch=1 for increasing mini_batch_size when random=1 is used --- include/darknet.h | 3 + src/convolutional_kernels.cu | 6 +- src/convolutional_layer.c | 2 +- src/detector.c | 108 +++++++++++++++++++++++------------ src/dropout_layer.c | 19 ++++-- src/dropout_layer_kernels.cu | 12 ++-- src/network.c | 17 +++++- src/network.h | 1 + src/network_kernels.cu | 6 +- src/parser.c | 4 ++ src/region_layer.c | 3 +- 11 files changed, 126 insertions(+), 55 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 429af213828..8eb40ebcb0c 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -218,6 +218,7 @@ struct layer { int batch_normalize; int shortcut; int batch; + int dynamic_minibatch; int forced; int flipped; int inputs; @@ -640,6 +641,7 @@ typedef struct network { int n; int batch; uint64_t *seen; + int *cur_iteration; int *t; float epoch; int subdivisions; @@ -739,6 +741,7 @@ typedef struct network { size_t max_delta_gpu_size; //#endif // GPU int optimized_memory; + int dynamic_minibatch; size_t workspace_size_limit; } network; diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 00d3961c4ed..d5c01d63e21 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -419,7 +419,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //#ifdef CUDNN_HALF //if (state.use_mixed_precision) { - int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); + int iteration_num = get_current_iteration(state.net); // (*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in) && (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 1 && l.size > 1) { @@ -671,7 +671,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state float alpha = 1, beta = 0; //#ifdef CUDNN_HALF - int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); + int iteration_num = get_current_iteration(state.net); //(*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in) && (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 1 && l.size > 1) { @@ -978,7 +978,7 @@ void assisted_activation2_gpu(float alpha, float *output, float *gt_gpu, float * void assisted_excitation_forward_gpu(convolutional_layer l, network_state state) { - const int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); + const int iteration_num = get_current_iteration(state.net); //(*state.net.seen) / (state.net.batch*state.net.subdivisions); // epoch //const float epoch = (float)(*state.net.seen) / state.net.train_images_num; diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index f6c4d3dca60..0e0f614efdd 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -786,7 +786,7 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) if (l->activation == SWISH || l->activation == MISH) l->activation_input = (float*)realloc(l->activation_input, total_batch*l->outputs * sizeof(float)); #ifdef GPU - if (old_w < w || old_h < h) { + if (old_w < w || old_h < h || l->dynamic_minibatch) { if (l->train) { cuda_free(l->delta_gpu); l->delta_gpu = cuda_make_array(l->delta, total_batch*l->outputs); diff --git a/src/detector.c b/src/detector.c index 2a1477240e7..8753d6412c2 100644 --- a/src/detector.c +++ b/src/detector.c @@ -66,19 +66,19 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i srand(time(0)); int seed = rand(); - int i; - for (i = 0; i < ngpus; ++i) { + int k; + for (k = 0; k < ngpus; ++k) { srand(seed); #ifdef GPU - cuda_set_device(gpus[i]); + cuda_set_device(gpus[k]); #endif - nets[i] = parse_network_cfg(cfgfile); - nets[i].benchmark_layers = benchmark_layers; + nets[k] = parse_network_cfg(cfgfile); + nets[k].benchmark_layers = benchmark_layers; if (weightfile) { - load_weights(&nets[i], weightfile); + load_weights(&nets[k], weightfile); } - if (clear) *nets[i].seen = 0; - nets[i].learning_rate *= ngpus; + if (clear) *nets[k].seen = 0; + nets[k].learning_rate *= ngpus; } srand(time(0)); network net = nets[0]; @@ -105,12 +105,13 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i int train_images_num = plist->size; char **paths = (char **)list_to_array(plist); - int init_w = net.w; - int init_h = net.h; + const int init_w = net.w; + const int init_h = net.h; + const int init_b = net.batch; int iter_save, iter_save_last, iter_map; - iter_save = get_current_batch(net); - iter_save_last = get_current_batch(net); - iter_map = get_current_batch(net); + iter_save = get_current_iteration(net); + iter_save_last = get_current_iteration(net); + iter_map = get_current_iteration(net); float mean_average_precision = -1; float best_map = mean_average_precision; @@ -165,7 +166,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i pthread_t load_thread = load_data(args); int count = 0; //while(i*imgs < N*120){ - while (get_current_batch(net) < net.max_batches) { + while (get_current_iteration(net) < net.max_batches) { if (l.random && count++ % 10 == 0) { float rand_coef = 1.4; if (l.random != 1.0) rand_coef = l.random; @@ -175,26 +176,48 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i int dim_h = roundl(random_val*init_h / net.resize_step + 1) * net.resize_step; if (random_val < 1 && (dim_w > init_w || dim_h > init_h)) dim_w = init_w, dim_h = init_h; - // at the beginning - if (avg_loss < 0) { - dim_w = roundl(rand_coef*init_w / net.resize_step + 1) * net.resize_step; - dim_h = roundl(rand_coef*init_h / net.resize_step + 1) * net.resize_step; + int max_dim_w = roundl(rand_coef*init_w / net.resize_step + 1) * net.resize_step; + int max_dim_h = roundl(rand_coef*init_h / net.resize_step + 1) * net.resize_step; + + // at the beginning (check if enough memory) and at the end (calc rolling mean/variance) + if (avg_loss < 0 || get_current_iteration(net) > net.max_batches - 100) { + dim_w = max_dim_w; + dim_h = max_dim_h; } if (dim_w < net.resize_step) dim_w = net.resize_step; if (dim_h < net.resize_step) dim_h = net.resize_step; + int dim_b = (init_b * max_dim_w * max_dim_h) / (dim_w * dim_h); + int new_dim_b = (int)(dim_b * 0.8); + if (new_dim_b > init_b) dim_b = new_dim_b; - printf("%d x %d \n", dim_w, dim_h); args.w = dim_w; args.h = dim_h; + int k; + if (net.dynamic_minibatch) { + for (k = 0; k < ngpus; ++k) { + (*nets[k].seen) = init_b * net.subdivisions * get_current_iteration(net); // remove this line, when you will save to weights-file both: seen & cur_iteration + nets[k].batch = dim_b; + int j; + for (j = 0; j < nets[k].n; ++j) + nets[k].layers[j].batch = dim_b; + } + net.batch = dim_b; + imgs = net.batch * net.subdivisions * ngpus; + args.n = imgs; + printf("\n %d x %d (batch = %d) \n", dim_w, dim_h, net.batch); + } + else + printf("\n %d x %d \n", dim_w, dim_h); + pthread_join(load_thread, 0); train = buffer; free_data(train); load_thread = load_data(args); - for (i = 0; i < ngpus; ++i) { - resize_network(nets + i, dim_w, dim_h); + for (k = 0; k < ngpus; ++k) { + resize_network(nets + k, dim_w, dim_h); } net = nets[0]; } @@ -246,7 +269,8 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i if (avg_loss < 0 || avg_loss != avg_loss) avg_loss = loss; // if(-inf or nan) avg_loss = avg_loss*.9 + loss*.1; - i = get_current_batch(net); + const int iteration = get_current_iteration(net); + //i = get_current_batch(net); int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions); // calculate mAP for each 4 Epochs calc_map_for_each = fmax(calc_map_for_each, 100); @@ -259,22 +283,36 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i } if (net.cudnn_half) { - if (i < net.burn_in * 3) fprintf(stderr, "\n Tensor Cores are disabled until the first %d iterations are reached.", 3 * net.burn_in); + if (iteration < net.burn_in * 3) fprintf(stderr, "\n Tensor Cores are disabled until the first %d iterations are reached.", 3 * net.burn_in); else fprintf(stderr, "\n Tensor Cores are used."); } - printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now() - time), i*imgs); + printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", iteration, loss, avg_loss, get_current_rate(net), (what_time_is_it_now() - time), iteration*imgs); int draw_precision = 0; - if (calc_map && (i >= next_map_calc || i == net.max_batches)) { + if (calc_map && (iteration >= next_map_calc || iteration == net.max_batches)) { if (l.random) { - printf("Resizing to initial size: %d x %d \n", init_w, init_h); + printf("Resizing to initial size: %d x %d ", init_w, init_h); args.w = init_w; args.h = init_h; + int k; + if (net.dynamic_minibatch) { + for (k = 0; k < ngpus; ++k) { + for (k = 0; k < ngpus; ++k) { + nets[k].batch = init_b; + int j; + for (j = 0; j < nets[k].n; ++j) + nets[k].layers[j].batch = init_b; + } + } + net.batch = init_b; + imgs = init_b * net.subdivisions * ngpus; + args.n = imgs; + printf("\n %d x %d (batch = %d) \n", init_w, init_h, init_b); + } pthread_join(load_thread, 0); free_data(train); train = buffer; load_thread = load_data(args); - int k; for (k = 0; k < ngpus; ++k) { resize_network(nets + k, init_w, init_h); } @@ -286,7 +324,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i // combine Training and Validation networks //network net_combined = combine_train_valid_networks(net, net_map); - iter_map = i; + iter_map = iteration; mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, 0, net.letter_box, &net_map);// &net_combined); printf("\n mean_average_precision (mAP@0.5) = %f \n", mean_average_precision); if (mean_average_precision > best_map) { @@ -300,23 +338,23 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i draw_precision = 1; } #ifdef OPENCV - draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, mean_average_precision, draw_precision, "mAP%", dont_show, mjpeg_port); + draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, iteration, net.max_batches, mean_average_precision, draw_precision, "mAP%", dont_show, mjpeg_port); #endif // OPENCV //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) { //if (i % 100 == 0) { - if (i >= (iter_save + 1000) || i % 1000 == 0) { - iter_save = i; + if (iteration >= (iter_save + 1000) || iteration % 1000 == 0) { + iter_save = iteration; #ifdef GPU if (ngpus != 1) sync_nets(nets, ngpus, 0); #endif char buff[256]; - sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); + sprintf(buff, "%s/%s_%d.weights", backup_directory, base, iteration); save_weights(net, buff); } - if (i >= (iter_save_last + 100) || i % 100 == 0) { - iter_save_last = i; + if (iteration >= (iter_save_last + 100) || iteration % 100 == 0) { + iter_save_last = iteration; #ifdef GPU if (ngpus != 1) sync_nets(nets, ngpus, 0); #endif @@ -350,7 +388,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i free_list_contents_kvp(options); free_list(options); - for (i = 0; i < ngpus; ++i) free_network(nets[i]); + for (k = 0; k < ngpus; ++k) free_network(nets[k]); free(nets); //free_network(net); diff --git a/src/dropout_layer.c b/src/dropout_layer.c index dccf7eeba50..3cc73ed24bd 100644 --- a/src/dropout_layer.c +++ b/src/dropout_layer.c @@ -33,8 +33,10 @@ dropout_layer make_dropout_layer(int batch, int inputs, float probability, int d l.forward_gpu = forward_dropout_layer_gpu; l.backward_gpu = backward_dropout_layer_gpu; l.rand_gpu = cuda_make_array(l.rand, inputs*batch); - l.drop_blocks_scale = cuda_make_array_pinned(l.rand, l.batch); - l.drop_blocks_scale_gpu = cuda_make_array(l.rand, l.batch); + if (l.dropblock) { + l.drop_blocks_scale = cuda_make_array_pinned(l.rand, l.batch); + l.drop_blocks_scale_gpu = cuda_make_array(l.rand, l.batch); + } #endif if (l.dropblock) { if(l.dropblock_size_abs) fprintf(stderr, "dropblock p = %.3f l.dropblock_size_abs = %d %4d -> %4d\n", probability, l.dropblock_size_abs, inputs, inputs); @@ -48,11 +50,18 @@ void resize_dropout_layer(dropout_layer *l, int inputs) { l->inputs = l->outputs = inputs; l->rand = (float*)xrealloc(l->rand, l->inputs * l->batch * sizeof(float)); - #ifdef GPU +#ifdef GPU cuda_free(l->rand_gpu); - l->rand_gpu = cuda_make_array(l->rand, l->inputs*l->batch); - #endif + + if (l->dropblock) { + cudaFreeHost(l->drop_blocks_scale); + l->drop_blocks_scale = cuda_make_array_pinned(l->rand, l->batch); + + cuda_free(l->drop_blocks_scale_gpu); + l->drop_blocks_scale_gpu = cuda_make_array(l->rand, l->batch); + } +#endif } void forward_dropout_layer(dropout_layer l, network_state state) diff --git a/src/dropout_layer_kernels.cu b/src/dropout_layer_kernels.cu index 346de4bbb10..dcf1d89c964 100644 --- a/src/dropout_layer_kernels.cu +++ b/src/dropout_layer_kernels.cu @@ -95,7 +95,7 @@ __global__ void yoloswag420blazeit360noscope(float *input, int size, float *rand void forward_dropout_layer_gpu(dropout_layer l, network_state state) { if (!state.train) return; - int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); + int iteration_num = get_current_iteration(state.net); // (*state.net.seen) / (state.net.batch*state.net.subdivisions); //if (iteration_num < state.net.burn_in) return; // We gradually increase the block size and the probability of dropout - during the first half of the training @@ -141,9 +141,9 @@ void forward_dropout_layer_gpu(dropout_layer l, network_state state) for (int b = 0; b < l.batch; ++b) { const float prob = l.drop_blocks_scale[b] * block_size * block_size / (float)l.outputs; const float scale = 1.0f / (1.0f - prob); - printf(" %d x %d - block_size = %d, block_size*block_size = %d , ", l.w, l.h, block_size, block_size*block_size); - printf(" , l.drop_blocks_scale[b] = %f, prob = %f, calc scale = %f \t cur_prob = %f, cur_scale = %f \n", - l.drop_blocks_scale[b], prob, scale, cur_prob, cur_scale); + //printf(" %d x %d - block_size = %d, block_size*block_size = %d , ", l.w, l.h, block_size, block_size*block_size); + //printf(" , l.drop_blocks_scale[b] = %f, prob = %f, calc scale = %f \t cur_prob = %f, cur_scale = %f \n", + // l.drop_blocks_scale[b], prob, scale, cur_prob, cur_scale); l.drop_blocks_scale[b] = scale; } @@ -176,14 +176,14 @@ void forward_dropout_layer_gpu(dropout_layer l, network_state state) void backward_dropout_layer_gpu(dropout_layer l, network_state state) { if(!state.delta) return; - //int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); + //int iteration_num = get_current_iteration(state.net); //(*state.net.seen) / (state.net.batch*state.net.subdivisions); //if (iteration_num < state.net.burn_in) return; int size = l.inputs*l.batch; // dropblock if (l.dropblock) { - int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions); + int iteration_num = get_current_iteration(state.net); //(*state.net.seen) / (state.net.batch*state.net.subdivisions); float multiplier = 1.0; if (iteration_num < (state.net.max_batches*0.85)) multiplier = (iteration_num / (float)(state.net.max_batches*0.85)); diff --git a/src/network.c b/src/network.c index 771df85c733..33d0001c66d 100644 --- a/src/network.c +++ b/src/network.c @@ -57,6 +57,11 @@ load_args get_base_args(network *net) return args; } +int64_t get_current_iteration(network net) +{ + return *net.cur_iteration; +} + int get_current_batch(network net) { int batch_num = (*net.seen)/(net.batch*net.subdivisions); @@ -240,6 +245,7 @@ network make_network(int n) net.n = n; net.layers = (layer*)xcalloc(net.n, sizeof(layer)); net.seen = (uint64_t*)xcalloc(1, sizeof(uint64_t)); + net.cur_iteration = (int*)xcalloc(1, sizeof(int)); #ifdef GPU net.input_gpu = (float**)xcalloc(1, sizeof(float*)); net.truth_gpu = (float**)xcalloc(1, sizeof(float*)); @@ -359,7 +365,7 @@ float train_network_datum(network net, float *x, float *y) forward_network(net, state); backward_network(net, state); float error = get_network_cost(net); - if(((*net.seen)/net.batch)%net.subdivisions == 0) update_network(net); + //if(((*net.seen)/net.batch)%net.subdivisions == 0) update_network(net); return error; } @@ -404,6 +410,12 @@ float train_network_waitkey(network net, data d, int wait_key) sum += err; if(wait_key) wait_key_cv(5); } + (*net.cur_iteration) += 1; +#ifdef GPU + update_network_gpu(net); +#else // GPU + update_network(net); +#endif // GPU free(X); free(y); return (float)sum/(n*batch); @@ -523,7 +535,7 @@ int resize_network(network *net, int w, int h) //fflush(stderr); for (i = 0; i < net->n; ++i){ layer l = net->layers[i]; - //printf(" %d: layer = %d,", i, l.type); + //printf(" (resize %d: layer = %d) , ", i, l.type); if(l.type == CONVOLUTIONAL){ resize_convolutional_layer(&l, w, h); } @@ -1048,6 +1060,7 @@ void free_network(network net) free(net.scales); free(net.steps); free(net.seen); + free(net.cur_iteration); #ifdef GPU if (gpu_index >= 0) cuda_free(net.workspace); diff --git a/src/network.h b/src/network.h index 7e51c37cc07..aad5021526f 100644 --- a/src/network.h +++ b/src/network.h @@ -108,6 +108,7 @@ float get_current_seq_subdivisions(network net); int get_sequence_value(network net); float get_current_rate(network net); int get_current_batch(network net); +int64_t get_current_iteration(network net); //void free_network(network net); // darknet.h void compare_networks(network n1, network n2, data d); char *get_layer_string(LAYER_TYPE a); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 34d9ec6a879..63d981c67b0 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -318,7 +318,7 @@ float train_network_datum_gpu(network net, float *x, float *y) float error = get_network_cost(net); //if (((*net.seen) / net.batch) % net.subdivisions == 0) update_network_gpu(net); const int sequence = get_sequence_value(net); - if (((*net.seen) / net.batch) % (net.subdivisions*sequence) == 0) update_network_gpu(net); + //if (((*net.seen) / net.batch) % (net.subdivisions*sequence) == 0) update_network_gpu(net); return error; } @@ -564,7 +564,9 @@ float train_networks(network *nets, int n, data d, int interval) sum += errors[i]; } //cudaDeviceSynchronize(); - if (get_current_batch(nets[0]) % interval == 0) { + *nets[0].cur_iteration += (n - 1); + if (get_current_iteration(nets[0]) % interval == 0) + { printf("Syncing... "); fflush(stdout); sync_nets(nets, n, interval); diff --git a/src/parser.c b/src/parser.c index f6f0fa0ea5e..4c337b29272 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1053,6 +1053,8 @@ void parse_net_options(list *options, network *net) net->batch *= net->time_steps; net->subdivisions = subdivs; + *net->cur_iteration = 0; + net->dynamic_minibatch = option_find_int_quiet(options, "dynamic_minibatch", 0); net->optimized_memory = option_find_int_quiet(options, "optimized_memory", 0); net->workspace_size_limit = (size_t)1024*1024 * option_find_float_quiet(options, "workspace_size_limit_MB", 1024); // 1024 MB by default @@ -1357,6 +1359,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) } #endif // GPU + l.dynamic_minibatch = net.dynamic_minibatch; l.onlyforward = option_find_int_quiet(options, "onlyforward", 0); l.stopbackward = option_find_int_quiet(options, "stopbackward", 0); l.dontload = option_find_int_quiet(options, "dontload", 0); @@ -1873,6 +1876,7 @@ void load_weights_upto(network *net, char *filename, int cutoff) fread(&iseen, sizeof(uint32_t), 1, fp); *net->seen = iseen; } + *net->cur_iteration = get_current_batch(*net); printf(", trained: %.0f K-images (%.0f Kilo-batches_64) \n", (float)(*net->seen / 1000), (float)(*net->seen / 64000)); int transpose = (major > 1000) || (minor > 1000); diff --git a/src/region_layer.c b/src/region_layer.c index 091b42f9576..3825b425141 100644 --- a/src/region_layer.c +++ b/src/region_layer.c @@ -67,7 +67,8 @@ void resize_region_layer(layer *l, int w, int h) l->delta = (float*)xrealloc(l->delta, l->batch * l->outputs * sizeof(float)); #ifdef GPU - if (old_w < w || old_h < h) { + //if (old_w < w || old_h < h) + { cuda_free(l->delta_gpu); cuda_free(l->output_gpu); From 81290b07376c5abb4988a492dda70913bb90133d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 2 Mar 2020 17:36:08 +0300 Subject: [PATCH 0849/1365] Fixed label_smoothing for Detector --- src/gaussian_yolo_layer.c | 16 ++++++++++++++-- src/yolo_layer.c | 16 ++++++++++++++-- 2 files changed, 28 insertions(+), 4 deletions(-) diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index 7e9efb21b34..101005ee427 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -371,13 +371,25 @@ void delta_gaussian_yolo_class(float *output, float *delta, int index, int class { int n; if (delta[index]){ - delta[index + stride*class_id] = (1 - label_smooth_eps) - output[index + stride*class_id]; + if (label_smooth_eps > 0) { + float out_val = output[index + stride*class_id] * (1 - label_smooth_eps) + 0.5*label_smooth_eps; + delta[index + stride*class_id] = 1 - out_val; + } + else { + delta[index + stride*class_id] = 1 - output[index + stride*class_id]; + } if (classes_multipliers) delta[index + stride*class_id] *= classes_multipliers[class_id]; if(avg_cat) *avg_cat += output[index + stride*class_id]; return; } for(n = 0; n < classes; ++n){ - delta[index + stride*n] = ((n == class_id) ? (1 - label_smooth_eps) : (0 + label_smooth_eps/classes)) - output[index + stride*n]; + if (label_smooth_eps > 0) { + float out_val = output[index + stride*class_id] * (1 - label_smooth_eps) + 0.5*label_smooth_eps; + delta[index + stride*n] = ((n == class_id) ? 1 : 0) - out_val; + } + else { + delta[index + stride*n] = ((n == class_id) ? 1 : 0) - output[index + stride*n]; + } if (classes_multipliers && n == class_id) delta[index + stride*class_id] *= classes_multipliers[class_id]; if(n == class_id && avg_cat) *avg_cat += output[index + stride*n]; } diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 86a78895b52..d80dcc1dd44 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -257,7 +257,13 @@ void delta_yolo_class(float *output, float *delta, int index, int class_id, int { int n; if (delta[index + stride*class_id]){ - delta[index + stride*class_id] = (1 - label_smooth_eps) - output[index + stride*class_id]; + if (label_smooth_eps > 0) { + float out_val = output[index + stride*class_id] * (1 - label_smooth_eps) + 0.5*label_smooth_eps; + delta[index + stride*class_id] = 1 - out_val; + } + else { + delta[index + stride*class_id] = 1 - output[index + stride*class_id]; + } if (classes_multipliers) delta[index + stride*class_id] *= classes_multipliers[class_id]; if(avg_cat) *avg_cat += output[index + stride*class_id]; return; @@ -285,7 +291,13 @@ void delta_yolo_class(float *output, float *delta, int index, int class_id, int else { // default for (n = 0; n < classes; ++n) { - delta[index + stride*n] = ((n == class_id) ? (1 - label_smooth_eps) : (0 + label_smooth_eps/classes)) - output[index + stride*n]; + if (label_smooth_eps > 0) { + float out_val = output[index + stride*class_id] * (1 - label_smooth_eps) + 0.5*label_smooth_eps; + delta[index + stride*n] = ((n == class_id) ? 1 : 0) - out_val; + } + else { + delta[index + stride*n] = ((n == class_id) ? 1 : 0) - output[index + stride*n]; + } if (classes_multipliers && n == class_id) delta[index + stride*class_id] *= classes_multipliers[class_id]; if (n == class_id && avg_cat) *avg_cat += output[index + stride*n]; } From 60e0950c74f40b6c7ce767c0a32c0c39459402b0 Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 2 Mar 2020 18:15:47 +0300 Subject: [PATCH 0850/1365] Update Readme.md --- README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 632f896272b..3ae52527e0e 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # Yolo-v3 and Yolo-v2 for Windows and Linux -### (neural network for object detection) - Tensor Cores can be used on [Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) and [Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows-using-vcpkg) +### (neural network for object detection) - Tensor Cores can be used on [Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) and [Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows-using-cmake-gui) More details: http://pjreddie.com/darknet/yolo/ @@ -8,7 +8,8 @@ More details: http://pjreddie.com/darknet/yolo/ [![TravisCI](https://travis-ci.org/AlexeyAB/darknet.svg?branch=master)](https://travis-ci.org/AlexeyAB/darknet) [![AppveyorCI](https://ci.appveyor.com/api/projects/status/594bwb5uoc1fxwiu/branch/master?svg=true)](https://ci.appveyor.com/project/AlexeyAB/darknet/branch/master) [![Contributors](https://img.shields.io/github/contributors/AlexeyAB/Darknet.svg)](https://github.com/AlexeyAB/darknet/graphs/contributors) -[![License: Unlicense](https://img.shields.io/badge/license-Unlicense-blue.svg)](https://github.com/AlexeyAB/darknet/blob/master/LICENSE) +[![License: Unlicense](https://img.shields.io/badge/license-Unlicense-blue.svg)](https://github.com/AlexeyAB/darknet/blob/master/LICENSE) +[![DOI](https://zenodo.org/badge/75388965.svg)](https://zenodo.org/badge/latestdoi/75388965) * [Requirements (and how to install dependecies)](#requirements) From 4f95c78f546e1e12525e5fa5f572813a0e86f1f2 Mon Sep 17 00:00:00 2001 From: Evert Arends Date: Tue, 3 Mar 2020 19:27:53 +0100 Subject: [PATCH 0851/1365] Update readme Updated the readme to prevent reading mistakes and hours of pointless searching --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 3ae52527e0e..a00d8924a45 100644 --- a/README.md +++ b/README.md @@ -396,7 +396,7 @@ Training Yolo v3: * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L610 * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L696 * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L783 - * change [`filters=255`] to filters=(classes + 5)x3 in the 3 `[convolutional]` before each `[yolo]` layer + * change [`filters=255`] to filters=(classes + 5)x3 in the 3 `[convolutional]` before each `[yolo]` layer, keep in mind that it only has to be the last `[convolutional]` before each of the `[yolo]` layers. * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L603 * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L689 * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L776 From 324cc881ee02ead024c5fe9936852d8a7ff0bdec Mon Sep 17 00:00:00 2001 From: Muhammad Maaz Date: Wed, 4 Mar 2020 20:54:30 +0500 Subject: [PATCH 0852/1365] Add support to display time remaining on the training chart. --- src/classifier.c | 3 ++- src/detector.c | 11 ++++++++++- src/image_opencv.cpp | 9 ++++----- src/image_opencv.h | 2 +- 4 files changed, 17 insertions(+), 8 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index 27aafad233d..1d86b2c8cf9 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -182,7 +182,8 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); #ifdef OPENCV - if (!dontuse_opencv) draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, topk_buff, dont_show, mjpeg_port); + double time1 = get_time_point(); + if (!dontuse_opencv) draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, topk_buff, dont_show, mjpeg_port, time1); #endif // OPENCV if (i >= (iter_save + 1000)) { diff --git a/src/detector.c b/src/detector.c index 2a1477240e7..d85df2ac124 100644 --- a/src/detector.c +++ b/src/detector.c @@ -164,6 +164,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i pthread_t load_thread = load_data(args); int count = 0; + double start, end, time_remaining[1000], tmp; //while(i*imgs < N*120){ while (get_current_batch(net) < net.max_batches) { if (l.random && count++ % 10 == 0) { @@ -300,7 +301,15 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i draw_precision = 1; } #ifdef OPENCV - draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, mean_average_precision, draw_precision, "mAP%", dont_show, mjpeg_port); + end = what_time_is_it_now(); + time_remaining[i%1000] = (net.max_batches - i)*(end - start) / 60 / 60; + tmp = 0.0; + int j; + for (j = 0; j < 1000; ++j){ + tmp += time_remaining[j]; + } + draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, mean_average_precision, draw_precision, "mAP%", dont_show, mjpeg_port, tmp/1000); + start = what_time_is_it_now(); #endif // OPENCV //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) { diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 73808477476..6b937827150 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1063,7 +1063,7 @@ extern "C" mat_cv* draw_train_chart(char *windows_name, float max_img_loss, int // ---------------------------------------- extern "C" void draw_train_loss(char *windows_name, mat_cv* img_src, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, - float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port) + float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port, double time_remaining) { try { cv::Mat &img = *(cv::Mat*)img_src; @@ -1104,11 +1104,10 @@ extern "C" void draw_train_loss(char *windows_name, mat_cv* img_src, int img_siz old_precision = precision; iteration_old = current_batch; } - - sprintf(char_buff, "current avg loss = %2.4f iteration = %d", avg_loss, current_batch); + sprintf(char_buff, "current avg loss = %2.4f iteration = %d Time Remaining = %2.2f hours", avg_loss, current_batch, time_remaining); pt1.x = 15, pt1.y = draw_size + 18; - pt2.x = pt1.x + 460, pt2.y = pt1.y + 20; - cv::rectangle(img, pt1, pt2, CV_RGB(255, 255, 255), CV_FILLED, 8, 0); + pt2.x = pt1.x + 800, pt2.y = pt1.y + 20; + cv::rectangle(img, pt1, pt2, CV_RGB(0, 255, 255), CV_FILLED, 8, 0); pt1.y += 15; cv::putText(img, char_buff, pt1, cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 100), 1, CV_AA); diff --git a/src/image_opencv.h b/src/image_opencv.h index 136b7388faa..a87593db908 100644 --- a/src/image_opencv.h +++ b/src/image_opencv.h @@ -94,7 +94,7 @@ void draw_detections_cv_v3(mat_cv* show_img, detection *dets, int num, float thr // Draw Loss & Accuracy chart mat_cv* draw_train_chart(char *windows_name, float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show); void draw_train_loss(char *windows_name, mat_cv* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, - float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port); + float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port, double time_remaining); // Data augmentation image image_data_augmentation(mat_cv* mat, int w, int h, From d124d552c0f9b27236cb2e364e4a6654a47dc052 Mon Sep 17 00:00:00 2001 From: Muhammad Maaz Date: Wed, 4 Mar 2020 20:54:30 +0500 Subject: [PATCH 0853/1365] Add feature to show approx. remaining training time to training chart. --- src/classifier.c | 17 ++++++++++++++++- src/detector.c | 17 ++++++++++++++++- src/image_opencv.cpp | 7 +++---- src/image_opencv.h | 2 +- 4 files changed, 36 insertions(+), 7 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index 27aafad233d..5f5ab1bff34 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -132,6 +132,10 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int iter_topk = get_current_batch(net); float topk = 0; + int count = 0; + double start, end, tmp, time_remaining[1000] = {0}; + start = what_time_is_it_now(); + while(get_current_batch(net) < net.max_batches || net.max_batches == 0){ time=clock(); @@ -182,7 +186,18 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); #ifdef OPENCV - if (!dontuse_opencv) draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, topk_buff, dont_show, mjpeg_port); + end = what_time_is_it_now(); + time_remaining[i%1000] = (net.max_batches - i)*(end - start) / 60 / 60; + tmp = 0.0; + int j, count = 0; + for (j = 0; j < 1000; ++j){ + if (time_remaining[j] != 0){ + tmp += time_remaining[j]; + count++; + } + } + if (!dontuse_opencv) draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, topk_buff, dont_show, mjpeg_port, tmp/count); + start = what_time_is_it_now(); #endif // OPENCV if (i >= (iter_save + 1000)) { diff --git a/src/detector.c b/src/detector.c index 2a1477240e7..8052c36ee35 100644 --- a/src/detector.c +++ b/src/detector.c @@ -163,7 +163,11 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i //printf(" imgs = %d \n", imgs); pthread_t load_thread = load_data(args); + int count = 0; + double start, end, tmp, time_remaining[1000] = {0}; + start = what_time_is_it_now(); + //while(i*imgs < N*120){ while (get_current_batch(net) < net.max_batches) { if (l.random && count++ % 10 == 0) { @@ -300,7 +304,18 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i draw_precision = 1; } #ifdef OPENCV - draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, mean_average_precision, draw_precision, "mAP%", dont_show, mjpeg_port); + end = what_time_is_it_now(); + time_remaining[i%1000] = (net.max_batches - i)*(end - start) / 60 / 60; + tmp = 0.0; + int j, count = 0; + for (j = 0; j < 1000; ++j){ + if (time_remaining[j] != 0){ + tmp += time_remaining[j]; + count++; + } + } + draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, mean_average_precision, draw_precision, "mAP%", dont_show, mjpeg_port, tmp/count); + start = what_time_is_it_now(); #endif // OPENCV //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) { diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 73808477476..3854084fc1e 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1063,7 +1063,7 @@ extern "C" mat_cv* draw_train_chart(char *windows_name, float max_img_loss, int // ---------------------------------------- extern "C" void draw_train_loss(char *windows_name, mat_cv* img_src, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, - float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port) + float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port, double time_remaining) { try { cv::Mat &img = *(cv::Mat*)img_src; @@ -1104,10 +1104,9 @@ extern "C" void draw_train_loss(char *windows_name, mat_cv* img_src, int img_siz old_precision = precision; iteration_old = current_batch; } - - sprintf(char_buff, "current avg loss = %2.4f iteration = %d", avg_loss, current_batch); + sprintf(char_buff, "current avg loss = %2.4f iteration = %d approx. time remaining = %2.2f hours", avg_loss, current_batch, time_remaining); pt1.x = 15, pt1.y = draw_size + 18; - pt2.x = pt1.x + 460, pt2.y = pt1.y + 20; + pt2.x = pt1.x + 800, pt2.y = pt1.y + 20; cv::rectangle(img, pt1, pt2, CV_RGB(255, 255, 255), CV_FILLED, 8, 0); pt1.y += 15; cv::putText(img, char_buff, pt1, cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 100), 1, CV_AA); diff --git a/src/image_opencv.h b/src/image_opencv.h index 136b7388faa..a87593db908 100644 --- a/src/image_opencv.h +++ b/src/image_opencv.h @@ -94,7 +94,7 @@ void draw_detections_cv_v3(mat_cv* show_img, detection *dets, int num, float thr // Draw Loss & Accuracy chart mat_cv* draw_train_chart(char *windows_name, float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show); void draw_train_loss(char *windows_name, mat_cv* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, - float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port); + float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port, double time_remaining); // Data augmentation image image_data_augmentation(mat_cv* mat, int w, int h, From 1174bb741763bba55eb74a9a8c71baa2ee66803e Mon Sep 17 00:00:00 2001 From: Muhammad Maaz Date: Fri, 6 Mar 2020 12:58:16 +0500 Subject: [PATCH 0854/1365] Add support to ignore first 50 iterations in calculating the remaining training time. For first 50 iterations chart will display 'nan hours' as remaining training time. --- src/detector.c | 4 +++- src/image_opencv.cpp | 2 +- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/src/detector.c b/src/detector.c index 8052c36ee35..ca1c157f101 100644 --- a/src/detector.c +++ b/src/detector.c @@ -308,11 +308,13 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i time_remaining[i%1000] = (net.max_batches - i)*(end - start) / 60 / 60; tmp = 0.0; int j, count = 0; - for (j = 0; j < 1000; ++j){ + if (i > 50){ + for (j = 0; j < 1000; ++j){ if (time_remaining[j] != 0){ tmp += time_remaining[j]; count++; } + } } draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, mean_average_precision, draw_precision, "mAP%", dont_show, mjpeg_port, tmp/count); start = what_time_is_it_now(); diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 3854084fc1e..7adf6231071 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1104,7 +1104,7 @@ extern "C" void draw_train_loss(char *windows_name, mat_cv* img_src, int img_siz old_precision = precision; iteration_old = current_batch; } - sprintf(char_buff, "current avg loss = %2.4f iteration = %d approx. time remaining = %2.2f hours", avg_loss, current_batch, time_remaining); + sprintf(char_buff, "current avg loss = %2.4f iteration = %d approx. time left = %2.2f hours", avg_loss, current_batch, time_remaining); pt1.x = 15, pt1.y = draw_size + 18; pt2.x = pt1.x + 800, pt2.y = pt1.y + 20; cv::rectangle(img, pt1, pt2, CV_RGB(255, 255, 255), CV_FILLED, 8, 0); From b56e8d11cfa4278ad5258eedb5207ae583d16e96 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 6 Mar 2020 13:46:17 +0300 Subject: [PATCH 0855/1365] Minor fix --- src/classifier.c | 6 +++--- src/network.c | 3 ++- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index 27aafad233d..15a76787db4 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -162,8 +162,8 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, calc_topk_for_each = fmax(calc_topk_for_each, 100); if (i % 10 == 0) { if (calc_topk) { - fprintf(stderr, "\n (next TOP5 calculation at %d iterations) ", calc_topk_for_each); - if (topk > 0) fprintf(stderr, " Last accuracy TOP5 = %2.2f %% \n", topk * 100); + fprintf(stderr, "\n (next TOP%d calculation at %d iterations) ", topk_data, calc_topk_for_each); + if (topk > 0) fprintf(stderr, " Last accuracy TOP%d = %2.2f %% \n", topk_data, topk * 100); } if (net.cudnn_half) { @@ -175,7 +175,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int draw_precision = 0; if (calc_topk && (i >= calc_topk_for_each || i == net.max_batches)) { iter_topk = i; - topk = validate_classifier_single(datacfg, cfgfile, weightfile, &net, topk_data); // calc TOP5 + topk = validate_classifier_single(datacfg, cfgfile, weightfile, &net, topk_data); // calc TOP-n printf("\n accuracy %s = %f \n", topk_buff, topk); draw_precision = 1; } diff --git a/src/network.c b/src/network.c index 33d0001c66d..9f58708221c 100644 --- a/src/network.c +++ b/src/network.c @@ -1133,7 +1133,8 @@ void fuse_conv_batchnorm(network net) { if (l->nweights > 0) { //cuda_pull_array(l.weights_gpu, l.weights, l.nweights); - for (int i = 0; i < l->nweights; ++i) printf(" w = %f,", l->weights[i]); + int i; + for (i = 0; i < l->nweights; ++i) printf(" w = %f,", l->weights[i]); printf(" l->nweights = %d \n", l->nweights); } From 992306b03154e1b799b3c757dc1caa2f1bce631b Mon Sep 17 00:00:00 2001 From: Tclack88 <39845330+Tclack88@users.noreply.github.com> Date: Mon, 9 Mar 2020 09:45:50 -0700 Subject: [PATCH 0856/1365] Update classifier.c declaring variables not allowed within `for loops` until C99. additionally. declaring int i multiple times within the same definition try_classifier isn't necessary --- src/classifier.c | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index 27aafad233d..7600d2c690f 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -753,13 +753,14 @@ void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filena float *predictions = network_predict(net, X); layer l = net.layers[layer_num]; - for(int i = 0; i < l.c; ++i){ + int i; + for(i = 0; i < l.c; ++i){ if(l.rolling_mean) printf("%f %f %f\n", l.rolling_mean[i], l.rolling_variance[i], l.scales[i]); } #ifdef GPU cuda_pull_array(l.output_gpu, l.output, l.outputs); #endif - for(int i = 0; i < l.outputs; ++i){ + for(i = 0; i < l.outputs; ++i){ printf("%f\n", l.output[i]); } /* @@ -777,7 +778,7 @@ void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filena top_predictions(net, top, indexes); printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); - for(int i = 0; i < top; ++i){ + for(i = 0; i < top; ++i){ int index = indexes[i]; printf("%s: %f\n", names[index], predictions[index]); } From 6c4886485d0bbb634507f06043c68639f6c69a62 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 10 Mar 2020 17:00:35 +0300 Subject: [PATCH 0857/1365] Minor speedup of DropBlock (training) --- src/data.c | 15 +++++++++++++-- src/dropout_layer_kernels.cu | 15 +++++++++++++++ src/image_opencv.cpp | 2 +- src/parser.c | 1 + 4 files changed, 30 insertions(+), 3 deletions(-) diff --git a/src/data.c b/src/data.c index 9e564e2cce8..b771b8a8232 100644 --- a/src/data.c +++ b/src/data.c @@ -950,7 +950,10 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo const int random_index = random_gen(); c = c ? c : 3; - assert(use_mixup != 2); + if (use_mixup == 2) { + printf("\n cutmix=1 - isn't supported for Detector \n"); + exit(0); + } if (use_mixup == 3 && letter_box) { printf("\n Combination: letter_box=1 & mosaic=1 - isn't supported, use only 1 of these parameters \n"); exit(0); @@ -1211,7 +1214,15 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo if(track) random_paths = get_sequential_paths(paths, n, m, mini_batch, augment_speed); else random_paths = get_random_paths(paths, n, m); - assert(use_mixup < 2); + //assert(use_mixup < 2); + if (use_mixup == 2) { + printf("\n cutmix=1 - isn't supported for Detector \n"); + exit(0); + } + if (use_mixup == 3) { + printf("\n mosaic=1 - compile Darknet with OpenCV for using mosaic=1 \n"); + exit(0); + } int mixup = use_mixup ? random_gen() % 2 : 0; //printf("\n mixup = %d \n", mixup); if (mixup) { diff --git a/src/dropout_layer_kernels.cu b/src/dropout_layer_kernels.cu index dcf1d89c964..a963235e8ae 100644 --- a/src/dropout_layer_kernels.cu +++ b/src/dropout_layer_kernels.cu @@ -74,6 +74,15 @@ __global__ void dropblock_fast_kernel(float *rand, float prob, int w, int h, int } +__global__ void set_scales_dropblock_kernel(float *drop_blocks_scale, int block_size_w, int block_size_h, int outputs, int batch) +{ + const int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index >= batch) return; + + const float prob = drop_blocks_scale[index] * block_size_w * block_size_h / (float)outputs; + const float scale = 1.0f / (1.0f - prob); + drop_blocks_scale[index] = scale; +} __global__ void scale_dropblock_kernel(float *output, int size, int outputs, float *drop_blocks_scale) { @@ -136,6 +145,11 @@ void forward_dropout_layer_gpu(dropout_layer l, network_state state) dropblock_fast_kernel << > > (l.rand_gpu, block_prob, l.w, l.h, l.w*l.h, l.c, block_size, l.drop_blocks_scale_gpu, state.input); CHECK_CUDA(cudaPeekAtLastError()); + num_blocks = get_number_of_blocks(l.batch, BLOCK); + set_scales_dropblock_kernel << > > (l.drop_blocks_scale_gpu, block_size, block_size, l.outputs, l.batch); + CHECK_CUDA(cudaPeekAtLastError()); + + /* cuda_pull_array(l.drop_blocks_scale_gpu, l.drop_blocks_scale, l.batch); for (int b = 0; b < l.batch; ++b) { @@ -148,6 +162,7 @@ void forward_dropout_layer_gpu(dropout_layer l, network_state state) } cuda_push_array(l.drop_blocks_scale_gpu, l.drop_blocks_scale, l.batch); + */ num_blocks = get_number_of_blocks(l.outputs * l.batch, BLOCK); scale_dropblock_kernel << > > (state.input, l.outputs * l.batch, l.outputs, l.drop_blocks_scale_gpu); diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 73808477476..5f7b72af7ab 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1345,7 +1345,7 @@ void show_opencv_info() #else // OPENCV extern "C" void show_opencv_info() { - std::cerr << " OpenCV isn't used \n"; + std::cerr << " OpenCV isn't used - data increase will run slowly \n"; } extern "C" int wait_key_cv(int delay) { return 0; } extern "C" int wait_until_press_key_cv() { return 0; } diff --git a/src/parser.c b/src/parser.c index 4c337b29272..2bd6270a984 100644 --- a/src/parser.c +++ b/src/parser.c @@ -997,6 +997,7 @@ route_layer parse_route(list *options, size_params params) if(next.out_w == first.out_w && next.out_h == first.out_h){ layer.out_c += next.out_c; }else{ + fprintf(stderr, " The width and height of the input layers are different. \n"); layer.out_h = layer.out_w = layer.out_c = 0; } } From a567a806a61b77eed177c2520bda8bb55924f8d7 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 11 Mar 2020 01:46:10 +0300 Subject: [PATCH 0858/1365] Set cur_iteration = 0; initially --- src/classifier.c | 5 ++++- src/darknet.c | 2 ++ src/detector.c | 5 ++++- src/parser.c | 10 ++++++++-- src/rnn.c | 5 ++++- src/tag.c | 5 ++++- 6 files changed, 26 insertions(+), 6 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index 07f48f18add..9924cf63e1a 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -48,7 +48,10 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, if(weightfile){ load_weights(&nets[i], weightfile); } - if(clear) *nets[i].seen = 0; + if (clear) { + *nets[i].seen = 0; + *nets[i].cur_iteration = 0; + } nets[i].learning_rate *= ngpus; } srand(time(0)); diff --git a/src/darknet.c b/src/darknet.c index d4f88b0249a..81e447274e4 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -165,6 +165,7 @@ void oneoff(char *cfgfile, char *weightfile, char *outfile) copy_cpu(l.n/3*l.c, l.weights, 1, l.weights + l.n/3*l.c, 1); copy_cpu(l.n/3*l.c, l.weights, 1, l.weights + 2*l.n/3*l.c, 1); *net.seen = 0; + *net.cur_iteration = 0; save_weights(net, outfile); } @@ -176,6 +177,7 @@ void partial(char *cfgfile, char *weightfile, char *outfile, int max) load_weights_upto(&net, weightfile, max); } *net.seen = 0; + *net.cur_iteration = 0; save_weights_upto(net, outfile, max); } diff --git a/src/detector.c b/src/detector.c index 8753d6412c2..536befb8794 100644 --- a/src/detector.c +++ b/src/detector.c @@ -77,7 +77,10 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i if (weightfile) { load_weights(&nets[k], weightfile); } - if (clear) *nets[k].seen = 0; + if (clear) { + *nets[k].seen = 0; + *nets[k].cur_iteration = 0; + } nets[k].learning_rate *= ngpus; } srand(time(0)); diff --git a/src/parser.c b/src/parser.c index 2bd6270a984..241fb932157 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1973,7 +1973,10 @@ network *load_network_custom(char *cfg, char *weights, int clear, int batch) load_weights(net, weights); } fuse_conv_batchnorm(*net); - if (clear) (*net->seen) = 0; + if (clear) { + (*net->seen) = 0; + (*net->cur_iteration) = 0; + } return net; } @@ -1987,6 +1990,9 @@ network *load_network(char *cfg, char *weights, int clear) printf(" Try to load weights: %s \n", weights); load_weights(net, weights); } - if (clear) (*net->seen) = 0; + if (clear) { + (*net->seen) = 0; + (*net->cur_iteration) = 0; + } return net; } diff --git a/src/rnn.c b/src/rnn.c index 6c9b38c878c..53fb8f320f4 100644 --- a/src/rnn.c +++ b/src/rnn.c @@ -155,7 +155,10 @@ void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear, fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); int batch = net.batch; int steps = net.time_steps; - if(clear) *net.seen = 0; + if (clear) { + *net.seen = 0; + *net.cur_iteration = 0; + } int i = (*net.seen)/net.batch; int streams = batch/steps; diff --git a/src/tag.c b/src/tag.c index ec9327af5a4..d7e1349a2a2 100644 --- a/src/tag.c +++ b/src/tag.c @@ -13,7 +13,10 @@ void train_tag(char *cfgfile, char *weightfile, int clear) if(weightfile){ load_weights(&net, weightfile); } - if(clear) *net.seen = 0; + if (clear) { + *net.seen = 0; + *net.cur_iteration = 0; + } printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); int imgs = 1024; list* plist = get_paths("tag/train.list"); From 8c13ca1619c85d0c118d908c8683abfaf1882717 Mon Sep 17 00:00:00 2001 From: Wastage Date: Wed, 11 Mar 2020 15:33:59 +0900 Subject: [PATCH 0859/1365] Class_id Garbage Value Check Added exception handling when Class_Id is garbage value (like negative) --- src/yolo_layer.c | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/yolo_layer.c b/src/yolo_layer.c index d80dcc1dd44..608daa8ffa2 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -374,11 +374,11 @@ void forward_yolo_layer(const layer l, network_state state) for (t = 0; t < l.max_boxes; ++t) { box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; - if (class_id >= l.classes) { + if (class_id >= l.classes || class_id < 0) { printf("\n Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1); printf("\n truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f, class_id = %d \n", truth.x, truth.y, truth.w, truth.h, class_id); if (check_mistakes) getchar(); - continue; // if label contains class_id more than number of classes in the cfg-file + continue; // if label contains class_id more than number of classes in the cfg-file and class_id check garbage value } if (!truth.x) break; // continue; @@ -427,7 +427,7 @@ void forward_yolo_layer(const layer l, network_state state) system(buff); } int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; - if (class_id >= l.classes) continue; // if label contains class_id more than number of classes in the cfg-file + if (class_id >= l.classes || class_id < 0) continue; // if label contains class_id more than number of classes in the cfg-file and class_id check garbage value if (!truth.x) break; // continue; float best_iou = 0; From 1a56ef588a37f047bbac938d677c42b91a2b80bd Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 11 Mar 2020 17:12:07 +0300 Subject: [PATCH 0860/1365] Added flag -cuda_debug_sync for checking CUDA errors --- src/dark_cuda.c | 23 +++++++++++++++++------ src/darknet.c | 13 +++++++++++-- src/detector.c | 2 +- src/parser.c | 6 ++++-- 4 files changed, 33 insertions(+), 11 deletions(-) diff --git a/src/dark_cuda.c b/src/dark_cuda.c index e51617af928..990870d2df5 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -29,6 +29,8 @@ int gpu_index = 0; #error "If you set CUDNN_HALF=1 then you must set CUDNN=1" #endif +extern int cuda_debug_sync; + void cuda_set_device(int n) { gpu_index = n; @@ -86,10 +88,13 @@ void check_error_extended(cudaError_t status, const char *file, int line, const check_error(status); } #if defined(DEBUG) || defined(CUDA_DEBUG) - status = cudaDeviceSynchronize(); - if (status != cudaSuccess) - printf("CUDA status = cudaDeviceSynchronize() Error: file: %s() : line: %d : build time: %s \n", file, line, date_time); + cuda_debug_sync = 1; #endif + if (cuda_debug_sync) { + status = cudaDeviceSynchronize(); + if (status != cudaSuccess) + printf("CUDA status = cudaDeviceSynchronize() Error: file: %s() : line: %d : build time: %s \n", file, line, date_time); + } check_error(status); } @@ -173,6 +178,9 @@ void cudnn_check_error(cudnnStatus_t status) #if defined(DEBUG) || defined(CUDA_DEBUG) cudaDeviceSynchronize(); #endif + if (cuda_debug_sync) { + cudaDeviceSynchronize(); + } cudnnStatus_t status2 = CUDNN_STATUS_SUCCESS; #ifdef CUDNN_ERRQUERY_RAWCODE cudnnStatus_t status_tmp = cudnnQueryRuntimeError(cudnn_handle(), &status2, CUDNN_ERRQUERY_RAWCODE, NULL); @@ -208,10 +216,13 @@ void cudnn_check_error_extended(cudnnStatus_t status, const char *file, int line cudnn_check_error(status); } #if defined(DEBUG) || defined(CUDA_DEBUG) - status = cudaDeviceSynchronize(); - if (status != CUDNN_STATUS_SUCCESS) - printf("\n cuDNN status = cudaDeviceSynchronize() Error in: file: %s() : line: %d : build time: %s \n", file, line, date_time); + cuda_debug_sync = 1; #endif + if (cuda_debug_sync) { + status = cudaDeviceSynchronize(); + if (status != CUDNN_STATUS_SUCCESS) + printf("\n cuDNN status = cudaDeviceSynchronize() Error in: file: %s() : line: %d : build time: %s \n", file, line, date_time); + } cudnn_check_error(status); } #endif diff --git a/src/darknet.c b/src/darknet.c index 81e447274e4..e500e808d06 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -12,6 +12,9 @@ #include "blas.h" #include "connected_layer.h" +int check_mistakes = 0; +int cuda_debug_sync = 0; + extern void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top); extern void run_voxel(int argc, char **argv); @@ -459,14 +462,20 @@ int main(int argc, char **argv) gpu_index = -1; printf(" GPU isn't used \n"); init_cpu(); -#else +#else // GPU if(gpu_index >= 0){ cuda_set_device(gpu_index); CHECK_CUDA(cudaSetDeviceFlags(cudaDeviceScheduleBlockingSync)); } show_cuda_cudnn_info(); -#endif + cuda_debug_sync = find_arg(argc, argv, "-cuda_debug_sync"); + +#ifdef CUDNN_HALF + printf(" CUDNN_HALF=1 \n"); +#endif // CUDNN_HALF + +#endif // GPU show_opencv_info(); diff --git a/src/detector.c b/src/detector.c index 536befb8794..4c0b586de72 100644 --- a/src/detector.c +++ b/src/detector.c @@ -18,7 +18,7 @@ typedef __compar_fn_t comparison_fn_t; #include "http_stream.h" -int check_mistakes = 0; +extern int check_mistakes; static int coco_ids[] = { 1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90 }; diff --git a/src/parser.c b/src/parser.c index 241fb932157..e7373ea4e8e 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1054,6 +1054,7 @@ void parse_net_options(list *options, network *net) net->batch *= net->time_steps; net->subdivisions = subdivs; + *net->seen = 0; *net->cur_iteration = 0; net->dynamic_minibatch = option_find_int_quiet(options, "dynamic_minibatch", 0); net->optimized_memory = option_find_int_quiet(options, "optimized_memory", 0); @@ -1096,7 +1097,7 @@ void parse_net_options(list *options, network *net) char *policy_s = option_find_str(options, "policy", "constant"); net->policy = get_policy(policy_s); net->burn_in = option_find_int_quiet(options, "burn_in", 0); -#ifdef CUDNN_HALF +#ifdef GPU if (net->gpu_index >= 0) { int compute_capability = get_gpu_compute_capability(net->gpu_index); if (get_gpu_compute_capability(net->gpu_index) >= 700) net->cudnn_half = 1; @@ -1104,7 +1105,7 @@ void parse_net_options(list *options, network *net) fprintf(stderr, " compute_capability = %d, cudnn_half = %d \n", compute_capability, net->cudnn_half); } else fprintf(stderr, " GPU isn't used \n"); -#endif +#endif// GPU if(net->policy == STEP){ net->step = option_find_int(options, "step", 1); net->scale = option_find_float(options, "scale", 1); @@ -1630,6 +1631,7 @@ void save_weights_upto(network net, char *filename, int cutoff) fwrite(&major, sizeof(int), 1, fp); fwrite(&minor, sizeof(int), 1, fp); fwrite(&revision, sizeof(int), 1, fp); + (*net.seen) = (*net.cur_iteration) * net.batch * net.subdivisions; fwrite(net.seen, sizeof(uint64_t), 1, fp); int i; From 49a7cdd26852e2f77be470874adc26fda7f89671 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 11 Mar 2020 17:20:27 +0300 Subject: [PATCH 0861/1365] Compile fix --- src/dark_cuda.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/dark_cuda.c b/src/dark_cuda.c index 990870d2df5..7e94f7c629b 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -219,9 +219,9 @@ void cudnn_check_error_extended(cudnnStatus_t status, const char *file, int line cuda_debug_sync = 1; #endif if (cuda_debug_sync) { - status = cudaDeviceSynchronize(); + cudaError_t status = cudaDeviceSynchronize(); if (status != CUDNN_STATUS_SUCCESS) - printf("\n cuDNN status = cudaDeviceSynchronize() Error in: file: %s() : line: %d : build time: %s \n", file, line, date_time); + printf("\n cudaError_t status = cudaDeviceSynchronize() Error in: file: %s() : line: %d : build time: %s \n", file, line, date_time); } cudnn_check_error(status); } From 5e54523960fd07e4a8893dc06b020aba91b592cf Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 11 Mar 2020 19:47:13 +0300 Subject: [PATCH 0862/1365] Added loss_scale for Mixed-Precision on Tensor-Cores --- include/darknet.h | 3 ++- src/batchnorm_layer.c | 4 ++-- src/connected_layer.c | 11 ++++++++++- src/conv_lstm_layer.c | 24 ++++++++++++------------ src/convolutional_kernels.cu | 17 ++++++++++++----- src/convolutional_layer.h | 2 +- src/crnn_layer.c | 8 ++++---- src/darknet.c | 1 - src/data.c | 3 ++- src/detector.c | 2 +- src/gru_layer.c | 14 +++++++------- src/local_layer.c | 2 +- src/lstm_layer.c | 18 +++++++++--------- src/network_kernels.cu | 4 ++-- src/parser.c | 3 ++- src/rnn_layer.c | 8 ++++---- src/shortcut_layer.c | 7 ++++++- src/yolo_layer.c | 2 +- 18 files changed, 78 insertions(+), 55 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 8eb40ebcb0c..0c6761a06a5 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -211,7 +211,7 @@ struct layer { void(*update) (struct layer, int, float, float, float); void(*forward_gpu) (struct layer, struct network_state); void(*backward_gpu) (struct layer, struct network_state); - void(*update_gpu) (struct layer, int, float, float, float); + void(*update_gpu) (struct layer, int, float, float, float, float); layer *share_layer; int train; int avgpool; @@ -642,6 +642,7 @@ typedef struct network { int batch; uint64_t *seen; int *cur_iteration; + float loss_scale; int *t; float epoch; int subdivisions; diff --git a/src/batchnorm_layer.c b/src/batchnorm_layer.c index 8920d414036..7432434cba2 100644 --- a/src/batchnorm_layer.c +++ b/src/batchnorm_layer.c @@ -387,9 +387,9 @@ void backward_batchnorm_layer_gpu(layer l, network_state state) } } -void update_batchnorm_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay) +void update_batchnorm_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay, float loss_scale) { - float learning_rate = learning_rate_init*l.learning_rate_scale; + float learning_rate = learning_rate_init * l.learning_rate_scale / loss_scale; //float momentum = a.momentum; //float decay = a.decay; //int batch = a.batch; diff --git a/src/connected_layer.c b/src/connected_layer.c index 08a16aaa9f0..25a5ffa1e34 100644 --- a/src/connected_layer.c +++ b/src/connected_layer.c @@ -308,8 +308,17 @@ void push_connected_layer(connected_layer l) CHECK_CUDA(cudaPeekAtLastError()); } -void update_connected_layer_gpu(connected_layer l, int batch, float learning_rate, float momentum, float decay) +void update_connected_layer_gpu(connected_layer l, int batch, float learning_rate_init, float momentum, float decay, float loss_scale) { + float learning_rate = learning_rate_init * l.learning_rate_scale; + + // Loss scale for Mixed-Precision on Tensor-Cores + if (loss_scale != 1.0) { + scal_ongpu(l.inputs*l.outputs, 1.0 / loss_scale, l.weight_updates_gpu, 1); + scal_ongpu(l.outputs, 1.0 / loss_scale, l.bias_updates_gpu, 1); + scal_ongpu(l.outputs, 1.0 / loss_scale, l.scale_updates_gpu, 1); + } + axpy_ongpu(l.outputs, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); scal_ongpu(l.outputs, momentum, l.bias_updates_gpu, 1); diff --git a/src/conv_lstm_layer.c b/src/conv_lstm_layer.c index bc53848a446..cb7e3fe176a 100644 --- a/src/conv_lstm_layer.c +++ b/src/conv_lstm_layer.c @@ -791,21 +791,21 @@ void push_conv_lstm_layer(layer l) push_convolutional_layer(*(l.uo)); } -void update_conv_lstm_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay) +void update_conv_lstm_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay, float loss_scale) { if (l.peephole) { - update_convolutional_layer_gpu(*(l.vf), batch, learning_rate, momentum, decay); - update_convolutional_layer_gpu(*(l.vi), batch, learning_rate, momentum, decay); - update_convolutional_layer_gpu(*(l.vo), batch, learning_rate, momentum, decay); + update_convolutional_layer_gpu(*(l.vf), batch, learning_rate, momentum, decay, loss_scale); + update_convolutional_layer_gpu(*(l.vi), batch, learning_rate, momentum, decay, loss_scale); + update_convolutional_layer_gpu(*(l.vo), batch, learning_rate, momentum, decay, loss_scale); } - update_convolutional_layer_gpu(*(l.wf), batch, learning_rate, momentum, decay); - update_convolutional_layer_gpu(*(l.wi), batch, learning_rate, momentum, decay); - update_convolutional_layer_gpu(*(l.wg), batch, learning_rate, momentum, decay); - update_convolutional_layer_gpu(*(l.wo), batch, learning_rate, momentum, decay); - update_convolutional_layer_gpu(*(l.uf), batch, learning_rate, momentum, decay); - update_convolutional_layer_gpu(*(l.ui), batch, learning_rate, momentum, decay); - update_convolutional_layer_gpu(*(l.ug), batch, learning_rate, momentum, decay); - update_convolutional_layer_gpu(*(l.uo), batch, learning_rate, momentum, decay); + update_convolutional_layer_gpu(*(l.wf), batch, learning_rate, momentum, decay, loss_scale); + update_convolutional_layer_gpu(*(l.wi), batch, learning_rate, momentum, decay, loss_scale); + update_convolutional_layer_gpu(*(l.wg), batch, learning_rate, momentum, decay, loss_scale); + update_convolutional_layer_gpu(*(l.wo), batch, learning_rate, momentum, decay, loss_scale); + update_convolutional_layer_gpu(*(l.uf), batch, learning_rate, momentum, decay, loss_scale); + update_convolutional_layer_gpu(*(l.ui), batch, learning_rate, momentum, decay, loss_scale); + update_convolutional_layer_gpu(*(l.ug), batch, learning_rate, momentum, decay, loss_scale); + update_convolutional_layer_gpu(*(l.uo), batch, learning_rate, momentum, decay, loss_scale); } void forward_conv_lstm_layer_gpu(layer l, network_state state) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index d5c01d63e21..372aa57a166 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -420,7 +420,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //#ifdef CUDNN_HALF //if (state.use_mixed_precision) { int iteration_num = get_current_iteration(state.net); // (*state.net.seen) / (state.net.batch*state.net.subdivisions); - if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in) && + if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3 * state.net.burn_in) && (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 1 && l.size > 1) { //printf("\n CUDNN_HALF!!! state.index = %d \n", state.index); @@ -672,7 +672,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state //#ifdef CUDNN_HALF int iteration_num = get_current_iteration(state.net); //(*state.net.seen) / (state.net.batch*state.net.subdivisions); - if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in) && + if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3 * state.net.burn_in) && (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 1 && l.size > 1) { const size_t input16_size = l.batch*l.c*l.w*l.h; @@ -717,7 +717,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state l.normDstTensorDescF16, delta16, // input l.normDstTensorDescF16, - l.x_norm_gpu, // output (new delta) + l.output_gpu, //l.x_norm_gpu, // output (new delta) l.normTensorDesc, l.scales_gpu, // input (should be FP32) l.scale_updates_gpu, // output (should be FP32) @@ -726,7 +726,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state l.mean_gpu, // input (should be FP32) l.variance_gpu)); // input (should be FP32) - simple_copy_ongpu(l.outputs*l.batch / 2, l.x_norm_gpu, delta16); + simple_copy_ongpu(l.outputs*l.batch / 2, l.output_gpu, delta16); //copy_ongpu(l.outputs*l.batch / 2, l.x_norm_gpu, 1, delta16, 1); //cudaMemcpyAsync(delta16, l.x_norm_gpu, l.outputs*l.batch * sizeof(half), cudaMemcpyDefault, get_cuda_stream()); } @@ -1188,7 +1188,7 @@ void push_convolutional_layer(convolutional_layer l) CHECK_CUDA(cudaPeekAtLastError()); } -void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay) +void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay, float loss_scale) { /* @@ -1232,6 +1232,13 @@ void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init //float decay = a.decay; //int batch = a.batch; + // Loss scale for Mixed-Precision on Tensor-Cores + if (loss_scale != 1.0) { + scal_ongpu(l.nweights, 1.0 / loss_scale, l.weight_updates_gpu, 1); + scal_ongpu(l.n, 1.0 / loss_scale, l.bias_updates_gpu, 1); + scal_ongpu(l.n, 1.0 / loss_scale, l.scale_updates_gpu, 1); + } + reset_nan_and_inf(l.weight_updates_gpu, l.nweights); fix_nan_and_inf(l.weights_gpu, l.nweights); diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h index 18135937137..e83ca873cc0 100644 --- a/src/convolutional_layer.h +++ b/src/convolutional_layer.h @@ -15,7 +15,7 @@ extern "C" { #ifdef GPU void forward_convolutional_layer_gpu(convolutional_layer layer, network_state state); void backward_convolutional_layer_gpu(convolutional_layer layer, network_state state); -void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay); +void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay, float loss_scale); void push_convolutional_layer(convolutional_layer layer); void pull_convolutional_layer(convolutional_layer layer); diff --git a/src/crnn_layer.c b/src/crnn_layer.c index 8b9ed2f9792..84646b4af48 100644 --- a/src/crnn_layer.c +++ b/src/crnn_layer.c @@ -265,11 +265,11 @@ void push_crnn_layer(layer l) push_convolutional_layer(*(l.output_layer)); } -void update_crnn_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay) +void update_crnn_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay, float loss_scale) { - update_convolutional_layer_gpu(*(l.input_layer), batch, learning_rate, momentum, decay); - update_convolutional_layer_gpu(*(l.self_layer), batch, learning_rate, momentum, decay); - update_convolutional_layer_gpu(*(l.output_layer), batch, learning_rate, momentum, decay); + update_convolutional_layer_gpu(*(l.input_layer), batch, learning_rate, momentum, decay, loss_scale); + update_convolutional_layer_gpu(*(l.self_layer), batch, learning_rate, momentum, decay, loss_scale); + update_convolutional_layer_gpu(*(l.output_layer), batch, learning_rate, momentum, decay, loss_scale); } void forward_crnn_layer_gpu(layer l, network_state state) diff --git a/src/darknet.c b/src/darknet.c index e500e808d06..94d5e2b0c6b 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -12,7 +12,6 @@ #include "blas.h" #include "connected_layer.h" -int check_mistakes = 0; int cuda_debug_sync = 0; diff --git a/src/data.c b/src/data.c index b771b8a8232..a103cc5a29f 100644 --- a/src/data.c +++ b/src/data.c @@ -8,6 +8,8 @@ #include #include +extern int check_mistakes; + #define NUMCHARS 37 pthread_mutex_t mutex = PTHREAD_MUTEX_INITIALIZER; @@ -178,7 +180,6 @@ matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int return X; } -extern int check_mistakes; box_label *read_boxes(char *filename, int *n) { diff --git a/src/detector.c b/src/detector.c index 4c0b586de72..536befb8794 100644 --- a/src/detector.c +++ b/src/detector.c @@ -18,7 +18,7 @@ typedef __compar_fn_t comparison_fn_t; #include "http_stream.h" -extern int check_mistakes; +int check_mistakes = 0; static int coco_ids[] = { 1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90 }; diff --git a/src/gru_layer.c b/src/gru_layer.c index 787f46ea65a..de301df3eb7 100644 --- a/src/gru_layer.c +++ b/src/gru_layer.c @@ -206,14 +206,14 @@ void push_gru_layer(layer l) { } -void update_gru_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay) +void update_gru_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay, float loss_scale) { - update_connected_layer_gpu(*(l.input_r_layer), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.input_z_layer), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.input_h_layer), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.state_r_layer), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.state_z_layer), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.state_h_layer), batch, learning_rate, momentum, decay); + update_connected_layer_gpu(*(l.input_r_layer), batch, learning_rate, momentum, decay, loss_scale); + update_connected_layer_gpu(*(l.input_z_layer), batch, learning_rate, momentum, decay, loss_scale); + update_connected_layer_gpu(*(l.input_h_layer), batch, learning_rate, momentum, decay, loss_scale); + update_connected_layer_gpu(*(l.state_r_layer), batch, learning_rate, momentum, decay, loss_scale); + update_connected_layer_gpu(*(l.state_z_layer), batch, learning_rate, momentum, decay, loss_scale); + update_connected_layer_gpu(*(l.state_h_layer), batch, learning_rate, momentum, decay, loss_scale); } void forward_gru_layer_gpu(layer l, network_state state) diff --git a/src/local_layer.c b/src/local_layer.c index 83b43c9a1b2..88c7b125228 100644 --- a/src/local_layer.c +++ b/src/local_layer.c @@ -253,7 +253,7 @@ void backward_local_layer_gpu(local_layer l, network_state state) } } -void update_local_layer_gpu(local_layer l, int batch, float learning_rate, float momentum, float decay) +void update_local_layer_gpu(local_layer l, int batch, float learning_rate, float momentum, float decay, float loss_scale) { int locations = l.out_w*l.out_h; int size = l.size*l.size*l.c*l.n*locations; diff --git a/src/lstm_layer.c b/src/lstm_layer.c index e0cf77972b5..a794556183a 100644 --- a/src/lstm_layer.c +++ b/src/lstm_layer.c @@ -401,16 +401,16 @@ void backward_lstm_layer(layer l, network_state state) } #ifdef GPU -void update_lstm_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay) +void update_lstm_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay, float loss_scale) { - update_connected_layer_gpu(*(l.wf), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.wi), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.wg), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.wo), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.uf), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.ui), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.ug), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.uo), batch, learning_rate, momentum, decay); + update_connected_layer_gpu(*(l.wf), batch, learning_rate, momentum, decay, loss_scale); + update_connected_layer_gpu(*(l.wi), batch, learning_rate, momentum, decay, loss_scale); + update_connected_layer_gpu(*(l.wg), batch, learning_rate, momentum, decay, loss_scale); + update_connected_layer_gpu(*(l.wo), batch, learning_rate, momentum, decay, loss_scale); + update_connected_layer_gpu(*(l.uf), batch, learning_rate, momentum, decay, loss_scale); + update_connected_layer_gpu(*(l.ui), batch, learning_rate, momentum, decay, loss_scale); + update_connected_layer_gpu(*(l.ug), batch, learning_rate, momentum, decay, loss_scale); + update_connected_layer_gpu(*(l.uo), batch, learning_rate, momentum, decay, loss_scale); } void forward_lstm_layer_gpu(layer l, network_state state) diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 63d981c67b0..ba65ff045f7 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -248,7 +248,7 @@ void update_network_gpu(network net) l.t = get_current_batch(net); if (iteration_num > (net.max_batches * 1 / 2)) l.deform = 0; if(l.update_gpu){ - l.update_gpu(l, update_batch, rate, net.momentum, net.decay); + l.update_gpu(l, update_batch, rate, net.momentum, net.decay, net.loss_scale); } } } @@ -379,7 +379,7 @@ void update_layer(layer l, network net) float rate = get_current_rate(net); l.t = get_current_batch(net); if(l.update_gpu){ - l.update_gpu(l, update_batch, rate, net.momentum, net.decay); + l.update_gpu(l, update_batch, rate, net.momentum, net.decay, net.loss_scale); } } diff --git a/src/parser.c b/src/parser.c index e7373ea4e8e..e20d98da994 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1056,6 +1056,7 @@ void parse_net_options(list *options, network *net) *net->seen = 0; *net->cur_iteration = 0; + net->loss_scale = option_find_float_quiet(options, "loss_scale", 1); net->dynamic_minibatch = option_find_int_quiet(options, "dynamic_minibatch", 0); net->optimized_memory = option_find_int_quiet(options, "optimized_memory", 0); net->workspace_size_limit = (size_t)1024*1024 * option_find_float_quiet(options, "workspace_size_limit_MB", 1024); // 1024 MB by default @@ -1205,7 +1206,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) params.batch = net.batch; params.time_steps = net.time_steps; params.net = net; - printf("batch = %d, time_steps = %d, train = %d \n", net.batch, net.time_steps, params.train); + printf("mini_batch = %d, batch = %d, time_steps = %d, train = %d \n", net.batch, net.batch * net.subdivisions, net.time_steps, params.train); int avg_outputs = 0; float bflops = 0; diff --git a/src/rnn_layer.c b/src/rnn_layer.c index 328bcbf07d9..98f0d489ed3 100644 --- a/src/rnn_layer.c +++ b/src/rnn_layer.c @@ -196,11 +196,11 @@ void push_rnn_layer(layer l) push_connected_layer(*(l.output_layer)); } -void update_rnn_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay) +void update_rnn_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay, float loss_scale) { - update_connected_layer_gpu(*(l.input_layer), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.self_layer), batch, learning_rate, momentum, decay); - update_connected_layer_gpu(*(l.output_layer), batch, learning_rate, momentum, decay); + update_connected_layer_gpu(*(l.input_layer), batch, learning_rate, momentum, decay, loss_scale); + update_connected_layer_gpu(*(l.self_layer), batch, learning_rate, momentum, decay, loss_scale); + update_connected_layer_gpu(*(l.output_layer), batch, learning_rate, momentum, decay, loss_scale); } void forward_rnn_layer_gpu(layer l, network_state state) diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index 0acb138a046..d703f8f8564 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -240,7 +240,7 @@ void backward_shortcut_layer_gpu(const layer l, network_state state) //shortcut_gpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta_gpu, l.w, l.h, l.c, state.net.layers[l.index].delta_gpu); } -void update_shortcut_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay) +void update_shortcut_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay, float loss_scale) { if (l.nweights > 0) { float learning_rate = learning_rate_init*l.learning_rate_scale; @@ -248,6 +248,11 @@ void update_shortcut_layer_gpu(layer l, int batch, float learning_rate_init, flo //float decay = a.decay; //int batch = a.batch; + // Loss scale for Mixed-Precision on Tensor-Cores + if (loss_scale != 1.0) { + scal_ongpu(l.nweights, 1.0 / loss_scale, l.weight_updates_gpu, 1); + } + reset_nan_and_inf(l.weight_updates_gpu, l.nweights); fix_nan_and_inf(l.weights_gpu, l.nweights); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 608daa8ffa2..8d25cd57385 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -822,6 +822,6 @@ void forward_yolo_layer_gpu(const layer l, network_state state) void backward_yolo_layer_gpu(const layer l, network_state state) { - axpy_ongpu(l.batch*l.inputs, 1, l.delta_gpu, 1, state.delta, 1); + axpy_ongpu(l.batch*l.inputs, state.net.loss_scale, l.delta_gpu, 1, state.delta, 1); } #endif From 374c769062b64c685cb673a0f5a2a3d3acef639d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 11 Mar 2020 19:54:39 +0300 Subject: [PATCH 0863/1365] Compile fix --- src/connected_layer.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/connected_layer.h b/src/connected_layer.h index 80805cc2abd..284c5125e50 100644 --- a/src/connected_layer.h +++ b/src/connected_layer.h @@ -22,7 +22,7 @@ void statistics_connected_layer(layer l); #ifdef GPU void forward_connected_layer_gpu(connected_layer layer, network_state state); void backward_connected_layer_gpu(connected_layer layer, network_state state); -void update_connected_layer_gpu(connected_layer layer, int batch, float learning_rate, float momentum, float decay); +void update_connected_layer_gpu(connected_layer layer, int batch, float learning_rate, float momentum, float decay, float loss_scale); void push_connected_layer(connected_layer layer); void pull_connected_layer(connected_layer layer); #endif From 56bd298297955b4ca80675374734158419c857a5 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 11 Mar 2020 19:58:28 +0300 Subject: [PATCH 0864/1365] another compile fix --- src/local_layer.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/local_layer.h b/src/local_layer.h index 8ef0dbad393..45e02a5c0a1 100644 --- a/src/local_layer.h +++ b/src/local_layer.h @@ -15,7 +15,7 @@ extern "C" { #ifdef GPU void forward_local_layer_gpu(local_layer layer, network_state state); void backward_local_layer_gpu(local_layer layer, network_state state); -void update_local_layer_gpu(local_layer layer, int batch, float learning_rate, float momentum, float decay); +void update_local_layer_gpu(local_layer layer, int batch, float learning_rate, float momentum, float decay, float loss_scale); void push_local_layer(local_layer layer); void pull_local_layer(local_layer layer); From 4dff7cb7698d6d2bb368b8ba6dff6613933dfc0c Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 11 Mar 2020 20:05:01 +0300 Subject: [PATCH 0865/1365] final compile fix --- src/batchnorm_layer.h | 2 +- src/conv_lstm_layer.h | 2 +- src/crnn_layer.h | 2 +- src/gru_layer.h | 2 +- src/lstm_layer.h | 2 +- src/rnn_layer.h | 2 +- src/shortcut_layer.h | 2 +- 7 files changed, 7 insertions(+), 7 deletions(-) diff --git a/src/batchnorm_layer.h b/src/batchnorm_layer.h index f5137465313..afdc54b7d93 100644 --- a/src/batchnorm_layer.h +++ b/src/batchnorm_layer.h @@ -18,7 +18,7 @@ void resize_batchnorm_layer(layer *l, int w, int h); #ifdef GPU void forward_batchnorm_layer_gpu(layer l, network_state state); void backward_batchnorm_layer_gpu(layer l, network_state state); -void update_batchnorm_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay); +void update_batchnorm_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay, float loss_scale); void pull_batchnorm_layer(layer l); void push_batchnorm_layer(layer l); #endif diff --git a/src/conv_lstm_layer.h b/src/conv_lstm_layer.h index 0a0438b04f5..14232cab2a6 100644 --- a/src/conv_lstm_layer.h +++ b/src/conv_lstm_layer.h @@ -23,7 +23,7 @@ void update_conv_lstm_layer(layer l, int batch, float learning_rate, float momen #ifdef GPU void forward_conv_lstm_layer_gpu(layer l, network_state state); void backward_conv_lstm_layer_gpu(layer l, network_state state); -void update_conv_lstm_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay); +void update_conv_lstm_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay, float loss_scale); #endif #ifdef __cplusplus diff --git a/src/crnn_layer.h b/src/crnn_layer.h index c2dc7a25258..b85df6ffaad 100644 --- a/src/crnn_layer.h +++ b/src/crnn_layer.h @@ -20,7 +20,7 @@ void update_crnn_layer(layer l, int batch, float learning_rate, float momentum, #ifdef GPU void forward_crnn_layer_gpu(layer l, network_state state); void backward_crnn_layer_gpu(layer l, network_state state); -void update_crnn_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay); +void update_crnn_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay, float loss_scale); void push_crnn_layer(layer l); void pull_crnn_layer(layer l); #endif diff --git a/src/gru_layer.h b/src/gru_layer.h index ae6b55f4adf..c13e46e8ea9 100644 --- a/src/gru_layer.h +++ b/src/gru_layer.h @@ -18,7 +18,7 @@ void update_gru_layer(layer l, int batch, float learning_rate, float momentum, f #ifdef GPU void forward_gru_layer_gpu(layer l, network_state state); void backward_gru_layer_gpu(layer l, network_state state); -void update_gru_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay); +void update_gru_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay, float loss_scale); void push_gru_layer(layer l); void pull_gru_layer(layer l); #endif diff --git a/src/lstm_layer.h b/src/lstm_layer.h index dc8eb37a38d..a116a83d635 100644 --- a/src/lstm_layer.h +++ b/src/lstm_layer.h @@ -18,7 +18,7 @@ void update_lstm_layer(layer l, int batch, float learning_rate, float momentum, #ifdef GPU void forward_lstm_layer_gpu(layer l, network_state state); void backward_lstm_layer_gpu(layer l, network_state state); -void update_lstm_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay); +void update_lstm_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay, float loss_scale); #endif #ifdef __cplusplus diff --git a/src/rnn_layer.h b/src/rnn_layer.h index 43b57d82e0f..a2aa0f9e4bf 100644 --- a/src/rnn_layer.h +++ b/src/rnn_layer.h @@ -19,7 +19,7 @@ void update_rnn_layer(layer l, int batch, float learning_rate, float momentum, f #ifdef GPU void forward_rnn_layer_gpu(layer l, network_state state); void backward_rnn_layer_gpu(layer l, network_state state); -void update_rnn_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay); +void update_rnn_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay, float loss_scale); void push_rnn_layer(layer l); void pull_rnn_layer(layer l); #endif diff --git a/src/shortcut_layer.h b/src/shortcut_layer.h index 291916200aa..f0bf33a79d4 100644 --- a/src/shortcut_layer.h +++ b/src/shortcut_layer.h @@ -18,7 +18,7 @@ void resize_shortcut_layer(layer *l, int w, int h, network *net); #ifdef GPU void forward_shortcut_layer_gpu(const layer l, network_state state); void backward_shortcut_layer_gpu(const layer l, network_state state); -void update_shortcut_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay); +void update_shortcut_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay, float loss_scale); void pull_shortcut_layer(layer l); void push_shortcut_layer(layer l); #endif From cf8409d7e101e4df257f083792482448189d0789 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 11 Mar 2020 21:41:14 +0300 Subject: [PATCH 0866/1365] Compile fix --- src/dark_cuda.c | 3 ++- src/dark_cuda.h | 2 ++ src/darknet.c | 2 -- 3 files changed, 4 insertions(+), 3 deletions(-) diff --git a/src/dark_cuda.c b/src/dark_cuda.c index 7e94f7c629b..e75943098f6 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -1,6 +1,7 @@ #ifdef __cplusplus extern "C" { #endif +int cuda_debug_sync = 0; int gpu_index = 0; #ifdef __cplusplus } @@ -19,6 +20,7 @@ int gpu_index = 0; #pragma comment(lib, "cuda.lib") + #ifdef CUDNN #ifndef USE_CMAKE_LIBS #pragma comment(lib, "cudnn.lib") @@ -29,7 +31,6 @@ int gpu_index = 0; #error "If you set CUDNN_HALF=1 then you must set CUDNN=1" #endif -extern int cuda_debug_sync; void cuda_set_device(int n) { diff --git a/src/dark_cuda.h b/src/dark_cuda.h index 4798d729c73..5f6978ae788 100644 --- a/src/dark_cuda.h +++ b/src/dark_cuda.h @@ -6,6 +6,8 @@ extern "C" { #endif + +extern int cuda_debug_sync; extern int gpu_index; #ifdef __cplusplus } diff --git a/src/darknet.c b/src/darknet.c index 94d5e2b0c6b..cba4ebcd704 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -12,8 +12,6 @@ #include "blas.h" #include "connected_layer.h" -int cuda_debug_sync = 0; - extern void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top); extern void run_voxel(int argc, char **argv); From 9ac401fa67132b127e585bd09f105ee3a5668261 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 12 Mar 2020 17:07:57 +0300 Subject: [PATCH 0867/1365] BiFPN fix --- src/blas_kernels.cu | 4 ++-- src/convolutional_kernels.cu | 6 +++--- src/shortcut_layer.c | 4 ++-- src/yolo_console_dll.cpp | 8 +++++--- 4 files changed, 12 insertions(+), 10 deletions(-) diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 331b53e7f95..77c6efdf21a 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -960,7 +960,7 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i else if (weights_normalizion == SOFTMAX_NORMALIZATION) grad = w*(1-w); delta_out[id] += delta_in[id] * w; // [0 or c or (c, h ,w)] - float weights_update_tmp = delta_in[id] * in[id] * grad; + float weights_update_tmp = delta_in[id] * in[id] * grad / step; if (layer_step == 1 && (size/32) > (id/32 + 1)) { float wu = warpAllReduceSum(weights_update_tmp); @@ -997,7 +997,7 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i else if (weights_normalizion == SOFTMAX_NORMALIZATION) grad = w*(1 - w); layer_delta[add_index] += delta_in[id] * w; - float weights_update_tmp = delta_in[id] * add[add_index] * grad; + float weights_update_tmp = delta_in[id] * add[add_index] * grad / step; if (layer_step == 1 && (size / 32) > (id / 32 + 1)) { float wu = warpAllReduceSum(weights_update_tmp); diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 372aa57a166..7f05551cc87 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -1287,9 +1287,9 @@ void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init //} } - //if (l.clip) { - // constrain_gpu(l.nweights, l.clip, l.weights_gpu, 1); - //} + if (l.clip) { + constrain_ongpu(l.nweights, l.clip, l.weights_gpu, 1); + } } /* diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index d703f8f8564..fedb9d8d0a3 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -53,7 +53,7 @@ layer make_shortcut_layer(int batch, int n, int *input_layers, int* input_sizes, if (l.nweights > 0) { l.weights = (float*)calloc(l.nweights, sizeof(float)); float scale = sqrt(2. / l.nweights); - for (i = 0; i < l.nweights; ++i) l.weights[i] = 1 + 0.01*rand_uniform(-1, 1);// scale*rand_uniform(-1, 1); // rand_normal(); + for (i = 0; i < l.nweights; ++i) l.weights[i] = 1;// +0.01*rand_uniform(-1, 1);// scale*rand_uniform(-1, 1); // rand_normal(); if (train) l.weight_updates = (float*)calloc(l.nweights, sizeof(float)); l.update = update_shortcut_layer; @@ -261,7 +261,7 @@ void update_shortcut_layer_gpu(layer l, int batch, float learning_rate_init, flo scal_ongpu(l.nweights, momentum, l.weight_updates_gpu, 1); //if (l.clip) { - // constrain_gpu(l.nweights, l.clip, l.weights_gpu, 1); + // constrain_ongpu(l.nweights, l.clip, l.weights_gpu, 1); //} } } diff --git a/src/yolo_console_dll.cpp b/src/yolo_console_dll.cpp index 8f73d869696..6d1bb2a580e 100644 --- a/src/yolo_console_dll.cpp +++ b/src/yolo_console_dll.cpp @@ -124,7 +124,7 @@ cv::Mat zed_capture_rgb(sl::Camera &zed) { cv::Mat zed_capture_3d(sl::Camera &zed) { sl::Mat cur_cloud; - zed.retrieveMeasure(cur_cloud, + zed.retrieveMeasure(cur_cloud, #ifdef ZED_STEREO_2_COMPAT_MODE sl::MEASURE_XYZ #else @@ -151,11 +151,13 @@ std::vector get_3d_coordinates(std::vector bbox_vect, cv::Mat xy #ifndef USE_CMAKE_LIBS #pragma comment(lib, "opencv_world" OPENCV_VERSION ".lib") #ifdef TRACK_OPTFLOW +/* #pragma comment(lib, "opencv_cudaoptflow" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_cudaimgproc" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_core" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib") #pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib") +*/ #endif // TRACK_OPTFLOW #endif // USE_CMAKE_LIBS #else // OpenCV 2.x @@ -342,7 +344,7 @@ int main(int argc, char *argv[]) #endif //init_params.sdk_cuda_ctx = (CUcontext)detector.get_cuda_context(); init_params.sdk_gpu_id = detector.cur_gpu_id; - + if (filename == "zed_camera" || file_ext == "svo") { std::cout << "ZED 3D Camera " << zed.open(init_params) << std::endl; if (!zed.isOpened()) { @@ -411,7 +413,7 @@ int main(int argc, char *argv[]) if (use_zed_camera) { while (zed.grab() != #ifdef ZED_STEREO_2_COMPAT_MODE - sl::SUCCESS + sl::SUCCESS #else sl::ERROR_CODE::SUCCESS #endif From 54aa887462daef3499ae1e57d42a6b560b254899 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 12 Mar 2020 17:40:52 +0300 Subject: [PATCH 0868/1365] Added clip=1 option for weighted-layers to constrain absolute weights value --- src/parser.c | 1 + 1 file changed, 1 insertion(+) diff --git a/src/parser.c b/src/parser.c index e20d98da994..74023ce6772 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1362,6 +1362,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) } #endif // GPU + l.clip = option_find_int_quiet(options, "clip", 0); l.dynamic_minibatch = net.dynamic_minibatch; l.onlyforward = option_find_int_quiet(options, "onlyforward", 0); l.stopbackward = option_find_int_quiet(options, "stopbackward", 0); From d79d2815b9b8f9172b988406bd2a4ccb3a2dff15 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 12 Mar 2020 17:43:01 +0300 Subject: [PATCH 0869/1365] clip float fix --- src/parser.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/parser.c b/src/parser.c index 74023ce6772..cd3f3d24c6a 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1362,7 +1362,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) } #endif // GPU - l.clip = option_find_int_quiet(options, "clip", 0); + l.clip = option_find_float_quiet(options, "clip", 0); l.dynamic_minibatch = net.dynamic_minibatch; l.onlyforward = option_find_int_quiet(options, "onlyforward", 0); l.stopbackward = option_find_int_quiet(options, "stopbackward", 0); From d11caf486da34a562347db53c140900aef42140f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 14 Mar 2020 03:24:46 +0300 Subject: [PATCH 0870/1365] Fixed BiFPN and label_smothing for Detection --- include/darknet.h | 2 ++ src/activation_kernels.cu | 2 ++ src/blas.h | 1 + src/blas_kernels.cu | 64 +++++++++++++++++++++++++++------------ src/darknet.c | 2 +- src/detector.c | 2 +- src/gaussian_yolo_layer.c | 23 ++++++-------- src/network.c | 2 +- src/network_kernels.cu | 6 ++-- src/parser.c | 10 +++++- src/shortcut_layer.c | 17 ++++++++++- src/yolo_layer.c | 26 ++++++++-------- 12 files changed, 102 insertions(+), 55 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 0c6761a06a5..ecc064020d1 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -324,6 +324,8 @@ struct layer { int onlyforward; int stopbackward; + int dont_update; + int burnin_update; int dontload; int dontsave; int dontloadscales; diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index c7cd49f2a6d..ef2ddbe4b7b 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -493,6 +493,7 @@ __global__ void activate_array_normalize_channels_softmax_kernel(float *x, int s for (k = 0; k < channels; ++k) { float val = x[wh_i + k * wh_step + b*wh_step*channels]; val = expf(val - max_val) / sum; + if (isnan(val) || isinf(val)) val = 0; output_gpu[wh_i + k * wh_step + b*wh_step*channels] = val; } } @@ -535,6 +536,7 @@ __global__ void gradient_array_normalize_channels_softmax_kernel(float *x, int s float delta = delta_gpu[index]; float grad = x[index] * (1 - x[index]); delta = delta * grad; + if (isnan(delta) || isinf(delta)) delta = 0; delta_gpu[index] = delta; } } diff --git a/src/blas.h b/src/blas.h index 9cf665d83c3..22048ec074d 100644 --- a/src/blas.h +++ b/src/blas.h @@ -60,6 +60,7 @@ void fix_nan_and_inf_cpu(float *input, size_t size); #ifdef GPU +void constrain_weight_updates_ongpu(int N, float learning_rate, float *weights_gpu, float *weight_updates_gpu); void axpy_ongpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY); void axpy_ongpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY); void simple_copy_ongpu(int size, float *src, float *dst); diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 77c6efdf21a..30354333088 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -418,6 +418,24 @@ __global__ void reorg_kernel(int N, float *x, int w, int h, int c, int batch, in //else out[0] = x[0]; } +__global__ void constrain_weight_updates_kernel(int N, float learning_rate, float *weights_gpu, float *weight_updates_gpu) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (i < N) { + const float w = weights_gpu[i]; + const float wu = weight_updates_gpu[i]; + const float wu_sign = (wu == 0) ? 0 : (wu / wu); + const float abs_limit = fabs(w*learning_rate); + if (fabs(wu) > abs_limit) weight_updates_gpu[i] = abs_limit * wu_sign; + } +} + +extern "C" void constrain_weight_updates_ongpu(int N, float learning_rate, float *weights_gpu, float *weight_updates_gpu) +{ + constrain_weight_updates_kernel << > >(N, learning_rate, weights_gpu, weight_updates_gpu); + CHECK_CUDA(cudaPeekAtLastError()); +} + __global__ void axpy_kernel(int N, float ALPHA, float *X, int OFFX, int INCX, float *Y, int OFFY, int INCY) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -801,6 +819,12 @@ __device__ float relu(float src) { return 0; } +__device__ float lrelu(float src) { + const float eps = 0.001; + if (src > eps) return src; + return eps; +} + __global__ void shortcut_singlelayer_simple_kernel(int size, int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights, WEIGHTS_NORMALIZATION_T weights_normalizion) { const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -852,7 +876,7 @@ __global__ void shortcut_multilayer_kernel(int size, int src_outputs, int batch, for (int i = 0; i < (n + 1); ++i) { const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] const float w = weights_gpu[weights_index]; - if (weights_normalizion == RELU_NORMALIZATION) sum += relu(w); + if (weights_normalizion == RELU_NORMALIZATION) sum += lrelu(w); else if (weights_normalizion == SOFTMAX_NORMALIZATION) sum += expf(w - max_val); } } @@ -861,7 +885,7 @@ __global__ void shortcut_multilayer_kernel(int size, int src_outputs, int batch, if (weights_gpu) { float w = weights_gpu[src_i / step]; - if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; + if (weights_normalizion == RELU_NORMALIZATION) w = lrelu(w) / sum; else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; out_val = in[id] * w; // [0 or c or (c, h ,w)] @@ -879,7 +903,7 @@ __global__ void shortcut_multilayer_kernel(int size, int src_outputs, int batch, if (weights_gpu) { const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)] float w = weights_gpu[weights_index]; - if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; + if (weights_normalizion == RELU_NORMALIZATION) w = lrelu(w) / sum; else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; out_val += add[add_index] * w; // [0 or c or (c, h ,w)] @@ -935,34 +959,27 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i for (i = 0; i < (n + 1); ++i) { const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] const float w = weights_gpu[weights_index]; - if (weights_normalizion == RELU_NORMALIZATION) sum += relu(w); + if (weights_normalizion == RELU_NORMALIZATION) sum += lrelu(w); else if (weights_normalizion == SOFTMAX_NORMALIZATION) sum += expf(w - max_val); } - /* - grad = 0; - for (i = 0; i < (n + 1); ++i) { - const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)] - const float delta_w = delta_in[id] * in[id]; - const float w = weights_gpu[weights_index]; - if (weights_normalizion == RELU_NORMALIZATION) grad += delta_w * relu(w) / sum; - else if (weights_normalizion == SOFTMAX_NORMALIZATION) grad += delta_w * expf(w - max_val) / sum; - } - */ } if (weights_gpu) { float w = weights_gpu[src_i / step]; - if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; + if (weights_normalizion == RELU_NORMALIZATION) w = lrelu(w) / sum; else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; if (weights_normalizion == RELU_NORMALIZATION) grad = w; else if (weights_normalizion == SOFTMAX_NORMALIZATION) grad = w*(1-w); delta_out[id] += delta_in[id] * w; // [0 or c or (c, h ,w)] - float weights_update_tmp = delta_in[id] * in[id] * grad / step; + float weights_update_tmp = delta_in[id] * in[id] * grad;// / step; if (layer_step == 1 && (size/32) > (id/32 + 1)) { + if (isnan(weights_update_tmp) || isinf(weights_update_tmp)) { + weights_update_tmp = 0; + } float wu = warpAllReduceSum(weights_update_tmp); if (threadIdx.x % 32 == 0) { if (!isnan(wu) && !isinf(wu)) @@ -997,13 +1014,18 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i else if (weights_normalizion == SOFTMAX_NORMALIZATION) grad = w*(1 - w); layer_delta[add_index] += delta_in[id] * w; - float weights_update_tmp = delta_in[id] * add[add_index] * grad / step; + float weights_update_tmp = delta_in[id] * add[add_index] * grad;// / step; if (layer_step == 1 && (size / 32) > (id / 32 + 1)) { + if (isnan(weights_update_tmp) || isinf(weights_update_tmp)) { + weights_update_tmp = 0; + } float wu = warpAllReduceSum(weights_update_tmp); if (threadIdx.x % 32 == 0) { if (!isnan(wu) && !isinf(wu)) atomicAdd(&weight_updates_gpu[weights_index], wu); + //if(weights_gpu[weights_index] != 1) printf(" wu = %f, weights_update_tmp = %f, w = %f, weights_gpu[weights_index] = %f, grad = %f, weights_normalizion = %d ", + // wu, weights_update_tmp, w, weights_gpu[weights_index], grad, weights_normalizion); } } else { @@ -1360,8 +1382,9 @@ __global__ void fix_nan_and_inf_kernel(float *input, size_t size) const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { float val = input[index]; - if (isnan(val) || isinf(val)) - input[index] = 1.0f / index; // pseudo random value + if (isnan(val) || isinf(val)) { + input[index] = 1.0f / (fabs((float)index) + 1); // pseudo random value + } } } @@ -1380,8 +1403,9 @@ __global__ void reset_nan_and_inf_kernel(float *input, size_t size) const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index < size) { float val = input[index]; - if (isnan(val) || isinf(val)) + if (isnan(val) || isinf(val)) { input[index] = 0; + } } } diff --git a/src/darknet.c b/src/darknet.c index cba4ebcd704..93d88b1b4c7 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -172,7 +172,7 @@ void oneoff(char *cfgfile, char *weightfile, char *outfile) void partial(char *cfgfile, char *weightfile, char *outfile, int max) { gpu_index = -1; - network net = parse_network_cfg(cfgfile); + network net = parse_network_cfg_custom(cfgfile, 1, 1); if(weightfile){ load_weights_upto(&net, weightfile, max); } diff --git a/src/detector.c b/src/detector.c index 536befb8794..d403f38cdd9 100644 --- a/src/detector.c +++ b/src/detector.c @@ -356,7 +356,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i save_weights(net, buff); } - if (iteration >= (iter_save_last + 100) || iteration % 100 == 0) { + if (iteration >= (iter_save_last + 100) || (iteration % 100 == 0 && iteration > 1)) { iter_save_last = iteration; #ifdef GPU if (ngpus != 1) sync_nets(nets, ngpus, 0); diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index 101005ee427..d3bd3f58181 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -371,25 +371,20 @@ void delta_gaussian_yolo_class(float *output, float *delta, int index, int class { int n; if (delta[index]){ - if (label_smooth_eps > 0) { - float out_val = output[index + stride*class_id] * (1 - label_smooth_eps) + 0.5*label_smooth_eps; - delta[index + stride*class_id] = 1 - out_val; - } - else { - delta[index + stride*class_id] = 1 - output[index + stride*class_id]; - } + float y_true = 1; + if (label_smooth_eps) y_true = y_true * (1 - label_smooth_eps) + 0.5*label_smooth_eps; + delta[index + stride*class_id] = y_true - output[index + stride*class_id]; + //delta[index + stride*class_id] = 1 - output[index + stride*class_id]; + if (classes_multipliers) delta[index + stride*class_id] *= classes_multipliers[class_id]; if(avg_cat) *avg_cat += output[index + stride*class_id]; return; } for(n = 0; n < classes; ++n){ - if (label_smooth_eps > 0) { - float out_val = output[index + stride*class_id] * (1 - label_smooth_eps) + 0.5*label_smooth_eps; - delta[index + stride*n] = ((n == class_id) ? 1 : 0) - out_val; - } - else { - delta[index + stride*n] = ((n == class_id) ? 1 : 0) - output[index + stride*n]; - } + float y_true = ((n == class_id) ? 1 : 0); + if (label_smooth_eps) y_true = y_true * (1 - label_smooth_eps) + 0.5*label_smooth_eps; + delta[index + stride*n] = y_true - output[index + stride*n]; + if (classes_multipliers && n == class_id) delta[index + stride*class_id] *= classes_multipliers[class_id]; if(n == class_id && avg_cat) *avg_cat += output[index + stride*n]; } diff --git a/src/network.c b/src/network.c index 9f58708221c..8f09dab711f 100644 --- a/src/network.c +++ b/src/network.c @@ -1135,7 +1135,7 @@ void fuse_conv_batchnorm(network net) //cuda_pull_array(l.weights_gpu, l.weights, l.nweights); int i; for (i = 0; i < l->nweights; ++i) printf(" w = %f,", l->weights[i]); - printf(" l->nweights = %d \n", l->nweights); + printf(" l->nweights = %d, j = %d \n", l->nweights, j); } // nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w) diff --git a/src/network_kernels.cu b/src/network_kernels.cu index ba65ff045f7..75b3d6a227e 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -169,7 +169,8 @@ void backward_network_gpu(network net, network_state state) for(i = net.n-1; i >= 0; --i){ state.index = i; layer l = net.layers[i]; - if (l.stopbackward) break; + if (l.stopbackward == 1) break; + if (l.stopbackward > get_current_iteration(net)) break; if(i == 0){ state.input = original_input; state.delta = original_delta; @@ -247,7 +248,8 @@ void update_network_gpu(network net) layer l = net.layers[i]; l.t = get_current_batch(net); if (iteration_num > (net.max_batches * 1 / 2)) l.deform = 0; - if(l.update_gpu){ + if (l.burnin_update && (l.burnin_update*net.burn_in > iteration_num)) continue; + if(l.update_gpu && l.dont_update < iteration_num){ l.update_gpu(l, update_batch, rate, net.momentum, net.decay, net.loss_scale); } } diff --git a/src/parser.c b/src/parser.c index cd3f3d24c6a..426e1481902 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1365,6 +1365,8 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) l.clip = option_find_float_quiet(options, "clip", 0); l.dynamic_minibatch = net.dynamic_minibatch; l.onlyforward = option_find_int_quiet(options, "onlyforward", 0); + l.dont_update = option_find_int_quiet(options, "dont_update", 0); + l.burnin_update = option_find_int_quiet(options, "burnin_update", 0); l.stopbackward = option_find_int_quiet(options, "stopbackward", 0); l.dontload = option_find_int_quiet(options, "dontload", 0); l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0); @@ -1556,8 +1558,14 @@ void save_shortcut_weights(layer l, FILE *fp) #ifdef GPU if (gpu_index >= 0) { pull_shortcut_layer(l); + printf("\n pull_shortcut_layer \n"); } #endif + for (int i = 0; i < l.nweights; ++i) printf(" %f, ", l.weight_updates[i]); + printf(" l.nweights = %d - update \n", l.nweights); + for (int i = 0; i < l.nweights; ++i) printf(" %f, ", l.weights[i]); + printf(" l.nweights = %d \n\n", l.nweights); + int num = l.nweights; fwrite(l.weights, sizeof(float), num, fp); } @@ -1843,7 +1851,7 @@ void load_shortcut_weights(layer l, FILE *fp) read_bytes = fread(l.weights, sizeof(float), num, fp); if (read_bytes > 0 && read_bytes < num) printf("\n Warning: Unexpected end of wights-file! l.weights - l.index = %d \n", l.index); //for (int i = 0; i < l.nweights; ++i) printf(" %f, ", l.weights[i]); - //printf("\n\n"); + //printf(" read_bytes = %d \n\n", read_bytes); #ifdef GPU if (gpu_index >= 0) { push_shortcut_layer(l); diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index fedb9d8d0a3..3e2521b34e5 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -256,10 +256,24 @@ void update_shortcut_layer_gpu(layer l, int batch, float learning_rate_init, flo reset_nan_and_inf(l.weight_updates_gpu, l.nweights); fix_nan_and_inf(l.weights_gpu, l.nweights); - axpy_ongpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); + constrain_weight_updates_ongpu(l.nweights, 0.001, l.weights_gpu, l.weight_updates_gpu); + + /* + cuda_pull_array_async(l.weights_gpu, l.weights, l.nweights); + cuda_pull_array_async(l.weight_updates_gpu, l.weight_updates, l.nweights); + CHECK_CUDA(cudaStreamSynchronize(get_cuda_stream())); + for (int i = 0; i < l.nweights; ++i) printf(" %f, ", l.weight_updates[i]); + printf(" l.nweights = %d - updates \n", l.nweights); + for (int i = 0; i < l.nweights; ++i) printf(" %f, ", l.weights[i]); + printf(" l.nweights = %d \n\n", l.nweights); + */ + + //axpy_ongpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); axpy_ongpu(l.nweights, learning_rate / batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); scal_ongpu(l.nweights, momentum, l.weight_updates_gpu, 1); + //fill_ongpu(l.nweights, 0, l.weight_updates_gpu, 1); + //if (l.clip) { // constrain_ongpu(l.nweights, l.clip, l.weights_gpu, 1); //} @@ -268,6 +282,7 @@ void update_shortcut_layer_gpu(layer l, int batch, float learning_rate_init, flo void pull_shortcut_layer(layer l) { + cuda_pull_array_async(l.weight_updates_gpu, l.weight_updates, l.nweights); cuda_pull_array_async(l.weights_gpu, l.weights, l.nweights); CHECK_CUDA(cudaPeekAtLastError()); CHECK_CUDA(cudaStreamSynchronize(get_cuda_stream())); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 8d25cd57385..31fe7249f61 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -257,13 +257,12 @@ void delta_yolo_class(float *output, float *delta, int index, int class_id, int { int n; if (delta[index + stride*class_id]){ - if (label_smooth_eps > 0) { - float out_val = output[index + stride*class_id] * (1 - label_smooth_eps) + 0.5*label_smooth_eps; - delta[index + stride*class_id] = 1 - out_val; - } - else { - delta[index + stride*class_id] = 1 - output[index + stride*class_id]; - } + float y_true = 1; + if(label_smooth_eps) y_true = y_true * (1 - label_smooth_eps) + 0.5*label_smooth_eps; + float result_delta = y_true - output[index + stride*class_id]; + if(!isnan(result_delta) && !isinf(result_delta)) delta[index + stride*class_id] = result_delta; + //delta[index + stride*class_id] = 1 - output[index + stride*class_id]; + if (classes_multipliers) delta[index + stride*class_id] *= classes_multipliers[class_id]; if(avg_cat) *avg_cat += output[index + stride*class_id]; return; @@ -291,13 +290,11 @@ void delta_yolo_class(float *output, float *delta, int index, int class_id, int else { // default for (n = 0; n < classes; ++n) { - if (label_smooth_eps > 0) { - float out_val = output[index + stride*class_id] * (1 - label_smooth_eps) + 0.5*label_smooth_eps; - delta[index + stride*n] = ((n == class_id) ? 1 : 0) - out_val; - } - else { - delta[index + stride*n] = ((n == class_id) ? 1 : 0) - output[index + stride*n]; - } + float y_true = ((n == class_id) ? 1 : 0); + if (label_smooth_eps) y_true = y_true * (1 - label_smooth_eps) + 0.5*label_smooth_eps; + float result_delta = y_true - output[index + stride*n]; + if (!isnan(result_delta) && !isinf(result_delta)) delta[index + stride*n] = result_delta; + if (classes_multipliers && n == class_id) delta[index + stride*class_id] *= classes_multipliers[class_id]; if (n == class_id && avg_cat) *avg_cat += output[index + stride*n]; } @@ -385,6 +382,7 @@ void forward_yolo_layer(const layer l, network_state state) int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4); float objectness = l.output[obj_index]; + if (isnan(objectness) || isinf(objectness)) l.output[obj_index] = 0; int class_id_match = compare_yolo_class(l.output, l.classes, class_index, l.w*l.h, objectness, class_id, 0.25f); float iou = box_iou(pred, truth); From 4ca7896402229f709eeab8d53992db55478e6c07 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 14 Mar 2020 04:14:56 +0300 Subject: [PATCH 0871/1365] Fixed constrain for BiFPN --- src/blas.h | 2 +- src/blas_kernels.cu | 10 +++++----- src/shortcut_layer.c | 3 ++- 3 files changed, 8 insertions(+), 7 deletions(-) diff --git a/src/blas.h b/src/blas.h index 22048ec074d..2f91451611e 100644 --- a/src/blas.h +++ b/src/blas.h @@ -60,7 +60,7 @@ void fix_nan_and_inf_cpu(float *input, size_t size); #ifdef GPU -void constrain_weight_updates_ongpu(int N, float learning_rate, float *weights_gpu, float *weight_updates_gpu); +void constrain_weight_updates_ongpu(int N, float coef, float *weights_gpu, float *weight_updates_gpu); void axpy_ongpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY); void axpy_ongpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY); void simple_copy_ongpu(int size, float *src, float *dst); diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 30354333088..a35177b509a 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -418,21 +418,21 @@ __global__ void reorg_kernel(int N, float *x, int w, int h, int c, int batch, in //else out[0] = x[0]; } -__global__ void constrain_weight_updates_kernel(int N, float learning_rate, float *weights_gpu, float *weight_updates_gpu) +__global__ void constrain_weight_updates_kernel(int N, float coef, float *weights_gpu, float *weight_updates_gpu) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (i < N) { const float w = weights_gpu[i]; const float wu = weight_updates_gpu[i]; - const float wu_sign = (wu == 0) ? 0 : (wu / wu); - const float abs_limit = fabs(w*learning_rate); + const float wu_sign = (wu == 0) ? 0 : (fabs(wu) / wu); + const float abs_limit = fabs(w * coef); if (fabs(wu) > abs_limit) weight_updates_gpu[i] = abs_limit * wu_sign; } } -extern "C" void constrain_weight_updates_ongpu(int N, float learning_rate, float *weights_gpu, float *weight_updates_gpu) +extern "C" void constrain_weight_updates_ongpu(int N, float coef, float *weights_gpu, float *weight_updates_gpu) { - constrain_weight_updates_kernel << > >(N, learning_rate, weights_gpu, weight_updates_gpu); + constrain_weight_updates_kernel << > >(N, coef, weights_gpu, weight_updates_gpu); CHECK_CUDA(cudaPeekAtLastError()); } diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index 3e2521b34e5..5de10a0b28c 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -256,7 +256,7 @@ void update_shortcut_layer_gpu(layer l, int batch, float learning_rate_init, flo reset_nan_and_inf(l.weight_updates_gpu, l.nweights); fix_nan_and_inf(l.weights_gpu, l.nweights); - constrain_weight_updates_ongpu(l.nweights, 0.001, l.weights_gpu, l.weight_updates_gpu); + constrain_weight_updates_ongpu(l.nweights, 1, l.weights_gpu, l.weight_updates_gpu); /* cuda_pull_array_async(l.weights_gpu, l.weights, l.nweights); @@ -282,6 +282,7 @@ void update_shortcut_layer_gpu(layer l, int batch, float learning_rate_init, flo void pull_shortcut_layer(layer l) { + constrain_weight_updates_ongpu(l.nweights, 1, l.weights_gpu, l.weight_updates_gpu); cuda_pull_array_async(l.weight_updates_gpu, l.weight_updates, l.nweights); cuda_pull_array_async(l.weights_gpu, l.weights, l.nweights); CHECK_CUDA(cudaPeekAtLastError()); From 002177de00a8f29d234eb761e277a988461f7710 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 14 Mar 2020 13:19:56 +0300 Subject: [PATCH 0872/1365] Minor Fix --- src/parser.c | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/parser.c b/src/parser.c index 426e1481902..56cab218450 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1101,8 +1101,10 @@ void parse_net_options(list *options, network *net) #ifdef GPU if (net->gpu_index >= 0) { int compute_capability = get_gpu_compute_capability(net->gpu_index); - if (get_gpu_compute_capability(net->gpu_index) >= 700) net->cudnn_half = 1; +#ifdef CUDNN_HALF + if (compute_capability >= 700) net->cudnn_half = 1; else net->cudnn_half = 0; +#endif// CUDNN_HALF fprintf(stderr, " compute_capability = %d, cudnn_half = %d \n", compute_capability, net->cudnn_half); } else fprintf(stderr, " GPU isn't used \n"); From 46ccac39e82f6ebf173ad5c53b0f659cfc9ead3f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 14 Mar 2020 13:43:33 +0300 Subject: [PATCH 0873/1365] Minor fix --- src/parser.c | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/parser.c b/src/parser.c index 56cab218450..53810f86c86 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1563,9 +1563,10 @@ void save_shortcut_weights(layer l, FILE *fp) printf("\n pull_shortcut_layer \n"); } #endif - for (int i = 0; i < l.nweights; ++i) printf(" %f, ", l.weight_updates[i]); + int i; + for (i = 0; i < l.nweights; ++i) printf(" %f, ", l.weight_updates[i]); printf(" l.nweights = %d - update \n", l.nweights); - for (int i = 0; i < l.nweights; ++i) printf(" %f, ", l.weights[i]); + for (i = 0; i < l.nweights; ++i) printf(" %f, ", l.weights[i]); printf(" l.nweights = %d \n\n", l.nweights); int num = l.nweights; From fb4da5ce5fe8339255a09974ed983fa44c3afbe9 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 14 Mar 2020 15:31:01 +0300 Subject: [PATCH 0874/1365] Clip for weight_updates for BiFPN --- src/shortcut_layer.c | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index 5de10a0b28c..d4a700232e1 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -256,7 +256,8 @@ void update_shortcut_layer_gpu(layer l, int batch, float learning_rate_init, flo reset_nan_and_inf(l.weight_updates_gpu, l.nweights); fix_nan_and_inf(l.weights_gpu, l.nweights); - constrain_weight_updates_ongpu(l.nweights, 1, l.weights_gpu, l.weight_updates_gpu); + //constrain_weight_updates_ongpu(l.nweights, 1, l.weights_gpu, l.weight_updates_gpu); + constrain_ongpu(l.nweights, 1, l.weight_updates_gpu, 1); /* cuda_pull_array_async(l.weights_gpu, l.weights, l.nweights); From 18d877a1ac0fdfbc82e0f32f5fefee6ef2c2f63b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 15 Mar 2020 00:04:18 +0300 Subject: [PATCH 0875/1365] Fixed loss_scale --- src/convolutional_kernels.cu | 10 +++++----- src/shortcut_layer.c | 2 +- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 7f05551cc87..eab6648a655 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -421,7 +421,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //if (state.use_mixed_precision) { int iteration_num = get_current_iteration(state.net); // (*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3 * state.net.burn_in) && - (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 1 && l.size > 1) + (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && l.groups <= 1 && l.size > 1) { //printf("\n CUDNN_HALF!!! state.index = %d \n", state.index); @@ -673,7 +673,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state //#ifdef CUDNN_HALF int iteration_num = get_current_iteration(state.net); //(*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3 * state.net.burn_in) && - (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 1 && l.size > 1) + (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && l.groups <= 1 && l.size > 1) { const size_t input16_size = l.batch*l.c*l.w*l.h; const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h; @@ -1234,9 +1234,9 @@ void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init // Loss scale for Mixed-Precision on Tensor-Cores if (loss_scale != 1.0) { - scal_ongpu(l.nweights, 1.0 / loss_scale, l.weight_updates_gpu, 1); - scal_ongpu(l.n, 1.0 / loss_scale, l.bias_updates_gpu, 1); - scal_ongpu(l.n, 1.0 / loss_scale, l.scale_updates_gpu, 1); + if (l.weight_updates_gpu && l.nweights > 0) scal_ongpu(l.nweights, 1.0 / loss_scale, l.weight_updates_gpu, 1); + if (l.bias_updates_gpu && l.n > 0) scal_ongpu(l.n, 1.0 / loss_scale, l.bias_updates_gpu, 1); + if (l.scale_updates_gpu && l.n > 0) scal_ongpu(l.n, 1.0 / loss_scale, l.scale_updates_gpu, 1); } reset_nan_and_inf(l.weight_updates_gpu, l.nweights); diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index d4a700232e1..1002a1a27c8 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -250,7 +250,7 @@ void update_shortcut_layer_gpu(layer l, int batch, float learning_rate_init, flo // Loss scale for Mixed-Precision on Tensor-Cores if (loss_scale != 1.0) { - scal_ongpu(l.nweights, 1.0 / loss_scale, l.weight_updates_gpu, 1); + if(l.weight_updates_gpu && l.nweights > 0) scal_ongpu(l.nweights, 1.0 / loss_scale, l.weight_updates_gpu, 1); } reset_nan_and_inf(l.weight_updates_gpu, l.nweights); From b24b61fb9707a9e9c9f12388b862ca3ec6379de9 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 15 Mar 2020 00:05:04 +0300 Subject: [PATCH 0876/1365] disable TC --- src/convolutional_kernels.cu | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index eab6648a655..3b4a343a9c2 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -421,7 +421,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //if (state.use_mixed_precision) { int iteration_num = get_current_iteration(state.net); // (*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3 * state.net.burn_in) && - (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && l.groups <= 1 && l.size > 1) + (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 1 && l.size > 1) { //printf("\n CUDNN_HALF!!! state.index = %d \n", state.index); @@ -673,7 +673,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state //#ifdef CUDNN_HALF int iteration_num = get_current_iteration(state.net); //(*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3 * state.net.burn_in) && - (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && l.groups <= 1 && l.size > 1) + (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 1 && l.size > 1) { const size_t input16_size = l.batch*l.c*l.w*l.h; const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h; From 0c8e9ce4b2cd770a5cbe68e7d61169c5a565e305 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 15 Mar 2020 00:50:27 +0300 Subject: [PATCH 0877/1365] Minor fix --- src/detector.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/detector.c b/src/detector.c index d403f38cdd9..92ce5864838 100644 --- a/src/detector.c +++ b/src/detector.c @@ -191,7 +191,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i if (dim_w < net.resize_step) dim_w = net.resize_step; if (dim_h < net.resize_step) dim_h = net.resize_step; int dim_b = (init_b * max_dim_w * max_dim_h) / (dim_w * dim_h); - int new_dim_b = (int)(dim_b * 0.8); + int new_dim_b = (int)(dim_b * 0.9); if (new_dim_b > init_b) dim_b = new_dim_b; args.w = dim_w; From 8ffc2b0b36b16cb0d9456a5f732191f56458e7ed Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 15 Mar 2020 14:06:03 +0300 Subject: [PATCH 0878/1365] Minor fix --- src/parser.c | 2 +- src/shortcut_layer.c | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/parser.c b/src/parser.c index 53810f86c86..a2084378999 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1644,7 +1644,7 @@ void save_weights_upto(network net, char *filename, int cutoff) fwrite(&major, sizeof(int), 1, fp); fwrite(&minor, sizeof(int), 1, fp); fwrite(&revision, sizeof(int), 1, fp); - (*net.seen) = (*net.cur_iteration) * net.batch * net.subdivisions; + //(*net.seen) = (*net.cur_iteration) * net.batch * net.subdivisions; fwrite(net.seen, sizeof(uint64_t), 1, fp); int i; diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c index 1002a1a27c8..17f0df94857 100644 --- a/src/shortcut_layer.c +++ b/src/shortcut_layer.c @@ -283,7 +283,7 @@ void update_shortcut_layer_gpu(layer l, int batch, float learning_rate_init, flo void pull_shortcut_layer(layer l) { - constrain_weight_updates_ongpu(l.nweights, 1, l.weights_gpu, l.weight_updates_gpu); + constrain_ongpu(l.nweights, 1, l.weight_updates_gpu, 1); cuda_pull_array_async(l.weight_updates_gpu, l.weight_updates, l.nweights); cuda_pull_array_async(l.weights_gpu, l.weights, l.nweights); CHECK_CUDA(cudaPeekAtLastError()); From 92e6e8eece3b789616430cad51a5afdb5a3153fc Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 15 Mar 2020 18:08:00 +0300 Subject: [PATCH 0879/1365] Minor fix --- src/detector.c | 2 +- src/yolo_layer.c | 3 +++ 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/src/detector.c b/src/detector.c index 92ce5864838..d403f38cdd9 100644 --- a/src/detector.c +++ b/src/detector.c @@ -191,7 +191,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i if (dim_w < net.resize_step) dim_w = net.resize_step; if (dim_h < net.resize_step) dim_h = net.resize_step; int dim_b = (init_b * max_dim_w * max_dim_h) / (dim_w * dim_h); - int new_dim_b = (int)(dim_b * 0.9); + int new_dim_b = (int)(dim_b * 0.8); if (new_dim_b > init_b) dim_b = new_dim_b; args.w = dim_w; diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 31fe7249f61..1ea6bf4d3f5 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -541,6 +541,9 @@ void forward_yolo_layer(const layer l, network_state state) } } + if (count == 0) count = 1; + if (class_count == 0) class_count = 1; + //*(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); //printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d\n", state.index, avg_iou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count); From b77add049e2203892c044dfd08f801ce5af49778 Mon Sep 17 00:00:00 2001 From: Muhammad Maaz Date: Thu, 19 Mar 2020 13:14:19 +0500 Subject: [PATCH 0880/1365] Logic Change: Implements rolling avg for remaining time calculation in detector.c --- src/detector.c | 19 ++++++++----------- 1 file changed, 8 insertions(+), 11 deletions(-) diff --git a/src/detector.c b/src/detector.c index ca1c157f101..3ececa5f677 100644 --- a/src/detector.c +++ b/src/detector.c @@ -165,7 +165,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i pthread_t load_thread = load_data(args); int count = 0; - double start, end, tmp, time_remaining[1000] = {0}; + double start, end, time_remaining, avg_t_minus_1, avg_t, alpha = 0.01; start = what_time_is_it_now(); //while(i*imgs < N*120){ @@ -305,18 +305,15 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i } #ifdef OPENCV end = what_time_is_it_now(); - time_remaining[i%1000] = (net.max_batches - i)*(end - start) / 60 / 60; - tmp = 0.0; - int j, count = 0; - if (i > 50){ - for (j = 0; j < 1000; ++j){ - if (time_remaining[j] != 0){ - tmp += time_remaining[j]; - count++; - } + time_remaining = (net.max_batches - i)*(end - start) / 60 / 60; + if (i > 1){ // ignore the first iteration + if (i == 2){ + avg_t_minus_1 = time_remaining; } + avg_t = alpha * time_remaining + (1 - alpha) * avg_t_minus_1; + avg_t_minus_1 = avg_t; } - draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, mean_average_precision, draw_precision, "mAP%", dont_show, mjpeg_port, tmp/count); + draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, mean_average_precision, draw_precision, "mAP%", dont_show, mjpeg_port, avg_t); start = what_time_is_it_now(); #endif // OPENCV From d6253d5cd714160784034fa3b3f3c3970c0d8cad Mon Sep 17 00:00:00 2001 From: Muhammad Maaz Date: Thu, 19 Mar 2020 13:19:30 +0500 Subject: [PATCH 0881/1365] Logic Change: Implements rolling avg for remaining time calculation in classifier.c --- src/classifier.c | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index 5f5ab1bff34..4cfbedb0c30 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -133,7 +133,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, float topk = 0; int count = 0; - double start, end, tmp, time_remaining[1000] = {0}; + double start, end, time_remaining, avg_t_minus_1, avg_t, alpha = 0.01; start = what_time_is_it_now(); while(get_current_batch(net) < net.max_batches || net.max_batches == 0){ @@ -187,16 +187,15 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); #ifdef OPENCV end = what_time_is_it_now(); - time_remaining[i%1000] = (net.max_batches - i)*(end - start) / 60 / 60; - tmp = 0.0; - int j, count = 0; - for (j = 0; j < 1000; ++j){ - if (time_remaining[j] != 0){ - tmp += time_remaining[j]; - count++; + time_remaining = (net.max_batches - i)*(end - start) / 60 / 60; + if (i > 1){ // ignore the first iteration + if (i == 2){ + avg_t_minus_1 = time_remaining; } + avg_t = alpha * time_remaining + (1 - alpha) * avg_t_minus_1; + avg_t_minus_1 = avg_t; } - if (!dontuse_opencv) draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, topk_buff, dont_show, mjpeg_port, tmp/count); + if (!dontuse_opencv) draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, topk_buff, dont_show, mjpeg_port, avg_t); start = what_time_is_it_now(); #endif // OPENCV From 1a6dd593c542b767147fabd796136040fc64076b Mon Sep 17 00:00:00 2001 From: Muhammad Maaz Date: Thu, 19 Mar 2020 17:15:42 +0500 Subject: [PATCH 0882/1365] Fix: Use iteration instead of i for current iteration. Simplified the time remaining calculation logic. --- src/detector.c | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git a/src/detector.c b/src/detector.c index 08874179ce6..81c4c39bc5a 100644 --- a/src/detector.c +++ b/src/detector.c @@ -169,8 +169,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i pthread_t load_thread = load_data(args); int count = 0; - double start, end, time_remaining, avg_t_minus_1, avg_t, alpha = 0.01; - start = what_time_is_it_now(); + double time_remaining, avg_t_minus_1, avg_t, alpha = 0.01; //while(i*imgs < N*120){ while (get_current_iteration(net) < net.max_batches) { @@ -345,17 +344,15 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i draw_precision = 1; } #ifdef OPENCV - end = what_time_is_it_now(); - time_remaining = (net.max_batches - i)*(end - start) / 60 / 60; - if (i > 1){ // ignore the first iteration - if (i == 2){ + time_remaining = (net.max_batches - iteration)*(what_time_is_it_now() - time + load_time) / 60 / 60; + if (iteration > 1){ // ignore the first iteration + if (iteration == 2){ avg_t_minus_1 = time_remaining; } avg_t = alpha * time_remaining + (1 - alpha) * avg_t_minus_1; avg_t_minus_1 = avg_t; } - draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, mean_average_precision, draw_precision, "mAP%", dont_show, mjpeg_port, avg_t); - start = what_time_is_it_now(); + draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, iteration, net.max_batches, mean_average_precision, draw_precision, "mAP%", dont_show, mjpeg_port, avg_t); #endif // OPENCV //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) { From 570f595247db2373e0ee23fe0d1af76697fd88df Mon Sep 17 00:00:00 2001 From: Muhammad Maaz Date: Fri, 20 Mar 2020 12:42:59 +0500 Subject: [PATCH 0883/1365] Remove redundant code from classifier.c --- src/classifier.c | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index a0c5f734049..41acca2a510 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -136,7 +136,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, float topk = 0; int count = 0; - double start, end, time_remaining, avg_t_minus_1, avg_t, alpha = 0.01; + double start, time_remaining, avg_t_minus_1, avg_t, alpha = 0.01; start = what_time_is_it_now(); while(get_current_batch(net) < net.max_batches || net.max_batches == 0){ @@ -189,8 +189,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); #ifdef OPENCV - end = what_time_is_it_now(); - time_remaining = (net.max_batches - i)*(end - start) / 60 / 60; + time_remaining = (net.max_batches - i)*(what_time_is_it_now() - start) / 60 / 60; if (i > 1){ // ignore the first iteration if (i == 2){ avg_t_minus_1 = time_remaining; From 5c2ddd301e51d60a700797d58bb2e8e686b4bd47 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 22 Mar 2020 17:38:07 +0300 Subject: [PATCH 0884/1365] Added param to cfg: [net] gaussian_noise=40 --- .../csresnext50-panet-spp-original-optimal.cfg | 8 ++------ cfg/csresnext50-panet-spp-original-optimal.cfg | 8 ++------ include/darknet.h | 2 ++ scripts/README.md | 4 ++++ src/dark_cuda.c | 4 ++-- src/data.c | 13 ++++++++----- src/data.h | 2 +- src/detector.c | 1 + src/image_opencv.cpp | 15 ++++++++++++++- src/image_opencv.h | 2 +- src/parser.c | 1 + 11 files changed, 38 insertions(+), 22 deletions(-) diff --git a/build/darknet/x64/cfg/csresnext50-panet-spp-original-optimal.cfg b/build/darknet/x64/cfg/csresnext50-panet-spp-original-optimal.cfg index 5722cf69828..148f8b3b472 100644 --- a/build/darknet/x64/cfg/csresnext50-panet-spp-original-optimal.cfg +++ b/build/darknet/x64/cfg/csresnext50-panet-spp-original-optimal.cfg @@ -863,11 +863,9 @@ scale_x_y = 1.2 iou_thresh=0.213 cls_normalizer=1.0 iou_normalizer=0.07 -uc_normalizer=0.07 iou_loss=ciou nms_kind=greedynms beta_nms=0.6 -beta1=0.6 [route] layers = -4 @@ -952,11 +950,10 @@ scale_x_y = 1.1 iou_thresh=0.213 cls_normalizer=1.0 iou_normalizer=0.07 -uc_normalizer=0.07 iou_loss=ciou nms_kind=greedynms beta_nms=0.6 -beta1=0.6 + [route] layers = -4 @@ -1041,8 +1038,7 @@ scale_x_y = 1.05 iou_thresh=0.213 cls_normalizer=1.0 iou_normalizer=0.07 -uc_normalizer=0.07 iou_loss=ciou nms_kind=greedynms beta_nms=0.6 -beta1=0.6 + diff --git a/cfg/csresnext50-panet-spp-original-optimal.cfg b/cfg/csresnext50-panet-spp-original-optimal.cfg index 5722cf69828..148f8b3b472 100644 --- a/cfg/csresnext50-panet-spp-original-optimal.cfg +++ b/cfg/csresnext50-panet-spp-original-optimal.cfg @@ -863,11 +863,9 @@ scale_x_y = 1.2 iou_thresh=0.213 cls_normalizer=1.0 iou_normalizer=0.07 -uc_normalizer=0.07 iou_loss=ciou nms_kind=greedynms beta_nms=0.6 -beta1=0.6 [route] layers = -4 @@ -952,11 +950,10 @@ scale_x_y = 1.1 iou_thresh=0.213 cls_normalizer=1.0 iou_normalizer=0.07 -uc_normalizer=0.07 iou_loss=ciou nms_kind=greedynms beta_nms=0.6 -beta1=0.6 + [route] layers = -4 @@ -1041,8 +1038,7 @@ scale_x_y = 1.05 iou_thresh=0.213 cls_normalizer=1.0 iou_normalizer=0.07 -uc_normalizer=0.07 iou_loss=ciou nms_kind=greedynms beta_nms=0.6 -beta1=0.6 + diff --git a/include/darknet.h b/include/darknet.h index ecc064020d1..f312dc2821f 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -691,6 +691,7 @@ typedef struct network { float min_ratio; int center; int flip; // horizontal flip 50% probability augmentaiont for classifier training (default = 1) + int gaussian_noise; int blur; int mixup; float label_smooth_eps; @@ -870,6 +871,7 @@ typedef struct load_args { int dontuse_opencv; float jitter; int flip; + int gaussian_noise; int blur; int mixup; float label_smooth_eps; diff --git a/scripts/README.md b/scripts/README.md index 1cefec957e7..1b184122a3b 100644 --- a/scripts/README.md +++ b/scripts/README.md @@ -2,6 +2,10 @@ ### Datasets: +59.26TB of research data: http://academictorrents.com/ + +ImageNet Torrent: http://academictorrents.com/browse.php?search=imagenet&page=0 + 25 thousand datasets on Kaggle: https://www.kaggle.com/datasets BDD100K - Diverse Driving Video: https://bair.berkeley.edu/blog/2018/05/30/bdd/ diff --git a/src/dark_cuda.c b/src/dark_cuda.c index e75943098f6..695925d8ac5 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -465,7 +465,7 @@ void cuda_push_array(float *x_gpu, float *x, size_t n) { size_t size = sizeof(float)*n; //cudaError_t status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice); - cudaError_t status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyDefault, get_cuda_stream()); + cudaError_t status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyHostToDevice, get_cuda_stream()); CHECK_CUDA(status); } @@ -473,7 +473,7 @@ void cuda_pull_array(float *x_gpu, float *x, size_t n) { size_t size = sizeof(float)*n; //cudaError_t status = cudaMemcpy(x, x_gpu, size, cudaMemcpyDeviceToHost); - cudaError_t status = cudaMemcpyAsync(x, x_gpu, size, cudaMemcpyDefault, get_cuda_stream()); + cudaError_t status = cudaMemcpyAsync(x, x_gpu, size, cudaMemcpyDeviceToHost, get_cuda_stream()); CHECK_CUDA(status); cudaStreamSynchronize(get_cuda_stream()); } diff --git a/src/data.c b/src/data.c index a103cc5a29f..f9d4d7df833 100644 --- a/src/data.c +++ b/src/data.c @@ -945,7 +945,7 @@ void blend_truth_mosaic(float *new_truth, int boxes, float *old_truth, int w, in #include "http_stream.h" -data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int use_blur, int use_mixup, +data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int use_gaussian_noise, int use_blur, int use_mixup, float jitter, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int show_imgs) { const int random_index = random_gen(); @@ -982,7 +982,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo float r1 = 0, r2 = 0, r3 = 0, r4 = 0, r_scale = 0; float dhue = 0, dsat = 0, dexp = 0, flip = 0, blur = 0; - int augmentation_calculated = 0; + int augmentation_calculated = 0, gaussian_noise = 0; d.y = make_matrix(n, 5*boxes); int i_mixup = 0; @@ -1036,6 +1036,9 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo else if (tmp_blur == 1) blur = 1; else blur = use_blur; } + + if (use_gaussian_noise && rand_int(0, 1) == 1) gaussian_noise = use_gaussian_noise; + else gaussian_noise = 0; } int pleft = rand_precalc_random(-dw, dw, r1); @@ -1085,7 +1088,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo if ((min_w_h / 8) < blur && blur > 1) blur = min_w_h / 8; // disable blur if one of the objects is too small image ai = image_data_augmentation(src, w, h, pleft, ptop, swidth, sheight, flip, dhue, dsat, dexp, - blur, boxes, truth); + gaussian_noise, blur, boxes, truth); if (use_mixup == 0) { d.X.vals[i] = ai.data; @@ -1205,7 +1208,7 @@ void blend_images(image new_img, float alpha, image old_img, float beta) new_img.data[i] = new_img.data[i] * alpha + old_img.data[i] * beta; } -data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int use_blur, int use_mixup, float jitter, +data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int gaussian_noise, int use_blur, int use_mixup, float jitter, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int show_imgs) { const int random_index = random_gen(); @@ -1395,7 +1398,7 @@ void *load_thread(void *ptr) } else if (a.type == REGION_DATA){ *a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure); } else if (a.type == DETECTION_DATA){ - *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.c, a.num_boxes, a.classes, a.flip, a.blur, a.mixup, a.jitter, + *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.c, a.num_boxes, a.classes, a.flip, a.gaussian_noise, a.blur, a.mixup, a.jitter, a.hue, a.saturation, a.exposure, a.mini_batch, a.track, a.augment_speed, a.letter_box, a.show_imgs); } else if (a.type == SWAG_DATA){ *a.d = load_data_swag(a.paths, a.n, a.classes, a.jitter); diff --git a/src/data.h b/src/data.h index e430363db01..b15a1ac9d86 100644 --- a/src/data.h +++ b/src/data.h @@ -86,7 +86,7 @@ void print_letters(float *pred, int n); data load_data_captcha(char **paths, int n, int m, int k, int w, int h); data load_data_captcha_encode(char **paths, int n, int m, int w, int h); data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h); -data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int use_blur, int use_mixup, +data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int gaussian_noise, int use_blur, int use_mixup, float jitter, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int show_imgs); data load_data_tag(char **paths, int n, int m, int k, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure); matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int dontuse_opencv); diff --git a/src/detector.c b/src/detector.c index d403f38cdd9..56d3d70687e 100644 --- a/src/detector.c +++ b/src/detector.c @@ -136,6 +136,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i args.threads = 64; // 16 or 64 args.angle = net.angle; + args.gaussian_noise = net.gaussian_noise; args.blur = net.blur; args.mixup = net.mixup; args.exposure = net.exposure; diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 5f7b72af7ab..6a96872b819 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1143,7 +1143,7 @@ extern "C" void draw_train_loss(char *windows_name, mat_cv* img_src, int img_siz extern "C" image image_data_augmentation(mat_cv* mat, int w, int h, int pleft, int ptop, int swidth, int sheight, int flip, float dhue, float dsat, float dexp, - int blur, int num_boxes, float *truth) + int gaussian_noise, int blur, int num_boxes, float *truth) { image out; try { @@ -1248,6 +1248,19 @@ extern "C" image image_data_augmentation(mat_cv* mat, int w, int h, dst.copyTo(sized); } + if (gaussian_noise) { + cv::Mat noise = cv::Mat(sized.size(), sized.type()); + gaussian_noise = std::min(gaussian_noise, 127); + gaussian_noise = std::max(gaussian_noise, 0); + cv::randn(noise, 0, gaussian_noise); //mean and variance + cv::Mat sized_norm = sized + noise; + //cv::normalize(sized_norm, sized_norm, 0.0, 255.0, cv::NORM_MINMAX, sized.type()); + //cv::imshow("source", sized); + //cv::imshow("gaussian noise", sized_norm); + //cv::waitKey(0); + sized = sized_norm; + } + //char txt[100]; //sprintf(txt, "blur = %d", blur); //cv::putText(sized, txt, cv::Point(100, 100), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.7, CV_RGB(255, 0, 0), 1, CV_AA); diff --git a/src/image_opencv.h b/src/image_opencv.h index 136b7388faa..416ec593f80 100644 --- a/src/image_opencv.h +++ b/src/image_opencv.h @@ -100,7 +100,7 @@ void draw_train_loss(char *windows_name, mat_cv* img, int img_size, float avg_lo image image_data_augmentation(mat_cv* mat, int w, int h, int pleft, int ptop, int swidth, int sheight, int flip, float dhue, float dsat, float dexp, - int blur, int num_boxes, float *truth); + int gaussian_noise, int blur, int num_boxes, float *truth); // blend two images with (alpha and beta) void blend_images_cv(image new_img, float alpha, image old_img, float beta); diff --git a/src/parser.c b/src/parser.c index a2084378999..91ad1988b42 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1076,6 +1076,7 @@ void parse_net_options(list *options, network *net) net->min_crop = option_find_int_quiet(options, "min_crop",net->w); net->flip = option_find_int_quiet(options, "flip", 1); net->blur = option_find_int_quiet(options, "blur", 0); + net->gaussian_noise = option_find_int_quiet(options, "gaussian_noise", 0); net->mixup = option_find_int_quiet(options, "mixup", 0); int cutmix = option_find_int_quiet(options, "cutmix", 0); int mosaic = option_find_int_quiet(options, "mosaic", 0); From bd7690b019e524624563f2d3e4db656034e7a40f Mon Sep 17 00:00:00 2001 From: Muhammad Maaz Date: Sun, 22 Mar 2020 20:38:47 +0500 Subject: [PATCH 0885/1365] Fix: Correct remaining training time in case someone resume training (src/detector.c) --- src/detector.c | 16 ++++++---------- 1 file changed, 6 insertions(+), 10 deletions(-) diff --git a/src/detector.c b/src/detector.c index 81c4c39bc5a..f14762a1773 100644 --- a/src/detector.c +++ b/src/detector.c @@ -169,7 +169,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i pthread_t load_thread = load_data(args); int count = 0; - double time_remaining, avg_t_minus_1, avg_t, alpha = 0.01; + double time_remaining, avg_time = -1, alpha_time = 0.01; //while(i*imgs < N*120){ while (get_current_iteration(net) < net.max_batches) { @@ -343,16 +343,12 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i draw_precision = 1; } -#ifdef OPENCV time_remaining = (net.max_batches - iteration)*(what_time_is_it_now() - time + load_time) / 60 / 60; - if (iteration > 1){ // ignore the first iteration - if (iteration == 2){ - avg_t_minus_1 = time_remaining; - } - avg_t = alpha * time_remaining + (1 - alpha) * avg_t_minus_1; - avg_t_minus_1 = avg_t; - } - draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, iteration, net.max_batches, mean_average_precision, draw_precision, "mAP%", dont_show, mjpeg_port, avg_t); + // set initial value, even if resume training from 10000 iteration + if (avg_time < 0) avg_time = time_remaining; + else avg_time = alpha_time * time_remaining + (1 - alpha_time) * avg_time; +#ifdef OPENCV + draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, iteration, net.max_batches, mean_average_precision, draw_precision, "mAP%", dont_show, mjpeg_port, avg_time); #endif // OPENCV //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) { From 8800212faa8afd86224be1c6cdaba4639e61677b Mon Sep 17 00:00:00 2001 From: Muhammad Maaz Date: Sun, 22 Mar 2020 20:40:26 +0500 Subject: [PATCH 0886/1365] Adds support to print remaining training time on console if darknet is not compiled with OpenCV --- src/detector.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/detector.c b/src/detector.c index f14762a1773..de98129b238 100644 --- a/src/detector.c +++ b/src/detector.c @@ -292,7 +292,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i if (iteration < net.burn_in * 3) fprintf(stderr, "\n Tensor Cores are disabled until the first %d iterations are reached.", 3 * net.burn_in); else fprintf(stderr, "\n Tensor Cores are used."); } - printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", iteration, loss, avg_loss, get_current_rate(net), (what_time_is_it_now() - time), iteration*imgs); + printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images, %f time left\n", iteration, loss, avg_loss, get_current_rate(net), (what_time_is_it_now() - time), iteration*imgs, avg_time); int draw_precision = 0; if (calc_map && (iteration >= next_map_calc || iteration == net.max_batches)) { From 9407a81c9d1c48a9cd8d23346ddff769565d6c27 Mon Sep 17 00:00:00 2001 From: Muhammad Maaz Date: Sun, 22 Mar 2020 20:49:21 +0500 Subject: [PATCH 0887/1365] Fix: Correct calculation of training time remaininig when resume training (src/classifier.c) Output training time remaining to console if darknet is not compiled with OpenCV --- src/classifier.c | 18 +++++++----------- 1 file changed, 7 insertions(+), 11 deletions(-) diff --git a/src/classifier.c b/src/classifier.c index 41acca2a510..1372cc0edf9 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -136,7 +136,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, float topk = 0; int count = 0; - double start, time_remaining, avg_t_minus_1, avg_t, alpha = 0.01; + double start, time_remaining, avg_time = -1, alpha_time = 0.01; start = what_time_is_it_now(); while(get_current_batch(net) < net.max_batches || net.max_batches == 0){ @@ -187,18 +187,14 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, draw_precision = 1; } - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); -#ifdef OPENCV time_remaining = (net.max_batches - i)*(what_time_is_it_now() - start) / 60 / 60; - if (i > 1){ // ignore the first iteration - if (i == 2){ - avg_t_minus_1 = time_remaining; - } - avg_t = alpha * time_remaining + (1 - alpha) * avg_t_minus_1; - avg_t_minus_1 = avg_t; - } - if (!dontuse_opencv) draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, topk_buff, dont_show, mjpeg_port, avg_t); + // set initial value, even if resume training from 10000 iteration + if (avg_time < 0) avg_time = time_remaining; + else avg_time = alpha_time * time_remaining + (1 - alpha_time) * avg_time; start = what_time_is_it_now(); + printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images, %f time left\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen, avg_time); +#ifdef OPENCV + if (!dontuse_opencv) draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, topk_buff, dont_show, mjpeg_port, avg_time); #endif // OPENCV if (i >= (iter_save + 1000)) { From 864d1062f875e7fb61be053c784d86f9105d4c6d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 23 Mar 2020 00:29:37 +0300 Subject: [PATCH 0888/1365] Minor fix --- src/detector.c | 1 + src/image_opencv.cpp | 73 +++++++++++++++++++++++--------------------- 2 files changed, 39 insertions(+), 35 deletions(-) diff --git a/src/detector.c b/src/detector.c index 8e02889fa51..28ae51758c2 100644 --- a/src/detector.c +++ b/src/detector.c @@ -42,6 +42,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i cuda_set_device(gpus[0]); printf(" Prepare additional network for mAP calculation...\n"); net_map = parse_network_cfg_custom(cfgfile, 1, 1); + net_map.benchmark_layers = benchmark_layers; const int net_classes = net_map.layers[net_map.n - 1].classes; int k; // free memory unnecessary arrays diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 7d1f22bd0e3..ccf55de8cab 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1005,11 +1005,6 @@ extern "C" void draw_detections_cv_v3(mat_cv* mat, detection *dets, int num, flo // ==================================================================== extern "C" mat_cv* draw_train_chart(char *windows_name, float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show, char* chart_path) { - if (chart_path != NULL && chart_path[0] != '\0'){ - cv::Mat* img_ptr = new cv::Mat(cv::imread(chart_path)); - return (mat_cv*)img_ptr; - } - int img_offset = 60; int draw_size = img_size - img_offset; cv::Mat *img_ptr = new cv::Mat(img_size, img_size, CV_8UC3, CV_RGB(255, 255, 255)); @@ -1017,40 +1012,48 @@ extern "C" mat_cv* draw_train_chart(char *windows_name, float max_img_loss, int cv::Point pt1, pt2, pt_text; try { - char char_buff[100]; - int i; - // vertical lines - pt1.x = img_offset; pt2.x = img_size, pt_text.x = 30; - for (i = 1; i <= number_of_lines; ++i) { - pt1.y = pt2.y = (float)i * draw_size / number_of_lines; - cv::line(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0); - if (i % 10 == 0) { - sprintf(char_buff, "%2.1f", max_img_loss*(number_of_lines - i) / number_of_lines); - pt_text.y = pt1.y + 3; - - cv::putText(img, char_buff, pt_text, cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); - cv::line(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0); - } + // load chart from file + if (chart_path != NULL && chart_path[0] != '\0') { + *img_ptr = cv::imread(chart_path); } - // horizontal lines - pt1.y = draw_size; pt2.y = 0, pt_text.y = draw_size + 15; - for (i = 0; i <= number_of_lines; ++i) { - pt1.x = pt2.x = img_offset + (float)i * draw_size / number_of_lines; - cv::line(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0); - if (i % 10 == 0) { - sprintf(char_buff, "%d", max_batches * i / number_of_lines); - pt_text.x = pt1.x - 20; - cv::putText(img, char_buff, pt_text, cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); - cv::line(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0); + else { + // draw new chart + char char_buff[100]; + int i; + // vertical lines + pt1.x = img_offset; pt2.x = img_size, pt_text.x = 30; + for (i = 1; i <= number_of_lines; ++i) { + pt1.y = pt2.y = (float)i * draw_size / number_of_lines; + cv::line(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0); + if (i % 10 == 0) { + sprintf(char_buff, "%2.1f", max_img_loss*(number_of_lines - i) / number_of_lines); + pt_text.y = pt1.y + 3; + + cv::putText(img, char_buff, pt_text, cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + cv::line(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0); + } + } + // horizontal lines + pt1.y = draw_size; pt2.y = 0, pt_text.y = draw_size + 15; + for (i = 0; i <= number_of_lines; ++i) { + pt1.x = pt2.x = img_offset + (float)i * draw_size / number_of_lines; + cv::line(img, pt1, pt2, CV_RGB(224, 224, 224), 1, 8, 0); + if (i % 10 == 0) { + sprintf(char_buff, "%d", max_batches * i / number_of_lines); + pt_text.x = pt1.x - 20; + cv::putText(img, char_buff, pt_text, cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + cv::line(img, pt1, pt2, CV_RGB(128, 128, 128), 1, 8, 0); + } } + + cv::putText(img, "Loss", cv::Point(10, 55), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 255), 1, CV_AA); + cv::putText(img, "Iteration number", cv::Point(draw_size / 2, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + char max_batches_buff[100]; + sprintf(max_batches_buff, "in cfg max_batches=%d", max_batches); + cv::putText(img, max_batches_buff, cv::Point(draw_size - 195, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); + cv::putText(img, "Press 's' to save : chart.png", cv::Point(5, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); } - cv::putText(img, "Loss", cv::Point(10, 55), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 255), 1, CV_AA); - cv::putText(img, "Iteration number", cv::Point(draw_size / 2, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); - char max_batches_buff[100]; - sprintf(max_batches_buff, "in cfg max_batches=%d", max_batches); - cv::putText(img, max_batches_buff, cv::Point(draw_size - 195, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); - cv::putText(img, "Press 's' to save : chart.png", cv::Point(5, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); if (!dont_show) { printf(" If error occurs - run training with flag: -dont_show \n"); cv::namedWindow(windows_name, cv::WINDOW_NORMAL); From bb5eb481d1b61cd47f9cec80ed23405932dd6103 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 23 Mar 2020 20:03:56 +0300 Subject: [PATCH 0889/1365] Minor fix --- src/blas_kernels.cu | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index a35177b509a..a5b952a7c75 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -482,8 +482,9 @@ __global__ void scal_add_kernel(int N, float ALPHA, float BETA, float *X, int IN __global__ void fill_kernel(int N, float ALPHA, float *X, int INCX) { - int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; - if(i < N) X[i*INCX] = ALPHA; + const int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index >= N) return; + X[index*INCX] = ALPHA; } __global__ void mask_kernel_new_api(int n, float *x, float mask_num, float *mask, float val) @@ -810,7 +811,9 @@ extern "C" void supp_ongpu(int N, float ALPHA, float * X, int INCX) extern "C" void fill_ongpu(int N, float ALPHA, float * X, int INCX) { - fill_kernel<<>>(N, ALPHA, X, INCX); + //fill_kernel<<>>(N, ALPHA, X, INCX); + //CHECK_CUDA(cudaPeekAtLastError()); + fill_kernel << > >(N, ALPHA, X, INCX); CHECK_CUDA(cudaPeekAtLastError()); } From a234a5022333c930de08f2470184ef4e0c68356e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 23 Mar 2020 21:56:32 +0300 Subject: [PATCH 0890/1365] Added efficientnet-lite3.cfg and activation=relu6 --- build/darknet/x64/cfg/efficientnet-lite3.cfg | 1009 ++++++++++++++++++ cfg/efficientnet-lite3.cfg | 1009 ++++++++++++++++++ include/darknet.h | 2 +- src/activation_kernels.cu | 25 +- src/activations.c | 1 + src/activations.h | 3 + 6 files changed, 2047 insertions(+), 2 deletions(-) create mode 100644 build/darknet/x64/cfg/efficientnet-lite3.cfg create mode 100644 cfg/efficientnet-lite3.cfg diff --git a/build/darknet/x64/cfg/efficientnet-lite3.cfg b/build/darknet/x64/cfg/efficientnet-lite3.cfg new file mode 100644 index 00000000000..e76bbe3085d --- /dev/null +++ b/build/darknet/x64/cfg/efficientnet-lite3.cfg @@ -0,0 +1,1009 @@ +# https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/lite/efficientnet_lite_builder.py +# (width_coefficient, depth_coefficient, resolution, dropout_rate) +# 'efficientnet-lite3': (1.2, 1.4, 280, 0.3), +# +#_DEFAULT_BLOCKS_ARGS = [ +# 'r1_k3_s11_e1_i32_o16_se0.25', 'r2_k3_s22_e6_i16_o24_se0.25', +# 'r2_k5_s22_e6_i24_o40_se0.25', 'r3_k3_s22_e6_i40_o80_se0.25', +# 'r3_k5_s11_e6_i80_o112_se0.25', 'r4_k5_s22_e6_i112_o192_se0.25', +# 'r1_k3_s11_e6_i192_o320_se0.25', +#] + +[net] +# Training +batch=120 +subdivisions=6 +height=288 +width=288 +channels=3 +momentum=0.9 +decay=0.0005 +max_crop=320 + +cutmix=1 +mosaic=1 +label_smooth_eps=0.1 + +burn_in=1000 +learning_rate=0.256 +policy=step +step=10000 +scale=0.96 +max_batches=1600000 +momentum=0.9 +decay=0.00005 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 + + +### CONV1 - 1 (1) +# conv1 +[convolutional] +filters=40 #32 +size=3 +pad=1 +stride=2 +batch_normalize=1 +activation=relu6 + + +### CONV2 - MBConv1 - 1 (2) +# conv2_1_expand +[convolutional] +filters=40 #32 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv2_1_dwise +[convolutional] +groups=40 #32 +filters=40 #32 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv2_1_linear +[convolutional] +filters=16 #16 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV2 - MBConv1 - 2 (2) +# conv2_1_expand +[convolutional] +filters=40 #32 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv2_1_dwise +[convolutional] +groups=40 #32 +filters=40 #32 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv2_1_linear +[convolutional] +filters=16 #16 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV3 - MBConv6 - 1 (3) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_3_1 +[shortcut] +from=-5 +activation=linear + +# conv2_2_expand +[convolutional] +filters=112 #96 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv2_2_dwise +[convolutional] +groups=112 #96 +filters=112 #96 +size=3 +pad=1 +stride=2 +batch_normalize=1 +activation=relu6 + +# conv2_2_linear +[convolutional] +filters=32 #24 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV3 - MBConv6 - 2 (3) +# conv3_1_expand +[convolutional] +filters=176 #144 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv3_1_dwise +[convolutional] +groups=176 #144 +filters=176 #144 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv3_1_linear +[convolutional] +filters=32 #24 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV3 - MBConv6 - 3 (3) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_3_1 +[shortcut] +from=-5 +activation=linear + +# conv3_1_expand +[convolutional] +filters=176 #144 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv3_1_dwise +[convolutional] +groups=176 #144 +filters=176 #144 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv3_1_linear +[convolutional] +filters=32 #24 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV4 - MBConv6 - 1 (3) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_3_1 +[shortcut] +from=-5 +activation=linear + +# conv_3_2_expand +[convolutional] +filters=176 #144 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_3_2_dwise +[convolutional] +groups=176 #144 +filters=176 #144 +size=5 +pad=1 +stride=2 +batch_normalize=1 +activation=relu6 + +# conv_3_2_linear +[convolutional] +filters=48 #40 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV4 - MBConv6 - 2 (3) +# conv_4_1_expand +[convolutional] +filters=232 #192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_4_1_dwise +[convolutional] +groups=232 #192 +filters=232 #192 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_4_1_linear +[convolutional] +filters=48 #40 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV4 - MBConv6 - 3 (3) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_4_2 +[shortcut] +from=-5 +activation=linear + +# conv_4_1_expand +[convolutional] +filters=232 #192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_4_1_dwise +[convolutional] +groups=232 #192 +filters=232 #192 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_4_1_linear +[convolutional] +filters=48 #40 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + + +### CONV5 - MBConv6 - 1 (5) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_4_2 +[shortcut] +from=-5 +activation=linear + +# conv_4_3_expand +[convolutional] +filters=232 #192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_4_3_dwise +[convolutional] +groups=232 #192 +filters=232 #192 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_4_3_linear +[convolutional] +filters=96 #80 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV5 - MBConv6 - 2 (5) +# conv_4_4_expand +[convolutional] +filters=464 #384 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_4_4_dwise +[convolutional] +groups=464 #384 +filters=464 #384 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_4_4_linear +[convolutional] +filters=96 #80 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV5 - MBConv6 - 3 (5) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_4_4 +[shortcut] +from=-5 +activation=linear + +# conv_4_5_expand +[convolutional] +filters=464 #384 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_4_5_dwise +[convolutional] +groups=464 #384 +filters=464 #384 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_4_5_linear +[convolutional] +filters=96 #80 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV5 - MBConv6 - 4 (5) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_4_4 +[shortcut] +from=-5 +activation=linear + +# conv_4_5_expand +[convolutional] +filters=464 #384 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_4_5_dwise +[convolutional] +groups=464 #384 +filters=464 #384 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_4_5_linear +[convolutional] +filters=96 #80 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV5 - MBConv6 - 5 (5) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_4_4 +[shortcut] +from=-5 +activation=linear + +# conv_4_5_expand +[convolutional] +filters=464 #384 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_4_5_dwise +[convolutional] +groups=464 #384 +filters=464 #384 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_4_5_linear +[convolutional] +filters=96 #80 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV6 - MBConv6 - 1 (5) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_4_6 +[shortcut] +from=-5 +activation=linear + +# conv_4_7_expand +[convolutional] +filters=464 #384 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_4_7_dwise +[convolutional] +groups=464 #384 +filters=464 #384 +size=5 +pad=1 +stride=2 +batch_normalize=1 +activation=relu6 + +# conv_4_7_linear +[convolutional] +filters=136 #112 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV6 - MBConv6 - 2 (5) +# conv_5_1_expand +[convolutional] +filters=688 #576 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_5_1_dwise +[convolutional] +groups=688 #576 +filters=688 #576 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_5_1_linear +[convolutional] +filters=136 #112 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV6 - MBConv6 - 3 (5) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_5_1 +[shortcut] +from=-5 +activation=linear + +# conv_5_2_expand +[convolutional] +filters=688 #576 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_5_2_dwise +[convolutional] +groups=688 #576 +filters=688 #576 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_5_2_linear +[convolutional] +filters=136 #112 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV6 - MBConv6 - 4 (5) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_5_1 +[shortcut] +from=-5 +activation=linear + +# conv_5_2_expand +[convolutional] +filters=688 #576 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_5_2_dwise +[convolutional] +groups=688 #576 +filters=688 #576 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_5_2_linear +[convolutional] +filters=136 #112 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV6 - MBConv6 - 5 (5) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_5_1 +[shortcut] +from=-5 +activation=linear + +# conv_5_2_expand +[convolutional] +filters=688 #576 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_5_2_dwise +[convolutional] +groups=688 #576 +filters=688 #576 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_5_2_linear +[convolutional] +filters=136 #112 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV7 - MBConv6 - 1 (6) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_5_2 +[shortcut] +from=-5 +activation=linear + +# conv_5_3_expand +[convolutional] +filters=688 #576 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_5_3_dwise +[convolutional] +groups=688 #576 +filters=688 #576 +size=5 +pad=1 +stride=2 +batch_normalize=1 +activation=relu6 + + +# conv_5_3_linear +[convolutional] +filters=232 #192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 2 (6) +# conv_6_1_expand +[convolutional] +filters=1152 #960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_6_1_dwise +[convolutional] +groups=1152 #960 +filters=1152 #960 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_6_1_linear +[convolutional] +filters=232 #192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 3 (6) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_6_1 +[shortcut] +from=-5 +activation=linear + +# conv_6_2_expand +[convolutional] +filters=1152 #960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_6_2_dwise +[convolutional] +groups=1152 #960 +filters=1152 #960 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_6_2_linear +[convolutional] +filters=232 #192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 4 (6) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_6_1 +[shortcut] +from=-5 +activation=linear + +# conv_6_2_expand +[convolutional] +filters=1152 #960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_6_2_dwise +[convolutional] +groups=1152 #960 +filters=1152 #960 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_6_2_linear +[convolutional] +filters=232 #192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 5 (6) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_6_1 +[shortcut] +from=-5 +activation=linear + +# conv_6_2_expand +[convolutional] +filters=1152 #960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_6_2_dwise +[convolutional] +groups=1152 #960 +filters=1152 #960 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_6_2_linear +[convolutional] +filters=232 #192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 6 (6) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_6_1 +[shortcut] +from=-5 +activation=linear + +# conv_6_2_expand +[convolutional] +filters=1152 #960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_6_2_dwise +[convolutional] +groups=1152 #960 +filters=1152 #960 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + + + +# conv_6_2_linear +[convolutional] +filters=232 #192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV8 - MBConv6 - 1 (1) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_6_2 +[shortcut] +from=-5 +activation=linear + +# conv_6_3_expand +[convolutional] +filters=1152 #960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_6_3_dwise +[convolutional] +groups=1152 #960 +filters=1152 #960 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + + + +# conv_6_3_linear +[convolutional] +filters=384 #320 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + + +### CONV9 - Conv2d 1x1 +# conv_6_4 +[convolutional] +filters=1536 #1280 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + + +[avgpool] + +[dropout] +probability=.3 + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=0 +activation=linear + +[softmax] +groups=1 + +#[cost] +#type=sse + diff --git a/cfg/efficientnet-lite3.cfg b/cfg/efficientnet-lite3.cfg new file mode 100644 index 00000000000..e76bbe3085d --- /dev/null +++ b/cfg/efficientnet-lite3.cfg @@ -0,0 +1,1009 @@ +# https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/lite/efficientnet_lite_builder.py +# (width_coefficient, depth_coefficient, resolution, dropout_rate) +# 'efficientnet-lite3': (1.2, 1.4, 280, 0.3), +# +#_DEFAULT_BLOCKS_ARGS = [ +# 'r1_k3_s11_e1_i32_o16_se0.25', 'r2_k3_s22_e6_i16_o24_se0.25', +# 'r2_k5_s22_e6_i24_o40_se0.25', 'r3_k3_s22_e6_i40_o80_se0.25', +# 'r3_k5_s11_e6_i80_o112_se0.25', 'r4_k5_s22_e6_i112_o192_se0.25', +# 'r1_k3_s11_e6_i192_o320_se0.25', +#] + +[net] +# Training +batch=120 +subdivisions=6 +height=288 +width=288 +channels=3 +momentum=0.9 +decay=0.0005 +max_crop=320 + +cutmix=1 +mosaic=1 +label_smooth_eps=0.1 + +burn_in=1000 +learning_rate=0.256 +policy=step +step=10000 +scale=0.96 +max_batches=1600000 +momentum=0.9 +decay=0.00005 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 + + +### CONV1 - 1 (1) +# conv1 +[convolutional] +filters=40 #32 +size=3 +pad=1 +stride=2 +batch_normalize=1 +activation=relu6 + + +### CONV2 - MBConv1 - 1 (2) +# conv2_1_expand +[convolutional] +filters=40 #32 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv2_1_dwise +[convolutional] +groups=40 #32 +filters=40 #32 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv2_1_linear +[convolutional] +filters=16 #16 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV2 - MBConv1 - 2 (2) +# conv2_1_expand +[convolutional] +filters=40 #32 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv2_1_dwise +[convolutional] +groups=40 #32 +filters=40 #32 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv2_1_linear +[convolutional] +filters=16 #16 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV3 - MBConv6 - 1 (3) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_3_1 +[shortcut] +from=-5 +activation=linear + +# conv2_2_expand +[convolutional] +filters=112 #96 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv2_2_dwise +[convolutional] +groups=112 #96 +filters=112 #96 +size=3 +pad=1 +stride=2 +batch_normalize=1 +activation=relu6 + +# conv2_2_linear +[convolutional] +filters=32 #24 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV3 - MBConv6 - 2 (3) +# conv3_1_expand +[convolutional] +filters=176 #144 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv3_1_dwise +[convolutional] +groups=176 #144 +filters=176 #144 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv3_1_linear +[convolutional] +filters=32 #24 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV3 - MBConv6 - 3 (3) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_3_1 +[shortcut] +from=-5 +activation=linear + +# conv3_1_expand +[convolutional] +filters=176 #144 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv3_1_dwise +[convolutional] +groups=176 #144 +filters=176 #144 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv3_1_linear +[convolutional] +filters=32 #24 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV4 - MBConv6 - 1 (3) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_3_1 +[shortcut] +from=-5 +activation=linear + +# conv_3_2_expand +[convolutional] +filters=176 #144 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_3_2_dwise +[convolutional] +groups=176 #144 +filters=176 #144 +size=5 +pad=1 +stride=2 +batch_normalize=1 +activation=relu6 + +# conv_3_2_linear +[convolutional] +filters=48 #40 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV4 - MBConv6 - 2 (3) +# conv_4_1_expand +[convolutional] +filters=232 #192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_4_1_dwise +[convolutional] +groups=232 #192 +filters=232 #192 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_4_1_linear +[convolutional] +filters=48 #40 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV4 - MBConv6 - 3 (3) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_4_2 +[shortcut] +from=-5 +activation=linear + +# conv_4_1_expand +[convolutional] +filters=232 #192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_4_1_dwise +[convolutional] +groups=232 #192 +filters=232 #192 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_4_1_linear +[convolutional] +filters=48 #40 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + + +### CONV5 - MBConv6 - 1 (5) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_4_2 +[shortcut] +from=-5 +activation=linear + +# conv_4_3_expand +[convolutional] +filters=232 #192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_4_3_dwise +[convolutional] +groups=232 #192 +filters=232 #192 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_4_3_linear +[convolutional] +filters=96 #80 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV5 - MBConv6 - 2 (5) +# conv_4_4_expand +[convolutional] +filters=464 #384 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_4_4_dwise +[convolutional] +groups=464 #384 +filters=464 #384 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_4_4_linear +[convolutional] +filters=96 #80 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV5 - MBConv6 - 3 (5) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_4_4 +[shortcut] +from=-5 +activation=linear + +# conv_4_5_expand +[convolutional] +filters=464 #384 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_4_5_dwise +[convolutional] +groups=464 #384 +filters=464 #384 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_4_5_linear +[convolutional] +filters=96 #80 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV5 - MBConv6 - 4 (5) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_4_4 +[shortcut] +from=-5 +activation=linear + +# conv_4_5_expand +[convolutional] +filters=464 #384 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_4_5_dwise +[convolutional] +groups=464 #384 +filters=464 #384 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_4_5_linear +[convolutional] +filters=96 #80 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV5 - MBConv6 - 5 (5) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_4_4 +[shortcut] +from=-5 +activation=linear + +# conv_4_5_expand +[convolutional] +filters=464 #384 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_4_5_dwise +[convolutional] +groups=464 #384 +filters=464 #384 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_4_5_linear +[convolutional] +filters=96 #80 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV6 - MBConv6 - 1 (5) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_4_6 +[shortcut] +from=-5 +activation=linear + +# conv_4_7_expand +[convolutional] +filters=464 #384 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_4_7_dwise +[convolutional] +groups=464 #384 +filters=464 #384 +size=5 +pad=1 +stride=2 +batch_normalize=1 +activation=relu6 + +# conv_4_7_linear +[convolutional] +filters=136 #112 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV6 - MBConv6 - 2 (5) +# conv_5_1_expand +[convolutional] +filters=688 #576 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_5_1_dwise +[convolutional] +groups=688 #576 +filters=688 #576 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_5_1_linear +[convolutional] +filters=136 #112 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV6 - MBConv6 - 3 (5) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_5_1 +[shortcut] +from=-5 +activation=linear + +# conv_5_2_expand +[convolutional] +filters=688 #576 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_5_2_dwise +[convolutional] +groups=688 #576 +filters=688 #576 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_5_2_linear +[convolutional] +filters=136 #112 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV6 - MBConv6 - 4 (5) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_5_1 +[shortcut] +from=-5 +activation=linear + +# conv_5_2_expand +[convolutional] +filters=688 #576 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_5_2_dwise +[convolutional] +groups=688 #576 +filters=688 #576 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_5_2_linear +[convolutional] +filters=136 #112 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV6 - MBConv6 - 5 (5) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_5_1 +[shortcut] +from=-5 +activation=linear + +# conv_5_2_expand +[convolutional] +filters=688 #576 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_5_2_dwise +[convolutional] +groups=688 #576 +filters=688 #576 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_5_2_linear +[convolutional] +filters=136 #112 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV7 - MBConv6 - 1 (6) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_5_2 +[shortcut] +from=-5 +activation=linear + +# conv_5_3_expand +[convolutional] +filters=688 #576 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_5_3_dwise +[convolutional] +groups=688 #576 +filters=688 #576 +size=5 +pad=1 +stride=2 +batch_normalize=1 +activation=relu6 + + +# conv_5_3_linear +[convolutional] +filters=232 #192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 2 (6) +# conv_6_1_expand +[convolutional] +filters=1152 #960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_6_1_dwise +[convolutional] +groups=1152 #960 +filters=1152 #960 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_6_1_linear +[convolutional] +filters=232 #192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 3 (6) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_6_1 +[shortcut] +from=-5 +activation=linear + +# conv_6_2_expand +[convolutional] +filters=1152 #960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_6_2_dwise +[convolutional] +groups=1152 #960 +filters=1152 #960 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_6_2_linear +[convolutional] +filters=232 #192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 4 (6) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_6_1 +[shortcut] +from=-5 +activation=linear + +# conv_6_2_expand +[convolutional] +filters=1152 #960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_6_2_dwise +[convolutional] +groups=1152 #960 +filters=1152 #960 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_6_2_linear +[convolutional] +filters=232 #192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 5 (6) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_6_1 +[shortcut] +from=-5 +activation=linear + +# conv_6_2_expand +[convolutional] +filters=1152 #960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_6_2_dwise +[convolutional] +groups=1152 #960 +filters=1152 #960 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + +# conv_6_2_linear +[convolutional] +filters=232 #192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + +### CONV7 - MBConv6 - 6 (6) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_6_1 +[shortcut] +from=-5 +activation=linear + +# conv_6_2_expand +[convolutional] +filters=1152 #960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_6_2_dwise +[convolutional] +groups=1152 #960 +filters=1152 #960 +size=5 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + + + +# conv_6_2_linear +[convolutional] +filters=232 #192 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + +### CONV8 - MBConv6 - 1 (1) +# dropout only before residual connection +[dropout] +probability=.3 + +# block_6_2 +[shortcut] +from=-5 +activation=linear + +# conv_6_3_expand +[convolutional] +filters=1152 #960 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + +# conv_6_3_dwise +[convolutional] +groups=1152 #960 +filters=1152 #960 +size=3 +stride=1 +pad=1 +batch_normalize=1 +activation=relu6 + + + +# conv_6_3_linear +[convolutional] +filters=384 #320 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=linear + + + + +### CONV9 - Conv2d 1x1 +# conv_6_4 +[convolutional] +filters=1536 #1280 +size=1 +stride=1 +pad=0 +batch_normalize=1 +activation=relu6 + + +[avgpool] + +[dropout] +probability=.3 + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=0 +activation=linear + +[softmax] +groups=1 + +#[cost] +#type=sse + diff --git a/include/darknet.h b/include/darknet.h index 9637d7b7e5c..d6bc9ee106b 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -104,7 +104,7 @@ typedef struct tree { // activations.h typedef enum { - LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, SWISH, MISH, NORM_CHAN, NORM_CHAN_SOFTMAX, NORM_CHAN_SOFTMAX_MAXVAL + LOGISTIC, RELU, RELU6, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, SWISH, MISH, NORM_CHAN, NORM_CHAN_SOFTMAX, NORM_CHAN_SOFTMAX_MAXVAL }ACTIVATION; // parser.h diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index ef2ddbe4b7b..ba1113fdb34 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -7,7 +7,6 @@ #include "activations.h" #include "dark_cuda.h" - __device__ float lhtan_activate_kernel(float x) { if(x < 0) return .001*x; @@ -30,6 +29,7 @@ __device__ float linear_activate_kernel(float x){return x;} __device__ float logistic_activate_kernel(float x){return 1.f/(1.f + expf(-x));} __device__ float loggy_activate_kernel(float x){return 2.f/(1.f + expf(-x)) - 1;} __device__ float relu_activate_kernel(float x){return x*(x>0);} +__device__ float relu6_activate_kernel(float x) { return min_val_cmp(max_val_cmp(x, 0), 6); } __device__ float elu_activate_kernel(float x){return (x >= 0)*x + (x < 0)*(expf(x)-1);} __device__ float selu_activate_kernel(float x) { return (x >= 0)*1.0507f*x + (x < 0)*1.0507f*1.6732f*(expf(x) - 1); } __device__ float relie_activate_kernel(float x){return (x>0) ? x : .01f*x;} @@ -68,6 +68,7 @@ __device__ float loggy_gradient_kernel(float x) return 2*(1-y)*y; } __device__ float relu_gradient_kernel(float x){return (x>0);} +__device__ float relu6_gradient_kernel(float x) { return (x > 0 && x < 6); } __device__ float elu_gradient_kernel(float x){return (x >= 0) + (x < 0)*(x + 1);} __device__ float selu_gradient_kernel(float x) { return (x >= 0)*1.0507f + (x < 0)*(x + 1.0507f*1.6732f); } __device__ float relie_gradient_kernel(float x){return (x>0) ? 1 : .01f;} @@ -92,6 +93,8 @@ __device__ float activate_kernel(float x, ACTIVATION a) return loggy_activate_kernel(x); case RELU: return relu_activate_kernel(x); + case RELU6: + return relu6_activate_kernel(x); case ELU: return elu_activate_kernel(x); case SELU: @@ -127,6 +130,8 @@ __device__ float gradient_kernel(float x, ACTIVATION a) return loggy_gradient_kernel(x); case RELU: return relu_gradient_kernel(x); + case RELU6: + return relu6_gradient_kernel(x); case NORM_CHAN: return relu_gradient_kernel(x); case ELU: @@ -272,6 +277,14 @@ __global__ void activate_array_relu_kernel(float *x, int n) } } +__global__ void activate_array_relu6_kernel(float *x, int n) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < n) { + x[index] = relu6_activate_kernel(x[index]); + } +} + __global__ void gradient_array_kernel(float *x, int n, ACTIVATION a, float *delta) { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -362,6 +375,14 @@ __global__ void gradient_array_relu_kernel(float *x, int n, float *delta) } } +__global__ void gradient_array_relu6_kernel(float *x, int n, float *delta) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < n) { + delta[index] *= relu6_gradient_kernel(x[index]); + } +} + extern "C" void activate_array_ongpu(float *x, int n, ACTIVATION a) { const int num_blocks = get_number_of_blocks(n, BLOCK); @@ -371,6 +392,7 @@ extern "C" void activate_array_ongpu(float *x, int n, ACTIVATION a) else if (a == TANH) activate_array_tanh_kernel << > >(x, n); else if (a == HARDTAN) activate_array_hardtan_kernel << > >(x, n); else if (a == RELU) activate_array_relu_kernel << > >(x, n); + else if (a == RELU6) activate_array_relu6_kernel << > >(x, n); else if (a == SELU) activate_array_selu_kernel << > >(x, n); else activate_array_kernel<<>>(x, n, a); @@ -400,6 +422,7 @@ extern "C" void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta else if (a == TANH) gradient_array_tanh_kernel << > >(x, n, delta); else if (a == HARDTAN) gradient_array_hardtan_kernel << > >(x, n, delta); else if (a == RELU) gradient_array_relu_kernel << > >(x, n, delta); + else if (a == RELU6) gradient_array_relu6_kernel << > >(x, n, delta); //else if (a == NORM_CHAN) gradient_array_relu_kernel << > >(x, n, delta); else if (a == NORM_CHAN_SOFTMAX || a == NORM_CHAN) { printf(" Error: should be used custom NORM_CHAN_SOFTMAX-function for gradient \n"); diff --git a/src/activations.c b/src/activations.c index fd586e2bb4c..0b68fda0ff4 100644 --- a/src/activations.c +++ b/src/activations.c @@ -53,6 +53,7 @@ ACTIVATION get_activation(char *s) if (strcmp(s, "normalize_channels_softmax_maxval") == 0) return NORM_CHAN_SOFTMAX_MAXVAL; if (strcmp(s, "loggy")==0) return LOGGY; if (strcmp(s, "relu")==0) return RELU; + if (strcmp(s, "relu6") == 0) return RELU6; if (strcmp(s, "elu")==0) return ELU; if (strcmp(s, "selu") == 0) return SELU; if (strcmp(s, "relie")==0) return RELIE; diff --git a/src/activations.h b/src/activations.h index 16285c0450a..9e9b0053c6c 100644 --- a/src/activations.h +++ b/src/activations.h @@ -3,6 +3,7 @@ #include "darknet.h" #include "dark_cuda.h" #include "math.h" +#include "utils.h" //typedef enum{ // LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, SWISH, MISH @@ -56,6 +57,7 @@ static inline float linear_activate(float x){return x;} static inline float logistic_activate(float x){return 1.f/(1.f + expf(-x));} static inline float loggy_activate(float x){return 2.f/(1.f + expf(-x)) - 1;} static inline float relu_activate(float x){return x*(x>0);} +static inline float relu6_activate(float x) { return min_val_cmp(max_val_cmp(x, 0), 6); } static inline float elu_activate(float x){return (x >= 0)*x + (x < 0)*(expf(x)-1);} static inline float selu_activate(float x) { return (x >= 0)*1.0507f*x + (x < 0)*1.0507f*1.6732f*(expf(x) - 1); } static inline float relie_activate(float x){return (x>0) ? x : .01f*x;} @@ -105,6 +107,7 @@ static inline float stair_gradient(float x) return 1.0f; } static inline float relu_gradient(float x){return (x>0);} +static inline float relu6_gradient(float x) { return (x > 0 && x < 6); } static inline float elu_gradient(float x){return (x >= 0) + (x < 0)*(x + 1);} static inline float selu_gradient(float x) { return (x >= 0)*1.0507f + (x < 0)*(x + 1.0507f*1.6732f); } static inline float relie_gradient(float x){return (x>0) ? 1 : .01f;} From 5e904d5b1e26c16b97312dd97997cfa9d1f4df82 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 24 Mar 2020 22:23:15 +0300 Subject: [PATCH 0891/1365] adversarial attack - experimental --- include/darknet.h | 2 + src/blas.h | 1 + src/blas_kernels.cu | 11 ++ src/convolutional_kernels.cu | 2 + src/detector.c | 193 +++++++++++++++++++++++++++++++++++ src/image.c | 7 ++ src/image_opencv.cpp | 125 +++++++++++++++++++++++ src/image_opencv.h | 3 + src/network.h | 1 + src/network_kernels.cu | 15 +++ src/parser.c | 1 + src/yolo_layer.c | 7 ++ 12 files changed, 368 insertions(+) diff --git a/include/darknet.h b/include/darknet.h index d6bc9ee106b..464d3cf3a47 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -696,6 +696,7 @@ typedef struct network { int mixup; float label_smooth_eps; int resize_step; + int adversarial; int letter_box; float angle; float aspect; @@ -950,6 +951,7 @@ LIB_API void optimize_picture(network *net, image orig, int max_layer, float sca // image.h LIB_API image resize_image(image im, int w, int h); +LIB_API image quantize_image(image im); LIB_API void copy_image_from_bytes(image im, char *pdata); LIB_API image letterbox_image(image im, int w, int h); LIB_API void rgbgr_image(image im); diff --git a/src/blas.h b/src/blas.h index 2f91451611e..efbee945ab5 100644 --- a/src/blas.h +++ b/src/blas.h @@ -21,6 +21,7 @@ void test_blas(); void const_cpu(int N, float ALPHA, float *X, int INCX); void constrain_ongpu(int N, float ALPHA, float * X, int INCX); +void constrain_min_max_ongpu(int N, float MIN, float MAX, float * X, int INCX); void pow_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY); void mul_cpu(int N, float *X, int INCX, float *Y, int INCY); diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index a5b952a7c75..db862c24a4e 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -459,6 +459,11 @@ __global__ void constrain_kernel(int N, float ALPHA, float *X, int INCX) int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if(i < N) X[i*INCX] = fminf(ALPHA, fmaxf(-ALPHA, X[i*INCX])); } +__global__ void constrain_min_max_kernel(int N, float MIN, float MAX, float *X, int INCX) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (i < N) X[i*INCX] = fminf(MAX, fmaxf(MIN, X[i*INCX])); +} __global__ void supp_kernel(int N, float ALPHA, float *X, int INCX) { @@ -790,6 +795,12 @@ extern "C" void constrain_ongpu(int N, float ALPHA, float * X, int INCX) CHECK_CUDA(cudaPeekAtLastError()); } +extern "C" void constrain_min_max_ongpu(int N, float MIN, float MAX, float * X, int INCX) +{ + constrain_min_max_kernel << > >(N, MIN, MAX, X, INCX); + CHECK_CUDA(cudaPeekAtLastError()); +} + extern "C" void scal_ongpu(int N, float ALPHA, float * X, int INCX) { diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 3b4a343a9c2..73be0c8929c 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -1292,6 +1292,8 @@ void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init } } + + /* void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay) { diff --git a/src/detector.c b/src/detector.c index 28ae51758c2..a20e57faafe 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1656,6 +1656,195 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam free_network(net); } +#ifdef OPENCV + +// adversarial attack dnn +void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, int dont_show, int it_num, + int letter_box, int benchmark_layers) +{ + list *options = read_data_cfg(datacfg); + char *name_list = option_find_str(options, "names", "data/names.list"); + int names_size = 0; + char **names = get_labels_custom(name_list, &names_size); //get_labels(name_list); + + image **alphabet = load_alphabet(); + network net = parse_network_cfg(cfgfile);// parse_network_cfg_custom(cfgfile, 1, 1); // set batch=1 + net.adversarial = 1; + set_batch_network(&net, 1); + if (weightfile) { + load_weights(&net, weightfile); + } + net.benchmark_layers = benchmark_layers; + //fuse_conv_batchnorm(net); + //calculate_binary_weights(net); + if (net.layers[net.n - 1].classes != names_size) { + printf("\n Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", + name_list, names_size, net.layers[net.n - 1].classes, cfgfile); + if (net.layers[net.n - 1].classes > names_size) getchar(); + } + + srand(2222222); + char buff[256]; + char *input = buff; + + int j; + float nms = .45; // 0.4F + while (1) { + if (filename) { + strncpy(input, filename, 256); + if (strlen(input) > 0) + if (input[strlen(input) - 1] == 0x0d) input[strlen(input) - 1] = 0; + } + else { + printf("Enter Image Path: "); + fflush(stdout); + input = fgets(input, 256, stdin); + if (!input) break; + strtok(input, "\n"); + } + //image im; + //image sized = load_image_resize(input, net.w, net.h, net.c, &im); + image im = load_image(input, 0, 0, net.c); + image sized; + if (letter_box) sized = letterbox_image(im, net.w, net.h); + else sized = resize_image(im, net.w, net.h); + + layer l = net.layers[net.n - 1]; + net.num_boxes = l.max_boxes; + int num_truth = l.truths; + float *truth_cpu = (float *)xcalloc(num_truth, sizeof(float)); + + int *it_num_set = (int *)xcalloc(1, sizeof(int)); + float *lr_set = (float *)xcalloc(1, sizeof(float)); + + cv_draw_object(sized, truth_cpu, net.num_boxes, num_truth, it_num_set, lr_set); + + net.learning_rate = *lr_set; + it_num = *it_num_set; + + float *X = sized.data; + + mat_cv* img = NULL; + float max_img_loss = 5; + int number_of_lines = 100; + int img_size = 1000; + char windows_name[100]; + char *base = basecfg(cfgfile); + sprintf(windows_name, "chart_%s.png", base); + img = draw_train_chart(windows_name, max_img_loss, it_num, number_of_lines, img_size, dont_show, NULL); + + int iteration; + for (iteration = 0; iteration < it_num; ++iteration) + { + /* + free_network(net); + net = parse_network_cfg(cfgfile);// parse_network_cfg_custom(cfgfile, 1, 1); // set batch=1 + net.learning_rate = *lr_set; + net.adversarial = 1; + set_batch_network(&net, 1); + if (weightfile) { + load_weights(&net, weightfile); + } + */ + + if (iteration == it_num - 1) { + net.train = 0; + quantize_image(sized); + network_predict(net, X); + } + else forward_backward_network_gpu(net, X, truth_cpu); + + float avg_loss = get_network_cost(net); + draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, iteration, it_num, 0, 0, "mAP%", dont_show, 0, 0); + //quantize_image(sized); + + show_image_cv(sized, "image_optimization"); + wait_key_cv(20); + } + + { + int nboxes = 0; + detection *dets = get_network_boxes(&net, im.w, im.h, thresh, 0, 0, 1, &nboxes, letter_box); + if (nms) { + if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms); + else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms); + } + draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, 1); + save_image(im, "pre_predictions"); + if (!dont_show) { + show_image(im, "pre_predictions"); + } + } + + quantize_image(sized); + save_image_png(sized, "drawn"); + //sized = load_image("drawn.png", 0, 0, net.c); + + free_network(net); + net = parse_network_cfg_custom(cfgfile, 1, 1); // set batch=1 + if (weightfile) { + load_weights(&net, weightfile); + } + net.benchmark_layers = benchmark_layers; + + + double time = get_time_point(); + network_predict(net, X); + printf("%s: Predicted in %lf milli-seconds.\n", input, ((double)get_time_point() - time) / 1000); + + int nboxes = 0; + detection *dets = get_network_boxes(&net, im.w, im.h, thresh, 0, 0, 1, &nboxes, letter_box); + if (nms) { + if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms); + else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms); + } + draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, 1); + save_image(im, "predictions"); + if (!dont_show) { + show_image(im, "predictions"); + } + + free_detections(dets, nboxes); + free_image(im); + free_image(sized); + + if (!dont_show) { + wait_until_press_key_cv(); + destroy_all_windows_cv(); + } + + free(lr_set); + free(it_num_set); + + if (filename) break; + } + + // free memory + free_ptrs((void**)names, net.layers[net.n - 1].classes); + free_list_contents_kvp(options); + free_list(options); + + int i; + const int nsize = 8; + for (j = 0; j < nsize; ++j) { + for (i = 32; i < 127; ++i) { + free_image(alphabet[j][i]); + } + free(alphabet[j]); + } + free(alphabet); + + free_network(net); +} +#else // OPENCV +void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, int dont_show, int it_num, + int letter_box, int benchmark_layers) +{ + printf(" ./darknet detector draw ... can't be used without OpenCV! \n"); + getchar(); +} +#endif // OPENCV + void run_detector(int argc, char **argv) { int dont_show = find_arg(argc, argv, "-dont_show"); @@ -1732,6 +1921,10 @@ void run_detector(int argc, char **argv) else if (0 == strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights); else if (0 == strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh, iou_thresh, map_points, letter_box, NULL); else if (0 == strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, height, show); + else if (0 == strcmp(argv[2], "draw")) { + int it_num = 100; + draw_object(datacfg, cfg, weights, filename, thresh, dont_show, it_num, letter_box, benchmark_layers); + } else if (0 == strcmp(argv[2], "demo")) { list *options = read_data_cfg(datacfg); int classes = option_find_int(options, "classes", 20); diff --git a/src/image.c b/src/image.c index 8c94b395fc9..fc5264df8de 100644 --- a/src/image.c +++ b/src/image.c @@ -1298,6 +1298,13 @@ float bilinear_interpolate(image im, float x, float y, int c) return val; } +image quantize_image(image im) +{ + int size = im.c * im.w * im.h; + int i; + for (i = 0; i < size; ++i) im.data[i] = (int)(im.data[i] * 255) / 255. + (0.5/255); +} + image resize_image(image im, int w, int h) { if (im.w == w && im.h == h) return copy_image(im); diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index ccf55de8cab..fa3a7c96aa8 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -11,6 +11,7 @@ #include #include #include +#include #include #include @@ -1304,6 +1305,130 @@ extern "C" image blur_image(image src_img, int ksize) return dst_img; } +// ==================================================================== +// Draw object - adversarial attack dnn +// ==================================================================== + +std::atomic x_start, y_start; +std::atomic x_end, y_end; +std::atomic x_size, y_size; +std::atomic draw_select, selected; + +void callback_mouse_click(int event, int x, int y, int flags, void* user_data) +{ + if (event == cv::EVENT_LBUTTONDOWN) + { + draw_select = true; + selected = false; + x_start = x; + y_start = y; + + //if (prev_img_rect.contains(Point2i(x, y))) add_id_img = -1; + //else if (next_img_rect.contains(Point2i(x, y))) add_id_img = 1; + //else add_id_img = 0; + //std::cout << "cv::EVENT_LBUTTONDOWN \n"; + } + else if (event == cv::EVENT_LBUTTONUP) + { + x_size = abs(x - x_start); + y_size = abs(y - y_start); + x_end = std::max(x, 0); + y_end = std::max(y, 0); + draw_select = false; + selected = true; + //std::cout << "cv::EVENT_LBUTTONUP \n"; + } + else if (event == cv::EVENT_MOUSEMOVE) + { + x_size = abs(x - x_start); + y_size = abs(y - y_start); + x_end = std::max(x, 0); + y_end = std::max(y, 0); + } +} + +extern "C" void cv_draw_object(image sized, float *truth_cpu, int max_boxes, int num_truth, int *it_num_set, float *lr_set) +{ + cv::Mat frame = image_to_mat(sized); + if(frame.channels() == 3) cv::cvtColor(frame, frame, cv::COLOR_RGB2BGR); + cv::Mat frame_clone = frame.clone(); + + + std::string const window_name = "Marking image"; + cv::namedWindow(window_name, cv::WINDOW_NORMAL); + cv::resizeWindow(window_name, 1280, 720); + cv::imshow(window_name, frame); + cv::moveWindow(window_name, 0, 0); + cv::setMouseCallback(window_name, callback_mouse_click); + + + int it_trackbar_value = 50; + std::string const it_trackbar_name = "iterations"; + int it_tb_res = cv::createTrackbar(it_trackbar_name, window_name, &it_trackbar_value, 1000); + + int lr_trackbar_value = 3; + std::string const lr_trackbar_name = "learning_rate exp"; + int lr_tb_res = cv::createTrackbar(lr_trackbar_name, window_name, &lr_trackbar_value, 20); + + int i = 0; + int id = 0; + + while (!selected) { +#ifndef CV_VERSION_EPOCH + int pressed_key = cv::waitKeyEx(20); // OpenCV 3.x +#else + int pressed_key = cv::waitKey(20); // OpenCV 2.x +#endif + if (pressed_key == 27 || pressed_key == 1048603) break;// break; // ESC - save & exit + + frame_clone = frame.clone(); + char buff[100]; + std::string lr_value = "learning_rage = " + std::to_string(1.0 / pow(2, lr_trackbar_value)); + cv::putText(frame_clone, lr_value, cv::Point2i(20, 20), cv::FONT_HERSHEY_SIMPLEX, 0.6, cv::Scalar(100, 50, 50), 2); + + if (draw_select) { + cv::Rect selected_rect( + cv::Point2i((int)min(x_start, x_end), (int)min(y_start, y_end)), + cv::Size(x_size, y_size)); + + rectangle(frame_clone, selected_rect, cv::Scalar(150, 200, 150)); + } + + + cv::imshow(window_name, frame_clone); + } + + if (selected) { + cv::Rect selected_rect( + cv::Point2i((int)min(x_start, x_end), (int)min(y_start, y_end)), + cv::Size(x_size, y_size)); + + printf(" x_start = %d, y_start = %d, x_size = %d, y_size = %d \n", + x_start, y_start, x_size, y_size); + + rectangle(frame, selected_rect, cv::Scalar(150, 200, 150)); + cv::imshow(window_name, frame); + cv::waitKey(100); + + float width = x_end - x_start; + float height = y_end - y_start; + + float const relative_center_x = (float)(x_start + width / 2) / frame.cols; + float const relative_center_y = (float)(y_start + height / 2) / frame.rows; + float const relative_width = (float)width / frame.cols; + float const relative_height = (float)height / frame.rows; + + truth_cpu[i * 5 + 0] = relative_center_x; + truth_cpu[i * 5 + 1] = relative_center_y; + truth_cpu[i * 5 + 2] = relative_width; + truth_cpu[i * 5 + 3] = relative_height; + truth_cpu[i * 5 + 4] = id; + } + + *it_num_set = it_trackbar_value; + *lr_set = 1.0 / pow(2, lr_trackbar_value); +} + // ==================================================================== // Show Anchors // ==================================================================== diff --git a/src/image_opencv.h b/src/image_opencv.h index 8af331804e1..2c599e9be61 100644 --- a/src/image_opencv.h +++ b/src/image_opencv.h @@ -108,6 +108,9 @@ void blend_images_cv(image new_img, float alpha, image old_img, float beta); // bilateralFilter bluring image blur_image(image src_img, int ksize); +// draw objects for Adversarial attacks +void cv_draw_object(image sized, float *truth_cpu, int max_boxes, int num_truth, int *it_num_set, float *lr_set); + // Show Anchors void show_acnhors(int number_of_boxes, int num_of_clusters, float *rel_width_height_array, model anchors_data, int width, int height); diff --git a/src/network.h b/src/network.h index aad5021526f..42d68d6c3c3 100644 --- a/src/network.h +++ b/src/network.h @@ -102,6 +102,7 @@ float *get_network_output_gpu(network net); void forward_network_gpu(network net, network_state state); void backward_network_gpu(network net, network_state state); void update_network_gpu(network net); +void forward_backward_network_gpu(network net, float *x, float *y); #endif float get_current_seq_subdivisions(network net); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 75b3d6a227e..2cb7954521d 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -227,6 +227,15 @@ void backward_network_gpu(network net, network_state state) */ } + if (net.adversarial) { + int x_size = get_network_input_size(net)*net.batch; + printf(" x_size = %d, original_delta = %p, original_input = %p, net.learning_rate = %d \n", + x_size, original_delta, original_input, x_size, net.learning_rate); + axpy_ongpu(x_size, net.learning_rate, original_delta, 1, original_input, 1); + //axpy_ongpu(x_size, 0.1, original_delta, 1, original_input, 1); + constrain_min_max_ongpu(x_size, 0, 1, original_input, 1); + } + if (net.benchmark_layers) { printf("\n\nSorted by time (backward):\n"); qsort(sorted_avg_time_per_layer, net.n, sizeof(time_benchmark_layers), time_comparator); @@ -272,6 +281,7 @@ void forward_backward_network_gpu(network net, float *x, float *y) } state.input = *net.input_gpu; state.delta = 0; + if (net.adversarial) state.delta = cuda_make_array(NULL, x_size); state.truth = *net.truth_gpu; state.train = 1; #if defined(CUDNN_HALF) && defined(CUDNN) @@ -311,6 +321,11 @@ void forward_backward_network_gpu(network net, float *x, float *y) forward_network_gpu(net, state); //cudaStreamSynchronize(get_cuda_stream()); backward_network_gpu(net, state); + + if (net.adversarial) { + cuda_free(state.delta); + cuda_pull_array(*net.input_gpu, x, x_size); + } } float train_network_datum_gpu(network net, float *x, float *y) diff --git a/src/parser.c b/src/parser.c index 91ad1988b42..41a9b68861d 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1086,6 +1086,7 @@ void parse_net_options(list *options, network *net) net->letter_box = option_find_int_quiet(options, "letter_box", 0); net->label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); net->resize_step = option_find_float_quiet(options, "resize_step", 32); + net->adversarial = option_find_int_quiet(options, "adversarial", 0); net->angle = option_find_float_quiet(options, "angle", 0); net->aspect = option_find_float_quiet(options, "aspect", 1); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 1ea6bf4d3f5..94a3cfe326b 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -171,6 +171,9 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, float tw = log(truth.w*w / biases[2 * n]); float th = log(truth.h*h / biases[2 * n + 1]); + printf(" tx = %f, ty = %f, tw = %f, th = %f \n", tx, ty, tw, th); + printf(" x = %f, y = %f, w = %f, h = %f \n", x[index + 0 * stride], x[index + 1 * stride], x[index + 2 * stride], x[index + 3 * stride]); + // accumulate delta delta[index + 0 * stride] += scale * (tx - x[index + 0 * stride]) * iou_normalizer; delta[index + 1 * stride] += scale * (ty - x[index + 1 * stride]) * iou_normalizer; @@ -474,6 +477,10 @@ void forward_yolo_layer(const layer l, network_state state) int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); + printf(" label: class_id = %d, truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f \n", class_id, truth.x, truth.y, truth.w, truth.h); + printf(" mask_n = %d, l.output[obj_index] = %f, l.output[class_index + class_id] = %f \n\n", mask_n, l.output[obj_index], l.output[class_index + class_id]); + + ++count; ++class_count; if (all_ious.iou > .5) recall += 1; From fdb61f8cb4a592e0b4690748dd1171ebc381927a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 24 Mar 2020 22:30:30 +0300 Subject: [PATCH 0892/1365] Minor fix --- src/image_opencv.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index fa3a7c96aa8..3a25ddb72ae 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1404,7 +1404,7 @@ extern "C" void cv_draw_object(image sized, float *truth_cpu, int max_boxes, int cv::Size(x_size, y_size)); printf(" x_start = %d, y_start = %d, x_size = %d, y_size = %d \n", - x_start, y_start, x_size, y_size); + x_start.load(), y_start.load(), x_size.load(), y_size.load()); rectangle(frame, selected_rect, cv::Scalar(150, 200, 150)); cv::imshow(window_name, frame); From aa4f9975a77855836922d66f46114569e8c06ae0 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 24 Mar 2020 23:02:44 +0300 Subject: [PATCH 0893/1365] adversarial attack - fixed --- src/detector.c | 12 ++++++------ src/image_opencv.cpp | 24 ++++++++++++++++++------ src/image_opencv.h | 2 +- src/network_kernels.cu | 5 ++++- 4 files changed, 29 insertions(+), 14 deletions(-) diff --git a/src/detector.c b/src/detector.c index a20e57faafe..ef220e0bef2 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1656,7 +1656,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam free_network(net); } -#ifdef OPENCV +#if defined(OPENCV) && defined(GPU) // adversarial attack dnn void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, int dont_show, int it_num, @@ -1675,7 +1675,7 @@ void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, load_weights(&net, weightfile); } net.benchmark_layers = benchmark_layers; - //fuse_conv_batchnorm(net); + fuse_conv_batchnorm(net); //calculate_binary_weights(net); if (net.layers[net.n - 1].classes != names_size) { printf("\n Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", @@ -1717,7 +1717,7 @@ void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, int *it_num_set = (int *)xcalloc(1, sizeof(int)); float *lr_set = (float *)xcalloc(1, sizeof(float)); - cv_draw_object(sized, truth_cpu, net.num_boxes, num_truth, it_num_set, lr_set); + cv_draw_object(sized, truth_cpu, net.num_boxes, num_truth, it_num_set, lr_set, l.classes, names); net.learning_rate = *lr_set; it_num = *it_num_set; @@ -1836,14 +1836,14 @@ void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, free_network(net); } -#else // OPENCV +#else // defined(OPENCV) && defined(GPU) void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, int dont_show, int it_num, int letter_box, int benchmark_layers) { - printf(" ./darknet detector draw ... can't be used without OpenCV! \n"); + printf(" ./darknet detector draw ... can't be used without OpenCV and CUDA! \n"); getchar(); } -#endif // OPENCV +#endif // defined(OPENCV) && defined(GPU) void run_detector(int argc, char **argv) { diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 3a25ddb72ae..030113ed728 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1347,7 +1347,7 @@ void callback_mouse_click(int event, int x, int y, int flags, void* user_data) } } -extern "C" void cv_draw_object(image sized, float *truth_cpu, int max_boxes, int num_truth, int *it_num_set, float *lr_set) +extern "C" void cv_draw_object(image sized, float *truth_cpu, int max_boxes, int num_truth, int *it_num_set, float *lr_set, int classes, char **names) { cv::Mat frame = image_to_mat(sized); if(frame.channels() == 3) cv::cvtColor(frame, frame, cv::COLOR_RGB2BGR); @@ -1366,12 +1366,15 @@ extern "C" void cv_draw_object(image sized, float *truth_cpu, int max_boxes, int std::string const it_trackbar_name = "iterations"; int it_tb_res = cv::createTrackbar(it_trackbar_name, window_name, &it_trackbar_value, 1000); - int lr_trackbar_value = 3; + int lr_trackbar_value = 12; std::string const lr_trackbar_name = "learning_rate exp"; int lr_tb_res = cv::createTrackbar(lr_trackbar_name, window_name, &lr_trackbar_value, 20); + int cl_trackbar_value = 0; + std::string const cl_trackbar_name = "class_id"; + int cl_tb_res = cv::createTrackbar(cl_trackbar_name, window_name, &cl_trackbar_value, classes-1); + int i = 0; - int id = 0; while (!selected) { #ifndef CV_VERSION_EPOCH @@ -1383,8 +1386,17 @@ extern "C" void cv_draw_object(image sized, float *truth_cpu, int max_boxes, int frame_clone = frame.clone(); char buff[100]; - std::string lr_value = "learning_rage = " + std::to_string(1.0 / pow(2, lr_trackbar_value)); - cv::putText(frame_clone, lr_value, cv::Point2i(20, 20), cv::FONT_HERSHEY_SIMPLEX, 0.6, cv::Scalar(100, 50, 50), 2); + std::string lr_value = "learning_rate = " + std::to_string(1.0 / pow(2, lr_trackbar_value)); + cv::putText(frame_clone, lr_value, cv::Point2i(10, 20), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(10, 50, 10), 3); + cv::putText(frame_clone, lr_value, cv::Point2i(10, 20), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(20, 120, 60), 2); + cv::putText(frame_clone, lr_value, cv::Point2i(10, 20), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(50, 200, 100), 1); + + if (names) { + std::string obj_name = names[cl_trackbar_value]; + cv::putText(frame_clone, obj_name, cv::Point2i(10, 40), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(10, 50, 10), 3); + cv::putText(frame_clone, obj_name, cv::Point2i(10, 40), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(20, 120, 60), 2); + cv::putText(frame_clone, obj_name, cv::Point2i(10, 40), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(50, 200, 100), 1); + } if (draw_select) { cv::Rect selected_rect( @@ -1422,7 +1434,7 @@ extern "C" void cv_draw_object(image sized, float *truth_cpu, int max_boxes, int truth_cpu[i * 5 + 1] = relative_center_y; truth_cpu[i * 5 + 2] = relative_width; truth_cpu[i * 5 + 3] = relative_height; - truth_cpu[i * 5 + 4] = id; + truth_cpu[i * 5 + 4] = cl_trackbar_value; } *it_num_set = it_trackbar_value; diff --git a/src/image_opencv.h b/src/image_opencv.h index 2c599e9be61..b8563a3ba71 100644 --- a/src/image_opencv.h +++ b/src/image_opencv.h @@ -109,7 +109,7 @@ void blend_images_cv(image new_img, float alpha, image old_img, float beta); image blur_image(image src_img, int ksize); // draw objects for Adversarial attacks -void cv_draw_object(image sized, float *truth_cpu, int max_boxes, int num_truth, int *it_num_set, float *lr_set); +void cv_draw_object(image sized, float *truth_cpu, int max_boxes, int num_truth, int *it_num_set, float *lr_set, int classes, char **names); // Show Anchors void show_acnhors(int number_of_boxes, int num_of_clusters, float *rel_width_height_array, model anchors_data, int width, int height); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 2cb7954521d..45f86a2f154 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -281,7 +281,10 @@ void forward_backward_network_gpu(network net, float *x, float *y) } state.input = *net.input_gpu; state.delta = 0; - if (net.adversarial) state.delta = cuda_make_array(NULL, x_size); + if (net.adversarial) { + state.train = 0; + state.delta = cuda_make_array(NULL, x_size); + } state.truth = *net.truth_gpu; state.train = 1; #if defined(CUDNN_HALF) && defined(CUDNN) From b3c9f373d176549cd336908443426669edad78fd Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 25 Mar 2020 02:35:05 +0300 Subject: [PATCH 0894/1365] adversarial attack - minor improvement --- src/detector.c | 59 ++++++++++++------------------------------ src/image.c | 5 ++++ src/image.h | 1 + src/image_opencv.cpp | 5 +++- src/image_opencv.h | 2 +- src/network_kernels.cu | 1 - src/yolo_layer.c | 9 +++---- 7 files changed, 32 insertions(+), 50 deletions(-) diff --git a/src/detector.c b/src/detector.c index ef220e0bef2..de4c6e398bc 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1709,6 +1709,8 @@ void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, if (letter_box) sized = letterbox_image(im, net.w, net.h); else sized = resize_image(im, net.w, net.h); + image src_sized = copy_image(sized); + layer l = net.layers[net.n - 1]; net.num_boxes = l.max_boxes; int num_truth = l.truths; @@ -1716,8 +1718,9 @@ void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, int *it_num_set = (int *)xcalloc(1, sizeof(int)); float *lr_set = (float *)xcalloc(1, sizeof(float)); + int *boxonly = (int *)xcalloc(1, sizeof(int)); - cv_draw_object(sized, truth_cpu, net.num_boxes, num_truth, it_num_set, lr_set, l.classes, names); + cv_draw_object(sized, truth_cpu, net.num_boxes, num_truth, it_num_set, lr_set, boxonly, l.classes, names); net.learning_rate = *lr_set; it_num = *it_num_set; @@ -1736,17 +1739,6 @@ void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, int iteration; for (iteration = 0; iteration < it_num; ++iteration) { - /* - free_network(net); - net = parse_network_cfg(cfgfile);// parse_network_cfg_custom(cfgfile, 1, 1); // set batch=1 - net.learning_rate = *lr_set; - net.adversarial = 1; - set_batch_network(&net, 1); - if (weightfile) { - load_weights(&net, weightfile); - } - */ - if (iteration == it_num - 1) { net.train = 0; quantize_image(sized); @@ -1758,55 +1750,38 @@ void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, iteration, it_num, 0, 0, "mAP%", dont_show, 0, 0); //quantize_image(sized); + if (*boxonly) { + int dw = truth_cpu[2] * sized.w, dh = truth_cpu[3] * sized.h; + int dx = truth_cpu[0] * sized.w - dw / 2, dy = truth_cpu[1] * sized.h - dh / 2; + image crop = crop_image(sized, dx, dy, dw, dh); + copy_image_inplace(src_sized, sized); + embed_image(crop, sized, dx, dy); + } + show_image_cv(sized, "image_optimization"); wait_key_cv(20); } - { - int nboxes = 0; - detection *dets = get_network_boxes(&net, im.w, im.h, thresh, 0, 0, 1, &nboxes, letter_box); - if (nms) { - if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms); - else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms); - } - draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, 1); - save_image(im, "pre_predictions"); - if (!dont_show) { - show_image(im, "pre_predictions"); - } - } - quantize_image(sized); save_image_png(sized, "drawn"); //sized = load_image("drawn.png", 0, 0, net.c); - free_network(net); - net = parse_network_cfg_custom(cfgfile, 1, 1); // set batch=1 - if (weightfile) { - load_weights(&net, weightfile); - } - net.benchmark_layers = benchmark_layers; - - - double time = get_time_point(); - network_predict(net, X); - printf("%s: Predicted in %lf milli-seconds.\n", input, ((double)get_time_point() - time) / 1000); - int nboxes = 0; - detection *dets = get_network_boxes(&net, im.w, im.h, thresh, 0, 0, 1, &nboxes, letter_box); + detection *dets = get_network_boxes(&net, sized.w, sized.h, thresh, 0, 0, 1, &nboxes, letter_box); if (nms) { if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms); else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms); } - draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, 1); - save_image(im, "predictions"); + draw_detections_v3(sized, dets, nboxes, thresh, names, alphabet, l.classes, 1); + save_image(sized, "pre_predictions"); if (!dont_show) { - show_image(im, "predictions"); + show_image(sized, "pre_predictions"); } free_detections(dets, nboxes); free_image(im); free_image(sized); + free_image(src_sized); if (!dont_show) { wait_until_press_key_cv(); diff --git a/src/image.c b/src/image.c index fc5264df8de..855a4f2f467 100644 --- a/src/image.c +++ b/src/image.c @@ -654,6 +654,11 @@ void normalize_image2(image p) free(max); } +void copy_image_inplace(image src, image dst) +{ + memcpy(dst.data, src.data, src.h*src.w*src.c * sizeof(float)); +} + image copy_image(image p) { image copy = p; diff --git a/src/image.h b/src/image.h index 904b87a8c98..70d5f98dde8 100644 --- a/src/image.h +++ b/src/image.h @@ -82,6 +82,7 @@ image make_empty_image(int w, int h, int c); image float_to_image_scaled(int w, int h, int c, float *data); image float_to_image(int w, int h, int c, float *data); image copy_image(image p); +void copy_image_inplace(image src, image dst); image load_image(char *filename, int w, int h, int c); image load_image_stb_resize(char *filename, int w, int h, int c); //LIB_API image load_image_color(char *filename, int w, int h); diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 030113ed728..c65eef61a4d 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1347,7 +1347,7 @@ void callback_mouse_click(int event, int x, int y, int flags, void* user_data) } } -extern "C" void cv_draw_object(image sized, float *truth_cpu, int max_boxes, int num_truth, int *it_num_set, float *lr_set, int classes, char **names) +extern "C" void cv_draw_object(image sized, float *truth_cpu, int max_boxes, int num_truth, int *it_num_set, float *lr_set, int *boxonly, int classes, char **names) { cv::Mat frame = image_to_mat(sized); if(frame.channels() == 3) cv::cvtColor(frame, frame, cv::COLOR_RGB2BGR); @@ -1374,6 +1374,9 @@ extern "C" void cv_draw_object(image sized, float *truth_cpu, int max_boxes, int std::string const cl_trackbar_name = "class_id"; int cl_tb_res = cv::createTrackbar(cl_trackbar_name, window_name, &cl_trackbar_value, classes-1); + std::string const bo_trackbar_name = "box-only"; + int bo_tb_res = cv::createTrackbar(bo_trackbar_name, window_name, boxonly, 1); + int i = 0; while (!selected) { diff --git a/src/image_opencv.h b/src/image_opencv.h index b8563a3ba71..b2dc7c82206 100644 --- a/src/image_opencv.h +++ b/src/image_opencv.h @@ -109,7 +109,7 @@ void blend_images_cv(image new_img, float alpha, image old_img, float beta); image blur_image(image src_img, int ksize); // draw objects for Adversarial attacks -void cv_draw_object(image sized, float *truth_cpu, int max_boxes, int num_truth, int *it_num_set, float *lr_set, int classes, char **names); +void cv_draw_object(image sized, float *truth_cpu, int max_boxes, int num_truth, int *it_num_set, float *lr_set, int *boxonly, int classes, char **names); // Show Anchors void show_acnhors(int number_of_boxes, int num_of_clusters, float *rel_width_height_array, model anchors_data, int width, int height); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 45f86a2f154..ae1a5c71fdd 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -232,7 +232,6 @@ void backward_network_gpu(network net, network_state state) printf(" x_size = %d, original_delta = %p, original_input = %p, net.learning_rate = %d \n", x_size, original_delta, original_input, x_size, net.learning_rate); axpy_ongpu(x_size, net.learning_rate, original_delta, 1, original_input, 1); - //axpy_ongpu(x_size, 0.1, original_delta, 1, original_input, 1); constrain_min_max_ongpu(x_size, 0, 1, original_input, 1); } diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 94a3cfe326b..41ae96c70a9 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -171,8 +171,8 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, float tw = log(truth.w*w / biases[2 * n]); float th = log(truth.h*h / biases[2 * n + 1]); - printf(" tx = %f, ty = %f, tw = %f, th = %f \n", tx, ty, tw, th); - printf(" x = %f, y = %f, w = %f, h = %f \n", x[index + 0 * stride], x[index + 1 * stride], x[index + 2 * stride], x[index + 3 * stride]); + //printf(" tx = %f, ty = %f, tw = %f, th = %f \n", tx, ty, tw, th); + //printf(" x = %f, y = %f, w = %f, h = %f \n", x[index + 0 * stride], x[index + 1 * stride], x[index + 2 * stride], x[index + 3 * stride]); // accumulate delta delta[index + 0 * stride] += scale * (tx - x[index + 0 * stride]) * iou_normalizer; @@ -477,9 +477,8 @@ void forward_yolo_layer(const layer l, network_state state) int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); - printf(" label: class_id = %d, truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f \n", class_id, truth.x, truth.y, truth.w, truth.h); - printf(" mask_n = %d, l.output[obj_index] = %f, l.output[class_index + class_id] = %f \n\n", mask_n, l.output[obj_index], l.output[class_index + class_id]); - + //printf(" label: class_id = %d, truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f \n", class_id, truth.x, truth.y, truth.w, truth.h); + //printf(" mask_n = %d, l.output[obj_index] = %f, l.output[class_index + class_id] = %f \n\n", mask_n, l.output[obj_index], l.output[class_index + class_id]); ++count; ++class_count; From 39d79785ba4e83b977d662373c2b67c4f6737060 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 25 Mar 2020 03:33:26 +0300 Subject: [PATCH 0895/1365] Minor fix --- src/detector.c | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/src/detector.c b/src/detector.c index de4c6e398bc..75066ca632c 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1739,16 +1739,10 @@ void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, int iteration; for (iteration = 0; iteration < it_num; ++iteration) { - if (iteration == it_num - 1) { - net.train = 0; - quantize_image(sized); - network_predict(net, X); - } - else forward_backward_network_gpu(net, X, truth_cpu); + forward_backward_network_gpu(net, X, truth_cpu); float avg_loss = get_network_cost(net); draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, iteration, it_num, 0, 0, "mAP%", dont_show, 0, 0); - //quantize_image(sized); if (*boxonly) { int dw = truth_cpu[2] * sized.w, dh = truth_cpu[3] * sized.h; @@ -1762,7 +1756,10 @@ void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, wait_key_cv(20); } + net.train = 0; quantize_image(sized); + network_predict(net, X); + save_image_png(sized, "drawn"); //sized = load_image("drawn.png", 0, 0, net.c); From 978c2e00ed21e137ee97044fa43f6f08642866b9 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 26 Mar 2020 00:16:57 +0300 Subject: [PATCH 0896/1365] self-adversarial training --- include/darknet.h | 1 + src/batchnorm_layer.c | 41 +++++++++++++++++------ src/blas.h | 1 + src/blas_kernels.cu | 17 ++++++++-- src/convolutional_kernels.cu | 64 +++++++++++++++++++----------------- src/detector.c | 2 +- src/image_opencv.cpp | 4 +-- src/network_kernels.cu | 22 +++++++++++++ src/parser.c | 3 +- 9 files changed, 109 insertions(+), 46 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 464d3cf3a47..181af1c071f 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -697,6 +697,7 @@ typedef struct network { float label_smooth_eps; int resize_step; int adversarial; + float adversarial_lr; int letter_box; float angle; float aspect; diff --git a/src/batchnorm_layer.c b/src/batchnorm_layer.c index 7432434cba2..eeba5cc57b9 100644 --- a/src/batchnorm_layer.c +++ b/src/batchnorm_layer.c @@ -231,17 +231,17 @@ void update_batchnorm_layer(layer l, int batch, float learning_rate, float momen void pull_batchnorm_layer(layer l) { - cuda_pull_array(l.biases_gpu, l.biases, l.c); - cuda_pull_array(l.scales_gpu, l.scales, l.c); - cuda_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.c); - cuda_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.c); + cuda_pull_array(l.biases_gpu, l.biases, l.out_c); + cuda_pull_array(l.scales_gpu, l.scales, l.out_c); + cuda_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.out_c); + cuda_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.out_c); } void push_batchnorm_layer(layer l) { - cuda_push_array(l.biases_gpu, l.biases, l.c); - cuda_push_array(l.scales_gpu, l.scales, l.c); - cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.c); - cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.c); + cuda_push_array(l.biases_gpu, l.biases, l.out_c); + cuda_push_array(l.scales_gpu, l.scales, l.out_c); + cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.out_c); + cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.out_c); } void forward_batchnorm_layer_gpu(layer l, network_state state) @@ -249,6 +249,13 @@ void forward_batchnorm_layer_gpu(layer l, network_state state) if (l.type == BATCHNORM) simple_copy_ongpu(l.outputs*l.batch, state.input, l.output_gpu); //copy_ongpu(l.outputs*l.batch, state.input, 1, l.output_gpu, 1); + if (state.net.adversarial) { + normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); + scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h); + return; + } + if (state.train) { simple_copy_ongpu(l.outputs*l.batch, l.output_gpu, l.x_gpu); @@ -339,9 +346,23 @@ void forward_batchnorm_layer_gpu(layer l, network_state state) void backward_batchnorm_layer_gpu(layer l, network_state state) { + if (state.net.adversarial) { + inverse_variance_ongpu(l.out_c, l.rolling_variance_gpu, l.variance_gpu, 0.00001); + + scale_bias_gpu(l.delta_gpu, l.variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); + scale_bias_gpu(l.delta_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); + return; + } + if (!state.train) { - l.mean_gpu = l.rolling_mean_gpu; - l.variance_gpu = l.rolling_variance_gpu; + //l.mean_gpu = l.rolling_mean_gpu; + //l.variance_gpu = l.rolling_variance_gpu; + simple_copy_ongpu(l.out_c, l.rolling_mean_gpu, l.mean_gpu); +#ifdef CUDNN + inverse_variance_ongpu(l.out_c, l.rolling_variance_gpu, l.variance_gpu, 0.00001); +#else + simple_copy_ongpu(l.out_c, l.rolling_variance_gpu, l.variance_gpu); +#endif } #ifdef CUDNN diff --git a/src/blas.h b/src/blas.h index efbee945ab5..819130a6404 100644 --- a/src/blas.h +++ b/src/blas.h @@ -91,6 +91,7 @@ void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean); void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance); void fast_v_cbn_gpu(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, int max_minibatch_index, float *m_avg, float *v_avg, float *variance, const float alpha, float *rolling_mean_gpu, float *rolling_variance_gpu, int inverse_variance, float epsilon); +void inverse_variance_ongpu(int size, float *src, float *dst, float epsilon); void normalize_scale_bias_gpu(float *x, float *mean, float *variance, float *scales, float *biases, int batch, int filters, int spatial, int inverse_variance, float epsilon); void compare_2_arrays_gpu(float *one, float *two, int size); void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out); diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index db862c24a4e..e63d15a97a8 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -641,9 +641,9 @@ __global__ void fast_v_cbn_kernel(const float *x, float *mean, int batch, int f //if (max_minibatch_index == minibatch_index) { - rolling_mean_gpu[filter] = alpha * mean[filter] + (1 - alpha) * rolling_mean_gpu[filter]; + if(rolling_mean_gpu) rolling_mean_gpu[filter] = alpha * mean[filter] + (1 - alpha) * rolling_mean_gpu[filter]; - rolling_variance_gpu[filter] = alpha * variance_tmp + (1 - alpha) * rolling_variance_gpu[filter]; + if(rolling_variance_gpu) rolling_variance_gpu[filter] = alpha * variance_tmp + (1 - alpha) * rolling_variance_gpu[filter]; } } } @@ -655,6 +655,19 @@ extern "C" void fast_v_cbn_gpu(const float *x, float *mean, int batch, int filte CHECK_CUDA(cudaPeekAtLastError()); } +__global__ void inverse_variance_kernel(int size, float *src, float *dst, float epsilon) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < size) + dst[index] = 1.0f / sqrtf(src[index] + epsilon); +} + +extern "C" void inverse_variance_ongpu(int size, float *src, float *dst, float epsilon) +{ + const int num_blocks = size / BLOCK + 1; + inverse_variance_kernel << > >(size, src, dst, epsilon); + CHECK_CUDA(cudaPeekAtLastError()); +} __global__ void normalize_scale_bias_kernel(int N, float *x, float *mean, float *variance, float *scales, float *biases, int batch, int filters, int spatial, int inverse_variance, float epsilon) { diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 73be0c8929c..7c8dddf4165 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -472,7 +472,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) if (l.batch_normalize) { - if (state.train) // Training + if (state.train && !state.net.adversarial) // Training { simple_copy_ongpu(l.outputs*l.batch / 2, output16, l.x_gpu); //copy_ongpu(l.outputs*l.batch / 2, output16, 1, l.x_gpu, 1); @@ -744,21 +744,23 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state assert((l.nweights) > 0); cuda_convert_f32_to_f16(l.weight_updates_gpu, l.nweights, l.weight_updates_gpu16); - CHECK_CUDNN(cudnnConvolutionBackwardFilter(cudnn_handle(), - &one, - l.srcTensorDesc16, - input16, //state.input, - l.ddstTensorDesc16, - delta16, //l.delta_gpu, - l.convDesc, - l.bf_algo16, - state.workspace, - l.workspace_size, - &one, - l.dweightDesc16, - l.weight_updates_gpu16)); // l.weight_updates_gpu); + if (!state.net.adversarial) { + CHECK_CUDNN(cudnnConvolutionBackwardFilter(cudnn_handle(), + &one, + l.srcTensorDesc16, + input16, //state.input, + l.ddstTensorDesc16, + delta16, //l.delta_gpu, + l.convDesc, + l.bf_algo16, + state.workspace, + l.workspace_size, + &one, + l.dweightDesc16, + l.weight_updates_gpu16)); // l.weight_updates_gpu); - cuda_convert_f16_to_f32(l.weight_updates_gpu16, l.nweights, l.weight_updates_gpu); + cuda_convert_f16_to_f32(l.weight_updates_gpu16, l.nweights, l.weight_updates_gpu); + } if (state.delta) { if (l.binary || l.xnor) swap_binary(&l); @@ -794,21 +796,23 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state backward_batchnorm_layer_gpu(l, state); } - // calculate conv weight updates - // if used: beta=1 then loss decreases faster - CHECK_CUDNN(cudnnConvolutionBackwardFilter(cudnn_handle(), - &one, - l.srcTensorDesc, - state.input, - l.ddstTensorDesc, - l.delta_gpu, - l.convDesc, - l.bf_algo, - state.workspace, - l.workspace_size, - &one, - l.dweightDesc, - l.weight_updates_gpu)); + if (!state.net.adversarial) { + // calculate conv weight updates + // if used: beta=1 then loss decreases faster + CHECK_CUDNN(cudnnConvolutionBackwardFilter(cudnn_handle(), + &one, + l.srcTensorDesc, + state.input, + l.ddstTensorDesc, + l.delta_gpu, + l.convDesc, + l.bf_algo, + state.workspace, + l.workspace_size, + &one, + l.dweightDesc, + l.weight_updates_gpu)); + } if (state.delta) { if (l.binary || l.xnor) swap_binary(&l); diff --git a/src/detector.c b/src/detector.c index 75066ca632c..d7e5e578d27 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1675,7 +1675,7 @@ void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, load_weights(&net, weightfile); } net.benchmark_layers = benchmark_layers; - fuse_conv_batchnorm(net); + //fuse_conv_batchnorm(net); //calculate_binary_weights(net); if (net.layers[net.n - 1].classes != names_size) { printf("\n Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index c65eef61a4d..90bdb37e8f0 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1362,11 +1362,11 @@ extern "C" void cv_draw_object(image sized, float *truth_cpu, int max_boxes, int cv::setMouseCallback(window_name, callback_mouse_click); - int it_trackbar_value = 50; + int it_trackbar_value = 200; std::string const it_trackbar_name = "iterations"; int it_tb_res = cv::createTrackbar(it_trackbar_name, window_name, &it_trackbar_value, 1000); - int lr_trackbar_value = 12; + int lr_trackbar_value = 10; std::string const lr_trackbar_name = "learning_rate exp"; int lr_tb_res = cv::createTrackbar(lr_trackbar_name, window_name, &lr_trackbar_value, 20); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index ae1a5c71fdd..152190111fd 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -333,6 +333,28 @@ void forward_backward_network_gpu(network net, float *x, float *y) float train_network_datum_gpu(network net, float *x, float *y) { *net.seen += net.batch; + if (net.adversarial_lr && rand_int(0, 1) == 1 && get_current_iteration(net) > net.burn_in) { + net.adversarial = 1; + float lr_old = net.learning_rate; + net.learning_rate = net.adversarial_lr; + layer l = net.layers[net.n - 1]; + float *truth_cpu = (float *)xcalloc(l.truths * l.batch, sizeof(float)); + + printf("\n adversarial training, adversarial_lr = %f \n", net.adversarial_lr); + + forward_backward_network_gpu(net, x, truth_cpu); + + image im; + im.w = net.w; + im.h = net.h; + im.c = net.c; + im.data = x; + //show_image(im, "adversarial data augmentation"); + + free(truth_cpu); + net.learning_rate = lr_old; + net.adversarial = 0; + } forward_backward_network_gpu(net, x, y); float error = get_network_cost(net); //if (((*net.seen) / net.batch) % net.subdivisions == 0) update_network_gpu(net); diff --git a/src/parser.c b/src/parser.c index 41a9b68861d..b997e56fab6 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1086,7 +1086,8 @@ void parse_net_options(list *options, network *net) net->letter_box = option_find_int_quiet(options, "letter_box", 0); net->label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); net->resize_step = option_find_float_quiet(options, "resize_step", 32); - net->adversarial = option_find_int_quiet(options, "adversarial", 0); + //net->adversarial = option_find_int_quiet(options, "adversarial", 0); + net->adversarial_lr = option_find_float_quiet(options, "adversarial_lr", 0); net->angle = option_find_float_quiet(options, "angle", 0); net->aspect = option_find_float_quiet(options, "aspect", 1); From 991961200694fca38c95400736d513aa396593a8 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 26 Mar 2020 01:25:04 +0300 Subject: [PATCH 0897/1365] Added attention during training: [net] adversarial_lr=0.05 attention=1 --- include/darknet.h | 3 ++- src/detector.c | 3 +++ src/network_kernels.cu | 49 ++++++++++++++++++++++++++++++++++++++++-- src/parser.c | 2 +- 4 files changed, 53 insertions(+), 4 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 181af1c071f..52d7ce5fdca 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -696,7 +696,8 @@ typedef struct network { int mixup; float label_smooth_eps; int resize_step; - int adversarial; + int attention; + int adversarial; float adversarial_lr; int letter_box; float angle; diff --git a/src/detector.c b/src/detector.c index d7e5e578d27..b2f8705c201 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1744,6 +1744,9 @@ void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, float avg_loss = get_network_cost(net); draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, iteration, it_num, 0, 0, "mAP%", dont_show, 0, 0); + float inv_loss = 1.0 / max_val_cmp(0.01, avg_loss); + //net.learning_rate = *lr_set * inv_loss; + if (*boxonly) { int dw = truth_cpu[2] * sized.w, dh = truth_cpu[3] * sized.h; int dx = truth_cpu[0] * sized.w - dw / 2, dy = truth_cpu[1] * sized.h - dh / 2; diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 152190111fd..d42948bb447 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -227,6 +227,49 @@ void backward_network_gpu(network net, network_state state) */ } + if (net.adversarial && net.attention) + { + int img_size = net.w * net.h * net.c; + float *original_input_cpu = (float *)xcalloc(img_size, sizeof(float)); + float *original_delta_cpu = (float *)xcalloc(img_size, sizeof(float)); + cuda_pull_array(original_input, original_input_cpu, img_size); + cuda_pull_array(original_delta, original_delta_cpu, img_size); + + image attention_img; + attention_img.w = net.w; + attention_img.h = net.h; + attention_img.c = net.c; + attention_img.data = original_delta_cpu; + int k; + float min_val = 999999, mean_val = 0, max_val = -999999; + for (k = 0; k < img_size; ++k) { + if (original_delta_cpu[k] < min_val) min_val = original_delta_cpu[k]; + if (original_delta_cpu[k] > max_val) max_val = original_delta_cpu[k]; + mean_val += original_delta_cpu[k]; + } + mean_val = mean_val / img_size; + float range = max_val - min_val; + + for (k = 0; k < img_size; ++k) { + float val = original_delta_cpu[k]; + val = fabs(mean_val - val) / range; + original_delta_cpu[k] = val * 4; + } + + image resized = resize_image(attention_img, net.w/4, net.w/4); + attention_img = resize_image(resized, net.w, net.w); + + for (k = 0; k < img_size; ++k) attention_img.data[k] += original_input_cpu[k]; + + //normalize_image(attention_img); + show_image(attention_img, "delta"); + + free_image(resized); + free_image(attention_img); + + free(original_input_cpu); + free(original_delta_cpu); + } if (net.adversarial) { int x_size = get_network_input_size(net)*net.batch; printf(" x_size = %d, original_delta = %p, original_input = %p, net.learning_rate = %d \n", @@ -333,12 +376,14 @@ void forward_backward_network_gpu(network net, float *x, float *y) float train_network_datum_gpu(network net, float *x, float *y) { *net.seen += net.batch; - if (net.adversarial_lr && rand_int(0, 1) == 1 && get_current_iteration(net) > net.burn_in) { + if (net.adversarial_lr && rand_int(0, 1) == 1) {// && get_current_iteration(net) > net.burn_in) { net.adversarial = 1; float lr_old = net.learning_rate; net.learning_rate = net.adversarial_lr; layer l = net.layers[net.n - 1]; - float *truth_cpu = (float *)xcalloc(l.truths * l.batch, sizeof(float)); + int y_size = get_network_output_size(net)*net.batch; + if (net.layers[net.n - 1].truths) y_size = net.layers[net.n - 1].truths*net.batch; + float *truth_cpu = (float *)xcalloc(y_size, sizeof(float)); printf("\n adversarial training, adversarial_lr = %f \n", net.adversarial_lr); diff --git a/src/parser.c b/src/parser.c index b997e56fab6..4afb28ea661 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1086,7 +1086,7 @@ void parse_net_options(list *options, network *net) net->letter_box = option_find_int_quiet(options, "letter_box", 0); net->label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); net->resize_step = option_find_float_quiet(options, "resize_step", 32); - //net->adversarial = option_find_int_quiet(options, "adversarial", 0); + net->attention = option_find_int_quiet(options, "attention", 0); net->adversarial_lr = option_find_float_quiet(options, "adversarial_lr", 0); net->angle = option_find_float_quiet(options, "angle", 0); From 8cb1f0c44fb805deacc935b2e07b3305bb73bcc0 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 26 Mar 2020 02:34:01 +0300 Subject: [PATCH 0898/1365] Minor fix --- include/darknet.h | 2 ++ src/image.c | 53 ++++++++++++++++++++++++++++++++++++++++++ src/network_kernels.cu | 33 +++----------------------- 3 files changed, 58 insertions(+), 30 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 52d7ce5fdca..7637a4f4261 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -952,6 +952,8 @@ LIB_API int network_height(network *net); LIB_API void optimize_picture(network *net, image orig, int max_layer, float scale, float rate, float thresh, int norm); // image.h +LIB_API void make_image_red(image im); +LIB_API image make_attention_image(int img_size, float *original_delta_cpu, float *original_input_cpu, int w, int h, int c); LIB_API image resize_image(image im, int w, int h); LIB_API image quantize_image(image im); LIB_API void copy_image_from_bytes(image im, char *pdata); diff --git a/src/image.c b/src/image.c index 855a4f2f467..5724507f8e2 100644 --- a/src/image.c +++ b/src/image.c @@ -1310,6 +1310,59 @@ image quantize_image(image im) for (i = 0; i < size; ++i) im.data[i] = (int)(im.data[i] * 255) / 255. + (0.5/255); } +void make_image_red(image im) +{ + int r, c, k; + for (r = 0; r < im.h; ++r) { + for (c = 0; c < im.w; ++c) { + float val = 0; + for (k = 0; k < im.c; ++k) { + val += get_pixel(im, c, r, k); + set_pixel(im, c, r, k, 0); + } + for (k = 0; k < im.c; ++k) { + //set_pixel(im, c, r, k, val); + } + set_pixel(im, c, r, 0, val); + } + } +} + +image make_attention_image(int img_size, float *original_delta_cpu, float *original_input_cpu, int w, int h, int c) +{ + image attention_img; + attention_img.w = w; + attention_img.h = h; + attention_img.c = c; + attention_img.data = original_delta_cpu; + make_image_red(attention_img); + + int k; + float min_val = 999999, mean_val = 0, max_val = -999999; + for (k = 0; k < img_size; ++k) { + if (original_delta_cpu[k] < min_val) min_val = original_delta_cpu[k]; + if (original_delta_cpu[k] > max_val) max_val = original_delta_cpu[k]; + mean_val += original_delta_cpu[k]; + } + mean_val = mean_val / img_size; + float range = max_val - min_val; + + for (k = 0; k < img_size; ++k) { + float val = original_delta_cpu[k]; + val = fabs(mean_val - val) / range; + original_delta_cpu[k] = val * 4; + } + + image resized = resize_image(attention_img, w / 4, h / 4); + attention_img = resize_image(resized, w, h); + free_image(resized); + for (k = 0; k < img_size; ++k) attention_img.data[k] += original_input_cpu[k]; + + //normalize_image(attention_img); + //show_image(attention_img, "delta"); + return attention_img; +} + image resize_image(image im, int w, int h) { if (im.w == w && im.h == h) return copy_image(im); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index d42948bb447..87b54ab27c9 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -235,36 +235,9 @@ void backward_network_gpu(network net, network_state state) cuda_pull_array(original_input, original_input_cpu, img_size); cuda_pull_array(original_delta, original_delta_cpu, img_size); - image attention_img; - attention_img.w = net.w; - attention_img.h = net.h; - attention_img.c = net.c; - attention_img.data = original_delta_cpu; - int k; - float min_val = 999999, mean_val = 0, max_val = -999999; - for (k = 0; k < img_size; ++k) { - if (original_delta_cpu[k] < min_val) min_val = original_delta_cpu[k]; - if (original_delta_cpu[k] > max_val) max_val = original_delta_cpu[k]; - mean_val += original_delta_cpu[k]; - } - mean_val = mean_val / img_size; - float range = max_val - min_val; - - for (k = 0; k < img_size; ++k) { - float val = original_delta_cpu[k]; - val = fabs(mean_val - val) / range; - original_delta_cpu[k] = val * 4; - } - - image resized = resize_image(attention_img, net.w/4, net.w/4); - attention_img = resize_image(resized, net.w, net.w); - - for (k = 0; k < img_size; ++k) attention_img.data[k] += original_input_cpu[k]; - - //normalize_image(attention_img); - show_image(attention_img, "delta"); + image attention_img = make_attention_image(img_size, original_delta_cpu, original_input_cpu, net.w, net.h, net.c); + show_image(attention_img, "attention_img"); - free_image(resized); free_image(attention_img); free(original_input_cpu); @@ -376,7 +349,7 @@ void forward_backward_network_gpu(network net, float *x, float *y) float train_network_datum_gpu(network net, float *x, float *y) { *net.seen += net.batch; - if (net.adversarial_lr && rand_int(0, 1) == 1) {// && get_current_iteration(net) > net.burn_in) { + if (net.adversarial_lr && rand_int(0, 1) == 1 && get_current_iteration(net) > net.burn_in) { net.adversarial = 1; float lr_old = net.learning_rate; net.learning_rate = net.adversarial_lr; From d6181c67cd72468a3b8c658586452a5c6fa8fcbf Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 26 Mar 2020 15:28:20 +0300 Subject: [PATCH 0899/1365] Fix one relu() to lrelu() for weighted-shortcut --- src/blas_kernels.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index e63d15a97a8..f6c5c67e25b 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -1034,7 +1034,7 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)] float w = weights_gpu[weights_index]; - if (weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; + if (weights_normalizion == RELU_NORMALIZATION) w = lrelu(w) / sum; else if (weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; if (weights_normalizion == RELU_NORMALIZATION) grad = w; From 2614a231f06894c1a89de0023735deb846958343 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 26 Mar 2020 15:37:18 +0300 Subject: [PATCH 0900/1365] Fixed relu to lrelu in fuse_conv_batchnorm() --- src/network.c | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/src/network.c b/src/network.c index 8f09dab711f..37463cec9bc 100644 --- a/src/network.c +++ b/src/network.c @@ -1092,6 +1092,12 @@ static float relu(float src) { return 0; } +static float lrelu(float src) { + const float eps = 0.001; + if (src > eps) return src; + return eps; +} + void fuse_conv_batchnorm(network net) { int j; @@ -1160,14 +1166,14 @@ void fuse_conv_batchnorm(network net) for (i = 0; i < (l->n + 1); ++i) { int w_index = chan + i * layer_step; float w = l->weights[w_index]; - if (l->weights_normalizion == RELU_NORMALIZATION) sum += relu(w); + if (l->weights_normalizion == RELU_NORMALIZATION) sum += lrelu(w); else if (l->weights_normalizion == SOFTMAX_NORMALIZATION) sum += expf(w - max_val); } for (i = 0; i < (l->n + 1); ++i) { int w_index = chan + i * layer_step; float w = l->weights[w_index]; - if (l->weights_normalizion == RELU_NORMALIZATION) w = relu(w) / sum; + if (l->weights_normalizion == RELU_NORMALIZATION) w = lrelu(w) / sum; else if (l->weights_normalizion == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum; l->weights[w_index] = w; } From 4f62a01cd338f4a9039559eba45b293264b6dbd8 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 27 Mar 2020 16:29:41 +0300 Subject: [PATCH 0901/1365] Fixed adversarial attack and training --- src/blas_kernels.cu | 9 +++++++++ src/convolutional_kernels.cu | 4 ++-- src/gaussian_yolo_layer.c | 12 ++++++++++++ src/network_kernels.cu | 3 ++- src/yolo_layer.c | 12 ++++++++++++ 5 files changed, 37 insertions(+), 3 deletions(-) diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index f6c5c67e25b..4eacc9e8afb 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -852,6 +852,15 @@ __device__ float lrelu(float src) { return eps; } +__device__ float grad_relu(float src) { + return (src > 0); +} + +__device__ float grad_lrelu(float src) { + const float eps = 0.001; + return (src > eps); +} + __global__ void shortcut_singlelayer_simple_kernel(int size, int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_output_gpu, float *out, float *in, float *weights_gpu, int nweights, WEIGHTS_NORMALIZATION_T weights_normalizion) { const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 7c8dddf4165..896189be1d5 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -421,7 +421,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //if (state.use_mixed_precision) { int iteration_num = get_current_iteration(state.net); // (*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3 * state.net.burn_in) && - (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 1 && l.size > 1) + (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && state.net.loss_scale != 1 && l.groups <= 1 && l.size > 1) { //printf("\n CUDNN_HALF!!! state.index = %d \n", state.index); @@ -673,7 +673,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state //#ifdef CUDNN_HALF int iteration_num = get_current_iteration(state.net); //(*state.net.seen) / (state.net.batch*state.net.subdivisions); if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3 * state.net.burn_in) && - (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 1 && l.size > 1) + (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && state.net.loss_scale != 1 && l.groups <= 1 && l.size > 1) { const size_t input16_size = l.batch*l.c*l.w*l.h; const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h; diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index d3bd3f58181..b9c7e898d9c 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -492,6 +492,18 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) if (best_match_iou > l.ignore_thresh) { l.delta[obj_index] = 0; } + else if (state.net.adversarial) { + int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9); + int stride = l.w*l.h; + float scale = pred.w * pred.h; + if (scale > 0) scale = sqrt(scale); + l.delta[obj_index] = scale * l.cls_normalizer * (0 - l.output[obj_index]); + int cl_id; + for (cl_id = 0; cl_id < l.classes; ++cl_id) { + if (l.output[class_index + stride*cl_id] * l.output[obj_index] > 0.25) + l.delta[class_index + stride*cl_id] = scale * (0 - l.output[class_index + stride*cl_id]); + } + } if (best_iou > l.truth_thresh) { l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 87b54ab27c9..83381da2c74 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -352,7 +352,8 @@ float train_network_datum_gpu(network net, float *x, float *y) if (net.adversarial_lr && rand_int(0, 1) == 1 && get_current_iteration(net) > net.burn_in) { net.adversarial = 1; float lr_old = net.learning_rate; - net.learning_rate = net.adversarial_lr; + float scale = ((float)net.max_batches) / get_current_iteration(net); + net.learning_rate = net.adversarial_lr * scale; layer l = net.layers[net.n - 1]; int y_size = get_network_output_size(net)*net.batch; if (net.layers[net.n - 1].truths) y_size = net.layers[net.n - 1].truths*net.batch; diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 41ae96c70a9..2929ec4f12c 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -404,6 +404,18 @@ void forward_yolo_layer(const layer l, network_state state) if (best_match_iou > l.ignore_thresh) { l.delta[obj_index] = 0; } + else if (state.net.adversarial) { + int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); + int stride = l.w*l.h; + float scale = pred.w * pred.h; + if (scale > 0) scale = sqrt(scale); + l.delta[obj_index] = scale * l.cls_normalizer * (0 - l.output[obj_index]); + int cl_id; + for (cl_id = 0; cl_id < l.classes; ++cl_id) { + if(l.output[class_index + stride*cl_id] * l.output[obj_index] > 0.25) + l.delta[class_index + stride*cl_id] = scale * (0 - l.output[class_index + stride*cl_id]); + } + } if (best_iou > l.truth_thresh) { l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); From 9a2344759b119faf9df98dbeed81650c03650ecd Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 27 Mar 2020 20:08:40 +0300 Subject: [PATCH 0902/1365] adversarial training learning rate fix --- src/network_kernels.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 83381da2c74..2b799b7d80c 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -352,7 +352,7 @@ float train_network_datum_gpu(network net, float *x, float *y) if (net.adversarial_lr && rand_int(0, 1) == 1 && get_current_iteration(net) > net.burn_in) { net.adversarial = 1; float lr_old = net.learning_rate; - float scale = ((float)net.max_batches) / get_current_iteration(net); + float scale = 1.0 - (get_current_iteration(net) / ((float)net.max_batches)); net.learning_rate = net.adversarial_lr * scale; layer l = net.layers[net.n - 1]; int y_size = get_network_output_size(net)*net.batch; From 6a77b8dc9afa3934be1072dc7e152761eafa3d73 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 29 Mar 2020 19:31:42 +0300 Subject: [PATCH 0903/1365] Show receptive field --- include/darknet.h | 5 +++++ src/parser.c | 57 +++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 62 insertions(+) diff --git a/include/darknet.h b/include/darknet.h index 7637a4f4261..44f8bfa1af7 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -338,6 +338,11 @@ struct layer { int dropblock; float scale; + int receptive_w; + int receptive_h; + int receptive_w_scale; + int receptive_h_scale; + char * cweights; int * indexes; int * input_layers; diff --git a/src/parser.c b/src/parser.c index 4afb28ea661..bb47cc59af7 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1096,6 +1096,8 @@ void parse_net_options(list *options, network *net) net->hue = option_find_float_quiet(options, "hue", 0); net->power = option_find_float_quiet(options, "power", 4); + const int show_receptive_field = option_find_float_quiet(options, "show_receptive_field", 0); + if(!net->inputs && !(net->h && net->w && net->c)) error("No input parameters supplied"); char *policy_s = option_find_str(options, "policy", "constant"); @@ -1218,6 +1220,9 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) size_t workspace_size = 0; size_t max_inputs = 0; size_t max_outputs = 0; + int receptive_w = 1, receptive_h = 1; + int receptive_w_scale = 1, receptive_h_scale = 1; + n = n->next; int count = 0; free_section(s); @@ -1332,6 +1337,58 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) fprintf(stderr, "Type not recognized: %s\n", s->type); } + // calculate receptive field + if(show_receptive_field) + { + int dilation = max_val_cmp(1, l.dilation); + int stride = max_val_cmp(1, l.stride); + int size = max_val_cmp(1, l.size); + + if (l.type == UPSAMPLE || (l.type == REORG)) + { + + l.receptive_w = receptive_w; + l.receptive_h = receptive_h; + l.receptive_w_scale = receptive_w_scale = receptive_w_scale / stride; + l.receptive_h_scale = receptive_h_scale = receptive_h_scale / stride; + + } + else { + if (l.type == ROUTE) { + receptive_w = receptive_h = receptive_w_scale = receptive_h_scale = 0; + int k; + for (k = 0; k < l.n; ++k) { + layer route_l = net.layers[l.input_layers[k]]; + receptive_w = max_val_cmp(receptive_w, route_l.receptive_w); + receptive_h = max_val_cmp(receptive_h, route_l.receptive_h); + receptive_w_scale = max_val_cmp(receptive_w_scale, route_l.receptive_w_scale); + receptive_h_scale = max_val_cmp(receptive_h_scale, route_l.receptive_h_scale); + } + } + else + { + int increase_receptive = size + (dilation - 1) * 2 - 1;// stride; + increase_receptive = max_val_cmp(0, increase_receptive); + + receptive_w += increase_receptive * receptive_w_scale; + receptive_h += increase_receptive * receptive_h_scale; + receptive_w_scale *= stride; + receptive_h_scale *= stride; + } + + l.receptive_w = receptive_w; + l.receptive_h = receptive_h; + l.receptive_w_scale = receptive_w_scale; + l.receptive_h_scale = receptive_h_scale; + } + //printf(" size = %d, dilation = %d, stride = %d, receptive_w = %d, receptive_w_scale = %d - ", size, dilation, stride, receptive_w, receptive_w_scale); + + int cur_receptive_w = receptive_w; + int cur_receptive_h = receptive_h; + + fprintf(stderr, "%4d - receptive field: %d x %d \n", count, cur_receptive_w, cur_receptive_h); + } + #ifdef GPU // futher GPU-memory optimization: net.optimized_memory == 2 if (net.optimized_memory >= 2 && params.train && l.type != DROPOUT) From 0e063371500bc998584aa58313cee04b5cf354c4 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 29 Mar 2020 20:03:21 +0300 Subject: [PATCH 0904/1365] minor fix --- src/parser.c | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/parser.c b/src/parser.c index bb47cc59af7..57a12ffc214 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1096,8 +1096,6 @@ void parse_net_options(list *options, network *net) net->hue = option_find_float_quiet(options, "hue", 0); net->power = option_find_float_quiet(options, "power", 4); - const int show_receptive_field = option_find_float_quiet(options, "show_receptive_field", 0); - if(!net->inputs && !(net->h && net->w && net->c)) error("No input parameters supplied"); char *policy_s = option_find_str(options, "policy", "constant"); @@ -1222,6 +1220,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) size_t max_outputs = 0; int receptive_w = 1, receptive_h = 1; int receptive_w_scale = 1, receptive_h_scale = 1; + const int show_receptive_field = option_find_float_quiet(options, "show_receptive_field", 0); n = n->next; int count = 0; From 07e4a7d7b65fdbe31ad72c19ed7a93d64cf3457a Mon Sep 17 00:00:00 2001 From: none Date: Tue, 31 Mar 2020 11:27:48 +0900 Subject: [PATCH 0905/1365] fix error quantize_image's result type. fix warning printf params, in backward_network_gpu. --- include/darknet.h | 2 +- src/image.c | 2 +- src/network_kernels.cu | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 44f8bfa1af7..f7172d2c14f 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -960,7 +960,7 @@ LIB_API void optimize_picture(network *net, image orig, int max_layer, float sca LIB_API void make_image_red(image im); LIB_API image make_attention_image(int img_size, float *original_delta_cpu, float *original_input_cpu, int w, int h, int c); LIB_API image resize_image(image im, int w, int h); -LIB_API image quantize_image(image im); +LIB_API void quantize_image(image im); LIB_API void copy_image_from_bytes(image im, char *pdata); LIB_API image letterbox_image(image im, int w, int h); LIB_API void rgbgr_image(image im); diff --git a/src/image.c b/src/image.c index 5724507f8e2..82896201190 100644 --- a/src/image.c +++ b/src/image.c @@ -1303,7 +1303,7 @@ float bilinear_interpolate(image im, float x, float y, int c) return val; } -image quantize_image(image im) +void quantize_image(image im) { int size = im.c * im.w * im.h; int i; diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 2b799b7d80c..dbf575319f9 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -246,7 +246,7 @@ void backward_network_gpu(network net, network_state state) if (net.adversarial) { int x_size = get_network_input_size(net)*net.batch; printf(" x_size = %d, original_delta = %p, original_input = %p, net.learning_rate = %d \n", - x_size, original_delta, original_input, x_size, net.learning_rate); + x_size, original_delta, original_input, net.learning_rate); axpy_ongpu(x_size, net.learning_rate, original_delta, 1, original_input, 1); constrain_min_max_ongpu(x_size, 0, 1, original_input, 1); } From 4cd21785fc9417247d36682f974ff6b764600a6e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 31 Mar 2020 19:15:33 +0300 Subject: [PATCH 0906/1365] Minor fix --- build/darknet/x64/cfg/cd53paspp-gamma.cfg | 1153 ++++++++++++++++ build/darknet/x64/cfg/cd53paspp-omega.cfg | 1156 +++++++++++++++++ build/darknet/x64/cfg/csdarknet53-omega.cfg | 762 +++++++++++ ...csresnext50-panet-spp-original-optimal.cfg | 2 - build/darknet/x64/partial.cmd | 2 + build/darknet/x64/yolov3.cfg | 2 + cfg/cd53paspp-gamma.cfg | 1153 ++++++++++++++++ cfg/cd53paspp-omega.cfg | 1156 +++++++++++++++++ cfg/csdarknet53-omega.cfg | 762 +++++++++++ ...csresnext50-panet-spp-original-optimal.cfg | 2 - include/darknet.h | 2 +- src/classifier.c | 2 +- src/detector.c | 2 +- src/image.c | 2 +- 14 files changed, 6150 insertions(+), 8 deletions(-) create mode 100644 build/darknet/x64/cfg/cd53paspp-gamma.cfg create mode 100644 build/darknet/x64/cfg/cd53paspp-omega.cfg create mode 100644 build/darknet/x64/cfg/csdarknet53-omega.cfg create mode 100644 cfg/cd53paspp-gamma.cfg create mode 100644 cfg/cd53paspp-omega.cfg create mode 100644 cfg/csdarknet53-omega.cfg diff --git a/build/darknet/x64/cfg/cd53paspp-gamma.cfg b/build/darknet/x64/cfg/cd53paspp-gamma.cfg new file mode 100644 index 00000000000..be3dbff6e5a --- /dev/null +++ b/build/darknet/x64/cfg/cd53paspp-gamma.cfg @@ -0,0 +1,1153 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=512 +height=512 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.00261 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +#cutmix=1 +mosaic=1 + +#:104x104 54:52x52 85:26x26 104:13x13 for 416 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-7 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-10 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +########################## + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 85 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 54 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +########################## + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +scale_x_y = 1.2 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=leaky + +[route] +layers = -1, -16 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +scale_x_y = 1.1 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=leaky + +[route] +layers = -1, -37 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 +scale_x_y = 1.05 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 diff --git a/build/darknet/x64/cfg/cd53paspp-omega.cfg b/build/darknet/x64/cfg/cd53paspp-omega.cfg new file mode 100644 index 00000000000..ef2b2ebdb65 --- /dev/null +++ b/build/darknet/x64/cfg/cd53paspp-omega.cfg @@ -0,0 +1,1156 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=512 +height=512 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.00261 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +#cutmix=1 +mosaic=1 + +#:104x104 54:52x52 85:26x26 104:13x13 for 416 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-7 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-10 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=mish + +########################## + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 85 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 54 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +########################## + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +scale_x_y = 1.2 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=leaky + +[route] +layers = -1, -16 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +scale_x_y = 1.1 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=leaky + +[route] +layers = -1, -37 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 +scale_x_y = 1.05 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + diff --git a/build/darknet/x64/cfg/csdarknet53-omega.cfg b/build/darknet/x64/cfg/csdarknet53-omega.cfg new file mode 100644 index 00000000000..3a3c0730f66 --- /dev/null +++ b/build/darknet/x64/cfg/csdarknet53-omega.cfg @@ -0,0 +1,762 @@ +[net] +# Training +batch=128 +subdivisions=4 + +label_smooth_eps=0.1 + +# Testing +# batch=1 +# subdivisions=1 + +height=256 +width=256 +channels=3 +min_crop=128 +max_crop=448 + +mosaic=1 +cutmix=1 + +burn_in=1000 +learning_rate=0.1 +policy=poly +power=4 +max_batches=1200000 +momentum=0.9 +decay=0.0005 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 + + + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-7 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-10 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=mish + +[avgpool] + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=1 +activation=linear + +[softmax] +groups=1 diff --git a/build/darknet/x64/cfg/csresnext50-panet-spp-original-optimal.cfg b/build/darknet/x64/cfg/csresnext50-panet-spp-original-optimal.cfg index 148f8b3b472..f0b78f973ef 100644 --- a/build/darknet/x64/cfg/csresnext50-panet-spp-original-optimal.cfg +++ b/build/darknet/x64/cfg/csresnext50-panet-spp-original-optimal.cfg @@ -858,7 +858,6 @@ num=9 jitter=.3 ignore_thresh = .7 truth_thresh = 1 -random=1 scale_x_y = 1.2 iou_thresh=0.213 cls_normalizer=1.0 @@ -945,7 +944,6 @@ num=9 jitter=.3 ignore_thresh = .7 truth_thresh = 1 -random=1 scale_x_y = 1.1 iou_thresh=0.213 cls_normalizer=1.0 diff --git a/build/darknet/x64/partial.cmd b/build/darknet/x64/partial.cmd index 79292b50cbd..3d2eeb7d652 100644 --- a/build/darknet/x64/partial.cmd +++ b/build/darknet/x64/partial.cmd @@ -6,6 +6,8 @@ rem Download Yolo9000: http://pjreddie.com/media/files/yolo9000.weights rem darknet.exe partial cfg/tiny-yolo-voc.cfg tiny-yolo-voc.weights tiny-yolo-voc.conv.13 13 +darknet.exe partial cfg/cd53paspp-omega.cfg cd53paspp-omega_final.weights cd53paspp-omega.conv.137 137 + darknet.exe partial cfg/csresnext50.cfg csresnext50.weights csresnext50.conv.75 75 diff --git a/build/darknet/x64/yolov3.cfg b/build/darknet/x64/yolov3.cfg index b2dd2b84e3e..1d94e666918 100644 --- a/build/darknet/x64/yolov3.cfg +++ b/build/darknet/x64/yolov3.cfg @@ -22,6 +22,8 @@ policy=steps steps=400000,450000 scales=.1,.1 +show_receptive_field=1 + [convolutional] batch_normalize=1 filters=32 diff --git a/cfg/cd53paspp-gamma.cfg b/cfg/cd53paspp-gamma.cfg new file mode 100644 index 00000000000..be3dbff6e5a --- /dev/null +++ b/cfg/cd53paspp-gamma.cfg @@ -0,0 +1,1153 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=512 +height=512 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.00261 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +#cutmix=1 +mosaic=1 + +#:104x104 54:52x52 85:26x26 104:13x13 for 416 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-7 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-10 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=leaky + +########################## + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 85 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 54 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +########################## + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +scale_x_y = 1.2 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=leaky + +[route] +layers = -1, -16 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +scale_x_y = 1.1 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=leaky + +[route] +layers = -1, -37 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 +scale_x_y = 1.05 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 diff --git a/cfg/cd53paspp-omega.cfg b/cfg/cd53paspp-omega.cfg new file mode 100644 index 00000000000..ef2b2ebdb65 --- /dev/null +++ b/cfg/cd53paspp-omega.cfg @@ -0,0 +1,1156 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=512 +height=512 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.00261 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +#cutmix=1 +mosaic=1 + +#:104x104 54:52x52 85:26x26 104:13x13 for 416 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-7 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-10 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=mish + +########################## + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 85 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 54 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +########################## + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +scale_x_y = 1.2 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=leaky + +[route] +layers = -1, -16 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +scale_x_y = 1.1 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=leaky + +[route] +layers = -1, -37 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 +scale_x_y = 1.05 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + diff --git a/cfg/csdarknet53-omega.cfg b/cfg/csdarknet53-omega.cfg new file mode 100644 index 00000000000..3a3c0730f66 --- /dev/null +++ b/cfg/csdarknet53-omega.cfg @@ -0,0 +1,762 @@ +[net] +# Training +batch=128 +subdivisions=4 + +label_smooth_eps=0.1 + +# Testing +# batch=1 +# subdivisions=1 + +height=256 +width=256 +channels=3 +min_crop=128 +max_crop=448 + +mosaic=1 +cutmix=1 + +burn_in=1000 +learning_rate=0.1 +policy=poly +power=4 +max_batches=1200000 +momentum=0.9 +decay=0.0005 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 + + + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-7 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-10 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=mish + +[avgpool] + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=1 +activation=linear + +[softmax] +groups=1 diff --git a/cfg/csresnext50-panet-spp-original-optimal.cfg b/cfg/csresnext50-panet-spp-original-optimal.cfg index 148f8b3b472..f0b78f973ef 100644 --- a/cfg/csresnext50-panet-spp-original-optimal.cfg +++ b/cfg/csresnext50-panet-spp-original-optimal.cfg @@ -858,7 +858,6 @@ num=9 jitter=.3 ignore_thresh = .7 truth_thresh = 1 -random=1 scale_x_y = 1.2 iou_thresh=0.213 cls_normalizer=1.0 @@ -945,7 +944,6 @@ num=9 jitter=.3 ignore_thresh = .7 truth_thresh = 1 -random=1 scale_x_y = 1.1 iou_thresh=0.213 cls_normalizer=1.0 diff --git a/include/darknet.h b/include/darknet.h index 44f8bfa1af7..f7172d2c14f 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -960,7 +960,7 @@ LIB_API void optimize_picture(network *net, image orig, int max_layer, float sca LIB_API void make_image_red(image im); LIB_API image make_attention_image(int img_size, float *original_delta_cpu, float *original_input_cpu, int w, int h, int c); LIB_API image resize_image(image im, int w, int h); -LIB_API image quantize_image(image im); +LIB_API void quantize_image(image im); LIB_API void copy_image_from_bytes(image im, char *pdata); LIB_API image letterbox_image(image im, int w, int h); LIB_API void rgbgr_image(image im); diff --git a/src/classifier.c b/src/classifier.c index 8aa50409113..5c1874886c0 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -192,7 +192,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, if (avg_time < 0) avg_time = time_remaining; else avg_time = alpha_time * time_remaining + (1 - alpha_time) * avg_time; start = what_time_is_it_now(); - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images, %f time left\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen, avg_time); + printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images, %f hours left\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen, avg_time); #ifdef OPENCV if (!dontuse_opencv) draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, topk_buff, dont_show, mjpeg_port, avg_time); #endif // OPENCV diff --git a/src/detector.c b/src/detector.c index b2f8705c201..cd7816339b2 100644 --- a/src/detector.c +++ b/src/detector.c @@ -294,7 +294,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i if (iteration < net.burn_in * 3) fprintf(stderr, "\n Tensor Cores are disabled until the first %d iterations are reached.", 3 * net.burn_in); else fprintf(stderr, "\n Tensor Cores are used."); } - printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images, %f time left\n", iteration, loss, avg_loss, get_current_rate(net), (what_time_is_it_now() - time), iteration*imgs, avg_time); + printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images, %f hours left\n", iteration, loss, avg_loss, get_current_rate(net), (what_time_is_it_now() - time), iteration*imgs, avg_time); int draw_precision = 0; if (calc_map && (iteration >= next_map_calc || iteration == net.max_batches)) { diff --git a/src/image.c b/src/image.c index 5724507f8e2..82896201190 100644 --- a/src/image.c +++ b/src/image.c @@ -1303,7 +1303,7 @@ float bilinear_interpolate(image im, float x, float y, int c) return val; } -image quantize_image(image im) +void quantize_image(image im) { int size = im.c * im.w * im.h; int i; From 68062fa4fbd721b351cbc843011345d0b025e66e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 31 Mar 2020 21:12:41 +0300 Subject: [PATCH 0907/1365] Added param to [yolo] iou_thresh_kind=giou iou_thresh=0.213 --- include/darknet.h | 1 + src/box.c | 12 ++++++++++++ src/box.h | 1 + src/gaussian_yolo_layer.c | 2 +- src/parser.c | 20 ++++++++++++++++++++ src/yolo_layer.c | 2 +- 6 files changed, 36 insertions(+), 2 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index f7172d2c14f..0fc9734d3ae 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -384,6 +384,7 @@ struct layer { float iou_normalizer; float cls_normalizer; IOU_LOSS iou_loss; + IOU_LOSS iou_thresh_kind; NMS_KIND nms_kind; float beta_nms; YOLO_POINT yolo_point; diff --git a/src/box.c b/src/box.c index f67a48ad634..8b8ff3bf3e4 100644 --- a/src/box.c +++ b/src/box.c @@ -148,6 +148,18 @@ float box_union(box a, box b) return u; } +float box_iou_kind(box a, box b, IOU_LOSS iou_kind) +{ + //IOU, GIOU, MSE, DIOU, CIOU + switch(iou_kind) { + case IOU: return box_iou(a, b); + case GIOU: return box_giou(a, b); + case DIOU: return box_diou(a, b); + case CIOU: return box_ciou(a, b); + } + return box_iou(a, b); +} + float box_iou(box a, box b) { //return box_intersection(a, b)/box_union(a, b); diff --git a/src/box.h b/src/box.h index 608a4c5869d..4b720653c60 100644 --- a/src/box.h +++ b/src/box.h @@ -33,6 +33,7 @@ extern "C" { box float_to_box(float *f); box float_to_box_stride(float *f, int stride); float box_iou(box a, box b); +float box_iou_kind(box a, box b, IOU_LOSS iou_kind); float box_rmse(box a, box b); dxrep dx_box_iou(box a, box b, IOU_LOSS iou_loss); float box_giou(box a, box b); diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index b9c7e898d9c..c6feb215e78 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -582,7 +582,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) box pred = { 0 }; pred.w = l.biases[2 * n] / state.net.w; pred.h = l.biases[2 * n + 1] / state.net.h; - float iou = box_iou(pred, truth_shift); + float iou = box_iou_kind(pred, truth_shift, l.iou_thresh_kind); // IOU, GIOU, MSE, DIOU, CIOU // iou, n if (iou > l.iou_thresh) { diff --git a/src/parser.c b/src/parser.c index 57a12ffc214..6a902d5ff30 100644 --- a/src/parser.c +++ b/src/parser.c @@ -426,6 +426,16 @@ layer parse_yolo(list *options, size_params params) fprintf(stderr, "[yolo] params: iou loss: %s (%d), iou_norm: %2.2f, cls_norm: %2.2f, scale_x_y: %2.2f\n", iou_loss, l.iou_loss, l.iou_normalizer, l.cls_normalizer, l.scale_x_y); + char *iou_thresh_kind_str = option_find_str_quiet(options, "iou_thresh_kind", "iou"); + if (strcmp(iou_loss, "IOU") == 0) l.iou_thresh_kind = IOU; + else if (strcmp(iou_loss, "giou") == 0) l.iou_thresh_kind = GIOU; + else if (strcmp(iou_loss, "diou") == 0) l.iou_thresh_kind = DIOU; + else if (strcmp(iou_loss, "ciou") == 0) l.iou_thresh_kind = CIOU; + else { + fprintf(stderr, " Wrong iou_thresh_kind = %s \n", iou_thresh_kind_str); + l.iou_thresh_kind = IOU; + } + l.beta_nms = option_find_float_quiet(options, "beta_nms", 0.6); char *nms_kind = option_find_str_quiet(options, "nms_kind", "default"); if (strcmp(nms_kind, "default") == 0) l.nms_kind = DEFAULT_NMS; @@ -521,6 +531,16 @@ layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3 else if (strcmp(iou_loss, "ciou") == 0) l.iou_loss = CIOU; else l.iou_loss = IOU; + char *iou_thresh_kind_str = option_find_str_quiet(options, "iou_thresh_kind", "iou"); + if (strcmp(iou_loss, "IOU") == 0) l.iou_thresh_kind = IOU; + else if (strcmp(iou_loss, "giou") == 0) l.iou_thresh_kind = GIOU; + else if (strcmp(iou_loss, "diou") == 0) l.iou_thresh_kind = DIOU; + else if (strcmp(iou_loss, "ciou") == 0) l.iou_thresh_kind = CIOU; + else { + fprintf(stderr, " Wrong iou_thresh_kind = %s \n", iou_thresh_kind_str); + l.iou_thresh_kind = IOU; + } + l.beta_nms = option_find_float_quiet(options, "beta_nms", 0.6); char *nms_kind = option_find_str_quiet(options, "nms_kind", "default"); if (strcmp(nms_kind, "default") == 0) l.nms_kind = DEFAULT_NMS; diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 2929ec4f12c..27d9bfd5bcd 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -505,7 +505,7 @@ void forward_yolo_layer(const layer l, network_state state) box pred = { 0 }; pred.w = l.biases[2 * n] / state.net.w; pred.h = l.biases[2 * n + 1] / state.net.h; - float iou = box_iou(pred, truth_shift); + float iou = box_iou_kind(pred, truth_shift, l.iou_thresh_kind); // IOU, GIOU, MSE, DIOU, CIOU // iou, n if (iou > l.iou_thresh) { From 2f9f4a40d34bb344e5c6667fa34dae4caa8e2aa3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 1 Apr 2020 15:02:21 +0300 Subject: [PATCH 0908/1365] Minor fix --- src/parser.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/parser.c b/src/parser.c index 6a902d5ff30..11e90f6f064 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1723,7 +1723,7 @@ void save_weights_upto(network net, char *filename, int cutoff) fwrite(&major, sizeof(int), 1, fp); fwrite(&minor, sizeof(int), 1, fp); fwrite(&revision, sizeof(int), 1, fp); - //(*net.seen) = (*net.cur_iteration) * net.batch * net.subdivisions; + (*net.seen) = get_current_iteration(net) * net.batch * net.subdivisions; // remove this line, when you will save to weights-file both: seen & cur_iteration fwrite(net.seen, sizeof(uint64_t), 1, fp); int i; From afb4cc4766eb8ab3686445448fa3cf652ab78eb8 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 2 Apr 2020 19:04:08 +0300 Subject: [PATCH 0909/1365] Fix for iou_thresh_kind= param --- src/parser.c | 24 ++++++++++++++---------- 1 file changed, 14 insertions(+), 10 deletions(-) diff --git a/src/parser.c b/src/parser.c index 11e90f6f064..28dc9f3db4f 100644 --- a/src/parser.c +++ b/src/parser.c @@ -427,10 +427,10 @@ layer parse_yolo(list *options, size_params params) iou_loss, l.iou_loss, l.iou_normalizer, l.cls_normalizer, l.scale_x_y); char *iou_thresh_kind_str = option_find_str_quiet(options, "iou_thresh_kind", "iou"); - if (strcmp(iou_loss, "IOU") == 0) l.iou_thresh_kind = IOU; - else if (strcmp(iou_loss, "giou") == 0) l.iou_thresh_kind = GIOU; - else if (strcmp(iou_loss, "diou") == 0) l.iou_thresh_kind = DIOU; - else if (strcmp(iou_loss, "ciou") == 0) l.iou_thresh_kind = CIOU; + if (strcmp(iou_thresh_kind_str, "iou") == 0) l.iou_thresh_kind = IOU; + else if (strcmp(iou_thresh_kind_str, "giou") == 0) l.iou_thresh_kind = GIOU; + else if (strcmp(iou_thresh_kind_str, "diou") == 0) l.iou_thresh_kind = DIOU; + else if (strcmp(iou_thresh_kind_str, "ciou") == 0) l.iou_thresh_kind = CIOU; else { fprintf(stderr, " Wrong iou_thresh_kind = %s \n", iou_thresh_kind_str); l.iou_thresh_kind = IOU; @@ -532,10 +532,10 @@ layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3 else l.iou_loss = IOU; char *iou_thresh_kind_str = option_find_str_quiet(options, "iou_thresh_kind", "iou"); - if (strcmp(iou_loss, "IOU") == 0) l.iou_thresh_kind = IOU; - else if (strcmp(iou_loss, "giou") == 0) l.iou_thresh_kind = GIOU; - else if (strcmp(iou_loss, "diou") == 0) l.iou_thresh_kind = DIOU; - else if (strcmp(iou_loss, "ciou") == 0) l.iou_thresh_kind = CIOU; + if (strcmp(iou_thresh_kind_str, "iou") == 0) l.iou_thresh_kind = IOU; + else if (strcmp(iou_thresh_kind_str, "giou") == 0) l.iou_thresh_kind = GIOU; + else if (strcmp(iou_thresh_kind_str, "diou") == 0) l.iou_thresh_kind = DIOU; + else if (strcmp(iou_thresh_kind_str, "ciou") == 0) l.iou_thresh_kind = CIOU; else { fprintf(stderr, " Wrong iou_thresh_kind = %s \n", iou_thresh_kind_str); l.iou_thresh_kind = IOU; @@ -1234,6 +1234,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) printf("mini_batch = %d, batch = %d, time_steps = %d, train = %d \n", net.batch, net.batch * net.subdivisions, net.time_steps, params.train); int avg_outputs = 0; + int avg_counter = 0; float bflops = 0; size_t workspace_size = 0; size_t max_inputs = 0; @@ -1476,7 +1477,10 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) } if (l.bflops > 0) bflops += l.bflops; - avg_outputs += l.outputs; + if (l.w > 1 && l.h > 1) { + avg_outputs += l.outputs; + avg_counter++; + } } free_list(sections); @@ -1520,7 +1524,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) net.outputs = get_network_output_size(net); net.output = get_network_output(net); - avg_outputs = avg_outputs / count; + avg_outputs = avg_outputs / avg_counter; fprintf(stderr, "Total BFLOPS %5.3f \n", bflops); fprintf(stderr, "avg_outputs = %d \n", avg_outputs); #ifdef GPU From a9bae4f0326b4a841756d5b1a6ed37821f6a9467 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 3 Apr 2020 20:33:36 +0300 Subject: [PATCH 0910/1365] Fixed DropBlock --- src/dropout_layer_kernels.cu | 129 ++++++++++++++++++++++++++--------- 1 file changed, 98 insertions(+), 31 deletions(-) diff --git a/src/dropout_layer_kernels.cu b/src/dropout_layer_kernels.cu index a963235e8ae..05cde594993 100644 --- a/src/dropout_layer_kernels.cu +++ b/src/dropout_layer_kernels.cu @@ -8,8 +8,11 @@ #include "utils.h" #include "blas.h" +#include "image_opencv.h" +#include "image.h" -__global__ void dropblock_fast_kernel(float *rand, float prob, int w, int h, int spatial, int filters, int block_size, float *drop_blocks_scale, float *output) + +__global__ void dropblock_fast_kernel(float *rand, float prob, int w, int h, int spatial, int filters, int batch, int block_size, float *drop_blocks_scale, float *output) { const int threads = BLOCK; const int id = threadIdx.x; @@ -32,6 +35,7 @@ __global__ void dropblock_fast_kernel(float *rand, float prob, int w, int h, int if (rand[index] < prob) { //Chose with the lowest rand[i] int new_val = rand[index] * 1000000; + rand[index] = 1; int old_val = atomicMin(&prob_block, new_val); if (new_val < old_val) { index_block = i; @@ -47,6 +51,9 @@ __global__ void dropblock_fast_kernel(float *rand, float prob, int w, int h, int int b_x = index_block % w; int b_y = index_block / w; + if (b_x > (w - block_size)) b_x = b_x - (w - block_size); + if (b_y > (h - block_size)) b_y = b_y - (h - block_size); + b_x = max(0, min(b_x, w - block_size)); b_y = max(0, min(b_y, h - block_size)); @@ -54,21 +61,24 @@ __global__ void dropblock_fast_kernel(float *rand, float prob, int w, int h, int for (i = id; i < block_square_size; i += threads) { - int i_x = i % w; - int i_y = i / w; + int i_x = i % block_size; + int i_y = i / block_size; int x = b_x + i_x; int y = b_y + i_y; - if (x < w && y < h) { - int index = b*spatial*f + f*spatial + y*w + x; + if (x >= 0 && x < w && y >= 0 && y < h) { + int new_index = b*filters*spatial + f*spatial + y*w + x; - output[index] = 0; + output[new_index] = 0; + rand[new_index] = 0; } } + //if (id == 0 && b == 0) printf(" f = %d, b = %d \n", f, b); + if (id == 0 && drop_blocks_scale) { - atomicAdd(&drop_blocks_scale[b], 1); + atomicAdd(&drop_blocks_scale[b], block_square_size); //if(b == 0) printf("\n index_block = %d \n", index_block); } @@ -79,7 +89,8 @@ __global__ void set_scales_dropblock_kernel(float *drop_blocks_scale, int block_ const int index = blockIdx.x*blockDim.x + threadIdx.x; if (index >= batch) return; - const float prob = drop_blocks_scale[index] * block_size_w * block_size_h / (float)outputs; + //printf(" drop_blocks_scale[index] = %f \n", drop_blocks_scale[index]); + const float prob = drop_blocks_scale[index] / (float)outputs; const float scale = 1.0f / (1.0f - prob); drop_blocks_scale[index] = scale; } @@ -94,6 +105,15 @@ __global__ void scale_dropblock_kernel(float *output, int size, int outputs, flo } +__global__ void backward_dropblock_kernel(float *pass, float *delta, int size) +{ + const int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index >= size) return; + + if (pass[index] == 0) delta[index] = 0; +} + + __global__ void yoloswag420blazeit360noscope(float *input, int size, float *rand, float prob, float scale) { int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; @@ -135,14 +155,17 @@ void forward_dropout_layer_gpu(dropout_layer l, network_state state) const int block_size = min_val_cmp(block_width, block_height); const float block_prob = cur_prob / (block_size*block_size); + assert(block_size <= l.w && block_size <= l.h); - int size = l.inputs*l.batch; + const int size = l.inputs*l.batch; cuda_random(l.rand_gpu, size); fill_ongpu(l.batch, 0, l.drop_blocks_scale_gpu, 1); + //fill_ongpu(l.outputs * l.batch, 1, state.input, 1); // remove!!! + int num_blocks = l.batch * l.c; - dropblock_fast_kernel << > > (l.rand_gpu, block_prob, l.w, l.h, l.w*l.h, l.c, block_size, l.drop_blocks_scale_gpu, state.input); + dropblock_fast_kernel << > > (l.rand_gpu, block_prob, l.w, l.h, l.w*l.h, l.c, l.batch, block_size, l.drop_blocks_scale_gpu, state.input); CHECK_CUDA(cudaPeekAtLastError()); num_blocks = get_number_of_blocks(l.batch, BLOCK); @@ -150,24 +173,39 @@ void forward_dropout_layer_gpu(dropout_layer l, network_state state) CHECK_CUDA(cudaPeekAtLastError()); /* - cuda_pull_array(l.drop_blocks_scale_gpu, l.drop_blocks_scale, l.batch); - - for (int b = 0; b < l.batch; ++b) { - const float prob = l.drop_blocks_scale[b] * block_size * block_size / (float)l.outputs; - const float scale = 1.0f / (1.0f - prob); - //printf(" %d x %d - block_size = %d, block_size*block_size = %d , ", l.w, l.h, block_size, block_size*block_size); - //printf(" , l.drop_blocks_scale[b] = %f, prob = %f, calc scale = %f \t cur_prob = %f, cur_scale = %f \n", - // l.drop_blocks_scale[b], prob, scale, cur_prob, cur_scale); - l.drop_blocks_scale[b] = scale; - } + { + cuda_pull_array(l.drop_blocks_scale_gpu, l.drop_blocks_scale, l.batch); + + float avg_scale = 0; + + for (int b = 0; b < l.batch; ++b) { + const float scale = l.drop_blocks_scale[b]; + avg_scale += scale; + printf(" %d x %d - block_size = %d, block_size*block_size = %d , ", l.w, l.h, block_size, block_size*block_size); + printf(" , l.drop_blocks_scale[b] = %f, scale = %f \t cur_prob = %f, cur_scale = %f \n", + l.drop_blocks_scale[b], scale, cur_prob, cur_scale); + } + avg_scale = avg_scale / l.batch; + printf(" avg_scale = %f \n", avg_scale); + + float *output = (float *)calloc(l.outputs * l.batch, sizeof(float)); + cuda_pull_array(state.input, output, l.outputs * l.batch); - cuda_push_array(l.drop_blocks_scale_gpu, l.drop_blocks_scale, l.batch); + printf(" l.w = %d, l.h = %d, l.c = %d \n", l.w, l.h, l.c); + + image img = float_to_image(l.w, l.h, l.c, output); + img = collapse_image_layers(img, 1); + //normalize_image(img); + + show_image(img, "dropout - forward"); + wait_key_cv(0); + //free_image(img); + //free(output); + } */ num_blocks = get_number_of_blocks(l.outputs * l.batch, BLOCK); scale_dropblock_kernel << > > (state.input, l.outputs * l.batch, l.outputs, l.drop_blocks_scale_gpu); - //scal_ongpu(l.outputs * l.batch, cur_scale, state.input, 1); - //scal_ongpu(l.outputs * l.batch, l.drop_blocks_scale[0], state.input, 1); CHECK_CUDA(cudaPeekAtLastError()); } @@ -194,7 +232,7 @@ void backward_dropout_layer_gpu(dropout_layer l, network_state state) //int iteration_num = get_current_iteration(state.net); //(*state.net.seen) / (state.net.batch*state.net.subdivisions); //if (iteration_num < state.net.burn_in) return; - int size = l.inputs*l.batch; + const int size = l.inputs*l.batch; // dropblock if (l.dropblock) { @@ -223,18 +261,47 @@ void backward_dropout_layer_gpu(dropout_layer l, network_state state) const int block_size = min_val_cmp(block_width, block_height); const float block_prob = cur_prob / (block_size*block_size); - int num_blocks = l.batch * l.c; - dropblock_fast_kernel << > > (l.rand_gpu, block_prob, l.w, l.h, l.w*l.h, l.c, block_size, NULL, state.delta); + //fill_ongpu(l.outputs * l.batch, 1, state.delta, 1); // remove!!! + + int num_blocks = get_number_of_blocks(l.outputs * l.batch, BLOCK); + backward_dropblock_kernel << > >(l.rand_gpu, state.delta, l.outputs * l.batch); CHECK_CUDA(cudaPeekAtLastError()); - num_blocks = get_number_of_blocks(l.outputs * l.batch, BLOCK); scale_dropblock_kernel << > > (state.delta, l.outputs * l.batch, l.outputs, l.drop_blocks_scale_gpu); - //scal_ongpu(l.outputs * l.batch, cur_scale, state.input, 1); - //scal_ongpu(l.outputs * l.batch, l.drop_blocks_scale[0], state.input, 1); CHECK_CUDA(cudaPeekAtLastError()); - //drop_block_kernel << > > (state.delta, size, l.rand_gpu, l.scale); - //CHECK_CUDA(cudaPeekAtLastError()); + /* + { + cuda_pull_array(l.drop_blocks_scale_gpu, l.drop_blocks_scale, l.batch); + + float avg_scale = 0; + + for (int b = 0; b < l.batch; ++b) { + const float scale = l.drop_blocks_scale[b]; + avg_scale += scale; + printf(" %d x %d - block_size = %d, block_size*block_size = %d , ", l.w, l.h, block_size, block_size*block_size); + printf(" , l.drop_blocks_scale[b] = %f, scale = %f \t cur_prob = %f, cur_scale = %f \n", + l.drop_blocks_scale[b], scale, cur_prob, cur_scale); + } + avg_scale = avg_scale / l.batch; + printf(" avg_scale = %f \n", avg_scale); + + float *output = (float *)calloc(l.outputs * l.batch, sizeof(float)); + cuda_pull_array(state.delta, output, l.outputs * l.batch); + + printf(" l.w = %d, l.h = %d, l.c = %d \n", l.w, l.h, l.c); + + image img = float_to_image(l.w, l.h, l.c, output); + img = collapse_image_layers(img, 1); + //normalize_image(img); + + show_image(img, "dropout - delta"); + wait_key_cv(0); + //free_image(img); + //free(output); + } + */ + } // dropout else { From 3d9aa2af4718a3bd5bbe23de2022987cb767e9c5 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 7 Apr 2020 22:40:20 +0300 Subject: [PATCH 0911/1365] `./darknet detector calc_anchors` calculates both anchors= and counters_per_class= --- README.md | 2 ++ src/detector.c | 35 ++++++++++++++++++++++++++++++++++- src/network_kernels.cu | 2 +- 3 files changed, 37 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index a00d8924a45..ce39448507a 100644 --- a/README.md +++ b/README.md @@ -104,8 +104,10 @@ You can get cfg-files by path: `darknet/cfg/` * **PyTorch > ONNX > CoreML > iOS** how to convert cfg/weights-files to pt-file: [ultralytics/yolov3](https://github.com/ultralytics/yolov3#darknet-conversion) and [iOS App](https://itunes.apple.com/app/id1452689527) * **TensorRT** for YOLOv3 (-70% faster inference): [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) * **TVM** - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backends (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about +* **OpenDataCam** - It detects, tracks and counts moving objects by using Yolo: https://github.com/opendatacam/opendatacam#-hardware-pre-requisite * **Netron** - Visualizer for neural networks: https://github.com/lutzroeder/netron + #### Datasets * MS COCO: use `./scripts/get_coco_dataset.sh` to get labeled MS COCO detection dataset diff --git a/src/detector.c b/src/detector.c index cd7816339b2..b1a56e5190f 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1369,6 +1369,9 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int int number_of_images = plist->size; char **paths = (char **)list_to_array(plist); + int classes = option_find_int(options, "classes", 1); + int* counter_per_class = (int*)xcalloc(classes, sizeof(int)); + srand(time(0)); int number_of_boxes = 0; printf(" read labels from %d images \n", number_of_images); @@ -1395,6 +1398,12 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int system(buff); if (check_mistakes) getchar(); } + if (truth[j].id >= classes) { + classes = truth[j].id + 1; + counter_per_class = (int*)xrealloc(counter_per_class, classes * sizeof(int)); + } + counter_per_class[truth[j].id]++; + number_of_boxes++; rel_width_height_array = (float*)xrealloc(rel_width_height_array, 2 * number_of_boxes * sizeof(float)); @@ -1461,10 +1470,33 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int } else avg_iou += best_iou; } + + char buff[1024]; + FILE* fwc = fopen("counters_per_class.txt", "wb"); + if (fwc) { + sprintf(buff, "counters_per_class = "); + printf("\n%s", buff); + fwrite(buff, sizeof(char), strlen(buff), fwc); + for (i = 0; i < classes; ++i) { + sprintf(buff, "%d", counter_per_class[i]); + printf("%s", buff); + fwrite(buff, sizeof(char), strlen(buff), fwc); + if (i < classes - 1) { + fwrite(", ", sizeof(char), 2, fwc); + printf(", "); + } + } + printf("\n"); + fclose(fwc); + } + else { + printf(" Error: file counters_per_class.txt can't be open \n"); + } + avg_iou = 100 * avg_iou / number_of_boxes; printf("\n avg IoU = %2.2f %% \n", avg_iou); - char buff[1024]; + FILE* fw = fopen("anchors.txt", "wb"); if (fw) { printf("\nSaving anchors to the file: anchors.txt \n"); @@ -1494,6 +1526,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int #endif // OPENCV } free(rel_width_height_array); + free(counter_per_class); getchar(); } diff --git a/src/network_kernels.cu b/src/network_kernels.cu index dbf575319f9..c611e2294cd 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -245,7 +245,7 @@ void backward_network_gpu(network net, network_state state) } if (net.adversarial) { int x_size = get_network_input_size(net)*net.batch; - printf(" x_size = %d, original_delta = %p, original_input = %p, net.learning_rate = %d \n", + printf(" x_size = %d, original_delta = %p, original_input = %p, net.learning_rate = %f \n", x_size, original_delta, original_input, net.learning_rate); axpy_ongpu(x_size, net.learning_rate, original_delta, 1, original_input, 1); constrain_min_max_ongpu(x_size, 0, 1, original_input, 1); From 08b1c20e2666ce8813054cfed6243ad48feaac1d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 11 Apr 2020 14:57:42 +0300 Subject: [PATCH 0912/1365] Fixed seen for multu-GPU training --- src/network_kernels.cu | 1 + 1 file changed, 1 insertion(+) diff --git a/src/network_kernels.cu b/src/network_kernels.cu index c611e2294cd..68c2a208059 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -625,6 +625,7 @@ float train_networks(network *nets, int n, data d, int interval) } //cudaDeviceSynchronize(); *nets[0].cur_iteration += (n - 1); + *nets[0].seen = nets[0].batch * nets[0].subdivisions * get_current_iteration(nets[0]); // remove this line, when you will save to weights-file both: seen & cur_iteration if (get_current_iteration(nets[0]) % interval == 0) { printf("Syncing... "); From 9e932d65c5cee9788f152ab677185e17c7fe08bf Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 11 Apr 2020 15:16:40 +0300 Subject: [PATCH 0913/1365] Fixed compilation for ZED camera --- src/yolo_console_dll.cpp | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/src/yolo_console_dll.cpp b/src/yolo_console_dll.cpp index 6d1bb2a580e..cb40ccf8af5 100644 --- a/src/yolo_console_dll.cpp +++ b/src/yolo_console_dll.cpp @@ -111,7 +111,13 @@ cv::Mat slMat2cvMat(sl::Mat &input) { ) { cv_type = CV_32FC4; } else cv_type = CV_8UC4; // sl::Mat used are either RGBA images or XYZ (4C) point clouds - return cv::Mat(input.getHeight(), input.getWidth(), cv_type, input.getPtr(sl::MEM::CPU)); + return cv::Mat(input.getHeight(), input.getWidth(), cv_type, input.getPtr( +#ifdef ZED_STEREO_2_COMPAT_MODE + sl::MEM::MEM_CPU +#else + sl::MEM::CPU +#endif + )); } cv::Mat zed_capture_rgb(sl::Camera &zed) { From 00193efd839029a7b79556ac692718be98091867 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 13 Apr 2020 22:52:20 +0300 Subject: [PATCH 0914/1365] Use C++ thread for demo.c --- src/demo.c | 12 ++++++------ src/http_stream.cpp | 21 +++++++++++++++++++++ src/http_stream.h | 6 ++++++ 3 files changed, 33 insertions(+), 6 deletions(-) diff --git a/src/demo.c b/src/demo.c index f261631dd51..a529bd385c0 100644 --- a/src/demo.c +++ b/src/demo.c @@ -156,8 +156,8 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int flag_exit = 0; - pthread_t fetch_thread; - pthread_t detect_thread; + custom_thread_t fetch_thread = NULL; + custom_thread_t detect_thread = NULL; fetch_in_thread(0); det_img = in_img; @@ -214,8 +214,8 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int int local_nboxes = nboxes; detection *local_dets = dets; - if (!benchmark) if (pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed"); - if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed"); + if (!benchmark) if (custom_create_thread(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed"); + if(custom_create_thread(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed"); //if (nms) do_nms_obj(local_dets, local_nboxes, l.classes, nms); // bad results if (nms) { @@ -285,9 +285,9 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int printf("\n cvWriteFrame \n"); } - pthread_join(detect_thread, 0); + custom_join(detect_thread, 0); if (!benchmark) { - pthread_join(fetch_thread, 0); + custom_join(fetch_thread, 0); free_image(det_s); } diff --git a/src/http_stream.cpp b/src/http_stream.cpp index c71a7f479fa..c8d4b583ad3 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -682,6 +682,27 @@ void show_total_time() { std::cout << " Total: " << total_time * 1000 << " msec" << std::endl; } + +int custom_create_thread(custom_thread_t * tid, const custom_attr_t * attr, void *(*func) (void *), void *arg) +{ + *tid = (custom_thread_t *)new std::thread(func, arg); + if (tid) return 0; + else return -1; +} + +int custom_join(custom_thread_t thread, void **value_ptr) +{ + std::thread *ptr = (std::thread *)thread; + if (ptr) { + ptr->join(); + delete ptr; + return 0; + } + else printf(" Error: ptr of thread is NULL in custom_join() \n"); + + return -1; +} + #else // C++11 #include diff --git a/src/http_stream.h b/src/http_stream.h index 92765647645..0aa63d65fe9 100644 --- a/src/http_stream.h +++ b/src/http_stream.h @@ -18,6 +18,12 @@ int send_http_post_request(char *http_post_host, int server_port, const char *vi #endif // OPENCV +typedef void* custom_thread_t; +typedef void* custom_attr_t; + +int custom_create_thread(custom_thread_t * tid, const custom_attr_t * attr, void *(*func) (void *), void *arg); +int custom_join(custom_thread_t thread, void **value_ptr); + #ifdef __cplusplus } #endif From 342a8d1561c19317f2d5fda0f099449b79b51716 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 13 Apr 2020 23:03:50 +0300 Subject: [PATCH 0915/1365] Fixed std thread --- src/http_stream.cpp | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/src/http_stream.cpp b/src/http_stream.cpp index c8d4b583ad3..76de4b29813 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -13,6 +13,7 @@ #include #include #include +#include #include using std::cerr; using std::endl; @@ -685,14 +686,15 @@ void show_total_time() { int custom_create_thread(custom_thread_t * tid, const custom_attr_t * attr, void *(*func) (void *), void *arg) { - *tid = (custom_thread_t *)new std::thread(func, arg); + std::thread *ptr = new std::thread(func, arg); + *tid = (custom_thread_t *)ptr; if (tid) return 0; else return -1; } -int custom_join(custom_thread_t thread, void **value_ptr) +int custom_join(custom_thread_t tid, void **value_ptr) { - std::thread *ptr = (std::thread *)thread; + std::thread *ptr = (std::thread *)tid; if (ptr) { ptr->join(); delete ptr; From d4b2ed9d22210d33e438d9557e976f8053f1cf9b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 17 Apr 2020 19:06:15 +0300 Subject: [PATCH 0916/1365] Added param train_only_bn=1 to train only Batch-normalization for this and previous layers --- build/darknet/x64/partial.cmd | 3 +++ include/darknet.h | 1 + src/convolutional_kernels.cu | 28 ++++++++++++++------------- src/network_kernels.cu | 2 ++ src/parser.c | 36 +++++++++++++++++++++++++++++++++++ 5 files changed, 57 insertions(+), 13 deletions(-) diff --git a/build/darknet/x64/partial.cmd b/build/darknet/x64/partial.cmd index 3d2eeb7d652..1248d1b750b 100644 --- a/build/darknet/x64/partial.cmd +++ b/build/darknet/x64/partial.cmd @@ -6,6 +6,9 @@ rem Download Yolo9000: http://pjreddie.com/media/files/yolo9000.weights rem darknet.exe partial cfg/tiny-yolo-voc.cfg tiny-yolo-voc.weights tiny-yolo-voc.conv.13 13 +darknet.exe partial cfg/csdarknet53-omega.cfg csdarknet53-omega_final.weights csdarknet53-omega.conv.105 105 + + darknet.exe partial cfg/cd53paspp-omega.cfg cd53paspp-omega_final.weights cd53paspp-omega.conv.137 137 diff --git a/include/darknet.h b/include/darknet.h index 0fc9734d3ae..92ad96a32ac 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -324,6 +324,7 @@ struct layer { int onlyforward; int stopbackward; + int train_only_bn; int dont_update; int burnin_update; int dontload; diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 896189be1d5..c63e0aaf65e 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -744,7 +744,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state assert((l.nweights) > 0); cuda_convert_f32_to_f16(l.weight_updates_gpu, l.nweights, l.weight_updates_gpu16); - if (!state.net.adversarial) { + if (!state.net.adversarial && !l.train_only_bn) { CHECK_CUDNN(cudnnConvolutionBackwardFilter(cudnn_handle(), &one, l.srcTensorDesc16, @@ -796,7 +796,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state backward_batchnorm_layer_gpu(l, state); } - if (!state.net.adversarial) { + if (!state.net.adversarial && !l.train_only_bn) { // calculate conv weight updates // if used: beta=1 then loss decreases faster CHECK_CUDNN(cudnnConvolutionBackwardFilter(cudnn_handle(), @@ -857,17 +857,19 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state float *im = state.input + (i*l.groups + j)*l.c / l.groups*l.h*l.w; - //im2col_ongpu(im, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, state.workspace); - im2col_gpu_ext(im, // input - l.c / l.groups, // input channels - l.h, l.w, // input size (h, w) - l.size, l.size, // kernel size (h, w) - l.pad * l.dilation, l.pad * l.dilation, // padding (h, w) - l.stride_y, l.stride_x, // stride (h, w) - l.dilation, l.dilation, // dilation (h, w) - state.workspace); // output - //gemm_ongpu(0, 1, m, n, k, 1, a + i*m*k, k, b, k, 1, c, n); - gemm_ongpu(0, 1, m, n, k, 1, a, k, b, k, 1, c, n); + if (!state.net.adversarial && !l.train_only_bn) { + //im2col_ongpu(im, l.c / l.groups, l.h, l.w, l.size, l.stride, l.pad, state.workspace); + im2col_gpu_ext(im, // input + l.c / l.groups, // input channels + l.h, l.w, // input size (h, w) + l.size, l.size, // kernel size (h, w) + l.pad * l.dilation, l.pad * l.dilation, // padding (h, w) + l.stride_y, l.stride_x, // stride (h, w) + l.dilation, l.dilation, // dilation (h, w) + state.workspace); // output + //gemm_ongpu(0, 1, m, n, k, 1, a + i*m*k, k, b, k, 1, c, n); + gemm_ongpu(0, 1, m, n, k, 1, a, k, b, k, 1, c, n); + } if (state.delta) { if (l.binary || l.xnor) swap_binary(&l); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 68c2a208059..7d12f077e4e 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -273,6 +273,8 @@ void update_network_gpu(network net) l.t = get_current_batch(net); if (iteration_num > (net.max_batches * 1 / 2)) l.deform = 0; if (l.burnin_update && (l.burnin_update*net.burn_in > iteration_num)) continue; + if (l.train_only_bn) continue; + if(l.update_gpu && l.dont_update < iteration_num){ l.update_gpu(l, update_batch, rate, net.momentum, net.decay, net.loss_scale); } diff --git a/src/parser.c b/src/parser.c index 28dc9f3db4f..1337836317f 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1190,6 +1190,39 @@ int is_network(section *s) || strcmp(s->type, "[network]")==0); } +void set_train_only_bn(network net) +{ + int train_only_bn = 0; + int i; + for (i = net.n - 1; i >= 0; --i) { + if (net.layers[i].train_only_bn) train_only_bn = net.layers[i].train_only_bn; // set l.train_only_bn for all previous layers + if (train_only_bn) { + net.layers[i].train_only_bn = train_only_bn; + + if (net.layers[i].type == CONV_LSTM) { + net.layers[i].wf->train_only_bn = train_only_bn; + net.layers[i].wi->train_only_bn = train_only_bn; + net.layers[i].wg->train_only_bn = train_only_bn; + net.layers[i].wo->train_only_bn = train_only_bn; + net.layers[i].uf->train_only_bn = train_only_bn; + net.layers[i].ui->train_only_bn = train_only_bn; + net.layers[i].ug->train_only_bn = train_only_bn; + net.layers[i].uo->train_only_bn = train_only_bn; + if (net.layers[i].peephole) { + net.layers[i].vf->train_only_bn = train_only_bn; + net.layers[i].vi->train_only_bn = train_only_bn; + net.layers[i].vo->train_only_bn = train_only_bn; + } + } + else if (net.layers[i].type == CRNN) { + net.layers[i].input_layer->train_only_bn = train_only_bn; + net.layers[i].self_layer->train_only_bn = train_only_bn; + net.layers[i].output_layer->train_only_bn = train_only_bn; + } + } + } +} + network parse_network_cfg(char *filename) { return parse_network_cfg_custom(filename, 0, 0); @@ -1450,6 +1483,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) l.dont_update = option_find_int_quiet(options, "dont_update", 0); l.burnin_update = option_find_int_quiet(options, "burnin_update", 0); l.stopbackward = option_find_int_quiet(options, "stopbackward", 0); + l.train_only_bn = option_find_int_quiet(options, "train_only_bn", 0); l.dontload = option_find_int_quiet(options, "dontload", 0); l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0); l.learning_rate_scale = option_find_float_quiet(options, "learning_rate", 1); @@ -1522,6 +1556,8 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) } #endif + set_train_only_bn(net); // set l.train_only_bn for all required layers + net.outputs = get_network_output_size(net); net.output = get_network_output(net); avg_outputs = avg_outputs / avg_counter; From 5dc7d8074d47347eff515f03a80c2d1e97eda95e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 18 Apr 2020 03:25:16 +0300 Subject: [PATCH 0917/1365] Tensor Cores fixed --- src/convolutional_kernels.cu | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index c63e0aaf65e..7f3459b4c51 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -420,8 +420,8 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //#ifdef CUDNN_HALF //if (state.use_mixed_precision) { int iteration_num = get_current_iteration(state.net); // (*state.net.seen) / (state.net.batch*state.net.subdivisions); - if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3 * state.net.burn_in) && - (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && state.net.loss_scale != 1 && l.groups <= 1 && l.size > 1) + if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || (iteration_num > 3 * state.net.burn_in) && state.net.loss_scale != 1) && + (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && l.groups <= 1 && l.size > 1) { //printf("\n CUDNN_HALF!!! state.index = %d \n", state.index); @@ -672,8 +672,8 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state //#ifdef CUDNN_HALF int iteration_num = get_current_iteration(state.net); //(*state.net.seen) / (state.net.batch*state.net.subdivisions); - if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3 * state.net.burn_in) && - (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && state.net.loss_scale != 1 && l.groups <= 1 && l.size > 1) + if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || (iteration_num > 3 * state.net.burn_in) && state.net.loss_scale != 1) && + (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && l.groups <= 1 && l.size > 1) { const size_t input16_size = l.batch*l.c*l.w*l.h; const size_t delta16_size = l.batch*l.n*l.out_w*l.out_h; From 56a7ccf07e21776c8f58f41c7cfc14d99a71dd26 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 18 Apr 2020 16:08:23 +0300 Subject: [PATCH 0918/1365] Improved FPS and and memory leak for ./darknet detector demo ... (bug in OpenCV - allocates memory for each thread without deallocation) --- src/demo.c | 114 ++++++++++++++++++++++++++++++-------------- src/http_stream.cpp | 24 ++++++++++ src/http_stream.h | 5 ++ 3 files changed, 106 insertions(+), 37 deletions(-) diff --git a/src/demo.c b/src/demo.c index a529bd385c0..ddd42a9a2b7 100644 --- a/src/demo.c +++ b/src/demo.c @@ -15,7 +15,6 @@ #include #endif - #ifdef OPENCV #include "http_stream.h" @@ -52,46 +51,79 @@ mat_cv* show_img; static volatile int flag_exit; static int letter_box = 0; -void *fetch_in_thread(void *ptr) +static const int thread_wait_ms = 1; +static volatile int run_fetch_in_thread = 0; +static volatile int run_detect_in_thread = 0; + + +int fetch_in_thread(void *ptr) { - int dont_close_stream = 0; // set 1 if your IP-camera periodically turns off and turns on video-stream - if(letter_box) - in_s = get_image_from_stream_letterbox(cap, net.w, net.h, net.c, &in_img, dont_close_stream); - else - in_s = get_image_from_stream_resize(cap, net.w, net.h, net.c, &in_img, dont_close_stream); - if(!in_s.data){ - printf("Stream closed.\n"); - flag_exit = 1; - //exit(EXIT_FAILURE); - return 0; - } - //in_s = resize_image(in, net.w, net.h); + while (!custom_atomic_load_int(&flag_exit)) { + while (!custom_atomic_load_int(&run_fetch_in_thread)) { + if (custom_atomic_load_int(&flag_exit)) return 0; + this_thread_yield(); + } + int dont_close_stream = 0; // set 1 if your IP-camera periodically turns off and turns on video-stream + if (letter_box) + in_s = get_image_from_stream_letterbox(cap, net.w, net.h, net.c, &in_img, dont_close_stream); + else + in_s = get_image_from_stream_resize(cap, net.w, net.h, net.c, &in_img, dont_close_stream); + if (!in_s.data) { + printf("Stream closed.\n"); + flag_exit = 1; + //exit(EXIT_FAILURE); + return 0; + } + //in_s = resize_image(in, net.w, net.h); + custom_atomic_store_int(&run_fetch_in_thread, 0); + } return 0; } -void *detect_in_thread(void *ptr) +int fetch_in_thread_sync(void *ptr) { - layer l = net.layers[net.n-1]; - float *X = det_s.data; - float *prediction = network_predict(net, X); + custom_atomic_store_int(&run_fetch_in_thread, 1); + while (custom_atomic_load_int(&run_fetch_in_thread)) this_thread_sleep_for(thread_wait_ms); +} - memcpy(predictions[demo_index], prediction, l.outputs*sizeof(float)); - mean_arrays(predictions, NFRAMES, l.outputs, avg); - l.output = avg; +int detect_in_thread(void *ptr) +{ + while (!custom_atomic_load_int(&flag_exit)) { + while (!custom_atomic_load_int(&run_detect_in_thread)) { + if (custom_atomic_load_int(&flag_exit)) return 0; + this_thread_yield(); + } + + layer l = net.layers[net.n - 1]; + float *X = det_s.data; + float *prediction = network_predict(net, X); + + memcpy(predictions[demo_index], prediction, l.outputs * sizeof(float)); + mean_arrays(predictions, NFRAMES, l.outputs, avg); + l.output = avg; - cv_images[demo_index] = det_img; - det_img = cv_images[(demo_index + NFRAMES / 2 + 1) % NFRAMES]; - demo_index = (demo_index + 1) % NFRAMES; + cv_images[demo_index] = det_img; + det_img = cv_images[(demo_index + NFRAMES / 2 + 1) % NFRAMES]; + demo_index = (demo_index + 1) % NFRAMES; - if (letter_box) - dets = get_network_boxes(&net, get_width_mat(in_img), get_height_mat(in_img), demo_thresh, demo_thresh, 0, 1, &nboxes, 1); // letter box - else - dets = get_network_boxes(&net, net.w, net.h, demo_thresh, demo_thresh, 0, 1, &nboxes, 0); // resized + if (letter_box) + dets = get_network_boxes(&net, get_width_mat(in_img), get_height_mat(in_img), demo_thresh, demo_thresh, 0, 1, &nboxes, 1); // letter box + else + dets = get_network_boxes(&net, net.w, net.h, demo_thresh, demo_thresh, 0, 1, &nboxes, 0); // resized + + custom_atomic_store_int(&run_detect_in_thread, 0); + } return 0; } +int detect_in_thread_sync(void *ptr) +{ + custom_atomic_store_int(&run_detect_in_thread, 1); + while (custom_atomic_load_int(&run_detect_in_thread)) this_thread_sleep_for(thread_wait_ms); +} + double get_wall_time() { struct timeval walltime; @@ -158,20 +190,22 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int custom_thread_t fetch_thread = NULL; custom_thread_t detect_thread = NULL; + if (custom_create_thread(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed"); + if (custom_create_thread(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed"); - fetch_in_thread(0); + fetch_in_thread_sync(0); //fetch_in_thread(0); det_img = in_img; det_s = in_s; - fetch_in_thread(0); - detect_in_thread(0); + fetch_in_thread_sync(0); //fetch_in_thread(0); + detect_in_thread_sync(0); //fetch_in_thread(0); det_img = in_img; det_s = in_s; for (j = 0; j < NFRAMES / 2; ++j) { free_detections(dets, nboxes); - fetch_in_thread(0); - detect_in_thread(0); + fetch_in_thread_sync(0); //fetch_in_thread(0); + detect_in_thread_sync(0); //fetch_in_thread(0); det_img = in_img; det_s = in_s; } @@ -213,9 +247,10 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int const float nms = .45; // 0.4F int local_nboxes = nboxes; detection *local_dets = dets; + this_thread_yield(); - if (!benchmark) if (custom_create_thread(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed"); - if(custom_create_thread(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed"); + if (!benchmark) custom_atomic_store_int(&run_fetch_in_thread, 1); // if (custom_create_thread(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed"); + custom_atomic_store_int(&run_detect_in_thread, 1); // if (custom_create_thread(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed"); //if (nms) do_nms_obj(local_dets, local_nboxes, l.classes, nms); // bad results if (nms) { @@ -285,9 +320,9 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int printf("\n cvWriteFrame \n"); } - custom_join(detect_thread, 0); + while (custom_atomic_load_int(&run_detect_in_thread)) this_thread_sleep_for(thread_wait_ms); // custom_join(detect_thread, 0); if (!benchmark) { - custom_join(fetch_thread, 0); + while (custom_atomic_load_int(&run_fetch_in_thread)) this_thread_sleep_for(thread_wait_ms); // custom_join(fetch_thread, 0); free_image(det_s); } @@ -332,6 +367,11 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int printf("output_video_writer closed. \n"); } + this_thread_sleep_for(thread_wait_ms); + + custom_join(detect_thread, 0); + custom_join(fetch_thread, 0); + // free memory free_image(in_s); free_detections(dets, nboxes); diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 76de4b29813..480f56e2734 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -14,6 +14,7 @@ #include #include #include +#include #include using std::cerr; using std::endl; @@ -705,6 +706,29 @@ int custom_join(custom_thread_t tid, void **value_ptr) return -1; } +int custom_atomic_load_int(volatile int* obj) +{ + const volatile std::atomic* ptr_a = (const volatile std::atomic*)obj; + return std::atomic_load(ptr_a); +} + +void custom_atomic_store_int(volatile int* obj, int desr) +{ + volatile std::atomic* ptr_a = (volatile std::atomic*)obj; + std::atomic_store(ptr_a, desr); +} + +void this_thread_sleep_for(int ms_time) +{ + std::chrono::milliseconds dura(ms_time); + std::this_thread::sleep_for(dura); +} + +void this_thread_yield() +{ + std::this_thread::yield(); +} + #else // C++11 #include diff --git a/src/http_stream.h b/src/http_stream.h index 0aa63d65fe9..0ad40ce4371 100644 --- a/src/http_stream.h +++ b/src/http_stream.h @@ -24,6 +24,11 @@ typedef void* custom_attr_t; int custom_create_thread(custom_thread_t * tid, const custom_attr_t * attr, void *(*func) (void *), void *arg); int custom_join(custom_thread_t thread, void **value_ptr); +int custom_atomic_load_int(volatile int* obj); +void custom_atomic_store_int(volatile int* obj, int desr); +void this_thread_sleep_for(int ms_time); +void this_thread_yield(); + #ifdef __cplusplus } #endif From 66c809fe78d9b2b0f51d5d65bdc8f83a1f530694 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 18 Apr 2020 16:55:53 +0300 Subject: [PATCH 0919/1365] Compile fix --- src/demo.c | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/src/demo.c b/src/demo.c index ddd42a9a2b7..dbb838c6b5b 100644 --- a/src/demo.c +++ b/src/demo.c @@ -56,7 +56,7 @@ static volatile int run_fetch_in_thread = 0; static volatile int run_detect_in_thread = 0; -int fetch_in_thread(void *ptr) +void *fetch_in_thread(void *ptr) { while (!custom_atomic_load_int(&flag_exit)) { while (!custom_atomic_load_int(&run_fetch_in_thread)) { @@ -81,13 +81,14 @@ int fetch_in_thread(void *ptr) return 0; } -int fetch_in_thread_sync(void *ptr) +void *fetch_in_thread_sync(void *ptr) { custom_atomic_store_int(&run_fetch_in_thread, 1); while (custom_atomic_load_int(&run_fetch_in_thread)) this_thread_sleep_for(thread_wait_ms); + return 0; } -int detect_in_thread(void *ptr) +void *detect_in_thread(void *ptr) { while (!custom_atomic_load_int(&flag_exit)) { while (!custom_atomic_load_int(&run_detect_in_thread)) { @@ -118,10 +119,11 @@ int detect_in_thread(void *ptr) return 0; } -int detect_in_thread_sync(void *ptr) +void *detect_in_thread_sync(void *ptr) { custom_atomic_store_int(&run_detect_in_thread, 1); while (custom_atomic_load_int(&run_detect_in_thread)) this_thread_sleep_for(thread_wait_ms); + return 0; } double get_wall_time() @@ -185,7 +187,6 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int exit(0); } - flag_exit = 0; custom_thread_t fetch_thread = NULL; From 4786d557f9ac7452ded74ce2f3df06fbe991924e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 18 Apr 2020 19:01:20 +0300 Subject: [PATCH 0920/1365] Fixed memory leak for Training with OPENCV=1 - create pool of CPU-threads only once (bug in OpenCV - it allocates memory for each thread without deallocation) --- include/darknet.h | 1 + src/data.c | 85 ++++++++++++++++++++++++++++++++++++++++++++++- src/detector.c | 2 ++ 3 files changed, 87 insertions(+), 1 deletion(-) diff --git a/include/darknet.h b/include/darknet.h index 92ad96a32ac..e8c226c9c44 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -979,6 +979,7 @@ LIB_API void free_layer(layer l); // data.c LIB_API void free_data(data d); LIB_API pthread_t load_data(load_args args); +LIB_API void free_load_threads(void *ptr); LIB_API pthread_t load_data_in_thread(load_args args); LIB_API void *load_thread(void *ptr); diff --git a/src/data.c b/src/data.c index f9d4d7df833..f5427595e1e 100644 --- a/src/data.c +++ b/src/data.c @@ -3,6 +3,7 @@ #include "image.h" #include "dark_cuda.h" #include "box.h" +#include "http_stream.h" #include #include @@ -1426,6 +1427,40 @@ pthread_t load_data_in_thread(load_args args) return thread; } +static const int thread_wait_ms = 5; +static volatile int flag_exit; +static volatile int * run_load_data = NULL; +static load_args * args_swap = NULL; +static pthread_t* threads = NULL; + +pthread_mutex_t mtx_load_data = PTHREAD_MUTEX_INITIALIZER; + +void *run_thread_loop(void *ptr) +{ + const int i = *(int *)ptr; + + while (!custom_atomic_load_int(&flag_exit)) { + while (!custom_atomic_load_int(&run_load_data[i])) { + if (custom_atomic_load_int(&flag_exit)) { + free(ptr); + return 0; + } + this_thread_sleep_for(thread_wait_ms); + } + + pthread_mutex_lock(&mtx_load_data); + load_args *args_local = (load_args *)xcalloc(1, sizeof(load_args)); + *args_local = args_swap[i]; + pthread_mutex_unlock(&mtx_load_data); + + load_thread(args_local); + + custom_atomic_store_int(&run_load_data[i], 0); + } + free(ptr); + return 0; +} + void *load_threads(void *ptr) { //srand(time(0)); @@ -1436,6 +1471,34 @@ void *load_threads(void *ptr) int total = args.n; free(ptr); data* buffers = (data*)xcalloc(args.threads, sizeof(data)); + if (!threads) { + threads = (pthread_t*)xcalloc(args.threads, sizeof(pthread_t)); + run_load_data = (volatile int *)xcalloc(args.threads, sizeof(int)); + args_swap = (load_args *)xcalloc(args.threads, sizeof(load_args)); + fprintf(stderr, " Create %d permanent cpu-threads \n", args.threads); + + for (i = 0; i < args.threads; ++i) { + int* ptr = (int*)xcalloc(1, sizeof(int)); + *ptr = i; + if (pthread_create(&threads[i], 0, run_thread_loop, ptr)) error("Thread creation failed"); + } + } + + for (i = 0; i < args.threads; ++i) { + args.d = buffers + i; + args.n = (i + 1) * total / args.threads - i * total / args.threads; + + pthread_mutex_lock(&mtx_load_data); + args_swap[i] = args; + pthread_mutex_unlock(&mtx_load_data); + + custom_atomic_store_int(&run_load_data[i], 1); // run thread + } + for (i = 0; i < args.threads; ++i) { + while (custom_atomic_load_int(&run_load_data[i])) this_thread_sleep_for(thread_wait_ms); // join + } + + /* pthread_t* threads = (pthread_t*)xcalloc(args.threads, sizeof(pthread_t)); for(i = 0; i < args.threads; ++i){ args.d = buffers + i; @@ -1445,6 +1508,8 @@ void *load_threads(void *ptr) for(i = 0; i < args.threads; ++i){ pthread_join(threads[i], 0); } + */ + *out = concat_datas(buffers, args.threads); out->shallow = 0; for(i = 0; i < args.threads; ++i){ @@ -1452,10 +1517,28 @@ void *load_threads(void *ptr) free_data(buffers[i]); } free(buffers); - free(threads); + //free(threads); return 0; } +void free_load_threads(void *ptr) +{ + load_args args = *(load_args *)ptr; + if (args.threads == 0) args.threads = 1; + int i; + if (threads) { + custom_atomic_store_int(&flag_exit, 1); + for (i = 0; i < args.threads; ++i) { + pthread_join(threads[i], 0); + } + free((void*)run_load_data); + free(args_swap); + free(threads); + threads = NULL; + custom_atomic_store_int(&flag_exit, 0); + } +} + pthread_t load_data(load_args args) { pthread_t thread; diff --git a/src/detector.c b/src/detector.c index b1a56e5190f..3b505a0af8c 100644 --- a/src/detector.c +++ b/src/detector.c @@ -392,6 +392,8 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i pthread_join(load_thread, 0); free_data(buffer); + free_load_threads(&args); + free(base); free(paths); free_list_contents(plist); From 1fb4606d5d04a0438887ca9a5681af25291fd240 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 19 Apr 2020 19:45:04 +0300 Subject: [PATCH 0921/1365] Minor fix --- src/demo.c | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/src/demo.c b/src/demo.c index dbb838c6b5b..7bca349d044 100644 --- a/src/demo.c +++ b/src/demo.c @@ -321,9 +321,15 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int printf("\n cvWriteFrame \n"); } - while (custom_atomic_load_int(&run_detect_in_thread)) this_thread_sleep_for(thread_wait_ms); // custom_join(detect_thread, 0); + while (custom_atomic_load_int(&run_detect_in_thread)) { + if(avg_fps > 200) this_thread_yield(); + else this_thread_sleep_for(thread_wait_ms); // custom_join(detect_thread, 0); + } if (!benchmark) { - while (custom_atomic_load_int(&run_fetch_in_thread)) this_thread_sleep_for(thread_wait_ms); // custom_join(fetch_thread, 0); + while (custom_atomic_load_int(&run_fetch_in_thread)) { + if (avg_fps > 200) this_thread_yield(); + else this_thread_sleep_for(thread_wait_ms); // custom_join(fetch_thread, 0); + } free_image(det_s); } From 7c1f9105f0d0a6abd1e12927bcd65d777a58146c Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 21 Apr 2020 04:25:43 +0300 Subject: [PATCH 0922/1365] Minor fix --- scripts/testdev2017.txt | 20288 ++++++++++++++++++++++++++++++++++++++ src/detector.c | 16 +- 2 files changed, 20301 insertions(+), 3 deletions(-) create mode 100644 scripts/testdev2017.txt diff --git a/scripts/testdev2017.txt b/scripts/testdev2017.txt new file mode 100644 index 00000000000..71f511ec696 --- /dev/null +++ b/scripts/testdev2017.txt @@ -0,0 +1,20288 @@ +F:/MSCOCO/test2017/000000466319.jpg +F:/MSCOCO/test2017/000000523573.jpg 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+F:/MSCOCO/test2017/000000099556.jpg +F:/MSCOCO/test2017/000000353600.jpg +F:/MSCOCO/test2017/000000532925.jpg +F:/MSCOCO/test2017/000000372750.jpg +F:/MSCOCO/test2017/000000017377.jpg +F:/MSCOCO/test2017/000000383354.jpg +F:/MSCOCO/test2017/000000422750.jpg +F:/MSCOCO/test2017/000000541056.jpg +F:/MSCOCO/test2017/000000155724.jpg diff --git a/src/detector.c b/src/detector.c index 3b505a0af8c..b7981ea9627 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1,3 +1,4 @@ +#include #include "darknet.h" #include "network.h" #include "region_layer.h" @@ -424,7 +425,9 @@ static int get_coco_image_id(char *filename) static void print_cocos(FILE *fp, char *image_path, detection *dets, int num_boxes, int classes, int w, int h) { int i, j; - int image_id = get_coco_image_id(image_path); + //int image_id = get_coco_image_id(image_path); + char *p = basecfg(image_path); + int image_id = atoi(p); for (i = 0; i < num_boxes; ++i) { float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.; float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.; @@ -442,7 +445,12 @@ static void print_cocos(FILE *fp, char *image_path, detection *dets, int num_box float bh = ymax - ymin; for (j = 0; j < classes; ++j) { - if (dets[i].prob[j] > 0) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, dets[i].prob[j]); + if (dets[i].prob[j] > 0) { + char buff[1024]; + sprintf(buff, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, dets[i].prob[j]); + fprintf(fp, buff); + //printf("%s", buff); + } } } } @@ -605,6 +613,8 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out load_weights(&net, weightfile); } //set_batch_network(&net, 1); + fuse_conv_batchnorm(net); + calculate_binary_weights(net); fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); srand(time(0)); @@ -668,7 +678,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out int i = 0; int t; - float thresh = .005; + float thresh = .001; float nms = .45; int nthreads = 4; From b573eab63f43bfb9c7570ceec953ebface5c8168 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 22 Apr 2020 20:49:27 +0300 Subject: [PATCH 0923/1365] Minor fix --- build/darknet/x64/darknet.py | 78 ++++++++++++++++++++++++++++++++++++ darknet.py | 4 +- include/darknet.h | 6 ++- results/tmp.txt | 0 src/demo.c | 4 +- src/network.c | 12 +++--- 6 files changed, 92 insertions(+), 12 deletions(-) create mode 100644 results/tmp.txt diff --git a/build/darknet/x64/darknet.py b/build/darknet/x64/darknet.py index da66e9c7fa1..ee561dac116 100644 --- a/build/darknet/x64/darknet.py +++ b/build/darknet/x64/darknet.py @@ -63,6 +63,9 @@ class DETECTION(Structure): ("uc", POINTER(c_float)), ("points", c_int)] +class DETNUMPAIR(Structure): + _fields_ = [("num", c_int), + ("dets", POINTER(DETECTION))] class IMAGE(Structure): _fields_ = [("w", c_int), @@ -161,6 +164,9 @@ def network_height(net): free_detections = lib.free_detections free_detections.argtypes = [POINTER(DETECTION), c_int] +free_batch_detections = lib.free_batch_detections +free_batch_detections.argtypes = [POINTER(DETNUMPAIR), c_int] + free_ptrs = lib.free_ptrs free_ptrs.argtypes = [POINTER(c_void_p), c_int] @@ -210,6 +216,11 @@ def network_height(net): predict_image_letterbox.argtypes = [c_void_p, IMAGE] predict_image_letterbox.restype = POINTER(c_float) +network_predict_batch = lib.network_predict_batch +network_predict_batch.argtypes = [c_void_p, IMAGE, c_int, c_int, c_int, + c_float, c_float, POINTER(c_int), c_int, c_int] +network_predict_batch.restype = POINTER(DETNUMPAIR) + def array_to_image(arr): import numpy as np # need to return old values to avoid python freeing memory @@ -445,5 +456,72 @@ def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yo print("Unable to show image: "+str(e)) return detections +def performBatchDetect(thresh= 0.25, configPath = "./cfg/yolov3.cfg", weightPath = "yolov3.weights", metaPath= "./cfg/coco.data", hier_thresh=.5, nms=.45, batch_size=3): + import cv2 + import numpy as np + # NB! Image sizes should be the same + # You can change the images, yet, be sure that they have the same width and height + img_samples = ['data/person.jpg', 'data/person.jpg', 'data/person.jpg'] + image_list = [cv2.imread(k) for k in img_samples] + + net = load_net_custom(configPath.encode('utf-8'), weightPath.encode('utf-8'), 0, batch_size) + meta = load_meta(metaPath.encode('utf-8')) + pred_height, pred_width, c = image_list[0].shape + net_width, net_height = (network_width(net), network_height(net)) + img_list = [] + for custom_image_bgr in image_list: + custom_image = cv2.cvtColor(custom_image_bgr, cv2.COLOR_BGR2RGB) + custom_image = cv2.resize( + custom_image, (net_width, net_height), interpolation=cv2.INTER_NEAREST) + custom_image = custom_image.transpose(2, 0, 1) + img_list.append(custom_image) + + arr = np.concatenate(img_list, axis=0) + arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0 + data = arr.ctypes.data_as(POINTER(c_float)) + im = IMAGE(net_width, net_height, c, data) + + batch_dets = network_predict_batch(net, im, batch_size, pred_width, + pred_height, thresh, hier_thresh, None, 0, 0) + batch_boxes = [] + batch_scores = [] + batch_classes = [] + for b in range(batch_size): + num = batch_dets[b].num + dets = batch_dets[b].dets + if nms: + do_nms_obj(dets, num, meta.classes, nms) + boxes = [] + scores = [] + classes = [] + for i in range(num): + det = dets[i] + score = -1 + label = None + for c in range(det.classes): + p = det.prob[c] + if p > score: + score = p + label = c + if score > thresh: + box = det.bbox + left, top, right, bottom = map(int,(box.x - box.w / 2, box.y - box.h / 2, + box.x + box.w / 2, box.y + box.h / 2)) + boxes.append((top, left, bottom, right)) + scores.append(score) + classes.append(label) + boxColor = (int(255 * (1 - (score ** 2))), int(255 * (score ** 2)), 0) + cv2.rectangle(image_list[b], (left, top), + (right, bottom), boxColor, 2) + cv2.imwrite(os.path.basename(img_samples[b]),image_list[b]) + + batch_boxes.append(boxes) + batch_scores.append(scores) + batch_classes.append(classes) + free_batch_detections(batch_dets, batch_size) + return batch_boxes, batch_scores, batch_classes + if __name__ == "__main__": print(performDetect()) + #Uncomment the following line to see batch inference working + #print(performBatchDetect()) \ No newline at end of file diff --git a/darknet.py b/darknet.py index 51876f6dad3..ee561dac116 100644 --- a/darknet.py +++ b/darknet.py @@ -522,6 +522,6 @@ def performBatchDetect(thresh= 0.25, configPath = "./cfg/yolov3.cfg", weightPath return batch_boxes, batch_scores, batch_classes if __name__ == "__main__": - #print(performDetect()) + print(performDetect()) #Uncomment the following line to see batch inference working - print(performBatchDetect()) \ No newline at end of file + #print(performBatchDetect()) \ No newline at end of file diff --git a/include/darknet.h b/include/darknet.h index cbae6bc48ff..ac83788ab8d 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -831,10 +831,10 @@ typedef struct detection{ } detection; // network.c -batch inference -typedef struct detNumPair { +typedef struct det_num_pair { int num; detection *dets; -} detNumPair, *pdetNumPair; +} det_num_pair, *pdet_num_pair; // matrix.h typedef struct matrix { @@ -945,7 +945,9 @@ LIB_API void diounms_sort(detection *dets, int total, int classes, float thresh, LIB_API float *network_predict(network net, float *input); LIB_API float *network_predict_ptr(network *net, float *input); LIB_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter); +LIB_API det_num_pair* network_predict_batch(network *net, image im, int batch_size, int w, int h, float thresh, float hier, int *map, int relative, int letter); LIB_API void free_detections(detection *dets, int n); +LIB_API void free_batch_detections(det_num_pair *det_num_pairs, int n); LIB_API void fuse_conv_batchnorm(network net); LIB_API void calculate_binary_weights(network net); LIB_API char *detection_to_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id, char *filename); diff --git a/results/tmp.txt b/results/tmp.txt new file mode 100644 index 00000000000..e69de29bb2d diff --git a/src/demo.c b/src/demo.c index 7bca349d044..6bd00303a0e 100644 --- a/src/demo.c +++ b/src/demo.c @@ -322,12 +322,12 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int } while (custom_atomic_load_int(&run_detect_in_thread)) { - if(avg_fps > 200) this_thread_yield(); + if(avg_fps > 180) this_thread_yield(); else this_thread_sleep_for(thread_wait_ms); // custom_join(detect_thread, 0); } if (!benchmark) { while (custom_atomic_load_int(&run_fetch_in_thread)) { - if (avg_fps > 200) this_thread_yield(); + if (avg_fps > 180) this_thread_yield(); else this_thread_sleep_for(thread_wait_ms); // custom_join(fetch_thread, 0); } free_image(det_s); diff --git a/src/network.c b/src/network.c index 8a791f0edd4..31574f90a8d 100644 --- a/src/network.c +++ b/src/network.c @@ -895,12 +895,12 @@ void free_detections(detection *dets, int n) free(dets); } -void free_batch_detections(detNumPair *detNumPairs, int n) +void free_batch_detections(det_num_pair *det_num_pairs, int n) { int i; for(i=0; i Date: Thu, 23 Apr 2020 14:49:06 +0300 Subject: [PATCH 0924/1365] Yolo v4 --- README.md | 83 +- build/darknet/x64/cfg/yolov4-custom.cfg | 1156 +++++++++++++++++ .../cfg/{cd53paspp-omega.cfg => yolov4.cfg} | 4 +- cfg/yolov4-custom.cfg | 1156 +++++++++++++++++ cfg/{cd53paspp-omega.cfg => yolov4.cfg} | 4 +- 5 files changed, 2362 insertions(+), 41 deletions(-) create mode 100644 build/darknet/x64/cfg/yolov4-custom.cfg rename build/darknet/x64/cfg/{cd53paspp-omega.cfg => yolov4.cfg} (99%) create mode 100644 cfg/yolov4-custom.cfg rename cfg/{cd53paspp-omega.cfg => yolov4.cfg} (99%) diff --git a/README.md b/README.md index ce39448507a..0c131787d96 100644 --- a/README.md +++ b/README.md @@ -49,25 +49,31 @@ More details: http://pjreddie.com/darknet/yolo/ There are weights-file for different cfg-files (trained for MS COCO dataset): -FPS on GeForce 1080Ti: +FPS on RTX 2070 (R) and Tesla V100 (V): -* [csresnext50-panet-spp-original-optimal.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/csresnext50-panet-spp-original-optimal.cfg) - **65.4% mAP@0.5 (43.2% AP@0.5..0.95) - 35 FPS** - 100.5 BFlops - 217 MB: [csresnext50-panet-spp-original-optimal_final.weights](https://drive.google.com/open?id=1_NnfVgj0EDtb_WLNoXV8Mo7WKgwdYZCc) +* [yolov4.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg) - 245 MB: [yolov4.weights](https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT) + * `width=608 height=608` in cfg: **65.7% mAP@0.5 (43.5% AP@0.5:0.95) - 34(R) FPS / 62(V) FPS** - 128.5 BFlops + * `width=512 height=512` in cfg: **64.9% mAP@0.5 (43.0% AP@0.5:0.95) - 45(R) FPS / 83(V) FPS** - 91.1 BFlops + * `width=416 height=416` in cfg: **62.8% mAP@0.5 (41.2% AP@0.5:0.95) - 55(R) FPS / 96(V) FPS** - 60.1 BFlops + * `width=320 height=320` in cfg: ** 60% mAP@0.5 ( 38% AP@0.5:0.95) - 63(R) FPS / 123(V) FPS** - 35.5 BFlops -* [yolov3-spp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-spp.cfg) - **60.6% mAP@0.5 - 30 FPS** - 141.5 BFlops - 240 MB: [yolov3-spp.weights](https://pjreddie.com/media/files/yolov3-spp.weights) +* [yolov3-tiny-prn.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny-prn.cfg) - **33.1% mAP@0.5 - 370(R) FPS** - 3.5 BFlops - 18.8 MB: [yolov3-tiny-prn.weights](https://drive.google.com/file/d/18yYZWyKbo4XSDVyztmsEcF9B_6bxrhUY/view?usp=sharing) -* [yolov3-tiny-prn.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny-prn.cfg) - **33.1% mAP@0.5 - 400 FPS** - 3.5 BFlops - 18.8 MB: [yolov3-tiny-prn.weights](https://drive.google.com/file/d/18yYZWyKbo4XSDVyztmsEcF9B_6bxrhUY/view?usp=sharing) +* [enet-coco.cfg (EfficientNetB0-Yolov3)](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/enet-coco.cfg) - **45.5% mAP@0.5 - 55(R) FPS** - 3.7 BFlops - 18.3 MB: [enetb0-coco_final.weights](https://drive.google.com/file/d/1FlHeQjWEQVJt0ay1PVsiuuMzmtNyv36m/view) -* [enet-coco.cfg (EfficientNetB0-Yolov3)](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/enet-coco.cfg) - **45.5% mAP@0.5 - 60 FPS** - 3.7 BFlops - 18.3 MB: [enetb0-coco_final.weights](https://drive.google.com/file/d/1FlHeQjWEQVJt0ay1PVsiuuMzmtNyv36m/view) - -* [yolov3-openimages.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-openimages.cfg) - 247 MB - OpenImages dataset: [yolov3-openimages.weights](https://pjreddie.com/media/files/yolov3-openimages.weights) +* [yolov3-openimages.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-openimages.cfg) - 247 MB - 18(R) FPS - OpenImages dataset: [yolov3-openimages.weights](https://pjreddie.com/media/files/yolov3-openimages.weights)
CLICK ME - Yolo v3 models +* [csresnext50-panet-spp-original-optimal.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/csresnext50-panet-spp-original-optimal.cfg) - **65.4% mAP@0.5 (43.2% AP@0.5:0.95) - 32(R) FPS** - 100.5 BFlops - 217 MB: [csresnext50-panet-spp-original-optimal_final.weights](https://drive.google.com/open?id=1_NnfVgj0EDtb_WLNoXV8Mo7WKgwdYZCc) + +* [yolov3-spp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-spp.cfg) - **60.6% mAP@0.5 - 38(R) FPS** - 141.5 BFlops - 240 MB: [yolov3-spp.weights](https://pjreddie.com/media/files/yolov3-spp.weights) + * [csresnext50-panet-spp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/csresnext50-panet-spp.cfg) - **60.0% mAP@0.5 - 44 FPS** - 71.3 BFlops - 217 MB: [csresnext50-panet-spp_final.weights](https://drive.google.com/file/d/1aNXdM8qVy11nqTcd2oaVB3mf7ckr258-/view?usp=sharing) -* [yolov3.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3.cfg) - **55.3% mAP@0.5 - 46 FPS** - 65.9 BFlops - 236 MB: [yolov3.weights](https://pjreddie.com/media/files/yolov3.weights) +* [yolov3.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3.cfg) - **55.3% mAP@0.5 - 66(R) FPS** - 65.9 BFlops - 236 MB: [yolov3.weights](https://pjreddie.com/media/files/yolov3.weights) -* [yolov3-tiny.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny.cfg) - **33.1% mAP@0.5 - 330 FPS** - 5.6 BFlops - 33.7 MB: [yolov3-tiny.weights](https://pjreddie.com/media/files/yolov3-tiny.weights) +* [yolov3-tiny.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny.cfg) - **33.1% mAP@0.5 - 345(R) FPS** - 5.6 BFlops - 33.7 MB: [yolov3-tiny.weights](https://pjreddie.com/media/files/yolov3-tiny.weights)
@@ -102,7 +108,7 @@ You can get cfg-files by path: `darknet/cfg/` * **Intel OpenVINO 2019 R1:** (Myriad X / USB Neural Compute Stick / Arria FPGA): read this [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) * **OpenCV-dnn** the fastest implementation for CPU (x86/ARM-Android), OpenCV can be compiled with [OpenVINO-backend](https://github.com/opencv/opencv/wiki/Intel's-Deep-Learning-Inference-Engine-backend) for running on (Myriad X / USB Neural Compute Stick / Arria FPGA), use `yolov3.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221) or [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) * **PyTorch > ONNX > CoreML > iOS** how to convert cfg/weights-files to pt-file: [ultralytics/yolov3](https://github.com/ultralytics/yolov3#darknet-conversion) and [iOS App](https://itunes.apple.com/app/id1452689527) -* **TensorRT** for YOLOv3 (-70% faster inference): [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) +* **TensorRT** for YOLOv3 (-70% faster inference): [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) read [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf) * **TVM** - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backends (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about * **OpenDataCam** - It detects, tracks and counts moving objects by using Yolo: https://github.com/opendatacam/opendatacam#-hardware-pre-requisite * **Netron** - Visualizer for neural networks: https://github.com/lutzroeder/netron @@ -153,30 +159,30 @@ Also, you might be interested in using a simplified repository where is implemen #### How to use on the command line -On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights` +On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights` On Linux find executable file `./darknet` in the root directory, while on Windows find it in the directory `\build\darknet\x64` -* Yolo v3 COCO - **image**: `darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights -thresh 0.25` -* **Output coordinates** of objects: `darknet.exe detector test cfg/coco.data yolov3.cfg yolov3.weights -ext_output dog.jpg` -* Yolo v3 COCO - **video**: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights -ext_output test.mp4` -* Yolo v3 COCO - **WebCam 0**: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights -c 0` -* Yolo v3 COCO for **net-videocam** - Smart WebCam: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg` -* Yolo v3 - **save result videofile res.avi**: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights test.mp4 -out_filename res.avi` +* Yolo v3 COCO - **image**: `darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25` +* **Output coordinates** of objects: `darknet.exe detector test cfg/coco.data yolov4.cfg yolov4.weights -ext_output dog.jpg` +* Yolo v3 COCO - **video**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output test.mp4` +* Yolo v3 COCO - **WebCam 0**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -c 0` +* Yolo v3 COCO for **net-videocam** - Smart WebCam: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg` +* Yolo v3 - **save result videofile res.avi**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -out_filename res.avi` * Yolo v3 **Tiny** COCO - video: `darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4` * **JSON and MJPEG server** that allows multiple connections from your soft or Web-browser `ip-address:8070` and 8090: `./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output` * Yolo v3 Tiny **on GPU #1**: `darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 1 test.mp4` -* Alternative method Yolo v3 COCO - image: `darknet.exe detect cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` +* Alternative method Yolo v3 COCO - image: `darknet.exe detect cfg/yolov4.cfg yolov4.weights -i 0 -thresh 0.25` * Train on **Amazon EC2**, to see mAP & Loss-chart using URL like: `http://ec2-35-160-228-91.us-west-2.compute.amazonaws.com:8090` in the Chrome/Firefox (**Darknet should be compiled with OpenCV**): - `./darknet detector train cfg/coco.data yolov3.cfg darknet53.conv.74 -dont_show -mjpeg_port 8090 -map` + `./darknet detector train cfg/coco.data yolov4.cfg yolov4.conv.137 -dont_show -mjpeg_port 8090 -map` * 186 MB Yolo9000 - image: `darknet.exe detector test cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights` * Remeber to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app * To process a list of images `data/train.txt` and save results of detection to `result.json` file use: - `darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights -ext_output -dont_show -out result.json < data/train.txt` + `darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output -dont_show -out result.json < data/train.txt` * To process a list of images `data/train.txt` and save results of detection to `result.txt` use: - `darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights -dont_show -ext_output < data/train.txt > result.txt` + `darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -dont_show -ext_output < data/train.txt > result.txt` * Pseudo-lableing - to process a list of images `data/new_train.txt` and save results of detection in Yolo training format for each image as label `.txt` (in this way you can increase the amount of training data) use: - `darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights -thresh 0.25 -dont_show -save_labels < data/new_train.txt` + `darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25 -dont_show -save_labels < data/new_train.txt` * To calculate anchors: `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416` * To check accuracy mAP@IoU=50: `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights` * To check accuracy mAP@IoU=75: `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights -iou_thresh 0.75` @@ -192,7 +198,7 @@ On Linux find executable file `./darknet` in the root directory, while on Window 3. Start Smart WebCam on your phone 4. Replace the address below, on shown in the phone application (Smart WebCam) and launch: -* Yolo v3 COCO-model: `darknet.exe detector demo data/coco.data yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` +* Yolo v3 COCO-model: `darknet.exe detector demo data/coco.data yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` ### How to compile on Linux (using `cmake`) @@ -222,9 +228,9 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c * `DEBUG=1` to bould debug version of Yolo * `OPENMP=1` to build with OpenMP support to accelerate Yolo by using multi-core CPU * `LIBSO=1` to build a library `darknet.so` and binary runable file `uselib` that uses this library. Or you can try to run so `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib test.mp4` How to use this SO-library from your own code - you can look at C++ example: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp - or use in such a way: `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov3.cfg yolov3.weights test.mp4` + or use in such a way: `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov4.cfg yolov4.weights test.mp4` * `ZED_CAMERA=1` to build a library with ZED-3D-camera support (should be ZED SDK installed), then run - `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov3.cfg yolov3.weights zed_camera` + `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov4.cfg yolov4.weights zed_camera` To run Darknet on Linux use examples from this article, just use `./darknet` instead of `darknet.exe`, i.e. use this command: `./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights` @@ -343,6 +349,8 @@ Then add to your created project: ## How to train (Pascal VOC Data): +
CLICK ME - How to train (Pascal VOC Data) + 1. Download pre-trained weights for the convolutional layers (154 MB): http://pjreddie.com/media/files/darknet53.conv.74 and put to the directory `build\darknet\x64` 2. Download The Pascal VOC Data and unpack it to directory `build\darknet\x64\data\voc` will be created dir `build\darknet\x64\data\voc\VOCdevkit\`: @@ -372,6 +380,8 @@ More information about training by the link: http://pjreddie.com/darknet/yolo/#t **Note:** If during training you see `nan` values for `avg` (loss) field - then training goes wrong, but if `nan` is in some other lines - then training goes well. +
+ ## How to train with multi-GPU: 1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74` @@ -387,7 +397,7 @@ https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ Training Yolo v3: -1. Create file `yolo-obj.cfg` with the same content as in `yolov3.cfg` (or copy `yolov3.cfg` to `yolo-obj.cfg)` and: +1. Create file `yolo-obj.cfg` with the same content as in `yolov4-custom.cfg` (or copy `yolov4-custom.cfg` to `yolo-obj.cfg)` and: * change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L3) * change line subdivisions to [`subdivisions=16`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4) @@ -413,7 +423,7 @@ Training Yolo v3: (Generally `filters` depends on the `classes`, `coords` and number of `mask`s, i.e. filters=`(classes + coords + 1)*`, where `mask` is indices of anchors. If `mask` is absence, then filters=`(classes + coords + 1)*num`) - So for example, for 2 objects, your file `yolo-obj.cfg` should differ from `yolov3.cfg` in such lines in each of **3** [yolo]-layers: + So for example, for 2 objects, your file `yolo-obj.cfg` should differ from `yolov4-custom.cfg` in such lines in each of **3** [yolo]-layers: ``` [convolutional] @@ -466,22 +476,23 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an ``` 7. Download pre-trained weights for the convolutional layers and put to the directory `build\darknet\x64` + * for `yolov4.cfg`, `yolov4-custom.cfg` (162 MB): [yolov4.conv.137](https://drive.google.com/open?id=1JKF-bdIklxOOVy-2Cr5qdvjgGpmGfcbp) * for `csresnext50-panet-spp.cfg` (133 MB): [csresnext50-panet-spp.conv.112](https://drive.google.com/file/d/16yMYCLQTY_oDlCIZPfn_sab6KD3zgzGq/view?usp=sharing) * for `yolov3.cfg, yolov3-spp.cfg` (154 MB): [darknet53.conv.74](https://pjreddie.com/media/files/darknet53.conv.74) * for `yolov3-tiny-prn.cfg , yolov3-tiny.cfg` (6 MB): [yolov3-tiny.conv.11](https://drive.google.com/file/d/18v36esoXCh-PsOKwyP2GWrpYDptDY8Zf/view?usp=sharing) * for `enet-coco.cfg (EfficientNetB0-Yolov3)` (14 MB): [enetb0-coco.conv.132](https://drive.google.com/file/d/1uhh3D6RSn0ekgmsaTcl-ZW53WBaUDo6j/view?usp=sharing) -8. Start training by using the command line: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74` +8. Start training by using the command line: `darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137` - To train on Linux use command: `./darknet detector train data/obj.data yolo-obj.cfg darknet53.conv.74` (just use `./darknet` instead of `darknet.exe`) + To train on Linux use command: `./darknet detector train data/obj.data yolo-obj.cfg yolov4.conv.137` (just use `./darknet` instead of `darknet.exe`) * (file `yolo-obj_last.weights` will be saved to the `build\darknet\x64\backup\` for each 100 iterations) * (file `yolo-obj_xxxx.weights` will be saved to the `build\darknet\x64\backup\` for each 1000 iterations) - * (to disable Loss-Window use `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -dont_show`, if you train on computer without monitor like a cloud Amazon EC2) - * (to see the mAP & Loss-chart during training on remote server without GUI, use command `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -dont_show -mjpeg_port 8090 -map` then open URL `http://ip-address:8090` in Chrome/Firefox browser) + * (to disable Loss-Window use `darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -dont_show`, if you train on computer without monitor like a cloud Amazon EC2) + * (to see the mAP & Loss-chart during training on remote server without GUI, use command `darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -dont_show -mjpeg_port 8090 -map` then open URL `http://ip-address:8090` in Chrome/Firefox browser) -8.1. For training with mAP (mean average precisions) calculation for each 4 Epochs (set `valid=valid.txt` or `train.txt` in `obj.data` file) and run: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -map` +8.1. For training with mAP (mean average precisions) calculation for each 4 Epochs (set `valid=valid.txt` or `train.txt` in `obj.data` file) and run: `darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -map` 9. After training is complete - get result `yolo-obj_final.weights` from path `build\darknet\x64\backup\` @@ -553,7 +564,7 @@ For example, **bigger mAP** gives weights `yolo-obj_8000.weights` - then **use t Or just train with `-map` flag: -`darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -map` +`darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -map` So you will see mAP-chart (red-line) in the Loss-chart Window. mAP will be calculated for each 4 Epochs using `valid=valid.txt` file that is specified in `obj.data` file (`1 Epoch = images_in_train_txt / batch` iterations) @@ -635,9 +646,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol That is, if only objects that occupied 80-90% of the image were present in the training set, then the trained network will not be able to detect objects that occupy 1-10% of the image. - * to speedup training (with decreasing detection accuracy) do Fine-Tuning instead of Transfer-Learning, set param `stopbackward=1` here: https://github.com/AlexeyAB/darknet/blob/6d44529cf93211c319813c90e0c1adb34426abe5/cfg/yolov3.cfg#L548 - then do this command: `./darknet partial cfg/yolov3.cfg yolov3.weights yolov3.conv.81 81` will be created file `yolov3.conv.81`, - then train by using weights file `yolov3.conv.81` instead of `darknet53.conv.74` + * to speedup training (with decreasing detection accuracy) set param `stopbackward=1` for layer-136 in cfg-file * each: `model of object, side, illimination, scale, each 30 grad` of the turn and inclination angles - these are *different objects* from an internal perspective of the neural network. So the more *different objects* you want to detect, the more complex network model should be used. @@ -720,7 +729,7 @@ There are 2 APIs: 2. To use Yolo as DLL-file in your C++ console application - open the solution `build\darknet\yolo_console_dll.sln`, set **x64** and **Release**, and do the: Build -> Build yolo_console_dll * you can run your console application from Windows Explorer `build\darknet\x64\yolo_console_dll.exe` - **use this command**: `yolo_console_dll.exe data/coco.names yolov3.cfg yolov3.weights test.mp4` + **use this command**: `yolo_console_dll.exe data/coco.names yolov4.cfg yolov4.weights test.mp4` * after launching your console application and entering the image file name - you will see info for each object: ` ` diff --git a/build/darknet/x64/cfg/yolov4-custom.cfg b/build/darknet/x64/cfg/yolov4-custom.cfg new file mode 100644 index 00000000000..e033fc8c571 --- /dev/null +++ b/build/darknet/x64/cfg/yolov4-custom.cfg @@ -0,0 +1,1156 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=16 +width=608 +height=608 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +#cutmix=1 +mosaic=1 + +#:104x104 54:52x52 85:26x26 104:13x13 for 416 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-7 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-10 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=mish + +########################## + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 85 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 54 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +########################## + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +scale_x_y = 1.2 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=leaky + +[route] +layers = -1, -16 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +scale_x_y = 1.1 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=leaky + +[route] +layers = -1, -37 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 +scale_x_y = 1.05 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + diff --git a/build/darknet/x64/cfg/cd53paspp-omega.cfg b/build/darknet/x64/cfg/yolov4.cfg similarity index 99% rename from build/darknet/x64/cfg/cd53paspp-omega.cfg rename to build/darknet/x64/cfg/yolov4.cfg index ef2b2ebdb65..7be5b456c7a 100644 --- a/build/darknet/x64/cfg/cd53paspp-omega.cfg +++ b/build/darknet/x64/cfg/yolov4.cfg @@ -5,8 +5,8 @@ # Training batch=64 subdivisions=8 -width=512 -height=512 +width=608 +height=608 channels=3 momentum=0.949 decay=0.0005 diff --git a/cfg/yolov4-custom.cfg b/cfg/yolov4-custom.cfg new file mode 100644 index 00000000000..e033fc8c571 --- /dev/null +++ b/cfg/yolov4-custom.cfg @@ -0,0 +1,1156 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=16 +width=608 +height=608 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +#cutmix=1 +mosaic=1 + +#:104x104 54:52x52 85:26x26 104:13x13 for 416 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-7 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-10 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=mish + +########################## + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 85 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 54 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +########################## + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +scale_x_y = 1.2 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=leaky + +[route] +layers = -1, -16 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +scale_x_y = 1.1 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=leaky + +[route] +layers = -1, -37 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 +scale_x_y = 1.05 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + diff --git a/cfg/cd53paspp-omega.cfg b/cfg/yolov4.cfg similarity index 99% rename from cfg/cd53paspp-omega.cfg rename to cfg/yolov4.cfg index ef2b2ebdb65..7be5b456c7a 100644 --- a/cfg/cd53paspp-omega.cfg +++ b/cfg/yolov4.cfg @@ -5,8 +5,8 @@ # Training batch=64 subdivisions=8 -width=512 -height=512 +width=608 +height=608 channels=3 momentum=0.949 decay=0.0005 From 3d4242a6e534fe44afd3c0bf0de92e0f4e9ce23f Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 23 Apr 2020 15:23:39 +0300 Subject: [PATCH 0925/1365] Update Readme.md --- README.md | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index 0c131787d96..9bb465e4308 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# Yolo-v3 and Yolo-v2 for Windows and Linux +# Yolo-v4 and Yolo-v3/v2 for Windows and Linux ### (neural network for object detection) - Tensor Cores can be used on [Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) and [Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows-using-cmake-gui) More details: http://pjreddie.com/darknet/yolo/ @@ -55,7 +55,7 @@ FPS on RTX 2070 (R) and Tesla V100 (V): * `width=608 height=608` in cfg: **65.7% mAP@0.5 (43.5% AP@0.5:0.95) - 34(R) FPS / 62(V) FPS** - 128.5 BFlops * `width=512 height=512` in cfg: **64.9% mAP@0.5 (43.0% AP@0.5:0.95) - 45(R) FPS / 83(V) FPS** - 91.1 BFlops * `width=416 height=416` in cfg: **62.8% mAP@0.5 (41.2% AP@0.5:0.95) - 55(R) FPS / 96(V) FPS** - 60.1 BFlops - * `width=320 height=320` in cfg: ** 60% mAP@0.5 ( 38% AP@0.5:0.95) - 63(R) FPS / 123(V) FPS** - 35.5 BFlops + * `width=320 height=320` in cfg: **60% mAP@0.5 ( 38% AP@0.5:0.95) - 63(R) FPS / 123(V) FPS** - 35.5 BFlops * [yolov3-tiny-prn.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny-prn.cfg) - **33.1% mAP@0.5 - 370(R) FPS** - 3.5 BFlops - 18.8 MB: [yolov3-tiny-prn.weights](https://drive.google.com/file/d/18yYZWyKbo4XSDVyztmsEcF9B_6bxrhUY/view?usp=sharing) @@ -153,7 +153,7 @@ Others: https://www.youtube.com/user/pjreddie/videos * run-time tips and warnings if you use incorrect cfg-file or dataset * many other fixes of code... -And added manual - [How to train Yolo v3/v2 (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects) +And added manual - [How to train Yolo v4-v2 (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects) Also, you might be interested in using a simplified repository where is implemented INT8-quantization (+30% speedup and -1% mAP reduced): https://github.com/AlexeyAB/yolo2_light @@ -163,12 +163,12 @@ On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector On Linux find executable file `./darknet` in the root directory, while on Windows find it in the directory `\build\darknet\x64` -* Yolo v3 COCO - **image**: `darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25` +* Yolo v4 COCO - **image**: `darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25` * **Output coordinates** of objects: `darknet.exe detector test cfg/coco.data yolov4.cfg yolov4.weights -ext_output dog.jpg` -* Yolo v3 COCO - **video**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output test.mp4` -* Yolo v3 COCO - **WebCam 0**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -c 0` -* Yolo v3 COCO for **net-videocam** - Smart WebCam: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg` -* Yolo v3 - **save result videofile res.avi**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -out_filename res.avi` +* Yolo v4 COCO - **video**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output test.mp4` +* Yolo v4 COCO - **WebCam 0**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -c 0` +* Yolo v4 COCO for **net-videocam** - Smart WebCam: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg` +* Yolo v4 - **save result videofile res.avi**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -out_filename res.avi` * Yolo v3 **Tiny** COCO - video: `darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4` * **JSON and MJPEG server** that allows multiple connections from your soft or Web-browser `ip-address:8070` and 8090: `./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output` * Yolo v3 Tiny **on GPU #1**: `darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 1 test.mp4` @@ -198,7 +198,7 @@ On Linux find executable file `./darknet` in the root directory, while on Window 3. Start Smart WebCam on your phone 4. Replace the address below, on shown in the phone application (Smart WebCam) and launch: -* Yolo v3 COCO-model: `darknet.exe detector demo data/coco.data yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` +* Yolo v4 COCO-model: `darknet.exe detector demo data/coco.data yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` ### How to compile on Linux (using `cmake`) @@ -232,7 +232,7 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c * `ZED_CAMERA=1` to build a library with ZED-3D-camera support (should be ZED SDK installed), then run `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov4.cfg yolov4.weights zed_camera` -To run Darknet on Linux use examples from this article, just use `./darknet` instead of `darknet.exe`, i.e. use this command: `./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights` +To run Darknet on Linux use examples from this article, just use `./darknet` instead of `darknet.exe`, i.e. use this command: `./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights` ### How to compile on Windows (using `CMake-GUI`) @@ -384,9 +384,9 @@ More information about training by the link: http://pjreddie.com/darknet/yolo/#t ## How to train with multi-GPU: -1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74` +1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train cfg/coco.data cfg/yolov4.cfg yolov4.conv.137` -2. Then stop and by using partially-trained model `/backup/yolov3-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train cfg/voc.data cfg/yolov3-voc.cfg /backup/yolov3-voc_1000.weights -gpus 0,1,2,3` +2. Then stop and by using partially-trained model `/backup/yolov4_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train cfg/coco.data cfg/yolov4.cfg /backup/yolov4_1000.weights -gpus 0,1,2,3` Only for small datasets sometimes better to decrease learning rate, for 4 GPUs set `learning_rate = 0.00025` (i.e. learning_rate = 0.001 / GPUs). In this case also increase 4x times `burn_in =` and `max_batches =` in your cfg-file. I.e. use `burn_in = 4000` instead of `1000`. Same goes for `steps=` if `policy=steps` is set. @@ -395,7 +395,7 @@ https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ ## How to train (to detect your custom objects): (to train old Yolo v2 `yolov2-voc.cfg`, `yolov2-tiny-voc.cfg`, `yolo-voc.cfg`, `yolo-voc.2.0.cfg`, ... [click by the link](https://github.com/AlexeyAB/darknet/tree/47c7af1cea5bbdedf1184963355e6418cb8b1b4f#how-to-train-pascal-voc-data)) -Training Yolo v3: +Training Yolo v4 (and v3): 1. Create file `yolo-obj.cfg` with the same content as in `yolov4-custom.cfg` (or copy `yolov4-custom.cfg` to `yolo-obj.cfg)` and: @@ -666,9 +666,9 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol ## How to mark bounded boxes of objects and create annotation files: -Here you can find repository with GUI-software for marking bounded boxes of objects and generating annotation files for Yolo v2 & v3: https://github.com/AlexeyAB/Yolo_mark +Here you can find repository with GUI-software for marking bounded boxes of objects and generating annotation files for Yolo v2 - v4: https://github.com/AlexeyAB/Yolo_mark -With example of: `train.txt`, `obj.names`, `obj.data`, `yolo-obj.cfg`, `air`1-6`.txt`, `bird`1-4`.txt` for 2 classes of objects (air, bird) and `train_obj.cmd` with example how to train this image-set with Yolo v2 & v3 +With example of: `train.txt`, `obj.names`, `obj.data`, `yolo-obj.cfg`, `air`1-6`.txt`, `bird`1-4`.txt` for 2 classes of objects (air, bird) and `train_obj.cmd` with example how to train this image-set with Yolo v2 - v4 Different tools for marking objects in images: 1. in C++: https://github.com/AlexeyAB/Yolo_mark From b1b2d5cfefe23b4b40043e2066ee22cd5e622e9c Mon Sep 17 00:00:00 2001 From: Alexey Date: Fri, 24 Apr 2020 04:02:02 +0300 Subject: [PATCH 0926/1365] Update Readme.md --- README.md | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 9bb465e4308..171e3d5ece6 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,8 @@ # Yolo-v4 and Yolo-v3/v2 for Windows and Linux ### (neural network for object detection) - Tensor Cores can be used on [Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) and [Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows-using-cmake-gui) +Paper Yolo v4: https://arxiv.org/abs/2004.10934 + More details: http://pjreddie.com/darknet/yolo/ @@ -37,9 +39,13 @@ More details: http://pjreddie.com/darknet/yolo/ 11. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) 12. [How to use Yolo as DLL and SO libraries](#how-to-use-yolo-as-dll-and-so-libraries) -| ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) |   ![map_fps](https://user-images.githubusercontent.com/4096485/71702416-6645dc00-2de0-11ea-8d65-de7d4b604021.png) mAP@0.5 (AP50) - FPS (GeForce 1080 Ti) https://arxiv.org/abs/1911.11929 https://github.com/WongKinYiu/CrossStagePartialNetworks - more models | + + +| ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) |   ![map_fps](https://user-images.githubusercontent.com/4096485/80163662-7ed04100-85df-11ea-8db7-1232b1158827.png) AP50:95 / AP50 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2004.10934 | |---|---| +* Yolo v4 Full comparison: [map_fps](https://user-images.githubusercontent.com/4096485/80163825-061db480-85e0-11ea-9ff9-13c7143789cb.png) +* CSPNet: [map_fps](https://user-images.githubusercontent.com/4096485/71702416-6645dc00-2de0-11ea-8d65-de7d4b604021.png) [paper](https://arxiv.org/abs/1911.11929) Comparison: https://github.com/WongKinYiu/CrossStagePartialNetworks * Yolo v3 on MS COCO: [Speed / Accuracy (mAP@0.5) chart](https://user-images.githubusercontent.com/4096485/52151356-e5d4a380-2683-11e9-9d7d-ac7bc192c477.jpg) * Yolo v3 on MS COCO (Yolo v3 vs RetinaNet) - Figure 3: https://arxiv.org/pdf/1804.02767v1.pdf * Yolo v2 on Pascal VOC 2007: https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg @@ -51,7 +57,7 @@ There are weights-file for different cfg-files (trained for MS COCO dataset): FPS on RTX 2070 (R) and Tesla V100 (V): -* [yolov4.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg) - 245 MB: [yolov4.weights](https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT) +* [yolov4.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg) - 245 MB: [yolov4.weights](https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT) paper [Yolo v4](https://arxiv.org/abs/2004.10934) * `width=608 height=608` in cfg: **65.7% mAP@0.5 (43.5% AP@0.5:0.95) - 34(R) FPS / 62(V) FPS** - 128.5 BFlops * `width=512 height=512` in cfg: **64.9% mAP@0.5 (43.0% AP@0.5:0.95) - 45(R) FPS / 83(V) FPS** - 91.1 BFlops * `width=416 height=416` in cfg: **62.8% mAP@0.5 (41.2% AP@0.5:0.95) - 55(R) FPS / 96(V) FPS** - 60.1 BFlops From 2fc7fbbc0ea001170b12d39b840b9f4d34905dd4 Mon Sep 17 00:00:00 2001 From: Alexey Date: Fri, 24 Apr 2020 16:22:13 +0300 Subject: [PATCH 0927/1365] Update Readme.md --- README.md | 34 ++++++++++++++++++++++++++++++++-- 1 file changed, 32 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 171e3d5ece6..d12aafe1fe4 100644 --- a/README.md +++ b/README.md @@ -41,16 +41,46 @@ More details: http://pjreddie.com/darknet/yolo/ -| ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) |   ![map_fps](https://user-images.githubusercontent.com/4096485/80163662-7ed04100-85df-11ea-8db7-1232b1158827.png) AP50:95 / AP50 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2004.10934 | +| ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) |   ![readme](https://user-images.githubusercontent.com/4096485/80213782-5f1e3480-8642-11ea-8fdf-0e6b9a6b5f4c.png) AP50:95 / AP50 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2004.10934 | |---|---| -* Yolo v4 Full comparison: [map_fps](https://user-images.githubusercontent.com/4096485/80163825-061db480-85e0-11ea-9ff9-13c7143789cb.png) +* Yolo v4 Full comparison: [map_fps](https://user-images.githubusercontent.com/4096485/80213824-6e9d7d80-8642-11ea-94a6-0be90c7d7cd5.png) * CSPNet: [map_fps](https://user-images.githubusercontent.com/4096485/71702416-6645dc00-2de0-11ea-8d65-de7d4b604021.png) [paper](https://arxiv.org/abs/1911.11929) Comparison: https://github.com/WongKinYiu/CrossStagePartialNetworks * Yolo v3 on MS COCO: [Speed / Accuracy (mAP@0.5) chart](https://user-images.githubusercontent.com/4096485/52151356-e5d4a380-2683-11e9-9d7d-ac7bc192c477.jpg) * Yolo v3 on MS COCO (Yolo v3 vs RetinaNet) - Figure 3: https://arxiv.org/pdf/1804.02767v1.pdf * Yolo v2 on Pascal VOC 2007: https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg * Yolo v2 on Pascal VOC 2012 (comp4): https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg +#### How to evaluate AP of YOLOv4 on the MS COCO evaluation server + +1. Download and unzip test-dev2017 dataset from MS COCO server: http://images.cocodataset.org/zips/test2017.zip +2. Download list of images for Detection taks and replace the paths with yours: https://raw.githubusercontent.com/AlexeyAB/darknet/master/scripts/testdev2017.txt +3. Download `yolov4.weights` file: https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT +4. Content of the file `cfg/coco.data` should be +``` +classes= 80 +train = /trainvalno5k.txt +valid = /testdev2017.txt +names = data/coco.names +backup = backup +eval=coco +``` +5. Create `/results/` folder near with `./darknet` executable file +6. Run validation: `./darknet detector valid cfg/coco.data cfg/yolov4.cfg yolov4.weights` +7. Rename the file `/results/coco_results.json` to `detections_test-dev2017_yolov4_results.json` and compress it to `detections_test-dev2017_yolov4_results.zip` +8. Submit file `detections_test-dev2017_yolov4_results.zip` to the MS COCO evaluation server for the `test-dev2019 (bbox)` + +#### How to evaluate FPS of YOLOv4 on GPU + +1. Compile Darknet with `GPU=1 CUDNN=1 CUDNN_HALF=1 OPENCV=1` in the `Makefile` (or use the same settings with Cmake) +2. Download `yolov4.weights` file: https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT +3. Get any .avi/.mp4 video file (preferably not more than 1920x1080 to avoid bottlenecks in CPU performance) +4. Run one of two commands and look at the AVG FPS: +* include video_capturing + NMS + drawing_bboxes: + `./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -dont_show -ext_output` +* exclude video_capturing + NMS + drawing_bboxes: + `./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -benchmark` + #### Pre-trained models There are weights-file for different cfg-files (trained for MS COCO dataset): From 893b889b2f62bff91d9c63931787d553ee19512f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 25 Apr 2020 14:58:36 +0300 Subject: [PATCH 0928/1365] Cmake is fixed for V100 (CC=7.0) --- CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index a624b592679..0b354dc1948 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -65,7 +65,7 @@ else() else() cuda_select_nvcc_arch_flags(CUDA_ARCH_FLAGS ${CUDA_ARCHITECTURES}) message(STATUS "Building with CUDA flags: " "${CUDA_ARCH_FLAGS}") - if (NOT "arch=compute_75,code=sm_75" IN_LIST CUDA_ARCH_FLAGS) + if (NOT "arch=compute_70,code=sm_70" IN_LIST CUDA_ARCH_FLAGS) set(ENABLE_CUDNN_HALF "FALSE" CACHE BOOL "Enable CUDNN Half precision" FORCE) message(STATUS "Your setup does not supports half precision (it requires CC >= 7.5)") endif() From 8db3fe4ef67150d28efb14260e301c548b91edf1 Mon Sep 17 00:00:00 2001 From: Alexey Date: Sat, 25 Apr 2020 18:05:36 +0300 Subject: [PATCH 0929/1365] Update Readme.md --- README.md | 69 ++++++++----------------------------------------------- 1 file changed, 10 insertions(+), 59 deletions(-) diff --git a/README.md b/README.md index d12aafe1fe4..10ce4720a42 100644 --- a/README.md +++ b/README.md @@ -29,23 +29,22 @@ More details: http://pjreddie.com/darknet/yolo/ * [Using CMake-GUI](#how-to-compile-on-windows-using-cmake-gui) * [Using vcpkg](#how-to-compile-on-windows-using-vcpkg) * [Legacy way](#how-to-compile-on-windows-legacy-way) -4. [How to train (Pascal VOC Data)](#how-to-train-pascal-voc-data) +4. [Training and Evaluation of speed and accuracy on MS COCO](https://github.com/AlexeyAB/darknet/wiki#training-and-evaluation-of-speed-and-accuracy-on-ms-coco) 5. [How to train with multi-GPU:](#how-to-train-with-multi-gpu) 6. [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects) 7. [How to train tiny-yolo (to detect your custom objects)](#how-to-train-tiny-yolo-to-detect-your-custom-objects) 8. [When should I stop training](#when-should-i-stop-training) -9. [How to calculate mAP on PascalVOC 2007](#how-to-calculate-map-on-pascalvoc-2007) -10. [How to improve object detection](#how-to-improve-object-detection) -11. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) -12. [How to use Yolo as DLL and SO libraries](#how-to-use-yolo-as-dll-and-so-libraries) +9. [How to improve object detection](#how-to-improve-object-detection) +10. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) +11. [How to use Yolo as DLL and SO libraries](#how-to-use-yolo-as-dll-and-so-libraries) | ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) |   ![readme](https://user-images.githubusercontent.com/4096485/80213782-5f1e3480-8642-11ea-8fdf-0e6b9a6b5f4c.png) AP50:95 / AP50 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2004.10934 | |---|---| -* Yolo v4 Full comparison: [map_fps](https://user-images.githubusercontent.com/4096485/80213824-6e9d7d80-8642-11ea-94a6-0be90c7d7cd5.png) -* CSPNet: [map_fps](https://user-images.githubusercontent.com/4096485/71702416-6645dc00-2de0-11ea-8d65-de7d4b604021.png) [paper](https://arxiv.org/abs/1911.11929) Comparison: https://github.com/WongKinYiu/CrossStagePartialNetworks +* Yolo v4 Full comparison: [map_fps](https://user-images.githubusercontent.com/4096485/80283279-0e303e00-871f-11ea-814c-870967d77fd1.png) +* CSPNet: [paper](https://arxiv.org/abs/1911.11929) and [map_fps](https://user-images.githubusercontent.com/4096485/71702416-6645dc00-2de0-11ea-8d65-de7d4b604021.png) comparison: https://github.com/WongKinYiu/CrossStagePartialNetworks * Yolo v3 on MS COCO: [Speed / Accuracy (mAP@0.5) chart](https://user-images.githubusercontent.com/4096485/52151356-e5d4a380-2683-11e9-9d7d-ac7bc192c477.jpg) * Yolo v3 on MS COCO (Yolo v3 vs RetinaNet) - Figure 3: https://arxiv.org/pdf/1804.02767v1.pdf * Yolo v2 on Pascal VOC 2007: https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg @@ -383,41 +382,6 @@ Then add to your created project: * For OpenCV 3.2: `opencv_world320.dll` and `opencv_ffmpeg320_64.dll` from `C:\opencv_3.0\opencv\build\x64\vc14\bin` * For OpenCV 2.4.13: `opencv_core2413.dll`, `opencv_highgui2413.dll` and `opencv_ffmpeg2413_64.dll` from `C:\opencv_2.4.13\opencv\build\x64\vc14\bin` -## How to train (Pascal VOC Data): - -
CLICK ME - How to train (Pascal VOC Data) - -1. Download pre-trained weights for the convolutional layers (154 MB): http://pjreddie.com/media/files/darknet53.conv.74 and put to the directory `build\darknet\x64` - -2. Download The Pascal VOC Data and unpack it to directory `build\darknet\x64\data\voc` will be created dir `build\darknet\x64\data\voc\VOCdevkit\`: - * http://pjreddie.com/media/files/VOCtrainval_11-May-2012.tar - * http://pjreddie.com/media/files/VOCtrainval_06-Nov-2007.tar - * http://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar - - 2.1 Download file `voc_label.py` to dir `build\darknet\x64\data\voc`: http://pjreddie.com/media/files/voc_label.py - -3. Download and install Python for Windows: https://www.python.org/ftp/python/3.5.2/python-3.5.2-amd64.exe - -4. Run command: `python build\darknet\x64\data\voc\voc_label.py` (to generate files: 2007_test.txt, 2007_train.txt, 2007_val.txt, 2012_train.txt, 2012_val.txt) - -5. Run command: `type 2007_train.txt 2007_val.txt 2012_*.txt > train.txt` - -6. Set `batch=64` and `subdivisions=8` in the file `yolov3-voc.cfg`: [link](https://github.com/AlexeyAB/darknet/blob/ee38c6e1513fb089b35be4ffa692afd9b3f65747/cfg/yolov3-voc.cfg#L3-L4) - -7. Start training by using `train_voc.cmd` or by using the command line: - - `darknet.exe detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74` - -(**Note:** To disable Loss-Window use flag `-dont_show`. If you are using CPU, try `darknet_no_gpu.exe` instead of `darknet.exe`.) - -If required change paths in the file `build\darknet\cfg\voc.data` - -More information about training by the link: http://pjreddie.com/darknet/yolo/#train-voc - - **Note:** If during training you see `nan` values for `avg` (loss) field - then training goes wrong, but if `nan` is in some other lines - then training goes well. - -
- ## How to train with multi-GPU: 1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train cfg/coco.data cfg/yolov4.cfg yolov4.conv.137` @@ -619,20 +583,6 @@ In terms of Wiki, indicators Precision and Recall have a slightly different mean ![precision_recall_iou](https://hsto.org/files/ca8/866/d76/ca8866d76fb840228940dbf442a7f06a.jpg) -### How to calculate mAP on PascalVOC 2007: - -1. To calculate mAP (mean average precision) on PascalVOC-2007-test: -* Download PascalVOC dataset, install Python 3.x and get file `2007_test.txt` as described here: https://github.com/AlexeyAB/darknet#how-to-train-pascal-voc-data -* Then download file https://raw.githubusercontent.com/AlexeyAB/darknet/master/scripts/voc_label_difficult.py to the dir `build\darknet\x64\data\` then run `voc_label_difficult.py` to get the file `difficult_2007_test.txt` -* Remove symbol `#` from this line to un-comment it: https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/data/voc.data#L4 -* Then there are 2 ways to get mAP: - 1. Using Darknet + Python: run the file `build/darknet/x64/calc_mAP_voc_py.cmd` - you will get mAP for `yolo-voc.cfg` model, mAP = 75.9% - 2. Using this fork of Darknet: run the file `build/darknet/x64/calc_mAP.cmd` - you will get mAP for `yolo-voc.cfg` model, mAP = 75.8% - - (The article specifies the value of mAP = 76.8% for YOLOv2 416×416, page-4 table-3: https://arxiv.org/pdf/1612.08242v1.pdf. We get values lower - perhaps due to the fact that the model was trained on a slightly different source code than the code on which the detection is was done) - -* if you want to get mAP for `tiny-yolo-voc.cfg` model, then un-comment line for tiny-yolo-voc.cfg and comment line for yolo-voc.cfg in the .cmd-file -* if you have Python 2.x instead of Python 3.x, and if you use Darknet+Python-way to get mAP, then in your cmd-file use `reval_voc.py` and `voc_eval.py` instead of `reval_voc_py3.py` and `voc_eval_py3.py` from this directory: https://github.com/AlexeyAB/darknet/tree/master/scripts ### Custom object detection: @@ -661,13 +611,14 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last `[yolo]`-layer or `[region]`-layer in your cfg-file (the global maximum number of objects that can be detected by YoloV3 is `0,0615234375*(width*height)` where are width and height are parameters from `[net]` section in cfg-file) - * for training for small objects (smaller than 16x16 after the image is resized to 416x416) - set `layers = -1, 11` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L720 - and set `stride=4` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L717 + * for training for small objects (smaller than 16x16 after the image is resized to 416x416) - set `layers = 23` instead of https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L895 + set `stride=4` instead of https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L892 + and set `stride=4` instead of https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L989 * for training for both small and large objects use modified models: * Full-model: 5 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3_5l.cfg * Tiny-model: 3 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny_3l.cfg - * Spatial-full-model: 3 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-spp.cfg + * YOLOv4: 3 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-custom.cfg * If you train the model to distinguish Left and Right objects as separate classes (left/right hand, left/right-turn on road signs, ...) then for disabling flip data augmentation - add `flip=0` here: https://github.com/AlexeyAB/darknet/blob/3d2d0a7c98dbc8923d9ff705b81ff4f7940ea6ff/cfg/yolov3.cfg#L17 From 379952208e2a8ee3daf16221558cb0ef9efd547b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 26 Apr 2020 14:19:20 +0300 Subject: [PATCH 0930/1365] Minor fix --- src/image_opencv.cpp | 2 +- src/network.c | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 90bdb37e8f0..4e852db2c6f 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1507,7 +1507,7 @@ void show_opencv_info() #else // OPENCV extern "C" void show_opencv_info() { - std::cerr << " OpenCV isn't used - data increase will run slowly \n"; + std::cerr << " OpenCV isn't used - data augmentation will be slow \n"; } extern "C" int wait_key_cv(int delay) { return 0; } extern "C" int wait_until_press_key_cv() { return 0; } diff --git a/src/network.c b/src/network.c index 31574f90a8d..46e99fc03e9 100644 --- a/src/network.c +++ b/src/network.c @@ -983,7 +983,8 @@ det_num_pair* network_predict_batch(network *net, image im, int batch_size, int network_predict(*net, im.data); det_num_pair *pdets = (struct det_num_pair *)calloc(batch_size, sizeof(det_num_pair)); int num; - for(int batch=0; batch Date: Sun, 26 Apr 2020 15:51:17 +0300 Subject: [PATCH 0931/1365] Minor fix - filename of chart (Loss & TopK) - training Classifier --- src/classifier.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/classifier.c b/src/classifier.c index 5c1874886c0..653222283d8 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -120,7 +120,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int number_of_lines = 100; int img_size = 1000; char windows_name[100]; - sprintf(windows_name, "average loss (id:%d)", random_gen()); + sprintf(windows_name, "chart_%s.png", base); if (!dontuse_opencv) img = draw_train_chart(windows_name, max_img_loss, net.max_batches, number_of_lines, img_size, dont_show, chart_path); #endif //OPENCV From 6ebaef30959133401b550a409953b27425250bab Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 27 Apr 2020 04:36:39 +0300 Subject: [PATCH 0932/1365] Added GELU activation --- include/darknet.h | 2 +- src/activation_kernels.cu | 28 ++++++++++++++++++++++++++++ src/activations.c | 7 +++++++ src/activations.h | 7 +++++++ 4 files changed, 43 insertions(+), 1 deletion(-) diff --git a/include/darknet.h b/include/darknet.h index ac83788ab8d..9b0ddeecbb0 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -104,7 +104,7 @@ typedef struct tree { // activations.h typedef enum { - LOGISTIC, RELU, RELU6, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, SWISH, MISH, NORM_CHAN, NORM_CHAN_SOFTMAX, NORM_CHAN_SOFTMAX_MAXVAL + LOGISTIC, RELU, RELU6, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, GELU, SWISH, MISH, NORM_CHAN, NORM_CHAN_SOFTMAX, NORM_CHAN_SOFTMAX_MAXVAL }ACTIVATION; // parser.h diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index ba1113fdb34..89e01c04604 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -36,6 +36,7 @@ __device__ float relie_activate_kernel(float x){return (x>0) ? x : .01f*x;} __device__ float ramp_activate_kernel(float x){return x*(x>0)+.1f*x;} __device__ float leaky_activate_kernel(float x){return (x>0) ? x : .1f*x;} __device__ float tanh_activate_kernel(float x){return (2/(1 + expf(-2*x)) - 1);} +__device__ float gelu_activate_kernel(float x){return (0.5*x*(1 + tanhf(0.797885*x + 0.035677*powf(x, 3))));} __device__ float softplus_kernel(float x, float threshold = 20) { if (x > threshold) return x; // too large else if (x < -threshold) return expf(x); // too small @@ -75,6 +76,11 @@ __device__ float relie_gradient_kernel(float x){return (x>0) ? 1 : .01f;} __device__ float ramp_gradient_kernel(float x){return (x>0)+.1f;} __device__ float leaky_gradient_kernel(float x){return (x>0) ? 1 : .1f;} __device__ float tanh_gradient_kernel(float x){return 1-x*x;} +__device__ float sech_gpu(float x) { return 2 / (expf(x) + expf(-x)); } +__device__ float gelu_gradient_kernel(float x) { + const float x3 = powf(x, 3); + return 0.5*tanhf(0.0356774*x3 + 0.797885*x) + (0.0535161*x3 + 0.398942*x) * powf(sech_gpu(0.0356774*x3 + 0.797885*x), 2) + 0.5; +} __device__ float plse_gradient_kernel(float x){return (x < 0 || x > 1) ? .01f : .125f;} __device__ float stair_gradient_kernel(float x) { @@ -99,6 +105,8 @@ __device__ float activate_kernel(float x, ACTIVATION a) return elu_activate_kernel(x); case SELU: return selu_activate_kernel(x); + case GELU: + return gelu_activate_kernel(x); case RELIE: return relie_activate_kernel(x); case RAMP: @@ -138,6 +146,8 @@ __device__ float gradient_kernel(float x, ACTIVATION a) return elu_gradient_kernel(x); case SELU: return selu_gradient_kernel(x); + case GELU: + return gelu_gradient_kernel(x); case RELIE: return relie_gradient_kernel(x); case RAMP: @@ -245,6 +255,14 @@ __global__ void activate_array_selu_kernel(float *x, int n) } } +__global__ void activate_array_gelu_kernel(float *x, int n) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < n) { + x[index] = gelu_activate_kernel(x[index]); + } +} + __global__ void activate_array_logistic_kernel(float *x, int n) { int index = blockIdx.x*blockDim.x + threadIdx.x; @@ -343,6 +361,14 @@ __global__ void gradient_array_selu_kernel(float *x, int n, float *delta) } } +__global__ void gradient_array_gelu_kernel(float *x, int n, float *delta) +{ + int index = blockIdx.x*blockDim.x + threadIdx.x; + if (index < n) { + delta[index] *= gelu_gradient_kernel(x[index]); + } +} + __global__ void gradient_array_logistic_kernel(float *x, int n, float *delta) { int index = blockIdx.x*blockDim.x + threadIdx.x; @@ -394,6 +420,7 @@ extern "C" void activate_array_ongpu(float *x, int n, ACTIVATION a) else if (a == RELU) activate_array_relu_kernel << > >(x, n); else if (a == RELU6) activate_array_relu6_kernel << > >(x, n); else if (a == SELU) activate_array_selu_kernel << > >(x, n); + else if (a == GELU) activate_array_gelu_kernel << > >(x, n); else activate_array_kernel<<>>(x, n, a); CHECK_CUDA(cudaPeekAtLastError()); @@ -429,6 +456,7 @@ extern "C" void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta exit(0); } else if (a == SELU) gradient_array_selu_kernel << > >(x, n, delta); + else if (a == GELU) gradient_array_gelu_kernel << > >(x, n, delta); else gradient_array_kernel << > > (x, n, a, delta); CHECK_CUDA(cudaPeekAtLastError()); diff --git a/src/activations.c b/src/activations.c index 0b68fda0ff4..dac252b6fcc 100644 --- a/src/activations.c +++ b/src/activations.c @@ -19,6 +19,8 @@ char *get_activation_string(ACTIVATION a) return "elu"; case SELU: return "selu"; + case GELU: + return "gelu"; case RELIE: return "relie"; case RAMP: @@ -56,6 +58,7 @@ ACTIVATION get_activation(char *s) if (strcmp(s, "relu6") == 0) return RELU6; if (strcmp(s, "elu")==0) return ELU; if (strcmp(s, "selu") == 0) return SELU; + if (strcmp(s, "gelu") == 0) return GELU; if (strcmp(s, "relie")==0) return RELIE; if (strcmp(s, "plse")==0) return PLSE; if (strcmp(s, "hardtan")==0) return HARDTAN; @@ -84,6 +87,8 @@ float activate(float x, ACTIVATION a) return elu_activate(x); case SELU: return selu_activate(x); + case GELU: + return gelu_activate(x); case RELIE: return relie_activate(x); case RAMP: @@ -300,6 +305,8 @@ float gradient(float x, ACTIVATION a) return elu_gradient(x); case SELU: return selu_gradient(x); + case GELU: + return gelu_gradient(x); case RELIE: return relie_gradient(x); case RAMP: diff --git a/src/activations.h b/src/activations.h index 9e9b0053c6c..8ac457d2d8d 100644 --- a/src/activations.h +++ b/src/activations.h @@ -65,6 +65,7 @@ static inline float ramp_activate(float x){return x*(x>0)+.1f*x;} static inline float leaky_activate(float x){return (x>0) ? x : .1f*x;} //static inline float tanh_activate(float x){return (expf(2*x)-1)/(expf(2*x)+1);} static inline float tanh_activate(float x) { return (2 / (1 + expf(-2 * x)) - 1); } +static inline float gelu_activate(float x) { return (0.5*x*(1 + tanhf(0.797885*x + 0.035677*powf(x, 3)))); } static inline float softplus_activate(float x, float threshold) { if (x > threshold) return x; // too large else if (x < -threshold) return expf(x); // too small @@ -114,6 +115,12 @@ static inline float relie_gradient(float x){return (x>0) ? 1 : .01f;} static inline float ramp_gradient(float x){return (x>0)+.1f;} static inline float leaky_gradient(float x){return (x>0) ? 1 : .1f;} static inline float tanh_gradient(float x){return 1-x*x;} + +static inline float sech(float x) { return 2 / (expf(x) + expf(-x)); } +static inline float gelu_gradient(float x) { + const float x3 = powf(x, 3); + return 0.5*tanhf(0.0356774*x3 + 0.797885*x) + (0.0535161*x3 + 0.398942*x) * powf(sech(0.0356774*x3 + 0.797885*x), 2) + 0.5; +} static inline float plse_gradient(float x){return (x < 0 || x > 1) ? .01f : .125f;} #ifdef __cplusplus From 8e272fa8680a79a20d3c6210ef8e001fbff160c0 Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 27 Apr 2020 15:48:34 +0300 Subject: [PATCH 0933/1365] Update Readme.md --- README.md | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 10ce4720a42..9678a1a534a 100644 --- a/README.md +++ b/README.md @@ -72,7 +72,7 @@ eval=coco #### How to evaluate FPS of YOLOv4 on GPU 1. Compile Darknet with `GPU=1 CUDNN=1 CUDNN_HALF=1 OPENCV=1` in the `Makefile` (or use the same settings with Cmake) -2. Download `yolov4.weights` file: https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT +2. Download `yolov4.weights` file 245 MB: [yolov4.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights) (Google-drive mirror [yolov4.weights](https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT) ) 3. Get any .avi/.mp4 video file (preferably not more than 1920x1080 to avoid bottlenecks in CPU performance) 4. Run one of two commands and look at the AVG FPS: * include video_capturing + NMS + drawing_bboxes: @@ -86,7 +86,7 @@ There are weights-file for different cfg-files (trained for MS COCO dataset): FPS on RTX 2070 (R) and Tesla V100 (V): -* [yolov4.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg) - 245 MB: [yolov4.weights](https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT) paper [Yolo v4](https://arxiv.org/abs/2004.10934) +* [yolov4.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg) - 245 MB: [yolov4.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights) (Google-drive mirror [yolov4.weights](https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT) ) paper [Yolo v4](https://arxiv.org/abs/2004.10934) * `width=608 height=608` in cfg: **65.7% mAP@0.5 (43.5% AP@0.5:0.95) - 34(R) FPS / 62(V) FPS** - 128.5 BFlops * `width=512 height=512` in cfg: **64.9% mAP@0.5 (43.0% AP@0.5:0.95) - 45(R) FPS / 83(V) FPS** - 91.1 BFlops * `width=416 height=416` in cfg: **62.8% mAP@0.5 (41.2% AP@0.5:0.95) - 55(R) FPS / 96(V) FPS** - 60.1 BFlops @@ -397,6 +397,8 @@ https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ Training Yolo v4 (and v3): +0. For training `cfg/yolov4-custom.cfg` download the pre-trained weights-file (162 MB): [yolov4.conv.137](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137) (Google drive mirror [yolov4.conv.137](https://drive.google.com/open?id=1JKF-bdIklxOOVy-2Cr5qdvjgGpmGfcbp) ) + 1. Create file `yolo-obj.cfg` with the same content as in `yolov4-custom.cfg` (or copy `yolov4-custom.cfg` to `yolo-obj.cfg)` and: * change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L3) @@ -476,7 +478,7 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an ``` 7. Download pre-trained weights for the convolutional layers and put to the directory `build\darknet\x64` - * for `yolov4.cfg`, `yolov4-custom.cfg` (162 MB): [yolov4.conv.137](https://drive.google.com/open?id=1JKF-bdIklxOOVy-2Cr5qdvjgGpmGfcbp) + * for `yolov4.cfg`, `yolov4-custom.cfg` (162 MB): [yolov4.conv.137](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137) (Google drive mirror [yolov4.conv.137](https://drive.google.com/open?id=1JKF-bdIklxOOVy-2Cr5qdvjgGpmGfcbp) ) * for `csresnext50-panet-spp.cfg` (133 MB): [csresnext50-panet-spp.conv.112](https://drive.google.com/file/d/16yMYCLQTY_oDlCIZPfn_sab6KD3zgzGq/view?usp=sharing) * for `yolov3.cfg, yolov3-spp.cfg` (154 MB): [darknet53.conv.74](https://pjreddie.com/media/files/darknet53.conv.74) * for `yolov3-tiny-prn.cfg , yolov3-tiny.cfg` (6 MB): [yolov3-tiny.conv.11](https://drive.google.com/file/d/18v36esoXCh-PsOKwyP2GWrpYDptDY8Zf/view?usp=sharing) From 83b3d46edeff5a0ee11c9bb4fef348f3fb6e1f5e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 27 Apr 2020 16:14:22 +0300 Subject: [PATCH 0934/1365] Fixed minor bug with closing ./darknet detector demo ... --- src/demo.c | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/demo.c b/src/demo.c index 6bd00303a0e..72a0ef4b723 100644 --- a/src/demo.c +++ b/src/demo.c @@ -70,7 +70,8 @@ void *fetch_in_thread(void *ptr) in_s = get_image_from_stream_resize(cap, net.w, net.h, net.c, &in_img, dont_close_stream); if (!in_s.data) { printf("Stream closed.\n"); - flag_exit = 1; + custom_atomic_store_int(&flag_exit, 1); + custom_atomic_store_int(&run_fetch_in_thread, 0); //exit(EXIT_FAILURE); return 0; } From 65506eb04acacc050a1903614066337071e866b8 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 27 Apr 2020 16:40:43 +0300 Subject: [PATCH 0935/1365] Minor fix --- .travis.yml | 18 +++++++++--------- README.md | 1 + build/darknet/x64/cfg/yolov4-custom.cfg | 3 +++ cfg/yolov4-custom.cfg | 3 +++ 4 files changed, 16 insertions(+), 9 deletions(-) diff --git a/.travis.yml b/.travis.yml index 32095e578c1..fae618333f4 100644 --- a/.travis.yml +++ b/.travis.yml @@ -74,15 +74,15 @@ matrix: - additional_defines="" - MATRIX_EVAL="brew install opencv" - - os: osx - name: macOS - clang - vcpkg - osx_image: xcode10.1 - env: - - additional_defines="-DBUILD_SHARED_LIBS=OFF" - - MATRIX_EVAL="brew install yasm && unset CC && unset CXX" - - USE_VCPKG=true - - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" - +# - os: osx +# name: macOS - clang - vcpkg +# osx_image: xcode10.1 +# env: +# - additional_defines="-DBUILD_SHARED_LIBS=OFF" +# - MATRIX_EVAL="brew install yasm && unset CC && unset CXX" +# - USE_VCPKG=true +# - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" +# - os: linux compiler: clang dist: bionic diff --git a/README.md b/README.md index 9678a1a534a..603a473cb30 100644 --- a/README.md +++ b/README.md @@ -87,6 +87,7 @@ There are weights-file for different cfg-files (trained for MS COCO dataset): FPS on RTX 2070 (R) and Tesla V100 (V): * [yolov4.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg) - 245 MB: [yolov4.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights) (Google-drive mirror [yolov4.weights](https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT) ) paper [Yolo v4](https://arxiv.org/abs/2004.10934) + just change `width=` and `height=` parameters in `yolov4.cfg` file and use the same `yolov4.weights` file for all cases: * `width=608 height=608` in cfg: **65.7% mAP@0.5 (43.5% AP@0.5:0.95) - 34(R) FPS / 62(V) FPS** - 128.5 BFlops * `width=512 height=512` in cfg: **64.9% mAP@0.5 (43.0% AP@0.5:0.95) - 45(R) FPS / 83(V) FPS** - 91.1 BFlops * `width=416 height=416` in cfg: **62.8% mAP@0.5 (41.2% AP@0.5:0.95) - 55(R) FPS / 96(V) FPS** - 60.1 BFlops diff --git a/build/darknet/x64/cfg/yolov4-custom.cfg b/build/darknet/x64/cfg/yolov4-custom.cfg index e033fc8c571..129c7a98ea0 100644 --- a/build/darknet/x64/cfg/yolov4-custom.cfg +++ b/build/darknet/x64/cfg/yolov4-custom.cfg @@ -978,6 +978,7 @@ iou_normalizer=0.07 iou_loss=ciou nms_kind=greedynms beta_nms=0.6 +max_delta=5 [route] @@ -1065,6 +1066,7 @@ iou_normalizer=0.07 iou_loss=ciou nms_kind=greedynms beta_nms=0.6 +max_delta=5 [route] @@ -1153,4 +1155,5 @@ iou_normalizer=0.07 iou_loss=ciou nms_kind=greedynms beta_nms=0.6 +max_delta=5 diff --git a/cfg/yolov4-custom.cfg b/cfg/yolov4-custom.cfg index e033fc8c571..129c7a98ea0 100644 --- a/cfg/yolov4-custom.cfg +++ b/cfg/yolov4-custom.cfg @@ -978,6 +978,7 @@ iou_normalizer=0.07 iou_loss=ciou nms_kind=greedynms beta_nms=0.6 +max_delta=5 [route] @@ -1065,6 +1066,7 @@ iou_normalizer=0.07 iou_loss=ciou nms_kind=greedynms beta_nms=0.6 +max_delta=5 [route] @@ -1153,4 +1155,5 @@ iou_normalizer=0.07 iou_loss=ciou nms_kind=greedynms beta_nms=0.6 +max_delta=5 From 6d38218a046995b789fdc8afbf09b73e4432f9ac Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 28 Apr 2020 19:20:22 +0300 Subject: [PATCH 0936/1365] Minor fix --- build/darknet/x64/darknet.py | 4 ++-- build/darknet/x64/darknet_video.py | 4 ++-- darknet.py | 4 ++-- darknet_video.py | 4 ++-- src/activations.c | 2 ++ 5 files changed, 10 insertions(+), 8 deletions(-) diff --git a/build/darknet/x64/darknet.py b/build/darknet/x64/darknet.py index ee561dac116..27a7a6797e6 100644 --- a/build/darknet/x64/darknet.py +++ b/build/darknet/x64/darknet.py @@ -313,7 +313,7 @@ def detect_image(net, meta, im, thresh=.5, hier_thresh=.5, nms=.45, debug= False metaMain = None altNames = None -def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yolov3.cfg", weightPath = "yolov3.weights", metaPath= "./cfg/coco.data", showImage= True, makeImageOnly = False, initOnly= False): +def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yolov4.cfg", weightPath = "yolov4.weights", metaPath= "./cfg/coco.data", showImage= True, makeImageOnly = False, initOnly= False): """ Convenience function to handle the detection and returns of objects. @@ -456,7 +456,7 @@ def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yo print("Unable to show image: "+str(e)) return detections -def performBatchDetect(thresh= 0.25, configPath = "./cfg/yolov3.cfg", weightPath = "yolov3.weights", metaPath= "./cfg/coco.data", hier_thresh=.5, nms=.45, batch_size=3): +def performBatchDetect(thresh= 0.25, configPath = "./cfg/yolov4.cfg", weightPath = "yolov4.weights", metaPath= "./cfg/coco.data", hier_thresh=.5, nms=.45, batch_size=3): import cv2 import numpy as np # NB! Image sizes should be the same diff --git a/build/darknet/x64/darknet_video.py b/build/darknet/x64/darknet_video.py index a5e462fcab6..aa9c669649f 100644 --- a/build/darknet/x64/darknet_video.py +++ b/build/darknet/x64/darknet_video.py @@ -42,8 +42,8 @@ def cvDrawBoxes(detections, img): def YOLO(): global metaMain, netMain, altNames - configPath = "./cfg/yolov3.cfg" - weightPath = "./yolov3.weights" + configPath = "./cfg/yolov4.cfg" + weightPath = "./yolov4.weights" metaPath = "./cfg/coco.data" if not os.path.exists(configPath): raise ValueError("Invalid config path `" + diff --git a/darknet.py b/darknet.py index ee561dac116..27a7a6797e6 100644 --- a/darknet.py +++ b/darknet.py @@ -313,7 +313,7 @@ def detect_image(net, meta, im, thresh=.5, hier_thresh=.5, nms=.45, debug= False metaMain = None altNames = None -def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yolov3.cfg", weightPath = "yolov3.weights", metaPath= "./cfg/coco.data", showImage= True, makeImageOnly = False, initOnly= False): +def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yolov4.cfg", weightPath = "yolov4.weights", metaPath= "./cfg/coco.data", showImage= True, makeImageOnly = False, initOnly= False): """ Convenience function to handle the detection and returns of objects. @@ -456,7 +456,7 @@ def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yo print("Unable to show image: "+str(e)) return detections -def performBatchDetect(thresh= 0.25, configPath = "./cfg/yolov3.cfg", weightPath = "yolov3.weights", metaPath= "./cfg/coco.data", hier_thresh=.5, nms=.45, batch_size=3): +def performBatchDetect(thresh= 0.25, configPath = "./cfg/yolov4.cfg", weightPath = "yolov4.weights", metaPath= "./cfg/coco.data", hier_thresh=.5, nms=.45, batch_size=3): import cv2 import numpy as np # NB! Image sizes should be the same diff --git a/darknet_video.py b/darknet_video.py index a5e462fcab6..aa9c669649f 100644 --- a/darknet_video.py +++ b/darknet_video.py @@ -42,8 +42,8 @@ def cvDrawBoxes(detections, img): def YOLO(): global metaMain, netMain, altNames - configPath = "./cfg/yolov3.cfg" - weightPath = "./yolov3.weights" + configPath = "./cfg/yolov4.cfg" + weightPath = "./yolov4.weights" metaPath = "./cfg/coco.data" if not os.path.exists(configPath): raise ValueError("Invalid config path `" + diff --git a/src/activations.c b/src/activations.c index dac252b6fcc..1a76433acaf 100644 --- a/src/activations.c +++ b/src/activations.c @@ -293,6 +293,8 @@ float gradient(float x, ACTIVATION a) return loggy_gradient(x); case RELU: return relu_gradient(x); + case RELU6: + return relu6_gradient(x); case NORM_CHAN: //return relu_gradient(x); case NORM_CHAN_SOFTMAX_MAXVAL: From 36c73c5b9e3f2e72049fb68566e32632f6c70e85 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 28 Apr 2020 19:20:50 +0300 Subject: [PATCH 0937/1365] Some explanations for training --- README.md | 2 +- build/darknet/x64/cfg/yolov4.cfg | 7 +++---- cfg/yolov4.cfg | 7 +++---- 3 files changed, 7 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 603a473cb30..8795e4603d0 100644 --- a/README.md +++ b/README.md @@ -389,7 +389,7 @@ Then add to your created project: 2. Then stop and by using partially-trained model `/backup/yolov4_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train cfg/coco.data cfg/yolov4.cfg /backup/yolov4_1000.weights -gpus 0,1,2,3` -Only for small datasets sometimes better to decrease learning rate, for 4 GPUs set `learning_rate = 0.00025` (i.e. learning_rate = 0.001 / GPUs). In this case also increase 4x times `burn_in =` and `max_batches =` in your cfg-file. I.e. use `burn_in = 4000` instead of `1000`. Same goes for `steps=` if `policy=steps` is set. +If you get a Nan, then for some datasets better to decrease learning rate, for 4 GPUs set `learning_rate = 0,00065` (i.e. learning_rate = 0.00261 / GPUs). In this case also increase 4x times `burn_in =` in your cfg-file. I.e. use `burn_in = 4000` instead of `1000`. https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ diff --git a/build/darknet/x64/cfg/yolov4.cfg b/build/darknet/x64/cfg/yolov4.cfg index 7be5b456c7a..47b9db61a6b 100644 --- a/build/darknet/x64/cfg/yolov4.cfg +++ b/build/darknet/x64/cfg/yolov4.cfg @@ -1,10 +1,9 @@ [net] -# Testing -#batch=1 -#subdivisions=1 -# Training batch=64 subdivisions=8 +# Training +#width=512 +#height=512 width=608 height=608 channels=3 diff --git a/cfg/yolov4.cfg b/cfg/yolov4.cfg index 7be5b456c7a..47b9db61a6b 100644 --- a/cfg/yolov4.cfg +++ b/cfg/yolov4.cfg @@ -1,10 +1,9 @@ [net] -# Testing -#batch=1 -#subdivisions=1 -# Training batch=64 subdivisions=8 +# Training +#width=512 +#height=512 width=608 height=608 channels=3 From 1c1275fe88b27f6d02a7d391fef5249d2ead8b56 Mon Sep 17 00:00:00 2001 From: Ali Babaei Date: Wed, 29 Apr 2020 10:23:40 +0430 Subject: [PATCH 0938/1365] [ fix ] - Not printing Iteration info in log file When we redirect output of training into log files, while the net.cudnn_half is True, iteration information line, which contains "avg loss" is missed. Using \n and fflush() fixed the issue. Each one of them may be enough to fix the issue. --- src/detector.c | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/src/detector.c b/src/detector.c index b7981ea9627..cf26107d1bc 100644 --- a/src/detector.c +++ b/src/detector.c @@ -292,10 +292,12 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i } if (net.cudnn_half) { - if (iteration < net.burn_in * 3) fprintf(stderr, "\n Tensor Cores are disabled until the first %d iterations are reached.", 3 * net.burn_in); - else fprintf(stderr, "\n Tensor Cores are used."); + if (iteration < net.burn_in * 3) fprintf(stderr, "\n Tensor Cores are disabled until the first %d iterations are reached.\n", 3 * net.burn_in); + else fprintf(stderr, "\n Tensor Cores are used.\n"); + fflush(stderr); } printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images, %f hours left\n", iteration, loss, avg_loss, get_current_rate(net), (what_time_is_it_now() - time), iteration*imgs, avg_time); + fflush(stdout); int draw_precision = 0; if (calc_map && (iteration >= next_map_calc || iteration == net.max_batches)) { From be49ab30cd5c8bef2b8c00cda3537b1ca3e12aa7 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Mon, 27 Apr 2020 18:11:19 +0200 Subject: [PATCH 0939/1365] omp support on macOS --- .travis.yml | 32 +++++++++++++++++++------------- CMakeLists.txt | 10 +++------- appveyor.yml | 1 + 3 files changed, 23 insertions(+), 20 deletions(-) diff --git a/.travis.yml b/.travis.yml index fae618333f4..9a0630a79ba 100644 --- a/.travis.yml +++ b/.travis.yml @@ -74,15 +74,22 @@ matrix: - additional_defines="" - MATRIX_EVAL="brew install opencv" -# - os: osx -# name: macOS - clang - vcpkg -# osx_image: xcode10.1 -# env: -# - additional_defines="-DBUILD_SHARED_LIBS=OFF" -# - MATRIX_EVAL="brew install yasm && unset CC && unset CXX" -# - USE_VCPKG=true -# - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" -# + - os: osx + compiler: clang + name: macOS - clang - opencv(latest) - libomp + env: + - additional_defines="" + - MATRIX_EVAL="brew install opencv libomp" + + - os: osx + name: macOS - clang - vcpkg + osx_image: xcode11.4 + env: + - additional_defines="-DBUILD_SHARED_LIBS=OFF" + - MATRIX_EVAL="brew install yasm && unset CC && unset CXX" + - USE_VCPKG=true + - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" + - os: linux compiler: clang dist: bionic @@ -201,10 +208,8 @@ matrix: - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" -# allow_failures: -# - name: macOS - vcpkg -# - name: ubuntu 18.04 - gcc - vcpkg -# - name: ubuntu 18.04 - gcc - CUDA 10 - vcpkg + allow_failures: + - name: macOS - clang - vcpkg before_install: - travis_retry eval "${MATRIX_EVAL}" @@ -222,6 +227,7 @@ install: - if [ "${USE_VCPKG}" = true ] ; then ./bootstrap-vcpkg.sh ; fi - if [ "${USE_VCPKG}" = true ] ; then echo "set(VCPKG_BUILD_TYPE release)" >> triplets/x64-osx.cmake ; fi - if [ "${USE_VCPKG}" = true ] ; then echo "set(VCPKG_BUILD_TYPE release)" >> triplets/x64-linux.cmake ; fi + - if [ "${USE_VCPKG}" = true ] ; then travis_wait 45 ./vcpkg upgrade --no-dry-run; fi - if [ "${USE_VCPKG}" = true ] ; then travis_wait ./vcpkg install stb pthreads --recurse; fi - if [ "${USE_VCPKG}" = true ] ; then travis_wait 45 ./vcpkg install ffmpeg --recurse; fi #- if [ "${USE_VCPKG}" = true ] && [[ ! -z "${CUDACXX}" ]] ; then travis_wait 45 ./vcpkg install opencv[ffmpeg,cuda] --recurse; fi diff --git a/CMakeLists.txt b/CMakeLists.txt index 0b354dc1948..342e7da6b04 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,9 +1,9 @@ -cmake_minimum_required(VERSION 3.8) +cmake_minimum_required(VERSION 3.12) set(Darknet_MAJOR_VERSION 0) set(Darknet_MINOR_VERSION 2) set(Darknet_PATCH_VERSION 5) -set(Darknet_TWEAK_VERSION 1) +set(Darknet_TWEAK_VERSION 2) set(Darknet_VERSION ${Darknet_MAJOR_VERSION}.${Darknet_MINOR_VERSION}.${Darknet_PATCH_VERSION}.${Darknet_TWEAK_VERSION}) option(CMAKE_VERBOSE_MAKEFILE "Create verbose makefile" OFF) @@ -127,11 +127,7 @@ if(ENABLE_OPENCV) endif() endif() find_package(Stb REQUIRED) -if(${CMAKE_VERSION} VERSION_LESS "3.11.0") - message(WARNING "To build with OpenMP support you need CMake 3.11.0+") -else() - find_package(OpenMP) -endif() +find_package(OpenMP) set(ADDITIONAL_CXX_FLAGS "-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -Wno-write-strings") set(ADDITIONAL_C_FLAGS "-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -Wno-write-strings") diff --git a/appveyor.yml b/appveyor.yml index 2abab7cd041..fbfd7f62764 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -61,6 +61,7 @@ install: - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] git pull - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] .\bootstrap-vcpkg.bat - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] echo set(VCPKG_BUILD_TYPE release) >> triplets\%VCPKG_DEFAULT_TRIPLET%.cmake + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg upgrade --no-dry-run - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] if [%USE_CUDA%]==[yes] vcpkg install cuda --recurse - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg install stb pthreads --recurse - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg install ffmpeg --recurse From fe879f3baf5229dd082ac6e17525d21b09770c50 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Mon, 27 Apr 2020 22:50:12 +0200 Subject: [PATCH 0940/1365] simplify cmake due to minimum project requirement already gone to 3.12 --- CMakeLists.txt | 40 ++++++++++++++++++---------------------- 1 file changed, 18 insertions(+), 22 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 342e7da6b04..7674b6bd435 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -48,31 +48,27 @@ set(INSTALL_LIB_DIR "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Path where lib set(INSTALL_INCLUDE_DIR "include/darknet" CACHE PATH "Path where headers will be installed") set(INSTALL_CMAKE_DIR "share/darknet" CACHE PATH "Path where cmake configs will be installed") -if(${CMAKE_VERSION} VERSION_LESS "3.9.0") - message(WARNING "To build with CUDA support you need CMake 3.9.0+") - set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA support" FORCE) -else() - include(CheckLanguage) - check_language(CUDA) - if(CMAKE_CUDA_COMPILER AND ENABLE_CUDA) - set(CUDA_ARCHITECTURES "Auto" CACHE STRING "\"Auto\" detects local machine GPU compute arch at runtime, \"Common\" and \"All\" cover common and entire subsets of architectures, \"Names\" is a list of architectures to enable by name, \"Numbers\" is a list of compute capabilities (version number) to enable") - set_property(CACHE CUDA_ARCHITECTURES PROPERTY STRINGS "Auto" "Common" "All" "Kepler Maxwell Kepler+Tegra Maxwell+Tegra Pascal" "3.0 7.5") - enable_language(CUDA) +include(CheckLanguage) +check_language(CUDA) +if(CMAKE_CUDA_COMPILER AND ENABLE_CUDA) + set(CUDA_ARCHITECTURES "Auto" CACHE STRING "\"Auto\" detects local machine GPU compute arch at runtime, \"Common\" and \"All\" cover common and entire subsets of architectures, \"Names\" is a list of architectures to enable by name, \"Numbers\" is a list of compute capabilities (version number) to enable") + set_property(CACHE CUDA_ARCHITECTURES PROPERTY STRINGS "Auto" "Common" "All" "Kepler Maxwell Kepler+Tegra Maxwell+Tegra Pascal" "3.0 7.5") + enable_language(CUDA) + find_package(CUDAToolkit REQUIRED) + if(CMAKE_CUDA_COMPILER_VERSION VERSION_LESS "9.0") + message(STATUS "Unsupported CUDA version, please upgrade to CUDA 9+. Disabling CUDA support") + set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA support" FORCE) + else() find_package(CUDA REQUIRED) - if(CUDA_VERSION VERSION_LESS "9.0") - message(STATUS "Unsupported CUDA version, please upgrade to CUDA 9+. Disabling CUDA support") - set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA support" FORCE) - else() - cuda_select_nvcc_arch_flags(CUDA_ARCH_FLAGS ${CUDA_ARCHITECTURES}) - message(STATUS "Building with CUDA flags: " "${CUDA_ARCH_FLAGS}") - if (NOT "arch=compute_70,code=sm_70" IN_LIST CUDA_ARCH_FLAGS) - set(ENABLE_CUDNN_HALF "FALSE" CACHE BOOL "Enable CUDNN Half precision" FORCE) - message(STATUS "Your setup does not supports half precision (it requires CC >= 7.5)") - endif() + cuda_select_nvcc_arch_flags(CUDA_ARCH_FLAGS ${CUDA_ARCHITECTURES}) + message(STATUS "Building with CUDA flags: " "${CUDA_ARCH_FLAGS}") + if (NOT "arch=compute_70,code=sm_70" IN_LIST CUDA_ARCH_FLAGS) + set(ENABLE_CUDNN_HALF "FALSE" CACHE BOOL "Enable CUDNN Half precision" FORCE) + message(STATUS "Your setup does not supports half precision (it requires CC >= 7.5)") endif() - else() - set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA support" FORCE) endif() +else() + set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA support" FORCE) endif() if (WIN32 AND ENABLE_CUDA AND CMAKE_MAKE_PROGRAM MATCHES "ninja") From 1c64c2d8924a0066dec6f8ef47d08462510ba59c Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 29 Apr 2020 17:33:03 +0200 Subject: [PATCH 0941/1365] enable cc70,cc72,cc75 for half_precision, add message for omp missing on macOS --- CMakeLists.txt | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 7674b6bd435..fbf15a1d9b7 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -54,7 +54,6 @@ if(CMAKE_CUDA_COMPILER AND ENABLE_CUDA) set(CUDA_ARCHITECTURES "Auto" CACHE STRING "\"Auto\" detects local machine GPU compute arch at runtime, \"Common\" and \"All\" cover common and entire subsets of architectures, \"Names\" is a list of architectures to enable by name, \"Numbers\" is a list of compute capabilities (version number) to enable") set_property(CACHE CUDA_ARCHITECTURES PROPERTY STRINGS "Auto" "Common" "All" "Kepler Maxwell Kepler+Tegra Maxwell+Tegra Pascal" "3.0 7.5") enable_language(CUDA) - find_package(CUDAToolkit REQUIRED) if(CMAKE_CUDA_COMPILER_VERSION VERSION_LESS "9.0") message(STATUS "Unsupported CUDA version, please upgrade to CUDA 9+. Disabling CUDA support") set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA support" FORCE) @@ -62,7 +61,7 @@ if(CMAKE_CUDA_COMPILER AND ENABLE_CUDA) find_package(CUDA REQUIRED) cuda_select_nvcc_arch_flags(CUDA_ARCH_FLAGS ${CUDA_ARCHITECTURES}) message(STATUS "Building with CUDA flags: " "${CUDA_ARCH_FLAGS}") - if (NOT "arch=compute_70,code=sm_70" IN_LIST CUDA_ARCH_FLAGS) + if (NOT "arch=compute_70,code=sm_70" IN_LIST CUDA_ARCH_FLAGS AND NOT "arch=compute_72,code=sm_72" IN_LIST CUDA_ARCH_FLAGS AND NOT "arch=compute_75,code=sm_75" IN_LIST CUDA_ARCH_FLAGS) set(ENABLE_CUDNN_HALF "FALSE" CACHE BOOL "Enable CUDNN Half precision" FORCE) message(STATUS "Your setup does not supports half precision (it requires CC >= 7.5)") endif() @@ -125,6 +124,10 @@ endif() find_package(Stb REQUIRED) find_package(OpenMP) +if(APPLE AND NOT OPENMP_FOUND) + message(STATUS " -> To enable OpenMP on macOS, please install libomp from Homebrew") +endif() + set(ADDITIONAL_CXX_FLAGS "-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -Wno-write-strings") set(ADDITIONAL_C_FLAGS "-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -Wno-write-strings") From 6afe964d4fbfc823a87f5298798c77482c76f221 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 29 Apr 2020 19:16:05 +0200 Subject: [PATCH 0942/1365] upgrade CUDA, cmake and VS on CI --- .travis.yml | 39 +++++++++++++++++++++------------------ CMakeLists.txt | 4 +++- README.md | 2 +- appveyor.yml | 28 +++++++++++++++------------- 4 files changed, 40 insertions(+), 33 deletions(-) diff --git a/.travis.yml b/.travis.yml index 9a0630a79ba..273f3cea78b 100644 --- a/.travis.yml +++ b/.travis.yml @@ -166,11 +166,12 @@ matrix: - libopencv-dev env: - additional_defines="" - - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - CUDA_PATH=/usr/local/cuda-10.0 - - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" + - INSTALL_CUDA_VERSION="10.2" + - CUDACXX=/usr/local/cuda-${INSTALL_CUDA_VERSION}/bin/nvcc + - CUDA_PATH=/usr/local/cuda-${INSTALL_CUDA_VERSION} + - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-${INSTALL_CUDA_VERSION} + - LD_LIBRARY_PATH="/usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64:/usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs:${LD_LIBRARY_PATH}" + - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.2.89-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1804_10.2.89-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-2 cuda-libraries-dev-10-2 cuda-driver-dev-10-2 cuda-cudart-dev-10-2 cuda-curand-dev-10-2 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs/libcuda.so.1" - os: linux compiler: gcc @@ -183,11 +184,12 @@ matrix: - libopencv-dev env: - additional_defines="" - - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - CUDA_PATH=/usr/local/cuda-10.0 - - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" + - INSTALL_CUDA_VERSION="10.2" + - CUDACXX=/usr/local/cuda-${INSTALL_CUDA_VERSION}/bin/nvcc + - CUDA_PATH=/usr/local/cuda-${INSTALL_CUDA_VERSION} + - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-${INSTALL_CUDA_VERSION} + - LD_LIBRARY_PATH="/usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64:/usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs:${LD_LIBRARY_PATH}" + - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.2.89-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1804_10.2.89-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-2 cuda-libraries-dev-10-2 cuda-driver-dev-10-2 cuda-cudart-dev-10-2 cuda-curand-dev-10-2 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs/libcuda.so.1" - os: linux compiler: gcc @@ -202,11 +204,12 @@ matrix: - additional_defines="-DBUILD_SHARED_LIBS=OFF" - USE_VCPKG=true - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" - - CUDACXX=/usr/local/cuda-10.0/bin/nvcc - - CUDA_PATH=/usr/local/cuda-10.0 - - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0 - - LD_LIBRARY_PATH="/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1404_10.0.130-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1404_4.0-2_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-0 cuda-libraries-dev-10-0 cuda-driver-dev-10-0 cuda-cudart-dev-10-0 cuda-cublas-dev-10-0 cuda-curand-dev-10-0 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-10.0/lib64/stubs/libcuda.so /usr/local/cuda-10.0/lib64/stubs/libcuda.so.1" + - INSTALL_CUDA_VERSION="10.2" + - CUDACXX=/usr/local/cuda-${INSTALL_CUDA_VERSION}/bin/nvcc + - CUDA_PATH=/usr/local/cuda-${INSTALL_CUDA_VERSION} + - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-${INSTALL_CUDA_VERSION} + - LD_LIBRARY_PATH="/usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64:/usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs:${LD_LIBRARY_PATH}" + - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.2.89-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1804_10.2.89-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-2 cuda-libraries-dev-10-2 cuda-driver-dev-10-2 cuda-cudart-dev-10-2 cuda-curand-dev-10-2 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs/libcuda.so.1" allow_failures: - name: macOS - clang - vcpkg @@ -216,9 +219,9 @@ before_install: install: # CMake upgrade on Linux - - if [[ "$TRAVIS_OS_NAME" == "linux" ]] ; then wget --no-check-certificate https://github.com/Kitware/CMake/releases/download/v3.13.4/cmake-3.13.4-Linux-x86_64.tar.gz ; fi - - if [[ "$TRAVIS_OS_NAME" == "linux" ]] ; then tar -xzf cmake-3.13.4-Linux-x86_64.tar.gz ; fi - - if [[ "$TRAVIS_OS_NAME" == "linux" ]] ; then export PATH=$PWD/cmake-3.13.4-Linux-x86_64/bin:$PATH ; fi + - if [[ "$TRAVIS_OS_NAME" == "linux" ]] ; then wget --no-check-certificate https://github.com/Kitware/CMake/releases/download/v3.17.2/cmake-3.17.2-Linux-x86_64.tar.gz ; fi + - if [[ "$TRAVIS_OS_NAME" == "linux" ]] ; then tar -xzf cmake-3.17.2-Linux-x86_64.tar.gz ; fi + - if [[ "$TRAVIS_OS_NAME" == "linux" ]] ; then export PATH=$PWD/cmake-3.17.2-Linux-x86_64/bin:$PATH ; fi - pushd $HOME - if [ -d "$HOME/vcpkg/.git" ] ; then echo vcpkg cached ; else rm -rf vcpkg ; git clone https://github.com/Microsoft/vcpkg ; fi - cd vcpkg diff --git a/CMakeLists.txt b/CMakeLists.txt index fbf15a1d9b7..4a10fc1d1a7 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -63,7 +63,9 @@ if(CMAKE_CUDA_COMPILER AND ENABLE_CUDA) message(STATUS "Building with CUDA flags: " "${CUDA_ARCH_FLAGS}") if (NOT "arch=compute_70,code=sm_70" IN_LIST CUDA_ARCH_FLAGS AND NOT "arch=compute_72,code=sm_72" IN_LIST CUDA_ARCH_FLAGS AND NOT "arch=compute_75,code=sm_75" IN_LIST CUDA_ARCH_FLAGS) set(ENABLE_CUDNN_HALF "FALSE" CACHE BOOL "Enable CUDNN Half precision" FORCE) - message(STATUS "Your setup does not supports half precision (it requires CC >= 7.5)") + message(STATUS "Your setup does not supports half precision (it requires CC >= 7.0)") + else() + message(STATUS "Your setup supports half precision (it requires CC >= 7.0)") endif() endif() else() diff --git a/README.md b/README.md index 603a473cb30..3c1bfe443b9 100644 --- a/README.md +++ b/README.md @@ -130,7 +130,7 @@ You can get cfg-files by path: `darknet/cfg/` ### Requirements * Windows or Linux -* **CMake >= 3.8** for modern CUDA support: https://cmake.org/download/ +* **CMake >= 3.12**: https://cmake.org/download/ * **CUDA 10.0**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) * **OpenCV >= 2.4**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or download from [OpenCV official site](https://opencv.org/releases.html) (on Windows set system variable `OpenCV_DIR` = `C:\opencv\build` - where are the `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png)) * **cuDNN >= 7.0 for CUDA 10.0** https://developer.nvidia.com/rdp/cudnn-archive (on **Linux** copy `cudnn.h`,`libcudnn.so`... as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on **Windows** copy `cudnn.h`,`cudnn64_7.dll`, `cudnn64_7.lib` as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows ) diff --git a/appveyor.yml b/appveyor.yml index fbfd7f62764..db6e63a729b 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -1,4 +1,4 @@ -image: Visual Studio 2017 +image: Visual Studio 2019 clone_folder: c:\projects\darknet cache: C:\Tools\vcpkg\installed\ @@ -6,6 +6,7 @@ environment: WORKSPACE: C:\projects matrix: - platform: Cygwin64 + USE_CUDA: no COMPILER: cygwin CYGWIN_NOWINPATH: yes CYGSH: C:\cygwin64\bin\bash -c @@ -22,10 +23,12 @@ environment: VCPKG_ROOT: C:\Tools\vcpkg VCPKG_DEFAULT_TRIPLET: x64-windows - platform: Win64 + USE_CUDA: no COMPILER: vs configuration: Release USE_INTEGRATED_LIBS: yes - platform: Win64 + USE_CUDA: no COMPILER: vs configuration: Release USE_INTEGRATED_LIBS: yes @@ -34,24 +37,23 @@ environment: install: - if [%COMPILER%]==[vs] cinst cmake ninja - if [%COMPILER%]==[vs] SET "PATH=C:\Program Files\CMake\bin;%PATH%" - - if [%COMPILER%]==[vs] call "C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\VC\Auxiliary\Build\vcvarsall.bat" x64 + - if [%COMPILER%]==[vs] call "C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Auxiliary\Build\vcvarsall.bat" x64 - if [%COMPILER%]==[cygwin] SET "PATH=C:\cygwin64\usr\local\bin;C:\cygwin64\bin;C:\cygwin64\usr\bin;%PATH%" - if [%COMPILER%]==[cygwin] SET PATH=%PATH:C:\Program Files\Git\usr\bin;=% - - git submodule -q update --init --recursive - cd %WORKSPACE%\ - - if [%USE_CUDA%]==[yes] curl -L https://developer.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda_10.1.105_418.96_win10.exe -o setup.exe - - if [%USE_CUDA%]==[yes] .\setup.exe -s nvcc_10.1 cuobjdump_10.1 nvprune_10.1 cupti_10.1 gpu_library_advisor_10.1 memcheck_10.1 nvdisasm_10.1 nvprof_10.1 visual_profiler_10.1 visual_studio_integration_10.1 cublas_10.1 cublas_dev_10.1 cudart_10.1 cufft_10.1 cufft_dev_10.1 curand_10.1 curand_dev_10.1 cusolver_10.1 cusolver_dev_10.1 cusparse_10.1 cusparse_dev_10.1 nvgraph_10.1 nvgraph_dev_10.1 npp_10.1 npp_dev_10.1 nvrtc_10.1 nvrtc_dev_10.1 nvml_dev_10.1 occupancy_calculator_10.1 fortran_examples_10.1 - - if [%USE_CUDA%]==[yes] set CUDA_PATH=%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v10.1 - - if [%USE_CUDA%]==[yes] set CUDA_PATH_V10_1=%CUDA_PATH% + - if [%USE_CUDA%]==[yes] curl -L http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_441.22_win10.exe -o setup.exe + - if [%USE_CUDA%]==[yes] .\setup.exe -s nvcc_10.2 cuobjdump_10.2 nvprune_10.2 cupti_10.2 gpu_library_advisor_10.2 memcheck_10.2 nvdisasm_10.2 nvprof_10.2 visual_profiler_10.2 visual_studio_integration_10.2 cublas_10.2 cublas_dev_10.2 cudart_10.2 cufft_10.2 cufft_dev_10.2 curand_10.2 curand_dev_10.2 cusolver_10.2 cusolver_dev_10.2 cusparse_10.2 cusparse_dev_10.2 nvgraph_10.2 nvgraph_dev_10.2 npp_10.2 npp_dev_10.2 nvrtc_10.2 nvrtc_dev_10.2 nvml_dev_10.2 occupancy_calculator_10.2 fortran_examples_10.2 + - if [%USE_CUDA%]==[yes] set CUDA_PATH=%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v10.2 + - if [%USE_CUDA%]==[yes] set CUDA_PATH_V10_2=%CUDA_PATH% - if [%USE_CUDA%]==[yes] set CUDA_TOOLKIT_ROOT_DIR=%CUDA_PATH% - if [%USE_CUDA%]==[yes] set PATH=%CUDA_PATH%\bin;%PATH% - cd %WORKSPACE%\ - mkdir cygwin-downloads - ps: if($env:COMPILER -eq "cygwin") { Invoke-WebRequest https://cygwin.com/setup-x86_64.exe -OutFile $env:WORKSPACE\cygwin-setup.exe } - if [%COMPILER%]==[cygwin] %WORKSPACE%\cygwin-setup.exe --quiet-mode --no-shortcuts --no-startmenu --no-desktop --upgrade-also --root C:\cygwin64 --local-package-dir %WORKSPACE%\cygwin-downloads --packages gcc-g++,cmake,libopencv-devel,libncurses-devel - - ps: if($env:COMPILER -eq "cygwin") { Invoke-WebRequest https://github.com/Kitware/CMake/releases/download/v3.14.0/cmake-3.14.0.tar.gz -OutFile $env:WORKSPACE\cmake-3.14.0.tar.gz } - - if [%COMPILER%]==[cygwin] %CYGSH% 'tar zxvf cmake-3.14.0.tar.gz' - - if [%COMPILER%]==[cygwin] cd %WORKSPACE%\cmake-3.14.0 + - ps: if($env:COMPILER -eq "cygwin") { Invoke-WebRequest https://github.com/Kitware/CMake/releases/download/v3.17.2/cmake-3.17.2.tar.gz -OutFile $env:WORKSPACE\cmake-3.17.2.tar.gz } + - if [%COMPILER%]==[cygwin] %CYGSH% 'tar zxvf cmake-3.17.2.tar.gz' + - if [%COMPILER%]==[cygwin] cd %WORKSPACE%\cmake-3.17.2 - if [%COMPILER%]==[cygwin] %CYGSH% 'cmake .' - if [%COMPILER%]==[cygwin] %CYGSH% 'make -j8' - if [%COMPILER%]==[cygwin] %CYGSH% 'make install' @@ -73,9 +75,9 @@ install: - if [%COMPILER%]==[cygwin] cd .. - mkdir build_release && cd build_release - if [%COMPILER%]==[cygwin] %CYGSH% 'cmake .. -G "Unix Makefiles" -DCMAKE_BUILD_TYPE="Release"' - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] if [%configuration%]==[Release] cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=%VCPKG_ROOT%\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=%VCPKG_DEFAULT_TRIPLET%" -DCMAKE_BUILD_TYPE="Release" .. - - if [%COMPILER%]==[vs] if [%USE_INTEGRATED_LIBS%]==[yes] if [%FORCE_CPP%]==[yes] cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" -DCMAKE_BUILD_TYPE="Release" "-DBUILD_AS_CPP:BOOL=TRUE" .. - - if [%COMPILER%]==[vs] if [%USE_INTEGRATED_LIBS%]==[yes] cmake -G "Visual Studio 15 2017" -T "host=x64" -A "x64" -DCMAKE_BUILD_TYPE="Release" .. + - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] if [%configuration%]==[Release] cmake -G "Visual Studio 16 2019" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=%VCPKG_ROOT%\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=%VCPKG_DEFAULT_TRIPLET%" -DCMAKE_BUILD_TYPE="Release" .. + - if [%COMPILER%]==[vs] if [%USE_INTEGRATED_LIBS%]==[yes] if [%FORCE_CPP%]==[yes] cmake -G "Visual Studio 16 2019" -T "host=x64" -A "x64" -DCMAKE_BUILD_TYPE="Release" "-DBUILD_AS_CPP:BOOL=TRUE" .. + - if [%COMPILER%]==[vs] if [%USE_INTEGRATED_LIBS%]==[yes] cmake -G "Visual Studio 16 2019" -T "host=x64" -A "x64" -DCMAKE_BUILD_TYPE="Release" .. - cd .. build_script: From 98e4d246e1507472924f7196118ed7466180a37f Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 29 Apr 2020 23:38:50 +0200 Subject: [PATCH 0943/1365] make cuda version parametric on appveyor --- appveyor.yml | 27 +++++++++++++++++++++++---- 1 file changed, 23 insertions(+), 4 deletions(-) diff --git a/appveyor.yml b/appveyor.yml index db6e63a729b..9c6f279fcf4 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -12,10 +12,25 @@ environment: CYGSH: C:\cygwin64\bin\bash -c - platform: Win64 USE_CUDA: yes + INSTALL_CUDA_VERSION: 10.0 COMPILER: vs configuration: Release VCPKG_ROOT: C:\Tools\vcpkg VCPKG_DEFAULT_TRIPLET: x64-windows + - platform: Win64 + USE_CUDA: yes + INSTALL_CUDA_VERSION: 10.1 + COMPILER: vs + configuration: Release + VCPKG_ROOT: C:\Tools\vcpkg + VCPKG_DEFAULT_TRIPLET: x64-windows + #- platform: Win64 + # USE_CUDA: yes + # INSTALL_CUDA_VERSION: 10.2 + # COMPILER: vs + # configuration: Release + # VCPKG_ROOT: C:\Tools\vcpkg + # VCPKG_DEFAULT_TRIPLET: x64-windows - platform: Win64 USE_CUDA: no COMPILER: vs @@ -41,10 +56,14 @@ install: - if [%COMPILER%]==[cygwin] SET "PATH=C:\cygwin64\usr\local\bin;C:\cygwin64\bin;C:\cygwin64\usr\bin;%PATH%" - if [%COMPILER%]==[cygwin] SET PATH=%PATH:C:\Program Files\Git\usr\bin;=% - cd %WORKSPACE%\ - - if [%USE_CUDA%]==[yes] curl -L http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_441.22_win10.exe -o setup.exe - - if [%USE_CUDA%]==[yes] .\setup.exe -s nvcc_10.2 cuobjdump_10.2 nvprune_10.2 cupti_10.2 gpu_library_advisor_10.2 memcheck_10.2 nvdisasm_10.2 nvprof_10.2 visual_profiler_10.2 visual_studio_integration_10.2 cublas_10.2 cublas_dev_10.2 cudart_10.2 cufft_10.2 cufft_dev_10.2 curand_10.2 curand_dev_10.2 cusolver_10.2 cusolver_dev_10.2 cusparse_10.2 cusparse_dev_10.2 nvgraph_10.2 nvgraph_dev_10.2 npp_10.2 npp_dev_10.2 nvrtc_10.2 nvrtc_dev_10.2 nvml_dev_10.2 occupancy_calculator_10.2 fortran_examples_10.2 - - if [%USE_CUDA%]==[yes] set CUDA_PATH=%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v10.2 - - if [%USE_CUDA%]==[yes] set CUDA_PATH_V10_2=%CUDA_PATH% + - if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.0] curl -L https://developer.nvidia.com/compute/cuda/10.0/Prod/local_installers/cuda_10.0.130_411.31_win10 -o setup.exe + - if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.1] curl -L https://developer.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda_10.1.105_418.96_win10.exe -o setup.exe + - if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.2] curl -L http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_441.22_win10.exe -o setup.exe + - if [%USE_CUDA%]==[yes] .\setup.exe -s nvcc_%INSTALL_CUDA_VERSION% cuobjdump_%INSTALL_CUDA_VERSION% nvprune_%INSTALL_CUDA_VERSION% cupti_%INSTALL_CUDA_VERSION% gpu_library_advisor_%INSTALL_CUDA_VERSION% memcheck_%INSTALL_CUDA_VERSION% nvdisasm_%INSTALL_CUDA_VERSION% nvprof_%INSTALL_CUDA_VERSION% visual_profiler_%INSTALL_CUDA_VERSION% visual_studio_integration_%INSTALL_CUDA_VERSION% cublas_%INSTALL_CUDA_VERSION% cublas_dev_%INSTALL_CUDA_VERSION% cudart_%INSTALL_CUDA_VERSION% cufft_%INSTALL_CUDA_VERSION% cufft_dev_%INSTALL_CUDA_VERSION% curand_%INSTALL_CUDA_VERSION% curand_dev_%INSTALL_CUDA_VERSION% cusolver_%INSTALL_CUDA_VERSION% cusolver_dev_%INSTALL_CUDA_VERSION% cusparse_%INSTALL_CUDA_VERSION% cusparse_dev_%INSTALL_CUDA_VERSION% nvgraph_%INSTALL_CUDA_VERSION% nvgraph_dev_%INSTALL_CUDA_VERSION% npp_%INSTALL_CUDA_VERSION% npp_dev_%INSTALL_CUDA_VERSION% nvrtc_%INSTALL_CUDA_VERSION% nvrtc_dev_%INSTALL_CUDA_VERSION% nvml_dev_%INSTALL_CUDA_VERSION% occupancy_calculator_%INSTALL_CUDA_VERSION% fortran_examples_%INSTALL_CUDA_VERSION% + - if [%USE_CUDA%]==[yes] set CUDA_PATH=%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v%INSTALL_CUDA_VERSION% + - if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.0] set CUDA_PATH_V10_0=%CUDA_PATH% + - if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.1] set CUDA_PATH_V10_1=%CUDA_PATH% + - if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.2] set CUDA_PATH_V10_2=%CUDA_PATH% - if [%USE_CUDA%]==[yes] set CUDA_TOOLKIT_ROOT_DIR=%CUDA_PATH% - if [%USE_CUDA%]==[yes] set PATH=%CUDA_PATH%\bin;%PATH% - cd %WORKSPACE%\ From f14054ec2b49440ad488c3e28612e7a76780bc5f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 30 Apr 2020 02:20:42 +0300 Subject: [PATCH 0944/1365] Fixed Cmake for CUDA CC 7.0, 7.2, 7.5 --- CMakeLists.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 0b354dc1948..700510fd977 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -65,9 +65,9 @@ else() else() cuda_select_nvcc_arch_flags(CUDA_ARCH_FLAGS ${CUDA_ARCHITECTURES}) message(STATUS "Building with CUDA flags: " "${CUDA_ARCH_FLAGS}") - if (NOT "arch=compute_70,code=sm_70" IN_LIST CUDA_ARCH_FLAGS) + if (NOT "arch=compute_70,code=sm_70" IN_LIST CUDA_ARCH_FLAGS AND NOT "arch=compute_72,code=sm_72" IN_LIST CUDA_ARCH_FLAGS AND NOT "arch=compute_75,code=sm_75" IN_LIST CUDA_ARCH_FLAGS) set(ENABLE_CUDNN_HALF "FALSE" CACHE BOOL "Enable CUDNN Half precision" FORCE) - message(STATUS "Your setup does not supports half precision (it requires CC >= 7.5)") + message(STATUS "Your setup does not supports half precision (it requires CC >= 7.0)") endif() endif() else() From 185213438eaca7ce4fab48c1fea2980c2cddaf9b Mon Sep 17 00:00:00 2001 From: qboticslabs Date: Thu, 30 Apr 2020 00:04:27 -0700 Subject: [PATCH 0945/1365] Added sample scripts for yolov4 --- image_yolov4.sh | 6 ++++++ net_cam_v4.sh | 6 ++++++ video_yolov4.sh | 6 ++++++ 3 files changed, 18 insertions(+) create mode 100755 image_yolov4.sh create mode 100755 net_cam_v4.sh create mode 100755 video_yolov4.sh diff --git a/image_yolov4.sh b/image_yolov4.sh new file mode 100755 index 00000000000..be3fd10459c --- /dev/null +++ b/image_yolov4.sh @@ -0,0 +1,6 @@ + + +./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights data/dog.jpg -i 0 -thresh 0.25 + + + diff --git a/net_cam_v4.sh b/net_cam_v4.sh new file mode 100755 index 00000000000..5b7520e6e6c --- /dev/null +++ b/net_cam_v4.sh @@ -0,0 +1,6 @@ +#rm test_dnn_out.avi + +./darknet detector demo ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights rtsp://admin:admin12345@192.168.0.228:554 -i 0 -thresh 0.25 + + + diff --git a/video_yolov4.sh b/video_yolov4.sh new file mode 100755 index 00000000000..159c8558177 --- /dev/null +++ b/video_yolov4.sh @@ -0,0 +1,6 @@ + + +./darknet detector demo ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights test50.mp4 -i 0 -thresh 0.25 + + + From 234218b961b948a469f3da9d218d9f4135c97452 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 30 Apr 2020 15:32:50 +0200 Subject: [PATCH 0946/1365] add GitHub Actions --- .github/workflows/ccpp.yml | 250 +++++++++++++++++++++++++++++++++++++ cmake/vcpkg_linux.diff | 6 + cmake/vcpkg_osx.diff | 6 + cmake/vcpkg_windows.diff | 6 + 4 files changed, 268 insertions(+) create mode 100644 .github/workflows/ccpp.yml create mode 100644 cmake/vcpkg_linux.diff create mode 100644 cmake/vcpkg_osx.diff create mode 100644 cmake/vcpkg_windows.diff diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml new file mode 100644 index 00000000000..d228877a6d6 --- /dev/null +++ b/.github/workflows/ccpp.yml @@ -0,0 +1,250 @@ +name: Darknet Continuous Integration + +on: [push, pull_request] + +jobs: + linux-build-vcpkg: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + + # Install system dependencies + - name: Update apt + run: sudo apt update + - name: Install dependencies + run: sudo apt install libopencv-dev yasm + + # Install latest CMake. + - uses: lukka/get-cmake@latest + + # Restore from cache the previously built ports. If "cache miss", then provision vcpkg, install desired ports, finally cache everything for the next run. + - name: Restore from cache and run vcpkg + env: + vcpkgResponseFile: ${{ github.workspace }}/cmake/vcpkg_linux.diff + uses: lukka/run-vcpkg@v2 + with: + # Response file stored in source control, it provides the list of ports and triplet(s). + vcpkgArguments: '@${{ env.vcpkgResponseFile }}' + # Location of the vcpkg as submodule of the repository. + vcpkgDirectory: '${{ github.workspace }}/vcpkg' + vcpkgGitCommitId: '2bc6cd714f931c32d3299a137d5abe0f86f803e1' + # Since the cache must be invalidated when content of the response file changes, let's + # compute its hash and append this to the computed cache's key. + appendedCacheKey: ${{ hashFiles(env.vcpkgResponseFile) }} + + - name: 'Build with CMake and Ninja' + uses: lukka/run-cmake@v2 + with: + cmakeListsOrSettingsJson: CMakeListsTxtAdvanced + cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' + useVcpkgToolchainFile: true + buildDirectory: '${{ runner.workspace }}/buildDirectory' + cmakeBuildType: 'Release' + cmakeAppendedArgs: "-DBUILD_SHARED_LIBS=OFF" + buildWithCMakeArgs: '--target install' + + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: cfg + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: data + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: ${{ github.workspace }}/*dark* + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: ${{ github.workspace }}/uselib* + + + linux-build: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + + # Install system dependencies + - name: Update apt + run: sudo apt update + - name: Install dependencies + run: sudo apt install libopencv-dev yasm + + # Install latest CMake. + - uses: lukka/get-cmake@latest + + - name: 'Build with CMake and Ninja' + uses: lukka/run-cmake@v2 + with: + cmakeListsOrSettingsJson: CMakeListsTxtAdvanced + cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' + useVcpkgToolchainFile: true + buildDirectory: '${{ runner.workspace }}/buildDirectory' + cmakeBuildType: 'Release' + buildWithCMakeArgs: '--target install' + + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: cfg + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: data + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: ${{ github.workspace }}/*dark* + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: ${{ github.workspace }}/uselib* + + + macos-build-vcpkg: + runs-on: macOS-latest + steps: + - uses: actions/checkout@v2 + + # Install system dependencies + - name: Install dependencies + run: brew install opencv libomp yasm + + # Install latest CMake. + - uses: lukka/get-cmake@latest + + # Restore from cache the previously built ports. If "cache miss", then provision vcpkg, install desired ports, finally cache everything for the next run. + - name: Restore from cache and run vcpkg + env: + vcpkgResponseFile: ${{ github.workspace }}/cmake/vcpkg_osx.diff + uses: lukka/run-vcpkg@v2 + with: + # Response file stored in source control, it provides the list of ports and triplet(s). + vcpkgArguments: '@${{ env.vcpkgResponseFile }}' + # Location of the vcpkg as submodule of the repository. + vcpkgDirectory: '${{ github.workspace }}/vcpkg' + vcpkgGitCommitId: '2bc6cd714f931c32d3299a137d5abe0f86f803e1' + # Since the cache must be invalidated when content of the response file changes, let's + # compute its hash and append this to the computed cache's key. + appendedCacheKey: ${{ hashFiles(env.vcpkgResponseFile) }} + + - name: 'Build with CMake and Ninja' + uses: lukka/run-cmake@v2 + with: + cmakeListsOrSettingsJson: CMakeListsTxtAdvanced + cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' + useVcpkgToolchainFile: true + buildDirectory: '${{ runner.workspace }}/buildDirectory' + cmakeBuildType: 'Release' + cmakeAppendedArgs: "-DBUILD_SHARED_LIBS=OFF" + buildWithCMakeArgs: '--target install' + + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: cfg + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: data + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: ${{ github.workspace }}/*dark* + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: ${{ github.workspace }}/uselib* + + + macos-build: + runs-on: macOS-latest + steps: + - uses: actions/checkout@v2 + + # Install system dependencies + - name: Install dependencies + run: brew install opencv libomp yasm + + # Install latest CMake. + - uses: lukka/get-cmake@latest + + - name: 'Build with CMake and Ninja' + uses: lukka/run-cmake@v2 + with: + cmakeListsOrSettingsJson: CMakeListsTxtAdvanced + cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' + useVcpkgToolchainFile: true + buildDirectory: '${{ runner.workspace }}/buildDirectory' + cmakeBuildType: 'Release' + buildWithCMakeArgs: '--target install' + + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: cfg + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: data + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: ${{ github.workspace }}/*dark* + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: ${{ github.workspace }}/uselib* + + + windows-build-vcpkg: + runs-on: windows-latest + steps: + - uses: actions/checkout@v2 + + # Install latest CMake. + - uses: lukka/get-cmake@latest + + # Restore from cache the previously built ports. If "cache miss", then provision vcpkg, install desired ports, finally cache everything for the next run. + - name: Restore from cache and run vcpkg + env: + vcpkgResponseFile: ${{ github.workspace }}/cmake/vcpkg_windows.diff + uses: lukka/run-vcpkg@v2 + with: + # Response file stored in source control, it provides the list of ports and triplet(s). + vcpkgArguments: '@${{ env.vcpkgResponseFile }}' + # Location of the vcpkg as submodule of the repository. + vcpkgDirectory: '${{ github.workspace }}/vcpkg' + vcpkgGitCommitId: '2bc6cd714f931c32d3299a137d5abe0f86f803e1' + # Since the cache must be invalidated when content of the response file changes, let's + # compute its hash and append this to the computed cache's key. + appendedCacheKey: ${{ hashFiles(env.vcpkgResponseFile) }} + + - name: 'Build with CMake and Ninja' + uses: lukka/run-cmake@v2 + with: + cmakeListsOrSettingsJson: CMakeListsTxtAdvanced + cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' + useVcpkgToolchainFile: true + buildDirectory: '${{ runner.workspace }}/buildDirectory' + cmakeBuildType: 'Release' + buildWithCMakeArgs: '--target install' + + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: cfg + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: data + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: ${{ github.workspace }}/*dark* + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: ${{ github.workspace }}/uselib* diff --git a/cmake/vcpkg_linux.diff b/cmake/vcpkg_linux.diff new file mode 100644 index 00000000000..dd4475bcf84 --- /dev/null +++ b/cmake/vcpkg_linux.diff @@ -0,0 +1,6 @@ +stb +pthreads +ffmpeg +opencv[ffmpeg] +--triplet +x64-linux \ No newline at end of file diff --git a/cmake/vcpkg_osx.diff b/cmake/vcpkg_osx.diff new file mode 100644 index 00000000000..12d2dfe4245 --- /dev/null +++ b/cmake/vcpkg_osx.diff @@ -0,0 +1,6 @@ +stb +pthreads +ffmpeg +opencv[ffmpeg] +--triplet +x64-osx \ No newline at end of file diff --git a/cmake/vcpkg_windows.diff b/cmake/vcpkg_windows.diff new file mode 100644 index 00000000000..ff554a81458 --- /dev/null +++ b/cmake/vcpkg_windows.diff @@ -0,0 +1,6 @@ +stb +pthreads +ffmpeg +opencv[ffmpeg] +--triplet +x64-windows \ No newline at end of file From c3dc2c4a0d633f42fac24f7a3d8ab922a7a379a6 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 30 Apr 2020 18:47:23 +0200 Subject: [PATCH 0947/1365] use Release as default build type --- CMakeLists.txt | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/CMakeLists.txt b/CMakeLists.txt index 4a10fc1d1a7..c2c7f7ae3e7 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -39,6 +39,16 @@ enable_language(CXX) set(CMAKE_CXX_STANDARD 11) set(CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/cmake/Modules/" ${CMAKE_MODULE_PATH}) +set(default_build_type "Release") +if(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES) + message(STATUS "Setting build type to '${default_build_type}' as none was specified.") + set(CMAKE_BUILD_TYPE "${default_build_type}" CACHE + STRING "Choose the type of build." FORCE) + # Set the possible values of build type for cmake-gui + set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS + "Debug" "Release" "MinSizeRel" "RelWithDebInfo") +endif() + if (CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT) set(CMAKE_INSTALL_PREFIX "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Install prefix" FORCE) endif() From a993060d09d2814a48ec5f8d6df5c46cf9b50fe9 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 30 Apr 2020 18:54:25 +0200 Subject: [PATCH 0948/1365] remove unnecessary system dependencies on github actions --- .github/workflows/ccpp.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index d228877a6d6..d291ce705a3 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -12,7 +12,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt install libopencv-dev yasm + run: sudo apt install yasm # Install latest CMake. - uses: lukka/get-cmake@latest @@ -70,7 +70,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt install libopencv-dev yasm + run: sudo apt install libopencv-dev # Install latest CMake. - uses: lukka/get-cmake@latest @@ -110,7 +110,7 @@ jobs: # Install system dependencies - name: Install dependencies - run: brew install opencv libomp yasm + run: brew install libomp yasm # Install latest CMake. - uses: lukka/get-cmake@latest @@ -166,7 +166,7 @@ jobs: # Install system dependencies - name: Install dependencies - run: brew install opencv libomp yasm + run: brew install opencv libomp # Install latest CMake. - uses: lukka/get-cmake@latest From 72b3712df84a2fa80273d90ac1950d61552d4a19 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 30 Apr 2020 19:55:55 +0200 Subject: [PATCH 0949/1365] force release mode --- .github/workflows/ccpp.yml | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index d291ce705a3..a673de68d64 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -40,7 +40,7 @@ jobs: useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' cmakeBuildType: 'Release' - cmakeAppendedArgs: "-DBUILD_SHARED_LIBS=OFF" + cmakeAppendedArgs: "-DBUILD_SHARED_LIBS=OFF -DCMAKE_BUILD_TYPE=Release" buildWithCMakeArgs: '--target install' - uses: actions/upload-artifact@v2 @@ -82,6 +82,7 @@ jobs: cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release" cmakeBuildType: 'Release' buildWithCMakeArgs: '--target install' @@ -138,7 +139,7 @@ jobs: useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' cmakeBuildType: 'Release' - cmakeAppendedArgs: "-DBUILD_SHARED_LIBS=OFF" + cmakeAppendedArgs: "-DBUILD_SHARED_LIBS=OFF -DCMAKE_BUILD_TYPE=Release" buildWithCMakeArgs: '--target install' - uses: actions/upload-artifact@v2 @@ -178,6 +179,7 @@ jobs: cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release" cmakeBuildType: 'Release' buildWithCMakeArgs: '--target install' @@ -229,6 +231,7 @@ jobs: cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release" cmakeBuildType: 'Release' buildWithCMakeArgs: '--target install' From f2786ba19dfa2922d2496fa58253fd388dee7de9 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 30 Apr 2020 20:36:36 +0200 Subject: [PATCH 0950/1365] expand github action ci coverage, leave only cygwin on appveyor. Next step will be cuda on github actions --- .github/workflows/ccpp.yml | 104 +++++++++++++++++++++++++++++++++++++ appveyor.yml | 62 +++++++++++----------- 2 files changed, 135 insertions(+), 31 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index a673de68d64..997dd9d19ae 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -104,6 +104,26 @@ jobs: path: ${{ github.workspace }}/uselib* + linux-build-no-opencv-cpp-mode: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + + # Install latest CMake. + - uses: lukka/get-cmake@latest + + - name: 'Build with CMake and Ninja' + uses: lukka/run-cmake@v2 + with: + cmakeListsOrSettingsJson: CMakeListsTxtAdvanced + cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' + useVcpkgToolchainFile: true + buildDirectory: '${{ runner.workspace }}/buildDirectory' + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DBUILD_AS_CPP:BOOL=TRUE" + cmakeBuildType: 'Release' + buildWithCMakeArgs: '--target install' + + macos-build-vcpkg: runs-on: macOS-latest steps: @@ -201,6 +221,26 @@ jobs: path: ${{ github.workspace }}/uselib* + macos-build-no-opencv-no-omp-cpp-mode: + runs-on: macOS-latest + steps: + - uses: actions/checkout@v2 + + # Install latest CMake. + - uses: lukka/get-cmake@latest + + - name: 'Build with CMake and Ninja' + uses: lukka/run-cmake@v2 + with: + cmakeListsOrSettingsJson: CMakeListsTxtAdvanced + cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' + useVcpkgToolchainFile: true + buildDirectory: '${{ runner.workspace }}/buildDirectory' + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DBUILD_AS_CPP:BOOL=TRUE" + cmakeBuildType: 'Release' + buildWithCMakeArgs: '--target install' + + windows-build-vcpkg: runs-on: windows-latest steps: @@ -247,7 +287,71 @@ jobs: with: name: darknet-vcpkg-${{ runner.os }} path: ${{ github.workspace }}/*dark* + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: ${{ runner.workspace }}/buildDirectory/*.dll - uses: actions/upload-artifact@v2 with: name: darknet-vcpkg-${{ runner.os }} path: ${{ github.workspace }}/uselib* + + + windows-build-integrated-libs: + runs-on: windows-latest + steps: + - uses: actions/checkout@v2 + + # Install latest CMake. + - uses: lukka/get-cmake@latest + + - name: 'Build with CMake and Ninja' + uses: lukka/run-cmake@v2 + with: + cmakeListsOrSettingsJson: CMakeListsTxtAdvanced + cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' + useVcpkgToolchainFile: true + buildDirectory: '${{ runner.workspace }}/buildDirectory' + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release" + cmakeBuildType: 'Release' + buildWithCMakeArgs: '--target install' + + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: cfg + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: data + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: ${{ github.workspace }}/*dark* + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: ${{ github.workspace }}/3rdparty/pthreads/bin/*.dll + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: ${{ github.workspace }}/uselib* + + windows-build-integrated-libs-cpp-mode: + runs-on: windows-latest + steps: + - uses: actions/checkout@v2 + + # Install latest CMake. + - uses: lukka/get-cmake@latest + + - name: 'Build with CMake and Ninja' + uses: lukka/run-cmake@v2 + with: + cmakeListsOrSettingsJson: CMakeListsTxtAdvanced + cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' + useVcpkgToolchainFile: true + buildDirectory: '${{ runner.workspace }}/buildDirectory' + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DBUILD_AS_CPP:BOOL=TRUE" + cmakeBuildType: 'Release' + buildWithCMakeArgs: '--target install' diff --git a/appveyor.yml b/appveyor.yml index 9c6f279fcf4..68aea5d503c 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -10,20 +10,20 @@ environment: COMPILER: cygwin CYGWIN_NOWINPATH: yes CYGSH: C:\cygwin64\bin\bash -c - - platform: Win64 - USE_CUDA: yes - INSTALL_CUDA_VERSION: 10.0 - COMPILER: vs - configuration: Release - VCPKG_ROOT: C:\Tools\vcpkg - VCPKG_DEFAULT_TRIPLET: x64-windows - - platform: Win64 - USE_CUDA: yes - INSTALL_CUDA_VERSION: 10.1 - COMPILER: vs - configuration: Release - VCPKG_ROOT: C:\Tools\vcpkg - VCPKG_DEFAULT_TRIPLET: x64-windows + #- platform: Win64 + # USE_CUDA: yes + # INSTALL_CUDA_VERSION: 10.0 + # COMPILER: vs + # configuration: Release + # VCPKG_ROOT: C:\Tools\vcpkg + # VCPKG_DEFAULT_TRIPLET: x64-windows + #- platform: Win64 + # USE_CUDA: yes + # INSTALL_CUDA_VERSION: 10.1 + # COMPILER: vs + # configuration: Release + # VCPKG_ROOT: C:\Tools\vcpkg + # VCPKG_DEFAULT_TRIPLET: x64-windows #- platform: Win64 # USE_CUDA: yes # INSTALL_CUDA_VERSION: 10.2 @@ -31,23 +31,23 @@ environment: # configuration: Release # VCPKG_ROOT: C:\Tools\vcpkg # VCPKG_DEFAULT_TRIPLET: x64-windows - - platform: Win64 - USE_CUDA: no - COMPILER: vs - configuration: Release - VCPKG_ROOT: C:\Tools\vcpkg - VCPKG_DEFAULT_TRIPLET: x64-windows - - platform: Win64 - USE_CUDA: no - COMPILER: vs - configuration: Release - USE_INTEGRATED_LIBS: yes - - platform: Win64 - USE_CUDA: no - COMPILER: vs - configuration: Release - USE_INTEGRATED_LIBS: yes - FORCE_CPP: yes + #- platform: Win64 + # USE_CUDA: no + # COMPILER: vs + # configuration: Release + # VCPKG_ROOT: C:\Tools\vcpkg + # VCPKG_DEFAULT_TRIPLET: x64-windows + #- platform: Win64 + # USE_CUDA: no + # COMPILER: vs + # configuration: Release + # USE_INTEGRATED_LIBS: yes + #- platform: Win64 + # USE_CUDA: no + # COMPILER: vs + # configuration: Release + # USE_INTEGRATED_LIBS: yes + # FORCE_CPP: yes install: - if [%COMPILER%]==[vs] cinst cmake ninja From 24ad413a79929280c7e83b961a08a3018e984e8f Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 30 Apr 2020 20:53:28 +0200 Subject: [PATCH 0951/1365] add mingw and cuda on github actions, restored cuda 10.2 on appveyor as a test --- .github/workflows/ccpp.yml | 223 +++++++++++++++++++++++++++++----- CMakeLists.txt | 4 +- README.md | 49 +++----- appveyor.yml | 18 +-- cmake/vcpkg_linux_cuda.diff | 8 ++ cmake/vcpkg_windows_cuda.diff | 8 ++ src/gemm.c | 5 +- src/getopt.c | 2 +- src/getopt.h | 2 +- src/gettimeofday.c | 2 +- src/gettimeofday.h | 2 +- 11 files changed, 239 insertions(+), 84 deletions(-) create mode 100644 cmake/vcpkg_linux_cuda.diff create mode 100644 cmake/vcpkg_windows_cuda.diff diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 997dd9d19ae..6d430e2a8af 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -8,28 +8,21 @@ jobs: steps: - uses: actions/checkout@v2 - # Install system dependencies - name: Update apt run: sudo apt update - name: Install dependencies run: sudo apt install yasm - # Install latest CMake. - uses: lukka/get-cmake@latest - # Restore from cache the previously built ports. If "cache miss", then provision vcpkg, install desired ports, finally cache everything for the next run. - name: Restore from cache and run vcpkg env: vcpkgResponseFile: ${{ github.workspace }}/cmake/vcpkg_linux.diff uses: lukka/run-vcpkg@v2 with: - # Response file stored in source control, it provides the list of ports and triplet(s). vcpkgArguments: '@${{ env.vcpkgResponseFile }}' - # Location of the vcpkg as submodule of the repository. vcpkgDirectory: '${{ github.workspace }}/vcpkg' vcpkgGitCommitId: '2bc6cd714f931c32d3299a137d5abe0f86f803e1' - # Since the cache must be invalidated when content of the response file changes, let's - # compute its hash and append this to the computed cache's key. appendedCacheKey: ${{ hashFiles(env.vcpkgResponseFile) }} - name: 'Build with CMake and Ninja' @@ -66,13 +59,11 @@ jobs: steps: - uses: actions/checkout@v2 - # Install system dependencies - name: Update apt run: sudo apt update - name: Install dependencies run: sudo apt install libopencv-dev - # Install latest CMake. - uses: lukka/get-cmake@latest - name: 'Build with CMake and Ninja' @@ -104,12 +95,76 @@ jobs: path: ${{ github.workspace }}/uselib* + linux-build-with-cuda: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + + - name: Update apt + run: sudo apt update + - name: Install dependencies + run: sudo apt install libopencv-dev + + - uses: lukka/get-cmake@latest + + - name: 'Install CUDA' + env: + INSTALL_CUDA_VERSION: "10.2" + CUDACXX: "/usr/local/cuda-10.2/bin/nvcc" + CUDA_PATH: "/usr/local/cuda-10.2" + CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda-10.2" + LD_LIBRARY_PATH: "/usr/local/cuda-10.2/lib64:/usr/local/cuda-10.2/lib64/stubs:$LD_LIBRARY_PATH" + run: | + wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.2.89-1_amd64.deb + sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub + sudo dpkg -i cuda-repo-ubuntu1804_10.2.89-1_amd64.deb + wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb + sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb + sudo apt update + sudo apt-get install -y --no-install-recommends cuda-compiler-10-2 cuda-libraries-dev-10-2 cuda-driver-dev-10-2 cuda-cudart-dev-10-2 cuda-curand-dev-10-2 + sudo apt-get install -y --no-install-recommends libcudnn7-dev + sudo ln -s /usr/local/cuda-${{ env.INSTALL_CUDA_VERSION }}/lib64/stubs/libcuda.so /usr/local/cuda-${{ env.INSTALL_CUDA_VERSION }}/lib64/stubs/libcuda.so.1 + + - name: 'Build with CMake and Ninja' + env: + INSTALL_CUDA_VERSION: "10.2" + CUDACXX: "/usr/local/cuda-10.2/bin/nvcc" + CUDA_PATH: "/usr/local/cuda-10.2" + CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda-10.2" + LD_LIBRARY_PATH: "/usr/local/cuda-10.2/lib64:/usr/local/cuda-10.2/lib64/stubs:$LD_LIBRARY_PATH" + uses: lukka/run-cmake@v2 + with: + cmakeListsOrSettingsJson: CMakeListsTxtAdvanced + cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' + useVcpkgToolchainFile: true + buildDirectory: '${{ runner.workspace }}/buildDirectory' + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release" + cmakeBuildType: 'Release' + buildWithCMakeArgs: '--target install' + + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: cfg + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: data + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: ${{ github.workspace }}/*dark* + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: ${{ github.workspace }}/uselib* + + linux-build-no-opencv-cpp-mode: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - # Install latest CMake. - uses: lukka/get-cmake@latest - name: 'Build with CMake and Ninja' @@ -129,26 +184,19 @@ jobs: steps: - uses: actions/checkout@v2 - # Install system dependencies - name: Install dependencies run: brew install libomp yasm - # Install latest CMake. - uses: lukka/get-cmake@latest - # Restore from cache the previously built ports. If "cache miss", then provision vcpkg, install desired ports, finally cache everything for the next run. - name: Restore from cache and run vcpkg env: vcpkgResponseFile: ${{ github.workspace }}/cmake/vcpkg_osx.diff uses: lukka/run-vcpkg@v2 with: - # Response file stored in source control, it provides the list of ports and triplet(s). vcpkgArguments: '@${{ env.vcpkgResponseFile }}' - # Location of the vcpkg as submodule of the repository. vcpkgDirectory: '${{ github.workspace }}/vcpkg' vcpkgGitCommitId: '2bc6cd714f931c32d3299a137d5abe0f86f803e1' - # Since the cache must be invalidated when content of the response file changes, let's - # compute its hash and append this to the computed cache's key. appendedCacheKey: ${{ hashFiles(env.vcpkgResponseFile) }} - name: 'Build with CMake and Ninja' @@ -185,11 +233,9 @@ jobs: steps: - uses: actions/checkout@v2 - # Install system dependencies - name: Install dependencies run: brew install opencv libomp - # Install latest CMake. - uses: lukka/get-cmake@latest - name: 'Build with CMake and Ninja' @@ -226,7 +272,6 @@ jobs: steps: - uses: actions/checkout@v2 - # Install latest CMake. - uses: lukka/get-cmake@latest - name: 'Build with CMake and Ninja' @@ -246,22 +291,16 @@ jobs: steps: - uses: actions/checkout@v2 - # Install latest CMake. - uses: lukka/get-cmake@latest - # Restore from cache the previously built ports. If "cache miss", then provision vcpkg, install desired ports, finally cache everything for the next run. - name: Restore from cache and run vcpkg env: vcpkgResponseFile: ${{ github.workspace }}/cmake/vcpkg_windows.diff uses: lukka/run-vcpkg@v2 with: - # Response file stored in source control, it provides the list of ports and triplet(s). vcpkgArguments: '@${{ env.vcpkgResponseFile }}' - # Location of the vcpkg as submodule of the repository. vcpkgDirectory: '${{ github.workspace }}/vcpkg' vcpkgGitCommitId: '2bc6cd714f931c32d3299a137d5abe0f86f803e1' - # Since the cache must be invalidated when content of the response file changes, let's - # compute its hash and append this to the computed cache's key. appendedCacheKey: ${{ hashFiles(env.vcpkgResponseFile) }} - name: 'Build with CMake and Ninja' @@ -273,7 +312,75 @@ jobs: buildDirectory: '${{ runner.workspace }}/buildDirectory' cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release" cmakeBuildType: 'Release' - buildWithCMakeArgs: '--target install' + buildWithCMakeArgs: '--config Release --target install' + + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: cfg + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: data + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: ${{ github.workspace }}/*dark* + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: ${{ github.workspace }}/buildDirectory/*.dll + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: ${{ github.workspace }}/uselib* + + + windows-build-vcpkg-with-cuda: + runs-on: windows-latest + steps: + - uses: actions/checkout@v2 + - name: 'Install CUDA' + run: | + choco install cuda -y + $env:ChocolateyInstall = Convert-Path "$((Get-Command choco).Path)\..\.." + Import-Module "$env:ChocolateyInstall\helpers\chocolateyProfile.psm1" + refreshenv + + - uses: lukka/get-cmake@latest + + - name: Restore from cache and run vcpkg + env: + vcpkgResponseFile: ${{ github.workspace }}/cmake/vcpkg_windows_cuda.diff + INSTALL_CUDA_VERSION: "10.2" + CUDA_PATH: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" + CUDA_PATH_V10_2: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" + CUDA_TOOLKIT_ROOT_DIR: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" + CUDACXX: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" + + uses: lukka/run-vcpkg@v2 + with: + vcpkgArguments: '@${{ env.vcpkgResponseFile }}' + vcpkgDirectory: '${{ github.workspace }}/vcpkg' + vcpkgGitCommitId: '2bc6cd714f931c32d3299a137d5abe0f86f803e1' + appendedCacheKey: ${{ hashFiles(env.vcpkgResponseFile) }} + + - name: 'Build with CMake and Ninja' + env: + INSTALL_CUDA_VERSION: "10.2" + CUDA_PATH: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" + CUDA_PATH_V10_2: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" + CUDA_TOOLKIT_ROOT_DIR: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" + CUDACXX: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" + uses: lukka/run-cmake@v2 + with: + cmakeListsOrSettingsJson: CMakeListsTxtAdvanced + cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' + useVcpkgToolchainFile: true + buildDirectory: '${{ runner.workspace }}/buildDirectory' + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release" + cmakeBuildType: 'Release' + buildWithCMakeArgs: '--config Release --target install' - uses: actions/upload-artifact@v2 with: @@ -302,7 +409,6 @@ jobs: steps: - uses: actions/checkout@v2 - # Install latest CMake. - uses: lukka/get-cmake@latest - name: 'Build with CMake and Ninja' @@ -314,7 +420,7 @@ jobs: buildDirectory: '${{ runner.workspace }}/buildDirectory' cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release" cmakeBuildType: 'Release' - buildWithCMakeArgs: '--target install' + buildWithCMakeArgs: '--config Release --target install' - uses: actions/upload-artifact@v2 with: @@ -337,12 +443,12 @@ jobs: name: darknet-vcpkg-${{ runner.os }} path: ${{ github.workspace }}/uselib* + windows-build-integrated-libs-cpp-mode: runs-on: windows-latest steps: - uses: actions/checkout@v2 - # Install latest CMake. - uses: lukka/get-cmake@latest - name: 'Build with CMake and Ninja' @@ -354,4 +460,59 @@ jobs: buildDirectory: '${{ runner.workspace }}/buildDirectory' cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DBUILD_AS_CPP:BOOL=TRUE" cmakeBuildType: 'Release' - buildWithCMakeArgs: '--target install' + buildWithCMakeArgs: '--config Release --target install' + + + windows-build-integrated-libs-with-cuda: + runs-on: windows-latest + steps: + - uses: actions/checkout@v2 + - name: 'Install CUDA' + run: | + choco install cuda -y + $env:ChocolateyInstall = Convert-Path "$((Get-Command choco).Path)\..\.." + Import-Module "$env:ChocolateyInstall\helpers\chocolateyProfile.psm1" + refreshenv + + - uses: lukka/get-cmake@latest + + - name: 'Build with CMake and Ninja' + env: + INSTALL_CUDA_VERSION: "10.2" + CUDA_PATH: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" + CUDA_PATH_V10_2: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" + CUDA_TOOLKIT_ROOT_DIR: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" + CUDACXX: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" + uses: lukka/run-cmake@v2 + with: + cmakeListsOrSettingsJson: CMakeListsTxtAdvanced + cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' + useVcpkgToolchainFile: true + buildDirectory: '${{ runner.workspace }}/buildDirectory' + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release" + cmakeBuildType: 'Release' + buildWithCMakeArgs: '--config Release --target install' + + + windows-mingw: + runs-on: windows-latest + steps: + - uses: actions/checkout@v2 + - name: fixPath + run: | + set PATH=%PATH:C:\Program Files\Git\bin;=% + set PATH=%PATH:C:\Program Files\Git\usr\bin;=% + shell: cmd + + - uses: lukka/get-cmake@latest + + - name: 'Build with CMake and Ninja' + uses: lukka/run-cmake@v2 + with: + cmakeListsOrSettingsJson: CMakeListsTxtAdvanced + cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' + useVcpkgToolchainFile: true + buildDirectory: '${{ runner.workspace }}/buildDirectory' + cmakeAppendedArgs: "-G\"MinGW Makefiles\" -DCMAKE_BUILD_TYPE=Release" + cmakeBuildType: 'Release' + buildWithCMakeArgs: '--config Release --target install' diff --git a/CMakeLists.txt b/CMakeLists.txt index c2c7f7ae3e7..5400cbbbacb 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -256,7 +256,7 @@ list(APPEND headers ${CMAKE_CURRENT_LIST_DIR}/include/darknet.h ) #remove windows only files -if(NOT WIN32) +if(NOT MSVC) list(REMOVE_ITEM headers ${CMAKE_CURRENT_LIST_DIR}/src/gettimeofday.h ${CMAKE_CURRENT_LIST_DIR}/src/getopt.h @@ -276,7 +276,7 @@ list(REMOVE_ITEM sources ${CMAKE_CURRENT_LIST_DIR}/src/darknet.c ) #remove windows only files -if(NOT WIN32) +if(NOT MSVC) list(REMOVE_ITEM sources ${CMAKE_CURRENT_LIST_DIR}/src/gettimeofday.c ${CMAKE_CURRENT_LIST_DIR}/src/getopt.c diff --git a/README.md b/README.md index d3fae87eb69..cb14f1485ba 100644 --- a/README.md +++ b/README.md @@ -242,14 +242,10 @@ The `CMakeLists.txt` will attempt to find installed optional dependencies like CUDA, cudnn, ZED and build against those. It will also create a shared object library file to use `darknet` for code development. -Do inside the cloned repository: +Open a bash terminal inside the cloned repository and launch: -``` -mkdir build-release -cd build-release -cmake .. -make -make install +```bash +./build.sh ``` ### How to compile on Linux (using `make`) @@ -270,51 +266,34 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c To run Darknet on Linux use examples from this article, just use `./darknet` instead of `darknet.exe`, i.e. use this command: `./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights` -### How to compile on Windows (using `CMake-GUI`) +### How to compile on Windows (using `CMake`) This is the recommended approach to build Darknet on Windows if you have already installed Visual Studio 2015/2017/2019, CUDA > 10.0, cuDNN > 7.0, and OpenCV > 2.4. -Use `CMake-GUI` as shown here on this [**IMAGE**](https://user-images.githubusercontent.com/4096485/55107892-6becf380-50e3-11e9-9a0a-556a943c429a.png): +Open a Powershell terminal inside the cloned repository and launch: -1. Configure -2. Optional platform for generator (Set: x64) -3. Finish -4. Generate -5. Open Project -6. Set: x64 & Release -7. Build -8. Build solution +```PowerShell +.\build.ps1 +``` ### How to compile on Windows (using `vcpkg`) -If you have already installed Visual Studio 2015/2017/2019, CUDA > 10.0, -cuDNN > 7.0, OpenCV > 2.4, then to compile Darknet it is recommended to use -[CMake-GUI](#how-to-compile-on-windows-using-cmake-gui). - -Otherwise, follow these steps: - 1. Install or update Visual Studio to at least version 2017, making sure to have it fully patched (run again the installer if not sure to automatically update to latest version). If you need to install from scratch, download VS from here: [Visual Studio Community](http://visualstudio.com) -2. Install CUDA and cuDNN - -3. Install `git` and `cmake`. Make sure they are on the Path at least for the current account - -4. Install [vcpkg](https://github.com/Microsoft/vcpkg) and try to install a test library to make sure everything is working, for example `vcpkg install opengl` - -5. Define an environment variables, `VCPKG_ROOT`, pointing to the install path of `vcpkg` +2. Install CUDA -6. Define another environment variable, with name `VCPKG_DEFAULT_TRIPLET` and value `x64-windows` +3. Install [vcpkg](https://github.com/Microsoft/vcpkg) and try to install a test library to make sure everything is working, for example `vcpkg install opengl` -7. Open Powershell and type these commands: +4. Open Powershell and type these commands: ```PowerShell -PS \> cd $env:VCPKG_ROOT -PS Code\vcpkg> .\vcpkg install pthreads opencv[ffmpeg] #replace with opencv[cuda,ffmpeg] in case you want to use cuda-accelerated openCV +PS \> cd vcpkg +PS Code\vcpkg> .\vcpkg install darknet[full]:x64-windows #replace with darknet[opencv-base,weights]:x64-windows for a quicker install; use --head if you want to build latest commit on master branch and not latest release ``` -8. Open Powershell, go to the `darknet` folder and build with the command `.\build.ps1`. If you want to use Visual Studio, you will find two custom solutions created for you by CMake after the build, one in `build_win_debug` and the other in `build_win_release`, containing all the appropriate config flags for your system. +5. You will find darknet inside the vcpkg\installed\x64-windows\tools\darknet folder, together with all the necessary weight and cfg files ### How to compile on Windows (legacy way) diff --git a/appveyor.yml b/appveyor.yml index 68aea5d503c..d2153460cab 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -24,13 +24,13 @@ environment: # configuration: Release # VCPKG_ROOT: C:\Tools\vcpkg # VCPKG_DEFAULT_TRIPLET: x64-windows - #- platform: Win64 - # USE_CUDA: yes - # INSTALL_CUDA_VERSION: 10.2 - # COMPILER: vs - # configuration: Release - # VCPKG_ROOT: C:\Tools\vcpkg - # VCPKG_DEFAULT_TRIPLET: x64-windows + - platform: Win64 + USE_CUDA: yes + INSTALL_CUDA_VERSION: 10.2 + COMPILER: vs + configuration: Release + VCPKG_ROOT: C:\Tools\vcpkg + VCPKG_DEFAULT_TRIPLET: x64-windows #- platform: Win64 # USE_CUDA: no # COMPILER: vs @@ -58,8 +58,8 @@ install: - cd %WORKSPACE%\ - if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.0] curl -L https://developer.nvidia.com/compute/cuda/10.0/Prod/local_installers/cuda_10.0.130_411.31_win10 -o setup.exe - if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.1] curl -L https://developer.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda_10.1.105_418.96_win10.exe -o setup.exe - - if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.2] curl -L http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_441.22_win10.exe -o setup.exe - - if [%USE_CUDA%]==[yes] .\setup.exe -s nvcc_%INSTALL_CUDA_VERSION% cuobjdump_%INSTALL_CUDA_VERSION% nvprune_%INSTALL_CUDA_VERSION% cupti_%INSTALL_CUDA_VERSION% gpu_library_advisor_%INSTALL_CUDA_VERSION% memcheck_%INSTALL_CUDA_VERSION% nvdisasm_%INSTALL_CUDA_VERSION% nvprof_%INSTALL_CUDA_VERSION% visual_profiler_%INSTALL_CUDA_VERSION% visual_studio_integration_%INSTALL_CUDA_VERSION% cublas_%INSTALL_CUDA_VERSION% cublas_dev_%INSTALL_CUDA_VERSION% cudart_%INSTALL_CUDA_VERSION% cufft_%INSTALL_CUDA_VERSION% cufft_dev_%INSTALL_CUDA_VERSION% curand_%INSTALL_CUDA_VERSION% curand_dev_%INSTALL_CUDA_VERSION% cusolver_%INSTALL_CUDA_VERSION% cusolver_dev_%INSTALL_CUDA_VERSION% cusparse_%INSTALL_CUDA_VERSION% cusparse_dev_%INSTALL_CUDA_VERSION% nvgraph_%INSTALL_CUDA_VERSION% nvgraph_dev_%INSTALL_CUDA_VERSION% npp_%INSTALL_CUDA_VERSION% npp_dev_%INSTALL_CUDA_VERSION% nvrtc_%INSTALL_CUDA_VERSION% nvrtc_dev_%INSTALL_CUDA_VERSION% nvml_dev_%INSTALL_CUDA_VERSION% occupancy_calculator_%INSTALL_CUDA_VERSION% fortran_examples_%INSTALL_CUDA_VERSION% + - if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.2] choco install cuda -y + - if [%USE_CUDA%]==[yes] if NOT [%INSTALL_CUDA_VERSION%]==[10.2] .\setup.exe -s nvcc_%INSTALL_CUDA_VERSION% cuobjdump_%INSTALL_CUDA_VERSION% nvprune_%INSTALL_CUDA_VERSION% cupti_%INSTALL_CUDA_VERSION% gpu_library_advisor_%INSTALL_CUDA_VERSION% memcheck_%INSTALL_CUDA_VERSION% nvdisasm_%INSTALL_CUDA_VERSION% nvprof_%INSTALL_CUDA_VERSION% visual_profiler_%INSTALL_CUDA_VERSION% visual_studio_integration_%INSTALL_CUDA_VERSION% cublas_%INSTALL_CUDA_VERSION% cublas_dev_%INSTALL_CUDA_VERSION% cudart_%INSTALL_CUDA_VERSION% cufft_%INSTALL_CUDA_VERSION% cufft_dev_%INSTALL_CUDA_VERSION% curand_%INSTALL_CUDA_VERSION% curand_dev_%INSTALL_CUDA_VERSION% cusolver_%INSTALL_CUDA_VERSION% cusolver_dev_%INSTALL_CUDA_VERSION% cusparse_%INSTALL_CUDA_VERSION% cusparse_dev_%INSTALL_CUDA_VERSION% nvgraph_%INSTALL_CUDA_VERSION% nvgraph_dev_%INSTALL_CUDA_VERSION% npp_%INSTALL_CUDA_VERSION% npp_dev_%INSTALL_CUDA_VERSION% nvrtc_%INSTALL_CUDA_VERSION% nvrtc_dev_%INSTALL_CUDA_VERSION% nvml_dev_%INSTALL_CUDA_VERSION% occupancy_calculator_%INSTALL_CUDA_VERSION% fortran_examples_%INSTALL_CUDA_VERSION% - if [%USE_CUDA%]==[yes] set CUDA_PATH=%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v%INSTALL_CUDA_VERSION% - if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.0] set CUDA_PATH_V10_0=%CUDA_PATH% - if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.1] set CUDA_PATH_V10_1=%CUDA_PATH% diff --git a/cmake/vcpkg_linux_cuda.diff b/cmake/vcpkg_linux_cuda.diff new file mode 100644 index 00000000000..635bd5f1c89 --- /dev/null +++ b/cmake/vcpkg_linux_cuda.diff @@ -0,0 +1,8 @@ +cuda +cudnn +stb +pthreads +ffmpeg +opencv[ffmpeg] +--triplet +x64-linux \ No newline at end of file diff --git a/cmake/vcpkg_windows_cuda.diff b/cmake/vcpkg_windows_cuda.diff new file mode 100644 index 00000000000..b757b8d0192 --- /dev/null +++ b/cmake/vcpkg_windows_cuda.diff @@ -0,0 +1,8 @@ +cuda +cudnn +stb +pthreads +ffmpeg +opencv[ffmpeg] +--triplet +x64-windows \ No newline at end of file diff --git a/src/gemm.c b/src/gemm.c index bc43b87fc8d..61f574bb1a7 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -513,10 +513,9 @@ static inline int popcnt_32(uint32_t val32) { } //---------------------------- - #if (defined(__AVX__) && defined(__x86_64__)) || (defined(_WIN64) && !defined(__MINGW32__)) -#ifdef _WIN64 +#if (defined(_WIN64) && !defined(__MINGW64__)) #include #include #include @@ -2863,4 +2862,4 @@ int test_gpu_blas() void init_cpu() { is_avx(); is_fma_avx2(); -} \ No newline at end of file +} diff --git a/src/getopt.c b/src/getopt.c index 2707e0a84fc..45d2b8e6375 100644 --- a/src/getopt.c +++ b/src/getopt.c @@ -1,4 +1,4 @@ -#ifdef _WIN32 +#ifdef _MSC_VER #include "getopt.h" #ifdef __cplusplus diff --git a/src/getopt.h b/src/getopt.h index 66ab5dfbe36..8266c734eec 100644 --- a/src/getopt.h +++ b/src/getopt.h @@ -1,4 +1,4 @@ -#ifdef _WIN32 +#ifdef _MSC_VER #ifndef __GETOPT_H__ /** * DISCLAIMER diff --git a/src/gettimeofday.c b/src/gettimeofday.c index 1db060cb4bd..74f6789b91b 100644 --- a/src/gettimeofday.c +++ b/src/gettimeofday.c @@ -1,4 +1,4 @@ -#ifdef _WIN32 +#ifdef _MSC_VER #include "gettimeofday.h" int gettimeofday(struct timeval* tp, struct timezone* tzp) diff --git a/src/gettimeofday.h b/src/gettimeofday.h index 2e96dd2b852..86fef1010ff 100644 --- a/src/gettimeofday.h +++ b/src/gettimeofday.h @@ -1,4 +1,4 @@ -#ifdef _WIN32 +#ifdef _MSC_VER #define WIN32_LEAN_AND_MEAN #include #include From ac7436dccd19d6caf93bb82d75b3df4b17c72883 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 1 May 2020 01:14:24 +0200 Subject: [PATCH 0952/1365] ported build configs from circleCI --- .github/workflows/ccpp.yml | 80 +++++++++++++++++++++++++++++++------- CMakeLists.txt | 9 ++++- appveyor.yml | 62 ++++++++++++++--------------- src/http_stream.cpp | 8 ++++ 4 files changed, 113 insertions(+), 46 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 6d430e2a8af..bdae8321342 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -3,7 +3,61 @@ name: Darknet Continuous Integration on: [push, pull_request] jobs: - linux-build-vcpkg: + linux-make: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + + - name: Update apt + run: sudo apt update + - name: Install dependencies + run: sudo apt install libopencv-dev + + - name: 'Install CUDA' + run: | + wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.2.89-1_amd64.deb + sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub + sudo dpkg -i cuda-repo-ubuntu1804_10.2.89-1_amd64.deb + wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb + sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb + sudo apt update + sudo apt-get install -y --no-install-recommends cuda-compiler-10-2 cuda-libraries-dev-10-2 cuda-driver-dev-10-2 cuda-cudart-dev-10-2 cuda-curand-dev-10-2 + sudo apt-get install -y --no-install-recommends libcudnn7-dev + sudo ln -s /usr/local/cuda-10.2/lib64/stubs/libcuda.so /usr/local/cuda-10.2/lib64/stubs/libcuda.so.1 + nvcc --version + gcc --version + + - name: 'LIBSO=1 GPU=0 CUDNN=0 OPENCV=0' + run: | + make LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 -j 8 + make clean + - name: 'LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 DEBUG=1' + run: | + make LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 DEBUG=1 -j 8 + make clean + - name: 'LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 AVX=1' + run: | + make LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 AVX=1 -j 8 + make clean + - name: 'LIBSO=1 GPU=0 CUDNN=0 OPENCV=1' + run: | + make LIBSO=1 GPU=0 CUDNN=0 OPENCV=1 -j 8 + make clean + - name: 'LIBSO=1 GPU=1 CUDNN=1 OPENCV=1' + run: | + make LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 -j 8 + make clean + - name: 'LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 CUDNN_HALF=1' + run: | + make LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 CUDNN_HALF=1 -j 8 + make clean + - name: 'LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 CUDNN_HALF=1 USE_CPP=1' + run: | + make LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 CUDNN_HALF=1 USE_CPP=1 -j 8 + make clean + + + linux-cmake-vcpkg: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 @@ -54,7 +108,7 @@ jobs: path: ${{ github.workspace }}/uselib* - linux-build: + linux-cmake: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 @@ -95,7 +149,7 @@ jobs: path: ${{ github.workspace }}/uselib* - linux-build-with-cuda: + linux-cmake-with-cuda: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 @@ -160,7 +214,7 @@ jobs: path: ${{ github.workspace }}/uselib* - linux-build-no-opencv-cpp-mode: + linux-cmake-no-opencv-cpp-mode: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 @@ -179,7 +233,7 @@ jobs: buildWithCMakeArgs: '--target install' - macos-build-vcpkg: + macos-cmake-vcpkg: runs-on: macOS-latest steps: - uses: actions/checkout@v2 @@ -228,7 +282,7 @@ jobs: path: ${{ github.workspace }}/uselib* - macos-build: + macos-cmake: runs-on: macOS-latest steps: - uses: actions/checkout@v2 @@ -267,7 +321,7 @@ jobs: path: ${{ github.workspace }}/uselib* - macos-build-no-opencv-no-omp-cpp-mode: + macos-cmake-no-opencv-no-omp-cpp-mode: runs-on: macOS-latest steps: - uses: actions/checkout@v2 @@ -286,7 +340,7 @@ jobs: buildWithCMakeArgs: '--target install' - windows-build-vcpkg: + windows-cmake-vcpkg: runs-on: windows-latest steps: - uses: actions/checkout@v2 @@ -336,7 +390,7 @@ jobs: path: ${{ github.workspace }}/uselib* - windows-build-vcpkg-with-cuda: + windows-cmake-vcpkg-with-cuda: runs-on: windows-latest steps: - uses: actions/checkout@v2 @@ -404,7 +458,7 @@ jobs: path: ${{ github.workspace }}/uselib* - windows-build-integrated-libs: + windows-cmake-integrated-libs: runs-on: windows-latest steps: - uses: actions/checkout@v2 @@ -444,7 +498,7 @@ jobs: path: ${{ github.workspace }}/uselib* - windows-build-integrated-libs-cpp-mode: + windows-cmake-integrated-libs-cpp-mode: runs-on: windows-latest steps: - uses: actions/checkout@v2 @@ -463,7 +517,7 @@ jobs: buildWithCMakeArgs: '--config Release --target install' - windows-build-integrated-libs-with-cuda: + windows-cmake-integrated-libs-with-cuda: runs-on: windows-latest steps: - uses: actions/checkout@v2 @@ -494,7 +548,7 @@ jobs: buildWithCMakeArgs: '--config Release --target install' - windows-mingw: + windows-cmake-mingw: runs-on: windows-latest steps: - uses: actions/checkout@v2 diff --git a/CMakeLists.txt b/CMakeLists.txt index 5400cbbbacb..dc37cb0ab37 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -364,9 +364,9 @@ endif() if(MSVC) target_link_libraries(darknet PRIVATE PThreads_windows::PThreads_windows) - target_link_libraries(darknet PRIVATE wsock32 ws2_32) + target_link_libraries(darknet PRIVATE wsock32) target_link_libraries(dark PUBLIC PThreads_windows::PThreads_windows) - target_link_libraries(dark PUBLIC wsock32 ws2_32) + target_link_libraries(dark PUBLIC wsock32) target_link_libraries(uselib PRIVATE PThreads_windows::PThreads_windows) target_compile_definitions(darknet PRIVATE -D_CRT_RAND_S -DNOMINMAX -D_USE_MATH_DEFINES) target_compile_definitions(dark PRIVATE -D_CRT_RAND_S -DNOMINMAX -D_USE_MATH_DEFINES) @@ -374,6 +374,11 @@ if(MSVC) target_compile_definitions(uselib PRIVATE -D_CRT_RAND_S -DNOMINMAX -D_USE_MATH_DEFINES) endif() +if(MSVC OR MINGW) + target_link_libraries(darknet PRIVATE ws2_32) + target_link_libraries(dark PUBLIC ws2_32) +endif() + target_link_libraries(darknet PRIVATE Threads::Threads) target_link_libraries(dark PUBLIC Threads::Threads) target_link_libraries(uselib PRIVATE Threads::Threads) diff --git a/appveyor.yml b/appveyor.yml index d2153460cab..841ecc506b8 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -10,20 +10,20 @@ environment: COMPILER: cygwin CYGWIN_NOWINPATH: yes CYGSH: C:\cygwin64\bin\bash -c - #- platform: Win64 - # USE_CUDA: yes - # INSTALL_CUDA_VERSION: 10.0 - # COMPILER: vs - # configuration: Release - # VCPKG_ROOT: C:\Tools\vcpkg - # VCPKG_DEFAULT_TRIPLET: x64-windows - #- platform: Win64 - # USE_CUDA: yes - # INSTALL_CUDA_VERSION: 10.1 - # COMPILER: vs - # configuration: Release - # VCPKG_ROOT: C:\Tools\vcpkg - # VCPKG_DEFAULT_TRIPLET: x64-windows + - platform: Win64 + USE_CUDA: yes + INSTALL_CUDA_VERSION: 10.0 + COMPILER: vs + configuration: Release + VCPKG_ROOT: C:\Tools\vcpkg + VCPKG_DEFAULT_TRIPLET: x64-windows + - platform: Win64 + USE_CUDA: yes + INSTALL_CUDA_VERSION: 10.1 + COMPILER: vs + configuration: Release + VCPKG_ROOT: C:\Tools\vcpkg + VCPKG_DEFAULT_TRIPLET: x64-windows - platform: Win64 USE_CUDA: yes INSTALL_CUDA_VERSION: 10.2 @@ -31,23 +31,23 @@ environment: configuration: Release VCPKG_ROOT: C:\Tools\vcpkg VCPKG_DEFAULT_TRIPLET: x64-windows - #- platform: Win64 - # USE_CUDA: no - # COMPILER: vs - # configuration: Release - # VCPKG_ROOT: C:\Tools\vcpkg - # VCPKG_DEFAULT_TRIPLET: x64-windows - #- platform: Win64 - # USE_CUDA: no - # COMPILER: vs - # configuration: Release - # USE_INTEGRATED_LIBS: yes - #- platform: Win64 - # USE_CUDA: no - # COMPILER: vs - # configuration: Release - # USE_INTEGRATED_LIBS: yes - # FORCE_CPP: yes + - platform: Win64 + USE_CUDA: no + COMPILER: vs + configuration: Release + VCPKG_ROOT: C:\Tools\vcpkg + VCPKG_DEFAULT_TRIPLET: x64-windows + - platform: Win64 + USE_CUDA: no + COMPILER: vs + configuration: Release + USE_INTEGRATED_LIBS: yes + - platform: Win64 + USE_CUDA: no + COMPILER: vs + configuration: Release + USE_INTEGRATED_LIBS: yes + FORCE_CPP: yes install: - if [%COMPILER%]==[vs] cinst cmake ninja diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 480f56e2734..2cf03aacbb3 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -718,11 +718,19 @@ void custom_atomic_store_int(volatile int* obj, int desr) std::atomic_store(ptr_a, desr); } +#if !defined(__MINGW64__) void this_thread_sleep_for(int ms_time) { std::chrono::milliseconds dura(ms_time); std::this_thread::sleep_for(dura); } +#else +void this_thread_sleep_for(int ms_time) +{ + std::cerr << " this_thread_sleep_for() isn't implemented \n"; + return; +} +#endif void this_thread_yield() { From 551e57dc8ba8a9e13e9156846cd7b2bbfcbd3f54 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 1 May 2020 09:49:40 +0200 Subject: [PATCH 0953/1365] fix linux make and add windows cygwin on github actions --- .github/workflows/ccpp.yml | 76 ++++++++++++++++++++++++-------------- Makefile | 10 ++--- README.md | 2 +- 3 files changed, 55 insertions(+), 33 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index bdae8321342..dc232003f46 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -24,6 +24,11 @@ jobs: sudo apt-get install -y --no-install-recommends cuda-compiler-10-2 cuda-libraries-dev-10-2 cuda-driver-dev-10-2 cuda-cudart-dev-10-2 cuda-curand-dev-10-2 sudo apt-get install -y --no-install-recommends libcudnn7-dev sudo ln -s /usr/local/cuda-10.2/lib64/stubs/libcuda.so /usr/local/cuda-10.2/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-10.2/lib64/stubs/libcuda.so /usr/local/cuda-10.2/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-10.2/lib64/stubs/libcuda.so /usr/local/cuda-10.2/lib64/libcuda.so + sudo ln -s /usr/local/cuda-10.2 /usr/local/cuda + export PATH=/usr/local/cuda/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH nvcc --version gcc --version @@ -45,14 +50,20 @@ jobs: make clean - name: 'LIBSO=1 GPU=1 CUDNN=1 OPENCV=1' run: | + export PATH=/usr/local/cuda/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH make LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 -j 8 make clean - name: 'LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 CUDNN_HALF=1' run: | + export PATH=/usr/local/cuda/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH make LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 CUDNN_HALF=1 -j 8 make clean - name: 'LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 CUDNN_HALF=1 USE_CPP=1' run: | + export PATH=/usr/local/cuda/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH make LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 CUDNN_HALF=1 USE_CPP=1 -j 8 make clean @@ -163,7 +174,6 @@ jobs: - name: 'Install CUDA' env: - INSTALL_CUDA_VERSION: "10.2" CUDACXX: "/usr/local/cuda-10.2/bin/nvcc" CUDA_PATH: "/usr/local/cuda-10.2" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda-10.2" @@ -177,11 +187,10 @@ jobs: sudo apt update sudo apt-get install -y --no-install-recommends cuda-compiler-10-2 cuda-libraries-dev-10-2 cuda-driver-dev-10-2 cuda-cudart-dev-10-2 cuda-curand-dev-10-2 sudo apt-get install -y --no-install-recommends libcudnn7-dev - sudo ln -s /usr/local/cuda-${{ env.INSTALL_CUDA_VERSION }}/lib64/stubs/libcuda.so /usr/local/cuda-${{ env.INSTALL_CUDA_VERSION }}/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-10.2/lib64/stubs/libcuda.so /usr/local/cuda-10.2/lib64/stubs/libcuda.so.1 - name: 'Build with CMake and Ninja' env: - INSTALL_CUDA_VERSION: "10.2" CUDACXX: "/usr/local/cuda-10.2/bin/nvcc" CUDA_PATH: "/usr/local/cuda-10.2" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda-10.2" @@ -198,19 +207,19 @@ jobs: - uses: actions/upload-artifact@v2 with: - name: darknet-${{ runner.os }} + name: darknet-cuda-${{ runner.os }} path: cfg - uses: actions/upload-artifact@v2 with: - name: darknet-${{ runner.os }} + name: darknet-cuda-${{ runner.os }} path: data - uses: actions/upload-artifact@v2 with: - name: darknet-${{ runner.os }} + name: darknet-cuda-${{ runner.os }} path: ${{ github.workspace }}/*dark* - uses: actions/upload-artifact@v2 with: - name: darknet-${{ runner.os }} + name: darknet-cuda-${{ runner.os }} path: ${{ github.workspace }}/uselib* @@ -383,7 +392,7 @@ jobs: - uses: actions/upload-artifact@v2 with: name: darknet-vcpkg-${{ runner.os }} - path: ${{ github.workspace }}/buildDirectory/*.dll + path: ${{ runner.workspace }}/buildDirectory/Release/*.dll - uses: actions/upload-artifact@v2 with: name: darknet-vcpkg-${{ runner.os }} @@ -406,7 +415,6 @@ jobs: - name: Restore from cache and run vcpkg env: vcpkgResponseFile: ${{ github.workspace }}/cmake/vcpkg_windows_cuda.diff - INSTALL_CUDA_VERSION: "10.2" CUDA_PATH: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" CUDA_PATH_V10_2: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" CUDA_TOOLKIT_ROOT_DIR: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" @@ -421,7 +429,6 @@ jobs: - name: 'Build with CMake and Ninja' env: - INSTALL_CUDA_VERSION: "10.2" CUDA_PATH: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" CUDA_PATH_V10_2: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" CUDA_TOOLKIT_ROOT_DIR: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" @@ -438,23 +445,23 @@ jobs: - uses: actions/upload-artifact@v2 with: - name: darknet-vcpkg-${{ runner.os }} + name: darknet-vcpkg-cuda-${{ runner.os }} path: cfg - uses: actions/upload-artifact@v2 with: - name: darknet-vcpkg-${{ runner.os }} + name: darknet-vcpkg-cuda-${{ runner.os }} path: data - uses: actions/upload-artifact@v2 with: - name: darknet-vcpkg-${{ runner.os }} + name: darknet-vcpkg-cuda-${{ runner.os }} path: ${{ github.workspace }}/*dark* - uses: actions/upload-artifact@v2 with: - name: darknet-vcpkg-${{ runner.os }} - path: ${{ runner.workspace }}/buildDirectory/*.dll + name: darknet-vcpkg-cuda-${{ runner.os }} + path: ${{ runner.workspace }}/buildDirectory/Release/*.dll - uses: actions/upload-artifact@v2 with: - name: darknet-vcpkg-${{ runner.os }} + name: darknet-vcpkg-cuda-${{ runner.os }} path: ${{ github.workspace }}/uselib* @@ -478,23 +485,23 @@ jobs: - uses: actions/upload-artifact@v2 with: - name: darknet-vcpkg-${{ runner.os }} + name: darknet-${{ runner.os }} path: cfg - uses: actions/upload-artifact@v2 with: - name: darknet-vcpkg-${{ runner.os }} + name: darknet-${{ runner.os }} path: data - uses: actions/upload-artifact@v2 with: - name: darknet-vcpkg-${{ runner.os }} + name: darknet-${{ runner.os }} path: ${{ github.workspace }}/*dark* - uses: actions/upload-artifact@v2 with: - name: darknet-vcpkg-${{ runner.os }} + name: darknet-${{ runner.os }} path: ${{ github.workspace }}/3rdparty/pthreads/bin/*.dll - uses: actions/upload-artifact@v2 with: - name: darknet-vcpkg-${{ runner.os }} + name: darknet-${{ runner.os }} path: ${{ github.workspace }}/uselib* @@ -532,7 +539,6 @@ jobs: - name: 'Build with CMake and Ninja' env: - INSTALL_CUDA_VERSION: "10.2" CUDA_PATH: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" CUDA_PATH_V10_2: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" CUDA_TOOLKIT_ROOT_DIR: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" @@ -552,11 +558,6 @@ jobs: runs-on: windows-latest steps: - uses: actions/checkout@v2 - - name: fixPath - run: | - set PATH=%PATH:C:\Program Files\Git\bin;=% - set PATH=%PATH:C:\Program Files\Git\usr\bin;=% - shell: cmd - uses: lukka/get-cmake@latest @@ -570,3 +571,24 @@ jobs: cmakeAppendedArgs: "-G\"MinGW Makefiles\" -DCMAKE_BUILD_TYPE=Release" cmakeBuildType: 'Release' buildWithCMakeArgs: '--config Release --target install' + + + windows-cmake-cygwin: + runs-on: windows-latest + steps: + - uses: actions/checkout@v2 + + - name: 'Install Cygwin' + run: | + choco install cygwin -y + choco install cyg-get -y + cyg-get gcc-g++ cmake make libopencv-devel libncurses-devel + + - name: 'Build' + run: | + mkdir buildDirectory + cd buildDirectory + path C:\tools\cygwin\bin + bash -c 'cmake .. -G "Unix Makefiles" -DCMAKE_BUILD_TYPE="Release"' + bash -c 'cmake --build . --target install -- -j8' + shell: cmd diff --git a/Makefile b/Makefile index d4dd53f9ff9..1d9d4422686 100644 --- a/Makefile +++ b/Makefile @@ -19,7 +19,7 @@ ARCH= -gencode arch=compute_30,code=sm_30 \ -gencode arch=compute_35,code=sm_35 \ -gencode arch=compute_50,code=[sm_50,compute_50] \ -gencode arch=compute_52,code=[sm_52,compute_52] \ - -gencode arch=compute_61,code=[sm_61,compute_61] + -gencode arch=compute_61,code=[sm_61,compute_61] OS := $(shell uname) @@ -127,16 +127,16 @@ ifeq ($(ZED_CAMERA), 1) CFLAGS+= -DZED_STEREO -I/usr/local/zed/include ifeq ($(ZED_CAMERA_v2_8), 1) LDFLAGS+= -L/usr/local/zed/lib -lsl_core -lsl_input -lsl_zed -#-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0 +#-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0 else LDFLAGS+= -L/usr/local/zed/lib -lsl_zed -#-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0 +#-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0 endif endif OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o gaussian_yolo_layer.o upsample_layer.o lstm_layer.o conv_lstm_layer.o scale_channels_layer.o sam_layer.o -ifeq ($(GPU), 1) -LDFLAGS+= -lstdc++ +ifeq ($(GPU), 1) +LDFLAGS+= -lstdc++ OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o endif diff --git a/README.md b/README.md index cb14f1485ba..d16119b376a 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ Paper Yolo v4: https://arxiv.org/abs/2004.10934 More details: http://pjreddie.com/darknet/yolo/ - +![Darknet Continuous Integration](https://github.com/AlexeyAB/darknet/workflows/Darknet%20Continuous%20Integration/badge.svg) [![CircleCI](https://circleci.com/gh/AlexeyAB/darknet.svg?style=svg)](https://circleci.com/gh/AlexeyAB/darknet) [![TravisCI](https://travis-ci.org/AlexeyAB/darknet.svg?branch=master)](https://travis-ci.org/AlexeyAB/darknet) [![AppveyorCI](https://ci.appveyor.com/api/projects/status/594bwb5uoc1fxwiu/branch/master?svg=true)](https://ci.appveyor.com/project/AlexeyAB/darknet/branch/master) From 6cbb75d10b43a95f11326a2475d64500b11fa64e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 1 May 2020 18:45:40 +0300 Subject: [PATCH 0954/1365] Minor fix for yolov4-custom.cfg --- build/darknet/x64/cfg/yolov4-custom.cfg | 1 + cfg/yolov4-custom.cfg | 1 + 2 files changed, 2 insertions(+) diff --git a/build/darknet/x64/cfg/yolov4-custom.cfg b/build/darknet/x64/cfg/yolov4-custom.cfg index 129c7a98ea0..32516bdc120 100644 --- a/build/darknet/x64/cfg/yolov4-custom.cfg +++ b/build/darknet/x64/cfg/yolov4-custom.cfg @@ -741,6 +741,7 @@ size=1 stride=1 pad=1 activation=mish +stopbackward=800 ########################## diff --git a/cfg/yolov4-custom.cfg b/cfg/yolov4-custom.cfg index 129c7a98ea0..32516bdc120 100644 --- a/cfg/yolov4-custom.cfg +++ b/cfg/yolov4-custom.cfg @@ -741,6 +741,7 @@ size=1 stride=1 pad=1 activation=mish +stopbackward=800 ########################## From 52c7314340b16d98b1726c07fa4e9be84c684507 Mon Sep 17 00:00:00 2001 From: JaledMC Date: Fri, 1 May 2020 18:30:53 +0200 Subject: [PATCH 0955/1365] python wrapper cleaning and new features a darknet module and both scripts for video and image inference, with the features of C code --- darknet.py | 475 ++++++++++++---------------------------------- darknet_images.py | 144 ++++++++++++++ darknet_video.py | 180 ++++++++---------- 3 files changed, 354 insertions(+), 445 deletions(-) create mode 100644 darknet_images.py diff --git a/darknet.py b/darknet.py index 27a7a6797e6..26a2bc409c5 100644 --- a/darknet.py +++ b/darknet.py @@ -4,7 +4,8 @@ Requires DLL compilation -Both the GPU and no-GPU version should be compiled; the no-GPU version should be renamed "yolo_cpp_dll_nogpu.dll". +Both the GPU and no-GPU version should be compiled; the no-GPU version should +be renamed "yolo_cpp_dll_nogpu.dll". On a GPU system, you can force CPU evaluation by any of: @@ -13,39 +14,22 @@ - Set environment variable "FORCE_CPU" to "true" -To use, either run performDetect() after import, or modify the end of this file. - -See the docstring of performDetect() for parameters. - -Directly viewing or returning bounding-boxed images requires scikit-image to be installed (`pip install scikit-image`) +Directly viewing or returning bounding-boxed images requires OpenCV +to be installed (`pip install opencv-python`) Original *nix 2.7: https://github.com/pjreddie/darknet/blob/0f110834f4e18b30d5f101bf8f1724c34b7b83db/python/darknet.py Windows Python 2.7 version: https://github.com/AlexeyAB/darknet/blob/fc496d52bf22a0bb257300d3c79be9cd80e722cb/build/darknet/x64/darknet.py @author: Philip Kahn -@date: 20180503 +@author: Jaled Moustafa Calvo +@date: 09/04/2020 """ -#pylint: disable=R, W0401, W0614, W0703 from ctypes import * -import math -import random import os +import re +import random -def sample(probs): - s = sum(probs) - probs = [a/s for a in probs] - r = random.uniform(0, 1) - for i in range(len(probs)): - r = r - probs[i] - if r <= 0: - return i - return len(probs)-1 - -def c_array(ctype, values): - arr = (ctype*len(values))() - arr[:] = values - return arr class BOX(Structure): _fields_ = [("x", c_float), @@ -63,9 +47,6 @@ class DETECTION(Structure): ("uc", POINTER(c_float)), ("points", c_int)] -class DETNUMPAIR(Structure): - _fields_ = [("num", c_int), - ("dets", POINTER(DETECTION))] class IMAGE(Structure): _fields_ = [("w", c_int), @@ -78,9 +59,125 @@ class METADATA(Structure): ("names", POINTER(c_char_p))] +def network_width(net): + return lib.network_width(net) + + +def network_height(net): + return lib.network_height(net) + + +def class_colors(names): + """ + Create a dict with one random BGR color for each + class name + """ + return {name:( + random.randint(0, 255), + random.randint(0, 255), + random.randint(0, 255)) for name in names} + + +def decode_detection(detections): + decoded = [] + for label, confidence, bbox in detections: + confidence = str(round(confidence * 100, 2)) + decoded.append((str(label), confidence, bbox)) + return decoded + + +def bbox2points(bbox): + """ + From bounding box yolo format + to corner points cv2 rectangle + """ + x, y, w, h = bbox + xmin = int(round(x - (w / 2))) + xmax = int(round(x + (w / 2))) + ymin = int(round(y - (h / 2))) + ymax = int(round(y + (h / 2))) + return (xmin, ymin), (xmax, ymax) + + +def draw_boxes(detections, image, colors): + import cv2 + for label, confidence, bbox in detections: + pt1, pt2 = bbox2points(bbox) + cv2.rectangle(image, pt1, pt2, colors[label], 1) + cv2.putText(image, "{} [{}]".format(label, confidence), + (pt1[0], pt1[1] - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, + colors[label], 2) + return image + + +def print_detections(detections, coordinates=False): + print("\nObjects:") + for label, confidence, bbox in detections: + x, y, w, h = bbox + if coordinates: + print("{}: {}% (left_x: {:.0f} top_y: {:.0f} width: {:.0f} height: {:.0f})".format(label, confidence, x, y, w, h)) + else: + print("{}: {}%".format(label, confidence)) + + +def load_network(config_file, data_file, weights): + """ + load model description and weights from config files + args: + config_file (str): path to .cfg model file + data_file (str): path to .data model file + weights (str): path to weights + returns: + network: trained model + class_names + class_colors + """ + network = load_net_custom( + config_file.encode("ascii"), + weights.encode("ascii"), 0, 1) # batch size = 1 + metadata = load_meta(data_file.encode("ascii")) + class_names = [metadata.names[i].decode("ascii") for i in range(metadata.classes)] + colors = class_colors(class_names) + return network, class_names, colors + + +def remove_negatives(detections, class_names, num): + """ + Remove all classes with 0% confidence within the detection + """ + predictions = [] + for j in range(num): + for idx, name in enumerate(class_names): + if detections[j].prob[idx] > 0: + bbox = detections[j].bbox + bbox = (bbox.x, bbox.y, bbox.w, bbox.h) + predictions.append((name, detections[j].prob[idx], (bbox))) + return predictions + + +def detect_image(network, class_names, image, thresh=.5, hier_thresh=.5, nms=.45): + """ + Returns a list with highest confidence class and their bbox + """ + pnum = pointer(c_int(0)) + predict_image(network, image) + detections = get_network_boxes(network, image.w, image.h, + thresh, hier_thresh, None, 0, pnum, 0) + num = pnum[0] + if nms: + do_nms_sort(detections, num, len(class_names), nms) + predictions = remove_negatives(detections, class_names, num) + predictions = decode_detection(predictions) + free_detections(detections, num) + return sorted(predictions, key=lambda x: x[1]) + + +def classify(network, meta, image): + detections = predict_image(network, image) + predictions = [(name, detection[idx]) for idx, name in enumerate(class_names)] + return sorted(predictions, key=lambda x: -x[1]) + -#lib = CDLL("/home/pjreddie/documents/darknet/libdarknet.so", RTLD_GLOBAL) -#lib = CDLL("libdarknet.so", RTLD_GLOBAL) hasGPU = True if os.name == "nt": cwd = os.path.dirname(__file__) @@ -108,7 +205,6 @@ class METADATA(Structure): raise ValueError("ForceCPU") except NameError: pass - # print(os.environ.keys()) # print("FORCE_CPU flag undefined, proceeding with GPU") if not os.path.exists(winGPUdll): raise ValueError("NoDLL") @@ -133,12 +229,6 @@ class METADATA(Structure): copy_image_from_bytes = lib.copy_image_from_bytes copy_image_from_bytes.argtypes = [IMAGE,c_char_p] -def network_width(net): - return lib.network_width(net) - -def network_height(net): - return lib.network_height(net) - predict = lib.network_predict_ptr predict.argtypes = [c_void_p, POINTER(c_float)] predict.restype = POINTER(c_float) @@ -164,9 +254,6 @@ def network_height(net): free_detections = lib.free_detections free_detections.argtypes = [POINTER(DETECTION), c_int] -free_batch_detections = lib.free_batch_detections -free_batch_detections.argtypes = [POINTER(DETNUMPAIR), c_int] - free_ptrs = lib.free_ptrs free_ptrs.argtypes = [POINTER(c_void_p), c_int] @@ -215,313 +302,3 @@ def network_height(net): predict_image_letterbox = lib.network_predict_image_letterbox predict_image_letterbox.argtypes = [c_void_p, IMAGE] predict_image_letterbox.restype = POINTER(c_float) - -network_predict_batch = lib.network_predict_batch -network_predict_batch.argtypes = [c_void_p, IMAGE, c_int, c_int, c_int, - c_float, c_float, POINTER(c_int), c_int, c_int] -network_predict_batch.restype = POINTER(DETNUMPAIR) - -def array_to_image(arr): - import numpy as np - # need to return old values to avoid python freeing memory - arr = arr.transpose(2,0,1) - c = arr.shape[0] - h = arr.shape[1] - w = arr.shape[2] - arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0 - data = arr.ctypes.data_as(POINTER(c_float)) - im = IMAGE(w,h,c,data) - return im, arr - -def classify(net, meta, im): - out = predict_image(net, im) - res = [] - for i in range(meta.classes): - if altNames is None: - nameTag = meta.names[i] - else: - nameTag = altNames[i] - res.append((nameTag, out[i])) - res = sorted(res, key=lambda x: -x[1]) - return res - -def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False): - """ - Performs the meat of the detection - """ - #pylint: disable= C0321 - im = load_image(image, 0, 0) - if debug: print("Loaded image") - ret = detect_image(net, meta, im, thresh, hier_thresh, nms, debug) - free_image(im) - if debug: print("freed image") - return ret - -def detect_image(net, meta, im, thresh=.5, hier_thresh=.5, nms=.45, debug= False): - #import cv2 - #custom_image_bgr = cv2.imread(image) # use: detect(,,imagePath,) - #custom_image = cv2.cvtColor(custom_image_bgr, cv2.COLOR_BGR2RGB) - #custom_image = cv2.resize(custom_image,(lib.network_width(net), lib.network_height(net)), interpolation = cv2.INTER_LINEAR) - #import scipy.misc - #custom_image = scipy.misc.imread(image) - #im, arr = array_to_image(custom_image) # you should comment line below: free_image(im) - num = c_int(0) - if debug: print("Assigned num") - pnum = pointer(num) - if debug: print("Assigned pnum") - predict_image(net, im) - letter_box = 0 - #predict_image_letterbox(net, im) - #letter_box = 1 - if debug: print("did prediction") - #dets = get_network_boxes(net, custom_image_bgr.shape[1], custom_image_bgr.shape[0], thresh, hier_thresh, None, 0, pnum, letter_box) # OpenCV - dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum, letter_box) - if debug: print("Got dets") - num = pnum[0] - if debug: print("got zeroth index of pnum") - if nms: - do_nms_sort(dets, num, meta.classes, nms) - if debug: print("did sort") - res = [] - if debug: print("about to range") - for j in range(num): - if debug: print("Ranging on "+str(j)+" of "+str(num)) - if debug: print("Classes: "+str(meta), meta.classes, meta.names) - for i in range(meta.classes): - if debug: print("Class-ranging on "+str(i)+" of "+str(meta.classes)+"= "+str(dets[j].prob[i])) - if dets[j].prob[i] > 0: - b = dets[j].bbox - if altNames is None: - nameTag = meta.names[i] - else: - nameTag = altNames[i] - if debug: - print("Got bbox", b) - print(nameTag) - print(dets[j].prob[i]) - print((b.x, b.y, b.w, b.h)) - res.append((nameTag, dets[j].prob[i], (b.x, b.y, b.w, b.h))) - if debug: print("did range") - res = sorted(res, key=lambda x: -x[1]) - if debug: print("did sort") - free_detections(dets, num) - if debug: print("freed detections") - return res - - -netMain = None -metaMain = None -altNames = None - -def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yolov4.cfg", weightPath = "yolov4.weights", metaPath= "./cfg/coco.data", showImage= True, makeImageOnly = False, initOnly= False): - """ - Convenience function to handle the detection and returns of objects. - - Displaying bounding boxes requires libraries scikit-image and numpy - - Parameters - ---------------- - imagePath: str - Path to the image to evaluate. Raises ValueError if not found - - thresh: float (default= 0.25) - The detection threshold - - configPath: str - Path to the configuration file. Raises ValueError if not found - - weightPath: str - Path to the weights file. Raises ValueError if not found - - metaPath: str - Path to the data file. Raises ValueError if not found - - showImage: bool (default= True) - Compute (and show) bounding boxes. Changes return. - - makeImageOnly: bool (default= False) - If showImage is True, this won't actually *show* the image, but will create the array and return it. - - initOnly: bool (default= False) - Only initialize globals. Don't actually run a prediction. - - Returns - ---------------------- - - - When showImage is False, list of tuples like - ('obj_label', confidence, (bounding_box_x_px, bounding_box_y_px, bounding_box_width_px, bounding_box_height_px)) - The X and Y coordinates are from the center of the bounding box. Subtract half the width or height to get the lower corner. - - Otherwise, a dict with - { - "detections": as above - "image": a numpy array representing an image, compatible with scikit-image - "caption": an image caption - } - """ - # Import the global variables. This lets us instance Darknet once, then just call performDetect() again without instancing again - global metaMain, netMain, altNames #pylint: disable=W0603 - assert 0 < thresh < 1, "Threshold should be a float between zero and one (non-inclusive)" - if not os.path.exists(configPath): - raise ValueError("Invalid config path `"+os.path.abspath(configPath)+"`") - if not os.path.exists(weightPath): - raise ValueError("Invalid weight path `"+os.path.abspath(weightPath)+"`") - if not os.path.exists(metaPath): - raise ValueError("Invalid data file path `"+os.path.abspath(metaPath)+"`") - if netMain is None: - netMain = load_net_custom(configPath.encode("ascii"), weightPath.encode("ascii"), 0, 1) # batch size = 1 - if metaMain is None: - metaMain = load_meta(metaPath.encode("ascii")) - if altNames is None: - # In Python 3, the metafile default access craps out on Windows (but not Linux) - # Read the names file and create a list to feed to detect - try: - with open(metaPath) as metaFH: - metaContents = metaFH.read() - import re - match = re.search("names *= *(.*)$", metaContents, re.IGNORECASE | re.MULTILINE) - if match: - result = match.group(1) - else: - result = None - try: - if os.path.exists(result): - with open(result) as namesFH: - namesList = namesFH.read().strip().split("\n") - altNames = [x.strip() for x in namesList] - except TypeError: - pass - except Exception: - pass - if initOnly: - print("Initialized detector") - return None - if not os.path.exists(imagePath): - raise ValueError("Invalid image path `"+os.path.abspath(imagePath)+"`") - # Do the detection - #detections = detect(netMain, metaMain, imagePath, thresh) # if is used cv2.imread(image) - detections = detect(netMain, metaMain, imagePath.encode("ascii"), thresh) - if showImage: - try: - from skimage import io, draw - import numpy as np - image = io.imread(imagePath) - print("*** "+str(len(detections))+" Results, color coded by confidence ***") - imcaption = [] - for detection in detections: - label = detection[0] - confidence = detection[1] - pstring = label+": "+str(np.rint(100 * confidence))+"%" - imcaption.append(pstring) - print(pstring) - bounds = detection[2] - shape = image.shape - # x = shape[1] - # xExtent = int(x * bounds[2] / 100) - # y = shape[0] - # yExtent = int(y * bounds[3] / 100) - yExtent = int(bounds[3]) - xEntent = int(bounds[2]) - # Coordinates are around the center - xCoord = int(bounds[0] - bounds[2]/2) - yCoord = int(bounds[1] - bounds[3]/2) - boundingBox = [ - [xCoord, yCoord], - [xCoord, yCoord + yExtent], - [xCoord + xEntent, yCoord + yExtent], - [xCoord + xEntent, yCoord] - ] - # Wiggle it around to make a 3px border - rr, cc = draw.polygon_perimeter([x[1] for x in boundingBox], [x[0] for x in boundingBox], shape= shape) - rr2, cc2 = draw.polygon_perimeter([x[1] + 1 for x in boundingBox], [x[0] for x in boundingBox], shape= shape) - rr3, cc3 = draw.polygon_perimeter([x[1] - 1 for x in boundingBox], [x[0] for x in boundingBox], shape= shape) - rr4, cc4 = draw.polygon_perimeter([x[1] for x in boundingBox], [x[0] + 1 for x in boundingBox], shape= shape) - rr5, cc5 = draw.polygon_perimeter([x[1] for x in boundingBox], [x[0] - 1 for x in boundingBox], shape= shape) - boxColor = (int(255 * (1 - (confidence ** 2))), int(255 * (confidence ** 2)), 0) - draw.set_color(image, (rr, cc), boxColor, alpha= 0.8) - draw.set_color(image, (rr2, cc2), boxColor, alpha= 0.8) - draw.set_color(image, (rr3, cc3), boxColor, alpha= 0.8) - draw.set_color(image, (rr4, cc4), boxColor, alpha= 0.8) - draw.set_color(image, (rr5, cc5), boxColor, alpha= 0.8) - if not makeImageOnly: - io.imshow(image) - io.show() - detections = { - "detections": detections, - "image": image, - "caption": "\n
".join(imcaption) - } - except Exception as e: - print("Unable to show image: "+str(e)) - return detections - -def performBatchDetect(thresh= 0.25, configPath = "./cfg/yolov4.cfg", weightPath = "yolov4.weights", metaPath= "./cfg/coco.data", hier_thresh=.5, nms=.45, batch_size=3): - import cv2 - import numpy as np - # NB! Image sizes should be the same - # You can change the images, yet, be sure that they have the same width and height - img_samples = ['data/person.jpg', 'data/person.jpg', 'data/person.jpg'] - image_list = [cv2.imread(k) for k in img_samples] - - net = load_net_custom(configPath.encode('utf-8'), weightPath.encode('utf-8'), 0, batch_size) - meta = load_meta(metaPath.encode('utf-8')) - pred_height, pred_width, c = image_list[0].shape - net_width, net_height = (network_width(net), network_height(net)) - img_list = [] - for custom_image_bgr in image_list: - custom_image = cv2.cvtColor(custom_image_bgr, cv2.COLOR_BGR2RGB) - custom_image = cv2.resize( - custom_image, (net_width, net_height), interpolation=cv2.INTER_NEAREST) - custom_image = custom_image.transpose(2, 0, 1) - img_list.append(custom_image) - - arr = np.concatenate(img_list, axis=0) - arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0 - data = arr.ctypes.data_as(POINTER(c_float)) - im = IMAGE(net_width, net_height, c, data) - - batch_dets = network_predict_batch(net, im, batch_size, pred_width, - pred_height, thresh, hier_thresh, None, 0, 0) - batch_boxes = [] - batch_scores = [] - batch_classes = [] - for b in range(batch_size): - num = batch_dets[b].num - dets = batch_dets[b].dets - if nms: - do_nms_obj(dets, num, meta.classes, nms) - boxes = [] - scores = [] - classes = [] - for i in range(num): - det = dets[i] - score = -1 - label = None - for c in range(det.classes): - p = det.prob[c] - if p > score: - score = p - label = c - if score > thresh: - box = det.bbox - left, top, right, bottom = map(int,(box.x - box.w / 2, box.y - box.h / 2, - box.x + box.w / 2, box.y + box.h / 2)) - boxes.append((top, left, bottom, right)) - scores.append(score) - classes.append(label) - boxColor = (int(255 * (1 - (score ** 2))), int(255 * (score ** 2)), 0) - cv2.rectangle(image_list[b], (left, top), - (right, bottom), boxColor, 2) - cv2.imwrite(os.path.basename(img_samples[b]),image_list[b]) - - batch_boxes.append(boxes) - batch_scores.append(scores) - batch_classes.append(classes) - free_batch_detections(batch_dets, batch_size) - return batch_boxes, batch_scores, batch_classes - -if __name__ == "__main__": - print(performDetect()) - #Uncomment the following line to see batch inference working - #print(performBatchDetect()) \ No newline at end of file diff --git a/darknet_images.py b/darknet_images.py new file mode 100644 index 00000000000..acf5f7ac822 --- /dev/null +++ b/darknet_images.py @@ -0,0 +1,144 @@ +import argparse +import os +import glob +import random +import darknet +import time +import cv2 + + +def parser(): + parser = argparse.ArgumentParser(description="YOLO Object Detection") + parser.add_argument("--input", type=str, default="", + help="image source. It can be a single image, a" + "txt with paths to them, or a folder. Image valid" + " formats are jpg, jpeg or png." + "If no input is given, ") + parser.add_argument("--weights", default="yolov4.weights", + help="yolo weights path") + parser.add_argument("--dont_show", action='store_true', + help="windown inference display. For headless systems") + parser.add_argument("--ext_output", action='store_true', + help="display bbox coordinates of detected objects") + parser.add_argument("--save_labels", action='store_true', + help="save detections bbox for each image in yolo format") + parser.add_argument("--config_file", default="./cfg/yolov4.cfg", + help="path to config file") + parser.add_argument("--data_file", default="./cfg/coco.data", + help="path to data file") + parser.add_argument("--thresh", type=float, default=.25, + help="remove detections with lower confidence") + return parser.parse_args() + + +def check_arguments_errors(args): + assert 0 < args.thresh < 1, "Threshold should be a float between zero and one (non-inclusive)" + if not os.path.exists(args.config_file): + raise(ValueError("Invalid config path {}".format(os.path.abspath(args.config_file)))) + if not os.path.exists(args.weights): + raise(ValueError("Invalid weight path {}".format(os.path.abspath(args.weights)))) + if not os.path.exists(args.data_file): + raise(ValueError("Invalid data file path {}".format(os.path.abspath(args.data_file)))) + if args.input and not os.path.exists(args.input): + raise(ValueError("Invalid image path {}".format(os.path.abspath(args.input)))) + + +def load_images(images_path): + """ + If image path is given, return it directly + For txt file, read it and return each line as image path + In other case, it's a folder, return a list with names of each + jpg, jpeg and png file + """ + input_path_extension = images_path.split('.')[-1] + if input_path_extension in ['jpg', 'jpeg', 'png']: + return [images_path] + elif input_path_extension == "txt": + with open(images_path, "r") as f: + return f.read().splitlines() + else: + return glob.glob( + os.path.join(images_path, "*.jpg")) + \ + glob.glob(os.path.join(images_path, "*.png")) + \ + glob.glob(os.path.join(images_path, "*.jpeg")) + + +def image_detection(image_path, network, class_names, class_colors, thresh): + # Darknet doesn't accept numpy images. + # Create one with image we reuse for each detect + width = darknet.network_width(network) + height = darknet.network_height(network) + darknet_image = darknet.make_image(width, height, 3) + + image = cv2.imread(image_path) + image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + image_resized = cv2.resize(image_rgb, (width, height), + interpolation=cv2.INTER_LINEAR) + + darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes()) + detections = darknet.detect_image(network, class_names, darknet_image, thresh=thresh) + image = darknet.draw_boxes(detections, image_resized, class_colors) + return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), detections + + +def convert2relative(image, bbox): + """ + YOLO format use relative coordinates for annotation + """ + x, y, w, h = bbox + width, height, _ = image.shape + return x/width, y/height, w/width, h/height + + +def save_annotations(name, image, detections, class_names): + """ + Files saved with image_name.txt and relative coordinates + """ + file_name = name.split(".")[:-1][0] + ".txt" + with open(file_name, "w") as f: + for label, confidence, bbox in detections: + x, y, w, h = convert2relative(image, bbox) + label = class_names.index(label) + f.write("{} {:.4f} {:.4f} {:.4f} {:.4f}\n".format(label, x, y, w, h)) + + +def main(): + args = parser() + check_arguments_errors(args) + + random.seed(3) # deterministic bbox colors + network, class_names, class_colors = darknet.load_network( + args.config_file, + args.data_file, + args.weights + ) + + images = load_images(args.input) + + index = 0 + while True: + # loop asking for new image paths if no list is given + if args.input: + if index >= len(images): + break + image_name = images[index] + else: + image_name = input("Enter Image Path: ") + prev_time = time.time() + image, detections = image_detection( + image_name, network, class_names, class_colors, args.thresh + ) + if args.save_labels: + save_annotations(image_name, image, detections, class_names) + darknet.print_detections(detections, args.ext_output) + fps = int(1/(time.time() - prev_time)) + print("FPS: {}".format(fps)) + if not args.dont_show: + cv2.imshow('Inference', image) + if cv2.waitKey() & 0xFF == ord('q'): + break + index += 1 + + +if __name__ == "__main__": + main() diff --git a/darknet_video.py b/darknet_video.py index aa9c669649f..e8368491d94 100644 --- a/darknet_video.py +++ b/darknet_video.py @@ -1,115 +1,103 @@ -from ctypes import * -import math -import random import os import cv2 -import numpy as np import time import darknet +import argparse +import random -def convertBack(x, y, w, h): - xmin = int(round(x - (w / 2))) - xmax = int(round(x + (w / 2))) - ymin = int(round(y - (h / 2))) - ymax = int(round(y + (h / 2))) - return xmin, ymin, xmax, ymax +def parser(): + parser = argparse.ArgumentParser(description="YOLO Object Detection") + parser.add_argument("--input", type=str, default=0, + help="video source. If empty, uses webcam 0 stream") + parser.add_argument("--output_file", type=str, default="", + help="inference video name. Not saved if empty") + parser.add_argument("--weights", default="yolov4.weights", + help="yolo weights path") + parser.add_argument("--dont_show", action='store_true', + help="windown inference display. For headless systems") + parser.add_argument("--ext_output", action='store_true', + help="display bbox coordinates of detected objects") + parser.add_argument("--config_file", default="./cfg/yolov4.cfg", + help="path to config file") + parser.add_argument("--data_file", default="./cfg/coco.data", + help="path to data file") + parser.add_argument("--thresh", type=float, default=.25, + help="remove detections with confidence below this value") + return parser.parse_args() -def cvDrawBoxes(detections, img): - for detection in detections: - x, y, w, h = detection[2][0],\ - detection[2][1],\ - detection[2][2],\ - detection[2][3] - xmin, ymin, xmax, ymax = convertBack( - float(x), float(y), float(w), float(h)) - pt1 = (xmin, ymin) - pt2 = (xmax, ymax) - cv2.rectangle(img, pt1, pt2, (0, 255, 0), 1) - cv2.putText(img, - detection[0].decode() + - " [" + str(round(detection[1] * 100, 2)) + "]", - (pt1[0], pt1[1] - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, - [0, 255, 0], 2) - return img +def check_arguments_errors(args): + assert 0 < args.thresh < 1, "Threshold should be a float between zero and one (non-inclusive)" + if not os.path.exists(args.config_file): + raise(ValueError("Invalid config path {}".format(os.path.abspath(args.config_file)))) + if not os.path.exists(args.weights): + raise(ValueError("Invalid weight path {}".format(os.path.abspath(args.weights)))) + if not os.path.exists(args.data_file): + raise(ValueError("Invalid data file path {}".format(os.path.abspath(args.data_file)))) + if args.input and not os.path.exists(args.input): + raise(ValueError("Invalid image path {}".format(os.path.abspath(args.input)))) -netMain = None -metaMain = None -altNames = None +def set_video(input_video, output_video, size, fps=15): + """ + Setup input and ouput (saved) video objects + args: + input_video (str): path to video, or webcam device number + output_video (str): inference video name to be saved + fps: frames per second for saved video (adjust this number for + player speed) + returns: + cap: cv2 input video object + video: cv2 output video object + """ + fourcc = cv2.VideoWriter_fourcc(*"MJPG") + video = cv2.VideoWriter(output_video, fourcc, fps, size) + return video -def YOLO(): - global metaMain, netMain, altNames - configPath = "./cfg/yolov4.cfg" - weightPath = "./yolov4.weights" - metaPath = "./cfg/coco.data" - if not os.path.exists(configPath): - raise ValueError("Invalid config path `" + - os.path.abspath(configPath)+"`") - if not os.path.exists(weightPath): - raise ValueError("Invalid weight path `" + - os.path.abspath(weightPath)+"`") - if not os.path.exists(metaPath): - raise ValueError("Invalid data file path `" + - os.path.abspath(metaPath)+"`") - if netMain is None: - netMain = darknet.load_net_custom(configPath.encode( - "ascii"), weightPath.encode("ascii"), 0, 1) # batch size = 1 - if metaMain is None: - metaMain = darknet.load_meta(metaPath.encode("ascii")) - if altNames is None: - try: - with open(metaPath) as metaFH: - metaContents = metaFH.read() - import re - match = re.search("names *= *(.*)$", metaContents, - re.IGNORECASE | re.MULTILINE) - if match: - result = match.group(1) - else: - result = None - try: - if os.path.exists(result): - with open(result) as namesFH: - namesList = namesFH.read().strip().split("\n") - altNames = [x.strip() for x in namesList] - except TypeError: - pass - except Exception: - pass - #cap = cv2.VideoCapture(0) - cap = cv2.VideoCapture("test.mp4") - cap.set(3, 1280) - cap.set(4, 720) - out = cv2.VideoWriter( - "output.avi", cv2.VideoWriter_fourcc(*"MJPG"), 10.0, - (darknet.network_width(netMain), darknet.network_height(netMain))) - print("Starting the YOLO loop...") +def main(): + args = parser() + check_arguments_errors(args) + random.seed(3) # deterministic bbox colors - # Create an image we reuse for each detect - darknet_image = darknet.make_image(darknet.network_width(netMain), - darknet.network_height(netMain),3) - while True: - prev_time = time.time() - ret, frame_read = cap.read() - frame_rgb = cv2.cvtColor(frame_read, cv2.COLOR_BGR2RGB) - frame_resized = cv2.resize(frame_rgb, - (darknet.network_width(netMain), - darknet.network_height(netMain)), - interpolation=cv2.INTER_LINEAR) + network, class_names, class_colors = darknet.load_network( + args.config_file, + args.data_file, + args.weights + ) + # Darknet doesn't accept numpy images. + # Create one with image we reuse for each detect + width = darknet.network_width(network) + height = darknet.network_height(network) + darknet_image = darknet.make_image(width, height, 3) - darknet.copy_image_from_bytes(darknet_image,frame_resized.tobytes()) + cap = cv2.VideoCapture(args.input) + video = set_video(args.input, args.output_file, (width, height)) - detections = darknet.detect_image(netMain, metaMain, darknet_image, thresh=0.25) - image = cvDrawBoxes(detections, frame_resized) + while cap.isOpened(): + prev_time = time.time() + ret, frame = cap.read() + if not ret: + break + frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + frame_resized = cv2.resize(frame_rgb, (width, height), + interpolation=cv2.INTER_LINEAR) + darknet.copy_image_from_bytes(darknet_image, frame_resized.tobytes()) + detections = darknet.detect_image(network, class_names, darknet_image, thresh=args.thresh) + image = darknet.draw_boxes(detections, frame_resized, class_colors) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) - print(1/(time.time()-prev_time)) - cv2.imshow('Demo', image) - cv2.waitKey(3) + if args.output_file is not None: + video.write(image) + fps = int(1/(time.time() - prev_time)) + print("FPS: {}".format(fps)) + darknet.print_detections(detections, args.ext_output) + if not args.dont_show: + cv2.imshow('Inference', image) + cv2.waitKey(fps) cap.release() - out.release() + video.release() + if __name__ == "__main__": - YOLO() + main() From 02da35eb509b71823ca765b663b48904020600b0 Mon Sep 17 00:00:00 2001 From: JaledMC Date: Fri, 1 May 2020 20:21:09 +0200 Subject: [PATCH 0956/1365] Change output_file to out_filename --- darknet_video.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/darknet_video.py b/darknet_video.py index e8368491d94..b5091da0ff4 100644 --- a/darknet_video.py +++ b/darknet_video.py @@ -10,7 +10,7 @@ def parser(): parser = argparse.ArgumentParser(description="YOLO Object Detection") parser.add_argument("--input", type=str, default=0, help="video source. If empty, uses webcam 0 stream") - parser.add_argument("--output_file", type=str, default="", + parser.add_argument("--out_filename", type=str, default="", help="inference video name. Not saved if empty") parser.add_argument("--weights", default="yolov4.weights", help="yolo weights path") From 2a9ef5246c065c1b606baaf254d93c4a3fd15e09 Mon Sep 17 00:00:00 2001 From: JaledMC Date: Fri, 1 May 2020 20:34:30 +0200 Subject: [PATCH 0957/1365] out_filename parameter bug The change from arparse parameter output_file to out_filename crash with this two lines --- darknet_video.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/darknet_video.py b/darknet_video.py index b5091da0ff4..e24e3b75736 100644 --- a/darknet_video.py +++ b/darknet_video.py @@ -73,7 +73,7 @@ def main(): darknet_image = darknet.make_image(width, height, 3) cap = cv2.VideoCapture(args.input) - video = set_video(args.input, args.output_file, (width, height)) + video = set_video(args.input, args.out_filename, (width, height)) while cap.isOpened(): prev_time = time.time() @@ -87,7 +87,7 @@ def main(): detections = darknet.detect_image(network, class_names, darknet_image, thresh=args.thresh) image = darknet.draw_boxes(detections, frame_resized, class_colors) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) - if args.output_file is not None: + if args.out_filename is not None: video.write(image) fps = int(1/(time.time() - prev_time)) print("FPS: {}".format(fps)) From a34682e67c9ff279019e17070a5d34d596cd8422 Mon Sep 17 00:00:00 2001 From: Juan Altmayer Pizzorno Date: Thu, 30 Apr 2020 14:04:54 -0400 Subject: [PATCH 0958/1365] - adjusted BatchNorm epsilon of normalize_cpu to be the same as normalize_gpu's kernel; --- src/blas.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/blas.c b/src/blas.c index b495a47e283..b588fb3369f 100644 --- a/src/blas.c +++ b/src/blas.c @@ -289,7 +289,7 @@ void normalize_cpu(float *x, float *mean, float *variance, int batch, int filter for(f = 0; f < filters; ++f){ for(i = 0; i < spatial; ++i){ int index = b*filters*spatial + f*spatial + i; - x[index] = (x[index] - mean[f])/(sqrt(variance[f] + .000001f)); + x[index] = (x[index] - mean[f])/(sqrt(variance[f] + .00001f)); } } } From 102e31ba660faa27bcde579db0c800f35d5726e4 Mon Sep 17 00:00:00 2001 From: Wang Xinyu Date: Sat, 2 May 2020 20:57:44 +0800 Subject: [PATCH 0959/1365] Update readme, add tensorrt yolov3-spp, yolov4 --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 8795e4603d0..e7a6f49e86d 100644 --- a/README.md +++ b/README.md @@ -144,7 +144,7 @@ You can get cfg-files by path: `darknet/cfg/` * **Intel OpenVINO 2019 R1:** (Myriad X / USB Neural Compute Stick / Arria FPGA): read this [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) * **OpenCV-dnn** the fastest implementation for CPU (x86/ARM-Android), OpenCV can be compiled with [OpenVINO-backend](https://github.com/opencv/opencv/wiki/Intel's-Deep-Learning-Inference-Engine-backend) for running on (Myriad X / USB Neural Compute Stick / Arria FPGA), use `yolov3.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221) or [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) * **PyTorch > ONNX > CoreML > iOS** how to convert cfg/weights-files to pt-file: [ultralytics/yolov3](https://github.com/ultralytics/yolov3#darknet-conversion) and [iOS App](https://itunes.apple.com/app/id1452689527) -* **TensorRT** for YOLOv3 (-70% faster inference): [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) read [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf) +* **TensorRT** for YOLOv3 (-70% faster inference): [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) read [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf). [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) implemented yolov3-spp, yolov4, etc. * **TVM** - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backends (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about * **OpenDataCam** - It detects, tracks and counts moving objects by using Yolo: https://github.com/opendatacam/opendatacam#-hardware-pre-requisite * **Netron** - Visualizer for neural networks: https://github.com/lutzroeder/netron From 861192df19e28c03d70a6aec59716dd16f390f26 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 3 May 2020 16:36:05 +0200 Subject: [PATCH 0960/1365] shorten github actions names --- .github/workflows/ccpp.yml | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index dc232003f46..e6ac0eb2e14 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -3,7 +3,7 @@ name: Darknet Continuous Integration on: [push, pull_request] jobs: - linux-make: + ubuntu-makefile: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 @@ -68,7 +68,7 @@ jobs: make clean - linux-cmake-vcpkg: + ubuntu-vcpkg: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 @@ -119,7 +119,7 @@ jobs: path: ${{ github.workspace }}/uselib* - linux-cmake: + ubuntu: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 @@ -160,7 +160,7 @@ jobs: path: ${{ github.workspace }}/uselib* - linux-cmake-with-cuda: + ubuntu-cuda: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 @@ -223,7 +223,7 @@ jobs: path: ${{ github.workspace }}/uselib* - linux-cmake-no-opencv-cpp-mode: + ubuntu-no-ocv-cpp: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 @@ -242,7 +242,7 @@ jobs: buildWithCMakeArgs: '--target install' - macos-cmake-vcpkg: + osx-vcpkg: runs-on: macOS-latest steps: - uses: actions/checkout@v2 @@ -291,7 +291,7 @@ jobs: path: ${{ github.workspace }}/uselib* - macos-cmake: + osx: runs-on: macOS-latest steps: - uses: actions/checkout@v2 @@ -330,7 +330,7 @@ jobs: path: ${{ github.workspace }}/uselib* - macos-cmake-no-opencv-no-omp-cpp-mode: + osx-no-ocv-no-omp-cpp: runs-on: macOS-latest steps: - uses: actions/checkout@v2 @@ -349,7 +349,7 @@ jobs: buildWithCMakeArgs: '--target install' - windows-cmake-vcpkg: + win-vcpkg: runs-on: windows-latest steps: - uses: actions/checkout@v2 @@ -399,7 +399,7 @@ jobs: path: ${{ github.workspace }}/uselib* - windows-cmake-vcpkg-with-cuda: + win-vcpkg-cuda: runs-on: windows-latest steps: - uses: actions/checkout@v2 @@ -465,7 +465,7 @@ jobs: path: ${{ github.workspace }}/uselib* - windows-cmake-integrated-libs: + win-integrated-libs: runs-on: windows-latest steps: - uses: actions/checkout@v2 @@ -505,7 +505,7 @@ jobs: path: ${{ github.workspace }}/uselib* - windows-cmake-integrated-libs-cpp-mode: + win-intlibs-cpp: runs-on: windows-latest steps: - uses: actions/checkout@v2 @@ -524,7 +524,7 @@ jobs: buildWithCMakeArgs: '--config Release --target install' - windows-cmake-integrated-libs-with-cuda: + win-intlibs-cuda: runs-on: windows-latest steps: - uses: actions/checkout@v2 @@ -554,7 +554,7 @@ jobs: buildWithCMakeArgs: '--config Release --target install' - windows-cmake-mingw: + mingw: runs-on: windows-latest steps: - uses: actions/checkout@v2 @@ -573,7 +573,7 @@ jobs: buildWithCMakeArgs: '--config Release --target install' - windows-cmake-cygwin: + cygwin: runs-on: windows-latest steps: - uses: actions/checkout@v2 From cba6153a4143e5a80d55581e11aa4d9970994379 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Mon, 4 May 2020 19:52:06 +0200 Subject: [PATCH 0961/1365] remove appveyor CI --- appveyor.yml | 109 --------------------------------------------------- 1 file changed, 109 deletions(-) delete mode 100644 appveyor.yml diff --git a/appveyor.yml b/appveyor.yml deleted file mode 100644 index 841ecc506b8..00000000000 --- a/appveyor.yml +++ /dev/null @@ -1,109 +0,0 @@ -image: Visual Studio 2019 -clone_folder: c:\projects\darknet -cache: C:\Tools\vcpkg\installed\ - -environment: - WORKSPACE: C:\projects - matrix: - - platform: Cygwin64 - USE_CUDA: no - COMPILER: cygwin - CYGWIN_NOWINPATH: yes - CYGSH: C:\cygwin64\bin\bash -c - - platform: Win64 - USE_CUDA: yes - INSTALL_CUDA_VERSION: 10.0 - COMPILER: vs - configuration: Release - VCPKG_ROOT: C:\Tools\vcpkg - VCPKG_DEFAULT_TRIPLET: x64-windows - - platform: Win64 - USE_CUDA: yes - INSTALL_CUDA_VERSION: 10.1 - COMPILER: vs - configuration: Release - VCPKG_ROOT: C:\Tools\vcpkg - VCPKG_DEFAULT_TRIPLET: x64-windows - - platform: Win64 - USE_CUDA: yes - INSTALL_CUDA_VERSION: 10.2 - COMPILER: vs - configuration: Release - VCPKG_ROOT: C:\Tools\vcpkg - VCPKG_DEFAULT_TRIPLET: x64-windows - - platform: Win64 - USE_CUDA: no - COMPILER: vs - configuration: Release - VCPKG_ROOT: C:\Tools\vcpkg - VCPKG_DEFAULT_TRIPLET: x64-windows - - platform: Win64 - USE_CUDA: no - COMPILER: vs - configuration: Release - USE_INTEGRATED_LIBS: yes - - platform: Win64 - USE_CUDA: no - COMPILER: vs - configuration: Release - USE_INTEGRATED_LIBS: yes - FORCE_CPP: yes - -install: - - if [%COMPILER%]==[vs] cinst cmake ninja - - if [%COMPILER%]==[vs] SET "PATH=C:\Program Files\CMake\bin;%PATH%" - - if [%COMPILER%]==[vs] call "C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Auxiliary\Build\vcvarsall.bat" x64 - - if [%COMPILER%]==[cygwin] SET "PATH=C:\cygwin64\usr\local\bin;C:\cygwin64\bin;C:\cygwin64\usr\bin;%PATH%" - - if [%COMPILER%]==[cygwin] SET PATH=%PATH:C:\Program Files\Git\usr\bin;=% - - cd %WORKSPACE%\ - - if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.0] curl -L https://developer.nvidia.com/compute/cuda/10.0/Prod/local_installers/cuda_10.0.130_411.31_win10 -o setup.exe - - if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.1] curl -L https://developer.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda_10.1.105_418.96_win10.exe -o setup.exe - - if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.2] choco install cuda -y - - if [%USE_CUDA%]==[yes] if NOT [%INSTALL_CUDA_VERSION%]==[10.2] .\setup.exe -s nvcc_%INSTALL_CUDA_VERSION% cuobjdump_%INSTALL_CUDA_VERSION% nvprune_%INSTALL_CUDA_VERSION% cupti_%INSTALL_CUDA_VERSION% gpu_library_advisor_%INSTALL_CUDA_VERSION% memcheck_%INSTALL_CUDA_VERSION% nvdisasm_%INSTALL_CUDA_VERSION% nvprof_%INSTALL_CUDA_VERSION% visual_profiler_%INSTALL_CUDA_VERSION% visual_studio_integration_%INSTALL_CUDA_VERSION% cublas_%INSTALL_CUDA_VERSION% cublas_dev_%INSTALL_CUDA_VERSION% cudart_%INSTALL_CUDA_VERSION% cufft_%INSTALL_CUDA_VERSION% cufft_dev_%INSTALL_CUDA_VERSION% curand_%INSTALL_CUDA_VERSION% curand_dev_%INSTALL_CUDA_VERSION% cusolver_%INSTALL_CUDA_VERSION% cusolver_dev_%INSTALL_CUDA_VERSION% cusparse_%INSTALL_CUDA_VERSION% cusparse_dev_%INSTALL_CUDA_VERSION% nvgraph_%INSTALL_CUDA_VERSION% nvgraph_dev_%INSTALL_CUDA_VERSION% npp_%INSTALL_CUDA_VERSION% npp_dev_%INSTALL_CUDA_VERSION% nvrtc_%INSTALL_CUDA_VERSION% nvrtc_dev_%INSTALL_CUDA_VERSION% nvml_dev_%INSTALL_CUDA_VERSION% occupancy_calculator_%INSTALL_CUDA_VERSION% fortran_examples_%INSTALL_CUDA_VERSION% - - if [%USE_CUDA%]==[yes] set CUDA_PATH=%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v%INSTALL_CUDA_VERSION% - - if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.0] set CUDA_PATH_V10_0=%CUDA_PATH% - - if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.1] set CUDA_PATH_V10_1=%CUDA_PATH% - - if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.2] set CUDA_PATH_V10_2=%CUDA_PATH% - - if [%USE_CUDA%]==[yes] set CUDA_TOOLKIT_ROOT_DIR=%CUDA_PATH% - - if [%USE_CUDA%]==[yes] set PATH=%CUDA_PATH%\bin;%PATH% - - cd %WORKSPACE%\ - - mkdir cygwin-downloads - - ps: if($env:COMPILER -eq "cygwin") { Invoke-WebRequest https://cygwin.com/setup-x86_64.exe -OutFile $env:WORKSPACE\cygwin-setup.exe } - - if [%COMPILER%]==[cygwin] %WORKSPACE%\cygwin-setup.exe --quiet-mode --no-shortcuts --no-startmenu --no-desktop --upgrade-also --root C:\cygwin64 --local-package-dir %WORKSPACE%\cygwin-downloads --packages gcc-g++,cmake,libopencv-devel,libncurses-devel - - ps: if($env:COMPILER -eq "cygwin") { Invoke-WebRequest https://github.com/Kitware/CMake/releases/download/v3.17.2/cmake-3.17.2.tar.gz -OutFile $env:WORKSPACE\cmake-3.17.2.tar.gz } - - if [%COMPILER%]==[cygwin] %CYGSH% 'tar zxvf cmake-3.17.2.tar.gz' - - if [%COMPILER%]==[cygwin] cd %WORKSPACE%\cmake-3.17.2 - - if [%COMPILER%]==[cygwin] %CYGSH% 'cmake .' - - if [%COMPILER%]==[cygwin] %CYGSH% 'make -j8' - - if [%COMPILER%]==[cygwin] %CYGSH% 'make install' - - if [%COMPILER%]==[cygwin] cd %WORKSPACE% - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] cd %VCPKG_ROOT% - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] git checkout . - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] git pull - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] .\bootstrap-vcpkg.bat - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] echo set(VCPKG_BUILD_TYPE release) >> triplets\%VCPKG_DEFAULT_TRIPLET%.cmake - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg upgrade --no-dry-run - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] if [%USE_CUDA%]==[yes] vcpkg install cuda --recurse - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg install stb pthreads --recurse - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg install ffmpeg --recurse - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] vcpkg install opencv[ffmpeg] --recurse ## opencv[ffmpeg,cuda] is too big to build, timing out (>1h). We use plain openCV also for CUDA builds (toolchain can manage this strange situation anyway) - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] rmdir "buildtrees" /S /Q - - cd %WORKSPACE%\darknet\ - - if [%COMPILER%]==[cygwin] mkdir build_debug && cd build_debug - - if [%COMPILER%]==[cygwin] %CYGSH% 'cmake .. -G "Unix Makefiles" -DCMAKE_BUILD_TYPE="Debug"' - - if [%COMPILER%]==[cygwin] cd .. - - mkdir build_release && cd build_release - - if [%COMPILER%]==[cygwin] %CYGSH% 'cmake .. -G "Unix Makefiles" -DCMAKE_BUILD_TYPE="Release"' - - if [%COMPILER%]==[vs] if NOT [%USE_INTEGRATED_LIBS%]==[yes] if [%configuration%]==[Release] cmake -G "Visual Studio 16 2019" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=%VCPKG_ROOT%\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=%VCPKG_DEFAULT_TRIPLET%" -DCMAKE_BUILD_TYPE="Release" .. - - if [%COMPILER%]==[vs] if [%USE_INTEGRATED_LIBS%]==[yes] if [%FORCE_CPP%]==[yes] cmake -G "Visual Studio 16 2019" -T "host=x64" -A "x64" -DCMAKE_BUILD_TYPE="Release" "-DBUILD_AS_CPP:BOOL=TRUE" .. - - if [%COMPILER%]==[vs] if [%USE_INTEGRATED_LIBS%]==[yes] cmake -G "Visual Studio 16 2019" -T "host=x64" -A "x64" -DCMAKE_BUILD_TYPE="Release" .. - - cd .. - -build_script: - - if [%COMPILER%]==[cygwin] cd build_debug && %CYGSH% 'cmake --build . --target install -- -j8' && cd .. - - if [%COMPILER%]==[cygwin] cd build_release && %CYGSH% 'cmake --build . --target install -- -j8' && cd .. - - if [%COMPILER%]==[vs] if [%configuration%]==[Release] cd build_release && cmake --build . --config Release --parallel 8 --target install && cd .. - -artifacts: - - path: lib - - path: '*.exe' From 502fd51cd15b2009aeb7467e578ed7d0fa735e59 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 5 May 2020 00:36:48 +0200 Subject: [PATCH 0962/1365] remove appveyor badge --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index d16119b376a..fdb120c5ce7 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,6 @@ More details: http://pjreddie.com/darknet/yolo/ ![Darknet Continuous Integration](https://github.com/AlexeyAB/darknet/workflows/Darknet%20Continuous%20Integration/badge.svg) [![CircleCI](https://circleci.com/gh/AlexeyAB/darknet.svg?style=svg)](https://circleci.com/gh/AlexeyAB/darknet) [![TravisCI](https://travis-ci.org/AlexeyAB/darknet.svg?branch=master)](https://travis-ci.org/AlexeyAB/darknet) -[![AppveyorCI](https://ci.appveyor.com/api/projects/status/594bwb5uoc1fxwiu/branch/master?svg=true)](https://ci.appveyor.com/project/AlexeyAB/darknet/branch/master) [![Contributors](https://img.shields.io/github/contributors/AlexeyAB/Darknet.svg)](https://github.com/AlexeyAB/darknet/graphs/contributors) [![License: Unlicense](https://img.shields.io/badge/license-Unlicense-blue.svg)](https://github.com/AlexeyAB/darknet/blob/master/LICENSE) [![DOI](https://zenodo.org/badge/75388965.svg)](https://zenodo.org/badge/latestdoi/75388965) From 08bc0c9373158da6c42f11b1359ca2c017cef1b5 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 6 May 2020 04:52:41 +0300 Subject: [PATCH 0963/1365] Fixed output of time_remaining for multi-GPU --- src/detector.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/detector.c b/src/detector.c index cf26107d1bc..d055d1ec50f 100644 --- a/src/detector.c +++ b/src/detector.c @@ -348,7 +348,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i draw_precision = 1; } - time_remaining = (net.max_batches - iteration)*(what_time_is_it_now() - time + load_time) / 60 / 60; + time_remaining = ((net.max_batches - iteration) / ngpus)*(what_time_is_it_now() - time + load_time) / 60 / 60; // set initial value, even if resume training from 10000 iteration if (avg_time < 0) avg_time = time_remaining; else avg_time = alpha_time * time_remaining + (1 - alpha_time) * avg_time; From 0fffb686fd568ff2a5a9da78a422f41e013913d4 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 7 May 2020 17:22:09 +0300 Subject: [PATCH 0964/1365] Added param [yolo] objectness_smooth=1 --- include/darknet.h | 1 + src/classifier.c | 2 +- src/darknet.c | 5 +++++ src/gaussian_yolo_layer.c | 32 ++++++++++++++++++++++---------- src/parser.c | 2 ++ src/yolo_layer.c | 30 +++++++++++++++++++++--------- 6 files changed, 52 insertions(+), 20 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 9b0ddeecbb0..07839ad665b 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -380,6 +380,7 @@ struct layer { float *weight_updates; float scale_x_y; + int objectness_smooth; float max_delta; float uc_normalizer; float iou_normalizer; diff --git a/src/classifier.c b/src/classifier.c index 653222283d8..cdffef5bfa1 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -187,7 +187,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, draw_precision = 1; } - time_remaining = (net.max_batches - i)*(what_time_is_it_now() - start) / 60 / 60; + time_remaining = ((net.max_batches - i) / ngpus) * (what_time_is_it_now() - start) / 60 / 60; // set initial value, even if resume training from 10000 iteration if (avg_time < 0) avg_time = time_remaining; else avg_time = alpha_time * time_remaining + (1 - alpha_time) * avg_time; diff --git a/src/darknet.c b/src/darknet.c index 93d88b1b4c7..92a9c193035 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -433,6 +433,11 @@ int main(int argc, char **argv) { #ifdef _DEBUG _CrtSetDbgFlag(_CRTDBG_ALLOC_MEM_DF | _CRTDBG_LEAK_CHECK_DF); + printf(" _DEBUG is used \n"); +#endif + +#ifdef DEBUG + printf(" DEBUG=1 \n"); #endif int i; diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index c6feb215e78..a6ef9c55e98 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -454,7 +454,10 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) for (j = 0; j < l.h; ++j) { for (i = 0; i < l.w; ++i) { for (n = 0; n < l.n; ++n) { - int box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0); + const int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9); + const int obj_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 8); + const int box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0); + const int stride = l.w*l.h; box pred = get_gaussian_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w*l.h, l.yolo_point); float best_match_iou = 0; int best_match_t = 0; @@ -471,8 +474,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) } if(!truth.x) break; - int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9); - int obj_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 8); + float objectness = l.output[obj_index]; int class_id_match = compare_gaussian_yolo_class(l.output, l.classes, class_index, l.w*l.h, objectness, class_id, 0.25f); @@ -486,15 +488,22 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) best_t = t; } } - int obj_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 8); + avg_anyobj += l.output[obj_index]; l.delta[obj_index] = l.cls_normalizer * (0 - l.output[obj_index]); if (best_match_iou > l.ignore_thresh) { - l.delta[obj_index] = 0; + const float iou_multiplier = best_match_iou*best_match_iou;// (best_match_iou - l.ignore_thresh) / (1.0 - l.ignore_thresh); + if (l.objectness_smooth) { + l.delta[obj_index] = l.cls_normalizer * (iou_multiplier - l.output[obj_index]); + + int class_id = state.truth[best_match_t*(4 + 1) + b*l.truths + 4]; + if (l.map) class_id = l.map[class_id]; + const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; + l.delta[class_index + stride*class_id] = class_multiplier * (iou_multiplier - l.output[class_index + stride*class_id]); + } + else l.delta[obj_index] = 0; } else if (state.net.adversarial) { - int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9); - int stride = l.w*l.h; float scale = pred.w * pred.h; if (scale > 0) scale = sqrt(scale); l.delta[obj_index] = scale * l.cls_normalizer * (0 - l.output[obj_index]); @@ -505,14 +514,17 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) } } if (best_iou > l.truth_thresh) { - l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); + const float iou_multiplier = best_iou*best_iou;// (best_iou - l.truth_thresh) / (1.0 - l.truth_thresh); + if (l.objectness_smooth) l.delta[obj_index] = l.cls_normalizer * (iou_multiplier - l.output[obj_index]); + else l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); + //l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); int class_id = state.truth[best_t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; - int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9); delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.label_smooth_eps, l.classes_multipliers); - box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; + if (l.objectness_smooth) l.delta[class_index + stride*class_id] = class_multiplier * (iou_multiplier - l.output[class_index + stride*class_id]); + box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); delta_gaussian_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, l.uc_normalizer, 1, l.yolo_point, l.max_delta); } } diff --git a/src/parser.c b/src/parser.c index 1337836317f..994361566e2 100644 --- a/src/parser.c +++ b/src/parser.c @@ -413,6 +413,7 @@ layer parse_yolo(list *options, size_params params) l.label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1); + l.objectness_smooth = option_find_int_quiet(options, "objectness_smooth", 0); l.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10 l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75); l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1); @@ -519,6 +520,7 @@ layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3 l.label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1); + l.objectness_smooth = option_find_int_quiet(options, "objectness_smooth", 0); l.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10 l.uc_normalizer = option_find_float_quiet(options, "uc_normalizer", 1.0); l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 3d8b98a6b88..486b17aafbe 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -365,7 +365,10 @@ void forward_yolo_layer(const layer l, network_state state) for (j = 0; j < l.h; ++j) { for (i = 0; i < l.w; ++i) { for (n = 0; n < l.n; ++n) { - int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); + const int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); + const int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4); + const int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); + const int stride = l.w*l.h; box pred = get_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w*l.h); float best_match_iou = 0; int best_match_t = 0; @@ -382,8 +385,6 @@ void forward_yolo_layer(const layer l, network_state state) } if (!truth.x) break; // continue; - int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); - int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4); float objectness = l.output[obj_index]; if (isnan(objectness) || isinf(objectness)) l.output[obj_index] = 0; int class_id_match = compare_yolo_class(l.output, l.classes, class_index, l.w*l.h, objectness, class_id, 0.25f); @@ -398,14 +399,22 @@ void forward_yolo_layer(const layer l, network_state state) best_t = t; } } - int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4); + avg_anyobj += l.output[obj_index]; l.delta[obj_index] = l.cls_normalizer * (0 - l.output[obj_index]); if (best_match_iou > l.ignore_thresh) { - l.delta[obj_index] = 0; + const float iou_multiplier = best_match_iou*best_match_iou;// (best_match_iou - l.ignore_thresh) / (1.0 - l.ignore_thresh); + if (l.objectness_smooth) { + l.delta[obj_index] = l.cls_normalizer * (iou_multiplier - l.output[obj_index]); + + int class_id = state.truth[best_match_t*(4 + 1) + b*l.truths + 4]; + if (l.map) class_id = l.map[class_id]; + const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; + l.delta[class_index + stride*class_id] = class_multiplier * (iou_multiplier - l.output[class_index + stride*class_id]); + } + else l.delta[obj_index] = 0; } else if (state.net.adversarial) { - int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); int stride = l.w*l.h; float scale = pred.w * pred.h; if (scale > 0) scale = sqrt(scale); @@ -417,14 +426,17 @@ void forward_yolo_layer(const layer l, network_state state) } } if (best_iou > l.truth_thresh) { - l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); + const float iou_multiplier = best_iou*best_iou;// (best_iou - l.truth_thresh) / (1.0 - l.truth_thresh); + if (l.objectness_smooth) l.delta[obj_index] = l.cls_normalizer * (iou_multiplier - l.output[obj_index]); + else l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); + //l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); int class_id = state.truth[best_t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; - int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); - box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; + if (l.objectness_smooth) l.delta[class_index + stride*class_id] = class_multiplier * (iou_multiplier - l.output[class_index + stride*class_id]); + box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta); } } From 1d9587046831d088ef52ad38cc9cf67154944990 Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 7 May 2020 17:53:05 +0300 Subject: [PATCH 0965/1365] Update Readme.md --- README.md | 31 ++++++------------------------- 1 file changed, 6 insertions(+), 25 deletions(-) diff --git a/README.md b/README.md index fdb120c5ce7..70ab6312fc8 100644 --- a/README.md +++ b/README.md @@ -5,12 +5,13 @@ Paper Yolo v4: https://arxiv.org/abs/2004.10934 More details: http://pjreddie.com/darknet/yolo/ -![Darknet Continuous Integration](https://github.com/AlexeyAB/darknet/workflows/Darknet%20Continuous%20Integration/badge.svg) +[![Darknet Continuous Integration](https://github.com/AlexeyAB/darknet/workflows/Darknet%20Continuous%20Integration/badge.svg)](https://github.com/AlexeyAB/darknet/actions?query=workflow%3A%22Darknet+Continuous+Integration%22) [![CircleCI](https://circleci.com/gh/AlexeyAB/darknet.svg?style=svg)](https://circleci.com/gh/AlexeyAB/darknet) [![TravisCI](https://travis-ci.org/AlexeyAB/darknet.svg?branch=master)](https://travis-ci.org/AlexeyAB/darknet) [![Contributors](https://img.shields.io/github/contributors/AlexeyAB/Darknet.svg)](https://github.com/AlexeyAB/darknet/graphs/contributors) [![License: Unlicense](https://img.shields.io/badge/license-Unlicense-blue.svg)](https://github.com/AlexeyAB/darknet/blob/master/LICENSE) [![DOI](https://zenodo.org/badge/75388965.svg)](https://zenodo.org/badge/latestdoi/75388965) +[![arxiv.org](http://img.shields.io/badge/cs.CV-arXiv%3A2004.10934-B31B1B.svg)](https://arxiv.org/abs/2004.10934) * [Requirements (and how to install dependecies)](#requirements) @@ -382,7 +383,7 @@ Training Yolo v4 (and v3): * change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L3) * change line subdivisions to [`subdivisions=16`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4) - * change line max_batches to (`classes*2000` but not less than `4000`), f.e. [`max_batches=6000`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L20) if you train for 3 classes + * change line max_batches to (`classes*2000` but not less than number of training images, and not less than `6000`), f.e. [`max_batches=6000`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L20) if you train for 3 classes * change line steps to 80% and 90% of max_batches, f.e. [`steps=4800,5400`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L22) * set network size `width=416 height=416` or any value multiple of 32: https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L8-L9 * change line `classes=80` to your number of objects in each of 3 `[yolo]`-layers: @@ -504,7 +505,7 @@ If you made you custom model that isn't based on other models, then you can trai ## When should I stop training: -Usually sufficient 2000 iterations for each class(object), but not less than 4000 iterations in total. But for a more precise definition when you should stop training, use the following manual: +Usually sufficient 2000 iterations for each class(object), but not less than number of training images and not less than 6000 iterations in total. But for a more precise definition when you should stop training, use the following manual: 1. During training, you will see varying indicators of error, and you should stop when no longer decreases **0.XXXXXXX avg**: @@ -518,6 +519,8 @@ Usually sufficient 2000 iterations for each class(object), but not less than 400 * **0.60730 avg** - average loss (error) - **the lower, the better** When you see that average loss **0.xxxxxx avg** no longer decreases at many iterations then you should stop training. The final avgerage loss can be from `0.05` (for a small model and easy dataset) to `3.0` (for a big model and a difficult dataset). + + Or if you train with flag `-map` then you will see mAP indicator `Last accuracy mAP@0.5 = 18.50%` in the console - this indicator is better than Loss, so train while mAP increases. 2. Once training is stopped, you should take some of last `.weights`-files from `darknet\build\darknet\x64\backup` and choose the best of them: @@ -646,28 +649,6 @@ Different tools for marking objects in images: 5. in JavaScript: https://github.com/opencv/cvat -## Using Yolo9000 - - Simultaneous detection and classification of 9000 objects: `darknet.exe detector test cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights data/dog.jpg` - -* `yolo9000.weights` - (186 MB Yolo9000 Model) requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo9000.weights - -* `yolo9000.cfg` - cfg-file of the Yolo9000, also there are paths to the `9k.tree` and `coco9k.map` https://github.com/AlexeyAB/darknet/blob/617cf313ccb1fe005db3f7d88dec04a04bd97cc2/cfg/yolo9000.cfg#L217-L218 - - * `9k.tree` - **WordTree** of 9418 categories - `
CLICK ME - Yolo v2 models From feaa5f62f4fc0828f2a773b97a293386c9942187 Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 25 Jun 2020 17:03:31 +0300 Subject: [PATCH 1045/1365] Update Readme.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c870ebf87e5..c047100d438 100644 --- a/README.md +++ b/README.md @@ -116,7 +116,7 @@ FPS on RTX 2070 (R) and Tesla V100 (V): * `width=416 height=416` in cfg: **62.8% mAP@0.5 (41.2% AP@0.5:0.95) - 55(R) FPS / 96(V) FPS** - 60.1 BFlops * `width=320 height=320` in cfg: **60% mAP@0.5 ( 38% AP@0.5:0.95) - 63(R) FPS / 123(V) FPS** - 35.5 BFlops -* [yolov4-tiny.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny-prn.cfg) - **33.1% mAP@0.5 - 371(1080Ti) FPS / 300(RTX2070) FPS** - 6.9 BFlops - 23.1 MB: [yolov4-tiny.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights) +* [yolov4-tiny.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny.cfg) - **33.1% mAP@0.5 - 371(1080Ti) FPS / 300(RTX2070) FPS** - 6.9 BFlops - 23.1 MB: [yolov4-tiny.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights) * [enet-coco.cfg (EfficientNetB0-Yolov3)](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/enet-coco.cfg) - **45.5% mAP@0.5 - 55(R) FPS** - 3.7 BFlops - 18.3 MB: [enetb0-coco_final.weights](https://drive.google.com/file/d/1FlHeQjWEQVJt0ay1PVsiuuMzmtNyv36m/view) From c044b98c645a8e3a41670fe3ae5666d4b7e5625f Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 25 Jun 2020 17:04:28 +0300 Subject: [PATCH 1046/1365] Minor fix --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c047100d438..9a79d4c7472 100644 --- a/README.md +++ b/README.md @@ -116,7 +116,7 @@ FPS on RTX 2070 (R) and Tesla V100 (V): * `width=416 height=416` in cfg: **62.8% mAP@0.5 (41.2% AP@0.5:0.95) - 55(R) FPS / 96(V) FPS** - 60.1 BFlops * `width=320 height=320` in cfg: **60% mAP@0.5 ( 38% AP@0.5:0.95) - 63(R) FPS / 123(V) FPS** - 35.5 BFlops -* [yolov4-tiny.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny.cfg) - **33.1% mAP@0.5 - 371(1080Ti) FPS / 300(RTX2070) FPS** - 6.9 BFlops - 23.1 MB: [yolov4-tiny.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights) +* [yolov4-tiny.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny.cfg) - **40.2% mAP@0.5 - 371(1080Ti) FPS / 300(RTX2070) FPS** - 6.9 BFlops - 23.1 MB: [yolov4-tiny.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights) * [enet-coco.cfg (EfficientNetB0-Yolov3)](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/enet-coco.cfg) - **45.5% mAP@0.5 - 55(R) FPS** - 3.7 BFlops - 18.3 MB: [enetb0-coco_final.weights](https://drive.google.com/file/d/1FlHeQjWEQVJt0ay1PVsiuuMzmtNyv36m/view) From dfb3773e7b467b6408943d43715d34da87c97c9e Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 25 Jun 2020 17:11:41 +0300 Subject: [PATCH 1047/1365] Minor fix --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9a79d4c7472..cc575da5afd 100644 --- a/README.md +++ b/README.md @@ -116,7 +116,7 @@ FPS on RTX 2070 (R) and Tesla V100 (V): * `width=416 height=416` in cfg: **62.8% mAP@0.5 (41.2% AP@0.5:0.95) - 55(R) FPS / 96(V) FPS** - 60.1 BFlops * `width=320 height=320` in cfg: **60% mAP@0.5 ( 38% AP@0.5:0.95) - 63(R) FPS / 123(V) FPS** - 35.5 BFlops -* [yolov4-tiny.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny.cfg) - **40.2% mAP@0.5 - 371(1080Ti) FPS / 300(RTX2070) FPS** - 6.9 BFlops - 23.1 MB: [yolov4-tiny.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights) +* [yolov4-tiny.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny.cfg) - **40.2% mAP@0.5 - 371(1080Ti) FPS / 330(RTX2070) FPS** - 6.9 BFlops - 23.1 MB: [yolov4-tiny.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights) * [enet-coco.cfg (EfficientNetB0-Yolov3)](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/enet-coco.cfg) - **45.5% mAP@0.5 - 55(R) FPS** - 3.7 BFlops - 18.3 MB: [enetb0-coco_final.weights](https://drive.google.com/file/d/1FlHeQjWEQVJt0ay1PVsiuuMzmtNyv36m/view) From dfbf42cfc05ed8a56dea2f37bb1ec5b1aa51f836 Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 25 Jun 2020 17:32:14 +0300 Subject: [PATCH 1048/1365] How to train tiny-yolo --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index cc575da5afd..5d6f777cdc5 100644 --- a/README.md +++ b/README.md @@ -521,10 +521,10 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an ### How to train tiny-yolo (to detect your custom objects): Do all the same steps as for the full yolo model as described above. With the exception of: -* Download default weights file for yolov3-tiny: https://pjreddie.com/media/files/yolov3-tiny.weights -* Get pre-trained weights `yolov3-tiny.conv.15` using command: `darknet.exe partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15` -* Make your custom model `yolov3-tiny-obj.cfg` based on `cfg/yolov3-tiny_obj.cfg` instead of `yolov3.cfg` -* Start training: `darknet.exe detector train data/obj.data yolov3-tiny-obj.cfg yolov3-tiny.conv.15` +* Download file with the first 29-convolutional layers of yolov4-tiny: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29 + (Or get this file from yolov4-tiny.weights file by using command: `darknet.exe partial cfg/yolov5-tiny.cfg yolov5-tiny.weights yolov5-tiny.conv.29 29` +* Make your custom model `yolov4-tiny-obj.cfg` based on `cfg/yolov4-tiny.cfg` instead of `yolov4.cfg` +* Start training: `darknet.exe detector train data/obj.data yolov4-tiny-obj.cfg yolov4-tiny.conv.29` For training Yolo based on other models ([DenseNet201-Yolo](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/densenet201_yolo.cfg) or [ResNet50-Yolo](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/resnet50_yolo.cfg)), you can download and get pre-trained weights as showed in this file: https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/partial.cmd If you made you custom model that isn't based on other models, then you can train it without pre-trained weights, then will be used random initial weights. From a71b9a6e9a009cf94900c53deb344c5204835700 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 26 Jun 2020 01:00:11 +0300 Subject: [PATCH 1049/1365] max_delta= affects counters_per_class= --- build/darknet/x64/partial.cmd | 1 + src/parser.c | 21 +++++++++++++-------- src/yolo_layer.c | 4 +++- 3 files changed, 17 insertions(+), 9 deletions(-) diff --git a/build/darknet/x64/partial.cmd b/build/darknet/x64/partial.cmd index f6225156cd1..26a701a9c40 100644 --- a/build/darknet/x64/partial.cmd +++ b/build/darknet/x64/partial.cmd @@ -5,6 +5,7 @@ rem Download Yolo9000: http://pjreddie.com/media/files/yolo9000.weights rem darknet.exe partial cfg/tiny-yolo-voc.cfg tiny-yolo-voc.weights tiny-yolo-voc.conv.13 13 +darknet.exe partial cfg/yolov4-tiny.cfg yolov4-tiny.weights yolov4-tiny.conv.29 29 darknet.exe partial cfg/yolov4-sam-mish.cfg cfg/yolov4-sam-mish.weights cfg/yolov4-sam-mish.conv.137 137 diff --git a/src/parser.c b/src/parser.c index 23c28383983..58a6ee3ff34 100644 --- a/src/parser.c +++ b/src/parser.c @@ -386,7 +386,7 @@ int *parse_yolo_mask(char *a, int *num) return mask; } -float *get_classes_multipliers(char *cpc, const int classes) +float *get_classes_multipliers(char *cpc, const int classes, const float max_delta) { float *classes_multipliers = NULL; if (cpc) { @@ -398,9 +398,15 @@ float *get_classes_multipliers(char *cpc, const int classes) } float max_counter = 0; int i; - for (i = 0; i < classes_counters; ++i) if (max_counter < counters_per_class[i]) max_counter = counters_per_class[i]; + for (i = 0; i < classes_counters; ++i) { + if (counters_per_class[i] < 1) counters_per_class[i] = 1; + if (max_counter < counters_per_class[i]) max_counter = counters_per_class[i]; + } classes_multipliers = (float *)calloc(classes_counters, sizeof(float)); - for (i = 0; i < classes_counters; ++i) classes_multipliers[i] = max_counter / counters_per_class[i]; + for (i = 0; i < classes_counters; ++i) { + classes_multipliers[i] = max_counter / counters_per_class[i]; + if(classes_multipliers[i] > max_delta) classes_multipliers[i] = max_delta; + } free(counters_per_class); printf(" classes_multipliers: "); for (i = 0; i < classes_counters; ++i) printf("%.1f, ", classes_multipliers[i]); @@ -425,13 +431,13 @@ layer parse_yolo(list *options, size_params params) } //assert(l.outputs == params.inputs); + l.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10 char *cpc = option_find_str(options, "counters_per_class", 0); - l.classes_multipliers = get_classes_multipliers(cpc, classes); + l.classes_multipliers = get_classes_multipliers(cpc, classes, l.max_delta); l.label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1); l.objectness_smooth = option_find_int_quiet(options, "objectness_smooth", 0); - l.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10 l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75); l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1); char *iou_loss = option_find_str_quiet(options, "iou_loss", "mse"); // "iou"); @@ -534,14 +540,13 @@ layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3 exit(EXIT_FAILURE); } //assert(l.outputs == params.inputs); - + l.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10 char *cpc = option_find_str(options, "counters_per_class", 0); - l.classes_multipliers = get_classes_multipliers(cpc, classes); + l.classes_multipliers = get_classes_multipliers(cpc, classes, l.max_delta); l.label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1); l.objectness_smooth = option_find_int_quiet(options, "objectness_smooth", 0); - l.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10 l.uc_normalizer = option_find_float_quiet(options, "uc_normalizer", 1.0); l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75); l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1.0); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index be56972c8bc..1f609f7a7f6 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -416,7 +416,9 @@ void forward_yolo_layer(const layer l, network_state state) int class_id = state.truth[best_match_t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; - delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); + if (class_id < l.classes && class_id >= 0) { + delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); + } } else l.delta[obj_index] = 0; } From 9b3fdb0a07a291ff882db65f41e4dba8a074a2a2 Mon Sep 17 00:00:00 2001 From: Alexey Date: Fri, 26 Jun 2020 20:19:19 +0300 Subject: [PATCH 1050/1365] Update Readme.md --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index 5d6f777cdc5..21877ca8386 100644 --- a/README.md +++ b/README.md @@ -5,6 +5,8 @@ Paper Yolo v4: https://arxiv.org/abs/2004.10934 More details: [medium link](https://medium.com/@alexeyab84/yolov4-the-most-accurate-real-time-neural-network-on-ms-coco-dataset-73adfd3602fe?source=friends_link&sk=6039748846bbcf1d960c3061542591d7) +Discussion: [Reddit](https://www.reddit.com/r/MachineLearning/comments/gydxzd/p_yolov4_the_most_accurate_realtime_neural/) + About Darknet framework: http://pjreddie.com/darknet/ [![Darknet Continuous Integration](https://github.com/AlexeyAB/darknet/workflows/Darknet%20Continuous%20Integration/badge.svg)](https://github.com/AlexeyAB/darknet/actions?query=workflow%3A%22Darknet+Continuous+Integration%22) @@ -14,6 +16,8 @@ About Darknet framework: http://pjreddie.com/darknet/ [![License: Unlicense](https://img.shields.io/badge/license-Unlicense-blue.svg)](https://github.com/AlexeyAB/darknet/blob/master/LICENSE) [![DOI](https://zenodo.org/badge/75388965.svg)](https://zenodo.org/badge/latestdoi/75388965) [![arxiv.org](http://img.shields.io/badge/cs.CV-arXiv%3A2004.10934-B31B1B.svg)](https://arxiv.org/abs/2004.10934) +[![colab](https://user-images.githubusercontent.com/4096485/85883812-41a84b00-b7ea-11ea-9559-a147cc410d61.png)](https://colab.research.google.com/drive/12QusaaRj_lUwCGDvQNfICpa7kA7_a2dE) + * [YOLOv4 model zoo](https://github.com/AlexeyAB/darknet/wiki/YOLOv4-model-zoo) * [Requirements (and how to install dependecies)](#requirements) From 320e6fd8d29f6f7825ef668f15f955f90131f782 Mon Sep 17 00:00:00 2001 From: Alexey Date: Sat, 27 Jun 2020 16:06:09 +0300 Subject: [PATCH 1051/1365] Minor fix --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 21877ca8386..d49102cc4e2 100644 --- a/README.md +++ b/README.md @@ -526,7 +526,7 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an Do all the same steps as for the full yolo model as described above. With the exception of: * Download file with the first 29-convolutional layers of yolov4-tiny: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29 - (Or get this file from yolov4-tiny.weights file by using command: `darknet.exe partial cfg/yolov5-tiny.cfg yolov5-tiny.weights yolov5-tiny.conv.29 29` + (Or get this file from yolov4-tiny.weights file by using command: `darknet.exe partial cfg/yolov4-tiny.cfg yolov4-tiny.weights yolov4-tiny.conv.29 29` * Make your custom model `yolov4-tiny-obj.cfg` based on `cfg/yolov4-tiny.cfg` instead of `yolov4.cfg` * Start training: `darknet.exe detector train data/obj.data yolov4-tiny-obj.cfg yolov4-tiny.conv.29` From 3af6781de273fa01f4a535c8e40c5056e809e22f Mon Sep 17 00:00:00 2001 From: Alexey Date: Tue, 30 Jun 2020 23:32:13 +0300 Subject: [PATCH 1052/1365] Update Readme.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index d49102cc4e2..457f8f39040 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,8 @@ About Darknet framework: http://pjreddie.com/darknet/ [![License: Unlicense](https://img.shields.io/badge/license-Unlicense-blue.svg)](https://github.com/AlexeyAB/darknet/blob/master/LICENSE) [![DOI](https://zenodo.org/badge/75388965.svg)](https://zenodo.org/badge/latestdoi/75388965) [![arxiv.org](http://img.shields.io/badge/cs.CV-arXiv%3A2004.10934-B31B1B.svg)](https://arxiv.org/abs/2004.10934) -[![colab](https://user-images.githubusercontent.com/4096485/85883812-41a84b00-b7ea-11ea-9559-a147cc410d61.png)](https://colab.research.google.com/drive/12QusaaRj_lUwCGDvQNfICpa7kA7_a2dE) +[![colab](https://user-images.githubusercontent.com/4096485/86174089-b2709f80-bb29-11ea-9faf-3d8dc668a1a5.png)](https://colab.research.google.com/drive/12QusaaRj_lUwCGDvQNfICpa7kA7_a2dE) +[![colab](https://user-images.githubusercontent.com/4096485/86174097-b56b9000-bb29-11ea-9240-c17f6bacfc34.png)](https://colab.research.google.com/drive/1_GdoqCJWXsChrOiY8sZMr_zbr_fH-0Fg) * [YOLOv4 model zoo](https://github.com/AlexeyAB/darknet/wiki/YOLOv4-model-zoo) From f7c13cf84aa616c40db929273c8964756ef76d81 Mon Sep 17 00:00:00 2001 From: Muhammad Maaz Date: Sat, 4 Jul 2020 17:20:48 +0500 Subject: [PATCH 1053/1365] Updates README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 457f8f39040..629bf98a90f 100644 --- a/README.md +++ b/README.md @@ -173,7 +173,7 @@ You can get cfg-files by path: `darknet/cfg/` * **TensorFlow:** YOLOv4 on TensorFlow 2.0 / TFlite / Andriod: https://github.com/hunglc007/tensorflow-yolov4-tflite For YOLOv3 - convert `yolov3.weights`/`cfg` files to `yolov3.ckpt`/`pb/meta`: by using [mystic123](https://github.com/mystic123/tensorflow-yolo-v3) project, and [TensorFlow-lite](https://www.tensorflow.org/lite/guide/get_started#2_convert_the_model_format) -* **OpenCV-dnn** the fastest implementation of YOLOv4 for CPU (x86/ARM-Android), OpenCV can be compiled with [OpenVINO-backend](https://github.com/opencv/opencv/wiki/Intel's-Deep-Learning-Inference-Engine-backend) for running on (Myriad X / USB Neural Compute Stick / Arria FPGA), use `yolov3.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221) or [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) +* **OpenCV-dnn** the fastest implementation of YOLOv4 for CPU (x86/ARM-Android), OpenCV can be compiled with [OpenVINO-backend](https://github.com/opencv/opencv/wiki/Intel's-Deep-Learning-Inference-Engine-backend) for running on (Myriad X / USB Neural Compute Stick / Arria FPGA), use `yolov4.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221) or [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) * **Intel OpenVINO 2019 R1:** (Myriad X / USB Neural Compute Stick / Arria FPGA): read this [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) * **Tencent/ncnn:** the fastest inference of YOLOv4 on mobile phone CPU: https://github.com/Tencent/ncnn * **PyTorch > ONNX > CoreML > iOS** how to convert cfg/weights-files to pt-file: [ultralytics/yolov3](https://github.com/ultralytics/yolov3#darknet-conversion) and [iOS App](https://itunes.apple.com/app/id1452689527) From f13532ce60f92859713cfa07cae24b0319e78bcf Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 6 Jul 2020 01:50:44 +0300 Subject: [PATCH 1054/1365] Contrastive learning for training Detector to track objects --- include/darknet.h | 17 +- src/blas.c | 175 +++++++++++++++++++- src/blas.h | 14 +- src/blas_kernels.cu | 10 +- src/classifier.c | 2 +- src/convolutional_kernels.cu | 13 +- src/data.c | 110 ++++++++----- src/data.h | 4 +- src/detector.c | 19 ++- src/image_opencv.cpp | 37 +++-- src/image_opencv.h | 4 +- src/layer.c | 4 +- src/network_kernels.cu | 2 +- src/parser.c | 21 ++- src/softmax_layer.c | 307 ++++++++++++++++++++++++++++------- src/softmax_layer.h | 2 +- src/utils.c | 11 ++ src/utils.h | 1 + src/yolo_layer.c | 32 ++-- 19 files changed, 629 insertions(+), 156 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 7b6f9f6e68c..a25e30010ee 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -142,6 +142,15 @@ typedef enum{ MULT, ADD, SUB, DIV } BINARY_ACTIVATION; +// blas.h +typedef struct contrastive_params { + float sim; + float P; + size_t i, j; + int time_step_i, time_step_j; +} contrastive_params; + + // layer.h typedef enum { CONVOLUTIONAL, @@ -234,6 +243,7 @@ struct layer { int out_h, out_w, out_c; int n; int max_boxes; + int truth_size; int groups; int group_id; int size; @@ -288,6 +298,8 @@ struct layer { int noloss; int softmax; int classes; + int detection; + int embedding_size; int coords; int background; int rescore; @@ -367,6 +379,8 @@ struct layer { int *labels; float *cos_sim; float *p_constrastive; + contrastive_params *contrast_p; + int *contrast_p_size; float * state; float * prev_state; float * forgot_state; @@ -894,6 +908,7 @@ typedef struct load_args { int nh; int nw; int num_boxes; + int truth_size; int min, max, size; int classes; int background; @@ -930,6 +945,7 @@ typedef struct load_args { // data.h typedef struct box_label { int id; + int track_id; float x, y, w, h; float left, right, top, bottom; } box_label; @@ -947,7 +963,6 @@ typedef struct box_label { // node *front; // node *back; //} list; - // ----------------------------------------------------- diff --git a/src/blas.c b/src/blas.c index 7bfc7528c83..6a36499f5f1 100644 --- a/src/blas.c +++ b/src/blas.c @@ -548,8 +548,163 @@ float cosine_similarity(float *A, float *B, unsigned int feature_size) return similarity; } +int check_sim(size_t i, size_t j, contrastive_params *contrast_p, int contrast_p_size) +{ + size_t z; + for (z = 0; z < contrast_p_size; ++z) { + if (contrast_p[z].i == i && contrast_p[z].j == j) break; + } + if (z == contrast_p_size) { + return 0; // not found + } + + return 1; // found +} + +float find_sim(size_t i, size_t j, contrastive_params *contrast_p, int contrast_p_size) +{ + size_t z; + for (z = 0; z < contrast_p_size; ++z) { + if (contrast_p[z].i == i && contrast_p[z].j == j) break; + } + if (z == contrast_p_size) { + printf(" Error: find_sim(): sim isn't found: i = %d, j = %d, z = %d \n", i, j, z); + getchar(); + } + + return contrast_p[z].sim; +} + +float find_P_constrastive(size_t i, size_t j, contrastive_params *contrast_p, int contrast_p_size) +{ + size_t z; + for (z = 0; z < contrast_p_size; ++z) { + if (contrast_p[z].i == i && contrast_p[z].j == j) break; + } + if (z == contrast_p_size) { + printf(" Error: find_P_constrastive(): P isn't found: i = %d, j = %d, z = %d \n", i, j, z); + getchar(); + } + + return contrast_p[z].P; +} + +// num_of_samples = 2 * loaded_images = mini_batch_size +float P_constrastive_f(size_t i, size_t l, int *labels, float **z, unsigned int feature_size, float temperature, contrastive_params *contrast_p, int contrast_p_size) +{ + if (i == l) { + fprintf(stderr, " Error: in P_constrastive must be i != l, while i = %d, l = %d \n", i, l); + getchar(); + } + + const float sim = find_sim(i, l, contrast_p, contrast_p_size); // cosine_similarity(z[i], z[l], feature_size); + const float numerator = expf(sim / temperature); + + float denominator = 0; + int k; + for (k = 0; k < contrast_p_size; ++k) { + contrastive_params cp = contrast_p[k]; + //if (k != i && labels[k] != labels[i]) { + //if (k != i) { + if (cp.i != i && cp.j == l) { + const float sim_den = cp.sim; + //const float sim_den = find_sim(k, l, contrast_p, contrast_p_size); // cosine_similarity(z[k], z[l], feature_size); + denominator += expf(sim_den / temperature); + } + } + + float result = numerator / denominator; + if (denominator == 0) result = 1; + if (result > 1) result = 0.9999; + return result; +} + +void grad_contrastive_loss_positive_f(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, contrastive_params *contrast_p, int contrast_p_size) +{ + const float vec_len = math_vector_length(z[i], feature_size); + size_t j; + float N = 0; + for (j = 0; j < num_of_samples; ++j) { + if (labels[i] == labels[j] && labels[i] > 0) N++; + } + if (N == 0 || temperature == 0 || vec_len == 0) { + fprintf(stderr, " Error: N == 0 || temperature == 0 || vec_len == 0. N=%f, temperature=%f, vec_len=%f \n", N, temperature, vec_len); + getchar(); + } + const float mult = 1 / ((N - 1) * temperature * vec_len); + + for (j = 0; j < num_of_samples; ++j) { + //if (i != j && (i/2) == (j/2)) { + if (i != j && labels[i] == labels[j] && labels[i] > 0) { + //printf(" i = %d, j = %d, num_of_samples = %d, labels[i] = %d, labels[j] = %d \n", + // i, j, num_of_samples, labels[i], labels[j]); + if (!check_sim(i, j, contrast_p, contrast_p_size)) continue; + const float sim = find_sim(i, j, contrast_p, contrast_p_size); //cos_sim[i*num_of_samples + j]; // cosine_similarity(z[i], z[j], feature_size); + const float P = find_P_constrastive(i, j, contrast_p, contrast_p_size); //p_constrastive[i*num_of_samples + j]; // P_constrastive(i, j, labels, num_of_samples, z, feature_size, temperature, cos_sim); + //const float custom_pos_mult = 1 - sim; + + + int m; + //const float d = mult*(sim * z[i][m] - z[j][m]) * (1 - P); // 1 + for (m = 0; m < feature_size; ++m) { + //const float d = mult*(sim * z[j][m] - z[j][m]) * (1 - P); // my + //const float d = mult*(sim * z[i][m] + sim * z[j][m] - z[j][m]) *(1 - P); // 1+2 + const float d = mult*(sim * z[i][m] - z[j][m]) *(1 - P); // 1 (70%) + //const float d = mult*(sim * z[j][m] - z[j][m]) * (1 - P); // 2 + // printf(" pos: z[j][m] = %f, z[i][m] = %f, d = %f, sim = %f \n", z[j][m], z[i][m], d, sim); + delta[m] -= d; + } + } + } +} + +void grad_contrastive_loss_negative_f(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, contrastive_params *contrast_p, int contrast_p_size) +{ + const float vec_len = math_vector_length(z[i], feature_size); + size_t j; + float N = 0; + for (j = 0; j < num_of_samples; ++j) { + if (labels[i] == labels[j] && labels[i] > 0) N++; + } + if (N == 0 || temperature == 0 || vec_len == 0) { + fprintf(stderr, " Error: N == 0 || temperature == 0 || vec_len == 0. N=%f, temperature=%f, vec_len=%f \n", N, temperature, vec_len); + getchar(); + } + const float mult = 1 / ((N - 1) * temperature * vec_len); + + for (j = 0; j < num_of_samples; ++j) { + //if (i != j && (i/2) == (j/2)) { + if (i != j && labels[i] == labels[j] && labels[i] > 0) { + + size_t k; + for (k = 0; k < num_of_samples; ++k) { + //if (k != i && k != j && labels[k] != labels[i]) { + if (k != i && k != j && labels[k] >= 0) { + if (!check_sim(i, k, contrast_p, contrast_p_size)) continue; + const float sim = find_sim(i, k, contrast_p, contrast_p_size); //cos_sim[i*num_of_samples + k]; // cosine_similarity(z[i], z[k], feature_size); + const float P = find_P_constrastive(i, k, contrast_p, contrast_p_size); //p_constrastive[i*num_of_samples + k]; // P_constrastive(i, k, labels, num_of_samples, z, feature_size, temperature, cos_sim); + //const float custom_pos_mult = 1 + sim; + + int m; + //const float d = mult*(z[k][m] + sim * z[i][m]) * P; // my1 + for (m = 0; m < feature_size; ++m) { + //const float d = mult*(z[k][m] + sim * z[i][m]) * P; // 1 (70%) + //const float d = mult*(z[k][m] - sim * z[k][m] - sim * z[i][m]) * P; // 1+2 + const float d = mult*(z[k][m] - sim * z[i][m]) * P; // 1 (70%) + //const float d = mult*(z[k][m] - sim * z[k][m]) * P; // 2 + //printf(" neg: z[k][m] = %f, z[i][m] = %f, d = %f, sim = %f \n", z[k][m], z[i][m], d, sim); + delta[m] -= d; + } + } + } + } + } +} + + + // num_of_samples = 2 * loaded_images = mini_batch_size -float P_constrastive(int i, int l, int *labels, int num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim) +float P_constrastive(size_t i, size_t l, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim) { if (i == l) { fprintf(stderr, " Error: in P_constrastive must be i != l, while i = %d, l = %d \n", i, l); @@ -578,11 +733,11 @@ float P_constrastive(int i, int l, int *labels, int num_of_samples, float **z, u // z[feature_size][num_of_samples] - array of arrays with contrastive features (output of conv-layer, f.e. 128 floats for each sample) // delta[feature_size] - array with deltas for backpropagation // temperature - scalar temperature param (temperature > 0), f.e. temperature = 0.07: Supervised Contrastive Learning -void grad_contrastive_loss_positive(int i, int *labels, int num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *p_constrastive, float *delta) +void grad_contrastive_loss_positive(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *p_constrastive, float *delta) { const float vec_len = math_vector_length(z[i], feature_size); - int j; - int N = 0; + size_t j; + float N = 0; for (j = 0; j < num_of_samples; ++j) { if (labels[i] == labels[j]) N++; } @@ -595,6 +750,8 @@ void grad_contrastive_loss_positive(int i, int *labels, int num_of_samples, floa for (j = 0; j < num_of_samples; ++j) { //if (i != j && (i/2) == (j/2)) { if (i != j && labels[i] == labels[j]) { + //printf(" i = %d, j = %d, num_of_samples = %d, labels[i] = %d, labels[j] = %d \n", + // i, j, num_of_samples, labels[i], labels[j]); const float sim = cos_sim[i*num_of_samples + j]; // cosine_similarity(z[i], z[j], feature_size); const float P = p_constrastive[i*num_of_samples + j]; // P_constrastive(i, j, labels, num_of_samples, z, feature_size, temperature, cos_sim); //const float custom_pos_mult = 1 - sim; @@ -615,11 +772,11 @@ void grad_contrastive_loss_positive(int i, int *labels, int num_of_samples, floa // z[feature_size][num_of_samples] - array of arrays with contrastive features (output of conv-layer, f.e. 128 floats for each sample) // delta[feature_size] - array with deltas for backpropagation // temperature - scalar temperature param (temperature > 0), f.e. temperature = 0.07: Supervised Contrastive Learning -void grad_contrastive_loss_negative(int i, int *labels, int num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *p_constrastive, float *delta) +void grad_contrastive_loss_negative(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *p_constrastive, float *delta) { const float vec_len = math_vector_length(z[i], feature_size); - int j; - int N = 0; + size_t j; + float N = 0; for (j = 0; j < num_of_samples; ++j) { if (labels[i] == labels[j]) N++; } @@ -633,10 +790,10 @@ void grad_contrastive_loss_negative(int i, int *labels, int num_of_samples, floa //if (i != j && (i/2) == (j/2)) { if (i != j && labels[i] == labels[j]) { - int k; + size_t k; for (k = 0; k < num_of_samples; ++k) { //if (k != i && k != j && labels[k] != labels[i]) { - if (k != i && k != j) { + if (k != i && k != j && labels[k] >= 0) { const float sim = cos_sim[i*num_of_samples + k]; // cosine_similarity(z[i], z[k], feature_size); const float P = p_constrastive[i*num_of_samples + k]; // P_constrastive(i, k, labels, num_of_samples, z, feature_size, temperature, cos_sim); //const float custom_pos_mult = 1 + sim; diff --git a/src/blas.h b/src/blas.h index 04ba00597af..ac630a23bb2 100644 --- a/src/blas.h +++ b/src/blas.h @@ -59,10 +59,18 @@ void softmax_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *er void constrain_cpu(int size, float ALPHA, float *X); void fix_nan_and_inf_cpu(float *input, size_t size); + +int check_sim(size_t i, size_t j, contrastive_params *contrast_p, int contrast_p_size); +float find_sim(size_t i, size_t j, contrastive_params *contrast_p, int contrast_p_size); +float find_P_constrastive(size_t i, size_t j, contrastive_params *contrast_p, int contrast_p_size); +float P_constrastive_f(size_t i, size_t l, int *labels, float **z, unsigned int feature_size, float temperature, contrastive_params *contrast_p, int contrast_p_size); +void grad_contrastive_loss_positive_f(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, contrastive_params *contrast_p, int contrast_p_size); +void grad_contrastive_loss_negative_f(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, contrastive_params *contrast_p, int contrast_p_size); + float cosine_similarity(float *A, float *B, unsigned int feature_size); -float P_constrastive(int i, int l, int *labels, int num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim); -void grad_contrastive_loss_positive(int i, int *labels, int num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *p_constrastive, float *delta); -void grad_contrastive_loss_negative(int i, int *labels, int num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *p_constrastive, float *delta); +float P_constrastive(size_t i, size_t l, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim); +void grad_contrastive_loss_positive(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *p_constrastive, float *delta); +void grad_contrastive_loss_negative(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *p_constrastive, float *delta); #ifdef GPU diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 5f96a33fe1a..97debb051e9 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -2321,10 +2321,12 @@ __global__ void mult_inverse_array_kernel(const float *src_gpu, float *dst_gpu, if (index < size) { float val = src_gpu[index]; - float sign = 1; - if (val < 0) sign = -1; - if (fabs(val) < fabs(eps)) val = eps * sign; - dst_gpu[index] = eps * 1.0f / val; + float sign = (val < 0) ? -1 : 1; + // eps = 1 by default + // eps = 2 - lower delta + // eps = 0 - higher delta (linear) + // eps = -1 - high delta (inverse number) + dst_gpu[index] = powf(fabs(val), eps) * sign; } } diff --git a/src/classifier.c b/src/classifier.c index 5368069648c..a0615f6443d 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -209,7 +209,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, start = what_time_is_it_now(); printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images, %f hours left\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen, avg_time); #ifdef OPENCV - if (!dontuse_opencv) draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, topk_buff, dont_show, mjpeg_port, avg_time); + if (!dontuse_opencv) draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, topk_buff, 0, dont_show, mjpeg_port, avg_time); #endif // OPENCV if (i >= (iter_save + 1000)) { diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index b4900b1ee49..d9f2e22485d 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -799,16 +799,18 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state if (!state.net.adversarial && !l.train_only_bn) { float *old_input = state.input; + /* if (l.reverse) { - if (*state.net.max_output16_size < l.inputs) { - *state.net.max_output16_size = l.inputs; + if (*state.net.max_output16_size < l.inputs*l.batch) { + *state.net.max_output16_size = l.inputs*l.batch; if (*state.net.output16_gpu) cuda_free(*state.net.output16_gpu); assert(*state.net.max_output16_size > 0); - *state.net.output16_gpu = cuda_make_array(NULL, l.inputs); + *state.net.output16_gpu = cuda_make_array(NULL, *state.net.max_output16_size); } - mult_inverse_array_gpu(state.input, *state.net.output16_gpu, l.inputs, l.reverse); + mult_inverse_array_gpu(state.input, *state.net.output16_gpu, l.inputs*l.batch, l.reverse); state.input = *state.net.output16_gpu; } + */ // calculate conv weight updates @@ -835,6 +837,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state if (l.binary || l.xnor) swap_binary(&l); float *old_weights = l.weights_gpu; + if (l.reverse) { if (*state.net.max_output16_size < l.nweights) { *state.net.max_output16_size = l.nweights; @@ -846,6 +849,8 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state l.weights_gpu = *state.net.output16_gpu; } + + // http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData // calculate delta for the next layer CHECK_CUDNN(cudnnConvolutionBackwardData(cudnn_handle(), diff --git a/src/data.c b/src/data.c index f0004906916..234926987d3 100644 --- a/src/data.c +++ b/src/data.c @@ -45,7 +45,7 @@ char **get_random_paths_indexes(char **paths, int n, int m, int *indexes) } */ -char **get_sequential_paths(char **paths, int n, int m, int mini_batch, int augment_speed) +char **get_sequential_paths(char **paths, int n, int m, int mini_batch, int augment_speed, int contrastive) { int speed = rand_int(1, augment_speed); if (speed < 1) speed = 1; @@ -55,7 +55,9 @@ char **get_sequential_paths(char **paths, int n, int m, int mini_batch, int augm //printf("n = %d, mini_batch = %d \n", n, mini_batch); unsigned int *start_time_indexes = (unsigned int *)xcalloc(mini_batch, sizeof(unsigned int)); for (i = 0; i < mini_batch; ++i) { - start_time_indexes[i] = random_gen() % m; + if (contrastive && (i % 2) == 1) start_time_indexes[i] = start_time_indexes[i - 1]; + else start_time_indexes[i] = random_gen() % m; + //printf(" start_time_indexes[i] = %u, ", start_time_indexes[i]); } @@ -67,6 +69,7 @@ char **get_sequential_paths(char **paths, int n, int m, int mini_batch, int augm //int index = random_gen() % m; sequentia_paths[i] = paths[index]; + //printf(" index = %d, ", index); //if(i == 0) printf("%s\n", paths[index]); //printf(" index = %u - grp: %s \n", index, paths[index]); if (strlen(sequentia_paths[i]) <= 4) printf(" Very small path to the image: %s \n", sequentia_paths[i]); @@ -77,15 +80,18 @@ char **get_sequential_paths(char **paths, int n, int m, int mini_batch, int augm return sequentia_paths; } -char **get_random_paths(char **paths, int n, int m) +char **get_random_paths_custom(char **paths, int n, int m, int contrastive) { char** random_paths = (char**)xcalloc(n, sizeof(char*)); int i; pthread_mutex_lock(&mutex); + int old_index = 0; //printf("n = %d \n", n); for(i = 0; i < n; ++i){ do { int index = random_gen() % m; + if (contrastive && (i % 2 == 1)) index = old_index; + else old_index = index; random_paths[i] = paths[index]; //if(i == 0) printf("%s\n", paths[index]); //printf("grp: %s\n", paths[index]); @@ -96,6 +102,11 @@ char **get_random_paths(char **paths, int n, int m) return random_paths; } +char **get_random_paths(char **paths, int n, int m) +{ + return get_random_paths_custom(paths, n, m, 0); +} + char **find_replace_paths(char **paths, int n, char *find, char *replace) { char** replace_paths = (char**)xcalloc(n, sizeof(char*)); @@ -203,11 +214,16 @@ box_label *read_boxes(char *filename, int *n) *n = 0; return boxes; } + const int max_obj_img = 4000;// 30000; + const int img_hash = (custom_hash(filename) % max_obj_img)*max_obj_img; + //printf(" img_hash = %d, filename = %s; ", img_hash, filename); float x, y, h, w; int id; int count = 0; while(fscanf(file, "%d %f %f %f %f", &id, &x, &y, &w, &h) == 5){ boxes = (box_label*)xrealloc(boxes, (count + 1) * sizeof(box_label)); + boxes[count].track_id = count + img_hash; + //printf(" boxes[count].track_id = %d, count = %d \n", boxes[count].track_id, count); boxes[count].id = id; boxes[count].x = x; boxes[count].y = y; @@ -358,7 +374,7 @@ void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int free(boxes); } -int fill_truth_detection(const char *path, int num_boxes, float *truth, int classes, int flip, float dx, float dy, float sx, float sy, +int fill_truth_detection(const char *path, int num_boxes, int truth_size, float *truth, int classes, int flip, float dx, float dy, float sx, float sy, int net_w, int net_h) { char labelpath[4096]; @@ -383,6 +399,7 @@ int fill_truth_detection(const char *path, int num_boxes, float *truth, int clas w = boxes[i].w; h = boxes[i].h; id = boxes[i].id; + int track_id = boxes[i].track_id; // not detect small objects //if ((w < 0.001F || h < 0.001F)) continue; @@ -435,11 +452,14 @@ int fill_truth_detection(const char *path, int num_boxes, float *truth, int clas if (x == 0) x += lowest_w; if (y == 0) y += lowest_h; - truth[(i-sub)*5+0] = x; - truth[(i-sub)*5+1] = y; - truth[(i-sub)*5+2] = w; - truth[(i-sub)*5+3] = h; - truth[(i-sub)*5+4] = id; + truth[(i-sub)*truth_size +0] = x; + truth[(i-sub)*truth_size +1] = y; + truth[(i-sub)*truth_size +2] = w; + truth[(i-sub)*truth_size +3] = h; + truth[(i-sub)*truth_size +4] = id; + truth[(i-sub)*truth_size +5] = track_id; + //float val = track_id; + //printf(" i = %d, sub = %d, truth_size = %d, track_id = %d, %f, %f\n", i, sub, truth_size, track_id, truth[(i - sub)*truth_size + 5], val); if (min_w_h == 0) min_w_h = w*net_w; if (min_w_h > w*net_w) min_w_h = w*net_w; @@ -848,20 +868,19 @@ data load_data_swag(char **paths, int n, int classes, float jitter) return d; } -void blend_truth(float *new_truth, int boxes, float *old_truth) +void blend_truth(float *new_truth, int boxes, int truth_size, float *old_truth) { - const int t_size = 4 + 1; int count_new_truth = 0; int t; for (t = 0; t < boxes; ++t) { - float x = new_truth[t*(4 + 1)]; + float x = new_truth[t*truth_size]; if (!x) break; count_new_truth++; } for (t = count_new_truth; t < boxes; ++t) { - float *new_truth_ptr = new_truth + t*t_size; - float *old_truth_ptr = old_truth + (t - count_new_truth)*t_size; + float *new_truth_ptr = new_truth + t*truth_size; + float *old_truth_ptr = old_truth + (t - count_new_truth)*truth_size; float x = old_truth_ptr[0]; if (!x) break; @@ -875,27 +894,26 @@ void blend_truth(float *new_truth, int boxes, float *old_truth) } -void blend_truth_mosaic(float *new_truth, int boxes, float *old_truth, int w, int h, float cut_x, float cut_y, int i_mixup, +void blend_truth_mosaic(float *new_truth, int boxes, int truth_size, float *old_truth, int w, int h, float cut_x, float cut_y, int i_mixup, int left_shift, int right_shift, int top_shift, int bot_shift, int net_w, int net_h, int mosaic_bound) { const float lowest_w = 1.F / net_w; const float lowest_h = 1.F / net_h; - const int t_size = 4 + 1; int count_new_truth = 0; int t; for (t = 0; t < boxes; ++t) { - float x = new_truth[t*(4 + 1)]; + float x = new_truth[t*truth_size]; if (!x) break; count_new_truth++; } int new_t = count_new_truth; for (t = count_new_truth; t < boxes; ++t) { - float *new_truth_ptr = new_truth + new_t*t_size; + float *new_truth_ptr = new_truth + new_t*truth_size; new_truth_ptr[0] = 0; - float *old_truth_ptr = old_truth + (t - count_new_truth)*t_size; + float *old_truth_ptr = old_truth + (t - count_new_truth)*truth_size; float x = old_truth_ptr[0]; if (!x) break; @@ -1031,8 +1049,8 @@ void blend_truth_mosaic(float *new_truth, int boxes, float *old_truth, int w, in #include "http_stream.h" -data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int use_gaussian_noise, int use_blur, int use_mixup, - float jitter, float resize, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int mosaic_bound, int show_imgs) +data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int truth_size, int classes, int use_flip, int use_gaussian_noise, int use_blur, int use_mixup, + float jitter, float resize, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int mosaic_bound, int contrastive, int show_imgs) { const int random_index = random_gen(); c = c ? c : 3; @@ -1074,17 +1092,17 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo float dhue = 0, dsat = 0, dexp = 0, flip = 0, blur = 0; int augmentation_calculated = 0, gaussian_noise = 0; - d.y = make_matrix(n, 5*boxes); + d.y = make_matrix(n, truth_size*boxes); int i_mixup = 0; for (i_mixup = 0; i_mixup <= use_mixup; i_mixup++) { if (i_mixup) augmentation_calculated = 0; // recalculate augmentation for the 2nd sequence if(track==1) char **random_paths; - if (track) random_paths = get_sequential_paths(paths, n, m, mini_batch, augment_speed); - else random_paths = get_random_paths(paths, n, m); + if (track) random_paths = get_sequential_paths(paths, n, m, mini_batch, augment_speed, contrastive); + else random_paths = get_random_paths_custom(paths, n, m, contrastive); for (i = 0; i < n; ++i) { - float *truth = (float*)xcalloc(5 * boxes, sizeof(float)); + float *truth = (float*)xcalloc(truth_size * boxes, sizeof(float)); const char *filename = random_paths[i]; int flag = (c >= 3); @@ -1221,21 +1239,23 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo float dy = ((float)ptop / oh) / sy; - int min_w_h = fill_truth_detection(filename, boxes, truth, classes, flip, dx, dy, 1. / sx, 1. / sy, w, h); + int min_w_h = fill_truth_detection(filename, boxes, truth_size, truth, classes, flip, dx, dy, 1. / sx, 1. / sy, w, h); + //for (int z = 0; z < boxes; ++z) if(truth[z*truth_size] > 0) printf(" track_id = %f \n", truth[z*truth_size + 5]); + //printf(" truth_size = %d \n", truth_size); if ((min_w_h / 8) < blur && blur > 1) blur = min_w_h / 8; // disable blur if one of the objects is too small image ai = image_data_augmentation(src, w, h, pleft, ptop, swidth, sheight, flip, dhue, dsat, dexp, - gaussian_noise, blur, boxes, truth); + gaussian_noise, blur, boxes, truth_size, truth); if (use_mixup == 0) { d.X.vals[i] = ai.data; - memcpy(d.y.vals[i], truth, 5 * boxes * sizeof(float)); + memcpy(d.y.vals[i], truth, truth_size * boxes * sizeof(float)); } else if (use_mixup == 1) { if (i_mixup == 0) { d.X.vals[i] = ai.data; - memcpy(d.y.vals[i], truth, 5 * boxes * sizeof(float)); + memcpy(d.y.vals[i], truth, truth_size * boxes * sizeof(float)); } else if (i_mixup == 1) { image old_img = make_empty_image(w, h, c); @@ -1244,7 +1264,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo //show_image(old_img, "old"); //wait_until_press_key_cv(); blend_images_cv(ai, 0.5, old_img, 0.5); - blend_truth(d.y.vals[i], boxes, truth); + blend_truth(d.y.vals[i], boxes, truth_size, truth); free_image(old_img); d.X.vals[i] = ai.data; } @@ -1291,7 +1311,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo } } - blend_truth_mosaic(d.y.vals[i], boxes, truth, w, h, cut_x[i], cut_y[i], i_mixup, left_shift, right_shift, top_shift, bot_shift, w, h, mosaic_bound); + blend_truth_mosaic(d.y.vals[i], boxes, truth_size, truth, w, h, cut_x[i], cut_y[i], i_mixup, left_shift, right_shift, top_shift, bot_shift, w, h, mosaic_bound); free_image(ai); ai.data = d.X.vals[i]; @@ -1306,7 +1326,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo sprintf(buff, "aug_%d_%d_%d", random_index, i, random_gen()); int t; for (t = 0; t < boxes; ++t) { - box b = float_to_box_stride(d.y.vals[i] + t*(4 + 1), 1); + box b = float_to_box_stride(d.y.vals[i] + t*truth_size, 1); if (!b.x) break; int left = (b.x - b.w / 2.)*ai.w; int right = (b.x + b.w / 2.)*ai.w; @@ -1346,15 +1366,15 @@ void blend_images(image new_img, float alpha, image old_img, float beta) new_img.data[i] = new_img.data[i] * alpha + old_img.data[i] * beta; } -data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int gaussian_noise, int use_blur, int use_mixup, - float jitter, float resize, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int mosaic_bound, int show_imgs) +data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int truth_size, int classes, int use_flip, int gaussian_noise, int use_blur, int use_mixup, + float jitter, float resize, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int mosaic_bound, int contrastive, int show_imgs) { const int random_index = random_gen(); c = c ? c : 3; char **random_paths; char **mixup_random_paths = NULL; - if(track) random_paths = get_sequential_paths(paths, n, m, mini_batch, augment_speed); - else random_paths = get_random_paths(paths, n, m); + if(track) random_paths = get_sequential_paths(paths, n, m, mini_batch, augment_speed, contrastive); + else random_paths = get_random_paths_custom(paths, n, m, contrastive); //assert(use_mixup < 2); if (use_mixup == 2) { @@ -1368,7 +1388,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo int mixup = use_mixup ? random_gen() % 2 : 0; //printf("\n mixup = %d \n", mixup); if (mixup) { - if (track) mixup_random_paths = get_sequential_paths(paths, n, m, mini_batch, augment_speed); + if (track) mixup_random_paths = get_sequential_paths(paths, n, m, mini_batch, augment_speed, contrastive); else mixup_random_paths = get_random_paths(paths, n, m); } @@ -1385,12 +1405,12 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo float dhue = 0, dsat = 0, dexp = 0, flip = 0; int augmentation_calculated = 0; - d.y = make_matrix(n, 5 * boxes); + d.y = make_matrix(n, truth_size * boxes); int i_mixup = 0; for (i_mixup = 0; i_mixup <= mixup; i_mixup++) { if (i_mixup) augmentation_calculated = 0; for (i = 0; i < n; ++i) { - float *truth = (float*)xcalloc(5 * boxes, sizeof(float)); + float *truth = (float*)xcalloc(truth_size * boxes, sizeof(float)); char *filename = (i_mixup) ? mixup_random_paths[i] : random_paths[i]; image orig = load_image(filename, 0, 0, c); @@ -1489,7 +1509,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo distort_image(sized, dhue, dsat, dexp); //random_distort_image(sized, hue, saturation, exposure); - fill_truth_detection(filename, boxes, truth, classes, flip, dx, dy, 1. / sx, 1. / sy, w, h); + fill_truth_detection(filename, boxes, truth_size, truth, classes, flip, dx, dy, 1. / sx, 1. / sy, w, h); if (i_mixup) { image old_img = sized; @@ -1498,12 +1518,12 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo //show_image(old_img, "old"); //wait_until_press_key_cv(); blend_images(sized, 0.5, old_img, 0.5); - blend_truth(truth, boxes, d.y.vals[i]); + blend_truth(truth, boxes, truth_size, d.y.vals[i]); free_image(old_img); } d.X.vals[i] = sized.data; - memcpy(d.y.vals[i], truth, 5 * boxes * sizeof(float)); + memcpy(d.y.vals[i], truth, truth_size * boxes * sizeof(float)); if (show_imgs)// && i_mixup) { @@ -1512,7 +1532,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo int t; for (t = 0; t < boxes; ++t) { - box b = float_to_box_stride(d.y.vals[i] + t*(4 + 1), 1); + box b = float_to_box_stride(d.y.vals[i] + t*truth_size, 1); if (!b.x) break; int left = (b.x - b.w / 2.)*sized.w; int right = (b.x + b.w / 2.)*sized.w; @@ -1561,8 +1581,8 @@ void *load_thread(void *ptr) } else if (a.type == REGION_DATA){ *a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure); } else if (a.type == DETECTION_DATA){ - *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.c, a.num_boxes, a.classes, a.flip, a.gaussian_noise, a.blur, a.mixup, a.jitter, a.resize, - a.hue, a.saturation, a.exposure, a.mini_batch, a.track, a.augment_speed, a.letter_box, a.mosaic_bound, a.show_imgs); + *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.c, a.num_boxes, a.truth_size, a.classes, a.flip, a.gaussian_noise, a.blur, a.mixup, a.jitter, a.resize, + a.hue, a.saturation, a.exposure, a.mini_batch, a.track, a.augment_speed, a.letter_box, a.mosaic_bound, a.contrastive, a.show_imgs); } else if (a.type == SWAG_DATA){ *a.d = load_data_swag(a.paths, a.n, a.classes, a.jitter); } else if (a.type == COMPARE_DATA){ diff --git a/src/data.h b/src/data.h index cfad6ca87b9..32320231c75 100644 --- a/src/data.h +++ b/src/data.h @@ -86,8 +86,8 @@ void print_letters(float *pred, int n); data load_data_captcha(char **paths, int n, int m, int k, int w, int h); data load_data_captcha_encode(char **paths, int n, int m, int w, int h); data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h); -data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int classes, int use_flip, int gaussian_noise, int use_blur, int use_mixup, - float jitter, float resize, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int mosaic_bound, int show_imgs); +data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int truth_size, int classes, int use_flip, int gaussian_noise, int use_blur, int use_mixup, + float jitter, float resize, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int mosaic_bound, int contrastive, int show_imgs); data load_data_tag(char **paths, int n, int m, int k, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure); matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int dontuse_opencv, int contrastive); data load_data_super(char **paths, int n, int m, int w, int h, int scale); diff --git a/src/detector.c b/src/detector.c index 6c1810218c7..cdfa7ee44ef 100644 --- a/src/detector.c +++ b/src/detector.c @@ -64,6 +64,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i char *base = basecfg(cfgfile); printf("%s\n", base); float avg_loss = -1; + float avg_contrastive_acc = 0; network* nets = (network*)xcalloc(ngpus, sizeof(network)); srand(time(0)); @@ -102,6 +103,13 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i data train, buffer; layer l = net.layers[net.n - 1]; + for (k = 0; k < net.n; ++k) { + layer lk = net.layers[k]; + if (lk.type == YOLO || lk.type == GAUSSIAN_YOLO || lk.type == REGION) { + l = lk; + printf(" Detection layer: %d - type = %d \n", k, l.type); + } + } int classes = l.classes; @@ -131,6 +139,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i args.jitter = l.jitter; args.resize = l.resize; args.num_boxes = l.max_boxes; + args.truth_size = l.truth_size; net.num_boxes = args.num_boxes; net.train_images_num = train_images_num; args.d = &buffer; @@ -244,6 +253,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i printf(" sequential_subdivisions = %d, sequence = %d \n", net.sequential_subdivisions, get_sequence_value(net)); } load_thread = load_data(args); + //wait_key_cv(500); /* int k; @@ -358,7 +368,12 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i if (avg_time < 0) avg_time = time_remaining; else avg_time = alpha_time * time_remaining + (1 - alpha_time) * avg_time; #ifdef OPENCV - draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, iteration, net.max_batches, mean_average_precision, draw_precision, "mAP%", dont_show, mjpeg_port, avg_time); + if (net.contrastive) { + float cur_con_acc = *net.layers[net.n - 1].loss; + if (cur_con_acc >= 0) avg_contrastive_acc = avg_contrastive_acc*0.99 + cur_con_acc * 0.01; + printf(" avg_contrastive_acc = %f \n", avg_contrastive_acc); + } + draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, iteration, net.max_batches, mean_average_precision, draw_precision, "mAP%", avg_contrastive_acc / 100, dont_show, mjpeg_port, avg_time); #endif // OPENCV //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) { @@ -1794,7 +1809,7 @@ void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, forward_backward_network_gpu(net, X, truth_cpu); float avg_loss = get_network_cost(net); - draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, iteration, it_num, 0, 0, "mAP%", dont_show, 0, 0); + draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, iteration, it_num, 0, 0, "mAP%", 0, dont_show, 0, 0); float inv_loss = 1.0 / max_val_cmp(0.01, avg_loss); //net.learning_rate = *lr_set * inv_loss; diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 4e852db2c6f..d3f24923a51 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1047,7 +1047,7 @@ extern "C" mat_cv* draw_train_chart(char *windows_name, float max_img_loss, int } } - cv::putText(img, "Loss", cv::Point(10, 55), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 255), 1, CV_AA); + cv::putText(img, "Loss", cv::Point(10, 60), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 255), 1, CV_AA); cv::putText(img, "Iteration number", cv::Point(draw_size / 2, img_size - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 0, 0), 1, CV_AA); char max_batches_buff[100]; sprintf(max_batches_buff, "in cfg max_batches=%d", max_batches); @@ -1072,7 +1072,7 @@ extern "C" mat_cv* draw_train_chart(char *windows_name, float max_img_loss, int // ---------------------------------------- extern "C" void draw_train_loss(char *windows_name, mat_cv* img_src, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, - float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port, double time_remaining) + float precision, int draw_precision, char *accuracy_name, float contr_acc, int dont_show, int mjpeg_port, double time_remaining) { try { cv::Mat &img = *(cv::Mat*)img_src; @@ -1085,6 +1085,23 @@ extern "C" void draw_train_loss(char *windows_name, mat_cv* img_src, int img_siz if (pt1.y < 0) pt1.y = 1; cv::circle(img, pt1, 1, CV_RGB(0, 0, 255), CV_FILLED, 8, 0); + // contrastive accuracy + if (contr_acc >= 0) { + static float old_contr_acc = 0; + + if (current_batch > 0) { + cv::line(img, + cv::Point(img_offset + draw_size * (float)(current_batch - 1) / max_batches, draw_size * (1 - old_contr_acc)), + cv::Point(img_offset + draw_size * (float)current_batch / max_batches, draw_size * (1 - contr_acc)), + CV_RGB(0, 150, 70), 1, 8, 0); + } + old_contr_acc = contr_acc; + + sprintf(char_buff, "C:%2.1f%% ", contr_acc * 100); + cv::putText(img, char_buff, cv::Point(1, 45), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 5, CV_AA); + cv::putText(img, char_buff, cv::Point(1, 45), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(0, 150, 70), 1, CV_AA); + } + // precision if (draw_precision) { static float old_precision = 0; @@ -1094,12 +1111,12 @@ extern "C" void draw_train_loss(char *windows_name, mat_cv* img_src, int img_siz if (iteration_old == 0) cv::putText(img, accuracy_name, cv::Point(10, 12), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 0, 0), 1, CV_AA); - if (iteration_old != 0){ - cv::line(img, - cv::Point(img_offset + draw_size * (float)iteration_old / max_batches, draw_size * (1 - old_precision)), - cv::Point(img_offset + draw_size * (float)current_batch / max_batches, draw_size * (1 - precision)), - CV_RGB(255, 0, 0), 1, 8, 0); - } + if (iteration_old != 0){ + cv::line(img, + cv::Point(img_offset + draw_size * (float)iteration_old / max_batches, draw_size * (1 - old_precision)), + cv::Point(img_offset + draw_size * (float)current_batch / max_batches, draw_size * (1 - precision)), + CV_RGB(255, 0, 0), 1, 8, 0); + } sprintf(char_buff, "%2.1f%% ", precision * 100); cv::putText(img, char_buff, cv::Point(10, 28), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 5, CV_AA); @@ -1153,7 +1170,7 @@ extern "C" void draw_train_loss(char *windows_name, mat_cv* img_src, int img_siz extern "C" image image_data_augmentation(mat_cv* mat, int w, int h, int pleft, int ptop, int swidth, int sheight, int flip, float dhue, float dsat, float dexp, - int gaussian_noise, int blur, int num_boxes, float *truth) + int gaussian_noise, int blur, int num_boxes, int truth_size, float *truth) { image out; try { @@ -1243,7 +1260,7 @@ extern "C" image image_data_augmentation(mat_cv* mat, int w, int h, cv::Rect img_rect(0, 0, sized.cols, sized.rows); int t; for (t = 0; t < num_boxes; ++t) { - box b = float_to_box_stride(truth + t*(4 + 1), 1); + box b = float_to_box_stride(truth + t*truth_size, 1); if (!b.x) break; int left = (b.x - b.w / 2.)*sized.cols; int width = b.w*sized.cols; diff --git a/src/image_opencv.h b/src/image_opencv.h index b2dc7c82206..6f25879612a 100644 --- a/src/image_opencv.h +++ b/src/image_opencv.h @@ -94,13 +94,13 @@ void draw_detections_cv_v3(mat_cv* show_img, detection *dets, int num, float thr // Draw Loss & Accuracy chart mat_cv* draw_train_chart(char *windows_name, float max_img_loss, int max_batches, int number_of_lines, int img_size, int dont_show, char* chart_path); void draw_train_loss(char *windows_name, mat_cv* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches, - float precision, int draw_precision, char *accuracy_name, int dont_show, int mjpeg_port, double time_remaining); + float precision, int draw_precision, char *accuracy_name, float contr_acc, int dont_show, int mjpeg_port, double time_remaining); // Data augmentation image image_data_augmentation(mat_cv* mat, int w, int h, int pleft, int ptop, int swidth, int sheight, int flip, float dhue, float dsat, float dexp, - int gaussian_noise, int blur, int num_boxes, float *truth); + int gaussian_noise, int blur, int num_boxes, int truth_size, float *truth); // blend two images with (alpha and beta) void blend_images_cv(image new_img, float alpha, image old_img, float beta); diff --git a/src/layer.c b/src/layer.c index 761bc8642bb..9dceb825204 100644 --- a/src/layer.c +++ b/src/layer.c @@ -74,9 +74,11 @@ void free_layer_custom(layer l, int keep_cudnn_desc) if (l.map) free(l.map); if (l.rand) free(l.rand); if (l.cost) free(l.cost); - if (l.labels) free(l.labels); + if (l.labels && !l.detection) free(l.labels); if (l.cos_sim) free(l.cos_sim); if (l.p_constrastive) free(l.p_constrastive); + if (l.contrast_p) free(l.contrast_p); + if (l.contrast_p_size) free(l.contrast_p_size); if (l.state) free(l.state); if (l.prev_state) free(l.prev_state); if (l.forgot_state) free(l.forgot_state); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 71fff2072ec..8ff33e4e327 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -386,7 +386,7 @@ float train_network_datum_gpu(network net, float *x, float *y) im.h = net.h; im.c = net.c; im.data = x; - //show_image(im, "adversarial data augmentation"); + show_image(im, "adversarial data augmentation"); //wait_key_cv(1); free(old_input); diff --git a/src/parser.c b/src/parser.c index 58a6ee3ff34..1452f85c160 100644 --- a/src/parser.c +++ b/src/parser.c @@ -359,8 +359,16 @@ softmax_layer parse_softmax(list *options, size_params params) contrastive_layer parse_contrastive(list *options, size_params params) { int classes = option_find_int(options, "classes", 1000); - contrastive_layer layer = make_contrastive_layer(params.batch, params.w, params.h, params.c, classes, params.inputs); + layer *yolo_layer = NULL; + int yolo_layer_id = option_find_int_quiet(options, "yolo_layer", 0); + if (yolo_layer_id < 0) yolo_layer_id = params.index + yolo_layer_id; + if(yolo_layer_id != 0) yolo_layer = params.net.layers + yolo_layer_id; + + contrastive_layer layer = make_contrastive_layer(params.batch, params.w, params.h, params.c, classes, params.inputs, yolo_layer); layer.temperature = option_find_float_quiet(options, "temperature", 1); + layer.steps = params.time_steps; + layer.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1); + layer.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10 return layer; } @@ -1100,9 +1108,10 @@ void parse_net_options(list *options, network *net) net->init_sequential_subdivisions = net->sequential_subdivisions = option_find_int_quiet(options, "sequential_subdivisions", subdivs); if (net->sequential_subdivisions > subdivs) net->init_sequential_subdivisions = net->sequential_subdivisions = subdivs; net->try_fix_nan = option_find_int_quiet(options, "try_fix_nan", 0); - net->batch /= subdivs; - net->batch *= net->time_steps; - net->subdivisions = subdivs; + net->batch /= subdivs; // mini_batch + const int mini_batch = net->batch; + net->batch *= net->time_steps; // mini_batch * time_steps + net->subdivisions = subdivs; // number of mini_batches *net->seen = 0; *net->cur_iteration = 0; @@ -1137,6 +1146,10 @@ void parse_net_options(list *options, network *net) net->mosaic_bound = option_find_int_quiet(options, "mosaic_bound", 0); net->contrastive = option_find_int_quiet(options, "contrastive", 0); net->unsupervised = option_find_int_quiet(options, "unsupervised", 0); + if (net->contrastive && mini_batch < 2) { + printf(" Error: mini_batch size (batch/subdivisions) should be higher than 1 for Contrastive loss \n"); + exit(0); + } net->label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); net->resize_step = option_find_float_quiet(options, "resize_step", 32); net->attention = option_find_int_quiet(options, "attention", 0); diff --git a/src/softmax_layer.c b/src/softmax_layer.c index f2f6acabf23..21d71235cc6 100644 --- a/src/softmax_layer.c +++ b/src/softmax_layer.c @@ -127,27 +127,47 @@ void backward_softmax_layer_gpu(const softmax_layer layer, network_state net) // ------------------------------------- -contrastive_layer make_contrastive_layer(int batch, int w, int h, int n, int classes, int inputs) +contrastive_layer make_contrastive_layer(int batch, int w, int h, int c, int classes, int inputs, layer *yolo_layer) { - fprintf(stderr, "contrastive %4d x%4d x%4d - batch: %4d \t classes = %4d \n", w, h, n, batch, classes); contrastive_layer l = { (LAYER_TYPE)0 }; l.type = CONTRASTIVE; l.batch = batch; l.inputs = inputs; - l.outputs = inputs; l.w = w; l.h = h; - l.c = n; - l.n = n; - l.classes = classes; + l.c = c; l.temperature = 1; + + if (yolo_layer) { + l.detection = 1; + l.labels = yolo_layer->labels; // track id + l.n = yolo_layer->n; // num of embeddings per cell = num of anchors + l.classes = yolo_layer->classes;// num of classes + l.embedding_size = l.inputs / (l.n*l.h*l.w); + l.truths = yolo_layer->truths; + } + else { + l.detection = 0; + l.labels = (int*)xcalloc(l.batch, sizeof(int)); // labels + l.n = 1; // num of embeddings per cell + l.classes = classes; // num of classes + l.embedding_size = l.c; + } + l.outputs = inputs; + l.loss = (float*)xcalloc(1, sizeof(float)); l.output = (float*)xcalloc(inputs * batch, sizeof(float)); l.delta = (float*)xcalloc(inputs * batch, sizeof(float)); l.cost = (float*)xcalloc(1, sizeof(float)); - l.labels = (int*)xcalloc(l.batch, sizeof(int)); - l.cos_sim = (float*)xcalloc(l.batch*l.batch, sizeof(float)); - l.p_constrastive = (float*)xcalloc(l.batch*l.batch, sizeof(float)); + + const size_t step = l.batch*l.n*l.h*l.w; + l.cos_sim = NULL; + l.p_constrastive = NULL; + //l.cos_sim = (float*)xcalloc(step*step, sizeof(float)); + //l.p_constrastive = (float*)xcalloc(step*step, sizeof(float)); + l.contrast_p_size = (int*)xcalloc(1, sizeof(int)); + *l.contrast_p_size = step; + l.contrast_p = (contrastive_params*)xcalloc(*l.contrast_p_size, sizeof(contrastive_params)); l.forward = forward_contrastive_layer; l.backward = backward_contrastive_layer; @@ -157,73 +177,178 @@ contrastive_layer make_contrastive_layer(int batch, int w, int h, int n, int cla l.output_gpu = cuda_make_array(l.output, inputs*batch); l.delta_gpu = cuda_make_array(l.delta, inputs*batch); - //l.cos_sim_gpu = cuda_make_array(l.cos_sim, l.batch*l.batch); #endif + fprintf(stderr, "contrastive %4d x%4d x%4d x emb_size %4d x batch: %4d classes = %4d \n", w, h, l.n, l.embedding_size, batch, classes); + if(l.detection) fprintf(stderr, "detection \n"); return l; } +static inline float clip_value(float val, const float max_val) +{ + if (val > max_val) { + //printf("\n val = %f > max_val = %f \n", val, max_val); + val = max_val; + } + else if (val < -max_val) { + //printf("\n val = %f < -max_val = %f \n", val, -max_val); + val = -max_val; + } + return val; +} void forward_contrastive_layer(contrastive_layer l, network_state state) { if (!state.train) return; const float truth_thresh = state.net.label_smooth_eps; - int b, w, h; + const int mini_batch = l.batch / l.steps; - memset(l.delta, 0, l.batch*l.inputs * sizeof(float)); - for (b = 0; b < l.batch; ++b) { - if (state.net.adversarial) l.labels[b] = b % 2; - else l.labels[b] = b / 2; - } + int b, n, w, h; + fill_cpu(l.batch*l.inputs, 0, l.delta, 1); - // set labels - for (b = 0; b < l.batch; ++b) { - for (h = 0; h < l.h; ++h) { - for (w = 0; w < l.w; ++w) - { - // find truth with max prob (only 1 label even if mosaic is used) - float max_truth = 0; - int n; - for (n = 0; n < l.classes; ++n) { - const float truth_prob = state.truth[b*l.classes + n]; - //printf(" truth_prob = %f, ", truth_prob); - //if (truth_prob > max_truth) - if (truth_prob > truth_thresh) - { - //printf(" truth_prob = %f, max_truth = %f, n = %d; ", truth_prob, max_truth, n); - max_truth = truth_prob; - l.labels[b] = n; + if (!l.detection) { + + for (b = 0; b < l.batch; ++b) { + if (state.net.adversarial) l.labels[b] = b % 2; + else l.labels[b] = b / 2; + } + + // set labels + for (b = 0; b < l.batch; ++b) { + for (h = 0; h < l.h; ++h) { + for (w = 0; w < l.w; ++w) + { + // find truth with max prob (only 1 label even if mosaic is used) + float max_truth = 0; + int n; + for (n = 0; n < l.classes; ++n) { + const float truth_prob = state.truth[b*l.classes + n]; + //printf(" truth_prob = %f, ", truth_prob); + //if (truth_prob > max_truth) + if (truth_prob > truth_thresh) + { + //printf(" truth_prob = %f, max_truth = %f, n = %d; ", truth_prob, max_truth, n); + max_truth = truth_prob; + l.labels[b] = n; + } } + //printf(", l.labels[b] = %d ", l.labels[b]); } - //printf(", l.labels[b] = %d ", l.labels[b]); } } + } //printf("\n\n"); // set pointers to features - float **z = (float**)xcalloc(l.batch, sizeof(float*)); + float **z = (float**)xcalloc(l.batch*l.n*l.h*l.w, sizeof(float*)); + for (b = 0; b < l.batch; ++b) { - for (h = 0; h < l.h; ++h) { - for (w = 0; w < l.w; ++w) - { - z[b] = state.input + b*l.inputs; + for (n = 0; n < l.n; ++n) { + for (h = 0; h < l.h; ++h) { + for (w = 0; w < l.w; ++w) + { + const int input_index = b*l.inputs + n*l.embedding_size*l.h*l.w + h*l.w + w; + + const int z_index = b*l.n*l.h*l.w + n*l.h*l.w + h*l.w + w; + + float *ptr = state.input + input_index; + + z[z_index] = ptr; + } } } } + int b2, n2, h2, w2; + int contrast_p_index = 0; + + float *max_sim_same = (float *)xcalloc(l.batch*l.inputs, sizeof(float)); + float *max_sim_diff = (float *)xcalloc(l.batch*l.inputs, sizeof(float)); + fill_cpu(l.batch*l.inputs, -10, max_sim_same, 1); + fill_cpu(l.batch*l.inputs, -10, max_sim_diff, 1); + // precalculate cosine similiraty for (b = 0; b < l.batch; ++b) { - int b2; - for (b2 = 0; b2 < l.batch; ++b2) { - const float sim = cosine_similarity(z[b], z[b2], l.n); - l.cos_sim[b*l.batch + b2] = sim; - //if (sim > 1.001 || sim < -1) { - // printf(" sim = %f, ", sim); getchar(); - //} + for (n = 0; n < l.n; ++n) { + for (h = 0; h < l.h; ++h) { + for (w = 0; w < l.w; ++w) + { + const int z_index = b*l.n*l.h*l.w + n*l.h*l.w + h*l.w + w; + if (l.labels[z_index] < 0) continue; + + for (b2 = 0; b2 < l.batch; ++b2) { + for (n2 = 0; n2 < l.n; ++n2) { + for (h2 = 0; h2 < l.h; ++h2) { + for (w2 = 0; w2 < l.w; ++w2) + { + const int z_index2 = b2*l.n*l.h*l.w + n2*l.h*l.w + h2*l.w + w2; + if (l.labels[z_index2] < 0) continue; + if (z_index == z_index2) continue; + + const int time_step_i = b / mini_batch; + const int time_step_j = b2 / mini_batch; + if (time_step_i != time_step_j) continue; + + const size_t step = l.batch*l.n*l.h*l.w; + + const float sim = cosine_similarity(z[z_index], z[z_index2], l.embedding_size); + //l.cos_sim[z_index*step + z_index2] = sim; + + // calc good sim + if (l.labels[z_index] == l.labels[z_index2] && max_sim_same[z_index] < sim) max_sim_same[z_index] = sim; + if (l.labels[z_index] != l.labels[z_index2] && max_sim_diff[z_index] < sim) max_sim_diff[z_index] = sim; + printf(" z_i = %d, z_i2 = %d, l = %d, l2 = %d, sim = %f \n", z_index, z_index2, l.labels[z_index], l.labels[z_index2], sim); + + l.contrast_p[contrast_p_index].sim = sim; + l.contrast_p[contrast_p_index].i = z_index; + l.contrast_p[contrast_p_index].j = z_index2; + l.contrast_p[contrast_p_index].time_step_i = time_step_i; + l.contrast_p[contrast_p_index].time_step_j = time_step_j; + contrast_p_index++; + if (contrast_p_index >= (*l.contrast_p_size)) { + *l.contrast_p_size = contrast_p_index + 1; + //printf(" *l.contrast_p_size = %d, z_index = %d, z_index2 = %d \n", *l.contrast_p_size, z_index, z_index2); + l.contrast_p = (contrastive_params*)xrealloc(l.contrast_p, (*l.contrast_p_size) * sizeof(contrastive_params)); + } + + if (sim > 1.001 || sim < -1) { + printf(" sim = %f, ", sim); getchar(); + } + } + } + } + } + } + } } } + // calc contrastive accuracy + int i; + int good_sims = 0, all_sims = 0, same_sim = 0, diff_sim = 0; + for (i = 0; i < l.batch*l.inputs; ++i) { + if (max_sim_same[i] >= -1 && max_sim_diff[i] >= -1) { + if (max_sim_same[i] >= -1) same_sim++; + if (max_sim_diff[i] >= -1) diff_sim++; + ++all_sims; + //printf(" max_sim_diff[i] = %f, max_sim_same[i] = %f \n", max_sim_diff[i], max_sim_same[i]); + if (max_sim_diff[i] < max_sim_same[i]) good_sims++; + } + } + if (all_sims > 0) { + *l.loss = 100 * good_sims / all_sims; + } + else *l.loss = -1; + printf(" Contrast accuracy = %f %%, all = %d, good = %d, same = %d, diff = %d \n", *l.loss, all_sims, good_sims, same_sim, diff_sim); + free(max_sim_same); + free(max_sim_diff); + + if (*l.loss > 100) { + getchar(); + } + + /* // show near sim float good_contrast = 0; for (b = 0; b < l.batch; b += 2) { @@ -236,13 +361,15 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) } *l.loss = 100 * good_contrast / (l.batch / 2); printf(" Contrast accuracy = %f %% \n", *l.loss); + */ + /* // precalculate P_contrastive for (b = 0; b < l.batch; ++b) { int b2; for (b2 = 0; b2 < l.batch; ++b2) { if (b != b2) { - const float P = P_constrastive(b, b2, l.labels, l.batch, z, l.n, l.temperature, l.cos_sim); + const float P = P_constrastive(b, b2, l.labels, l.batch, z, l.embedding_size, l.temperature, l.cos_sim); l.p_constrastive[b*l.batch + b2] = P; if (P > 1 || P < -1) { printf(" p = %f, ", P); getchar(); @@ -250,26 +377,93 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) } } } + */ + + + // precalculate cosine similiraty + for (b = 0; b < l.batch; ++b) { + for (n = 0; n < l.n; ++n) { + for (h = 0; h < l.h; ++h) { + for (w = 0; w < l.w; ++w) + { + const int z_index = b*l.n*l.h*l.w + n*l.h*l.w + h*l.w + w; + if (l.labels[z_index] < 0) continue; + + for (b2 = 0; b2 < l.batch; ++b2) { + for (n2 = 0; n2 < l.n; ++n2) { + for (h2 = 0; h2 < l.h; ++h2) { + for (w2 = 0; w2 < l.w; ++w2) + { + const int z_index2 = b2*l.n*l.h*l.w + n2*l.h*l.w + h2*l.w + w2; + if (l.labels[z_index2] < 0) continue; + if (z_index == z_index2) continue; + + const int time_step_i = b / mini_batch; + const int time_step_j = b2 / mini_batch; + if (time_step_i != time_step_j) continue; + + const size_t step = l.batch*l.n*l.h*l.w; + + if (z_index != z_index2) { + //const float P = P_constrastive(z_index, z_index2, l.labels, l.batch, z, l.embedding_size, l.temperature, l.cos_sim); + const float P = P_constrastive_f(z_index, z_index2, l.labels, z, l.embedding_size, l.temperature, l.contrast_p, contrast_p_index); + //l.p_constrastive[z_index*step + z_index2] = P; + + int q; + for(q = 0; q < contrast_p_index; ++q) + if (l.contrast_p[q].i == z_index && l.contrast_p[q].j == z_index2) { + l.contrast_p[q].P = P; + } + + //if (P > 1 || P < -1) { + // printf(" p = %f, z_index = %d, z_index2 = %d ", P, z_index, z_index2); getchar(); + //} + } + } + } + } + } + } + } + } + } + // calc deltas for (b = 0; b < l.batch; ++b) { - for (h = 0; h < l.h; ++h) { - for (w = 0; w < l.w; ++w) - { - //printf(" b = %d, ", b); - // positive - grad_contrastive_loss_positive(b, l.labels, l.batch, z, l.n, l.temperature, l.cos_sim, l.p_constrastive, l.delta + b*l.inputs); - - // negative - grad_contrastive_loss_negative(b, l.labels, l.batch, z, l.n, l.temperature, l.cos_sim, l.p_constrastive, l.delta + b*l.inputs); + for (n = 0; n < l.n; ++n) { + for (h = 0; h < l.h; ++h) { + for (w = 0; w < l.w; ++w) + { + const int z_index = b*l.n*l.h*l.w + n*l.h*l.w + h*l.w + w; + const size_t step = l.batch*l.n*l.h*l.w; + if (l.labels[z_index] < 0) continue; + + // positive + grad_contrastive_loss_positive_f(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.delta + z_index, l.contrast_p, contrast_p_index); + //grad_contrastive_loss_positive(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.cos_sim, l.p_constrastive, l.delta + b*l.inputs); + + // negative + grad_contrastive_loss_negative_f(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.delta + z_index, l.contrast_p, contrast_p_index); + //grad_contrastive_loss_negative(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.cos_sim, l.p_constrastive, l.delta + b*l.inputs); + } } } } + scal_cpu(l.inputs * l.batch, l.cls_normalizer, l.delta, 1); + + for (i = 0; i < l.inputs * l.batch; ++i) { + l.delta[i] = clip_value(l.delta[i], l.max_delta); + } + *(l.cost) = pow(mag_array(l.delta, l.inputs * l.batch), 2); if (state.net.adversarial) { printf(" adversarial contrastive loss = %f \n\n", *(l.cost)); } + else { + printf(" contrastive loss = %f \n\n", *(l.cost)); + } free(z); } @@ -304,6 +498,7 @@ void forward_contrastive_layer_gpu(contrastive_layer l, network_state state) float *truth_cpu = 0; if (state.truth) { int num_truth = l.batch*l.classes; + if (l.detection) num_truth = l.batch*l.truths; truth_cpu = (float *)xcalloc(num_truth, sizeof(float)); cuda_pull_array(state.truth, truth_cpu, num_truth); } diff --git a/src/softmax_layer.h b/src/softmax_layer.h index f1985a99316..c86997b3c95 100644 --- a/src/softmax_layer.h +++ b/src/softmax_layer.h @@ -22,7 +22,7 @@ void backward_softmax_layer_gpu(const softmax_layer l, network_state state); //----------------------- -contrastive_layer make_contrastive_layer(int batch, int w, int h, int n, int classes, int inputs); +contrastive_layer make_contrastive_layer(int batch, int w, int h, int n, int classes, int inputs, layer *yolo_layer); void forward_contrastive_layer(contrastive_layer l, network_state state); void backward_contrastive_layer(contrastive_layer l, network_state net); diff --git a/src/utils.c b/src/utils.c index bbed913fe0a..2fd6375d557 100644 --- a/src/utils.c +++ b/src/utils.c @@ -1031,3 +1031,14 @@ int make_directory(char *path, int mode) return mkdir(path, mode); #endif } + +unsigned long custom_hash(char *str) +{ + unsigned long hash = 5381; + int c; + + while (c = *str++) + hash = ((hash << 5) + hash) + c; /* hash * 33 + c */ + + return hash; +} \ No newline at end of file diff --git a/src/utils.h b/src/utils.h index 29da5c50818..9a154ea6267 100644 --- a/src/utils.h +++ b/src/utils.h @@ -94,6 +94,7 @@ int *random_index_order(int min, int max); int max_int_index(int *a, int n); boxabs box_to_boxabs(const box* b, const int img_w, const int img_h, const int bounds_check); int make_directory(char *path, int mode); +unsigned long custom_hash(char *str); #define max_val_cmp(a,b) (((a) > (b)) ? (a) : (b)) #define min_val_cmp(a,b) (((a) < (b)) ? (a) : (b)) diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 1f609f7a7f6..2214d292fce 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -42,7 +42,11 @@ layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int l.outputs = h*w*n*(classes + 4 + 1); l.inputs = l.outputs; l.max_boxes = max_boxes; - l.truths = l.max_boxes*(4 + 1); // 90*(4 + 1); + l.truth_size = 4 + 2; + l.truths = l.max_boxes*l.truth_size; // 90*(4 + 1); + l.labels = (int*)xcalloc(batch * l.w*l.h*l.n, sizeof(int)); + for (i = 0; i < batch * l.w*l.h*l.n; ++i) l.labels[i] = -1; + l.delta = (float*)xcalloc(batch * l.outputs, sizeof(float)); l.output = (float*)xcalloc(batch * l.outputs, sizeof(float)); for(i = 0; i < total*2; ++i){ @@ -351,6 +355,8 @@ void forward_yolo_layer(const layer l, network_state state) // delta is zeroed memset(l.delta, 0, l.outputs * l.batch * sizeof(float)); if (!state.train) return; + + for (i = 0; i < l.batch * l.w*l.h*l.n; ++i) l.labels[i] = -1; //float avg_iou = 0; float tot_iou = 0; float tot_giou = 0; @@ -382,8 +388,8 @@ void forward_yolo_layer(const layer l, network_state state) float best_iou = 0; int best_t = 0; for (t = 0; t < l.max_boxes; ++t) { - box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); - int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; + box truth = float_to_box_stride(state.truth + t*l.truth_size + b*l.truths, 1); + int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; if (class_id >= l.classes || class_id < 0) { printf("\n Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1); printf("\n truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f, class_id = %d \n", truth.x, truth.y, truth.w, truth.h, class_id); @@ -414,7 +420,7 @@ void forward_yolo_layer(const layer l, network_state state) if (l.objectness_smooth) { l.delta[obj_index] = l.cls_normalizer * (iou_multiplier - l.output[obj_index]); - int class_id = state.truth[best_match_t*(4 + 1) + b*l.truths + 4]; + int class_id = state.truth[best_match_t*l.truth_size + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; if (class_id < l.classes && class_id >= 0) { delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); @@ -453,12 +459,12 @@ void forward_yolo_layer(const layer l, network_state state) else l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); //l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); - int class_id = state.truth[best_t*(4 + 1) + b*l.truths + 4]; + int class_id = state.truth[best_t*l.truth_size + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; if (l.objectness_smooth) l.delta[class_index + stride*class_id] = class_multiplier * (iou_multiplier - l.output[class_index + stride*class_id]); - box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); + box truth = float_to_box_stride(state.truth + best_t*l.truth_size + b*l.truths, 1); delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox); (*state.net.total_bbox)++; } @@ -466,7 +472,7 @@ void forward_yolo_layer(const layer l, network_state state) } } for (t = 0; t < l.max_boxes; ++t) { - box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); + box truth = float_to_box_stride(state.truth + t*l.truth_size + b*l.truths, 1); if (truth.x < 0 || truth.y < 0 || truth.x > 1 || truth.y > 1 || truth.w < 0 || truth.h < 0) { char buff[256]; printf(" Wrong label: truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f \n", truth.x, truth.y, truth.w, truth.h); @@ -474,7 +480,7 @@ void forward_yolo_layer(const layer l, network_state state) truth.x, truth.y, truth.w, truth.h); system(buff); } - int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; + int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; if (class_id >= l.classes || class_id < 0) continue; // if label contains class_id more than number of classes in the cfg-file and class_id check garbage value if (!truth.x) break; // continue; @@ -497,7 +503,7 @@ void forward_yolo_layer(const layer l, network_state state) int mask_n = int_index(l.mask, best_n, l.n); if (mask_n >= 0) { - int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; + int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); @@ -505,6 +511,12 @@ void forward_yolo_layer(const layer l, network_state state) ious all_ious = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox); (*state.net.total_bbox)++; + const int truth_in_index = t*l.truth_size + b*l.truths + 5; + const int track_id = state.truth[truth_in_index]; + const int truth_out_index = b*l.n*l.w*l.h + mask_n*l.w*l.h + j*l.w + i; + l.labels[truth_out_index] = track_id; + //printf(" track_id = %d, t = %d, b = %d, truth_in_index = %d, truth_out_index = %d \n", track_id, t, b, truth_in_index, truth_out_index); + // range is 0 <= 1 tot_iou += all_ious.iou; tot_iou_loss += 1 - all_ious.iou; @@ -545,7 +557,7 @@ void forward_yolo_layer(const layer l, network_state state) // iou, n if (iou > l.iou_thresh) { - int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; + int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); From 76a316cec509dffb5f91b52fc880597ab2dfbdb2 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 6 Jul 2020 03:32:14 +0300 Subject: [PATCH 1055/1365] Disable jitter and flip augmentation for contrastive detector learning --- src/data.c | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/src/data.c b/src/data.c index 234926987d3..cadbdb0947b 100644 --- a/src/data.c +++ b/src/data.c @@ -1139,10 +1139,14 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo resize_r1 = random_float(); resize_r2 = random_float(); - r1 = random_float(); - r2 = random_float(); - r3 = random_float(); - r4 = random_float(); + if (!contrastive || i % 2 == 0) { + r1 = random_float(); + r2 = random_float(); + r3 = random_float(); + r4 = random_float(); + + flip = use_flip ? random_gen() % 2 : 0; + } r_scale = random_float(); @@ -1150,8 +1154,6 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo dsat = rand_scale(saturation); dexp = rand_scale(exposure); - flip = use_flip ? random_gen() % 2 : 0; - if (use_blur) { int tmp_blur = rand_int(0, 2); // 0 - disable, 1 - blur background, 2 - blur the whole image if (tmp_blur == 0) blur = 0; From fc6b3d0ad2a6a440222b40d1b0ae4f13f4971742 Mon Sep 17 00:00:00 2001 From: oneWayOut Date: Mon, 6 Jul 2020 12:49:57 +0800 Subject: [PATCH 1056/1365] fix makefile comment bug spaces between variable declaration and comment will assign unexpected value to the variable --- Makefile | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/Makefile b/Makefile index 1d9d4422686..5635f8f49cc 100644 --- a/Makefile +++ b/Makefile @@ -5,12 +5,14 @@ OPENCV=0 AVX=0 OPENMP=0 LIBSO=0 -ZED_CAMERA=0 # ZED SDK 3.0 and above -ZED_CAMERA_v2_8=0 # ZED SDK 2.X +ZED_CAMERA=0 +ZED_CAMERA_v2_8=0 # set GPU=1 and CUDNN=1 to speedup on GPU # set CUDNN_HALF=1 to further speedup 3 x times (Mixed-precision on Tensor Cores) GPU: Volta, Xavier, Turing and higher # set AVX=1 and OPENMP=1 to speedup on CPU (if error occurs then set AVX=0) +# set ZED_CAMERA=1 to enable ZED SDK 3.0 and above +# set ZED_CAMERA_v2_8=1 to enable ZED SDK 2.X USE_CPP=0 DEBUG=0 From 7753a965b4fdca748dea67681d5e455bc32e44c8 Mon Sep 17 00:00:00 2001 From: adityap27 Date: Mon, 6 Jul 2020 22:14:30 +0530 Subject: [PATCH 1057/1365] fixed mAP calculation for points=0 --- src/detector.c | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/detector.c b/src/detector.c index 6c1810218c7..28bd712fddf 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1253,6 +1253,12 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa avg_precision += delta_recall * last_precision; } + //add remaining area of PR curve when recall isn't 0 at rank-1 + if(pr[i][0].recall!=0) + { + //no need of delta_recall=last_recall-0; + avg_precision += last_recall * last_precision; + } } // MSCOCO - 101 Recall-points, PascalVOC - 11 Recall-points else From 73beba671d7b26f2bc9a9f8015628857145c59e5 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 6 Jul 2020 16:03:04 +0300 Subject: [PATCH 1058/1365] Contrastive loss is fixed to work with both Classifier and Detector --- include/darknet.h | 4 +- src/blas.c | 66 ++++++++++++++++------- src/blas.h | 2 +- src/classifier.c | 11 +++- src/detector.c | 4 +- src/image_opencv.cpp | 11 ++++ src/image_opencv.h | 1 + src/layer.c | 3 +- src/network_kernels.cu | 6 ++- src/softmax_layer.c | 120 ++++++++++++++++++++++++++--------------- 10 files changed, 155 insertions(+), 73 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index a25e30010ee..0ad60edf306 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -145,6 +145,7 @@ typedef enum{ // blas.h typedef struct contrastive_params { float sim; + float exp_sim; float P; size_t i, j; int time_step_i, time_step_j; @@ -378,9 +379,8 @@ struct layer { float * cost; int *labels; float *cos_sim; + float *exp_cos_sim; float *p_constrastive; - contrastive_params *contrast_p; - int *contrast_p_size; float * state; float * prev_state; float * forgot_state; diff --git a/src/blas.c b/src/blas.c index 6a36499f5f1..c6c716d696d 100644 --- a/src/blas.c +++ b/src/blas.c @@ -548,6 +548,19 @@ float cosine_similarity(float *A, float *B, unsigned int feature_size) return similarity; } +int get_sim_P_index(size_t i, size_t j, contrastive_params *contrast_p, int contrast_p_size) +{ + size_t z; + for (z = 0; z < contrast_p_size; ++z) { + if (contrast_p[z].i == i && contrast_p[z].j == j) break; + } + if (z == contrast_p_size) { + return -1; // not found + } + + return z; // found +} + int check_sim(size_t i, size_t j, contrastive_params *contrast_p, int contrast_p_size) { size_t z; @@ -607,9 +620,10 @@ float P_constrastive_f(size_t i, size_t l, int *labels, float **z, unsigned int //if (k != i && labels[k] != labels[i]) { //if (k != i) { if (cp.i != i && cp.j == l) { - const float sim_den = cp.sim; - //const float sim_den = find_sim(k, l, contrast_p, contrast_p_size); // cosine_similarity(z[k], z[l], feature_size); - denominator += expf(sim_den / temperature); + //const float sim_den = cp.sim; + ////const float sim_den = find_sim(k, l, contrast_p, contrast_p_size); // cosine_similarity(z[k], z[l], feature_size); + //denominator += expf(sim_den / temperature); + denominator += cp.exp_sim; } } @@ -625,22 +639,27 @@ void grad_contrastive_loss_positive_f(size_t i, int *labels, size_t num_of_sampl size_t j; float N = 0; for (j = 0; j < num_of_samples; ++j) { - if (labels[i] == labels[j] && labels[i] > 0) N++; + if (labels[i] == labels[j] && labels[i] >= 0) N++; } if (N == 0 || temperature == 0 || vec_len == 0) { - fprintf(stderr, " Error: N == 0 || temperature == 0 || vec_len == 0. N=%f, temperature=%f, vec_len=%f \n", N, temperature, vec_len); + fprintf(stderr, " Error: N == 0 || temperature == 0 || vec_len == 0. N=%f, temperature=%f, vec_len=%f, labels[i] = %d \n", + N, temperature, vec_len, labels[i]); getchar(); } const float mult = 1 / ((N - 1) * temperature * vec_len); for (j = 0; j < num_of_samples; ++j) { //if (i != j && (i/2) == (j/2)) { - if (i != j && labels[i] == labels[j] && labels[i] > 0) { + if (i != j && labels[i] == labels[j] && labels[i] >= 0) { //printf(" i = %d, j = %d, num_of_samples = %d, labels[i] = %d, labels[j] = %d \n", // i, j, num_of_samples, labels[i], labels[j]); - if (!check_sim(i, j, contrast_p, contrast_p_size)) continue; - const float sim = find_sim(i, j, contrast_p, contrast_p_size); //cos_sim[i*num_of_samples + j]; // cosine_similarity(z[i], z[j], feature_size); - const float P = find_P_constrastive(i, j, contrast_p, contrast_p_size); //p_constrastive[i*num_of_samples + j]; // P_constrastive(i, j, labels, num_of_samples, z, feature_size, temperature, cos_sim); + const int sim_P_i = get_sim_P_index(i, j, contrast_p, contrast_p_size); + if (sim_P_i < 0) continue; + const float sim = contrast_p[sim_P_i].sim; + const float P = contrast_p[sim_P_i].P; + //if (!check_sim(i, j, contrast_p, contrast_p_size)) continue; + //const float sim = find_sim(i, j, contrast_p, contrast_p_size); //cos_sim[i*num_of_samples + j]; // cosine_similarity(z[i], z[j], feature_size); + //const float P = find_P_constrastive(i, j, contrast_p, contrast_p_size); //p_constrastive[i*num_of_samples + j]; // P_constrastive(i, j, labels, num_of_samples, z, feature_size, temperature, cos_sim); //const float custom_pos_mult = 1 - sim; @@ -664,25 +683,30 @@ void grad_contrastive_loss_negative_f(size_t i, int *labels, size_t num_of_sampl size_t j; float N = 0; for (j = 0; j < num_of_samples; ++j) { - if (labels[i] == labels[j] && labels[i] > 0) N++; + if (labels[i] == labels[j] && labels[i] >= 0) N++; } if (N == 0 || temperature == 0 || vec_len == 0) { - fprintf(stderr, " Error: N == 0 || temperature == 0 || vec_len == 0. N=%f, temperature=%f, vec_len=%f \n", N, temperature, vec_len); + fprintf(stderr, " Error: N == 0 || temperature == 0 || vec_len == 0. N=%f, temperature=%f, vec_len=%f, labels[i] = %d \n", + N, temperature, vec_len, labels[i]); getchar(); } const float mult = 1 / ((N - 1) * temperature * vec_len); for (j = 0; j < num_of_samples; ++j) { //if (i != j && (i/2) == (j/2)) { - if (i != j && labels[i] == labels[j] && labels[i] > 0) { + if (i != j && labels[i] == labels[j] && labels[i] >= 0) { size_t k; for (k = 0; k < num_of_samples; ++k) { //if (k != i && k != j && labels[k] != labels[i]) { if (k != i && k != j && labels[k] >= 0) { - if (!check_sim(i, k, contrast_p, contrast_p_size)) continue; - const float sim = find_sim(i, k, contrast_p, contrast_p_size); //cos_sim[i*num_of_samples + k]; // cosine_similarity(z[i], z[k], feature_size); - const float P = find_P_constrastive(i, k, contrast_p, contrast_p_size); //p_constrastive[i*num_of_samples + k]; // P_constrastive(i, k, labels, num_of_samples, z, feature_size, temperature, cos_sim); + const int sim_P_i = get_sim_P_index(i, k, contrast_p, contrast_p_size); + if (sim_P_i < 0) continue; + const float sim = contrast_p[sim_P_i].sim; + const float P = contrast_p[sim_P_i].P; + //if (!check_sim(i, k, contrast_p, contrast_p_size)) continue; + //const float sim = find_sim(i, k, contrast_p, contrast_p_size); //cos_sim[i*num_of_samples + k]; // cosine_similarity(z[i], z[k], feature_size); + //const float P = find_P_constrastive(i, k, contrast_p, contrast_p_size); //p_constrastive[i*num_of_samples + k]; // P_constrastive(i, k, labels, num_of_samples, z, feature_size, temperature, cos_sim); //const float custom_pos_mult = 1 + sim; int m; @@ -704,23 +728,25 @@ void grad_contrastive_loss_negative_f(size_t i, int *labels, size_t num_of_sampl // num_of_samples = 2 * loaded_images = mini_batch_size -float P_constrastive(size_t i, size_t l, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim) +float P_constrastive(size_t i, size_t l, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *exp_cos_sim) { if (i == l) { fprintf(stderr, " Error: in P_constrastive must be i != l, while i = %d, l = %d \n", i, l); getchar(); } - const float sim = cos_sim[i*num_of_samples + l]; // cosine_similarity(z[i], z[l], feature_size); - const float numerator = expf(sim / temperature); + //const float sim = cos_sim[i*num_of_samples + l]; // cosine_similarity(z[i], z[l], feature_size); + //const float numerator = expf(sim / temperature); + const float numerator = exp_cos_sim[i*num_of_samples + l]; float denominator = 0; int k; for (k = 0; k < num_of_samples; ++k) { //if (k != i && labels[k] != labels[i]) { if (k != i) { - const float sim_den = cos_sim[k*num_of_samples + l]; // cosine_similarity(z[k], z[l], feature_size); - denominator += expf(sim_den / temperature); + //const float sim_den = cos_sim[k*num_of_samples + l]; // cosine_similarity(z[k], z[l], feature_size); + //denominator += expf(sim_den / temperature); + denominator += exp_cos_sim[k*num_of_samples + l]; } } diff --git a/src/blas.h b/src/blas.h index ac630a23bb2..8ba147ff8e7 100644 --- a/src/blas.h +++ b/src/blas.h @@ -68,7 +68,7 @@ void grad_contrastive_loss_positive_f(size_t i, int *labels, size_t num_of_sampl void grad_contrastive_loss_negative_f(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, contrastive_params *contrast_p, int contrast_p_size); float cosine_similarity(float *A, float *B, unsigned int feature_size); -float P_constrastive(size_t i, size_t l, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim); +float P_constrastive(size_t i, size_t l, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *exp_cos_sim); void grad_contrastive_loss_positive(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *p_constrastive, float *delta); void grad_contrastive_loss_negative(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *p_constrastive, float *delta); diff --git a/src/classifier.c b/src/classifier.c index a0615f6443d..6e2095c7d6e 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -32,6 +32,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int i; float avg_loss = -1; + float avg_contrastive_acc = 0; char *base = basecfg(cfgfile); printf("%s\n", base); printf("%d\n", ngpus); @@ -209,7 +210,15 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, start = what_time_is_it_now(); printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images, %f hours left\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen, avg_time); #ifdef OPENCV - if (!dontuse_opencv) draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, topk_buff, 0, dont_show, mjpeg_port, avg_time); + if (net.contrastive) { + float cur_con_acc = -1; + int k; + for (k = 0; k < net.n; ++k) + if (net.layers[k].type == CONTRASTIVE) cur_con_acc = *net.layers[k].loss; + if (cur_con_acc >= 0) avg_contrastive_acc = avg_contrastive_acc*0.99 + cur_con_acc * 0.01; + printf(" avg_contrastive_acc = %f \n", avg_contrastive_acc); + } + if (!dontuse_opencv) draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, topk_buff, avg_contrastive_acc / 100, dont_show, mjpeg_port, avg_time); #endif // OPENCV if (i >= (iter_save + 1000)) { diff --git a/src/detector.c b/src/detector.c index cdfa7ee44ef..a1c5d939e77 100644 --- a/src/detector.c +++ b/src/detector.c @@ -369,7 +369,9 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i else avg_time = alpha_time * time_remaining + (1 - alpha_time) * avg_time; #ifdef OPENCV if (net.contrastive) { - float cur_con_acc = *net.layers[net.n - 1].loss; + float cur_con_acc = -1; + for (k = 0; k < net.n; ++k) + if (net.layers[k].type == CONTRASTIVE) cur_con_acc = *net.layers[k].loss; if (cur_con_acc >= 0) avg_contrastive_acc = avg_contrastive_acc*0.99 + cur_con_acc * 0.01; printf(" avg_contrastive_acc = %f \n", avg_contrastive_acc); } diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index d3f24923a51..8b8b321c97f 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -374,6 +374,17 @@ extern "C" void create_window_cv(char const* window_name, int full_screen, int w } // ---------------------------------------- +extern "C" void resize_window_cv(char const* window_name, int width, int height) +{ + try { + cv::resizeWindow(window_name, width, height); + } + catch (...) { + cerr << "OpenCV exception: create_window_cv \n"; + } +} +// ---------------------------------------- + extern "C" void destroy_all_windows_cv() { try { diff --git a/src/image_opencv.h b/src/image_opencv.h index 6f25879612a..a825a3c9789 100644 --- a/src/image_opencv.h +++ b/src/image_opencv.h @@ -47,6 +47,7 @@ image mat_to_image_cv(mat_cv *mat); // Window void create_window_cv(char const* window_name, int full_screen, int width, int height); +void resize_window_cv(char const* window_name, int width, int height); void destroy_all_windows_cv(); int wait_key_cv(int delay); int wait_until_press_key_cv(); diff --git a/src/layer.c b/src/layer.c index 9dceb825204..3dcef7ac25a 100644 --- a/src/layer.c +++ b/src/layer.c @@ -76,9 +76,8 @@ void free_layer_custom(layer l, int keep_cudnn_desc) if (l.cost) free(l.cost); if (l.labels && !l.detection) free(l.labels); if (l.cos_sim) free(l.cos_sim); + if (l.exp_cos_sim) free(l.exp_cos_sim); if (l.p_constrastive) free(l.p_constrastive); - if (l.contrast_p) free(l.contrast_p); - if (l.contrast_p_size) free(l.contrast_p_size); if (l.state) free(l.state); if (l.prev_state) free(l.prev_state); if (l.forgot_state) free(l.forgot_state); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 8ff33e4e327..e6e24d2c214 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -237,6 +237,7 @@ void backward_network_gpu(network net, network_state state) image attention_img = make_attention_image(img_size, original_delta_cpu, original_input_cpu, net.w, net.h, net.c); show_image(attention_img, "attention_img"); + resize_window_cv("attention_img", 500, 500); free_image(attention_img); @@ -376,7 +377,7 @@ float train_network_datum_gpu(network net, float *x, float *y) int b; for (b = 0; b < net.batch; ++b) { if (b % 2 == 1 && net.contrastive) { - printf(" b = %d old img, ", b); + //printf(" b = %d old img, ", b); memcpy(x + img_size*b, old_input + img_size*b, img_size * sizeof(float)); } } @@ -387,7 +388,8 @@ float train_network_datum_gpu(network net, float *x, float *y) im.c = net.c; im.data = x; show_image(im, "adversarial data augmentation"); - //wait_key_cv(1); + resize_window_cv("adversarial data augmentation", 500, 500); + wait_key_cv(1); free(old_input); free(truth_cpu); diff --git a/src/softmax_layer.c b/src/softmax_layer.c index 21d71235cc6..ec0dadbe279 100644 --- a/src/softmax_layer.c +++ b/src/softmax_layer.c @@ -117,9 +117,9 @@ void forward_softmax_layer_gpu(const softmax_layer l, network_state net) } } -void backward_softmax_layer_gpu(const softmax_layer layer, network_state net) +void backward_softmax_layer_gpu(const softmax_layer layer, network_state state) { - axpy_ongpu(layer.batch*layer.inputs, 1, layer.delta_gpu, 1, net.delta, 1); + axpy_ongpu(layer.batch*layer.inputs, state.net.loss_scale, layer.delta_gpu, 1, state.delta, 1); } #endif @@ -162,12 +162,17 @@ contrastive_layer make_contrastive_layer(int batch, int w, int h, int c, int cla const size_t step = l.batch*l.n*l.h*l.w; l.cos_sim = NULL; + l.exp_cos_sim = NULL; l.p_constrastive = NULL; - //l.cos_sim = (float*)xcalloc(step*step, sizeof(float)); + if (!l.detection) { + l.cos_sim = (float*)xcalloc(step*step, sizeof(float)); + l.exp_cos_sim = (float*)xcalloc(step*step, sizeof(float)); + l.p_constrastive = (float*)xcalloc(step*step, sizeof(float)); + } //l.p_constrastive = (float*)xcalloc(step*step, sizeof(float)); - l.contrast_p_size = (int*)xcalloc(1, sizeof(int)); - *l.contrast_p_size = step; - l.contrast_p = (contrastive_params*)xcalloc(*l.contrast_p_size, sizeof(contrastive_params)); + //l.contrast_p_size = (int*)xcalloc(1, sizeof(int)); + //*l.contrast_p_size = step; + //l.contrast_p = (contrastive_params*)xcalloc(*l.contrast_p_size, sizeof(contrastive_params)); l.forward = forward_contrastive_layer; l.backward = backward_contrastive_layer; @@ -178,7 +183,7 @@ contrastive_layer make_contrastive_layer(int batch, int w, int h, int c, int cla l.output_gpu = cuda_make_array(l.output, inputs*batch); l.delta_gpu = cuda_make_array(l.delta, inputs*batch); #endif - fprintf(stderr, "contrastive %4d x%4d x%4d x emb_size %4d x batch: %4d classes = %4d \n", w, h, l.n, l.embedding_size, batch, classes); + fprintf(stderr, "contrastive %4d x%4d x%4d x emb_size %4d x batch: %4d classes = %4d, step = %4d \n", w, h, l.n, l.embedding_size, batch, classes, step); if(l.detection) fprintf(stderr, "detection \n"); return l; } @@ -263,6 +268,11 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) int b2, n2, h2, w2; int contrast_p_index = 0; + const size_t step = l.batch*l.n*l.h*l.w; + size_t contrast_p_size = step; + if (!l.detection) contrast_p_size = l.batch*l.batch; + contrastive_params *contrast_p = (contrastive_params*)xcalloc(contrast_p_size, sizeof(contrastive_params)); + float *max_sim_same = (float *)xcalloc(l.batch*l.inputs, sizeof(float)); float *max_sim_diff = (float *)xcalloc(l.batch*l.inputs, sizeof(float)); fill_cpu(l.batch*l.inputs, -10, max_sim_same, 1); @@ -293,23 +303,29 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) const size_t step = l.batch*l.n*l.h*l.w; const float sim = cosine_similarity(z[z_index], z[z_index2], l.embedding_size); - //l.cos_sim[z_index*step + z_index2] = sim; + const float exp_sim = expf(sim / l.temperature); + if (!l.detection) { + l.cos_sim[z_index*step + z_index2] = sim; + l.exp_cos_sim[z_index*step + z_index2] = exp_sim; + } // calc good sim if (l.labels[z_index] == l.labels[z_index2] && max_sim_same[z_index] < sim) max_sim_same[z_index] = sim; if (l.labels[z_index] != l.labels[z_index2] && max_sim_diff[z_index] < sim) max_sim_diff[z_index] = sim; - printf(" z_i = %d, z_i2 = %d, l = %d, l2 = %d, sim = %f \n", z_index, z_index2, l.labels[z_index], l.labels[z_index2], sim); - - l.contrast_p[contrast_p_index].sim = sim; - l.contrast_p[contrast_p_index].i = z_index; - l.contrast_p[contrast_p_index].j = z_index2; - l.contrast_p[contrast_p_index].time_step_i = time_step_i; - l.contrast_p[contrast_p_index].time_step_j = time_step_j; + //printf(" z_i = %d, z_i2 = %d, l = %d, l2 = %d, sim = %f \n", z_index, z_index2, l.labels[z_index], l.labels[z_index2], sim); + + contrast_p[contrast_p_index].sim = sim; + contrast_p[contrast_p_index].exp_sim = exp_sim; + contrast_p[contrast_p_index].i = z_index; + contrast_p[contrast_p_index].j = z_index2; + contrast_p[contrast_p_index].time_step_i = time_step_i; + contrast_p[contrast_p_index].time_step_j = time_step_j; contrast_p_index++; - if (contrast_p_index >= (*l.contrast_p_size)) { - *l.contrast_p_size = contrast_p_index + 1; - //printf(" *l.contrast_p_size = %d, z_index = %d, z_index2 = %d \n", *l.contrast_p_size, z_index, z_index2); - l.contrast_p = (contrastive_params*)xrealloc(l.contrast_p, (*l.contrast_p_size) * sizeof(contrastive_params)); + //printf(" contrast_p_index = %d, contrast_p_size = %d \n", contrast_p_index, contrast_p_size); + if ((contrast_p_index+1) >= contrast_p_size) { + contrast_p_size = contrast_p_index + 1; + //printf(" contrast_p_size = %d, z_index = %d, z_index2 = %d \n", contrast_p_size, z_index, z_index2); + contrast_p = (contrastive_params*)xrealloc(contrast_p, contrast_p_size * sizeof(contrastive_params)); } if (sim > 1.001 || sim < -1) { @@ -344,9 +360,6 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) free(max_sim_same); free(max_sim_diff); - if (*l.loss > 100) { - getchar(); - } /* // show near sim @@ -379,8 +392,7 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) } */ - - // precalculate cosine similiraty + // precalculate P-contrastive for (b = 0; b < l.batch; ++b) { for (n = 0; n < l.n; ++n) { for (h = 0; h < l.h; ++h) { @@ -404,21 +416,28 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) const size_t step = l.batch*l.n*l.h*l.w; - if (z_index != z_index2) { - //const float P = P_constrastive(z_index, z_index2, l.labels, l.batch, z, l.embedding_size, l.temperature, l.cos_sim); - const float P = P_constrastive_f(z_index, z_index2, l.labels, z, l.embedding_size, l.temperature, l.contrast_p, contrast_p_index); - //l.p_constrastive[z_index*step + z_index2] = P; + float P = -10; + if (l.detection) { + P = P_constrastive_f(z_index, z_index2, l.labels, z, l.embedding_size, l.temperature, contrast_p, contrast_p_index); + } + else { + P = P_constrastive(z_index, z_index2, l.labels, step, z, l.embedding_size, l.temperature, l.cos_sim, l.exp_cos_sim); + l.p_constrastive[z_index*step + z_index2] = P; + } + + int q; + for (q = 0; q < contrast_p_index; ++q) + if (contrast_p[q].i == z_index && contrast_p[q].j == z_index2) { + contrast_p[q].P = P; + break; + } - int q; - for(q = 0; q < contrast_p_index; ++q) - if (l.contrast_p[q].i == z_index && l.contrast_p[q].j == z_index2) { - l.contrast_p[q].P = P; - } + //if (q == contrast_p_index) getchar(); - //if (P > 1 || P < -1) { - // printf(" p = %f, z_index = %d, z_index2 = %d ", P, z_index, z_index2); getchar(); - //} - } + + //if (P > 1 || P < -1) { + // printf(" p = %f, z_index = %d, z_index2 = %d ", P, z_index, z_index2); getchar(); + //} } } } @@ -439,13 +458,25 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) const size_t step = l.batch*l.n*l.h*l.w; if (l.labels[z_index] < 0) continue; - // positive - grad_contrastive_loss_positive_f(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.delta + z_index, l.contrast_p, contrast_p_index); - //grad_contrastive_loss_positive(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.cos_sim, l.p_constrastive, l.delta + b*l.inputs); + if (l.detection) { + // detector + + // positive + grad_contrastive_loss_positive_f(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.delta + z_index, contrast_p, contrast_p_index); + + // negative + grad_contrastive_loss_negative_f(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.delta + z_index, contrast_p, contrast_p_index); + } + else { + // classifier + + // positive + grad_contrastive_loss_positive(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.cos_sim, l.p_constrastive, l.delta + b*l.inputs); + + // negative + grad_contrastive_loss_negative(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.cos_sim, l.p_constrastive, l.delta + b*l.inputs); + } - // negative - grad_contrastive_loss_negative_f(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.delta + z_index, l.contrast_p, contrast_p_index); - //grad_contrastive_loss_negative(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.cos_sim, l.p_constrastive, l.delta + b*l.inputs); } } } @@ -465,6 +496,7 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) printf(" contrastive loss = %f \n\n", *(l.cost)); } + free(contrast_p); free(z); } @@ -518,7 +550,7 @@ void forward_contrastive_layer_gpu(contrastive_layer l, network_state state) void backward_contrastive_layer_gpu(contrastive_layer layer, network_state state) { - axpy_ongpu(layer.batch*layer.inputs, 1, layer.delta_gpu, 1, state.delta, 1); + axpy_ongpu(layer.batch*layer.inputs, state.net.loss_scale, layer.delta_gpu, 1, state.delta, 1); } #endif \ No newline at end of file From 2fca3c399b722934d24b9b86963215fc40ac6bcd Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 7 Jul 2020 00:14:25 +0300 Subject: [PATCH 1059/1365] Fixed bug in contrastive learning --- include/darknet.h | 4 ++++ src/blas.c | 33 +++++++++++++++++++++++++-------- src/blas.h | 10 ++++++---- src/data.c | 24 ++++++++++++++---------- src/data.h | 2 +- src/detector.c | 8 +++----- src/network.c | 7 +++++++ src/parser.c | 10 ++++++++++ src/softmax_layer.c | 36 +++++++++++++++++++++++++++--------- src/yolo_layer.c | 10 ++++++++++ 10 files changed, 107 insertions(+), 37 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 0ad60edf306..9cfd2ac4c44 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -300,6 +300,7 @@ struct layer { int softmax; int classes; int detection; + layer *embedding_layer; int embedding_size; int coords; int background; @@ -741,6 +742,7 @@ typedef struct network { int letter_box; int mosaic_bound; int contrastive; + int contrastive_jit_flip; int unsupervised; float angle; float aspect; @@ -863,6 +865,7 @@ typedef struct detection{ int sort_class; float *uc; // Gaussian_YOLOv3 - tx,ty,tw,th uncertainty int points; // bit-0 - center, bit-1 - top-left-corner, bit-2 - bottom-right-corner + float *embeddings; // embeddings for tracking } detection; // network.c -batch inference @@ -923,6 +926,7 @@ typedef struct load_args { int show_imgs; int dontuse_opencv; int contrastive; + int contrastive_jit_flip; float jitter; float resize; int flip; diff --git a/src/blas.c b/src/blas.c index c6c716d696d..5c3b99c8e4d 100644 --- a/src/blas.c +++ b/src/blas.c @@ -516,6 +516,19 @@ void fix_nan_and_inf_cpu(float *input, size_t size) } } +void get_embedding(float *src, int src_w, int src_h, int src_c, int embedding_size, int cur_w, int cur_h, int cur_n, int cur_b, float *dst) +{ + int i; + for (i = 0; i < embedding_size; ++i) { + const int src_index = cur_b*(src_c*src_h*src_w) + cur_n*(embedding_size*src_h*src_w) + i*src_h*src_w + cur_h*(src_w) + cur_w; + + const float val = src[src_index]; + dst[i] = val; + //printf(" val = %f, ", val); + } +} + + // Euclidean_norm float math_vector_length(float *A, unsigned int feature_size) { @@ -633,7 +646,7 @@ float P_constrastive_f(size_t i, size_t l, int *labels, float **z, unsigned int return result; } -void grad_contrastive_loss_positive_f(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, contrastive_params *contrast_p, int contrast_p_size) +void grad_contrastive_loss_positive_f(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, int wh, contrastive_params *contrast_p, int contrast_p_size) { const float vec_len = math_vector_length(z[i], feature_size); size_t j; @@ -671,13 +684,14 @@ void grad_contrastive_loss_positive_f(size_t i, int *labels, size_t num_of_sampl const float d = mult*(sim * z[i][m] - z[j][m]) *(1 - P); // 1 (70%) //const float d = mult*(sim * z[j][m] - z[j][m]) * (1 - P); // 2 // printf(" pos: z[j][m] = %f, z[i][m] = %f, d = %f, sim = %f \n", z[j][m], z[i][m], d, sim); - delta[m] -= d; + const int out_i = m * wh; + delta[out_i] -= d; } } } } -void grad_contrastive_loss_negative_f(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, contrastive_params *contrast_p, int contrast_p_size) +void grad_contrastive_loss_negative_f(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, int wh, contrastive_params *contrast_p, int contrast_p_size) { const float vec_len = math_vector_length(z[i], feature_size); size_t j; @@ -717,7 +731,8 @@ void grad_contrastive_loss_negative_f(size_t i, int *labels, size_t num_of_sampl const float d = mult*(z[k][m] - sim * z[i][m]) * P; // 1 (70%) //const float d = mult*(z[k][m] - sim * z[k][m]) * P; // 2 //printf(" neg: z[k][m] = %f, z[i][m] = %f, d = %f, sim = %f \n", z[k][m], z[i][m], d, sim); - delta[m] -= d; + const int out_i = m * wh; + delta[out_i] -= d; } } } @@ -759,7 +774,7 @@ float P_constrastive(size_t i, size_t l, int *labels, size_t num_of_samples, flo // z[feature_size][num_of_samples] - array of arrays with contrastive features (output of conv-layer, f.e. 128 floats for each sample) // delta[feature_size] - array with deltas for backpropagation // temperature - scalar temperature param (temperature > 0), f.e. temperature = 0.07: Supervised Contrastive Learning -void grad_contrastive_loss_positive(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *p_constrastive, float *delta) +void grad_contrastive_loss_positive(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *p_constrastive, float *delta, int wh) { const float vec_len = math_vector_length(z[i], feature_size); size_t j; @@ -787,7 +802,8 @@ void grad_contrastive_loss_positive(size_t i, int *labels, size_t num_of_samples const float d = mult*(sim * z[i][m] - z[j][m]) * (1 - P); // good //const float d = mult*(sim * z[j][m] - z[j][m]) * (1 - P); // bad // printf(" pos: z[j][m] = %f, z[i][m] = %f, d = %f, sim = %f \n", z[j][m], z[i][m], d, sim); - delta[m] -= d; + const int out_i = m * wh; + delta[out_i] -= d; } } } @@ -798,7 +814,7 @@ void grad_contrastive_loss_positive(size_t i, int *labels, size_t num_of_samples // z[feature_size][num_of_samples] - array of arrays with contrastive features (output of conv-layer, f.e. 128 floats for each sample) // delta[feature_size] - array with deltas for backpropagation // temperature - scalar temperature param (temperature > 0), f.e. temperature = 0.07: Supervised Contrastive Learning -void grad_contrastive_loss_negative(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *p_constrastive, float *delta) +void grad_contrastive_loss_negative(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *p_constrastive, float *delta, int wh) { const float vec_len = math_vector_length(z[i], feature_size); size_t j; @@ -829,7 +845,8 @@ void grad_contrastive_loss_negative(size_t i, int *labels, size_t num_of_samples const float d = mult*(z[k][m] - sim * z[i][m]) * P; // good //const float d = mult*(z[k][m] - sim * z[k][m]) * P; // bad //printf(" neg: z[k][m] = %f, z[i][m] = %f, d = %f, sim = %f \n", z[k][m], z[i][m], d, sim); - delta[m] -= d; + const int out_i = m * wh; + delta[out_i] -= d; } } } diff --git a/src/blas.h b/src/blas.h index 8ba147ff8e7..198bdb71eb9 100644 --- a/src/blas.h +++ b/src/blas.h @@ -64,13 +64,15 @@ int check_sim(size_t i, size_t j, contrastive_params *contrast_p, int contrast_p float find_sim(size_t i, size_t j, contrastive_params *contrast_p, int contrast_p_size); float find_P_constrastive(size_t i, size_t j, contrastive_params *contrast_p, int contrast_p_size); float P_constrastive_f(size_t i, size_t l, int *labels, float **z, unsigned int feature_size, float temperature, contrastive_params *contrast_p, int contrast_p_size); -void grad_contrastive_loss_positive_f(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, contrastive_params *contrast_p, int contrast_p_size); -void grad_contrastive_loss_negative_f(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, contrastive_params *contrast_p, int contrast_p_size); +void grad_contrastive_loss_positive_f(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, int wh, contrastive_params *contrast_p, int contrast_p_size); +void grad_contrastive_loss_negative_f(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, int wh, contrastive_params *contrast_p, int contrast_p_size); +void get_embedding(float *src, int src_w, int src_h, int src_c, int embedding_size, int cur_w, int cur_h, int cur_n, int cur_b, float *dst); +float math_vector_length(float *A, unsigned int feature_size); float cosine_similarity(float *A, float *B, unsigned int feature_size); float P_constrastive(size_t i, size_t l, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *exp_cos_sim); -void grad_contrastive_loss_positive(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *p_constrastive, float *delta); -void grad_contrastive_loss_negative(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *p_constrastive, float *delta); +void grad_contrastive_loss_positive(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *p_constrastive, float *delta, int wh); +void grad_contrastive_loss_negative(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *p_constrastive, float *delta, int wh); #ifdef GPU diff --git a/src/data.c b/src/data.c index cadbdb0947b..d386f371bcd 100644 --- a/src/data.c +++ b/src/data.c @@ -1050,7 +1050,7 @@ void blend_truth_mosaic(float *new_truth, int boxes, int truth_size, float *old_ #include "http_stream.h" data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int truth_size, int classes, int use_flip, int use_gaussian_noise, int use_blur, int use_mixup, - float jitter, float resize, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int mosaic_bound, int contrastive, int show_imgs) + float jitter, float resize, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int mosaic_bound, int contrastive, int contrastive_jit_flip, int show_imgs) { const int random_index = random_gen(); c = c ? c : 3; @@ -1139,7 +1139,8 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo resize_r1 = random_float(); resize_r2 = random_float(); - if (!contrastive || i % 2 == 0) { + if (!contrastive || contrastive_jit_flip || i % 2 == 0) + { r1 = random_float(); r2 = random_float(); r3 = random_float(); @@ -1369,7 +1370,7 @@ void blend_images(image new_img, float alpha, image old_img, float beta) } data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int truth_size, int classes, int use_flip, int gaussian_noise, int use_blur, int use_mixup, - float jitter, float resize, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int mosaic_bound, int contrastive, int show_imgs) + float jitter, float resize, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int mosaic_bound, int contrastive, int contrastive_jit_flip, int show_imgs) { const int random_index = random_gen(); c = c ? c : 3; @@ -1438,18 +1439,21 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo resize_r1 = random_float(); resize_r2 = random_float(); - r1 = random_float(); - r2 = random_float(); - r3 = random_float(); - r4 = random_float(); + if (!contrastive || contrastive_jit_flip || i % 2 == 0) + { + r1 = random_float(); + r2 = random_float(); + r3 = random_float(); + r4 = random_float(); + + flip = use_flip ? random_gen() % 2 : 0; + } r_scale = random_float(); dhue = rand_uniform_strong(-hue, hue); dsat = rand_scale(saturation); dexp = rand_scale(exposure); - - flip = use_flip ? random_gen() % 2 : 0; } int pleft = rand_precalc_random(-dw, dw, r1); @@ -1584,7 +1588,7 @@ void *load_thread(void *ptr) *a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure); } else if (a.type == DETECTION_DATA){ *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.c, a.num_boxes, a.truth_size, a.classes, a.flip, a.gaussian_noise, a.blur, a.mixup, a.jitter, a.resize, - a.hue, a.saturation, a.exposure, a.mini_batch, a.track, a.augment_speed, a.letter_box, a.mosaic_bound, a.contrastive, a.show_imgs); + a.hue, a.saturation, a.exposure, a.mini_batch, a.track, a.augment_speed, a.letter_box, a.mosaic_bound, a.contrastive, a.contrastive_jit_flip, a.show_imgs); } else if (a.type == SWAG_DATA){ *a.d = load_data_swag(a.paths, a.n, a.classes, a.jitter); } else if (a.type == COMPARE_DATA){ diff --git a/src/data.h b/src/data.h index 32320231c75..3c6e66f56cd 100644 --- a/src/data.h +++ b/src/data.h @@ -87,7 +87,7 @@ data load_data_captcha(char **paths, int n, int m, int k, int w, int h); data load_data_captcha_encode(char **paths, int n, int m, int w, int h); data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h); data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int truth_size, int classes, int use_flip, int gaussian_noise, int use_blur, int use_mixup, - float jitter, float resize, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int mosaic_bound, int contrastive, int show_imgs); + float jitter, float resize, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int mosaic_bound, int contrastive, int contrastive_jit_flip, int show_imgs); data load_data_tag(char **paths, int n, int m, int k, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure); matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int dontuse_opencv, int contrastive); data load_data_super(char **paths, int n, int m, int w, int h, int scale); diff --git a/src/detector.c b/src/detector.c index 76872342431..17b2f820565 100644 --- a/src/detector.c +++ b/src/detector.c @@ -156,6 +156,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i args.letter_box = net.letter_box; args.mosaic_bound = net.mosaic_bound; args.contrastive = net.contrastive; + args.contrastive_jit_flip = net.contrastive_jit_flip; if (dont_show && show_imgs) show_imgs = 2; args.show_imgs = show_imgs; @@ -1271,11 +1272,8 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa avg_precision += delta_recall * last_precision; } //add remaining area of PR curve when recall isn't 0 at rank-1 - if(pr[i][0].recall!=0) - { - //no need of delta_recall=last_recall-0; - avg_precision += last_recall * last_precision; - } + double delta_recall = last_recall - 0; + avg_precision += delta_recall * last_precision; } // MSCOCO - 101 Recall-points, PascalVOC - 11 Recall-points else diff --git a/src/network.c b/src/network.c index d8ab07c6879..aa68a642400 100644 --- a/src/network.c +++ b/src/network.c @@ -781,6 +781,9 @@ detection *make_network_boxes(network *net, float thresh, int *num) if (l.coords > 4) dets[i].mask = (float*)xcalloc(l.coords - 4, sizeof(float)); else dets[i].mask = NULL; + + if(l.embedding_layer) dets[i].embeddings = (float*)xcalloc(l.embedding_size, sizeof(float)); + else dets[i].embeddings = NULL; } return dets; } @@ -801,6 +804,9 @@ detection *make_network_boxes_batch(network *net, float thresh, int *num, int ba if (l.coords > 4) dets[i].mask = (float*)xcalloc(l.coords - 4, sizeof(float)); else dets[i].mask = NULL; + + if (l.embedding_layer) dets[i].embeddings = (float*)xcalloc(l.embedding_size, sizeof(float)); + else dets[i].embeddings = NULL; } return dets; } @@ -900,6 +906,7 @@ void free_detections(detection *dets, int n) free(dets[i].prob); if (dets[i].uc) free(dets[i].uc); if (dets[i].mask) free(dets[i].mask); + if (dets[i].embeddings) free(dets[i].embeddings); } free(dets); } diff --git a/src/parser.c b/src/parser.c index 1452f85c160..b11997d7099 100644 --- a/src/parser.c +++ b/src/parser.c @@ -487,6 +487,15 @@ layer parse_yolo(list *options, size_params params) l.iou_thresh = option_find_float_quiet(options, "iou_thresh", 1); // recommended to use iou_thresh=0.213 in [yolo] l.random = option_find_float_quiet(options, "random", 0); + int embedding_layer_id = option_find_int_quiet(options, "embedding_layer", 999999); + if (embedding_layer_id < 0) embedding_layer_id = params.index + embedding_layer_id; + if (embedding_layer_id != 999999) { + printf(" embedding_layer_id = %d, ", embedding_layer_id); + l.embedding_layer = ¶ms.net.layers[embedding_layer_id]; + l.embedding_size = l.embedding_layer->n / l.n; + printf(" embedding_size = %d \n", l.embedding_size); + } + char *map_file = option_find_str(options, "map", 0); if (map_file) l.map = read_map(map_file); @@ -1145,6 +1154,7 @@ void parse_net_options(list *options, network *net) net->letter_box = option_find_int_quiet(options, "letter_box", 0); net->mosaic_bound = option_find_int_quiet(options, "mosaic_bound", 0); net->contrastive = option_find_int_quiet(options, "contrastive", 0); + net->contrastive_jit_flip = option_find_int_quiet(options, "contrastive_jit_flip", 0); net->unsupervised = option_find_int_quiet(options, "unsupervised", 0); if (net->contrastive && mini_batch < 2) { printf(" Error: mini_batch size (batch/subdivisions) should be higher than 1 for Contrastive loss \n"); diff --git a/src/softmax_layer.c b/src/softmax_layer.c index ec0dadbe279..43059704598 100644 --- a/src/softmax_layer.c +++ b/src/softmax_layer.c @@ -253,13 +253,15 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) for (h = 0; h < l.h; ++h) { for (w = 0; w < l.w; ++w) { - const int input_index = b*l.inputs + n*l.embedding_size*l.h*l.w + h*l.w + w; - const int z_index = b*l.n*l.h*l.w + n*l.h*l.w + h*l.w + w; + if (l.labels[z_index] < 0) continue; - float *ptr = state.input + input_index; + //const int input_index = b*l.inputs + n*l.embedding_size*l.h*l.w + h*l.w + w; + //float *ptr = state.input + input_index; + //z[z_index] = ptr; - z[z_index] = ptr; + z[z_index] = (float*)xcalloc(l.embedding_size, sizeof(float)); + get_embedding(state.input, l.w, l.h, l.c, l.embedding_size, w, h, n, b, z[z_index]); } } } @@ -328,7 +330,7 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) contrast_p = (contrastive_params*)xrealloc(contrast_p, contrast_p_size * sizeof(contrastive_params)); } - if (sim > 1.001 || sim < -1) { + if (sim > 1.001 || sim < -1.001) { printf(" sim = %f, ", sim); getchar(); } } @@ -458,23 +460,26 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) const size_t step = l.batch*l.n*l.h*l.w; if (l.labels[z_index] < 0) continue; + const int delta_index = b*l.embedding_size*l.n*l.h*l.w + n*l.embedding_size*l.h*l.w + h*l.w + w; + const int wh = l.w*l.h; + if (l.detection) { // detector // positive - grad_contrastive_loss_positive_f(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.delta + z_index, contrast_p, contrast_p_index); + grad_contrastive_loss_positive_f(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.delta + delta_index, wh, contrast_p, contrast_p_index); // negative - grad_contrastive_loss_negative_f(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.delta + z_index, contrast_p, contrast_p_index); + grad_contrastive_loss_negative_f(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.delta + delta_index, wh, contrast_p, contrast_p_index); } else { // classifier // positive - grad_contrastive_loss_positive(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.cos_sim, l.p_constrastive, l.delta + b*l.inputs); + grad_contrastive_loss_positive(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.cos_sim, l.p_constrastive, l.delta + delta_index, wh); // negative - grad_contrastive_loss_negative(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.cos_sim, l.p_constrastive, l.delta + b*l.inputs); + grad_contrastive_loss_negative(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.cos_sim, l.p_constrastive, l.delta + delta_index, wh); } } @@ -496,6 +501,19 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) printf(" contrastive loss = %f \n\n", *(l.cost)); } + for (b = 0; b < l.batch; ++b) { + for (n = 0; n < l.n; ++n) { + for (h = 0; h < l.h; ++h) { + for (w = 0; w < l.w; ++w) + { + const int z_index = b*l.n*l.h*l.w + n*l.h*l.w + h*l.w + w; + //if (l.labels[z_index] < 0) continue; + if (z[z_index]) free(z[z_index]); + } + } + } + } + free(contrast_p); free(z); } diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 2214d292fce..970a3897222 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -841,6 +841,11 @@ int get_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, dets[count].bbox = get_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h); dets[count].objectness = objectness; dets[count].classes = l.classes; + if (l.embedding_layer) { + layer le = *l.embedding_layer; + get_embedding(le.output, le.w, le.h, le.n, l.embedding_size, col, row, n, 0, dets[count].embeddings); + } + for (j = 0; j < l.classes; ++j) { int class_index = entry_index(l, 0, n*l.w*l.h + i, 4 + 1 + j); float prob = objectness*predictions[class_index]; @@ -873,6 +878,11 @@ int get_yolo_detections_batch(layer l, int w, int h, int netw, int neth, float t dets[count].bbox = get_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h); dets[count].objectness = objectness; dets[count].classes = l.classes; + if (l.embedding_layer) { + layer le = *l.embedding_layer; + get_embedding(le.output, le.w, le.h, le.n, l.embedding_size, col, row, n, batch, dets[count].embeddings); + } + for (j = 0; j < l.classes; ++j) { int class_index = entry_index(l, batch, n*l.w*l.h + i, 4 + 1 + j); float prob = objectness*predictions[class_index]; From ce2ae2c5c3849cdb6f9b66680c2ef0c2f9d2dad2 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 7 Jul 2020 02:49:43 +0300 Subject: [PATCH 1060/1365] put embeddings to the detections --- include/darknet.h | 3 ++- src/layer.c | 1 + src/network.c | 22 +++++++++++++++++++--- src/parser.c | 6 ++++-- src/yolo_layer.c | 15 +++++++++------ 5 files changed, 35 insertions(+), 12 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 9cfd2ac4c44..c278c2ffc6c 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -300,7 +300,8 @@ struct layer { int softmax; int classes; int detection; - layer *embedding_layer; + int embedding_layer_id; + float *embedding_output; int embedding_size; int coords; int background; diff --git a/src/layer.c b/src/layer.c index 3dcef7ac25a..ae60254bbc1 100644 --- a/src/layer.c +++ b/src/layer.c @@ -78,6 +78,7 @@ void free_layer_custom(layer l, int keep_cudnn_desc) if (l.cos_sim) free(l.cos_sim); if (l.exp_cos_sim) free(l.exp_cos_sim); if (l.p_constrastive) free(l.p_constrastive); + if (l.embedding_output) free(l.embedding_output); if (l.state) free(l.state); if (l.prev_state) free(l.prev_state); if (l.forgot_state) free(l.forgot_state); diff --git a/src/network.c b/src/network.c index aa68a642400..81349aba3d6 100644 --- a/src/network.c +++ b/src/network.c @@ -768,8 +768,16 @@ int num_detections_batch(network *net, float thresh, int batch) detection *make_network_boxes(network *net, float thresh, int *num) { - layer l = net->layers[net->n - 1]; int i; + layer l = net->layers[net->n - 1]; + for (i = 0; i < net->n; ++i) { + layer l_tmp = net->layers[i]; + if (l_tmp.type == YOLO || l_tmp.type == GAUSSIAN_YOLO || l_tmp.type == DETECTION || l_tmp.type == REGION) { + l = l_tmp; + break; + } + } + int nboxes = num_detections(net, thresh); if (num) *num = nboxes; detection* dets = (detection*)xcalloc(nboxes, sizeof(detection)); @@ -782,7 +790,7 @@ detection *make_network_boxes(network *net, float thresh, int *num) if (l.coords > 4) dets[i].mask = (float*)xcalloc(l.coords - 4, sizeof(float)); else dets[i].mask = NULL; - if(l.embedding_layer) dets[i].embeddings = (float*)xcalloc(l.embedding_size, sizeof(float)); + if(l.embedding_output) dets[i].embeddings = (float*)xcalloc(l.embedding_size, sizeof(float)); else dets[i].embeddings = NULL; } return dets; @@ -792,6 +800,14 @@ detection *make_network_boxes_batch(network *net, float thresh, int *num, int ba { int i; layer l = net->layers[net->n - 1]; + for (i = 0; i < net->n; ++i) { + layer l_tmp = net->layers[i]; + if (l_tmp.type == YOLO || l_tmp.type == GAUSSIAN_YOLO || l_tmp.type == DETECTION || l_tmp.type == REGION) { + l = l_tmp; + break; + } + } + int nboxes = num_detections_batch(net, thresh, batch); assert(num != NULL); *num = nboxes; @@ -805,7 +821,7 @@ detection *make_network_boxes_batch(network *net, float thresh, int *num, int ba if (l.coords > 4) dets[i].mask = (float*)xcalloc(l.coords - 4, sizeof(float)); else dets[i].mask = NULL; - if (l.embedding_layer) dets[i].embeddings = (float*)xcalloc(l.embedding_size, sizeof(float)); + if (l.embedding_output) dets[i].embeddings = (float*)xcalloc(l.embedding_size, sizeof(float)); else dets[i].embeddings = NULL; } return dets; diff --git a/src/parser.c b/src/parser.c index b11997d7099..1e5dec4a577 100644 --- a/src/parser.c +++ b/src/parser.c @@ -491,8 +491,10 @@ layer parse_yolo(list *options, size_params params) if (embedding_layer_id < 0) embedding_layer_id = params.index + embedding_layer_id; if (embedding_layer_id != 999999) { printf(" embedding_layer_id = %d, ", embedding_layer_id); - l.embedding_layer = ¶ms.net.layers[embedding_layer_id]; - l.embedding_size = l.embedding_layer->n / l.n; + layer le = params.net.layers[embedding_layer_id]; + l.embedding_layer_id = embedding_layer_id; + l.embedding_output = (float*)xcalloc(le.batch * le.outputs, sizeof(float)); + l.embedding_size = le.n / l.n; printf(" embedding_size = %d \n", l.embedding_size); } diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 970a3897222..82bd451f189 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -841,9 +841,8 @@ int get_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, dets[count].bbox = get_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h); dets[count].objectness = objectness; dets[count].classes = l.classes; - if (l.embedding_layer) { - layer le = *l.embedding_layer; - get_embedding(le.output, le.w, le.h, le.n, l.embedding_size, col, row, n, 0, dets[count].embeddings); + if (l.embedding_output) { + get_embedding(l.embedding_output, l.w, l.h, l.n*l.embedding_size, l.embedding_size, col, row, n, 0, dets[count].embeddings); } for (j = 0; j < l.classes; ++j) { @@ -878,9 +877,8 @@ int get_yolo_detections_batch(layer l, int w, int h, int netw, int neth, float t dets[count].bbox = get_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h); dets[count].objectness = objectness; dets[count].classes = l.classes; - if (l.embedding_layer) { - layer le = *l.embedding_layer; - get_embedding(le.output, le.w, le.h, le.n, l.embedding_size, col, row, n, batch, dets[count].embeddings); + if (l.embedding_output) { + get_embedding(l.embedding_output, l.w, l.h, l.n*l.embedding_size, l.embedding_size, col, row, n, batch, dets[count].embeddings); } for (j = 0; j < l.classes; ++j) { @@ -900,6 +898,11 @@ int get_yolo_detections_batch(layer l, int w, int h, int netw, int neth, float t void forward_yolo_layer_gpu(const layer l, network_state state) { + if (l.embedding_output) { + layer le = state.net.layers[l.embedding_layer_id]; + cuda_pull_array_async(le.output_gpu, l.embedding_output, le.batch*le.outputs); + } + //copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1); simple_copy_ongpu(l.batch*l.inputs, state.input, l.output_gpu); int b, n; From 8c77a3d532d6408cede5e5ecce70755ad248ca54 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 7 Jul 2020 03:07:42 +0300 Subject: [PATCH 1061/1365] Fixed random=1 --- src/yolo_layer.c | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 82bd451f189..adf14f69319 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -91,6 +91,9 @@ void resize_yolo_layer(layer *l, int w, int h) l->outputs = h*w*l->n*(l->classes + 4 + 1); l->inputs = l->outputs; + if (l->embedding_output) l->embedding_output = (float*)xrealloc(l->output, l->batch * l->embedding_size * l->n * l->h * l->w * sizeof(float)); + if (l->labels) l->labels = (int*)xrealloc(l->labels, l->batch * l->n * l->h * l->w * sizeof(int)); + if (!l->output_pinned) l->output = (float*)xrealloc(l->output, l->batch*l->outputs * sizeof(float)); if (!l->delta_pinned) l->delta = (float*)xrealloc(l->delta, l->batch*l->outputs*sizeof(float)); From ccb392ddf2ab49f66989e6318a2e379c9238068c Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 7 Jul 2020 19:30:49 +0300 Subject: [PATCH 1062/1365] Show track_id for models with contrastive learning --- build/darknet/x64/darknet.py | 6 +- darknet.py | 6 +- include/darknet.h | 9 +++ src/demo.c | 9 ++- src/http_stream.cpp | 126 +++++++++++++++++++++++++++++++++++ src/image_opencv.cpp | 8 ++- src/image_opencv.h | 1 + src/network.c | 2 + src/parser.c | 2 + 9 files changed, 164 insertions(+), 5 deletions(-) diff --git a/build/darknet/x64/darknet.py b/build/darknet/x64/darknet.py index 27a7a6797e6..74058a19c78 100644 --- a/build/darknet/x64/darknet.py +++ b/build/darknet/x64/darknet.py @@ -61,7 +61,11 @@ class DETECTION(Structure): ("objectness", c_float), ("sort_class", c_int), ("uc", POINTER(c_float)), - ("points", c_int)] + ("points", c_int), + ("embeddings", POINTER(c_float)), + ("embedding_size", c_int), + ("sim", c_float), + ("track_id", c_int)] class DETNUMPAIR(Structure): _fields_ = [("num", c_int), diff --git a/darknet.py b/darknet.py index 27a7a6797e6..74058a19c78 100644 --- a/darknet.py +++ b/darknet.py @@ -61,7 +61,11 @@ class DETECTION(Structure): ("objectness", c_float), ("sort_class", c_int), ("uc", POINTER(c_float)), - ("points", c_int)] + ("points", c_int), + ("embeddings", POINTER(c_float)), + ("embedding_size", c_int), + ("sim", c_float), + ("track_id", c_int)] class DETNUMPAIR(Structure): _fields_ = [("num", c_int), diff --git a/include/darknet.h b/include/darknet.h index c278c2ffc6c..fa213bde571 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -303,6 +303,8 @@ struct layer { int embedding_layer_id; float *embedding_output; int embedding_size; + float sim_thresh; + int track_history_size; int coords; int background; int rescore; @@ -867,6 +869,9 @@ typedef struct detection{ float *uc; // Gaussian_YOLOv3 - tx,ty,tw,th uncertainty int points; // bit-0 - center, bit-1 - top-left-corner, bit-2 - bottom-right-corner float *embeddings; // embeddings for tracking + int embedding_size; + float sim; + int track_id; } detection; // network.c -batch inference @@ -1063,6 +1068,10 @@ void stop_timer_and_show(); void stop_timer_and_show_name(char *name); void show_total_time(); +void set_track_id(detection *new_dets, int new_dets_num, float thresh, float sim_thresh, int deque_size); +int fill_remaining_id(detection *new_dets, int new_dets_num, int new_track_id, float thresh); + + // gemm.h LIB_API void init_cpu(); diff --git a/src/demo.c b/src/demo.c index 943c8704098..458dc6af293 100644 --- a/src/demo.c +++ b/src/demo.c @@ -179,8 +179,11 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int int i; for (i = 0; i < net.n; ++i) { - layer l = net.layers[i]; - if (l.type == YOLO) l.mean_alpha = 1.0 / avg_frames; + layer lc = net.layers[i]; + if (lc.type == YOLO) { + lc.mean_alpha = 1.0 / avg_frames; + l = lc; + } } if (l.classes != demo_classes) { @@ -262,6 +265,8 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int else diounms_sort(local_dets, local_nboxes, l.classes, nms, l.nms_kind, l.beta_nms); } + if (l.embedding_size) set_track_id(local_dets, local_nboxes, demo_thresh, l.sim_thresh, l.track_history_size); + //printf("\033[2J"); //printf("\033[1;1H"); //printf("\nFPS:%.1f\n", fps); diff --git a/src/http_stream.cpp b/src/http_stream.cpp index d6a11c567e9..2168dbb1172 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -755,3 +755,129 @@ void stop_timer_and_show() { void stop_timer_and_show_name(char *name) { stop_timer_and_show(); } void total_time() {} #endif // C++11 + +#include +#include +#include +#include "blas.h" +#include "utils.h" + +struct similarity_detections_t { + int old_id, new_id; + float sim; +}; + +int check_prob(detection det, float thresh) +{ + for (int i = 0; i < det.classes; ++i) { + if (det.prob[i] > thresh) return 1; + } + return 0; +} + +int fill_remaining_id(detection *new_dets, int new_dets_num, int new_track_id, float thresh) +{ + for (int i = 0; i < new_dets_num; ++i) { + if (new_dets[i].track_id == 0 && check_prob(new_dets[i], thresh)) { + //printf(" old_tid = %d, new_tid = %d, sim = %f \n", new_dets[i].track_id, new_track_id, new_dets[i].sim); + new_dets[i].track_id = new_track_id; + new_track_id++; + } + } + return new_track_id; +} + +float *make_float_array(float* src, size_t size) +{ + float *dst = (float*)xcalloc(size, sizeof(float)); + memcpy(dst, src, size*sizeof(float)); + return dst; +} + +struct detection_t : detection { + detection_t(detection det) : detection(det) + { + if (embeddings) embeddings = make_float_array(det.embeddings, embedding_size); + if (prob) prob = make_float_array(det.prob, classes); + if (uc) uc = make_float_array(det.uc, 4); + } + + detection_t(detection_t const& det) : detection(det) + { + if (embeddings) embeddings = make_float_array(det.embeddings, embedding_size); + if (prob) prob = make_float_array(det.prob, classes); + if (uc) uc = make_float_array(det.uc, 4); + } + + ~detection_t() { + if (embeddings) free(embeddings); + if (prob) free(prob); + if (uc) free(uc); + } +}; + + + +void set_track_id(detection *new_dets, int new_dets_num, float thresh, float sim_thresh, int deque_size) +{ + static int new_track_id = 1; + static std::deque> old_dets_dq; + + // copy detections from queue of vectors to the one vector + std::vector old_dets; + for (std::vector &v : old_dets_dq) { + for (int i = 0; i < v.size(); ++i) { + old_dets.push_back(v[i]); + } + } + + std::vector sim_det(old_dets.size() * new_dets_num); + + // calculate similarity + for (int old_id = 0; old_id < old_dets.size(); ++old_id) { + for (int new_id = 0; new_id < new_dets_num; ++new_id) { + const int index = old_id*new_dets_num + new_id; + const float sim = cosine_similarity(new_dets[new_id].embeddings, old_dets[old_id].embeddings, old_dets[0].embedding_size); + sim_det[index].new_id = new_id; + sim_det[index].old_id = old_id; + sim_det[index].sim = sim; + } + } + + // sort similarity + std::sort(sim_det.begin(), sim_det.end(), [](similarity_detections_t v1, similarity_detections_t v2) { return v1.sim > v2.sim; }); + if(sim_det.size() > 0) printf(" sim_det_first = %f, sim_det_end = %f \n", sim_det.begin()->sim, sim_det.rbegin()->sim); + + + std::vector new_idx(new_dets_num, 1); + std::vector old_idx(old_dets.size(), 1); + std::vector track_idx(new_track_id, 1); + + // match objects + for (int index = 0; index < new_dets_num*old_dets.size(); ++index) { + const int new_id = sim_det[index].new_id; + const int old_id = sim_det[index].old_id; + const int track_id = old_dets[old_id].track_id; + if (check_prob(new_dets[new_id], thresh) && track_idx[track_id] && new_idx[new_id] && old_idx[old_id] && sim_thresh < sim_det[index].sim) { + new_dets[new_id].sim = sim_det[index].sim; + new_dets[new_id].track_id = track_id; + new_idx[new_id] = 0; + old_idx[old_id] = 0; + track_idx[track_id] = 0; + } + } + + // set new track_id + new_track_id = fill_remaining_id(new_dets, new_dets_num, new_track_id, thresh); + + // store new_detections to the queue of vectors + std::vector new_det_vec; + for (int i = 0; i < new_dets_num; ++i) { + if(check_prob(new_dets[i], thresh)) + new_det_vec.push_back(new_dets[i]); + } + + old_dets_dq.push_back(new_det_vec); // add new + if (old_dets_dq.size() > deque_size) old_dets_dq.pop_front(); // remove old +} + diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 8b8b321c97f..512cc0e1582 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -893,10 +893,15 @@ extern "C" void draw_detections_cv_v3(mat_cv* mat, detection *dets, int num, flo if (class_id < 0) { strcat(labelstr, names[j]); class_id = j; - char buff[10]; + char buff[20]; + if (dets[i].track_id) { + sprintf(buff, " (track = %d)", dets[i].track_id); + strcat(labelstr, buff); + } sprintf(buff, " (%2.0f%%)", dets[i].prob[j] * 100); strcat(labelstr, buff); printf("%s: %.0f%% ", names[j], dets[i].prob[j] * 100); + if (dets[i].track_id) printf("(track = %d, sim = %f) ", dets[i].track_id, dets[i].sim); } else { strcat(labelstr, ", "); @@ -1540,4 +1545,5 @@ extern "C" void show_opencv_info() extern "C" int wait_key_cv(int delay) { return 0; } extern "C" int wait_until_press_key_cv() { return 0; } extern "C" void destroy_all_windows_cv() {} +extern "C" void resize_window_cv(char const* window_name, int width, int height) {} #endif // OPENCV diff --git a/src/image_opencv.h b/src/image_opencv.h index a825a3c9789..6fa6cb5c6b0 100644 --- a/src/image_opencv.h +++ b/src/image_opencv.h @@ -123,6 +123,7 @@ void show_opencv_info(); int wait_key_cv(int delay); int wait_until_press_key_cv(); void destroy_all_windows_cv(); +void resize_window_cv(char const* window_name, int width, int height); #endif // OPENCV diff --git a/src/network.c b/src/network.c index 81349aba3d6..c3c08cee971 100644 --- a/src/network.c +++ b/src/network.c @@ -792,6 +792,7 @@ detection *make_network_boxes(network *net, float thresh, int *num) if(l.embedding_output) dets[i].embeddings = (float*)xcalloc(l.embedding_size, sizeof(float)); else dets[i].embeddings = NULL; + dets[i].embedding_size = l.embedding_size; } return dets; } @@ -823,6 +824,7 @@ detection *make_network_boxes_batch(network *net, float thresh, int *num, int ba if (l.embedding_output) dets[i].embeddings = (float*)xcalloc(l.embedding_size, sizeof(float)); else dets[i].embeddings = NULL; + dets[i].embedding_size = l.embedding_size; } return dets; } diff --git a/src/parser.c b/src/parser.c index 1e5dec4a577..f403fef8fe0 100644 --- a/src/parser.c +++ b/src/parser.c @@ -487,6 +487,8 @@ layer parse_yolo(list *options, size_params params) l.iou_thresh = option_find_float_quiet(options, "iou_thresh", 1); // recommended to use iou_thresh=0.213 in [yolo] l.random = option_find_float_quiet(options, "random", 0); + l.track_history_size = option_find_int_quiet(options, "track_history_size", 5); + l.sim_thresh = option_find_int_quiet(options, "sim_thresh", 0.8); int embedding_layer_id = option_find_int_quiet(options, "embedding_layer", 999999); if (embedding_layer_id < 0) embedding_layer_id = params.index + embedding_layer_id; if (embedding_layer_id != 999999) { From 37753e0cb4da93077446d4846582b9ee23cd3efc Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 8 Jul 2020 00:08:45 +0300 Subject: [PATCH 1063/1365] Improved tracking (new params) --- include/darknet.h | 5 ++++- src/demo.c | 2 +- src/http_stream.cpp | 51 ++++++++++++++++++++++++++++++++------------- src/parser.c | 3 +++ 4 files changed, 44 insertions(+), 17 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index fa213bde571..892ad71b2f1 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -305,6 +305,9 @@ struct layer { int embedding_size; float sim_thresh; int track_history_size; + int dets_for_track; + int dets_for_show; + float track_ciou_norm; int coords; int background; int rescore; @@ -1068,7 +1071,7 @@ void stop_timer_and_show(); void stop_timer_and_show_name(char *name); void show_total_time(); -void set_track_id(detection *new_dets, int new_dets_num, float thresh, float sim_thresh, int deque_size); +void set_track_id(detection *new_dets, int new_dets_num, float thresh, float sim_thresh, float track_ciou_norm, int deque_size, int dets_for_track, int dets_for_show); int fill_remaining_id(detection *new_dets, int new_dets_num, int new_track_id, float thresh); diff --git a/src/demo.c b/src/demo.c index 458dc6af293..5fff2360bbd 100644 --- a/src/demo.c +++ b/src/demo.c @@ -265,7 +265,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int else diounms_sort(local_dets, local_nboxes, l.classes, nms, l.nms_kind, l.beta_nms); } - if (l.embedding_size) set_track_id(local_dets, local_nboxes, demo_thresh, l.sim_thresh, l.track_history_size); + if (l.embedding_size) set_track_id(local_dets, local_nboxes, demo_thresh, l.sim_thresh, l.track_ciou_norm, l.track_history_size, l.dets_for_track, l.dets_for_show); //printf("\033[2J"); //printf("\033[1;1H"); diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 2168dbb1172..3fd3c94cf51 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -775,13 +775,15 @@ int check_prob(detection det, float thresh) return 0; } -int fill_remaining_id(detection *new_dets, int new_dets_num, int new_track_id, float thresh) +int fill_remaining_id(detection *new_dets, int new_dets_num, int new_track_id, float thresh, int detection_count) { for (int i = 0; i < new_dets_num; ++i) { if (new_dets[i].track_id == 0 && check_prob(new_dets[i], thresh)) { //printf(" old_tid = %d, new_tid = %d, sim = %f \n", new_dets[i].track_id, new_track_id, new_dets[i].sim); - new_dets[i].track_id = new_track_id; - new_track_id++; + if (new_dets[i].sort_class > detection_count) { + new_dets[i].track_id = new_track_id; + new_track_id++; + } } } return new_track_id; @@ -795,7 +797,8 @@ float *make_float_array(float* src, size_t size) } struct detection_t : detection { - detection_t(detection det) : detection(det) + int det_count; + detection_t(detection det) : detection(det), det_count(0) { if (embeddings) embeddings = make_float_array(det.embeddings, embedding_size); if (prob) prob = make_float_array(det.prob, classes); @@ -818,7 +821,7 @@ struct detection_t : detection { -void set_track_id(detection *new_dets, int new_dets_num, float thresh, float sim_thresh, int deque_size) +void set_track_id(detection *new_dets, int new_dets_num, float thresh, float sim_thresh, float track_ciou_norm, int deque_size, int dets_for_track, int dets_for_show) { static int new_track_id = 1; static std::deque> old_dets_dq; @@ -858,26 +861,44 @@ void set_track_id(detection *new_dets, int new_dets_num, float thresh, float sim const int new_id = sim_det[index].new_id; const int old_id = sim_det[index].old_id; const int track_id = old_dets[old_id].track_id; - if (check_prob(new_dets[new_id], thresh) && track_idx[track_id] && new_idx[new_id] && old_idx[old_id] && sim_thresh < sim_det[index].sim) { - new_dets[new_id].sim = sim_det[index].sim; - new_dets[new_id].track_id = track_id; - new_idx[new_id] = 0; - old_idx[old_id] = 0; - track_idx[track_id] = 0; + const int det_count = old_dets[old_id].sort_class; + if (check_prob(new_dets[new_id], thresh) && track_idx[track_id] && new_idx[new_id] && old_idx[old_id]) { + float sim = sim_det[index].sim; + float ciou = box_ciou(new_dets[new_id].bbox, old_dets[old_id].bbox); + sim = sim * (1 - track_ciou_norm) + ciou * track_ciou_norm; + if (sim_thresh < sim) { + new_dets[new_id].sim = sim; + new_dets[new_id].track_id = track_id; + new_dets[new_id].sort_class = det_count + 1; + new_idx[new_id] = 0; + old_idx[old_id] = 0; + if(track_id) track_idx[track_id] = 0; + } } } // set new track_id - new_track_id = fill_remaining_id(new_dets, new_dets_num, new_track_id, thresh); + new_track_id = fill_remaining_id(new_dets, new_dets_num, new_track_id, thresh, dets_for_track); // store new_detections to the queue of vectors std::vector new_det_vec; for (int i = 0; i < new_dets_num; ++i) { - if(check_prob(new_dets[i], thresh)) + if (check_prob(new_dets[i], thresh)) { new_det_vec.push_back(new_dets[i]); + } } - old_dets_dq.push_back(new_det_vec); // add new - if (old_dets_dq.size() > deque_size) old_dets_dq.pop_front(); // remove old + // add new + old_dets_dq.push_back(new_det_vec); + // remove old + if (old_dets_dq.size() > deque_size) old_dets_dq.pop_front(); + + // remove detection which were detected only on few frames + for (int i = 0; i < new_dets_num; ++i) { + if (new_dets[i].sort_class < dets_for_show) + for (int j = 0; j < new_dets[i].classes; ++j) { + new_dets[i].prob[j] = 0; + } + } } diff --git a/src/parser.c b/src/parser.c index f403fef8fe0..4ac1912c39f 100644 --- a/src/parser.c +++ b/src/parser.c @@ -489,6 +489,9 @@ layer parse_yolo(list *options, size_params params) l.track_history_size = option_find_int_quiet(options, "track_history_size", 5); l.sim_thresh = option_find_int_quiet(options, "sim_thresh", 0.8); + l.dets_for_track = option_find_int_quiet(options, "dets_for_track", 1); + l.dets_for_show = option_find_int_quiet(options, "dets_for_show", 1); + l.track_ciou_norm = option_find_float_quiet(options, "track_ciou_norm", 0.01); int embedding_layer_id = option_find_int_quiet(options, "embedding_layer", 999999); if (embedding_layer_id < 0) embedding_layer_id = params.index + embedding_layer_id; if (embedding_layer_id != 999999) { From 5f598eecdfa62ddf65569e42a79b765fc6c65c6a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 8 Jul 2020 00:20:31 +0300 Subject: [PATCH 1064/1365] minor fix --- src/image_opencv.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 512cc0e1582..add27c96ef0 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -895,7 +895,7 @@ extern "C" void draw_detections_cv_v3(mat_cv* mat, detection *dets, int num, flo class_id = j; char buff[20]; if (dets[i].track_id) { - sprintf(buff, " (track = %d)", dets[i].track_id); + sprintf(buff, " (id: %d)", dets[i].track_id); strcat(labelstr, buff); } sprintf(buff, " (%2.0f%%)", dets[i].prob[j] * 100); From 1a1a4afcdfd3afb304f2182f7769a2af6cb4b0fc Mon Sep 17 00:00:00 2001 From: Alexey Date: Wed, 8 Jul 2020 00:28:50 +0300 Subject: [PATCH 1065/1365] Update Readme.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 457f8f39040..516db940ca8 100644 --- a/README.md +++ b/README.md @@ -490,6 +490,7 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an 7. Download pre-trained weights for the convolutional layers and put to the directory `build\darknet\x64` * for `yolov4.cfg`, `yolov4-custom.cfg` (162 MB): [yolov4.conv.137](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137) (Google drive mirror [yolov4.conv.137](https://drive.google.com/open?id=1JKF-bdIklxOOVy-2Cr5qdvjgGpmGfcbp) ) + * for `yolov4-tiny.cfg`, `yolov4-tiny-custom.cfg` (19 MB): [yolov4-tiny.conv.29](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29) * for `csresnext50-panet-spp.cfg` (133 MB): [csresnext50-panet-spp.conv.112](https://drive.google.com/file/d/16yMYCLQTY_oDlCIZPfn_sab6KD3zgzGq/view?usp=sharing) * for `yolov3.cfg, yolov3-spp.cfg` (154 MB): [darknet53.conv.74](https://pjreddie.com/media/files/darknet53.conv.74) * for `yolov3-tiny-prn.cfg , yolov3-tiny.cfg` (6 MB): [yolov3-tiny.conv.11](https://drive.google.com/file/d/18v36esoXCh-PsOKwyP2GWrpYDptDY8Zf/view?usp=sharing) From 504fc76509ca6ed3275cf06685331027d4ebcbee Mon Sep 17 00:00:00 2001 From: Alexey Date: Wed, 8 Jul 2020 14:37:34 +0300 Subject: [PATCH 1066/1365] Update issue templates --- ...aining-issue---no-detections---nan-avg-loss---low-accuracy.md | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/ISSUE_TEMPLATE/training-issue---no-detections---nan-avg-loss---low-accuracy.md b/.github/ISSUE_TEMPLATE/training-issue---no-detections---nan-avg-loss---low-accuracy.md index d18385e9a25..b826cdd8c40 100644 --- a/.github/ISSUE_TEMPLATE/training-issue---no-detections---nan-avg-loss---low-accuracy.md +++ b/.github/ISSUE_TEMPLATE/training-issue---no-detections---nan-avg-loss---low-accuracy.md @@ -8,6 +8,7 @@ assignees: '' --- If you have an issue with training - no-detections / Nan avg-loss / low accuracy: + * read FAQ: https://github.com/AlexeyAB/darknet/wiki/FAQ---frequently-asked-questions * what command do you use? * what dataset do you use? * what Loss and mAP did you get? From a47422a30ea947bcbd1753b2c10c41058024a0ba Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 8 Jul 2020 15:45:44 +0300 Subject: [PATCH 1067/1365] Added scripts --- scripts/install_OpenCV4.sh | 66 +++++++++++++++++ scripts/kitti2yolo.py | 141 +++++++++++++++++++++++++++++++++++++ 2 files changed, 207 insertions(+) create mode 100644 scripts/install_OpenCV4.sh create mode 100644 scripts/kitti2yolo.py diff --git a/scripts/install_OpenCV4.sh b/scripts/install_OpenCV4.sh new file mode 100644 index 00000000000..f57c87b4c7e --- /dev/null +++ b/scripts/install_OpenCV4.sh @@ -0,0 +1,66 @@ +#!/bin/bash + +#based on https://devtalk.nvidia.com/default/topic/1042035/installing-opencv4-on-xavier/ & https://github.com/markste-in/OpenCV4XAVIER/blob/master/buildOpenCV4.sh + +# Compute Capabilities can be found here https://developer.nvidia.com/cuda-gpus#compute +ARCH_BIN=7.2 # AGX Xavier +#ARCH_BIN=6.2 # Tx2 + +cd ~/Downloads +sudo apt-get install -y build-essential \ + unzip \ + pkg-config \ + libjpeg-dev \ + libpng-dev \ + libtiff-dev \ + libavcodec-dev \ + libavformat-dev \ + libswscale-dev \ + libv4l-dev \ + libxvidcore-dev \ + libx264-dev \ + libgtk-3-dev \ + libatlas-base-dev \ + gfortran \ + python3-dev \ + python3-venv \ + libgstreamer1.0-dev \ + libgstreamer-plugins-base1.0-dev \ + libdc1394-22-dev \ + libavresample-dev + +git clone https://github.com/opencv/opencv.git +git clone https://github.com/opencv/opencv_contrib.git + +python3 -m venv opencv4 +source opencv4/bin/activate +pip install wheel +pip install numpy + +cd opencv && mkdir build && cd build + +cmake -D CMAKE_BUILD_TYPE=RELEASE \ + -D CMAKE_INSTALL_PREFIX=/usr/local \ + -D INSTALL_PYTHON_EXAMPLES=ON \ + -D INSTALL_C_EXAMPLES=OFF \ + -D OPENCV_EXTRA_MODULES_PATH='~/Downloads/opencv_contrib/modules' \ + -D PYTHON_EXECUTABLE='~/Downloads/opencv4/bin/python' \ + -D BUILD_EXAMPLES=ON \ + -D WITH_CUDA=ON \ + -D CUDA_ARCH_BIN=${ARCH_BIN} \ + -D CUDA_ARCH_PTX="" \ + -D ENABLE_FAST_MATH=ON \ + -D CUDA_FAST_MATH=ON \ + -D WITH_CUBLAS=ON \ + -D WITH_LIBV4L=ON \ + -D WITH_GSTREAMER=ON \ + -D WITH_GSTREAMER_0_10=OFF \ + -D WITH_TBB=ON \ + ../ + +make -j4 +sudo make install +sudo ldconfig + +cd ~/Downloads/opencv4/lib/python3.6/site-packages +ln -s /usr/local/lib/python3.6/site-packages/cv2.cpython-36m-aarch64-linux-gnu.so cv2.so diff --git a/scripts/kitti2yolo.py b/scripts/kitti2yolo.py new file mode 100644 index 00000000000..4bd69db8b12 --- /dev/null +++ b/scripts/kitti2yolo.py @@ -0,0 +1,141 @@ +#!/usr/bin/env python +from __future__ import print_function +from __future__ import division +# +# This is a utility for converting ground truth data from the kitti format +# to the YOLO format. +# +# +# +# YOLO FORMAT +# .txt for each .jpg - in the same directory and with the same name +# +# +# Where: +# +# - integer number of object from 0 to (classes-1) +# - floats relative to image width/height 0.0 to 1.0 +# eg. = / +# Note: - are center of rectangle (not top-left corner) +# +# For example for img1.jpg you will be created img1.txt containing: +# +# 1 0.716797 0.395833 0.216406 0.147222 +# 0 0.687109 0.379167 0.255469 0.158333 +# 1 0.420312 0.395833 0.140625 0.166667 +# +# KITTI FORMAT +# +# All images as .png in a separate folder to the .txt labels of the same name +# One label line is as follows: +# +# 1 type Describes the type of object: Car, Van, Truck, +# Pedestrian, Person_sitting, Cyclist, Tram, +# Misc or DontCare +# 1 truncated Float from 0 (non-truncated) to 1 (truncated), where +# truncated refers to the object leaving image boundaries +# 1 occluded Integer (0,1,2,3) indicating occlusion state: +# 0 = fully visible, 1 = partly occluded +# 2 = largely occluded, 3 = unknown +# 1 alpha Observation angle of object, ranging [-pi..pi] +# 4 bbox 2D bounding box of object in the image (0-based index): +# contains left, top, right, bottom pixel coordinates +# 3 dimensions 3D object dimensions: height, width, length (in meters) +# 3 location 3D object location x,y,z in camera coordinates (in meters) +# 1 rotation_y Rotation ry around Y-axis in camera coordinates [-pi..pi] +# 1 score Only for results: Float, indicating confidence in +# detection, needed for p/r curves, higher is better. +# +# Car 0.0 0 -1.5 57.0 17.3 614.1 200.12 1.65 1.67 3.64 -0.65 1.71 46.70 -1.59 +# Cyclist 0.0 0 -2.46 665.45 160.00 717.9 217.9 1.7 0.4 1.6 2.4 1.3 22.1 -2.35 +# Pedestrian 0.00 2 0.2 42.1 17.6 433.1 24.0 1.6 0.38 0.30 -5.8 1.6 23.1 -0.03 +# DontCare -1 -1 -10 650.19 175.02 668.98 210.48 -1 -1 -1 -1000 -1000 -1000 -10 + +# core imports +import argparse +import sys +import os +import shutil +import cv2 + + +kitti2yolotype_dict = {'Car': '0', + 'Van': '0', + 'Pedestrian': '1', + 'Person_sitting': '1', + 'Cyclist': '2', + 'Truck': '3', + 'Tram': '6', + 'Misc': '6', + 'DontCare': '6'} + + +def kitti2yolo(kitti_label, img_height, img_width): + + kitti_label_arr = kitti_label.split(' ') + x1 = float(kitti_label_arr[4]) + y1 = float(kitti_label_arr[5]) + x2 = float(kitti_label_arr[6]) + y2 = float(kitti_label_arr[7]) + + bb_width = x2 - x1 + bb_height = y2 - y1 + yolo_x = (x1 + 0.5*bb_width) / img_width + yolo_y = (y1 + 0.5*bb_height) / img_height + yolo_bb_width = bb_width / img_width + yolo_bb_height = bb_height / img_height + yolo_label = kitti2yolotype_dict[kitti_label_arr[0]] + + return (yolo_label + ' ' + + str(yolo_x) + ' ' + + str(yolo_y) + ' ' + + str(yolo_bb_width) + ' ' + + str(yolo_bb_height)) + + +def main(args): + + # parse command line arguments + parser = argparse.ArgumentParser() + parser.add_argument("--kitti", + help="path to kitti-format images and labels, images\ + should be under images_path/images and labels should\ + be under images_path/labels") + parser.add_argument("--yolo", + help="path to output yolo-ready training data") + # kitti paths + args = parser.parse_args() + root_path = args.kitti + yolo_path = args.yolo + if root_path is None: + root_path = os.getcwd() + if (root_path[-1] != os.sep): + root_path += os.sep + kitti_images_path = root_path + 'image_2' + os.sep + kitti_labels_path = root_path + 'label_2' + os.sep + + # yolo paths + if yolo_path is None: + yolo_path = root_path + 'yolo_labels' + os.sep + + if not os.path.exists(yolo_path): + os.makedirs(yolo_path) + + # load each kitti label, convert to yolo and save + for labelfilename in os.listdir(kitti_labels_path): + yolo_labels = [] + with open(kitti_labels_path + labelfilename, 'r') as kittilabelfile: + cvimage = cv2.imread(kitti_images_path + + labelfilename.split('.txt')[0] + '.png') + height, width, frame_depth = cvimage.shape + for kitti_label in kittilabelfile: + yolo_labels.append(kitti2yolo(kitti_label, + img_height=height, + img_width=width)) + with open(yolo_path + labelfilename, 'w+') as yololabelfile: + for label in yolo_labels: + yololabelfile.write(label + '\n') + + +if __name__ == '__main__': + main(sys.argv) From 8872a860b34067756ecf1105006489cf2d5b885a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 8 Jul 2020 20:23:20 +0300 Subject: [PATCH 1068/1365] Minor fix --- src/detector.c | 37 ++++++++++++++++++++++++++++++++++++- src/http_stream.cpp | 5 +++-- 2 files changed, 39 insertions(+), 3 deletions(-) diff --git a/src/detector.c b/src/detector.c index 17b2f820565..6017f584249 100644 --- a/src/detector.c +++ b/src/detector.c @@ -647,6 +647,14 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out char **paths = (char **)list_to_array(plist); layer l = net.layers[net.n - 1]; + int k; + for (k = 0; k < net.n; ++k) { + layer lk = net.layers[k]; + if (lk.type == YOLO || lk.type == GAUSSIAN_YOLO || lk.type == REGION) { + l = lk; + printf(" Detection layer: %d - type = %d \n", k, l.type); + } + } int classes = l.classes; char buff[1024]; @@ -960,6 +968,14 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa layer l = net.layers[net.n - 1]; + int k; + for (k = 0; k < net.n; ++k) { + layer lk = net.layers[k]; + if (lk.type == YOLO || lk.type == GAUSSIAN_YOLO || lk.type == REGION) { + l = lk; + printf(" Detection layer: %d - type = %d \n", k, l.type); + } + } int classes = l.classes; int m = plist->size; @@ -1042,7 +1058,8 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms); else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms); } - //if (nms) do_nms_obj(dets, nboxes, l.classes, nms); + + //if (l.embedding_size) set_track_id(dets, nboxes, thresh, l.sim_thresh, l.track_ciou_norm, l.track_history_size, l.dets_for_track, l.dets_for_show); char labelpath[4096]; replace_image_to_label(path, labelpath); @@ -1628,7 +1645,16 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam image sized; if(letter_box) sized = letterbox_image(im, net.w, net.h); else sized = resize_image(im, net.w, net.h); + layer l = net.layers[net.n - 1]; + int k; + for (k = 0; k < net.n; ++k) { + layer lk = net.layers[k]; + if (lk.type == YOLO || lk.type == GAUSSIAN_YOLO || lk.type == REGION) { + l = lk; + printf(" Detection layer: %d - type = %d \n", k, l.type); + } + } //box *boxes = calloc(l.w*l.h*l.n, sizeof(box)); //float **probs = calloc(l.w*l.h*l.n, sizeof(float*)); @@ -1785,6 +1811,15 @@ void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, image src_sized = copy_image(sized); layer l = net.layers[net.n - 1]; + int k; + for (k = 0; k < net.n; ++k) { + layer lk = net.layers[k]; + if (lk.type == YOLO || lk.type == GAUSSIAN_YOLO || lk.type == REGION) { + l = lk; + printf(" Detection layer: %d - type = %d \n", k, l.type); + } + } + net.num_boxes = l.max_boxes; int num_truth = l.truths; float *truth_cpu = (float *)xcalloc(num_truth, sizeof(float)); diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 3fd3c94cf51..0a231937fc0 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -849,7 +849,7 @@ void set_track_id(detection *new_dets, int new_dets_num, float thresh, float sim // sort similarity std::sort(sim_det.begin(), sim_det.end(), [](similarity_detections_t v1, similarity_detections_t v2) { return v1.sim > v2.sim; }); - if(sim_det.size() > 0) printf(" sim_det_first = %f, sim_det_end = %f \n", sim_det.begin()->sim, sim_det.rbegin()->sim); + //if(sim_det.size() > 0) printf(" sim_det_first = %f, sim_det_end = %f \n", sim_det.begin()->sim, sim_det.rbegin()->sim); std::vector new_idx(new_dets_num, 1); @@ -895,10 +895,11 @@ void set_track_id(detection *new_dets, int new_dets_num, float thresh, float sim // remove detection which were detected only on few frames for (int i = 0; i < new_dets_num; ++i) { - if (new_dets[i].sort_class < dets_for_show) + if (new_dets[i].sort_class < dets_for_show) { for (int j = 0; j < new_dets[i].classes; ++j) { new_dets[i].prob[j] = 0; } + } } } From aa002ea1f8fbce6e139210ee1d936ce58ce120e1 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 9 Jul 2020 14:45:06 +0300 Subject: [PATCH 1069/1365] Minor fix --- src/blas.c | 2 ++ src/http_stream.cpp | 1 + src/yolo_layer.c | 4 ++-- 3 files changed, 5 insertions(+), 2 deletions(-) diff --git a/src/blas.c b/src/blas.c index 5c3b99c8e4d..b294f755055 100644 --- a/src/blas.c +++ b/src/blas.c @@ -658,6 +658,7 @@ void grad_contrastive_loss_positive_f(size_t i, int *labels, size_t num_of_sampl fprintf(stderr, " Error: N == 0 || temperature == 0 || vec_len == 0. N=%f, temperature=%f, vec_len=%f, labels[i] = %d \n", N, temperature, vec_len, labels[i]); getchar(); + return; } const float mult = 1 / ((N - 1) * temperature * vec_len); @@ -703,6 +704,7 @@ void grad_contrastive_loss_negative_f(size_t i, int *labels, size_t num_of_sampl fprintf(stderr, " Error: N == 0 || temperature == 0 || vec_len == 0. N=%f, temperature=%f, vec_len=%f, labels[i] = %d \n", N, temperature, vec_len, labels[i]); getchar(); + return; } const float mult = 1 / ((N - 1) * temperature * vec_len); diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 0a231937fc0..c8e9b5e113c 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -862,6 +862,7 @@ void set_track_id(detection *new_dets, int new_dets_num, float thresh, float sim const int old_id = sim_det[index].old_id; const int track_id = old_dets[old_id].track_id; const int det_count = old_dets[old_id].sort_class; + //printf(" ciou = %f \n", box_ciou(new_dets[new_id].bbox, old_dets[old_id].bbox)); if (check_prob(new_dets[new_id], thresh) && track_idx[track_id] && new_idx[new_id] && old_idx[old_id]) { float sim = sim_det[index].sim; float ciou = box_ciou(new_dets[new_id].bbox, old_dets[old_id].bbox); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index adf14f69319..dba7831b9b4 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -392,6 +392,7 @@ void forward_yolo_layer(const layer l, network_state state) int best_t = 0; for (t = 0; t < l.max_boxes; ++t) { box truth = float_to_box_stride(state.truth + t*l.truth_size + b*l.truths, 1); + if (!truth.x) break; // continue; int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; if (class_id >= l.classes || class_id < 0) { printf("\n Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1); @@ -399,7 +400,6 @@ void forward_yolo_layer(const layer l, network_state state) if (check_mistakes) getchar(); continue; // if label contains class_id more than number of classes in the cfg-file and class_id check garbage value } - if (!truth.x) break; // continue; float objectness = l.output[obj_index]; if (isnan(objectness) || isinf(objectness)) l.output[obj_index] = 0; @@ -476,6 +476,7 @@ void forward_yolo_layer(const layer l, network_state state) } for (t = 0; t < l.max_boxes; ++t) { box truth = float_to_box_stride(state.truth + t*l.truth_size + b*l.truths, 1); + if (!truth.x) break; // continue; if (truth.x < 0 || truth.y < 0 || truth.x > 1 || truth.y > 1 || truth.w < 0 || truth.h < 0) { char buff[256]; printf(" Wrong label: truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f \n", truth.x, truth.y, truth.w, truth.h); @@ -486,7 +487,6 @@ void forward_yolo_layer(const layer l, network_state state) int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; if (class_id >= l.classes || class_id < 0) continue; // if label contains class_id more than number of classes in the cfg-file and class_id check garbage value - if (!truth.x) break; // continue; float best_iou = 0; int best_n = 0; i = (truth.x * l.w); From d8db9743dbde5b94b86e04ef038f195828ea123f Mon Sep 17 00:00:00 2001 From: Alexey Date: Fri, 10 Jul 2020 15:08:38 +0300 Subject: [PATCH 1070/1365] Update README.md --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 516db940ca8..e2661477ec5 100644 --- a/README.md +++ b/README.md @@ -52,17 +52,17 @@ About Darknet framework: http://pjreddie.com/darknet/ |---|---| tkDNN-TensorRT accelerates YOLOv4 **~2x** times for batch=1 and **3x-4x** times for batch=4. -OpenCV-dnn is ~10% slower than tkDNN-TensorRT. * tkDNN: https://github.com/ceccocats/tkDNN * OpenCV: https://gist.github.com/YashasSamaga/48bdb167303e10f4d07b754888ddbdcf **GeForce RTX 2080 Ti:** | Network Size | Darknet, FPS (avg)| tkDNN TensorRT FP32, FPS | tkDNN TensorRT FP16, FPS | OpenCV FP16, FPS | tkDNN TensorRT FP16 batch=4, FPS | OpenCV FP16 batch=4, FPS | tkDNN Speedup | |:-----:|:--------:|--------:|--------:|--------:|--------:|--------:|------:| -|320 | 100 | 116 | **202** | 171 | **423** | 384 | **4.2x** | -|416 | 82 | 103 | **162** | 146 | **284** | 260 | **3.5x** | -|512 | 69 | 91 | **134** | 125 | **206** | 190 | **2.9x** | -|608 | 53 | 62 | **103** | 100 | **150** | 133 | **2.8x** | +|320 | 100 | 116 | **202** | 183 | 423 | **430** | **4.3x** | +|416 | 82 | 103 | **162** | 159 | 284 | **294** | **3.6x** | +|512 | 69 | 91 | 134 | **138** | 206 | **216** | **3.1x** | +|608 | 53 | 62 | 103 | **115**| 150 | **150** | **2.8x** | +|Tiny 416 | 500 | 609 | **790** | 773 | **1774** | 1353 | **3.5x** | * Yolo v4 Full comparison: [map_fps](https://user-images.githubusercontent.com/4096485/80283279-0e303e00-871f-11ea-814c-870967d77fd1.png) * Yolo v4 tiny comparison: [tiny_fps](https://user-images.githubusercontent.com/4096485/85734112-6e366700-b705-11ea-95d1-fcba0de76d72.png) From f5258a83e29ed9159d58be6bd5be6fa9bb6227d6 Mon Sep 17 00:00:00 2001 From: Juuso Alasuutari Date: Fri, 10 Jul 2020 15:08:30 +0300 Subject: [PATCH 1071/1365] Fix member initialization ordering Signed-off-by: Juuso Alasuutari --- include/yolo_v2_class.hpp | 3 +-- src/yolo_console_dll.cpp | 2 +- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/include/yolo_v2_class.hpp b/include/yolo_v2_class.hpp index 5543df7f8fc..360c3320bc7 100644 --- a/include/yolo_v2_class.hpp +++ b/include/yolo_v2_class.hpp @@ -851,8 +851,7 @@ class track_kalman_t track_kalman_t(int _max_objects = 1000, int _min_frames = 3, float _max_dist = 40, cv::Size _img_size = cv::Size(10000, 10000)) : - max_objects(_max_objects), min_frames(_min_frames), max_dist(_max_dist), img_size(_img_size), - track_id_counter(0) + track_id_counter(0), max_objects(_max_objects), min_frames(_min_frames), max_dist(_max_dist), img_size(_img_size) { kalman_vec.resize(max_objects); track_id_state_id_time.resize(max_objects); diff --git a/src/yolo_console_dll.cpp b/src/yolo_console_dll.cpp index cb40ccf8af5..06286062657 100644 --- a/src/yolo_console_dll.cpp +++ b/src/yolo_console_dll.cpp @@ -398,7 +398,7 @@ int main(int argc, char *argv[]) bool exit_flag; cv::Mat zed_cloud; std::queue track_optflow_queue; - detection_data_t() : exit_flag(false), new_detection(false) {} + detection_data_t() : new_detection(false), exit_flag(false) {} }; const bool sync = detection_sync; // sync data exchange From f13d7d217599ff99f9516185105ce00cfd58865d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 11 Jul 2020 00:23:21 +0300 Subject: [PATCH 1072/1365] Minor fix --- src/http_stream.cpp | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/src/http_stream.cpp b/src/http_stream.cpp index c8e9b5e113c..9dff2a32775 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -775,6 +775,19 @@ int check_prob(detection det, float thresh) return 0; } +int check_classes_id(detection det1, detection det2, float thresh) +{ + if (det1.classes != det2.classes) { + printf(" Error: det1.classes != det2.classes \n"); + getchar(); + } + + for (int i = 0; i < det1.classes; ++i) { + if (det1.prob[i] > thresh && det2.prob[i] > thresh) return 1; + } + return 0; +} + int fill_remaining_id(detection *new_dets, int new_dets_num, int new_track_id, float thresh, int detection_count) { for (int i = 0; i < new_dets_num; ++i) { @@ -863,7 +876,7 @@ void set_track_id(detection *new_dets, int new_dets_num, float thresh, float sim const int track_id = old_dets[old_id].track_id; const int det_count = old_dets[old_id].sort_class; //printf(" ciou = %f \n", box_ciou(new_dets[new_id].bbox, old_dets[old_id].bbox)); - if (check_prob(new_dets[new_id], thresh) && track_idx[track_id] && new_idx[new_id] && old_idx[old_id]) { + if (track_idx[track_id] && new_idx[new_id] && old_idx[old_id] && check_classes_id(new_dets[new_id], old_dets[old_id], thresh)) { float sim = sim_det[index].sim; float ciou = box_ciou(new_dets[new_id].bbox, old_dets[old_id].bbox); sim = sim * (1 - track_ciou_norm) + ciou * track_ciou_norm; From cb9148b38f8d2c5cec9ba717916268745235850c Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 11 Jul 2020 03:29:29 +0300 Subject: [PATCH 1073/1365] cuDNN 8.0 supported (but works slow) --- src/convolutional_layer.c | 161 ++++++++++++++++++++++++++++++++++---- 1 file changed, 147 insertions(+), 14 deletions(-) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index bf628cf56a7..7926a42827a 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -283,6 +283,135 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference, size_t workspace_ #else CHECK_CUDNN(cudnnSetConvolution2dDescriptor(l->convDesc, l->pad * l->dilation, l->pad * l->dilation, l->stride_y, l->stride_x, l->dilation, l->dilation, CUDNN_CROSS_CORRELATION)); // cudnn 5.1 #endif + + +#if CUDNN_MAJOR >= 8 + + if (cudnn_preference == cudnn_smallest) + { + workspace_size_specify = 0; + } + + size_t free_memory, total_memory; + int requested_algo_count = 0, returned_algo_count = 0; + int found_conv_algorithm = 0; + float min_time = 1000000; // 1000 sec + + // FWD + cudnnConvolutionFwdAlgoPerf_t conv_fwd_results[100]; + CHECK_CUDNN(cudnnGetConvolutionForwardAlgorithmMaxCount(cudnn_handle(), &requested_algo_count)); + + CHECK_CUDNN(cudnnGetConvolutionForwardAlgorithm_v7(cudnn_handle(), + l->srcTensorDesc, + l->weightDesc, + l->convDesc, + l->dstTensorDesc, + requested_algo_count, // (cudnnConvolutionFwdPreference_t)forward_algo, + &returned_algo_count, // workspace_size_specify, + conv_fwd_results)); + + CHECK_CUDNN(cudaMemGetInfo(&free_memory, &total_memory)); + + found_conv_algorithm = 0; + min_time = 1000000; // 1000 sec + for (int i = 0; i < returned_algo_count; i++) + { + if (conv_fwd_results[i].status == CUDNN_STATUS_SUCCESS && + conv_fwd_results[i].algo != CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED && + conv_fwd_results[i].memory < free_memory && + conv_fwd_results[i].memory <= workspace_size_specify && + conv_fwd_results[i].time < min_time) + { + found_conv_algorithm = 1; + l->fw_algo = conv_fwd_results[i].algo; + min_time = conv_fwd_results[i].time; + //printf(" - cuDNN FWD algo: %d, time = %f ms \n", l->fw_algo, min_time); + } + } + + if (!found_conv_algorithm) { + printf(" Error: cuDNN isn't found FWD algo for convolution.\n"); + getchar(); + exit(0); + } + //printf(" cuDNN FWD algo: %d, time = %f ms \n", l->fw_algo, min_time); + + // Bwd-Data + cudnnConvolutionBwdDataAlgoPerf_t conv_bwd_data_results[100]; + CHECK_CUDNN(cudnnGetConvolutionBackwardDataAlgorithmMaxCount(cudnn_handle(), &requested_algo_count)); + + CHECK_CUDNN(cudnnGetConvolutionBackwardDataAlgorithm_v7(cudnn_handle(), + l->weightDesc, + l->ddstTensorDesc, + l->convDesc, + l->dsrcTensorDesc, + requested_algo_count, // (cudnnConvolutionFwdPreference_t)forward_algo, + &returned_algo_count, // workspace_size_specify, + &conv_bwd_data_results[0])); + + CHECK_CUDNN(cudaMemGetInfo(&free_memory, &total_memory)); + + found_conv_algorithm = 0; + min_time = 1000000; // 1000 sec + for (int i = 0; i < returned_algo_count; i++) + { + if (conv_bwd_data_results[i].status == CUDNN_STATUS_SUCCESS && + conv_bwd_data_results[i].memory < free_memory && + conv_bwd_data_results[i].memory <= workspace_size_specify && + conv_bwd_data_results[i].time < min_time) + { + found_conv_algorithm = 1; + l->bd_algo = conv_bwd_data_results[i].algo; + min_time = conv_bwd_data_results[i].time; + } + } + + if (!found_conv_algorithm) { + printf(" Error: cuDNN isn't found BWD-data algo for convolution.\n"); + getchar(); + exit(0); + } + //printf(" cuDNN BWD-data algo: %d \n", l->bd_algo); + + // Bwd-Filters + cudnnConvolutionBwdFilterAlgoPerf_t conv_bwd_filter_results[100]; + CHECK_CUDNN(cudnnGetConvolutionBackwardFilterAlgorithmMaxCount(cudnn_handle(), &requested_algo_count)); + + CHECK_CUDNN(cudnnGetConvolutionBackwardFilterAlgorithm_v7(cudnn_handle(), + l->srcTensorDesc, + l->ddstTensorDesc, + l->convDesc, + l->dweightDesc, + requested_algo_count, // (cudnnConvolutionFwdPreference_t)forward_algo, + &returned_algo_count, // workspace_size_specify, + &conv_bwd_filter_results[0])); + + CHECK_CUDNN(cudaMemGetInfo(&free_memory, &total_memory)); + + found_conv_algorithm = 0; + min_time = 1000000; // 1000 sec + for (int i = 0; i < returned_algo_count; i++) + { + if (conv_bwd_filter_results[i].status == CUDNN_STATUS_SUCCESS && + conv_bwd_filter_results[i].memory < free_memory && + conv_bwd_filter_results[i].memory <= workspace_size_specify && + conv_bwd_filter_results[i].time < min_time) + { + found_conv_algorithm = 1; + l->bf_algo = conv_bwd_filter_results[i].algo; + min_time = conv_bwd_filter_results[i].time; + } + } + + if (!found_conv_algorithm) { + printf(" Error: cuDNN isn't found BWD-filter algo for convolution.\n"); + getchar(); + exit(0); + } + //printf(" cuDNN BWD-filter algo: %d \n", l->bf_algo); + +#else // CUDNN_MAJOR >= 8 + int forward_algo = CUDNN_CONVOLUTION_FWD_PREFER_FASTEST; int backward_algo = CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST; int backward_filter = CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST; @@ -309,22 +438,26 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference, size_t workspace_ (cudnnConvolutionFwdPreference_t)forward_algo, workspace_size_specify, &l->fw_algo)); + CHECK_CUDNN(cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(), - l->weightDesc, - l->ddstTensorDesc, - l->convDesc, - l->dsrcTensorDesc, - (cudnnConvolutionBwdDataPreference_t)backward_algo, - workspace_size_specify, - &l->bd_algo)); + l->weightDesc, + l->ddstTensorDesc, + l->convDesc, + l->dsrcTensorDesc, + (cudnnConvolutionBwdDataPreference_t)backward_algo, + workspace_size_specify, + &l->bd_algo)); + CHECK_CUDNN(cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(), - l->srcTensorDesc, - l->ddstTensorDesc, - l->convDesc, - l->dweightDesc, - (cudnnConvolutionBwdFilterPreference_t)backward_filter, - workspace_size_specify, - &l->bf_algo)); + l->srcTensorDesc, + l->ddstTensorDesc, + l->convDesc, + l->dweightDesc, + (cudnnConvolutionBwdFilterPreference_t)backward_filter, + workspace_size_specify, + &l->bf_algo)); +#endif // CUDNN_MAJOR >= 8 + //if (data_type == CUDNN_DATA_HALF) { From 5e9cc2710cd3f7987a54f324e1f0bf34ef5a8bf7 Mon Sep 17 00:00:00 2001 From: Alexey Date: Sat, 11 Jul 2020 04:08:41 +0300 Subject: [PATCH 1074/1365] Update README.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index e2661477ec5..d3b67f9749d 100644 --- a/README.md +++ b/README.md @@ -62,7 +62,8 @@ tkDNN-TensorRT accelerates YOLOv4 **~2x** times for batch=1 and **3x-4x** times |416 | 82 | 103 | **162** | 159 | 284 | **294** | **3.6x** | |512 | 69 | 91 | 134 | **138** | 206 | **216** | **3.1x** | |608 | 53 | 62 | 103 | **115**| 150 | **150** | **2.8x** | -|Tiny 416 | 500 | 609 | **790** | 773 | **1774** | 1353 | **3.5x** | +|Tiny 416 | 443 | 609 | **790** | 773 | **1774** | 1353 | **3.5x** | +|Tiny 416 CPU Core i7 7700HQ | 6 | - | - | 42 | - | 39 | **7.0x** | * Yolo v4 Full comparison: [map_fps](https://user-images.githubusercontent.com/4096485/80283279-0e303e00-871f-11ea-814c-870967d77fd1.png) * Yolo v4 tiny comparison: [tiny_fps](https://user-images.githubusercontent.com/4096485/85734112-6e366700-b705-11ea-95d1-fcba0de76d72.png) From de68e19cc627f642023f09513ac2306fbcbc1e4b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 11 Jul 2020 17:20:24 +0300 Subject: [PATCH 1075/1365] Added yolov4-tiny-3l.cfg and yolov4-tiny-custom.cfg --- build/darknet/x64/cfg/yolov4-tiny-3l.cfg | 332 +++++++++++++++++++ build/darknet/x64/cfg/yolov4-tiny-custom.cfg | 281 ++++++++++++++++ cfg/yolov4-tiny-3l.cfg | 332 +++++++++++++++++++ cfg/yolov4-tiny-custom.cfg | 281 ++++++++++++++++ 4 files changed, 1226 insertions(+) create mode 100644 build/darknet/x64/cfg/yolov4-tiny-3l.cfg create mode 100644 build/darknet/x64/cfg/yolov4-tiny-custom.cfg create mode 100644 cfg/yolov4-tiny-3l.cfg create mode 100644 cfg/yolov4-tiny-custom.cfg diff --git a/build/darknet/x64/cfg/yolov4-tiny-3l.cfg b/build/darknet/x64/cfg/yolov4-tiny-3l.cfg new file mode 100644 index 00000000000..116407066a3 --- /dev/null +++ b/build/darknet/x64/cfg/yolov4-tiny-3l.cfg @@ -0,0 +1,332 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=1 +width=608 +height=608 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.00261 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1 +groups=2 +group_id=1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -6,-1 + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1 +groups=2 +group_id=1 + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -6,-1 + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1 +groups=2 +group_id=1 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -6,-1 + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +################################## + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +scale_x_y = 1.05 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +ignore_thresh = .7 +truth_thresh = 1 +random=0 +resize=1.5 +nms_kind=greedynms +beta_nms=0.6 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 23 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +scale_x_y = 1.05 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +ignore_thresh = .7 +truth_thresh = 1 +random=0 +resize=1.5 +nms_kind=greedynms +beta_nms=0.6 + + +[route] +layers = -3 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 15 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +scale_x_y = 1.05 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +ignore_thresh = .7 +truth_thresh = 1 +random=0 +resize=1.5 +nms_kind=greedynms +beta_nms=0.6 + diff --git a/build/darknet/x64/cfg/yolov4-tiny-custom.cfg b/build/darknet/x64/cfg/yolov4-tiny-custom.cfg new file mode 100644 index 00000000000..5c83be0e19c --- /dev/null +++ b/build/darknet/x64/cfg/yolov4-tiny-custom.cfg @@ -0,0 +1,281 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=1 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.00261 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1 +groups=2 +group_id=1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -6,-1 + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1 +groups=2 +group_id=1 + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -6,-1 + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1 +groups=2 +group_id=1 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -6,-1 + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +################################## + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + + +[yolo] +mask = 3,4,5 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +scale_x_y = 1.05 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +ignore_thresh = .7 +truth_thresh = 1 +random=0 +resize=1.5 +nms_kind=greedynms +beta_nms=0.6 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 23 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + +[yolo] +mask = 0,1,2 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +scale_x_y = 1.05 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +ignore_thresh = .7 +truth_thresh = 1 +random=0 +resize=1.5 +nms_kind=greedynms +beta_nms=0.6 diff --git a/cfg/yolov4-tiny-3l.cfg b/cfg/yolov4-tiny-3l.cfg new file mode 100644 index 00000000000..116407066a3 --- /dev/null +++ b/cfg/yolov4-tiny-3l.cfg @@ -0,0 +1,332 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=1 +width=608 +height=608 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.00261 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1 +groups=2 +group_id=1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -6,-1 + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1 +groups=2 +group_id=1 + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -6,-1 + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1 +groups=2 +group_id=1 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -6,-1 + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +################################## + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +scale_x_y = 1.05 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +ignore_thresh = .7 +truth_thresh = 1 +random=0 +resize=1.5 +nms_kind=greedynms +beta_nms=0.6 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 23 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +scale_x_y = 1.05 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +ignore_thresh = .7 +truth_thresh = 1 +random=0 +resize=1.5 +nms_kind=greedynms +beta_nms=0.6 + + +[route] +layers = -3 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 15 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.3 +scale_x_y = 1.05 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +ignore_thresh = .7 +truth_thresh = 1 +random=0 +resize=1.5 +nms_kind=greedynms +beta_nms=0.6 + diff --git a/cfg/yolov4-tiny-custom.cfg b/cfg/yolov4-tiny-custom.cfg new file mode 100644 index 00000000000..5c83be0e19c --- /dev/null +++ b/cfg/yolov4-tiny-custom.cfg @@ -0,0 +1,281 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=1 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.00261 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1 +groups=2 +group_id=1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -6,-1 + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1 +groups=2 +group_id=1 + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -6,-1 + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1 +groups=2 +group_id=1 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -6,-1 + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +################################## + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + + +[yolo] +mask = 3,4,5 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +scale_x_y = 1.05 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +ignore_thresh = .7 +truth_thresh = 1 +random=0 +resize=1.5 +nms_kind=greedynms +beta_nms=0.6 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 23 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + +[yolo] +mask = 0,1,2 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +scale_x_y = 1.05 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +ignore_thresh = .7 +truth_thresh = 1 +random=0 +resize=1.5 +nms_kind=greedynms +beta_nms=0.6 From 20760d29715bb34bb2fbd0a05318dafe8150b325 Mon Sep 17 00:00:00 2001 From: Alexey Date: Sat, 11 Jul 2020 17:24:56 +0300 Subject: [PATCH 1076/1365] Update README.md --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index d3b67f9749d..249f1a6a6f1 100644 --- a/README.md +++ b/README.md @@ -63,7 +63,7 @@ tkDNN-TensorRT accelerates YOLOv4 **~2x** times for batch=1 and **3x-4x** times |512 | 69 | 91 | 134 | **138** | 206 | **216** | **3.1x** | |608 | 53 | 62 | 103 | **115**| 150 | **150** | **2.8x** | |Tiny 416 | 443 | 609 | **790** | 773 | **1774** | 1353 | **3.5x** | -|Tiny 416 CPU Core i7 7700HQ | 6 | - | - | 42 | - | 39 | **7.0x** | +|Tiny 416 CPU Core i7 7700HQ | 3.4 | - | - | 42 | - | 39 | **12x** | * Yolo v4 Full comparison: [map_fps](https://user-images.githubusercontent.com/4096485/80283279-0e303e00-871f-11ea-814c-870967d77fd1.png) * Yolo v4 tiny comparison: [tiny_fps](https://user-images.githubusercontent.com/4096485/85734112-6e366700-b705-11ea-95d1-fcba0de76d72.png) @@ -491,7 +491,7 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an 7. Download pre-trained weights for the convolutional layers and put to the directory `build\darknet\x64` * for `yolov4.cfg`, `yolov4-custom.cfg` (162 MB): [yolov4.conv.137](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137) (Google drive mirror [yolov4.conv.137](https://drive.google.com/open?id=1JKF-bdIklxOOVy-2Cr5qdvjgGpmGfcbp) ) - * for `yolov4-tiny.cfg`, `yolov4-tiny-custom.cfg` (19 MB): [yolov4-tiny.conv.29](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29) + * for `yolov4-tiny.cfg`, `yolov4-tiny-3l.cfg`, `yolov4-tiny-custom.cfg` (19 MB): [yolov4-tiny.conv.29](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29) * for `csresnext50-panet-spp.cfg` (133 MB): [csresnext50-panet-spp.conv.112](https://drive.google.com/file/d/16yMYCLQTY_oDlCIZPfn_sab6KD3zgzGq/view?usp=sharing) * for `yolov3.cfg, yolov3-spp.cfg` (154 MB): [darknet53.conv.74](https://pjreddie.com/media/files/darknet53.conv.74) * for `yolov3-tiny-prn.cfg , yolov3-tiny.cfg` (6 MB): [yolov3-tiny.conv.11](https://drive.google.com/file/d/18v36esoXCh-PsOKwyP2GWrpYDptDY8Zf/view?usp=sharing) @@ -529,8 +529,8 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an Do all the same steps as for the full yolo model as described above. With the exception of: * Download file with the first 29-convolutional layers of yolov4-tiny: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29 - (Or get this file from yolov4-tiny.weights file by using command: `darknet.exe partial cfg/yolov4-tiny.cfg yolov4-tiny.weights yolov4-tiny.conv.29 29` -* Make your custom model `yolov4-tiny-obj.cfg` based on `cfg/yolov4-tiny.cfg` instead of `yolov4.cfg` + (Or get this file from yolov4-tiny.weights file by using command: `darknet.exe partial cfg/yolov4-tiny-custom.cfg yolov4-tiny.weights yolov4-tiny.conv.29 29` +* Make your custom model `yolov4-tiny-obj.cfg` based on `cfg/yolov4-tiny-custom.cfg` instead of `yolov4.cfg` * Start training: `darknet.exe detector train data/obj.data yolov4-tiny-obj.cfg yolov4-tiny.conv.29` For training Yolo based on other models ([DenseNet201-Yolo](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/densenet201_yolo.cfg) or [ResNet50-Yolo](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/resnet50_yolo.cfg)), you can download and get pre-trained weights as showed in this file: https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/partial.cmd @@ -634,7 +634,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * for training for both small and large objects use modified models: * Full-model: 5 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3_5l.cfg - * Tiny-model: 3 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny_3l.cfg + * Tiny-model: 3 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny-3l.cfg * YOLOv4: 3 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-custom.cfg * If you train the model to distinguish Left and Right objects as separate classes (left/right hand, left/right-turn on road signs, ...) then for disabling flip data augmentation - add `flip=0` here: https://github.com/AlexeyAB/darknet/blob/3d2d0a7c98dbc8923d9ff705b81ff4f7940ea6ff/cfg/yolov3.cfg#L17 From fe4b2d008e7aa168e031400e531d4b462ee9d326 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 19 Jul 2020 01:19:49 +0300 Subject: [PATCH 1077/1365] Minor fix --- README.md | 6 +++++- src/convolutional_layer.c | 2 +- src/softmax_layer.c | 3 ++- 3 files changed, 8 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 249f1a6a6f1..3330edd08d4 100644 --- a/README.md +++ b/README.md @@ -177,7 +177,10 @@ You can get cfg-files by path: `darknet/cfg/` * **OpenCV-dnn** the fastest implementation of YOLOv4 for CPU (x86/ARM-Android), OpenCV can be compiled with [OpenVINO-backend](https://github.com/opencv/opencv/wiki/Intel's-Deep-Learning-Inference-Engine-backend) for running on (Myriad X / USB Neural Compute Stick / Arria FPGA), use `yolov3.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221) or [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) * **Intel OpenVINO 2019 R1:** (Myriad X / USB Neural Compute Stick / Arria FPGA): read this [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) * **Tencent/ncnn:** the fastest inference of YOLOv4 on mobile phone CPU: https://github.com/Tencent/ncnn -* **PyTorch > ONNX > CoreML > iOS** how to convert cfg/weights-files to pt-file: [ultralytics/yolov3](https://github.com/ultralytics/yolov3#darknet-conversion) and [iOS App](https://itunes.apple.com/app/id1452689527) +* **PyTorch > ONNX > CoreML > iOS** how to convert cfg/weights-files to pt-file: + * [WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4) + * [maudzung/3D-YOLOv4](https://github.com/maudzung/Complex-YOLOv4-Pytorch) + * [ultralytics/yolov3](https://github.com/ultralytics/yolov3#darknet-conversion) and [iOS App](https://itunes.apple.com/app/id1452689527) * **TensorRT** YOLOv4 on TensorRT+tkDNN: https://github.com/ceccocats/tkDNN For YOLOv3 (-70% faster inference): [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) read [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf). [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) implemented yolov3-spp, yolov4, etc. * **TVM** - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backends (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about @@ -680,6 +683,7 @@ Different tools for marking objects in images: 3. in Python: https://github.com/Cartucho/OpenLabeling 4. in C++: https://www.ccoderun.ca/darkmark/ 5. in JavaScript: https://github.com/opencv/cvat +6. DL-Annotator for Windows ($30): [url](https://www.microsoft.com/en-us/p/dlannotator/9nsx79m7t8fn?activetab=pivot:overviewtab) ## How to use Yolo as DLL and SO libraries diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 7926a42827a..3a1c3bc59f2 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -310,7 +310,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference, size_t workspace_ &returned_algo_count, // workspace_size_specify, conv_fwd_results)); - CHECK_CUDNN(cudaMemGetInfo(&free_memory, &total_memory)); + CHECK_CUDA(cudaMemGetInfo(&free_memory, &total_memory)); found_conv_algorithm = 0; min_time = 1000000; // 1000 sec diff --git a/src/softmax_layer.c b/src/softmax_layer.c index 43059704598..b6b48624500 100644 --- a/src/softmax_layer.c +++ b/src/softmax_layer.c @@ -143,6 +143,7 @@ contrastive_layer make_contrastive_layer(int batch, int w, int h, int c, int cla l.labels = yolo_layer->labels; // track id l.n = yolo_layer->n; // num of embeddings per cell = num of anchors l.classes = yolo_layer->classes;// num of classes + classes = l.classes; l.embedding_size = l.inputs / (l.n*l.h*l.w); l.truths = yolo_layer->truths; } @@ -183,7 +184,7 @@ contrastive_layer make_contrastive_layer(int batch, int w, int h, int c, int cla l.output_gpu = cuda_make_array(l.output, inputs*batch); l.delta_gpu = cuda_make_array(l.delta, inputs*batch); #endif - fprintf(stderr, "contrastive %4d x%4d x%4d x emb_size %4d x batch: %4d classes = %4d, step = %4d \n", w, h, l.n, l.embedding_size, batch, classes, step); + fprintf(stderr, "contrastive %4d x%4d x%4d x emb_size %4d x batch: %4d classes = %4d, step = %4d \n", w, h, l.n, l.embedding_size, batch, l.classes, step); if(l.detection) fprintf(stderr, "detection \n"); return l; } From d3234c8cee82fa6e22ff6c38ccfd2ae28926e3a2 Mon Sep 17 00:00:00 2001 From: JaledMC Date: Thu, 23 Jul 2020 22:07:26 +0200 Subject: [PATCH 1078/1365] Update darknet_images.py --- darknet_images.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/darknet_images.py b/darknet_images.py index acf5f7ac822..a3079b49240 100644 --- a/darknet_images.py +++ b/darknet_images.py @@ -99,7 +99,7 @@ def save_annotations(name, image, detections, class_names): for label, confidence, bbox in detections: x, y, w, h = convert2relative(image, bbox) label = class_names.index(label) - f.write("{} {:.4f} {:.4f} {:.4f} {:.4f}\n".format(label, x, y, w, h)) + f.write("{} {:.4f} {:.4f} {:.4f} {:.4f} {:.4f}\n".format(label, x, y, w, h, confidence)) def main(): From 40ee64ea82def57f76170ee38e549d23d0e58f60 Mon Sep 17 00:00:00 2001 From: Will Battel Date: Thu, 23 Jul 2020 15:52:15 -0500 Subject: [PATCH 1079/1365] Add nvcc flag for known Ampere GPUs --- Makefile | 3 +++ 1 file changed, 3 insertions(+) diff --git a/Makefile b/Makefile index 5635f8f49cc..af19371400c 100644 --- a/Makefile +++ b/Makefile @@ -25,6 +25,9 @@ ARCH= -gencode arch=compute_30,code=sm_30 \ OS := $(shell uname) +# Tesla A100 (GA100), DGX-A100, RTX 3080 +# ARCH= -gencode arch=compute_80,code=[sm_80,compute_80] + # Tesla V100 # ARCH= -gencode arch=compute_70,code=[sm_70,compute_70] From f96f2a30d57ce57516961c163108c5459be2cc9e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 25 Jul 2020 14:27:41 +0300 Subject: [PATCH 1080/1365] Updated README.md --- README.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 3330edd08d4..32f79ff29f6 100644 --- a/README.md +++ b/README.md @@ -177,10 +177,10 @@ You can get cfg-files by path: `darknet/cfg/` * **OpenCV-dnn** the fastest implementation of YOLOv4 for CPU (x86/ARM-Android), OpenCV can be compiled with [OpenVINO-backend](https://github.com/opencv/opencv/wiki/Intel's-Deep-Learning-Inference-Engine-backend) for running on (Myriad X / USB Neural Compute Stick / Arria FPGA), use `yolov3.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221) or [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) * **Intel OpenVINO 2019 R1:** (Myriad X / USB Neural Compute Stick / Arria FPGA): read this [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) * **Tencent/ncnn:** the fastest inference of YOLOv4 on mobile phone CPU: https://github.com/Tencent/ncnn -* **PyTorch > ONNX > CoreML > iOS** how to convert cfg/weights-files to pt-file: +* **PyTorch > ONNX**: * [WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4) * [maudzung/3D-YOLOv4](https://github.com/maudzung/Complex-YOLOv4-Pytorch) - * [ultralytics/yolov3](https://github.com/ultralytics/yolov3#darknet-conversion) and [iOS App](https://itunes.apple.com/app/id1452689527) + * [Tianxiaomo/pytorch-YOLOv4](https://github.com/Tianxiaomo/pytorch-YOLOv4) * **TensorRT** YOLOv4 on TensorRT+tkDNN: https://github.com/ceccocats/tkDNN For YOLOv3 (-70% faster inference): [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) read [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf). [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) implemented yolov3-spp, yolov4, etc. * **TVM** - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backends (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about @@ -683,7 +683,8 @@ Different tools for marking objects in images: 3. in Python: https://github.com/Cartucho/OpenLabeling 4. in C++: https://www.ccoderun.ca/darkmark/ 5. in JavaScript: https://github.com/opencv/cvat -6. DL-Annotator for Windows ($30): [url](https://www.microsoft.com/en-us/p/dlannotator/9nsx79m7t8fn?activetab=pivot:overviewtab) +6. in C++: https://github.com/jveitchmichaelis/deeplabel +7. DL-Annotator for Windows ($30): [url](https://www.microsoft.com/en-us/p/dlannotator/9nsx79m7t8fn?activetab=pivot:overviewtab) ## How to use Yolo as DLL and SO libraries From 4343ee8394594ac307000ef6a36d30f41e3bc1fe Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 25 Jul 2020 14:55:48 +0300 Subject: [PATCH 1081/1365] Fix scripts/README.md --- scripts/README.md | 21 ++++++++++++--------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/scripts/README.md b/scripts/README.md index 1b184122a3b..a641e673d0e 100644 --- a/scripts/README.md +++ b/scripts/README.md @@ -4,17 +4,23 @@ 59.26TB of research data: http://academictorrents.com/ -ImageNet Torrent: http://academictorrents.com/browse.php?search=imagenet&page=0 +ImageNet Torrent (Stanford): http://academictorrents.com/browse.php?search=imagenet&page=0 25 thousand datasets on Kaggle: https://www.kaggle.com/datasets -BDD100K - Diverse Driving Video: https://bair.berkeley.edu/blog/2018/05/30/bdd/ +BDD100K - Diverse Driving Video (Berkeley): https://bair.berkeley.edu/blog/2018/05/30/bdd/ -Pascal VOC: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html +KITTI - for autonomous driving (Toyota): http://www.cvlibs.net/datasets/kitti/ -MS COCO: http://cocodataset.org/#download +A2D2 - for autonomous driving (Audi): https://www.a2d2.audi/a2d2/en.html -ImageNet: http://imagenet.stanford.edu/download.php +nuScenes (for autonomous driving): https://www.nuscenes.org/overview + +Pascal VOC (Oxford): http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html + +MS COCO (Microsoft): http://cocodataset.org/#download + +ImageNet (Stanford): http://imagenet.stanford.edu/download.php ImageNet (ILSVRC2012): http://www.image-net.org/challenges/LSVRC/2012/nonpub-downloads @@ -22,7 +28,7 @@ ImageNet (ILSVRC2015): http://image-net.org/small/download.php ImageNet VID: http://bvisionweb1.cs.unc.edu/ilsvrc2015/download-videos-3j16.php -Open Images: https://storage.googleapis.com/openimages/web/download.html +Open Images (Google): https://storage.googleapis.com/openimages/web/download.html Cityscapes: https://www.cityscapes-dataset.com/ @@ -48,9 +54,6 @@ Visual Question Answering: https://visualqa.org/download.html Large Movie Review Dataset: http://ai.stanford.edu/~amaas/data/sentiment/ -KITTI (for autonomous driving): http://www.cvlibs.net/datasets/kitti/ - -nuScenes (for autonomous driving): https://www.nuscenes.org/overview ---- From 74f32a39286b7abe87972c6d8163393b97d0f8ef Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 25 Jul 2020 14:56:48 +0300 Subject: [PATCH 1082/1365] Improved speed of [contrastive] layer. Added Hard-Mish activation. Added [maxpool] maxpool_zero_nonmax=1 param --- include/darknet.h | 3 +- src/activation_kernels.cu | 52 ++++++++++++++++++ src/activations.c | 40 ++++++++++++++ src/activations.h | 4 ++ src/blas.c | 29 ++++++++++ src/blas.h | 1 + src/convolutional_kernels.cu | 3 ++ src/convolutional_layer.c | 15 +++--- src/detector.c | 1 + src/maxpool_layer_kernels.cu | 26 +++++++++ src/parser.c | 10 ++++ src/softmax_layer.c | 102 +++++++++++++++++++++-------------- 12 files changed, 238 insertions(+), 48 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 892ad71b2f1..b08ae70884d 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -104,7 +104,7 @@ typedef struct tree { // activations.h typedef enum { - LOGISTIC, RELU, RELU6, RELIE, LINEAR, RAMP, TANH, PLSE, REVLEAKY, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, GELU, SWISH, MISH, NORM_CHAN, NORM_CHAN_SOFTMAX, NORM_CHAN_SOFTMAX_MAXVAL + LOGISTIC, RELU, RELU6, RELIE, LINEAR, RAMP, TANH, PLSE, REVLEAKY, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, GELU, SWISH, MISH, HARD_MISH, NORM_CHAN, NORM_CHAN_SOFTMAX, NORM_CHAN_SOFTMAX_MAXVAL }ACTIVATION; // parser.h @@ -255,6 +255,7 @@ struct layer { int dilation; int antialiasing; int maxpool_depth; + int maxpool_zero_nonmax; int out_channels; float reverse; int flatten; diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index 2b23d7c8977..529a421a08f 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -274,6 +274,25 @@ __global__ void activate_array_mish_kernel(float *x, int n, float *activation_in } } +__device__ float hard_mish_yashas(float x) +{ + if (x > 0) + return x; + if (x > -2) + return x * x / 2 + x; + return 0; +} + +__global__ void activate_array_hard_mish_kernel(float *x, int n, float *activation_input, float *output_gpu) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (i < n) { + + float x_val = x[i]; + if (activation_input) activation_input[i] = x_val; // store value before activation + output_gpu[i] = hard_mish_yashas(x_val); + } +} __global__ void activate_array_leaky_kernel(float *x, int n) { int index = blockIdx.x*blockDim.x + threadIdx.x; @@ -381,6 +400,25 @@ __global__ void gradient_array_mish_kernel(int n, float *activation_input_gpu, f } } +__device__ float hard_mish_yashas_grad(float x) +{ + if (x > 0) + return x; + if (x > -2) + return x * x / 2 + x; + return 0; +} + +__global__ void gradient_array_hard_mish_kernel(int n, float *activation_input_gpu, float *delta) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (i < n) { + + const float x = activation_input_gpu[i]; + delta[i] *= hard_mish_yashas_grad(x); + } +} + __global__ void gradient_array_leaky_kernel(float *x, int n, float *delta) { int index = blockIdx.x*blockDim.x + threadIdx.x; @@ -484,6 +522,13 @@ extern "C" void activate_array_mish_ongpu(float *x, int n, float *activation_inp CHECK_CUDA(cudaPeekAtLastError()); } +extern "C" void activate_array_hard_mish_ongpu(float *x, int n, float *activation_input_gpu, float *output_gpu) +{ + const int num_blocks = get_number_of_blocks(n, BLOCK); + activate_array_hard_mish_kernel << > >(x, n, activation_input_gpu, output_gpu); + CHECK_CUDA(cudaPeekAtLastError()); +} + extern "C" void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta) { const int num_blocks = get_number_of_blocks(n, BLOCK); @@ -522,6 +567,13 @@ extern "C" void gradient_array_mish_ongpu(int n, float *activation_input_gpu, fl CHECK_CUDA(cudaPeekAtLastError()); } +extern "C" void gradient_array_hard_mish_ongpu(int n, float *activation_input_gpu, float *delta) +{ + const int num_blocks = get_number_of_blocks(n, BLOCK); + gradient_array_hard_mish_kernel << > > (n, activation_input_gpu, delta); + CHECK_CUDA(cudaPeekAtLastError()); +} + __global__ void activate_array_normalize_channels_kernel(float *x, int size, int batch, int channels, int wh_step, float *output_gpu) { diff --git a/src/activations.c b/src/activations.c index 7328ddff574..87ba1d9e3d9 100644 --- a/src/activations.c +++ b/src/activations.c @@ -50,6 +50,7 @@ ACTIVATION get_activation(char *s) if (strcmp(s, "logistic")==0) return LOGISTIC; if (strcmp(s, "swish") == 0) return SWISH; if (strcmp(s, "mish") == 0) return MISH; + if (strcmp(s, "hard_mish") == 0) return HARD_MISH; if (strcmp(s, "normalize_channels") == 0) return NORM_CHAN; if (strcmp(s, "normalize_channels_softmax") == 0) return NORM_CHAN_SOFTMAX; if (strcmp(s, "normalize_channels_softmax_maxval") == 0) return NORM_CHAN_SOFTMAX_MAXVAL; @@ -159,6 +160,26 @@ void activate_array_mish(float *x, const int n, float * activation_input, float } } +static float hard_mish_yashas(float x) +{ + if (x > 0) + return x; + if (x > -2) + return x * x / 2 + x; + return 0; +} + +void activate_array_hard_mish(float *x, const int n, float * activation_input, float * output) +{ + int i; + #pragma omp parallel for + for (i = 0; i < n; ++i) { + float x_val = x[i]; + activation_input[i] = x_val; // store value before activation + output[i] = hard_mish_yashas(x_val); + } +} + void activate_array_normalize_channels(float *x, const int n, int batch, int channels, int wh_step, float *output) { int size = n / channels; @@ -378,3 +399,22 @@ void gradient_array_mish(const int n, const float * activation_input, float * de //delta[i] *= derivative; } } + +static float hard_mish_yashas_grad(float x) +{ + if (x > 0) + return 1; + if (x > -2) + return x + 1; + return 0; +} + +void gradient_array_hard_mish(const int n, const float * activation_input, float * delta) +{ + int i; + #pragma omp parallel for + for (i = 0; i < n; ++i) { + float inp = activation_input[i]; + delta[i] *= hard_mish_yashas_grad(inp); + } +} diff --git a/src/activations.h b/src/activations.h index 8ac457d2d8d..95c2c2c138b 100644 --- a/src/activations.h +++ b/src/activations.h @@ -20,9 +20,11 @@ float gradient(float x, ACTIVATION a); void gradient_array(const float *x, const int n, const ACTIVATION a, float *delta); void gradient_array_swish(const float *x, const int n, const float * sigmoid, float * delta); void gradient_array_mish(const int n, const float * activation_input, float * delta); +void gradient_array_hard_mish(const int n, const float * activation_input, float * delta); void activate_array(float *x, const int n, const ACTIVATION a); void activate_array_swish(float *x, const int n, float * output_sigmoid, float * output); void activate_array_mish(float *x, const int n, float * activation_input, float * output); +void activate_array_hard_mish(float *x, const int n, float * activation_input, float * output); void activate_array_normalize_channels(float *x, const int n, int batch, int channels, int wh_step, float *output); void gradient_array_normalize_channels(float *x, const int n, int batch, int channels, int wh_step, float *delta); void activate_array_normalize_channels_softmax(float *x, const int n, int batch, int channels, int wh_step, float *output, int use_max_val); @@ -31,9 +33,11 @@ void gradient_array_normalize_channels_softmax(float *x, const int n, int batch, void activate_array_ongpu(float *x, int n, ACTIVATION a); void activate_array_swish_ongpu(float *x, int n, float *output_sigmoid_gpu, float *output_gpu); void activate_array_mish_ongpu(float *x, int n, float *activation_input_gpu, float *output_gpu); +void activate_array_hard_mish_ongpu(float *x, int n, float *activation_input_gpu, float *output_gpu); void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta); void gradient_array_swish_ongpu(float *x, int n, float *sigmoid_gpu, float *delta); void gradient_array_mish_ongpu(int n, float *activation_input_gpu, float *delta); +void gradient_array_hard_mish_ongpu(int n, float *activation_input_gpu, float *delta); void activate_array_normalize_channels_ongpu(float *x, int n, int batch, int channels, int wh_step, float *output_gpu); void gradient_array_normalize_channels_ongpu(float *output_gpu, int n, int batch, int channels, int wh_step, float *delta_gpu); void activate_array_normalize_channels_softmax_ongpu(float *x, int n, int batch, int channels, int wh_step, float *output_gpu, int use_max_val); diff --git a/src/blas.c b/src/blas.c index b294f755055..7311875edb8 100644 --- a/src/blas.c +++ b/src/blas.c @@ -615,6 +615,35 @@ float find_P_constrastive(size_t i, size_t j, contrastive_params *contrast_p, in return contrast_p[z].P; } +// num_of_samples = 2 * loaded_images = mini_batch_size +float P_constrastive_f_det(size_t il, int *labels, float **z, unsigned int feature_size, float temperature, contrastive_params *contrast_p, int contrast_p_size) +{ + const float sim = contrast_p[il].sim; + const size_t i = contrast_p[il].i; + const size_t j = contrast_p[il].j; + + const float numerator = expf(sim / temperature); + + float denominator = 0; + int k; + for (k = 0; k < contrast_p_size; ++k) { + contrastive_params cp = contrast_p[k]; + //if (k != i && labels[k] != labels[i]) { + //if (k != i) { + if (cp.i != i && cp.j == j) { + //const float sim_den = cp.sim; + ////const float sim_den = find_sim(k, l, contrast_p, contrast_p_size); // cosine_similarity(z[k], z[l], feature_size); + //denominator += expf(sim_den / temperature); + denominator += cp.exp_sim; + } + } + + float result = numerator / denominator; + if (denominator == 0) result = 1; + if (result > 1) result = 0.9999; + return result; +} + // num_of_samples = 2 * loaded_images = mini_batch_size float P_constrastive_f(size_t i, size_t l, int *labels, float **z, unsigned int feature_size, float temperature, contrastive_params *contrast_p, int contrast_p_size) { diff --git a/src/blas.h b/src/blas.h index 198bdb71eb9..35ed25e1975 100644 --- a/src/blas.h +++ b/src/blas.h @@ -63,6 +63,7 @@ void fix_nan_and_inf_cpu(float *input, size_t size); int check_sim(size_t i, size_t j, contrastive_params *contrast_p, int contrast_p_size); float find_sim(size_t i, size_t j, contrastive_params *contrast_p, int contrast_p_size); float find_P_constrastive(size_t i, size_t j, contrastive_params *contrast_p, int contrast_p_size); +float P_constrastive_f_det(size_t il, int *labels, float **z, unsigned int feature_size, float temperature, contrastive_params *contrast_p, int contrast_p_size); float P_constrastive_f(size_t i, size_t l, int *labels, float **z, unsigned int feature_size, float temperature, contrastive_params *contrast_p, int contrast_p_size); void grad_contrastive_loss_positive_f(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, int wh, contrastive_params *contrast_p, int contrast_p_size); void grad_contrastive_loss_negative_f(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, int wh, contrastive_params *contrast_p, int contrast_p_size); diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index d9f2e22485d..f64793f23f7 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -394,6 +394,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == MISH) activate_array_mish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); + else if (l.activation == HARD_MISH) activate_array_hard_mish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == NORM_CHAN) activate_array_normalize_channels_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu); else if (l.activation == NORM_CHAN_SOFTMAX) activate_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu, 0); else if (l.activation == NORM_CHAN_SOFTMAX_MAXVAL) activate_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu, 1); @@ -602,6 +603,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) if (l.activation == SWISH) activate_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == MISH) activate_array_mish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); + else if (l.activation == HARD_MISH) activate_array_hard_mish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.output_gpu); else if (l.activation == NORM_CHAN) activate_array_normalize_channels_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu); else if (l.activation == NORM_CHAN_SOFTMAX) activate_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu, 0); else if (l.activation == NORM_CHAN_SOFTMAX_MAXVAL) activate_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output_gpu, 1); @@ -649,6 +651,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state if (l.activation == SWISH) gradient_array_swish_ongpu(l.output_gpu, l.outputs*l.batch, l.activation_input_gpu, l.delta_gpu); else if (l.activation == MISH) gradient_array_mish_ongpu(l.outputs*l.batch, l.activation_input_gpu, l.delta_gpu); + else if (l.activation == HARD_MISH) gradient_array_hard_mish_ongpu(l.outputs*l.batch, l.activation_input_gpu, l.delta_gpu); else if (l.activation == NORM_CHAN_SOFTMAX || l.activation == NORM_CHAN_SOFTMAX_MAXVAL) gradient_array_normalize_channels_softmax_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu); else if (l.activation == NORM_CHAN) gradient_array_normalize_channels_ongpu(l.output_gpu, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu); else gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 3a1c3bc59f2..a169f3fe87c 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -349,7 +349,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference, size_t workspace_ &returned_algo_count, // workspace_size_specify, &conv_bwd_data_results[0])); - CHECK_CUDNN(cudaMemGetInfo(&free_memory, &total_memory)); + CHECK_CUDA(cudaMemGetInfo(&free_memory, &total_memory)); found_conv_algorithm = 0; min_time = 1000000; // 1000 sec @@ -386,7 +386,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference, size_t workspace_ &returned_algo_count, // workspace_size_specify, &conv_bwd_filter_results[0])); - CHECK_CUDNN(cudaMemGetInfo(&free_memory, &total_memory)); + CHECK_CUDA(cudaMemGetInfo(&free_memory, &total_memory)); found_conv_algorithm = 0; min_time = 1000000; // 1000 sec @@ -658,7 +658,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, } #ifndef GPU - if (l.activation == SWISH || l.activation == MISH) l.activation_input = (float*)calloc(total_batch*l.outputs, sizeof(float)); + if (l.activation == SWISH || l.activation == MISH || l.activation == HARD_MISH) l.activation_input = (float*)calloc(total_batch*l.outputs, sizeof(float)); #endif // not GPU if(adam){ @@ -680,7 +680,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, if(gpu_index >= 0){ - if (train && (l.activation == SWISH || l.activation == MISH)) { + if (train && (l.activation == SWISH || l.activation == MISH || l.activation == HARD_MISH)) { l.activation_input_gpu = cuda_make_array(l.activation_input, total_batch*l.outputs); } @@ -917,7 +917,7 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) //l->binary_input = realloc(l->inputs*l->batch, sizeof(float)); } - if (l->activation == SWISH || l->activation == MISH) l->activation_input = (float*)realloc(l->activation_input, total_batch*l->outputs * sizeof(float)); + if (l->activation == SWISH || l->activation == MISH || l->activation == HARD_MISH) l->activation_input = (float*)realloc(l->activation_input, total_batch*l->outputs * sizeof(float)); #ifdef GPU if (old_w < w || old_h < h || l->dynamic_minibatch) { if (l->train) { @@ -943,7 +943,7 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h) l->binary_input_gpu = cuda_make_array(0, l->inputs*l->batch); } - if (l->activation == SWISH || l->activation == MISH) { + if (l->activation == SWISH || l->activation == MISH || l->activation == HARD_MISH) { cuda_free(l->activation_input_gpu); l->activation_input_gpu = cuda_make_array(l->activation_input, total_batch*l->outputs); } @@ -1353,6 +1353,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //activate_array(l.output, m*n*l.batch, l.activation); if (l.activation == SWISH) activate_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.output); else if (l.activation == MISH) activate_array_mish(l.output, l.outputs*l.batch, l.activation_input, l.output); + else if (l.activation == HARD_MISH) activate_array_hard_mish(l.output, l.outputs*l.batch, l.activation_input, l.output); else if (l.activation == NORM_CHAN) activate_array_normalize_channels(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output); else if (l.activation == NORM_CHAN_SOFTMAX) activate_array_normalize_channels_softmax(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output, 0); else if (l.activation == NORM_CHAN_SOFTMAX_MAXVAL) activate_array_normalize_channels_softmax(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output, 1); @@ -1398,6 +1399,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) //activate_array(l.output, m*n*l.batch, l.activation); if (l.activation == SWISH) activate_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.output); else if (l.activation == MISH) activate_array_mish(l.output, l.outputs*l.batch, l.activation_input, l.output); + else if (l.activation == HARD_MISH) activate_array_hard_mish(l.output, l.outputs*l.batch, l.activation_input, l.output); else if (l.activation == NORM_CHAN) activate_array_normalize_channels(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output); else if (l.activation == NORM_CHAN_SOFTMAX) activate_array_normalize_channels_softmax(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output, 0); else if (l.activation == NORM_CHAN_SOFTMAX_MAXVAL) activate_array_normalize_channels_softmax(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.output, 1); @@ -1539,6 +1541,7 @@ void backward_convolutional_layer(convolutional_layer l, network_state state) if (l.activation == SWISH) gradient_array_swish(l.output, l.outputs*l.batch, l.activation_input, l.delta); else if (l.activation == MISH) gradient_array_mish(l.outputs*l.batch, l.activation_input, l.delta); + else if (l.activation == HARD_MISH) gradient_array_hard_mish(l.outputs*l.batch, l.activation_input, l.delta); else if (l.activation == NORM_CHAN_SOFTMAX || l.activation == NORM_CHAN_SOFTMAX_MAXVAL) gradient_array_normalize_channels_softmax(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.delta); else if (l.activation == NORM_CHAN) gradient_array_normalize_channels(l.output, l.outputs*l.batch, l.batch, l.out_c, l.out_w*l.out_h, l.delta); else gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); diff --git a/src/detector.c b/src/detector.c index 6017f584249..0f1e1816ecd 100644 --- a/src/detector.c +++ b/src/detector.c @@ -408,6 +408,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i char buff[256]; sprintf(buff, "%s/%s_final.weights", backup_directory, base); save_weights(net, buff); + printf("If you want to train from the beginning, then use flag in the end of training command: -clear \n"); #ifdef OPENCV release_mat(&img); diff --git a/src/maxpool_layer_kernels.cu b/src/maxpool_layer_kernels.cu index 7436a19e7cb..d6a815fcbe8 100644 --- a/src/maxpool_layer_kernels.cu +++ b/src/maxpool_layer_kernels.cu @@ -91,6 +91,15 @@ __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c if (indexes) indexes[out_index] = max_i; } +__global__ void forward_zero_nonmax_kernel(int n, int *indexes, float *output) +{ + + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= n) return; + + if (indexes[id] != id) output[id] = 0; +} + __global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride_x, int stride_y, int size, int pad, float *delta, float *prev_delta, int *indexes) { int h = (in_h + pad - size) / stride_y + 1; @@ -129,7 +138,14 @@ __global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_ prev_delta[index] += d; } +__global__ void backward_zero_nonmax_kernel(int n, int *indexes, float *prev_delta) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= n) return; + + if (indexes[id] != id) prev_delta[id] = 0; +} extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network_state state) { if (layer.maxpool_depth) { @@ -178,6 +194,11 @@ extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network_state sta forward_maxpool_layer_kernel << > > (n, layer.h, layer.w, layer.c, layer.stride_x, layer.stride_y, layer.size, layer.pad, state.input, layer.output_gpu, layer.indexes_gpu); CHECK_CUDA(cudaPeekAtLastError()); + + if (layer.maxpool_zero_nonmax) { + forward_zero_nonmax_kernel << > > (n, layer.indexes_gpu, layer.output_gpu); + CHECK_CUDA(cudaPeekAtLastError()); + } } if (layer.antialiasing) { @@ -225,6 +246,11 @@ extern "C" void backward_maxpool_layer_gpu(maxpool_layer layer, network_state st backward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride_x, layer.stride_y, layer.size, layer.pad, layer.delta_gpu, state.delta, layer.indexes_gpu); CHECK_CUDA(cudaPeekAtLastError()); + + if (layer.maxpool_zero_nonmax) { + backward_zero_nonmax_kernel << > > (n, layer.indexes_gpu, state.delta); + CHECK_CUDA(cudaPeekAtLastError()); + } } diff --git a/src/parser.c b/src/parser.c index 4ac1912c39f..1c914450fba 100644 --- a/src/parser.c +++ b/src/parser.c @@ -363,6 +363,11 @@ contrastive_layer parse_contrastive(list *options, size_params params) int yolo_layer_id = option_find_int_quiet(options, "yolo_layer", 0); if (yolo_layer_id < 0) yolo_layer_id = params.index + yolo_layer_id; if(yolo_layer_id != 0) yolo_layer = params.net.layers + yolo_layer_id; + if (yolo_layer->type != YOLO) { + printf(" Error: [contrastive] layer should point to the [yolo] layer instead of %d layer! \n", yolo_layer_id); + getchar(); + exit(0); + } contrastive_layer layer = make_contrastive_layer(params.batch, params.w, params.h, params.c, classes, params.inputs, yolo_layer); layer.temperature = option_find_float_quiet(options, "temperature", 1); @@ -501,6 +506,10 @@ layer parse_yolo(list *options, size_params params) l.embedding_output = (float*)xcalloc(le.batch * le.outputs, sizeof(float)); l.embedding_size = le.n / l.n; printf(" embedding_size = %d \n", l.embedding_size); + if (le.n % l.n != 0) { + printf(" Warning: filters=%d number in embedding_layer=%d isn't divisable by number of anchors %d \n", le.n, embedding_layer_id, l.n); + getchar(); + } } char *map_file = option_find_str(options, "map", 0); @@ -832,6 +841,7 @@ maxpool_layer parse_maxpool(list *options, size_params params) if(!(h && w && c)) error("Layer before [maxpool] layer must output image."); maxpool_layer layer = make_maxpool_layer(batch, h, w, c, size, stride_x, stride_y, padding, maxpool_depth, out_channels, antialiasing, avgpool, params.train); + layer.maxpool_zero_nonmax = option_find_int_quiet(options, "maxpool_zero_nonmax", 0); return layer; } diff --git a/src/softmax_layer.c b/src/softmax_layer.c index b6b48624500..158c4061d02 100644 --- a/src/softmax_layer.c +++ b/src/softmax_layer.c @@ -146,6 +146,15 @@ contrastive_layer make_contrastive_layer(int batch, int w, int h, int c, int cla classes = l.classes; l.embedding_size = l.inputs / (l.n*l.h*l.w); l.truths = yolo_layer->truths; + if (l.embedding_size != yolo_layer->embedding_size) { + printf(" Error: [contrastive] embedding_size=%d isn't equal to [yolo] embedding_size=%d. They should use the same [convolutional] layer \n", l.embedding_size, yolo_layer->embedding_size); + getchar(); + exit(0); + } + if (l.inputs % (l.n*l.h*l.w) != 0) { + printf(" Warning: filters= number in the previous (embedding) layer isn't divisable by number of anchors %d \n", l.n); + getchar(); + } } else { l.detection = 0; @@ -395,52 +404,62 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) } */ - // precalculate P-contrastive - for (b = 0; b < l.batch; ++b) { - for (n = 0; n < l.n; ++n) { - for (h = 0; h < l.h; ++h) { - for (w = 0; w < l.w; ++w) - { - const int z_index = b*l.n*l.h*l.w + n*l.h*l.w + h*l.w + w; - if (l.labels[z_index] < 0) continue; - - for (b2 = 0; b2 < l.batch; ++b2) { - for (n2 = 0; n2 < l.n; ++n2) { - for (h2 = 0; h2 < l.h; ++h2) { - for (w2 = 0; w2 < l.w; ++w2) - { - const int z_index2 = b2*l.n*l.h*l.w + n2*l.h*l.w + h2*l.w + w2; - if (l.labels[z_index2] < 0) continue; - if (z_index == z_index2) continue; - - const int time_step_i = b / mini_batch; - const int time_step_j = b2 / mini_batch; - if (time_step_i != time_step_j) continue; - const size_t step = l.batch*l.n*l.h*l.w; - - float P = -10; - if (l.detection) { - P = P_constrastive_f(z_index, z_index2, l.labels, z, l.embedding_size, l.temperature, contrast_p, contrast_p_index); - } - else { - P = P_constrastive(z_index, z_index2, l.labels, step, z, l.embedding_size, l.temperature, l.cos_sim, l.exp_cos_sim); - l.p_constrastive[z_index*step + z_index2] = P; - } - - int q; - for (q = 0; q < contrast_p_index; ++q) - if (contrast_p[q].i == z_index && contrast_p[q].j == z_index2) { - contrast_p[q].P = P; - break; + if (l.detection) { + int k; + #pragma omp parallel for + for (k = 0; k < contrast_p_index; ++k) { + contrast_p[k].P = P_constrastive_f_det(k, l.labels, z, l.embedding_size, l.temperature, contrast_p, contrast_p_index); + } + } + else { + // precalculate P-contrastive + for (b = 0; b < l.batch; ++b) { + for (n = 0; n < l.n; ++n) { + for (h = 0; h < l.h; ++h) { + for (w = 0; w < l.w; ++w) + { + const int z_index = b*l.n*l.h*l.w + n*l.h*l.w + h*l.w + w; + if (l.labels[z_index] < 0) continue; + + for (b2 = 0; b2 < l.batch; ++b2) { + for (n2 = 0; n2 < l.n; ++n2) { + for (h2 = 0; h2 < l.h; ++h2) { + for (w2 = 0; w2 < l.w; ++w2) + { + const int z_index2 = b2*l.n*l.h*l.w + n2*l.h*l.w + h2*l.w + w2; + if (l.labels[z_index2] < 0) continue; + if (z_index == z_index2) continue; + + const int time_step_i = b / mini_batch; + const int time_step_j = b2 / mini_batch; + if (time_step_i != time_step_j) continue; + + const size_t step = l.batch*l.n*l.h*l.w; + + float P = -10; + if (l.detection) { + P = P_constrastive_f(z_index, z_index2, l.labels, z, l.embedding_size, l.temperature, contrast_p, contrast_p_index); + } + else { + P = P_constrastive(z_index, z_index2, l.labels, step, z, l.embedding_size, l.temperature, l.cos_sim, l.exp_cos_sim); + l.p_constrastive[z_index*step + z_index2] = P; } - //if (q == contrast_p_index) getchar(); + int q; + for (q = 0; q < contrast_p_index; ++q) + if (contrast_p[q].i == z_index && contrast_p[q].j == z_index2) { + contrast_p[q].P = P; + break; + } + //if (q == contrast_p_index) getchar(); - //if (P > 1 || P < -1) { - // printf(" p = %f, z_index = %d, z_index2 = %d ", P, z_index, z_index2); getchar(); - //} + + //if (P > 1 || P < -1) { + // printf(" p = %f, z_index = %d, z_index2 = %d ", P, z_index, z_index2); getchar(); + //} + } } } } @@ -452,6 +471,7 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) // calc deltas + #pragma omp parallel for for (b = 0; b < l.batch; ++b) { for (n = 0; n < l.n; ++n) { for (h = 0; h < l.h; ++h) { From a953d068d67abb6938f5f90908328b19e1c60b40 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 25 Jul 2020 14:59:34 +0300 Subject: [PATCH 1083/1365] Minor fix --- image_yolov2.sh | 6 ------ video_v2.sh | 6 ------ 2 files changed, 12 deletions(-) delete mode 100755 image_yolov2.sh delete mode 100755 video_v2.sh diff --git a/image_yolov2.sh b/image_yolov2.sh deleted file mode 100755 index b1d518640a8..00000000000 --- a/image_yolov2.sh +++ /dev/null @@ -1,6 +0,0 @@ - - -./darknet detector test ./cfg/voc.data ./cfg/yolov2.cfg ./yolov2.weights data/dog.jpg -i 0 -thresh 0.2 - - - diff --git a/video_v2.sh b/video_v2.sh deleted file mode 100755 index 919c5791c34..00000000000 --- a/video_v2.sh +++ /dev/null @@ -1,6 +0,0 @@ - - -./darknet detector demo ./cfg/coco.data ./cfg/yolov2.cfg ./yolov2.weights test50.mp4 -i 0 -thresh 0.25 - - - From 2c17d67e5ac43bc0dfc00e72f0d9dc168928b7c1 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 25 Jul 2020 15:09:29 +0300 Subject: [PATCH 1084/1365] Minor fix --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 2d8949bdbe6..f352d6fd6f5 100644 --- a/README.md +++ b/README.md @@ -55,7 +55,7 @@ tkDNN-TensorRT accelerates YOLOv4 **~2x** times for batch=1 and **3x-4x** times * tkDNN: https://github.com/ceccocats/tkDNN * OpenCV: https://gist.github.com/YashasSamaga/48bdb167303e10f4d07b754888ddbdcf -**GeForce RTX 2080 Ti:** +#### GeForce RTX 2080 Ti: | Network Size | Darknet, FPS (avg)| tkDNN TensorRT FP32, FPS | tkDNN TensorRT FP16, FPS | OpenCV FP16, FPS | tkDNN TensorRT FP16 batch=4, FPS | OpenCV FP16 batch=4, FPS | tkDNN Speedup | |:-----:|:--------:|--------:|--------:|--------:|--------:|--------:|------:| |320 | 100 | 116 | **202** | 183 | 423 | **430** | **4.3x** | From 49bff0e24da36f7c1a2471b12089efb27a0173b1 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 25 Jul 2020 16:33:35 +0300 Subject: [PATCH 1085/1365] fixed maxpool_zero_nonmax=1 --- src/maxpool_layer_kernels.cu | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/maxpool_layer_kernels.cu b/src/maxpool_layer_kernels.cu index d6a815fcbe8..ab39d6b57b2 100644 --- a/src/maxpool_layer_kernels.cu +++ b/src/maxpool_layer_kernels.cu @@ -91,13 +91,13 @@ __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c if (indexes) indexes[out_index] = max_i; } -__global__ void forward_zero_nonmax_kernel(int n, int *indexes, float *output) +__global__ void forward_zero_nonmax_kernel(int n, float *input, float *output) { int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (id >= n) return; - if (indexes[id] != id) output[id] = 0; + if (input[id] != output[id]) output[id] = 0; } __global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride_x, int stride_y, int size, int pad, float *delta, float *prev_delta, int *indexes) @@ -196,7 +196,7 @@ extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network_state sta CHECK_CUDA(cudaPeekAtLastError()); if (layer.maxpool_zero_nonmax) { - forward_zero_nonmax_kernel << > > (n, layer.indexes_gpu, layer.output_gpu); + forward_zero_nonmax_kernel << > > (n, state.input, layer.output_gpu); CHECK_CUDA(cudaPeekAtLastError()); } } From 139fe141a542a8b73943b20bce3e3a96780f6e75 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 27 Jul 2020 15:42:00 +0300 Subject: [PATCH 1086/1365] Bug is fixed --- src/image.c | 3 +++ src/region_layer.c | 17 +++++++++-------- 2 files changed, 12 insertions(+), 8 deletions(-) diff --git a/src/image.c b/src/image.c index 7b5d39f2d64..07942c71e6e 100644 --- a/src/image.c +++ b/src/image.c @@ -434,6 +434,9 @@ void draw_detections_v3(image im, detection *dets, int num, float thresh, char * if (alphabet) { char labelstr[4096] = { 0 }; strcat(labelstr, names[selected_detections[i].best_class]); + char prob_str[10]; + sprintf(prob_str, ": %.2f", selected_detections[i].det.prob[selected_detections[i].best_class]); + strcat(labelstr, prob_str); int j; for (j = 0; j < classes; ++j) { if (selected_detections[i].det.prob[j] > thresh && j != selected_detections[i].best_class) { diff --git a/src/region_layer.c b/src/region_layer.c index 3825b425141..7aa1a196f80 100644 --- a/src/region_layer.c +++ b/src/region_layer.c @@ -28,7 +28,8 @@ region_layer make_region_layer(int batch, int w, int h, int n, int classes, int l.outputs = h*w*n*(classes + coords + 1); l.inputs = l.outputs; l.max_boxes = max_boxes; - l.truths = max_boxes*(5); + l.truth_size = 4 + 2; + l.truths = max_boxes*l.truth_size; l.delta = (float*)xcalloc(batch * l.outputs, sizeof(float)); l.output = (float*)xcalloc(batch * l.outputs, sizeof(float)); int i; @@ -226,9 +227,9 @@ void forward_region_layer(const region_layer l, network_state state) if(l.softmax_tree){ int onlyclass_id = 0; for(t = 0; t < l.max_boxes; ++t){ - box truth = float_to_box(state.truth + t*5 + b*l.truths); + box truth = float_to_box(state.truth + t*l.truth_size + b*l.truths); if(!truth.x) break; // continue; - int class_id = state.truth[t*5 + b*l.truths + 4]; + int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; float maxp = 0; int maxi = 0; if(truth.x > 100000 && truth.y > 100000){ @@ -258,13 +259,13 @@ void forward_region_layer(const region_layer l, network_state state) float best_iou = 0; int best_class_id = -1; for(t = 0; t < l.max_boxes; ++t){ - box truth = float_to_box(state.truth + t*5 + b*l.truths); - int class_id = state.truth[t * 5 + b*l.truths + 4]; + box truth = float_to_box(state.truth + t*l.truth_size + b*l.truths); + int class_id = state.truth[t * l.truth_size + b*l.truths + 4]; if (class_id >= l.classes) continue; // if label contains class_id more than number of classes in the cfg-file if(!truth.x) break; // continue; float iou = box_iou(pred, truth); if (iou > best_iou) { - best_class_id = state.truth[t*5 + b*l.truths + 4]; + best_class_id = state.truth[t*l.truth_size + b*l.truths + 4]; best_iou = iou; } } @@ -297,8 +298,8 @@ void forward_region_layer(const region_layer l, network_state state) } } for(t = 0; t < l.max_boxes; ++t){ - box truth = float_to_box(state.truth + t*5 + b*l.truths); - int class_id = state.truth[t * 5 + b*l.truths + 4]; + box truth = float_to_box(state.truth + t*l.truth_size + b*l.truths); + int class_id = state.truth[t * l.truth_size + b*l.truths + 4]; if (class_id >= l.classes) { printf("\n Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes-1); getchar(); From 6af4370c3fe0f43a8c2fea6e04fc3dd2930b1da5 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 28 Jul 2020 04:28:46 +0300 Subject: [PATCH 1087/1365] Hard_mish is fixed --- README.md | 3 ++- src/activation_kernels.cu | 4 ++-- src/convolutional_kernels.cu | 2 +- src/parser.c | 2 ++ src/yolo_layer.c | 20 +++++++++++--------- 5 files changed, 18 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index f352d6fd6f5..29c767b70d2 100644 --- a/README.md +++ b/README.md @@ -684,7 +684,8 @@ Different tools for marking objects in images: 4. in C++: https://www.ccoderun.ca/darkmark/ 5. in JavaScript: https://github.com/opencv/cvat 6. in C++: https://github.com/jveitchmichaelis/deeplabel -7. DL-Annotator for Windows ($30): [url](https://www.microsoft.com/en-us/p/dlannotator/9nsx79m7t8fn?activetab=pivot:overviewtab) +7. in C#: https://github.com/BMW-InnovationLab/BMW-Labeltool-Lite +8. DL-Annotator for Windows ($30): [url](https://www.microsoft.com/en-us/p/dlannotator/9nsx79m7t8fn?activetab=pivot:overviewtab) ## How to use Yolo as DLL and SO libraries diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index 529a421a08f..272453c6dd3 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -403,9 +403,9 @@ __global__ void gradient_array_mish_kernel(int n, float *activation_input_gpu, f __device__ float hard_mish_yashas_grad(float x) { if (x > 0) - return x; + return 1; if (x > -2) - return x * x / 2 + x; + return x + 1; return 0; } diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index f64793f23f7..eaa8fcd8ca7 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -844,7 +844,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state if (l.reverse) { if (*state.net.max_output16_size < l.nweights) { *state.net.max_output16_size = l.nweights; - if (*state.net.output16_gpu) cuda_free(*state.net.output16_gpu); + if (*state.net.output16_gpu && *state.net.max_output16_size > 0) cuda_free(*state.net.output16_gpu); assert(*state.net.max_output16_size > 0); *state.net.output16_gpu = cuda_make_array(NULL, l.nweights); } diff --git a/src/parser.c b/src/parser.c index 1c914450fba..474a525882d 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1656,6 +1656,8 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) } // pre-allocate memory for inference on Tensor Cores (fp16) + *net.max_input16_size = 0; + *net.max_output16_size = 0; if (net.cudnn_half) { *net.max_input16_size = max_inputs; CHECK_CUDA(cudaMalloc((void **)net.input16_gpu, *net.max_input16_size * sizeof(short))); //sizeof(half) diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 236ec4c3a14..0287524f673 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -419,15 +419,10 @@ void forward_yolo_layer(const layer l, network_state state) avg_anyobj += l.output[obj_index]; l.delta[obj_index] = l.cls_normalizer * (0 - l.output[obj_index]); if (best_match_iou > l.ignore_thresh) { - const float iou_multiplier = best_match_iou*best_match_iou;// (best_match_iou - l.ignore_thresh) / (1.0 - l.ignore_thresh); if (l.objectness_smooth) { - l.delta[obj_index] = l.cls_normalizer * (iou_multiplier - l.output[obj_index]); + const float delta_obj = l.cls_normalizer * (best_match_iou - l.output[obj_index]); + if (delta_obj > l.delta[obj_index]) l.delta[obj_index] = delta_obj; - int class_id = state.truth[best_match_t*l.truth_size + b*l.truths + 4]; - if (l.map) class_id = l.map[class_id]; - if (class_id < l.classes && class_id >= 0) { - delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); - } } else l.delta[obj_index] = 0; } @@ -535,7 +530,10 @@ void forward_yolo_layer(const layer l, network_state state) int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4); avg_obj += l.output[obj_index]; - l.delta[obj_index] = class_multiplier * l.cls_normalizer * (1 - l.output[obj_index]); + if (l.objectness_smooth) { + float delta_obj = class_multiplier * l.cls_normalizer * (1 - l.output[obj_index]); + if (l.delta[obj_index] == 0) l.delta[obj_index] = delta_obj; + } else l.delta[obj_index] = class_multiplier * l.cls_normalizer * (1 - l.output[obj_index]); int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); @@ -583,7 +581,11 @@ void forward_yolo_layer(const layer l, network_state state) int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4); avg_obj += l.output[obj_index]; - l.delta[obj_index] = class_multiplier * l.cls_normalizer * (1 - l.output[obj_index]); + if (l.objectness_smooth) { + float delta_obj = class_multiplier * l.cls_normalizer * (1 - l.output[obj_index]); + if (l.delta[obj_index] == 0) l.delta[obj_index] = delta_obj; + } + else l.delta[obj_index] = class_multiplier * l.cls_normalizer * (1 - l.output[obj_index]); int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); From edd04355cc932ab0e1708ba50dc31294e813d846 Mon Sep 17 00:00:00 2001 From: JaledMC Date: Wed, 29 Jul 2020 17:16:06 +0200 Subject: [PATCH 1088/1365] Added multithreading One thread for inference, another for frame video capture, and one more for drawing --- darknet_video.py | 115 +++++++++++++++++++++++++++++------------------ 1 file changed, 72 insertions(+), 43 deletions(-) diff --git a/darknet_video.py b/darknet_video.py index 19740d8bfb3..de96d709ed1 100644 --- a/darknet_video.py +++ b/darknet_video.py @@ -1,9 +1,12 @@ +from ctypes import * +import random import os import cv2 import time import darknet import argparse -import random +from threading import Thread, enumerate +from queue import Queue def parser(): @@ -27,6 +30,17 @@ def parser(): return parser.parse_args() +def str2int(video_path): + """ + argparse returns and string althout webcam uses int (0, 1 ...) + Cast to int if needed + """ + try: + return int(video_path) + except ValueError: + return video_path + + def check_arguments_errors(args): assert 0 < args.thresh < 1, "Threshold should be a float between zero and one (non-inclusive)" if not os.path.exists(args.config_file): @@ -39,67 +53,82 @@ def check_arguments_errors(args): raise(ValueError("Invalid video path {}".format(os.path.abspath(args.input)))) -def str2int(video_path): - """ - argparse returns and string althout webcam uses int (0, 1 ...) - Cast to int if needed - """ - try: - return int(video_path) - except ValueError: - return video_path - - def set_saved_video(input_video, output_video, size): fourcc = cv2.VideoWriter_fourcc(*"MJPG") fps = int(input_video.get(cv2.CAP_PROP_FPS)) - video = cv2.VideoWriter(output_video, fourcc, fps, size,) + video = cv2.VideoWriter(output_video, fourcc, fps, size) return video -def main(): - args = parser() - check_arguments_errors(args) - random.seed(3) # deterministic bbox colors - - network, class_names, class_colors = darknet.load_network( - args.config_file, - args.data_file, - args.weights - ) - # Darknet doesn't accept numpy images. - # Create one with image we reuse for each detect - width = darknet.network_width(network) - height = darknet.network_height(network) - darknet_image = darknet.make_image(width, height, 3) - - input_path = str2int(args.input) - cap = cv2.VideoCapture(input_path) - video = set_saved_video(cap, args.out_filename, (width, height)) - +def video_capture(frame_queue, darknet_image_queue): while cap.isOpened(): - prev_time = time.time() ret, frame = cap.read() if not ret: break frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frame_resized = cv2.resize(frame_rgb, (width, height), interpolation=cv2.INTER_LINEAR) + frame_queue.put(frame_resized) darknet.copy_image_from_bytes(darknet_image, frame_resized.tobytes()) + darknet_image_queue.put(darknet_image) + cap.release() + + +def inference(darknet_image_queue, detections_queue, fps_queue): + while cap.isOpened(): + darknet_image = darknet_image_queue.get() + prev_time = time.time() detections = darknet.detect_image(network, class_names, darknet_image, thresh=args.thresh) - image = darknet.draw_boxes(detections, frame_resized, class_colors) - image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) - if args.out_filename is not None: - video.write(image) + detections_queue.put(detections) fps = int(1/(time.time() - prev_time)) + fps_queue.put(fps) print("FPS: {}".format(fps)) darknet.print_detections(detections, args.ext_output) - if not args.dont_show: - cv2.imshow('Inference', image) - cv2.waitKey(fps) + cap.release() + + +def drawing(frame_queue, detections_queue, fps_queue): + random.seed(3) # deterministic bbox colors + video = set_saved_video(cap, args.out_filename, (width, height)) + while cap.isOpened(): + frame_resized = frame_queue.get() + detections = detections_queue.get() + fps = fps_queue.get() + if frame_resized is not None: + image = darknet.draw_boxes(detections, frame_resized, class_colors) + image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + if args.out_filename is not None: + video.write(image) + if not args.dont_show: + cv2.imshow('Inference', image) + if cv2.waitKey(fps) == 27: + break cap.release() video.release() + cv2.destroyAllWindows() -if __name__ == "__main__": - main() +if __name__ == '__main__': + frame_queue = Queue() + darknet_image_queue = Queue(maxsize=1) + detections_queue = Queue(maxsize=1) + fps_queue = Queue(maxsize=1) + + args = parser() + check_arguments_errors(args) + network, class_names, class_colors = darknet.load_network( + args.config_file, + args.data_file, + args.weights, + batch_size=1 + ) + # Darknet doesn't accept numpy images. + # Create one with image we reuse for each detect + width = darknet.network_width(network) + height = darknet.network_height(network) + darknet_image = darknet.make_image(width, height, 3) + input_path = str2int(args.input) + cap = cv2.VideoCapture(input_path) + Thread(target=video_capture, args=(frame_queue, darknet_image_queue)).start() + Thread(target=inference, args=(darknet_image_queue, detections_queue, fps_queue)).start() + Thread(target=drawing, args=(frame_queue, detections_queue, fps_queue)).start() From ce7e79e650a2ef6f41c9f6f9eab97b7e7f740bc1 Mon Sep 17 00:00:00 2001 From: JaledMC Date: Wed, 29 Jul 2020 17:16:56 +0200 Subject: [PATCH 1089/1365] Update darknet_images.py --- darknet_images.py | 80 +++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 78 insertions(+), 2 deletions(-) diff --git a/darknet_images.py b/darknet_images.py index a3079b49240..471a99ba75b 100644 --- a/darknet_images.py +++ b/darknet_images.py @@ -5,6 +5,8 @@ import darknet import time import cv2 +import numpy as np +import darknet def parser(): @@ -14,6 +16,8 @@ def parser(): "txt with paths to them, or a folder. Image valid" " formats are jpg, jpeg or png." "If no input is given, ") + parser.add_argument("--batch_size", default=1, type=int, + help="number of images to be processed at the same time") parser.add_argument("--weights", default="yolov4.weights", help="yolo weights path") parser.add_argument("--dont_show", action='store_true', @@ -43,6 +47,18 @@ def check_arguments_errors(args): raise(ValueError("Invalid image path {}".format(os.path.abspath(args.input)))) +def check_batch_shape(images, batch_size): + """ + Image sizes should be the same width and height + """ + shapes = [image.shape for image in images] + if len(set(shapes)) > 1: + raise ValueError("Images don't have same shape") + if len(shapes) > batch_size: + raise ValueError("Batch size higher than number of images") + return shapes[0] + + def load_images(images_path): """ If image path is given, return it directly @@ -63,6 +79,24 @@ def load_images(images_path): glob.glob(os.path.join(images_path, "*.jpeg")) +def prepare_batch(images, network, channels=3): + width = darknet.network_width(network) + height = darknet.network_height(network) + + darknet_images = [] + for image in images: + image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + image_resized = cv2.resize(image_rgb, (width, height), + interpolation=cv2.INTER_LINEAR) + custom_image = image_resized.transpose(2, 0, 1) + darknet_images.append(custom_image) + + batch_array = np.concatenate(darknet_images, axis=0) + batch_array = np.ascontiguousarray(batch_array.flat, dtype=np.float32)/255.0 + darknet_images = batch_array.ctypes.data_as(darknet.POINTER(darknet.c_float)) + return darknet.IMAGE(width, height, channels, darknet_images) + + def image_detection(image_path, network, class_names, class_colors, thresh): # Darknet doesn't accept numpy images. # Create one with image we reuse for each detect @@ -81,6 +115,25 @@ def image_detection(image_path, network, class_names, class_colors, thresh): return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), detections +def batch_detection(network, images, class_names, class_colors, + thresh=0.25, hier_thresh=.5, nms=.45, batch_size=4): + image_height, image_width, _ = check_batch_shape(images, batch_size) + darknet_images = prepare_batch(images, network) + batch_detections = darknet.network_predict_batch(network, darknet_images, batch_size, image_width, + image_height, thresh, hier_thresh, None, 0, 0) + batch_predictions = [] + for idx in range(batch_size): + num = batch_detections[idx].num + detections = batch_detections[idx].dets + if nms: + darknet.do_nms_obj(detections, num, len(class_names), nms) + predictions = darknet.remove_negatives(detections, class_names, num) + images[idx] = darknet.draw_boxes(predictions, images[idx], class_colors) + batch_predictions.append(predictions) + darknet.free_batch_detections(batch_detections, batch_size) + return images, batch_predictions + + def convert2relative(image, bbox): """ YOLO format use relative coordinates for annotation @@ -99,7 +152,27 @@ def save_annotations(name, image, detections, class_names): for label, confidence, bbox in detections: x, y, w, h = convert2relative(image, bbox) label = class_names.index(label) - f.write("{} {:.4f} {:.4f} {:.4f} {:.4f} {:.4f}\n".format(label, x, y, w, h, confidence)) + f.write("{} {:.4f} {:.4f} {:.4f} {:.4f} {:.4f}\n".format(label, x, y, w, h, float(confidence))) + + +def batch_detection_example(): + args = parser() + check_arguments_errors(args) + batch_size = 3 + random.seed(3) # deterministic bbox colors + network, class_names, class_colors = darknet.load_network( + args.config_file, + args.data_file, + args.weights, + batch_size=batch_size + ) + image_names = ['data/horses.jpg', 'data/horses.jpg', 'data/eagle.jpg'] + images = [cv2.imread(image) for image in image_names] + images, detections, = batch_detection(network, images, class_names, + class_colors, batch_size=batch_size) + for name, image in zip(image_names, images): + cv2.imwrite(name.replace("data/", ""), image) + print(detections) def main(): @@ -110,7 +183,8 @@ def main(): network, class_names, class_colors = darknet.load_network( args.config_file, args.data_file, - args.weights + args.weights, + batch_size=args.batch_size ) images = load_images(args.input) @@ -141,4 +215,6 @@ def main(): if __name__ == "__main__": + # unconmment next line for an example of batch processing + # batch_detection_example() main() From 1360070bf54913ee8e5ddf62bd8f27608085af8d Mon Sep 17 00:00:00 2001 From: JaledMC Date: Wed, 29 Jul 2020 17:17:40 +0200 Subject: [PATCH 1090/1365] Update darknet.py --- darknet.py | 142 ++++++++++++++++++++++++++++------------------------- 1 file changed, 75 insertions(+), 67 deletions(-) diff --git a/darknet.py b/darknet.py index 26a2bc409c5..832f407883b 100644 --- a/darknet.py +++ b/darknet.py @@ -4,8 +4,7 @@ Requires DLL compilation -Both the GPU and no-GPU version should be compiled; the no-GPU version should -be renamed "yolo_cpp_dll_nogpu.dll". +Both the GPU and no-GPU version should be compiled; the no-GPU version should be renamed "yolo_cpp_dll_nogpu.dll". On a GPU system, you can force CPU evaluation by any of: @@ -13,22 +12,18 @@ - Set environment variable CUDA_VISIBLE_DEVICES to -1 - Set environment variable "FORCE_CPU" to "true" - -Directly viewing or returning bounding-boxed images requires OpenCV -to be installed (`pip install opencv-python`) - +Directly viewing or returning bounding-boxed images requires scikit-image to be installed (`pip install scikit-image`) Original *nix 2.7: https://github.com/pjreddie/darknet/blob/0f110834f4e18b30d5f101bf8f1724c34b7b83db/python/darknet.py Windows Python 2.7 version: https://github.com/AlexeyAB/darknet/blob/fc496d52bf22a0bb257300d3c79be9cd80e722cb/build/darknet/x64/darknet.py @author: Philip Kahn -@author: Jaled Moustafa Calvo -@date: 09/04/2020 +@date: 20180503 """ from ctypes import * -import os -import re +import math import random +import os class BOX(Structure): @@ -37,6 +32,7 @@ class BOX(Structure): ("w", c_float), ("h", c_float)] + class DETECTION(Structure): _fields_ = [("bbox", BOX), ("classes", c_int), @@ -45,7 +41,16 @@ class DETECTION(Structure): ("objectness", c_float), ("sort_class", c_int), ("uc", POINTER(c_float)), - ("points", c_int)] + ("points", c_int), + ("embeddings", POINTER(c_float)), + ("embedding_size", c_int), + ("sim", c_float), + ("track_id", c_int)] + + +class DETNUMPAIR(Structure): + _fields_ = [("num", c_int), + ("dets", POINTER(DETECTION))] class IMAGE(Structure): @@ -54,6 +59,7 @@ class IMAGE(Structure): ("c", c_int), ("data", POINTER(c_float))] + class METADATA(Structure): _fields_ = [("classes", c_int), ("names", POINTER(c_char_p))] @@ -67,28 +73,9 @@ def network_height(net): return lib.network_height(net) -def class_colors(names): - """ - Create a dict with one random BGR color for each - class name - """ - return {name:( - random.randint(0, 255), - random.randint(0, 255), - random.randint(0, 255)) for name in names} - - -def decode_detection(detections): - decoded = [] - for label, confidence, bbox in detections: - confidence = str(round(confidence * 100, 2)) - decoded.append((str(label), confidence, bbox)) - return decoded - - def bbox2points(bbox): """ - From bounding box yolo format + From bounding box yolo format to corner points cv2 rectangle """ x, y, w, h = bbox @@ -96,31 +83,21 @@ def bbox2points(bbox): xmax = int(round(x + (w / 2))) ymin = int(round(y - (h / 2))) ymax = int(round(y + (h / 2))) - return (xmin, ymin), (xmax, ymax) + return xmin, ymin, xmax, ymax -def draw_boxes(detections, image, colors): - import cv2 - for label, confidence, bbox in detections: - pt1, pt2 = bbox2points(bbox) - cv2.rectangle(image, pt1, pt2, colors[label], 1) - cv2.putText(image, "{} [{}]".format(label, confidence), - (pt1[0], pt1[1] - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, - colors[label], 2) - return image - - -def print_detections(detections, coordinates=False): - print("\nObjects:") - for label, confidence, bbox in detections: - x, y, w, h = bbox - if coordinates: - print("{}: {}% (left_x: {:.0f} top_y: {:.0f} width: {:.0f} height: {:.0f})".format(label, confidence, x, y, w, h)) - else: - print("{}: {}%".format(label, confidence)) +def class_colors(names): + """ + Create a dict with one random BGR color for each + class name + """ + return {name: ( + random.randint(0, 255), + random.randint(0, 255), + random.randint(0, 255)) for name in names} -def load_network(config_file, data_file, weights): +def load_network(config_file, data_file, weights, batch_size=1): """ load model description and weights from config files args: @@ -130,17 +107,46 @@ def load_network(config_file, data_file, weights): returns: network: trained model class_names - class_colors + class_colors """ network = load_net_custom( config_file.encode("ascii"), - weights.encode("ascii"), 0, 1) # batch size = 1 + weights.encode("ascii"), 0, batch_size) metadata = load_meta(data_file.encode("ascii")) class_names = [metadata.names[i].decode("ascii") for i in range(metadata.classes)] colors = class_colors(class_names) return network, class_names, colors +def print_detections(detections, coordinates=False): + print("\nObjects:") + for label, confidence, bbox in detections: + x, y, w, h = bbox + if coordinates: + print("{}: {}% (left_x: {:.0f} top_y: {:.0f} width: {:.0f} height: {:.0f})".format(label, confidence, x, y, w, h)) + else: + print("{}: {}%".format(label, confidence)) + + +def draw_boxes(detections, image, colors): + import cv2 + for label, confidence, bbox in detections: + left, top, right, bottom = bbox2points(bbox) + cv2.rectangle(image, (left, top), (right, bottom), colors[label], 1) + cv2.putText(image, "{} [{:.2f}]".format(label, float(confidence)), + (left, top - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, + colors[label], 2) + return image + + +def decode_detection(detections): + decoded = [] + for label, confidence, bbox in detections: + confidence = str(round(confidence * 100, 2)) + decoded.append((str(label), confidence, bbox)) + return decoded + + def remove_negatives(detections, class_names, num): """ Remove all classes with 0% confidence within the detection @@ -172,12 +178,8 @@ def detect_image(network, class_names, image, thresh=.5, hier_thresh=.5, nms=.45 return sorted(predictions, key=lambda x: x[1]) -def classify(network, meta, image): - detections = predict_image(network, image) - predictions = [(name, detection[idx]) for idx, name in enumerate(class_names)] - return sorted(predictions, key=lambda x: -x[1]) - - +# lib = CDLL("/home/pjreddie/documents/darknet/libdarknet.so", RTLD_GLOBAL) +# lib = CDLL("libdarknet.so", RTLD_GLOBAL) hasGPU = True if os.name == "nt": cwd = os.path.dirname(__file__) @@ -193,7 +195,7 @@ def classify(network, meta, image): if tmp in ["1", "true", "yes", "on"]: raise ValueError("ForceCPU") else: - print("Flag value '"+tmp+"' not forcing CPU mode") + print("Flag value {} not forcing CPU mode".format(tmp)) except KeyError: # We never set the flag if 'CUDA_VISIBLE_DEVICES' in envKeys: @@ -203,9 +205,8 @@ def classify(network, meta, image): global DARKNET_FORCE_CPU if DARKNET_FORCE_CPU: raise ValueError("ForceCPU") - except NameError: - pass - # print("FORCE_CPU flag undefined, proceeding with GPU") + except NameError as cpu_error: + print(cpu_error) if not os.path.exists(winGPUdll): raise ValueError("NoDLL") lib = CDLL(winGPUdll, RTLD_GLOBAL) @@ -215,10 +216,9 @@ def classify(network, meta, image): lib = CDLL(winNoGPUdll, RTLD_GLOBAL) print("Notice: CPU-only mode") else: - # Try the other way, in case no_gpu was - # compile but not renamed + # Try the other way, in case no_gpu was compile but not renamed lib = CDLL(winGPUdll, RTLD_GLOBAL) - print("Environment variables indicated a CPU run, but we didn't find `"+winNoGPUdll+"`. Trying a GPU run anyway.") + print("Environment variables indicated a CPU run, but we didn't find {}. Trying a GPU run anyway.".format(winNoGPUdll)) else: lib = CDLL("./libdarknet.so", RTLD_GLOBAL) lib.network_width.argtypes = [c_void_p] @@ -254,6 +254,9 @@ def classify(network, meta, image): free_detections = lib.free_detections free_detections.argtypes = [POINTER(DETECTION), c_int] +free_batch_detections = lib.free_batch_detections +free_batch_detections.argtypes = [POINTER(DETNUMPAIR), c_int] + free_ptrs = lib.free_ptrs free_ptrs.argtypes = [POINTER(c_void_p), c_int] @@ -302,3 +305,8 @@ def classify(network, meta, image): predict_image_letterbox = lib.network_predict_image_letterbox predict_image_letterbox.argtypes = [c_void_p, IMAGE] predict_image_letterbox.restype = POINTER(c_float) + +network_predict_batch = lib.network_predict_batch +network_predict_batch.argtypes = [c_void_p, IMAGE, c_int, c_int, c_int, + c_float, c_float, POINTER(c_int), c_int, c_int] +network_predict_batch.restype = POINTER(DETNUMPAIR) From 1bb2d70bf9c5b65ab2571692ae61062b49a02778 Mon Sep 17 00:00:00 2001 From: JaledMC Date: Wed, 29 Jul 2020 17:34:03 +0200 Subject: [PATCH 1091/1365] Update darknet.py --- darknet.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/darknet.py b/darknet.py index 832f407883b..5d76afd1151 100644 --- a/darknet.py +++ b/darknet.py @@ -48,6 +48,9 @@ class DETECTION(Structure): ("track_id", c_int)] + + + class DETNUMPAIR(Structure): _fields_ = [("num", c_int), ("dets", POINTER(DETECTION))] From d0b6bae7316b576a221c4b4c1a461862bdf9cf43 Mon Sep 17 00:00:00 2001 From: JaledMC Date: Wed, 29 Jul 2020 17:35:39 +0200 Subject: [PATCH 1092/1365] Update darknet.py --- darknet.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/darknet.py b/darknet.py index 5d76afd1151..bb0ee183688 100644 --- a/darknet.py +++ b/darknet.py @@ -47,10 +47,6 @@ class DETECTION(Structure): ("sim", c_float), ("track_id", c_int)] - - - - class DETNUMPAIR(Structure): _fields_ = [("num", c_int), ("dets", POINTER(DETECTION))] From f2eb30b52cedeab0e48fc42ddfac26c711e6eabb Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 2 Aug 2020 17:37:03 +0300 Subject: [PATCH 1093/1365] Contrastive loss minor fix --- src/blas.c | 8 ++++---- src/softmax_layer.c | 16 +++++++++------- 2 files changed, 13 insertions(+), 11 deletions(-) diff --git a/src/blas.c b/src/blas.c index a21bca69ad4..b3c67bbf7fb 100644 --- a/src/blas.c +++ b/src/blas.c @@ -638,8 +638,8 @@ float P_constrastive_f_det(size_t il, int *labels, float **z, unsigned int featu } } - float result = numerator / denominator; - if (denominator == 0) result = 1; + float result = 0.9999; + if (denominator != 0) result = numerator / denominator; if (result > 1) result = 0.9999; return result; } @@ -669,8 +669,8 @@ float P_constrastive_f(size_t i, size_t l, int *labels, float **z, unsigned int } } - float result = numerator / denominator; - if (denominator == 0) result = 1; + float result = 0.9999; + if (denominator != 0) result = numerator / denominator; if (result > 1) result = 0.9999; return result; } diff --git a/src/softmax_layer.c b/src/softmax_layer.c index 158c4061d02..a80ddeafef9 100644 --- a/src/softmax_layer.c +++ b/src/softmax_layer.c @@ -405,11 +405,13 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) */ + const size_t contr_size = contrast_p_index; + if (l.detection) { int k; #pragma omp parallel for - for (k = 0; k < contrast_p_index; ++k) { - contrast_p[k].P = P_constrastive_f_det(k, l.labels, z, l.embedding_size, l.temperature, contrast_p, contrast_p_index); + for (k = 0; k < contr_size; ++k) { + contrast_p[k].P = P_constrastive_f_det(k, l.labels, z, l.embedding_size, l.temperature, contrast_p, contr_size); } } else { @@ -439,7 +441,7 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) float P = -10; if (l.detection) { - P = P_constrastive_f(z_index, z_index2, l.labels, z, l.embedding_size, l.temperature, contrast_p, contrast_p_index); + P = P_constrastive_f(z_index, z_index2, l.labels, z, l.embedding_size, l.temperature, contrast_p, contr_size); } else { P = P_constrastive(z_index, z_index2, l.labels, step, z, l.embedding_size, l.temperature, l.cos_sim, l.exp_cos_sim); @@ -447,13 +449,13 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) } int q; - for (q = 0; q < contrast_p_index; ++q) + for (q = 0; q < contr_size; ++q) if (contrast_p[q].i == z_index && contrast_p[q].j == z_index2) { contrast_p[q].P = P; break; } - //if (q == contrast_p_index) getchar(); + //if (q == contr_size) getchar(); //if (P > 1 || P < -1) { @@ -488,10 +490,10 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) // detector // positive - grad_contrastive_loss_positive_f(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.delta + delta_index, wh, contrast_p, contrast_p_index); + grad_contrastive_loss_positive_f(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.delta + delta_index, wh, contrast_p, contr_size); // negative - grad_contrastive_loss_negative_f(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.delta + delta_index, wh, contrast_p, contrast_p_index); + grad_contrastive_loss_negative_f(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.delta + delta_index, wh, contrast_p, contr_size); } else { // classifier From 0b36b29b6aa6eddc2134581dd8f666d5db4276a9 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 2 Aug 2020 17:56:17 +0300 Subject: [PATCH 1094/1365] Minor fix --- include/darknet.h | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index c42cba10b09..b9418177893 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -1073,8 +1073,8 @@ void stop_timer_and_show(); void stop_timer_and_show_name(char *name); void show_total_time(); -void set_track_id(detection *new_dets, int new_dets_num, float thresh, float sim_thresh, float track_ciou_norm, int deque_size, int dets_for_track, int dets_for_show); -int fill_remaining_id(detection *new_dets, int new_dets_num, int new_track_id, float thresh); +LIB_API void set_track_id(detection *new_dets, int new_dets_num, float thresh, float sim_thresh, float track_ciou_norm, int deque_size, int dets_for_track, int dets_for_show); +LIB_API int fill_remaining_id(detection *new_dets, int new_dets_num, int new_track_id, float thresh); // gemm.h From 666e26196ee785eb7976b38f1e379e4ca9d79ddc Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 2 Aug 2020 18:22:03 +0300 Subject: [PATCH 1095/1365] Minor fix --- build/darknet/x64/darknet.py | 464 ++++++++-------------------- build/darknet/x64/darknet_video.cmd | 4 +- build/darknet/x64/darknet_video.py | 225 ++++++++------ 3 files changed, 246 insertions(+), 447 deletions(-) diff --git a/build/darknet/x64/darknet.py b/build/darknet/x64/darknet.py index 74058a19c78..bb0ee183688 100644 --- a/build/darknet/x64/darknet.py +++ b/build/darknet/x64/darknet.py @@ -12,40 +12,19 @@ - Set environment variable CUDA_VISIBLE_DEVICES to -1 - Set environment variable "FORCE_CPU" to "true" - -To use, either run performDetect() after import, or modify the end of this file. - -See the docstring of performDetect() for parameters. - Directly viewing or returning bounding-boxed images requires scikit-image to be installed (`pip install scikit-image`) - Original *nix 2.7: https://github.com/pjreddie/darknet/blob/0f110834f4e18b30d5f101bf8f1724c34b7b83db/python/darknet.py Windows Python 2.7 version: https://github.com/AlexeyAB/darknet/blob/fc496d52bf22a0bb257300d3c79be9cd80e722cb/build/darknet/x64/darknet.py @author: Philip Kahn @date: 20180503 """ -#pylint: disable=R, W0401, W0614, W0703 from ctypes import * import math import random import os -def sample(probs): - s = sum(probs) - probs = [a/s for a in probs] - r = random.uniform(0, 1) - for i in range(len(probs)): - r = r - probs[i] - if r <= 0: - return i - return len(probs)-1 - -def c_array(ctype, values): - arr = (ctype*len(values))() - arr[:] = values - return arr class BOX(Structure): _fields_ = [("x", c_float), @@ -53,6 +32,7 @@ class BOX(Structure): ("w", c_float), ("h", c_float)] + class DETECTION(Structure): _fields_ = [("bbox", BOX), ("classes", c_int), @@ -71,20 +51,134 @@ class DETNUMPAIR(Structure): _fields_ = [("num", c_int), ("dets", POINTER(DETECTION))] + class IMAGE(Structure): _fields_ = [("w", c_int), ("h", c_int), ("c", c_int), ("data", POINTER(c_float))] + class METADATA(Structure): _fields_ = [("classes", c_int), ("names", POINTER(c_char_p))] +def network_width(net): + return lib.network_width(net) + + +def network_height(net): + return lib.network_height(net) + + +def bbox2points(bbox): + """ + From bounding box yolo format + to corner points cv2 rectangle + """ + x, y, w, h = bbox + xmin = int(round(x - (w / 2))) + xmax = int(round(x + (w / 2))) + ymin = int(round(y - (h / 2))) + ymax = int(round(y + (h / 2))) + return xmin, ymin, xmax, ymax + + +def class_colors(names): + """ + Create a dict with one random BGR color for each + class name + """ + return {name: ( + random.randint(0, 255), + random.randint(0, 255), + random.randint(0, 255)) for name in names} + + +def load_network(config_file, data_file, weights, batch_size=1): + """ + load model description and weights from config files + args: + config_file (str): path to .cfg model file + data_file (str): path to .data model file + weights (str): path to weights + returns: + network: trained model + class_names + class_colors + """ + network = load_net_custom( + config_file.encode("ascii"), + weights.encode("ascii"), 0, batch_size) + metadata = load_meta(data_file.encode("ascii")) + class_names = [metadata.names[i].decode("ascii") for i in range(metadata.classes)] + colors = class_colors(class_names) + return network, class_names, colors + + +def print_detections(detections, coordinates=False): + print("\nObjects:") + for label, confidence, bbox in detections: + x, y, w, h = bbox + if coordinates: + print("{}: {}% (left_x: {:.0f} top_y: {:.0f} width: {:.0f} height: {:.0f})".format(label, confidence, x, y, w, h)) + else: + print("{}: {}%".format(label, confidence)) + + +def draw_boxes(detections, image, colors): + import cv2 + for label, confidence, bbox in detections: + left, top, right, bottom = bbox2points(bbox) + cv2.rectangle(image, (left, top), (right, bottom), colors[label], 1) + cv2.putText(image, "{} [{:.2f}]".format(label, float(confidence)), + (left, top - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, + colors[label], 2) + return image -#lib = CDLL("/home/pjreddie/documents/darknet/libdarknet.so", RTLD_GLOBAL) -#lib = CDLL("libdarknet.so", RTLD_GLOBAL) + +def decode_detection(detections): + decoded = [] + for label, confidence, bbox in detections: + confidence = str(round(confidence * 100, 2)) + decoded.append((str(label), confidence, bbox)) + return decoded + + +def remove_negatives(detections, class_names, num): + """ + Remove all classes with 0% confidence within the detection + """ + predictions = [] + for j in range(num): + for idx, name in enumerate(class_names): + if detections[j].prob[idx] > 0: + bbox = detections[j].bbox + bbox = (bbox.x, bbox.y, bbox.w, bbox.h) + predictions.append((name, detections[j].prob[idx], (bbox))) + return predictions + + +def detect_image(network, class_names, image, thresh=.5, hier_thresh=.5, nms=.45): + """ + Returns a list with highest confidence class and their bbox + """ + pnum = pointer(c_int(0)) + predict_image(network, image) + detections = get_network_boxes(network, image.w, image.h, + thresh, hier_thresh, None, 0, pnum, 0) + num = pnum[0] + if nms: + do_nms_sort(detections, num, len(class_names), nms) + predictions = remove_negatives(detections, class_names, num) + predictions = decode_detection(predictions) + free_detections(detections, num) + return sorted(predictions, key=lambda x: x[1]) + + +# lib = CDLL("/home/pjreddie/documents/darknet/libdarknet.so", RTLD_GLOBAL) +# lib = CDLL("libdarknet.so", RTLD_GLOBAL) hasGPU = True if os.name == "nt": cwd = os.path.dirname(__file__) @@ -100,7 +194,7 @@ class METADATA(Structure): if tmp in ["1", "true", "yes", "on"]: raise ValueError("ForceCPU") else: - print("Flag value '"+tmp+"' not forcing CPU mode") + print("Flag value {} not forcing CPU mode".format(tmp)) except KeyError: # We never set the flag if 'CUDA_VISIBLE_DEVICES' in envKeys: @@ -110,10 +204,8 @@ class METADATA(Structure): global DARKNET_FORCE_CPU if DARKNET_FORCE_CPU: raise ValueError("ForceCPU") - except NameError: - pass - # print(os.environ.keys()) - # print("FORCE_CPU flag undefined, proceeding with GPU") + except NameError as cpu_error: + print(cpu_error) if not os.path.exists(winGPUdll): raise ValueError("NoDLL") lib = CDLL(winGPUdll, RTLD_GLOBAL) @@ -123,10 +215,9 @@ class METADATA(Structure): lib = CDLL(winNoGPUdll, RTLD_GLOBAL) print("Notice: CPU-only mode") else: - # Try the other way, in case no_gpu was - # compile but not renamed + # Try the other way, in case no_gpu was compile but not renamed lib = CDLL(winGPUdll, RTLD_GLOBAL) - print("Environment variables indicated a CPU run, but we didn't find `"+winNoGPUdll+"`. Trying a GPU run anyway.") + print("Environment variables indicated a CPU run, but we didn't find {}. Trying a GPU run anyway.".format(winNoGPUdll)) else: lib = CDLL("./libdarknet.so", RTLD_GLOBAL) lib.network_width.argtypes = [c_void_p] @@ -137,12 +228,6 @@ class METADATA(Structure): copy_image_from_bytes = lib.copy_image_from_bytes copy_image_from_bytes.argtypes = [IMAGE,c_char_p] -def network_width(net): - return lib.network_width(net) - -def network_height(net): - return lib.network_height(net) - predict = lib.network_predict_ptr predict.argtypes = [c_void_p, POINTER(c_float)] predict.restype = POINTER(c_float) @@ -224,308 +309,3 @@ def network_height(net): network_predict_batch.argtypes = [c_void_p, IMAGE, c_int, c_int, c_int, c_float, c_float, POINTER(c_int), c_int, c_int] network_predict_batch.restype = POINTER(DETNUMPAIR) - -def array_to_image(arr): - import numpy as np - # need to return old values to avoid python freeing memory - arr = arr.transpose(2,0,1) - c = arr.shape[0] - h = arr.shape[1] - w = arr.shape[2] - arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0 - data = arr.ctypes.data_as(POINTER(c_float)) - im = IMAGE(w,h,c,data) - return im, arr - -def classify(net, meta, im): - out = predict_image(net, im) - res = [] - for i in range(meta.classes): - if altNames is None: - nameTag = meta.names[i] - else: - nameTag = altNames[i] - res.append((nameTag, out[i])) - res = sorted(res, key=lambda x: -x[1]) - return res - -def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False): - """ - Performs the meat of the detection - """ - #pylint: disable= C0321 - im = load_image(image, 0, 0) - if debug: print("Loaded image") - ret = detect_image(net, meta, im, thresh, hier_thresh, nms, debug) - free_image(im) - if debug: print("freed image") - return ret - -def detect_image(net, meta, im, thresh=.5, hier_thresh=.5, nms=.45, debug= False): - #import cv2 - #custom_image_bgr = cv2.imread(image) # use: detect(,,imagePath,) - #custom_image = cv2.cvtColor(custom_image_bgr, cv2.COLOR_BGR2RGB) - #custom_image = cv2.resize(custom_image,(lib.network_width(net), lib.network_height(net)), interpolation = cv2.INTER_LINEAR) - #import scipy.misc - #custom_image = scipy.misc.imread(image) - #im, arr = array_to_image(custom_image) # you should comment line below: free_image(im) - num = c_int(0) - if debug: print("Assigned num") - pnum = pointer(num) - if debug: print("Assigned pnum") - predict_image(net, im) - letter_box = 0 - #predict_image_letterbox(net, im) - #letter_box = 1 - if debug: print("did prediction") - #dets = get_network_boxes(net, custom_image_bgr.shape[1], custom_image_bgr.shape[0], thresh, hier_thresh, None, 0, pnum, letter_box) # OpenCV - dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum, letter_box) - if debug: print("Got dets") - num = pnum[0] - if debug: print("got zeroth index of pnum") - if nms: - do_nms_sort(dets, num, meta.classes, nms) - if debug: print("did sort") - res = [] - if debug: print("about to range") - for j in range(num): - if debug: print("Ranging on "+str(j)+" of "+str(num)) - if debug: print("Classes: "+str(meta), meta.classes, meta.names) - for i in range(meta.classes): - if debug: print("Class-ranging on "+str(i)+" of "+str(meta.classes)+"= "+str(dets[j].prob[i])) - if dets[j].prob[i] > 0: - b = dets[j].bbox - if altNames is None: - nameTag = meta.names[i] - else: - nameTag = altNames[i] - if debug: - print("Got bbox", b) - print(nameTag) - print(dets[j].prob[i]) - print((b.x, b.y, b.w, b.h)) - res.append((nameTag, dets[j].prob[i], (b.x, b.y, b.w, b.h))) - if debug: print("did range") - res = sorted(res, key=lambda x: -x[1]) - if debug: print("did sort") - free_detections(dets, num) - if debug: print("freed detections") - return res - - -netMain = None -metaMain = None -altNames = None - -def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yolov4.cfg", weightPath = "yolov4.weights", metaPath= "./cfg/coco.data", showImage= True, makeImageOnly = False, initOnly= False): - """ - Convenience function to handle the detection and returns of objects. - - Displaying bounding boxes requires libraries scikit-image and numpy - - Parameters - ---------------- - imagePath: str - Path to the image to evaluate. Raises ValueError if not found - - thresh: float (default= 0.25) - The detection threshold - - configPath: str - Path to the configuration file. Raises ValueError if not found - - weightPath: str - Path to the weights file. Raises ValueError if not found - - metaPath: str - Path to the data file. Raises ValueError if not found - - showImage: bool (default= True) - Compute (and show) bounding boxes. Changes return. - - makeImageOnly: bool (default= False) - If showImage is True, this won't actually *show* the image, but will create the array and return it. - - initOnly: bool (default= False) - Only initialize globals. Don't actually run a prediction. - - Returns - ---------------------- - - - When showImage is False, list of tuples like - ('obj_label', confidence, (bounding_box_x_px, bounding_box_y_px, bounding_box_width_px, bounding_box_height_px)) - The X and Y coordinates are from the center of the bounding box. Subtract half the width or height to get the lower corner. - - Otherwise, a dict with - { - "detections": as above - "image": a numpy array representing an image, compatible with scikit-image - "caption": an image caption - } - """ - # Import the global variables. This lets us instance Darknet once, then just call performDetect() again without instancing again - global metaMain, netMain, altNames #pylint: disable=W0603 - assert 0 < thresh < 1, "Threshold should be a float between zero and one (non-inclusive)" - if not os.path.exists(configPath): - raise ValueError("Invalid config path `"+os.path.abspath(configPath)+"`") - if not os.path.exists(weightPath): - raise ValueError("Invalid weight path `"+os.path.abspath(weightPath)+"`") - if not os.path.exists(metaPath): - raise ValueError("Invalid data file path `"+os.path.abspath(metaPath)+"`") - if netMain is None: - netMain = load_net_custom(configPath.encode("ascii"), weightPath.encode("ascii"), 0, 1) # batch size = 1 - if metaMain is None: - metaMain = load_meta(metaPath.encode("ascii")) - if altNames is None: - # In Python 3, the metafile default access craps out on Windows (but not Linux) - # Read the names file and create a list to feed to detect - try: - with open(metaPath) as metaFH: - metaContents = metaFH.read() - import re - match = re.search("names *= *(.*)$", metaContents, re.IGNORECASE | re.MULTILINE) - if match: - result = match.group(1) - else: - result = None - try: - if os.path.exists(result): - with open(result) as namesFH: - namesList = namesFH.read().strip().split("\n") - altNames = [x.strip() for x in namesList] - except TypeError: - pass - except Exception: - pass - if initOnly: - print("Initialized detector") - return None - if not os.path.exists(imagePath): - raise ValueError("Invalid image path `"+os.path.abspath(imagePath)+"`") - # Do the detection - #detections = detect(netMain, metaMain, imagePath, thresh) # if is used cv2.imread(image) - detections = detect(netMain, metaMain, imagePath.encode("ascii"), thresh) - if showImage: - try: - from skimage import io, draw - import numpy as np - image = io.imread(imagePath) - print("*** "+str(len(detections))+" Results, color coded by confidence ***") - imcaption = [] - for detection in detections: - label = detection[0] - confidence = detection[1] - pstring = label+": "+str(np.rint(100 * confidence))+"%" - imcaption.append(pstring) - print(pstring) - bounds = detection[2] - shape = image.shape - # x = shape[1] - # xExtent = int(x * bounds[2] / 100) - # y = shape[0] - # yExtent = int(y * bounds[3] / 100) - yExtent = int(bounds[3]) - xEntent = int(bounds[2]) - # Coordinates are around the center - xCoord = int(bounds[0] - bounds[2]/2) - yCoord = int(bounds[1] - bounds[3]/2) - boundingBox = [ - [xCoord, yCoord], - [xCoord, yCoord + yExtent], - [xCoord + xEntent, yCoord + yExtent], - [xCoord + xEntent, yCoord] - ] - # Wiggle it around to make a 3px border - rr, cc = draw.polygon_perimeter([x[1] for x in boundingBox], [x[0] for x in boundingBox], shape= shape) - rr2, cc2 = draw.polygon_perimeter([x[1] + 1 for x in boundingBox], [x[0] for x in boundingBox], shape= shape) - rr3, cc3 = draw.polygon_perimeter([x[1] - 1 for x in boundingBox], [x[0] for x in boundingBox], shape= shape) - rr4, cc4 = draw.polygon_perimeter([x[1] for x in boundingBox], [x[0] + 1 for x in boundingBox], shape= shape) - rr5, cc5 = draw.polygon_perimeter([x[1] for x in boundingBox], [x[0] - 1 for x in boundingBox], shape= shape) - boxColor = (int(255 * (1 - (confidence ** 2))), int(255 * (confidence ** 2)), 0) - draw.set_color(image, (rr, cc), boxColor, alpha= 0.8) - draw.set_color(image, (rr2, cc2), boxColor, alpha= 0.8) - draw.set_color(image, (rr3, cc3), boxColor, alpha= 0.8) - draw.set_color(image, (rr4, cc4), boxColor, alpha= 0.8) - draw.set_color(image, (rr5, cc5), boxColor, alpha= 0.8) - if not makeImageOnly: - io.imshow(image) - io.show() - detections = { - "detections": detections, - "image": image, - "caption": "\n
".join(imcaption) - } - except Exception as e: - print("Unable to show image: "+str(e)) - return detections - -def performBatchDetect(thresh= 0.25, configPath = "./cfg/yolov4.cfg", weightPath = "yolov4.weights", metaPath= "./cfg/coco.data", hier_thresh=.5, nms=.45, batch_size=3): - import cv2 - import numpy as np - # NB! Image sizes should be the same - # You can change the images, yet, be sure that they have the same width and height - img_samples = ['data/person.jpg', 'data/person.jpg', 'data/person.jpg'] - image_list = [cv2.imread(k) for k in img_samples] - - net = load_net_custom(configPath.encode('utf-8'), weightPath.encode('utf-8'), 0, batch_size) - meta = load_meta(metaPath.encode('utf-8')) - pred_height, pred_width, c = image_list[0].shape - net_width, net_height = (network_width(net), network_height(net)) - img_list = [] - for custom_image_bgr in image_list: - custom_image = cv2.cvtColor(custom_image_bgr, cv2.COLOR_BGR2RGB) - custom_image = cv2.resize( - custom_image, (net_width, net_height), interpolation=cv2.INTER_NEAREST) - custom_image = custom_image.transpose(2, 0, 1) - img_list.append(custom_image) - - arr = np.concatenate(img_list, axis=0) - arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0 - data = arr.ctypes.data_as(POINTER(c_float)) - im = IMAGE(net_width, net_height, c, data) - - batch_dets = network_predict_batch(net, im, batch_size, pred_width, - pred_height, thresh, hier_thresh, None, 0, 0) - batch_boxes = [] - batch_scores = [] - batch_classes = [] - for b in range(batch_size): - num = batch_dets[b].num - dets = batch_dets[b].dets - if nms: - do_nms_obj(dets, num, meta.classes, nms) - boxes = [] - scores = [] - classes = [] - for i in range(num): - det = dets[i] - score = -1 - label = None - for c in range(det.classes): - p = det.prob[c] - if p > score: - score = p - label = c - if score > thresh: - box = det.bbox - left, top, right, bottom = map(int,(box.x - box.w / 2, box.y - box.h / 2, - box.x + box.w / 2, box.y + box.h / 2)) - boxes.append((top, left, bottom, right)) - scores.append(score) - classes.append(label) - boxColor = (int(255 * (1 - (score ** 2))), int(255 * (score ** 2)), 0) - cv2.rectangle(image_list[b], (left, top), - (right, bottom), boxColor, 2) - cv2.imwrite(os.path.basename(img_samples[b]),image_list[b]) - - batch_boxes.append(boxes) - batch_scores.append(scores) - batch_classes.append(classes) - free_batch_detections(batch_dets, batch_size) - return batch_boxes, batch_scores, batch_classes - -if __name__ == "__main__": - print(performDetect()) - #Uncomment the following line to see batch inference working - #print(performBatchDetect()) \ No newline at end of file diff --git a/build/darknet/x64/darknet_video.cmd b/build/darknet/x64/darknet_video.cmd index 8311dc0c941..1ed338401ee 100644 --- a/build/darknet/x64/darknet_video.cmd +++ b/build/darknet/x64/darknet_video.cmd @@ -1,5 +1,5 @@ -rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\python.exe darknet_video.py +python.exe darknet_video.py --data_file cfg/coco.data --config_file cfg/yolov4.cfg --weights yolov4.weights --input test.mp4 -C:\Python27\python.exe darknet_video.py +rem C:\Python27\python.exe darknet_video.py --data_file cfg/coco.data --config_file cfg/yolov4.cfg --weights yolov4.weights --input test.mp4 pause \ No newline at end of file diff --git a/build/darknet/x64/darknet_video.py b/build/darknet/x64/darknet_video.py index aa9c669649f..de96d709ed1 100644 --- a/build/darknet/x64/darknet_video.py +++ b/build/darknet/x64/darknet_video.py @@ -1,115 +1,134 @@ from ctypes import * -import math import random import os import cv2 -import numpy as np import time import darknet +import argparse +from threading import Thread, enumerate +from queue import Queue -def convertBack(x, y, w, h): - xmin = int(round(x - (w / 2))) - xmax = int(round(x + (w / 2))) - ymin = int(round(y - (h / 2))) - ymax = int(round(y + (h / 2))) - return xmin, ymin, xmax, ymax - - -def cvDrawBoxes(detections, img): - for detection in detections: - x, y, w, h = detection[2][0],\ - detection[2][1],\ - detection[2][2],\ - detection[2][3] - xmin, ymin, xmax, ymax = convertBack( - float(x), float(y), float(w), float(h)) - pt1 = (xmin, ymin) - pt2 = (xmax, ymax) - cv2.rectangle(img, pt1, pt2, (0, 255, 0), 1) - cv2.putText(img, - detection[0].decode() + - " [" + str(round(detection[1] * 100, 2)) + "]", - (pt1[0], pt1[1] - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, - [0, 255, 0], 2) - return img - - -netMain = None -metaMain = None -altNames = None - - -def YOLO(): - - global metaMain, netMain, altNames - configPath = "./cfg/yolov4.cfg" - weightPath = "./yolov4.weights" - metaPath = "./cfg/coco.data" - if not os.path.exists(configPath): - raise ValueError("Invalid config path `" + - os.path.abspath(configPath)+"`") - if not os.path.exists(weightPath): - raise ValueError("Invalid weight path `" + - os.path.abspath(weightPath)+"`") - if not os.path.exists(metaPath): - raise ValueError("Invalid data file path `" + - os.path.abspath(metaPath)+"`") - if netMain is None: - netMain = darknet.load_net_custom(configPath.encode( - "ascii"), weightPath.encode("ascii"), 0, 1) # batch size = 1 - if metaMain is None: - metaMain = darknet.load_meta(metaPath.encode("ascii")) - if altNames is None: - try: - with open(metaPath) as metaFH: - metaContents = metaFH.read() - import re - match = re.search("names *= *(.*)$", metaContents, - re.IGNORECASE | re.MULTILINE) - if match: - result = match.group(1) - else: - result = None - try: - if os.path.exists(result): - with open(result) as namesFH: - namesList = namesFH.read().strip().split("\n") - altNames = [x.strip() for x in namesList] - except TypeError: - pass - except Exception: - pass - #cap = cv2.VideoCapture(0) - cap = cv2.VideoCapture("test.mp4") - cap.set(3, 1280) - cap.set(4, 720) - out = cv2.VideoWriter( - "output.avi", cv2.VideoWriter_fourcc(*"MJPG"), 10.0, - (darknet.network_width(netMain), darknet.network_height(netMain))) - print("Starting the YOLO loop...") - - # Create an image we reuse for each detect - darknet_image = darknet.make_image(darknet.network_width(netMain), - darknet.network_height(netMain),3) - while True: - prev_time = time.time() - ret, frame_read = cap.read() - frame_rgb = cv2.cvtColor(frame_read, cv2.COLOR_BGR2RGB) - frame_resized = cv2.resize(frame_rgb, - (darknet.network_width(netMain), - darknet.network_height(netMain)), + +def parser(): + parser = argparse.ArgumentParser(description="YOLO Object Detection") + parser.add_argument("--input", type=str, default=0, + help="video source. If empty, uses webcam 0 stream") + parser.add_argument("--out_filename", type=str, default="", + help="inference video name. Not saved if empty") + parser.add_argument("--weights", default="yolov4.weights", + help="yolo weights path") + parser.add_argument("--dont_show", action='store_true', + help="windown inference display. For headless systems") + parser.add_argument("--ext_output", action='store_true', + help="display bbox coordinates of detected objects") + parser.add_argument("--config_file", default="./cfg/yolov4.cfg", + help="path to config file") + parser.add_argument("--data_file", default="./cfg/coco.data", + help="path to data file") + parser.add_argument("--thresh", type=float, default=.25, + help="remove detections with confidence below this value") + return parser.parse_args() + + +def str2int(video_path): + """ + argparse returns and string althout webcam uses int (0, 1 ...) + Cast to int if needed + """ + try: + return int(video_path) + except ValueError: + return video_path + + +def check_arguments_errors(args): + assert 0 < args.thresh < 1, "Threshold should be a float between zero and one (non-inclusive)" + if not os.path.exists(args.config_file): + raise(ValueError("Invalid config path {}".format(os.path.abspath(args.config_file)))) + if not os.path.exists(args.weights): + raise(ValueError("Invalid weight path {}".format(os.path.abspath(args.weights)))) + if not os.path.exists(args.data_file): + raise(ValueError("Invalid data file path {}".format(os.path.abspath(args.data_file)))) + if str2int(args.input) == str and not os.path.exists(args.input): + raise(ValueError("Invalid video path {}".format(os.path.abspath(args.input)))) + + +def set_saved_video(input_video, output_video, size): + fourcc = cv2.VideoWriter_fourcc(*"MJPG") + fps = int(input_video.get(cv2.CAP_PROP_FPS)) + video = cv2.VideoWriter(output_video, fourcc, fps, size) + return video + + +def video_capture(frame_queue, darknet_image_queue): + while cap.isOpened(): + ret, frame = cap.read() + if not ret: + break + frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + frame_resized = cv2.resize(frame_rgb, (width, height), interpolation=cv2.INTER_LINEAR) + frame_queue.put(frame_resized) + darknet.copy_image_from_bytes(darknet_image, frame_resized.tobytes()) + darknet_image_queue.put(darknet_image) + cap.release() + + +def inference(darknet_image_queue, detections_queue, fps_queue): + while cap.isOpened(): + darknet_image = darknet_image_queue.get() + prev_time = time.time() + detections = darknet.detect_image(network, class_names, darknet_image, thresh=args.thresh) + detections_queue.put(detections) + fps = int(1/(time.time() - prev_time)) + fps_queue.put(fps) + print("FPS: {}".format(fps)) + darknet.print_detections(detections, args.ext_output) + cap.release() - darknet.copy_image_from_bytes(darknet_image,frame_resized.tobytes()) - detections = darknet.detect_image(netMain, metaMain, darknet_image, thresh=0.25) - image = cvDrawBoxes(detections, frame_resized) - image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) - print(1/(time.time()-prev_time)) - cv2.imshow('Demo', image) - cv2.waitKey(3) +def drawing(frame_queue, detections_queue, fps_queue): + random.seed(3) # deterministic bbox colors + video = set_saved_video(cap, args.out_filename, (width, height)) + while cap.isOpened(): + frame_resized = frame_queue.get() + detections = detections_queue.get() + fps = fps_queue.get() + if frame_resized is not None: + image = darknet.draw_boxes(detections, frame_resized, class_colors) + image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + if args.out_filename is not None: + video.write(image) + if not args.dont_show: + cv2.imshow('Inference', image) + if cv2.waitKey(fps) == 27: + break cap.release() - out.release() + video.release() + cv2.destroyAllWindows() + + +if __name__ == '__main__': + frame_queue = Queue() + darknet_image_queue = Queue(maxsize=1) + detections_queue = Queue(maxsize=1) + fps_queue = Queue(maxsize=1) -if __name__ == "__main__": - YOLO() + args = parser() + check_arguments_errors(args) + network, class_names, class_colors = darknet.load_network( + args.config_file, + args.data_file, + args.weights, + batch_size=1 + ) + # Darknet doesn't accept numpy images. + # Create one with image we reuse for each detect + width = darknet.network_width(network) + height = darknet.network_height(network) + darknet_image = darknet.make_image(width, height, 3) + input_path = str2int(args.input) + cap = cv2.VideoCapture(input_path) + Thread(target=video_capture, args=(frame_queue, darknet_image_queue)).start() + Thread(target=inference, args=(darknet_image_queue, detections_queue, fps_queue)).start() + Thread(target=drawing, args=(frame_queue, detections_queue, fps_queue)).start() From a1ea4d5df36d0974451f17af09ec26a133d8f28e Mon Sep 17 00:00:00 2001 From: lineofbestgit <23038409+lineofbestgit@users.noreply.github.com> Date: Mon, 3 Aug 2020 19:23:22 +0200 Subject: [PATCH 1096/1365] Update README.md Fixed repeating sentence --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 29c767b70d2..e73fef7d7d3 100644 --- a/README.md +++ b/README.md @@ -418,7 +418,7 @@ Training Yolo v4 (and v3): * change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L3) * change line subdivisions to [`subdivisions=16`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4) - * change line max_batches to (`classes*2000` but not less than number of training images, but not less than number of training images and not less than `6000`), f.e. [`max_batches=6000`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L20) if you train for 3 classes + * change line max_batches to (`classes*2000` but not less than number of training images and not less than `6000`), f.e. [`max_batches=6000`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L20) if you train for 3 classes * change line steps to 80% and 90% of max_batches, f.e. [`steps=4800,5400`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L22) * set network size `width=416 height=416` or any value multiple of 32: https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L8-L9 * change line `classes=80` to your number of objects in each of 3 `[yolo]`-layers: From 10fdf9a852f9d9639ce38f6b10e4bb0bb382f3d4 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 5 Aug 2020 03:18:33 +0300 Subject: [PATCH 1097/1365] P_constrastive_f_det_gpu() --- include/darknet.h | 3 ++- src/blas.h | 1 + src/blas_kernels.cu | 45 +++++++++++++++++++++++++++++++++++++++++++++ src/layer.c | 1 + src/softmax_layer.c | 24 ++++++++++++++++++++++-- 5 files changed, 71 insertions(+), 3 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index b9418177893..4483d166a3c 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -147,7 +147,7 @@ typedef struct contrastive_params { float sim; float exp_sim; float P; - size_t i, j; + int i, j; int time_step_i, time_step_j; } contrastive_params; @@ -389,6 +389,7 @@ struct layer { float *cos_sim; float *exp_cos_sim; float *p_constrastive; + contrastive_params *contrast_p_gpu; float * state; float * prev_state; float * forgot_state; diff --git a/src/blas.h b/src/blas.h index 35ed25e1975..02ada92ca28 100644 --- a/src/blas.h +++ b/src/blas.h @@ -171,6 +171,7 @@ void rotate_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, i void reduce_and_expand_array_gpu(const float *src_gpu, float *dst_gpu, int size, int groups); void expand_array_gpu(const float *src_gpu, float *dst_gpu, int size, int groups); void mult_inverse_array_gpu(const float *src_gpu, float *dst_gpu, int size, float eps); +void P_constrastive_f_det_gpu(int *labels, unsigned int feature_size, float temperature, contrastive_params *contrast_p, const int contrast_p_size); #endif // GPU #ifdef __cplusplus diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 97debb051e9..bbf50eb1405 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -2336,5 +2336,50 @@ extern "C" void mult_inverse_array_gpu(const float *src_gpu, float *dst_gpu, int const int num_blocks = get_number_of_blocks(size, block_size); mult_inverse_array_kernel << > > (src_gpu, dst_gpu, size, eps); + CHECK_CUDA(cudaPeekAtLastError()); +} + + + +__global__ void P_constrastive_f_det_kernel(int *labels, unsigned int feature_size, float temperature, contrastive_params *contrast_p, const int contrast_p_size) +{ + const int il = blockIdx.x*blockDim.x + threadIdx.x; + + if (il < contrast_p_size) { + const float sim = contrast_p[il].sim; + const size_t i = contrast_p[il].i; + const size_t j = contrast_p[il].j; + + const float numerator = expf(sim / temperature); + + float denominator = 0; + int k; + for (k = 0; k < contrast_p_size; ++k) { + contrastive_params cp = contrast_p[k]; + //if (k != i && labels[k] != labels[i]) { + //if (k != i) { + if (cp.i != i && cp.j == j) { + //const float sim_den = cp.sim; + ////const float sim_den = find_sim(k, l, contrast_p, contrast_p_size); // cosine_similarity(z[k], z[l], feature_size); + //denominator += expf(sim_den / temperature); + denominator += cp.exp_sim; + } + } + + float result = 0.9999; + if (denominator != 0) result = numerator / denominator; + if (result > 1) result = 0.9999; + + contrast_p[il].P = result; + } +} + + +extern "C" void P_constrastive_f_det_gpu(int *labels, unsigned int feature_size, float temperature, contrastive_params *contrast_p, const int contrast_p_size) +{ + const int block_size = BLOCK; + const int num_blocks = get_number_of_blocks(contrast_p_size, block_size); + P_constrastive_f_det_kernel << > > (labels, feature_size, temperature, contrast_p, contrast_p_size); + CHECK_CUDA(cudaPeekAtLastError()); } \ No newline at end of file diff --git a/src/layer.c b/src/layer.c index ae60254bbc1..a31b8e89003 100644 --- a/src/layer.c +++ b/src/layer.c @@ -149,6 +149,7 @@ void free_layer_custom(layer l, int keep_cudnn_desc) #ifdef GPU if (l.indexes_gpu) cuda_free((float *)l.indexes_gpu); + if (l.contrast_p_gpu) cuda_free(l.contrast_p_gpu); if (l.z_gpu) cuda_free(l.z_gpu); if (l.r_gpu) cuda_free(l.r_gpu); if (l.m_gpu) cuda_free(l.m_gpu); diff --git a/src/softmax_layer.c b/src/softmax_layer.c index a80ddeafef9..56347fe61ba 100644 --- a/src/softmax_layer.c +++ b/src/softmax_layer.c @@ -126,7 +126,7 @@ void backward_softmax_layer_gpu(const softmax_layer layer, network_state state) // ------------------------------------- - +// Supervised Contrastive Learning: https://arxiv.org/pdf/2004.11362.pdf contrastive_layer make_contrastive_layer(int batch, int w, int h, int c, int classes, int inputs, layer *yolo_layer) { contrastive_layer l = { (LAYER_TYPE)0 }; @@ -138,8 +138,10 @@ contrastive_layer make_contrastive_layer(int batch, int w, int h, int c, int cla l.c = c; l.temperature = 1; + l.max_boxes = 0; if (yolo_layer) { l.detection = 1; + l.max_boxes = yolo_layer->max_boxes; l.labels = yolo_layer->labels; // track id l.n = yolo_layer->n; // num of embeddings per cell = num of anchors l.classes = yolo_layer->classes;// num of classes @@ -192,6 +194,10 @@ contrastive_layer make_contrastive_layer(int batch, int w, int h, int c, int cla l.output_gpu = cuda_make_array(l.output, inputs*batch); l.delta_gpu = cuda_make_array(l.delta, inputs*batch); + + const int max_contr_size = (l.max_boxes*l.batch)*(l.max_boxes*l.batch) * sizeof(contrastive_params)/4; + printf(" max_contr_size = %d MB \n", max_contr_size / (1024*1024)); + l.contrast_p_gpu = cuda_make_array(NULL, max_contr_size); #endif fprintf(stderr, "contrastive %4d x%4d x%4d x emb_size %4d x batch: %4d classes = %4d, step = %4d \n", w, h, l.n, l.embedding_size, batch, l.classes, step); if(l.detection) fprintf(stderr, "detection \n"); @@ -408,11 +414,25 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) const size_t contr_size = contrast_p_index; if (l.detection) { +#ifdef GPU + const int max_contr_size = (l.max_boxes*l.batch)*(l.max_boxes*l.batch); + if (max_contr_size < contr_size) { + printf(" Error: too large number of bboxes: contr_size = %d > max_contr_size = %d \n", contr_size, max_contr_size); + exit(0); + } + int *labels = NULL; + if (contr_size > 2) { + cuda_push_array(l.contrast_p_gpu, contrast_p, contr_size * sizeof(contrastive_params) / 4); + P_constrastive_f_det_gpu(labels, l.embedding_size, l.temperature, l.contrast_p_gpu, contr_size); + cuda_pull_array(l.contrast_p_gpu, contrast_p, contr_size * sizeof(contrastive_params) / 4); + } +#else // GPU int k; - #pragma omp parallel for + //#pragma omp parallel for for (k = 0; k < contr_size; ++k) { contrast_p[k].P = P_constrastive_f_det(k, l.labels, z, l.embedding_size, l.temperature, contrast_p, contr_size); } +#endif // GPU } else { // precalculate P-contrastive From cfcb37a303be861b961986fa6f5a9bb739847982 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 5 Aug 2020 03:27:39 +0300 Subject: [PATCH 1098/1365] Compile fix for CPP --- src/softmax_layer.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/softmax_layer.c b/src/softmax_layer.c index 56347fe61ba..8e119e803ea 100644 --- a/src/softmax_layer.c +++ b/src/softmax_layer.c @@ -197,7 +197,7 @@ contrastive_layer make_contrastive_layer(int batch, int w, int h, int c, int cla const int max_contr_size = (l.max_boxes*l.batch)*(l.max_boxes*l.batch) * sizeof(contrastive_params)/4; printf(" max_contr_size = %d MB \n", max_contr_size / (1024*1024)); - l.contrast_p_gpu = cuda_make_array(NULL, max_contr_size); + l.contrast_p_gpu = (contrastive_params *)cuda_make_array(NULL, max_contr_size); #endif fprintf(stderr, "contrastive %4d x%4d x%4d x emb_size %4d x batch: %4d classes = %4d, step = %4d \n", w, h, l.n, l.embedding_size, batch, l.classes, step); if(l.detection) fprintf(stderr, "detection \n"); From 737c3553573ea8a1450a897e40369865e60ba5e4 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 5 Aug 2020 03:36:39 +0300 Subject: [PATCH 1099/1365] Compile fix for CPP --- src/softmax_layer.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/softmax_layer.c b/src/softmax_layer.c index 8e119e803ea..3ad358efb9b 100644 --- a/src/softmax_layer.c +++ b/src/softmax_layer.c @@ -422,9 +422,9 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) } int *labels = NULL; if (contr_size > 2) { - cuda_push_array(l.contrast_p_gpu, contrast_p, contr_size * sizeof(contrastive_params) / 4); + cuda_push_array((contrastive_params *)l.contrast_p_gpu, (contrastive_params *)contrast_p, contr_size * sizeof(contrastive_params) / 4); P_constrastive_f_det_gpu(labels, l.embedding_size, l.temperature, l.contrast_p_gpu, contr_size); - cuda_pull_array(l.contrast_p_gpu, contrast_p, contr_size * sizeof(contrastive_params) / 4); + cuda_pull_array((contrastive_params *)l.contrast_p_gpu, (contrastive_params *)contrast_p, contr_size * sizeof(contrastive_params) / 4); } #else // GPU int k; From 14b44a846bb0adaaceb668050d485c8b09337e02 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 5 Aug 2020 03:47:59 +0300 Subject: [PATCH 1100/1365] Another compile fix --- src/softmax_layer.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/softmax_layer.c b/src/softmax_layer.c index 3ad358efb9b..8e74b1de2f2 100644 --- a/src/softmax_layer.c +++ b/src/softmax_layer.c @@ -422,9 +422,9 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) } int *labels = NULL; if (contr_size > 2) { - cuda_push_array((contrastive_params *)l.contrast_p_gpu, (contrastive_params *)contrast_p, contr_size * sizeof(contrastive_params) / 4); + cuda_push_array((float *)l.contrast_p_gpu, (float *)contrast_p, contr_size * sizeof(contrastive_params) / 4); P_constrastive_f_det_gpu(labels, l.embedding_size, l.temperature, l.contrast_p_gpu, contr_size); - cuda_pull_array((contrastive_params *)l.contrast_p_gpu, (contrastive_params *)contrast_p, contr_size * sizeof(contrastive_params) / 4); + cuda_pull_array((float *)l.contrast_p_gpu, (float *)contrast_p, contr_size * sizeof(contrastive_params) / 4); } #else // GPU int k; From 2b0b412eec553f493c084ffdbe3c09c48c986e83 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 5 Aug 2020 04:04:59 +0300 Subject: [PATCH 1101/1365] Final fix --- build/darknet/yolo_cpp_dll.vcxproj | 4 ++-- src/layer.c | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/build/darknet/yolo_cpp_dll.vcxproj b/build/darknet/yolo_cpp_dll.vcxproj index 813cae31e9d..4bd09206caa 100644 --- a/build/darknet/yolo_cpp_dll.vcxproj +++ b/build/darknet/yolo_cpp_dll.vcxproj @@ -52,7 +52,7 @@ - + @@ -306,6 +306,6 @@ - + \ No newline at end of file diff --git a/src/layer.c b/src/layer.c index a31b8e89003..1808ea9fc12 100644 --- a/src/layer.c +++ b/src/layer.c @@ -149,7 +149,7 @@ void free_layer_custom(layer l, int keep_cudnn_desc) #ifdef GPU if (l.indexes_gpu) cuda_free((float *)l.indexes_gpu); - if (l.contrast_p_gpu) cuda_free(l.contrast_p_gpu); + if (l.contrast_p_gpu) cuda_free((float *)l.contrast_p_gpu); if (l.z_gpu) cuda_free(l.z_gpu); if (l.r_gpu) cuda_free(l.r_gpu); if (l.m_gpu) cuda_free(l.m_gpu); From 725d9092e41e2d481efd47dc8d8f63856acfb24c Mon Sep 17 00:00:00 2001 From: Mark Peng Date: Sat, 8 Aug 2020 12:57:52 +0800 Subject: [PATCH 1102/1365] Added free_network_ptr() support for darknet.py. --- darknet.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/darknet.py b/darknet.py index 74058a19c78..cd5bde34527 100644 --- a/darknet.py +++ b/darknet.py @@ -188,6 +188,10 @@ def network_height(net): load_net_custom.argtypes = [c_char_p, c_char_p, c_int, c_int] load_net_custom.restype = c_void_p +free_network_ptr = lib.free_network_ptr +free_network_ptr.argtypes = [c_void_p] +free_network_ptr.restype = c_void_p + do_nms_obj = lib.do_nms_obj do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float] From 9ecc4f7d6b24dec4d56a1d5c480fe842625dc57b Mon Sep 17 00:00:00 2001 From: Dontcare Date: Sat, 8 Aug 2020 12:46:20 +0300 Subject: [PATCH 1103/1365] Update darknet.py # Example code import os import sys os.environ['DARKNET_PATH'] = '/usr/local/darknet' sys.path.append(os.environ['DARKNET_PATH']) import darknet def main(): network, class_names, class_colors = darknet.load_network( '../nscan-train/data/cfg/yolov4-tiny-3l-numbers-12.cfg', '../nscan-train/data/numbers.data', './data/weights/numbers/yolov4-tiny-3l-numbers-12_last.weights', batch_size=1 ) if __name__ == '__main__': main() --- darknet.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/darknet.py b/darknet.py index bb0ee183688..467b777452c 100644 --- a/darknet.py +++ b/darknet.py @@ -11,6 +11,7 @@ - Set global variable DARKNET_FORCE_CPU to True - Set environment variable CUDA_VISIBLE_DEVICES to -1 - Set environment variable "FORCE_CPU" to "true" +- Set environment variable "DARKNET_PATH" to path darknet lib .so (for Linux) Directly viewing or returning bounding-boxed images requires scikit-image to be installed (`pip install scikit-image`) @@ -219,7 +220,9 @@ def detect_image(network, class_names, image, thresh=.5, hier_thresh=.5, nms=.45 lib = CDLL(winGPUdll, RTLD_GLOBAL) print("Environment variables indicated a CPU run, but we didn't find {}. Trying a GPU run anyway.".format(winNoGPUdll)) else: - lib = CDLL("./libdarknet.so", RTLD_GLOBAL) + lib = CDLL(os.path.join( + os.environ.get('DARKNET_PATH', './'), + "libdarknet.so"), RTLD_GLOBAL) lib.network_width.argtypes = [c_void_p] lib.network_width.restype = c_int lib.network_height.argtypes = [c_void_p] From 78293335dfb1713a3c74b4d1f9425f29ea9043ef Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sat, 8 Aug 2020 13:15:03 +0200 Subject: [PATCH 1104/1365] unify lib naming with makefile --- CMakeLists.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/CMakeLists.txt b/CMakeLists.txt index 6f10e70802a..30380062882 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -313,6 +313,7 @@ endif() if(BUILD_AS_CPP) set_target_properties(dark PROPERTIES LINKER_LANGUAGE CXX) endif() +set_target_properties(dark PROPERTIES OUTPUT_NAME "darknet") if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND BUILD_USELIB_TRACK) add_executable(uselib_track ${CMAKE_CURRENT_LIST_DIR}/src/yolo_console_dll.cpp) From c00dbbb9fa614980eb1a62f0efb7648323f0a459 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sat, 8 Aug 2020 15:35:46 +0200 Subject: [PATCH 1105/1365] bump minimum compute capability to 5.0 --- CMakeLists.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 30380062882..29bbcd9fa29 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -62,7 +62,7 @@ include(CheckLanguage) check_language(CUDA) if(CMAKE_CUDA_COMPILER AND ENABLE_CUDA) set(CUDA_ARCHITECTURES "Auto" CACHE STRING "\"Auto\" detects local machine GPU compute arch at runtime, \"Common\" and \"All\" cover common and entire subsets of architectures, \"Names\" is a list of architectures to enable by name, \"Numbers\" is a list of compute capabilities (version number) to enable") - set_property(CACHE CUDA_ARCHITECTURES PROPERTY STRINGS "Auto" "Common" "All" "Kepler Maxwell Kepler+Tegra Maxwell+Tegra Pascal" "3.0 7.5") + set_property(CACHE CUDA_ARCHITECTURES PROPERTY STRINGS "Auto" "Common" "All" "Kepler Maxwell Kepler+Tegra Maxwell+Tegra Pascal" "5.0 7.5") enable_language(CUDA) if(CMAKE_CUDA_COMPILER_VERSION VERSION_LESS "9.0") message(STATUS "Unsupported CUDA version, please upgrade to CUDA 9+. Disabling CUDA support") @@ -71,7 +71,7 @@ if(CMAKE_CUDA_COMPILER AND ENABLE_CUDA) find_package(CUDA REQUIRED) cuda_select_nvcc_arch_flags(CUDA_ARCH_FLAGS ${CUDA_ARCHITECTURES}) message(STATUS "Building with CUDA flags: " "${CUDA_ARCH_FLAGS}") - if (NOT "arch=compute_70,code=sm_70" IN_LIST CUDA_ARCH_FLAGS AND NOT "arch=compute_72,code=sm_72" IN_LIST CUDA_ARCH_FLAGS AND NOT "arch=compute_75,code=sm_75" IN_LIST CUDA_ARCH_FLAGS) + if (NOT "arch=compute_70,code=sm_70" IN_LIST CUDA_ARCH_FLAGS AND NOT "arch=compute_72,code=sm_72" IN_LIST CUDA_ARCH_FLAGS AND NOT "arch=compute_75,code=sm_75" IN_LIST CUDA_ARCH_FLAGS AND NOT "arch=compute_80,code=sm_80" IN_LIST CUDA_ARCH_FLAGS) set(ENABLE_CUDNN_HALF "FALSE" CACHE BOOL "Enable CUDNN Half precision" FORCE) message(STATUS "Your setup does not supports half precision (it requires CC >= 7.0)") else() From 8cddef3466d53a838d284f339dec7780914549ad Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sat, 8 Aug 2020 15:35:57 +0200 Subject: [PATCH 1106/1365] upgrade vcpkg commit ref --- .github/workflows/ccpp.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index c89653257d9..60482a5a355 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -87,7 +87,7 @@ jobs: with: vcpkgArguments: '@${{ env.vcpkgResponseFile }}' vcpkgDirectory: '${{ github.workspace }}/vcpkg' - vcpkgGitCommitId: '091e190117613ffc4917f83c6e13ca1095e9cb2f' + vcpkgGitCommitId: '8121b4ec3d6a11353daf7639ed9082a78e617a2e' appendedCacheKey: ${{ hashFiles(env.vcpkgResponseFile) }} - name: 'Build with CMake and Ninja' @@ -259,7 +259,7 @@ jobs: with: vcpkgArguments: '@${{ env.vcpkgResponseFile }}' vcpkgDirectory: '${{ github.workspace }}/vcpkg' - vcpkgGitCommitId: '091e190117613ffc4917f83c6e13ca1095e9cb2f' + vcpkgGitCommitId: '8121b4ec3d6a11353daf7639ed9082a78e617a2e' appendedCacheKey: ${{ hashFiles(env.vcpkgResponseFile) }} - name: 'Build with CMake and Ninja' @@ -363,7 +363,7 @@ jobs: with: vcpkgArguments: '@${{ env.vcpkgResponseFile }}' vcpkgDirectory: '${{ github.workspace }}/vcpkg' - vcpkgGitCommitId: '091e190117613ffc4917f83c6e13ca1095e9cb2f' + vcpkgGitCommitId: '8121b4ec3d6a11353daf7639ed9082a78e617a2e' appendedCacheKey: ${{ hashFiles(env.vcpkgResponseFile) }} - name: 'Build with CMake and Ninja' @@ -424,7 +424,7 @@ jobs: with: vcpkgArguments: '@${{ env.vcpkgResponseFile }}' vcpkgDirectory: '${{ github.workspace }}/vcpkg' - vcpkgGitCommitId: '091e190117613ffc4917f83c6e13ca1095e9cb2f' + vcpkgGitCommitId: '8121b4ec3d6a11353daf7639ed9082a78e617a2e' appendedCacheKey: ${{ hashFiles(env.vcpkgResponseFile) }} - name: 'Build with CMake and Ninja' From 86b1bf74977e129d78099cd178df0bdf35fd9e5f Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sat, 8 Aug 2020 16:25:53 +0200 Subject: [PATCH 1107/1365] restore missing infos in README.md after merging with master --- CMakeLists.txt | 2 +- README.md | 8 +++++--- scripts/setup.ps1 | 4 ++-- scripts/setup.sh | 4 ++-- 4 files changed, 10 insertions(+), 8 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 29bbcd9fa29..4c7224e4e8b 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -3,7 +3,7 @@ cmake_minimum_required(VERSION 3.12) set(Darknet_MAJOR_VERSION 0) set(Darknet_MINOR_VERSION 2) set(Darknet_PATCH_VERSION 5) -set(Darknet_TWEAK_VERSION 2) +set(Darknet_TWEAK_VERSION 3) set(Darknet_VERSION ${Darknet_MAJOR_VERSION}.${Darknet_MINOR_VERSION}.${Darknet_PATCH_VERSION}.${Darknet_TWEAK_VERSION}) option(CMAKE_VERBOSE_MAKEFILE "Create verbose makefile" OFF) diff --git a/README.md b/README.md index 1d8eb5813a5..064cf8f115a 100644 --- a/README.md +++ b/README.md @@ -166,7 +166,7 @@ You can get cfg-files by path: `darknet/cfg/` ### Requirements * Windows or Linux -* **CMake >= 3.17**: https://cmake.org/download/ +* **CMake >= 3.12**: https://cmake.org/download/ * **CUDA >= 10.0**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) * **OpenCV >= 2.4**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or download from [OpenCV official site](https://opencv.org/releases.html) (on Windows set system variable `OpenCV_DIR` = `C:\opencv\build` - where are the `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png)) * **cuDNN >= 7.0** https://developer.nvidia.com/rdp/cudnn-archive (on **Linux** copy `cudnn.h`,`libcudnn.so`... as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on **Windows** copy `cudnn.h`,`cudnn64_7.dll`, `cudnn64_7.lib` as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows ) @@ -562,7 +562,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * for training for both small and large objects use modified models: * Full-model: 5 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3_5l.cfg - * Tiny-model: 3 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny_3l.cfg + * Tiny-model: 3 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny_3l.cfg * YOLOv4: 3 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-custom.cfg * If you train the model to distinguish Left and Right objects as separate classes (left/right hand, left/right-turn on road signs, ...) then for disabling flip data augmentation - add `flip=0` here: https://github.com/AlexeyAB/darknet/blob/3d2d0a7c98dbc8923d9ff705b81ff4f7940ea6ff/cfg/yolov3.cfg#L17 @@ -581,9 +581,11 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * each: `model of object, side, illimination, scale, each 30 grad` of the turn and inclination angles - these are *different objects* from an internal perspective of the neural network. So the more *different objects* you want to detect, the more complex network model should be used. +* to make the detected bounded boxes more accurate, you can add 3 parameters `ignore_thresh = .9 iou_normalizer=0.5 iou_loss=giou` to each `[yolo]` layer and train, it will increase mAP@0.9, but decrease mAP@0.5. + * Only if you are an **expert** in neural detection networks - recalculate anchors for your dataset for `width` and `height` from cfg-file: `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416` -then set the same 9 `anchors` in each of 3 `[yolo]`-layers in your cfg-file. But you should change indexes of anchors `masks=` for each [yolo]-layer, so that 1st-[yolo]-layer has anchors larger than 60x60, 2nd larger than 30x30, 3rd remaining. Also you should change the `filters=(classes + 5)*` before each [yolo]-layer. If many of the calculated anchors do not fit under the appropriate layers - then just try using all the default anchors. +then set the same 9 `anchors` in each of 3 `[yolo]`-layers in your cfg-file. But you should change indexes of anchors `masks=` for each [yolo]-layer, so for YOLOv4 the 1st-[yolo]-layer has anchors smaller than 30x30, 2nd smaller than 60x60, 3rd remaining, and vice versa for YOLOv3. Also you should change the `filters=(classes + 5)*` before each [yolo]-layer. If many of the calculated anchors do not fit under the appropriate layers - then just try using all the default anchors. 2. After training - for detection: diff --git a/scripts/setup.ps1 b/scripts/setup.ps1 index 71890a943ea..4c201cb7c41 100755 --- a/scripts/setup.ps1 +++ b/scripts/setup.ps1 @@ -21,7 +21,7 @@ else { Remove-Item -r $temp_folder Set-Location .. Set-Location $vcpkg_folder\ -git.exe clone https://github.com/Microsoft/vcpkg +git.exe clone https://github.com/microsoft/vcpkg Set-Location vcpkg -.\bootstrap-vcpkg.bat +.\bootstrap-vcpkg.bat -disableMetrics .\vcpkg.exe install darknet[${features}]:x64-windows diff --git a/scripts/setup.sh b/scripts/setup.sh index 1ab4f31e1e5..b5889f6a802 100755 --- a/scripts/setup.sh +++ b/scripts/setup.sh @@ -43,7 +43,7 @@ fi rm -rf $temp_folder cd .. cd $vcpkg_folder -git clone https://github.com/Microsoft/vcpkg +git clone https://github.com/microsoft/vcpkg cd vcpkg -./bootstrap-vcpkg.sh +./bootstrap-vcpkg.sh -disableMetrics ./vcpkg install darknet[${features}] From a2ccbd47ff5c29cf831c6529fb960c36a7dc6180 Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 10 Aug 2020 16:15:25 +0300 Subject: [PATCH 1108/1365] Update Readme.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 064cf8f115a..8fef5582632 100644 --- a/README.md +++ b/README.md @@ -178,7 +178,7 @@ You can get cfg-files by path: `darknet/cfg/` * **TensorFlow:** YOLOv4 on TensorFlow 2.0 / TFlite / Andriod: https://github.com/hunglc007/tensorflow-yolov4-tflite For YOLOv3 - convert `yolov3.weights`/`cfg` files to `yolov3.ckpt`/`pb/meta`: by using [mystic123](https://github.com/mystic123/tensorflow-yolo-v3) project, and [TensorFlow-lite](https://www.tensorflow.org/lite/guide/get_started#2_convert_the_model_format) * **OpenCV-dnn** the fastest implementation of YOLOv4 for CPU (x86/ARM-Android), OpenCV can be compiled with [OpenVINO-backend](https://github.com/opencv/opencv/wiki/Intel's-Deep-Learning-Inference-Engine-backend) for running on (Myriad X / USB Neural Compute Stick / Arria FPGA), use `yolov4.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221) or [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) -* **Intel OpenVINO 2019 R1:** (Myriad X / USB Neural Compute Stick / Arria FPGA): read this [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) +* **Intel OpenVINO 2020 R4:** (NPU Myriad X / USB Neural Compute Stick / Arria FPGA): read this [manual](https://github.com/TNTWEN/OpenVINO-YOLOV4) (old [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) ) * **Tencent/ncnn:** the fastest inference of YOLOv4 on mobile phone CPU: https://github.com/Tencent/ncnn * **PyTorch > ONNX**: * [WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4) From 38a164bcb9e017f8c9c3645a39419320e217545e Mon Sep 17 00:00:00 2001 From: Alexey Date: Tue, 11 Aug 2020 21:01:40 +0300 Subject: [PATCH 1109/1365] Update README.md --- README.md | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 8fef5582632..2e5ab8fdf7c 100644 --- a/README.md +++ b/README.md @@ -2,13 +2,16 @@ ## (neural networks for object detection) -### Tensor Cores can be used on [Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux-using-cmake) and [Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows-using-cmake) - Paper Yolo v4: https://arxiv.org/abs/2004.10934 More details: [medium link](https://medium.com/@alexeyab84/yolov4-the-most-accurate-real-time-neural-network-on-ms-coco-dataset-73adfd3602fe?source=friends_link&sk=6039748846bbcf1d960c3061542591d7) -Discussion: [Reddit](https://www.reddit.com/r/MachineLearning/comments/gydxzd/p_yolov4_the_most_accurate_realtime_neural/) +Manual: https://github.com/AlexeyAB/darknet/wiki + +Discussion: + - [Reddit](https://www.reddit.com/r/MachineLearning/comments/gydxzd/p_yolov4_the_most_accurate_realtime_neural/) + - [Google-groups](https://groups.google.com/forum/#!forum/darknet) + - [Discord](https://discord.gg/zSq8rtW) About Darknet framework: http://pjreddie.com/darknet/ @@ -49,9 +52,10 @@ About Darknet framework: http://pjreddie.com/darknet/ 10. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) 11. [How to use Yolo as DLL and SO libraries](#how-to-use-yolo-as-dll-and-so-libraries) -| ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) |   ![modern_gpus](https://user-images.githubusercontent.com/4096485/82835867-f1c62380-9ecd-11ea-9134-1598ed2abc4b.png) AP50:95 / AP50 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2004.10934 | +![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) + +![modern_gpus](https://user-images.githubusercontent.com/4096485/82835867-f1c62380-9ecd-11ea-9134-1598ed2abc4b.png) AP50:95 / AP50 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2004.10934 -|---|---| tkDNN-TensorRT accelerates YOLOv4 **~2x** times for batch=1 and **3x-4x** times for batch=4. * tkDNN: https://github.com/ceccocats/tkDNN @@ -85,7 +89,7 @@ Others: https://www.youtube.com/user/pjreddie/videos 1. Download and unzip test-dev2017 dataset from MS COCO server: http://images.cocodataset.org/zips/test2017.zip 2. Download list of images for Detection taks and replace the paths with yours: https://raw.githubusercontent.com/AlexeyAB/darknet/master/scripts/testdev2017.txt -3. Download `yolov4.weights` file: https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT +3. Download `yolov4.weights` file 245 MB: [yolov4.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights) (Google-drive mirror [yolov4.weights](https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT) ) 4. Content of the file `cfg/coco.data` should be ```ini From 0d8718a43413a30ad3df8573a5d6929d45491326 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 16 Aug 2020 17:55:10 +0300 Subject: [PATCH 1110/1365] Added layer: [history] history_size=4 --- include/darknet.h | 2 + src/conv_lstm_layer.c | 174 +++++++++++++++++++++++++++++++++++ src/conv_lstm_layer.h | 7 ++ src/convolutional_kernels.cu | 2 +- src/convolutional_layer.c | 2 +- src/parser.c | 10 ++ 6 files changed, 195 insertions(+), 2 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 4483d166a3c..69d459694f6 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -176,6 +176,7 @@ typedef enum { GRU, LSTM, CONV_LSTM, + HISTORY, CRNN, BATCHNORM, NETWORK, @@ -272,6 +273,7 @@ struct layer { int keep_delta_gpu; int optimized_memory; int steps; + int history_size; int bottleneck; float time_normalizer; int state_constrain; diff --git a/src/conv_lstm_layer.c b/src/conv_lstm_layer.c index 9aec1874dc0..d56eb1c13e3 100644 --- a/src/conv_lstm_layer.c +++ b/src/conv_lstm_layer.c @@ -240,6 +240,180 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i return l; } +layer make_history_layer(int batch, int h, int w, int c, int history_size, int steps, int train) +{ + //steps = 1; + layer l = { (LAYER_TYPE)0 }; + l.train = train; + l.batch = batch; + l.type = HISTORY; + l.steps = steps; + l.history_size = history_size; + l.h = h; + l.w = w; + l.c = c; + l.out_h = h; + l.out_w = w; + l.out_c = c * history_size; + l.inputs = h * w * c; + l.outputs = h * w * c * history_size; + + l.forward = forward_history_layer; + l.backward = backward_history_layer; + + fprintf(stderr, "HISTORY b = %d, s = %2d, steps = %2d %4d x%4d x%4d -> %4d x%4d x%4d \n", l.batch / l.steps, l.history_size, l.steps, w, h, c, l.out_w, l.out_h, l.out_c); + + l.output = (float*)xcalloc(l.batch * l.outputs, sizeof(float)); + l.delta = (float*)xcalloc(l.batch * l.outputs, sizeof(float)); + + l.prev_state_cpu = (float*)xcalloc(l.batch*l.outputs, sizeof(float)); + +#ifdef GPU + + l.forward_gpu = forward_history_layer_gpu; + l.backward_gpu = backward_history_layer_gpu; + + l.output_gpu = cuda_make_array(0, l.batch * l.outputs); + l.delta_gpu = cuda_make_array(0, l.batch * l.outputs); + + l.prev_state_gpu = cuda_make_array(0, l.batch*l.outputs); + +#endif // GPU + + //l.batch = 4; + //l.steps = 1; + + return l; +} + +void forward_history_layer(layer l, network_state state) +{ + const int batch = l.batch / l.steps; + + float *prev_output = l.prev_state_cpu; + + int i; + for (i = 0; i < l.steps; ++i) { + // shift cell + int shift_size = l.inputs * (l.history_size - 1); + int output_sift = l.inputs; + + int b; + for (b = 0; b < batch; ++b) { + int input_start = b*l.inputs + i*l.inputs*batch; + int output_start = b*l.outputs + i*l.outputs*batch; + float *input = state.input + input_start; + float *output = l.output + output_start; + + copy_cpu(shift_size, prev_output + b*l.outputs, 1, output + output_sift, 1); + + copy_cpu(l.inputs, input, 1, output, 1); + + } + prev_output = l.output + i*l.outputs*batch; + } + + int output_start = (l.steps-1)*l.outputs*batch; + copy_cpu(batch*l.outputs, l.output + output_start, 1, l.prev_state_cpu, 1); +} + +void backward_history_layer(layer l, network_state state) +{ + const int batch = l.batch / l.steps; + + // l.delta -> state.delta + int i; + for (i = 0; i < l.steps; ++i) { + int b; + for (b = 0; b < batch; ++b) { + int input_start = b*l.inputs + i*l.inputs*batch; + int output_start = b*l.outputs + i*l.outputs*batch; + float *state_delta = state.delta + input_start; + float *l_delta = l.delta + output_start; + + //copy_cpu(l.inputs, l_delta, 1, state_delta, 1); + axpy_cpu(l.inputs, 1, l_delta, 1, state_delta, 1); + } + } +} + +#ifdef GPU +void forward_history_layer_gpu(const layer l, network_state state) +{ + const int batch = l.batch / l.steps; + + //int copy_size = l.inputs*batch*l.steps; + //printf(" copy_size = %d, inputs = %d, batch = %d, steps = %d, l.history_size = %d \n", copy_size, l.inputs, batch, l.steps, l.history_size); + //simple_copy_ongpu(copy_size, state.input, l.output_gpu); + //return; + + //fill_ongpu(batch*l.outputs, 0, l.prev_state_gpu, 1); + float *prev_output = l.prev_state_gpu; + + int i; + for (i = 0; i < l.steps; ++i) { + // shift cell + int shift_size = l.inputs * (l.history_size - 1); + int output_sift = l.inputs; + + int b; + for (b = 0; b < batch; ++b) { + //printf(" hist-fw: i = %d, b = %d \n", i, b); + + int input_start = b*l.inputs + i*l.inputs*batch; + int output_start = b*l.outputs + i*l.outputs*batch; + float *input = state.input + input_start; + float *output = l.output_gpu + output_start; + + //copy_cpu(shift_size, prev_output + b*l.outputs, 1, output + output_sift, 1); + simple_copy_ongpu(shift_size, prev_output + b*l.outputs, output + output_sift); + + //copy_cpu(l.inputs, input, 1, output, 1); + simple_copy_ongpu(l.inputs, input, output); + + int h; + for (h = 1; h < l.history_size; ++h) { + //scal_ongpu(l.inputs, (l.history_size - h)/ (float)l.history_size, output + h*l.inputs, 1); + //scal_ongpu(l.inputs, 0, output + h*l.inputs, 1); + } + } + prev_output = l.output_gpu + i*l.outputs*batch; + } + + int output_start = (l.steps - 1)*l.outputs*batch; + //copy_cpu(batch*l.outputs, l.output + output_start, 1, l.prev_state_cpu, 1); + simple_copy_ongpu(batch*l.outputs, l.output_gpu + output_start, l.prev_state_gpu); +} + +void backward_history_layer_gpu(const layer l, network_state state) +{ + const int batch = l.batch / l.steps; + + //int copy_size = l.inputs*batch*l.steps; + //printf(" copy_size = %d, inputs = %d, batch = %d, steps = %d, l.history_size = %d \n", copy_size, l.inputs, batch, l.steps, l.history_size); + //axpy_ongpu(copy_size, 1, l.delta_gpu, 1, state.delta, 1); + //return; + + // l.delta -> state.delta + int i; + for (i = 0; i < l.steps; ++i) { + int b; + for (b = 0; b < batch; ++b) { + //printf(" hist-bw: i = %d, b = %d \n", i, b); + + int input_start = b*l.inputs + i*l.inputs*batch; + int output_start = b*l.outputs + i*l.outputs*batch; + float *state_delta = state.delta + input_start; + float *l_delta = l.delta_gpu + output_start; + + //copy_cpu(l.inputs, l_delta, 1, state_delta, 1); + axpy_ongpu(l.inputs, 1, l_delta, 1, state_delta, 1); + } + } +} +#endif + + void update_conv_lstm_layer(layer l, int batch, float learning_rate, float momentum, float decay) { if (l.peephole) { diff --git a/src/conv_lstm_layer.h b/src/conv_lstm_layer.h index a79c06f6453..fae59f14996 100644 --- a/src/conv_lstm_layer.h +++ b/src/conv_lstm_layer.h @@ -20,10 +20,17 @@ void forward_conv_lstm_layer(layer l, network_state state); void backward_conv_lstm_layer(layer l, network_state state); void update_conv_lstm_layer(layer l, int batch, float learning_rate, float momentum, float decay); +layer make_history_layer(int batch, int h, int w, int c, int history_size, int steps, int train); +void forward_history_layer(layer l, network_state state); +void backward_history_layer(layer l, network_state state); + #ifdef GPU void forward_conv_lstm_layer_gpu(layer l, network_state state); void backward_conv_lstm_layer_gpu(layer l, network_state state); void update_conv_lstm_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay, float loss_scale); + +void forward_history_layer_gpu(const layer l, network_state state); +void backward_history_layer_gpu(const layer l, network_state state); #endif #ifdef __cplusplus diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index eaa8fcd8ca7..ae922eef921 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -569,7 +569,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) float *a = l.weights_gpu + j*l.nweights / l.groups; float *b = state.workspace; float *c = l.output_gpu + (i*l.groups + j)*n*m; - if (l.size == 1) { + if (l.size == 1 && l.stride == 1 && l.dilation == 1) { b = im; } else { diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index a169f3fe87c..1ee241ea3a9 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -1364,7 +1364,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state) else { //printf(" l.index = %d - FP32 \n", l.index); float *im = state.input + (i*l.groups + j)*(l.c / l.groups)*l.h*l.w; - if (l.size == 1) { + if (l.size == 1 && l.stride == 1 && l.dilation == 1) { b = im; } else { diff --git a/src/parser.c b/src/parser.c index 474a525882d..59d71bed45b 100644 --- a/src/parser.c +++ b/src/parser.c @@ -69,6 +69,7 @@ LAYER_TYPE string_to_layer_type(char * type) if (strcmp(type, "[gru]")==0) return GRU; if (strcmp(type, "[lstm]")==0) return LSTM; if (strcmp(type, "[conv_lstm]") == 0) return CONV_LSTM; + if (strcmp(type, "[history]") == 0) return HISTORY; if (strcmp(type, "[rnn]")==0) return RNN; if (strcmp(type, "[conn]")==0 || strcmp(type, "[connected]")==0) return CONNECTED; @@ -329,6 +330,13 @@ layer parse_conv_lstm(list *options, size_params params) return l; } +layer parse_history(list *options, size_params params) +{ + int history_size = option_find_int(options, "history_size", 4); + layer l = make_history_layer(params.batch, params.h, params.w, params.c, history_size, params.time_steps, params.train); + return l; +} + connected_layer parse_connected(list *options, size_params params) { int output = option_find_int(options, "output",1); @@ -1377,6 +1385,8 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) l = parse_lstm(options, params); }else if (lt == CONV_LSTM) { l = parse_conv_lstm(options, params); + }else if (lt == HISTORY) { + l = parse_history(options, params); }else if(lt == CRNN){ l = parse_crnn(options, params); }else if(lt == CONNECTED){ From 05dee78fa3c41d92eb322d8d57fb065ddebc00b4 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 17 Aug 2020 02:13:46 +0300 Subject: [PATCH 1111/1365] Added [convolutional] coordconv=1 for GPU-only --- build/darknet/darknet.vcxproj | 6 ++--- include/darknet.h | 1 + src/blas.h | 1 + src/blas_kernels.cu | 47 +++++++++++++++++++++++++++++++++++ src/conv_lstm_layer.c | 1 - src/convolutional_kernels.cu | 8 ++++++ src/parser.c | 1 + 7 files changed, 61 insertions(+), 4 deletions(-) diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj index b685bebd50b..824fb57e96e 100644 --- a/build/darknet/darknet.vcxproj +++ b/build/darknet/darknet.vcxproj @@ -52,7 +52,7 @@ - + @@ -153,7 +153,7 @@ 64 - compute_30,sm_30;compute_75,sm_75 + compute_35,sm_35;compute_75,sm_75 @@ -302,6 +302,6 @@ - + \ No newline at end of file diff --git a/include/darknet.h b/include/darknet.h index 69d459694f6..0a4dec8efae 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -259,6 +259,7 @@ struct layer { int maxpool_zero_nonmax; int out_channels; float reverse; + int coordconv; int flatten; int spatial; int pad; diff --git a/src/blas.h b/src/blas.h index 02ada92ca28..86c9801df58 100644 --- a/src/blas.h +++ b/src/blas.h @@ -172,6 +172,7 @@ void reduce_and_expand_array_gpu(const float *src_gpu, float *dst_gpu, int size, void expand_array_gpu(const float *src_gpu, float *dst_gpu, int size, int groups); void mult_inverse_array_gpu(const float *src_gpu, float *dst_gpu, int size, float eps); void P_constrastive_f_det_gpu(int *labels, unsigned int feature_size, float temperature, contrastive_params *contrast_p, const int contrast_p_size); +void coord_conv_gpu(float *dst, int size, int w, int h, int chan, int b, int type); #endif // GPU #ifdef __cplusplus diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index bbf50eb1405..b8f7aed557f 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -2381,5 +2381,52 @@ extern "C" void P_constrastive_f_det_gpu(int *labels, unsigned int feature_size, const int num_blocks = get_number_of_blocks(contrast_p_size, block_size); P_constrastive_f_det_kernel << > > (labels, feature_size, temperature, contrast_p, contrast_p_size); + CHECK_CUDA(cudaPeekAtLastError()); +} + + + + +__global__ void coord_conv_kernel(float *dst, int w, int h, int chan, int batch, int type) +{ + int i = blockIdx.x*blockDim.x + threadIdx.x; + + const int x = i % w; + i = i / w; + const int y = i % h; + i = i / h; + const int c = i % chan; + //i = i / chan; + //const int b = i % batch; + + if (type == 0) { + if (c == 0) { + const float x_val = (2.0f * x) / w - 1.0f; // [-1; 1) + dst[i] = x_val; // x - coord + } + else if (c == 1) { + const float y_val = (2.0f * y) / h - 1.0f; // [-1; 1) + dst[i] = y_val; // y - coord + } + else if (c == 2) { + const float x_val = (2.0f * x) / w - 1.0f; // [-1; 1) + const float y_val = (2.0f * y) / h - 1.0f; // [-1; 1) + const float rad_val = sqrtf(x_val*x_val + y_val*y_val); // [0; 1.414) + dst[i] = rad_val; // rad - coord + } + } + else if (type == 1) { + if (c >= 0 && c <= 2) { + dst[i] = 0; + } + } +} + +extern "C" void coord_conv_gpu(float *dst, int size, int w, int h, int chan, int b, int type) +{ + const int block_size = BLOCK; + const int num_blocks = get_number_of_blocks(size, block_size); + coord_conv_kernel << > > (dst, w, h, chan, b, type); + CHECK_CUDA(cudaPeekAtLastError()); } \ No newline at end of file diff --git a/src/conv_lstm_layer.c b/src/conv_lstm_layer.c index d56eb1c13e3..4b0f2511ecf 100644 --- a/src/conv_lstm_layer.c +++ b/src/conv_lstm_layer.c @@ -242,7 +242,6 @@ layer make_conv_lstm_layer(int batch, int h, int w, int c, int output_filters, i layer make_history_layer(int batch, int h, int w, int c, int history_size, int steps, int train) { - //steps = 1; layer l = { (LAYER_TYPE)0 }; l.train = train; l.batch = batch; diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index ae922eef921..b1aa4e69809 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -629,10 +629,18 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) simple_copy_ongpu(l.outputs*l.batch, l.output_gpu, l.input_antialiasing_gpu); simple_copy_ongpu(l.input_layer->outputs*l.input_layer->batch, l.input_layer->output_gpu, l.output_gpu); } + + if (l.coordconv) { + coord_conv_gpu(l.output_gpu, l.outputs*l.batch, l.out_w, l.out_h, l.out_c, l.batch, 0); + } } void backward_convolutional_layer_gpu(convolutional_layer l, network_state state) { + if (l.coordconv) { + coord_conv_gpu(l.delta_gpu, l.outputs*l.batch, l.out_w, l.out_h, l.out_c, l.batch, 1); + } + if (l.antialiasing) { network_state s = { 0 }; s.train = state.train; diff --git a/src/parser.c b/src/parser.c index 59d71bed45b..fc562595751 100644 --- a/src/parser.c +++ b/src/parser.c @@ -229,6 +229,7 @@ convolutional_layer parse_convolutional(list *options, size_params params) layer.angle = option_find_float_quiet(options, "angle", 15); layer.grad_centr = option_find_int_quiet(options, "grad_centr", 0); layer.reverse = option_find_float_quiet(options, "reverse", 0); + layer.coordconv = option_find_int_quiet(options, "coordconv", 0); if(params.net.adam){ layer.B1 = params.net.B1; From 6a2d50f2431baee9ffcdf7f9daa2c779d8df3c29 Mon Sep 17 00:00:00 2001 From: sandy Date: Tue, 18 Aug 2020 16:56:45 -0700 Subject: [PATCH 1112/1365] Precompute part of expensive multiplication --- src/network.c | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/network.c b/src/network.c index a28a3429fa8..68a73fffbbd 100644 --- a/src/network.c +++ b/src/network.c @@ -1240,12 +1240,14 @@ void fuse_conv_batchnorm(network net) { l->biases[f] = l->biases[f] - (double)l->scales[f] * l->rolling_mean[f] / (sqrt((double)l->rolling_variance[f] + .00001)); + double precomputed = l->scales[f] / (sqrt((double)l->rolling_variance[f] + .00001)); + const size_t filter_size = l->size*l->size*l->c / l->groups; int i; for (i = 0; i < filter_size; ++i) { int w_index = f*filter_size + i; - l->weights[w_index] = (double)l->weights[w_index] * l->scales[f] / (sqrt((double)l->rolling_variance[f] + .00001)); + l->weights[w_index] *= precomputed; } } From 519fc8b11b981bfcb6eee3fa028f47b0c4881df2 Mon Sep 17 00:00:00 2001 From: sandy Date: Tue, 18 Aug 2020 16:57:02 -0700 Subject: [PATCH 1113/1365] Remove expensive unnecessary memset --- src/utils.c | 1 - 1 file changed, 1 deletion(-) diff --git a/src/utils.c b/src/utils.c index 2fd6375d557..a7cb19d8d44 100644 --- a/src/utils.c +++ b/src/utils.c @@ -38,7 +38,6 @@ void *xcalloc(size_t nmemb, size_t size) { if(!ptr) { calloc_error(); } - memset(ptr, 0, nmemb * size); return ptr; } From acd8f31969c88f2c929089aa7993cb72e2e65fdc Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 30 Aug 2020 01:55:49 +0300 Subject: [PATCH 1114/1365] Added [yolo] obj_normalizer=1.0 delta_normalizer=1.0 --- include/darknet.h | 2 ++ src/gaussian_yolo_layer.c | 18 +++++++++--------- src/parser.c | 14 +++++++++----- src/yolo_layer.c | 26 +++++++++++++------------- 4 files changed, 33 insertions(+), 27 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 0a4dec8efae..634674725b5 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -420,7 +420,9 @@ struct layer { float max_delta; float uc_normalizer; float iou_normalizer; + float obj_normalizer; float cls_normalizer; + float delta_normalizer; IOU_LOSS iou_loss; IOU_LOSS iou_thresh_kind; NMS_KIND nms_kind; diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index 72673960594..df2a3b9f363 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -490,11 +490,11 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) } avg_anyobj += l.output[obj_index]; - l.delta[obj_index] = l.cls_normalizer * (0 - l.output[obj_index]); + l.delta[obj_index] = l.obj_normalizer * (0 - l.output[obj_index]); if (best_match_iou > l.ignore_thresh) { const float iou_multiplier = best_match_iou*best_match_iou;// (best_match_iou - l.ignore_thresh) / (1.0 - l.ignore_thresh); if (l.objectness_smooth) { - l.delta[obj_index] = l.cls_normalizer * (iou_multiplier - l.output[obj_index]); + l.delta[obj_index] = l.obj_normalizer * (iou_multiplier - l.output[obj_index]); int class_id = state.truth[best_match_t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; @@ -505,7 +505,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) else if (state.net.adversarial) { float scale = pred.w * pred.h; if (scale > 0) scale = sqrt(scale); - l.delta[obj_index] = scale * l.cls_normalizer * (0 - l.output[obj_index]); + l.delta[obj_index] = scale * l.obj_normalizer * (0 - l.output[obj_index]); int cl_id; for (cl_id = 0; cl_id < l.classes; ++cl_id) { if (l.output[class_index + stride*cl_id] * l.output[obj_index] > 0.25) @@ -514,9 +514,9 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) } if (best_iou > l.truth_thresh) { const float iou_multiplier = best_iou*best_iou;// (best_iou - l.truth_thresh) / (1.0 - l.truth_thresh); - if (l.objectness_smooth) l.delta[obj_index] = l.cls_normalizer * (iou_multiplier - l.output[obj_index]); - else l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); - //l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); + if (l.objectness_smooth) l.delta[obj_index] = l.obj_normalizer * (iou_multiplier - l.output[obj_index]); + else l.delta[obj_index] = l.obj_normalizer * (1 - l.output[obj_index]); + //l.delta[obj_index] = l.obj_normalizer * (1 - l.output[obj_index]); int class_id = state.truth[best_t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; @@ -573,7 +573,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8); avg_obj += l.output[obj_index]; - l.delta[obj_index] = class_multiplier * l.cls_normalizer * (1 - l.output[obj_index]); + l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); int class_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 9); delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.label_smooth_eps, l.classes_multipliers); @@ -606,7 +606,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8); avg_obj += l.output[obj_index]; - l.delta[obj_index] = class_multiplier * l.cls_normalizer * (1 - l.output[obj_index]); + l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); int class_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 9); delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.label_smooth_eps, l.classes_multipliers); @@ -893,6 +893,6 @@ void forward_gaussian_yolo_layer_gpu(const layer l, network_state state) void backward_gaussian_yolo_layer_gpu(const layer l, network_state state) { - axpy_ongpu(l.batch*l.inputs, 1, l.delta_gpu, 1, state.delta, 1); + axpy_ongpu(l.batch*l.inputs, l.delta_normalizer, l.delta_gpu, 1, state.delta, 1); } #endif diff --git a/src/parser.c b/src/parser.c index fc562595751..68ca28f71ae 100644 --- a/src/parser.c +++ b/src/parser.c @@ -461,7 +461,9 @@ layer parse_yolo(list *options, size_params params) l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1); l.objectness_smooth = option_find_int_quiet(options, "objectness_smooth", 0); l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75); + l.obj_normalizer = option_find_float_quiet(options, "obj_normalizer", 1); l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1); + l.delta_normalizer = option_find_float_quiet(options, "delta_normalizer", 1); char *iou_loss = option_find_str_quiet(options, "iou_loss", "mse"); // "iou"); if (strcmp(iou_loss, "mse") == 0) l.iou_loss = MSE; @@ -469,8 +471,8 @@ layer parse_yolo(list *options, size_params params) else if (strcmp(iou_loss, "diou") == 0) l.iou_loss = DIOU; else if (strcmp(iou_loss, "ciou") == 0) l.iou_loss = CIOU; else l.iou_loss = IOU; - fprintf(stderr, "[yolo] params: iou loss: %s (%d), iou_norm: %2.2f, cls_norm: %2.2f, scale_x_y: %2.2f\n", - iou_loss, l.iou_loss, l.iou_normalizer, l.cls_normalizer, l.scale_x_y); + fprintf(stderr, "[yolo] params: iou loss: %s (%d), iou_norm: %2.2f, obj_norm: %2.2f, cls_norm: %2.2f, delta_norm: %2.2f, scale_x_y: %2.2f\n", + iou_loss, l.iou_loss, l.iou_normalizer, l.obj_normalizer, l.cls_normalizer, l.delta_normalizer, l.scale_x_y); char *iou_thresh_kind_str = option_find_str_quiet(options, "iou_thresh_kind", "iou"); if (strcmp(iou_thresh_kind_str, "iou") == 0) l.iou_thresh_kind = IOU; @@ -591,7 +593,9 @@ layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3 l.objectness_smooth = option_find_int_quiet(options, "objectness_smooth", 0); l.uc_normalizer = option_find_float_quiet(options, "uc_normalizer", 1.0); l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75); - l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1.0); + l.obj_normalizer = option_find_float_quiet(options, "obj_normalizer", 1.0); + l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1); + l.delta_normalizer = option_find_float_quiet(options, "delta_normalizer", 1); char *iou_loss = option_find_str_quiet(options, "iou_loss", "mse"); // "iou"); if (strcmp(iou_loss, "mse") == 0) l.iou_loss = MSE; @@ -626,8 +630,8 @@ layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3 else if (strcmp(yolo_point, "right_bottom") == 0) l.yolo_point = YOLO_RIGHT_BOTTOM; else l.yolo_point = YOLO_CENTER; - fprintf(stderr, "[Gaussian_yolo] iou loss: %s (%d), iou_norm: %2.2f, cls_norm: %2.2f, scale: %2.2f, point: %d\n", - iou_loss, l.iou_loss, l.iou_normalizer, l.cls_normalizer, l.scale_x_y, l.yolo_point); + fprintf(stderr, "[Gaussian_yolo] iou loss: %s (%d), iou_norm: %2.2f, obj_norm: %2.2f, cls_norm: %2.2f, delta_norm: %2.2f, scale: %2.2f, point: %d\n", + iou_loss, l.iou_loss, l.iou_normalizer, l.obj_normalizer, l.cls_normalizer, l.delta_normalizer, l.scale_x_y, l.yolo_point); l.jitter = option_find_float(options, "jitter", .2); l.resize = option_find_float_quiet(options, "resize", 1.0); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 0287524f673..e0a5bcf683f 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -417,10 +417,10 @@ void forward_yolo_layer(const layer l, network_state state) } avg_anyobj += l.output[obj_index]; - l.delta[obj_index] = l.cls_normalizer * (0 - l.output[obj_index]); + l.delta[obj_index] = l.obj_normalizer * (0 - l.output[obj_index]); if (best_match_iou > l.ignore_thresh) { if (l.objectness_smooth) { - const float delta_obj = l.cls_normalizer * (best_match_iou - l.output[obj_index]); + const float delta_obj = l.obj_normalizer * (best_match_iou - l.output[obj_index]); if (delta_obj > l.delta[obj_index]) l.delta[obj_index] = delta_obj; } @@ -430,7 +430,7 @@ void forward_yolo_layer(const layer l, network_state state) int stride = l.w*l.h; float scale = pred.w * pred.h; if (scale > 0) scale = sqrt(scale); - l.delta[obj_index] = scale * l.cls_normalizer * (0 - l.output[obj_index]); + l.delta[obj_index] = scale * l.obj_normalizer * (0 - l.output[obj_index]); int cl_id; int found_object = 0; for (cl_id = 0; cl_id < l.classes; ++cl_id) { @@ -453,9 +453,9 @@ void forward_yolo_layer(const layer l, network_state state) } if (best_iou > l.truth_thresh) { const float iou_multiplier = best_iou*best_iou;// (best_iou - l.truth_thresh) / (1.0 - l.truth_thresh); - if (l.objectness_smooth) l.delta[obj_index] = l.cls_normalizer * (iou_multiplier - l.output[obj_index]); - else l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); - //l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]); + if (l.objectness_smooth) l.delta[obj_index] = l.obj_normalizer * (iou_multiplier - l.output[obj_index]); + else l.delta[obj_index] = l.obj_normalizer * (1 - l.output[obj_index]); + //l.delta[obj_index] = l.obj_normalizer * (1 - l.output[obj_index]); int class_id = state.truth[best_t*l.truth_size + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; @@ -531,9 +531,9 @@ void forward_yolo_layer(const layer l, network_state state) int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4); avg_obj += l.output[obj_index]; if (l.objectness_smooth) { - float delta_obj = class_multiplier * l.cls_normalizer * (1 - l.output[obj_index]); + float delta_obj = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); if (l.delta[obj_index] == 0) l.delta[obj_index] = delta_obj; - } else l.delta[obj_index] = class_multiplier * l.cls_normalizer * (1 - l.output[obj_index]); + } else l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); @@ -582,10 +582,10 @@ void forward_yolo_layer(const layer l, network_state state) int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4); avg_obj += l.output[obj_index]; if (l.objectness_smooth) { - float delta_obj = class_multiplier * l.cls_normalizer * (1 - l.output[obj_index]); + float delta_obj = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); if (l.delta[obj_index] == 0) l.delta[obj_index] = delta_obj; } - else l.delta[obj_index] = class_multiplier * l.cls_normalizer * (1 - l.output[obj_index]); + else l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); @@ -635,7 +635,7 @@ void forward_yolo_layer(const layer l, network_state state) } } } - float classification_loss = l.cls_normalizer * pow(mag_array(no_iou_loss_delta, l.outputs * l.batch), 2); + float classification_loss = l.obj_normalizer * pow(mag_array(no_iou_loss_delta, l.outputs * l.batch), 2); free(no_iou_loss_delta); float loss = pow(mag_array(l.delta, l.outputs * l.batch), 2); float iou_loss = loss - classification_loss; @@ -663,7 +663,7 @@ void forward_yolo_layer(const layer l, network_state state) iou_loss /= l.batch; fprintf(stderr, "v3 (%s loss, Normalizer: (iou: %.2f, cls: %.2f) Region %d Avg (IOU: %f, GIOU: %f), Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d, class_loss = %f, iou_loss = %f, total_loss = %f \n", - (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.cls_normalizer, state.index, tot_iou / count, tot_giou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count, + (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.obj_normalizer, state.index, tot_iou / count, tot_giou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count, classification_loss, iou_loss, loss); } @@ -956,6 +956,6 @@ void forward_yolo_layer_gpu(const layer l, network_state state) void backward_yolo_layer_gpu(const layer l, network_state state) { - axpy_ongpu(l.batch*l.inputs, state.net.loss_scale, l.delta_gpu, 1, state.delta, 1); + axpy_ongpu(l.batch*l.inputs, state.net.loss_scale * l.delta_normalizer, l.delta_gpu, 1, state.delta, 1); } #endif From ecad770071eb776208a46977347e6d2410d4f50e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 30 Aug 2020 02:34:29 +0300 Subject: [PATCH 1115/1365] Added [yolo] l.cls_normalizer --- src/gaussian_yolo_layer.c | 12 ++++++------ src/yolo_layer.c | 10 +++++----- 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index df2a3b9f363..bfef6991634 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -367,7 +367,7 @@ void averages_gaussian_yolo_deltas(int class_index, int box_index, int stride, i } } -void delta_gaussian_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, float label_smooth_eps, float *classes_multipliers) +void delta_gaussian_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, float label_smooth_eps, float *classes_multipliers, float cls_normalizer) { int n; if (delta[index]){ @@ -385,7 +385,7 @@ void delta_gaussian_yolo_class(float *output, float *delta, int index, int class if (label_smooth_eps) y_true = y_true * (1 - label_smooth_eps) + 0.5*label_smooth_eps; delta[index + stride*n] = y_true - output[index + stride*n]; - if (classes_multipliers && n == class_id) delta[index + stride*class_id] *= classes_multipliers[class_id]; + if (classes_multipliers && n == class_id) delta[index + stride*class_id] *= classes_multipliers[class_id] * cls_normalizer; if(n == class_id && avg_cat) *avg_cat += output[index + stride*n]; } } @@ -498,7 +498,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int class_id = state.truth[best_match_t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; - delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.label_smooth_eps, l.classes_multipliers); + delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); } else l.delta[obj_index] = 0; } @@ -520,7 +520,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int class_id = state.truth[best_t*(4 + 1) + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; - delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.label_smooth_eps, l.classes_multipliers); + delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; if (l.objectness_smooth) l.delta[class_index + stride*class_id] = class_multiplier * (iou_multiplier - l.output[class_index + stride*class_id]); box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); @@ -576,7 +576,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); int class_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 9); - delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.label_smooth_eps, l.classes_multipliers); + delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); ++count; ++class_count; @@ -609,7 +609,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); int class_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 9); - delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.label_smooth_eps, l.classes_multipliers); + delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); ++count; ++class_count; diff --git a/src/yolo_layer.c b/src/yolo_layer.c index e0a5bcf683f..834fbf80b11 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -270,7 +270,7 @@ void averages_yolo_deltas(int class_index, int box_index, int stride, int classe } } -void delta_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, int focal_loss, float label_smooth_eps, float *classes_multipliers) +void delta_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, int focal_loss, float label_smooth_eps, float *classes_multipliers, float cls_normalizer) { int n; if (delta[index + stride*class_id]){ @@ -312,7 +312,7 @@ void delta_yolo_class(float *output, float *delta, int index, int class_id, int float result_delta = y_true - output[index + stride*n]; if (!isnan(result_delta) && !isinf(result_delta)) delta[index + stride*n] = result_delta; - if (classes_multipliers && n == class_id) delta[index + stride*class_id] *= classes_multipliers[class_id]; + if (classes_multipliers && n == class_id) delta[index + stride*class_id] *= classes_multipliers[class_id] * cls_normalizer; if (n == class_id && avg_cat) *avg_cat += output[index + stride*n]; } } @@ -459,7 +459,7 @@ void forward_yolo_layer(const layer l, network_state state) int class_id = state.truth[best_t*l.truth_size + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; - delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); + delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; if (l.objectness_smooth) l.delta[class_index + stride*class_id] = class_multiplier * (iou_multiplier - l.output[class_index + stride*class_id]); box truth = float_to_box_stride(state.truth + best_t*l.truth_size + b*l.truths, 1); @@ -536,7 +536,7 @@ void forward_yolo_layer(const layer l, network_state state) } else l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); - delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); + delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); //printf(" label: class_id = %d, truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f \n", class_id, truth.x, truth.y, truth.w, truth.h); //printf(" mask_n = %d, l.output[obj_index] = %f, l.output[class_index + class_id] = %f \n\n", mask_n, l.output[obj_index], l.output[class_index + class_id]); @@ -588,7 +588,7 @@ void forward_yolo_layer(const layer l, network_state state) else l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); - delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers); + delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); ++count; ++class_count; From eb0272f27acda1982fe4d30acd838fca427785a9 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 6 Sep 2020 04:54:20 +0300 Subject: [PATCH 1116/1365] Fixed cuDNN 8.x usage --- scripts/get_coco2017.sh | 25 +++++++++++++++++++++++++ src/convolutional_layer.c | 6 +++--- 2 files changed, 28 insertions(+), 3 deletions(-) create mode 100644 scripts/get_coco2017.sh diff --git a/scripts/get_coco2017.sh b/scripts/get_coco2017.sh new file mode 100644 index 00000000000..fed57473d5c --- /dev/null +++ b/scripts/get_coco2017.sh @@ -0,0 +1,25 @@ +#!/bin/bash +# Zip coco folder +# zip -r coco.zip coco +# tar -czvf coco.tar.gz coco + +# Download labels from Google Drive, accepting presented query +filename="coco2017labels.zip" +fileid="1cXZR_ckHki6nddOmcysCuuJFM--T-Q6L" +curl -c ./cookie -s -L "https://drive.google.com/uc?export=download&id=${fileid}" > /dev/null +curl -Lb ./cookie "https://drive.google.com/uc?export=download&confirm=`awk '/download/ {print $NF}' ./cookie`&id=${fileid}" -o ${filename} +rm ./cookie + +# Unzip labels +unzip -q ${filename} # for coco.zip +# tar -xzf ${filename} # for coco.tar.gz +rm ${filename} + +# Download and unzip images +cd coco/images +f="train2017.zip" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f # 19G, 118k images +f="val2017.zip" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f # 1G, 5k images +# f="test2017.zip" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f # 7G, 41k images + +# cd out +cd ../.. diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 1ee241ea3a9..130f57fabb0 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -319,7 +319,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference, size_t workspace_ if (conv_fwd_results[i].status == CUDNN_STATUS_SUCCESS && conv_fwd_results[i].algo != CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED && conv_fwd_results[i].memory < free_memory && - conv_fwd_results[i].memory <= workspace_size_specify && + (conv_fwd_results[i].memory <= workspace_size_specify || cudnn_preference == cudnn_fastest) && conv_fwd_results[i].time < min_time) { found_conv_algorithm = 1; @@ -357,7 +357,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference, size_t workspace_ { if (conv_bwd_data_results[i].status == CUDNN_STATUS_SUCCESS && conv_bwd_data_results[i].memory < free_memory && - conv_bwd_data_results[i].memory <= workspace_size_specify && + (conv_bwd_data_results[i].memory <= workspace_size_specify || cudnn_preference == cudnn_fastest) && conv_bwd_data_results[i].time < min_time) { found_conv_algorithm = 1; @@ -394,7 +394,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference, size_t workspace_ { if (conv_bwd_filter_results[i].status == CUDNN_STATUS_SUCCESS && conv_bwd_filter_results[i].memory < free_memory && - conv_bwd_filter_results[i].memory <= workspace_size_specify && + (conv_bwd_filter_results[i].memory <= workspace_size_specify || cudnn_preference == cudnn_fastest) && conv_bwd_filter_results[i].time < min_time) { found_conv_algorithm = 1; From 05a88b261439c70565cd6a862dd7fa5f7382f221 Mon Sep 17 00:00:00 2001 From: zk2ly <56818387+zk2ly@users.noreply.github.com> Date: Thu, 10 Sep 2020 15:28:21 +0800 Subject: [PATCH 1117/1365] Update darknet_images.py --- darknet_images.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/darknet_images.py b/darknet_images.py index 471a99ba75b..113489e2340 100644 --- a/darknet_images.py +++ b/darknet_images.py @@ -139,7 +139,7 @@ def convert2relative(image, bbox): YOLO format use relative coordinates for annotation """ x, y, w, h = bbox - width, height, _ = image.shape + height, width, _ = image.shape return x/width, y/height, w/width, h/height From 5aa9fddce6c6133b3feeda8a8b34eea5255ff4fb Mon Sep 17 00:00:00 2001 From: AllenH Date: Tue, 15 Sep 2020 15:47:05 +0800 Subject: [PATCH 1118/1365] fix: python API that caused Memory leak --- darknet_images.py | 1 + darknet_video.py | 1 + 2 files changed, 2 insertions(+) diff --git a/darknet_images.py b/darknet_images.py index 471a99ba75b..ced13ac7941 100644 --- a/darknet_images.py +++ b/darknet_images.py @@ -111,6 +111,7 @@ def image_detection(image_path, network, class_names, class_colors, thresh): darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes()) detections = darknet.detect_image(network, class_names, darknet_image, thresh=thresh) + darknet.free_image(darknet_image) image = darknet.draw_boxes(detections, image_resized, class_colors) return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), detections diff --git a/darknet_video.py b/darknet_video.py index de96d709ed1..44e4b4fc1e4 100644 --- a/darknet_video.py +++ b/darknet_video.py @@ -84,6 +84,7 @@ def inference(darknet_image_queue, detections_queue, fps_queue): fps_queue.put(fps) print("FPS: {}".format(fps)) darknet.print_detections(detections, args.ext_output) + darknet.free_image(darknet_image) cap.release() From 8993a247479192462d22b7ed8a26ae59c04d2735 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 21 Sep 2020 05:48:15 +0300 Subject: [PATCH 1119/1365] Added: [net] contrastive_color=0 and [contrastive] contrastive_neg_max --- include/darknet.h | 4 ++++ src/blas.c | 13 +++++++++---- src/blas.h | 4 ++-- src/data.c | 24 +++++++++++++++--------- src/data.h | 2 +- src/detector.c | 1 + src/layer.c | 1 + src/parser.c | 2 ++ src/softmax_layer.c | 9 +++++++-- src/yolo_layer.c | 9 ++++++++- 10 files changed, 50 insertions(+), 19 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 634674725b5..62c0f9a2ae5 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -389,6 +389,8 @@ struct layer { float * rand; float * cost; int *labels; + int *class_ids; + int contrastive_neg_max; float *cos_sim; float *exp_cos_sim; float *p_constrastive; @@ -756,6 +758,7 @@ typedef struct network { int mosaic_bound; int contrastive; int contrastive_jit_flip; + int contrastive_color; int unsupervised; float angle; float aspect; @@ -943,6 +946,7 @@ typedef struct load_args { int dontuse_opencv; int contrastive; int contrastive_jit_flip; + int contrastive_color; float jitter; float resize; int flip; diff --git a/src/blas.c b/src/blas.c index b3c67bbf7fb..f814b8ab402 100644 --- a/src/blas.c +++ b/src/blas.c @@ -675,7 +675,7 @@ float P_constrastive_f(size_t i, size_t l, int *labels, float **z, unsigned int return result; } -void grad_contrastive_loss_positive_f(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, int wh, contrastive_params *contrast_p, int contrast_p_size) +void grad_contrastive_loss_positive_f(size_t i, int *class_ids, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, int wh, contrastive_params *contrast_p, int contrast_p_size) { const float vec_len = math_vector_length(z[i], feature_size); size_t j; @@ -721,7 +721,7 @@ void grad_contrastive_loss_positive_f(size_t i, int *labels, size_t num_of_sampl } } -void grad_contrastive_loss_negative_f(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, int wh, contrastive_params *contrast_p, int contrast_p_size) +void grad_contrastive_loss_negative_f(size_t i, int *class_ids, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, int wh, contrastive_params *contrast_p, int contrast_p_size, int neg_max) { const float vec_len = math_vector_length(z[i], feature_size); size_t j; @@ -737,14 +737,17 @@ void grad_contrastive_loss_negative_f(size_t i, int *labels, size_t num_of_sampl } const float mult = 1 / ((N - 1) * temperature * vec_len); + int neg_counter = 0; + for (j = 0; j < num_of_samples; ++j) { //if (i != j && (i/2) == (j/2)) { - if (i != j && labels[i] == labels[j] && labels[i] >= 0) { + if (labels[i] >= 0 && labels[i] == labels[j] && i != j) { size_t k; for (k = 0; k < num_of_samples; ++k) { //if (k != i && k != j && labels[k] != labels[i]) { - if (k != i && k != j && labels[k] >= 0) { + if (k != i && k != j && labels[k] != labels[i] && class_ids[j] == class_ids[k]) { + neg_counter++; const int sim_P_i = get_sim_P_index(i, k, contrast_p, contrast_p_size); if (sim_P_i < 0) continue; const float sim = contrast_p[sim_P_i].sim; @@ -765,6 +768,8 @@ void grad_contrastive_loss_negative_f(size_t i, int *labels, size_t num_of_sampl const int out_i = m * wh; delta[out_i] -= d; } + + if (neg_counter >= neg_max) return; } } } diff --git a/src/blas.h b/src/blas.h index 86c9801df58..eddca47b4c8 100644 --- a/src/blas.h +++ b/src/blas.h @@ -65,8 +65,8 @@ float find_sim(size_t i, size_t j, contrastive_params *contrast_p, int contrast_ float find_P_constrastive(size_t i, size_t j, contrastive_params *contrast_p, int contrast_p_size); float P_constrastive_f_det(size_t il, int *labels, float **z, unsigned int feature_size, float temperature, contrastive_params *contrast_p, int contrast_p_size); float P_constrastive_f(size_t i, size_t l, int *labels, float **z, unsigned int feature_size, float temperature, contrastive_params *contrast_p, int contrast_p_size); -void grad_contrastive_loss_positive_f(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, int wh, contrastive_params *contrast_p, int contrast_p_size); -void grad_contrastive_loss_negative_f(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, int wh, contrastive_params *contrast_p, int contrast_p_size); +void grad_contrastive_loss_positive_f(size_t i, int *class_ids, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, int wh, contrastive_params *contrast_p, int contrast_p_size); +void grad_contrastive_loss_negative_f(size_t i, int *class_ids, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, int wh, contrastive_params *contrast_p, int contrast_p_size, int neg_max); void get_embedding(float *src, int src_w, int src_h, int src_c, int embedding_size, int cur_w, int cur_h, int cur_n, int cur_b, float *dst); float math_vector_length(float *A, unsigned int feature_size); diff --git a/src/data.c b/src/data.c index d386f371bcd..d33f2199082 100644 --- a/src/data.c +++ b/src/data.c @@ -1050,7 +1050,7 @@ void blend_truth_mosaic(float *new_truth, int boxes, int truth_size, float *old_ #include "http_stream.h" data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int truth_size, int classes, int use_flip, int use_gaussian_noise, int use_blur, int use_mixup, - float jitter, float resize, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int mosaic_bound, int contrastive, int contrastive_jit_flip, int show_imgs) + float jitter, float resize, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int mosaic_bound, int contrastive, int contrastive_jit_flip, int contrastive_color, int show_imgs) { const int random_index = random_gen(); c = c ? c : 3; @@ -1151,9 +1151,12 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo r_scale = random_float(); - dhue = rand_uniform_strong(-hue, hue); - dsat = rand_scale(saturation); - dexp = rand_scale(exposure); + if (!contrastive || contrastive_color || i % 2 == 0) + { + dhue = rand_uniform_strong(-hue, hue); + dsat = rand_scale(saturation); + dexp = rand_scale(exposure); + } if (use_blur) { int tmp_blur = rand_int(0, 2); // 0 - disable, 1 - blur background, 2 - blur the whole image @@ -1370,7 +1373,7 @@ void blend_images(image new_img, float alpha, image old_img, float beta) } data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int truth_size, int classes, int use_flip, int gaussian_noise, int use_blur, int use_mixup, - float jitter, float resize, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int mosaic_bound, int contrastive, int contrastive_jit_flip, int show_imgs) + float jitter, float resize, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int mosaic_bound, int contrastive, int contrastive_jit_flip, int contrastive_color, int show_imgs) { const int random_index = random_gen(); c = c ? c : 3; @@ -1451,9 +1454,12 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo r_scale = random_float(); - dhue = rand_uniform_strong(-hue, hue); - dsat = rand_scale(saturation); - dexp = rand_scale(exposure); + if (!contrastive || contrastive_color || i % 2 == 0) + { + dhue = rand_uniform_strong(-hue, hue); + dsat = rand_scale(saturation); + dexp = rand_scale(exposure); + } } int pleft = rand_precalc_random(-dw, dw, r1); @@ -1588,7 +1594,7 @@ void *load_thread(void *ptr) *a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure); } else if (a.type == DETECTION_DATA){ *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.c, a.num_boxes, a.truth_size, a.classes, a.flip, a.gaussian_noise, a.blur, a.mixup, a.jitter, a.resize, - a.hue, a.saturation, a.exposure, a.mini_batch, a.track, a.augment_speed, a.letter_box, a.mosaic_bound, a.contrastive, a.contrastive_jit_flip, a.show_imgs); + a.hue, a.saturation, a.exposure, a.mini_batch, a.track, a.augment_speed, a.letter_box, a.mosaic_bound, a.contrastive, a.contrastive_jit_flip, a.contrastive_color, a.show_imgs); } else if (a.type == SWAG_DATA){ *a.d = load_data_swag(a.paths, a.n, a.classes, a.jitter); } else if (a.type == COMPARE_DATA){ diff --git a/src/data.h b/src/data.h index 3c6e66f56cd..ecbd0188e7f 100644 --- a/src/data.h +++ b/src/data.h @@ -87,7 +87,7 @@ data load_data_captcha(char **paths, int n, int m, int k, int w, int h); data load_data_captcha_encode(char **paths, int n, int m, int w, int h); data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h); data load_data_detection(int n, char **paths, int m, int w, int h, int c, int boxes, int truth_size, int classes, int use_flip, int gaussian_noise, int use_blur, int use_mixup, - float jitter, float resize, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int mosaic_bound, int contrastive, int contrastive_jit_flip, int show_imgs); + float jitter, float resize, float hue, float saturation, float exposure, int mini_batch, int track, int augment_speed, int letter_box, int mosaic_bound, int contrastive, int contrastive_jit_flip, int contrastive_color, int show_imgs); data load_data_tag(char **paths, int n, int m, int k, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure); matrix load_image_augment_paths(char **paths, int n, int use_flip, int min, int max, int w, int h, float angle, float aspect, float hue, float saturation, float exposure, int dontuse_opencv, int contrastive); data load_data_super(char **paths, int n, int m, int w, int h, int scale); diff --git a/src/detector.c b/src/detector.c index 0f1e1816ecd..52511fb0dfb 100644 --- a/src/detector.c +++ b/src/detector.c @@ -157,6 +157,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i args.mosaic_bound = net.mosaic_bound; args.contrastive = net.contrastive; args.contrastive_jit_flip = net.contrastive_jit_flip; + args.contrastive_color = net.contrastive_color; if (dont_show && show_imgs) show_imgs = 2; args.show_imgs = show_imgs; diff --git a/src/layer.c b/src/layer.c index 1808ea9fc12..fe3827d2ad5 100644 --- a/src/layer.c +++ b/src/layer.c @@ -75,6 +75,7 @@ void free_layer_custom(layer l, int keep_cudnn_desc) if (l.rand) free(l.rand); if (l.cost) free(l.cost); if (l.labels && !l.detection) free(l.labels); + if (l.class_ids && !l.detection) free(l.class_ids); if (l.cos_sim) free(l.cos_sim); if (l.exp_cos_sim) free(l.exp_cos_sim); if (l.p_constrastive) free(l.p_constrastive); diff --git a/src/parser.c b/src/parser.c index 68ca28f71ae..66c185518d7 100644 --- a/src/parser.c +++ b/src/parser.c @@ -383,6 +383,7 @@ contrastive_layer parse_contrastive(list *options, size_params params) layer.steps = params.time_steps; layer.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1); layer.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10 + layer.contrastive_neg_max = option_find_int_quiet(options, "contrastive_neg_max", 3); return layer; } @@ -1185,6 +1186,7 @@ void parse_net_options(list *options, network *net) net->mosaic_bound = option_find_int_quiet(options, "mosaic_bound", 0); net->contrastive = option_find_int_quiet(options, "contrastive", 0); net->contrastive_jit_flip = option_find_int_quiet(options, "contrastive_jit_flip", 0); + net->contrastive_color = option_find_int_quiet(options, "contrastive_color", 0); net->unsupervised = option_find_int_quiet(options, "unsupervised", 0); if (net->contrastive && mini_batch < 2) { printf(" Error: mini_batch size (batch/subdivisions) should be higher than 1 for Contrastive loss \n"); diff --git a/src/softmax_layer.c b/src/softmax_layer.c index 8e74b1de2f2..59f51117e77 100644 --- a/src/softmax_layer.c +++ b/src/softmax_layer.c @@ -143,6 +143,7 @@ contrastive_layer make_contrastive_layer(int batch, int w, int h, int c, int cla l.detection = 1; l.max_boxes = yolo_layer->max_boxes; l.labels = yolo_layer->labels; // track id + l.class_ids = yolo_layer->class_ids; // class_ids l.n = yolo_layer->n; // num of embeddings per cell = num of anchors l.classes = yolo_layer->classes;// num of classes classes = l.classes; @@ -313,6 +314,8 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) const int z_index2 = b2*l.n*l.h*l.w + n2*l.h*l.w + h2*l.w + w2; if (l.labels[z_index2] < 0) continue; if (z_index == z_index2) continue; + if (l.detection) + if (l.class_ids[z_index] != l.class_ids[z_index2]) continue; const int time_step_i = b / mini_batch; const int time_step_j = b2 / mini_batch; @@ -452,6 +455,8 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) const int z_index2 = b2*l.n*l.h*l.w + n2*l.h*l.w + h2*l.w + w2; if (l.labels[z_index2] < 0) continue; if (z_index == z_index2) continue; + if (l.detection) + if (l.class_ids[z_index] != l.class_ids[z_index2]) continue; const int time_step_i = b / mini_batch; const int time_step_j = b2 / mini_batch; @@ -510,10 +515,10 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) // detector // positive - grad_contrastive_loss_positive_f(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.delta + delta_index, wh, contrast_p, contr_size); + grad_contrastive_loss_positive_f(z_index, l.class_ids, l.labels, step, z, l.embedding_size, l.temperature, l.delta + delta_index, wh, contrast_p, contr_size); // negative - grad_contrastive_loss_negative_f(z_index, l.labels, step, z, l.embedding_size, l.temperature, l.delta + delta_index, wh, contrast_p, contr_size); + grad_contrastive_loss_negative_f(z_index, l.class_ids, l.labels, step, z, l.embedding_size, l.temperature, l.delta + delta_index, wh, contrast_p, contr_size, l.contrastive_neg_max); } else { // classifier diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 834fbf80b11..d46b824f713 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -46,6 +46,8 @@ layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int l.truths = l.max_boxes*l.truth_size; // 90*(4 + 1); l.labels = (int*)xcalloc(batch * l.w*l.h*l.n, sizeof(int)); for (i = 0; i < batch * l.w*l.h*l.n; ++i) l.labels[i] = -1; + l.class_ids = (int*)xcalloc(batch * l.w*l.h*l.n, sizeof(int)); + for (i = 0; i < batch * l.w*l.h*l.n; ++i) l.class_ids[i] = -1; l.delta = (float*)xcalloc(batch * l.outputs, sizeof(float)); l.output = (float*)xcalloc(batch * l.outputs, sizeof(float)); @@ -93,6 +95,7 @@ void resize_yolo_layer(layer *l, int w, int h) if (l->embedding_output) l->embedding_output = (float*)xrealloc(l->output, l->batch * l->embedding_size * l->n * l->h * l->w * sizeof(float)); if (l->labels) l->labels = (int*)xrealloc(l->labels, l->batch * l->n * l->h * l->w * sizeof(int)); + if (l->class_ids) l->class_ids = (int*)xrealloc(l->class_ids, l->batch * l->n * l->h * l->w * sizeof(int)); if (!l->output_pinned) l->output = (float*)xrealloc(l->output, l->batch*l->outputs * sizeof(float)); if (!l->delta_pinned) l->delta = (float*)xrealloc(l->delta, l->batch*l->outputs*sizeof(float)); @@ -360,6 +363,7 @@ void forward_yolo_layer(const layer l, network_state state) if (!state.train) return; for (i = 0; i < l.batch * l.w*l.h*l.n; ++i) l.labels[i] = -1; + for (i = 0; i < l.batch * l.w*l.h*l.n; ++i) l.class_ids[i] = -1; //float avg_iou = 0; float tot_iou = 0; float tot_giou = 0; @@ -513,6 +517,7 @@ void forward_yolo_layer(const layer l, network_state state) const int track_id = state.truth[truth_in_index]; const int truth_out_index = b*l.n*l.w*l.h + mask_n*l.w*l.h + j*l.w + i; l.labels[truth_out_index] = track_id; + l.class_ids[truth_out_index] = class_id; //printf(" track_id = %d, t = %d, b = %d, truth_in_index = %d, truth_out_index = %d \n", track_id, t, b, truth_in_index, truth_out_index); // range is 0 <= 1 @@ -603,11 +608,13 @@ void forward_yolo_layer(const layer l, network_state state) for (j = 0; j < l.h; ++j) { for (i = 0; i < l.w; ++i) { for (n = 0; n < l.n; ++n) { + int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4); int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); const int stride = l.w*l.h; - averages_yolo_deltas(class_index, box_index, stride, l.classes, l.delta); + if(l.delta[obj_index] != 0) + averages_yolo_deltas(class_index, box_index, stride, l.classes, l.delta); } } } From fdb1841eb1699cf24a193787c2da7b2d9bd10070 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 21 Sep 2020 06:11:20 +0300 Subject: [PATCH 1120/1365] Ignore behavior of [history] layer for training on non-sequential images --- src/conv_lstm_layer.c | 21 ++++++++++++++++++++- 1 file changed, 20 insertions(+), 1 deletion(-) diff --git a/src/conv_lstm_layer.c b/src/conv_lstm_layer.c index 4b0f2511ecf..72e7eac3865 100644 --- a/src/conv_lstm_layer.c +++ b/src/conv_lstm_layer.c @@ -287,6 +287,11 @@ layer make_history_layer(int batch, int h, int w, int c, int history_size, int s void forward_history_layer(layer l, network_state state) { + if (l.steps == 1) { + copy_cpu(l.inputs*l.batch, state.input, 1, l.output, 1); + return; + } + const int batch = l.batch / l.steps; float *prev_output = l.prev_state_cpu; @@ -307,7 +312,6 @@ void forward_history_layer(layer l, network_state state) copy_cpu(shift_size, prev_output + b*l.outputs, 1, output + output_sift, 1); copy_cpu(l.inputs, input, 1, output, 1); - } prev_output = l.output + i*l.outputs*batch; } @@ -318,6 +322,11 @@ void forward_history_layer(layer l, network_state state) void backward_history_layer(layer l, network_state state) { + if (l.steps == 1) { + axpy_cpu(l.inputs*l.batch, 1, l.delta, 1, state.delta, 1); + return; + } + const int batch = l.batch / l.steps; // l.delta -> state.delta @@ -339,6 +348,11 @@ void backward_history_layer(layer l, network_state state) #ifdef GPU void forward_history_layer_gpu(const layer l, network_state state) { + if (l.steps == 1) { + simple_copy_ongpu(l.inputs*l.batch, state.input, l.output_gpu); + return; + } + const int batch = l.batch / l.steps; //int copy_size = l.inputs*batch*l.steps; @@ -386,6 +400,11 @@ void forward_history_layer_gpu(const layer l, network_state state) void backward_history_layer_gpu(const layer l, network_state state) { + if (l.steps == 1) { + axpy_ongpu(l.inputs*l.batch, 1, l.delta_gpu, 1, state.delta, 1); + return; + } + const int batch = l.batch / l.steps; //int copy_size = l.inputs*batch*l.steps; From 860d5291d46ececb7ec97a44a98e99c56931fbb5 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 23 Sep 2020 11:03:47 +0200 Subject: [PATCH 1121/1365] fix compatibility with newer clang --- src/gemm.c | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/src/gemm.c b/src/gemm.c index 0b06d987086..519751c0622 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -531,7 +531,7 @@ static inline __int32 _mm256_extract_epi32(__m256i a, const int index) { } #endif -static inline float _castu32_f32(uint32_t a) { +static inline float _dn_castu32_f32(uint32_t a) { return *((float *)&a); } @@ -546,30 +546,30 @@ static inline float _mm256_extract_float32(__m256 a, const int index) { #include #include -static inline float _castu32_f32(uint32_t a) { +static inline float _dn_castu32_f32(uint32_t a) { return *((float *)&a); } static inline float _mm256_extract_float32(__m256 a, const int index) { switch(index) { case 0: - return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 0)); + return _dn_castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 0)); case 1: - return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 1)); + return _dn_castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 1)); case 2: - return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 2)); + return _dn_castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 2)); case 3: - return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 3)); + return _dn_castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 3)); case 4: - return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 4)); + return _dn_castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 4)); case 5: - return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 5)); + return _dn_castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 5)); case 6: - return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 6)); + return _dn_castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 6)); case 7: - return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 7)); + return _dn_castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 7)); default: - return _castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 0)); + return _dn_castu32_f32(_mm256_extract_epi32(_mm256_castps_si256(a), 0)); } } From 91b5dd2da7dcb9acc69d129e504ad9ef83045c4c Mon Sep 17 00:00:00 2001 From: Alexey Date: Wed, 7 Oct 2020 01:14:00 +0300 Subject: [PATCH 1122/1365] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 2e5ab8fdf7c..6fe35295906 100644 --- a/README.md +++ b/README.md @@ -615,6 +615,7 @@ Different tools for marking objects in images: 6. in C++: https://github.com/jveitchmichaelis/deeplabel 7. in C#: https://github.com/BMW-InnovationLab/BMW-Labeltool-Lite 8. DL-Annotator for Windows ($30): [url](https://www.microsoft.com/en-us/p/dlannotator/9nsx79m7t8fn?activetab=pivot:overviewtab) +9. v7labs - the greatest cloud labeling tool ($1.5 per hour): https://www.v7labs.com/ ## How to use Yolo as DLL and SO libraries From d65909fbea471d06e52a2e4a41132380dc2edaa6 Mon Sep 17 00:00:00 2001 From: Alexey Date: Sun, 11 Oct 2020 15:54:55 +0300 Subject: [PATCH 1123/1365] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 6fe35295906..f754cd88222 100644 --- a/README.md +++ b/README.md @@ -190,6 +190,8 @@ You can get cfg-files by path: `darknet/cfg/` * [Tianxiaomo/pytorch-YOLOv4](https://github.com/Tianxiaomo/pytorch-YOLOv4) * **TensorRT** YOLOv4 on TensorRT+tkDNN: https://github.com/ceccocats/tkDNN For YOLOv3 (-70% faster inference): [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) read [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf). [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) implemented yolov3-spp, yolov4, etc. +* **Deepstream 5.0 / TensorRT for YOLOv4** https://github.com/NVIDIA-AI-IOT/yolov4_deepstream +* **Amazon Neurochip / Amazon EC2 Inf1 instances** 1.85 times higher throughput and 37% lower cost per image for TensorFlow based YOLOv4 model, using Keras [URL](https://aws.amazon.com/ru/blogs/machine-learning/improving-performance-for-deep-learning-based-object-detection-with-an-aws-neuron-compiled-yolov4-model-on-aws-inferentia/) * **TVM** - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backends (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about * **OpenDataCam** - It detects, tracks and counts moving objects by using YOLOv4: https://github.com/opendatacam/opendatacam#-hardware-pre-requisite * **Netron** - Visualizer for neural networks: https://github.com/lutzroeder/netron From 7a28aade28394c355f7aa999bfe217b20ba0daf0 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 20 Oct 2020 02:49:24 +0300 Subject: [PATCH 1124/1365] yolov4-tiny_contrastive.cfg --- .../x64/cfg/yolov4-tiny_contrastive.cfg | 446 ++++++++++++++++++ cfg/yolov4-tiny_contrastive.cfg | 446 ++++++++++++++++++ 2 files changed, 892 insertions(+) create mode 100644 build/darknet/x64/cfg/yolov4-tiny_contrastive.cfg create mode 100644 cfg/yolov4-tiny_contrastive.cfg diff --git a/build/darknet/x64/cfg/yolov4-tiny_contrastive.cfg b/build/darknet/x64/cfg/yolov4-tiny_contrastive.cfg new file mode 100644 index 00000000000..8969f167593 --- /dev/null +++ b/build/darknet/x64/cfg/yolov4-tiny_contrastive.cfg @@ -0,0 +1,446 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=2 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +contrastive=1 +contrastive_jit_flip=0 + +learning_rate=0.00261 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1 +groups=2 +group_id=1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -6,-1 + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1 +groups=2 +group_id=1 + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -6,-1 + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1 +groups=2 +group_id=1 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -6,-1 + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +################################## + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +#[conv_lstm] +#batch_normalize=1 +#size=3 +#pad=1 +#output=128 +#groups=1 +#peephole=0 +#bottleneck=1 +##time_normalizer=0.5 +#lstm_activation=tanh +#activation=leaky + + +########### + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + + +########### to [yolo-3] + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 23 + +########### + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +#[conv_lstm] +#batch_normalize=1 +#size=3 +#pad=1 +#output=128 +#groups=1 +#peephole=0 +#bottleneck=1 +##time_normalizer=0.5 +#lstm_activation=tanh +#activation=leaky + +########### + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +########### to [yolo-2] + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 15 + +########### + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +#[conv_lstm] +#batch_normalize=1 +#size=3 +#pad=1 +#output=64 +#groups=1 +#peephole=0 +#bottleneck=1 +##time_normalizer=0.5 +#lstm_activation=tanh +#activation=leaky + +########### + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers= 0 +onlyforward=1 + +[local_avgpool] +size=4 +stride=4 + +[route] +layers= -1,-3 +onlyforward=1 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +# Contrastive embeddings +[convolutional] +batch_normalize=1 +filters=1152 +size=1 +stride=1 +pad=1 +#activation=tanh +#activation=leaky +activation=linear + +[route] +layers= -9 +#onlyforward=1 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=765 +activation=linear + + + +[yolo] +mask = 0,1,2,3,4,5,6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +scale_x_y = 1.05 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +ignore_thresh = .7 +truth_thresh = 1 +random=0 +resize=1.5 +nms_kind=greedynms +beta_nms=0.6 +max_delta=5 +embedding_layer = -4 + + +[route] +layers= -5 + + +[contrastive] +classes=1 +temperature=1.0 +yolo_layer= -2 +cls_normalizer=1.0 +max_delta=10 diff --git a/cfg/yolov4-tiny_contrastive.cfg b/cfg/yolov4-tiny_contrastive.cfg new file mode 100644 index 00000000000..8969f167593 --- /dev/null +++ b/cfg/yolov4-tiny_contrastive.cfg @@ -0,0 +1,446 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=2 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +contrastive=1 +contrastive_jit_flip=0 + +learning_rate=0.00261 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1 +groups=2 +group_id=1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -6,-1 + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1 +groups=2 +group_id=1 + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -6,-1 + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-1 +groups=2 +group_id=1 + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -6,-1 + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +################################## + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +#[conv_lstm] +#batch_normalize=1 +#size=3 +#pad=1 +#output=128 +#groups=1 +#peephole=0 +#bottleneck=1 +##time_normalizer=0.5 +#lstm_activation=tanh +#activation=leaky + + +########### + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + + +########### to [yolo-3] + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 23 + +########### + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +#[conv_lstm] +#batch_normalize=1 +#size=3 +#pad=1 +#output=128 +#groups=1 +#peephole=0 +#bottleneck=1 +##time_normalizer=0.5 +#lstm_activation=tanh +#activation=leaky + +########### + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +########### to [yolo-2] + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 15 + +########### + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +#[conv_lstm] +#batch_normalize=1 +#size=3 +#pad=1 +#output=64 +#groups=1 +#peephole=0 +#bottleneck=1 +##time_normalizer=0.5 +#lstm_activation=tanh +#activation=leaky + +########### + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers= 0 +onlyforward=1 + +[local_avgpool] +size=4 +stride=4 + +[route] +layers= -1,-3 +onlyforward=1 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +# Contrastive embeddings +[convolutional] +batch_normalize=1 +filters=1152 +size=1 +stride=1 +pad=1 +#activation=tanh +#activation=leaky +activation=linear + +[route] +layers= -9 +#onlyforward=1 + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=765 +activation=linear + + + +[yolo] +mask = 0,1,2,3,4,5,6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +scale_x_y = 1.05 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +ignore_thresh = .7 +truth_thresh = 1 +random=0 +resize=1.5 +nms_kind=greedynms +beta_nms=0.6 +max_delta=5 +embedding_layer = -4 + + +[route] +layers= -5 + + +[contrastive] +classes=1 +temperature=1.0 +yolo_layer= -2 +cls_normalizer=1.0 +max_delta=10 From 88936e06a943e950d885d0a88df558f1eab8528a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 20 Oct 2020 05:34:37 +0300 Subject: [PATCH 1125/1365] Added some hyper-params to the yolov4-tiny_contrastive.cfg --- .../x64/cfg/yolov4-tiny_contrastive.cfg | 5 ++++ src/http_stream.cpp | 28 +++++++++++++++---- 2 files changed, 28 insertions(+), 5 deletions(-) diff --git a/build/darknet/x64/cfg/yolov4-tiny_contrastive.cfg b/build/darknet/x64/cfg/yolov4-tiny_contrastive.cfg index 8969f167593..a23dc5b9e2f 100644 --- a/build/darknet/x64/cfg/yolov4-tiny_contrastive.cfg +++ b/build/darknet/x64/cfg/yolov4-tiny_contrastive.cfg @@ -433,6 +433,11 @@ beta_nms=0.6 max_delta=5 embedding_layer = -4 +track_history_size = 20 +sim_thresh = 0.7 +dets_for_show = 2 +dets_for_track = 8 +track_ciou_norm = 0.2 [route] layers= -5 diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 9dff2a32775..3ec7e851593 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -782,9 +782,27 @@ int check_classes_id(detection det1, detection det2, float thresh) getchar(); } + int det1_id = -1; + float det1_prob = 0; + int det2_id = -1; + float det2_prob = 0; + for (int i = 0; i < det1.classes; ++i) { - if (det1.prob[i] > thresh && det2.prob[i] > thresh) return 1; + if (det1.prob[i] > thresh && det1.prob[i] > det1_prob) { + det1_prob = det1.prob[i]; + det1_id = i; + } + if (det2.prob[i] > thresh && det2.prob[i] > det2_prob) { + det2_prob = det2.prob[i]; + det2_id = i; + } } + + if (det1_id == det2_id && det2_id != -1) return 1; + + //for (int i = 0; i < det1.classes; ++i) { + // if (det1.prob[i] > thresh && det2.prob[i] > thresh) return 1; + //} return 0; } @@ -864,7 +882,6 @@ void set_track_id(detection *new_dets, int new_dets_num, float thresh, float sim std::sort(sim_det.begin(), sim_det.end(), [](similarity_detections_t v1, similarity_detections_t v2) { return v1.sim > v2.sim; }); //if(sim_det.size() > 0) printf(" sim_det_first = %f, sim_det_end = %f \n", sim_det.begin()->sim, sim_det.rbegin()->sim); - std::vector new_idx(new_dets_num, 1); std::vector old_idx(old_dets.size(), 1); std::vector track_idx(new_track_id, 1); @@ -878,13 +895,14 @@ void set_track_id(detection *new_dets, int new_dets_num, float thresh, float sim //printf(" ciou = %f \n", box_ciou(new_dets[new_id].bbox, old_dets[old_id].bbox)); if (track_idx[track_id] && new_idx[new_id] && old_idx[old_id] && check_classes_id(new_dets[new_id], old_dets[old_id], thresh)) { float sim = sim_det[index].sim; - float ciou = box_ciou(new_dets[new_id].bbox, old_dets[old_id].bbox); + //float ciou = box_ciou(new_dets[new_id].bbox, old_dets[old_id].bbox); + float ciou = box_iou(new_dets[new_id].bbox, old_dets[old_id].bbox); sim = sim * (1 - track_ciou_norm) + ciou * track_ciou_norm; - if (sim_thresh < sim) { + if (sim_thresh < sim && new_dets[new_id].sim < sim) { new_dets[new_id].sim = sim; new_dets[new_id].track_id = track_id; new_dets[new_id].sort_class = det_count + 1; - new_idx[new_id] = 0; + //new_idx[new_id] = 0; old_idx[old_id] = 0; if(track_id) track_idx[track_id] = 0; } From 8297b1be3108f47d82b97d322d5f1aef5150018f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 20 Oct 2020 05:37:49 +0300 Subject: [PATCH 1126/1365] Added some hyper-params to the yolov4-tiny_contrastive.cfg --- build/darknet/x64/cfg/yolov4-tiny_contrastive.cfg | 3 ++- cfg/yolov4-tiny_contrastive.cfg | 8 +++++++- 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/build/darknet/x64/cfg/yolov4-tiny_contrastive.cfg b/build/darknet/x64/cfg/yolov4-tiny_contrastive.cfg index a23dc5b9e2f..1fed0433cf0 100644 --- a/build/darknet/x64/cfg/yolov4-tiny_contrastive.cfg +++ b/build/darknet/x64/cfg/yolov4-tiny_contrastive.cfg @@ -16,7 +16,8 @@ exposure = 1.5 hue=.1 contrastive=1 -contrastive_jit_flip=0 +contrastive_jit_flip=1 +contrastive_color=0 learning_rate=0.00261 burn_in=1000 diff --git a/cfg/yolov4-tiny_contrastive.cfg b/cfg/yolov4-tiny_contrastive.cfg index 8969f167593..1fed0433cf0 100644 --- a/cfg/yolov4-tiny_contrastive.cfg +++ b/cfg/yolov4-tiny_contrastive.cfg @@ -16,7 +16,8 @@ exposure = 1.5 hue=.1 contrastive=1 -contrastive_jit_flip=0 +contrastive_jit_flip=1 +contrastive_color=0 learning_rate=0.00261 burn_in=1000 @@ -433,6 +434,11 @@ beta_nms=0.6 max_delta=5 embedding_layer = -4 +track_history_size = 20 +sim_thresh = 0.7 +dets_for_show = 2 +dets_for_track = 8 +track_ciou_norm = 0.2 [route] layers= -5 From f04f418d27732c05de91cea0cf2fc1d35b36b8ce Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 3 Nov 2020 20:28:52 +0300 Subject: [PATCH 1127/1365] cspx-p7-mish_hp.cfg experimental --- build/darknet/x64/cfg/cspx-p7-mish-omega.cfg | 1459 ++++++++++ build/darknet/x64/cfg/cspx-p7-mish_hp.cfg | 2638 ++++++++++++++++++ cfg/cspx-p7-mish-omega.cfg | 1459 ++++++++++ cfg/cspx-p7-mish_hp.cfg | 2638 ++++++++++++++++++ 4 files changed, 8194 insertions(+) create mode 100644 build/darknet/x64/cfg/cspx-p7-mish-omega.cfg create mode 100644 build/darknet/x64/cfg/cspx-p7-mish_hp.cfg create mode 100644 cfg/cspx-p7-mish-omega.cfg create mode 100644 cfg/cspx-p7-mish_hp.cfg diff --git a/build/darknet/x64/cfg/cspx-p7-mish-omega.cfg b/build/darknet/x64/cfg/cspx-p7-mish-omega.cfg new file mode 100644 index 00000000000..93c4aefa94c --- /dev/null +++ b/build/darknet/x64/cfg/cspx-p7-mish-omega.cfg @@ -0,0 +1,1459 @@ +[net] +# Training +batch=100 +subdivisions=2 + +label_smooth_eps=0.1 + +# Testing +# batch=1 +# subdivisions=1 + +height=320 +width=320 +channels=3 +min_crop=320 +max_crop=640 + +mosaic=1 +cutmix=1 + +burn_in=2000 +learning_rate=0.1 +policy=poly +power=4 +max_batches=1200000 +#max_batches=400000 +momentum=0.9 +decay=0.0005 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=40 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-13 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-49 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-49 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-25 + +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-25 + +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-25 + +[convolutional] +batch_normalize=1 +filters=1600 +size=1 +stride=1 +pad=1 +activation=mish + +[avgpool] + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=1 +activation=linear + +[softmax] +groups=1 \ No newline at end of file diff --git a/build/darknet/x64/cfg/cspx-p7-mish_hp.cfg b/build/darknet/x64/cfg/cspx-p7-mish_hp.cfg new file mode 100644 index 00000000000..209fa8f81cb --- /dev/null +++ b/build/darknet/x64/cfg/cspx-p7-mish_hp.cfg @@ -0,0 +1,2638 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=16 +#width=1536 +#height=1536 +width=896 +height=896 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 +letter_box=1 + +### Start of Backbone ### + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=40 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-13 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-49 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-49 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-25 + +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-25 + +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-25 + +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=mish + +### End of backbone ### + +### Start of CSPSPP ### + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -13 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +### End of CSPSPP ### + +### Start of CSPPAN ### + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 180 ###P6 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 152 ###P5 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 124 ###P4 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 72 ###P3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1, 271 ###S4 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, 255 ###S5 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, 239 ###S6 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, 223 ###S7 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +### End of CSPPAN ### + +### Start of YOLO ### + +[route] +layers = 287 ### + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=320 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +#filters=340 +filters=170 +activation=linear + +#[route] +#layers=-1 + +[yolo] +mask = 0,1 +anchors = 13,17, 31,25, 24,51, 61,45 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 800,800 +classes=80 +num=16 +#jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +resize=1.5 +scale_x_y = 1.05 +##iou_thresh=0.213 +#cls_normalizer=1.0 +#iou_normalizer=0.07 + +jitter=.1 +#objectness_smooth=1 +##iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=4.0 + +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + +#counters_per_class = 26575, 7113, 44338, 8730, 5154, 6100, 4550, 10038, 10832, 13016, 1883, 1974, 1287, 9787, 10883, 4776, 5507, 6562, 9724, 8171, 5491, 1312, 5296, 5193, 8707, 11531, 12415, 6470, 6214, 2688, 6605, 2665, 6393, 9159, 3300, 3756, 5508, 6143, 4854, 24516, 7914, 20710, 5466, 7810, 6168, 14398, 9603, 5941, 4403, 6561, 7308, 7885, 2908, 5892, 7401, 6466, 38878, 5804, 8585, 4160, 15789, 4157, 5836, 4964, 2273, 5746, 2891, 6411, 1641, 3335, 223, 5624, 2658, 25244, 6320, 6539, 1464, 4775, 198, 1937 +#max_delta=3 + +[route] +layers = 300 ### + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=640 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +#filters=340 +filters=255 +activation=linear + +[yolo] +mask = 1,2,3 +anchors = 13,17, 31,25, 24,51, 61,45 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 800,800 +classes=80 +num=16 +#jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +resize=1.5 +scale_x_y = 1.05 +##iou_thresh=0.213 +#cls_normalizer=1.0 +#iou_normalizer=0.07 + +jitter=.1 +#objectness_smooth=1 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 + +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + +#counters_per_class = 26575, 7113, 44338, 8730, 5154, 6100, 4550, 10038, 10832, 13016, 1883, 1974, 1287, 9787, 10883, 4776, 5507, 6562, 9724, 8171, 5491, 1312, 5296, 5193, 8707, 11531, 12415, 6470, 6214, 2688, 6605, 2665, 6393, 9159, 3300, 3756, 5508, 6143, 4854, 24516, 7914, 20710, 5466, 7810, 6168, 14398, 9603, 5941, 4403, 6561, 7308, 7885, 2908, 5892, 7401, 6466, 38878, 5804, 8585, 4160, 15789, 4157, 5836, 4964, 2273, 5746, 2891, 6411, 1641, 3335, 223, 5624, 2658, 25244, 6320, 6539, 1464, 4775, 198, 1937 +#max_delta=3 + +[route] +layers = 313 ### + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=linear + +[yolo] +mask = 4,5,6,7 +anchors = 13,17, 31,25, 24,51, 61,45 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 800,800 +classes=80 +num=16 +#jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +resize=1.5 +scale_x_y = 1.05 +##iou_thresh=0.213 +#cls_normalizer=1.0 +#iou_normalizer=0.07 + +jitter=.1 +objectness_smooth=1 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.5 + +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + +counters_per_class = 26575, 7113, 44338, 8730, 5154, 6100, 4550, 10038, 10832, 13016, 1883, 1974, 1287, 9787, 10883, 4776, 5507, 6562, 9724, 8171, 5491, 1312, 5296, 5193, 8707, 11531, 12415, 6470, 6214, 2688, 6605, 2665, 6393, 9159, 3300, 3756, 5508, 6143, 4854, 24516, 7914, 20710, 5466, 7810, 6168, 14398, 9603, 5941, 4403, 6561, 7308, 7885, 2908, 5892, 7401, 6466, 38878, 5804, 8585, 4160, 15789, 4157, 5836, 4964, 2273, 5746, 2891, 6411, 1641, 3335, 223, 5624, 2658, 25244, 6320, 6539, 1464, 4775, 198, 1937 +max_delta=3 + +[route] +layers = 326 ### + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=linear + +[yolo] +mask = 8,9,10,11 +anchors = 13,17, 31,25, 24,51, 61,45 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 800,800 +classes=80 +num=16 +#jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +resize=1.5 +scale_x_y = 1.05 +##iou_thresh=0.213 +#cls_normalizer=1.0 +#iou_normalizer=0.07 + +jitter=.1 +objectness_smooth=1 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 + +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + +counters_per_class = 26575, 7113, 44338, 8730, 5154, 6100, 4550, 10038, 10832, 13016, 1883, 1974, 1287, 9787, 10883, 4776, 5507, 6562, 9724, 8171, 5491, 1312, 5296, 5193, 8707, 11531, 12415, 6470, 6214, 2688, 6605, 2665, 6393, 9159, 3300, 3756, 5508, 6143, 4854, 24516, 7914, 20710, 5466, 7810, 6168, 14398, 9603, 5941, 4403, 6561, 7308, 7885, 2908, 5892, 7401, 6466, 38878, 5804, 8585, 4160, 15789, 4157, 5836, 4964, 2273, 5746, 2891, 6411, 1641, 3335, 223, 5624, 2658, 25244, 6320, 6539, 1464, 4775, 198, 1937 +max_delta=3 + +[route] +layers = 339 ### + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=linear + +[yolo] +mask = 12,13,14,15 +anchors = 13,17, 31,25, 24,51, 61,45 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 800,800 +classes=80 +num=16 +#jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +resize=1.5 +scale_x_y = 1.05 +##iou_thresh=0.213 +#cls_normalizer=1.0 +#iou_normalizer=0.07 + +jitter=.1 +objectness_smooth=1 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.1 + +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + +#counters_per_class = 26575, 7113, 44338, 8730, 5154, 6100, 4550, 10038, 10832, 13016, 1883, 1974, 1287, 9787, 10883, 4776, 5507, 6562, 9724, 8171, 5491, 1312, 5296, 5193, 8707, 11531, 12415, 6470, 6214, 2688, 6605, 2665, 6393, 9159, 3300, 3756, 5508, 6143, 4854, 24516, 7914, 20710, 5466, 7810, 6168, 14398, 9603, 5941, 4403, 6561, 7308, 7885, 2908, 5892, 7401, 6466, 38878, 5804, 8585, 4160, 15789, 4157, 5836, 4964, 2273, 5746, 2891, 6411, 1641, 3335, 223, 5624, 2658, 25244, 6320, 6539, 1464, 4775, 198, 1937 +max_delta=3 + +### End of YOLO ### \ No newline at end of file diff --git a/cfg/cspx-p7-mish-omega.cfg b/cfg/cspx-p7-mish-omega.cfg new file mode 100644 index 00000000000..93c4aefa94c --- /dev/null +++ b/cfg/cspx-p7-mish-omega.cfg @@ -0,0 +1,1459 @@ +[net] +# Training +batch=100 +subdivisions=2 + +label_smooth_eps=0.1 + +# Testing +# batch=1 +# subdivisions=1 + +height=320 +width=320 +channels=3 +min_crop=320 +max_crop=640 + +mosaic=1 +cutmix=1 + +burn_in=2000 +learning_rate=0.1 +policy=poly +power=4 +max_batches=1200000 +#max_batches=400000 +momentum=0.9 +decay=0.0005 + +angle=7 +hue=.1 +saturation=.75 +exposure=.75 +aspect=.75 + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=40 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-13 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-49 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-49 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-25 + +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-25 + +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-25 + +[convolutional] +batch_normalize=1 +filters=1600 +size=1 +stride=1 +pad=1 +activation=mish + +[avgpool] + +[convolutional] +filters=1000 +size=1 +stride=1 +pad=1 +activation=linear + +[softmax] +groups=1 \ No newline at end of file diff --git a/cfg/cspx-p7-mish_hp.cfg b/cfg/cspx-p7-mish_hp.cfg new file mode 100644 index 00000000000..209fa8f81cb --- /dev/null +++ b/cfg/cspx-p7-mish_hp.cfg @@ -0,0 +1,2638 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=16 +#width=1536 +#height=1536 +width=896 +height=896 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 +letter_box=1 + +### Start of Backbone ### + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=40 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-13 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-49 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-49 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-25 + +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-25 + +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-25 + +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=mish + +### End of backbone ### + +### Start of CSPSPP ### + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -13 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +### End of CSPSPP ### + +### Start of CSPPAN ### + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 180 ###P6 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 152 ###P5 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 124 ###P4 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 72 ###P3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1, 271 ###S4 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, 255 ###S5 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, 239 ###S6 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, 223 ###S7 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +### End of CSPPAN ### + +### Start of YOLO ### + +[route] +layers = 287 ### + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=320 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +#filters=340 +filters=170 +activation=linear + +#[route] +#layers=-1 + +[yolo] +mask = 0,1 +anchors = 13,17, 31,25, 24,51, 61,45 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 800,800 +classes=80 +num=16 +#jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +resize=1.5 +scale_x_y = 1.05 +##iou_thresh=0.213 +#cls_normalizer=1.0 +#iou_normalizer=0.07 + +jitter=.1 +#objectness_smooth=1 +##iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=4.0 + +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + +#counters_per_class = 26575, 7113, 44338, 8730, 5154, 6100, 4550, 10038, 10832, 13016, 1883, 1974, 1287, 9787, 10883, 4776, 5507, 6562, 9724, 8171, 5491, 1312, 5296, 5193, 8707, 11531, 12415, 6470, 6214, 2688, 6605, 2665, 6393, 9159, 3300, 3756, 5508, 6143, 4854, 24516, 7914, 20710, 5466, 7810, 6168, 14398, 9603, 5941, 4403, 6561, 7308, 7885, 2908, 5892, 7401, 6466, 38878, 5804, 8585, 4160, 15789, 4157, 5836, 4964, 2273, 5746, 2891, 6411, 1641, 3335, 223, 5624, 2658, 25244, 6320, 6539, 1464, 4775, 198, 1937 +#max_delta=3 + +[route] +layers = 300 ### + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=640 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +#filters=340 +filters=255 +activation=linear + +[yolo] +mask = 1,2,3 +anchors = 13,17, 31,25, 24,51, 61,45 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 800,800 +classes=80 +num=16 +#jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +resize=1.5 +scale_x_y = 1.05 +##iou_thresh=0.213 +#cls_normalizer=1.0 +#iou_normalizer=0.07 + +jitter=.1 +#objectness_smooth=1 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 + +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + +#counters_per_class = 26575, 7113, 44338, 8730, 5154, 6100, 4550, 10038, 10832, 13016, 1883, 1974, 1287, 9787, 10883, 4776, 5507, 6562, 9724, 8171, 5491, 1312, 5296, 5193, 8707, 11531, 12415, 6470, 6214, 2688, 6605, 2665, 6393, 9159, 3300, 3756, 5508, 6143, 4854, 24516, 7914, 20710, 5466, 7810, 6168, 14398, 9603, 5941, 4403, 6561, 7308, 7885, 2908, 5892, 7401, 6466, 38878, 5804, 8585, 4160, 15789, 4157, 5836, 4964, 2273, 5746, 2891, 6411, 1641, 3335, 223, 5624, 2658, 25244, 6320, 6539, 1464, 4775, 198, 1937 +#max_delta=3 + +[route] +layers = 313 ### + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=linear + +[yolo] +mask = 4,5,6,7 +anchors = 13,17, 31,25, 24,51, 61,45 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 800,800 +classes=80 +num=16 +#jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +resize=1.5 +scale_x_y = 1.05 +##iou_thresh=0.213 +#cls_normalizer=1.0 +#iou_normalizer=0.07 + +jitter=.1 +objectness_smooth=1 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.5 + +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + +counters_per_class = 26575, 7113, 44338, 8730, 5154, 6100, 4550, 10038, 10832, 13016, 1883, 1974, 1287, 9787, 10883, 4776, 5507, 6562, 9724, 8171, 5491, 1312, 5296, 5193, 8707, 11531, 12415, 6470, 6214, 2688, 6605, 2665, 6393, 9159, 3300, 3756, 5508, 6143, 4854, 24516, 7914, 20710, 5466, 7810, 6168, 14398, 9603, 5941, 4403, 6561, 7308, 7885, 2908, 5892, 7401, 6466, 38878, 5804, 8585, 4160, 15789, 4157, 5836, 4964, 2273, 5746, 2891, 6411, 1641, 3335, 223, 5624, 2658, 25244, 6320, 6539, 1464, 4775, 198, 1937 +max_delta=3 + +[route] +layers = 326 ### + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=linear + +[yolo] +mask = 8,9,10,11 +anchors = 13,17, 31,25, 24,51, 61,45 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 800,800 +classes=80 +num=16 +#jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +resize=1.5 +scale_x_y = 1.05 +##iou_thresh=0.213 +#cls_normalizer=1.0 +#iou_normalizer=0.07 + +jitter=.1 +objectness_smooth=1 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 + +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + +counters_per_class = 26575, 7113, 44338, 8730, 5154, 6100, 4550, 10038, 10832, 13016, 1883, 1974, 1287, 9787, 10883, 4776, 5507, 6562, 9724, 8171, 5491, 1312, 5296, 5193, 8707, 11531, 12415, 6470, 6214, 2688, 6605, 2665, 6393, 9159, 3300, 3756, 5508, 6143, 4854, 24516, 7914, 20710, 5466, 7810, 6168, 14398, 9603, 5941, 4403, 6561, 7308, 7885, 2908, 5892, 7401, 6466, 38878, 5804, 8585, 4160, 15789, 4157, 5836, 4964, 2273, 5746, 2891, 6411, 1641, 3335, 223, 5624, 2658, 25244, 6320, 6539, 1464, 4775, 198, 1937 +max_delta=3 + +[route] +layers = 339 ### + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=linear + +[yolo] +mask = 12,13,14,15 +anchors = 13,17, 31,25, 24,51, 61,45 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 800,800 +classes=80 +num=16 +#jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +resize=1.5 +scale_x_y = 1.05 +##iou_thresh=0.213 +#cls_normalizer=1.0 +#iou_normalizer=0.07 + +jitter=.1 +objectness_smooth=1 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.1 + +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + +#counters_per_class = 26575, 7113, 44338, 8730, 5154, 6100, 4550, 10038, 10832, 13016, 1883, 1974, 1287, 9787, 10883, 4776, 5507, 6562, 9724, 8171, 5491, 1312, 5296, 5193, 8707, 11531, 12415, 6470, 6214, 2688, 6605, 2665, 6393, 9159, 3300, 3756, 5508, 6143, 4854, 24516, 7914, 20710, 5466, 7810, 6168, 14398, 9603, 5941, 4403, 6561, 7308, 7885, 2908, 5892, 7401, 6466, 38878, 5804, 8585, 4160, 15789, 4157, 5836, 4964, 2273, 5746, 2891, 6411, 1641, 3335, 223, 5624, 2658, 25244, 6320, 6539, 1464, 4775, 198, 1937 +max_delta=3 + +### End of YOLO ### \ No newline at end of file From 14b196d4f2f73fb2f6f8c4019de9a0de00c5a27e Mon Sep 17 00:00:00 2001 From: Alexey Date: Wed, 4 Nov 2020 22:01:46 +0300 Subject: [PATCH 1128/1365] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index f754cd88222..ff19f3320d5 100644 --- a/README.md +++ b/README.md @@ -188,6 +188,7 @@ You can get cfg-files by path: `darknet/cfg/` * [WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4) * [maudzung/3D-YOLOv4](https://github.com/maudzung/Complex-YOLOv4-Pytorch) * [Tianxiaomo/pytorch-YOLOv4](https://github.com/Tianxiaomo/pytorch-YOLOv4) +* **ONNX** on Jetson for YOLOv4: https://developer.nvidia.com/blog/announcing-onnx-runtime-for-jetson/ * **TensorRT** YOLOv4 on TensorRT+tkDNN: https://github.com/ceccocats/tkDNN For YOLOv3 (-70% faster inference): [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) read [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf). [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) implemented yolov3-spp, yolov4, etc. * **Deepstream 5.0 / TensorRT for YOLOv4** https://github.com/NVIDIA-AI-IOT/yolov4_deepstream From 5ebcf63fcee73b2b94e3df71d7ead0a07ecc217c Mon Sep 17 00:00:00 2001 From: MarvinKlemp Date: Sun, 8 Nov 2020 16:30:21 +0100 Subject: [PATCH 1129/1365] Added a hint for installation with CUDA version >= 11 --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index ff19f3320d5..9a5b653e10e 100644 --- a/README.md +++ b/README.md @@ -303,6 +303,7 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c or use in such a way: `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov4.cfg yolov4.weights test.mp4` * `ZED_CAMERA=1` to build a library with ZED-3D-camera support (should be ZED SDK installed), then run `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov4.cfg yolov4.weights zed_camera` +* If you use a never version than CUDA 11 you further need to edit line 20 from Makefile and remove `-gencode arch=compute_30,code=sm_30 \` To run Darknet on Linux use examples from this article, just use `./darknet` instead of `darknet.exe`, i.e. use this command: `./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights` From 236205879dcc9dc01f0f5b220751ffcbfb665279 Mon Sep 17 00:00:00 2001 From: JaledMC Date: Mon, 9 Nov 2020 00:31:42 +0100 Subject: [PATCH 1130/1365] Add image_classification function to darknet_images Enable darknet classifier, without bbox detection --- darknet_images.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/darknet_images.py b/darknet_images.py index 0b07689e4c6..a06f44bf47f 100644 --- a/darknet_images.py +++ b/darknet_images.py @@ -135,6 +135,19 @@ def batch_detection(network, images, class_names, class_colors, return images, batch_predictions +def image_classification(image, network, class_names): + width = darknet.network_width(network) + height = darknet.network_height(network) + image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + image_resized = cv2.resize(image_rgb, (width, height), + interpolation=cv2.INTER_LINEAR) + darknet_image = darknet.make_image(width, height, 3) + darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes()) + detections = darknet.predict_image(network, darknet_image) + predictions = [(name, detections[idx]) for idx, name in enumerate(class_names)] + return sorted(predictions, key=lambda x: -x[1]) + + def convert2relative(image, bbox): """ YOLO format use relative coordinates for annotation From 682210bc722873f2d6d8fc6eed21dba3e70e020b Mon Sep 17 00:00:00 2001 From: JaledMC Date: Mon, 9 Nov 2020 00:36:36 +0100 Subject: [PATCH 1131/1365] Free image memory --- darknet_images.py | 1 + 1 file changed, 1 insertion(+) diff --git a/darknet_images.py b/darknet_images.py index a06f44bf47f..0f1fca9b7ae 100644 --- a/darknet_images.py +++ b/darknet_images.py @@ -145,6 +145,7 @@ def image_classification(image, network, class_names): darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes()) detections = darknet.predict_image(network, darknet_image) predictions = [(name, detections[idx]) for idx, name in enumerate(class_names)] + darknet.free_image(darknet_image) return sorted(predictions, key=lambda x: -x[1]) From 3d33bbfecc61376fb81a2ad10ea0f972b3850446 Mon Sep 17 00:00:00 2001 From: MarvinKlemp Date: Thu, 12 Nov 2020 10:07:54 +0100 Subject: [PATCH 1132/1365] generalized statement --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9a5b653e10e..f1e6ab4ec2a 100644 --- a/README.md +++ b/README.md @@ -303,7 +303,7 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c or use in such a way: `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov4.cfg yolov4.weights test.mp4` * `ZED_CAMERA=1` to build a library with ZED-3D-camera support (should be ZED SDK installed), then run `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov4.cfg yolov4.weights zed_camera` -* If you use a never version than CUDA 11 you further need to edit line 20 from Makefile and remove `-gencode arch=compute_30,code=sm_30 \` +* You also need to specify for which graphics card the code is generated. This is done by setting `ARCH=`. If you use a never version than CUDA 11 you further need to edit line 20 from Makefile and remove `-gencode arch=compute_30,code=sm_30 \` as Kepler GPU support was dropped in CUDA 11. You can also drop the general `ARCH=` and just uncomment `ARCH=` for your graphics card. To run Darknet on Linux use examples from this article, just use `./darknet` instead of `darknet.exe`, i.e. use this command: `./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights` From fe8e9edd29488508b8125bfcb061ed19f87d8a9d Mon Sep 17 00:00:00 2001 From: MarvinKlemp Date: Thu, 12 Nov 2020 10:12:50 +0100 Subject: [PATCH 1133/1365] Added Ampere Gencode --- Makefile | 3 +++ 1 file changed, 3 insertions(+) diff --git a/Makefile b/Makefile index e6a4ad73884..414ef099c26 100644 --- a/Makefile +++ b/Makefile @@ -49,6 +49,9 @@ OS := $(shell uname) # For Jetson Tx2 or Drive-PX2 uncomment: # ARCH= -gencode arch=compute_62,code=[sm_62,compute_62] +# For Tesla GA10x cards, RTX 3090, RTX 3080, RTX 3070, RTX A6000, RTX A40 uncomment: +# ARCH= -gencode arch=compute_86,code=[sm_86,compute_86] + VPATH=./src/ EXEC=darknet From c7e3e2ee9ec2d8fff447d83736e13bce0938015f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 16 Nov 2020 15:17:18 +0300 Subject: [PATCH 1134/1365] Fix nms for valid, coco 2014 -> 2017 --- src/box.c | 2 +- src/demo.c | 8 +++++++- src/detector.c | 2 +- src/utils.c | 10 +++++----- 4 files changed, 14 insertions(+), 8 deletions(-) diff --git a/src/box.c b/src/box.c index 8b8ff3bf3e4..201ab13341d 100644 --- a/src/box.c +++ b/src/box.c @@ -913,7 +913,7 @@ void diounms_sort(detection *dets, int total, int classes, float thresh, NMS_KIN */ dets[j].prob[k] = 0; } - else if (box_iou(a, b) > thresh && nms_kind == GREEDY_NMS) { + else if (box_diou(a, b) > thresh && nms_kind == GREEDY_NMS) { dets[j].prob[k] = 0; } else { diff --git a/src/demo.c b/src/demo.c index 5fff2360bbd..604b6990bcf 100644 --- a/src/demo.c +++ b/src/demo.c @@ -107,7 +107,13 @@ void *detect_in_thread(void *ptr) dets = get_network_boxes(&net, get_width_mat(in_img), get_height_mat(in_img), demo_thresh, demo_thresh, 0, 1, &nboxes, 1); // letter box else dets = get_network_boxes(&net, net.w, net.h, demo_thresh, demo_thresh, 0, 1, &nboxes, 0); // resized - + + //const float nms = .45; + //if (nms) { + // if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms); + // else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms); + //} + custom_atomic_store_int(&run_detect_in_thread, 0); } diff --git a/src/detector.c b/src/detector.c index 52511fb0dfb..5d284e02d9a 100644 --- a/src/detector.c +++ b/src/detector.c @@ -714,7 +714,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out int t; float thresh = .001; - float nms = .45; + float nms = .6; int nthreads = 4; if (m < 4) nthreads = m; diff --git a/src/utils.c b/src/utils.c index a7cb19d8d44..6e004acdb41 100644 --- a/src/utils.c +++ b/src/utils.c @@ -260,11 +260,11 @@ void find_replace_extension(char *str, char *orig, char *rep, char *output) void replace_image_to_label(const char* input_path, char* output_path) { - find_replace(input_path, "/images/train2014/", "/labels/train2014/", output_path); // COCO - find_replace(output_path, "/images/val2014/", "/labels/val2014/", output_path); // COCO + find_replace(input_path, "/images/train2017/", "/labels/train2017/", output_path); // COCO + find_replace(output_path, "/images/val2017/", "/labels/val2017/", output_path); // COCO find_replace(output_path, "/JPEGImages/", "/labels/", output_path); // PascalVOC - find_replace(output_path, "\\images\\train2014\\", "\\labels\\train2014\\", output_path); // COCO - find_replace(output_path, "\\images\\val2014\\", "\\labels\\val2014\\", output_path); // COCO + find_replace(output_path, "\\images\\train2017\\", "\\labels\\train2017\\", output_path); // COCO + find_replace(output_path, "\\images\\val2017\\", "\\labels\\val2017\\", output_path); // COCO find_replace(output_path, "\\JPEGImages\\", "\\labels\\", output_path); // PascalVOC //find_replace(output_path, "/images/", "/labels/", output_path); // COCO //find_replace(output_path, "/VOC2007/JPEGImages/", "/VOC2007/labels/", output_path); // PascalVOC @@ -1040,4 +1040,4 @@ unsigned long custom_hash(char *str) hash = ((hash << 5) + hash) + c; /* hash * 33 + c */ return hash; -} \ No newline at end of file +} From 8c9c5171891ea92b0cbf5c7fddf935df0b854540 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 16 Nov 2020 16:29:06 +0300 Subject: [PATCH 1135/1365] [yolo] new_coords=1 --- include/darknet.h | 1 + src/parser.c | 1 + src/yolo_layer.c | 58 ++++++++++++++++++++++++++++++++++------------- 3 files changed, 44 insertions(+), 16 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 62c0f9a2ae5..bec90bef471 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -419,6 +419,7 @@ struct layer { float scale_x_y; int objectness_smooth; + int new_coords; float max_delta; float uc_normalizer; float iou_normalizer; diff --git a/src/parser.c b/src/parser.c index 66c185518d7..c500cdbb620 100644 --- a/src/parser.c +++ b/src/parser.c @@ -461,6 +461,7 @@ layer parse_yolo(list *options, size_params params) l.label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1); l.objectness_smooth = option_find_int_quiet(options, "objectness_smooth", 0); + l.new_coords = option_find_int_quiet(options, "new_coords", 0); l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75); l.obj_normalizer = option_find_float_quiet(options, "obj_normalizer", 1); l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index d46b824f713..8693342aafb 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -129,7 +129,7 @@ void resize_yolo_layer(layer *l, int w, int h) #endif } -box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, int stride) +box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, int stride, int new_coords) { box b; // ln - natural logarithm (base = e) @@ -137,10 +137,18 @@ box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw // y` = t.y * lh - i; // y = ln(y`/(1-y`)) // y - output of previous conv-layer // w = ln(t.w * net.w / anchors_w); // w - output of previous conv-layer // h = ln(t.h * net.h / anchors_h); // h - output of previous conv-layer - b.x = (i + x[index + 0*stride]) / lw; - b.y = (j + x[index + 1*stride]) / lh; - b.w = exp(x[index + 2*stride]) * biases[2*n] / w; - b.h = exp(x[index + 3*stride]) * biases[2*n+1] / h; + if (new_coords) { + b.x = (i + x[index + 0 * stride] * 2 - 0.5) / lw; + b.y = (j + x[index + 1 * stride] * 2 - 0.5) / lh; + b.w = x[index + 2 * stride] * x[index + 2 * stride] * 4 * biases[2 * n] / w; + b.h = x[index + 3 * stride] * x[index + 3 * stride] * 4 * biases[2 * n + 1] / h; + } + else { + b.x = (i + x[index + 0*stride]) / lw; + b.y = (j + x[index + 1*stride]) / lh; + b.w = exp(x[index + 2*stride]) * biases[2*n] / w; + b.h = exp(x[index + 3*stride]) * biases[2*n+1] / h; + } return b; } @@ -163,7 +171,7 @@ static inline float clip_value(float val, const float max_val) return val; } -ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss, int accumulate, float max_delta, int *rewritten_bbox) +ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss, int accumulate, float max_delta, int *rewritten_bbox, int new_coords) { if (delta[index + 0 * stride] || delta[index + 1 * stride] || delta[index + 2 * stride] || delta[index + 3 * stride]) { (*rewritten_bbox)++; @@ -173,7 +181,7 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, // i - step in layer width // j - step in layer height // Returns a box in absolute coordinates - box pred = get_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride); + box pred = get_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride, new_coords); all_ious.iou = box_iou(pred, truth); all_ious.giou = box_giou(pred, truth); all_ious.diou = box_diou(pred, truth); @@ -188,6 +196,13 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, float tw = log(truth.w*w / biases[2 * n]); float th = log(truth.h*h / biases[2 * n + 1]); + if (new_coords) { + tx = (truth.x*lw - i + 0.5) / 2; + ty = (truth.y*lh - j + 0.5) / 2; + tw = sqrt(truth.w*w / (4 * biases[2 * n])); + th = sqrt(truth.h*h / (4 * biases[2 * n + 1])); + } + //printf(" tx = %f, ty = %f, tw = %f, th = %f \n", tx, ty, tw, th); //printf(" x = %f, y = %f, w = %f, h = %f \n", x[index + 0 * stride], x[index + 1 * stride], x[index + 2 * stride], x[index + 3 * stride]); @@ -216,8 +231,14 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, float dh = all_ious.dx_iou.dr; // predict exponential, apply gradient of e^delta_t ONLY for w,h - dw *= exp(x[index + 2 * stride]); - dh *= exp(x[index + 3 * stride]); + if (new_coords) { + dw *= 8 * x[index + 2 * stride]; + dh *= 8 * x[index + 3 * stride]; + } + else { + dw *= exp(x[index + 2 * stride]); + dh *= exp(x[index + 3 * stride]); + } // normalize iou weight dx *= iou_normalizer; @@ -389,7 +410,7 @@ void forward_yolo_layer(const layer l, network_state state) const int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4); const int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); const int stride = l.w*l.h; - box pred = get_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w*l.h); + box pred = get_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w*l.h, l.new_coords); float best_match_iou = 0; int best_match_t = 0; float best_iou = 0; @@ -467,7 +488,7 @@ void forward_yolo_layer(const layer l, network_state state) const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; if (l.objectness_smooth) l.delta[class_index + stride*class_id] = class_multiplier * (iou_multiplier - l.output[class_index + stride*class_id]); box truth = float_to_box_stride(state.truth + best_t*l.truth_size + b*l.truths, 1); - delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox); + delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords); (*state.net.total_bbox)++; } } @@ -510,7 +531,7 @@ void forward_yolo_layer(const layer l, network_state state) int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; - ious all_ious = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox); + ious all_ious = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords); (*state.net.total_bbox)++; const int truth_in_index = t*l.truth_size + b*l.truths + 5; @@ -568,7 +589,7 @@ void forward_yolo_layer(const layer l, network_state state) int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; - ious all_ious = delta_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox); + ious all_ious = delta_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords); (*state.net.total_bbox)++; // range is 0 <= 1 @@ -850,7 +871,7 @@ int get_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, if (objectness > thresh) { //printf("\n objectness = %f, thresh = %f, i = %d, n = %d \n", objectness, thresh, i, n); int box_index = entry_index(l, 0, n*l.w*l.h + i, 0); - dets[count].bbox = get_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h); + dets[count].bbox = get_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h, l.new_coords); dets[count].objectness = objectness; dets[count].classes = l.classes; if (l.embedding_output) { @@ -886,7 +907,7 @@ int get_yolo_detections_batch(layer l, int w, int h, int netw, int neth, float t if (objectness > thresh) { //printf("\n objectness = %f, thresh = %f, i = %d, n = %d \n", objectness, thresh, i, n); int box_index = entry_index(l, batch, n*l.w*l.h + i, 0); - dets[count].bbox = get_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h); + dets[count].bbox = get_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h, l.new_coords); dets[count].objectness = objectness; dets[count].classes = l.classes; if (l.embedding_output) { @@ -925,7 +946,12 @@ void forward_yolo_layer_gpu(const layer l, network_state state) // x = ln(y/(1-y)) // ln - natural logarithm (base = e) // if(y->1) x -> inf // if(y->0) x -> -inf - activate_array_ongpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC); // x,y + if (l.new_coords) { + activate_array_ongpu(l.output_gpu + index, 4 * l.w*l.h, LOGISTIC); // x,y + } + else { + activate_array_ongpu(l.output_gpu + index, 2 * l.w*l.h, LOGISTIC); // x,y + } if (l.scale_x_y != 1) scal_add_ongpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output_gpu + index, 1); // scale x,y index = entry_index(l, b, n*l.w*l.h, 4); activate_array_ongpu(l.output_gpu + index, (1+l.classes)*l.w*l.h, LOGISTIC); // classes and objectness From 17cf4bfbfc49a588e1ef3da5c5d706f72cd1cb10 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 16 Nov 2020 20:36:02 +0300 Subject: [PATCH 1136/1365] added yolov4x-mish.cfg --- build/darknet/x64/cfg/yolov4x-mish.cfg | 1427 ++++++++++++++++++++++++ 1 file changed, 1427 insertions(+) create mode 100644 build/darknet/x64/cfg/yolov4x-mish.cfg diff --git a/build/darknet/x64/cfg/yolov4x-mish.cfg b/build/darknet/x64/cfg/yolov4x-mish.cfg new file mode 100644 index 00000000000..89f2564cb43 --- /dev/null +++ b/build/darknet/x64/cfg/yolov4x-mish.cfg @@ -0,0 +1,1427 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=672 +height=672 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.00261 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=40 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-13 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-34 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-34 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-19 + +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=mish + +########################## 6 0 6 6 3 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -15 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 94 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 57 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +########################## + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +objectness_smooth=0 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=4.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1, -22 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1,-8 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -55 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1,-8 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 From 5c046bea96f560865aac0150c67ebde6b3aabfce Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 18 Nov 2020 04:06:28 +0300 Subject: [PATCH 1137/1365] minor fix --- build/darknet/x64/cfg/yolov4x-mish.cfg | 1 + cfg/yolov4x-mish.cfg | 1428 ++++++++++++++++++++++++ src/yolo_layer.c | 2 +- 3 files changed, 1430 insertions(+), 1 deletion(-) create mode 100644 cfg/yolov4x-mish.cfg diff --git a/build/darknet/x64/cfg/yolov4x-mish.cfg b/build/darknet/x64/cfg/yolov4x-mish.cfg index 89f2564cb43..86840e46efd 100644 --- a/build/darknet/x64/cfg/yolov4x-mish.cfg +++ b/build/darknet/x64/cfg/yolov4x-mish.cfg @@ -1131,6 +1131,7 @@ size=1 stride=1 pad=1 activation=mish +stopbackward=800 ########################## diff --git a/cfg/yolov4x-mish.cfg b/cfg/yolov4x-mish.cfg new file mode 100644 index 00000000000..86840e46efd --- /dev/null +++ b/cfg/yolov4x-mish.cfg @@ -0,0 +1,1428 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=672 +height=672 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.00261 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=40 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-13 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-34 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-34 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-19 + +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=mish + +########################## 6 0 6 6 3 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -15 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 94 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 57 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish +stopbackward=800 + +########################## + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +objectness_smooth=0 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=4.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1, -22 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1,-8 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -55 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1,-8 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 8693342aafb..f49267c456b 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -947,7 +947,7 @@ void forward_yolo_layer_gpu(const layer l, network_state state) // if(y->1) x -> inf // if(y->0) x -> -inf if (l.new_coords) { - activate_array_ongpu(l.output_gpu + index, 4 * l.w*l.h, LOGISTIC); // x,y + activate_array_ongpu(l.output_gpu + index, 4 * l.w*l.h, LOGISTIC); // x,y,w,h } else { activate_array_ongpu(l.output_gpu + index, 2 * l.w*l.h, LOGISTIC); // x,y From 9db8609e7a07409fe31bf484fb08db385350a5f9 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 20 Nov 2020 04:25:41 +0300 Subject: [PATCH 1138/1365] Added cspx-p7-mish.cfg --- build/darknet/x64/cfg/cspx-p7-mish.cfg | 2604 ++++++++++++++++++++++++ cfg/cspx-p7-mish.cfg | 2604 ++++++++++++++++++++++++ 2 files changed, 5208 insertions(+) create mode 100644 build/darknet/x64/cfg/cspx-p7-mish.cfg create mode 100644 cfg/cspx-p7-mish.cfg diff --git a/build/darknet/x64/cfg/cspx-p7-mish.cfg b/build/darknet/x64/cfg/cspx-p7-mish.cfg new file mode 100644 index 00000000000..b5713391bcc --- /dev/null +++ b/build/darknet/x64/cfg/cspx-p7-mish.cfg @@ -0,0 +1,2604 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=1536 +height=1536 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +steps=400000,450000 +#max_batches = 770500 +#steps=700000,750000 +policy=steps +scales=.1,.1 + +mosaic=1 +letter_box=1 + +### Start of Backbone ### + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=40 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-13 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-49 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-49 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-25 + +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-25 + +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-25 + +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=mish + +### End of backbone ### + +### Start of CSPSPP ### + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -13 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +### End of CSPSPP ### + +### Start of CSPPAN ### + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 180 ###P6 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 152 ###P5 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 124 ###P4 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 72 ###P3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1, 271 ###S4 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, 255 ###S5 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, 239 ###S6 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, 223 ###S7 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +### End of CSPPAN ### + +### Start of YOLO ### + +[route] +layers = 287 ### + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=320 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=linear + + +[yolo] +mask = 0,1,2,3 +anchors = 13,17, 22,25, 27,66, 55,41, 57,88, 112,69, 69,177, 136,138, 136,138, 287,114, 134,275, 268,248, 268,248, 232,504, 445,416, 640,640, 812,393, 477,808, 1070,908, 1408,1408 +classes=80 +num=16 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +scale_x_y = 1.05 +jitter=.1 +objectness_smooth=1 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=4.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +max_delta=40 +new_coords=1 + +[route] +layers = 300 ### + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=640 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=linear + +[yolo] +mask = 4,5,6,7 +anchors = 13,17, 22,25, 27,66, 55,41, 57,88, 112,69, 69,177, 136,138, 136,138, 287,114, 134,275, 268,248, 268,248, 232,504, 445,416, 640,640, 812,393, 477,808, 1070,908, 1408,1408 +classes=80 +num=16 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +scale_x_y = 1.05 +jitter=.1 +objectness_smooth=1 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +max_delta=40 +new_coords=1 + +[route] +layers = 313 ### + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=linear + +[yolo] +mask = 8,9,10,11 +anchors = 13,17, 22,25, 27,66, 55,41, 57,88, 112,69, 69,177, 136,138, 136,138, 287,114, 134,275, 268,248, 268,248, 232,504, 445,416, 640,640, 812,393, 477,808, 1070,908, 1408,1408 +classes=80 +num=16 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +scale_x_y = 1.05 +jitter=.1 +objectness_smooth=1 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.5 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +max_delta=40 +new_coords=1 + +[route] +layers = 326 ### + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=linear + +[yolo] +mask = 12,13,14,15 +anchors = 13,17, 22,25, 27,66, 55,41, 57,88, 112,69, 69,177, 136,138, 136,138, 287,114, 134,275, 268,248, 268,248, 232,504, 445,416, 640,640, 812,393, 477,808, 1070,908, 1408,1408 +classes=80 +num=16 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +scale_x_y = 1.05 +jitter=.1 +objectness_smooth=1 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +max_delta=40 +new_coords=1 + +[route] +layers = 339 ### + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=linear + +[yolo] +mask = 16,17,18,19 +anchors = 13,17, 22,25, 27,66, 55,41, 57,88, 112,69, 69,177, 136,138, 136,138, 287,114, 134,275, 268,248, 268,248, 232,504, 445,416, 640,640, 812,393, 477,808, 1070,908, 1408,1408 +classes=80 +num=16 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +scale_x_y = 1.05 +jitter=.1 +objectness_smooth=1 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.1 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +max_delta=40 +new_coords=1 + +### End of YOLO ### \ No newline at end of file diff --git a/cfg/cspx-p7-mish.cfg b/cfg/cspx-p7-mish.cfg new file mode 100644 index 00000000000..b5713391bcc --- /dev/null +++ b/cfg/cspx-p7-mish.cfg @@ -0,0 +1,2604 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=1536 +height=1536 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +steps=400000,450000 +#max_batches = 770500 +#steps=700000,750000 +policy=steps +scales=.1,.1 + +mosaic=1 +letter_box=1 + +### Start of Backbone ### + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=40 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-13 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-49 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-49 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-25 + +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-25 + +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-25 + +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=mish + +### End of backbone ### + +### Start of CSPSPP ### + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -13 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +### End of CSPSPP ### + +### Start of CSPPAN ### + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 180 ###P6 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 152 ###P5 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 124 ###P4 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 72 ###P3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1, 271 ###S4 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, 255 ###S5 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, 239 ###S6 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, 223 ###S7 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +### End of CSPPAN ### + +### Start of YOLO ### + +[route] +layers = 287 ### + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=320 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=linear + + +[yolo] +mask = 0,1,2,3 +anchors = 13,17, 22,25, 27,66, 55,41, 57,88, 112,69, 69,177, 136,138, 136,138, 287,114, 134,275, 268,248, 268,248, 232,504, 445,416, 640,640, 812,393, 477,808, 1070,908, 1408,1408 +classes=80 +num=16 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +scale_x_y = 1.05 +jitter=.1 +objectness_smooth=1 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=4.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +max_delta=40 +new_coords=1 + +[route] +layers = 300 ### + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=640 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=linear + +[yolo] +mask = 4,5,6,7 +anchors = 13,17, 22,25, 27,66, 55,41, 57,88, 112,69, 69,177, 136,138, 136,138, 287,114, 134,275, 268,248, 268,248, 232,504, 445,416, 640,640, 812,393, 477,808, 1070,908, 1408,1408 +classes=80 +num=16 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +scale_x_y = 1.05 +jitter=.1 +objectness_smooth=1 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +max_delta=40 +new_coords=1 + +[route] +layers = 313 ### + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=linear + +[yolo] +mask = 8,9,10,11 +anchors = 13,17, 22,25, 27,66, 55,41, 57,88, 112,69, 69,177, 136,138, 136,138, 287,114, 134,275, 268,248, 268,248, 232,504, 445,416, 640,640, 812,393, 477,808, 1070,908, 1408,1408 +classes=80 +num=16 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +scale_x_y = 1.05 +jitter=.1 +objectness_smooth=1 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.5 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +max_delta=40 +new_coords=1 + +[route] +layers = 326 ### + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=linear + +[yolo] +mask = 12,13,14,15 +anchors = 13,17, 22,25, 27,66, 55,41, 57,88, 112,69, 69,177, 136,138, 136,138, 287,114, 134,275, 268,248, 268,248, 232,504, 445,416, 640,640, 812,393, 477,808, 1070,908, 1408,1408 +classes=80 +num=16 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +scale_x_y = 1.05 +jitter=.1 +objectness_smooth=1 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +max_delta=40 +new_coords=1 + +[route] +layers = 339 ### + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=linear + +[yolo] +mask = 16,17,18,19 +anchors = 13,17, 22,25, 27,66, 55,41, 57,88, 112,69, 69,177, 136,138, 136,138, 287,114, 134,275, 268,248, 268,248, 232,504, 445,416, 640,640, 812,393, 477,808, 1070,908, 1408,1408 +classes=80 +num=16 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +scale_x_y = 1.05 +jitter=.1 +objectness_smooth=1 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.1 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +max_delta=40 +new_coords=1 + +### End of YOLO ### \ No newline at end of file From d82cf7e684356c4bd5b7f5f384779f9f8434afbc Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 20 Nov 2020 18:45:30 +0300 Subject: [PATCH 1139/1365] max_delta=20 for yolov4x-mish.cfg --- build/darknet/x64/cfg/yolov4x-mish.cfg | 3 +++ cfg/yolov4x-mish.cfg | 3 +++ 2 files changed, 6 insertions(+) diff --git a/build/darknet/x64/cfg/yolov4x-mish.cfg b/build/darknet/x64/cfg/yolov4x-mish.cfg index 86840e46efd..39629ee2a42 100644 --- a/build/darknet/x64/cfg/yolov4x-mish.cfg +++ b/build/darknet/x64/cfg/yolov4x-mish.cfg @@ -1170,6 +1170,7 @@ iou_loss=ciou nms_kind=diounms beta_nms=0.6 new_coords=1 +max_delta=20 [route] layers = -4 @@ -1298,6 +1299,7 @@ iou_loss=ciou nms_kind=diounms beta_nms=0.6 new_coords=1 +max_delta=20 [route] layers = -4 @@ -1426,3 +1428,4 @@ iou_loss=ciou nms_kind=diounms beta_nms=0.6 new_coords=1 +max_delta=20 diff --git a/cfg/yolov4x-mish.cfg b/cfg/yolov4x-mish.cfg index 86840e46efd..39629ee2a42 100644 --- a/cfg/yolov4x-mish.cfg +++ b/cfg/yolov4x-mish.cfg @@ -1170,6 +1170,7 @@ iou_loss=ciou nms_kind=diounms beta_nms=0.6 new_coords=1 +max_delta=20 [route] layers = -4 @@ -1298,6 +1299,7 @@ iou_loss=ciou nms_kind=diounms beta_nms=0.6 new_coords=1 +max_delta=20 [route] layers = -4 @@ -1426,3 +1428,4 @@ iou_loss=ciou nms_kind=diounms beta_nms=0.6 new_coords=1 +max_delta=20 From d18e22ae1bd46e57618af925b56eb187d4485de9 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 20 Nov 2020 21:14:31 +0300 Subject: [PATCH 1140/1365] fix new_coords for CPU --- src/yolo_layer.c | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/src/yolo_layer.c b/src/yolo_layer.c index f49267c456b..776386b5a90 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -371,7 +371,12 @@ void forward_yolo_layer(const layer l, network_state state) for (b = 0; b < l.batch; ++b) { for (n = 0; n < l.n; ++n) { int index = entry_index(l, b, n*l.w*l.h, 0); - activate_array(l.output + index, 2 * l.w*l.h, LOGISTIC); // x,y, + if (l.new_coords) { + activate_array(l.output + index, 4 * l.w*l.h, LOGISTIC); // x,y,w,h + } + else { + activate_array(l.output + index, 2 * l.w*l.h, LOGISTIC); // x,y, + } scal_add_cpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale x,y index = entry_index(l, b, n*l.w*l.h, 4); activate_array(l.output + index, (1 + l.classes)*l.w*l.h, LOGISTIC); From be906dfa0e1d24f5ba61963d16dd0dd00b32f317 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 21 Nov 2020 05:51:31 +0300 Subject: [PATCH 1141/1365] Minor fix --- build/darknet/x64/cfg/yolov4x-mish.cfg | 4 ++-- cfg/yolov4x-mish.cfg | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/build/darknet/x64/cfg/yolov4x-mish.cfg b/build/darknet/x64/cfg/yolov4x-mish.cfg index 39629ee2a42..a7e75cf793d 100644 --- a/build/darknet/x64/cfg/yolov4x-mish.cfg +++ b/build/darknet/x64/cfg/yolov4x-mish.cfg @@ -5,8 +5,8 @@ # Training batch=64 subdivisions=8 -width=672 -height=672 +width=640 +height=640 channels=3 momentum=0.949 decay=0.0005 diff --git a/cfg/yolov4x-mish.cfg b/cfg/yolov4x-mish.cfg index 39629ee2a42..a7e75cf793d 100644 --- a/cfg/yolov4x-mish.cfg +++ b/cfg/yolov4x-mish.cfg @@ -5,8 +5,8 @@ # Training batch=64 subdivisions=8 -width=672 -height=672 +width=640 +height=640 channels=3 momentum=0.949 decay=0.0005 From d2703b0ecfe5bd8a93621b65cc62b1be93e7cecd Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 23 Nov 2020 19:55:26 +0300 Subject: [PATCH 1142/1365] optimized_memory=1, learning_rate=0.001, max_delta=20, 5, 2 for yolov4x-mish.cfg --- build/darknet/darknet.vcxproj | 6 +++--- build/darknet/x64/cfg/yolov4x-mish.cfg | 8 +++++--- cfg/yolov4x-mish.cfg | 8 +++++--- src/utils.c | 6 ++++++ 4 files changed, 19 insertions(+), 9 deletions(-) diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj index 824fb57e96e..5282de26d91 100644 --- a/build/darknet/darknet.vcxproj +++ b/build/darknet/darknet.vcxproj @@ -52,7 +52,7 @@ - + @@ -153,7 +153,7 @@ 64 - compute_35,sm_35;compute_75,sm_75 + compute_35,sm_35;compute_86,sm_86 @@ -302,6 +302,6 @@ - + \ No newline at end of file diff --git a/build/darknet/x64/cfg/yolov4x-mish.cfg b/build/darknet/x64/cfg/yolov4x-mish.cfg index a7e75cf793d..0e9fb44132d 100644 --- a/build/darknet/x64/cfg/yolov4x-mish.cfg +++ b/build/darknet/x64/cfg/yolov4x-mish.cfg @@ -15,7 +15,7 @@ saturation = 1.5 exposure = 1.5 hue=.1 -learning_rate=0.00261 +learning_rate=0.001 burn_in=1000 max_batches = 500500 policy=steps @@ -26,6 +26,8 @@ mosaic=1 letter_box=1 +optimized_memory=1 + [convolutional] batch_normalize=1 filters=32 @@ -1299,7 +1301,7 @@ iou_loss=ciou nms_kind=diounms beta_nms=0.6 new_coords=1 -max_delta=20 +max_delta=5 [route] layers = -4 @@ -1428,4 +1430,4 @@ iou_loss=ciou nms_kind=diounms beta_nms=0.6 new_coords=1 -max_delta=20 +max_delta=2 diff --git a/cfg/yolov4x-mish.cfg b/cfg/yolov4x-mish.cfg index a7e75cf793d..0e9fb44132d 100644 --- a/cfg/yolov4x-mish.cfg +++ b/cfg/yolov4x-mish.cfg @@ -15,7 +15,7 @@ saturation = 1.5 exposure = 1.5 hue=.1 -learning_rate=0.00261 +learning_rate=0.001 burn_in=1000 max_batches = 500500 policy=steps @@ -26,6 +26,8 @@ mosaic=1 letter_box=1 +optimized_memory=1 + [convolutional] batch_normalize=1 filters=32 @@ -1299,7 +1301,7 @@ iou_loss=ciou nms_kind=diounms beta_nms=0.6 new_coords=1 -max_delta=20 +max_delta=5 [route] layers = -4 @@ -1428,4 +1430,4 @@ iou_loss=ciou nms_kind=diounms beta_nms=0.6 new_coords=1 -max_delta=20 +max_delta=2 diff --git a/src/utils.c b/src/utils.c index 6e004acdb41..fe5c2062148 100644 --- a/src/utils.c +++ b/src/utils.c @@ -265,6 +265,12 @@ void replace_image_to_label(const char* input_path, char* output_path) find_replace(output_path, "/JPEGImages/", "/labels/", output_path); // PascalVOC find_replace(output_path, "\\images\\train2017\\", "\\labels\\train2017\\", output_path); // COCO find_replace(output_path, "\\images\\val2017\\", "\\labels\\val2017\\", output_path); // COCO + + find_replace(output_path, "\\images\\train2014\\", "\\labels\\train2014\\", output_path); // COCO + find_replace(output_path, "\\images\\val2014\\", "\\labels\\val2014\\", output_path); // COCO + find_replace(output_path, "/images/train2014/", "/labels/train2014/", output_path); // COCO + find_replace(output_path, "/images/val2014/", "/labels/val2014/", output_path); // COCO + find_replace(output_path, "\\JPEGImages\\", "\\labels\\", output_path); // PascalVOC //find_replace(output_path, "/images/", "/labels/", output_path); // COCO //find_replace(output_path, "/VOC2007/JPEGImages/", "/VOC2007/labels/", output_path); // PascalVOC From f056fc3b6a11528fa0522a468eca1e909b7004b7 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 23 Nov 2020 23:32:55 +0300 Subject: [PATCH 1143/1365] Speedup [yolo] layer --- include/darknet.h | 1 + src/parser.c | 1 + src/yolo_layer.c | 384 +++++++++++++++++++++++++++++----------------- 3 files changed, 246 insertions(+), 140 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index bec90bef471..bb9f2dcd539 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -420,6 +420,7 @@ struct layer { float scale_x_y; int objectness_smooth; int new_coords; + int show_details; float max_delta; float uc_normalizer; float iou_normalizer; diff --git a/src/parser.c b/src/parser.c index c500cdbb620..160cffdd52d 100644 --- a/src/parser.c +++ b/src/parser.c @@ -454,6 +454,7 @@ layer parse_yolo(list *options, size_params params) } //assert(l.outputs == params.inputs); + l.show_details = option_find_int_quiet(options, "show_details", 0); l.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10 char *cpc = option_find_str(options, "counters_per_class", 0); l.classes_multipliers = get_classes_multipliers(cpc, classes, l.max_delta); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 776386b5a90..8644ae45701 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -135,8 +135,8 @@ box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw // ln - natural logarithm (base = e) // x` = t.x * lw - i; // x = ln(x`/(1-x`)) // x - output of previous conv-layer // y` = t.y * lh - i; // y = ln(y`/(1-y`)) // y - output of previous conv-layer - // w = ln(t.w * net.w / anchors_w); // w - output of previous conv-layer - // h = ln(t.h * net.h / anchors_h); // h - output of previous conv-layer + // w = ln(t.w * net.w / anchors_w); // w - output of previous conv-layer + // h = ln(t.h * net.h / anchors_h); // h - output of previous conv-layer if (new_coords) { b.x = (i + x[index + 0 * stride] * 2 - 0.5) / lw; b.y = (j + x[index + 1 * stride] * 2 - 0.5) / lh; @@ -144,10 +144,10 @@ box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw b.h = x[index + 3 * stride] * x[index + 3 * stride] * 4 * biases[2 * n + 1] / h; } else { - b.x = (i + x[index + 0*stride]) / lw; - b.y = (j + x[index + 1*stride]) / lh; - b.w = exp(x[index + 2*stride]) * biases[2*n] / w; - b.h = exp(x[index + 3*stride]) * biases[2*n+1] / h; + b.x = (i + x[index + 0 * stride]) / lw; + b.y = (j + x[index + 1 * stride]) / lh; + b.w = exp(x[index + 2 * stride]) * biases[2 * n] / w; + b.h = exp(x[index + 3 * stride]) * biases[2 * n + 1] / h; } return b; } @@ -362,68 +362,60 @@ static int entry_index(layer l, int batch, int location, int entry) return batch*l.outputs + n*l.w*l.h*(4+l.classes+1) + entry*l.w*l.h + loc; } -void forward_yolo_layer(const layer l, network_state state) -{ - int i, j, b, t, n; - memcpy(l.output, state.input, l.outputs*l.batch * sizeof(float)); +typedef struct { + layer l; + network_state state; + int b; -#ifndef GPU - for (b = 0; b < l.batch; ++b) { - for (n = 0; n < l.n; ++n) { - int index = entry_index(l, b, n*l.w*l.h, 0); - if (l.new_coords) { - activate_array(l.output + index, 4 * l.w*l.h, LOGISTIC); // x,y,w,h - } - else { - activate_array(l.output + index, 2 * l.w*l.h, LOGISTIC); // x,y, - } - scal_add_cpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale x,y - index = entry_index(l, b, n*l.w*l.h, 4); - activate_array(l.output + index, (1 + l.classes)*l.w*l.h, LOGISTIC); - } - } -#endif + float tot_iou; + int count; + int class_count; +} train_yolo_args; - // delta is zeroed - memset(l.delta, 0, l.outputs * l.batch * sizeof(float)); - if (!state.train) return; +void process_batch(train_yolo_args* ptr) +{ + { + train_yolo_args *args = (train_yolo_args*)ptr; + const layer l = args->l; + network_state state = args->state; + int b = args->b; + + int i, j, t, n; + + //printf(" b = %d \n", b, b); + + //float tot_iou = 0; + float tot_giou = 0; + float tot_diou = 0; + float tot_ciou = 0; + float tot_iou_loss = 0; + float tot_giou_loss = 0; + float tot_diou_loss = 0; + float tot_ciou_loss = 0; + float recall = 0; + float recall75 = 0; + float avg_cat = 0; + float avg_obj = 0; + float avg_anyobj = 0; + //int count = 0; + //int class_count = 0; - for (i = 0; i < l.batch * l.w*l.h*l.n; ++i) l.labels[i] = -1; - for (i = 0; i < l.batch * l.w*l.h*l.n; ++i) l.class_ids[i] = -1; - //float avg_iou = 0; - float tot_iou = 0; - float tot_giou = 0; - float tot_diou = 0; - float tot_ciou = 0; - float tot_iou_loss = 0; - float tot_giou_loss = 0; - float tot_diou_loss = 0; - float tot_ciou_loss = 0; - float recall = 0; - float recall75 = 0; - float avg_cat = 0; - float avg_obj = 0; - float avg_anyobj = 0; - int count = 0; - int class_count = 0; - *(l.cost) = 0; - for (b = 0; b < l.batch; ++b) { for (j = 0; j < l.h; ++j) { for (i = 0; i < l.w; ++i) { for (n = 0; n < l.n; ++n) { - const int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); - const int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4); - const int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); - const int stride = l.w*l.h; - box pred = get_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w*l.h, l.new_coords); + const int class_index = entry_index(l, b, n * l.w * l.h + j * l.w + i, 4 + 1); + const int obj_index = entry_index(l, b, n * l.w * l.h + j * l.w + i, 4); + const int box_index = entry_index(l, b, n * l.w * l.h + j * l.w + i, 0); + const int stride = l.w * l.h; + box pred = get_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w * l.h, l.new_coords); float best_match_iou = 0; int best_match_t = 0; float best_iou = 0; int best_t = 0; for (t = 0; t < l.max_boxes; ++t) { - box truth = float_to_box_stride(state.truth + t*l.truth_size + b*l.truths, 1); + box truth = float_to_box_stride(state.truth + t * l.truth_size + b * l.truths, 1); if (!truth.x) break; // continue; - int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; + int class_id = state.truth[t * l.truth_size + b * l.truths + 4]; if (class_id >= l.classes || class_id < 0) { printf("\n Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1); printf("\n truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f, class_id = %d \n", truth.x, truth.y, truth.w, truth.h, class_id); @@ -433,7 +425,7 @@ void forward_yolo_layer(const layer l, network_state state) float objectness = l.output[obj_index]; if (isnan(objectness) || isinf(objectness)) l.output[obj_index] = 0; - int class_id_match = compare_yolo_class(l.output, l.classes, class_index, l.w*l.h, objectness, class_id, 0.25f); + int class_id_match = compare_yolo_class(l.output, l.classes, class_index, l.w * l.h, objectness, class_id, 0.25f); float iou = box_iou(pred, truth); if (iou > best_match_iou && class_id_match == 1) { @@ -457,23 +449,23 @@ void forward_yolo_layer(const layer l, network_state state) else l.delta[obj_index] = 0; } else if (state.net.adversarial) { - int stride = l.w*l.h; + int stride = l.w * l.h; float scale = pred.w * pred.h; if (scale > 0) scale = sqrt(scale); l.delta[obj_index] = scale * l.obj_normalizer * (0 - l.output[obj_index]); int cl_id; int found_object = 0; for (cl_id = 0; cl_id < l.classes; ++cl_id) { - if (l.output[class_index + stride*cl_id] * l.output[obj_index] > 0.25) { - l.delta[class_index + stride*cl_id] = scale * (0 - l.output[class_index + stride*cl_id]); + if (l.output[class_index + stride * cl_id] * l.output[obj_index] > 0.25) { + l.delta[class_index + stride * cl_id] = scale * (0 - l.output[class_index + stride * cl_id]); found_object = 1; } } if (found_object) { // don't use this loop for adversarial attack drawing for (cl_id = 0; cl_id < l.classes; ++cl_id) - if (l.output[class_index + stride*cl_id] * l.output[obj_index] < 0.25) - l.delta[class_index + stride*cl_id] = scale * (1 - l.output[class_index + stride*cl_id]); + if (l.output[class_index + stride * cl_id] * l.output[obj_index] < 0.25) + l.delta[class_index + stride * cl_id] = scale * (1 - l.output[class_index + stride * cl_id]); l.delta[box_index + 0 * stride] += scale * (0 - l.output[box_index + 0 * stride]); l.delta[box_index + 1 * stride] += scale * (0 - l.output[box_index + 1 * stride]); @@ -482,25 +474,25 @@ void forward_yolo_layer(const layer l, network_state state) } } if (best_iou > l.truth_thresh) { - const float iou_multiplier = best_iou*best_iou;// (best_iou - l.truth_thresh) / (1.0 - l.truth_thresh); + const float iou_multiplier = best_iou * best_iou;// (best_iou - l.truth_thresh) / (1.0 - l.truth_thresh); if (l.objectness_smooth) l.delta[obj_index] = l.obj_normalizer * (iou_multiplier - l.output[obj_index]); else l.delta[obj_index] = l.obj_normalizer * (1 - l.output[obj_index]); //l.delta[obj_index] = l.obj_normalizer * (1 - l.output[obj_index]); - int class_id = state.truth[best_t*l.truth_size + b*l.truths + 4]; + int class_id = state.truth[best_t * l.truth_size + b * l.truths + 4]; if (l.map) class_id = l.map[class_id]; - delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); + delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w * l.h, 0, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; - if (l.objectness_smooth) l.delta[class_index + stride*class_id] = class_multiplier * (iou_multiplier - l.output[class_index + stride*class_id]); - box truth = float_to_box_stride(state.truth + best_t*l.truth_size + b*l.truths, 1); - delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords); + if (l.objectness_smooth) l.delta[class_index + stride * class_id] = class_multiplier * (iou_multiplier - l.output[class_index + stride * class_id]); + box truth = float_to_box_stride(state.truth + best_t * l.truth_size + b * l.truths, 1); + delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w * truth.h), l.w * l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords); (*state.net.total_bbox)++; } } } } for (t = 0; t < l.max_boxes; ++t) { - box truth = float_to_box_stride(state.truth + t*l.truth_size + b*l.truths, 1); + box truth = float_to_box_stride(state.truth + t * l.truth_size + b * l.truths, 1); if (!truth.x) break; // continue; if (truth.x < 0 || truth.y < 0 || truth.x > 1 || truth.y > 1 || truth.w < 0 || truth.h < 0) { char buff[256]; @@ -509,7 +501,7 @@ void forward_yolo_layer(const layer l, network_state state) truth.x, truth.y, truth.w, truth.h); system(buff); } - int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; + int class_id = state.truth[t * l.truth_size + b * l.truths + 4]; if (class_id >= l.classes || class_id < 0) continue; // if label contains class_id more than number of classes in the cfg-file and class_id check garbage value float best_iou = 0; @@ -531,23 +523,23 @@ void forward_yolo_layer(const layer l, network_state state) int mask_n = int_index(l.mask, best_n, l.n); if (mask_n >= 0) { - int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; + int class_id = state.truth[t * l.truth_size + b * l.truths + 4]; if (l.map) class_id = l.map[class_id]; - int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); + int box_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 0); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; - ious all_ious = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords); + ious all_ious = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w * truth.h), l.w * l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords); (*state.net.total_bbox)++; - const int truth_in_index = t*l.truth_size + b*l.truths + 5; + const int truth_in_index = t * l.truth_size + b * l.truths + 5; const int track_id = state.truth[truth_in_index]; - const int truth_out_index = b*l.n*l.w*l.h + mask_n*l.w*l.h + j*l.w + i; + const int truth_out_index = b * l.n * l.w * l.h + mask_n * l.w * l.h + j * l.w + i; l.labels[truth_out_index] = track_id; l.class_ids[truth_out_index] = class_id; //printf(" track_id = %d, t = %d, b = %d, truth_in_index = %d, truth_out_index = %d \n", track_id, t, b, truth_in_index, truth_out_index); // range is 0 <= 1 - tot_iou += all_ious.iou; + args->tot_iou += all_ious.iou; tot_iou_loss += 1 - all_ious.iou; // range is -1 <= giou <= 1 tot_giou += all_ious.giou; @@ -559,21 +551,22 @@ void forward_yolo_layer(const layer l, network_state state) tot_ciou += all_ious.ciou; tot_ciou_loss += 1 - all_ious.ciou; - int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4); + int obj_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 4); avg_obj += l.output[obj_index]; if (l.objectness_smooth) { float delta_obj = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); if (l.delta[obj_index] == 0) l.delta[obj_index] = delta_obj; - } else l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); + } + else l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); - int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); - delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); + int class_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 4 + 1); + delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w * l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); //printf(" label: class_id = %d, truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f \n", class_id, truth.x, truth.y, truth.w, truth.h); //printf(" mask_n = %d, l.output[obj_index] = %f, l.output[class_index + class_id] = %f \n\n", mask_n, l.output[obj_index], l.output[class_index + class_id]); - ++count; - ++class_count; + ++(args->count); + ++(args->class_count); if (all_ious.iou > .5) recall += 1; if (all_ious.iou > .75) recall75 += 1; } @@ -589,16 +582,16 @@ void forward_yolo_layer(const layer l, network_state state) // iou, n if (iou > l.iou_thresh) { - int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; + int class_id = state.truth[t * l.truth_size + b * l.truths + 4]; if (l.map) class_id = l.map[class_id]; - int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); + int box_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 0); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; - ious all_ious = delta_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords); + ious all_ious = delta_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w * truth.h), l.w * l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords); (*state.net.total_bbox)++; // range is 0 <= 1 - tot_iou += all_ious.iou; + args->tot_iou += all_ious.iou; tot_iou_loss += 1 - all_ious.iou; // range is -1 <= giou <= 1 tot_giou += all_ious.giou; @@ -610,7 +603,7 @@ void forward_yolo_layer(const layer l, network_state state) tot_ciou += all_ious.ciou; tot_ciou_loss += 1 - all_ious.ciou; - int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4); + int obj_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 4); avg_obj += l.output[obj_index]; if (l.objectness_smooth) { float delta_obj = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); @@ -618,11 +611,11 @@ void forward_yolo_layer(const layer l, network_state state) } else l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); - int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); - delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); + int class_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 4 + 1); + delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w * l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); - ++count; - ++class_count; + ++(args->count); + ++(args->class_count); if (all_ious.iou > .5) recall += 1; if (all_ious.iou > .75) recall75 += 1; } @@ -630,74 +623,185 @@ void forward_yolo_layer(const layer l, network_state state) } } - // averages the deltas obtained by the function: delta_yolo_box()_accumulate - for (j = 0; j < l.h; ++j) { - for (i = 0; i < l.w; ++i) { - for (n = 0; n < l.n; ++n) { - int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4); - int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); - int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); - const int stride = l.w*l.h; - - if(l.delta[obj_index] != 0) - averages_yolo_deltas(class_index, box_index, stride, l.classes, l.delta); + if (l.iou_thresh < 1.0f) { + // averages the deltas obtained by the function: delta_yolo_box()_accumulate + for (j = 0; j < l.h; ++j) { + for (i = 0; i < l.w; ++i) { + for (n = 0; n < l.n; ++n) { + int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4); + int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); + int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); + const int stride = l.w*l.h; + + if (l.delta[obj_index] != 0) + averages_yolo_deltas(class_index, box_index, stride, l.classes, l.delta); + } } } } + } - if (count == 0) count = 1; - if (class_count == 0) class_count = 1; + //free(ptr); +} + - //(*l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); - //printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d\n", state.index, avg_iou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count); - int stride = l.w*l.h; - float* no_iou_loss_delta = (float *)calloc(l.batch * l.outputs, sizeof(float)); - memcpy(no_iou_loss_delta, l.delta, l.batch * l.outputs * sizeof(float)); +void forward_yolo_layer(const layer l, network_state state) +{ + //int i, j, b, t, n; + memcpy(l.output, state.input, l.outputs*l.batch * sizeof(float)); + +#ifndef GPU for (b = 0; b < l.batch; ++b) { - for (j = 0; j < l.h; ++j) { - for (i = 0; i < l.w; ++i) { - for (n = 0; n < l.n; ++n) { - int index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); - no_iou_loss_delta[index + 0 * stride] = 0; - no_iou_loss_delta[index + 1 * stride] = 0; - no_iou_loss_delta[index + 2 * stride] = 0; - no_iou_loss_delta[index + 3 * stride] = 0; - } + for (n = 0; n < l.n; ++n) { + int index = entry_index(l, b, n*l.w*l.h, 0); + if (l.new_coords) { + activate_array(l.output + index, 4 * l.w*l.h, LOGISTIC); // x,y,w,h } + else { + activate_array(l.output + index, 2 * l.w*l.h, LOGISTIC); // x,y, + }pr + scal_add_cpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale x,y + index = entry_index(l, b, n*l.w*l.h, 4); + activate_array(l.output + index, (1 + l.classes)*l.w*l.h, LOGISTIC); } } - float classification_loss = l.obj_normalizer * pow(mag_array(no_iou_loss_delta, l.outputs * l.batch), 2); - free(no_iou_loss_delta); - float loss = pow(mag_array(l.delta, l.outputs * l.batch), 2); - float iou_loss = loss - classification_loss; +#endif + + // delta is zeroed + memset(l.delta, 0, l.outputs * l.batch * sizeof(float)); + if (!state.train) return; + + int i; + for (i = 0; i < l.batch * l.w*l.h*l.n; ++i) l.labels[i] = -1; + for (i = 0; i < l.batch * l.w*l.h*l.n; ++i) l.class_ids[i] = -1; + //float avg_iou = 0; + float tot_iou = 0; + float tot_giou = 0; + float tot_diou = 0; + float tot_ciou = 0; + float tot_iou_loss = 0; + float tot_giou_loss = 0; + float tot_diou_loss = 0; + float tot_ciou_loss = 0; + float recall = 0; + float recall75 = 0; + float avg_cat = 0; + float avg_obj = 0; + float avg_anyobj = 0; + int count = 0; + int class_count = 0; + *(l.cost) = 0; + + + + int b; + + int num_threads = l.batch; + pthread_t* threads = (pthread_t*)calloc(num_threads, sizeof(pthread_t)); + + train_yolo_args* ptr = (train_yolo_args*)calloc(l.batch, sizeof(train_yolo_args)); + + for (b = 0; b < l.batch; b++) + { + //train_yolo_args* ptr = (train_yolo_args*)calloc(1, sizeof(train_yolo_args)); + ptr[b].l = l; + ptr[b].state = state; + ptr[b].b = b; + + ptr[b].tot_iou = 0; + ptr[b].count = 0; + ptr[b].class_count = 0; + + if (pthread_create(&threads[b], 0, process_batch, &ptr[b])) error("Thread creation failed"); + } + + for (b = 0; b < l.batch; b++) + { + pthread_join(threads[b], 0); - float avg_iou_loss = 0; - // gIOU loss + MSE (objectness) loss - if (l.iou_loss == MSE) { - *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); + tot_iou += ptr[b].tot_iou; + count += ptr[b].count; + class_count += ptr[b].class_count; + } + + free(ptr); + free(threads); + + + if (count == 0) count = 1; + if (class_count == 0) class_count = 1; + + if (l.show_details == 0) { + float loss = pow(mag_array(l.delta, l.outputs * l.batch), 2); + *(l.cost) = loss; + + loss /= l.batch; + + fprintf(stderr, "v3 (%s loss, Normalizer: (iou: %.2f, obj: %.2f, cls: %.2f) Region %d Avg (IOU: %f), count: %d, total_loss = %f \n", + (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.obj_normalizer, l.cls_normalizer, state.index, tot_iou / count, count, loss); } else { - // Always compute classification loss both for iou + cls loss and for logging with mse loss - // TODO: remove IOU loss fields before computing MSE on class - // probably split into two arrays - if (l.iou_loss == GIOU) { - avg_iou_loss = count > 0 ? l.iou_normalizer * (tot_giou_loss / count) : 0; + // show detailed output + + int stride = l.w*l.h; + float* no_iou_loss_delta = (float *)calloc(l.batch * l.outputs, sizeof(float)); + memcpy(no_iou_loss_delta, l.delta, l.batch * l.outputs * sizeof(float)); + + + int j, n; + for (b = 0; b < l.batch; ++b) { + for (j = 0; j < l.h; ++j) { + for (i = 0; i < l.w; ++i) { + for (n = 0; n < l.n; ++n) { + int index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); + no_iou_loss_delta[index + 0 * stride] = 0; + no_iou_loss_delta[index + 1 * stride] = 0; + no_iou_loss_delta[index + 2 * stride] = 0; + no_iou_loss_delta[index + 3 * stride] = 0; + } + } + } + } + + float classification_loss = l.obj_normalizer * pow(mag_array(no_iou_loss_delta, l.outputs * l.batch), 2); + free(no_iou_loss_delta); + float loss = pow(mag_array(l.delta, l.outputs * l.batch), 2); + float iou_loss = loss - classification_loss; + + float avg_iou_loss = 0; + *(l.cost) = loss; + /* + // gIOU loss + MSE (objectness) loss + if (l.iou_loss == MSE) { + *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); } else { - avg_iou_loss = count > 0 ? l.iou_normalizer * (tot_iou_loss / count) : 0; + // Always compute classification loss both for iou + cls loss and for logging with mse loss + // TODO: remove IOU loss fields before computing MSE on class + // probably split into two arrays + if (l.iou_loss == GIOU) { + avg_iou_loss = count > 0 ? l.iou_normalizer * (tot_giou_loss / count) : 0; + } + else { + avg_iou_loss = count > 0 ? l.iou_normalizer * (tot_iou_loss / count) : 0; + } + *(l.cost) = avg_iou_loss + classification_loss; } - *(l.cost) = avg_iou_loss + classification_loss; - } + */ - loss /= l.batch; - classification_loss /= l.batch; - iou_loss /= l.batch; + loss /= l.batch; + classification_loss /= l.batch; + iou_loss /= l.batch; - fprintf(stderr, "v3 (%s loss, Normalizer: (iou: %.2f, cls: %.2f) Region %d Avg (IOU: %f, GIOU: %f), Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d, class_loss = %f, iou_loss = %f, total_loss = %f \n", - (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.obj_normalizer, state.index, tot_iou / count, tot_giou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count, - classification_loss, iou_loss, loss); + fprintf(stderr, "v3 (%s loss, Normalizer: (iou: %.2f, obj: %.2f, cls: %.2f) Region %d Avg (IOU: %f), count: %d, class_loss = %f, iou_loss = %f, total_loss = %f \n", + (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.obj_normalizer, l.cls_normalizer, state.index, tot_iou / count, count, classification_loss, iou_loss, loss); + + //fprintf(stderr, "v3 (%s loss, Normalizer: (iou: %.2f, cls: %.2f) Region %d Avg (IOU: %f, GIOU: %f), Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d, class_loss = %f, iou_loss = %f, total_loss = %f \n", + // (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.obj_normalizer, state.index, tot_iou / count, tot_giou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count, + // classification_loss, iou_loss, loss); + } } void backward_yolo_layer(const layer l, network_state state) From 333cc14a06ca903afa6bbcb67bd2d88222f75cc7 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 24 Nov 2020 01:41:00 +0300 Subject: [PATCH 1144/1365] [net] equidistant_point=1 instead of LR-scheduler --- include/darknet.h | 2 ++ src/detector.c | 2 +- src/network.c | 2 ++ src/parser.c | 2 ++ src/yolo_layer.c | 58 +++++++++++++++++++++++++++++++---------------- 5 files changed, 45 insertions(+), 21 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index bb9f2dcd539..3f90f0ad875 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -697,6 +697,8 @@ typedef struct network { int n; int batch; uint64_t *seen; + float *delta_rolling_avg; + int equidistant_point; int *cur_iteration; float loss_scale; int *t; diff --git a/src/detector.c b/src/detector.c index 5d284e02d9a..8ec3f1ae2d7 100644 --- a/src/detector.c +++ b/src/detector.c @@ -301,7 +301,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions); // calculate mAP for each 4 Epochs calc_map_for_each = fmax(calc_map_for_each, 100); int next_map_calc = iter_map + calc_map_for_each; - next_map_calc = fmax(next_map_calc, net.burn_in); + next_map_calc = 1000;// fmax(next_map_calc, net.burn_in); //next_map_calc = fmax(next_map_calc, 400); if (calc_map) { printf("\n (next mAP calculation at %d iterations) ", next_map_calc); diff --git a/src/network.c b/src/network.c index 68a73fffbbd..a59f2e478b1 100644 --- a/src/network.c +++ b/src/network.c @@ -245,6 +245,7 @@ network make_network(int n) net.n = n; net.layers = (layer*)xcalloc(net.n, sizeof(layer)); net.seen = (uint64_t*)xcalloc(1, sizeof(uint64_t)); + net.delta_rolling_avg = (float*)xcalloc(1, sizeof(float)); net.cur_iteration = (int*)xcalloc(1, sizeof(int)); net.total_bbox = (int*)xcalloc(1, sizeof(int)); net.rewritten_bbox = (int*)xcalloc(1, sizeof(int)); @@ -1181,6 +1182,7 @@ void free_network(network net) free(net.scales); free(net.steps); free(net.seen); + free(net.delta_rolling_avg); free(net.cur_iteration); free(net.total_bbox); free(net.rewritten_bbox); diff --git a/src/parser.c b/src/parser.c index 160cffdd52d..74d60c8e306 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1155,6 +1155,8 @@ void parse_net_options(list *options, network *net) net->batch *= net->time_steps; // mini_batch * time_steps net->subdivisions = subdivs; // number of mini_batches + net->equidistant_point = option_find_int_quiet(options, "equidistant_point", 0); + *net->delta_rolling_avg = 0; *net->seen = 0; *net->cur_iteration = 0; net->loss_scale = option_find_float_quiet(options, "loss_scale", 1); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 8644ae45701..d756d12242a 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -362,7 +362,7 @@ static int entry_index(layer l, int batch, int location, int entry) return batch*l.outputs + n*l.w*l.h*(4+l.classes+1) + entry*l.w*l.h + loc; } -typedef struct { +typedef struct train_yolo_args { layer l; network_state state; int b; @@ -372,7 +372,7 @@ typedef struct { int class_count; } train_yolo_args; -void process_batch(train_yolo_args* ptr) +void *process_batch(void* ptr) { { train_yolo_args *args = (train_yolo_args*)ptr; @@ -642,7 +642,7 @@ void process_batch(train_yolo_args* ptr) } - //free(ptr); + return 0; } @@ -651,6 +651,7 @@ void forward_yolo_layer(const layer l, network_state state) { //int i, j, b, t, n; memcpy(l.output, state.input, l.outputs*l.batch * sizeof(float)); + int b, n; #ifndef GPU for (b = 0; b < l.batch; ++b) { @@ -661,7 +662,7 @@ void forward_yolo_layer(const layer l, network_state state) } else { activate_array(l.output + index, 2 * l.w*l.h, LOGISTIC); // x,y, - }pr + } scal_add_cpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale x,y index = entry_index(l, b, n*l.w*l.h, 4); activate_array(l.output + index, (1 + l.classes)*l.w*l.h, LOGISTIC); @@ -695,40 +696,57 @@ void forward_yolo_layer(const layer l, network_state state) *(l.cost) = 0; - - int b; - int num_threads = l.batch; pthread_t* threads = (pthread_t*)calloc(num_threads, sizeof(pthread_t)); - train_yolo_args* ptr = (train_yolo_args*)calloc(l.batch, sizeof(train_yolo_args)); + struct train_yolo_args* yolo_args = (train_yolo_args*)xcalloc(l.batch, sizeof(struct train_yolo_args)); for (b = 0; b < l.batch; b++) { - //train_yolo_args* ptr = (train_yolo_args*)calloc(1, sizeof(train_yolo_args)); - ptr[b].l = l; - ptr[b].state = state; - ptr[b].b = b; + yolo_args[b].l = l; + yolo_args[b].state = state; + yolo_args[b].b = b; - ptr[b].tot_iou = 0; - ptr[b].count = 0; - ptr[b].class_count = 0; + yolo_args[b].tot_iou = 0; + yolo_args[b].count = 0; + yolo_args[b].class_count = 0; - if (pthread_create(&threads[b], 0, process_batch, &ptr[b])) error("Thread creation failed"); + if (pthread_create(&threads[b], 0, process_batch, &(yolo_args[b]))) error("Thread creation failed"); } for (b = 0; b < l.batch; b++) { pthread_join(threads[b], 0); - tot_iou += ptr[b].tot_iou; - count += ptr[b].count; - class_count += ptr[b].class_count; + tot_iou += yolo_args[b].tot_iou; + count += yolo_args[b].count; + class_count += yolo_args[b].class_count; } - free(ptr); + free(yolo_args); free(threads); + // Search for an equidistant point from the distant boundaries of the local minimum + int iteration_num = get_current_iteration(state.net); + //printf(" equidistant_point ep = %d, it = %d \n", state.net.equidistant_point, iteration_num); + + if (state.net.equidistant_point && state.net.equidistant_point < iteration_num) { + float progress_it = iteration_num - state.net.equidistant_point; + float progress = progress_it / (state.net.max_batches - state.net.equidistant_point); + float loss_threshold = (*state.net.delta_rolling_avg) * progress; + printf(" RUN equidistant_point loss_threshold = %f, ep = %d, it = %d \n", loss_threshold, state.net.equidistant_point, iteration_num); + + float cur_max = 0; + for (i = 0; i < l.batch * l.outputs; ++i) { + if (cur_max < fabs(l.delta[i])) + cur_max = fabs(l.delta[i]); + + if (fabs(l.delta[i]) < loss_threshold) + l.delta[i] = 0; + } + + *state.net.delta_rolling_avg = *state.net.delta_rolling_avg * 0.99 + cur_max * 0.01; + } if (count == 0) count = 1; if (class_count == 0) class_count = 1; From 181967937ddfdd86c3f2c1259be45cc66880225e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 24 Nov 2020 14:24:35 +0300 Subject: [PATCH 1145/1365] Minor fix --- src/detector.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/detector.c b/src/detector.c index 8ec3f1ae2d7..5d284e02d9a 100644 --- a/src/detector.c +++ b/src/detector.c @@ -301,7 +301,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions); // calculate mAP for each 4 Epochs calc_map_for_each = fmax(calc_map_for_each, 100); int next_map_calc = iter_map + calc_map_for_each; - next_map_calc = 1000;// fmax(next_map_calc, net.burn_in); + next_map_calc = fmax(next_map_calc, net.burn_in); //next_map_calc = fmax(next_map_calc, 400); if (calc_map) { printf("\n (next mAP calculation at %d iterations) ", next_map_calc); From bf5084934ff97100844d2b3cd281b992b5b735ae Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 28 Nov 2020 02:57:12 +0300 Subject: [PATCH 1146/1365] Added yolov4-csp.cfg --- build/darknet/x64/cfg/yolov4-csp.cfg | 1274 ++++++++++++++++++++++++++ cfg/yolov4-csp.cfg | 1274 ++++++++++++++++++++++++++ 2 files changed, 2548 insertions(+) create mode 100644 build/darknet/x64/cfg/yolov4-csp.cfg create mode 100644 cfg/yolov4-csp.cfg diff --git a/build/darknet/x64/cfg/yolov4-csp.cfg b/build/darknet/x64/cfg/yolov4-csp.cfg new file mode 100644 index 00000000000..2d1f9c21302 --- /dev/null +++ b/build/darknet/x64/cfg/yolov4-csp.cfg @@ -0,0 +1,1274 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=512 +height=512 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +optimized_memory=1 + +#23:104x104 54:52x52 85:26x26 104:13x13 for 416 + + + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=mish + +#[convolutional] +#batch_normalize=1 +#filters=64 +#size=1 +#stride=1 +#pad=1 +#activation=mish + +#[route] +#layers = -2 + +#[convolutional] +#batch_normalize=1 +#filters=64 +#size=1 +#stride=1 +#pad=1 +#activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +#[convolutional] +#batch_normalize=1 +#filters=64 +#size=1 +#stride=1 +#pad=1 +#activation=mish + +#[route] +#layers = -1,-7 + +#[convolutional] +#batch_normalize=1 +#filters=64 +#size=1 +#stride=1 +#pad=1 +#activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-10 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=mish + +########################## + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1, -13 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 79 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1, -6 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 48 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[route] +layers = -1, -6 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +########################## + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +objectness_smooth=0 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=4.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=20 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1, -20 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1,-6 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=5 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1, -49 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1,-6 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 diff --git a/cfg/yolov4-csp.cfg b/cfg/yolov4-csp.cfg new file mode 100644 index 00000000000..2d1f9c21302 --- /dev/null +++ b/cfg/yolov4-csp.cfg @@ -0,0 +1,1274 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=512 +height=512 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +optimized_memory=1 + +#23:104x104 54:52x52 85:26x26 104:13x13 for 416 + + + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=mish + +#[convolutional] +#batch_normalize=1 +#filters=64 +#size=1 +#stride=1 +#pad=1 +#activation=mish + +#[route] +#layers = -2 + +#[convolutional] +#batch_normalize=1 +#filters=64 +#size=1 +#stride=1 +#pad=1 +#activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +#[convolutional] +#batch_normalize=1 +#filters=64 +#size=1 +#stride=1 +#pad=1 +#activation=mish + +#[route] +#layers = -1,-7 + +#[convolutional] +#batch_normalize=1 +#filters=64 +#size=1 +#stride=1 +#pad=1 +#activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-10 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=mish + +########################## + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1, -13 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 79 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1, -6 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 48 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[route] +layers = -1, -6 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +########################## + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +objectness_smooth=0 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=4.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=20 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1, -20 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1,-6 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=5 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1, -49 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1,-6 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 From 332ec6496ea3b63919ba53f52982544c2aea9a7f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 28 Nov 2020 17:35:54 +0300 Subject: [PATCH 1147/1365] [net] equidistant_point=200000 num_sigmas_reject_badlabels=3 badlabels_rejection_percentage=0.1 --- build/darknet/x64/cfg/yolov4-tiny.cfg | 5 ++ include/darknet.h | 6 ++ src/network.c | 10 ++- src/parser.c | 6 ++ src/yolo_layer.c | 111 +++++++++++++++++++++++--- 5 files changed, 125 insertions(+), 13 deletions(-) diff --git a/build/darknet/x64/cfg/yolov4-tiny.cfg b/build/darknet/x64/cfg/yolov4-tiny.cfg index dc6f5bfb8c6..1dd0ebdaeb0 100644 --- a/build/darknet/x64/cfg/yolov4-tiny.cfg +++ b/build/darknet/x64/cfg/yolov4-tiny.cfg @@ -22,6 +22,11 @@ policy=steps steps=400000,450000 scales=.1,.1 +#equidistant_point=1000 +#num_sigmas_reject_badlabels=3 +#badlabels_rejection_percentage=0.1 + + [convolutional] batch_normalize=1 filters=32 diff --git a/include/darknet.h b/include/darknet.h index 3f90f0ad875..0a245f0babe 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -697,8 +697,14 @@ typedef struct network { int n; int batch; uint64_t *seen; + float *badlabels_reject_threshold; + float *delta_rolling_max; float *delta_rolling_avg; + float *delta_rolling_std; int equidistant_point; + float badlabels_rejection_percentage; + float num_sigmas_reject_badlabels; + float ema_alpha; int *cur_iteration; float loss_scale; int *t; diff --git a/src/network.c b/src/network.c index a59f2e478b1..2c6c5679bd5 100644 --- a/src/network.c +++ b/src/network.c @@ -245,7 +245,10 @@ network make_network(int n) net.n = n; net.layers = (layer*)xcalloc(net.n, sizeof(layer)); net.seen = (uint64_t*)xcalloc(1, sizeof(uint64_t)); - net.delta_rolling_avg = (float*)xcalloc(1, sizeof(float)); + net.badlabels_reject_threshold = (float*)xcalloc(1, sizeof(float)); + net.delta_rolling_max = (float*)xcalloc(1, sizeof(float)); + net.delta_rolling_avg = (float*)xcalloc(1, sizeof(float)); + net.delta_rolling_std = (float*)xcalloc(1, sizeof(float)); net.cur_iteration = (int*)xcalloc(1, sizeof(int)); net.total_bbox = (int*)xcalloc(1, sizeof(int)); net.rewritten_bbox = (int*)xcalloc(1, sizeof(int)); @@ -1182,7 +1185,10 @@ void free_network(network net) free(net.scales); free(net.steps); free(net.seen); - free(net.delta_rolling_avg); + free(net.badlabels_reject_threshold); + free(net.delta_rolling_max); + free(net.delta_rolling_avg); + free(net.delta_rolling_std); free(net.cur_iteration); free(net.total_bbox); free(net.rewritten_bbox); diff --git a/src/parser.c b/src/parser.c index 74d60c8e306..5617fb508d2 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1156,7 +1156,13 @@ void parse_net_options(list *options, network *net) net->subdivisions = subdivs; // number of mini_batches net->equidistant_point = option_find_int_quiet(options, "equidistant_point", 0); + net->badlabels_rejection_percentage = option_find_float_quiet(options, "badlabels_rejection_percentage", 0); + net->num_sigmas_reject_badlabels = option_find_float_quiet(options, "num_sigmas_reject_badlabels", 0); + net->ema_alpha = option_find_float_quiet(options, "ema_alpha", 0); + *net->badlabels_reject_threshold = 0; + *net->delta_rolling_max = 0; *net->delta_rolling_avg = 0; + *net->delta_rolling_std = 0; *net->seen = 0; *net->cur_iteration = 0; net->loss_scale = option_find_float_quiet(options, "loss_scale", 1); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index d756d12242a..b8f7a23f1e8 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -728,24 +728,113 @@ void forward_yolo_layer(const layer l, network_state state) // Search for an equidistant point from the distant boundaries of the local minimum int iteration_num = get_current_iteration(state.net); + const int start_point = state.net.max_batches * 3 / 4; //printf(" equidistant_point ep = %d, it = %d \n", state.net.equidistant_point, iteration_num); - if (state.net.equidistant_point && state.net.equidistant_point < iteration_num) { - float progress_it = iteration_num - state.net.equidistant_point; - float progress = progress_it / (state.net.max_batches - state.net.equidistant_point); - float loss_threshold = (*state.net.delta_rolling_avg) * progress; - printf(" RUN equidistant_point loss_threshold = %f, ep = %d, it = %d \n", loss_threshold, state.net.equidistant_point, iteration_num); + if ((state.net.badlabels_rejection_percentage && start_point < iteration_num) || + (state.net.num_sigmas_reject_badlabels && start_point < iteration_num) || + (state.net.equidistant_point && state.net.equidistant_point < iteration_num)) + { + const float progress_it = iteration_num - state.net.equidistant_point; + const float progress = progress_it / (state.net.max_batches - state.net.equidistant_point); + float ep_loss_threshold = (*state.net.delta_rolling_avg) * progress; float cur_max = 0; + float cur_avg = 0; + float counter = 0; for (i = 0; i < l.batch * l.outputs; ++i) { - if (cur_max < fabs(l.delta[i])) - cur_max = fabs(l.delta[i]); - if (fabs(l.delta[i]) < loss_threshold) - l.delta[i] = 0; + if (l.delta[i] != 0) { + counter++; + cur_avg += fabs(l.delta[i]); + + if (cur_max < fabs(l.delta[i])) + cur_max = fabs(l.delta[i]); + } + } + + cur_avg = cur_avg / counter; + + if (*state.net.delta_rolling_max == 0) *state.net.delta_rolling_max = cur_max; + *state.net.delta_rolling_max = *state.net.delta_rolling_max * 0.99 + cur_max * 0.01; + *state.net.delta_rolling_avg = *state.net.delta_rolling_avg * 0.99 + cur_avg * 0.01; + + // reject high loss to filter bad labels + if (state.net.num_sigmas_reject_badlabels && start_point < iteration_num) + { + const float rolling_std = (*state.net.delta_rolling_std); + const float rolling_max = (*state.net.delta_rolling_max); + const float rolling_avg = (*state.net.delta_rolling_avg); + const float progress_badlabels = (float)(iteration_num - start_point) / (start_point); + + float cur_std = 0; + float counter = 0; + for (i = 0; i < l.batch * l.outputs; ++i) { + if (l.delta[i] != 0) { + counter++; + cur_std += pow(l.delta[i] - rolling_avg, 2); + } + } + cur_std = sqrt(cur_std / counter); + + *state.net.delta_rolling_std = *state.net.delta_rolling_std * 0.99 + cur_std * 0.01; + + float final_badlebels_threshold = rolling_avg + rolling_std * state.net.num_sigmas_reject_badlabels; + float badlabels_threshold = rolling_max - progress_badlabels * fabs(rolling_max - final_badlebels_threshold); + badlabels_threshold = max_val_cmp(final_badlebels_threshold, badlabels_threshold); + for (i = 0; i < l.batch * l.outputs; ++i) { + if (fabs(l.delta[i]) > badlabels_threshold) + l.delta[i] = 0; + } + printf(" rolling_std = %f, rolling_max = %f, rolling_avg = %f \n", rolling_std, rolling_max, rolling_avg); + printf(" badlabels loss_threshold = %f, start_it = %d, progress = %f \n", badlabels_threshold, start_point, progress_badlabels *100); + + ep_loss_threshold = min_val_cmp(final_badlebels_threshold, rolling_avg) * progress; + } + + + // reject some percent of the highest deltas to filter bad labels + if (state.net.badlabels_rejection_percentage && start_point < iteration_num) { + if (*state.net.badlabels_reject_threshold == 0) + *state.net.badlabels_reject_threshold = *state.net.delta_rolling_max; + + printf(" badlabels_reject_threshold = %f \n", *state.net.badlabels_reject_threshold); + + const float num_deltas_per_anchor = (l.classes + 4 + 1); + float counter_reject = 0; + float counter_all = 0; + for (i = 0; i < l.batch * l.outputs; ++i) { + if (l.delta[i] != 0) { + counter_all++; + if (fabs(l.delta[i]) > (*state.net.badlabels_reject_threshold)) { + counter_reject++; + l.delta[i] = 0; + } + } + } + float cur_percent = 100 * (counter_reject*num_deltas_per_anchor / counter_all); + if (cur_percent > state.net.badlabels_rejection_percentage) { + *state.net.badlabels_reject_threshold += 0.01; + printf(" increase!!! \n"); + } + else if (*state.net.badlabels_reject_threshold > 0.01) { + *state.net.badlabels_reject_threshold -= 0.01; + printf(" decrease!!! \n"); + } + + printf(" badlabels_reject_threshold = %f, cur_percent = %f, badlabels_rejection_percentage = %f, delta_rolling_max = %f \n", + *state.net.badlabels_reject_threshold, cur_percent, state.net.badlabels_rejection_percentage, *state.net.delta_rolling_max); + } + + + // reject low loss to find equidistant point + if (state.net.equidistant_point && state.net.equidistant_point < iteration_num) { + printf(" equidistant_point loss_threshold = %f, start_it = %d, progress = %3.1f %% \n", ep_loss_threshold, state.net.equidistant_point, progress * 100); + for (i = 0; i < l.batch * l.outputs; ++i) { + if (fabs(l.delta[i]) < ep_loss_threshold) + l.delta[i] = 0; + } } - - *state.net.delta_rolling_avg = *state.net.delta_rolling_avg * 0.99 + cur_max * 0.01; } if (count == 0) count = 1; From 80e1da22eedf1d73845f180583c6f0c1da6e4aec Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 29 Nov 2020 21:30:41 +0300 Subject: [PATCH 1148/1365] Added [net] ema_alpha=0.998 --- build/darknet/x64/cfg/yolov4-tiny.cfg | 7 ++ include/darknet.h | 4 + src/convolutional_layer.c | 4 + src/darknet.c | 2 +- src/detector.c | 6 ++ src/layer.c | 3 + src/network.c | 102 ++++++++++++++++++++++++++ src/network.h | 4 + src/parser.c | 36 ++++++++- src/parser.h | 2 +- 10 files changed, 165 insertions(+), 5 deletions(-) diff --git a/build/darknet/x64/cfg/yolov4-tiny.cfg b/build/darknet/x64/cfg/yolov4-tiny.cfg index 1dd0ebdaeb0..e1b53b3af98 100644 --- a/build/darknet/x64/cfg/yolov4-tiny.cfg +++ b/build/darknet/x64/cfg/yolov4-tiny.cfg @@ -17,11 +17,18 @@ hue=.1 learning_rate=0.00261 burn_in=1000 + max_batches = 500200 policy=steps steps=400000,450000 scales=.1,.1 +#max_batches = 501000 +#policy=steps +#steps=500000 +#scales=0.01 + +#ema_alpha=0.998 #equidistant_point=1000 #num_sigmas_reject_badlabels=3 #badlabels_rejection_percentage=0.1 diff --git a/include/darknet.h b/include/darknet.h index 0a245f0babe..4ebf47db06b 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -414,6 +414,10 @@ struct layer { float *scales; float *scale_updates; + float *weights_ema; + float *biases_ema; + float *scales_ema; + float *weights; float *weight_updates; diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 130f57fabb0..1d52dd1d23c 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -566,6 +566,9 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, if (train) { l.weight_updates = (float*)xcalloc(l.nweights, sizeof(float)); l.bias_updates = (float*)xcalloc(n, sizeof(float)); + + l.weights_ema = (float*)xcalloc(l.nweights, sizeof(float)); + l.biases_ema = (float*)xcalloc(n, sizeof(float)); } } @@ -637,6 +640,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, l.scales[i] = 1; } if (train) { + l.scales_ema = (float*)xcalloc(n, sizeof(float)); l.scale_updates = (float*)xcalloc(n, sizeof(float)); l.mean = (float*)xcalloc(n, sizeof(float)); diff --git a/src/darknet.c b/src/darknet.c index 92a9c193035..13ab75f3d38 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -178,7 +178,7 @@ void partial(char *cfgfile, char *weightfile, char *outfile, int max) } *net.seen = 0; *net.cur_iteration = 0; - save_weights_upto(net, outfile, max); + save_weights_upto(net, outfile, max, 0); } #include "convolutional_layer.h" diff --git a/src/detector.c b/src/detector.c index 5d284e02d9a..0b2c4a652ab 100644 --- a/src/detector.c +++ b/src/detector.c @@ -400,6 +400,12 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i char buff[256]; sprintf(buff, "%s/%s_last.weights", backup_directory, base); save_weights(net, buff); + + if (net.ema_alpha && is_ema_initialized(net)) { + sprintf(buff, "%s/%s_ema.weights", backup_directory, base); + save_weights_upto(net, buff, net.n, 1); + printf(" EMA weights are saved to the file: %s \n", buff); + } } free_data(train); } diff --git a/src/layer.c b/src/layer.c index fe3827d2ad5..032a24e0f2c 100644 --- a/src/layer.c +++ b/src/layer.c @@ -92,6 +92,9 @@ void free_layer_custom(layer l, int keep_cudnn_desc) if (l.bias_updates) free(l.bias_updates), l.bias_updates = NULL; if (l.scales) free(l.scales), l.scales = NULL; if (l.scale_updates) free(l.scale_updates), l.scale_updates = NULL; + if (l.biases_ema) free(l.biases_ema), l.biases = NULL; + if (l.scales_ema) free(l.scales_ema), l.scales = NULL; + if (l.weights_ema) free(l.weights_ema), l.weights = NULL; if (l.weights) free(l.weights), l.weights = NULL; if (l.weight_updates) free(l.weight_updates), l.weight_updates = NULL; if (l.align_bit_weights) free(l.align_bit_weights); diff --git a/src/network.c b/src/network.c index 2c6c5679bd5..df78b43c8c0 100644 --- a/src/network.c +++ b/src/network.c @@ -425,6 +425,34 @@ float train_network_waitkey(network net, data d, int wait_key) #else // GPU update_network(net); #endif // GPU + + int ema_start_point = net.max_batches / 2; + + if (net.ema_alpha && (*net.cur_iteration) >= ema_start_point) + { + int ema_period = (net.max_batches - ema_start_point - 1000) * (1.0 - net.ema_alpha); + int ema_apply_point = net.max_batches - 1000; + + if (!is_ema_initialized(net)) + { + ema_update(net, 0); // init EMA + printf(" EMA initialization \n"); + } + + if ((*net.cur_iteration) >= ema_apply_point) + { + ema_apply(net); // apply EMA (BN rolling mean/var recalculation is required) + printf(" ema_apply() \n"); + } + else + if ((*net.cur_iteration) < ema_apply_point && + (*net.cur_iteration) % ema_period == 0) + { + ema_update(net, net.ema_alpha); // update EMA + printf(" ema_update(), ema_alpha = %f \n", net.ema_alpha); + } + } + free(X); free(y); return (float)sum/(n*batch); @@ -1489,3 +1517,77 @@ void restore_network_recurrent_state(network net) if (net.layers[k].type == CRNN) free_state_crnn(net.layers[k]); } } + + +int is_ema_initialized(network net) +{ + int i; + for (i = 0; i < net.n; ++i) { + layer l = net.layers[i]; + if (l.type == CONVOLUTIONAL) { + int k; + if (l.weights_ema) { + for (k = 0; k < l.nweights; ++k) { + if (l.weights_ema[k] != 0) return 1; + } + } + } + } + + return 0; +} + +void ema_update(network net, float ema_alpha) +{ + int i; + for (i = 0; i < net.n; ++i) { + layer l = net.layers[i]; + if (l.type == CONVOLUTIONAL) { +#ifdef GPU + if (gpu_index >= 0) { + pull_convolutional_layer(l); + } +#endif + int k; + if (l.weights_ema) { + for (k = 0; k < l.nweights; ++k) { + l.weights_ema[k] = ema_alpha * l.weights_ema[k] + (1 - ema_alpha) * l.weights[k]; + } + } + + for (k = 0; k < l.n; ++k) { + if (l.biases_ema) l.biases_ema[k] = ema_alpha * l.biases_ema[k] + (1 - ema_alpha) * l.biases[k]; + if (l.scales_ema) l.scales_ema[k] = ema_alpha * l.scales_ema[k] + (1 - ema_alpha) * l.scales[k]; + } + } + } +} + + +void ema_apply(network net) +{ + int i; + for (i = 0; i < net.n; ++i) { + layer l = net.layers[i]; + if (l.type == CONVOLUTIONAL) { + int k; + if (l.weights_ema) { + for (k = 0; k < l.nweights; ++k) { + l.weights[k] = l.weights_ema[k]; + } + } + + for (k = 0; k < l.n; ++k) { + if (l.biases_ema) l.biases[k] = l.biases_ema[k]; + if (l.scales_ema) l.scales[k] = l.scales_ema[k]; + } + +#ifdef GPU + if (gpu_index >= 0) { + push_convolutional_layer(l); + } +#endif + } + } +} + diff --git a/src/network.h b/src/network.h index 42d68d6c3c3..f5e81befb3e 100644 --- a/src/network.h +++ b/src/network.h @@ -171,6 +171,10 @@ void free_network_recurrent_state(network net); void randomize_network_recurrent_state(network net); void remember_network_recurrent_state(network net); void restore_network_recurrent_state(network net); +int is_ema_initialized(network net); +void ema_update(network net, float ema_alpha); +void ema_apply(network net); + #ifdef __cplusplus } diff --git a/src/parser.c b/src/parser.c index 5617fb508d2..9f4b639e35c 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1825,6 +1825,31 @@ void save_convolutional_weights(layer l, FILE *fp) //} } +void save_convolutional_weights_ema(layer l, FILE *fp) +{ + if (l.binary) { + //save_convolutional_weights_binary(l, fp); + //return; + } +#ifdef GPU + if (gpu_index >= 0) { + pull_convolutional_layer(l); + } +#endif + int num = l.nweights; + fwrite(l.biases_ema, sizeof(float), l.n, fp); + if (l.batch_normalize) { + fwrite(l.scales_ema, sizeof(float), l.n, fp); + fwrite(l.rolling_mean, sizeof(float), l.n, fp); + fwrite(l.rolling_variance, sizeof(float), l.n, fp); + } + fwrite(l.weights_ema, sizeof(float), num, fp); + //if(l.adam){ + // fwrite(l.m, sizeof(float), num, fp); + // fwrite(l.v, sizeof(float), num, fp); + //} +} + void save_batchnorm_weights(layer l, FILE *fp) { #ifdef GPU @@ -1854,7 +1879,7 @@ void save_connected_weights(layer l, FILE *fp) } } -void save_weights_upto(network net, char *filename, int cutoff) +void save_weights_upto(network net, char *filename, int cutoff, int save_ema) { #ifdef GPU if(net.gpu_index >= 0){ @@ -1878,7 +1903,12 @@ void save_weights_upto(network net, char *filename, int cutoff) for(i = 0; i < net.n && i < cutoff; ++i){ layer l = net.layers[i]; if (l.type == CONVOLUTIONAL && l.share_layer == NULL) { - save_convolutional_weights(l, fp); + if (save_ema) { + save_convolutional_weights_ema(l, fp); + } + else { + save_convolutional_weights(l, fp); + } } if (l.type == SHORTCUT && l.nweights > 0) { save_shortcut_weights(l, fp); } if(l.type == CONNECTED){ @@ -1941,7 +1971,7 @@ void save_weights_upto(network net, char *filename, int cutoff) } void save_weights(network net, char *filename) { - save_weights_upto(net, filename, net.n); + save_weights_upto(net, filename, net.n, 0); } void transpose_matrix(float *a, int rows, int cols) diff --git a/src/parser.h b/src/parser.h index 5e0d3fd1295..05241167d20 100644 --- a/src/parser.h +++ b/src/parser.h @@ -9,7 +9,7 @@ network parse_network_cfg(char *filename); network parse_network_cfg_custom(char *filename, int batch, int time_steps); void save_network(network net, char *filename); void save_weights(network net, char *filename); -void save_weights_upto(network net, char *filename, int cutoff); +void save_weights_upto(network net, char *filename, int cutoff, int save_ema); void save_weights_double(network net, char *filename); void load_weights(network *net, char *filename); void load_weights_upto(network *net, char *filename, int cutoff); From e87042a49a9e9196482f8bad6374b5e4f6ad364b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 1 Dec 2020 05:14:02 +0300 Subject: [PATCH 1149/1365] Fixed Makefile and CMakeLists.txt for RTX 3090 --- CMakeLists.txt | 4 ++-- Makefile | 9 ++++++--- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 4c7224e4e8b..ea7c4a64760 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -62,7 +62,7 @@ include(CheckLanguage) check_language(CUDA) if(CMAKE_CUDA_COMPILER AND ENABLE_CUDA) set(CUDA_ARCHITECTURES "Auto" CACHE STRING "\"Auto\" detects local machine GPU compute arch at runtime, \"Common\" and \"All\" cover common and entire subsets of architectures, \"Names\" is a list of architectures to enable by name, \"Numbers\" is a list of compute capabilities (version number) to enable") - set_property(CACHE CUDA_ARCHITECTURES PROPERTY STRINGS "Auto" "Common" "All" "Kepler Maxwell Kepler+Tegra Maxwell+Tegra Pascal" "5.0 7.5") + set_property(CACHE CUDA_ARCHITECTURES PROPERTY STRINGS "Auto" "Common" "All" "Kepler Maxwell Kepler+Tegra Maxwell+Tegra Pascal" "5.0 7.5 8.6") enable_language(CUDA) if(CMAKE_CUDA_COMPILER_VERSION VERSION_LESS "9.0") message(STATUS "Unsupported CUDA version, please upgrade to CUDA 9+. Disabling CUDA support") @@ -71,7 +71,7 @@ if(CMAKE_CUDA_COMPILER AND ENABLE_CUDA) find_package(CUDA REQUIRED) cuda_select_nvcc_arch_flags(CUDA_ARCH_FLAGS ${CUDA_ARCHITECTURES}) message(STATUS "Building with CUDA flags: " "${CUDA_ARCH_FLAGS}") - if (NOT "arch=compute_70,code=sm_70" IN_LIST CUDA_ARCH_FLAGS AND NOT "arch=compute_72,code=sm_72" IN_LIST CUDA_ARCH_FLAGS AND NOT "arch=compute_75,code=sm_75" IN_LIST CUDA_ARCH_FLAGS AND NOT "arch=compute_80,code=sm_80" IN_LIST CUDA_ARCH_FLAGS) + if (NOT "arch=compute_70,code=sm_70" IN_LIST CUDA_ARCH_FLAGS AND NOT "arch=compute_72,code=sm_72" IN_LIST CUDA_ARCH_FLAGS AND NOT "arch=compute_75,code=sm_75" IN_LIST CUDA_ARCH_FLAGS AND NOT "arch=compute_80,code=sm_80" IN_LIST CUDA_ARCH_FLAGS AND NOT "arch=compute_86,code=sm_86" IN_LIST CUDA_ARCH_FLAGS) set(ENABLE_CUDNN_HALF "FALSE" CACHE BOOL "Enable CUDNN Half precision" FORCE) message(STATUS "Your setup does not supports half precision (it requires CC >= 7.0)") else() diff --git a/Makefile b/Makefile index 414ef099c26..816aaab329d 100644 --- a/Makefile +++ b/Makefile @@ -17,14 +17,17 @@ ZED_CAMERA_v2_8=0 USE_CPP=0 DEBUG=0 -ARCH= -gencode arch=compute_30,code=sm_30 \ - -gencode arch=compute_35,code=sm_35 \ +ARCH= -gencode arch=compute_35,code=sm_35 \ -gencode arch=compute_50,code=[sm_50,compute_50] \ -gencode arch=compute_52,code=[sm_52,compute_52] \ - -gencode arch=compute_61,code=[sm_61,compute_61] + -gencode arch=compute_61,code=[sm_61,compute_61] \ + -gencode arch=compute_86,code=[sm_86,compute_86] OS := $(shell uname) +# Kepler GeForce GTX 770, GTX 760, GT 740 +# ARCH= -gencode arch=compute_30,code=sm_30 + # Tesla A100 (GA100), DGX-A100, RTX 3080 # ARCH= -gencode arch=compute_80,code=[sm_80,compute_80] From eb208de417b0c1919dedab9e2a59fc26e93f1b9e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 1 Dec 2020 16:45:28 +0300 Subject: [PATCH 1150/1365] Commented in the Makefile: -gencode arch=compute_86,code=[sm_86,compute_86] --- Makefile | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/Makefile b/Makefile index 816aaab329d..5fb70543746 100644 --- a/Makefile +++ b/Makefile @@ -20,11 +20,13 @@ DEBUG=0 ARCH= -gencode arch=compute_35,code=sm_35 \ -gencode arch=compute_50,code=[sm_50,compute_50] \ -gencode arch=compute_52,code=[sm_52,compute_52] \ - -gencode arch=compute_61,code=[sm_61,compute_61] \ - -gencode arch=compute_86,code=[sm_86,compute_86] + -gencode arch=compute_61,code=[sm_61,compute_61] OS := $(shell uname) +# GeForce RTX 3070, 3080, 3090 +# ARCH= -gencode arch=compute_86,code=[sm_86,compute_86] + # Kepler GeForce GTX 770, GTX 760, GT 740 # ARCH= -gencode arch=compute_30,code=sm_30 From 6d5addd1764a61ddb1877fe7b06d0429cab6b9d8 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 1 Dec 2020 17:28:28 +0300 Subject: [PATCH 1151/1365] Fixed new_coords=1 scale_x_y = 2.0 for yolov4x-mish.cfg and yolov4-csp.cfg --- build/darknet/x64/cfg/yolov4-csp.cfg | 5 ++++- build/darknet/x64/cfg/yolov4x-mish.cfg | 5 ++++- cfg/yolov4-csp.cfg | 5 ++++- cfg/yolov4x-mish.cfg | 5 ++++- src/yolo_layer.c | 8 ++++---- 5 files changed, 20 insertions(+), 8 deletions(-) diff --git a/build/darknet/x64/cfg/yolov4-csp.cfg b/build/darknet/x64/cfg/yolov4-csp.cfg index 2d1f9c21302..1c977dd2d8b 100644 --- a/build/darknet/x64/cfg/yolov4-csp.cfg +++ b/build/darknet/x64/cfg/yolov4-csp.cfg @@ -26,7 +26,7 @@ mosaic=1 letter_box=1 -optimized_memory=1 +#optimized_memory=1 #23:104x104 54:52x52 85:26x26 104:13x13 for 416 @@ -1032,6 +1032,7 @@ anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 4 classes=80 num=9 jitter=.1 +scale_x_y = 2.0 objectness_smooth=0 ignore_thresh = .7 truth_thresh = 1 @@ -1145,6 +1146,7 @@ anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 4 classes=80 num=9 jitter=.1 +scale_x_y = 2.0 objectness_smooth=1 ignore_thresh = .7 truth_thresh = 1 @@ -1258,6 +1260,7 @@ anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 4 classes=80 num=9 jitter=.1 +scale_x_y = 2.0 objectness_smooth=1 ignore_thresh = .7 truth_thresh = 1 diff --git a/build/darknet/x64/cfg/yolov4x-mish.cfg b/build/darknet/x64/cfg/yolov4x-mish.cfg index 0e9fb44132d..2e6779ca6e6 100644 --- a/build/darknet/x64/cfg/yolov4x-mish.cfg +++ b/build/darknet/x64/cfg/yolov4x-mish.cfg @@ -26,7 +26,7 @@ mosaic=1 letter_box=1 -optimized_memory=1 +#optimized_memory=1 [convolutional] batch_normalize=1 @@ -1159,6 +1159,7 @@ anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 4 classes=80 num=9 jitter=.1 +scale_x_y = 2.0 objectness_smooth=0 ignore_thresh = .7 truth_thresh = 1 @@ -1288,6 +1289,7 @@ anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 4 classes=80 num=9 jitter=.1 +scale_x_y = 2.0 objectness_smooth=1 ignore_thresh = .7 truth_thresh = 1 @@ -1417,6 +1419,7 @@ anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 4 classes=80 num=9 jitter=.1 +scale_x_y = 2.0 objectness_smooth=1 ignore_thresh = .7 truth_thresh = 1 diff --git a/cfg/yolov4-csp.cfg b/cfg/yolov4-csp.cfg index 2d1f9c21302..1c977dd2d8b 100644 --- a/cfg/yolov4-csp.cfg +++ b/cfg/yolov4-csp.cfg @@ -26,7 +26,7 @@ mosaic=1 letter_box=1 -optimized_memory=1 +#optimized_memory=1 #23:104x104 54:52x52 85:26x26 104:13x13 for 416 @@ -1032,6 +1032,7 @@ anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 4 classes=80 num=9 jitter=.1 +scale_x_y = 2.0 objectness_smooth=0 ignore_thresh = .7 truth_thresh = 1 @@ -1145,6 +1146,7 @@ anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 4 classes=80 num=9 jitter=.1 +scale_x_y = 2.0 objectness_smooth=1 ignore_thresh = .7 truth_thresh = 1 @@ -1258,6 +1260,7 @@ anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 4 classes=80 num=9 jitter=.1 +scale_x_y = 2.0 objectness_smooth=1 ignore_thresh = .7 truth_thresh = 1 diff --git a/cfg/yolov4x-mish.cfg b/cfg/yolov4x-mish.cfg index 0e9fb44132d..2e6779ca6e6 100644 --- a/cfg/yolov4x-mish.cfg +++ b/cfg/yolov4x-mish.cfg @@ -26,7 +26,7 @@ mosaic=1 letter_box=1 -optimized_memory=1 +#optimized_memory=1 [convolutional] batch_normalize=1 @@ -1159,6 +1159,7 @@ anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 4 classes=80 num=9 jitter=.1 +scale_x_y = 2.0 objectness_smooth=0 ignore_thresh = .7 truth_thresh = 1 @@ -1288,6 +1289,7 @@ anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 4 classes=80 num=9 jitter=.1 +scale_x_y = 2.0 objectness_smooth=1 ignore_thresh = .7 truth_thresh = 1 @@ -1417,6 +1419,7 @@ anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 4 classes=80 num=9 jitter=.1 +scale_x_y = 2.0 objectness_smooth=1 ignore_thresh = .7 truth_thresh = 1 diff --git a/src/yolo_layer.c b/src/yolo_layer.c index b8f7a23f1e8..fa0f8e934e0 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -138,8 +138,8 @@ box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw // w = ln(t.w * net.w / anchors_w); // w - output of previous conv-layer // h = ln(t.h * net.h / anchors_h); // h - output of previous conv-layer if (new_coords) { - b.x = (i + x[index + 0 * stride] * 2 - 0.5) / lw; - b.y = (j + x[index + 1 * stride] * 2 - 0.5) / lh; + b.x = (i + x[index + 0 * stride]) / lw; + b.y = (j + x[index + 1 * stride]) / lh; b.w = x[index + 2 * stride] * x[index + 2 * stride] * 4 * biases[2 * n] / w; b.h = x[index + 3 * stride] * x[index + 3 * stride] * 4 * biases[2 * n + 1] / h; } @@ -197,8 +197,8 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, float th = log(truth.h*h / biases[2 * n + 1]); if (new_coords) { - tx = (truth.x*lw - i + 0.5) / 2; - ty = (truth.y*lh - j + 0.5) / 2; + //tx = (truth.x*lw - i + 0.5) / 2; + //ty = (truth.y*lh - j + 0.5) / 2; tw = sqrt(truth.w*w / (4 * biases[2 * n])); th = sqrt(truth.h*h / (4 * biases[2 * n + 1])); } From 560fda34947fe842805b49a1eeda8f2c162cdca9 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 2 Dec 2020 14:42:05 +0300 Subject: [PATCH 1152/1365] [net] weights_reject_freq=1001 --- build/darknet/x64/cfg/yolov4-tiny.cfg | 5 ++ include/darknet.h | 1 + src/network.c | 75 ++++++++++++++++++++++++++- src/network.h | 1 + src/parser.c | 1 + 5 files changed, 82 insertions(+), 1 deletion(-) diff --git a/build/darknet/x64/cfg/yolov4-tiny.cfg b/build/darknet/x64/cfg/yolov4-tiny.cfg index e1b53b3af98..821a34bff72 100644 --- a/build/darknet/x64/cfg/yolov4-tiny.cfg +++ b/build/darknet/x64/cfg/yolov4-tiny.cfg @@ -28,6 +28,7 @@ scales=.1,.1 #steps=500000 #scales=0.01 +#weights_reject_freq=1001 #ema_alpha=0.998 #equidistant_point=1000 #num_sigmas_reject_badlabels=3 @@ -242,6 +243,8 @@ random=0 resize=1.5 nms_kind=greedynms beta_nms=0.6 +#new_coords=1 +#scale_x_y = 2.0 [route] layers = -4 @@ -291,3 +294,5 @@ random=0 resize=1.5 nms_kind=greedynms beta_nms=0.6 +#new_coords=1 +#scale_x_y = 2.0 diff --git a/include/darknet.h b/include/darknet.h index 4ebf47db06b..040d9e35999 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -705,6 +705,7 @@ typedef struct network { float *delta_rolling_max; float *delta_rolling_avg; float *delta_rolling_std; + int weights_reject_freq; int equidistant_point; float badlabels_rejection_percentage; float num_sigmas_reject_badlabels; diff --git a/src/network.c b/src/network.c index df78b43c8c0..d42a212b44d 100644 --- a/src/network.c +++ b/src/network.c @@ -439,7 +439,7 @@ float train_network_waitkey(network net, data d, int wait_key) printf(" EMA initialization \n"); } - if ((*net.cur_iteration) >= ema_apply_point) + if ((*net.cur_iteration) == ema_apply_point) { ema_apply(net); // apply EMA (BN rolling mean/var recalculation is required) printf(" ema_apply() \n"); @@ -453,6 +453,18 @@ float train_network_waitkey(network net, data d, int wait_key) } } + + int reject_stop_point = net.max_batches*3/4; + + if ((*net.cur_iteration) < reject_stop_point && + net.weights_reject_freq && + (*net.cur_iteration) % net.weights_reject_freq == 0) + { + float sim_threshold = 0.4; + reject_similar_weights(net, sim_threshold); + } + + free(X); free(y); return (float)sum/(n*batch); @@ -1591,3 +1603,64 @@ void ema_apply(network net) } } + + +void reject_similar_weights(network net, float sim_threshold) +{ + int i; + for (i = 0; i < net.n; ++i) { + layer l = net.layers[i]; + if (i == 0) continue; + if (net.n > i + 1) if (net.layers[i + 1].type == YOLO) continue; + if (net.n > i + 2) if (net.layers[i + 2].type == YOLO) continue; + if (net.n > i + 3) if (net.layers[i + 3].type == YOLO) continue; + + if (l.type == CONVOLUTIONAL && l.activation != LINEAR) { +#ifdef GPU + if (gpu_index >= 0) { + pull_convolutional_layer(l); + } +#endif + int k, j; + float max_sim = -1000; + int max_sim_index = 0; + int max_sim_index2 = 0; + int filter_size = l.size*l.size*l.c; + for (k = 0; k < l.n; ++k) + { + for (j = k+1; j < l.n; ++j) + { + int w1 = k; + int w2 = j; + + float sim = cosine_similarity(&l.weights[filter_size*w1], &l.weights[filter_size*w2], filter_size); + if (sim > max_sim) { + max_sim = sim; + max_sim_index = w1; + max_sim_index2 = w2; + } + } + } + + printf(" reject_similar_weights: i = %d, l.n = %d, w1 = %d, w2 = %d, sim = %f, thresh = %f \n", + i, l.n, max_sim_index, max_sim_index2, max_sim, sim_threshold); + + if (max_sim > sim_threshold) { + printf(" rejecting... \n"); + float scale = sqrt(2. / (l.size*l.size*l.c / l.groups)); + + for (k = 0; k < filter_size; ++k) { + l.weights[max_sim_index*filter_size + k] = scale*rand_uniform(-1, 1); + } + if (l.biases) l.biases[max_sim_index] = 0.0f; + if (l.scales) l.scales[max_sim_index] = 1.0f; + } + +#ifdef GPU + if (gpu_index >= 0) { + push_convolutional_layer(l); + } +#endif + } + } +} diff --git a/src/network.h b/src/network.h index f5e81befb3e..7661c8ef806 100644 --- a/src/network.h +++ b/src/network.h @@ -174,6 +174,7 @@ void restore_network_recurrent_state(network net); int is_ema_initialized(network net); void ema_update(network net, float ema_alpha); void ema_apply(network net); +void reject_similar_weights(network net, float sim_threshold); #ifdef __cplusplus diff --git a/src/parser.c b/src/parser.c index 9f4b639e35c..be566f223f9 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1155,6 +1155,7 @@ void parse_net_options(list *options, network *net) net->batch *= net->time_steps; // mini_batch * time_steps net->subdivisions = subdivs; // number of mini_batches + net->weights_reject_freq = option_find_int_quiet(options, "weights_reject_freq", 0); net->equidistant_point = option_find_int_quiet(options, "equidistant_point", 0); net->badlabels_rejection_percentage = option_find_float_quiet(options, "badlabels_rejection_percentage", 0); net->num_sigmas_reject_badlabels = option_find_float_quiet(options, "num_sigmas_reject_badlabels", 0); From 312fd2e99a765949e468e18277d41f7992f08860 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 3 Dec 2020 02:33:53 +0300 Subject: [PATCH 1153/1365] Fix letter_box for yolov4x-mish/csp.cfg: letter_box = net.letter_box; And force for Detection: if (net.letter_box) letter_box = 1; --- src/demo.c | 5 +++-- src/detector.c | 2 ++ 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/src/demo.c b/src/demo.c index 604b6990bcf..08e06f19651 100644 --- a/src/demo.c +++ b/src/demo.c @@ -107,13 +107,13 @@ void *detect_in_thread(void *ptr) dets = get_network_boxes(&net, get_width_mat(in_img), get_height_mat(in_img), demo_thresh, demo_thresh, 0, 1, &nboxes, 1); // letter box else dets = get_network_boxes(&net, net.w, net.h, demo_thresh, demo_thresh, 0, 1, &nboxes, 0); // resized - + //const float nms = .45; //if (nms) { // if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms); // else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms); //} - + custom_atomic_store_int(&run_detect_in_thread, 0); } @@ -158,6 +158,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int if(weightfile){ load_weights(&net, weightfile); } + if (net.letter_box) letter_box = 1; net.benchmark_layers = benchmark_layers; fuse_conv_batchnorm(net); calculate_binary_weights(net); diff --git a/src/detector.c b/src/detector.c index 0b2c4a652ab..176d31de0d3 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1006,6 +1006,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa args.w = net.w; args.h = net.h; args.c = net.c; + letter_box = net.letter_box; if (letter_box) args.type = LETTERBOX_DATA; else args.type = IMAGE_DATA; @@ -1610,6 +1611,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam if (weightfile) { load_weights(&net, weightfile); } + if (net.letter_box) letter_box = 1; net.benchmark_layers = benchmark_layers; fuse_conv_batchnorm(net); calculate_binary_weights(net); From 8d6e56e7b41961f5e1015ebc0fa4bb04cd0fad4e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 4 Dec 2020 03:06:08 +0300 Subject: [PATCH 1154/1365] Fixed new_coords=1 training --- src/yolo_layer.c | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/src/yolo_layer.c b/src/yolo_layer.c index fa0f8e934e0..4c15790dd0a 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -232,8 +232,13 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, // predict exponential, apply gradient of e^delta_t ONLY for w,h if (new_coords) { - dw *= 8 * x[index + 2 * stride]; - dh *= 8 * x[index + 3 * stride]; + dw *= 8 * x[index + 2 * stride] * biases[2 * n] / w; + dh *= 8 * x[index + 3 * stride] * biases[2 * n + 1] / h; + + //float grad_w = 8 * exp(-x[index + 2 * stride]) / pow(exp(-x[index + 2 * stride]) + 1, 3); + //float grad_h = 8 * exp(-x[index + 3 * stride]) / pow(exp(-x[index + 3 * stride]) + 1, 3); + //dw *= grad_w; + //dh *= grad_h; } else { dw *= exp(x[index + 2 * stride]); From 4709f6156ca1c1baf8d9178576ddef7bb597c9c3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 4 Dec 2020 21:01:24 +0300 Subject: [PATCH 1155/1365] Use default gradient for new_coords=1 --- src/yolo_layer.c | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 4c15790dd0a..3a6e1e2a294 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -230,6 +230,7 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, float dw = all_ious.dx_iou.dl; float dh = all_ious.dx_iou.dr; + /* // predict exponential, apply gradient of e^delta_t ONLY for w,h if (new_coords) { dw *= 8 * x[index + 2 * stride] * biases[2 * n] / w; @@ -244,6 +245,10 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, dw *= exp(x[index + 2 * stride]); dh *= exp(x[index + 3 * stride]); } + */ + + dw *= exp(x[index + 2 * stride]); + dh *= exp(x[index + 3 * stride]); // normalize iou weight dx *= iou_normalizer; From c47b24a81d8b6762ead064d80e4de3629cd0f3ab Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 7 Dec 2020 00:08:30 +0300 Subject: [PATCH 1156/1365] Fixed new_coords=1, yolov4-csp.cfg, yolov4x-mish.cfg --- build/darknet/x64/cfg/yolov4-csp.cfg | 8 ++++---- build/darknet/x64/cfg/yolov4x-mish.cfg | 8 ++++---- src/detector.c | 2 +- src/yolo_layer.c | 27 ++++++++++++++------------ src/yolo_layer.h | 2 +- 5 files changed, 25 insertions(+), 22 deletions(-) diff --git a/build/darknet/x64/cfg/yolov4-csp.cfg b/build/darknet/x64/cfg/yolov4-csp.cfg index 1c977dd2d8b..23ebfd7461d 100644 --- a/build/darknet/x64/cfg/yolov4-csp.cfg +++ b/build/darknet/x64/cfg/yolov4-csp.cfg @@ -1023,7 +1023,7 @@ size=1 stride=1 pad=1 filters=255 -activation=linear +activation=logistic [yolo] @@ -1046,7 +1046,7 @@ iou_loss=ciou nms_kind=diounms beta_nms=0.6 new_coords=1 -max_delta=20 +max_delta=5 [route] layers = -4 @@ -1137,7 +1137,7 @@ size=1 stride=1 pad=1 filters=255 -activation=linear +activation=logistic [yolo] @@ -1251,7 +1251,7 @@ size=1 stride=1 pad=1 filters=255 -activation=linear +activation=logistic [yolo] diff --git a/build/darknet/x64/cfg/yolov4x-mish.cfg b/build/darknet/x64/cfg/yolov4x-mish.cfg index 2e6779ca6e6..2ff854f6dcc 100644 --- a/build/darknet/x64/cfg/yolov4x-mish.cfg +++ b/build/darknet/x64/cfg/yolov4x-mish.cfg @@ -1150,7 +1150,7 @@ size=1 stride=1 pad=1 filters=255 -activation=linear +activation=logistic [yolo] @@ -1173,7 +1173,7 @@ iou_loss=ciou nms_kind=diounms beta_nms=0.6 new_coords=1 -max_delta=20 +max_delta=5 [route] layers = -4 @@ -1280,7 +1280,7 @@ size=1 stride=1 pad=1 filters=255 -activation=linear +activation=logistic [yolo] @@ -1410,7 +1410,7 @@ size=1 stride=1 pad=1 filters=255 -activation=linear +activation=logistic [yolo] diff --git a/src/detector.c b/src/detector.c index 176d31de0d3..d9aeed3b97a 100644 --- a/src/detector.c +++ b/src/detector.c @@ -382,7 +382,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) { //if (i % 100 == 0) { - if (iteration >= (iter_save + 1000) || iteration % 1000 == 0) { + if (iteration >= (iter_save + 10000) || iteration % 10000 == 0) { iter_save = iteration; #ifdef GPU if (ngpus != 1) sync_nets(nets, ngpus, 0); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 3a6e1e2a294..7b1f5f13c4e 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -230,11 +230,13 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, float dw = all_ious.dx_iou.dl; float dh = all_ious.dx_iou.dr; - /* + // predict exponential, apply gradient of e^delta_t ONLY for w,h if (new_coords) { - dw *= 8 * x[index + 2 * stride] * biases[2 * n] / w; - dh *= 8 * x[index + 3 * stride] * biases[2 * n + 1] / h; + //dw *= 8 * x[index + 2 * stride]; + //dh *= 8 * x[index + 3 * stride]; + //dw *= 8 * x[index + 2 * stride] * biases[2 * n] / w; + //dh *= 8 * x[index + 3 * stride] * biases[2 * n + 1] / h; //float grad_w = 8 * exp(-x[index + 2 * stride]) / pow(exp(-x[index + 2 * stride]) + 1, 3); //float grad_h = 8 * exp(-x[index + 3 * stride]) / pow(exp(-x[index + 3 * stride]) + 1, 3); @@ -245,10 +247,10 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, dw *= exp(x[index + 2 * stride]); dh *= exp(x[index + 3 * stride]); } - */ - dw *= exp(x[index + 2 * stride]); - dh *= exp(x[index + 3 * stride]); + + //dw *= exp(x[index + 2 * stride]); + //dh *= exp(x[index + 3 * stride]); // normalize iou weight dx *= iou_normalizer; @@ -668,14 +670,14 @@ void forward_yolo_layer(const layer l, network_state state) for (n = 0; n < l.n; ++n) { int index = entry_index(l, b, n*l.w*l.h, 0); if (l.new_coords) { - activate_array(l.output + index, 4 * l.w*l.h, LOGISTIC); // x,y,w,h + //activate_array(l.output + index, 4 * l.w*l.h, LOGISTIC); // x,y,w,h } else { activate_array(l.output + index, 2 * l.w*l.h, LOGISTIC); // x,y, + index = entry_index(l, b, n*l.w*l.h, 4); + activate_array(l.output + index, (1 + l.classes)*l.w*l.h, LOGISTIC); } scal_add_cpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale x,y - index = entry_index(l, b, n*l.w*l.h, 4); - activate_array(l.output + index, (1 + l.classes)*l.w*l.h, LOGISTIC); } } #endif @@ -1173,14 +1175,15 @@ void forward_yolo_layer_gpu(const layer l, network_state state) // if(y->1) x -> inf // if(y->0) x -> -inf if (l.new_coords) { - activate_array_ongpu(l.output_gpu + index, 4 * l.w*l.h, LOGISTIC); // x,y,w,h + //activate_array_ongpu(l.output_gpu + index, 4 * l.w*l.h, LOGISTIC); // x,y,w,h } else { activate_array_ongpu(l.output_gpu + index, 2 * l.w*l.h, LOGISTIC); // x,y + + index = entry_index(l, b, n*l.w*l.h, 4); + activate_array_ongpu(l.output_gpu + index, (1 + l.classes)*l.w*l.h, LOGISTIC); // classes and objectness } if (l.scale_x_y != 1) scal_add_ongpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output_gpu + index, 1); // scale x,y - index = entry_index(l, b, n*l.w*l.h, 4); - activate_array_ongpu(l.output_gpu + index, (1+l.classes)*l.w*l.h, LOGISTIC); // classes and objectness } } if(!state.train || l.onlyforward){ diff --git a/src/yolo_layer.h b/src/yolo_layer.h index 8de44b45517..08883b0f244 100644 --- a/src/yolo_layer.h +++ b/src/yolo_layer.h @@ -20,7 +20,7 @@ void correct_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth #ifdef GPU void forward_yolo_layer_gpu(const layer l, network_state state); -void backward_yolo_layer_gpu(layer l, network_state state); +void backward_yolo_layer_gpu(const layer l, network_state state); #endif #ifdef __cplusplus From ef279575c5132d770b68693dcd1948f96029ec69 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 7 Dec 2020 02:10:09 +0300 Subject: [PATCH 1157/1365] Fixed yolov4-csp.cfg, yolov4x-mish.cfg --- cfg/yolov4-csp.cfg | 8 ++++---- cfg/yolov4x-mish.cfg | 8 ++++---- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/cfg/yolov4-csp.cfg b/cfg/yolov4-csp.cfg index 1c977dd2d8b..23ebfd7461d 100644 --- a/cfg/yolov4-csp.cfg +++ b/cfg/yolov4-csp.cfg @@ -1023,7 +1023,7 @@ size=1 stride=1 pad=1 filters=255 -activation=linear +activation=logistic [yolo] @@ -1046,7 +1046,7 @@ iou_loss=ciou nms_kind=diounms beta_nms=0.6 new_coords=1 -max_delta=20 +max_delta=5 [route] layers = -4 @@ -1137,7 +1137,7 @@ size=1 stride=1 pad=1 filters=255 -activation=linear +activation=logistic [yolo] @@ -1251,7 +1251,7 @@ size=1 stride=1 pad=1 filters=255 -activation=linear +activation=logistic [yolo] diff --git a/cfg/yolov4x-mish.cfg b/cfg/yolov4x-mish.cfg index 2e6779ca6e6..2ff854f6dcc 100644 --- a/cfg/yolov4x-mish.cfg +++ b/cfg/yolov4x-mish.cfg @@ -1150,7 +1150,7 @@ size=1 stride=1 pad=1 filters=255 -activation=linear +activation=logistic [yolo] @@ -1173,7 +1173,7 @@ iou_loss=ciou nms_kind=diounms beta_nms=0.6 new_coords=1 -max_delta=20 +max_delta=5 [route] layers = -4 @@ -1280,7 +1280,7 @@ size=1 stride=1 pad=1 filters=255 -activation=linear +activation=logistic [yolo] @@ -1410,7 +1410,7 @@ size=1 stride=1 pad=1 filters=255 -activation=linear +activation=logistic [yolo] From 66dfa5bba4f5a01362e3741013a8ebe6e1151d90 Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 7 Dec 2020 17:22:17 +0300 Subject: [PATCH 1158/1365] Update README.md --- README.md | 21 +++++++++++++++++++-- 1 file changed, 19 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index f1e6ab4ec2a..7fe5e2281c7 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,9 @@ ## (neural networks for object detection) -Paper Yolo v4: https://arxiv.org/abs/2004.10934 +Paper YOLO v4: https://arxiv.org/abs/2004.10934 + +Paper Scaled YOLO v4: https://arxiv.org/abs/2011.08036 More details: [medium link](https://medium.com/@alexeyab84/yolov4-the-most-accurate-real-time-neural-network-on-ms-coco-dataset-73adfd3602fe?source=friends_link&sk=6039748846bbcf1d960c3061542591d7) @@ -54,6 +56,10 @@ About Darknet framework: http://pjreddie.com/darknet/ ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) +![scaled_yolov4](https://user-images.githubusercontent.com/4096485/101356322-f1f5a180-38a8-11eb-9907-4fe4f188d887.png) AP50:95 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2011.08036 + +---- + ![modern_gpus](https://user-images.githubusercontent.com/4096485/82835867-f1c62380-9ecd-11ea-9134-1598ed2abc4b.png) AP50:95 / AP50 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2004.10934 @@ -81,7 +87,8 @@ tkDNN-TensorRT accelerates YOLOv4 **~2x** times for batch=1 and **3x-4x** times #### Youtube video of results -[![Yolo v4](http://img.youtube.com/vi/1_SiUOYUoOI/0.jpg)](https://youtu.be/1_SiUOYUoOI "Yolo v4") +| [![Yolo v4](https://user-images.githubusercontent.com/4096485/101360000-1a33cf00-38ae-11eb-9e5e-b29c5fb0afbe.png)](https://youtu.be/1_SiUOYUoOI "Yolo v4") | [![Scaled Yolo v4](https://user-images.githubusercontent.com/4096485/101359389-43a02b00-38ad-11eb-866c-f813e96bf61a.png)](https://youtu.be/YDFf-TqJOFE "Scaled Yolo v4") | +|---|---| Others: https://www.youtube.com/user/pjreddie/videos @@ -124,6 +131,16 @@ There are weights-file for different cfg-files (trained for MS COCO dataset): FPS on RTX 2070 (R) and Tesla V100 (V): +* [yolov4x-mish.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4x-mish.cfg) - **67.9% mAP@0.5 (49.4% AP@0.5:0.95) - 23(R) FPS / 50(V) FPS** - 221 BFlops (110 FMA) - 381 MB: [yolov4x-mish.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.weights) + * pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.conv.166 + +* [yolov4-csp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-csp.cfg) - 202 MB: [yolov4-csp.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.weights) paper [Scaled Yolo v4](https://arxiv.org/abs/2011.08036) + + just change `width=` and `height=` parameters in `yolov4-csp.cfg` file and use the same `yolov4-csp.weights` file for all cases: + * `width=608 height=608` in cfg: **66.2% mAP@0.5 (47.5% AP@0.5:0.95) - 70(V) FPS** - 120 (60 FMA) BFlops + * `width=512 height=512` in cfg: **64.8% mAP@0.5 (46.2% AP@0.5:0.95) - 93(V) FPS** - 77 (39 FMA) BFlops + * pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.conv.142 + * [yolov4.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg) - 245 MB: [yolov4.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights) (Google-drive mirror [yolov4.weights](https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT) ) paper [Yolo v4](https://arxiv.org/abs/2004.10934) just change `width=` and `height=` parameters in `yolov4.cfg` file and use the same `yolov4.weights` file for all cases: * `width=608 height=608` in cfg: **65.7% mAP@0.5 (43.5% AP@0.5:0.95) - 34(R) FPS / 62(V) FPS** - 128.5 BFlops From 4ee3be7e68fb9c7eda5cc390e47e59f01e40dded Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 7 Dec 2020 17:27:02 +0300 Subject: [PATCH 1159/1365] Update README.md --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 7fe5e2281c7..062f1c2ed20 100644 --- a/README.md +++ b/README.md @@ -196,7 +196,8 @@ You can get cfg-files by path: `darknet/cfg/` #### Yolo v4 in other frameworks -* **TensorFlow:** YOLOv4 on TensorFlow 2.0 / TFlite / Andriod: https://github.com/hunglc007/tensorflow-yolov4-tflite +* **Pytorch - Scaled-YOLOv4:** https://github.com/WongKinYiu/ScaledYOLOv4 +* **TensorFlow:** `pip install yolov4` YOLOv4 on TensorFlow 2.0 / TFlite / Andriod: https://github.com/hunglc007/tensorflow-yolov4-tflite For YOLOv3 - convert `yolov3.weights`/`cfg` files to `yolov3.ckpt`/`pb/meta`: by using [mystic123](https://github.com/mystic123/tensorflow-yolo-v3) project, and [TensorFlow-lite](https://www.tensorflow.org/lite/guide/get_started#2_convert_the_model_format) * **OpenCV-dnn** the fastest implementation of YOLOv4 for CPU (x86/ARM-Android), OpenCV can be compiled with [OpenVINO-backend](https://github.com/opencv/opencv/wiki/Intel's-Deep-Learning-Inference-Engine-backend) for running on (Myriad X / USB Neural Compute Stick / Arria FPGA), use `yolov4.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221) or [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) * **Intel OpenVINO 2020 R4:** (NPU Myriad X / USB Neural Compute Stick / Arria FPGA): read this [manual](https://github.com/TNTWEN/OpenVINO-YOLOV4) (old [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) ) @@ -205,6 +206,7 @@ You can get cfg-files by path: `darknet/cfg/` * [WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4) * [maudzung/3D-YOLOv4](https://github.com/maudzung/Complex-YOLOv4-Pytorch) * [Tianxiaomo/pytorch-YOLOv4](https://github.com/Tianxiaomo/pytorch-YOLOv4) + * [YOLOv5](https://github.com/ultralytics/yolov5) * **ONNX** on Jetson for YOLOv4: https://developer.nvidia.com/blog/announcing-onnx-runtime-for-jetson/ * **TensorRT** YOLOv4 on TensorRT+tkDNN: https://github.com/ceccocats/tkDNN For YOLOv3 (-70% faster inference): [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) read [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf). [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) implemented yolov3-spp, yolov4, etc. From a72bb059d88bb7a4a0ec2451eec243e9de4f62b0 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 10 Dec 2020 22:12:45 +0300 Subject: [PATCH 1160/1365] Show GIoU/IoU loss instead of RMSE by default --- src/parser.c | 2 +- src/yolo_layer.c | 7 +++++-- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/src/parser.c b/src/parser.c index be566f223f9..eb41e91457b 100644 --- a/src/parser.c +++ b/src/parser.c @@ -454,7 +454,7 @@ layer parse_yolo(list *options, size_params params) } //assert(l.outputs == params.inputs); - l.show_details = option_find_int_quiet(options, "show_details", 0); + l.show_details = option_find_int_quiet(options, "show_details", 1); l.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10 char *cpc = option_find_str(options, "counters_per_class", 0); l.classes_multipliers = get_classes_multipliers(cpc, classes, l.max_delta); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 7b1f5f13c4e..f31f6f57e14 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -380,6 +380,7 @@ typedef struct train_yolo_args { int b; float tot_iou; + float tot_giou_loss; int count; int class_count; } train_yolo_args; @@ -720,6 +721,7 @@ void forward_yolo_layer(const layer l, network_state state) yolo_args[b].b = b; yolo_args[b].tot_iou = 0; + yolo_args[b].tot_giou_loss = 0; yolo_args[b].count = 0; yolo_args[b].class_count = 0; @@ -731,6 +733,7 @@ void forward_yolo_layer(const layer l, network_state state) pthread_join(threads[b], 0); tot_iou += yolo_args[b].tot_iou; + tot_giou_loss += yolo_args[b].tot_giou_loss; count += yolo_args[b].count; class_count += yolo_args[b].class_count; } @@ -891,7 +894,7 @@ void forward_yolo_layer(const layer l, network_state state) float avg_iou_loss = 0; *(l.cost) = loss; - /* + // gIOU loss + MSE (objectness) loss if (l.iou_loss == MSE) { *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); @@ -908,7 +911,7 @@ void forward_yolo_layer(const layer l, network_state state) } *(l.cost) = avg_iou_loss + classification_loss; } - */ + loss /= l.batch; classification_loss /= l.batch; From 1b936ab9ad4b19254e96a0fe02c0b2a0812a88dd Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 10 Dec 2020 22:56:54 +0300 Subject: [PATCH 1161/1365] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 062f1c2ed20..04b3709161a 100644 --- a/README.md +++ b/README.md @@ -4,9 +4,9 @@ Paper YOLO v4: https://arxiv.org/abs/2004.10934 -Paper Scaled YOLO v4: https://arxiv.org/abs/2011.08036 +Paper Scaled YOLO v4: https://arxiv.org/abs/2011.08036 use to reproduce results: https://github.com/WongKinYiu/ScaledYOLOv4 -More details: [medium link](https://medium.com/@alexeyab84/yolov4-the-most-accurate-real-time-neural-network-on-ms-coco-dataset-73adfd3602fe?source=friends_link&sk=6039748846bbcf1d960c3061542591d7) +More details: [medium link_Scaled_YOLOv4](https://alexeyab84.medium.com/scaled-yolo-v4-is-the-best-neural-network-for-object-detection-on-ms-coco-dataset-39dfa22fa982?source=friends_link&sk=c8553bfed861b1a7932f739d26f487c8) and [medium link_YOLOv4](https://medium.com/@alexeyab84/yolov4-the-most-accurate-real-time-neural-network-on-ms-coco-dataset-73adfd3602fe?source=friends_link&sk=6039748846bbcf1d960c3061542591d7) Manual: https://github.com/AlexeyAB/darknet/wiki From b5ff7f4cd3803caf35d932f5a1d2529e445bbe3f Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Thu, 10 Dec 2020 23:13:26 +0300 Subject: [PATCH 1162/1365] Save weights for each 1000 iterations if max_batches < 10000 --- README.md | 6 ++++-- src/detector.c | 4 +++- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 04b3709161a..203f77c7591 100644 --- a/README.md +++ b/README.md @@ -4,9 +4,11 @@ Paper YOLO v4: https://arxiv.org/abs/2004.10934 -Paper Scaled YOLO v4: https://arxiv.org/abs/2011.08036 use to reproduce results: https://github.com/WongKinYiu/ScaledYOLOv4 +Paper Scaled YOLO v4: https://arxiv.org/abs/2011.08036 use to reproduce results: [ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4) -More details: [medium link_Scaled_YOLOv4](https://alexeyab84.medium.com/scaled-yolo-v4-is-the-best-neural-network-for-object-detection-on-ms-coco-dataset-39dfa22fa982?source=friends_link&sk=c8553bfed861b1a7932f739d26f487c8) and [medium link_YOLOv4](https://medium.com/@alexeyab84/yolov4-the-most-accurate-real-time-neural-network-on-ms-coco-dataset-73adfd3602fe?source=friends_link&sk=6039748846bbcf1d960c3061542591d7) +More details in articles on medium: + * [Scaled_YOLOv4](https://alexeyab84.medium.com/scaled-yolo-v4-is-the-best-neural-network-for-object-detection-on-ms-coco-dataset-39dfa22fa982?source=friends_link&sk=c8553bfed861b1a7932f739d26f487c8) + * [YOLOv4](https://medium.com/@alexeyab84/yolov4-the-most-accurate-real-time-neural-network-on-ms-coco-dataset-73adfd3602fe?source=friends_link&sk=6039748846bbcf1d960c3061542591d7) Manual: https://github.com/AlexeyAB/darknet/wiki diff --git a/src/detector.c b/src/detector.c index d9aeed3b97a..a2fdf0bf3cd 100644 --- a/src/detector.c +++ b/src/detector.c @@ -382,7 +382,9 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) { //if (i % 100 == 0) { - if (iteration >= (iter_save + 10000) || iteration % 10000 == 0) { + if ((iteration >= (iter_save + 10000) || iteration % 10000 == 0) || + (iteration >= (iter_save + 1000) || iteration % 1000 == 0) && net.max_batches < 10000) + { iter_save = iteration; #ifdef GPU if (ngpus != 1) sync_nets(nets, ngpus, 0); From 6be487b51f720fad926b6e2fff5685b3328561c5 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 11 Dec 2020 05:56:07 +0300 Subject: [PATCH 1163/1365] Fixed cspx-p7-mish.cfg --- build/darknet/x64/cfg/cspx-p7-mish.cfg | 20 ++++++++++---------- cfg/cspx-p7-mish.cfg | 20 ++++++++++---------- 2 files changed, 20 insertions(+), 20 deletions(-) diff --git a/build/darknet/x64/cfg/cspx-p7-mish.cfg b/build/darknet/x64/cfg/cspx-p7-mish.cfg index b5713391bcc..01be7283680 100644 --- a/build/darknet/x64/cfg/cspx-p7-mish.cfg +++ b/build/darknet/x64/cfg/cspx-p7-mish.cfg @@ -2340,7 +2340,7 @@ size=1 stride=1 pad=1 filters=340 -activation=linear +activation=logistic [yolo] @@ -2352,7 +2352,7 @@ ignore_thresh = .7 truth_thresh = 1 #random=1 resize=1.5 -scale_x_y = 1.05 +scale_x_y = 2.0 jitter=.1 objectness_smooth=1 iou_thresh=0.2 @@ -2400,7 +2400,7 @@ size=1 stride=1 pad=1 filters=340 -activation=linear +activation=logistic [yolo] mask = 4,5,6,7 @@ -2411,7 +2411,7 @@ ignore_thresh = .7 truth_thresh = 1 #random=1 resize=1.5 -scale_x_y = 1.05 +scale_x_y = 2.0 jitter=.1 objectness_smooth=1 iou_thresh=0.2 @@ -2459,7 +2459,7 @@ size=1 stride=1 pad=1 filters=340 -activation=linear +activation=logistic [yolo] mask = 8,9,10,11 @@ -2470,7 +2470,7 @@ ignore_thresh = .7 truth_thresh = 1 #random=1 resize=1.5 -scale_x_y = 1.05 +scale_x_y = 2.0 jitter=.1 objectness_smooth=1 iou_thresh=0.2 @@ -2518,7 +2518,7 @@ size=1 stride=1 pad=1 filters=340 -activation=linear +activation=logistic [yolo] mask = 12,13,14,15 @@ -2529,7 +2529,7 @@ ignore_thresh = .7 truth_thresh = 1 #random=1 resize=1.5 -scale_x_y = 1.05 +scale_x_y = 2.0 jitter=.1 objectness_smooth=1 iou_thresh=0.2 @@ -2577,7 +2577,7 @@ size=1 stride=1 pad=1 filters=340 -activation=linear +activation=logistic [yolo] mask = 16,17,18,19 @@ -2588,7 +2588,7 @@ ignore_thresh = .7 truth_thresh = 1 #random=1 resize=1.5 -scale_x_y = 1.05 +scale_x_y = 2.0 jitter=.1 objectness_smooth=1 iou_thresh=0.2 diff --git a/cfg/cspx-p7-mish.cfg b/cfg/cspx-p7-mish.cfg index b5713391bcc..01be7283680 100644 --- a/cfg/cspx-p7-mish.cfg +++ b/cfg/cspx-p7-mish.cfg @@ -2340,7 +2340,7 @@ size=1 stride=1 pad=1 filters=340 -activation=linear +activation=logistic [yolo] @@ -2352,7 +2352,7 @@ ignore_thresh = .7 truth_thresh = 1 #random=1 resize=1.5 -scale_x_y = 1.05 +scale_x_y = 2.0 jitter=.1 objectness_smooth=1 iou_thresh=0.2 @@ -2400,7 +2400,7 @@ size=1 stride=1 pad=1 filters=340 -activation=linear +activation=logistic [yolo] mask = 4,5,6,7 @@ -2411,7 +2411,7 @@ ignore_thresh = .7 truth_thresh = 1 #random=1 resize=1.5 -scale_x_y = 1.05 +scale_x_y = 2.0 jitter=.1 objectness_smooth=1 iou_thresh=0.2 @@ -2459,7 +2459,7 @@ size=1 stride=1 pad=1 filters=340 -activation=linear +activation=logistic [yolo] mask = 8,9,10,11 @@ -2470,7 +2470,7 @@ ignore_thresh = .7 truth_thresh = 1 #random=1 resize=1.5 -scale_x_y = 1.05 +scale_x_y = 2.0 jitter=.1 objectness_smooth=1 iou_thresh=0.2 @@ -2518,7 +2518,7 @@ size=1 stride=1 pad=1 filters=340 -activation=linear +activation=logistic [yolo] mask = 12,13,14,15 @@ -2529,7 +2529,7 @@ ignore_thresh = .7 truth_thresh = 1 #random=1 resize=1.5 -scale_x_y = 1.05 +scale_x_y = 2.0 jitter=.1 objectness_smooth=1 iou_thresh=0.2 @@ -2577,7 +2577,7 @@ size=1 stride=1 pad=1 filters=340 -activation=linear +activation=logistic [yolo] mask = 16,17,18,19 @@ -2588,7 +2588,7 @@ ignore_thresh = .7 truth_thresh = 1 #random=1 resize=1.5 -scale_x_y = 1.05 +scale_x_y = 2.0 jitter=.1 objectness_smooth=1 iou_thresh=0.2 From 1eda55d7919e8e6394bcd275eb5cb8f23f38621e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 11 Dec 2020 21:53:34 +0300 Subject: [PATCH 1164/1365] Fixed EMA. Fixed display of GIoU / IoU loss. --- src/network.c | 3 +-- src/yolo_layer.c | 15 +++++++++------ 2 files changed, 10 insertions(+), 8 deletions(-) diff --git a/src/network.c b/src/network.c index d42a212b44d..9dec1df5761 100644 --- a/src/network.c +++ b/src/network.c @@ -445,8 +445,7 @@ float train_network_waitkey(network net, data d, int wait_key) printf(" ema_apply() \n"); } else - if ((*net.cur_iteration) < ema_apply_point && - (*net.cur_iteration) % ema_period == 0) + if ((*net.cur_iteration) < ema_apply_point)// && (*net.cur_iteration) % ema_period == 0) { ema_update(net, net.ema_alpha); // update EMA printf(" ema_update(), ema_alpha = %f \n", net.ema_alpha); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index f31f6f57e14..ecb3fff29bc 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -381,6 +381,7 @@ typedef struct train_yolo_args { float tot_iou; float tot_giou_loss; + float tot_iou_loss; int count; int class_count; } train_yolo_args; @@ -401,8 +402,8 @@ void *process_batch(void* ptr) float tot_giou = 0; float tot_diou = 0; float tot_ciou = 0; - float tot_iou_loss = 0; - float tot_giou_loss = 0; + //float tot_iou_loss = 0; + //float tot_giou_loss = 0; float tot_diou_loss = 0; float tot_ciou_loss = 0; float recall = 0; @@ -553,10 +554,10 @@ void *process_batch(void* ptr) // range is 0 <= 1 args->tot_iou += all_ious.iou; - tot_iou_loss += 1 - all_ious.iou; + args->tot_iou_loss += 1 - all_ious.iou; // range is -1 <= giou <= 1 tot_giou += all_ious.giou; - tot_giou_loss += 1 - all_ious.giou; + args->tot_giou_loss += 1 - all_ious.giou; tot_diou += all_ious.diou; tot_diou_loss += 1 - all_ious.diou; @@ -605,10 +606,10 @@ void *process_batch(void* ptr) // range is 0 <= 1 args->tot_iou += all_ious.iou; - tot_iou_loss += 1 - all_ious.iou; + args->tot_iou_loss += 1 - all_ious.iou; // range is -1 <= giou <= 1 tot_giou += all_ious.giou; - tot_giou_loss += 1 - all_ious.giou; + args->tot_giou_loss += 1 - all_ious.giou; tot_diou += all_ious.diou; tot_diou_loss += 1 - all_ious.diou; @@ -721,6 +722,7 @@ void forward_yolo_layer(const layer l, network_state state) yolo_args[b].b = b; yolo_args[b].tot_iou = 0; + yolo_args[b].tot_iou_loss = 0; yolo_args[b].tot_giou_loss = 0; yolo_args[b].count = 0; yolo_args[b].class_count = 0; @@ -733,6 +735,7 @@ void forward_yolo_layer(const layer l, network_state state) pthread_join(threads[b], 0); tot_iou += yolo_args[b].tot_iou; + tot_iou_loss += yolo_args[b].tot_iou_loss; tot_giou_loss += yolo_args[b].tot_giou_loss; count += yolo_args[b].count; class_count += yolo_args[b].class_count; From 2da4f0de842bb98a65cb93129408a699142a49f1 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 12 Dec 2020 04:37:31 +0300 Subject: [PATCH 1165/1365] Reverted some changes in yolo_layer.c to fix a bug --- src/yolo_layer.c | 467 ++++++++++++----------------------------------- 1 file changed, 121 insertions(+), 346 deletions(-) diff --git a/src/yolo_layer.c b/src/yolo_layer.c index ecb3fff29bc..2814f30ed0c 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -230,7 +230,6 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, float dw = all_ious.dx_iou.dl; float dh = all_ious.dx_iou.dr; - // predict exponential, apply gradient of e^delta_t ONLY for w,h if (new_coords) { //dw *= 8 * x[index + 2 * stride]; @@ -248,10 +247,6 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, dh *= exp(x[index + 3 * stride]); } - - //dw *= exp(x[index + 2 * stride]); - //dh *= exp(x[index + 3 * stride]); - // normalize iou weight dx *= iou_normalizer; dy *= iou_normalizer; @@ -374,62 +369,63 @@ static int entry_index(layer l, int batch, int location, int entry) return batch*l.outputs + n*l.w*l.h*(4+l.classes+1) + entry*l.w*l.h + loc; } -typedef struct train_yolo_args { - layer l; - network_state state; - int b; +void forward_yolo_layer(const layer l, network_state state) +{ + int i, j, b, t, n; + memcpy(l.output, state.input, l.outputs*l.batch * sizeof(float)); - float tot_iou; - float tot_giou_loss; - float tot_iou_loss; - int count; - int class_count; -} train_yolo_args; +#ifndef GPU + for (b = 0; b < l.batch; ++b) { + for (n = 0; n < l.n; ++n) { + int index = entry_index(l, b, n*l.w*l.h, 0); + activate_array(l.output + index, 2 * l.w*l.h, LOGISTIC); // x,y, + scal_add_cpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale x,y + index = entry_index(l, b, n*l.w*l.h, 4); + activate_array(l.output + index, (1 + l.classes)*l.w*l.h, LOGISTIC); + } + } +#endif -void *process_batch(void* ptr) -{ - { - train_yolo_args *args = (train_yolo_args*)ptr; - const layer l = args->l; - network_state state = args->state; - int b = args->b; - - int i, j, t, n; - - //printf(" b = %d \n", b, b); - - //float tot_iou = 0; - float tot_giou = 0; - float tot_diou = 0; - float tot_ciou = 0; - //float tot_iou_loss = 0; - //float tot_giou_loss = 0; - float tot_diou_loss = 0; - float tot_ciou_loss = 0; - float recall = 0; - float recall75 = 0; - float avg_cat = 0; - float avg_obj = 0; - float avg_anyobj = 0; - //int count = 0; - //int class_count = 0; + // delta is zeroed + memset(l.delta, 0, l.outputs * l.batch * sizeof(float)); + if (!state.train) return; + for (i = 0; i < l.batch * l.w*l.h*l.n; ++i) l.labels[i] = -1; + for (i = 0; i < l.batch * l.w*l.h*l.n; ++i) l.class_ids[i] = -1; + //float avg_iou = 0; + float tot_iou = 0; + float tot_giou = 0; + float tot_diou = 0; + float tot_ciou = 0; + float tot_iou_loss = 0; + float tot_giou_loss = 0; + float tot_diou_loss = 0; + float tot_ciou_loss = 0; + float recall = 0; + float recall75 = 0; + float avg_cat = 0; + float avg_obj = 0; + float avg_anyobj = 0; + int count = 0; + int class_count = 0; + *(l.cost) = 0; + for (b = 0; b < l.batch; ++b) { for (j = 0; j < l.h; ++j) { for (i = 0; i < l.w; ++i) { for (n = 0; n < l.n; ++n) { - const int class_index = entry_index(l, b, n * l.w * l.h + j * l.w + i, 4 + 1); - const int obj_index = entry_index(l, b, n * l.w * l.h + j * l.w + i, 4); - const int box_index = entry_index(l, b, n * l.w * l.h + j * l.w + i, 0); - const int stride = l.w * l.h; - box pred = get_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w * l.h, l.new_coords); + const int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); + const int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4); + const int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); + const int stride = l.w*l.h; + box pred = get_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w*l.h, l.new_coords); float best_match_iou = 0; int best_match_t = 0; float best_iou = 0; int best_t = 0; for (t = 0; t < l.max_boxes; ++t) { - box truth = float_to_box_stride(state.truth + t * l.truth_size + b * l.truths, 1); + box truth = float_to_box_stride(state.truth + t*l.truth_size + b*l.truths, 1); if (!truth.x) break; // continue; - int class_id = state.truth[t * l.truth_size + b * l.truths + 4]; + int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; if (class_id >= l.classes || class_id < 0) { printf("\n Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1); printf("\n truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f, class_id = %d \n", truth.x, truth.y, truth.w, truth.h, class_id); @@ -439,7 +435,7 @@ void *process_batch(void* ptr) float objectness = l.output[obj_index]; if (isnan(objectness) || isinf(objectness)) l.output[obj_index] = 0; - int class_id_match = compare_yolo_class(l.output, l.classes, class_index, l.w * l.h, objectness, class_id, 0.25f); + int class_id_match = compare_yolo_class(l.output, l.classes, class_index, l.w*l.h, objectness, class_id, 0.25f); float iou = box_iou(pred, truth); if (iou > best_match_iou && class_id_match == 1) { @@ -463,23 +459,23 @@ void *process_batch(void* ptr) else l.delta[obj_index] = 0; } else if (state.net.adversarial) { - int stride = l.w * l.h; + int stride = l.w*l.h; float scale = pred.w * pred.h; if (scale > 0) scale = sqrt(scale); l.delta[obj_index] = scale * l.obj_normalizer * (0 - l.output[obj_index]); int cl_id; int found_object = 0; for (cl_id = 0; cl_id < l.classes; ++cl_id) { - if (l.output[class_index + stride * cl_id] * l.output[obj_index] > 0.25) { - l.delta[class_index + stride * cl_id] = scale * (0 - l.output[class_index + stride * cl_id]); + if (l.output[class_index + stride*cl_id] * l.output[obj_index] > 0.25) { + l.delta[class_index + stride*cl_id] = scale * (0 - l.output[class_index + stride*cl_id]); found_object = 1; } } if (found_object) { // don't use this loop for adversarial attack drawing for (cl_id = 0; cl_id < l.classes; ++cl_id) - if (l.output[class_index + stride * cl_id] * l.output[obj_index] < 0.25) - l.delta[class_index + stride * cl_id] = scale * (1 - l.output[class_index + stride * cl_id]); + if (l.output[class_index + stride*cl_id] * l.output[obj_index] < 0.25) + l.delta[class_index + stride*cl_id] = scale * (1 - l.output[class_index + stride*cl_id]); l.delta[box_index + 0 * stride] += scale * (0 - l.output[box_index + 0 * stride]); l.delta[box_index + 1 * stride] += scale * (0 - l.output[box_index + 1 * stride]); @@ -488,25 +484,25 @@ void *process_batch(void* ptr) } } if (best_iou > l.truth_thresh) { - const float iou_multiplier = best_iou * best_iou;// (best_iou - l.truth_thresh) / (1.0 - l.truth_thresh); + const float iou_multiplier = best_iou*best_iou;// (best_iou - l.truth_thresh) / (1.0 - l.truth_thresh); if (l.objectness_smooth) l.delta[obj_index] = l.obj_normalizer * (iou_multiplier - l.output[obj_index]); else l.delta[obj_index] = l.obj_normalizer * (1 - l.output[obj_index]); //l.delta[obj_index] = l.obj_normalizer * (1 - l.output[obj_index]); - int class_id = state.truth[best_t * l.truth_size + b * l.truths + 4]; + int class_id = state.truth[best_t*l.truth_size + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; - delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w * l.h, 0, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); + delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; - if (l.objectness_smooth) l.delta[class_index + stride * class_id] = class_multiplier * (iou_multiplier - l.output[class_index + stride * class_id]); - box truth = float_to_box_stride(state.truth + best_t * l.truth_size + b * l.truths, 1); - delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w * truth.h), l.w * l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords); + if (l.objectness_smooth) l.delta[class_index + stride*class_id] = class_multiplier * (iou_multiplier - l.output[class_index + stride*class_id]); + box truth = float_to_box_stride(state.truth + best_t*l.truth_size + b*l.truths, 1); + delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords); (*state.net.total_bbox)++; } } } } for (t = 0; t < l.max_boxes; ++t) { - box truth = float_to_box_stride(state.truth + t * l.truth_size + b * l.truths, 1); + box truth = float_to_box_stride(state.truth + t*l.truth_size + b*l.truths, 1); if (!truth.x) break; // continue; if (truth.x < 0 || truth.y < 0 || truth.x > 1 || truth.y > 1 || truth.w < 0 || truth.h < 0) { char buff[256]; @@ -515,7 +511,7 @@ void *process_batch(void* ptr) truth.x, truth.y, truth.w, truth.h); system(buff); } - int class_id = state.truth[t * l.truth_size + b * l.truths + 4]; + int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; if (class_id >= l.classes || class_id < 0) continue; // if label contains class_id more than number of classes in the cfg-file and class_id check garbage value float best_iou = 0; @@ -537,27 +533,27 @@ void *process_batch(void* ptr) int mask_n = int_index(l.mask, best_n, l.n); if (mask_n >= 0) { - int class_id = state.truth[t * l.truth_size + b * l.truths + 4]; + int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; - int box_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 0); + int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; - ious all_ious = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w * truth.h), l.w * l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords); + ious all_ious = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords); (*state.net.total_bbox)++; - const int truth_in_index = t * l.truth_size + b * l.truths + 5; + const int truth_in_index = t*l.truth_size + b*l.truths + 5; const int track_id = state.truth[truth_in_index]; - const int truth_out_index = b * l.n * l.w * l.h + mask_n * l.w * l.h + j * l.w + i; + const int truth_out_index = b*l.n*l.w*l.h + mask_n*l.w*l.h + j*l.w + i; l.labels[truth_out_index] = track_id; l.class_ids[truth_out_index] = class_id; //printf(" track_id = %d, t = %d, b = %d, truth_in_index = %d, truth_out_index = %d \n", track_id, t, b, truth_in_index, truth_out_index); // range is 0 <= 1 - args->tot_iou += all_ious.iou; - args->tot_iou_loss += 1 - all_ious.iou; + tot_iou += all_ious.iou; + tot_iou_loss += 1 - all_ious.iou; // range is -1 <= giou <= 1 tot_giou += all_ious.giou; - args->tot_giou_loss += 1 - all_ious.giou; + tot_giou_loss += 1 - all_ious.giou; tot_diou += all_ious.diou; tot_diou_loss += 1 - all_ious.diou; @@ -565,22 +561,21 @@ void *process_batch(void* ptr) tot_ciou += all_ious.ciou; tot_ciou_loss += 1 - all_ious.ciou; - int obj_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 4); + int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4); avg_obj += l.output[obj_index]; if (l.objectness_smooth) { float delta_obj = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); if (l.delta[obj_index] == 0) l.delta[obj_index] = delta_obj; - } - else l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); + } else l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); - int class_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 4 + 1); - delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w * l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); + int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); + delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); //printf(" label: class_id = %d, truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f \n", class_id, truth.x, truth.y, truth.w, truth.h); //printf(" mask_n = %d, l.output[obj_index] = %f, l.output[class_index + class_id] = %f \n\n", mask_n, l.output[obj_index], l.output[class_index + class_id]); - ++(args->count); - ++(args->class_count); + ++count; + ++class_count; if (all_ious.iou > .5) recall += 1; if (all_ious.iou > .75) recall75 += 1; } @@ -596,20 +591,20 @@ void *process_batch(void* ptr) // iou, n if (iou > l.iou_thresh) { - int class_id = state.truth[t * l.truth_size + b * l.truths + 4]; + int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; - int box_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 0); + int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; - ious all_ious = delta_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w * truth.h), l.w * l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords); + ious all_ious = delta_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords); (*state.net.total_bbox)++; // range is 0 <= 1 - args->tot_iou += all_ious.iou; - args->tot_iou_loss += 1 - all_ious.iou; + tot_iou += all_ious.iou; + tot_iou_loss += 1 - all_ious.iou; // range is -1 <= giou <= 1 tot_giou += all_ious.giou; - args->tot_giou_loss += 1 - all_ious.giou; + tot_giou_loss += 1 - all_ious.giou; tot_diou += all_ious.diou; tot_diou_loss += 1 - all_ious.diou; @@ -617,7 +612,7 @@ void *process_batch(void* ptr) tot_ciou += all_ious.ciou; tot_ciou_loss += 1 - all_ious.ciou; - int obj_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 4); + int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4); avg_obj += l.output[obj_index]; if (l.objectness_smooth) { float delta_obj = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); @@ -625,11 +620,11 @@ void *process_batch(void* ptr) } else l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); - int class_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 4 + 1); - delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w * l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); + int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); + delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); - ++(args->count); - ++(args->class_count); + ++count; + ++class_count; if (all_ious.iou > .5) recall += 1; if (all_ious.iou > .75) recall75 += 1; } @@ -653,280 +648,60 @@ void *process_batch(void* ptr) } } } - } - return 0; -} - - + if (count == 0) count = 1; + if (class_count == 0) class_count = 1; -void forward_yolo_layer(const layer l, network_state state) -{ - //int i, j, b, t, n; - memcpy(l.output, state.input, l.outputs*l.batch * sizeof(float)); - int b, n; + //(*l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); + //printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d\n", state.index, avg_iou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count); -#ifndef GPU + int stride = l.w*l.h; + float* no_iou_loss_delta = (float *)calloc(l.batch * l.outputs, sizeof(float)); + memcpy(no_iou_loss_delta, l.delta, l.batch * l.outputs * sizeof(float)); for (b = 0; b < l.batch; ++b) { - for (n = 0; n < l.n; ++n) { - int index = entry_index(l, b, n*l.w*l.h, 0); - if (l.new_coords) { - //activate_array(l.output + index, 4 * l.w*l.h, LOGISTIC); // x,y,w,h - } - else { - activate_array(l.output + index, 2 * l.w*l.h, LOGISTIC); // x,y, - index = entry_index(l, b, n*l.w*l.h, 4); - activate_array(l.output + index, (1 + l.classes)*l.w*l.h, LOGISTIC); - } - scal_add_cpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale x,y - } - } -#endif - - // delta is zeroed - memset(l.delta, 0, l.outputs * l.batch * sizeof(float)); - if (!state.train) return; - - int i; - for (i = 0; i < l.batch * l.w*l.h*l.n; ++i) l.labels[i] = -1; - for (i = 0; i < l.batch * l.w*l.h*l.n; ++i) l.class_ids[i] = -1; - //float avg_iou = 0; - float tot_iou = 0; - float tot_giou = 0; - float tot_diou = 0; - float tot_ciou = 0; - float tot_iou_loss = 0; - float tot_giou_loss = 0; - float tot_diou_loss = 0; - float tot_ciou_loss = 0; - float recall = 0; - float recall75 = 0; - float avg_cat = 0; - float avg_obj = 0; - float avg_anyobj = 0; - int count = 0; - int class_count = 0; - *(l.cost) = 0; - - - int num_threads = l.batch; - pthread_t* threads = (pthread_t*)calloc(num_threads, sizeof(pthread_t)); - - struct train_yolo_args* yolo_args = (train_yolo_args*)xcalloc(l.batch, sizeof(struct train_yolo_args)); - - for (b = 0; b < l.batch; b++) - { - yolo_args[b].l = l; - yolo_args[b].state = state; - yolo_args[b].b = b; - - yolo_args[b].tot_iou = 0; - yolo_args[b].tot_iou_loss = 0; - yolo_args[b].tot_giou_loss = 0; - yolo_args[b].count = 0; - yolo_args[b].class_count = 0; - - if (pthread_create(&threads[b], 0, process_batch, &(yolo_args[b]))) error("Thread creation failed"); - } - - for (b = 0; b < l.batch; b++) - { - pthread_join(threads[b], 0); - - tot_iou += yolo_args[b].tot_iou; - tot_iou_loss += yolo_args[b].tot_iou_loss; - tot_giou_loss += yolo_args[b].tot_giou_loss; - count += yolo_args[b].count; - class_count += yolo_args[b].class_count; - } - - free(yolo_args); - free(threads); - - // Search for an equidistant point from the distant boundaries of the local minimum - int iteration_num = get_current_iteration(state.net); - const int start_point = state.net.max_batches * 3 / 4; - //printf(" equidistant_point ep = %d, it = %d \n", state.net.equidistant_point, iteration_num); - - if ((state.net.badlabels_rejection_percentage && start_point < iteration_num) || - (state.net.num_sigmas_reject_badlabels && start_point < iteration_num) || - (state.net.equidistant_point && state.net.equidistant_point < iteration_num)) - { - const float progress_it = iteration_num - state.net.equidistant_point; - const float progress = progress_it / (state.net.max_batches - state.net.equidistant_point); - float ep_loss_threshold = (*state.net.delta_rolling_avg) * progress; - - float cur_max = 0; - float cur_avg = 0; - float counter = 0; - for (i = 0; i < l.batch * l.outputs; ++i) { - - if (l.delta[i] != 0) { - counter++; - cur_avg += fabs(l.delta[i]); - - if (cur_max < fabs(l.delta[i])) - cur_max = fabs(l.delta[i]); - } - } - - cur_avg = cur_avg / counter; - - if (*state.net.delta_rolling_max == 0) *state.net.delta_rolling_max = cur_max; - *state.net.delta_rolling_max = *state.net.delta_rolling_max * 0.99 + cur_max * 0.01; - *state.net.delta_rolling_avg = *state.net.delta_rolling_avg * 0.99 + cur_avg * 0.01; - - // reject high loss to filter bad labels - if (state.net.num_sigmas_reject_badlabels && start_point < iteration_num) - { - const float rolling_std = (*state.net.delta_rolling_std); - const float rolling_max = (*state.net.delta_rolling_max); - const float rolling_avg = (*state.net.delta_rolling_avg); - const float progress_badlabels = (float)(iteration_num - start_point) / (start_point); - - float cur_std = 0; - float counter = 0; - for (i = 0; i < l.batch * l.outputs; ++i) { - if (l.delta[i] != 0) { - counter++; - cur_std += pow(l.delta[i] - rolling_avg, 2); - } - } - cur_std = sqrt(cur_std / counter); - - *state.net.delta_rolling_std = *state.net.delta_rolling_std * 0.99 + cur_std * 0.01; - - float final_badlebels_threshold = rolling_avg + rolling_std * state.net.num_sigmas_reject_badlabels; - float badlabels_threshold = rolling_max - progress_badlabels * fabs(rolling_max - final_badlebels_threshold); - badlabels_threshold = max_val_cmp(final_badlebels_threshold, badlabels_threshold); - for (i = 0; i < l.batch * l.outputs; ++i) { - if (fabs(l.delta[i]) > badlabels_threshold) - l.delta[i] = 0; - } - printf(" rolling_std = %f, rolling_max = %f, rolling_avg = %f \n", rolling_std, rolling_max, rolling_avg); - printf(" badlabels loss_threshold = %f, start_it = %d, progress = %f \n", badlabels_threshold, start_point, progress_badlabels *100); - - ep_loss_threshold = min_val_cmp(final_badlebels_threshold, rolling_avg) * progress; - } - - - // reject some percent of the highest deltas to filter bad labels - if (state.net.badlabels_rejection_percentage && start_point < iteration_num) { - if (*state.net.badlabels_reject_threshold == 0) - *state.net.badlabels_reject_threshold = *state.net.delta_rolling_max; - - printf(" badlabels_reject_threshold = %f \n", *state.net.badlabels_reject_threshold); - - const float num_deltas_per_anchor = (l.classes + 4 + 1); - float counter_reject = 0; - float counter_all = 0; - for (i = 0; i < l.batch * l.outputs; ++i) { - if (l.delta[i] != 0) { - counter_all++; - if (fabs(l.delta[i]) > (*state.net.badlabels_reject_threshold)) { - counter_reject++; - l.delta[i] = 0; - } + for (j = 0; j < l.h; ++j) { + for (i = 0; i < l.w; ++i) { + for (n = 0; n < l.n; ++n) { + int index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); + no_iou_loss_delta[index + 0 * stride] = 0; + no_iou_loss_delta[index + 1 * stride] = 0; + no_iou_loss_delta[index + 2 * stride] = 0; + no_iou_loss_delta[index + 3 * stride] = 0; } } - float cur_percent = 100 * (counter_reject*num_deltas_per_anchor / counter_all); - if (cur_percent > state.net.badlabels_rejection_percentage) { - *state.net.badlabels_reject_threshold += 0.01; - printf(" increase!!! \n"); - } - else if (*state.net.badlabels_reject_threshold > 0.01) { - *state.net.badlabels_reject_threshold -= 0.01; - printf(" decrease!!! \n"); - } - - printf(" badlabels_reject_threshold = %f, cur_percent = %f, badlabels_rejection_percentage = %f, delta_rolling_max = %f \n", - *state.net.badlabels_reject_threshold, cur_percent, state.net.badlabels_rejection_percentage, *state.net.delta_rolling_max); - } - - - // reject low loss to find equidistant point - if (state.net.equidistant_point && state.net.equidistant_point < iteration_num) { - printf(" equidistant_point loss_threshold = %f, start_it = %d, progress = %3.1f %% \n", ep_loss_threshold, state.net.equidistant_point, progress * 100); - for (i = 0; i < l.batch * l.outputs; ++i) { - if (fabs(l.delta[i]) < ep_loss_threshold) - l.delta[i] = 0; - } } } + float classification_loss = l.obj_normalizer * pow(mag_array(no_iou_loss_delta, l.outputs * l.batch), 2); + free(no_iou_loss_delta); + float loss = pow(mag_array(l.delta, l.outputs * l.batch), 2); + float iou_loss = loss - classification_loss; - if (count == 0) count = 1; - if (class_count == 0) class_count = 1; - - if (l.show_details == 0) { - float loss = pow(mag_array(l.delta, l.outputs * l.batch), 2); - *(l.cost) = loss; - - loss /= l.batch; - - fprintf(stderr, "v3 (%s loss, Normalizer: (iou: %.2f, obj: %.2f, cls: %.2f) Region %d Avg (IOU: %f), count: %d, total_loss = %f \n", - (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.obj_normalizer, l.cls_normalizer, state.index, tot_iou / count, count, loss); + float avg_iou_loss = 0; + // gIOU loss + MSE (objectness) loss + if (l.iou_loss == MSE) { + *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); } else { - // show detailed output - - int stride = l.w*l.h; - float* no_iou_loss_delta = (float *)calloc(l.batch * l.outputs, sizeof(float)); - memcpy(no_iou_loss_delta, l.delta, l.batch * l.outputs * sizeof(float)); - - - int j, n; - for (b = 0; b < l.batch; ++b) { - for (j = 0; j < l.h; ++j) { - for (i = 0; i < l.w; ++i) { - for (n = 0; n < l.n; ++n) { - int index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); - no_iou_loss_delta[index + 0 * stride] = 0; - no_iou_loss_delta[index + 1 * stride] = 0; - no_iou_loss_delta[index + 2 * stride] = 0; - no_iou_loss_delta[index + 3 * stride] = 0; - } - } - } - } - - float classification_loss = l.obj_normalizer * pow(mag_array(no_iou_loss_delta, l.outputs * l.batch), 2); - free(no_iou_loss_delta); - float loss = pow(mag_array(l.delta, l.outputs * l.batch), 2); - float iou_loss = loss - classification_loss; - - float avg_iou_loss = 0; - *(l.cost) = loss; - - // gIOU loss + MSE (objectness) loss - if (l.iou_loss == MSE) { - *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); + // Always compute classification loss both for iou + cls loss and for logging with mse loss + // TODO: remove IOU loss fields before computing MSE on class + // probably split into two arrays + if (l.iou_loss == GIOU) { + avg_iou_loss = count > 0 ? l.iou_normalizer * (tot_giou_loss / count) : 0; } else { - // Always compute classification loss both for iou + cls loss and for logging with mse loss - // TODO: remove IOU loss fields before computing MSE on class - // probably split into two arrays - if (l.iou_loss == GIOU) { - avg_iou_loss = count > 0 ? l.iou_normalizer * (tot_giou_loss / count) : 0; - } - else { - avg_iou_loss = count > 0 ? l.iou_normalizer * (tot_iou_loss / count) : 0; - } - *(l.cost) = avg_iou_loss + classification_loss; + avg_iou_loss = count > 0 ? l.iou_normalizer * (tot_iou_loss / count) : 0; } + *(l.cost) = avg_iou_loss + classification_loss; + } + loss /= l.batch; + classification_loss /= l.batch; + iou_loss /= l.batch; - loss /= l.batch; - classification_loss /= l.batch; - iou_loss /= l.batch; - - fprintf(stderr, "v3 (%s loss, Normalizer: (iou: %.2f, obj: %.2f, cls: %.2f) Region %d Avg (IOU: %f), count: %d, class_loss = %f, iou_loss = %f, total_loss = %f \n", - (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.obj_normalizer, l.cls_normalizer, state.index, tot_iou / count, count, classification_loss, iou_loss, loss); - - //fprintf(stderr, "v3 (%s loss, Normalizer: (iou: %.2f, cls: %.2f) Region %d Avg (IOU: %f, GIOU: %f), Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d, class_loss = %f, iou_loss = %f, total_loss = %f \n", - // (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.obj_normalizer, state.index, tot_iou / count, tot_giou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count, - // classification_loss, iou_loss, loss); - } + fprintf(stderr, "v3 (%s loss, Normalizer: (iou: %.2f, cls: %.2f) Region %d Avg (IOU: %f, GIOU: %f), Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d, class_loss = %f, iou_loss = %f, total_loss = %f \n", + (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.obj_normalizer, state.index, tot_iou / count, tot_giou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count, + classification_loss, iou_loss, loss); } void backward_yolo_layer(const layer l, network_state state) From b25c2c6cbdef3a849fd1f17eddfb5aa1387d868d Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 12 Dec 2020 05:52:58 +0300 Subject: [PATCH 1166/1365] Final training fix --- src/yolo_layer.c | 479 ++++++++++++++++++++++++++++++++++------------- 1 file changed, 352 insertions(+), 127 deletions(-) diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 2814f30ed0c..8a7df31dae6 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -230,6 +230,7 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, float dw = all_ious.dx_iou.dl; float dh = all_ious.dx_iou.dr; + // predict exponential, apply gradient of e^delta_t ONLY for w,h if (new_coords) { //dw *= 8 * x[index + 2 * stride]; @@ -247,6 +248,10 @@ ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, dh *= exp(x[index + 3 * stride]); } + + //dw *= exp(x[index + 2 * stride]); + //dh *= exp(x[index + 3 * stride]); + // normalize iou weight dx *= iou_normalizer; dy *= iou_normalizer; @@ -369,63 +374,62 @@ static int entry_index(layer l, int batch, int location, int entry) return batch*l.outputs + n*l.w*l.h*(4+l.classes+1) + entry*l.w*l.h + loc; } -void forward_yolo_layer(const layer l, network_state state) -{ - int i, j, b, t, n; - memcpy(l.output, state.input, l.outputs*l.batch * sizeof(float)); +typedef struct train_yolo_args { + layer l; + network_state state; + int b; -#ifndef GPU - for (b = 0; b < l.batch; ++b) { - for (n = 0; n < l.n; ++n) { - int index = entry_index(l, b, n*l.w*l.h, 0); - activate_array(l.output + index, 2 * l.w*l.h, LOGISTIC); // x,y, - scal_add_cpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale x,y - index = entry_index(l, b, n*l.w*l.h, 4); - activate_array(l.output + index, (1 + l.classes)*l.w*l.h, LOGISTIC); - } - } -#endif + float tot_iou; + float tot_giou_loss; + float tot_iou_loss; + int count; + int class_count; +} train_yolo_args; - // delta is zeroed - memset(l.delta, 0, l.outputs * l.batch * sizeof(float)); - if (!state.train) return; +void *process_batch(void* ptr) +{ + { + train_yolo_args *args = (train_yolo_args*)ptr; + const layer l = args->l; + network_state state = args->state; + int b = args->b; + + int i, j, t, n; + + //printf(" b = %d \n", b, b); + + //float tot_iou = 0; + float tot_giou = 0; + float tot_diou = 0; + float tot_ciou = 0; + //float tot_iou_loss = 0; + //float tot_giou_loss = 0; + float tot_diou_loss = 0; + float tot_ciou_loss = 0; + float recall = 0; + float recall75 = 0; + float avg_cat = 0; + float avg_obj = 0; + float avg_anyobj = 0; + //int count = 0; + //int class_count = 0; - for (i = 0; i < l.batch * l.w*l.h*l.n; ++i) l.labels[i] = -1; - for (i = 0; i < l.batch * l.w*l.h*l.n; ++i) l.class_ids[i] = -1; - //float avg_iou = 0; - float tot_iou = 0; - float tot_giou = 0; - float tot_diou = 0; - float tot_ciou = 0; - float tot_iou_loss = 0; - float tot_giou_loss = 0; - float tot_diou_loss = 0; - float tot_ciou_loss = 0; - float recall = 0; - float recall75 = 0; - float avg_cat = 0; - float avg_obj = 0; - float avg_anyobj = 0; - int count = 0; - int class_count = 0; - *(l.cost) = 0; - for (b = 0; b < l.batch; ++b) { for (j = 0; j < l.h; ++j) { for (i = 0; i < l.w; ++i) { for (n = 0; n < l.n; ++n) { - const int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); - const int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4); - const int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); - const int stride = l.w*l.h; - box pred = get_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w*l.h, l.new_coords); + const int class_index = entry_index(l, b, n * l.w * l.h + j * l.w + i, 4 + 1); + const int obj_index = entry_index(l, b, n * l.w * l.h + j * l.w + i, 4); + const int box_index = entry_index(l, b, n * l.w * l.h + j * l.w + i, 0); + const int stride = l.w * l.h; + box pred = get_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w * l.h, l.new_coords); float best_match_iou = 0; int best_match_t = 0; float best_iou = 0; int best_t = 0; for (t = 0; t < l.max_boxes; ++t) { - box truth = float_to_box_stride(state.truth + t*l.truth_size + b*l.truths, 1); + box truth = float_to_box_stride(state.truth + t * l.truth_size + b * l.truths, 1); if (!truth.x) break; // continue; - int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; + int class_id = state.truth[t * l.truth_size + b * l.truths + 4]; if (class_id >= l.classes || class_id < 0) { printf("\n Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1); printf("\n truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f, class_id = %d \n", truth.x, truth.y, truth.w, truth.h, class_id); @@ -435,7 +439,7 @@ void forward_yolo_layer(const layer l, network_state state) float objectness = l.output[obj_index]; if (isnan(objectness) || isinf(objectness)) l.output[obj_index] = 0; - int class_id_match = compare_yolo_class(l.output, l.classes, class_index, l.w*l.h, objectness, class_id, 0.25f); + int class_id_match = compare_yolo_class(l.output, l.classes, class_index, l.w * l.h, objectness, class_id, 0.25f); float iou = box_iou(pred, truth); if (iou > best_match_iou && class_id_match == 1) { @@ -459,23 +463,23 @@ void forward_yolo_layer(const layer l, network_state state) else l.delta[obj_index] = 0; } else if (state.net.adversarial) { - int stride = l.w*l.h; + int stride = l.w * l.h; float scale = pred.w * pred.h; if (scale > 0) scale = sqrt(scale); l.delta[obj_index] = scale * l.obj_normalizer * (0 - l.output[obj_index]); int cl_id; int found_object = 0; for (cl_id = 0; cl_id < l.classes; ++cl_id) { - if (l.output[class_index + stride*cl_id] * l.output[obj_index] > 0.25) { - l.delta[class_index + stride*cl_id] = scale * (0 - l.output[class_index + stride*cl_id]); + if (l.output[class_index + stride * cl_id] * l.output[obj_index] > 0.25) { + l.delta[class_index + stride * cl_id] = scale * (0 - l.output[class_index + stride * cl_id]); found_object = 1; } } if (found_object) { // don't use this loop for adversarial attack drawing for (cl_id = 0; cl_id < l.classes; ++cl_id) - if (l.output[class_index + stride*cl_id] * l.output[obj_index] < 0.25) - l.delta[class_index + stride*cl_id] = scale * (1 - l.output[class_index + stride*cl_id]); + if (l.output[class_index + stride * cl_id] * l.output[obj_index] < 0.25) + l.delta[class_index + stride * cl_id] = scale * (1 - l.output[class_index + stride * cl_id]); l.delta[box_index + 0 * stride] += scale * (0 - l.output[box_index + 0 * stride]); l.delta[box_index + 1 * stride] += scale * (0 - l.output[box_index + 1 * stride]); @@ -484,25 +488,25 @@ void forward_yolo_layer(const layer l, network_state state) } } if (best_iou > l.truth_thresh) { - const float iou_multiplier = best_iou*best_iou;// (best_iou - l.truth_thresh) / (1.0 - l.truth_thresh); + const float iou_multiplier = best_iou * best_iou;// (best_iou - l.truth_thresh) / (1.0 - l.truth_thresh); if (l.objectness_smooth) l.delta[obj_index] = l.obj_normalizer * (iou_multiplier - l.output[obj_index]); else l.delta[obj_index] = l.obj_normalizer * (1 - l.output[obj_index]); //l.delta[obj_index] = l.obj_normalizer * (1 - l.output[obj_index]); - int class_id = state.truth[best_t*l.truth_size + b*l.truths + 4]; + int class_id = state.truth[best_t * l.truth_size + b * l.truths + 4]; if (l.map) class_id = l.map[class_id]; - delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); + delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w * l.h, 0, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; - if (l.objectness_smooth) l.delta[class_index + stride*class_id] = class_multiplier * (iou_multiplier - l.output[class_index + stride*class_id]); - box truth = float_to_box_stride(state.truth + best_t*l.truth_size + b*l.truths, 1); - delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords); + if (l.objectness_smooth) l.delta[class_index + stride * class_id] = class_multiplier * (iou_multiplier - l.output[class_index + stride * class_id]); + box truth = float_to_box_stride(state.truth + best_t * l.truth_size + b * l.truths, 1); + delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w * truth.h), l.w * l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords); (*state.net.total_bbox)++; } } } } for (t = 0; t < l.max_boxes; ++t) { - box truth = float_to_box_stride(state.truth + t*l.truth_size + b*l.truths, 1); + box truth = float_to_box_stride(state.truth + t * l.truth_size + b * l.truths, 1); if (!truth.x) break; // continue; if (truth.x < 0 || truth.y < 0 || truth.x > 1 || truth.y > 1 || truth.w < 0 || truth.h < 0) { char buff[256]; @@ -511,7 +515,7 @@ void forward_yolo_layer(const layer l, network_state state) truth.x, truth.y, truth.w, truth.h); system(buff); } - int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; + int class_id = state.truth[t * l.truth_size + b * l.truths + 4]; if (class_id >= l.classes || class_id < 0) continue; // if label contains class_id more than number of classes in the cfg-file and class_id check garbage value float best_iou = 0; @@ -533,27 +537,27 @@ void forward_yolo_layer(const layer l, network_state state) int mask_n = int_index(l.mask, best_n, l.n); if (mask_n >= 0) { - int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; + int class_id = state.truth[t * l.truth_size + b * l.truths + 4]; if (l.map) class_id = l.map[class_id]; - int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); + int box_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 0); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; - ious all_ious = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords); + ious all_ious = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w * truth.h), l.w * l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords); (*state.net.total_bbox)++; - const int truth_in_index = t*l.truth_size + b*l.truths + 5; + const int truth_in_index = t * l.truth_size + b * l.truths + 5; const int track_id = state.truth[truth_in_index]; - const int truth_out_index = b*l.n*l.w*l.h + mask_n*l.w*l.h + j*l.w + i; + const int truth_out_index = b * l.n * l.w * l.h + mask_n * l.w * l.h + j * l.w + i; l.labels[truth_out_index] = track_id; l.class_ids[truth_out_index] = class_id; //printf(" track_id = %d, t = %d, b = %d, truth_in_index = %d, truth_out_index = %d \n", track_id, t, b, truth_in_index, truth_out_index); // range is 0 <= 1 - tot_iou += all_ious.iou; - tot_iou_loss += 1 - all_ious.iou; + args->tot_iou += all_ious.iou; + args->tot_iou_loss += 1 - all_ious.iou; // range is -1 <= giou <= 1 tot_giou += all_ious.giou; - tot_giou_loss += 1 - all_ious.giou; + args->tot_giou_loss += 1 - all_ious.giou; tot_diou += all_ious.diou; tot_diou_loss += 1 - all_ious.diou; @@ -561,21 +565,22 @@ void forward_yolo_layer(const layer l, network_state state) tot_ciou += all_ious.ciou; tot_ciou_loss += 1 - all_ious.ciou; - int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4); + int obj_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 4); avg_obj += l.output[obj_index]; if (l.objectness_smooth) { float delta_obj = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); if (l.delta[obj_index] == 0) l.delta[obj_index] = delta_obj; - } else l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); + } + else l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); - int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); - delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); + int class_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 4 + 1); + delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w * l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); //printf(" label: class_id = %d, truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f \n", class_id, truth.x, truth.y, truth.w, truth.h); //printf(" mask_n = %d, l.output[obj_index] = %f, l.output[class_index + class_id] = %f \n\n", mask_n, l.output[obj_index], l.output[class_index + class_id]); - ++count; - ++class_count; + ++(args->count); + ++(args->class_count); if (all_ious.iou > .5) recall += 1; if (all_ious.iou > .75) recall75 += 1; } @@ -591,20 +596,20 @@ void forward_yolo_layer(const layer l, network_state state) // iou, n if (iou > l.iou_thresh) { - int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; + int class_id = state.truth[t * l.truth_size + b * l.truths + 4]; if (l.map) class_id = l.map[class_id]; - int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); + int box_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 0); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; - ious all_ious = delta_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords); + ious all_ious = delta_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w * truth.h), l.w * l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords); (*state.net.total_bbox)++; // range is 0 <= 1 - tot_iou += all_ious.iou; - tot_iou_loss += 1 - all_ious.iou; + args->tot_iou += all_ious.iou; + args->tot_iou_loss += 1 - all_ious.iou; // range is -1 <= giou <= 1 tot_giou += all_ious.giou; - tot_giou_loss += 1 - all_ious.giou; + args->tot_giou_loss += 1 - all_ious.giou; tot_diou += all_ious.diou; tot_diou_loss += 1 - all_ious.diou; @@ -612,7 +617,7 @@ void forward_yolo_layer(const layer l, network_state state) tot_ciou += all_ious.ciou; tot_ciou_loss += 1 - all_ious.ciou; - int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4); + int obj_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 4); avg_obj += l.output[obj_index]; if (l.objectness_smooth) { float delta_obj = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); @@ -620,11 +625,11 @@ void forward_yolo_layer(const layer l, network_state state) } else l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]); - int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); - delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); + int class_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 4 + 1); + delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w * l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); - ++count; - ++class_count; + ++(args->count); + ++(args->class_count); if (all_ious.iou > .5) recall += 1; if (all_ious.iou > .75) recall75 += 1; } @@ -648,60 +653,280 @@ void forward_yolo_layer(const layer l, network_state state) } } } + } - if (count == 0) count = 1; - if (class_count == 0) class_count = 1; + return 0; +} + + - //(*l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); - //printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d\n", state.index, avg_iou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count); +void forward_yolo_layer(const layer l, network_state state) +{ + //int i, j, b, t, n; + memcpy(l.output, state.input, l.outputs*l.batch * sizeof(float)); + int b, n; - int stride = l.w*l.h; - float* no_iou_loss_delta = (float *)calloc(l.batch * l.outputs, sizeof(float)); - memcpy(no_iou_loss_delta, l.delta, l.batch * l.outputs * sizeof(float)); +#ifndef GPU for (b = 0; b < l.batch; ++b) { - for (j = 0; j < l.h; ++j) { - for (i = 0; i < l.w; ++i) { - for (n = 0; n < l.n; ++n) { - int index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); - no_iou_loss_delta[index + 0 * stride] = 0; - no_iou_loss_delta[index + 1 * stride] = 0; - no_iou_loss_delta[index + 2 * stride] = 0; - no_iou_loss_delta[index + 3 * stride] = 0; + for (n = 0; n < l.n; ++n) { + int index = entry_index(l, b, n*l.w*l.h, 0); + if (l.new_coords) { + //activate_array(l.output + index, 4 * l.w*l.h, LOGISTIC); // x,y,w,h + } + else { + activate_array(l.output + index, 2 * l.w*l.h, LOGISTIC); // x,y, + index = entry_index(l, b, n*l.w*l.h, 4); + activate_array(l.output + index, (1 + l.classes)*l.w*l.h, LOGISTIC); + } + scal_add_cpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale x,y + } + } +#endif + + // delta is zeroed + memset(l.delta, 0, l.outputs * l.batch * sizeof(float)); + if (!state.train) return; + + int i; + for (i = 0; i < l.batch * l.w*l.h*l.n; ++i) l.labels[i] = -1; + for (i = 0; i < l.batch * l.w*l.h*l.n; ++i) l.class_ids[i] = -1; + //float avg_iou = 0; + float tot_iou = 0; + float tot_giou = 0; + float tot_diou = 0; + float tot_ciou = 0; + float tot_iou_loss = 0; + float tot_giou_loss = 0; + float tot_diou_loss = 0; + float tot_ciou_loss = 0; + float recall = 0; + float recall75 = 0; + float avg_cat = 0; + float avg_obj = 0; + float avg_anyobj = 0; + int count = 0; + int class_count = 0; + *(l.cost) = 0; + + + int num_threads = l.batch; + pthread_t* threads = (pthread_t*)calloc(num_threads, sizeof(pthread_t)); + + struct train_yolo_args* yolo_args = (train_yolo_args*)xcalloc(l.batch, sizeof(struct train_yolo_args)); + + for (b = 0; b < l.batch; b++) + { + yolo_args[b].l = l; + yolo_args[b].state = state; + yolo_args[b].b = b; + + yolo_args[b].tot_iou = 0; + yolo_args[b].tot_iou_loss = 0; + yolo_args[b].tot_giou_loss = 0; + yolo_args[b].count = 0; + yolo_args[b].class_count = 0; + + if (pthread_create(&threads[b], 0, process_batch, &(yolo_args[b]))) error("Thread creation failed"); + } + + for (b = 0; b < l.batch; b++) + { + pthread_join(threads[b], 0); + + tot_iou += yolo_args[b].tot_iou; + tot_iou_loss += yolo_args[b].tot_iou_loss; + tot_giou_loss += yolo_args[b].tot_giou_loss; + count += yolo_args[b].count; + class_count += yolo_args[b].class_count; + } + + free(yolo_args); + free(threads); + + // Search for an equidistant point from the distant boundaries of the local minimum + int iteration_num = get_current_iteration(state.net); + const int start_point = state.net.max_batches * 3 / 4; + //printf(" equidistant_point ep = %d, it = %d \n", state.net.equidistant_point, iteration_num); + + if ((state.net.badlabels_rejection_percentage && start_point < iteration_num) || + (state.net.num_sigmas_reject_badlabels && start_point < iteration_num) || + (state.net.equidistant_point && state.net.equidistant_point < iteration_num)) + { + const float progress_it = iteration_num - state.net.equidistant_point; + const float progress = progress_it / (state.net.max_batches - state.net.equidistant_point); + float ep_loss_threshold = (*state.net.delta_rolling_avg) * progress; + + float cur_max = 0; + float cur_avg = 0; + float counter = 0; + for (i = 0; i < l.batch * l.outputs; ++i) { + + if (l.delta[i] != 0) { + counter++; + cur_avg += fabs(l.delta[i]); + + if (cur_max < fabs(l.delta[i])) + cur_max = fabs(l.delta[i]); + } + } + + cur_avg = cur_avg / counter; + + if (*state.net.delta_rolling_max == 0) *state.net.delta_rolling_max = cur_max; + *state.net.delta_rolling_max = *state.net.delta_rolling_max * 0.99 + cur_max * 0.01; + *state.net.delta_rolling_avg = *state.net.delta_rolling_avg * 0.99 + cur_avg * 0.01; + + // reject high loss to filter bad labels + if (state.net.num_sigmas_reject_badlabels && start_point < iteration_num) + { + const float rolling_std = (*state.net.delta_rolling_std); + const float rolling_max = (*state.net.delta_rolling_max); + const float rolling_avg = (*state.net.delta_rolling_avg); + const float progress_badlabels = (float)(iteration_num - start_point) / (start_point); + + float cur_std = 0; + float counter = 0; + for (i = 0; i < l.batch * l.outputs; ++i) { + if (l.delta[i] != 0) { + counter++; + cur_std += pow(l.delta[i] - rolling_avg, 2); + } + } + cur_std = sqrt(cur_std / counter); + + *state.net.delta_rolling_std = *state.net.delta_rolling_std * 0.99 + cur_std * 0.01; + + float final_badlebels_threshold = rolling_avg + rolling_std * state.net.num_sigmas_reject_badlabels; + float badlabels_threshold = rolling_max - progress_badlabels * fabs(rolling_max - final_badlebels_threshold); + badlabels_threshold = max_val_cmp(final_badlebels_threshold, badlabels_threshold); + for (i = 0; i < l.batch * l.outputs; ++i) { + if (fabs(l.delta[i]) > badlabels_threshold) + l.delta[i] = 0; + } + printf(" rolling_std = %f, rolling_max = %f, rolling_avg = %f \n", rolling_std, rolling_max, rolling_avg); + printf(" badlabels loss_threshold = %f, start_it = %d, progress = %f \n", badlabels_threshold, start_point, progress_badlabels *100); + + ep_loss_threshold = min_val_cmp(final_badlebels_threshold, rolling_avg) * progress; + } + + + // reject some percent of the highest deltas to filter bad labels + if (state.net.badlabels_rejection_percentage && start_point < iteration_num) { + if (*state.net.badlabels_reject_threshold == 0) + *state.net.badlabels_reject_threshold = *state.net.delta_rolling_max; + + printf(" badlabels_reject_threshold = %f \n", *state.net.badlabels_reject_threshold); + + const float num_deltas_per_anchor = (l.classes + 4 + 1); + float counter_reject = 0; + float counter_all = 0; + for (i = 0; i < l.batch * l.outputs; ++i) { + if (l.delta[i] != 0) { + counter_all++; + if (fabs(l.delta[i]) > (*state.net.badlabels_reject_threshold)) { + counter_reject++; + l.delta[i] = 0; + } } } + float cur_percent = 100 * (counter_reject*num_deltas_per_anchor / counter_all); + if (cur_percent > state.net.badlabels_rejection_percentage) { + *state.net.badlabels_reject_threshold += 0.01; + printf(" increase!!! \n"); + } + else if (*state.net.badlabels_reject_threshold > 0.01) { + *state.net.badlabels_reject_threshold -= 0.01; + printf(" decrease!!! \n"); + } + + printf(" badlabels_reject_threshold = %f, cur_percent = %f, badlabels_rejection_percentage = %f, delta_rolling_max = %f \n", + *state.net.badlabels_reject_threshold, cur_percent, state.net.badlabels_rejection_percentage, *state.net.delta_rolling_max); + } + + + // reject low loss to find equidistant point + if (state.net.equidistant_point && state.net.equidistant_point < iteration_num) { + printf(" equidistant_point loss_threshold = %f, start_it = %d, progress = %3.1f %% \n", ep_loss_threshold, state.net.equidistant_point, progress * 100); + for (i = 0; i < l.batch * l.outputs; ++i) { + if (fabs(l.delta[i]) < ep_loss_threshold) + l.delta[i] = 0; + } } } - float classification_loss = l.obj_normalizer * pow(mag_array(no_iou_loss_delta, l.outputs * l.batch), 2); - free(no_iou_loss_delta); - float loss = pow(mag_array(l.delta, l.outputs * l.batch), 2); - float iou_loss = loss - classification_loss; - float avg_iou_loss = 0; - // gIOU loss + MSE (objectness) loss - if (l.iou_loss == MSE) { - *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); + if (count == 0) count = 1; + if (class_count == 0) class_count = 1; + + if (l.show_details == 0) { + float loss = pow(mag_array(l.delta, l.outputs * l.batch), 2); + *(l.cost) = loss; + + loss /= l.batch; + + fprintf(stderr, "v3 (%s loss, Normalizer: (iou: %.2f, obj: %.2f, cls: %.2f) Region %d Avg (IOU: %f), count: %d, total_loss = %f \n", + (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.obj_normalizer, l.cls_normalizer, state.index, tot_iou / count, count, loss); } else { - // Always compute classification loss both for iou + cls loss and for logging with mse loss - // TODO: remove IOU loss fields before computing MSE on class - // probably split into two arrays - if (l.iou_loss == GIOU) { - avg_iou_loss = count > 0 ? l.iou_normalizer * (tot_giou_loss / count) : 0; + // show detailed output + + int stride = l.w*l.h; + float* no_iou_loss_delta = (float *)calloc(l.batch * l.outputs, sizeof(float)); + memcpy(no_iou_loss_delta, l.delta, l.batch * l.outputs * sizeof(float)); + + + int j, n; + for (b = 0; b < l.batch; ++b) { + for (j = 0; j < l.h; ++j) { + for (i = 0; i < l.w; ++i) { + for (n = 0; n < l.n; ++n) { + int index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); + no_iou_loss_delta[index + 0 * stride] = 0; + no_iou_loss_delta[index + 1 * stride] = 0; + no_iou_loss_delta[index + 2 * stride] = 0; + no_iou_loss_delta[index + 3 * stride] = 0; + } + } + } + } + + float classification_loss = l.obj_normalizer * pow(mag_array(no_iou_loss_delta, l.outputs * l.batch), 2); + free(no_iou_loss_delta); + float loss = pow(mag_array(l.delta, l.outputs * l.batch), 2); + float iou_loss = loss - classification_loss; + + float avg_iou_loss = 0; + *(l.cost) = loss; + + // gIOU loss + MSE (objectness) loss + if (l.iou_loss == MSE) { + *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); } else { - avg_iou_loss = count > 0 ? l.iou_normalizer * (tot_iou_loss / count) : 0; + // Always compute classification loss both for iou + cls loss and for logging with mse loss + // TODO: remove IOU loss fields before computing MSE on class + // probably split into two arrays + if (l.iou_loss == GIOU) { + avg_iou_loss = count > 0 ? l.iou_normalizer * (tot_giou_loss / count) : 0; + } + else { + avg_iou_loss = count > 0 ? l.iou_normalizer * (tot_iou_loss / count) : 0; + } + *(l.cost) = avg_iou_loss + classification_loss; } - *(l.cost) = avg_iou_loss + classification_loss; - } - loss /= l.batch; - classification_loss /= l.batch; - iou_loss /= l.batch; - fprintf(stderr, "v3 (%s loss, Normalizer: (iou: %.2f, cls: %.2f) Region %d Avg (IOU: %f, GIOU: %f), Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d, class_loss = %f, iou_loss = %f, total_loss = %f \n", - (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.obj_normalizer, state.index, tot_iou / count, tot_giou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count, - classification_loss, iou_loss, loss); + loss /= l.batch; + classification_loss /= l.batch; + iou_loss /= l.batch; + + fprintf(stderr, "v3 (%s loss, Normalizer: (iou: %.2f, obj: %.2f, cls: %.2f) Region %d Avg (IOU: %f), count: %d, class_loss = %f, iou_loss = %f, total_loss = %f \n", + (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.obj_normalizer, l.cls_normalizer, state.index, tot_iou / count, count, classification_loss, iou_loss, loss); + + //fprintf(stderr, "v3 (%s loss, Normalizer: (iou: %.2f, cls: %.2f) Region %d Avg (IOU: %f, GIOU: %f), Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d, class_loss = %f, iou_loss = %f, total_loss = %f \n", + // (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.obj_normalizer, state.index, tot_iou / count, tot_giou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count, + // classification_loss, iou_loss, loss); + } } void backward_yolo_layer(const layer l, network_state state) @@ -950,21 +1175,21 @@ void forward_yolo_layer_gpu(const layer l, network_state state) int b, n; for (b = 0; b < l.batch; ++b){ for(n = 0; n < l.n; ++n){ - int index = entry_index(l, b, n*l.w*l.h, 0); + int bbox_index = entry_index(l, b, n*l.w*l.h, 0); // y = 1./(1. + exp(-x)) // x = ln(y/(1-y)) // ln - natural logarithm (base = e) // if(y->1) x -> inf // if(y->0) x -> -inf if (l.new_coords) { - //activate_array_ongpu(l.output_gpu + index, 4 * l.w*l.h, LOGISTIC); // x,y,w,h + //activate_array_ongpu(l.output_gpu + bbox_index, 4 * l.w*l.h, LOGISTIC); // x,y,w,h } else { - activate_array_ongpu(l.output_gpu + index, 2 * l.w*l.h, LOGISTIC); // x,y + activate_array_ongpu(l.output_gpu + bbox_index, 2 * l.w*l.h, LOGISTIC); // x,y - index = entry_index(l, b, n*l.w*l.h, 4); - activate_array_ongpu(l.output_gpu + index, (1 + l.classes)*l.w*l.h, LOGISTIC); // classes and objectness + int obj_index = entry_index(l, b, n*l.w*l.h, 4); + activate_array_ongpu(l.output_gpu + obj_index, (1 + l.classes)*l.w*l.h, LOGISTIC); // classes and objectness } - if (l.scale_x_y != 1) scal_add_ongpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output_gpu + index, 1); // scale x,y + if (l.scale_x_y != 1) scal_add_ongpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output_gpu + bbox_index, 1); // scale x,y } } if(!state.train || l.onlyforward){ From a488131a1158665ec421ef258f55292127da1487 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 13 Dec 2020 05:02:24 +0300 Subject: [PATCH 1167/1365] Fix scale_xy for CPU --- src/yolo_layer.c | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 8a7df31dae6..cd06c0fb7ad 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -670,16 +670,16 @@ void forward_yolo_layer(const layer l, network_state state) #ifndef GPU for (b = 0; b < l.batch; ++b) { for (n = 0; n < l.n; ++n) { - int index = entry_index(l, b, n*l.w*l.h, 0); + int bbox_index = entry_index(l, b, n*l.w*l.h, 0); if (l.new_coords) { - //activate_array(l.output + index, 4 * l.w*l.h, LOGISTIC); // x,y,w,h + //activate_array(l.output + bbox_index, 4 * l.w*l.h, LOGISTIC); // x,y,w,h } else { - activate_array(l.output + index, 2 * l.w*l.h, LOGISTIC); // x,y, - index = entry_index(l, b, n*l.w*l.h, 4); - activate_array(l.output + index, (1 + l.classes)*l.w*l.h, LOGISTIC); + activate_array(l.output + bbox_index, 2 * l.w*l.h, LOGISTIC); // x,y, + int obj_index = entry_index(l, b, n*l.w*l.h, 4); + activate_array(l.output + obj_index, (1 + l.classes)*l.w*l.h, LOGISTIC); } - scal_add_cpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale x,y + scal_add_cpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + bbox_index, 1); // scale x,y } } #endif From 972b25c5c8acfe438b3559afd9ce7eb868b92dab Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 14 Dec 2020 18:19:49 +0300 Subject: [PATCH 1168/1365] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 203f77c7591..eda15ff1465 100644 --- a/README.md +++ b/README.md @@ -133,13 +133,13 @@ There are weights-file for different cfg-files (trained for MS COCO dataset): FPS on RTX 2070 (R) and Tesla V100 (V): -* [yolov4x-mish.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4x-mish.cfg) - **67.9% mAP@0.5 (49.4% AP@0.5:0.95) - 23(R) FPS / 50(V) FPS** - 221 BFlops (110 FMA) - 381 MB: [yolov4x-mish.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.weights) +* [yolov4x-mish.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4x-mish.cfg) - 640x640 - **67.9% mAP@0.5 (49.4% AP@0.5:0.95) - 23(R) FPS / 50(V) FPS** - 221 BFlops (110 FMA) - 381 MB: [yolov4x-mish.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.weights) * pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.conv.166 * [yolov4-csp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-csp.cfg) - 202 MB: [yolov4-csp.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.weights) paper [Scaled Yolo v4](https://arxiv.org/abs/2011.08036) just change `width=` and `height=` parameters in `yolov4-csp.cfg` file and use the same `yolov4-csp.weights` file for all cases: - * `width=608 height=608` in cfg: **66.2% mAP@0.5 (47.5% AP@0.5:0.95) - 70(V) FPS** - 120 (60 FMA) BFlops + * `width=640 height=640` in cfg: **66.2% mAP@0.5 (47.5% AP@0.5:0.95) - 70(V) FPS** - 120 (60 FMA) BFlops * `width=512 height=512` in cfg: **64.8% mAP@0.5 (46.2% AP@0.5:0.95) - 93(V) FPS** - 77 (39 FMA) BFlops * pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.conv.142 From 360206f04f2f78dd6ab967c1f99777271ff8755a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 15 Dec 2020 01:34:12 +0300 Subject: [PATCH 1169/1365] Fixed equidistant point threshold --- src/yolo_layer.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/yolo_layer.c b/src/yolo_layer.c index cd06c0fb7ad..883d755e305 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -755,7 +755,7 @@ void forward_yolo_layer(const layer l, network_state state) { const float progress_it = iteration_num - state.net.equidistant_point; const float progress = progress_it / (state.net.max_batches - state.net.equidistant_point); - float ep_loss_threshold = (*state.net.delta_rolling_avg) * progress; + float ep_loss_threshold = (*state.net.delta_rolling_avg) * progress * 1.4; float cur_max = 0; float cur_avg = 0; From a298f94255a20a3198d80ea512755d9e5dddbf02 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 15 Dec 2020 07:09:58 +0300 Subject: [PATCH 1170/1365] Minor fix for cfg-files --- build/darknet/x64/cfg/yolov4-csp.cfg | 2 ++ build/darknet/x64/cfg/yolov4-tiny.cfg | 12 ++++-------- cfg/yolov4-csp.cfg | 2 ++ cfg/yolov4-tiny.cfg | 17 +++++++++++++++-- 4 files changed, 23 insertions(+), 10 deletions(-) diff --git a/build/darknet/x64/cfg/yolov4-csp.cfg b/build/darknet/x64/cfg/yolov4-csp.cfg index 23ebfd7461d..691ec03b04a 100644 --- a/build/darknet/x64/cfg/yolov4-csp.cfg +++ b/build/darknet/x64/cfg/yolov4-csp.cfg @@ -26,6 +26,8 @@ mosaic=1 letter_box=1 +ema_alpha=0.9998 + #optimized_memory=1 #23:104x104 54:52x52 85:26x26 104:13x13 for 416 diff --git a/build/darknet/x64/cfg/yolov4-tiny.cfg b/build/darknet/x64/cfg/yolov4-tiny.cfg index 821a34bff72..d990b5134d8 100644 --- a/build/darknet/x64/cfg/yolov4-tiny.cfg +++ b/build/darknet/x64/cfg/yolov4-tiny.cfg @@ -18,21 +18,17 @@ hue=.1 learning_rate=0.00261 burn_in=1000 -max_batches = 500200 +max_batches = 2000200 policy=steps -steps=400000,450000 +steps=1600000,1800000 scales=.1,.1 -#max_batches = 501000 -#policy=steps -#steps=500000 -#scales=0.01 #weights_reject_freq=1001 -#ema_alpha=0.998 +#ema_alpha=0.9998 #equidistant_point=1000 #num_sigmas_reject_badlabels=3 -#badlabels_rejection_percentage=0.1 +#badlabels_rejection_percentage=0.2 [convolutional] diff --git a/cfg/yolov4-csp.cfg b/cfg/yolov4-csp.cfg index 23ebfd7461d..691ec03b04a 100644 --- a/cfg/yolov4-csp.cfg +++ b/cfg/yolov4-csp.cfg @@ -26,6 +26,8 @@ mosaic=1 letter_box=1 +ema_alpha=0.9998 + #optimized_memory=1 #23:104x104 54:52x52 85:26x26 104:13x13 for 416 diff --git a/cfg/yolov4-tiny.cfg b/cfg/yolov4-tiny.cfg index dc6f5bfb8c6..d990b5134d8 100644 --- a/cfg/yolov4-tiny.cfg +++ b/cfg/yolov4-tiny.cfg @@ -17,11 +17,20 @@ hue=.1 learning_rate=0.00261 burn_in=1000 -max_batches = 500200 + +max_batches = 2000200 policy=steps -steps=400000,450000 +steps=1600000,1800000 scales=.1,.1 + +#weights_reject_freq=1001 +#ema_alpha=0.9998 +#equidistant_point=1000 +#num_sigmas_reject_badlabels=3 +#badlabels_rejection_percentage=0.2 + + [convolutional] batch_normalize=1 filters=32 @@ -230,6 +239,8 @@ random=0 resize=1.5 nms_kind=greedynms beta_nms=0.6 +#new_coords=1 +#scale_x_y = 2.0 [route] layers = -4 @@ -279,3 +290,5 @@ random=0 resize=1.5 nms_kind=greedynms beta_nms=0.6 +#new_coords=1 +#scale_x_y = 2.0 From d18d73ff5ff1c76db8fa1848b13f2771b01b4a6a Mon Sep 17 00:00:00 2001 From: Alexey Date: Fri, 18 Dec 2020 02:25:53 +0300 Subject: [PATCH 1171/1365] Readme.md for Xilinx Zynq Ultrascale+ (DPU) ZCU102/ZCU104 --- README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index eda15ff1465..7a06b2046c5 100644 --- a/README.md +++ b/README.md @@ -196,13 +196,13 @@ You can get cfg-files by path: `darknet/cfg/` * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported * on Linux **GCC or Clang**, on Windows **MSVC 2017/2019** https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community -#### Yolo v4 in other frameworks +### Yolo v4 in other frameworks * **Pytorch - Scaled-YOLOv4:** https://github.com/WongKinYiu/ScaledYOLOv4 * **TensorFlow:** `pip install yolov4` YOLOv4 on TensorFlow 2.0 / TFlite / Andriod: https://github.com/hunglc007/tensorflow-yolov4-tflite For YOLOv3 - convert `yolov3.weights`/`cfg` files to `yolov3.ckpt`/`pb/meta`: by using [mystic123](https://github.com/mystic123/tensorflow-yolo-v3) project, and [TensorFlow-lite](https://www.tensorflow.org/lite/guide/get_started#2_convert_the_model_format) * **OpenCV-dnn** the fastest implementation of YOLOv4 for CPU (x86/ARM-Android), OpenCV can be compiled with [OpenVINO-backend](https://github.com/opencv/opencv/wiki/Intel's-Deep-Learning-Inference-Engine-backend) for running on (Myriad X / USB Neural Compute Stick / Arria FPGA), use `yolov4.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221) or [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) -* **Intel OpenVINO 2020 R4:** (NPU Myriad X / USB Neural Compute Stick / Arria FPGA): read this [manual](https://github.com/TNTWEN/OpenVINO-YOLOV4) (old [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) ) +* **Intel OpenVINO 2021.2:** supports YOLOv4 (NPU Myriad X / USB Neural Compute Stick / Arria FPGA): https://devmesh.intel.com/projects/openvino-yolov4-49c756 read this [manual](https://github.com/TNTWEN/OpenVINO-YOLOV4) (old [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) ) * **Tencent/ncnn:** the fastest inference of YOLOv4 on mobile phone CPU: https://github.com/Tencent/ncnn * **PyTorch > ONNX**: * [WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4) @@ -213,6 +213,7 @@ You can get cfg-files by path: `darknet/cfg/` * **TensorRT** YOLOv4 on TensorRT+tkDNN: https://github.com/ceccocats/tkDNN For YOLOv3 (-70% faster inference): [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) read [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf). [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) implemented yolov3-spp, yolov4, etc. * **Deepstream 5.0 / TensorRT for YOLOv4** https://github.com/NVIDIA-AI-IOT/yolov4_deepstream +* **Xilinx Zynq Ultrascale+ Deep Learning Processor (DPU) ZCU102/ZCU104:** https://github.com/Xilinx/Vitis-In-Depth-Tutorial/tree/master/Machine_Learning/Design_Tutorials/07-yolov4-tutorial * **Amazon Neurochip / Amazon EC2 Inf1 instances** 1.85 times higher throughput and 37% lower cost per image for TensorFlow based YOLOv4 model, using Keras [URL](https://aws.amazon.com/ru/blogs/machine-learning/improving-performance-for-deep-learning-based-object-detection-with-an-aws-neuron-compiled-yolov4-model-on-aws-inferentia/) * **TVM** - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backends (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about * **OpenDataCam** - It detects, tracks and counts moving objects by using YOLOv4: https://github.com/opendatacam/opendatacam#-hardware-pre-requisite From 84fa42a966cc1d01573894ab697639a2856197da Mon Sep 17 00:00:00 2001 From: Alexey Date: Fri, 18 Dec 2020 05:45:09 +0300 Subject: [PATCH 1172/1365] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7a06b2046c5..70b5be65bca 100644 --- a/README.md +++ b/README.md @@ -212,7 +212,7 @@ You can get cfg-files by path: `darknet/cfg/` * **ONNX** on Jetson for YOLOv4: https://developer.nvidia.com/blog/announcing-onnx-runtime-for-jetson/ * **TensorRT** YOLOv4 on TensorRT+tkDNN: https://github.com/ceccocats/tkDNN For YOLOv3 (-70% faster inference): [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) read [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf). [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) implemented yolov3-spp, yolov4, etc. -* **Deepstream 5.0 / TensorRT for YOLOv4** https://github.com/NVIDIA-AI-IOT/yolov4_deepstream +* **Deepstream 5.0 / TensorRT for YOLOv4** https://github.com/NVIDIA-AI-IOT/yolov4_deepstream or https://github.com/marcoslucianops/DeepStream-Yolo * **Xilinx Zynq Ultrascale+ Deep Learning Processor (DPU) ZCU102/ZCU104:** https://github.com/Xilinx/Vitis-In-Depth-Tutorial/tree/master/Machine_Learning/Design_Tutorials/07-yolov4-tutorial * **Amazon Neurochip / Amazon EC2 Inf1 instances** 1.85 times higher throughput and 37% lower cost per image for TensorFlow based YOLOv4 model, using Keras [URL](https://aws.amazon.com/ru/blogs/machine-learning/improving-performance-for-deep-learning-based-object-detection-with-an-aws-neuron-compiled-yolov4-model-on-aws-inferentia/) * **TVM** - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backends (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about From c5b8bc7f2472c32636a9c7122d78229219b84482 Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 21 Dec 2020 20:39:35 +0300 Subject: [PATCH 1173/1365] Update README.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 70b5be65bca..67867b65007 100644 --- a/README.md +++ b/README.md @@ -200,7 +200,8 @@ You can get cfg-files by path: `darknet/cfg/` * **Pytorch - Scaled-YOLOv4:** https://github.com/WongKinYiu/ScaledYOLOv4 * **TensorFlow:** `pip install yolov4` YOLOv4 on TensorFlow 2.0 / TFlite / Andriod: https://github.com/hunglc007/tensorflow-yolov4-tflite - For YOLOv3 - convert `yolov3.weights`/`cfg` files to `yolov3.ckpt`/`pb/meta`: by using [mystic123](https://github.com/mystic123/tensorflow-yolo-v3) project, and [TensorFlow-lite](https://www.tensorflow.org/lite/guide/get_started#2_convert_the_model_format) + Official TF models: https://github.com/tensorflow/models/tree/master/official/vision/beta/projects/yolo + For YOLOv4 - convert `yolov4.weights`/`cfg` files to `yolov4.pb` by using [TNTWEN](https://github.com/TNTWEN/OpenVINO-YOLOV4) project, and to `yolov4.tflite` [TensorFlow-lite](https://www.tensorflow.org/lite/guide/get_started#2_convert_the_model_format) * **OpenCV-dnn** the fastest implementation of YOLOv4 for CPU (x86/ARM-Android), OpenCV can be compiled with [OpenVINO-backend](https://github.com/opencv/opencv/wiki/Intel's-Deep-Learning-Inference-Engine-backend) for running on (Myriad X / USB Neural Compute Stick / Arria FPGA), use `yolov4.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221) or [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) * **Intel OpenVINO 2021.2:** supports YOLOv4 (NPU Myriad X / USB Neural Compute Stick / Arria FPGA): https://devmesh.intel.com/projects/openvino-yolov4-49c756 read this [manual](https://github.com/TNTWEN/OpenVINO-YOLOV4) (old [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) ) * **Tencent/ncnn:** the fastest inference of YOLOv4 on mobile phone CPU: https://github.com/Tencent/ncnn From dd02b915be26bb981a382486f76dab37cd5d9e07 Mon Sep 17 00:00:00 2001 From: BlackHill de Eagle Date: Wed, 23 Dec 2020 15:18:56 +0800 Subject: [PATCH 1174/1365] Fix bug OSError: exception: access violation writing #6897 Avoid read and write image at the same time. --- darknet_video.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/darknet_video.py b/darknet_video.py index 44e4b4fc1e4..cc20b266606 100644 --- a/darknet_video.py +++ b/darknet_video.py @@ -69,8 +69,9 @@ def video_capture(frame_queue, darknet_image_queue): frame_resized = cv2.resize(frame_rgb, (width, height), interpolation=cv2.INTER_LINEAR) frame_queue.put(frame_resized) - darknet.copy_image_from_bytes(darknet_image, frame_resized.tobytes()) - darknet_image_queue.put(darknet_image) + img_for_detect = darknet.make_image(width, height, 3) + darknet.copy_image_from_bytes(img_for_detect, frame_resized.tobytes()) + darknet_image_queue.put(img_for_detect) cap.release() @@ -123,11 +124,8 @@ def drawing(frame_queue, detections_queue, fps_queue): args.weights, batch_size=1 ) - # Darknet doesn't accept numpy images. - # Create one with image we reuse for each detect width = darknet.network_width(network) height = darknet.network_height(network) - darknet_image = darknet.make_image(width, height, 3) input_path = str2int(args.input) cap = cv2.VideoCapture(input_path) Thread(target=video_capture, args=(frame_queue, darknet_image_queue)).start() From f83231fa1db7e8ca7141700f5c45fe90b015778b Mon Sep 17 00:00:00 2001 From: Alexey Date: Sat, 26 Dec 2020 17:45:07 +0300 Subject: [PATCH 1175/1365] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 67867b65007..bfae04c8b19 100644 --- a/README.md +++ b/README.md @@ -214,6 +214,7 @@ You can get cfg-files by path: `darknet/cfg/` * **TensorRT** YOLOv4 on TensorRT+tkDNN: https://github.com/ceccocats/tkDNN For YOLOv3 (-70% faster inference): [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) read [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf). [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) implemented yolov3-spp, yolov4, etc. * **Deepstream 5.0 / TensorRT for YOLOv4** https://github.com/NVIDIA-AI-IOT/yolov4_deepstream or https://github.com/marcoslucianops/DeepStream-Yolo +* **Triton Inference Server / TensorRT** https://github.com/isarsoft/yolov4-triton-tensorrt * **Xilinx Zynq Ultrascale+ Deep Learning Processor (DPU) ZCU102/ZCU104:** https://github.com/Xilinx/Vitis-In-Depth-Tutorial/tree/master/Machine_Learning/Design_Tutorials/07-yolov4-tutorial * **Amazon Neurochip / Amazon EC2 Inf1 instances** 1.85 times higher throughput and 37% lower cost per image for TensorFlow based YOLOv4 model, using Keras [URL](https://aws.amazon.com/ru/blogs/machine-learning/improving-performance-for-deep-learning-based-object-detection-with-an-aws-neuron-compiled-yolov4-model-on-aws-inferentia/) * **TVM** - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backends (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about From 95339f2df57624ae1b27a560ff643e49eff238eb Mon Sep 17 00:00:00 2001 From: Alexey Date: Sun, 27 Dec 2020 04:05:20 +0300 Subject: [PATCH 1176/1365] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index bfae04c8b19..eaea54df440 100644 --- a/README.md +++ b/README.md @@ -212,7 +212,7 @@ You can get cfg-files by path: `darknet/cfg/` * [YOLOv5](https://github.com/ultralytics/yolov5) * **ONNX** on Jetson for YOLOv4: https://developer.nvidia.com/blog/announcing-onnx-runtime-for-jetson/ * **TensorRT** YOLOv4 on TensorRT+tkDNN: https://github.com/ceccocats/tkDNN - For YOLOv3 (-70% faster inference): [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) read [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf). [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) implemented yolov3-spp, yolov4, etc. + For YOLOv3 (-70% faster inference): [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) read [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf). [jkjung-avt/tensorrt_demos](https://github.com/jkjung-avt/tensorrt_demos) or [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) implemented yolov3-spp, yolov4, etc. * **Deepstream 5.0 / TensorRT for YOLOv4** https://github.com/NVIDIA-AI-IOT/yolov4_deepstream or https://github.com/marcoslucianops/DeepStream-Yolo * **Triton Inference Server / TensorRT** https://github.com/isarsoft/yolov4-triton-tensorrt * **Xilinx Zynq Ultrascale+ Deep Learning Processor (DPU) ZCU102/ZCU104:** https://github.com/Xilinx/Vitis-In-Depth-Tutorial/tree/master/Machine_Learning/Design_Tutorials/07-yolov4-tutorial From b11af9e93b4ec225450b0dd73bdd7aaa1f2912b8 Mon Sep 17 00:00:00 2001 From: Alexey Date: Tue, 5 Jan 2021 16:48:02 +0300 Subject: [PATCH 1177/1365] Update README.md --- README.md | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/README.md b/README.md index eaea54df440..93539ffa0a1 100644 --- a/README.md +++ b/README.md @@ -333,6 +333,28 @@ To run Darknet on Linux use examples from this article, just use `./darknet` ins ### How to compile on Windows (using `CMake`) +Requires: +* MSVS: https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community +* Cmake GUI: `Windows win64-x64 Installer`https://cmake.org/download/ +* Download Darknet zip-archive with the latest commit and uncompress it: [master.zip](https://github.com/AlexeyAB/darknet/archive/master.zip) + +In the Windows: + + +* Start (button) -> All programms -> Cmake -> Cmake (gui) -> + +* [look at image](https://habrastorage.org/webt/pz/s1/uu/pzs1uu4heb7vflfcjqn-lxy-aqu.jpeg) In Cmake: Enter input path to the darknet Source, and output path to the Binaries -> Configure (button) -> Optional platform for generator: `x64` -> Finish -> Generate -> Open Project -> + +* in MS Visual Studio: Select: x64 and Release -> Build -> Build solution + +* find the executable file `darknet.exe` in the output path to the binaries you specified + +![x64 and Release](https://habrastorage.org/webt/ay/ty/f-/aytyf-8bufe7q-16yoecommlwys.jpeg) + + + +### How to compile on Windows (using `vcpkg`) + This is the recommended approach to build Darknet on Windows. 1. Install Visual Studio 2017 or 2019. In case you need to download it, please go here: [Visual Studio Community](http://visualstudio.com) From 103d301ccbc19e47e002005bdfdbaf07a92cd880 Mon Sep 17 00:00:00 2001 From: Alexey Date: Sat, 9 Jan 2021 05:49:43 +0300 Subject: [PATCH 1178/1365] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 93539ffa0a1..220eeab054b 100644 --- a/README.md +++ b/README.md @@ -372,7 +372,7 @@ PS Code\vcpkg> .\vcpkg install darknet[full]:x64-windows #replace with d PS Code\vcpkg> cd .. PS Code\> git clone https://github.com/AlexeyAB/darknet PS Code\> cd darknet -PS Code\darknet> .\build.ps1 +PS Code\darknet> powershell -ExecutionPolicy Bypass -File .\build.ps1 ``` ## How to train with multi-GPU From f57636ad655bd6ff0505c96b6254c58cf9ccc465 Mon Sep 17 00:00:00 2001 From: Ievgen Popovych Date: Thu, 21 Jan 2021 18:31:21 +0200 Subject: [PATCH 1179/1365] CMake CUDA configuration improvements (#7164) * CMake: Fail generation if CUDA was requested but not found ..instead of resorting to CPU build silently. This makes builds predictable and removes the confusion of `ENABLE_CUDA` option being merely a suggestion. Signed-off-by: Ievgen Popovych * CMake: Be more precise with ZED SDK messages CMake generation with `ENABLE_CUDA=OFF` and `ENABLE_ZED_CAMERA=OFF` would spit out "ZED SDK not enabled, since it requires CUDA" messages even though ZED SDK was explicitly disabled. Fix this by being a bit more precise with messages. Signed-off-by: Ievgen Popovych * CMake: Honor CUDNN_ENABLE option Setting CUDNN_ENABLE to OFF would not disable CUDNN support if CUDNN libraries are available. Fix this behavior by only looking for CUDNN if it was requested (CUDNN_ENABLE) and predicate actual settings on CUDNN_FOUND. Signed-off-by: Ievgen Popovych * github: actions: Explicitly set CUDA state in CI --- .github/workflows/ccpp.yml | 28 ++++++++++++++-------------- CMakeLists.txt | 38 ++++++++++++++++++-------------------- 2 files changed, 32 insertions(+), 34 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 60482a5a355..a7e2d7072fb 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -98,7 +98,7 @@ jobs: useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' cmakeBuildType: 'Release' - cmakeAppendedArgs: "-DBUILD_SHARED_LIBS=OFF -DCMAKE_BUILD_TYPE=Release" + cmakeAppendedArgs: "-DBUILD_SHARED_LIBS=OFF -DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF" buildWithCMakeArgs: '--target install' - uses: actions/upload-artifact@v2 @@ -138,7 +138,7 @@ jobs: cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release" + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF" cmakeBuildType: 'Release' buildWithCMakeArgs: '--target install' @@ -201,7 +201,7 @@ jobs: cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release" + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=ON" cmakeBuildType: 'Release' buildWithCMakeArgs: '--target install' @@ -237,7 +237,7 @@ jobs: cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DBUILD_AS_CPP:BOOL=TRUE" + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DBUILD_AS_CPP:BOOL=TRUE -DENABLE_CUDA=OFF" cmakeBuildType: 'Release' buildWithCMakeArgs: '--target install' @@ -270,7 +270,7 @@ jobs: useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' cmakeBuildType: 'Release' - cmakeAppendedArgs: "-DBUILD_SHARED_LIBS=OFF -DCMAKE_BUILD_TYPE=Release" + cmakeAppendedArgs: "-DBUILD_SHARED_LIBS=OFF -DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF" buildWithCMakeArgs: '--target install' - uses: actions/upload-artifact@v2 @@ -308,7 +308,7 @@ jobs: cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release" + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF" cmakeBuildType: 'Release' buildWithCMakeArgs: '--target install' @@ -344,7 +344,7 @@ jobs: cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DBUILD_AS_CPP:BOOL=TRUE" + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DBUILD_AS_CPP:BOOL=TRUE -DENABLE_CUDA=OFF" cmakeBuildType: 'Release' buildWithCMakeArgs: '--target install' @@ -373,7 +373,7 @@ jobs: cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release" + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF" cmakeBuildType: 'Release' buildWithCMakeArgs: '--config Release --target install' @@ -439,7 +439,7 @@ jobs: cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release" + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=ON" cmakeBuildType: 'Release' buildWithCMakeArgs: '--config Release --target install' @@ -479,7 +479,7 @@ jobs: cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release" + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF" cmakeBuildType: 'Release' buildWithCMakeArgs: '--config Release --target install' @@ -519,7 +519,7 @@ jobs: cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DBUILD_AS_CPP:BOOL=TRUE" + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DBUILD_AS_CPP:BOOL=TRUE -DENABLE_CUDA=OFF" cmakeBuildType: 'Release' buildWithCMakeArgs: '--config Release --target install' @@ -549,7 +549,7 @@ jobs: cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release" + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=ON" cmakeBuildType: 'Release' buildWithCMakeArgs: '--config Release --target install' @@ -568,7 +568,7 @@ jobs: cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-G\"MinGW Makefiles\" -DCMAKE_BUILD_TYPE=Release" + cmakeAppendedArgs: "-G\"MinGW Makefiles\" -DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF" cmakeBuildType: 'Release' buildWithCMakeArgs: '--config Release --target install' @@ -589,6 +589,6 @@ jobs: mkdir buildDirectory cd buildDirectory path C:\tools\cygwin\bin - bash -c 'cmake .. -G "Unix Makefiles" -DCMAKE_BUILD_TYPE="Release"' + bash -c 'cmake .. -G "Unix Makefiles" -DCMAKE_BUILD_TYPE="Release" -DENABLE_CUDA=OFF' bash -c 'cmake --build . --target install -- -j8' shell: cmd diff --git a/CMakeLists.txt b/CMakeLists.txt index ea7c4a64760..d7ffd8d10fa 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -65,8 +65,7 @@ if(CMAKE_CUDA_COMPILER AND ENABLE_CUDA) set_property(CACHE CUDA_ARCHITECTURES PROPERTY STRINGS "Auto" "Common" "All" "Kepler Maxwell Kepler+Tegra Maxwell+Tegra Pascal" "5.0 7.5 8.6") enable_language(CUDA) if(CMAKE_CUDA_COMPILER_VERSION VERSION_LESS "9.0") - message(STATUS "Unsupported CUDA version, please upgrade to CUDA 9+. Disabling CUDA support") - set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA support" FORCE) + message(FATAL_ERROR "Unsupported CUDA version, please upgrade to CUDA 9+ or set ENABLE_CUDA to OFF to disable CUDA.") else() find_package(CUDA REQUIRED) cuda_select_nvcc_arch_flags(CUDA_ARCH_FLAGS ${CUDA_ARCHITECTURES}) @@ -86,7 +85,9 @@ if(CMAKE_CUDA_COMPILER AND ENABLE_CUDA) endif() endif() else() - set(ENABLE_CUDA "FALSE" CACHE BOOL "Enable CUDA support" FORCE) + if(ENABLE_CUDA AND NOT CMAKE_CUDA_COMPILER) + message(FATAL_ERROR "CUDA requested but CUDA Toolkit was not found (set ENABLE_CUDA to OFF if not needed).") + endif() endif() if (WIN32 AND ENABLE_CUDA AND CMAKE_MAKE_PROGRAM MATCHES "ninja") @@ -191,11 +192,8 @@ if(OpenCV_FOUND) endif() endif() -if(ENABLE_CUDA) +if(ENABLE_CUDA AND ENABLE_CUDNN) find_package(CUDNN) - if(NOT CUDNN_FOUND) - set(ENABLE_CUDNN "FALSE" CACHE BOOL "Enable CUDNN" FORCE) - endif() endif() if(ENABLE_CUDA) @@ -229,21 +227,21 @@ if(ENABLE_CUDA) message(STATUS "CMAKE_CUDA_FLAGS: ${CMAKE_CUDA_FLAGS}") endif() -if(ENABLE_CUDA) - if(ENABLE_ZED_CAMERA) - find_package(ZED 2 QUIET) - if(ZED_FOUND) - include_directories(${ZED_INCLUDE_DIRS}) - link_directories(${ZED_LIBRARY_DIR}) - message(STATUS "ZED SDK enabled") - else() - message(STATUS "ZED SDK not found") - set(ENABLE_ZED_CAMERA "FALSE" CACHE BOOL "Enable ZED Camera support" FORCE) - endif() +if(ENABLE_CUDA AND ENABLE_ZED_CAMERA) + find_package(ZED 2 QUIET) + if(ZED_FOUND) + include_directories(${ZED_INCLUDE_DIRS}) + link_directories(${ZED_LIBRARY_DIR}) + message(STATUS "ZED SDK enabled") + else() + message(STATUS "ZED SDK not found") + set(ENABLE_ZED_CAMERA "FALSE" CACHE BOOL "Enable ZED Camera support" FORCE) endif() else() - message(STATUS "ZED SDK not enabled, since it requires CUDA") - set(ENABLE_ZED_CAMERA "FALSE" CACHE BOOL "Enable ZED Camera support" FORCE) + if(ENABLE_ZED_CAMERA AND NOT ENABLE_CUDA) + message(STATUS "ZED SDK not enabled, since it requires CUDA") + set(ENABLE_ZED_CAMERA "FALSE" CACHE BOOL "Enable ZED Camera support" FORCE) + endif() endif() set(DARKNET_INSTALL_INCLUDE_DIR ${INSTALL_INCLUDE_DIR}) From 00d578e327c22638ea12e73c4efb74c798c08de5 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 21 Jan 2021 17:36:00 +0100 Subject: [PATCH 1180/1365] update GitHub actions (#7095) * update to run-vcpkg@v6 * bump to cmake 3.18 * use newer cuda syntax, fail if cuda enabled but not found * update win build script * improve travis with non-cuda-enabled builds * improve windows building process --- .github/workflows/ccpp.yml | 165 ++++++++--------------- .gitignore | 1 + .travis.yml | 82 ++++++----- CMakeLists.txt | 115 ++++++++++------ DarknetConfig.cmake.in | 6 +- build.ps1 | 113 ++++++++-------- build.sh | 27 ++-- cmake/Modules/FindPThreads_windows.cmake | 4 +- cmake/vcpkg_linux.diff | 6 - cmake/vcpkg_linux_cuda.diff | 8 -- cmake/vcpkg_osx.diff | 6 - cmake/vcpkg_windows.diff | 6 - cmake/vcpkg_windows_cuda.diff | 8 -- vcpkg.json | 19 +++ 14 files changed, 265 insertions(+), 301 deletions(-) delete mode 100644 cmake/vcpkg_linux.diff delete mode 100644 cmake/vcpkg_linux_cuda.diff delete mode 100644 cmake/vcpkg_osx.diff delete mode 100644 cmake/vcpkg_windows.diff delete mode 100644 cmake/vcpkg_windows_cuda.diff create mode 100644 vcpkg.json diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index a7e2d7072fb..3e56cc2739b 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -68,7 +68,7 @@ jobs: make clean - ubuntu-vcpkg: + ubuntu-vcpkg-cuda: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 @@ -80,18 +80,44 @@ jobs: - uses: lukka/get-cmake@latest + - name: 'Install CUDA' + env: + CUDACXX: "/usr/local/cuda-10.2/bin/nvcc" + CUDA_PATH: "/usr/local/cuda-10.2" + CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda-10.2" + LD_LIBRARY_PATH: "/usr/local/cuda-10.2/lib64:/usr/local/cuda-10.2/lib64/stubs:$LD_LIBRARY_PATH" + run: | + wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.2.89-1_amd64.deb + sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub + sudo dpkg -i cuda-repo-ubuntu1804_10.2.89-1_amd64.deb + wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb + sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb + sudo apt update + sudo apt-get install -y --no-install-recommends cuda-compiler-10-2 cuda-libraries-dev-10-2 cuda-driver-dev-10-2 cuda-cudart-dev-10-2 cuda-curand-dev-10-2 + sudo apt-get install -y --no-install-recommends libcudnn7-dev + sudo ln -s /usr/local/cuda-10.2/lib64/stubs/libcuda.so /usr/local/cuda-10.2/lib64/stubs/libcuda.so.1 + - name: Restore from cache and run vcpkg env: - vcpkgResponseFile: ${{ github.workspace }}/cmake/vcpkg_linux.diff - uses: lukka/run-vcpkg@v2 + CUDACXX: "/usr/local/cuda-10.2/bin/nvcc" + CUDA_PATH: "/usr/local/cuda-10.2" + CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda-10.2" + LD_LIBRARY_PATH: "/usr/local/cuda-10.2/lib64:/usr/local/cuda-10.2/lib64/stubs:$LD_LIBRARY_PATH" + uses: lukka/run-vcpkg@v6 with: - vcpkgArguments: '@${{ env.vcpkgResponseFile }}' + setupOnly: true vcpkgDirectory: '${{ github.workspace }}/vcpkg' - vcpkgGitCommitId: '8121b4ec3d6a11353daf7639ed9082a78e617a2e' - appendedCacheKey: ${{ hashFiles(env.vcpkgResponseFile) }} + vcpkgGitCommitId: '50ea8c0ab7aca3bb9245bba7fc877ad2f2a4464c' + appendedCacheKey: ${{ hashFiles( '**/vcpkg.json' ) }} + vcpkgTriplet: 'x64-linux' - name: 'Build with CMake and Ninja' - uses: lukka/run-cmake@v2 + env: + CUDACXX: "/usr/local/cuda-10.2/bin/nvcc" + CUDA_PATH: "/usr/local/cuda-10.2" + CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda-10.2" + LD_LIBRARY_PATH: "/usr/local/cuda-10.2/lib64:/usr/local/cuda-10.2/lib64/stubs:$LD_LIBRARY_PATH" + uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' @@ -132,7 +158,7 @@ jobs: - uses: lukka/get-cmake@latest - name: 'Build with CMake and Ninja' - uses: lukka/run-cmake@v2 + uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' @@ -195,7 +221,7 @@ jobs: CUDA_PATH: "/usr/local/cuda-10.2" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda-10.2" LD_LIBRARY_PATH: "/usr/local/cuda-10.2/lib64:/usr/local/cuda-10.2/lib64/stubs:$LD_LIBRARY_PATH" - uses: lukka/run-cmake@v2 + uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' @@ -231,7 +257,7 @@ jobs: - uses: lukka/get-cmake@latest - name: 'Build with CMake and Ninja' - uses: lukka/run-cmake@v2 + uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' @@ -243,7 +269,7 @@ jobs: osx-vcpkg: - runs-on: macOS-latest + runs-on: macos-latest steps: - uses: actions/checkout@v2 @@ -253,17 +279,16 @@ jobs: - uses: lukka/get-cmake@latest - name: Restore from cache and run vcpkg - env: - vcpkgResponseFile: ${{ github.workspace }}/cmake/vcpkg_osx.diff - uses: lukka/run-vcpkg@v2 + uses: lukka/run-vcpkg@v6 with: - vcpkgArguments: '@${{ env.vcpkgResponseFile }}' + setupOnly: true vcpkgDirectory: '${{ github.workspace }}/vcpkg' - vcpkgGitCommitId: '8121b4ec3d6a11353daf7639ed9082a78e617a2e' - appendedCacheKey: ${{ hashFiles(env.vcpkgResponseFile) }} + vcpkgGitCommitId: '50ea8c0ab7aca3bb9245bba7fc877ad2f2a4464c' + appendedCacheKey: ${{ hashFiles( '**/vcpkg.json' ) }} + vcpkgTriplet: 'x64-osx' - name: 'Build with CMake and Ninja' - uses: lukka/run-cmake@v2 + uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' @@ -292,7 +317,7 @@ jobs: osx: - runs-on: macOS-latest + runs-on: macos-latest steps: - uses: actions/checkout@v2 @@ -302,7 +327,7 @@ jobs: - uses: lukka/get-cmake@latest - name: 'Build with CMake and Ninja' - uses: lukka/run-cmake@v2 + uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' @@ -331,14 +356,14 @@ jobs: osx-no-ocv-no-omp-cpp: - runs-on: macOS-latest + runs-on: macos-latest steps: - uses: actions/checkout@v2 - uses: lukka/get-cmake@latest - name: 'Build with CMake and Ninja' - uses: lukka/run-cmake@v2 + uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' @@ -349,56 +374,6 @@ jobs: buildWithCMakeArgs: '--target install' - win-vcpkg: - runs-on: windows-latest - steps: - - uses: actions/checkout@v2 - - - uses: lukka/get-cmake@latest - - - name: Restore from cache and run vcpkg - env: - vcpkgResponseFile: ${{ github.workspace }}/cmake/vcpkg_windows.diff - uses: lukka/run-vcpkg@v2 - with: - vcpkgArguments: '@${{ env.vcpkgResponseFile }}' - vcpkgDirectory: '${{ github.workspace }}/vcpkg' - vcpkgGitCommitId: '8121b4ec3d6a11353daf7639ed9082a78e617a2e' - appendedCacheKey: ${{ hashFiles(env.vcpkgResponseFile) }} - - - name: 'Build with CMake and Ninja' - uses: lukka/run-cmake@v2 - with: - cmakeListsOrSettingsJson: CMakeListsTxtAdvanced - cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' - useVcpkgToolchainFile: true - buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF" - cmakeBuildType: 'Release' - buildWithCMakeArgs: '--config Release --target install' - - - uses: actions/upload-artifact@v2 - with: - name: darknet-vcpkg-${{ runner.os }} - path: cfg - - uses: actions/upload-artifact@v2 - with: - name: darknet-vcpkg-${{ runner.os }} - path: data - - uses: actions/upload-artifact@v2 - with: - name: darknet-vcpkg-${{ runner.os }} - path: ${{ github.workspace }}/*dark* - - uses: actions/upload-artifact@v2 - with: - name: darknet-vcpkg-${{ runner.os }} - path: ${{ runner.workspace }}/buildDirectory/Release/*.dll - - uses: actions/upload-artifact@v2 - with: - name: darknet-vcpkg-${{ runner.os }} - path: ${{ github.workspace }}/uselib* - - win-vcpkg-cuda: runs-on: windows-latest steps: @@ -414,18 +389,17 @@ jobs: - name: Restore from cache and run vcpkg env: - vcpkgResponseFile: ${{ github.workspace }}/cmake/vcpkg_windows_cuda.diff CUDA_PATH: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" CUDA_PATH_V10_2: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" CUDA_TOOLKIT_ROOT_DIR: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" CUDACXX: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" - - uses: lukka/run-vcpkg@v2 + uses: lukka/run-vcpkg@v6 with: - vcpkgArguments: '@${{ env.vcpkgResponseFile }}' + setupOnly: true vcpkgDirectory: '${{ github.workspace }}/vcpkg' - vcpkgGitCommitId: '8121b4ec3d6a11353daf7639ed9082a78e617a2e' - appendedCacheKey: ${{ hashFiles(env.vcpkgResponseFile) }} + vcpkgGitCommitId: '50ea8c0ab7aca3bb9245bba7fc877ad2f2a4464c' + appendedCacheKey: ${{ hashFiles( '**/vcpkg.json' ) }} + vcpkgTriplet: 'x64-windows' - name: 'Build with CMake and Ninja' env: @@ -433,13 +407,13 @@ jobs: CUDA_PATH_V10_2: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" CUDA_TOOLKIT_ROOT_DIR: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" CUDACXX: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" - uses: lukka/run-cmake@v2 + uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=ON" + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DSELECT_OPENCV_MODULES=ON" cmakeBuildType: 'Release' buildWithCMakeArgs: '--config Release --target install' @@ -465,7 +439,7 @@ jobs: path: ${{ github.workspace }}/uselib* - win-integrated-libs: + win-intlibs: runs-on: windows-latest steps: - uses: actions/checkout@v2 @@ -473,7 +447,7 @@ jobs: - uses: lukka/get-cmake@latest - name: 'Build with CMake and Ninja' - uses: lukka/run-cmake@v2 + uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' @@ -513,7 +487,7 @@ jobs: - uses: lukka/get-cmake@latest - name: 'Build with CMake and Ninja' - uses: lukka/run-cmake@v2 + uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' @@ -543,7 +517,7 @@ jobs: CUDA_PATH_V10_2: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" CUDA_TOOLKIT_ROOT_DIR: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" CUDACXX: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" - uses: lukka/run-cmake@v2 + uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' @@ -562,7 +536,7 @@ jobs: - uses: lukka/get-cmake@latest - name: 'Build with CMake and Ninja' - uses: lukka/run-cmake@v2 + uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' @@ -571,24 +545,3 @@ jobs: cmakeAppendedArgs: "-G\"MinGW Makefiles\" -DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF" cmakeBuildType: 'Release' buildWithCMakeArgs: '--config Release --target install' - - - cygwin: - runs-on: windows-latest - steps: - - uses: actions/checkout@v2 - - - name: 'Install Cygwin' - run: | - choco install cygwin -y - choco install cyg-get -y - cyg-get gcc-g++ cmake make libopencv-devel libncurses-devel - - - name: 'Build' - run: | - mkdir buildDirectory - cd buildDirectory - path C:\tools\cygwin\bin - bash -c 'cmake .. -G "Unix Makefiles" -DCMAKE_BUILD_TYPE="Release" -DENABLE_CUDA=OFF' - bash -c 'cmake --build . --target install -- -j8' - shell: cmd diff --git a/.gitignore b/.gitignore index 2a1cf1b2143..dabf6a771e3 100644 --- a/.gitignore +++ b/.gitignore @@ -55,3 +55,4 @@ include/darknet/ uselib uselib_track darknet +vcpkg/ diff --git a/.travis.yml b/.travis.yml index 273f3cea78b..059f8116235 100644 --- a/.travis.yml +++ b/.travis.yml @@ -3,6 +3,7 @@ cache: timeout: 1000 directories: - $HOME/vcpkg + - $HOME/.cache/vcpkg matrix: include: @@ -10,84 +11,95 @@ matrix: - os: osx compiler: gcc name: macOS - gcc (llvm backend) + osx_image: xcode12.3 env: - - additional_defines="" + - additional_defines=" -DENABLE_CUDA=OFF" - MATRIX_EVAL="" - os: osx compiler: gcc name: macOS - gcc (llvm backend) - opencv@2 + osx_image: xcode12.3 env: - OpenCV_DIR="/usr/local/opt/opencv@2/" - - additional_defines="-DOpenCV_DIR=${OpenCV_DIR}" + - additional_defines="-DOpenCV_DIR=${OpenCV_DIR} -DENABLE_CUDA=OFF" - MATRIX_EVAL="brew install opencv@2" - os: osx compiler: gcc name: macOS - gcc (llvm backend) - opencv@3 + osx_image: xcode12.3 env: - OpenCV_DIR="/usr/local/opt/opencv@3/" - - additional_defines="-DOpenCV_DIR=${OpenCV_DIR}" + - additional_defines="-DOpenCV_DIR=${OpenCV_DIR} -DENABLE_CUDA=OFF" - MATRIX_EVAL="brew install opencv@3" - os: osx compiler: gcc name: macOS - gcc (llvm backend) - opencv(latest) + osx_image: xcode12.3 env: - - additional_defines="" + - additional_defines=" -DENABLE_CUDA=OFF" - MATRIX_EVAL="brew install opencv" - os: osx compiler: clang name: macOS - clang + osx_image: xcode12.3 env: - - additional_defines="" + - additional_defines=" -DENABLE_CUDA=OFF" - MATRIX_EVAL="" - os: osx compiler: clang name: macOS - clang - C++ mode + osx_image: xcode12.3 env: - - additional_defines="-DBUILD_AS_CPP:BOOL=TRUE" + - additional_defines="-DBUILD_AS_CPP:BOOL=TRUE -DENABLE_CUDA=OFF" - MATRIX_EVAL="" - os: osx compiler: clang name: macOS - clang - opencv@2 + osx_image: xcode12.3 env: - OpenCV_DIR="/usr/local/opt/opencv@2/" - - additional_defines="-DOpenCV_DIR=${OpenCV_DIR}" + - additional_defines="-DOpenCV_DIR=${OpenCV_DIR} -DENABLE_CUDA=OFF" - MATRIX_EVAL="brew install opencv@2" - os: osx compiler: clang name: macOS - clang - opencv@3 + osx_image: xcode12.3 env: - OpenCV_DIR="/usr/local/opt/opencv@3/" - - additional_defines="-DOpenCV_DIR=${OpenCV_DIR}" + - additional_defines="-DOpenCV_DIR=${OpenCV_DIR} -DENABLE_CUDA=OFF" - MATRIX_EVAL="brew install opencv@3" - os: osx compiler: clang name: macOS - clang - opencv(latest) + osx_image: xcode12.3 env: - - additional_defines="" + - additional_defines=" -DENABLE_CUDA=OFF" - MATRIX_EVAL="brew install opencv" - os: osx compiler: clang name: macOS - clang - opencv(latest) - libomp + osx_image: xcode12.3 env: - - additional_defines="" + - additional_defines=" -DENABLE_CUDA=OFF" - MATRIX_EVAL="brew install opencv libomp" - os: osx name: macOS - clang - vcpkg - osx_image: xcode11.4 + osx_image: xcode12.3 env: - - additional_defines="-DBUILD_SHARED_LIBS=OFF" + - additional_defines="-DBUILD_SHARED_LIBS=OFF -DENABLE_CUDA=OFF" - MATRIX_EVAL="brew install yasm && unset CC && unset CXX" - USE_VCPKG=true + - VCPKG_ROOT="$HOME/vcpkg" - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" - os: linux @@ -95,7 +107,7 @@ matrix: dist: bionic name: ubuntu 18.04 - clang env: - - additional_defines="" + - additional_defines=" -DENABLE_CUDA=OFF" - MATRIX_EVAL="" - os: linux @@ -103,7 +115,7 @@ matrix: dist: bionic name: ubuntu 18.04 - gcc env: - - additional_defines="" + - additional_defines=" -DENABLE_CUDA=OFF" - MATRIX_EVAL="" - os: linux @@ -111,22 +123,7 @@ matrix: dist: bionic name: ubuntu 18.04 - g++ env: - - additional_defines="-DBUILD_AS_CPP:BOOL=TRUE" - - MATRIX_EVAL="" - - - os: linux - compiler: gcc - dist: bionic - name: ubuntu 18.04 - gcc - vcpkg - addons: - apt: - update: true - packages: - - yasm - env: - - additional_defines="-DBUILD_SHARED_LIBS=OFF" - - USE_VCPKG=true - - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" + - additional_defines="-DBUILD_AS_CPP:BOOL=TRUE -DENABLE_CUDA=OFF" - MATRIX_EVAL="" - os: linux @@ -139,7 +136,7 @@ matrix: packages: - libopencv-dev env: - - additional_defines="" + - additional_defines=" -DENABLE_CUDA=OFF" - MATRIX_EVAL="" - os: linux @@ -152,7 +149,7 @@ matrix: packages: - libopencv-dev env: - - additional_defines="" + - additional_defines=" -DENABLE_CUDA=OFF" - MATRIX_EVAL="" - os: linux @@ -203,6 +200,7 @@ matrix: env: - additional_defines="-DBUILD_SHARED_LIBS=OFF" - USE_VCPKG=true + - VCPKG_ROOT="$HOME/vcpkg" - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" - INSTALL_CUDA_VERSION="10.2" - CUDACXX=/usr/local/cuda-${INSTALL_CUDA_VERSION}/bin/nvcc @@ -211,17 +209,17 @@ matrix: - LD_LIBRARY_PATH="/usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64:/usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.2.89-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1804_10.2.89-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-2 cuda-libraries-dev-10-2 cuda-driver-dev-10-2 cuda-cudart-dev-10-2 cuda-curand-dev-10-2 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs/libcuda.so.1" - allow_failures: - - name: macOS - clang - vcpkg +# allow_failures: +# - name: macOS - clang - vcpkg before_install: - travis_retry eval "${MATRIX_EVAL}" install: # CMake upgrade on Linux - - if [[ "$TRAVIS_OS_NAME" == "linux" ]] ; then wget --no-check-certificate https://github.com/Kitware/CMake/releases/download/v3.17.2/cmake-3.17.2-Linux-x86_64.tar.gz ; fi - - if [[ "$TRAVIS_OS_NAME" == "linux" ]] ; then tar -xzf cmake-3.17.2-Linux-x86_64.tar.gz ; fi - - if [[ "$TRAVIS_OS_NAME" == "linux" ]] ; then export PATH=$PWD/cmake-3.17.2-Linux-x86_64/bin:$PATH ; fi + - if [[ "$TRAVIS_OS_NAME" == "linux" ]] ; then wget --no-check-certificate https://github.com/Kitware/CMake/releases/download/v3.19.2/cmake-3.19.2-Linux-x86_64.tar.gz ; fi + - if [[ "$TRAVIS_OS_NAME" == "linux" ]] ; then tar -xzf cmake-3.19.2-Linux-x86_64.tar.gz ; fi + - if [[ "$TRAVIS_OS_NAME" == "linux" ]] ; then export PATH=$PWD/cmake-3.19.2-Linux-x86_64/bin:$PATH ; fi - pushd $HOME - if [ -d "$HOME/vcpkg/.git" ] ; then echo vcpkg cached ; else rm -rf vcpkg ; git clone https://github.com/Microsoft/vcpkg ; fi - cd vcpkg @@ -230,19 +228,15 @@ install: - if [ "${USE_VCPKG}" = true ] ; then ./bootstrap-vcpkg.sh ; fi - if [ "${USE_VCPKG}" = true ] ; then echo "set(VCPKG_BUILD_TYPE release)" >> triplets/x64-osx.cmake ; fi - if [ "${USE_VCPKG}" = true ] ; then echo "set(VCPKG_BUILD_TYPE release)" >> triplets/x64-linux.cmake ; fi - - if [ "${USE_VCPKG}" = true ] ; then travis_wait 45 ./vcpkg upgrade --no-dry-run; fi - - if [ "${USE_VCPKG}" = true ] ; then travis_wait ./vcpkg install stb pthreads --recurse; fi - - if [ "${USE_VCPKG}" = true ] ; then travis_wait 45 ./vcpkg install ffmpeg --recurse; fi - #- if [ "${USE_VCPKG}" = true ] && [[ ! -z "${CUDACXX}" ]] ; then travis_wait 45 ./vcpkg install opencv[ffmpeg,cuda] --recurse; fi - #- if [ "${USE_VCPKG}" = true ] && [[ -z "${CUDACXX}" ]] ; then travis_wait 45 ./vcpkg install opencv[ffmpeg] --recurse; fi - - if [ "${USE_VCPKG}" = true ] ; then travis_wait 45 ./vcpkg install opencv[ffmpeg] --recurse; fi - if [ "${USE_VCPKG}" = true ] ; then rm -rf buildtrees; fi + - if [ "${USE_VCPKG}" = true ] ; then rm -rf installed; fi + - if [ "${USE_VCPKG}" = true ] ; then rm -rf packages; fi - popd before_script: - mkdir build_release - cd build_release - - cmake .. -DCMAKE_BUILD_TYPE="Release" ${VCPKG_DEFINES} ${additional_defines} + - travis_wait 50 cmake .. -DCMAKE_BUILD_TYPE="Release" ${additional_defines} - cd .. script: diff --git a/CMakeLists.txt b/CMakeLists.txt index d7ffd8d10fa..9b57d739606 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,13 +1,14 @@ -cmake_minimum_required(VERSION 3.12) +cmake_minimum_required(VERSION 3.18) +include(CMakeDependentOption) set(Darknet_MAJOR_VERSION 0) set(Darknet_MINOR_VERSION 2) set(Darknet_PATCH_VERSION 5) -set(Darknet_TWEAK_VERSION 3) +set(Darknet_TWEAK_VERSION 4) set(Darknet_VERSION ${Darknet_MAJOR_VERSION}.${Darknet_MINOR_VERSION}.${Darknet_PATCH_VERSION}.${Darknet_TWEAK_VERSION}) -option(CMAKE_VERBOSE_MAKEFILE "Create verbose makefile" OFF) -option(CUDA_VERBOSE_BUILD "Create verbose CUDA build" OFF) +option(CMAKE_VERBOSE_MAKEFILE "Create verbose makefile" ON) +option(CUDA_VERBOSE_BUILD "Create verbose CUDA build" ON) option(BUILD_SHARED_LIBS "Create dark as a shared library" ON) option(BUILD_AS_CPP "Build Darknet using C++ compiler also for C files" OFF) option(BUILD_USELIB_TRACK "Build uselib_track" ON) @@ -19,10 +20,36 @@ option(ENABLE_CUDNN_HALF "Enable CUDNN Half precision" ON) option(ENABLE_ZED_CAMERA "Enable ZED Camera support" ON) option(ENABLE_VCPKG_INTEGRATION "Enable VCPKG integration" ON) +if(CMAKE_COMPILER_IS_GNUCC OR "${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang") + set(CMAKE_COMPILER_IS_GNUCC_OR_CLANG TRUE) + if("${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang") + set(CMAKE_COMPILER_IS_CLANG TRUE) + else() + set(CMAKE_COMPILER_IS_CLANG FALSE) + endif() +else() + set(CMAKE_COMPILER_IS_GNUCC_OR_CLANG FALSE) + set(CMAKE_COMPILER_IS_CLANG FALSE) +endif() + +if(NOT CMAKE_HOST_SYSTEM_PROCESSOR AND NOT WIN32) + execute_process(COMMAND "uname" "-m" OUTPUT_VARIABLE CMAKE_HOST_SYSTEM_PROCESSOR OUTPUT_STRIP_TRAILING_WHITESPACE) +endif() + +if(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "x86") + set(IS_X86 TRUE) +else() + set(IS_X86 FALSE) +endif() + +cmake_dependent_option(ENABLE_SSE_AND_AVX_FLAGS "Enable AVX and SSE optimizations (x86-only)" ON "CMAKE_COMPILER_IS_GNUCC_OR_CLANG;IS_X86" OFF) + if(ENABLE_VCPKG_INTEGRATION AND DEFINED ENV{VCPKG_ROOT} AND NOT DEFINED CMAKE_TOOLCHAIN_FILE) set(CMAKE_TOOLCHAIN_FILE "$ENV{VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake" CACHE STRING "") + set(_VCPKG_INSTALLED_DIR ${CMAKE_CURRENT_LIST_DIR}/vcpkg CACHE STRING "") message(STATUS "VCPKG found: $ENV{VCPKG_ROOT}") message(STATUS "Using VCPKG integration") + message(STATUS "Installing dependencies in ${_VCPKG_INSTALLED_DIR}") endif() project(Darknet VERSION ${Darknet_VERSION}) @@ -49,7 +76,7 @@ if(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES) "Debug" "Release" "MinSizeRel" "RelWithDebInfo") endif() -if (CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT) +if(CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT) set(CMAKE_INSTALL_PREFIX "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Install prefix" FORCE) endif() @@ -58,39 +85,38 @@ set(INSTALL_LIB_DIR "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Path where lib set(INSTALL_INCLUDE_DIR "include/darknet" CACHE PATH "Path where headers will be installed") set(INSTALL_CMAKE_DIR "share/darknet" CACHE PATH "Path where cmake configs will be installed") -include(CheckLanguage) -check_language(CUDA) -if(CMAKE_CUDA_COMPILER AND ENABLE_CUDA) - set(CUDA_ARCHITECTURES "Auto" CACHE STRING "\"Auto\" detects local machine GPU compute arch at runtime, \"Common\" and \"All\" cover common and entire subsets of architectures, \"Names\" is a list of architectures to enable by name, \"Numbers\" is a list of compute capabilities (version number) to enable") - set_property(CACHE CUDA_ARCHITECTURES PROPERTY STRINGS "Auto" "Common" "All" "Kepler Maxwell Kepler+Tegra Maxwell+Tegra Pascal" "5.0 7.5 8.6") - enable_language(CUDA) - if(CMAKE_CUDA_COMPILER_VERSION VERSION_LESS "9.0") - message(FATAL_ERROR "Unsupported CUDA version, please upgrade to CUDA 9+ or set ENABLE_CUDA to OFF to disable CUDA.") - else() - find_package(CUDA REQUIRED) - cuda_select_nvcc_arch_flags(CUDA_ARCH_FLAGS ${CUDA_ARCHITECTURES}) - message(STATUS "Building with CUDA flags: " "${CUDA_ARCH_FLAGS}") - if (NOT "arch=compute_70,code=sm_70" IN_LIST CUDA_ARCH_FLAGS AND NOT "arch=compute_72,code=sm_72" IN_LIST CUDA_ARCH_FLAGS AND NOT "arch=compute_75,code=sm_75" IN_LIST CUDA_ARCH_FLAGS AND NOT "arch=compute_80,code=sm_80" IN_LIST CUDA_ARCH_FLAGS AND NOT "arch=compute_86,code=sm_86" IN_LIST CUDA_ARCH_FLAGS) - set(ENABLE_CUDNN_HALF "FALSE" CACHE BOOL "Enable CUDNN Half precision" FORCE) - message(STATUS "Your setup does not supports half precision (it requires CC >= 7.0)") +if(ENABLE_CUDA) + include(CheckLanguage) + check_language(CUDA) + if(NOT CMAKE_CUDA_COMPILER) + message(FATAL_ERROR "CUDA not found, please build explicitly with -DENABLE_CUDA=OFF if you do not want CUDA") + elseif(ENABLE_CUDA) + enable_language(CUDA) + if(CMAKE_CUDA_COMPILER_VERSION VERSION_LESS "9.0") + message(FATAL_ERROR "Unsupported CUDA version, please upgrade to CUDA 9+ or disable CUDA with explicitly with -DENABLE_CUDA=OFF") else() - message(STATUS "Your setup supports half precision (it requires CC >= 7.0)") + message(STATUS "Selected CMAKE_CUDA_ARCHITECTURES: ${CMAKE_CUDA_ARCHITECTURES}") + if(70 IN_LIST CMAKE_CUDA_ARCHITECTURES OR + 72 IN_LIST CMAKE_CUDA_ARCHITECTURES OR + 75 IN_LIST CMAKE_CUDA_ARCHITECTURES OR + 80 IN_LIST CMAKE_CUDA_ARCHITECTURES OR + 86 IN_LIST CMAKE_CUDA_ARCHITECTURES) + set(ENABLE_CUDNN_HALF "TRUE" CACHE BOOL "Enable CUDNN Half precision" FORCE) + message(STATUS "Your setup supports half precision (it requires CC >= 7.0)") + else() + set(ENABLE_CUDNN_HALF "FALSE" CACHE BOOL "Enable CUDNN Half precision" FORCE) + message(STATUS "Your setup does not support half precision (it requires CC >= 7.0)") + endif() endif() - endif() - if (CMAKE_VERSION VERSION_GREATER_EQUAL "3.17") if(BUILD_SHARED_LIBS) set(CMAKE_CUDA_RUNTIME_LIBRARY "Shared") else() set(CMAKE_CUDA_RUNTIME_LIBRARY "Static") endif() endif() -else() - if(ENABLE_CUDA AND NOT CMAKE_CUDA_COMPILER) - message(FATAL_ERROR "CUDA requested but CUDA Toolkit was not found (set ENABLE_CUDA to OFF if not needed).") - endif() endif() -if (WIN32 AND ENABLE_CUDA AND CMAKE_MAKE_PROGRAM MATCHES "ninja") +if(WIN32 AND ENABLE_CUDA AND CMAKE_MAKE_PROGRAM MATCHES "ninja") option(SELECT_OPENCV_MODULES "Use only few selected OpenCV modules to circumvent 8192 char limit when using Ninja on Windows" ON) else() option(SELECT_OPENCV_MODULES "Use only few selected OpenCV modules to circumvent 8192 char limit when using Ninja on Windows" OFF) @@ -160,9 +186,9 @@ if(MSVC) string(REGEX REPLACE "/O2" "/Ox" CMAKE_C_FLAGS_RELEASE ${CMAKE_C_FLAGS_RELEASE}) endif() -if(CMAKE_COMPILER_IS_GNUCC OR "${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang") - if ("${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang") - if (UNIX AND NOT APPLE) +if(CMAKE_COMPILER_IS_GNUCC_OR_CLANG) + if(CMAKE_COMPILER_IS_CLANG) + if(UNIX AND NOT APPLE) set(CMAKE_CXX_FLAGS "-pthread ${CMAKE_CXX_FLAGS}") #force pthread to avoid bugs in some cmake setups set(CMAKE_C_FLAGS "-pthread ${CMAKE_C_FLAGS}") endif() @@ -173,8 +199,10 @@ if(CMAKE_COMPILER_IS_GNUCC OR "${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang") string(REGEX REPLACE "-O3" "-Ofast" CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) string(REGEX REPLACE "-O0" "-Og" CMAKE_C_FLAGS_DEBUG ${CMAKE_C_FLAGS_DEBUG}) string(REGEX REPLACE "-O3" "-Ofast" CMAKE_C_FLAGS_RELEASE ${CMAKE_C_FLAGS_RELEASE}) - set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a") - set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a") + if(ENABLE_SSE_AND_AVX_FLAGS) + set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a") + set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a") + endif() endif() if(OpenCV_FOUND) @@ -197,7 +225,7 @@ if(ENABLE_CUDA AND ENABLE_CUDNN) endif() if(ENABLE_CUDA) - if (MSVC) + if(MSVC) set(ADDITIONAL_CXX_FLAGS "${ADDITIONAL_CXX_FLAGS} /DGPU") if(CUDNN_FOUND) set(ADDITIONAL_CXX_FLAGS "${ADDITIONAL_CXX_FLAGS} /DCUDNN") @@ -215,7 +243,7 @@ if(ENABLE_CUDA) if(OpenCV_FOUND) set(ADDITIONAL_CXX_FLAGS "${ADDITIONAL_CXX_FLAGS} -DOPENCV") endif() - if (APPLE) + if(APPLE) set(CUDA_HOST_COMPILER_FLAGS "--compiler-options \" ${ADDITIONAL_CXX_FLAGS} -fPIC -Xpreprocessor -fopenmp -Ofast \"") else() set(CUDA_HOST_COMPILER_FLAGS "--compiler-options \" ${ADDITIONAL_CXX_FLAGS} -fPIC -fopenmp -Ofast \"") @@ -238,18 +266,17 @@ if(ENABLE_CUDA AND ENABLE_ZED_CAMERA) set(ENABLE_ZED_CAMERA "FALSE" CACHE BOOL "Enable ZED Camera support" FORCE) endif() else() - if(ENABLE_ZED_CAMERA AND NOT ENABLE_CUDA) + if(ENABLE_ZED_CAMERA) message(STATUS "ZED SDK not enabled, since it requires CUDA") - set(ENABLE_ZED_CAMERA "FALSE" CACHE BOOL "Enable ZED Camera support" FORCE) endif() + set(ENABLE_ZED_CAMERA "FALSE" CACHE BOOL "Enable ZED Camera support" FORCE) endif() -set(DARKNET_INSTALL_INCLUDE_DIR ${INSTALL_INCLUDE_DIR}) # Make relative paths absolute (needed later on) foreach(p LIB BIN INCLUDE CMAKE) set(var INSTALL_${p}_DIR) if(NOT IS_ABSOLUTE "${${var}}") - set(${var} "${CMAKE_INSTALL_PREFIX}/${${var}}") + set(FULLPATH_${var} "${CMAKE_INSTALL_PREFIX}/${${var}}") endif() endforeach() @@ -328,9 +355,9 @@ if(BUILD_AS_CPP) set_target_properties(darknet PROPERTIES LINKER_LANGUAGE CXX) endif() -target_include_directories(darknet PUBLIC $ $ $ $) -target_include_directories(dark PUBLIC $ $ $ $) -target_include_directories(uselib PUBLIC $ $ $ $) +target_include_directories(darknet PUBLIC $ $ $ $) +target_include_directories(dark PUBLIC $ $ $ $) +target_include_directories(uselib PUBLIC $ $ $ $) target_compile_definitions(darknet PRIVATE -DUSE_CMAKE_LIBS) target_compile_definitions(dark PRIVATE -DUSE_CMAKE_LIBS) @@ -355,7 +382,7 @@ if(OpenCV_FOUND) target_link_libraries(darknet PRIVATE ${OpenCV_LINKED_COMPONENTS}) target_link_libraries(uselib PRIVATE ${OpenCV_LINKED_COMPONENTS}) target_link_libraries(dark PUBLIC ${OpenCV_LINKED_COMPONENTS}) - target_include_directories(dark PUBLIC ${OpenCV_INCLUDE_DIRS}) + target_include_directories(dark PRIVATE ${OpenCV_INCLUDE_DIRS}) target_compile_definitions(darknet PRIVATE -DOPENCV) target_compile_definitions(dark PUBLIC -DOPENCV) endif() @@ -470,7 +497,7 @@ export(PACKAGE Darknet) # Create the DarknetConfig.cmake # First of all we compute the relative path between the cmake config file and the include path -file(RELATIVE_PATH REL_INCLUDE_DIR "${INSTALL_CMAKE_DIR}" "${INSTALL_INCLUDE_DIR}") +file(RELATIVE_PATH REL_INCLUDE_DIR "${FULLPATH_INSTALL_CMAKE_DIR}" "${FULLPATH_INSTALL_INCLUDE_DIR}") set(CONF_INCLUDE_DIRS "${PROJECT_SOURCE_DIR}" "${PROJECT_BINARY_DIR}") configure_file(DarknetConfig.cmake.in "${PROJECT_BINARY_DIR}/DarknetConfig.cmake" @ONLY) set(CONF_INCLUDE_DIRS "\${Darknet_CMAKE_DIR}/${REL_INCLUDE_DIR}") diff --git a/DarknetConfig.cmake.in b/DarknetConfig.cmake.in index c8f4787db55..703a59e3956 100644 --- a/DarknetConfig.cmake.in +++ b/DarknetConfig.cmake.in @@ -15,8 +15,6 @@ if(@ENABLE_CUDA@) if(NOT CMAKE_CUDA_COMPILER) message(STATUS " --> WARNING: Unable to find native CUDA integration!") endif() - find_dependency(CUDA) - cuda_select_nvcc_arch_flags(CUDA_ARCH_FLAGS ${CUDA_ARCHITECTURES}) if(@CUDNN_FOUND@) find_dependency(CUDNN) endif() @@ -38,6 +36,10 @@ endif() include("${Darknet_CMAKE_DIR}/DarknetTargets.cmake") include("${Darknet_CMAKE_DIR}/DarknetConfigVersion.cmake") +if(@OpenCV_FOUND@) + target_include_directories(Darknet::dark PRIVATE ${OpenCV_INCLUDE_DIRS}) +endif() + get_target_property(FULL_DARKNET_INCLUDE_DIRS Darknet::dark INTERFACE_INCLUDE_DIRECTORIES) list(GET FULL_DARKNET_INCLUDE_DIRS 0 Darknet_INCLUDE_DIR) get_filename_component(Darknet_INCLUDE_DIR "${Darknet_INCLUDE_DIR}" REALPATH) diff --git a/build.ps1 b/build.ps1 index 15ff77e0ffa..85a85dd35de 100755 --- a/build.ps1 +++ b/build.ps1 @@ -1,9 +1,30 @@ #!/usr/bin/env pwsh -$number_of_build_workers=8 -$use_vcpkg=$true -$use_ninja=$false -$force_cpp_build=$false +$number_of_build_workers = 8 +$enable_cuda = $true +$use_vcpkg = $true +$use_ninja = $true +$force_cpp_build = $false + +#$additional_build_setup = " -DCMAKE_CUDA_ARCHITECTURES=30" + + +$CMAKE_EXE = Get-Command cmake | Select-Object -ExpandProperty Definition +$NINJA_EXE = Get-Command ninja | Select-Object -ExpandProperty Definition + +if (-Not $CMAKE_EXE) { + throw "Could not find CMake, please install it" +} +else { + Write-Host "Using CMake from ${CMAKE_EXE}" +} + +if (-Not $NINJA_EXE -and $use_ninja) { + throw "Could not find Ninja, please install it" +} +else { + Write-Host "Using Ninja from ${NINJA_EXE}" +} function getProgramFiles32bit() { $out = ${env:PROGRAMFILES(X86)} @@ -80,11 +101,13 @@ if ((Test-Path env:VCPKG_ROOT) -and $use_vcpkg) { } elseif ((Test-Path "${env:WORKSPACE}\vcpkg") -and $use_vcpkg) { $vcpkg_path = "${env:WORKSPACE}\vcpkg" + $env:VCPKG_ROOT = "${env:WORKSPACE}\vcpkg" Write-Host "Found vcpkg in WORKSPACE\vcpkg: $vcpkg_path" } else { - $use_vcpkg=$false + $use_vcpkg = $false Write-Host "Skipping vcpkg-enabled builds because the VCPKG_ROOT environment variable is not defined or you requested to avoid VCPKG, using self-distributed libs`n" -ForegroundColor Yellow + $additional_build_setup = $additional_build_setup + " -DENABLE_VCPKG_INTEGRATION:BOOL=OFF" } if ($null -eq $env:VCPKG_DEFAULT_TRIPLET -and $use_vcpkg) { @@ -115,7 +138,7 @@ if ($null -eq (Get-Command "cl.exe" -ErrorAction SilentlyContinue)) { $tokens = getLatestVisualStudioWithDesktopWorkloadVersion $tokens = $tokens.split('.') -if($use_ninja) { +if ($use_ninja) { $generator = "Ninja" } else { @@ -155,75 +178,49 @@ if (Test-Path env:CUDA_PATH) { } } -if($force_cpp_build) { - $additional_build_setup="-DBUILD_AS_CPP:BOOL=TRUE" +if ($force_cpp_build) { + $additional_build_setup = $additional_build_setup + " -DBUILD_AS_CPP:BOOL=ON" +} + +if (-Not($enable_cuda)) { + $additional_build_setup = $additional_build_setup + " -DENABLE_CUDA:BOOL=OFF" } if ($use_vcpkg) { - ## DEBUG - #New-Item -Path .\build_win_debug -ItemType directory -Force - #Set-Location build_win_debug - #if ($use_ninja) { - #cmake -G "$generator" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" #"-DCMAKE_BUILD_TYPE=Debug" $additional_build_setup .. - #$dllfolder = "." - #} - #else { - #cmake -G "$generator" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Debug" $additional_build_setup .. - #$dllfolder = "Debug" - #} - #cmake --build . --config Debug --target install - ##cmake --build . --config Debug --parallel ${number_of_build_workers} --target install #valid only for CMake 3.12+ - #Remove-Item DarknetConfig.cmake - #Remove-Item DarknetConfigVersion.cmake - #$dllfiles = Get-ChildItem ${dllfolder}\*.dll - #if ($dllfiles) { - # Copy-Item $dllfiles .. - #} - #Set-Location .. - #Copy-Item cmake\Modules\*.cmake share\darknet\ - - # RELEASE New-Item -Path .\build_win_release -ItemType directory -Force Set-Location build_win_release - if($use_ninja) { - cmake -G "$generator" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Release" $additional_build_setup .. + if ($use_ninja) { + $cmake_args = "-G `"$generator`" `"-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet`" `"-DCMAKE_BUILD_TYPE=Release`" ${additional_build_setup} -S .." $dllfolder = "." } else { - cmake -G "$generator" -T "host=x64" -A "x64" "-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake" "-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" "-DCMAKE_BUILD_TYPE=Release" $additional_build_setup .. + $cmake_args = "-G `"$generator`" -T `"host=x64`" -A `"x64`" `"-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet`" `"-DCMAKE_BUILD_TYPE=Release`" ${additional_build_setup} -S .." $dllfolder = "Release" } - cmake --build . --config Release --target install - #cmake --build . --config Release --parallel ${number_of_build_workers} --target install #valid only for CMake 3.12+ - Remove-Item DarknetConfig.cmake - Remove-Item DarknetConfigVersion.cmake - $dllfiles = Get-ChildItem ${dllfolder}\*.dll - if ($dllfiles) { - Copy-Item $dllfiles .. - } - Set-Location .. - Copy-Item cmake\Modules\*.cmake share\darknet\ } else { # USE LOCAL PTHREAD LIB AND LOCAL STB HEADER, NO VCPKG, ONLY RELEASE MODE SUPPORTED # if you want to manually force this case, remove VCPKG_ROOT env variable and remember to use "vcpkg integrate remove" in case you had enabled user-wide vcpkg integration New-Item -Path .\build_win_release_novcpkg -ItemType directory -Force Set-Location build_win_release_novcpkg - if($use_ninja) { - cmake -G "$generator" $additional_build_setup .. + if ($use_ninja) { + $cmake_args = "-G `"$generator`" ${additional_build_setup} -S .." + $dllfolder = "..\3rdparty\pthreads\bin" } else { - cmake -G "$generator" -T "host=x64" -A "x64" $additional_build_setup .. - } - cmake --build . --config Release --target install - #cmake --build . --config Release --parallel ${number_of_build_workers} --target install #valid only for CMake 3.12+ - Remove-Item DarknetConfig.cmake - Remove-Item DarknetConfigVersion.cmake - $dllfolder = "..\3rdparty\pthreads\bin" - $dllfiles = Get-ChildItem ${dllfolder}\*.dll - if ($dllfiles) { - Copy-Item $dllfiles .. + $cmake_args = "-G `"$generator`" -T `"host=x64`" -A `"x64`" ${additional_build_setup} -S .." + $dllfolder = "..\3rdparty\pthreads\bin" } - Set-Location .. - Copy-Item cmake\Modules\*.cmake share\darknet\ } + +Write-Host "CMake args: $cmake_args" +Start-Process -NoNewWindow -Wait -FilePath $CMAKE_EXE -ArgumentList $cmake_args +Start-Process -NoNewWindow -Wait -FilePath $CMAKE_EXE -ArgumentList "--build . --config Release --parallel ${number_of_build_workers} --target install" +Remove-Item DarknetConfig.cmake +Remove-Item DarknetConfigVersion.cmake +$dllfiles = Get-ChildItem ${dllfolder}\*.dll +if ($dllfiles) { + Copy-Item $dllfiles .. +} +Set-Location .. +Copy-Item cmake\Modules\*.cmake share\darknet\ diff --git a/build.sh b/build.sh index dfd5446b47d..51275834932 100755 --- a/build.sh +++ b/build.sh @@ -1,8 +1,9 @@ #!/usr/bin/env bash number_of_build_workers=8 -bypass_vcpkg=true +use_vcpkg=false force_cpp_build=false +enable_cuda=false if [[ "$OSTYPE" == "darwin"* ]]; then vcpkg_triplet="x64-osx" @@ -10,36 +11,41 @@ else vcpkg_triplet="x64-linux" fi -if [[ ! -z "${VCPKG_ROOT}" ]] && [ -d ${VCPKG_ROOT} ] && [ ! "$bypass_vcpkg" = true ] +if [[ ! -z "${VCPKG_ROOT}" ]] && [ -d ${VCPKG_ROOT} ] && [ "$use_vcpkg" = true ] then vcpkg_path="${VCPKG_ROOT}" - vcpkg_define="-DCMAKE_TOOLCHAIN_FILE=${vcpkg_path}/scripts/buildsystems/vcpkg.cmake" vcpkg_triplet_define="-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" echo "Found vcpkg in VCPKG_ROOT: ${vcpkg_path}" additional_defines="-DBUILD_SHARED_LIBS=OFF" -elif [[ ! -z "${WORKSPACE}" ]] && [ -d ${WORKSPACE}/vcpkg ] && [ ! "$bypass_vcpkg" = true ] +elif [[ ! -z "${WORKSPACE}" ]] && [ -d ${WORKSPACE}/vcpkg ] && [ "$use_vcpkg" = true ] then + export VCPKG_ROOT="${WORKSPACE}/vcpkg" vcpkg_path="${WORKSPACE}/vcpkg" - vcpkg_define="-DCMAKE_TOOLCHAIN_FILE=${vcpkg_path}/scripts/buildsystems/vcpkg.cmake" vcpkg_triplet_define="-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" echo "Found vcpkg in WORKSPACE/vcpkg: ${vcpkg_path}" additional_defines="-DBUILD_SHARED_LIBS=OFF" -elif [ ! "$bypass_vcpkg" = true ] +elif [ "$use_vcpkg" = true ] then (>&2 echo "darknet is unsupported without vcpkg, use at your own risk!") +else + additional_build_setup=${additional_build_setup}" -DENABLE_VCPKG_INTEGRATION:BOOL=FALSE" fi if [ "$force_cpp_build" = true ] then - additional_build_setup="-DBUILD_AS_CPP:BOOL=TRUE" + additional_build_setup=${additional_build_setup}" -DBUILD_AS_CPP:BOOL=TRUE" +fi + +if [ "$enable_cuda" = false ] +then + additional_build_setup=${additional_build_setup}" -DENABLE_CUDA:BOOL=FALSE" fi ## DEBUG #mkdir -p build_debug #cd build_debug #cmake .. -DCMAKE_BUILD_TYPE=Debug ${vcpkg_define} ${vcpkg_triplet_define} ${additional_defines} ${additional_build_setup} -#cmake --build . --target install -- -j${number_of_build_workers} -##cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ +#cmake --build . --target install --parallel ${number_of_build_workers} #rm -f DarknetConfig.cmake #rm -f DarknetConfigVersion.cmake #cd .. @@ -49,8 +55,7 @@ fi mkdir -p build_release cd build_release cmake .. -DCMAKE_BUILD_TYPE=Release ${vcpkg_define} ${vcpkg_triplet_define} ${additional_defines} ${additional_build_setup} -cmake --build . --target install -- -j${number_of_build_workers} -#cmake --build . --target install --parallel ${number_of_build_workers} #valid only for CMake 3.12+ +cmake --build . --target install --parallel ${number_of_build_workers} rm -f DarknetConfig.cmake rm -f DarknetConfigVersion.cmake cd .. diff --git a/cmake/Modules/FindPThreads_windows.cmake b/cmake/Modules/FindPThreads_windows.cmake index 02d8a72ded7..64e314005ff 100644 --- a/cmake/Modules/FindPThreads_windows.cmake +++ b/cmake/Modules/FindPThreads_windows.cmake @@ -32,7 +32,7 @@ endif() # Allow libraries to be set manually if(NOT PThreads_windows_LIBRARY) find_library(PThreads_windows_LIBRARY_RELEASE NAMES pthreadsVC2 pthreadVC2 PATHS ${PThreads_windows_DIR} PATH_SUFFIXES lib) - find_library(PThreads_windows_LIBRARY_DEBUG NAMES pthreadsVC2d pthreadVC2d PATHS ${PThreads_windows_DIR} PATH_SUFFIXES lib) + find_library(PThreads_windows_LIBRARY_DEBUG NAMES pthreadsVC2d pthreadVC2d pthreadsVC2 pthreadVC2 PATHS ${PThreads_windows_DIR} PATH_SUFFIXES lib) select_library_configurations(PThreads_windows) endif() @@ -44,7 +44,7 @@ list(TRANSFORM PThreads_windows_DLL_DIR APPEND "/../bin") message(STATUS "PThreads_windows_DLL_DIR: ${PThreads_windows_DLL_DIR}") find_file(PThreads_windows_LIBRARY_RELEASE_DLL NAMES pthreadVC2.dll PATHS ${PThreads_windows_DLL_DIR}) -find_file(PThreads_windows_LIBRARY_DEBUG_DLL NAMES pthreadVC2d.dll PATHS ${PThreads_windows_DLL_DIR}) +find_file(PThreads_windows_LIBRARY_DEBUG_DLL NAMES pthreadVC2d.dll pthreadVC2.dll PATHS ${PThreads_windows_DLL_DIR}) # Register imported libraries: # 1. If we can find a Windows .dll file (or if we can find both Debug and diff --git a/cmake/vcpkg_linux.diff b/cmake/vcpkg_linux.diff deleted file mode 100644 index dd4475bcf84..00000000000 --- a/cmake/vcpkg_linux.diff +++ /dev/null @@ -1,6 +0,0 @@ -stb -pthreads -ffmpeg -opencv[ffmpeg] ---triplet -x64-linux \ No newline at end of file diff --git a/cmake/vcpkg_linux_cuda.diff b/cmake/vcpkg_linux_cuda.diff deleted file mode 100644 index 635bd5f1c89..00000000000 --- a/cmake/vcpkg_linux_cuda.diff +++ /dev/null @@ -1,8 +0,0 @@ -cuda -cudnn -stb -pthreads -ffmpeg -opencv[ffmpeg] ---triplet -x64-linux \ No newline at end of file diff --git a/cmake/vcpkg_osx.diff b/cmake/vcpkg_osx.diff deleted file mode 100644 index 12d2dfe4245..00000000000 --- a/cmake/vcpkg_osx.diff +++ /dev/null @@ -1,6 +0,0 @@ -stb -pthreads -ffmpeg -opencv[ffmpeg] ---triplet -x64-osx \ No newline at end of file diff --git a/cmake/vcpkg_windows.diff b/cmake/vcpkg_windows.diff deleted file mode 100644 index ff554a81458..00000000000 --- a/cmake/vcpkg_windows.diff +++ /dev/null @@ -1,6 +0,0 @@ -stb -pthreads -ffmpeg -opencv[ffmpeg] ---triplet -x64-windows \ No newline at end of file diff --git a/cmake/vcpkg_windows_cuda.diff b/cmake/vcpkg_windows_cuda.diff deleted file mode 100644 index b757b8d0192..00000000000 --- a/cmake/vcpkg_windows_cuda.diff +++ /dev/null @@ -1,8 +0,0 @@ -cuda -cudnn -stb -pthreads -ffmpeg -opencv[ffmpeg] ---triplet -x64-windows \ No newline at end of file diff --git a/vcpkg.json b/vcpkg.json new file mode 100644 index 00000000000..7f55c4d9f5a --- /dev/null +++ b/vcpkg.json @@ -0,0 +1,19 @@ +{ + "name": "darknet", + "version-string": "0.2.5.4", + "port-version": 1, + "homepage": "https://github.com/alexeyab/darknet", + "description": "Neural networks for object detection", + "dependencies": [ + "stb", + "pthreads", + { + "name": "opencv", + "features": [ "ffmpeg" ] + }, + { + "name": "cudnn", + "platform": "!osx" + } + ] +} From 4de12a1e7edf91c36bcf1aafea444e20b8e7d5f3 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sat, 23 Jan 2021 09:02:22 +0100 Subject: [PATCH 1181/1365] default on opencv4.5 on vcpkg --- .github/workflows/ccpp.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 3e56cc2739b..a544351638a 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -107,7 +107,7 @@ jobs: with: setupOnly: true vcpkgDirectory: '${{ github.workspace }}/vcpkg' - vcpkgGitCommitId: '50ea8c0ab7aca3bb9245bba7fc877ad2f2a4464c' + vcpkgGitCommitId: 'e4ce66eecfd3e5cca5eac06c971921bf8e37cf88' appendedCacheKey: ${{ hashFiles( '**/vcpkg.json' ) }} vcpkgTriplet: 'x64-linux' @@ -283,7 +283,7 @@ jobs: with: setupOnly: true vcpkgDirectory: '${{ github.workspace }}/vcpkg' - vcpkgGitCommitId: '50ea8c0ab7aca3bb9245bba7fc877ad2f2a4464c' + vcpkgGitCommitId: 'e4ce66eecfd3e5cca5eac06c971921bf8e37cf88' appendedCacheKey: ${{ hashFiles( '**/vcpkg.json' ) }} vcpkgTriplet: 'x64-osx' @@ -397,7 +397,7 @@ jobs: with: setupOnly: true vcpkgDirectory: '${{ github.workspace }}/vcpkg' - vcpkgGitCommitId: '50ea8c0ab7aca3bb9245bba7fc877ad2f2a4464c' + vcpkgGitCommitId: 'e4ce66eecfd3e5cca5eac06c971921bf8e37cf88' appendedCacheKey: ${{ hashFiles( '**/vcpkg.json' ) }} vcpkgTriplet: 'x64-windows' From a8013ebcfba3416e230df8d60c2a82f04a8591ee Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sat, 23 Jan 2021 09:02:33 +0100 Subject: [PATCH 1182/1365] do not default on ninja on windows --- build.ps1 | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/build.ps1 b/build.ps1 index 85a85dd35de..ab17585c84c 100755 --- a/build.ps1 +++ b/build.ps1 @@ -3,14 +3,12 @@ $number_of_build_workers = 8 $enable_cuda = $true $use_vcpkg = $true -$use_ninja = $true +$use_ninja = $false $force_cpp_build = $false #$additional_build_setup = " -DCMAKE_CUDA_ARCHITECTURES=30" - $CMAKE_EXE = Get-Command cmake | Select-Object -ExpandProperty Definition -$NINJA_EXE = Get-Command ninja | Select-Object -ExpandProperty Definition if (-Not $CMAKE_EXE) { throw "Could not find CMake, please install it" @@ -19,11 +17,14 @@ else { Write-Host "Using CMake from ${CMAKE_EXE}" } -if (-Not $NINJA_EXE -and $use_ninja) { - throw "Could not find Ninja, please install it" -} -else { - Write-Host "Using Ninja from ${NINJA_EXE}" +if ($use_ninja) { + $NINJA_EXE = Get-Command ninja | Select-Object -ExpandProperty Definition + if (-Not $NINJA_EXE) { + throw "Could not find Ninja, please install it" + } + else { + Write-Host "Using Ninja from ${NINJA_EXE}" + } } function getProgramFiles32bit() { From 1735d57ad1b0166fa44bcd4d4226eaa7bdb706e2 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sat, 23 Jan 2021 16:06:36 +0100 Subject: [PATCH 1183/1365] rework integrated libs logic --- .github/workflows/ccpp.yml | 5 ++++- CMakeLists.txt | 17 ++++++++++------- 2 files changed, 14 insertions(+), 8 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index a544351638a..807126d1d33 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -110,6 +110,7 @@ jobs: vcpkgGitCommitId: 'e4ce66eecfd3e5cca5eac06c971921bf8e37cf88' appendedCacheKey: ${{ hashFiles( '**/vcpkg.json' ) }} vcpkgTriplet: 'x64-linux' + additionalCachedPaths: ${{ env.buildDir }}/vcpkg_installed - name: 'Build with CMake and Ninja' env: @@ -286,6 +287,7 @@ jobs: vcpkgGitCommitId: 'e4ce66eecfd3e5cca5eac06c971921bf8e37cf88' appendedCacheKey: ${{ hashFiles( '**/vcpkg.json' ) }} vcpkgTriplet: 'x64-osx' + additionalCachedPaths: ${{ env.buildDir }}/vcpkg_installed - name: 'Build with CMake and Ninja' uses: lukka/run-cmake@v3 @@ -400,6 +402,7 @@ jobs: vcpkgGitCommitId: 'e4ce66eecfd3e5cca5eac06c971921bf8e37cf88' appendedCacheKey: ${{ hashFiles( '**/vcpkg.json' ) }} vcpkgTriplet: 'x64-windows' + additionalCachedPaths: ${{ env.buildDir }}/vcpkg_installed - name: 'Build with CMake and Ninja' env: @@ -413,7 +416,7 @@ jobs: cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DSELECT_OPENCV_MODULES=ON" + cmakeAppendedArgs: "-GNinja -DCMAKE_BUILD_TYPE=Release -DSELECT_OPENCV_MODULES=ON" cmakeBuildType: 'Release' buildWithCMakeArgs: '--config Release --target install' diff --git a/CMakeLists.txt b/CMakeLists.txt index 9b57d739606..96d0dae618d 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -46,20 +46,23 @@ cmake_dependent_option(ENABLE_SSE_AND_AVX_FLAGS "Enable AVX and SSE optimization if(ENABLE_VCPKG_INTEGRATION AND DEFINED ENV{VCPKG_ROOT} AND NOT DEFINED CMAKE_TOOLCHAIN_FILE) set(CMAKE_TOOLCHAIN_FILE "$ENV{VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake" CACHE STRING "") - set(_VCPKG_INSTALLED_DIR ${CMAKE_CURRENT_LIST_DIR}/vcpkg CACHE STRING "") + #set(_VCPKG_INSTALLED_DIR ${CMAKE_CURRENT_LIST_DIR}/vcpkg CACHE STRING "") #folder for manifest-installed dependencies message(STATUS "VCPKG found: $ENV{VCPKG_ROOT}") message(STATUS "Using VCPKG integration") - message(STATUS "Installing dependencies in ${_VCPKG_INSTALLED_DIR}") -endif() - -project(Darknet VERSION ${Darknet_VERSION}) - -if(WIN32 AND NOT DEFINED CMAKE_TOOLCHAIN_FILE) + set(USE_INTEGRATED_LIBS "FALSE" CACHE BOOL "Use libs distributed with this repo") +elseif(DEFINED CMAKE_TOOLCHAIN_FILE) + message(STATUS "Using toolchain: ${CMAKE_TOOLCHAIN_FILE}") + set(USE_INTEGRATED_LIBS "FALSE" CACHE BOOL "Use libs distributed with this repo") +elseif(WIN32) + message(STATUS "vcpkg not found, toolchain not defined, using integrated libs on win32") set(USE_INTEGRATED_LIBS "TRUE" CACHE BOOL "Use libs distributed with this repo") else() + message(WARNING "vcpkg not found, toolchain not defined, system not win32 so build might fail") set(USE_INTEGRATED_LIBS "FALSE" CACHE BOOL "Use libs distributed with this repo") endif() +project(Darknet VERSION ${Darknet_VERSION}) + enable_language(C) enable_language(CXX) From e72a11a04c1b8aa805b78d4c49ef2664c6057f67 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sat, 23 Jan 2021 16:29:51 +0100 Subject: [PATCH 1184/1365] update README --- README.md | 63 +++++++++++++++++++++++++++++++++++-------------------- 1 file changed, 40 insertions(+), 23 deletions(-) diff --git a/README.md b/README.md index 220eeab054b..0065eca6fa8 100644 --- a/README.md +++ b/README.md @@ -38,23 +38,31 @@ About Darknet framework: http://pjreddie.com/darknet/ * [Yolo v4 in other frameworks (TensorRT, TensorFlow, PyTorch, OpenVINO, OpenCV-dnn, TVM,...)](#yolo-v4-in-other-frameworks) * [Datasets](#datasets) -0. [Improvements in this repository](#improvements-in-this-repository) -1. [How to use](#how-to-use-on-the-command-line) -2. How to compile on Linux - * [Using cmake](#how-to-compile-on-linux-using-cmake) - * [Using make](#how-to-compile-on-linux-using-make) -3. How to compile on Windows - * [Using cmake](#how-to-compile-on-windows-using-cmake) - * [Using vcpkg](#how-to-compile-on-windows-using-vcpkg) - * [Legacy way](#how-to-compile-on-windows-legacy-way) -4. [Training and Evaluation of speed and accuracy on MS COCO](https://github.com/AlexeyAB/darknet/wiki#training-and-evaluation-of-speed-and-accuracy-on-ms-coco) -5. [How to train with multi-GPU:](#how-to-train-with-multi-gpu) -6. [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects) -7. [How to train tiny-yolo (to detect your custom objects)](#how-to-train-tiny-yolo-to-detect-your-custom-objects) -8. [When should I stop training](#when-should-i-stop-training) -9. [How to improve object detection](#how-to-improve-object-detection) -10. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) -11. [How to use Yolo as DLL and SO libraries](#how-to-use-yolo-as-dll-and-so-libraries) +- [Yolo v4, v3 and v2 for Windows and Linux](#yolo-v4-v3-and-v2-for-windows-and-linux) + - [(neural networks for object detection)](#neural-networks-for-object-detection) + - [GeForce RTX 2080 Ti:](#geforce-rtx-2080-ti) + - [Youtube video of results](#youtube-video-of-results) + - [How to evaluate AP of YOLOv4 on the MS COCO evaluation server](#how-to-evaluate-ap-of-yolov4-on-the-ms-coco-evaluation-server) + - [How to evaluate FPS of YOLOv4 on GPU](#how-to-evaluate-fps-of-yolov4-on-gpu) + - [Pre-trained models](#pre-trained-models) + - [Requirements](#requirements) + - [Yolo v4 in other frameworks](#yolo-v4-in-other-frameworks) + - [Datasets](#datasets) + - [Improvements in this repository](#improvements-in-this-repository) + - [How to use on the command line](#how-to-use-on-the-command-line) + - [For using network video-camera mjpeg-stream with any Android smartphone](#for-using-network-video-camera-mjpeg-stream-with-any-android-smartphone) + - [How to compile on Linux/macOS (using `CMake`)](#how-to-compile-on-linuxmacos-using-cmake) + - [How to compile on Linux (using `make`)](#how-to-compile-on-linux-using-make) + - [How to compile on Windows (using `CMake`)](#how-to-compile-on-windows-using-cmake) + - [How to compile on Windows (using `vcpkg`)](#how-to-compile-on-windows-using-vcpkg) + - [How to train with multi-GPU](#how-to-train-with-multi-gpu) + - [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects) + - [How to train tiny-yolo (to detect your custom objects):](#how-to-train-tiny-yolo-to-detect-your-custom-objects) + - [When should I stop training:](#when-should-i-stop-training) + - [Custom object detection:](#custom-object-detection) + - [How to improve object detection:](#how-to-improve-object-detection) + - [How to mark bounded boxes of objects and create annotation files:](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) + - [How to use Yolo as DLL and SO libraries](#how-to-use-yolo-as-dll-and-so-libraries) ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) @@ -189,7 +197,7 @@ You can get cfg-files by path: `darknet/cfg/` ### Requirements * Windows or Linux -* **CMake >= 3.12**: https://cmake.org/download/ +* **CMake >= 3.18**: https://cmake.org/download/ * **CUDA >= 10.0**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) * **OpenCV >= 2.4**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or download from [OpenCV official site](https://opencv.org/releases.html) (on Windows set system variable `OpenCV_DIR` = `C:\opencv\build` - where are the `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png)) * **cuDNN >= 7.0** https://developer.nvidia.com/rdp/cudnn-archive (on **Linux** copy `cudnn.h`,`libcudnn.so`... as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on **Windows** copy `cudnn.h`,`cudnn64_7.dll`, `cudnn64_7.lib` as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows ) @@ -335,15 +343,15 @@ To run Darknet on Linux use examples from this article, just use `./darknet` ins Requires: * MSVS: https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community -* Cmake GUI: `Windows win64-x64 Installer`https://cmake.org/download/ +* CMake GUI: `Windows win64-x64 Installer`https://cmake.org/download/ * Download Darknet zip-archive with the latest commit and uncompress it: [master.zip](https://github.com/AlexeyAB/darknet/archive/master.zip) In the Windows: -* Start (button) -> All programms -> Cmake -> Cmake (gui) -> +* Start (button) -> All programms -> CMake -> CMake (gui) -> -* [look at image](https://habrastorage.org/webt/pz/s1/uu/pzs1uu4heb7vflfcjqn-lxy-aqu.jpeg) In Cmake: Enter input path to the darknet Source, and output path to the Binaries -> Configure (button) -> Optional platform for generator: `x64` -> Finish -> Generate -> Open Project -> +* [look at image](https://habrastorage.org/webt/pz/s1/uu/pzs1uu4heb7vflfcjqn-lxy-aqu.jpeg) In CMake: Enter input path to the darknet Source, and output path to the Binaries -> Configure (button) -> Optional platform for generator: `x64` -> Finish -> Generate -> Open Project -> * in MS Visual Studio: Select: x64 and Release -> Build -> Build solution @@ -366,9 +374,18 @@ This is the recommended approach to build Darknet on Windows. ```PowerShell PS Code\> git clone https://github.com/microsoft/vcpkg PS Code\> cd vcpkg -PS Code\vcpkg> $env:VCPKG_ROOT=$PWD PS Code\vcpkg> .\bootstrap-vcpkg.bat -PS Code\vcpkg> .\vcpkg install darknet[full]:x64-windows #replace with darknet[opencv-base,cuda,cudnn]:x64-windows for a quicker install of dependencies +PS Code\vcpkg> .\vcpkg.exe install darknet[full]:x64-windows #replace with darknet[opencv-base,cuda,cudnn,weights,weights-train]:x64-windows for a quicker install of dependencies +PS Code\vcpkg> cd .. +``` + +If you prefer to download the most recent, maybe unreleased version, on the last line you need to add `--head` tag, like `.\vcpkg.exe install darknet[full]:x64-windows --head`. After completion, you can find darknet executables, networks and weights inside `installed\x64-windows\tools\darknet`; lib file is inside `installed\x64-windows\lib` and dll file is inside `installed\x64-windows\bin`. + +To manually build a version of darknet, after completing the previous step, you can also run + +```PowerShell +PS Code\> cd vcpkg +PS Code\vcpkg> $env:VCPKG_ROOT=$PWD PS Code\vcpkg> cd .. PS Code\> git clone https://github.com/AlexeyAB/darknet PS Code\> cd darknet From 63caceab0d3591b992c6d192d9382a03c6476b64 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sat, 23 Jan 2021 17:04:23 +0100 Subject: [PATCH 1185/1365] add manual trigger for github actions --- .github/workflows/ccpp.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 807126d1d33..239971c3773 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -1,6 +1,6 @@ name: Darknet Continuous Integration -on: [push, pull_request] +on: [push, pull_request, workflow_dispatch] jobs: ubuntu-makefile: From da15b141f0027114c2cf4052ac4faba4d4aaba8b Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sat, 23 Jan 2021 20:06:55 +0100 Subject: [PATCH 1186/1365] trying to fix cuda path --- .github/workflows/ccpp.yml | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 239971c3773..d397aceba91 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -391,10 +391,10 @@ jobs: - name: Restore from cache and run vcpkg env: - CUDA_PATH: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" - CUDA_PATH_V10_2: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" - CUDA_TOOLKIT_ROOT_DIR: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" - CUDACXX: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" + CUDA_PATH: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2" + CUDA_PATH_V10_2: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2" + CUDA_TOOLKIT_ROOT_DIR: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2" + CUDACXX: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" uses: lukka/run-vcpkg@v6 with: setupOnly: true @@ -406,10 +406,10 @@ jobs: - name: 'Build with CMake and Ninja' env: - CUDA_PATH: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" - CUDA_PATH_V10_2: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" - CUDA_TOOLKIT_ROOT_DIR: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" - CUDACXX: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" + CUDA_PATH: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2" + CUDA_PATH_V10_2: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2" + CUDA_TOOLKIT_ROOT_DIR: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2" + CUDACXX: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced @@ -516,10 +516,10 @@ jobs: - name: 'Build with CMake and Ninja' env: - CUDA_PATH: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" - CUDA_PATH_V10_2: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" - CUDA_TOOLKIT_ROOT_DIR: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" - CUDACXX: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" + CUDA_PATH: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2" + CUDA_PATH_V10_2: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2" + CUDA_TOOLKIT_ROOT_DIR: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2" + CUDACXX: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced From dcc38312fe1acf77d754715631b9795b763c7f90 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 24 Jan 2021 01:05:28 +0100 Subject: [PATCH 1187/1365] trying with forward slashes --- .github/workflows/ccpp.yml | 25 +++++++++++-------------- .travis.yml | 4 ++-- 2 files changed, 13 insertions(+), 16 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index d397aceba91..bf90f26197b 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -376,7 +376,7 @@ jobs: buildWithCMakeArgs: '--target install' - win-vcpkg-cuda: + win-vcpkg-cuda-ninja: runs-on: windows-latest steps: - uses: actions/checkout@v2 @@ -386,15 +386,17 @@ jobs: $env:ChocolateyInstall = Convert-Path "$((Get-Command choco).Path)\..\.." Import-Module "$env:ChocolateyInstall\helpers\chocolateyProfile.psm1" refreshenv + #echo "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2/bin/" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append + echo $env:PATH - uses: lukka/get-cmake@latest - name: Restore from cache and run vcpkg env: - CUDA_PATH: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2" - CUDA_PATH_V10_2: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2" - CUDA_TOOLKIT_ROOT_DIR: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2" - CUDACXX: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" + CUDA_PATH: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2" + CUDA_PATH_V10_2: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2" + CUDA_TOOLKIT_ROOT_DIR: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2" + CUDACXX: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2/bin/nvcc.exe" uses: lukka/run-vcpkg@v6 with: setupOnly: true @@ -406,10 +408,10 @@ jobs: - name: 'Build with CMake and Ninja' env: - CUDA_PATH: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2" - CUDA_PATH_V10_2: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2" - CUDA_TOOLKIT_ROOT_DIR: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2" - CUDACXX: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" + CUDA_PATH: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2" + CUDA_PATH_V10_2: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2" + CUDA_TOOLKIT_ROOT_DIR: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2" + CUDACXX: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2/bin/nvcc.exe" uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced @@ -515,11 +517,6 @@ jobs: - uses: lukka/get-cmake@latest - name: 'Build with CMake and Ninja' - env: - CUDA_PATH: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2" - CUDA_PATH_V10_2: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2" - CUDA_TOOLKIT_ROOT_DIR: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2" - CUDACXX: "C:\\Program\ Files\\NVIDIA\ GPU\ Computing\ Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced diff --git a/.travis.yml b/.travis.yml index 059f8116235..4268f1343e4 100644 --- a/.travis.yml +++ b/.travis.yml @@ -209,8 +209,8 @@ matrix: - LD_LIBRARY_PATH="/usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64:/usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.2.89-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1804_10.2.89-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-2 cuda-libraries-dev-10-2 cuda-driver-dev-10-2 cuda-cudart-dev-10-2 cuda-curand-dev-10-2 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs/libcuda.so.1" -# allow_failures: -# - name: macOS - clang - vcpkg + allow_failures: + - name: macOS - clang - vcpkg before_install: - travis_retry eval "${MATRIX_EVAL}" From e2a4253bb086d15c5a145c7bca06725de73c5fae Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 24 Jan 2021 15:31:13 +0100 Subject: [PATCH 1188/1365] add cuda to path to github ci; improve diagnostic messages --- .github/workflows/ccpp.yml | 8 +++++++- CMakeLists.txt | 12 ++++++++++-- 2 files changed, 17 insertions(+), 3 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index bf90f26197b..fb1459f8150 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -386,7 +386,8 @@ jobs: $env:ChocolateyInstall = Convert-Path "$((Get-Command choco).Path)\..\.." Import-Module "$env:ChocolateyInstall\helpers\chocolateyProfile.psm1" refreshenv - #echo "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2/bin/" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append + echo "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2/bin/" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append + refreshenv echo $env:PATH - uses: lukka/get-cmake@latest @@ -517,6 +518,11 @@ jobs: - uses: lukka/get-cmake@latest - name: 'Build with CMake and Ninja' + env: + CUDA_PATH: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2" + CUDA_PATH_V10_2: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2" + CUDA_TOOLKIT_ROOT_DIR: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2" + CUDACXX: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2/bin/nvcc.exe" uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced diff --git a/CMakeLists.txt b/CMakeLists.txt index 96d0dae618d..a5a3a6aba3b 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -92,10 +92,18 @@ if(ENABLE_CUDA) include(CheckLanguage) check_language(CUDA) if(NOT CMAKE_CUDA_COMPILER) - message(FATAL_ERROR "CUDA not found, please build explicitly with -DENABLE_CUDA=OFF if you do not want CUDA") - elseif(ENABLE_CUDA) + message(STATUS "PATH: $ENV{PATH}") + message(STATUS "CUDA_PATH: $ENV{CUDA_PATH}") + message(STATUS "CUDA_TOOLKIT_ROOT_DIR: $ENV{CUDA_TOOLKIT_ROOT_DIR}") + message(STATUS "CUDACXX: $ENV{CUDACXX}") + message(FATAL_ERROR "CUDA not found, please build explicitly with -DENABLE_CUDA=OFF if you do not want CUDA.") + else() enable_language(CUDA) if(CMAKE_CUDA_COMPILER_VERSION VERSION_LESS "9.0") + message(STATUS "PATH: $ENV{PATH}") + message(STATUS "CUDA_PATH: $ENV{CUDA_PATH}") + message(STATUS "CUDA_TOOLKIT_ROOT_DIR: $ENV{CUDA_TOOLKIT_ROOT_DIR}") + message(STATUS "CUDACXX: $ENV{CUDACXX}") message(FATAL_ERROR "Unsupported CUDA version, please upgrade to CUDA 9+ or disable CUDA with explicitly with -DENABLE_CUDA=OFF") else() message(STATUS "Selected CMAKE_CUDA_ARCHITECTURES: ${CMAKE_CUDA_ARCHITECTURES}") From 5f84f909095b3653474c9b0def7e89db2768dd33 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 24 Jan 2021 17:07:10 +0100 Subject: [PATCH 1189/1365] revert to double backslash --- .github/workflows/ccpp.yml | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index fb1459f8150..a510a398448 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -386,7 +386,7 @@ jobs: $env:ChocolateyInstall = Convert-Path "$((Get-Command choco).Path)\..\.." Import-Module "$env:ChocolateyInstall\helpers\chocolateyProfile.psm1" refreshenv - echo "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2/bin/" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append + echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append refreshenv echo $env:PATH @@ -394,10 +394,10 @@ jobs: - name: Restore from cache and run vcpkg env: - CUDA_PATH: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2" - CUDA_PATH_V10_2: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2" - CUDA_TOOLKIT_ROOT_DIR: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2" - CUDACXX: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2/bin/nvcc.exe" + CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" + CUDA_PATH_V10_2: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" + CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" + CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" uses: lukka/run-vcpkg@v6 with: setupOnly: true @@ -409,10 +409,10 @@ jobs: - name: 'Build with CMake and Ninja' env: - CUDA_PATH: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2" - CUDA_PATH_V10_2: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2" - CUDA_TOOLKIT_ROOT_DIR: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2" - CUDACXX: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2/bin/nvcc.exe" + CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" + CUDA_PATH_V10_2: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" + CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" + CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced @@ -519,10 +519,10 @@ jobs: - name: 'Build with CMake and Ninja' env: - CUDA_PATH: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2" - CUDA_PATH_V10_2: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2" - CUDA_TOOLKIT_ROOT_DIR: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2" - CUDACXX: "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2/bin/nvcc.exe" + CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" + CUDA_PATH_V10_2: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" + CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" + CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced From 05bda64c92d0850f6f5195f20b56e73b68efba21 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 29 Jan 2021 08:30:06 +0100 Subject: [PATCH 1190/1365] Update vcpkg refs to OpenCV 4.5.1 --- .github/workflows/ccpp.yml | 6 +++--- CMakeLists.txt | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index a510a398448..a0be9c89ad5 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -107,7 +107,7 @@ jobs: with: setupOnly: true vcpkgDirectory: '${{ github.workspace }}/vcpkg' - vcpkgGitCommitId: 'e4ce66eecfd3e5cca5eac06c971921bf8e37cf88' + vcpkgGitCommitId: '865de0c130f3d34174ac7a410789df3c44a27fd2' appendedCacheKey: ${{ hashFiles( '**/vcpkg.json' ) }} vcpkgTriplet: 'x64-linux' additionalCachedPaths: ${{ env.buildDir }}/vcpkg_installed @@ -284,7 +284,7 @@ jobs: with: setupOnly: true vcpkgDirectory: '${{ github.workspace }}/vcpkg' - vcpkgGitCommitId: 'e4ce66eecfd3e5cca5eac06c971921bf8e37cf88' + vcpkgGitCommitId: '865de0c130f3d34174ac7a410789df3c44a27fd2' appendedCacheKey: ${{ hashFiles( '**/vcpkg.json' ) }} vcpkgTriplet: 'x64-osx' additionalCachedPaths: ${{ env.buildDir }}/vcpkg_installed @@ -402,7 +402,7 @@ jobs: with: setupOnly: true vcpkgDirectory: '${{ github.workspace }}/vcpkg' - vcpkgGitCommitId: 'e4ce66eecfd3e5cca5eac06c971921bf8e37cf88' + vcpkgGitCommitId: '865de0c130f3d34174ac7a410789df3c44a27fd2' appendedCacheKey: ${{ hashFiles( '**/vcpkg.json' ) }} vcpkgTriplet: 'x64-windows' additionalCachedPaths: ${{ env.buildDir }}/vcpkg_installed diff --git a/CMakeLists.txt b/CMakeLists.txt index a5a3a6aba3b..05496258a46 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -113,10 +113,10 @@ if(ENABLE_CUDA) 80 IN_LIST CMAKE_CUDA_ARCHITECTURES OR 86 IN_LIST CMAKE_CUDA_ARCHITECTURES) set(ENABLE_CUDNN_HALF "TRUE" CACHE BOOL "Enable CUDNN Half precision" FORCE) - message(STATUS "Your setup supports half precision (it requires CC >= 7.0)") + message(STATUS "Your setup supports half precision (CUDA_ARCHITECTURES >= 70)") else() set(ENABLE_CUDNN_HALF "FALSE" CACHE BOOL "Enable CUDNN Half precision" FORCE) - message(STATUS "Your setup does not support half precision (it requires CC >= 7.0)") + message(STATUS "Your setup does not support half precision (it requires CUDA_ARCHITECTURES >= 70)") endif() endif() if(BUILD_SHARED_LIBS) From dc71b14e77a5d0e1e48be061b0816f014970968d Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 29 Jan 2021 17:08:09 +0100 Subject: [PATCH 1191/1365] fix interface included directory for opencv --- DarknetConfig.cmake.in | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DarknetConfig.cmake.in b/DarknetConfig.cmake.in index 703a59e3956..c016b220bd0 100644 --- a/DarknetConfig.cmake.in +++ b/DarknetConfig.cmake.in @@ -37,7 +37,7 @@ include("${Darknet_CMAKE_DIR}/DarknetTargets.cmake") include("${Darknet_CMAKE_DIR}/DarknetConfigVersion.cmake") if(@OpenCV_FOUND@) - target_include_directories(Darknet::dark PRIVATE ${OpenCV_INCLUDE_DIRS}) + set_property(TARGET Darknet::dark APPEND PROPERTY INTERFACE_INCLUDE_DIRECTORIES "${OpenCV_INCLUDE_DIRS}") endif() get_target_property(FULL_DARKNET_INCLUDE_DIRS Darknet::dark INTERFACE_INCLUDE_DIRECTORIES) From 661e8ab26bda8588b6c926f221059a0f00f65e34 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 29 Jan 2021 17:39:25 +0100 Subject: [PATCH 1192/1365] add nasm dependency --- .github/workflows/ccpp.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index a0be9c89ad5..69cfc77d000 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -76,7 +76,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt install yasm + run: sudo apt install yasm nasm - uses: lukka/get-cmake@latest @@ -275,7 +275,7 @@ jobs: - uses: actions/checkout@v2 - name: Install dependencies - run: brew install libomp yasm + run: brew install libomp yasm nasm - uses: lukka/get-cmake@latest From b5d852057e5d8569f6bcb1d9cbde47aa6c326fad Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 29 Jan 2021 17:40:29 +0100 Subject: [PATCH 1193/1365] fix --- .travis.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 4268f1343e4..56dc996055a 100644 --- a/.travis.yml +++ b/.travis.yml @@ -97,7 +97,7 @@ matrix: osx_image: xcode12.3 env: - additional_defines="-DBUILD_SHARED_LIBS=OFF -DENABLE_CUDA=OFF" - - MATRIX_EVAL="brew install yasm && unset CC && unset CXX" + - MATRIX_EVAL="brew install yasm nasm libomp && unset CC && unset CXX" - USE_VCPKG=true - VCPKG_ROOT="$HOME/vcpkg" - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" @@ -197,6 +197,7 @@ matrix: update: true packages: - yasm + - nasm env: - additional_defines="-DBUILD_SHARED_LIBS=OFF" - USE_VCPKG=true From 9284d0280926865cb6f841dfb587f36cac3d9247 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 18 Feb 2021 22:22:39 +0100 Subject: [PATCH 1194/1365] comment out windows-vcpkg, improve setup scripts --- .github/workflows/ccpp.yml | 134 ++++++++++++++++++------------------- scripts/setup.ps1 | 18 ++++- scripts/setup.sh | 7 +- 3 files changed, 88 insertions(+), 71 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 69cfc77d000..c6930d9141b 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -376,73 +376,73 @@ jobs: buildWithCMakeArgs: '--target install' - win-vcpkg-cuda-ninja: - runs-on: windows-latest - steps: - - uses: actions/checkout@v2 - - name: 'Install CUDA' - run: | - choco install cuda --version=10.2.89.20191206 -y - $env:ChocolateyInstall = Convert-Path "$((Get-Command choco).Path)\..\.." - Import-Module "$env:ChocolateyInstall\helpers\chocolateyProfile.psm1" - refreshenv - echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append - refreshenv - echo $env:PATH - - - uses: lukka/get-cmake@latest - - - name: Restore from cache and run vcpkg - env: - CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" - CUDA_PATH_V10_2: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" - CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" - CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" - uses: lukka/run-vcpkg@v6 - with: - setupOnly: true - vcpkgDirectory: '${{ github.workspace }}/vcpkg' - vcpkgGitCommitId: '865de0c130f3d34174ac7a410789df3c44a27fd2' - appendedCacheKey: ${{ hashFiles( '**/vcpkg.json' ) }} - vcpkgTriplet: 'x64-windows' - additionalCachedPaths: ${{ env.buildDir }}/vcpkg_installed - - - name: 'Build with CMake and Ninja' - env: - CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" - CUDA_PATH_V10_2: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" - CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" - CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" - uses: lukka/run-cmake@v3 - with: - cmakeListsOrSettingsJson: CMakeListsTxtAdvanced - cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' - useVcpkgToolchainFile: true - buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-GNinja -DCMAKE_BUILD_TYPE=Release -DSELECT_OPENCV_MODULES=ON" - cmakeBuildType: 'Release' - buildWithCMakeArgs: '--config Release --target install' - - - uses: actions/upload-artifact@v2 - with: - name: darknet-vcpkg-cuda-${{ runner.os }} - path: cfg - - uses: actions/upload-artifact@v2 - with: - name: darknet-vcpkg-cuda-${{ runner.os }} - path: data - - uses: actions/upload-artifact@v2 - with: - name: darknet-vcpkg-cuda-${{ runner.os }} - path: ${{ github.workspace }}/*dark* - - uses: actions/upload-artifact@v2 - with: - name: darknet-vcpkg-cuda-${{ runner.os }} - path: ${{ runner.workspace }}/buildDirectory/Release/*.dll - - uses: actions/upload-artifact@v2 - with: - name: darknet-vcpkg-cuda-${{ runner.os }} - path: ${{ github.workspace }}/uselib* +# win-vcpkg-cuda-ninja: +# runs-on: windows-latest +# steps: +# - uses: actions/checkout@v2 +# - name: 'Install CUDA' +# run: | +# choco install cuda --version=10.2.89.20191206 -y +# $env:ChocolateyInstall = Convert-Path "$((Get-Command choco).Path)\..\.." +# Import-Module "$env:ChocolateyInstall\helpers\chocolateyProfile.psm1" +# refreshenv +# echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append +# refreshenv +# echo $env:PATH +# +# - uses: lukka/get-cmake@latest +# +# - name: Restore from cache and run vcpkg +# env: +# CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" +# CUDA_PATH_V10_2: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" +# CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" +# CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" +# uses: lukka/run-vcpkg@v6 +# with: +# setupOnly: true +# vcpkgDirectory: '${{ github.workspace }}/vcpkg' +# vcpkgGitCommitId: '865de0c130f3d34174ac7a410789df3c44a27fd2' +# appendedCacheKey: ${{ hashFiles( '**/vcpkg.json' ) }} +# vcpkgTriplet: 'x64-windows' +# additionalCachedPaths: ${{ env.buildDir }}/vcpkg_installed +# +# - name: 'Build with CMake and Ninja' +# env: +# CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" +# CUDA_PATH_V10_2: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" +# CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" +# CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" +# uses: lukka/run-cmake@v3 +# with: +# cmakeListsOrSettingsJson: CMakeListsTxtAdvanced +# cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' +# useVcpkgToolchainFile: true +# buildDirectory: '${{ runner.workspace }}/buildDirectory' +# cmakeAppendedArgs: "-GNinja -DCMAKE_BUILD_TYPE=Release -DSELECT_OPENCV_MODULES=ON" +# cmakeBuildType: 'Release' +# buildWithCMakeArgs: '--config Release --target install' +# +# - uses: actions/upload-artifact@v2 +# with: +# name: darknet-vcpkg-cuda-${{ runner.os }} +# path: cfg +# - uses: actions/upload-artifact@v2 +# with: +# name: darknet-vcpkg-cuda-${{ runner.os }} +# path: data +# - uses: actions/upload-artifact@v2 +# with: +# name: darknet-vcpkg-cuda-${{ runner.os }} +# path: ${{ github.workspace }}/*dark* +# - uses: actions/upload-artifact@v2 +# with: +# name: darknet-vcpkg-cuda-${{ runner.os }} +# path: ${{ runner.workspace }}/buildDirectory/Release/*.dll +# - uses: actions/upload-artifact@v2 +# with: +# name: darknet-vcpkg-cuda-${{ runner.os }} +# path: ${{ github.workspace }}/uselib* win-intlibs: diff --git a/scripts/setup.ps1 b/scripts/setup.ps1 index 4c201cb7c41..4de939bae92 100755 --- a/scripts/setup.ps1 +++ b/scripts/setup.ps1 @@ -1,12 +1,19 @@ ## enable or disable installed components -$install_cuda=$true +$install_choco = $true +$install_cuda = $true + +if ($install_cuda -and -not $install_choco) { + Write-Host "If you want to install cuda without letting the script install also choco, be sure Chocolatey is already installed" +} ########################### # Download and install Chocolatey Set-ExecutionPolicy unrestricted -Invoke-Expression ((New-Object System.Net.WebClient).DownloadString('https://chocolatey.org/install.ps1')) +if ($install_choco) { + Invoke-Expression ((New-Object System.Net.WebClient).DownloadString('https://chocolatey.org/install.ps1')) +} choco.exe install -y cmake ninja powershell git vscode choco-exe install -y visualstudio2019buildtools --package-parameters "--add Microsoft.VisualStudio.Component.VC.CoreBuildTools --includeRecommended --includeOptional --passive --locale en-US --lang en-US" @@ -15,7 +22,12 @@ if ($install_cuda) { $features = "full" } else { - $features = "opencv-base,weights,weights-train" + if (-not $null -eq $env:CUDA_PATH) { + $features = "full" + } + else{ + $features = "opencv-base,weights,weights-train" + } } Remove-Item -r $temp_folder diff --git a/scripts/setup.sh b/scripts/setup.sh index b5889f6a802..0680f876236 100755 --- a/scripts/setup.sh +++ b/scripts/setup.sh @@ -37,9 +37,14 @@ if [ "$install_cuda" = true ] ; then features = "full" fi else - features = "opencv-base,weights,weights-train" + if [[ -v CUDA_PATH ]]; then + features = "full" + else + features = "opencv-base,weights,weights-train" + fi fi + rm -rf $temp_folder cd .. cd $vcpkg_folder From b9d296e3080beb022ced7c3faddfb7836b57a265 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sat, 20 Feb 2021 09:28:07 +0100 Subject: [PATCH 1195/1365] improve manifest mode --- .github/workflows/ccpp.yml | 115 ++++++++++++++++--------------------- .gitignore | 2 + CMakeLists.txt | 16 ++++-- build.ps1 | 5 +- scripts/setup.ps1 | 29 ++++------ vcpkg.json | 111 +++++++++++++++++++++++++++++++---- 6 files changed, 177 insertions(+), 101 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index c6930d9141b..29e2df1fbdf 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -376,73 +376,54 @@ jobs: buildWithCMakeArgs: '--target install' -# win-vcpkg-cuda-ninja: -# runs-on: windows-latest -# steps: -# - uses: actions/checkout@v2 -# - name: 'Install CUDA' -# run: | -# choco install cuda --version=10.2.89.20191206 -y -# $env:ChocolateyInstall = Convert-Path "$((Get-Command choco).Path)\..\.." -# Import-Module "$env:ChocolateyInstall\helpers\chocolateyProfile.psm1" -# refreshenv -# echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append -# refreshenv -# echo $env:PATH -# -# - uses: lukka/get-cmake@latest -# -# - name: Restore from cache and run vcpkg -# env: -# CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" -# CUDA_PATH_V10_2: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" -# CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" -# CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" -# uses: lukka/run-vcpkg@v6 -# with: -# setupOnly: true -# vcpkgDirectory: '${{ github.workspace }}/vcpkg' -# vcpkgGitCommitId: '865de0c130f3d34174ac7a410789df3c44a27fd2' -# appendedCacheKey: ${{ hashFiles( '**/vcpkg.json' ) }} -# vcpkgTriplet: 'x64-windows' -# additionalCachedPaths: ${{ env.buildDir }}/vcpkg_installed -# -# - name: 'Build with CMake and Ninja' -# env: -# CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" -# CUDA_PATH_V10_2: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" -# CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" -# CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" -# uses: lukka/run-cmake@v3 -# with: -# cmakeListsOrSettingsJson: CMakeListsTxtAdvanced -# cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' -# useVcpkgToolchainFile: true -# buildDirectory: '${{ runner.workspace }}/buildDirectory' -# cmakeAppendedArgs: "-GNinja -DCMAKE_BUILD_TYPE=Release -DSELECT_OPENCV_MODULES=ON" -# cmakeBuildType: 'Release' -# buildWithCMakeArgs: '--config Release --target install' -# -# - uses: actions/upload-artifact@v2 -# with: -# name: darknet-vcpkg-cuda-${{ runner.os }} -# path: cfg -# - uses: actions/upload-artifact@v2 -# with: -# name: darknet-vcpkg-cuda-${{ runner.os }} -# path: data -# - uses: actions/upload-artifact@v2 -# with: -# name: darknet-vcpkg-cuda-${{ runner.os }} -# path: ${{ github.workspace }}/*dark* -# - uses: actions/upload-artifact@v2 -# with: -# name: darknet-vcpkg-cuda-${{ runner.os }} -# path: ${{ runner.workspace }}/buildDirectory/Release/*.dll -# - uses: actions/upload-artifact@v2 -# with: -# name: darknet-vcpkg-cuda-${{ runner.os }} -# path: ${{ github.workspace }}/uselib* + win-vcpkg-ninja: + runs-on: windows-latest + steps: + - uses: actions/checkout@v2 + + - uses: lukka/get-cmake@latest + + - name: Restore from cache and run vcpkg + uses: lukka/run-vcpkg@v6 + with: + setupOnly: true + vcpkgDirectory: '${{ github.workspace }}/vcpkg' + vcpkgGitCommitId: '865de0c130f3d34174ac7a410789df3c44a27fd2' + appendedCacheKey: ${{ hashFiles( '**/vcpkg.json' ) }} + vcpkgTriplet: 'x64-windows' + additionalCachedPaths: ${{ env.buildDir }}/vcpkg_installed + + - name: 'Build with CMake and Ninja' + uses: lukka/run-cmake@v3 + with: + cmakeListsOrSettingsJson: CMakeListsTxtAdvanced + cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' + useVcpkgToolchainFile: true + buildDirectory: '${{ runner.workspace }}/buildDirectory' + cmakeAppendedArgs: "-GNinja -DCMAKE_BUILD_TYPE=Release -DSELECT_OPENCV_MODULES=ON -DENABLE_CUDA=OFF" + cmakeBuildType: 'Release' + buildWithCMakeArgs: '--config Release --target install' + + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: cfg + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: data + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: ${{ github.workspace }}/*dark* + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: ${{ runner.workspace }}/buildDirectory/Release/*.dll + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: ${{ github.workspace }}/uselib* win-intlibs: diff --git a/.gitignore b/.gitignore index dabf6a771e3..386c5583ac2 100644 --- a/.gitignore +++ b/.gitignore @@ -21,6 +21,7 @@ submission/ cfg/ build/darknet/* build_*/ +vcpkg_installed/ !build/darknet/YoloWrapper.cs .fuse* *.weights @@ -33,6 +34,7 @@ build/detect_cuda_compute_capabilities.cu build/.ninja_deps build/.ninja_log build/Makefile +*/vcpkg-manifest-install.log # OS Generated # .DS_Store* diff --git a/CMakeLists.txt b/CMakeLists.txt index 05496258a46..25d41f46fcf 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -20,6 +20,17 @@ option(ENABLE_CUDNN_HALF "Enable CUDNN Half precision" ON) option(ENABLE_ZED_CAMERA "Enable ZED Camera support" ON) option(ENABLE_VCPKG_INTEGRATION "Enable VCPKG integration" ON) +if(ENABLE_OPENCV AND ENABLE_CUDA AND NOT APPLE) + list(APPEND VCPKG_MANIFEST_FEATURES "opencv-cuda") +elseif(ENABLE_CUDA AND NOT APPLE) + list(APPEND VCPKG_MANIFEST_FEATURES "cuda") +elseif(ENABLE_OPENCV) + list(APPEND VCPKG_MANIFEST_FEATURES "opencv-base") +endif() +if(ENABLE_CUDNN AND NOT APPLE) + list(APPEND VCPKG_MANIFEST_FEATURES "cudnn") +endif() + if(CMAKE_COMPILER_IS_GNUCC OR "${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang") set(CMAKE_COMPILER_IS_GNUCC_OR_CLANG TRUE) if("${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang") @@ -49,6 +60,7 @@ if(ENABLE_VCPKG_INTEGRATION AND DEFINED ENV{VCPKG_ROOT} AND NOT DEFINED CMAKE_TO #set(_VCPKG_INSTALLED_DIR ${CMAKE_CURRENT_LIST_DIR}/vcpkg CACHE STRING "") #folder for manifest-installed dependencies message(STATUS "VCPKG found: $ENV{VCPKG_ROOT}") message(STATUS "Using VCPKG integration") + message(STATUS "VCPKG_MANIFEST_FEATURES: ${VCPKG_MANIFEST_FEATURES}") set(USE_INTEGRATED_LIBS "FALSE" CACHE BOOL "Use libs distributed with this repo") elseif(DEFINED CMAKE_TOOLCHAIN_FILE) message(STATUS "Using toolchain: ${CMAKE_TOOLCHAIN_FILE}") @@ -92,17 +104,13 @@ if(ENABLE_CUDA) include(CheckLanguage) check_language(CUDA) if(NOT CMAKE_CUDA_COMPILER) - message(STATUS "PATH: $ENV{PATH}") message(STATUS "CUDA_PATH: $ENV{CUDA_PATH}") - message(STATUS "CUDA_TOOLKIT_ROOT_DIR: $ENV{CUDA_TOOLKIT_ROOT_DIR}") message(STATUS "CUDACXX: $ENV{CUDACXX}") message(FATAL_ERROR "CUDA not found, please build explicitly with -DENABLE_CUDA=OFF if you do not want CUDA.") else() enable_language(CUDA) if(CMAKE_CUDA_COMPILER_VERSION VERSION_LESS "9.0") - message(STATUS "PATH: $ENV{PATH}") message(STATUS "CUDA_PATH: $ENV{CUDA_PATH}") - message(STATUS "CUDA_TOOLKIT_ROOT_DIR: $ENV{CUDA_TOOLKIT_ROOT_DIR}") message(STATUS "CUDACXX: $ENV{CUDACXX}") message(FATAL_ERROR "Unsupported CUDA version, please upgrade to CUDA 9+ or disable CUDA with explicitly with -DENABLE_CUDA=OFF") else() diff --git a/build.ps1 b/build.ps1 index ab17585c84c..a213bbe48a8 100755 --- a/build.ps1 +++ b/build.ps1 @@ -3,7 +3,7 @@ $number_of_build_workers = 8 $enable_cuda = $true $use_vcpkg = $true -$use_ninja = $false +$use_ninja = $true $force_cpp_build = $false #$additional_build_setup = " -DCMAKE_CUDA_ARCHITECTURES=30" @@ -20,7 +20,8 @@ else { if ($use_ninja) { $NINJA_EXE = Get-Command ninja | Select-Object -ExpandProperty Definition if (-Not $NINJA_EXE) { - throw "Could not find Ninja, please install it" + $use_ninja = $false + Write-Host "Could not find Ninja, using msbuild as a fallback" -ForegroundColor Yellow } else { Write-Host "Using Ninja from ${NINJA_EXE}" diff --git a/scripts/setup.ps1 b/scripts/setup.ps1 index 4de939bae92..acc29458b62 100755 --- a/scripts/setup.ps1 +++ b/scripts/setup.ps1 @@ -1,24 +1,20 @@ -## enable or disable installed components +#!/usr/bin/env pwsh -$install_choco = $true -$install_cuda = $true +$install_cuda = $false -if ($install_cuda -and -not $install_choco) { - Write-Host "If you want to install cuda without letting the script install also choco, be sure Chocolatey is already installed" -} - -########################### - -# Download and install Chocolatey -Set-ExecutionPolicy unrestricted -if ($install_choco) { +if ($null -eq (Get-Command "choco.exe" -ErrorAction SilentlyContinue)) { + # Download and install Chocolatey + Set-ExecutionPolicy unrestricted -Scope CurrentUser Invoke-Expression ((New-Object System.Net.WebClient).DownloadString('https://chocolatey.org/install.ps1')) + Write-Host "Please close and re-open powershell and then re-run setup.ps1 script" + Break } -choco.exe install -y cmake ninja powershell git vscode -choco-exe install -y visualstudio2019buildtools --package-parameters "--add Microsoft.VisualStudio.Component.VC.CoreBuildTools --includeRecommended --includeOptional --passive --locale en-US --lang en-US" + +Start-Process -FilePath "choco" -Verb runAs -ArgumentList " install -y cmake ninja powershell git vscode" +Start-Process -FilePath "choco" -Verb runAs -ArgumentList " install -y visualstudio2019buildtools --package-parameters `"--add Microsoft.VisualStudio.Component.VC.CoreBuildTools --includeRecommended --includeOptional --passive --locale en-US --lang en-US`"" if ($install_cuda) { - choco-exe install -y cuda + Start-Process -FilePath "choco" -Verb runAs -ArgumentList " install -y cuda" $features = "full" } else { @@ -30,9 +26,6 @@ else { } } -Remove-Item -r $temp_folder -Set-Location .. -Set-Location $vcpkg_folder\ git.exe clone https://github.com/microsoft/vcpkg Set-Location vcpkg .\bootstrap-vcpkg.bat -disableMetrics diff --git a/vcpkg.json b/vcpkg.json index 7f55c4d9f5a..fa593c6c083 100644 --- a/vcpkg.json +++ b/vcpkg.json @@ -1,19 +1,110 @@ { "name": "darknet", "version-string": "0.2.5.4", - "port-version": 1, + "port-version": 2, "homepage": "https://github.com/alexeyab/darknet", - "description": "Neural networks for object detection", + "description": "Darknet is an open source neural network framework written in C and CUDA. You only look once (YOLO) is a state-of-the-art, real-time object detection system, best example of darknet functionalities.", "dependencies": [ "stb", - "pthreads", - { - "name": "opencv", - "features": [ "ffmpeg" ] + "pthreads" + ], + "features": { + "cuda": { + "description": "Build darknet with support for CUDA", + "dependencies": [ + "cuda" + ] }, - { - "name": "cudnn", - "platform": "!osx" + "cudnn": { + "description": "Build darknet with support for cuDNN", + "dependencies": [ + "cuda", + "cudnn" + ] + }, + "opencv-base": { + "description": "Build darknet with support for latest version of OpenCV", + "dependencies": [ + { + "name": "opencv", + "features": [ + "ffmpeg" + ] + } + ] + }, + "opencv-cuda": { + "description": "Build darknet with support for latest version of CUDA-enabled OpenCV", + "dependencies": [ + { + "name": "opencv", + "features": [ + "ffmpeg" + ] + }, + { + "name": "opencv", + "features": [ + "cuda" + ] + } + ] + }, + "opencv2-base": { + "description": "Build darknet with support for OpenCV2", + "dependencies": [ + { + "name": "opencv2", + "features": [ + "ffmpeg" + ] + } + ] + }, + "opencv2-cuda": { + "description": "Build darknet with support for CUDA-enabled OpenCV2", + "dependencies": [ + { + "name": "opencv2", + "features": [ + "ffmpeg" + ] + }, + { + "name": "opencv2", + "features": [ + "cuda" + ] + } + ] + }, + "opencv3-base": { + "description": "Build darknet with support for OpenCV3", + "dependencies": [ + { + "name": "opencv3", + "features": [ + "ffmpeg" + ] + } + ] + }, + "opencv3-cuda": { + "description": "Build darknet with support for CUDA-enabled OpenCV3", + "dependencies": [ + { + "name": "opencv3", + "features": [ + "ffmpeg" + ] + }, + { + "name": "opencv3", + "features": [ + "cuda" + ] + } + ] } - ] + } } From fd6ea9ba60f2256cdeee5d40bb7e9b708a773a52 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sat, 20 Feb 2021 11:22:23 +0100 Subject: [PATCH 1196/1365] use same logic for all enables: if on, then package is required --- .github/workflows/ccpp.yml | 6 +++--- CMakeLists.txt | 8 ++++++-- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 29e2df1fbdf..1c30fb8a1db 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -107,7 +107,7 @@ jobs: with: setupOnly: true vcpkgDirectory: '${{ github.workspace }}/vcpkg' - vcpkgGitCommitId: '865de0c130f3d34174ac7a410789df3c44a27fd2' + vcpkgGitCommitId: '99dc49dae7e170c3be63dd097230007f3bb73c4f' appendedCacheKey: ${{ hashFiles( '**/vcpkg.json' ) }} vcpkgTriplet: 'x64-linux' additionalCachedPaths: ${{ env.buildDir }}/vcpkg_installed @@ -284,7 +284,7 @@ jobs: with: setupOnly: true vcpkgDirectory: '${{ github.workspace }}/vcpkg' - vcpkgGitCommitId: '865de0c130f3d34174ac7a410789df3c44a27fd2' + vcpkgGitCommitId: '99dc49dae7e170c3be63dd097230007f3bb73c4f' appendedCacheKey: ${{ hashFiles( '**/vcpkg.json' ) }} vcpkgTriplet: 'x64-osx' additionalCachedPaths: ${{ env.buildDir }}/vcpkg_installed @@ -388,7 +388,7 @@ jobs: with: setupOnly: true vcpkgDirectory: '${{ github.workspace }}/vcpkg' - vcpkgGitCommitId: '865de0c130f3d34174ac7a410789df3c44a27fd2' + vcpkgGitCommitId: '99dc49dae7e170c3be63dd097230007f3bb73c4f' appendedCacheKey: ${{ hashFiles( '**/vcpkg.json' ) }} vcpkgTriplet: 'x64-windows' additionalCachedPaths: ${{ env.buildDir }}/vcpkg_installed diff --git a/CMakeLists.txt b/CMakeLists.txt index 25d41f46fcf..7ff659e3260 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -64,6 +64,10 @@ if(ENABLE_VCPKG_INTEGRATION AND DEFINED ENV{VCPKG_ROOT} AND NOT DEFINED CMAKE_TO set(USE_INTEGRATED_LIBS "FALSE" CACHE BOOL "Use libs distributed with this repo") elseif(DEFINED CMAKE_TOOLCHAIN_FILE) message(STATUS "Using toolchain: ${CMAKE_TOOLCHAIN_FILE}") + if(CMAKE_TOOLCHAIN_FILE MATCHES "vcpkg.cmake") + message(STATUS "Toolchain uses VCPKG integration") + message(STATUS "VCPKG_MANIFEST_FEATURES: ${VCPKG_MANIFEST_FEATURES}") + endif() set(USE_INTEGRATED_LIBS "FALSE" CACHE BOOL "Use libs distributed with this repo") elseif(WIN32) message(STATUS "vcpkg not found, toolchain not defined, using integrated libs on win32") @@ -153,7 +157,7 @@ if(MSVC) find_package(PThreads_windows REQUIRED) endif() if(ENABLE_OPENCV) - find_package(OpenCV) + find_package(OpenCV REQUIRED) if(OpenCV_FOUND) if(SELECT_OPENCV_MODULES) if(TARGET opencv_world) @@ -240,7 +244,7 @@ if(OpenCV_FOUND) endif() if(ENABLE_CUDA AND ENABLE_CUDNN) - find_package(CUDNN) + find_package(CUDNN REQUIRED) endif() if(ENABLE_CUDA) From 8f3cd2840e0440bacf812b340e559987f21f0816 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sat, 20 Feb 2021 17:05:27 +0100 Subject: [PATCH 1197/1365] fixes --- .github/workflows/ccpp.yml | 14 ++++----- .travis.yml | 61 +++++--------------------------------- build.ps1 | 8 +++++ build.sh | 10 +++++++ 4 files changed, 32 insertions(+), 61 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 1c30fb8a1db..e4c45e8a47a 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -125,7 +125,7 @@ jobs: useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' cmakeBuildType: 'Release' - cmakeAppendedArgs: "-DBUILD_SHARED_LIBS=OFF -DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF" + cmakeAppendedArgs: "-DBUILD_SHARED_LIBS=OFF -DCMAKE_BUILD_TYPE=Release" buildWithCMakeArgs: '--target install' - uses: actions/upload-artifact@v2 @@ -264,7 +264,7 @@ jobs: cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DBUILD_AS_CPP:BOOL=TRUE -DENABLE_CUDA=OFF" + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DBUILD_AS_CPP:BOOL=TRUE -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" cmakeBuildType: 'Release' buildWithCMakeArgs: '--target install' @@ -371,7 +371,7 @@ jobs: cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DBUILD_AS_CPP:BOOL=TRUE -DENABLE_CUDA=OFF" + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DBUILD_AS_CPP:BOOL=TRUE -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" cmakeBuildType: 'Release' buildWithCMakeArgs: '--target install' @@ -440,7 +440,7 @@ jobs: cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF" + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" cmakeBuildType: 'Release' buildWithCMakeArgs: '--config Release --target install' @@ -480,7 +480,7 @@ jobs: cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DBUILD_AS_CPP:BOOL=TRUE -DENABLE_CUDA=OFF" + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DBUILD_AS_CPP:BOOL=TRUE -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" cmakeBuildType: 'Release' buildWithCMakeArgs: '--config Release --target install' @@ -510,7 +510,7 @@ jobs: cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=ON" + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" cmakeBuildType: 'Release' buildWithCMakeArgs: '--config Release --target install' @@ -529,6 +529,6 @@ jobs: cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-G\"MinGW Makefiles\" -DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF" + cmakeAppendedArgs: "-G\"MinGW Makefiles\" -DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" cmakeBuildType: 'Release' buildWithCMakeArgs: '--config Release --target install' diff --git a/.travis.yml b/.travis.yml index 56dc996055a..447a72a179d 100644 --- a/.travis.yml +++ b/.travis.yml @@ -13,7 +13,7 @@ matrix: name: macOS - gcc (llvm backend) osx_image: xcode12.3 env: - - additional_defines=" -DENABLE_CUDA=OFF" + - additional_defines=" -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" - MATRIX_EVAL="" - os: osx @@ -47,7 +47,7 @@ matrix: name: macOS - clang osx_image: xcode12.3 env: - - additional_defines=" -DENABLE_CUDA=OFF" + - additional_defines=" -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" - MATRIX_EVAL="" - os: osx @@ -55,7 +55,7 @@ matrix: name: macOS - clang - C++ mode osx_image: xcode12.3 env: - - additional_defines="-DBUILD_AS_CPP:BOOL=TRUE -DENABLE_CUDA=OFF" + - additional_defines="-DBUILD_AS_CPP:BOOL=TRUE -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" - MATRIX_EVAL="" - os: osx @@ -92,22 +92,12 @@ matrix: - additional_defines=" -DENABLE_CUDA=OFF" - MATRIX_EVAL="brew install opencv libomp" - - os: osx - name: macOS - clang - vcpkg - osx_image: xcode12.3 - env: - - additional_defines="-DBUILD_SHARED_LIBS=OFF -DENABLE_CUDA=OFF" - - MATRIX_EVAL="brew install yasm nasm libomp && unset CC && unset CXX" - - USE_VCPKG=true - - VCPKG_ROOT="$HOME/vcpkg" - - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" - - os: linux compiler: clang dist: bionic name: ubuntu 18.04 - clang env: - - additional_defines=" -DENABLE_CUDA=OFF" + - additional_defines=" -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" - MATRIX_EVAL="" - os: linux @@ -115,7 +105,7 @@ matrix: dist: bionic name: ubuntu 18.04 - gcc env: - - additional_defines=" -DENABLE_CUDA=OFF" + - additional_defines=" -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" - MATRIX_EVAL="" - os: linux @@ -123,7 +113,7 @@ matrix: dist: bionic name: ubuntu 18.04 - g++ env: - - additional_defines="-DBUILD_AS_CPP:BOOL=TRUE -DENABLE_CUDA=OFF" + - additional_defines="-DBUILD_AS_CPP:BOOL=TRUE -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" - MATRIX_EVAL="" - os: linux @@ -188,31 +178,6 @@ matrix: - LD_LIBRARY_PATH="/usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64:/usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs:${LD_LIBRARY_PATH}" - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.2.89-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1804_10.2.89-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-2 cuda-libraries-dev-10-2 cuda-driver-dev-10-2 cuda-cudart-dev-10-2 cuda-curand-dev-10-2 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs/libcuda.so.1" - - os: linux - compiler: gcc - dist: bionic - name: ubuntu 18.04 - gcc - CUDA 10 - vcpkg - addons: - apt: - update: true - packages: - - yasm - - nasm - env: - - additional_defines="-DBUILD_SHARED_LIBS=OFF" - - USE_VCPKG=true - - VCPKG_ROOT="$HOME/vcpkg" - - VCPKG_DEFINES="-DCMAKE_TOOLCHAIN_FILE=$HOME/vcpkg/scripts/buildsystems/vcpkg.cmake" - - INSTALL_CUDA_VERSION="10.2" - - CUDACXX=/usr/local/cuda-${INSTALL_CUDA_VERSION}/bin/nvcc - - CUDA_PATH=/usr/local/cuda-${INSTALL_CUDA_VERSION} - - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-${INSTALL_CUDA_VERSION} - - LD_LIBRARY_PATH="/usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64:/usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.2.89-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1804_10.2.89-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-2 cuda-libraries-dev-10-2 cuda-driver-dev-10-2 cuda-cudart-dev-10-2 cuda-curand-dev-10-2 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs/libcuda.so.1" - - allow_failures: - - name: macOS - clang - vcpkg - before_install: - travis_retry eval "${MATRIX_EVAL}" @@ -221,23 +186,11 @@ install: - if [[ "$TRAVIS_OS_NAME" == "linux" ]] ; then wget --no-check-certificate https://github.com/Kitware/CMake/releases/download/v3.19.2/cmake-3.19.2-Linux-x86_64.tar.gz ; fi - if [[ "$TRAVIS_OS_NAME" == "linux" ]] ; then tar -xzf cmake-3.19.2-Linux-x86_64.tar.gz ; fi - if [[ "$TRAVIS_OS_NAME" == "linux" ]] ; then export PATH=$PWD/cmake-3.19.2-Linux-x86_64/bin:$PATH ; fi - - pushd $HOME - - if [ -d "$HOME/vcpkg/.git" ] ; then echo vcpkg cached ; else rm -rf vcpkg ; git clone https://github.com/Microsoft/vcpkg ; fi - - cd vcpkg - - git checkout . - - git pull - - if [ "${USE_VCPKG}" = true ] ; then ./bootstrap-vcpkg.sh ; fi - - if [ "${USE_VCPKG}" = true ] ; then echo "set(VCPKG_BUILD_TYPE release)" >> triplets/x64-osx.cmake ; fi - - if [ "${USE_VCPKG}" = true ] ; then echo "set(VCPKG_BUILD_TYPE release)" >> triplets/x64-linux.cmake ; fi - - if [ "${USE_VCPKG}" = true ] ; then rm -rf buildtrees; fi - - if [ "${USE_VCPKG}" = true ] ; then rm -rf installed; fi - - if [ "${USE_VCPKG}" = true ] ; then rm -rf packages; fi - - popd before_script: - mkdir build_release - cd build_release - - travis_wait 50 cmake .. -DCMAKE_BUILD_TYPE="Release" ${additional_defines} + - cmake .. -DCMAKE_BUILD_TYPE="Release" ${additional_defines} - cd .. script: diff --git a/build.ps1 b/build.ps1 index a213bbe48a8..b07e3d27599 100755 --- a/build.ps1 +++ b/build.ps1 @@ -188,6 +188,14 @@ if (-Not($enable_cuda)) { $additional_build_setup = $additional_build_setup + " -DENABLE_CUDA:BOOL=OFF" } +if (-Not($enable_cudnn)) { + $additional_build_setup = $additional_build_setup + " -DENABLE_CUDNN:BOOL=OFF" +} + +if (-Not($enable_opencv)) { + $additional_build_setup = $additional_build_setup + " -DENABLE_OPENCV:BOOL=OFF" +} + if ($use_vcpkg) { New-Item -Path .\build_win_release -ItemType directory -Force Set-Location build_win_release diff --git a/build.sh b/build.sh index 51275834932..41d7634e0a9 100755 --- a/build.sh +++ b/build.sh @@ -4,6 +4,8 @@ number_of_build_workers=8 use_vcpkg=false force_cpp_build=false enable_cuda=false +enable_cudnn=false +enable_opencv=false if [[ "$OSTYPE" == "darwin"* ]]; then vcpkg_triplet="x64-osx" @@ -40,6 +42,14 @@ if [ "$enable_cuda" = false ] then additional_build_setup=${additional_build_setup}" -DENABLE_CUDA:BOOL=FALSE" fi +if [ "$enable_cudnn" = false ] +then + additional_build_setup=${additional_build_setup}" -DENABLE_CUDNN:BOOL=FALSE" +fi +if [ "$enable_opencv" = false ] +then + additional_build_setup=${additional_build_setup}" -DENABLE_OPENCV:BOOL=FALSE" +fi ## DEBUG #mkdir -p build_debug From 44d29d598266f7e934c756d8d57a042dd1453023 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 21 Feb 2021 10:31:29 +0100 Subject: [PATCH 1198/1365] do not enable cudnn feature if cuda is not enabled --- CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 7ff659e3260..0cdc25c94a9 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -27,7 +27,7 @@ elseif(ENABLE_CUDA AND NOT APPLE) elseif(ENABLE_OPENCV) list(APPEND VCPKG_MANIFEST_FEATURES "opencv-base") endif() -if(ENABLE_CUDNN AND NOT APPLE) +if(ENABLE_CUDNN AND ENABLE_CUDA AND NOT APPLE) list(APPEND VCPKG_MANIFEST_FEATURES "cudnn") endif() From 6a2cab982a0e7e84fccecf84a804c9483d4f2632 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 21 Feb 2021 12:47:05 +0100 Subject: [PATCH 1199/1365] use same opencv feature set independently of cuda --- README.md | 4 ++-- build.sh | 4 ++-- vcpkg.json | 33 +++++++++++++++------------------ 3 files changed, 19 insertions(+), 22 deletions(-) diff --git a/README.md b/README.md index 0065eca6fa8..e96ca63fe2d 100644 --- a/README.md +++ b/README.md @@ -196,13 +196,11 @@ You can get cfg-files by path: `darknet/cfg/` ### Requirements -* Windows or Linux * **CMake >= 3.18**: https://cmake.org/download/ * **CUDA >= 10.0**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) * **OpenCV >= 2.4**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or download from [OpenCV official site](https://opencv.org/releases.html) (on Windows set system variable `OpenCV_DIR` = `C:\opencv\build` - where are the `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png)) * **cuDNN >= 7.0** https://developer.nvidia.com/rdp/cudnn-archive (on **Linux** copy `cudnn.h`,`libcudnn.so`... as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on **Windows** copy `cudnn.h`,`cudnn64_7.dll`, `cudnn64_7.lib` as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows ) * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported -* on Linux **GCC or Clang**, on Windows **MSVC 2017/2019** https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community ### Yolo v4 in other frameworks @@ -320,6 +318,8 @@ Open a shell terminal inside the cloned repository and launch: ./build.sh ``` +To update CMake on Ubuntu, it's better to follow guide here: https://apt.kitware.com/ + ### How to compile on Linux (using `make`) Just do `make` in the darknet directory. (You can try to compile and run it on Google Colab in cloud [link](https://colab.research.google.com/drive/12QusaaRj_lUwCGDvQNfICpa7kA7_a2dE) (press «Open in Playground» button at the top-left corner) and watch the video [link](https://www.youtube.com/watch?v=mKAEGSxwOAY) ) diff --git a/build.sh b/build.sh index 41d7634e0a9..183fc878e2f 100755 --- a/build.sh +++ b/build.sh @@ -1,11 +1,11 @@ #!/usr/bin/env bash number_of_build_workers=8 -use_vcpkg=false +use_vcpkg=true force_cpp_build=false enable_cuda=false enable_cudnn=false -enable_opencv=false +enable_opencv=true if [[ "$OSTYPE" == "darwin"* ]]; then vcpkg_triplet="x64-osx" diff --git a/vcpkg.json b/vcpkg.json index fa593c6c083..dacc49ed56f 100644 --- a/vcpkg.json +++ b/vcpkg.json @@ -28,6 +28,8 @@ { "name": "opencv", "features": [ + "contrib", + "dnn", "ffmpeg" ] } @@ -39,14 +41,11 @@ { "name": "opencv", "features": [ + "contrib", + "cuda", + "dnn", "ffmpeg" ] - }, - { - "name": "opencv", - "features": [ - "cuda" - ] } ] }, @@ -56,6 +55,8 @@ { "name": "opencv2", "features": [ + "contrib", + "dnn", "ffmpeg" ] } @@ -67,14 +68,11 @@ { "name": "opencv2", "features": [ + "contrib", + "cuda", + "dnn", "ffmpeg" ] - }, - { - "name": "opencv2", - "features": [ - "cuda" - ] } ] }, @@ -84,6 +82,8 @@ { "name": "opencv3", "features": [ + "contrib", + "dnn", "ffmpeg" ] } @@ -95,14 +95,11 @@ { "name": "opencv3", "features": [ + "contrib", + "cuda", + "dnn", "ffmpeg" ] - }, - { - "name": "opencv3", - "features": [ - "cuda" - ] } ] } From 552bddc140dd33e8f8672f895984a00c73a81e3c Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 21 Feb 2021 18:00:59 +0100 Subject: [PATCH 1200/1365] move to ubuntu 20.04 on github actions --- .github/workflows/ccpp.yml | 146 +++++++++++++++++++------------------ .gitignore | 1 + scripts/setup.sh | 86 ++++++++++++++-------- vcpkg.json | 2 - 4 files changed, 131 insertions(+), 104 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index e4c45e8a47a..c0c7b8d0fd0 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -4,7 +4,7 @@ on: [push, pull_request, workflow_dispatch] jobs: ubuntu-makefile: - runs-on: ubuntu-latest + runs-on: ubuntu-20.04 steps: - uses: actions/checkout@v2 @@ -15,18 +15,18 @@ jobs: - name: 'Install CUDA' run: | - wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.2.89-1_amd64.deb - sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub - sudo dpkg -i cuda-repo-ubuntu1804_10.2.89-1_amd64.deb - wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb - sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb sudo apt update - sudo apt-get install -y --no-install-recommends cuda-compiler-10-2 cuda-libraries-dev-10-2 cuda-driver-dev-10-2 cuda-cudart-dev-10-2 cuda-curand-dev-10-2 - sudo apt-get install -y --no-install-recommends libcudnn7-dev - sudo ln -s /usr/local/cuda-10.2/lib64/stubs/libcuda.so /usr/local/cuda-10.2/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-10.2/lib64/stubs/libcuda.so /usr/local/cuda-10.2/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-10.2/lib64/stubs/libcuda.so /usr/local/cuda-10.2/lib64/libcuda.so - sudo ln -s /usr/local/cuda-10.2 /usr/local/cuda + sudo apt-get dist-upgrade -y + sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin + sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /" + sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/ /" + sudo apt-get install -y --no-install-recommends cuda-compiler-11-2 cuda-libraries-dev-11-2 cuda-driver-dev-11-2 cuda-cudart-dev-11-2 + sudo apt-get install -y --no-install-recommends libcudnn8-dev + sudo rm -rf /usr/local/cuda + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + sudo ln -s /usr/local/cuda-11.2 /usr/local/cuda export PATH=/usr/local/cuda/bin:$PATH export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH nvcc --version @@ -69,7 +69,7 @@ jobs: ubuntu-vcpkg-cuda: - runs-on: ubuntu-latest + runs-on: ubuntu-20.04 steps: - uses: actions/checkout@v2 @@ -81,28 +81,30 @@ jobs: - uses: lukka/get-cmake@latest - name: 'Install CUDA' - env: - CUDACXX: "/usr/local/cuda-10.2/bin/nvcc" - CUDA_PATH: "/usr/local/cuda-10.2" - CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda-10.2" - LD_LIBRARY_PATH: "/usr/local/cuda-10.2/lib64:/usr/local/cuda-10.2/lib64/stubs:$LD_LIBRARY_PATH" run: | - wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.2.89-1_amd64.deb - sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub - sudo dpkg -i cuda-repo-ubuntu1804_10.2.89-1_amd64.deb - wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb - sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb sudo apt update - sudo apt-get install -y --no-install-recommends cuda-compiler-10-2 cuda-libraries-dev-10-2 cuda-driver-dev-10-2 cuda-cudart-dev-10-2 cuda-curand-dev-10-2 - sudo apt-get install -y --no-install-recommends libcudnn7-dev - sudo ln -s /usr/local/cuda-10.2/lib64/stubs/libcuda.so /usr/local/cuda-10.2/lib64/stubs/libcuda.so.1 + sudo apt-get dist-upgrade -y + sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin + sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /" + sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/ /" + sudo apt-get install -y --no-install-recommends cuda-compiler-11-2 cuda-libraries-dev-11-2 cuda-driver-dev-11-2 cuda-cudart-dev-11-2 + sudo apt-get install -y --no-install-recommends libcudnn8-dev + sudo rm -rf /usr/local/cuda + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + sudo ln -s /usr/local/cuda-11.2 /usr/local/cuda + export PATH=/usr/local/cuda/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH + nvcc --version + gcc --version - name: Restore from cache and run vcpkg env: - CUDACXX: "/usr/local/cuda-10.2/bin/nvcc" - CUDA_PATH: "/usr/local/cuda-10.2" - CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda-10.2" - LD_LIBRARY_PATH: "/usr/local/cuda-10.2/lib64:/usr/local/cuda-10.2/lib64/stubs:$LD_LIBRARY_PATH" + CUDACXX: "/usr/local/cuda/bin/nvcc" + CUDA_PATH: "/usr/local/cuda" + CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" + LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" uses: lukka/run-vcpkg@v6 with: setupOnly: true @@ -112,12 +114,12 @@ jobs: vcpkgTriplet: 'x64-linux' additionalCachedPaths: ${{ env.buildDir }}/vcpkg_installed - - name: 'Build with CMake and Ninja' + - name: 'Build with CMake' env: - CUDACXX: "/usr/local/cuda-10.2/bin/nvcc" - CUDA_PATH: "/usr/local/cuda-10.2" - CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda-10.2" - LD_LIBRARY_PATH: "/usr/local/cuda-10.2/lib64:/usr/local/cuda-10.2/lib64/stubs:$LD_LIBRARY_PATH" + CUDACXX: "/usr/local/cuda/bin/nvcc" + CUDA_PATH: "/usr/local/cuda" + CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" + LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced @@ -125,7 +127,7 @@ jobs: useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' cmakeBuildType: 'Release' - cmakeAppendedArgs: "-DBUILD_SHARED_LIBS=OFF -DCMAKE_BUILD_TYPE=Release" + cmakeAppendedArgs: "-DBUILD_SHARED_LIBS=OFF -DCMAKE_BUILD_TYPE=Release -DENABLE_CUDNN=OFF" buildWithCMakeArgs: '--target install' - uses: actions/upload-artifact@v2 @@ -147,7 +149,7 @@ jobs: ubuntu: - runs-on: ubuntu-latest + runs-on: ubuntu-20.04 steps: - uses: actions/checkout@v2 @@ -158,14 +160,14 @@ jobs: - uses: lukka/get-cmake@latest - - name: 'Build with CMake and Ninja' + - name: 'Build with CMake' uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF" + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DENABLE_CUDNN=OFF -DENABLE_CUDA=OFF" cmakeBuildType: 'Release' buildWithCMakeArgs: '--target install' @@ -188,7 +190,7 @@ jobs: ubuntu-cuda: - runs-on: ubuntu-latest + runs-on: ubuntu-20.04 steps: - uses: actions/checkout@v2 @@ -200,28 +202,30 @@ jobs: - uses: lukka/get-cmake@latest - name: 'Install CUDA' - env: - CUDACXX: "/usr/local/cuda-10.2/bin/nvcc" - CUDA_PATH: "/usr/local/cuda-10.2" - CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda-10.2" - LD_LIBRARY_PATH: "/usr/local/cuda-10.2/lib64:/usr/local/cuda-10.2/lib64/stubs:$LD_LIBRARY_PATH" run: | - wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.2.89-1_amd64.deb - sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub - sudo dpkg -i cuda-repo-ubuntu1804_10.2.89-1_amd64.deb - wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb - sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb sudo apt update - sudo apt-get install -y --no-install-recommends cuda-compiler-10-2 cuda-libraries-dev-10-2 cuda-driver-dev-10-2 cuda-cudart-dev-10-2 cuda-curand-dev-10-2 - sudo apt-get install -y --no-install-recommends libcudnn7-dev - sudo ln -s /usr/local/cuda-10.2/lib64/stubs/libcuda.so /usr/local/cuda-10.2/lib64/stubs/libcuda.so.1 + sudo apt-get dist-upgrade -y + sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin + sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /" + sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/ /" + sudo apt-get install -y --no-install-recommends cuda-compiler-11-2 cuda-libraries-dev-11-2 cuda-driver-dev-11-2 cuda-cudart-dev-11-2 + sudo apt-get install -y --no-install-recommends libcudnn8-dev + sudo rm -rf /usr/local/cuda + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + sudo ln -s /usr/local/cuda-11.2 /usr/local/cuda + export PATH=/usr/local/cuda/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH + nvcc --version + gcc --version - - name: 'Build with CMake and Ninja' + - name: 'Build with CMake' env: - CUDACXX: "/usr/local/cuda-10.2/bin/nvcc" - CUDA_PATH: "/usr/local/cuda-10.2" - CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda-10.2" - LD_LIBRARY_PATH: "/usr/local/cuda-10.2/lib64:/usr/local/cuda-10.2/lib64/stubs:$LD_LIBRARY_PATH" + CUDACXX: "/usr/local/cuda/bin/nvcc" + CUDA_PATH: "/usr/local/cuda" + CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" + LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced @@ -251,13 +255,13 @@ jobs: ubuntu-no-ocv-cpp: - runs-on: ubuntu-latest + runs-on: ubuntu-20.04 steps: - uses: actions/checkout@v2 - uses: lukka/get-cmake@latest - - name: 'Build with CMake and Ninja' + - name: 'Build with CMake' uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced @@ -289,7 +293,7 @@ jobs: vcpkgTriplet: 'x64-osx' additionalCachedPaths: ${{ env.buildDir }}/vcpkg_installed - - name: 'Build with CMake and Ninja' + - name: 'Build with CMake' uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced @@ -297,7 +301,7 @@ jobs: useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' cmakeBuildType: 'Release' - cmakeAppendedArgs: "-DBUILD_SHARED_LIBS=OFF -DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF" + cmakeAppendedArgs: "-DBUILD_SHARED_LIBS=OFF -DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF" buildWithCMakeArgs: '--target install' - uses: actions/upload-artifact@v2 @@ -328,14 +332,14 @@ jobs: - uses: lukka/get-cmake@latest - - name: 'Build with CMake and Ninja' + - name: 'Build with CMake' uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF" + cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF" cmakeBuildType: 'Release' buildWithCMakeArgs: '--target install' @@ -364,7 +368,7 @@ jobs: - uses: lukka/get-cmake@latest - - name: 'Build with CMake and Ninja' + - name: 'Build with CMake' uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced @@ -393,14 +397,14 @@ jobs: vcpkgTriplet: 'x64-windows' additionalCachedPaths: ${{ env.buildDir }}/vcpkg_installed - - name: 'Build with CMake and Ninja' + - name: 'Build with CMake' uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' useVcpkgToolchainFile: true buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-GNinja -DCMAKE_BUILD_TYPE=Release -DSELECT_OPENCV_MODULES=ON -DENABLE_CUDA=OFF" + cmakeAppendedArgs: "-GNinja -DCMAKE_BUILD_TYPE=Release -DSELECT_OPENCV_MODULES=ON -DENABLE_CUDNN=OFF -DENABLE_CUDA=OFF" cmakeBuildType: 'Release' buildWithCMakeArgs: '--config Release --target install' @@ -433,7 +437,7 @@ jobs: - uses: lukka/get-cmake@latest - - name: 'Build with CMake and Ninja' + - name: 'Build with CMake' uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced @@ -473,7 +477,7 @@ jobs: - uses: lukka/get-cmake@latest - - name: 'Build with CMake and Ninja' + - name: 'Build with CMake' uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced @@ -498,7 +502,7 @@ jobs: - uses: lukka/get-cmake@latest - - name: 'Build with CMake and Ninja' + - name: 'Build with CMake' env: CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" CUDA_PATH_V10_2: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" @@ -522,7 +526,7 @@ jobs: - uses: lukka/get-cmake@latest - - name: 'Build with CMake and Ninja' + - name: 'Build with CMake' uses: lukka/run-cmake@v3 with: cmakeListsOrSettingsJson: CMakeListsTxtAdvanced diff --git a/.gitignore b/.gitignore index 386c5583ac2..174f0b5a378 100644 --- a/.gitignore +++ b/.gitignore @@ -19,6 +19,7 @@ convnet/ decaf/ submission/ cfg/ +temp/ build/darknet/* build_*/ vcpkg_installed/ diff --git a/scripts/setup.sh b/scripts/setup.sh index 0680f876236..30c2f2a0014 100755 --- a/scripts/setup.sh +++ b/scripts/setup.sh @@ -2,53 +2,77 @@ ## enable or disable installed components -install_cuda=false -vcpkg_folder="." -temp_folder="./temp" +install_cuda=true ########################### -mkdir $temp_folder +temp_folder="./temp" +mkdir -p $temp_folder cd $temp_folder -sudo apt-get install cmake git ninja-build build-essentials g++ +sudo apt-get install cmake git ninja-build build-essential g++ if [ "$install_cuda" = true ] ; then if [[ "$OSTYPE" == "darwin"* ]]; then echo "Unable to provide CUDA on macOS" else # Download and install CUDA - - wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.2.89-1_amd64.deb - sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub - sudo dpkg -i cuda-repo-ubuntu1804_10.2.89-1_amd64.deb - wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb - sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb - sudo apt update - sudo apt-get install -y --no-install-recommends cuda-compiler-10-2 cuda-libraries-dev-10-2 cuda-driver-dev-10-2 cuda-cudart-dev-10-2 cuda-curand-dev-10-2 - sudo apt-get install -y --no-install-recommends libcudnn7-dev - sudo ln -s /usr/local/cuda-10.2 /usr/local/cuda - export PATH=/usr/local/cuda/bin:$PATH - export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH - export CUDACXX=/usr/local/cuda/bin/nvcc - export CUDA_PATH=/usr/local/cuda - export CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda - - features = "full" + if [[ $(cut -f2 <<< $(lsb_release -r)) == "18.04" ]]; then + wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.2.89-1_amd64.deb + sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub + sudo dpkg -i cuda-repo-ubuntu1804_10.2.89-1_amd64.deb + wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb + sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb + sudo apt update + sudo apt-get dist-upgrade -y + sudo apt-get install -y --no-install-recommends cuda-compiler-10-2 cuda-libraries-dev-10-2 cuda-driver-dev-10-2 cuda-cudart-dev-10-2 cuda-curand-dev-10-2 + sudo apt-get install -y --no-install-recommends libcudnn7-dev + sudo rm -rf /usr/local/cuda + sudo ln -s /usr/local/cuda-10.2 /usr/local/cuda + export PATH=/usr/local/cuda/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH + export CUDACXX=/usr/local/cuda/bin/nvcc + export CUDA_PATH=/usr/local/cuda + export CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda + features="full" + elif [[ $(cut -f2 <<< $(lsb_release -r)) == "20.04" ]]; then + sudo apt update + sudo apt-get dist-upgrade -y + #sudo apt-get install -y --no-install-recommends nvidia-cuda-dev nvidia-cuda-toolkit + sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin + sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /" + sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/ /" + sudo apt-get install -y --no-install-recommends cuda-compiler-11-2 cuda-libraries-dev-11-2 cuda-driver-dev-11-2 cuda-cudart-dev-11-2 + sudo apt-get install -y --no-install-recommends libcudnn8-dev + sudo rm -rf /usr/local/cuda + sudo ln -s /usr/local/cuda-11.2 /usr/local/cuda + export PATH=/usr/local/cuda/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH + export CUDACXX=/usr/local/cuda/bin/nvcc + export CUDA_PATH=/usr/local/cuda + export CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda + features="full" + else + echo "Unable to auto-install CUDA on this Linux OS" + features="opencv-base,weights,weights-train" + fi fi else if [[ -v CUDA_PATH ]]; then - features = "full" + features="full" else - features = "opencv-base,weights,weights-train" + features="opencv-base,weights,weights-train" fi fi - -rm -rf $temp_folder cd .. -cd $vcpkg_folder -git clone https://github.com/microsoft/vcpkg -cd vcpkg -./bootstrap-vcpkg.sh -disableMetrics -./vcpkg install darknet[${features}] +rm -rf $temp_folder + +if [[ ! -v VCPKG_ROOT ]]; then + git clone https://github.com/microsoft/vcpkg + cd vcpkg + ./bootstrap-vcpkg.sh -disableMetrics + export VCPKG_ROOT=$(pwd) +fi + +$VCPKG_ROOT/vcpkg install darknet[${features}] diff --git a/vcpkg.json b/vcpkg.json index dacc49ed56f..a7f66f260fc 100644 --- a/vcpkg.json +++ b/vcpkg.json @@ -56,7 +56,6 @@ "name": "opencv2", "features": [ "contrib", - "dnn", "ffmpeg" ] } @@ -70,7 +69,6 @@ "features": [ "contrib", "cuda", - "dnn", "ffmpeg" ] } From 44c868bacadd1a110ecc48cbd88b13740338cbfa Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 21 Feb 2021 18:11:02 +0100 Subject: [PATCH 1201/1365] add missing pubkey install step --- .github/workflows/ccpp.yml | 3 +++ scripts/setup.sh | 1 + 2 files changed, 4 insertions(+) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index c0c7b8d0fd0..d2a59775197 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -18,6 +18,7 @@ jobs: sudo apt update sudo apt-get dist-upgrade -y sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin + sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /" sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/ /" sudo apt-get install -y --no-install-recommends cuda-compiler-11-2 cuda-libraries-dev-11-2 cuda-driver-dev-11-2 cuda-cudart-dev-11-2 @@ -85,6 +86,7 @@ jobs: sudo apt update sudo apt-get dist-upgrade -y sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin + sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /" sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/ /" sudo apt-get install -y --no-install-recommends cuda-compiler-11-2 cuda-libraries-dev-11-2 cuda-driver-dev-11-2 cuda-cudart-dev-11-2 @@ -206,6 +208,7 @@ jobs: sudo apt update sudo apt-get dist-upgrade -y sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin + sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /" sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/ /" sudo apt-get install -y --no-install-recommends cuda-compiler-11-2 cuda-libraries-dev-11-2 cuda-driver-dev-11-2 cuda-cudart-dev-11-2 diff --git a/scripts/setup.sh b/scripts/setup.sh index 30c2f2a0014..f0970907323 100755 --- a/scripts/setup.sh +++ b/scripts/setup.sh @@ -40,6 +40,7 @@ if [ "$install_cuda" = true ] ; then sudo apt-get dist-upgrade -y #sudo apt-get install -y --no-install-recommends nvidia-cuda-dev nvidia-cuda-toolkit sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin + sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /" sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/ /" sudo apt-get install -y --no-install-recommends cuda-compiler-11-2 cuda-libraries-dev-11-2 cuda-driver-dev-11-2 cuda-cudart-dev-11-2 From 94fdfa45903db162c89c5ebf354e012f1e5dd099 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 21 Feb 2021 22:59:36 +0100 Subject: [PATCH 1202/1365] use ninja whenever possible in build.ps1 --- build.ps1 | 22 ++++++++++------------ 1 file changed, 10 insertions(+), 12 deletions(-) diff --git a/build.ps1 b/build.ps1 index b07e3d27599..cf96d0daa06 100755 --- a/build.ps1 +++ b/build.ps1 @@ -2,14 +2,14 @@ $number_of_build_workers = 8 $enable_cuda = $true +$enable_cudnn = $true +$enable_opencv = $true $use_vcpkg = $true -$use_ninja = $true $force_cpp_build = $false #$additional_build_setup = " -DCMAKE_CUDA_ARCHITECTURES=30" -$CMAKE_EXE = Get-Command cmake | Select-Object -ExpandProperty Definition - +$CMAKE_EXE = Get-Command cmake 2> $null | Select-Object -ExpandProperty Definition if (-Not $CMAKE_EXE) { throw "Could not find CMake, please install it" } @@ -17,15 +17,13 @@ else { Write-Host "Using CMake from ${CMAKE_EXE}" } -if ($use_ninja) { - $NINJA_EXE = Get-Command ninja | Select-Object -ExpandProperty Definition - if (-Not $NINJA_EXE) { - $use_ninja = $false - Write-Host "Could not find Ninja, using msbuild as a fallback" -ForegroundColor Yellow - } - else { - Write-Host "Using Ninja from ${NINJA_EXE}" - } +$NINJA_EXE = Get-Command ninja 2> $null | Select-Object -ExpandProperty Definition +if (-Not $NINJA_EXE) { + $use_ninja = $false + Write-Host "Could not find Ninja, using msbuild as a fallback" -ForegroundColor Yellow +} +else { + Write-Host "Using Ninja from ${NINJA_EXE}" } function getProgramFiles32bit() { From 53db0163ebf1afd46892fc962ff9d85089b67ca5 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 28 Feb 2021 08:57:44 +0300 Subject: [PATCH 1203/1365] Added CUDA graphs/streams. To speed up the inference use: [net] use_cuda_graph = 1 --- include/darknet.h | 8 +++ src/blas.h | 2 +- src/blas_kernels.cu | 14 ++-- src/convolutional_kernels.cu | 42 ++++++++++-- src/dark_cuda.c | 129 +++++++++++++++++++++++++++++------ src/dark_cuda.h | 6 +- src/demo.c | 3 +- src/network.c | 2 + src/network_kernels.cu | 43 +++++++++++- src/parser.c | 11 ++- src/route_layer.c | 8 +++ 11 files changed, 230 insertions(+), 38 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 040d9e35999..1a5fdf75586 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -557,6 +557,9 @@ struct layer { //#ifdef GPU int *indexes_gpu; + int stream; + int wait_stream_id; + float *z_gpu; float *r_gpu; float *h_gpu; @@ -817,6 +820,11 @@ typedef struct network { size_t *max_input16_size; size_t *max_output16_size; int wait_stream; + + void *cuda_graph; + void *cuda_graph_exec; + int use_cuda_graph; + int *cuda_graph_ready; float *global_delta_gpu; float *state_delta_gpu; diff --git a/src/blas.h b/src/blas.h index eddca47b4c8..ab888903b54 100644 --- a/src/blas.h +++ b/src/blas.h @@ -170,7 +170,7 @@ void stretch_sway_flip_weights_gpu(const float *src_weight_gpu, float *weight_de void rotate_weights_gpu(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int size, int reverse); void reduce_and_expand_array_gpu(const float *src_gpu, float *dst_gpu, int size, int groups); void expand_array_gpu(const float *src_gpu, float *dst_gpu, int size, int groups); -void mult_inverse_array_gpu(const float *src_gpu, float *dst_gpu, int size, float eps); +void mult_inverse_array_gpu(const float *src_gpu, float *dst_gpu, int size, float eps, float divider, float clip, float abs_add); void P_constrastive_f_det_gpu(int *labels, unsigned int feature_size, float temperature, contrastive_params *contrast_p, const int contrast_p_size); void coord_conv_gpu(float *dst, int size, int w, int h, int chan, int b, int type); diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index b8f7aed557f..189b2daae2c 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -2315,7 +2315,8 @@ extern "C" void expand_array_gpu(const float *src_gpu, float *dst_gpu, int size, -__global__ void mult_inverse_array_kernel(const float *src_gpu, float *dst_gpu, int size, const float eps) +__global__ void mult_inverse_array_kernel(const float *src_gpu, float *dst_gpu, int size, const float eps, + float divider, const float clip, const float abs_add) { const int index = blockIdx.x*blockDim.x + threadIdx.x; @@ -2326,15 +2327,20 @@ __global__ void mult_inverse_array_kernel(const float *src_gpu, float *dst_gpu, // eps = 2 - lower delta // eps = 0 - higher delta (linear) // eps = -1 - high delta (inverse number) - dst_gpu[index] = powf(fabs(val), eps) * sign; + // = (abs(x)*10+1)^(-1) + float unsigned_val = powf(fabs(val)*10 + abs_add, eps); + unsigned_val = unsigned_val / divider; + if (unsigned_val > clip && clip != 0.0) unsigned_val = clip; + if (isnan(unsigned_val) || isinf(unsigned_val)) unsigned_val = 0; + dst_gpu[index] = unsigned_val * sign; } } -extern "C" void mult_inverse_array_gpu(const float *src_gpu, float *dst_gpu, int size, float eps) +extern "C" void mult_inverse_array_gpu(const float *src_gpu, float *dst_gpu, int size, float eps, float divider, float clip, float abs_add) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); - mult_inverse_array_kernel << > > (src_gpu, dst_gpu, size, eps); + mult_inverse_array_kernel << > > (src_gpu, dst_gpu, size, eps, divider, clip, abs_add); CHECK_CUDA(cudaPeekAtLastError()); } diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index b1aa4e69809..d15019ae7dc 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -165,6 +165,14 @@ half *cuda_make_f16_from_f32_array(float *src, size_t n) void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) { + if (l.stream >= 0) { + switch_stream(l.stream); + } + + if (l.wait_stream_id >= 0) { + wait_stream(l.wait_stream_id); + } + //fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1); if(l.binary){ binarize_weights_gpu(l.weights_gpu, l.n, (l.c / l.groups)*l.size*l.size, l.binary_weights_gpu); @@ -810,6 +818,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state if (!state.net.adversarial && !l.train_only_bn) { float *old_input = state.input; + /* if (l.reverse) { if (*state.net.max_output16_size < l.inputs*l.batch) { @@ -818,12 +827,14 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state assert(*state.net.max_output16_size > 0); *state.net.output16_gpu = cuda_make_array(NULL, *state.net.max_output16_size); } - mult_inverse_array_gpu(state.input, *state.net.output16_gpu, l.inputs*l.batch, l.reverse); + float clip = 0.0; + float divider = 1.0; + float abs_add = 1.0; + mult_inverse_array_gpu(state.input, *state.net.output16_gpu, l.inputs*l.batch, l.reverse, divider, clip, abs_add); state.input = *state.net.output16_gpu; } */ - // calculate conv weight updates // if used: beta=1 then loss decreases faster CHECK_CUDNN(cudnnConvolutionBackwardFilter(cudnn_handle(), @@ -849,6 +860,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state float *old_weights = l.weights_gpu; + /* if (l.reverse) { if (*state.net.max_output16_size < l.nweights) { *state.net.max_output16_size = l.nweights; @@ -856,11 +868,13 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state assert(*state.net.max_output16_size > 0); *state.net.output16_gpu = cuda_make_array(NULL, l.nweights); } - mult_inverse_array_gpu(l.weights_gpu, *state.net.output16_gpu, l.nweights, l.reverse); + float clip = 0.0; + float divider = 1.0; + float abs_add = 1.0; + mult_inverse_array_gpu(l.weights_gpu, *state.net.output16_gpu, l.nweights, l.reverse, divider, clip, abs_add); l.weights_gpu = *state.net.output16_gpu; } - - + */ // http://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnConvolutionBackwardData // calculate delta for the next layer @@ -958,6 +972,8 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state reset_nan_and_inf(l.weight_updates_gpu, size); fix_nan_and_inf(l.weights_gpu, size); } + + } __global__ void calc_avg_activation_kernel(float *src, float *dst, int size, int channels, int batches) @@ -1311,8 +1327,24 @@ void update_convolutional_layer_gpu(layer l, int batch, float learning_rate_init //axpy_ongpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); //axpy_ongpu(l.nweights, learning_rate / batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); //scal_ongpu(l.nweights, momentum, l.weight_updates_gpu, 1); + + float *old_weight_updates_gpu = l.weight_updates_gpu; + + + if (l.reverse) { + float clip = 0.0; + float divider = 1.0; + float abs_add = 1.0; + mult_inverse_array_gpu(l.weight_updates_gpu, l.output_gpu, l.inputs*l.batch, l.reverse, divider, clip, abs_add); + l.weight_updates_gpu = l.output_gpu; + } + + axpy_ongpu(l.nweights, -decay*batch*loss_scale, l.weights_gpu, 1, l.weight_updates_gpu, 1); axpy_ongpu(l.nweights, learning_rate / batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); + + l.weight_updates_gpu = old_weight_updates_gpu; + scal_ongpu(l.nweights, momentum, l.weight_updates_gpu, 1); axpy_ongpu(l.n, learning_rate / batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); diff --git a/src/dark_cuda.c b/src/dark_cuda.c index fec06473880..55272724b23 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -122,14 +122,14 @@ static int streamInit[16] = { 0 }; cudaStream_t get_cuda_stream() { int i = cuda_get_device(); if (!streamInit[i]) { - //printf("Create CUDA-stream \n"); - cudaError_t status = cudaStreamCreate(&streamsArray[i]); - //cudaError_t status = cudaStreamCreateWithFlags(&streamsArray[i], cudaStreamNonBlocking); + printf("Create CUDA-stream - %d \n", i); + //cudaError_t status = cudaStreamCreate(&streamsArray[i], cudaStreamNonBlocking); + cudaError_t status = cudaStreamCreateWithFlags(&streamsArray[i], cudaStreamNonBlocking); if (status != cudaSuccess) { printf(" cudaStreamCreate error: %d \n", status); const char *s = cudaGetErrorString(status); printf("CUDA Error: %s\n", s); - status = cudaStreamCreateWithFlags(&streamsArray[i], cudaStreamDefault); + status = cudaStreamCreateWithFlags(&streamsArray[i], cudaStreamNonBlocking); // cudaStreamDefault CHECK_CUDA(status); } streamInit[i] = 1; @@ -137,40 +137,44 @@ cudaStream_t get_cuda_stream() { return streamsArray[i]; } +/* static cudaStream_t streamsArray2[16]; // cudaStreamSynchronize( get_cuda_memcpy_stream() ); static int streamInit2[16] = { 0 }; cudaStream_t get_cuda_memcpy_stream() { int i = cuda_get_device(); if (!streamInit2[i]) { - cudaError_t status = cudaStreamCreate(&streamsArray2[i]); - //cudaError_t status = cudaStreamCreateWithFlags(&streamsArray2[i], cudaStreamNonBlocking); + printf(" Create COPY stream %d \n", i); + //cudaError_t status = cudaStreamCreate(&streamsArray2[i], cudaStreamNonBlocking); + cudaError_t status = cudaStreamCreateWithFlags(&streamsArray2[i], cudaStreamNonBlocking); if (status != cudaSuccess) { printf(" cudaStreamCreate-Memcpy error: %d \n", status); const char *s = cudaGetErrorString(status); printf("CUDA Error: %s\n", s); - status = cudaStreamCreateWithFlags(&streamsArray2[i], cudaStreamDefault); + status = cudaStreamCreateWithFlags(&streamsArray2[i], cudaStreamNonBlocking); CHECK_CUDA(status); } streamInit2[i] = 1; } return streamsArray2[i]; } - +*/ #ifdef CUDNN +static int cudnnInit[16] = { 0 }; +static cudnnHandle_t cudnnHandle[16]; + cudnnHandle_t cudnn_handle() { - static int init[16] = {0}; - static cudnnHandle_t handle[16]; int i = cuda_get_device(); - if(!init[i]) { - cudnnCreate(&handle[i]); - init[i] = 1; - cudnnStatus_t status = cudnnSetStream(handle[i], get_cuda_stream()); + if(!cudnnInit[i]) { + cudnnCreate(&cudnnHandle[i]); + cudnnInit[i] = 1; + cudnnStatus_t status = cudnnSetStream(cudnnHandle[i], get_cuda_stream()); CHECK_CUDNN(status); + printf(" Create cudnn-handle %d \n", i); } - return handle[i]; + return cudnnHandle[i]; } @@ -226,22 +230,102 @@ void cudnn_check_error_extended(cudnnStatus_t status, const char *file, int line } cudnn_check_error(status); } + +static cudnnHandle_t switchCudnnHandle[16]; +static int switchCudnnInit[16]; #endif + +static int blasInit[16] = { 0 }; +static cublasHandle_t blasHandle[16]; + cublasHandle_t blas_handle() { - static int init[16] = {0}; - static cublasHandle_t handle[16]; int i = cuda_get_device(); - if(!init[i]) { - cublasCreate(&handle[i]); - cublasStatus_t status = cublasSetStream(handle[i], get_cuda_stream()); + if (!blasInit[i]) { + cublasCreate(&blasHandle[i]); + cublasStatus_t status = cublasSetStream(blasHandle[i], get_cuda_stream()); CHECK_CUDA((cudaError_t)status); - init[i] = 1; + blasInit[i] = 1; + } + return blasHandle[i]; +} + + +static int switchBlasInit[16] = { 0 }; +static cublasHandle_t switchBlasHandle[16]; + +static cudaStream_t switchStreamsArray[16]; +static int switchStreamInit[16] = { 0 }; + +cudaStream_t switch_stream(int i) { + int dev_id = cuda_get_device(); + + //printf(" switch_stream = %d \n", i); + if (!switchStreamInit[i]) { + CHECK_CUDA(cudaStreamCreateWithFlags(&switchStreamsArray[i], cudaStreamNonBlocking)); + switchStreamInit[i] = 1; + printf(" Create stream %d \n", i); + } + + //cudaStreamQuery(streamsArray[0]); // Flush previous stream queue + streamsArray[dev_id] = switchStreamsArray[i]; + streamInit[dev_id] = switchStreamInit[i]; + + //printf("switch_stream %d - get_cuda_stream() = %d \n", i, get_cuda_stream()); + + /* + if (!switchBlasInit[i]) { + CHECK_CUDA( cublasCreate(&switchBlasHandle[i]) ); + switchBlasInit[i] = 1; + CHECK_CUDA( cublasSetStream(switchBlasHandle[i], switchStreamsArray[i]) ); + printf(" Create blas-handle %d \n", i); } - return handle[i]; + blasHandle[dev_id] = switchBlasHandle[i]; + blasInit[dev_id] = switchBlasInit[i]; + */ + +#ifdef CUDNN + if (!switchCudnnInit[i]) { + CHECK_CUDNN( cudnnCreate(&switchCudnnHandle[i]) ); + switchCudnnInit[i] = 1; + CHECK_CUDNN(cudnnSetStream(switchCudnnHandle[i], switchStreamsArray[i])); + printf(" Create cudnn-handle %d \n", i); + } + cudnnHandle[dev_id] = switchCudnnHandle[i]; + cudnnInit[dev_id] = switchCudnnInit[i]; +#endif + + return switchStreamsArray[i]; } +static const max_events = 1024; +static cudaEvent_t switchEventsArray[1024]; +static volatile int event_counter = 0; + +void wait_stream(int i) { + int dev_id = cuda_get_device(); + if (event_counter >= max_events) error("CUDA max_events exceeded \n"); + + CHECK_CUDA( cudaEventCreateWithFlags(&switchEventsArray[event_counter], cudaEventDisableTiming) ); + //printf(" create event = %d (wait for stream = %d) \n", event_counter, i); + + //CHECK_CUDA(cudaEventRecordWithFlags(switchEventsArray[i], switchStreamsArray[i], cudaEventRecordExternal) ); + CHECK_CUDA( cudaEventRecord(switchEventsArray[event_counter], switchStreamsArray[i]) ); + CHECK_CUDA( cudaStreamWaitEvent(streamsArray[dev_id], switchEventsArray[event_counter], cudaEventWaitDefault) ); + //cudaStreamWaitEvent(streamsArray[dev_id], switchEventsArray[i], cudaEventWaitExternal); + event_counter++; +} + +void reset_wait_stream_events() { + int i; + for (i = 0; i < event_counter; ++i) { + CHECK_CUDA(cudaEventDestroy(switchEventsArray[i])); + } + event_counter = 0; +} + + static float **pinned_ptr = NULL; static size_t pinned_num_of_blocks = 0; static size_t pinned_index = 0; @@ -473,6 +557,7 @@ void cuda_pull_array(float *x_gpu, float *x, size_t n) { size_t size = sizeof(float)*n; //cudaError_t status = cudaMemcpy(x, x_gpu, size, cudaMemcpyDeviceToHost); + //printf("cuda_pull_array - get_cuda_stream() = %d \n", get_cuda_stream()); cudaError_t status = cudaMemcpyAsync(x, x_gpu, size, cudaMemcpyDeviceToHost, get_cuda_stream()); CHECK_CUDA(status); cudaStreamSynchronize(get_cuda_stream()); diff --git a/src/dark_cuda.h b/src/dark_cuda.h index 0e5f39f9a35..10f6d89e385 100644 --- a/src/dark_cuda.h +++ b/src/dark_cuda.h @@ -77,11 +77,15 @@ extern "C" { float cuda_compare(float *x_gpu, float *x, size_t n, char *s); dim3 cuda_gridsize(size_t n); cudaStream_t get_cuda_stream(); - cudaStream_t get_cuda_memcpy_stream(); + //cudaStream_t get_cuda_memcpy_stream(); int get_number_of_blocks(int array_size, int block_size); int get_gpu_compute_capability(int i, char *device_name); void show_cuda_cudnn_info(); + cudaStream_t switch_stream(int i); + void wait_stream(int i); + void reset_wait_stream_events(); + #ifdef CUDNN cudnnHandle_t cudnn_handle(); enum {cudnn_fastest, cudnn_smallest, cudnn_specify}; diff --git a/src/demo.c b/src/demo.c index 08e06f19651..d503aa7fcfc 100644 --- a/src/demo.c +++ b/src/demo.c @@ -97,7 +97,8 @@ void *detect_in_thread(void *ptr) layer l = net.layers[net.n - 1]; float *X = det_s.data; - float *prediction = network_predict(net, X); + //float *prediction = + network_predict(net, X); cv_images[demo_index] = det_img; det_img = cv_images[(demo_index + avg_frames / 2 + 1) % avg_frames]; diff --git a/src/network.c b/src/network.c index 9dec1df5761..600be1b2d7d 100644 --- a/src/network.c +++ b/src/network.c @@ -245,6 +245,7 @@ network make_network(int n) net.n = n; net.layers = (layer*)xcalloc(net.n, sizeof(layer)); net.seen = (uint64_t*)xcalloc(1, sizeof(uint64_t)); + net.cuda_graph_ready = (int*)xcalloc(1, sizeof(int)); net.badlabels_reject_threshold = (float*)xcalloc(1, sizeof(float)); net.delta_rolling_max = (float*)xcalloc(1, sizeof(float)); net.delta_rolling_avg = (float*)xcalloc(1, sizeof(float)); @@ -1224,6 +1225,7 @@ void free_network(network net) free(net.scales); free(net.steps); free(net.seen); + free(net.cuda_graph_ready); free(net.badlabels_reject_threshold); free(net.delta_rolling_max); free(net.delta_rolling_avg); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index e6e24d2c214..9f4c640b3e9 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -664,7 +664,7 @@ float train_networks(network *nets, int n, data d, int interval) float *get_network_output_layer_gpu(network net, int i) { layer l = net.layers[i]; - if(l.type != REGION) cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch); + if(l.type != REGION && l.type != YOLO && (*net.cuda_graph_ready) == 0) cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch); return l.output; } @@ -686,12 +686,49 @@ float *network_predict_gpu(network net, float *input) //state.input = cuda_make_array(input, size); // memory will be allocated in the parse_network_cfg_custom() state.input = net.input_state_gpu; memcpy(net.input_pinned_cpu, input, size * sizeof(float)); - cuda_push_array(state.input, net.input_pinned_cpu, size); state.truth = 0; state.train = 0; state.delta = 0; - forward_network_gpu(net, state); + + //cudaGraphExec_t instance = (cudaGraphExec_t)net.cuda_graph_exec; + static cudaGraphExec_t instance; + + if ((*net.cuda_graph_ready) == 0) { + static cudaGraph_t graph; + if (net.use_cuda_graph == 1) { + int i; + for (i = 0; i < 16; ++i) switch_stream(i); + + cudaStream_t stream0 = switch_stream(0); + CHECK_CUDA(cudaDeviceSynchronize()); + printf("Try to capture graph... \n"); + //cudaGraph_t graph = (cudaGraph_t)net.cuda_graph; + CHECK_CUDA(cudaStreamBeginCapture(stream0, cudaStreamCaptureModeGlobal)); + } + + cuda_push_array(state.input, net.input_pinned_cpu, size); + forward_network_gpu(net, state); + + if (net.use_cuda_graph == 1) { + cudaStream_t stream0 = switch_stream(0); + CHECK_CUDA(cudaStreamEndCapture(stream0, &graph)); + CHECK_CUDA(cudaGraphInstantiate(&instance, graph, NULL, NULL, 0)); + (*net.cuda_graph_ready) = 1; + printf(" graph is captured... \n"); + CHECK_CUDA(cudaDeviceSynchronize()); + } + CHECK_CUDA(cudaStreamSynchronize(get_cuda_stream())); + } + else { + cudaStream_t stream0 = switch_stream(0); + //printf(" cudaGraphLaunch \n"); + CHECK_CUDA( cudaGraphLaunch(instance, stream0) ); + CHECK_CUDA( cudaStreamSynchronize(stream0) ); + //printf(" ~cudaGraphLaunch \n"); + } + float *out = get_network_output_gpu(net); + reset_wait_stream_events(); //cuda_free(state.input); // will be freed in the free_network() return out; } diff --git a/src/parser.c b/src/parser.c index eb41e91457b..e7498d9daee 100644 --- a/src/parser.c +++ b/src/parser.c @@ -231,6 +231,9 @@ convolutional_layer parse_convolutional(list *options, size_params params) layer.reverse = option_find_float_quiet(options, "reverse", 0); layer.coordconv = option_find_int_quiet(options, "coordconv", 0); + layer.stream = option_find_int_quiet(options, "stream", -1); + layer.wait_stream_id = option_find_int_quiet(options, "wait_stream", -1); + if(params.net.adam){ layer.B1 = params.net.B1; layer.B2 = params.net.B2; @@ -1107,6 +1110,9 @@ route_layer parse_route(list *options, size_params params) layer.h = first.h; layer.c = layer.out_c; + layer.stream = option_find_int_quiet(options, "stream", -1); + layer.wait_stream_id = option_find_int_quiet(options, "wait_stream", -1); + if (n > 3) fprintf(stderr, " \t "); else if (n > 1) fprintf(stderr, " \t "); else fprintf(stderr, " \t\t "); @@ -1166,11 +1172,14 @@ void parse_net_options(list *options, network *net) *net->delta_rolling_std = 0; *net->seen = 0; *net->cur_iteration = 0; + *net->cuda_graph_ready = 0; + net->use_cuda_graph = option_find_int_quiet(options, "use_cuda_graph", 0); net->loss_scale = option_find_float_quiet(options, "loss_scale", 1); net->dynamic_minibatch = option_find_int_quiet(options, "dynamic_minibatch", 0); net->optimized_memory = option_find_int_quiet(options, "optimized_memory", 0); net->workspace_size_limit = (size_t)1024*1024 * option_find_float_quiet(options, "workspace_size_limit_MB", 1024); // 1024 MB by default + net->adam = option_find_int_quiet(options, "adam", 0); if(net->adam){ net->B1 = option_find_float(options, "B1", .9); @@ -1672,7 +1681,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) fprintf(stderr, "avg_outputs = %d \n", avg_outputs); #ifdef GPU get_cuda_stream(); - get_cuda_memcpy_stream(); + //get_cuda_memcpy_stream(); if (gpu_index >= 0) { int size = get_network_input_size(net) * net.batch; diff --git a/src/route_layer.c b/src/route_layer.c index 2e0699d59a5..9a3410c3190 100644 --- a/src/route_layer.c +++ b/src/route_layer.c @@ -116,6 +116,14 @@ void backward_route_layer(const route_layer l, network_state state) #ifdef GPU void forward_route_layer_gpu(const route_layer l, network_state state) { + if (l.stream >= 0) { + switch_stream(l.stream); + } + + if (l.wait_stream_id >= 0) { + wait_stream(l.wait_stream_id); + } + int i, j; int offset = 0; for(i = 0; i < l.n; ++i){ From f8df52ad72f79a1bc97fdc72309668fe1dbb58fd Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 28 Feb 2021 09:11:22 +0300 Subject: [PATCH 1204/1365] minor fix --- src/dark_cuda.c | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/dark_cuda.c b/src/dark_cuda.c index 55272724b23..65ffcc4c19d 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -299,6 +299,10 @@ cudaStream_t switch_stream(int i) { return switchStreamsArray[i]; } +#ifndef cudaEventWaitDefault +#define cudaEventWaitDefault 0x00 +#endif // cudaEventWaitDefault + static const max_events = 1024; static cudaEvent_t switchEventsArray[1024]; static volatile int event_counter = 0; From 7bcac1c84bd76b9f2a14e918ffdaeafc036fcd78 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 28 Feb 2021 09:30:31 +0300 Subject: [PATCH 1205/1365] Try new docker for circle-ci --- .circleci/config.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 4360aa0cb60..aaa88b49c56 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -2,7 +2,8 @@ version: 2.0 jobs: build: docker: - - image: alantrrs/cuda-opencv:latest + - image: witekkq/cuda-opencv:latest +# - image: alantrrs/cuda-opencv:latest # - image: nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04 working_directory: ~/work steps: From 92c92017b2a7cb202d5b585e490d41357ed0a454 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 28 Feb 2021 18:38:42 +0300 Subject: [PATCH 1206/1365] Try new docker image for circle-ci --- .circleci/config.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index aaa88b49c56..c93960f7188 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -2,7 +2,7 @@ version: 2.0 jobs: build: docker: - - image: witekkq/cuda-opencv:latest + - image: julianassmann/opencv-cuda:cuda-11.1-opencv-4.5 # - image: alantrrs/cuda-opencv:latest # - image: nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04 working_directory: ~/work From 1a874378e49ba9540409b85af6bccbda7cedb6f8 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 28 Feb 2021 18:43:16 +0300 Subject: [PATCH 1207/1365] Try new docker image for circle-ci --- .circleci/config.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index c93960f7188..671ed4c25c4 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -2,7 +2,7 @@ version: 2.0 jobs: build: docker: - - image: julianassmann/opencv-cuda:cuda-11.1-opencv-4.5 + - image: julianassmann/opencv-cuda:cuda-10.2-opencv-4.3-devel # - image: alantrrs/cuda-opencv:latest # - image: nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04 working_directory: ~/work From f058d26a8aafbb2e2a542293dd15af04b85ae08e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 1 Mar 2021 05:35:05 +0300 Subject: [PATCH 1208/1365] circle ci fix --- .circleci/config.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 671ed4c25c4..85846c983c5 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -2,7 +2,7 @@ version: 2.0 jobs: build: docker: - - image: julianassmann/opencv-cuda:cuda-10.2-opencv-4.3-devel + - image: alexeyab84/dockerfiles:latest # - image: alantrrs/cuda-opencv:latest # - image: nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04 working_directory: ~/work From b8c9c9d457a47d27710082c6e16206fc50af21f3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 1 Mar 2021 05:44:04 +0300 Subject: [PATCH 1209/1365] minor fix --- .circleci/config.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.circleci/config.yml b/.circleci/config.yml index 85846c983c5..d89ce46b785 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -10,6 +10,8 @@ jobs: - checkout - run: nvcc --version - run: gcc --version + - run: export PATH=$PATH:/usr/local/include/opencv4/ + - run: export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib/:/usr/lib/:/usr/lib64/ - run: make LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 -j 8 - run: make clean - run: make LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 DEBUG=1 -j 8 From 0bd7e6713db7e7dd8fb4da22d5b721832b01b895 Mon Sep 17 00:00:00 2001 From: hainq Date: Fri, 19 Mar 2021 12:13:26 +0700 Subject: [PATCH 1210/1365] cuda: floor -> floorf, ceil -> ceilf --- src/activation_kernels.cu | 2 +- src/blas_kernels.cu | 40 ++++++++++++++++++------------------ src/convolutional_kernels.cu | 8 ++++---- 3 files changed, 25 insertions(+), 25 deletions(-) diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index 272453c6dd3..7a4d06f5ac0 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -85,7 +85,7 @@ __device__ float gelu_gradient_kernel(float x) { __device__ float plse_gradient_kernel(float x){return (x < 0 || x > 1) ? .01f : .125f;} __device__ float stair_gradient_kernel(float x) { - if (floor(x) == x) return 0; + if (floorf(x) == x) return 0; return 1; } diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 189b2daae2c..bc327995eed 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -1758,11 +1758,11 @@ __global__ void smooth_rotate_weights_kernel(const float *src_weight_gpu, float float x_s = x_c + (x - x_c)*cos_a + (y - y_c)*sin_a; float y_s = y_c - (x - x_c)*sin_a + (y - y_c)*cos_a; - int x_0 = floor(x_s); // round down - int x_1 = ceil(x_s); // round up + int x_0 = floorf(x_s); // round down + int x_1 = ceilf(x_s); // round up if (x_0 == x_1) x_1 = x_0 + 1; - int y_0 = floor(y_s); - int y_1 = ceil(y_s); + int y_0 = floorf(y_s); + int y_1 = ceilf(y_s); if (y_0 == y_1) y_1 = y_0 + 1; float c_x_0 = x_1 - x_s; @@ -1855,11 +1855,11 @@ __global__ void stretch_weights_kernel(const float *src_weight_gpu, float *weig float x_s = x_c + (x - x_c) / scale; float y_s = y_c + (y - y_c) / scale; - int x_0 = floor(x_s); // round down - int x_1 = ceil(x_s); // round up + int x_0 = floorf(x_s); // round down + int x_1 = ceilf(x_s); // round up if (x_0 == x_1) x_1 = x_0 + 1; - int y_0 = floor(y_s); - int y_1 = ceil(y_s); + int y_0 = floorf(y_s); + int y_1 = ceilf(y_s); if (y_0 == y_1) y_1 = y_0 + 1; float c_x_0 = x_1 - x_s; @@ -1953,11 +1953,11 @@ __global__ void sway_and_flip_weights_kernel(const float *src_weight_gpu, float float x_s = x_c + (x - x_c)*cos_a + (y - y_c)*sin_a; float y_s = y_c - (x - x_c)*sin_a + (y - y_c)*cos_a; - int x_0 = floor(x_s); // round down - int x_1 = ceil(x_s); // round up + int x_0 = floorf(x_s); // round down + int x_1 = ceilf(x_s); // round up if (x_0 == x_1) x_1 = x_0 + 1; - int y_0 = floor(y_s); - int y_1 = ceil(y_s); + int y_0 = floorf(y_s); + int y_1 = ceilf(y_s); if (y_0 == y_1) y_1 = y_0 + 1; float c_x_0 = x_1 - x_s; @@ -2144,11 +2144,11 @@ __global__ void stretch_sway_flip_weights_kernel(const float *src_weight_gpu, f float x_s = x_c + (x - x_c) / scale; float y_s = y_c + (y - y_c) / scale; - int x_0 = floor(x_s); // round down - int x_1 = ceil(x_s); // round up + int x_0 = floorf(x_s); // round down + int x_1 = ceilf(x_s); // round up if (x_0 == x_1) x_1 = x_0 + 1; - int y_0 = floor(y_s); - int y_1 = ceil(y_s); + int y_0 = floorf(y_s); + int y_1 = ceilf(y_s); if (y_0 == y_1) y_1 = y_0 + 1; float c_x_0 = x_1 - x_s; @@ -2203,11 +2203,11 @@ __global__ void stretch_sway_flip_weights_kernel(const float *src_weight_gpu, f float x_s = x_c + (x - x_c)*cos_a + (y - y_c)*sin_a; float y_s = y_c - (x - x_c)*sin_a + (y - y_c)*cos_a; - int x_0 = floor(x_s); // round down - int x_1 = ceil(x_s); // round up + int x_0 = floorf(x_s); // round down + int x_1 = ceilf(x_s); // round up if (x_0 == x_1) x_1 = x_0 + 1; - int y_0 = floor(y_s); - int y_1 = ceil(y_s); + int y_0 = floorf(y_s); + int y_1 = ceilf(y_s); if (y_0 == y_1) y_1 = y_0 + 1; float c_x_0 = x_1 - x_s; diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index d15019ae7dc..ddd140c3088 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -1103,10 +1103,10 @@ void assisted_excitation_forward_gpu(convolutional_layer l, network_state state) float dh = (1 - truth.h) * beta; //printf(" alpha = %f, beta = %f, truth.w = %f, dw = %f, tw+dw = %f, l.out_w = %d \n", alpha, beta, truth.w, dw, truth.w+dw, l.out_w); - int left = floor((truth.x - (dw + truth.w) / 2) * l.out_w); - int right = ceil((truth.x + (dw + truth.w) / 2) * l.out_w); - int top = floor((truth.y - (dh + truth.h) / 2) * l.out_h); - int bottom = ceil((truth.y + (dh + truth.h) / 2) * l.out_h); + int left = floorf((truth.x - (dw + truth.w) / 2) * l.out_w); + int right = ceilf((truth.x + (dw + truth.w) / 2) * l.out_w); + int top = floorf((truth.y - (dh + truth.h) / 2) * l.out_h); + int bottom = ceilf((truth.y + (dh + truth.h) / 2) * l.out_h); if (left < 0) left = 0; if (top < 0) top = 0; if (right > l.out_w) right = l.out_w; From 730e5c1c6a099d6f579cbf61448939be460b0efe Mon Sep 17 00:00:00 2001 From: Alexey Date: Sun, 28 Mar 2021 17:51:02 +0300 Subject: [PATCH 1211/1365] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 220eeab054b..09c0befdc79 100644 --- a/README.md +++ b/README.md @@ -190,9 +190,9 @@ You can get cfg-files by path: `darknet/cfg/` * Windows or Linux * **CMake >= 3.12**: https://cmake.org/download/ -* **CUDA >= 10.0**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) +* **CUDA >= 10.2**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) * **OpenCV >= 2.4**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or download from [OpenCV official site](https://opencv.org/releases.html) (on Windows set system variable `OpenCV_DIR` = `C:\opencv\build` - where are the `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png)) -* **cuDNN >= 7.0** https://developer.nvidia.com/rdp/cudnn-archive (on **Linux** copy `cudnn.h`,`libcudnn.so`... as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on **Windows** copy `cudnn.h`,`cudnn64_7.dll`, `cudnn64_7.lib` as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows ) +* **cuDNN >= 8.0.2** https://developer.nvidia.com/rdp/cudnn-archive (on **Linux** copy `cudnn.h`,`libcudnn.so`... as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on **Windows** copy `cudnn.h`,`cudnn64_7.dll`, `cudnn64_7.lib` as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows ) * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported * on Linux **GCC or Clang**, on Windows **MSVC 2017/2019** https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community From 4949b6cd84886320e2dc8893352e18110c51175c Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 28 Mar 2021 18:13:43 +0300 Subject: [PATCH 1212/1365] minor fix --- .circleci/config.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index d89ce46b785..d9e2e5dc886 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -2,7 +2,8 @@ version: 2.0 jobs: build: docker: - - image: alexeyab84/dockerfiles:latest + - image: datamachines/cudnn_tensorflow_opencv:11.2.0_2.4.1_4.5.1-20210211 +# - image: alexeyab84/dockerfiles:latest # - image: alantrrs/cuda-opencv:latest # - image: nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04 working_directory: ~/work From 1e3a616ed6cefc517db6c8c106c83de24fad275c Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 28 Mar 2021 18:49:55 +0300 Subject: [PATCH 1213/1365] compile fix --- src/dark_cuda.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/dark_cuda.c b/src/dark_cuda.c index 65ffcc4c19d..30509c51ee4 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -303,7 +303,7 @@ cudaStream_t switch_stream(int i) { #define cudaEventWaitDefault 0x00 #endif // cudaEventWaitDefault -static const max_events = 1024; +static const int max_events = 1024; static cudaEvent_t switchEventsArray[1024]; static volatile int event_counter = 0; From e83d6526cd3ccabf68594e434cc8f593b6fd1e3d Mon Sep 17 00:00:00 2001 From: Alexey Date: Wed, 7 Apr 2021 03:16:01 +0300 Subject: [PATCH 1214/1365] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 09c0befdc79..74ff9430be0 100644 --- a/README.md +++ b/README.md @@ -58,7 +58,7 @@ About Darknet framework: http://pjreddie.com/darknet/ ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) -![scaled_yolov4](https://user-images.githubusercontent.com/4096485/101356322-f1f5a180-38a8-11eb-9907-4fe4f188d887.png) AP50:95 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2011.08036 +![scaled_yolov4](https://user-images.githubusercontent.com/4096485/112776361-281d8380-9048-11eb-8083-8728b12dcd55.png) AP50:95 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2011.08036 ---- From b409569ee4f4fc5506423185418973d66d4487f7 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 11 Apr 2021 19:23:49 +0200 Subject: [PATCH 1215/1365] upgrade to run-vcpkg@v7 --- .github/workflows/ccpp.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index d2a59775197..d69bc756af0 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -107,7 +107,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - uses: lukka/run-vcpkg@v6 + uses: lukka/run-vcpkg@v7 with: setupOnly: true vcpkgDirectory: '${{ github.workspace }}/vcpkg' @@ -287,7 +287,7 @@ jobs: - uses: lukka/get-cmake@latest - name: Restore from cache and run vcpkg - uses: lukka/run-vcpkg@v6 + uses: lukka/run-vcpkg@v7 with: setupOnly: true vcpkgDirectory: '${{ github.workspace }}/vcpkg' @@ -391,7 +391,7 @@ jobs: - uses: lukka/get-cmake@latest - name: Restore from cache and run vcpkg - uses: lukka/run-vcpkg@v6 + uses: lukka/run-vcpkg@v7 with: setupOnly: true vcpkgDirectory: '${{ github.workspace }}/vcpkg' From fa8d21dee58ad425327d16820e4d38ebc166f618 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 14 Apr 2021 11:50:54 +0200 Subject: [PATCH 1216/1365] use build.ps1 also in CI to better test it --- .github/workflows/ccpp.yml | 246 +++++++++++++++---------------------- build.ps1 | 238 +++++++++++++++++++++-------------- 2 files changed, 243 insertions(+), 241 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index d69bc756af0..73bf8871682 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -2,6 +2,9 @@ name: Darknet Continuous Integration on: [push, pull_request, workflow_dispatch] +env: + VCPKG_BINARY_SOURCES: 'clear;nuget,vcpkgbinarycache,readwrite' + jobs: ubuntu-makefile: runs-on: ubuntu-20.04 @@ -101,52 +104,44 @@ jobs: nvcc --version gcc --version - - name: Restore from cache and run vcpkg - env: - CUDACXX: "/usr/local/cuda/bin/nvcc" - CUDA_PATH: "/usr/local/cuda" - CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" - LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - uses: lukka/run-vcpkg@v7 - with: - setupOnly: true - vcpkgDirectory: '${{ github.workspace }}/vcpkg' - vcpkgGitCommitId: '99dc49dae7e170c3be63dd097230007f3bb73c4f' - appendedCacheKey: ${{ hashFiles( '**/vcpkg.json' ) }} - vcpkgTriplet: 'x64-linux' - additionalCachedPaths: ${{ env.buildDir }}/vcpkg_installed - - - name: 'Build with CMake' + - name: 'Setup vcpkg and NuGet artifacts backend' + shell: bash + run: > + git clone https://github.com/microsoft/vcpkg; + ./vcpkg/bootstrap-vcpkg.sh; + mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) + sources add + -source "https://nuget.pkg.github.com/cenit/index.json" + -storepasswordincleartext + -name "vcpkgbinarycache" + -username "cenit" + -password "${{ secrets.GITHUB_TOKEN }}" + + - name: 'Build' + shell: pwsh env: CUDACXX: "/usr/local/cuda/bin/nvcc" CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - uses: lukka/run-cmake@v3 - with: - cmakeListsOrSettingsJson: CMakeListsTxtAdvanced - cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' - useVcpkgToolchainFile: true - buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeBuildType: 'Release' - cmakeAppendedArgs: "-DBUILD_SHARED_LIBS=OFF -DCMAKE_BUILD_TYPE=Release -DENABLE_CUDNN=OFF" - buildWithCMakeArgs: '--target install' + run: | + "./build.ps1 -DoNotSetupVS -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN" - uses: actions/upload-artifact@v2 with: - name: darknet-vcpkg-${{ runner.os }} + name: darknet-vcpkg-cuda-${{ runner.os }} path: cfg - uses: actions/upload-artifact@v2 with: - name: darknet-vcpkg-${{ runner.os }} + name: darknet-vcpkg-cuda-${{ runner.os }} path: data - uses: actions/upload-artifact@v2 with: - name: darknet-vcpkg-${{ runner.os }} + name: darknet-vcpkg-cuda-${{ runner.os }} path: ${{ github.workspace }}/*dark* - uses: actions/upload-artifact@v2 with: - name: darknet-vcpkg-${{ runner.os }} + name: darknet-vcpkg-cuda-${{ runner.os }} path: ${{ github.workspace }}/uselib* @@ -162,16 +157,15 @@ jobs: - uses: lukka/get-cmake@latest - - name: 'Build with CMake' - uses: lukka/run-cmake@v3 - with: - cmakeListsOrSettingsJson: CMakeListsTxtAdvanced - cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' - useVcpkgToolchainFile: true - buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DENABLE_CUDNN=OFF -DENABLE_CUDA=OFF" - cmakeBuildType: 'Release' - buildWithCMakeArgs: '--target install' + - name: 'Build' + shell: pwsh + env: + CUDACXX: "/usr/local/cuda/bin/nvcc" + CUDA_PATH: "/usr/local/cuda" + CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" + LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" + run: | + "./build.ps1 -DoNotSetupVS -EnableOPENCV" - uses: actions/upload-artifact@v2 with: @@ -223,21 +217,15 @@ jobs: nvcc --version gcc --version - - name: 'Build with CMake' + - name: 'Build' + shell: pwsh env: CUDACXX: "/usr/local/cuda/bin/nvcc" CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - uses: lukka/run-cmake@v3 - with: - cmakeListsOrSettingsJson: CMakeListsTxtAdvanced - cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' - useVcpkgToolchainFile: true - buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=ON" - cmakeBuildType: 'Release' - buildWithCMakeArgs: '--target install' + run: | + "./build.ps1 -DoNotSetupVS -EnableOPENCV -EnableCUDA" - uses: actions/upload-artifact@v2 with: @@ -264,16 +252,10 @@ jobs: - uses: lukka/get-cmake@latest - - name: 'Build with CMake' - uses: lukka/run-cmake@v3 - with: - cmakeListsOrSettingsJson: CMakeListsTxtAdvanced - cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' - useVcpkgToolchainFile: true - buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DBUILD_AS_CPP:BOOL=TRUE -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" - cmakeBuildType: 'Release' - buildWithCMakeArgs: '--target install' + - name: 'Build' + shell: pwsh + run: | + "./build.ps1 -DoNotSetupVS -ForceCPP" osx-vcpkg: @@ -286,26 +268,23 @@ jobs: - uses: lukka/get-cmake@latest - - name: Restore from cache and run vcpkg - uses: lukka/run-vcpkg@v7 - with: - setupOnly: true - vcpkgDirectory: '${{ github.workspace }}/vcpkg' - vcpkgGitCommitId: '99dc49dae7e170c3be63dd097230007f3bb73c4f' - appendedCacheKey: ${{ hashFiles( '**/vcpkg.json' ) }} - vcpkgTriplet: 'x64-osx' - additionalCachedPaths: ${{ env.buildDir }}/vcpkg_installed - - - name: 'Build with CMake' - uses: lukka/run-cmake@v3 - with: - cmakeListsOrSettingsJson: CMakeListsTxtAdvanced - cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' - useVcpkgToolchainFile: true - buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeBuildType: 'Release' - cmakeAppendedArgs: "-DBUILD_SHARED_LIBS=OFF -DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF" - buildWithCMakeArgs: '--target install' + - name: 'Setup vcpkg and NuGet artifacts backend' + shell: bash + run: > + git clone https://github.com/microsoft/vcpkg; + ./vcpkg/bootstrap-vcpkg.sh; + mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) + sources add + -source "https://nuget.pkg.github.com/cenit/index.json" + -storepasswordincleartext + -name "vcpkgbinarycache" + -username "cenit" + -password "${{ secrets.GITHUB_TOKEN }}" + + - name: 'Build' + shell: pwsh + run: | + "./build.ps1 -DoNotSetupVS -UseVCPKG -EnableOPENCV" - uses: actions/upload-artifact@v2 with: @@ -335,16 +314,10 @@ jobs: - uses: lukka/get-cmake@latest - - name: 'Build with CMake' - uses: lukka/run-cmake@v3 - with: - cmakeListsOrSettingsJson: CMakeListsTxtAdvanced - cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' - useVcpkgToolchainFile: true - buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF" - cmakeBuildType: 'Release' - buildWithCMakeArgs: '--target install' + - name: 'Build' + shell: pwsh + run: | + "./build.ps1 -DoNotSetupVS -EnableOPENCV" - uses: actions/upload-artifact@v2 with: @@ -371,45 +344,36 @@ jobs: - uses: lukka/get-cmake@latest - - name: 'Build with CMake' - uses: lukka/run-cmake@v3 - with: - cmakeListsOrSettingsJson: CMakeListsTxtAdvanced - cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' - useVcpkgToolchainFile: true - buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DBUILD_AS_CPP:BOOL=TRUE -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" - cmakeBuildType: 'Release' - buildWithCMakeArgs: '--target install' + - name: 'Build' + shell: pwsh + run: | + "./build.ps1 -DoNotSetupVS -ForceCPP" - win-vcpkg-ninja: + win-vcpkg: runs-on: windows-latest steps: - uses: actions/checkout@v2 - uses: lukka/get-cmake@latest - - name: Restore from cache and run vcpkg - uses: lukka/run-vcpkg@v7 - with: - setupOnly: true - vcpkgDirectory: '${{ github.workspace }}/vcpkg' - vcpkgGitCommitId: '99dc49dae7e170c3be63dd097230007f3bb73c4f' - appendedCacheKey: ${{ hashFiles( '**/vcpkg.json' ) }} - vcpkgTriplet: 'x64-windows' - additionalCachedPaths: ${{ env.buildDir }}/vcpkg_installed - - - name: 'Build with CMake' - uses: lukka/run-cmake@v3 - with: - cmakeListsOrSettingsJson: CMakeListsTxtAdvanced - cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' - useVcpkgToolchainFile: true - buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-GNinja -DCMAKE_BUILD_TYPE=Release -DSELECT_OPENCV_MODULES=ON -DENABLE_CUDNN=OFF -DENABLE_CUDA=OFF" - cmakeBuildType: 'Release' - buildWithCMakeArgs: '--config Release --target install' + - name: 'Setup vcpkg and NuGet artifacts backend' + shell: bash + run: > + git clone https://github.com/microsoft/vcpkg; + ./vcpkg/bootstrap-vcpkg.sh; + mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) + sources add + -source "https://nuget.pkg.github.com/cenit/index.json" + -storepasswordincleartext + -name "vcpkgbinarycache" + -username "cenit" + -password "${{ secrets.GITHUB_TOKEN }}" + + - name: 'Build' + shell: pwsh + run: | + "./build.ps1 -UseVCPKG -EnableOPENCV" - uses: actions/upload-artifact@v2 with: @@ -440,16 +404,10 @@ jobs: - uses: lukka/get-cmake@latest - - name: 'Build with CMake' - uses: lukka/run-cmake@v3 - with: - cmakeListsOrSettingsJson: CMakeListsTxtAdvanced - cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' - useVcpkgToolchainFile: true - buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" - cmakeBuildType: 'Release' - buildWithCMakeArgs: '--config Release --target install' + - name: 'Build' + shell: pwsh + run: | + "./build.ps1" - uses: actions/upload-artifact@v2 with: @@ -480,16 +438,10 @@ jobs: - uses: lukka/get-cmake@latest - - name: 'Build with CMake' - uses: lukka/run-cmake@v3 - with: - cmakeListsOrSettingsJson: CMakeListsTxtAdvanced - cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' - useVcpkgToolchainFile: true - buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DBUILD_AS_CPP:BOOL=TRUE -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" - cmakeBuildType: 'Release' - buildWithCMakeArgs: '--config Release --target install' + - name: 'Build' + shell: pwsh + run: | + "./build.ps1 -ForceCPP" win-intlibs-cuda: @@ -505,21 +457,15 @@ jobs: - uses: lukka/get-cmake@latest - - name: 'Build with CMake' + - name: 'Build' env: CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" CUDA_PATH_V10_2: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" - uses: lukka/run-cmake@v3 - with: - cmakeListsOrSettingsJson: CMakeListsTxtAdvanced - cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' - useVcpkgToolchainFile: true - buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" - cmakeBuildType: 'Release' - buildWithCMakeArgs: '--config Release --target install' + shell: pwsh + run: | + "./build.ps1 -EnableCUDA" mingw: diff --git a/build.ps1 b/build.ps1 index cf96d0daa06..b9176759450 100755 --- a/build.ps1 +++ b/build.ps1 @@ -1,14 +1,78 @@ #!/usr/bin/env pwsh -$number_of_build_workers = 8 -$enable_cuda = $true -$enable_cudnn = $true -$enable_opencv = $true -$use_vcpkg = $true -$force_cpp_build = $false +param ( + [switch]$EnableCUDA = $false, + [switch]$EnableCUDNN = $false, + [switch]$EnableOPENCV = $false, + [switch]$UseVCPKG = $false, + [switch]$DoNotSetupVS = $false, + [switch]$DoNotUseNinja = $false, + [switch]$ForceCPP = $false +) +$number_of_build_workers = 8 #$additional_build_setup = " -DCMAKE_CUDA_ARCHITECTURES=30" + +if ($EnableCUDA) { + Write-Host "CUDA is enabled" +} +else { + Write-Host "CUDA is disabled, please pass -EnableCUDA to the script to enable" +} + +if ($EnableCUDNN) { + Write-Host "CUDNN is enabled" +} +else { + Write-Host "CUDNN is disabled, please pass -EnableCUDNN to the script to enable" +} + +if ($EnableOPENCV) { + Write-Host "OPENCV is enabled" +} +else { + Write-Host "OPENCV is disabled, please pass -EnableOPENCV to the script to enable" +} + +if ($UseVCPKG) { + Write-Host "VCPKG is enabled" +} +else { + Write-Host "VCPKG is disabled, please pass -UseVCPKG to the script to enable" +} + +if ($DoNotSetupVS) { + Write-Host "VisualStudio integration is disabled" +} +else { + Write-Host "VisualStudio integration is enabled, please pass -DoNotSetupVS to the script to disable" +} + +if ($DoNotUseNinja) { + Write-Host "Ninja is disabled" +} +else { + Write-Host "Ninja is enabled, please pass -DoNotUseNinja to the script to disable" +} + +if ($ForceCPP) { + Write-Host "ForceCPP build mode is enabled" +} +else { + Write-Host "ForceCPP build mode is disabled, please pass -ForceCPP to the script to enable" +} + +if ($IsWindows) { + $vcpkg_triplet = "x64-windows" +} +if ($IsMacOS) { + $vcpkg_triplet = "x64-osx" +} +if ($IsLinux) { + $vcpkg_triplet = "x64-linux" +} + $CMAKE_EXE = Get-Command cmake 2> $null | Select-Object -ExpandProperty Definition if (-Not $CMAKE_EXE) { throw "Could not find CMake, please install it" @@ -17,13 +81,16 @@ else { Write-Host "Using CMake from ${CMAKE_EXE}" } -$NINJA_EXE = Get-Command ninja 2> $null | Select-Object -ExpandProperty Definition -if (-Not $NINJA_EXE) { - $use_ninja = $false - Write-Host "Could not find Ninja, using msbuild as a fallback" -ForegroundColor Yellow -} -else { - Write-Host "Using Ninja from ${NINJA_EXE}" +if (-Not $DoNotUseNinja) { + $NINJA_EXE = Get-Command ninja 2> $null | Select-Object -ExpandProperty Definition + if (-Not $NINJA_EXE) { + $DoNotUseNinja = $true + Write-Host "Could not find Ninja, using msbuild or make backends as a fallback" -ForegroundColor Yellow + } + else { + Write-Host "Using Ninja from ${NINJA_EXE}" + $generator = "Ninja" + } } function getProgramFiles32bit() { @@ -95,71 +162,76 @@ function getLatestVisualStudioWithDesktopWorkloadVersion() { } -if ((Test-Path env:VCPKG_ROOT) -and $use_vcpkg) { +if ((Test-Path env:VCPKG_ROOT) -and $UseVCPKG) { $vcpkg_path = "$env:VCPKG_ROOT" Write-Host "Found vcpkg in VCPKG_ROOT: $vcpkg_path" + $additional_build_setup = $additional_build_setup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" +} +elseif ((Test-Path "${env:WORKSPACE}/vcpkg") -and $UseVCPKG) { + $vcpkg_path = "${env:WORKSPACE}/vcpkg" + $env:VCPKG_ROOT = "${env:WORKSPACE}/vcpkg" + Write-Host "Found vcpkg in WORKSPACE/vcpkg: $vcpkg_path" + $additional_build_setup = $additional_build_setup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" } -elseif ((Test-Path "${env:WORKSPACE}\vcpkg") -and $use_vcpkg) { - $vcpkg_path = "${env:WORKSPACE}\vcpkg" - $env:VCPKG_ROOT = "${env:WORKSPACE}\vcpkg" - Write-Host "Found vcpkg in WORKSPACE\vcpkg: $vcpkg_path" +elseif ((Test-Path "${RUNVCPKG_VCPKG_ROOT_OUT}") -and $UseVCPKG) { + $vcpkg_path = "${RUNVCPKG_VCPKG_ROOT_OUT}" + $env:VCPKG_ROOT = "${RUNVCPKG_VCPKG_ROOT_OUT}" + Write-Host "Found vcpkg in RUNVCPKG_VCPKG_ROOT_OUT: ${RUNVCPKG_VCPKG_ROOT_OUT}" + $additional_build_setup = $additional_build_setup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" } else { - $use_vcpkg = $false - Write-Host "Skipping vcpkg-enabled builds because the VCPKG_ROOT environment variable is not defined or you requested to avoid VCPKG, using self-distributed libs`n" -ForegroundColor Yellow + Write-Host "Skipping vcpkg integration`n" -ForegroundColor Yellow $additional_build_setup = $additional_build_setup + " -DENABLE_VCPKG_INTEGRATION:BOOL=OFF" } -if ($null -eq $env:VCPKG_DEFAULT_TRIPLET -and $use_vcpkg) { - Write-Host "No default triplet has been set-up for vcpkg. Defaulting to x64-windows" -ForegroundColor Yellow - $vcpkg_triplet = "x64-windows" -} -elseif ($use_vcpkg) { - $vcpkg_triplet = $env:VCPKG_DEFAULT_TRIPLET -} - -if ($vcpkg_triplet -Match "x86" -and $use_vcpkg) { - Throw "darknet is supported only in x64 builds!" -} - -if ($null -eq (Get-Command "cl.exe" -ErrorAction SilentlyContinue)) { - $vsfound = getLatestVisualStudioWithDesktopWorkloadPath - Write-Host "Found VS in ${vsfound}" - Push-Location "${vsfound}\Common7\Tools" - cmd.exe /c "VsDevCmd.bat -arch=x64 & set" | - ForEach-Object { - if ($_ -match "=") { - $v = $_.split("="); Set-Item -force -path "ENV:\$($v[0])" -value "$($v[1])" +if (-Not $DoNotSetupVS) { + if ($null -eq (Get-Command "cl.exe" -ErrorAction SilentlyContinue)) { + $vsfound = getLatestVisualStudioWithDesktopWorkloadPath + Write-Host "Found VS in ${vsfound}" + Push-Location "${vsfound}\Common7\Tools" + cmd.exe /c "VsDevCmd.bat -arch=x64 & set" | + ForEach-Object { + if ($_ -match "=") { + $v = $_.split("="); Set-Item -force -path "ENV:\$($v[0])" -value "$($v[1])" + } } + Pop-Location + Write-Host "Visual Studio Command Prompt variables set" -ForegroundColor Yellow } - Pop-Location - Write-Host "Visual Studio Command Prompt variables set" -ForegroundColor Yellow -} -$tokens = getLatestVisualStudioWithDesktopWorkloadVersion -$tokens = $tokens.split('.') -if ($use_ninja) { - $generator = "Ninja" -} -else { - if ($tokens[0] -eq "14") { - $generator = "Visual Studio 14 2015" - } - elseif ($tokens[0] -eq "15") { - $generator = "Visual Studio 15 2017" - } - elseif ($tokens[0] -eq "16") { - $generator = "Visual Studio 16 2019" + $tokens = getLatestVisualStudioWithDesktopWorkloadVersion + $tokens = $tokens.split('.') + if ($DoNotUseNinja) { + $dllfolder = "Release" + $selectConfig = " --config Release " + if ($tokens[0] -eq "14") { + $generator = "Visual Studio 14 2015" + $additional_build_setup = $additional_build_setup + " -T `"host=x64`" -A `"x64`"" + } + elseif ($tokens[0] -eq "15") { + $generator = "Visual Studio 15 2017" + $additional_build_setup = $additional_build_setup + " -T `"host=x64`" -A `"x64`"" + } + elseif ($tokens[0] -eq "16") { + $generator = "Visual Studio 16 2019" + $additional_build_setup = $additional_build_setup + " -T `"host=x64`" -A `"x64`"" + } + else { + throw "Unknown Visual Studio version, unsupported configuration" + } } - else { - throw "Unknown Visual Studio version, unsupported configuration" + if (-Not $UseVCPKG) { + $dllfolder = "../3rdparty/pthreads/bin" } } +if ($DoNotSetupVS -and $DoNotUseNinja) { + $generator = "Unix Makefiles" +} Write-Host "Setting up environment to use CMake generator: $generator" -ForegroundColor Yellow -if ($null -eq (Get-Command "nvcc.exe" -ErrorAction SilentlyContinue)) { +if ($null -eq (Get-Command "nvcc" -ErrorAction SilentlyContinue)) { if (Test-Path env:CUDA_PATH) { - $env:PATH += ";${env:CUDA_PATH}\bin" + $env:PATH += ";${env:CUDA_PATH}/bin" Write-Host "Found cuda in ${env:CUDA_PATH}" -ForegroundColor Yellow } else { @@ -173,62 +245,46 @@ if (Test-Path env:CUDA_PATH) { Write-Host "Added missing env variable CUDA_TOOLKIT_ROOT_DIR" -ForegroundColor Yellow } if (-Not(Test-Path env:CUDACXX)) { - $env:CUDACXX = "${env:CUDA_PATH}\bin\nvcc.exe" + $env:CUDACXX = "${env:CUDA_PATH}/bin/nvcc" Write-Host "Added missing env variable CUDACXX" -ForegroundColor Yellow } } -if ($force_cpp_build) { +if ($ForceCPP) { $additional_build_setup = $additional_build_setup + " -DBUILD_AS_CPP:BOOL=ON" } -if (-Not($enable_cuda)) { +if (-Not($EnableCUDA)) { $additional_build_setup = $additional_build_setup + " -DENABLE_CUDA:BOOL=OFF" } -if (-Not($enable_cudnn)) { +if (-Not($EnableCUDNN)) { $additional_build_setup = $additional_build_setup + " -DENABLE_CUDNN:BOOL=OFF" } -if (-Not($enable_opencv)) { +if (-Not($EnableOPENCV)) { $additional_build_setup = $additional_build_setup + " -DENABLE_OPENCV:BOOL=OFF" } -if ($use_vcpkg) { - New-Item -Path .\build_win_release -ItemType directory -Force - Set-Location build_win_release - if ($use_ninja) { +Push-Location $PSScriptRoot +New-Item -Path ./build_release -ItemType directory -Force +Set-Location build_release + +if ($UseVCPKG) { $cmake_args = "-G `"$generator`" `"-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet`" `"-DCMAKE_BUILD_TYPE=Release`" ${additional_build_setup} -S .." - $dllfolder = "." - } - else { - $cmake_args = "-G `"$generator`" -T `"host=x64`" -A `"x64`" `"-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet`" `"-DCMAKE_BUILD_TYPE=Release`" ${additional_build_setup} -S .." - $dllfolder = "Release" - } } else { - # USE LOCAL PTHREAD LIB AND LOCAL STB HEADER, NO VCPKG, ONLY RELEASE MODE SUPPORTED - # if you want to manually force this case, remove VCPKG_ROOT env variable and remember to use "vcpkg integrate remove" in case you had enabled user-wide vcpkg integration - New-Item -Path .\build_win_release_novcpkg -ItemType directory -Force - Set-Location build_win_release_novcpkg - if ($use_ninja) { $cmake_args = "-G `"$generator`" ${additional_build_setup} -S .." - $dllfolder = "..\3rdparty\pthreads\bin" - } - else { - $cmake_args = "-G `"$generator`" -T `"host=x64`" -A `"x64`" ${additional_build_setup} -S .." - $dllfolder = "..\3rdparty\pthreads\bin" - } } - Write-Host "CMake args: $cmake_args" Start-Process -NoNewWindow -Wait -FilePath $CMAKE_EXE -ArgumentList $cmake_args -Start-Process -NoNewWindow -Wait -FilePath $CMAKE_EXE -ArgumentList "--build . --config Release --parallel ${number_of_build_workers} --target install" +Start-Process -NoNewWindow -Wait -FilePath $CMAKE_EXE -ArgumentList "--build . ${selectConfig} --parallel ${number_of_build_workers} --target install" Remove-Item DarknetConfig.cmake Remove-Item DarknetConfigVersion.cmake -$dllfiles = Get-ChildItem ${dllfolder}\*.dll +$dllfiles = Get-ChildItem ./${dllfolder}/*.dll if ($dllfiles) { Copy-Item $dllfiles .. } Set-Location .. -Copy-Item cmake\Modules\*.cmake share\darknet\ +Copy-Item cmake/Modules/*.cmake share/darknet/ +Pop-Location From 42ecbfa4b4171d0a7324733f72cd6e6a3653cec5 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 14 Apr 2021 13:30:50 +0200 Subject: [PATCH 1217/1365] improve non-windows handling in ps1 script --- .github/workflows/ccpp.yml | 35 ++++++++++++----------------------- build.ps1 | 25 ++++++++----------------- 2 files changed, 20 insertions(+), 40 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 73bf8871682..09d5f070314 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -124,8 +124,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: | - "./build.ps1 -DoNotSetupVS -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN" + run: ./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA - uses: actions/upload-artifact@v2 with: @@ -164,8 +163,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: | - "./build.ps1 -DoNotSetupVS -EnableOPENCV" + run: ./build.ps1 -EnableOPENCV - uses: actions/upload-artifact@v2 with: @@ -224,8 +222,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: | - "./build.ps1 -DoNotSetupVS -EnableOPENCV -EnableCUDA" + run: ./build.ps1 -EnableOPENCV -EnableCUDA - uses: actions/upload-artifact@v2 with: @@ -254,8 +251,7 @@ jobs: - name: 'Build' shell: pwsh - run: | - "./build.ps1 -DoNotSetupVS -ForceCPP" + run: ./build.ps1 -ForceCPP osx-vcpkg: @@ -283,8 +279,7 @@ jobs: - name: 'Build' shell: pwsh - run: | - "./build.ps1 -DoNotSetupVS -UseVCPKG -EnableOPENCV" + run: ./build.ps1 -UseVCPKG -EnableOPENCV - uses: actions/upload-artifact@v2 with: @@ -316,8 +311,7 @@ jobs: - name: 'Build' shell: pwsh - run: | - "./build.ps1 -DoNotSetupVS -EnableOPENCV" + run: ./build.ps1 -EnableOPENCV - uses: actions/upload-artifact@v2 with: @@ -346,8 +340,7 @@ jobs: - name: 'Build' shell: pwsh - run: | - "./build.ps1 -DoNotSetupVS -ForceCPP" + run: ./build.ps1 -ForceCPP win-vcpkg: @@ -362,7 +355,7 @@ jobs: run: > git clone https://github.com/microsoft/vcpkg; ./vcpkg/bootstrap-vcpkg.sh; - mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) + $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -source "https://nuget.pkg.github.com/cenit/index.json" -storepasswordincleartext @@ -372,8 +365,7 @@ jobs: - name: 'Build' shell: pwsh - run: | - "./build.ps1 -UseVCPKG -EnableOPENCV" + run: ./build.ps1 -UseVCPKG -EnableOPENCV - uses: actions/upload-artifact@v2 with: @@ -406,8 +398,7 @@ jobs: - name: 'Build' shell: pwsh - run: | - "./build.ps1" + run: ./build.ps1 - uses: actions/upload-artifact@v2 with: @@ -440,8 +431,7 @@ jobs: - name: 'Build' shell: pwsh - run: | - "./build.ps1 -ForceCPP" + run: ./build.ps1 -ForceCPP win-intlibs-cuda: @@ -464,8 +454,7 @@ jobs: CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" shell: pwsh - run: | - "./build.ps1 -EnableCUDA" + run: ./build.ps1 -EnableCUDA mingw: diff --git a/build.ps1 b/build.ps1 index b9176759450..896d2b53d2e 100755 --- a/build.ps1 +++ b/build.ps1 @@ -13,6 +13,13 @@ param ( $number_of_build_workers = 8 #$additional_build_setup = " -DCMAKE_CUDA_ARCHITECTURES=30" +if (-Not $IsWindows) { + $DoNotSetupVS = $true +} + +if ($IsWindows -and -Not $env:VCPKG_DEFAULT_TRIPLET) { + $env:VCPKG_DEFAULT_TRIPLET = "x64-windows" +} if ($EnableCUDA) { Write-Host "CUDA is enabled" @@ -63,16 +70,6 @@ else { Write-Host "ForceCPP build mode is disabled, please pass -ForceCPP to the script to enable" } -if ($IsWindows) { - $vcpkg_triplet = "x64-windows" -} -if ($IsMacOS) { - $vcpkg_triplet = "x64-osx" -} -if ($IsLinux) { - $vcpkg_triplet = "x64-linux" -} - $CMAKE_EXE = Get-Command cmake 2> $null | Select-Object -ExpandProperty Definition if (-Not $CMAKE_EXE) { throw "Could not find CMake, please install it" @@ -269,13 +266,7 @@ if (-Not($EnableOPENCV)) { Push-Location $PSScriptRoot New-Item -Path ./build_release -ItemType directory -Force Set-Location build_release - -if ($UseVCPKG) { - $cmake_args = "-G `"$generator`" `"-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet`" `"-DCMAKE_BUILD_TYPE=Release`" ${additional_build_setup} -S .." -} -else { - $cmake_args = "-G `"$generator`" ${additional_build_setup} -S .." -} +$cmake_args = "-G `"$generator`" ${additional_build_setup} -S .." Write-Host "CMake args: $cmake_args" Start-Process -NoNewWindow -Wait -FilePath $CMAKE_EXE -ArgumentList $cmake_args Start-Process -NoNewWindow -Wait -FilePath $CMAKE_EXE -ArgumentList "--build . ${selectConfig} --parallel ${number_of_build_workers} --target install" From 4a774e08369089d69f458fc6e9c7a4afa9a60dd7 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 14 Apr 2021 13:40:42 +0200 Subject: [PATCH 1218/1365] find also vcpkg nested inside darknet folder --- build.ps1 | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/build.ps1 b/build.ps1 index 896d2b53d2e..6006576f8d8 100755 --- a/build.ps1 +++ b/build.ps1 @@ -70,6 +70,8 @@ else { Write-Host "ForceCPP build mode is disabled, please pass -ForceCPP to the script to enable" } +Push-Location $PSScriptRoot + $CMAKE_EXE = Get-Command cmake 2> $null | Select-Object -ExpandProperty Definition if (-Not $CMAKE_EXE) { throw "Could not find CMake, please install it" @@ -176,6 +178,12 @@ elseif ((Test-Path "${RUNVCPKG_VCPKG_ROOT_OUT}") -and $UseVCPKG) { Write-Host "Found vcpkg in RUNVCPKG_VCPKG_ROOT_OUT: ${RUNVCPKG_VCPKG_ROOT_OUT}" $additional_build_setup = $additional_build_setup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" } +elseif ((Test-Path "$PWD/vcpkg") -and $UseVCPKG) { + $vcpkg_path = "$PWD/vcpkg" + $env:VCPKG_ROOT = "$PWD/vcpkg" + Write-Host "Found vcpkg in $PWD/vcpkg: $PWD/vcpkg" + $additional_build_setup = $additional_build_setup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" +} else { Write-Host "Skipping vcpkg integration`n" -ForegroundColor Yellow $additional_build_setup = $additional_build_setup + " -DENABLE_VCPKG_INTEGRATION:BOOL=OFF" @@ -263,7 +271,6 @@ if (-Not($EnableOPENCV)) { $additional_build_setup = $additional_build_setup + " -DENABLE_OPENCV:BOOL=OFF" } -Push-Location $PSScriptRoot New-Item -Path ./build_release -ItemType directory -Force Set-Location build_release $cmake_args = "-G `"$generator`" ${additional_build_setup} -S .." From 013d5e676dc918e79618eb9a52dc5b64d7344847 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 14 Apr 2021 15:06:10 +0200 Subject: [PATCH 1219/1365] use only build.ps1 --- CMakeLists.txt | 9 ++++--- README.md | 51 ++++++++++++++++++++--------------- build.ps1 | 46 +++++++++++++++++++------------- build.sh | 72 -------------------------------------------------- 4 files changed, 63 insertions(+), 115 deletions(-) delete mode 100755 build.sh diff --git a/CMakeLists.txt b/CMakeLists.txt index 0cdc25c94a9..a62d5bafc7d 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -20,11 +20,12 @@ option(ENABLE_CUDNN_HALF "Enable CUDNN Half precision" ON) option(ENABLE_ZED_CAMERA "Enable ZED Camera support" ON) option(ENABLE_VCPKG_INTEGRATION "Enable VCPKG integration" ON) -if(ENABLE_OPENCV AND ENABLE_CUDA AND NOT APPLE) - list(APPEND VCPKG_MANIFEST_FEATURES "opencv-cuda") -elseif(ENABLE_CUDA AND NOT APPLE) +#if(ENABLE_OPENCV AND ENABLE_CUDA AND NOT APPLE) +# list(APPEND VCPKG_MANIFEST_FEATURES "opencv-cuda") +if(ENABLE_CUDA AND NOT APPLE) list(APPEND VCPKG_MANIFEST_FEATURES "cuda") -elseif(ENABLE_OPENCV) +endif() +if(ENABLE_OPENCV) list(APPEND VCPKG_MANIFEST_FEATURES "opencv-base") endif() if(ENABLE_CUDNN AND ENABLE_CUDA AND NOT APPLE) diff --git a/README.md b/README.md index e445d1b711e..5bf7c0faf6b 100644 --- a/README.md +++ b/README.md @@ -312,12 +312,31 @@ On Linux find executable file `./darknet` in the root directory, while on Window The `CMakeLists.txt` will attempt to find installed optional dependencies like CUDA, cudnn, ZED and build against those. It will also create a shared object library file to use `darknet` for code development. -Open a shell terminal inside the cloned repository and launch: +Install powershell if you do not already have it ([guide here](https://docs.microsoft.com/en-us/powershell/scripting/install/installing-powershell)). -```bash -./build.sh +Then, open a powershell terminal inside the darknet repository and type these commands (using `vcpkg` to automatically download dependencies): + +```PowerShell +PS Code/> git clone https://github.com/microsoft/vcpkg +PS Code/> cd vcpkg +PS Code/vcpkg> ./bootstrap-vcpkg.bat +PS Code/vcpkg> $env:VCPKG_ROOT=$PWD +PS Code/vcpkg> cd .. +PS Code/> git clone https://github.com/AlexeyAB/darknet +PS Code/> cd darknet +PS Code/darknet> ./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN +``` + +or these commands (without `vcpkg` - please install required libraries manually) + +```PowerShell +PS Code/> git clone https://github.com/AlexeyAB/darknet +PS Code/> cd darknet +PS Code/darknet> ./build.ps1 -EnableOPENCV -EnableCUDA -EnableCUDNN ``` +(remove options like `-EnableCUDA` or `-EnableCUDNN` if you are not interested into) + To update CMake on Ubuntu, it's better to follow guide here: https://apt.kitware.com/ ### How to compile on Linux (using `make`) @@ -372,24 +391,14 @@ This is the recommended approach to build Darknet on Windows. 3. Open Powershell (Start -> All programs -> Windows Powershell) and type these commands: ```PowerShell -PS Code\> git clone https://github.com/microsoft/vcpkg -PS Code\> cd vcpkg -PS Code\vcpkg> .\bootstrap-vcpkg.bat -PS Code\vcpkg> .\vcpkg.exe install darknet[full]:x64-windows #replace with darknet[opencv-base,cuda,cudnn,weights,weights-train]:x64-windows for a quicker install of dependencies -PS Code\vcpkg> cd .. -``` - -If you prefer to download the most recent, maybe unreleased version, on the last line you need to add `--head` tag, like `.\vcpkg.exe install darknet[full]:x64-windows --head`. After completion, you can find darknet executables, networks and weights inside `installed\x64-windows\tools\darknet`; lib file is inside `installed\x64-windows\lib` and dll file is inside `installed\x64-windows\bin`. - -To manually build a version of darknet, after completing the previous step, you can also run - -```PowerShell -PS Code\> cd vcpkg -PS Code\vcpkg> $env:VCPKG_ROOT=$PWD -PS Code\vcpkg> cd .. -PS Code\> git clone https://github.com/AlexeyAB/darknet -PS Code\> cd darknet -PS Code\darknet> powershell -ExecutionPolicy Bypass -File .\build.ps1 +PS Code/> git clone https://github.com/microsoft/vcpkg +PS Code/> cd vcpkg +PS Code/vcpkg> ./bootstrap-vcpkg.bat +PS Code/vcpkg> $env:VCPKG_ROOT=$PWD +PS Code/vcpkg> cd .. +PS Code/> git clone https://github.com/AlexeyAB/darknet +PS Code/> cd darknet +PS Code/darknet> ./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN ``` ## How to train with multi-GPU diff --git a/build.ps1 b/build.ps1 index 6006576f8d8..8eedc23dad0 100755 --- a/build.ps1 +++ b/build.ps1 @@ -22,16 +22,24 @@ if ($IsWindows -and -Not $env:VCPKG_DEFAULT_TRIPLET) { } if ($EnableCUDA) { + if($IsMacOS) { + Write-Host "Cannot enable CUDA on macOS" -ForegroundColor Yellow + $EnableCUDA = $false + } Write-Host "CUDA is enabled" } -else { +elseif (-Not $IsMacOS) { Write-Host "CUDA is disabled, please pass -EnableCUDA to the script to enable" } if ($EnableCUDNN) { + if ($IsMacOS) { + Write-Host "Cannot enable CUDNN on macOS" -ForegroundColor Yellow + $EnableCUDNN = $false + } Write-Host "CUDNN is enabled" } -else { +elseif (-Not $IsMacOS) { Write-Host "CUDNN is disabled, please pass -EnableCUDNN to the script to enable" } @@ -234,24 +242,26 @@ if ($DoNotSetupVS -and $DoNotUseNinja) { } Write-Host "Setting up environment to use CMake generator: $generator" -ForegroundColor Yellow -if ($null -eq (Get-Command "nvcc" -ErrorAction SilentlyContinue)) { - if (Test-Path env:CUDA_PATH) { - $env:PATH += ";${env:CUDA_PATH}/bin" - Write-Host "Found cuda in ${env:CUDA_PATH}" -ForegroundColor Yellow - } - else { - Write-Host "Unable to find CUDA, if necessary please install it or define a CUDA_PATH env variable pointing to the install folder" -ForegroundColor Yellow +if (-Not $IsMacOS -and $EnableCUDA) { + if ($null -eq (Get-Command "nvcc" -ErrorAction SilentlyContinue)) { + if (Test-Path env:CUDA_PATH) { + $env:PATH += ";${env:CUDA_PATH}/bin" + Write-Host "Found cuda in ${env:CUDA_PATH}" -ForegroundColor Yellow + } + else { + Write-Host "Unable to find CUDA, if necessary please install it or define a CUDA_PATH env variable pointing to the install folder" -ForegroundColor Yellow + } } -} -if (Test-Path env:CUDA_PATH) { - if (-Not(Test-Path env:CUDA_TOOLKIT_ROOT_DIR)) { - $env:CUDA_TOOLKIT_ROOT_DIR = "${env:CUDA_PATH}" - Write-Host "Added missing env variable CUDA_TOOLKIT_ROOT_DIR" -ForegroundColor Yellow - } - if (-Not(Test-Path env:CUDACXX)) { - $env:CUDACXX = "${env:CUDA_PATH}/bin/nvcc" - Write-Host "Added missing env variable CUDACXX" -ForegroundColor Yellow + if (Test-Path env:CUDA_PATH) { + if (-Not(Test-Path env:CUDA_TOOLKIT_ROOT_DIR)) { + $env:CUDA_TOOLKIT_ROOT_DIR = "${env:CUDA_PATH}" + Write-Host "Added missing env variable CUDA_TOOLKIT_ROOT_DIR" -ForegroundColor Yellow + } + if (-Not(Test-Path env:CUDACXX)) { + $env:CUDACXX = "${env:CUDA_PATH}/bin/nvcc" + Write-Host "Added missing env variable CUDACXX" -ForegroundColor Yellow + } } } diff --git a/build.sh b/build.sh deleted file mode 100755 index 183fc878e2f..00000000000 --- a/build.sh +++ /dev/null @@ -1,72 +0,0 @@ -#!/usr/bin/env bash - -number_of_build_workers=8 -use_vcpkg=true -force_cpp_build=false -enable_cuda=false -enable_cudnn=false -enable_opencv=true - -if [[ "$OSTYPE" == "darwin"* ]]; then - vcpkg_triplet="x64-osx" -else - vcpkg_triplet="x64-linux" -fi - -if [[ ! -z "${VCPKG_ROOT}" ]] && [ -d ${VCPKG_ROOT} ] && [ "$use_vcpkg" = true ] -then - vcpkg_path="${VCPKG_ROOT}" - vcpkg_triplet_define="-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" - echo "Found vcpkg in VCPKG_ROOT: ${vcpkg_path}" - additional_defines="-DBUILD_SHARED_LIBS=OFF" -elif [[ ! -z "${WORKSPACE}" ]] && [ -d ${WORKSPACE}/vcpkg ] && [ "$use_vcpkg" = true ] -then - export VCPKG_ROOT="${WORKSPACE}/vcpkg" - vcpkg_path="${WORKSPACE}/vcpkg" - vcpkg_triplet_define="-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet" - echo "Found vcpkg in WORKSPACE/vcpkg: ${vcpkg_path}" - additional_defines="-DBUILD_SHARED_LIBS=OFF" -elif [ "$use_vcpkg" = true ] -then - (>&2 echo "darknet is unsupported without vcpkg, use at your own risk!") -else - additional_build_setup=${additional_build_setup}" -DENABLE_VCPKG_INTEGRATION:BOOL=FALSE" -fi - -if [ "$force_cpp_build" = true ] -then - additional_build_setup=${additional_build_setup}" -DBUILD_AS_CPP:BOOL=TRUE" -fi - -if [ "$enable_cuda" = false ] -then - additional_build_setup=${additional_build_setup}" -DENABLE_CUDA:BOOL=FALSE" -fi -if [ "$enable_cudnn" = false ] -then - additional_build_setup=${additional_build_setup}" -DENABLE_CUDNN:BOOL=FALSE" -fi -if [ "$enable_opencv" = false ] -then - additional_build_setup=${additional_build_setup}" -DENABLE_OPENCV:BOOL=FALSE" -fi - -## DEBUG -#mkdir -p build_debug -#cd build_debug -#cmake .. -DCMAKE_BUILD_TYPE=Debug ${vcpkg_define} ${vcpkg_triplet_define} ${additional_defines} ${additional_build_setup} -#cmake --build . --target install --parallel ${number_of_build_workers} -#rm -f DarknetConfig.cmake -#rm -f DarknetConfigVersion.cmake -#cd .. -#cp cmake/Modules/*.cmake share/darknet/ - -# RELEASE -mkdir -p build_release -cd build_release -cmake .. -DCMAKE_BUILD_TYPE=Release ${vcpkg_define} ${vcpkg_triplet_define} ${additional_defines} ${additional_build_setup} -cmake --build . --target install --parallel ${number_of_build_workers} -rm -f DarknetConfig.cmake -rm -f DarknetConfigVersion.cmake -cd .. -cp cmake/Modules/*.cmake share/darknet/ From 0ee4ff36b219f6ae45fee38633a44db509357149 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 14 Apr 2021 15:19:06 +0200 Subject: [PATCH 1220/1365] download weights using a local script, do not rely on vcpkg --- scripts/download_weights.ps1 | 37 ++++++++++++++++++++++++++++++++++++ scripts/setup.ps1 | 2 +- scripts/setup.sh | 4 ++-- 3 files changed, 40 insertions(+), 3 deletions(-) create mode 100755 scripts/download_weights.ps1 diff --git a/scripts/download_weights.ps1 b/scripts/download_weights.ps1 new file mode 100755 index 00000000000..bc352606512 --- /dev/null +++ b/scripts/download_weights.ps1 @@ -0,0 +1,37 @@ +#!/usr/bin/env pwsh + +$url = "https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights" +Invoke-WebRequest -Uri $url -OutFile "yolov4-tiny.weights" + +$url = "https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights" +Invoke-WebRequest -Uri $url -OutFile "yolov4.weights" + +$url = "https://drive.google.com/u/0/uc?id=18yYZWyKbo4XSDVyztmsEcF9B_6bxrhUY&export=download" +Invoke-WebRequest -Uri $url -OutFile "yolov3-tiny-prn.weights" + +$url = "https://pjreddie.com/media/files/yolov3.weights" +Invoke-WebRequest -Uri $url -OutFile "yolov3.weights" + +$url = "https://pjreddie.com/media/files/yolov3-openimages.weights" +Invoke-WebRequest -Uri $url -OutFile "yolov3-openimages.weights" + +$url = "https://pjreddie.com/media/files/yolov2.weights" +Invoke-WebRequest -Uri $url -OutFile "yolov2.weights" + +$url = "https://pjreddie.com/media/files/yolov3-tiny.weights" +Invoke-WebRequest -Uri $url -OutFile "yolov3-tiny.weights" + +$url = "https://pjreddie.com/media/files/yolov2-tiny.weights" +Invoke-WebRequest -Uri $url -OutFile "yolov2-tiny.weights" + +$url = "https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29" +Invoke-WebRequest -Uri $url -OutFile "yolov4-tiny.conv.29" + +$url = "https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137" +Invoke-WebRequest -Uri $url -OutFile "yolov4.conv.137" + +$url = "https://pjreddie.com/media/files/darknet53.conv.74" +Invoke-WebRequest -Uri $url -OutFile "darknet53.conv.74" + +$url = "https://pjreddie.com/media/files/darknet19_448.conv.23" +Invoke-WebRequest -Uri $url -OutFile "darknet19_448.conv.23" diff --git a/scripts/setup.ps1 b/scripts/setup.ps1 index acc29458b62..b846bab1656 100755 --- a/scripts/setup.ps1 +++ b/scripts/setup.ps1 @@ -22,7 +22,7 @@ else { $features = "full" } else{ - $features = "opencv-base,weights,weights-train" + $features = "opencv-base" } } diff --git a/scripts/setup.sh b/scripts/setup.sh index f0970907323..12a769b8881 100755 --- a/scripts/setup.sh +++ b/scripts/setup.sh @@ -55,14 +55,14 @@ if [ "$install_cuda" = true ] ; then features="full" else echo "Unable to auto-install CUDA on this Linux OS" - features="opencv-base,weights,weights-train" + features="opencv-base" fi fi else if [[ -v CUDA_PATH ]]; then features="full" else - features="opencv-base,weights,weights-train" + features="opencv-base" fi fi From 56cd9c6e8c7b65596ff41901ed8b43b583d9ef11 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 14 Apr 2021 17:15:36 +0200 Subject: [PATCH 1221/1365] enable opencv w/cuda in build.ps1 --- CMakeLists.txt | 5 +++-- build.ps1 | 21 +++++++++++++++++++++ 2 files changed, 24 insertions(+), 2 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index a62d5bafc7d..00f446fcccf 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -20,8 +20,9 @@ option(ENABLE_CUDNN_HALF "Enable CUDNN Half precision" ON) option(ENABLE_ZED_CAMERA "Enable ZED Camera support" ON) option(ENABLE_VCPKG_INTEGRATION "Enable VCPKG integration" ON) -#if(ENABLE_OPENCV AND ENABLE_CUDA AND NOT APPLE) -# list(APPEND VCPKG_MANIFEST_FEATURES "opencv-cuda") +if(ENABLE_OPENCV_WITH_CUDA AND NOT APPLE) + list(APPEND VCPKG_MANIFEST_FEATURES "opencv-cuda") +endif() if(ENABLE_CUDA AND NOT APPLE) list(APPEND VCPKG_MANIFEST_FEATURES "cuda") endif() diff --git a/build.ps1 b/build.ps1 index 8eedc23dad0..bb953622e7b 100755 --- a/build.ps1 +++ b/build.ps1 @@ -4,6 +4,7 @@ param ( [switch]$EnableCUDA = $false, [switch]$EnableCUDNN = $false, [switch]$EnableOPENCV = $false, + [switch]$EnableOPENCV_CUDA = $false, [switch]$UseVCPKG = $false, [switch]$DoNotSetupVS = $false, [switch]$DoNotUseNinja = $false, @@ -50,6 +51,22 @@ else { Write-Host "OPENCV is disabled, please pass -EnableOPENCV to the script to enable" } +if ($EnableCUDA -and $EnableOPENCV -and -not $EnableOPENCV_CUDA) { + Write-Host "OPENCV with CUDA extension is not enabled, you can enable it passing -EnableOPENCV_CUDA" +} +elseif ($EnableOPENCV -and $EnableOPENCV_CUDA -and -not $EnableCUDA) { + Write-Host "OPENCV with CUDA extension was requested, but CUDA is not enabled, you can enable it passing -EnableCUDA" + $EnableOPENCV_CUDA = $false +} +elseif ($EnableCUDA -and $EnableOPENCV_CUDA -and -not $EnableOPENCV) { + Write-Host "OPENCV with CUDA extension was requested, but OPENCV is not enabled, you can enable it passing -EnableOPENCV" + $EnableOPENCV_CUDA = $false +} +elseif ($EnableOPENCV_CUDA -and -not $EnableCUDA -and -not $EnableOPENCV) { + Write-Host "OPENCV with CUDA extension was requested, but OPENCV and CUDA are not enabled, you can enable them passing -EnableOPENCV -EnableCUDA" + $EnableOPENCV_CUDA = $false +} + if ($UseVCPKG) { Write-Host "VCPKG is enabled" } @@ -281,6 +298,10 @@ if (-Not($EnableOPENCV)) { $additional_build_setup = $additional_build_setup + " -DENABLE_OPENCV:BOOL=OFF" } +if ($EnableOPENCV_CUDA) { + $additional_build_setup = $additional_build_setup + " -DENABLE_OPENCV_WITH_CUDA:BOOL=ON" +} + New-Item -Path ./build_release -ItemType directory -Force Set-Location build_release $cmake_args = "-G `"$generator`" ${additional_build_setup} -S .." From e1c02e02daa6e76b06762629247881eabb205088 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 15 Apr 2021 08:15:27 +0200 Subject: [PATCH 1222/1365] circumvent current upstream bugs --- .github/workflows/ccpp.yml | 4 ++-- README.md | 1 + build.ps1 | 15 ++++++++++++++- 3 files changed, 17 insertions(+), 3 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 09d5f070314..077b3cf99f8 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -124,7 +124,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA + run: ./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -ForceStaticLib - uses: actions/upload-artifact@v2 with: @@ -279,7 +279,7 @@ jobs: - name: 'Build' shell: pwsh - run: ./build.ps1 -UseVCPKG -EnableOPENCV + run: ./build.ps1 -UseVCPKG - uses: actions/upload-artifact@v2 with: diff --git a/README.md b/README.md index 5bf7c0faf6b..02b729aef5c 100644 --- a/README.md +++ b/README.md @@ -197,6 +197,7 @@ You can get cfg-files by path: `darknet/cfg/` ### Requirements * **CMake >= 3.18**: https://cmake.org/download/ +* **Powershell**: https://docs.microsoft.com/en-us/powershell/scripting/install/installing-powershell * **CUDA >= 10.2**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) * **OpenCV >= 2.4**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or download from [OpenCV official site](https://opencv.org/releases.html) (on Windows set system variable `OpenCV_DIR` = `C:\opencv\build` - where are the `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png)) * **cuDNN >= 8.0.2** https://developer.nvidia.com/rdp/cudnn-archive (on **Linux** copy `cudnn.h`,`libcudnn.so`... as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on **Windows** copy `cudnn.h`,`cudnn64_7.dll`, `cudnn64_7.lib` as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows ) diff --git a/build.ps1 b/build.ps1 index bb953622e7b..7a38d0d250d 100755 --- a/build.ps1 +++ b/build.ps1 @@ -8,7 +8,9 @@ param ( [switch]$UseVCPKG = $false, [switch]$DoNotSetupVS = $false, [switch]$DoNotUseNinja = $false, - [switch]$ForceCPP = $false + [switch]$ForceCPP = $false, + [switch]$ForceStaticLib = $false, + [switch]$ForceGCC8 = $false ) $number_of_build_workers = 8 @@ -18,6 +20,17 @@ if (-Not $IsWindows) { $DoNotSetupVS = $true } +if ($ForceStaticLib) { + Write-Host "Forced CMake to produce a static library" + $additional_build_setup = " -DBUILD_SHARED_LIBS=OFF " +} + +if ($IsLinux -and $ForceGCC8) { + Write-Host "Manually setting CC and CXX variables to gcc-8 and g++-8" + $env:CC = "gcc-8" + $env:CXX = "g++-8" +} + if ($IsWindows -and -Not $env:VCPKG_DEFAULT_TRIPLET) { $env:VCPKG_DEFAULT_TRIPLET = "x64-windows" } From b064668bbb0a5711a78d41da21279ee20a9d584a Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 15 Apr 2021 14:53:58 +0200 Subject: [PATCH 1223/1365] improve readme wording about build.ps1 --- README.md | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 02b729aef5c..6d99ef21031 100644 --- a/README.md +++ b/README.md @@ -197,7 +197,7 @@ You can get cfg-files by path: `darknet/cfg/` ### Requirements * **CMake >= 3.18**: https://cmake.org/download/ -* **Powershell**: https://docs.microsoft.com/en-us/powershell/scripting/install/installing-powershell +* **Powershell** (already installed on windows): https://docs.microsoft.com/en-us/powershell/scripting/install/installing-powershell * **CUDA >= 10.2**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) * **OpenCV >= 2.4**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or download from [OpenCV official site](https://opencv.org/releases.html) (on Windows set system variable `OpenCV_DIR` = `C:\opencv\build` - where are the `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png)) * **cuDNN >= 8.0.2** https://developer.nvidia.com/rdp/cudnn-archive (on **Linux** copy `cudnn.h`,`libcudnn.so`... as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on **Windows** copy `cudnn.h`,`cudnn64_7.dll`, `cudnn64_7.lib` as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows ) @@ -315,12 +315,12 @@ The `CMakeLists.txt` will attempt to find installed optional dependencies like C Install powershell if you do not already have it ([guide here](https://docs.microsoft.com/en-us/powershell/scripting/install/installing-powershell)). -Then, open a powershell terminal inside the darknet repository and type these commands (using `vcpkg` to automatically download dependencies): +Then, open a shell inside the darknet repository and type these commands (using `vcpkg` to automatically download dependencies): ```PowerShell PS Code/> git clone https://github.com/microsoft/vcpkg PS Code/> cd vcpkg -PS Code/vcpkg> ./bootstrap-vcpkg.bat +PS Code/vcpkg> ./bootstrap-vcpkg.sh PS Code/vcpkg> $env:VCPKG_ROOT=$PWD PS Code/vcpkg> cd .. PS Code/> git clone https://github.com/AlexeyAB/darknet @@ -328,6 +328,8 @@ PS Code/> cd darknet PS Code/darknet> ./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN ``` +(add option `-EnableOPENCV_CUDA` if you want to build OpenCV with CUDA support - very slow to build!) + or these commands (without `vcpkg` - please install required libraries manually) ```PowerShell @@ -336,7 +338,8 @@ PS Code/> cd darknet PS Code/darknet> ./build.ps1 -EnableOPENCV -EnableCUDA -EnableCUDNN ``` -(remove options like `-EnableCUDA` or `-EnableCUDNN` if you are not interested into) +(remove options like `-EnableCUDA` or `-EnableCUDNN` if you are not interested into). +If you open the `build.ps1` script at the beginning you will find all available switches. To update CMake on Ubuntu, it's better to follow guide here: https://apt.kitware.com/ @@ -394,14 +397,16 @@ This is the recommended approach to build Darknet on Windows. ```PowerShell PS Code/> git clone https://github.com/microsoft/vcpkg PS Code/> cd vcpkg -PS Code/vcpkg> ./bootstrap-vcpkg.bat +PS Code/vcpkg> .\bootstrap-vcpkg.bat PS Code/vcpkg> $env:VCPKG_ROOT=$PWD PS Code/vcpkg> cd .. PS Code/> git clone https://github.com/AlexeyAB/darknet PS Code/> cd darknet -PS Code/darknet> ./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN +PS Code/darknet> .\build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN ``` +(add option `-EnableOPENCV_CUDA` if you want to build OpenCV with CUDA support - very slow to build! - or remove options like `-EnableCUDA` or `-EnableCUDNN` if you are not interested in them). If you open the `build.ps1` script at the beginning you will find all available switches. + ## How to train with multi-GPU 1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train cfg/coco.data cfg/yolov4.cfg yolov4.conv.137` From 580a5a6c9b4f58b639002200ee630f95b5ec6187 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 15 Apr 2021 17:59:23 +0200 Subject: [PATCH 1224/1365] improve readme more --- README.md | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 6d99ef21031..7844ea63317 100644 --- a/README.md +++ b/README.md @@ -52,6 +52,8 @@ About Darknet framework: http://pjreddie.com/darknet/ - [How to use on the command line](#how-to-use-on-the-command-line) - [For using network video-camera mjpeg-stream with any Android smartphone](#for-using-network-video-camera-mjpeg-stream-with-any-android-smartphone) - [How to compile on Linux/macOS (using `CMake`)](#how-to-compile-on-linuxmacos-using-cmake) + - [Using `vcpkg`](#using-vcpkg) + - [Using libraries manually provided](#using-libraries-manually-provided) - [How to compile on Linux (using `make`)](#how-to-compile-on-linux-using-make) - [How to compile on Windows (using `CMake`)](#how-to-compile-on-windows-using-cmake) - [How to compile on Windows (using `vcpkg`)](#how-to-compile-on-windows-using-vcpkg) @@ -315,7 +317,11 @@ The `CMakeLists.txt` will attempt to find installed optional dependencies like C Install powershell if you do not already have it ([guide here](https://docs.microsoft.com/en-us/powershell/scripting/install/installing-powershell)). -Then, open a shell inside the darknet repository and type these commands (using `vcpkg` to automatically download dependencies): +To update CMake on Ubuntu, it's better to follow guide here: https://apt.kitware.com/ + +### Using `vcpkg` + +Open a shell and type these commands ```PowerShell PS Code/> git clone https://github.com/microsoft/vcpkg @@ -329,8 +335,11 @@ PS Code/darknet> ./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCU ``` (add option `-EnableOPENCV_CUDA` if you want to build OpenCV with CUDA support - very slow to build!) +If you open the `build.ps1` script at the beginning you will find all available switches. + +### Using libraries manually provided -or these commands (without `vcpkg` - please install required libraries manually) +Open a shell and type these commands ```PowerShell PS Code/> git clone https://github.com/AlexeyAB/darknet @@ -341,8 +350,6 @@ PS Code/darknet> ./build.ps1 -EnableOPENCV -EnableCUDA -EnableCUDNN (remove options like `-EnableCUDA` or `-EnableCUDNN` if you are not interested into). If you open the `build.ps1` script at the beginning you will find all available switches. -To update CMake on Ubuntu, it's better to follow guide here: https://apt.kitware.com/ - ### How to compile on Linux (using `make`) Just do `make` in the darknet directory. (You can try to compile and run it on Google Colab in cloud [link](https://colab.research.google.com/drive/12QusaaRj_lUwCGDvQNfICpa7kA7_a2dE) (press «Open in Playground» button at the top-left corner) and watch the video [link](https://www.youtube.com/watch?v=mKAEGSxwOAY) ) @@ -369,8 +376,7 @@ Requires: * CMake GUI: `Windows win64-x64 Installer`https://cmake.org/download/ * Download Darknet zip-archive with the latest commit and uncompress it: [master.zip](https://github.com/AlexeyAB/darknet/archive/master.zip) -In the Windows: - +In Windows: * Start (button) -> All programms -> CMake -> CMake (gui) -> @@ -383,7 +389,6 @@ In the Windows: ![x64 and Release](https://habrastorage.org/webt/ay/ty/f-/aytyf-8bufe7q-16yoecommlwys.jpeg) - ### How to compile on Windows (using `vcpkg`) This is the recommended approach to build Darknet on Windows. From ba8f7bf756241be5b766d198a69184fb8095213e Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 18 Apr 2021 15:11:36 +0200 Subject: [PATCH 1225/1365] improve build process even more (#7610) * test for shared libs * improve flag handling * build so lib in ci * clone vcpkg if not found * fix * improve clang handling * bump vcpkg.json version * gemm.c compatible with ARM * remove unnecessary vcpkg.json field * do not unnecessarily rebuild vcpkg in the CI build stage * use alexeyab nuget cache * enable self-removal of build folder * add interactivity in build.ps1 if not opt-out * spellcheck README * fix another leftover spelling error * also auto-update darknet if possible * do not self-update darknet in CI --- .github/workflows/ccpp.yml | 34 +++++++------- CMakeLists.txt | 38 +++++++++------- README.md | 55 ++++++++++------------ build.ps1 | 92 +++++++++++++++++++++++++++++++++---- src/gemm.c | 93 ++++++++++++++------------------------ src/http_stream.cpp | 5 +- src/httplib.h | 5 +- vcpkg.json | 22 +++++++-- 8 files changed, 204 insertions(+), 140 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 077b3cf99f8..d6c18fda3fd 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -111,10 +111,10 @@ jobs: ./vcpkg/bootstrap-vcpkg.sh; mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add - -source "https://nuget.pkg.github.com/cenit/index.json" + -source "https://nuget.pkg.github.com/AlexeyAB/index.json" -storepasswordincleartext -name "vcpkgbinarycache" - -username "cenit" + -username "AlexeyAB" -password "${{ secrets.GITHUB_TOKEN }}" - name: 'Build' @@ -124,7 +124,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -ForceStaticLib + run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET - uses: actions/upload-artifact@v2 with: @@ -163,7 +163,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ./build.ps1 -EnableOPENCV + run: ./build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET - uses: actions/upload-artifact@v2 with: @@ -222,7 +222,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ./build.ps1 -EnableOPENCV -EnableCUDA + run: ./build.ps1 -EnableOPENCV -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET - uses: actions/upload-artifact@v2 with: @@ -251,7 +251,7 @@ jobs: - name: 'Build' shell: pwsh - run: ./build.ps1 -ForceCPP + run: ./build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET osx-vcpkg: @@ -271,15 +271,15 @@ jobs: ./vcpkg/bootstrap-vcpkg.sh; mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add - -source "https://nuget.pkg.github.com/cenit/index.json" + -source "https://nuget.pkg.github.com/AlexeyAB/index.json" -storepasswordincleartext -name "vcpkgbinarycache" - -username "cenit" + -username "AlexeyAB" -password "${{ secrets.GITHUB_TOKEN }}" - name: 'Build' shell: pwsh - run: ./build.ps1 -UseVCPKG + run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -DisableInteractive -DoNotUpdateDARKNET - uses: actions/upload-artifact@v2 with: @@ -311,7 +311,7 @@ jobs: - name: 'Build' shell: pwsh - run: ./build.ps1 -EnableOPENCV + run: ./build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET - uses: actions/upload-artifact@v2 with: @@ -340,7 +340,7 @@ jobs: - name: 'Build' shell: pwsh - run: ./build.ps1 -ForceCPP + run: ./build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET win-vcpkg: @@ -357,15 +357,15 @@ jobs: ./vcpkg/bootstrap-vcpkg.sh; $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add - -source "https://nuget.pkg.github.com/cenit/index.json" + -source "https://nuget.pkg.github.com/AlexeyAB/index.json" -storepasswordincleartext -name "vcpkgbinarycache" - -username "cenit" + -username "AlexeyAB" -password "${{ secrets.GITHUB_TOKEN }}" - name: 'Build' shell: pwsh - run: ./build.ps1 -UseVCPKG -EnableOPENCV + run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET - uses: actions/upload-artifact@v2 with: @@ -398,7 +398,7 @@ jobs: - name: 'Build' shell: pwsh - run: ./build.ps1 + run: ./build.ps1 -DisableInteractive -DoNotUpdateDARKNET - uses: actions/upload-artifact@v2 with: @@ -431,7 +431,7 @@ jobs: - name: 'Build' shell: pwsh - run: ./build.ps1 -ForceCPP + run: ./build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET win-intlibs-cuda: @@ -454,7 +454,7 @@ jobs: CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" shell: pwsh - run: ./build.ps1 -EnableCUDA + run: ./build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET mingw: diff --git a/CMakeLists.txt b/CMakeLists.txt index 00f446fcccf..0029abe78ee 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -19,8 +19,9 @@ option(ENABLE_CUDNN "Enable CUDNN" ON) option(ENABLE_CUDNN_HALF "Enable CUDNN Half precision" ON) option(ENABLE_ZED_CAMERA "Enable ZED Camera support" ON) option(ENABLE_VCPKG_INTEGRATION "Enable VCPKG integration" ON) +option(VCPKG_BUILD_OPENCV_WITH_CUDA "Build OpenCV with CUDA extension integration" ON) -if(ENABLE_OPENCV_WITH_CUDA AND NOT APPLE) +if(VCPKG_BUILD_OPENCV_WITH_CUDA AND NOT APPLE) list(APPEND VCPKG_MANIFEST_FEATURES "opencv-cuda") endif() if(ENABLE_CUDA AND NOT APPLE) @@ -33,18 +34,6 @@ if(ENABLE_CUDNN AND ENABLE_CUDA AND NOT APPLE) list(APPEND VCPKG_MANIFEST_FEATURES "cudnn") endif() -if(CMAKE_COMPILER_IS_GNUCC OR "${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang") - set(CMAKE_COMPILER_IS_GNUCC_OR_CLANG TRUE) - if("${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang") - set(CMAKE_COMPILER_IS_CLANG TRUE) - else() - set(CMAKE_COMPILER_IS_CLANG FALSE) - endif() -else() - set(CMAKE_COMPILER_IS_GNUCC_OR_CLANG FALSE) - set(CMAKE_COMPILER_IS_CLANG FALSE) -endif() - if(NOT CMAKE_HOST_SYSTEM_PROCESSOR AND NOT WIN32) execute_process(COMMAND "uname" "-m" OUTPUT_VARIABLE CMAKE_HOST_SYSTEM_PROCESSOR OUTPUT_STRIP_TRAILING_WHITESPACE) endif() @@ -87,6 +76,18 @@ enable_language(CXX) set(CMAKE_CXX_STANDARD 11) set(CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/cmake/Modules/" ${CMAKE_MODULE_PATH}) +if(CMAKE_COMPILER_IS_GNUCC OR "${CMAKE_C_COMPILER_ID}" MATCHES "Clang" OR "${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang") + set(CMAKE_COMPILER_IS_GNUCC_OR_CLANG TRUE) + if("${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang" OR "${CMAKE_CXX_COMPILER_ID}" MATCHES "clang") + set(CMAKE_COMPILER_IS_CLANG TRUE) + else() + set(CMAKE_COMPILER_IS_CLANG FALSE) + endif() +else() + set(CMAKE_COMPILER_IS_GNUCC_OR_CLANG FALSE) + set(CMAKE_COMPILER_IS_CLANG FALSE) +endif() + set(default_build_type "Release") if(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES) message(STATUS "Setting build type to '${default_build_type}' as none was specified.") @@ -201,12 +202,14 @@ endif() set(ADDITIONAL_CXX_FLAGS "-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -Wno-write-strings") set(ADDITIONAL_C_FLAGS "-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -Wno-deprecated-declarations -Wno-write-strings") +if(UNIX AND BUILD_SHARED_LIBS AND NOT CMAKE_COMPILER_IS_CLANG) + set(SHAREDLIB_CXX_FLAGS "-Wl,-Bsymbolic") + set(SHAREDLIB_C_FLAGS "-Wl,-Bsymbolic") +endif() if(MSVC) set(ADDITIONAL_CXX_FLAGS "/wd4013 /wd4018 /wd4028 /wd4047 /wd4068 /wd4090 /wd4101 /wd4113 /wd4133 /wd4190 /wd4244 /wd4267 /wd4305 /wd4477 /wd4996 /wd4819 /fp:fast") set(ADDITIONAL_C_FLAGS "/wd4013 /wd4018 /wd4028 /wd4047 /wd4068 /wd4090 /wd4101 /wd4113 /wd4133 /wd4190 /wd4244 /wd4267 /wd4305 /wd4477 /wd4996 /wd4819 /fp:fast") - set(CMAKE_CXX_FLAGS "${ADDITIONAL_CXX_FLAGS} ${CMAKE_CXX_FLAGS}") - set(CMAKE_C_FLAGS "${ADDITIONAL_C_FLAGS} ${CMAKE_C_FLAGS}") string(REGEX REPLACE "/O2" "/Ox" CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) string(REGEX REPLACE "/O2" "/Ox" CMAKE_C_FLAGS_RELEASE ${CMAKE_C_FLAGS_RELEASE}) endif() @@ -218,8 +221,6 @@ if(CMAKE_COMPILER_IS_GNUCC_OR_CLANG) set(CMAKE_C_FLAGS "-pthread ${CMAKE_C_FLAGS}") endif() endif() - set(CMAKE_CXX_FLAGS "${ADDITIONAL_CXX_FLAGS} ${CMAKE_CXX_FLAGS}") - set(CMAKE_C_FLAGS "${ADDITIONAL_C_FLAGS} ${CMAKE_C_FLAGS}") string(REGEX REPLACE "-O0" "-Og" CMAKE_CXX_FLAGS_DEBUG ${CMAKE_CXX_FLAGS_DEBUG}) string(REGEX REPLACE "-O3" "-Ofast" CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) string(REGEX REPLACE "-O0" "-Og" CMAKE_C_FLAGS_DEBUG ${CMAKE_C_FLAGS_DEBUG}) @@ -230,6 +231,9 @@ if(CMAKE_COMPILER_IS_GNUCC_OR_CLANG) endif() endif() +set(CMAKE_CXX_FLAGS "${ADDITIONAL_CXX_FLAGS} ${SHAREDLIB_CXX_FLAGS} ${CMAKE_CXX_FLAGS}") +set(CMAKE_C_FLAGS "${ADDITIONAL_C_FLAGS} ${SHAREDLIB_C_FLAGS} ${CMAKE_C_FLAGS}") + if(OpenCV_FOUND) if(ENABLE_CUDA AND NOT OpenCV_CUDA_VERSION) set(BUILD_USELIB_TRACK "FALSE" CACHE BOOL "Build uselib_track" FORCE) diff --git a/README.md b/README.md index 2de8c7bf980..1e839f8a44a 100644 --- a/README.md +++ b/README.md @@ -31,7 +31,7 @@ About Darknet framework: http://pjreddie.com/darknet/ * [YOLOv4 model zoo](https://github.com/AlexeyAB/darknet/wiki/YOLOv4-model-zoo) -* [Requirements (and how to install dependecies)](#requirements) +* [Requirements (and how to install dependencies)](#requirements) * [Pre-trained models](#pre-trained-models) * [FAQ - frequently asked questions](https://github.com/AlexeyAB/darknet/wiki/FAQ---frequently-asked-questions) * [Explanations in issues](https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3AExplanations) @@ -107,7 +107,7 @@ Others: https://www.youtube.com/user/pjreddie/videos #### How to evaluate AP of YOLOv4 on the MS COCO evaluation server 1. Download and unzip test-dev2017 dataset from MS COCO server: http://images.cocodataset.org/zips/test2017.zip -2. Download list of images for Detection taks and replace the paths with yours: https://raw.githubusercontent.com/AlexeyAB/darknet/master/scripts/testdev2017.txt +2. Download list of images for Detection tasks and replace the paths with yours: https://raw.githubusercontent.com/AlexeyAB/darknet/master/scripts/testdev2017.txt 3. Download `yolov4.weights` file 245 MB: [yolov4.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights) (Google-drive mirror [yolov4.weights](https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT) ) 4. Content of the file `cfg/coco.data` should be @@ -202,13 +202,13 @@ You can get cfg-files by path: `darknet/cfg/` * **Powershell** (already installed on windows): https://docs.microsoft.com/en-us/powershell/scripting/install/installing-powershell * **CUDA >= 10.2**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) * **OpenCV >= 2.4**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or download from [OpenCV official site](https://opencv.org/releases.html) (on Windows set system variable `OpenCV_DIR` = `C:\opencv\build` - where are the `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png)) -* **cuDNN >= 8.0.2** https://developer.nvidia.com/rdp/cudnn-archive (on **Linux** copy `cudnn.h`,`libcudnn.so`... as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on **Windows** copy `cudnn.h`,`cudnn64_7.dll`, `cudnn64_7.lib` as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows ) +* **cuDNN >= 8.0.2** https://developer.nvidia.com/rdp/cudnn-archive (on **Linux** copy `cudnn.h`,`libcudnn.so`... as described here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on **Windows** copy `cudnn.h`,`cudnn64_7.dll`, `cudnn64_7.lib` as described here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows ) * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported ### Yolo v4 in other frameworks * **Pytorch - Scaled-YOLOv4:** https://github.com/WongKinYiu/ScaledYOLOv4 -* **TensorFlow:** `pip install yolov4` YOLOv4 on TensorFlow 2.0 / TFlite / Andriod: https://github.com/hunglc007/tensorflow-yolov4-tflite +* **TensorFlow:** `pip install yolov4` YOLOv4 on TensorFlow 2.0 / TFlite / Android: https://github.com/hunglc007/tensorflow-yolov4-tflite Official TF models: https://github.com/tensorflow/models/tree/master/official/vision/beta/projects/yolo For YOLOv4 - convert `yolov4.weights`/`cfg` files to `yolov4.pb` by using [TNTWEN](https://github.com/TNTWEN/OpenVINO-YOLOV4) project, and to `yolov4.tflite` [TensorFlow-lite](https://www.tensorflow.org/lite/guide/get_started#2_convert_the_model_format) * **OpenCV-dnn** the fastest implementation of YOLOv4 for CPU (x86/ARM-Android), OpenCV can be compiled with [OpenVINO-backend](https://github.com/opencv/opencv/wiki/Intel's-Deep-Learning-Inference-Engine-backend) for running on (Myriad X / USB Neural Compute Stick / Arria FPGA), use `yolov4.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221) or [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) @@ -226,7 +226,7 @@ You can get cfg-files by path: `darknet/cfg/` * **Triton Inference Server / TensorRT** https://github.com/isarsoft/yolov4-triton-tensorrt * **Xilinx Zynq Ultrascale+ Deep Learning Processor (DPU) ZCU102/ZCU104:** https://github.com/Xilinx/Vitis-In-Depth-Tutorial/tree/master/Machine_Learning/Design_Tutorials/07-yolov4-tutorial * **Amazon Neurochip / Amazon EC2 Inf1 instances** 1.85 times higher throughput and 37% lower cost per image for TensorFlow based YOLOv4 model, using Keras [URL](https://aws.amazon.com/ru/blogs/machine-learning/improving-performance-for-deep-learning-based-object-detection-with-an-aws-neuron-compiled-yolov4-model-on-aws-inferentia/) -* **TVM** - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backends (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about +* **TVM** - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backend (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about * **OpenDataCam** - It detects, tracks and counts moving objects by using YOLOv4: https://github.com/opendatacam/opendatacam#-hardware-pre-requisite * **Netron** - Visualizer for neural networks: https://github.com/lutzroeder/netron @@ -287,12 +287,12 @@ On Linux find executable file `./darknet` in the root directory, while on Window * Train on **Amazon EC2**, to see mAP & Loss-chart using URL like: `http://ec2-35-160-228-91.us-west-2.compute.amazonaws.com:8090` in the Chrome/Firefox (**Darknet should be compiled with OpenCV**): `./darknet detector train cfg/coco.data yolov4.cfg yolov4.conv.137 -dont_show -mjpeg_port 8090 -map` * 186 MB Yolo9000 - image: `darknet.exe detector test cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights` -* Remeber to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app +* Remember to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app * To process a list of images `data/train.txt` and save results of detection to `result.json` file use: `darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output -dont_show -out result.json < data/train.txt` * To process a list of images `data/train.txt` and save results of detection to `result.txt` use: `darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -dont_show -ext_output < data/train.txt > result.txt` -* Pseudo-lableing - to process a list of images `data/new_train.txt` and save results of detection in Yolo training format for each image as label `.txt` (in this way you can increase the amount of training data) use: +* Pseudo-labelling - to process a list of images `data/new_train.txt` and save results of detection in Yolo training format for each image as label `.txt` (in this way you can increase the amount of training data) use: `darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25 -dont_show -save_labels < data/new_train.txt` * To calculate anchors: `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416` * To check accuracy mAP@IoU=50: `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights` @@ -324,11 +324,6 @@ To update CMake on Ubuntu, it's better to follow guide here: https://apt.kitware Open a shell and type these commands ```PowerShell -PS Code/> git clone https://github.com/microsoft/vcpkg -PS Code/> cd vcpkg -PS Code/vcpkg> ./bootstrap-vcpkg.sh -PS Code/vcpkg> $env:VCPKG_ROOT=$PWD -PS Code/vcpkg> cd .. PS Code/> git clone https://github.com/AlexeyAB/darknet PS Code/> cd darknet PS Code/darknet> ./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN @@ -359,9 +354,9 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c * `CUDNN=1` to build with cuDNN v5-v7 to accelerate training by using GPU (cuDNN should be in `/usr/local/cudnn`) * `CUDNN_HALF=1` to build for Tensor Cores (on Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x * `OPENCV=1` to build with OpenCV 4.x/3.x/2.4.x - allows to detect on video files and video streams from network cameras or web-cams -* `DEBUG=1` to bould debug version of Yolo +* `DEBUG=1` to build debug version of Yolo * `OPENMP=1` to build with OpenMP support to accelerate Yolo by using multi-core CPU -* `LIBSO=1` to build a library `darknet.so` and binary runable file `uselib` that uses this library. Or you can try to run so `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib test.mp4` How to use this SO-library from your own code - you can look at C++ example: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp +* `LIBSO=1` to build a library `darknet.so` and binary runnable file `uselib` that uses this library. Or you can try to run so `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib test.mp4` How to use this SO-library from your own code - you can look at C++ example: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp or use in such a way: `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov4.cfg yolov4.weights test.mp4` * `ZED_CAMERA=1` to build a library with ZED-3D-camera support (should be ZED SDK installed), then run `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov4.cfg yolov4.weights zed_camera` @@ -371,16 +366,17 @@ To run Darknet on Linux use examples from this article, just use `./darknet` ins ### How to compile on Windows (using `CMake`) -Requires: -* MSVS: https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community +Requires: + +* MSVC: https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community * CMake GUI: `Windows win64-x64 Installer`https://cmake.org/download/ * Download Darknet zip-archive with the latest commit and uncompress it: [master.zip](https://github.com/AlexeyAB/darknet/archive/master.zip) -In Windows: +In Windows: -* Start (button) -> All programms -> CMake -> CMake (gui) -> +* Start (button) -> All programs -> CMake -> CMake (gui) -> -* [look at image](https://habrastorage.org/webt/pz/s1/uu/pzs1uu4heb7vflfcjqn-lxy-aqu.jpeg) In CMake: Enter input path to the darknet Source, and output path to the Binaries -> Configure (button) -> Optional platform for generator: `x64` -> Finish -> Generate -> Open Project -> +* [look at image](https://habrastorage.org/webt/pz/s1/uu/pzs1uu4heb7vflfcjqn-lxy-aqu.jpeg) In CMake: Enter input path to the darknet Source, and output path to the Binaries -> Configure (button) -> Optional platform for generator: `x64` -> Finish -> Generate -> Open Project -> * in MS Visual Studio: Select: x64 and Release -> Build -> Build solution @@ -400,11 +396,6 @@ This is the recommended approach to build Darknet on Windows. 3. Open Powershell (Start -> All programs -> Windows Powershell) and type these commands: ```PowerShell -PS Code/> git clone https://github.com/microsoft/vcpkg -PS Code/> cd vcpkg -PS Code/vcpkg> .\bootstrap-vcpkg.bat -PS Code/vcpkg> $env:VCPKG_ROOT=$PWD -PS Code/vcpkg> cd .. PS Code/> git clone https://github.com/AlexeyAB/darknet PS Code/> cd darknet PS Code/darknet> .\build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN @@ -490,7 +481,7 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an * `` - integer object number from `0` to `(classes-1)` * ` ` - float values **relative** to width and height of image, it can be equal from `(0.0 to 1.0]` * for example: ` = / ` or ` = / ` - * atention: ` ` - are center of rectangle (are not top-left corner) + * attention: ` ` - are center of rectangle (are not top-left corner) For example for `img1.jpg` you will be created `img1.txt` containing: @@ -570,15 +561,15 @@ Usually sufficient 2000 iterations for each class(object), but not less than num * **9002** - iteration number (number of batch) * **0.60730 avg** - average loss (error) - **the lower, the better** - When you see that average loss **0.xxxxxx avg** no longer decreases at many iterations then you should stop training. The final avgerage loss can be from `0.05` (for a small model and easy dataset) to `3.0` (for a big model and a difficult dataset). + When you see that average loss **0.xxxxxx avg** no longer decreases at many iterations then you should stop training. The final average loss can be from `0.05` (for a small model and easy dataset) to `3.0` (for a big model and a difficult dataset). Or if you train with flag `-map` then you will see mAP indicator `Last accuracy mAP@0.5 = 18.50%` in the console - this indicator is better than Loss, so train while mAP increases. 2. Once training is stopped, you should take some of last `.weights`-files from `darknet\build\darknet\x64\backup` and choose the best of them: -For example, you stopped training after 9000 iterations, but the best result can give one of previous weights (7000, 8000, 9000). It can happen due to overfitting. **Overfitting** - is case when you can detect objects on images from training-dataset, but can't detect objects on any others images. You should get weights from **Early Stopping Point**: +For example, you stopped training after 9000 iterations, but the best result can give one of previous weights (7000, 8000, 9000). It can happen due to over-fitting. **Over-fitting** - is case when you can detect objects on images from training-dataset, but can't detect objects on any others images. You should get weights from **Early Stopping Point**: -![Overfitting](https://hsto.org/files/5dc/7ae/7fa/5dc7ae7fad9d4e3eb3a484c58bfc1ff5.png) +![Over-fitting](https://hsto.org/files/5dc/7ae/7fa/5dc7ae7fad9d4e3eb3a484c58bfc1ff5.png) To get weights from Early Stopping Point: @@ -592,7 +583,7 @@ To get weights from Early Stopping Point: * `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_8000.weights` * `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_9000.weights` -And comapre last output lines for each weights (7000, 8000, 9000): +And compare last output lines for each weights (7000, 8000, 9000): Choose weights-file **with the highest mAP (mean average precision)** or IoU (intersect over union) @@ -610,7 +601,7 @@ So you will see mAP-chart (red-line) in the Loss-chart Window. mAP will be calcu Example of custom object detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights` -* **IoU** (intersect over union) - average instersect over union of objects and detections for a certain threshold = 0.24 +* **IoU** (intersect over union) - average intersect over union of objects and detections for a certain threshold = 0.24 * **mAP** (mean average precision) - mean value of `average precisions` for each class, where `average precision` is average value of 11 points on PR-curve for each possible threshold (each probability of detection) for the same class (Precision-Recall in terms of PascalVOC, where Precision=TP/(TP+FP) and Recall=TP/(TP+FN) ), page-11: http://homepages.inf.ed.ac.uk/ckiw/postscript/ijcv_voc09.pdf @@ -639,7 +630,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * my Loss is very high and mAP is very low, is training wrong? Run training with ` -show_imgs` flag at the end of training command, do you see correct bounded boxes of objects (in windows or in files `aug_...jpg`)? If no - your training dataset is wrong. -* for each object which you want to detect - there must be at least 1 similar object in the Training dataset with about the same: shape, side of object, relative size, angle of rotation, tilt, illumination. So desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 different images for each class or more, and you should train `2000*classes` iterations or more +* for each object which you want to detect - there must be at least 1 similar object in the Training dataset with about the same: shape, side of object, relative size, angle of rotation, tilt, illumination. So desirable that your training dataset include images with objects at different: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 different images for each class or more, and you should train `2000*classes` iterations or more * desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty `.txt` files) - use as many images of negative samples as there are images with objects @@ -670,7 +661,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol * to speedup training (with decreasing detection accuracy) set param `stopbackward=1` for layer-136 in cfg-file -* each: `model of object, side, illimination, scale, each 30 grad` of the turn and inclination angles - these are *different objects* from an internal perspective of the neural network. So the more *different objects* you want to detect, the more complex network model should be used. +* each: `model of object, side, illumination, scale, each 30 grad` of the turn and inclination angles - these are *different objects* from an internal perspective of the neural network. So the more *different objects* you want to detect, the more complex network model should be used. * to make the detected bounded boxes more accurate, you can add 3 parameters `ignore_thresh = .9 iou_normalizer=0.5 iou_loss=giou` to each `[yolo]` layer and train, it will increase mAP@0.9, but decrease mAP@0.5. diff --git a/build.ps1 b/build.ps1 index 7a38d0d250d..e147fb878ef 100755 --- a/build.ps1 +++ b/build.ps1 @@ -1,11 +1,15 @@ #!/usr/bin/env pwsh param ( + [switch]$DisableInteractive = $false, [switch]$EnableCUDA = $false, [switch]$EnableCUDNN = $false, [switch]$EnableOPENCV = $false, [switch]$EnableOPENCV_CUDA = $false, [switch]$UseVCPKG = $false, + [switch]$DoNotUpdateVCPKG = $false, + [switch]$DoNotUpdateDARKNET = $false, + [switch]$DoNotDeleteBuildFolder = $false, [switch]$DoNotSetupVS = $false, [switch]$DoNotUseNinja = $false, [switch]$ForceCPP = $false, @@ -13,9 +17,45 @@ param ( [switch]$ForceGCC8 = $false ) +if (-Not $DisableInteractive -and -Not $UseVCPKG) { + $Result = Read-Host "Enable vcpkg to install darknet dependencies (yes/no)" + if ($Result -eq 'Yes' -or $Result -eq 'Y' -or $Result -eq 'yes' -or $Result -eq 'y') { + $UseVCPKG = $true + } +} + +if (-Not $DisableInteractive -and -Not $EnableCUDA -and -Not $IsMacOS) { + $Result = Read-Host "Enable CUDA integration (yes/no)" + if ($Result -eq 'Yes' -or $Result -eq 'Y' -or $Result -eq 'yes' -or $Result -eq 'y') { + $EnableCUDA = $true + } +} + +if ($EnableCUDA -and -Not $DisableInteractive -and -Not $EnableCUDNN) { + $Result = Read-Host "Enable CUDNN optional dependency (yes/no)" + if ($Result -eq 'Yes' -or $Result -eq 'Y' -or $Result -eq 'yes' -or $Result -eq 'y') { + $EnableCUDNN = $true + } +} + +if (-Not $DisableInteractive -and -Not $EnableOPENCV) { + $Result = Read-Host "Enable OpenCV optional dependency (yes/no)" + if ($Result -eq 'Yes' -or $Result -eq 'Y' -or $Result -eq 'yes' -or $Result -eq 'y') { + $EnableOPENCV = $true + } +} + $number_of_build_workers = 8 #$additional_build_setup = " -DCMAKE_CUDA_ARCHITECTURES=30" +if ($IsLinux -or $IsMacOS) { + $bootstrap_ext = ".sh" +} +elseif ($IsWindows) { + $bootstrap_ext = ".bat" +} +Write-Host "Native shell script extension: ${bootstrap_ext}" + if (-Not $IsWindows) { $DoNotSetupVS = $true } @@ -36,7 +76,7 @@ if ($IsWindows -and -Not $env:VCPKG_DEFAULT_TRIPLET) { } if ($EnableCUDA) { - if($IsMacOS) { + if ($IsMacOS) { Write-Host "Cannot enable CUDA on macOS" -ForegroundColor Yellow $EnableCUDA = $false } @@ -82,6 +122,12 @@ elseif ($EnableOPENCV_CUDA -and -not $EnableCUDA -and -not $EnableOPENCV) { if ($UseVCPKG) { Write-Host "VCPKG is enabled" + if ($DoNotUpdateVCPKG) { + Write-Host "VCPKG will not be updated to latest version if found" -ForegroundColor Yellow + } + else { + Write-Host "VCPKG will be updated to latest version if found" + } } else { Write-Host "VCPKG is disabled, please pass -UseVCPKG to the script to enable" @@ -110,6 +156,18 @@ else { Push-Location $PSScriptRoot +$GIT_EXE = Get-Command git 2> $null | Select-Object -ExpandProperty Definition +if (-Not $GIT_EXE) { + throw "Could not find git, please install it" +} +else { + Write-Host "Using git from ${GIT_EXE}" +} + +if ((Test-Path "$PSScriptRoot/.git") -and -not $DoNotUpdateDARKNET) { + & $GIT_EXE pull +} + $CMAKE_EXE = Get-Command cmake 2> $null | Select-Object -ExpandProperty Definition if (-Not $CMAKE_EXE) { throw "Could not find CMake, please install it" @@ -216,7 +274,10 @@ elseif ((Test-Path "${RUNVCPKG_VCPKG_ROOT_OUT}") -and $UseVCPKG) { Write-Host "Found vcpkg in RUNVCPKG_VCPKG_ROOT_OUT: ${RUNVCPKG_VCPKG_ROOT_OUT}" $additional_build_setup = $additional_build_setup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" } -elseif ((Test-Path "$PWD/vcpkg") -and $UseVCPKG) { +elseif ($UseVCPKG) { + if (-Not (Test-Path "$PWD/vcpkg")) { + & $GIT_EXE clone https://github.com/microsoft/vcpkg + } $vcpkg_path = "$PWD/vcpkg" $env:VCPKG_ROOT = "$PWD/vcpkg" Write-Host "Found vcpkg in $PWD/vcpkg: $PWD/vcpkg" @@ -227,6 +288,13 @@ else { $additional_build_setup = $additional_build_setup + " -DENABLE_VCPKG_INTEGRATION:BOOL=OFF" } +if ($UseVCPKG -and (Test-Path "$vcpkg_path/.git") -and -not $DoNotUpdateVCPKG) { + Push-Location $vcpkg_path + & $GIT_EXE pull + & $PWD/bootstrap-vcpkg${bootstrap_ext} -disableMetrics + Pop-Location +} + if (-Not $DoNotSetupVS) { if ($null -eq (Get-Command "cl.exe" -ErrorAction SilentlyContinue)) { $vsfound = getLatestVisualStudioWithDesktopWorkloadPath @@ -239,7 +307,7 @@ if (-Not $DoNotSetupVS) { } } Pop-Location - Write-Host "Visual Studio Command Prompt variables set" -ForegroundColor Yellow + Write-Host "Visual Studio Command Prompt variables set" } $tokens = getLatestVisualStudioWithDesktopWorkloadVersion @@ -270,13 +338,13 @@ if (-Not $DoNotSetupVS) { if ($DoNotSetupVS -and $DoNotUseNinja) { $generator = "Unix Makefiles" } -Write-Host "Setting up environment to use CMake generator: $generator" -ForegroundColor Yellow +Write-Host "Setting up environment to use CMake generator: $generator" if (-Not $IsMacOS -and $EnableCUDA) { if ($null -eq (Get-Command "nvcc" -ErrorAction SilentlyContinue)) { if (Test-Path env:CUDA_PATH) { $env:PATH += ";${env:CUDA_PATH}/bin" - Write-Host "Found cuda in ${env:CUDA_PATH}" -ForegroundColor Yellow + Write-Host "Found cuda in ${env:CUDA_PATH}" } else { Write-Host "Unable to find CUDA, if necessary please install it or define a CUDA_PATH env variable pointing to the install folder" -ForegroundColor Yellow @@ -311,12 +379,18 @@ if (-Not($EnableOPENCV)) { $additional_build_setup = $additional_build_setup + " -DENABLE_OPENCV:BOOL=OFF" } -if ($EnableOPENCV_CUDA) { - $additional_build_setup = $additional_build_setup + " -DENABLE_OPENCV_WITH_CUDA:BOOL=ON" +if (-Not($EnableOPENCV_CUDA)) { + $additional_build_setup = $additional_build_setup + " -DVCPKG_BUILD_OPENCV_WITH_CUDA:BOOL=OFF" +} + +$build_folder = "./build_release" +if (-Not $DoNotDeleteBuildFolder) { + Write-Host "Removing folder $build_folder" -ForegroundColor Yellow + Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $build_folder } -New-Item -Path ./build_release -ItemType directory -Force -Set-Location build_release +New-Item -Path $build_folder -ItemType directory -Force +Set-Location $build_folder $cmake_args = "-G `"$generator`" ${additional_build_setup} -S .." Write-Host "CMake args: $cmake_args" Start-Process -NoNewWindow -Wait -FilePath $CMAKE_EXE -ArgumentList $cmake_args diff --git a/src/gemm.c b/src/gemm.c index 519751c0622..84a7e9a6815 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -8,13 +8,29 @@ #include #include #include -#ifdef _WIN32 -#include -#endif #if defined(_OPENMP) #include #endif +#if defined(_MSC_VER) +#if defined(_M_ARM) || defined(_M_ARM64) +static inline uint32_t popcnt(uint32_t v) { + v = v - ((v >> 1) & 0x55555555); + v = (v & 0x33333333) + ((v >> 2) & 0x33333333); + return ((v + (v >> 4) & 0xF0F0F0F) * 0x1010101) >> 24; +} +#define POPCNT(x) popcnt((x)) +#define POPCNT64(x) (popcnt((unsigned)(x)) + popcnt((unsigned)((uint64_t)(x) >> 32))) +#else +#include +#define POPCNT(x) __popcnt(x) +#define POPCNT64(x) __popcnt64(x) +#endif +#elif defined(__GNUC__) +#define POPCNT(x) __builtin_popcount(x) +#define POPCNT64(x) __builtin_popcountll(x) +#endif + #define TILE_M 4 // 4 ops #define TILE_N 16 // AVX2 = 2 ops * 8 floats #define TILE_K 16 // loop @@ -230,7 +246,7 @@ void gemm_nn_custom_bin_mean(int M, int N, int K, float ALPHA_UNUSED, uint64_t b_bit64 = *((uint64_t *)(B + (k_ldb + j) / 8)); uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); //printf("\n %d \n",__builtin_popcountll(c_bit64)); // gcc - printf("\n %d \n", __popcnt64(c_bit64)); // msvs + printf("\n %d \n", POPCNT64(c_bit64)); // msvs int h; for (h = 0; h < 64; ++h) @@ -298,11 +314,7 @@ void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); -#ifdef WIN32 - int tmp_count = __popcnt64(c_bit64); -#else - int tmp_count = __builtin_popcountll(c_bit64); -#endif + int tmp_count = POPCNT64(c_bit64); if (K - k < 64) tmp_count = tmp_count - (64 - (K - k)); // remove extra bits count += tmp_count; @@ -503,16 +515,6 @@ void transpose_bin(uint32_t *A, uint32_t *B, const int n, const int m, } } -static inline int popcnt_32(uint32_t val32) { -#ifdef WIN32 // Windows MSVS - int tmp_count = __popcnt(val32); -#else // Linux GCC - int tmp_count = __builtin_popcount(val32); -#endif - return tmp_count; -} -//---------------------------- - #if (defined(__AVX__) && defined(__x86_64__)) || (defined(_WIN64) && !defined(__MINGW32__)) #if (defined(_WIN64) && !defined(__MINGW64__)) @@ -925,14 +927,14 @@ void gemm_nn_bin_32bit_packed(int M, int N, int K, float ALPHA, // waiting for - CPUID Flags: AVX512VPOPCNTDQ: __m512i _mm512_popcnt_epi32(__m512i a) __m256 count = _mm256_setr_ps( - popcnt_32(_mm256_extract_epi32(xnor256, 0)), - popcnt_32(_mm256_extract_epi32(xnor256, 1)), - popcnt_32(_mm256_extract_epi32(xnor256, 2)), - popcnt_32(_mm256_extract_epi32(xnor256, 3)), - popcnt_32(_mm256_extract_epi32(xnor256, 4)), - popcnt_32(_mm256_extract_epi32(xnor256, 5)), - popcnt_32(_mm256_extract_epi32(xnor256, 6)), - popcnt_32(_mm256_extract_epi32(xnor256, 7))); + POPCNT(_mm256_extract_epi32(xnor256, 0)), + POPCNT(_mm256_extract_epi32(xnor256, 1)), + POPCNT(_mm256_extract_epi32(xnor256, 2)), + POPCNT(_mm256_extract_epi32(xnor256, 3)), + POPCNT(_mm256_extract_epi32(xnor256, 4)), + POPCNT(_mm256_extract_epi32(xnor256, 5)), + POPCNT(_mm256_extract_epi32(xnor256, 6)), + POPCNT(_mm256_extract_epi32(xnor256, 7))); __m256 val2 = _mm256_set1_ps(2); count = _mm256_mul_ps(count, val2); // count * 2 @@ -952,7 +954,7 @@ void gemm_nn_bin_32bit_packed(int M, int N, int K, float ALPHA, { PUT_IN_REGISTER uint32_t B_PART = B[s*ldb + j]; uint32_t xnor_result = ~(A_PART ^ B_PART); - int32_t count = popcnt_32(xnor_result); // must be Signed int + int32_t count = POPCNT(xnor_result); // must be Signed int C[i*ldc + j] += (2 * count - 32) * mean_val; } @@ -1140,13 +1142,7 @@ void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride, static inline int popcnt128(__m128i n) { const __m128i n_hi = _mm_unpackhi_epi64(n, n); -#if defined(_MSC_VER) - return __popcnt64(_mm_cvtsi128_si64(n)) + __popcnt64(_mm_cvtsi128_si64(n_hi)); -#elif defined(__APPLE__) && defined(__clang__) - return _mm_popcnt_u64(_mm_cvtsi128_si64(n)) + _mm_popcnt_u64(_mm_cvtsi128_si64(n_hi)); -#else - return __popcntq(_mm_cvtsi128_si64(n)) + __popcntq(_mm_cvtsi128_si64(n_hi)); -#endif + return POPCNT64(_mm_cvtsi128_si64(n)) + POPCNT64(_mm_cvtsi128_si64(n_hi)); } static inline int popcnt256(__m256i n) { @@ -2021,7 +2017,7 @@ void gemm_nn_bin_32bit_packed(int M, int N, int K, float ALPHA, PUT_IN_REGISTER uint32_t B_PART = B[s * ldb + j]; uint32_t xnor_result = ~(A_PART ^ B_PART); //printf(" xnor_result = %d, ", xnor_result); - int32_t count = popcnt_32(xnor_result); // must be Signed int + int32_t count = POPCNT(xnor_result); // must be Signed int C[i*ldc + j] += (2 * count - 32) * mean_val; //c[i*n + j] += count*mean; @@ -2079,25 +2075,6 @@ void convolution_2d(int w, int h, int ksize, int n, int c, int pad, int stride, } } -static inline int popcnt_64(uint64_t val64) { -#ifdef WIN32 // Windows -#ifdef _WIN64 // Windows 64-bit - int tmp_count = __popcnt64(val64); -#else // Windows 32-bit - int tmp_count = __popcnt(val64); - tmp_count += __popcnt(val64 >> 32); -#endif -#else // Linux -#if defined(__x86_64__) || defined(__aarch64__) // Linux 64-bit - int tmp_count = __builtin_popcountll(val64); -#else // Linux 32-bit - int tmp_count = __builtin_popcount(val64); - tmp_count += __builtin_popcount(val64 >> 32); -#endif -#endif - return tmp_count; -} - void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, unsigned char *A, int lda, unsigned char *B, int ldb, @@ -2118,7 +2095,7 @@ void gemm_nn_custom_bin_mean_transposed(int M, int N, int K, float ALPHA_UNUSED, uint64_t b_bit64 = *((uint64_t *)(B + (j*ldb + k) / 8)); uint64_t c_bit64 = xnor_int64(a_bit64, b_bit64); - int tmp_count = popcnt_64(c_bit64); + int tmp_count = POPCNT64(c_bit64); if (K - k < 64) tmp_count = tmp_count - (64 - (K - k)); // remove extra bits count += tmp_count; @@ -2518,7 +2495,7 @@ void gemm_nn_bin_transposed_32bit_packed(int M, int N, int K, float ALPHA, PUT_IN_REGISTER uint32_t A_PART = ((uint32_t*)A)[i*lda + s]; PUT_IN_REGISTER uint32_t B_PART = ((uint32_t*)B)[j * ldb + s]; uint32_t xnor_result = ~(A_PART ^ B_PART); - int32_t count = popcnt_32(xnor_result); // must be Signed int + int32_t count = POPCNT(xnor_result); // must be Signed int val += (2 * count - 32) * mean_val; } @@ -2581,7 +2558,7 @@ void convolution_repacked(uint32_t *packed_input, uint32_t *packed_weights, floa uint32_t weight = ((uint32_t *)packed_weights)[fil*new_lda / 32 + chan*size*size + f_y*size + f_x]; uint32_t xnor_result = ~(input ^ weight); - int32_t count = popcnt_32(xnor_result); // mandatory Signed int + int32_t count = POPCNT(xnor_result); // mandatory Signed int sum += (2 * count - 32) * mean_val; } } diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 3ec7e851593..b17edfb5d36 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -70,8 +70,12 @@ static int close_socket(SOCKET s) { #define SOCKADDR struct sockaddr #define SOCKADDR_IN struct sockaddr_in #define ADDRPOINTER unsigned int* +#ifndef INVALID_SOCKET #define INVALID_SOCKET -1 +#endif +#ifndef SOCKET_ERROR #define SOCKET_ERROR -1 +#endif struct _IGNORE_PIPE_SIGNAL { struct sigaction new_actn, old_actn; @@ -934,4 +938,3 @@ void set_track_id(detection *new_dets, int new_dets_num, float thresh, float sim } } } - diff --git a/src/httplib.h b/src/httplib.h index 41fbfb19419..e4678faeeb2 100644 --- a/src/httplib.h +++ b/src/httplib.h @@ -126,7 +126,10 @@ using socket_t = SOCKET; #include using socket_t = int; -#define INVALID_SOCKET (-1) + +#ifndef INVALID_SOCKET +#define INVALID_SOCKET -1 +#endif #endif //_WIN32 #include diff --git a/vcpkg.json b/vcpkg.json index a7f66f260fc..c57fe9175bf 100644 --- a/vcpkg.json +++ b/vcpkg.json @@ -1,12 +1,11 @@ { "name": "darknet", - "version-string": "0.2.5.4", - "port-version": 2, - "homepage": "https://github.com/alexeyab/darknet", + "version-date": "2021-04-16", "description": "Darknet is an open source neural network framework written in C and CUDA. You only look once (YOLO) is a state-of-the-art, real-time object detection system, best example of darknet functionalities.", + "homepage": "https://github.com/alexeyab/darknet", "dependencies": [ - "stb", - "pthreads" + "pthreads", + "stb" ], "features": { "cuda": { @@ -22,6 +21,19 @@ "cudnn" ] }, + "full": { + "description": "Build darknet fully featured", + "dependencies": [ + { + "name": "darknet", + "features": [ + "cuda", + "cudnn", + "opencv-cuda" + ] + } + ] + }, "opencv-base": { "description": "Build darknet with support for latest version of OpenCV", "dependencies": [ From b6cd7592183043613f4b41c25e3385e7f752845c Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 18 Apr 2021 16:49:54 +0200 Subject: [PATCH 1226/1365] apply feedback from vcpkg ci --- src/gemm.c | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/src/gemm.c b/src/gemm.c index 84a7e9a6815..5f5c9689c67 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -23,8 +23,18 @@ static inline uint32_t popcnt(uint32_t v) { #define POPCNT64(x) (popcnt((unsigned)(x)) + popcnt((unsigned)((uint64_t)(x) >> 32))) #else #include +#ifdef _WIN64 #define POPCNT(x) __popcnt(x) #define POPCNT64(x) __popcnt64(x) +#else +static inline int popcnt_64(uint64_t val64) { + int tmp_count = __popcnt(val64); + tmp_count += __popcnt(val64 >> 32); + return tmp_count; +} +#define POPCNT(x) __popcnt(x) +#define POPCNT64(x) popcnt_64(x) +#endif #endif #elif defined(__GNUC__) #define POPCNT(x) __builtin_popcount(x) @@ -515,7 +525,7 @@ void transpose_bin(uint32_t *A, uint32_t *B, const int n, const int m, } } -#if (defined(__AVX__) && defined(__x86_64__)) || (defined(_WIN64) && !defined(__MINGW32__)) +#if (defined(__AVX__) && defined(__x86_64__)) || (defined(_WIN64) && !defined(__MINGW32__) && !defined(_M_ARM64)) #if (defined(_WIN64) && !defined(__MINGW64__)) #include From 8bc512dca323ccace7c4ad7b702d9b30c2866c02 Mon Sep 17 00:00:00 2001 From: Renault Fernandes <32931110+renaultfernandes@users.noreply.github.com> Date: Tue, 20 Apr 2021 04:28:00 +0530 Subject: [PATCH 1227/1365] Fix no inference detections bug when cudnn is disabled. (#7617) Fixes issue described here: https://github.com/AlexeyAB/darknet/issues/7616 --- src/dark_cuda.c | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/src/dark_cuda.c b/src/dark_cuda.c index 30509c51ee4..a0afff493fe 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -123,8 +123,11 @@ cudaStream_t get_cuda_stream() { int i = cuda_get_device(); if (!streamInit[i]) { printf("Create CUDA-stream - %d \n", i); - //cudaError_t status = cudaStreamCreate(&streamsArray[i], cudaStreamNonBlocking); +#ifdef CUDNN cudaError_t status = cudaStreamCreateWithFlags(&streamsArray[i], cudaStreamNonBlocking); +#else + cudaError_t status = cudaStreamCreate(&streamsArray[i]); +#endif if (status != cudaSuccess) { printf(" cudaStreamCreate error: %d \n", status); const char *s = cudaGetErrorString(status); From 96971deaa79e69b6deb1c77f546bf44bee2bf3f6 Mon Sep 17 00:00:00 2001 From: Alexey Date: Wed, 21 Apr 2021 03:11:31 +0300 Subject: [PATCH 1228/1365] Update README.md --- README.md | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 1e839f8a44a..4a4a67f49f8 100644 --- a/README.md +++ b/README.md @@ -211,22 +211,25 @@ You can get cfg-files by path: `darknet/cfg/` * **TensorFlow:** `pip install yolov4` YOLOv4 on TensorFlow 2.0 / TFlite / Android: https://github.com/hunglc007/tensorflow-yolov4-tflite Official TF models: https://github.com/tensorflow/models/tree/master/official/vision/beta/projects/yolo For YOLOv4 - convert `yolov4.weights`/`cfg` files to `yolov4.pb` by using [TNTWEN](https://github.com/TNTWEN/OpenVINO-YOLOV4) project, and to `yolov4.tflite` [TensorFlow-lite](https://www.tensorflow.org/lite/guide/get_started#2_convert_the_model_format) -* **OpenCV-dnn** the fastest implementation of YOLOv4 for CPU (x86/ARM-Android), OpenCV can be compiled with [OpenVINO-backend](https://github.com/opencv/opencv/wiki/Intel's-Deep-Learning-Inference-Engine-backend) for running on (Myriad X / USB Neural Compute Stick / Arria FPGA), use `yolov4.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221) or [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) -* **Intel OpenVINO 2021.2:** supports YOLOv4 (NPU Myriad X / USB Neural Compute Stick / Arria FPGA): https://devmesh.intel.com/projects/openvino-yolov4-49c756 read this [manual](https://github.com/TNTWEN/OpenVINO-YOLOV4) (old [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) ) -* **Tencent/ncnn:** the fastest inference of YOLOv4 on mobile phone CPU: https://github.com/Tencent/ncnn +* **OpenCV** the fastest implementation of YOLOv4 for CPU (x86/ARM-Android), OpenCV can be compiled with [OpenVINO-backend](https://github.com/opencv/opencv/wiki/Intel's-Deep-Learning-Inference-Engine-backend) for running on (Myriad X / USB Neural Compute Stick / Arria FPGA), use `yolov4.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221) or [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) +* **Intel OpenVINO 2021.2:** supports YOLOv4 (NPU Myriad X / USB Neural Compute Stick / Arria FPGA): https://devmesh.intel.com/projects/openvino-yolov4-49c756 read this [manual](https://github.com/TNTWEN/OpenVINO-YOLOV4) (old [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) ) (for [Scaled-YOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-large) models use https://github.com/Chen-MingChang/pytorch_YOLO_OpenVINO_demo ) * **PyTorch > ONNX**: * [WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4) * [maudzung/3D-YOLOv4](https://github.com/maudzung/Complex-YOLOv4-Pytorch) * [Tianxiaomo/pytorch-YOLOv4](https://github.com/Tianxiaomo/pytorch-YOLOv4) * [YOLOv5](https://github.com/ultralytics/yolov5) * **ONNX** on Jetson for YOLOv4: https://developer.nvidia.com/blog/announcing-onnx-runtime-for-jetson/ -* **TensorRT** YOLOv4 on TensorRT+tkDNN: https://github.com/ceccocats/tkDNN - For YOLOv3 (-70% faster inference): [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) read [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf). [jkjung-avt/tensorrt_demos](https://github.com/jkjung-avt/tensorrt_demos) or [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) implemented yolov3-spp, yolov4, etc. -* **Deepstream 5.0 / TensorRT for YOLOv4** https://github.com/NVIDIA-AI-IOT/yolov4_deepstream or https://github.com/marcoslucianops/DeepStream-Yolo +* **TensorRT+tkDNN**: https://github.com/ceccocats/tkDNN#fps-results +* **Deepstream 5.0 / TensorRT for YOLOv4** https://github.com/NVIDIA-AI-IOT/yolov4_deepstream or https://github.com/marcoslucianops/DeepStream-Yolo read [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) and [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf). Additionally [jkjung-avt/tensorrt_demos](https://github.com/jkjung-avt/tensorrt_demos) or [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) * **Triton Inference Server / TensorRT** https://github.com/isarsoft/yolov4-triton-tensorrt +* **DirectML** https://github.com/microsoft/DirectML/tree/master/Samples/yolov4 +* **OpenCL** (Intel, AMD, Mali GPUs for macOS & GNU/Linux) https://github.com/sowson/darknet +* **HIP** for Training and Detection on AMD GPU https://github.com/os-hackathon/darknet +* **ROS** (Robot Operating System) https://github.com/engcang/ros-yolo-sort * **Xilinx Zynq Ultrascale+ Deep Learning Processor (DPU) ZCU102/ZCU104:** https://github.com/Xilinx/Vitis-In-Depth-Tutorial/tree/master/Machine_Learning/Design_Tutorials/07-yolov4-tutorial * **Amazon Neurochip / Amazon EC2 Inf1 instances** 1.85 times higher throughput and 37% lower cost per image for TensorFlow based YOLOv4 model, using Keras [URL](https://aws.amazon.com/ru/blogs/machine-learning/improving-performance-for-deep-learning-based-object-detection-with-an-aws-neuron-compiled-yolov4-model-on-aws-inferentia/) * **TVM** - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backend (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about +* **Tencent/ncnn:** the fastest inference of YOLOv4 on mobile phone CPU: https://github.com/Tencent/ncnn * **OpenDataCam** - It detects, tracks and counts moving objects by using YOLOv4: https://github.com/opendatacam/opendatacam#-hardware-pre-requisite * **Netron** - Visualizer for neural networks: https://github.com/lutzroeder/netron From 91efb7c7f6c00a4616ed58735f7c670908d0a849 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 21 Apr 2021 18:25:46 +0200 Subject: [PATCH 1229/1365] Deploy CUDA 11.3 in CI (#7630) * [nuget] use personal baget repo * update cuda to 11.3 on windows * add error messages during build to not have green badge when failed * remove cygwin script, is untested now; use deploy-cuda.ps1 script also inside setup.ps1 --- .github/workflows/ccpp.yml | 77 +++++++++++++++----------- build.ps1 | 12 +++- scripts/README.md | 9 +-- scripts/deploy-cuda.ps1 | 26 +++++++++ scripts/setup.ps1 | 13 +++-- scripts/setup.sh | 6 ++ scripts/windows/win_install_cygwin.cmd | 12 ---- vcpkg.json | 2 - 8 files changed, 97 insertions(+), 60 deletions(-) create mode 100644 scripts/deploy-cuda.ps1 delete mode 100644 scripts/windows/win_install_cygwin.cmd diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index d6c18fda3fd..ac44c9f67f1 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -3,6 +3,7 @@ name: Darknet Continuous Integration on: [push, pull_request, workflow_dispatch] env: + #VCPKG_BINARY_SOURCES: 'clear;nuget,githubpackages,readwrite' VCPKG_BINARY_SOURCES: 'clear;nuget,vcpkgbinarycache,readwrite' jobs: @@ -107,15 +108,20 @@ jobs: - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash run: > - git clone https://github.com/microsoft/vcpkg; - ./vcpkg/bootstrap-vcpkg.sh; + git clone https://github.com/microsoft/vcpkg ; + ./vcpkg/bootstrap-vcpkg.sh ; + mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add + -Name "githubpackages" + -Source https://nuget.pkg.github.com/${{ github.repository_owner }} ; mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) - sources add - -source "https://nuget.pkg.github.com/AlexeyAB/index.json" - -storepasswordincleartext - -name "vcpkgbinarycache" - -username "AlexeyAB" - -password "${{ secrets.GITHUB_TOKEN }}" + setapikey ${{ secrets.GITHUB_TOKEN }} + -Source https://nuget.pkg.github.com/${{ github.repository_owner }} ; + mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add + -Name "vcpkgbinarycache" + -Source http://93.49.111.10:5555/v3/index.json ; + mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) + setapikey ${{ secrets.BAGET_API_KEY }} + -Source http://93.49.111.10:5555/v3/index.json - name: 'Build' shell: pwsh @@ -267,15 +273,20 @@ jobs: - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash run: > - git clone https://github.com/microsoft/vcpkg; - ./vcpkg/bootstrap-vcpkg.sh; + git clone https://github.com/microsoft/vcpkg ; + ./vcpkg/bootstrap-vcpkg.sh ; + mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add + -Name "githubpackages" + -Source https://nuget.pkg.github.com/${{ github.repository_owner }} ; + mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) + setapikey ${{ secrets.GITHUB_TOKEN }} + -Source https://nuget.pkg.github.com/${{ github.repository_owner }} ; + mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add + -Name "vcpkgbinarycache" + -Source http://93.49.111.10:5555/v3/index.json ; mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) - sources add - -source "https://nuget.pkg.github.com/AlexeyAB/index.json" - -storepasswordincleartext - -name "vcpkgbinarycache" - -username "AlexeyAB" - -password "${{ secrets.GITHUB_TOKEN }}" + setapikey ${{ secrets.BAGET_API_KEY }} + -Source http://93.49.111.10:5555/v3/index.json - name: 'Build' shell: pwsh @@ -353,15 +364,20 @@ jobs: - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash run: > - git clone https://github.com/microsoft/vcpkg; - ./vcpkg/bootstrap-vcpkg.sh; + git clone https://github.com/microsoft/vcpkg ; + ./vcpkg/bootstrap-vcpkg.sh ; + $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add + -Name "githubpackages" + -Source https://nuget.pkg.github.com/${{ github.repository_owner }} ; $(./vcpkg/vcpkg fetch nuget | tail -n 1) - sources add - -source "https://nuget.pkg.github.com/AlexeyAB/index.json" - -storepasswordincleartext - -name "vcpkgbinarycache" - -username "AlexeyAB" - -password "${{ secrets.GITHUB_TOKEN }}" + setapikey ${{ secrets.GITHUB_TOKEN }} + -Source https://nuget.pkg.github.com/${{ github.repository_owner }} ; + $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add + -Name "vcpkgbinarycache" + -Source http://93.49.111.10:5555/v3/index.json ; + $(./vcpkg/vcpkg fetch nuget | tail -n 1) + setapikey ${{ secrets.BAGET_API_KEY }} + -Source http://93.49.111.10:5555/v3/index.json - name: 'Build' shell: pwsh @@ -439,20 +455,15 @@ jobs: steps: - uses: actions/checkout@v2 - name: 'Install CUDA' - run: | - choco install cuda --version=10.2.89.20191206 -y - $env:ChocolateyInstall = Convert-Path "$((Get-Command choco).Path)\..\.." - Import-Module "$env:ChocolateyInstall\helpers\chocolateyProfile.psm1" - refreshenv + run: ./scripts/deploy-cuda.ps1 - uses: lukka/get-cmake@latest - name: 'Build' env: - CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" - CUDA_PATH_V10_2: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" - CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2" - CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe" + CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3" + CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3" + CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3\\bin\\nvcc.exe" shell: pwsh run: ./build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET diff --git a/build.ps1 b/build.ps1 index e147fb878ef..9574023efd6 100755 --- a/build.ps1 +++ b/build.ps1 @@ -393,8 +393,16 @@ New-Item -Path $build_folder -ItemType directory -Force Set-Location $build_folder $cmake_args = "-G `"$generator`" ${additional_build_setup} -S .." Write-Host "CMake args: $cmake_args" -Start-Process -NoNewWindow -Wait -FilePath $CMAKE_EXE -ArgumentList $cmake_args -Start-Process -NoNewWindow -Wait -FilePath $CMAKE_EXE -ArgumentList "--build . ${selectConfig} --parallel ${number_of_build_workers} --target install" +$proc = Start-Process -NoNewWindow -Wait -PassThru -FilePath $CMAKE_EXE -ArgumentList $cmake_args +$exitCode = $proc.ExitCode +if (-not $exitCode -eq 0) { + Throw "Config failed! Exited with $exitCode." +} +$proc = Start-Process -NoNewWindow -Wait -PassThru -FilePath $CMAKE_EXE -ArgumentList "--build . ${selectConfig} --parallel ${number_of_build_workers} --target install" +$exitCode = $proc.ExitCode +if (-not $exitCode -eq 0) { + Throw "Config failed! Exited with $exitCode." +} Remove-Item DarknetConfig.cmake Remove-Item DarknetConfigVersion.cmake $dllfiles = Get-ChildItem ./${dllfolder}/*.dll diff --git a/scripts/README.md b/scripts/README.md index a641e673d0e..91b17224361 100644 --- a/scripts/README.md +++ b/scripts/README.md @@ -1,8 +1,6 @@ +# Datasets - -### Datasets: - -59.26TB of research data: http://academictorrents.com/ +59.26TB of research data: http://academictorrents.com/ ImageNet Torrent (Stanford): http://academictorrents.com/browse.php?search=imagenet&page=0 @@ -54,7 +52,6 @@ Visual Question Answering: https://visualqa.org/download.html Large Movie Review Dataset: http://ai.stanford.edu/~amaas/data/sentiment/ - ---- Wikipedia's List of datasets: https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research @@ -71,4 +68,4 @@ Pedestrian DATASETs for Vision based Detection and Tracking: https://hemprasad.w TrackingNet: https://tracking-net.org/ -RGB, RGBD, Texture-mapped 3D mesh models: http://www.ycbbenchmarks.com/ \ No newline at end of file +RGB, RGBD, Texture-mapped 3D mesh models: http://www.ycbbenchmarks.com/ diff --git a/scripts/deploy-cuda.ps1 b/scripts/deploy-cuda.ps1 new file mode 100644 index 00000000000..29847578d3e --- /dev/null +++ b/scripts/deploy-cuda.ps1 @@ -0,0 +1,26 @@ +#!/usr/bin/env pwsh + +$url = 'https://developer.download.nvidia.com/compute/cuda/11.3.0/network_installers/cuda_11.3.0_win10_network.exe' + +$CudaFeatures = 'nvcc_11.3 cuobjdump_11.3 nvprune_11.3 cupti_11.3 memcheck_11.3 nvdisasm_11.3 nvprof_11.3 ' + ` + 'visual_studio_integration_11.3 visual_profiler_11.3 visual_profiler_11.3 cublas_11.3 cublas_dev_11.3 ' + ` + 'cudart_11.3 cufft_11.3 cufft_dev_11.3 curand_11.3 curand_dev_11.3 cusolver_11.3 cusolver_dev_11.3 ' + ` + 'cusparse_11.3 cusparse_dev_11.3 npp_11.3 npp_dev_11.3 nvrtc_11.3 nvrtc_dev_11.3 nvml_dev_11.3 ' + ` + 'occupancy_calculator_11.3 ' + +try { + Write-Host 'Downloading CUDA...' + Invoke-WebRequest -Uri $url -OutFile "cuda_11.3.0_win10_network.exe" + Write-Host 'Installing CUDA...' + $proc = Start-Process -Wait -PassThru -FilePath "./cuda_11.3.0_win10_network.exe" -ArgumentList @('-s ' + $CudaFeatures) + $exitCode = $proc.ExitCode + if ($exitCode -eq 0) { + Write-Host 'Installation successful!' + } + else { + Throw "Installation failed! Exited with $exitCode." + } +} +catch { + Throw "Failed to install CUDA! $($_.Exception.Message)" +} diff --git a/scripts/setup.ps1 b/scripts/setup.ps1 index b846bab1656..c5c2ae22b21 100755 --- a/scripts/setup.ps1 +++ b/scripts/setup.ps1 @@ -6,15 +6,15 @@ if ($null -eq (Get-Command "choco.exe" -ErrorAction SilentlyContinue)) { # Download and install Chocolatey Set-ExecutionPolicy unrestricted -Scope CurrentUser Invoke-Expression ((New-Object System.Net.WebClient).DownloadString('https://chocolatey.org/install.ps1')) - Write-Host "Please close and re-open powershell and then re-run setup.ps1 script" - Break + Throw "Please close and re-open powershell and then re-run setup.ps1 script" } Start-Process -FilePath "choco" -Verb runAs -ArgumentList " install -y cmake ninja powershell git vscode" Start-Process -FilePath "choco" -Verb runAs -ArgumentList " install -y visualstudio2019buildtools --package-parameters `"--add Microsoft.VisualStudio.Component.VC.CoreBuildTools --includeRecommended --includeOptional --passive --locale en-US --lang en-US`"" +Push-Location $PSScriptRoot if ($install_cuda) { - Start-Process -FilePath "choco" -Verb runAs -ArgumentList " install -y cuda" + & ./deploy-cuda.ps1 $features = "full" } else { @@ -26,7 +26,10 @@ else { } } -git.exe clone https://github.com/microsoft/vcpkg -Set-Location vcpkg +git.exe clone https://github.com/microsoft/vcpkg ../vcpkg +Set-Location ..\vcpkg .\bootstrap-vcpkg.bat -disableMetrics .\vcpkg.exe install darknet[${features}]:x64-windows +Pop-Location + +Write-Host "Darknet installed in $pwd\x64-windows\tools\darknet" -ForegroundColor Yellow diff --git a/scripts/setup.sh b/scripts/setup.sh index 12a769b8881..c33379e92ef 100755 --- a/scripts/setup.sh +++ b/scripts/setup.sh @@ -77,3 +77,9 @@ if [[ ! -v VCPKG_ROOT ]]; then fi $VCPKG_ROOT/vcpkg install darknet[${features}] + +if [[ "$OSTYPE" == "darwin"* ]]; then + echo "Darknet installed in $VCPKG_ROOT/installed/x64-osx/tools/darknet" +else + echo "Darknet installed in $VCPKG_ROOT/installed/x64-linux/tools/darknet" +fi diff --git a/scripts/windows/win_install_cygwin.cmd b/scripts/windows/win_install_cygwin.cmd deleted file mode 100644 index 6a2bda4a812..00000000000 --- a/scripts/windows/win_install_cygwin.cmd +++ /dev/null @@ -1,12 +0,0 @@ -echo Download file: https://www.cygwin.com/setup-x86_64.exe - - -setup-x86_64.exe -q -P dos2unix,wget,tar,untar,gzip,unzip,qawk,bzip2,git,vim,gcc-g++,make,grep,sed,find - - -# wget rawgit.com/transcode-open/apt-cyg/master/apt-cyg -# install apt-cyg /bin - -echo Finished - -pause \ No newline at end of file diff --git a/vcpkg.json b/vcpkg.json index c57fe9175bf..30caffb7f88 100644 --- a/vcpkg.json +++ b/vcpkg.json @@ -67,7 +67,6 @@ { "name": "opencv2", "features": [ - "contrib", "ffmpeg" ] } @@ -79,7 +78,6 @@ { "name": "opencv2", "features": [ - "contrib", "cuda", "ffmpeg" ] From 4f794aa15265d2a068d9f452e5efd9d7a3574524 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 22 Apr 2021 17:03:23 +0200 Subject: [PATCH 1230/1365] almost revert last PR (#7634) * [darknet] split pr/ci pipelines to use fast caching only when accessible * restore original pipeline names * restore original ccpp file name * speed up scripts avoiding -wait flag * restore fast cache in readonly * clean up scripts removing references to github packages, which are often down/broken --- .github/workflows/ccpp.yml | 21 +- .github/workflows/on_pr.yml | 459 ++++++++++++++++++++++++++++++++++++ build.ps1 | 6 +- scripts/deploy-cuda.ps1 | 3 +- 4 files changed, 466 insertions(+), 23 deletions(-) create mode 100644 .github/workflows/on_pr.yml diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index ac44c9f67f1..dd8a98dd856 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -1,9 +1,8 @@ name: Darknet Continuous Integration -on: [push, pull_request, workflow_dispatch] +on: [push, workflow_dispatch] env: - #VCPKG_BINARY_SOURCES: 'clear;nuget,githubpackages,readwrite' VCPKG_BINARY_SOURCES: 'clear;nuget,vcpkgbinarycache,readwrite' jobs: @@ -111,12 +110,6 @@ jobs: git clone https://github.com/microsoft/vcpkg ; ./vcpkg/bootstrap-vcpkg.sh ; mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add - -Name "githubpackages" - -Source https://nuget.pkg.github.com/${{ github.repository_owner }} ; - mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) - setapikey ${{ secrets.GITHUB_TOKEN }} - -Source https://nuget.pkg.github.com/${{ github.repository_owner }} ; - mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" -Source http://93.49.111.10:5555/v3/index.json ; mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) @@ -276,12 +269,6 @@ jobs: git clone https://github.com/microsoft/vcpkg ; ./vcpkg/bootstrap-vcpkg.sh ; mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add - -Name "githubpackages" - -Source https://nuget.pkg.github.com/${{ github.repository_owner }} ; - mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) - setapikey ${{ secrets.GITHUB_TOKEN }} - -Source https://nuget.pkg.github.com/${{ github.repository_owner }} ; - mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" -Source http://93.49.111.10:5555/v3/index.json ; mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) @@ -367,12 +354,6 @@ jobs: git clone https://github.com/microsoft/vcpkg ; ./vcpkg/bootstrap-vcpkg.sh ; $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add - -Name "githubpackages" - -Source https://nuget.pkg.github.com/${{ github.repository_owner }} ; - $(./vcpkg/vcpkg fetch nuget | tail -n 1) - setapikey ${{ secrets.GITHUB_TOKEN }} - -Source https://nuget.pkg.github.com/${{ github.repository_owner }} ; - $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" -Source http://93.49.111.10:5555/v3/index.json ; $(./vcpkg/vcpkg fetch nuget | tail -n 1) diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml new file mode 100644 index 00000000000..198d84fc4e0 --- /dev/null +++ b/.github/workflows/on_pr.yml @@ -0,0 +1,459 @@ +name: Darknet Pull Requests + +on: [pull_request] + +env: + VCPKG_BINARY_SOURCES: 'clear;nuget,vcpkgbinarycache,read' + +jobs: + ubuntu-makefile: + runs-on: ubuntu-20.04 + steps: + - uses: actions/checkout@v2 + + - name: Update apt + run: sudo apt update + - name: Install dependencies + run: sudo apt install libopencv-dev + + - name: 'Install CUDA' + run: | + sudo apt update + sudo apt-get dist-upgrade -y + sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin + sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub + sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /" + sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/ /" + sudo apt-get install -y --no-install-recommends cuda-compiler-11-2 cuda-libraries-dev-11-2 cuda-driver-dev-11-2 cuda-cudart-dev-11-2 + sudo apt-get install -y --no-install-recommends libcudnn8-dev + sudo rm -rf /usr/local/cuda + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + sudo ln -s /usr/local/cuda-11.2 /usr/local/cuda + export PATH=/usr/local/cuda/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH + nvcc --version + gcc --version + + - name: 'LIBSO=1 GPU=0 CUDNN=0 OPENCV=0' + run: | + make LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 -j 8 + make clean + - name: 'LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 DEBUG=1' + run: | + make LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 DEBUG=1 -j 8 + make clean + - name: 'LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 AVX=1' + run: | + make LIBSO=1 GPU=0 CUDNN=0 OPENCV=0 AVX=1 -j 8 + make clean + - name: 'LIBSO=1 GPU=0 CUDNN=0 OPENCV=1' + run: | + make LIBSO=1 GPU=0 CUDNN=0 OPENCV=1 -j 8 + make clean + - name: 'LIBSO=1 GPU=1 CUDNN=1 OPENCV=1' + run: | + export PATH=/usr/local/cuda/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH + make LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 -j 8 + make clean + - name: 'LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 CUDNN_HALF=1' + run: | + export PATH=/usr/local/cuda/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH + make LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 CUDNN_HALF=1 -j 8 + make clean + - name: 'LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 CUDNN_HALF=1 USE_CPP=1' + run: | + export PATH=/usr/local/cuda/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH + make LIBSO=1 GPU=1 CUDNN=1 OPENCV=1 CUDNN_HALF=1 USE_CPP=1 -j 8 + make clean + + + ubuntu-vcpkg-cuda: + runs-on: ubuntu-20.04 + steps: + - uses: actions/checkout@v2 + + - name: Update apt + run: sudo apt update + - name: Install dependencies + run: sudo apt install yasm nasm + + - uses: lukka/get-cmake@latest + + - name: 'Install CUDA' + run: | + sudo apt update + sudo apt-get dist-upgrade -y + sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin + sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub + sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /" + sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/ /" + sudo apt-get install -y --no-install-recommends cuda-compiler-11-2 cuda-libraries-dev-11-2 cuda-driver-dev-11-2 cuda-cudart-dev-11-2 + sudo apt-get install -y --no-install-recommends libcudnn8-dev + sudo rm -rf /usr/local/cuda + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + sudo ln -s /usr/local/cuda-11.2 /usr/local/cuda + export PATH=/usr/local/cuda/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH + nvcc --version + gcc --version + + - name: 'Setup vcpkg and NuGet artifacts backend' + shell: bash + run: > + git clone https://github.com/microsoft/vcpkg ; + ./vcpkg/bootstrap-vcpkg.sh ; + mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add + -Name "vcpkgbinarycache" + -Source http://93.49.111.10:5555/v3/index.json + + - name: 'Build' + shell: pwsh + env: + CUDACXX: "/usr/local/cuda/bin/nvcc" + CUDA_PATH: "/usr/local/cuda" + CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" + LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" + run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET + + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-cuda-${{ runner.os }} + path: cfg + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-cuda-${{ runner.os }} + path: data + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-cuda-${{ runner.os }} + path: ${{ github.workspace }}/*dark* + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-cuda-${{ runner.os }} + path: ${{ github.workspace }}/uselib* + + + ubuntu: + runs-on: ubuntu-20.04 + steps: + - uses: actions/checkout@v2 + + - name: Update apt + run: sudo apt update + - name: Install dependencies + run: sudo apt install libopencv-dev + + - uses: lukka/get-cmake@latest + + - name: 'Build' + shell: pwsh + env: + CUDACXX: "/usr/local/cuda/bin/nvcc" + CUDA_PATH: "/usr/local/cuda" + CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" + LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" + run: ./build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET + + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: cfg + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: data + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: ${{ github.workspace }}/*dark* + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: ${{ github.workspace }}/uselib* + + + ubuntu-cuda: + runs-on: ubuntu-20.04 + steps: + - uses: actions/checkout@v2 + + - name: Update apt + run: sudo apt update + - name: Install dependencies + run: sudo apt install libopencv-dev + + - uses: lukka/get-cmake@latest + + - name: 'Install CUDA' + run: | + sudo apt update + sudo apt-get dist-upgrade -y + sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin + sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub + sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /" + sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/ /" + sudo apt-get install -y --no-install-recommends cuda-compiler-11-2 cuda-libraries-dev-11-2 cuda-driver-dev-11-2 cuda-cudart-dev-11-2 + sudo apt-get install -y --no-install-recommends libcudnn8-dev + sudo rm -rf /usr/local/cuda + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + sudo ln -s /usr/local/cuda-11.2 /usr/local/cuda + export PATH=/usr/local/cuda/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH + nvcc --version + gcc --version + + - name: 'Build' + shell: pwsh + env: + CUDACXX: "/usr/local/cuda/bin/nvcc" + CUDA_PATH: "/usr/local/cuda" + CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" + LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" + run: ./build.ps1 -EnableOPENCV -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET + + - uses: actions/upload-artifact@v2 + with: + name: darknet-cuda-${{ runner.os }} + path: cfg + - uses: actions/upload-artifact@v2 + with: + name: darknet-cuda-${{ runner.os }} + path: data + - uses: actions/upload-artifact@v2 + with: + name: darknet-cuda-${{ runner.os }} + path: ${{ github.workspace }}/*dark* + - uses: actions/upload-artifact@v2 + with: + name: darknet-cuda-${{ runner.os }} + path: ${{ github.workspace }}/uselib* + + + ubuntu-no-ocv-cpp: + runs-on: ubuntu-20.04 + steps: + - uses: actions/checkout@v2 + + - uses: lukka/get-cmake@latest + + - name: 'Build' + shell: pwsh + run: ./build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET + + + osx-vcpkg: + runs-on: macos-latest + steps: + - uses: actions/checkout@v2 + + - name: Install dependencies + run: brew install libomp yasm nasm + + - uses: lukka/get-cmake@latest + + - name: 'Setup vcpkg and NuGet artifacts backend' + shell: bash + run: > + git clone https://github.com/microsoft/vcpkg ; + ./vcpkg/bootstrap-vcpkg.sh ; + mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add + -Name "vcpkgbinarycache" + -Source http://93.49.111.10:5555/v3/index.json + + - name: 'Build' + shell: pwsh + run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -DisableInteractive -DoNotUpdateDARKNET + + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: cfg + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: data + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: ${{ github.workspace }}/*dark* + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: ${{ github.workspace }}/uselib* + + + osx: + runs-on: macos-latest + steps: + - uses: actions/checkout@v2 + + - name: Install dependencies + run: brew install opencv libomp + + - uses: lukka/get-cmake@latest + + - name: 'Build' + shell: pwsh + run: ./build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET + + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: cfg + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: data + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: ${{ github.workspace }}/*dark* + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: ${{ github.workspace }}/uselib* + + + osx-no-ocv-no-omp-cpp: + runs-on: macos-latest + steps: + - uses: actions/checkout@v2 + + - uses: lukka/get-cmake@latest + + - name: 'Build' + shell: pwsh + run: ./build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET + + + win-vcpkg: + runs-on: windows-latest + steps: + - uses: actions/checkout@v2 + + - uses: lukka/get-cmake@latest + + - name: 'Setup vcpkg and NuGet artifacts backend' + shell: bash + run: > + git clone https://github.com/microsoft/vcpkg ; + ./vcpkg/bootstrap-vcpkg.sh ; + $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add + -Name "vcpkgbinarycache" + -Source http://93.49.111.10:5555/v3/index.json + + - name: 'Build' + shell: pwsh + run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET + + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: cfg + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: data + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: ${{ github.workspace }}/*dark* + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: ${{ runner.workspace }}/buildDirectory/Release/*.dll + - uses: actions/upload-artifact@v2 + with: + name: darknet-vcpkg-${{ runner.os }} + path: ${{ github.workspace }}/uselib* + + + win-intlibs: + runs-on: windows-latest + steps: + - uses: actions/checkout@v2 + + - uses: lukka/get-cmake@latest + + - name: 'Build' + shell: pwsh + run: ./build.ps1 -DisableInteractive -DoNotUpdateDARKNET + + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: cfg + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: data + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: ${{ github.workspace }}/*dark* + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: ${{ github.workspace }}/3rdparty/pthreads/bin/*.dll + - uses: actions/upload-artifact@v2 + with: + name: darknet-${{ runner.os }} + path: ${{ github.workspace }}/uselib* + + + win-intlibs-cpp: + runs-on: windows-latest + steps: + - uses: actions/checkout@v2 + + - uses: lukka/get-cmake@latest + + - name: 'Build' + shell: pwsh + run: ./build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET + + + win-intlibs-cuda: + runs-on: windows-latest + steps: + - uses: actions/checkout@v2 + - name: 'Install CUDA' + run: ./scripts/deploy-cuda.ps1 + + - uses: lukka/get-cmake@latest + + - name: 'Build' + env: + CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3" + CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3" + CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3\\bin\\nvcc.exe" + shell: pwsh + run: ./build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET + + + mingw: + runs-on: windows-latest + steps: + - uses: actions/checkout@v2 + + - uses: lukka/get-cmake@latest + + - name: 'Build with CMake' + uses: lukka/run-cmake@v3 + with: + cmakeListsOrSettingsJson: CMakeListsTxtAdvanced + cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' + useVcpkgToolchainFile: true + buildDirectory: '${{ runner.workspace }}/buildDirectory' + cmakeAppendedArgs: "-G\"MinGW Makefiles\" -DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" + cmakeBuildType: 'Release' + buildWithCMakeArgs: '--config Release --target install' diff --git a/build.ps1 b/build.ps1 index 9574023efd6..8cf090f0a12 100755 --- a/build.ps1 +++ b/build.ps1 @@ -393,12 +393,14 @@ New-Item -Path $build_folder -ItemType directory -Force Set-Location $build_folder $cmake_args = "-G `"$generator`" ${additional_build_setup} -S .." Write-Host "CMake args: $cmake_args" -$proc = Start-Process -NoNewWindow -Wait -PassThru -FilePath $CMAKE_EXE -ArgumentList $cmake_args +$proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList $cmake_args +$proc.WaitForExit() $exitCode = $proc.ExitCode if (-not $exitCode -eq 0) { Throw "Config failed! Exited with $exitCode." } -$proc = Start-Process -NoNewWindow -Wait -PassThru -FilePath $CMAKE_EXE -ArgumentList "--build . ${selectConfig} --parallel ${number_of_build_workers} --target install" +$proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList "--build . ${selectConfig} --parallel ${number_of_build_workers} --target install" +$proc.WaitForExit() $exitCode = $proc.ExitCode if (-not $exitCode -eq 0) { Throw "Config failed! Exited with $exitCode." diff --git a/scripts/deploy-cuda.ps1 b/scripts/deploy-cuda.ps1 index 29847578d3e..83b64d29559 100644 --- a/scripts/deploy-cuda.ps1 +++ b/scripts/deploy-cuda.ps1 @@ -12,7 +12,8 @@ try { Write-Host 'Downloading CUDA...' Invoke-WebRequest -Uri $url -OutFile "cuda_11.3.0_win10_network.exe" Write-Host 'Installing CUDA...' - $proc = Start-Process -Wait -PassThru -FilePath "./cuda_11.3.0_win10_network.exe" -ArgumentList @('-s ' + $CudaFeatures) + $proc = Start-Process -PassThru -FilePath "./cuda_11.3.0_win10_network.exe" -ArgumentList @('-s ' + $CudaFeatures) + $proc.WaitForExit() $exitCode = $proc.ExitCode if ($exitCode -eq 0) { Write-Host 'Installation successful!' From c9f2c5f0e9b65a5bf12b5110851489e9159f1988 Mon Sep 17 00:00:00 2001 From: deoksangkim <49770966+deoksangkim@users.noreply.github.com> Date: Mon, 26 Apr 2021 06:58:22 +0900 Subject: [PATCH 1231/1365] fix memory error in batchnorm layer (#7619) --- src/batchnorm_layer.c | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/batchnorm_layer.c b/src/batchnorm_layer.c index eeba5cc57b9..6729b031923 100644 --- a/src/batchnorm_layer.c +++ b/src/batchnorm_layer.c @@ -36,6 +36,12 @@ layer make_batchnorm_layer(int batch, int w, int h, int c, int train) layer.rolling_mean = (float*)xcalloc(c, sizeof(float)); layer.rolling_variance = (float*)xcalloc(c, sizeof(float)); + layer.mean_delta = (float*)xcalloc(c, sizeof(float)); + layer.variance_delta = (float*)xcalloc(c, sizeof(float)); + + layer.x = (float*)xcalloc(layer.batch*layer.outputs, sizeof(float)); + layer.x_norm = (float*)xcalloc(layer.batch*layer.outputs, sizeof(float)); + layer.forward = forward_batchnorm_layer; layer.backward = backward_batchnorm_layer; layer.update = update_batchnorm_layer; From 3a0dc55cada5c04d4a7e5292d4dcc1e429609611 Mon Sep 17 00:00:00 2001 From: Renault Fernandes <32931110+renaultfernandes@users.noreply.github.com> Date: Mon, 26 Apr 2021 03:29:41 +0530 Subject: [PATCH 1232/1365] Use cublasStatus_t instead of cudaError_t when error checking cublas calls (#7626) Cublas success is indicated by CUBLAS_STATUS_SUCCESS. This must be used to determine if a cublas function call was successful. Although Cuda does not complain if we use cudaError_t, when bulding with ROCm support it is absolutely necessary to use the correct enum (cublasStatus_t), else ROCm will always interpret the returned status as an error and crash. --- src/dark_cuda.c | 33 +++++++++++++++++++++++++++++++-- src/dark_cuda.h | 2 ++ 2 files changed, 33 insertions(+), 2 deletions(-) diff --git a/src/dark_cuda.c b/src/dark_cuda.c index a0afff493fe..ceb43c8878b 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -239,6 +239,35 @@ static int switchCudnnInit[16]; #endif +void cublas_check_error(cublasStatus_t status) +{ +#if defined(DEBUG) || defined(CUDA_DEBUG) + cudaDeviceSynchronize(); +#endif + if (cuda_debug_sync) { + cudaDeviceSynchronize(); + } + if (status != CUBLAS_STATUS_SUCCESS) { + printf("cuBLAS Error\n"); + } +} + +void cublas_check_error_extended(cublasStatus_t status, const char *file, int line, const char *date_time) +{ + if (status != CUBLAS_STATUS_SUCCESS) { + printf("\n cuBLAS status Error in: file: %s() : line: %d : build time: %s \n", file, line, date_time); + } +#if defined(DEBUG) || defined(CUDA_DEBUG) + cuda_debug_sync = 1; +#endif + if (cuda_debug_sync) { + cudaError_t status = cudaDeviceSynchronize(); + if (status != CUDA_SUCCESS) + printf("\n cudaError_t status = cudaDeviceSynchronize() Error in: file: %s() : line: %d : build time: %s \n", file, line, date_time); + } + cublas_check_error(status); +} + static int blasInit[16] = { 0 }; static cublasHandle_t blasHandle[16]; @@ -246,9 +275,9 @@ cublasHandle_t blas_handle() { int i = cuda_get_device(); if (!blasInit[i]) { - cublasCreate(&blasHandle[i]); + CHECK_CUBLAS(cublasCreate(&blasHandle[i])); cublasStatus_t status = cublasSetStream(blasHandle[i], get_cuda_stream()); - CHECK_CUDA((cudaError_t)status); + CHECK_CUBLAS(status); blasInit[i] = 1; } return blasHandle[i]; diff --git a/src/dark_cuda.h b/src/dark_cuda.h index 10f6d89e385..9251e877672 100644 --- a/src/dark_cuda.h +++ b/src/dark_cuda.h @@ -56,7 +56,9 @@ extern "C" { #endif // __cplusplus void check_error(cudaError_t status); void check_error_extended(cudaError_t status, const char *file, int line, const char *date_time); + void cublas_check_error_extended(cublasStatus_t status, const char *file, int line, const char *date_time); #define CHECK_CUDA(X) check_error_extended(X, __FILE__ " : " __FUNCTION__, __LINE__, __DATE__ " - " __TIME__ ); +#define CHECK_CUBLAS(X) cublas_check_error_extended(X, __FILE__ " : " __FUNCTION__, __LINE__, __DATE__ " - " __TIME__ ); cublasHandle_t blas_handle(); void free_pinned_memory(); From 5c927228de38f4a57256c63944923f1a41f391d5 Mon Sep 17 00:00:00 2001 From: Martin Date: Mon, 26 Apr 2021 00:01:33 +0200 Subject: [PATCH 1233/1365] Fix python video (#7611) * some bbox conversion functions * output video in original resolution * fixing drawing of bboxes into orig sized video --- darknet_video.py | 71 ++++++++++++++++++++++++++++++++++++++++-------- 1 file changed, 60 insertions(+), 11 deletions(-) diff --git a/darknet_video.py b/darknet_video.py index cc20b266606..04895133f97 100644 --- a/darknet_video.py +++ b/darknet_video.py @@ -60,16 +60,61 @@ def set_saved_video(input_video, output_video, size): return video +def convert2relative(bbox): + """ + YOLO format use relative coordinates for annotation + """ + x, y, w, h = bbox + _height = darknet_height + _width = darknet_width + return x/_width, y/_height, w/_width, h/_height + + +def convert2original(image, bbox): + x, y, w, h = convert2relative(bbox) + + image_h, image_w, __ = image.shape + + orig_x = int(x * image_w) + orig_y = int(y * image_h) + orig_width = int(w * image_w) + orig_height = int(h * image_h) + + bbox_converted = (orig_x, orig_y, orig_width, orig_height) + + return bbox_converted + + +def convert4cropping(image, bbox): + x, y, w, h = convert2relative(bbox) + + image_h, image_w, __ = image.shape + + orig_left = int((x - w / 2.) * image_w) + orig_right = int((x + w / 2.) * image_w) + orig_top = int((y - h / 2.) * image_h) + orig_bottom = int((y + h / 2.) * image_h) + + if (orig_left < 0): orig_left = 0 + if (orig_right > image_w - 1): orig_right = image_w - 1 + if (orig_top < 0): orig_top = 0 + if (orig_bottom > image_h - 1): orig_bottom = image_h - 1 + + bbox_cropping = (orig_left, orig_top, orig_right, orig_bottom) + + return bbox_cropping + + def video_capture(frame_queue, darknet_image_queue): while cap.isOpened(): ret, frame = cap.read() if not ret: break frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) - frame_resized = cv2.resize(frame_rgb, (width, height), + frame_resized = cv2.resize(frame_rgb, (darknet_width, darknet_height), interpolation=cv2.INTER_LINEAR) - frame_queue.put(frame_resized) - img_for_detect = darknet.make_image(width, height, 3) + frame_queue.put(frame) + img_for_detect = darknet.make_image(darknet_width, darknet_height, 3) darknet.copy_image_from_bytes(img_for_detect, frame_resized.tobytes()) darknet_image_queue.put(img_for_detect) cap.release() @@ -91,18 +136,22 @@ def inference(darknet_image_queue, detections_queue, fps_queue): def drawing(frame_queue, detections_queue, fps_queue): random.seed(3) # deterministic bbox colors - video = set_saved_video(cap, args.out_filename, (width, height)) + video = set_saved_video(cap, args.out_filename, (darknet_width, darknet_height)) while cap.isOpened(): - frame_resized = frame_queue.get() + frame = frame_queue.get() detections = detections_queue.get() fps = fps_queue.get() - if frame_resized is not None: - image = darknet.draw_boxes(detections, frame_resized, class_colors) + detections_adjusted = [] + if frame is not None: + for label, confidence, bbox in detections: + bbox_adjusted = convert2original(frame, bbox) + detections_adjusted.append((str(label), confidence, bbox_adjusted)) + image = darknet.draw_boxes(detections_adjusted, frame, class_colors) + if not args.dont_show: + cv2.imshow('Inference', image) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) if args.out_filename is not None: video.write(image) - if not args.dont_show: - cv2.imshow('Inference', image) if cv2.waitKey(fps) == 27: break cap.release() @@ -124,8 +173,8 @@ def drawing(frame_queue, detections_queue, fps_queue): args.weights, batch_size=1 ) - width = darknet.network_width(network) - height = darknet.network_height(network) + darknet_width = darknet.network_width(network) + darknet_height = darknet.network_height(network) input_path = str2int(args.input) cap = cv2.VideoCapture(input_path) Thread(target=video_capture, args=(frame_queue, darknet_image_queue)).start() From 98fa47c844d76e7a15f0f3f2bcc94fd222fcf78b Mon Sep 17 00:00:00 2001 From: Juan Toca Mateo Date: Mon, 26 Apr 2021 00:01:52 +0200 Subject: [PATCH 1234/1365] Fixed relative paths in darknet_images.py (#7566) --- darknet_images.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/darknet_images.py b/darknet_images.py index 0f1fca9b7ae..17ac91731a7 100644 --- a/darknet_images.py +++ b/darknet_images.py @@ -162,7 +162,7 @@ def save_annotations(name, image, detections, class_names): """ Files saved with image_name.txt and relative coordinates """ - file_name = name.split(".")[:-1][0] + ".txt" + file_name = os.path.splitext(name)[0] + ".txt" with open(file_name, "w") as f: for label, confidence, bbox in detections: x, y, w, h = convert2relative(image, bbox) From e2a128737bcee224088a0076629983e2a4beca01 Mon Sep 17 00:00:00 2001 From: ILLO YOON Date: Mon, 26 Apr 2021 07:05:41 +0900 Subject: [PATCH 1235/1365] gaussian_yolo_layer truth size mismatch fixed due to the update of track_id (#7578) --- src/gaussian_yolo_layer.c | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index bfef6991634..bd99a89dc6b 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -51,7 +51,8 @@ layer make_gaussian_yolo_layer(int batch, int w, int h, int n, int total, int *m l.outputs = h*w*n*(classes + 8 + 1); l.inputs = l.outputs; l.max_boxes = max_boxes; - l.truths = l.max_boxes*(4 + 1); + l.truth_size = 4 + 2; + l.truths = l.max_boxes*l.truth_size; l.delta = (float*)calloc(batch*l.outputs, sizeof(float)); l.output = (float*)calloc(batch*l.outputs, sizeof(float)); for(i = 0; i < total*2; ++i){ @@ -464,8 +465,8 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) float best_iou = 0; int best_t = 0; for(t = 0; t < l.max_boxes; ++t){ - box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); - int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; + box truth = float_to_box_stride(state.truth + t*l.truth_size + b*l.truths, 1); + int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; if (class_id >= l.classes) { printf("\n Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1); printf(" truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f, class_id = %d \n", truth.x, truth.y, truth.w, truth.h, class_id); @@ -496,7 +497,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) if (l.objectness_smooth) { l.delta[obj_index] = l.obj_normalizer * (iou_multiplier - l.output[obj_index]); - int class_id = state.truth[best_match_t*(4 + 1) + b*l.truths + 4]; + int class_id = state.truth[best_match_t*l.truth_size + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); } @@ -518,19 +519,19 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) else l.delta[obj_index] = l.obj_normalizer * (1 - l.output[obj_index]); //l.delta[obj_index] = l.obj_normalizer * (1 - l.output[obj_index]); - int class_id = state.truth[best_t*(4 + 1) + b*l.truths + 4]; + int class_id = state.truth[best_t*l.truth_size + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer); const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f; if (l.objectness_smooth) l.delta[class_index + stride*class_id] = class_multiplier * (iou_multiplier - l.output[class_index + stride*class_id]); - box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1); + box truth = float_to_box_stride(state.truth + best_t*l.truth_size + b*l.truths, 1); delta_gaussian_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, l.uc_normalizer, 1, l.yolo_point, l.max_delta); } } } } for(t = 0; t < l.max_boxes; ++t){ - box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1); + box truth = float_to_box_stride(state.truth + t*l.truth_size + b*l.truths, 1); if(!truth.x) break; float best_iou = 0; @@ -564,7 +565,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) int mask_n = int_index(l.mask, best_n, l.n); if(mask_n >= 0){ - int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; + int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; int box_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); @@ -597,7 +598,7 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) // iou, n if (iou > l.iou_thresh) { - int class_id = state.truth[t*(4 + 1) + b*l.truths + 4]; + int class_id = state.truth[t*l.truth_size + b*l.truths + 4]; if (l.map) class_id = l.map[class_id]; int box_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); From 846c79b6d4cf78f27fa4adb5341ad88cf581f0c3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 11 May 2021 22:59:21 +0300 Subject: [PATCH 1236/1365] Added [empty]/[silence] and [implicit] layers --- Makefile | 2 +- build/darknet/darknet.vcxproj | 2 + .../x64/cfg/yolov4-sam-mish-csp-reorg-bfm.cfg | 1429 +++++++++++++++++ build/darknet/x64/darknet.py | 2 + build/darknet/x64/darknet_python.cmd | 2 +- build/darknet/x64/partial.cmd | 5 +- build/darknet/yolo_cpp_dll.vcxproj | 8 +- cfg/yolov4-sam-mish-csp-reorg-bfm.cfg | 1429 +++++++++++++++++ include/darknet.h | 3 +- src/blas.h | 3 + src/blas_kernels.cu | 35 +- src/parser.c | 76 +- src/representation_layer.c | 160 ++ src/representation_layer.h | 29 + 14 files changed, 3173 insertions(+), 12 deletions(-) create mode 100644 build/darknet/x64/cfg/yolov4-sam-mish-csp-reorg-bfm.cfg create mode 100644 cfg/yolov4-sam-mish-csp-reorg-bfm.cfg create mode 100644 src/representation_layer.c create mode 100644 src/representation_layer.h diff --git a/Makefile b/Makefile index 5fb70543746..431933ca058 100644 --- a/Makefile +++ b/Makefile @@ -151,7 +151,7 @@ LDFLAGS+= -L/usr/local/zed/lib -lsl_zed endif endif -OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o gaussian_yolo_layer.o upsample_layer.o lstm_layer.o conv_lstm_layer.o scale_channels_layer.o sam_layer.o +OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o representation_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o gaussian_yolo_layer.o upsample_layer.o lstm_layer.o conv_lstm_layer.o scale_channels_layer.o sam_layer.o ifeq ($(GPU), 1) LDFLAGS+= -lstdc++ OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj index 5282de26d91..5c8a7c3e1b9 100644 --- a/build/darknet/darknet.vcxproj +++ b/build/darknet/darknet.vcxproj @@ -223,6 +223,7 @@ + @@ -286,6 +287,7 @@ + diff --git a/build/darknet/x64/cfg/yolov4-sam-mish-csp-reorg-bfm.cfg b/build/darknet/x64/cfg/yolov4-sam-mish-csp-reorg-bfm.cfg new file mode 100644 index 00000000000..1461d88838e --- /dev/null +++ b/build/darknet/x64/cfg/yolov4-sam-mish-csp-reorg-bfm.cfg @@ -0,0 +1,1429 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=512 +height=512 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +#:104x104 54:52x52 85:26x26 104:13x13 for 416 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-7 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-10 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=mish +stopbackward=800 + + +########################## + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1, -13 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 79 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[reorg3d] +stride=2 + +[route] +layers = -1, -4, -7 + +[upsample] +stride=2 + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 79 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = 48 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[reorg3d] +stride=2 + +[route] +layers = -1, -4, -6 + +[shortcut] +from= -10 +activation=linear + +[upsample] +stride=2 + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1, -6 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 48 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = 17 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[reorg3d] +stride=2 + +[route] +layers = -1, -4, -6 + +[shortcut] +from= -19 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +#### BFM-1 + +[route] +layers = 17 + +[reorg3d] +stride=2 + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[route] +layers = -1, -6 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=256 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + + +########################## + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.2 +ignore_thresh = .7 +truth_thresh = 1 +random=0 +resize=1.5 +scale_x_y = 1.2 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +uc_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 +beta1=0.6 +max_delta=5 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1, 137 +#layers = -1, -20 + +[route] +layers = -17 + +[reorg3d] +stride=2 + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1,-6 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=512 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.2 +ignore_thresh = .7 +truth_thresh = 1 +random=0 +resize=1.5 +scale_x_y = 1.1 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +uc_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 +beta1=0.6 +max_delta=5 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1, 126 +# layers = -1, -49 + +[route] +layers = -17 + +[reorg3d] +stride=2 + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1,-6 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1024 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +ignore_thresh = .7 +truth_thresh = 1 +random=0 +resize=1.5 +scale_x_y = 1.05 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +uc_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 +beta1=0.6 +max_delta=5 diff --git a/build/darknet/x64/darknet.py b/build/darknet/x64/darknet.py index bb0ee183688..23539e0b64d 100644 --- a/build/darknet/x64/darknet.py +++ b/build/darknet/x64/darknet.py @@ -25,6 +25,8 @@ import random import os +print("Run: darknet_images.py or:\n") +print("python.exe darknet_video.py --data_file cfg/coco.data --config_file cfg/yolov4.cfg --weights yolov4.weights --input test.mp4 \n") class BOX(Structure): _fields_ = [("x", c_float), diff --git a/build/darknet/x64/darknet_python.cmd b/build/darknet/x64/darknet_python.cmd index b2df11d8207..74556582e82 100644 --- a/build/darknet/x64/darknet_python.cmd +++ b/build/darknet/x64/darknet_python.cmd @@ -14,6 +14,6 @@ rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\Scripts\pip install sci rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\Scripts\pip install scipy rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\Scripts\pip install opencv-python -C:\Users\Alex\AppData\Local\Programs\Python\Python36\python.exe darknet.py +C:\Users\Alex\AppData\Local\Programs\Python\Python36\python.exe darknet_images.py pause \ No newline at end of file diff --git a/build/darknet/x64/partial.cmd b/build/darknet/x64/partial.cmd index 26a701a9c40..a9b06ca86b7 100644 --- a/build/darknet/x64/partial.cmd +++ b/build/darknet/x64/partial.cmd @@ -8,7 +8,10 @@ rem darknet.exe partial cfg/tiny-yolo-voc.cfg tiny-yolo-voc.weights tiny-yolo-vo darknet.exe partial cfg/yolov4-tiny.cfg yolov4-tiny.weights yolov4-tiny.conv.29 29 -darknet.exe partial cfg/yolov4-sam-mish.cfg cfg/yolov4-sam-mish.weights cfg/yolov4-sam-mish.conv.137 137 +rem darknet.exe partial cfg/yolov4-sam-mish.cfg cfg/yolov4-sam-mish.weights cfg/yolov4-sam-mish.conv.137 137 + +rem darknet.exe partial cfg/yolov4-sam-mish.cfg cfg/yolov4-sam-mish.weights cfg/yolov4-sam-mish.conv.105 105 + pause diff --git a/build/darknet/yolo_cpp_dll.vcxproj b/build/darknet/yolo_cpp_dll.vcxproj index 4bd09206caa..35742aa2be4 100644 --- a/build/darknet/yolo_cpp_dll.vcxproj +++ b/build/darknet/yolo_cpp_dll.vcxproj @@ -52,7 +52,7 @@ - + @@ -155,7 +155,7 @@ 64 - compute_30,sm_30;compute_75,sm_75 + compute_35,sm_35;compute_75,sm_75 @@ -225,6 +225,7 @@ + @@ -290,6 +291,7 @@ + @@ -306,6 +308,6 @@ - + \ No newline at end of file diff --git a/cfg/yolov4-sam-mish-csp-reorg-bfm.cfg b/cfg/yolov4-sam-mish-csp-reorg-bfm.cfg new file mode 100644 index 00000000000..1461d88838e --- /dev/null +++ b/cfg/yolov4-sam-mish-csp-reorg-bfm.cfg @@ -0,0 +1,1429 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=512 +height=512 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +#:104x104 54:52x52 85:26x26 104:13x13 for 416 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-7 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-10 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=mish +stopbackward=800 + + +########################## + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1, -13 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 79 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[reorg3d] +stride=2 + +[route] +layers = -1, -4, -7 + +[upsample] +stride=2 + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 79 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = 48 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[reorg3d] +stride=2 + +[route] +layers = -1, -4, -6 + +[shortcut] +from= -10 +activation=linear + +[upsample] +stride=2 + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1, -6 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 48 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = 17 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[reorg3d] +stride=2 + +[route] +layers = -1, -4, -6 + +[shortcut] +from= -19 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +#### BFM-1 + +[route] +layers = 17 + +[reorg3d] +stride=2 + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[route] +layers = -1, -6 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=256 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + + +########################## + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.2 +ignore_thresh = .7 +truth_thresh = 1 +random=0 +resize=1.5 +scale_x_y = 1.2 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +uc_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 +beta1=0.6 +max_delta=5 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1, 137 +#layers = -1, -20 + +[route] +layers = -17 + +[reorg3d] +stride=2 + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1,-6 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=512 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.2 +ignore_thresh = .7 +truth_thresh = 1 +random=0 +resize=1.5 +scale_x_y = 1.1 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +uc_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 +beta1=0.6 +max_delta=5 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1, 126 +# layers = -1, -49 + +[route] +layers = -17 + +[reorg3d] +stride=2 + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1,-6 + +[convolutional] +batch_normalize=1 +size=1 +stride=1 +pad=1 +filters=1024 +activation=logistic + +[sam] +from=-2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +ignore_thresh = .7 +truth_thresh = 1 +random=0 +resize=1.5 +scale_x_y = 1.05 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +uc_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 +beta1=0.6 +max_delta=5 diff --git a/include/darknet.h b/include/darknet.h index 1a5fdf75586..f5170f375cd 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -192,7 +192,8 @@ typedef enum { L2NORM, EMPTY, BLANK, - CONTRASTIVE + CONTRASTIVE, + IMPLICIT } LAYER_TYPE; // layer.h diff --git a/src/blas.h b/src/blas.h index ab888903b54..b69a702fa93 100644 --- a/src/blas.h +++ b/src/blas.h @@ -174,6 +174,9 @@ void mult_inverse_array_gpu(const float *src_gpu, float *dst_gpu, int size, floa void P_constrastive_f_det_gpu(int *labels, unsigned int feature_size, float temperature, contrastive_params *contrast_p, const int contrast_p_size); void coord_conv_gpu(float *dst, int size, int w, int h, int chan, int b, int type); +void forward_implicit_gpu(int batch, int nweights, float *weight_gpu, float *output_gpu); +void backward_implicit_gpu(int batch, int nweights, float *weight_updates_gpu, float *delta_gpu); + #endif // GPU #ifdef __cplusplus } diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index bc327995eed..21dbfbba575 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -2435,4 +2435,37 @@ extern "C" void coord_conv_gpu(float *dst, int size, int w, int h, int chan, int coord_conv_kernel << > > (dst, w, h, chan, b, type); CHECK_CUDA(cudaPeekAtLastError()); -} \ No newline at end of file +} + + +__global__ void forward_implicit_kernel(int size, int batch, int nweights, float *weight_gpu, float *output_gpu) +{ + const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= size) return; + + output_gpu[id] = weight_gpu[id % nweights]; +} + +extern "C" void forward_implicit_gpu(int batch, int nweights, float *weight_gpu, float *output_gpu) +{ + int size = batch * nweights; + forward_implicit_kernel << > > (size, batch, nweights, weight_gpu, output_gpu); + CHECK_CUDA(cudaPeekAtLastError()); +} + + + +__global__ void backward_implicit_kernel(int size, int batch, int nweights, float *weight_updates_gpu, float *delta_gpu) +{ + const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= size) return; + + weight_updates_gpu[id % nweights] += delta_gpu[id]; +} + +extern "C" void backward_implicit_gpu(int batch, int nweights, float *weight_updates_gpu, float *delta_gpu) +{ + int size = batch * nweights; + backward_implicit_kernel << > > (size, batch, nweights, weight_updates_gpu, delta_gpu); + CHECK_CUDA(cudaPeekAtLastError()); +} diff --git a/src/parser.c b/src/parser.c index e7498d9daee..8f8f584268e 100644 --- a/src/parser.c +++ b/src/parser.c @@ -39,6 +39,11 @@ #include "version.h" #include "yolo_layer.h" #include "gaussian_yolo_layer.h" +#include "representation_layer.h" + +void empty_func(dropout_layer l, network_state state) { + //l.output_gpu = state.input; +} typedef struct{ char *type; @@ -90,7 +95,9 @@ LAYER_TYPE string_to_layer_type(char * type) if (strcmp(type, "[contrastive]") == 0) return CONTRASTIVE; if (strcmp(type, "[route]")==0) return ROUTE; if (strcmp(type, "[upsample]") == 0) return UPSAMPLE; - if (strcmp(type, "[empty]") == 0) return EMPTY; + if (strcmp(type, "[empty]") == 0 + || strcmp(type, "[silence]") == 0) return EMPTY; + if (strcmp(type, "[implicit]") == 0) return IMPLICIT; return BLANK; } @@ -1036,6 +1043,17 @@ layer parse_sam(list *options, size_params params, network net) return s; } +layer parse_implicit(list *options, size_params params, network net) +{ + float mean_init = option_find_float(options, "mean", 0.0); + float std_init = option_find_float(options, "std", 0.2); + int filters = option_find_int(options, "filters", 128); + int atoms = option_find_int_quiet(options, "atoms", 1); + + layer s = make_implicit_layer(params.batch, params.index, mean_init, std_init, filters, atoms); + + return s; +} layer parse_activation(list *options, size_params params) { @@ -1480,6 +1498,8 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) net.layers[count - 1].use_bin_output = 0; net.layers[l.index].use_bin_output = 0; net.layers[l.index].keep_delta_gpu = 1; + } else if (lt == IMPLICIT) { + l = parse_implicit(options, params, net); }else if(lt == DROPOUT){ l = parse_dropout(options, params); l.output = net.layers[count-1].output; @@ -1492,16 +1512,25 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) } else if (lt == EMPTY) { layer empty_layer = {(LAYER_TYPE)0}; - empty_layer.out_w = params.w; - empty_layer.out_h = params.h; - empty_layer.out_c = params.c; l = empty_layer; + l.type = EMPTY; + l.w = l.out_w = params.w; + l.h = l.out_h = params.h; + l.c = l.out_c = params.c; + l.batch = params.batch; + l.inputs = l.outputs = params.inputs; l.output = net.layers[count - 1].output; l.delta = net.layers[count - 1].delta; + l.forward = empty_func; + l.backward = empty_func; #ifdef GPU l.output_gpu = net.layers[count - 1].output_gpu; l.delta_gpu = net.layers[count - 1].delta_gpu; + l.keep_delta_gpu = 1; + l.forward_gpu = empty_func; + l.backward_gpu = empty_func; #endif + fprintf(stderr, "empty \n"); }else{ fprintf(stderr, "Type not recognized: %s\n", s->type); } @@ -1604,6 +1633,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0); l.learning_rate_scale = option_find_float_quiet(options, "learning_rate", 1); option_unused(options); + net.layers[count] = l; if (l.workspace_size > workspace_size) workspace_size = l.workspace_size; if (l.inputs > max_inputs) max_inputs = l.inputs; @@ -1810,6 +1840,24 @@ void save_shortcut_weights(layer l, FILE *fp) fwrite(l.weights, sizeof(float), num, fp); } +void save_implicit_weights(layer l, FILE *fp) +{ +#ifdef GPU + if (gpu_index >= 0) { + pull_implicit_layer(l); + //printf("\n pull_implicit_layer \n"); + } +#endif + int i; + //if(l.weight_updates) for (i = 0; i < l.nweights; ++i) printf(" %f, ", l.weight_updates[i]); + //printf(" l.nweights = %d - update \n", l.nweights); + //for (i = 0; i < l.nweights; ++i) printf(" %f, ", l.weights[i]); + //printf(" l.nweights = %d \n\n", l.nweights); + + int num = l.nweights; + fwrite(l.weights, sizeof(float), num, fp); +} + void save_convolutional_weights(layer l, FILE *fp) { if(l.binary){ @@ -1921,6 +1969,8 @@ void save_weights_upto(network net, char *filename, int cutoff, int save_ema) } } if (l.type == SHORTCUT && l.nweights > 0) { save_shortcut_weights(l, fp); + } if (l.type == IMPLICIT) { + save_implicit_weights(l, fp); } if(l.type == CONNECTED){ save_connected_weights(l, fp); } if(l.type == BATCHNORM){ @@ -2131,6 +2181,21 @@ void load_shortcut_weights(layer l, FILE *fp) #endif } +void load_implicit_weights(layer l, FILE *fp) +{ + int num = l.nweights; + int read_bytes; + read_bytes = fread(l.weights, sizeof(float), num, fp); + if (read_bytes > 0 && read_bytes < num) printf("\n Warning: Unexpected end of wights-file! l.weights - l.index = %d \n", l.index); + //for (int i = 0; i < l.nweights; ++i) printf(" %f, ", l.weights[i]); + //printf(" read_bytes = %d \n\n", read_bytes); +#ifdef GPU + if (gpu_index >= 0) { + push_implicit_layer(l); + } +#endif +} + void load_weights_upto(network *net, char *filename, int cutoff) { #ifdef GPU @@ -2175,6 +2240,9 @@ void load_weights_upto(network *net, char *filename, int cutoff) if (l.type == SHORTCUT && l.nweights > 0) { load_shortcut_weights(l, fp); } + if (l.type == IMPLICIT) { + load_implicit_weights(l, fp); + } if(l.type == CONNECTED){ load_connected_weights(l, fp, transpose); } diff --git a/src/representation_layer.c b/src/representation_layer.c new file mode 100644 index 00000000000..a6cc5c5f3f8 --- /dev/null +++ b/src/representation_layer.c @@ -0,0 +1,160 @@ +#include "representation_layer.h" +#include "utils.h" +#include "dark_cuda.h" +#include "blas.h" +#include +#include + +layer make_implicit_layer(int batch, int index, float mean_init, float std_init, int filters, int atoms) +{ + fprintf(stderr,"implicit Layer: %d x %d \t mean=%.2f, std=%.2f \n", filters, atoms, mean_init, std_init); + layer l = { (LAYER_TYPE)0 }; + l.type = IMPLICIT; + l.batch = batch; + l.w = 1; + l.h = 1; + l.c = 1; + + l.out_w = 1; + l.out_h = atoms; + l.out_c = filters; + + l.outputs = l.out_w*l.out_h*l.out_c; + l.inputs = 1; + l.index = index; + + l.nweights = l.out_w * l.out_h * l.out_c; + + l.weight_updates = (float*)xcalloc(l.nweights, sizeof(float)); + l.weights = (float*)xcalloc(l.nweights, sizeof(float)); + int i; + for (i = 0; i < l.nweights; ++i) l.weights[i] = mean_init + rand_uniform(-std_init, std_init); + + + l.delta = (float*)xcalloc(l.outputs * batch, sizeof(float)); + l.output = (float*)xcalloc(l.outputs * batch, sizeof(float)); + + l.forward = forward_implicit_layer; + l.backward = backward_implicit_layer; + l.update = update_implicit_layer; +#ifdef GPU + l.forward_gpu = forward_implicit_layer_gpu; + l.backward_gpu = backward_implicit_layer_gpu; + l.update_gpu = update_implicit_layer_gpu; + + l.delta_gpu = cuda_make_array(l.delta, l.outputs*batch); + l.output_gpu = cuda_make_array(l.output, l.outputs*batch); + + l.weight_updates_gpu = cuda_make_array(l.weight_updates, l.nweights); + l.weights_gpu = cuda_make_array(l.weights, l.nweights); +#endif + return l; +} + +void resize_implicit_layer(layer *l, int w, int h) +{ +} + +void forward_implicit_layer(const layer l, network_state state) +{ + int i; + #pragma omp parallel for + for (i = 0; i < l.nweights * l.batch; ++i) { + l.output[i] = l.weights[i % l.nweights]; + } +} + +void backward_implicit_layer(const layer l, network_state state) +{ + int i; + #pragma omp parallel for + for (i = 0; i < l.nweights * l.batch; ++i) { + l.weight_updates[i % l.nweights] += l.delta[i]; + } +} + +void update_implicit_layer(layer l, int batch, float learning_rate_init, float momentum, float decay) +{ + float learning_rate = learning_rate_init*l.learning_rate_scale; + //float momentum = a.momentum; + //float decay = a.decay; + //int batch = a.batch; + + axpy_cpu(l.nweights, -decay*batch, l.weights, 1, l.weight_updates, 1); + axpy_cpu(l.nweights, learning_rate / batch, l.weight_updates, 1, l.weights, 1); + scal_cpu(l.nweights, momentum, l.weight_updates, 1); + +} + + +#ifdef GPU +void forward_implicit_layer_gpu(const layer l, network_state state) +{ + forward_implicit_gpu(l.batch, l.nweights, l.weights_gpu, l.output_gpu); +} + +void backward_implicit_layer_gpu(const layer l, network_state state) +{ + backward_implicit_gpu(l.batch, l.nweights, l.weight_updates_gpu, l.delta_gpu); +} + +void update_implicit_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay, float loss_scale) +{ + // Loss scale for Mixed-Precision on Tensor-Cores + float learning_rate = learning_rate_init*l.learning_rate_scale / loss_scale; + //float momentum = a.momentum; + //float decay = a.decay; + //int batch = a.batch; + + reset_nan_and_inf(l.weight_updates_gpu, l.nweights); + fix_nan_and_inf(l.weights_gpu, l.nweights); + + if (l.adam) { + //adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.nweights, batch, a.t); + adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, l.B1, l.B2, l.eps, decay, learning_rate, l.nweights, batch, l.t); + } + else { + //axpy_ongpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); + //axpy_ongpu(l.nweights, learning_rate / batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); + //scal_ongpu(l.nweights, momentum, l.weight_updates_gpu, 1); + + axpy_ongpu(l.nweights, -decay*batch*loss_scale, l.weights_gpu, 1, l.weight_updates_gpu, 1); + axpy_ongpu(l.nweights, learning_rate / batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); + + scal_ongpu(l.nweights, momentum, l.weight_updates_gpu, 1); + } + + if (l.clip) { + constrain_ongpu(l.nweights, l.clip, l.weights_gpu, 1); + } +} + +void pull_implicit_layer(layer l) +{ + cuda_pull_array_async(l.weights_gpu, l.weights, l.nweights); + cuda_pull_array_async(l.weight_updates_gpu, l.weight_updates, l.nweights); + + if (l.adam) { + cuda_pull_array_async(l.m_gpu, l.m, l.nweights); + cuda_pull_array_async(l.v_gpu, l.v, l.nweights); + } + CHECK_CUDA(cudaPeekAtLastError()); + cudaStreamSynchronize(get_cuda_stream()); +} + +void push_implicit_layer(layer l) +{ + cuda_push_array(l.weights_gpu, l.weights, l.nweights); + + if (l.train) { + cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.nweights); + } + if (l.adam) { + cuda_push_array(l.m_gpu, l.m, l.nweights); + cuda_push_array(l.v_gpu, l.v, l.nweights); + } + CHECK_CUDA(cudaPeekAtLastError()); +} +#endif + + diff --git a/src/representation_layer.h b/src/representation_layer.h new file mode 100644 index 00000000000..8b2a9da3082 --- /dev/null +++ b/src/representation_layer.h @@ -0,0 +1,29 @@ +#ifndef REPRESENTATION_LAYER_H +#define REPRESENTATION_LAYER_H + +#include "layer.h" +#include "network.h" + +#ifdef __cplusplus +extern "C" { +#endif +layer make_implicit_layer(int batch, int index, float mean_init, float std_init, int filters, int atoms); +void forward_implicit_layer(const layer l, network_state state); +void backward_implicit_layer(const layer l, network_state state); +void update_implicit_layer(layer l, int batch, float learning_rate_init, float momentum, float decay); + +void resize_implicit_layer(layer *l, int w, int h); + +#ifdef GPU +void forward_implicit_layer_gpu(const layer l, network_state state); +void backward_implicit_layer_gpu(const layer l, network_state state); + +void update_implicit_layer_gpu(layer l, int batch, float learning_rate_init, float momentum, float decay, float loss_scale); +void pull_implicit_layer(layer l); +void push_implicit_layer(layer l); +#endif + +#ifdef __cplusplus +} +#endif +#endif // REPRESENTATION_LAYER_H From 432623aa715393125196dbf94a964c1519b1739e Mon Sep 17 00:00:00 2001 From: Double Date: Wed, 12 May 2021 04:11:20 +0800 Subject: [PATCH 1237/1365] fix: frame delay (#7659) * fix: frame delay when use low performance device such as raspberry pi or jetson nano execute darknet demo, the streaming will be delayed more and more * fix: operate only in live stream Co-authored-by: Double.c --- src/demo.c | 5 +++++ src/image_opencv.cpp | 9 +++++++++ src/image_opencv.h | 2 +- src/utils.c | 5 +++++ src/utils.h | 2 ++ 5 files changed, 22 insertions(+), 1 deletion(-) diff --git a/src/demo.c b/src/demo.c index d503aa7fcfc..ad1cc6e041c 100644 --- a/src/demo.c +++ b/src/demo.c @@ -36,6 +36,7 @@ static float demo_thresh = 0; static int demo_ext_output = 0; static long long int frame_id = 0; static int demo_json_port = -1; +static bool demo_skip_frame = false; static int avg_frames; @@ -59,6 +60,8 @@ void *fetch_in_thread(void *ptr) while (!custom_atomic_load_int(&flag_exit)) { while (!custom_atomic_load_int(&run_fetch_in_thread)) { if (custom_atomic_load_int(&flag_exit)) return 0; + if (demo_skip_frame) + consume_frame(cap); this_thread_yield(); } int dont_close_stream = 0; // set 1 if your IP-camera periodically turns off and turns on video-stream @@ -168,9 +171,11 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int if(filename){ printf("video file: %s\n", filename); cap = get_capture_video_stream(filename); + demo_skip_frame = is_live_stream(filename); }else{ printf("Webcam index: %d\n", cam_index); cap = get_capture_webcam(cam_index); + demo_skip_frame = true; } if (!cap) { diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index add27c96ef0..f9615cb00ae 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -834,6 +834,15 @@ extern "C" image get_image_from_stream_letterbox(cap_cv *cap, int w, int h, int } // ---------------------------------------- +extern "C" void consume_frame(cap_cv *cap){ + cv::Mat *src = NULL; + src = (cv::Mat *)get_capture_frame_cv(cap); + if (src) + delete src; +} +// ---------------------------------------- + + // ==================================================================== // Image Saving // ==================================================================== diff --git a/src/image_opencv.h b/src/image_opencv.h index 6fa6cb5c6b0..3aeb4478390 100644 --- a/src/image_opencv.h +++ b/src/image_opencv.h @@ -83,7 +83,7 @@ int set_capture_position_frame_cv(cap_cv *cap, int index); image get_image_from_stream_cpp(cap_cv *cap); image get_image_from_stream_resize(cap_cv *cap, int w, int h, int c, mat_cv** in_img, int dont_close); image get_image_from_stream_letterbox(cap_cv *cap, int w, int h, int c, mat_cv** in_img, int dont_close); - +void consume_frame(cap_cv *cap); // Image Saving void save_cv_png(mat_cv *img, const char *name); diff --git a/src/utils.c b/src/utils.c index fe5c2062148..e4a2298e762 100644 --- a/src/utils.c +++ b/src/utils.c @@ -1047,3 +1047,8 @@ unsigned long custom_hash(char *str) return hash; } + +bool is_live_stream(const char * path){ + const char *url_schema = "://"; + return (NULL != strstr(path, url_schema)); +} diff --git a/src/utils.h b/src/utils.h index 9a154ea6267..a217b425a2e 100644 --- a/src/utils.h +++ b/src/utils.h @@ -5,6 +5,7 @@ #include #include +#include #ifndef M_PI #define M_PI 3.14159265358979323846 // pi @@ -95,6 +96,7 @@ int max_int_index(int *a, int n); boxabs box_to_boxabs(const box* b, const int img_w, const int img_h, const int bounds_check); int make_directory(char *path, int mode); unsigned long custom_hash(char *str); +bool is_live_stream(const char * path); #define max_val_cmp(a,b) (((a) > (b)) ? (a) : (b)) #define min_val_cmp(a,b) (((a) < (b)) ? (a) : (b)) From 81b768bae0017d386e95247d47ff14898b055308 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Wed, 12 May 2021 04:16:19 +0300 Subject: [PATCH 1238/1365] Fix [implicit] layer --- src/blas_kernels.cu | 6 ++++-- src/representation_layer.c | 1 - 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 21dbfbba575..85c55adfb82 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -2460,12 +2460,14 @@ __global__ void backward_implicit_kernel(int size, int batch, int nweights, floa const int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (id >= size) return; - weight_updates_gpu[id % nweights] += delta_gpu[id]; + for (int i = 0; i < batch; ++i) { + weight_updates_gpu[id] += delta_gpu[id + i * nweights]; + } } extern "C" void backward_implicit_gpu(int batch, int nweights, float *weight_updates_gpu, float *delta_gpu) { - int size = batch * nweights; + int size = nweights; backward_implicit_kernel << > > (size, batch, nweights, weight_updates_gpu, delta_gpu); CHECK_CUDA(cudaPeekAtLastError()); } diff --git a/src/representation_layer.c b/src/representation_layer.c index a6cc5c5f3f8..fe7741fa9db 100644 --- a/src/representation_layer.c +++ b/src/representation_layer.c @@ -67,7 +67,6 @@ void forward_implicit_layer(const layer l, network_state state) void backward_implicit_layer(const layer l, network_state state) { int i; - #pragma omp parallel for for (i = 0; i < l.nweights * l.batch; ++i) { l.weight_updates[i % l.nweights] += l.delta[i]; } From 179be4dcb4a70b74859df084cd5b590c9d9a805e Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 13 May 2021 22:31:59 +0200 Subject: [PATCH 1239/1365] add support for Windows PowerShell (#7702) * add support for powershell v5 * revert opencv enabled on osx * improve error handling in initial stages * intercept failures also when updating darknet sources themselves * press a key before exiting script, to preserve console host window if not running in a shell * check path length --- build.ps1 | 139 +++++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 117 insertions(+), 22 deletions(-) diff --git a/build.ps1 b/build.ps1 index 8cf090f0a12..07fb47aafa1 100755 --- a/build.ps1 +++ b/build.ps1 @@ -14,9 +14,70 @@ param ( [switch]$DoNotUseNinja = $false, [switch]$ForceCPP = $false, [switch]$ForceStaticLib = $false, + [switch]$ForceSetupVS = $false, [switch]$ForceGCC8 = $false ) +Function MyThrow ($Message) { + if ($DisableInteractive) { + Throw $Message + } + else { + # Check if running in PowerShell ISE + if ($psISE) { + # "ReadKey" not supported in PowerShell ISE. + # Show MessageBox UI + $Shell = New-Object -ComObject "WScript.Shell" + $Shell.Popup($Message, 0, "OK", 0) + return + } + + $Ignore = + 16, # Shift (left or right) + 17, # Ctrl (left or right) + 18, # Alt (left or right) + 20, # Caps lock + 91, # Windows key (left) + 92, # Windows key (right) + 93, # Menu key + 144, # Num lock + 145, # Scroll lock + 166, # Back + 167, # Forward + 168, # Refresh + 169, # Stop + 170, # Search + 171, # Favorites + 172, # Start/Home + 173, # Mute + 174, # Volume Down + 175, # Volume Up + 176, # Next Track + 177, # Previous Track + 178, # Stop Media + 179, # Play + 180, # Mail + 181, # Select Media + 182, # Application 1 + 183 # Application 2 + + Write-Host $Message + Write-Host -NoNewline "Press any key to continue..." + while ($null -eq $KeyInfo.VirtualKeyCode -or $Ignore -contains $KeyInfo.VirtualKeyCode) { + $KeyInfo = $Host.UI.RawUI.ReadKey("NoEcho, IncludeKeyDown") + } + exit + } +} + +if ($PSVersionTable.PSVersion.Major -eq 5) { + $IsWindowsPowerShell = $true +} + +if ($PSVersionTable.PSVersion.Major -lt 5) { + MyThrow("Your PowerShell version is too old, please update it.") +} + if (-Not $DisableInteractive -and -Not $UseVCPKG) { $Result = Read-Host "Enable vcpkg to install darknet dependencies (yes/no)" if ($Result -eq 'Yes' -or $Result -eq 'Y' -or $Result -eq 'yes' -or $Result -eq 'y') { @@ -51,12 +112,12 @@ $number_of_build_workers = 8 if ($IsLinux -or $IsMacOS) { $bootstrap_ext = ".sh" } -elseif ($IsWindows) { +elseif ($IsWindows -or $IsWindowsPowerShell) { $bootstrap_ext = ".bat" } Write-Host "Native shell script extension: ${bootstrap_ext}" -if (-Not $IsWindows) { +if (-Not $IsWindows -and -not $IsWindowsPowerShell -and -Not $ForceSetupVS) { $DoNotSetupVS = $true } @@ -71,7 +132,7 @@ if ($IsLinux -and $ForceGCC8) { $env:CXX = "g++-8" } -if ($IsWindows -and -Not $env:VCPKG_DEFAULT_TRIPLET) { +if (($IsWindows -or $IsWindowsPowerShell) -and -Not $env:VCPKG_DEFAULT_TRIPLET) { $env:VCPKG_DEFAULT_TRIPLET = "x64-windows" } @@ -158,19 +219,24 @@ Push-Location $PSScriptRoot $GIT_EXE = Get-Command git 2> $null | Select-Object -ExpandProperty Definition if (-Not $GIT_EXE) { - throw "Could not find git, please install it" + MyThrow("Could not find git, please install it") } else { Write-Host "Using git from ${GIT_EXE}" } if ((Test-Path "$PSScriptRoot/.git") -and -not $DoNotUpdateDARKNET) { - & $GIT_EXE pull + $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "pull" + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-not $exitCode -eq 0) { + MyThrow("Updating darknet sources failed! Exited with $exitCode.") + } } $CMAKE_EXE = Get-Command cmake 2> $null | Select-Object -ExpandProperty Definition if (-Not $CMAKE_EXE) { - throw "Could not find CMake, please install it" + MyThrow("Could not find CMake, please install it") } else { Write-Host "Using CMake from ${CMAKE_EXE}" @@ -195,7 +261,7 @@ function getProgramFiles32bit() { } if ($null -eq $out) { - throw "Could not find [Program Files 32-bit]" + MyThrow("Could not find [Program Files 32-bit]") } return $out @@ -219,11 +285,11 @@ function getLatestVisualStudioWithDesktopWorkloadPath() { } } if (!$installationPath) { - Throw "Could not locate any installation of Visual Studio" + MyThrow("Could not locate any installation of Visual Studio") } } else { - Throw "Could not locate vswhere at $vswhereExe" + MyThrow("Could not locate vswhere at $vswhereExe") } return $installationPath } @@ -247,11 +313,11 @@ function getLatestVisualStudioWithDesktopWorkloadVersion() { } } if (!$installationVersion) { - Throw "Could not locate any installation of Visual Studio" + MyThrow("Could not locate any installation of Visual Studio") } } else { - Throw "Could not locate vswhere at $vswhereExe" + MyThrow("Could not locate vswhere at $vswhereExe") } return $installationVersion } @@ -268,15 +334,22 @@ elseif ((Test-Path "${env:WORKSPACE}/vcpkg") -and $UseVCPKG) { Write-Host "Found vcpkg in WORKSPACE/vcpkg: $vcpkg_path" $additional_build_setup = $additional_build_setup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" } -elseif ((Test-Path "${RUNVCPKG_VCPKG_ROOT_OUT}") -and $UseVCPKG) { - $vcpkg_path = "${RUNVCPKG_VCPKG_ROOT_OUT}" - $env:VCPKG_ROOT = "${RUNVCPKG_VCPKG_ROOT_OUT}" - Write-Host "Found vcpkg in RUNVCPKG_VCPKG_ROOT_OUT: ${RUNVCPKG_VCPKG_ROOT_OUT}" - $additional_build_setup = $additional_build_setup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" +elseif (-not($null -eq ${RUNVCPKG_VCPKG_ROOT_OUT})) { + if((Test-Path "${RUNVCPKG_VCPKG_ROOT_OUT}") -and $UseVCPKG) { + $vcpkg_path = "${RUNVCPKG_VCPKG_ROOT_OUT}" + $env:VCPKG_ROOT = "${RUNVCPKG_VCPKG_ROOT_OUT}" + Write-Host "Found vcpkg in RUNVCPKG_VCPKG_ROOT_OUT: ${vcpkg_path}" + $additional_build_setup = $additional_build_setup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" + } } elseif ($UseVCPKG) { if (-Not (Test-Path "$PWD/vcpkg")) { - & $GIT_EXE clone https://github.com/microsoft/vcpkg + $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "clone https://github.com/microsoft/vcpkg" + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-not $exitCode -eq 0) { + MyThrow("Cloning vcpkg sources failed! Exited with $exitCode.") + } } $vcpkg_path = "$PWD/vcpkg" $env:VCPKG_ROOT = "$PWD/vcpkg" @@ -290,11 +363,33 @@ else { if ($UseVCPKG -and (Test-Path "$vcpkg_path/.git") -and -not $DoNotUpdateVCPKG) { Push-Location $vcpkg_path - & $GIT_EXE pull - & $PWD/bootstrap-vcpkg${bootstrap_ext} -disableMetrics + $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "pull" + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-not $exitCode -eq 0) { + MyThrow("Updating vcpkg sources failed! Exited with $exitCode.") + } + $proc = Start-Process -NoNewWindow -PassThru -FilePath $PWD/bootstrap-vcpkg${bootstrap_ext} -ArgumentList "-disableMetrics" + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-not $exitCode -eq 0) { + MyThrow("Bootstrapping vcpkg failed! Exited with $exitCode.") + } Pop-Location } +if ($UseVCPKG -and ($vcpkg_path.length -gt 40) -and ($IsWindows -or $IsWindowsPowerShell)) { + Write-Host "vcpkg path is very long and might fail. Please move it or" -ForegroundColor Yellow + Write-Host "the entire darknet folder to a shorter path, like C:\darknet" -ForegroundColor Yellow + Write-Host "You can use the subst command to ease the process if necessary" -ForegroundColor Yellow + if (-Not $DisableInteractive) { + $Result = Read-Host "Do you still want to continue? (yes/no)" + if ($Result -eq 'No' -or $Result -eq 'N' -or $Result -eq 'no' -or $Result -eq 'n') { + MyThrow("Build aborted") + } + } +} + if (-Not $DoNotSetupVS) { if ($null -eq (Get-Command "cl.exe" -ErrorAction SilentlyContinue)) { $vsfound = getLatestVisualStudioWithDesktopWorkloadPath @@ -328,7 +423,7 @@ if (-Not $DoNotSetupVS) { $additional_build_setup = $additional_build_setup + " -T `"host=x64`" -A `"x64`"" } else { - throw "Unknown Visual Studio version, unsupported configuration" + MyThrow("Unknown Visual Studio version, unsupported configuration") } } if (-Not $UseVCPKG) { @@ -397,13 +492,13 @@ $proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList $proc.WaitForExit() $exitCode = $proc.ExitCode if (-not $exitCode -eq 0) { - Throw "Config failed! Exited with $exitCode." + MyThrow("Config failed! Exited with $exitCode.") } $proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList "--build . ${selectConfig} --parallel ${number_of_build_workers} --target install" $proc.WaitForExit() $exitCode = $proc.ExitCode if (-not $exitCode -eq 0) { - Throw "Config failed! Exited with $exitCode." + MyThrow("Config failed! Exited with $exitCode.") } Remove-Item DarknetConfig.cmake Remove-Item DarknetConfigVersion.cmake From 0024a6a3c8dd029dac2211277ca0f9b633872cfe Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 16 May 2021 21:16:49 +0200 Subject: [PATCH 1240/1365] [build.ps1] improvements (#7711) * use parentheses to make clear operator order, print out versions * add english language pack note in README.md * add even more build info --- CMakeLists.txt | 2 + README.md | 95 +++++++++++++++++------------------ build.ps1 | 133 +++++++++++++++++++++++++++++++------------------ 3 files changed, 132 insertions(+), 98 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 0029abe78ee..49df87cf3a8 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -7,6 +7,8 @@ set(Darknet_PATCH_VERSION 5) set(Darknet_TWEAK_VERSION 4) set(Darknet_VERSION ${Darknet_MAJOR_VERSION}.${Darknet_MINOR_VERSION}.${Darknet_PATCH_VERSION}.${Darknet_TWEAK_VERSION}) +message("Darknet_VERSION: ${Darknet_VERSION}") + option(CMAKE_VERBOSE_MAKEFILE "Create verbose makefile" ON) option(CUDA_VERBOSE_BUILD "Create verbose CUDA build" ON) option(BUILD_SHARED_LIBS "Create dark as a shared library" ON) diff --git a/README.md b/README.md index 4a4a67f49f8..acd2506d16c 100644 --- a/README.md +++ b/README.md @@ -6,16 +6,18 @@ Paper YOLO v4: https://arxiv.org/abs/2004.10934 Paper Scaled YOLO v4: https://arxiv.org/abs/2011.08036 use to reproduce results: [ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4) -More details in articles on medium: - * [Scaled_YOLOv4](https://alexeyab84.medium.com/scaled-yolo-v4-is-the-best-neural-network-for-object-detection-on-ms-coco-dataset-39dfa22fa982?source=friends_link&sk=c8553bfed861b1a7932f739d26f487c8) - * [YOLOv4](https://medium.com/@alexeyab84/yolov4-the-most-accurate-real-time-neural-network-on-ms-coco-dataset-73adfd3602fe?source=friends_link&sk=6039748846bbcf1d960c3061542591d7) +More details in articles on medium: + +* [Scaled_YOLOv4](https://alexeyab84.medium.com/scaled-yolo-v4-is-the-best-neural-network-for-object-detection-on-ms-coco-dataset-39dfa22fa982?source=friends_link&sk=c8553bfed861b1a7932f739d26f487c8) +* [YOLOv4](https://medium.com/@alexeyab84/yolov4-the-most-accurate-real-time-neural-network-on-ms-coco-dataset-73adfd3602fe?source=friends_link&sk=6039748846bbcf1d960c3061542591d7) Manual: https://github.com/AlexeyAB/darknet/wiki -Discussion: - - [Reddit](https://www.reddit.com/r/MachineLearning/comments/gydxzd/p_yolov4_the_most_accurate_realtime_neural/) - - [Google-groups](https://groups.google.com/forum/#!forum/darknet) - - [Discord](https://discord.gg/zSq8rtW) +Discussion: + +* [Reddit](https://www.reddit.com/r/MachineLearning/comments/gydxzd/p_yolov4_the_most_accurate_realtime_neural/) +* [Google-groups](https://groups.google.com/forum/#!forum/darknet) +* [Discord](https://discord.gg/zSq8rtW) About Darknet framework: http://pjreddie.com/darknet/ @@ -29,7 +31,6 @@ About Darknet framework: http://pjreddie.com/darknet/ [![colab](https://user-images.githubusercontent.com/4096485/86174089-b2709f80-bb29-11ea-9faf-3d8dc668a1a5.png)](https://colab.research.google.com/drive/12QusaaRj_lUwCGDvQNfICpa7kA7_a2dE) [![colab](https://user-images.githubusercontent.com/4096485/86174097-b56b9000-bb29-11ea-9240-c17f6bacfc34.png)](https://colab.research.google.com/drive/1_GdoqCJWXsChrOiY8sZMr_zbr_fH-0Fg) - * [YOLOv4 model zoo](https://github.com/AlexeyAB/darknet/wiki/YOLOv4-model-zoo) * [Requirements (and how to install dependencies)](#requirements) * [Pre-trained models](#pre-trained-models) @@ -40,7 +41,7 @@ About Darknet framework: http://pjreddie.com/darknet/ - [Yolo v4, v3 and v2 for Windows and Linux](#yolo-v4-v3-and-v2-for-windows-and-linux) - [(neural networks for object detection)](#neural-networks-for-object-detection) - - [GeForce RTX 2080 Ti:](#geforce-rtx-2080-ti) + - [GeForce RTX 2080 Ti](#geforce-rtx-2080-ti) - [Youtube video of results](#youtube-video-of-results) - [How to evaluate AP of YOLOv4 on the MS COCO evaluation server](#how-to-evaluate-ap-of-yolov4-on-the-ms-coco-evaluation-server) - [How to evaluate FPS of YOLOv4 on GPU](#how-to-evaluate-fps-of-yolov4-on-gpu) @@ -61,9 +62,9 @@ About Darknet framework: http://pjreddie.com/darknet/ - [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects) - [How to train tiny-yolo (to detect your custom objects):](#how-to-train-tiny-yolo-to-detect-your-custom-objects) - [When should I stop training:](#when-should-i-stop-training) - - [Custom object detection:](#custom-object-detection) - - [How to improve object detection:](#how-to-improve-object-detection) - - [How to mark bounded boxes of objects and create annotation files:](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) + - [Custom object detection](#custom-object-detection) + - [How to improve object detection](#how-to-improve-object-detection) + - [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) - [How to use Yolo as DLL and SO libraries](#how-to-use-yolo-as-dll-and-so-libraries) ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) @@ -74,20 +75,21 @@ About Darknet framework: http://pjreddie.com/darknet/ ![modern_gpus](https://user-images.githubusercontent.com/4096485/82835867-f1c62380-9ecd-11ea-9134-1598ed2abc4b.png) AP50:95 / AP50 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2004.10934 - tkDNN-TensorRT accelerates YOLOv4 **~2x** times for batch=1 and **3x-4x** times for batch=4. + * tkDNN: https://github.com/ceccocats/tkDNN * OpenCV: https://gist.github.com/YashasSamaga/48bdb167303e10f4d07b754888ddbdcf -#### GeForce RTX 2080 Ti: -| Network Size | Darknet, FPS (avg)| tkDNN TensorRT FP32, FPS | tkDNN TensorRT FP16, FPS | OpenCV FP16, FPS | tkDNN TensorRT FP16 batch=4, FPS | OpenCV FP16 batch=4, FPS | tkDNN Speedup | -|:-----:|:--------:|--------:|--------:|--------:|--------:|--------:|------:| -|320 | 100 | 116 | **202** | 183 | 423 | **430** | **4.3x** | -|416 | 82 | 103 | **162** | 159 | 284 | **294** | **3.6x** | -|512 | 69 | 91 | 134 | **138** | 206 | **216** | **3.1x** | -|608 | 53 | 62 | 103 | **115**| 150 | **150** | **2.8x** | -|Tiny 416 | 443 | 609 | **790** | 773 | **1774** | 1353 | **3.5x** | -|Tiny 416 CPU Core i7 7700HQ | 3.4 | - | - | 42 | - | 39 | **12x** | +### GeForce RTX 2080 Ti + +| Network Size | Darknet, FPS (avg) | tkDNN TensorRT FP32, FPS | tkDNN TensorRT FP16, FPS | OpenCV FP16, FPS | tkDNN TensorRT FP16 batch=4, FPS | OpenCV FP16 batch=4, FPS | tkDNN Speedup | +|:--------------------------:|:------------------:|-------------------------:|-------------------------:|-----------------:|---------------------------------:|-------------------------:|--------------:| +|320 | 100 | 116 | **202** | 183 | 423 | **430** | **4.3x** | +|416 | 82 | 103 | **162** | 159 | 284 | **294** | **3.6x** | +|512 | 69 | 91 | 134 | **138** | 206 | **216** | **3.1x** | +|608 | 53 | 62 | 103 | **115** | 150 | **150** | **2.8x** | +|Tiny 416 | 443 | 609 | **790** | 773 | **1774** | 1353 | **3.5x** | +|Tiny 416 CPU Core i7 7700HQ | 3.4 | - | - | 42 | - | 39 | **12x** | * Yolo v4 Full comparison: [map_fps](https://user-images.githubusercontent.com/4096485/80283279-0e303e00-871f-11ea-814c-870967d77fd1.png) * Yolo v4 tiny comparison: [tiny_fps](https://user-images.githubusercontent.com/4096485/85734112-6e366700-b705-11ea-95d1-fcba0de76d72.png) @@ -144,15 +146,15 @@ There are weights-file for different cfg-files (trained for MS COCO dataset): FPS on RTX 2070 (R) and Tesla V100 (V): * [yolov4x-mish.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4x-mish.cfg) - 640x640 - **67.9% mAP@0.5 (49.4% AP@0.5:0.95) - 23(R) FPS / 50(V) FPS** - 221 BFlops (110 FMA) - 381 MB: [yolov4x-mish.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.weights) - * pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.conv.166 + * pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.conv.166 * [yolov4-csp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-csp.cfg) - 202 MB: [yolov4-csp.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.weights) paper [Scaled Yolo v4](https://arxiv.org/abs/2011.08036) just change `width=` and `height=` parameters in `yolov4-csp.cfg` file and use the same `yolov4-csp.weights` file for all cases: * `width=640 height=640` in cfg: **66.2% mAP@0.5 (47.5% AP@0.5:0.95) - 70(V) FPS** - 120 (60 FMA) BFlops * `width=512 height=512` in cfg: **64.8% mAP@0.5 (46.2% AP@0.5:0.95) - 93(V) FPS** - 77 (39 FMA) BFlops - * pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.conv.142 - + * pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.conv.142 + * [yolov4.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg) - 245 MB: [yolov4.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights) (Google-drive mirror [yolov4.weights](https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT) ) paper [Yolo v4](https://arxiv.org/abs/2004.10934) just change `width=` and `height=` parameters in `yolov4.cfg` file and use the same `yolov4.weights` file for all cases: * `width=608 height=608` in cfg: **65.7% mAP@0.5 (43.5% AP@0.5:0.95) - 34(R) FPS / 62(V) FPS** - 128.5 BFlops @@ -213,11 +215,11 @@ You can get cfg-files by path: `darknet/cfg/` For YOLOv4 - convert `yolov4.weights`/`cfg` files to `yolov4.pb` by using [TNTWEN](https://github.com/TNTWEN/OpenVINO-YOLOV4) project, and to `yolov4.tflite` [TensorFlow-lite](https://www.tensorflow.org/lite/guide/get_started#2_convert_the_model_format) * **OpenCV** the fastest implementation of YOLOv4 for CPU (x86/ARM-Android), OpenCV can be compiled with [OpenVINO-backend](https://github.com/opencv/opencv/wiki/Intel's-Deep-Learning-Inference-Engine-backend) for running on (Myriad X / USB Neural Compute Stick / Arria FPGA), use `yolov4.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221) or [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) * **Intel OpenVINO 2021.2:** supports YOLOv4 (NPU Myriad X / USB Neural Compute Stick / Arria FPGA): https://devmesh.intel.com/projects/openvino-yolov4-49c756 read this [manual](https://github.com/TNTWEN/OpenVINO-YOLOV4) (old [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) ) (for [Scaled-YOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-large) models use https://github.com/Chen-MingChang/pytorch_YOLO_OpenVINO_demo ) -* **PyTorch > ONNX**: - * [WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4) - * [maudzung/3D-YOLOv4](https://github.com/maudzung/Complex-YOLOv4-Pytorch) - * [Tianxiaomo/pytorch-YOLOv4](https://github.com/Tianxiaomo/pytorch-YOLOv4) - * [YOLOv5](https://github.com/ultralytics/yolov5) +* **PyTorch > ONNX**: + * [WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4) + * [maudzung/3D-YOLOv4](https://github.com/maudzung/Complex-YOLOv4-Pytorch) + * [Tianxiaomo/pytorch-YOLOv4](https://github.com/Tianxiaomo/pytorch-YOLOv4) + * [YOLOv5](https://github.com/ultralytics/yolov5) * **ONNX** on Jetson for YOLOv4: https://developer.nvidia.com/blog/announcing-onnx-runtime-for-jetson/ * **TensorRT+tkDNN**: https://github.com/ceccocats/tkDNN#fps-results * **Deepstream 5.0 / TensorRT for YOLOv4** https://github.com/NVIDIA-AI-IOT/yolov4_deepstream or https://github.com/marcoslucianops/DeepStream-Yolo read [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) and [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf). Additionally [jkjung-avt/tensorrt_demos](https://github.com/jkjung-avt/tensorrt_demos) or [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) @@ -392,7 +394,7 @@ In Windows: This is the recommended approach to build Darknet on Windows. -1. Install Visual Studio 2017 or 2019. In case you need to download it, please go here: [Visual Studio Community](http://visualstudio.com) +1. Install Visual Studio 2017 or 2019. In case you need to download it, please go here: [Visual Studio Community](http://visualstudio.com). Remember to install English language pack, this is mandatory for vcpkg! 2. Install CUDA (at least v10.0) enabling VS Integration during installation. @@ -423,7 +425,6 @@ https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ Training Yolo v4 (and v3): 0. For training `cfg/yolov4-custom.cfg` download the pre-trained weights-file (162 MB): [yolov4.conv.137](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137) (Google drive mirror [yolov4.conv.137](https://drive.google.com/open?id=1JKF-bdIklxOOVy-2Cr5qdvjgGpmGfcbp) ) - 1. Create file `yolo-obj.cfg` with the same content as in `yolov4-custom.cfg` (or copy `yolov4-custom.cfg` to `yolo-obj.cfg)` and: * change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L3) @@ -445,9 +446,8 @@ Training Yolo v4 (and v3): * https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L789 So if `classes=1` then should be `filters=18`. If `classes=2` then write `filters=21`. - **(Do not write in the cfg-file: filters=(classes + 5)x3)** - + (Generally `filters` depends on the `classes`, `coords` and number of `mask`s, i.e. filters=`(classes + coords + 1)*`, where `mask` is indices of anchors. If `mask` is absence, then filters=`(classes + coords + 1)*num`) So for example, for 2 objects, your file `yolo-obj.cfg` should differ from `yolov4-custom.cfg` in such lines in each of **3** [yolo]-layers: @@ -461,7 +461,6 @@ classes=2 ``` 2. Create file `obj.names` in the directory `build\darknet\x64\data\`, with objects names - each in new line - 3. Create file `obj.data` in the directory `build\darknet\x64\data\`, containing (where **classes = number of objects**): ```ini @@ -473,7 +472,6 @@ classes=2 ``` 4. Put image-files (.jpg) of your objects in the directory `build\darknet\x64\data\obj\` - 5. You should label each object on images from your dataset. Use this visual GUI-software for marking bounded boxes of objects and generating annotation files for Yolo v2 & v3: https://github.com/AlexeyAB/Yolo_mark It will create `.txt`-file for each `.jpg`-image-file - in the same directory and with the same name, but with `.txt`-extension, and put to file: object number and object coordinates on this image, for each object in new line: @@ -509,12 +507,11 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an * for `yolov3.cfg, yolov3-spp.cfg` (154 MB): [darknet53.conv.74](https://pjreddie.com/media/files/darknet53.conv.74) * for `yolov3-tiny-prn.cfg , yolov3-tiny.cfg` (6 MB): [yolov3-tiny.conv.11](https://drive.google.com/file/d/18v36esoXCh-PsOKwyP2GWrpYDptDY8Zf/view?usp=sharing) * for `enet-coco.cfg (EfficientNetB0-Yolov3)` (14 MB): [enetb0-coco.conv.132](https://drive.google.com/file/d/1uhh3D6RSn0ekgmsaTcl-ZW53WBaUDo6j/view?usp=sharing) - 8. Start training by using the command line: `darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137` - + To train on Linux use command: `./darknet detector train data/obj.data yolo-obj.cfg yolov4.conv.137` (just use `./darknet` instead of `darknet.exe`) - + * (file `yolo-obj_last.weights` will be saved to the `build\darknet\x64\backup\` for each 100 iterations) * (file `yolo-obj_xxxx.weights` will be saved to the `build\darknet\x64\backup\` for each 1000 iterations) * (to disable Loss-Window use `darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -dont_show`, if you train on computer without monitor like a cloud Amazon EC2) @@ -529,18 +526,19 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an (in the original repository https://github.com/pjreddie/darknet the weights-file is saved only once every 10 000 iterations `if(iterations > 1000)`) * Also you can get result earlier than all 45000 iterations. - + **Note:** If during training you see `nan` values for `avg` (loss) field - then training goes wrong, but if `nan` is in some other lines - then training goes well. - + **Note:** If you changed width= or height= in your cfg-file, then new width and height must be divisible by 32. - + **Note:** After training use such command for detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights` - + **Note:** if error `Out of memory` occurs then in `.cfg`-file you should increase `subdivisions=16`, 32 or 64: [link](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4) - + ### How to train tiny-yolo (to detect your custom objects): Do all the same steps as for the full yolo model as described above. With the exception of: + * Download file with the first 29-convolutional layers of yolov4-tiny: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29 (Or get this file from yolov4-tiny.weights file by using command: `darknet.exe partial cfg/yolov4-tiny-custom.cfg yolov4-tiny.weights yolov4-tiny.conv.29 29` * Make your custom model `yolov4-tiny-obj.cfg` based on `cfg/yolov4-tiny-custom.cfg` instead of `yolov4.cfg` @@ -548,7 +546,7 @@ Do all the same steps as for the full yolo model as described above. With the ex For training Yolo based on other models ([DenseNet201-Yolo](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/densenet201_yolo.cfg) or [ResNet50-Yolo](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/resnet50_yolo.cfg)), you can download and get pre-trained weights as showed in this file: https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/partial.cmd If you made you custom model that isn't based on other models, then you can train it without pre-trained weights, then will be used random initial weights. - + ## When should I stop training: Usually sufficient 2000 iterations for each class(object), but not less than number of training images and not less than 6000 iterations in total. But for a more precise definition when you should stop training, use the following manual: @@ -613,15 +611,14 @@ In terms of Wiki, indicators Precision and Recall have a slightly different mean ![precision_recall_iou](https://hsto.org/files/ca8/866/d76/ca8866d76fb840228940dbf442a7f06a.jpg) - -### Custom object detection: +### Custom object detection Example of custom object detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights` | ![Yolo_v2_training](https://hsto.org/files/d12/1e7/515/d121e7515f6a4eb694913f10de5f2b61.jpg) | ![Yolo_v2_training](https://hsto.org/files/727/c7e/5e9/727c7e5e99bf4d4aa34027bb6a5e4bab.jpg) | |---|---| -## How to improve object detection: +## How to improve object detection 1. Before training: @@ -680,7 +677,7 @@ then set the same 9 `anchors` in each of 3 `[yolo]`-layers in your cfg-file. But * to get even greater accuracy you should train with higher resolution 608x608 or 832x832, note: if error `Out of memory` occurs then in `.cfg`-file you should increase `subdivisions=16`, 32 or 64: [link](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4) -## How to mark bounded boxes of objects and create annotation files: +## How to mark bounded boxes of objects and create annotation files Here you can find repository with GUI-software for marking bounded boxes of objects and generating annotation files for Yolo v2 - v4: https://github.com/AlexeyAB/Yolo_mark diff --git a/build.ps1 b/build.ps1 index 07fb47aafa1..6c7d297e9a1 100755 --- a/build.ps1 +++ b/build.ps1 @@ -1,5 +1,6 @@ #!/usr/bin/env pwsh + param ( [switch]$DisableInteractive = $false, [switch]$EnableCUDA = $false, @@ -15,12 +16,14 @@ param ( [switch]$ForceCPP = $false, [switch]$ForceStaticLib = $false, [switch]$ForceSetupVS = $false, - [switch]$ForceGCC8 = $false + [Int32]$ForceGCCVersion = 0 ) +$build_ps1_version = "0.9" Function MyThrow ($Message) { if ($DisableInteractive) { - Throw $Message + Write-Host $Message -ForegroundColor Red + exit } else { # Check if running in PowerShell ISE @@ -61,15 +64,19 @@ Function MyThrow ($Message) { 182, # Application 1 183 # Application 2 - Write-Host $Message + Write-Host $Message -ForegroundColor Red Write-Host -NoNewline "Press any key to continue..." - while ($null -eq $KeyInfo.VirtualKeyCode -or $Ignore -contains $KeyInfo.VirtualKeyCode) { + while (($null -eq $KeyInfo.VirtualKeyCode) -or ($Ignore -contains $KeyInfo.VirtualKeyCode)) { $KeyInfo = $Host.UI.RawUI.ReadKey("NoEcho, IncludeKeyDown") } exit } } +Write-Host "Darknet build script version ${build_ps1_version}" +Write-Host -NoNewLine "PowerShell version:" +$PSVersionTable.PSVersion + if ($PSVersionTable.PSVersion.Major -eq 5) { $IsWindowsPowerShell = $true } @@ -78,30 +85,30 @@ if ($PSVersionTable.PSVersion.Major -lt 5) { MyThrow("Your PowerShell version is too old, please update it.") } -if (-Not $DisableInteractive -and -Not $UseVCPKG) { +if ((-Not $DisableInteractive) -and (-Not $UseVCPKG)) { $Result = Read-Host "Enable vcpkg to install darknet dependencies (yes/no)" - if ($Result -eq 'Yes' -or $Result -eq 'Y' -or $Result -eq 'yes' -or $Result -eq 'y') { + if (($Result -eq 'Yes') -or ($Result -eq 'Y') -or ($Result -eq 'yes') -or ($Result -eq 'y')) { $UseVCPKG = $true } } -if (-Not $DisableInteractive -and -Not $EnableCUDA -and -Not $IsMacOS) { +if ((-Not $DisableInteractive) -and (-Not $EnableCUDA) -and (-Not $IsMacOS)) { $Result = Read-Host "Enable CUDA integration (yes/no)" - if ($Result -eq 'Yes' -or $Result -eq 'Y' -or $Result -eq 'yes' -or $Result -eq 'y') { + if (($Result -eq 'Yes') -or ($Result -eq 'Y') -or ($Result -eq 'yes') -or ($Result -eq 'y')) { $EnableCUDA = $true } } -if ($EnableCUDA -and -Not $DisableInteractive -and -Not $EnableCUDNN) { +if ($EnableCUDA -and (-Not $DisableInteractive) -and (-Not $EnableCUDNN)) { $Result = Read-Host "Enable CUDNN optional dependency (yes/no)" - if ($Result -eq 'Yes' -or $Result -eq 'Y' -or $Result -eq 'yes' -or $Result -eq 'y') { + if (($Result -eq 'Yes') -or ($Result -eq 'Y') -or ($Result -eq 'yes') -or ($Result -eq 'y')) { $EnableCUDNN = $true } } -if (-Not $DisableInteractive -and -Not $EnableOPENCV) { +if ((-Not $DisableInteractive) -and (-Not $EnableOPENCV)) { $Result = Read-Host "Enable OpenCV optional dependency (yes/no)" - if ($Result -eq 'Yes' -or $Result -eq 'Y' -or $Result -eq 'yes' -or $Result -eq 'y') { + if (($Result -eq 'Yes') -or ($Result -eq 'Y') -or ($Result -eq 'yes') -or ($Result -eq 'y')) { $EnableOPENCV = $true } } @@ -115,9 +122,11 @@ if ($IsLinux -or $IsMacOS) { elseif ($IsWindows -or $IsWindowsPowerShell) { $bootstrap_ext = ".bat" } -Write-Host "Native shell script extension: ${bootstrap_ext}" +if ($UseVCPKG) { + Write-Host "vcpkg bootstrap script: bootstrap-vcpkg${bootstrap_ext}" +} -if (-Not $IsWindows -and -not $IsWindowsPowerShell -and -Not $ForceSetupVS) { +if ((-Not $IsWindows) -and (-Not $IsWindowsPowerShell) -and (-Not $ForceSetupVS)) { $DoNotSetupVS = $true } @@ -126,13 +135,13 @@ if ($ForceStaticLib) { $additional_build_setup = " -DBUILD_SHARED_LIBS=OFF " } -if ($IsLinux -and $ForceGCC8) { - Write-Host "Manually setting CC and CXX variables to gcc-8 and g++-8" - $env:CC = "gcc-8" - $env:CXX = "g++-8" +if (($IsLinux -or $IsMacOS) -and ($ForceGCCVersion -gt 0)) { + Write-Host "Manually setting CC and CXX variables to gcc version $ForceGCCVersion" + $env:CC = "gcc-$ForceGCCVersion" + $env:CXX = "g++-$ForceGCCVersion" } -if (($IsWindows -or $IsWindowsPowerShell) -and -Not $env:VCPKG_DEFAULT_TRIPLET) { +if (($IsWindows -or $IsWindowsPowerShell) -and (-Not $env:VCPKG_DEFAULT_TRIPLET)) { $env:VCPKG_DEFAULT_TRIPLET = "x64-windows" } @@ -165,18 +174,18 @@ else { Write-Host "OPENCV is disabled, please pass -EnableOPENCV to the script to enable" } -if ($EnableCUDA -and $EnableOPENCV -and -not $EnableOPENCV_CUDA) { +if ($EnableCUDA -and $EnableOPENCV -and (-Not $EnableOPENCV_CUDA)) { Write-Host "OPENCV with CUDA extension is not enabled, you can enable it passing -EnableOPENCV_CUDA" } -elseif ($EnableOPENCV -and $EnableOPENCV_CUDA -and -not $EnableCUDA) { +elseif ($EnableOPENCV -and $EnableOPENCV_CUDA -and (-Not $EnableCUDA)) { Write-Host "OPENCV with CUDA extension was requested, but CUDA is not enabled, you can enable it passing -EnableCUDA" $EnableOPENCV_CUDA = $false } -elseif ($EnableCUDA -and $EnableOPENCV_CUDA -and -not $EnableOPENCV) { +elseif ($EnableCUDA -and $EnableOPENCV_CUDA -and (-Not $EnableOPENCV)) { Write-Host "OPENCV with CUDA extension was requested, but OPENCV is not enabled, you can enable it passing -EnableOPENCV" $EnableOPENCV_CUDA = $false } -elseif ($EnableOPENCV_CUDA -and -not $EnableCUDA -and -not $EnableOPENCV) { +elseif ($EnableOPENCV_CUDA -and (-Not $EnableCUDA) -and (-Not $EnableOPENCV)) { Write-Host "OPENCV with CUDA extension was requested, but OPENCV and CUDA are not enabled, you can enable them passing -EnableOPENCV -EnableCUDA" $EnableOPENCV_CUDA = $false } @@ -225,12 +234,19 @@ else { Write-Host "Using git from ${GIT_EXE}" } -if ((Test-Path "$PSScriptRoot/.git") -and -not $DoNotUpdateDARKNET) { - $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "pull" - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-not $exitCode -eq 0) { - MyThrow("Updating darknet sources failed! Exited with $exitCode.") +if (Test-Path "$PSScriptRoot/.git") { + Write-Host "Darknet has been cloned with git and supports self-updating mechanism" + if ($DoNotUpdateDARKNET) { + Write-Host "Darknet will not self-update sources" -ForegroundColor Yellow + } + else { + Write-Host "Darknet will self-update sources, please pass -DoNotUpdateDARKNET to the script to disable" + $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "pull" + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-Not ($exitCode -eq 0)) { + MyThrow("Updating darknet sources failed! Exited with error code $exitCode.") + } } } @@ -240,6 +256,12 @@ if (-Not $CMAKE_EXE) { } else { Write-Host "Using CMake from ${CMAKE_EXE}" + $proc = Start-Process -NoNewWindow -PassThru -FilePath ${CMAKE_EXE} -ArgumentList "--version" + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-Not ($exitCode -eq 0)) { + MyThrow("CMake version check failed! Exited with error code $exitCode.") + } } if (-Not $DoNotUseNinja) { @@ -250,7 +272,17 @@ if (-Not $DoNotUseNinja) { } else { Write-Host "Using Ninja from ${NINJA_EXE}" - $generator = "Ninja" + Write-Host -NoNewLine "Ninja version " + $proc = Start-Process -NoNewWindow -PassThru -FilePath ${NINJA_EXE} -ArgumentList "--version" + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-Not ($exitCode -eq 0)) { + $DoNotUseNinja = $true + Write-Host "Unable to run Ninja previously found, using msbuild or make backends as a fallback" -ForegroundColor Yellow + } + else { + $generator = "Ninja" + } } } @@ -347,8 +379,8 @@ elseif ($UseVCPKG) { $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "clone https://github.com/microsoft/vcpkg" $proc.WaitForExit() $exitCode = $proc.ExitCode - if (-not $exitCode -eq 0) { - MyThrow("Cloning vcpkg sources failed! Exited with $exitCode.") + if (-not ($exitCode -eq 0)) { + MyThrow("Cloning vcpkg sources failed! Exited with error code $exitCode.") } } $vcpkg_path = "$PWD/vcpkg" @@ -361,19 +393,19 @@ else { $additional_build_setup = $additional_build_setup + " -DENABLE_VCPKG_INTEGRATION:BOOL=OFF" } -if ($UseVCPKG -and (Test-Path "$vcpkg_path/.git") -and -not $DoNotUpdateVCPKG) { +if ($UseVCPKG -and (Test-Path "$vcpkg_path/.git") -and (-Not $DoNotUpdateVCPKG)) { Push-Location $vcpkg_path $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "pull" $proc.WaitForExit() $exitCode = $proc.ExitCode - if (-not $exitCode -eq 0) { - MyThrow("Updating vcpkg sources failed! Exited with $exitCode.") + if (-Not ($exitCode -eq 0)) { + MyThrow("Updating vcpkg sources failed! Exited with error code $exitCode.") } $proc = Start-Process -NoNewWindow -PassThru -FilePath $PWD/bootstrap-vcpkg${bootstrap_ext} -ArgumentList "-disableMetrics" $proc.WaitForExit() $exitCode = $proc.ExitCode - if (-not $exitCode -eq 0) { - MyThrow("Bootstrapping vcpkg failed! Exited with $exitCode.") + if (-Not ($exitCode -eq 0)) { + MyThrow("Bootstrapping vcpkg failed! Exited with error code $exitCode.") } Pop-Location } @@ -384,7 +416,7 @@ if ($UseVCPKG -and ($vcpkg_path.length -gt 40) -and ($IsWindows -or $IsWindowsPo Write-Host "You can use the subst command to ease the process if necessary" -ForegroundColor Yellow if (-Not $DisableInteractive) { $Result = Read-Host "Do you still want to continue? (yes/no)" - if ($Result -eq 'No' -or $Result -eq 'N' -or $Result -eq 'no' -or $Result -eq 'n') { + if (($Result -eq 'No') -or ($Result -eq 'N') -or ($Result -eq 'no') -or ($Result -eq 'n')) { MyThrow("Build aborted") } } @@ -462,19 +494,19 @@ if ($ForceCPP) { $additional_build_setup = $additional_build_setup + " -DBUILD_AS_CPP:BOOL=ON" } -if (-Not($EnableCUDA)) { +if (-Not $EnableCUDA) { $additional_build_setup = $additional_build_setup + " -DENABLE_CUDA:BOOL=OFF" } -if (-Not($EnableCUDNN)) { +if (-Not $EnableCUDNN) { $additional_build_setup = $additional_build_setup + " -DENABLE_CUDNN:BOOL=OFF" } -if (-Not($EnableOPENCV)) { +if (-Not $EnableOPENCV) { $additional_build_setup = $additional_build_setup + " -DENABLE_OPENCV:BOOL=OFF" } -if (-Not($EnableOPENCV_CUDA)) { +if (-Not $EnableOPENCV_CUDA) { $additional_build_setup = $additional_build_setup + " -DVCPKG_BUILD_OPENCV_WITH_CUDA:BOOL=OFF" } @@ -484,28 +516,31 @@ if (-Not $DoNotDeleteBuildFolder) { Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $build_folder } -New-Item -Path $build_folder -ItemType directory -Force +New-Item -Path $build_folder -ItemType directory -Force | Out-Null Set-Location $build_folder $cmake_args = "-G `"$generator`" ${additional_build_setup} -S .." +Write-Host "Configuring CMake project" -ForegroundColor Green Write-Host "CMake args: $cmake_args" $proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList $cmake_args $proc.WaitForExit() $exitCode = $proc.ExitCode -if (-not $exitCode -eq 0) { - MyThrow("Config failed! Exited with $exitCode.") +if (-Not ($exitCode -eq 0)) { + MyThrow("Config failed! Exited with error code $exitCode.") } +Write-Host "Building CMake project" -ForegroundColor Green $proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList "--build . ${selectConfig} --parallel ${number_of_build_workers} --target install" $proc.WaitForExit() $exitCode = $proc.ExitCode -if (-not $exitCode -eq 0) { - MyThrow("Config failed! Exited with $exitCode.") +if (-Not ($exitCode -eq 0)) { + MyThrow("Config failed! Exited with error code $exitCode.") } -Remove-Item DarknetConfig.cmake -Remove-Item DarknetConfigVersion.cmake +Remove-Item -Force -ErrorAction SilentlyContinue DarknetConfig.cmake +Remove-Item -Force -ErrorAction SilentlyContinue DarknetConfigVersion.cmake $dllfiles = Get-ChildItem ./${dllfolder}/*.dll if ($dllfiles) { Copy-Item $dllfiles .. } Set-Location .. Copy-Item cmake/Modules/*.cmake share/darknet/ +Write-Host "Build complete!" -ForegroundColor Green Pop-Location From bdb5fe3e91988b586382fa1674e6315032f142cb Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 18 May 2021 21:47:15 +0200 Subject: [PATCH 1241/1365] fixes for PowerShell ISE (#7715) --- build.ps1 | 76 +++++++++++++++++++++++++++++++------------------------ 1 file changed, 43 insertions(+), 33 deletions(-) diff --git a/build.ps1 b/build.ps1 index 6c7d297e9a1..38282cf8329 100755 --- a/build.ps1 +++ b/build.ps1 @@ -16,14 +16,16 @@ param ( [switch]$ForceCPP = $false, [switch]$ForceStaticLib = $false, [switch]$ForceSetupVS = $false, - [Int32]$ForceGCCVersion = 0 + [Int32]$ForceGCCVersion = 0, + [Int32]$NumberOfBuildWorkers = 8, + [string]$AdditionalBuildSetup = "" # "-DCMAKE_CUDA_ARCHITECTURES=30" ) -$build_ps1_version = "0.9" +$build_ps1_version = "0.9.1" Function MyThrow ($Message) { if ($DisableInteractive) { Write-Host $Message -ForegroundColor Red - exit + throw } else { # Check if running in PowerShell ISE @@ -32,7 +34,7 @@ Function MyThrow ($Message) { # Show MessageBox UI $Shell = New-Object -ComObject "WScript.Shell" $Shell.Popup($Message, 0, "OK", 0) - return + throw } $Ignore = @@ -69,21 +71,12 @@ Function MyThrow ($Message) { while (($null -eq $KeyInfo.VirtualKeyCode) -or ($Ignore -contains $KeyInfo.VirtualKeyCode)) { $KeyInfo = $Host.UI.RawUI.ReadKey("NoEcho, IncludeKeyDown") } - exit + Write-Host "" + throw } } Write-Host "Darknet build script version ${build_ps1_version}" -Write-Host -NoNewLine "PowerShell version:" -$PSVersionTable.PSVersion - -if ($PSVersionTable.PSVersion.Major -eq 5) { - $IsWindowsPowerShell = $true -} - -if ($PSVersionTable.PSVersion.Major -lt 5) { - MyThrow("Your PowerShell version is too old, please update it.") -} if ((-Not $DisableInteractive) -and (-Not $UseVCPKG)) { $Result = Read-Host "Enable vcpkg to install darknet dependencies (yes/no)" @@ -113,8 +106,17 @@ if ((-Not $DisableInteractive) -and (-Not $EnableOPENCV)) { } } -$number_of_build_workers = 8 -#$additional_build_setup = " -DCMAKE_CUDA_ARCHITECTURES=30" +Write-Host -NoNewLine "PowerShell version:" +$PSVersionTable.PSVersion + +if ($PSVersionTable.PSVersion.Major -eq 5) { + $IsWindowsPowerShell = $true +} + +if ($PSVersionTable.PSVersion.Major -lt 5) { + MyThrow("Your PowerShell version is too old, please update it.") +} + if ($IsLinux -or $IsMacOS) { $bootstrap_ext = ".sh" @@ -132,7 +134,7 @@ if ((-Not $IsWindows) -and (-Not $IsWindowsPowerShell) -and (-Not $ForceSetupVS) if ($ForceStaticLib) { Write-Host "Forced CMake to produce a static library" - $additional_build_setup = " -DBUILD_SHARED_LIBS=OFF " + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DBUILD_SHARED_LIBS=OFF " } if (($IsLinux -or $IsMacOS) -and ($ForceGCCVersion -gt 0)) { @@ -242,6 +244,7 @@ if (Test-Path "$PSScriptRoot/.git") { else { Write-Host "Darknet will self-update sources, please pass -DoNotUpdateDARKNET to the script to disable" $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "pull" + $handle = $proc.Handle $proc.WaitForExit() $exitCode = $proc.ExitCode if (-Not ($exitCode -eq 0)) { @@ -257,6 +260,7 @@ if (-Not $CMAKE_EXE) { else { Write-Host "Using CMake from ${CMAKE_EXE}" $proc = Start-Process -NoNewWindow -PassThru -FilePath ${CMAKE_EXE} -ArgumentList "--version" + $handle = $proc.Handle $proc.WaitForExit() $exitCode = $proc.ExitCode if (-Not ($exitCode -eq 0)) { @@ -274,6 +278,7 @@ if (-Not $DoNotUseNinja) { Write-Host "Using Ninja from ${NINJA_EXE}" Write-Host -NoNewLine "Ninja version " $proc = Start-Process -NoNewWindow -PassThru -FilePath ${NINJA_EXE} -ArgumentList "--version" + $handle = $proc.Handle $proc.WaitForExit() $exitCode = $proc.ExitCode if (-Not ($exitCode -eq 0)) { @@ -358,25 +363,26 @@ function getLatestVisualStudioWithDesktopWorkloadVersion() { if ((Test-Path env:VCPKG_ROOT) -and $UseVCPKG) { $vcpkg_path = "$env:VCPKG_ROOT" Write-Host "Found vcpkg in VCPKG_ROOT: $vcpkg_path" - $additional_build_setup = $additional_build_setup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" } elseif ((Test-Path "${env:WORKSPACE}/vcpkg") -and $UseVCPKG) { $vcpkg_path = "${env:WORKSPACE}/vcpkg" $env:VCPKG_ROOT = "${env:WORKSPACE}/vcpkg" Write-Host "Found vcpkg in WORKSPACE/vcpkg: $vcpkg_path" - $additional_build_setup = $additional_build_setup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" } elseif (-not($null -eq ${RUNVCPKG_VCPKG_ROOT_OUT})) { if((Test-Path "${RUNVCPKG_VCPKG_ROOT_OUT}") -and $UseVCPKG) { $vcpkg_path = "${RUNVCPKG_VCPKG_ROOT_OUT}" $env:VCPKG_ROOT = "${RUNVCPKG_VCPKG_ROOT_OUT}" Write-Host "Found vcpkg in RUNVCPKG_VCPKG_ROOT_OUT: ${vcpkg_path}" - $additional_build_setup = $additional_build_setup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" } } elseif ($UseVCPKG) { if (-Not (Test-Path "$PWD/vcpkg")) { $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "clone https://github.com/microsoft/vcpkg" + $handle = $proc.Handle $proc.WaitForExit() $exitCode = $proc.ExitCode if (-not ($exitCode -eq 0)) { @@ -386,22 +392,24 @@ elseif ($UseVCPKG) { $vcpkg_path = "$PWD/vcpkg" $env:VCPKG_ROOT = "$PWD/vcpkg" Write-Host "Found vcpkg in $PWD/vcpkg: $PWD/vcpkg" - $additional_build_setup = $additional_build_setup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" } else { Write-Host "Skipping vcpkg integration`n" -ForegroundColor Yellow - $additional_build_setup = $additional_build_setup + " -DENABLE_VCPKG_INTEGRATION:BOOL=OFF" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=OFF" } if ($UseVCPKG -and (Test-Path "$vcpkg_path/.git") -and (-Not $DoNotUpdateVCPKG)) { Push-Location $vcpkg_path $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "pull" + $handle = $proc.Handle $proc.WaitForExit() $exitCode = $proc.ExitCode if (-Not ($exitCode -eq 0)) { MyThrow("Updating vcpkg sources failed! Exited with error code $exitCode.") } $proc = Start-Process -NoNewWindow -PassThru -FilePath $PWD/bootstrap-vcpkg${bootstrap_ext} -ArgumentList "-disableMetrics" + $handle = $proc.Handle $proc.WaitForExit() $exitCode = $proc.ExitCode if (-Not ($exitCode -eq 0)) { @@ -444,15 +452,15 @@ if (-Not $DoNotSetupVS) { $selectConfig = " --config Release " if ($tokens[0] -eq "14") { $generator = "Visual Studio 14 2015" - $additional_build_setup = $additional_build_setup + " -T `"host=x64`" -A `"x64`"" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -T `"host=x64`" -A `"x64`"" } elseif ($tokens[0] -eq "15") { $generator = "Visual Studio 15 2017" - $additional_build_setup = $additional_build_setup + " -T `"host=x64`" -A `"x64`"" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -T `"host=x64`" -A `"x64`"" } elseif ($tokens[0] -eq "16") { $generator = "Visual Studio 16 2019" - $additional_build_setup = $additional_build_setup + " -T `"host=x64`" -A `"x64`"" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -T `"host=x64`" -A `"x64`"" } else { MyThrow("Unknown Visual Studio version, unsupported configuration") @@ -491,23 +499,23 @@ if (-Not $IsMacOS -and $EnableCUDA) { } if ($ForceCPP) { - $additional_build_setup = $additional_build_setup + " -DBUILD_AS_CPP:BOOL=ON" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DBUILD_AS_CPP:BOOL=ON" } if (-Not $EnableCUDA) { - $additional_build_setup = $additional_build_setup + " -DENABLE_CUDA:BOOL=OFF" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_CUDA:BOOL=OFF" } if (-Not $EnableCUDNN) { - $additional_build_setup = $additional_build_setup + " -DENABLE_CUDNN:BOOL=OFF" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_CUDNN:BOOL=OFF" } if (-Not $EnableOPENCV) { - $additional_build_setup = $additional_build_setup + " -DENABLE_OPENCV:BOOL=OFF" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_OPENCV:BOOL=OFF" } if (-Not $EnableOPENCV_CUDA) { - $additional_build_setup = $additional_build_setup + " -DVCPKG_BUILD_OPENCV_WITH_CUDA:BOOL=OFF" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DVCPKG_BUILD_OPENCV_WITH_CUDA:BOOL=OFF" } $build_folder = "./build_release" @@ -518,17 +526,19 @@ if (-Not $DoNotDeleteBuildFolder) { New-Item -Path $build_folder -ItemType directory -Force | Out-Null Set-Location $build_folder -$cmake_args = "-G `"$generator`" ${additional_build_setup} -S .." +$cmake_args = "-G `"$generator`" ${AdditionalBuildSetup} -S .." Write-Host "Configuring CMake project" -ForegroundColor Green Write-Host "CMake args: $cmake_args" $proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList $cmake_args +$handle = $proc.Handle $proc.WaitForExit() $exitCode = $proc.ExitCode if (-Not ($exitCode -eq 0)) { MyThrow("Config failed! Exited with error code $exitCode.") } Write-Host "Building CMake project" -ForegroundColor Green -$proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList "--build . ${selectConfig} --parallel ${number_of_build_workers} --target install" +$proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList "--build . ${selectConfig} --parallel ${NumberOfBuildWorkers} --target install" +$handle = $proc.Handle $proc.WaitForExit() $exitCode = $proc.ExitCode if (-Not ($exitCode -eq 0)) { From ac8ebca0639f445ae456a1da05a13496ae0fcdc2 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 19 May 2021 22:46:20 +0200 Subject: [PATCH 1242/1365] [build.ps1] download ninja automatically if missing + other small updates (#7721) * self download ninja if not found on system and not requested to opt-out * add a flag to clean up vcpkg local binary cache to force restart from scratch * really delete if requested --- .gitignore | 2 ++ build.ps1 | 60 ++++++++++++++++++++++++++++++++++++++++++++++++++---- 2 files changed, 58 insertions(+), 4 deletions(-) diff --git a/.gitignore b/.gitignore index 174f0b5a378..716233d08c3 100644 --- a/.gitignore +++ b/.gitignore @@ -22,6 +22,8 @@ cfg/ temp/ build/darknet/* build_*/ +ninja/ +ninja.zip vcpkg_installed/ !build/darknet/YoloWrapper.cs .fuse* diff --git a/build.ps1 b/build.ps1 index 38282cf8329..9e33c3f5186 100755 --- a/build.ps1 +++ b/build.ps1 @@ -15,13 +15,15 @@ param ( [switch]$DoNotUseNinja = $false, [switch]$ForceCPP = $false, [switch]$ForceStaticLib = $false, + [switch]$ForceVCPKGCacheRemoval = $false, [switch]$ForceSetupVS = $false, [Int32]$ForceGCCVersion = 0, [Int32]$NumberOfBuildWorkers = 8, [string]$AdditionalBuildSetup = "" # "-DCMAKE_CUDA_ARCHITECTURES=30" ) -$build_ps1_version = "0.9.1" +$build_ps1_version = "0.9.2" + Function MyThrow ($Message) { if ($DisableInteractive) { Write-Host $Message -ForegroundColor Red @@ -76,6 +78,28 @@ Function MyThrow ($Message) { } } +Function DownloadNinja() { + Write-Host "Unable to find Ninja, downloading a portable version on-the-fly" -ForegroundColor Yellow + Remove-Item -Force -Recurse -ErrorAction SilentlyContinue ninja + Remove-Item -Force -ErrorAction SilentlyContinue ninja.zip + if ($IsWindows -or $IsWindowsPowerShell) { + $url = "https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-win.zip" + } + elseif ($IsLinux) { + $url = "https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-linux.zip" + } + elseif ($IsMacOS) { + $url = "https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-mac.zip" + } + else { + MyThrow("Unknown OS, unsupported") + } + Invoke-RestMethod -Uri $url -Method Get -ContentType application/zip -OutFile "ninja.zip" + Expand-Archive -Path ninja.zip + Remove-Item -Force -ErrorAction SilentlyContinue ninja.zip +} + + Write-Host "Darknet build script version ${build_ps1_version}" if ((-Not $DisableInteractive) -and (-Not $UseVCPKG)) { @@ -271,10 +295,15 @@ else { if (-Not $DoNotUseNinja) { $NINJA_EXE = Get-Command ninja 2> $null | Select-Object -ExpandProperty Definition if (-Not $NINJA_EXE) { - $DoNotUseNinja = $true - Write-Host "Could not find Ninja, using msbuild or make backends as a fallback" -ForegroundColor Yellow + DownloadNinja + $env:PATH += ";${PSScriptRoot}/ninja" + $NINJA_EXE = Get-Command ninja 2> $null | Select-Object -ExpandProperty Definition + if (-Not $NINJA_EXE) { + $DoNotUseNinja = $true + Write-Host "Could not find Ninja, unable to download a portable ninja, using msbuild or make backends as a fallback" -ForegroundColor Yellow + } } - else { + if ($NINJA_EXE) { Write-Host "Using Ninja from ${NINJA_EXE}" Write-Host -NoNewLine "Ninja version " $proc = Start-Process -NoNewWindow -PassThru -FilePath ${NINJA_EXE} -ArgumentList "--version" @@ -287,6 +316,7 @@ if (-Not $DoNotUseNinja) { } else { $generator = "Ninja" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DCMAKE_BUILD_TYPE=Release" } } } @@ -430,6 +460,27 @@ if ($UseVCPKG -and ($vcpkg_path.length -gt 40) -and ($IsWindows -or $IsWindowsPo } } +if ($ForceVCPKGCacheRemoval -and (-Not $UseVCPKG)) { + Write-Host "VCPKG is not enabled, so local vcpkg binary cache will not be deleted even if requested" -ForegroundColor Yellow +} + +if ($UseVCPKG -and $ForceVCPKGCacheRemoval) { + if ($IsWindows -or $IsWindowsPowerShell) { + $vcpkgbinarycachepath = "$env:LOCALAPPDATA/vcpkg/archive" + } + elseif ($IsLinux) { + $vcpkgbinarycachepath = "$env:HOME/.cache/vcpkg/archive" + } + elseif ($IsMacOS) { + $vcpkgbinarycachepath = "$env:HOME/.cache/vcpkg/archive" + } + else { + MyThrow("Unknown OS, unsupported") + } + Write-Host "Removing local vcpkg binary cache from $vcpkgbinarycachepath" -ForegroundColor Yellow + Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $vcpkgbinarycachepath +} + if (-Not $DoNotSetupVS) { if ($null -eq (Get-Command "cl.exe" -ErrorAction SilentlyContinue)) { $vsfound = getLatestVisualStudioWithDesktopWorkloadPath @@ -472,6 +523,7 @@ if (-Not $DoNotSetupVS) { } if ($DoNotSetupVS -and $DoNotUseNinja) { $generator = "Unix Makefiles" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DCMAKE_BUILD_TYPE=Release" } Write-Host "Setting up environment to use CMake generator: $generator" From 5853e51d604712918bd2fb23bab0ec82d19a88f9 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 26 May 2021 22:58:29 +0200 Subject: [PATCH 1243/1365] [build.ps1] fix 64bit build with developer powershell for VS19 (#7740) * [build.ps1] use x64 also on developer powershell for VS * improve handling of errors on windows platform * [build.ps1] bump version * improve logic handling in uselib_track --- CMakeLists.txt | 12 +++++++----- build.ps1 | 16 +++++++++------- 2 files changed, 16 insertions(+), 12 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 49df87cf3a8..adfc709ac30 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -237,17 +237,19 @@ set(CMAKE_CXX_FLAGS "${ADDITIONAL_CXX_FLAGS} ${SHAREDLIB_CXX_FLAGS} ${CMAKE_CXX_ set(CMAKE_C_FLAGS "${ADDITIONAL_C_FLAGS} ${SHAREDLIB_C_FLAGS} ${CMAKE_C_FLAGS}") if(OpenCV_FOUND) - if(ENABLE_CUDA AND NOT OpenCV_CUDA_VERSION) - set(BUILD_USELIB_TRACK "FALSE" CACHE BOOL "Build uselib_track" FORCE) - message(STATUS " -> darknet is fine for now, but uselib_track has been disabled!") - message(STATUS " -> Please rebuild OpenCV from sources with CUDA support to enable it") - elseif(ENABLE_CUDA AND OpenCV_CUDA_VERSION) + if(ENABLE_CUDA AND OpenCV_CUDA_VERSION) if(TARGET opencv_cudaoptflow) list(APPEND OpenCV_LINKED_COMPONENTS "opencv_cudaoptflow") endif() if(TARGET opencv_cudaimgproc) list(APPEND OpenCV_LINKED_COMPONENTS "opencv_cudaimgproc") endif() + elseif(ENABLE_CUDA AND NOT OpenCV_CUDA_VERSION) + set(BUILD_USELIB_TRACK "FALSE" CACHE BOOL "Build uselib_track" FORCE) + message(STATUS " -> darknet is fine for now, but uselib_track has been disabled!") + message(STATUS " -> Please rebuild OpenCV from sources with CUDA support to enable it") + else() + set(BUILD_USELIB_TRACK "FALSE" CACHE BOOL "Build uselib_track" FORCE) endif() endif() diff --git a/build.ps1 b/build.ps1 index 9e33c3f5186..61e70425783 100755 --- a/build.ps1 +++ b/build.ps1 @@ -22,7 +22,7 @@ param ( [string]$AdditionalBuildSetup = "" # "-DCMAKE_CUDA_ARCHITECTURES=30" ) -$build_ps1_version = "0.9.2" +$build_ps1_version = "0.9.3" Function MyThrow ($Message) { if ($DisableInteractive) { @@ -252,7 +252,7 @@ else { Push-Location $PSScriptRoot -$GIT_EXE = Get-Command git 2> $null | Select-Object -ExpandProperty Definition +$GIT_EXE = Get-Command "git" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition if (-Not $GIT_EXE) { MyThrow("Could not find git, please install it") } @@ -277,7 +277,7 @@ if (Test-Path "$PSScriptRoot/.git") { } } -$CMAKE_EXE = Get-Command cmake 2> $null | Select-Object -ExpandProperty Definition +$CMAKE_EXE = Get-Command "cmake" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition if (-Not $CMAKE_EXE) { MyThrow("Could not find CMake, please install it") } @@ -293,11 +293,11 @@ else { } if (-Not $DoNotUseNinja) { - $NINJA_EXE = Get-Command ninja 2> $null | Select-Object -ExpandProperty Definition + $NINJA_EXE = Get-Command "ninja" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition if (-Not $NINJA_EXE) { DownloadNinja $env:PATH += ";${PSScriptRoot}/ninja" - $NINJA_EXE = Get-Command ninja 2> $null | Select-Object -ExpandProperty Definition + $NINJA_EXE = Get-Command "ninja" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition if (-Not $NINJA_EXE) { $DoNotUseNinja = $true Write-Host "Could not find Ninja, unable to download a portable ninja, using msbuild or make backends as a fallback" -ForegroundColor Yellow @@ -482,7 +482,8 @@ if ($UseVCPKG -and $ForceVCPKGCacheRemoval) { } if (-Not $DoNotSetupVS) { - if ($null -eq (Get-Command "cl.exe" -ErrorAction SilentlyContinue)) { + $CL_EXE = Get-Command "cl" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition + if ((-Not $CL_EXE) -or ($CL_EXE -match "HostX86\\x86") -or ($CL_EXE -match "HostX64\\x86")) { $vsfound = getLatestVisualStudioWithDesktopWorkloadPath Write-Host "Found VS in ${vsfound}" Push-Location "${vsfound}\Common7\Tools" @@ -528,7 +529,8 @@ if ($DoNotSetupVS -and $DoNotUseNinja) { Write-Host "Setting up environment to use CMake generator: $generator" if (-Not $IsMacOS -and $EnableCUDA) { - if ($null -eq (Get-Command "nvcc" -ErrorAction SilentlyContinue)) { + $NVCC_EXE = Get-Command "nvcc" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition + if (-Not $NVCC_EXE) { if (Test-Path env:CUDA_PATH) { $env:PATH += ";${env:CUDA_PATH}/bin" Write-Host "Found cuda in ${env:CUDA_PATH}" From 8c85eb7784f0d85a1515dc2499479bbf118f2210 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 4 Jun 2021 14:02:07 +0200 Subject: [PATCH 1244/1365] [build.ps1/setup.sh] do not export vcpkg_root anymore, add setup scripts to CI (#7751) * [build.ps1] do not export vcpkg_root anymore * [README.md] add a build step related to Set-ExecutionPolicy on Windows * use scope currentuser to avoid admin powershell * simplify even more set-executionpolicy command order * remove unnecessary decoration to shell commands to improve user experience with copy button * write instructions to build using only cmake * Update README.md * add target install * improve setup scripts for future tests * improve setup.sh script for more tool installations and add command line flags handling * add CI tests for setup scripts * use nuget artifact cache to improve CI build times * disable interactivity for build script when called from setup script, add kitware gpg key in CI * enable cudnn in setup.sh * add a workaround for missing nvidia drivers in CI * setup CUDA env variables * remove some very slow builds from travis and move them to github actions * chmod sh scripts * use deploy-cuda scripts also in PR pipelines * install asm tools * restore links missing for CI when using deploy-cuda.sh * enable cudnn on ubuntu ci pipelines * pass CUDA env variables * add missing cuda install for opencv2 and opencv3 ci pipelines * remove unnecessary steps Co-authored-by: Alexey --- .github/workflows/ccpp.yml | 182 ++++++++--- .github/workflows/on_pr.yml | 170 ++++++++--- .gitignore | 1 + .travis.yml | 60 ---- CMakeLists.txt | 39 ++- README.md | 555 +++++++++++++++++----------------- build.ps1 | 30 +- scripts/deploy-cuda.sh | 38 +++ scripts/dice_label.sh | 0 scripts/get_coco2017.sh | 0 scripts/get_coco_dataset.sh | 0 scripts/get_imagenet_train.sh | 0 scripts/imagenet_label.sh | 0 scripts/install_OpenCV4.sh | 0 scripts/setup.ps1 | 32 +- scripts/setup.sh | 119 ++++---- 16 files changed, 718 insertions(+), 508 deletions(-) create mode 100755 scripts/deploy-cuda.sh mode change 100644 => 100755 scripts/dice_label.sh mode change 100644 => 100755 scripts/get_coco2017.sh mode change 100644 => 100755 scripts/get_coco_dataset.sh mode change 100644 => 100755 scripts/get_imagenet_train.sh mode change 100644 => 100755 scripts/imagenet_label.sh mode change 100644 => 100755 scripts/install_OpenCV4.sh diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index dd8a98dd856..b26e0701ac0 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -17,24 +17,13 @@ jobs: run: sudo apt install libopencv-dev - name: 'Install CUDA' + run: ./scripts/deploy-cuda.sh + + - name: 'Create softlinks for CUDA' run: | - sudo apt update - sudo apt-get dist-upgrade -y - sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin - sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub - sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /" - sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/ /" - sudo apt-get install -y --no-install-recommends cuda-compiler-11-2 cuda-libraries-dev-11-2 cuda-driver-dev-11-2 cuda-cudart-dev-11-2 - sudo apt-get install -y --no-install-recommends libcudnn8-dev - sudo rm -rf /usr/local/cuda sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so - sudo ln -s /usr/local/cuda-11.2 /usr/local/cuda - export PATH=/usr/local/cuda/bin:$PATH - export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH - nvcc --version - gcc --version - name: 'LIBSO=1 GPU=0 CUDNN=0 OPENCV=0' run: | @@ -72,37 +61,21 @@ jobs: make clean - ubuntu-vcpkg-cuda: + ubuntu-vcpkg-opencv4-cuda: runs-on: ubuntu-20.04 steps: - uses: actions/checkout@v2 - - name: Update apt - run: sudo apt update - - name: Install dependencies - run: sudo apt install yasm nasm - - uses: lukka/get-cmake@latest - name: 'Install CUDA' + run: ./scripts/deploy-cuda.sh + + - name: 'Create softlinks for CUDA' run: | - sudo apt update - sudo apt-get dist-upgrade -y - sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin - sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub - sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /" - sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/ /" - sudo apt-get install -y --no-install-recommends cuda-compiler-11-2 cuda-libraries-dev-11-2 cuda-driver-dev-11-2 cuda-cudart-dev-11-2 - sudo apt-get install -y --no-install-recommends libcudnn8-dev - sudo rm -rf /usr/local/cuda sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so - sudo ln -s /usr/local/cuda-11.2 /usr/local/cuda - export PATH=/usr/local/cuda/bin:$PATH - export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH - nvcc --version - gcc --version - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -123,7 +96,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET + run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateDARKNET - uses: actions/upload-artifact@v2 with: @@ -143,6 +116,82 @@ jobs: path: ${{ github.workspace }}/uselib* + ubuntu-vcpkg-opencv3-cuda: + runs-on: ubuntu-20.04 + steps: + - uses: actions/checkout@v2 + + - uses: lukka/get-cmake@latest + + - name: 'Install CUDA' + run: ./scripts/deploy-cuda.sh + + - name: 'Create softlinks for CUDA' + run: | + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + + - name: 'Setup vcpkg and NuGet artifacts backend' + shell: bash + run: > + git clone https://github.com/microsoft/vcpkg ; + ./vcpkg/bootstrap-vcpkg.sh ; + mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add + -Name "vcpkgbinarycache" + -Source http://93.49.111.10:5555/v3/index.json ; + mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) + setapikey ${{ secrets.BAGET_API_KEY }} + -Source http://93.49.111.10:5555/v3/index.json + + - name: 'Build' + shell: pwsh + env: + CUDACXX: "/usr/local/cuda/bin/nvcc" + CUDA_PATH: "/usr/local/cuda" + CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" + LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" + run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 3 -DisableInteractive -DoNotUpdateDARKNET + + + ubuntu-vcpkg-opencv2-cuda: + runs-on: ubuntu-20.04 + steps: + - uses: actions/checkout@v2 + + - uses: lukka/get-cmake@latest + + - name: 'Install CUDA' + run: ./scripts/deploy-cuda.sh + + - name: 'Create softlinks for CUDA' + run: | + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + + - name: 'Setup vcpkg and NuGet artifacts backend' + shell: bash + run: > + git clone https://github.com/microsoft/vcpkg ; + ./vcpkg/bootstrap-vcpkg.sh ; + mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add + -Name "vcpkgbinarycache" + -Source http://93.49.111.10:5555/v3/index.json ; + mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) + setapikey ${{ secrets.BAGET_API_KEY }} + -Source http://93.49.111.10:5555/v3/index.json + + - name: 'Build' + shell: pwsh + env: + CUDACXX: "/usr/local/cuda/bin/nvcc" + CUDA_PATH: "/usr/local/cuda" + CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" + LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" + run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 2 -DisableInteractive -DoNotUpdateDARKNET + + ubuntu: runs-on: ubuntu-20.04 steps: @@ -195,24 +244,13 @@ jobs: - uses: lukka/get-cmake@latest - name: 'Install CUDA' + run: ./scripts/deploy-cuda.sh + + - name: 'Create softlinks for CUDA' run: | - sudo apt update - sudo apt-get dist-upgrade -y - sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin - sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub - sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /" - sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/ /" - sudo apt-get install -y --no-install-recommends cuda-compiler-11-2 cuda-libraries-dev-11-2 cuda-driver-dev-11-2 cuda-cudart-dev-11-2 - sudo apt-get install -y --no-install-recommends libcudnn8-dev - sudo rm -rf /usr/local/cuda sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so - sudo ln -s /usr/local/cuda-11.2 /usr/local/cuda - export PATH=/usr/local/cuda/bin:$PATH - export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH - nvcc --version - gcc --version - name: 'Build' shell: pwsh @@ -221,7 +259,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ./build.ps1 -EnableOPENCV -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET + run: ./build.ps1 -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateDARKNET - uses: actions/upload-artifact@v2 with: @@ -253,6 +291,28 @@ jobs: run: ./build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET + ubuntu-setup-sh: + runs-on: ubuntu-20.04 + steps: + - uses: actions/checkout@v2 + + - name: 'Setup vcpkg and NuGet artifacts backend' + shell: bash + run: > + git clone https://github.com/microsoft/vcpkg ; + ./vcpkg/bootstrap-vcpkg.sh ; + mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add + -Name "vcpkgbinarycache" + -Source http://93.49.111.10:5555/v3/index.json ; + mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) + setapikey ${{ secrets.BAGET_API_KEY }} + -Source http://93.49.111.10:5555/v3/index.json + + - name: 'Setup' + shell: bash + run: ./scripts/setup.sh -InstallCUDA -BypassDRIVER + + osx-vcpkg: runs-on: macos-latest steps: @@ -419,6 +479,28 @@ jobs: path: ${{ github.workspace }}/uselib* + win-setup-ps1: + runs-on: windows-latest + steps: + - uses: actions/checkout@v2 + + - name: 'Setup vcpkg and NuGet artifacts backend' + shell: bash + run: > + git clone https://github.com/microsoft/vcpkg ; + ./vcpkg/bootstrap-vcpkg.sh ; + $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add + -Name "vcpkgbinarycache" + -Source http://93.49.111.10:5555/v3/index.json ; + $(./vcpkg/vcpkg fetch nuget | tail -n 1) + setapikey ${{ secrets.BAGET_API_KEY }} + -Source http://93.49.111.10:5555/v3/index.json + + - name: 'Setup' + shell: pwsh + run: ./scripts/setup.ps1 -InstallCUDA + + win-intlibs-cpp: runs-on: windows-latest steps: diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index 198d84fc4e0..42c03d696be 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -17,24 +17,13 @@ jobs: run: sudo apt install libopencv-dev - name: 'Install CUDA' + run: ./scripts/deploy-cuda.sh + + - name: 'Create softlinks for CUDA' run: | - sudo apt update - sudo apt-get dist-upgrade -y - sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin - sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub - sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /" - sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/ /" - sudo apt-get install -y --no-install-recommends cuda-compiler-11-2 cuda-libraries-dev-11-2 cuda-driver-dev-11-2 cuda-cudart-dev-11-2 - sudo apt-get install -y --no-install-recommends libcudnn8-dev - sudo rm -rf /usr/local/cuda sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so - sudo ln -s /usr/local/cuda-11.2 /usr/local/cuda - export PATH=/usr/local/cuda/bin:$PATH - export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH - nvcc --version - gcc --version - name: 'LIBSO=1 GPU=0 CUDNN=0 OPENCV=0' run: | @@ -72,37 +61,21 @@ jobs: make clean - ubuntu-vcpkg-cuda: + ubuntu-vcpkg-opencv4-cuda: runs-on: ubuntu-20.04 steps: - uses: actions/checkout@v2 - - name: Update apt - run: sudo apt update - - name: Install dependencies - run: sudo apt install yasm nasm - - uses: lukka/get-cmake@latest - name: 'Install CUDA' + run: ./scripts/deploy-cuda.sh + + - name: 'Create softlinks for CUDA' run: | - sudo apt update - sudo apt-get dist-upgrade -y - sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin - sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub - sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /" - sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/ /" - sudo apt-get install -y --no-install-recommends cuda-compiler-11-2 cuda-libraries-dev-11-2 cuda-driver-dev-11-2 cuda-cudart-dev-11-2 - sudo apt-get install -y --no-install-recommends libcudnn8-dev - sudo rm -rf /usr/local/cuda sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so - sudo ln -s /usr/local/cuda-11.2 /usr/local/cuda - export PATH=/usr/local/cuda/bin:$PATH - export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH - nvcc --version - gcc --version - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -120,7 +93,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET + run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateDARKNET - uses: actions/upload-artifact@v2 with: @@ -140,6 +113,76 @@ jobs: path: ${{ github.workspace }}/uselib* + ubuntu-vcpkg-opencv3-cuda: + runs-on: ubuntu-20.04 + steps: + - uses: actions/checkout@v2 + + - uses: lukka/get-cmake@latest + + - name: 'Install CUDA' + run: ./scripts/deploy-cuda.sh + + - name: 'Create softlinks for CUDA' + run: | + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + + - name: 'Setup vcpkg and NuGet artifacts backend' + shell: bash + run: > + git clone https://github.com/microsoft/vcpkg ; + ./vcpkg/bootstrap-vcpkg.sh ; + mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add + -Name "vcpkgbinarycache" + -Source http://93.49.111.10:5555/v3/index.json + + - name: 'Build' + shell: pwsh + env: + CUDACXX: "/usr/local/cuda/bin/nvcc" + CUDA_PATH: "/usr/local/cuda" + CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" + LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" + run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 3 -DisableInteractive -DoNotUpdateDARKNET + + + ubuntu-vcpkg-opencv2-cuda: + runs-on: ubuntu-20.04 + steps: + - uses: actions/checkout@v2 + + - uses: lukka/get-cmake@latest + + - name: 'Install CUDA' + run: ./scripts/deploy-cuda.sh + + - name: 'Create softlinks for CUDA' + run: | + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + + - name: 'Setup vcpkg and NuGet artifacts backend' + shell: bash + run: > + git clone https://github.com/microsoft/vcpkg ; + ./vcpkg/bootstrap-vcpkg.sh ; + mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add + -Name "vcpkgbinarycache" + -Source http://93.49.111.10:5555/v3/index.json + + - name: 'Build' + shell: pwsh + env: + CUDACXX: "/usr/local/cuda/bin/nvcc" + CUDA_PATH: "/usr/local/cuda" + CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" + LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" + run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 2 -DisableInteractive -DoNotUpdateDARKNET + + ubuntu: runs-on: ubuntu-20.04 steps: @@ -192,24 +235,13 @@ jobs: - uses: lukka/get-cmake@latest - name: 'Install CUDA' + run: ./scripts/deploy-cuda.sh + + - name: 'Create softlinks for CUDA' run: | - sudo apt update - sudo apt-get dist-upgrade -y - sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin - sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub - sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /" - sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/ /" - sudo apt-get install -y --no-install-recommends cuda-compiler-11-2 cuda-libraries-dev-11-2 cuda-driver-dev-11-2 cuda-cudart-dev-11-2 - sudo apt-get install -y --no-install-recommends libcudnn8-dev - sudo rm -rf /usr/local/cuda sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so - sudo ln -s /usr/local/cuda-11.2 /usr/local/cuda - export PATH=/usr/local/cuda/bin:$PATH - export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH - nvcc --version - gcc --version - name: 'Build' shell: pwsh @@ -218,7 +250,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ./build.ps1 -EnableOPENCV -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET + run: ./build.ps1 -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateDARKNET - uses: actions/upload-artifact@v2 with: @@ -250,6 +282,25 @@ jobs: run: ./build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET + ubuntu-setup-sh: + runs-on: ubuntu-20.04 + steps: + - uses: actions/checkout@v2 + + - name: 'Setup vcpkg and NuGet artifacts backend' + shell: bash + run: > + git clone https://github.com/microsoft/vcpkg ; + ./vcpkg/bootstrap-vcpkg.sh ; + mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add + -Name "vcpkgbinarycache" + -Source http://93.49.111.10:5555/v3/index.json + + - name: 'Setup' + shell: bash + run: ./scripts/setup.sh -InstallCUDA -BypassDRIVER + + osx-vcpkg: runs-on: macos-latest steps: @@ -410,6 +461,25 @@ jobs: path: ${{ github.workspace }}/uselib* + win-setup-ps1: + runs-on: windows-latest + steps: + - uses: actions/checkout@v2 + + - name: 'Setup vcpkg and NuGet artifacts backend' + shell: bash + run: > + git clone https://github.com/microsoft/vcpkg ; + ./vcpkg/bootstrap-vcpkg.sh ; + $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add + -Name "vcpkgbinarycache" + -Source http://93.49.111.10:5555/v3/index.json + + - name: 'Setup' + shell: pwsh + run: ./scripts/setup.ps1 -InstallCUDA + + win-intlibs-cpp: runs-on: windows-latest steps: diff --git a/.gitignore b/.gitignore index 716233d08c3..916cfb88461 100644 --- a/.gitignore +++ b/.gitignore @@ -38,6 +38,7 @@ build/.ninja_deps build/.ninja_log build/Makefile */vcpkg-manifest-install.log +build.log # OS Generated # .DS_Store* diff --git a/.travis.yml b/.travis.yml index 447a72a179d..f208498dbcd 100644 --- a/.travis.yml +++ b/.travis.yml @@ -16,32 +16,6 @@ matrix: - additional_defines=" -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" - MATRIX_EVAL="" - - os: osx - compiler: gcc - name: macOS - gcc (llvm backend) - opencv@2 - osx_image: xcode12.3 - env: - - OpenCV_DIR="/usr/local/opt/opencv@2/" - - additional_defines="-DOpenCV_DIR=${OpenCV_DIR} -DENABLE_CUDA=OFF" - - MATRIX_EVAL="brew install opencv@2" - - - os: osx - compiler: gcc - name: macOS - gcc (llvm backend) - opencv@3 - osx_image: xcode12.3 - env: - - OpenCV_DIR="/usr/local/opt/opencv@3/" - - additional_defines="-DOpenCV_DIR=${OpenCV_DIR} -DENABLE_CUDA=OFF" - - MATRIX_EVAL="brew install opencv@3" - - - os: osx - compiler: gcc - name: macOS - gcc (llvm backend) - opencv(latest) - osx_image: xcode12.3 - env: - - additional_defines=" -DENABLE_CUDA=OFF" - - MATRIX_EVAL="brew install opencv" - - os: osx compiler: clang name: macOS - clang @@ -58,40 +32,6 @@ matrix: - additional_defines="-DBUILD_AS_CPP:BOOL=TRUE -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" - MATRIX_EVAL="" - - os: osx - compiler: clang - name: macOS - clang - opencv@2 - osx_image: xcode12.3 - env: - - OpenCV_DIR="/usr/local/opt/opencv@2/" - - additional_defines="-DOpenCV_DIR=${OpenCV_DIR} -DENABLE_CUDA=OFF" - - MATRIX_EVAL="brew install opencv@2" - - - os: osx - compiler: clang - name: macOS - clang - opencv@3 - osx_image: xcode12.3 - env: - - OpenCV_DIR="/usr/local/opt/opencv@3/" - - additional_defines="-DOpenCV_DIR=${OpenCV_DIR} -DENABLE_CUDA=OFF" - - MATRIX_EVAL="brew install opencv@3" - - - os: osx - compiler: clang - name: macOS - clang - opencv(latest) - osx_image: xcode12.3 - env: - - additional_defines=" -DENABLE_CUDA=OFF" - - MATRIX_EVAL="brew install opencv" - - - os: osx - compiler: clang - name: macOS - clang - opencv(latest) - libomp - osx_image: xcode12.3 - env: - - additional_defines=" -DENABLE_CUDA=OFF" - - MATRIX_EVAL="brew install opencv libomp" - - os: linux compiler: clang dist: bionic diff --git a/CMakeLists.txt b/CMakeLists.txt index adfc709ac30..b3630f516dc 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -22,19 +22,48 @@ option(ENABLE_CUDNN_HALF "Enable CUDNN Half precision" ON) option(ENABLE_ZED_CAMERA "Enable ZED Camera support" ON) option(ENABLE_VCPKG_INTEGRATION "Enable VCPKG integration" ON) option(VCPKG_BUILD_OPENCV_WITH_CUDA "Build OpenCV with CUDA extension integration" ON) +option(VCPKG_USE_OPENCV2 "Use legacy OpenCV 2" OFF) +option(VCPKG_USE_OPENCV3 "Use legacy OpenCV 3" OFF) +option(VCPKG_USE_OPENCV4 "Use OpenCV 4" ON) -if(VCPKG_BUILD_OPENCV_WITH_CUDA AND NOT APPLE) - list(APPEND VCPKG_MANIFEST_FEATURES "opencv-cuda") +if(VCPKG_USE_OPENCV4 AND VCPKG_USE_OPENCV2) + message(STATUS "You required vcpkg feature related to OpenCV 2 but forgot to turn off those for OpenCV 4, doing that for you") + set(VCPKG_USE_OPENCV4 OFF CACHE BOOL "Use OpenCV 4" FORCE) endif() +if(VCPKG_USE_OPENCV4 AND VCPKG_USE_OPENCV3) + message(STATUS "You required vcpkg feature related to OpenCV 3 but forgot to turn off those for OpenCV 4, doing that for you") + set(VCPKG_USE_OPENCV4 OFF CACHE BOOL "Use OpenCV 4" FORCE) +endif() +if(VCPKG_USE_OPENCV2 AND VCPKG_USE_OPENCV3) + message(STATUS "You required vcpkg features related to both OpenCV 2 and OpenCV 3. Impossible to satisfy, keeping only OpenCV 3") + set(VCPKG_USE_OPENCV2 OFF CACHE BOOL "Use legacy OpenCV 2" FORCE) +endif() + if(ENABLE_CUDA AND NOT APPLE) list(APPEND VCPKG_MANIFEST_FEATURES "cuda") endif() -if(ENABLE_OPENCV) - list(APPEND VCPKG_MANIFEST_FEATURES "opencv-base") -endif() if(ENABLE_CUDNN AND ENABLE_CUDA AND NOT APPLE) list(APPEND VCPKG_MANIFEST_FEATURES "cudnn") endif() +if(ENABLE_OPENCV) + if(VCPKG_BUILD_OPENCV_WITH_CUDA AND NOT APPLE) + if(VCPKG_USE_OPENCV4) + list(APPEND VCPKG_MANIFEST_FEATURES "opencv-cuda") + elseif(VCPKG_USE_OPENCV3) + list(APPEND VCPKG_MANIFEST_FEATURES "opencv3-cuda") + elseif(VCPKG_USE_OPENCV2) + list(APPEND VCPKG_MANIFEST_FEATURES "opencv2-cuda") + endif() + else() + if(VCPKG_USE_OPENCV4) + list(APPEND VCPKG_MANIFEST_FEATURES "opencv-base") + elseif(VCPKG_USE_OPENCV3) + list(APPEND VCPKG_MANIFEST_FEATURES "opencv3-base") + elseif(VCPKG_USE_OPENCV2) + list(APPEND VCPKG_MANIFEST_FEATURES "opencv2-base") + endif() + endif() +endif() if(NOT CMAKE_HOST_SYSTEM_PROCESSOR AND NOT WIN32) execute_process(COMMAND "uname" "-m" OUTPUT_VARIABLE CMAKE_HOST_SYSTEM_PROCESSOR OUTPUT_STRIP_TRAILING_WHITESPACE) diff --git a/README.md b/README.md index acd2506d16c..cc85070b24a 100644 --- a/README.md +++ b/README.md @@ -8,16 +8,16 @@ Paper Scaled YOLO v4: https://arxiv.org/abs/2011.08036 use to reproduce results More details in articles on medium: -* [Scaled_YOLOv4](https://alexeyab84.medium.com/scaled-yolo-v4-is-the-best-neural-network-for-object-detection-on-ms-coco-dataset-39dfa22fa982?source=friends_link&sk=c8553bfed861b1a7932f739d26f487c8) -* [YOLOv4](https://medium.com/@alexeyab84/yolov4-the-most-accurate-real-time-neural-network-on-ms-coco-dataset-73adfd3602fe?source=friends_link&sk=6039748846bbcf1d960c3061542591d7) +- [Scaled_YOLOv4](https://alexeyab84.medium.com/scaled-yolo-v4-is-the-best-neural-network-for-object-detection-on-ms-coco-dataset-39dfa22fa982?source=friends_link&sk=c8553bfed861b1a7932f739d26f487c8) +- [YOLOv4](https://medium.com/@alexeyab84/yolov4-the-most-accurate-real-time-neural-network-on-ms-coco-dataset-73adfd3602fe?source=friends_link&sk=6039748846bbcf1d960c3061542591d7) Manual: https://github.com/AlexeyAB/darknet/wiki Discussion: -* [Reddit](https://www.reddit.com/r/MachineLearning/comments/gydxzd/p_yolov4_the_most_accurate_realtime_neural/) -* [Google-groups](https://groups.google.com/forum/#!forum/darknet) -* [Discord](https://discord.gg/zSq8rtW) +- [Reddit](https://www.reddit.com/r/MachineLearning/comments/gydxzd/p_yolov4_the_most_accurate_realtime_neural/) +- [Google-groups](https://groups.google.com/forum/#!forum/darknet) +- [Discord](https://discord.gg/zSq8rtW) About Darknet framework: http://pjreddie.com/darknet/ @@ -31,13 +31,13 @@ About Darknet framework: http://pjreddie.com/darknet/ [![colab](https://user-images.githubusercontent.com/4096485/86174089-b2709f80-bb29-11ea-9faf-3d8dc668a1a5.png)](https://colab.research.google.com/drive/12QusaaRj_lUwCGDvQNfICpa7kA7_a2dE) [![colab](https://user-images.githubusercontent.com/4096485/86174097-b56b9000-bb29-11ea-9240-c17f6bacfc34.png)](https://colab.research.google.com/drive/1_GdoqCJWXsChrOiY8sZMr_zbr_fH-0Fg) -* [YOLOv4 model zoo](https://github.com/AlexeyAB/darknet/wiki/YOLOv4-model-zoo) -* [Requirements (and how to install dependencies)](#requirements) -* [Pre-trained models](#pre-trained-models) -* [FAQ - frequently asked questions](https://github.com/AlexeyAB/darknet/wiki/FAQ---frequently-asked-questions) -* [Explanations in issues](https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3AExplanations) -* [Yolo v4 in other frameworks (TensorRT, TensorFlow, PyTorch, OpenVINO, OpenCV-dnn, TVM,...)](#yolo-v4-in-other-frameworks) -* [Datasets](#datasets) +- [YOLOv4 model zoo](https://github.com/AlexeyAB/darknet/wiki/YOLOv4-model-zoo) +- [Requirements (and how to install dependencies)](#requirements) +- [Pre-trained models](#pre-trained-models) +- [FAQ - frequently asked questions](https://github.com/AlexeyAB/darknet/wiki/FAQ---frequently-asked-questions) +- [Explanations in issues](https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3AExplanations) +- [Yolo v4 in other frameworks (TensorRT, TensorFlow, PyTorch, OpenVINO, OpenCV-dnn, TVM,...)](#yolo-v4-in-other-frameworks) +- [Datasets](#datasets) - [Yolo v4, v3 and v2 for Windows and Linux](#yolo-v4-v3-and-v2-for-windows-and-linux) - [(neural networks for object detection)](#neural-networks-for-object-detection) @@ -46,39 +46,38 @@ About Darknet framework: http://pjreddie.com/darknet/ - [How to evaluate AP of YOLOv4 on the MS COCO evaluation server](#how-to-evaluate-ap-of-yolov4-on-the-ms-coco-evaluation-server) - [How to evaluate FPS of YOLOv4 on GPU](#how-to-evaluate-fps-of-yolov4-on-gpu) - [Pre-trained models](#pre-trained-models) - - [Requirements](#requirements) + - [Requirements for Windows, Linux and macOS](#requirements-for-windows-linux-and-macos) - [Yolo v4 in other frameworks](#yolo-v4-in-other-frameworks) - [Datasets](#datasets) - [Improvements in this repository](#improvements-in-this-repository) - [How to use on the command line](#how-to-use-on-the-command-line) - [For using network video-camera mjpeg-stream with any Android smartphone](#for-using-network-video-camera-mjpeg-stream-with-any-android-smartphone) - [How to compile on Linux/macOS (using `CMake`)](#how-to-compile-on-linuxmacos-using-cmake) - - [Using `vcpkg`](#using-vcpkg) - - [Using libraries manually provided](#using-libraries-manually-provided) + - [Using also PowerShell](#using-also-powershell) - [How to compile on Linux (using `make`)](#how-to-compile-on-linux-using-make) - [How to compile on Windows (using `CMake`)](#how-to-compile-on-windows-using-cmake) - [How to compile on Windows (using `vcpkg`)](#how-to-compile-on-windows-using-vcpkg) - [How to train with multi-GPU](#how-to-train-with-multi-gpu) - [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects) - - [How to train tiny-yolo (to detect your custom objects):](#how-to-train-tiny-yolo-to-detect-your-custom-objects) - - [When should I stop training:](#when-should-i-stop-training) + - [How to train tiny-yolo (to detect your custom objects)](#how-to-train-tiny-yolo-to-detect-your-custom-objects) + - [When should I stop training](#when-should-i-stop-training) - [Custom object detection](#custom-object-detection) - [How to improve object detection](#how-to-improve-object-detection) - [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) - [How to use Yolo as DLL and SO libraries](#how-to-use-yolo-as-dll-and-so-libraries) -![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) +![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) ![scaled_yolov4](https://user-images.githubusercontent.com/4096485/112776361-281d8380-9048-11eb-8083-8728b12dcd55.png) AP50:95 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2011.08036 ---- -![modern_gpus](https://user-images.githubusercontent.com/4096485/82835867-f1c62380-9ecd-11ea-9134-1598ed2abc4b.png) AP50:95 / AP50 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2004.10934 +![modern_gpus](https://user-images.githubusercontent.com/4096485/82835867-f1c62380-9ecd-11ea-9134-1598ed2abc4b.png) AP50:95 / AP50 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2004.10934 tkDNN-TensorRT accelerates YOLOv4 **~2x** times for batch=1 and **3x-4x** times for batch=4. -* tkDNN: https://github.com/ceccocats/tkDNN -* OpenCV: https://gist.github.com/YashasSamaga/48bdb167303e10f4d07b754888ddbdcf +- tkDNN: https://github.com/ceccocats/tkDNN +- OpenCV: https://gist.github.com/YashasSamaga/48bdb167303e10f4d07b754888ddbdcf ### GeForce RTX 2080 Ti @@ -91,13 +90,13 @@ tkDNN-TensorRT accelerates YOLOv4 **~2x** times for batch=1 and **3x-4x** times |Tiny 416 | 443 | 609 | **790** | 773 | **1774** | 1353 | **3.5x** | |Tiny 416 CPU Core i7 7700HQ | 3.4 | - | - | 42 | - | 39 | **12x** | -* Yolo v4 Full comparison: [map_fps](https://user-images.githubusercontent.com/4096485/80283279-0e303e00-871f-11ea-814c-870967d77fd1.png) -* Yolo v4 tiny comparison: [tiny_fps](https://user-images.githubusercontent.com/4096485/85734112-6e366700-b705-11ea-95d1-fcba0de76d72.png) -* CSPNet: [paper](https://arxiv.org/abs/1911.11929) and [map_fps](https://user-images.githubusercontent.com/4096485/71702416-6645dc00-2de0-11ea-8d65-de7d4b604021.png) comparison: https://github.com/WongKinYiu/CrossStagePartialNetworks -* Yolo v3 on MS COCO: [Speed / Accuracy (mAP@0.5) chart](https://user-images.githubusercontent.com/4096485/52151356-e5d4a380-2683-11e9-9d7d-ac7bc192c477.jpg) -* Yolo v3 on MS COCO (Yolo v3 vs RetinaNet) - Figure 3: https://arxiv.org/pdf/1804.02767v1.pdf -* Yolo v2 on Pascal VOC 2007: https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg -* Yolo v2 on Pascal VOC 2012 (comp4): https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg +- Yolo v4 Full comparison: [map_fps](https://user-images.githubusercontent.com/4096485/80283279-0e303e00-871f-11ea-814c-870967d77fd1.png) +- Yolo v4 tiny comparison: [tiny_fps](https://user-images.githubusercontent.com/4096485/85734112-6e366700-b705-11ea-95d1-fcba0de76d72.png) +- CSPNet: [paper](https://arxiv.org/abs/1911.11929) and [map_fps](https://user-images.githubusercontent.com/4096485/71702416-6645dc00-2de0-11ea-8d65-de7d4b604021.png) comparison: https://github.com/WongKinYiu/CrossStagePartialNetworks +- Yolo v3 on MS COCO: [Speed / Accuracy (mAP@0.5) chart](https://user-images.githubusercontent.com/4096485/52151356-e5d4a380-2683-11e9-9d7d-ac7bc192c477.jpg) +- Yolo v3 on MS COCO (Yolo v3 vs RetinaNet) - Figure 3: https://arxiv.org/pdf/1804.02767v1.pdf +- Yolo v2 on Pascal VOC 2007: https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg +- Yolo v2 on Pascal VOC 2012 (comp4): https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg #### Youtube video of results @@ -134,9 +133,9 @@ eval=coco 3. Get any .avi/.mp4 video file (preferably not more than 1920x1080 to avoid bottlenecks in CPU performance) 4. Run one of two commands and look at the AVG FPS: -* include video_capturing + NMS + drawing_bboxes: +- include video_capturing + NMS + drawing_bboxes: `./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -dont_show -ext_output` -* exclude video_capturing + NMS + drawing_bboxes: +- exclude video_capturing + NMS + drawing_bboxes: `./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -benchmark` #### Pre-trained models @@ -145,52 +144,52 @@ There are weights-file for different cfg-files (trained for MS COCO dataset): FPS on RTX 2070 (R) and Tesla V100 (V): -* [yolov4x-mish.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4x-mish.cfg) - 640x640 - **67.9% mAP@0.5 (49.4% AP@0.5:0.95) - 23(R) FPS / 50(V) FPS** - 221 BFlops (110 FMA) - 381 MB: [yolov4x-mish.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.weights) - * pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.conv.166 +- [yolov4x-mish.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4x-mish.cfg) - 640x640 - **67.9% mAP@0.5 (49.4% AP@0.5:0.95) - 23(R) FPS / 50(V) FPS** - 221 BFlops (110 FMA) - 381 MB: [yolov4x-mish.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.weights) + - pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.conv.166 -* [yolov4-csp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-csp.cfg) - 202 MB: [yolov4-csp.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.weights) paper [Scaled Yolo v4](https://arxiv.org/abs/2011.08036) +- [yolov4-csp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-csp.cfg) - 202 MB: [yolov4-csp.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.weights) paper [Scaled Yolo v4](https://arxiv.org/abs/2011.08036) just change `width=` and `height=` parameters in `yolov4-csp.cfg` file and use the same `yolov4-csp.weights` file for all cases: - * `width=640 height=640` in cfg: **66.2% mAP@0.5 (47.5% AP@0.5:0.95) - 70(V) FPS** - 120 (60 FMA) BFlops - * `width=512 height=512` in cfg: **64.8% mAP@0.5 (46.2% AP@0.5:0.95) - 93(V) FPS** - 77 (39 FMA) BFlops - * pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.conv.142 + - `width=640 height=640` in cfg: **66.2% mAP@0.5 (47.5% AP@0.5:0.95) - 70(V) FPS** - 120 (60 FMA) BFlops + - `width=512 height=512` in cfg: **64.8% mAP@0.5 (46.2% AP@0.5:0.95) - 93(V) FPS** - 77 (39 FMA) BFlops + - pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.conv.142 -* [yolov4.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg) - 245 MB: [yolov4.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights) (Google-drive mirror [yolov4.weights](https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT) ) paper [Yolo v4](https://arxiv.org/abs/2004.10934) +- [yolov4.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg) - 245 MB: [yolov4.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights) (Google-drive mirror [yolov4.weights](https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT) ) paper [Yolo v4](https://arxiv.org/abs/2004.10934) just change `width=` and `height=` parameters in `yolov4.cfg` file and use the same `yolov4.weights` file for all cases: - * `width=608 height=608` in cfg: **65.7% mAP@0.5 (43.5% AP@0.5:0.95) - 34(R) FPS / 62(V) FPS** - 128.5 BFlops - * `width=512 height=512` in cfg: **64.9% mAP@0.5 (43.0% AP@0.5:0.95) - 45(R) FPS / 83(V) FPS** - 91.1 BFlops - * `width=416 height=416` in cfg: **62.8% mAP@0.5 (41.2% AP@0.5:0.95) - 55(R) FPS / 96(V) FPS** - 60.1 BFlops - * `width=320 height=320` in cfg: **60% mAP@0.5 ( 38% AP@0.5:0.95) - 63(R) FPS / 123(V) FPS** - 35.5 BFlops + - `width=608 height=608` in cfg: **65.7% mAP@0.5 (43.5% AP@0.5:0.95) - 34(R) FPS / 62(V) FPS** - 128.5 BFlops + - `width=512 height=512` in cfg: **64.9% mAP@0.5 (43.0% AP@0.5:0.95) - 45(R) FPS / 83(V) FPS** - 91.1 BFlops + - `width=416 height=416` in cfg: **62.8% mAP@0.5 (41.2% AP@0.5:0.95) - 55(R) FPS / 96(V) FPS** - 60.1 BFlops + - `width=320 height=320` in cfg: **60% mAP@0.5 ( 38% AP@0.5:0.95) - 63(R) FPS / 123(V) FPS** - 35.5 BFlops -* [yolov4-tiny.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny.cfg) - **40.2% mAP@0.5 - 371(1080Ti) FPS / 330(RTX2070) FPS** - 6.9 BFlops - 23.1 MB: [yolov4-tiny.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights) +- [yolov4-tiny.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny.cfg) - **40.2% mAP@0.5 - 371(1080Ti) FPS / 330(RTX2070) FPS** - 6.9 BFlops - 23.1 MB: [yolov4-tiny.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights) -* [enet-coco.cfg (EfficientNetB0-Yolov3)](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/enet-coco.cfg) - **45.5% mAP@0.5 - 55(R) FPS** - 3.7 BFlops - 18.3 MB: [enetb0-coco_final.weights](https://drive.google.com/file/d/1FlHeQjWEQVJt0ay1PVsiuuMzmtNyv36m/view) +- [enet-coco.cfg (EfficientNetB0-Yolov3)](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/enet-coco.cfg) - **45.5% mAP@0.5 - 55(R) FPS** - 3.7 BFlops - 18.3 MB: [enetb0-coco_final.weights](https://drive.google.com/file/d/1FlHeQjWEQVJt0ay1PVsiuuMzmtNyv36m/view) -* [yolov3-openimages.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-openimages.cfg) - 247 MB - 18(R) FPS - OpenImages dataset: [yolov3-openimages.weights](https://pjreddie.com/media/files/yolov3-openimages.weights) +- [yolov3-openimages.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-openimages.cfg) - 247 MB - 18(R) FPS - OpenImages dataset: [yolov3-openimages.weights](https://pjreddie.com/media/files/yolov3-openimages.weights)
CLICK ME - Yolo v3 models -* [csresnext50-panet-spp-original-optimal.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/csresnext50-panet-spp-original-optimal.cfg) - **65.4% mAP@0.5 (43.2% AP@0.5:0.95) - 32(R) FPS** - 100.5 BFlops - 217 MB: [csresnext50-panet-spp-original-optimal_final.weights](https://drive.google.com/open?id=1_NnfVgj0EDtb_WLNoXV8Mo7WKgwdYZCc) +- [csresnext50-panet-spp-original-optimal.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/csresnext50-panet-spp-original-optimal.cfg) - **65.4% mAP@0.5 (43.2% AP@0.5:0.95) - 32(R) FPS** - 100.5 BFlops - 217 MB: [csresnext50-panet-spp-original-optimal_final.weights](https://drive.google.com/open?id=1_NnfVgj0EDtb_WLNoXV8Mo7WKgwdYZCc) -* [yolov3-spp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-spp.cfg) - **60.6% mAP@0.5 - 38(R) FPS** - 141.5 BFlops - 240 MB: [yolov3-spp.weights](https://pjreddie.com/media/files/yolov3-spp.weights) +- [yolov3-spp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-spp.cfg) - **60.6% mAP@0.5 - 38(R) FPS** - 141.5 BFlops - 240 MB: [yolov3-spp.weights](https://pjreddie.com/media/files/yolov3-spp.weights) -* [csresnext50-panet-spp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/csresnext50-panet-spp.cfg) - **60.0% mAP@0.5 - 44 FPS** - 71.3 BFlops - 217 MB: [csresnext50-panet-spp_final.weights](https://drive.google.com/file/d/1aNXdM8qVy11nqTcd2oaVB3mf7ckr258-/view?usp=sharing) +- [csresnext50-panet-spp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/csresnext50-panet-spp.cfg) - **60.0% mAP@0.5 - 44 FPS** - 71.3 BFlops - 217 MB: [csresnext50-panet-spp_final.weights](https://drive.google.com/file/d/1aNXdM8qVy11nqTcd2oaVB3mf7ckr258-/view?usp=sharing) -* [yolov3.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3.cfg) - **55.3% mAP@0.5 - 66(R) FPS** - 65.9 BFlops - 236 MB: [yolov3.weights](https://pjreddie.com/media/files/yolov3.weights) +- [yolov3.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3.cfg) - **55.3% mAP@0.5 - 66(R) FPS** - 65.9 BFlops - 236 MB: [yolov3.weights](https://pjreddie.com/media/files/yolov3.weights) -* [yolov3-tiny.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny.cfg) - **33.1% mAP@0.5 - 345(R) FPS** - 5.6 BFlops - 33.7 MB: [yolov3-tiny.weights](https://pjreddie.com/media/files/yolov3-tiny.weights) +- [yolov3-tiny.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny.cfg) - **33.1% mAP@0.5 - 345(R) FPS** - 5.6 BFlops - 33.7 MB: [yolov3-tiny.weights](https://pjreddie.com/media/files/yolov3-tiny.weights) -* [yolov3-tiny-prn.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny-prn.cfg) - **33.1% mAP@0.5 - 370(R) FPS** - 3.5 BFlops - 18.8 MB: [yolov3-tiny-prn.weights](https://drive.google.com/file/d/18yYZWyKbo4XSDVyztmsEcF9B_6bxrhUY/view?usp=sharing) +- [yolov3-tiny-prn.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny-prn.cfg) - **33.1% mAP@0.5 - 370(R) FPS** - 3.5 BFlops - 18.8 MB: [yolov3-tiny-prn.weights](https://drive.google.com/file/d/18yYZWyKbo4XSDVyztmsEcF9B_6bxrhUY/view?usp=sharing)
CLICK ME - Yolo v2 models -* `yolov2.cfg` (194 MB COCO Yolo v2) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov2.weights -* `yolo-voc.cfg` (194 MB VOC Yolo v2) - requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo-voc.weights -* `yolov2-tiny.cfg` (43 MB COCO Yolo v2) - requires 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov2-tiny.weights -* `yolov2-tiny-voc.cfg` (60 MB VOC Yolo v2) - requires 1 GB GPU-RAM: http://pjreddie.com/media/files/yolov2-tiny-voc.weights -* `yolo9000.cfg` (186 MB Yolo9000-model) - requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo9000.weights +- `yolov2.cfg` (194 MB COCO Yolo v2) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov2.weights +- `yolo-voc.cfg` (194 MB VOC Yolo v2) - requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo-voc.weights +- `yolov2-tiny.cfg` (43 MB COCO Yolo v2) - requires 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov2-tiny.weights +- `yolov2-tiny-voc.cfg` (60 MB VOC Yolo v2) - requires 1 GB GPU-RAM: http://pjreddie.com/media/files/yolov2-tiny-voc.weights +- `yolo9000.cfg` (186 MB Yolo9000-model) - requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo9000.weights
@@ -198,76 +197,76 @@ Put it near compiled: darknet.exe You can get cfg-files by path: `darknet/cfg/` -### Requirements +### Requirements for Windows, Linux and macOS -* **CMake >= 3.18**: https://cmake.org/download/ -* **Powershell** (already installed on windows): https://docs.microsoft.com/en-us/powershell/scripting/install/installing-powershell -* **CUDA >= 10.2**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) -* **OpenCV >= 2.4**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or download from [OpenCV official site](https://opencv.org/releases.html) (on Windows set system variable `OpenCV_DIR` = `C:\opencv\build` - where are the `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png)) -* **cuDNN >= 8.0.2** https://developer.nvidia.com/rdp/cudnn-archive (on **Linux** copy `cudnn.h`,`libcudnn.so`... as described here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on **Windows** copy `cudnn.h`,`cudnn64_7.dll`, `cudnn64_7.lib` as described here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows ) -* **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported +- **CMake >= 3.18**: https://cmake.org/download/ +- **Powershell** (already installed on windows): https://docs.microsoft.com/en-us/powershell/scripting/install/installing-powershell +- **CUDA >= 10.2**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) +- **OpenCV >= 2.4**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or download from [OpenCV official site](https://opencv.org/releases.html) (on Windows set system variable `OpenCV_DIR` = `C:\opencv\build` - where are the `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png)) +- **cuDNN >= 8.0.2** https://developer.nvidia.com/rdp/cudnn-archive (on **Linux** copy `cudnn.h`,`libcudnn.so`... as described here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on **Windows** copy `cudnn.h`,`cudnn64_7.dll`, `cudnn64_7.lib` as described here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows ) +- **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported ### Yolo v4 in other frameworks -* **Pytorch - Scaled-YOLOv4:** https://github.com/WongKinYiu/ScaledYOLOv4 -* **TensorFlow:** `pip install yolov4` YOLOv4 on TensorFlow 2.0 / TFlite / Android: https://github.com/hunglc007/tensorflow-yolov4-tflite +- **Pytorch - Scaled-YOLOv4:** https://github.com/WongKinYiu/ScaledYOLOv4 +- **TensorFlow:** `pip install yolov4` YOLOv4 on TensorFlow 2.0 / TFlite / Android: https://github.com/hunglc007/tensorflow-yolov4-tflite Official TF models: https://github.com/tensorflow/models/tree/master/official/vision/beta/projects/yolo For YOLOv4 - convert `yolov4.weights`/`cfg` files to `yolov4.pb` by using [TNTWEN](https://github.com/TNTWEN/OpenVINO-YOLOV4) project, and to `yolov4.tflite` [TensorFlow-lite](https://www.tensorflow.org/lite/guide/get_started#2_convert_the_model_format) -* **OpenCV** the fastest implementation of YOLOv4 for CPU (x86/ARM-Android), OpenCV can be compiled with [OpenVINO-backend](https://github.com/opencv/opencv/wiki/Intel's-Deep-Learning-Inference-Engine-backend) for running on (Myriad X / USB Neural Compute Stick / Arria FPGA), use `yolov4.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221) or [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) -* **Intel OpenVINO 2021.2:** supports YOLOv4 (NPU Myriad X / USB Neural Compute Stick / Arria FPGA): https://devmesh.intel.com/projects/openvino-yolov4-49c756 read this [manual](https://github.com/TNTWEN/OpenVINO-YOLOV4) (old [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) ) (for [Scaled-YOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-large) models use https://github.com/Chen-MingChang/pytorch_YOLO_OpenVINO_demo ) -* **PyTorch > ONNX**: - * [WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4) - * [maudzung/3D-YOLOv4](https://github.com/maudzung/Complex-YOLOv4-Pytorch) - * [Tianxiaomo/pytorch-YOLOv4](https://github.com/Tianxiaomo/pytorch-YOLOv4) - * [YOLOv5](https://github.com/ultralytics/yolov5) -* **ONNX** on Jetson for YOLOv4: https://developer.nvidia.com/blog/announcing-onnx-runtime-for-jetson/ -* **TensorRT+tkDNN**: https://github.com/ceccocats/tkDNN#fps-results -* **Deepstream 5.0 / TensorRT for YOLOv4** https://github.com/NVIDIA-AI-IOT/yolov4_deepstream or https://github.com/marcoslucianops/DeepStream-Yolo read [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) and [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf). Additionally [jkjung-avt/tensorrt_demos](https://github.com/jkjung-avt/tensorrt_demos) or [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) -* **Triton Inference Server / TensorRT** https://github.com/isarsoft/yolov4-triton-tensorrt -* **DirectML** https://github.com/microsoft/DirectML/tree/master/Samples/yolov4 -* **OpenCL** (Intel, AMD, Mali GPUs for macOS & GNU/Linux) https://github.com/sowson/darknet -* **HIP** for Training and Detection on AMD GPU https://github.com/os-hackathon/darknet -* **ROS** (Robot Operating System) https://github.com/engcang/ros-yolo-sort -* **Xilinx Zynq Ultrascale+ Deep Learning Processor (DPU) ZCU102/ZCU104:** https://github.com/Xilinx/Vitis-In-Depth-Tutorial/tree/master/Machine_Learning/Design_Tutorials/07-yolov4-tutorial -* **Amazon Neurochip / Amazon EC2 Inf1 instances** 1.85 times higher throughput and 37% lower cost per image for TensorFlow based YOLOv4 model, using Keras [URL](https://aws.amazon.com/ru/blogs/machine-learning/improving-performance-for-deep-learning-based-object-detection-with-an-aws-neuron-compiled-yolov4-model-on-aws-inferentia/) -* **TVM** - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backend (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about -* **Tencent/ncnn:** the fastest inference of YOLOv4 on mobile phone CPU: https://github.com/Tencent/ncnn -* **OpenDataCam** - It detects, tracks and counts moving objects by using YOLOv4: https://github.com/opendatacam/opendatacam#-hardware-pre-requisite -* **Netron** - Visualizer for neural networks: https://github.com/lutzroeder/netron +- **OpenCV** the fastest implementation of YOLOv4 for CPU (x86/ARM-Android), OpenCV can be compiled with [OpenVINO-backend](https://github.com/opencv/opencv/wiki/Intel's-Deep-Learning-Inference-Engine-backend) for running on (Myriad X / USB Neural Compute Stick / Arria FPGA), use `yolov4.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221) or [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) +- **Intel OpenVINO 2021.2:** supports YOLOv4 (NPU Myriad X / USB Neural Compute Stick / Arria FPGA): https://devmesh.intel.com/projects/openvino-yolov4-49c756 read this [manual](https://github.com/TNTWEN/OpenVINO-YOLOV4) (old [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) ) (for [Scaled-YOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-large) models use https://github.com/Chen-MingChang/pytorch_YOLO_OpenVINO_demo ) +- **PyTorch > ONNX**: + - [WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4) + - [maudzung/3D-YOLOv4](https://github.com/maudzung/Complex-YOLOv4-Pytorch) + - [Tianxiaomo/pytorch-YOLOv4](https://github.com/Tianxiaomo/pytorch-YOLOv4) + - [YOLOv5](https://github.com/ultralytics/yolov5) +- **ONNX** on Jetson for YOLOv4: https://developer.nvidia.com/blog/announcing-onnx-runtime-for-jetson/ +- **TensorRT+tkDNN**: https://github.com/ceccocats/tkDNN#fps-results +- **Deepstream 5.0 / TensorRT for YOLOv4** https://github.com/NVIDIA-AI-IOT/yolov4_deepstream or https://github.com/marcoslucianops/DeepStream-Yolo read [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) and [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf). Additionally [jkjung-avt/tensorrt_demos](https://github.com/jkjung-avt/tensorrt_demos) or [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) +- **Triton Inference Server / TensorRT** https://github.com/isarsoft/yolov4-triton-tensorrt +- **DirectML** https://github.com/microsoft/DirectML/tree/master/Samples/yolov4 +- **OpenCL** (Intel, AMD, Mali GPUs for macOS & GNU/Linux) https://github.com/sowson/darknet +- **HIP** for Training and Detection on AMD GPU https://github.com/os-hackathon/darknet +- **ROS** (Robot Operating System) https://github.com/engcang/ros-yolo-sort +- **Xilinx Zynq Ultrascale+ Deep Learning Processor (DPU) ZCU102/ZCU104:** https://github.com/Xilinx/Vitis-In-Depth-Tutorial/tree/master/Machine_Learning/Design_Tutorials/07-yolov4-tutorial +- **Amazon Neurochip / Amazon EC2 Inf1 instances** 1.85 times higher throughput and 37% lower cost per image for TensorFlow based YOLOv4 model, using Keras [URL](https://aws.amazon.com/ru/blogs/machine-learning/improving-performance-for-deep-learning-based-object-detection-with-an-aws-neuron-compiled-yolov4-model-on-aws-inferentia/) +- **TVM** - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backend (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about +- **Tencent/ncnn:** the fastest inference of YOLOv4 on mobile phone CPU: https://github.com/Tencent/ncnn +- **OpenDataCam** - It detects, tracks and counts moving objects by using YOLOv4: https://github.com/opendatacam/opendatacam#-hardware-pre-requisite +- **Netron** - Visualizer for neural networks: https://github.com/lutzroeder/netron #### Datasets -* MS COCO: use `./scripts/get_coco_dataset.sh` to get labeled MS COCO detection dataset -* OpenImages: use `python ./scripts/get_openimages_dataset.py` for labeling train detection dataset -* Pascal VOC: use `python ./scripts/voc_label.py` for labeling Train/Test/Val detection datasets -* ILSVRC2012 (ImageNet classification): use `./scripts/get_imagenet_train.sh` (also `imagenet_label.sh` for labeling valid set) -* German/Belgium/Russian/LISA/MASTIF Traffic Sign Datasets for Detection - use this parsers: https://github.com/angeligareta/Datasets2Darknet#detection-task -* List of other datasets: https://github.com/AlexeyAB/darknet/tree/master/scripts#datasets +- MS COCO: use `./scripts/get_coco_dataset.sh` to get labeled MS COCO detection dataset +- OpenImages: use `python ./scripts/get_openimages_dataset.py` for labeling train detection dataset +- Pascal VOC: use `python ./scripts/voc_label.py` for labeling Train/Test/Val detection datasets +- ILSVRC2012 (ImageNet classification): use `./scripts/get_imagenet_train.sh` (also `imagenet_label.sh` for labeling valid set) +- German/Belgium/Russian/LISA/MASTIF Traffic Sign Datasets for Detection - use this parsers: https://github.com/angeligareta/Datasets2Darknet#detection-task +- List of other datasets: https://github.com/AlexeyAB/darknet/tree/master/scripts#datasets ### Improvements in this repository -* developed State-of-the-Art object detector YOLOv4 -* added State-of-Art models: CSP, PRN, EfficientNet -* added layers: [conv_lstm], [scale_channels] SE/ASFF/BiFPN, [local_avgpool], [sam], [Gaussian_yolo], [reorg3d] (fixed [reorg]), fixed [batchnorm] -* added the ability for training recurrent models (with layers conv-lstm`[conv_lstm]`/conv-rnn`[crnn]`) for accurate detection on video -* added data augmentation: `[net] mixup=1 cutmix=1 mosaic=1 blur=1`. Added activations: SWISH, MISH, NORM_CHAN, NORM_CHAN_SOFTMAX -* added the ability for training with GPU-processing using CPU-RAM to increase the mini_batch_size and increase accuracy (instead of batch-norm sync) -* improved binary neural network performance **2x-4x times** for Detection on CPU and GPU if you trained your own weights by using this XNOR-net model (bit-1 inference) : https://github.com/AlexeyAB/darknet/blob/master/cfg/yolov3-tiny_xnor.cfg -* improved neural network performance **~7%** by fusing 2 layers into 1: Convolutional + Batch-norm -* improved performance: Detection **2x times**, on GPU Volta/Turing (Tesla V100, GeForce RTX, ...) using Tensor Cores if `CUDNN_HALF` defined in the `Makefile` or `darknet.sln` -* improved performance **~1.2x** times on FullHD, **~2x** times on 4K, for detection on the video (file/stream) using `darknet detector demo`... -* improved performance **3.5 X times** of data augmentation for training (using OpenCV SSE/AVX functions instead of hand-written functions) - removes bottleneck for training on multi-GPU or GPU Volta -* improved performance of detection and training on Intel CPU with AVX (Yolo v3 **~85%**) -* optimized memory allocation during network resizing when `random=1` -* optimized GPU initialization for detection - we use batch=1 initially instead of re-init with batch=1 -* added correct calculation of **mAP, F1, IoU, Precision-Recall** using command `darknet detector map`... -* added drawing of chart of average-Loss and accuracy-mAP (`-map` flag) during training -* run `./darknet detector demo ... -json_port 8070 -mjpeg_port 8090` as JSON and MJPEG server to get results online over the network by using your soft or Web-browser -* added calculation of anchors for training -* added example of Detection and Tracking objects: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp -* run-time tips and warnings if you use incorrect cfg-file or dataset -* added support for Windows -* many other fixes of code... +- developed State-of-the-Art object detector YOLOv4 +- added State-of-Art models: CSP, PRN, EfficientNet +- added layers: [conv_lstm], [scale_channels] SE/ASFF/BiFPN, [local_avgpool], [sam], [Gaussian_yolo], [reorg3d] (fixed [reorg]), fixed [batchnorm] +- added the ability for training recurrent models (with layers conv-lstm`[conv_lstm]`/conv-rnn`[crnn]`) for accurate detection on video +- added data augmentation: `[net] mixup=1 cutmix=1 mosaic=1 blur=1`. Added activations: SWISH, MISH, NORM_CHAN, NORM_CHAN_SOFTMAX +- added the ability for training with GPU-processing using CPU-RAM to increase the mini_batch_size and increase accuracy (instead of batch-norm sync) +- improved binary neural network performance **2x-4x times** for Detection on CPU and GPU if you trained your own weights by using this XNOR-net model (bit-1 inference) : https://github.com/AlexeyAB/darknet/blob/master/cfg/yolov3-tiny_xnor.cfg +- improved neural network performance **~7%** by fusing 2 layers into 1: Convolutional + Batch-norm +- improved performance: Detection **2x times**, on GPU Volta/Turing (Tesla V100, GeForce RTX, ...) using Tensor Cores if `CUDNN_HALF` defined in the `Makefile` or `darknet.sln` +- improved performance **~1.2x** times on FullHD, **~2x** times on 4K, for detection on the video (file/stream) using `darknet detector demo`... +- improved performance **3.5 X times** of data augmentation for training (using OpenCV SSE/AVX functions instead of hand-written functions) - removes bottleneck for training on multi-GPU or GPU Volta +- improved performance of detection and training on Intel CPU with AVX (Yolo v3 **~85%**) +- optimized memory allocation during network resizing when `random=1` +- optimized GPU initialization for detection - we use batch=1 initially instead of re-init with batch=1 +- added correct calculation of **mAP, F1, IoU, Precision-Recall** using command `darknet detector map`... +- added drawing of chart of average-Loss and accuracy-mAP (`-map` flag) during training +- run `./darknet detector demo ... -json_port 8070 -mjpeg_port 8090` as JSON and MJPEG server to get results online over the network by using your soft or Web-browser +- added calculation of anchors for training +- added example of Detection and Tracking objects: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp +- run-time tips and warnings if you use incorrect cfg-file or dataset +- added support for Windows +- many other fixes of code... And added manual - [How to train Yolo v4-v2 (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects) @@ -277,77 +276,78 @@ Also, you might be interested in using a simplified repository where is implemen On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights` -On Linux find executable file `./darknet` in the root directory, while on Windows find it in the directory `\build\darknet\x64` - -* Yolo v4 COCO - **image**: `darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25` -* **Output coordinates** of objects: `darknet.exe detector test cfg/coco.data yolov4.cfg yolov4.weights -ext_output dog.jpg` -* Yolo v4 COCO - **video**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output test.mp4` -* Yolo v4 COCO - **WebCam 0**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -c 0` -* Yolo v4 COCO for **net-videocam** - Smart WebCam: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg` -* Yolo v4 - **save result videofile res.avi**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -out_filename res.avi` -* Yolo v3 **Tiny** COCO - video: `darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4` -* **JSON and MJPEG server** that allows multiple connections from your soft or Web-browser `ip-address:8070` and 8090: `./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output` -* Yolo v3 Tiny **on GPU #1**: `darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 1 test.mp4` -* Alternative method Yolo v3 COCO - image: `darknet.exe detect cfg/yolov4.cfg yolov4.weights -i 0 -thresh 0.25` -* Train on **Amazon EC2**, to see mAP & Loss-chart using URL like: `http://ec2-35-160-228-91.us-west-2.compute.amazonaws.com:8090` in the Chrome/Firefox (**Darknet should be compiled with OpenCV**): +On Linux find executable file `./darknet` in the root directory, while on Windows find it in the directory `\build\darknet\x64` + +- Yolo v4 COCO - **image**: `darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25` +- **Output coordinates** of objects: `darknet.exe detector test cfg/coco.data yolov4.cfg yolov4.weights -ext_output dog.jpg` +- Yolo v4 COCO - **video**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output test.mp4` +- Yolo v4 COCO - **WebCam 0**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -c 0` +- Yolo v4 COCO for **net-videocam** - Smart WebCam: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg` +- Yolo v4 - **save result videofile res.avi**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -out_filename res.avi` +- Yolo v3 **Tiny** COCO - video: `darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4` +- **JSON and MJPEG server** that allows multiple connections from your soft or Web-browser `ip-address:8070` and 8090: `./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output` +- Yolo v3 Tiny **on GPU #1**: `darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 1 test.mp4` +- Alternative method Yolo v3 COCO - image: `darknet.exe detect cfg/yolov4.cfg yolov4.weights -i 0 -thresh 0.25` +- Train on **Amazon EC2**, to see mAP & Loss-chart using URL like: `http://ec2-35-160-228-91.us-west-2.compute.amazonaws.com:8090` in the Chrome/Firefox (**Darknet should be compiled with OpenCV**): `./darknet detector train cfg/coco.data yolov4.cfg yolov4.conv.137 -dont_show -mjpeg_port 8090 -map` -* 186 MB Yolo9000 - image: `darknet.exe detector test cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights` -* Remember to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app -* To process a list of images `data/train.txt` and save results of detection to `result.json` file use: +- 186 MB Yolo9000 - image: `darknet.exe detector test cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights` +- Remember to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app +- To process a list of images `data/train.txt` and save results of detection to `result.json` file use: `darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output -dont_show -out result.json < data/train.txt` -* To process a list of images `data/train.txt` and save results of detection to `result.txt` use: +- To process a list of images `data/train.txt` and save results of detection to `result.txt` use: `darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -dont_show -ext_output < data/train.txt > result.txt` -* Pseudo-labelling - to process a list of images `data/new_train.txt` and save results of detection in Yolo training format for each image as label `.txt` (in this way you can increase the amount of training data) use: +- Pseudo-labelling - to process a list of images `data/new_train.txt` and save results of detection in Yolo training format for each image as label `.txt` (in this way you can increase the amount of training data) use: `darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25 -dont_show -save_labels < data/new_train.txt` -* To calculate anchors: `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416` -* To check accuracy mAP@IoU=50: `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights` -* To check accuracy mAP@IoU=75: `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights -iou_thresh 0.75` +- To calculate anchors: `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416` +- To check accuracy mAP@IoU=50: `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights` +- To check accuracy mAP@IoU=75: `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights -iou_thresh 0.75` ##### For using network video-camera mjpeg-stream with any Android smartphone 1. Download for Android phone mjpeg-stream soft: IP Webcam / Smart WebCam - * Smart WebCam - preferably: https://play.google.com/store/apps/details?id=com.acontech.android.SmartWebCam2 - * IP Webcam: https://play.google.com/store/apps/details?id=com.pas.webcam + - Smart WebCam - preferably: https://play.google.com/store/apps/details?id=com.acontech.android.SmartWebCam2 + - IP Webcam: https://play.google.com/store/apps/details?id=com.pas.webcam 2. Connect your Android phone to computer by WiFi (through a WiFi-router) or USB 3. Start Smart WebCam on your phone 4. Replace the address below, on shown in the phone application (Smart WebCam) and launch: -* Yolo v4 COCO-model: `darknet.exe detector demo data/coco.data yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` +- Yolo v4 COCO-model: `darknet.exe detector demo data/coco.data yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` ### How to compile on Linux/macOS (using `CMake`) The `CMakeLists.txt` will attempt to find installed optional dependencies like CUDA, cudnn, ZED and build against those. It will also create a shared object library file to use `darknet` for code development. -Install powershell if you do not already have it ([guide here](https://docs.microsoft.com/en-us/powershell/scripting/install/installing-powershell)). +To update CMake on Ubuntu, it's better to follow guide here: https://apt.kitware.com/ or https://cmake.org/download/ -To update CMake on Ubuntu, it's better to follow guide here: https://apt.kitware.com/ - -### Using `vcpkg` - -Open a shell and type these commands - -```PowerShell -PS Code/> git clone https://github.com/AlexeyAB/darknet -PS Code/> cd darknet -PS Code/darknet> ./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN +```bash +git clone https://github.com/AlexeyAB/darknet +cd darknet +mkdir build_release +cd build_release +cmake .. +cmake --build . --target install --parallel 8 ``` -(add option `-EnableOPENCV_CUDA` if you want to build OpenCV with CUDA support - very slow to build!) -If you open the `build.ps1` script at the beginning you will find all available switches. +### Using also PowerShell + +Install: `Cmake`, `CUDA`, `cuDNN` [How to install dependencies](#requirements) -### Using libraries manually provided +Install powershell for your OS (Linux or MacOS) ([guide here](https://docs.microsoft.com/en-us/powershell/scripting/install/installing-powershell)). -Open a shell and type these commands +Open PowerShell type these commands ```PowerShell -PS Code/> git clone https://github.com/AlexeyAB/darknet -PS Code/> cd darknet -PS Code/darknet> ./build.ps1 -EnableOPENCV -EnableCUDA -EnableCUDNN +git clone https://github.com/AlexeyAB/darknet +cd darknet +./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN ``` -(remove options like `-EnableCUDA` or `-EnableCUDNN` if you are not interested into). +- remove options like `-EnableCUDA` or `-EnableCUDNN` if you are not interested into +- remove option `-UseVCPKG` if you plan to manually provide OpenCV library to darknet or if you do not want to enable OpenCV integration +- add option `-EnableOPENCV_CUDA` if you want to build OpenCV with CUDA support - very slow to build! (requires `-UseVCPKG`) + If you open the `build.ps1` script at the beginning you will find all available switches. ### How to compile on Linux (using `make`) @@ -355,17 +355,17 @@ If you open the `build.ps1` script at the beginning you will find all available Just do `make` in the darknet directory. (You can try to compile and run it on Google Colab in cloud [link](https://colab.research.google.com/drive/12QusaaRj_lUwCGDvQNfICpa7kA7_a2dE) (press «Open in Playground» button at the top-left corner) and watch the video [link](https://www.youtube.com/watch?v=mKAEGSxwOAY) ) Before make, you can set such options in the `Makefile`: [link](https://github.com/AlexeyAB/darknet/blob/9c1b9a2cf6363546c152251be578a21f3c3caec6/Makefile#L1) -* `GPU=1` to build with CUDA to accelerate by using GPU (CUDA should be in `/usr/local/cuda`) -* `CUDNN=1` to build with cuDNN v5-v7 to accelerate training by using GPU (cuDNN should be in `/usr/local/cudnn`) -* `CUDNN_HALF=1` to build for Tensor Cores (on Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x -* `OPENCV=1` to build with OpenCV 4.x/3.x/2.4.x - allows to detect on video files and video streams from network cameras or web-cams -* `DEBUG=1` to build debug version of Yolo -* `OPENMP=1` to build with OpenMP support to accelerate Yolo by using multi-core CPU -* `LIBSO=1` to build a library `darknet.so` and binary runnable file `uselib` that uses this library. Or you can try to run so `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib test.mp4` How to use this SO-library from your own code - you can look at C++ example: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp +- `GPU=1` to build with CUDA to accelerate by using GPU (CUDA should be in `/usr/local/cuda`) +- `CUDNN=1` to build with cuDNN v5-v7 to accelerate training by using GPU (cuDNN should be in `/usr/local/cudnn`) +- `CUDNN_HALF=1` to build for Tensor Cores (on Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x +- `OPENCV=1` to build with OpenCV 4.x/3.x/2.4.x - allows to detect on video files and video streams from network cameras or web-cams +- `DEBUG=1` to build debug version of Yolo +- `OPENMP=1` to build with OpenMP support to accelerate Yolo by using multi-core CPU +- `LIBSO=1` to build a library `darknet.so` and binary runnable file `uselib` that uses this library. Or you can try to run so `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib test.mp4` How to use this SO-library from your own code - you can look at C++ example: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp or use in such a way: `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov4.cfg yolov4.weights test.mp4` -* `ZED_CAMERA=1` to build a library with ZED-3D-camera support (should be ZED SDK installed), then run +- `ZED_CAMERA=1` to build a library with ZED-3D-camera support (should be ZED SDK installed), then run `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov4.cfg yolov4.weights zed_camera` -* You also need to specify for which graphics card the code is generated. This is done by setting `ARCH=`. If you use a never version than CUDA 11 you further need to edit line 20 from Makefile and remove `-gencode arch=compute_30,code=sm_30 \` as Kepler GPU support was dropped in CUDA 11. You can also drop the general `ARCH=` and just uncomment `ARCH=` for your graphics card. +- You also need to specify for which graphics card the code is generated. This is done by setting `ARCH=`. If you use a never version than CUDA 11 you further need to edit line 20 from Makefile and remove `-gencode arch=compute_30,code=sm_30 \` as Kepler GPU support was dropped in CUDA 11. You can also drop the general `ARCH=` and just uncomment `ARCH=` for your graphics card. To run Darknet on Linux use examples from this article, just use `./darknet` instead of `darknet.exe`, i.e. use this command: `./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights` @@ -373,37 +373,37 @@ To run Darknet on Linux use examples from this article, just use `./darknet` ins Requires: -* MSVC: https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community -* CMake GUI: `Windows win64-x64 Installer`https://cmake.org/download/ -* Download Darknet zip-archive with the latest commit and uncompress it: [master.zip](https://github.com/AlexeyAB/darknet/archive/master.zip) +- MSVC: https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community +- CMake GUI: `Windows win64-x64 Installer`https://cmake.org/download/ +- Download Darknet zip-archive with the latest commit and uncompress it: [master.zip](https://github.com/AlexeyAB/darknet/archive/master.zip) In Windows: -* Start (button) -> All programs -> CMake -> CMake (gui) -> +- Start (button) -> All programs -> CMake -> CMake (gui) -> -* [look at image](https://habrastorage.org/webt/pz/s1/uu/pzs1uu4heb7vflfcjqn-lxy-aqu.jpeg) In CMake: Enter input path to the darknet Source, and output path to the Binaries -> Configure (button) -> Optional platform for generator: `x64` -> Finish -> Generate -> Open Project -> +- [look at image](https://habrastorage.org/webt/pz/s1/uu/pzs1uu4heb7vflfcjqn-lxy-aqu.jpeg) In CMake: Enter input path to the darknet Source, and output path to the Binaries -> Configure (button) -> Optional platform for generator: `x64` -> Finish -> Generate -> Open Project -> -* in MS Visual Studio: Select: x64 and Release -> Build -> Build solution +- in MS Visual Studio: Select: x64 and Release -> Build -> Build solution -* find the executable file `darknet.exe` in the output path to the binaries you specified +- find the executable file `darknet.exe` in the output path to the binaries you specified ![x64 and Release](https://habrastorage.org/webt/ay/ty/f-/aytyf-8bufe7q-16yoecommlwys.jpeg) - ### How to compile on Windows (using `vcpkg`) This is the recommended approach to build Darknet on Windows. 1. Install Visual Studio 2017 or 2019. In case you need to download it, please go here: [Visual Studio Community](http://visualstudio.com). Remember to install English language pack, this is mandatory for vcpkg! -2. Install CUDA (at least v10.0) enabling VS Integration during installation. +2. Install CUDA enabling VS Integration during installation. 3. Open Powershell (Start -> All programs -> Windows Powershell) and type these commands: ```PowerShell -PS Code/> git clone https://github.com/AlexeyAB/darknet -PS Code/> cd darknet -PS Code/darknet> .\build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN +Set-ExecutionPolicy unrestricted -Scope CurrentUser -Force +git clone https://github.com/AlexeyAB/darknet +cd darknet +.\build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN ``` (add option `-EnableOPENCV_CUDA` if you want to build OpenCV with CUDA support - very slow to build! - or remove options like `-EnableCUDA` or `-EnableCUDNN` if you are not interested in them). If you open the `build.ps1` script at the beginning you will find all available switches. @@ -427,23 +427,23 @@ Training Yolo v4 (and v3): 0. For training `cfg/yolov4-custom.cfg` download the pre-trained weights-file (162 MB): [yolov4.conv.137](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137) (Google drive mirror [yolov4.conv.137](https://drive.google.com/open?id=1JKF-bdIklxOOVy-2Cr5qdvjgGpmGfcbp) ) 1. Create file `yolo-obj.cfg` with the same content as in `yolov4-custom.cfg` (or copy `yolov4-custom.cfg` to `yolo-obj.cfg)` and: -* change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L3) -* change line subdivisions to [`subdivisions=16`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4) -* change line max_batches to (`classes*2000`, but not less than number of training images and not less than `6000`), f.e. [`max_batches=6000`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L20) if you train for 3 classes -* change line steps to 80% and 90% of max_batches, f.e. [`steps=4800,5400`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L22) -* set network size `width=416 height=416` or any value multiple of 32: https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L8-L9 -* change line `classes=80` to your number of objects in each of 3 `[yolo]`-layers: - * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L610 - * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L696 - * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L783 -* change [`filters=255`] to filters=(classes + 5)x3 in the 3 `[convolutional]` before each `[yolo]` layer, keep in mind that it only has to be the last `[convolutional]` before each of the `[yolo]` layers. - * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L603 - * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L689 - * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L776 -* when using [`[Gaussian_yolo]`](https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L608) layers, change [`filters=57`] filters=(classes + 9)x3 in the 3 `[convolutional]` before each `[Gaussian_yolo]` layer - * https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L604 - * https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L696 - * https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L789 +- change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L3) +- change line subdivisions to [`subdivisions=16`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4) +- change line max_batches to (`classes*2000`, but not less than number of training images and not less than `6000`), f.e. [`max_batches=6000`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L20) if you train for 3 classes +- change line steps to 80% and 90% of max_batches, f.e. [`steps=4800,5400`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L22) +- set network size `width=416 height=416` or any value multiple of 32: https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L8-L9 +- change line `classes=80` to your number of objects in each of 3 `[yolo]`-layers: + - https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L610 + - https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L696 + - https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L783 +- change [`filters=255`] to filters=(classes + 5)x3 in the 3 `[convolutional]` before each `[yolo]` layer, keep in mind that it only has to be the last `[convolutional]` before each of the `[yolo]` layers. + - https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L603 + - https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L689 + - https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L776 +- when using [`[Gaussian_yolo]`](https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L608) layers, change [`filters=57`] filters=(classes + 9)x3 in the 3 `[convolutional]` before each `[Gaussian_yolo]` layer + - https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L604 + - https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L696 + - https://github.com/AlexeyAB/darknet/blob/6e5bdf1282ad6b06ed0e962c3f5be67cf63d96dc/cfg/Gaussian_yolov3_BDD.cfg#L789 So if `classes=1` then should be `filters=18`. If `classes=2` then write `filters=21`. **(Do not write in the cfg-file: filters=(classes + 5)x3)** @@ -474,19 +474,20 @@ classes=2 4. Put image-files (.jpg) of your objects in the directory `build\darknet\x64\data\obj\` 5. You should label each object on images from your dataset. Use this visual GUI-software for marking bounded boxes of objects and generating annotation files for Yolo v2 & v3: https://github.com/AlexeyAB/Yolo_mark -It will create `.txt`-file for each `.jpg`-image-file - in the same directory and with the same name, but with `.txt`-extension, and put to file: object number and object coordinates on this image, for each object in new line: +It will create `.txt`-file for each `.jpg`-image-file - in the same directory and with the same name, but with `.txt`-extension, and put to file: object number and object coordinates on this image, for each object in new line: ` ` - Where: - * `` - integer object number from `0` to `(classes-1)` - * ` ` - float values **relative** to width and height of image, it can be equal from `(0.0 to 1.0]` - * for example: ` = / ` or ` = / ` - * attention: ` ` - are center of rectangle (are not top-left corner) + Where: + +- `` - integer object number from `0` to `(classes-1)` +- ` ` - float values **relative** to width and height of image, it can be equal from `(0.0 to 1.0]` +- for example: ` = / ` or ` = / ` +- attention: ` ` - are center of rectangle (are not top-left corner) For example for `img1.jpg` you will be created `img1.txt` containing: - ``` + ```csv 1 0.716797 0.395833 0.216406 0.147222 0 0.687109 0.379167 0.255469 0.158333 1 0.420312 0.395833 0.140625 0.166667 @@ -494,38 +495,38 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an 6. Create file `train.txt` in directory `build\darknet\x64\data\`, with filenames of your images, each filename in new line, with path relative to `darknet.exe`, for example containing: - ``` + ```csv data/obj/img1.jpg data/obj/img2.jpg data/obj/img3.jpg ``` 7. Download pre-trained weights for the convolutional layers and put to the directory `build\darknet\x64` - * for `yolov4.cfg`, `yolov4-custom.cfg` (162 MB): [yolov4.conv.137](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137) (Google drive mirror [yolov4.conv.137](https://drive.google.com/open?id=1JKF-bdIklxOOVy-2Cr5qdvjgGpmGfcbp) ) - * for `yolov4-tiny.cfg`, `yolov4-tiny-3l.cfg`, `yolov4-tiny-custom.cfg` (19 MB): [yolov4-tiny.conv.29](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29) - * for `csresnext50-panet-spp.cfg` (133 MB): [csresnext50-panet-spp.conv.112](https://drive.google.com/file/d/16yMYCLQTY_oDlCIZPfn_sab6KD3zgzGq/view?usp=sharing) - * for `yolov3.cfg, yolov3-spp.cfg` (154 MB): [darknet53.conv.74](https://pjreddie.com/media/files/darknet53.conv.74) - * for `yolov3-tiny-prn.cfg , yolov3-tiny.cfg` (6 MB): [yolov3-tiny.conv.11](https://drive.google.com/file/d/18v36esoXCh-PsOKwyP2GWrpYDptDY8Zf/view?usp=sharing) - * for `enet-coco.cfg (EfficientNetB0-Yolov3)` (14 MB): [enetb0-coco.conv.132](https://drive.google.com/file/d/1uhh3D6RSn0ekgmsaTcl-ZW53WBaUDo6j/view?usp=sharing) + - for `yolov4.cfg`, `yolov4-custom.cfg` (162 MB): [yolov4.conv.137](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137) (Google drive mirror [yolov4.conv.137](https://drive.google.com/open?id=1JKF-bdIklxOOVy-2Cr5qdvjgGpmGfcbp) ) + - for `yolov4-tiny.cfg`, `yolov4-tiny-3l.cfg`, `yolov4-tiny-custom.cfg` (19 MB): [yolov4-tiny.conv.29](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29) + - for `csresnext50-panet-spp.cfg` (133 MB): [csresnext50-panet-spp.conv.112](https://drive.google.com/file/d/16yMYCLQTY_oDlCIZPfn_sab6KD3zgzGq/view?usp=sharing) + - for `yolov3.cfg, yolov3-spp.cfg` (154 MB): [darknet53.conv.74](https://pjreddie.com/media/files/darknet53.conv.74) + - for `yolov3-tiny-prn.cfg , yolov3-tiny.cfg` (6 MB): [yolov3-tiny.conv.11](https://drive.google.com/file/d/18v36esoXCh-PsOKwyP2GWrpYDptDY8Zf/view?usp=sharing) + - for `enet-coco.cfg (EfficientNetB0-Yolov3)` (14 MB): [enetb0-coco.conv.132](https://drive.google.com/file/d/1uhh3D6RSn0ekgmsaTcl-ZW53WBaUDo6j/view?usp=sharing) 8. Start training by using the command line: `darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137` To train on Linux use command: `./darknet detector train data/obj.data yolo-obj.cfg yolov4.conv.137` (just use `./darknet` instead of `darknet.exe`) - * (file `yolo-obj_last.weights` will be saved to the `build\darknet\x64\backup\` for each 100 iterations) - * (file `yolo-obj_xxxx.weights` will be saved to the `build\darknet\x64\backup\` for each 1000 iterations) - * (to disable Loss-Window use `darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -dont_show`, if you train on computer without monitor like a cloud Amazon EC2) - * (to see the mAP & Loss-chart during training on remote server without GUI, use command `darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -dont_show -mjpeg_port 8090 -map` then open URL `http://ip-address:8090` in Chrome/Firefox browser) + - (file `yolo-obj_last.weights` will be saved to the `build\darknet\x64\backup\` for each 100 iterations) + - (file `yolo-obj_xxxx.weights` will be saved to the `build\darknet\x64\backup\` for each 1000 iterations) + - (to disable Loss-Window use `darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -dont_show`, if you train on computer without monitor like a cloud Amazon EC2) + - (to see the mAP & Loss-chart during training on remote server without GUI, use command `darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -dont_show -mjpeg_port 8090 -map` then open URL `http://ip-address:8090` in Chrome/Firefox browser) 8.1. For training with mAP (mean average precisions) calculation for each 4 Epochs (set `valid=valid.txt` or `train.txt` in `obj.data` file) and run: `darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -map` 9. After training is complete - get result `yolo-obj_final.weights` from path `build\darknet\x64\backup\` - * After each 100 iterations you can stop and later start training from this point. For example, after 2000 iterations you can stop training, and later just start training using: `darknet.exe detector train data/obj.data yolo-obj.cfg backup\yolo-obj_2000.weights` + - After each 100 iterations you can stop and later start training from this point. For example, after 2000 iterations you can stop training, and later just start training using: `darknet.exe detector train data/obj.data yolo-obj.cfg backup\yolo-obj_2000.weights` (in the original repository https://github.com/pjreddie/darknet the weights-file is saved only once every 10 000 iterations `if(iterations > 1000)`) - * Also you can get result earlier than all 45000 iterations. + - Also you can get result earlier than all 45000 iterations. **Note:** If during training you see `nan` values for `avg` (loss) field - then training goes wrong, but if `nan` is in some other lines - then training goes well. @@ -535,19 +536,19 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an **Note:** if error `Out of memory` occurs then in `.cfg`-file you should increase `subdivisions=16`, 32 or 64: [link](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4) -### How to train tiny-yolo (to detect your custom objects): +### How to train tiny-yolo (to detect your custom objects) Do all the same steps as for the full yolo model as described above. With the exception of: -* Download file with the first 29-convolutional layers of yolov4-tiny: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29 +- Download file with the first 29-convolutional layers of yolov4-tiny: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29 (Or get this file from yolov4-tiny.weights file by using command: `darknet.exe partial cfg/yolov4-tiny-custom.cfg yolov4-tiny.weights yolov4-tiny.conv.29 29` -* Make your custom model `yolov4-tiny-obj.cfg` based on `cfg/yolov4-tiny-custom.cfg` instead of `yolov4.cfg` -* Start training: `darknet.exe detector train data/obj.data yolov4-tiny-obj.cfg yolov4-tiny.conv.29` +- Make your custom model `yolov4-tiny-obj.cfg` based on `cfg/yolov4-tiny-custom.cfg` instead of `yolov4.cfg` +- Start training: `darknet.exe detector train data/obj.data yolov4-tiny-obj.cfg yolov4-tiny.conv.29` For training Yolo based on other models ([DenseNet201-Yolo](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/densenet201_yolo.cfg) or [ResNet50-Yolo](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/resnet50_yolo.cfg)), you can download and get pre-trained weights as showed in this file: https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/partial.cmd If you made you custom model that isn't based on other models, then you can train it without pre-trained weights, then will be used random initial weights. -## When should I stop training: +## When should I stop training Usually sufficient 2000 iterations for each class(object), but not less than number of training images and not less than 6000 iterations in total. But for a more precise definition when you should stop training, use the following manual: @@ -559,18 +560,18 @@ Usually sufficient 2000 iterations for each class(object), but not less than num > **9002**: 0.211667, **0.60730 avg**, 0.001000 rate, 3.868000 seconds, 576128 images > Loaded: 0.000000 seconds - * **9002** - iteration number (number of batch) - * **0.60730 avg** - average loss (error) - **the lower, the better** +- **9002** - iteration number (number of batch) +- **0.60730 avg** - average loss (error) - **the lower, the better** When you see that average loss **0.xxxxxx avg** no longer decreases at many iterations then you should stop training. The final average loss can be from `0.05` (for a small model and easy dataset) to `3.0` (for a big model and a difficult dataset). - Or if you train with flag `-map` then you will see mAP indicator `Last accuracy mAP@0.5 = 18.50%` in the console - this indicator is better than Loss, so train while mAP increases. + Or if you train with flag `-map` then you will see mAP indicator `Last accuracy mAP@0.5 = 18.50%` in the console - this indicator is better than Loss, so train while mAP increases. 2. Once training is stopped, you should take some of last `.weights`-files from `darknet\build\darknet\x64\backup` and choose the best of them: For example, you stopped training after 9000 iterations, but the best result can give one of previous weights (7000, 8000, 9000). It can happen due to over-fitting. **Over-fitting** - is case when you can detect objects on images from training-dataset, but can't detect objects on any others images. You should get weights from **Early Stopping Point**: -![Over-fitting](https://hsto.org/files/5dc/7ae/7fa/5dc7ae7fad9d4e3eb3a484c58bfc1ff5.png) +![Over-fitting](https://hsto.org/files/5dc/7ae/7fa/5dc7ae7fad9d4e3eb3a484c58bfc1ff5.png) To get weights from Early Stopping Point: @@ -580,9 +581,9 @@ To get weights from Early Stopping Point: (If you use another GitHub repository, then use `darknet.exe detector recall`... instead of `darknet.exe detector map`...) -* `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights` -* `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_8000.weights` -* `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_9000.weights` +- `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights` +- `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_8000.weights` +- `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_9000.weights` And compare last output lines for each weights (7000, 8000, 9000): @@ -590,9 +591,9 @@ Choose weights-file **with the highest mAP (mean average precision)** or IoU (in For example, **bigger mAP** gives weights `yolo-obj_8000.weights` - then **use this weights for detection**. -Or just train with `-map` flag: +Or just train with `-map` flag: -`darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -map` +`darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -map` So you will see mAP-chart (red-line) in the Loss-chart Window. mAP will be calculated for each 4 Epochs using `valid=valid.txt` file that is specified in `obj.data` file (`1 Epoch = images_in_train_txt / batch` iterations) @@ -602,9 +603,9 @@ So you will see mAP-chart (red-line) in the Loss-chart Window. mAP will be calcu Example of custom object detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights` -* **IoU** (intersect over union) - average intersect over union of objects and detections for a certain threshold = 0.24 +- **IoU** (intersect over union) - average intersect over union of objects and detections for a certain threshold = 0.24 -* **mAP** (mean average precision) - mean value of `average precisions` for each class, where `average precision` is average value of 11 points on PR-curve for each possible threshold (each probability of detection) for the same class (Precision-Recall in terms of PascalVOC, where Precision=TP/(TP+FP) and Recall=TP/(TP+FN) ), page-11: http://homepages.inf.ed.ac.uk/ckiw/postscript/ijcv_voc09.pdf +- **mAP** (mean average precision) - mean value of `average precisions` for each class, where `average precision` is average value of 11 points on PR-curve for each possible threshold (each probability of detection) for the same class (Precision-Recall in terms of PascalVOC, where Precision=TP/(TP+FP) and Recall=TP/(TP+FN) ), page-11: http://homepages.inf.ed.ac.uk/ckiw/postscript/ijcv_voc09.pdf **mAP** is default metric of precision in the PascalVOC competition, **this is the same as AP50** metric in the MS COCO competition. In terms of Wiki, indicators Precision and Recall have a slightly different meaning than in the PascalVOC competition, but **IoU always has the same meaning**. @@ -622,60 +623,60 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol 1. Before training: -* set flag `random=1` in your `.cfg`-file - it will increase precision by training Yolo for different resolutions: [link](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L788) +- set flag `random=1` in your `.cfg`-file - it will increase precision by training Yolo for different resolutions: [link](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L788) -* increase network resolution in your `.cfg`-file (`height=608`, `width=608` or any value multiple of 32) - it will increase precision +- increase network resolution in your `.cfg`-file (`height=608`, `width=608` or any value multiple of 32) - it will increase precision -* check that each object that you want to detect is mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: https://github.com/AlexeyAB/Yolo_mark +- check that each object that you want to detect is mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: https://github.com/AlexeyAB/Yolo_mark -* my Loss is very high and mAP is very low, is training wrong? Run training with ` -show_imgs` flag at the end of training command, do you see correct bounded boxes of objects (in windows or in files `aug_...jpg`)? If no - your training dataset is wrong. +- my Loss is very high and mAP is very low, is training wrong? Run training with `-show_imgs` flag at the end of training command, do you see correct bounded boxes of objects (in windows or in files `aug_...jpg`)? If no - your training dataset is wrong. -* for each object which you want to detect - there must be at least 1 similar object in the Training dataset with about the same: shape, side of object, relative size, angle of rotation, tilt, illumination. So desirable that your training dataset include images with objects at different: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 different images for each class or more, and you should train `2000*classes` iterations or more +- for each object which you want to detect - there must be at least 1 similar object in the Training dataset with about the same: shape, side of object, relative size, angle of rotation, tilt, illumination. So desirable that your training dataset include images with objects at different: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 different images for each class or more, and you should train `2000*classes` iterations or more -* desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty `.txt` files) - use as many images of negative samples as there are images with objects +- desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty `.txt` files) - use as many images of negative samples as there are images with objects -* What is the best way to mark objects: label only the visible part of the object, or label the visible and overlapped part of the object, or label a little more than the entire object (with a little gap)? Mark as you like - how would you like it to be detected. +- What is the best way to mark objects: label only the visible part of the object, or label the visible and overlapped part of the object, or label a little more than the entire object (with a little gap)? Mark as you like - how would you like it to be detected. -* for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last `[yolo]`-layer or `[region]`-layer in your cfg-file (the global maximum number of objects that can be detected by YoloV3 is `0,0615234375*(width*height)` where are width and height are parameters from `[net]` section in cfg-file) +- for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last `[yolo]`-layer or `[region]`-layer in your cfg-file (the global maximum number of objects that can be detected by YoloV3 is `0,0615234375*(width*height)` where are width and height are parameters from `[net]` section in cfg-file) -* for training for small objects (smaller than 16x16 after the image is resized to 416x416) - set `layers = 23` instead of https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L895 - * set `stride=4` instead of https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L892 - * set `stride=4` instead of https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L989 +- for training for small objects (smaller than 16x16 after the image is resized to 416x416) - set `layers = 23` instead of https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L895 + - set `stride=4` instead of https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L892 + - set `stride=4` instead of https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L989 -* for training for both small and large objects use modified models: - * Full-model: 5 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3_5l.cfg - * Tiny-model: 3 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny_3l.cfg - * YOLOv4: 3 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-custom.cfg +- for training for both small and large objects use modified models: + - Full-model: 5 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3_5l.cfg + - Tiny-model: 3 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny_3l.cfg + - YOLOv4: 3 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-custom.cfg -* If you train the model to distinguish Left and Right objects as separate classes (left/right hand, left/right-turn on road signs, ...) then for disabling flip data augmentation - add `flip=0` here: https://github.com/AlexeyAB/darknet/blob/3d2d0a7c98dbc8923d9ff705b81ff4f7940ea6ff/cfg/yolov3.cfg#L17 +- If you train the model to distinguish Left and Right objects as separate classes (left/right hand, left/right-turn on road signs, ...) then for disabling flip data augmentation - add `flip=0` here: https://github.com/AlexeyAB/darknet/blob/3d2d0a7c98dbc8923d9ff705b81ff4f7940ea6ff/cfg/yolov3.cfg#L17 -* General rule - your training dataset should include such a set of relative sizes of objects that you want to detect: - * `train_network_width * train_obj_width / train_image_width ~= detection_network_width * detection_obj_width / detection_image_width` - * `train_network_height * train_obj_height / train_image_height ~= detection_network_height * detection_obj_height / detection_image_height` +- General rule - your training dataset should include such a set of relative sizes of objects that you want to detect: + - `train_network_width * train_obj_width / train_image_width ~= detection_network_width * detection_obj_width / detection_image_width` + - `train_network_height * train_obj_height / train_image_height ~= detection_network_height * detection_obj_height / detection_image_height` I.e. for each object from Test dataset there must be at least 1 object in the Training dataset with the same class_id and about the same relative size: - `object width in percent from Training dataset` ~= `object width in percent from Test dataset` + `object width in percent from Training dataset` ~= `object width in percent from Test dataset` That is, if only objects that occupied 80-90% of the image were present in the training set, then the trained network will not be able to detect objects that occupy 1-10% of the image. -* to speedup training (with decreasing detection accuracy) set param `stopbackward=1` for layer-136 in cfg-file +- to speedup training (with decreasing detection accuracy) set param `stopbackward=1` for layer-136 in cfg-file -* each: `model of object, side, illumination, scale, each 30 grad` of the turn and inclination angles - these are *different objects* from an internal perspective of the neural network. So the more *different objects* you want to detect, the more complex network model should be used. +- each: `model of object, side, illumination, scale, each 30 grad` of the turn and inclination angles - these are *different objects* from an internal perspective of the neural network. So the more *different objects* you want to detect, the more complex network model should be used. -* to make the detected bounded boxes more accurate, you can add 3 parameters `ignore_thresh = .9 iou_normalizer=0.5 iou_loss=giou` to each `[yolo]` layer and train, it will increase mAP@0.9, but decrease mAP@0.5. +- to make the detected bounded boxes more accurate, you can add 3 parameters `ignore_thresh = .9 iou_normalizer=0.5 iou_loss=giou` to each `[yolo]` layer and train, it will increase mAP@0.9, but decrease mAP@0.5. -* Only if you are an **expert** in neural detection networks - recalculate anchors for your dataset for `width` and `height` from cfg-file: +- Only if you are an **expert** in neural detection networks - recalculate anchors for your dataset for `width` and `height` from cfg-file: `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416` then set the same 9 `anchors` in each of 3 `[yolo]`-layers in your cfg-file. But you should change indexes of anchors `masks=` for each [yolo]-layer, so for YOLOv4 the 1st-[yolo]-layer has anchors smaller than 30x30, 2nd smaller than 60x60, 3rd remaining, and vice versa for YOLOv3. Also you should change the `filters=(classes + 5)*` before each [yolo]-layer. If many of the calculated anchors do not fit under the appropriate layers - then just try using all the default anchors. 2. After training - for detection: -* Increase network-resolution by set in your `.cfg`-file (`height=608` and `width=608`) or (`height=832` and `width=832`) or (any value multiple of 32) - this increases the precision and makes it possible to detect small objects: [link](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L8-L9) +- Increase network-resolution by set in your `.cfg`-file (`height=608` and `width=608`) or (`height=832` and `width=832`) or (any value multiple of 32) - this increases the precision and makes it possible to detect small objects: [link](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L8-L9) -* it is not necessary to train the network again, just use `.weights`-file already trained for 416x416 resolution +- it is not necessary to train the network again, just use `.weights`-file already trained for 416x416 resolution -* to get even greater accuracy you should train with higher resolution 608x608 or 832x832, note: if error `Out of memory` occurs then in `.cfg`-file you should increase `subdivisions=16`, 32 or 64: [link](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4) +- to get even greater accuracy you should train with higher resolution 608x608 or 832x832, note: if error `Out of memory` occurs then in `.cfg`-file you should increase `subdivisions=16`, 32 or 64: [link](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4) ## How to mark bounded boxes of objects and create annotation files @@ -697,40 +698,40 @@ Different tools for marking objects in images: ## How to use Yolo as DLL and SO libraries -* on Linux - * using `build.sh` or - * build `darknet` using `cmake` or - * set `LIBSO=1` in the `Makefile` and do `make` -* on Windows - * using `build.ps1` or - * build `darknet` using `cmake` or - * compile `build\darknet\yolo_cpp_dll.sln` solution or `build\darknet\yolo_cpp_dll_no_gpu.sln` solution +- on Linux + - using `build.sh` or + - build `darknet` using `cmake` or + - set `LIBSO=1` in the `Makefile` and do `make` +- on Windows + - using `build.ps1` or + - build `darknet` using `cmake` or + - compile `build\darknet\yolo_cpp_dll.sln` solution or `build\darknet\yolo_cpp_dll_no_gpu.sln` solution There are 2 APIs: -* C API: https://github.com/AlexeyAB/darknet/blob/master/include/darknet.h - * Python examples using the C API: - * https://github.com/AlexeyAB/darknet/blob/master/darknet.py - * https://github.com/AlexeyAB/darknet/blob/master/darknet_video.py +- C API: https://github.com/AlexeyAB/darknet/blob/master/include/darknet.h + - Python examples using the C API: + - https://github.com/AlexeyAB/darknet/blob/master/darknet.py + - https://github.com/AlexeyAB/darknet/blob/master/darknet_video.py -* C++ API: https://github.com/AlexeyAB/darknet/blob/master/include/yolo_v2_class.hpp - * C++ example that uses C++ API: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp +- C++ API: https://github.com/AlexeyAB/darknet/blob/master/include/yolo_v2_class.hpp + - C++ example that uses C++ API: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp ---- 1. To compile Yolo as C++ DLL-file `yolo_cpp_dll.dll` - open the solution `build\darknet\yolo_cpp_dll.sln`, set **x64** and **Release**, and do the: Build -> Build yolo_cpp_dll - * You should have installed **CUDA 10.0** - * To use cuDNN do: (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add at the beginning of line: `CUDNN;` + - You should have installed **CUDA 10.2** + - To use cuDNN do: (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add at the beginning of line: `CUDNN;` 2. To use Yolo as DLL-file in your C++ console application - open the solution `build\darknet\yolo_console_dll.sln`, set **x64** and **Release**, and do the: Build -> Build yolo_console_dll - * you can run your console application from Windows Explorer `build\darknet\x64\yolo_console_dll.exe` + - you can run your console application from Windows Explorer `build\darknet\x64\yolo_console_dll.exe` **use this command**: `yolo_console_dll.exe data/coco.names yolov4.cfg yolov4.weights test.mp4` - * after launching your console application and entering the image file name - you will see info for each object: + - after launching your console application and entering the image file name - you will see info for each object: ` ` - * to use simple OpenCV-GUI you should uncomment line `//#define OPENCV` in `yolo_console_dll.cpp`-file: [link](https://github.com/AlexeyAB/darknet/blob/a6cbaeecde40f91ddc3ea09aa26a03ab5bbf8ba8/src/yolo_console_dll.cpp#L5) - * you can see source code of simple example for detection on the video file: [link](https://github.com/AlexeyAB/darknet/blob/ab1c5f9e57b4175f29a6ef39e7e68987d3e98704/src/yolo_console_dll.cpp#L75) + - to use simple OpenCV-GUI you should uncomment line `//#define OPENCV` in `yolo_console_dll.cpp`-file: [link](https://github.com/AlexeyAB/darknet/blob/a6cbaeecde40f91ddc3ea09aa26a03ab5bbf8ba8/src/yolo_console_dll.cpp#L5) + - you can see source code of simple example for detection on the video file: [link](https://github.com/AlexeyAB/darknet/blob/ab1c5f9e57b4175f29a6ef39e7e68987d3e98704/src/yolo_console_dll.cpp#L75) `yolo_cpp_dll.dll`-API: [link](https://github.com/AlexeyAB/darknet/blob/master/src/yolo_v2_class.hpp#L42) diff --git a/build.ps1 b/build.ps1 index 61e70425783..86c9acf4ba3 100755 --- a/build.ps1 +++ b/build.ps1 @@ -18,11 +18,17 @@ param ( [switch]$ForceVCPKGCacheRemoval = $false, [switch]$ForceSetupVS = $false, [Int32]$ForceGCCVersion = 0, + [Int32]$ForceOpenCVVersion = 0, [Int32]$NumberOfBuildWorkers = 8, [string]$AdditionalBuildSetup = "" # "-DCMAKE_CUDA_ARCHITECTURES=30" ) -$build_ps1_version = "0.9.3" +$build_ps1_version = "0.9.4" + +$ErrorActionPreference = "SilentlyContinue" +Stop-Transcript | out-null +$ErrorActionPreference = "Continue" +Start-Transcript -Path $PSScriptRoot/build.log Function MyThrow ($Message) { if ($DisableInteractive) { @@ -389,6 +395,7 @@ function getLatestVisualStudioWithDesktopWorkloadVersion() { return $installationVersion } +$vcpkg_root_set_by_this_script = $false if ((Test-Path env:VCPKG_ROOT) -and $UseVCPKG) { $vcpkg_path = "$env:VCPKG_ROOT" @@ -398,6 +405,7 @@ if ((Test-Path env:VCPKG_ROOT) -and $UseVCPKG) { elseif ((Test-Path "${env:WORKSPACE}/vcpkg") -and $UseVCPKG) { $vcpkg_path = "${env:WORKSPACE}/vcpkg" $env:VCPKG_ROOT = "${env:WORKSPACE}/vcpkg" + $vcpkg_root_set_by_this_script = $true Write-Host "Found vcpkg in WORKSPACE/vcpkg: $vcpkg_path" $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" } @@ -405,6 +413,7 @@ elseif (-not($null -eq ${RUNVCPKG_VCPKG_ROOT_OUT})) { if((Test-Path "${RUNVCPKG_VCPKG_ROOT_OUT}") -and $UseVCPKG) { $vcpkg_path = "${RUNVCPKG_VCPKG_ROOT_OUT}" $env:VCPKG_ROOT = "${RUNVCPKG_VCPKG_ROOT_OUT}" + $vcpkg_root_set_by_this_script = $true Write-Host "Found vcpkg in RUNVCPKG_VCPKG_ROOT_OUT: ${vcpkg_path}" $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" } @@ -421,6 +430,7 @@ elseif ($UseVCPKG) { } $vcpkg_path = "$PWD/vcpkg" $env:VCPKG_ROOT = "$PWD/vcpkg" + $vcpkg_root_set_by_this_script = $true Write-Host "Found vcpkg in $PWD/vcpkg: $PWD/vcpkg" $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" } @@ -464,6 +474,16 @@ if ($ForceVCPKGCacheRemoval -and (-Not $UseVCPKG)) { Write-Host "VCPKG is not enabled, so local vcpkg binary cache will not be deleted even if requested" -ForegroundColor Yellow } +if (($ForceOpenCVVersion -eq 2) -and $UseVCPKG) { + Write-Host "You requested OpenCV version 2, so vcpkg will install that version" -ForegroundColor Yellow + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DVCPKG_USE_OPENCV4=OFF -DVCPKG_USE_OPENCV2=ON" +} + +if (($ForceOpenCVVersion -eq 3) -and $UseVCPKG) { + Write-Host "You requested OpenCV version 3, so vcpkg will install that version" -ForegroundColor Yellow + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DVCPKG_USE_OPENCV4=OFF -DVCPKG_USE_OPENCV3=ON" +} + if ($UseVCPKG -and $ForceVCPKGCacheRemoval) { if ($IsWindows -or $IsWindowsPowerShell) { $vcpkgbinarycachepath = "$env:LOCALAPPDATA/vcpkg/archive" @@ -608,3 +628,11 @@ Set-Location .. Copy-Item cmake/Modules/*.cmake share/darknet/ Write-Host "Build complete!" -ForegroundColor Green Pop-Location + +if ($vcpkg_root_set_by_this_script) { + $env:VCPKG_ROOT = $null +} + +$ErrorActionPreference = "SilentlyContinue" +Stop-Transcript | out-null +$ErrorActionPreference = "Continue" diff --git a/scripts/deploy-cuda.sh b/scripts/deploy-cuda.sh new file mode 100755 index 00000000000..65f173aabaf --- /dev/null +++ b/scripts/deploy-cuda.sh @@ -0,0 +1,38 @@ +#!/usr/bin/env bash + +if [[ "$OSTYPE" == "darwin"* ]]; then + echo "Unable to deploy CUDA on macOS, please wait for a future script update" +else + if [[ $(cut -f2 <<< $(lsb_release -r)) == "18.04" ]]; then + sudo apt-get update + sudo apt-get install build-essential g++ + sudo apt-get install apt-transport-https ca-certificates gnupg software-properties-common wget + wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.2.89-1_amd64.deb + sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub + sudo dpkg -i cuda-repo-ubuntu1804_10.2.89-1_amd64.deb + wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb + sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb + sudo apt-get update + sudo apt-get dist-upgrade -y + sudo apt-get install -y --no-install-recommends cuda-compiler-10-2 cuda-libraries-dev-10-2 cuda-driver-dev-10-2 cuda-cudart-dev-10-2 cuda-curand-dev-10-2 + sudo apt-get install -y --no-install-recommends libcudnn7-dev + sudo rm -rf /usr/local/cuda + sudo ln -s /usr/local/cuda-10.2 /usr/local/cuda + elif [[ $(cut -f2 <<< $(lsb_release -r)) == "20.04" ]]; then + sudo apt-get update + sudo apt-get install build-essential g++ + sudo apt-get install apt-transport-https ca-certificates gnupg software-properties-common wget + sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin + sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub + sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /" + sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/ /" + sudo apt-get update + sudo apt-get dist-upgrade -y + sudo apt-get install -y --no-install-recommends cuda-compiler-11-2 cuda-libraries-dev-11-2 cuda-driver-dev-11-2 cuda-cudart-dev-11-2 + sudo apt-get install -y --no-install-recommends libcudnn8-dev + sudo rm -rf /usr/local/cuda + sudo ln -s /usr/local/cuda-11.2 /usr/local/cuda + else + echo "Unable to deploy CUDA on this Linux version, please wait for a future script update" + fi +fi diff --git a/scripts/dice_label.sh b/scripts/dice_label.sh old mode 100644 new mode 100755 diff --git a/scripts/get_coco2017.sh b/scripts/get_coco2017.sh old mode 100644 new mode 100755 diff --git a/scripts/get_coco_dataset.sh b/scripts/get_coco_dataset.sh old mode 100644 new mode 100755 diff --git a/scripts/get_imagenet_train.sh b/scripts/get_imagenet_train.sh old mode 100644 new mode 100755 diff --git a/scripts/imagenet_label.sh b/scripts/imagenet_label.sh old mode 100644 new mode 100755 diff --git a/scripts/install_OpenCV4.sh b/scripts/install_OpenCV4.sh old mode 100644 new mode 100755 diff --git a/scripts/setup.ps1 b/scripts/setup.ps1 index c5c2ae22b21..ca54dba9754 100755 --- a/scripts/setup.ps1 +++ b/scripts/setup.ps1 @@ -1,10 +1,11 @@ #!/usr/bin/env pwsh -$install_cuda = $false +param ( + [switch]$InstallCUDA = $false +) if ($null -eq (Get-Command "choco.exe" -ErrorAction SilentlyContinue)) { # Download and install Chocolatey - Set-ExecutionPolicy unrestricted -Scope CurrentUser Invoke-Expression ((New-Object System.Net.WebClient).DownloadString('https://chocolatey.org/install.ps1')) Throw "Please close and re-open powershell and then re-run setup.ps1 script" } @@ -13,23 +14,26 @@ Start-Process -FilePath "choco" -Verb runAs -ArgumentList " install -y cmake nin Start-Process -FilePath "choco" -Verb runAs -ArgumentList " install -y visualstudio2019buildtools --package-parameters `"--add Microsoft.VisualStudio.Component.VC.CoreBuildTools --includeRecommended --includeOptional --passive --locale en-US --lang en-US`"" Push-Location $PSScriptRoot -if ($install_cuda) { - & ./deploy-cuda.ps1 - $features = "full" +if ($InstallCUDA) { + & $PSScriptRoot/deploy-cuda.ps1 + $env:CUDA_PATH="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3" + $env:CUDA_TOOLKIT_ROOT_DIR="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3" + $env:CUDACXX="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3\\bin\\nvcc.exe" + $CUDAisAvailable = $true } else { if (-not $null -eq $env:CUDA_PATH) { - $features = "full" + $CUDAisAvailable = $true } else{ - $features = "opencv-base" + $CUDAisAvailable = $false } } -git.exe clone https://github.com/microsoft/vcpkg ../vcpkg -Set-Location ..\vcpkg -.\bootstrap-vcpkg.bat -disableMetrics -.\vcpkg.exe install darknet[${features}]:x64-windows -Pop-Location - -Write-Host "Darknet installed in $pwd\x64-windows\tools\darknet" -ForegroundColor Yellow +if ($CUDAisAvailable) { + & $PSScriptRoot/../build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET + #& $PSScriptRoot/../build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableOPENCV_CUDA -DisableInteractive -DoNotUpdateDARKNET +} +else { + & $PSScriptRoot/../build.ps1 -UseVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET +} diff --git a/scripts/setup.sh b/scripts/setup.sh index c33379e92ef..51d641bcea4 100755 --- a/scripts/setup.sh +++ b/scripts/setup.sh @@ -1,85 +1,102 @@ #!/usr/bin/env bash -## enable or disable installed components +install_tools=false +bypass_driver_installation=false -install_cuda=true +POSITIONAL=() +while [[ $# -gt 0 ]] +do +key="$1" -########################### +case $key in + -InstallCUDA|--InstallCUDA) + install_tools=true + shift + ;; + -BypassDRIVER|--BypassDRIVER) + bypass_driver_installation=true + shift + ;; + *) # unknown option + POSITIONAL+=("$1") # save it in an array for later + shift # past argument + ;; +esac +done +set -- "${POSITIONAL[@]}" # restore positional parameters +script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" +echo "This script is located in $script_dir" +cd $script_dir/.. temp_folder="./temp" mkdir -p $temp_folder cd $temp_folder -sudo apt-get install cmake git ninja-build build-essential g++ - -if [ "$install_cuda" = true ] ; then +if [ "$install_tools" = true ] ; then + $script_dir/deploy-cuda.sh if [[ "$OSTYPE" == "darwin"* ]]; then - echo "Unable to provide CUDA on macOS" + echo "Unable to provide tools on macOS, please wait for a future script update or do not put -InstallCUDA command line flag to continue" else - # Download and install CUDA if [[ $(cut -f2 <<< $(lsb_release -r)) == "18.04" ]]; then - wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.2.89-1_amd64.deb - sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub - sudo dpkg -i cuda-repo-ubuntu1804_10.2.89-1_amd64.deb - wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb - sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb - sudo apt update + sudo apt-get update + sudo apt-get install git ninja-build build-essential g++ nasm yasm + sudo apt-get install apt-transport-https ca-certificates gnupg software-properties-common wget + wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | gpg --dearmor - | sudo tee /etc/apt/trusted.gpg.d/kitware.gpg >/dev/null + sudo apt-add-repository 'deb https://apt.kitware.com/ubuntu/ bionic main' + wget -q https://packages.microsoft.com/config/ubuntu/18.04/packages-microsoft-prod.deb + sudo dpkg -i packages-microsoft-prod.deb + sudo add-apt-repository universe + sudo apt-get update sudo apt-get dist-upgrade -y - sudo apt-get install -y --no-install-recommends cuda-compiler-10-2 cuda-libraries-dev-10-2 cuda-driver-dev-10-2 cuda-cudart-dev-10-2 cuda-curand-dev-10-2 - sudo apt-get install -y --no-install-recommends libcudnn7-dev - sudo rm -rf /usr/local/cuda - sudo ln -s /usr/local/cuda-10.2 /usr/local/cuda + sudo apt-get install -y cmake + sudo apt-get install -y powershell + if [ "$bypass_driver_installation" = true ] ; then + sudo ln -s /usr/local/cuda-10.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-10.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-10.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + fi export PATH=/usr/local/cuda/bin:$PATH export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH export CUDACXX=/usr/local/cuda/bin/nvcc export CUDA_PATH=/usr/local/cuda export CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda - features="full" + cuda_is_available=true elif [[ $(cut -f2 <<< $(lsb_release -r)) == "20.04" ]]; then - sudo apt update + sudo apt-get update + sudo apt-get install git ninja-build build-essential g++ nasm yasm + sudo apt-get install apt-transport-https ca-certificates gnupg software-properties-common wget + wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | gpg --dearmor - | sudo tee /etc/apt/trusted.gpg.d/kitware.gpg >/dev/null + sudo apt-add-repository 'deb https://apt.kitware.com/ubuntu/ focal main' + wget -q https://packages.microsoft.com/config/ubuntu/20.04/packages-microsoft-prod.deb + sudo dpkg -i packages-microsoft-prod.deb + sudo add-apt-repository universe + sudo apt-get update sudo apt-get dist-upgrade -y - #sudo apt-get install -y --no-install-recommends nvidia-cuda-dev nvidia-cuda-toolkit - sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin - sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub - sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /" - sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/ /" - sudo apt-get install -y --no-install-recommends cuda-compiler-11-2 cuda-libraries-dev-11-2 cuda-driver-dev-11-2 cuda-cudart-dev-11-2 - sudo apt-get install -y --no-install-recommends libcudnn8-dev - sudo rm -rf /usr/local/cuda - sudo ln -s /usr/local/cuda-11.2 /usr/local/cuda + sudo apt-get install -y cmake + sudo apt-get install -y powershell + if [ "$bypass_driver_installation" = true ] ; then + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + fi export PATH=/usr/local/cuda/bin:$PATH export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH export CUDACXX=/usr/local/cuda/bin/nvcc export CUDA_PATH=/usr/local/cuda export CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda - features="full" + cuda_is_available=true else - echo "Unable to auto-install CUDA on this Linux OS" - features="opencv-base" + echo "Unable to provide tools on macOS, please wait for a future script update or do not put -InstallCUDA command line flag to continue" fi fi -else - if [[ -v CUDA_PATH ]]; then - features="full" - else - features="opencv-base" - fi fi cd .. -rm -rf $temp_folder - -if [[ ! -v VCPKG_ROOT ]]; then - git clone https://github.com/microsoft/vcpkg - cd vcpkg - ./bootstrap-vcpkg.sh -disableMetrics - export VCPKG_ROOT=$(pwd) -fi - -$VCPKG_ROOT/vcpkg install darknet[${features}] +rm -rf "$temp_folder" -if [[ "$OSTYPE" == "darwin"* ]]; then - echo "Darknet installed in $VCPKG_ROOT/installed/x64-osx/tools/darknet" +if [[ -v CUDA_PATH ]]; then + ./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateDARKNET + #./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -EnableOPENCV_CUDA -DisableInteractive -DoNotUpdateDARKNET else - echo "Darknet installed in $VCPKG_ROOT/installed/x64-linux/tools/darknet" + ./build.ps1 -UseVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET fi From 83e377989d1a7b86249425dc1e025ad4acff7cb7 Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 7 Jun 2021 19:52:22 +0300 Subject: [PATCH 1245/1365] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index cc85070b24a..337d095d44f 100644 --- a/README.md +++ b/README.md @@ -220,6 +220,7 @@ You can get cfg-files by path: `darknet/cfg/` - [Tianxiaomo/pytorch-YOLOv4](https://github.com/Tianxiaomo/pytorch-YOLOv4) - [YOLOv5](https://github.com/ultralytics/yolov5) - **ONNX** on Jetson for YOLOv4: https://developer.nvidia.com/blog/announcing-onnx-runtime-for-jetson/ +- **nVidia Transfer Learning Toolkit (TLT>=3.0)** Training and Detection https://docs.nvidia.com/metropolis/TLT/tlt-user-guide/text/object_detection/yolo_v4.html - **TensorRT+tkDNN**: https://github.com/ceccocats/tkDNN#fps-results - **Deepstream 5.0 / TensorRT for YOLOv4** https://github.com/NVIDIA-AI-IOT/yolov4_deepstream or https://github.com/marcoslucianops/DeepStream-Yolo read [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) and [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf). Additionally [jkjung-avt/tensorrt_demos](https://github.com/jkjung-avt/tensorrt_demos) or [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) - **Triton Inference Server / TensorRT** https://github.com/isarsoft/yolov4-triton-tensorrt From 7ff80b1a4762e9bf6c477e8197b7d8b330488763 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 11 Jun 2021 02:35:26 +0200 Subject: [PATCH 1246/1365] add missing asm tools in CI, run it every night (#7790) * add missing asm tools * run CI every night * add csharp tool --- .github/workflows/ccpp.yml | 33 +++++++++++++- .github/workflows/on_pr.yml | 27 +++++++++++ CMakeLists.txt | 5 +++ build.ps1 | 18 +++++++- src/csharp/CMakeLists.txt | 19 ++++++++ src/csharp/YoloWrapper.cs | 89 +++++++++++++++++++++++++++++++++++++ 6 files changed, 188 insertions(+), 3 deletions(-) create mode 100644 src/csharp/CMakeLists.txt create mode 100644 src/csharp/YoloWrapper.cs diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index b26e0701ac0..21ff07296fb 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -1,6 +1,10 @@ name: Darknet Continuous Integration -on: [push, workflow_dispatch] +on: + push: + workflow_dispatch: + schedule: + - cron: '0 0 * * *' env: VCPKG_BINARY_SOURCES: 'clear;nuget,vcpkgbinarycache,readwrite' @@ -68,6 +72,11 @@ jobs: - uses: lukka/get-cmake@latest + - name: Update apt + run: sudo apt update + - name: Install dependencies + run: sudo apt install yasm nasm + - name: 'Install CUDA' run: ./scripts/deploy-cuda.sh @@ -123,6 +132,11 @@ jobs: - uses: lukka/get-cmake@latest + - name: Update apt + run: sudo apt update + - name: Install dependencies + run: sudo apt install yasm nasm + - name: 'Install CUDA' run: ./scripts/deploy-cuda.sh @@ -161,6 +175,11 @@ jobs: - uses: lukka/get-cmake@latest + - name: Update apt + run: sudo apt update + - name: Install dependencies + run: sudo apt install yasm nasm + - name: 'Install CUDA' run: ./scripts/deploy-cuda.sh @@ -513,6 +532,18 @@ jobs: run: ./build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET + win-csharp: + runs-on: windows-latest + steps: + - uses: actions/checkout@v2 + + - uses: lukka/get-cmake@latest + + - name: 'Build' + shell: pwsh + run: ./build.ps1 -EnableCSharpWrapper -DisableInteractive -DoNotUpdateDARKNET + + win-intlibs-cuda: runs-on: windows-latest steps: diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index 42c03d696be..6b6aface5b8 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -68,6 +68,11 @@ jobs: - uses: lukka/get-cmake@latest + - name: Update apt + run: sudo apt update + - name: Install dependencies + run: sudo apt install yasm nasm + - name: 'Install CUDA' run: ./scripts/deploy-cuda.sh @@ -120,6 +125,11 @@ jobs: - uses: lukka/get-cmake@latest + - name: Update apt + run: sudo apt update + - name: Install dependencies + run: sudo apt install yasm nasm + - name: 'Install CUDA' run: ./scripts/deploy-cuda.sh @@ -155,6 +165,11 @@ jobs: - uses: lukka/get-cmake@latest + - name: Update apt + run: sudo apt update + - name: Install dependencies + run: sudo apt install yasm nasm + - name: 'Install CUDA' run: ./scripts/deploy-cuda.sh @@ -492,6 +507,18 @@ jobs: run: ./build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET + win-csharp: + runs-on: windows-latest + steps: + - uses: actions/checkout@v2 + + - uses: lukka/get-cmake@latest + + - name: 'Build' + shell: pwsh + run: ./build.ps1 -EnableCSharpWrapper -DisableInteractive -DoNotUpdateDARKNET + + win-intlibs-cuda: runs-on: windows-latest steps: diff --git a/CMakeLists.txt b/CMakeLists.txt index b3630f516dc..0e1abf32d9c 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -21,6 +21,7 @@ option(ENABLE_CUDNN "Enable CUDNN" ON) option(ENABLE_CUDNN_HALF "Enable CUDNN Half precision" ON) option(ENABLE_ZED_CAMERA "Enable ZED Camera support" ON) option(ENABLE_VCPKG_INTEGRATION "Enable VCPKG integration" ON) +option(ENABLE_CSHARP_WRAPPER "Enable building a csharp wrapper" OFF) option(VCPKG_BUILD_OPENCV_WITH_CUDA "Build OpenCV with CUDA extension integration" ON) option(VCPKG_USE_OPENCV2 "Use legacy OpenCV 2" OFF) option(VCPKG_USE_OPENCV3 "Use legacy OpenCV 3" OFF) @@ -576,3 +577,7 @@ install(FILES "${PROJECT_BINARY_DIR}/DarknetConfigVersion.cmake" DESTINATION "${INSTALL_CMAKE_DIR}" ) + +if(ENABLE_CSHARP_WRAPPER) + add_subdirectory(src/csharp) +endif() diff --git a/build.ps1 b/build.ps1 index 86c9acf4ba3..d230622e9a5 100755 --- a/build.ps1 +++ b/build.ps1 @@ -17,13 +17,14 @@ param ( [switch]$ForceStaticLib = $false, [switch]$ForceVCPKGCacheRemoval = $false, [switch]$ForceSetupVS = $false, + [switch]$EnableCSharpWrapper = $false, [Int32]$ForceGCCVersion = 0, [Int32]$ForceOpenCVVersion = 0, [Int32]$NumberOfBuildWorkers = 8, [string]$AdditionalBuildSetup = "" # "-DCMAKE_CUDA_ARCHITECTURES=30" ) -$build_ps1_version = "0.9.4" +$build_ps1_version = "0.9.5" $ErrorActionPreference = "SilentlyContinue" Stop-Transcript | out-null @@ -242,6 +243,15 @@ else { Write-Host "VisualStudio integration is enabled, please pass -DoNotSetupVS to the script to disable" } +if ($EnableCSharpWrapper -and ($IsWindowsPowerShell -or $IsWindows)) { + Write-Host "Yolo C# wrapper integration is enabled. Will be built with Visual Studio generator. Disabling Ninja" + $DoNotUseNinja = $true +} +else { + $EnableCSharpWrapper = $false + Write-Host "Yolo C# wrapper integration is disabled, please pass -EnableCSharpWrapper to the script to enable. You must be on Windows!" +} + if ($DoNotUseNinja) { Write-Host "Ninja is disabled" } @@ -410,7 +420,7 @@ elseif ((Test-Path "${env:WORKSPACE}/vcpkg") -and $UseVCPKG) { $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" } elseif (-not($null -eq ${RUNVCPKG_VCPKG_ROOT_OUT})) { - if((Test-Path "${RUNVCPKG_VCPKG_ROOT_OUT}") -and $UseVCPKG) { + if ((Test-Path "${RUNVCPKG_VCPKG_ROOT_OUT}") -and $UseVCPKG) { $vcpkg_path = "${RUNVCPKG_VCPKG_ROOT_OUT}" $env:VCPKG_ROOT = "${RUNVCPKG_VCPKG_ROOT_OUT}" $vcpkg_root_set_by_this_script = $true @@ -592,6 +602,10 @@ if (-Not $EnableOPENCV_CUDA) { $AdditionalBuildSetup = $AdditionalBuildSetup + " -DVCPKG_BUILD_OPENCV_WITH_CUDA:BOOL=OFF" } +if ($EnableCSharpWrapper) { + $additional_build_setup = $additional_build_setup + " -DENABLE_CSHARP_WRAPPER:BOOL=ON" +} + $build_folder = "./build_release" if (-Not $DoNotDeleteBuildFolder) { Write-Host "Removing folder $build_folder" -ForegroundColor Yellow diff --git a/src/csharp/CMakeLists.txt b/src/csharp/CMakeLists.txt new file mode 100644 index 00000000000..971725b227d --- /dev/null +++ b/src/csharp/CMakeLists.txt @@ -0,0 +1,19 @@ + +project(YoloWrapper LANGUAGES CSharp) +include(CSharpUtilities) + +add_library(${PROJECT_NAME} + ${PROJECT_NAME}.cs +) + +target_link_libraries(${PROJECT_NAME} PRIVATE dark) + +set_property(TARGET ${PROJECT_NAME} PROPERTY VS_DOTNET_REFERENCES + "System" + "System.Runtime.InteropServices" +) + +install(TARGETS ${PROJECT_NAME} + RUNTIME DESTINATION "${INSTALL_BIN_DIR}" + COMPONENT dev +) diff --git a/src/csharp/YoloWrapper.cs b/src/csharp/YoloWrapper.cs new file mode 100644 index 00000000000..52c12adb80f --- /dev/null +++ b/src/csharp/YoloWrapper.cs @@ -0,0 +1,89 @@ +using System; +using System.Runtime.InteropServices; + +namespace Darknet +{ + public class YoloWrapper : IDisposable + { + private const string YoloLibraryName = "yolo_cpp_dll.dll"; + private const int MaxObjects = 1000; + + [DllImport(YoloLibraryName, EntryPoint = "init")] + private static extern int InitializeYolo(string configurationFilename, string weightsFilename, int gpu); + + [DllImport(YoloLibraryName, EntryPoint = "detect_image")] + private static extern int DetectImage(string filename, ref BboxContainer container); + + [DllImport(YoloLibraryName, EntryPoint = "detect_mat")] + private static extern int DetectImage(IntPtr pArray, int nSize, ref BboxContainer container); + + [DllImport(YoloLibraryName, EntryPoint = "dispose")] + private static extern int DisposeYolo(); + + [StructLayout(LayoutKind.Sequential)] + public struct bbox_t + { + public UInt32 x, y, w, h; // (x,y) - top-left corner, (w, h) - width & height of bounded box + public float prob; // confidence - probability that the object was found correctly + public UInt32 obj_id; // class of object - from range [0, classes-1] + public UInt32 track_id; // tracking id for video (0 - untracked, 1 - inf - tracked object) + public UInt32 frames_counter; + public float x_3d, y_3d, z_3d; // 3-D coordinates, if there is used 3D-stereo camera + }; + + [StructLayout(LayoutKind.Sequential)] + public struct BboxContainer + { + [MarshalAs(UnmanagedType.ByValArray, SizeConst = MaxObjects)] + public bbox_t[] candidates; + } + + public YoloWrapper(string configurationFilename, string weightsFilename, int gpu) + { + InitializeYolo(configurationFilename, weightsFilename, gpu); + } + + public void Dispose() + { + DisposeYolo(); + } + + public bbox_t[] Detect(string filename) + { + var container = new BboxContainer(); + var count = DetectImage(filename, ref container); + + return container.candidates; + } + + public bbox_t[] Detect(byte[] imageData) + { + var container = new BboxContainer(); + + var size = Marshal.SizeOf(imageData[0]) * imageData.Length; + var pnt = Marshal.AllocHGlobal(size); + + try + { + // Copy the array to unmanaged memory. + Marshal.Copy(imageData, 0, pnt, imageData.Length); + var count = DetectImage(pnt, imageData.Length, ref container); + if (count == -1) + { + throw new NotSupportedException($"{YoloLibraryName} has no OpenCV support"); + } + } + catch (Exception exception) + { + return null; + } + finally + { + // Free the unmanaged memory. + Marshal.FreeHGlobal(pnt); + } + + return container.candidates; + } + } +} From 894aada70bcc78a224b7675a19068b6353dd6fbe Mon Sep 17 00:00:00 2001 From: Ievgen Popovych Date: Fri, 11 Jun 2021 03:36:12 +0300 Subject: [PATCH 1247/1365] region_layer: Populate out_h/w/c fileds for consitency (#7783) --- src/region_layer.c | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/region_layer.c b/src/region_layer.c index 7aa1a196f80..b7aba32e1a9 100644 --- a/src/region_layer.c +++ b/src/region_layer.c @@ -20,6 +20,10 @@ region_layer make_region_layer(int batch, int w, int h, int n, int classes, int l.batch = batch; l.h = h; l.w = w; + l.c = n*(classes + coords + 1); + l.out_w = l.w; + l.out_h = l.h; + l.out_c = l.c; l.classes = classes; l.coords = coords; l.cost = (float*)xcalloc(1, sizeof(float)); From 67543e27aa151dea522ff2a8b287c709c0397749 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 11 Jun 2021 03:38:20 +0300 Subject: [PATCH 1248/1365] fix darknet_no_gpu.vcxproj --- build/darknet/darknet_no_gpu.vcxproj | 2 ++ 1 file changed, 2 insertions(+) diff --git a/build/darknet/darknet_no_gpu.vcxproj b/build/darknet/darknet_no_gpu.vcxproj index 72d23af3d09..fadf7289694 100644 --- a/build/darknet/darknet_no_gpu.vcxproj +++ b/build/darknet/darknet_no_gpu.vcxproj @@ -227,6 +227,7 @@ + @@ -290,6 +291,7 @@ + From 00b8e29b6802cc085e30f64b48f4a693e6019235 Mon Sep 17 00:00:00 2001 From: Alexey Date: Sat, 12 Jun 2021 02:53:08 +0300 Subject: [PATCH 1249/1365] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 337d095d44f..2d1bbc52e6c 100644 --- a/README.md +++ b/README.md @@ -28,6 +28,7 @@ About Darknet framework: http://pjreddie.com/darknet/ [![License: Unlicense](https://img.shields.io/badge/license-Unlicense-blue.svg)](https://github.com/AlexeyAB/darknet/blob/master/LICENSE) [![DOI](https://zenodo.org/badge/75388965.svg)](https://zenodo.org/badge/latestdoi/75388965) [![arxiv.org](http://img.shields.io/badge/cs.CV-arXiv%3A2004.10934-B31B1B.svg)](https://arxiv.org/abs/2004.10934) +[![arxiv.org](http://img.shields.io/badge/cs.CV-arXiv%3A2011.08036-B31B1B.svg)](https://arxiv.org/abs/2011.08036) [![colab](https://user-images.githubusercontent.com/4096485/86174089-b2709f80-bb29-11ea-9faf-3d8dc668a1a5.png)](https://colab.research.google.com/drive/12QusaaRj_lUwCGDvQNfICpa7kA7_a2dE) [![colab](https://user-images.githubusercontent.com/4096485/86174097-b56b9000-bb29-11ea-9240-c17f6bacfc34.png)](https://colab.research.google.com/drive/1_GdoqCJWXsChrOiY8sZMr_zbr_fH-0Fg) From 396b5299a79f6401c0657128ec6ff6d78816d78f Mon Sep 17 00:00:00 2001 From: Double Date: Thu, 17 Jun 2021 00:25:00 +0800 Subject: [PATCH 1250/1365] fix: flush buffer to weights-file when writing each layer (#7747) Co-authored-by: Double.c --- src/parser.c | 1 + 1 file changed, 1 insertion(+) diff --git a/src/parser.c b/src/parser.c index 8f8f584268e..a2d133382c4 100644 --- a/src/parser.c +++ b/src/parser.c @@ -2026,6 +2026,7 @@ void save_weights_upto(network net, char *filename, int cutoff, int save_ema) fwrite(l.biases, sizeof(float), l.outputs, fp); fwrite(l.weights, sizeof(float), size, fp); } + fflush(fp); } fclose(fp); } From 6c171a4ceaaf508568a90fe8f2d01014c2c62f34 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 18 Jun 2021 00:15:55 +0200 Subject: [PATCH 1251/1365] add automatic rebase github action (#7809) * add automatic rebase action * do not upload artifacts from PRs * use proper symbol in build.ps1 * fix lib naming * fix folder for dll artifacts --- .github/workflows/ccpp.yml | 2 +- .github/workflows/on_pr.yml | 127 ------------------ .github/workflows/rebase.yml | 19 +++ build.ps1 | 2 +- src/csharp/CMakeLists.txt | 2 +- .../{YoloWrapper.cs => YoloCSharpWrapper.cs} | 2 +- 6 files changed, 23 insertions(+), 131 deletions(-) create mode 100644 .github/workflows/rebase.yml rename src/csharp/{YoloWrapper.cs => YoloCSharpWrapper.cs} (97%) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 21ff07296fb..92fb50375b3 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -458,7 +458,7 @@ jobs: - uses: actions/upload-artifact@v2 with: name: darknet-vcpkg-${{ runner.os }} - path: ${{ runner.workspace }}/buildDirectory/Release/*.dll + path: ${{ github.workspace }}/build_release/*.dll - uses: actions/upload-artifact@v2 with: name: darknet-vcpkg-${{ runner.os }} diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index 6b6aface5b8..9f0a664ebcb 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -100,23 +100,6 @@ jobs: LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateDARKNET - - uses: actions/upload-artifact@v2 - with: - name: darknet-vcpkg-cuda-${{ runner.os }} - path: cfg - - uses: actions/upload-artifact@v2 - with: - name: darknet-vcpkg-cuda-${{ runner.os }} - path: data - - uses: actions/upload-artifact@v2 - with: - name: darknet-vcpkg-cuda-${{ runner.os }} - path: ${{ github.workspace }}/*dark* - - uses: actions/upload-artifact@v2 - with: - name: darknet-vcpkg-cuda-${{ runner.os }} - path: ${{ github.workspace }}/uselib* - ubuntu-vcpkg-opencv3-cuda: runs-on: ubuntu-20.04 @@ -219,23 +202,6 @@ jobs: LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" run: ./build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET - - uses: actions/upload-artifact@v2 - with: - name: darknet-${{ runner.os }} - path: cfg - - uses: actions/upload-artifact@v2 - with: - name: darknet-${{ runner.os }} - path: data - - uses: actions/upload-artifact@v2 - with: - name: darknet-${{ runner.os }} - path: ${{ github.workspace }}/*dark* - - uses: actions/upload-artifact@v2 - with: - name: darknet-${{ runner.os }} - path: ${{ github.workspace }}/uselib* - ubuntu-cuda: runs-on: ubuntu-20.04 @@ -267,23 +233,6 @@ jobs: LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" run: ./build.ps1 -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateDARKNET - - uses: actions/upload-artifact@v2 - with: - name: darknet-cuda-${{ runner.os }} - path: cfg - - uses: actions/upload-artifact@v2 - with: - name: darknet-cuda-${{ runner.os }} - path: data - - uses: actions/upload-artifact@v2 - with: - name: darknet-cuda-${{ runner.os }} - path: ${{ github.workspace }}/*dark* - - uses: actions/upload-artifact@v2 - with: - name: darknet-cuda-${{ runner.os }} - path: ${{ github.workspace }}/uselib* - ubuntu-no-ocv-cpp: runs-on: ubuntu-20.04 @@ -339,23 +288,6 @@ jobs: shell: pwsh run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -DisableInteractive -DoNotUpdateDARKNET - - uses: actions/upload-artifact@v2 - with: - name: darknet-vcpkg-${{ runner.os }} - path: cfg - - uses: actions/upload-artifact@v2 - with: - name: darknet-vcpkg-${{ runner.os }} - path: data - - uses: actions/upload-artifact@v2 - with: - name: darknet-vcpkg-${{ runner.os }} - path: ${{ github.workspace }}/*dark* - - uses: actions/upload-artifact@v2 - with: - name: darknet-vcpkg-${{ runner.os }} - path: ${{ github.workspace }}/uselib* - osx: runs-on: macos-latest @@ -371,23 +303,6 @@ jobs: shell: pwsh run: ./build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET - - uses: actions/upload-artifact@v2 - with: - name: darknet-${{ runner.os }} - path: cfg - - uses: actions/upload-artifact@v2 - with: - name: darknet-${{ runner.os }} - path: data - - uses: actions/upload-artifact@v2 - with: - name: darknet-${{ runner.os }} - path: ${{ github.workspace }}/*dark* - - uses: actions/upload-artifact@v2 - with: - name: darknet-${{ runner.os }} - path: ${{ github.workspace }}/uselib* - osx-no-ocv-no-omp-cpp: runs-on: macos-latest @@ -421,27 +336,6 @@ jobs: shell: pwsh run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET - - uses: actions/upload-artifact@v2 - with: - name: darknet-vcpkg-${{ runner.os }} - path: cfg - - uses: actions/upload-artifact@v2 - with: - name: darknet-vcpkg-${{ runner.os }} - path: data - - uses: actions/upload-artifact@v2 - with: - name: darknet-vcpkg-${{ runner.os }} - path: ${{ github.workspace }}/*dark* - - uses: actions/upload-artifact@v2 - with: - name: darknet-vcpkg-${{ runner.os }} - path: ${{ runner.workspace }}/buildDirectory/Release/*.dll - - uses: actions/upload-artifact@v2 - with: - name: darknet-vcpkg-${{ runner.os }} - path: ${{ github.workspace }}/uselib* - win-intlibs: runs-on: windows-latest @@ -454,27 +348,6 @@ jobs: shell: pwsh run: ./build.ps1 -DisableInteractive -DoNotUpdateDARKNET - - uses: actions/upload-artifact@v2 - with: - name: darknet-${{ runner.os }} - path: cfg - - uses: actions/upload-artifact@v2 - with: - name: darknet-${{ runner.os }} - path: data - - uses: actions/upload-artifact@v2 - with: - name: darknet-${{ runner.os }} - path: ${{ github.workspace }}/*dark* - - uses: actions/upload-artifact@v2 - with: - name: darknet-${{ runner.os }} - path: ${{ github.workspace }}/3rdparty/pthreads/bin/*.dll - - uses: actions/upload-artifact@v2 - with: - name: darknet-${{ runner.os }} - path: ${{ github.workspace }}/uselib* - win-setup-ps1: runs-on: windows-latest diff --git a/.github/workflows/rebase.yml b/.github/workflows/rebase.yml new file mode 100644 index 00000000000..608904374ef --- /dev/null +++ b/.github/workflows/rebase.yml @@ -0,0 +1,19 @@ +name: Automatic Rebase +on: + issue_comment: + types: [created] +jobs: + rebase: + name: Rebase + if: github.event.issue.pull_request != '' && contains(github.event.comment.body, '/rebase') && github.event.comment.author_association == 'MEMBER' + runs-on: ubuntu-latest + steps: + - name: Checkout the latest code + uses: actions/checkout@v2 + with: + token: ${{ secrets.GITHUB_TOKEN }} + fetch-depth: 0 # otherwise, you will fail to push refs to dest repo + - name: Automatic Rebase + uses: cirrus-actions/rebase@1.4 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/build.ps1 b/build.ps1 index d230622e9a5..b5115cabc15 100755 --- a/build.ps1 +++ b/build.ps1 @@ -603,7 +603,7 @@ if (-Not $EnableOPENCV_CUDA) { } if ($EnableCSharpWrapper) { - $additional_build_setup = $additional_build_setup + " -DENABLE_CSHARP_WRAPPER:BOOL=ON" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_CSHARP_WRAPPER:BOOL=ON" } $build_folder = "./build_release" diff --git a/src/csharp/CMakeLists.txt b/src/csharp/CMakeLists.txt index 971725b227d..1b591a16dab 100644 --- a/src/csharp/CMakeLists.txt +++ b/src/csharp/CMakeLists.txt @@ -1,5 +1,5 @@ -project(YoloWrapper LANGUAGES CSharp) +project(YoloCSharpWrapper LANGUAGES CSharp) include(CSharpUtilities) add_library(${PROJECT_NAME} diff --git a/src/csharp/YoloWrapper.cs b/src/csharp/YoloCSharpWrapper.cs similarity index 97% rename from src/csharp/YoloWrapper.cs rename to src/csharp/YoloCSharpWrapper.cs index 52c12adb80f..35d23a9c2e1 100644 --- a/src/csharp/YoloWrapper.cs +++ b/src/csharp/YoloCSharpWrapper.cs @@ -5,7 +5,7 @@ namespace Darknet { public class YoloWrapper : IDisposable { - private const string YoloLibraryName = "yolo_cpp_dll.dll"; + private const string YoloLibraryName = "darknet.dll"; private const int MaxObjects = 1000; [DllImport(YoloLibraryName, EntryPoint = "init")] From 3fafd211b908fd84a66f3723c77119351ed656a5 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Mon, 21 Jun 2021 11:07:31 +0200 Subject: [PATCH 1252/1365] [autorebase] bump to v1.5 (#7819) --- .github/workflows/rebase.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/rebase.yml b/.github/workflows/rebase.yml index 608904374ef..251a259ffcf 100644 --- a/.github/workflows/rebase.yml +++ b/.github/workflows/rebase.yml @@ -5,7 +5,7 @@ on: jobs: rebase: name: Rebase - if: github.event.issue.pull_request != '' && contains(github.event.comment.body, '/rebase') && github.event.comment.author_association == 'MEMBER' + if: github.event.issue.pull_request != '' && contains(github.event.comment.body, '/rebase') && (github.event.comment.author_association == 'MEMBER' || github.event.comment.author_association == 'OWNER' || github.event.comment.author_association == 'CONTRIBUTOR') runs-on: ubuntu-latest steps: - name: Checkout the latest code @@ -14,6 +14,6 @@ jobs: token: ${{ secrets.GITHUB_TOKEN }} fetch-depth: 0 # otherwise, you will fail to push refs to dest repo - name: Automatic Rebase - uses: cirrus-actions/rebase@1.4 + uses: cirrus-actions/rebase@1.5 env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} From 070ed01e26e5568ad5183321881d3b26380f5345 Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 21 Jun 2021 23:47:53 +0300 Subject: [PATCH 1253/1365] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 2d1bbc52e6c..42007e975d6 100644 --- a/README.md +++ b/README.md @@ -33,7 +33,7 @@ About Darknet framework: http://pjreddie.com/darknet/ [![colab](https://user-images.githubusercontent.com/4096485/86174097-b56b9000-bb29-11ea-9240-c17f6bacfc34.png)](https://colab.research.google.com/drive/1_GdoqCJWXsChrOiY8sZMr_zbr_fH-0Fg) - [YOLOv4 model zoo](https://github.com/AlexeyAB/darknet/wiki/YOLOv4-model-zoo) -- [Requirements (and how to install dependencies)](#requirements) +- [Requirements (and how to install dependencies)](#requirements-for-windows-linux-and-macos) - [Pre-trained models](#pre-trained-models) - [FAQ - frequently asked questions](https://github.com/AlexeyAB/darknet/wiki/FAQ---frequently-asked-questions) - [Explanations in issues](https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3AExplanations) From 46dea82bced8e7e2aa571a57cd819e97f19356da Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 22 Jun 2021 00:31:38 +0300 Subject: [PATCH 1254/1365] Added yolov4-p5.cfg and yolov4-p6.cfg --- build/darknet/x64/cfg/yolov4-p5.cfg | 1837 +++++++++++++++++++++ build/darknet/x64/cfg/yolov4-p6.cfg | 2293 +++++++++++++++++++++++++++ cfg/yolov4-p5.cfg | 1837 +++++++++++++++++++++ cfg/yolov4-p6.cfg | 2293 +++++++++++++++++++++++++++ 4 files changed, 8260 insertions(+) create mode 100644 build/darknet/x64/cfg/yolov4-p5.cfg create mode 100644 build/darknet/x64/cfg/yolov4-p6.cfg create mode 100644 cfg/yolov4-p5.cfg create mode 100644 cfg/yolov4-p6.cfg diff --git a/build/darknet/x64/cfg/yolov4-p5.cfg b/build/darknet/x64/cfg/yolov4-p5.cfg new file mode 100644 index 00000000000..3a7848177fd --- /dev/null +++ b/build/darknet/x64/cfg/yolov4-p5.cfg @@ -0,0 +1,1837 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=896 +height=896 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +ema_alpha=0.9998 + +#use_cuda_graph = 1 + + +# ============ Backbone ============ # + +# Stem + +# 0 +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + + +# P1 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-7 + +# Transition last + +# 10 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + + +# P2 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-13 + +# Transition last + +# 26 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + + +# P3 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-49 + +# Transition last + +# 78 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + + +# P4 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-49 + +# Transition last + +# 130 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + + +# P5 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-25 + +# Transition last + +# 158 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=mish + +# ============ End of Backbone ============ # + +# ============ Neck ============ # + +# CSPSPP + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1, -13 + +# 173 (previous+6+5+2k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# End of CSPSPP + + +# FPN-4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 130 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 189 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + + +# FPN-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 78 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 205 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + + +# PAN-4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1, 189 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1,-8 + +# Transition last + +# 218 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + + +# PAN-5 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1, 173 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1,-8 + +# Transition last + +# 231 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# ============ End of Neck ============ # + +# ============ Head ============ # + +# YOLO-3 + +[route] +layers = 205 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=logistic +#activation=linear +# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet + +[yolo] +mask = 0,1,2,3 +anchors = 13,17, 31,25, 24,51, 61,45, 48,102, 119,96, 97,189, 217,184, 171,384, 324,451, 616,618, 800,800 +classes=80 +num=12 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-4 + +[route] +layers = 218 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=logistic +#activation=linear +# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet + +[yolo] +mask = 4,5,6,7 +anchors = 13,17, 31,25, 24,51, 61,45, 48,102, 119,96, 97,189, 217,184, 171,384, 324,451, 616,618, 800,800 +classes=80 +num=12 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-5 + +[route] +layers = 231 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=logistic +#activation=linear +# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet + +[yolo] +mask = 8,9,10,11 +anchors = 13,17, 31,25, 24,51, 61,45, 48,102, 119,96, 97,189, 217,184, 171,384, 324,451, 616,618, 800,800 +classes=80 +num=12 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + +# ============ End of Head ============ # \ No newline at end of file diff --git a/build/darknet/x64/cfg/yolov4-p6.cfg b/build/darknet/x64/cfg/yolov4-p6.cfg new file mode 100644 index 00000000000..6e241e12e01 --- /dev/null +++ b/build/darknet/x64/cfg/yolov4-p6.cfg @@ -0,0 +1,2293 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=1280 +height=1280 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +ema_alpha=0.9998 + +#use_cuda_graph = 1 + + +# ============ Backbone ============ # + +# Stem + +# 0 +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + + +# P1 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-7 + +# Transition last + +# 10 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + + +# P2 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-13 + +# Transition last + +# 26 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + + +# P3 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-49 + +# Transition last + +# 78 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + + +# P4 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-49 + +# Transition last + +# 130 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + + +# P5 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-25 + +# Transition last + +# 158 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=mish + + +# P6 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-25 + +# Transition last + +# 186 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=mish + +# ============ End of Backbone ============ # + +# ============ Neck ============ # + +# CSPSPP + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1, -13 + +# 201 (previous+6+5+2k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# End of CSPSPP + + +# FPN-5 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 158 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 217 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + + +# FPN-4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 130 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 233 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + + +# FPN-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 78 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 249 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + + +# PAN-4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1, 233 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1,-8 + +# Transition last + +# 262 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + + +# PAN-5 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1, 217 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1,-8 + +# Transition last + +# 275 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + + +# PAN-6 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1, 201 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1,-8 + +# Transition last + +# 288 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# ============ End of Neck ============ # + +# ============ Head ============ # + +# YOLO-3 + +[route] +layers = 249 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=logistic +#activation=linear +# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet + +[yolo] +mask = 0,1,2,3 +anchors = 13,17, 31,25, 24,51, 61,45, 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 1024,1024 +classes=80 +num=16 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 +scale_x_y = 1.05 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + + +# YOLO-4 + +[route] +layers = 262 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=logistic +#activation=linear +# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet + +[yolo] +mask = 4,5,6,7 +anchors = 13,17, 31,25, 24,51, 61,45, 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 1024,1024 +classes=80 +num=16 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-5 + +[route] +layers = 275 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=logistic +#activation=linear +# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet + +[yolo] +mask = 8,9,10,11 +anchors = 13,17, 31,25, 24,51, 61,45, 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 1024,1024 +classes=80 +num=16 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-6 + +[route] +layers = 288 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=logistic +#activation=linear +# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet + +[yolo] +mask = 12,13,14,15 +anchors = 13,17, 31,25, 24,51, 61,45, 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 1024,1024 +classes=80 +num=16 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + +# ============ End of Head ============ # \ No newline at end of file diff --git a/cfg/yolov4-p5.cfg b/cfg/yolov4-p5.cfg new file mode 100644 index 00000000000..3a7848177fd --- /dev/null +++ b/cfg/yolov4-p5.cfg @@ -0,0 +1,1837 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=896 +height=896 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +ema_alpha=0.9998 + +#use_cuda_graph = 1 + + +# ============ Backbone ============ # + +# Stem + +# 0 +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + + +# P1 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-7 + +# Transition last + +# 10 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + + +# P2 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-13 + +# Transition last + +# 26 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + + +# P3 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-49 + +# Transition last + +# 78 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + + +# P4 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-49 + +# Transition last + +# 130 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + + +# P5 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-25 + +# Transition last + +# 158 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=mish + +# ============ End of Backbone ============ # + +# ============ Neck ============ # + +# CSPSPP + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1, -13 + +# 173 (previous+6+5+2k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# End of CSPSPP + + +# FPN-4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 130 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 189 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + + +# FPN-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 78 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 205 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + + +# PAN-4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1, 189 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1,-8 + +# Transition last + +# 218 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + + +# PAN-5 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1, 173 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1,-8 + +# Transition last + +# 231 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# ============ End of Neck ============ # + +# ============ Head ============ # + +# YOLO-3 + +[route] +layers = 205 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=logistic +#activation=linear +# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet + +[yolo] +mask = 0,1,2,3 +anchors = 13,17, 31,25, 24,51, 61,45, 48,102, 119,96, 97,189, 217,184, 171,384, 324,451, 616,618, 800,800 +classes=80 +num=12 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-4 + +[route] +layers = 218 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=logistic +#activation=linear +# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet + +[yolo] +mask = 4,5,6,7 +anchors = 13,17, 31,25, 24,51, 61,45, 48,102, 119,96, 97,189, 217,184, 171,384, 324,451, 616,618, 800,800 +classes=80 +num=12 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-5 + +[route] +layers = 231 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=logistic +#activation=linear +# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet + +[yolo] +mask = 8,9,10,11 +anchors = 13,17, 31,25, 24,51, 61,45, 48,102, 119,96, 97,189, 217,184, 171,384, 324,451, 616,618, 800,800 +classes=80 +num=12 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + +# ============ End of Head ============ # \ No newline at end of file diff --git a/cfg/yolov4-p6.cfg b/cfg/yolov4-p6.cfg new file mode 100644 index 00000000000..6e241e12e01 --- /dev/null +++ b/cfg/yolov4-p6.cfg @@ -0,0 +1,2293 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=1280 +height=1280 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +ema_alpha=0.9998 + +#use_cuda_graph = 1 + + +# ============ Backbone ============ # + +# Stem + +# 0 +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + + +# P1 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-7 + +# Transition last + +# 10 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + + +# P2 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-13 + +# Transition last + +# 26 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + + +# P3 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-49 + +# Transition last + +# 78 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + + +# P4 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-49 + +# Transition last + +# 130 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + + +# P5 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-25 + +# Transition last + +# 158 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=mish + + +# P6 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-25 + +# Transition last + +# 186 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=mish + +# ============ End of Backbone ============ # + +# ============ Neck ============ # + +# CSPSPP + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1, -13 + +# 201 (previous+6+5+2k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# End of CSPSPP + + +# FPN-5 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 158 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 217 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + + +# FPN-4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 130 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 233 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + + +# FPN-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 78 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 249 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + + +# PAN-4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1, 233 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1,-8 + +# Transition last + +# 262 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + + +# PAN-5 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1, 217 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1,-8 + +# Transition last + +# 275 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + + +# PAN-6 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1, 201 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1,-8 + +# Transition last + +# 288 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# ============ End of Neck ============ # + +# ============ Head ============ # + +# YOLO-3 + +[route] +layers = 249 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=logistic +#activation=linear +# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet + +[yolo] +mask = 0,1,2,3 +anchors = 13,17, 31,25, 24,51, 61,45, 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 1024,1024 +classes=80 +num=16 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 +scale_x_y = 1.05 +iou_thresh=0.213 +cls_normalizer=1.0 +iou_normalizer=0.07 +iou_loss=ciou +nms_kind=greedynms +beta_nms=0.6 + + +# YOLO-4 + +[route] +layers = 262 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=logistic +#activation=linear +# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet + +[yolo] +mask = 4,5,6,7 +anchors = 13,17, 31,25, 24,51, 61,45, 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 1024,1024 +classes=80 +num=16 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-5 + +[route] +layers = 275 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=logistic +#activation=linear +# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet + +[yolo] +mask = 8,9,10,11 +anchors = 13,17, 31,25, 24,51, 61,45, 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 1024,1024 +classes=80 +num=16 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-6 + +[route] +layers = 288 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=logistic +#activation=linear +# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet + +[yolo] +mask = 12,13,14,15 +anchors = 13,17, 31,25, 24,51, 61,45, 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 1024,1024 +classes=80 +num=16 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + +# ============ End of Head ============ # \ No newline at end of file From 8bbdb6b77c400106cf9232d226e2cda2bf5f54b1 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 24 Jun 2021 10:23:45 +0200 Subject: [PATCH 1255/1365] [CI] test vcpkg nightly builds (#7826) --- .github/workflows/ccpp.yml | 22 +++++ build.ps1 | 166 +++++++++++++++++++++++++++++-------- 2 files changed, 152 insertions(+), 36 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 92fb50375b3..c8d65b796b4 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -465,6 +465,28 @@ jobs: path: ${{ github.workspace }}/uselib* + win-vcpkg-port: + runs-on: windows-latest + steps: + - uses: actions/checkout@v2 + + - name: 'Setup vcpkg and NuGet artifacts backend' + shell: bash + run: > + git clone https://github.com/microsoft/vcpkg ; + ./vcpkg/bootstrap-vcpkg.sh ; + $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add + -Name "vcpkgbinarycache" + -Source http://93.49.111.10:5555/v3/index.json ; + $(./vcpkg/vcpkg fetch nuget | tail -n 1) + setapikey ${{ secrets.BAGET_API_KEY }} + -Source http://93.49.111.10:5555/v3/index.json + + - name: 'Build' + shell: pwsh + run: ./build.ps1 -UseVCPKG -InstallDARKNETthroughVCPKG -ForceVCPKGDarknetHEAD -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET + + win-intlibs: runs-on: windows-latest steps: diff --git a/build.ps1 b/build.ps1 index b5115cabc15..92342afd8b5 100755 --- a/build.ps1 +++ b/build.ps1 @@ -8,6 +8,9 @@ param ( [switch]$EnableOPENCV = $false, [switch]$EnableOPENCV_CUDA = $false, [switch]$UseVCPKG = $false, + [switch]$InstallDARKNETthroughVCPKG = $false, + [switch]$InstallDARKNETdependenciesThroughVCPKGManifest = $false, + [switch]$ForceVCPKGDarknetHEAD = $false, [switch]$DoNotUpdateVCPKG = $false, [switch]$DoNotUpdateDARKNET = $false, [switch]$DoNotDeleteBuildFolder = $false, @@ -18,13 +21,14 @@ param ( [switch]$ForceVCPKGCacheRemoval = $false, [switch]$ForceSetupVS = $false, [switch]$EnableCSharpWrapper = $false, + [switch]$DownloadWeights = $false, [Int32]$ForceGCCVersion = 0, [Int32]$ForceOpenCVVersion = 0, [Int32]$NumberOfBuildWorkers = 8, [string]$AdditionalBuildSetup = "" # "-DCMAKE_CUDA_ARCHITECTURES=30" ) -$build_ps1_version = "0.9.5" +$build_ps1_version = "0.9.6" $ErrorActionPreference = "SilentlyContinue" Stop-Transcript | out-null @@ -151,10 +155,28 @@ if ($PSVersionTable.PSVersion.Major -lt 5) { if ($IsLinux -or $IsMacOS) { $bootstrap_ext = ".sh" + $exe_ext = "" } elseif ($IsWindows -or $IsWindowsPowerShell) { $bootstrap_ext = ".bat" + $exe_ext = ".exe" } + +if ($InstallDARKNETdependenciesThroughVCPKGManifest -and -not $InstallDARKNETthroughVCPKG) { + Write-Host "You requested darknet dependencies to be installed by vcpkg in manifest mode but you didn't enable installation through vcpkg, doing that for you" + $InstallDARKNETthroughVCPKG = $true +} + +if ($InstallDARKNETthroughVCPKG -and -not $UseVCPKG) { + Write-Host "You requested darknet to be installed by vcpkg but you didn't enable vcpkg, doing that for you" + $UseVCPKG = $true +} + +if ($InstallDARKNETthroughVCPKG -and -not $EnableOPENCV) { + Write-Host "You requested darknet to be installed by vcpkg but you didn't enable OpenCV, doing that for you" + $EnableOPENCV = $true +} + if ($UseVCPKG) { Write-Host "vcpkg bootstrap script: bootstrap-vcpkg${bootstrap_ext}" } @@ -606,42 +628,114 @@ if ($EnableCSharpWrapper) { $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_CSHARP_WRAPPER:BOOL=ON" } -$build_folder = "./build_release" -if (-Not $DoNotDeleteBuildFolder) { - Write-Host "Removing folder $build_folder" -ForegroundColor Yellow - Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $build_folder -} - -New-Item -Path $build_folder -ItemType directory -Force | Out-Null -Set-Location $build_folder -$cmake_args = "-G `"$generator`" ${AdditionalBuildSetup} -S .." -Write-Host "Configuring CMake project" -ForegroundColor Green -Write-Host "CMake args: $cmake_args" -$proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList $cmake_args -$handle = $proc.Handle -$proc.WaitForExit() -$exitCode = $proc.ExitCode -if (-Not ($exitCode -eq 0)) { - MyThrow("Config failed! Exited with error code $exitCode.") -} -Write-Host "Building CMake project" -ForegroundColor Green -$proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList "--build . ${selectConfig} --parallel ${NumberOfBuildWorkers} --target install" -$handle = $proc.Handle -$proc.WaitForExit() -$exitCode = $proc.ExitCode -if (-Not ($exitCode -eq 0)) { - MyThrow("Config failed! Exited with error code $exitCode.") -} -Remove-Item -Force -ErrorAction SilentlyContinue DarknetConfig.cmake -Remove-Item -Force -ErrorAction SilentlyContinue DarknetConfigVersion.cmake -$dllfiles = Get-ChildItem ./${dllfolder}/*.dll -if ($dllfiles) { - Copy-Item $dllfiles .. -} -Set-Location .. -Copy-Item cmake/Modules/*.cmake share/darknet/ +if ($InstallDARKNETthroughVCPKG) { + if ($ForceVCPKGDarknetHEAD) { + $headMode = " --head " + } + $features = "opencv-base" + $feature_manifest_opencv = "--x-feature=opencv-base" + if ($EnableCUDA) { + $features = $features + ",cuda" + $feature_manifest_cuda = "--x-feature=cuda" + } + if ($EnableCUDNN) { + $features = $features + ",cudnn" + $feature_manifest_cudnn = "--x-feature=cudnn" + } + if (-not (Test-Path "${env:VCPKG_ROOT}/vcpkg${exe_ext}")) { + $proc = Start-Process -NoNewWindow -PassThru -FilePath ${env:VCPKG_ROOT}/bootstrap-vcpkg${bootstrap_ext} -ArgumentList "-disableMetrics" + $handle = $proc.Handle + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-Not ($exitCode -eq 0)) { + MyThrow("Bootstrapping vcpkg failed! Exited with error code $exitCode.") + } + } + if ($InstallDARKNETdependenciesThroughVCPKGManifest) { + Write-Host "Running vcpkg in manifest mode to install darknet dependencies" + Write-Host "vcpkg install --x-no-default-features $feature_manifest_opencv $feature_manifest_cuda $feature_manifest_cudnn $headMode" + $proc = Start-Process -NoNewWindow -PassThru -FilePath "${env:VCPKG_ROOT}/vcpkg${exe_ext}" -ArgumentList " install --x-no-default-features $feature_manifest_opencv $feature_manifest_cuda $feature_manifest_cudnn $headMode " + $handle = $proc.Handle + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-Not ($exitCode -eq 0)) { + MyThrow("Installing darknet through vcpkg failed! Exited with error code $exitCode.") + } + } + else { + Write-Host "Running vcpkg to install darknet" + Write-Host "vcpkg install darknet[${features}] $headMode --recurse" + Push-Location ${env:VCPKG_ROOT} + if ($ForceVCPKGDarknetHEAD) { + $proc = Start-Process -NoNewWindow -PassThru -FilePath "${env:VCPKG_ROOT}/vcpkg${exe_ext}" -ArgumentList " --feature-flags=-manifests remove darknet --recurse " + $handle = $proc.Handle + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-Not ($exitCode -eq 0)) { + MyThrow("Removing darknet through vcpkg failed! Exited with error code $exitCode.") + } + } + $proc = Start-Process -NoNewWindow -PassThru -FilePath "${env:VCPKG_ROOT}/vcpkg${exe_ext}" -ArgumentList " --feature-flags=-manifests upgrade --no-dry-run " + $handle = $proc.Handle + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-Not ($exitCode -eq 0)) { + MyThrow("Upgrading vcpkg installed ports failed! Exited with error code $exitCode.") + } + $proc = Start-Process -NoNewWindow -PassThru -FilePath "${env:VCPKG_ROOT}/vcpkg${exe_ext}" -ArgumentList " --feature-flags=-manifests install darknet[${features}] $headMode --recurse " # "-manifest" disables the manifest feature, so that if vcpkg is a subfolder of darknet, the vcpkg.json inside darknet folder does not trigger errors due to automatic manifest mode + $handle = $proc.Handle + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-Not ($exitCode -eq 0)) { + MyThrow("Installing darknet dependencies through vcpkg failed! Exited with error code $exitCode.") + } + Pop-Location + } +} +else { + $build_folder = "./build_release" + if (-Not $DoNotDeleteBuildFolder) { + Write-Host "Removing folder $build_folder" -ForegroundColor Yellow + Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $build_folder + } + New-Item -Path $build_folder -ItemType directory -Force | Out-Null + Set-Location $build_folder + $cmake_args = "-G `"$generator`" ${AdditionalBuildSetup} -S .." + Write-Host "Configuring CMake project" -ForegroundColor Green + Write-Host "CMake args: $cmake_args" + $proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList $cmake_args + $handle = $proc.Handle + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-Not ($exitCode -eq 0)) { + MyThrow("Config failed! Exited with error code $exitCode.") + } + Write-Host "Building CMake project" -ForegroundColor Green + $proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList "--build . ${selectConfig} --parallel ${NumberOfBuildWorkers} --target install" + $handle = $proc.Handle + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-Not ($exitCode -eq 0)) { + MyThrow("Config failed! Exited with error code $exitCode.") + } + Remove-Item -Force -ErrorAction SilentlyContinue DarknetConfig.cmake + Remove-Item -Force -ErrorAction SilentlyContinue DarknetConfigVersion.cmake + $dllfiles = Get-ChildItem ./${dllfolder}/*.dll + if ($dllfiles) { + Copy-Item $dllfiles .. + } + Set-Location .. + Copy-Item cmake/Modules/*.cmake share/darknet/ + Pop-Location +} + Write-Host "Build complete!" -ForegroundColor Green -Pop-Location + +if ($DownloadWeights) { + Write-Host "Downloading weights..." -ForegroundColor Yellow + & $PSScriptRoot/scripts/download_weights.ps1 + Write-Host "Weights downloaded" -ForegroundColor Green +} if ($vcpkg_root_set_by_this_script) { $env:VCPKG_ROOT = $null From 28952006b010146344319fd14f9e41b5ee5d828b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 27 Jun 2021 04:46:03 +0300 Subject: [PATCH 1256/1365] Fixed cfg: yolov4-p5.cfg, yolov4-p6.cfg. Added new Weights-files, for: yolov4-csp, yolov4-p5, yolov4-p6, yolov4x-mish --- build/darknet/x64/cfg/yolov4-p5.cfg | 6 +++--- build/darknet/x64/cfg/yolov4-p6.cfg | 25 +++++++++++++++---------- build/darknet/x64/partial.cmd | 14 +++++++++++++- cfg/yolov4-p5.cfg | 6 +++--- cfg/yolov4-p6.cfg | 25 +++++++++++++++---------- 5 files changed, 49 insertions(+), 27 deletions(-) diff --git a/build/darknet/x64/cfg/yolov4-p5.cfg b/build/darknet/x64/cfg/yolov4-p5.cfg index 3a7848177fd..14bce30ebd2 100644 --- a/build/darknet/x64/cfg/yolov4-p5.cfg +++ b/build/darknet/x64/cfg/yolov4-p5.cfg @@ -1737,7 +1737,7 @@ resize=1.5 iou_thresh=0.2 iou_normalizer=0.05 cls_normalizer=0.5 -obj_normalizer=0.4 +obj_normalizer=1.0 iou_loss=ciou nms_kind=diounms beta_nms=0.6 @@ -1782,7 +1782,7 @@ resize=1.5 iou_thresh=0.2 iou_normalizer=0.05 cls_normalizer=0.5 -obj_normalizer=0.4 +obj_normalizer=1.0 iou_loss=ciou nms_kind=diounms beta_nms=0.6 @@ -1827,7 +1827,7 @@ resize=1.5 iou_thresh=0.2 iou_normalizer=0.05 cls_normalizer=0.5 -obj_normalizer=0.4 +obj_normalizer=1.0 iou_loss=ciou nms_kind=diounms beta_nms=0.6 diff --git a/build/darknet/x64/cfg/yolov4-p6.cfg b/build/darknet/x64/cfg/yolov4-p6.cfg index 6e241e12e01..8defa150b6f 100644 --- a/build/darknet/x64/cfg/yolov4-p6.cfg +++ b/build/darknet/x64/cfg/yolov4-p6.cfg @@ -2143,17 +2143,22 @@ mask = 0,1,2,3 anchors = 13,17, 31,25, 24,51, 61,45, 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 1024,1024 classes=80 num=16 -jitter=.3 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 ignore_thresh = .7 truth_thresh = 1 -random=1 -scale_x_y = 1.05 -iou_thresh=0.213 -cls_normalizer=1.0 -iou_normalizer=0.07 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 iou_loss=ciou -nms_kind=greedynms +nms_kind=diounms beta_nms=0.6 +new_coords=1 +max_delta=2 # YOLO-4 @@ -2193,7 +2198,7 @@ resize=1.5 iou_thresh=0.2 iou_normalizer=0.05 cls_normalizer=0.5 -obj_normalizer=0.4 +obj_normalizer=1.0 iou_loss=ciou nms_kind=diounms beta_nms=0.6 @@ -2238,7 +2243,7 @@ resize=1.5 iou_thresh=0.2 iou_normalizer=0.05 cls_normalizer=0.5 -obj_normalizer=0.4 +obj_normalizer=1.0 iou_loss=ciou nms_kind=diounms beta_nms=0.6 @@ -2283,7 +2288,7 @@ resize=1.5 iou_thresh=0.2 iou_normalizer=0.05 cls_normalizer=0.5 -obj_normalizer=0.4 +obj_normalizer=1.0 iou_loss=ciou nms_kind=diounms beta_nms=0.6 diff --git a/build/darknet/x64/partial.cmd b/build/darknet/x64/partial.cmd index a9b06ca86b7..6080cf68b89 100644 --- a/build/darknet/x64/partial.cmd +++ b/build/darknet/x64/partial.cmd @@ -2,10 +2,22 @@ rem Download weights for - DenseNet201, ResNet50 and ResNet152 by this link: htt rem Download Yolo/Tiny-yolo: https://pjreddie.com/darknet/yolo/ rem Download Yolo9000: http://pjreddie.com/media/files/yolo9000.weights +darknet.exe partial cfg/yolov4-csp.cfg yolov4-csp.weights yolov4-csp.conv.142 142 + +darknet.exe partial cfg/yolov4x-mish.cfg yolov4x-mish.weights yolov4x-mish.conv.166 166 + + + + +rem darknet.exe partial cfg/yolov4-p5.cfg yolov4-p5.weights yolov4-p5.conv.232 232 + +rem darknet.exe partial cfg/yolov4-p6.cfg yolov4-p6.weights yolov4-p6.conv.289 289 + + rem darknet.exe partial cfg/tiny-yolo-voc.cfg tiny-yolo-voc.weights tiny-yolo-voc.conv.13 13 -darknet.exe partial cfg/yolov4-tiny.cfg yolov4-tiny.weights yolov4-tiny.conv.29 29 +rem darknet.exe partial cfg/yolov4-tiny.cfg yolov4-tiny.weights yolov4-tiny.conv.29 29 rem darknet.exe partial cfg/yolov4-sam-mish.cfg cfg/yolov4-sam-mish.weights cfg/yolov4-sam-mish.conv.137 137 diff --git a/cfg/yolov4-p5.cfg b/cfg/yolov4-p5.cfg index 3a7848177fd..14bce30ebd2 100644 --- a/cfg/yolov4-p5.cfg +++ b/cfg/yolov4-p5.cfg @@ -1737,7 +1737,7 @@ resize=1.5 iou_thresh=0.2 iou_normalizer=0.05 cls_normalizer=0.5 -obj_normalizer=0.4 +obj_normalizer=1.0 iou_loss=ciou nms_kind=diounms beta_nms=0.6 @@ -1782,7 +1782,7 @@ resize=1.5 iou_thresh=0.2 iou_normalizer=0.05 cls_normalizer=0.5 -obj_normalizer=0.4 +obj_normalizer=1.0 iou_loss=ciou nms_kind=diounms beta_nms=0.6 @@ -1827,7 +1827,7 @@ resize=1.5 iou_thresh=0.2 iou_normalizer=0.05 cls_normalizer=0.5 -obj_normalizer=0.4 +obj_normalizer=1.0 iou_loss=ciou nms_kind=diounms beta_nms=0.6 diff --git a/cfg/yolov4-p6.cfg b/cfg/yolov4-p6.cfg index 6e241e12e01..8defa150b6f 100644 --- a/cfg/yolov4-p6.cfg +++ b/cfg/yolov4-p6.cfg @@ -2143,17 +2143,22 @@ mask = 0,1,2,3 anchors = 13,17, 31,25, 24,51, 61,45, 61,45, 48,102, 119,96, 97,189, 97,189, 217,184, 171,384, 324,451, 324,451, 545,357, 616,618, 1024,1024 classes=80 num=16 -jitter=.3 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 ignore_thresh = .7 truth_thresh = 1 -random=1 -scale_x_y = 1.05 -iou_thresh=0.213 -cls_normalizer=1.0 -iou_normalizer=0.07 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 iou_loss=ciou -nms_kind=greedynms +nms_kind=diounms beta_nms=0.6 +new_coords=1 +max_delta=2 # YOLO-4 @@ -2193,7 +2198,7 @@ resize=1.5 iou_thresh=0.2 iou_normalizer=0.05 cls_normalizer=0.5 -obj_normalizer=0.4 +obj_normalizer=1.0 iou_loss=ciou nms_kind=diounms beta_nms=0.6 @@ -2238,7 +2243,7 @@ resize=1.5 iou_thresh=0.2 iou_normalizer=0.05 cls_normalizer=0.5 -obj_normalizer=0.4 +obj_normalizer=1.0 iou_loss=ciou nms_kind=diounms beta_nms=0.6 @@ -2283,7 +2288,7 @@ resize=1.5 iou_thresh=0.2 iou_normalizer=0.05 cls_normalizer=0.5 -obj_normalizer=0.4 +obj_normalizer=1.0 iou_loss=ciou nms_kind=diounms beta_nms=0.6 From 24bb6a8ebf5ec32bff4f30ec0801d9a9a9fca3f6 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 29 Jun 2021 21:32:38 +0300 Subject: [PATCH 1257/1365] Fixed [] stopbackward=1, for frozen layers: don't calc batch-norm statistic and don't allocate training-buffers. --- src/convolutional_kernels.cu | 5 +++-- src/network.c | 3 ++- src/network_kernels.cu | 4 +++- src/parser.c | 43 ++++++++++++++++++++++++++++++++++-- 4 files changed, 49 insertions(+), 6 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index ddd140c3088..f44016c52ca 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -165,6 +165,7 @@ half *cuda_make_f16_from_f32_array(float *src, size_t n) void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) { + state.train = l.train; if (l.stream >= 0) { switch_stream(l.stream); } @@ -1219,8 +1220,8 @@ void pull_convolutional_layer(convolutional_layer l) { cuda_pull_array_async(l.weights_gpu, l.weights, l.nweights); cuda_pull_array_async(l.biases_gpu, l.biases, l.n); - cuda_pull_array_async(l.weight_updates_gpu, l.weight_updates, l.nweights); - cuda_pull_array_async(l.bias_updates_gpu, l.bias_updates, l.n); + if (l.weight_updates_gpu) cuda_pull_array_async(l.weight_updates_gpu, l.weight_updates, l.nweights); + if (l.bias_updates_gpu) cuda_pull_array_async(l.bias_updates_gpu, l.bias_updates, l.n); if (l.batch_normalize){ cuda_pull_array_async(l.scales_gpu, l.scales, l.n); cuda_pull_array_async(l.rolling_mean_gpu, l.rolling_mean, l.n); diff --git a/src/network.c b/src/network.c index 600be1b2d7d..39d9d1ca8f0 100644 --- a/src/network.c +++ b/src/network.c @@ -273,7 +273,7 @@ void forward_network(network net, network_state state) for(i = 0; i < net.n; ++i){ state.index = i; layer l = net.layers[i]; - if(l.delta && state.train){ + if(l.delta && state.train && l.train){ scal_cpu(l.outputs * l.batch, 0, l.delta, 1); } //double time = get_time_point(); @@ -297,6 +297,7 @@ void update_network(network net) float rate = get_current_rate(net); for(i = 0; i < net.n; ++i){ layer l = net.layers[i]; + if (l.train == 0) continue; if(l.update){ l.update(l, update_batch, rate, net.momentum, net.decay); } diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 9f4c640b3e9..6f3a651968d 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -76,7 +76,7 @@ void forward_network_gpu(network net, network_state state) for(i = 0; i < net.n; ++i){ state.index = i; layer l = net.layers[i]; - if(l.delta_gpu && state.train){ + if(l.delta_gpu && state.train && l.train){ fill_ongpu(l.outputs * l.batch, 0, l.delta_gpu, 1); } @@ -271,6 +271,8 @@ void update_network_gpu(network net) float rate = get_current_rate(net); for(i = 0; i < net.n; ++i){ layer l = net.layers[i]; + if (l.train == 0) continue; + l.t = get_current_batch(net); if (iteration_num > (net.max_batches * 1 / 2)) l.deform = 0; if (l.burnin_update && (l.burnin_update*net.burn_in > iteration_num)) continue; diff --git a/src/parser.c b/src/parser.c index a2d133382c4..d1f2f744510 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1366,8 +1366,8 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) net.gpu_index = gpu_index; size_params params; - if (batch > 0) params.train = 0; // allocates memory for Detection only - else params.train = 1; // allocates memory for Detection & Training + if (batch > 0) params.train = 0; // allocates memory for Inference only + else params.train = 1; // allocates memory for Inference & Training section *s = (section *)n->val; list *options = s->options; @@ -1395,6 +1395,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) params.net = net; printf("mini_batch = %d, batch = %d, time_steps = %d, train = %d \n", net.batch, net.batch * net.subdivisions, net.time_steps, params.train); + int last_stop_backward = -1; int avg_outputs = 0; int avg_counter = 0; float bflops = 0; @@ -1408,8 +1409,28 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) n = n->next; int count = 0; free_section(s); + + // find l.stopbackward = option_find_int_quiet(options, "stopbackward", 0); + node *n_tmp = n; + int count_tmp = 0; + while (n_tmp) { + s = (section *)n_tmp->val; + options = s->options; + int stopbackward = option_find_int_quiet(options, "stopbackward", 0); + if (stopbackward == 1) { + last_stop_backward = count_tmp; + printf("last_stop_backward = %d \n", last_stop_backward); + } + n_tmp = n_tmp->next; + ++count_tmp; + } + fprintf(stderr, " layer filters size/strd(dil) input output\n"); while(n){ + + if (count < last_stop_backward) params.train = 0; + else params.train = 1; + params.index = count; fprintf(stderr, "%4d ", count); s = (section *)n->val; @@ -1662,6 +1683,24 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) avg_counter++; } } + + if (last_stop_backward > -1) { + int k; + for (k = 0; k < last_stop_backward; ++k) { + layer l = net.layers[k]; + if (l.keep_delta_gpu) { + if (!l.delta) l.delta = (float*)xcalloc(l.outputs, sizeof(float)); +#ifdef GPU + if (!l.delta_gpu) l.delta_gpu = (float *)cuda_make_array(NULL, l.outputs); +#endif + } + + l.onlyforward = 1; + l.train = 0; + net.layers[k] = l; + } + } + free_list(sections); #ifdef GPU From 85c6278ef1f8a0bed424567c5ac86da44a65474a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 29 Jun 2021 22:09:26 +0300 Subject: [PATCH 1258/1365] Added cfg/yolov4-p5-frozen.cfg for training with pre-trained COCO-weights file yolov4-p5.weights --- build/darknet/x64/cfg/yolov4-p5-frozen.cfg | 1838 ++++++++++++++++++++ cfg/yolov4-p5-frozen.cfg | 1838 ++++++++++++++++++++ src/parser.c | 8 +- 3 files changed, 3682 insertions(+), 2 deletions(-) create mode 100644 build/darknet/x64/cfg/yolov4-p5-frozen.cfg create mode 100644 cfg/yolov4-p5-frozen.cfg diff --git a/build/darknet/x64/cfg/yolov4-p5-frozen.cfg b/build/darknet/x64/cfg/yolov4-p5-frozen.cfg new file mode 100644 index 00000000000..38bebe32a21 --- /dev/null +++ b/build/darknet/x64/cfg/yolov4-p5-frozen.cfg @@ -0,0 +1,1838 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=896 +height=896 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +ema_alpha=0.9998 + +#use_cuda_graph = 1 + + +# ============ Backbone ============ # + +# Stem + +# 0 +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + + +# P1 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-7 + +# Transition last + +# 10 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + + +# P2 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-13 + +# Transition last + +# 26 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + + +# P3 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-49 + +# Transition last + +# 78 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + + +# P4 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-49 + +# Transition last + +# 130 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + + +# P5 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-25 + +# Transition last + +# 158 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=mish + +# ============ End of Backbone ============ # + +# ============ Neck ============ # + +# CSPSPP + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1, -13 + +# 173 (previous+6+5+2k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# End of CSPSPP + + +# FPN-4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 130 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 189 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + + +# FPN-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 78 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 205 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + + +# PAN-4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1, 189 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1,-8 + +# Transition last + +# 218 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + + +# PAN-5 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1, 173 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1,-8 + +# Transition last + +# 231 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish +stopbackward=1 + +# ============ End of Neck ============ # + +# ============ Head ============ # + +# YOLO-3 + +[route] +layers = 205 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=logistic +#activation=linear +# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet + +[yolo] +mask = 0,1,2,3 +anchors = 13,17, 31,25, 24,51, 61,45, 48,102, 119,96, 97,189, 217,184, 171,384, 324,451, 616,618, 800,800 +classes=80 +num=12 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-4 + +[route] +layers = 218 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=logistic +#activation=linear +# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet + +[yolo] +mask = 4,5,6,7 +anchors = 13,17, 31,25, 24,51, 61,45, 48,102, 119,96, 97,189, 217,184, 171,384, 324,451, 616,618, 800,800 +classes=80 +num=12 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-5 + +[route] +layers = 231 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=logistic +#activation=linear +# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet + +[yolo] +mask = 8,9,10,11 +anchors = 13,17, 31,25, 24,51, 61,45, 48,102, 119,96, 97,189, 217,184, 171,384, 324,451, 616,618, 800,800 +classes=80 +num=12 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + +# ============ End of Head ============ # \ No newline at end of file diff --git a/cfg/yolov4-p5-frozen.cfg b/cfg/yolov4-p5-frozen.cfg new file mode 100644 index 00000000000..38bebe32a21 --- /dev/null +++ b/cfg/yolov4-p5-frozen.cfg @@ -0,0 +1,1838 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=896 +height=896 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +ema_alpha=0.9998 + +#use_cuda_graph = 1 + + +# ============ Backbone ============ # + +# Stem + +# 0 +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + + +# P1 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-7 + +# Transition last + +# 10 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + + +# P2 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-13 + +# Transition last + +# 26 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + + +# P3 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-49 + +# Transition last + +# 78 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + + +# P4 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-49 + +# Transition last + +# 130 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + + +# P5 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-25 + +# Transition last + +# 158 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=mish + +# ============ End of Backbone ============ # + +# ============ Neck ============ # + +# CSPSPP + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1, -13 + +# 173 (previous+6+5+2k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# End of CSPSPP + + +# FPN-4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 130 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 189 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + + +# FPN-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 78 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 205 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + + +# PAN-4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1, 189 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1,-8 + +# Transition last + +# 218 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + + +# PAN-5 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1, 173 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[route] +layers = -1,-8 + +# Transition last + +# 231 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish +stopbackward=1 + +# ============ End of Neck ============ # + +# ============ Head ============ # + +# YOLO-3 + +[route] +layers = 205 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=logistic +#activation=linear +# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet + +[yolo] +mask = 0,1,2,3 +anchors = 13,17, 31,25, 24,51, 61,45, 48,102, 119,96, 97,189, 217,184, 171,384, 324,451, 616,618, 800,800 +classes=80 +num=12 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-4 + +[route] +layers = 218 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=logistic +#activation=linear +# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet + +[yolo] +mask = 4,5,6,7 +anchors = 13,17, 31,25, 24,51, 61,45, 48,102, 119,96, 97,189, 217,184, 171,384, 324,451, 616,618, 800,800 +classes=80 +num=12 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-5 + +[route] +layers = 231 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=340 +activation=logistic +#activation=linear +# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet + +[yolo] +mask = 8,9,10,11 +anchors = 13,17, 31,25, 24,51, 61,45, 48,102, 119,96, 97,189, 217,184, 171,384, 324,451, 616,618, 800,800 +classes=80 +num=12 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + +# ============ End of Head ============ # \ No newline at end of file diff --git a/src/parser.c b/src/parser.c index d1f2f744510..37c8e0776b0 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1499,7 +1499,8 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) int k; for (k = 0; k < l.n; ++k) { net.layers[l.input_layers[k]].use_bin_output = 0; - net.layers[l.input_layers[k]].keep_delta_gpu = 1; + if (count >= last_stop_backward) + net.layers[l.input_layers[k]].keep_delta_gpu = 1; } }else if (lt == UPSAMPLE) { l = parse_upsample(options, params, net); @@ -1507,7 +1508,8 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) l = parse_shortcut(options, params, net); net.layers[count - 1].use_bin_output = 0; net.layers[l.index].use_bin_output = 0; - net.layers[l.index].keep_delta_gpu = 1; + if (count >= last_stop_backward) + net.layers[l.index].keep_delta_gpu = 1; }else if (lt == SCALE_CHANNELS) { l = parse_scale_channels(options, params, net); net.layers[count - 1].use_bin_output = 0; @@ -1655,6 +1657,8 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) l.learning_rate_scale = option_find_float_quiet(options, "learning_rate", 1); option_unused(options); + if (l.stopbackward == 1) printf(" ------- previous layers are frozen ------- \n"); + net.layers[count] = l; if (l.workspace_size > workspace_size) workspace_size = l.workspace_size; if (l.inputs > max_inputs) max_inputs = l.inputs; From c2221f07f88c9ff189c77c10a7381c22b59c7d58 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 29 Jun 2021 23:54:58 +0300 Subject: [PATCH 1259/1365] Fixed adversarial training --- include/darknet.h | 2 +- src/image.c | 4 ++-- src/network_kernels.cu | 17 +++++++++++++++-- 3 files changed, 18 insertions(+), 5 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index f5170f375cd..6daaaf1ca65 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -1056,7 +1056,7 @@ LIB_API void optimize_picture(network *net, image orig, int max_layer, float sca // image.h LIB_API void make_image_red(image im); -LIB_API image make_attention_image(int img_size, float *original_delta_cpu, float *original_input_cpu, int w, int h, int c); +LIB_API image make_attention_image(int img_size, float *original_delta_cpu, float *original_input_cpu, int w, int h, int c, float alpha); LIB_API image resize_image(image im, int w, int h); LIB_API void quantize_image(image im); LIB_API void copy_image_from_bytes(image im, char *pdata); diff --git a/src/image.c b/src/image.c index 07942c71e6e..8ef026d33a2 100644 --- a/src/image.c +++ b/src/image.c @@ -1351,7 +1351,7 @@ void make_image_red(image im) } } -image make_attention_image(int img_size, float *original_delta_cpu, float *original_input_cpu, int w, int h, int c) +image make_attention_image(int img_size, float *original_delta_cpu, float *original_input_cpu, int w, int h, int c, float alpha) { image attention_img; attention_img.w = w; @@ -1379,7 +1379,7 @@ image make_attention_image(int img_size, float *original_delta_cpu, float *origi image resized = resize_image(attention_img, w / 4, h / 4); attention_img = resize_image(resized, w, h); free_image(resized); - for (k = 0; k < img_size; ++k) attention_img.data[k] += original_input_cpu[k]; + for (k = 0; k < img_size; ++k) attention_img.data[k] = attention_img.data[k]*alpha + (1-alpha)*original_input_cpu[k]; //normalize_image(attention_img); //show_image(attention_img, "delta"); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 6f3a651968d..ac3403cf547 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -76,7 +76,7 @@ void forward_network_gpu(network net, network_state state) for(i = 0; i < net.n; ++i){ state.index = i; layer l = net.layers[i]; - if(l.delta_gpu && state.train && l.train){ + if(l.delta_gpu && state.train){ fill_ongpu(l.outputs * l.batch, 0, l.delta_gpu, 1); } @@ -235,12 +235,25 @@ void backward_network_gpu(network net, network_state state) cuda_pull_array(original_input, original_input_cpu, img_size); cuda_pull_array(original_delta, original_delta_cpu, img_size); - image attention_img = make_attention_image(img_size, original_delta_cpu, original_input_cpu, net.w, net.h, net.c); + image attention_img = make_attention_image(img_size, original_delta_cpu, original_input_cpu, net.w, net.h, net.c, 0.7); show_image(attention_img, "attention_img"); resize_window_cv("attention_img", 500, 500); + //static int img_counter = 0; + //img_counter++; + //char buff[256]; + //sprintf(buff, "attention_img_%d.png", img_counter); + //save_image_png(attention_img, buff); free_image(attention_img); + image attention_mask_img = make_attention_image(img_size, original_delta_cpu, original_delta_cpu, net.w, net.h, net.c, 1.0); + show_image(attention_mask_img, "attention_mask_img"); + resize_window_cv("attention_mask_img", 500, 500); + + //sprintf(buff, "attention_mask_img_%d.png", img_counter); + //save_image_png(attention_mask_img, buff); + free_image(attention_mask_img); + free(original_input_cpu); free(original_delta_cpu); } From 97ccee56283ad92118153f0cd1036d1ea07ba115 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 29 Jun 2021 23:55:34 +0300 Subject: [PATCH 1260/1365] minor fix - added: move_window_cv() function --- src/image_opencv.cpp | 11 +++++++++++ src/image_opencv.h | 1 + 2 files changed, 12 insertions(+) diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index f9615cb00ae..2524f1593ca 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -385,6 +385,17 @@ extern "C" void resize_window_cv(char const* window_name, int width, int height) } // ---------------------------------------- +extern "C" void move_window_cv(char const* window_name, int x, int y) +{ + try { + cv::moveWindow(window_name, x, y); + } + catch (...) { + cerr << "OpenCV exception: create_window_cv \n"; + } +} +// ---------------------------------------- + extern "C" void destroy_all_windows_cv() { try { diff --git a/src/image_opencv.h b/src/image_opencv.h index 3aeb4478390..19d16e1d9b5 100644 --- a/src/image_opencv.h +++ b/src/image_opencv.h @@ -48,6 +48,7 @@ image mat_to_image_cv(mat_cv *mat); // Window void create_window_cv(char const* window_name, int full_screen, int width, int height); void resize_window_cv(char const* window_name, int width, int height); +void move_window_cv(char const* window_name, int x, int y); void destroy_all_windows_cv(); int wait_key_cv(int delay); int wait_until_press_key_cv(); From 90c15f8f04ec46a855dd10d55f2c296bf6b13567 Mon Sep 17 00:00:00 2001 From: Alexey Date: Wed, 30 Jun 2021 06:37:57 +0300 Subject: [PATCH 1261/1365] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 42007e975d6..edf899bf6d8 100644 --- a/README.md +++ b/README.md @@ -220,7 +220,7 @@ You can get cfg-files by path: `darknet/cfg/` - [maudzung/3D-YOLOv4](https://github.com/maudzung/Complex-YOLOv4-Pytorch) - [Tianxiaomo/pytorch-YOLOv4](https://github.com/Tianxiaomo/pytorch-YOLOv4) - [YOLOv5](https://github.com/ultralytics/yolov5) -- **ONNX** on Jetson for YOLOv4: https://developer.nvidia.com/blog/announcing-onnx-runtime-for-jetson/ +- **ONNX** on Jetson for YOLOv4: https://developer.nvidia.com/blog/announcing-onnx-runtime-for-jetson/ and https://github.com/ttanzhiqiang/onnx_tensorrt_project - **nVidia Transfer Learning Toolkit (TLT>=3.0)** Training and Detection https://docs.nvidia.com/metropolis/TLT/tlt-user-guide/text/object_detection/yolo_v4.html - **TensorRT+tkDNN**: https://github.com/ceccocats/tkDNN#fps-results - **Deepstream 5.0 / TensorRT for YOLOv4** https://github.com/NVIDIA-AI-IOT/yolov4_deepstream or https://github.com/marcoslucianops/DeepStream-Yolo read [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) and [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf). Additionally [jkjung-avt/tensorrt_demos](https://github.com/jkjung-avt/tensorrt_demos) or [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) From 86ced7151a71c05fab57bc14f77d6a4bb97b9ee6 Mon Sep 17 00:00:00 2001 From: Alexey Date: Wed, 30 Jun 2021 06:51:24 +0300 Subject: [PATCH 1262/1365] Update README.md --- README.md | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index edf899bf6d8..6912b30dc89 100644 --- a/README.md +++ b/README.md @@ -145,13 +145,19 @@ There are weights-file for different cfg-files (trained for MS COCO dataset): FPS on RTX 2070 (R) and Tesla V100 (V): -- [yolov4x-mish.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4x-mish.cfg) - 640x640 - **67.9% mAP@0.5 (49.4% AP@0.5:0.95) - 23(R) FPS / 50(V) FPS** - 221 BFlops (110 FMA) - 381 MB: [yolov4x-mish.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.weights) +- [yolov4-p6.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-p6.cfg) - 1280x1280 - **72.1% mAP@0.5 (54.0% AP@0.5:0.95) - 32(V) FPS** - xxx BFlops (xxx FMA) - 487 MB: [yolov4-p6.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-p6.weights) + - pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-p6.conv.289 + +- [yolov4-p5.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-p5.cfg) - 896x896 - **70.0% mAP@0.5 (51.6% AP@0.5:0.95) - 43(V) FPS** - xxx BFlops (xxx FMA) - 271 MB: [yolov4-p5.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-p5.weights) + - pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-p5.conv.232 + +- [yolov4x-mish.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4x-mish.cfg) - 640x640 - **68.5% mAP@0.5 (50.1% AP@0.5:0.95) - 23(R) FPS / 50(V) FPS** - 221 BFlops (110 FMA) - 381 MB: [yolov4x-mish.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.weights) - pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.conv.166 - [yolov4-csp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-csp.cfg) - 202 MB: [yolov4-csp.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.weights) paper [Scaled Yolo v4](https://arxiv.org/abs/2011.08036) just change `width=` and `height=` parameters in `yolov4-csp.cfg` file and use the same `yolov4-csp.weights` file for all cases: - - `width=640 height=640` in cfg: **66.2% mAP@0.5 (47.5% AP@0.5:0.95) - 70(V) FPS** - 120 (60 FMA) BFlops + - `width=640 height=640` in cfg: **67.4% mAP@0.5 (48.7% AP@0.5:0.95) - 70(V) FPS** - 120 (60 FMA) BFlops - `width=512 height=512` in cfg: **64.8% mAP@0.5 (46.2% AP@0.5:0.95) - 93(V) FPS** - 77 (39 FMA) BFlops - pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.conv.142 From 9c9232d1c3f0f80e40bf347643a542903d6703ca Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 3 Jul 2021 04:18:25 +0300 Subject: [PATCH 1263/1365] Bug fix --- src/parser.c | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/src/parser.c b/src/parser.c index 37c8e0776b0..6d5590e8f55 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1429,7 +1429,6 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) while(n){ if (count < last_stop_backward) params.train = 0; - else params.train = 1; params.index = count; fprintf(stderr, "%4d ", count); @@ -1693,9 +1692,9 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) for (k = 0; k < last_stop_backward; ++k) { layer l = net.layers[k]; if (l.keep_delta_gpu) { - if (!l.delta) l.delta = (float*)xcalloc(l.outputs, sizeof(float)); + if (!l.delta) l.delta = (float*)xcalloc(l.outputs*l.batch, sizeof(float)); #ifdef GPU - if (!l.delta_gpu) l.delta_gpu = (float *)cuda_make_array(NULL, l.outputs); + if (!l.delta_gpu) l.delta_gpu = (float *)cuda_make_array(NULL, l.outputs*l.batch); #endif } From b2cb64dffbcf706ac9f1d12d7fe699c40eacc40b Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 3 Jul 2021 04:25:42 +0300 Subject: [PATCH 1264/1365] bug fix --- src/parser.c | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/parser.c b/src/parser.c index 6d5590e8f55..8930791787f 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1425,9 +1425,12 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) ++count_tmp; } + int old_params_train = params.train; + fprintf(stderr, " layer filters size/strd(dil) input output\n"); while(n){ + params.train = old_params_train; if (count < last_stop_backward) params.train = 0; params.index = count; From 335ac662c84421982ca3a54a47e64926b88297d5 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 3 Jul 2021 04:46:55 +0300 Subject: [PATCH 1265/1365] Improved [net] optimized_memory=1 to reduce GPU memory usage. --- src/parser.c | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/src/parser.c b/src/parser.c index 8930791787f..db28e737571 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1615,7 +1615,12 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) #ifdef GPU // futher GPU-memory optimization: net.optimized_memory == 2 l.optimized_memory = net.optimized_memory; - if (net.optimized_memory >= 2 && params.train && l.type != DROPOUT) + if (net.optimized_memory == 1 && params.train && l.type != DROPOUT) { + if (l.delta_gpu) { + cuda_free(l.delta_gpu); + l.delta_gpu = NULL; + } + } else if (net.optimized_memory >= 2 && params.train && l.type != DROPOUT) { if (l.output_gpu) { cuda_free(l.output_gpu); @@ -1732,6 +1737,9 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) } l.delta_gpu = net.global_delta_gpu; } + else { + if (!l.delta_gpu) l.delta_gpu = (float *)cuda_make_array(NULL, l.outputs*l.batch); + } // maximum optimization if (net.optimized_memory >= 3 && l.type != DROPOUT) { From be3985210ed336477b2e1317fd13c6cf1494adb1 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 4 Jul 2021 06:31:45 +0300 Subject: [PATCH 1266/1365] fix possible bug --- src/convolutional_kernels.cu | 3 ++- src/parser.c | 33 +++++++++++++++++++-------------- 2 files changed, 21 insertions(+), 15 deletions(-) diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index f44016c52ca..1e9cdd9c739 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -165,7 +165,8 @@ half *cuda_make_f16_from_f32_array(float *src, size_t n) void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) { - state.train = l.train; + if (l.train == 0) state.train = 0; + if (l.stream >= 0) { switch_stream(l.stream); } diff --git a/src/parser.c b/src/parser.c index db28e737571..1a345b5cc1b 100644 --- a/src/parser.c +++ b/src/parser.c @@ -1413,16 +1413,18 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) // find l.stopbackward = option_find_int_quiet(options, "stopbackward", 0); node *n_tmp = n; int count_tmp = 0; - while (n_tmp) { - s = (section *)n_tmp->val; - options = s->options; - int stopbackward = option_find_int_quiet(options, "stopbackward", 0); - if (stopbackward == 1) { - last_stop_backward = count_tmp; - printf("last_stop_backward = %d \n", last_stop_backward); + if (params.train == 1) { + while (n_tmp) { + s = (section *)n_tmp->val; + options = s->options; + int stopbackward = option_find_int_quiet(options, "stopbackward", 0); + if (stopbackward == 1) { + last_stop_backward = count_tmp; + printf("last_stop_backward = %d \n", last_stop_backward); + } + n_tmp = n_tmp->next; + ++count_tmp; } - n_tmp = n_tmp->next; - ++count_tmp; } int old_params_train = params.train; @@ -1700,15 +1702,18 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) for (k = 0; k < last_stop_backward; ++k) { layer l = net.layers[k]; if (l.keep_delta_gpu) { - if (!l.delta) l.delta = (float*)xcalloc(l.outputs*l.batch, sizeof(float)); + if (!l.delta) { + net.layers[k].delta = (float*)xcalloc(l.outputs*l.batch, sizeof(float)); + } #ifdef GPU - if (!l.delta_gpu) l.delta_gpu = (float *)cuda_make_array(NULL, l.outputs*l.batch); + if (!l.delta_gpu) { + net.layers[k].delta_gpu = (float *)cuda_make_array(NULL, l.outputs*l.batch); + } #endif } - l.onlyforward = 1; - l.train = 0; - net.layers[k] = l; + net.layers[k].onlyforward = 1; + net.layers[k].train = 0; } } From 005513a9db14878579adfbb61083962c99bb0a89 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sun, 4 Jul 2021 06:38:49 +0300 Subject: [PATCH 1267/1365] minor fix --- src/network.c | 1 + 1 file changed, 1 insertion(+) diff --git a/src/network.c b/src/network.c index 39d9d1ca8f0..c41932479fd 100644 --- a/src/network.c +++ b/src/network.c @@ -1459,6 +1459,7 @@ void copy_weights_net(network net_train, network *net_map) } net_map->layers[k].batch = 1; net_map->layers[k].steps = 1; + net_map->layers[k].train = 0; } } From 842a6025846640c110654b3bdfdb64fe3d10d5c9 Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 8 Jul 2021 01:13:29 +0300 Subject: [PATCH 1268/1365] Update README.md --- README.md | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/README.md b/README.md index 6912b30dc89..3b5a4e1bff4 100644 --- a/README.md +++ b/README.md @@ -768,3 +768,27 @@ public: #endif }; ``` + +## Citation + +``` +@misc{bochkovskiy2020yolov4, + title={YOLOv4: Optimal Speed and Accuracy of Object Detection}, + author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao}, + year={2020}, + eprint={2004.10934}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + +``` +@InProceedings{Wang_2021_CVPR, + author = {Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark}, + title = {{Scaled-YOLOv4}: Scaling Cross Stage Partial Network}, + booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, + month = {June}, + year = {2021}, + pages = {13029-13038} +} +``` From 08088dccbbae7bb68c41d5ae3355380ce6654663 Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 8 Jul 2021 01:14:28 +0300 Subject: [PATCH 1269/1365] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 3b5a4e1bff4..bb545cf097a 100644 --- a/README.md +++ b/README.md @@ -66,6 +66,7 @@ About Darknet framework: http://pjreddie.com/darknet/ - [How to improve object detection](#how-to-improve-object-detection) - [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) - [How to use Yolo as DLL and SO libraries](#how-to-use-yolo-as-dll-and-so-libraries) +- [Citation](#citation) ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) From 9c26b291fabad663656e1e52d125134564f539d1 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 9 Jul 2021 13:50:38 +0200 Subject: [PATCH 1270/1365] fix python main module to be compiled with recent build scripts (#7876) * fix python main module to be compiled with recent build scripts * fixes for posix systems --- .gitignore | 2 ++ darknet.py | 78 ++++++++++-------------------------------------------- 2 files changed, 16 insertions(+), 64 deletions(-) diff --git a/.gitignore b/.gitignore index 916cfb88461..a1d890429b3 100644 --- a/.gitignore +++ b/.gitignore @@ -9,6 +9,7 @@ *.dll *.lib *.dylib +*.pyc mnist/ data/ caffe/ @@ -39,6 +40,7 @@ build/.ninja_log build/Makefile */vcpkg-manifest-install.log build.log +__pycache__/ # OS Generated # .DS_Store* diff --git a/darknet.py b/darknet.py index ad526f99d95..698f0469ad5 100644 --- a/darknet.py +++ b/darknet.py @@ -1,26 +1,13 @@ -#!python3 +#!/usr/bin/env python3 + """ Python 3 wrapper for identifying objects in images -Requires DLL compilation - -Both the GPU and no-GPU version should be compiled; the no-GPU version should be renamed "yolo_cpp_dll_nogpu.dll". - -On a GPU system, you can force CPU evaluation by any of: - -- Set global variable DARKNET_FORCE_CPU to True -- Set environment variable CUDA_VISIBLE_DEVICES to -1 -- Set environment variable "FORCE_CPU" to "true" -- Set environment variable "DARKNET_PATH" to path darknet lib .so (for Linux) - +Running the script requires opencv-python to be installed (`pip install opencv-python`) Directly viewing or returning bounding-boxed images requires scikit-image to be installed (`pip install scikit-image`) - -Original *nix 2.7: https://github.com/pjreddie/darknet/blob/0f110834f4e18b30d5f101bf8f1724c34b7b83db/python/darknet.py -Windows Python 2.7 version: https://github.com/AlexeyAB/darknet/blob/fc496d52bf22a0bb257300d3c79be9cd80e722cb/build/darknet/x64/darknet.py - -@author: Philip Kahn -@date: 20180503 +Use pip3 instead of pip on some systems to be sure to install modules for python3 """ + from ctypes import * import math import random @@ -178,51 +165,17 @@ def detect_image(network, class_names, image, thresh=.5, hier_thresh=.5, nms=.45 return sorted(predictions, key=lambda x: x[1]) -# lib = CDLL("/home/pjreddie/documents/darknet/libdarknet.so", RTLD_GLOBAL) -# lib = CDLL("libdarknet.so", RTLD_GLOBAL) -hasGPU = True -if os.name == "nt": +if os.name == "posix": + cwd = os.path.dirname(__file__) + lib = CDLL(cwd + "/libdarknet.so", RTLD_GLOBAL) +elif os.name == "nt": cwd = os.path.dirname(__file__) os.environ['PATH'] = cwd + ';' + os.environ['PATH'] - winGPUdll = os.path.join(cwd, "yolo_cpp_dll.dll") - winNoGPUdll = os.path.join(cwd, "yolo_cpp_dll_nogpu.dll") - envKeys = list() - for k, v in os.environ.items(): - envKeys.append(k) - try: - try: - tmp = os.environ["FORCE_CPU"].lower() - if tmp in ["1", "true", "yes", "on"]: - raise ValueError("ForceCPU") - else: - print("Flag value {} not forcing CPU mode".format(tmp)) - except KeyError: - # We never set the flag - if 'CUDA_VISIBLE_DEVICES' in envKeys: - if int(os.environ['CUDA_VISIBLE_DEVICES']) < 0: - raise ValueError("ForceCPU") - try: - global DARKNET_FORCE_CPU - if DARKNET_FORCE_CPU: - raise ValueError("ForceCPU") - except NameError as cpu_error: - print(cpu_error) - if not os.path.exists(winGPUdll): - raise ValueError("NoDLL") - lib = CDLL(winGPUdll, RTLD_GLOBAL) - except (KeyError, ValueError): - hasGPU = False - if os.path.exists(winNoGPUdll): - lib = CDLL(winNoGPUdll, RTLD_GLOBAL) - print("Notice: CPU-only mode") - else: - # Try the other way, in case no_gpu was compile but not renamed - lib = CDLL(winGPUdll, RTLD_GLOBAL) - print("Environment variables indicated a CPU run, but we didn't find {}. Trying a GPU run anyway.".format(winNoGPUdll)) + lib = CDLL("darknet.dll", RTLD_GLOBAL) else: - lib = CDLL(os.path.join( - os.environ.get('DARKNET_PATH', './'), - "libdarknet.so"), RTLD_GLOBAL) + print("Unsupported OS") + exit + lib.network_width.argtypes = [c_void_p] lib.network_width.restype = c_int lib.network_height.argtypes = [c_void_p] @@ -235,10 +188,7 @@ def detect_image(network, class_names, image, thresh=.5, hier_thresh=.5, nms=.45 predict.argtypes = [c_void_p, POINTER(c_float)] predict.restype = POINTER(c_float) -if hasGPU: - set_gpu = lib.cuda_set_device - set_gpu.argtypes = [c_int] - +set_gpu = lib.cuda_set_device init_cpu = lib.init_cpu make_image = lib.make_image From 2418fa7b08eb7d42635420780e78187eb0b42e35 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Mon, 12 Jul 2021 05:30:51 +0300 Subject: [PATCH 1271/1365] Added: yolov4-csp-swish.cfg, yolov4-csp-x-swish.cfg, yolov4-csp-x-swish-frozen.cfg --- build/darknet/x64/cfg/yolov4-csp-swish.cfg | 1355 ++++++++++++++ .../x64/cfg/yolov4-csp-x-swish-frozen.cfg | 1556 +++++++++++++++++ build/darknet/x64/cfg/yolov4-csp-x-swish.cfg | 1556 +++++++++++++++++ build/darknet/x64/partial.cmd | 9 + cfg/yolov4-csp-swish.cfg | 1355 ++++++++++++++ cfg/yolov4-csp-x-swish-frozen.cfg | 1556 +++++++++++++++++ cfg/yolov4-csp-x-swish.cfg | 1556 +++++++++++++++++ 7 files changed, 8943 insertions(+) create mode 100644 build/darknet/x64/cfg/yolov4-csp-swish.cfg create mode 100644 build/darknet/x64/cfg/yolov4-csp-x-swish-frozen.cfg create mode 100644 build/darknet/x64/cfg/yolov4-csp-x-swish.cfg create mode 100644 cfg/yolov4-csp-swish.cfg create mode 100644 cfg/yolov4-csp-x-swish-frozen.cfg create mode 100644 cfg/yolov4-csp-x-swish.cfg diff --git a/build/darknet/x64/cfg/yolov4-csp-swish.cfg b/build/darknet/x64/cfg/yolov4-csp-swish.cfg new file mode 100644 index 00000000000..2aab444ecd5 --- /dev/null +++ b/build/darknet/x64/cfg/yolov4-csp-swish.cfg @@ -0,0 +1,1355 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=640 +height=640 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +ema_alpha=0.9998 + +#optimized_memory=1 + + +# ============ Backbone ============ # + +# Stem + +# 0 +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=swish + +# P1 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=swish + +# 4 (previous+1+3k) +[shortcut] +from=-3 +activation=linear + +# P2 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-10 + +# Transition last + +# 17 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +# P3 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1 -(4+3k)] + +[route] +layers = -1,-28 + +# Transition last + +# 48 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +# P4 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1 -(3k+4)] + +[route] +layers = -1,-28 + +# Transition last + +# 79 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +# P5 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1 -(3k+4)] + +[route] +layers = -1,-16 + +# Transition last + +# 98 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=swish + +# ============ End of Backbone ============ # + +# ============ Neck ============ # + +# CSPSPP + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=swish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=swish + +[route] +layers = -1, -13 + +# 113 (previous+6+5+2k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +# End of CSPSPP + + +# FPN-4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[upsample] +stride=2 + +[route] +layers = 79 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=swish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=swish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -6 + +# Transition last + +# 127 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + + +# FPN-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[upsample] +stride=2 + +[route] +layers = 48 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=swish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=swish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -6 + +# Transition last + +# 141 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + + +# PAN-4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=swish + +[route] +layers = -1, 127 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=swish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=swish + +[route] +layers = -1,-6 + +# Transition last + +# 152 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + + +# PAN-5 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=swish + +[route] +layers = -1, 113 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=swish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=swish + +[route] +layers = -1,-6 + +# Transition last + +# 163 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish +stopbackward=900 + +# ============ End of Neck ============ # + +# ============ Head ============ # + +# YOLO-3 + +[route] +layers = 141 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=swish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-4 + +[route] +layers = 152 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=swish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-5 + +[route] +layers = 163 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=swish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 diff --git a/build/darknet/x64/cfg/yolov4-csp-x-swish-frozen.cfg b/build/darknet/x64/cfg/yolov4-csp-x-swish-frozen.cfg new file mode 100644 index 00000000000..838e0e1ac72 --- /dev/null +++ b/build/darknet/x64/cfg/yolov4-csp-x-swish-frozen.cfg @@ -0,0 +1,1556 @@ + +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=640 +height=640 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +ema_alpha=0.9998 + +#optimized_memory=1 + + +# ============ Backbone ============ # + +# Stem + +# 0 +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=swish + +# P1 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=2 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=40 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=swish + +# 4 (previous+1+3k) +[shortcut] +from=-3 +activation=linear + +# P2 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-13 + +# Transition last + +# 20 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +# P3 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1 -(4+3k)] + +[route] +layers = -1,-34 + +# Transition last + +# 57 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +# P4 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1 -(3k+4)] + +[route] +layers = -1,-34 + +# Transition last + +# 94 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +# P5 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1 -(3k+4)] + +[route] +layers = -1,-19 + +# Transition last + +# 116 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=swish + +# ============ End of Backbone ============ # + +# ============ Neck ============ # + +# CSPSPP + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[route] +layers = -1, -15 + +# 133 (previous+6+5+2k) +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +# End of CSPSPP + + +# FPN-4 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[upsample] +stride=2 + +[route] +layers = 94 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 149 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + + +# FPN-3 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[upsample] +stride=2 + +[route] +layers = 57 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=swish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 165 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + + +# PAN-4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=320 +activation=swish + +[route] +layers = -1, 149 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[route] +layers = -1,-8 + +# Transition last + +# 178 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + + +# PAN-5 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=640 +activation=swish + +[route] +layers = -1, 133 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[route] +layers = -1,-8 + +# Transition last + +# 191 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish +stopbackward=1 + +# ============ End of Neck ============ # + +# ============ Head ============ # + +# YOLO-3 + +[route] +layers = 165 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-4 + +[route] +layers = 178 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-5 + +[route] +layers = 191 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1280 +activation=swish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 diff --git a/build/darknet/x64/cfg/yolov4-csp-x-swish.cfg b/build/darknet/x64/cfg/yolov4-csp-x-swish.cfg new file mode 100644 index 00000000000..015db22c8b5 --- /dev/null +++ b/build/darknet/x64/cfg/yolov4-csp-x-swish.cfg @@ -0,0 +1,1556 @@ + +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=640 +height=640 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +ema_alpha=0.9998 + +#optimized_memory=1 + + +# ============ Backbone ============ # + +# Stem + +# 0 +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=swish + +# P1 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=2 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=40 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=swish + +# 4 (previous+1+3k) +[shortcut] +from=-3 +activation=linear + +# P2 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-13 + +# Transition last + +# 20 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +# P3 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1 -(4+3k)] + +[route] +layers = -1,-34 + +# Transition last + +# 57 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +# P4 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1 -(3k+4)] + +[route] +layers = -1,-34 + +# Transition last + +# 94 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +# P5 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1 -(3k+4)] + +[route] +layers = -1,-19 + +# Transition last + +# 116 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=swish + +# ============ End of Backbone ============ # + +# ============ Neck ============ # + +# CSPSPP + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[route] +layers = -1, -15 + +# 133 (previous+6+5+2k) +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +# End of CSPSPP + + +# FPN-4 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[upsample] +stride=2 + +[route] +layers = 94 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 149 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + + +# FPN-3 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[upsample] +stride=2 + +[route] +layers = 57 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=swish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 165 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + + +# PAN-4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=320 +activation=swish + +[route] +layers = -1, 149 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[route] +layers = -1,-8 + +# Transition last + +# 178 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + + +# PAN-5 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=640 +activation=swish + +[route] +layers = -1, 133 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[route] +layers = -1,-8 + +# Transition last + +# 191 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish +stopbackward=900 + +# ============ End of Neck ============ # + +# ============ Head ============ # + +# YOLO-3 + +[route] +layers = 165 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-4 + +[route] +layers = 178 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-5 + +[route] +layers = 191 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1280 +activation=swish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 diff --git a/build/darknet/x64/partial.cmd b/build/darknet/x64/partial.cmd index 6080cf68b89..ee0cb0d2437 100644 --- a/build/darknet/x64/partial.cmd +++ b/build/darknet/x64/partial.cmd @@ -2,6 +2,15 @@ rem Download weights for - DenseNet201, ResNet50 and ResNet152 by this link: htt rem Download Yolo/Tiny-yolo: https://pjreddie.com/darknet/yolo/ rem Download Yolo9000: http://pjreddie.com/media/files/yolo9000.weights + +darknet.exe partial cfg/yolov4-csp-swish.cfg yolov4-csp-swish.weights yolov4-csp-swish.conv.164 164 + + +darknet.exe partial cfg/yolov4-csp-x-swish.cfg yolov4-csp-x-swish.weights yolov4-csp-x-swish.conv.192 192 + + +pause + darknet.exe partial cfg/yolov4-csp.cfg yolov4-csp.weights yolov4-csp.conv.142 142 darknet.exe partial cfg/yolov4x-mish.cfg yolov4x-mish.weights yolov4x-mish.conv.166 166 diff --git a/cfg/yolov4-csp-swish.cfg b/cfg/yolov4-csp-swish.cfg new file mode 100644 index 00000000000..2aab444ecd5 --- /dev/null +++ b/cfg/yolov4-csp-swish.cfg @@ -0,0 +1,1355 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=640 +height=640 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +ema_alpha=0.9998 + +#optimized_memory=1 + + +# ============ Backbone ============ # + +# Stem + +# 0 +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=swish + +# P1 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=swish + +# 4 (previous+1+3k) +[shortcut] +from=-3 +activation=linear + +# P2 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-10 + +# Transition last + +# 17 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +# P3 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1 -(4+3k)] + +[route] +layers = -1,-28 + +# Transition last + +# 48 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +# P4 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1 -(3k+4)] + +[route] +layers = -1,-28 + +# Transition last + +# 79 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +# P5 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1 -(3k+4)] + +[route] +layers = -1,-16 + +# Transition last + +# 98 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=swish + +# ============ End of Backbone ============ # + +# ============ Neck ============ # + +# CSPSPP + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=swish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=swish + +[route] +layers = -1, -13 + +# 113 (previous+6+5+2k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +# End of CSPSPP + + +# FPN-4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[upsample] +stride=2 + +[route] +layers = 79 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=swish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=swish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -6 + +# Transition last + +# 127 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + + +# FPN-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[upsample] +stride=2 + +[route] +layers = 48 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=swish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=swish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -6 + +# Transition last + +# 141 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=swish + + +# PAN-4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=swish + +[route] +layers = -1, 127 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=swish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=swish + +[route] +layers = -1,-6 + +# Transition last + +# 152 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=swish + + +# PAN-5 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=swish + +[route] +layers = -1, 113 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=swish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=swish + +[route] +layers = -1,-6 + +# Transition last + +# 163 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=swish +stopbackward=900 + +# ============ End of Neck ============ # + +# ============ Head ============ # + +# YOLO-3 + +[route] +layers = 141 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=swish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-4 + +[route] +layers = 152 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=swish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-5 + +[route] +layers = 163 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=swish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 diff --git a/cfg/yolov4-csp-x-swish-frozen.cfg b/cfg/yolov4-csp-x-swish-frozen.cfg new file mode 100644 index 00000000000..838e0e1ac72 --- /dev/null +++ b/cfg/yolov4-csp-x-swish-frozen.cfg @@ -0,0 +1,1556 @@ + +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=640 +height=640 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +ema_alpha=0.9998 + +#optimized_memory=1 + + +# ============ Backbone ============ # + +# Stem + +# 0 +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=swish + +# P1 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=2 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=40 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=swish + +# 4 (previous+1+3k) +[shortcut] +from=-3 +activation=linear + +# P2 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-13 + +# Transition last + +# 20 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +# P3 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1 -(4+3k)] + +[route] +layers = -1,-34 + +# Transition last + +# 57 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +# P4 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1 -(3k+4)] + +[route] +layers = -1,-34 + +# Transition last + +# 94 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +# P5 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1 -(3k+4)] + +[route] +layers = -1,-19 + +# Transition last + +# 116 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=swish + +# ============ End of Backbone ============ # + +# ============ Neck ============ # + +# CSPSPP + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[route] +layers = -1, -15 + +# 133 (previous+6+5+2k) +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +# End of CSPSPP + + +# FPN-4 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[upsample] +stride=2 + +[route] +layers = 94 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 149 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + + +# FPN-3 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[upsample] +stride=2 + +[route] +layers = 57 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=swish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 165 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + + +# PAN-4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=320 +activation=swish + +[route] +layers = -1, 149 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[route] +layers = -1,-8 + +# Transition last + +# 178 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + + +# PAN-5 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=640 +activation=swish + +[route] +layers = -1, 133 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[route] +layers = -1,-8 + +# Transition last + +# 191 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish +stopbackward=1 + +# ============ End of Neck ============ # + +# ============ Head ============ # + +# YOLO-3 + +[route] +layers = 165 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-4 + +[route] +layers = 178 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-5 + +[route] +layers = 191 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1280 +activation=swish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 diff --git a/cfg/yolov4-csp-x-swish.cfg b/cfg/yolov4-csp-x-swish.cfg new file mode 100644 index 00000000000..015db22c8b5 --- /dev/null +++ b/cfg/yolov4-csp-x-swish.cfg @@ -0,0 +1,1556 @@ + +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=640 +height=640 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +ema_alpha=0.9998 + +#optimized_memory=1 + + +# ============ Backbone ============ # + +# Stem + +# 0 +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=swish + +# P1 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=2 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=40 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=swish + +# 4 (previous+1+3k) +[shortcut] +from=-3 +activation=linear + +# P2 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-13 + +# Transition last + +# 20 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +# P3 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1 -(4+3k)] + +[route] +layers = -1,-34 + +# Transition last + +# 57 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +# P4 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1 -(3k+4)] + +[route] +layers = -1,-34 + +# Transition last + +# 94 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +# P5 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=swish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +# Merge [-1 -(3k+4)] + +[route] +layers = -1,-19 + +# Transition last + +# 116 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=swish + +# ============ End of Backbone ============ # + +# ============ Neck ============ # + +# CSPSPP + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[route] +layers = -1, -15 + +# 133 (previous+6+5+2k) +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +# End of CSPSPP + + +# FPN-4 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[upsample] +stride=2 + +[route] +layers = 94 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 149 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + + +# FPN-3 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[upsample] +stride=2 + +[route] +layers = 57 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=swish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=swish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 165 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=swish + + +# PAN-4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=320 +activation=swish + +[route] +layers = -1, 149 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[route] +layers = -1,-8 + +# Transition last + +# 178 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=swish + + +# PAN-5 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=640 +activation=swish + +[route] +layers = -1, 133 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +# Split + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[route] +layers = -1,-8 + +# Transition last + +# 191 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=swish +stopbackward=900 + +# ============ End of Neck ============ # + +# ============ Head ============ # + +# YOLO-3 + +[route] +layers = 165 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=swish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-4 + +[route] +layers = 178 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=swish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-5 + +[route] +layers = 191 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1280 +activation=swish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 From 1be96802a030ae2216c7e19b7523e1e2f0948aaf Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 12 Jul 2021 05:40:11 +0300 Subject: [PATCH 1272/1365] Update README.md --- README.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/README.md b/README.md index bb545cf097a..29cdf564d1c 100644 --- a/README.md +++ b/README.md @@ -152,6 +152,12 @@ FPS on RTX 2070 (R) and Tesla V100 (V): - [yolov4-p5.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-p5.cfg) - 896x896 - **70.0% mAP@0.5 (51.6% AP@0.5:0.95) - 43(V) FPS** - xxx BFlops (xxx FMA) - 271 MB: [yolov4-p5.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-p5.weights) - pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-p5.conv.232 +- [yolov4-csp-x-swish.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-csp-x-swish.cfg) - 640x640 - **69.9% mAP@0.5 (51.5% AP@0.5:0.95) - 23(R) FPS / 50(V) FPS** - 221 BFlops (110 FMA) - 381 MB: [yolov4-csp-x-swish.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp-x-swish.weights) + - pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp-x-swish.conv.192 + +- [yolov4-csp-swish.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-csp-swish.cfg) - 640x640 - **68.7% mAP@0.5 (50.0% AP@0.5:0.95) - 70(V) FPS** - 120 (60 FMA) - 202 MB: [yolov4-csp-swish.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp-swish.weights) + - pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp-swish.conv.164 + - [yolov4x-mish.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4x-mish.cfg) - 640x640 - **68.5% mAP@0.5 (50.1% AP@0.5:0.95) - 23(R) FPS / 50(V) FPS** - 221 BFlops (110 FMA) - 381 MB: [yolov4x-mish.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.weights) - pre-trained weights for training: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.conv.166 From 7d75fe3c344eb280f1a9534f6b91a87aa61fa34d Mon Sep 17 00:00:00 2001 From: Yusei-Yagi <45314822+Yamahitsuji@users.noreply.github.com> Date: Fri, 16 Jul 2021 06:09:42 +0900 Subject: [PATCH 1273/1365] Fix bugs in darknet_video.py (#7908) * fix VideoWriter frame size * fix saved image color --- darknet_video.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/darknet_video.py b/darknet_video.py index 04895133f97..5e7036203a7 100644 --- a/darknet_video.py +++ b/darknet_video.py @@ -136,7 +136,7 @@ def inference(darknet_image_queue, detections_queue, fps_queue): def drawing(frame_queue, detections_queue, fps_queue): random.seed(3) # deterministic bbox colors - video = set_saved_video(cap, args.out_filename, (darknet_width, darknet_height)) + video = set_saved_video(cap, args.out_filename, (video_width, video_height)) while cap.isOpened(): frame = frame_queue.get() detections = detections_queue.get() @@ -149,7 +149,6 @@ def drawing(frame_queue, detections_queue, fps_queue): image = darknet.draw_boxes(detections_adjusted, frame, class_colors) if not args.dont_show: cv2.imshow('Inference', image) - image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) if args.out_filename is not None: video.write(image) if cv2.waitKey(fps) == 27: @@ -177,6 +176,8 @@ def drawing(frame_queue, detections_queue, fps_queue): darknet_height = darknet.network_height(network) input_path = str2int(args.input) cap = cv2.VideoCapture(input_path) + video_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + video_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) Thread(target=video_capture, args=(frame_queue, darknet_image_queue)).start() Thread(target=inference, args=(darknet_image_queue, detections_queue, fps_queue)).start() Thread(target=drawing, args=(frame_queue, detections_queue, fps_queue)).start() From 9e839c597cf0b48c9d550b25bb30cab9548506f0 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Fri, 16 Jul 2021 00:11:43 +0300 Subject: [PATCH 1274/1365] darknet_video.py --- build/darknet/x64/darknet_video.py | 81 ++++++++++++++++++++++++------ 1 file changed, 65 insertions(+), 16 deletions(-) diff --git a/build/darknet/x64/darknet_video.py b/build/darknet/x64/darknet_video.py index de96d709ed1..5e7036203a7 100644 --- a/build/darknet/x64/darknet_video.py +++ b/build/darknet/x64/darknet_video.py @@ -60,17 +60,63 @@ def set_saved_video(input_video, output_video, size): return video +def convert2relative(bbox): + """ + YOLO format use relative coordinates for annotation + """ + x, y, w, h = bbox + _height = darknet_height + _width = darknet_width + return x/_width, y/_height, w/_width, h/_height + + +def convert2original(image, bbox): + x, y, w, h = convert2relative(bbox) + + image_h, image_w, __ = image.shape + + orig_x = int(x * image_w) + orig_y = int(y * image_h) + orig_width = int(w * image_w) + orig_height = int(h * image_h) + + bbox_converted = (orig_x, orig_y, orig_width, orig_height) + + return bbox_converted + + +def convert4cropping(image, bbox): + x, y, w, h = convert2relative(bbox) + + image_h, image_w, __ = image.shape + + orig_left = int((x - w / 2.) * image_w) + orig_right = int((x + w / 2.) * image_w) + orig_top = int((y - h / 2.) * image_h) + orig_bottom = int((y + h / 2.) * image_h) + + if (orig_left < 0): orig_left = 0 + if (orig_right > image_w - 1): orig_right = image_w - 1 + if (orig_top < 0): orig_top = 0 + if (orig_bottom > image_h - 1): orig_bottom = image_h - 1 + + bbox_cropping = (orig_left, orig_top, orig_right, orig_bottom) + + return bbox_cropping + + def video_capture(frame_queue, darknet_image_queue): while cap.isOpened(): ret, frame = cap.read() if not ret: break frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) - frame_resized = cv2.resize(frame_rgb, (width, height), + frame_resized = cv2.resize(frame_rgb, (darknet_width, darknet_height), interpolation=cv2.INTER_LINEAR) - frame_queue.put(frame_resized) - darknet.copy_image_from_bytes(darknet_image, frame_resized.tobytes()) - darknet_image_queue.put(darknet_image) + frame_queue.put(frame) + img_for_detect = darknet.make_image(darknet_width, darknet_height, 3) + darknet.copy_image_from_bytes(img_for_detect, frame_resized.tobytes()) + darknet_image_queue.put(img_for_detect) cap.release() @@ -84,23 +130,27 @@ def inference(darknet_image_queue, detections_queue, fps_queue): fps_queue.put(fps) print("FPS: {}".format(fps)) darknet.print_detections(detections, args.ext_output) + darknet.free_image(darknet_image) cap.release() def drawing(frame_queue, detections_queue, fps_queue): random.seed(3) # deterministic bbox colors - video = set_saved_video(cap, args.out_filename, (width, height)) + video = set_saved_video(cap, args.out_filename, (video_width, video_height)) while cap.isOpened(): - frame_resized = frame_queue.get() + frame = frame_queue.get() detections = detections_queue.get() fps = fps_queue.get() - if frame_resized is not None: - image = darknet.draw_boxes(detections, frame_resized, class_colors) - image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) - if args.out_filename is not None: - video.write(image) + detections_adjusted = [] + if frame is not None: + for label, confidence, bbox in detections: + bbox_adjusted = convert2original(frame, bbox) + detections_adjusted.append((str(label), confidence, bbox_adjusted)) + image = darknet.draw_boxes(detections_adjusted, frame, class_colors) if not args.dont_show: cv2.imshow('Inference', image) + if args.out_filename is not None: + video.write(image) if cv2.waitKey(fps) == 27: break cap.release() @@ -122,13 +172,12 @@ def drawing(frame_queue, detections_queue, fps_queue): args.weights, batch_size=1 ) - # Darknet doesn't accept numpy images. - # Create one with image we reuse for each detect - width = darknet.network_width(network) - height = darknet.network_height(network) - darknet_image = darknet.make_image(width, height, 3) + darknet_width = darknet.network_width(network) + darknet_height = darknet.network_height(network) input_path = str2int(args.input) cap = cv2.VideoCapture(input_path) + video_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + video_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) Thread(target=video_capture, args=(frame_queue, darknet_image_queue)).start() Thread(target=inference, args=(darknet_image_queue, detections_queue, fps_queue)).start() Thread(target=drawing, args=(frame_queue, detections_queue, fps_queue)).start() From 01cf2b4c86cdbef568430ef86ed5721317bc9730 Mon Sep 17 00:00:00 2001 From: Marcus Turewicz <24448509+marcusturewicz@users.noreply.github.com> Date: Fri, 16 Jul 2021 07:15:47 +1000 Subject: [PATCH 1275/1365] Adds probability to precision recall curve (#7650) --- src/detector.c | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/src/detector.c b/src/detector.c index a2fdf0bf3cd..23d43cc2474 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1204,6 +1204,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa qsort(detections, detections_count, sizeof(box_prob), detections_comparator); typedef struct { + double prob; double precision; double recall; int tp, fp, fn; @@ -1238,6 +1239,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa } box_prob d = detections[rank]; + pr[d.class_id][rank].prob = d.p; // if (detected && isn't detected before) if (d.truth_flag == 1) { if (truth_flags[d.unique_truth_index] == 0) @@ -1310,15 +1312,17 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa for (point = 0; point < map_points; ++point) { double cur_recall = point * 1.0 / (map_points-1); double cur_precision = 0; + double cur_prob = 0; for (rank = 0; rank < detections_count; ++rank) { if (pr[i][rank].recall >= cur_recall) { // > or >= if (pr[i][rank].precision > cur_precision) { cur_precision = pr[i][rank].precision; + cur_prob = pr[i][rank].prob; } } } - //printf("class_id = %d, point = %d, cur_recall = %.4f, cur_precision = %.4f \n", i, point, cur_recall, cur_precision); + //printf("class_id = %d, point = %d, cur_prob = %.4f, cur_recall = %.4f, cur_precision = %.4f \n", i, point, cur_prob, cur_recall, cur_precision); avg_precision += cur_precision; } From 4361b7a3227faeed99c3e949677fa68dff441fe9 Mon Sep 17 00:00:00 2001 From: 8k <5159915+7680x4320@users.noreply.github.com> Date: Fri, 16 Jul 2021 06:28:25 +0900 Subject: [PATCH 1276/1365] add thresholds (#6831) --- include/darknet.h | 2 +- src/detector.c | 10 +++++----- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 6daaaf1ca65..9ded8dfeaf5 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -1047,7 +1047,7 @@ LIB_API void reset_rnn(network *net); LIB_API float *network_predict_image(network *net, image im); LIB_API float *network_predict_image_letterbox(network *net, image im); LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, const int map_points, int letter_box, network *existing_net); -LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port, int show_imgs, int benchmark_layers, char* chart_path); +LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, float thresh, float iou_thresh, int mjpeg_port, int show_imgs, int benchmark_layers, char* chart_path); LIB_API void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile, int letter_box, int benchmark_layers); LIB_API int network_width(network *net); diff --git a/src/detector.c b/src/detector.c index 23d43cc2474..ec7f72a3c32 100644 --- a/src/detector.c +++ b/src/detector.c @@ -23,7 +23,7 @@ int check_mistakes = 0; static int coco_ids[] = { 1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90 }; -void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port, int show_imgs, int benchmark_layers, char* chart_path) +void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, float thresh, float iou_thresh, int mjpeg_port, int show_imgs, int benchmark_layers, char* chart_path) { list *options = read_data_cfg(datacfg); char *train_images = option_find_str(options, "train", "data/train.txt"); @@ -305,7 +305,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i //next_map_calc = fmax(next_map_calc, 400); if (calc_map) { printf("\n (next mAP calculation at %d iterations) ", next_map_calc); - if (mean_average_precision > 0) printf("\n Last accuracy mAP@0.5 = %2.2f %%, best = %2.2f %% ", mean_average_precision * 100, best_map * 100); + if (mean_average_precision > 0) printf("\n Last accuracy mAP@%0.2f = %2.2f %%, best = %2.2f %% ", iou_thresh, mean_average_precision * 100, best_map * 100); } if (net.cudnn_half) { @@ -353,8 +353,8 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i //network net_combined = combine_train_valid_networks(net, net_map); iter_map = iteration; - mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, 0, net.letter_box, &net_map);// &net_combined); - printf("\n mean_average_precision (mAP@0.5) = %f \n", mean_average_precision); + mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, thresh, iou_thresh, 0, net.letter_box, &net_map);// &net_combined); + printf("\n mean_average_precision (mAP@%0.2f) = %f \n", iou_thresh, mean_average_precision); if (mean_average_precision > best_map) { best_map = mean_average_precision; printf("New best mAP!\n"); @@ -2017,7 +2017,7 @@ void run_detector(int argc, char **argv) if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0; char *filename = (argc > 6) ? argv[6] : 0; if (0 == strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels, outfile, letter_box, benchmark_layers); - else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map, mjpeg_port, show_imgs, benchmark_layers, chart_path); + else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map, thresh, iou_thresh, mjpeg_port, show_imgs, benchmark_layers, chart_path); else if (0 == strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile); else if (0 == strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights); else if (0 == strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh, iou_thresh, map_points, letter_box, NULL); From cf7b4585438b61d4f8568b5a24b5e93a89461c13 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 15 Jul 2021 23:30:40 +0200 Subject: [PATCH 1277/1365] [cmake] update modules (#7891) * [cmake] update modules * [cmake] use proper module name when using vcpkg libs * apply fixes from review --- CMakeLists.txt | 8 +- cmake/Modules/FindCUDNN.cmake | 68 ++++--- cmake/Modules/FindPThreads4W.cmake | 229 +++++++++++++++++++++++ cmake/Modules/FindPThreads_windows.cmake | 83 -------- 4 files changed, 276 insertions(+), 112 deletions(-) create mode 100644 cmake/Modules/FindPThreads4W.cmake delete mode 100644 cmake/Modules/FindPThreads_windows.cmake diff --git a/CMakeLists.txt b/CMakeLists.txt index 0e1abf32d9c..26e435e4263 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -181,15 +181,17 @@ else() endif() if(USE_INTEGRATED_LIBS) - set(PThreads_windows_DIR ${CMAKE_CURRENT_LIST_DIR}/3rdparty/pthreads CACHE PATH "Path where pthreads for windows can be located") + set(PThreads4W_ROOT ${CMAKE_CURRENT_LIST_DIR}/3rdparty/pthreads CACHE PATH "Path where pthreads for windows can be located") endif() set(Stb_DIR ${CMAKE_CURRENT_LIST_DIR}/3rdparty/stb CACHE PATH "Path where Stb image library can be located") set(CMAKE_DEBUG_POSTFIX d) set(CMAKE_THREAD_PREFER_PTHREAD ON) find_package(Threads REQUIRED) -if(MSVC) - find_package(PThreads_windows REQUIRED) +if(MSVC AND USE_INTEGRATED_LIBS) + find_package(PThreads4W REQUIRED) +elseif(MSVC) + find_package(pthreads REQUIRED) endif() if(ENABLE_OPENCV) find_package(OpenCV REQUIRED) diff --git a/cmake/Modules/FindCUDNN.cmake b/cmake/Modules/FindCUDNN.cmake index 871295faaee..7a692b055d5 100644 --- a/cmake/Modules/FindCUDNN.cmake +++ b/cmake/Modules/FindCUDNN.cmake @@ -19,39 +19,55 @@ # ``CUDNN_LIBRARIES`` # The CUDNN libraries. # +# ``CuDNN::CuDNN`` +# The CUDNN target +# include(FindPackageHandleStandardArgs) -if(NOT CUDNN_INCLUDE_DIR) - find_path(CUDNN_INCLUDE_DIR cudnn.h - HINTS ${CUDA_HOME} ${CUDA_TOOLKIT_ROOT_DIR} $ENV{cudnn} $ENV{CUDNN} - PATH_SUFFIXES cuda/include include) -endif() - -if(NOT CUDNN_LIBRARY) - find_library(CUDNN_LIBRARY cudnn - HINTS ${CUDA_HOME} ${CUDA_TOOLKIT_ROOT_DIR} $ENV{cudnn} $ENV{CUDNN} - PATH_SUFFIXES lib lib64 cuda/lib cuda/lib64 lib/x64) -endif() - +find_path(CUDNN_INCLUDE_DIR NAMES cudnn.h cudnn_v8.h cudnn_v7.h + HINTS $ENV{CUDA_PATH} $ENV{CUDA_TOOLKIT_ROOT_DIR} $ENV{CUDA_HOME} $ENV{CUDNN_ROOT_DIR} /usr/include + PATH_SUFFIXES cuda/include include) +find_library(CUDNN_LIBRARY NAMES cudnn cudnn8 cudnn7 + HINTS $ENV{CUDA_PATH} $ENV{CUDA_TOOLKIT_ROOT_DIR} $ENV{CUDA_HOME} $ENV{CUDNN_ROOT_DIR} /usr/lib/x86_64-linux-gnu/ + PATH_SUFFIXES lib lib64 cuda/lib cuda/lib64 lib/x64 cuda/lib/x64) if(EXISTS "${CUDNN_INCLUDE_DIR}/cudnn.h") file(READ ${CUDNN_INCLUDE_DIR}/cudnn.h CUDNN_HEADER_CONTENTS) - string(REGEX MATCH "define CUDNN_MAJOR * +([0-9]+)" - CUDNN_VERSION_MAJOR "${CUDNN_HEADER_CONTENTS}") - string(REGEX REPLACE "define CUDNN_MAJOR * +([0-9]+)" "\\1" - CUDNN_VERSION_MAJOR "${CUDNN_VERSION_MAJOR}") - string(REGEX MATCH "define CUDNN_MINOR * +([0-9]+)" - CUDNN_VERSION_MINOR "${CUDNN_HEADER_CONTENTS}") - string(REGEX REPLACE "define CUDNN_MINOR * +([0-9]+)" "\\1" - CUDNN_VERSION_MINOR "${CUDNN_VERSION_MINOR}") - string(REGEX MATCH "define CUDNN_PATCHLEVEL * +([0-9]+)" - CUDNN_VERSION_PATCH "${CUDNN_HEADER_CONTENTS}") - string(REGEX REPLACE "define CUDNN_PATCHLEVEL * +([0-9]+)" "\\1" - CUDNN_VERSION_PATCH "${CUDNN_VERSION_PATCH}") - if(NOT CUDNN_VERSION_MAJOR) +elseif(EXISTS "${CUDNN_INCLUDE_DIR}/cudnn_v8.h") + file(READ ${CUDNN_INCLUDE_DIR}/cudnn_v8.h CUDNN_HEADER_CONTENTS) +elseif(EXISTS "${CUDNN_INCLUDE_DIR}/cudnn_v7.h") + file(READ ${CUDNN_INCLUDE_DIR}/cudnn_v7.h CUDNN_HEADER_CONTENTS) +endif() +if(EXISTS "${CUDNN_INCLUDE_DIR}/cudnn_version.h") + file(READ "${CUDNN_INCLUDE_DIR}/cudnn_version.h" CUDNN_VERSION_H_CONTENTS) + string(APPEND CUDNN_HEADER_CONTENTS "${CUDNN_VERSION_H_CONTENTS}") + unset(CUDNN_VERSION_H_CONTENTS) +elseif(EXISTS "${CUDNN_INCLUDE_DIR}/cudnn_version_v8.h") + file(READ "${CUDNN_INCLUDE_DIR}/cudnn_version_v8.h" CUDNN_VERSION_H_CONTENTS) + string(APPEND CUDNN_HEADER_CONTENTS "${CUDNN_VERSION_H_CONTENTS}") + unset(CUDNN_VERSION_H_CONTENTS) +elseif(EXISTS "${CUDNN_INCLUDE_DIR}/cudnn_version_v7.h") + file(READ "${CUDNN_INCLUDE_DIR}/cudnn_version_v7.h" CUDNN_VERSION_H_CONTENTS) + string(APPEND CUDNN_HEADER_CONTENTS "${CUDNN_VERSION_H_CONTENTS}") + unset(CUDNN_VERSION_H_CONTENTS) +endif() +if(CUDNN_HEADER_CONTENTS) + string(REGEX MATCH "define CUDNN_MAJOR * +([0-9]+)" + _CUDNN_VERSION_MAJOR "${CUDNN_HEADER_CONTENTS}") + string(REGEX REPLACE "define CUDNN_MAJOR * +([0-9]+)" "\\1" + _CUDNN_VERSION_MAJOR "${_CUDNN_VERSION_MAJOR}") + string(REGEX MATCH "define CUDNN_MINOR * +([0-9]+)" + _CUDNN_VERSION_MINOR "${CUDNN_HEADER_CONTENTS}") + string(REGEX REPLACE "define CUDNN_MINOR * +([0-9]+)" "\\1" + _CUDNN_VERSION_MINOR "${_CUDNN_VERSION_MINOR}") + string(REGEX MATCH "define CUDNN_PATCHLEVEL * +([0-9]+)" + _CUDNN_VERSION_PATCH "${CUDNN_HEADER_CONTENTS}") + string(REGEX REPLACE "define CUDNN_PATCHLEVEL * +([0-9]+)" "\\1" + _CUDNN_VERSION_PATCH "${_CUDNN_VERSION_PATCH}") + if(NOT _CUDNN_VERSION_MAJOR) set(CUDNN_VERSION "?") else() - set(CUDNN_VERSION "${CUDNN_VERSION_MAJOR}.${CUDNN_VERSION_MINOR}.${CUDNN_VERSION_PATCH}") + set(CUDNN_VERSION "${_CUDNN_VERSION_MAJOR}.${_CUDNN_VERSION_MINOR}.${_CUDNN_VERSION_PATCH}") endif() endif() diff --git a/cmake/Modules/FindPThreads4W.cmake b/cmake/Modules/FindPThreads4W.cmake new file mode 100644 index 00000000000..18e2b77c814 --- /dev/null +++ b/cmake/Modules/FindPThreads4W.cmake @@ -0,0 +1,229 @@ +# Distributed under the OSI-approved BSD 3-Clause License. +# Copyright Stefano Sinigardi + +#.rst: +# FindPThreads4W +# ------------ +# +# Find the PThread4W includes and library. +# +# Result Variables +# ^^^^^^^^^^^^^^^^ +# +# This script defines the following variables: +# +# ``PThreads4W_FOUND`` +# True if PThreads4W library found +# +# ``PThreads4W_VERSION`` +# Containing the PThreads4W version tag (manually defined) +# +# ``PThreads4W_INCLUDE_DIR`` +# Location of PThreads4W headers +# +# ``PThreads4W_LIBRARY`` +# List of libraries to link with when using PThreads4W (no exception handling) +# +# ``PThreads4W_CXXEXC_LIBRARY`` +# List of libraries to link with when using PThreads4W (C++ exception handling) +# +# ``PThreads4W_STRUCTEXC_LIBRARY`` +# List of libraries to link with when using PThreads4W (struct exception handling) +# +# Result Targets +# ^^^^^^^^^^^^^^^^ +# +# This script defines the following targets: +# +# ``PThreads4W::PThreads4W`` +# Target to use PThreads4W (no exception handling) +# +# ``PThreads4W::PThreads4W_CXXEXC`` +# Target to use PThreads4W (C++ exception handling) +# +# ``PThreads4W::PThreads4W_STRUCTEXC`` +# Target to use PThreads4W (struct exception handling) +# + +include(${CMAKE_ROOT}/Modules/FindPackageHandleStandardArgs.cmake) +include(${CMAKE_ROOT}/Modules/SelectLibraryConfigurations.cmake) + +if(NOT PThreads4W_INCLUDE_DIR) + find_path(PThreads4W_INCLUDE_DIR NAMES pthread.h) +endif() + +set(PThreads4W_MAJOR_VERSION 2) +set(PThreads4W_MINOR_VERSION 0) +set(PThreads4W_PATCH_VERSION 0) +set(PThreads4W_VERSION "${PThreads4W_MAJOR_VERSION}.${PThreads4W_MINOR_VERSION}.${PThreads4W_PATCH_VERSION}") + +# Allow libraries to be set manually +if(NOT PThreads4W_LIBRARY) + find_library(PThreads4W_LIBRARY NAMES pthreadVC${PThreads4W_MAJOR_VERSION}) + find_library(PThreads4W_LIBRARY_RELEASE NAMES pthreadVC${PThreads4W_MAJOR_VERSION}) +endif() +if(NOT PThreads4W_CXXEXC_LIBRARY) + find_library(PThreads4W_CXXEXC_LIBRARY_RELEASE NAMES pthreadVCE${PThreads4W_MAJOR_VERSION}) + find_library(PThreads4W_CXXEXC_LIBRARY_DEBUG NAMES pthreadVCE${PThreads4W_MAJOR_VERSION}d) + select_library_configurations(PThreads4W_CXXEXC) +endif() +if(NOT PThreads4W_STRUCTEXC_LIBRARY) + find_library(PThreads4W_STRUCTEXC_LIBRARY_RELEASE NAMES pthreadVSE${PThreads4W_MAJOR_VERSION}) + find_library(PThreads4W_STRUCTEXC_LIBRARY_DEBUG NAMES pthreadVSE${PThreads4W_MAJOR_VERSION}d) + select_library_configurations(PThreads4W_STRUCTEXC) +endif() + +find_package_handle_standard_args(PThreads4W DEFAULT_MSG PThreads4W_LIBRARY PThreads4W_INCLUDE_DIR) +mark_as_advanced(PThreads4W_INCLUDE_DIR PThreads4W_LIBRARY) + +set(PThreads4W_DLL_DIR ${PThreads4W_INCLUDE_DIR}) +list(TRANSFORM PThreads4W_DLL_DIR APPEND "/../bin") +message(STATUS "PThreads4W_DLL_DIR: ${PThreads4W_DLL_DIR}") +set(PThreads4W_DEBUG_DLL_DIR ${PThreads4W_INCLUDE_DIR}) +list(TRANSFORM PThreads4W_DEBUG_DLL_DIR APPEND "/../debug/bin") +message(STATUS "PThreads4W_DEBUG_DLL_DIR: ${PThreads4W_DEBUG_DLL_DIR}") + +find_file(PThreads4W_LIBRARY_RELEASE_DLL NAMES pthreadVC${PThreads4W_MAJOR_VERSION}.dll PATHS ${PThreads4W_DLL_DIR}) +find_file(PThreads4W_LIBRARY_DEBUG_DLL NAMES pthreadVC${PThreads4W_MAJOR_VERSION}d.dll PATHS ${PThreads4W_DEBUG_DLL_DIR}) +find_file(PThreads4W_CXXEXC_LIBRARY_RELEASE_DLL NAMES pthreadVCE${PThreads4W_MAJOR_VERSION}.dll PATHS ${PThreads4W_DLL_DIR}) +find_file(PThreads4W_CXXEXC_LIBRARY_DEBUG_DLL NAMES pthreadVCE${PThreads4W_MAJOR_VERSION}d.dll PATHS ${PThreads4W_DEBUG_DLL_DIR}) +find_file(PThreads4W_STRUCTEXC_LIBRARY_RELEASE_DLL NAMES pthreadVSE${PThreads4W_MAJOR_VERSION}.dll PATHS ${PThreads4W_DLL_DIR}) +find_file(PThreads4W_STRUCTEXC_LIBRARY_DEBUG_DLL NAMES pthreadVSE${PThreads4W_MAJOR_VERSION}d.dll PATHS ${PThreads4W_DEBUG_DLL_DIR}) + +#Compatibility definitions, deprecated +set(PTHREAD_INCLUDE_DIR ${PThreads4W_INCLUDE_DIR} CACHE PATH "") +set(PTHREADS_INCLUDE_DIR ${PThreads4W_INCLUDE_DIR} CACHE PATH "") +set(PThreads_windows_INCLUDE_DIR ${PThreads4W_INCLUDE_DIR} CACHE PATH "") +set(PTHREAD_LIBRARIES ${PThreads4W_LIBRARY} CACHE STRING "") +set(PTHREADS_LIBRARIES ${PThreads4W_LIBRARY} CACHE STRING "") +set(PTHREAD_LIBRARY ${PThreads4W_LIBRARY} CACHE STRING "") +set(PTHREADS_LIBRARY ${PThreads4W_LIBRARY} CACHE STRING "") +set(LIBPTHREAD ${PThreads4W_LIBRARY} CACHE STRING "") +set(LIBPTHREADS ${PThreads4W_LIBRARY} CACHE STRING "") +set(PThreads_windows_LIBRARY ${PThreads4W_LIBRARY} CACHE STRING "") +set(PThreads_VERSION "${PThreads4W_VERSION}") +if(PThreads4W_FOUND) + set(PThreads_windows_FOUND TRUE) +endif() + +#TARGETS +if( PThreads4W_FOUND AND NOT TARGET PThreads4W::PThreads4W_CXXEXC ) + if( EXISTS "${PThreads4W_CXXEXC_LIBRARY_RELEASE_DLL}" ) + add_library( PThreads4W::PThreads4W_CXXEXC SHARED IMPORTED ) + set_target_properties( PThreads4W::PThreads4W_CXXEXC PROPERTIES + IMPORTED_LOCATION_RELEASE "${PThreads4W_CXXEXC_LIBRARY_RELEASE_DLL}" + IMPORTED_IMPLIB "${PThreads4W_CXXEXC_LIBRARY_RELEASE}" + INTERFACE_INCLUDE_DIRECTORIES "${PThreads4W_INCLUDE_DIR}" + IMPORTED_CONFIGURATIONS Release + IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) + if( EXISTS "${PThreads4W_CXXEXC_LIBRARY_DEBUG_DLL}" ) + set_property( TARGET PThreads4W::PThreads4W_CXXEXC APPEND PROPERTY IMPORTED_CONFIGURATIONS Debug ) + set_target_properties( PThreads4W::PThreads4W_CXXEXC PROPERTIES + IMPORTED_LOCATION_DEBUG "${PThreads4W_CXXEXC_LIBRARY_DEBUG_DLL}" + IMPORTED_IMPLIB_DEBUG "${PThreads4W_CXXEXC_LIBRARY_DEBUG}" ) + endif() + else() + add_library( PThreads4W::PThreads4W_CXXEXC UNKNOWN IMPORTED ) + set_target_properties( PThreads4W::PThreads4W_CXXEXC PROPERTIES + IMPORTED_LOCATION_RELEASE "${PThreads4W_CXXEXC_LIBRARY_RELEASE}" + INTERFACE_INCLUDE_DIRECTORIES "${PThreads4W_INCLUDE_DIR}" + IMPORTED_CONFIGURATIONS Release + IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) + if( EXISTS "${PThreads4W_CXXEXC_LIBRARY_DEBUG}" ) + set_property( TARGET PThreads4W::PThreads4W_CXXEXC APPEND PROPERTY IMPORTED_CONFIGURATIONS Debug ) + set_target_properties( PThreads4W::PThreads4W_CXXEXC PROPERTIES + IMPORTED_LOCATION_DEBUG "${PThreads4W_CXXEXC_LIBRARY_DEBUG}" ) + endif() + endif() +endif() + +if( PThreads4W_FOUND AND NOT TARGET PThreads4W::PThreads4W_STRUCTEXC ) + if( EXISTS "${PThreads4W_STRUCTEXC_LIBRARY_RELEASE_DLL}" ) + add_library( PThreads4W::PThreads4W_STRUCTEXC SHARED IMPORTED ) + set_target_properties( PThreads4W::PThreads4W_STRUCTEXC PROPERTIES + IMPORTED_LOCATION_RELEASE "${PThreads4W_STRUCTEXC_LIBRARY_RELEASE_DLL}" + IMPORTED_IMPLIB "${PThreads4W_STRUCTEXC_LIBRARY_RELEASE}" + INTERFACE_INCLUDE_DIRECTORIES "${PThreads4W_INCLUDE_DIR}" + IMPORTED_CONFIGURATIONS Release + IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) + if( EXISTS "${PThreads4W_STRUCTEXC_LIBRARY_DEBUG_DLL}" ) + set_property( TARGET PThreads4W::PThreads4W_STRUCTEXC APPEND PROPERTY IMPORTED_CONFIGURATIONS Debug ) + set_target_properties( PThreads4W::PThreads4W_STRUCTEXC PROPERTIES + IMPORTED_LOCATION_DEBUG "${PThreads4W_STRUCTEXC_LIBRARY_DEBUG_DLL}" + IMPORTED_IMPLIB_DEBUG "${PThreads4W_STRUCTEXC_LIBRARY_DEBUG}" ) + endif() + else() + add_library( PThreads4W::PThreads4W_STRUCTEXC UNKNOWN IMPORTED ) + set_target_properties( PThreads4W::PThreads4W_STRUCTEXC PROPERTIES + IMPORTED_LOCATION_RELEASE "${PThreads4W_STRUCTEXC_LIBRARY_RELEASE}" + INTERFACE_INCLUDE_DIRECTORIES "${PThreads4W_INCLUDE_DIR}" + IMPORTED_CONFIGURATIONS Release + IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) + if( EXISTS "${PThreads4W_STRUCTEXC_LIBRARY_DEBUG}" ) + set_property( TARGET PThreads4W::PThreads4W_STRUCTEXC APPEND PROPERTY IMPORTED_CONFIGURATIONS Debug ) + set_target_properties( PThreads4W::PThreads4W_STRUCTEXC PROPERTIES + IMPORTED_LOCATION_DEBUG "${PThreads4W_STRUCTEXC_LIBRARY_DEBUG}" ) + endif() + endif() +endif() + +if( PThreads4W_FOUND AND NOT TARGET PThreads4W::PThreads4W ) + if( EXISTS "${PThreads4W_LIBRARY_RELEASE_DLL}" ) + add_library( PThreads4W::PThreads4W SHARED IMPORTED ) + set_target_properties( PThreads4W::PThreads4W PROPERTIES + IMPORTED_LOCATION_RELEASE "${PThreads4W_LIBRARY_RELEASE_DLL}" + IMPORTED_IMPLIB "${PThreads4W_LIBRARY_RELEASE}" + INTERFACE_INCLUDE_DIRECTORIES "${PThreads4W_INCLUDE_DIR}" + IMPORTED_CONFIGURATIONS Release + IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) + if( EXISTS "${PThreads4W_LIBRARY_DEBUG_DLL}" ) + set_property( TARGET PThreads4W::PThreads4W APPEND PROPERTY IMPORTED_CONFIGURATIONS Debug ) + set_target_properties( PThreads4W::PThreads4W PROPERTIES + IMPORTED_LOCATION_DEBUG "${PThreads4W_LIBRARY_DEBUG_DLL}" + IMPORTED_IMPLIB_DEBUG "${PThreads4W_LIBRARY_DEBUG}" ) + endif() + else() + add_library( PThreads4W::PThreads4W UNKNOWN IMPORTED ) + set_target_properties( PThreads4W::PThreads4W PROPERTIES + IMPORTED_LOCATION_RELEASE "${PThreads4W_LIBRARY_RELEASE}" + INTERFACE_INCLUDE_DIRECTORIES "${PThreads4W_INCLUDE_DIR}" + IMPORTED_CONFIGURATIONS Release + IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) + if( EXISTS "${PThreads4W_LIBRARY_DEBUG}" ) + set_property( TARGET PThreads4W::PThreads4W APPEND PROPERTY IMPORTED_CONFIGURATIONS Debug ) + set_target_properties( PThreads4W::PThreads4W PROPERTIES + IMPORTED_LOCATION_DEBUG "${PThreads4W_LIBRARY_DEBUG}" ) + endif() + endif() +endif() + +#Compatibility targets, deprecated +if( PThreads4W_FOUND AND NOT TARGET PThreads_windows::PThreads_windows ) + if( EXISTS "${PThreads4W_LIBRARY_RELEASE_DLL}" ) + add_library( PThreads_windows::PThreads_windows SHARED IMPORTED ) + set_target_properties( PThreads_windows::PThreads_windows PROPERTIES + IMPORTED_LOCATION_RELEASE "${PThreads4W_LIBRARY_RELEASE_DLL}" + IMPORTED_IMPLIB "${PThreads4W_LIBRARY_RELEASE}" + INTERFACE_INCLUDE_DIRECTORIES "${PThreads4W_INCLUDE_DIR}" + IMPORTED_CONFIGURATIONS Release + IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) + if( EXISTS "${PThreads4W_LIBRARY_DEBUG_DLL}" ) + set_property( TARGET PThreads_windows::PThreads_windows APPEND PROPERTY IMPORTED_CONFIGURATIONS Debug ) + set_target_properties( PThreads_windows::PThreads_windows PROPERTIES + IMPORTED_LOCATION_DEBUG "${PThreads4W_LIBRARY_DEBUG_DLL}" + IMPORTED_IMPLIB_DEBUG "${PThreads4W_LIBRARY_DEBUG}" ) + endif() + else() + add_library( PThreads_windows::PThreads_windows UNKNOWN IMPORTED ) + set_target_properties( PThreads_windows::PThreads_windows PROPERTIES + IMPORTED_LOCATION_RELEASE "${PThreads4W_LIBRARY_RELEASE}" + INTERFACE_INCLUDE_DIRECTORIES "${PThreads4W_INCLUDE_DIR}" + IMPORTED_CONFIGURATIONS Release + IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) + if( EXISTS "${PThreads4W_LIBRARY_DEBUG}" ) + set_property( TARGET PThreads_windows::PThreads_windows APPEND PROPERTY IMPORTED_CONFIGURATIONS Debug ) + set_target_properties( PThreads_windows::PThreads_windows PROPERTIES + IMPORTED_LOCATION_DEBUG "${PThreads4W_LIBRARY_DEBUG}" ) + endif() + endif() +endif() diff --git a/cmake/Modules/FindPThreads_windows.cmake b/cmake/Modules/FindPThreads_windows.cmake deleted file mode 100644 index 64e314005ff..00000000000 --- a/cmake/Modules/FindPThreads_windows.cmake +++ /dev/null @@ -1,83 +0,0 @@ -# Distributed under the OSI-approved BSD 3-Clause License. -# Copyright Stefano Sinigardi - -#.rst: -# FindPThreads -# ------------ -# -# Find the PThreads includes and library. -# -# Result Variables -# ^^^^^^^^^^^^^^^^ -# -# This module defines the following variables: -# -# ``PThreads_windows_FOUND`` -# True if PThreads_windows library found -# -# ``PThreads_windows_INCLUDE_DIR`` -# Location of PThreads_windows headers -# -# ``PThreads_windows_LIBRARY`` -# List of libraries to link with when using PThreads_windows -# - -include(${CMAKE_ROOT}/Modules/FindPackageHandleStandardArgs.cmake) -include(${CMAKE_ROOT}/Modules/SelectLibraryConfigurations.cmake) - -if(NOT PThreads_windows_INCLUDE_DIR) - find_path(PThreads_windows_INCLUDE_DIR NAMES pthread.h PATHS ${PThreads_windows_DIR} PATH_SUFFIXES include) -endif() - -# Allow libraries to be set manually -if(NOT PThreads_windows_LIBRARY) - find_library(PThreads_windows_LIBRARY_RELEASE NAMES pthreadsVC2 pthreadVC2 PATHS ${PThreads_windows_DIR} PATH_SUFFIXES lib) - find_library(PThreads_windows_LIBRARY_DEBUG NAMES pthreadsVC2d pthreadVC2d pthreadsVC2 pthreadVC2 PATHS ${PThreads_windows_DIR} PATH_SUFFIXES lib) - select_library_configurations(PThreads_windows) -endif() - -find_package_handle_standard_args(PThreads_windows DEFAULT_MSG PThreads_windows_LIBRARY PThreads_windows_INCLUDE_DIR) -mark_as_advanced(PThreads_windows_INCLUDE_DIR PThreads_windows_LIBRARY) - -set(PThreads_windows_DLL_DIR ${PThreads_windows_INCLUDE_DIR}) -list(TRANSFORM PThreads_windows_DLL_DIR APPEND "/../bin") -message(STATUS "PThreads_windows_DLL_DIR: ${PThreads_windows_DLL_DIR}") - -find_file(PThreads_windows_LIBRARY_RELEASE_DLL NAMES pthreadVC2.dll PATHS ${PThreads_windows_DLL_DIR}) -find_file(PThreads_windows_LIBRARY_DEBUG_DLL NAMES pthreadVC2d.dll pthreadVC2.dll PATHS ${PThreads_windows_DLL_DIR}) - -# Register imported libraries: -# 1. If we can find a Windows .dll file (or if we can find both Debug and -# Release libraries), we will set appropriate target properties for these. -# 2. However, for most systems, we will only register the import location and -# include directory. - -if( PThreads_windows_FOUND AND NOT TARGET PThreads_windows::PThreads_windows ) - if( EXISTS "${PThreads_windows_LIBRARY_RELEASE_DLL}" ) - add_library( PThreads_windows::PThreads_windows SHARED IMPORTED ) - set_target_properties( PThreads_windows::PThreads_windows PROPERTIES - IMPORTED_LOCATION_RELEASE "${PThreads_windows_LIBRARY_RELEASE_DLL}" - IMPORTED_IMPLIB "${PThreads_windows_LIBRARY_RELEASE}" - INTERFACE_INCLUDE_DIRECTORIES "${PThreads_windows_INCLUDE_DIR}" - IMPORTED_CONFIGURATIONS Release - IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) - if( EXISTS "${PThreads_windows_LIBRARY_DEBUG_DLL}" ) - set_property( TARGET PThreads_windows::PThreads_windows APPEND PROPERTY IMPORTED_CONFIGURATIONS Debug ) - set_target_properties( PThreads_windows::PThreads_windows PROPERTIES - IMPORTED_LOCATION_DEBUG "${PThreads_windows_LIBRARY_DEBUG_DLL}" - IMPORTED_IMPLIB_DEBUG "${PThreads_windows_LIBRARY_DEBUG}" ) - endif() - else() - add_library( PThreads_windows::PThreads_windows UNKNOWN IMPORTED ) - set_target_properties( PThreads_windows::PThreads_windows PROPERTIES - IMPORTED_LOCATION_RELEASE "${PThreads_windows_LIBRARY_RELEASE}" - INTERFACE_INCLUDE_DIRECTORIES "${PThreads_windows_INCLUDE_DIR}" - IMPORTED_CONFIGURATIONS Release - IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) - if( EXISTS "${PThreads_windows_LIBRARY_DEBUG}" ) - set_property( TARGET PThreads_windows::PThreads_windows APPEND PROPERTY IMPORTED_CONFIGURATIONS Debug ) - set_target_properties( PThreads_windows::PThreads_windows PROPERTIES - IMPORTED_LOCATION_DEBUG "${PThreads_windows_LIBRARY_DEBUG}" ) - endif() - endif() -endif() From 5e73447fa8948e4b64a726d45c1b4e0f0bd0304b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?St=C3=A9phane=20Charette?= Date: Sat, 17 Jul 2021 20:01:10 -0700 Subject: [PATCH 1278/1365] allocation and error functions now take a filename, function, and line number to better diagnose where problems happen (#7925) --- include/darknet.h | 7 ++-- src/art.c | 2 +- src/classifier.c | 6 ++-- src/convolutional_kernels.cu | 2 +- src/convolutional_layer.c | 2 +- src/dark_cuda.c | 22 ++++++------- src/data.c | 6 ++-- src/demo.c | 10 +++--- src/detector.c | 2 +- src/network.c | 2 +- src/network_kernels.cu | 4 +-- src/parser.c | 28 ++++++++-------- src/rnn.c | 8 ++--- src/utils.c | 62 +++++++++++++++++++++++------------- src/utils.h | 27 +++++++++++----- src/yolo_layer.c | 2 +- 16 files changed, 109 insertions(+), 83 deletions(-) diff --git a/include/darknet.h b/include/darknet.h index 9ded8dfeaf5..5f225ab2763 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -770,7 +770,7 @@ typedef struct network { float label_smooth_eps; int resize_step; int attention; - int adversarial; + int adversarial; float adversarial_lr; float max_chart_loss; int letter_box; @@ -821,7 +821,7 @@ typedef struct network { size_t *max_input16_size; size_t *max_output16_size; int wait_stream; - + void *cuda_graph; void *cuda_graph_exec; int use_cuda_graph; @@ -908,7 +908,7 @@ typedef struct detection{ float *embeddings; // embeddings for tracking int embedding_size; float sim; - int track_id; + int track_id; } detection; // network.c -batch inference @@ -1017,7 +1017,6 @@ typedef struct box_label { // parser.c LIB_API network *load_network(char *cfg, char *weights, int clear); LIB_API network *load_network_custom(char *cfg, char *weights, int clear, int batch); -LIB_API network *load_network(char *cfg, char *weights, int clear); LIB_API void free_network(network net); LIB_API void free_network_ptr(network* net); diff --git a/src/art.c b/src/art.c index 22e8ab86f3b..5fbcfceada9 100644 --- a/src/art.c +++ b/src/art.c @@ -27,7 +27,7 @@ void demo_art(char *cfgfile, char *weightfile, int cam_index) cap = get_capture_webcam(cam_index); char *window = "ArtJudgementBot9000!!!"; - if(!cap) error("Couldn't connect to webcam.\n"); + if(!cap) error("Couldn't connect to webcam.", DARKNET_LOC); create_window_cv(window, 0, 512, 512); int i; int idx[] = {37, 401, 434}; diff --git a/src/classifier.c b/src/classifier.c index f84e1dc2aa8..e42b1ea3ab2 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -1051,7 +1051,7 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i int* indexes = (int*)xcalloc(top, sizeof(int)); - if(!cap) error("Couldn't connect to webcam.\n"); + if(!cap) error("Couldn't connect to webcam.", DARKNET_LOC); create_window_cv("Threat", 0, 512, 512); float fps = 0; int i; @@ -1190,7 +1190,7 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde int* indexes = (int*)xcalloc(top, sizeof(int)); - if(!cap) error("Couldn't connect to webcam.\n"); + if(!cap) error("Couldn't connect to webcam.", DARKNET_LOC); cvNamedWindow("Threat Detection", CV_WINDOW_NORMAL); cvResizeWindow("Threat Detection", 512, 512); float fps = 0; @@ -1274,7 +1274,7 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind int* indexes = (int*)xcalloc(top, sizeof(int)); - if(!cap) error("Couldn't connect to webcam.\n"); + if(!cap) error("Couldn't connect to webcam.", DARKNET_LOC); if (!benchmark) create_window_cv("Classifier", 0, 512, 512); float fps = 0; int i; diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 1e9cdd9c739..188399bb7e6 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -159,7 +159,7 @@ half *cuda_make_f16_from_f32_array(float *src, size_t n) assert(n > 0); cuda_convert_f32_to_f16(src, n, (float *)dst16); } - if (!dst16) error("Cuda malloc failed\n"); + if (!dst16) error("Cuda malloc failed", DARKNET_LOC); return dst16; } diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 1d52dd1d23c..7d64e1d51be 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -241,7 +241,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference, size_t workspace_ #endif #else //if(CUDNN_MAJOR >= 7) if (l->groups > 1) { - error("CUDNN < 7 doesn't support groups, please upgrade!"); + error("CUDNN < 7 doesn't support groups, please upgrade!", DARKNET_LOC); } #endif diff --git a/src/dark_cuda.c b/src/dark_cuda.c index ceb43c8878b..8fdaee3fc7b 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -67,7 +67,7 @@ void check_error(cudaError_t status) #ifdef WIN32 getchar(); #endif - error(buffer); + error(buffer, DARKNET_LOC); } if (status2 != cudaSuccess) { @@ -78,7 +78,7 @@ void check_error(cudaError_t status) #ifdef WIN32 getchar(); #endif - error(buffer); + error(buffer, DARKNET_LOC); } } @@ -202,7 +202,7 @@ void cudnn_check_error(cudnnStatus_t status) #ifdef WIN32 getchar(); #endif - error(buffer); + error(buffer, DARKNET_LOC); } if (status2 != CUDNN_STATUS_SUCCESS) { @@ -213,7 +213,7 @@ void cudnn_check_error(cudnnStatus_t status) #ifdef WIN32 getchar(); #endif - error(buffer); + error(buffer, DARKNET_LOC); } } @@ -341,7 +341,7 @@ static volatile int event_counter = 0; void wait_stream(int i) { int dev_id = cuda_get_device(); - if (event_counter >= max_events) error("CUDA max_events exceeded \n"); + if (event_counter >= max_events) error("CUDA max_events exceeded", DARKNET_LOC); CHECK_CUDA( cudaEventCreateWithFlags(&switchEventsArray[event_counter], cudaEventDisableTiming) ); //printf(" create event = %d (wait for stream = %d) \n", event_counter, i); @@ -391,7 +391,7 @@ void pre_allocate_pinned_memory(const size_t size) pthread_mutex_lock(&mutex_pinned); if (!pinned_ptr) { pinned_ptr = (float **)calloc(num_of_blocks, sizeof(float *)); - if(!pinned_ptr) error("calloc failed in pre_allocate() \n"); + if(!pinned_ptr) error("calloc failed in pre_allocate()", DARKNET_LOC); printf("pre_allocate: size = %Iu MB, num_of_blocks = %Iu, block_size = %Iu MB \n", size / (1024*1024), num_of_blocks, pinned_block_size / (1024 * 1024)); @@ -401,7 +401,7 @@ void pre_allocate_pinned_memory(const size_t size) cudaError_t status = cudaHostAlloc((void **)&pinned_ptr[k], pinned_block_size, cudaHostRegisterMapped); if (status != cudaSuccess) fprintf(stderr, " Can't pre-allocate CUDA-pinned buffer on CPU-RAM \n"); CHECK_CUDA(status); - if (!pinned_ptr[k]) error("cudaHostAlloc failed\n"); + if (!pinned_ptr[k]) error("cudaHostAlloc failed", DARKNET_LOC); else { printf(" Allocated %d pinned block \n", pinned_block_size); } @@ -478,7 +478,7 @@ float *cuda_make_array_pinned(float *x, size_t n) status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyDefault, get_cuda_stream()); CHECK_CUDA(status); } - if (!x_gpu) error("cudaHostAlloc failed\n"); + if (!x_gpu) error("cudaHostAlloc failed", DARKNET_LOC); return x_gpu; } @@ -496,7 +496,7 @@ float *cuda_make_array(float *x, size_t n) status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyDefault, get_cuda_stream()); CHECK_CUDA(status); } - if(!x_gpu) error("Cuda malloc failed\n"); + if(!x_gpu) error("Cuda malloc failed", DARKNET_LOC); return x_gpu; } @@ -511,7 +511,7 @@ void **cuda_make_array_pointers(void **x, size_t n) status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyDefault, get_cuda_stream()); CHECK_CUDA(status); } - if (!x_gpu) error("Cuda malloc failed\n"); + if (!x_gpu) error("Cuda malloc failed", DARKNET_LOC); return x_gpu; } @@ -563,7 +563,7 @@ int *cuda_make_int_array_new_api(int *x, size_t n) cudaError_t status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyHostToDevice, get_cuda_stream()); CHECK_CUDA(status); } - if (!x_gpu) error("Cuda malloc failed\n"); + if (!x_gpu) error("Cuda malloc failed", DARKNET_LOC); return x_gpu; } diff --git a/src/data.c b/src/data.c index d33f2199082..7d2a4659906 100644 --- a/src/data.c +++ b/src/data.c @@ -1617,7 +1617,7 @@ pthread_t load_data_in_thread(load_args args) pthread_t thread; struct load_args* ptr = (load_args*)xcalloc(1, sizeof(struct load_args)); *ptr = args; - if(pthread_create(&thread, 0, load_thread, ptr)) error("Thread creation failed"); + if(pthread_create(&thread, 0, load_thread, ptr)) error("Thread creation failed", DARKNET_LOC); return thread; } @@ -1674,7 +1674,7 @@ void *load_threads(void *ptr) for (i = 0; i < args.threads; ++i) { int* ptr = (int*)xcalloc(1, sizeof(int)); *ptr = i; - if (pthread_create(&threads[i], 0, run_thread_loop, ptr)) error("Thread creation failed"); + if (pthread_create(&threads[i], 0, run_thread_loop, ptr)) error("Thread creation failed", DARKNET_LOC); } } @@ -1738,7 +1738,7 @@ pthread_t load_data(load_args args) pthread_t thread; struct load_args* ptr = (load_args*)xcalloc(1, sizeof(struct load_args)); *ptr = args; - if(pthread_create(&thread, 0, load_threads, ptr)) error("Thread creation failed"); + if(pthread_create(&thread, 0, load_threads, ptr)) error("Thread creation failed", DARKNET_LOC); return thread; } diff --git a/src/demo.c b/src/demo.c index ad1cc6e041c..0c40333513e 100644 --- a/src/demo.c +++ b/src/demo.c @@ -182,7 +182,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int #ifdef WIN32 printf("Check that you have copied file opencv_ffmpeg340_64.dll to the same directory where is darknet.exe \n"); #endif - error("Couldn't connect to webcam.\n"); + error("Couldn't connect to webcam.", DARKNET_LOC); } layer l = net.layers[net.n-1]; @@ -209,8 +209,8 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int custom_thread_t fetch_thread = NULL; custom_thread_t detect_thread = NULL; - if (custom_create_thread(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed"); - if (custom_create_thread(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed"); + if (custom_create_thread(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed", DARKNET_LOC); + if (custom_create_thread(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed", DARKNET_LOC); fetch_in_thread_sync(0); //fetch_in_thread(0); det_img = in_img; @@ -269,8 +269,8 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int detection *local_dets = dets; this_thread_yield(); - if (!benchmark) custom_atomic_store_int(&run_fetch_in_thread, 1); // if (custom_create_thread(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed"); - custom_atomic_store_int(&run_detect_in_thread, 1); // if (custom_create_thread(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed"); + if (!benchmark) custom_atomic_store_int(&run_fetch_in_thread, 1); // if (custom_create_thread(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed", DARKNET_LOC); + custom_atomic_store_int(&run_detect_in_thread, 1); // if (custom_create_thread(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed", DARKNET_LOC); //if (nms) do_nms_obj(local_dets, local_nboxes, l.classes, nms); // bad results if (nms) { diff --git a/src/detector.c b/src/detector.c index ec7f72a3c32..73b7bedc158 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1635,7 +1635,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam if (outfile) { json_file = fopen(outfile, "wb"); if(!json_file) { - error("fopen failed"); + error("fopen failed", DARKNET_LOC); } char *tmp = "[\n"; fwrite(tmp, sizeof(char), strlen(tmp), json_file); diff --git a/src/network.c b/src/network.c index c41932479fd..f45c7902c2c 100644 --- a/src/network.c +++ b/src/network.c @@ -639,7 +639,7 @@ int resize_network(network *net, int w, int h) resize_cost_layer(&l, inputs); }else{ fprintf(stderr, "Resizing type %d \n", (int)l.type); - error("Cannot resize this type of layer"); + error("Cannot resize this type of layer", DARKNET_LOC); } if(l.workspace_size > workspace_size) workspace_size = l.workspace_size; inputs = l.outputs; diff --git a/src/network_kernels.cu b/src/network_kernels.cu index ac3403cf547..7fbc03cf5f5 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -442,7 +442,7 @@ pthread_t train_network_in_thread(network net, data d, float *err) ptr->net = net; ptr->d = d; ptr->err = err; - if(pthread_create(&thread, 0, train_thread, ptr)) error("Thread creation failed"); + if(pthread_create(&thread, 0, train_thread, ptr)) error("Thread creation failed", DARKNET_LOC); return thread; } @@ -616,7 +616,7 @@ pthread_t sync_layer_in_thread(network *nets, int n, int j) ptr->nets = nets; ptr->n = n; ptr->j = j; - if(pthread_create(&thread, 0, sync_layer_thread, ptr)) error("Thread creation failed"); + if(pthread_create(&thread, 0, sync_layer_thread, ptr)) error("Thread creation failed", DARKNET_LOC); return thread; } diff --git a/src/parser.c b/src/parser.c index 1a345b5cc1b..8c645e6c13e 100644 --- a/src/parser.c +++ b/src/parser.c @@ -159,7 +159,7 @@ local_layer parse_local(list *options, size_params params) w = params.w; c = params.c; batch=params.batch; - if(!(h && w && c)) error("Layer before local layer must output image."); + if(!(h && w && c)) error("Layer before local layer must output image.", DARKNET_LOC); local_layer layer = make_local_layer(batch,h,w,c,n,size,stride,pad,activation); @@ -205,7 +205,7 @@ convolutional_layer parse_convolutional(list *options, size_params params) w = params.w; c = params.c; batch=params.batch; - if(!(h && w && c)) error("Layer before convolutional layer must output image."); + if(!(h && w && c)) error("Layer before convolutional layer must output image.", DARKNET_LOC); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); int cbn = option_find_int_quiet(options, "cbn", 0); if (cbn) batch_normalize = 2; @@ -781,7 +781,7 @@ crop_layer parse_crop(list *options, size_params params) w = params.w; c = params.c; batch=params.batch; - if(!(h && w && c)) error("Layer before crop layer must output image."); + if(!(h && w && c)) error("Layer before crop layer must output image.", DARKNET_LOC); int noadjust = option_find_int_quiet(options, "noadjust",0); @@ -801,7 +801,7 @@ layer parse_reorg(list *options, size_params params) w = params.w; c = params.c; batch=params.batch; - if(!(h && w && c)) error("Layer before reorg layer must output image."); + if(!(h && w && c)) error("Layer before reorg layer must output image.", DARKNET_LOC); layer layer = make_reorg_layer(batch,w,h,c,stride,reverse); return layer; @@ -818,7 +818,7 @@ layer parse_reorg_old(list *options, size_params params) w = params.w; c = params.c; batch = params.batch; - if (!(h && w && c)) error("Layer before reorg layer must output image."); + if (!(h && w && c)) error("Layer before reorg layer must output image.", DARKNET_LOC); layer layer = make_reorg_old_layer(batch, w, h, c, stride, reverse); return layer; @@ -841,7 +841,7 @@ maxpool_layer parse_local_avgpool(list *options, size_params params) w = params.w; c = params.c; batch = params.batch; - if (!(h && w && c)) error("Layer before [local_avgpool] layer must output image."); + if (!(h && w && c)) error("Layer before [local_avgpool] layer must output image.", DARKNET_LOC); maxpool_layer layer = make_maxpool_layer(batch, h, w, c, size, stride_x, stride_y, padding, maxpool_depth, out_channels, antialiasing, avgpool, params.train); return layer; @@ -864,7 +864,7 @@ maxpool_layer parse_maxpool(list *options, size_params params) w = params.w; c = params.c; batch=params.batch; - if(!(h && w && c)) error("Layer before [maxpool] layer must output image."); + if(!(h && w && c)) error("Layer before [maxpool] layer must output image.", DARKNET_LOC); maxpool_layer layer = make_maxpool_layer(batch, h, w, c, size, stride_x, stride_y, padding, maxpool_depth, out_channels, antialiasing, avgpool, params.train); layer.maxpool_zero_nonmax = option_find_int_quiet(options, "maxpool_zero_nonmax", 0); @@ -878,7 +878,7 @@ avgpool_layer parse_avgpool(list *options, size_params params) h = params.h; c = params.c; batch=params.batch; - if(!(h && w && c)) error("Layer before avgpool layer must output image."); + if(!(h && w && c)) error("Layer before avgpool layer must output image.", DARKNET_LOC); avgpool_layer layer = make_avgpool_layer(batch,w,h,c); return layer; @@ -952,7 +952,7 @@ layer parse_shortcut(list *options, size_params params, network net) char *l = option_find(options, "from"); int len = strlen(l); - if (!l) error("Route Layer must specify input layers: from = ..."); + if (!l) error("Route Layer must specify input layers: from = ...", DARKNET_LOC); int n = 1; int i; for (i = 0; i < len; ++i) { @@ -1084,7 +1084,7 @@ layer parse_upsample(list *options, size_params params, network net) route_layer parse_route(list *options, size_params params) { char *l = option_find(options, "layers"); - if(!l) error("Route Layer must specify input layers"); + if(!l) error("Route Layer must specify input layers", DARKNET_LOC); int len = strlen(l); int n = 1; int i; @@ -1243,7 +1243,7 @@ void parse_net_options(list *options, network *net) net->hue = option_find_float_quiet(options, "hue", 0); net->power = option_find_float_quiet(options, "power", 4); - if(!net->inputs && !(net->h && net->w && net->c)) error("No input parameters supplied"); + if(!net->inputs && !(net->h && net->w && net->c)) error("No input parameters supplied", DARKNET_LOC); char *policy_s = option_find_str(options, "policy", "constant"); net->policy = get_policy(policy_s); @@ -1267,7 +1267,7 @@ void parse_net_options(list *options, network *net) char *l = option_find(options, "steps"); char *p = option_find(options, "scales"); char *s = option_find(options, "seq_scales"); - if(net->policy == STEPS && (!l || !p)) error("STEPS policy must have steps and scales in cfg file"); + if(net->policy == STEPS && (!l || !p)) error("STEPS policy must have steps and scales in cfg file", DARKNET_LOC); if (l) { int len = strlen(l); @@ -1361,7 +1361,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) { list *sections = read_cfg(filename); node *n = sections->front; - if(!n) error("Config file has no sections"); + if(!n) error("Config file has no sections", DARKNET_LOC); network net = make_network(sections->size - 1); net.gpu_index = gpu_index; size_params params; @@ -1371,7 +1371,7 @@ network parse_network_cfg_custom(char *filename, int batch, int time_steps) section *s = (section *)n->val; list *options = s->options; - if(!is_network(s)) error("First section must be [net] or [network]"); + if(!is_network(s)) error("First section must be [net] or [network]", DARKNET_LOC); parse_net_options(options, &net); #ifdef GPU diff --git a/src/rnn.c b/src/rnn.c index 53fb8f320f4..ef2c7cca737 100644 --- a/src/rnn.c +++ b/src/rnn.c @@ -69,7 +69,7 @@ float_pair get_rnn_token_data(int *tokens, size_t *offsets, int characters, size offsets[i] = (offsets[i] + 1) % len; if(curr >= characters || curr < 0 || next >= characters || next < 0){ - error("Bad char"); + error("Bad char", DARKNET_LOC); } } } @@ -99,7 +99,7 @@ float_pair get_rnn_data(unsigned char *text, size_t *offsets, int characters, si printf("%ld %d %d %d %d\n", index, j, len, (int)text[index+j], (int)text[index+j+1]); printf("%s", text+index); */ - error("Bad char"); + error("Bad char", DARKNET_LOC); } } } @@ -360,7 +360,7 @@ void valid_tactic_rnn(char *cfgfile, char *weightfile, char *seed) while(c != EOF){ int next = getc(stdin); if(next == EOF) break; - if(next < 0 || next >= 255) error("Out of range character"); + if(next < 0 || next >= 255) error("Out of range character", DARKNET_LOC); input[c] = 1; float *out = network_predict(net, input); @@ -411,7 +411,7 @@ void valid_char_rnn(char *cfgfile, char *weightfile, char *seed) while(c != EOF){ int next = getc(stdin); if(next == EOF) break; - if(next < 0 || next >= 255) error("Out of range character"); + if(next < 0 || next >= 255) error("Out of range character", DARKNET_LOC); ++count; if(next == ' ' || next == '\n' || next == '\t') ++words; input[c] = 1; diff --git a/src/utils.c b/src/utils.c index e4a2298e762..0c271b7bf41 100644 --- a/src/utils.c +++ b/src/utils.c @@ -25,26 +25,26 @@ #pragma warning(disable: 4996) #endif -void *xmalloc(size_t size) { +void *xmalloc_location(const size_t size, const char * const filename, const char * const funcname, const int line) { void *ptr=malloc(size); if(!ptr) { - malloc_error(); + malloc_error(size, filename, funcname, line); } return ptr; } -void *xcalloc(size_t nmemb, size_t size) { - void *ptr=calloc(nmemb,size); +void *xcalloc_location(const size_t nmemb, const size_t size, const char * const filename, const char * const funcname, const int line) { + void *ptr=calloc(nmemb, size); if(!ptr) { - calloc_error(); + calloc_error(nmemb * size, filename, funcname, line); } return ptr; } -void *xrealloc(void *ptr, size_t size) { +void *xrealloc_location(void *ptr, const size_t size, const char * const filename, const char * const funcname, const int line) { ptr=realloc(ptr,size); if(!ptr) { - realloc_error(); + realloc_error(size, filename, funcname, line); } return ptr; } @@ -325,32 +325,48 @@ void top_k(float *a, int n, int k, int *index) } } -void error(const char *s) +void error(const char * const msg, const char * const filename, const char * const funcname, const int line) { - perror(s); - assert(0); + fprintf(stderr, "Darknet error location: %s, %s, line #%d\n", filename, funcname, line); + perror(msg); exit(EXIT_FAILURE); } -void malloc_error() +const char * size_to_IEC_string(const size_t size) { - fprintf(stderr, "xMalloc error - possibly out of CPU RAM \n"); - exit(EXIT_FAILURE); + const float bytes = (double)size; + const float KiB = 1024; + const float MiB = 1024 * KiB; + const float GiB = 1024 * MiB; + + static char buffer[25]; + if (size < KiB) sprintf(buffer, "%ld bytes", size); + else if (size < MiB) sprintf(buffer, "%1.1f KiB", bytes / KiB); + else if (size < GiB) sprintf(buffer, "%1.1f MiB", bytes / MiB); + else sprintf(buffer, "%1.1f GiB", bytes / GiB); + + return buffer; } -void calloc_error() +void malloc_error(const size_t size, const char * const filename, const char * const funcname, const int line) { - fprintf(stderr, "Calloc error - possibly out of CPU RAM \n"); - exit(EXIT_FAILURE); + fprintf(stderr, "Failed to malloc %s\n", size_to_IEC_string(size)); + error("Malloc error - possibly out of CPU RAM", filename, funcname, line); } -void realloc_error() +void calloc_error(const size_t size, const char * const filename, const char * const funcname, const int line) { - fprintf(stderr, "Realloc error - possibly out of CPU RAM \n"); - exit(EXIT_FAILURE); + fprintf(stderr, "Failed to calloc %s\n", size_to_IEC_string(size)); + error("Calloc error - possibly out of CPU RAM", filename, funcname, line); +} + +void realloc_error(const size_t size, const char * const filename, const char * const funcname, const int line) +{ + fprintf(stderr, "Failed to realloc %s\n", size_to_IEC_string(size)); + error("Realloc error - possibly out of CPU RAM", filename, funcname, line); } -void file_error(char *s) +void file_error(const char * const s) { fprintf(stderr, "Couldn't open file: %s\n", s); exit(EXIT_FAILURE); @@ -460,7 +476,7 @@ int read_int(int fd) void write_int(int fd, int n) { int next = write(fd, &n, sizeof(int)); - if(next <= 0) error("read failed"); + if(next <= 0) error("read failed", DARKNET_LOC); } int read_all_fail(int fd, char *buffer, size_t bytes) @@ -490,7 +506,7 @@ void read_all(int fd, char *buffer, size_t bytes) size_t n = 0; while(n < bytes){ int next = read(fd, buffer + n, bytes-n); - if(next <= 0) error("read failed"); + if(next <= 0) error("read failed", DARKNET_LOC); n += next; } } @@ -500,7 +516,7 @@ void write_all(int fd, char *buffer, size_t bytes) size_t n = 0; while(n < bytes){ size_t next = write(fd, buffer + n, bytes-n); - if(next <= 0) error("write failed"); + if(next <= 0) error("write failed", DARKNET_LOC); n += next; } } diff --git a/src/utils.h b/src/utils.h index a217b425a2e..f84054b8b39 100644 --- a/src/utils.h +++ b/src/utils.h @@ -15,12 +15,24 @@ extern "C" { #endif +#define DARKNET_LOC __FILE__, __func__, __LINE__ + LIB_API void free_ptrs(void **ptrs, int n); LIB_API void top_k(float *a, int n, int k, int *index); -void *xmalloc(size_t size); -void *xcalloc(size_t nmemb, size_t size); -void *xrealloc(void *ptr, size_t size); +/* The "location" is the file, function, and line as defined by the DARKNET_LOC macro. + * This is then printed when error() is called to terminate the instance of darknet. + */ +void *xmalloc_location(const size_t size, const char * const filename, const char * const funcname, const int line); +void *xcalloc_location(const size_t nmemb, const size_t size, const char * const filename, const char * const funcname, const int line); +void *xrealloc_location(void *ptr, const size_t size, const char * const filename, const char * const funcname, const int line); + +#define xmalloc(s) xmalloc_location(s, DARKNET_LOC) +#define xcalloc(m, s) xcalloc_location(m, s, DARKNET_LOC) +#define xrealloc(p, s) xrealloc_location(p, s, DARKNET_LOC) + +void error(const char * const msg, const char * const filename, const char * const funcname, const int line); + double what_time_is_it_now(); int *read_map(char *filename); void shuffle(void *arr, size_t n, size_t size); @@ -36,11 +48,10 @@ int read_all_fail(int fd, char *buffer, size_t bytes); int write_all_fail(int fd, char *buffer, size_t bytes); LIB_API void find_replace(const char* str, char* orig, char* rep, char* output); void replace_image_to_label(const char* input_path, char* output_path); -void error(const char *s); -void malloc_error(); -void calloc_error(); -void realloc_error(); -void file_error(char *s); +void malloc_error(const size_t size, const char * const filename, const char * const funcname, const int line); +void calloc_error(const size_t size, const char * const filename, const char * const funcname, const int line); +void realloc_error(const size_t size, const char * const filename, const char * const funcname, const int line); +void file_error(const char * const s); void strip(char *s); void strip_args(char *s); void strip_char(char *s, char bad); diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 883d755e305..0eae2fc8feb 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -727,7 +727,7 @@ void forward_yolo_layer(const layer l, network_state state) yolo_args[b].count = 0; yolo_args[b].class_count = 0; - if (pthread_create(&threads[b], 0, process_batch, &(yolo_args[b]))) error("Thread creation failed"); + if (pthread_create(&threads[b], 0, process_batch, &(yolo_args[b]))) error("Thread creation failed", DARKNET_LOC); } for (b = 0; b < l.batch; b++) From d669680879f72e58a5bc4d8de98c2e3c0aab0b62 Mon Sep 17 00:00:00 2001 From: Sergey Nuzhny Date: Sun, 18 Jul 2021 18:58:01 +0300 Subject: [PATCH 1279/1365] Add batch inference on C++ (#7915) * Add batch inference on C++ * Return default params * Add make_nms parameter --- include/yolo_v2_class.hpp | 5 ++- src/yolo_v2_class.cpp | 81 ++++++++++++++++++++++++++++++++++++--- 2 files changed, 78 insertions(+), 8 deletions(-) diff --git a/include/yolo_v2_class.hpp b/include/yolo_v2_class.hpp index 360c3320bc7..1d70a2c2e9b 100644 --- a/include/yolo_v2_class.hpp +++ b/include/yolo_v2_class.hpp @@ -56,7 +56,7 @@ struct bbox_t_container { #include // C #endif -extern "C" LIB_API int init(const char *configurationFilename, const char *weightsFilename, int gpu); +extern "C" LIB_API int init(const char *configurationFilename, const char *weightsFilename, int gpu, int batch_size); extern "C" LIB_API int detect_image(const char *filename, bbox_t_container &container); extern "C" LIB_API int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container &container); extern "C" LIB_API int dispose(); @@ -76,11 +76,12 @@ class Detector { float nms = .4; bool wait_stream; - LIB_API Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0); + LIB_API Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0, int batch_size = 1); LIB_API ~Detector(); LIB_API std::vector detect(std::string image_filename, float thresh = 0.2, bool use_mean = false); LIB_API std::vector detect(image_t img, float thresh = 0.2, bool use_mean = false); + LIB_API std::vector> detectBatch(image_t img, int batch_size, int width, int height, float thresh, bool make_nms = true); static LIB_API image_t load_image(std::string image_filename); static LIB_API void free_image(image_t m); LIB_API int get_net_width() const; diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index 93812e7fabe..f7c1ea6907f 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -27,9 +27,9 @@ extern "C" { //static Detector* detector = NULL; static std::unique_ptr detector; -int init(const char *configurationFilename, const char *weightsFilename, int gpu) +int init(const char *configurationFilename, const char *weightsFilename, int gpu, int batch_size) { - detector.reset(new Detector(configurationFilename, weightsFilename, gpu)); + detector.reset(new Detector(configurationFilename, weightsFilename, gpu, batch_size)); return 1; } @@ -127,7 +127,8 @@ struct detector_gpu_t { unsigned int *track_id; }; -LIB_API Detector::Detector(std::string cfg_filename, std::string weight_filename, int gpu_id) : cur_gpu_id(gpu_id) +LIB_API Detector::Detector(std::string cfg_filename, std::string weight_filename, int gpu_id, int batch_size) + : cur_gpu_id(gpu_id) { wait_stream = 0; #ifdef GPU @@ -153,11 +154,11 @@ LIB_API Detector::Detector(std::string cfg_filename, std::string weight_filename char *cfgfile = const_cast(_cfg_filename.c_str()); char *weightfile = const_cast(_weight_filename.c_str()); - net = parse_network_cfg_custom(cfgfile, 1, 1); + net = parse_network_cfg_custom(cfgfile, batch_size, batch_size); if (weightfile) { load_weights(&net, weightfile); } - set_batch_network(&net, 1); + set_batch_network(&net, batch_size); net.gpu_index = cur_gpu_id; fuse_conv_batchnorm(net); @@ -354,6 +355,74 @@ LIB_API std::vector Detector::detect(image_t img, float thresh, bool use return bbox_vec; } +LIB_API std::vector> Detector::detectBatch(image_t img, int batch_size, int width, int height, float thresh, bool make_nms) +{ + detector_gpu_t &detector_gpu = *static_cast(detector_gpu_ptr.get()); + network &net = detector_gpu.net; +#ifdef GPU + int old_gpu_index; + cudaGetDevice(&old_gpu_index); + if(cur_gpu_id != old_gpu_index) + cudaSetDevice(net.gpu_index); + + net.wait_stream = wait_stream; // 1 - wait CUDA-stream, 0 - not to wait +#endif + //std::cout << "net.gpu_index = " << net.gpu_index << std::endl; + + layer l = net.layers[net.n - 1]; + + float hier_thresh = 0.5; + image in_img; + in_img.c = img.c; + in_img.w = img.w; + in_img.h = img.h; + in_img.data = img.data; + det_num_pair* prediction = network_predict_batch(&net, in_img, batch_size, width, height, thresh, hier_thresh, 0, 0, 0); + + std::vector> bbox_vec(batch_size); + + for (int bi = 0; bi < batch_size; ++bi) + { + auto dets = prediction[bi].dets; + + if (make_nms && nms) + do_nms_sort(dets, prediction[bi].num, l.classes, nms); + + for (int i = 0; i < prediction[bi].num; ++i) + { + box b = dets[i].bbox; + int const obj_id = max_index(dets[i].prob, l.classes); + float const prob = dets[i].prob[obj_id]; + + if (prob > thresh) + { + bbox_t bbox; + bbox.x = std::max((double)0, (b.x - b.w / 2.)); + bbox.y = std::max((double)0, (b.y - b.h / 2.)); + bbox.w = b.w; + bbox.h = b.h; + bbox.obj_id = obj_id; + bbox.prob = prob; + bbox.track_id = 0; + bbox.frames_counter = 0; + bbox.x_3d = NAN; + bbox.y_3d = NAN; + bbox.z_3d = NAN; + + bbox_vec[bi].push_back(bbox); + } + } + } + free_batch_detections(prediction, batch_size); + +#ifdef GPU + if (cur_gpu_id != old_gpu_index) + cudaSetDevice(old_gpu_index); +#endif + + return bbox_vec; +} + LIB_API std::vector Detector::tracking_id(std::vector cur_bbox_vec, bool const change_history, int const frames_story, int const max_dist) { @@ -430,4 +499,4 @@ void *Detector::get_cuda_context() #else // GPU return NULL; #endif // GPU -} \ No newline at end of file +} From f5007cd0d40cf6207aceda564458d3fa021872b3 Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 27 Jul 2021 18:43:40 +0300 Subject: [PATCH 1280/1365] Fixed for-loop var initialization for OpenMP, for Contrastive-loss --- src/softmax_layer.c | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/src/softmax_layer.c b/src/softmax_layer.c index 59f51117e77..df51ff942ce 100644 --- a/src/softmax_layer.c +++ b/src/softmax_layer.c @@ -498,17 +498,18 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) // calc deltas + int bd = 0; #pragma omp parallel for - for (b = 0; b < l.batch; ++b) { - for (n = 0; n < l.n; ++n) { - for (h = 0; h < l.h; ++h) { - for (w = 0; w < l.w; ++w) + for (bd = 0; bd < l.batch; ++bd) { + for (int nd = 0; nd < l.n; ++nd) { + for (int hd = 0; hd < l.h; ++hd) { + for (int wd = 0; wd < l.w; ++wd) { - const int z_index = b*l.n*l.h*l.w + n*l.h*l.w + h*l.w + w; + const int z_index = bd*l.n*l.h*l.w + nd*l.h*l.w + hd*l.w + wd; const size_t step = l.batch*l.n*l.h*l.w; if (l.labels[z_index] < 0) continue; - const int delta_index = b*l.embedding_size*l.n*l.h*l.w + n*l.embedding_size*l.h*l.w + h*l.w + w; + const int delta_index = bd*l.embedding_size*l.n*l.h*l.w + nd*l.embedding_size*l.h*l.w + hd*l.w + wd; const int wh = l.w*l.h; if (l.detection) { From b8dceb7ed055b1ab2094bdbd0756b61473db3ef6 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 30 Jul 2021 03:05:23 +0200 Subject: [PATCH 1281/1365] [CI] update to cuda11.4 (#7914) * [CI] update to cuda11.4 * update setup scripts to cuda 11.4 --- .github/workflows/ccpp.yml | 36 ++++++++++++++++++------------------ .github/workflows/on_pr.yml | 36 ++++++++++++++++++------------------ scripts/deploy-cuda.ps1 | 16 ++++++++-------- scripts/deploy-cuda.sh | 19 +++++++++---------- scripts/setup.ps1 | 6 +++--- scripts/setup.sh | 12 ++++++------ 6 files changed, 62 insertions(+), 63 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index c8d65b796b4..d3e9d253b54 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -25,9 +25,9 @@ jobs: - name: 'Create softlinks for CUDA' run: | - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so - name: 'LIBSO=1 GPU=0 CUDNN=0 OPENCV=0' run: | @@ -82,9 +82,9 @@ jobs: - name: 'Create softlinks for CUDA' run: | - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -142,9 +142,9 @@ jobs: - name: 'Create softlinks for CUDA' run: | - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -185,9 +185,9 @@ jobs: - name: 'Create softlinks for CUDA' run: | - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -267,9 +267,9 @@ jobs: - name: 'Create softlinks for CUDA' run: | - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so - name: 'Build' shell: pwsh @@ -577,9 +577,9 @@ jobs: - name: 'Build' env: - CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3" - CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3" - CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3\\bin\\nvcc.exe" + CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.4" + CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.4" + CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.4\\bin\\nvcc.exe" shell: pwsh run: ./build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index 9f0a664ebcb..090e5acea7d 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -21,9 +21,9 @@ jobs: - name: 'Create softlinks for CUDA' run: | - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so - name: 'LIBSO=1 GPU=0 CUDNN=0 OPENCV=0' run: | @@ -78,9 +78,9 @@ jobs: - name: 'Create softlinks for CUDA' run: | - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -118,9 +118,9 @@ jobs: - name: 'Create softlinks for CUDA' run: | - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -158,9 +158,9 @@ jobs: - name: 'Create softlinks for CUDA' run: | - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -220,9 +220,9 @@ jobs: - name: 'Create softlinks for CUDA' run: | - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so - name: 'Build' shell: pwsh @@ -403,9 +403,9 @@ jobs: - name: 'Build' env: - CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3" - CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3" - CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3\\bin\\nvcc.exe" + CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.4" + CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.4" + CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.4\\bin\\nvcc.exe" shell: pwsh run: ./build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET diff --git a/scripts/deploy-cuda.ps1 b/scripts/deploy-cuda.ps1 index 83b64d29559..78ce6e507f4 100644 --- a/scripts/deploy-cuda.ps1 +++ b/scripts/deploy-cuda.ps1 @@ -1,18 +1,18 @@ #!/usr/bin/env pwsh -$url = 'https://developer.download.nvidia.com/compute/cuda/11.3.0/network_installers/cuda_11.3.0_win10_network.exe' +$url = 'https://developer.download.nvidia.com/compute/cuda/11.4.0/network_installers/cuda_11.4.0_win10_network.exe' -$CudaFeatures = 'nvcc_11.3 cuobjdump_11.3 nvprune_11.3 cupti_11.3 memcheck_11.3 nvdisasm_11.3 nvprof_11.3 ' + ` - 'visual_studio_integration_11.3 visual_profiler_11.3 visual_profiler_11.3 cublas_11.3 cublas_dev_11.3 ' + ` - 'cudart_11.3 cufft_11.3 cufft_dev_11.3 curand_11.3 curand_dev_11.3 cusolver_11.3 cusolver_dev_11.3 ' + ` - 'cusparse_11.3 cusparse_dev_11.3 npp_11.3 npp_dev_11.3 nvrtc_11.3 nvrtc_dev_11.3 nvml_dev_11.3 ' + ` - 'occupancy_calculator_11.3 ' +$CudaFeatures = 'nvcc_11.4 cuobjdump_11.4 nvprune_11.4 cupti_11.4 memcheck_11.4 nvdisasm_11.4 nvprof_11.4 ' + ` + 'visual_studio_integration_11.4 visual_profiler_11.4 visual_profiler_11.4 cublas_11.4 cublas_dev_11.4 ' + ` + 'cudart_11.4 cufft_11.4 cufft_dev_11.4 curand_11.4 curand_dev_11.4 cusolver_11.4 cusolver_dev_11.4 ' + ` + 'cusparse_11.4 cusparse_dev_11.4 npp_11.4 npp_dev_11.4 nvrtc_11.4 nvrtc_dev_11.4 nvml_dev_11.4 ' + ` + 'occupancy_calculator_11.4 ' try { Write-Host 'Downloading CUDA...' - Invoke-WebRequest -Uri $url -OutFile "cuda_11.3.0_win10_network.exe" + Invoke-WebRequest -Uri $url -OutFile "cuda_11.4.0_win10_network.exe" Write-Host 'Installing CUDA...' - $proc = Start-Process -PassThru -FilePath "./cuda_11.3.0_win10_network.exe" -ArgumentList @('-s ' + $CudaFeatures) + $proc = Start-Process -PassThru -FilePath "./cuda_11.4.0_win10_network.exe" -ArgumentList @('-s ' + $CudaFeatures) $proc.WaitForExit() $exitCode = $proc.ExitCode if ($exitCode -eq 0) { diff --git a/scripts/deploy-cuda.sh b/scripts/deploy-cuda.sh index 65f173aabaf..92da66c0b16 100755 --- a/scripts/deploy-cuda.sh +++ b/scripts/deploy-cuda.sh @@ -7,17 +7,16 @@ else sudo apt-get update sudo apt-get install build-essential g++ sudo apt-get install apt-transport-https ca-certificates gnupg software-properties-common wget - wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.2.89-1_amd64.deb - sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub - sudo dpkg -i cuda-repo-ubuntu1804_10.2.89-1_amd64.deb - wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb - sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb + sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin + sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub + sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /" + sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/ /" sudo apt-get update sudo apt-get dist-upgrade -y - sudo apt-get install -y --no-install-recommends cuda-compiler-10-2 cuda-libraries-dev-10-2 cuda-driver-dev-10-2 cuda-cudart-dev-10-2 cuda-curand-dev-10-2 - sudo apt-get install -y --no-install-recommends libcudnn7-dev + sudo apt-get install -y --no-install-recommends cuda-compiler-11-4 cuda-libraries-dev-11-4 cuda-driver-dev-11-4 cuda-cudart-dev-11-4 + sudo apt-get install -y --no-install-recommends libcudnn8-dev sudo rm -rf /usr/local/cuda - sudo ln -s /usr/local/cuda-10.2 /usr/local/cuda + sudo ln -s /usr/local/cuda-11.4 /usr/local/cuda elif [[ $(cut -f2 <<< $(lsb_release -r)) == "20.04" ]]; then sudo apt-get update sudo apt-get install build-essential g++ @@ -28,10 +27,10 @@ else sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/ /" sudo apt-get update sudo apt-get dist-upgrade -y - sudo apt-get install -y --no-install-recommends cuda-compiler-11-2 cuda-libraries-dev-11-2 cuda-driver-dev-11-2 cuda-cudart-dev-11-2 + sudo apt-get install -y --no-install-recommends cuda-compiler-11-4 cuda-libraries-dev-11-4 cuda-driver-dev-11-4 cuda-cudart-dev-11-4 sudo apt-get install -y --no-install-recommends libcudnn8-dev sudo rm -rf /usr/local/cuda - sudo ln -s /usr/local/cuda-11.2 /usr/local/cuda + sudo ln -s /usr/local/cuda-11.4 /usr/local/cuda else echo "Unable to deploy CUDA on this Linux version, please wait for a future script update" fi diff --git a/scripts/setup.ps1 b/scripts/setup.ps1 index ca54dba9754..1ebd9f02eed 100755 --- a/scripts/setup.ps1 +++ b/scripts/setup.ps1 @@ -16,9 +16,9 @@ Push-Location $PSScriptRoot if ($InstallCUDA) { & $PSScriptRoot/deploy-cuda.ps1 - $env:CUDA_PATH="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3" - $env:CUDA_TOOLKIT_ROOT_DIR="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3" - $env:CUDACXX="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3\\bin\\nvcc.exe" + $env:CUDA_PATH="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.4" + $env:CUDA_TOOLKIT_ROOT_DIR="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.4" + $env:CUDACXX="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.4\\bin\\nvcc.exe" $CUDAisAvailable = $true } else { diff --git a/scripts/setup.sh b/scripts/setup.sh index 51d641bcea4..67aa744871b 100755 --- a/scripts/setup.sh +++ b/scripts/setup.sh @@ -51,9 +51,9 @@ if [ "$install_tools" = true ] ; then sudo apt-get install -y cmake sudo apt-get install -y powershell if [ "$bypass_driver_installation" = true ] ; then - sudo ln -s /usr/local/cuda-10.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-10.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-10.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so fi export PATH=/usr/local/cuda/bin:$PATH export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH @@ -75,9 +75,9 @@ if [ "$install_tools" = true ] ; then sudo apt-get install -y cmake sudo apt-get install -y powershell if [ "$bypass_driver_installation" = true ] ; then - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.2/lib64/stubs/libcuda.so /usr/local/cuda-11.2/lib64/libcuda.so + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so fi export PATH=/usr/local/cuda/bin:$PATH export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH From 2495bd7166ed3b016e45cd9578c2699a86ba5bfb Mon Sep 17 00:00:00 2001 From: Alexey Date: Sun, 22 Aug 2021 02:52:15 +0300 Subject: [PATCH 1282/1365] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 29cdf564d1c..65b518726a3 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ Paper YOLO v4: https://arxiv.org/abs/2004.10934 -Paper Scaled YOLO v4: https://arxiv.org/abs/2011.08036 use to reproduce results: [ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4) +Paper Scaled YOLO v4: * **[CVPR 2021](https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.html)**: use to reproduce results: [ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4) More details in articles on medium: From 9ff8653d999c8a22bc8f1ff4f4a8a3cc5b63d255 Mon Sep 17 00:00:00 2001 From: Sarim Mehdi Date: Wed, 25 Aug 2021 17:10:21 +0200 Subject: [PATCH 1283/1365] Made inference faster (this is especially useful when using Yolo9000) (#8009) * Update network.c custom_get_region_detections function now keeps track of class index with the highest probability. * Update darknet.h Added best_class_idx to detection struct * Update darknet.py added python code for faster negative removal and also faster non-max suppression --- darknet.py | 66 +++++++++++++++++++++++++++++++++++++++++++++++ include/darknet.h | 1 + src/network.c | 6 +++++ 3 files changed, 73 insertions(+) diff --git a/darknet.py b/darknet.py index 698f0469ad5..ebb0eede210 100644 --- a/darknet.py +++ b/darknet.py @@ -24,6 +24,7 @@ class BOX(Structure): class DETECTION(Structure): _fields_ = [("bbox", BOX), ("classes", c_int), + ("best_class_idx", c_int), ("prob", POINTER(c_float)), ("mask", POINTER(c_float)), ("objectness", c_float), @@ -133,6 +134,56 @@ def decode_detection(detections): decoded.append((str(label), confidence, bbox)) return decoded +# https://www.pyimagesearch.com/2015/02/16/faster-non-maximum-suppression-python/ +# Malisiewicz et al. +def non_max_suppression_fast(detections, overlap_thresh): + boxes = [] + for detection in detections: + _, _, _, (x, y, w, h) = detection + x1 = x - w / 2 + y1 = y - h / 2 + x2 = x + w / 2 + y2 = y + h / 2 + boxes.append(np.array([x1, y1, x2, y2])) + boxes_array = np.array(boxes) + + # initialize the list of picked indexes + pick = [] + # grab the coordinates of the bounding boxes + x1 = boxes_array[:, 0] + y1 = boxes_array[:, 1] + x2 = boxes_array[:, 2] + y2 = boxes_array[:, 3] + # compute the area of the bounding boxes and sort the bounding + # boxes by the bottom-right y-coordinate of the bounding box + area = (x2 - x1 + 1) * (y2 - y1 + 1) + idxs = np.argsort(y2) + # keep looping while some indexes still remain in the indexes + # list + while len(idxs) > 0: + # grab the last index in the indexes list and add the + # index value to the list of picked indexes + last = len(idxs) - 1 + i = idxs[last] + pick.append(i) + # find the largest (x, y) coordinates for the start of + # the bounding box and the smallest (x, y) coordinates + # for the end of the bounding box + xx1 = np.maximum(x1[i], x1[idxs[:last]]) + yy1 = np.maximum(y1[i], y1[idxs[:last]]) + xx2 = np.minimum(x2[i], x2[idxs[:last]]) + yy2 = np.minimum(y2[i], y2[idxs[:last]]) + # compute the width and height of the bounding box + w = np.maximum(0, xx2 - xx1 + 1) + h = np.maximum(0, yy2 - yy1 + 1) + # compute the ratio of overlap + overlap = (w * h) / area[idxs[:last]] + # delete all indexes from the index list that have + idxs = np.delete(idxs, np.concatenate(([last], + np.where(overlap > overlap_thresh)[0]))) + # return only the bounding boxes that were picked using the + # integer data type + return [detections[i] for i in pick] def remove_negatives(detections, class_names, num): """ @@ -148,6 +199,21 @@ def remove_negatives(detections, class_names, num): return predictions +def remove_negatives_faster(detections, class_names, num): + """ + Faster version of remove_negatives (very useful when using yolo9000) + """ + predictions = [] + for j in range(num): + if detections[j].best_class_idx == -1: + continue + name = class_names[detections[j].best_class_idx] + bbox = detections[j].bbox + bbox = (bbox.x, bbox.y, bbox.w, bbox.h) + predictions.append((name, detections[j].prob[detections[j].best_class_idx], bbox)) + return predictions + + def detect_image(network, class_names, image, thresh=.5, hier_thresh=.5, nms=.45): """ Returns a list with highest confidence class and their bbox diff --git a/include/darknet.h b/include/darknet.h index 5f225ab2763..d72027cc45f 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -899,6 +899,7 @@ typedef struct ious { typedef struct detection{ box bbox; int classes; + int best_class_idx; float *prob; float *mask; float objectness; diff --git a/src/network.c b/src/network.c index f45c7902c2c..92379f053f4 100644 --- a/src/network.c +++ b/src/network.c @@ -885,7 +885,13 @@ void custom_get_region_detections(layer l, int w, int h, int net_w, int net_h, f dets[j].classes = l.classes; dets[j].bbox = boxes[j]; dets[j].objectness = 1; + float highest_prob = 0; + dets[j].best_class_idx = -1; for (i = 0; i < l.classes; ++i) { + if (probs[j][i] > highest_prob) { + highest_prob = probs[j][i]; + dets[j].best_class_idx = i; + } dets[j].prob[i] = probs[j][i]; } } From 568d8c18ab7323b68b16e5844040cd8d779dc7df Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Tue, 28 Sep 2021 23:02:02 +0300 Subject: [PATCH 1284/1365] fix for CUDA streams --- src/convolutional_layer.c | 1 + src/route_layer.c | 1 + 2 files changed, 2 insertions(+) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 7d64e1d51be..88bb260ab5b 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -523,6 +523,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, stride_x = stride_y = l.stride = l.stride_x = l.stride_y = 1; // use stride=1 in host-layer } + l.wait_stream_id = -1; l.deform = deform; l.assisted_excitation = assisted_excitation; l.share_layer = share_layer; diff --git a/src/route_layer.c b/src/route_layer.c index 9a3410c3190..c9232101867 100644 --- a/src/route_layer.c +++ b/src/route_layer.c @@ -15,6 +15,7 @@ route_layer make_route_layer(int batch, int n, int *input_layers, int *input_siz l.input_sizes = input_sizes; l.groups = groups; l.group_id = group_id; + l.wait_stream_id = -1; int i; int outputs = 0; for(i = 0; i < n; ++i){ From aef928cc0c0394f9e07232e037ea191b18ab3431 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 8 Oct 2021 00:20:20 +0200 Subject: [PATCH 1285/1365] remove travis ci (#8143) --- .travis.yml | 137 ---------------------------------------------------- README.md | 3 +- 2 files changed, 1 insertion(+), 139 deletions(-) delete mode 100644 .travis.yml diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index f208498dbcd..00000000000 --- a/.travis.yml +++ /dev/null @@ -1,137 +0,0 @@ -language: cpp -cache: - timeout: 1000 - directories: - - $HOME/vcpkg - - $HOME/.cache/vcpkg - -matrix: - include: - - - os: osx - compiler: gcc - name: macOS - gcc (llvm backend) - osx_image: xcode12.3 - env: - - additional_defines=" -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" - - MATRIX_EVAL="" - - - os: osx - compiler: clang - name: macOS - clang - osx_image: xcode12.3 - env: - - additional_defines=" -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" - - MATRIX_EVAL="" - - - os: osx - compiler: clang - name: macOS - clang - C++ mode - osx_image: xcode12.3 - env: - - additional_defines="-DBUILD_AS_CPP:BOOL=TRUE -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" - - MATRIX_EVAL="" - - - os: linux - compiler: clang - dist: bionic - name: ubuntu 18.04 - clang - env: - - additional_defines=" -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" - - MATRIX_EVAL="" - - - os: linux - compiler: gcc - dist: bionic - name: ubuntu 18.04 - gcc - env: - - additional_defines=" -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" - - MATRIX_EVAL="" - - - os: linux - compiler: gcc - dist: bionic - name: ubuntu 18.04 - g++ - env: - - additional_defines="-DBUILD_AS_CPP:BOOL=TRUE -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" - - MATRIX_EVAL="" - - - os: linux - compiler: clang - dist: bionic - name: ubuntu 18.04 - clang - opencv@3 - addons: - apt: - update: true - packages: - - libopencv-dev - env: - - additional_defines=" -DENABLE_CUDA=OFF" - - MATRIX_EVAL="" - - - os: linux - compiler: gcc - dist: bionic - name: ubuntu 18.04 - gcc - opencv@3 - addons: - apt: - update: true - packages: - - libopencv-dev - env: - - additional_defines=" -DENABLE_CUDA=OFF" - - MATRIX_EVAL="" - - - os: linux - compiler: clang - dist: bionic - name: ubuntu 18.04 - clang - CUDA 10 - opencv@3 - addons: - apt: - update: true - packages: - - libopencv-dev - env: - - additional_defines="" - - INSTALL_CUDA_VERSION="10.2" - - CUDACXX=/usr/local/cuda-${INSTALL_CUDA_VERSION}/bin/nvcc - - CUDA_PATH=/usr/local/cuda-${INSTALL_CUDA_VERSION} - - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-${INSTALL_CUDA_VERSION} - - LD_LIBRARY_PATH="/usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64:/usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.2.89-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1804_10.2.89-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-2 cuda-libraries-dev-10-2 cuda-driver-dev-10-2 cuda-cudart-dev-10-2 cuda-curand-dev-10-2 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs/libcuda.so.1" - - - os: linux - compiler: gcc - dist: bionic - name: ubuntu 18.04 - gcc - CUDA 10 - opencv@3 - addons: - apt: - update: true - packages: - - libopencv-dev - env: - - additional_defines="" - - INSTALL_CUDA_VERSION="10.2" - - CUDACXX=/usr/local/cuda-${INSTALL_CUDA_VERSION}/bin/nvcc - - CUDA_PATH=/usr/local/cuda-${INSTALL_CUDA_VERSION} - - CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-${INSTALL_CUDA_VERSION} - - LD_LIBRARY_PATH="/usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64:/usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs:${LD_LIBRARY_PATH}" - - MATRIX_EVAL="wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.2.89-1_amd64.deb && sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub && sudo dpkg -i cuda-repo-ubuntu1804_10.2.89-1_amd64.deb && wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb && sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb && sudo apt-get -y update && sudo apt-get install -y --no-install-recommends cuda-compiler-10-2 cuda-libraries-dev-10-2 cuda-driver-dev-10-2 cuda-cudart-dev-10-2 cuda-curand-dev-10-2 && sudo apt-get install -y --no-install-recommends libcudnn7-dev && sudo ln -s /usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${INSTALL_CUDA_VERSION}/lib64/stubs/libcuda.so.1" - -before_install: - - travis_retry eval "${MATRIX_EVAL}" - -install: - # CMake upgrade on Linux - - if [[ "$TRAVIS_OS_NAME" == "linux" ]] ; then wget --no-check-certificate https://github.com/Kitware/CMake/releases/download/v3.19.2/cmake-3.19.2-Linux-x86_64.tar.gz ; fi - - if [[ "$TRAVIS_OS_NAME" == "linux" ]] ; then tar -xzf cmake-3.19.2-Linux-x86_64.tar.gz ; fi - - if [[ "$TRAVIS_OS_NAME" == "linux" ]] ; then export PATH=$PWD/cmake-3.19.2-Linux-x86_64/bin:$PATH ; fi - -before_script: - - mkdir build_release - - cd build_release - - cmake .. -DCMAKE_BUILD_TYPE="Release" ${additional_defines} - - cd .. - -script: - - cd build_release && cmake --build . --target install -- -j8 && cd .. diff --git a/README.md b/README.md index 65b518726a3..5ce2ef76549 100644 --- a/README.md +++ b/README.md @@ -23,7 +23,6 @@ About Darknet framework: http://pjreddie.com/darknet/ [![Darknet Continuous Integration](https://github.com/AlexeyAB/darknet/workflows/Darknet%20Continuous%20Integration/badge.svg)](https://github.com/AlexeyAB/darknet/actions?query=workflow%3A%22Darknet+Continuous+Integration%22) [![CircleCI](https://circleci.com/gh/AlexeyAB/darknet.svg?style=svg)](https://circleci.com/gh/AlexeyAB/darknet) -[![TravisCI](https://travis-ci.org/AlexeyAB/darknet.svg?branch=master)](https://travis-ci.org/AlexeyAB/darknet) [![Contributors](https://img.shields.io/github/contributors/AlexeyAB/Darknet.svg)](https://github.com/AlexeyAB/darknet/graphs/contributors) [![License: Unlicense](https://img.shields.io/badge/license-Unlicense-blue.svg)](https://github.com/AlexeyAB/darknet/blob/master/LICENSE) [![DOI](https://zenodo.org/badge/75388965.svg)](https://zenodo.org/badge/latestdoi/75388965) @@ -66,7 +65,7 @@ About Darknet framework: http://pjreddie.com/darknet/ - [How to improve object detection](#how-to-improve-object-detection) - [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) - [How to use Yolo as DLL and SO libraries](#how-to-use-yolo-as-dll-and-so-libraries) -- [Citation](#citation) + - [Citation](#citation) ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) From 04b0b15ee42c3b54ae457eb639d9b666a9f5ca8d Mon Sep 17 00:00:00 2001 From: Raul Tambre Date: Sun, 24 Oct 2021 19:04:17 +0300 Subject: [PATCH 1286/1365] Link cuda.lib using a preprocessor macro only for non-CMake (#8173) Debian CUDA packages don't ship a shim for the Windows SO cuda.lib, so compilation fails. For CMake it's actually linked using targets, so avoid the preprocessor macro in that case. --- src/dark_cuda.c | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/src/dark_cuda.c b/src/dark_cuda.c index 8fdaee3fc7b..d155b408295 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -18,14 +18,13 @@ int gpu_index = 0; #include #include +#ifndef USE_CMAKE_LIBS #pragma comment(lib, "cuda.lib") - #ifdef CUDNN -#ifndef USE_CMAKE_LIBS #pragma comment(lib, "cudnn.lib") -#endif // USE_CMAKE_LIBS #endif // CUDNN +#endif // USE_CMAKE_LIBS #if defined(CUDNN_HALF) && !defined(CUDNN) #error "If you set CUDNN_HALF=1 then you must set CUDNN=1" From 354baa0ce3cfbe2e1ee06e217595f38cd74b7a8e Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 30 Oct 2021 05:43:46 +0300 Subject: [PATCH 1287/1365] Added yolov4-csp-s-mish.cfg, yolov4-csp-x-mish.cfg --- cfg/yolov4-csp-s-mish.cfg | 914 ++++++++++++++++++++++ cfg/yolov4-csp-x-mish.cfg | 1554 +++++++++++++++++++++++++++++++++++++ 2 files changed, 2468 insertions(+) create mode 100644 cfg/yolov4-csp-s-mish.cfg create mode 100644 cfg/yolov4-csp-x-mish.cfg diff --git a/cfg/yolov4-csp-s-mish.cfg b/cfg/yolov4-csp-s-mish.cfg new file mode 100644 index 00000000000..82efbf3a5ab --- /dev/null +++ b/cfg/yolov4-csp-s-mish.cfg @@ -0,0 +1,914 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=640 +height=640 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.00261 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +ema_alpha=0.9998 + +#optimized_memory=1 + + +# ============ Backbone ============ # + +# Stem + +# 0 +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +# P1 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=2 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +# 4 (previous+1+3k) +[shortcut] +from=-3 +activation=linear + +# P2 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-7 + +# Transition last + +# 14 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# P3 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1 -(4+3k)] + +[route] +layers = -1,-7 + +# Transition last + +# 24 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# P4 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1 -(3k+4)] + +[route] +layers = -1,-7 + +# Transition last + +# 34 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# P5 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1 -(3k+4)] + +[route] +layers = -1,-7 + +# Transition last + +# 44 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# ============ End of Backbone ============ # + +# ============ Neck ============ # + +# CSPSPP + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1, -11 + +# 57 (previous+6+5+2k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# End of CSPSPP + + +# FPN-4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 34 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -4 + +# Transition last + +# 69 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + + +# FPN-3 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 24 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=64 +activation=mish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -4 + +# Transition last + +# 81 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + + +# PAN-4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=128 +activation=mish + +[route] +layers = -1, 69 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[route] +layers = -1,-4 + +# Transition last + +# 90 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + + +# PAN-5 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1, 57 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1,-4 + +# Transition last + +# 99 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# ============ End of Neck ============ # + +# ============ Head ============ # + +# YOLO-3 + +[route] +layers = 81 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-4 + +[route] +layers = 90 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-5 + +[route] +layers = 99 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 \ No newline at end of file diff --git a/cfg/yolov4-csp-x-mish.cfg b/cfg/yolov4-csp-x-mish.cfg new file mode 100644 index 00000000000..aec8f53188b --- /dev/null +++ b/cfg/yolov4-csp-x-mish.cfg @@ -0,0 +1,1554 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=640 +height=640 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.00261 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +ema_alpha=0.9998 + +#optimized_memory=1 + + +# ============ Backbone ============ # + +# Stem + +# 0 +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +# P1 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=2 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=40 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +# 4 (previous+1+3k) +[shortcut] +from=-3 +activation=linear + +# P2 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-13 + +# Transition last + +# 20 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +# P3 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1 -(4+3k)] + +[route] +layers = -1,-34 + +# Transition last + +# 57 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +# P4 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1 -(3k+4)] + +[route] +layers = -1,-34 + +# Transition last + +# 94 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +# P5 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1 -(3k+4)] + +[route] +layers = -1,-19 + +# Transition last + +# 116 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=mish + +# ============ End of Backbone ============ # + +# ============ Neck ============ # + +# CSPSPP + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -15 + +# 133 (previous+6+5+2k) +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +# End of CSPSPP + + +# FPN-4 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 94 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 149 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + + +# FPN-3 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 57 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=mish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 165 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + + +# PAN-4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1, 149 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1,-8 + +# Transition last + +# 178 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + + +# PAN-5 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, 133 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1,-8 + +# Transition last + +# 191 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +# ============ End of Neck ============ # + +# ============ Head ============ # + +# YOLO-3 + +[route] +layers = 165 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-4 + +[route] +layers = 178 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-5 + +[route] +layers = 191 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 \ No newline at end of file From 9d40b619756be9521bc2ccd81808f502daaa3e9a Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 30 Oct 2021 05:44:32 +0300 Subject: [PATCH 1288/1365] Added yolov4-csp-s-mish.cfg, yolov4-csp-x-mish.cfg --- build/darknet/x64/cfg/yolov4-csp-s-mish.cfg | 914 +++++++++++ build/darknet/x64/cfg/yolov4-csp-x-mish.cfg | 1554 +++++++++++++++++++ 2 files changed, 2468 insertions(+) create mode 100644 build/darknet/x64/cfg/yolov4-csp-s-mish.cfg create mode 100644 build/darknet/x64/cfg/yolov4-csp-x-mish.cfg diff --git a/build/darknet/x64/cfg/yolov4-csp-s-mish.cfg b/build/darknet/x64/cfg/yolov4-csp-s-mish.cfg new file mode 100644 index 00000000000..82efbf3a5ab --- /dev/null +++ b/build/darknet/x64/cfg/yolov4-csp-s-mish.cfg @@ -0,0 +1,914 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=640 +height=640 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.00261 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +ema_alpha=0.9998 + +#optimized_memory=1 + + +# ============ Backbone ============ # + +# Stem + +# 0 +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +# P1 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=2 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +# 4 (previous+1+3k) +[shortcut] +from=-3 +activation=linear + +# P2 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-7 + +# Transition last + +# 14 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# P3 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1 -(4+3k)] + +[route] +layers = -1,-7 + +# Transition last + +# 24 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# P4 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1 -(3k+4)] + +[route] +layers = -1,-7 + +# Transition last + +# 34 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# P5 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1 -(3k+4)] + +[route] +layers = -1,-7 + +# Transition last + +# 44 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# ============ End of Backbone ============ # + +# ============ Neck ============ # + +# CSPSPP + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1, -11 + +# 57 (previous+6+5+2k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# End of CSPSPP + + +# FPN-4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 34 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -4 + +# Transition last + +# 69 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + + +# FPN-3 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 24 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=64 +activation=mish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -4 + +# Transition last + +# 81 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + + +# PAN-4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=128 +activation=mish + +[route] +layers = -1, 69 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[route] +layers = -1,-4 + +# Transition last + +# 90 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + + +# PAN-5 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1, 57 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[route] +layers = -1,-4 + +# Transition last + +# 99 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# ============ End of Neck ============ # + +# ============ Head ============ # + +# YOLO-3 + +[route] +layers = 81 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-4 + +[route] +layers = 90 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-5 + +[route] +layers = 99 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 \ No newline at end of file diff --git a/build/darknet/x64/cfg/yolov4-csp-x-mish.cfg b/build/darknet/x64/cfg/yolov4-csp-x-mish.cfg new file mode 100644 index 00000000000..aec8f53188b --- /dev/null +++ b/build/darknet/x64/cfg/yolov4-csp-x-mish.cfg @@ -0,0 +1,1554 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=8 +width=640 +height=640 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.00261 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +ema_alpha=0.9998 + +#optimized_memory=1 + + +# ============ Backbone ============ # + +# Stem + +# 0 +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +# P1 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=2 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=40 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +# 4 (previous+1+3k) +[shortcut] +from=-3 +activation=linear + +# P2 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1, -(3k+4)] + +[route] +layers = -1,-13 + +# Transition last + +# 20 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +# P3 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1 -(4+3k)] + +[route] +layers = -1,-34 + +# Transition last + +# 57 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +# P4 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1 -(3k+4)] + +[route] +layers = -1,-34 + +# Transition last + +# 94 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +# P5 + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +# Residual Block + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Transition first + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +# Merge [-1 -(3k+4)] + +[route] +layers = -1,-19 + +# Transition last + +# 116 (previous+7+3k) +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=mish + +# ============ End of Backbone ============ # + +# ============ Neck ============ # + +# CSPSPP + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -15 + +# 133 (previous+6+5+2k) +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +# End of CSPSPP + + +# FPN-4 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 94 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 149 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + + +# FPN-3 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 57 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=mish + +# Merge [-1, -(2k+2)] + +[route] +layers = -1, -8 + +# Transition last + +# 165 (previous+6+4+2k) +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + + +# PAN-4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1, 149 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1,-8 + +# Transition last + +# 178 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + + +# PAN-5 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, 133 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +# Split + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +# Plain Block + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1,-8 + +# Transition last + +# 191 (previous+3+4+2k) +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +# ============ End of Neck ============ # + +# ============ Head ============ # + +# YOLO-3 + +[route] +layers = 165 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-4 + +[route] +layers = 178 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# YOLO-5 + +[route] +layers = 191 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 \ No newline at end of file From 359001d360df2fe8b77ce56c60bb3d48b6d1faea Mon Sep 17 00:00:00 2001 From: AlexeyAB Date: Sat, 30 Oct 2021 06:04:18 +0300 Subject: [PATCH 1289/1365] Added yolov4_new.cfg --- build/darknet/x64/cfg/yolov4_new.cfg | 1176 ++++++++++++++++++++++++++ cfg/yolov4_new.cfg | 1176 ++++++++++++++++++++++++++ 2 files changed, 2352 insertions(+) create mode 100644 build/darknet/x64/cfg/yolov4_new.cfg create mode 100644 cfg/yolov4_new.cfg diff --git a/build/darknet/x64/cfg/yolov4_new.cfg b/build/darknet/x64/cfg/yolov4_new.cfg new file mode 100644 index 00000000000..2c28cf097ab --- /dev/null +++ b/build/darknet/x64/cfg/yolov4_new.cfg @@ -0,0 +1,1176 @@ +[net] +batch=64 +subdivisions=8 +# Training +#width=512 +#height=512 +width=608 +height=608 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.0013 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +ema_alpha=0.9998 + +#optimized_memory=1 + +#:104x104 54:52x52 85:26x26 104:13x13 for 416 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-7 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-10 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=mish + +########################## + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 85 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 54 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +########################## + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=leaky + +[route] +layers = -1, -16 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=leaky + +[route] +layers = -1, -37 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 diff --git a/cfg/yolov4_new.cfg b/cfg/yolov4_new.cfg new file mode 100644 index 00000000000..2c28cf097ab --- /dev/null +++ b/cfg/yolov4_new.cfg @@ -0,0 +1,1176 @@ +[net] +batch=64 +subdivisions=8 +# Training +#width=512 +#height=512 +width=608 +height=608 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.0013 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +ema_alpha=0.9998 + +#optimized_memory=1 + +#:104x104 54:52x52 85:26x26 104:13x13 for 416 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-7 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-10 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-28 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-16 + +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=mish + +########################## + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 85 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 54 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +########################## + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=leaky + +[route] +layers = -1, -16 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=512 +activation=leaky + +[route] +layers = -1, -37 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +#iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 From 4b35dbbf9a0608a928ea92f58fc83408464f86ce Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 12 Nov 2021 04:58:00 +0100 Subject: [PATCH 1290/1365] update to cuda 11.5 (#8174) --- .github/workflows/ccpp.yml | 157 ++++++++++++++++++++---------- .github/workflows/on_pr.yml | 85 ++++++++-------- README.md | 44 ++++----- build.ps1 | 119 +++++++++++++++++++++- scripts/deploy-cuda.ps1 | 16 +-- scripts/deploy-cuda.sh | 59 ++++++----- scripts/requested_cuda_version.sh | 4 + scripts/setup.ps1 | 6 +- scripts/setup.sh | 111 ++++++++++----------- 9 files changed, 389 insertions(+), 212 deletions(-) create mode 100755 scripts/requested_cuda_version.sh diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index d3e9d253b54..379da275549 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -21,13 +21,14 @@ jobs: run: sudo apt install libopencv-dev - name: 'Install CUDA' - run: ./scripts/deploy-cuda.sh + run: ${{ github.workspace }}/scripts/deploy-cuda.sh - name: 'Create softlinks for CUDA' run: | - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so + source ${{ github.workspace }}/scripts/requested_cuda_version.sh + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so - name: 'LIBSO=1 GPU=0 CUDNN=0 OPENCV=0' run: | @@ -78,13 +79,14 @@ jobs: run: sudo apt install yasm nasm - name: 'Install CUDA' - run: ./scripts/deploy-cuda.sh + run: ${{ github.workspace }}/scripts/deploy-cuda.sh - name: 'Create softlinks for CUDA' run: | - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so + source ${{ github.workspace }}/scripts/requested_cuda_version.sh + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -93,7 +95,13 @@ jobs: ./vcpkg/bootstrap-vcpkg.sh ; mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" - -Source http://93.49.111.10:5555/v3/index.json ; + -Source http://93.49.111.10:5555/v3/index.json + + - name: Setup NuGet API key if found + env: + BAGET_API_KEY: ${{ secrets.BAGET_API_KEY }} + if: env.BAGET_API_KEY != null + run: > mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) setapikey ${{ secrets.BAGET_API_KEY }} -Source http://93.49.111.10:5555/v3/index.json @@ -105,7 +113,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateDARKNET - uses: actions/upload-artifact@v2 with: @@ -138,13 +146,14 @@ jobs: run: sudo apt install yasm nasm - name: 'Install CUDA' - run: ./scripts/deploy-cuda.sh + run: ${{ github.workspace }}/scripts/deploy-cuda.sh - name: 'Create softlinks for CUDA' run: | - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so + source ${{ github.workspace }}/scripts/requested_cuda_version.sh + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -153,7 +162,13 @@ jobs: ./vcpkg/bootstrap-vcpkg.sh ; mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" - -Source http://93.49.111.10:5555/v3/index.json ; + -Source http://93.49.111.10:5555/v3/index.json + + - name: Setup NuGet API key if found + env: + BAGET_API_KEY: ${{ secrets.BAGET_API_KEY }} + if: env.BAGET_API_KEY != null + run: > mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) setapikey ${{ secrets.BAGET_API_KEY }} -Source http://93.49.111.10:5555/v3/index.json @@ -165,7 +180,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 3 -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 3 -DisableInteractive -DoNotUpdateDARKNET ubuntu-vcpkg-opencv2-cuda: @@ -181,13 +196,14 @@ jobs: run: sudo apt install yasm nasm - name: 'Install CUDA' - run: ./scripts/deploy-cuda.sh + run: ${{ github.workspace }}/scripts/deploy-cuda.sh - name: 'Create softlinks for CUDA' run: | - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so + source ${{ github.workspace }}/scripts/requested_cuda_version.sh + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -196,7 +212,13 @@ jobs: ./vcpkg/bootstrap-vcpkg.sh ; mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" - -Source http://93.49.111.10:5555/v3/index.json ; + -Source http://93.49.111.10:5555/v3/index.json + + - name: Setup NuGet API key if found + env: + BAGET_API_KEY: ${{ secrets.BAGET_API_KEY }} + if: env.BAGET_API_KEY != null + run: > mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) setapikey ${{ secrets.BAGET_API_KEY }} -Source http://93.49.111.10:5555/v3/index.json @@ -208,7 +230,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 2 -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 2 -DisableInteractive -DoNotUpdateDARKNET ubuntu: @@ -230,7 +252,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ./build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET - uses: actions/upload-artifact@v2 with: @@ -263,13 +285,14 @@ jobs: - uses: lukka/get-cmake@latest - name: 'Install CUDA' - run: ./scripts/deploy-cuda.sh + run: ${{ github.workspace }}/scripts/deploy-cuda.sh - name: 'Create softlinks for CUDA' run: | - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so + source ${{ github.workspace }}/scripts/requested_cuda_version.sh + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so - name: 'Build' shell: pwsh @@ -278,7 +301,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ./build.ps1 -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateDARKNET - uses: actions/upload-artifact@v2 with: @@ -307,7 +330,7 @@ jobs: - name: 'Build' shell: pwsh - run: ./build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET ubuntu-setup-sh: @@ -322,14 +345,20 @@ jobs: ./vcpkg/bootstrap-vcpkg.sh ; mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" - -Source http://93.49.111.10:5555/v3/index.json ; + -Source http://93.49.111.10:5555/v3/index.json + + - name: Setup NuGet API key if found + env: + BAGET_API_KEY: ${{ secrets.BAGET_API_KEY }} + if: env.BAGET_API_KEY != null + run: > mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) setapikey ${{ secrets.BAGET_API_KEY }} -Source http://93.49.111.10:5555/v3/index.json - name: 'Setup' shell: bash - run: ./scripts/setup.sh -InstallCUDA -BypassDRIVER + run: ${{ github.workspace }}/scripts/setup.sh -InstallTOOLS -InstallCUDA -BypassDRIVER osx-vcpkg: @@ -349,14 +378,20 @@ jobs: ./vcpkg/bootstrap-vcpkg.sh ; mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" - -Source http://93.49.111.10:5555/v3/index.json ; + -Source http://93.49.111.10:5555/v3/index.json + + - name: Setup NuGet API key if found + env: + BAGET_API_KEY: ${{ secrets.BAGET_API_KEY }} + if: env.BAGET_API_KEY != null + run: > mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) setapikey ${{ secrets.BAGET_API_KEY }} -Source http://93.49.111.10:5555/v3/index.json - name: 'Build' shell: pwsh - run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -DisableInteractive -DoNotUpdateDARKNET - uses: actions/upload-artifact@v2 with: @@ -388,7 +423,7 @@ jobs: - name: 'Build' shell: pwsh - run: ./build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET - uses: actions/upload-artifact@v2 with: @@ -417,7 +452,7 @@ jobs: - name: 'Build' shell: pwsh - run: ./build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET win-vcpkg: @@ -434,14 +469,20 @@ jobs: ./vcpkg/bootstrap-vcpkg.sh ; $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" - -Source http://93.49.111.10:5555/v3/index.json ; - $(./vcpkg/vcpkg fetch nuget | tail -n 1) + -Source http://93.49.111.10:5555/v3/index.json + + - name: Setup NuGet API key if found + env: + BAGET_API_KEY: ${{ secrets.BAGET_API_KEY }} + if: env.BAGET_API_KEY != null + run: > + mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) setapikey ${{ secrets.BAGET_API_KEY }} -Source http://93.49.111.10:5555/v3/index.json - name: 'Build' shell: pwsh - run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET - uses: actions/upload-artifact@v2 with: @@ -477,14 +518,20 @@ jobs: ./vcpkg/bootstrap-vcpkg.sh ; $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" - -Source http://93.49.111.10:5555/v3/index.json ; - $(./vcpkg/vcpkg fetch nuget | tail -n 1) + -Source http://93.49.111.10:5555/v3/index.json + + - name: Setup NuGet API key if found + env: + BAGET_API_KEY: ${{ secrets.BAGET_API_KEY }} + if: env.BAGET_API_KEY != null + run: > + mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) setapikey ${{ secrets.BAGET_API_KEY }} -Source http://93.49.111.10:5555/v3/index.json - name: 'Build' shell: pwsh - run: ./build.ps1 -UseVCPKG -InstallDARKNETthroughVCPKG -ForceVCPKGDarknetHEAD -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -InstallDARKNETthroughVCPKG -ForceVCPKGDarknetHEAD -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET win-intlibs: @@ -496,7 +543,7 @@ jobs: - name: 'Build' shell: pwsh - run: ./build.ps1 -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -DisableInteractive -DoNotUpdateDARKNET - uses: actions/upload-artifact@v2 with: @@ -532,14 +579,20 @@ jobs: ./vcpkg/bootstrap-vcpkg.sh ; $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" - -Source http://93.49.111.10:5555/v3/index.json ; - $(./vcpkg/vcpkg fetch nuget | tail -n 1) + -Source http://93.49.111.10:5555/v3/index.json + + - name: Setup NuGet API key if found + env: + BAGET_API_KEY: ${{ secrets.BAGET_API_KEY }} + if: env.BAGET_API_KEY != null + run: > + mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) setapikey ${{ secrets.BAGET_API_KEY }} -Source http://93.49.111.10:5555/v3/index.json - name: 'Setup' shell: pwsh - run: ./scripts/setup.ps1 -InstallCUDA + run: ${{ github.workspace }}/scripts/setup.ps1 -InstallCUDA win-intlibs-cpp: @@ -551,7 +604,7 @@ jobs: - name: 'Build' shell: pwsh - run: ./build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET win-csharp: @@ -563,7 +616,7 @@ jobs: - name: 'Build' shell: pwsh - run: ./build.ps1 -EnableCSharpWrapper -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -EnableCSharpWrapper -DisableInteractive -DoNotUpdateDARKNET win-intlibs-cuda: @@ -571,17 +624,17 @@ jobs: steps: - uses: actions/checkout@v2 - name: 'Install CUDA' - run: ./scripts/deploy-cuda.ps1 + run: ${{ github.workspace }}/scripts/deploy-cuda.ps1 - uses: lukka/get-cmake@latest - name: 'Build' env: - CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.4" - CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.4" - CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.4\\bin\\nvcc.exe" + CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.5" + CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.5" + CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.5\\bin\\nvcc.exe" shell: pwsh - run: ./build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET mingw: diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index 090e5acea7d..9e88aa7f6b2 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -17,13 +17,14 @@ jobs: run: sudo apt install libopencv-dev - name: 'Install CUDA' - run: ./scripts/deploy-cuda.sh + run: ${{ github.workspace }}/scripts/deploy-cuda.sh - name: 'Create softlinks for CUDA' run: | - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so + source ${{ github.workspace }}/scripts/requested_cuda_version.sh + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so - name: 'LIBSO=1 GPU=0 CUDNN=0 OPENCV=0' run: | @@ -74,13 +75,14 @@ jobs: run: sudo apt install yasm nasm - name: 'Install CUDA' - run: ./scripts/deploy-cuda.sh + run: ${{ github.workspace }}/scripts/deploy-cuda.sh - name: 'Create softlinks for CUDA' run: | - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so + source ${{ github.workspace }}/scripts/requested_cuda_version.sh + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -98,7 +100,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateDARKNET ubuntu-vcpkg-opencv3-cuda: @@ -114,13 +116,14 @@ jobs: run: sudo apt install yasm nasm - name: 'Install CUDA' - run: ./scripts/deploy-cuda.sh + run: ${{ github.workspace }}/scripts/deploy-cuda.sh - name: 'Create softlinks for CUDA' run: | - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so + source ${{ github.workspace }}/scripts/requested_cuda_version.sh + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -138,7 +141,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 3 -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 3 -DisableInteractive -DoNotUpdateDARKNET ubuntu-vcpkg-opencv2-cuda: @@ -154,13 +157,14 @@ jobs: run: sudo apt install yasm nasm - name: 'Install CUDA' - run: ./scripts/deploy-cuda.sh + run: ${{ github.workspace }}/scripts/deploy-cuda.sh - name: 'Create softlinks for CUDA' run: | - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so + source ${{ github.workspace }}/scripts/requested_cuda_version.sh + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -178,7 +182,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 2 -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 2 -DisableInteractive -DoNotUpdateDARKNET ubuntu: @@ -200,7 +204,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ./build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET ubuntu-cuda: @@ -216,13 +220,14 @@ jobs: - uses: lukka/get-cmake@latest - name: 'Install CUDA' - run: ./scripts/deploy-cuda.sh + run: ${{ github.workspace }}/scripts/deploy-cuda.sh - name: 'Create softlinks for CUDA' run: | - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so + source ${{ github.workspace }}/scripts/requested_cuda_version.sh + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so - name: 'Build' shell: pwsh @@ -231,7 +236,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ./build.ps1 -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateDARKNET ubuntu-no-ocv-cpp: @@ -243,7 +248,7 @@ jobs: - name: 'Build' shell: pwsh - run: ./build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET ubuntu-setup-sh: @@ -262,7 +267,7 @@ jobs: - name: 'Setup' shell: bash - run: ./scripts/setup.sh -InstallCUDA -BypassDRIVER + run: ${{ github.workspace }}/scripts/setup.sh -InstallTOOLS -InstallCUDA -BypassDRIVER osx-vcpkg: @@ -286,7 +291,7 @@ jobs: - name: 'Build' shell: pwsh - run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -DisableInteractive -DoNotUpdateDARKNET osx: @@ -301,7 +306,7 @@ jobs: - name: 'Build' shell: pwsh - run: ./build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET osx-no-ocv-no-omp-cpp: @@ -313,7 +318,7 @@ jobs: - name: 'Build' shell: pwsh - run: ./build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET win-vcpkg: @@ -334,7 +339,7 @@ jobs: - name: 'Build' shell: pwsh - run: ./build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET win-intlibs: @@ -346,7 +351,7 @@ jobs: - name: 'Build' shell: pwsh - run: ./build.ps1 -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -DisableInteractive -DoNotUpdateDARKNET win-setup-ps1: @@ -365,7 +370,7 @@ jobs: - name: 'Setup' shell: pwsh - run: ./scripts/setup.ps1 -InstallCUDA + run: ${{ github.workspace }}/scripts/setup.ps1 -InstallCUDA win-intlibs-cpp: @@ -377,7 +382,7 @@ jobs: - name: 'Build' shell: pwsh - run: ./build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET win-csharp: @@ -389,7 +394,7 @@ jobs: - name: 'Build' shell: pwsh - run: ./build.ps1 -EnableCSharpWrapper -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -EnableCSharpWrapper -DisableInteractive -DoNotUpdateDARKNET win-intlibs-cuda: @@ -397,17 +402,17 @@ jobs: steps: - uses: actions/checkout@v2 - name: 'Install CUDA' - run: ./scripts/deploy-cuda.ps1 + run: ${{ github.workspace }}/scripts/deploy-cuda.ps1 - uses: lukka/get-cmake@latest - name: 'Build' env: - CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.4" - CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.4" - CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.4\\bin\\nvcc.exe" + CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.5" + CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.5" + CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.5\\bin\\nvcc.exe" shell: pwsh - run: ./build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET mingw: diff --git a/README.md b/README.md index 5ce2ef76549..7567592575c 100644 --- a/README.md +++ b/README.md @@ -216,7 +216,7 @@ You can get cfg-files by path: `darknet/cfg/` - **Powershell** (already installed on windows): https://docs.microsoft.com/en-us/powershell/scripting/install/installing-powershell - **CUDA >= 10.2**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) - **OpenCV >= 2.4**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or download from [OpenCV official site](https://opencv.org/releases.html) (on Windows set system variable `OpenCV_DIR` = `C:\opencv\build` - where are the `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png)) -- **cuDNN >= 8.0.2** https://developer.nvidia.com/rdp/cudnn-archive (on **Linux** copy `cudnn.h`,`libcudnn.so`... as described here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on **Windows** copy `cudnn.h`,`cudnn64_7.dll`, `cudnn64_7.lib` as described here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows ) +- **cuDNN >= 8.0.2** https://developer.nvidia.com/rdp/cudnn-archive (on **Linux** follow steps described here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on **Windows** follow steps described here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows) - **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported ### Yolo v4 in other frameworks @@ -288,33 +288,35 @@ Also, you might be interested in using a simplified repository where is implemen #### How to use on the command line -On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights` +If you use `build.ps1` script or the makefile (Linux only) you will find `darknet` in the root directory. -On Linux find executable file `./darknet` in the root directory, while on Windows find it in the directory `\build\darknet\x64` +If you use the deprecated Visual Studio solutions, you will find `darknet` in the directory `\build\darknet\x64`. -- Yolo v4 COCO - **image**: `darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25` -- **Output coordinates** of objects: `darknet.exe detector test cfg/coco.data yolov4.cfg yolov4.weights -ext_output dog.jpg` -- Yolo v4 COCO - **video**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output test.mp4` -- Yolo v4 COCO - **WebCam 0**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -c 0` -- Yolo v4 COCO for **net-videocam** - Smart WebCam: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg` -- Yolo v4 - **save result videofile res.avi**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -out_filename res.avi` -- Yolo v3 **Tiny** COCO - video: `darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4` +If you customize build with CMake GUI, darknet executable will be installed in your preferred folder. + +- Yolo v4 COCO - **image**: `./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25` +- **Output coordinates** of objects: `./darknet detector test cfg/coco.data yolov4.cfg yolov4.weights -ext_output dog.jpg` +- Yolo v4 COCO - **video**: `./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output test.mp4` +- Yolo v4 COCO - **WebCam 0**: `./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -c 0` +- Yolo v4 COCO for **net-videocam** - Smart WebCam: `./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg` +- Yolo v4 - **save result videofile res.avi**: `./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -out_filename res.avi` +- Yolo v3 **Tiny** COCO - video: `./darknet detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4` - **JSON and MJPEG server** that allows multiple connections from your soft or Web-browser `ip-address:8070` and 8090: `./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output` -- Yolo v3 Tiny **on GPU #1**: `darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 1 test.mp4` -- Alternative method Yolo v3 COCO - image: `darknet.exe detect cfg/yolov4.cfg yolov4.weights -i 0 -thresh 0.25` +- Yolo v3 Tiny **on GPU #1**: `./darknet detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 1 test.mp4` +- Alternative method Yolo v3 COCO - image: `./darknet detect cfg/yolov4.cfg yolov4.weights -i 0 -thresh 0.25` - Train on **Amazon EC2**, to see mAP & Loss-chart using URL like: `http://ec2-35-160-228-91.us-west-2.compute.amazonaws.com:8090` in the Chrome/Firefox (**Darknet should be compiled with OpenCV**): `./darknet detector train cfg/coco.data yolov4.cfg yolov4.conv.137 -dont_show -mjpeg_port 8090 -map` -- 186 MB Yolo9000 - image: `darknet.exe detector test cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights` +- 186 MB Yolo9000 - image: `./darknet detector test cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights` - Remember to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app - To process a list of images `data/train.txt` and save results of detection to `result.json` file use: - `darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output -dont_show -out result.json < data/train.txt` + `./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output -dont_show -out result.json < data/train.txt` - To process a list of images `data/train.txt` and save results of detection to `result.txt` use: - `darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -dont_show -ext_output < data/train.txt > result.txt` + `./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -dont_show -ext_output < data/train.txt > result.txt` - Pseudo-labelling - to process a list of images `data/new_train.txt` and save results of detection in Yolo training format for each image as label `.txt` (in this way you can increase the amount of training data) use: - `darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25 -dont_show -save_labels < data/new_train.txt` -- To calculate anchors: `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416` -- To check accuracy mAP@IoU=50: `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights` -- To check accuracy mAP@IoU=75: `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights -iou_thresh 0.75` + `./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25 -dont_show -save_labels < data/new_train.txt` +- To calculate anchors: `./darknet detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416` +- To check accuracy mAP@IoU=50: `./darknet detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights` +- To check accuracy mAP@IoU=75: `./darknet detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights -iou_thresh 0.75` ##### For using network video-camera mjpeg-stream with any Android smartphone @@ -327,7 +329,7 @@ On Linux find executable file `./darknet` in the root directory, while on Window 3. Start Smart WebCam on your phone 4. Replace the address below, on shown in the phone application (Smart WebCam) and launch: -- Yolo v4 COCO-model: `darknet.exe detector demo data/coco.data yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` +- Yolo v4 COCO-model: `./darknet detector demo data/coco.data yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` ### How to compile on Linux/macOS (using `CMake`) @@ -381,8 +383,6 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov4.cfg yolov4.weights zed_camera` - You also need to specify for which graphics card the code is generated. This is done by setting `ARCH=`. If you use a never version than CUDA 11 you further need to edit line 20 from Makefile and remove `-gencode arch=compute_30,code=sm_30 \` as Kepler GPU support was dropped in CUDA 11. You can also drop the general `ARCH=` and just uncomment `ARCH=` for your graphics card. -To run Darknet on Linux use examples from this article, just use `./darknet` instead of `darknet.exe`, i.e. use this command: `./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights` - ### How to compile on Windows (using `CMake`) Requires: diff --git a/build.ps1 b/build.ps1 index 92342afd8b5..e2b4b7f102f 100755 --- a/build.ps1 +++ b/build.ps1 @@ -1,5 +1,95 @@ #!/usr/bin/env pwsh +<# + +.SYNOPSIS + build + Created By: Stefano Sinigardi + Created Date: February 18, 2019 + Last Modified Date: November 10, 2021 + +.DESCRIPTION +Build darknet using CMake, trying to properly setup the environment around compiler + +.PARAMETER DisableInteractive +Disable script interactivity (useful for CI runs) + +.PARAMETER EnableCUDA +Enable CUDA feature + +.PARAMETER EnableCUDNN +Enable CUDNN feature + +.PARAMETER EnableOPENCV +Build darknet linking to OpenCV + +.PARAMETER EnableOPENCV_CUDA +Use a CUDA-enabled OpenCV build + +.PARAMETER UseVCPKG +Use VCPKG to build darknet dependencies. Clone it if not already found on system + +.PARAMETER InstallDARKNETthroughVCPKG +Use VCPKG to install darknet thanks to the port integrated in it + +.PARAMETER InstallDARKNETdependenciesThroughVCPKGManifest +Use VCPKG to install darknet dependencies using vcpkg manifest feature + +.PARAMETER ForceVCPKGDarknetHEAD +Install darknet from vcpkg and force it to HEAD version, not latest port release + +.PARAMETER DoNotUpdateVCPKG +Do not update vcpkg before running the build (valid only if vcpkg is cloned by this script or the version found on the system is git-enabled) + +.PARAMETER DoNotUpdateDARKNET +Do not update darknet before running the build (valid only if darknet is git-enabled) + +.PARAMETER DoNotDeleteBuildFolder +Do not delete temporary cmake build folder at the end of the script + +.PARAMETER DoNotSetupVS +Do not setup VisualStudio environment using the vcvars script + +.PARAMETER DoNotUseNinja +Do not use Ninja for build + +.PARAMETER ForceCPP +Force building darknet using C++ compiler also for plain C code + +.PARAMETER ForceStaticLib +Create darknet library as static instead of the default linking mode of your system + +.PARAMETER ForceVCPKGCacheRemoval +Force clean up of the local vcpkg binary cache before building + +.PARAMETER DoNotDeleteBuildtreesFolder +Do not delete vcpkg buildtrees temp folder at the end of the script + +.PARAMETER ForceSetupVS +Forces Visual Studio setup, also on systems on which it would not have been enabled automatically + +.PARAMETER EnableCSharpWrapper +Enables building C# darknet wrapper + +.PARAMETER DownloadWeights +Download pre-trained weight files + +.PARAMETER ForceGCCVersion +Force a specific GCC version + +.PARAMETER ForceOpenCVVersion +Force a specific OpenCV version (valid only with vcpkg-enabled builds) + +.PARAMETER NumberOfBuildWorkers +Forces a specific number of threads for parallel building + +.PARAMETER AdditionalBuildSetup +Additional setup parameters to manually pass to CMake + +.EXAMPLE +.\build -DisableInteractive -DoNotDeleteBuildFolder -UseVCPKG + +#> param ( [switch]$DisableInteractive = $false, @@ -19,6 +109,7 @@ param ( [switch]$ForceCPP = $false, [switch]$ForceStaticLib = $false, [switch]$ForceVCPKGCacheRemoval = $false, + [switch]$DoNotDeleteBuildtreesFolder = $false, [switch]$ForceSetupVS = $false, [switch]$EnableCSharpWrapper = $false, [switch]$DownloadWeights = $false, @@ -28,7 +119,7 @@ param ( [string]$AdditionalBuildSetup = "" # "-DCMAKE_CUDA_ARCHITECTURES=30" ) -$build_ps1_version = "0.9.6" +$build_ps1_version = "0.9.7" $ErrorActionPreference = "SilentlyContinue" Stop-Transcript | out-null @@ -389,6 +480,14 @@ function getLatestVisualStudioWithDesktopWorkloadPath() { $installationPath = $instance.InstallationPath -replace "\\$" # Remove potential trailing backslash } } + if (!$installationPath) { + Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also pre-release installations" -ForegroundColor Yellow + $output = & $vswhereExe -prerelease -products * -latest -format xml + [xml]$asXml = $output + foreach ($instance in $asXml.instances.instance) { + $installationPath = $instance.InstallationPath -replace "\\$" # Remove potential trailing backslash + } + } if (!$installationPath) { MyThrow("Could not locate any installation of Visual Studio") } @@ -417,6 +516,14 @@ function getLatestVisualStudioWithDesktopWorkloadVersion() { $installationVersion = $instance.installationVersion } } + if (!$installationVersion) { + Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also pre-release installations" -ForegroundColor Yellow + $output = & $vswhereExe -prerelease -products * -latest -format xml + [xml]$asXml = $output + foreach ($instance in $asXml.instances.instance) { + $installationVersion = $instance.installationVersion + } + } if (!$installationVersion) { MyThrow("Could not locate any installation of Visual Studio") } @@ -506,6 +613,11 @@ if ($ForceVCPKGCacheRemoval -and (-Not $UseVCPKG)) { Write-Host "VCPKG is not enabled, so local vcpkg binary cache will not be deleted even if requested" -ForegroundColor Yellow } +if ($UseVCPKG -and (-Not $DoNotDeleteBuildtreesFolder)) { + Write-Host "Cleaning folder buildtrees inside vcpkg" -ForegroundColor Yellow + Remove-Item -Force -Recurse -ErrorAction SilentlyContinue "$env:VCPKG_ROOT/buildtrees" +} + if (($ForceOpenCVVersion -eq 2) -and $UseVCPKG) { Write-Host "You requested OpenCV version 2, so vcpkg will install that version" -ForegroundColor Yellow $AdditionalBuildSetup = $AdditionalBuildSetup + " -DVCPKG_USE_OPENCV4=OFF -DVCPKG_USE_OPENCV2=ON" @@ -729,6 +841,11 @@ else { Pop-Location } +if ($UseVCPKG -and (-Not $DoNotDeleteBuildtreesFolder)) { + Write-Host "Cleaning folder buildtrees inside vcpkg" -ForegroundColor Yellow + Remove-Item -Force -Recurse -ErrorAction SilentlyContinue "$env:VCPKG_ROOT/buildtrees" +} + Write-Host "Build complete!" -ForegroundColor Green if ($DownloadWeights) { diff --git a/scripts/deploy-cuda.ps1 b/scripts/deploy-cuda.ps1 index 78ce6e507f4..36682ba3a8b 100644 --- a/scripts/deploy-cuda.ps1 +++ b/scripts/deploy-cuda.ps1 @@ -1,18 +1,18 @@ #!/usr/bin/env pwsh -$url = 'https://developer.download.nvidia.com/compute/cuda/11.4.0/network_installers/cuda_11.4.0_win10_network.exe' +$url = 'https://developer.download.nvidia.com/compute/cuda/11.5.0/network_installers/cuda_11.5.0_win10_network.exe' -$CudaFeatures = 'nvcc_11.4 cuobjdump_11.4 nvprune_11.4 cupti_11.4 memcheck_11.4 nvdisasm_11.4 nvprof_11.4 ' + ` - 'visual_studio_integration_11.4 visual_profiler_11.4 visual_profiler_11.4 cublas_11.4 cublas_dev_11.4 ' + ` - 'cudart_11.4 cufft_11.4 cufft_dev_11.4 curand_11.4 curand_dev_11.4 cusolver_11.4 cusolver_dev_11.4 ' + ` - 'cusparse_11.4 cusparse_dev_11.4 npp_11.4 npp_dev_11.4 nvrtc_11.4 nvrtc_dev_11.4 nvml_dev_11.4 ' + ` - 'occupancy_calculator_11.4 ' +$CudaFeatures = 'nvcc_11.5 cuobjdump_11.5 nvprune_11.5 cupti_11.5 memcheck_11.5 nvdisasm_11.5 nvprof_11.5 ' + ` + 'visual_studio_integration_11.5 visual_profiler_11.5 visual_profiler_11.5 cublas_11.5 cublas_dev_11.5 ' + ` + 'cudart_11.5 cufft_11.5 cufft_dev_11.5 curand_11.5 curand_dev_11.5 cusolver_11.5 cusolver_dev_11.5 ' + ` + 'cusparse_11.5 cusparse_dev_11.5 npp_11.5 npp_dev_11.5 nvrtc_11.5 nvrtc_dev_11.5 nvml_dev_11.5 ' + ` + 'occupancy_calculator_11.5 ' try { Write-Host 'Downloading CUDA...' - Invoke-WebRequest -Uri $url -OutFile "cuda_11.4.0_win10_network.exe" + Invoke-WebRequest -Uri $url -OutFile "cuda_11.5.0_win10_network.exe" Write-Host 'Installing CUDA...' - $proc = Start-Process -PassThru -FilePath "./cuda_11.4.0_win10_network.exe" -ArgumentList @('-s ' + $CudaFeatures) + $proc = Start-Process -PassThru -FilePath "./cuda_11.5.0_win10_network.exe" -ArgumentList @('-s ' + $CudaFeatures) $proc.WaitForExit() $exitCode = $proc.ExitCode if ($exitCode -eq 0) { diff --git a/scripts/deploy-cuda.sh b/scripts/deploy-cuda.sh index 92da66c0b16..267e3270981 100755 --- a/scripts/deploy-cuda.sh +++ b/scripts/deploy-cuda.sh @@ -2,36 +2,33 @@ if [[ "$OSTYPE" == "darwin"* ]]; then echo "Unable to deploy CUDA on macOS, please wait for a future script update" + exit 1 +elif [[ $(cut -f2 <<< $(lsb_release -i)) == "Ubuntu" ]]; then + distr_name="$(cut -f2 <<< $(lsb_release -i) | tr '[:upper:]' '[:lower:]')$(cut -f2 <<< $(lsb_release -r) | tr -d '.')" else - if [[ $(cut -f2 <<< $(lsb_release -r)) == "18.04" ]]; then - sudo apt-get update - sudo apt-get install build-essential g++ - sudo apt-get install apt-transport-https ca-certificates gnupg software-properties-common wget - sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin - sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub - sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /" - sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/ /" - sudo apt-get update - sudo apt-get dist-upgrade -y - sudo apt-get install -y --no-install-recommends cuda-compiler-11-4 cuda-libraries-dev-11-4 cuda-driver-dev-11-4 cuda-cudart-dev-11-4 - sudo apt-get install -y --no-install-recommends libcudnn8-dev - sudo rm -rf /usr/local/cuda - sudo ln -s /usr/local/cuda-11.4 /usr/local/cuda - elif [[ $(cut -f2 <<< $(lsb_release -r)) == "20.04" ]]; then - sudo apt-get update - sudo apt-get install build-essential g++ - sudo apt-get install apt-transport-https ca-certificates gnupg software-properties-common wget - sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin - sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub - sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /" - sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/ /" - sudo apt-get update - sudo apt-get dist-upgrade -y - sudo apt-get install -y --no-install-recommends cuda-compiler-11-4 cuda-libraries-dev-11-4 cuda-driver-dev-11-4 cuda-cudart-dev-11-4 - sudo apt-get install -y --no-install-recommends libcudnn8-dev - sudo rm -rf /usr/local/cuda - sudo ln -s /usr/local/cuda-11.4 /usr/local/cuda - else - echo "Unable to deploy CUDA on this Linux version, please wait for a future script update" - fi + echo "Unable to deploy CUDA on this OS, please wait for a future script update" + exit 3 fi + +script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" + +if [ -f $script_dir/requested_cuda_version.sh ]; then + source $script_dir/requested_cuda_version.sh +else + echo "Unable to find requested_cuda_version.sh script" + exit 1 +fi + +sudo apt-get update +sudo apt-get install build-essential g++ +sudo apt-get install apt-transport-https ca-certificates gnupg software-properties-common wget +sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/$distr_name/x86_64/cuda-$distr_name.pin +sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/$distr_name/x86_64/7fa2af80.pub +sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/$distr_name/x86_64/ /" +sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/$distr_name/x86_64/ /" +sudo apt-get update +sudo apt-get dist-upgrade -y +sudo apt-get install -y --no-install-recommends cuda-compiler-${CUDA_VERSION_DASHED} cuda-libraries-dev-${CUDA_VERSION_DASHED} cuda-driver-dev-${CUDA_VERSION_DASHED} cuda-cudart-dev-${CUDA_VERSION_DASHED} +sudo apt-get install -y --no-install-recommends libcudnn8-dev +sudo rm -rf /usr/local/cuda +sudo ln -s /usr/local/cuda-${CUDA_VERSION} /usr/local/cuda diff --git a/scripts/requested_cuda_version.sh b/scripts/requested_cuda_version.sh new file mode 100755 index 00000000000..190a5f5b743 --- /dev/null +++ b/scripts/requested_cuda_version.sh @@ -0,0 +1,4 @@ +#!/usr/bin/env bash + +export CUDA_VERSION="11.5" +export CUDA_VERSION_DASHED="${CUDA_VERSION//./-}" diff --git a/scripts/setup.ps1 b/scripts/setup.ps1 index 1ebd9f02eed..913c8f747c2 100755 --- a/scripts/setup.ps1 +++ b/scripts/setup.ps1 @@ -16,9 +16,9 @@ Push-Location $PSScriptRoot if ($InstallCUDA) { & $PSScriptRoot/deploy-cuda.ps1 - $env:CUDA_PATH="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.4" - $env:CUDA_TOOLKIT_ROOT_DIR="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.4" - $env:CUDACXX="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.4\\bin\\nvcc.exe" + $env:CUDA_PATH="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.5" + $env:CUDA_TOOLKIT_ROOT_DIR="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.5" + $env:CUDACXX="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.5\\bin\\nvcc.exe" $CUDAisAvailable = $true } else { diff --git a/scripts/setup.sh b/scripts/setup.sh index 67aa744871b..7b57ca5b8dc 100755 --- a/scripts/setup.sh +++ b/scripts/setup.sh @@ -1,6 +1,7 @@ #!/usr/bin/env bash install_tools=false +install_cuda=false bypass_driver_installation=false POSITIONAL=() @@ -10,6 +11,10 @@ key="$1" case $key in -InstallCUDA|--InstallCUDA) + install_cuda=true + shift + ;; + -InstallTOOLS|--InstallTOOLS) install_tools=true shift ;; @@ -32,62 +37,58 @@ temp_folder="./temp" mkdir -p $temp_folder cd $temp_folder -if [ "$install_tools" = true ] ; then - $script_dir/deploy-cuda.sh - if [[ "$OSTYPE" == "darwin"* ]]; then - echo "Unable to provide tools on macOS, please wait for a future script update or do not put -InstallCUDA command line flag to continue" - else - if [[ $(cut -f2 <<< $(lsb_release -r)) == "18.04" ]]; then - sudo apt-get update - sudo apt-get install git ninja-build build-essential g++ nasm yasm - sudo apt-get install apt-transport-https ca-certificates gnupg software-properties-common wget - wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | gpg --dearmor - | sudo tee /etc/apt/trusted.gpg.d/kitware.gpg >/dev/null - sudo apt-add-repository 'deb https://apt.kitware.com/ubuntu/ bionic main' - wget -q https://packages.microsoft.com/config/ubuntu/18.04/packages-microsoft-prod.deb - sudo dpkg -i packages-microsoft-prod.deb - sudo add-apt-repository universe - sudo apt-get update - sudo apt-get dist-upgrade -y - sudo apt-get install -y cmake - sudo apt-get install -y powershell - if [ "$bypass_driver_installation" = true ] ; then - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so - fi - export PATH=/usr/local/cuda/bin:$PATH - export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH - export CUDACXX=/usr/local/cuda/bin/nvcc - export CUDA_PATH=/usr/local/cuda - export CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda - cuda_is_available=true - elif [[ $(cut -f2 <<< $(lsb_release -r)) == "20.04" ]]; then - sudo apt-get update - sudo apt-get install git ninja-build build-essential g++ nasm yasm - sudo apt-get install apt-transport-https ca-certificates gnupg software-properties-common wget - wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | gpg --dearmor - | sudo tee /etc/apt/trusted.gpg.d/kitware.gpg >/dev/null - sudo apt-add-repository 'deb https://apt.kitware.com/ubuntu/ focal main' - wget -q https://packages.microsoft.com/config/ubuntu/20.04/packages-microsoft-prod.deb - sudo dpkg -i packages-microsoft-prod.deb - sudo add-apt-repository universe - sudo apt-get update - sudo apt-get dist-upgrade -y - sudo apt-get install -y cmake - sudo apt-get install -y powershell - if [ "$bypass_driver_installation" = true ] ; then - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-11.4/lib64/stubs/libcuda.so /usr/local/cuda-11.4/lib64/libcuda.so - fi - export PATH=/usr/local/cuda/bin:$PATH - export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH - export CUDACXX=/usr/local/cuda/bin/nvcc - export CUDA_PATH=/usr/local/cuda - export CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda - cuda_is_available=true - else - echo "Unable to provide tools on macOS, please wait for a future script update or do not put -InstallCUDA command line flag to continue" +if [ -f $script_dir/requested_cuda_version.sh ]; then + source $script_dir/requested_cuda_version.sh +else + echo "Unable to find requested_cuda_version.sh script" + exit 1 +fi + +if [[ "$OSTYPE" == "darwin"* ]]; then + if [ "$install_cuda" = true ] ; then + echo "Unable to install CUDA on macOS, please wait for a future script update or do not put -InstallCUDA command line flag to continue" + exit 2 + fi + if [ "$install_tools" = true ] ; then + echo "Unable to provide tools on macOS, please wait for a future script update or do not put -InstallTOOLS command line flag to continue" + exit 3 + fi +elif [[ $(cut -f2 <<< $(lsb_release -i)) == "Ubuntu" ]]; then + if [ "$install_cuda" = true ] ; then + $script_dir/deploy-cuda.sh + if [ "$bypass_driver_installation" = true ] ; then + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so.1 + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so.1 + sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so fi + export PATH=/usr/local/cuda/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH + export CUDACXX=/usr/local/cuda/bin/nvcc + export CUDA_PATH=/usr/local/cuda + export CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda + fi + if [ "$install_tools" = true ] ; then + sudo apt-get update + sudo apt-get install -y git ninja-build build-essential g++ nasm yasm + sudo apt-get install -y apt-transport-https ca-certificates gnupg software-properties-common wget + wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | gpg --dearmor - | sudo tee /etc/apt/trusted.gpg.d/kitware.gpg >/dev/null + sudo apt-add-repository "deb https://apt.kitware.com/ubuntu/ $(cut -f2 <<< $(lsb_release -c)) main" + wget -q https://packages.microsoft.com/config/ubuntu/$(cut -f2 <<< $(lsb_release -r))/packages-microsoft-prod.deb + sudo dpkg -i packages-microsoft-prod.deb + sudo add-apt-repository universe + sudo apt-get update + sudo apt-get dist-upgrade -y + sudo apt-get install -y cmake + sudo apt-get install -y powershell + fi +else + if [ "$install_cuda" = true ] ; then + echo "Unable to install CUDA on this OS, please wait for a future script update or do not put -InstallCUDA command line flag to continue" + exit 4 + fi + if [ "$install_tools" = true ] ; then + echo "Unable to install tools on this OS, please wait for a future script update or do not put -InstallTOOLS command line flag to continue" + exit 5 fi fi From 29c78e9e9fed059f311b935480bc431fbe5496ab Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 14 Nov 2021 11:35:50 +0100 Subject: [PATCH 1291/1365] fix unnecessary mono call on windows (#8229) --- .github/workflows/ccpp.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 379da275549..f9d79c6b76a 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -476,7 +476,7 @@ jobs: BAGET_API_KEY: ${{ secrets.BAGET_API_KEY }} if: env.BAGET_API_KEY != null run: > - mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) + $(./vcpkg/vcpkg fetch nuget | tail -n 1) setapikey ${{ secrets.BAGET_API_KEY }} -Source http://93.49.111.10:5555/v3/index.json @@ -525,7 +525,7 @@ jobs: BAGET_API_KEY: ${{ secrets.BAGET_API_KEY }} if: env.BAGET_API_KEY != null run: > - mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) + $(./vcpkg/vcpkg fetch nuget | tail -n 1) setapikey ${{ secrets.BAGET_API_KEY }} -Source http://93.49.111.10:5555/v3/index.json @@ -586,7 +586,7 @@ jobs: BAGET_API_KEY: ${{ secrets.BAGET_API_KEY }} if: env.BAGET_API_KEY != null run: > - mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) + $(./vcpkg/vcpkg fetch nuget | tail -n 1) setapikey ${{ secrets.BAGET_API_KEY }} -Source http://93.49.111.10:5555/v3/index.json From 108dc8b7feb7fb2d9679d23a216e46e9a5fbc35b Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 14 Nov 2021 13:03:43 +0100 Subject: [PATCH 1292/1365] force ash shell for nuget setup --- .github/workflows/ccpp.yml | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index f9d79c6b76a..5a6b8757774 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -98,6 +98,7 @@ jobs: -Source http://93.49.111.10:5555/v3/index.json - name: Setup NuGet API key if found + shell: bash env: BAGET_API_KEY: ${{ secrets.BAGET_API_KEY }} if: env.BAGET_API_KEY != null @@ -165,6 +166,7 @@ jobs: -Source http://93.49.111.10:5555/v3/index.json - name: Setup NuGet API key if found + shell: bash env: BAGET_API_KEY: ${{ secrets.BAGET_API_KEY }} if: env.BAGET_API_KEY != null @@ -215,6 +217,7 @@ jobs: -Source http://93.49.111.10:5555/v3/index.json - name: Setup NuGet API key if found + shell: bash env: BAGET_API_KEY: ${{ secrets.BAGET_API_KEY }} if: env.BAGET_API_KEY != null @@ -348,6 +351,7 @@ jobs: -Source http://93.49.111.10:5555/v3/index.json - name: Setup NuGet API key if found + shell: bash env: BAGET_API_KEY: ${{ secrets.BAGET_API_KEY }} if: env.BAGET_API_KEY != null @@ -381,6 +385,7 @@ jobs: -Source http://93.49.111.10:5555/v3/index.json - name: Setup NuGet API key if found + shell: bash env: BAGET_API_KEY: ${{ secrets.BAGET_API_KEY }} if: env.BAGET_API_KEY != null @@ -472,6 +477,7 @@ jobs: -Source http://93.49.111.10:5555/v3/index.json - name: Setup NuGet API key if found + shell: bash env: BAGET_API_KEY: ${{ secrets.BAGET_API_KEY }} if: env.BAGET_API_KEY != null @@ -521,6 +527,7 @@ jobs: -Source http://93.49.111.10:5555/v3/index.json - name: Setup NuGet API key if found + shell: bash env: BAGET_API_KEY: ${{ secrets.BAGET_API_KEY }} if: env.BAGET_API_KEY != null @@ -582,6 +589,7 @@ jobs: -Source http://93.49.111.10:5555/v3/index.json - name: Setup NuGet API key if found + shell: bash env: BAGET_API_KEY: ${{ secrets.BAGET_API_KEY }} if: env.BAGET_API_KEY != null From 0b2ebb7b9d87725120449f93ecd00102d0dd83f1 Mon Sep 17 00:00:00 2001 From: Johann Klemmack Date: Tue, 23 Nov 2021 06:39:12 -0800 Subject: [PATCH 1293/1365] Update YoloCSharpWrapper to support batch_size (#8249) YoloCSharpWrapper `InitializeYolo` declaration now matches the parameters of yolo_v2_class.hpp "init". Resolves "attempting to read/write corrupted memory" error when calling darknet.dll "detect" methods from C#. --- src/csharp/YoloCSharpWrapper.cs | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/csharp/YoloCSharpWrapper.cs b/src/csharp/YoloCSharpWrapper.cs index 35d23a9c2e1..8fc8567e5a6 100644 --- a/src/csharp/YoloCSharpWrapper.cs +++ b/src/csharp/YoloCSharpWrapper.cs @@ -9,7 +9,7 @@ public class YoloWrapper : IDisposable private const int MaxObjects = 1000; [DllImport(YoloLibraryName, EntryPoint = "init")] - private static extern int InitializeYolo(string configurationFilename, string weightsFilename, int gpu); + private static extern int InitializeYolo(string configurationFilename, string weightsFilename, int gpu, int batch_size); [DllImport(YoloLibraryName, EntryPoint = "detect_image")] private static extern int DetectImage(string filename, ref BboxContainer container); @@ -38,9 +38,9 @@ public struct BboxContainer public bbox_t[] candidates; } - public YoloWrapper(string configurationFilename, string weightsFilename, int gpu) + public YoloWrapper(string configurationFilename, string weightsFilename, int gpu, int batch_size = 1) { - InitializeYolo(configurationFilename, weightsFilename, gpu); + InitializeYolo(configurationFilename, weightsFilename, gpu, batch_size); } public void Dispose() From 7fe4fb7c457c789fe2c00195b6d8f851083b1514 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 3 Dec 2021 18:33:58 +0100 Subject: [PATCH 1294/1365] [github actions] add missing tool from base image (#8271) --- .github/workflows/ccpp.yml | 6 +++--- .github/workflows/on_pr.yml | 6 +++--- scripts/setup.sh | 13 +++++++++++-- 3 files changed, 17 insertions(+), 8 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 5a6b8757774..846cabb20bc 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -76,7 +76,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt install yasm nasm + run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.sh @@ -144,7 +144,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt install yasm nasm + run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.sh @@ -195,7 +195,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt install yasm nasm + run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.sh diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index 9e88aa7f6b2..046b4370fb7 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -72,7 +72,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt install yasm nasm + run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.sh @@ -113,7 +113,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt install yasm nasm + run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.sh @@ -154,7 +154,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt install yasm nasm + run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.sh diff --git a/scripts/setup.sh b/scripts/setup.sh index 7b57ca5b8dc..ad8e12ad3a5 100755 --- a/scripts/setup.sh +++ b/scripts/setup.sh @@ -38,6 +38,7 @@ mkdir -p $temp_folder cd $temp_folder if [ -f $script_dir/requested_cuda_version.sh ]; then + echo "Loading $script_dir/requested_cuda_version.sh" source $script_dir/requested_cuda_version.sh else echo "Unable to find requested_cuda_version.sh script" @@ -54,7 +55,11 @@ if [[ "$OSTYPE" == "darwin"* ]]; then exit 3 fi elif [[ $(cut -f2 <<< $(lsb_release -i)) == "Ubuntu" ]]; then + echo "Running in $(cut -f2 <<< $(lsb_release -i))" + echo "InstallCUDA: $install_cuda" + echo "InstallTOOLS: $install_tools" if [ "$install_cuda" = true ] ; then + echo "Running $script_dir/deploy-cuda.sh" $script_dir/deploy-cuda.sh if [ "$bypass_driver_installation" = true ] ; then sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so.1 @@ -68,9 +73,11 @@ elif [[ $(cut -f2 <<< $(lsb_release -i)) == "Ubuntu" ]]; then export CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda fi if [ "$install_tools" = true ] ; then + echo "Installing tools" sudo apt-get update - sudo apt-get install -y git ninja-build build-essential g++ nasm yasm + sudo apt-get install -y git ninja-build build-essential g++ nasm yasm gperf sudo apt-get install -y apt-transport-https ca-certificates gnupg software-properties-common wget + sudo apt-get install -y libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | gpg --dearmor - | sudo tee /etc/apt/trusted.gpg.d/kitware.gpg >/dev/null sudo apt-add-repository "deb https://apt.kitware.com/ubuntu/ $(cut -f2 <<< $(lsb_release -c)) main" wget -q https://packages.microsoft.com/config/ubuntu/$(cut -f2 <<< $(lsb_release -r))/packages-microsoft-prod.deb @@ -80,6 +87,8 @@ elif [[ $(cut -f2 <<< $(lsb_release -i)) == "Ubuntu" ]]; then sudo apt-get dist-upgrade -y sudo apt-get install -y cmake sudo apt-get install -y powershell + sudo apt-get install -y curl zip unzip tar + sudo apt-get install -y pkg-config autoconf libtool bison fi else if [ "$install_cuda" = true ] ; then @@ -94,7 +103,7 @@ fi cd .. rm -rf "$temp_folder" - +echo "Building darknet" if [[ -v CUDA_PATH ]]; then ./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateDARKNET #./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -EnableOPENCV_CUDA -DisableInteractive -DoNotUpdateDARKNET From 967b72db114a60b254a4bb62091558a4ee04946b Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 17 Dec 2021 15:05:24 +0100 Subject: [PATCH 1295/1365] improve compatibility with vs2022 (#8295) --- build.ps1 | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/build.ps1 b/build.ps1 index e2b4b7f102f..370ef5fc3f1 100755 --- a/build.ps1 +++ b/build.ps1 @@ -119,7 +119,7 @@ param ( [string]$AdditionalBuildSetup = "" # "-DCMAKE_CUDA_ARCHITECTURES=30" ) -$build_ps1_version = "0.9.7" +$build_ps1_version = "0.9.8" $ErrorActionPreference = "SilentlyContinue" Stop-Transcript | out-null @@ -678,6 +678,10 @@ if (-Not $DoNotSetupVS) { $generator = "Visual Studio 16 2019" $AdditionalBuildSetup = $AdditionalBuildSetup + " -T `"host=x64`" -A `"x64`"" } + elseif ($tokens[0] -eq "17") { + $generator = "Visual Studio 17 2022" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -T `"host=x64`" -A `"x64`"" + } else { MyThrow("Unknown Visual Studio version, unsupported configuration") } From 57910b1fa918ee16548590c4c870035c416987dd Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 22 Dec 2021 12:20:27 +0100 Subject: [PATCH 1296/1365] [CI] build dependencies in release-only mode (#8304) * enable release-only triplet on windows * add debug tmate * use release-only triplets also for linux and osx --- .github/workflows/ccpp.yml | 17 +++++++++++++++++ CMakeLists.txt | 5 +++++ build.ps1 | 8 +++++++- 3 files changed, 29 insertions(+), 1 deletion(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 846cabb20bc..8e110aa60db 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -3,6 +3,11 @@ name: Darknet Continuous Integration on: push: workflow_dispatch: + inputs: + debug_enabled: + description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)' + required: false + default: false schedule: - cron: '0 0 * * *' @@ -71,6 +76,10 @@ jobs: steps: - uses: actions/checkout@v2 + - name: Setup tmate session + uses: mxschmitt/action-tmate@v3 + if: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.debug_enabled }} + - uses: lukka/get-cmake@latest - name: Update apt @@ -370,6 +379,10 @@ jobs: steps: - uses: actions/checkout@v2 + - name: Setup tmate session + uses: mxschmitt/action-tmate@v3 + if: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.debug_enabled }} + - name: Install dependencies run: brew install libomp yasm nasm @@ -465,6 +478,10 @@ jobs: steps: - uses: actions/checkout@v2 + - name: Setup tmate session + uses: mxschmitt/action-tmate@v3 + if: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.debug_enabled }} + - uses: lukka/get-cmake@latest - name: 'Setup vcpkg and NuGet artifacts backend' diff --git a/CMakeLists.txt b/CMakeLists.txt index 26e435e4263..9753e8f7870 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -27,6 +27,11 @@ option(VCPKG_USE_OPENCV2 "Use legacy OpenCV 2" OFF) option(VCPKG_USE_OPENCV3 "Use legacy OpenCV 3" OFF) option(VCPKG_USE_OPENCV4 "Use OpenCV 4" ON) +if(DEFINED ENV{VCPKG_DEFAULT_TRIPLET}) + message(STATUS "Setting default vcpkg target triplet to $ENV{VCPKG_DEFAULT_TRIPLET}") + set(VCPKG_TARGET_TRIPLET $ENV{VCPKG_DEFAULT_TRIPLET}) +endif() + if(VCPKG_USE_OPENCV4 AND VCPKG_USE_OPENCV2) message(STATUS "You required vcpkg feature related to OpenCV 2 but forgot to turn off those for OpenCV 4, doing that for you") set(VCPKG_USE_OPENCV4 OFF CACHE BOOL "Use OpenCV 4" FORCE) diff --git a/build.ps1 b/build.ps1 index 370ef5fc3f1..c9b63dd918f 100755 --- a/build.ps1 +++ b/build.ps1 @@ -288,7 +288,13 @@ if (($IsLinux -or $IsMacOS) -and ($ForceGCCVersion -gt 0)) { } if (($IsWindows -or $IsWindowsPowerShell) -and (-Not $env:VCPKG_DEFAULT_TRIPLET)) { - $env:VCPKG_DEFAULT_TRIPLET = "x64-windows" + $env:VCPKG_DEFAULT_TRIPLET = "x64-windows-release" +} +elseif ($IsMacOS -and (-Not $env:VCPKG_DEFAULT_TRIPLET)) { + $env:VCPKG_DEFAULT_TRIPLET = "x64-osx-release" +} +elseif ($IsLinux -and (-Not $env:VCPKG_DEFAULT_TRIPLET)) { + $env:VCPKG_DEFAULT_TRIPLET = "x64-linux-release" } if ($EnableCUDA) { From 47aa6cceb6c3a9c01b877110d79f47610e24ec07 Mon Sep 17 00:00:00 2001 From: jenhaoyang Date: Sat, 8 Jan 2022 10:13:03 +0800 Subject: [PATCH 1297/1365] Update Readme.txt URL Typo (#8285) Update Readme.txt Typo --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7567592575c..9270951e0a5 100644 --- a/README.md +++ b/README.md @@ -659,7 +659,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol - for training for both small and large objects use modified models: - Full-model: 5 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3_5l.cfg - - Tiny-model: 3 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny_3l.cfg + - Tiny-model: 3 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny-3l.cfg - YOLOv4: 3 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-custom.cfg - If you train the model to distinguish Left and Right objects as separate classes (left/right hand, left/right-turn on road signs, ...) then for disabling flip data augmentation - add `flip=0` here: https://github.com/AlexeyAB/darknet/blob/3d2d0a7c98dbc8923d9ff705b81ff4f7940ea6ff/cfg/yolov3.cfg#L17 From b4d03f88028594c3073c41f60cd469d03fb3ee8e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?St=C3=A9phane=20Charette?= Date: Sat, 8 Jan 2022 18:50:40 -0800 Subject: [PATCH 1298/1365] issue #8308: memory leaks in map (#8314) * issue #8308: memory leaks in map * update the window title with some training stats --- src/coco.c | 1 + src/detector.c | 27 +++++++++++++++++++++++---- src/yolo.c | 1 + 3 files changed, 25 insertions(+), 4 deletions(-) diff --git a/src/coco.c b/src/coco.c index 8c386a224bd..605e9ac1355 100644 --- a/src/coco.c +++ b/src/coco.c @@ -320,6 +320,7 @@ void validate_coco_recall(char *cfgfile, char *weightfile) //if (fps) free(fps); if (id) free(id); + free(truth); free_image(orig); free_image(sized); } diff --git a/src/detector.c b/src/detector.c index 73b7bedc158..c6bc788b96f 100644 --- a/src/detector.c +++ b/src/detector.c @@ -308,6 +308,15 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i if (mean_average_precision > 0) printf("\n Last accuracy mAP@%0.2f = %2.2f %%, best = %2.2f %% ", iou_thresh, mean_average_precision * 100, best_map * 100); } + #ifndef WIN32 + if (mean_average_precision > 0.0) { + printf("\033]2;%d/%d: loss=%0.1f map=%0.2f best=%0.2f hours left=%0.1f\007", iteration, net.max_batches, loss, mean_average_precision, best_map, avg_time); + } + else { + printf("\033]2;%d/%d: loss=%0.1f hours left=%0.1f\007", iteration, net.max_batches, loss, avg_time); + } + #endif + if (net.cudnn_half) { if (iteration < net.burn_in * 3) fprintf(stderr, "\n Tensor Cores are disabled until the first %d iterations are reached.\n", 3 * net.burn_in); else fprintf(stderr, "\n Tensor Cores are used.\n"); @@ -902,6 +911,7 @@ void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile) } //fprintf(stderr, " %s - %s - ", paths[i], labelpath); fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals / (i + 1), avg_iou * 100 / total, 100.*correct / total); + free(truth); free(id); free_image(orig); free_image(sized); @@ -970,13 +980,13 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa list *plist = get_paths(valid_images); char **paths = (char **)list_to_array(plist); + list *plist_dif = NULL; char **paths_dif = NULL; if (difficult_valid_images) { - list *plist_dif = get_paths(difficult_valid_images); + plist_dif = get_paths(difficult_valid_images); paths_dif = (char **)list_to_array(plist_dif); } - layer l = net.layers[net.n - 1]; int k; for (k = 0; k < net.n; ++k) { @@ -1181,6 +1191,8 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa //if(errors_in_this_image > 0) fwrite(buff, sizeof(char), strlen(buff), reinforcement_fd); free_detections(dets, nboxes); + free(truth); + free(truth_dif); free(id); free_image(val[t]); free_image(val_resized[t]); @@ -1274,7 +1286,6 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa free(truth_flags); - double mean_average_precision = 0; for (i = 0; i < classes; ++i) { @@ -1362,7 +1373,14 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa free(detections); free(truth_classes_count); free(detection_per_class_count); - + free(paths); + free(paths_dif); + free_list_contents(plist); + free_list(plist); + if (plist_dif) { + free_list_contents(plist_dif); + free_list(plist_dif); + } free(avg_iou_per_class); free(tp_for_thresh_per_class); free(fp_for_thresh_per_class); @@ -1484,6 +1502,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int printf("\r loaded \t image: %d \t box: %d", i + 1, number_of_boxes); } free(buff); + free(truth); } printf("\n all loaded. \n"); printf("\n calculating k-means++ ..."); diff --git a/src/yolo.c b/src/yolo.c index 384c487291e..dd5e2a161de 100644 --- a/src/yolo.c +++ b/src/yolo.c @@ -277,6 +277,7 @@ void validate_yolo_recall(char *cfgfile, char *weightfile) fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total); free(id); + free(truth); free_image(orig); free_image(sized); } From 2c137d16f60aca5e9cbcef505472c3833447971c Mon Sep 17 00:00:00 2001 From: Adam Cuellar <40183888+AdamCuellar@users.noreply.github.com> Date: Wed, 2 Mar 2022 22:12:18 -0500 Subject: [PATCH 1299/1365] Fix conv_lstm cuda errors and nan's (#8388) --- src/conv_lstm_layer.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/conv_lstm_layer.c b/src/conv_lstm_layer.c index 72e7eac3865..a7804e736c3 100644 --- a/src/conv_lstm_layer.c +++ b/src/conv_lstm_layer.c @@ -1454,7 +1454,7 @@ void backward_conv_lstm_layer_gpu(layer l, network_state state) axpy_ongpu(l.outputs*l.batch, 1, l.bottelneck_delta_gpu + l.outputs*l.batch, 1, state.delta, 1); // lead to nan } else { - axpy_ongpu(l.outputs*l.batch, l.time_normalizer, l.temp3_gpu, 1, l.dh_gpu, 1); + // if (l.dh_gpu) axpy_ongpu(l.outputs*l.batch, l.time_normalizer, l.temp3_gpu, 1, l.dh_gpu, 1); } // c From 8a0bf84c19e38214219dbd3345f04ce778426c57 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 6 Mar 2022 23:41:08 +0100 Subject: [PATCH 1300/1365] various fixes (#8398) * re-enable opencv on osx * enable all and all-major architecture flags for cuda * update build script * use proper path separator * [windows-cuda] bump to 11.6.1 * [windows-cuda] some fixes from CI runs * make deploy-cuda.ps1 work also when called from outside its folder * bump to cuda 11.6 also on linux * deploy custom cmake modules if requested by the user * fix cmake module source path * use proper clause for dll copy --- .github/workflows/ccpp.yml | 8 ++-- .github/workflows/on_pr.yml | 8 ++-- CMakeLists.txt | 16 ++++++- DarknetConfig.cmake.in | 6 ++- build.ps1 | 75 +++++++++++++++++++++++-------- scripts/deploy-cuda.ps1 | 34 ++++++++++---- scripts/requested_cuda_version.sh | 2 +- scripts/setup.ps1 | 8 ++-- 8 files changed, 115 insertions(+), 42 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 8e110aa60db..f9d1788b16c 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -409,7 +409,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET - uses: actions/upload-artifact@v2 with: @@ -655,9 +655,9 @@ jobs: - name: 'Build' env: - CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.5" - CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.5" - CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.5\\bin\\nvcc.exe" + CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.6" + CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.6" + CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.6\\bin\\nvcc.exe" shell: pwsh run: ${{ github.workspace }}/build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index 046b4370fb7..e8bafb1717d 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -291,7 +291,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET osx: @@ -408,9 +408,9 @@ jobs: - name: 'Build' env: - CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.5" - CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.5" - CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.5\\bin\\nvcc.exe" + CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.6" + CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.6" + CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.6\\bin\\nvcc.exe" shell: pwsh run: ${{ github.workspace }}/build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET diff --git a/CMakeLists.txt b/CMakeLists.txt index 9753e8f7870..d34b8f80233 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -21,6 +21,7 @@ option(ENABLE_CUDNN "Enable CUDNN" ON) option(ENABLE_CUDNN_HALF "Enable CUDNN Half precision" ON) option(ENABLE_ZED_CAMERA "Enable ZED Camera support" ON) option(ENABLE_VCPKG_INTEGRATION "Enable VCPKG integration" ON) +option(ENABLE_DEPLOY_CUSTOM_CMAKE_MODULES "Copy custom CMake modules for downstream integration" OFF) option(ENABLE_CSHARP_WRAPPER "Enable building a csharp wrapper" OFF) option(VCPKG_BUILD_OPENCV_WITH_CUDA "Build OpenCV with CUDA extension integration" ON) option(VCPKG_USE_OPENCV2 "Use legacy OpenCV 2" OFF) @@ -158,8 +159,13 @@ if(ENABLE_CUDA) message(STATUS "CUDACXX: $ENV{CUDACXX}") message(FATAL_ERROR "Unsupported CUDA version, please upgrade to CUDA 9+ or disable CUDA with explicitly with -DENABLE_CUDA=OFF") else() + if(CMAKE_VERSION VERSION_GREATER_EQUAL "3.23.0" AND NOT DEFINED CMAKE_CUDA_ARCHITECTURES) + set(CMAKE_CUDA_ARCHITECTURES all-major) + endif() message(STATUS "Selected CMAKE_CUDA_ARCHITECTURES: ${CMAKE_CUDA_ARCHITECTURES}") - if(70 IN_LIST CMAKE_CUDA_ARCHITECTURES OR + if("all-major" IN_LIST CMAKE_CUDA_ARCHITECTURES OR + "all" IN_LIST CMAKE_CUDA_ARCHITECTURES OR + 70 IN_LIST CMAKE_CUDA_ARCHITECTURES OR 72 IN_LIST CMAKE_CUDA_ARCHITECTURES OR 75 IN_LIST CMAKE_CUDA_ARCHITECTURES OR 80 IN_LIST CMAKE_CUDA_ARCHITECTURES OR @@ -585,6 +591,14 @@ install(FILES DESTINATION "${INSTALL_CMAKE_DIR}" ) +if (ENABLE_DEPLOY_CUSTOM_CMAKE_MODULES) + install(FILES + "${CMAKE_CURRENT_LIST_DIR}/cmake/Modules/FindCUDNN.cmake" + "${CMAKE_CURRENT_LIST_DIR}/cmake/Modules/FindPThreads4W.cmake" + DESTINATION "${INSTALL_CMAKE_DIR}" + ) +endif() + if(ENABLE_CSHARP_WRAPPER) add_subdirectory(src/csharp) endif() diff --git a/DarknetConfig.cmake.in b/DarknetConfig.cmake.in index c016b220bd0..122120695e0 100644 --- a/DarknetConfig.cmake.in +++ b/DarknetConfig.cmake.in @@ -24,7 +24,11 @@ set(CMAKE_THREAD_PREFER_PTHREAD ON) find_dependency(Threads) if(MSVC) - find_dependency(PThreads_windows) + if(@USE_INTEGRATED_LIBS@) + find_dependency(PThreads4W) + else() + find_dependency(pthreads) + endif() set(CMAKE_CXX_FLAGS "/wd4018 /wd4244 /wd4267 /wd4305 ${CMAKE_CXX_FLAGS}") endif() diff --git a/build.ps1 b/build.ps1 index c9b63dd918f..9b774aa812b 100755 --- a/build.ps1 +++ b/build.ps1 @@ -14,6 +14,9 @@ Build darknet using CMake, trying to properly setup the environment around compi .PARAMETER DisableInteractive Disable script interactivity (useful for CI runs) +.PARAMETER DisableDLLcopy +Disable automatic DLL deployment through vcpkg at the end + .PARAMETER EnableCUDA Enable CUDA feature @@ -62,8 +65,11 @@ Create darknet library as static instead of the default linking mode of your sys .PARAMETER ForceVCPKGCacheRemoval Force clean up of the local vcpkg binary cache before building -.PARAMETER DoNotDeleteBuildtreesFolder -Do not delete vcpkg buildtrees temp folder at the end of the script +.PARAMETER ForceVCPKGBuildtreesRemoval +Force clean up of vcpkg buildtrees temp folder at the end of the script + +.PARAMETER ForceVCPKGPackagesRemoval +Force clean up of vcpkg packages folder at the end of the script .PARAMETER ForceSetupVS Forces Visual Studio setup, also on systems on which it would not have been enabled automatically @@ -93,6 +99,7 @@ Additional setup parameters to manually pass to CMake param ( [switch]$DisableInteractive = $false, + [switch]$DisableDLLcopy = $false, [switch]$EnableCUDA = $false, [switch]$EnableCUDNN = $false, [switch]$EnableOPENCV = $false, @@ -109,7 +116,8 @@ param ( [switch]$ForceCPP = $false, [switch]$ForceStaticLib = $false, [switch]$ForceVCPKGCacheRemoval = $false, - [switch]$DoNotDeleteBuildtreesFolder = $false, + [switch]$ForceVCPKGBuildtreesRemoval = $false, + [switch]$ForceVCPKGPackagesRemoval = $false, [switch]$ForceSetupVS = $false, [switch]$EnableCSharpWrapper = $false, [switch]$DownloadWeights = $false, @@ -119,7 +127,7 @@ param ( [string]$AdditionalBuildSetup = "" # "-DCMAKE_CUDA_ARCHITECTURES=30" ) -$build_ps1_version = "0.9.8" +$build_ps1_version = "1.9.8" $ErrorActionPreference = "SilentlyContinue" Stop-Transcript | out-null @@ -202,10 +210,10 @@ Function DownloadNinja() { } -Write-Host "Darknet build script version ${build_ps1_version}" +Write-Host "Build script version ${build_ps1_version}" if ((-Not $DisableInteractive) -and (-Not $UseVCPKG)) { - $Result = Read-Host "Enable vcpkg to install darknet dependencies (yes/no)" + $Result = Read-Host "Enable vcpkg to install dependencies (yes/no)" if (($Result -eq 'Yes') -or ($Result -eq 'Y') -or ($Result -eq 'yes') -or ($Result -eq 'y')) { $UseVCPKG = $true } @@ -431,7 +439,7 @@ if (-Not $DoNotUseNinja) { $NINJA_EXE = Get-Command "ninja" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition if (-Not $NINJA_EXE) { DownloadNinja - $env:PATH += ";${PSScriptRoot}/ninja" + $env:PATH = '{0}{1}{2}' -f $env:PATH, [IO.Path]::PathSeparator, "${PSScriptRoot}/ninja" $NINJA_EXE = Get-Command "ninja" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition if (-Not $NINJA_EXE) { $DoNotUseNinja = $true @@ -555,7 +563,7 @@ elseif ((Test-Path "${env:WORKSPACE}/vcpkg") -and $UseVCPKG) { $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" } elseif (-not($null -eq ${RUNVCPKG_VCPKG_ROOT_OUT})) { - if ((Test-Path "${RUNVCPKG_VCPKG_ROOT_OUT}") -and $UseVCPKG) { + if((Test-Path "${RUNVCPKG_VCPKG_ROOT_OUT}") -and $UseVCPKG) { $vcpkg_path = "${RUNVCPKG_VCPKG_ROOT_OUT}" $env:VCPKG_ROOT = "${RUNVCPKG_VCPKG_ROOT_OUT}" $vcpkg_root_set_by_this_script = $true @@ -619,7 +627,7 @@ if ($ForceVCPKGCacheRemoval -and (-Not $UseVCPKG)) { Write-Host "VCPKG is not enabled, so local vcpkg binary cache will not be deleted even if requested" -ForegroundColor Yellow } -if ($UseVCPKG -and (-Not $DoNotDeleteBuildtreesFolder)) { +if ($UseVCPKG -and $ForceVCPKGBuildtreesRemoval) { Write-Host "Cleaning folder buildtrees inside vcpkg" -ForegroundColor Yellow Remove-Item -Force -Recurse -ErrorAction SilentlyContinue "$env:VCPKG_ROOT/buildtrees" } @@ -706,7 +714,7 @@ if (-Not $IsMacOS -and $EnableCUDA) { $NVCC_EXE = Get-Command "nvcc" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition if (-Not $NVCC_EXE) { if (Test-Path env:CUDA_PATH) { - $env:PATH += ";${env:CUDA_PATH}/bin" + $env:PATH = '{0}{1}{2}' -f $env:PATH, [IO.Path]::PathSeparator, "${env:CUDA_PATH}/bin" Write-Host "Found cuda in ${env:CUDA_PATH}" } else { @@ -726,6 +734,10 @@ if (-Not $IsMacOS -and $EnableCUDA) { } } +if (-Not $DisableDLLcopy) { + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DX_VCPKG_APPLOCAL_DEPS_INSTALL=ON" +} + if ($ForceCPP) { $AdditionalBuildSetup = $AdditionalBuildSetup + " -DBUILD_AS_CPP:BOOL=ON" } @@ -750,6 +762,10 @@ if ($EnableCSharpWrapper) { $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_CSHARP_WRAPPER:BOOL=ON" } +if (-Not $InstallDARKNETthroughVCPKG) { + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_DEPLOY_CUSTOM_CMAKE_MODULES:BOOL=ON" +} + if ($InstallDARKNETthroughVCPKG) { if ($ForceVCPKGDarknetHEAD) { $headMode = " --head " @@ -811,7 +827,6 @@ if ($InstallDARKNETthroughVCPKG) { if (-Not ($exitCode -eq 0)) { MyThrow("Installing darknet dependencies through vcpkg failed! Exited with error code $exitCode.") } - Pop-Location } } else { @@ -842,21 +857,43 @@ else { } Remove-Item -Force -ErrorAction SilentlyContinue DarknetConfig.cmake Remove-Item -Force -ErrorAction SilentlyContinue DarknetConfigVersion.cmake - $dllfiles = Get-ChildItem ./${dllfolder}/*.dll - if ($dllfiles) { - Copy-Item $dllfiles .. + if (-Not $UseVCPKG -And -Not $DisableDLLcopy) { + $dllfiles = Get-ChildItem ./${dllfolder}/*.dll + if ($dllfiles) { + Copy-Item $dllfiles .. + } } Set-Location .. - Copy-Item cmake/Modules/*.cmake share/darknet/ - Pop-Location } -if ($UseVCPKG -and (-Not $DoNotDeleteBuildtreesFolder)) { - Write-Host "Cleaning folder buildtrees inside vcpkg" -ForegroundColor Yellow - Remove-Item -Force -Recurse -ErrorAction SilentlyContinue "$env:VCPKG_ROOT/buildtrees" + +if (-Not $DoNotDeleteBuildFolder) { + Write-Host "Removing folder $build_folder" -ForegroundColor Yellow + Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $build_folder } Write-Host "Build complete!" -ForegroundColor Green +Pop-Location + +if ($ForceVCPKGBuildtreesRemoval -and (-Not $UseVCPKG)) { + Write-Host "VCPKG is not enabled, so local vcpkg buildtrees folder will not be deleted even if requested" -ForegroundColor Yellow +} + +if ($UseVCPKG -and $ForceVCPKGBuildtreesRemoval) { + $vcpkgbuildtreespath = "$vcpkg_path/buildtrees" + Write-Host "Removing local vcpkg buildtrees folder from $vcpkgbuildtreespath" -ForegroundColor Yellow + Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $vcpkgbuildtreespath +} + +if ($ForceVCPKGPackagesRemoval -and (-Not $UseVCPKG)) { + Write-Host "VCPKG is not enabled, so local vcpkg packages folder will not be deleted even if requested" -ForegroundColor Yellow +} + +if ($UseVCPKG -and $ForceVCPKGPackagesRemoval) { + $vcpkgpackagespath = "$vcpkg_path/packages" + Write-Host "Removing local vcpkg packages folder from $vcpkgpackagespath" -ForegroundColor Yellow + Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $vcpkgpackagespath +} if ($DownloadWeights) { Write-Host "Downloading weights..." -ForegroundColor Yellow diff --git a/scripts/deploy-cuda.ps1 b/scripts/deploy-cuda.ps1 index 36682ba3a8b..a460e8b3127 100644 --- a/scripts/deploy-cuda.ps1 +++ b/scripts/deploy-cuda.ps1 @@ -1,20 +1,38 @@ #!/usr/bin/env pwsh -$url = 'https://developer.download.nvidia.com/compute/cuda/11.5.0/network_installers/cuda_11.5.0_win10_network.exe' +param ( + [switch]$DisableVisualStudioFeatures = $false, + [switch]$DisableSilentMode = $false +) -$CudaFeatures = 'nvcc_11.5 cuobjdump_11.5 nvprune_11.5 cupti_11.5 memcheck_11.5 nvdisasm_11.5 nvprof_11.5 ' + ` - 'visual_studio_integration_11.5 visual_profiler_11.5 visual_profiler_11.5 cublas_11.5 cublas_dev_11.5 ' + ` - 'cudart_11.5 cufft_11.5 cufft_dev_11.5 curand_11.5 curand_dev_11.5 cusolver_11.5 cusolver_dev_11.5 ' + ` - 'cusparse_11.5 cusparse_dev_11.5 npp_11.5 npp_dev_11.5 nvrtc_11.5 nvrtc_dev_11.5 nvml_dev_11.5 ' + ` - 'occupancy_calculator_11.5 ' +$url = 'https://developer.download.nvidia.com/compute/cuda/11.6.1/network_installers/cuda_11.6.1_windows_network.exe' + +$CudaFeatures = 'nvcc_11.6 cuobjdump_11.6 nvprune_11.6 cupti_11.6 memcheck_11.6 nvdisasm_11.6 nvprof_11.6 ' + ` + 'cublas_11.6 cublas_dev_11.6 nvjpeg_11.6 nvjpeg_dev_11.6 nvtx_11.6 cuxxfilt_11.6 sanitizer_11.6 ' + ` + 'cudart_11.6 cufft_11.6 cufft_dev_11.6 curand_11.6 curand_dev_11.6 cusolver_11.6 cusolver_dev_11.6 ' + ` + 'cusparse_11.6 cusparse_dev_11.6 npp_11.6 npp_dev_11.6 nvrtc_11.6 nvrtc_dev_11.6 nvml_dev_11.6 ' + ` + 'occupancy_calculator_11.6 documentation_11.6 ' + +if (-Not $DisableVisualStudioFeatures) { + $CudaFeatures = $CudaFeatures + 'visual_studio_integration_11.6 visual_profiler_11.6 ' +} + +if ($DisableSilentMode) { + $SilentFlag = ' ' +} +else { + $SilentFlag = '-s ' +} try { + Push-Location $PSScriptRoot Write-Host 'Downloading CUDA...' - Invoke-WebRequest -Uri $url -OutFile "cuda_11.5.0_win10_network.exe" + Invoke-WebRequest -Uri $url -OutFile "cuda_11.6.1_windows_network.exe" Write-Host 'Installing CUDA...' - $proc = Start-Process -PassThru -FilePath "./cuda_11.5.0_win10_network.exe" -ArgumentList @('-s ' + $CudaFeatures) + $proc = Start-Process -PassThru -FilePath "./cuda_11.6.1_windows_network.exe" -ArgumentList @($SilentFlag + $CudaFeatures) $proc.WaitForExit() $exitCode = $proc.ExitCode + Pop-Location if ($exitCode -eq 0) { Write-Host 'Installation successful!' } diff --git a/scripts/requested_cuda_version.sh b/scripts/requested_cuda_version.sh index 190a5f5b743..90f54b25a26 100755 --- a/scripts/requested_cuda_version.sh +++ b/scripts/requested_cuda_version.sh @@ -1,4 +1,4 @@ #!/usr/bin/env bash -export CUDA_VERSION="11.5" +export CUDA_VERSION="11.6" export CUDA_VERSION_DASHED="${CUDA_VERSION//./-}" diff --git a/scripts/setup.ps1 b/scripts/setup.ps1 index 913c8f747c2..865080382e8 100755 --- a/scripts/setup.ps1 +++ b/scripts/setup.ps1 @@ -11,14 +11,14 @@ if ($null -eq (Get-Command "choco.exe" -ErrorAction SilentlyContinue)) { } Start-Process -FilePath "choco" -Verb runAs -ArgumentList " install -y cmake ninja powershell git vscode" -Start-Process -FilePath "choco" -Verb runAs -ArgumentList " install -y visualstudio2019buildtools --package-parameters `"--add Microsoft.VisualStudio.Component.VC.CoreBuildTools --includeRecommended --includeOptional --passive --locale en-US --lang en-US`"" +Start-Process -FilePath "choco" -Verb runAs -ArgumentList " install -y visualstudio2022buildtools --package-parameters `"--add Microsoft.VisualStudio.Component.VC.CoreBuildTools --includeRecommended --includeOptional --passive --locale en-US --lang en-US`"" Push-Location $PSScriptRoot if ($InstallCUDA) { & $PSScriptRoot/deploy-cuda.ps1 - $env:CUDA_PATH="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.5" - $env:CUDA_TOOLKIT_ROOT_DIR="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.5" - $env:CUDACXX="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.5\\bin\\nvcc.exe" + $env:CUDA_PATH="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.6" + $env:CUDA_TOOLKIT_ROOT_DIR="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.6" + $env:CUDACXX="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.6\\bin\\nvcc.exe" $CUDAisAvailable = $true } else { From 695d562918d6cd878ffe34104a9c1b518a2de740 Mon Sep 17 00:00:00 2001 From: Sergey Nuzhny Date: Tue, 17 May 2022 01:57:30 +0300 Subject: [PATCH 1301/1365] Fixed issues 8510 and 8511 (#8516) * Fixed issues 8510 and 8511 * Update yolo.c Return fprintf --- src/parser.c | 2 +- src/yolo.c | 9 +++++---- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/src/parser.c b/src/parser.c index 8c645e6c13e..09abf2d4beb 100644 --- a/src/parser.c +++ b/src/parser.c @@ -517,7 +517,7 @@ layer parse_yolo(list *options, size_params params) l.random = option_find_float_quiet(options, "random", 0); l.track_history_size = option_find_int_quiet(options, "track_history_size", 5); - l.sim_thresh = option_find_int_quiet(options, "sim_thresh", 0.8); + l.sim_thresh = option_find_float_quiet(options, "sim_thresh", 0.8); l.dets_for_track = option_find_int_quiet(options, "dets_for_track", 1); l.dets_for_show = option_find_int_quiet(options, "dets_for_show", 1); l.track_ciou_norm = option_find_float_quiet(options, "track_ciou_norm", 0.01); diff --git a/src/yolo.c b/src/yolo.c index dd5e2a161de..44f7c5a5438 100644 --- a/src/yolo.c +++ b/src/yolo.c @@ -190,7 +190,6 @@ void validate_yolo(char *cfgfile, char *weightfile) } } - if (fps) free(fps); if (val) free(val); if (val_resized) free(val_resized); if (buf) free(buf); @@ -198,10 +197,12 @@ void validate_yolo(char *cfgfile, char *weightfile) if (thr) free(thr); fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start)); - for(j = 0; j < classes; ++j){ - fclose(fps[j]); + if (fps) { + for(j = 0; j < classes; ++j){ + fclose(fps[j]); + } + free(fps); } - free(fps); } void validate_yolo_recall(char *cfgfile, char *weightfile) From 4a7c2802e0a2d782dfd73ccb00a20412d0564842 Mon Sep 17 00:00:00 2001 From: Scott Martin Date: Mon, 16 May 2022 19:04:54 -0400 Subject: [PATCH 1302/1365] Allow image_detection to receive a cv2 image; remove duplicate darknet import (#8387) --- darknet_images.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/darknet_images.py b/darknet_images.py index 17ac91731a7..65129678b0a 100644 --- a/darknet_images.py +++ b/darknet_images.py @@ -2,7 +2,6 @@ import os import glob import random -import darknet import time import cv2 import numpy as np @@ -97,14 +96,17 @@ def prepare_batch(images, network, channels=3): return darknet.IMAGE(width, height, channels, darknet_images) -def image_detection(image_path, network, class_names, class_colors, thresh): +def image_detection(image_or_path, network, class_names, class_colors, thresh): # Darknet doesn't accept numpy images. # Create one with image we reuse for each detect width = darknet.network_width(network) height = darknet.network_height(network) darknet_image = darknet.make_image(width, height, 3) - image = cv2.imread(image_path) + if type(image_or_path) == "str": + image = cv2.imread(image_or_path) + else: + image = image_or_path image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) image_resized = cv2.resize(image_rgb, (width, height), interpolation=cv2.INTER_LINEAR) From eaee5060d55377f566310b9dc84e5605e1cccccf Mon Sep 17 00:00:00 2001 From: Akash Patel <17132214+acxz@users.noreply.github.com> Date: Wed, 8 Jun 2022 09:38:53 -0400 Subject: [PATCH 1303/1365] - Fix warning -Werror=format-security (#8525) Co-authored-by: Jeremy Prater --- src/detector.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/detector.c b/src/detector.c index c6bc788b96f..0b947b69089 100644 --- a/src/detector.c +++ b/src/detector.c @@ -492,7 +492,7 @@ static void print_cocos(FILE *fp, char *image_path, detection *dets, int num_box if (dets[i].prob[j] > 0) { char buff[1024]; sprintf(buff, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, dets[i].prob[j]); - fprintf(fp, buff); + fprintf(fp, "%s", buff); //printf("%s", buff); } } From 1ee19442ee4e003a30a861dc96af078c923c66d3 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 1 Jul 2022 16:46:41 +0200 Subject: [PATCH 1304/1365] update to cuda 11.7, update pthreads4w cmake module, upgrade ps1 scripts (#8586) --- .github/workflows/ccpp.yml | 6 +- .github/workflows/on_pr.yml | 6 +- CMakeLists.txt | 22 ++-- DarknetConfig.cmake.in | 6 +- build.ps1 | 165 +----------------------- cmake/Modules/FindPThreads4W.cmake | 160 +---------------------- scripts/deploy-cuda.ps1 | 26 ++-- scripts/deploy-cuda.sh | 23 +++- scripts/requested_cuda_version.sh | 4 +- scripts/setup.ps1 | 8 +- scripts/utils.psm1 | 200 +++++++++++++++++++++++++++++ 11 files changed, 266 insertions(+), 360 deletions(-) create mode 100644 scripts/utils.psm1 diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index f9d1788b16c..fa6e74c2acc 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -655,9 +655,9 @@ jobs: - name: 'Build' env: - CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.6" - CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.6" - CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.6\\bin\\nvcc.exe" + CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.7" + CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.7" + CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.7\\bin\\nvcc.exe" shell: pwsh run: ${{ github.workspace }}/build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index e8bafb1717d..b777d315adf 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -408,9 +408,9 @@ jobs: - name: 'Build' env: - CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.6" - CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.6" - CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.6\\bin\\nvcc.exe" + CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.7" + CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.7" + CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.7\\bin\\nvcc.exe" shell: pwsh run: ${{ github.workspace }}/build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET diff --git a/CMakeLists.txt b/CMakeLists.txt index d34b8f80233..128415cc92a 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -103,7 +103,7 @@ elseif(WIN32) set(USE_INTEGRATED_LIBS "TRUE" CACHE BOOL "Use libs distributed with this repo") else() message(WARNING "vcpkg not found, toolchain not defined, system not win32 so build might fail") - set(USE_INTEGRATED_LIBS "FALSE" CACHE BOOL "Use libs distributed with this repo") + set(USE_INTEGRATED_LIBS "TRUE" CACHE BOOL "Use libs distributed with this repo") endif() project(Darknet VERSION ${Darknet_VERSION}) @@ -112,7 +112,9 @@ enable_language(C) enable_language(CXX) set(CMAKE_CXX_STANDARD 11) -set(CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/cmake/Modules/" ${CMAKE_MODULE_PATH}) +if(USE_INTEGRATED_LIBS) + set(CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/cmake/Modules/" ${CMAKE_MODULE_PATH}) +endif() if(CMAKE_COMPILER_IS_GNUCC OR "${CMAKE_C_COMPILER_ID}" MATCHES "Clang" OR "${CMAKE_CXX_COMPILER_ID}" MATCHES "Clang") set(CMAKE_COMPILER_IS_GNUCC_OR_CLANG TRUE) @@ -193,16 +195,14 @@ endif() if(USE_INTEGRATED_LIBS) set(PThreads4W_ROOT ${CMAKE_CURRENT_LIST_DIR}/3rdparty/pthreads CACHE PATH "Path where pthreads for windows can be located") + set(Stb_DIR ${CMAKE_CURRENT_LIST_DIR}/3rdparty/stb CACHE PATH "Path where Stb image library can be located") endif() -set(Stb_DIR ${CMAKE_CURRENT_LIST_DIR}/3rdparty/stb CACHE PATH "Path where Stb image library can be located") set(CMAKE_DEBUG_POSTFIX d) set(CMAKE_THREAD_PREFER_PTHREAD ON) find_package(Threads REQUIRED) -if(MSVC AND USE_INTEGRATED_LIBS) +if(MSVC) find_package(PThreads4W REQUIRED) -elseif(MSVC) - find_package(pthreads REQUIRED) endif() if(ENABLE_OPENCV) find_package(OpenCV REQUIRED) @@ -476,11 +476,11 @@ if(CMAKE_COMPILER_IS_GNUCC) endif() if(MSVC) - target_link_libraries(darknet PRIVATE PThreads_windows::PThreads_windows) + target_link_libraries(darknet PRIVATE PThreads4W::PThreads4W) target_link_libraries(darknet PRIVATE wsock32) - target_link_libraries(dark PUBLIC PThreads_windows::PThreads_windows) + target_link_libraries(dark PUBLIC PThreads4W::PThreads4W) target_link_libraries(dark PUBLIC wsock32) - target_link_libraries(uselib PRIVATE PThreads_windows::PThreads_windows) + target_link_libraries(uselib PRIVATE PThreads4W::PThreads4W) target_compile_definitions(darknet PRIVATE -D_CRT_RAND_S -DNOMINMAX -D_USE_MATH_DEFINES) target_compile_definitions(dark PRIVATE -D_CRT_RAND_S -DNOMINMAX -D_USE_MATH_DEFINES) target_compile_definitions(dark PUBLIC -D_CRT_SECURE_NO_WARNINGS) @@ -515,7 +515,7 @@ if(ENABLE_CUDA) target_compile_definitions(dark PUBLIC -DGPU) endif() -if(USE_INTEGRATED_LIBS) +if(USE_INTEGRATED_LIBS AND WIN32) target_compile_definitions(darknet PRIVATE -D_TIMESPEC_DEFINED) target_compile_definitions(dark PRIVATE -D_TIMESPEC_DEFINED) endif() @@ -535,7 +535,7 @@ if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND BUILD_USELIB_TRACK) target_compile_definitions(uselib_track PRIVATE -DZED_STEREO) endif() if(MSVC) - target_link_libraries(uselib_track PRIVATE PThreads_windows::PThreads_windows) + target_link_libraries(uselib_track PRIVATE PThreads4W::PThreads4W) target_compile_definitions(uselib_track PRIVATE -D_CRT_RAND_S -DNOMINMAX -D_USE_MATH_DEFINES) endif() target_link_libraries(uselib_track PRIVATE Threads::Threads) diff --git a/DarknetConfig.cmake.in b/DarknetConfig.cmake.in index 122120695e0..ffd34098a8b 100644 --- a/DarknetConfig.cmake.in +++ b/DarknetConfig.cmake.in @@ -24,11 +24,7 @@ set(CMAKE_THREAD_PREFER_PTHREAD ON) find_dependency(Threads) if(MSVC) - if(@USE_INTEGRATED_LIBS@) - find_dependency(PThreads4W) - else() - find_dependency(pthreads) - endif() + find_dependency(PThreads4W) set(CMAKE_CXX_FLAGS "/wd4018 /wd4244 /wd4267 /wd4305 ${CMAKE_CXX_FLAGS}") endif() diff --git a/build.ps1 b/build.ps1 index 9b774aa812b..c9efadddd06 100755 --- a/build.ps1 +++ b/build.ps1 @@ -127,90 +127,15 @@ param ( [string]$AdditionalBuildSetup = "" # "-DCMAKE_CUDA_ARCHITECTURES=30" ) -$build_ps1_version = "1.9.8" +$build_ps1_version = "2.0.0" +Import-Module -Name $PSScriptRoot/scripts/utils.psm1 -Force $ErrorActionPreference = "SilentlyContinue" Stop-Transcript | out-null $ErrorActionPreference = "Continue" Start-Transcript -Path $PSScriptRoot/build.log -Function MyThrow ($Message) { - if ($DisableInteractive) { - Write-Host $Message -ForegroundColor Red - throw - } - else { - # Check if running in PowerShell ISE - if ($psISE) { - # "ReadKey" not supported in PowerShell ISE. - # Show MessageBox UI - $Shell = New-Object -ComObject "WScript.Shell" - $Shell.Popup($Message, 0, "OK", 0) - throw - } - - $Ignore = - 16, # Shift (left or right) - 17, # Ctrl (left or right) - 18, # Alt (left or right) - 20, # Caps lock - 91, # Windows key (left) - 92, # Windows key (right) - 93, # Menu key - 144, # Num lock - 145, # Scroll lock - 166, # Back - 167, # Forward - 168, # Refresh - 169, # Stop - 170, # Search - 171, # Favorites - 172, # Start/Home - 173, # Mute - 174, # Volume Down - 175, # Volume Up - 176, # Next Track - 177, # Previous Track - 178, # Stop Media - 179, # Play - 180, # Mail - 181, # Select Media - 182, # Application 1 - 183 # Application 2 - - Write-Host $Message -ForegroundColor Red - Write-Host -NoNewline "Press any key to continue..." - while (($null -eq $KeyInfo.VirtualKeyCode) -or ($Ignore -contains $KeyInfo.VirtualKeyCode)) { - $KeyInfo = $Host.UI.RawUI.ReadKey("NoEcho, IncludeKeyDown") - } - Write-Host "" - throw - } -} - -Function DownloadNinja() { - Write-Host "Unable to find Ninja, downloading a portable version on-the-fly" -ForegroundColor Yellow - Remove-Item -Force -Recurse -ErrorAction SilentlyContinue ninja - Remove-Item -Force -ErrorAction SilentlyContinue ninja.zip - if ($IsWindows -or $IsWindowsPowerShell) { - $url = "https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-win.zip" - } - elseif ($IsLinux) { - $url = "https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-linux.zip" - } - elseif ($IsMacOS) { - $url = "https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-mac.zip" - } - else { - MyThrow("Unknown OS, unsupported") - } - Invoke-RestMethod -Uri $url -Method Get -ContentType application/zip -OutFile "ninja.zip" - Expand-Archive -Path ninja.zip - Remove-Item -Force -ErrorAction SilentlyContinue ninja.zip -} - - -Write-Host "Build script version ${build_ps1_version}" +Write-Host "Build script version ${build_ps1_version}, utils module version ${utils_psm1_version}" if ((-Not $DisableInteractive) -and (-Not $UseVCPKG)) { $Result = Read-Host "Enable vcpkg to install dependencies (yes/no)" @@ -464,90 +389,6 @@ if (-Not $DoNotUseNinja) { } } -function getProgramFiles32bit() { - $out = ${env:PROGRAMFILES(X86)} - if ($null -eq $out) { - $out = ${env:PROGRAMFILES} - } - - if ($null -eq $out) { - MyThrow("Could not find [Program Files 32-bit]") - } - - return $out -} - -function getLatestVisualStudioWithDesktopWorkloadPath() { - $programFiles = getProgramFiles32bit - $vswhereExe = "$programFiles\Microsoft Visual Studio\Installer\vswhere.exe" - if (Test-Path $vswhereExe) { - $output = & $vswhereExe -products * -latest -requires Microsoft.VisualStudio.Workload.NativeDesktop -format xml - [xml]$asXml = $output - foreach ($instance in $asXml.instances.instance) { - $installationPath = $instance.InstallationPath -replace "\\$" # Remove potential trailing backslash - } - if (!$installationPath) { - Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also partial installations" -ForegroundColor Yellow - $output = & $vswhereExe -products * -latest -format xml - [xml]$asXml = $output - foreach ($instance in $asXml.instances.instance) { - $installationPath = $instance.InstallationPath -replace "\\$" # Remove potential trailing backslash - } - } - if (!$installationPath) { - Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also pre-release installations" -ForegroundColor Yellow - $output = & $vswhereExe -prerelease -products * -latest -format xml - [xml]$asXml = $output - foreach ($instance in $asXml.instances.instance) { - $installationPath = $instance.InstallationPath -replace "\\$" # Remove potential trailing backslash - } - } - if (!$installationPath) { - MyThrow("Could not locate any installation of Visual Studio") - } - } - else { - MyThrow("Could not locate vswhere at $vswhereExe") - } - return $installationPath -} - - -function getLatestVisualStudioWithDesktopWorkloadVersion() { - $programFiles = getProgramFiles32bit - $vswhereExe = "$programFiles\Microsoft Visual Studio\Installer\vswhere.exe" - if (Test-Path $vswhereExe) { - $output = & $vswhereExe -products * -latest -requires Microsoft.VisualStudio.Workload.NativeDesktop -format xml - [xml]$asXml = $output - foreach ($instance in $asXml.instances.instance) { - $installationVersion = $instance.InstallationVersion - } - if (!$installationVersion) { - Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also partial installations" -ForegroundColor Yellow - $output = & $vswhereExe -products * -latest -format xml - [xml]$asXml = $output - foreach ($instance in $asXml.instances.instance) { - $installationVersion = $instance.installationVersion - } - } - if (!$installationVersion) { - Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also pre-release installations" -ForegroundColor Yellow - $output = & $vswhereExe -prerelease -products * -latest -format xml - [xml]$asXml = $output - foreach ($instance in $asXml.instances.instance) { - $installationVersion = $instance.installationVersion - } - } - if (!$installationVersion) { - MyThrow("Could not locate any installation of Visual Studio") - } - } - else { - MyThrow("Could not locate vswhere at $vswhereExe") - } - return $installationVersion -} - $vcpkg_root_set_by_this_script = $false if ((Test-Path env:VCPKG_ROOT) -and $UseVCPKG) { diff --git a/cmake/Modules/FindPThreads4W.cmake b/cmake/Modules/FindPThreads4W.cmake index 18e2b77c814..ef20cc2cbb4 100644 --- a/cmake/Modules/FindPThreads4W.cmake +++ b/cmake/Modules/FindPThreads4W.cmake @@ -24,12 +24,6 @@ # ``PThreads4W_LIBRARY`` # List of libraries to link with when using PThreads4W (no exception handling) # -# ``PThreads4W_CXXEXC_LIBRARY`` -# List of libraries to link with when using PThreads4W (C++ exception handling) -# -# ``PThreads4W_STRUCTEXC_LIBRARY`` -# List of libraries to link with when using PThreads4W (struct exception handling) -# # Result Targets # ^^^^^^^^^^^^^^^^ # @@ -38,12 +32,6 @@ # ``PThreads4W::PThreads4W`` # Target to use PThreads4W (no exception handling) # -# ``PThreads4W::PThreads4W_CXXEXC`` -# Target to use PThreads4W (C++ exception handling) -# -# ``PThreads4W::PThreads4W_STRUCTEXC`` -# Target to use PThreads4W (struct exception handling) -# include(${CMAKE_ROOT}/Modules/FindPackageHandleStandardArgs.cmake) include(${CMAKE_ROOT}/Modules/SelectLibraryConfigurations.cmake) @@ -60,170 +48,32 @@ set(PThreads4W_VERSION "${PThreads4W_MAJOR_VERSION}.${PThreads4W_MINOR_VERSION}. # Allow libraries to be set manually if(NOT PThreads4W_LIBRARY) find_library(PThreads4W_LIBRARY NAMES pthreadVC${PThreads4W_MAJOR_VERSION}) - find_library(PThreads4W_LIBRARY_RELEASE NAMES pthreadVC${PThreads4W_MAJOR_VERSION}) -endif() -if(NOT PThreads4W_CXXEXC_LIBRARY) - find_library(PThreads4W_CXXEXC_LIBRARY_RELEASE NAMES pthreadVCE${PThreads4W_MAJOR_VERSION}) - find_library(PThreads4W_CXXEXC_LIBRARY_DEBUG NAMES pthreadVCE${PThreads4W_MAJOR_VERSION}d) - select_library_configurations(PThreads4W_CXXEXC) -endif() -if(NOT PThreads4W_STRUCTEXC_LIBRARY) - find_library(PThreads4W_STRUCTEXC_LIBRARY_RELEASE NAMES pthreadVSE${PThreads4W_MAJOR_VERSION}) - find_library(PThreads4W_STRUCTEXC_LIBRARY_DEBUG NAMES pthreadVSE${PThreads4W_MAJOR_VERSION}d) - select_library_configurations(PThreads4W_STRUCTEXC) endif() find_package_handle_standard_args(PThreads4W DEFAULT_MSG PThreads4W_LIBRARY PThreads4W_INCLUDE_DIR) -mark_as_advanced(PThreads4W_INCLUDE_DIR PThreads4W_LIBRARY) +mark_as_advanced(PThreads4W_INCLUDE_DIR PThreads4W_LIBRARY ) set(PThreads4W_DLL_DIR ${PThreads4W_INCLUDE_DIR}) list(TRANSFORM PThreads4W_DLL_DIR APPEND "/../bin") message(STATUS "PThreads4W_DLL_DIR: ${PThreads4W_DLL_DIR}") -set(PThreads4W_DEBUG_DLL_DIR ${PThreads4W_INCLUDE_DIR}) -list(TRANSFORM PThreads4W_DEBUG_DLL_DIR APPEND "/../debug/bin") -message(STATUS "PThreads4W_DEBUG_DLL_DIR: ${PThreads4W_DEBUG_DLL_DIR}") - -find_file(PThreads4W_LIBRARY_RELEASE_DLL NAMES pthreadVC${PThreads4W_MAJOR_VERSION}.dll PATHS ${PThreads4W_DLL_DIR}) -find_file(PThreads4W_LIBRARY_DEBUG_DLL NAMES pthreadVC${PThreads4W_MAJOR_VERSION}d.dll PATHS ${PThreads4W_DEBUG_DLL_DIR}) -find_file(PThreads4W_CXXEXC_LIBRARY_RELEASE_DLL NAMES pthreadVCE${PThreads4W_MAJOR_VERSION}.dll PATHS ${PThreads4W_DLL_DIR}) -find_file(PThreads4W_CXXEXC_LIBRARY_DEBUG_DLL NAMES pthreadVCE${PThreads4W_MAJOR_VERSION}d.dll PATHS ${PThreads4W_DEBUG_DLL_DIR}) -find_file(PThreads4W_STRUCTEXC_LIBRARY_RELEASE_DLL NAMES pthreadVSE${PThreads4W_MAJOR_VERSION}.dll PATHS ${PThreads4W_DLL_DIR}) -find_file(PThreads4W_STRUCTEXC_LIBRARY_DEBUG_DLL NAMES pthreadVSE${PThreads4W_MAJOR_VERSION}d.dll PATHS ${PThreads4W_DEBUG_DLL_DIR}) -#Compatibility definitions, deprecated -set(PTHREAD_INCLUDE_DIR ${PThreads4W_INCLUDE_DIR} CACHE PATH "") -set(PTHREADS_INCLUDE_DIR ${PThreads4W_INCLUDE_DIR} CACHE PATH "") -set(PThreads_windows_INCLUDE_DIR ${PThreads4W_INCLUDE_DIR} CACHE PATH "") -set(PTHREAD_LIBRARIES ${PThreads4W_LIBRARY} CACHE STRING "") -set(PTHREADS_LIBRARIES ${PThreads4W_LIBRARY} CACHE STRING "") -set(PTHREAD_LIBRARY ${PThreads4W_LIBRARY} CACHE STRING "") -set(PTHREADS_LIBRARY ${PThreads4W_LIBRARY} CACHE STRING "") -set(LIBPTHREAD ${PThreads4W_LIBRARY} CACHE STRING "") -set(LIBPTHREADS ${PThreads4W_LIBRARY} CACHE STRING "") -set(PThreads_windows_LIBRARY ${PThreads4W_LIBRARY} CACHE STRING "") -set(PThreads_VERSION "${PThreads4W_VERSION}") -if(PThreads4W_FOUND) - set(PThreads_windows_FOUND TRUE) -endif() - -#TARGETS -if( PThreads4W_FOUND AND NOT TARGET PThreads4W::PThreads4W_CXXEXC ) - if( EXISTS "${PThreads4W_CXXEXC_LIBRARY_RELEASE_DLL}" ) - add_library( PThreads4W::PThreads4W_CXXEXC SHARED IMPORTED ) - set_target_properties( PThreads4W::PThreads4W_CXXEXC PROPERTIES - IMPORTED_LOCATION_RELEASE "${PThreads4W_CXXEXC_LIBRARY_RELEASE_DLL}" - IMPORTED_IMPLIB "${PThreads4W_CXXEXC_LIBRARY_RELEASE}" - INTERFACE_INCLUDE_DIRECTORIES "${PThreads4W_INCLUDE_DIR}" - IMPORTED_CONFIGURATIONS Release - IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) - if( EXISTS "${PThreads4W_CXXEXC_LIBRARY_DEBUG_DLL}" ) - set_property( TARGET PThreads4W::PThreads4W_CXXEXC APPEND PROPERTY IMPORTED_CONFIGURATIONS Debug ) - set_target_properties( PThreads4W::PThreads4W_CXXEXC PROPERTIES - IMPORTED_LOCATION_DEBUG "${PThreads4W_CXXEXC_LIBRARY_DEBUG_DLL}" - IMPORTED_IMPLIB_DEBUG "${PThreads4W_CXXEXC_LIBRARY_DEBUG}" ) - endif() - else() - add_library( PThreads4W::PThreads4W_CXXEXC UNKNOWN IMPORTED ) - set_target_properties( PThreads4W::PThreads4W_CXXEXC PROPERTIES - IMPORTED_LOCATION_RELEASE "${PThreads4W_CXXEXC_LIBRARY_RELEASE}" - INTERFACE_INCLUDE_DIRECTORIES "${PThreads4W_INCLUDE_DIR}" - IMPORTED_CONFIGURATIONS Release - IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) - if( EXISTS "${PThreads4W_CXXEXC_LIBRARY_DEBUG}" ) - set_property( TARGET PThreads4W::PThreads4W_CXXEXC APPEND PROPERTY IMPORTED_CONFIGURATIONS Debug ) - set_target_properties( PThreads4W::PThreads4W_CXXEXC PROPERTIES - IMPORTED_LOCATION_DEBUG "${PThreads4W_CXXEXC_LIBRARY_DEBUG}" ) - endif() - endif() -endif() - -if( PThreads4W_FOUND AND NOT TARGET PThreads4W::PThreads4W_STRUCTEXC ) - if( EXISTS "${PThreads4W_STRUCTEXC_LIBRARY_RELEASE_DLL}" ) - add_library( PThreads4W::PThreads4W_STRUCTEXC SHARED IMPORTED ) - set_target_properties( PThreads4W::PThreads4W_STRUCTEXC PROPERTIES - IMPORTED_LOCATION_RELEASE "${PThreads4W_STRUCTEXC_LIBRARY_RELEASE_DLL}" - IMPORTED_IMPLIB "${PThreads4W_STRUCTEXC_LIBRARY_RELEASE}" - INTERFACE_INCLUDE_DIRECTORIES "${PThreads4W_INCLUDE_DIR}" - IMPORTED_CONFIGURATIONS Release - IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) - if( EXISTS "${PThreads4W_STRUCTEXC_LIBRARY_DEBUG_DLL}" ) - set_property( TARGET PThreads4W::PThreads4W_STRUCTEXC APPEND PROPERTY IMPORTED_CONFIGURATIONS Debug ) - set_target_properties( PThreads4W::PThreads4W_STRUCTEXC PROPERTIES - IMPORTED_LOCATION_DEBUG "${PThreads4W_STRUCTEXC_LIBRARY_DEBUG_DLL}" - IMPORTED_IMPLIB_DEBUG "${PThreads4W_STRUCTEXC_LIBRARY_DEBUG}" ) - endif() - else() - add_library( PThreads4W::PThreads4W_STRUCTEXC UNKNOWN IMPORTED ) - set_target_properties( PThreads4W::PThreads4W_STRUCTEXC PROPERTIES - IMPORTED_LOCATION_RELEASE "${PThreads4W_STRUCTEXC_LIBRARY_RELEASE}" - INTERFACE_INCLUDE_DIRECTORIES "${PThreads4W_INCLUDE_DIR}" - IMPORTED_CONFIGURATIONS Release - IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) - if( EXISTS "${PThreads4W_STRUCTEXC_LIBRARY_DEBUG}" ) - set_property( TARGET PThreads4W::PThreads4W_STRUCTEXC APPEND PROPERTY IMPORTED_CONFIGURATIONS Debug ) - set_target_properties( PThreads4W::PThreads4W_STRUCTEXC PROPERTIES - IMPORTED_LOCATION_DEBUG "${PThreads4W_STRUCTEXC_LIBRARY_DEBUG}" ) - endif() - endif() -endif() +find_file(PThreads4W_LIBRARY_DLL NAMES pthreadVC${PThreads4W_MAJOR_VERSION}.dll PATHS ${PThreads4W_DLL_DIR}) if( PThreads4W_FOUND AND NOT TARGET PThreads4W::PThreads4W ) if( EXISTS "${PThreads4W_LIBRARY_RELEASE_DLL}" ) add_library( PThreads4W::PThreads4W SHARED IMPORTED ) set_target_properties( PThreads4W::PThreads4W PROPERTIES - IMPORTED_LOCATION_RELEASE "${PThreads4W_LIBRARY_RELEASE_DLL}" - IMPORTED_IMPLIB "${PThreads4W_LIBRARY_RELEASE}" + IMPORTED_LOCATION_RELEASE "${PThreads4W_LIBRARY_DLL}" + IMPORTED_IMPLIB "${PThreads4W_LIBRARY}" INTERFACE_INCLUDE_DIRECTORIES "${PThreads4W_INCLUDE_DIR}" IMPORTED_CONFIGURATIONS Release IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) - if( EXISTS "${PThreads4W_LIBRARY_DEBUG_DLL}" ) - set_property( TARGET PThreads4W::PThreads4W APPEND PROPERTY IMPORTED_CONFIGURATIONS Debug ) - set_target_properties( PThreads4W::PThreads4W PROPERTIES - IMPORTED_LOCATION_DEBUG "${PThreads4W_LIBRARY_DEBUG_DLL}" - IMPORTED_IMPLIB_DEBUG "${PThreads4W_LIBRARY_DEBUG}" ) - endif() else() add_library( PThreads4W::PThreads4W UNKNOWN IMPORTED ) set_target_properties( PThreads4W::PThreads4W PROPERTIES - IMPORTED_LOCATION_RELEASE "${PThreads4W_LIBRARY_RELEASE}" - INTERFACE_INCLUDE_DIRECTORIES "${PThreads4W_INCLUDE_DIR}" - IMPORTED_CONFIGURATIONS Release - IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) - if( EXISTS "${PThreads4W_LIBRARY_DEBUG}" ) - set_property( TARGET PThreads4W::PThreads4W APPEND PROPERTY IMPORTED_CONFIGURATIONS Debug ) - set_target_properties( PThreads4W::PThreads4W PROPERTIES - IMPORTED_LOCATION_DEBUG "${PThreads4W_LIBRARY_DEBUG}" ) - endif() - endif() -endif() - -#Compatibility targets, deprecated -if( PThreads4W_FOUND AND NOT TARGET PThreads_windows::PThreads_windows ) - if( EXISTS "${PThreads4W_LIBRARY_RELEASE_DLL}" ) - add_library( PThreads_windows::PThreads_windows SHARED IMPORTED ) - set_target_properties( PThreads_windows::PThreads_windows PROPERTIES - IMPORTED_LOCATION_RELEASE "${PThreads4W_LIBRARY_RELEASE_DLL}" - IMPORTED_IMPLIB "${PThreads4W_LIBRARY_RELEASE}" - INTERFACE_INCLUDE_DIRECTORIES "${PThreads4W_INCLUDE_DIR}" - IMPORTED_CONFIGURATIONS Release - IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) - if( EXISTS "${PThreads4W_LIBRARY_DEBUG_DLL}" ) - set_property( TARGET PThreads_windows::PThreads_windows APPEND PROPERTY IMPORTED_CONFIGURATIONS Debug ) - set_target_properties( PThreads_windows::PThreads_windows PROPERTIES - IMPORTED_LOCATION_DEBUG "${PThreads4W_LIBRARY_DEBUG_DLL}" - IMPORTED_IMPLIB_DEBUG "${PThreads4W_LIBRARY_DEBUG}" ) - endif() - else() - add_library( PThreads_windows::PThreads_windows UNKNOWN IMPORTED ) - set_target_properties( PThreads_windows::PThreads_windows PROPERTIES - IMPORTED_LOCATION_RELEASE "${PThreads4W_LIBRARY_RELEASE}" + IMPORTED_LOCATION_RELEASE "${PThreads4W_LIBRARY}" INTERFACE_INCLUDE_DIRECTORIES "${PThreads4W_INCLUDE_DIR}" IMPORTED_CONFIGURATIONS Release IMPORTED_LINK_INTERFACE_LANGUAGES "C" ) - if( EXISTS "${PThreads4W_LIBRARY_DEBUG}" ) - set_property( TARGET PThreads_windows::PThreads_windows APPEND PROPERTY IMPORTED_CONFIGURATIONS Debug ) - set_target_properties( PThreads_windows::PThreads_windows PROPERTIES - IMPORTED_LOCATION_DEBUG "${PThreads4W_LIBRARY_DEBUG}" ) - endif() endif() endif() diff --git a/scripts/deploy-cuda.ps1 b/scripts/deploy-cuda.ps1 index a460e8b3127..769b0974eb5 100644 --- a/scripts/deploy-cuda.ps1 +++ b/scripts/deploy-cuda.ps1 @@ -5,16 +5,22 @@ param ( [switch]$DisableSilentMode = $false ) -$url = 'https://developer.download.nvidia.com/compute/cuda/11.6.1/network_installers/cuda_11.6.1_windows_network.exe' +Import-Module -Name $PSScriptRoot/utils.psm1 -Force -$CudaFeatures = 'nvcc_11.6 cuobjdump_11.6 nvprune_11.6 cupti_11.6 memcheck_11.6 nvdisasm_11.6 nvprof_11.6 ' + ` - 'cublas_11.6 cublas_dev_11.6 nvjpeg_11.6 nvjpeg_dev_11.6 nvtx_11.6 cuxxfilt_11.6 sanitizer_11.6 ' + ` - 'cudart_11.6 cufft_11.6 cufft_dev_11.6 curand_11.6 curand_dev_11.6 cusolver_11.6 cusolver_dev_11.6 ' + ` - 'cusparse_11.6 cusparse_dev_11.6 npp_11.6 npp_dev_11.6 nvrtc_11.6 nvrtc_dev_11.6 nvml_dev_11.6 ' + ` - 'occupancy_calculator_11.6 documentation_11.6 ' +$url = "https://developer.download.nvidia.com/compute/cuda/${cuda_version_full}/network_installers/cuda_${cuda_version_full}_windows_network.exe" + +$CudaFeatures = "nvcc_${cuda_version_short} cuobjdump_${cuda_version_short} nvprune_${cuda_version_short} " + ` + " cupti_${cuda_version_short} memcheck_${cuda_version_short} nvdisasm_${cuda_version_short} nvprof_${cuda_version_short} " + ` + " cublas_${cuda_version_short} cublas_dev_${cuda_version_short} nvjpeg_${cuda_version_short} nvjpeg_dev_${cuda_version_short} " + ` + " nvtx_${cuda_version_short} cuxxfilt_${cuda_version_short} sanitizer_${cuda_version_short} " + ` + " cudart_${cuda_version_short} cufft_${cuda_version_short} cufft_dev_${cuda_version_short} curand_${cuda_version_short} " + ` + " curand_dev_${cuda_version_short} cusolver_${cuda_version_short} cusolver_dev_${cuda_version_short} " + ` + " cusparse_${cuda_version_short} cusparse_dev_${cuda_version_short} npp_${cuda_version_short} npp_dev_${cuda_version_short} " + ` + " nvrtc_${cuda_version_short} nvrtc_dev_${cuda_version_short} nvml_dev_${cuda_version_short} " + ` + " occupancy_calculator_${cuda_version_short} documentation_${cuda_version_short} " if (-Not $DisableVisualStudioFeatures) { - $CudaFeatures = $CudaFeatures + 'visual_studio_integration_11.6 visual_profiler_11.6 ' + $CudaFeatures = $CudaFeatures + "visual_studio_integration_${cuda_version_short} visual_profiler_${cuda_version_short} " } if ($DisableSilentMode) { @@ -26,10 +32,10 @@ else { try { Push-Location $PSScriptRoot - Write-Host 'Downloading CUDA...' - Invoke-WebRequest -Uri $url -OutFile "cuda_11.6.1_windows_network.exe" + Write-Host "Downloading CUDA from $url..." + Invoke-WebRequest -Uri $url -OutFile "cuda_${cuda_version_full}_windows_network.exe" Write-Host 'Installing CUDA...' - $proc = Start-Process -PassThru -FilePath "./cuda_11.6.1_windows_network.exe" -ArgumentList @($SilentFlag + $CudaFeatures) + $proc = Start-Process -PassThru -FilePath "./cuda_${cuda_version_full}_windows_network.exe" -ArgumentList @($SilentFlag + $CudaFeatures) $proc.WaitForExit() $exitCode = $proc.ExitCode Pop-Location diff --git a/scripts/deploy-cuda.sh b/scripts/deploy-cuda.sh index 267e3270981..1370dd556ec 100755 --- a/scripts/deploy-cuda.sh +++ b/scripts/deploy-cuda.sh @@ -19,16 +19,25 @@ else exit 1 fi -sudo apt-get update -sudo apt-get install build-essential g++ -sudo apt-get install apt-transport-https ca-certificates gnupg software-properties-common wget -sudo wget -O /etc/apt/preferences.d/cuda-repository-pin-600 https://developer.download.nvidia.com/compute/cuda/repos/$distr_name/x86_64/cuda-$distr_name.pin -sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/$distr_name/x86_64/7fa2af80.pub +sudo apt-key del 7fa2af80 +wget https://developer.download.nvidia.com/compute/cuda/repos/$distr_name/x86_64/cuda-keyring_1.0-1_all.deb +sudo dpkg -i cuda-keyring_1.0-1_all.deb sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/$distr_name/x86_64/ /" sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/$distr_name/x86_64/ /" +wget https://developer.download.nvidia.com/compute/cuda/repos/$distr_name/x86_64/cuda-$distr_name.pin +sudo mv cuda-${OS}.pin /etc/apt/preferences.d/cuda-repository-pin-600 +sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/$distr_name/x86_64/7fa2af80.pub +sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/$distr_name/x86_64/ /" sudo apt-get update +sudo apt-get install build-essential g++ +sudo apt-get install apt-transport-https ca-certificates gnupg software-properties-common wget +sudo apt-get install zlib1g sudo apt-get dist-upgrade -y -sudo apt-get install -y --no-install-recommends cuda-compiler-${CUDA_VERSION_DASHED} cuda-libraries-dev-${CUDA_VERSION_DASHED} cuda-driver-dev-${CUDA_VERSION_DASHED} cuda-cudart-dev-${CUDA_VERSION_DASHED} -sudo apt-get install -y --no-install-recommends libcudnn8-dev +sudo apt-get install -y --no-install-recommends cuda-${CUDA_VERSION_DASHED} +#sudo apt-get install libcudnn8=${cudnn_version}-1+${cuda_version} +#sudo apt-get install libcudnn8-dev=${cudnn_version}-1+cuda${CUDA_VERSION} +sudo apt-get install libcudnn8 +sudo apt-get install libcudnn8-dev + sudo rm -rf /usr/local/cuda sudo ln -s /usr/local/cuda-${CUDA_VERSION} /usr/local/cuda diff --git a/scripts/requested_cuda_version.sh b/scripts/requested_cuda_version.sh index 90f54b25a26..6c95e056850 100755 --- a/scripts/requested_cuda_version.sh +++ b/scripts/requested_cuda_version.sh @@ -1,4 +1,6 @@ #!/usr/bin/env bash -export CUDA_VERSION="11.6" +export CUDA_VERSION="11.7" export CUDA_VERSION_DASHED="${CUDA_VERSION//./-}" +export CUDNN_VERSION="8.4.1" +export CUDNN_VERSION_DASHED="${CUDNN_VERSION//./-}" diff --git a/scripts/setup.ps1 b/scripts/setup.ps1 index 865080382e8..526221916bb 100755 --- a/scripts/setup.ps1 +++ b/scripts/setup.ps1 @@ -4,6 +4,8 @@ param ( [switch]$InstallCUDA = $false ) +Import-Module -Name $PSScriptRoot/utils.psm1 -Force + if ($null -eq (Get-Command "choco.exe" -ErrorAction SilentlyContinue)) { # Download and install Chocolatey Invoke-Expression ((New-Object System.Net.WebClient).DownloadString('https://chocolatey.org/install.ps1')) @@ -16,9 +18,9 @@ Push-Location $PSScriptRoot if ($InstallCUDA) { & $PSScriptRoot/deploy-cuda.ps1 - $env:CUDA_PATH="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.6" - $env:CUDA_TOOLKIT_ROOT_DIR="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.6" - $env:CUDACXX="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.6\\bin\\nvcc.exe" + $env:CUDA_PATH = "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v${cuda_version_short}" + $env:CUDA_TOOLKIT_ROOT_DIR = "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v${cuda_version_short}" + $env:CUDACXX = "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v${cuda_version_short}\\bin\\nvcc.exe" $CUDAisAvailable = $true } else { diff --git a/scripts/utils.psm1 b/scripts/utils.psm1 new file mode 100644 index 00000000000..87a57d8ed76 --- /dev/null +++ b/scripts/utils.psm1 @@ -0,0 +1,200 @@ +<# +Copyright (c) Stefano Sinigardi + +MIT License + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +#> + +$utils_psm1_version = "0.0.2" +$cuda_version_full = "11.7.0" +$cuda_version_short = "11.7" +$cuda_version_full_dashed = $cuda_version_full.replace('.', '-') +$cuda_version_short_dashed = $cuda_version_short.replace('.', '-') + +function getProgramFiles32bit() { + $out = ${env:PROGRAMFILES(X86)} + if ($null -eq $out) { + $out = ${env:PROGRAMFILES} + } + + if ($null -eq $out) { + MyThrow("Could not find [Program Files 32-bit]") + } + + return $out +} + +function getLatestVisualStudioWithDesktopWorkloadPath() { + $programFiles = getProgramFiles32bit + $vswhereExe = "$programFiles\Microsoft Visual Studio\Installer\vswhere.exe" + if (Test-Path $vswhereExe) { + $output = & $vswhereExe -products * -latest -requires Microsoft.VisualStudio.Workload.NativeDesktop -format xml + [xml]$asXml = $output + foreach ($instance in $asXml.instances.instance) { + $installationPath = $instance.InstallationPath -replace "\\$" # Remove potential trailing backslash + } + if (!$installationPath) { + Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also partial installations" -ForegroundColor Yellow + $output = & $vswhereExe -products * -latest -format xml + [xml]$asXml = $output + foreach ($instance in $asXml.instances.instance) { + $installationPath = $instance.InstallationPath -replace "\\$" # Remove potential trailing backslash + } + } + if (!$installationPath) { + Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also pre-release installations" -ForegroundColor Yellow + $output = & $vswhereExe -prerelease -products * -latest -format xml + [xml]$asXml = $output + foreach ($instance in $asXml.instances.instance) { + $installationPath = $instance.InstallationPath -replace "\\$" # Remove potential trailing backslash + } + } + if (!$installationPath) { + MyThrow("Could not locate any installation of Visual Studio") + } + } + else { + MyThrow("Could not locate vswhere at $vswhereExe") + } + return $installationPath +} + +function getLatestVisualStudioWithDesktopWorkloadVersion() { + $programFiles = getProgramFiles32bit + $vswhereExe = "$programFiles\Microsoft Visual Studio\Installer\vswhere.exe" + if (Test-Path $vswhereExe) { + $output = & $vswhereExe -products * -latest -requires Microsoft.VisualStudio.Workload.NativeDesktop -format xml + [xml]$asXml = $output + foreach ($instance in $asXml.instances.instance) { + $installationVersion = $instance.InstallationVersion + } + if (!$installationVersion) { + Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also partial installations" -ForegroundColor Yellow + $output = & $vswhereExe -products * -latest -format xml + [xml]$asXml = $output + foreach ($instance in $asXml.instances.instance) { + $installationVersion = $instance.installationVersion + } + } + if (!$installationVersion) { + Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also pre-release installations" -ForegroundColor Yellow + $output = & $vswhereExe -prerelease -products * -latest -format xml + [xml]$asXml = $output + foreach ($instance in $asXml.instances.instance) { + $installationVersion = $instance.installationVersion + } + } + if (!$installationVersion) { + MyThrow("Could not locate any installation of Visual Studio") + } + } + else { + MyThrow("Could not locate vswhere at $vswhereExe") + } + return $installationVersion +} + +function DownloadNinja() { + Write-Host "Unable to find Ninja, downloading a portable version on-the-fly" -ForegroundColor Yellow + Remove-Item -Force -Recurse -ErrorAction SilentlyContinue ninja + Remove-Item -Force -ErrorAction SilentlyContinue ninja.zip + if ($IsWindows -or $IsWindowsPowerShell) { + $url = "https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-win.zip" + } + elseif ($IsLinux) { + $url = "https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-linux.zip" + } + elseif ($IsMacOS) { + $url = "https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-mac.zip" + } + else { + MyThrow("Unknown OS, unsupported") + } + Invoke-RestMethod -Uri $url -Method Get -ContentType application/zip -OutFile "ninja.zip" + Expand-Archive -Path ninja.zip + Remove-Item -Force -ErrorAction SilentlyContinue ninja.zip +} + +Function MyThrow ($Message) { + if ($DisableInteractive) { + Write-Host $Message -ForegroundColor Red + throw + } + else { + # Check if running in PowerShell ISE + if ($psISE) { + # "ReadKey" not supported in PowerShell ISE. + # Show MessageBox UI + $Shell = New-Object -ComObject "WScript.Shell" + $Shell.Popup($Message, 0, "OK", 0) + throw + } + + $Ignore = + 16, # Shift (left or right) + 17, # Ctrl (left or right) + 18, # Alt (left or right) + 20, # Caps lock + 91, # Windows key (left) + 92, # Windows key (right) + 93, # Menu key + 144, # Num lock + 145, # Scroll lock + 166, # Back + 167, # Forward + 168, # Refresh + 169, # Stop + 170, # Search + 171, # Favorites + 172, # Start/Home + 173, # Mute + 174, # Volume Down + 175, # Volume Up + 176, # Next Track + 177, # Previous Track + 178, # Stop Media + 179, # Play + 180, # Mail + 181, # Select Media + 182, # Application 1 + 183 # Application 2 + + Write-Host $Message -ForegroundColor Red + Write-Host -NoNewline "Press any key to continue..." + while (($null -eq $KeyInfo.VirtualKeyCode) -or ($Ignore -contains $KeyInfo.VirtualKeyCode)) { + $KeyInfo = $Host.UI.RawUI.ReadKey("NoEcho, IncludeKeyDown") + } + Write-Host "" + throw + } +} + +Export-ModuleMember -Variable utils_psm1_version +Export-ModuleMember -Variable cuda_version_full +Export-ModuleMember -Variable cuda_version_short +Export-ModuleMember -Variable cuda_version_full_dashed +Export-ModuleMember -Variable cuda_version_short_dashed + +Export-ModuleMember -Function getProgramFiles32bit +Export-ModuleMember -Function getLatestVisualStudioWithDesktopWorkloadPath +Export-ModuleMember -Function getLatestVisualStudioWithDesktopWorkloadVersion +Export-ModuleMember -Function DownloadNinja +Export-ModuleMember -Function MyThrow +Export-ModuleMember -Function CopyTexFile From 69514cac9ed9830fda5ab0ecdb9b37617d9e8892 Mon Sep 17 00:00:00 2001 From: Alexey Date: Thu, 7 Jul 2022 05:44:53 +0300 Subject: [PATCH 1305/1365] Update README.md --- README.md | 49 +++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 47 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 9270951e0a5..61a2d69ebe5 100644 --- a/README.md +++ b/README.md @@ -2,9 +2,54 @@ ## (neural networks for object detection) -Paper YOLO v4: https://arxiv.org/abs/2004.10934 +* **Paper YOLOv7**: https://arxiv.org/abs/2207.02696 -Paper Scaled YOLO v4: * **[CVPR 2021](https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.html)**: use to reproduce results: [ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4) +* **source code YOLOv7 - Pytorch (use to reproduce results):** https://github.com/WongKinYiu/yolov7 + +---- + +* **Paper YOLOv4**: https://arxiv.org/abs/2004.10934 + +* **source code YOLOv4 - Darknet (use to reproduce results):** https://github.com/AlexeyAB/darknet + +---- + +* **Paper Scaled-YOLOv4 (CVPR 2021)**: https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.html + +* **source code Scaled-YOLOv4 - Pytorch (use to reproduce results):** https://github.com/WongKinYiu/ScaledYOLOv4 + +---- + +### YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors + +* **Paper**: https://arxiv.org/abs/2207.02696 + +* **source code - Pytorch (use to reproduce results):** https://github.com/WongKinYiu/yolov7 + + +YOLOv7 is more accurate and faster than YOLOv5 by **120%** FPS, than YOLOX by **180%** FPS, than Dual-Swin-T by **1200%** FPS, than ConvNext by **550%** FPS, than SWIN-L by **500%** FPS, than PPYOLOE-X by **150%** FPS. + +YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100, batch=1. + +* YOLOv7-e6 (55.9% AP, 56 FPS V100 b=1) by `+500%` FPS faster than SWIN-L C-M-RCNN (53.9% AP, 9.2 FPS A100 b=1) +* YOLOv7-e6 (55.9% AP, 56 FPS V100 b=1) by `+550%` FPS faster than ConvNeXt-XL C-M-RCNN (55.2% AP, 8.6 FPS A100 b=1) +* YOLOv7-w6 (54.6% AP, 84 FPS V100 b=1) by `+120%` FPS faster than YOLOv5-X6-r6.1 (55.0% AP, 38 FPS V100 b=1) +* YOLOv7-w6 (54.6% AP, 84 FPS V100 b=1) by `+1200%` FPS faster than Dual-Swin-T C-M-RCNN (53.6% AP, 6.5 FPS V100 b=1) +* YOLOv7x (52.9% AP, 114 FPS V100 b=1) by `+150%` FPS faster than PPYOLOE-X (51.9% AP, 45 FPS V100 b=1) +* YOLOv7 (51.2% AP, 161 FPS V100 b=1) by `+180%` FPS faster than YOLOX-X (51.1% AP, 58 FPS V100 b=1) + + +---- + +![image](https://user-images.githubusercontent.com/4096485/177674970-0c633f50-405d-4a20-a8d9-bd467d09d6ab.png) + + +---- + +![image](https://user-images.githubusercontent.com/4096485/177675030-a929ee00-0eba-4d93-95c2-225231d0fd61.png) + + +---- More details in articles on medium: From a02b5b83a62e1d809d06899fc2a89aeaf9753bc4 Mon Sep 17 00:00:00 2001 From: AlexeyAB84 Date: Thu, 7 Jul 2022 22:19:18 +0300 Subject: [PATCH 1306/1365] Added yolov7-tiny.cfg --- build/darknet/x64/cfg/yolov7-tiny.cfg | 706 ++++++++++++++++++++++++++ cfg/yolov7-tiny.cfg | 706 ++++++++++++++++++++++++++ 2 files changed, 1412 insertions(+) create mode 100644 build/darknet/x64/cfg/yolov7-tiny.cfg create mode 100644 cfg/yolov7-tiny.cfg diff --git a/build/darknet/x64/cfg/yolov7-tiny.cfg b/build/darknet/x64/cfg/yolov7-tiny.cfg new file mode 100644 index 00000000000..8fe8c58cf1b --- /dev/null +++ b/build/darknet/x64/cfg/yolov7-tiny.cfg @@ -0,0 +1,706 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=1 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.00261 +burn_in=1000 + +max_batches = 2000200 +policy=steps +steps=1600000,1800000 +scales=.1,.1 + +# 0 +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=2 +pad=1 +activation=leaky + +# 1 +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -5,-3,-2,-1 + +# 8 +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -5,-3,-2,-1 + +# 16 +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -5,-3,-2,-1 + +# 24 +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -5,-3,-2,-1 + +# 32 +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + + +################################## + +### SPPCSP ### +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -10,-1 + +# 44 +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky +### End SPPCSP ### + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 24 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-3 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -5,-3,-2,-1 + +# 56 +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 16 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-3 + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -5,-3,-2,-1 + +# 68 +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +########################## + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=128 +activation=leaky + +[route] +layers = -1,56 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -5,-3,-2,-1 + +# 77 +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=leaky + +[route] +layers = -1,44 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -5,-3,-2,-1 + +# 86 +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +############################# + +# ============ End of Neck ============ # + +# ============ Head ============ # + + +# P3 +[route] +layers = 68 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +#activation=linear +activation=logistic + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# P4 +[route] +layers = 77 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +#activation=linear +activation=logistic + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# P5 +[route] +layers = 86 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +#activation=linear +activation=logistic + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 diff --git a/cfg/yolov7-tiny.cfg b/cfg/yolov7-tiny.cfg new file mode 100644 index 00000000000..8fe8c58cf1b --- /dev/null +++ b/cfg/yolov7-tiny.cfg @@ -0,0 +1,706 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=1 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.00261 +burn_in=1000 + +max_batches = 2000200 +policy=steps +steps=1600000,1800000 +scales=.1,.1 + +# 0 +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=2 +pad=1 +activation=leaky + +# 1 +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -5,-3,-2,-1 + +# 8 +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -5,-3,-2,-1 + +# 16 +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -5,-3,-2,-1 + +# 24 +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -5,-3,-2,-1 + +# 32 +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + + +################################## + +### SPPCSP ### +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -10,-1 + +# 44 +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky +### End SPPCSP ### + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 24 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-3 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -5,-3,-2,-1 + +# 56 +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = 16 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -1,-3 + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -5,-3,-2,-1 + +# 68 +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +########################## + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=128 +activation=leaky + +[route] +layers = -1,56 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -5,-3,-2,-1 + +# 77 +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=256 +activation=leaky + +[route] +layers = -1,44 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[route] +layers = -5,-3,-2,-1 + +# 86 +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +############################# + +# ============ End of Neck ============ # + +# ============ Head ============ # + + +# P3 +[route] +layers = 68 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=128 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +#activation=linear +activation=logistic + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# P4 +[route] +layers = 77 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +#activation=linear +activation=logistic + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# P5 +[route] +layers = 86 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +#activation=linear +activation=logistic + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 From ecace9a36d98e505f3c42cd66413b9614afb2eb0 Mon Sep 17 00:00:00 2001 From: AlexeyAB84 Date: Thu, 7 Jul 2022 23:33:45 +0300 Subject: [PATCH 1307/1365] minor fix --- src/demo.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/demo.c b/src/demo.c index 0c40333513e..2c48bb78749 100644 --- a/src/demo.c +++ b/src/demo.c @@ -344,12 +344,12 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int } while (custom_atomic_load_int(&run_detect_in_thread)) { - if(avg_fps > 180) this_thread_yield(); + if(avg_fps > 50) this_thread_yield(); else this_thread_sleep_for(thread_wait_ms); // custom_join(detect_thread, 0); } if (!benchmark) { while (custom_atomic_load_int(&run_fetch_in_thread)) { - if (avg_fps > 180) this_thread_yield(); + if (avg_fps > 50) this_thread_yield(); else this_thread_sleep_for(thread_wait_ms); // custom_join(fetch_thread, 0); } free_image(det_s); From 25505164a3bd6235c75deaad325878ceda90249a Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 18 Jul 2022 00:24:59 +0300 Subject: [PATCH 1308/1365] Update README.md --- README.md | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 61a2d69ebe5..0bbf462d99e 100644 --- a/README.md +++ b/README.md @@ -2,21 +2,21 @@ ## (neural networks for object detection) -* **Paper YOLOv7**: https://arxiv.org/abs/2207.02696 +* Paper **YOLOv7**: https://arxiv.org/abs/2207.02696 -* **source code YOLOv7 - Pytorch (use to reproduce results):** https://github.com/WongKinYiu/yolov7 +* source code YOLOv7 - Pytorch (use to reproduce results): https://github.com/WongKinYiu/yolov7 ---- -* **Paper YOLOv4**: https://arxiv.org/abs/2004.10934 +* Paper **YOLOv4**: https://arxiv.org/abs/2004.10934 -* **source code YOLOv4 - Darknet (use to reproduce results):** https://github.com/AlexeyAB/darknet +* source code YOLOv4 - Darknet (use to reproduce results): https://github.com/AlexeyAB/darknet ---- -* **Paper Scaled-YOLOv4 (CVPR 2021)**: https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.html +* Paper **Scaled-YOLOv4 (CVPR 2021)**: https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.html -* **source code Scaled-YOLOv4 - Pytorch (use to reproduce results):** https://github.com/WongKinYiu/ScaledYOLOv4 +* source code Scaled-YOLOv4 - Pytorch (use to reproduce results): https://github.com/WongKinYiu/ScaledYOLOv4 ---- @@ -41,8 +41,7 @@ YOLOv7 surpasses all known object detectors in both speed and accuracy in the ra ---- -![image](https://user-images.githubusercontent.com/4096485/177674970-0c633f50-405d-4a20-a8d9-bd467d09d6ab.png) - +![more5](https://user-images.githubusercontent.com/4096485/179425274-f55a36d4-8450-4471-816b-8c105841effd.jpg) ---- From c484fcca49be30a80e58855d4010c44b593737c4 Mon Sep 17 00:00:00 2001 From: AlexeyAB84 Date: Thu, 11 Aug 2022 14:40:04 +0300 Subject: [PATCH 1309/1365] Added yolov7.cfg and yolov7x.cfg --- build/darknet/x64/cfg/yolov7.cfg | 1024 +++++++++++++++++++++++++ build/darknet/x64/cfg/yolov7x.cfg | 1152 +++++++++++++++++++++++++++++ cfg/yolov7.cfg | 1024 +++++++++++++++++++++++++ cfg/yolov7x.cfg | 1152 +++++++++++++++++++++++++++++ 4 files changed, 4352 insertions(+) create mode 100644 build/darknet/x64/cfg/yolov7.cfg create mode 100644 build/darknet/x64/cfg/yolov7x.cfg create mode 100644 cfg/yolov7.cfg create mode 100644 cfg/yolov7x.cfg diff --git a/build/darknet/x64/cfg/yolov7.cfg b/build/darknet/x64/cfg/yolov7.cfg new file mode 100644 index 00000000000..62e3a144660 --- /dev/null +++ b/build/darknet/x64/cfg/yolov7.cfg @@ -0,0 +1,1024 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=8 +subdivisions=1 +width=640 +height=640 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.00261 +burn_in=1000 + +max_batches = 2000200 +policy=steps +steps=1600000,1800000 +scales=.1,.1 + +# 0 +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=silu + + +# 1 +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + + +# 3 +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7 + +# 12 +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=silu + +# 18 +[route] +layers = -1,-4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7 + +# 27 +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=silu + +# 33 +[route] +layers = -1,-4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7 + +# 42 +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=silu + + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-3 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=silu + +# 48 +[route] +layers = -1,-4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7 + +# 57 +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=silu + +################################## + +### SPPCSP ### +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=silu + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-6,-5,-3,-1 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=silu + +[route] +layers = -1, -13 + +# 72 +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu +### End SPPCSP ### + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[upsample] +stride=2 + +[route] +layers = 42 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3 + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-2,-3,-4,-5,-7 + +# 86 +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[upsample] +stride=2 + +[route] +layers = 27 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3 + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-2,-3,-4,-5,-7 + +# 100 +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=silu + +[route] +layers = -1,-4,86 + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-2,-3,-4,-5,-7 + +# 115 +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=silu + +[route] +layers = -1,-4,72 + + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-2,-3,-4,-5,-7 + +# 130 +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + +############################# + +# ============ End of Neck ============ # + +# ============ Head ============ # + + +# P3 +[route] +layers = 100 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=silu + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +#activation=linear +activation=logistic + +[yolo] +mask = 0,1,2 +anchors = 12,16, 19,36, 40,28, 36,75, 76,55, 72,146, 142,110, 192,243, 459,401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# P4 +[route] +layers = 115 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=silu + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +#activation=linear +activation=logistic + +[yolo] +mask = 3,4,5 +anchors = 12,16, 19,36, 40,28, 36,75, 76,55, 72,146, 142,110, 192,243, 459,401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# P5 +[route] +layers = 130 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=silu + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +#activation=linear +activation=logistic + +[yolo] +mask = 6,7,8 +anchors = 12,16, 19,36, 40,28, 36,75, 76,55, 72,146, 142,110, 192,243, 459,401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 diff --git a/build/darknet/x64/cfg/yolov7x.cfg b/build/darknet/x64/cfg/yolov7x.cfg new file mode 100644 index 00000000000..46eec57d893 --- /dev/null +++ b/build/darknet/x64/cfg/yolov7x.cfg @@ -0,0 +1,1152 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=8 +subdivisions=1 +width=640 +height=640 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.00261 +burn_in=1000 + +max_batches = 2000200 +policy=steps +steps=1600000,1800000 +scales=.1,.1 + + +# 0 +[convolutional] +batch_normalize=1 +filters=40 +size=3 +stride=1 +pad=1 +activation=silu + + +# 1 +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=2 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=silu + + +# 3 +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=2 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7,-9 + +# 14 +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=silu + + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-3 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=2 +pad=1 +activation=silu + +# 20 +[route] +layers = -1,-4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7,-9 + +# 31 +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=silu + + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-3 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=2 +pad=1 +activation=silu + +# 37 +[route] +layers = -1,-4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7,-9 + +# 48 +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=silu + + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-3 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=2 +pad=1 +activation=silu + +# 54 +[route] +layers = -1,-4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7,-9 + +# 65 +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=silu + +################################## + +### SPPCSP ### +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=silu + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=silu + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-6,-5,-3,-1 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=silu + +[route] +layers = -1, -13 + +# 80 +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=silu + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=silu + +[upsample] +stride=2 + +[route] +layers = 48 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3 + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7,-9 + +# 96 +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=silu + + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=silu + +[upsample] +stride=2 + +[route] +layers = 31 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3 + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7,-9 + +# 112 +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=silu + + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-3 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=2 +pad=1 +activation=silu + +[route] +layers = -1,-4,96 + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7,-9 + +# 129 +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=silu + + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-3 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=2 +pad=1 +activation=silu + +[route] +layers = -1,-4,80 + + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7,-9 + +# 146 +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=silu + +############################# + +# ============ End of Neck ============ # + +# ============ Head ============ # + + +# P3 +[route] +layers = 112 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=silu + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +#activation=linear +activation=logistic + +[yolo] +mask = 0,1,2 +anchors = 12,16, 19,36, 40,28, 36,75, 76,55, 72,146, 142,110, 192,243, 459,401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# P4 +[route] +layers = 129 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=silu + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +#activation=linear +activation=logistic + +[yolo] +mask = 3,4,5 +anchors = 12,16, 19,36, 40,28, 36,75, 76,55, 72,146, 142,110, 192,243, 459,401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# P5 +[route] +layers = 146 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1280 +activation=silu + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +#activation=linear +activation=logistic + +[yolo] +mask = 6,7,8 +anchors = 12,16, 19,36, 40,28, 36,75, 76,55, 72,146, 142,110, 192,243, 459,401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 diff --git a/cfg/yolov7.cfg b/cfg/yolov7.cfg new file mode 100644 index 00000000000..62e3a144660 --- /dev/null +++ b/cfg/yolov7.cfg @@ -0,0 +1,1024 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=8 +subdivisions=1 +width=640 +height=640 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.00261 +burn_in=1000 + +max_batches = 2000200 +policy=steps +steps=1600000,1800000 +scales=.1,.1 + +# 0 +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=silu + + +# 1 +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + + +# 3 +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7 + +# 12 +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=silu + +# 18 +[route] +layers = -1,-4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7 + +# 27 +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=silu + +# 33 +[route] +layers = -1,-4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7 + +# 42 +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=silu + + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-3 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=silu + +# 48 +[route] +layers = -1,-4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7 + +# 57 +[convolutional] +batch_normalize=1 +filters=1024 +size=1 +stride=1 +pad=1 +activation=silu + +################################## + +### SPPCSP ### +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=silu + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-6,-5,-3,-1 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=silu + +[route] +layers = -1, -13 + +# 72 +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu +### End SPPCSP ### + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[upsample] +stride=2 + +[route] +layers = 42 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3 + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-2,-3,-4,-5,-7 + +# 86 +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[upsample] +stride=2 + +[route] +layers = 27 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3 + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-2,-3,-4,-5,-7 + +# 100 +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-3 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=silu + +[route] +layers = -1,-4,86 + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-2,-3,-4,-5,-7 + +# 115 +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-3 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=silu + +[route] +layers = -1,-4,72 + + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-2,-3,-4,-5,-7 + +# 130 +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + +############################# + +# ============ End of Neck ============ # + +# ============ Head ============ # + + +# P3 +[route] +layers = 100 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=silu + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +#activation=linear +activation=logistic + +[yolo] +mask = 0,1,2 +anchors = 12,16, 19,36, 40,28, 36,75, 76,55, 72,146, 142,110, 192,243, 459,401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# P4 +[route] +layers = 115 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=silu + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +#activation=linear +activation=logistic + +[yolo] +mask = 3,4,5 +anchors = 12,16, 19,36, 40,28, 36,75, 76,55, 72,146, 142,110, 192,243, 459,401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# P5 +[route] +layers = 130 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=silu + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +#activation=linear +activation=logistic + +[yolo] +mask = 6,7,8 +anchors = 12,16, 19,36, 40,28, 36,75, 76,55, 72,146, 142,110, 192,243, 459,401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 diff --git a/cfg/yolov7x.cfg b/cfg/yolov7x.cfg new file mode 100644 index 00000000000..46eec57d893 --- /dev/null +++ b/cfg/yolov7x.cfg @@ -0,0 +1,1152 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=8 +subdivisions=1 +width=640 +height=640 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.00261 +burn_in=1000 + +max_batches = 2000200 +policy=steps +steps=1600000,1800000 +scales=.1,.1 + + +# 0 +[convolutional] +batch_normalize=1 +filters=40 +size=3 +stride=1 +pad=1 +activation=silu + + +# 1 +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=2 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=silu + + +# 3 +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=2 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7,-9 + +# 14 +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=silu + + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-3 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=2 +pad=1 +activation=silu + +# 20 +[route] +layers = -1,-4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7,-9 + +# 31 +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=silu + + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-3 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=2 +pad=1 +activation=silu + +# 37 +[route] +layers = -1,-4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7,-9 + +# 48 +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=silu + + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-3 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=2 +pad=1 +activation=silu + +# 54 +[route] +layers = -1,-4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7,-9 + +# 65 +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=silu + +################################## + +### SPPCSP ### +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=silu + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=silu + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-6,-5,-3,-1 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=silu + +[route] +layers = -1, -13 + +# 80 +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=silu + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=silu + +[upsample] +stride=2 + +[route] +layers = 48 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3 + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7,-9 + +# 96 +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=silu + + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=silu + +[upsample] +stride=2 + +[route] +layers = 31 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3 + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7,-9 + +# 112 +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=silu + + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-3 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=2 +pad=1 +activation=silu + +[route] +layers = -1,-4,96 + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7,-9 + +# 129 +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=silu + + +[maxpool] +size=2 +stride=2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-3 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=2 +pad=1 +activation=silu + +[route] +layers = -1,-4,80 + + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + +[route] +layers=-2 + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=silu + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=silu + +[route] +layers = -1,-3,-5,-7,-9 + +# 146 +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=silu + +############################# + +# ============ End of Neck ============ # + +# ============ Head ============ # + + +# P3 +[route] +layers = 112 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=silu + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +#activation=linear +activation=logistic + +[yolo] +mask = 0,1,2 +anchors = 12,16, 19,36, 40,28, 36,75, 76,55, 72,146, 142,110, 192,243, 459,401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# P4 +[route] +layers = 129 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=silu + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +#activation=linear +activation=logistic + +[yolo] +mask = 3,4,5 +anchors = 12,16, 19,36, 40,28, 36,75, 76,55, 72,146, 142,110, 192,243, 459,401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 + + +# P5 +[route] +layers = 146 + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1280 +activation=silu + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +#activation=linear +activation=logistic + +[yolo] +mask = 6,7,8 +anchors = 12,16, 19,36, 40,28, 36,75, 76,55, 72,146, 142,110, 192,243, 459,401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 From b10dd9387354d4d3d8b1c2c099394a215d36867e Mon Sep 17 00:00:00 2001 From: AlexeyAB84 Date: Thu, 11 Aug 2022 14:44:15 +0300 Subject: [PATCH 1310/1365] fix activation silu to swish for yolov7/x.cfg --- build/darknet/x64/cfg/yolov7.cfg | 178 ++++++++++++------------- build/darknet/x64/cfg/yolov7x.cfg | 210 +++++++++++++++--------------- cfg/yolov7.cfg | 178 ++++++++++++------------- cfg/yolov7x.cfg | 210 +++++++++++++++--------------- 4 files changed, 388 insertions(+), 388 deletions(-) diff --git a/build/darknet/x64/cfg/yolov7.cfg b/build/darknet/x64/cfg/yolov7.cfg index 62e3a144660..b4aea08dc82 100644 --- a/build/darknet/x64/cfg/yolov7.cfg +++ b/build/darknet/x64/cfg/yolov7.cfg @@ -30,7 +30,7 @@ filters=32 size=3 stride=1 pad=1 -activation=silu +activation=swish # 1 @@ -40,7 +40,7 @@ filters=64 size=3 stride=2 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -48,7 +48,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish # 3 @@ -58,7 +58,7 @@ filters=128 size=3 stride=2 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -66,7 +66,7 @@ filters=64 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -77,7 +77,7 @@ filters=64 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -85,7 +85,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -93,7 +93,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -101,7 +101,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -109,7 +109,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7 @@ -121,7 +121,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [maxpool] @@ -134,7 +134,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-3 @@ -145,7 +145,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -153,7 +153,7 @@ filters=128 size=3 stride=2 pad=1 -activation=silu +activation=swish # 18 [route] @@ -165,7 +165,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -176,7 +176,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -184,7 +184,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -192,7 +192,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -200,7 +200,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -208,7 +208,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7 @@ -220,7 +220,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish [maxpool] @@ -233,7 +233,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-3 @@ -244,7 +244,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -252,7 +252,7 @@ filters=256 size=3 stride=2 pad=1 -activation=silu +activation=swish # 33 [route] @@ -264,7 +264,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -275,7 +275,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -283,7 +283,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -291,7 +291,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -299,7 +299,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -307,7 +307,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7 @@ -319,7 +319,7 @@ filters=1024 size=1 stride=1 pad=1 -activation=silu +activation=swish [maxpool] @@ -332,7 +332,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-3 @@ -343,7 +343,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -351,7 +351,7 @@ filters=512 size=3 stride=2 pad=1 -activation=silu +activation=swish # 48 [route] @@ -363,7 +363,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -374,7 +374,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -382,7 +382,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -390,7 +390,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -398,7 +398,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -406,7 +406,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7 @@ -418,7 +418,7 @@ filters=1024 size=1 stride=1 pad=1 -activation=silu +activation=swish ################################## @@ -429,7 +429,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -2 @@ -440,7 +440,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -448,7 +448,7 @@ size=3 stride=1 pad=1 filters=512 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -456,7 +456,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish ### SPP ### [maxpool] @@ -487,7 +487,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -495,7 +495,7 @@ size=3 stride=1 pad=1 filters=512 -activation=silu +activation=swish [route] layers = -1, -13 @@ -507,7 +507,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish ### End SPPCSP ### @@ -517,7 +517,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [upsample] stride=2 @@ -531,7 +531,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3 @@ -543,7 +543,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -554,7 +554,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -562,7 +562,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -570,7 +570,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -578,7 +578,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -586,7 +586,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-2,-3,-4,-5,-7 @@ -598,7 +598,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] @@ -607,7 +607,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [upsample] stride=2 @@ -621,7 +621,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3 @@ -633,7 +633,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -644,7 +644,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -652,7 +652,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -660,7 +660,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -668,7 +668,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -676,7 +676,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-2,-3,-4,-5,-7 @@ -688,7 +688,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [maxpool] @@ -701,7 +701,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-3 @@ -712,7 +712,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -720,7 +720,7 @@ filters=128 size=3 stride=2 pad=1 -activation=silu +activation=swish [route] layers = -1,-4,86 @@ -732,7 +732,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -743,7 +743,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -751,7 +751,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -759,7 +759,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -767,7 +767,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -775,7 +775,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-2,-3,-4,-5,-7 @@ -787,7 +787,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [maxpool] @@ -800,7 +800,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-3 @@ -811,7 +811,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -819,7 +819,7 @@ filters=256 size=3 stride=2 pad=1 -activation=silu +activation=swish [route] layers = -1,-4,72 @@ -831,7 +831,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -842,7 +842,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -850,7 +850,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -858,7 +858,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -866,7 +866,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -874,7 +874,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-2,-3,-4,-5,-7 @@ -886,7 +886,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish ############################# @@ -905,7 +905,7 @@ size=3 stride=1 pad=1 filters=256 -activation=silu +activation=swish [convolutional] size=1 @@ -948,7 +948,7 @@ size=3 stride=1 pad=1 filters=512 -activation=silu +activation=swish [convolutional] size=1 @@ -991,7 +991,7 @@ size=3 stride=1 pad=1 filters=1024 -activation=silu +activation=swish [convolutional] size=1 diff --git a/build/darknet/x64/cfg/yolov7x.cfg b/build/darknet/x64/cfg/yolov7x.cfg index 46eec57d893..d489728af9a 100644 --- a/build/darknet/x64/cfg/yolov7x.cfg +++ b/build/darknet/x64/cfg/yolov7x.cfg @@ -31,7 +31,7 @@ filters=40 size=3 stride=1 pad=1 -activation=silu +activation=swish # 1 @@ -41,7 +41,7 @@ filters=80 size=3 stride=2 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -49,7 +49,7 @@ filters=80 size=3 stride=1 pad=1 -activation=silu +activation=swish # 3 @@ -59,7 +59,7 @@ filters=160 size=3 stride=2 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -67,7 +67,7 @@ filters=64 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -78,7 +78,7 @@ filters=64 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -86,7 +86,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -94,7 +94,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -102,7 +102,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -110,7 +110,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -118,7 +118,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -126,7 +126,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7,-9 @@ -138,7 +138,7 @@ filters=320 size=1 stride=1 pad=1 -activation=silu +activation=swish [maxpool] @@ -151,7 +151,7 @@ filters=160 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-3 @@ -162,7 +162,7 @@ filters=160 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -170,7 +170,7 @@ filters=160 size=3 stride=2 pad=1 -activation=silu +activation=swish # 20 [route] @@ -182,7 +182,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -193,7 +193,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -201,7 +201,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -209,7 +209,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -217,7 +217,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -225,7 +225,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -233,7 +233,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -241,7 +241,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7,-9 @@ -253,7 +253,7 @@ filters=640 size=1 stride=1 pad=1 -activation=silu +activation=swish [maxpool] @@ -266,7 +266,7 @@ filters=320 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-3 @@ -277,7 +277,7 @@ filters=320 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -285,7 +285,7 @@ filters=320 size=3 stride=2 pad=1 -activation=silu +activation=swish # 37 [route] @@ -297,7 +297,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -308,7 +308,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -316,7 +316,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -324,7 +324,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -332,7 +332,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -340,7 +340,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -348,7 +348,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -356,7 +356,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7,-9 @@ -368,7 +368,7 @@ filters=1280 size=1 stride=1 pad=1 -activation=silu +activation=swish [maxpool] @@ -381,7 +381,7 @@ filters=640 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-3 @@ -392,7 +392,7 @@ filters=640 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -400,7 +400,7 @@ filters=640 size=3 stride=2 pad=1 -activation=silu +activation=swish # 54 [route] @@ -412,7 +412,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -423,7 +423,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -431,7 +431,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -439,7 +439,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -447,7 +447,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -455,7 +455,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -463,7 +463,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -471,7 +471,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7,-9 @@ -483,7 +483,7 @@ filters=1280 size=1 stride=1 pad=1 -activation=silu +activation=swish ################################## @@ -494,7 +494,7 @@ filters=640 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -2 @@ -505,7 +505,7 @@ filters=640 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -513,7 +513,7 @@ size=3 stride=1 pad=1 filters=640 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -521,7 +521,7 @@ filters=640 size=1 stride=1 pad=1 -activation=silu +activation=swish ### SPP ### [maxpool] @@ -552,7 +552,7 @@ filters=640 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -560,7 +560,7 @@ size=3 stride=1 pad=1 filters=640 -activation=silu +activation=swish [route] layers = -1, -13 @@ -572,7 +572,7 @@ filters=640 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] @@ -581,7 +581,7 @@ filters=320 size=1 stride=1 pad=1 -activation=silu +activation=swish [upsample] stride=2 @@ -595,7 +595,7 @@ filters=320 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3 @@ -607,7 +607,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -618,7 +618,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -626,7 +626,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -634,7 +634,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -642,7 +642,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -650,7 +650,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -658,7 +658,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -666,7 +666,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7,-9 @@ -678,7 +678,7 @@ filters=320 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] @@ -687,7 +687,7 @@ filters=160 size=1 stride=1 pad=1 -activation=silu +activation=swish [upsample] stride=2 @@ -701,7 +701,7 @@ filters=160 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3 @@ -713,7 +713,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -724,7 +724,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -732,7 +732,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -740,7 +740,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -748,7 +748,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -756,7 +756,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -764,7 +764,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -772,7 +772,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7,-9 @@ -784,7 +784,7 @@ filters=160 size=1 stride=1 pad=1 -activation=silu +activation=swish [maxpool] @@ -797,7 +797,7 @@ filters=160 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-3 @@ -808,7 +808,7 @@ filters=160 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -816,7 +816,7 @@ filters=160 size=3 stride=2 pad=1 -activation=silu +activation=swish [route] layers = -1,-4,96 @@ -828,7 +828,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -839,7 +839,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -847,7 +847,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -855,7 +855,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -863,7 +863,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -871,7 +871,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -879,7 +879,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -887,7 +887,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7,-9 @@ -899,7 +899,7 @@ filters=320 size=1 stride=1 pad=1 -activation=silu +activation=swish [maxpool] @@ -912,7 +912,7 @@ filters=320 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-3 @@ -923,7 +923,7 @@ filters=320 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -931,7 +931,7 @@ filters=320 size=3 stride=2 pad=1 -activation=silu +activation=swish [route] layers = -1,-4,80 @@ -943,7 +943,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -954,7 +954,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -962,7 +962,7 @@ filters=512 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -970,7 +970,7 @@ filters=512 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -978,7 +978,7 @@ filters=512 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -986,7 +986,7 @@ filters=512 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -994,7 +994,7 @@ filters=512 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -1002,7 +1002,7 @@ filters=512 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7,-9 @@ -1014,7 +1014,7 @@ filters=640 size=1 stride=1 pad=1 -activation=silu +activation=swish ############################# @@ -1033,7 +1033,7 @@ size=3 stride=1 pad=1 filters=320 -activation=silu +activation=swish [convolutional] size=1 @@ -1076,7 +1076,7 @@ size=3 stride=1 pad=1 filters=640 -activation=silu +activation=swish [convolutional] size=1 @@ -1119,7 +1119,7 @@ size=3 stride=1 pad=1 filters=1280 -activation=silu +activation=swish [convolutional] size=1 diff --git a/cfg/yolov7.cfg b/cfg/yolov7.cfg index 62e3a144660..b4aea08dc82 100644 --- a/cfg/yolov7.cfg +++ b/cfg/yolov7.cfg @@ -30,7 +30,7 @@ filters=32 size=3 stride=1 pad=1 -activation=silu +activation=swish # 1 @@ -40,7 +40,7 @@ filters=64 size=3 stride=2 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -48,7 +48,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish # 3 @@ -58,7 +58,7 @@ filters=128 size=3 stride=2 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -66,7 +66,7 @@ filters=64 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -77,7 +77,7 @@ filters=64 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -85,7 +85,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -93,7 +93,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -101,7 +101,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -109,7 +109,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7 @@ -121,7 +121,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [maxpool] @@ -134,7 +134,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-3 @@ -145,7 +145,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -153,7 +153,7 @@ filters=128 size=3 stride=2 pad=1 -activation=silu +activation=swish # 18 [route] @@ -165,7 +165,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -176,7 +176,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -184,7 +184,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -192,7 +192,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -200,7 +200,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -208,7 +208,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7 @@ -220,7 +220,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish [maxpool] @@ -233,7 +233,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-3 @@ -244,7 +244,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -252,7 +252,7 @@ filters=256 size=3 stride=2 pad=1 -activation=silu +activation=swish # 33 [route] @@ -264,7 +264,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -275,7 +275,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -283,7 +283,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -291,7 +291,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -299,7 +299,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -307,7 +307,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7 @@ -319,7 +319,7 @@ filters=1024 size=1 stride=1 pad=1 -activation=silu +activation=swish [maxpool] @@ -332,7 +332,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-3 @@ -343,7 +343,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -351,7 +351,7 @@ filters=512 size=3 stride=2 pad=1 -activation=silu +activation=swish # 48 [route] @@ -363,7 +363,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -374,7 +374,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -382,7 +382,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -390,7 +390,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -398,7 +398,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -406,7 +406,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7 @@ -418,7 +418,7 @@ filters=1024 size=1 stride=1 pad=1 -activation=silu +activation=swish ################################## @@ -429,7 +429,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -2 @@ -440,7 +440,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -448,7 +448,7 @@ size=3 stride=1 pad=1 filters=512 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -456,7 +456,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish ### SPP ### [maxpool] @@ -487,7 +487,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -495,7 +495,7 @@ size=3 stride=1 pad=1 filters=512 -activation=silu +activation=swish [route] layers = -1, -13 @@ -507,7 +507,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish ### End SPPCSP ### @@ -517,7 +517,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [upsample] stride=2 @@ -531,7 +531,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3 @@ -543,7 +543,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -554,7 +554,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -562,7 +562,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -570,7 +570,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -578,7 +578,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -586,7 +586,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-2,-3,-4,-5,-7 @@ -598,7 +598,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] @@ -607,7 +607,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [upsample] stride=2 @@ -621,7 +621,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3 @@ -633,7 +633,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -644,7 +644,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -652,7 +652,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -660,7 +660,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -668,7 +668,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -676,7 +676,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-2,-3,-4,-5,-7 @@ -688,7 +688,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [maxpool] @@ -701,7 +701,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-3 @@ -712,7 +712,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -720,7 +720,7 @@ filters=128 size=3 stride=2 pad=1 -activation=silu +activation=swish [route] layers = -1,-4,86 @@ -732,7 +732,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -743,7 +743,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -751,7 +751,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -759,7 +759,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -767,7 +767,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -775,7 +775,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-2,-3,-4,-5,-7 @@ -787,7 +787,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [maxpool] @@ -800,7 +800,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-3 @@ -811,7 +811,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -819,7 +819,7 @@ filters=256 size=3 stride=2 pad=1 -activation=silu +activation=swish [route] layers = -1,-4,72 @@ -831,7 +831,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -842,7 +842,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -850,7 +850,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -858,7 +858,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -866,7 +866,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -874,7 +874,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-2,-3,-4,-5,-7 @@ -886,7 +886,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish ############################# @@ -905,7 +905,7 @@ size=3 stride=1 pad=1 filters=256 -activation=silu +activation=swish [convolutional] size=1 @@ -948,7 +948,7 @@ size=3 stride=1 pad=1 filters=512 -activation=silu +activation=swish [convolutional] size=1 @@ -991,7 +991,7 @@ size=3 stride=1 pad=1 filters=1024 -activation=silu +activation=swish [convolutional] size=1 diff --git a/cfg/yolov7x.cfg b/cfg/yolov7x.cfg index 46eec57d893..d489728af9a 100644 --- a/cfg/yolov7x.cfg +++ b/cfg/yolov7x.cfg @@ -31,7 +31,7 @@ filters=40 size=3 stride=1 pad=1 -activation=silu +activation=swish # 1 @@ -41,7 +41,7 @@ filters=80 size=3 stride=2 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -49,7 +49,7 @@ filters=80 size=3 stride=1 pad=1 -activation=silu +activation=swish # 3 @@ -59,7 +59,7 @@ filters=160 size=3 stride=2 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -67,7 +67,7 @@ filters=64 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -78,7 +78,7 @@ filters=64 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -86,7 +86,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -94,7 +94,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -102,7 +102,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -110,7 +110,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -118,7 +118,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -126,7 +126,7 @@ filters=64 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7,-9 @@ -138,7 +138,7 @@ filters=320 size=1 stride=1 pad=1 -activation=silu +activation=swish [maxpool] @@ -151,7 +151,7 @@ filters=160 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-3 @@ -162,7 +162,7 @@ filters=160 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -170,7 +170,7 @@ filters=160 size=3 stride=2 pad=1 -activation=silu +activation=swish # 20 [route] @@ -182,7 +182,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -193,7 +193,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -201,7 +201,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -209,7 +209,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -217,7 +217,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -225,7 +225,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -233,7 +233,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -241,7 +241,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7,-9 @@ -253,7 +253,7 @@ filters=640 size=1 stride=1 pad=1 -activation=silu +activation=swish [maxpool] @@ -266,7 +266,7 @@ filters=320 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-3 @@ -277,7 +277,7 @@ filters=320 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -285,7 +285,7 @@ filters=320 size=3 stride=2 pad=1 -activation=silu +activation=swish # 37 [route] @@ -297,7 +297,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -308,7 +308,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -316,7 +316,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -324,7 +324,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -332,7 +332,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -340,7 +340,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -348,7 +348,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -356,7 +356,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7,-9 @@ -368,7 +368,7 @@ filters=1280 size=1 stride=1 pad=1 -activation=silu +activation=swish [maxpool] @@ -381,7 +381,7 @@ filters=640 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-3 @@ -392,7 +392,7 @@ filters=640 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -400,7 +400,7 @@ filters=640 size=3 stride=2 pad=1 -activation=silu +activation=swish # 54 [route] @@ -412,7 +412,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -423,7 +423,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -431,7 +431,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -439,7 +439,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -447,7 +447,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -455,7 +455,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -463,7 +463,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -471,7 +471,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7,-9 @@ -483,7 +483,7 @@ filters=1280 size=1 stride=1 pad=1 -activation=silu +activation=swish ################################## @@ -494,7 +494,7 @@ filters=640 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -2 @@ -505,7 +505,7 @@ filters=640 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -513,7 +513,7 @@ size=3 stride=1 pad=1 filters=640 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -521,7 +521,7 @@ filters=640 size=1 stride=1 pad=1 -activation=silu +activation=swish ### SPP ### [maxpool] @@ -552,7 +552,7 @@ filters=640 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -560,7 +560,7 @@ size=3 stride=1 pad=1 filters=640 -activation=silu +activation=swish [route] layers = -1, -13 @@ -572,7 +572,7 @@ filters=640 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] @@ -581,7 +581,7 @@ filters=320 size=1 stride=1 pad=1 -activation=silu +activation=swish [upsample] stride=2 @@ -595,7 +595,7 @@ filters=320 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3 @@ -607,7 +607,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -618,7 +618,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -626,7 +626,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -634,7 +634,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -642,7 +642,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -650,7 +650,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -658,7 +658,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -666,7 +666,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7,-9 @@ -678,7 +678,7 @@ filters=320 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] @@ -687,7 +687,7 @@ filters=160 size=1 stride=1 pad=1 -activation=silu +activation=swish [upsample] stride=2 @@ -701,7 +701,7 @@ filters=160 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3 @@ -713,7 +713,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -724,7 +724,7 @@ filters=128 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -732,7 +732,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -740,7 +740,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -748,7 +748,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -756,7 +756,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -764,7 +764,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -772,7 +772,7 @@ filters=128 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7,-9 @@ -784,7 +784,7 @@ filters=160 size=1 stride=1 pad=1 -activation=silu +activation=swish [maxpool] @@ -797,7 +797,7 @@ filters=160 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-3 @@ -808,7 +808,7 @@ filters=160 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -816,7 +816,7 @@ filters=160 size=3 stride=2 pad=1 -activation=silu +activation=swish [route] layers = -1,-4,96 @@ -828,7 +828,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -839,7 +839,7 @@ filters=256 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -847,7 +847,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -855,7 +855,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -863,7 +863,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -871,7 +871,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -879,7 +879,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -887,7 +887,7 @@ filters=256 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7,-9 @@ -899,7 +899,7 @@ filters=320 size=1 stride=1 pad=1 -activation=silu +activation=swish [maxpool] @@ -912,7 +912,7 @@ filters=320 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-3 @@ -923,7 +923,7 @@ filters=320 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -931,7 +931,7 @@ filters=320 size=3 stride=2 pad=1 -activation=silu +activation=swish [route] layers = -1,-4,80 @@ -943,7 +943,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish [route] layers=-2 @@ -954,7 +954,7 @@ filters=512 size=1 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -962,7 +962,7 @@ filters=512 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -970,7 +970,7 @@ filters=512 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -978,7 +978,7 @@ filters=512 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -986,7 +986,7 @@ filters=512 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -994,7 +994,7 @@ filters=512 size=3 stride=1 pad=1 -activation=silu +activation=swish [convolutional] batch_normalize=1 @@ -1002,7 +1002,7 @@ filters=512 size=3 stride=1 pad=1 -activation=silu +activation=swish [route] layers = -1,-3,-5,-7,-9 @@ -1014,7 +1014,7 @@ filters=640 size=1 stride=1 pad=1 -activation=silu +activation=swish ############################# @@ -1033,7 +1033,7 @@ size=3 stride=1 pad=1 filters=320 -activation=silu +activation=swish [convolutional] size=1 @@ -1076,7 +1076,7 @@ size=3 stride=1 pad=1 filters=640 -activation=silu +activation=swish [convolutional] size=1 @@ -1119,7 +1119,7 @@ size=3 stride=1 pad=1 filters=1280 -activation=silu +activation=swish [convolutional] size=1 From 96f08de6839eb1c125c7b86bffe1d3dde9570e5b Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 16 Aug 2022 20:20:48 +0200 Subject: [PATCH 1311/1365] restore Windows PowerShell compatibility (#8607) * fix IsWindowsPowershell visibility * add missing closing braces * use proper flag * non-interactive deploy of cuda * set default host triplet * add a CI test for legacy Windows PowerShell * add powershell as shell name explicitly, otherwise github actions are already defaulting to pwsh --- .github/workflows/ccpp.yml | 42 +++-- .github/workflows/on_pr.yml | 40 +++-- build.ps1 | 321 +++++++++++++++++++++++++++++------- scripts/deploy-cuda.sh | 12 +- scripts/setup.ps1 | 6 +- scripts/setup.sh | 6 +- scripts/utils.psm1 | 12 +- 7 files changed, 338 insertions(+), 101 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index fa6e74c2acc..70610779c3e 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -123,7 +123,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL - uses: actions/upload-artifact@v2 with: @@ -191,7 +191,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 3 -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 3 -DisableInteractive -DoNotUpdateTOOL ubuntu-vcpkg-opencv2-cuda: @@ -242,7 +242,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 2 -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 2 -DisableInteractive -DoNotUpdateTOOL ubuntu: @@ -264,7 +264,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL - uses: actions/upload-artifact@v2 with: @@ -313,7 +313,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL - uses: actions/upload-artifact@v2 with: @@ -342,7 +342,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateTOOL ubuntu-setup-sh: @@ -409,7 +409,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL - uses: actions/upload-artifact@v2 with: @@ -441,7 +441,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL - uses: actions/upload-artifact@v2 with: @@ -470,7 +470,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateTOOL win-vcpkg: @@ -505,7 +505,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL - uses: actions/upload-artifact@v2 with: @@ -555,7 +555,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -InstallDARKNETthroughVCPKG -ForceVCPKGDarknetHEAD -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -InstallDARKNETthroughVCPKG -ForceVCPKGDarknetHEAD -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL win-intlibs: @@ -567,7 +567,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -DisableInteractive -DoNotUpdateTOOL - uses: actions/upload-artifact@v2 with: @@ -629,7 +629,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateTOOL win-csharp: @@ -641,7 +641,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -EnableCSharpWrapper -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -EnableCSharpWrapper -DisableInteractive -DoNotUpdateTOOL win-intlibs-cuda: @@ -659,7 +659,19 @@ jobs: CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.7" CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.7\\bin\\nvcc.exe" shell: pwsh - run: ${{ github.workspace }}/build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateTOOL + + + win-powershell51: + runs-on: windows-latest + steps: + - uses: actions/checkout@v2 + + - uses: lukka/get-cmake@latest + + - name: 'Build' + shell: powershell + run: ${{ github.workspace }}/build.ps1 -DisableInteractive -DoNotUpdateTOOL mingw: diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index b777d315adf..db6a10658fb 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -100,7 +100,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL ubuntu-vcpkg-opencv3-cuda: @@ -141,7 +141,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 3 -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 3 -DisableInteractive -DoNotUpdateTOOL ubuntu-vcpkg-opencv2-cuda: @@ -182,7 +182,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 2 -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 2 -DisableInteractive -DoNotUpdateTOOL ubuntu: @@ -204,7 +204,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL ubuntu-cuda: @@ -236,7 +236,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL ubuntu-no-ocv-cpp: @@ -248,7 +248,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateTOOL ubuntu-setup-sh: @@ -291,7 +291,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL osx: @@ -306,7 +306,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL osx-no-ocv-no-omp-cpp: @@ -318,7 +318,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateTOOL win-vcpkg: @@ -339,7 +339,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL win-intlibs: @@ -351,7 +351,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -DisableInteractive -DoNotUpdateTOOL win-setup-ps1: @@ -382,7 +382,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateTOOL win-csharp: @@ -394,7 +394,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -EnableCSharpWrapper -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -EnableCSharpWrapper -DisableInteractive -DoNotUpdateTOOL win-intlibs-cuda: @@ -412,7 +412,19 @@ jobs: CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.7" CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.7\\bin\\nvcc.exe" shell: pwsh - run: ${{ github.workspace }}/build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET + run: ${{ github.workspace }}/build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateTOOL + + + win-powershell51: + runs-on: windows-latest + steps: + - uses: actions/checkout@v2 + + - uses: lukka/get-cmake@latest + + - name: 'Build' + shell: powershell + run: ${{ github.workspace }}/build.ps1 -DisableInteractive -DoNotUpdateTOOL mingw: diff --git a/build.ps1 b/build.ps1 index c9efadddd06..aae41a7c98b 100755 --- a/build.ps1 +++ b/build.ps1 @@ -6,10 +6,10 @@ build Created By: Stefano Sinigardi Created Date: February 18, 2019 - Last Modified Date: November 10, 2021 + Last Modified Date: May 10, 2022 .DESCRIPTION -Build darknet using CMake, trying to properly setup the environment around compiler +Build tool using CMake, trying to properly setup the environment around compiler .PARAMETER DisableInteractive Disable script interactivity (useful for CI runs) @@ -18,7 +18,7 @@ Disable script interactivity (useful for CI runs) Disable automatic DLL deployment through vcpkg at the end .PARAMETER EnableCUDA -Enable CUDA feature +Build tool with CUDA support .PARAMETER EnableCUDNN Enable CUDNN feature @@ -30,7 +30,7 @@ Build darknet linking to OpenCV Use a CUDA-enabled OpenCV build .PARAMETER UseVCPKG -Use VCPKG to build darknet dependencies. Clone it if not already found on system +Use VCPKG to build tool dependencies. Clone it if not already found on system .PARAMETER InstallDARKNETthroughVCPKG Use VCPKG to install darknet thanks to the port integrated in it @@ -44,8 +44,17 @@ Install darknet from vcpkg and force it to HEAD version, not latest port release .PARAMETER DoNotUpdateVCPKG Do not update vcpkg before running the build (valid only if vcpkg is cloned by this script or the version found on the system is git-enabled) -.PARAMETER DoNotUpdateDARKNET -Do not update darknet before running the build (valid only if darknet is git-enabled) +.PARAMETER VCPKGSuffix +Specify a suffix to the vcpkg local folder for searching, useful to point to a custom version + +.PARAMETER VCPKGFork +Specify a fork username to point to a custom version of vcpkg (ex: -VCPKGFork "custom" to point to github.com/custom/vcpkg) + +.PARAMETER VCPKGBranch +Specify a branch to checkout in the vcpkg folder, useful to point to a custom version especially for forked vcpkg versions + +.PARAMETER DoNotUpdateTOOL +Do not update the tool before running the build (valid only if tool is git-enabled) .PARAMETER DoNotDeleteBuildFolder Do not delete temporary cmake build folder at the end of the script @@ -60,7 +69,7 @@ Do not use Ninja for build Force building darknet using C++ compiler also for plain C code .PARAMETER ForceStaticLib -Create darknet library as static instead of the default linking mode of your system +Create library as static instead of the default linking mode of your system .PARAMETER ForceVCPKGCacheRemoval Force clean up of the local vcpkg binary cache before building @@ -74,6 +83,13 @@ Force clean up of vcpkg packages folder at the end of the script .PARAMETER ForceSetupVS Forces Visual Studio setup, also on systems on which it would not have been enabled automatically + +.PARAMETER ForceCMakeFromVS +Forces usage of CMake from Visual Studio instead of the system-wide/user installed one + +.PARAMETER ForceNinjaFromVS +Forces usage of Ninja from Visual Studio instead of the system-wide/user installed one + .PARAMETER EnableCSharpWrapper Enables building C# darknet wrapper @@ -97,6 +113,30 @@ Additional setup parameters to manually pass to CMake #> +<# +Copyright (c) Stefano Sinigardi + +MIT License + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +#> + param ( [switch]$DisableInteractive = $false, [switch]$DisableDLLcopy = $false, @@ -109,7 +149,10 @@ param ( [switch]$InstallDARKNETdependenciesThroughVCPKGManifest = $false, [switch]$ForceVCPKGDarknetHEAD = $false, [switch]$DoNotUpdateVCPKG = $false, - [switch]$DoNotUpdateDARKNET = $false, + [string]$VCPKGSuffix = "", + [string]$VCPKGFork = "", + [string]$VCPKGBranch = "", + [switch]$DoNotUpdateTOOL = $false, [switch]$DoNotDeleteBuildFolder = $false, [switch]$DoNotSetupVS = $false, [switch]$DoNotUseNinja = $false, @@ -119,6 +162,8 @@ param ( [switch]$ForceVCPKGBuildtreesRemoval = $false, [switch]$ForceVCPKGPackagesRemoval = $false, [switch]$ForceSetupVS = $false, + [switch]$ForceCMakeFromVS = $false, + [switch]$ForceNinjaFromVS = $false, [switch]$EnableCSharpWrapper = $false, [switch]$DownloadWeights = $false, [Int32]$ForceGCCVersion = 0, @@ -127,17 +172,21 @@ param ( [string]$AdditionalBuildSetup = "" # "-DCMAKE_CUDA_ARCHITECTURES=30" ) -$build_ps1_version = "2.0.0" +$global:DisableInteractive = $DisableInteractive + +$build_ps1_version = "2.3.3" + Import-Module -Name $PSScriptRoot/scripts/utils.psm1 -Force $ErrorActionPreference = "SilentlyContinue" Stop-Transcript | out-null $ErrorActionPreference = "Continue" -Start-Transcript -Path $PSScriptRoot/build.log +$BuildLogPath = "$PSScriptRoot/build.log" +Start-Transcript -Path $BuildLogPath Write-Host "Build script version ${build_ps1_version}, utils module version ${utils_psm1_version}" -if ((-Not $DisableInteractive) -and (-Not $UseVCPKG)) { +if ((-Not $global:DisableInteractive) -and (-Not $UseVCPKG)) { $Result = Read-Host "Enable vcpkg to install dependencies (yes/no)" if (($Result -eq 'Yes') -or ($Result -eq 'Y') -or ($Result -eq 'yes') -or ($Result -eq 'y')) { $UseVCPKG = $true @@ -168,15 +217,14 @@ if ((-Not $DisableInteractive) -and (-Not $EnableOPENCV)) { Write-Host -NoNewLine "PowerShell version:" $PSVersionTable.PSVersion -if ($PSVersionTable.PSVersion.Major -eq 5) { - $IsWindowsPowerShell = $true +if ($IsWindowsPowerShell) { + Write-Host "Running on Windows Powershell, please consider update and running on newer Powershell versions" } if ($PSVersionTable.PSVersion.Major -lt 5) { MyThrow("Your PowerShell version is too old, please update it.") } - if ($IsLinux -or $IsMacOS) { $bootstrap_ext = ".sh" $exe_ext = "" @@ -220,14 +268,49 @@ if (($IsLinux -or $IsMacOS) -and ($ForceGCCVersion -gt 0)) { $env:CXX = "g++-$ForceGCCVersion" } +$vcpkg_triplet_set_by_this_script = $false +$vcpkg_host_triplet_set_by_this_script = $false + if (($IsWindows -or $IsWindowsPowerShell) -and (-Not $env:VCPKG_DEFAULT_TRIPLET)) { $env:VCPKG_DEFAULT_TRIPLET = "x64-windows-release" + $vcpkg_triplet_set_by_this_script = $true +} +if (($IsWindows -or $IsWindowsPowerShell) -and (-Not $env:VCPKG_DEFAULT_HOST_TRIPLET)) { + $env:VCPKG_DEFAULT_HOST_TRIPLET = "x64-windows-release" + $vcpkg_host_triplet_set_by_this_script = $true } -elseif ($IsMacOS -and (-Not $env:VCPKG_DEFAULT_TRIPLET)) { + +if ($IsMacOS -and (-Not $env:VCPKG_DEFAULT_TRIPLET)) { $env:VCPKG_DEFAULT_TRIPLET = "x64-osx-release" + $vcpkg_triplet_set_by_this_script = $true +} +if ($IsMacOS -and (-Not $env:VCPKG_DEFAULT_HOST_TRIPLET)) { + $env:VCPKG_DEFAULT_HOST_TRIPLET = "x64-osx-release" + $vcpkg_host_triplet_set_by_this_script = $true } -elseif ($IsLinux -and (-Not $env:VCPKG_DEFAULT_TRIPLET)) { + +if ($IsLinux -and (-Not $env:VCPKG_DEFAULT_TRIPLET)) { $env:VCPKG_DEFAULT_TRIPLET = "x64-linux-release" + $vcpkg_triplet_set_by_this_script = $true +} +if ($IsLinux -and (-Not $env:VCPKG_DEFAULT_HOST_TRIPLET)) { + $env:VCPKG_DEFAULT_HOST_TRIPLET = "x64-linux-release" + $vcpkg_host_triplet_set_by_this_script = $true +} + +if ($VCPKGSuffix -ne "" -and -not $UseVCPKG) { + Write-Host "You specified a vcpkg folder suffix but didn't enable vcpkg integration, doing that for you" -ForegroundColor Yellow + $UseVCPKG = $true +} + +if ($VCPKGFork -ne "" -and -not $UseVCPKG) { + Write-Host "You specified a vcpkg fork but didn't enable vcpkg integration, doing that for you" -ForegroundColor Yellow + $UseVCPKG = $true +} + +if ($VCPKGBranch -ne "" -and -not $UseVCPKG) { + Write-Host "You specified a vcpkg branch but didn't enable vcpkg integration, doing that for you" -ForegroundColor Yellow + $UseVCPKG = $true } if ($EnableCUDA) { @@ -328,23 +411,41 @@ else { Write-Host "Using git from ${GIT_EXE}" } -if (Test-Path "$PSScriptRoot/.git") { - Write-Host "Darknet has been cloned with git and supports self-updating mechanism" - if ($DoNotUpdateDARKNET) { - Write-Host "Darknet will not self-update sources" -ForegroundColor Yellow +$GitRepoPath = Resolve-Path "$PSScriptRoot/.git" +if (Test-Path "$GitRepoPath") { + Write-Host "This tool has been cloned with git and supports self-updating mechanism" + if ($DoNotUpdateTOOL) { + Write-Host "This tool will not self-update sources" -ForegroundColor Yellow } else { - Write-Host "Darknet will self-update sources, please pass -DoNotUpdateDARKNET to the script to disable" + Write-Host "This tool will self-update sources, please pass -DoNotUpdateTOOL to the script to disable" $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "pull" $handle = $proc.Handle $proc.WaitForExit() $exitCode = $proc.ExitCode if (-Not ($exitCode -eq 0)) { - MyThrow("Updating darknet sources failed! Exited with error code $exitCode.") + MyThrow("Updating this tool sources failed! Exited with error code $exitCode.") } } } +if ($ForceCmakeFromVS) { + $vsfound = getLatestVisualStudioWithDesktopWorkloadPath + $cmakePath = "${vsfound}\Common7\IDE\CommonExtensions\Microsoft\CMake\CMake\bin" + $vsCmakePath = "${vsfound}\Common7\IDE\CommonExtensions\Microsoft\CMake\CMake\bin\cmake.exe" + $CMAKE_EXE = Get-Command "cmake" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition + if ((Test-Path "$vsCmakePath") -and -not ($vsCmakePath -eq $CMAKE_EXE)) { + Write-Host "Adding CMake from Visual Studio to PATH" + $env:PATH = '{0}{1}{2}' -f "$cmakePath", [IO.Path]::PathSeparator, $env:PATH + } + elseif ($vsCmakePath -eq $CMAKE_EXE) { + Write-Host "CMake from Visual Studio was already the preferred choice" -ForegroundColor Yellow + } + else { + Write-Host "Unable to find CMake integrated in Visual Studio" -ForegroundColor Red + } +} + $CMAKE_EXE = Get-Command "cmake" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition if (-Not $CMAKE_EXE) { MyThrow("Could not find CMake, please install it") @@ -361,10 +462,27 @@ else { } if (-Not $DoNotUseNinja) { + if ($ForceNinjaFromVS) { + $vsfound = getLatestVisualStudioWithDesktopWorkloadPath + $ninjaPath = "${vsfound}\Common7\IDE\CommonExtensions\Microsoft\CMake\Ninja" + $vsninjaPath = "${vsfound}\Common7\IDE\CommonExtensions\Microsoft\CMake\Ninja\ninja.exe" + $NINJA_EXE = Get-Command "ninja" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition + if ((Test-Path "$vsninjaPath") -and -not ($vsninjaPath -eq $NINJA_EXE) -and (-not $DoNotUseNinja)) { + Write-Host "Adding Ninja from Visual Studio to PATH" + $env:PATH = '{0}{1}{2}' -f "$ninjaPath", [IO.Path]::PathSeparator, $env:PATH + } + elseif ($vsninjaPath -eq $NINJA_EXE) { + Write-Host "Ninja from Visual Studio was already the preferred choice" -ForegroundColor Yellow + } + else { + Write-Host "Unable to find Ninja integrated in Visual Studio" -ForegroundColor Red + } + } $NINJA_EXE = Get-Command "ninja" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition if (-Not $NINJA_EXE) { DownloadNinja - $env:PATH = '{0}{1}{2}' -f $env:PATH, [IO.Path]::PathSeparator, "${PSScriptRoot}/ninja" + $NinjaPath = Join-Path (${PSScriptRoot}) 'ninja' + $env:PATH = '{0}{1}{2}' -f $env:PATH, [IO.Path]::PathSeparator, "$NinjaPath" $NINJA_EXE = Get-Command "ninja" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition if (-Not $NINJA_EXE) { $DoNotUseNinja = $true @@ -391,30 +509,33 @@ if (-Not $DoNotUseNinja) { $vcpkg_root_set_by_this_script = $false -if ((Test-Path env:VCPKG_ROOT) -and $UseVCPKG) { +if ((Test-Path env:VCPKG_ROOT) -and $UseVCPKG -and $VCPKGSuffix -eq "") { $vcpkg_path = "$env:VCPKG_ROOT" + $vcpkg_path = Resolve-Path $vcpkg_path Write-Host "Found vcpkg in VCPKG_ROOT: $vcpkg_path" $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" } -elseif ((Test-Path "${env:WORKSPACE}/vcpkg") -and $UseVCPKG) { - $vcpkg_path = "${env:WORKSPACE}/vcpkg" - $env:VCPKG_ROOT = "${env:WORKSPACE}/vcpkg" +elseif (-not($null -eq ${env:WORKSPACE}) -and (Test-Path "${env:WORKSPACE}/vcpkg${VCPKGSuffix}") -and $UseVCPKG) { + $vcpkg_path = "${env:WORKSPACE}/vcpkg${VCPKGSuffix}" + $vcpkg_path = Resolve-Path $vcpkg_path + $env:VCPKG_ROOT = "$vcpkg_path" $vcpkg_root_set_by_this_script = $true - Write-Host "Found vcpkg in WORKSPACE/vcpkg: $vcpkg_path" + Write-Host "Found vcpkg in WORKSPACE/vcpkg${VCPKGSuffix}: $vcpkg_path" $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" } elseif (-not($null -eq ${RUNVCPKG_VCPKG_ROOT_OUT})) { - if((Test-Path "${RUNVCPKG_VCPKG_ROOT_OUT}") -and $UseVCPKG) { + if ((Test-Path "${RUNVCPKG_VCPKG_ROOT_OUT}") -and $UseVCPKG) { $vcpkg_path = "${RUNVCPKG_VCPKG_ROOT_OUT}" - $env:VCPKG_ROOT = "${RUNVCPKG_VCPKG_ROOT_OUT}" + $vcpkg_path = Resolve-Path $vcpkg_path + $env:VCPKG_ROOT = "$vcpkg_path" $vcpkg_root_set_by_this_script = $true - Write-Host "Found vcpkg in RUNVCPKG_VCPKG_ROOT_OUT: ${vcpkg_path}" + Write-Host "Found vcpkg in RUNVCPKG_VCPKG_ROOT_OUT: $vcpkg_path" $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" } } elseif ($UseVCPKG) { - if (-Not (Test-Path "$PWD/vcpkg")) { - $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "clone https://github.com/microsoft/vcpkg" + if (-Not (Test-Path "$PWD/vcpkg${VCPKGSuffix}")) { + $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "clone https://github.com/microsoft/vcpkg vcpkg${VCPKGSuffix}" $handle = $proc.Handle $proc.WaitForExit() $exitCode = $proc.ExitCode @@ -422,41 +543,97 @@ elseif ($UseVCPKG) { MyThrow("Cloning vcpkg sources failed! Exited with error code $exitCode.") } } - $vcpkg_path = "$PWD/vcpkg" - $env:VCPKG_ROOT = "$PWD/vcpkg" + $vcpkg_path = "$PWD/vcpkg${VCPKGSuffix}" + $vcpkg_path = Resolve-Path $vcpkg_path + $env:VCPKG_ROOT = "$vcpkg_path" $vcpkg_root_set_by_this_script = $true - Write-Host "Found vcpkg in $PWD/vcpkg: $PWD/vcpkg" + Write-Host "Found vcpkg in $PWD/vcpkg${VCPKGSuffix}: $vcpkg_path" $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" } else { - Write-Host "Skipping vcpkg integration`n" -ForegroundColor Yellow - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=OFF" + if (-not ($VCPKGSuffix -eq "")) { + MyThrow("Unable to find vcpkg${VCPKGSuffix}") + } + else { + Write-Host "Skipping vcpkg integration`n" -ForegroundColor Yellow + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=OFF" + } } -if ($UseVCPKG -and (Test-Path "$vcpkg_path/.git") -and (-Not $DoNotUpdateVCPKG)) { +$vcpkg_branch_set_by_this_script = $false + +if ($UseVCPKG -and (Test-Path "$vcpkg_path/.git")) { Push-Location $vcpkg_path - $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "pull" - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-Not ($exitCode -eq 0)) { - MyThrow("Updating vcpkg sources failed! Exited with error code $exitCode.") + if ($VCPKGFork -ne "") { + $vcpkgfork_already_setup = $false + $remotes = & $GIT_EXE 'remote' + ForEach ($remote in $remotes) { + if ($remote -eq "vcpkgfork") { + $vcpkgfork_already_setup = $true + Write-Host "remote vcpkgfork already setup" + } + } + if (-Not $vcpkgfork_already_setup) { + $git_args = "remote add vcpkgfork https://github.com/${VCPKGFork}/vcpkg" + Write-Host "setting up remote vcpkgfork" + $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "$git_args" + $handle = $proc.Handle + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-Not ($exitCode -eq 0)) { + MyThrow("Adding remote https://github.com/${VCPKGFork}/vcpkg failed! Exited with error code $exitCode.") + } + } + $git_args = "fetch vcpkgfork" + $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "$git_args" + $handle = $proc.Handle + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-Not ($exitCode -eq 0)) { + MyThrow("Fetching from remote https://github.com/${VCPKGFork}/vcpkg failed! Exited with error code $exitCode.") + } } - $proc = Start-Process -NoNewWindow -PassThru -FilePath $PWD/bootstrap-vcpkg${bootstrap_ext} -ArgumentList "-disableMetrics" - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-Not ($exitCode -eq 0)) { - MyThrow("Bootstrapping vcpkg failed! Exited with error code $exitCode.") + if ($VCPKGBranch -ne "") { + if ($VCPKGFork -ne "") { + $git_args = "checkout vcpkgfork/$VCPKGBranch" + } + else { + $git_args = "checkout $VCPKGBranch" + } + $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "$git_args" + $handle = $proc.Handle + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-Not ($exitCode -eq 0)) { + MyThrow("Checking out branch $VCPKGBranch failed! Exited with error code $exitCode.") + } + $vcpkg_branch_set_by_this_script = $true + } + if (-Not $DoNotUpdateVCPKG -and $VCPKGFork -eq "") { + $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "pull" + $handle = $proc.Handle + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-Not ($exitCode -eq 0)) { + MyThrow("Updating vcpkg sources failed! Exited with error code $exitCode.") + } + $VcpkgBootstrapScript = Join-Path $PWD "bootstrap-vcpkg${bootstrap_ext}" + $proc = Start-Process -NoNewWindow -PassThru -FilePath $VcpkgBootstrapScript -ArgumentList "-disableMetrics" + $handle = $proc.Handle + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-Not ($exitCode -eq 0)) { + MyThrow("Bootstrapping vcpkg failed! Exited with error code $exitCode.") + } } Pop-Location } if ($UseVCPKG -and ($vcpkg_path.length -gt 40) -and ($IsWindows -or $IsWindowsPowerShell)) { Write-Host "vcpkg path is very long and might fail. Please move it or" -ForegroundColor Yellow - Write-Host "the entire darknet folder to a shorter path, like C:\darknet" -ForegroundColor Yellow + Write-Host "the entire tool folder to a shorter path, like C:\src" -ForegroundColor Yellow Write-Host "You can use the subst command to ease the process if necessary" -ForegroundColor Yellow - if (-Not $DisableInteractive) { + if (-Not $global:DisableInteractive) { $Result = Read-Host "Do you still want to continue? (yes/no)" if (($Result -eq 'No') -or ($Result -eq 'N') -or ($Result -eq 'no') -or ($Result -eq 'n')) { MyThrow("Build aborted") @@ -470,7 +647,7 @@ if ($ForceVCPKGCacheRemoval -and (-Not $UseVCPKG)) { if ($UseVCPKG -and $ForceVCPKGBuildtreesRemoval) { Write-Host "Cleaning folder buildtrees inside vcpkg" -ForegroundColor Yellow - Remove-Item -Force -Recurse -ErrorAction SilentlyContinue "$env:VCPKG_ROOT/buildtrees" + Remove-Item -Force -Recurse -ErrorAction SilentlyContinue "$vcpkgbuildtreespath" } if (($ForceOpenCVVersion -eq 2) -and $UseVCPKG) { @@ -519,7 +696,6 @@ if (-Not $DoNotSetupVS) { $tokens = getLatestVisualStudioWithDesktopWorkloadVersion $tokens = $tokens.split('.') if ($DoNotUseNinja) { - $dllfolder = "Release" $selectConfig = " --config Release " if ($tokens[0] -eq "14") { $generator = "Visual Studio 14 2015" @@ -707,6 +883,7 @@ else { Set-Location .. } +Pop-Location if (-Not $DoNotDeleteBuildFolder) { Write-Host "Removing folder $build_folder" -ForegroundColor Yellow @@ -714,14 +891,13 @@ if (-Not $DoNotDeleteBuildFolder) { } Write-Host "Build complete!" -ForegroundColor Green -Pop-Location if ($ForceVCPKGBuildtreesRemoval -and (-Not $UseVCPKG)) { Write-Host "VCPKG is not enabled, so local vcpkg buildtrees folder will not be deleted even if requested" -ForegroundColor Yellow } +$vcpkgbuildtreespath = "$vcpkg_path/buildtrees" if ($UseVCPKG -and $ForceVCPKGBuildtreesRemoval) { - $vcpkgbuildtreespath = "$vcpkg_path/buildtrees" Write-Host "Removing local vcpkg buildtrees folder from $vcpkgbuildtreespath" -ForegroundColor Yellow Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $vcpkgbuildtreespath } @@ -746,6 +922,37 @@ if ($vcpkg_root_set_by_this_script) { $env:VCPKG_ROOT = $null } +if ($vcpkg_triplet_set_by_this_script) { + $env:VCPKG_DEFAULT_TRIPLET = $null +} +if ($vcpkg_host_triplet_set_by_this_script) { + $env:VCPKG_DEFAULT_HOST_TRIPLET = $null +} + + +if ($vcpkg_branch_set_by_this_script) { + Push-Location $vcpkg_path + $git_args = "checkout -" + $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "$git_args" + $handle = $proc.Handle + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-Not ($exitCode -eq 0)) { + MyThrow("Checking out previous branch failed! Exited with error code $exitCode.") + } + if ($VCPKGFork -ne "") { + $git_args = "remote rm vcpkgfork" + $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "$git_args" + $handle = $proc.Handle + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-Not ($exitCode -eq 0)) { + MyThrow("Checking out previous branch failed! Exited with error code $exitCode.") + } + } + Pop-Location +} + $ErrorActionPreference = "SilentlyContinue" Stop-Transcript | out-null $ErrorActionPreference = "Continue" diff --git a/scripts/deploy-cuda.sh b/scripts/deploy-cuda.sh index 1370dd556ec..535e0012126 100755 --- a/scripts/deploy-cuda.sh +++ b/scripts/deploy-cuda.sh @@ -29,15 +29,13 @@ sudo mv cuda-${OS}.pin /etc/apt/preferences.d/cuda-repository-pin-600 sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/$distr_name/x86_64/7fa2af80.pub sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/$distr_name/x86_64/ /" sudo apt-get update -sudo apt-get install build-essential g++ -sudo apt-get install apt-transport-https ca-certificates gnupg software-properties-common wget -sudo apt-get install zlib1g +sudo apt-get install -y build-essential g++ +sudo apt-get install -y apt-transport-https ca-certificates gnupg software-properties-common wget +sudo apt-get install -y zlib1g sudo apt-get dist-upgrade -y sudo apt-get install -y --no-install-recommends cuda-${CUDA_VERSION_DASHED} -#sudo apt-get install libcudnn8=${cudnn_version}-1+${cuda_version} -#sudo apt-get install libcudnn8-dev=${cudnn_version}-1+cuda${CUDA_VERSION} -sudo apt-get install libcudnn8 -sudo apt-get install libcudnn8-dev +sudo apt-get install -y libcudnn8 +sudo apt-get install -y libcudnn8-dev sudo rm -rf /usr/local/cuda sudo ln -s /usr/local/cuda-${CUDA_VERSION} /usr/local/cuda diff --git a/scripts/setup.ps1 b/scripts/setup.ps1 index 526221916bb..6345d40960f 100755 --- a/scripts/setup.ps1 +++ b/scripts/setup.ps1 @@ -33,9 +33,9 @@ else { } if ($CUDAisAvailable) { - & $PSScriptRoot/../build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -DisableInteractive -DoNotUpdateDARKNET - #& $PSScriptRoot/../build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableOPENCV_CUDA -DisableInteractive -DoNotUpdateDARKNET + & $PSScriptRoot/../build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -DisableInteractive -DoNotUpdateTOOL + #& $PSScriptRoot/../build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableOPENCV_CUDA -DisableInteractive -DoNotUpdateTOOL } else { - & $PSScriptRoot/../build.ps1 -UseVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET + & $PSScriptRoot/../build.ps1 -UseVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL } diff --git a/scripts/setup.sh b/scripts/setup.sh index ad8e12ad3a5..1a180e8740d 100755 --- a/scripts/setup.sh +++ b/scripts/setup.sh @@ -105,8 +105,8 @@ cd .. rm -rf "$temp_folder" echo "Building darknet" if [[ -v CUDA_PATH ]]; then - ./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateDARKNET - #./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -EnableOPENCV_CUDA -DisableInteractive -DoNotUpdateDARKNET + ./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL + #./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -EnableOPENCV_CUDA -DisableInteractive -DoNotUpdateTOOL else - ./build.ps1 -UseVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateDARKNET + ./build.ps1 -UseVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL fi diff --git a/scripts/utils.psm1 b/scripts/utils.psm1 index 87a57d8ed76..ed80855153f 100644 --- a/scripts/utils.psm1 +++ b/scripts/utils.psm1 @@ -22,7 +22,14 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. #> -$utils_psm1_version = "0.0.2" +$utils_psm1_version = "0.0.4" +$IsWindowsPowerShell = switch ( $PSVersionTable.PSVersion.Major ) { + 5 { $true } + 4 { $true } + 3 { $true } + 2 { $true } + default { $false } +} $cuda_version_full = "11.7.0" $cuda_version_short = "11.7" $cuda_version_full_dashed = $cuda_version_full.replace('.', '-') @@ -133,7 +140,7 @@ function DownloadNinja() { } Function MyThrow ($Message) { - if ($DisableInteractive) { + if ($global:DisableInteractive) { Write-Host $Message -ForegroundColor Red throw } @@ -187,6 +194,7 @@ Function MyThrow ($Message) { } Export-ModuleMember -Variable utils_psm1_version +Export-ModuleMember -Variable IsWindowsPowerShell Export-ModuleMember -Variable cuda_version_full Export-ModuleMember -Variable cuda_version_short Export-ModuleMember -Variable cuda_version_full_dashed From 62e5549cef53bd11890808615e0bfae59cfd4491 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?St=C3=A9phane=20Charette?= Date: Wed, 21 Sep 2022 04:03:47 -0700 Subject: [PATCH 1312/1365] Make sure best.weights is the most recent weights for a given mAP% (#8670) * issue #8308: memory leaks in map * update the window title with some training stats * make sure _best.weights is the most recent weights with that mAP% --- src/detector.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/detector.c b/src/detector.c index 0b947b69089..61864967e7e 100644 --- a/src/detector.c +++ b/src/detector.c @@ -364,7 +364,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i iter_map = iteration; mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, thresh, iou_thresh, 0, net.letter_box, &net_map);// &net_combined); printf("\n mean_average_precision (mAP@%0.2f) = %f \n", iou_thresh, mean_average_precision); - if (mean_average_precision > best_map) { + if (mean_average_precision >= best_map) { best_map = mean_average_precision; printf("New best mAP!\n"); char buff[256]; From 0faed3e60e52f742bbef43b83f6be51dd30f373e Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 4 Nov 2022 14:27:17 +0100 Subject: [PATCH 1313/1365] add missing dependencies (#8694) --- .github/workflows/ccpp.yml | 6 +++--- .github/workflows/on_pr.yml | 6 +++--- scripts/setup.sh | 2 +- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 70610779c3e..82f46a9a49f 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -85,7 +85,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev + run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.sh @@ -153,7 +153,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev + run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.sh @@ -204,7 +204,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev + run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.sh diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index db6a10658fb..7b0a5721a21 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -72,7 +72,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev + run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.sh @@ -113,7 +113,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev + run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.sh @@ -154,7 +154,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev + run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.sh diff --git a/scripts/setup.sh b/scripts/setup.sh index 1a180e8740d..4ca00f749ba 100755 --- a/scripts/setup.sh +++ b/scripts/setup.sh @@ -77,7 +77,7 @@ elif [[ $(cut -f2 <<< $(lsb_release -i)) == "Ubuntu" ]]; then sudo apt-get update sudo apt-get install -y git ninja-build build-essential g++ nasm yasm gperf sudo apt-get install -y apt-transport-https ca-certificates gnupg software-properties-common wget - sudo apt-get install -y libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev + sudo apt-get install -y libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | gpg --dearmor - | sudo tee /etc/apt/trusted.gpg.d/kitware.gpg >/dev/null sudo apt-add-repository "deb https://apt.kitware.com/ubuntu/ $(cut -f2 <<< $(lsb_release -c)) main" wget -q https://packages.microsoft.com/config/ubuntu/$(cut -f2 <<< $(lsb_release -r))/packages-microsoft-prod.deb From ed59050950b5a890a2a1d1c69547250c436a5968 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 3 Mar 2023 18:24:13 +0100 Subject: [PATCH 1314/1365] fix errors in CI (#8753) * update build scripts * add pkg-config on osx * re-enable building with older opencv versions --- .github/workflows/ccpp.yml | 2 +- .github/workflows/on_pr.yml | 2 +- .gitignore | 2 + CMakeLists.txt | 4 +- build.ps1 | 205 ++++++++++++++++++++++++++---------- scripts/utils.psm1 | 148 ++++++++++++++++++++++++-- vcpkg.json.opencv23 | 122 +++++++++++++++++++++ 7 files changed, 419 insertions(+), 66 deletions(-) create mode 100644 vcpkg.json.opencv23 diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 82f46a9a49f..9765ad2e25e 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -384,7 +384,7 @@ jobs: if: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.debug_enabled }} - name: Install dependencies - run: brew install libomp yasm nasm + run: brew install libomp yasm nasm pkg-config - uses: lukka/get-cmake@latest diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index 7b0a5721a21..23a9c23dd54 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -276,7 +276,7 @@ jobs: - uses: actions/checkout@v2 - name: Install dependencies - run: brew install libomp yasm nasm + run: brew install libomp yasm nasm pkg-config - uses: lukka/get-cmake@latest diff --git a/.gitignore b/.gitignore index a1d890429b3..48f5a7819a8 100644 --- a/.gitignore +++ b/.gitignore @@ -10,6 +10,7 @@ *.lib *.dylib *.pyc +*.bak mnist/ data/ caffe/ @@ -23,6 +24,7 @@ cfg/ temp/ build/darknet/* build_*/ +debug/ ninja/ ninja.zip vcpkg_installed/ diff --git a/CMakeLists.txt b/CMakeLists.txt index 128415cc92a..d3afb968447 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -253,8 +253,8 @@ if(UNIX AND BUILD_SHARED_LIBS AND NOT CMAKE_COMPILER_IS_CLANG) endif() if(MSVC) - set(ADDITIONAL_CXX_FLAGS "/wd4013 /wd4018 /wd4028 /wd4047 /wd4068 /wd4090 /wd4101 /wd4113 /wd4133 /wd4190 /wd4244 /wd4267 /wd4305 /wd4477 /wd4996 /wd4819 /fp:fast") - set(ADDITIONAL_C_FLAGS "/wd4013 /wd4018 /wd4028 /wd4047 /wd4068 /wd4090 /wd4101 /wd4113 /wd4133 /wd4190 /wd4244 /wd4267 /wd4305 /wd4477 /wd4996 /wd4819 /fp:fast") + set(ADDITIONAL_CXX_FLAGS " /nologo /wd4013 /wd4018 /wd4028 /wd4047 /wd4068 /wd4090 /wd4101 /wd4113 /wd4133 /wd4190 /wd4244 /wd4267 /wd4305 /wd4477 /wd4996 /wd4819 /fp:fast") + set(ADDITIONAL_C_FLAGS " /nologo /wd4013 /wd4018 /wd4028 /wd4047 /wd4068 /wd4090 /wd4101 /wd4113 /wd4133 /wd4190 /wd4244 /wd4267 /wd4305 /wd4477 /wd4996 /wd4819 /fp:fast") string(REGEX REPLACE "/O2" "/Ox" CMAKE_CXX_FLAGS_RELEASE ${CMAKE_CXX_FLAGS_RELEASE}) string(REGEX REPLACE "/O2" "/Ox" CMAKE_C_FLAGS_RELEASE ${CMAKE_C_FLAGS_RELEASE}) endif() diff --git a/build.ps1 b/build.ps1 index aae41a7c98b..daef705cfff 100755 --- a/build.ps1 +++ b/build.ps1 @@ -6,7 +6,7 @@ build Created By: Stefano Sinigardi Created Date: February 18, 2019 - Last Modified Date: May 10, 2022 + Last Modified Date: February 20, 2023 .DESCRIPTION Build tool using CMake, trying to properly setup the environment around compiler @@ -14,17 +14,23 @@ Build tool using CMake, trying to properly setup the environment around compiler .PARAMETER DisableInteractive Disable script interactivity (useful for CI runs) +.PARAMETER BuildDebug +Build using the debug toolchain + .PARAMETER DisableDLLcopy Disable automatic DLL deployment through vcpkg at the end .PARAMETER EnableCUDA -Build tool with CUDA support +Enable CUDA feature .PARAMETER EnableCUDNN Enable CUDNN feature .PARAMETER EnableOPENCV -Build darknet linking to OpenCV +Enable OpenCV feature + +.PARAMETER ForceOpenCVVersion +Force a specific OpenCV version .PARAMETER EnableOPENCV_CUDA Use a CUDA-enabled OpenCV build @@ -83,7 +89,6 @@ Force clean up of vcpkg packages folder at the end of the script .PARAMETER ForceSetupVS Forces Visual Studio setup, also on systems on which it would not have been enabled automatically - .PARAMETER ForceCMakeFromVS Forces usage of CMake from Visual Studio instead of the system-wide/user installed one @@ -99,9 +104,6 @@ Download pre-trained weight files .PARAMETER ForceGCCVersion Force a specific GCC version -.PARAMETER ForceOpenCVVersion -Force a specific OpenCV version (valid only with vcpkg-enabled builds) - .PARAMETER NumberOfBuildWorkers Forces a specific number of threads for parallel building @@ -109,7 +111,7 @@ Forces a specific number of threads for parallel building Additional setup parameters to manually pass to CMake .EXAMPLE -.\build -DisableInteractive -DoNotDeleteBuildFolder -UseVCPKG +./build -DisableInteractive -DoNotDeleteBuildFolder -UseVCPKG #> @@ -139,10 +141,12 @@ SOFTWARE. param ( [switch]$DisableInteractive = $false, + [switch]$BuildDebug = $false, [switch]$DisableDLLcopy = $false, [switch]$EnableCUDA = $false, [switch]$EnableCUDNN = $false, [switch]$EnableOPENCV = $false, + [Int32]$ForceOpenCVVersion = 0, [switch]$EnableOPENCV_CUDA = $false, [switch]$UseVCPKG = $false, [switch]$InstallDARKNETthroughVCPKG = $false, @@ -167,24 +171,31 @@ param ( [switch]$EnableCSharpWrapper = $false, [switch]$DownloadWeights = $false, [Int32]$ForceGCCVersion = 0, - [Int32]$ForceOpenCVVersion = 0, [Int32]$NumberOfBuildWorkers = 8, [string]$AdditionalBuildSetup = "" # "-DCMAKE_CUDA_ARCHITECTURES=30" ) $global:DisableInteractive = $DisableInteractive -$build_ps1_version = "2.3.3" +$build_ps1_version = "3.1.0" +$script_name = $MyInvocation.MyCommand.Name Import-Module -Name $PSScriptRoot/scripts/utils.psm1 -Force $ErrorActionPreference = "SilentlyContinue" Stop-Transcript | out-null $ErrorActionPreference = "Continue" -$BuildLogPath = "$PSScriptRoot/build.log" +$PSCustomScriptRoot = $PSScriptRoot +$BuildLogPath = "$PSCustomScriptRoot/build.log" +$ReleaseInstallPrefix = "$PSCustomScriptRoot" +$DebugInstallPrefix = "$PSCustomScriptRoot/debug" +$DebugBuildSetup = " -DCMAKE_INSTALL_PREFIX=$DebugInstallPrefix -DCMAKE_BUILD_TYPE=Debug" +$ReleaseBuildSetup = " -DCMAKE_INSTALL_PREFIX=$ReleaseInstallPrefix -DCMAKE_BUILD_TYPE=Release" + Start-Transcript -Path $BuildLogPath Write-Host "Build script version ${build_ps1_version}, utils module version ${utils_psm1_version}" +Write-Host "Working directory: $PSCustomScriptRoot, log file: $BuildLogPath, $script_name is in submodule: $IsInGitSubmodule" if ((-Not $global:DisableInteractive) -and (-Not $UseVCPKG)) { $Result = Read-Host "Enable vcpkg to install dependencies (yes/no)" @@ -272,30 +283,66 @@ $vcpkg_triplet_set_by_this_script = $false $vcpkg_host_triplet_set_by_this_script = $false if (($IsWindows -or $IsWindowsPowerShell) -and (-Not $env:VCPKG_DEFAULT_TRIPLET)) { - $env:VCPKG_DEFAULT_TRIPLET = "x64-windows-release" - $vcpkg_triplet_set_by_this_script = $true + if($BuildDebug) { + $env:VCPKG_DEFAULT_TRIPLET = "x64-windows" + $vcpkg_triplet_set_by_this_script = $true + } + else { + $env:VCPKG_DEFAULT_TRIPLET = "x64-windows-release" + $vcpkg_triplet_set_by_this_script = $true + } } if (($IsWindows -or $IsWindowsPowerShell) -and (-Not $env:VCPKG_DEFAULT_HOST_TRIPLET)) { - $env:VCPKG_DEFAULT_HOST_TRIPLET = "x64-windows-release" - $vcpkg_host_triplet_set_by_this_script = $true + if ($BuildDebug) { + $env:VCPKG_DEFAULT_HOST_TRIPLET = "x64-windows" + $vcpkg_host_triplet_set_by_this_script = $true + } + else { + $env:VCPKG_DEFAULT_HOST_TRIPLET = "x64-windows-release" + $vcpkg_host_triplet_set_by_this_script = $true + } } if ($IsMacOS -and (-Not $env:VCPKG_DEFAULT_TRIPLET)) { - $env:VCPKG_DEFAULT_TRIPLET = "x64-osx-release" - $vcpkg_triplet_set_by_this_script = $true + if ($BuildDebug) { + $env:VCPKG_DEFAULT_TRIPLET = "x64-osx" + $vcpkg_triplet_set_by_this_script = $true + } + else { + $env:VCPKG_DEFAULT_TRIPLET = "x64-osx-release" + $vcpkg_triplet_set_by_this_script = $true + } } if ($IsMacOS -and (-Not $env:VCPKG_DEFAULT_HOST_TRIPLET)) { - $env:VCPKG_DEFAULT_HOST_TRIPLET = "x64-osx-release" - $vcpkg_host_triplet_set_by_this_script = $true + if ($BuildDebug) { + $env:VCPKG_DEFAULT_HOST_TRIPLET = "x64-osx" + $vcpkg_host_triplet_set_by_this_script = $true + } + else { + $env:VCPKG_DEFAULT_HOST_TRIPLET = "x64-osx-release" + $vcpkg_host_triplet_set_by_this_script = $true + } } if ($IsLinux -and (-Not $env:VCPKG_DEFAULT_TRIPLET)) { - $env:VCPKG_DEFAULT_TRIPLET = "x64-linux-release" - $vcpkg_triplet_set_by_this_script = $true + if ($BuildDebug) { + $env:VCPKG_DEFAULT_TRIPLET = "x64-linux" + $vcpkg_triplet_set_by_this_script = $true + } + else { + $env:VCPKG_DEFAULT_TRIPLET = "x64-linux-release" + $vcpkg_triplet_set_by_this_script = $true + } } if ($IsLinux -and (-Not $env:VCPKG_DEFAULT_HOST_TRIPLET)) { - $env:VCPKG_DEFAULT_HOST_TRIPLET = "x64-linux-release" - $vcpkg_host_triplet_set_by_this_script = $true + if ($BuildDebug) { + $env:VCPKG_DEFAULT_HOST_TRIPLET = "x64-linux" + $vcpkg_host_triplet_set_by_this_script = $true + } + else { + $env:VCPKG_DEFAULT_HOST_TRIPLET = "x64-linux-release" + $vcpkg_host_triplet_set_by_this_script = $true + } } if ($VCPKGSuffix -ne "" -and -not $UseVCPKG) { @@ -360,6 +407,9 @@ elseif ($EnableOPENCV_CUDA -and (-Not $EnableCUDA) -and (-Not $EnableOPENCV)) { if ($UseVCPKG) { Write-Host "VCPKG is enabled" + if ($BuildDebug -and ($env:VCPKG_DEFAULT_TRIPLET -match "release")) { + MyThrow("You asked to build also debug binaries but VCPKG_DEFAULT_TRIPLET is set to a release triplet") + } if ($DoNotUpdateVCPKG) { Write-Host "VCPKG will not be updated to latest version if found" -ForegroundColor Yellow } @@ -401,7 +451,7 @@ else { Write-Host "ForceCPP build mode is disabled, please pass -ForceCPP to the script to enable" } -Push-Location $PSScriptRoot +Push-Location $PSCustomScriptRoot $GIT_EXE = Get-Command "git" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition if (-Not $GIT_EXE) { @@ -411,7 +461,8 @@ else { Write-Host "Using git from ${GIT_EXE}" } -$GitRepoPath = Resolve-Path "$PSScriptRoot/.git" +$GitRepoPath = Resolve-Path "$PSCustomScriptRoot/.git" -ErrorAction SilentlyContinue +$GitModulesPath = Resolve-Path "$PSCustomScriptRoot/.gitmodules" -ErrorAction SilentlyContinue if (Test-Path "$GitRepoPath") { Write-Host "This tool has been cloned with git and supports self-updating mechanism" if ($DoNotUpdateTOOL) { @@ -431,8 +482,8 @@ if (Test-Path "$GitRepoPath") { if ($ForceCmakeFromVS) { $vsfound = getLatestVisualStudioWithDesktopWorkloadPath - $cmakePath = "${vsfound}\Common7\IDE\CommonExtensions\Microsoft\CMake\CMake\bin" - $vsCmakePath = "${vsfound}\Common7\IDE\CommonExtensions\Microsoft\CMake\CMake\bin\cmake.exe" + $cmakePath = "${vsfound}/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/bin" + $vsCmakePath = "${vsfound}/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/bin/cmake.exe" $CMAKE_EXE = Get-Command "cmake" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition if ((Test-Path "$vsCmakePath") -and -not ($vsCmakePath -eq $CMAKE_EXE)) { Write-Host "Adding CMake from Visual Studio to PATH" @@ -464,8 +515,8 @@ else { if (-Not $DoNotUseNinja) { if ($ForceNinjaFromVS) { $vsfound = getLatestVisualStudioWithDesktopWorkloadPath - $ninjaPath = "${vsfound}\Common7\IDE\CommonExtensions\Microsoft\CMake\Ninja" - $vsninjaPath = "${vsfound}\Common7\IDE\CommonExtensions\Microsoft\CMake\Ninja\ninja.exe" + $ninjaPath = "${vsfound}/Common7/IDE/CommonExtensions/Microsoft/CMake/Ninja" + $vsninjaPath = "${vsfound}/Common7/IDE/CommonExtensions/Microsoft/CMake/Ninja/ninja.exe" $NINJA_EXE = Get-Command "ninja" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition if ((Test-Path "$vsninjaPath") -and -not ($vsninjaPath -eq $NINJA_EXE) -and (-not $DoNotUseNinja)) { Write-Host "Adding Ninja from Visual Studio to PATH" @@ -481,7 +532,7 @@ if (-Not $DoNotUseNinja) { $NINJA_EXE = Get-Command "ninja" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition if (-Not $NINJA_EXE) { DownloadNinja - $NinjaPath = Join-Path (${PSScriptRoot}) 'ninja' + $NinjaPath = Join-Path (${PSCustomScriptRoot}) 'ninja' $env:PATH = '{0}{1}{2}' -f $env:PATH, [IO.Path]::PathSeparator, "$NinjaPath" $NINJA_EXE = Get-Command "ninja" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition if (-Not $NINJA_EXE) { @@ -502,7 +553,6 @@ if (-Not $DoNotUseNinja) { } else { $generator = "Ninja" - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DCMAKE_BUILD_TYPE=Release" } } } @@ -652,14 +702,21 @@ if ($UseVCPKG -and $ForceVCPKGBuildtreesRemoval) { if (($ForceOpenCVVersion -eq 2) -and $UseVCPKG) { Write-Host "You requested OpenCV version 2, so vcpkg will install that version" -ForegroundColor Yellow + Write-Host "This requires using vcpkg.json.opencv23 as manifest file" -ForegroundColor Yellow $AdditionalBuildSetup = $AdditionalBuildSetup + " -DVCPKG_USE_OPENCV4=OFF -DVCPKG_USE_OPENCV2=ON" } if (($ForceOpenCVVersion -eq 3) -and $UseVCPKG) { Write-Host "You requested OpenCV version 3, so vcpkg will install that version" -ForegroundColor Yellow + Write-Host "This requires using vcpkg.json.opencv23 as manifest file" -ForegroundColor Yellow $AdditionalBuildSetup = $AdditionalBuildSetup + " -DVCPKG_USE_OPENCV4=OFF -DVCPKG_USE_OPENCV3=ON" } +if($ForceOpenCVVersion -gt 0) { + Move-Item $PSCustomScriptRoot/vcpkg.json $PSCustomScriptRoot/vcpkg.json.bak + Move-Item $PSCustomScriptRoot/vcpkg.json.opencv23 $PSCustomScriptRoot/vcpkg.json +} + if ($UseVCPKG -and $ForceVCPKGCacheRemoval) { if ($IsWindows -or $IsWindowsPowerShell) { $vcpkgbinarycachepath = "$env:LOCALAPPDATA/vcpkg/archive" @@ -682,7 +739,7 @@ if (-Not $DoNotSetupVS) { if ((-Not $CL_EXE) -or ($CL_EXE -match "HostX86\\x86") -or ($CL_EXE -match "HostX64\\x86")) { $vsfound = getLatestVisualStudioWithDesktopWorkloadPath Write-Host "Found VS in ${vsfound}" - Push-Location "${vsfound}\Common7\Tools" + Push-Location "${vsfound}/Common7/Tools" cmd.exe /c "VsDevCmd.bat -arch=x64 & set" | ForEach-Object { if ($_ -match "=") { @@ -696,7 +753,8 @@ if (-Not $DoNotSetupVS) { $tokens = getLatestVisualStudioWithDesktopWorkloadVersion $tokens = $tokens.split('.') if ($DoNotUseNinja) { - $selectConfig = " --config Release " + $debugConfig = " --config Debug " + $releaseConfig = " --config Release " if ($tokens[0] -eq "14") { $generator = "Visual Studio 14 2015" $AdditionalBuildSetup = $AdditionalBuildSetup + " -T `"host=x64`" -A `"x64`"" @@ -723,7 +781,6 @@ if (-Not $DoNotSetupVS) { } if ($DoNotSetupVS -and $DoNotUseNinja) { $generator = "Unix Makefiles" - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DCMAKE_BUILD_TYPE=Release" } Write-Host "Setting up environment to use CMake generator: $generator" @@ -820,9 +877,10 @@ if ($InstallDARKNETthroughVCPKG) { else { Write-Host "Running vcpkg to install darknet" Write-Host "vcpkg install darknet[${features}] $headMode --recurse" + Move-Item $PSCustomScriptRoot/vcpkg.json $PSCustomScriptRoot/vcpkg.json.bak Push-Location ${env:VCPKG_ROOT} if ($ForceVCPKGDarknetHEAD) { - $proc = Start-Process -NoNewWindow -PassThru -FilePath "${env:VCPKG_ROOT}/vcpkg${exe_ext}" -ArgumentList " --feature-flags=-manifests remove darknet --recurse " + $proc = Start-Process -NoNewWindow -PassThru -FilePath "${env:VCPKG_ROOT}/vcpkg${exe_ext}" -ArgumentList " remove darknet --recurse " $handle = $proc.Handle $proc.WaitForExit() $exitCode = $proc.ExitCode @@ -830,32 +888,71 @@ if ($InstallDARKNETthroughVCPKG) { MyThrow("Removing darknet through vcpkg failed! Exited with error code $exitCode.") } } - $proc = Start-Process -NoNewWindow -PassThru -FilePath "${env:VCPKG_ROOT}/vcpkg${exe_ext}" -ArgumentList " --feature-flags=-manifests upgrade --no-dry-run " + $proc = Start-Process -NoNewWindow -PassThru -FilePath "${env:VCPKG_ROOT}/vcpkg${exe_ext}" -ArgumentList " upgrade --no-dry-run " $handle = $proc.Handle $proc.WaitForExit() $exitCode = $proc.ExitCode if (-Not ($exitCode -eq 0)) { MyThrow("Upgrading vcpkg installed ports failed! Exited with error code $exitCode.") } - $proc = Start-Process -NoNewWindow -PassThru -FilePath "${env:VCPKG_ROOT}/vcpkg${exe_ext}" -ArgumentList " --feature-flags=-manifests install darknet[${features}] $headMode --recurse " # "-manifest" disables the manifest feature, so that if vcpkg is a subfolder of darknet, the vcpkg.json inside darknet folder does not trigger errors due to automatic manifest mode + $proc = Start-Process -NoNewWindow -PassThru -FilePath "${env:VCPKG_ROOT}/vcpkg${exe_ext}" -ArgumentList " install darknet[${features}] $headMode --recurse " # "-manifest" disables the manifest feature, so that if vcpkg is a subfolder of darknet, the vcpkg.json inside darknet folder does not trigger errors due to automatic manifest mode $handle = $proc.Handle $proc.WaitForExit() $exitCode = $proc.ExitCode if (-Not ($exitCode -eq 0)) { MyThrow("Installing darknet dependencies through vcpkg failed! Exited with error code $exitCode.") } + Move-Item $PSCustomScriptRoot/vcpkg.json.bak $PSCustomScriptRoot/vcpkg.json } } else { - $build_folder = "./build_release" + if ($BuildDebug -and $UseVCPKG) { + $debug_build_folder = "$PSCustomScriptRoot/build_debug" + if (-Not $DoNotDeleteBuildFolder) { + Write-Host "Removing folder $debug_build_folder" -ForegroundColor Yellow + Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $debug_build_folder + } + + New-Item -Path $debug_build_folder -ItemType directory -Force | Out-Null + Set-Location $debug_build_folder + $cmake_args = "-G `"$generator`" ${DebugBuildSetup} ${AdditionalBuildSetup} -S .." + Write-Host "Configuring debug CMake project" -ForegroundColor Green + Write-Host "CMake args: $cmake_args" + $proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList $cmake_args + $handle = $proc.Handle + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-Not ($exitCode -eq 0)) { + MyThrow("Config failed! Exited with error code $exitCode.") + } + Write-Host "Building debug CMake project" -ForegroundColor Green + $proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList "--build . ${debugConfig} --parallel ${NumberOfBuildWorkers} --target install" + $handle = $proc.Handle + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-Not ($exitCode -eq 0)) { + MyThrow("Build failed! Exited with error code $exitCode.") + } + Get-ChildItem "${debug_build_folder}" -Filter *.pdb | + Foreach-Object { + Write-Host "-- Copying $_ to $DebugInstallPrefix/bin" + Copy-Item $_ $DebugInstallPrefix/bin + } + if (-Not $DoNotDeleteBuildFolder) { + Write-Host "Removing folder $debug_build_folder" -ForegroundColor Yellow + Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $debug_build_folder + } + } + $release_build_folder = "$PSCustomScriptRoot/build_release" if (-Not $DoNotDeleteBuildFolder) { - Write-Host "Removing folder $build_folder" -ForegroundColor Yellow - Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $build_folder + Write-Host "Removing folder $release_build_folder" -ForegroundColor Yellow + Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $release_build_folder } - New-Item -Path $build_folder -ItemType directory -Force | Out-Null - Set-Location $build_folder - $cmake_args = "-G `"$generator`" ${AdditionalBuildSetup} -S .." - Write-Host "Configuring CMake project" -ForegroundColor Green + + New-Item -Path $release_build_folder -ItemType directory -Force | Out-Null + Set-Location $release_build_folder + $cmake_args = "-G `"$generator`" ${ReleaseBuildSetup} ${AdditionalBuildSetup} -S .." + Write-Host "Configuring release CMake project" -ForegroundColor Green Write-Host "CMake args: $cmake_args" $proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList $cmake_args $handle = $proc.Handle @@ -864,13 +961,13 @@ else { if (-Not ($exitCode -eq 0)) { MyThrow("Config failed! Exited with error code $exitCode.") } - Write-Host "Building CMake project" -ForegroundColor Green - $proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList "--build . ${selectConfig} --parallel ${NumberOfBuildWorkers} --target install" + Write-Host "Building release CMake project" -ForegroundColor Green + $proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList "--build . ${releaseConfig} --parallel ${NumberOfBuildWorkers} --target install" $handle = $proc.Handle $proc.WaitForExit() $exitCode = $proc.ExitCode if (-Not ($exitCode -eq 0)) { - MyThrow("Config failed! Exited with error code $exitCode.") + MyThrow("Build failed! Exited with error code $exitCode.") } Remove-Item -Force -ErrorAction SilentlyContinue DarknetConfig.cmake Remove-Item -Force -ErrorAction SilentlyContinue DarknetConfigVersion.cmake @@ -880,16 +977,14 @@ else { Copy-Item $dllfiles .. } } + if (-Not $DoNotDeleteBuildFolder) { + Write-Host "Removing folder $build_folder" -ForegroundColor Yellow + Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $build_folder + } Set-Location .. } Pop-Location - -if (-Not $DoNotDeleteBuildFolder) { - Write-Host "Removing folder $build_folder" -ForegroundColor Yellow - Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $build_folder -} - Write-Host "Build complete!" -ForegroundColor Green if ($ForceVCPKGBuildtreesRemoval -and (-Not $UseVCPKG)) { @@ -929,6 +1024,10 @@ if ($vcpkg_host_triplet_set_by_this_script) { $env:VCPKG_DEFAULT_HOST_TRIPLET = $null } +if($ForceOpenCVVersion -gt 0) { + Move-Item $PSCustomScriptRoot/vcpkg.json $PSCustomScriptRoot/vcpkg.json.opencv23 + Move-Item $PSCustomScriptRoot/vcpkg.json.bak $PSCustomScriptRoot/vcpkg.json +} if ($vcpkg_branch_set_by_this_script) { Push-Location $vcpkg_path diff --git a/scripts/utils.psm1 b/scripts/utils.psm1 index ed80855153f..ba2d5ee4139 100644 --- a/scripts/utils.psm1 +++ b/scripts/utils.psm1 @@ -22,7 +22,7 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. #> -$utils_psm1_version = "0.0.4" +$utils_psm1_version = "0.3.0" $IsWindowsPowerShell = switch ( $PSVersionTable.PSVersion.Major ) { 5 { $true } 4 { $true } @@ -30,6 +30,28 @@ $IsWindowsPowerShell = switch ( $PSVersionTable.PSVersion.Major ) { 2 { $true } default { $false } } + +$ExecutableSuffix = "" +if ($IsWindowsPowerShell -or $IsWindows) { + $ExecutableSuffix = ".exe" +} + +Push-Location $PSScriptRoot +$GIT_EXE = Get-Command "git" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition +if ($GIT_EXE) { + $IsInGitSubmoduleString = $(git rev-parse --show-superproject-working-tree 2> $null) + if ($IsInGitSubmoduleString.Length -eq 0) { + $IsInGitSubmodule = $false + } + else { + $IsInGitSubmodule = $true + } +} +else { + $IsInGitSubmodule = $false +} +Pop-Location + $cuda_version_full = "11.7.0" $cuda_version_short = "11.7" $cuda_version_full_dashed = $cuda_version_full.replace('.', '-') @@ -48,7 +70,7 @@ function getProgramFiles32bit() { return $out } -function getLatestVisualStudioWithDesktopWorkloadPath() { +function getLatestVisualStudioWithDesktopWorkloadPath([bool]$required = $true) { $programFiles = getProgramFiles32bit $vswhereExe = "$programFiles\Microsoft Visual Studio\Installer\vswhere.exe" if (Test-Path $vswhereExe) { @@ -74,16 +96,28 @@ function getLatestVisualStudioWithDesktopWorkloadPath() { } } if (!$installationPath) { - MyThrow("Could not locate any installation of Visual Studio") + if ($required) { + MyThrow("Could not locate any installation of Visual Studio") + } + else { + Write-Host "Could not locate any installation of Visual Studio" -ForegroundColor Red + return $null + } } } else { - MyThrow("Could not locate vswhere at $vswhereExe") + if ($required) { + MyThrow("Could not locate vswhere at $vswhereExe") + } + else { + Write-Host "Could not locate vswhere at $vswhereExe" -ForegroundColor Red + return $null + } } return $installationPath } -function getLatestVisualStudioWithDesktopWorkloadVersion() { +function getLatestVisualStudioWithDesktopWorkloadVersion([bool]$required = $true) { $programFiles = getProgramFiles32bit $vswhereExe = "$programFiles\Microsoft Visual Studio\Installer\vswhere.exe" if (Test-Path $vswhereExe) { @@ -109,17 +143,29 @@ function getLatestVisualStudioWithDesktopWorkloadVersion() { } } if (!$installationVersion) { - MyThrow("Could not locate any installation of Visual Studio") + if ($required) { + MyThrow("Could not locate any installation of Visual Studio") + } + else { + Write-Host "Could not locate any installation of Visual Studio" -ForegroundColor Red + return $null + } } } else { - MyThrow("Could not locate vswhere at $vswhereExe") + if ($required) { + MyThrow("Could not locate vswhere at $vswhereExe") + } + else { + Write-Host "Could not locate vswhere at $vswhereExe" -ForegroundColor Red + return $null + } } return $installationVersion } function DownloadNinja() { - Write-Host "Unable to find Ninja, downloading a portable version on-the-fly" -ForegroundColor Yellow + Write-Host "Downloading a portable version of Ninja" -ForegroundColor Yellow Remove-Item -Force -Recurse -ErrorAction SilentlyContinue ninja Remove-Item -Force -ErrorAction SilentlyContinue ninja.zip if ($IsWindows -or $IsWindowsPowerShell) { @@ -137,6 +183,88 @@ function DownloadNinja() { Invoke-RestMethod -Uri $url -Method Get -ContentType application/zip -OutFile "ninja.zip" Expand-Archive -Path ninja.zip Remove-Item -Force -ErrorAction SilentlyContinue ninja.zip + return "./ninja${ExecutableSuffix}" +} + +function DownloadAria2() { + Write-Host "Downloading a portable version of Aria2" -ForegroundColor Yellow + if ($IsWindows -or $IsWindowsPowerShell) { + $basename = "aria2-1.35.0-win-32bit-build1" + $zipName = "${basename}.zip" + $outFolder = "$basename/$basename" + Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $outFolder + Remove-Item -Force -ErrorAction SilentlyContinue $zipName + $url = "https://github.com/aria2/aria2/releases/download/release-1.35.0/$zipName" + Invoke-RestMethod -Uri $url -Method Get -ContentType application/zip -OutFile $zipName + Expand-Archive -Path $zipName + } + elseif ($IsLinux) { + $basename = "aria2-1.36.0-linux-gnu-64bit-build1" + $zipName = "${basename}.tar.bz2" + $outFolder = $basename + Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $outFolder + Remove-Item -Force -ErrorAction SilentlyContinue $zipName + $url = "https://github.com/q3aql/aria2-static-builds/releases/download/v1.36.0/$zipName" + Invoke-RestMethod -Uri $url -Method Get -ContentType application/zip -OutFile $zipName + tar xf $zipName + } + elseif ($IsMacOS) { + $basename = "aria2-1.35.0-osx-darwin" + $zipName = "${basename}.tar.bz2" + $outFolder = "aria2-1.35.0/bin" + Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $outFolder + Remove-Item -Force -ErrorAction SilentlyContinue $zipName + $url = "https://github.com/aria2/aria2/releases/download/release-1.35.0/$zipName" + Invoke-RestMethod -Uri $url -Method Get -ContentType application/zip -OutFile $zipName + tar xf $zipName + } + else { + MyThrow("Unknown OS, unsupported") + } + Remove-Item -Force -ErrorAction SilentlyContinue $zipName + return "./$outFolder/aria2c${ExecutableSuffix}" +} + +function Download7Zip() { + Write-Host "Downloading a portable version of 7-Zip" -ForegroundColor Yellow + if ($IsWindows -or $IsWindowsPowerShell) { + $basename = "7za920" + $zipName = "${basename}.zip" + $outFolder = "$basename" + $outSuffix = "a" + Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $outFolder + Remove-Item -Force -ErrorAction SilentlyContinue $zipName + $url = "https://www.7-zip.org/a/$zipName" + Invoke-RestMethod -Uri $url -Method Get -ContentType application/zip -OutFile $zipName + Expand-Archive -Path $zipName + } + elseif ($IsLinux) { + $basename = "7z2201-linux-x64" + $zipName = "${basename}.tar.xz" + $outFolder = $basename + $outSuffix = "z" + Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $outFolder + Remove-Item -Force -ErrorAction SilentlyContinue $zipName + $url = "https://www.7-zip.org/a/$zipName" + Invoke-RestMethod -Uri $url -Method Get -ContentType application/zip -OutFile $zipName + tar xf $zipName + } + elseif ($IsMacOS) { + $basename = "7z2107-mac" + $zipName = "${basename}.tar.xz" + $outFolder = $basename + $outSuffix = "z" + Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $outFolder + Remove-Item -Force -ErrorAction SilentlyContinue $zipName + $url = "https://www.7-zip.org/a/$zipName" + Invoke-RestMethod -Uri $url -Method Get -ContentType application/zip -OutFile $zipName + tar xf $zipName + } + else { + MyThrow("Unknown OS, unsupported") + } + Remove-Item -Force -ErrorAction SilentlyContinue $zipName + return "./$outFolder/7z${outSuffix}${ExecutableSuffix}" } Function MyThrow ($Message) { @@ -195,14 +323,16 @@ Function MyThrow ($Message) { Export-ModuleMember -Variable utils_psm1_version Export-ModuleMember -Variable IsWindowsPowerShell +Export-ModuleMember -Variable IsInGitSubmodule Export-ModuleMember -Variable cuda_version_full Export-ModuleMember -Variable cuda_version_short Export-ModuleMember -Variable cuda_version_full_dashed Export-ModuleMember -Variable cuda_version_short_dashed - Export-ModuleMember -Function getProgramFiles32bit Export-ModuleMember -Function getLatestVisualStudioWithDesktopWorkloadPath Export-ModuleMember -Function getLatestVisualStudioWithDesktopWorkloadVersion Export-ModuleMember -Function DownloadNinja +Export-ModuleMember -Function DownloadAria2 +Export-ModuleMember -Function Download7Zip Export-ModuleMember -Function MyThrow Export-ModuleMember -Function CopyTexFile diff --git a/vcpkg.json.opencv23 b/vcpkg.json.opencv23 new file mode 100644 index 00000000000..ccfb9afa289 --- /dev/null +++ b/vcpkg.json.opencv23 @@ -0,0 +1,122 @@ +{ + "name": "darknet", + "version-date": "2021-04-16", + "description": "Darknet is an open source neural network framework written in C and CUDA. You only look once (YOLO) is a state-of-the-art, real-time object detection system, best example of darknet functionalities.", + "homepage": "https://github.com/alexeyab/darknet", + "dependencies": [ + "pthreads", + "stb" + ], + "features": { + "cuda": { + "description": "Build darknet with support for CUDA", + "dependencies": [ + "cuda" + ] + }, + "cudnn": { + "description": "Build darknet with support for cuDNN", + "dependencies": [ + "cuda", + "cudnn" + ] + }, + "full": { + "description": "Build darknet fully featured", + "dependencies": [ + { + "name": "darknet", + "features": [ + "cuda", + "cudnn", + "opencv-cuda" + ] + } + ] + }, + "opencv-base": { + "description": "Build darknet with support for latest version of OpenCV", + "dependencies": [ + { + "name": "opencv", + "features": [ + "contrib", + "dnn", + "ffmpeg" + ] + } + ] + }, + "opencv-cuda": { + "description": "Build darknet with support for latest version of CUDA-enabled OpenCV", + "dependencies": [ + { + "name": "opencv", + "features": [ + "contrib", + "cuda", + "dnn", + "ffmpeg" + ] + } + ] + }, + "opencv2-base": { + "description": "Build darknet with support for OpenCV2", + "dependencies": [ + { + "name": "opencv2", + "features": [ + "ffmpeg" + ] + } + ] + }, + "opencv2-cuda": { + "description": "Build darknet with support for CUDA-enabled OpenCV2", + "dependencies": [ + { + "name": "opencv2", + "features": [ + "cuda", + "ffmpeg" + ] + } + ] + }, + "opencv3-base": { + "description": "Build darknet with support for OpenCV3", + "dependencies": [ + { + "name": "opencv3", + "features": [ + "contrib", + "dnn", + "ffmpeg" + ] + } + ] + }, + "opencv3-cuda": { + "description": "Build darknet with support for CUDA-enabled OpenCV3", + "dependencies": [ + { + "name": "opencv3", + "features": [ + "contrib", + "cuda", + "dnn", + "ffmpeg" + ] + } + ] + } + }, + "overrides": [ + { + "name": "ffmpeg", + "version": "4.4.3" + } + ], + "builtin-baseline": "54cc53c43430c73f489e52af5fadd032c1aced16" +} From 59c86222c5387bffd9108a21885f80e980ece234 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?St=C3=A9phane=20Charette?= Date: Sat, 6 May 2023 11:19:30 -0700 Subject: [PATCH 1315/1365] better logging for filename/function/line on CUDA and CUDNN error; logging of full backtrace on Linux when an error causes Darknet to exit (#8791) --- Makefile | 2 +- src/dark_cuda.c | 40 ++++++++++++++++++++-------------------- src/dark_cuda.h | 14 +++++++------- src/utils.c | 23 ++++++++++++++++++++++- 4 files changed, 50 insertions(+), 29 deletions(-) diff --git a/Makefile b/Makefile index 431933ca058..1154880fdee 100644 --- a/Makefile +++ b/Makefile @@ -78,7 +78,7 @@ NVCC=nvcc OPTS=-Ofast LDFLAGS= -lm -pthread COMMON= -Iinclude/ -I3rdparty/stb/include -CFLAGS=-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC +CFLAGS=-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -rdynamic ifeq ($(DEBUG), 1) #OPTS= -O0 -g diff --git a/src/dark_cuda.c b/src/dark_cuda.c index d155b408295..246192f9475 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -54,7 +54,7 @@ void *cuda_get_context() return (void *)pctx; } -void check_error(cudaError_t status) +void check_error(cudaError_t status, const char * const filename, const char * const funcname, const int line) { cudaError_t status2 = cudaGetLastError(); if (status != cudaSuccess) @@ -66,7 +66,7 @@ void check_error(cudaError_t status) #ifdef WIN32 getchar(); #endif - error(buffer, DARKNET_LOC); + error(buffer, filename, funcname, line); } if (status2 != cudaSuccess) { @@ -77,15 +77,15 @@ void check_error(cudaError_t status) #ifdef WIN32 getchar(); #endif - error(buffer, DARKNET_LOC); + error(buffer, filename, funcname, line); } } -void check_error_extended(cudaError_t status, const char *file, int line, const char *date_time) +void check_error_extended(cudaError_t status, const char * const filename, const char * const funcname, const int line) { if (status != cudaSuccess) { - printf("CUDA status Error: file: %s() : line: %d : build time: %s \n", file, line, date_time); - check_error(status); + printf("CUDA status Error: file: %s: func: %s() line: %d\n", filename, funcname, line); + check_error(status, filename, funcname, line); } #if defined(DEBUG) || defined(CUDA_DEBUG) cuda_debug_sync = 1; @@ -93,9 +93,9 @@ void check_error_extended(cudaError_t status, const char *file, int line, const if (cuda_debug_sync) { status = cudaDeviceSynchronize(); if (status != cudaSuccess) - printf("CUDA status = cudaDeviceSynchronize() Error: file: %s() : line: %d : build time: %s \n", file, line, date_time); + printf("CUDA status = cudaDeviceSynchronize() Error: file: %s: func: %s() line: %d\n", filename, funcname, line); } - check_error(status); + check_error(status, filename, funcname, line); } dim3 cuda_gridsize(size_t n){ @@ -180,7 +180,7 @@ cudnnHandle_t cudnn_handle() } -void cudnn_check_error(cudnnStatus_t status) +void cudnn_check_error(cudnnStatus_t status, const char * const filename, const char * const function, const int line) { #if defined(DEBUG) || defined(CUDA_DEBUG) cudaDeviceSynchronize(); @@ -201,7 +201,7 @@ void cudnn_check_error(cudnnStatus_t status) #ifdef WIN32 getchar(); #endif - error(buffer, DARKNET_LOC); + error(buffer, filename, function, line); } if (status2 != CUDNN_STATUS_SUCCESS) { @@ -212,15 +212,15 @@ void cudnn_check_error(cudnnStatus_t status) #ifdef WIN32 getchar(); #endif - error(buffer, DARKNET_LOC); + error(buffer, filename, function, line); } } -void cudnn_check_error_extended(cudnnStatus_t status, const char *file, int line, const char *date_time) +void cudnn_check_error_extended(cudnnStatus_t status, const char * const filename, const char * const function, const int line) { if (status != CUDNN_STATUS_SUCCESS) { - printf("\n cuDNN status Error in: file: %s() : line: %d : build time: %s \n", file, line, date_time); - cudnn_check_error(status); + printf("\n cuDNN status Error in: file: %s function: %s() line: %d\n", filename, function, line); + cudnn_check_error(status, filename, function, line); } #if defined(DEBUG) || defined(CUDA_DEBUG) cuda_debug_sync = 1; @@ -228,9 +228,9 @@ void cudnn_check_error_extended(cudnnStatus_t status, const char *file, int line if (cuda_debug_sync) { cudaError_t status = cudaDeviceSynchronize(); if (status != CUDNN_STATUS_SUCCESS) - printf("\n cudaError_t status = cudaDeviceSynchronize() Error in: file: %s() : line: %d : build time: %s \n", file, line, date_time); + printf("\n cudaError_t status = cudaDeviceSynchronize() Error in: file: %s function: %s() line: %d\n", filename, function, line); } - cudnn_check_error(status); + cudnn_check_error(status, filename, function, line); } static cudnnHandle_t switchCudnnHandle[16]; @@ -251,10 +251,10 @@ void cublas_check_error(cublasStatus_t status) } } -void cublas_check_error_extended(cublasStatus_t status, const char *file, int line, const char *date_time) +void cublas_check_error_extended(cublasStatus_t status, const char * const filename, const char * const function, const int line) { if (status != CUBLAS_STATUS_SUCCESS) { - printf("\n cuBLAS status Error in: file: %s() : line: %d : build time: %s \n", file, line, date_time); + printf("\n cuBLAS status Error in: file: %s function: %s() line: %d\n", filename, function, line); } #if defined(DEBUG) || defined(CUDA_DEBUG) cuda_debug_sync = 1; @@ -262,7 +262,7 @@ void cublas_check_error_extended(cublasStatus_t status, const char *file, int li if (cuda_debug_sync) { cudaError_t status = cudaDeviceSynchronize(); if (status != CUDA_SUCCESS) - printf("\n cudaError_t status = cudaDeviceSynchronize() Error in: file: %s() : line: %d : build time: %s \n", file, line, date_time); + printf("\n cudaError_t status = cudaDeviceSynchronize() Error in: file: %s function: %s() line: %d\n", filename, function, line); } cublas_check_error(status); } @@ -602,7 +602,7 @@ void cuda_pull_array_async(float *x_gpu, float *x, size_t n) { size_t size = sizeof(float)*n; cudaError_t status = cudaMemcpyAsync(x, x_gpu, size, cudaMemcpyDefault, get_cuda_stream()); - check_error(status); + check_error(status, DARKNET_LOC); //cudaStreamSynchronize(get_cuda_stream()); } diff --git a/src/dark_cuda.h b/src/dark_cuda.h index 9251e877672..4f7c087a84d 100644 --- a/src/dark_cuda.h +++ b/src/dark_cuda.h @@ -54,11 +54,11 @@ extern int gpu_index; #ifdef __cplusplus extern "C" { #endif // __cplusplus - void check_error(cudaError_t status); - void check_error_extended(cudaError_t status, const char *file, int line, const char *date_time); - void cublas_check_error_extended(cublasStatus_t status, const char *file, int line, const char *date_time); -#define CHECK_CUDA(X) check_error_extended(X, __FILE__ " : " __FUNCTION__, __LINE__, __DATE__ " - " __TIME__ ); -#define CHECK_CUBLAS(X) cublas_check_error_extended(X, __FILE__ " : " __FUNCTION__, __LINE__, __DATE__ " - " __TIME__ ); + void check_error(cudaError_t status, const char * const filename, const char * const funcname, const int line); + void check_error_extended(cudaError_t status, const char * const filename, const char * const funcname, const int line); + void cublas_check_error_extended(cublasStatus_t status, const char * const filename, const char * const funcname, const int line); +#define CHECK_CUDA(X) check_error_extended(X, __FILE__, __func__, __LINE__ ); +#define CHECK_CUBLAS(X) cublas_check_error_extended(X, __FILE__, __func__, __LINE__ ); cublasHandle_t blas_handle(); void free_pinned_memory(); @@ -92,8 +92,8 @@ extern "C" { cudnnHandle_t cudnn_handle(); enum {cudnn_fastest, cudnn_smallest, cudnn_specify}; -void cudnn_check_error_extended(cudnnStatus_t status, const char *file, int line, const char *date_time); -#define CHECK_CUDNN(X) cudnn_check_error_extended(X, __FILE__ " : " __FUNCTION__, __LINE__, __DATE__ " - " __TIME__ ); +void cudnn_check_error_extended(cudnnStatus_t status, const char * const filename, const char * const function, const int line); +#define CHECK_CUDNN(X) cudnn_check_error_extended(X, __FILE__, __func__, __LINE__); #endif #ifdef __cplusplus diff --git a/src/utils.c b/src/utils.c index 0c271b7bf41..3aad28714ad 100644 --- a/src/utils.c +++ b/src/utils.c @@ -18,6 +18,7 @@ #else #include #include +#include #endif @@ -325,10 +326,30 @@ void top_k(float *a, int n, int k, int *index) } } + +void log_backtrace() +{ +#ifndef WIN32 + void * buffer[50]; + int count = backtrace(buffer, sizeof(buffer)); + char **symbols = backtrace_symbols(buffer, count); + + fprintf(stderr, "backtrace (%d entries)\n", count); + + for (int idx = 0; idx < count; idx ++) + { + fprintf(stderr, "%d/%d: %s\n", idx + 1, count, symbols[idx]); + } + + free(symbols); +#endif +} + void error(const char * const msg, const char * const filename, const char * const funcname, const int line) { - fprintf(stderr, "Darknet error location: %s, %s, line #%d\n", filename, funcname, line); + fprintf(stderr, "Darknet error location: %s, %s(), line #%d\n", filename, funcname, line); perror(msg); + log_backtrace(); exit(EXIT_FAILURE); } From 8c2e3ad5b810b7ecff659a0ea0c4068e5e073cd9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?St=C3=A9phane=20Charette?= Date: Tue, 20 Jun 2023 12:01:49 -0700 Subject: [PATCH 1316/1365] Update README.md link to FAQ and Discord --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index 0bbf462d99e..7833c9d43e6 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,8 @@ # Yolo v4, v3 and v2 for Windows and Linux +* See the FAQ: https://www.ccoderun.ca/programming/darknet_faq/ +* Join the Darknet/YOLO Discord: https://discord.gg/zSq8rtW + ## (neural networks for object detection) * Paper **YOLOv7**: https://arxiv.org/abs/2207.02696 From 69005fb5980586dcfa5b50212750f42e72b09cde Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?St=C3=A9phane=20Charette?= Date: Tue, 20 Jun 2023 12:09:53 -0700 Subject: [PATCH 1317/1365] Update README.md Link to new repo in readme --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 7833c9d43e6..1cdf49d7315 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,9 @@ # Yolo v4, v3 and v2 for Windows and Linux -* See the FAQ: https://www.ccoderun.ca/programming/darknet_faq/ +* Read the FAQ: https://www.ccoderun.ca/programming/darknet_faq/ * Join the Darknet/YOLO Discord: https://discord.gg/zSq8rtW +* Recommended GitHub repo for Darknet/YOLO: https://github.com/hank-ai/darknetcv/ +* Hank.ai and Darknet/YOLO: https://hank.ai/darknet-welcomes-hank-ai-as-official-sponsor-and-commercial-entity/ ## (neural networks for object detection) From fb2c3b0d6d25b7019af6c541e998d1716e56d7a7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?St=C3=A9phane=20Charette?= Date: Tue, 20 Jun 2023 12:25:59 -0700 Subject: [PATCH 1318/1365] Update README.md remove bad links to google groups and reddit --- README.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/README.md b/README.md index 1cdf49d7315..7745e582a95 100644 --- a/README.md +++ b/README.md @@ -64,8 +64,6 @@ Manual: https://github.com/AlexeyAB/darknet/wiki Discussion: -- [Reddit](https://www.reddit.com/r/MachineLearning/comments/gydxzd/p_yolov4_the_most_accurate_realtime_neural/) -- [Google-groups](https://groups.google.com/forum/#!forum/darknet) - [Discord](https://discord.gg/zSq8rtW) About Darknet framework: http://pjreddie.com/darknet/ From 9bf13a09d87705563deaf2b704cbd7c4429afe1f Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 2 Aug 2023 08:50:59 +0200 Subject: [PATCH 1319/1365] update build script to workaround vcpkg integrated in vs (#8818) --- .github/workflows/ccpp.yml | 4 +- .github/workflows/on_pr.yml | 2 +- build.ps1 | 282 +++++++++++++++++++++--------------- scripts/setup.ps1 | 2 +- scripts/utils.psm1 | 5 + 5 files changed, 174 insertions(+), 121 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 9765ad2e25e..70b6e5e764c 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -505,7 +505,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -ForceLocalVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL - uses: actions/upload-artifact@v2 with: @@ -555,7 +555,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -InstallDARKNETthroughVCPKG -ForceVCPKGDarknetHEAD -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -ForceLocalVCPKG -InstallDARKNETthroughVCPKG -ForceVCPKGDarknetHEAD -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL win-intlibs: diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index 23a9c23dd54..d7c3447824f 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -339,7 +339,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -ForceLocalVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL win-intlibs: diff --git a/build.ps1 b/build.ps1 index daef705cfff..9a4bb9ecabb 100755 --- a/build.ps1 +++ b/build.ps1 @@ -6,7 +6,7 @@ build Created By: Stefano Sinigardi Created Date: February 18, 2019 - Last Modified Date: February 20, 2023 + Last Modified Date: August 1, 2023 .DESCRIPTION Build tool using CMake, trying to properly setup the environment around compiler @@ -36,7 +36,10 @@ Force a specific OpenCV version Use a CUDA-enabled OpenCV build .PARAMETER UseVCPKG -Use VCPKG to build tool dependencies. Clone it if not already found on system +Use vcpkg to build tool dependencies. Clone it if not already found on system + +.PARAMETER ForceLocalVCPKG +Use a copy of vcpkg in a subfolder of the tool folder, even if there might be another copy already provided by the system .PARAMETER InstallDARKNETthroughVCPKG Use VCPKG to install darknet thanks to the port integrated in it @@ -83,6 +86,9 @@ Force clean up of the local vcpkg binary cache before building .PARAMETER ForceVCPKGBuildtreesRemoval Force clean up of vcpkg buildtrees temp folder at the end of the script +.PARAMETER ForceVCPKGBuildtreesPath +Force using a different buildtrees dir for vcpkg + .PARAMETER ForceVCPKGPackagesRemoval Force clean up of vcpkg packages folder at the end of the script @@ -101,6 +107,9 @@ Enables building C# darknet wrapper .PARAMETER DownloadWeights Download pre-trained weight files +.PARAMETER Use32bitTriplet +Use 32 bit triplet for target build (windows-only) + .PARAMETER ForceGCCVersion Force a specific GCC version @@ -149,6 +158,7 @@ param ( [Int32]$ForceOpenCVVersion = 0, [switch]$EnableOPENCV_CUDA = $false, [switch]$UseVCPKG = $false, + [switch]$ForceLocalVCPKG = $false, [switch]$InstallDARKNETthroughVCPKG = $false, [switch]$InstallDARKNETdependenciesThroughVCPKGManifest = $false, [switch]$ForceVCPKGDarknetHEAD = $false, @@ -164,12 +174,14 @@ param ( [switch]$ForceStaticLib = $false, [switch]$ForceVCPKGCacheRemoval = $false, [switch]$ForceVCPKGBuildtreesRemoval = $false, + [string]$ForceVCPKGBuildtreesPath = "", [switch]$ForceVCPKGPackagesRemoval = $false, [switch]$ForceSetupVS = $false, [switch]$ForceCMakeFromVS = $false, [switch]$ForceNinjaFromVS = $false, [switch]$EnableCSharpWrapper = $false, [switch]$DownloadWeights = $false, + [switch]$Use32bitTriplet = $false, [Int32]$ForceGCCVersion = 0, [Int32]$NumberOfBuildWorkers = 8, [string]$AdditionalBuildSetup = "" # "-DCMAKE_CUDA_ARCHITECTURES=30" @@ -177,7 +189,7 @@ param ( $global:DisableInteractive = $DisableInteractive -$build_ps1_version = "3.1.0" +$build_ps1_version = "3.4.0" $script_name = $MyInvocation.MyCommand.Name Import-Module -Name $PSScriptRoot/scripts/utils.psm1 -Force @@ -185,7 +197,12 @@ Import-Module -Name $PSScriptRoot/scripts/utils.psm1 -Force $ErrorActionPreference = "SilentlyContinue" Stop-Transcript | out-null $ErrorActionPreference = "Continue" -$PSCustomScriptRoot = $PSScriptRoot +if($IsInGitSubmodule) { + $PSCustomScriptRoot = Split-Path $PSScriptRoot -Parent +} +else { + $PSCustomScriptRoot = $PSScriptRoot +} $BuildLogPath = "$PSCustomScriptRoot/build.log" $ReleaseInstallPrefix = "$PSCustomScriptRoot" $DebugInstallPrefix = "$PSCustomScriptRoot/debug" @@ -244,7 +261,10 @@ elseif ($IsWindows -or $IsWindowsPowerShell) { $bootstrap_ext = ".bat" $exe_ext = ".exe" } - +if ($ForceLocalVCPKG -And -Not $UseVCPKG) { + $UseVCPKG = $true + Write-Host "ForceLocalVCPKG was true but UseVCPKG was false, setting UseVCPKG to true" +} if ($InstallDARKNETdependenciesThroughVCPKGManifest -and -not $InstallDARKNETthroughVCPKG) { Write-Host "You requested darknet dependencies to be installed by vcpkg in manifest mode but you didn't enable installation through vcpkg, doing that for you" $InstallDARKNETthroughVCPKG = $true @@ -283,13 +303,27 @@ $vcpkg_triplet_set_by_this_script = $false $vcpkg_host_triplet_set_by_this_script = $false if (($IsWindows -or $IsWindowsPowerShell) -and (-Not $env:VCPKG_DEFAULT_TRIPLET)) { - if($BuildDebug) { - $env:VCPKG_DEFAULT_TRIPLET = "x64-windows" + if ($Use32bitTriplet) { + if (-Not $BuildDebug) { + $BuildDebug = $true + Write-Host "Warning: when building for 32bit windows target, only builds with also debug version are possible. Debug has been enabled on your behalf!" -ForegroundColor Yellow + } + if (-Not $DoNotUseNinja) { + $DoNotUseNinja = $true + Write-Host "Warning: when building for 32bit windows target, only msbuild can be used and ninja will be disabled. Doing that for you!" -ForegroundColor Yellow + } + $env:VCPKG_DEFAULT_TRIPLET = "x86-windows" $vcpkg_triplet_set_by_this_script = $true } else { - $env:VCPKG_DEFAULT_TRIPLET = "x64-windows-release" - $vcpkg_triplet_set_by_this_script = $true + if($BuildDebug) { + $env:VCPKG_DEFAULT_TRIPLET = "x64-windows" + $vcpkg_triplet_set_by_this_script = $true + } + else { + $env:VCPKG_DEFAULT_TRIPLET = "x64-windows-release" + $vcpkg_triplet_set_by_this_script = $true + } } } if (($IsWindows -or $IsWindowsPowerShell) -and (-Not $env:VCPKG_DEFAULT_HOST_TRIPLET)) { @@ -325,13 +359,15 @@ if ($IsMacOS -and (-Not $env:VCPKG_DEFAULT_HOST_TRIPLET)) { } if ($IsLinux -and (-Not $env:VCPKG_DEFAULT_TRIPLET)) { - if ($BuildDebug) { - $env:VCPKG_DEFAULT_TRIPLET = "x64-linux" - $vcpkg_triplet_set_by_this_script = $true - } - else { - $env:VCPKG_DEFAULT_TRIPLET = "x64-linux-release" - $vcpkg_triplet_set_by_this_script = $true + if ($true) { + if ($BuildDebug) { + $env:VCPKG_DEFAULT_TRIPLET = "x64-linux" + $vcpkg_triplet_set_by_this_script = $true + } + else { + $env:VCPKG_DEFAULT_TRIPLET = "x64-linux-release" + $vcpkg_triplet_set_by_this_script = $true + } } } if ($IsLinux -and (-Not $env:VCPKG_DEFAULT_HOST_TRIPLET)) { @@ -470,6 +506,7 @@ if (Test-Path "$GitRepoPath") { } else { Write-Host "This tool will self-update sources, please pass -DoNotUpdateTOOL to the script to disable" + Set-Location "$PSCustomScriptRoot" $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "pull" $handle = $proc.Handle $proc.WaitForExit() @@ -477,6 +514,16 @@ if (Test-Path "$GitRepoPath") { if (-Not ($exitCode -eq 0)) { MyThrow("Updating this tool sources failed! Exited with error code $exitCode.") } + if (Test-Path "$GitModulesPath") { + $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "submodule update --init --recursive" + $handle = $proc.Handle + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-Not ($exitCode -eq 0)) { + MyThrow("Updating this tool submodule sources failed! Exited with error code $exitCode.") + } + } + Set-Location "$PSCustomScriptRoot" } } @@ -557,33 +604,115 @@ if (-Not $DoNotUseNinja) { } } -$vcpkg_root_set_by_this_script = $false +if (-Not $DoNotSetupVS) { + $CL_EXE = Get-Command "cl" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition + if ((-Not $CL_EXE) -or ($CL_EXE -match "HostX86\\x86") -or ($CL_EXE -match "HostX64\\x86")) { + $vsfound = getLatestVisualStudioWithDesktopWorkloadPath + Write-Host "Found VS in ${vsfound}" + Push-Location "${vsfound}/Common7/Tools" + cmd.exe /c "VsDevCmd.bat -arch=x64 & set" | + ForEach-Object { + if ($_ -match "=") { + $v = $_.split("="); Set-Item -force -path "ENV:\$($v[0])" -value "$($v[1])" + } + } + Pop-Location + Write-Host "Visual Studio Command Prompt variables set" + } -if ((Test-Path env:VCPKG_ROOT) -and $UseVCPKG -and $VCPKGSuffix -eq "") { - $vcpkg_path = "$env:VCPKG_ROOT" - $vcpkg_path = Resolve-Path $vcpkg_path - Write-Host "Found vcpkg in VCPKG_ROOT: $vcpkg_path" - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" + $tokens = getLatestVisualStudioWithDesktopWorkloadVersion + $tokens = $tokens.split('.') + if ($DoNotUseNinja) { + $debugConfig = " --config Debug " + $releaseConfig = " --config Release " + if ($Use32bitTriplet) { + $targetArchitecture = "`"Win32`"" + } + else { + $targetArchitecture = "`"x64`"" + } + if ($tokens[0] -eq "14") { + $generator = "Visual Studio 14 2015" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -T `"host=x64`" -A $targetArchitecture" + } + elseif ($tokens[0] -eq "15") { + $generator = "Visual Studio 15 2017" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -T `"host=x64`" -A $targetArchitecture" + } + elseif ($tokens[0] -eq "16") { + $generator = "Visual Studio 16 2019" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -T `"host=x64`" -A $targetArchitecture" + } + elseif ($tokens[0] -eq "17") { + $generator = "Visual Studio 17 2022" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -T `"host=x64`" -A $targetArchitecture" + } + else { + MyThrow("Unknown Visual Studio version, unsupported configuration") + } + } + if (-Not $UseVCPKG) { + $dllfolder = "../3rdparty/pthreads/bin" + } } -elseif (-not($null -eq ${env:WORKSPACE}) -and (Test-Path "${env:WORKSPACE}/vcpkg${VCPKGSuffix}") -and $UseVCPKG) { - $vcpkg_path = "${env:WORKSPACE}/vcpkg${VCPKGSuffix}" - $vcpkg_path = Resolve-Path $vcpkg_path - $env:VCPKG_ROOT = "$vcpkg_path" - $vcpkg_root_set_by_this_script = $true - Write-Host "Found vcpkg in WORKSPACE/vcpkg${VCPKGSuffix}: $vcpkg_path" - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" +if ($DoNotSetupVS -and $DoNotUseNinja) { + $generator = "Unix Makefiles" +} +Write-Host "Setting up environment to use CMake generator: $generator" + +if (-Not $IsMacOS -and $EnableCUDA) { + $NVCC_EXE = Get-Command "nvcc" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition + if (-Not $NVCC_EXE) { + if (Test-Path env:CUDA_PATH) { + $env:PATH = '{0}{1}{2}' -f $env:PATH, [IO.Path]::PathSeparator, "${env:CUDA_PATH}/bin" + Write-Host "Found cuda in ${env:CUDA_PATH}" + } + else { + Write-Host "Unable to find CUDA, if necessary please install it or define a CUDA_PATH env variable pointing to the install folder" -ForegroundColor Yellow + } + } + + if (Test-Path env:CUDA_PATH) { + if (-Not(Test-Path env:CUDA_TOOLKIT_ROOT_DIR)) { + $env:CUDA_TOOLKIT_ROOT_DIR = "${env:CUDA_PATH}" + Write-Host "Added missing env variable CUDA_TOOLKIT_ROOT_DIR" -ForegroundColor Yellow + } + if (-Not(Test-Path env:CUDACXX)) { + $env:CUDACXX = "${env:CUDA_PATH}/bin/nvcc" + Write-Host "Added missing env variable CUDACXX" -ForegroundColor Yellow + } + } } -elseif (-not($null -eq ${RUNVCPKG_VCPKG_ROOT_OUT})) { - if ((Test-Path "${RUNVCPKG_VCPKG_ROOT_OUT}") -and $UseVCPKG) { - $vcpkg_path = "${RUNVCPKG_VCPKG_ROOT_OUT}" + +$vcpkg_root_set_by_this_script = $false + +if ($UseVCPKG -And -Not $ForceLocalVCPKG) { + if ((Test-Path env:VCPKG_ROOT) -and $VCPKGSuffix -eq "") { + $vcpkg_path = "$env:VCPKG_ROOT" + $vcpkg_path = Resolve-Path $vcpkg_path + Write-Host "Found vcpkg in VCPKG_ROOT: $vcpkg_path" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" + } + elseif (-not($null -eq ${env:WORKSPACE}) -and (Test-Path "${env:WORKSPACE}/vcpkg${VCPKGSuffix}")) { + $vcpkg_path = "${env:WORKSPACE}/vcpkg${VCPKGSuffix}" $vcpkg_path = Resolve-Path $vcpkg_path $env:VCPKG_ROOT = "$vcpkg_path" $vcpkg_root_set_by_this_script = $true - Write-Host "Found vcpkg in RUNVCPKG_VCPKG_ROOT_OUT: $vcpkg_path" + Write-Host "Found vcpkg in WORKSPACE/vcpkg${VCPKGSuffix}: $vcpkg_path" $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" } + elseif (-not($null -eq ${RUNVCPKG_VCPKG_ROOT_OUT})) { + if ((Test-Path "${RUNVCPKG_VCPKG_ROOT_OUT}") -and $UseVCPKG) { + $vcpkg_path = "${RUNVCPKG_VCPKG_ROOT_OUT}" + $vcpkg_path = Resolve-Path $vcpkg_path + $env:VCPKG_ROOT = "$vcpkg_path" + $vcpkg_root_set_by_this_script = $true + Write-Host "Found vcpkg in RUNVCPKG_VCPKG_ROOT_OUT: $vcpkg_path" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" + } + } } -elseif ($UseVCPKG) { +if (($null -eq $vcpkg_path) -and $UseVCPKG) { if (-Not (Test-Path "$PWD/vcpkg${VCPKGSuffix}")) { $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "clone https://github.com/microsoft/vcpkg vcpkg${VCPKGSuffix}" $handle = $proc.Handle @@ -695,11 +824,6 @@ if ($ForceVCPKGCacheRemoval -and (-Not $UseVCPKG)) { Write-Host "VCPKG is not enabled, so local vcpkg binary cache will not be deleted even if requested" -ForegroundColor Yellow } -if ($UseVCPKG -and $ForceVCPKGBuildtreesRemoval) { - Write-Host "Cleaning folder buildtrees inside vcpkg" -ForegroundColor Yellow - Remove-Item -Force -Recurse -ErrorAction SilentlyContinue "$vcpkgbuildtreespath" -} - if (($ForceOpenCVVersion -eq 2) -and $UseVCPKG) { Write-Host "You requested OpenCV version 2, so vcpkg will install that version" -ForegroundColor Yellow Write-Host "This requires using vcpkg.json.opencv23 as manifest file" -ForegroundColor Yellow @@ -734,80 +858,6 @@ if ($UseVCPKG -and $ForceVCPKGCacheRemoval) { Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $vcpkgbinarycachepath } -if (-Not $DoNotSetupVS) { - $CL_EXE = Get-Command "cl" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition - if ((-Not $CL_EXE) -or ($CL_EXE -match "HostX86\\x86") -or ($CL_EXE -match "HostX64\\x86")) { - $vsfound = getLatestVisualStudioWithDesktopWorkloadPath - Write-Host "Found VS in ${vsfound}" - Push-Location "${vsfound}/Common7/Tools" - cmd.exe /c "VsDevCmd.bat -arch=x64 & set" | - ForEach-Object { - if ($_ -match "=") { - $v = $_.split("="); Set-Item -force -path "ENV:\$($v[0])" -value "$($v[1])" - } - } - Pop-Location - Write-Host "Visual Studio Command Prompt variables set" - } - - $tokens = getLatestVisualStudioWithDesktopWorkloadVersion - $tokens = $tokens.split('.') - if ($DoNotUseNinja) { - $debugConfig = " --config Debug " - $releaseConfig = " --config Release " - if ($tokens[0] -eq "14") { - $generator = "Visual Studio 14 2015" - $AdditionalBuildSetup = $AdditionalBuildSetup + " -T `"host=x64`" -A `"x64`"" - } - elseif ($tokens[0] -eq "15") { - $generator = "Visual Studio 15 2017" - $AdditionalBuildSetup = $AdditionalBuildSetup + " -T `"host=x64`" -A `"x64`"" - } - elseif ($tokens[0] -eq "16") { - $generator = "Visual Studio 16 2019" - $AdditionalBuildSetup = $AdditionalBuildSetup + " -T `"host=x64`" -A `"x64`"" - } - elseif ($tokens[0] -eq "17") { - $generator = "Visual Studio 17 2022" - $AdditionalBuildSetup = $AdditionalBuildSetup + " -T `"host=x64`" -A `"x64`"" - } - else { - MyThrow("Unknown Visual Studio version, unsupported configuration") - } - } - if (-Not $UseVCPKG) { - $dllfolder = "../3rdparty/pthreads/bin" - } -} -if ($DoNotSetupVS -and $DoNotUseNinja) { - $generator = "Unix Makefiles" -} -Write-Host "Setting up environment to use CMake generator: $generator" - -if (-Not $IsMacOS -and $EnableCUDA) { - $NVCC_EXE = Get-Command "nvcc" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition - if (-Not $NVCC_EXE) { - if (Test-Path env:CUDA_PATH) { - $env:PATH = '{0}{1}{2}' -f $env:PATH, [IO.Path]::PathSeparator, "${env:CUDA_PATH}/bin" - Write-Host "Found cuda in ${env:CUDA_PATH}" - } - else { - Write-Host "Unable to find CUDA, if necessary please install it or define a CUDA_PATH env variable pointing to the install folder" -ForegroundColor Yellow - } - } - - if (Test-Path env:CUDA_PATH) { - if (-Not(Test-Path env:CUDA_TOOLKIT_ROOT_DIR)) { - $env:CUDA_TOOLKIT_ROOT_DIR = "${env:CUDA_PATH}" - Write-Host "Added missing env variable CUDA_TOOLKIT_ROOT_DIR" -ForegroundColor Yellow - } - if (-Not(Test-Path env:CUDACXX)) { - $env:CUDACXX = "${env:CUDA_PATH}/bin/nvcc" - Write-Host "Added missing env variable CUDACXX" -ForegroundColor Yellow - } - } -} - if (-Not $DisableDLLcopy) { $AdditionalBuildSetup = $AdditionalBuildSetup + " -DX_VCPKG_APPLOCAL_DEPS_INSTALL=ON" } @@ -978,8 +1028,8 @@ else { } } if (-Not $DoNotDeleteBuildFolder) { - Write-Host "Removing folder $build_folder" -ForegroundColor Yellow - Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $build_folder + Write-Host "Removing folder $release_build_folder" -ForegroundColor Yellow + Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $release_build_folder } Set-Location .. } @@ -991,7 +1041,6 @@ if ($ForceVCPKGBuildtreesRemoval -and (-Not $UseVCPKG)) { Write-Host "VCPKG is not enabled, so local vcpkg buildtrees folder will not be deleted even if requested" -ForegroundColor Yellow } -$vcpkgbuildtreespath = "$vcpkg_path/buildtrees" if ($UseVCPKG -and $ForceVCPKGBuildtreesRemoval) { Write-Host "Removing local vcpkg buildtrees folder from $vcpkgbuildtreespath" -ForegroundColor Yellow Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $vcpkgbuildtreespath @@ -1016,7 +1065,6 @@ if ($DownloadWeights) { if ($vcpkg_root_set_by_this_script) { $env:VCPKG_ROOT = $null } - if ($vcpkg_triplet_set_by_this_script) { $env:VCPKG_DEFAULT_TRIPLET = $null } diff --git a/scripts/setup.ps1 b/scripts/setup.ps1 index 6345d40960f..125cf6faef4 100755 --- a/scripts/setup.ps1 +++ b/scripts/setup.ps1 @@ -33,7 +33,7 @@ else { } if ($CUDAisAvailable) { - & $PSScriptRoot/../build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -DisableInteractive -DoNotUpdateTOOL + & $PSScriptRoot/../build.ps1 -UseVCPKG -ForceLocalVCPKG -EnableOPENCV -EnableCUDA -DisableInteractive -DoNotUpdateTOOL #& $PSScriptRoot/../build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableOPENCV_CUDA -DisableInteractive -DoNotUpdateTOOL } else { diff --git a/scripts/utils.psm1 b/scripts/utils.psm1 index ba2d5ee4139..6c7313c3ef4 100644 --- a/scripts/utils.psm1 +++ b/scripts/utils.psm1 @@ -36,6 +36,9 @@ if ($IsWindowsPowerShell -or $IsWindows) { $ExecutableSuffix = ".exe" } +$64bitPwsh = $([Environment]::Is64BitProcess) +$64bitOS = $([Environment]::Is64BitOperatingSystem) + Push-Location $PSScriptRoot $GIT_EXE = Get-Command "git" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition if ($GIT_EXE) { @@ -324,6 +327,8 @@ Function MyThrow ($Message) { Export-ModuleMember -Variable utils_psm1_version Export-ModuleMember -Variable IsWindowsPowerShell Export-ModuleMember -Variable IsInGitSubmodule +Export-ModuleMember -Variable 64bitPwsh +Export-ModuleMember -Variable 64bitOS Export-ModuleMember -Variable cuda_version_full Export-ModuleMember -Variable cuda_version_short Export-ModuleMember -Variable cuda_version_full_dashed From 5915c2e09a7e6f8650a1b875f11f556a21b40f99 Mon Sep 17 00:00:00 2001 From: Tobit Flatscher <53856473+2b-t@users.noreply.github.com> Date: Wed, 2 Aug 2023 07:53:25 +0100 Subject: [PATCH 1320/1365] feat: Allow package to be used directly with ROS/ROS 2 by providing package manifest (#8783) --- package.xml | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 package.xml diff --git a/package.xml b/package.xml new file mode 100644 index 00000000000..f68d3bcc7b2 --- /dev/null +++ b/package.xml @@ -0,0 +1,26 @@ + + + darknet + 0.1.0 + Darknet, an open source neural network framework. + + Joseph Redmon + Alexey Bochkovskiy + + YOLO license + + cmake + + libopencv-dev + + + nvidia-cuda-dev + + + nvidia-cuda + + + cmake + + + From d2c3788539bb73d3cfcdb7a1d5fa6b29ae1ec0e1 Mon Sep 17 00:00:00 2001 From: Tobit Flatscher <53856473+2b-t@users.noreply.github.com> Date: Wed, 2 Aug 2023 07:54:08 +0100 Subject: [PATCH 1321/1365] feat: Move set_batch_network to public API (#8782) --- include/darknet.h | 1 + src/network.h | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/include/darknet.h b/include/darknet.h index d72027cc45f..2517d3fbd05 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -1032,6 +1032,7 @@ LIB_API void diounms_sort(detection *dets, int total, int classes, float thresh, // network.h LIB_API float *network_predict(network net, float *input); LIB_API float *network_predict_ptr(network *net, float *input); +LIB_API void set_batch_network(network *net, int b); LIB_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter); LIB_API det_num_pair* network_predict_batch(network *net, image im, int batch_size, int w, int h, float thresh, float hier, int *map, int relative, int letter); LIB_API void free_detections(detection *dets, int n); diff --git a/src/network.h b/src/network.h index 7661c8ef806..9cfce4e940a 100644 --- a/src/network.h +++ b/src/network.h @@ -144,7 +144,7 @@ int get_predicted_class_network(network net); void print_network(network net); void visualize_network(network net); int resize_network(network *net, int w, int h); -void set_batch_network(network *net, int b); +//LIB_API void set_batch_network(network *net, int b); int get_network_input_size(network net); float get_network_cost(network net); //LIB_API layer* get_network_layer(network* net, int i); From a87fe0b40da37e65c4c3337c9e7213db8c05fdc4 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 24 Aug 2023 19:16:28 +0200 Subject: [PATCH 1322/1365] update github actions (#8829) * update github actions * consolidate versions * bump minimum cmake version --- .github/workflows/ccpp.yml | 114 +++++++++++++++++------------------ .github/workflows/on_pr.yml | 52 ++++++++-------- .github/workflows/rebase.yml | 13 +++- CMakeLists.txt | 52 ++++++++++++---- vcpkg.json | 2 +- vcpkg.json.opencv23 | 2 +- 6 files changed, 132 insertions(+), 103 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 70b6e5e764c..daaa9b0dba3 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -18,7 +18,7 @@ jobs: ubuntu-makefile: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Update apt run: sudo apt update @@ -74,7 +74,7 @@ jobs: ubuntu-vcpkg-opencv4-cuda: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Setup tmate session uses: mxschmitt/action-tmate@v3 @@ -125,19 +125,19 @@ jobs: LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-vcpkg-cuda-${{ runner.os }} path: cfg - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-vcpkg-cuda-${{ runner.os }} path: data - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-vcpkg-cuda-${{ runner.os }} path: ${{ github.workspace }}/*dark* - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-vcpkg-cuda-${{ runner.os }} path: ${{ github.workspace }}/uselib* @@ -146,7 +146,7 @@ jobs: ubuntu-vcpkg-opencv3-cuda: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: lukka/get-cmake@latest @@ -197,7 +197,7 @@ jobs: ubuntu-vcpkg-opencv2-cuda: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: lukka/get-cmake@latest @@ -248,7 +248,7 @@ jobs: ubuntu: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Update apt run: sudo apt update @@ -266,19 +266,19 @@ jobs: LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-${{ runner.os }} path: cfg - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-${{ runner.os }} path: data - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-${{ runner.os }} path: ${{ github.workspace }}/*dark* - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-${{ runner.os }} path: ${{ github.workspace }}/uselib* @@ -287,7 +287,7 @@ jobs: ubuntu-cuda: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Update apt run: sudo apt update @@ -315,19 +315,19 @@ jobs: LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-cuda-${{ runner.os }} path: cfg - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-cuda-${{ runner.os }} path: data - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-cuda-${{ runner.os }} path: ${{ github.workspace }}/*dark* - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-cuda-${{ runner.os }} path: ${{ github.workspace }}/uselib* @@ -336,7 +336,7 @@ jobs: ubuntu-no-ocv-cpp: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: lukka/get-cmake@latest @@ -348,7 +348,7 @@ jobs: ubuntu-setup-sh: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -377,7 +377,7 @@ jobs: osx-vcpkg: runs-on: macos-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Setup tmate session uses: mxschmitt/action-tmate@v3 @@ -411,19 +411,19 @@ jobs: shell: pwsh run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-vcpkg-${{ runner.os }} path: cfg - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-vcpkg-${{ runner.os }} path: data - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-vcpkg-${{ runner.os }} path: ${{ github.workspace }}/*dark* - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-vcpkg-${{ runner.os }} path: ${{ github.workspace }}/uselib* @@ -432,7 +432,7 @@ jobs: osx: runs-on: macos-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Install dependencies run: brew install opencv libomp @@ -443,19 +443,19 @@ jobs: shell: pwsh run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-${{ runner.os }} path: cfg - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-${{ runner.os }} path: data - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-${{ runner.os }} path: ${{ github.workspace }}/*dark* - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-${{ runner.os }} path: ${{ github.workspace }}/uselib* @@ -464,7 +464,7 @@ jobs: osx-no-ocv-no-omp-cpp: runs-on: macos-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: lukka/get-cmake@latest @@ -476,7 +476,7 @@ jobs: win-vcpkg: runs-on: windows-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Setup tmate session uses: mxschmitt/action-tmate@v3 @@ -507,23 +507,23 @@ jobs: shell: pwsh run: ${{ github.workspace }}/build.ps1 -UseVCPKG -ForceLocalVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-vcpkg-${{ runner.os }} path: cfg - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-vcpkg-${{ runner.os }} path: data - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-vcpkg-${{ runner.os }} path: ${{ github.workspace }}/*dark* - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-vcpkg-${{ runner.os }} path: ${{ github.workspace }}/build_release/*.dll - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-vcpkg-${{ runner.os }} path: ${{ github.workspace }}/uselib* @@ -532,7 +532,7 @@ jobs: win-vcpkg-port: runs-on: windows-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -561,7 +561,7 @@ jobs: win-intlibs: runs-on: windows-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: lukka/get-cmake@latest @@ -569,23 +569,23 @@ jobs: shell: pwsh run: ${{ github.workspace }}/build.ps1 -DisableInteractive -DoNotUpdateTOOL - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-${{ runner.os }} path: cfg - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-${{ runner.os }} path: data - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-${{ runner.os }} path: ${{ github.workspace }}/*dark* - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-${{ runner.os }} path: ${{ github.workspace }}/3rdparty/pthreads/bin/*.dll - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: name: darknet-${{ runner.os }} path: ${{ github.workspace }}/uselib* @@ -594,7 +594,7 @@ jobs: win-setup-ps1: runs-on: windows-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -623,7 +623,7 @@ jobs: win-intlibs-cpp: runs-on: windows-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: lukka/get-cmake@latest @@ -635,7 +635,7 @@ jobs: win-csharp: runs-on: windows-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: lukka/get-cmake@latest @@ -647,7 +647,7 @@ jobs: win-intlibs-cuda: runs-on: windows-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.ps1 @@ -665,7 +665,7 @@ jobs: win-powershell51: runs-on: windows-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: lukka/get-cmake@latest @@ -677,17 +677,13 @@ jobs: mingw: runs-on: windows-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: lukka/get-cmake@latest - name: 'Build with CMake' - uses: lukka/run-cmake@v3 - with: - cmakeListsOrSettingsJson: CMakeListsTxtAdvanced - cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' - useVcpkgToolchainFile: true - buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-G\"MinGW Makefiles\" -DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" - cmakeBuildType: 'Release' - buildWithCMakeArgs: '--config Release --target install' + run: | + mkdir build_release + cd build_release + cmake .. -G"MinGW Makefiles" -DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF + cmake --build . --config Release --target install diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index d7c3447824f..c97875046fe 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -9,7 +9,7 @@ jobs: ubuntu-makefile: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Update apt run: sudo apt update @@ -65,7 +65,7 @@ jobs: ubuntu-vcpkg-opencv4-cuda: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: lukka/get-cmake@latest @@ -106,7 +106,7 @@ jobs: ubuntu-vcpkg-opencv3-cuda: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: lukka/get-cmake@latest @@ -147,7 +147,7 @@ jobs: ubuntu-vcpkg-opencv2-cuda: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: lukka/get-cmake@latest @@ -188,7 +188,7 @@ jobs: ubuntu: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Update apt run: sudo apt update @@ -210,7 +210,7 @@ jobs: ubuntu-cuda: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Update apt run: sudo apt update @@ -242,7 +242,7 @@ jobs: ubuntu-no-ocv-cpp: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: lukka/get-cmake@latest @@ -254,7 +254,7 @@ jobs: ubuntu-setup-sh: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -273,7 +273,7 @@ jobs: osx-vcpkg: runs-on: macos-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Install dependencies run: brew install libomp yasm nasm pkg-config @@ -297,7 +297,7 @@ jobs: osx: runs-on: macos-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Install dependencies run: brew install opencv libomp @@ -312,7 +312,7 @@ jobs: osx-no-ocv-no-omp-cpp: runs-on: macos-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: lukka/get-cmake@latest @@ -324,7 +324,7 @@ jobs: win-vcpkg: runs-on: windows-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: lukka/get-cmake@latest @@ -345,7 +345,7 @@ jobs: win-intlibs: runs-on: windows-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: lukka/get-cmake@latest @@ -357,7 +357,7 @@ jobs: win-setup-ps1: runs-on: windows-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -376,7 +376,7 @@ jobs: win-intlibs-cpp: runs-on: windows-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: lukka/get-cmake@latest @@ -388,7 +388,7 @@ jobs: win-csharp: runs-on: windows-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: lukka/get-cmake@latest @@ -400,7 +400,7 @@ jobs: win-intlibs-cuda: runs-on: windows-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.ps1 @@ -418,7 +418,7 @@ jobs: win-powershell51: runs-on: windows-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: lukka/get-cmake@latest @@ -430,17 +430,13 @@ jobs: mingw: runs-on: windows-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - uses: lukka/get-cmake@latest - name: 'Build with CMake' - uses: lukka/run-cmake@v3 - with: - cmakeListsOrSettingsJson: CMakeListsTxtAdvanced - cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt' - useVcpkgToolchainFile: true - buildDirectory: '${{ runner.workspace }}/buildDirectory' - cmakeAppendedArgs: "-G\"MinGW Makefiles\" -DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF" - cmakeBuildType: 'Release' - buildWithCMakeArgs: '--config Release --target install' + run: | + mkdir build_release + cd build_release + cmake .. -G"MinGW Makefiles" -DCMAKE_BUILD_TYPE=Release -DENABLE_CUDA=OFF -DENABLE_CUDNN=OFF -DENABLE_OPENCV=OFF + cmake --build . --config Release --target install diff --git a/.github/workflows/rebase.yml b/.github/workflows/rebase.yml index 251a259ffcf..f9e853e439b 100644 --- a/.github/workflows/rebase.yml +++ b/.github/workflows/rebase.yml @@ -5,15 +5,22 @@ on: jobs: rebase: name: Rebase - if: github.event.issue.pull_request != '' && contains(github.event.comment.body, '/rebase') && (github.event.comment.author_association == 'MEMBER' || github.event.comment.author_association == 'OWNER' || github.event.comment.author_association == 'CONTRIBUTOR') runs-on: ubuntu-latest + if: >- + github.event.issue.pull_request != '' && + ( + contains(github.event.comment.body, '/rebase') || + contains(github.event.comment.body, '/autosquash') + ) steps: - name: Checkout the latest code - uses: actions/checkout@v2 + uses: actions/checkout@v3 with: token: ${{ secrets.GITHUB_TOKEN }} fetch-depth: 0 # otherwise, you will fail to push refs to dest repo - name: Automatic Rebase - uses: cirrus-actions/rebase@1.5 + uses: cirrus-actions/rebase@1.8 + with: + autosquash: ${{ contains(github.event.comment.body, '/autosquash') || contains(github.event.comment.body, '/rebase-autosquash') }} env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/CMakeLists.txt b/CMakeLists.txt index d3afb968447..412a93f9e1a 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,14 +1,6 @@ -cmake_minimum_required(VERSION 3.18) +cmake_minimum_required(VERSION 3.19) include(CMakeDependentOption) -set(Darknet_MAJOR_VERSION 0) -set(Darknet_MINOR_VERSION 2) -set(Darknet_PATCH_VERSION 5) -set(Darknet_TWEAK_VERSION 4) -set(Darknet_VERSION ${Darknet_MAJOR_VERSION}.${Darknet_MINOR_VERSION}.${Darknet_PATCH_VERSION}.${Darknet_TWEAK_VERSION}) - -message("Darknet_VERSION: ${Darknet_VERSION}") - option(CMAKE_VERBOSE_MAKEFILE "Create verbose makefile" ON) option(CUDA_VERBOSE_BUILD "Create verbose CUDA build" ON) option(BUILD_SHARED_LIBS "Create dark as a shared library" ON) @@ -85,17 +77,23 @@ endif() cmake_dependent_option(ENABLE_SSE_AND_AVX_FLAGS "Enable AVX and SSE optimizations (x86-only)" ON "CMAKE_COMPILER_IS_GNUCC_OR_CLANG;IS_X86" OFF) if(ENABLE_VCPKG_INTEGRATION AND DEFINED ENV{VCPKG_ROOT} AND NOT DEFINED CMAKE_TOOLCHAIN_FILE) + set(CMAKE_POLICY_DEFAULT_CMP0077 NEW) + set(X_VCPKG_APPLOCAL_DEPS_INSTALL ON) set(CMAKE_TOOLCHAIN_FILE "$ENV{VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake" CACHE STRING "") #set(_VCPKG_INSTALLED_DIR ${CMAKE_CURRENT_LIST_DIR}/vcpkg CACHE STRING "") #folder for manifest-installed dependencies message(STATUS "VCPKG found: $ENV{VCPKG_ROOT}") message(STATUS "Using VCPKG integration") - message(STATUS "VCPKG_MANIFEST_FEATURES: ${VCPKG_MANIFEST_FEATURES}") set(USE_INTEGRATED_LIBS "FALSE" CACHE BOOL "Use libs distributed with this repo") + if(VCPKG_MANIFEST_FEATURES) + message(STATUS "VCPKG_MANIFEST_FEATURES: ${VCPKG_MANIFEST_FEATURES}") + endif() elseif(DEFINED CMAKE_TOOLCHAIN_FILE) message(STATUS "Using toolchain: ${CMAKE_TOOLCHAIN_FILE}") if(CMAKE_TOOLCHAIN_FILE MATCHES "vcpkg.cmake") message(STATUS "Toolchain uses VCPKG integration") - message(STATUS "VCPKG_MANIFEST_FEATURES: ${VCPKG_MANIFEST_FEATURES}") + if(VCPKG_MANIFEST_FEATURES) + message(STATUS "VCPKG_MANIFEST_FEATURES: ${VCPKG_MANIFEST_FEATURES}") + endif() endif() set(USE_INTEGRATED_LIBS "FALSE" CACHE BOOL "Use libs distributed with this repo") elseif(WIN32) @@ -106,6 +104,30 @@ else() set(USE_INTEGRATED_LIBS "TRUE" CACHE BOOL "Use libs distributed with this repo") endif() +if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/vcpkg.json) + file(READ ${CMAKE_CURRENT_SOURCE_DIR}/vcpkg.json VCPKG_JSON_STRING) + string(JSON VERSION_STRING GET ${VCPKG_JSON_STRING} version) +else() + set(VERSION_STRING "0.2.5.4") +endif() + +string(REPLACE "." ";" VERSION_LIST ${VERSION_STRING}) +list(LENGTH VERSION_LIST VERSION_LIST_LENGTH) +if(VERSION_LIST_LENGTH LESS 3) + message(FATAL_ERROR "Darknet needs at least major.minor.patch version numbers to properly configure") +endif() +list(GET VERSION_LIST 0 Darknet_MAJOR_VERSION) +list(GET VERSION_LIST 1 Darknet_MINOR_VERSION) +list(GET VERSION_LIST 2 Darknet_PATCH_VERSION) +if(VERSION_LIST_LENGTH GREATER 3) + list(GET VERSION_LIST 3 Darknet_TWEAK_VERSION) +else() + set(Darknet_TWEAK_VERSION 0) +endif() + +set(Darknet_VERSION ${Darknet_MAJOR_VERSION}.${Darknet_MINOR_VERSION}.${Darknet_PATCH_VERSION}.${Darknet_TWEAK_VERSION}) +message("Darknet_VERSION: ${Darknet_VERSION}") + project(Darknet VERSION ${Darknet_VERSION}) enable_language(C) @@ -602,3 +624,11 @@ endif() if(ENABLE_CSHARP_WRAPPER) add_subdirectory(src/csharp) endif() + +set(CMAKE_INSTALL_SYSTEM_RUNTIME_LIBS_SKIP TRUE) +include(InstallRequiredSystemLibraries) + +install( + PROGRAMS ${CMAKE_INSTALL_SYSTEM_RUNTIME_LIBS} + DESTINATION ${INSTALL_BIN_DIR} +) diff --git a/vcpkg.json b/vcpkg.json index 30caffb7f88..045049cca21 100644 --- a/vcpkg.json +++ b/vcpkg.json @@ -1,6 +1,6 @@ { "name": "darknet", - "version-date": "2021-04-16", + "version": "0.2.5.5", "description": "Darknet is an open source neural network framework written in C and CUDA. You only look once (YOLO) is a state-of-the-art, real-time object detection system, best example of darknet functionalities.", "homepage": "https://github.com/alexeyab/darknet", "dependencies": [ diff --git a/vcpkg.json.opencv23 b/vcpkg.json.opencv23 index ccfb9afa289..11c1a5da21e 100644 --- a/vcpkg.json.opencv23 +++ b/vcpkg.json.opencv23 @@ -1,6 +1,6 @@ { "name": "darknet", - "version-date": "2021-04-16", + "version": "0.2.5.5", "description": "Darknet is an open source neural network framework written in C and CUDA. You only look once (YOLO) is a state-of-the-art, real-time object detection system, best example of darknet functionalities.", "homepage": "https://github.com/alexeyab/darknet", "dependencies": [ From 38a5b489da9b333d51d3a4b7b3012002dd07a411 Mon Sep 17 00:00:00 2001 From: Yuriy Savitskiy Date: Fri, 25 Aug 2023 20:30:33 +0700 Subject: [PATCH 1323/1365] Fix segfault for batch_detection_example (#8478) Co-authored-by: Yuriy Savitskiy --- darknet_images.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/darknet_images.py b/darknet_images.py index 65129678b0a..8ec3f8ee384 100644 --- a/darknet_images.py +++ b/darknet_images.py @@ -78,7 +78,7 @@ def load_images(images_path): glob.glob(os.path.join(images_path, "*.jpeg")) -def prepare_batch(images, network, channels=3): +def prepare_batch(images, network): width = darknet.network_width(network) height = darknet.network_height(network) @@ -92,9 +92,7 @@ def prepare_batch(images, network, channels=3): batch_array = np.concatenate(darknet_images, axis=0) batch_array = np.ascontiguousarray(batch_array.flat, dtype=np.float32)/255.0 - darknet_images = batch_array.ctypes.data_as(darknet.POINTER(darknet.c_float)) - return darknet.IMAGE(width, height, channels, darknet_images) - + return batch_array def image_detection(image_or_path, network, class_names, class_colors, thresh): # Darknet doesn't accept numpy images. @@ -121,7 +119,9 @@ def image_detection(image_or_path, network, class_names, class_colors, thresh): def batch_detection(network, images, class_names, class_colors, thresh=0.25, hier_thresh=.5, nms=.45, batch_size=4): image_height, image_width, _ = check_batch_shape(images, batch_size) - darknet_images = prepare_batch(images, network) + batch_array = prepare_batch(images, network) + batch_array = batch_array.ctypes.data_as(darknet.POINTER(darknet.c_float)) + darknet_images = darknet.IMAGE(image_width, image_height, 3, batch_array) batch_detections = darknet.network_predict_batch(network, darknet_images, batch_size, image_width, image_height, thresh, hier_thresh, None, 0, 0) batch_predictions = [] From 2bb27c182c8eed033dde57e3d67f8685da6a61db Mon Sep 17 00:00:00 2001 From: juraev Date: Fri, 25 Aug 2023 18:32:52 +0500 Subject: [PATCH 1324/1365] Update darknet_images.py (#8559) Safer and better type check. --- darknet_images.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/darknet_images.py b/darknet_images.py index 8ec3f8ee384..bfdd88c958a 100644 --- a/darknet_images.py +++ b/darknet_images.py @@ -101,7 +101,7 @@ def image_detection(image_or_path, network, class_names, class_colors, thresh): height = darknet.network_height(network) darknet_image = darknet.make_image(width, height, 3) - if type(image_or_path) == "str": + if isinstance(image_or_path, str): image = cv2.imread(image_or_path) else: image = image_or_path From 6a9dc6a6f8a32be6abe88cdd32900a2508ec26a7 Mon Sep 17 00:00:00 2001 From: Juuso Alasuutari Date: Fri, 25 Aug 2023 16:56:43 +0300 Subject: [PATCH 1325/1365] Update stb libraries to latest from upstream (#5941) * Update stb_image_write to v1.14 Source-commit: 2bb4a0accd4003c1db4c24533981e01b1adfd656 Signed-off-by: Juuso Alasuutari * Update stb_image to v2.25 Source-commit: 0224a44a10564a214595797b4c88323f79a5f934 Signed-off-by: Juuso Alasuutari --------- Signed-off-by: Juuso Alasuutari --- 3rdparty/stb/include/stb_image.h | 881 +++++++++++++++++++------ 3rdparty/stb/include/stb_image_write.h | 470 +++++++++---- 2 files changed, 1014 insertions(+), 337 deletions(-) diff --git a/3rdparty/stb/include/stb_image.h b/3rdparty/stb/include/stb_image.h index a056138dd04..2857f05d385 100644 --- a/3rdparty/stb/include/stb_image.h +++ b/3rdparty/stb/include/stb_image.h @@ -1,5 +1,5 @@ -/* stb_image - v2.16 - public domain image loader - http://nothings.org/stb_image.h - no warranty implied; use at your own risk +/* stb_image - v2.25 - public domain image loader - http://nothings.org/stb + no warranty implied; use at your own risk Do this: #define STB_IMAGE_IMPLEMENTATION @@ -48,6 +48,15 @@ LICENSE RECENT REVISION HISTORY: + 2.25 (2020-02-02) fix warnings + 2.24 (2020-02-02) fix warnings; thread-local failure_reason and flip_vertically + 2.23 (2019-08-11) fix clang static analysis warning + 2.22 (2019-03-04) gif fixes, fix warnings + 2.21 (2019-02-25) fix typo in comment + 2.20 (2019-02-07) support utf8 filenames in Windows; fix warnings and platform ifdefs + 2.19 (2018-02-11) fix warning + 2.18 (2018-01-30) fix warnings + 2.17 (2018-01-29) bugfix, 1-bit BMP, 16-bitness query, fix warnings 2.16 (2017-07-23) all functions have 16-bit variants; optimizations; bugfixes 2.15 (2017-03-18) fix png-1,2,4; all Imagenet JPGs; no runtime SSE detection on GCC 2.14 (2017-03-03) remove deprecated STBI_JPEG_OLD; fixes for Imagenet JPGs @@ -74,29 +83,32 @@ RECENT REVISION HISTORY: Thatcher Ulrich (psd) Nicolas Guillemot (vertical flip) Ken Miller (pgm, ppm) Richard Mitton (16-bit PSD) github:urraka (animated gif) Junggon Kim (PNM comments) - Daniel Gibson (16-bit TGA) + Christopher Forseth (animated gif) Daniel Gibson (16-bit TGA) socks-the-fox (16-bit PNG) Jeremy Sawicki (handle all ImageNet JPGs) - Optimizations & bugfixes - Fabian "ryg" Giesen + Optimizations & bugfixes Mikhail Morozov (1-bit BMP) + Fabian "ryg" Giesen Anael Seghezzi (is-16-bit query) Arseny Kapoulkine John-Mark Allen + Carmelo J Fdez-Aguera Bug & warning fixes Marc LeBlanc David Woo Guillaume George Martins Mozeiko Christpher Lloyd Jerry Jansson Joseph Thomson Phil Jordan Dave Moore Roy Eltham Hayaki Saito Nathan Reed Won Chun Luke Graham Johan Duparc Nick Verigakis - the Horde3D community Thomas Ruf Ronny Chevalier Baldur Karlsson - Janez Zemva John Bartholomew Michal Cichon github:rlyeh - Jonathan Blow Ken Hamada Tero Hanninen github:romigrou - Laurent Gomila Cort Stratton Sergio Gonzalez github:svdijk - Aruelien Pocheville Thibault Reuille Cass Everitt github:snagar - Ryamond Barbiero Paul Du Bois Engin Manap github:Zelex - Michaelangel007@github Philipp Wiesemann Dale Weiler github:grim210 - Oriol Ferrer Mesia Josh Tobin Matthew Gregan github:sammyhw - Blazej Dariusz Roszkowski Gregory Mullen github:phprus - Christian Floisand Kevin Schmidt github:poppolopoppo + the Horde3D community Thomas Ruf Ronny Chevalier github:rlyeh + Janez Zemva John Bartholomew Michal Cichon github:romigrou + Jonathan Blow Ken Hamada Tero Hanninen github:svdijk + Laurent Gomila Cort Stratton Sergio Gonzalez github:snagar + Aruelien Pocheville Thibault Reuille Cass Everitt github:Zelex + Ryamond Barbiero Paul Du Bois Engin Manap github:grim210 + Aldo Culquicondor Philipp Wiesemann Dale Weiler github:sammyhw + Oriol Ferrer Mesia Josh Tobin Matthew Gregan github:phprus + Julian Raschke Gregory Mullen Baldur Karlsson github:poppolopoppo + Christian Floisand Kevin Schmidt JR Smith github:darealshinji + Brad Weinberger Matvey Cherevko github:Michaelangel007 + Blazej Dariusz Roszkowski Alexander Veselov */ #ifndef STBI_INCLUDE_STB_IMAGE_H @@ -105,10 +117,8 @@ RECENT REVISION HISTORY: // DOCUMENTATION // // Limitations: -// - no 16-bit-per-channel PNG // - no 12-bit-per-channel JPEG // - no JPEGs with arithmetic coding -// - no 1-bit BMP // - GIF always returns *comp=4 // // Basic usage (see HDR discussion below for HDR usage): @@ -159,6 +169,16 @@ RECENT REVISION HISTORY: // // =========================================================================== // +// UNICODE: +// +// If compiling for Windows and you wish to use Unicode filenames, compile +// with +// #define STBI_WINDOWS_UTF8 +// and pass utf8-encoded filenames. Call stbi_convert_wchar_to_utf8 to convert +// Windows wchar_t filenames to utf8. +// +// =========================================================================== +// // Philosophy // // stb libraries are designed with the following priorities: @@ -169,12 +189,12 @@ RECENT REVISION HISTORY: // // Sometimes I let "good performance" creep up in priority over "easy to maintain", // and for best performance I may provide less-easy-to-use APIs that give higher -// performance, in addition to the easy to use ones. Nevertheless, it's important +// performance, in addition to the easy-to-use ones. Nevertheless, it's important // to keep in mind that from the standpoint of you, a client of this library, // all you care about is #1 and #3, and stb libraries DO NOT emphasize #3 above all. // // Some secondary priorities arise directly from the first two, some of which -// make more explicit reasons why performance can't be emphasized. +// provide more explicit reasons why performance can't be emphasized. // // - Portable ("ease of use") // - Small source code footprint ("easy to maintain") @@ -217,11 +237,10 @@ RECENT REVISION HISTORY: // // HDR image support (disable by defining STBI_NO_HDR) // -// stb_image now supports loading HDR images in general, and currently -// the Radiance .HDR file format, although the support is provided -// generically. You can still load any file through the existing interface; -// if you attempt to load an HDR file, it will be automatically remapped to -// LDR, assuming gamma 2.2 and an arbitrary scale factor defaulting to 1; +// stb_image supports loading HDR images in general, and currently the Radiance +// .HDR file format specifically. You can still load any file through the existing +// interface; if you attempt to load an HDR file, it will be automatically remapped +// to LDR, assuming gamma 2.2 and an arbitrary scale factor defaulting to 1; // both of these constants can be reconfigured through this interface: // // stbi_hdr_to_ldr_gamma(2.2f); @@ -255,7 +274,7 @@ RECENT REVISION HISTORY: // // By default we convert iphone-formatted PNGs back to RGB, even though // they are internally encoded differently. You can disable this conversion -// by by calling stbi_convert_iphone_png_to_rgb(0), in which case +// by calling stbi_convert_iphone_png_to_rgb(0), in which case // you will always just get the native iphone "format" through (which // is BGR stored in RGB). // @@ -317,6 +336,7 @@ enum STBI_rgb_alpha = 4 }; +#include typedef unsigned char stbi_uc; typedef unsigned short stbi_us; @@ -324,11 +344,13 @@ typedef unsigned short stbi_us; extern "C" { #endif +#ifndef STBIDEF #ifdef STB_IMAGE_STATIC #define STBIDEF static #else #define STBIDEF extern #endif +#endif ////////////////////////////////////////////////////////////////////////////// // @@ -360,6 +382,14 @@ STBIDEF stbi_uc *stbi_load_from_file (FILE *f, int *x, int *y, int *channels_in // for stbi_load_from_file, file pointer is left pointing immediately after image #endif +#ifndef STBI_NO_GIF +STBIDEF stbi_uc *stbi_load_gif_from_memory(stbi_uc const *buffer, int len, int **delays, int *x, int *y, int *z, int *comp, int req_comp); +#endif + +#ifdef STBI_WINDOWS_UTF8 +STBIDEF int stbi_convert_wchar_to_utf8(char *buffer, size_t bufferlen, const wchar_t* input); +#endif + //////////////////////////////////// // // 16-bits-per-channel interface @@ -407,7 +437,7 @@ STBIDEF int stbi_is_hdr_from_file(FILE *f); // get a VERY brief reason for failure -// NOT THREADSAFE +// on most compilers (and ALL modern mainstream compilers) this is threadsafe STBIDEF const char *stbi_failure_reason (void); // free the loaded image -- this is just free() @@ -416,11 +446,14 @@ STBIDEF void stbi_image_free (void *retval_from_stbi_load); // get image dimensions & components without fully decoding STBIDEF int stbi_info_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp); STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp); +STBIDEF int stbi_is_16_bit_from_memory(stbi_uc const *buffer, int len); +STBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const *clbk, void *user); #ifndef STBI_NO_STDIO -STBIDEF int stbi_info (char const *filename, int *x, int *y, int *comp); -STBIDEF int stbi_info_from_file (FILE *f, int *x, int *y, int *comp); - +STBIDEF int stbi_info (char const *filename, int *x, int *y, int *comp); +STBIDEF int stbi_info_from_file (FILE *f, int *x, int *y, int *comp); +STBIDEF int stbi_is_16_bit (char const *filename); +STBIDEF int stbi_is_16_bit_from_file(FILE *f); #endif @@ -437,6 +470,11 @@ STBIDEF void stbi_convert_iphone_png_to_rgb(int flag_true_if_should_convert); // flip the image vertically, so the first pixel in the output array is the bottom left STBIDEF void stbi_set_flip_vertically_on_load(int flag_true_if_should_flip); +// as above, but only applies to images loaded on the thread that calls the function +// this function is only available if your compiler supports thread-local variables; +// calling it will fail to link if your compiler doesn't +STBIDEF void stbi_set_flip_vertically_on_load_thread(int flag_true_if_should_flip); + // ZLIB client - used by PNG, available for other purposes STBIDEF char *stbi_zlib_decode_malloc_guesssize(const char *buffer, int len, int initial_size, int *outlen); @@ -504,7 +542,7 @@ STBIDEF int stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const ch #include #if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) -#include // ldexp +#include // ldexp, pow #endif #ifndef STBI_NO_STDIO @@ -516,6 +554,12 @@ STBIDEF int stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const ch #define STBI_ASSERT(x) assert(x) #endif +#ifdef __cplusplus +#define STBI_EXTERN extern "C" +#else +#define STBI_EXTERN extern +#endif + #ifndef _MSC_VER #ifdef __cplusplus @@ -527,6 +571,17 @@ STBIDEF int stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const ch #define stbi_inline __forceinline #endif +#ifndef STBI_NO_THREAD_LOCALS + #if defined(__cplusplus) && __cplusplus >= 201103L + #define STBI_THREAD_LOCAL thread_local + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 201112L + #define STBI_THREAD_LOCAL _Thread_local + #elif defined(__GNUC__) + #define STBI_THREAD_LOCAL __thread + #elif defined(_MSC_VER) + #define STBI_THREAD_LOCAL __declspec(thread) +#endif +#endif #ifdef _MSC_VER typedef unsigned short stbi__uint16; @@ -640,14 +695,18 @@ static int stbi__cpuid3(void) #define STBI_SIMD_ALIGN(type, name) __declspec(align(16)) type name +#if !defined(STBI_NO_JPEG) && defined(STBI_SSE2) static int stbi__sse2_available(void) { int info3 = stbi__cpuid3(); return ((info3 >> 26) & 1) != 0; } +#endif + #else // assume GCC-style if not VC++ #define STBI_SIMD_ALIGN(type, name) type name __attribute__((aligned(16))) +#if !defined(STBI_NO_JPEG) && defined(STBI_SSE2) static int stbi__sse2_available(void) { // If we're even attempting to compile this on GCC/Clang, that means @@ -655,6 +714,8 @@ static int stbi__sse2_available(void) // instructions at will, and so are we. return 1; } +#endif + #endif #endif @@ -784,6 +845,7 @@ static int stbi__jpeg_info(stbi__context *s, int *x, int *y, int *comp); static int stbi__png_test(stbi__context *s); static void *stbi__png_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); static int stbi__png_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__png_is16(stbi__context *s); #endif #ifndef STBI_NO_BMP @@ -802,6 +864,7 @@ static int stbi__tga_info(stbi__context *s, int *x, int *y, int *comp); static int stbi__psd_test(stbi__context *s); static void *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc); static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__psd_is16(stbi__context *s); #endif #ifndef STBI_NO_HDR @@ -819,6 +882,7 @@ static int stbi__pic_info(stbi__context *s, int *x, int *y, int *comp); #ifndef STBI_NO_GIF static int stbi__gif_test(stbi__context *s); static void *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static void *stbi__load_gif_main(stbi__context *s, int **delays, int *x, int *y, int *z, int *comp, int req_comp); static int stbi__gif_info(stbi__context *s, int *x, int *y, int *comp); #endif @@ -828,19 +892,24 @@ static void *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int static int stbi__pnm_info(stbi__context *s, int *x, int *y, int *comp); #endif -// this is not threadsafe -static const char *stbi__g_failure_reason; +static +#ifdef STBI_THREAD_LOCAL +STBI_THREAD_LOCAL +#endif +const char *stbi__g_failure_reason; STBIDEF const char *stbi_failure_reason(void) { return stbi__g_failure_reason; } +#ifndef STBI_NO_FAILURE_STRINGS static int stbi__err(const char *str) { stbi__g_failure_reason = str; return 0; } +#endif static void *stbi__malloc(size_t size) { @@ -879,11 +948,13 @@ static int stbi__mul2sizes_valid(int a, int b) return a <= INT_MAX/b; } +#if !defined(STBI_NO_JPEG) || !defined(STBI_NO_PNG) || !defined(STBI_NO_TGA) || !defined(STBI_NO_HDR) // returns 1 if "a*b + add" has no negative terms/factors and doesn't overflow static int stbi__mad2sizes_valid(int a, int b, int add) { return stbi__mul2sizes_valid(a, b) && stbi__addsizes_valid(a*b, add); } +#endif // returns 1 if "a*b*c + add" has no negative terms/factors and doesn't overflow static int stbi__mad3sizes_valid(int a, int b, int c, int add) @@ -893,18 +964,22 @@ static int stbi__mad3sizes_valid(int a, int b, int c, int add) } // returns 1 if "a*b*c*d + add" has no negative terms/factors and doesn't overflow +#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) static int stbi__mad4sizes_valid(int a, int b, int c, int d, int add) { return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a*b, c) && stbi__mul2sizes_valid(a*b*c, d) && stbi__addsizes_valid(a*b*c*d, add); } +#endif +#if !defined(STBI_NO_JPEG) || !defined(STBI_NO_PNG) || !defined(STBI_NO_TGA) || !defined(STBI_NO_HDR) // mallocs with size overflow checking static void *stbi__malloc_mad2(int a, int b, int add) { if (!stbi__mad2sizes_valid(a, b, add)) return NULL; return stbi__malloc(a*b + add); } +#endif static void *stbi__malloc_mad3(int a, int b, int c, int add) { @@ -912,11 +987,13 @@ static void *stbi__malloc_mad3(int a, int b, int c, int add) return stbi__malloc(a*b*c + add); } +#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) static void *stbi__malloc_mad4(int a, int b, int c, int d, int add) { if (!stbi__mad4sizes_valid(a, b, c, d, add)) return NULL; return stbi__malloc(a*b*c*d + add); } +#endif // stbi__err - error // stbi__errpf - error returning pointer to float @@ -946,13 +1023,29 @@ static float *stbi__ldr_to_hdr(stbi_uc *data, int x, int y, int comp); static stbi_uc *stbi__hdr_to_ldr(float *data, int x, int y, int comp); #endif -static int stbi__vertically_flip_on_load = 0; +static int stbi__vertically_flip_on_load_global = 0; STBIDEF void stbi_set_flip_vertically_on_load(int flag_true_if_should_flip) { - stbi__vertically_flip_on_load = flag_true_if_should_flip; + stbi__vertically_flip_on_load_global = flag_true_if_should_flip; } +#ifndef STBI_THREAD_LOCAL +#define stbi__vertically_flip_on_load stbi__vertically_flip_on_load_global +#else +static STBI_THREAD_LOCAL int stbi__vertically_flip_on_load_local, stbi__vertically_flip_on_load_set; + +STBIDEF void stbi_set_flip_vertically_on_load_thread(int flag_true_if_should_flip) +{ + stbi__vertically_flip_on_load_local = flag_true_if_should_flip; + stbi__vertically_flip_on_load_set = 1; +} + +#define stbi__vertically_flip_on_load (stbi__vertically_flip_on_load_set \ + ? stbi__vertically_flip_on_load_local \ + : stbi__vertically_flip_on_load_global) +#endif // STBI_THREAD_LOCAL + static void *stbi__load_main(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc) { memset(ri, 0, sizeof(*ri)); // make sure it's initialized if we add new fields @@ -974,6 +1067,8 @@ static void *stbi__load_main(stbi__context *s, int *x, int *y, int *comp, int re #endif #ifndef STBI_NO_PSD if (stbi__psd_test(s)) return stbi__psd_load(s,x,y,comp,req_comp, ri, bpc); + #else + STBI_NOTUSED(bpc); #endif #ifndef STBI_NO_PIC if (stbi__pic_test(s)) return stbi__pic_load(s,x,y,comp,req_comp, ri); @@ -1054,6 +1149,20 @@ static void stbi__vertical_flip(void *image, int w, int h, int bytes_per_pixel) } } +#ifndef STBI_NO_GIF +static void stbi__vertical_flip_slices(void *image, int w, int h, int z, int bytes_per_pixel) +{ + int slice; + int slice_size = w * h * bytes_per_pixel; + + stbi_uc *bytes = (stbi_uc *)image; + for (slice = 0; slice < z; ++slice) { + stbi__vertical_flip(bytes, w, h, bytes_per_pixel); + bytes += slice_size; + } +} +#endif + static unsigned char *stbi__load_and_postprocess_8bit(stbi__context *s, int *x, int *y, int *comp, int req_comp) { stbi__result_info ri; @@ -1103,7 +1212,7 @@ static stbi__uint16 *stbi__load_and_postprocess_16bit(stbi__context *s, int *x, return (stbi__uint16 *) result; } -#ifndef STBI_NO_HDR +#if !defined(STBI_NO_HDR) && !defined(STBI_NO_LINEAR) static void stbi__float_postprocess(float *result, int *x, int *y, int *comp, int req_comp) { if (stbi__vertically_flip_on_load && result != NULL) { @@ -1115,10 +1224,38 @@ static void stbi__float_postprocess(float *result, int *x, int *y, int *comp, in #ifndef STBI_NO_STDIO +#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) +STBI_EXTERN __declspec(dllimport) int __stdcall MultiByteToWideChar(unsigned int cp, unsigned long flags, const char *str, int cbmb, wchar_t *widestr, int cchwide); +STBI_EXTERN __declspec(dllimport) int __stdcall WideCharToMultiByte(unsigned int cp, unsigned long flags, const wchar_t *widestr, int cchwide, char *str, int cbmb, const char *defchar, int *used_default); +#endif + +#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) +STBIDEF int stbi_convert_wchar_to_utf8(char *buffer, size_t bufferlen, const wchar_t* input) +{ + return WideCharToMultiByte(65001 /* UTF8 */, 0, input, -1, buffer, (int) bufferlen, NULL, NULL); +} +#endif + static FILE *stbi__fopen(char const *filename, char const *mode) { FILE *f; -#if defined(_MSC_VER) && _MSC_VER >= 1400 +#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) + wchar_t wMode[64]; + wchar_t wFilename[1024]; + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, filename, -1, wFilename, sizeof(wFilename))) + return 0; + + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, mode, -1, wMode, sizeof(wMode))) + return 0; + +#if _MSC_VER >= 1400 + if (0 != _wfopen_s(&f, wFilename, wMode)) + f = 0; +#else + f = _wfopen(wFilename, wMode); +#endif + +#elif defined(_MSC_VER) && _MSC_VER >= 1400 if (0 != fopen_s(&f, filename, mode)) f=0; #else @@ -1205,6 +1342,22 @@ STBIDEF stbi_uc *stbi_load_from_callbacks(stbi_io_callbacks const *clbk, void *u return stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp); } +#ifndef STBI_NO_GIF +STBIDEF stbi_uc *stbi_load_gif_from_memory(stbi_uc const *buffer, int len, int **delays, int *x, int *y, int *z, int *comp, int req_comp) +{ + unsigned char *result; + stbi__context s; + stbi__start_mem(&s,buffer,len); + + result = (unsigned char*) stbi__load_gif_main(&s, delays, x, y, z, comp, req_comp); + if (stbi__vertically_flip_on_load) { + stbi__vertical_flip_slices( result, *x, *y, *z, *comp ); + } + + return result; +} +#endif + #ifndef STBI_NO_LINEAR static float *stbi__loadf_main(stbi__context *s, int *x, int *y, int *comp, int req_comp) { @@ -1288,12 +1441,16 @@ STBIDEF int stbi_is_hdr (char const *filename) return result; } -STBIDEF int stbi_is_hdr_from_file(FILE *f) +STBIDEF int stbi_is_hdr_from_file(FILE *f) { #ifndef STBI_NO_HDR + long pos = ftell(f); + int res; stbi__context s; stbi__start_file(&s,f); - return stbi__hdr_test(&s); + res = stbi__hdr_test(&s); + fseek(f, pos, SEEK_SET); + return res; #else STBI_NOTUSED(f); return 0; @@ -1366,6 +1523,9 @@ stbi_inline static stbi_uc stbi__get8(stbi__context *s) return 0; } +#if defined(STBI_NO_JPEG) && defined(STBI_NO_HDR) && defined(STBI_NO_PIC) && defined(STBI_NO_PNM) +// nothing +#else stbi_inline static int stbi__at_eof(stbi__context *s) { if (s->io.read) { @@ -1377,7 +1537,11 @@ stbi_inline static int stbi__at_eof(stbi__context *s) return s->img_buffer >= s->img_buffer_end; } +#endif +#if defined(STBI_NO_JPEG) && defined(STBI_NO_PNG) && defined(STBI_NO_BMP) && defined(STBI_NO_PSD) && defined(STBI_NO_TGA) && defined(STBI_NO_GIF) && defined(STBI_NO_PIC) +// nothing +#else static void stbi__skip(stbi__context *s, int n) { if (n < 0) { @@ -1394,7 +1558,11 @@ static void stbi__skip(stbi__context *s, int n) } s->img_buffer += n; } +#endif +#if defined(STBI_NO_PNG) && defined(STBI_NO_TGA) && defined(STBI_NO_HDR) && defined(STBI_NO_PNM) +// nothing +#else static int stbi__getn(stbi__context *s, stbi_uc *buffer, int n) { if (s->io.read) { @@ -1418,18 +1586,27 @@ static int stbi__getn(stbi__context *s, stbi_uc *buffer, int n) } else return 0; } +#endif +#if defined(STBI_NO_JPEG) && defined(STBI_NO_PNG) && defined(STBI_NO_PSD) && defined(STBI_NO_PIC) +// nothing +#else static int stbi__get16be(stbi__context *s) { int z = stbi__get8(s); return (z << 8) + stbi__get8(s); } +#endif +#if defined(STBI_NO_PNG) && defined(STBI_NO_PSD) && defined(STBI_NO_PIC) +// nothing +#else static stbi__uint32 stbi__get32be(stbi__context *s) { stbi__uint32 z = stbi__get16be(s); return (z << 16) + stbi__get16be(s); } +#endif #if defined(STBI_NO_BMP) && defined(STBI_NO_TGA) && defined(STBI_NO_GIF) // nothing @@ -1451,7 +1628,9 @@ static stbi__uint32 stbi__get32le(stbi__context *s) #define STBI__BYTECAST(x) ((stbi_uc) ((x) & 255)) // truncate int to byte without warnings - +#if defined(STBI_NO_JPEG) && defined(STBI_NO_PNG) && defined(STBI_NO_BMP) && defined(STBI_NO_PSD) && defined(STBI_NO_TGA) && defined(STBI_NO_GIF) && defined(STBI_NO_PIC) && defined(STBI_NO_PNM) +// nothing +#else ////////////////////////////////////////////////////////////////////////////// // // generic converter from built-in img_n to req_comp @@ -1467,7 +1646,11 @@ static stbi_uc stbi__compute_y(int r, int g, int b) { return (stbi_uc) (((r*77) + (g*150) + (29*b)) >> 8); } +#endif +#if defined(STBI_NO_PNG) && defined(STBI_NO_BMP) && defined(STBI_NO_PSD) && defined(STBI_NO_TGA) && defined(STBI_NO_GIF) && defined(STBI_NO_PIC) && defined(STBI_NO_PNM) +// nothing +#else static unsigned char *stbi__convert_format(unsigned char *data, int img_n, int req_comp, unsigned int x, unsigned int y) { int i,j; @@ -1491,18 +1674,18 @@ static unsigned char *stbi__convert_format(unsigned char *data, int img_n, int r // convert source image with img_n components to one with req_comp components; // avoid switch per pixel, so use switch per scanline and massive macros switch (STBI__COMBO(img_n, req_comp)) { - STBI__CASE(1,2) { dest[0]=src[0], dest[1]=255; } break; + STBI__CASE(1,2) { dest[0]=src[0]; dest[1]=255; } break; STBI__CASE(1,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; - STBI__CASE(1,4) { dest[0]=dest[1]=dest[2]=src[0], dest[3]=255; } break; + STBI__CASE(1,4) { dest[0]=dest[1]=dest[2]=src[0]; dest[3]=255; } break; STBI__CASE(2,1) { dest[0]=src[0]; } break; STBI__CASE(2,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; - STBI__CASE(2,4) { dest[0]=dest[1]=dest[2]=src[0], dest[3]=src[1]; } break; - STBI__CASE(3,4) { dest[0]=src[0],dest[1]=src[1],dest[2]=src[2],dest[3]=255; } break; + STBI__CASE(2,4) { dest[0]=dest[1]=dest[2]=src[0]; dest[3]=src[1]; } break; + STBI__CASE(3,4) { dest[0]=src[0];dest[1]=src[1];dest[2]=src[2];dest[3]=255; } break; STBI__CASE(3,1) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); } break; - STBI__CASE(3,2) { dest[0]=stbi__compute_y(src[0],src[1],src[2]), dest[1] = 255; } break; + STBI__CASE(3,2) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); dest[1] = 255; } break; STBI__CASE(4,1) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); } break; - STBI__CASE(4,2) { dest[0]=stbi__compute_y(src[0],src[1],src[2]), dest[1] = src[3]; } break; - STBI__CASE(4,3) { dest[0]=src[0],dest[1]=src[1],dest[2]=src[2]; } break; + STBI__CASE(4,2) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); dest[1] = src[3]; } break; + STBI__CASE(4,3) { dest[0]=src[0];dest[1]=src[1];dest[2]=src[2]; } break; default: STBI_ASSERT(0); } #undef STBI__CASE @@ -1511,12 +1694,20 @@ static unsigned char *stbi__convert_format(unsigned char *data, int img_n, int r STBI_FREE(data); return good; } +#endif +#if defined(STBI_NO_PNG) && defined(STBI_NO_PSD) +// nothing +#else static stbi__uint16 stbi__compute_y_16(int r, int g, int b) { return (stbi__uint16) (((r*77) + (g*150) + (29*b)) >> 8); } +#endif +#if defined(STBI_NO_PNG) && defined(STBI_NO_PSD) +// nothing +#else static stbi__uint16 *stbi__convert_format16(stbi__uint16 *data, int img_n, int req_comp, unsigned int x, unsigned int y) { int i,j; @@ -1540,18 +1731,18 @@ static stbi__uint16 *stbi__convert_format16(stbi__uint16 *data, int img_n, int r // convert source image with img_n components to one with req_comp components; // avoid switch per pixel, so use switch per scanline and massive macros switch (STBI__COMBO(img_n, req_comp)) { - STBI__CASE(1,2) { dest[0]=src[0], dest[1]=0xffff; } break; + STBI__CASE(1,2) { dest[0]=src[0]; dest[1]=0xffff; } break; STBI__CASE(1,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; - STBI__CASE(1,4) { dest[0]=dest[1]=dest[2]=src[0], dest[3]=0xffff; } break; + STBI__CASE(1,4) { dest[0]=dest[1]=dest[2]=src[0]; dest[3]=0xffff; } break; STBI__CASE(2,1) { dest[0]=src[0]; } break; STBI__CASE(2,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; - STBI__CASE(2,4) { dest[0]=dest[1]=dest[2]=src[0], dest[3]=src[1]; } break; - STBI__CASE(3,4) { dest[0]=src[0],dest[1]=src[1],dest[2]=src[2],dest[3]=0xffff; } break; + STBI__CASE(2,4) { dest[0]=dest[1]=dest[2]=src[0]; dest[3]=src[1]; } break; + STBI__CASE(3,4) { dest[0]=src[0];dest[1]=src[1];dest[2]=src[2];dest[3]=0xffff; } break; STBI__CASE(3,1) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); } break; - STBI__CASE(3,2) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]), dest[1] = 0xffff; } break; + STBI__CASE(3,2) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); dest[1] = 0xffff; } break; STBI__CASE(4,1) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); } break; - STBI__CASE(4,2) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]), dest[1] = src[3]; } break; - STBI__CASE(4,3) { dest[0]=src[0],dest[1]=src[1],dest[2]=src[2]; } break; + STBI__CASE(4,2) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); dest[1] = src[3]; } break; + STBI__CASE(4,3) { dest[0]=src[0];dest[1]=src[1];dest[2]=src[2]; } break; default: STBI_ASSERT(0); } #undef STBI__CASE @@ -1560,6 +1751,7 @@ static stbi__uint16 *stbi__convert_format16(stbi__uint16 *data, int img_n, int r STBI_FREE(data); return good; } +#endif #ifndef STBI_NO_LINEAR static float *stbi__ldr_to_hdr(stbi_uc *data, int x, int y, int comp) @@ -1575,7 +1767,11 @@ static float *stbi__ldr_to_hdr(stbi_uc *data, int x, int y, int comp) for (k=0; k < n; ++k) { output[i*comp + k] = (float) (pow(data[i*comp+k]/255.0f, stbi__l2h_gamma) * stbi__l2h_scale); } - if (k < comp) output[i*comp + k] = data[i*comp+k]/255.0f; + } + if (n < comp) { + for (i=0; i < x*y; ++i) { + output[i*comp + n] = data[i*comp + n]/255.0f; + } } STBI_FREE(data); return output; @@ -1705,7 +1901,8 @@ typedef struct static int stbi__build_huffman(stbi__huffman *h, int *count) { - int i,j,k=0,code; + int i,j,k=0; + unsigned int code; // build size list for each symbol (from JPEG spec) for (i=0; i < 16; ++i) for (j=0; j < count[i]; ++j) @@ -1721,7 +1918,7 @@ static int stbi__build_huffman(stbi__huffman *h, int *count) if (h->size[k] == j) { while (h->size[k] == j) h->code[k++] = (stbi__uint16) (code++); - if (code-1 >= (1 << j)) return stbi__err("bad code lengths","Corrupt JPEG"); + if (code-1 >= (1u << j)) return stbi__err("bad code lengths","Corrupt JPEG"); } // compute largest code + 1 for this size, preshifted as needed later h->maxcode[j] = code << (16-j); @@ -1765,7 +1962,7 @@ static void stbi__build_fast_ac(stbi__int16 *fast_ac, stbi__huffman *h) if (k < m) k += (~0U << magbits) + 1; // if the result is small enough, we can fit it in fast_ac table if (k >= -128 && k <= 127) - fast_ac[i] = (stbi__int16) ((k << 8) + (run << 4) + (len + magbits)); + fast_ac[i] = (stbi__int16) ((k * 256) + (run * 16) + (len + magbits)); } } } @@ -1774,7 +1971,7 @@ static void stbi__build_fast_ac(stbi__int16 *fast_ac, stbi__huffman *h) static void stbi__grow_buffer_unsafe(stbi__jpeg *j) { do { - int b = j->nomore ? 0 : stbi__get8(j->s); + unsigned int b = j->nomore ? 0 : stbi__get8(j->s); if (b == 0xff) { int c = stbi__get8(j->s); while (c == 0xff) c = stbi__get8(j->s); // consume fill bytes @@ -1790,7 +1987,7 @@ static void stbi__grow_buffer_unsafe(stbi__jpeg *j) } // (1 << n) - 1 -static stbi__uint32 stbi__bmask[17]={0,1,3,7,15,31,63,127,255,511,1023,2047,4095,8191,16383,32767,65535}; +static const stbi__uint32 stbi__bmask[17]={0,1,3,7,15,31,63,127,255,511,1023,2047,4095,8191,16383,32767,65535}; // decode a jpeg huffman value from the bitstream stbi_inline static int stbi__jpeg_huff_decode(stbi__jpeg *j, stbi__huffman *h) @@ -1843,7 +2040,7 @@ stbi_inline static int stbi__jpeg_huff_decode(stbi__jpeg *j, stbi__huffman *h) } // bias[n] = (-1<rgb = 0; for (i=0; i < s->img_n; ++i) { - static unsigned char rgb[3] = { 'R', 'G', 'B' }; + static const unsigned char rgb[3] = { 'R', 'G', 'B' }; z->img_comp[i].id = stbi__get8(s); if (s->img_n == 3 && z->img_comp[i].id == rgb[i]) ++z->rgb; @@ -3093,8 +3290,8 @@ static int stbi__decode_jpeg_image(stbi__jpeg *j) } else if (stbi__DNL(m)) { int Ld = stbi__get16be(j->s); stbi__uint32 NL = stbi__get16be(j->s); - if (Ld != 4) stbi__err("bad DNL len", "Corrupt JPEG"); - if (NL != j->s->img_y) stbi__err("bad DNL height", "Corrupt JPEG"); + if (Ld != 4) return stbi__err("bad DNL len", "Corrupt JPEG"); + if (NL != j->s->img_y) return stbi__err("bad DNL height", "Corrupt JPEG"); } else { if (!stbi__process_marker(j, m)) return 0; } @@ -3547,7 +3744,7 @@ static stbi_uc *load_jpeg_image(stbi__jpeg *z, int *out_x, int *out_y, int *comp int k; unsigned int i,j; stbi_uc *output; - stbi_uc *coutput[4]; + stbi_uc *coutput[4] = { NULL, NULL, NULL, NULL }; stbi__resample res_comp[4]; @@ -3668,7 +3865,7 @@ static stbi_uc *load_jpeg_image(stbi__jpeg *z, int *out_x, int *out_y, int *comp if (n == 1) for (i=0; i < z->s->img_x; ++i) out[i] = y[i]; else - for (i=0; i < z->s->img_x; ++i) *out++ = y[i], *out++ = 255; + for (i=0; i < z->s->img_x; ++i) { *out++ = y[i]; *out++ = 255; } } } } @@ -3912,18 +4109,18 @@ static int stbi__zexpand(stbi__zbuf *z, char *zout, int n) // need to make room return 1; } -static int stbi__zlength_base[31] = { +static const int stbi__zlength_base[31] = { 3,4,5,6,7,8,9,10,11,13, 15,17,19,23,27,31,35,43,51,59, 67,83,99,115,131,163,195,227,258,0,0 }; -static int stbi__zlength_extra[31]= +static const int stbi__zlength_extra[31]= { 0,0,0,0,0,0,0,0,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,0,0,0 }; -static int stbi__zdist_base[32] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193, +static const int stbi__zdist_base[32] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193, 257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577,0,0}; -static int stbi__zdist_extra[32] = +static const int stbi__zdist_extra[32] = { 0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13}; static int stbi__parse_huffman_block(stbi__zbuf *a) @@ -3970,7 +4167,7 @@ static int stbi__parse_huffman_block(stbi__zbuf *a) static int stbi__compute_huffman_codes(stbi__zbuf *a) { - static stbi_uc length_dezigzag[19] = { 16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15 }; + static const stbi_uc length_dezigzag[19] = { 16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15 }; stbi__zhuffman z_codelength; stbi_uc lencodes[286+32+137];//padding for maximum single op stbi_uc codelength_sizes[19]; @@ -4229,7 +4426,7 @@ static stbi__pngchunk stbi__get_chunk_header(stbi__context *s) static int stbi__check_png_header(stbi__context *s) { - static stbi_uc png_sig[8] = { 137,80,78,71,13,10,26,10 }; + static const stbi_uc png_sig[8] = { 137,80,78,71,13,10,26,10 }; int i; for (i=0; i < 8; ++i) if (stbi__get8(s) != png_sig[i]) return stbi__err("bad png sig","Not a PNG"); @@ -4275,7 +4472,7 @@ static int stbi__paeth(int a, int b, int c) return c; } -static stbi_uc stbi__depth_scale_table[9] = { 0, 0xff, 0x55, 0, 0x11, 0,0,0, 0x01 }; +static const stbi_uc stbi__depth_scale_table[9] = { 0, 0xff, 0x55, 0, 0x11, 0,0,0, 0x01 }; // create the png data from post-deflated data static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 raw_len, int out_n, stbi__uint32 x, stbi__uint32 y, int depth, int color) @@ -4295,8 +4492,10 @@ static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 r a->out = (stbi_uc *) stbi__malloc_mad3(x, y, output_bytes, 0); // extra bytes to write off the end into if (!a->out) return stbi__err("outofmem", "Out of memory"); + if (!stbi__mad3sizes_valid(img_n, x, depth, 7)) return stbi__err("too large", "Corrupt PNG"); img_width_bytes = (((img_n * x * depth) + 7) >> 3); img_len = (img_width_bytes + 1) * y; + // we used to check for exact match between raw_len and img_len on non-interlaced PNGs, // but issue #276 reported a PNG in the wild that had extra data at the end (all zeros), // so just check for raw_len < img_len always. @@ -4675,12 +4874,12 @@ static void stbi__de_iphone(stbi__png *z) } } -#define STBI__PNG_TYPE(a,b,c,d) (((a) << 24) + ((b) << 16) + ((c) << 8) + (d)) +#define STBI__PNG_TYPE(a,b,c,d) (((unsigned) (a) << 24) + ((unsigned) (b) << 16) + ((unsigned) (c) << 8) + (unsigned) (d)) static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) { stbi_uc palette[1024], pal_img_n=0; - stbi_uc has_trans=0, tc[3]; + stbi_uc has_trans=0, tc[3]={0}; stbi__uint16 tc16[3]; stbi__uint32 ioff=0, idata_limit=0, i, pal_len=0; int first=1,k,interlace=0, color=0, is_iphone=0; @@ -4824,6 +5023,8 @@ static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) ++s->img_n; } STBI_FREE(z->expanded); z->expanded = NULL; + // end of PNG chunk, read and skip CRC + stbi__get32be(s); return 1; } @@ -4912,6 +5113,19 @@ static int stbi__png_info(stbi__context *s, int *x, int *y, int *comp) p.s = s; return stbi__png_info_raw(&p, x, y, comp); } + +static int stbi__png_is16(stbi__context *s) +{ + stbi__png p; + p.s = s; + if (!stbi__png_info_raw(&p, NULL, NULL, NULL)) + return 0; + if (p.depth != 16) { + stbi__rewind(p.s); + return 0; + } + return 1; +} #endif // Microsoft/Windows BMP image @@ -4945,11 +5159,11 @@ static int stbi__high_bit(unsigned int z) { int n=0; if (z == 0) return -1; - if (z >= 0x10000) n += 16, z >>= 16; - if (z >= 0x00100) n += 8, z >>= 8; - if (z >= 0x00010) n += 4, z >>= 4; - if (z >= 0x00004) n += 2, z >>= 2; - if (z >= 0x00002) n += 1, z >>= 1; + if (z >= 0x10000) { n += 16; z >>= 16; } + if (z >= 0x00100) { n += 8; z >>= 8; } + if (z >= 0x00010) { n += 4; z >>= 4; } + if (z >= 0x00004) { n += 2; z >>= 2; } + if (z >= 0x00002) { n += 1;/* >>= 1;*/ } return n; } @@ -4963,27 +5177,34 @@ static int stbi__bitcount(unsigned int a) return a & 0xff; } -static int stbi__shiftsigned(int v, int shift, int bits) -{ - int result; - int z=0; - - if (shift < 0) v <<= -shift; - else v >>= shift; - result = v; - - z = bits; - while (z < 8) { - result += v >> z; - z += bits; - } - return result; +// extract an arbitrarily-aligned N-bit value (N=bits) +// from v, and then make it 8-bits long and fractionally +// extend it to full full range. +static int stbi__shiftsigned(unsigned int v, int shift, int bits) +{ + static unsigned int mul_table[9] = { + 0, + 0xff/*0b11111111*/, 0x55/*0b01010101*/, 0x49/*0b01001001*/, 0x11/*0b00010001*/, + 0x21/*0b00100001*/, 0x41/*0b01000001*/, 0x81/*0b10000001*/, 0x01/*0b00000001*/, + }; + static unsigned int shift_table[9] = { + 0, 0,0,1,0,2,4,6,0, + }; + if (shift < 0) + v <<= -shift; + else + v >>= shift; + STBI_ASSERT(v < 256); + v >>= (8-bits); + STBI_ASSERT(bits >= 0 && bits <= 8); + return (int) ((unsigned) v * mul_table[bits]) >> shift_table[bits]; } typedef struct { int bpp, offset, hsz; unsigned int mr,mg,mb,ma, all_a; + int extra_read; } stbi__bmp_data; static void *stbi__bmp_parse_header(stbi__context *s, stbi__bmp_data *info) @@ -4996,6 +5217,7 @@ static void *stbi__bmp_parse_header(stbi__context *s, stbi__bmp_data *info) info->offset = stbi__get32le(s); info->hsz = hsz = stbi__get32le(s); info->mr = info->mg = info->mb = info->ma = 0; + info->extra_read = 14; if (hsz != 12 && hsz != 40 && hsz != 56 && hsz != 108 && hsz != 124) return stbi__errpuc("unknown BMP", "BMP type not supported: unknown"); if (hsz == 12) { @@ -5007,7 +5229,6 @@ static void *stbi__bmp_parse_header(stbi__context *s, stbi__bmp_data *info) } if (stbi__get16le(s) != 1) return stbi__errpuc("bad BMP", "bad BMP"); info->bpp = stbi__get16le(s); - if (info->bpp == 1) return stbi__errpuc("monochrome", "BMP type not supported: 1-bit"); if (hsz != 12) { int compress = stbi__get32le(s); if (compress == 1 || compress == 2) return stbi__errpuc("BMP RLE", "BMP type not supported: RLE"); @@ -5040,6 +5261,7 @@ static void *stbi__bmp_parse_header(stbi__context *s, stbi__bmp_data *info) info->mr = stbi__get32le(s); info->mg = stbi__get32le(s); info->mb = stbi__get32le(s); + info->extra_read += 12; // not documented, but generated by photoshop and handled by mspaint if (info->mr == info->mg && info->mg == info->mb) { // ?!?!? @@ -5096,13 +5318,19 @@ static void *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req if (info.hsz == 12) { if (info.bpp < 24) - psize = (info.offset - 14 - 24) / 3; + psize = (info.offset - info.extra_read - 24) / 3; } else { if (info.bpp < 16) - psize = (info.offset - 14 - info.hsz) >> 2; + psize = (info.offset - info.extra_read - info.hsz) >> 2; + } + if (psize == 0) { + STBI_ASSERT(info.offset == (s->img_buffer - s->buffer_start)); } - s->img_n = ma ? 4 : 3; + if (info.bpp == 24 && ma == 0xff000000) + s->img_n = 3; + else + s->img_n = ma ? 4 : 3; if (req_comp && req_comp >= 3) // we can directly decode 3 or 4 target = req_comp; else @@ -5124,36 +5352,56 @@ static void *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req if (info.hsz != 12) stbi__get8(s); pal[i][3] = 255; } - stbi__skip(s, info.offset - 14 - info.hsz - psize * (info.hsz == 12 ? 3 : 4)); - if (info.bpp == 4) width = (s->img_x + 1) >> 1; + stbi__skip(s, info.offset - info.extra_read - info.hsz - psize * (info.hsz == 12 ? 3 : 4)); + if (info.bpp == 1) width = (s->img_x + 7) >> 3; + else if (info.bpp == 4) width = (s->img_x + 1) >> 1; else if (info.bpp == 8) width = s->img_x; else { STBI_FREE(out); return stbi__errpuc("bad bpp", "Corrupt BMP"); } pad = (-width)&3; - for (j=0; j < (int) s->img_y; ++j) { - for (i=0; i < (int) s->img_x; i += 2) { - int v=stbi__get8(s),v2=0; - if (info.bpp == 4) { - v2 = v & 15; - v >>= 4; + if (info.bpp == 1) { + for (j=0; j < (int) s->img_y; ++j) { + int bit_offset = 7, v = stbi__get8(s); + for (i=0; i < (int) s->img_x; ++i) { + int color = (v>>bit_offset)&0x1; + out[z++] = pal[color][0]; + out[z++] = pal[color][1]; + out[z++] = pal[color][2]; + if (target == 4) out[z++] = 255; + if (i+1 == (int) s->img_x) break; + if((--bit_offset) < 0) { + bit_offset = 7; + v = stbi__get8(s); + } } - out[z++] = pal[v][0]; - out[z++] = pal[v][1]; - out[z++] = pal[v][2]; - if (target == 4) out[z++] = 255; - if (i+1 == (int) s->img_x) break; - v = (info.bpp == 8) ? stbi__get8(s) : v2; - out[z++] = pal[v][0]; - out[z++] = pal[v][1]; - out[z++] = pal[v][2]; - if (target == 4) out[z++] = 255; + stbi__skip(s, pad); + } + } else { + for (j=0; j < (int) s->img_y; ++j) { + for (i=0; i < (int) s->img_x; i += 2) { + int v=stbi__get8(s),v2=0; + if (info.bpp == 4) { + v2 = v & 15; + v >>= 4; + } + out[z++] = pal[v][0]; + out[z++] = pal[v][1]; + out[z++] = pal[v][2]; + if (target == 4) out[z++] = 255; + if (i+1 == (int) s->img_x) break; + v = (info.bpp == 8) ? stbi__get8(s) : v2; + out[z++] = pal[v][0]; + out[z++] = pal[v][1]; + out[z++] = pal[v][2]; + if (target == 4) out[z++] = 255; + } + stbi__skip(s, pad); } - stbi__skip(s, pad); } } else { int rshift=0,gshift=0,bshift=0,ashift=0,rcount=0,gcount=0,bcount=0,acount=0; int z = 0; int easy=0; - stbi__skip(s, info.offset - 14 - info.hsz); + stbi__skip(s, info.offset - info.extra_read - info.hsz); if (info.bpp == 24) width = 3 * s->img_x; else if (info.bpp == 16) width = 2*s->img_x; else /* bpp = 32 and pad = 0 */ width=0; @@ -5188,7 +5436,7 @@ static void *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req int bpp = info.bpp; for (i=0; i < (int) s->img_x; ++i) { stbi__uint32 v = (bpp == 16 ? (stbi__uint32) stbi__get16le(s) : stbi__get32le(s)); - int a; + unsigned int a; out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mr, rshift, rcount)); out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mg, gshift, gcount)); out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mb, bshift, bcount)); @@ -5212,7 +5460,7 @@ static void *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req stbi_uc *p1 = out + j *s->img_x*target; stbi_uc *p2 = out + (s->img_y-1-j)*s->img_x*target; for (i=0; i < (int) s->img_x*target; ++i) { - t = p1[i], p1[i] = p2[i], p2[i] = t; + t = p1[i]; p1[i] = p2[i]; p2[i] = t; } } } @@ -5236,14 +5484,14 @@ static void *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req static int stbi__tga_get_comp(int bits_per_pixel, int is_grey, int* is_rgb16) { // only RGB or RGBA (incl. 16bit) or grey allowed - if(is_rgb16) *is_rgb16 = 0; + if (is_rgb16) *is_rgb16 = 0; switch(bits_per_pixel) { case 8: return STBI_grey; case 16: if(is_grey) return STBI_grey_alpha; - // else: fall-through + // fallthrough case 15: if(is_rgb16) *is_rgb16 = 1; - return STBI_rgb; - case 24: // fall-through + return STBI_rgb; + case 24: // fallthrough case 32: return bits_per_pixel/8; default: return 0; } @@ -5392,6 +5640,8 @@ static void *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req int RLE_repeating = 0; int read_next_pixel = 1; STBI_NOTUSED(ri); + STBI_NOTUSED(tga_x_origin); // @TODO + STBI_NOTUSED(tga_y_origin); // @TODO // do a tiny bit of precessing if ( tga_image_type >= 8 ) @@ -5555,6 +5805,7 @@ static void *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req // Microsoft's C compilers happy... [8^( tga_palette_start = tga_palette_len = tga_palette_bits = tga_x_origin = tga_y_origin = 0; + STBI_NOTUSED(tga_palette_start); // OK, done return tga_data; } @@ -5702,7 +5953,7 @@ static void *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req // Else if n is 128, noop. // Endloop - // The RLE-compressed data is preceeded by a 2-byte data count for each row in the data, + // The RLE-compressed data is preceded by a 2-byte data count for each row in the data, // which we're going to just skip. stbi__skip(s, h * channelCount * 2 ); @@ -6038,11 +6289,13 @@ typedef struct typedef struct { int w,h; - stbi_uc *out, *old_out; // output buffer (always 4 components) - int flags, bgindex, ratio, transparent, eflags, delay; + stbi_uc *out; // output buffer (always 4 components) + stbi_uc *background; // The current "background" as far as a gif is concerned + stbi_uc *history; + int flags, bgindex, ratio, transparent, eflags; stbi_uc pal[256][4]; stbi_uc lpal[256][4]; - stbi__gif_lzw codes[4096]; + stbi__gif_lzw codes[8192]; stbi_uc *color_table; int parse, step; int lflags; @@ -6050,6 +6303,7 @@ typedef struct int max_x, max_y; int cur_x, cur_y; int line_size; + int delay; } stbi__gif; static int stbi__gif_test_raw(stbi__context *s) @@ -6125,6 +6379,7 @@ static int stbi__gif_info_raw(stbi__context *s, int *x, int *y, int *comp) static void stbi__out_gif_code(stbi__gif *g, stbi__uint16 code) { stbi_uc *p, *c; + int idx; // recurse to decode the prefixes, since the linked-list is backwards, // and working backwards through an interleaved image would be nasty @@ -6133,10 +6388,12 @@ static void stbi__out_gif_code(stbi__gif *g, stbi__uint16 code) if (g->cur_y >= g->max_y) return; - p = &g->out[g->cur_x + g->cur_y]; - c = &g->color_table[g->codes[code].suffix * 4]; + idx = g->cur_x + g->cur_y; + p = &g->out[idx]; + g->history[idx / 4] = 1; - if (c[3] >= 128) { + c = &g->color_table[g->codes[code].suffix * 4]; + if (c[3] > 128) { // don't render transparent pixels; p[0] = c[2]; p[1] = c[1]; p[2] = c[0]; @@ -6210,11 +6467,16 @@ static stbi_uc *stbi__process_gif_raster(stbi__context *s, stbi__gif *g) stbi__skip(s,len); return g->out; } else if (code <= avail) { - if (first) return stbi__errpuc("no clear code", "Corrupt GIF"); + if (first) { + return stbi__errpuc("no clear code", "Corrupt GIF"); + } if (oldcode >= 0) { p = &g->codes[avail++]; - if (avail > 4096) return stbi__errpuc("too many codes", "Corrupt GIF"); + if (avail > 8192) { + return stbi__errpuc("too many codes", "Corrupt GIF"); + } + p->prefix = (stbi__int16) oldcode; p->first = g->codes[oldcode].first; p->suffix = (code == avail) ? p->first : g->codes[code].first; @@ -6236,62 +6498,77 @@ static stbi_uc *stbi__process_gif_raster(stbi__context *s, stbi__gif *g) } } -static void stbi__fill_gif_background(stbi__gif *g, int x0, int y0, int x1, int y1) -{ - int x, y; - stbi_uc *c = g->pal[g->bgindex]; - for (y = y0; y < y1; y += 4 * g->w) { - for (x = x0; x < x1; x += 4) { - stbi_uc *p = &g->out[y + x]; - p[0] = c[2]; - p[1] = c[1]; - p[2] = c[0]; - p[3] = 0; - } - } -} - // this function is designed to support animated gifs, although stb_image doesn't support it -static stbi_uc *stbi__gif_load_next(stbi__context *s, stbi__gif *g, int *comp, int req_comp) +// two back is the image from two frames ago, used for a very specific disposal format +static stbi_uc *stbi__gif_load_next(stbi__context *s, stbi__gif *g, int *comp, int req_comp, stbi_uc *two_back) { - int i; - stbi_uc *prev_out = 0; - - if (g->out == 0 && !stbi__gif_header(s, g, comp,0)) - return 0; // stbi__g_failure_reason set by stbi__gif_header + int dispose; + int first_frame; + int pi; + int pcount; + STBI_NOTUSED(req_comp); - if (!stbi__mad3sizes_valid(g->w, g->h, 4, 0)) - return stbi__errpuc("too large", "GIF too large"); + // on first frame, any non-written pixels get the background colour (non-transparent) + first_frame = 0; + if (g->out == 0) { + if (!stbi__gif_header(s, g, comp,0)) return 0; // stbi__g_failure_reason set by stbi__gif_header + if (!stbi__mad3sizes_valid(4, g->w, g->h, 0)) + return stbi__errpuc("too large", "GIF image is too large"); + pcount = g->w * g->h; + g->out = (stbi_uc *) stbi__malloc(4 * pcount); + g->background = (stbi_uc *) stbi__malloc(4 * pcount); + g->history = (stbi_uc *) stbi__malloc(pcount); + if (!g->out || !g->background || !g->history) + return stbi__errpuc("outofmem", "Out of memory"); + + // image is treated as "transparent" at the start - ie, nothing overwrites the current background; + // background colour is only used for pixels that are not rendered first frame, after that "background" + // color refers to the color that was there the previous frame. + memset(g->out, 0x00, 4 * pcount); + memset(g->background, 0x00, 4 * pcount); // state of the background (starts transparent) + memset(g->history, 0x00, pcount); // pixels that were affected previous frame + first_frame = 1; + } else { + // second frame - how do we dispoase of the previous one? + dispose = (g->eflags & 0x1C) >> 2; + pcount = g->w * g->h; - prev_out = g->out; - g->out = (stbi_uc *) stbi__malloc_mad3(4, g->w, g->h, 0); - if (g->out == 0) return stbi__errpuc("outofmem", "Out of memory"); + if ((dispose == 3) && (two_back == 0)) { + dispose = 2; // if I don't have an image to revert back to, default to the old background + } - switch ((g->eflags & 0x1C) >> 2) { - case 0: // unspecified (also always used on 1st frame) - stbi__fill_gif_background(g, 0, 0, 4 * g->w, 4 * g->w * g->h); - break; - case 1: // do not dispose - if (prev_out) memcpy(g->out, prev_out, 4 * g->w * g->h); - g->old_out = prev_out; - break; - case 2: // dispose to background - if (prev_out) memcpy(g->out, prev_out, 4 * g->w * g->h); - stbi__fill_gif_background(g, g->start_x, g->start_y, g->max_x, g->max_y); - break; - case 3: // dispose to previous - if (g->old_out) { - for (i = g->start_y; i < g->max_y; i += 4 * g->w) - memcpy(&g->out[i + g->start_x], &g->old_out[i + g->start_x], g->max_x - g->start_x); + if (dispose == 3) { // use previous graphic + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi]) { + memcpy( &g->out[pi * 4], &two_back[pi * 4], 4 ); + } } - break; + } else if (dispose == 2) { + // restore what was changed last frame to background before that frame; + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi]) { + memcpy( &g->out[pi * 4], &g->background[pi * 4], 4 ); + } + } + } else { + // This is a non-disposal case eithe way, so just + // leave the pixels as is, and they will become the new background + // 1: do not dispose + // 0: not specified. + } + + // background is what out is after the undoing of the previou frame; + memcpy( g->background, g->out, 4 * g->w * g->h ); } + // clear my history; + memset( g->history, 0x00, g->w * g->h ); // pixels that were affected previous frame + for (;;) { - switch (stbi__get8(s)) { + int tag = stbi__get8(s); + switch (tag) { case 0x2C: /* Image Descriptor */ { - int prev_trans = -1; stbi__int32 x, y, w, h; stbi_uc *o; @@ -6310,6 +6587,13 @@ static stbi_uc *stbi__gif_load_next(stbi__context *s, stbi__gif *g, int *comp, i g->cur_x = g->start_x; g->cur_y = g->start_y; + // if the width of the specified rectangle is 0, that means + // we may not see *any* pixels or the image is malformed; + // to make sure this is caught, move the current y down to + // max_y (which is what out_gif_code checks). + if (w == 0) + g->cur_y = g->max_y; + g->lflags = stbi__get8(s); if (g->lflags & 0x40) { @@ -6324,19 +6608,24 @@ static stbi_uc *stbi__gif_load_next(stbi__context *s, stbi__gif *g, int *comp, i stbi__gif_parse_colortable(s,g->lpal, 2 << (g->lflags & 7), g->eflags & 0x01 ? g->transparent : -1); g->color_table = (stbi_uc *) g->lpal; } else if (g->flags & 0x80) { - if (g->transparent >= 0 && (g->eflags & 0x01)) { - prev_trans = g->pal[g->transparent][3]; - g->pal[g->transparent][3] = 0; - } g->color_table = (stbi_uc *) g->pal; } else return stbi__errpuc("missing color table", "Corrupt GIF"); o = stbi__process_gif_raster(s, g); - if (o == NULL) return NULL; - - if (prev_trans != -1) - g->pal[g->transparent][3] = (stbi_uc) prev_trans; + if (!o) return NULL; + + // if this was the first frame, + pcount = g->w * g->h; + if (first_frame && (g->bgindex > 0)) { + // if first frame, any pixel not drawn to gets the background color + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi] == 0) { + g->pal[g->bgindex][3] = 255; // just in case it was made transparent, undo that; It will be reset next frame if need be; + memcpy( &g->out[pi * 4], &g->pal[g->bgindex], 4 ); + } + } + } return o; } @@ -6344,19 +6633,35 @@ static stbi_uc *stbi__gif_load_next(stbi__context *s, stbi__gif *g, int *comp, i case 0x21: // Comment Extension. { int len; - if (stbi__get8(s) == 0xF9) { // Graphic Control Extension. + int ext = stbi__get8(s); + if (ext == 0xF9) { // Graphic Control Extension. len = stbi__get8(s); if (len == 4) { g->eflags = stbi__get8(s); - g->delay = stbi__get16le(s); - g->transparent = stbi__get8(s); + g->delay = 10 * stbi__get16le(s); // delay - 1/100th of a second, saving as 1/1000ths. + + // unset old transparent + if (g->transparent >= 0) { + g->pal[g->transparent][3] = 255; + } + if (g->eflags & 0x01) { + g->transparent = stbi__get8(s); + if (g->transparent >= 0) { + g->pal[g->transparent][3] = 0; + } + } else { + // don't need transparent + stbi__skip(s, 1); + g->transparent = -1; + } } else { stbi__skip(s, len); break; } } - while ((len = stbi__get8(s)) != 0) + while ((len = stbi__get8(s)) != 0) { stbi__skip(s, len); + } break; } @@ -6367,28 +6672,104 @@ static stbi_uc *stbi__gif_load_next(stbi__context *s, stbi__gif *g, int *comp, i return stbi__errpuc("unknown code", "Corrupt GIF"); } } +} - STBI_NOTUSED(req_comp); +static void *stbi__load_gif_main(stbi__context *s, int **delays, int *x, int *y, int *z, int *comp, int req_comp) +{ + if (stbi__gif_test(s)) { + int layers = 0; + stbi_uc *u = 0; + stbi_uc *out = 0; + stbi_uc *two_back = 0; + stbi__gif g; + int stride; + memset(&g, 0, sizeof(g)); + if (delays) { + *delays = 0; + } + + do { + u = stbi__gif_load_next(s, &g, comp, req_comp, two_back); + if (u == (stbi_uc *) s) u = 0; // end of animated gif marker + + if (u) { + *x = g.w; + *y = g.h; + ++layers; + stride = g.w * g.h * 4; + + if (out) { + void *tmp = (stbi_uc*) STBI_REALLOC( out, layers * stride ); + if (NULL == tmp) { + STBI_FREE(g.out); + STBI_FREE(g.history); + STBI_FREE(g.background); + return stbi__errpuc("outofmem", "Out of memory"); + } + else + out = (stbi_uc*) tmp; + if (delays) { + *delays = (int*) STBI_REALLOC( *delays, sizeof(int) * layers ); + } + } else { + out = (stbi_uc*)stbi__malloc( layers * stride ); + if (delays) { + *delays = (int*) stbi__malloc( layers * sizeof(int) ); + } + } + memcpy( out + ((layers - 1) * stride), u, stride ); + if (layers >= 2) { + two_back = out - 2 * stride; + } + + if (delays) { + (*delays)[layers - 1U] = g.delay; + } + } + } while (u != 0); + + // free temp buffer; + STBI_FREE(g.out); + STBI_FREE(g.history); + STBI_FREE(g.background); + + // do the final conversion after loading everything; + if (req_comp && req_comp != 4) + out = stbi__convert_format(out, 4, req_comp, layers * g.w, g.h); + + *z = layers; + return out; + } else { + return stbi__errpuc("not GIF", "Image was not as a gif type."); + } } static void *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) { stbi_uc *u = 0; - stbi__gif* g = (stbi__gif*) stbi__malloc(sizeof(stbi__gif)); - memset(g, 0, sizeof(*g)); + stbi__gif g; + memset(&g, 0, sizeof(g)); STBI_NOTUSED(ri); - u = stbi__gif_load_next(s, g, comp, req_comp); + u = stbi__gif_load_next(s, &g, comp, req_comp, 0); if (u == (stbi_uc *) s) u = 0; // end of animated gif marker if (u) { - *x = g->w; - *y = g->h; + *x = g.w; + *y = g.h; + + // moved conversion to after successful load so that the same + // can be done for multiple frames. if (req_comp && req_comp != 4) - u = stbi__convert_format(u, 4, req_comp, g->w, g->h); + u = stbi__convert_format(u, 4, req_comp, g.w, g.h); + } else if (g.out) { + // if there was an error and we allocated an image buffer, free it! + STBI_FREE(g.out); } - else if (g->out) - STBI_FREE(g->out); - STBI_FREE(g); + + // free buffers needed for multiple frame loading; + STBI_FREE(g.history); + STBI_FREE(g.background); + return u; } @@ -6659,7 +7040,12 @@ static int stbi__bmp_info(stbi__context *s, int *x, int *y, int *comp) return 0; if (x) *x = s->img_x; if (y) *y = s->img_y; - if (comp) *comp = info.ma ? 4 : 3; + if (comp) { + if (info.bpp == 24 && info.ma == 0xff000000) + *comp = 3; + else + *comp = info.ma ? 4 : 3; + } return 1; } #endif @@ -6667,7 +7053,7 @@ static int stbi__bmp_info(stbi__context *s, int *x, int *y, int *comp) #ifndef STBI_NO_PSD static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp) { - int channelCount, dummy; + int channelCount, dummy, depth; if (!x) x = &dummy; if (!y) y = &dummy; if (!comp) comp = &dummy; @@ -6687,7 +7073,8 @@ static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp) } *y = stbi__get32be(s); *x = stbi__get32be(s); - if (stbi__get16be(s) != 8) { + depth = stbi__get16be(s); + if (depth != 8 && depth != 16) { stbi__rewind( s ); return 0; } @@ -6698,6 +7085,33 @@ static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp) *comp = 4; return 1; } + +static int stbi__psd_is16(stbi__context *s) +{ + int channelCount, depth; + if (stbi__get32be(s) != 0x38425053) { + stbi__rewind( s ); + return 0; + } + if (stbi__get16be(s) != 1) { + stbi__rewind( s ); + return 0; + } + stbi__skip(s, 6); + channelCount = stbi__get16be(s); + if (channelCount < 0 || channelCount > 16) { + stbi__rewind( s ); + return 0; + } + (void) stbi__get32be(s); + (void) stbi__get32be(s); + depth = stbi__get16be(s); + if (depth != 16) { + stbi__rewind( s ); + return 0; + } + return 1; +} #endif #ifndef STBI_NO_PIC @@ -6928,6 +7342,19 @@ static int stbi__info_main(stbi__context *s, int *x, int *y, int *comp) return stbi__err("unknown image type", "Image not of any known type, or corrupt"); } +static int stbi__is_16_main(stbi__context *s) +{ + #ifndef STBI_NO_PNG + if (stbi__png_is16(s)) return 1; + #endif + + #ifndef STBI_NO_PSD + if (stbi__psd_is16(s)) return 1; + #endif + + return 0; +} + #ifndef STBI_NO_STDIO STBIDEF int stbi_info(char const *filename, int *x, int *y, int *comp) { @@ -6949,6 +7376,27 @@ STBIDEF int stbi_info_from_file(FILE *f, int *x, int *y, int *comp) fseek(f,pos,SEEK_SET); return r; } + +STBIDEF int stbi_is_16_bit(char const *filename) +{ + FILE *f = stbi__fopen(filename, "rb"); + int result; + if (!f) return stbi__err("can't fopen", "Unable to open file"); + result = stbi_is_16_bit_from_file(f); + fclose(f); + return result; +} + +STBIDEF int stbi_is_16_bit_from_file(FILE *f) +{ + int r; + stbi__context s; + long pos = ftell(f); + stbi__start_file(&s, f); + r = stbi__is_16_main(&s); + fseek(f,pos,SEEK_SET); + return r; +} #endif // !STBI_NO_STDIO STBIDEF int stbi_info_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp) @@ -6965,10 +7413,31 @@ STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const *c, void *user, int return stbi__info_main(&s,x,y,comp); } +STBIDEF int stbi_is_16_bit_from_memory(stbi_uc const *buffer, int len) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__is_16_main(&s); +} + +STBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const *c, void *user) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) c, user); + return stbi__is_16_main(&s); +} + #endif // STB_IMAGE_IMPLEMENTATION /* revision history: + 2.20 (2019-02-07) support utf8 filenames in Windows; fix warnings and platform ifdefs + 2.19 (2018-02-11) fix warning + 2.18 (2018-01-30) fix warnings + 2.17 (2018-01-29) change sbti__shiftsigned to avoid clang -O2 bug + 1-bit BMP + *_is_16_bit api + avoid warnings 2.16 (2017-07-23) all functions have 16-bit variants; STBI_NO_STDIO works again; compilation fixes; diff --git a/3rdparty/stb/include/stb_image_write.h b/3rdparty/stb/include/stb_image_write.h index 274b1d81ccf..cffd473c1fb 100644 --- a/3rdparty/stb/include/stb_image_write.h +++ b/3rdparty/stb/include/stb_image_write.h @@ -1,4 +1,4 @@ -/* stb_image_write - v1.07 - public domain - http://nothings.org/stb/stb_image_write.h +/* stb_image_write - v1.14 - public domain - http://nothings.org/stb writes out PNG/BMP/TGA/JPEG/HDR images to C stdio - Sean Barrett 2010-2015 no warranty implied; use at your own risk @@ -12,32 +12,47 @@ ABOUT: - This header file is a library for writing images to C stdio. It could be - adapted to write to memory or a general streaming interface; let me know. + This header file is a library for writing images to C stdio or a callback. The PNG output is not optimal; it is 20-50% larger than the file - written by a decent optimizing implementation. This library is designed - for source code compactness and simplicity, not optimal image file size - or run-time performance. + written by a decent optimizing implementation; though providing a custom + zlib compress function (see STBIW_ZLIB_COMPRESS) can mitigate that. + This library is designed for source code compactness and simplicity, + not optimal image file size or run-time performance. BUILDING: You can #define STBIW_ASSERT(x) before the #include to avoid using assert.h. You can #define STBIW_MALLOC(), STBIW_REALLOC(), and STBIW_FREE() to replace malloc,realloc,free. - You can define STBIW_MEMMOVE() to replace memmove() + You can #define STBIW_MEMMOVE() to replace memmove() + You can #define STBIW_ZLIB_COMPRESS to use a custom zlib-style compress function + for PNG compression (instead of the builtin one), it must have the following signature: + unsigned char * my_compress(unsigned char *data, int data_len, int *out_len, int quality); + The returned data will be freed with STBIW_FREE() (free() by default), + so it must be heap allocated with STBIW_MALLOC() (malloc() by default), + +UNICODE: + + If compiling for Windows and you wish to use Unicode filenames, compile + with + #define STBIW_WINDOWS_UTF8 + and pass utf8-encoded filenames. Call stbiw_convert_wchar_to_utf8 to convert + Windows wchar_t filenames to utf8. USAGE: - There are four functions, one for each image file format: + There are five functions, one for each image file format: int stbi_write_png(char const *filename, int w, int h, int comp, const void *data, int stride_in_bytes); int stbi_write_bmp(char const *filename, int w, int h, int comp, const void *data); int stbi_write_tga(char const *filename, int w, int h, int comp, const void *data); + int stbi_write_jpg(char const *filename, int w, int h, int comp, const void *data, int quality); int stbi_write_hdr(char const *filename, int w, int h, int comp, const float *data); - int stbi_write_jpg(char const *filename, int w, int h, int comp, const float *data); - There are also four equivalent functions that use an arbitrary write function. You are + void stbi_flip_vertically_on_write(int flag); // flag is non-zero to flip data vertically + + There are also five equivalent functions that use an arbitrary write function. You are expected to open/close your file-equivalent before and after calling these: int stbi_write_png_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data, int stride_in_bytes); @@ -49,6 +64,12 @@ where the callback is: void stbi_write_func(void *context, void *data, int size); + You can configure it with these global variables: + int stbi_write_tga_with_rle; // defaults to true; set to 0 to disable RLE + int stbi_write_png_compression_level; // defaults to 8; set to higher for more compression + int stbi_write_force_png_filter; // defaults to -1; set to 0..5 to force a filter mode + + You can define STBI_WRITE_NO_STDIO to disable the file variant of these functions, so the library will not use stdio.h at all. However, this will also disable HDR writing, because it requires stdio for formatted output. @@ -75,6 +96,9 @@ writer, both because it is in BGR order and because it may have padding at the end of the line.) + PNG allows you to set the deflate compression level by setting the global + variable 'stbi_write_png_compression_level' (it defaults to 8). + HDR expects linear float data. Since the format is always 32-bit rgb(e) data, alpha (if provided) is discarded, and for monochrome data it is replicated across all three channels. @@ -88,21 +112,17 @@ CREDITS: - PNG/BMP/TGA - Sean Barrett - HDR - Baldur Karlsson - TGA monochrome: - Jean-Sebastien Guay - misc enhancements: - Tim Kelsey - TGA RLE - Alan Hickman - initial file IO callback implementation - Emmanuel Julien - JPEG - Jon Olick (original jo_jpeg.cpp code) - Daniel Gibson + + Sean Barrett - PNG/BMP/TGA + Baldur Karlsson - HDR + Jean-Sebastien Guay - TGA monochrome + Tim Kelsey - misc enhancements + Alan Hickman - TGA RLE + Emmanuel Julien - initial file IO callback implementation + Jon Olick - original jo_jpeg.cpp code + Daniel Gibson - integrate JPEG, allow external zlib + Aarni Koskela - allow choosing PNG filter + bugfixes: github:Chribba Guillaume Chereau @@ -114,6 +134,12 @@ Thatcher Ulrich github:poppolopoppo Patrick Boettcher + github:xeekworx + Cap Petschulat + Simon Rodriguez + Ivan Tikhonov + github:ignotion + Adam Schackart LICENSE @@ -124,15 +150,25 @@ LICENSE #ifndef INCLUDE_STB_IMAGE_WRITE_H #define INCLUDE_STB_IMAGE_WRITE_H -#ifdef __cplusplus -extern "C" { -#endif +#include +// if STB_IMAGE_WRITE_STATIC causes problems, try defining STBIWDEF to 'inline' or 'static inline' +#ifndef STBIWDEF #ifdef STB_IMAGE_WRITE_STATIC -#define STBIWDEF static +#define STBIWDEF static +#else +#ifdef __cplusplus +#define STBIWDEF extern "C" #else -#define STBIWDEF extern +#define STBIWDEF extern +#endif +#endif +#endif + +#ifndef STB_IMAGE_WRITE_STATIC // C++ forbids static forward declarations extern int stbi_write_tga_with_rle; +extern int stbi_write_png_compression_level; +extern int stbi_write_force_png_filter; #endif #ifndef STBI_WRITE_NO_STDIO @@ -141,6 +177,10 @@ STBIWDEF int stbi_write_bmp(char const *filename, int w, int h, int comp, const STBIWDEF int stbi_write_tga(char const *filename, int w, int h, int comp, const void *data); STBIWDEF int stbi_write_hdr(char const *filename, int w, int h, int comp, const float *data); STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const void *data, int quality); + +#ifdef STBI_WINDOWS_UTF8 +STBIWDEF int stbiw_convert_wchar_to_utf8(char *buffer, size_t bufferlen, const wchar_t* input); +#endif #endif typedef void stbi_write_func(void *context, void *data, int size); @@ -151,9 +191,7 @@ STBIWDEF int stbi_write_tga_to_func(stbi_write_func *func, void *context, int w, STBIWDEF int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const float *data); STBIWDEF int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int quality); -#ifdef __cplusplus -} -#endif +STBIWDEF void stbi_flip_vertically_on_write(int flip_boolean); #endif//INCLUDE_STB_IMAGE_WRITE_H @@ -208,6 +246,23 @@ STBIWDEF int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, #define STBIW_UCHAR(x) (unsigned char) ((x) & 0xff) +#ifdef STB_IMAGE_WRITE_STATIC +static int stbi_write_png_compression_level = 8; +static int stbi_write_tga_with_rle = 1; +static int stbi_write_force_png_filter = -1; +#else +int stbi_write_png_compression_level = 8; +int stbi_write_tga_with_rle = 1; +int stbi_write_force_png_filter = -1; +#endif + +static int stbi__flip_vertically_on_write = 0; + +STBIWDEF void stbi_flip_vertically_on_write(int flag) +{ + stbi__flip_vertically_on_write = flag; +} + typedef struct { stbi_write_func *func; @@ -228,9 +283,52 @@ static void stbi__stdio_write(void *context, void *data, int size) fwrite(data,1,size,(FILE*) context); } +#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) +#ifdef __cplusplus +#define STBIW_EXTERN extern "C" +#else +#define STBIW_EXTERN extern +#endif +STBIW_EXTERN __declspec(dllimport) int __stdcall MultiByteToWideChar(unsigned int cp, unsigned long flags, const char *str, int cbmb, wchar_t *widestr, int cchwide); +STBIW_EXTERN __declspec(dllimport) int __stdcall WideCharToMultiByte(unsigned int cp, unsigned long flags, const wchar_t *widestr, int cchwide, char *str, int cbmb, const char *defchar, int *used_default); + +STBIWDEF int stbiw_convert_wchar_to_utf8(char *buffer, size_t bufferlen, const wchar_t* input) +{ + return WideCharToMultiByte(65001 /* UTF8 */, 0, input, -1, buffer, (int) bufferlen, NULL, NULL); +} +#endif + +static FILE *stbiw__fopen(char const *filename, char const *mode) +{ + FILE *f; +#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) + wchar_t wMode[64]; + wchar_t wFilename[1024]; + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, filename, -1, wFilename, sizeof(wFilename))) + return 0; + + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, mode, -1, wMode, sizeof(wMode))) + return 0; + +#if _MSC_VER >= 1400 + if (0 != _wfopen_s(&f, wFilename, wMode)) + f = 0; +#else + f = _wfopen(wFilename, wMode); +#endif + +#elif defined(_MSC_VER) && _MSC_VER >= 1400 + if (0 != fopen_s(&f, filename, mode)) + f=0; +#else + f = fopen(filename, mode); +#endif + return f; +} + static int stbi__start_write_file(stbi__write_context *s, const char *filename) { - FILE *f = fopen(filename, "wb"); + FILE *f = stbiw__fopen(filename, "wb"); stbi__start_write_callbacks(s, stbi__stdio_write, (void *) f); return f != NULL; } @@ -245,12 +343,6 @@ static void stbi__end_write_file(stbi__write_context *s) typedef unsigned int stbiw_uint32; typedef int stb_image_write_test[sizeof(stbiw_uint32)==4 ? 1 : -1]; -#ifdef STB_IMAGE_WRITE_STATIC -static int stbi_write_tga_with_rle = 1; -#else -int stbi_write_tga_with_rle = 1; -#endif - static void stbiw__writefv(stbi__write_context *s, const char *fmt, va_list v) { while (*fmt) { @@ -296,7 +388,7 @@ static void stbiw__putc(stbi__write_context *s, unsigned char c) static void stbiw__write3(stbi__write_context *s, unsigned char a, unsigned char b, unsigned char c) { unsigned char arr[3]; - arr[0] = a, arr[1] = b, arr[2] = c; + arr[0] = a; arr[1] = b; arr[2] = c; s->func(s->context, arr, 3); } @@ -341,10 +433,14 @@ static void stbiw__write_pixels(stbi__write_context *s, int rgb_dir, int vdir, i if (y <= 0) return; - if (vdir < 0) - j_end = -1, j = y-1; - else - j_end = y, j = 0; + if (stbi__flip_vertically_on_write) + vdir *= -1; + + if (vdir < 0) { + j_end = -1; j = y-1; + } else { + j_end = y; j = 0; + } for (; j != j_end; j += vdir) { for (i=0; i < x; ++i) { @@ -412,11 +508,21 @@ static int stbi_write_tga_core(stbi__write_context *s, int x, int y, int comp, v "111 221 2222 11", 0, 0, format, 0, 0, 0, 0, 0, x, y, (colorbytes + has_alpha) * 8, has_alpha * 8); } else { int i,j,k; + int jend, jdir; stbiw__writef(s, "111 221 2222 11", 0,0,format+8, 0,0,0, 0,0,x,y, (colorbytes + has_alpha) * 8, has_alpha * 8); - for (j = y - 1; j >= 0; --j) { - unsigned char *row = (unsigned char *) data + j * x * comp; + if (stbi__flip_vertically_on_write) { + j = 0; + jend = y; + jdir = 1; + } else { + j = y-1; + jend = -1; + jdir = -1; + } + for (; j != jend; j += jdir) { + unsigned char *row = (unsigned char *) data + j * x * comp; int len; for (i = 0; i < x; i += len) { @@ -492,7 +598,7 @@ STBIWDEF int stbi_write_tga(char const *filename, int x, int y, int comp, const #define stbiw__max(a, b) ((a) > (b) ? (a) : (b)) -void stbiw__linear_to_rgbe(unsigned char *rgbe, float *linear) +static void stbiw__linear_to_rgbe(unsigned char *rgbe, float *linear) { int exponent; float maxcomp = stbiw__max(linear[0], stbiw__max(linear[1], linear[2])); @@ -509,7 +615,7 @@ void stbiw__linear_to_rgbe(unsigned char *rgbe, float *linear) } } -void stbiw__write_run_data(stbi__write_context *s, int length, unsigned char databyte) +static void stbiw__write_run_data(stbi__write_context *s, int length, unsigned char databyte) { unsigned char lengthbyte = STBIW_UCHAR(length+128); STBIW_ASSERT(length+128 <= 255); @@ -517,7 +623,7 @@ void stbiw__write_run_data(stbi__write_context *s, int length, unsigned char dat s->func(s->context, &databyte, 1); } -void stbiw__write_dump_data(stbi__write_context *s, int length, unsigned char *data) +static void stbiw__write_dump_data(stbi__write_context *s, int length, unsigned char *data) { unsigned char lengthbyte = STBIW_UCHAR(length); STBIW_ASSERT(length <= 128); // inconsistent with spec but consistent with official code @@ -525,7 +631,7 @@ void stbiw__write_dump_data(stbi__write_context *s, int length, unsigned char *d s->func(s->context, data, length); } -void stbiw__write_hdr_scanline(stbi__write_context *s, int width, int ncomp, unsigned char *scratch, float *scanline) +static void stbiw__write_hdr_scanline(stbi__write_context *s, int width, int ncomp, unsigned char *scratch, float *scanline) { unsigned char scanlineheader[4] = { 2, 2, 0, 0 }; unsigned char rgbe[4]; @@ -626,11 +732,15 @@ static int stbi_write_hdr_core(stbi__write_context *s, int x, int y, int comp, f char header[] = "#?RADIANCE\n# Written by stb_image_write.h\nFORMAT=32-bit_rle_rgbe\n"; s->func(s->context, header, sizeof(header)-1); +#ifdef __STDC_WANT_SECURE_LIB__ + len = sprintf_s(buffer, sizeof(buffer), "EXPOSURE= 1.0000000000000\n\n-Y %d +X %d\n", y, x); +#else len = sprintf(buffer, "EXPOSURE= 1.0000000000000\n\n-Y %d +X %d\n", y, x); +#endif s->func(s->context, buffer, len); for(i=0; i < y; i++) - stbiw__write_hdr_scanline(s, x, comp, scratch, data + comp*i*x); + stbiw__write_hdr_scanline(s, x, comp, scratch, data + comp*x*(stbi__flip_vertically_on_write ? y-1-i : i)); STBIW_FREE(scratch); return 1; } @@ -662,8 +772,9 @@ STBIWDEF int stbi_write_hdr(char const *filename, int x, int y, int comp, const // PNG writer // +#ifndef STBIW_ZLIB_COMPRESS // stretchy buffer; stbiw__sbpush() == vector<>::push_back() -- stbiw__sbcount() == vector<>::size() -#define stbiw__sbraw(a) ((int *) (a) - 2) +#define stbiw__sbraw(a) ((int *) (void *) (a) - 2) #define stbiw__sbm(a) stbiw__sbraw(a)[0] #define stbiw__sbn(a) stbiw__sbraw(a)[1] @@ -742,8 +853,14 @@ static unsigned int stbiw__zhash(unsigned char *data) #define stbiw__ZHASH 16384 -unsigned char * stbi_zlib_compress(unsigned char *data, int data_len, int *out_len, int quality) +#endif // STBIW_ZLIB_COMPRESS + +STBIWDEF unsigned char * stbi_zlib_compress(unsigned char *data, int data_len, int *out_len, int quality) { +#ifdef STBIW_ZLIB_COMPRESS + // user provided a zlib compress implementation, use that + return STBIW_ZLIB_COMPRESS(data, data_len, out_len, quality); +#else // use builtin static unsigned short lengthc[] = { 3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258, 259 }; static unsigned char lengtheb[]= { 0,0,0,0,0,0,0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 0 }; static unsigned short distc[] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577, 32768 }; @@ -751,7 +868,9 @@ unsigned char * stbi_zlib_compress(unsigned char *data, int data_len, int *out_l unsigned int bitbuf=0; int i,j, bitcount=0; unsigned char *out = NULL; - unsigned char ***hash_table = (unsigned char***) STBIW_MALLOC(stbiw__ZHASH * sizeof(char**)); + unsigned char ***hash_table = (unsigned char***) STBIW_MALLOC(stbiw__ZHASH * sizeof(unsigned char**)); + if (hash_table == NULL) + return NULL; if (quality < 5) quality = 5; stbiw__sbpush(out, 0x78); // DEFLATE 32K window @@ -772,7 +891,7 @@ unsigned char * stbi_zlib_compress(unsigned char *data, int data_len, int *out_l for (j=0; j < n; ++j) { if (hlist[j]-data > i-32768) { // if entry lies within window int d = stbiw__zlib_countm(hlist[j], data+i, data_len-i); - if (d >= best) best=d,bestloc=hlist[j]; + if (d >= best) { best=d; bestloc=hlist[j]; } } } // when hash table entry is too long, delete half the entries @@ -831,8 +950,8 @@ unsigned char * stbi_zlib_compress(unsigned char *data, int data_len, int *out_l int blocklen = (int) (data_len % 5552); j=0; while (j < data_len) { - for (i=0; i < blocklen; ++i) s1 += data[j+i], s2 += s1; - s1 %= 65521, s2 %= 65521; + for (i=0; i < blocklen; ++i) { s1 += data[j+i]; s2 += s1; } + s1 %= 65521; s2 %= 65521; j += blocklen; blocklen = 5552; } @@ -845,10 +964,14 @@ unsigned char * stbi_zlib_compress(unsigned char *data, int data_len, int *out_l // make returned pointer freeable STBIW_MEMMOVE(stbiw__sbraw(out), out, *out_len); return (unsigned char *) stbiw__sbraw(out); +#endif // STBIW_ZLIB_COMPRESS } static unsigned int stbiw__crc32(unsigned char *buffer, int len) { +#ifdef STBIW_CRC32 + return STBIW_CRC32(buffer, len); +#else static unsigned int crc_table[256] = { 0x00000000, 0x77073096, 0xEE0E612C, 0x990951BA, 0x076DC419, 0x706AF48F, 0xE963A535, 0x9E6495A3, @@ -890,6 +1013,7 @@ static unsigned int stbiw__crc32(unsigned char *buffer, int len) for (i=0; i < len; ++i) crc = (crc >> 8) ^ crc_table[buffer[i] ^ (crc & 0xff)]; return ~crc; +#endif } #define stbiw__wpng4(o,a,b,c,d) ((o)[0]=STBIW_UCHAR(a),(o)[1]=STBIW_UCHAR(b),(o)[2]=STBIW_UCHAR(c),(o)[3]=STBIW_UCHAR(d),(o)+=4) @@ -911,61 +1035,91 @@ static unsigned char stbiw__paeth(int a, int b, int c) } // @OPTIMIZE: provide an option that always forces left-predict or paeth predict -unsigned char *stbi_write_png_to_mem(unsigned char *pixels, int stride_bytes, int x, int y, int n, int *out_len) +static void stbiw__encode_png_line(unsigned char *pixels, int stride_bytes, int width, int height, int y, int n, int filter_type, signed char *line_buffer) { + static int mapping[] = { 0,1,2,3,4 }; + static int firstmap[] = { 0,1,0,5,6 }; + int *mymap = (y != 0) ? mapping : firstmap; + int i; + int type = mymap[filter_type]; + unsigned char *z = pixels + stride_bytes * (stbi__flip_vertically_on_write ? height-1-y : y); + int signed_stride = stbi__flip_vertically_on_write ? -stride_bytes : stride_bytes; + + if (type==0) { + memcpy(line_buffer, z, width*n); + return; + } + + // first loop isn't optimized since it's just one pixel + for (i = 0; i < n; ++i) { + switch (type) { + case 1: line_buffer[i] = z[i]; break; + case 2: line_buffer[i] = z[i] - z[i-signed_stride]; break; + case 3: line_buffer[i] = z[i] - (z[i-signed_stride]>>1); break; + case 4: line_buffer[i] = (signed char) (z[i] - stbiw__paeth(0,z[i-signed_stride],0)); break; + case 5: line_buffer[i] = z[i]; break; + case 6: line_buffer[i] = z[i]; break; + } + } + switch (type) { + case 1: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - z[i-n]; break; + case 2: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - z[i-signed_stride]; break; + case 3: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - ((z[i-n] + z[i-signed_stride])>>1); break; + case 4: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - stbiw__paeth(z[i-n], z[i-signed_stride], z[i-signed_stride-n]); break; + case 5: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - (z[i-n]>>1); break; + case 6: for (i=n; i < width*n; ++i) line_buffer[i] = z[i] - stbiw__paeth(z[i-n], 0,0); break; + } +} + +STBIWDEF unsigned char *stbi_write_png_to_mem(const unsigned char *pixels, int stride_bytes, int x, int y, int n, int *out_len) +{ + int force_filter = stbi_write_force_png_filter; int ctype[5] = { -1, 0, 4, 2, 6 }; unsigned char sig[8] = { 137,80,78,71,13,10,26,10 }; unsigned char *out,*o, *filt, *zlib; signed char *line_buffer; - int i,j,k,p,zlen; + int j,zlen; if (stride_bytes == 0) stride_bytes = x * n; + if (force_filter >= 5) { + force_filter = -1; + } + filt = (unsigned char *) STBIW_MALLOC((x*n+1) * y); if (!filt) return 0; line_buffer = (signed char *) STBIW_MALLOC(x * n); if (!line_buffer) { STBIW_FREE(filt); return 0; } for (j=0; j < y; ++j) { - static int mapping[] = { 0,1,2,3,4 }; - static int firstmap[] = { 0,1,0,5,6 }; - int *mymap = (j != 0) ? mapping : firstmap; - int best = 0, bestval = 0x7fffffff; - for (p=0; p < 2; ++p) { - for (k= p?best:0; k < 5; ++k) { // @TODO: clarity: rewrite this to go 0..5, and 'continue' the unwanted ones during 2nd pass - int type = mymap[k],est=0; - unsigned char *z = pixels + stride_bytes*j; - for (i=0; i < n; ++i) - switch (type) { - case 0: line_buffer[i] = z[i]; break; - case 1: line_buffer[i] = z[i]; break; - case 2: line_buffer[i] = z[i] - z[i-stride_bytes]; break; - case 3: line_buffer[i] = z[i] - (z[i-stride_bytes]>>1); break; - case 4: line_buffer[i] = (signed char) (z[i] - stbiw__paeth(0,z[i-stride_bytes],0)); break; - case 5: line_buffer[i] = z[i]; break; - case 6: line_buffer[i] = z[i]; break; - } - for (i=n; i < x*n; ++i) { - switch (type) { - case 0: line_buffer[i] = z[i]; break; - case 1: line_buffer[i] = z[i] - z[i-n]; break; - case 2: line_buffer[i] = z[i] - z[i-stride_bytes]; break; - case 3: line_buffer[i] = z[i] - ((z[i-n] + z[i-stride_bytes])>>1); break; - case 4: line_buffer[i] = z[i] - stbiw__paeth(z[i-n], z[i-stride_bytes], z[i-stride_bytes-n]); break; - case 5: line_buffer[i] = z[i] - (z[i-n]>>1); break; - case 6: line_buffer[i] = z[i] - stbiw__paeth(z[i-n], 0,0); break; - } - } - if (p) break; - for (i=0; i < x*n; ++i) + int filter_type; + if (force_filter > -1) { + filter_type = force_filter; + stbiw__encode_png_line((unsigned char*)(pixels), stride_bytes, x, y, j, n, force_filter, line_buffer); + } else { // Estimate the best filter by running through all of them: + int best_filter = 0, best_filter_val = 0x7fffffff, est, i; + for (filter_type = 0; filter_type < 5; filter_type++) { + stbiw__encode_png_line((unsigned char*)(pixels), stride_bytes, x, y, j, n, filter_type, line_buffer); + + // Estimate the entropy of the line using this filter; the less, the better. + est = 0; + for (i = 0; i < x*n; ++i) { est += abs((signed char) line_buffer[i]); - if (est < bestval) { bestval = est; best = k; } + } + if (est < best_filter_val) { + best_filter_val = est; + best_filter = filter_type; + } + } + if (filter_type != best_filter) { // If the last iteration already got us the best filter, don't redo it + stbiw__encode_png_line((unsigned char*)(pixels), stride_bytes, x, y, j, n, best_filter, line_buffer); + filter_type = best_filter; } } - // when we get here, best contains the filter type, and line_buffer contains the data - filt[j*(x*n+1)] = (unsigned char) best; + // when we get here, filter_type contains the filter type, and line_buffer contains the data + filt[j*(x*n+1)] = (unsigned char) filter_type; STBIW_MEMMOVE(filt+j*(x*n+1)+1, line_buffer, x*n); } STBIW_FREE(line_buffer); - zlib = stbi_zlib_compress(filt, y*( x*n+1), &zlen, 8); // increase 8 to get smaller but use more memory + zlib = stbi_zlib_compress(filt, y*( x*n+1), &zlen, stbi_write_png_compression_level); STBIW_FREE(filt); if (!zlib) return 0; @@ -1008,9 +1162,10 @@ STBIWDEF int stbi_write_png(char const *filename, int x, int y, int comp, const { FILE *f; int len; - unsigned char *png = stbi_write_png_to_mem((unsigned char *) data, stride_bytes, x, y, comp, &len); + unsigned char *png = stbi_write_png_to_mem((const unsigned char *) data, stride_bytes, x, y, comp, &len); if (png == NULL) return 0; - f = fopen(filename, "wb"); + + f = stbiw__fopen(filename, "wb"); if (!f) { STBIW_FREE(png); return 0; } fwrite(png, 1, len, f); fclose(f); @@ -1022,7 +1177,7 @@ STBIWDEF int stbi_write_png(char const *filename, int x, int y, int comp, const STBIWDEF int stbi_write_png_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int stride_bytes) { int len; - unsigned char *png = stbi_write_png_to_mem((unsigned char *) data, stride_bytes, x, y, comp, &len); + unsigned char *png = stbi_write_png_to_mem((const unsigned char *) data, stride_bytes, x, y, comp, &len); if (png == NULL) return 0; func(context, png, len); STBIW_FREE(png); @@ -1116,26 +1271,31 @@ static void stbiw__jpg_calcBits(int val, unsigned short bits[2]) { bits[0] = val & ((1< 100 ? 100 : quality; quality = quality < 50 ? 5000 / quality : 200 - quality * 2; @@ -1284,7 +1445,7 @@ static int stbi_write_jpg_core(stbi__write_context *s, int width, int height, in static const unsigned char head0[] = { 0xFF,0xD8,0xFF,0xE0,0,0x10,'J','F','I','F',0,1,1,0,0,1,0,1,0,0,0xFF,0xDB,0,0x84,0 }; static const unsigned char head2[] = { 0xFF,0xDA,0,0xC,3,1,0,2,0x11,3,0x11,0,0x3F,0 }; const unsigned char head1[] = { 0xFF,0xC0,0,0x11,8,(unsigned char)(height>>8),STBIW_UCHAR(height),(unsigned char)(width>>8),STBIW_UCHAR(width), - 3,1,0x11,0,2,0x11,1,3,0x11,1,0xFF,0xC4,0x01,0xA2,0 }; + 3,1,(unsigned char)(subsample?0x22:0x11),0,2,0x11,1,3,0x11,1,0xFF,0xC4,0x01,0xA2,0 }; s->func(s->context, (void*)head0, sizeof(head0)); s->func(s->context, (void*)YTable, sizeof(YTable)); stbiw__putc(s, 1); @@ -1307,38 +1468,74 @@ static int stbi_write_jpg_core(stbi__write_context *s, int width, int height, in // Encode 8x8 macroblocks { static const unsigned short fillBits[] = {0x7F, 7}; - const unsigned char *imageData = (const unsigned char *)data; int DCY=0, DCU=0, DCV=0; int bitBuf=0, bitCnt=0; // comp == 2 is grey+alpha (alpha is ignored) int ofsG = comp > 2 ? 1 : 0, ofsB = comp > 2 ? 2 : 0; + const unsigned char *dataR = (const unsigned char *)data; + const unsigned char *dataG = dataR + ofsG; + const unsigned char *dataB = dataR + ofsB; int x, y, pos; - for(y = 0; y < height; y += 8) { - for(x = 0; x < width; x += 8) { - float YDU[64], UDU[64], VDU[64]; - for(row = y, pos = 0; row < y+8; ++row) { - for(col = x; col < x+8; ++col, ++pos) { - int p = row*width*comp + col*comp; - float r, g, b; - if(row >= height) { - p -= width*comp*(row+1 - height); + if(subsample) { + for(y = 0; y < height; y += 16) { + for(x = 0; x < width; x += 16) { + float Y[256], U[256], V[256]; + for(row = y, pos = 0; row < y+16; ++row) { + // row >= height => use last input row + int clamped_row = (row < height) ? row : height - 1; + int base_p = (stbi__flip_vertically_on_write ? (height-1-clamped_row) : clamped_row)*width*comp; + for(col = x; col < x+16; ++col, ++pos) { + // if col >= width => use pixel from last input column + int p = base_p + ((col < width) ? col : (width-1))*comp; + float r = dataR[p], g = dataG[p], b = dataB[p]; + Y[pos]= +0.29900f*r + 0.58700f*g + 0.11400f*b - 128; + U[pos]= -0.16874f*r - 0.33126f*g + 0.50000f*b; + V[pos]= +0.50000f*r - 0.41869f*g - 0.08131f*b; } - if(col >= width) { - p -= comp*(col+1 - width); + } + DCY = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, Y+0, 16, fdtbl_Y, DCY, YDC_HT, YAC_HT); + DCY = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, Y+8, 16, fdtbl_Y, DCY, YDC_HT, YAC_HT); + DCY = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, Y+128, 16, fdtbl_Y, DCY, YDC_HT, YAC_HT); + DCY = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, Y+136, 16, fdtbl_Y, DCY, YDC_HT, YAC_HT); + + // subsample U,V + { + float subU[64], subV[64]; + int yy, xx; + for(yy = 0, pos = 0; yy < 8; ++yy) { + for(xx = 0; xx < 8; ++xx, ++pos) { + int j = yy*32+xx*2; + subU[pos] = (U[j+0] + U[j+1] + U[j+16] + U[j+17]) * 0.25f; + subV[pos] = (V[j+0] + V[j+1] + V[j+16] + V[j+17]) * 0.25f; + } } - - r = imageData[p+0]; - g = imageData[p+ofsG]; - b = imageData[p+ofsB]; - YDU[pos]=+0.29900f*r+0.58700f*g+0.11400f*b-128; - UDU[pos]=-0.16874f*r-0.33126f*g+0.50000f*b; - VDU[pos]=+0.50000f*r-0.41869f*g-0.08131f*b; + DCU = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, subU, 8, fdtbl_UV, DCU, UVDC_HT, UVAC_HT); + DCV = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, subV, 8, fdtbl_UV, DCV, UVDC_HT, UVAC_HT); } } + } + } else { + for(y = 0; y < height; y += 8) { + for(x = 0; x < width; x += 8) { + float Y[64], U[64], V[64]; + for(row = y, pos = 0; row < y+8; ++row) { + // row >= height => use last input row + int clamped_row = (row < height) ? row : height - 1; + int base_p = (stbi__flip_vertically_on_write ? (height-1-clamped_row) : clamped_row)*width*comp; + for(col = x; col < x+8; ++col, ++pos) { + // if col >= width => use pixel from last input column + int p = base_p + ((col < width) ? col : (width-1))*comp; + float r = dataR[p], g = dataG[p], b = dataB[p]; + Y[pos]= +0.29900f*r + 0.58700f*g + 0.11400f*b - 128; + U[pos]= -0.16874f*r - 0.33126f*g + 0.50000f*b; + V[pos]= +0.50000f*r - 0.41869f*g - 0.08131f*b; + } + } - DCY = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, YDU, fdtbl_Y, DCY, YDC_HT, YAC_HT); - DCU = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, UDU, fdtbl_UV, DCU, UVDC_HT, UVAC_HT); - DCV = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, VDU, fdtbl_UV, DCV, UVDC_HT, UVAC_HT); + DCY = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, Y, 8, fdtbl_Y, DCY, YDC_HT, YAC_HT); + DCU = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, U, 8, fdtbl_UV, DCU, UVDC_HT, UVAC_HT); + DCV = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, V, 8, fdtbl_UV, DCV, UVDC_HT, UVAC_HT); + } } } @@ -1377,6 +1574,17 @@ STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const #endif // STB_IMAGE_WRITE_IMPLEMENTATION /* Revision history + 1.14 (2020-02-02) updated JPEG writer to downsample chroma channels + 1.13 + 1.12 + 1.11 (2019-08-11) + + 1.10 (2019-02-07) + support utf8 filenames in Windows; fix warnings and platform ifdefs + 1.09 (2018-02-11) + fix typo in zlib quality API, improve STB_I_W_STATIC in C++ + 1.08 (2018-01-29) + add stbi__flip_vertically_on_write, external zlib, zlib quality, choose PNG filter 1.07 (2017-07-24) doc fix 1.06 (2017-07-23) From d47d72cb707d4d9adeb98368900889d59cfc4451 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 25 Aug 2023 16:47:31 +0200 Subject: [PATCH 1326/1365] minor spelling fixes (#8830) * minor spelling fixes * fix spacing * bump stb to latest and greatest --- .github/workflows/ccpp.yml | 1 + 3rdparty/stb/include/stb_image.h | 607 +++++++++++++++++++------ 3rdparty/stb/include/stb_image_write.h | 132 ++++-- README.md | 10 +- darknet_video.py | 2 +- scripts/kmeansiou.c | 11 +- src/.editorconfig | 8 - src/activation_kernels.cu | 2 +- src/art.c | 2 +- src/blas.c | 2 +- src/blas_kernels.cu | 112 ++--- src/box.c | 18 +- src/box.h | 20 +- src/coco.c | 14 +- src/col2im.c | 2 +- src/col2im_kernels.cu | 2 +- src/connected_layer.c | 12 +- src/convolutional_layer.c | 1 - src/cost_layer.c | 4 +- src/dark_cuda.c | 16 +- src/dark_cuda.h | 2 +- src/darknet.c | 20 +- src/data.h | 6 +- src/detection_layer.c | 50 +- src/gaussian_yolo_layer.c | 2 +- src/getopt.c | 132 +++--- src/getopt.h | 20 +- src/go.c | 12 +- src/im2col_kernels.cu | 8 +- src/image.c | 2 +- src/image_opencv.cpp | 10 +- src/layer.h | 56 +-- src/maxpool_layer.c | 4 +- src/network.c | 4 +- src/network.h | 14 +- src/option_list.h | 4 +- src/parser.c | 22 +- src/representation_layer.c | 2 - src/rnn_vid.c | 2 +- src/softmax_layer.c | 52 +-- src/tree.h | 2 +- src/yolo.c | 14 +- src/yolo_v2_class.cpp | 4 +- 43 files changed, 900 insertions(+), 522 deletions(-) delete mode 100644 src/.editorconfig diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index daaa9b0dba3..5d5ec8f0f39 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -5,6 +5,7 @@ on: workflow_dispatch: inputs: debug_enabled: + type: boolean description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)' required: false default: false diff --git a/3rdparty/stb/include/stb_image.h b/3rdparty/stb/include/stb_image.h index 2857f05d385..5e807a0a6e7 100644 --- a/3rdparty/stb/include/stb_image.h +++ b/3rdparty/stb/include/stb_image.h @@ -1,4 +1,4 @@ -/* stb_image - v2.25 - public domain image loader - http://nothings.org/stb +/* stb_image - v2.28 - public domain image loader - http://nothings.org/stb no warranty implied; use at your own risk Do this: @@ -48,6 +48,9 @@ LICENSE RECENT REVISION HISTORY: + 2.28 (2023-01-29) many error fixes, security errors, just tons of stuff + 2.27 (2021-07-11) document stbi_info better, 16-bit PNM support, bug fixes + 2.26 (2020-07-13) many minor fixes 2.25 (2020-02-02) fix warnings 2.24 (2020-02-02) fix warnings; thread-local failure_reason and flip_vertically 2.23 (2019-08-11) fix clang static analysis warning @@ -88,27 +91,37 @@ RECENT REVISION HISTORY: Jeremy Sawicki (handle all ImageNet JPGs) Optimizations & bugfixes Mikhail Morozov (1-bit BMP) Fabian "ryg" Giesen Anael Seghezzi (is-16-bit query) - Arseny Kapoulkine + Arseny Kapoulkine Simon Breuss (16-bit PNM) John-Mark Allen Carmelo J Fdez-Aguera Bug & warning fixes - Marc LeBlanc David Woo Guillaume George Martins Mozeiko - Christpher Lloyd Jerry Jansson Joseph Thomson Phil Jordan - Dave Moore Roy Eltham Hayaki Saito Nathan Reed - Won Chun Luke Graham Johan Duparc Nick Verigakis - the Horde3D community Thomas Ruf Ronny Chevalier github:rlyeh - Janez Zemva John Bartholomew Michal Cichon github:romigrou - Jonathan Blow Ken Hamada Tero Hanninen github:svdijk - Laurent Gomila Cort Stratton Sergio Gonzalez github:snagar - Aruelien Pocheville Thibault Reuille Cass Everitt github:Zelex - Ryamond Barbiero Paul Du Bois Engin Manap github:grim210 - Aldo Culquicondor Philipp Wiesemann Dale Weiler github:sammyhw - Oriol Ferrer Mesia Josh Tobin Matthew Gregan github:phprus - Julian Raschke Gregory Mullen Baldur Karlsson github:poppolopoppo - Christian Floisand Kevin Schmidt JR Smith github:darealshinji - Brad Weinberger Matvey Cherevko github:Michaelangel007 - Blazej Dariusz Roszkowski Alexander Veselov + Marc LeBlanc David Woo Guillaume George Martins Mozeiko + Christpher Lloyd Jerry Jansson Joseph Thomson Blazej Dariusz Roszkowski + Phil Jordan Dave Moore Roy Eltham + Hayaki Saito Nathan Reed Won Chun + Luke Graham Johan Duparc Nick Verigakis the Horde3D community + Thomas Ruf Ronny Chevalier github:rlyeh + Janez Zemva John Bartholomew Michal Cichon github:romigrou + Jonathan Blow Ken Hamada Tero Hanninen github:svdijk + Eugene Golushkov Laurent Gomila Cort Stratton github:snagar + Aruelien Pocheville Sergio Gonzalez Thibault Reuille github:Zelex + Cass Everitt Ryamond Barbiero github:grim210 + Paul Du Bois Engin Manap Aldo Culquicondor github:sammyhw + Philipp Wiesemann Dale Weiler Oriol Ferrer Mesia github:phprus + Josh Tobin Neil Bickford Matthew Gregan github:poppolopoppo + Julian Raschke Gregory Mullen Christian Floisand github:darealshinji + Baldur Karlsson Kevin Schmidt JR Smith github:Michaelangel007 + Brad Weinberger Matvey Cherevko github:mosra + Luca Sas Alexander Veselov Zack Middleton [reserved] + Ryan C. Gordon [reserved] [reserved] + DO NOT ADD YOUR NAME HERE + + Jacko Dirks + + To add your name to the credits, pick a random blank space in the middle and fill it. + 80% of merge conflicts on stb PRs are due to people adding their name at the end + of the credits. */ #ifndef STBI_INCLUDE_STB_IMAGE_H @@ -128,7 +141,7 @@ RECENT REVISION HISTORY: // // ... x = width, y = height, n = # 8-bit components per pixel ... // // ... replace '0' with '1'..'4' to force that many components per pixel // // ... but 'n' will always be the number that it would have been if you said 0 -// stbi_image_free(data) +// stbi_image_free(data); // // Standard parameters: // int *x -- outputs image width in pixels @@ -167,6 +180,32 @@ RECENT REVISION HISTORY: // // Paletted PNG, BMP, GIF, and PIC images are automatically depalettized. // +// To query the width, height and component count of an image without having to +// decode the full file, you can use the stbi_info family of functions: +// +// int x,y,n,ok; +// ok = stbi_info(filename, &x, &y, &n); +// // returns ok=1 and sets x, y, n if image is a supported format, +// // 0 otherwise. +// +// Note that stb_image pervasively uses ints in its public API for sizes, +// including sizes of memory buffers. This is now part of the API and thus +// hard to change without causing breakage. As a result, the various image +// loaders all have certain limits on image size; these differ somewhat +// by format but generally boil down to either just under 2GB or just under +// 1GB. When the decoded image would be larger than this, stb_image decoding +// will fail. +// +// Additionally, stb_image will reject image files that have any of their +// dimensions set to a larger value than the configurable STBI_MAX_DIMENSIONS, +// which defaults to 2**24 = 16777216 pixels. Due to the above memory limit, +// the only way to have an image with such dimensions load correctly +// is for it to have a rather extreme aspect ratio. Either way, the +// assumption here is that such larger images are likely to be malformed +// or malicious. If you do need to load an image with individual dimensions +// larger than that, and it still fits in the overall size limit, you can +// #define STBI_MAX_DIMENSIONS on your own to be something larger. +// // =========================================================================== // // UNICODE: @@ -272,11 +311,10 @@ RECENT REVISION HISTORY: // // iPhone PNG support: // -// By default we convert iphone-formatted PNGs back to RGB, even though -// they are internally encoded differently. You can disable this conversion -// by calling stbi_convert_iphone_png_to_rgb(0), in which case -// you will always just get the native iphone "format" through (which -// is BGR stored in RGB). +// We optionally support converting iPhone-formatted PNGs (which store +// premultiplied BGRA) back to RGB, even though they're internally encoded +// differently. To enable this conversion, call +// stbi_convert_iphone_png_to_rgb(1). // // Call stbi_set_unpremultiply_on_load(1) as well to force a divide per // pixel to remove any premultiplied alpha *only* if the image file explicitly @@ -318,7 +356,14 @@ RECENT REVISION HISTORY: // - If you use STBI_NO_PNG (or _ONLY_ without PNG), and you still // want the zlib decoder to be available, #define STBI_SUPPORT_ZLIB // - +// - If you define STBI_MAX_DIMENSIONS, stb_image will reject images greater +// than that size (in either width or height) without further processing. +// This is to let programs in the wild set an upper bound to prevent +// denial-of-service attacks on untrusted data, as one could generate a +// valid image of gigantic dimensions and force stb_image to allocate a +// huge block of memory and spend disproportionate time decoding it. By +// default this is set to (1 << 24), which is 16777216, but that's still +// very big. #ifndef STBI_NO_STDIO #include @@ -473,6 +518,8 @@ STBIDEF void stbi_set_flip_vertically_on_load(int flag_true_if_should_flip); // as above, but only applies to images loaded on the thread that calls the function // this function is only available if your compiler supports thread-local variables; // calling it will fail to link if your compiler doesn't +STBIDEF void stbi_set_unpremultiply_on_load_thread(int flag_true_if_should_unpremultiply); +STBIDEF void stbi_convert_iphone_png_to_rgb_thread(int flag_true_if_should_convert); STBIDEF void stbi_set_flip_vertically_on_load_thread(int flag_true_if_should_flip); // ZLIB client - used by PNG, available for other purposes @@ -574,16 +621,22 @@ STBIDEF int stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const ch #ifndef STBI_NO_THREAD_LOCALS #if defined(__cplusplus) && __cplusplus >= 201103L #define STBI_THREAD_LOCAL thread_local - #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 201112L - #define STBI_THREAD_LOCAL _Thread_local - #elif defined(__GNUC__) + #elif defined(__GNUC__) && __GNUC__ < 5 #define STBI_THREAD_LOCAL __thread #elif defined(_MSC_VER) #define STBI_THREAD_LOCAL __declspec(thread) -#endif + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 201112L && !defined(__STDC_NO_THREADS__) + #define STBI_THREAD_LOCAL _Thread_local + #endif + + #ifndef STBI_THREAD_LOCAL + #if defined(__GNUC__) + #define STBI_THREAD_LOCAL __thread + #endif + #endif #endif -#ifdef _MSC_VER +#if defined(_MSC_VER) || defined(__SYMBIAN32__) typedef unsigned short stbi__uint16; typedef signed short stbi__int16; typedef unsigned int stbi__uint32; @@ -612,7 +665,7 @@ typedef unsigned char validate_uint32[sizeof(stbi__uint32)==4 ? 1 : -1]; #ifdef STBI_HAS_LROTL #define stbi_lrot(x,y) _lrotl(x,y) #else - #define stbi_lrot(x,y) (((x) << (y)) | ((x) >> (32 - (y)))) + #define stbi_lrot(x,y) (((x) << (y)) | ((x) >> (-(y) & 31))) #endif #if defined(STBI_MALLOC) && defined(STBI_FREE) && (defined(STBI_REALLOC) || defined(STBI_REALLOC_SIZED)) @@ -726,14 +779,21 @@ static int stbi__sse2_available(void) #ifdef STBI_NEON #include -// assume GCC or Clang on ARM targets +#ifdef _MSC_VER +#define STBI_SIMD_ALIGN(type, name) __declspec(align(16)) type name +#else #define STBI_SIMD_ALIGN(type, name) type name __attribute__((aligned(16))) #endif +#endif #ifndef STBI_SIMD_ALIGN #define STBI_SIMD_ALIGN(type, name) type name #endif +#ifndef STBI_MAX_DIMENSIONS +#define STBI_MAX_DIMENSIONS (1 << 24) +#endif + /////////////////////////////////////////////// // // stbi__context struct and start_xxx functions @@ -751,6 +811,7 @@ typedef struct int read_from_callbacks; int buflen; stbi_uc buffer_start[128]; + int callback_already_read; stbi_uc *img_buffer, *img_buffer_end; stbi_uc *img_buffer_original, *img_buffer_original_end; @@ -764,6 +825,7 @@ static void stbi__start_mem(stbi__context *s, stbi_uc const *buffer, int len) { s->io.read = NULL; s->read_from_callbacks = 0; + s->callback_already_read = 0; s->img_buffer = s->img_buffer_original = (stbi_uc *) buffer; s->img_buffer_end = s->img_buffer_original_end = (stbi_uc *) buffer+len; } @@ -775,7 +837,8 @@ static void stbi__start_callbacks(stbi__context *s, stbi_io_callbacks *c, void * s->io_user_data = user; s->buflen = sizeof(s->buffer_start); s->read_from_callbacks = 1; - s->img_buffer_original = s->buffer_start; + s->callback_already_read = 0; + s->img_buffer = s->img_buffer_original = s->buffer_start; stbi__refill_buffer(s); s->img_buffer_original_end = s->img_buffer_end; } @@ -789,12 +852,17 @@ static int stbi__stdio_read(void *user, char *data, int size) static void stbi__stdio_skip(void *user, int n) { + int ch; fseek((FILE*) user, n, SEEK_CUR); + ch = fgetc((FILE*) user); /* have to read a byte to reset feof()'s flag */ + if (ch != EOF) { + ungetc(ch, (FILE *) user); /* push byte back onto stream if valid. */ + } } static int stbi__stdio_eof(void *user) { - return feof((FILE*) user); + return feof((FILE*) user) || ferror((FILE *) user); } static stbi_io_callbacks stbi__stdio_callbacks = @@ -890,6 +958,7 @@ static int stbi__gif_info(stbi__context *s, int *x, int *y, int *comp); static int stbi__pnm_test(stbi__context *s); static void *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); static int stbi__pnm_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__pnm_is16(stbi__context *s); #endif static @@ -964,7 +1033,7 @@ static int stbi__mad3sizes_valid(int a, int b, int c, int add) } // returns 1 if "a*b*c*d + add" has no negative terms/factors and doesn't overflow -#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) +#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) || !defined(STBI_NO_PNM) static int stbi__mad4sizes_valid(int a, int b, int c, int d, int add) { return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a*b, c) && @@ -987,7 +1056,7 @@ static void *stbi__malloc_mad3(int a, int b, int c, int add) return stbi__malloc(a*b*c + add); } -#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) +#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) || !defined(STBI_NO_PNM) static void *stbi__malloc_mad4(int a, int b, int c, int d, int add) { if (!stbi__mad4sizes_valid(a, b, c, d, add)) return NULL; @@ -995,6 +1064,23 @@ static void *stbi__malloc_mad4(int a, int b, int c, int d, int add) } #endif +// returns 1 if the sum of two signed ints is valid (between -2^31 and 2^31-1 inclusive), 0 on overflow. +static int stbi__addints_valid(int a, int b) +{ + if ((a >= 0) != (b >= 0)) return 1; // a and b have different signs, so no overflow + if (a < 0 && b < 0) return a >= INT_MIN - b; // same as a + b >= INT_MIN; INT_MIN - b cannot overflow since b < 0. + return a <= INT_MAX - b; +} + +// returns 1 if the product of two signed shorts is valid, 0 on overflow. +static int stbi__mul2shorts_valid(short a, short b) +{ + if (b == 0 || b == -1) return 1; // multiplication by 0 is always 0; check for -1 so SHRT_MIN/b doesn't overflow + if ((a >= 0) == (b >= 0)) return a <= SHRT_MAX/b; // product is positive, so similar to mul2sizes_valid + if (b < 0) return a <= SHRT_MIN / b; // same as a * b >= SHRT_MIN + return a >= SHRT_MIN / b; +} + // stbi__err - error // stbi__errpf - error returning pointer to float // stbi__errpuc - error returning pointer to unsigned char @@ -1053,9 +1139,8 @@ static void *stbi__load_main(stbi__context *s, int *x, int *y, int *comp, int re ri->channel_order = STBI_ORDER_RGB; // all current input & output are this, but this is here so we can add BGR order ri->num_channels = 0; - #ifndef STBI_NO_JPEG - if (stbi__jpeg_test(s)) return stbi__jpeg_load(s,x,y,comp,req_comp, ri); - #endif + // test the formats with a very explicit header first (at least a FOURCC + // or distinctive magic number first) #ifndef STBI_NO_PNG if (stbi__png_test(s)) return stbi__png_load(s,x,y,comp,req_comp, ri); #endif @@ -1073,6 +1158,13 @@ static void *stbi__load_main(stbi__context *s, int *x, int *y, int *comp, int re #ifndef STBI_NO_PIC if (stbi__pic_test(s)) return stbi__pic_load(s,x,y,comp,req_comp, ri); #endif + + // then the formats that can end up attempting to load with just 1 or 2 + // bytes matching expectations; these are prone to false positives, so + // try them later + #ifndef STBI_NO_JPEG + if (stbi__jpeg_test(s)) return stbi__jpeg_load(s,x,y,comp,req_comp, ri); + #endif #ifndef STBI_NO_PNM if (stbi__pnm_test(s)) return stbi__pnm_load(s,x,y,comp,req_comp, ri); #endif @@ -1171,8 +1263,10 @@ static unsigned char *stbi__load_and_postprocess_8bit(stbi__context *s, int *x, if (result == NULL) return NULL; + // it is the responsibility of the loaders to make sure we get either 8 or 16 bit. + STBI_ASSERT(ri.bits_per_channel == 8 || ri.bits_per_channel == 16); + if (ri.bits_per_channel != 8) { - STBI_ASSERT(ri.bits_per_channel == 16); result = stbi__convert_16_to_8((stbi__uint16 *) result, *x, *y, req_comp == 0 ? *comp : req_comp); ri.bits_per_channel = 8; } @@ -1195,8 +1289,10 @@ static stbi__uint16 *stbi__load_and_postprocess_16bit(stbi__context *s, int *x, if (result == NULL) return NULL; + // it is the responsibility of the loaders to make sure we get either 8 or 16 bit. + STBI_ASSERT(ri.bits_per_channel == 8 || ri.bits_per_channel == 16); + if (ri.bits_per_channel != 16) { - STBI_ASSERT(ri.bits_per_channel == 8); result = stbi__convert_8_to_16((stbi_uc *) result, *x, *y, req_comp == 0 ? *comp : req_comp); ri.bits_per_channel = 16; } @@ -1224,12 +1320,12 @@ static void stbi__float_postprocess(float *result, int *x, int *y, int *comp, in #ifndef STBI_NO_STDIO -#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) +#if defined(_WIN32) && defined(STBI_WINDOWS_UTF8) STBI_EXTERN __declspec(dllimport) int __stdcall MultiByteToWideChar(unsigned int cp, unsigned long flags, const char *str, int cbmb, wchar_t *widestr, int cchwide); STBI_EXTERN __declspec(dllimport) int __stdcall WideCharToMultiByte(unsigned int cp, unsigned long flags, const wchar_t *widestr, int cchwide, char *str, int cbmb, const char *defchar, int *used_default); #endif -#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) +#if defined(_WIN32) && defined(STBI_WINDOWS_UTF8) STBIDEF int stbi_convert_wchar_to_utf8(char *buffer, size_t bufferlen, const wchar_t* input) { return WideCharToMultiByte(65001 /* UTF8 */, 0, input, -1, buffer, (int) bufferlen, NULL, NULL); @@ -1239,16 +1335,16 @@ STBIDEF int stbi_convert_wchar_to_utf8(char *buffer, size_t bufferlen, const wch static FILE *stbi__fopen(char const *filename, char const *mode) { FILE *f; -#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) +#if defined(_WIN32) && defined(STBI_WINDOWS_UTF8) wchar_t wMode[64]; wchar_t wFilename[1024]; - if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, filename, -1, wFilename, sizeof(wFilename))) + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, filename, -1, wFilename, sizeof(wFilename)/sizeof(*wFilename))) return 0; - if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, mode, -1, wMode, sizeof(wMode))) + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, mode, -1, wMode, sizeof(wMode)/sizeof(*wMode))) return 0; -#if _MSC_VER >= 1400 +#if defined(_MSC_VER) && _MSC_VER >= 1400 if (0 != _wfopen_s(&f, wFilename, wMode)) f = 0; #else @@ -1499,6 +1595,7 @@ enum static void stbi__refill_buffer(stbi__context *s) { int n = (s->io.read)(s->io_user_data,(char*)s->buffer_start,s->buflen); + s->callback_already_read += (int) (s->img_buffer - s->img_buffer_original); if (n == 0) { // at end of file, treat same as if from memory, but need to handle case // where s->img_buffer isn't pointing to safe memory, e.g. 0-byte file @@ -1544,6 +1641,7 @@ stbi_inline static int stbi__at_eof(stbi__context *s) #else static void stbi__skip(stbi__context *s, int n) { + if (n == 0) return; // already there! if (n < 0) { s->img_buffer = s->img_buffer_end; return; @@ -1622,7 +1720,8 @@ static int stbi__get16le(stbi__context *s) static stbi__uint32 stbi__get32le(stbi__context *s) { stbi__uint32 z = stbi__get16le(s); - return z + (stbi__get16le(s) << 16); + z += (stbi__uint32)stbi__get16le(s) << 16; + return z; } #endif @@ -1686,7 +1785,7 @@ static unsigned char *stbi__convert_format(unsigned char *data, int img_n, int r STBI__CASE(4,1) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); } break; STBI__CASE(4,2) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); dest[1] = src[3]; } break; STBI__CASE(4,3) { dest[0]=src[0];dest[1]=src[1];dest[2]=src[2]; } break; - default: STBI_ASSERT(0); + default: STBI_ASSERT(0); STBI_FREE(data); STBI_FREE(good); return stbi__errpuc("unsupported", "Unsupported format conversion"); } #undef STBI__CASE } @@ -1743,7 +1842,7 @@ static stbi__uint16 *stbi__convert_format16(stbi__uint16 *data, int img_n, int r STBI__CASE(4,1) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); } break; STBI__CASE(4,2) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); dest[1] = src[3]; } break; STBI__CASE(4,3) { dest[0]=src[0];dest[1]=src[1];dest[2]=src[2]; } break; - default: STBI_ASSERT(0); + default: STBI_ASSERT(0); STBI_FREE(data); STBI_FREE(good); return (stbi__uint16*) stbi__errpuc("unsupported", "Unsupported format conversion"); } #undef STBI__CASE } @@ -1904,9 +2003,12 @@ static int stbi__build_huffman(stbi__huffman *h, int *count) int i,j,k=0; unsigned int code; // build size list for each symbol (from JPEG spec) - for (i=0; i < 16; ++i) - for (j=0; j < count[i]; ++j) + for (i=0; i < 16; ++i) { + for (j=0; j < count[i]; ++j) { h->size[k++] = (stbi_uc) (i+1); + if(k >= 257) return stbi__err("bad size list","Corrupt JPEG"); + } + } h->size[k] = 0; // compute actual symbols (from jpeg spec) @@ -2031,6 +2133,8 @@ stbi_inline static int stbi__jpeg_huff_decode(stbi__jpeg *j, stbi__huffman *h) // convert the huffman code to the symbol id c = ((j->code_buffer >> (32 - k)) & stbi__bmask[k]) + h->delta[k]; + if(c < 0 || c >= 256) // symbol id out of bounds! + return -1; STBI_ASSERT((((j->code_buffer) >> (32 - h->size[c])) & stbi__bmask[h->size[c]]) == h->code[c]); // convert the id to a symbol @@ -2049,14 +2153,14 @@ stbi_inline static int stbi__extend_receive(stbi__jpeg *j, int n) unsigned int k; int sgn; if (j->code_bits < n) stbi__grow_buffer_unsafe(j); + if (j->code_bits < n) return 0; // ran out of bits from stream, return 0s intead of continuing - sgn = (stbi__int32)j->code_buffer >> 31; // sign bit is always in MSB + sgn = j->code_buffer >> 31; // sign bit always in MSB; 0 if MSB clear (positive), 1 if MSB set (negative) k = stbi_lrot(j->code_buffer, n); - STBI_ASSERT(n >= 0 && n < (int) (sizeof(stbi__bmask)/sizeof(*stbi__bmask))); j->code_buffer = k & ~stbi__bmask[n]; k &= stbi__bmask[n]; j->code_bits -= n; - return k + (stbi__jbias[n] & ~sgn); + return k + (stbi__jbias[n] & (sgn - 1)); } // get some unsigned bits @@ -2064,6 +2168,7 @@ stbi_inline static int stbi__jpeg_get_bits(stbi__jpeg *j, int n) { unsigned int k; if (j->code_bits < n) stbi__grow_buffer_unsafe(j); + if (j->code_bits < n) return 0; // ran out of bits from stream, return 0s intead of continuing k = stbi_lrot(j->code_buffer, n); j->code_buffer = k & ~stbi__bmask[n]; k &= stbi__bmask[n]; @@ -2075,6 +2180,7 @@ stbi_inline static int stbi__jpeg_get_bit(stbi__jpeg *j) { unsigned int k; if (j->code_bits < 1) stbi__grow_buffer_unsafe(j); + if (j->code_bits < 1) return 0; // ran out of bits from stream, return 0s intead of continuing k = j->code_buffer; j->code_buffer <<= 1; --j->code_bits; @@ -2106,14 +2212,16 @@ static int stbi__jpeg_decode_block(stbi__jpeg *j, short data[64], stbi__huffman if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); t = stbi__jpeg_huff_decode(j, hdc); - if (t < 0) return stbi__err("bad huffman code","Corrupt JPEG"); + if (t < 0 || t > 15) return stbi__err("bad huffman code","Corrupt JPEG"); // 0 all the ac values now so we can do it 32-bits at a time memset(data,0,64*sizeof(data[0])); diff = t ? stbi__extend_receive(j, t) : 0; + if (!stbi__addints_valid(j->img_comp[b].dc_pred, diff)) return stbi__err("bad delta","Corrupt JPEG"); dc = j->img_comp[b].dc_pred + diff; j->img_comp[b].dc_pred = dc; + if (!stbi__mul2shorts_valid(dc, dequant[0])) return stbi__err("can't merge dc and ac", "Corrupt JPEG"); data[0] = (short) (dc * dequant[0]); // decode AC components, see JPEG spec @@ -2127,6 +2235,7 @@ static int stbi__jpeg_decode_block(stbi__jpeg *j, short data[64], stbi__huffman if (r) { // fast-AC path k += (r >> 4) & 15; // run s = r & 15; // combined length + if (s > j->code_bits) return stbi__err("bad huffman code", "Combined length longer than code bits available"); j->code_buffer <<= s; j->code_bits -= s; // decode into unzigzag'd location @@ -2163,11 +2272,14 @@ static int stbi__jpeg_decode_block_prog_dc(stbi__jpeg *j, short data[64], stbi__ // first scan for DC coefficient, must be first memset(data,0,64*sizeof(data[0])); // 0 all the ac values now t = stbi__jpeg_huff_decode(j, hdc); + if (t < 0 || t > 15) return stbi__err("can't merge dc and ac", "Corrupt JPEG"); diff = t ? stbi__extend_receive(j, t) : 0; + if (!stbi__addints_valid(j->img_comp[b].dc_pred, diff)) return stbi__err("bad delta", "Corrupt JPEG"); dc = j->img_comp[b].dc_pred + diff; j->img_comp[b].dc_pred = dc; - data[0] = (short) (dc << j->succ_low); + if (!stbi__mul2shorts_valid(dc, 1 << j->succ_low)) return stbi__err("can't merge dc and ac", "Corrupt JPEG"); + data[0] = (short) (dc * (1 << j->succ_low)); } else { // refinement scan for DC coefficient if (stbi__jpeg_get_bit(j)) @@ -2201,10 +2313,11 @@ static int stbi__jpeg_decode_block_prog_ac(stbi__jpeg *j, short data[64], stbi__ if (r) { // fast-AC path k += (r >> 4) & 15; // run s = r & 15; // combined length + if (s > j->code_bits) return stbi__err("bad huffman code", "Combined length longer than code bits available"); j->code_buffer <<= s; j->code_bits -= s; zig = stbi__jpeg_dezigzag[k++]; - data[zig] = (short) ((r >> 8) << shift); + data[zig] = (short) ((r >> 8) * (1 << shift)); } else { int rs = stbi__jpeg_huff_decode(j, hac); if (rs < 0) return stbi__err("bad huffman code","Corrupt JPEG"); @@ -2222,7 +2335,7 @@ static int stbi__jpeg_decode_block_prog_ac(stbi__jpeg *j, short data[64], stbi__ } else { k += r; zig = stbi__jpeg_dezigzag[k++]; - data[zig] = (short) (stbi__extend_receive(j,s) << shift); + data[zig] = (short) (stbi__extend_receive(j,s) * (1 << shift)); } } } while (k <= j->spec_end); @@ -3021,6 +3134,7 @@ static int stbi__process_marker(stbi__jpeg *z, int m) sizes[i] = stbi__get8(z->s); n += sizes[i]; } + if(n > 256) return stbi__err("bad DHT header","Corrupt JPEG"); // Loop over i < n would write past end of values! L -= 17; if (tc == 0) { if (!stbi__build_huffman(z->huff_dc+th, sizes)) return 0; @@ -3153,6 +3267,8 @@ static int stbi__process_frame_header(stbi__jpeg *z, int scan) p = stbi__get8(s); if (p != 8) return stbi__err("only 8-bit","JPEG format not supported: 8-bit only"); // JPEG baseline s->img_y = stbi__get16be(s); if (s->img_y == 0) return stbi__err("no header height", "JPEG format not supported: delayed height"); // Legal, but we don't handle it--but neither does IJG s->img_x = stbi__get16be(s); if (s->img_x == 0) return stbi__err("0 width","Corrupt JPEG"); // JPEG requires + if (s->img_y > STBI_MAX_DIMENSIONS) return stbi__err("too large","Very large image (corrupt?)"); + if (s->img_x > STBI_MAX_DIMENSIONS) return stbi__err("too large","Very large image (corrupt?)"); c = stbi__get8(s); if (c != 3 && c != 1 && c != 4) return stbi__err("bad component count","Corrupt JPEG"); s->img_n = c; @@ -3184,6 +3300,13 @@ static int stbi__process_frame_header(stbi__jpeg *z, int scan) if (z->img_comp[i].v > v_max) v_max = z->img_comp[i].v; } + // check that plane subsampling factors are integer ratios; our resamplers can't deal with fractional ratios + // and I've never seen a non-corrupted JPEG file actually use them + for (i=0; i < s->img_n; ++i) { + if (h_max % z->img_comp[i].h != 0) return stbi__err("bad H","Corrupt JPEG"); + if (v_max % z->img_comp[i].v != 0) return stbi__err("bad V","Corrupt JPEG"); + } + // compute interleaved mcu info z->img_h_max = h_max; z->img_v_max = v_max; @@ -3261,6 +3384,28 @@ static int stbi__decode_jpeg_header(stbi__jpeg *z, int scan) return 1; } +static int stbi__skip_jpeg_junk_at_end(stbi__jpeg *j) +{ + // some JPEGs have junk at end, skip over it but if we find what looks + // like a valid marker, resume there + while (!stbi__at_eof(j->s)) { + int x = stbi__get8(j->s); + while (x == 255) { // might be a marker + if (stbi__at_eof(j->s)) return STBI__MARKER_none; + x = stbi__get8(j->s); + if (x != 0x00 && x != 0xff) { + // not a stuffed zero or lead-in to another marker, looks + // like an actual marker, return it + return x; + } + // stuffed zero has x=0 now which ends the loop, meaning we go + // back to regular scan loop. + // repeated 0xff keeps trying to read the next byte of the marker. + } + } + return STBI__MARKER_none; +} + // decode image to YCbCr format static int stbi__decode_jpeg_image(stbi__jpeg *j) { @@ -3277,25 +3422,22 @@ static int stbi__decode_jpeg_image(stbi__jpeg *j) if (!stbi__process_scan_header(j)) return 0; if (!stbi__parse_entropy_coded_data(j)) return 0; if (j->marker == STBI__MARKER_none ) { - // handle 0s at the end of image data from IP Kamera 9060 - while (!stbi__at_eof(j->s)) { - int x = stbi__get8(j->s); - if (x == 255) { - j->marker = stbi__get8(j->s); - break; - } - } + j->marker = stbi__skip_jpeg_junk_at_end(j); // if we reach eof without hitting a marker, stbi__get_marker() below will fail and we'll eventually return 0 } + m = stbi__get_marker(j); + if (STBI__RESTART(m)) + m = stbi__get_marker(j); } else if (stbi__DNL(m)) { int Ld = stbi__get16be(j->s); stbi__uint32 NL = stbi__get16be(j->s); if (Ld != 4) return stbi__err("bad DNL len", "Corrupt JPEG"); if (NL != j->s->img_y) return stbi__err("bad DNL height", "Corrupt JPEG"); + m = stbi__get_marker(j); } else { - if (!stbi__process_marker(j, m)) return 0; + if (!stbi__process_marker(j, m)) return 1; + m = stbi__get_marker(j); } - m = stbi__get_marker(j); } if (j->progressive) stbi__jpeg_finish(j); @@ -3739,6 +3881,10 @@ static stbi_uc *load_jpeg_image(stbi__jpeg *z, int *out_x, int *out_y, int *comp else decode_n = z->s->img_n; + // nothing to do if no components requested; check this now to avoid + // accessing uninitialized coutput[0] later + if (decode_n <= 0) { stbi__cleanup_jpeg(z); return NULL; } + // resample and color-convert { int k; @@ -3881,6 +4027,8 @@ static void *stbi__jpeg_load(stbi__context *s, int *x, int *y, int *comp, int re { unsigned char* result; stbi__jpeg* j = (stbi__jpeg*) stbi__malloc(sizeof(stbi__jpeg)); + if (!j) return stbi__errpuc("outofmem", "Out of memory"); + memset(j, 0, sizeof(stbi__jpeg)); STBI_NOTUSED(ri); j->s = s; stbi__setup_jpeg(j); @@ -3893,6 +4041,8 @@ static int stbi__jpeg_test(stbi__context *s) { int r; stbi__jpeg* j = (stbi__jpeg*)stbi__malloc(sizeof(stbi__jpeg)); + if (!j) return stbi__err("outofmem", "Out of memory"); + memset(j, 0, sizeof(stbi__jpeg)); j->s = s; stbi__setup_jpeg(j); r = stbi__decode_jpeg_header(j, STBI__SCAN_type); @@ -3917,6 +4067,8 @@ static int stbi__jpeg_info(stbi__context *s, int *x, int *y, int *comp) { int result; stbi__jpeg* j = (stbi__jpeg*) (stbi__malloc(sizeof(stbi__jpeg))); + if (!j) return stbi__err("outofmem", "Out of memory"); + memset(j, 0, sizeof(stbi__jpeg)); j->s = s; result = stbi__jpeg_info_raw(j, x, y, comp); STBI_FREE(j); @@ -3936,6 +4088,7 @@ static int stbi__jpeg_info(stbi__context *s, int *x, int *y, int *comp) // fast-way is faster to check than jpeg huffman, but slow way is slower #define STBI__ZFAST_BITS 9 // accelerate all cases in default tables #define STBI__ZFAST_MASK ((1 << STBI__ZFAST_BITS) - 1) +#define STBI__ZNSYMS 288 // number of symbols in literal/length alphabet // zlib-style huffman encoding // (jpegs packs from left, zlib from right, so can't share code) @@ -3945,8 +4098,8 @@ typedef struct stbi__uint16 firstcode[16]; int maxcode[17]; stbi__uint16 firstsymbol[16]; - stbi_uc size[288]; - stbi__uint16 value[288]; + stbi_uc size[STBI__ZNSYMS]; + stbi__uint16 value[STBI__ZNSYMS]; } stbi__zhuffman; stbi_inline static int stbi__bitreverse16(int n) @@ -4033,16 +4186,23 @@ typedef struct stbi__zhuffman z_length, z_distance; } stbi__zbuf; +stbi_inline static int stbi__zeof(stbi__zbuf *z) +{ + return (z->zbuffer >= z->zbuffer_end); +} + stbi_inline static stbi_uc stbi__zget8(stbi__zbuf *z) { - if (z->zbuffer >= z->zbuffer_end) return 0; - return *z->zbuffer++; + return stbi__zeof(z) ? 0 : *z->zbuffer++; } static void stbi__fill_bits(stbi__zbuf *z) { do { - STBI_ASSERT(z->code_buffer < (1U << z->num_bits)); + if (z->code_buffer >= (1U << z->num_bits)) { + z->zbuffer = z->zbuffer_end; /* treat this as EOF so we fail. */ + return; + } z->code_buffer |= (unsigned int) stbi__zget8(z) << z->num_bits; z->num_bits += 8; } while (z->num_bits <= 24); @@ -4067,10 +4227,11 @@ static int stbi__zhuffman_decode_slowpath(stbi__zbuf *a, stbi__zhuffman *z) for (s=STBI__ZFAST_BITS+1; ; ++s) if (k < z->maxcode[s]) break; - if (s == 16) return -1; // invalid code! + if (s >= 16) return -1; // invalid code! // code size is s, so: b = (k >> (16-s)) - z->firstcode[s] + z->firstsymbol[s]; - STBI_ASSERT(z->size[b] == s); + if (b >= STBI__ZNSYMS) return -1; // some data was corrupt somewhere! + if (z->size[b] != s) return -1; // was originally an assert, but report failure instead. a->code_buffer >>= s; a->num_bits -= s; return z->value[b]; @@ -4079,7 +4240,12 @@ static int stbi__zhuffman_decode_slowpath(stbi__zbuf *a, stbi__zhuffman *z) stbi_inline static int stbi__zhuffman_decode(stbi__zbuf *a, stbi__zhuffman *z) { int b,s; - if (a->num_bits < 16) stbi__fill_bits(a); + if (a->num_bits < 16) { + if (stbi__zeof(a)) { + return -1; /* report error for unexpected end of data. */ + } + stbi__fill_bits(a); + } b = z->fast[a->code_buffer & STBI__ZFAST_MASK]; if (b) { s = b >> 9; @@ -4093,13 +4259,16 @@ stbi_inline static int stbi__zhuffman_decode(stbi__zbuf *a, stbi__zhuffman *z) static int stbi__zexpand(stbi__zbuf *z, char *zout, int n) // need to make room for n bytes { char *q; - int cur, limit, old_limit; + unsigned int cur, limit, old_limit; z->zout = zout; if (!z->z_expandable) return stbi__err("output buffer limit","Corrupt PNG"); - cur = (int) (z->zout - z->zout_start); - limit = old_limit = (int) (z->zout_end - z->zout_start); - while (cur + n > limit) + cur = (unsigned int) (z->zout - z->zout_start); + limit = old_limit = (unsigned) (z->zout_end - z->zout_start); + if (UINT_MAX - cur < (unsigned) n) return stbi__err("outofmem", "Out of memory"); + while (cur + n > limit) { + if(limit > UINT_MAX / 2) return stbi__err("outofmem", "Out of memory"); limit *= 2; + } q = (char *) STBI_REALLOC_SIZED(z->zout_start, old_limit, limit); STBI_NOTUSED(old_limit); if (q == NULL) return stbi__err("outofmem", "Out of memory"); @@ -4142,11 +4311,12 @@ static int stbi__parse_huffman_block(stbi__zbuf *a) a->zout = zout; return 1; } + if (z >= 286) return stbi__err("bad huffman code","Corrupt PNG"); // per DEFLATE, length codes 286 and 287 must not appear in compressed data z -= 257; len = stbi__zlength_base[z]; if (stbi__zlength_extra[z]) len += stbi__zreceive(a, stbi__zlength_extra[z]); z = stbi__zhuffman_decode(a, &a->z_distance); - if (z < 0) return stbi__err("bad huffman code","Corrupt PNG"); + if (z < 0 || z >= 30) return stbi__err("bad huffman code","Corrupt PNG"); // per DEFLATE, distance codes 30 and 31 must not appear in compressed data dist = stbi__zdist_base[z]; if (stbi__zdist_extra[z]) dist += stbi__zreceive(a, stbi__zdist_extra[z]); if (zout - a->zout_start < dist) return stbi__err("bad dist","Corrupt PNG"); @@ -4197,11 +4367,12 @@ static int stbi__compute_huffman_codes(stbi__zbuf *a) c = stbi__zreceive(a,2)+3; if (n == 0) return stbi__err("bad codelengths", "Corrupt PNG"); fill = lencodes[n-1]; - } else if (c == 17) + } else if (c == 17) { c = stbi__zreceive(a,3)+3; - else { - STBI_ASSERT(c == 18); + } else if (c == 18) { c = stbi__zreceive(a,7)+11; + } else { + return stbi__err("bad codelengths", "Corrupt PNG"); } if (ntot - n < c) return stbi__err("bad codelengths", "Corrupt PNG"); memset(lencodes+n, fill, c); @@ -4227,7 +4398,7 @@ static int stbi__parse_uncompressed_block(stbi__zbuf *a) a->code_buffer >>= 8; a->num_bits -= 8; } - STBI_ASSERT(a->num_bits == 0); + if (a->num_bits < 0) return stbi__err("zlib corrupt","Corrupt PNG"); // now fill header the normal way while (k < 4) header[k++] = stbi__zget8(a); @@ -4249,6 +4420,7 @@ static int stbi__parse_zlib_header(stbi__zbuf *a) int cm = cmf & 15; /* int cinfo = cmf >> 4; */ int flg = stbi__zget8(a); + if (stbi__zeof(a)) return stbi__err("bad zlib header","Corrupt PNG"); // zlib spec if ((cmf*256+flg) % 31 != 0) return stbi__err("bad zlib header","Corrupt PNG"); // zlib spec if (flg & 32) return stbi__err("no preset dict","Corrupt PNG"); // preset dictionary not allowed in png if (cm != 8) return stbi__err("bad compression","Corrupt PNG"); // DEFLATE required for png @@ -4256,7 +4428,7 @@ static int stbi__parse_zlib_header(stbi__zbuf *a) return 1; } -static const stbi_uc stbi__zdefault_length[288] = +static const stbi_uc stbi__zdefault_length[STBI__ZNSYMS] = { 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, @@ -4302,7 +4474,7 @@ static int stbi__parse_zlib(stbi__zbuf *a, int parse_header) } else { if (type == 1) { // use fixed code lengths - if (!stbi__zbuild_huffman(&a->z_length , stbi__zdefault_length , 288)) return 0; + if (!stbi__zbuild_huffman(&a->z_length , stbi__zdefault_length , STBI__ZNSYMS)) return 0; if (!stbi__zbuild_huffman(&a->z_distance, stbi__zdefault_distance, 32)) return 0; } else { if (!stbi__compute_huffman_codes(a)) return 0; @@ -4510,7 +4682,7 @@ static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 r return stbi__err("invalid filter","Corrupt PNG"); if (depth < 8) { - STBI_ASSERT(img_width_bytes <= x); + if (img_width_bytes > x) return stbi__err("invalid width","Corrupt PNG"); cur += x*out_n - img_width_bytes; // store output to the rightmost img_len bytes, so we can decode in place filter_bytes = 1; width = img_width_bytes; @@ -4698,6 +4870,7 @@ static int stbi__create_png_image(stbi__png *a, stbi_uc *image_data, stbi__uint3 // de-interlacing final = (stbi_uc *) stbi__malloc_mad3(a->s->img_x, a->s->img_y, out_bytes, 0); + if (!final) return stbi__err("outofmem", "Out of memory"); for (p=0; p < 7; ++p) { int xorig[] = { 0,4,0,2,0,1,0 }; int yorig[] = { 0,0,4,0,2,0,1 }; @@ -4818,19 +4991,46 @@ static int stbi__expand_png_palette(stbi__png *a, stbi_uc *palette, int len, int return 1; } -static int stbi__unpremultiply_on_load = 0; -static int stbi__de_iphone_flag = 0; +static int stbi__unpremultiply_on_load_global = 0; +static int stbi__de_iphone_flag_global = 0; STBIDEF void stbi_set_unpremultiply_on_load(int flag_true_if_should_unpremultiply) { - stbi__unpremultiply_on_load = flag_true_if_should_unpremultiply; + stbi__unpremultiply_on_load_global = flag_true_if_should_unpremultiply; } STBIDEF void stbi_convert_iphone_png_to_rgb(int flag_true_if_should_convert) { - stbi__de_iphone_flag = flag_true_if_should_convert; + stbi__de_iphone_flag_global = flag_true_if_should_convert; } +#ifndef STBI_THREAD_LOCAL +#define stbi__unpremultiply_on_load stbi__unpremultiply_on_load_global +#define stbi__de_iphone_flag stbi__de_iphone_flag_global +#else +static STBI_THREAD_LOCAL int stbi__unpremultiply_on_load_local, stbi__unpremultiply_on_load_set; +static STBI_THREAD_LOCAL int stbi__de_iphone_flag_local, stbi__de_iphone_flag_set; + +STBIDEF void stbi_set_unpremultiply_on_load_thread(int flag_true_if_should_unpremultiply) +{ + stbi__unpremultiply_on_load_local = flag_true_if_should_unpremultiply; + stbi__unpremultiply_on_load_set = 1; +} + +STBIDEF void stbi_convert_iphone_png_to_rgb_thread(int flag_true_if_should_convert) +{ + stbi__de_iphone_flag_local = flag_true_if_should_convert; + stbi__de_iphone_flag_set = 1; +} + +#define stbi__unpremultiply_on_load (stbi__unpremultiply_on_load_set \ + ? stbi__unpremultiply_on_load_local \ + : stbi__unpremultiply_on_load_global) +#define stbi__de_iphone_flag (stbi__de_iphone_flag_set \ + ? stbi__de_iphone_flag_local \ + : stbi__de_iphone_flag_global) +#endif // STBI_THREAD_LOCAL + static void stbi__de_iphone(stbi__png *z) { stbi__context *s = z->s; @@ -4905,8 +5105,10 @@ static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) if (!first) return stbi__err("multiple IHDR","Corrupt PNG"); first = 0; if (c.length != 13) return stbi__err("bad IHDR len","Corrupt PNG"); - s->img_x = stbi__get32be(s); if (s->img_x > (1 << 24)) return stbi__err("too large","Very large image (corrupt?)"); - s->img_y = stbi__get32be(s); if (s->img_y > (1 << 24)) return stbi__err("too large","Very large image (corrupt?)"); + s->img_x = stbi__get32be(s); + s->img_y = stbi__get32be(s); + if (s->img_y > STBI_MAX_DIMENSIONS) return stbi__err("too large","Very large image (corrupt?)"); + if (s->img_x > STBI_MAX_DIMENSIONS) return stbi__err("too large","Very large image (corrupt?)"); z->depth = stbi__get8(s); if (z->depth != 1 && z->depth != 2 && z->depth != 4 && z->depth != 8 && z->depth != 16) return stbi__err("1/2/4/8/16-bit only","PNG not supported: 1/2/4/8/16-bit only"); color = stbi__get8(s); if (color > 6) return stbi__err("bad ctype","Corrupt PNG"); if (color == 3 && z->depth == 16) return stbi__err("bad ctype","Corrupt PNG"); @@ -4918,14 +5120,13 @@ static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) if (!pal_img_n) { s->img_n = (color & 2 ? 3 : 1) + (color & 4 ? 1 : 0); if ((1 << 30) / s->img_x / s->img_n < s->img_y) return stbi__err("too large", "Image too large to decode"); - if (scan == STBI__SCAN_header) return 1; } else { // if paletted, then pal_n is our final components, and // img_n is # components to decompress/filter. s->img_n = 1; if ((1 << 30) / s->img_x / 4 < s->img_y) return stbi__err("too large","Corrupt PNG"); - // if SCAN_header, have to scan to see if we have a tRNS } + // even with SCAN_header, have to scan to see if we have a tRNS break; } @@ -4957,6 +5158,8 @@ static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) if (!(s->img_n & 1)) return stbi__err("tRNS with alpha","Corrupt PNG"); if (c.length != (stbi__uint32) s->img_n*2) return stbi__err("bad tRNS len","Corrupt PNG"); has_trans = 1; + // non-paletted with tRNS = constant alpha. if header-scanning, we can stop now. + if (scan == STBI__SCAN_header) { ++s->img_n; return 1; } if (z->depth == 16) { for (k = 0; k < s->img_n; ++k) tc16[k] = (stbi__uint16)stbi__get16be(s); // copy the values as-is } else { @@ -4969,7 +5172,13 @@ static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) case STBI__PNG_TYPE('I','D','A','T'): { if (first) return stbi__err("first not IHDR", "Corrupt PNG"); if (pal_img_n && !pal_len) return stbi__err("no PLTE","Corrupt PNG"); - if (scan == STBI__SCAN_header) { s->img_n = pal_img_n; return 1; } + if (scan == STBI__SCAN_header) { + // header scan definitely stops at first IDAT + if (pal_img_n) + s->img_n = pal_img_n; + return 1; + } + if (c.length > (1u << 30)) return stbi__err("IDAT size limit", "IDAT section larger than 2^30 bytes"); if ((int)(ioff + c.length) < (int)ioff) return 0; if (ioff + c.length > idata_limit) { stbi__uint32 idata_limit_old = idata_limit; @@ -5055,10 +5264,12 @@ static void *stbi__do_png(stbi__png *p, int *x, int *y, int *n, int req_comp, st void *result=NULL; if (req_comp < 0 || req_comp > 4) return stbi__errpuc("bad req_comp", "Internal error"); if (stbi__parse_png_file(p, STBI__SCAN_load, req_comp)) { - if (p->depth < 8) + if (p->depth <= 8) ri->bits_per_channel = 8; + else if (p->depth == 16) + ri->bits_per_channel = 16; else - ri->bits_per_channel = p->depth; + return stbi__errpuc("bad bits_per_channel", "PNG not supported: unsupported color depth"); result = p->out; p->out = NULL; if (req_comp && req_comp != p->s->img_out_n) { @@ -5207,6 +5418,32 @@ typedef struct int extra_read; } stbi__bmp_data; +static int stbi__bmp_set_mask_defaults(stbi__bmp_data *info, int compress) +{ + // BI_BITFIELDS specifies masks explicitly, don't override + if (compress == 3) + return 1; + + if (compress == 0) { + if (info->bpp == 16) { + info->mr = 31u << 10; + info->mg = 31u << 5; + info->mb = 31u << 0; + } else if (info->bpp == 32) { + info->mr = 0xffu << 16; + info->mg = 0xffu << 8; + info->mb = 0xffu << 0; + info->ma = 0xffu << 24; + info->all_a = 0; // if all_a is 0 at end, then we loaded alpha channel but it was all 0 + } else { + // otherwise, use defaults, which is all-0 + info->mr = info->mg = info->mb = info->ma = 0; + } + return 1; + } + return 0; // error +} + static void *stbi__bmp_parse_header(stbi__context *s, stbi__bmp_data *info) { int hsz; @@ -5219,6 +5456,8 @@ static void *stbi__bmp_parse_header(stbi__context *s, stbi__bmp_data *info) info->mr = info->mg = info->mb = info->ma = 0; info->extra_read = 14; + if (info->offset < 0) return stbi__errpuc("bad BMP", "bad BMP"); + if (hsz != 12 && hsz != 40 && hsz != 56 && hsz != 108 && hsz != 124) return stbi__errpuc("unknown BMP", "BMP type not supported: unknown"); if (hsz == 12) { s->img_x = stbi__get16le(s); @@ -5232,6 +5471,8 @@ static void *stbi__bmp_parse_header(stbi__context *s, stbi__bmp_data *info) if (hsz != 12) { int compress = stbi__get32le(s); if (compress == 1 || compress == 2) return stbi__errpuc("BMP RLE", "BMP type not supported: RLE"); + if (compress >= 4) return stbi__errpuc("BMP JPEG/PNG", "BMP type not supported: unsupported compression"); // this includes PNG/JPEG modes + if (compress == 3 && info->bpp != 16 && info->bpp != 32) return stbi__errpuc("bad BMP", "bad BMP"); // bitfields requires 16 or 32 bits/pixel stbi__get32le(s); // discard sizeof stbi__get32le(s); // discard hres stbi__get32le(s); // discard vres @@ -5246,17 +5487,7 @@ static void *stbi__bmp_parse_header(stbi__context *s, stbi__bmp_data *info) } if (info->bpp == 16 || info->bpp == 32) { if (compress == 0) { - if (info->bpp == 32) { - info->mr = 0xffu << 16; - info->mg = 0xffu << 8; - info->mb = 0xffu << 0; - info->ma = 0xffu << 24; - info->all_a = 0; // if all_a is 0 at end, then we loaded alpha channel but it was all 0 - } else { - info->mr = 31u << 10; - info->mg = 31u << 5; - info->mb = 31u << 0; - } + stbi__bmp_set_mask_defaults(info, compress); } else if (compress == 3) { info->mr = stbi__get32le(s); info->mg = stbi__get32le(s); @@ -5271,6 +5502,7 @@ static void *stbi__bmp_parse_header(stbi__context *s, stbi__bmp_data *info) return stbi__errpuc("bad BMP", "bad BMP"); } } else { + // V4/V5 header int i; if (hsz != 108 && hsz != 124) return stbi__errpuc("bad BMP", "bad BMP"); @@ -5278,6 +5510,8 @@ static void *stbi__bmp_parse_header(stbi__context *s, stbi__bmp_data *info) info->mg = stbi__get32le(s); info->mb = stbi__get32le(s); info->ma = stbi__get32le(s); + if (compress != 3) // override mr/mg/mb unless in BI_BITFIELDS mode, as per docs + stbi__bmp_set_mask_defaults(info, compress); stbi__get32le(s); // discard color space for (i=0; i < 12; ++i) stbi__get32le(s); // discard color space parameters @@ -5310,6 +5544,9 @@ static void *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req flip_vertically = ((int) s->img_y) > 0; s->img_y = abs((int) s->img_y); + if (s->img_y > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large","Very large image (corrupt?)"); + if (s->img_x > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large","Very large image (corrupt?)"); + mr = info.mr; mg = info.mg; mb = info.mb; @@ -5324,7 +5561,23 @@ static void *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req psize = (info.offset - info.extra_read - info.hsz) >> 2; } if (psize == 0) { - STBI_ASSERT(info.offset == (s->img_buffer - s->buffer_start)); + // accept some number of extra bytes after the header, but if the offset points either to before + // the header ends or implies a large amount of extra data, reject the file as malformed + int bytes_read_so_far = s->callback_already_read + (int)(s->img_buffer - s->img_buffer_original); + int header_limit = 1024; // max we actually read is below 256 bytes currently. + int extra_data_limit = 256*4; // what ordinarily goes here is a palette; 256 entries*4 bytes is its max size. + if (bytes_read_so_far <= 0 || bytes_read_so_far > header_limit) { + return stbi__errpuc("bad header", "Corrupt BMP"); + } + // we established that bytes_read_so_far is positive and sensible. + // the first half of this test rejects offsets that are either too small positives, or + // negative, and guarantees that info.offset >= bytes_read_so_far > 0. this in turn + // ensures the number computed in the second half of the test can't overflow. + if (info.offset < bytes_read_so_far || info.offset - bytes_read_so_far > extra_data_limit) { + return stbi__errpuc("bad offset", "Corrupt BMP"); + } else { + stbi__skip(s, info.offset - bytes_read_so_far); + } } if (info.bpp == 24 && ma == 0xff000000) @@ -5419,6 +5672,7 @@ static void *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req gshift = stbi__high_bit(mg)-7; gcount = stbi__bitcount(mg); bshift = stbi__high_bit(mb)-7; bcount = stbi__bitcount(mb); ashift = stbi__high_bit(ma)-7; acount = stbi__bitcount(ma); + if (rcount > 8 || gcount > 8 || bcount > 8 || acount > 8) { STBI_FREE(out); return stbi__errpuc("bad masks", "Corrupt BMP"); } } for (j=0; j < (int) s->img_y; ++j) { if (easy) { @@ -5643,6 +5897,9 @@ static void *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req STBI_NOTUSED(tga_x_origin); // @TODO STBI_NOTUSED(tga_y_origin); // @TODO + if (tga_height > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large","Very large image (corrupt?)"); + if (tga_width > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large","Very large image (corrupt?)"); + // do a tiny bit of precessing if ( tga_image_type >= 8 ) { @@ -5682,6 +5939,11 @@ static void *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req // do I need to load a palette? if ( tga_indexed) { + if (tga_palette_len == 0) { /* you have to have at least one entry! */ + STBI_FREE(tga_data); + return stbi__errpuc("bad palette", "Corrupt TGA"); + } + // any data to skip? (offset usually = 0) stbi__skip(s, tga_palette_start ); // load the palette @@ -5890,6 +6152,9 @@ static void *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req h = stbi__get32be(s); w = stbi__get32be(s); + if (h > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large","Very large image (corrupt?)"); + if (w > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large","Very large image (corrupt?)"); + // Make sure the depth is 8 bits. bitdepth = stbi__get16be(s); if (bitdepth != 8 && bitdepth != 16) @@ -6244,6 +6509,10 @@ static void *stbi__pic_load(stbi__context *s,int *px,int *py,int *comp,int req_c x = stbi__get16be(s); y = stbi__get16be(s); + + if (y > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large","Very large image (corrupt?)"); + if (x > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large","Very large image (corrupt?)"); + if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (pic header)"); if (!stbi__mad3sizes_valid(x, y, 4, 0)) return stbi__errpuc("too large", "PIC image too large to decode"); @@ -6253,6 +6522,7 @@ static void *stbi__pic_load(stbi__context *s,int *px,int *py,int *comp,int req_c // intermediate buffer is RGBA result = (stbi_uc *) stbi__malloc_mad3(x, y, 4, 0); + if (!result) return stbi__errpuc("outofmem", "Out of memory"); memset(result, 0xff, x*y*4); if (!stbi__pic_load_core(s,x,y,comp, result)) { @@ -6352,6 +6622,9 @@ static int stbi__gif_header(stbi__context *s, stbi__gif *g, int *comp, int is_in g->ratio = stbi__get8(s); g->transparent = -1; + if (g->w > STBI_MAX_DIMENSIONS) return stbi__err("too large","Very large image (corrupt?)"); + if (g->h > STBI_MAX_DIMENSIONS) return stbi__err("too large","Very large image (corrupt?)"); + if (comp != 0) *comp = 4; // can't actually tell whether it's 3 or 4 until we parse the comments if (is_info) return 1; @@ -6365,6 +6638,7 @@ static int stbi__gif_header(stbi__context *s, stbi__gif *g, int *comp, int is_in static int stbi__gif_info_raw(stbi__context *s, int *x, int *y, int *comp) { stbi__gif* g = (stbi__gif*) stbi__malloc(sizeof(stbi__gif)); + if (!g) return stbi__err("outofmem", "Out of memory"); if (!stbi__gif_header(s, g, comp, 1)) { STBI_FREE(g); stbi__rewind( s ); @@ -6529,7 +6803,7 @@ static stbi_uc *stbi__gif_load_next(stbi__context *s, stbi__gif *g, int *comp, i memset(g->history, 0x00, pcount); // pixels that were affected previous frame first_frame = 1; } else { - // second frame - how do we dispoase of the previous one? + // second frame - how do we dispose of the previous one? dispose = (g->eflags & 0x1C) >> 2; pcount = g->w * g->h; @@ -6674,6 +6948,17 @@ static stbi_uc *stbi__gif_load_next(stbi__context *s, stbi__gif *g, int *comp, i } } +static void *stbi__load_gif_main_outofmem(stbi__gif *g, stbi_uc *out, int **delays) +{ + STBI_FREE(g->out); + STBI_FREE(g->history); + STBI_FREE(g->background); + + if (out) STBI_FREE(out); + if (delays && *delays) STBI_FREE(*delays); + return stbi__errpuc("outofmem", "Out of memory"); +} + static void *stbi__load_gif_main(stbi__context *s, int **delays, int *x, int *y, int *z, int *comp, int req_comp) { if (stbi__gif_test(s)) { @@ -6683,6 +6968,12 @@ static void *stbi__load_gif_main(stbi__context *s, int **delays, int *x, int *y, stbi_uc *two_back = 0; stbi__gif g; int stride; + int out_size = 0; + int delays_size = 0; + + STBI_NOTUSED(out_size); + STBI_NOTUSED(delays_size); + memset(&g, 0, sizeof(g)); if (delays) { *delays = 0; @@ -6699,22 +6990,31 @@ static void *stbi__load_gif_main(stbi__context *s, int **delays, int *x, int *y, stride = g.w * g.h * 4; if (out) { - void *tmp = (stbi_uc*) STBI_REALLOC( out, layers * stride ); - if (NULL == tmp) { - STBI_FREE(g.out); - STBI_FREE(g.history); - STBI_FREE(g.background); - return stbi__errpuc("outofmem", "Out of memory"); + void *tmp = (stbi_uc*) STBI_REALLOC_SIZED( out, out_size, layers * stride ); + if (!tmp) + return stbi__load_gif_main_outofmem(&g, out, delays); + else { + out = (stbi_uc*) tmp; + out_size = layers * stride; } - else - out = (stbi_uc*) tmp; + if (delays) { - *delays = (int*) STBI_REALLOC( *delays, sizeof(int) * layers ); + int *new_delays = (int*) STBI_REALLOC_SIZED( *delays, delays_size, sizeof(int) * layers ); + if (!new_delays) + return stbi__load_gif_main_outofmem(&g, out, delays); + *delays = new_delays; + delays_size = layers * sizeof(int); } } else { out = (stbi_uc*)stbi__malloc( layers * stride ); + if (!out) + return stbi__load_gif_main_outofmem(&g, out, delays); + out_size = layers * stride; if (delays) { *delays = (int*) stbi__malloc( layers * sizeof(int) ); + if (!*delays) + return stbi__load_gif_main_outofmem(&g, out, delays); + delays_size = layers * sizeof(int); } } memcpy( out + ((layers - 1) * stride), u, stride ); @@ -6893,6 +7193,9 @@ static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int re token += 3; width = (int) strtol(token, NULL, 10); + if (height > STBI_MAX_DIMENSIONS) return stbi__errpf("too large","Very large image (corrupt?)"); + if (width > STBI_MAX_DIMENSIONS) return stbi__errpf("too large","Very large image (corrupt?)"); + *x = width; *y = height; @@ -6961,12 +7264,12 @@ static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int re // Run value = stbi__get8(s); count -= 128; - if (count > nleft) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("corrupt", "bad RLE data in HDR"); } + if ((count == 0) || (count > nleft)) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("corrupt", "bad RLE data in HDR"); } for (z = 0; z < count; ++z) scanline[i++ * 4 + k] = value; } else { // Dump - if (count > nleft) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("corrupt", "bad RLE data in HDR"); } + if ((count == 0) || (count > nleft)) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("corrupt", "bad RLE data in HDR"); } for (z = 0; z < count; ++z) scanline[i++ * 4 + k] = stbi__get8(s); } @@ -7035,9 +7338,10 @@ static int stbi__bmp_info(stbi__context *s, int *x, int *y, int *comp) info.all_a = 255; p = stbi__bmp_parse_header(s, &info); - stbi__rewind( s ); - if (p == NULL) + if (p == NULL) { + stbi__rewind( s ); return 0; + } if (x) *x = s->img_x; if (y) *y = s->img_y; if (comp) { @@ -7103,8 +7407,8 @@ static int stbi__psd_is16(stbi__context *s) stbi__rewind( s ); return 0; } - (void) stbi__get32be(s); - (void) stbi__get32be(s); + STBI_NOTUSED(stbi__get32be(s)); + STBI_NOTUSED(stbi__get32be(s)); depth = stbi__get16be(s); if (depth != 16) { stbi__rewind( s ); @@ -7183,7 +7487,6 @@ static int stbi__pic_info(stbi__context *s, int *x, int *y, int *comp) // Known limitations: // Does not support comments in the header section // Does not support ASCII image data (formats P2 and P3) -// Does not support 16-bit-per-channel #ifndef STBI_NO_PNM @@ -7204,22 +7507,33 @@ static void *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req stbi_uc *out; STBI_NOTUSED(ri); - if (!stbi__pnm_info(s, (int *)&s->img_x, (int *)&s->img_y, (int *)&s->img_n)) + ri->bits_per_channel = stbi__pnm_info(s, (int *)&s->img_x, (int *)&s->img_y, (int *)&s->img_n); + if (ri->bits_per_channel == 0) return 0; + if (s->img_y > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large","Very large image (corrupt?)"); + if (s->img_x > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large","Very large image (corrupt?)"); + *x = s->img_x; *y = s->img_y; if (comp) *comp = s->img_n; - if (!stbi__mad3sizes_valid(s->img_n, s->img_x, s->img_y, 0)) + if (!stbi__mad4sizes_valid(s->img_n, s->img_x, s->img_y, ri->bits_per_channel / 8, 0)) return stbi__errpuc("too large", "PNM too large"); - out = (stbi_uc *) stbi__malloc_mad3(s->img_n, s->img_x, s->img_y, 0); + out = (stbi_uc *) stbi__malloc_mad4(s->img_n, s->img_x, s->img_y, ri->bits_per_channel / 8, 0); if (!out) return stbi__errpuc("outofmem", "Out of memory"); - stbi__getn(s, out, s->img_n * s->img_x * s->img_y); + if (!stbi__getn(s, out, s->img_n * s->img_x * s->img_y * (ri->bits_per_channel / 8))) { + STBI_FREE(out); + return stbi__errpuc("bad PNM", "PNM file truncated"); + } if (req_comp && req_comp != s->img_n) { - out = stbi__convert_format(out, s->img_n, req_comp, s->img_x, s->img_y); + if (ri->bits_per_channel == 16) { + out = (stbi_uc *) stbi__convert_format16((stbi__uint16 *) out, s->img_n, req_comp, s->img_x, s->img_y); + } else { + out = stbi__convert_format(out, s->img_n, req_comp, s->img_x, s->img_y); + } if (out == NULL) return out; // stbi__convert_format frees input on failure } return out; @@ -7256,6 +7570,8 @@ static int stbi__pnm_getinteger(stbi__context *s, char *c) while (!stbi__at_eof(s) && stbi__pnm_isdigit(*c)) { value = value*10 + (*c - '0'); *c = (char) stbi__get8(s); + if((value > 214748364) || (value == 214748364 && *c > '7')) + return stbi__err("integer parse overflow", "Parsing an integer in the PPM header overflowed a 32-bit int"); } return value; @@ -7286,17 +7602,29 @@ static int stbi__pnm_info(stbi__context *s, int *x, int *y, int *comp) stbi__pnm_skip_whitespace(s, &c); *x = stbi__pnm_getinteger(s, &c); // read width + if(*x == 0) + return stbi__err("invalid width", "PPM image header had zero or overflowing width"); stbi__pnm_skip_whitespace(s, &c); *y = stbi__pnm_getinteger(s, &c); // read height + if (*y == 0) + return stbi__err("invalid width", "PPM image header had zero or overflowing width"); stbi__pnm_skip_whitespace(s, &c); maxv = stbi__pnm_getinteger(s, &c); // read max value - - if (maxv > 255) - return stbi__err("max value > 255", "PPM image not 8-bit"); + if (maxv > 65535) + return stbi__err("max value > 65535", "PPM image supports only 8-bit and 16-bit images"); + else if (maxv > 255) + return 16; else - return 1; + return 8; +} + +static int stbi__pnm_is16(stbi__context *s) +{ + if (stbi__pnm_info(s, NULL, NULL, NULL) == 16) + return 1; + return 0; } #endif @@ -7352,6 +7680,9 @@ static int stbi__is_16_main(stbi__context *s) if (stbi__psd_is16(s)) return 1; #endif + #ifndef STBI_NO_PNM + if (stbi__pnm_is16(s)) return 1; + #endif return 0; } diff --git a/3rdparty/stb/include/stb_image_write.h b/3rdparty/stb/include/stb_image_write.h index cffd473c1fb..e4b32ed1bc3 100644 --- a/3rdparty/stb/include/stb_image_write.h +++ b/3rdparty/stb/include/stb_image_write.h @@ -1,4 +1,4 @@ -/* stb_image_write - v1.14 - public domain - http://nothings.org/stb +/* stb_image_write - v1.16 - public domain - http://nothings.org/stb writes out PNG/BMP/TGA/JPEG/HDR images to C stdio - Sean Barrett 2010-2015 no warranty implied; use at your own risk @@ -140,6 +140,7 @@ Ivan Tikhonov github:ignotion Adam Schackart + Andrew Kensler LICENSE @@ -166,9 +167,9 @@ LICENSE #endif #ifndef STB_IMAGE_WRITE_STATIC // C++ forbids static forward declarations -extern int stbi_write_tga_with_rle; -extern int stbi_write_png_compression_level; -extern int stbi_write_force_png_filter; +STBIWDEF int stbi_write_tga_with_rle; +STBIWDEF int stbi_write_png_compression_level; +STBIWDEF int stbi_write_force_png_filter; #endif #ifndef STBI_WRITE_NO_STDIO @@ -178,7 +179,7 @@ STBIWDEF int stbi_write_tga(char const *filename, int w, int h, int comp, const STBIWDEF int stbi_write_hdr(char const *filename, int w, int h, int comp, const float *data); STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const void *data, int quality); -#ifdef STBI_WINDOWS_UTF8 +#ifdef STBIW_WINDOWS_UTF8 STBIWDEF int stbiw_convert_wchar_to_utf8(char *buffer, size_t bufferlen, const wchar_t* input); #endif #endif @@ -267,6 +268,8 @@ typedef struct { stbi_write_func *func; void *context; + unsigned char buffer[64]; + int buf_used; } stbi__write_context; // initialize a callback-based context @@ -283,7 +286,7 @@ static void stbi__stdio_write(void *context, void *data, int size) fwrite(data,1,size,(FILE*) context); } -#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) +#if defined(_WIN32) && defined(STBIW_WINDOWS_UTF8) #ifdef __cplusplus #define STBIW_EXTERN extern "C" #else @@ -294,25 +297,25 @@ STBIW_EXTERN __declspec(dllimport) int __stdcall WideCharToMultiByte(unsigned in STBIWDEF int stbiw_convert_wchar_to_utf8(char *buffer, size_t bufferlen, const wchar_t* input) { - return WideCharToMultiByte(65001 /* UTF8 */, 0, input, -1, buffer, (int) bufferlen, NULL, NULL); + return WideCharToMultiByte(65001 /* UTF8 */, 0, input, -1, buffer, (int) bufferlen, NULL, NULL); } #endif static FILE *stbiw__fopen(char const *filename, char const *mode) { FILE *f; -#if defined(_MSC_VER) && defined(STBI_WINDOWS_UTF8) +#if defined(_WIN32) && defined(STBIW_WINDOWS_UTF8) wchar_t wMode[64]; wchar_t wFilename[1024]; - if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, filename, -1, wFilename, sizeof(wFilename))) + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, filename, -1, wFilename, sizeof(wFilename)/sizeof(*wFilename))) return 0; - if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, mode, -1, wMode, sizeof(wMode))) + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, mode, -1, wMode, sizeof(wMode)/sizeof(*wMode))) return 0; -#if _MSC_VER >= 1400 - if (0 != _wfopen_s(&f, wFilename, wMode)) - f = 0; +#if defined(_MSC_VER) && _MSC_VER >= 1400 + if (0 != _wfopen_s(&f, wFilename, wMode)) + f = 0; #else f = _wfopen(wFilename, wMode); #endif @@ -380,16 +383,36 @@ static void stbiw__writef(stbi__write_context *s, const char *fmt, ...) va_end(v); } +static void stbiw__write_flush(stbi__write_context *s) +{ + if (s->buf_used) { + s->func(s->context, &s->buffer, s->buf_used); + s->buf_used = 0; + } +} + static void stbiw__putc(stbi__write_context *s, unsigned char c) { s->func(s->context, &c, 1); } +static void stbiw__write1(stbi__write_context *s, unsigned char a) +{ + if ((size_t)s->buf_used + 1 > sizeof(s->buffer)) + stbiw__write_flush(s); + s->buffer[s->buf_used++] = a; +} + static void stbiw__write3(stbi__write_context *s, unsigned char a, unsigned char b, unsigned char c) { - unsigned char arr[3]; - arr[0] = a; arr[1] = b; arr[2] = c; - s->func(s->context, arr, 3); + int n; + if ((size_t)s->buf_used + 3 > sizeof(s->buffer)) + stbiw__write_flush(s); + n = s->buf_used; + s->buf_used = n+3; + s->buffer[n+0] = a; + s->buffer[n+1] = b; + s->buffer[n+2] = c; } static void stbiw__write_pixel(stbi__write_context *s, int rgb_dir, int comp, int write_alpha, int expand_mono, unsigned char *d) @@ -398,7 +421,7 @@ static void stbiw__write_pixel(stbi__write_context *s, int rgb_dir, int comp, in int k; if (write_alpha < 0) - s->func(s->context, &d[comp - 1], 1); + stbiw__write1(s, d[comp - 1]); switch (comp) { case 2: // 2 pixels = mono + alpha, alpha is written separately, so same as 1-channel case @@ -406,7 +429,7 @@ static void stbiw__write_pixel(stbi__write_context *s, int rgb_dir, int comp, in if (expand_mono) stbiw__write3(s, d[0], d[0], d[0]); // monochrome bmp else - s->func(s->context, d, 1); // monochrome TGA + stbiw__write1(s, d[0]); // monochrome TGA break; case 4: if (!write_alpha) { @@ -422,7 +445,7 @@ static void stbiw__write_pixel(stbi__write_context *s, int rgb_dir, int comp, in break; } if (write_alpha > 0) - s->func(s->context, &d[comp - 1], 1); + stbiw__write1(s, d[comp - 1]); } static void stbiw__write_pixels(stbi__write_context *s, int rgb_dir, int vdir, int x, int y, int comp, void *data, int write_alpha, int scanline_pad, int expand_mono) @@ -447,6 +470,7 @@ static void stbiw__write_pixels(stbi__write_context *s, int rgb_dir, int vdir, i unsigned char *d = (unsigned char *) data + (j*x+i)*comp; stbiw__write_pixel(s, rgb_dir, comp, write_alpha, expand_mono, d); } + stbiw__write_flush(s); s->func(s->context, &zero, scanline_pad); } } @@ -467,16 +491,27 @@ static int stbiw__outfile(stbi__write_context *s, int rgb_dir, int vdir, int x, static int stbi_write_bmp_core(stbi__write_context *s, int x, int y, int comp, const void *data) { - int pad = (-x*3) & 3; - return stbiw__outfile(s,-1,-1,x,y,comp,1,(void *) data,0,pad, - "11 4 22 4" "4 44 22 444444", - 'B', 'M', 14+40+(x*3+pad)*y, 0,0, 14+40, // file header - 40, x,y, 1,24, 0,0,0,0,0,0); // bitmap header + if (comp != 4) { + // write RGB bitmap + int pad = (-x*3) & 3; + return stbiw__outfile(s,-1,-1,x,y,comp,1,(void *) data,0,pad, + "11 4 22 4" "4 44 22 444444", + 'B', 'M', 14+40+(x*3+pad)*y, 0,0, 14+40, // file header + 40, x,y, 1,24, 0,0,0,0,0,0); // bitmap header + } else { + // RGBA bitmaps need a v4 header + // use BI_BITFIELDS mode with 32bpp and alpha mask + // (straight BI_RGB with alpha mask doesn't work in most readers) + return stbiw__outfile(s,-1,-1,x,y,comp,1,(void *)data,1,0, + "11 4 22 4" "4 44 22 444444 4444 4 444 444 444 444", + 'B', 'M', 14+108+x*y*4, 0, 0, 14+108, // file header + 108, x,y, 1,32, 3,0,0,0,0,0, 0xff0000,0xff00,0xff,0xff000000u, 0, 0,0,0, 0,0,0, 0,0,0, 0,0,0); // bitmap V4 header + } } STBIWDEF int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data) { - stbi__write_context s; + stbi__write_context s = { 0 }; stbi__start_write_callbacks(&s, func, context); return stbi_write_bmp_core(&s, x, y, comp, data); } @@ -484,7 +519,7 @@ STBIWDEF int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int x, #ifndef STBI_WRITE_NO_STDIO STBIWDEF int stbi_write_bmp(char const *filename, int x, int y, int comp, const void *data) { - stbi__write_context s; + stbi__write_context s = { 0 }; if (stbi__start_write_file(&s,filename)) { int r = stbi_write_bmp_core(&s, x, y, comp, data); stbi__end_write_file(&s); @@ -557,24 +592,25 @@ static int stbi_write_tga_core(stbi__write_context *s, int x, int y, int comp, v if (diff) { unsigned char header = STBIW_UCHAR(len - 1); - s->func(s->context, &header, 1); + stbiw__write1(s, header); for (k = 0; k < len; ++k) { stbiw__write_pixel(s, -1, comp, has_alpha, 0, begin + k * comp); } } else { unsigned char header = STBIW_UCHAR(len - 129); - s->func(s->context, &header, 1); + stbiw__write1(s, header); stbiw__write_pixel(s, -1, comp, has_alpha, 0, begin); } } } + stbiw__write_flush(s); } return 1; } STBIWDEF int stbi_write_tga_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data) { - stbi__write_context s; + stbi__write_context s = { 0 }; stbi__start_write_callbacks(&s, func, context); return stbi_write_tga_core(&s, x, y, comp, (void *) data); } @@ -582,7 +618,7 @@ STBIWDEF int stbi_write_tga_to_func(stbi_write_func *func, void *context, int x, #ifndef STBI_WRITE_NO_STDIO STBIWDEF int stbi_write_tga(char const *filename, int x, int y, int comp, const void *data) { - stbi__write_context s; + stbi__write_context s = { 0 }; if (stbi__start_write_file(&s,filename)) { int r = stbi_write_tga_core(&s, x, y, comp, (void *) data); stbi__end_write_file(&s); @@ -598,6 +634,8 @@ STBIWDEF int stbi_write_tga(char const *filename, int x, int y, int comp, const #define stbiw__max(a, b) ((a) > (b) ? (a) : (b)) +#ifndef STBI_WRITE_NO_STDIO + static void stbiw__linear_to_rgbe(unsigned char *rgbe, float *linear) { int exponent; @@ -732,7 +770,7 @@ static int stbi_write_hdr_core(stbi__write_context *s, int x, int y, int comp, f char header[] = "#?RADIANCE\n# Written by stb_image_write.h\nFORMAT=32-bit_rle_rgbe\n"; s->func(s->context, header, sizeof(header)-1); -#ifdef __STDC_WANT_SECURE_LIB__ +#ifdef __STDC_LIB_EXT1__ len = sprintf_s(buffer, sizeof(buffer), "EXPOSURE= 1.0000000000000\n\n-Y %d +X %d\n", y, x); #else len = sprintf(buffer, "EXPOSURE= 1.0000000000000\n\n-Y %d +X %d\n", y, x); @@ -748,15 +786,14 @@ static int stbi_write_hdr_core(stbi__write_context *s, int x, int y, int comp, f STBIWDEF int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const float *data) { - stbi__write_context s; + stbi__write_context s = { 0 }; stbi__start_write_callbacks(&s, func, context); return stbi_write_hdr_core(&s, x, y, comp, (float *) data); } -#ifndef STBI_WRITE_NO_STDIO STBIWDEF int stbi_write_hdr(char const *filename, int x, int y, int comp, const float *data) { - stbi__write_context s; + stbi__write_context s = { 0 }; if (stbi__start_write_file(&s,filename)) { int r = stbi_write_hdr_core(&s, x, y, comp, (float *) data); stbi__end_write_file(&s); @@ -944,6 +981,23 @@ STBIWDEF unsigned char * stbi_zlib_compress(unsigned char *data, int data_len, i (void) stbiw__sbfree(hash_table[i]); STBIW_FREE(hash_table); + // store uncompressed instead if compression was worse + if (stbiw__sbn(out) > data_len + 2 + ((data_len+32766)/32767)*5) { + stbiw__sbn(out) = 2; // truncate to DEFLATE 32K window and FLEVEL = 1 + for (j = 0; j < data_len;) { + int blocklen = data_len - j; + if (blocklen > 32767) blocklen = 32767; + stbiw__sbpush(out, data_len - j == blocklen); // BFINAL = ?, BTYPE = 0 -- no compression + stbiw__sbpush(out, STBIW_UCHAR(blocklen)); // LEN + stbiw__sbpush(out, STBIW_UCHAR(blocklen >> 8)); + stbiw__sbpush(out, STBIW_UCHAR(~blocklen)); // NLEN + stbiw__sbpush(out, STBIW_UCHAR(~blocklen >> 8)); + memcpy(out+stbiw__sbn(out), data+j, blocklen); + stbiw__sbn(out) += blocklen; + j += blocklen; + } + } + { // compute adler32 on input unsigned int s1=1, s2=0; @@ -1552,7 +1606,7 @@ static int stbi_write_jpg_core(stbi__write_context *s, int width, int height, in STBIWDEF int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int quality) { - stbi__write_context s; + stbi__write_context s = { 0 }; stbi__start_write_callbacks(&s, func, context); return stbi_write_jpg_core(&s, x, y, comp, (void *) data, quality); } @@ -1561,7 +1615,7 @@ STBIWDEF int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, #ifndef STBI_WRITE_NO_STDIO STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const void *data, int quality) { - stbi__write_context s; + stbi__write_context s = { 0 }; if (stbi__start_write_file(&s,filename)) { int r = stbi_write_jpg_core(&s, x, y, comp, data, quality); stbi__end_write_file(&s); @@ -1574,6 +1628,10 @@ STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const #endif // STB_IMAGE_WRITE_IMPLEMENTATION /* Revision history + 1.16 (2021-07-11) + make Deflate code emit uncompressed blocks when it would otherwise expand + support writing BMPs with alpha channel + 1.15 (2020-07-13) unknown 1.14 (2020-02-02) updated JPEG writer to downsample chroma channels 1.13 1.12 @@ -1611,7 +1669,7 @@ STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const add HDR output fix monochrome BMP 0.95 (2014-08-17) - add monochrome TGA output + add monochrome TGA output 0.94 (2014-05-31) rename private functions to avoid conflicts with stb_image.h 0.93 (2014-05-27) diff --git a/README.md b/README.md index 7745e582a95..64a7ff4e150 100644 --- a/README.md +++ b/README.md @@ -301,7 +301,7 @@ You can get cfg-files by path: `darknet/cfg/` - OpenImages: use `python ./scripts/get_openimages_dataset.py` for labeling train detection dataset - Pascal VOC: use `python ./scripts/voc_label.py` for labeling Train/Test/Val detection datasets - ILSVRC2012 (ImageNet classification): use `./scripts/get_imagenet_train.sh` (also `imagenet_label.sh` for labeling valid set) -- German/Belgium/Russian/LISA/MASTIF Traffic Sign Datasets for Detection - use this parsers: https://github.com/angeligareta/Datasets2Darknet#detection-task +- German/Belgium/Russian/LISA/MASTIF Traffic Sign Datasets for Detection - use this parser: https://github.com/angeligareta/Datasets2Darknet#detection-task - List of other datasets: https://github.com/AlexeyAB/darknet/tree/master/scripts#datasets ### Improvements in this repository @@ -372,9 +372,9 @@ If you customize build with CMake GUI, darknet executable will be installed in y - Smart WebCam - preferably: https://play.google.com/store/apps/details?id=com.acontech.android.SmartWebCam2 - IP Webcam: https://play.google.com/store/apps/details?id=com.pas.webcam -2. Connect your Android phone to computer by WiFi (through a WiFi-router) or USB +2. Connect your Android phone to the computer by WiFi (through a WiFi-router) or USB 3. Start Smart WebCam on your phone -4. Replace the address below, on shown in the phone application (Smart WebCam) and launch: +4. Replace the address below, shown in the phone application (Smart WebCam) and launch: - Yolo v4 COCO-model: `./darknet detector demo data/coco.data yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` @@ -428,7 +428,7 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c or use in such a way: `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov4.cfg yolov4.weights test.mp4` - `ZED_CAMERA=1` to build a library with ZED-3D-camera support (should be ZED SDK installed), then run `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov4.cfg yolov4.weights zed_camera` -- You also need to specify for which graphics card the code is generated. This is done by setting `ARCH=`. If you use a never version than CUDA 11 you further need to edit line 20 from Makefile and remove `-gencode arch=compute_30,code=sm_30 \` as Kepler GPU support was dropped in CUDA 11. You can also drop the general `ARCH=` and just uncomment `ARCH=` for your graphics card. +- You also need to specify for which graphics card the code is generated. This is done by setting `ARCH=`. If you use a newer version than CUDA 11 you further need to edit line 20 from Makefile and remove `-gencode arch=compute_30,code=sm_30 \` as Kepler GPU support was dropped in CUDA 11. You can also drop the general `ARCH=` and just uncomment `ARCH=` for your graphics card. ### How to compile on Windows (using `CMake`) @@ -611,7 +611,7 @@ If you made you custom model that isn't based on other models, then you can trai ## When should I stop training -Usually sufficient 2000 iterations for each class(object), but not less than number of training images and not less than 6000 iterations in total. But for a more precise definition when you should stop training, use the following manual: +Usually sufficient 2000 iterations for each class(object), but not less than number of training images and not less than 6000 iterations in total. But for a more precise definition of when you should stop training, use the following manual: 1. During training, you will see varying indicators of error, and you should stop when no longer decreases **0.XXXXXXX avg**: diff --git a/darknet_video.py b/darknet_video.py index 5e7036203a7..eab6e9e763a 100644 --- a/darknet_video.py +++ b/darknet_video.py @@ -32,7 +32,7 @@ def parser(): def str2int(video_path): """ - argparse returns and string althout webcam uses int (0, 1 ...) + argparse returns strings although webcam uses int (0, 1 ...) Cast to int if needed """ try: diff --git a/scripts/kmeansiou.c b/scripts/kmeansiou.c index 783d09a0b4a..30f33914f20 100644 --- a/scripts/kmeansiou.c +++ b/scripts/kmeansiou.c @@ -79,7 +79,7 @@ double WCSS(matrix data, int *assignments, matrix centers) { int i, j; double sum = 0; - + for(i = 0; i < data.rows; ++i){ int ci = assignments[i]; sum += (1 - dist(data.vals[i], centers.vals[ci], data.cols)); @@ -143,10 +143,10 @@ model do_kmeans(matrix data, int k) int main(int argc, char *argv[]) { - if(argc < 3){ + if(argc < 3){ fprintf(stderr, "usage: %s [points/centers/stats]\n", argv[0]); return 0; - } + } int i,j; srand(time(0)); matrix data = csv_to_matrix(argv[1], 0); @@ -159,7 +159,7 @@ int main(int argc, char *argv[]) int *assignments = m.assignments; for(i = 0; i < k; ++i){ if(i != 0) printf("-\n"); - for(j = 0; j < data.rows; ++j){ + for(j = 0; j < data.rows; ++j){ if(!(assignments[j] == i)) continue; printf("%f, %f\n", data.vals[j][0], data.vals[j][1]); } @@ -323,7 +323,7 @@ matrix csv_to_matrix(char *filename, int header) return m; } -// Arguement parsing +// Argument parsing void del_arg(int argc, char **argv, int index) { @@ -389,4 +389,3 @@ char *find_char_arg(int argc, char **argv, char *arg, char *def) } return def; } - diff --git a/src/.editorconfig b/src/.editorconfig deleted file mode 100644 index 2eb162b28f6..00000000000 --- a/src/.editorconfig +++ /dev/null @@ -1,8 +0,0 @@ -root=true - -[*] -trim_trailing_whitespace = true -indent_style = space -indent_size = 4 - - diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu index 7a4d06f5ac0..25b5cfdc8d1 100644 --- a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -743,4 +743,4 @@ extern "C" void gradient_array_normalize_channels_ongpu(float *output_gpu, int n gradient_array_normalize_channels_kernel << > > (output_gpu, size, batch, channels, wh_step, delta_gpu); CHECK_CUDA(cudaPeekAtLastError()); -} \ No newline at end of file +} diff --git a/src/art.c b/src/art.c index 5fbcfceada9..d90290fcbc2 100644 --- a/src/art.c +++ b/src/art.c @@ -51,7 +51,7 @@ void demo_art(char *cfgfile, char *weightfile, int cam_index) score = score; printf("I APPRECIATE THIS ARTWORK: %10.7f%%\n", score*100); printf("["); - int upper = 30; + int upper = 30; for(i = 0; i < upper; ++i){ printf("%c", ((i+.5) < score*upper) ? 219 : ' '); } diff --git a/src/blas.c b/src/blas.c index f814b8ab402..6cd1deb06a3 100644 --- a/src/blas.c +++ b/src/blas.c @@ -888,4 +888,4 @@ void grad_contrastive_loss_negative(size_t i, int *labels, size_t num_of_samples } } } -} \ No newline at end of file +} diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index 85c55adfb82..cefb49b1eb3 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -512,8 +512,8 @@ __global__ void fill_kernel(int N, float ALPHA, float *X, int INCX) __global__ void mask_kernel_new_api(int n, float *x, float mask_num, float *mask, float val) { - int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; - if (i < n && mask[i] == mask_num) x[i] = val; + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (i < n && mask[i] == mask_num) x[i] = val; } __global__ void mask_kernel(int n, float *x, float mask_num, float *mask) @@ -804,7 +804,7 @@ extern "C" void reorg_ongpu(float *x, int w, int h, int c, int batch, int stride extern "C" void mask_gpu_new_api(int N, float * X, float mask_num, float * mask, float val) { - mask_kernel_new_api <<>>(N, X, mask_num, mask, val); + mask_kernel_new_api <<>>(N, X, mask_num, mask, val); CHECK_CUDA(cudaPeekAtLastError()); } @@ -1250,18 +1250,18 @@ extern "C" void smooth_l1_gpu(int n, float *pred, float *truth, float *delta, fl __global__ void softmax_x_ent_kernel(int n, float *pred, float *truth, float *delta, float *error) { - int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; - if (i < n) { - float t = truth[i]; - float p = pred[i]; - error[i] = (t) ? -log(p) : 0; - delta[i] = t - p; - } + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (i < n) { + float t = truth[i]; + float p = pred[i]; + error[i] = (t) ? -log(p) : 0; + delta[i] = t - p; + } } extern "C" void softmax_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error) { - softmax_x_ent_kernel << > >(n, pred, truth, delta, error); + softmax_x_ent_kernel << > >(n, pred, truth, delta, error); CHECK_CUDA(cudaPeekAtLastError()); } @@ -1364,35 +1364,35 @@ extern "C" void softmax_gpu(float *input, int n, int offset, int groups, float t __device__ void softmax_device_new_api(float *input, int n, float temp, int stride, float *output) { - int i; - float sum = 0; - float largest = -INFINITY; - for (i = 0; i < n; ++i) { - int val = input[i*stride]; - largest = (val>largest) ? val : largest; - } - for (i = 0; i < n; ++i) { - float e = expf(input[i*stride] / temp - largest / temp); - sum += e; - output[i*stride] = e; - } - for (i = 0; i < n; ++i) { - output[i*stride] /= sum; - } + int i; + float sum = 0; + float largest = -INFINITY; + for (i = 0; i < n; ++i) { + int val = input[i*stride]; + largest = (val>largest) ? val : largest; + } + for (i = 0; i < n; ++i) { + float e = expf(input[i*stride] / temp - largest / temp); + sum += e; + output[i*stride] = e; + } + for (i = 0; i < n; ++i) { + output[i*stride] /= sum; + } } __global__ void softmax_kernel_new_api(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output) { - int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; - if (id >= batch*groups) return; - int b = id / groups; - int g = id % groups; - softmax_device_new_api(input + b*batch_offset + g*group_offset, n, temp, stride, output + b*batch_offset + g*group_offset); + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= batch*groups) return; + int b = id / groups; + int g = id % groups; + softmax_device_new_api(input + b*batch_offset + g*group_offset, n, temp, stride, output + b*batch_offset + g*group_offset); } extern "C" void softmax_gpu_new_api(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output) { - softmax_kernel_new_api << > >(input, n, batch, batch_offset, groups, group_offset, stride, temp, output); + softmax_kernel_new_api << > >(input, n, batch, batch_offset, groups, group_offset, stride, temp, output); CHECK_CUDA(cudaPeekAtLastError()); } @@ -1430,34 +1430,34 @@ extern "C" void upsample_gpu(float *in, int w, int h, int c, int batch, int stri __global__ void softmax_tree_kernel(float *input, int spatial, int batch, int stride, float temp, float *output, int groups, int *group_size, int *group_offset) { - int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; - if (id >= spatial*batch*groups) return; - int s = id % spatial; - id = id / spatial; - int g = id % groups; - int b = id / groups; - int goff = group_offset[g] * spatial; - int boff = b*stride; - softmax_device_new_api(input + goff + boff + s, group_size[g], temp, spatial, output + goff + boff + s); + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= spatial*batch*groups) return; + int s = id % spatial; + id = id / spatial; + int g = id % groups; + int b = id / groups; + int goff = group_offset[g] * spatial; + int boff = b*stride; + softmax_device_new_api(input + goff + boff + s, group_size[g], temp, spatial, output + goff + boff + s); } extern "C" void softmax_tree_gpu(float *input, int spatial, int batch, int stride, float temp, float *output, tree hier) { - int *tree_groups_size = cuda_make_int_array_new_api(hier.group_size, hier.groups); - int *tree_groups_offset = cuda_make_int_array_new_api(hier.group_offset, hier.groups); - /* - static int *tree_groups_size = 0; - static int *tree_groups_offset = 0; - if(!tree_groups_size){ - tree_groups_size = cuda_make_int_array(hier.group_size, hier.groups); - tree_groups_offset = cuda_make_int_array(hier.group_offset, hier.groups); - } - */ - int num = spatial*batch*hier.groups; - softmax_tree_kernel <<>>(input, spatial, batch, stride, temp, output, hier.groups, tree_groups_size, tree_groups_offset); - CHECK_CUDA(cudaPeekAtLastError()); - cuda_free((float *)tree_groups_size); - cuda_free((float *)tree_groups_offset); + int *tree_groups_size = cuda_make_int_array_new_api(hier.group_size, hier.groups); + int *tree_groups_offset = cuda_make_int_array_new_api(hier.group_offset, hier.groups); + /* + static int *tree_groups_size = 0; + static int *tree_groups_offset = 0; + if(!tree_groups_size){ + tree_groups_size = cuda_make_int_array(hier.group_size, hier.groups); + tree_groups_offset = cuda_make_int_array(hier.group_offset, hier.groups); + } + */ + int num = spatial*batch*hier.groups; + softmax_tree_kernel <<>>(input, spatial, batch, stride, temp, output, hier.groups, tree_groups_size, tree_groups_offset); + CHECK_CUDA(cudaPeekAtLastError()); + cuda_free((float *)tree_groups_size); + cuda_free((float *)tree_groups_offset); } diff --git a/src/box.c b/src/box.c index 201ab13341d..0ad1263e56f 100644 --- a/src/box.c +++ b/src/box.c @@ -345,12 +345,12 @@ dxrep dx_box_iou(box pred, box truth, IOU_LOSS iou_loss) { p_dl += ((giou_C * dU_wrt_l) - (U * dC_wrt_l)) / (giou_C * giou_C); p_dr += ((giou_C * dU_wrt_r) - (U * dC_wrt_r)) / (giou_C * giou_C); } - if (Iw<=0||Ih<=0) { - p_dt = ((giou_C * dU_wrt_t) - (U * dC_wrt_t)) / (giou_C * giou_C); + if (Iw<=0||Ih<=0) { + p_dt = ((giou_C * dU_wrt_t) - (U * dC_wrt_t)) / (giou_C * giou_C); p_db = ((giou_C * dU_wrt_b) - (U * dC_wrt_b)) / (giou_C * giou_C); p_dl = ((giou_C * dU_wrt_l) - (U * dC_wrt_l)) / (giou_C * giou_C); p_dr = ((giou_C * dU_wrt_r) - (U * dC_wrt_r)) / (giou_C * giou_C); - } + } } float Ct = fmin(pred.y - pred.h / 2,truth.y - truth.h / 2); @@ -418,21 +418,21 @@ dxrep dx_box_iou(box pred, box truth, IOU_LOSS iou_loss) { p_dw += (2*Cw*dCw_dw+2*Ch*dCh_dw)*S / (C * C); p_dh += (2*Cw*dCw_dh+2*Ch*dCh_dh)*S / (C * C); } - if (Iw<=0||Ih<=0){ + if (Iw<=0||Ih<=0){ p_dx = (2*(truth.x-pred.x)*C-(2*Cw*dCw_dx+2*Ch*dCh_dx)*S) / (C * C); p_dy = (2*(truth.y-pred.y)*C-(2*Cw*dCw_dy+2*Ch*dCh_dy)*S) / (C * C); p_dw = (2*Cw*dCw_dw+2*Ch*dCh_dw)*S / (C * C); p_dh = (2*Cw*dCw_dh+2*Ch*dCh_dh)*S / (C * C); } } - //The following codes are calculating the gradient of ciou. + //The following codes are calculating the gradient of ciou. if (iou_loss == CIOU) { - float ar_gt = truth.w / truth.h; + float ar_gt = truth.w / truth.h; float ar_pred = pred.w / pred.h; float ar_loss = 4 / (M_PI * M_PI) * (atan(ar_gt) - atan(ar_pred)) * (atan(ar_gt) - atan(ar_pred)); - float alpha = ar_loss / (1 - I/U + ar_loss + 0.000001); - float ar_dw=8/(M_PI*M_PI)*(atan(ar_gt)-atan(ar_pred))*pred.h; + float alpha = ar_loss / (1 - I/U + ar_loss + 0.000001); + float ar_dw=8/(M_PI*M_PI)*(atan(ar_gt)-atan(ar_pred))*pred.h; float ar_dh=-8/(M_PI*M_PI)*(atan(ar_gt)-atan(ar_pred))*pred.w; if (C > 0) { // dar* @@ -441,7 +441,7 @@ dxrep dx_box_iou(box pred, box truth, IOU_LOSS iou_loss) { p_dw += (2*Cw*dCw_dw+2*Ch*dCh_dw)*S / (C * C) + alpha * ar_dw; p_dh += (2*Cw*dCw_dh+2*Ch*dCh_dh)*S / (C * C) + alpha * ar_dh; } - if (Iw<=0||Ih<=0){ + if (Iw<=0||Ih<=0){ p_dx = (2*(truth.x-pred.x)*C-(2*Cw*dCw_dx+2*Ch*dCh_dx)*S) / (C * C); p_dy = (2*(truth.y-pred.y)*C-(2*Cw*dCw_dy+2*Ch*dCh_dy)*S) / (C * C); p_dw = (2*Cw*dCw_dw+2*Ch*dCh_dw)*S / (C * C) + alpha * ar_dw; diff --git a/src/box.h b/src/box.h index 4b720653c60..f72e26c46dc 100644 --- a/src/box.h +++ b/src/box.h @@ -12,19 +12,19 @@ typedef struct{ } dbox; //typedef struct detection { -// box bbox; -// int classes; -// float *prob; -// float *mask; -// float objectness; -// int sort_class; +// box bbox; +// int classes; +// float *prob; +// float *mask; +// float objectness; +// int sort_class; //} detection; typedef struct detection_with_class { - detection det; - // The most probable class id: the best class index in this->prob. - // Is filled temporary when processing results, otherwise not initialized - int best_class; + detection det; + // The most probable class id: the best class index in this->prob. + // Is filled temporary when processing results, otherwise not initialized + int best_class; } detection_with_class; #ifdef __cplusplus diff --git a/src/coco.c b/src/coco.c index 605e9ac1355..ec49ae013d3 100644 --- a/src/coco.c +++ b/src/coco.c @@ -291,7 +291,7 @@ void validate_coco_recall(char *cfgfile, char *weightfile) if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms_thresh); char labelpath[4096]; - replace_image_to_label(path, labelpath); + replace_image_to_label(path, labelpath); int num_labels = 0; box_label *truth = read_boxes(labelpath, &num_labels); @@ -389,16 +389,16 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh) void run_coco(int argc, char **argv) { - int dont_show = find_arg(argc, argv, "-dont_show"); - int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1); + int dont_show = find_arg(argc, argv, "-dont_show"); + int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1); int json_port = find_int_arg(argc, argv, "-json_port", -1); - char *out_filename = find_char_arg(argc, argv, "-out_filename", 0); + char *out_filename = find_char_arg(argc, argv, "-out_filename", 0); char *prefix = find_char_arg(argc, argv, "-prefix", 0); float thresh = find_float_arg(argc, argv, "-thresh", .2); - float hier_thresh = find_float_arg(argc, argv, "-hier", .5); + float hier_thresh = find_float_arg(argc, argv, "-hier", .5); int cam_index = find_int_arg(argc, argv, "-c", 0); int frame_skip = find_int_arg(argc, argv, "-s", 0); - int ext_output = find_arg(argc, argv, "-ext_output"); + int ext_output = find_arg(argc, argv, "-ext_output"); if(argc < 4){ fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); @@ -413,5 +413,5 @@ void run_coco(int argc, char **argv) else if(0==strcmp(argv[2], "valid")) validate_coco(cfg, weights); else if(0==strcmp(argv[2], "recall")) validate_coco_recall(cfg, weights); else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, hier_thresh, cam_index, filename, coco_classes, 80, 1, frame_skip, - prefix, out_filename, mjpeg_port, 0, json_port, dont_show, ext_output, 0, 0, 0, 0, 0); + prefix, out_filename, mjpeg_port, 0, json_port, dont_show, ext_output, 0, 0, 0, 0, 0); } diff --git a/src/col2im.c b/src/col2im.c index 4b8cb592e6f..10138a04603 100644 --- a/src/col2im.c +++ b/src/col2im.c @@ -92,4 +92,4 @@ void col2im_cpu_ext(const float* data_col, const int channels, } } } -} \ No newline at end of file +} diff --git a/src/col2im_kernels.cu b/src/col2im_kernels.cu index 0e07bc37ae2..4e6342ad220 100644 --- a/src/col2im_kernels.cu +++ b/src/col2im_kernels.cu @@ -133,4 +133,4 @@ void col2im_gpu_ext(const float* data_col, const int channels, height_col, width_col, data_im); CHECK_CUDA(cudaPeekAtLastError()); -} \ No newline at end of file +} diff --git a/src/connected_layer.c b/src/connected_layer.c index 25a5ffa1e34..244e82fbb86 100644 --- a/src/connected_layer.c +++ b/src/connected_layer.c @@ -363,12 +363,12 @@ void forward_connected_layer_gpu(connected_layer l, network_state state) gemm_ongpu(0,1,m,n,k,1,a,k,b,k,1,c,n); #endif // CUDNN - if (l.batch_normalize) { - forward_batchnorm_layer_gpu(l, state); - } - else { - add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.outputs, 1); - } + if (l.batch_normalize) { + forward_batchnorm_layer_gpu(l, state); + } + else { + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.outputs, 1); + } //for(i = 0; i < l.batch; ++i) axpy_ongpu(l.outputs, 1, l.biases_gpu, 1, l.output_gpu + i*l.outputs, 1); activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); } diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 88bb260ab5b..9267a4a3935 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -1688,4 +1688,3 @@ image *visualize_convolutional_layer(convolutional_layer l, char *window, image free_image(dc); return single_weights; } - diff --git a/src/cost_layer.c b/src/cost_layer.c index ed1cc134479..60080b1204e 100644 --- a/src/cost_layer.c +++ b/src/cost_layer.c @@ -25,8 +25,8 @@ char *get_cost_string(COST_TYPE a) return "masked"; case SMOOTH: return "smooth"; - default: - return "sse"; + default: + return "sse"; } } diff --git a/src/dark_cuda.c b/src/dark_cuda.c index 246192f9475..3b21a75eb66 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -553,17 +553,17 @@ int *cuda_make_int_array(size_t n) int *cuda_make_int_array_new_api(int *x, size_t n) { - int *x_gpu; - size_t size = sizeof(int)*n; - cudaError_t status = cudaMalloc((void **)&x_gpu, size); + int *x_gpu; + size_t size = sizeof(int)*n; + cudaError_t status = cudaMalloc((void **)&x_gpu, size); CHECK_CUDA(status); - if (x) { - //status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice); + if (x) { + //status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice); cudaError_t status = cudaMemcpyAsync(x_gpu, x, size, cudaMemcpyHostToDevice, get_cuda_stream()); CHECK_CUDA(status); - } - if (!x_gpu) error("Cuda malloc failed", DARKNET_LOC); - return x_gpu; + } + if (!x_gpu) error("Cuda malloc failed", DARKNET_LOC); + return x_gpu; } void cuda_free(float *x_gpu) diff --git a/src/dark_cuda.h b/src/dark_cuda.h index 4f7c087a84d..3e1c1e3bef4 100644 --- a/src/dark_cuda.h +++ b/src/dark_cuda.h @@ -68,7 +68,7 @@ extern "C" { float *cuda_make_array(float *x, size_t n); void **cuda_make_array_pointers(void **x, size_t n); int *cuda_make_int_array(size_t n); - int *cuda_make_int_array_new_api(int *x, size_t n); + int *cuda_make_int_array_new_api(int *x, size_t n); void cuda_push_array(float *x_gpu, float *x, size_t n); //LIB_API void cuda_pull_array(float *x_gpu, float *x, size_t n); //LIB_API void cuda_set_device(int n); diff --git a/src/darknet.c b/src/darknet.c index 13ab75f3d38..e30baddbbd0 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -117,7 +117,7 @@ void operations(char *cfgfile) ops += 2l * l.n * l.size*l.size*l.c * l.out_h*l.out_w; } else if(l.type == CONNECTED){ ops += 2l * l.inputs * l.outputs; - } else if (l.type == RNN){ + } else if (l.type == RNN){ ops += 2l * l.input_layer->inputs * l.input_layer->outputs; ops += 2l * l.self_layer->inputs * l.self_layer->outputs; ops += 2l * l.output_layer->inputs * l.output_layer->outputs; @@ -251,7 +251,7 @@ void reset_normalize_net(char *cfgfile, char *weightfile, char *outfile) denormalize_connected_layer(*l.ui); denormalize_connected_layer(*l.ug); denormalize_connected_layer(*l.uo); - } + } } save_weights(net, outfile); } @@ -412,7 +412,7 @@ void denormalize_net(char *cfgfile, char *weightfile, char *outfile) l.ug->batch_normalize = 0; l.uo->batch_normalize = 0; net.layers[i].batch_normalize=0; - } + } } save_weights(net, outfile); } @@ -432,7 +432,7 @@ void visualize(char *cfgfile, char *weightfile) int main(int argc, char **argv) { #ifdef _DEBUG - _CrtSetDbgFlag(_CRTDBG_ALLOC_MEM_DF | _CRTDBG_LEAK_CHECK_DF); + _CrtSetDbgFlag(_CRTDBG_ALLOC_MEM_DF | _CRTDBG_LEAK_CHECK_DF); printf(" _DEBUG is used \n"); #endif @@ -440,11 +440,11 @@ int main(int argc, char **argv) printf(" DEBUG=1 \n"); #endif - int i; - for (i = 0; i < argc; ++i) { - if (!argv[i]) continue; - strip_args(argv[i]); - } + int i; + for (i = 0; i < argc; ++i) { + if (!argv[i]) continue; + strip_args(argv[i]); + } //test_resize("data/bad.jpg"); //test_box(); @@ -493,7 +493,7 @@ int main(int argc, char **argv) run_detector(argc, argv); } else if (0 == strcmp(argv[1], "detect")){ float thresh = find_float_arg(argc, argv, "-thresh", .24); - int ext_output = find_arg(argc, argv, "-ext_output"); + int ext_output = find_arg(argc, argv, "-ext_output"); char *filename = (argc > 4) ? argv[4]: 0; test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, 0.5, 0, ext_output, 0, NULL, 0, 0); } else if (0 == strcmp(argv[1], "cifar")){ diff --git a/src/data.h b/src/data.h index ecbd0188e7f..9f12343a0a9 100644 --- a/src/data.h +++ b/src/data.h @@ -45,8 +45,8 @@ typedef struct load_args{ char **labels; int h; int w; - int c; // color depth - int out_w; + int c; // color depth + int out_w; int out_h; int nh; int nw; @@ -55,7 +55,7 @@ typedef struct load_args{ int classes; int background; int scale; - int small_object; + int small_object; float jitter; int flip; float angle; diff --git a/src/detection_layer.c b/src/detection_layer.c index 3c6528a9b77..d3b9af8a443 100644 --- a/src/detection_layer.c +++ b/src/detection_layer.c @@ -287,29 +287,29 @@ void backward_detection_layer_gpu(detection_layer l, network_state state) void get_detection_detections(layer l, int w, int h, float thresh, detection *dets) { - int i, j, n; - float *predictions = l.output; - //int per_cell = 5*num+classes; - for (i = 0; i < l.side*l.side; ++i) { - int row = i / l.side; - int col = i % l.side; - for (n = 0; n < l.n; ++n) { - int index = i*l.n + n; - int p_index = l.side*l.side*l.classes + i*l.n + n; - float scale = predictions[p_index]; - int box_index = l.side*l.side*(l.classes + l.n) + (i*l.n + n) * 4; - box b; - b.x = (predictions[box_index + 0] + col) / l.side * w; - b.y = (predictions[box_index + 1] + row) / l.side * h; - b.w = pow(predictions[box_index + 2], (l.sqrt ? 2 : 1)) * w; - b.h = pow(predictions[box_index + 3], (l.sqrt ? 2 : 1)) * h; - dets[index].bbox = b; - dets[index].objectness = scale; - for (j = 0; j < l.classes; ++j) { - int class_index = i*l.classes; - float prob = scale*predictions[class_index + j]; - dets[index].prob[j] = (prob > thresh) ? prob : 0; - } - } - } + int i, j, n; + float *predictions = l.output; + //int per_cell = 5*num+classes; + for (i = 0; i < l.side*l.side; ++i) { + int row = i / l.side; + int col = i % l.side; + for (n = 0; n < l.n; ++n) { + int index = i*l.n + n; + int p_index = l.side*l.side*l.classes + i*l.n + n; + float scale = predictions[p_index]; + int box_index = l.side*l.side*(l.classes + l.n) + (i*l.n + n) * 4; + box b; + b.x = (predictions[box_index + 0] + col) / l.side * w; + b.y = (predictions[box_index + 1] + row) / l.side * h; + b.w = pow(predictions[box_index + 2], (l.sqrt ? 2 : 1)) * w; + b.h = pow(predictions[box_index + 3], (l.sqrt ? 2 : 1)) * h; + dets[index].bbox = b; + dets[index].objectness = scale; + for (j = 0; j < l.classes; ++j) { + int class_index = i*l.classes; + float prob = scale*predictions[class_index + j]; + dets[index].prob[j] = (prob > thresh) ? prob : 0; + } + } + } } diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index bd99a89dc6b..b016a231d19 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -51,7 +51,7 @@ layer make_gaussian_yolo_layer(int batch, int w, int h, int n, int total, int *m l.outputs = h*w*n*(classes + 8 + 1); l.inputs = l.outputs; l.max_boxes = max_boxes; - l.truth_size = 4 + 2; + l.truth_size = 4 + 2; l.truths = l.max_boxes*l.truth_size; l.delta = (float*)calloc(batch*l.outputs, sizeof(float)); l.output = (float*)calloc(batch*l.outputs, sizeof(float)); diff --git a/src/getopt.c b/src/getopt.c index 45d2b8e6375..61aa096ab5d 100644 --- a/src/getopt.c +++ b/src/getopt.c @@ -64,8 +64,8 @@ permute_args(int panonopt_start, int panonopt_end, int opt_end, char* swap; /* - * compute lengths of blocks and number and size of cycles - */ + * compute lengths of blocks and number and size of cycles + */ nnonopts = panonopt_end - panonopt_start; nopts = opt_end - panonopt_end; ncycle = gcd(nnonopts, nopts); @@ -91,7 +91,7 @@ permute_args(int panonopt_start, int panonopt_end, int opt_end, #ifdef REPLACE_GETOPT /* * getopt -- - * Parse argc/argv argument vector. + * Parse argc/argv argument vector. * * [eventually this will replace the BSD getopt] */ @@ -99,13 +99,13 @@ int getopt(int nargc, char* const* nargv, const char* options) { /* - * We don't pass FLAG_PERMUTE to getopt_internal() since - * the BSD getopt(3) (unlike GNU) has never done this. - * - * Furthermore, since many privileged programs call getopt() - * before dropping privileges it makes sense to keep things - * as simple (and bug-free) as possible. - */ + * We don't pass FLAG_PERMUTE to getopt_internal() since + * the BSD getopt(3) (unlike GNU) has never done this. + * + * Furthermore, since many privileged programs call getopt() + * before dropping privileges it makes sense to keep things + * as simple (and bug-free) as possible. + */ return (getopt_internal(nargc, nargv, options, NULL, NULL, 0)); } #endif /* REPLACE_GETOPT */ @@ -129,24 +129,24 @@ int getopt(int nargc, char* const* nargv, const char* options) extern "C" { #endif -struct option /* specification for a long form option... */ +struct option /* specification for a long form option... */ { const char* name; /* option name, without leading hyphens */ - int has_arg; /* does it take an argument? */ - int* flag; /* where to save its status, or NULL */ - int val; /* its associated status value */ + int has_arg; /* does it take an argument? */ + int* flag; /* where to save its status, or NULL */ + int val; /* its associated status value */ }; -enum /* permitted values for its `has_arg' field... */ +enum /* permitted values for its `has_arg' field... */ { - no_argument = 0, /* option never takes an argument */ - required_argument, /* option always requires an argument */ - optional_argument /* option may take an argument */ + no_argument = 0, /* option never takes an argument */ + required_argument, /* option always requires an argument */ + optional_argument /* option may take an argument */ }; /* * parse_long_options -- - * Parse long options in argc/argv argument vector. + * Parse long options in argc/argv argument vector. * Returns -1 if short_too is set and the option does not match long_options. */ static int @@ -186,9 +186,9 @@ parse_long_options(char* const* nargv, const char* options, break; } /* - * If this is a known short option, don't allow - * a partial match of a single character. - */ + * If this is a known short option, don't allow + * a partial match of a single character. + */ if (short_too && current_argv_len == 1) continue; @@ -212,8 +212,8 @@ parse_long_options(char* const* nargv, const char* options, warnx(noarg, (int)current_argv_len, current_argv); /* - * XXX: GNU sets optopt to val regardless of flag - */ + * XXX: GNU sets optopt to val regardless of flag + */ if (long_options[match].flag == NULL) optopt = long_options[match].val; else @@ -225,23 +225,23 @@ parse_long_options(char* const* nargv, const char* options, optarg = has_equal; else if (long_options[match].has_arg == required_argument) { /* - * optional argument doesn't use next nargv - */ + * optional argument doesn't use next nargv + */ optarg = nargv[optind++]; } } if ((long_options[match].has_arg == required_argument) && (optarg == NULL)) { /* - * Missing argument; leading ':' indicates no error - * should be generated. - */ + * Missing argument; leading ':' indicates no error + * should be generated. + */ if (PRINT_ERROR) warnx(recargstring, current_argv); /* - * XXX: GNU sets optopt to val regardless of flag - */ + * XXX: GNU sets optopt to val regardless of flag + */ if (long_options[match].flag == NULL) optopt = long_options[match].val; else @@ -271,7 +271,7 @@ parse_long_options(char* const* nargv, const char* options, /* * getopt_internal -- - * Parse argc/argv argument vector. Called by user level routines. + * Parse argc/argv argument vector. Called by user level routines. */ static int getopt_internal(int nargc, char* const* nargv, const char* options, @@ -285,19 +285,19 @@ getopt_internal(int nargc, char* const* nargv, const char* options, return (-1); /* - * XXX Some GNU programs (like cvs) set optind to 0 instead of - * XXX using optreset. Work around this braindamage. - */ + * XXX Some GNU programs (like cvs) set optind to 0 instead of + * XXX using optreset. Work around this braindamage. + */ if (optind == 0) optind = optreset = 1; /* - * Disable GNU extensions if POSIXLY_CORRECT is set or options - * string begins with a '+'. - * - * CV, 2009-12-14: Check POSIXLY_CORRECT anew if optind == 0 or - * optreset != 0 for GNU compatibility. - */ + * Disable GNU extensions if POSIXLY_CORRECT is set or options + * string begins with a '+'. + * + * CV, 2009-12-14: Check POSIXLY_CORRECT anew if optind == 0 or + * optreset != 0 for GNU compatibility. + */ if (posixly_correct == -1 || optreset != 0) posixly_correct = (getenv("POSIXLY_CORRECT") != NULL); if (*options == '-') @@ -322,9 +322,9 @@ getopt_internal(int nargc, char* const* nargv, const char* options, optind -= nonopt_end - nonopt_start; } else if (nonopt_start != -1) { /* - * If we skipped non-options, set optind - * to the first of them. - */ + * If we skipped non-options, set optind + * to the first of them. + */ optind = nonopt_start; } nonopt_start = nonopt_end = -1; @@ -334,17 +334,17 @@ getopt_internal(int nargc, char* const* nargv, const char* options, place = EMSG; /* found non-option */ if (flags & FLAG_ALLARGS) { /* - * GNU extension: - * return non-option as argument to option 1 - */ + * GNU extension: + * return non-option as argument to option 1 + */ optarg = nargv[optind++]; return (INORDER); } if (!(flags & FLAG_PERMUTE)) { /* - * If no permutation wanted, stop parsing - * at first non-option. - */ + * If no permutation wanted, stop parsing + * at first non-option. + */ return (-1); } /* do permutation */ @@ -364,15 +364,15 @@ getopt_internal(int nargc, char* const* nargv, const char* options, nonopt_end = optind; /* - * If we have "-" do nothing, if "--" we are done. - */ + * If we have "-" do nothing, if "--" we are done. + */ if (place[1] != '\0' && *++place == '-' && place[1] == '\0') { optind++; place = EMSG; /* - * We found an option (--), so if we skipped - * non-options, we have to permute. - */ + * We found an option (--), so if we skipped + * non-options, we have to permute. + */ if (nonopt_end != -1) { permute_args(nonopt_start, nonopt_end, optind, nargv); @@ -384,11 +384,11 @@ getopt_internal(int nargc, char* const* nargv, const char* options, } /* - * Check long options if: - * 1) we were passed some - * 2) the arg is not just "-" - * 3) either the arg starts with -- we are getopt_long_only() - */ + * Check long options if: + * 1) we were passed some + * 2) the arg is not just "-" + * 3) either the arg starts with -- we are getopt_long_only() + */ if (long_options != NULL && place != nargv[optind] && (*place == '-' || (flags & FLAG_LONGONLY))) { short_too = 0; if (*place == '-') @@ -406,10 +406,10 @@ getopt_internal(int nargc, char* const* nargv, const char* options, if ((optchar = (int)*place++) == (int)':' || (optchar == (int)'-' && *place != '\0') || (oli = (char*)strchr(options, optchar)) == NULL) { /* - * If the user specified "-" and '-' isn't listed in - * options, return -1 (non-option) as per POSIX. - * Otherwise, it is an unknown option character (or ':'). - */ + * If the user specified "-" and '-' isn't listed in + * options, return -1 (non-option) as per POSIX. + * Otherwise, it is an unknown option character (or ':'). + */ if (optchar == (int)'-' && *place == '\0') return (-1); if (!*place) @@ -462,7 +462,7 @@ getopt_internal(int nargc, char* const* nargv, const char* options, /* * getopt_long -- - * Parse argc/argv argument vector. + * Parse argc/argv argument vector. */ int getopt_long(int nargc, char* const* nargv, const char* options, const struct option* long_options, int* idx) @@ -474,7 +474,7 @@ int getopt_long(int nargc, char* const* nargv, const char* options, /* * getopt_long_only -- - * Parse argc/argv argument vector. + * Parse argc/argv argument vector. */ int getopt_long_only(int nargc, char* const* nargv, const char* options, const struct option* long_options, int* idx) diff --git a/src/getopt.h b/src/getopt.h index 8266c734eec..a9f913d4f3d 100644 --- a/src/getopt.h +++ b/src/getopt.h @@ -75,12 +75,12 @@ extern "C" { #define REPLACE_GETOPT /* use this getopt as the system getopt(3) */ -//extern int optind; /* index of first non-option in argv */ -//extern int optopt; /* single option character, as parsed */ -//extern int opterr; /* flag to enable built-in diagnostics... */ -// /* (user may set to zero, to suppress) */ +//extern int optind; /* index of first non-option in argv */ +//extern int optopt; /* single option character, as parsed */ +//extern int opterr; /* flag to enable built-in diagnostics... */ +// /* (user may set to zero, to suppress) */ // -//extern char *optarg; /* pointer to argument of current option */ +//extern char *optarg; /* pointer to argument of current option */ #define PRINT_ERROR ((opterr) && (*options != ':')) @@ -145,7 +145,7 @@ static void permute_args(int panonopt_start, int panonopt_end, int opt_end, char #ifdef REPLACE_GETOPT /* * getopt -- - * Parse argc/argv argument vector. + * Parse argc/argv argument vector. * * [eventually this will replace the BSD getopt] */ @@ -187,26 +187,26 @@ extern "C" { /* * parse_long_options -- - * Parse long options in argc/argv argument vector. + * Parse long options in argc/argv argument vector. * Returns -1 if short_too is set and the option does not match long_options. */ /* static int parse_long_options(char* const* nargv, const char* options, const struct option* long_options, int* idx, int short_too); */ /* * getopt_internal -- - * Parse argc/argv argument vector. Called by user level routines. + * Parse argc/argv argument vector. Called by user level routines. */ /* static int getopt_internal(int nargc, char* const* nargv, const char* options, const struct option* long_options, int* idx, int flags); */ /* * getopt_long -- - * Parse argc/argv argument vector. + * Parse argc/argv argument vector. */ int getopt_long(int nargc, char* const* nargv, const char* options, const struct option* long_options, int* idx); /* * getopt_long_only -- - * Parse argc/argv argument vector. + * Parse argc/argv argument vector. */ int getopt_long_only(int nargc, char* const* nargv, const char* options, const struct option* long_options, int* idx); diff --git a/src/go.c b/src/go.c index 1c7a1a083dc..4ed73dcbca8 100644 --- a/src/go.c +++ b/src/go.c @@ -580,9 +580,9 @@ void engine_go(char *filename, char *weightfile, int multi) fprintf(f, "final_status_list dead\n"); fclose(f); #ifdef _WIN32 - FILE *p = _popen("./gnugo --mode gtp < game.txt", "r"); + FILE *p = _popen("./gnugo --mode gtp < game.txt", "r"); #else - FILE *p = popen("./gnugo --mode gtp < game.txt", "r"); + FILE *p = popen("./gnugo --mode gtp < game.txt", "r"); #endif for(i = 0; i < count; ++i){ free(fgetl(p)); @@ -721,9 +721,9 @@ float score_game(float *board) fprintf(f, "final_score\n"); fclose(f); #ifdef _WIN32 - FILE *p = _popen("./gnugo --mode gtp < game.txt", "r"); + FILE *p = _popen("./gnugo --mode gtp < game.txt", "r"); #else - FILE *p = popen("./gnugo --mode gtp < game.txt", "r"); + FILE *p = popen("./gnugo --mode gtp < game.txt", "r"); #endif for(i = 0; i < count; ++i){ free(fgetl(p)); @@ -740,9 +740,9 @@ float score_game(float *board) } if(player == 'W') score = -score; #ifdef _WIN32 - _pclose(p); + _pclose(p); #else - pclose(p); + pclose(p); #endif return score; } diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index f924b3e5cd2..37551a761b8 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -500,7 +500,7 @@ __device__ void transpose8rS32_reversed_diagonale(unsigned char* A, unsigned cha B[7 * n] = reverse_byte_CUDA(x >> 24); B[6 * n] = reverse_byte_CUDA(x >> 16); B[5 * n] = reverse_byte_CUDA(x >> 8); B[4 * n] = reverse_byte_CUDA(x); B[3 * n] = reverse_byte_CUDA(y >> 24); B[2 * n] = reverse_byte_CUDA(y >> 16); B[1 * n] = reverse_byte_CUDA(y >> 8); B[0 * n] = reverse_byte_CUDA(y); - //__device__ ​ unsigned int __brev(unsigned int x) + //__device__ ​ unsigned int __brev(unsigned int x) //Reverse the bit order of a 32 bit unsigned integer. // https://docs.nvidia.com/cuda/cuda-math-api/group__CUDA__MATH__INTRINSIC__INT.html } @@ -1750,8 +1750,8 @@ __global__ void gemm_nn_custom_bin_mean_transposed_gpu_kernel(int M, int N, int // https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#wmma-subbyte // nvcuda::wmma::col_major -> cutlass::MatrixLayout::kColumnMajor (matrix is not transposed) -// Matrix A Matrix B Accumulator Matrix Size (m-n-k) -// precision::b1 precision::b1 int 8x8x128 +// Matrix A Matrix B Accumulator Matrix Size (m-n-k) +// precision::b1 precision::b1 int 8x8x128 // The only dimensions currently supported by WMMA for XNOR // const int WMMA_M = 8; @@ -2285,4 +2285,4 @@ void im2col_gpu_ext(const float* data_im, const int channels, width_col, data_col); CHECK_CUDA(cudaPeekAtLastError()); -} \ No newline at end of file +} diff --git a/src/image.c b/src/image.c index 8ef026d33a2..ca9f26651eb 100644 --- a/src/image.c +++ b/src/image.c @@ -705,7 +705,7 @@ void show_image(image p, const char *name) #ifdef OPENCV show_image_cv(p, name); #else - fprintf(stderr, "Not compiled with OpenCV, saving to %s.png instead\n", name); + fprintf(stderr, "Not compiled with OpenCV, saving to %s.jpg instead\n", name); save_image(p, name); #endif // OPENCV } diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 2524f1593ca..7ff18bfe862 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1147,12 +1147,12 @@ extern "C" void draw_train_loss(char *windows_name, mat_cv* img_src, int img_siz if (iteration_old == 0) cv::putText(img, accuracy_name, cv::Point(10, 12), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 0, 0), 1, CV_AA); - if (iteration_old != 0){ - cv::line(img, + if (iteration_old != 0){ + cv::line(img, cv::Point(img_offset + draw_size * (float)iteration_old / max_batches, draw_size * (1 - old_precision)), cv::Point(img_offset + draw_size * (float)current_batch / max_batches, draw_size * (1 - precision)), CV_RGB(255, 0, 0), 1, 8, 0); - } + } sprintf(char_buff, "%2.1f%% ", precision * 100); cv::putText(img, char_buff, cv::Point(10, 28), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, CV_RGB(255, 255, 255), 5, CV_AA); @@ -1434,9 +1434,9 @@ extern "C" void cv_draw_object(image sized, float *truth_cpu, int max_boxes, int while (!selected) { #ifndef CV_VERSION_EPOCH - int pressed_key = cv::waitKeyEx(20); // OpenCV 3.x + int pressed_key = cv::waitKeyEx(20); // OpenCV 3.x #else - int pressed_key = cv::waitKey(20); // OpenCV 2.x + int pressed_key = cv::waitKey(20); // OpenCV 2.x #endif if (pressed_key == 27 || pressed_key == 1048603) break;// break; // ESC - save & exit diff --git a/src/layer.h b/src/layer.h index e92d3b4a95f..5b3d228b158 100644 --- a/src/layer.h +++ b/src/layer.h @@ -36,10 +36,10 @@ extern "C" { // NETWORK, // XNOR, // REGION, -// YOLO, +// YOLO, // REORG, -// UPSAMPLE, -// REORG_OLD, +// UPSAMPLE, +// REORG_OLD, // BLANK //} LAYER_TYPE; @@ -48,15 +48,15 @@ extern "C" { //} COST_TYPE; //typedef struct { -// int batch; -// float learning_rate; -// float momentum; -// float decay; -// int adam; -// float B1; -// float B2; -// float eps; -// int t; +// int batch; +// float learning_rate; +// float momentum; +// float decay; +// int adam; +// float B1; +// float B2; +// float eps; +// int t; //} update_args; /* @@ -87,7 +87,7 @@ struct layer{ int side; int stride; int reverse; - int spatial; + int spatial; int pad; int sqrt; int flip; @@ -105,8 +105,8 @@ struct layer{ float shift; float ratio; float learning_rate_scale; - int focal_loss; - int noloss; + int focal_loss; + int noloss; int softmax; int classes; int coords; @@ -118,10 +118,10 @@ struct layer{ int noadjust; int reorg; int log; - int tanh; - int *mask; - int total; - float bflops; + int tanh; + int *mask; + int total; + float bflops; int adam; float B1; @@ -146,14 +146,14 @@ struct layer{ float coord_scale; float object_scale; float noobject_scale; - float mask_scale; + float mask_scale; float class_scale; int bias_match; int random; - float ignore_thresh; - float truth_thresh; + float ignore_thresh; + float truth_thresh; float thresh; - float focus; + float focus; int classfix; int absolute; @@ -208,7 +208,7 @@ struct layer{ int * input_sizes; float * delta; float * output; - float * loss; + float * loss; float * squared; float * norms; @@ -298,8 +298,8 @@ struct layer{ float * weights_gpu; float * weight_updates_gpu; - float * weights_gpu16; - float * weight_updates_gpu16; + float * weights_gpu16; + float * weight_updates_gpu16; float * biases_gpu; float * bias_updates_gpu; @@ -308,7 +308,7 @@ struct layer{ float * scale_updates_gpu; float * output_gpu; - float * loss_gpu; + float * loss_gpu; float * delta_gpu; float * rand_gpu; float * squared_gpu; @@ -318,7 +318,7 @@ struct layer{ cudnnTensorDescriptor_t srcTensorDesc16, dstTensorDesc16; cudnnTensorDescriptor_t dsrcTensorDesc, ddstTensorDesc; cudnnTensorDescriptor_t dsrcTensorDesc16, ddstTensorDesc16; - cudnnTensorDescriptor_t normTensorDesc, normDstTensorDesc, normDstTensorDescF16; + cudnnTensorDescriptor_t normTensorDesc, normDstTensorDesc, normDstTensorDescF16; cudnnFilterDescriptor_t weightDesc, weightDesc16; cudnnFilterDescriptor_t dweightDesc, dweightDesc16; cudnnConvolutionDescriptor_t convDesc; diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c index 89ae55d4645..0e7dfd5b549 100644 --- a/src/maxpool_layer.c +++ b/src/maxpool_layer.c @@ -142,7 +142,7 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s else cudnn_maxpool_setup(&l); #endif // GPU - l.bflops = (l.size*l.size*l.c * l.out_h*l.out_w) / 1000000000.; + l.bflops = (l.size*l.size*l.c * l.out_h*l.out_w) / 1000000000.; if (avgpool) { if (stride_x == stride_y) fprintf(stderr, "avg %2dx%2d/%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BF\n", size, size, stride_x, w, h, c, l.out_w, l.out_h, l.out_c, l.bflops); @@ -411,4 +411,4 @@ void backward_local_avgpool_layer(const maxpool_layer l, network_state state) } } -} \ No newline at end of file +} diff --git a/src/network.c b/src/network.c index 92379f053f4..7741e3b1969 100644 --- a/src/network.c +++ b/src/network.c @@ -889,8 +889,8 @@ void custom_get_region_detections(layer l, int w, int h, int net_w, int net_h, f dets[j].best_class_idx = -1; for (i = 0; i < l.classes; ++i) { if (probs[j][i] > highest_prob) { - highest_prob = probs[j][i]; - dets[j].best_class_idx = i; + highest_prob = probs[j][i]; + dets[j].best_class_idx = i; } dets[j].prob[i] = probs[j][i]; } diff --git a/src/network.h b/src/network.h index 9cfce4e940a..f6850656e0e 100644 --- a/src/network.h +++ b/src/network.h @@ -23,7 +23,7 @@ typedef struct network{ float *workspace; int n; int batch; - uint64_t *seen; + uint64_t *seen; float epoch; int subdivisions; float momentum; @@ -61,7 +61,7 @@ typedef struct network{ float exposure; float saturation; float hue; - int small_object; + int small_object; int gpu_index; tree *hierarchy; @@ -71,11 +71,11 @@ typedef struct network{ float **input_gpu; float **truth_gpu; - float **input16_gpu; - float **output16_gpu; - size_t *max_input16_size; - size_t *max_output16_size; - int wait_stream; + float **input16_gpu; + float **output16_gpu; + size_t *max_input16_size; + size_t *max_output16_size; + int wait_stream; #endif } network; diff --git a/src/option_list.h b/src/option_list.h index 726b559aa4e..3dd66e4459d 100644 --- a/src/option_list.h +++ b/src/option_list.h @@ -26,8 +26,8 @@ float option_find_float_quiet(list *l, char *key, float def); void option_unused(list *l); //typedef struct { -// int classes; -// char **names; +// int classes; +// char **names; //} metadata; //LIB_API metadata get_metadata(char *file); diff --git a/src/parser.c b/src/parser.c index 09abf2d4beb..3bf4683d996 100644 --- a/src/parser.c +++ b/src/parser.c @@ -362,17 +362,17 @@ connected_layer parse_connected(list *options, size_params params) softmax_layer parse_softmax(list *options, size_params params) { - int groups = option_find_int_quiet(options, "groups", 1); - softmax_layer layer = make_softmax_layer(params.batch, params.inputs, groups); - layer.temperature = option_find_float_quiet(options, "temperature", 1); - char *tree_file = option_find_str(options, "tree", 0); - if (tree_file) layer.softmax_tree = read_tree(tree_file); - layer.w = params.w; - layer.h = params.h; - layer.c = params.c; - layer.spatial = option_find_float_quiet(options, "spatial", 0); - layer.noloss = option_find_int_quiet(options, "noloss", 0); - return layer; + int groups = option_find_int_quiet(options, "groups", 1); + softmax_layer layer = make_softmax_layer(params.batch, params.inputs, groups); + layer.temperature = option_find_float_quiet(options, "temperature", 1); + char *tree_file = option_find_str(options, "tree", 0); + if (tree_file) layer.softmax_tree = read_tree(tree_file); + layer.w = params.w; + layer.h = params.h; + layer.c = params.c; + layer.spatial = option_find_float_quiet(options, "spatial", 0); + layer.noloss = option_find_int_quiet(options, "noloss", 0); + return layer; } contrastive_layer parse_contrastive(list *options, size_params params) diff --git a/src/representation_layer.c b/src/representation_layer.c index fe7741fa9db..3efe74f58b7 100644 --- a/src/representation_layer.c +++ b/src/representation_layer.c @@ -155,5 +155,3 @@ void push_implicit_layer(layer l) CHECK_CUDA(cudaPeekAtLastError()); } #endif - - diff --git a/src/rnn_vid.c b/src/rnn_vid.c index a5ff5278435..c521c758879 100644 --- a/src/rnn_vid.c +++ b/src/rnn_vid.c @@ -160,7 +160,7 @@ void generate_vid_rnn(char *cfgfile, char *weightfile) //CvCapture* cap = cvCaptureFromFile("extra/vid/ILSVRC2015/Data/VID/snippets/val/ILSVRC2015_val_00007030.mp4"); float *feat; float *next; - next = NULL; + next = NULL; image last; for(i = 0; i < 25; ++i){ image im = get_image_from_stream_cpp(cap); diff --git a/src/softmax_layer.c b/src/softmax_layer.c index df51ff942ce..44ec537f9cc 100644 --- a/src/softmax_layer.c +++ b/src/softmax_layer.c @@ -14,16 +14,16 @@ void softmax_tree(float *input, int batch, int inputs, float temp, tree *hierarchy, float *output) { - int b; - for (b = 0; b < batch; ++b) { - int i; - int count = 0; - for (i = 0; i < hierarchy->groups; ++i) { - int group_size = hierarchy->group_size[i]; - softmax(input + b*inputs + count, group_size, temp, output + b*inputs + count, 1); - count += group_size; - } - } + int b; + for (b = 0; b < batch; ++b) { + int i; + int count = 0; + for (i = 0; i < hierarchy->groups; ++i) { + int group_size = hierarchy->group_size[i]; + softmax(input + b*inputs + count, group_size, temp, output + b*inputs + count, 1); + count += group_size; + } + } } softmax_layer make_softmax_layer(int batch, int inputs, int groups) @@ -89,28 +89,28 @@ void pull_softmax_layer_output(const softmax_layer layer) void forward_softmax_layer_gpu(const softmax_layer l, network_state net) { if(l.softmax_tree){ - softmax_tree_gpu(net.input, 1, l.batch, l.inputs, l.temperature, l.output_gpu, *l.softmax_tree); - /* - int i; - int count = 0; - for (i = 0; i < l.softmax_tree->groups; ++i) { - int group_size = l.softmax_tree->group_size[i]; - softmax_gpu(net.input_gpu + count, group_size, l.batch, l.inputs, 1, 0, 1, l.temperature, l.output_gpu + count); - count += group_size; - } - */ + softmax_tree_gpu(net.input, 1, l.batch, l.inputs, l.temperature, l.output_gpu, *l.softmax_tree); + /* + int i; + int count = 0; + for (i = 0; i < l.softmax_tree->groups; ++i) { + int group_size = l.softmax_tree->group_size[i]; + softmax_gpu(net.input_gpu + count, group_size, l.batch, l.inputs, 1, 0, 1, l.temperature, l.output_gpu + count); + count += group_size; + } + */ } else { if(l.spatial){ - softmax_gpu_new_api(net.input, l.c, l.batch*l.c, l.inputs/l.c, l.w*l.h, 1, l.w*l.h, 1, l.output_gpu); + softmax_gpu_new_api(net.input, l.c, l.batch*l.c, l.inputs/l.c, l.w*l.h, 1, l.w*l.h, 1, l.output_gpu); }else{ - softmax_gpu_new_api(net.input, l.inputs/l.groups, l.batch, l.inputs, l.groups, l.inputs/l.groups, 1, l.temperature, l.output_gpu); + softmax_gpu_new_api(net.input, l.inputs/l.groups, l.batch, l.inputs, l.groups, l.inputs/l.groups, 1, l.temperature, l.output_gpu); } } if(net.truth && !l.noloss){ softmax_x_ent_gpu(l.batch*l.inputs, l.output_gpu, net.truth, l.delta_gpu, l.loss_gpu); if(l.softmax_tree){ - mask_gpu_new_api(l.batch*l.inputs, l.delta_gpu, SECRET_NUM, net.truth, 0); - mask_gpu_new_api(l.batch*l.inputs, l.loss_gpu, SECRET_NUM, net.truth, 0); + mask_gpu_new_api(l.batch*l.inputs, l.delta_gpu, SECRET_NUM, net.truth, 0); + mask_gpu_new_api(l.batch*l.inputs, l.loss_gpu, SECRET_NUM, net.truth, 0); } cuda_pull_array(l.loss_gpu, l.loss, l.batch*l.inputs); l.cost[0] = sum_array(l.loss, l.batch*l.inputs); @@ -119,7 +119,7 @@ void forward_softmax_layer_gpu(const softmax_layer l, network_state net) void backward_softmax_layer_gpu(const softmax_layer layer, network_state state) { - axpy_ongpu(layer.batch*layer.inputs, state.net.loss_scale, layer.delta_gpu, 1, state.delta, 1); + axpy_ongpu(layer.batch*layer.inputs, state.net.loss_scale, layer.delta_gpu, 1, state.delta, 1); } #endif @@ -620,4 +620,4 @@ void backward_contrastive_layer_gpu(contrastive_layer layer, network_state state axpy_ongpu(layer.batch*layer.inputs, state.net.loss_scale, layer.delta_gpu, 1, state.delta, 1); } -#endif \ No newline at end of file +#endif diff --git a/src/tree.h b/src/tree.h index 86379738710..c5a09aa97a8 100644 --- a/src/tree.h +++ b/src/tree.h @@ -6,7 +6,7 @@ // int *leaf; // int n; // int *parent; -// int *child; +// int *child; // int *group; // char **name; // diff --git a/src/yolo.c b/src/yolo.c index 44f7c5a5438..f13d460aca0 100644 --- a/src/yolo.c +++ b/src/yolo.c @@ -251,7 +251,7 @@ void validate_yolo_recall(char *cfgfile, char *weightfile) if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms); char labelpath[4096]; - replace_image_to_label(path, labelpath); + replace_image_to_label(path, labelpath); int num_labels = 0; box_label *truth = read_boxes(labelpath, &num_labels); @@ -343,16 +343,16 @@ void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh) void run_yolo(int argc, char **argv) { - int dont_show = find_arg(argc, argv, "-dont_show"); - int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1); + int dont_show = find_arg(argc, argv, "-dont_show"); + int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1); int json_port = find_int_arg(argc, argv, "-json_port", -1); - char *out_filename = find_char_arg(argc, argv, "-out_filename", 0); + char *out_filename = find_char_arg(argc, argv, "-out_filename", 0); char *prefix = find_char_arg(argc, argv, "-prefix", 0); float thresh = find_float_arg(argc, argv, "-thresh", .2); - float hier_thresh = find_float_arg(argc, argv, "-hier", .5); + float hier_thresh = find_float_arg(argc, argv, "-hier", .5); int cam_index = find_int_arg(argc, argv, "-c", 0); int frame_skip = find_int_arg(argc, argv, "-s", 0); - int ext_output = find_arg(argc, argv, "-ext_output"); + int ext_output = find_arg(argc, argv, "-ext_output"); if(argc < 4){ fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); return; @@ -366,5 +366,5 @@ void run_yolo(int argc, char **argv) else if(0==strcmp(argv[2], "valid")) validate_yolo(cfg, weights); else if(0==strcmp(argv[2], "recall")) validate_yolo_recall(cfg, weights); else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, hier_thresh, cam_index, filename, voc_names, 20, 1, frame_skip, - prefix, out_filename, mjpeg_port, 0, json_port, dont_show, ext_output, 0, 0, 0, 0, 0); + prefix, out_filename, mjpeg_port, 0, json_port, dont_show, ext_output, 0, 0, 0, 0, 0); } diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index f7c1ea6907f..b0c17e8d93f 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -69,7 +69,7 @@ int get_device_count() { return count; #else return -1; -#endif // GPU +#endif // GPU } bool built_with_cuda(){ @@ -106,7 +106,7 @@ int get_device_name(int gpu, char* deviceName) { return 1; #else return -1; -#endif // GPU +#endif // GPU } #ifdef GPU From f3a50b9aee0164896f9314b82dda4c6836dfa81a Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 25 Aug 2023 22:11:50 +0200 Subject: [PATCH 1327/1365] add kmeansiou to build targets (#8831) --- CMakeLists.txt | 21 ++++++++++++++------- build.ps1 | 13 ++----------- scripts/utils.psm1 | 9 ++++----- 3 files changed, 20 insertions(+), 23 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 412a93f9e1a..ee9b2ec1e0b 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,8 +1,8 @@ cmake_minimum_required(VERSION 3.19) include(CMakeDependentOption) -option(CMAKE_VERBOSE_MAKEFILE "Create verbose makefile" ON) -option(CUDA_VERBOSE_BUILD "Create verbose CUDA build" ON) +option(CMAKE_VERBOSE_MAKEFILE "Create verbose makefile" OFF) +option(CUDA_VERBOSE_BUILD "Create verbose CUDA build" OFF) option(BUILD_SHARED_LIBS "Create dark as a shared library" ON) option(BUILD_AS_CPP "Build Darknet using C++ compiler also for C files" OFF) option(BUILD_USELIB_TRACK "Build uselib_track" ON) @@ -12,7 +12,7 @@ option(ENABLE_CUDA "Enable CUDA support" ON) option(ENABLE_CUDNN "Enable CUDNN" ON) option(ENABLE_CUDNN_HALF "Enable CUDNN Half precision" ON) option(ENABLE_ZED_CAMERA "Enable ZED Camera support" ON) -option(ENABLE_VCPKG_INTEGRATION "Enable VCPKG integration" ON) +option(ENABLE_VCPKG_INTEGRATION "Enable VCPKG integration" OFF) option(ENABLE_DEPLOY_CUSTOM_CMAKE_MODULES "Copy custom CMake modules for downstream integration" OFF) option(ENABLE_CSHARP_WRAPPER "Enable building a csharp wrapper" OFF) option(VCPKG_BUILD_OPENCV_WITH_CUDA "Build OpenCV with CUDA extension integration" ON) @@ -169,6 +169,8 @@ set(INSTALL_LIB_DIR "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Path where lib set(INSTALL_INCLUDE_DIR "include/darknet" CACHE PATH "Path where headers will be installed") set(INSTALL_CMAKE_DIR "share/darknet" CACHE PATH "Path where cmake configs will be installed") +find_library(MATH_LIBRARY m) + if(ENABLE_CUDA) include(CheckLanguage) check_language(CUDA) @@ -453,6 +455,11 @@ if(BUILD_AS_CPP) set_target_properties(darknet PROPERTIES LINKER_LANGUAGE CXX) endif() +add_executable(kmeansiou ${CMAKE_CURRENT_LIST_DIR}/scripts/kmeansiou.c) +if(MATH_LIBRARY) + target_link_libraries(kmeansiou PRIVATE ${MATH_LIBRARY}) +endif() + target_include_directories(darknet PUBLIC $ $ $ $) target_include_directories(dark PUBLIC $ $ $ $) target_include_directories(uselib PUBLIC $ $ $ $) @@ -492,9 +499,9 @@ if(OPENMP_FOUND) target_link_libraries(dark PUBLIC OpenMP::OpenMP_C) endif() -if(CMAKE_COMPILER_IS_GNUCC) - target_link_libraries(darknet PRIVATE m) - target_link_libraries(dark PUBLIC m) +if(CMAKE_COMPILER_IS_GNUCC AND MATH_LIBRARY) + target_link_libraries(darknet PRIVATE ${MATH_LIBRARY}) + target_link_libraries(dark PUBLIC ${MATH_LIBRARY}) endif() if(MSVC) @@ -575,7 +582,7 @@ install(TARGETS dark EXPORT DarknetTargets PUBLIC_HEADER DESTINATION "${INSTALL_INCLUDE_DIR}" COMPONENT dev ) -install(TARGETS uselib darknet +install(TARGETS uselib darknet kmeansiou DESTINATION "${INSTALL_BIN_DIR}" ) if(OpenCV_FOUND AND OpenCV_VERSION VERSION_GREATER "3.0" AND BUILD_USELIB_TRACK) diff --git a/build.ps1 b/build.ps1 index 9a4bb9ecabb..354f05df5a1 100755 --- a/build.ps1 +++ b/build.ps1 @@ -6,7 +6,7 @@ build Created By: Stefano Sinigardi Created Date: February 18, 2019 - Last Modified Date: August 1, 2023 + Last Modified Date: August 10, 2023 .DESCRIPTION Build tool using CMake, trying to properly setup the environment around compiler @@ -189,7 +189,7 @@ param ( $global:DisableInteractive = $DisableInteractive -$build_ps1_version = "3.4.0" +$build_ps1_version = "3.4.1" $script_name = $MyInvocation.MyCommand.Name Import-Module -Name $PSScriptRoot/scripts/utils.psm1 -Force @@ -729,15 +729,6 @@ if (($null -eq $vcpkg_path) -and $UseVCPKG) { Write-Host "Found vcpkg in $PWD/vcpkg${VCPKGSuffix}: $vcpkg_path" $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" } -else { - if (-not ($VCPKGSuffix -eq "")) { - MyThrow("Unable to find vcpkg${VCPKGSuffix}") - } - else { - Write-Host "Skipping vcpkg integration`n" -ForegroundColor Yellow - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=OFF" - } -} $vcpkg_branch_set_by_this_script = $false diff --git a/scripts/utils.psm1 b/scripts/utils.psm1 index 6c7313c3ef4..8fe34b596ae 100644 --- a/scripts/utils.psm1 +++ b/scripts/utils.psm1 @@ -83,7 +83,7 @@ function getLatestVisualStudioWithDesktopWorkloadPath([bool]$required = $true) { $installationPath = $instance.InstallationPath -replace "\\$" # Remove potential trailing backslash } if (!$installationPath) { - Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also partial installations" -ForegroundColor Yellow + #Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also partial installations" -ForegroundColor Yellow $output = & $vswhereExe -products * -latest -format xml [xml]$asXml = $output foreach ($instance in $asXml.instances.instance) { @@ -91,7 +91,7 @@ function getLatestVisualStudioWithDesktopWorkloadPath([bool]$required = $true) { } } if (!$installationPath) { - Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also pre-release installations" -ForegroundColor Yellow + #Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also pre-release installations" -ForegroundColor Yellow $output = & $vswhereExe -prerelease -products * -latest -format xml [xml]$asXml = $output foreach ($instance in $asXml.instances.instance) { @@ -130,7 +130,7 @@ function getLatestVisualStudioWithDesktopWorkloadVersion([bool]$required = $true $installationVersion = $instance.InstallationVersion } if (!$installationVersion) { - Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also partial installations" -ForegroundColor Yellow + #Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also partial installations" -ForegroundColor Yellow $output = & $vswhereExe -products * -latest -format xml [xml]$asXml = $output foreach ($instance in $asXml.instances.instance) { @@ -138,7 +138,7 @@ function getLatestVisualStudioWithDesktopWorkloadVersion([bool]$required = $true } } if (!$installationVersion) { - Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also pre-release installations" -ForegroundColor Yellow + #Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also pre-release installations" -ForegroundColor Yellow $output = & $vswhereExe -prerelease -products * -latest -format xml [xml]$asXml = $output foreach ($instance in $asXml.instances.instance) { @@ -340,4 +340,3 @@ Export-ModuleMember -Function DownloadNinja Export-ModuleMember -Function DownloadAria2 Export-ModuleMember -Function Download7Zip Export-ModuleMember -Function MyThrow -Export-ModuleMember -Function CopyTexFile From d5e04502906f589370a30f1c70181ef368e75bc2 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sat, 26 Aug 2023 01:41:27 +0200 Subject: [PATCH 1328/1365] upgrade to cuda 12.2 (#8832) --- .github/workflows/ccpp.yml | 6 ++-- .github/workflows/on_pr.yml | 6 ++-- Makefile | 6 +--- scripts/deploy-cuda.ps1 | 47 ++++++++++++++++++++++++------- scripts/deploy-cuda.sh | 10 ++----- scripts/requested_cuda_version.sh | 4 +-- scripts/utils.psm1 | 4 +-- 7 files changed, 49 insertions(+), 34 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 5d5ec8f0f39..0fd2abe6ea3 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -656,9 +656,9 @@ jobs: - name: 'Build' env: - CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.7" - CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.7" - CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.7\\bin\\nvcc.exe" + CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.2" + CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.2" + CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.2\\bin\\nvcc.exe" shell: pwsh run: ${{ github.workspace }}/build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateTOOL diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index c97875046fe..25fa153cbca 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -408,9 +408,9 @@ jobs: - name: 'Build' env: - CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.7" - CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.7" - CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.7\\bin\\nvcc.exe" + CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.2" + CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.2" + CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.2\\bin\\nvcc.exe" shell: pwsh run: ${{ github.workspace }}/build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateTOOL diff --git a/Makefile b/Makefile index 1154880fdee..8aa2a509809 100644 --- a/Makefile +++ b/Makefile @@ -17,8 +17,7 @@ ZED_CAMERA_v2_8=0 USE_CPP=0 DEBUG=0 -ARCH= -gencode arch=compute_35,code=sm_35 \ - -gencode arch=compute_50,code=[sm_50,compute_50] \ +ARCH= -gencode arch=compute_50,code=[sm_50,compute_50] \ -gencode arch=compute_52,code=[sm_52,compute_52] \ -gencode arch=compute_61,code=[sm_61,compute_61] @@ -27,9 +26,6 @@ OS := $(shell uname) # GeForce RTX 3070, 3080, 3090 # ARCH= -gencode arch=compute_86,code=[sm_86,compute_86] -# Kepler GeForce GTX 770, GTX 760, GT 740 -# ARCH= -gencode arch=compute_30,code=sm_30 - # Tesla A100 (GA100), DGX-A100, RTX 3080 # ARCH= -gencode arch=compute_80,code=[sm_80,compute_80] diff --git a/scripts/deploy-cuda.ps1 b/scripts/deploy-cuda.ps1 index 769b0974eb5..ab6b4f2fea5 100644 --- a/scripts/deploy-cuda.ps1 +++ b/scripts/deploy-cuda.ps1 @@ -9,15 +9,42 @@ Import-Module -Name $PSScriptRoot/utils.psm1 -Force $url = "https://developer.download.nvidia.com/compute/cuda/${cuda_version_full}/network_installers/cuda_${cuda_version_full}_windows_network.exe" -$CudaFeatures = "nvcc_${cuda_version_short} cuobjdump_${cuda_version_short} nvprune_${cuda_version_short} " + ` - " cupti_${cuda_version_short} memcheck_${cuda_version_short} nvdisasm_${cuda_version_short} nvprof_${cuda_version_short} " + ` - " cublas_${cuda_version_short} cublas_dev_${cuda_version_short} nvjpeg_${cuda_version_short} nvjpeg_dev_${cuda_version_short} " + ` - " nvtx_${cuda_version_short} cuxxfilt_${cuda_version_short} sanitizer_${cuda_version_short} " + ` - " cudart_${cuda_version_short} cufft_${cuda_version_short} cufft_dev_${cuda_version_short} curand_${cuda_version_short} " + ` - " curand_dev_${cuda_version_short} cusolver_${cuda_version_short} cusolver_dev_${cuda_version_short} " + ` - " cusparse_${cuda_version_short} cusparse_dev_${cuda_version_short} npp_${cuda_version_short} npp_dev_${cuda_version_short} " + ` - " nvrtc_${cuda_version_short} nvrtc_dev_${cuda_version_short} nvml_dev_${cuda_version_short} " + ` - " occupancy_calculator_${cuda_version_short} documentation_${cuda_version_short} " +$CudaFeatures = " nvcc_${cuda_version_short} " + ` + " cublas_${cuda_version_short} " + ` + " cublas_dev_${cuda_version_short} " + ` + " cuda_profiler_api_${cuda_version_short} " + ` + " cudart_${cuda_version_short} " + ` + " cufft_${cuda_version_short} " + ` + " cufft_dev_${cuda_version_short} " + ` + " cuobjdump_${cuda_version_short} " + ` + " cupti_${cuda_version_short} " + ` + " curand_${cuda_version_short} " + ` + " curand_dev_${cuda_version_short} " + ` + " cusolver_${cuda_version_short} " + ` + " cusolver_dev_${cuda_version_short} " + ` + " cusparse_${cuda_version_short} " + ` + " cusparse_dev_${cuda_version_short} " + ` + " cuxxfilt_${cuda_version_short} " + ` + " npp_${cuda_version_short} " + ` + " npp_dev_${cuda_version_short} " + ` + " nsight_compute_${cuda_version_short} " + ` + " nsight_systems_${cuda_version_short} " + ` + " nsight_vse_${cuda_version_short} " + ` + " nvdisasm_${cuda_version_short} " + ` + " nvjitlink_${cuda_version_short} " + ` + " nvjpeg_${cuda_version_short} " + ` + " nvjpeg_dev_${cuda_version_short} " + ` + " nvml_dev_${cuda_version_short} " + ` + " nvprof_${cuda_version_short} " + ` + " nvprune_${cuda_version_short} " + ` + " nvrtc_${cuda_version_short} " + ` + " nvrtc_dev_${cuda_version_short} " + ` + " nvtx_${cuda_version_short} " + ` + " occupancy_calculator_${cuda_version_short} " + ` + " opencl_${cuda_version_short} " + ` + " sanitizer_${cuda_version_short} " + ` + " thrust_${cuda_version_short} " + ` + " visual_profiler_${cuda_version_short} " if (-Not $DisableVisualStudioFeatures) { $CudaFeatures = $CudaFeatures + "visual_studio_integration_${cuda_version_short} visual_profiler_${cuda_version_short} " @@ -33,7 +60,7 @@ else { try { Push-Location $PSScriptRoot Write-Host "Downloading CUDA from $url..." - Invoke-WebRequest -Uri $url -OutFile "cuda_${cuda_version_full}_windows_network.exe" + curl.exe -L -o cuda_${cuda_version_full}_windows_network.exe -s -S $url Write-Host 'Installing CUDA...' $proc = Start-Process -PassThru -FilePath "./cuda_${cuda_version_full}_windows_network.exe" -ArgumentList @($SilentFlag + $CudaFeatures) $proc.WaitForExit() diff --git a/scripts/deploy-cuda.sh b/scripts/deploy-cuda.sh index 535e0012126..952669cb87f 100755 --- a/scripts/deploy-cuda.sh +++ b/scripts/deploy-cuda.sh @@ -20,14 +20,8 @@ else fi sudo apt-key del 7fa2af80 -wget https://developer.download.nvidia.com/compute/cuda/repos/$distr_name/x86_64/cuda-keyring_1.0-1_all.deb -sudo dpkg -i cuda-keyring_1.0-1_all.deb -sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/$distr_name/x86_64/ /" -sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/machine-learning/repos/$distr_name/x86_64/ /" -wget https://developer.download.nvidia.com/compute/cuda/repos/$distr_name/x86_64/cuda-$distr_name.pin -sudo mv cuda-${OS}.pin /etc/apt/preferences.d/cuda-repository-pin-600 -sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/$distr_name/x86_64/7fa2af80.pub -sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/$distr_name/x86_64/ /" +wget https://developer.download.nvidia.com/compute/cuda/repos/$distr_name/x86_64/cuda-keyring_1.1-1_all.deb +sudo dpkg -i cuda-keyring_1.1-1_all.deb sudo apt-get update sudo apt-get install -y build-essential g++ sudo apt-get install -y apt-transport-https ca-certificates gnupg software-properties-common wget diff --git a/scripts/requested_cuda_version.sh b/scripts/requested_cuda_version.sh index 6c95e056850..ae9ea2a4f55 100755 --- a/scripts/requested_cuda_version.sh +++ b/scripts/requested_cuda_version.sh @@ -1,6 +1,4 @@ #!/usr/bin/env bash -export CUDA_VERSION="11.7" +export CUDA_VERSION="12.2" export CUDA_VERSION_DASHED="${CUDA_VERSION//./-}" -export CUDNN_VERSION="8.4.1" -export CUDNN_VERSION_DASHED="${CUDNN_VERSION//./-}" diff --git a/scripts/utils.psm1 b/scripts/utils.psm1 index 8fe34b596ae..928d9339ae8 100644 --- a/scripts/utils.psm1 +++ b/scripts/utils.psm1 @@ -55,8 +55,8 @@ else { } Pop-Location -$cuda_version_full = "11.7.0" -$cuda_version_short = "11.7" +$cuda_version_full = "12.2.0" +$cuda_version_short = "12.2" $cuda_version_full_dashed = $cuda_version_full.replace('.', '-') $cuda_version_short_dashed = $cuda_version_short.replace('.', '-') From ea4f126cea650507713de8483f1547f14dcf0c0d Mon Sep 17 00:00:00 2001 From: mhwahdan <61120913+mhwahdan@users.noreply.github.com> Date: Sat, 26 Aug 2023 02:49:28 +0300 Subject: [PATCH 1329/1365] Added docker support + 1 command installation using docker-compose (#8578) * Added cpu docker support * add gpu docker support + docker compose for ease of use --- Dockerfile.cpu | 49 ++++++++++++++++++++++++++++++++++++++++++++++ Dockerfile.gpu | 47 ++++++++++++++++++++++++++++++++++++++++++++ docker-compose.yml | 20 +++++++++++++++++++ 3 files changed, 116 insertions(+) create mode 100644 Dockerfile.cpu create mode 100644 Dockerfile.gpu create mode 100644 docker-compose.yml diff --git a/Dockerfile.cpu b/Dockerfile.cpu new file mode 100644 index 00000000000..7a15cfb499e --- /dev/null +++ b/Dockerfile.cpu @@ -0,0 +1,49 @@ +FROM ubuntu:latest AS builder + +ENV DEBIAN_FRONTEND noninteractive + +RUN apt-get update -y + +RUN apt-get install -y g++ make pkg-config libopencv-dev + +COPY . /darknet + +WORKDIR /darknet + +RUN rm Dockerfile.cpu + +RUN rm Dockerfile.gpu + +RUN rm Docker-compose.yml + +RUN make + +FROM ubuntu:latest + +ENV DEBIAN_FRONTEND noninteractive + +RUN apt-get update -y + +RUN apt-get install -y sudo libgomp1 + +RUN useradd -U -m yolo + +RUN usermod -aG sudo yolo + +RUN usermod --shell /bin/bash yolo + +RUN echo "yolo:yolo" | chpasswd + +COPY --from=builder /darknet /home/yolo/darknet + +RUN cp /home/yolo/darknet/libdarknet.so /usr/local/lib/libdarknet.so || echo "libso not used" + +RUN cp /home/yolo/darknet/include/darknet.h /usr/local/include/darknet.h + +RUN ldconfig + +WORKDIR /home/yolo/darknet + +USER yolo + + diff --git a/Dockerfile.gpu b/Dockerfile.gpu new file mode 100644 index 00000000000..c4a9effbbcc --- /dev/null +++ b/Dockerfile.gpu @@ -0,0 +1,47 @@ +FROM nvidia/cuda:11.6.0-cudnn8-devel-ubuntu20.04 AS builder + +ENV DEBIAN_FRONTEND noninteractive + +RUN apt-get update -y + +RUN apt-get install -y g++ make pkg-config libopencv-dev + +COPY . /darknet + +WORKDIR /darknet + +RUN rm Dockerfile.cpu + +RUN rm Dockerfile.gpu + +RUN rm Docker-compose.yml + +RUN make + +FROM nvidia/cuda:11.6.0-cudnn8-devel-ubuntu20.04 + +ENV DEBIAN_FRONTEND noninteractive + +RUN apt-get update -y + +RUN apt-get install -y sudo libgomp1 + +RUN useradd -U -m yolo + +RUN usermod -aG sudo yolo + +RUN usermod --shell /bin/bash yolo + +RUN echo "yolo:yolo" | chpasswd + +COPY --from=builder /darknet /home/yolo/darknet + +RUN cp /home/yolo/darknet/libdarknet.so /usr/local/lib/libdarknet.so || echo "libso not used" + +RUN cp /home/yolo/darknet/include/darknet.h /usr/local/include/darknet.h + +RUN ldconfig + +WORKDIR /home/yolo/darknet + +USER yolo \ No newline at end of file diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 00000000000..968fbccaaf9 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,20 @@ +version: '2' + +services: + yolo-gpu: + build: + context: . + dockerfile: Dockerfile.gpu + image: yolo:gpu + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: 1 + capabilities: [gpu] + yolo-cpu: + build: + context: . + dockerfile: Dockerfile.cpu + image: yolo:cpu \ No newline at end of file From ef8ad4ae2d974b67883798164c271aac02732c95 Mon Sep 17 00:00:00 2001 From: Cristi Fati Date: Sat, 26 Aug 2023 02:55:36 +0300 Subject: [PATCH 1330/1365] Fix CPU only project build failure (#8555) --- build/darknet/yolo_cpp_dll_no_gpu.vcxproj | 2 ++ 1 file changed, 2 insertions(+) diff --git a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj index c9f3ddfa29c..ea04b5497e6 100644 --- a/build/darknet/yolo_cpp_dll_no_gpu.vcxproj +++ b/build/darknet/yolo_cpp_dll_no_gpu.vcxproj @@ -214,6 +214,7 @@ + @@ -275,6 +276,7 @@ + From c87e33eb0662bd1b6883fd37b40e64f5a9e389ac Mon Sep 17 00:00:00 2001 From: Cristi Fati Date: Sat, 26 Aug 2023 02:56:16 +0300 Subject: [PATCH 1331/1365] Fix thread synchronization bug + many more (#8562) * Minor: float fps (useful for small values - e.g. CPU) * Fix multiple code readability issues * Better synchoronize threads (when user interrupts video) * Better thread objects handling * Pass data via arguments instead of global like variables - 0 * Minor code readability fixes * CTypes definitions * Pass data via arguments instead of global like variables - 1 * Code reordering + (minor) renames * Pass data via arguments instead of global like variables - 2 --- darknet.py | 183 +++++++++++++++++++++++++++-------------------- darknet_video.py | 164 +++++++++++++++++++++++++----------------- 2 files changed, 202 insertions(+), 145 deletions(-) diff --git a/darknet.py b/darknet.py index ebb0eede210..603ecae92dc 100644 --- a/darknet.py +++ b/darknet.py @@ -8,49 +8,71 @@ Use pip3 instead of pip on some systems to be sure to install modules for python3 """ -from ctypes import * -import math +import ctypes as ct import random import os +import cv2 +import numpy as np -class BOX(Structure): - _fields_ = [("x", c_float), - ("y", c_float), - ("w", c_float), - ("h", c_float)] +class BOX(ct.Structure): + _fields_ = ( + ("x", ct.c_float), + ("y", ct.c_float), + ("w", ct.c_float), + ("h", ct.c_float), + ) -class DETECTION(Structure): - _fields_ = [("bbox", BOX), - ("classes", c_int), - ("best_class_idx", c_int), - ("prob", POINTER(c_float)), - ("mask", POINTER(c_float)), - ("objectness", c_float), - ("sort_class", c_int), - ("uc", POINTER(c_float)), - ("points", c_int), - ("embeddings", POINTER(c_float)), - ("embedding_size", c_int), - ("sim", c_float), - ("track_id", c_int)] +FloatPtr = ct.POINTER(ct.c_float) +IntPtr = ct.POINTER(ct.c_int) -class DETNUMPAIR(Structure): - _fields_ = [("num", c_int), - ("dets", POINTER(DETECTION))] +class DETECTION(ct.Structure): + _fields_ = ( + ("bbox", BOX), + ("classes", ct.c_int), + ("best_class_idx", ct.c_int), + ("prob", FloatPtr), + ("mask", FloatPtr), + ("objectness", ct.c_float), + ("sort_class", ct.c_int), + ("uc", FloatPtr), + ("points", ct.c_int), + ("embeddings", FloatPtr), + ("embedding_size", ct.c_int), + ("sim", ct.c_float), + ("track_id", ct.c_int), + ) -class IMAGE(Structure): - _fields_ = [("w", c_int), - ("h", c_int), - ("c", c_int), - ("data", POINTER(c_float))] +DETECTIONPtr = ct.POINTER(DETECTION) -class METADATA(Structure): - _fields_ = [("classes", c_int), - ("names", POINTER(c_char_p))] + +class DETNUMPAIR(ct.Structure): + _fields_ = ( + ("num", ct.c_int), + ("dets", DETECTIONPtr), + ) + + +DETNUMPAIRPtr = ct.POINTER(DETNUMPAIR) + + +class IMAGE(ct.Structure): + _fields_ = ( + ("w", ct.c_int), + ("h", ct.c_int), + ("c", ct.c_int), + ("data", FloatPtr), + ) + + +class METADATA(ct.Structure): + _fields_ = ( + ("classes", ct.c_int), + ("names", ct.POINTER(ct.c_char_p)), + ) def network_width(net): @@ -67,10 +89,10 @@ def bbox2points(bbox): to corner points cv2 rectangle """ x, y, w, h = bbox - xmin = int(round(x - (w / 2))) - xmax = int(round(x + (w / 2))) - ymin = int(round(y - (h / 2))) - ymax = int(round(y + (h / 2))) + xmin = round(x - (w / 2)) + xmax = round(x + (w / 2)) + ymin = round(y - (h / 2)) + ymax = round(y + (h / 2)) return xmin, ymin, xmax, ymax @@ -134,6 +156,7 @@ def decode_detection(detections): decoded.append((str(label), confidence, bbox)) return decoded + # https://www.pyimagesearch.com/2015/02/16/faster-non-maximum-suppression-python/ # Malisiewicz et al. def non_max_suppression_fast(detections, overlap_thresh): @@ -185,6 +208,7 @@ def non_max_suppression_fast(detections, overlap_thresh): # integer data type return [detections[i] for i in pick] + def remove_negatives(detections, class_names, num): """ Remove all classes with 0% confidence within the detection @@ -218,7 +242,7 @@ def detect_image(network, class_names, image, thresh=.5, hier_thresh=.5, nms=.45 """ Returns a list with highest confidence class and their bbox """ - pnum = pointer(c_int(0)) + pnum = ct.pointer(ct.c_int(0)) predict_image(network, image) detections = get_network_boxes(network, image.w, image.h, thresh, hier_thresh, None, 0, pnum, 0) @@ -233,102 +257,105 @@ def detect_image(network, class_names, image, thresh=.5, hier_thresh=.5, nms=.45 if os.name == "posix": cwd = os.path.dirname(__file__) - lib = CDLL(cwd + "/libdarknet.so", RTLD_GLOBAL) + lib = ct.CDLL(cwd + "/libdarknet.so", ct.RTLD_GLOBAL) elif os.name == "nt": cwd = os.path.dirname(__file__) - os.environ['PATH'] = cwd + ';' + os.environ['PATH'] - lib = CDLL("darknet.dll", RTLD_GLOBAL) + os.environ["PATH"] = os.path.pathsep.join((cwd, os.environ["PATH"])) + lib = ct.CDLL("darknet.dll", ct.RTLD_GLOBAL) else: + lib = None # Intellisense print("Unsupported OS") - exit + exit() -lib.network_width.argtypes = [c_void_p] -lib.network_width.restype = c_int -lib.network_height.argtypes = [c_void_p] -lib.network_height.restype = c_int +lib.network_width.argtypes = (ct.c_void_p,) +lib.network_width.restype = ct.c_int +lib.network_height.argtypes = (ct.c_void_p,) +lib.network_height.restype = ct.c_int copy_image_from_bytes = lib.copy_image_from_bytes -copy_image_from_bytes.argtypes = [IMAGE,c_char_p] +copy_image_from_bytes.argtypes = (IMAGE, ct.c_char_p) predict = lib.network_predict_ptr -predict.argtypes = [c_void_p, POINTER(c_float)] -predict.restype = POINTER(c_float) +predict.argtypes = (ct.c_void_p, FloatPtr) +predict.restype = FloatPtr set_gpu = lib.cuda_set_device init_cpu = lib.init_cpu make_image = lib.make_image -make_image.argtypes = [c_int, c_int, c_int] +make_image.argtypes = (ct.c_int, ct.c_int, ct.c_int) make_image.restype = IMAGE get_network_boxes = lib.get_network_boxes -get_network_boxes.argtypes = [c_void_p, c_int, c_int, c_float, c_float, POINTER(c_int), c_int, POINTER(c_int), c_int] -get_network_boxes.restype = POINTER(DETECTION) +get_network_boxes.argtypes = (ct.c_void_p, ct.c_int, ct.c_int, ct.c_float, ct.c_float, IntPtr, ct.c_int, IntPtr, + ct.c_int) +get_network_boxes.restype = DETECTIONPtr make_network_boxes = lib.make_network_boxes -make_network_boxes.argtypes = [c_void_p] -make_network_boxes.restype = POINTER(DETECTION) +make_network_boxes.argtypes = (ct.c_void_p,) +make_network_boxes.restype = DETECTIONPtr free_detections = lib.free_detections -free_detections.argtypes = [POINTER(DETECTION), c_int] +free_detections.argtypes = (DETECTIONPtr, ct.c_int) free_batch_detections = lib.free_batch_detections -free_batch_detections.argtypes = [POINTER(DETNUMPAIR), c_int] +free_batch_detections.argtypes = (DETNUMPAIRPtr, ct.c_int) free_ptrs = lib.free_ptrs -free_ptrs.argtypes = [POINTER(c_void_p), c_int] +free_ptrs.argtypes = (ct.POINTER(ct.c_void_p), ct.c_int) network_predict = lib.network_predict_ptr -network_predict.argtypes = [c_void_p, POINTER(c_float)] +network_predict.argtypes = (ct.c_void_p, FloatPtr) reset_rnn = lib.reset_rnn -reset_rnn.argtypes = [c_void_p] +reset_rnn.argtypes = (ct.c_void_p,) load_net = lib.load_network -load_net.argtypes = [c_char_p, c_char_p, c_int] -load_net.restype = c_void_p +load_net.argtypes = (ct.c_char_p, ct.c_char_p, ct.c_int) +load_net.restype = ct.c_void_p load_net_custom = lib.load_network_custom -load_net_custom.argtypes = [c_char_p, c_char_p, c_int, c_int] -load_net_custom.restype = c_void_p +load_net_custom.argtypes = (ct.c_char_p, ct.c_char_p, ct.c_int, ct.c_int) +load_net_custom.restype = ct.c_void_p free_network_ptr = lib.free_network_ptr -free_network_ptr.argtypes = [c_void_p] -free_network_ptr.restype = c_void_p +free_network_ptr.argtypes = (ct.c_void_p,) +free_network_ptr.restype = ct.c_void_p do_nms_obj = lib.do_nms_obj -do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float] +do_nms_obj.argtypes = (DETECTIONPtr, ct.c_int, ct.c_int, ct.c_float) do_nms_sort = lib.do_nms_sort -do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float] +do_nms_sort.argtypes = (DETECTIONPtr, ct.c_int, ct.c_int, ct.c_float) free_image = lib.free_image -free_image.argtypes = [IMAGE] +free_image.argtypes = (IMAGE,) letterbox_image = lib.letterbox_image -letterbox_image.argtypes = [IMAGE, c_int, c_int] +letterbox_image.argtypes = (IMAGE, ct.c_int, ct.c_int) letterbox_image.restype = IMAGE load_meta = lib.get_metadata -lib.get_metadata.argtypes = [c_char_p] +lib.get_metadata.argtypes = (ct.c_char_p,) lib.get_metadata.restype = METADATA load_image = lib.load_image_color -load_image.argtypes = [c_char_p, c_int, c_int] +load_image.argtypes = (ct.c_char_p, ct.c_int, ct.c_int) load_image.restype = IMAGE rgbgr_image = lib.rgbgr_image -rgbgr_image.argtypes = [IMAGE] +rgbgr_image.argtypes = (IMAGE,) predict_image = lib.network_predict_image -predict_image.argtypes = [c_void_p, IMAGE] -predict_image.restype = POINTER(c_float) +predict_image.argtypes = (ct.c_void_p, IMAGE) +predict_image.restype = FloatPtr predict_image_letterbox = lib.network_predict_image_letterbox -predict_image_letterbox.argtypes = [c_void_p, IMAGE] -predict_image_letterbox.restype = POINTER(c_float) +predict_image_letterbox.argtypes = (ct.c_void_p, IMAGE) +predict_image_letterbox.restype = FloatPtr network_predict_batch = lib.network_predict_batch -network_predict_batch.argtypes = [c_void_p, IMAGE, c_int, c_int, c_int, - c_float, c_float, POINTER(c_int), c_int, c_int] -network_predict_batch.restype = POINTER(DETNUMPAIR) +network_predict_batch.argtypes = (ct.c_void_p, IMAGE, ct.c_int, ct.c_int, ct.c_int, + ct.c_float, ct.c_float, IntPtr, ct.c_int, ct.c_int) +network_predict_batch.restype = DETNUMPAIRPtr + diff --git a/darknet_video.py b/darknet_video.py index eab6e9e763a..c6682a24eae 100644 --- a/darknet_video.py +++ b/darknet_video.py @@ -1,12 +1,11 @@ -from ctypes import * import random import os import cv2 import time import darknet import argparse -from threading import Thread, enumerate -from queue import Queue +import threading +import queue def parser(): @@ -17,9 +16,9 @@ def parser(): help="inference video name. Not saved if empty") parser.add_argument("--weights", default="yolov4.weights", help="yolo weights path") - parser.add_argument("--dont_show", action='store_true', - help="windown inference display. For headless systems") - parser.add_argument("--ext_output", action='store_true', + parser.add_argument("--dont_show", action="store_true", + help="window inference display. For headless systems") + parser.add_argument("--ext_output", action="store_true", help="display bbox coordinates of detected objects") parser.add_argument("--config_file", default="./cfg/yolov4.cfg", help="path to config file") @@ -53,131 +52,162 @@ def check_arguments_errors(args): raise(ValueError("Invalid video path {}".format(os.path.abspath(args.input)))) -def set_saved_video(input_video, output_video, size): +def set_saved_video(output_video, size, fps): fourcc = cv2.VideoWriter_fourcc(*"MJPG") - fps = int(input_video.get(cv2.CAP_PROP_FPS)) - video = cv2.VideoWriter(output_video, fourcc, fps, size) - return video + return cv2.VideoWriter(output_video, fourcc, fps, size) -def convert2relative(bbox): +def convert2relative(bbox, preproc_h, preproc_w): """ YOLO format use relative coordinates for annotation """ - x, y, w, h = bbox - _height = darknet_height - _width = darknet_width - return x/_width, y/_height, w/_width, h/_height + x, y, w, h = bbox + return x / preproc_w, y / preproc_h, w / preproc_w, h / preproc_h -def convert2original(image, bbox): - x, y, w, h = convert2relative(bbox) +def convert2original(image, bbox, preproc_h, preproc_w): + x, y, w, h = convert2relative(bbox, preproc_h, preproc_w) image_h, image_w, __ = image.shape - orig_x = int(x * image_w) - orig_y = int(y * image_h) - orig_width = int(w * image_w) - orig_height = int(h * image_h) + orig_x = int(x * image_w) + orig_y = int(y * image_h) + orig_width = int(w * image_w) + orig_height = int(h * image_h) bbox_converted = (orig_x, orig_y, orig_width, orig_height) return bbox_converted -def convert4cropping(image, bbox): - x, y, w, h = convert2relative(bbox) +# @TODO - cfati: Unused +def convert4cropping(image, bbox, preproc_h, preproc_w): + x, y, w, h = convert2relative(bbox, preproc_h, preproc_w) image_h, image_w, __ = image.shape - orig_left = int((x - w / 2.) * image_w) - orig_right = int((x + w / 2.) * image_w) - orig_top = int((y - h / 2.) * image_h) - orig_bottom = int((y + h / 2.) * image_h) + orig_left = int((x - w / 2.) * image_w) + orig_right = int((x + w / 2.) * image_w) + orig_top = int((y - h / 2.) * image_h) + orig_bottom = int((y + h / 2.) * image_h) - if (orig_left < 0): orig_left = 0 - if (orig_right > image_w - 1): orig_right = image_w - 1 - if (orig_top < 0): orig_top = 0 - if (orig_bottom > image_h - 1): orig_bottom = image_h - 1 + if orig_left < 0: + orig_left = 0 + if orig_right > image_w - 1: + orig_right = image_w - 1 + if orig_top < 0: + orig_top = 0 + if orig_bottom > image_h - 1: + orig_bottom = image_h - 1 bbox_cropping = (orig_left, orig_top, orig_right, orig_bottom) return bbox_cropping -def video_capture(frame_queue, darknet_image_queue): - while cap.isOpened(): +def video_capture(stop_flag, input_path, raw_frame_queue, preprocessed_frame_queue, preproc_h, preproc_w): + cap = cv2.VideoCapture(input_path) + while cap.isOpened() and not stop_flag.is_set(): ret, frame = cap.read() if not ret: break frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) - frame_resized = cv2.resize(frame_rgb, (darknet_width, darknet_height), + frame_resized = cv2.resize(frame_rgb, (preproc_w, preproc_h), interpolation=cv2.INTER_LINEAR) - frame_queue.put(frame) - img_for_detect = darknet.make_image(darknet_width, darknet_height, 3) + raw_frame_queue.put(frame) + img_for_detect = darknet.make_image(preproc_w, preproc_h, 3) darknet.copy_image_from_bytes(img_for_detect, frame_resized.tobytes()) - darknet_image_queue.put(img_for_detect) + preprocessed_frame_queue.put(img_for_detect) + stop_flag.set() cap.release() -def inference(darknet_image_queue, detections_queue, fps_queue): - while cap.isOpened(): - darknet_image = darknet_image_queue.get() +def inference(stop_flag, preprocessed_frame_queue, detections_queue, fps_queue, + network, class_names, threshold): + while not stop_flag.is_set(): + darknet_image = preprocessed_frame_queue.get() prev_time = time.time() - detections = darknet.detect_image(network, class_names, darknet_image, thresh=args.thresh) + detections = darknet.detect_image(network, class_names, darknet_image, thresh=threshold) + fps = 1 / (time.time() - prev_time) detections_queue.put(detections) - fps = int(1/(time.time() - prev_time)) - fps_queue.put(fps) - print("FPS: {}".format(fps)) + fps_queue.put(int(fps)) + print("FPS: {:.2f}".format(fps)) darknet.print_detections(detections, args.ext_output) darknet.free_image(darknet_image) - cap.release() -def drawing(frame_queue, detections_queue, fps_queue): +def drawing(stop_flag, input_video_fps, queues, preproc_h, preproc_w, vid_h, vid_w): random.seed(3) # deterministic bbox colors - video = set_saved_video(cap, args.out_filename, (video_width, video_height)) - while cap.isOpened(): - frame = frame_queue.get() + raw_frame_queue, preprocessed_frame_queue, detections_queue, fps_queue = queues + video = set_saved_video(args.out_filename, (vid_w, vid_h), input_video_fps) + fps = 1 + while not stop_flag.is_set(): + frame = raw_frame_queue.get() detections = detections_queue.get() fps = fps_queue.get() detections_adjusted = [] if frame is not None: for label, confidence, bbox in detections: - bbox_adjusted = convert2original(frame, bbox) + bbox_adjusted = convert2original(frame, bbox, preproc_h, preproc_w) detections_adjusted.append((str(label), confidence, bbox_adjusted)) image = darknet.draw_boxes(detections_adjusted, frame, class_colors) if not args.dont_show: - cv2.imshow('Inference', image) + cv2.imshow("Inference", image) if args.out_filename is not None: video.write(image) if cv2.waitKey(fps) == 27: break - cap.release() + stop_flag.set() video.release() cv2.destroyAllWindows() + timeout = 1 / (fps if fps > 0 else 0.5) + for q in (preprocessed_frame_queue, detections_queue, fps_queue): + try: + q.get(block=True, timeout=timeout) + except queue.Empty: + pass -if __name__ == '__main__': - frame_queue = Queue() - darknet_image_queue = Queue(maxsize=1) - detections_queue = Queue(maxsize=1) - fps_queue = Queue(maxsize=1) - +if __name__ == "__main__": args = parser() check_arguments_errors(args) network, class_names, class_colors = darknet.load_network( - args.config_file, - args.data_file, - args.weights, - batch_size=1 - ) + args.config_file, + args.data_file, + args.weights, + batch_size=1) darknet_width = darknet.network_width(network) darknet_height = darknet.network_height(network) input_path = str2int(args.input) - cap = cv2.VideoCapture(input_path) + cap = cv2.VideoCapture(input_path) # Open video twice :( video_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) video_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) - Thread(target=video_capture, args=(frame_queue, darknet_image_queue)).start() - Thread(target=inference, args=(darknet_image_queue, detections_queue, fps_queue)).start() - Thread(target=drawing, args=(frame_queue, detections_queue, fps_queue)).start() + video_fps = int(cap.get(cv2.CAP_PROP_FPS)) + cap.release() + del cap + + ExecUnit = threading.Thread + Queue = queue.Queue + stop_flag = threading.Event() + + raw_frame_queue = Queue() + preprocessed_frame_queue = Queue(maxsize=1) + detections_queue = Queue(maxsize=1) + fps_queue = Queue(maxsize=1) + + exec_units = ( + ExecUnit(target=video_capture, args=(stop_flag, input_path, raw_frame_queue, preprocessed_frame_queue, + darknet_height, darknet_width)), + ExecUnit(target=inference, args=(stop_flag, preprocessed_frame_queue, detections_queue, fps_queue, + network, class_names, args.thresh)), + ExecUnit(target=drawing, args=(stop_flag, video_fps, + (raw_frame_queue, preprocessed_frame_queue, detections_queue, fps_queue), + darknet_height, darknet_width, video_height, video_width)), + ) + for exec_unit in exec_units: + exec_unit.start() + for exec_unit in exec_units: + exec_unit.join() + + print("\nDone.") + From 68100fb3a8f995b0e36e5258a723de88f32c84d5 Mon Sep 17 00:00:00 2001 From: Juuso Alasuutari Date: Sat, 26 Aug 2023 03:41:03 +0300 Subject: [PATCH 1332/1365] Fix format string conversion specifiers (#6192) Signed-off-by: Juuso Alasuutari Co-authored-by: Stefano Sinigardi --- src/blas.c | 8 ++++---- src/captcha.c | 2 +- src/cifar.c | 4 ++-- src/classifier.c | 2 +- src/compare.c | 6 +++--- src/dark_cuda.c | 12 ++++++------ src/dice.c | 2 +- src/go.c | 2 +- src/http_stream.cpp | 2 +- src/network.h | 11 +++++++++++ src/softmax_layer.c | 2 +- src/tag.c | 2 +- src/writing.c | 2 +- 13 files changed, 34 insertions(+), 23 deletions(-) diff --git a/src/blas.c b/src/blas.c index 6cd1deb06a3..a3024d11bb3 100644 --- a/src/blas.c +++ b/src/blas.c @@ -594,7 +594,7 @@ float find_sim(size_t i, size_t j, contrastive_params *contrast_p, int contrast_ if (contrast_p[z].i == i && contrast_p[z].j == j) break; } if (z == contrast_p_size) { - printf(" Error: find_sim(): sim isn't found: i = %d, j = %d, z = %d \n", i, j, z); + printf(" Error: find_sim(): sim isn't found: i = %zu, j = %zu, z = %zu \n", i, j, z); getchar(); } @@ -608,7 +608,7 @@ float find_P_constrastive(size_t i, size_t j, contrastive_params *contrast_p, in if (contrast_p[z].i == i && contrast_p[z].j == j) break; } if (z == contrast_p_size) { - printf(" Error: find_P_constrastive(): P isn't found: i = %d, j = %d, z = %d \n", i, j, z); + printf(" Error: find_P_constrastive(): P isn't found: i = %zu, j = %zu, z = %zu \n", i, j, z); getchar(); } @@ -648,7 +648,7 @@ float P_constrastive_f_det(size_t il, int *labels, float **z, unsigned int featu float P_constrastive_f(size_t i, size_t l, int *labels, float **z, unsigned int feature_size, float temperature, contrastive_params *contrast_p, int contrast_p_size) { if (i == l) { - fprintf(stderr, " Error: in P_constrastive must be i != l, while i = %d, l = %d \n", i, l); + fprintf(stderr, " Error: in P_constrastive must be i != l, while i = %zu, l = %zu \n", i, l); getchar(); } @@ -782,7 +782,7 @@ void grad_contrastive_loss_negative_f(size_t i, int *class_ids, int *labels, siz float P_constrastive(size_t i, size_t l, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *exp_cos_sim) { if (i == l) { - fprintf(stderr, " Error: in P_constrastive must be i != l, while i = %d, l = %d \n", i, l); + fprintf(stderr, " Error: in P_constrastive must be i != l, while i = %zu, l = %zu \n", i, l); getchar(); } diff --git a/src/captcha.c b/src/captcha.c index 0cc15915257..5fd565d0442 100644 --- a/src/captcha.c +++ b/src/captcha.c @@ -85,7 +85,7 @@ void train_captcha(char *cfgfile, char *weightfile) float loss = train_network(net, train); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%d: %f, %f avg, %lf seconds, %ld images\n", i, loss, avg_loss, sec(clock()-time), *net.seen); + printf("%d: %f, %f avg, %lf seconds, %" PRIu64 " images\n", i, loss, avg_loss, sec(clock()-time), *net.seen); free_data(train); if(i%100==0){ char buff[256]; diff --git a/src/cifar.c b/src/cifar.c index 1b87cb5f016..1ef221e8709 100644 --- a/src/cifar.c +++ b/src/cifar.c @@ -29,7 +29,7 @@ void train_cifar(char *cfgfile, char *weightfile) float loss = train_network_sgd(net, train, 1); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.95 + loss*.05; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); + printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %" PRIu64 " images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); if(*net.seen/N > epoch){ epoch = *net.seen/N; char buff[256]; @@ -85,7 +85,7 @@ void train_cifar_distill(char *cfgfile, char *weightfile) float loss = train_network_sgd(net, train, 1); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.95 + loss*.05; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); + printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %" PRIu64 " images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); if(*net.seen/N > epoch){ epoch = *net.seen/N; char buff[256]; diff --git a/src/classifier.c b/src/classifier.c index e42b1ea3ab2..c17e1b1ffe7 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -208,7 +208,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, if (avg_time < 0) avg_time = time_remaining; else avg_time = alpha_time * time_remaining + (1 - alpha_time) * avg_time; start = what_time_is_it_now(); - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images, %f hours left\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen, avg_time); + printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %" PRIu64 " images, %f hours left\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen, avg_time); #ifdef OPENCV if (net.contrastive) { float cur_con_acc = -1; diff --git a/src/compare.c b/src/compare.c index 62edabe965c..4bacda4c233 100644 --- a/src/compare.c +++ b/src/compare.c @@ -1,5 +1,3 @@ -#include - #include "network.h" #include "detection_layer.h" #include "cost_layer.h" @@ -7,6 +5,8 @@ #include "parser.h" #include "box.h" +#include + void train_compare(char *cfgfile, char *weightfile) { srand(time(0)); @@ -54,7 +54,7 @@ void train_compare(char *cfgfile, char *weightfile) float loss = train_network(net, train); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%.3f: %f, %f avg, %lf seconds, %ld images\n", (float)*net.seen/N, loss, avg_loss, sec(clock()-time), *net.seen); + printf("%.3f: %f, %f avg, %lf seconds, %" PRIu64 " images\n", (float)*net.seen/N, loss, avg_loss, sec(clock()-time), *net.seen); free_data(train); if(i%100 == 0){ char buff[256]; diff --git a/src/dark_cuda.c b/src/dark_cuda.c index 3b21a75eb66..09b757f26fa 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -385,14 +385,14 @@ void free_pinned_memory() void pre_allocate_pinned_memory(const size_t size) { const size_t num_of_blocks = size / pinned_block_size + ((size % pinned_block_size) ? 1 : 0); - printf("pre_allocate... pinned_ptr = %p \n", pinned_ptr); + printf("pre_allocate... pinned_ptr = %p \n", (void *)pinned_ptr); pthread_mutex_lock(&mutex_pinned); if (!pinned_ptr) { pinned_ptr = (float **)calloc(num_of_blocks, sizeof(float *)); if(!pinned_ptr) error("calloc failed in pre_allocate()", DARKNET_LOC); - printf("pre_allocate: size = %Iu MB, num_of_blocks = %Iu, block_size = %Iu MB \n", + printf("pre_allocate: size = %zu MB, num_of_blocks = %zu, block_size = %zu MB \n", size / (1024*1024), num_of_blocks, pinned_block_size / (1024 * 1024)); int k; @@ -402,7 +402,7 @@ void pre_allocate_pinned_memory(const size_t size) CHECK_CUDA(status); if (!pinned_ptr[k]) error("cudaHostAlloc failed", DARKNET_LOC); else { - printf(" Allocated %d pinned block \n", pinned_block_size); + printf(" Allocated %zu pinned block \n", pinned_block_size); } } pinned_num_of_blocks = num_of_blocks; @@ -423,7 +423,7 @@ float *cuda_make_array_pinned_preallocated(float *x, size_t n) { if ((allocation_size + pinned_index) > pinned_block_size) { const float filled = (float)100 * pinned_index / pinned_block_size; - printf("\n Pinned block_id = %d, filled = %f %% \n", pinned_block_id, filled); + printf("\n Pinned block_id = %zu, filled = %f %% \n", pinned_block_id, filled); pinned_block_id++; pinned_index = 0; } @@ -438,13 +438,13 @@ float *cuda_make_array_pinned_preallocated(float *x, size_t n) if(!x_cpu) { if (allocation_size > pinned_block_size / 2) { - printf("Try to allocate new pinned memory, size = %d MB \n", size / (1024 * 1024)); + printf("Try to allocate new pinned memory, size = %zu MB \n", size / (1024 * 1024)); cudaError_t status = cudaHostAlloc((void **)&x_cpu, size, cudaHostRegisterMapped); if (status != cudaSuccess) fprintf(stderr, " Can't allocate CUDA-pinned memory on CPU-RAM (pre-allocated memory is over too) \n"); CHECK_CUDA(status); } else { - printf("Try to allocate new pinned BLOCK, size = %d MB \n", size / (1024 * 1024)); + printf("Try to allocate new pinned BLOCK, size = %zu MB \n", size / (1024 * 1024)); pinned_num_of_blocks++; pinned_block_id = pinned_num_of_blocks - 1; pinned_index = 0; diff --git a/src/dice.c b/src/dice.c index 8a0393a8bdf..bb5d643796e 100644 --- a/src/dice.c +++ b/src/dice.c @@ -33,7 +33,7 @@ void train_dice(char *cfgfile, char *weightfile) float loss = train_network(net, train); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%d: %f, %f avg, %lf seconds, %ld images\n", i, loss, avg_loss, sec(clock()-time), *net.seen); + printf("%d: %f, %f avg, %lf seconds, %" PRIu64 " images\n", i, loss, avg_loss, sec(clock()-time), *net.seen); free_data(train); if((i % 100) == 0) net.learning_rate *= .1; if(i%100==0){ diff --git a/src/go.c b/src/go.c index 4ed73dcbca8..5107125e784 100644 --- a/src/go.c +++ b/src/go.c @@ -142,7 +142,7 @@ void train_go(char *cfgfile, char *weightfile) float loss = train_network_datum(net, board, move) / net.batch; if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.95 + loss*.05; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); + printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %" PRIu64 " images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); if(*net.seen/N > epoch){ epoch = *net.seen/N; char buff[256]; diff --git a/src/http_stream.cpp b/src/http_stream.cpp index b17edfb5d36..580b5fe367e 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -508,7 +508,7 @@ class MJPG_sender } char head[400]; - sprintf(head, "--mjpegstream\r\nContent-Type: image/jpeg\r\nContent-Length: %zu\r\n\r\n", outlen); + sprintf(head, "--mjpegstream\r\nContent-Type: image/jpeg\r\nContent-Length: %d\r\n\r\n", outlen); _write(s, head, 0); int n = _write(s, (char*)(&outbuf[0]), outlen); cerr << "known client: " << s << ", sent = " << n << ", must be sent outlen = " << outlen << endl; diff --git a/src/network.h b/src/network.h index f6850656e0e..e5b19ca7775 100644 --- a/src/network.h +++ b/src/network.h @@ -1,6 +1,17 @@ // Oh boy, why am I about to do this.... #ifndef NETWORK_H #define NETWORK_H + +/* + * Necessary in C++ to get format macros out of inttypes.h + */ +#ifdef __cplusplus +#ifndef __STDC_FORMAT_MACROS +#define __STDC_FORMAT_MACROS 1 +#endif +#endif +#include + #include "darknet.h" #include diff --git a/src/softmax_layer.c b/src/softmax_layer.c index 44ec537f9cc..5b747fa4719 100644 --- a/src/softmax_layer.c +++ b/src/softmax_layer.c @@ -200,7 +200,7 @@ contrastive_layer make_contrastive_layer(int batch, int w, int h, int c, int cla printf(" max_contr_size = %d MB \n", max_contr_size / (1024*1024)); l.contrast_p_gpu = (contrastive_params *)cuda_make_array(NULL, max_contr_size); #endif - fprintf(stderr, "contrastive %4d x%4d x%4d x emb_size %4d x batch: %4d classes = %4d, step = %4d \n", w, h, l.n, l.embedding_size, batch, l.classes, step); + fprintf(stderr, "contrastive %4d x%4d x%4d x emb_size %4d x batch: %4d classes = %4d, step = %4zu \n", w, h, l.n, l.embedding_size, batch, l.classes, step); if(l.detection) fprintf(stderr, "detection \n"); return l; } diff --git a/src/tag.c b/src/tag.c index d7e1349a2a2..c1e031b2c8e 100644 --- a/src/tag.c +++ b/src/tag.c @@ -63,7 +63,7 @@ void train_tag(char *cfgfile, char *weightfile, int clear) float loss = train_network(net, train); if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); + printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %" PRIu64 " images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); free_data(train); if(*net.seen/N > epoch){ epoch = *net.seen/N; diff --git a/src/writing.c b/src/writing.c index 29785b7b759..1fed538fa14 100644 --- a/src/writing.c +++ b/src/writing.c @@ -65,7 +65,7 @@ void train_writing(char *cfgfile, char *weightfile) if(avg_loss == -1) avg_loss = loss; avg_loss = avg_loss*.9 + loss*.1; - printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); + printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %" PRIu64 " images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen); free_data(train); if(get_current_batch(net)%100 == 0){ char buff[256]; From 2fa6307b1748eb42ff8e9fe734fb8d7f63192ff9 Mon Sep 17 00:00:00 2001 From: "dekk.Mars" Date: Sat, 26 Aug 2023 08:50:44 +0800 Subject: [PATCH 1333/1365] mini fix (#5662) FileNotFoundError: Could not find module Co-authored-by: Stefano Sinigardi --- darknet.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/darknet.py b/darknet.py index 603ecae92dc..59953bc036f 100644 --- a/darknet.py +++ b/darknet.py @@ -261,7 +261,7 @@ def detect_image(network, class_names, image, thresh=.5, hier_thresh=.5, nms=.45 elif os.name == "nt": cwd = os.path.dirname(__file__) os.environ["PATH"] = os.path.pathsep.join((cwd, os.environ["PATH"])) - lib = ct.CDLL("darknet.dll", ct.RTLD_GLOBAL) + lib = ct.CDLL("darknet.dll", winmode = 0, mode = ct.RTLD_GLOBAL) else: lib = None # Intellisense print("Unsupported OS") @@ -358,4 +358,3 @@ def detect_image(network, class_names, image, thresh=.5, hier_thresh=.5, nms=.45 network_predict_batch.argtypes = (ct.c_void_p, IMAGE, ct.c_int, ct.c_int, ct.c_int, ct.c_float, ct.c_float, IntPtr, ct.c_int, ct.c_int) network_predict_batch.restype = DETNUMPAIRPtr - From eb313288a5990fbb5445cb524e92981a3bc16007 Mon Sep 17 00:00:00 2001 From: mwohlm <98554770+mwohlm@users.noreply.github.com> Date: Sat, 26 Aug 2023 02:54:28 +0200 Subject: [PATCH 1334/1365] Set option ENABLE_SSE_AND_AVX_FLAGS after all dependent variables have been defined. (#8597) --- CMakeLists.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index ee9b2ec1e0b..05bf93ffdec 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -74,8 +74,6 @@ else() set(IS_X86 FALSE) endif() -cmake_dependent_option(ENABLE_SSE_AND_AVX_FLAGS "Enable AVX and SSE optimizations (x86-only)" ON "CMAKE_COMPILER_IS_GNUCC_OR_CLANG;IS_X86" OFF) - if(ENABLE_VCPKG_INTEGRATION AND DEFINED ENV{VCPKG_ROOT} AND NOT DEFINED CMAKE_TOOLCHAIN_FILE) set(CMAKE_POLICY_DEFAULT_CMP0077 NEW) set(X_VCPKG_APPLOCAL_DEPS_INSTALL ON) @@ -150,6 +148,8 @@ else() set(CMAKE_COMPILER_IS_CLANG FALSE) endif() +cmake_dependent_option(ENABLE_SSE_AND_AVX_FLAGS "Enable AVX and SSE optimizations (x86-only)" ON "CMAKE_COMPILER_IS_GNUCC_OR_CLANG;IS_X86" OFF) + set(default_build_type "Release") if(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES) message(STATUS "Setting build type to '${default_build_type}' as none was specified.") From dfc70de68260a3ff1fdc8478f084e734b88628df Mon Sep 17 00:00:00 2001 From: CSTEZCAN <33690601+CSTEZCAN@users.noreply.github.com> Date: Sat, 26 Aug 2023 04:41:40 +0300 Subject: [PATCH 1335/1365] Adding new cards, sorting cuda compute (#8792) Co-authored-by: Stefano Sinigardi --- Makefile | 58 +++++++++++++++++++++++++++++++++++++------------------- 1 file changed, 39 insertions(+), 19 deletions(-) diff --git a/Makefile b/Makefile index 8aa2a509809..167d071585b 100644 --- a/Makefile +++ b/Makefile @@ -9,7 +9,7 @@ ZED_CAMERA=0 ZED_CAMERA_v2_8=0 # set GPU=1 and CUDNN=1 to speedup on GPU -# set CUDNN_HALF=1 to further speedup 3 x times (Mixed-precision on Tensor Cores) GPU: Volta, Xavier, Turing and higher +# set CUDNN_HALF=1 to further speedup 3 x times (Mixed-precision on Tensor Cores) GPU: Volta, Xavier, Turing, Ampere, Ada and higher # set AVX=1 and OPENMP=1 to speedup on CPU (if error occurs then set AVX=0) # set ZED_CAMERA=1 to enable ZED SDK 3.0 and above # set ZED_CAMERA_v2_8=1 to enable ZED SDK 2.X @@ -23,36 +23,56 @@ ARCH= -gencode arch=compute_50,code=[sm_50,compute_50] \ OS := $(shell uname) -# GeForce RTX 3070, 3080, 3090 -# ARCH= -gencode arch=compute_86,code=[sm_86,compute_86] - -# Tesla A100 (GA100), DGX-A100, RTX 3080 -# ARCH= -gencode arch=compute_80,code=[sm_80,compute_80] +# Naming confusion with recent RTX cards. +# "NVIDIA Quadro RTX x000" and T1000/Tx00 are Turing Architecture Family with Compute Capability of 7.5 +# "NVIDIA RTX Ax000" are Ampere Architecture Family with Compute Capability of 8.6 +# NVIDIA "RTX x000 Ada" are Ada Lovelace Architecture Family with Compute Capability of 8.9 +# Source https://developer.nvidia.com/cuda-gpus -# Tesla V100 -# ARCH= -gencode arch=compute_70,code=[sm_70,compute_70] +# KEPLER, GeForce GTX 770, GTX 760, GT 740 +# ARCH= -gencode arch=compute_30,code=sm_30 -# GeForce RTX 2080 Ti, RTX 2080, RTX 2070, Quadro RTX 8000, Quadro RTX 6000, Quadro RTX 5000, Tesla T4, XNOR Tensor Cores -# ARCH= -gencode arch=compute_75,code=[sm_75,compute_75] +# MAXWELL, GeForce GTX 950, 960, 970, 980, 980 Ti, "GTX" Titan X +# ARCH= -gencode arch=compute_52,code=sm_52 -# Jetson XAVIER -# ARCH= -gencode arch=compute_72,code=[sm_72,compute_72] - -# GTX 1080, GTX 1070, GTX 1060, GTX 1050, GTX 1030, Titan Xp, Tesla P40, Tesla P4 -# ARCH= -gencode arch=compute_61,code=sm_61 -gencode arch=compute_61,code=compute_61 +# Jetson TX1, Tegra X1, DRIVE CX, DRIVE PX, Jetson Nano (2GB, 4GB) +# ARCH= -gencode arch=compute_53,code=[sm_53,compute_53] # GP100/Tesla P100 - DGX-1 # ARCH= -gencode arch=compute_60,code=sm_60 -# For Jetson TX1, Tegra X1, DRIVE CX, DRIVE PX - uncomment: -# ARCH= -gencode arch=compute_53,code=[sm_53,compute_53] +# PASCAL, GTX 10x0, GTX 10x0 Ti, Titan Xp, Tesla P40, Tesla P4 +# ARCH= -gencode arch=compute_61,code=[sm_61,compute_61] -# For Jetson Tx2 or Drive-PX2 uncomment: +# For Jetson TX2, Jetson Nano TX2 or Drive-PX2 uncomment: # ARCH= -gencode arch=compute_62,code=[sm_62,compute_62] -# For Tesla GA10x cards, RTX 3090, RTX 3080, RTX 3070, RTX A6000, RTX A40 uncomment: +# Tesla V100 +# ARCH= -gencode arch=compute_70,code=[sm_70,compute_70] + +# Jetson XAVIER, XAVIER NX +# ARCH= -gencode arch=compute_72,code=[sm_72,compute_72] + +# GeForce Titan RTX, RTX 20x0, RTX 20x0 Ti, Quadro RTX x000, Tesla T4, XNOR Tensor Cores +# ARCH= -gencode arch=compute_75,code=[sm_75,compute_75] + +# Tesla A100 (GA100), DGX-A100, A30, A100, RTX 3080 +# ARCH= -gencode arch=compute_80,code=[sm_80,compute_80] + +# GeForce RTX 30x0, 30x0 Ti, Tesla GA10x, RTX Axxxx, A2, A10, A16, A40 # ARCH= -gencode arch=compute_86,code=[sm_86,compute_86] +# NOT TESTED, THEORETICAL +# Jetson ORIN, ORIN NX, ORIN NANO +# ARCH= -gencode arch=compute_87,code=[sm_87,compute_87] + +# NOT TESTED, THEORETICAL +# GeForce RTX 4070 Ti, 4080, 4090, L4, L40 +# ARCH= -gencode arch=compute_89,code=[sm_89,compute_89] + +# NOT TESTED, THEORETICAL +# Nvidia H100 +# ARCH= -gencode arch=compute_90,code=[sm_90,compute_90] VPATH=./src/ EXEC=darknet From 2f4ab0887ce355185fdf8b9df5ed6dc8f616f4cb Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sat, 26 Aug 2023 03:50:00 +0200 Subject: [PATCH 1336/1365] Gl integration (#8833) * Start adding support for running darknet from OpenGL textures * Get CUDA-GL interop working (at least without segfaults...) * Copy the GL texture to the CUDA input buffer * Remove printf statements * Code cleanup * Fix a function call * Remove the #ifdef GPU guard in darknet.h * Use the width and height from the network instead of hard-coding * Install libgles2-mesa-dev for some ubuntu targets * Get the build working on Windows * Add an OpenGL-CUDA integration option for CMake * Bump the version-date in vcpkg.json * Remove ifdef in darknet.h for the network_predict_gl_texture() function * Use target_compile_definitions to set CUDA_OPENGL_INTEGRATION flag * Re-add the #ifdef CUDA_OPENGL_INTEGRATION guard --------- Co-authored-by: Brian Schwind --- .github/workflows/ccpp.yml | 4 +- .github/workflows/on_pr.yml | 4 +- CMakeLists.txt | 16 +++++++ include/darknet.h | 4 ++ src/blas.c | 12 ++--- src/dark_cuda.h | 12 +++++ src/network.c | 15 +++++++ src/network.h | 1 + src/network_kernels.cu | 89 +++++++++++++++++++++++++++++++++++++ vcpkg.json | 12 ++++- 10 files changed, 157 insertions(+), 12 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 0fd2abe6ea3..1f33a47b114 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -24,7 +24,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt install libopencv-dev + run: sudo apt install libopencv-dev libgles2-mesa-dev - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.sh @@ -293,7 +293,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt install libopencv-dev + run: sudo apt install libopencv-dev libgles2-mesa-dev - uses: lukka/get-cmake@latest diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index 25fa153cbca..f32c2797550 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -14,7 +14,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt install libopencv-dev + run: sudo apt install libopencv-dev libgles2-mesa-dev - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.sh @@ -215,7 +215,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt install libopencv-dev + run: sudo apt install libopencv-dev libgles2-mesa-dev - uses: lukka/get-cmake@latest diff --git a/CMakeLists.txt b/CMakeLists.txt index 05bf93ffdec..5c3331654ac 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -9,6 +9,7 @@ option(BUILD_USELIB_TRACK "Build uselib_track" ON) option(MANUALLY_EXPORT_TRACK_OPTFLOW "Manually export the TRACK_OPTFLOW=1 define" OFF) option(ENABLE_OPENCV "Enable OpenCV integration" ON) option(ENABLE_CUDA "Enable CUDA support" ON) +cmake_dependent_option(ENABLE_CUDA_OPENGL_INTEGRATION "Build darknet with support for running networks straight from OpenGL textures" ON "ENABLE_CUDA" OFF) option(ENABLE_CUDNN "Enable CUDNN" ON) option(ENABLE_CUDNN_HALF "Enable CUDNN Half precision" ON) option(ENABLE_ZED_CAMERA "Enable ZED Camera support" ON) @@ -327,6 +328,11 @@ endif() if(ENABLE_CUDA) if(MSVC) set(ADDITIONAL_CXX_FLAGS "${ADDITIONAL_CXX_FLAGS} /DGPU") + + if(ENABLE_CUDA_OPENGL_INTEGRATION) + set(ADDITIONAL_CXX_FLAGS "${ADDITIONAL_CXX_FLAGS} /DCUDA_OPENGL_INTEGRATION") + endif() + if(CUDNN_FOUND) set(ADDITIONAL_CXX_FLAGS "${ADDITIONAL_CXX_FLAGS} /DCUDNN") endif() @@ -337,6 +343,11 @@ if(ENABLE_CUDA) set(CUDA_HOST_COMPILER_FLAGS "-Wno-deprecated-declarations -Xcompiler=\"${ADDITIONAL_CXX_FLAGS_COMMA_SEPARATED}\"") else() set(ADDITIONAL_CXX_FLAGS "${ADDITIONAL_CXX_FLAGS} -DGPU") + + if(ENABLE_CUDA_OPENGL_INTEGRATION) + set(ADDITIONAL_CXX_FLAGS "${ADDITIONAL_CXX_FLAGS} -DCUDA_OPENGL_INTEGRATION") + endif() + if(CUDNN_FOUND) set(ADDITIONAL_CXX_FLAGS "${ADDITIONAL_CXX_FLAGS} -DCUDNN") endif() @@ -544,6 +555,11 @@ if(ENABLE_CUDA) target_compile_definitions(dark PUBLIC -DGPU) endif() +if(ENABLE_CUDA_OPENGL_INTEGRATION) + target_compile_definitions(darknet PRIVATE -DCUDA_OPENGL_INTEGRATION) + target_compile_definitions(dark PUBLIC -DCUDA_OPENGL_INTEGRATION) +endif() + if(USE_INTEGRATED_LIBS AND WIN32) target_compile_definitions(darknet PRIVATE -D_TIMESPEC_DEFINED) target_compile_definitions(dark PRIVATE -D_TIMESPEC_DEFINED) diff --git a/include/darknet.h b/include/darknet.h index 2517d3fbd05..d82cda2841a 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -1032,6 +1032,10 @@ LIB_API void diounms_sort(detection *dets, int total, int classes, float thresh, // network.h LIB_API float *network_predict(network net, float *input); LIB_API float *network_predict_ptr(network *net, float *input); +#ifdef CUDA_OPENGL_INTEGRATION +LIB_API float *network_predict_gl_texture(network *net, uint32_t texture_id); +#endif // CUDA_OPENGL_INTEGRATION + LIB_API void set_batch_network(network *net, int b); LIB_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter); LIB_API det_num_pair* network_predict_batch(network *net, image im, int batch_size, int w, int h, float thresh, float hier, int *map, int relative, int letter); diff --git a/src/blas.c b/src/blas.c index a3024d11bb3..483cf9f43f2 100644 --- a/src/blas.c +++ b/src/blas.c @@ -342,32 +342,32 @@ void fill_cpu(int N, float ALPHA, float *X, int INCX) } } -void deinter_cpu(int NX, float *X, int NY, float *Y, int B, float *OUT) +void deinter_cpu(int NX, float *X, int NY, float *Y, int B, float *OUTPUT) { int i, j; int index = 0; for(j = 0; j < B; ++j) { for(i = 0; i < NX; ++i){ - if(X) X[j*NX + i] += OUT[index]; + if(X) X[j*NX + i] += OUTPUT[index]; ++index; } for(i = 0; i < NY; ++i){ - if(Y) Y[j*NY + i] += OUT[index]; + if(Y) Y[j*NY + i] += OUTPUT[index]; ++index; } } } -void inter_cpu(int NX, float *X, int NY, float *Y, int B, float *OUT) +void inter_cpu(int NX, float *X, int NY, float *Y, int B, float *OUTPUT) { int i, j; int index = 0; for(j = 0; j < B; ++j) { for(i = 0; i < NX; ++i){ - OUT[index++] = X[j*NX + i]; + OUTPUT[index++] = X[j*NX + i]; } for(i = 0; i < NY; ++i){ - OUT[index++] = Y[j*NY + i]; + OUTPUT[index++] = Y[j*NY + i]; } } } diff --git a/src/dark_cuda.h b/src/dark_cuda.h index 3e1c1e3bef4..ffe3836b6eb 100644 --- a/src/dark_cuda.h +++ b/src/dark_cuda.h @@ -25,6 +25,18 @@ extern int gpu_index; #include #include #include + +#ifdef CUDA_OPENGL_INTEGRATION +// On Windows, we need to include before +// including OpenGL headers or else we will get various +// compiler errors due to missing macros. +#ifdef _WIN32 +#define WIN32_LEAN_AND_MEAN +#include +#endif // _WIN32 + +#include +#endif // CUDA_OPENGL_INTEGRATION //#include #ifdef CUDNN diff --git a/src/network.c b/src/network.c index 7741e3b1969..ba59d69202f 100644 --- a/src/network.c +++ b/src/network.c @@ -776,6 +776,21 @@ float *network_predict(network net, float *input) return out; } +#ifdef CUDA_OPENGL_INTEGRATION +float *network_predict_gl_texture(network *net, uint32_t texture_id) +{ + if(net->batch != 1) { + set_batch_network(net, 1); + } + + if(gpu_index >= 0) { + return network_predict_gpu_gl_texture(*net, texture_id); + } + + return NULL; +} +#endif // CUDA_OPENGL_INTEGRATION + int num_detections(network *net, float thresh) { int i; diff --git a/src/network.h b/src/network.h index e5b19ca7775..c8a77064253 100644 --- a/src/network.h +++ b/src/network.h @@ -107,6 +107,7 @@ float train_networks(network *nets, int n, data d, int interval); void sync_nets(network *nets, int n, int interval); float train_network_datum_gpu(network net, float *x, float *y); float *network_predict_gpu(network net, float *input); +float *network_predict_gpu_gl_texture(network net, uint32_t texture_id); float * get_network_output_gpu_layer(network net, int i); float * get_network_delta_gpu_layer(network net, int i); float *get_network_output_gpu(network net); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 7fbc03cf5f5..58f9d5bac38 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -747,3 +747,92 @@ float *network_predict_gpu(network net, float *input) //cuda_free(state.input); // will be freed in the free_network() return out; } + +#ifdef CUDA_OPENGL_INTEGRATION +float *network_predict_gpu_gl_texture(network net, uint32_t texture_id) +{ + if (net.gpu_index != cuda_get_device()) + cuda_set_device(net.gpu_index); + int size = get_network_input_size(net) * net.batch; + + // Map the OpenGL texture resource so CUDA can access it. + cudaGraphicsResource_t graphics_resource = NULL; + unsigned int flags = cudaGraphicsRegisterFlagsReadOnly; + CHECK_CUDA(cudaGraphicsGLRegisterImage(&graphics_resource, texture_id, GL_TEXTURE_2D, flags)); + CHECK_CUDA(cudaGraphicsMapResources(1, &graphics_resource, 0)); + + void* dev_ptr = NULL; + cudaArray_t dev_array = NULL; + CHECK_CUDA(cudaGraphicsSubResourceGetMappedArray(&dev_array, graphics_resource, 0, 0)); + + size_t width = net.w; + size_t height = net.h; + size_t pitch = width * sizeof(float); + + CHECK_CUDA(cudaMemcpy2DFromArray( + net.input_state_gpu, // dst + pitch, // dst_pitch + dev_array, // src + 0, // width offset + 0, // height offset + width * sizeof(float), // width (in bytes) + height * net.c, // height (in rows) + cudaMemcpyDeviceToDevice // Transfer type + )); + + network_state state; + state.index = 0; + state.net = net; + state.input = net.input_state_gpu; + state.truth = 0; + state.train = 0; + state.delta = 0; + + //cudaGraphExec_t instance = (cudaGraphExec_t)net.cuda_graph_exec; + static cudaGraphExec_t instance; + + if ((*net.cuda_graph_ready) == 0) { + static cudaGraph_t graph; + if (net.use_cuda_graph == 1) { + int i; + for (i = 0; i < 16; ++i) switch_stream(i); + + cudaStream_t stream0 = switch_stream(0); + CHECK_CUDA(cudaDeviceSynchronize()); + printf("Try to capture graph... \n"); + //cudaGraph_t graph = (cudaGraph_t)net.cuda_graph; + CHECK_CUDA(cudaStreamBeginCapture(stream0, cudaStreamCaptureModeGlobal)); + } + + // cuda_push_array(state.input, net.input_pinned_cpu, size); + forward_network_gpu(net, state); + + if (net.use_cuda_graph == 1) { + cudaStream_t stream0 = switch_stream(0); + CHECK_CUDA(cudaStreamEndCapture(stream0, &graph)); + CHECK_CUDA(cudaGraphInstantiate(&instance, graph, NULL, NULL, 0)); + (*net.cuda_graph_ready) = 1; + printf(" graph is captured... \n"); + CHECK_CUDA(cudaDeviceSynchronize()); + } + CHECK_CUDA(cudaStreamSynchronize(get_cuda_stream())); + } + else { + cudaStream_t stream0 = switch_stream(0); + //printf(" cudaGraphLaunch \n"); + CHECK_CUDA( cudaGraphLaunch(instance, stream0) ); + CHECK_CUDA( cudaStreamSynchronize(stream0) ); + //printf(" ~cudaGraphLaunch \n"); + } + + float *out = get_network_output_gpu(net); + reset_wait_stream_events(); + //cuda_free(state.input); // will be freed in the free_network() + + // Unmap the OpenGL texture. + cudaGraphicsUnmapResources(1, &graphics_resource, 0); + cudaGraphicsUnregisterResource(graphics_resource); + + return out; +} +#endif // CUDA_OPENGL_INTEGRATION diff --git a/vcpkg.json b/vcpkg.json index 045049cca21..1ef26f470d2 100644 --- a/vcpkg.json +++ b/vcpkg.json @@ -1,6 +1,6 @@ { "name": "darknet", - "version": "0.2.5.5", + "version": "0.2.5.6", "description": "Darknet is an open source neural network framework written in C and CUDA. You only look once (YOLO) is a state-of-the-art, real-time object detection system, best example of darknet functionalities.", "homepage": "https://github.com/alexeyab/darknet", "dependencies": [ @@ -21,6 +21,13 @@ "cudnn" ] }, + "cuda-opengl-integration": { + "description": "Build darknet with support for running networks straight from OpenGL textures", + "dependencies": [ + "cuda", + "opengl" + ] + }, "full": { "description": "Build darknet fully featured", "dependencies": [ @@ -29,7 +36,8 @@ "features": [ "cuda", "cudnn", - "opencv-cuda" + "opencv-cuda", + "cuda-opengl-integration" ] } ] From 82eb049fdcb3bd02d3082085ea34d0b3b12447e7 Mon Sep 17 00:00:00 2001 From: zhiliu6 Date: Sat, 26 Aug 2023 09:52:25 +0800 Subject: [PATCH 1337/1365] Support label image filename with multiple extension. (#6667) --- src/utils.c | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/src/utils.c b/src/utils.c index 3aad28714ad..2ad33d5e5ae 100644 --- a/src/utils.c +++ b/src/utils.c @@ -240,12 +240,23 @@ void trim(char *str) free(buffer); } +char *strlaststr(char *haystack, char *needle) +{ + char *p = strstr(haystack, needle), *r = NULL; + while (p != NULL) + { + r = p; + p = strstr(p + 1, needle); + } + return r; +} + void find_replace_extension(char *str, char *orig, char *rep, char *output) { char* buffer = (char*)calloc(8192, sizeof(char)); sprintf(buffer, "%s", str); - char *p = strstr(buffer, orig); + char *p = strlaststr(buffer, orig); int offset = (p - buffer); int chars_from_end = strlen(buffer) - offset; if (!p || chars_from_end != strlen(orig)) { // Is 'orig' even in 'str' AND is 'orig' found at the end of 'str'? From 538c4353ff6fd8e22071b77137fd5a7ddc74b306 Mon Sep 17 00:00:00 2001 From: "David C. Schedl" Date: Sat, 26 Aug 2023 04:05:40 +0200 Subject: [PATCH 1338/1365] Configure when to write weights. (#7932) Co-authored-by: schedldave Co-authored-by: Schedl David Co-authored-by: Stefano Sinigardi --- src/detector.c | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/src/detector.c b/src/detector.c index 61864967e7e..6f050986615 100644 --- a/src/detector.c +++ b/src/detector.c @@ -30,6 +30,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i char *valid_images = option_find_str(options, "valid", train_images); char *backup_directory = option_find_str(options, "backup", "/backup/"); + network net_map; if (calc_map) { FILE* valid_file = fopen(valid_images, "r"); @@ -98,6 +99,11 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i printf("\n Warning: You set batch=%d lower than 64! It is recommended to set batch=64 subdivision=64 \n", actual_batch_size); } + int save_after_iterations = option_find_int(options, "saveweights", (net.max_batches < 10000) ? 1000 : 10000 ); // configure when to write weights. Very useful for smaller datasets! + int save_last_weights_after = option_find_int(options, "savelast", 100); + printf("Weights are saved after: %d iterations. Last weights (*_last.weight) are stored every %d iterations. \n", save_after_iterations, save_last_weights_after ); + + int imgs = net.batch * net.subdivisions * ngpus; printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); data train, buffer; @@ -389,10 +395,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, iteration, net.max_batches, mean_average_precision, draw_precision, "mAP%", avg_contrastive_acc / 100, dont_show, mjpeg_port, avg_time); #endif // OPENCV - //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) { - //if (i % 100 == 0) { - if ((iteration >= (iter_save + 10000) || iteration % 10000 == 0) || - (iteration >= (iter_save + 1000) || iteration % 1000 == 0) && net.max_batches < 10000) + if ( (iteration >= (iter_save + save_after_iterations) || iteration % save_after_iterations == 0) ) { iter_save = iteration; #ifdef GPU @@ -403,7 +406,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i save_weights(net, buff); } - if (iteration >= (iter_save_last + 100) || (iteration % 100 == 0 && iteration > 1)) { + if ( (save_after_iterations > save_last_weights_after) && (iteration >= (iter_save_last + save_last_weights_after) || (iteration % save_last_weights_after == 0 && iteration > 1))) { iter_save_last = iteration; #ifdef GPU if (ngpus != 1) sync_nets(nets, ngpus, 0); From a2f20d7069883287f6a1bdf073c70667a91b3fbe Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sun, 27 Aug 2023 23:49:28 +0200 Subject: [PATCH 1339/1365] use ansi escape sequence vt100 ESC[H ESC[J to clear screen in a portable way (win10+ supported!) (#8834) --- src/art.c | 3 +-- src/classifier.c | 16 ++++------------ src/demo.c | 3 +-- src/detector.c | 7 +++---- 4 files changed, 9 insertions(+), 20 deletions(-) diff --git a/src/art.c b/src/art.c index d90290fcbc2..748e19038dc 100644 --- a/src/art.c +++ b/src/art.c @@ -40,8 +40,7 @@ void demo_art(char *cfgfile, char *weightfile, int cam_index) float *p = network_predict(net, in_s.data); - printf("\033[2J"); - printf("\033[1;1H"); + printf("\033[H\033[J"); float score = 0; for(i = 0; i < n; ++i){ diff --git a/src/classifier.c b/src/classifier.c index c17e1b1ffe7..0c81aee4e27 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -1129,11 +1129,8 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i sprintf(buff, "tmp/threat_%06d", count); //save_image(out, buff); -#ifndef _WIN32 - printf("\033[2J"); - printf("\033[1;1H"); -#endif - printf("\nFPS:%.0f\n",fps); + printf("\033[H\033[J"); + printf("\nFPS:%.0f\n", fps); for(i = 0; i < top; ++i){ int index = indexes[i]; @@ -1208,8 +1205,7 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde float *predictions = network_predict(net, in_s.data); top_predictions(net, top, indexes); - printf("\033[2J"); - printf("\033[1;1H"); + printf("\033[H\033[J"); int threat = 0; for(i = 0; i < sizeof(bad_cats)/sizeof(bad_cats[0]); ++i){ @@ -1308,11 +1304,7 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 1); top_predictions(net, top, indexes); -#ifndef _WIN32 - printf("\033[2J"); - printf("\033[1;1H"); -#endif - + printf("\033[H\033[J"); if (!benchmark) { printf("\rFPS: %.2f (use -benchmark command line flag for correct measurement)\n", fps); diff --git a/src/demo.c b/src/demo.c index 2c48bb78749..e9017a0bd8c 100644 --- a/src/demo.c +++ b/src/demo.c @@ -280,8 +280,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int if (l.embedding_size) set_track_id(local_dets, local_nboxes, demo_thresh, l.sim_thresh, l.track_ciou_norm, l.track_history_size, l.dets_for_track, l.dets_for_show); - //printf("\033[2J"); - //printf("\033[1;1H"); + printf("\033[H\033[J"); //printf("\nFPS:%.1f\n", fps); printf("Objects:\n\n"); diff --git a/src/detector.c b/src/detector.c index 6f050986615..3799063187b 100644 --- a/src/detector.c +++ b/src/detector.c @@ -314,14 +314,13 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i if (mean_average_precision > 0) printf("\n Last accuracy mAP@%0.2f = %2.2f %%, best = %2.2f %% ", iou_thresh, mean_average_precision * 100, best_map * 100); } - #ifndef WIN32 + printf("\033[H\033[J"); if (mean_average_precision > 0.0) { - printf("\033]2;%d/%d: loss=%0.1f map=%0.2f best=%0.2f hours left=%0.1f\007", iteration, net.max_batches, loss, mean_average_precision, best_map, avg_time); + printf("%d/%d: loss=%0.1f map=%0.2f best=%0.2f hours left=%0.1f\007", iteration, net.max_batches, loss, mean_average_precision, best_map, avg_time); } else { - printf("\033]2;%d/%d: loss=%0.1f hours left=%0.1f\007", iteration, net.max_batches, loss, avg_time); + printf("%d/%d: loss=%0.1f hours left=%0.1f\007", iteration, net.max_batches, loss, avg_time); } - #endif if (net.cudnn_half) { if (iteration < net.burn_in * 3) fprintf(stderr, "\n Tensor Cores are disabled until the first %d iterations are reached.\n", 3 * net.burn_in); From 291cdb9ccd02e5a5ef262ea7133d004a5cb6ec90 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 31 Aug 2023 11:32:51 +0200 Subject: [PATCH 1340/1365] enable packaging (#8837) --- CMakeLists.txt | 52 +++++++++++++++++++++++++++++++++++++++++++----- build.ps1 | 36 ++++++++++++++++++++++----------- src/darknet.ico | Bin 0 -> 410598 bytes src/darknet.rc | 1 + 4 files changed, 72 insertions(+), 17 deletions(-) create mode 100644 src/darknet.ico create mode 100644 src/darknet.rc diff --git a/CMakeLists.txt b/CMakeLists.txt index 5c3331654ac..8587793345a 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -16,6 +16,7 @@ option(ENABLE_ZED_CAMERA "Enable ZED Camera support" ON) option(ENABLE_VCPKG_INTEGRATION "Enable VCPKG integration" OFF) option(ENABLE_DEPLOY_CUSTOM_CMAKE_MODULES "Copy custom CMake modules for downstream integration" OFF) option(ENABLE_CSHARP_WRAPPER "Enable building a csharp wrapper" OFF) +option(ENABLE_INSTALLER "Enable building an installer" OFF) option(VCPKG_BUILD_OPENCV_WITH_CUDA "Build OpenCV with CUDA extension integration" ON) option(VCPKG_USE_OPENCV2 "Use legacy OpenCV 2" OFF) option(VCPKG_USE_OPENCV3 "Use legacy OpenCV 3" OFF) @@ -161,12 +162,17 @@ if(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES) "Debug" "Release" "MinSizeRel" "RelWithDebInfo") endif() -if(CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT) - set(CMAKE_INSTALL_PREFIX "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Install prefix" FORCE) +if(NOT ENABLE_INSTALLER) + if(CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT) + set(CMAKE_INSTALL_PREFIX "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Install prefix" FORCE) + endif() + set(INSTALL_BIN_DIR "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Path where exe and dll will be installed") + set(INSTALL_LIB_DIR "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Path where lib will be installed") +else() + set(INSTALL_BIN_DIR "bin" CACHE PATH "Path where exe and dll will be installed") + set(INSTALL_LIB_DIR "lib" CACHE PATH "Path where lib will be installed") endif() -set(INSTALL_BIN_DIR "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Path where exe and dll will be installed") -set(INSTALL_LIB_DIR "${CMAKE_CURRENT_LIST_DIR}" CACHE PATH "Path where lib will be installed") set(INSTALL_INCLUDE_DIR "include/darknet" CACHE PATH "Path where headers will be installed") set(INSTALL_CMAKE_DIR "share/darknet" CACHE PATH "Path where cmake configs will be installed") @@ -383,7 +389,6 @@ else() set(ENABLE_ZED_CAMERA "FALSE" CACHE BOOL "Enable ZED Camera support" FORCE) endif() -# Make relative paths absolute (needed later on) foreach(p LIB BIN INCLUDE CMAKE) set(var INSTALL_${p}_DIR) if(NOT IS_ABSOLUTE "${${var}}") @@ -465,6 +470,9 @@ if(BUILD_AS_CPP) set_source_files_properties(${CMAKE_CURRENT_LIST_DIR}/src/darknet.c PROPERTIES LANGUAGE CXX) set_target_properties(darknet PROPERTIES LINKER_LANGUAGE CXX) endif() +if(MSVC) + target_sources(darknet PRIVATE ${CMAKE_CURRENT_LIST_DIR}/src/darknet.rc) +endif() add_executable(kmeansiou ${CMAKE_CURRENT_LIST_DIR}/scripts/kmeansiou.c) if(MATH_LIBRARY) @@ -655,3 +663,37 @@ install( PROGRAMS ${CMAKE_INSTALL_SYSTEM_RUNTIME_LIBS} DESTINATION ${INSTALL_BIN_DIR} ) + +if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/vcpkg.json) + file(READ ${CMAKE_CURRENT_SOURCE_DIR}/vcpkg.json VCPKG_JSON_STRING) + string(JSON CPACK_PACKAGE_NAME GET ${VCPKG_JSON_STRING} name) + string(JSON CPACK_BUNDLE_NAME GET ${VCPKG_JSON_STRING} name) + string(JSON CPACK_PACKAGE_HOMEPAGE_URL GET ${VCPKG_JSON_STRING} homepage) + string(JSON CPACK_PACKAGE_DESCRIPTION GET ${VCPKG_JSON_STRING} description) + string(JSON CPACK_NSIS_DISPLAY_NAME GET ${VCPKG_JSON_STRING} description) + string(JSON CPACK_NSIS_PACKAGE_NAME GET ${VCPKG_JSON_STRING} description) + set(CPACK_RESOURCE_FILE_LICENSE ${CMAKE_CURRENT_SOURCE_DIR}/LICENSE) + set(CPACK_NSIS_MUI_ICON "${CMAKE_CURRENT_SOURCE_DIR}/src/darknet.ico") + set(CPACK_NSIS_MUI_UNIICON "${CMAKE_CURRENT_SOURCE_DIR}/src/darknet.ico") + set(CPACK_BUNDLE_UNIICON "${CMAKE_CURRENT_SOURCE_DIR}/src/darknet.ico") + + if(UNIX AND NOT APPLE) + find_program(LSB_RELEASE_EXEC lsb_release) + execute_process(COMMAND ${LSB_RELEASE_EXEC} -is + OUTPUT_VARIABLE LSB_RELEASE_ID_SHORT + OUTPUT_STRIP_TRAILING_WHITESPACE + ) + if(LSB_RELEASE_ID_SHORT STREQUAL "Ubuntu") + set(CPACK_GENERATOR "DEB") + else() + set(CPACK_GENERATOR "RPM") + endif() + elseif(WIN32) + set(CPACK_PACKAGE_INSTALL_DIRECTORY ${CPACK_PACKAGE_NAME}) + set(CPACK_NSIS_MODIFY_PATH "ON") + set(CPACK_NSIS_ENABLE_UNINSTALL_BEFORE_INSTALL "ON") + set(CPACK_GENERATOR "NSIS") + endif() + + include(CPack) +endif() diff --git a/build.ps1 b/build.ps1 index 354f05df5a1..b9e3205569e 100755 --- a/build.ps1 +++ b/build.ps1 @@ -6,7 +6,7 @@ build Created By: Stefano Sinigardi Created Date: February 18, 2019 - Last Modified Date: August 10, 2023 + Last Modified Date: August 31, 2023 .DESCRIPTION Build tool using CMake, trying to properly setup the environment around compiler @@ -110,6 +110,9 @@ Download pre-trained weight files .PARAMETER Use32bitTriplet Use 32 bit triplet for target build (windows-only) +.PARAMETER BuildInstaller +Build an installer using CPack + .PARAMETER ForceGCCVersion Force a specific GCC version @@ -182,6 +185,7 @@ param ( [switch]$EnableCSharpWrapper = $false, [switch]$DownloadWeights = $false, [switch]$Use32bitTriplet = $false, + [switch]$BuildInstaller = $false, [Int32]$ForceGCCVersion = 0, [Int32]$NumberOfBuildWorkers = 8, [string]$AdditionalBuildSetup = "" # "-DCMAKE_CUDA_ARCHITECTURES=30" @@ -189,7 +193,7 @@ param ( $global:DisableInteractive = $DisableInteractive -$build_ps1_version = "3.4.1" +$build_ps1_version = "3.5.0" $script_name = $MyInvocation.MyCommand.Name Import-Module -Name $PSScriptRoot/scripts/utils.psm1 -Force @@ -881,6 +885,15 @@ if (-Not $InstallDARKNETthroughVCPKG) { $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_DEPLOY_CUSTOM_CMAKE_MODULES:BOOL=ON" } +if ($ForceVCPKGBuildtreesPath -ne "") { + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DVCPKG_INSTALL_OPTIONS=`"--x-buildtrees-root=$ForceVCPKGBuildtreesPath`" " + New-Item -Path $ForceVCPKGBuildtreesPath -ItemType directory -Force | Out-Null + $vcpkgbuildtreespath = "$ForceVCPKGBuildtreesPath" +} +else { + $vcpkgbuildtreespath = "$vcpkg_path/buildtrees" +} + if ($InstallDARKNETthroughVCPKG) { if ($ForceVCPKGDarknetHEAD) { $headMode = " --head " @@ -979,10 +992,6 @@ else { Write-Host "-- Copying $_ to $DebugInstallPrefix/bin" Copy-Item $_ $DebugInstallPrefix/bin } - if (-Not $DoNotDeleteBuildFolder) { - Write-Host "Removing folder $debug_build_folder" -ForegroundColor Yellow - Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $debug_build_folder - } } $release_build_folder = "$PSCustomScriptRoot/build_release" if (-Not $DoNotDeleteBuildFolder) { @@ -1010,19 +1019,22 @@ else { if (-Not ($exitCode -eq 0)) { MyThrow("Build failed! Exited with error code $exitCode.") } - Remove-Item -Force -ErrorAction SilentlyContinue DarknetConfig.cmake - Remove-Item -Force -ErrorAction SilentlyContinue DarknetConfigVersion.cmake if (-Not $UseVCPKG -And -Not $DisableDLLcopy) { $dllfiles = Get-ChildItem ./${dllfolder}/*.dll if ($dllfiles) { Copy-Item $dllfiles .. } } - if (-Not $DoNotDeleteBuildFolder) { - Write-Host "Removing folder $release_build_folder" -ForegroundColor Yellow - Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $release_build_folder + if ($BuildInstaller) { + Write-Host "Building package with CPack" -ForegroundColor Green + $proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList "--build . --target package" + $handle = $proc.Handle + $proc.WaitForExit() + $exitCode = $proc.ExitCode + if (-Not ($exitCode -eq 0)) { + MyThrow("Packaging failed! Exited with error code $exitCode.") + } } - Set-Location .. } Pop-Location diff --git a/src/darknet.ico b/src/darknet.ico new file mode 100644 index 0000000000000000000000000000000000000000..358c53f308998b8d2d2ebe62337ae8bf47137e3e GIT binary patch literal 410598 zcmeF42b5MvcIO+BbB-c(Lz6+|oSK|-7OBZOiX;T0$wA~G5GWvl5CTakYdqPW(ad

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5a9970b24a719c67141ca203fe4cc5e416a7b3b5 Mon Sep 17 00:00:00 2001 From: Muhammad Maaz Date: Fri, 1 Sep 2023 18:20:25 +0400 Subject: [PATCH 1341/1365] Add support for less or more frequent mAP calculation while training (#4918) * Feature: Add support to pass '-mAP_epochs' from command line. '-mAP_epochs' decide after how many epochs mAP should be calculated. Default value is 4 which means the mAP will be calculated after each 4 epochs. --- README.md | 2 ++ include/darknet.h | 2 +- src/detector.c | 8 +++++--- 3 files changed, 8 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 64a7ff4e150..d9de132a86d 100644 --- a/README.md +++ b/README.md @@ -581,6 +581,8 @@ It will create `.txt`-file for each `.jpg`-image-file - in the same directory an 8.1. For training with mAP (mean average precisions) calculation for each 4 Epochs (set `valid=valid.txt` or `train.txt` in `obj.data` file) and run: `darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -map` +8.2. One can also set the `-mAP_epochs` in the training command if less or more frequent mAP calculation is needed. For example in order to calculate mAP for each 2 Epochs run `darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -map -mAP_epochs 2` + 9. After training is complete - get result `yolo-obj_final.weights` from path `build\darknet\x64\backup\` - After each 100 iterations you can stop and later start training from this point. For example, after 2000 iterations you can stop training, and later just start training using: `darknet.exe detector train data/obj.data yolo-obj.cfg backup\yolo-obj_2000.weights` diff --git a/include/darknet.h b/include/darknet.h index d82cda2841a..55ab50d5da8 100644 --- a/include/darknet.h +++ b/include/darknet.h @@ -1052,7 +1052,7 @@ LIB_API void reset_rnn(network *net); LIB_API float *network_predict_image(network *net, image im); LIB_API float *network_predict_image_letterbox(network *net, image im); LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, const int map_points, int letter_box, network *existing_net); -LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, float thresh, float iou_thresh, int mjpeg_port, int show_imgs, int benchmark_layers, char* chart_path); +LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, float thresh, float iou_thresh, int mjpeg_port, int show_imgs, int benchmark_layers, char* chart_path, int mAP_epochs); LIB_API void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile, int letter_box, int benchmark_layers); LIB_API int network_width(network *net); diff --git a/src/detector.c b/src/detector.c index 3799063187b..5ea7fc0077f 100644 --- a/src/detector.c +++ b/src/detector.c @@ -23,7 +23,7 @@ int check_mistakes = 0; static int coco_ids[] = { 1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90 }; -void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, float thresh, float iou_thresh, int mjpeg_port, int show_imgs, int benchmark_layers, char* chart_path) +void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, float thresh, float iou_thresh, int mjpeg_port, int show_imgs, int benchmark_layers, char* chart_path, int mAP_epochs) { list *options = read_data_cfg(datacfg); char *train_images = option_find_str(options, "train", "data/train.txt"); @@ -304,7 +304,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i const int iteration = get_current_iteration(net); //i = get_current_batch(net); - int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions); // calculate mAP for each 4 Epochs + int calc_map_for_each = mAP_epochs * train_images_num / (net.batch * net.subdivisions); // calculate mAP every mAP_epochs calc_map_for_each = fmax(calc_map_for_each, 100); int next_map_calc = iter_map + calc_map_for_each; next_map_calc = fmax(next_map_calc, net.burn_in); @@ -2000,6 +2000,8 @@ void run_detector(int argc, char **argv) int ext_output = find_arg(argc, argv, "-ext_output"); int save_labels = find_arg(argc, argv, "-save_labels"); char* chart_path = find_char_arg(argc, argv, "-chart", 0); + // While training, decide after how many epochs mAP will be calculated. Default value is 4 which means the mAP will be calculated after each 4 epochs + int mAP_epochs = find_int_arg(argc, argv, "-mAP_epochs", 4); if (argc < 4) { fprintf(stderr, "usage: %s %s [train/test/valid/demo/map] [data] [cfg] [weights (optional)]\n", argv[0], argv[1]); return; @@ -2038,7 +2040,7 @@ void run_detector(int argc, char **argv) if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0; char *filename = (argc > 6) ? argv[6] : 0; if (0 == strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels, outfile, letter_box, benchmark_layers); - else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map, thresh, iou_thresh, mjpeg_port, show_imgs, benchmark_layers, chart_path); + else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map, thresh, iou_thresh, mjpeg_port, show_imgs, benchmark_layers, chart_path, mAP_epochs); else if (0 == strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile); else if (0 == strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights); else if (0 == strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh, iou_thresh, map_points, letter_box, NULL); From 640edd27782697fd8355c524346b14e35af7f73a Mon Sep 17 00:00:00 2001 From: Matthew Jones <87321304+jonesmwhv@users.noreply.github.com> Date: Fri, 1 Sep 2023 16:25:48 +0100 Subject: [PATCH 1342/1365] Fix unhandled negative resize augmentation. Propagate OpenCV errors back to C logger. (#8565) --- src/data.c | 9 +++++++++ src/image_opencv.cpp | 4 ++-- 2 files changed, 11 insertions(+), 2 deletions(-) diff --git a/src/data.c b/src/data.c index 7d2a4659906..17b675cc667 100644 --- a/src/data.c +++ b/src/data.c @@ -1238,6 +1238,15 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo int swidth = ow - pleft - pright; int sheight = oh - ptop - pbot; + if (swidth <= 0 || sheight <= 0 || (ow - pleft) <= 0 || (oh - ptop) <= 0 ) { + printf("\n WARNING: invalid resize. Resetting swidth: %d , sheight: %d, pleft: %d, ptop: %d \n", dw, dh, 0 ,0); + printf("\n Original values: \n swidth = %d, sheight = %d, pleft = %d, pright = %d, ptop = %d, pbot = %d, ow = %d, oh = %d \n", swidth, sheight, pleft, pright, ptop, pbot, ow, oh); + swidth = ow; + sheight = oh; + pleft = 0; + ptop = 0; + } + float sx = (float)swidth / ow; float sy = (float)sheight / oh; diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 7ff18bfe862..459d9bf0f37 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -1331,8 +1331,8 @@ extern "C" image image_data_augmentation(mat_cv* mat, int w, int h, // Mat -> image out = mat_to_image(sized); } - catch (...) { - cerr << "OpenCV can't augment image: " << w << " x " << h << " \n"; + catch (const std::exception& e) { + cerr << "OpenCV can't augment image: " << w << " x " << h << " \n" << e.what() << " \n"; out = mat_to_image(*(cv::Mat*)mat); } return out; From 30b044abd623578d76e56f094af0b1d64e42395d Mon Sep 17 00:00:00 2001 From: ck Date: Fri, 1 Sep 2023 23:26:36 +0800 Subject: [PATCH 1343/1365] improve code for parser.c using int32_t for major, minor and revision of .weights file's header. (#7983) Co-authored-by: Stefano Sinigardi --- src/parser.c | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/src/parser.c b/src/parser.c index 3bf4683d996..16a2b905f7b 100644 --- a/src/parser.c +++ b/src/parser.c @@ -2006,12 +2006,12 @@ void save_weights_upto(network net, char *filename, int cutoff, int save_ema) FILE *fp = fopen(filename, "wb"); if(!fp) file_error(filename); - int major = MAJOR_VERSION; - int minor = MINOR_VERSION; - int revision = PATCH_VERSION; - fwrite(&major, sizeof(int), 1, fp); - fwrite(&minor, sizeof(int), 1, fp); - fwrite(&revision, sizeof(int), 1, fp); + int32_t major = MAJOR_VERSION; + int32_t minor = MINOR_VERSION; + int32_t revision = PATCH_VERSION; + fwrite(&major, sizeof(int32_t), 1, fp); + fwrite(&minor, sizeof(int32_t), 1, fp); + fwrite(&revision, sizeof(int32_t), 1, fp); (*net.seen) = get_current_iteration(net) * net.batch * net.subdivisions; // remove this line, when you will save to weights-file both: seen & cur_iteration fwrite(net.seen, sizeof(uint64_t), 1, fp); @@ -2267,12 +2267,12 @@ void load_weights_upto(network *net, char *filename, int cutoff) FILE *fp = fopen(filename, "rb"); if(!fp) file_error(filename); - int major; - int minor; - int revision; - fread(&major, sizeof(int), 1, fp); - fread(&minor, sizeof(int), 1, fp); - fread(&revision, sizeof(int), 1, fp); + int32_t major; + int32_t minor; + int32_t revision; + fread(&major, sizeof(int32_t), 1, fp); + fread(&minor, sizeof(int32_t), 1, fp); + fread(&revision, sizeof(int32_t), 1, fp); if ((major * 10 + minor) >= 2) { printf("\n seen 64"); uint64_t iseen = 0; From ac4e765a4ae9ee3a150e75af1114191ff701a5e5 Mon Sep 17 00:00:00 2001 From: umbralada <30407896+umbralada@users.noreply.github.com> Date: Sat, 2 Sep 2023 00:15:01 +0300 Subject: [PATCH 1344/1365] added free_alphabet function; this function used for free alphabet. (#1805) --- src/coco.c | 3 +-- src/demo.c | 9 +-------- src/detector.c | 12 +----------- src/image.c | 13 +++++++++++++ src/image.h | 1 + src/yolo.c | 4 ++-- 6 files changed, 19 insertions(+), 23 deletions(-) diff --git a/src/coco.c b/src/coco.c index ec49ae013d3..1ccb3bc8b63 100644 --- a/src/coco.c +++ b/src/coco.c @@ -374,10 +374,9 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh) show_image(im, "predictions"); free_image(im); free_image(sized); - + free_alphabet(alphabet); wait_until_press_key_cv(); destroy_all_windows_cv(); - if (filename) break; } free(boxes); diff --git a/src/demo.c b/src/demo.c index e9017a0bd8c..7a258f3e70f 100644 --- a/src/demo.c +++ b/src/demo.c @@ -413,14 +413,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int free_ptrs((void **)names, net.layers[net.n - 1].classes); - const int nsize = 8; - for (j = 0; j < nsize; ++j) { - for (i = 32; i < 127; ++i) { - free_image(alphabet[j][i]); - } - free(alphabet[j]); - } - free(alphabet); + free_alphabet(alphabet); free_network(net); //cudaProfilerStop(); } diff --git a/src/detector.c b/src/detector.c index 5ea7fc0077f..5171ee86a49 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1778,17 +1778,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam free_ptrs((void**)names, net.layers[net.n - 1].classes); free_list_contents_kvp(options); free_list(options); - - int i; - const int nsize = 8; - for (j = 0; j < nsize; ++j) { - for (i = 32; i < 127; ++i) { - free_image(alphabet[j][i]); - } - free(alphabet[j]); - } - free(alphabet); - + free_alphabet(alphabet); free_network(net); } diff --git a/src/image.c b/src/image.c index ca9f26651eb..3775837b2b4 100644 --- a/src/image.c +++ b/src/image.c @@ -280,6 +280,19 @@ image **load_alphabet() return alphabets; } +void free_alphabet(image **alphabet) +{ + int i, j; + const int nsize = 8; + for (j = 0; j < nsize; ++j) { + for (i = 32; i < 127; ++i) { + free_image(alphabet[j][i]); + } + free(alphabet[j]); + } + free(alphabet); +} + // Creates array of detections with prob > thresh and fills best_class for them diff --git a/src/image.h b/src/image.h index 90e6a04868b..65ccb7c7122 100644 --- a/src/image.h +++ b/src/image.h @@ -88,6 +88,7 @@ image load_image(char *filename, int w, int h, int c); image load_image_stb_resize(char *filename, int w, int h, int c); //LIB_API image load_image_color(char *filename, int w, int h); image **load_alphabet(); +void free_alphabet(image **alphabet); //float get_pixel(image m, int x, int y, int c); //float get_pixel_extend(image m, int x, int y, int c); diff --git a/src/yolo.c b/src/yolo.c index f13d460aca0..dfd497dfc80 100644 --- a/src/yolo.c +++ b/src/yolo.c @@ -328,11 +328,11 @@ void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh) free_image(im); free_image(sized); - + free_alphabet(alphabet); wait_until_press_key_cv(); destroy_all_windows_cv(); - if (filename) break; + if (filename) break; } free(boxes); for(j = 0; j < l.side*l.side*l.n; ++j) { From d30c13c38f37d8cbd4234621042a075bf3aedb56 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Mon, 11 Sep 2023 21:09:53 +0200 Subject: [PATCH 1345/1365] cpack update (#8842) * enable building installers in CI * [build.ps1] improve installer handling * do not install directly when building installer --- .github/workflows/ccpp.yml | 6 +++--- CMakeLists.txt | 25 ++++++++++++++++--------- build.ps1 | 25 +++++++++++++++++++------ 3 files changed, 38 insertions(+), 18 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 1f33a47b114..b59a4710719 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -124,7 +124,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL -BuildInstaller - uses: actions/upload-artifact@v3 with: @@ -410,7 +410,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL -BuildInstaller - uses: actions/upload-artifact@v3 with: @@ -506,7 +506,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -ForceLocalVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -ForceLocalVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL -BuildInstaller - uses: actions/upload-artifact@v3 with: diff --git a/CMakeLists.txt b/CMakeLists.txt index 8587793345a..8766e59524f 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -21,6 +21,7 @@ option(VCPKG_BUILD_OPENCV_WITH_CUDA "Build OpenCV with CUDA extension integratio option(VCPKG_USE_OPENCV2 "Use legacy OpenCV 2" OFF) option(VCPKG_USE_OPENCV3 "Use legacy OpenCV 3" OFF) option(VCPKG_USE_OPENCV4 "Use OpenCV 4" ON) +option(USE_NSIS "Use NSIS as a CPack backend on Windows" ON) if(DEFINED ENV{VCPKG_DEFAULT_TRIPLET}) message(STATUS "Setting default vcpkg target triplet to $ENV{VCPKG_DEFAULT_TRIPLET}") @@ -667,15 +668,9 @@ install( if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/vcpkg.json) file(READ ${CMAKE_CURRENT_SOURCE_DIR}/vcpkg.json VCPKG_JSON_STRING) string(JSON CPACK_PACKAGE_NAME GET ${VCPKG_JSON_STRING} name) - string(JSON CPACK_BUNDLE_NAME GET ${VCPKG_JSON_STRING} name) string(JSON CPACK_PACKAGE_HOMEPAGE_URL GET ${VCPKG_JSON_STRING} homepage) string(JSON CPACK_PACKAGE_DESCRIPTION GET ${VCPKG_JSON_STRING} description) - string(JSON CPACK_NSIS_DISPLAY_NAME GET ${VCPKG_JSON_STRING} description) - string(JSON CPACK_NSIS_PACKAGE_NAME GET ${VCPKG_JSON_STRING} description) set(CPACK_RESOURCE_FILE_LICENSE ${CMAKE_CURRENT_SOURCE_DIR}/LICENSE) - set(CPACK_NSIS_MUI_ICON "${CMAKE_CURRENT_SOURCE_DIR}/src/darknet.ico") - set(CPACK_NSIS_MUI_UNIICON "${CMAKE_CURRENT_SOURCE_DIR}/src/darknet.ico") - set(CPACK_BUNDLE_UNIICON "${CMAKE_CURRENT_SOURCE_DIR}/src/darknet.ico") if(UNIX AND NOT APPLE) find_program(LSB_RELEASE_EXEC lsb_release) @@ -685,14 +680,26 @@ if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/vcpkg.json) ) if(LSB_RELEASE_ID_SHORT STREQUAL "Ubuntu") set(CPACK_GENERATOR "DEB") + set(CPACK_DEBIAN_PACKAGE_MAINTAINER "Darknet") else() set(CPACK_GENERATOR "RPM") endif() + elseif(APPLE) + set(CPACK_GENERATOR "DragNDrop") elseif(WIN32) set(CPACK_PACKAGE_INSTALL_DIRECTORY ${CPACK_PACKAGE_NAME}) - set(CPACK_NSIS_MODIFY_PATH "ON") - set(CPACK_NSIS_ENABLE_UNINSTALL_BEFORE_INSTALL "ON") - set(CPACK_GENERATOR "NSIS") + if(USE_NSIS) + set(CPACK_GENERATOR "NSIS") + string(JSON CPACK_NSIS_PACKAGE_NAME GET ${VCPKG_JSON_STRING} name) + string(JSON CPACK_NSIS_DISPLAY_NAME GET ${VCPKG_JSON_STRING} name) + set(CPACK_NSIS_ENABLE_UNINSTALL_BEFORE_INSTALL "ON") + set(CPACK_NSIS_MODIFY_PATH OFF) #disable extra page for adding to PATH, because it's broken on Win10+ due to NSIS not supporting MAX_PATH + set(CPACK_NSIS_MUI_ICON "${CMAKE_CURRENT_SOURCE_DIR}/src/darknet.ico") + set(CPACK_NSIS_MUI_UNIICON "${CMAKE_CURRENT_SOURCE_DIR}/src/darknet.ico") + else() + set(CPACK_GENERATOR "WIX") + #set(CPACK_WIX_UPGRADE_GUID "") # IMPORTANT! It has to be unique for every project!! + endif() endif() include(CPack) diff --git a/build.ps1 b/build.ps1 index b9e3205569e..a85fcca7b8b 100755 --- a/build.ps1 +++ b/build.ps1 @@ -6,7 +6,7 @@ build Created By: Stefano Sinigardi Created Date: February 18, 2019 - Last Modified Date: August 31, 2023 + Last Modified Date: September 10, 2023 .DESCRIPTION Build tool using CMake, trying to properly setup the environment around compiler @@ -193,7 +193,7 @@ param ( $global:DisableInteractive = $DisableInteractive -$build_ps1_version = "3.5.0" +$build_ps1_version = "3.5.1" $script_name = $MyInvocation.MyCommand.Name Import-Module -Name $PSScriptRoot/scripts/utils.psm1 -Force @@ -210,9 +210,12 @@ else { $BuildLogPath = "$PSCustomScriptRoot/build.log" $ReleaseInstallPrefix = "$PSCustomScriptRoot" $DebugInstallPrefix = "$PSCustomScriptRoot/debug" -$DebugBuildSetup = " -DCMAKE_INSTALL_PREFIX=$DebugInstallPrefix -DCMAKE_BUILD_TYPE=Debug" -$ReleaseBuildSetup = " -DCMAKE_INSTALL_PREFIX=$ReleaseInstallPrefix -DCMAKE_BUILD_TYPE=Release" - +$DebugBuildSetup = " -DCMAKE_BUILD_TYPE=Debug " +$ReleaseBuildSetup = " -DCMAKE_BUILD_TYPE=Release " +if (-Not $BuildInstaller) { + $DebugBuildSetup = $DebugBuildSetup + " -DCMAKE_INSTALL_PREFIX=$DebugInstallPrefix " + $ReleaseBuildSetup = $ReleaseBuildSetup + " -DCMAKE_INSTALL_PREFIX=$ReleaseInstallPrefix " +} Start-Transcript -Path $BuildLogPath Write-Host "Build script version ${build_ps1_version}, utils module version ${utils_psm1_version}" @@ -819,6 +822,11 @@ if ($ForceVCPKGCacheRemoval -and (-Not $UseVCPKG)) { Write-Host "VCPKG is not enabled, so local vcpkg binary cache will not be deleted even if requested" -ForegroundColor Yellow } +if ($BuildInstaller) { + Write-Host "You requested to build an installer, so enabling this option if supported" -ForegroundColor Yellow + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_INSTALLER=ON" +} + if (($ForceOpenCVVersion -eq 2) -and $UseVCPKG) { Write-Host "You requested OpenCV version 2, so vcpkg will install that version" -ForegroundColor Yellow Write-Host "This requires using vcpkg.json.opencv23 as manifest file" -ForegroundColor Yellow @@ -1012,7 +1020,12 @@ else { MyThrow("Config failed! Exited with error code $exitCode.") } Write-Host "Building release CMake project" -ForegroundColor Green - $proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList "--build . ${releaseConfig} --parallel ${NumberOfBuildWorkers} --target install" + if ($BuildInstaller) { + $proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList "--build . ${releaseConfig} --parallel ${NumberOfBuildWorkers}" + } + else { + $proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList "--build . ${releaseConfig} --parallel ${NumberOfBuildWorkers} --target install" + } $handle = $proc.Handle $proc.WaitForExit() $exitCode = $proc.ExitCode From 4da0cd6d7939aec7d396dad93954993be6ca0b0b Mon Sep 17 00:00:00 2001 From: Thibault Durand Date: Thu, 21 Sep 2023 01:46:17 -0300 Subject: [PATCH 1346/1365] Evo: add output to a json file option when running on a video (#3723) --- README.md | 1 + src/coco.c | 3 ++- src/demo.c | 28 ++++++++++++++++++++++++++-- src/demo.h | 2 +- src/detector.c | 3 ++- src/yolo.c | 3 ++- 6 files changed, 34 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index d9de132a86d..0a1ae52226a 100644 --- a/README.md +++ b/README.md @@ -359,6 +359,7 @@ If you customize build with CMake GUI, darknet executable will be installed in y `./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output -dont_show -out result.json < data/train.txt` - To process a list of images `data/train.txt` and save results of detection to `result.txt` use: `./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -dont_show -ext_output < data/train.txt > result.txt` +- To process a video and output results to a json file use: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights file.mp4 -dont_show -json_file_output results.json` - Pseudo-labelling - to process a list of images `data/new_train.txt` and save results of detection in Yolo training format for each image as label `.txt` (in this way you can increase the amount of training data) use: `./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25 -dont_show -save_labels < data/new_train.txt` - To calculate anchors: `./darknet detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416` diff --git a/src/coco.c b/src/coco.c index 1ccb3bc8b63..8ad13834b01 100644 --- a/src/coco.c +++ b/src/coco.c @@ -398,6 +398,7 @@ void run_coco(int argc, char **argv) int cam_index = find_int_arg(argc, argv, "-c", 0); int frame_skip = find_int_arg(argc, argv, "-s", 0); int ext_output = find_arg(argc, argv, "-ext_output"); + char *json_file_output = find_char_arg(argc, argv, "-json_file_output", 0); if(argc < 4){ fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); @@ -412,5 +413,5 @@ void run_coco(int argc, char **argv) else if(0==strcmp(argv[2], "valid")) validate_coco(cfg, weights); else if(0==strcmp(argv[2], "recall")) validate_coco_recall(cfg, weights); else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, hier_thresh, cam_index, filename, coco_classes, 80, 1, frame_skip, - prefix, out_filename, mjpeg_port, 0, json_port, dont_show, ext_output, 0, 0, 0, 0, 0); + prefix, out_filename, mjpeg_port, 0, json_port, dont_show, ext_output, 0, 0, 0, 0, 0, json_file_output); } diff --git a/src/demo.c b/src/demo.c index 7a258f3e70f..0e4d3d90cd3 100644 --- a/src/demo.c +++ b/src/demo.c @@ -142,7 +142,7 @@ double get_wall_time() void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, int avgframes, int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int dontdraw_bbox, int json_port, int dont_show, int ext_output, int letter_box_in, int time_limit_sec, char *http_post_host, - int benchmark, int benchmark_layers) + int benchmark, int benchmark_layers, char *json_file_output) { if (avgframes < 1) avgframes = 1; avg_frames = avgframes; @@ -157,6 +157,15 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int demo_thresh = thresh; demo_ext_output = ext_output; demo_json_port = json_port; + char *json_buf = NULL; + FILE* json_file = NULL; + + if (json_file_output) { + json_file = fopen(json_file_output, "wb"); + char *tmp = "[\n"; + fwrite(tmp, sizeof(char), strlen(tmp), json_file); + } + printf("Demo\n"); net = parse_network_cfg_custom(cfgfile, 1, 1); // set batch=1 if(weightfile){ @@ -290,6 +299,16 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int send_json(local_dets, local_nboxes, l.classes, demo_names, frame_id, demo_json_port, timeout); } + if (json_file_output) { + if (json_buf) { + char *tmp = ", \n"; + fwrite(tmp, sizeof(char), strlen(tmp), json_file); + } + json_buf = detection_to_json(local_dets, local_nboxes, l.classes, demo_names, frame_id, NULL); + fwrite(json_buf, sizeof(char), strlen(json_buf), json_file); + free(json_buf); + } + //char *http_post_server = "webhook.site/898bbd9b-0ddd-49cf-b81d-1f56be98d870"; if (http_post_host && !send_http_post_once) { int timeout = 3; // 3 seconds @@ -396,6 +415,11 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int printf("output_video_writer closed. \n"); } + if (json_file_output) { + char *tmp = "\n]"; + fwrite(tmp, sizeof(char), strlen(tmp), json_file); + fclose(json_file); + } this_thread_sleep_for(thread_wait_ms); custom_join(detect_thread, 0); @@ -420,7 +444,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int #else void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, int avgframes, int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int dontdraw_bbox, int json_port, int dont_show, int ext_output, int letter_box_in, int time_limit_sec, char *http_post_host, - int benchmark, int benchmark_layers) + int benchmark, int benchmark_layers, char *json_file_output) { fprintf(stderr, "Demo needs OpenCV for webcam images.\n"); } diff --git a/src/demo.h b/src/demo.h index 380b72feddc..15f359dd04c 100644 --- a/src/demo.h +++ b/src/demo.h @@ -6,7 +6,7 @@ extern "C" { #endif void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int cam_index, const char *filename, char **names, int classes, int avgframes, - int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int dontdraw_bbox, int json_port, int dont_show, int ext_output, int letter_box_in, int time_limit_sec, char *http_post_host, int benchmark, int benchmark_layers); + int frame_skip, char *prefix, char *out_filename, int mjpeg_port, int dontdraw_bbox, int json_port, int dont_show, int ext_output, int letter_box_in, int time_limit_sec, char *http_post_host, int benchmark, int benchmark_layers, char *json_file_output); #ifdef __cplusplus } #endif diff --git a/src/detector.c b/src/detector.c index 5171ee86a49..8f3023d5eaa 100644 --- a/src/detector.c +++ b/src/detector.c @@ -1975,6 +1975,7 @@ void run_detector(int argc, char **argv) char *http_post_host = find_char_arg(argc, argv, "-http_post_host", 0); int time_limit_sec = find_int_arg(argc, argv, "-time_limit_sec", 0); char *out_filename = find_char_arg(argc, argv, "-out_filename", 0); + char *json_file_output = find_char_arg(argc, argv, "-json_file_output", 0); char *outfile = find_char_arg(argc, argv, "-out", 0); char *prefix = find_char_arg(argc, argv, "-prefix", 0); float thresh = find_float_arg(argc, argv, "-thresh", .25); // 0.24 @@ -2048,7 +2049,7 @@ void run_detector(int argc, char **argv) if (strlen(filename) > 0) if (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0; demo(cfg, weights, thresh, hier_thresh, cam_index, filename, names, classes, avgframes, frame_skip, prefix, out_filename, - mjpeg_port, dontdraw_bbox, json_port, dont_show, ext_output, letter_box, time_limit_sec, http_post_host, benchmark, benchmark_layers); + mjpeg_port, dontdraw_bbox, json_port, dont_show, ext_output, letter_box, time_limit_sec, http_post_host, benchmark, benchmark_layers, json_file_output); free_list_contents_kvp(options); free_list(options); diff --git a/src/yolo.c b/src/yolo.c index dfd497dfc80..ef68acabc51 100644 --- a/src/yolo.c +++ b/src/yolo.c @@ -353,6 +353,7 @@ void run_yolo(int argc, char **argv) int cam_index = find_int_arg(argc, argv, "-c", 0); int frame_skip = find_int_arg(argc, argv, "-s", 0); int ext_output = find_arg(argc, argv, "-ext_output"); + char *json_file_output = find_char_arg(argc, argv, "-json_file_output", 0); if(argc < 4){ fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); return; @@ -366,5 +367,5 @@ void run_yolo(int argc, char **argv) else if(0==strcmp(argv[2], "valid")) validate_yolo(cfg, weights); else if(0==strcmp(argv[2], "recall")) validate_yolo_recall(cfg, weights); else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, hier_thresh, cam_index, filename, voc_names, 20, 1, frame_skip, - prefix, out_filename, mjpeg_port, 0, json_port, dont_show, ext_output, 0, 0, 0, 0, 0); + prefix, out_filename, mjpeg_port, 0, json_port, dont_show, ext_output, 0, 0, 0, 0, 0, json_file_output); } From 39b854d0ac339a8804442c43014583729d72281d Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 21 Sep 2023 08:06:26 +0200 Subject: [PATCH 1347/1365] lower disk space usage in CI to remove spurious errors (#8843) Co-authored-by: vsaw --- .github/workflows/ccpp.yml | 24 +++++++++++++++--------- .github/workflows/on_pr.yml | 22 ++++++++++++++-------- build.ps1 | 6 +++++- scripts/deploy-cuda.sh | 12 +++++++----- scripts/setup.sh | 15 ++++++++------- 5 files changed, 49 insertions(+), 30 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index b59a4710719..cb231b989ba 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -86,7 +86,9 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + run: sudo apt-get install -y --no-install-recommends yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + - name: Clean downloads + run: sudo apt-get clean - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.sh @@ -101,7 +103,7 @@ jobs: - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash run: > - git clone https://github.com/microsoft/vcpkg ; + git clone --depth 1 https://github.com/microsoft/vcpkg ; ./vcpkg/bootstrap-vcpkg.sh ; mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" @@ -154,7 +156,9 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + run: sudo apt-get install -y --no-install-recommends yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + - name: Clean downloads + run: sudo apt-get clean - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.sh @@ -205,7 +209,9 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + run: sudo apt-get install -y --no-install-recommends yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + - name: Clean downloads + run: sudo apt-get clean - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.sh @@ -354,7 +360,7 @@ jobs: - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash run: > - git clone https://github.com/microsoft/vcpkg ; + git clone --depth 1 https://github.com/microsoft/vcpkg ; ./vcpkg/bootstrap-vcpkg.sh ; mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" @@ -392,7 +398,7 @@ jobs: - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash run: > - git clone https://github.com/microsoft/vcpkg ; + git clone --depth 1 https://github.com/microsoft/vcpkg ; ./vcpkg/bootstrap-vcpkg.sh ; mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" @@ -488,7 +494,7 @@ jobs: - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash run: > - git clone https://github.com/microsoft/vcpkg ; + git clone --depth 1 https://github.com/microsoft/vcpkg ; ./vcpkg/bootstrap-vcpkg.sh ; $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" @@ -538,7 +544,7 @@ jobs: - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash run: > - git clone https://github.com/microsoft/vcpkg ; + git clone --depth 1 https://github.com/microsoft/vcpkg ; ./vcpkg/bootstrap-vcpkg.sh ; $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" @@ -600,7 +606,7 @@ jobs: - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash run: > - git clone https://github.com/microsoft/vcpkg ; + git clone --depth 1 https://github.com/microsoft/vcpkg ; ./vcpkg/bootstrap-vcpkg.sh ; $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index f32c2797550..2af8e96c7ee 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -72,7 +72,9 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + run: sudo apt-get install -y --no-install-recommends yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + - name: Clean downloads + run: sudo apt-get clean - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.sh @@ -87,7 +89,7 @@ jobs: - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash run: > - git clone https://github.com/microsoft/vcpkg ; + git clone --depth 1 https://github.com/microsoft/vcpkg ; ./vcpkg/bootstrap-vcpkg.sh ; mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" @@ -113,7 +115,9 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + run: sudo apt-get install -y --no-install-recommends yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + - name: Clean downloads + run: sudo apt-get clean - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.sh @@ -154,7 +158,9 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt-get install -y yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + run: sudo apt-get install -y --no-install-recommends yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + - name: Clean downloads + run: sudo apt-get clean - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.sh @@ -259,7 +265,7 @@ jobs: - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash run: > - git clone https://github.com/microsoft/vcpkg ; + git clone --depth 1 https://github.com/microsoft/vcpkg ; ./vcpkg/bootstrap-vcpkg.sh ; mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" @@ -283,7 +289,7 @@ jobs: - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash run: > - git clone https://github.com/microsoft/vcpkg ; + git clone --depth 1 https://github.com/microsoft/vcpkg ; ./vcpkg/bootstrap-vcpkg.sh ; mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" @@ -331,7 +337,7 @@ jobs: - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash run: > - git clone https://github.com/microsoft/vcpkg ; + git clone --depth 1 https://github.com/microsoft/vcpkg ; ./vcpkg/bootstrap-vcpkg.sh ; $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" @@ -362,7 +368,7 @@ jobs: - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash run: > - git clone https://github.com/microsoft/vcpkg ; + git clone --depth 1 https://github.com/microsoft/vcpkg ; ./vcpkg/bootstrap-vcpkg.sh ; $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" diff --git a/build.ps1 b/build.ps1 index a85fcca7b8b..768a478442b 100755 --- a/build.ps1 +++ b/build.ps1 @@ -721,7 +721,11 @@ if ($UseVCPKG -And -Not $ForceLocalVCPKG) { } if (($null -eq $vcpkg_path) -and $UseVCPKG) { if (-Not (Test-Path "$PWD/vcpkg${VCPKGSuffix}")) { - $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "clone https://github.com/microsoft/vcpkg vcpkg${VCPKGSuffix}" + $shallow_copy = "" + if($ForceOpenCVVersion -eq 0) { + $shallow_copy = " --depth 1 " + } + $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "clone $shallow_copy https://github.com/microsoft/vcpkg vcpkg${VCPKGSuffix}" $handle = $proc.Handle $proc.WaitForExit() $exitCode = $proc.ExitCode diff --git a/scripts/deploy-cuda.sh b/scripts/deploy-cuda.sh index 952669cb87f..b95258c2875 100755 --- a/scripts/deploy-cuda.sh +++ b/scripts/deploy-cuda.sh @@ -23,13 +23,15 @@ sudo apt-key del 7fa2af80 wget https://developer.download.nvidia.com/compute/cuda/repos/$distr_name/x86_64/cuda-keyring_1.1-1_all.deb sudo dpkg -i cuda-keyring_1.1-1_all.deb sudo apt-get update -sudo apt-get install -y build-essential g++ -sudo apt-get install -y apt-transport-https ca-certificates gnupg software-properties-common wget -sudo apt-get install -y zlib1g +sudo apt-get install -y --no-install-recommends build-essential g++ +sudo apt-get install -y --no-install-recommends apt-transport-https ca-certificates gnupg software-properties-common wget +sudo apt-get install -y --no-install-recommends zlib1g sudo apt-get dist-upgrade -y sudo apt-get install -y --no-install-recommends cuda-${CUDA_VERSION_DASHED} -sudo apt-get install -y libcudnn8 -sudo apt-get install -y libcudnn8-dev +sudo apt-get install -y --no-install-recommends libcudnn8 +sudo apt-get install -y --no-install-recommends libcudnn8-dev sudo rm -rf /usr/local/cuda sudo ln -s /usr/local/cuda-${CUDA_VERSION} /usr/local/cuda + +sudo apt-get clean diff --git a/scripts/setup.sh b/scripts/setup.sh index 4ca00f749ba..e583e972369 100755 --- a/scripts/setup.sh +++ b/scripts/setup.sh @@ -75,9 +75,9 @@ elif [[ $(cut -f2 <<< $(lsb_release -i)) == "Ubuntu" ]]; then if [ "$install_tools" = true ] ; then echo "Installing tools" sudo apt-get update - sudo apt-get install -y git ninja-build build-essential g++ nasm yasm gperf - sudo apt-get install -y apt-transport-https ca-certificates gnupg software-properties-common wget - sudo apt-get install -y libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + sudo apt-get install -y --no-install-recommends git ninja-build build-essential g++ nasm yasm gperf + sudo apt-get install -y --no-install-recommends apt-transport-https ca-certificates gnupg software-properties-common wget + sudo apt-get install -y --no-install-recommends libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | gpg --dearmor - | sudo tee /etc/apt/trusted.gpg.d/kitware.gpg >/dev/null sudo apt-add-repository "deb https://apt.kitware.com/ubuntu/ $(cut -f2 <<< $(lsb_release -c)) main" wget -q https://packages.microsoft.com/config/ubuntu/$(cut -f2 <<< $(lsb_release -r))/packages-microsoft-prod.deb @@ -85,10 +85,11 @@ elif [[ $(cut -f2 <<< $(lsb_release -i)) == "Ubuntu" ]]; then sudo add-apt-repository universe sudo apt-get update sudo apt-get dist-upgrade -y - sudo apt-get install -y cmake - sudo apt-get install -y powershell - sudo apt-get install -y curl zip unzip tar - sudo apt-get install -y pkg-config autoconf libtool bison + sudo apt-get install -y --no-install-recommends cmake + sudo apt-get install -y --no-install-recommends powershell + sudo apt-get install -y --no-install-recommends curl zip unzip tar + sudo apt-get install -y --no-install-recommends pkg-config autoconf libtool bison + sudo apt-get clean fi else if [ "$install_cuda" = true ] ; then From 9a9fcdb1117a0fb852eeb5e7134d76759dfddab0 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 27 Sep 2023 02:04:56 +0200 Subject: [PATCH 1348/1365] minor code cleaning (#8844) * minor cleanup * vcpkg force internal tools to reduce cache misses * move getopt to 3rdparty * add a very small test on a couple of platform in CI --- .github/workflows/ccpp.yml | 35 +- .github/workflows/on_pr.yml | 25 +- .gitignore | 2 + {src => 3rdparty/getopt}/getopt.c | 0 {src => 3rdparty/getopt}/getopt.h | 0 CMakeLists.txt | 19 +- DarknetConfig.cmake.in | 3 + build.ps1 | 42 +- results/tmp.txt | 0 scripts/deploy-cuda.sh | 4 +- scripts/install_OpenCV4.sh | 66 - scripts/log_parser/plot.jpg | Bin 32981 -> 0 bytes scripts/log_parser/readme.md | 20 +- scripts/log_parser/run_log_parser_windows.cmd | 6 - scripts/log_parser/test.log | 75621 --------------- scripts/log_parser/test_new.log | 76285 ---------------- scripts/testdev2017.txt | 20288 ---- src/activation_kernels.cu | 67 +- src/activations.c | 4 +- src/blas.c | 18 +- src/blas_kernels.cu | 111 +- src/classifier.c | 4 +- src/col2im_kernels.cu | 4 +- src/convolutional_kernels.cu | 20 +- src/convolutional_layer.c | 15 +- src/dark_cuda.c | 12 - src/darknet.c | 5 - src/darkunistd.h | 2 +- src/data.c | 28 +- src/demo.c | 4 +- src/detector.c | 20 +- src/dropout_layer.c | 2 +- src/dropout_layer_kernels.cu | 14 +- src/gaussian_yolo_layer.c | 3 - src/gemm.c | 2 - src/http_stream.cpp | 3 +- src/im2col_kernels.cu | 53 +- src/image.c | 17 +- src/image_opencv.cpp | 1 - src/maxpool_layer_kernels.cu | 14 +- src/network.c | 2 +- src/network_kernels.cu | 6 +- src/parser.c | 36 +- src/region_layer.c | 1 - src/route_layer.c | 3 +- src/softmax_layer.c | 15 +- src/utils.h | 2 +- src/yolo_console_dll.cpp | 12 +- src/yolo_layer.c | 3 - src/yolo_v2_class.cpp | 2 +- vcpkg.json | 6 +- vcpkg.json.opencv23 | 6 +- 52 files changed, 327 insertions(+), 172606 deletions(-) rename {src => 3rdparty/getopt}/getopt.c (100%) rename {src => 3rdparty/getopt}/getopt.h (100%) delete mode 100644 results/tmp.txt delete mode 100755 scripts/install_OpenCV4.sh delete mode 100644 scripts/log_parser/plot.jpg delete mode 100644 scripts/log_parser/run_log_parser_windows.cmd delete mode 100644 scripts/log_parser/test.log delete mode 100644 scripts/log_parser/test_new.log delete mode 100644 scripts/testdev2017.txt diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index cb231b989ba..ad386f42c91 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -14,6 +14,7 @@ on: env: VCPKG_BINARY_SOURCES: 'clear;nuget,vcpkgbinarycache,readwrite' + VCPKG_FORCE_DOWNLOADED_BINARIES: "TRUE" jobs: ubuntu-makefile: @@ -351,6 +352,12 @@ jobs: shell: pwsh run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateTOOL + - name: Test on data/dog.jpg + shell: bash + run: > + wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights -O ${{ github.workspace }}/yolov4-tiny.weights; + ${{ github.workspace }}/build_release/darknet detect ${{ github.workspace }}/cfg/yolov4-tiny.cfg ${{ github.workspace }}/yolov4-tiny.weights ${{ github.workspace }}/data/dog.jpg -dont_show + ubuntu-setup-sh: runs-on: ubuntu-20.04 @@ -627,16 +634,40 @@ jobs: run: ${{ github.workspace }}/scripts/setup.ps1 -InstallCUDA - win-intlibs-cpp: + win-vcpkg-base-cpp: runs-on: windows-latest steps: - uses: actions/checkout@v3 - uses: lukka/get-cmake@latest + - name: 'Setup vcpkg and NuGet artifacts backend' + shell: bash + run: > + git clone --depth 1 https://github.com/microsoft/vcpkg ; + ./vcpkg/bootstrap-vcpkg.sh ; + $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add + -Name "vcpkgbinarycache" + -Source http://93.49.111.10:5555/v3/index.json + + - name: Setup NuGet API key if found + shell: bash + env: + BAGET_API_KEY: ${{ secrets.BAGET_API_KEY }} + if: env.BAGET_API_KEY != null + run: > + $(./vcpkg/vcpkg fetch nuget | tail -n 1) + setapikey ${{ secrets.BAGET_API_KEY }} + -Source http://93.49.111.10:5555/v3/index.json + - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -ForceLocalVCPKG -DoNotUpdateVCPKG -ForceCPP -DisableInteractive -DoNotUpdateTOOL + + - name: Download yolov4-tiny.weights + run: curl.exe -L https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights -o ${{ github.workspace }}\yolov4-tiny.weights + - name: Test on data/dog.jpg + run: ${{ github.workspace }}\build_release\darknet.exe detect ${{ github.workspace }}\cfg\yolov4-tiny.cfg ${{ github.workspace }}\yolov4-tiny.weights ${{ github.workspace }}\data\dog.jpg win-csharp: diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index 2af8e96c7ee..6e7d36b807c 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -4,6 +4,7 @@ on: [pull_request] env: VCPKG_BINARY_SOURCES: 'clear;nuget,vcpkgbinarycache,read' + VCPKG_FORCE_DOWNLOADED_BINARIES: "TRUE" jobs: ubuntu-makefile: @@ -256,6 +257,12 @@ jobs: shell: pwsh run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateTOOL + - name: Test on data/dog.jpg + shell: bash + run: > + wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights -O ${{ github.workspace }}/yolov4-tiny.weights; + ${{ github.workspace }}/build_release/darknet detect ${{ github.workspace }}/cfg/yolov4-tiny.cfg ${{ github.workspace }}/yolov4-tiny.weights ${{ github.workspace }}/data/dog.jpg -dont_show + ubuntu-setup-sh: runs-on: ubuntu-20.04 @@ -379,16 +386,30 @@ jobs: run: ${{ github.workspace }}/scripts/setup.ps1 -InstallCUDA - win-intlibs-cpp: + win-vcpkg-base-cpp: runs-on: windows-latest steps: - uses: actions/checkout@v3 - uses: lukka/get-cmake@latest + - name: 'Setup vcpkg and NuGet artifacts backend' + shell: bash + run: > + git clone --depth 1 https://github.com/microsoft/vcpkg ; + ./vcpkg/bootstrap-vcpkg.sh ; + $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add + -Name "vcpkgbinarycache" + -Source http://93.49.111.10:5555/v3/index.json + - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -ForceLocalVCPKG -DoNotUpdateVCPKG -ForceCPP -DisableInteractive -DoNotUpdateTOOL + + - name: Download yolov4-tiny.weights + run: curl.exe -L https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights -o ${{ github.workspace }}\yolov4-tiny.weights + - name: Test on data/dog.jpg + run: ${{ github.workspace }}\build_release\darknet.exe detect ${{ github.workspace }}\cfg\yolov4-tiny.cfg ${{ github.workspace }}\yolov4-tiny.weights ${{ github.workspace }}\data\dog.jpg win-csharp: diff --git a/.gitignore b/.gitignore index 48f5a7819a8..bb62a6060b2 100644 --- a/.gitignore +++ b/.gitignore @@ -4,6 +4,7 @@ *.csv *.out *.png +*.jpg *.so *.exe *.dll @@ -65,4 +66,5 @@ include/darknet/ uselib uselib_track darknet +kmeansiou vcpkg/ diff --git a/src/getopt.c b/3rdparty/getopt/getopt.c similarity index 100% rename from src/getopt.c rename to 3rdparty/getopt/getopt.c diff --git a/src/getopt.h b/3rdparty/getopt/getopt.h similarity index 100% rename from src/getopt.h rename to 3rdparty/getopt/getopt.h diff --git a/CMakeLists.txt b/CMakeLists.txt index 8766e59524f..4710e39def8 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -412,7 +412,6 @@ list(APPEND headers if(NOT MSVC) list(REMOVE_ITEM headers ${CMAKE_CURRENT_LIST_DIR}/src/gettimeofday.h - ${CMAKE_CURRENT_LIST_DIR}/src/getopt.h ) endif() #set(exported_headers ${headers}) @@ -432,10 +431,19 @@ list(REMOVE_ITEM sources if(NOT MSVC) list(REMOVE_ITEM sources ${CMAKE_CURRENT_LIST_DIR}/src/gettimeofday.c - ${CMAKE_CURRENT_LIST_DIR}/src/getopt.c ) endif() +if(USE_INTEGRATED_LIBS AND MSVC) + list(APPEND sources + ${CMAKE_CURRENT_LIST_DIR}/3rdparty/getopt/getopt.c + ${CMAKE_CURRENT_LIST_DIR}/3rdparty/getopt/getopt.h + ) +endif() +if((NOT USE_INTEGRATED_LIBS) AND MSVC) + find_package(unofficial-getopt-win32 REQUIRED) +endif() + if(ENABLE_CUDA) file(GLOB cuda_sources "${CMAKE_CURRENT_LIST_DIR}/src/*.cu") endif() @@ -530,6 +538,13 @@ if(MSVC) target_link_libraries(dark PUBLIC PThreads4W::PThreads4W) target_link_libraries(dark PUBLIC wsock32) target_link_libraries(uselib PRIVATE PThreads4W::PThreads4W) + if(USE_INTEGRATED_LIBS) + target_include_directories(dark PRIVATE ${CMAKE_CURRENT_LIST_DIR}/3rdparty/getopt) + target_include_directories(darknet PRIVATE ${CMAKE_CURRENT_LIST_DIR}/3rdparty/getopt) + else() + target_link_libraries(dark PRIVATE unofficial::getopt-win32::getopt) + target_link_libraries(darknet PRIVATE unofficial::getopt-win32::getopt) + endif() target_compile_definitions(darknet PRIVATE -D_CRT_RAND_S -DNOMINMAX -D_USE_MATH_DEFINES) target_compile_definitions(dark PRIVATE -D_CRT_RAND_S -DNOMINMAX -D_USE_MATH_DEFINES) target_compile_definitions(dark PUBLIC -D_CRT_SECURE_NO_WARNINGS) diff --git a/DarknetConfig.cmake.in b/DarknetConfig.cmake.in index ffd34098a8b..ab6388516f3 100644 --- a/DarknetConfig.cmake.in +++ b/DarknetConfig.cmake.in @@ -26,6 +26,9 @@ find_dependency(Threads) if(MSVC) find_dependency(PThreads4W) set(CMAKE_CXX_FLAGS "/wd4018 /wd4244 /wd4267 /wd4305 ${CMAKE_CXX_FLAGS}") + if(@unofficial-getopt-win32_FOUND@) + find_dependency(unofficial-getopt-win32) + endif() endif() if(@OPENMP_FOUND@) diff --git a/build.ps1 b/build.ps1 index 768a478442b..e378e4b4536 100755 --- a/build.ps1 +++ b/build.ps1 @@ -6,7 +6,7 @@ build Created By: Stefano Sinigardi Created Date: February 18, 2019 - Last Modified Date: September 10, 2023 + Last Modified Date: September 25, 2023 .DESCRIPTION Build tool using CMake, trying to properly setup the environment around compiler @@ -193,10 +193,24 @@ param ( $global:DisableInteractive = $DisableInteractive -$build_ps1_version = "3.5.1" +$build_ps1_version = "3.6.1" $script_name = $MyInvocation.MyCommand.Name +$utils_psm1_avail = $false -Import-Module -Name $PSScriptRoot/scripts/utils.psm1 -Force +if (Test-Path $PSScriptRoot/scripts/utils.psm1) { + Import-Module -Name $PSScriptRoot/scripts/utils.psm1 -Force + $utils_psm1_avail = $true +} +else { + $utils_psm1_version = "unavail" + $IsWindowsPowerShell = $false + $IsInGitSubmodule = $false +} + +if (-Not $utils_psm1_avail) { + $DoNotSetupVS = $true + $ForceCMakeFromVS = $false +} $ErrorActionPreference = "SilentlyContinue" Stop-Transcript | out-null @@ -219,6 +233,9 @@ if (-Not $BuildInstaller) { Start-Transcript -Path $BuildLogPath Write-Host "Build script version ${build_ps1_version}, utils module version ${utils_psm1_version}" +if (-Not $utils_psm1_avail) { + Write-Host "utils.psm1 is not available, so VS integration is forcefully disabled" -ForegroundColor Yellow +} Write-Host "Working directory: $PSCustomScriptRoot, log file: $BuildLogPath, $script_name is in submodule: $IsInGitSubmodule" if ((-Not $global:DisableInteractive) -and (-Not $UseVCPKG)) { @@ -722,7 +739,7 @@ if ($UseVCPKG -And -Not $ForceLocalVCPKG) { if (($null -eq $vcpkg_path) -and $UseVCPKG) { if (-Not (Test-Path "$PWD/vcpkg${VCPKGSuffix}")) { $shallow_copy = "" - if($ForceOpenCVVersion -eq 0) { + if(($ForceOpenCVVersion -eq 0)) { $shallow_copy = " --depth 1 " } $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "clone $shallow_copy https://github.com/microsoft/vcpkg vcpkg${VCPKGSuffix}" @@ -897,13 +914,16 @@ if (-Not $InstallDARKNETthroughVCPKG) { $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_DEPLOY_CUSTOM_CMAKE_MODULES:BOOL=ON" } -if ($ForceVCPKGBuildtreesPath -ne "") { - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DVCPKG_INSTALL_OPTIONS=`"--x-buildtrees-root=$ForceVCPKGBuildtreesPath`" " - New-Item -Path $ForceVCPKGBuildtreesPath -ItemType directory -Force | Out-Null - $vcpkgbuildtreespath = "$ForceVCPKGBuildtreesPath" -} -else { - $vcpkgbuildtreespath = "$vcpkg_path/buildtrees" +if($UseVCPKG) { + if ($ForceVCPKGBuildtreesPath -ne "") { + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DVCPKG_INSTALL_OPTIONS=--clean-buildtrees-after-build;--x-buildtrees-root=`"$ForceVCPKGBuildtreesPath`"" + New-Item -Path $ForceVCPKGBuildtreesPath -ItemType directory -Force | Out-Null + $vcpkgbuildtreespath = "$ForceVCPKGBuildtreesPath" + } + else { + $AdditionalBuildSetup = $AdditionalBuildSetup + " -DVCPKG_INSTALL_OPTIONS=--clean-buildtrees-after-build" + $vcpkgbuildtreespath = "$vcpkg_path/buildtrees" + } } if ($InstallDARKNETthroughVCPKG) { diff --git a/results/tmp.txt b/results/tmp.txt deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/scripts/deploy-cuda.sh b/scripts/deploy-cuda.sh index b95258c2875..fb85d52f559 100755 --- a/scripts/deploy-cuda.sh +++ b/scripts/deploy-cuda.sh @@ -7,7 +7,7 @@ elif [[ $(cut -f2 <<< $(lsb_release -i)) == "Ubuntu" ]]; then distr_name="$(cut -f2 <<< $(lsb_release -i) | tr '[:upper:]' '[:lower:]')$(cut -f2 <<< $(lsb_release -r) | tr -d '.')" else echo "Unable to deploy CUDA on this OS, please wait for a future script update" - exit 3 + exit 2 fi script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" @@ -16,7 +16,7 @@ if [ -f $script_dir/requested_cuda_version.sh ]; then source $script_dir/requested_cuda_version.sh else echo "Unable to find requested_cuda_version.sh script" - exit 1 + exit 3 fi sudo apt-key del 7fa2af80 diff --git a/scripts/install_OpenCV4.sh b/scripts/install_OpenCV4.sh deleted file mode 100755 index f57c87b4c7e..00000000000 --- a/scripts/install_OpenCV4.sh +++ /dev/null @@ -1,66 +0,0 @@ -#!/bin/bash - -#based on https://devtalk.nvidia.com/default/topic/1042035/installing-opencv4-on-xavier/ & https://github.com/markste-in/OpenCV4XAVIER/blob/master/buildOpenCV4.sh - -# Compute Capabilities can be found here https://developer.nvidia.com/cuda-gpus#compute -ARCH_BIN=7.2 # AGX Xavier -#ARCH_BIN=6.2 # Tx2 - -cd ~/Downloads -sudo apt-get install -y build-essential \ - unzip \ - pkg-config \ - libjpeg-dev \ - libpng-dev \ - libtiff-dev \ - libavcodec-dev \ - libavformat-dev \ - libswscale-dev \ - libv4l-dev \ - libxvidcore-dev \ - libx264-dev \ - libgtk-3-dev \ - libatlas-base-dev \ - gfortran \ - python3-dev \ - python3-venv \ - libgstreamer1.0-dev \ - libgstreamer-plugins-base1.0-dev \ - libdc1394-22-dev \ - libavresample-dev - -git clone https://github.com/opencv/opencv.git -git clone https://github.com/opencv/opencv_contrib.git - -python3 -m venv opencv4 -source opencv4/bin/activate -pip install wheel -pip install numpy - -cd opencv && mkdir build && cd build - -cmake -D CMAKE_BUILD_TYPE=RELEASE \ - -D CMAKE_INSTALL_PREFIX=/usr/local \ - -D INSTALL_PYTHON_EXAMPLES=ON \ - -D INSTALL_C_EXAMPLES=OFF \ - -D OPENCV_EXTRA_MODULES_PATH='~/Downloads/opencv_contrib/modules' \ - -D PYTHON_EXECUTABLE='~/Downloads/opencv4/bin/python' \ - -D BUILD_EXAMPLES=ON \ - -D WITH_CUDA=ON \ - -D CUDA_ARCH_BIN=${ARCH_BIN} \ - -D CUDA_ARCH_PTX="" \ - -D ENABLE_FAST_MATH=ON \ - -D CUDA_FAST_MATH=ON \ - -D WITH_CUBLAS=ON \ - -D WITH_LIBV4L=ON \ - -D WITH_GSTREAMER=ON \ - -D WITH_GSTREAMER_0_10=OFF \ - -D WITH_TBB=ON \ - ../ - -make -j4 -sudo make install -sudo ldconfig - -cd ~/Downloads/opencv4/lib/python3.6/site-packages -ln -s /usr/local/lib/python3.6/site-packages/cv2.cpython-36m-aarch64-linux-gnu.so cv2.so diff --git a/scripts/log_parser/plot.jpg b/scripts/log_parser/plot.jpg 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and csv file 3. --log-file log file name to be parsed @@ -11,11 +14,4 @@ `python log_parser.py --source-dir ./ --save-dir ./ --log-file test.log --show true` -![plot](https://github.com/Adesun/darknet/blob/log_parser/scripts/log_parser/plot.jpg) - - - - - - - +![plot](https://github.com/AlexeyAB/darknet/blob/master/scripts/log_parser/test_new.svg) diff --git a/scripts/log_parser/run_log_parser_windows.cmd b/scripts/log_parser/run_log_parser_windows.cmd deleted file mode 100644 index d62320cbdd0..00000000000 --- a/scripts/log_parser/run_log_parser_windows.cmd +++ /dev/null @@ -1,6 +0,0 @@ -C:\Python27\Scripts\pip install numpy -C:\Python27\Scripts\pip install matplotlib - -C:\Python27\python.exe log_parser.py --source-dir ./ --save-dir ./ --log-file test.log --show true - -pause \ No newline at end of file diff --git a/scripts/log_parser/test.log b/scripts/log_parser/test.log deleted file mode 100644 index c7890446361..00000000000 --- a/scripts/log_parser/test.log +++ /dev/null @@ -1,75621 +0,0 @@ - -47: 1.047464, 1.522159 avg, 0.000100 rate, 30.347219 seconds, 12032 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.428715, Class: 1.000000, Obj: 0.003723, No Obj: 0.001716, Avg Recall: 0.404959, count: 121 -Region Avg IOU: 0.438329, Class: 1.000000, Obj: 0.002941, No Obj: 0.001655, Avg Recall: 0.402174, count: 92 -Region Avg IOU: 0.407593, Class: 1.000000, Obj: 0.003534, No Obj: 0.001688, Avg Recall: 0.315789, count: 95 -Region Avg IOU: 0.449705, Class: 1.000000, Obj: 0.002977, No Obj: 0.001781, Avg Recall: 0.395604, count: 91 -Region Avg IOU: 0.456085, Class: 1.000000, Obj: 0.004364, No Obj: 0.001758, Avg Recall: 0.407767, count: 103 -Region Avg IOU: 0.450921, Class: 1.000000, Obj: 0.003457, No Obj: 0.001733, Avg Recall: 0.386139, count: 101 -Region Avg IOU: 0.443686, Class: 1.000000, Obj: 0.003580, No Obj: 0.001665, Avg Recall: 0.416667, count: 120 -Region Avg IOU: 0.432210, Class: 1.000000, Obj: 0.004021, No Obj: 0.001600, Avg Recall: 0.392308, count: 130 -48: 1.072136, 1.477157 avg, 0.000100 rate, 29.550329 seconds, 12288 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.475444, Class: 1.000000, Obj: 0.003698, No Obj: 0.001953, Avg Recall: 0.422222, count: 90 -Region Avg IOU: 0.423659, Class: 1.000000, Obj: 0.004283, No Obj: 0.001903, Avg Recall: 0.336207, count: 116 -Region Avg IOU: 0.419662, Class: 1.000000, Obj: 0.004069, No Obj: 0.001837, Avg Recall: 0.367816, count: 87 -Region Avg IOU: 0.449789, Class: 1.000000, Obj: 0.003738, No Obj: 0.001742, Avg Recall: 0.483516, count: 91 -Region Avg IOU: 0.462331, Class: 1.000000, Obj: 0.003469, No Obj: 0.001698, Avg Recall: 0.422222, count: 90 -Region Avg IOU: 0.438536, Class: 1.000000, Obj: 0.003820, No Obj: 0.001724, Avg Recall: 0.411111, count: 90 -Region Avg IOU: 0.446077, Class: 1.000000, Obj: 0.003930, No Obj: 0.001742, Avg Recall: 0.380165, count: 121 -Region Avg IOU: 0.464327, Class: 1.000000, Obj: 0.004471, No Obj: 0.001844, Avg Recall: 0.409091, count: 88 -49: 1.002402, 1.429681 avg, 0.000100 rate, 29.563101 seconds, 12544 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.463229, Class: 1.000000, Obj: 0.005016, No Obj: 0.001920, Avg Recall: 0.442478, count: 113 -Region Avg IOU: 0.461912, Class: 1.000000, Obj: 0.004873, No Obj: 0.001887, Avg Recall: 0.444444, count: 117 -Region Avg IOU: 0.473730, Class: 1.000000, Obj: 0.004588, No Obj: 0.001852, Avg Recall: 0.450450, count: 111 -Region Avg IOU: 0.426187, Class: 1.000000, Obj: 0.003231, No Obj: 0.001851, Avg Recall: 0.330435, count: 115 -Region Avg IOU: 0.419271, Class: 1.000000, Obj: 0.003881, No Obj: 0.001808, Avg Recall: 0.346154, count: 130 -Region Avg IOU: 0.466242, Class: 1.000000, Obj: 0.004225, No Obj: 0.001865, Avg Recall: 0.409091, count: 88 -Region Avg IOU: 0.435481, Class: 1.000000, Obj: 0.003961, No Obj: 0.001786, Avg Recall: 0.401961, count: 102 -Region Avg IOU: 0.467318, Class: 1.000000, Obj: 0.004206, No Obj: 0.001768, Avg Recall: 0.436893, count: 103 -50: 1.132416, 1.399955 avg, 0.000100 rate, 30.103252 seconds, 12800 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 16.127846 seconds -Region Avg IOU: 0.446533, Class: 1.000000, Obj: 0.003721, No Obj: 0.001788, Avg Recall: 0.443396, count: 106 -Region Avg IOU: 0.450646, Class: 1.000000, Obj: 0.004063, No Obj: 0.001808, Avg Recall: 0.440678, count: 118 -Region Avg IOU: 0.466007, Class: 1.000000, Obj: 0.005437, No Obj: 0.002014, Avg Recall: 0.467626, count: 139 -Region Avg IOU: 0.425697, Class: 1.000000, Obj: 0.003647, No Obj: 0.001834, Avg Recall: 0.435897, count: 78 -Region Avg IOU: 0.481089, Class: 1.000000, Obj: 0.004279, No Obj: 0.001822, Avg Recall: 0.481203, count: 133 -Region Avg IOU: 0.454378, Class: 1.000000, Obj: 0.004522, No Obj: 0.001955, Avg Recall: 0.418367, count: 98 -Region Avg IOU: 0.460797, Class: 1.000000, Obj: 0.003981, No Obj: 0.001881, Avg Recall: 0.412500, count: 80 -Region Avg IOU: 0.429492, Class: 1.000000, Obj: 0.005626, No Obj: 0.001894, Avg Recall: 0.343137, count: 102 -51: 0.987677, 1.358727 avg, 0.000100 rate, 28.519953 seconds, 13056 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.488753, Class: 1.000000, Obj: 0.004752, No Obj: 0.001937, Avg Recall: 0.507937, count: 126 -Region Avg IOU: 0.433132, Class: 1.000000, Obj: 0.004270, No Obj: 0.001860, Avg Recall: 0.381356, count: 118 -Region Avg IOU: 0.487905, Class: 1.000000, Obj: 0.004661, No Obj: 0.001817, Avg Recall: 0.516129, count: 124 -Region Avg IOU: 0.440912, Class: 1.000000, Obj: 0.004548, No Obj: 0.001934, Avg Recall: 0.411215, count: 107 -Region Avg IOU: 0.497909, Class: 1.000000, Obj: 0.004529, No Obj: 0.001958, Avg Recall: 0.495652, count: 115 -Region Avg IOU: 0.454877, Class: 1.000000, Obj: 0.005121, No Obj: 0.001894, Avg Recall: 0.433962, count: 106 -Region Avg IOU: 0.428856, Class: 1.000000, Obj: 0.005765, No Obj: 0.001898, Avg Recall: 0.356522, count: 115 -Region Avg IOU: 0.485691, Class: 1.000000, Obj: 0.004285, No Obj: 0.001865, Avg Recall: 0.473684, count: 76 -52: 1.041295, 1.326984 avg, 0.000100 rate, 30.541143 seconds, 13312 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.427167, Class: 1.000000, Obj: 0.004219, No Obj: 0.001811, Avg Recall: 0.472868, count: 129 -Region Avg IOU: 0.423692, Class: 1.000000, Obj: 0.003715, No Obj: 0.001857, Avg Recall: 0.351852, count: 108 -Region Avg IOU: 0.490241, Class: 1.000000, Obj: 0.004206, No Obj: 0.002049, Avg Recall: 0.453846, count: 130 -Region Avg IOU: 0.444881, Class: 1.000000, Obj: 0.004942, No Obj: 0.002108, Avg Recall: 0.427184, count: 103 -Region Avg IOU: 0.465927, Class: 1.000000, Obj: 0.004213, No Obj: 0.002151, Avg Recall: 0.441860, count: 129 -Region Avg IOU: 0.420400, Class: 1.000000, Obj: 0.004070, No Obj: 0.001869, Avg Recall: 0.351351, count: 148 -Region Avg IOU: 0.427397, Class: 1.000000, Obj: 0.003762, No Obj: 0.002007, Avg Recall: 0.350000, count: 80 -Region Avg IOU: 0.427353, Class: 1.000000, Obj: 0.004326, No Obj: 0.001971, Avg Recall: 0.358491, count: 106 -53: 1.064999, 1.300785 avg, 0.000100 rate, 28.955660 seconds, 13568 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.424366, Class: 1.000000, Obj: 0.003911, No Obj: 0.002015, Avg Recall: 0.387097, count: 93 -Region Avg IOU: 0.418162, Class: 1.000000, Obj: 0.004191, No Obj: 0.001935, Avg Recall: 0.307692, count: 78 -Region Avg IOU: 0.476894, Class: 1.000000, Obj: 0.004202, No Obj: 0.001905, Avg Recall: 0.522727, count: 88 -Region Avg IOU: 0.452994, Class: 1.000000, Obj: 0.004254, No Obj: 0.001862, Avg Recall: 0.420561, count: 107 -Region Avg IOU: 0.467134, Class: 1.000000, Obj: 0.004412, No Obj: 0.001961, Avg Recall: 0.396396, count: 111 -Region Avg IOU: 0.442964, Class: 1.000000, Obj: 0.004942, No Obj: 0.002051, Avg Recall: 0.393617, count: 94 -Region Avg IOU: 0.436745, Class: 1.000000, Obj: 0.004403, No Obj: 0.001894, Avg Recall: 0.333333, count: 117 -Region Avg IOU: 0.471488, Class: 1.000000, Obj: 0.004428, No Obj: 0.002187, Avg Recall: 0.412371, count: 97 -54: 0.975343, 1.268241 avg, 0.000100 rate, 28.240408 seconds, 13824 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.446179, Class: 1.000000, Obj: 0.004307, No Obj: 0.001994, Avg Recall: 0.413223, count: 121 -Region Avg IOU: 0.399145, Class: 1.000000, Obj: 0.004525, No Obj: 0.002008, Avg Recall: 0.274510, count: 102 -Region Avg IOU: 0.465781, Class: 1.000000, Obj: 0.005256, No Obj: 0.002012, Avg Recall: 0.469388, count: 98 -Region Avg IOU: 0.438915, Class: 1.000000, Obj: 0.004326, No Obj: 0.002004, Avg Recall: 0.380165, count: 121 -Region Avg IOU: 0.487065, Class: 1.000000, Obj: 0.005217, No Obj: 0.002095, Avg Recall: 0.485437, count: 103 -Region Avg IOU: 0.461664, Class: 1.000000, Obj: 0.005514, No Obj: 0.002048, Avg Recall: 0.422018, count: 109 -Region Avg IOU: 0.444623, Class: 1.000000, Obj: 0.004557, No Obj: 0.002066, Avg Recall: 0.380435, count: 92 -Region Avg IOU: 0.472902, Class: 1.000000, Obj: 0.005269, No Obj: 0.002112, Avg Recall: 0.450450, count: 111 -55: 1.056194, 1.247036 avg, 0.000100 rate, 29.117577 seconds, 14080 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.463027, Class: 1.000000, Obj: 0.004656, No Obj: 0.002040, Avg Recall: 0.432000, count: 125 -Region Avg IOU: 0.455686, Class: 1.000000, Obj: 0.005231, No Obj: 0.002109, Avg Recall: 0.398374, count: 123 -Region Avg IOU: 0.464345, Class: 1.000000, Obj: 0.005373, No Obj: 0.002150, Avg Recall: 0.470000, count: 100 -Region Avg IOU: 0.457154, Class: 1.000000, Obj: 0.004793, No Obj: 0.002099, Avg Recall: 0.413043, count: 92 -Region Avg IOU: 0.439416, Class: 1.000000, Obj: 0.004268, No Obj: 0.002059, Avg Recall: 0.407767, count: 103 -Region Avg IOU: 0.481529, Class: 1.000000, Obj: 0.005517, No Obj: 0.002034, Avg Recall: 0.494845, count: 97 -Region Avg IOU: 0.434703, Class: 1.000000, Obj: 0.003961, No Obj: 0.002076, Avg Recall: 0.405172, count: 116 -Region Avg IOU: 0.435745, Class: 1.000000, Obj: 0.004408, No Obj: 0.002057, Avg Recall: 0.366972, count: 109 -56: 0.928000, 1.215132 avg, 0.000100 rate, 30.770716 seconds, 14336 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.455057, Class: 1.000000, Obj: 0.004458, No Obj: 0.002047, Avg Recall: 0.478992, count: 119 -Region Avg IOU: 0.446574, Class: 1.000000, Obj: 0.003384, No Obj: 0.001928, Avg Recall: 0.414894, count: 94 -Region Avg IOU: 0.491699, Class: 1.000000, Obj: 0.005386, No Obj: 0.002087, Avg Recall: 0.457143, count: 105 -Region Avg IOU: 0.485587, Class: 1.000000, Obj: 0.004681, No Obj: 0.002145, Avg Recall: 0.508621, count: 116 -Region Avg IOU: 0.416717, Class: 1.000000, Obj: 0.004486, No Obj: 0.002231, Avg Recall: 0.358491, count: 106 -Region Avg IOU: 0.486551, Class: 1.000000, Obj: 0.004961, No Obj: 0.002113, Avg Recall: 0.515152, count: 99 -Region Avg IOU: 0.445127, Class: 1.000000, Obj: 0.003916, No Obj: 0.001962, Avg Recall: 0.407767, count: 103 -Region Avg IOU: 0.421841, Class: 1.000000, Obj: 0.004925, No Obj: 0.002052, Avg Recall: 0.372727, count: 110 -57: 0.877792, 1.181399 avg, 0.000100 rate, 29.464731 seconds, 14592 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.502660, Class: 1.000000, Obj: 0.003847, No Obj: 0.002238, Avg Recall: 0.511628, count: 86 -Region Avg IOU: 0.478753, Class: 1.000000, Obj: 0.005949, No Obj: 0.002120, Avg Recall: 0.487180, count: 117 -Region Avg IOU: 0.456793, Class: 1.000000, Obj: 0.004884, No Obj: 0.002106, Avg Recall: 0.475806, count: 124 -Region Avg IOU: 0.459018, Class: 1.000000, Obj: 0.004597, No Obj: 0.002107, Avg Recall: 0.433333, count: 90 -Region Avg IOU: 0.449195, Class: 1.000000, Obj: 0.003572, No Obj: 0.002067, Avg Recall: 0.376147, count: 109 -Region Avg IOU: 0.461955, Class: 1.000000, Obj: 0.005321, No Obj: 0.002113, Avg Recall: 0.410714, count: 112 -Region Avg IOU: 0.448568, Class: 1.000000, Obj: 0.004650, No Obj: 0.002071, Avg Recall: 0.413223, count: 121 -Region Avg IOU: 0.435186, Class: 1.000000, Obj: 0.004205, No Obj: 0.002109, Avg Recall: 0.347107, count: 121 -58: 0.919538, 1.155213 avg, 0.000100 rate, 29.205755 seconds, 14848 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.480027, Class: 1.000000, Obj: 0.004029, No Obj: 0.002175, Avg Recall: 0.440860, count: 93 -Region Avg IOU: 0.458573, Class: 1.000000, Obj: 0.004553, No Obj: 0.002320, Avg Recall: 0.417476, count: 103 -Region Avg IOU: 0.406209, Class: 1.000000, Obj: 0.005043, No Obj: 0.002270, Avg Recall: 0.398601, count: 143 -Region Avg IOU: 0.502930, Class: 1.000000, Obj: 0.005065, No Obj: 0.002309, Avg Recall: 0.505747, count: 87 -Region Avg IOU: 0.436542, Class: 1.000000, Obj: 0.005034, No Obj: 0.002186, Avg Recall: 0.416000, count: 125 -Region Avg IOU: 0.459570, Class: 1.000000, Obj: 0.005460, No Obj: 0.002203, Avg Recall: 0.438776, count: 98 -Region Avg IOU: 0.430338, Class: 1.000000, Obj: 0.005381, No Obj: 0.002028, Avg Recall: 0.431034, count: 116 -Region Avg IOU: 0.467244, Class: 1.000000, Obj: 0.005544, No Obj: 0.002309, Avg Recall: 0.450000, count: 120 -59: 1.040989, 1.143790 avg, 0.000100 rate, 30.519863 seconds, 15104 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.464597, Class: 1.000000, Obj: 0.005249, No Obj: 0.002217, Avg Recall: 0.419643, count: 112 -Region Avg IOU: 0.442365, Class: 1.000000, Obj: 0.005105, No Obj: 0.002230, Avg Recall: 0.390000, count: 100 -Region Avg IOU: 0.484116, Class: 1.000000, Obj: 0.005686, No Obj: 0.002165, Avg Recall: 0.458716, count: 109 -Region Avg IOU: 0.471340, Class: 1.000000, Obj: 0.005272, No Obj: 0.002092, Avg Recall: 0.460526, count: 76 -Region Avg IOU: 0.446636, Class: 1.000000, Obj: 0.004868, No Obj: 0.002296, Avg Recall: 0.383929, count: 112 -Region Avg IOU: 0.446759, Class: 1.000000, Obj: 0.004475, No Obj: 0.002194, Avg Recall: 0.388235, count: 85 -Region Avg IOU: 0.466474, Class: 1.000000, Obj: 0.005421, No Obj: 0.002183, Avg Recall: 0.424528, count: 106 -Region Avg IOU: 0.460601, Class: 1.000000, Obj: 0.005320, No Obj: 0.002270, Avg Recall: 0.394495, count: 109 -60: 0.973860, 1.126797 avg, 0.000100 rate, 31.211624 seconds, 15360 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 20.242279 seconds -Region Avg IOU: 0.449306, Class: 1.000000, Obj: 0.006209, No Obj: 0.002448, Avg Recall: 0.387387, count: 111 -Region Avg IOU: 0.446552, Class: 1.000000, Obj: 0.005887, No Obj: 0.002205, Avg Recall: 0.430108, count: 93 -Region Avg IOU: 0.443146, Class: 1.000000, Obj: 0.005156, No Obj: 0.002387, Avg Recall: 0.448529, count: 136 -Region Avg IOU: 0.430750, Class: 1.000000, Obj: 0.005968, No Obj: 0.002304, Avg Recall: 0.403509, count: 114 -Region Avg IOU: 0.443198, Class: 1.000000, Obj: 0.004402, No Obj: 0.002099, Avg Recall: 0.382979, count: 94 -Region Avg IOU: 0.480529, Class: 1.000000, Obj: 0.005185, No Obj: 0.002315, Avg Recall: 0.487805, count: 123 -Region Avg IOU: 0.431505, Class: 1.000000, Obj: 0.005212, No Obj: 0.002229, Avg Recall: 0.373626, count: 91 -Region Avg IOU: 0.477782, Class: 1.000000, Obj: 0.006799, No Obj: 0.002390, Avg Recall: 0.528736, count: 87 -61: 0.940142, 1.108132 avg, 0.000100 rate, 29.632563 seconds, 15616 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.458874, Class: 1.000000, Obj: 0.005216, No Obj: 0.002177, Avg Recall: 0.341176, count: 85 -Region Avg IOU: 0.480681, Class: 1.000000, Obj: 0.006658, No Obj: 0.002347, Avg Recall: 0.468254, count: 126 -Region Avg IOU: 0.464254, Class: 1.000000, Obj: 0.005977, No Obj: 0.002284, Avg Recall: 0.475806, count: 124 -Region Avg IOU: 0.424609, Class: 1.000000, Obj: 0.005201, No Obj: 0.002163, Avg Recall: 0.318681, count: 91 -Region Avg IOU: 0.454752, Class: 1.000000, Obj: 0.004660, No Obj: 0.002194, Avg Recall: 0.390244, count: 123 -Region Avg IOU: 0.443953, Class: 1.000000, Obj: 0.005300, No Obj: 0.002213, Avg Recall: 0.404040, count: 99 -Region Avg IOU: 0.465569, Class: 1.000000, Obj: 0.005236, No Obj: 0.002242, Avg Recall: 0.466667, count: 75 -Region Avg IOU: 0.463640, Class: 1.000000, Obj: 0.004988, No Obj: 0.002169, Avg Recall: 0.427350, count: 117 -62: 0.824160, 1.079735 avg, 0.000100 rate, 29.879017 seconds, 15872 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.450519, Class: 1.000000, Obj: 0.005477, No Obj: 0.002333, Avg Recall: 0.359551, count: 89 -Region Avg IOU: 0.476553, Class: 1.000000, Obj: 0.006012, No Obj: 0.002476, Avg Recall: 0.439560, count: 91 -Region Avg IOU: 0.457751, Class: 1.000000, Obj: 0.005468, No Obj: 0.002243, Avg Recall: 0.480000, count: 100 -Region Avg IOU: 0.413168, Class: 1.000000, Obj: 0.006259, No Obj: 0.002351, Avg Recall: 0.349398, count: 83 -Region Avg IOU: 0.461453, Class: 1.000000, Obj: 0.005536, No Obj: 0.002252, Avg Recall: 0.412371, count: 97 -Region Avg IOU: 0.449934, Class: 1.000000, Obj: 0.006613, No Obj: 0.002015, Avg Recall: 0.458333, count: 96 -Region Avg IOU: 0.470539, Class: 1.000000, Obj: 0.006083, No Obj: 0.002369, Avg Recall: 0.494505, count: 91 -Region Avg IOU: 0.456777, Class: 1.000000, Obj: 0.005258, No Obj: 0.002120, Avg Recall: 0.421053, count: 114 -63: 0.747286, 1.046490 avg, 0.000100 rate, 27.742975 seconds, 16128 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.435400, Class: 1.000000, Obj: 0.005325, No Obj: 0.002315, Avg Recall: 0.389474, count: 95 -Region Avg IOU: 0.461539, Class: 1.000000, Obj: 0.005157, No Obj: 0.002269, Avg Recall: 0.477273, count: 88 -Region Avg IOU: 0.474930, Class: 1.000000, Obj: 0.006347, No Obj: 0.002416, Avg Recall: 0.476635, count: 107 -Region Avg IOU: 0.452175, Class: 1.000000, Obj: 0.007624, No Obj: 0.002575, Avg Recall: 0.449612, count: 129 -Region Avg IOU: 0.461076, Class: 1.000000, Obj: 0.007455, No Obj: 0.002490, Avg Recall: 0.490741, count: 108 -Region Avg IOU: 0.425447, Class: 1.000000, Obj: 0.004533, No Obj: 0.002235, Avg Recall: 0.414141, count: 99 -Region Avg IOU: 0.481115, Class: 1.000000, Obj: 0.005358, No Obj: 0.002336, Avg Recall: 0.483146, count: 89 -Region Avg IOU: 0.455682, Class: 1.000000, Obj: 0.005909, No Obj: 0.002506, Avg Recall: 0.439560, count: 91 -64: 0.815265, 1.023367 avg, 0.000100 rate, 29.579117 seconds, 16384 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.462917, Class: 1.000000, Obj: 0.005996, No Obj: 0.002401, Avg Recall: 0.427350, count: 117 -Region Avg IOU: 0.494266, Class: 1.000000, Obj: 0.006057, No Obj: 0.002285, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.449500, Class: 1.000000, Obj: 0.005906, No Obj: 0.002373, Avg Recall: 0.385827, count: 127 -Region Avg IOU: 0.472407, Class: 1.000000, Obj: 0.006655, No Obj: 0.002392, Avg Recall: 0.443548, count: 124 -Region Avg IOU: 0.463600, Class: 1.000000, Obj: 0.005666, No Obj: 0.002384, Avg Recall: 0.450000, count: 60 -Region Avg IOU: 0.425534, Class: 1.000000, Obj: 0.005165, No Obj: 0.002332, Avg Recall: 0.339286, count: 112 -Region Avg IOU: 0.438209, Class: 1.000000, Obj: 0.005291, No Obj: 0.002216, Avg Recall: 0.421569, count: 102 -Region Avg IOU: 0.459652, Class: 1.000000, Obj: 0.006248, No Obj: 0.002325, Avg Recall: 0.432432, count: 111 -65: 0.871888, 1.008219 avg, 0.000100 rate, 32.835602 seconds, 16640 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.469220, Class: 1.000000, Obj: 0.005856, No Obj: 0.002399, Avg Recall: 0.447368, count: 114 -Region Avg IOU: 0.461166, Class: 1.000000, Obj: 0.005932, No Obj: 0.002248, Avg Recall: 0.416000, count: 125 -Region Avg IOU: 0.396831, Class: 1.000000, Obj: 0.004851, No Obj: 0.002402, Avg Recall: 0.385417, count: 96 -Region Avg IOU: 0.482093, Class: 1.000000, Obj: 0.005952, No Obj: 0.002291, Avg Recall: 0.491803, count: 122 -Region Avg IOU: 0.445798, Class: 1.000000, Obj: 0.006188, No Obj: 0.002376, Avg Recall: 0.441176, count: 102 -Region Avg IOU: 0.450064, Class: 1.000000, Obj: 0.005405, No Obj: 0.002238, Avg Recall: 0.438095, count: 105 -Region Avg IOU: 0.478171, Class: 1.000000, Obj: 0.004727, No Obj: 0.002323, Avg Recall: 0.505155, count: 97 -Region Avg IOU: 0.491278, Class: 1.000000, Obj: 0.006083, No Obj: 0.002248, Avg Recall: 0.469565, count: 115 -66: 0.967755, 1.004173 avg, 0.000100 rate, 28.552547 seconds, 16896 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.463518, Class: 1.000000, Obj: 0.005900, No Obj: 0.002337, Avg Recall: 0.495798, count: 119 -Region Avg IOU: 0.430182, Class: 1.000000, Obj: 0.005599, No Obj: 0.002471, Avg Recall: 0.392157, count: 102 -Region Avg IOU: 0.468784, Class: 1.000000, Obj: 0.006265, No Obj: 0.002222, Avg Recall: 0.489796, count: 98 -Region Avg IOU: 0.441953, Class: 1.000000, Obj: 0.005593, No Obj: 0.002523, Avg Recall: 0.403670, count: 109 -Region Avg IOU: 0.492594, Class: 1.000000, Obj: 0.006697, No Obj: 0.002429, Avg Recall: 0.513274, count: 113 -Region Avg IOU: 0.436817, Class: 1.000000, Obj: 0.005276, No Obj: 0.002331, Avg Recall: 0.443182, count: 88 -Region Avg IOU: 0.458139, Class: 1.000000, Obj: 0.005119, No Obj: 0.002354, Avg Recall: 0.436975, count: 119 -Region Avg IOU: 0.468970, Class: 1.000000, Obj: 0.005014, No Obj: 0.002243, Avg Recall: 0.423423, count: 111 -67: 0.870157, 0.990771 avg, 0.000100 rate, 28.145737 seconds, 17152 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.451768, Class: 1.000000, Obj: 0.005125, No Obj: 0.002409, Avg Recall: 0.398305, count: 118 -Region Avg IOU: 0.477322, Class: 1.000000, Obj: 0.006140, No Obj: 0.002480, Avg Recall: 0.484211, count: 95 -Region Avg IOU: 0.468788, Class: 1.000000, Obj: 0.004967, No Obj: 0.002464, Avg Recall: 0.472441, count: 127 -Region Avg IOU: 0.455319, Class: 1.000000, Obj: 0.005964, No Obj: 0.002310, Avg Recall: 0.429630, count: 135 -Region Avg IOU: 0.450046, Class: 1.000000, Obj: 0.006142, No Obj: 0.002556, Avg Recall: 0.421875, count: 128 -Region Avg IOU: 0.458760, Class: 1.000000, Obj: 0.006565, No Obj: 0.002555, Avg Recall: 0.432624, count: 141 -Region Avg IOU: 0.444594, Class: 1.000000, Obj: 0.005563, No Obj: 0.002324, Avg Recall: 0.407767, count: 103 -Region Avg IOU: 0.448235, Class: 1.000000, Obj: 0.006236, No Obj: 0.002338, Avg Recall: 0.438596, count: 114 -68: 0.955265, 0.987221 avg, 0.000100 rate, 29.493307 seconds, 17408 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.501178, Class: 1.000000, Obj: 0.006902, No Obj: 0.002355, Avg Recall: 0.508197, count: 122 -Region Avg IOU: 0.447999, Class: 1.000000, Obj: 0.006434, No Obj: 0.002344, Avg Recall: 0.442623, count: 122 -Region Avg IOU: 0.412312, Class: 1.000000, Obj: 0.005537, No Obj: 0.002241, Avg Recall: 0.382979, count: 94 -Region Avg IOU: 0.419776, Class: 1.000000, Obj: 0.005523, No Obj: 0.002324, Avg Recall: 0.379310, count: 116 -Region Avg IOU: 0.449767, Class: 1.000000, Obj: 0.005476, No Obj: 0.002273, Avg Recall: 0.425926, count: 108 -Region Avg IOU: 0.413625, Class: 1.000000, Obj: 0.005927, No Obj: 0.002326, Avg Recall: 0.369748, count: 119 -Region Avg IOU: 0.490231, Class: 1.000000, Obj: 0.007453, No Obj: 0.002520, Avg Recall: 0.463235, count: 136 -Region Avg IOU: 0.440044, Class: 1.000000, Obj: 0.004845, No Obj: 0.002289, Avg Recall: 0.446602, count: 103 -69: 0.996621, 0.988161 avg, 0.000100 rate, 29.148563 seconds, 17664 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.471018, Class: 1.000000, Obj: 0.005533, No Obj: 0.002393, Avg Recall: 0.500000, count: 106 -Region Avg IOU: 0.499434, Class: 1.000000, Obj: 0.006874, No Obj: 0.002721, Avg Recall: 0.455446, count: 101 -Region Avg IOU: 0.473926, Class: 1.000000, Obj: 0.006265, No Obj: 0.002430, Avg Recall: 0.461538, count: 91 -Region Avg IOU: 0.487269, Class: 1.000000, Obj: 0.007206, No Obj: 0.002656, Avg Recall: 0.513158, count: 76 -Region Avg IOU: 0.462740, Class: 1.000000, Obj: 0.005528, No Obj: 0.002267, Avg Recall: 0.436620, count: 142 -Region Avg IOU: 0.430079, Class: 1.000000, Obj: 0.006445, No Obj: 0.002315, Avg Recall: 0.344262, count: 122 -Region Avg IOU: 0.441048, Class: 1.000000, Obj: 0.007204, No Obj: 0.002601, Avg Recall: 0.400000, count: 95 -Region Avg IOU: 0.447752, Class: 1.000000, Obj: 0.006320, No Obj: 0.002478, Avg Recall: 0.428571, count: 119 -70: 0.763747, 0.965719 avg, 0.000100 rate, 31.316036 seconds, 17920 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 16.358551 seconds -Region Avg IOU: 0.376903, Class: 1.000000, Obj: 0.004452, No Obj: 0.001838, Avg Recall: 0.271028, count: 107 -Region Avg IOU: 0.389303, Class: 1.000000, Obj: 0.005097, No Obj: 0.001854, Avg Recall: 0.309091, count: 110 -Region Avg IOU: 0.386589, Class: 1.000000, Obj: 0.004156, No Obj: 0.001822, Avg Recall: 0.314815, count: 108 -Region Avg IOU: 0.421835, Class: 1.000000, Obj: 0.004093, No Obj: 0.001841, Avg Recall: 0.380000, count: 100 -Region Avg IOU: 0.426775, Class: 1.000000, Obj: 0.005538, No Obj: 0.001969, Avg Recall: 0.370079, count: 127 -Region Avg IOU: 0.398809, Class: 1.000000, Obj: 0.004081, No Obj: 0.001897, Avg Recall: 0.300000, count: 100 -Region Avg IOU: 0.389488, Class: 1.000000, Obj: 0.004882, No Obj: 0.001871, Avg Recall: 0.357798, count: 109 -Region Avg IOU: 0.455727, Class: 1.000000, Obj: 0.005288, No Obj: 0.001833, Avg Recall: 0.445652, count: 92 -71: 1.038662, 0.973014 avg, 0.000100 rate, 21.776632 seconds, 18176 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.412311, Class: 1.000000, Obj: 0.004795, No Obj: 0.001896, Avg Recall: 0.355140, count: 107 -Region Avg IOU: 0.389860, Class: 1.000000, Obj: 0.004465, No Obj: 0.001897, Avg Recall: 0.344828, count: 87 -Region Avg IOU: 0.409433, Class: 1.000000, Obj: 0.004463, No Obj: 0.001912, Avg Recall: 0.361905, count: 105 -Region Avg IOU: 0.435932, Class: 1.000000, Obj: 0.003988, No Obj: 0.001961, Avg Recall: 0.405405, count: 111 -Region Avg IOU: 0.377729, Class: 1.000000, Obj: 0.005111, No Obj: 0.001836, Avg Recall: 0.280702, count: 114 -Region Avg IOU: 0.405559, Class: 1.000000, Obj: 0.004570, No Obj: 0.001918, Avg Recall: 0.322581, count: 93 -Region Avg IOU: 0.406919, Class: 1.000000, Obj: 0.004564, No Obj: 0.001817, Avg Recall: 0.385321, count: 109 -Region Avg IOU: 0.393971, Class: 1.000000, Obj: 0.004929, No Obj: 0.001829, Avg Recall: 0.362745, count: 102 -72: 0.942164, 0.969929 avg, 0.000100 rate, 23.125082 seconds, 18432 images -Loaded: 0.004993 seconds -Region Avg IOU: 0.380303, Class: 1.000000, Obj: 0.004660, No Obj: 0.001850, Avg Recall: 0.325397, count: 126 -Region Avg IOU: 0.399602, Class: 1.000000, Obj: 0.004868, No Obj: 0.001885, Avg Recall: 0.340659, count: 91 -Region Avg IOU: 0.369473, Class: 1.000000, Obj: 0.003985, No Obj: 0.001956, Avg Recall: 0.327103, count: 107 -Region Avg IOU: 0.404897, Class: 1.000000, Obj: 0.004192, No Obj: 0.001941, Avg Recall: 0.293103, count: 116 -Region Avg IOU: 0.407292, Class: 1.000000, Obj: 0.005029, No Obj: 0.001941, Avg Recall: 0.346154, count: 104 -Region Avg IOU: 0.422099, Class: 1.000000, Obj: 0.003701, No Obj: 0.001763, Avg Recall: 0.388235, count: 85 -Region Avg IOU: 0.423409, Class: 1.000000, Obj: 0.004887, No Obj: 0.002025, Avg Recall: 0.405797, count: 138 -Region Avg IOU: 0.396031, Class: 1.000000, Obj: 0.005051, No Obj: 0.001988, Avg Recall: 0.347826, count: 115 -73: 0.872876, 0.960223 avg, 0.000100 rate, 23.445032 seconds, 18688 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.427547, Class: 1.000000, Obj: 0.005214, No Obj: 0.002095, Avg Recall: 0.407407, count: 135 -Region Avg IOU: 0.383960, Class: 1.000000, Obj: 0.004520, No Obj: 0.001825, Avg Recall: 0.316667, count: 120 -Region Avg IOU: 0.399633, Class: 1.000000, Obj: 0.004654, No Obj: 0.001895, Avg Recall: 0.318681, count: 91 -Region Avg IOU: 0.356324, Class: 1.000000, Obj: 0.005073, No Obj: 0.001834, Avg Recall: 0.245283, count: 106 -Region Avg IOU: 0.405195, Class: 1.000000, Obj: 0.004343, No Obj: 0.001923, Avg Recall: 0.362832, count: 113 -Region Avg IOU: 0.380349, Class: 1.000000, Obj: 0.005279, No Obj: 0.001965, Avg Recall: 0.325758, count: 132 -Region Avg IOU: 0.382672, Class: 1.000000, Obj: 0.003882, No Obj: 0.001887, Avg Recall: 0.285714, count: 105 -Region Avg IOU: 0.405336, Class: 1.000000, Obj: 0.004842, No Obj: 0.001778, Avg Recall: 0.351064, count: 94 -74: 0.901284, 0.954329 avg, 0.000100 rate, 23.831539 seconds, 18944 images -Loaded: 0.234075 seconds -Region Avg IOU: 0.397398, Class: 1.000000, Obj: 0.004780, No Obj: 0.001898, Avg Recall: 0.300971, count: 103 -Region Avg IOU: 0.377595, Class: 1.000000, Obj: 0.004065, No Obj: 0.001732, Avg Recall: 0.346535, count: 101 -Region Avg IOU: 0.378780, Class: 1.000000, Obj: 0.004273, No Obj: 0.001833, Avg Recall: 0.267327, count: 101 -Region Avg IOU: 0.340115, Class: 1.000000, Obj: 0.005108, No Obj: 0.001900, Avg Recall: 0.196429, count: 112 -Region Avg IOU: 0.383317, Class: 1.000000, Obj: 0.004024, No Obj: 0.002007, Avg Recall: 0.330189, count: 106 -Region Avg IOU: 0.400968, Class: 1.000000, Obj: 0.003973, No Obj: 0.001879, Avg Recall: 0.347458, count: 118 -Region Avg IOU: 0.400346, Class: 1.000000, Obj: 0.005391, No Obj: 0.002006, Avg Recall: 0.393701, count: 127 -Region Avg IOU: 0.427451, Class: 1.000000, Obj: 0.004242, No Obj: 0.001826, Avg Recall: 0.329897, count: 97 -75: 0.914889, 0.950385 avg, 0.000100 rate, 23.968409 seconds, 19200 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.399317, Class: 1.000000, Obj: 0.003748, No Obj: 0.001848, Avg Recall: 0.291262, count: 103 -Region Avg IOU: 0.401285, Class: 1.000000, Obj: 0.005205, No Obj: 0.001995, Avg Recall: 0.353535, count: 99 -Region Avg IOU: 0.386112, Class: 1.000000, Obj: 0.005992, No Obj: 0.001981, Avg Recall: 0.345133, count: 113 -Region Avg IOU: 0.393823, Class: 1.000000, Obj: 0.005116, No Obj: 0.001905, Avg Recall: 0.347107, count: 121 -Region Avg IOU: 0.451357, Class: 1.000000, Obj: 0.004814, No Obj: 0.001941, Avg Recall: 0.476744, count: 86 -Region Avg IOU: 0.427074, Class: 1.000000, Obj: 0.003856, No Obj: 0.001888, Avg Recall: 0.379310, count: 116 -Region Avg IOU: 0.417250, Class: 1.000000, Obj: 0.005572, No Obj: 0.001914, Avg Recall: 0.434783, count: 92 -Region Avg IOU: 0.420569, Class: 1.000000, Obj: 0.004643, No Obj: 0.001891, Avg Recall: 0.364583, count: 96 -76: 0.818747, 0.937222 avg, 0.000100 rate, 22.878044 seconds, 19456 images -Loaded: 0.000042 seconds -Region Avg IOU: 0.414949, Class: 1.000000, Obj: 0.005273, No Obj: 0.001955, Avg Recall: 0.390476, count: 105 -Region Avg IOU: 0.423672, Class: 1.000000, Obj: 0.004646, No Obj: 0.001893, Avg Recall: 0.428571, count: 119 -Region Avg IOU: 0.426337, Class: 1.000000, Obj: 0.005487, No Obj: 0.001983, Avg Recall: 0.408451, count: 142 -Region Avg IOU: 0.413504, Class: 1.000000, Obj: 0.004273, No Obj: 0.001902, Avg Recall: 0.371134, count: 97 -Region Avg IOU: 0.404427, Class: 1.000000, Obj: 0.006446, No Obj: 0.001979, Avg Recall: 0.373984, count: 123 -Region Avg IOU: 0.397120, Class: 1.000000, Obj: 0.004916, No Obj: 0.001903, Avg Recall: 0.424779, count: 113 -Region Avg IOU: 0.412270, Class: 1.000000, Obj: 0.003831, No Obj: 0.001906, Avg Recall: 0.355556, count: 90 -Region Avg IOU: 0.430922, Class: 1.000000, Obj: 0.005149, No Obj: 0.001972, Avg Recall: 0.396552, count: 116 -77: 0.898306, 0.933330 avg, 0.000100 rate, 22.610891 seconds, 19712 images -Loaded: 1.866748 seconds -Region Avg IOU: 0.449533, Class: 1.000000, Obj: 0.005253, No Obj: 0.001969, Avg Recall: 0.429688, count: 128 -Region Avg IOU: 0.403980, Class: 1.000000, Obj: 0.005068, No Obj: 0.001975, Avg Recall: 0.363636, count: 121 -Region Avg IOU: 0.403167, Class: 1.000000, Obj: 0.004747, No Obj: 0.002004, Avg Recall: 0.362069, count: 116 -Region Avg IOU: 0.364143, Class: 1.000000, Obj: 0.004259, No Obj: 0.001900, Avg Recall: 0.333333, count: 108 -Region Avg IOU: 0.384844, Class: 1.000000, Obj: 0.004268, No Obj: 0.001986, Avg Recall: 0.315789, count: 114 -Region Avg IOU: 0.344057, Class: 1.000000, Obj: 0.004086, No Obj: 0.001906, Avg Recall: 0.295652, count: 115 -Region Avg IOU: 0.421288, Class: 1.000000, Obj: 0.004863, No Obj: 0.001945, Avg Recall: 0.358974, count: 117 -Region Avg IOU: 0.425248, Class: 1.000000, Obj: 0.005392, No Obj: 0.001967, Avg Recall: 0.354839, count: 93 -78: 0.895824, 0.929579 avg, 0.000100 rate, 22.500811 seconds, 19968 images -Loaded: 0.499702 seconds -Region Avg IOU: 0.385485, Class: 1.000000, Obj: 0.005663, No Obj: 0.001924, Avg Recall: 0.342105, count: 76 -Region Avg IOU: 0.368274, Class: 1.000000, Obj: 0.004986, No Obj: 0.002084, Avg Recall: 0.277778, count: 108 -Region Avg IOU: 0.412787, Class: 1.000000, Obj: 0.004899, No Obj: 0.001931, Avg Recall: 0.346535, count: 101 -Region Avg IOU: 0.405165, Class: 1.000000, Obj: 0.005261, No Obj: 0.001941, Avg Recall: 0.351351, count: 111 -Region Avg IOU: 0.414258, Class: 1.000000, Obj: 0.005727, No Obj: 0.001943, Avg Recall: 0.483871, count: 93 -Region Avg IOU: 0.393839, Class: 1.000000, Obj: 0.004600, No Obj: 0.001910, Avg Recall: 0.392523, count: 107 -Region Avg IOU: 0.414041, Class: 1.000000, Obj: 0.005101, No Obj: 0.002006, Avg Recall: 0.407407, count: 108 -Region Avg IOU: 0.429402, Class: 1.000000, Obj: 0.005262, No Obj: 0.001944, Avg Recall: 0.428571, count: 119 -79: 0.819469, 0.918568 avg, 0.000100 rate, 23.409573 seconds, 20224 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.397193, Class: 1.000000, Obj: 0.006352, No Obj: 0.002081, Avg Recall: 0.369748, count: 119 -Region Avg IOU: 0.398612, Class: 1.000000, Obj: 0.005139, No Obj: 0.002028, Avg Recall: 0.303448, count: 145 -Region Avg IOU: 0.355564, Class: 1.000000, Obj: 0.004521, No Obj: 0.001946, Avg Recall: 0.306306, count: 111 -Region Avg IOU: 0.399350, Class: 1.000000, Obj: 0.004990, No Obj: 0.001985, Avg Recall: 0.340000, count: 100 -Region Avg IOU: 0.426017, Class: 1.000000, Obj: 0.004600, No Obj: 0.001860, Avg Recall: 0.441441, count: 111 -Region Avg IOU: 0.463416, Class: 1.000000, Obj: 0.005992, No Obj: 0.002072, Avg Recall: 0.455556, count: 90 -Region Avg IOU: 0.409541, Class: 1.000000, Obj: 0.005879, No Obj: 0.001903, Avg Recall: 0.355140, count: 107 -Region Avg IOU: 0.379582, Class: 1.000000, Obj: 0.005605, No Obj: 0.001859, Avg Recall: 0.263736, count: 91 -80: 0.932312, 0.919943 avg, 0.000100 rate, 22.332439 seconds, 20480 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000076 seconds -Region Avg IOU: 0.453897, Class: 1.000000, Obj: 0.008274, No Obj: 0.002845, Avg Recall: 0.460177, count: 113 -Region Avg IOU: 0.464090, Class: 1.000000, Obj: 0.007217, No Obj: 0.003134, Avg Recall: 0.427083, count: 96 -Region Avg IOU: 0.485967, Class: 1.000000, Obj: 0.008364, No Obj: 0.003295, Avg Recall: 0.467391, count: 92 -Region Avg IOU: 0.491343, Class: 1.000000, Obj: 0.007904, No Obj: 0.002995, Avg Recall: 0.478571, count: 140 -Region Avg IOU: 0.478002, Class: 1.000000, Obj: 0.009611, No Obj: 0.003046, Avg Recall: 0.449541, count: 109 -Region Avg IOU: 0.458325, Class: 1.000000, Obj: 0.008591, No Obj: 0.002973, Avg Recall: 0.432000, count: 125 -Region Avg IOU: 0.495640, Class: 1.000000, Obj: 0.007386, No Obj: 0.003187, Avg Recall: 0.517241, count: 116 -Region Avg IOU: 0.476973, Class: 1.000000, Obj: 0.009212, No Obj: 0.003316, Avg Recall: 0.446602, count: 103 -81: 0.895833, 0.917532 avg, 0.000100 rate, 31.506176 seconds, 20736 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.455633, Class: 1.000000, Obj: 0.008846, No Obj: 0.003116, Avg Recall: 0.506329, count: 79 -Region Avg IOU: 0.437387, Class: 1.000000, Obj: 0.008299, No Obj: 0.003024, Avg Recall: 0.388350, count: 103 -Region Avg IOU: 0.479266, Class: 1.000000, Obj: 0.007811, No Obj: 0.003009, Avg Recall: 0.555556, count: 144 -Region Avg IOU: 0.482754, Class: 1.000000, Obj: 0.007473, No Obj: 0.002941, Avg Recall: 0.475728, count: 103 -Region Avg IOU: 0.449492, Class: 1.000000, Obj: 0.008347, No Obj: 0.003023, Avg Recall: 0.384615, count: 104 -Region Avg IOU: 0.425540, Class: 1.000000, Obj: 0.008086, No Obj: 0.002884, Avg Recall: 0.416667, count: 120 -Region Avg IOU: 0.484657, Class: 1.000000, Obj: 0.009054, No Obj: 0.002974, Avg Recall: 0.484848, count: 99 -Region Avg IOU: 0.475754, Class: 1.000000, Obj: 0.009937, No Obj: 0.002994, Avg Recall: 0.538462, count: 104 -82: 0.783562, 0.904135 avg, 0.000100 rate, 33.641228 seconds, 20992 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.507417, Class: 1.000000, Obj: 0.007703, No Obj: 0.003095, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.491101, Class: 1.000000, Obj: 0.007786, No Obj: 0.002995, Avg Recall: 0.558333, count: 120 -Region Avg IOU: 0.462527, Class: 1.000000, Obj: 0.009181, No Obj: 0.002755, Avg Recall: 0.458716, count: 109 -Region Avg IOU: 0.441697, Class: 1.000000, Obj: 0.008632, No Obj: 0.003353, Avg Recall: 0.420635, count: 126 -Region Avg IOU: 0.503517, Class: 1.000000, Obj: 0.009503, No Obj: 0.003216, Avg Recall: 0.544554, count: 101 -Region Avg IOU: 0.474263, Class: 1.000000, Obj: 0.009790, No Obj: 0.003102, Avg Recall: 0.516393, count: 122 -Region Avg IOU: 0.468307, Class: 1.000000, Obj: 0.007205, No Obj: 0.003366, Avg Recall: 0.511111, count: 90 -Region Avg IOU: 0.505400, Class: 1.000000, Obj: 0.009325, No Obj: 0.003592, Avg Recall: 0.505618, count: 89 -83: 0.777444, 0.891466 avg, 0.000100 rate, 32.136700 seconds, 21248 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.460883, Class: 1.000000, Obj: 0.008283, No Obj: 0.003011, Avg Recall: 0.425373, count: 134 -Region Avg IOU: 0.477417, Class: 1.000000, Obj: 0.009926, No Obj: 0.003172, Avg Recall: 0.482759, count: 116 -Region Avg IOU: 0.464328, Class: 1.000000, Obj: 0.007132, No Obj: 0.002842, Avg Recall: 0.450450, count: 111 -Region Avg IOU: 0.479981, Class: 1.000000, Obj: 0.008828, No Obj: 0.002904, Avg Recall: 0.546296, count: 108 -Region Avg IOU: 0.460226, Class: 1.000000, Obj: 0.007717, No Obj: 0.003023, Avg Recall: 0.481481, count: 108 -Region Avg IOU: 0.484935, Class: 1.000000, Obj: 0.010352, No Obj: 0.003057, Avg Recall: 0.514851, count: 101 -Region Avg IOU: 0.484038, Class: 1.000000, Obj: 0.006865, No Obj: 0.002614, Avg Recall: 0.465116, count: 86 -Region Avg IOU: 0.514697, Class: 1.000000, Obj: 0.009493, No Obj: 0.002934, Avg Recall: 0.509434, count: 106 -84: 0.906994, 0.893019 avg, 0.000100 rate, 34.343327 seconds, 21504 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.449291, Class: 1.000000, Obj: 0.007546, No Obj: 0.003105, Avg Recall: 0.461538, count: 104 -Region Avg IOU: 0.488447, Class: 1.000000, Obj: 0.007590, No Obj: 0.002815, Avg Recall: 0.510638, count: 94 -Region Avg IOU: 0.473922, Class: 1.000000, Obj: 0.008101, No Obj: 0.002946, Avg Recall: 0.513274, count: 113 -Region Avg IOU: 0.497035, Class: 1.000000, Obj: 0.006646, No Obj: 0.002861, Avg Recall: 0.523364, count: 107 -Region Avg IOU: 0.473706, Class: 1.000000, Obj: 0.008789, No Obj: 0.002966, Avg Recall: 0.467290, count: 107 -Region Avg IOU: 0.467067, Class: 1.000000, Obj: 0.009797, No Obj: 0.003178, Avg Recall: 0.452830, count: 106 -Region Avg IOU: 0.497692, Class: 1.000000, Obj: 0.007462, No Obj: 0.002994, Avg Recall: 0.500000, count: 68 -Region Avg IOU: 0.459852, Class: 1.000000, Obj: 0.008145, No Obj: 0.002943, Avg Recall: 0.480392, count: 102 -85: 0.715092, 0.875226 avg, 0.000100 rate, 30.834787 seconds, 21760 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.502348, Class: 1.000000, Obj: 0.012495, No Obj: 0.003061, Avg Recall: 0.509615, count: 104 -Region Avg IOU: 0.468247, Class: 1.000000, Obj: 0.008602, No Obj: 0.002957, Avg Recall: 0.465909, count: 88 -Region Avg IOU: 0.483515, Class: 1.000000, Obj: 0.010266, No Obj: 0.003134, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.477158, Class: 1.000000, Obj: 0.007653, No Obj: 0.003045, Avg Recall: 0.492647, count: 136 -Region Avg IOU: 0.511802, Class: 1.000000, Obj: 0.010386, No Obj: 0.003353, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.476694, Class: 1.000000, Obj: 0.009483, No Obj: 0.003276, Avg Recall: 0.477064, count: 109 -Region Avg IOU: 0.478697, Class: 1.000000, Obj: 0.010024, No Obj: 0.003070, Avg Recall: 0.504348, count: 115 -Region Avg IOU: 0.499056, Class: 1.000000, Obj: 0.007879, No Obj: 0.002733, Avg Recall: 0.528302, count: 106 -86: 0.853785, 0.873082 avg, 0.000100 rate, 31.375292 seconds, 22016 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.477822, Class: 1.000000, Obj: 0.010757, No Obj: 0.003459, Avg Recall: 0.494505, count: 91 -Region Avg IOU: 0.477973, Class: 1.000000, Obj: 0.009124, No Obj: 0.003175, Avg Recall: 0.467391, count: 92 -Region Avg IOU: 0.525650, Class: 1.000000, Obj: 0.010018, No Obj: 0.003040, Avg Recall: 0.530864, count: 81 -Region Avg IOU: 0.492615, Class: 1.000000, Obj: 0.007360, No Obj: 0.002839, Avg Recall: 0.537634, count: 93 -Region Avg IOU: 0.452419, Class: 1.000000, Obj: 0.008424, No Obj: 0.003028, Avg Recall: 0.427083, count: 96 -Region Avg IOU: 0.474133, Class: 1.000000, Obj: 0.008893, No Obj: 0.002849, Avg Recall: 0.460938, count: 128 -Region Avg IOU: 0.463998, Class: 1.000000, Obj: 0.008556, No Obj: 0.003400, Avg Recall: 0.462264, count: 106 -Region Avg IOU: 0.506610, Class: 1.000000, Obj: 0.008459, No Obj: 0.003033, Avg Recall: 0.582418, count: 91 -87: 0.731969, 0.858971 avg, 0.000100 rate, 31.147476 seconds, 22272 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.465251, Class: 1.000000, Obj: 0.008841, No Obj: 0.003161, Avg Recall: 0.483607, count: 122 -Region Avg IOU: 0.496167, Class: 1.000000, Obj: 0.010391, No Obj: 0.003409, Avg Recall: 0.512397, count: 121 -Region Avg IOU: 0.477838, Class: 1.000000, Obj: 0.010027, No Obj: 0.003310, Avg Recall: 0.516949, count: 118 -Region Avg IOU: 0.486395, Class: 1.000000, Obj: 0.007821, No Obj: 0.003025, Avg Recall: 0.489130, count: 92 -Region Avg IOU: 0.444412, Class: 1.000000, Obj: 0.008594, No Obj: 0.002959, Avg Recall: 0.403361, count: 119 -Region Avg IOU: 0.506697, Class: 1.000000, Obj: 0.009555, No Obj: 0.003241, Avg Recall: 0.607843, count: 102 -Region Avg IOU: 0.513008, Class: 1.000000, Obj: 0.011271, No Obj: 0.003552, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.431296, Class: 1.000000, Obj: 0.007914, No Obj: 0.002877, Avg Recall: 0.384615, count: 117 -88: 0.738589, 0.846932 avg, 0.000100 rate, 33.892986 seconds, 22528 images -Loaded: 0.429681 seconds -Region Avg IOU: 0.496481, Class: 1.000000, Obj: 0.007924, No Obj: 0.002931, Avg Recall: 0.533333, count: 90 -Region Avg IOU: 0.468890, Class: 1.000000, Obj: 0.007889, No Obj: 0.002924, Avg Recall: 0.451923, count: 104 -Region Avg IOU: 0.458980, Class: 1.000000, Obj: 0.010328, No Obj: 0.003091, Avg Recall: 0.367347, count: 98 -Region Avg IOU: 0.494080, Class: 1.000000, Obj: 0.007494, No Obj: 0.002892, Avg Recall: 0.562500, count: 96 -Region Avg IOU: 0.488302, Class: 1.000000, Obj: 0.010377, No Obj: 0.003200, Avg Recall: 0.526786, count: 112 -Region Avg IOU: 0.455216, Class: 1.000000, Obj: 0.008731, No Obj: 0.003032, Avg Recall: 0.433735, count: 83 -Region Avg IOU: 0.520353, Class: 1.000000, Obj: 0.008331, No Obj: 0.002942, Avg Recall: 0.563636, count: 110 -Region Avg IOU: 0.480235, Class: 1.000000, Obj: 0.007905, No Obj: 0.002750, Avg Recall: 0.531915, count: 94 -89: 0.739573, 0.836196 avg, 0.000100 rate, 32.383438 seconds, 22784 images -Loaded: 1.123800 seconds -Region Avg IOU: 0.469929, Class: 1.000000, Obj: 0.011840, No Obj: 0.003404, Avg Recall: 0.459016, count: 122 -Region Avg IOU: 0.471599, Class: 1.000000, Obj: 0.009687, No Obj: 0.003095, Avg Recall: 0.485981, count: 107 -Region Avg IOU: 0.531577, Class: 1.000000, Obj: 0.007816, No Obj: 0.003075, Avg Recall: 0.588235, count: 85 -Region Avg IOU: 0.471019, Class: 1.000000, Obj: 0.008680, No Obj: 0.002967, Avg Recall: 0.437500, count: 112 -Region Avg IOU: 0.496343, Class: 1.000000, Obj: 0.009093, No Obj: 0.003350, Avg Recall: 0.543478, count: 92 -Region Avg IOU: 0.465509, Class: 1.000000, Obj: 0.007688, No Obj: 0.003114, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.490754, Class: 1.000000, Obj: 0.009509, No Obj: 0.003072, Avg Recall: 0.527473, count: 91 -Region Avg IOU: 0.494460, Class: 1.000000, Obj: 0.008910, No Obj: 0.002853, Avg Recall: 0.485437, count: 103 -90: 0.713576, 0.823934 avg, 0.000100 rate, 31.484388 seconds, 23040 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.730658 seconds -Region Avg IOU: 0.338446, Class: 1.000000, Obj: 0.003412, No Obj: 0.001591, Avg Recall: 0.224000, count: 125 -Region Avg IOU: 0.300414, Class: 1.000000, Obj: 0.003058, No Obj: 0.001678, Avg Recall: 0.178862, count: 123 -Region Avg IOU: 0.315763, Class: 1.000000, Obj: 0.002500, No Obj: 0.001524, Avg Recall: 0.194690, count: 113 -Region Avg IOU: 0.308711, Class: 1.000000, Obj: 0.003992, No Obj: 0.001688, Avg Recall: 0.217391, count: 115 -Region Avg IOU: 0.347021, Class: 1.000000, Obj: 0.003581, No Obj: 0.001597, Avg Recall: 0.284091, count: 88 -Region Avg IOU: 0.320414, Class: 1.000000, Obj: 0.003897, No Obj: 0.001726, Avg Recall: 0.261261, count: 111 -Region Avg IOU: 0.342663, Class: 1.000000, Obj: 0.004560, No Obj: 0.001678, Avg Recall: 0.263158, count: 114 -Region Avg IOU: 0.330086, Class: 1.000000, Obj: 0.003702, No Obj: 0.001726, Avg Recall: 0.210526, count: 95 -91: 1.072456, 0.848787 avg, 0.000100 rate, 16.839100 seconds, 23296 images -Loaded: 4.669958 seconds -Region Avg IOU: 0.348397, Class: 1.000000, Obj: 0.004182, No Obj: 0.001758, Avg Recall: 0.237624, count: 101 -Region Avg IOU: 0.344290, Class: 1.000000, Obj: 0.002793, No Obj: 0.001597, Avg Recall: 0.277108, count: 83 -Region Avg IOU: 0.319710, Class: 1.000000, Obj: 0.003367, No Obj: 0.001614, Avg Recall: 0.267442, count: 86 -Region Avg IOU: 0.304407, Class: 1.000000, Obj: 0.002591, No Obj: 0.001610, Avg Recall: 0.185484, count: 124 -Region Avg IOU: 0.341941, Class: 1.000000, Obj: 0.004226, No Obj: 0.001749, Avg Recall: 0.223214, count: 112 -Region Avg IOU: 0.340288, Class: 1.000000, Obj: 0.003630, No Obj: 0.001627, Avg Recall: 0.250000, count: 80 -Region Avg IOU: 0.338004, Class: 1.000000, Obj: 0.003311, No Obj: 0.001593, Avg Recall: 0.223404, count: 94 -Region Avg IOU: 0.298400, Class: 1.000000, Obj: 0.003254, No Obj: 0.001550, Avg Recall: 0.192308, count: 104 -92: 0.934407, 0.857349 avg, 0.000100 rate, 15.165589 seconds, 23552 images -Loaded: 3.632566 seconds -Region Avg IOU: 0.336140, Class: 1.000000, Obj: 0.003547, No Obj: 0.001631, Avg Recall: 0.237113, count: 97 -Region Avg IOU: 0.305364, Class: 1.000000, Obj: 0.003652, No Obj: 0.001570, Avg Recall: 0.156250, count: 96 -Region Avg IOU: 0.321589, Class: 1.000000, Obj: 0.004088, No Obj: 0.001735, Avg Recall: 0.256410, count: 117 -Region Avg IOU: 0.299121, Class: 1.000000, Obj: 0.003120, No Obj: 0.001627, Avg Recall: 0.193548, count: 93 -Region Avg IOU: 0.276892, Class: 1.000000, Obj: 0.003383, No Obj: 0.001699, Avg Recall: 0.144231, count: 104 -Region Avg IOU: 0.321857, Class: 1.000000, Obj: 0.003612, No Obj: 0.001619, Avg Recall: 0.208791, count: 91 -Region Avg IOU: 0.367148, Class: 1.000000, Obj: 0.003225, No Obj: 0.001665, Avg Recall: 0.306818, count: 88 -Region Avg IOU: 0.311217, Class: 1.000000, Obj: 0.004516, No Obj: 0.001727, Avg Recall: 0.205674, count: 141 -93: 1.138570, 0.885471 avg, 0.000100 rate, 15.627140 seconds, 23808 images -Loaded: 4.099127 seconds -Region Avg IOU: 0.360947, Class: 1.000000, Obj: 0.003400, No Obj: 0.001604, Avg Recall: 0.313043, count: 115 -Region Avg IOU: 0.310741, Class: 1.000000, Obj: 0.003226, No Obj: 0.001577, Avg Recall: 0.161538, count: 130 -Region Avg IOU: 0.281053, Class: 1.000000, Obj: 0.003571, No Obj: 0.001606, Avg Recall: 0.186275, count: 102 -Region Avg IOU: 0.318549, Class: 1.000000, Obj: 0.003044, No Obj: 0.001591, Avg Recall: 0.204918, count: 122 -Region Avg IOU: 0.340211, Class: 1.000000, Obj: 0.003316, No Obj: 0.001605, Avg Recall: 0.245098, count: 102 -Region Avg IOU: 0.334926, Class: 1.000000, Obj: 0.003634, No Obj: 0.001618, Avg Recall: 0.239437, count: 71 -Region Avg IOU: 0.335955, Class: 1.000000, Obj: 0.004738, No Obj: 0.001779, Avg Recall: 0.284483, count: 116 -Region Avg IOU: 0.379780, Class: 1.000000, Obj: 0.003419, No Obj: 0.001549, Avg Recall: 0.351648, count: 91 -94: 1.128912, 0.909815 avg, 0.000100 rate, 18.165020 seconds, 24064 images -Loaded: 3.385909 seconds -Region Avg IOU: 0.269131, Class: 1.000000, Obj: 0.002875, No Obj: 0.001631, Avg Recall: 0.180000, count: 100 -Region Avg IOU: 0.400673, Class: 1.000000, Obj: 0.003885, No Obj: 0.001609, Avg Recall: 0.333333, count: 78 -Region Avg IOU: 0.331471, Class: 1.000000, Obj: 0.004380, No Obj: 0.001612, Avg Recall: 0.240741, count: 108 -Region Avg IOU: 0.326119, Class: 1.000000, Obj: 0.003805, No Obj: 0.001605, Avg Recall: 0.204724, count: 127 -Region Avg IOU: 0.341853, Class: 1.000000, Obj: 0.004026, No Obj: 0.001679, Avg Recall: 0.235849, count: 106 -Region Avg IOU: 0.339166, Class: 1.000000, Obj: 0.004868, No Obj: 0.001740, Avg Recall: 0.200000, count: 100 -Region Avg IOU: 0.287769, Class: 1.000000, Obj: 0.003269, No Obj: 0.001580, Avg Recall: 0.155172, count: 116 -Region Avg IOU: 0.331047, Class: 1.000000, Obj: 0.004582, No Obj: 0.001678, Avg Recall: 0.216867, count: 83 -95: 1.043438, 0.923177 avg, 0.000100 rate, 17.756952 seconds, 24320 images -Loaded: 1.833889 seconds -Region Avg IOU: 0.317123, Class: 1.000000, Obj: 0.003174, No Obj: 0.001591, Avg Recall: 0.228814, count: 118 -Region Avg IOU: 0.320495, Class: 1.000000, Obj: 0.003490, No Obj: 0.001864, Avg Recall: 0.226415, count: 106 -Region Avg IOU: 0.326956, Class: 1.000000, Obj: 0.003551, No Obj: 0.001653, Avg Recall: 0.247788, count: 113 -Region Avg IOU: 0.304915, Class: 1.000000, Obj: 0.003442, No Obj: 0.001618, Avg Recall: 0.196581, count: 117 -Region Avg IOU: 0.307746, Class: 1.000000, Obj: 0.003086, No Obj: 0.001591, Avg Recall: 0.239316, count: 117 -Region Avg IOU: 0.280347, Class: 1.000000, Obj: 0.003608, No Obj: 0.001604, Avg Recall: 0.233645, count: 107 -Region Avg IOU: 0.316579, Class: 1.000000, Obj: 0.003084, No Obj: 0.001622, Avg Recall: 0.200000, count: 100 -Region Avg IOU: 0.340601, Class: 1.000000, Obj: 0.003749, No Obj: 0.001635, Avg Recall: 0.292683, count: 82 -96: 1.039548, 0.934814 avg, 0.000100 rate, 18.991085 seconds, 24576 images -Loaded: 1.487245 seconds -Region Avg IOU: 0.373135, Class: 1.000000, Obj: 0.004025, No Obj: 0.001686, Avg Recall: 0.265823, count: 79 -Region Avg IOU: 0.327623, Class: 1.000000, Obj: 0.003427, No Obj: 0.001651, Avg Recall: 0.155963, count: 109 -Region Avg IOU: 0.309509, Class: 1.000000, Obj: 0.003791, No Obj: 0.001687, Avg Recall: 0.198529, count: 136 -Region Avg IOU: 0.345068, Class: 1.000000, Obj: 0.004184, No Obj: 0.001708, Avg Recall: 0.207692, count: 130 -Region Avg IOU: 0.312876, Class: 1.000000, Obj: 0.003592, No Obj: 0.001714, Avg Recall: 0.224000, count: 125 -Region Avg IOU: 0.317825, Class: 1.000000, Obj: 0.003842, No Obj: 0.001577, Avg Recall: 0.193548, count: 93 -Region Avg IOU: 0.292304, Class: 1.000000, Obj: 0.003084, No Obj: 0.001564, Avg Recall: 0.212121, count: 132 -Region Avg IOU: 0.331968, Class: 1.000000, Obj: 0.004780, No Obj: 0.001737, Avg Recall: 0.258333, count: 120 -97: 1.091317, 0.950465 avg, 0.000100 rate, 18.226433 seconds, 24832 images -Loaded: 3.768637 seconds -Region Avg IOU: 0.308435, Class: 1.000000, Obj: 0.003798, No Obj: 0.001668, Avg Recall: 0.239130, count: 92 -Region Avg IOU: 0.292578, Class: 1.000000, Obj: 0.003942, No Obj: 0.001674, Avg Recall: 0.155738, count: 122 -Region Avg IOU: 0.334198, Class: 1.000000, Obj: 0.002822, No Obj: 0.001601, Avg Recall: 0.261364, count: 88 -Region Avg IOU: 0.336979, Class: 1.000000, Obj: 0.004464, No Obj: 0.001875, Avg Recall: 0.235772, count: 123 -Region Avg IOU: 0.286421, Class: 1.000000, Obj: 0.003674, No Obj: 0.001733, Avg Recall: 0.213483, count: 89 -Region Avg IOU: 0.323806, Class: 1.000000, Obj: 0.002585, No Obj: 0.001618, Avg Recall: 0.250000, count: 128 -Region Avg IOU: 0.347644, Class: 1.000000, Obj: 0.004302, No Obj: 0.001764, Avg Recall: 0.262411, count: 141 -Region Avg IOU: 0.316108, Class: 1.000000, Obj: 0.003409, No Obj: 0.001710, Avg Recall: 0.188679, count: 106 -98: 1.353004, 0.990718 avg, 0.000100 rate, 15.735616 seconds, 25088 images -Loaded: 5.863623 seconds -Region Avg IOU: 0.305805, Class: 1.000000, Obj: 0.003468, No Obj: 0.001552, Avg Recall: 0.222222, count: 99 -Region Avg IOU: 0.324997, Class: 1.000000, Obj: 0.003871, No Obj: 0.001707, Avg Recall: 0.260000, count: 100 -Region Avg IOU: 0.328944, Class: 1.000000, Obj: 0.003615, No Obj: 0.001804, Avg Recall: 0.266187, count: 139 -Region Avg IOU: 0.348217, Class: 1.000000, Obj: 0.003425, No Obj: 0.001693, Avg Recall: 0.257426, count: 101 -Region Avg IOU: 0.342464, Class: 1.000000, Obj: 0.003825, No Obj: 0.001601, Avg Recall: 0.218182, count: 110 -Region Avg IOU: 0.329581, Class: 1.000000, Obj: 0.004327, No Obj: 0.001614, Avg Recall: 0.234234, count: 111 -Region Avg IOU: 0.354932, Class: 1.000000, Obj: 0.004172, No Obj: 0.001793, Avg Recall: 0.244898, count: 98 -Region Avg IOU: 0.376834, Class: 1.000000, Obj: 0.003856, No Obj: 0.001779, Avg Recall: 0.350000, count: 100 -99: 0.961120, 0.987759 avg, 0.000100 rate, 15.273914 seconds, 25344 images -Loaded: 6.481336 seconds -Region Avg IOU: 0.338976, Class: 1.000000, Obj: 0.004807, No Obj: 0.001818, Avg Recall: 0.286713, count: 143 -Region Avg IOU: 0.362520, Class: 1.000000, Obj: 0.003964, No Obj: 0.001630, Avg Recall: 0.236364, count: 110 -Region Avg IOU: 0.331326, Class: 1.000000, Obj: 0.004246, No Obj: 0.001621, Avg Recall: 0.240000, count: 100 -Region Avg IOU: 0.329278, Class: 1.000000, Obj: 0.003860, No Obj: 0.001698, Avg Recall: 0.267327, count: 101 -Region Avg IOU: 0.310898, Class: 1.000000, Obj: 0.003548, No Obj: 0.001582, Avg Recall: 0.290698, count: 86 -Region Avg IOU: 0.313557, Class: 1.000000, Obj: 0.005275, No Obj: 0.001820, Avg Recall: 0.209091, count: 110 -Region Avg IOU: 0.313736, Class: 1.000000, Obj: 0.004089, No Obj: 0.001687, Avg Recall: 0.205479, count: 146 -Region Avg IOU: 0.330880, Class: 1.000000, Obj: 0.004151, No Obj: 0.001608, Avg Recall: 0.260000, count: 100 -100: 1.164829, 1.005466 avg, 0.001000 rate, 15.569431 seconds, 25600 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000070 seconds -Region Avg IOU: 0.473403, Class: 1.000000, Obj: 0.010709, No Obj: 0.003608, Avg Recall: 0.480519, count: 154 -Region Avg IOU: 0.492487, Class: 1.000000, Obj: 0.010309, No Obj: 0.003282, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.537464, Class: 1.000000, Obj: 0.011640, No Obj: 0.003578, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.478520, Class: 1.000000, Obj: 0.011308, No Obj: 0.003426, Avg Recall: 0.485437, count: 103 -Region Avg IOU: 0.509087, Class: 1.000000, Obj: 0.012098, No Obj: 0.003628, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.494641, Class: 1.000000, Obj: 0.011138, No Obj: 0.003409, Avg Recall: 0.515464, count: 97 -Region Avg IOU: 0.433739, Class: 1.000000, Obj: 0.010377, No Obj: 0.003384, Avg Recall: 0.418605, count: 86 -Region Avg IOU: 0.458220, Class: 1.000000, Obj: 0.010531, No Obj: 0.003322, Avg Recall: 0.450820, count: 122 -101: 0.858552, 0.990774 avg, 0.001000 rate, 32.183750 seconds, 25856 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.490164, Class: 1.000000, Obj: 0.014882, No Obj: 0.003975, Avg Recall: 0.513514, count: 111 -Region Avg IOU: 0.458854, Class: 1.000000, Obj: 0.011274, No Obj: 0.003503, Avg Recall: 0.504587, count: 109 -Region Avg IOU: 0.508361, Class: 1.000000, Obj: 0.010710, No Obj: 0.003309, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.503583, Class: 1.000000, Obj: 0.012211, No Obj: 0.003594, Avg Recall: 0.546219, count: 119 -Region Avg IOU: 0.468503, Class: 1.000000, Obj: 0.011470, No Obj: 0.003431, Avg Recall: 0.438095, count: 105 -Region Avg IOU: 0.498476, Class: 1.000000, Obj: 0.012982, No Obj: 0.003628, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.481645, Class: 1.000000, Obj: 0.010064, No Obj: 0.003237, Avg Recall: 0.507463, count: 134 -Region Avg IOU: 0.465567, Class: 1.000000, Obj: 0.012043, No Obj: 0.003480, Avg Recall: 0.468085, count: 94 -102: 0.876983, 0.979395 avg, 0.001000 rate, 31.162479 seconds, 26112 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.473590, Class: 1.000000, Obj: 0.013820, No Obj: 0.003555, Avg Recall: 0.465517, count: 116 -Region Avg IOU: 0.512463, Class: 1.000000, Obj: 0.012312, No Obj: 0.003400, Avg Recall: 0.577778, count: 90 -Region Avg IOU: 0.443306, Class: 1.000000, Obj: 0.011813, No Obj: 0.003711, Avg Recall: 0.418803, count: 117 -Region Avg IOU: 0.480336, Class: 1.000000, Obj: 0.015263, No Obj: 0.003735, Avg Recall: 0.468085, count: 94 -Region Avg IOU: 0.487930, Class: 1.000000, Obj: 0.014088, No Obj: 0.003748, Avg Recall: 0.540816, count: 98 -Region Avg IOU: 0.494643, Class: 1.000000, Obj: 0.012510, No Obj: 0.003687, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.485737, Class: 1.000000, Obj: 0.014310, No Obj: 0.003593, Avg Recall: 0.533333, count: 105 -Region Avg IOU: 0.462756, Class: 1.000000, Obj: 0.014439, No Obj: 0.003615, Avg Recall: 0.512821, count: 117 -103: 0.814016, 0.962857 avg, 0.001000 rate, 30.262449 seconds, 26368 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.482073, Class: 1.000000, Obj: 0.013979, No Obj: 0.003548, Avg Recall: 0.540984, count: 122 -Region Avg IOU: 0.483201, Class: 1.000000, Obj: 0.013488, No Obj: 0.003768, Avg Recall: 0.535714, count: 140 -Region Avg IOU: 0.492224, Class: 1.000000, Obj: 0.017730, No Obj: 0.003938, Avg Recall: 0.508065, count: 124 -Region Avg IOU: 0.488048, Class: 1.000000, Obj: 0.015811, No Obj: 0.004127, Avg Recall: 0.472222, count: 108 -Region Avg IOU: 0.489120, Class: 1.000000, Obj: 0.015106, No Obj: 0.004090, Avg Recall: 0.525424, count: 118 -Region Avg IOU: 0.461814, Class: 1.000000, Obj: 0.014361, No Obj: 0.004093, Avg Recall: 0.443299, count: 97 -Region Avg IOU: 0.485790, Class: 1.000000, Obj: 0.012550, No Obj: 0.003767, Avg Recall: 0.514085, count: 142 -Region Avg IOU: 0.499346, Class: 1.000000, Obj: 0.015812, No Obj: 0.004319, Avg Recall: 0.537037, count: 108 -104: 0.922208, 0.958792 avg, 0.001000 rate, 33.577679 seconds, 26624 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.460412, Class: 1.000000, Obj: 0.014700, No Obj: 0.003654, Avg Recall: 0.478261, count: 115 -Region Avg IOU: 0.496341, Class: 1.000000, Obj: 0.015551, No Obj: 0.003895, Avg Recall: 0.537815, count: 119 -Region Avg IOU: 0.464317, Class: 1.000000, Obj: 0.014603, No Obj: 0.003861, Avg Recall: 0.441860, count: 86 -Region Avg IOU: 0.465991, Class: 1.000000, Obj: 0.013922, No Obj: 0.003684, Avg Recall: 0.456311, count: 103 -Region Avg IOU: 0.495067, Class: 1.000000, Obj: 0.016132, No Obj: 0.004141, Avg Recall: 0.514286, count: 105 -Region Avg IOU: 0.490284, Class: 1.000000, Obj: 0.013197, No Obj: 0.004130, Avg Recall: 0.532609, count: 92 -Region Avg IOU: 0.465651, Class: 1.000000, Obj: 0.013389, No Obj: 0.003735, Avg Recall: 0.470588, count: 119 -Region Avg IOU: 0.507589, Class: 1.000000, Obj: 0.016537, No Obj: 0.004047, Avg Recall: 0.526316, count: 133 -105: 0.789676, 0.941881 avg, 0.001000 rate, 33.200737 seconds, 26880 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.484895, Class: 1.000000, Obj: 0.015519, No Obj: 0.003681, Avg Recall: 0.544444, count: 90 -Region Avg IOU: 0.475354, Class: 1.000000, Obj: 0.017847, No Obj: 0.003941, Avg Recall: 0.445946, count: 74 -Region Avg IOU: 0.466499, Class: 1.000000, Obj: 0.017357, No Obj: 0.003919, Avg Recall: 0.489796, count: 98 -Region Avg IOU: 0.497689, Class: 1.000000, Obj: 0.019212, No Obj: 0.004099, Avg Recall: 0.555556, count: 90 -Region Avg IOU: 0.514123, Class: 1.000000, Obj: 0.017002, No Obj: 0.003706, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.514059, Class: 1.000000, Obj: 0.017553, No Obj: 0.004410, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.500603, Class: 1.000000, Obj: 0.021433, No Obj: 0.004731, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.491062, Class: 1.000000, Obj: 0.014540, No Obj: 0.003677, Avg Recall: 0.578431, count: 102 -106: 0.630742, 0.910767 avg, 0.001000 rate, 33.297947 seconds, 27136 images -Loaded: 1.539596 seconds -Region Avg IOU: 0.498127, Class: 1.000000, Obj: 0.015847, No Obj: 0.004066, Avg Recall: 0.476191, count: 105 -Region Avg IOU: 0.468621, Class: 1.000000, Obj: 0.019156, No Obj: 0.004161, Avg Recall: 0.447154, count: 123 -Region Avg IOU: 0.510051, Class: 1.000000, Obj: 0.014895, No Obj: 0.003977, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.490292, Class: 1.000000, Obj: 0.015497, No Obj: 0.003787, Avg Recall: 0.536765, count: 136 -Region Avg IOU: 0.528536, Class: 1.000000, Obj: 0.017138, No Obj: 0.004159, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.492874, Class: 1.000000, Obj: 0.016763, No Obj: 0.004000, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.555063, Class: 1.000000, Obj: 0.014949, No Obj: 0.004478, Avg Recall: 0.619718, count: 71 -Region Avg IOU: 0.501277, Class: 1.000000, Obj: 0.021127, No Obj: 0.004207, Avg Recall: 0.544554, count: 101 -107: 0.723685, 0.892059 avg, 0.001000 rate, 31.039200 seconds, 27392 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.508239, Class: 1.000000, Obj: 0.020024, No Obj: 0.004603, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.541477, Class: 1.000000, Obj: 0.022302, No Obj: 0.004159, Avg Recall: 0.670886, count: 79 -Region Avg IOU: 0.495955, Class: 1.000000, Obj: 0.020174, No Obj: 0.004289, Avg Recall: 0.500000, count: 94 -Region Avg IOU: 0.482237, Class: 1.000000, Obj: 0.020650, No Obj: 0.004293, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.496838, Class: 1.000000, Obj: 0.020659, No Obj: 0.003952, Avg Recall: 0.511111, count: 90 -Region Avg IOU: 0.495713, Class: 1.000000, Obj: 0.022260, No Obj: 0.004236, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.478969, Class: 1.000000, Obj: 0.019927, No Obj: 0.004260, Avg Recall: 0.511628, count: 129 -Region Avg IOU: 0.483265, Class: 1.000000, Obj: 0.017276, No Obj: 0.003750, Avg Recall: 0.530864, count: 81 -108: 0.708579, 0.873711 avg, 0.001000 rate, 33.582127 seconds, 27648 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.510429, Class: 1.000000, Obj: 0.022848, No Obj: 0.004273, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.513329, Class: 1.000000, Obj: 0.018233, No Obj: 0.004310, Avg Recall: 0.538462, count: 143 -Region Avg IOU: 0.482651, Class: 1.000000, Obj: 0.020126, No Obj: 0.004401, Avg Recall: 0.470000, count: 100 -Region Avg IOU: 0.552829, Class: 1.000000, Obj: 0.018517, No Obj: 0.004528, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.544977, Class: 1.000000, Obj: 0.019974, No Obj: 0.004552, Avg Recall: 0.625000, count: 120 -Region Avg IOU: 0.513361, Class: 1.000000, Obj: 0.020663, No Obj: 0.004643, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.484044, Class: 1.000000, Obj: 0.021311, No Obj: 0.004056, Avg Recall: 0.538462, count: 130 -Region Avg IOU: 0.474699, Class: 1.000000, Obj: 0.020424, No Obj: 0.003957, Avg Recall: 0.485149, count: 101 -109: 0.716756, 0.858015 avg, 0.001000 rate, 30.614384 seconds, 27904 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.542077, Class: 1.000000, Obj: 0.021685, No Obj: 0.004439, Avg Recall: 0.664000, count: 125 -Region Avg IOU: 0.508916, Class: 1.000000, Obj: 0.022551, No Obj: 0.004356, Avg Recall: 0.550000, count: 100 -Region Avg IOU: 0.478160, Class: 1.000000, Obj: 0.019255, No Obj: 0.003876, Avg Recall: 0.478992, count: 119 -Region Avg IOU: 0.517913, Class: 1.000000, Obj: 0.018103, No Obj: 0.004220, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.516146, Class: 1.000000, Obj: 0.021445, No Obj: 0.004823, Avg Recall: 0.551471, count: 136 -Region Avg IOU: 0.514736, Class: 1.000000, Obj: 0.023655, No Obj: 0.004405, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.492352, Class: 1.000000, Obj: 0.023733, No Obj: 0.004344, Avg Recall: 0.522727, count: 132 -Region Avg IOU: 0.497115, Class: 1.000000, Obj: 0.023492, No Obj: 0.004301, Avg Recall: 0.500000, count: 72 -110: 0.771625, 0.849376 avg, 0.001000 rate, 32.344833 seconds, 28160 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 13.227507 seconds -Region Avg IOU: 0.492701, Class: 1.000000, Obj: 0.019377, No Obj: 0.003204, Avg Recall: 0.540230, count: 87 -Region Avg IOU: 0.434928, Class: 1.000000, Obj: 0.017053, No Obj: 0.003191, Avg Recall: 0.442308, count: 104 -Region Avg IOU: 0.499384, Class: 1.000000, Obj: 0.020305, No Obj: 0.003443, Avg Recall: 0.532787, count: 122 -Region Avg IOU: 0.477680, Class: 1.000000, Obj: 0.018739, No Obj: 0.003616, Avg Recall: 0.485437, count: 103 -Region Avg IOU: 0.485611, Class: 1.000000, Obj: 0.017560, No Obj: 0.003267, Avg Recall: 0.592593, count: 81 -Region Avg IOU: 0.457257, Class: 1.000000, Obj: 0.018978, No Obj: 0.003186, Avg Recall: 0.451923, count: 104 -Region Avg IOU: 0.487153, Class: 1.000000, Obj: 0.024658, No Obj: 0.003781, Avg Recall: 0.558333, count: 120 -Region Avg IOU: 0.487143, Class: 1.000000, Obj: 0.021876, No Obj: 0.003489, Avg Recall: 0.532609, count: 92 -111: 0.694467, 0.833885 avg, 0.001000 rate, 27.710236 seconds, 28416 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.502132, Class: 1.000000, Obj: 0.028710, No Obj: 0.003562, Avg Recall: 0.490566, count: 106 -Region Avg IOU: 0.473027, Class: 1.000000, Obj: 0.017587, No Obj: 0.003443, Avg Recall: 0.470000, count: 100 -Region Avg IOU: 0.469256, Class: 1.000000, Obj: 0.022721, No Obj: 0.003252, Avg Recall: 0.432692, count: 104 -Region Avg IOU: 0.456139, Class: 1.000000, Obj: 0.017929, No Obj: 0.003293, Avg Recall: 0.443299, count: 97 -Region Avg IOU: 0.496859, Class: 1.000000, Obj: 0.021643, No Obj: 0.003603, Avg Recall: 0.495050, count: 101 -Region Avg IOU: 0.486553, Class: 1.000000, Obj: 0.021928, No Obj: 0.003657, Avg Recall: 0.565657, count: 99 -Region Avg IOU: 0.473226, Class: 1.000000, Obj: 0.020802, No Obj: 0.003663, Avg Recall: 0.470000, count: 100 -Region Avg IOU: 0.459093, Class: 1.000000, Obj: 0.025701, No Obj: 0.003519, Avg Recall: 0.453704, count: 108 -112: 0.675548, 0.818052 avg, 0.001000 rate, 28.707180 seconds, 28672 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.447120, Class: 1.000000, Obj: 0.022765, No Obj: 0.003583, Avg Recall: 0.419355, count: 124 -Region Avg IOU: 0.483591, Class: 1.000000, Obj: 0.028294, No Obj: 0.003619, Avg Recall: 0.495727, count: 117 -Region Avg IOU: 0.522916, Class: 1.000000, Obj: 0.024939, No Obj: 0.003524, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.501675, Class: 1.000000, Obj: 0.025509, No Obj: 0.003642, Avg Recall: 0.556522, count: 115 -Region Avg IOU: 0.474642, Class: 1.000000, Obj: 0.022004, No Obj: 0.003380, Avg Recall: 0.486486, count: 111 -Region Avg IOU: 0.487420, Class: 1.000000, Obj: 0.028938, No Obj: 0.003818, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.508610, Class: 1.000000, Obj: 0.023995, No Obj: 0.003642, Avg Recall: 0.507353, count: 136 -Region Avg IOU: 0.475693, Class: 1.000000, Obj: 0.027080, No Obj: 0.003546, Avg Recall: 0.531915, count: 94 -113: 0.674044, 0.803651 avg, 0.001000 rate, 28.496719 seconds, 28928 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.425568, Class: 1.000000, Obj: 0.021522, No Obj: 0.003133, Avg Recall: 0.394737, count: 76 -Region Avg IOU: 0.498711, Class: 1.000000, Obj: 0.018210, No Obj: 0.003437, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.469195, Class: 1.000000, Obj: 0.025221, No Obj: 0.003385, Avg Recall: 0.464286, count: 112 -Region Avg IOU: 0.457633, Class: 1.000000, Obj: 0.027643, No Obj: 0.003462, Avg Recall: 0.448980, count: 98 -Region Avg IOU: 0.505752, Class: 1.000000, Obj: 0.025009, No Obj: 0.003505, Avg Recall: 0.532609, count: 92 -Region Avg IOU: 0.466559, Class: 1.000000, Obj: 0.022457, No Obj: 0.003764, Avg Recall: 0.438596, count: 114 -Region Avg IOU: 0.473095, Class: 1.000000, Obj: 0.024195, No Obj: 0.003801, Avg Recall: 0.456000, count: 125 -Region Avg IOU: 0.440601, Class: 1.000000, Obj: 0.020406, No Obj: 0.003036, Avg Recall: 0.457944, count: 107 -114: 0.723953, 0.795681 avg, 0.001000 rate, 27.374653 seconds, 29184 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.472947, Class: 1.000000, Obj: 0.029132, No Obj: 0.003754, Avg Recall: 0.476191, count: 105 -Region Avg IOU: 0.473463, Class: 1.000000, Obj: 0.027530, No Obj: 0.003721, Avg Recall: 0.524194, count: 124 -Region Avg IOU: 0.476955, Class: 1.000000, Obj: 0.023228, No Obj: 0.003428, Avg Recall: 0.478633, count: 117 -Region Avg IOU: 0.441256, Class: 1.000000, Obj: 0.028178, No Obj: 0.003548, Avg Recall: 0.434343, count: 99 -Region Avg IOU: 0.462792, Class: 1.000000, Obj: 0.024346, No Obj: 0.003567, Avg Recall: 0.447917, count: 96 -Region Avg IOU: 0.483679, Class: 1.000000, Obj: 0.025092, No Obj: 0.003639, Avg Recall: 0.567568, count: 111 -Region Avg IOU: 0.450649, Class: 1.000000, Obj: 0.026107, No Obj: 0.003265, Avg Recall: 0.420000, count: 100 -Region Avg IOU: 0.487443, Class: 1.000000, Obj: 0.026368, No Obj: 0.003443, Avg Recall: 0.533981, count: 103 -115: 0.642179, 0.780331 avg, 0.001000 rate, 28.398939 seconds, 29440 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.501817, Class: 1.000000, Obj: 0.029192, No Obj: 0.003818, Avg Recall: 0.530612, count: 98 -Region Avg IOU: 0.507305, Class: 1.000000, Obj: 0.031552, No Obj: 0.004054, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.450179, Class: 1.000000, Obj: 0.028819, No Obj: 0.003603, Avg Recall: 0.430108, count: 93 -Region Avg IOU: 0.483394, Class: 1.000000, Obj: 0.027642, No Obj: 0.003733, Avg Recall: 0.544444, count: 90 -Region Avg IOU: 0.492505, Class: 1.000000, Obj: 0.031215, No Obj: 0.003963, Avg Recall: 0.524590, count: 122 -Region Avg IOU: 0.474759, Class: 1.000000, Obj: 0.030525, No Obj: 0.003952, Avg Recall: 0.485981, count: 107 -Region Avg IOU: 0.458649, Class: 1.000000, Obj: 0.030446, No Obj: 0.003687, Avg Recall: 0.465347, count: 101 -Region Avg IOU: 0.513490, Class: 1.000000, Obj: 0.022572, No Obj: 0.003690, Avg Recall: 0.520000, count: 100 -116: 0.663788, 0.768677 avg, 0.001000 rate, 26.878147 seconds, 29696 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.513865, Class: 1.000000, Obj: 0.027800, No Obj: 0.003751, Avg Recall: 0.584270, count: 89 -Region Avg IOU: 0.469640, Class: 1.000000, Obj: 0.034400, No Obj: 0.003896, Avg Recall: 0.438462, count: 130 -Region Avg IOU: 0.475702, Class: 1.000000, Obj: 0.045417, No Obj: 0.003806, Avg Recall: 0.465909, count: 88 -Region Avg IOU: 0.482892, Class: 1.000000, Obj: 0.030513, No Obj: 0.003606, Avg Recall: 0.495575, count: 113 -Region Avg IOU: 0.491577, Class: 1.000000, Obj: 0.041599, No Obj: 0.004036, Avg Recall: 0.517544, count: 114 -Region Avg IOU: 0.456538, Class: 1.000000, Obj: 0.029984, No Obj: 0.003739, Avg Recall: 0.429688, count: 128 -Region Avg IOU: 0.459118, Class: 1.000000, Obj: 0.031308, No Obj: 0.003652, Avg Recall: 0.464646, count: 99 -Region Avg IOU: 0.432701, Class: 1.000000, Obj: 0.033430, No Obj: 0.003475, Avg Recall: 0.414062, count: 128 -117: 0.634998, 0.755309 avg, 0.001000 rate, 27.727591 seconds, 29952 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.465084, Class: 1.000000, Obj: 0.026935, No Obj: 0.003893, Avg Recall: 0.506173, count: 81 -Region Avg IOU: 0.461054, Class: 1.000000, Obj: 0.044455, No Obj: 0.003743, Avg Recall: 0.471264, count: 87 -Region Avg IOU: 0.488565, Class: 1.000000, Obj: 0.036397, No Obj: 0.003983, Avg Recall: 0.518182, count: 110 -Region Avg IOU: 0.464534, Class: 1.000000, Obj: 0.026316, No Obj: 0.003784, Avg Recall: 0.495798, count: 119 -Region Avg IOU: 0.452751, Class: 1.000000, Obj: 0.032176, No Obj: 0.003603, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.455764, Class: 1.000000, Obj: 0.033836, No Obj: 0.003591, Avg Recall: 0.477778, count: 90 -Region Avg IOU: 0.393808, Class: 1.000000, Obj: 0.033396, No Obj: 0.003669, Avg Recall: 0.346939, count: 98 -Region Avg IOU: 0.459751, Class: 1.000000, Obj: 0.030395, No Obj: 0.004011, Avg Recall: 0.492537, count: 134 -118: 0.653468, 0.745125 avg, 0.001000 rate, 28.422272 seconds, 30208 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.476523, Class: 1.000000, Obj: 0.039307, No Obj: 0.004209, Avg Recall: 0.524272, count: 103 -Region Avg IOU: 0.481350, Class: 1.000000, Obj: 0.042598, No Obj: 0.004230, Avg Recall: 0.526316, count: 95 -Region Avg IOU: 0.487669, Class: 1.000000, Obj: 0.045177, No Obj: 0.004210, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.472326, Class: 1.000000, Obj: 0.037210, No Obj: 0.004113, Avg Recall: 0.513761, count: 109 -Region Avg IOU: 0.498056, Class: 1.000000, Obj: 0.037385, No Obj: 0.003612, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.472917, Class: 1.000000, Obj: 0.036490, No Obj: 0.003831, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.450334, Class: 1.000000, Obj: 0.042678, No Obj: 0.003910, Avg Recall: 0.490909, count: 110 -Region Avg IOU: 0.476352, Class: 1.000000, Obj: 0.046285, No Obj: 0.004063, Avg Recall: 0.571429, count: 98 -119: 0.607879, 0.731400 avg, 0.001000 rate, 28.431032 seconds, 30464 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.493094, Class: 1.000000, Obj: 0.043892, No Obj: 0.004551, Avg Recall: 0.536000, count: 125 -Region Avg IOU: 0.466774, Class: 1.000000, Obj: 0.040423, No Obj: 0.004298, Avg Recall: 0.537736, count: 106 -Region Avg IOU: 0.472661, Class: 1.000000, Obj: 0.039514, No Obj: 0.003922, Avg Recall: 0.482759, count: 116 -Region Avg IOU: 0.488830, Class: 1.000000, Obj: 0.036934, No Obj: 0.004118, Avg Recall: 0.495238, count: 105 -Region Avg IOU: 0.485488, Class: 1.000000, Obj: 0.049812, No Obj: 0.004606, Avg Recall: 0.483871, count: 124 -Region Avg IOU: 0.454754, Class: 1.000000, Obj: 0.043175, No Obj: 0.004187, Avg Recall: 0.486486, count: 111 -Region Avg IOU: 0.485339, Class: 1.000000, Obj: 0.054828, No Obj: 0.004416, Avg Recall: 0.508621, count: 116 -Region Avg IOU: 0.478240, Class: 1.000000, Obj: 0.039305, No Obj: 0.004551, Avg Recall: 0.504762, count: 105 -120: 0.650806, 0.723341 avg, 0.001000 rate, 28.695120 seconds, 30720 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.910725 seconds -Region Avg IOU: 0.379824, Class: 1.000000, Obj: 0.027464, No Obj: 0.002958, Avg Recall: 0.355140, count: 107 -Region Avg IOU: 0.366606, Class: 1.000000, Obj: 0.025522, No Obj: 0.003221, Avg Recall: 0.349057, count: 106 -Region Avg IOU: 0.400886, Class: 1.000000, Obj: 0.025245, No Obj: 0.003275, Avg Recall: 0.362963, count: 135 -Region Avg IOU: 0.319271, Class: 1.000000, Obj: 0.028294, No Obj: 0.002999, Avg Recall: 0.260870, count: 115 -Region Avg IOU: 0.315897, Class: 1.000000, Obj: 0.025045, No Obj: 0.003126, Avg Recall: 0.257812, count: 128 -Region Avg IOU: 0.352306, Class: 1.000000, Obj: 0.035922, No Obj: 0.003073, Avg Recall: 0.346154, count: 104 -Region Avg IOU: 0.387872, Class: 1.000000, Obj: 0.035030, No Obj: 0.003107, Avg Recall: 0.324561, count: 114 -Region Avg IOU: 0.354147, Class: 1.000000, Obj: 0.021562, No Obj: 0.002892, Avg Recall: 0.281818, count: 110 -121: 0.856592, 0.736666 avg, 0.001000 rate, 19.899715 seconds, 30976 images -Loaded: 2.306033 seconds -Region Avg IOU: 0.377000, Class: 1.000000, Obj: 0.027964, No Obj: 0.003220, Avg Recall: 0.358491, count: 106 -Region Avg IOU: 0.334051, Class: 1.000000, Obj: 0.025411, No Obj: 0.003017, Avg Recall: 0.265487, count: 113 -Region Avg IOU: 0.303617, Class: 1.000000, Obj: 0.036285, No Obj: 0.003726, Avg Recall: 0.192857, count: 140 -Region Avg IOU: 0.351437, Class: 1.000000, Obj: 0.031680, No Obj: 0.003352, Avg Recall: 0.327586, count: 116 -Region Avg IOU: 0.360675, Class: 1.000000, Obj: 0.040180, No Obj: 0.004065, Avg Recall: 0.307692, count: 130 -Region Avg IOU: 0.349048, Class: 1.000000, Obj: 0.025255, No Obj: 0.002962, Avg Recall: 0.277311, count: 119 -Region Avg IOU: 0.405749, Class: 1.000000, Obj: 0.036428, No Obj: 0.003453, Avg Recall: 0.418033, count: 122 -Region Avg IOU: 0.361616, Class: 1.000000, Obj: 0.028325, No Obj: 0.002845, Avg Recall: 0.348315, count: 89 -122: 0.931278, 0.756127 avg, 0.001000 rate, 17.272308 seconds, 31232 images -Loaded: 3.253275 seconds -Region Avg IOU: 0.372328, Class: 1.000000, Obj: 0.036480, No Obj: 0.003480, Avg Recall: 0.383929, count: 112 -Region Avg IOU: 0.399682, Class: 1.000000, Obj: 0.032141, No Obj: 0.003377, Avg Recall: 0.366071, count: 112 -Region Avg IOU: 0.349786, Class: 1.000000, Obj: 0.029745, No Obj: 0.003213, Avg Recall: 0.284314, count: 102 -Region Avg IOU: 0.381365, Class: 1.000000, Obj: 0.030885, No Obj: 0.003405, Avg Recall: 0.361538, count: 130 -Region Avg IOU: 0.310204, Class: 1.000000, Obj: 0.019208, No Obj: 0.002697, Avg Recall: 0.178571, count: 84 -Region Avg IOU: 0.357406, Class: 1.000000, Obj: 0.025767, No Obj: 0.003054, Avg Recall: 0.293233, count: 133 -Region Avg IOU: 0.371999, Class: 1.000000, Obj: 0.027923, No Obj: 0.003463, Avg Recall: 0.349650, count: 143 -Region Avg IOU: 0.350587, Class: 1.000000, Obj: 0.028743, No Obj: 0.003356, Avg Recall: 0.266129, count: 124 -123: 0.814409, 0.761955 avg, 0.001000 rate, 16.773745 seconds, 31488 images -Loaded: 3.799472 seconds -Region Avg IOU: 0.307406, Class: 1.000000, Obj: 0.021321, No Obj: 0.002978, Avg Recall: 0.195652, count: 92 -Region Avg IOU: 0.328855, Class: 1.000000, Obj: 0.048787, No Obj: 0.003678, Avg Recall: 0.247788, count: 113 -Region Avg IOU: 0.400134, Class: 1.000000, Obj: 0.035601, No Obj: 0.003452, Avg Recall: 0.370000, count: 100 -Region Avg IOU: 0.366448, Class: 1.000000, Obj: 0.038510, No Obj: 0.003652, Avg Recall: 0.298851, count: 87 -Region Avg IOU: 0.343342, Class: 1.000000, Obj: 0.031572, No Obj: 0.003565, Avg Recall: 0.280992, count: 121 -Region Avg IOU: 0.394078, Class: 1.000000, Obj: 0.052149, No Obj: 0.004003, Avg Recall: 0.328000, count: 125 -Region Avg IOU: 0.380763, Class: 1.000000, Obj: 0.031768, No Obj: 0.003532, Avg Recall: 0.325581, count: 86 -Region Avg IOU: 0.419190, Class: 1.000000, Obj: 0.042506, No Obj: 0.003128, Avg Recall: 0.378378, count: 74 -124: 0.692343, 0.754994 avg, 0.001000 rate, 19.073656 seconds, 31744 images -Loaded: 2.599506 seconds -Region Avg IOU: 0.370054, Class: 1.000000, Obj: 0.041912, No Obj: 0.004211, Avg Recall: 0.314516, count: 124 -Region Avg IOU: 0.416409, Class: 1.000000, Obj: 0.066073, No Obj: 0.004618, Avg Recall: 0.424242, count: 132 -Region Avg IOU: 0.311124, Class: 1.000000, Obj: 0.033019, No Obj: 0.003658, Avg Recall: 0.214953, count: 107 -Region Avg IOU: 0.344313, Class: 1.000000, Obj: 0.041906, No Obj: 0.003630, Avg Recall: 0.247619, count: 105 -Region Avg IOU: 0.392143, Class: 1.000000, Obj: 0.048318, No Obj: 0.004082, Avg Recall: 0.369748, count: 119 -Region Avg IOU: 0.360869, Class: 1.000000, Obj: 0.057455, No Obj: 0.004768, Avg Recall: 0.347826, count: 138 -Region Avg IOU: 0.311539, Class: 1.000000, Obj: 0.036555, No Obj: 0.003674, Avg Recall: 0.237037, count: 135 -Region Avg IOU: 0.378528, Class: 1.000000, Obj: 0.039357, No Obj: 0.004163, Avg Recall: 0.370787, count: 89 -125: 0.817249, 0.761220 avg, 0.001000 rate, 18.500572 seconds, 32000 images -Loaded: 2.207225 seconds -Region Avg IOU: 0.377712, Class: 1.000000, Obj: 0.057516, No Obj: 0.004203, Avg Recall: 0.358333, count: 120 -Region Avg IOU: 0.386326, Class: 1.000000, Obj: 0.035243, No Obj: 0.003805, Avg Recall: 0.339450, count: 109 -Region Avg IOU: 0.359749, Class: 1.000000, Obj: 0.047613, No Obj: 0.004525, Avg Recall: 0.360656, count: 122 -Region Avg IOU: 0.398777, Class: 1.000000, Obj: 0.030397, No Obj: 0.003177, Avg Recall: 0.348837, count: 86 -Region Avg IOU: 0.337757, Class: 1.000000, Obj: 0.055311, No Obj: 0.004579, Avg Recall: 0.286885, count: 122 -Region Avg IOU: 0.338158, Class: 1.000000, Obj: 0.029801, No Obj: 0.003793, Avg Recall: 0.165138, count: 109 -Region Avg IOU: 0.360532, Class: 1.000000, Obj: 0.040243, No Obj: 0.004431, Avg Recall: 0.297710, count: 131 -Region Avg IOU: 0.375157, Class: 1.000000, Obj: 0.051371, No Obj: 0.004562, Avg Recall: 0.330769, count: 130 -126: 0.757124, 0.760810 avg, 0.001000 rate, 16.497831 seconds, 32256 images -Loaded: 4.514653 seconds -Region Avg IOU: 0.419890, Class: 1.000000, Obj: 0.064319, No Obj: 0.004790, Avg Recall: 0.427184, count: 103 -Region Avg IOU: 0.362334, Class: 1.000000, Obj: 0.046956, No Obj: 0.004528, Avg Recall: 0.307018, count: 114 -Region Avg IOU: 0.401455, Class: 1.000000, Obj: 0.050322, No Obj: 0.004531, Avg Recall: 0.382979, count: 94 -Region Avg IOU: 0.393687, Class: 1.000000, Obj: 0.057089, No Obj: 0.004619, Avg Recall: 0.391304, count: 92 -Region Avg IOU: 0.367807, Class: 1.000000, Obj: 0.037011, No Obj: 0.004533, Avg Recall: 0.310924, count: 119 -Region Avg IOU: 0.367920, Class: 1.000000, Obj: 0.062993, No Obj: 0.004861, Avg Recall: 0.292453, count: 106 -Region Avg IOU: 0.361710, Class: 1.000000, Obj: 0.062781, No Obj: 0.004446, Avg Recall: 0.347107, count: 121 -Region Avg IOU: 0.441494, Class: 1.000000, Obj: 0.064274, No Obj: 0.004692, Avg Recall: 0.453704, count: 108 -127: 0.711874, 0.755916 avg, 0.001000 rate, 19.132076 seconds, 32512 images -Loaded: 3.212510 seconds -Region Avg IOU: 0.339858, Class: 1.000000, Obj: 0.057389, No Obj: 0.004301, Avg Recall: 0.277108, count: 83 -Region Avg IOU: 0.356179, Class: 1.000000, Obj: 0.043672, No Obj: 0.004892, Avg Recall: 0.253731, count: 134 -Region Avg IOU: 0.380031, Class: 1.000000, Obj: 0.045195, No Obj: 0.004294, Avg Recall: 0.266667, count: 90 -Region Avg IOU: 0.359571, Class: 1.000000, Obj: 0.044317, No Obj: 0.004298, Avg Recall: 0.321101, count: 109 -Region Avg IOU: 0.381797, Class: 1.000000, Obj: 0.053631, No Obj: 0.004626, Avg Recall: 0.315217, count: 92 -Region Avg IOU: 0.399019, Class: 1.000000, Obj: 0.070788, No Obj: 0.005474, Avg Recall: 0.378378, count: 148 -Region Avg IOU: 0.401653, Class: 1.000000, Obj: 0.068788, No Obj: 0.004888, Avg Recall: 0.379630, count: 108 -Region Avg IOU: 0.418314, Class: 1.000000, Obj: 0.066202, No Obj: 0.005270, Avg Recall: 0.409836, count: 122 -128: 0.791887, 0.759513 avg, 0.001000 rate, 18.695660 seconds, 32768 images -Loaded: 2.072731 seconds -Region Avg IOU: 0.406785, Class: 1.000000, Obj: 0.076798, No Obj: 0.005283, Avg Recall: 0.375000, count: 120 -Region Avg IOU: 0.373859, Class: 1.000000, Obj: 0.061303, No Obj: 0.005820, Avg Recall: 0.268908, count: 119 -Region Avg IOU: 0.400680, Class: 1.000000, Obj: 0.059768, No Obj: 0.005332, Avg Recall: 0.400000, count: 115 -Region Avg IOU: 0.348630, Class: 1.000000, Obj: 0.060233, No Obj: 0.005134, Avg Recall: 0.305556, count: 108 -Region Avg IOU: 0.365933, Class: 1.000000, Obj: 0.071051, No Obj: 0.005468, Avg Recall: 0.314815, count: 108 -Region Avg IOU: 0.378623, Class: 1.000000, Obj: 0.071793, No Obj: 0.005564, Avg Recall: 0.299145, count: 117 -Region Avg IOU: 0.393678, Class: 1.000000, Obj: 0.061131, No Obj: 0.005182, Avg Recall: 0.341880, count: 117 -Region Avg IOU: 0.348250, Class: 1.000000, Obj: 0.074887, No Obj: 0.005637, Avg Recall: 0.258333, count: 120 -129: 0.772739, 0.760836 avg, 0.001000 rate, 19.576620 seconds, 33024 images -Loaded: 2.470951 seconds -Region Avg IOU: 0.386718, Class: 1.000000, Obj: 0.087471, No Obj: 0.005757, Avg Recall: 0.326531, count: 98 -Region Avg IOU: 0.395936, Class: 1.000000, Obj: 0.063521, No Obj: 0.005356, Avg Recall: 0.336634, count: 101 -Region Avg IOU: 0.390947, Class: 1.000000, Obj: 0.062256, No Obj: 0.005160, Avg Recall: 0.333333, count: 93 -Region Avg IOU: 0.401832, Class: 1.000000, Obj: 0.055530, No Obj: 0.005728, Avg Recall: 0.382979, count: 94 -Region Avg IOU: 0.402577, Class: 1.000000, Obj: 0.105227, No Obj: 0.005214, Avg Recall: 0.380952, count: 105 -Region Avg IOU: 0.379722, Class: 1.000000, Obj: 0.081773, No Obj: 0.006621, Avg Recall: 0.335484, count: 155 -Region Avg IOU: 0.385051, Class: 1.000000, Obj: 0.079096, No Obj: 0.005317, Avg Recall: 0.358974, count: 117 -Region Avg IOU: 0.368038, Class: 1.000000, Obj: 0.057244, No Obj: 0.004611, Avg Recall: 0.318182, count: 110 -130: 0.809060, 0.765658 avg, 0.001000 rate, 20.313522 seconds, 33280 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000068 seconds -Region Avg IOU: 0.516677, Class: 1.000000, Obj: 0.122879, No Obj: 0.007906, Avg Recall: 0.555556, count: 90 -Region Avg IOU: 0.510134, Class: 1.000000, Obj: 0.114593, No Obj: 0.008168, Avg Recall: 0.533898, count: 118 -Region Avg IOU: 0.504655, Class: 1.000000, Obj: 0.119635, No Obj: 0.008058, Avg Recall: 0.563025, count: 119 -Region Avg IOU: 0.471292, Class: 1.000000, Obj: 0.121513, No Obj: 0.007494, Avg Recall: 0.482143, count: 112 -Region Avg IOU: 0.524670, Class: 1.000000, Obj: 0.123341, No Obj: 0.007574, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.507923, Class: 1.000000, Obj: 0.120252, No Obj: 0.007777, Avg Recall: 0.593985, count: 133 -Region Avg IOU: 0.479084, Class: 1.000000, Obj: 0.098741, No Obj: 0.007527, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.517199, Class: 1.000000, Obj: 0.093235, No Obj: 0.007158, Avg Recall: 0.560976, count: 82 -131: 0.634533, 0.752546 avg, 0.001000 rate, 31.323376 seconds, 33536 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.505869, Class: 1.000000, Obj: 0.105966, No Obj: 0.006882, Avg Recall: 0.605042, count: 119 -Region Avg IOU: 0.551265, Class: 1.000000, Obj: 0.068216, No Obj: 0.006953, Avg Recall: 0.666667, count: 117 -Region Avg IOU: 0.486102, Class: 1.000000, Obj: 0.097874, No Obj: 0.006830, Avg Recall: 0.512195, count: 82 -Region Avg IOU: 0.491295, Class: 1.000000, Obj: 0.094801, No Obj: 0.007324, Avg Recall: 0.525424, count: 118 -Region Avg IOU: 0.538617, Class: 1.000000, Obj: 0.093465, No Obj: 0.007440, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.511990, Class: 1.000000, Obj: 0.104708, No Obj: 0.007369, Avg Recall: 0.607143, count: 84 -Region Avg IOU: 0.539830, Class: 1.000000, Obj: 0.077689, No Obj: 0.006969, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.551389, Class: 1.000000, Obj: 0.094509, No Obj: 0.006796, Avg Recall: 0.642105, count: 95 -132: 0.598115, 0.737103 avg, 0.001000 rate, 30.938555 seconds, 33792 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.536077, Class: 1.000000, Obj: 0.058047, No Obj: 0.005288, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.468354, Class: 1.000000, Obj: 0.061300, No Obj: 0.005690, Avg Recall: 0.482143, count: 112 -Region Avg IOU: 0.513540, Class: 1.000000, Obj: 0.070944, No Obj: 0.005208, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.462697, Class: 1.000000, Obj: 0.073604, No Obj: 0.005417, Avg Recall: 0.528736, count: 87 -Region Avg IOU: 0.536111, Class: 1.000000, Obj: 0.087038, No Obj: 0.005789, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.518188, Class: 1.000000, Obj: 0.060476, No Obj: 0.005819, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.507075, Class: 1.000000, Obj: 0.073068, No Obj: 0.005625, Avg Recall: 0.526786, count: 112 -Region Avg IOU: 0.484777, Class: 1.000000, Obj: 0.061646, No Obj: 0.005590, Avg Recall: 0.555556, count: 108 -133: 0.574090, 0.720802 avg, 0.001000 rate, 31.306450 seconds, 34048 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.492939, Class: 1.000000, Obj: 0.052438, No Obj: 0.005014, Avg Recall: 0.496063, count: 127 -Region Avg IOU: 0.524321, Class: 1.000000, Obj: 0.067316, No Obj: 0.004220, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.548043, Class: 1.000000, Obj: 0.061905, No Obj: 0.004824, Avg Recall: 0.647541, count: 122 -Region Avg IOU: 0.522370, Class: 1.000000, Obj: 0.053462, No Obj: 0.004003, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.473826, Class: 1.000000, Obj: 0.053413, No Obj: 0.004792, Avg Recall: 0.480916, count: 131 -Region Avg IOU: 0.496211, Class: 1.000000, Obj: 0.055441, No Obj: 0.004432, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.465141, Class: 1.000000, Obj: 0.046293, No Obj: 0.004439, Avg Recall: 0.515385, count: 130 -Region Avg IOU: 0.521645, Class: 1.000000, Obj: 0.061785, No Obj: 0.004556, Avg Recall: 0.583333, count: 96 -134: 0.645973, 0.713319 avg, 0.001000 rate, 30.709581 seconds, 34304 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.507180, Class: 1.000000, Obj: 0.050491, No Obj: 0.003688, Avg Recall: 0.524752, count: 101 -Region Avg IOU: 0.541542, Class: 1.000000, Obj: 0.050432, No Obj: 0.003632, Avg Recall: 0.622047, count: 127 -Region Avg IOU: 0.511567, Class: 1.000000, Obj: 0.040233, No Obj: 0.003531, Avg Recall: 0.601695, count: 118 -Region Avg IOU: 0.507495, Class: 1.000000, Obj: 0.039961, No Obj: 0.003660, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.534033, Class: 1.000000, Obj: 0.047673, No Obj: 0.003989, Avg Recall: 0.603053, count: 131 -Region Avg IOU: 0.547915, Class: 1.000000, Obj: 0.033575, No Obj: 0.003679, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.523699, Class: 1.000000, Obj: 0.039637, No Obj: 0.003863, Avg Recall: 0.572727, count: 110 -Region Avg IOU: 0.480927, Class: 1.000000, Obj: 0.035794, No Obj: 0.003312, Avg Recall: 0.468750, count: 96 -135: 0.591350, 0.701122 avg, 0.001000 rate, 32.683910 seconds, 34560 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.462474, Class: 1.000000, Obj: 0.037725, No Obj: 0.002896, Avg Recall: 0.454545, count: 99 -Region Avg IOU: 0.458847, Class: 1.000000, Obj: 0.030322, No Obj: 0.002818, Avg Recall: 0.504132, count: 121 -Region Avg IOU: 0.517839, Class: 1.000000, Obj: 0.034261, No Obj: 0.002884, Avg Recall: 0.597561, count: 82 -Region Avg IOU: 0.473644, Class: 1.000000, Obj: 0.037605, No Obj: 0.003067, Avg Recall: 0.504202, count: 119 -Region Avg IOU: 0.488178, Class: 1.000000, Obj: 0.033206, No Obj: 0.003016, Avg Recall: 0.512605, count: 119 -Region Avg IOU: 0.490420, Class: 1.000000, Obj: 0.038288, No Obj: 0.003054, Avg Recall: 0.495327, count: 107 -Region Avg IOU: 0.502465, Class: 1.000000, Obj: 0.031704, No Obj: 0.003195, Avg Recall: 0.503937, count: 127 -Region Avg IOU: 0.536565, Class: 1.000000, Obj: 0.036112, No Obj: 0.003372, Avg Recall: 0.626087, count: 115 -136: 0.607675, 0.691777 avg, 0.001000 rate, 31.655128 seconds, 34816 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.494715, Class: 1.000000, Obj: 0.026787, No Obj: 0.003000, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.500869, Class: 1.000000, Obj: 0.028239, No Obj: 0.002617, Avg Recall: 0.544118, count: 136 -Region Avg IOU: 0.497426, Class: 1.000000, Obj: 0.025768, No Obj: 0.002826, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.459040, Class: 1.000000, Obj: 0.025855, No Obj: 0.002424, Avg Recall: 0.500000, count: 116 -Region Avg IOU: 0.545545, Class: 1.000000, Obj: 0.031548, No Obj: 0.002934, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.477405, Class: 1.000000, Obj: 0.022805, No Obj: 0.002751, Avg Recall: 0.485981, count: 107 -Region Avg IOU: 0.535716, Class: 1.000000, Obj: 0.034843, No Obj: 0.002731, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.541091, Class: 1.000000, Obj: 0.033698, No Obj: 0.002740, Avg Recall: 0.629921, count: 127 -137: 0.546887, 0.677288 avg, 0.001000 rate, 32.261562 seconds, 35072 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.488295, Class: 1.000000, Obj: 0.027166, No Obj: 0.002338, Avg Recall: 0.447059, count: 85 -Region Avg IOU: 0.530225, Class: 1.000000, Obj: 0.023800, No Obj: 0.002403, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.545909, Class: 1.000000, Obj: 0.028991, No Obj: 0.002517, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.517156, Class: 1.000000, Obj: 0.027672, No Obj: 0.002398, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.529235, Class: 1.000000, Obj: 0.025494, No Obj: 0.002462, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.547537, Class: 1.000000, Obj: 0.025554, No Obj: 0.002392, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.479024, Class: 1.000000, Obj: 0.019810, No Obj: 0.002138, Avg Recall: 0.496454, count: 141 -Region Avg IOU: 0.465915, Class: 1.000000, Obj: 0.025682, No Obj: 0.002572, Avg Recall: 0.500000, count: 114 -138: 0.516661, 0.661225 avg, 0.001000 rate, 33.624508 seconds, 35328 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.494327, Class: 1.000000, Obj: 0.022446, No Obj: 0.002391, Avg Recall: 0.524194, count: 124 -Region Avg IOU: 0.485278, Class: 1.000000, Obj: 0.023870, No Obj: 0.002312, Avg Recall: 0.550000, count: 120 -Region Avg IOU: 0.499530, Class: 1.000000, Obj: 0.019371, No Obj: 0.002034, Avg Recall: 0.550562, count: 89 -Region Avg IOU: 0.505603, Class: 1.000000, Obj: 0.029886, No Obj: 0.002444, Avg Recall: 0.581395, count: 86 -Region Avg IOU: 0.506909, Class: 1.000000, Obj: 0.019191, No Obj: 0.002200, Avg Recall: 0.577778, count: 90 -Region Avg IOU: 0.494124, Class: 1.000000, Obj: 0.019450, No Obj: 0.002048, Avg Recall: 0.534884, count: 86 -Region Avg IOU: 0.501969, Class: 1.000000, Obj: 0.022001, No Obj: 0.002359, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.479663, Class: 1.000000, Obj: 0.021517, No Obj: 0.002229, Avg Recall: 0.510000, count: 100 -139: 0.593137, 0.654417 avg, 0.001000 rate, 35.129295 seconds, 35584 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.537925, Class: 1.000000, Obj: 0.018194, No Obj: 0.002224, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.546285, Class: 1.000000, Obj: 0.029105, No Obj: 0.002190, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.507195, Class: 1.000000, Obj: 0.022794, No Obj: 0.002296, Avg Recall: 0.524590, count: 122 -Region Avg IOU: 0.496953, Class: 1.000000, Obj: 0.025121, No Obj: 0.002213, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.540722, Class: 1.000000, Obj: 0.019250, No Obj: 0.002071, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.562344, Class: 1.000000, Obj: 0.022623, No Obj: 0.002004, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.531222, Class: 1.000000, Obj: 0.023500, No Obj: 0.002092, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.519273, Class: 1.000000, Obj: 0.023049, No Obj: 0.002212, Avg Recall: 0.615385, count: 104 -140: 0.546675, 0.643642 avg, 0.001000 rate, 30.236712 seconds, 35840 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 12.053537 seconds -Region Avg IOU: 0.497861, Class: 1.000000, Obj: 0.020027, No Obj: 0.001881, Avg Recall: 0.474227, count: 97 -Region Avg IOU: 0.497135, Class: 1.000000, Obj: 0.018804, No Obj: 0.001817, Avg Recall: 0.514851, count: 101 -Region Avg IOU: 0.503749, Class: 1.000000, Obj: 0.018216, No Obj: 0.001842, Avg Recall: 0.571429, count: 77 -Region Avg IOU: 0.496829, Class: 1.000000, Obj: 0.020048, No Obj: 0.001822, Avg Recall: 0.534483, count: 116 -Region Avg IOU: 0.504274, Class: 1.000000, Obj: 0.021135, No Obj: 0.001990, Avg Recall: 0.554688, count: 128 -Region Avg IOU: 0.528826, Class: 1.000000, Obj: 0.021516, No Obj: 0.001988, Avg Recall: 0.630435, count: 92 -Region Avg IOU: 0.466830, Class: 1.000000, Obj: 0.021056, No Obj: 0.001842, Avg Recall: 0.440000, count: 150 -Region Avg IOU: 0.510068, Class: 1.000000, Obj: 0.022385, No Obj: 0.001939, Avg Recall: 0.572816, count: 103 -141: 0.589267, 0.638205 avg, 0.001000 rate, 28.299179 seconds, 36096 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.446818, Class: 1.000000, Obj: 0.018016, No Obj: 0.001775, Avg Recall: 0.450549, count: 91 -Region Avg IOU: 0.503000, Class: 1.000000, Obj: 0.020767, No Obj: 0.001785, Avg Recall: 0.582418, count: 91 -Region Avg IOU: 0.480620, Class: 1.000000, Obj: 0.016669, No Obj: 0.001734, Avg Recall: 0.461538, count: 91 -Region Avg IOU: 0.488193, Class: 1.000000, Obj: 0.019188, No Obj: 0.001955, Avg Recall: 0.463158, count: 95 -Region Avg IOU: 0.457398, Class: 1.000000, Obj: 0.021198, No Obj: 0.001803, Avg Recall: 0.485149, count: 101 -Region Avg IOU: 0.476619, Class: 1.000000, Obj: 0.026003, No Obj: 0.001916, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.514033, Class: 1.000000, Obj: 0.019904, No Obj: 0.001886, Avg Recall: 0.548780, count: 82 -Region Avg IOU: 0.495558, Class: 1.000000, Obj: 0.021151, No Obj: 0.002095, Avg Recall: 0.529851, count: 134 -142: 0.467865, 0.621171 avg, 0.001000 rate, 30.287752 seconds, 36352 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.491935, Class: 1.000000, Obj: 0.021392, No Obj: 0.002019, Avg Recall: 0.495727, count: 117 -Region Avg IOU: 0.489205, Class: 1.000000, Obj: 0.027913, No Obj: 0.001913, Avg Recall: 0.488189, count: 127 -Region Avg IOU: 0.493705, Class: 1.000000, Obj: 0.028538, No Obj: 0.001879, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.502902, Class: 1.000000, Obj: 0.022749, No Obj: 0.001905, Avg Recall: 0.546667, count: 75 -Region Avg IOU: 0.561880, Class: 1.000000, Obj: 0.024418, No Obj: 0.002123, Avg Recall: 0.650794, count: 126 -Region Avg IOU: 0.506119, Class: 1.000000, Obj: 0.028548, No Obj: 0.001934, Avg Recall: 0.494505, count: 91 -Region Avg IOU: 0.500048, Class: 1.000000, Obj: 0.028808, No Obj: 0.001981, Avg Recall: 0.533898, count: 118 -Region Avg IOU: 0.485825, Class: 1.000000, Obj: 0.020191, No Obj: 0.001869, Avg Recall: 0.522059, count: 136 -143: 0.543332, 0.613387 avg, 0.001000 rate, 29.331120 seconds, 36608 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.550880, Class: 1.000000, Obj: 0.021441, No Obj: 0.001882, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.532431, Class: 1.000000, Obj: 0.020139, No Obj: 0.002008, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.486084, Class: 1.000000, Obj: 0.022426, No Obj: 0.001875, Avg Recall: 0.495413, count: 109 -Region Avg IOU: 0.513148, Class: 1.000000, Obj: 0.025495, No Obj: 0.001929, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.488690, Class: 1.000000, Obj: 0.024211, No Obj: 0.001948, Avg Recall: 0.470000, count: 100 -Region Avg IOU: 0.499034, Class: 1.000000, Obj: 0.028363, No Obj: 0.002010, Avg Recall: 0.556522, count: 115 -Region Avg IOU: 0.505785, Class: 1.000000, Obj: 0.029733, No Obj: 0.001840, Avg Recall: 0.573034, count: 89 -Region Avg IOU: 0.475319, Class: 1.000000, Obj: 0.019951, No Obj: 0.001895, Avg Recall: 0.494845, count: 97 -144: 0.528971, 0.604945 avg, 0.001000 rate, 28.314968 seconds, 36864 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.493813, Class: 1.000000, Obj: 0.028984, No Obj: 0.002129, Avg Recall: 0.492064, count: 126 -Region Avg IOU: 0.497389, Class: 1.000000, Obj: 0.022854, No Obj: 0.001981, Avg Recall: 0.528846, count: 104 -Region Avg IOU: 0.492721, Class: 1.000000, Obj: 0.028089, No Obj: 0.001998, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.515901, Class: 1.000000, Obj: 0.036282, No Obj: 0.002073, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.482904, Class: 1.000000, Obj: 0.030667, No Obj: 0.001914, Avg Recall: 0.500000, count: 110 -Region Avg IOU: 0.502817, Class: 1.000000, Obj: 0.036260, No Obj: 0.002222, Avg Recall: 0.536913, count: 149 -Region Avg IOU: 0.500422, Class: 1.000000, Obj: 0.023984, No Obj: 0.002109, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.537634, Class: 1.000000, Obj: 0.033710, No Obj: 0.002196, Avg Recall: 0.641304, count: 92 -145: 0.564799, 0.600931 avg, 0.001000 rate, 28.638388 seconds, 37120 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.533345, Class: 1.000000, Obj: 0.022902, No Obj: 0.002120, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.466951, Class: 1.000000, Obj: 0.037391, No Obj: 0.002253, Avg Recall: 0.520000, count: 125 -Region Avg IOU: 0.483566, Class: 1.000000, Obj: 0.029571, No Obj: 0.002039, Avg Recall: 0.525862, count: 116 -Region Avg IOU: 0.491813, Class: 1.000000, Obj: 0.033827, No Obj: 0.002061, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.509863, Class: 1.000000, Obj: 0.037914, No Obj: 0.002146, Avg Recall: 0.625000, count: 120 -Region Avg IOU: 0.492755, Class: 1.000000, Obj: 0.035902, No Obj: 0.002312, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.477506, Class: 1.000000, Obj: 0.035977, No Obj: 0.002211, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.524553, Class: 1.000000, Obj: 0.028321, No Obj: 0.002223, Avg Recall: 0.596639, count: 119 -146: 0.586349, 0.599473 avg, 0.001000 rate, 30.241899 seconds, 37376 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.507123, Class: 1.000000, Obj: 0.033784, No Obj: 0.002165, Avg Recall: 0.534483, count: 116 -Region Avg IOU: 0.470511, Class: 1.000000, Obj: 0.029382, No Obj: 0.002142, Avg Recall: 0.469388, count: 98 -Region Avg IOU: 0.564251, Class: 1.000000, Obj: 0.038236, No Obj: 0.002172, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.505197, Class: 1.000000, Obj: 0.037997, No Obj: 0.002336, Avg Recall: 0.566929, count: 127 -Region Avg IOU: 0.486190, Class: 1.000000, Obj: 0.042089, No Obj: 0.002358, Avg Recall: 0.524590, count: 122 -Region Avg IOU: 0.477246, Class: 1.000000, Obj: 0.044619, No Obj: 0.002440, Avg Recall: 0.486111, count: 144 -Region Avg IOU: 0.532468, Class: 1.000000, Obj: 0.033742, No Obj: 0.002495, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.501362, Class: 1.000000, Obj: 0.040250, No Obj: 0.002428, Avg Recall: 0.607407, count: 135 -147: 0.583872, 0.597913 avg, 0.001000 rate, 29.809404 seconds, 37632 images -Loaded: 0.163738 seconds -Region Avg IOU: 0.491635, Class: 1.000000, Obj: 0.038393, No Obj: 0.002325, Avg Recall: 0.522727, count: 88 -Region Avg IOU: 0.474129, Class: 1.000000, Obj: 0.039940, No Obj: 0.002485, Avg Recall: 0.452381, count: 126 -Region Avg IOU: 0.524925, Class: 1.000000, Obj: 0.048134, No Obj: 0.002490, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.512083, Class: 1.000000, Obj: 0.048082, No Obj: 0.002851, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.508141, Class: 1.000000, Obj: 0.038014, No Obj: 0.002591, Avg Recall: 0.579365, count: 126 -Region Avg IOU: 0.531065, Class: 1.000000, Obj: 0.046844, No Obj: 0.002582, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.470696, Class: 1.000000, Obj: 0.038992, No Obj: 0.002612, Avg Recall: 0.507937, count: 126 -Region Avg IOU: 0.497891, Class: 1.000000, Obj: 0.061499, No Obj: 0.002604, Avg Recall: 0.559702, count: 134 -148: 0.547938, 0.592915 avg, 0.001000 rate, 29.141829 seconds, 37888 images -Loaded: 0.731051 seconds -Region Avg IOU: 0.489052, Class: 1.000000, Obj: 0.051958, No Obj: 0.002799, Avg Recall: 0.518519, count: 108 -Region Avg IOU: 0.526129, Class: 1.000000, Obj: 0.053559, No Obj: 0.002976, Avg Recall: 0.597826, count: 92 -Region Avg IOU: 0.496294, Class: 1.000000, Obj: 0.066338, No Obj: 0.002993, Avg Recall: 0.544715, count: 123 -Region Avg IOU: 0.521133, Class: 1.000000, Obj: 0.064307, No Obj: 0.003072, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.519500, Class: 1.000000, Obj: 0.074515, No Obj: 0.003036, Avg Recall: 0.618321, count: 131 -Region Avg IOU: 0.494841, Class: 1.000000, Obj: 0.054282, No Obj: 0.002744, Avg Recall: 0.528736, count: 87 -Region Avg IOU: 0.491468, Class: 1.000000, Obj: 0.061731, No Obj: 0.002737, Avg Recall: 0.568182, count: 88 -Region Avg IOU: 0.494992, Class: 1.000000, Obj: 0.050777, No Obj: 0.002468, Avg Recall: 0.574713, count: 87 -149: 0.505035, 0.584127 avg, 0.001000 rate, 28.015266 seconds, 38144 images -Loaded: 2.526531 seconds -Region Avg IOU: 0.467868, Class: 1.000000, Obj: 0.057943, No Obj: 0.002959, Avg Recall: 0.446154, count: 130 -Region Avg IOU: 0.534901, Class: 1.000000, Obj: 0.055598, No Obj: 0.003098, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.466076, Class: 1.000000, Obj: 0.066514, No Obj: 0.002920, Avg Recall: 0.541667, count: 96 -Region Avg IOU: 0.550995, Class: 1.000000, Obj: 0.057437, No Obj: 0.003107, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.537386, Class: 1.000000, Obj: 0.075446, No Obj: 0.002955, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.494717, Class: 1.000000, Obj: 0.050496, No Obj: 0.002890, Avg Recall: 0.562500, count: 80 -Region Avg IOU: 0.487325, Class: 1.000000, Obj: 0.063691, No Obj: 0.002952, Avg Recall: 0.512605, count: 119 -Region Avg IOU: 0.530312, Class: 1.000000, Obj: 0.054930, No Obj: 0.003116, Avg Recall: 0.588710, count: 124 -150: 0.515060, 0.577220 avg, 0.001000 rate, 30.049229 seconds, 38400 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000083 seconds -Region Avg IOU: 0.539302, Class: 1.000000, Obj: 0.066652, No Obj: 0.003499, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.484976, Class: 1.000000, Obj: 0.059552, No Obj: 0.003426, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.500703, Class: 1.000000, Obj: 0.053303, No Obj: 0.003248, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.536940, Class: 1.000000, Obj: 0.065722, No Obj: 0.003570, Avg Recall: 0.580645, count: 124 -Region Avg IOU: 0.515068, Class: 1.000000, Obj: 0.082121, No Obj: 0.003876, Avg Recall: 0.619048, count: 126 -Region Avg IOU: 0.466405, Class: 1.000000, Obj: 0.056241, No Obj: 0.003241, Avg Recall: 0.509259, count: 108 -Region Avg IOU: 0.502868, Class: 1.000000, Obj: 0.098569, No Obj: 0.003750, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.516984, Class: 1.000000, Obj: 0.085909, No Obj: 0.004194, Avg Recall: 0.564885, count: 131 -151: 0.534028, 0.572901 avg, 0.001000 rate, 32.063976 seconds, 38656 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.515546, Class: 1.000000, Obj: 0.074570, No Obj: 0.003926, Avg Recall: 0.537931, count: 145 -Region Avg IOU: 0.531558, Class: 1.000000, Obj: 0.086221, No Obj: 0.004300, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.534684, Class: 1.000000, Obj: 0.081070, No Obj: 0.003942, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.551527, Class: 1.000000, Obj: 0.086500, No Obj: 0.004284, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.499836, Class: 1.000000, Obj: 0.096238, No Obj: 0.004311, Avg Recall: 0.566667, count: 120 -Region Avg IOU: 0.532538, Class: 1.000000, Obj: 0.089522, No Obj: 0.004355, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.525819, Class: 1.000000, Obj: 0.111369, No Obj: 0.004053, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.492348, Class: 1.000000, Obj: 0.105890, No Obj: 0.003626, Avg Recall: 0.523364, count: 107 -152: 0.539169, 0.569528 avg, 0.001000 rate, 31.646067 seconds, 38912 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.542054, Class: 1.000000, Obj: 0.110181, No Obj: 0.004337, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.535458, Class: 1.000000, Obj: 0.078923, No Obj: 0.004156, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.508345, Class: 1.000000, Obj: 0.094356, No Obj: 0.004223, Avg Recall: 0.547826, count: 115 -Region Avg IOU: 0.542588, Class: 1.000000, Obj: 0.090251, No Obj: 0.004288, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.539676, Class: 1.000000, Obj: 0.096645, No Obj: 0.004301, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.520879, Class: 1.000000, Obj: 0.109793, No Obj: 0.003894, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.504818, Class: 1.000000, Obj: 0.110869, No Obj: 0.004586, Avg Recall: 0.557971, count: 138 -Region Avg IOU: 0.493220, Class: 1.000000, Obj: 0.097117, No Obj: 0.003937, Avg Recall: 0.528302, count: 106 -153: 0.609064, 0.573482 avg, 0.001000 rate, 31.883533 seconds, 39168 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.527616, Class: 1.000000, Obj: 0.098685, No Obj: 0.004002, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.531714, Class: 1.000000, Obj: 0.134717, No Obj: 0.004531, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.534400, Class: 1.000000, Obj: 0.100440, No Obj: 0.004792, Avg Recall: 0.614286, count: 140 -Region Avg IOU: 0.526301, Class: 1.000000, Obj: 0.117553, No Obj: 0.004536, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.530916, Class: 1.000000, Obj: 0.075591, No Obj: 0.004360, Avg Recall: 0.616000, count: 125 -Region Avg IOU: 0.511797, Class: 1.000000, Obj: 0.076432, No Obj: 0.004381, Avg Recall: 0.570248, count: 121 -Region Avg IOU: 0.511063, Class: 1.000000, Obj: 0.102521, No Obj: 0.004389, Avg Recall: 0.585366, count: 82 -Region Avg IOU: 0.512627, Class: 1.000000, Obj: 0.078976, No Obj: 0.003942, Avg Recall: 0.612903, count: 93 -154: 0.583522, 0.574486 avg, 0.001000 rate, 32.500729 seconds, 39424 images -Loaded: 0.000059 seconds -Region Avg IOU: 0.512210, Class: 1.000000, Obj: 0.084312, No Obj: 0.003647, Avg Recall: 0.544554, count: 101 -Region Avg IOU: 0.518981, Class: 1.000000, Obj: 0.112264, No Obj: 0.004154, Avg Recall: 0.621951, count: 82 -Region Avg IOU: 0.525402, Class: 1.000000, Obj: 0.090198, No Obj: 0.004245, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.524754, Class: 1.000000, Obj: 0.111543, No Obj: 0.004511, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.562303, Class: 1.000000, Obj: 0.098797, No Obj: 0.004129, Avg Recall: 0.670732, count: 82 -Region Avg IOU: 0.547055, Class: 1.000000, Obj: 0.114471, No Obj: 0.004306, Avg Recall: 0.714286, count: 84 -Region Avg IOU: 0.524562, Class: 1.000000, Obj: 0.112990, No Obj: 0.004132, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.532801, Class: 1.000000, Obj: 0.099294, No Obj: 0.004251, Avg Recall: 0.646154, count: 130 -155: 0.504289, 0.567466 avg, 0.001000 rate, 32.500492 seconds, 39680 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.499766, Class: 1.000000, Obj: 0.063159, No Obj: 0.004325, Avg Recall: 0.571429, count: 119 -Region Avg IOU: 0.503205, Class: 1.000000, Obj: 0.080677, No Obj: 0.003929, Avg Recall: 0.552083, count: 96 -Region Avg IOU: 0.509778, Class: 1.000000, Obj: 0.072777, No Obj: 0.003982, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.534968, Class: 1.000000, Obj: 0.105729, No Obj: 0.004372, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.501030, Class: 1.000000, Obj: 0.087849, No Obj: 0.003586, Avg Recall: 0.558140, count: 86 -Region Avg IOU: 0.505898, Class: 1.000000, Obj: 0.083681, No Obj: 0.003924, Avg Recall: 0.608247, count: 97 -Region Avg IOU: 0.518291, Class: 1.000000, Obj: 0.100173, No Obj: 0.004470, Avg Recall: 0.604478, count: 134 -Region Avg IOU: 0.525684, Class: 1.000000, Obj: 0.096388, No Obj: 0.003885, Avg Recall: 0.590909, count: 88 -156: 0.558925, 0.566612 avg, 0.001000 rate, 32.313061 seconds, 39936 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.508899, Class: 1.000000, Obj: 0.105904, No Obj: 0.003699, Avg Recall: 0.537815, count: 119 -Region Avg IOU: 0.518522, Class: 1.000000, Obj: 0.106111, No Obj: 0.003717, Avg Recall: 0.573034, count: 89 -Region Avg IOU: 0.514758, Class: 1.000000, Obj: 0.086305, No Obj: 0.003695, Avg Recall: 0.546219, count: 119 -Region Avg IOU: 0.533483, Class: 1.000000, Obj: 0.115211, No Obj: 0.003967, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.494310, Class: 1.000000, Obj: 0.121048, No Obj: 0.003630, Avg Recall: 0.500000, count: 88 -Region Avg IOU: 0.497480, Class: 1.000000, Obj: 0.097722, No Obj: 0.003869, Avg Recall: 0.536364, count: 110 -Region Avg IOU: 0.533582, Class: 1.000000, Obj: 0.101161, No Obj: 0.003767, Avg Recall: 0.650000, count: 120 -Region Avg IOU: 0.527904, Class: 1.000000, Obj: 0.080462, No Obj: 0.004051, Avg Recall: 0.634615, count: 104 -157: 0.570134, 0.566964 avg, 0.001000 rate, 32.124958 seconds, 40192 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.529792, Class: 1.000000, Obj: 0.090387, No Obj: 0.003212, Avg Recall: 0.642857, count: 84 -Region Avg IOU: 0.520920, Class: 1.000000, Obj: 0.074789, No Obj: 0.003667, Avg Recall: 0.608000, count: 125 -Region Avg IOU: 0.525770, Class: 1.000000, Obj: 0.074268, No Obj: 0.003694, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.502048, Class: 1.000000, Obj: 0.068361, No Obj: 0.003487, Avg Recall: 0.562500, count: 128 -Region Avg IOU: 0.554582, Class: 1.000000, Obj: 0.069789, No Obj: 0.003498, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.533564, Class: 1.000000, Obj: 0.062711, No Obj: 0.003581, Avg Recall: 0.609756, count: 123 -Region Avg IOU: 0.492386, Class: 1.000000, Obj: 0.059449, No Obj: 0.003472, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.555264, Class: 1.000000, Obj: 0.092935, No Obj: 0.003911, Avg Recall: 0.666667, count: 126 -158: 0.604453, 0.570713 avg, 0.001000 rate, 32.056892 seconds, 40448 images -Loaded: 0.000057 seconds -Region Avg IOU: 0.530881, Class: 1.000000, Obj: 0.075982, No Obj: 0.003282, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.460049, Class: 1.000000, Obj: 0.050842, No Obj: 0.003099, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.532193, Class: 1.000000, Obj: 0.073411, No Obj: 0.003460, Avg Recall: 0.605042, count: 119 -Region Avg IOU: 0.515073, Class: 1.000000, Obj: 0.078916, No Obj: 0.003325, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.496349, Class: 1.000000, Obj: 0.075204, No Obj: 0.003188, Avg Recall: 0.512605, count: 119 -Region Avg IOU: 0.529895, Class: 1.000000, Obj: 0.096703, No Obj: 0.003197, Avg Recall: 0.644068, count: 118 -Region Avg IOU: 0.538268, Class: 1.000000, Obj: 0.071526, No Obj: 0.003349, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.508892, Class: 1.000000, Obj: 0.071253, No Obj: 0.003464, Avg Recall: 0.564815, count: 108 -159: 0.574743, 0.571116 avg, 0.001000 rate, 30.706553 seconds, 40704 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.500289, Class: 1.000000, Obj: 0.089487, No Obj: 0.003135, Avg Recall: 0.534091, count: 88 -Region Avg IOU: 0.534560, Class: 1.000000, Obj: 0.054443, No Obj: 0.003116, Avg Recall: 0.656250, count: 128 -Region Avg IOU: 0.550825, Class: 1.000000, Obj: 0.088766, No Obj: 0.003274, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.534184, Class: 1.000000, Obj: 0.066264, No Obj: 0.003217, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.527884, Class: 1.000000, Obj: 0.067274, No Obj: 0.003180, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.538322, Class: 1.000000, Obj: 0.073208, No Obj: 0.003152, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.505669, Class: 1.000000, Obj: 0.064512, No Obj: 0.003218, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.500915, Class: 1.000000, Obj: 0.063642, No Obj: 0.003086, Avg Recall: 0.570000, count: 100 -160: 0.539768, 0.567981 avg, 0.001000 rate, 32.419369 seconds, 40960 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 13.027225 seconds -Region Avg IOU: 0.552720, Class: 1.000000, Obj: 0.075524, No Obj: 0.003325, Avg Recall: 0.638462, count: 130 -Region Avg IOU: 0.538686, Class: 1.000000, Obj: 0.060536, No Obj: 0.003117, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.492181, Class: 1.000000, Obj: 0.071652, No Obj: 0.002827, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.543564, Class: 1.000000, Obj: 0.048729, No Obj: 0.002923, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.570209, Class: 1.000000, Obj: 0.055701, No Obj: 0.003032, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.523337, Class: 1.000000, Obj: 0.063701, No Obj: 0.003208, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.543532, Class: 1.000000, Obj: 0.083431, No Obj: 0.003032, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.542412, Class: 1.000000, Obj: 0.067720, No Obj: 0.003128, Avg Recall: 0.619048, count: 105 -161: 0.600494, 0.571232 avg, 0.001000 rate, 31.039751 seconds, 41216 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.492011, Class: 1.000000, Obj: 0.091098, No Obj: 0.002822, Avg Recall: 0.532787, count: 122 -Region Avg IOU: 0.504138, Class: 1.000000, Obj: 0.065430, No Obj: 0.002840, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.533247, Class: 1.000000, Obj: 0.087007, No Obj: 0.002917, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.592559, Class: 1.000000, Obj: 0.086041, No Obj: 0.002753, Avg Recall: 0.736264, count: 91 -Region Avg IOU: 0.519615, Class: 1.000000, Obj: 0.047021, No Obj: 0.002720, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.545996, Class: 1.000000, Obj: 0.073181, No Obj: 0.003149, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.528385, Class: 1.000000, Obj: 0.082035, No Obj: 0.003040, Avg Recall: 0.589552, count: 134 -Region Avg IOU: 0.516704, Class: 1.000000, Obj: 0.082300, No Obj: 0.002947, Avg Recall: 0.630631, count: 111 -162: 0.528764, 0.566986 avg, 0.001000 rate, 31.026333 seconds, 41472 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.507078, Class: 1.000000, Obj: 0.066948, No Obj: 0.002928, Avg Recall: 0.543307, count: 127 -Region Avg IOU: 0.538023, Class: 1.000000, Obj: 0.057249, No Obj: 0.003025, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.497984, Class: 1.000000, Obj: 0.056191, No Obj: 0.002745, Avg Recall: 0.559140, count: 93 -Region Avg IOU: 0.506980, Class: 1.000000, Obj: 0.069114, No Obj: 0.003021, Avg Recall: 0.532787, count: 122 -Region Avg IOU: 0.524068, Class: 1.000000, Obj: 0.076260, No Obj: 0.003107, Avg Recall: 0.590551, count: 127 -Region Avg IOU: 0.545414, Class: 1.000000, Obj: 0.080071, No Obj: 0.002880, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.508892, Class: 1.000000, Obj: 0.080213, No Obj: 0.002574, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.521999, Class: 1.000000, Obj: 0.057551, No Obj: 0.002723, Avg Recall: 0.604651, count: 86 -163: 0.564536, 0.566741 avg, 0.001000 rate, 29.750328 seconds, 41728 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.541630, Class: 1.000000, Obj: 0.080075, No Obj: 0.002992, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.558578, Class: 1.000000, Obj: 0.071850, No Obj: 0.002804, Avg Recall: 0.642857, count: 84 -Region Avg IOU: 0.545821, Class: 1.000000, Obj: 0.059978, No Obj: 0.002765, Avg Recall: 0.648352, count: 91 -Region Avg IOU: 0.512866, Class: 1.000000, Obj: 0.083598, No Obj: 0.002689, Avg Recall: 0.541667, count: 96 -Region Avg IOU: 0.543180, Class: 1.000000, Obj: 0.082911, No Obj: 0.003074, Avg Recall: 0.637037, count: 135 -Region Avg IOU: 0.499041, Class: 1.000000, Obj: 0.065326, No Obj: 0.002956, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.521909, Class: 1.000000, Obj: 0.075398, No Obj: 0.003172, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.548703, Class: 1.000000, Obj: 0.078988, No Obj: 0.002900, Avg Recall: 0.720430, count: 93 -164: 0.522745, 0.562341 avg, 0.001000 rate, 32.642021 seconds, 41984 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.511362, Class: 1.000000, Obj: 0.100622, No Obj: 0.003103, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.522237, Class: 1.000000, Obj: 0.087617, No Obj: 0.002950, Avg Recall: 0.533333, count: 105 -Region Avg IOU: 0.535280, Class: 1.000000, Obj: 0.086362, No Obj: 0.002833, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.545629, Class: 1.000000, Obj: 0.065150, No Obj: 0.003187, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.487483, Class: 1.000000, Obj: 0.061341, No Obj: 0.002887, Avg Recall: 0.490385, count: 104 -Region Avg IOU: 0.529668, Class: 1.000000, Obj: 0.069259, No Obj: 0.003005, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.488181, Class: 1.000000, Obj: 0.066730, No Obj: 0.003176, Avg Recall: 0.538462, count: 143 -Region Avg IOU: 0.510140, Class: 1.000000, Obj: 0.067549, No Obj: 0.003084, Avg Recall: 0.551724, count: 87 -165: 0.537195, 0.559826 avg, 0.001000 rate, 31.907608 seconds, 42240 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.558318, Class: 1.000000, Obj: 0.099586, No Obj: 0.003348, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.528202, Class: 1.000000, Obj: 0.094951, No Obj: 0.003061, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.520706, Class: 1.000000, Obj: 0.080602, No Obj: 0.002944, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.538547, Class: 1.000000, Obj: 0.081430, No Obj: 0.003243, Avg Recall: 0.620690, count: 116 -Region Avg IOU: 0.491448, Class: 1.000000, Obj: 0.097689, No Obj: 0.003249, Avg Recall: 0.534884, count: 129 -Region Avg IOU: 0.540166, Class: 1.000000, Obj: 0.086449, No Obj: 0.003000, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.565204, Class: 1.000000, Obj: 0.083454, No Obj: 0.003373, Avg Recall: 0.691729, count: 133 -Region Avg IOU: 0.530147, Class: 1.000000, Obj: 0.058718, No Obj: 0.002684, Avg Recall: 0.558140, count: 86 -166: 0.554594, 0.559303 avg, 0.001000 rate, 32.395161 seconds, 42496 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.524574, Class: 1.000000, Obj: 0.095970, No Obj: 0.003174, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.524033, Class: 1.000000, Obj: 0.082817, No Obj: 0.003021, Avg Recall: 0.590164, count: 122 -Region Avg IOU: 0.500970, Class: 1.000000, Obj: 0.092753, No Obj: 0.003194, Avg Recall: 0.564103, count: 117 -Region Avg IOU: 0.530242, Class: 1.000000, Obj: 0.126039, No Obj: 0.002916, Avg Recall: 0.576471, count: 85 -Region Avg IOU: 0.500746, Class: 1.000000, Obj: 0.073777, No Obj: 0.003028, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.559327, Class: 1.000000, Obj: 0.074921, No Obj: 0.003146, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.505294, Class: 1.000000, Obj: 0.093640, No Obj: 0.002916, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.527683, Class: 1.000000, Obj: 0.103963, No Obj: 0.003418, Avg Recall: 0.609524, count: 105 -167: 0.483168, 0.551690 avg, 0.001000 rate, 33.337273 seconds, 42752 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.502031, Class: 1.000000, Obj: 0.091353, No Obj: 0.003156, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.527305, Class: 1.000000, Obj: 0.096483, No Obj: 0.003252, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.536963, Class: 1.000000, Obj: 0.080081, No Obj: 0.003239, Avg Recall: 0.609756, count: 123 -Region Avg IOU: 0.496490, Class: 1.000000, Obj: 0.066945, No Obj: 0.003177, Avg Recall: 0.536765, count: 136 -Region Avg IOU: 0.534384, Class: 1.000000, Obj: 0.090634, No Obj: 0.003309, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.487862, Class: 1.000000, Obj: 0.099315, No Obj: 0.003296, Avg Recall: 0.534091, count: 88 -Region Avg IOU: 0.458095, Class: 1.000000, Obj: 0.082164, No Obj: 0.003019, Avg Recall: 0.495050, count: 101 -Region Avg IOU: 0.497465, Class: 1.000000, Obj: 0.075694, No Obj: 0.003059, Avg Recall: 0.573034, count: 89 -168: 0.637723, 0.560293 avg, 0.001000 rate, 29.821156 seconds, 43008 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.482069, Class: 1.000000, Obj: 0.065072, No Obj: 0.003526, Avg Recall: 0.564885, count: 131 -Region Avg IOU: 0.532380, Class: 1.000000, Obj: 0.082589, No Obj: 0.003022, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.490234, Class: 1.000000, Obj: 0.117781, No Obj: 0.003495, Avg Recall: 0.509804, count: 102 -Region Avg IOU: 0.486839, Class: 1.000000, Obj: 0.107984, No Obj: 0.003530, Avg Recall: 0.528455, count: 123 -Region Avg IOU: 0.538617, Class: 1.000000, Obj: 0.090405, No Obj: 0.003543, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.529210, Class: 1.000000, Obj: 0.083186, No Obj: 0.003171, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.545495, Class: 1.000000, Obj: 0.092151, No Obj: 0.003545, Avg Recall: 0.672000, count: 125 -Region Avg IOU: 0.559233, Class: 1.000000, Obj: 0.095137, No Obj: 0.003200, Avg Recall: 0.653846, count: 104 -169: 0.600893, 0.564353 avg, 0.001000 rate, 31.981733 seconds, 43264 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.559671, Class: 1.000000, Obj: 0.100404, No Obj: 0.003284, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.567718, Class: 1.000000, Obj: 0.115654, No Obj: 0.003492, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.540717, Class: 1.000000, Obj: 0.095295, No Obj: 0.003216, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.515490, Class: 1.000000, Obj: 0.099445, No Obj: 0.003344, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.511879, Class: 1.000000, Obj: 0.108558, No Obj: 0.003385, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.497839, Class: 1.000000, Obj: 0.094809, No Obj: 0.003667, Avg Recall: 0.583893, count: 149 -Region Avg IOU: 0.530061, Class: 1.000000, Obj: 0.102372, No Obj: 0.003426, Avg Recall: 0.635659, count: 129 -Region Avg IOU: 0.515046, Class: 1.000000, Obj: 0.091203, No Obj: 0.003442, Avg Recall: 0.578431, count: 102 -170: 0.541840, 0.562102 avg, 0.001000 rate, 30.115776 seconds, 43520 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 15.138480 seconds -Region Avg IOU: 0.481129, Class: 1.000000, Obj: 0.052385, No Obj: 0.002819, Avg Recall: 0.510204, count: 98 -Region Avg IOU: 0.453805, Class: 1.000000, Obj: 0.083183, No Obj: 0.003313, Avg Recall: 0.449612, count: 129 -Region Avg IOU: 0.464892, Class: 1.000000, Obj: 0.059794, No Obj: 0.003011, Avg Recall: 0.470588, count: 119 -Region Avg IOU: 0.511702, Class: 1.000000, Obj: 0.078970, No Obj: 0.003157, Avg Recall: 0.583333, count: 84 -Region Avg IOU: 0.468541, Class: 1.000000, Obj: 0.120349, No Obj: 0.003492, Avg Recall: 0.517544, count: 114 -Region Avg IOU: 0.457607, Class: 1.000000, Obj: 0.092984, No Obj: 0.002687, Avg Recall: 0.507692, count: 65 -Region Avg IOU: 0.448584, Class: 1.000000, Obj: 0.114023, No Obj: 0.003136, Avg Recall: 0.470588, count: 102 -Region Avg IOU: 0.476880, Class: 1.000000, Obj: 0.079898, No Obj: 0.003024, Avg Recall: 0.495327, count: 107 -171: 0.518857, 0.557777 avg, 0.001000 rate, 26.218065 seconds, 43776 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.440347, Class: 1.000000, Obj: 0.090272, No Obj: 0.003154, Avg Recall: 0.500000, count: 94 -Region Avg IOU: 0.457757, Class: 1.000000, Obj: 0.088676, No Obj: 0.003194, Avg Recall: 0.471698, count: 106 -Region Avg IOU: 0.467811, Class: 1.000000, Obj: 0.086524, No Obj: 0.003243, Avg Recall: 0.457944, count: 107 -Region Avg IOU: 0.450551, Class: 1.000000, Obj: 0.072710, No Obj: 0.003217, Avg Recall: 0.478261, count: 92 -Region Avg IOU: 0.463561, Class: 1.000000, Obj: 0.112269, No Obj: 0.003536, Avg Recall: 0.486726, count: 113 -Region Avg IOU: 0.487443, Class: 1.000000, Obj: 0.095110, No Obj: 0.003299, Avg Recall: 0.560748, count: 107 -Region Avg IOU: 0.460879, Class: 1.000000, Obj: 0.106613, No Obj: 0.003122, Avg Recall: 0.538462, count: 91 -Region Avg IOU: 0.442149, Class: 1.000000, Obj: 0.104308, No Obj: 0.002787, Avg Recall: 0.453488, count: 86 -172: 0.538244, 0.555824 avg, 0.001000 rate, 27.013451 seconds, 44032 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.497189, Class: 1.000000, Obj: 0.125336, No Obj: 0.003528, Avg Recall: 0.546296, count: 108 -Region Avg IOU: 0.481278, Class: 1.000000, Obj: 0.094412, No Obj: 0.003688, Avg Recall: 0.519481, count: 154 -Region Avg IOU: 0.491219, Class: 1.000000, Obj: 0.108787, No Obj: 0.003177, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.506714, Class: 1.000000, Obj: 0.106317, No Obj: 0.003184, Avg Recall: 0.581395, count: 86 -Region Avg IOU: 0.451300, Class: 1.000000, Obj: 0.120251, No Obj: 0.003223, Avg Recall: 0.452632, count: 95 -Region Avg IOU: 0.448628, Class: 1.000000, Obj: 0.076415, No Obj: 0.003230, Avg Recall: 0.438849, count: 139 -Region Avg IOU: 0.470177, Class: 1.000000, Obj: 0.100821, No Obj: 0.003072, Avg Recall: 0.473118, count: 93 -Region Avg IOU: 0.469248, Class: 1.000000, Obj: 0.122461, No Obj: 0.003680, Avg Recall: 0.516949, count: 118 -173: 0.624248, 0.562666 avg, 0.001000 rate, 26.746489 seconds, 44288 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.454131, Class: 1.000000, Obj: 0.107042, No Obj: 0.003354, Avg Recall: 0.518519, count: 108 -Region Avg IOU: 0.461449, Class: 1.000000, Obj: 0.092815, No Obj: 0.003294, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.461958, Class: 1.000000, Obj: 0.105094, No Obj: 0.003111, Avg Recall: 0.479167, count: 96 -Region Avg IOU: 0.476845, Class: 1.000000, Obj: 0.075338, No Obj: 0.003232, Avg Recall: 0.525424, count: 118 -Region Avg IOU: 0.458042, Class: 1.000000, Obj: 0.087644, No Obj: 0.002918, Avg Recall: 0.464646, count: 99 -Region Avg IOU: 0.481640, Class: 1.000000, Obj: 0.080338, No Obj: 0.003329, Avg Recall: 0.500000, count: 118 -Region Avg IOU: 0.444117, Class: 1.000000, Obj: 0.099309, No Obj: 0.003353, Avg Recall: 0.493333, count: 75 -Region Avg IOU: 0.517418, Class: 1.000000, Obj: 0.128920, No Obj: 0.003616, Avg Recall: 0.620370, count: 108 -174: 0.604688, 0.566868 avg, 0.001000 rate, 26.486298 seconds, 44544 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.505844, Class: 1.000000, Obj: 0.113091, No Obj: 0.003308, Avg Recall: 0.534653, count: 101 -Region Avg IOU: 0.488278, Class: 1.000000, Obj: 0.123296, No Obj: 0.003725, Avg Recall: 0.577586, count: 116 -Region Avg IOU: 0.447411, Class: 1.000000, Obj: 0.124084, No Obj: 0.003321, Avg Recall: 0.465909, count: 88 -Region Avg IOU: 0.450528, Class: 1.000000, Obj: 0.123453, No Obj: 0.003657, Avg Recall: 0.466667, count: 120 -Region Avg IOU: 0.417613, Class: 1.000000, Obj: 0.105254, No Obj: 0.002963, Avg Recall: 0.389381, count: 113 -Region Avg IOU: 0.473997, Class: 1.000000, Obj: 0.113191, No Obj: 0.003555, Avg Recall: 0.514286, count: 105 -Region Avg IOU: 0.488334, Class: 1.000000, Obj: 0.107833, No Obj: 0.003584, Avg Recall: 0.536364, count: 110 -Region Avg IOU: 0.494724, Class: 1.000000, Obj: 0.149208, No Obj: 0.003138, Avg Recall: 0.526882, count: 93 -175: 0.576241, 0.567806 avg, 0.001000 rate, 26.456499 seconds, 44800 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.429550, Class: 1.000000, Obj: 0.098495, No Obj: 0.003134, Avg Recall: 0.410526, count: 95 -Region Avg IOU: 0.472329, Class: 1.000000, Obj: 0.102012, No Obj: 0.003307, Avg Recall: 0.516949, count: 118 -Region Avg IOU: 0.462459, Class: 1.000000, Obj: 0.088257, No Obj: 0.003167, Avg Recall: 0.412844, count: 109 -Region Avg IOU: 0.499383, Class: 1.000000, Obj: 0.103046, No Obj: 0.003918, Avg Recall: 0.560345, count: 116 -Region Avg IOU: 0.476051, Class: 1.000000, Obj: 0.138559, No Obj: 0.003426, Avg Recall: 0.500000, count: 84 -Region Avg IOU: 0.407691, Class: 1.000000, Obj: 0.091248, No Obj: 0.002915, Avg Recall: 0.324074, count: 108 -Region Avg IOU: 0.442676, Class: 1.000000, Obj: 0.125793, No Obj: 0.003326, Avg Recall: 0.469565, count: 115 -Region Avg IOU: 0.433698, Class: 1.000000, Obj: 0.104483, No Obj: 0.003747, Avg Recall: 0.421429, count: 140 -176: 0.632938, 0.574319 avg, 0.001000 rate, 25.325441 seconds, 45056 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.473222, Class: 1.000000, Obj: 0.107376, No Obj: 0.003719, Avg Recall: 0.480620, count: 129 -Region Avg IOU: 0.454991, Class: 1.000000, Obj: 0.118958, No Obj: 0.003770, Avg Recall: 0.510791, count: 139 -Region Avg IOU: 0.482222, Class: 1.000000, Obj: 0.155214, No Obj: 0.003503, Avg Recall: 0.514851, count: 101 -Region Avg IOU: 0.490107, Class: 1.000000, Obj: 0.127569, No Obj: 0.003200, Avg Recall: 0.510870, count: 92 -Region Avg IOU: 0.468924, Class: 1.000000, Obj: 0.106723, No Obj: 0.003077, Avg Recall: 0.485714, count: 105 -Region Avg IOU: 0.439609, Class: 1.000000, Obj: 0.110397, No Obj: 0.003083, Avg Recall: 0.482759, count: 87 -Region Avg IOU: 0.507612, Class: 1.000000, Obj: 0.096243, No Obj: 0.003600, Avg Recall: 0.600000, count: 135 -Region Avg IOU: 0.503114, Class: 1.000000, Obj: 0.120481, No Obj: 0.003397, Avg Recall: 0.574257, count: 101 -177: 0.610473, 0.577934 avg, 0.001000 rate, 25.403477 seconds, 45312 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.515381, Class: 1.000000, Obj: 0.119341, No Obj: 0.003791, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.446205, Class: 1.000000, Obj: 0.105606, No Obj: 0.003490, Avg Recall: 0.504132, count: 121 -Region Avg IOU: 0.457075, Class: 1.000000, Obj: 0.126279, No Obj: 0.003156, Avg Recall: 0.430000, count: 100 -Region Avg IOU: 0.469621, Class: 1.000000, Obj: 0.102930, No Obj: 0.003858, Avg Recall: 0.472441, count: 127 -Region Avg IOU: 0.459906, Class: 1.000000, Obj: 0.078400, No Obj: 0.003433, Avg Recall: 0.467742, count: 124 -Region Avg IOU: 0.473479, Class: 1.000000, Obj: 0.133160, No Obj: 0.003688, Avg Recall: 0.510204, count: 98 -Region Avg IOU: 0.475128, Class: 1.000000, Obj: 0.100418, No Obj: 0.003175, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.441854, Class: 1.000000, Obj: 0.105324, No Obj: 0.003423, Avg Recall: 0.431034, count: 116 -178: 0.602541, 0.580395 avg, 0.001000 rate, 27.730932 seconds, 45568 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.443358, Class: 1.000000, Obj: 0.099901, No Obj: 0.003775, Avg Recall: 0.523077, count: 130 -Region Avg IOU: 0.486039, Class: 1.000000, Obj: 0.095059, No Obj: 0.003418, Avg Recall: 0.519231, count: 104 -Region Avg IOU: 0.411321, Class: 1.000000, Obj: 0.114616, No Obj: 0.003368, Avg Recall: 0.436782, count: 87 -Region Avg IOU: 0.415361, Class: 1.000000, Obj: 0.075015, No Obj: 0.003136, Avg Recall: 0.413793, count: 116 -Region Avg IOU: 0.405699, Class: 1.000000, Obj: 0.097763, No Obj: 0.002946, Avg Recall: 0.356322, count: 87 -Region Avg IOU: 0.463564, Class: 1.000000, Obj: 0.111392, No Obj: 0.003479, Avg Recall: 0.490909, count: 110 -Region Avg IOU: 0.456576, Class: 1.000000, Obj: 0.117606, No Obj: 0.003415, Avg Recall: 0.505263, count: 95 -Region Avg IOU: 0.456468, Class: 1.000000, Obj: 0.090431, No Obj: 0.003302, Avg Recall: 0.464286, count: 112 -179: 0.576261, 0.579982 avg, 0.001000 rate, 27.303535 seconds, 45824 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.465798, Class: 1.000000, Obj: 0.097557, No Obj: 0.003475, Avg Recall: 0.482759, count: 116 -Region Avg IOU: 0.499483, Class: 1.000000, Obj: 0.144702, No Obj: 0.004088, Avg Recall: 0.530303, count: 132 -Region Avg IOU: 0.481615, Class: 1.000000, Obj: 0.104441, No Obj: 0.003306, Avg Recall: 0.505155, count: 97 -Region Avg IOU: 0.471461, Class: 1.000000, Obj: 0.075226, No Obj: 0.003286, Avg Recall: 0.495327, count: 107 -Region Avg IOU: 0.480685, Class: 1.000000, Obj: 0.158093, No Obj: 0.003433, Avg Recall: 0.555556, count: 99 -Region Avg IOU: 0.464440, Class: 1.000000, Obj: 0.133661, No Obj: 0.003760, Avg Recall: 0.489209, count: 139 -Region Avg IOU: 0.470093, Class: 1.000000, Obj: 0.094332, No Obj: 0.003170, Avg Recall: 0.478723, count: 94 -Region Avg IOU: 0.439196, Class: 1.000000, Obj: 0.123699, No Obj: 0.003541, Avg Recall: 0.481481, count: 108 -180: 0.579148, 0.579898 avg, 0.001000 rate, 26.453299 seconds, 46080 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 17.926586 seconds -Region Avg IOU: 0.500209, Class: 1.000000, Obj: 0.113284, No Obj: 0.003474, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.463103, Class: 1.000000, Obj: 0.090147, No Obj: 0.003256, Avg Recall: 0.489583, count: 96 -Region Avg IOU: 0.446880, Class: 1.000000, Obj: 0.086222, No Obj: 0.002855, Avg Recall: 0.453333, count: 75 -Region Avg IOU: 0.490225, Class: 1.000000, Obj: 0.080806, No Obj: 0.003219, Avg Recall: 0.595745, count: 94 -Region Avg IOU: 0.479181, Class: 1.000000, Obj: 0.088091, No Obj: 0.003247, Avg Recall: 0.463636, count: 110 -Region Avg IOU: 0.447914, Class: 1.000000, Obj: 0.105986, No Obj: 0.003241, Avg Recall: 0.488889, count: 135 -Region Avg IOU: 0.418938, Class: 1.000000, Obj: 0.070635, No Obj: 0.003163, Avg Recall: 0.419048, count: 105 -Region Avg IOU: 0.444998, Class: 1.000000, Obj: 0.096170, No Obj: 0.003571, Avg Recall: 0.481203, count: 133 -181: 0.581560, 0.580064 avg, 0.001000 rate, 24.689314 seconds, 46336 images -Loaded: 0.131877 seconds -Region Avg IOU: 0.480170, Class: 1.000000, Obj: 0.107316, No Obj: 0.003158, Avg Recall: 0.500000, count: 84 -Region Avg IOU: 0.447835, Class: 1.000000, Obj: 0.101756, No Obj: 0.003351, Avg Recall: 0.470588, count: 119 -Region Avg IOU: 0.533532, Class: 1.000000, Obj: 0.101988, No Obj: 0.003325, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.473670, Class: 1.000000, Obj: 0.123709, No Obj: 0.003357, Avg Recall: 0.481132, count: 106 -Region Avg IOU: 0.463010, Class: 1.000000, Obj: 0.137231, No Obj: 0.003766, Avg Recall: 0.486301, count: 146 -Region Avg IOU: 0.447094, Class: 1.000000, Obj: 0.116388, No Obj: 0.003811, Avg Recall: 0.466216, count: 148 -Region Avg IOU: 0.448963, Class: 1.000000, Obj: 0.107238, No Obj: 0.003465, Avg Recall: 0.455224, count: 134 -Region Avg IOU: 0.438521, Class: 1.000000, Obj: 0.112710, No Obj: 0.003408, Avg Recall: 0.504425, count: 113 -182: 0.713998, 0.593458 avg, 0.001000 rate, 26.254196 seconds, 46592 images -Loaded: 0.014915 seconds -Region Avg IOU: 0.425564, Class: 1.000000, Obj: 0.103665, No Obj: 0.003048, Avg Recall: 0.423913, count: 92 -Region Avg IOU: 0.465626, Class: 1.000000, Obj: 0.101386, No Obj: 0.003244, Avg Recall: 0.477477, count: 111 -Region Avg IOU: 0.464784, Class: 1.000000, Obj: 0.129287, No Obj: 0.003645, Avg Recall: 0.475410, count: 122 -Region Avg IOU: 0.431915, Class: 1.000000, Obj: 0.130362, No Obj: 0.003351, Avg Recall: 0.409091, count: 88 -Region Avg IOU: 0.417298, Class: 1.000000, Obj: 0.095562, No Obj: 0.003174, Avg Recall: 0.440000, count: 100 -Region Avg IOU: 0.482607, Class: 1.000000, Obj: 0.092467, No Obj: 0.003300, Avg Recall: 0.529412, count: 102 -Region Avg IOU: 0.486104, Class: 1.000000, Obj: 0.098715, No Obj: 0.004233, Avg Recall: 0.562500, count: 144 -Region Avg IOU: 0.438563, Class: 1.000000, Obj: 0.105387, No Obj: 0.003610, Avg Recall: 0.415929, count: 113 -183: 0.610063, 0.595118 avg, 0.001000 rate, 25.105371 seconds, 46848 images -Loaded: 0.138609 seconds -Region Avg IOU: 0.494117, Class: 1.000000, Obj: 0.097967, No Obj: 0.003372, Avg Recall: 0.527273, count: 110 -Region Avg IOU: 0.477352, Class: 1.000000, Obj: 0.108471, No Obj: 0.003202, Avg Recall: 0.550562, count: 89 -Region Avg IOU: 0.451031, Class: 1.000000, Obj: 0.121437, No Obj: 0.003703, Avg Recall: 0.446429, count: 112 -Region Avg IOU: 0.459631, Class: 1.000000, Obj: 0.105033, No Obj: 0.003123, Avg Recall: 0.493151, count: 73 -Region Avg IOU: 0.462910, Class: 1.000000, Obj: 0.121629, No Obj: 0.003583, Avg Recall: 0.500000, count: 110 -Region Avg IOU: 0.494166, Class: 1.000000, Obj: 0.111216, No Obj: 0.003183, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.434141, Class: 1.000000, Obj: 0.103568, No Obj: 0.003614, Avg Recall: 0.496296, count: 135 -Region Avg IOU: 0.439582, Class: 1.000000, Obj: 0.104772, No Obj: 0.003414, Avg Recall: 0.477477, count: 111 -184: 0.548821, 0.590489 avg, 0.001000 rate, 23.672352 seconds, 47104 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.426647, Class: 1.000000, Obj: 0.131934, No Obj: 0.003380, Avg Recall: 0.413793, count: 87 -Region Avg IOU: 0.428895, Class: 1.000000, Obj: 0.148367, No Obj: 0.003529, Avg Recall: 0.418803, count: 117 -Region Avg IOU: 0.453168, Class: 1.000000, Obj: 0.127040, No Obj: 0.003701, Avg Recall: 0.467391, count: 92 -Region Avg IOU: 0.476311, Class: 1.000000, Obj: 0.102059, No Obj: 0.003327, Avg Recall: 0.471264, count: 87 -Region Avg IOU: 0.469608, Class: 1.000000, Obj: 0.149679, No Obj: 0.003665, Avg Recall: 0.495146, count: 103 -Region Avg IOU: 0.483301, Class: 1.000000, Obj: 0.159973, No Obj: 0.003962, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.477573, Class: 1.000000, Obj: 0.132802, No Obj: 0.003914, Avg Recall: 0.524590, count: 122 -Region Avg IOU: 0.491724, Class: 1.000000, Obj: 0.111686, No Obj: 0.003671, Avg Recall: 0.527778, count: 108 -185: 0.503760, 0.581816 avg, 0.001000 rate, 24.680389 seconds, 47360 images -Loaded: 1.088855 seconds -Region Avg IOU: 0.472014, Class: 1.000000, Obj: 0.103644, No Obj: 0.003710, Avg Recall: 0.553571, count: 112 -Region Avg IOU: 0.487874, Class: 1.000000, Obj: 0.125537, No Obj: 0.004164, Avg Recall: 0.526316, count: 133 -Region Avg IOU: 0.433588, Class: 1.000000, Obj: 0.103982, No Obj: 0.003144, Avg Recall: 0.409091, count: 110 -Region Avg IOU: 0.454500, Class: 1.000000, Obj: 0.075830, No Obj: 0.003186, Avg Recall: 0.500000, count: 110 -Region Avg IOU: 0.462794, Class: 1.000000, Obj: 0.127424, No Obj: 0.003703, Avg Recall: 0.510638, count: 94 -Region Avg IOU: 0.442027, Class: 1.000000, Obj: 0.097186, No Obj: 0.003937, Avg Recall: 0.438849, count: 139 -Region Avg IOU: 0.470627, Class: 1.000000, Obj: 0.113175, No Obj: 0.003698, Avg Recall: 0.480392, count: 102 -Region Avg IOU: 0.477619, Class: 1.000000, Obj: 0.120795, No Obj: 0.003948, Avg Recall: 0.533333, count: 120 -186: 0.632330, 0.586867 avg, 0.001000 rate, 23.243134 seconds, 47616 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.447154, Class: 1.000000, Obj: 0.120302, No Obj: 0.003517, Avg Recall: 0.457944, count: 107 -Region Avg IOU: 0.473084, Class: 1.000000, Obj: 0.120254, No Obj: 0.004256, Avg Recall: 0.500000, count: 118 -Region Avg IOU: 0.458803, Class: 1.000000, Obj: 0.108338, No Obj: 0.003661, Avg Recall: 0.456311, count: 103 -Region Avg IOU: 0.463878, Class: 1.000000, Obj: 0.126643, No Obj: 0.003207, Avg Recall: 0.494845, count: 97 -Region Avg IOU: 0.478364, Class: 1.000000, Obj: 0.125895, No Obj: 0.003716, Avg Recall: 0.510204, count: 98 -Region Avg IOU: 0.468985, Class: 1.000000, Obj: 0.140309, No Obj: 0.003402, Avg Recall: 0.510204, count: 98 -Region Avg IOU: 0.419872, Class: 1.000000, Obj: 0.100322, No Obj: 0.003288, Avg Recall: 0.416667, count: 108 -Region Avg IOU: 0.487514, Class: 1.000000, Obj: 0.128420, No Obj: 0.003905, Avg Recall: 0.525000, count: 120 -187: 0.577012, 0.585882 avg, 0.001000 rate, 25.505939 seconds, 47872 images -Loaded: 0.000052 seconds -Region Avg IOU: 0.423586, Class: 1.000000, Obj: 0.085614, No Obj: 0.003592, Avg Recall: 0.446429, count: 112 -Region Avg IOU: 0.466008, Class: 1.000000, Obj: 0.162833, No Obj: 0.004007, Avg Recall: 0.472527, count: 91 -Region Avg IOU: 0.438556, Class: 1.000000, Obj: 0.105876, No Obj: 0.003583, Avg Recall: 0.421053, count: 95 -Region Avg IOU: 0.424805, Class: 1.000000, Obj: 0.111049, No Obj: 0.003919, Avg Recall: 0.413223, count: 121 -Region Avg IOU: 0.468941, Class: 1.000000, Obj: 0.155970, No Obj: 0.003106, Avg Recall: 0.467532, count: 77 -Region Avg IOU: 0.476988, Class: 1.000000, Obj: 0.128134, No Obj: 0.004264, Avg Recall: 0.516129, count: 124 -Region Avg IOU: 0.462442, Class: 1.000000, Obj: 0.141031, No Obj: 0.004074, Avg Recall: 0.525862, count: 116 -Region Avg IOU: 0.461978, Class: 1.000000, Obj: 0.127972, No Obj: 0.004025, Avg Recall: 0.521008, count: 119 -188: 0.634507, 0.590744 avg, 0.001000 rate, 25.240644 seconds, 48128 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.447367, Class: 1.000000, Obj: 0.117257, No Obj: 0.003829, Avg Recall: 0.486239, count: 109 -Region Avg IOU: 0.436301, Class: 1.000000, Obj: 0.112827, No Obj: 0.003130, Avg Recall: 0.430233, count: 86 -Region Avg IOU: 0.463606, Class: 1.000000, Obj: 0.122693, No Obj: 0.003952, Avg Recall: 0.447619, count: 105 -Region Avg IOU: 0.490176, Class: 1.000000, Obj: 0.132643, No Obj: 0.004450, Avg Recall: 0.522388, count: 134 -Region Avg IOU: 0.426716, Class: 1.000000, Obj: 0.118543, No Obj: 0.003881, Avg Recall: 0.410256, count: 117 -Region Avg IOU: 0.476822, Class: 1.000000, Obj: 0.097372, No Obj: 0.003723, Avg Recall: 0.474359, count: 78 -Region Avg IOU: 0.506245, Class: 1.000000, Obj: 0.123808, No Obj: 0.004100, Avg Recall: 0.604839, count: 124 -Region Avg IOU: 0.437213, Class: 1.000000, Obj: 0.119725, No Obj: 0.003760, Avg Recall: 0.451327, count: 113 -189: 0.647084, 0.596378 avg, 0.001000 rate, 24.633060 seconds, 48384 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.441234, Class: 1.000000, Obj: 0.080884, No Obj: 0.003457, Avg Recall: 0.471264, count: 87 -Region Avg IOU: 0.494793, Class: 1.000000, Obj: 0.114607, No Obj: 0.003851, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.453271, Class: 1.000000, Obj: 0.135807, No Obj: 0.003788, Avg Recall: 0.457944, count: 107 -Region Avg IOU: 0.437457, Class: 1.000000, Obj: 0.108388, No Obj: 0.003723, Avg Recall: 0.486842, count: 76 -Region Avg IOU: 0.419907, Class: 1.000000, Obj: 0.119169, No Obj: 0.004039, Avg Recall: 0.443478, count: 115 -Region Avg IOU: 0.442240, Class: 1.000000, Obj: 0.123235, No Obj: 0.003838, Avg Recall: 0.467213, count: 122 -Region Avg IOU: 0.444937, Class: 1.000000, Obj: 0.094446, No Obj: 0.003759, Avg Recall: 0.522124, count: 113 -Region Avg IOU: 0.411552, Class: 1.000000, Obj: 0.087384, No Obj: 0.003403, Avg Recall: 0.442105, count: 95 -190: 0.567882, 0.593529 avg, 0.001000 rate, 24.922815 seconds, 48640 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.389280 seconds -Region Avg IOU: 0.357753, Class: 1.000000, Obj: 0.081710, No Obj: 0.003895, Avg Recall: 0.308824, count: 136 -Region Avg IOU: 0.361736, Class: 1.000000, Obj: 0.051902, No Obj: 0.002895, Avg Recall: 0.310680, count: 103 -Region Avg IOU: 0.336708, Class: 1.000000, Obj: 0.071547, No Obj: 0.003330, Avg Recall: 0.300971, count: 103 -Region Avg IOU: 0.385596, Class: 1.000000, Obj: 0.075226, No Obj: 0.003704, Avg Recall: 0.321429, count: 112 -Region Avg IOU: 0.398148, Class: 1.000000, Obj: 0.066442, No Obj: 0.003747, Avg Recall: 0.394958, count: 119 -Region Avg IOU: 0.376871, Class: 1.000000, Obj: 0.057388, No Obj: 0.003057, Avg Recall: 0.310680, count: 103 -Region Avg IOU: 0.340765, Class: 1.000000, Obj: 0.083329, No Obj: 0.003283, Avg Recall: 0.274725, count: 91 -Region Avg IOU: 0.381157, Class: 1.000000, Obj: 0.055527, No Obj: 0.003397, Avg Recall: 0.366972, count: 109 -191: 0.727277, 0.606903 avg, 0.001000 rate, 16.856405 seconds, 48896 images -Loaded: 4.240136 seconds -Region Avg IOU: 0.359623, Class: 1.000000, Obj: 0.066097, No Obj: 0.003667, Avg Recall: 0.319149, count: 94 -Region Avg IOU: 0.363995, Class: 1.000000, Obj: 0.083958, No Obj: 0.003414, Avg Recall: 0.283333, count: 120 -Region Avg IOU: 0.419348, Class: 1.000000, Obj: 0.088396, No Obj: 0.003964, Avg Recall: 0.464646, count: 99 -Region Avg IOU: 0.352398, Class: 1.000000, Obj: 0.066927, No Obj: 0.003456, Avg Recall: 0.330189, count: 106 -Region Avg IOU: 0.353639, Class: 1.000000, Obj: 0.068691, No Obj: 0.003286, Avg Recall: 0.322222, count: 90 -Region Avg IOU: 0.325874, Class: 1.000000, Obj: 0.055531, No Obj: 0.003208, Avg Recall: 0.277228, count: 101 -Region Avg IOU: 0.372916, Class: 1.000000, Obj: 0.077257, No Obj: 0.003424, Avg Recall: 0.308411, count: 107 -Region Avg IOU: 0.365903, Class: 1.000000, Obj: 0.086379, No Obj: 0.003331, Avg Recall: 0.375000, count: 104 -192: 0.633843, 0.609597 avg, 0.001000 rate, 14.622401 seconds, 49152 images -Loaded: 3.502695 seconds -Region Avg IOU: 0.342467, Class: 1.000000, Obj: 0.061541, No Obj: 0.003150, Avg Recall: 0.287129, count: 101 -Region Avg IOU: 0.330145, Class: 1.000000, Obj: 0.082801, No Obj: 0.003862, Avg Recall: 0.279661, count: 118 -Region Avg IOU: 0.362561, Class: 1.000000, Obj: 0.075847, No Obj: 0.003158, Avg Recall: 0.376238, count: 101 -Region Avg IOU: 0.339829, Class: 1.000000, Obj: 0.083013, No Obj: 0.003459, Avg Recall: 0.276316, count: 76 -Region Avg IOU: 0.418529, Class: 1.000000, Obj: 0.107623, No Obj: 0.004342, Avg Recall: 0.370370, count: 135 -Region Avg IOU: 0.355010, Class: 1.000000, Obj: 0.092132, No Obj: 0.003719, Avg Recall: 0.336538, count: 104 -Region Avg IOU: 0.360217, Class: 1.000000, Obj: 0.085981, No Obj: 0.004245, Avg Recall: 0.304688, count: 128 -Region Avg IOU: 0.413974, Class: 1.000000, Obj: 0.079189, No Obj: 0.003926, Avg Recall: 0.443548, count: 124 -193: 0.691112, 0.617749 avg, 0.001000 rate, 18.883982 seconds, 49408 images -Loaded: 1.808375 seconds -Region Avg IOU: 0.369474, Class: 1.000000, Obj: 0.112061, No Obj: 0.004345, Avg Recall: 0.338983, count: 118 -Region Avg IOU: 0.330758, Class: 1.000000, Obj: 0.055576, No Obj: 0.003486, Avg Recall: 0.292453, count: 106 -Region Avg IOU: 0.367592, Class: 1.000000, Obj: 0.085063, No Obj: 0.004129, Avg Recall: 0.333333, count: 144 -Region Avg IOU: 0.337230, Class: 1.000000, Obj: 0.072858, No Obj: 0.003769, Avg Recall: 0.280702, count: 114 -Region Avg IOU: 0.378224, Class: 1.000000, Obj: 0.086360, No Obj: 0.004378, Avg Recall: 0.317568, count: 148 -Region Avg IOU: 0.314713, Class: 1.000000, Obj: 0.078959, No Obj: 0.003699, Avg Recall: 0.310680, count: 103 -Region Avg IOU: 0.405721, Class: 1.000000, Obj: 0.093427, No Obj: 0.003440, Avg Recall: 0.377193, count: 114 -Region Avg IOU: 0.411972, Class: 1.000000, Obj: 0.108228, No Obj: 0.003948, Avg Recall: 0.341880, count: 117 -194: 0.828466, 0.638820 avg, 0.001000 rate, 15.501010 seconds, 49664 images -Loaded: 6.187396 seconds -Region Avg IOU: 0.357360, Class: 1.000000, Obj: 0.086853, No Obj: 0.003257, Avg Recall: 0.291667, count: 72 -Region Avg IOU: 0.365177, Class: 1.000000, Obj: 0.070723, No Obj: 0.003717, Avg Recall: 0.302083, count: 96 -Region Avg IOU: 0.378155, Class: 1.000000, Obj: 0.105902, No Obj: 0.003755, Avg Recall: 0.350000, count: 80 -Region Avg IOU: 0.361464, Class: 1.000000, Obj: 0.075428, No Obj: 0.003763, Avg Recall: 0.394495, count: 109 -Region Avg IOU: 0.350907, Class: 1.000000, Obj: 0.086328, No Obj: 0.004092, Avg Recall: 0.336000, count: 125 -Region Avg IOU: 0.377233, Class: 1.000000, Obj: 0.077090, No Obj: 0.004082, Avg Recall: 0.362205, count: 127 -Region Avg IOU: 0.393884, Class: 1.000000, Obj: 0.095154, No Obj: 0.004161, Avg Recall: 0.393162, count: 117 -Region Avg IOU: 0.327261, Class: 1.000000, Obj: 0.101087, No Obj: 0.003872, Avg Recall: 0.298969, count: 97 -195: 0.586162, 0.633555 avg, 0.001000 rate, 13.714569 seconds, 49920 images -Loaded: 8.035774 seconds -Region Avg IOU: 0.379192, Class: 1.000000, Obj: 0.091563, No Obj: 0.004212, Avg Recall: 0.352941, count: 119 -Region Avg IOU: 0.337301, Class: 1.000000, Obj: 0.082258, No Obj: 0.004341, Avg Recall: 0.317308, count: 104 -Region Avg IOU: 0.363574, Class: 1.000000, Obj: 0.084063, No Obj: 0.004620, Avg Recall: 0.344444, count: 90 -Region Avg IOU: 0.363876, Class: 1.000000, Obj: 0.096150, No Obj: 0.003974, Avg Recall: 0.333333, count: 96 -Region Avg IOU: 0.380768, Class: 1.000000, Obj: 0.094307, No Obj: 0.003449, Avg Recall: 0.320513, count: 78 -Region Avg IOU: 0.349963, Class: 1.000000, Obj: 0.091002, No Obj: 0.004015, Avg Recall: 0.310345, count: 87 -Region Avg IOU: 0.393962, Class: 1.000000, Obj: 0.141130, No Obj: 0.004381, Avg Recall: 0.326087, count: 92 -Region Avg IOU: 0.382323, Class: 1.000000, Obj: 0.113491, No Obj: 0.004180, Avg Recall: 0.369565, count: 92 -196: 0.673955, 0.637595 avg, 0.001000 rate, 17.881556 seconds, 50176 images -Loaded: 2.167227 seconds -Region Avg IOU: 0.336170, Class: 1.000000, Obj: 0.123769, No Obj: 0.004700, Avg Recall: 0.233645, count: 107 -Region Avg IOU: 0.358022, Class: 1.000000, Obj: 0.110727, No Obj: 0.004211, Avg Recall: 0.342105, count: 114 -Region Avg IOU: 0.338237, Class: 1.000000, Obj: 0.099150, No Obj: 0.004520, Avg Recall: 0.287129, count: 101 -Region Avg IOU: 0.380898, Class: 1.000000, Obj: 0.085897, No Obj: 0.004333, Avg Recall: 0.375000, count: 104 -Region Avg IOU: 0.380200, Class: 1.000000, Obj: 0.125273, No Obj: 0.005050, Avg Recall: 0.320000, count: 125 -Region Avg IOU: 0.383340, Class: 1.000000, Obj: 0.104173, No Obj: 0.004312, Avg Recall: 0.353982, count: 113 -Region Avg IOU: 0.388301, Class: 1.000000, Obj: 0.145859, No Obj: 0.004350, Avg Recall: 0.342105, count: 114 -Region Avg IOU: 0.377442, Class: 1.000000, Obj: 0.112184, No Obj: 0.004255, Avg Recall: 0.345133, count: 113 -197: 0.683811, 0.642216 avg, 0.001000 rate, 14.917688 seconds, 50432 images -Loaded: 3.782606 seconds -Region Avg IOU: 0.367873, Class: 1.000000, Obj: 0.088640, No Obj: 0.004553, Avg Recall: 0.336538, count: 104 -Region Avg IOU: 0.359729, Class: 1.000000, Obj: 0.118585, No Obj: 0.005014, Avg Recall: 0.342105, count: 114 -Region Avg IOU: 0.371009, Class: 1.000000, Obj: 0.122101, No Obj: 0.004886, Avg Recall: 0.325926, count: 135 -Region Avg IOU: 0.370362, Class: 1.000000, Obj: 0.112385, No Obj: 0.004745, Avg Recall: 0.353448, count: 116 -Region Avg IOU: 0.355853, Class: 1.000000, Obj: 0.127635, No Obj: 0.004247, Avg Recall: 0.300000, count: 100 -Region Avg IOU: 0.322251, Class: 1.000000, Obj: 0.098767, No Obj: 0.004471, Avg Recall: 0.284483, count: 116 -Region Avg IOU: 0.378648, Class: 1.000000, Obj: 0.103531, No Obj: 0.005145, Avg Recall: 0.310000, count: 100 -Region Avg IOU: 0.382721, Class: 1.000000, Obj: 0.100594, No Obj: 0.004937, Avg Recall: 0.379310, count: 116 -198: 0.721391, 0.650134 avg, 0.001000 rate, 14.267272 seconds, 50688 images -Loaded: 6.200374 seconds -Region Avg IOU: 0.388631, Class: 1.000000, Obj: 0.150475, No Obj: 0.005102, Avg Recall: 0.351852, count: 108 -Region Avg IOU: 0.372265, Class: 1.000000, Obj: 0.136388, No Obj: 0.004938, Avg Recall: 0.336957, count: 92 -Region Avg IOU: 0.331873, Class: 1.000000, Obj: 0.117507, No Obj: 0.003932, Avg Recall: 0.296296, count: 108 -Region Avg IOU: 0.425035, Class: 1.000000, Obj: 0.128877, No Obj: 0.006038, Avg Recall: 0.427536, count: 138 -Region Avg IOU: 0.367144, Class: 1.000000, Obj: 0.118568, No Obj: 0.005402, Avg Recall: 0.389706, count: 136 -Region Avg IOU: 0.353911, Class: 1.000000, Obj: 0.096323, No Obj: 0.004398, Avg Recall: 0.359223, count: 103 -Region Avg IOU: 0.378827, Class: 1.000000, Obj: 0.124610, No Obj: 0.005113, Avg Recall: 0.344086, count: 93 -Region Avg IOU: 0.351715, Class: 1.000000, Obj: 0.119414, No Obj: 0.005280, ASaving weights to ./person9300/backup/tyvp_93_9c512_200.weights -vg Recall: 0.314815, count: 108 -199: 0.639123, 0.649033 avg, 0.001000 rate, 15.981627 seconds, 50944 images -Loaded: 5.454707 seconds -Region Avg IOU: 0.353175, Class: 1.000000, Obj: 0.100851, No Obj: 0.004510, Avg Recall: 0.329268, count: 82 -Region Avg IOU: 0.372944, Class: 1.000000, Obj: 0.145822, No Obj: 0.005489, Avg Recall: 0.322581, count: 124 -Region Avg IOU: 0.365239, Class: 1.000000, Obj: 0.124562, No Obj: 0.005111, Avg Recall: 0.330189, count: 106 -Region Avg IOU: 0.321408, Class: 1.000000, Obj: 0.107163, No Obj: 0.004974, Avg Recall: 0.243697, count: 119 -Region Avg IOU: 0.355257, Class: 1.000000, Obj: 0.107952, No Obj: 0.005056, Avg Recall: 0.324074, count: 108 -Region Avg IOU: 0.393713, Class: 1.000000, Obj: 0.131490, No Obj: 0.005353, Avg Recall: 0.288000, count: 125 -Region Avg IOU: 0.370179, Class: 1.000000, Obj: 0.100964, No Obj: 0.005206, Avg Recall: 0.366337, count: 101 -Region Avg IOU: 0.416135, Class: 1.000000, Obj: 0.130614, No Obj: 0.005238, Avg Recall: 0.385965, count: 114 -200: 0.621944, 0.646324 avg, 0.001000 rate, 15.257389 seconds, 51200 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000070 seconds -Region Avg IOU: 0.510920, Class: 1.000000, Obj: 0.168813, No Obj: 0.006069, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.564670, Class: 1.000000, Obj: 0.200826, No Obj: 0.006938, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.504491, Class: 1.000000, Obj: 0.206146, No Obj: 0.006280, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.530886, Class: 1.000000, Obj: 0.186233, No Obj: 0.006940, Avg Recall: 0.616000, count: 125 -Region Avg IOU: 0.497538, Class: 1.000000, Obj: 0.184725, No Obj: 0.006433, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.488644, Class: 1.000000, Obj: 0.184380, No Obj: 0.005851, Avg Recall: 0.500000, count: 88 -Region Avg IOU: 0.516573, Class: 1.000000, Obj: 0.199401, No Obj: 0.006349, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.488183, Class: 1.000000, Obj: 0.165242, No Obj: 0.006670, Avg Recall: 0.546763, count: 139 -201: 0.610726, 0.642764 avg, 0.001000 rate, 29.768259 seconds, 51456 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.508311, Class: 1.000000, Obj: 0.152385, No Obj: 0.005425, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.487715, Class: 1.000000, Obj: 0.137765, No Obj: 0.005715, Avg Recall: 0.494382, count: 89 -Region Avg IOU: 0.529200, Class: 1.000000, Obj: 0.134366, No Obj: 0.005265, Avg Recall: 0.630435, count: 92 -Region Avg IOU: 0.525160, Class: 1.000000, Obj: 0.169075, No Obj: 0.005269, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.488279, Class: 1.000000, Obj: 0.174032, No Obj: 0.005478, Avg Recall: 0.515152, count: 99 -Region Avg IOU: 0.503109, Class: 1.000000, Obj: 0.150726, No Obj: 0.005599, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.500708, Class: 1.000000, Obj: 0.177296, No Obj: 0.005870, Avg Recall: 0.544643, count: 112 -Region Avg IOU: 0.543844, Class: 1.000000, Obj: 0.118472, No Obj: 0.006658, Avg Recall: 0.656934, count: 137 -202: 0.572908, 0.635778 avg, 0.001000 rate, 27.959282 seconds, 51712 images -Loaded: 2.548845 seconds -Region Avg IOU: 0.520723, Class: 1.000000, Obj: 0.183255, No Obj: 0.004960, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.486383, Class: 1.000000, Obj: 0.136282, No Obj: 0.005037, Avg Recall: 0.471154, count: 104 -Region Avg IOU: 0.516166, Class: 1.000000, Obj: 0.137171, No Obj: 0.005225, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.554633, Class: 1.000000, Obj: 0.143834, No Obj: 0.005066, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.556122, Class: 1.000000, Obj: 0.102217, No Obj: 0.006127, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.512723, Class: 1.000000, Obj: 0.160860, No Obj: 0.005820, Avg Recall: 0.601770, count: 113 -Region Avg IOU: 0.534755, Class: 1.000000, Obj: 0.151295, No Obj: 0.005837, Avg Recall: 0.633987, count: 153 -Region Avg IOU: 0.486005, Class: 1.000000, Obj: 0.128550, No Obj: 0.004931, Avg Recall: 0.558824, count: 102 -203: 0.623899, 0.634590 avg, 0.001000 rate, 24.666975 seconds, 51968 images -Loaded: 3.877415 seconds -Region Avg IOU: 0.524696, Class: 1.000000, Obj: 0.132280, No Obj: 0.004708, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.512190, Class: 1.000000, Obj: 0.096886, No Obj: 0.004491, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.501237, Class: 1.000000, Obj: 0.105402, No Obj: 0.004449, Avg Recall: 0.541667, count: 120 -Region Avg IOU: 0.501914, Class: 1.000000, Obj: 0.116178, No Obj: 0.004627, Avg Recall: 0.573770, count: 122 -Region Avg IOU: 0.525541, Class: 1.000000, Obj: 0.122167, No Obj: 0.005006, Avg Recall: 0.605263, count: 152 -Region Avg IOU: 0.545277, Class: 1.000000, Obj: 0.122461, No Obj: 0.004946, Avg Recall: 0.666667, count: 141 -Region Avg IOU: 0.476652, Class: 1.000000, Obj: 0.111177, No Obj: 0.004118, Avg Recall: 0.483516, count: 91 -Region Avg IOU: 0.543673, Class: 1.000000, Obj: 0.134424, No Obj: 0.004373, Avg Recall: 0.698795, count: 83 -204: 0.645394, 0.635671 avg, 0.001000 rate, 26.091185 seconds, 52224 images -Loaded: 5.899188 seconds -Region Avg IOU: 0.498546, Class: 1.000000, Obj: 0.090411, No Obj: 0.003821, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.541562, Class: 1.000000, Obj: 0.076638, No Obj: 0.004175, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.495047, Class: 1.000000, Obj: 0.108002, No Obj: 0.003724, Avg Recall: 0.594937, count: 79 -Region Avg IOU: 0.537912, Class: 1.000000, Obj: 0.125514, No Obj: 0.003657, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.460002, Class: 1.000000, Obj: 0.094237, No Obj: 0.003755, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.512551, Class: 1.000000, Obj: 0.081061, No Obj: 0.003901, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.477188, Class: 1.000000, Obj: 0.085657, No Obj: 0.003792, Avg Recall: 0.474576, count: 118 -Region Avg IOU: 0.510497, Class: 1.000000, Obj: 0.092049, No Obj: 0.003951, Avg Recall: 0.607143, count: 84 -205: 0.526996, 0.624803 avg, 0.001000 rate, 25.296806 seconds, 52480 images -Loaded: 3.683450 seconds -Region Avg IOU: 0.522717, Class: 1.000000, Obj: 0.070292, No Obj: 0.003667, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.501466, Class: 1.000000, Obj: 0.065345, No Obj: 0.003562, Avg Recall: 0.549180, count: 122 -Region Avg IOU: 0.520845, Class: 1.000000, Obj: 0.066814, No Obj: 0.003351, Avg Recall: 0.621212, count: 132 -Region Avg IOU: 0.527293, Class: 1.000000, Obj: 0.076853, No Obj: 0.003532, Avg Recall: 0.659091, count: 88 -Region Avg IOU: 0.541367, Class: 1.000000, Obj: 0.096330, No Obj: 0.003522, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.494084, Class: 1.000000, Obj: 0.063684, No Obj: 0.003528, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.518261, Class: 1.000000, Obj: 0.080638, No Obj: 0.003510, Avg Recall: 0.591549, count: 142 -Region Avg IOU: 0.529723, Class: 1.000000, Obj: 0.096617, No Obj: 0.003396, Avg Recall: 0.574468, count: 94 -206: 0.541817, 0.616505 avg, 0.001000 rate, 26.890984 seconds, 52736 images -Loaded: 3.652765 seconds -Region Avg IOU: 0.503980, Class: 1.000000, Obj: 0.062107, No Obj: 0.003276, Avg Recall: 0.548872, count: 133 -Region Avg IOU: 0.517589, Class: 1.000000, Obj: 0.072578, No Obj: 0.003258, Avg Recall: 0.613445, count: 119 -Region Avg IOU: 0.537664, Class: 1.000000, Obj: 0.064330, No Obj: 0.003393, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.482198, Class: 1.000000, Obj: 0.073250, No Obj: 0.003171, Avg Recall: 0.500000, count: 92 -Region Avg IOU: 0.557132, Class: 1.000000, Obj: 0.093880, No Obj: 0.003732, Avg Recall: 0.645161, count: 124 -Region Avg IOU: 0.509297, Class: 1.000000, Obj: 0.069535, No Obj: 0.003270, Avg Recall: 0.568182, count: 88 -Region Avg IOU: 0.477448, Class: 1.000000, Obj: 0.059538, No Obj: 0.003115, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.522015, Class: 1.000000, Obj: 0.074077, No Obj: 0.003083, Avg Recall: 0.639640, count: 111 -207: 0.523543, 0.607208 avg, 0.001000 rate, 23.632019 seconds, 52992 images -Loaded: 4.426599 seconds -Region Avg IOU: 0.515631, Class: 1.000000, Obj: 0.062006, No Obj: 0.003058, Avg Recall: 0.561983, count: 121 -Region Avg IOU: 0.520346, Class: 1.000000, Obj: 0.071002, No Obj: 0.003061, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.501662, Class: 1.000000, Obj: 0.070303, No Obj: 0.003161, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.527914, Class: 1.000000, Obj: 0.077663, No Obj: 0.003110, Avg Recall: 0.609756, count: 123 -Region Avg IOU: 0.533140, Class: 1.000000, Obj: 0.075625, No Obj: 0.003166, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.528469, Class: 1.000000, Obj: 0.058241, No Obj: 0.003183, Avg Recall: 0.603175, count: 126 -Region Avg IOU: 0.539966, Class: 1.000000, Obj: 0.074406, No Obj: 0.003212, Avg Recall: 0.612903, count: 124 -Region Avg IOU: 0.516990, Class: 1.000000, Obj: 0.080651, No Obj: 0.003007, Avg Recall: 0.580645, count: 93 -208: 0.552290, 0.601717 avg, 0.001000 rate, 27.507956 seconds, 53248 images -Loaded: 3.220212 seconds -Region Avg IOU: 0.513091, Class: 1.000000, Obj: 0.086720, No Obj: 0.003108, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.527546, Class: 1.000000, Obj: 0.076249, No Obj: 0.002850, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.503333, Class: 1.000000, Obj: 0.069563, No Obj: 0.003117, Avg Recall: 0.572650, count: 117 -Region Avg IOU: 0.519992, Class: 1.000000, Obj: 0.063635, No Obj: 0.002813, Avg Recall: 0.576087, count: 92 -Region Avg IOU: 0.531808, Class: 1.000000, Obj: 0.068176, No Obj: 0.003090, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.543060, Class: 1.000000, Obj: 0.078916, No Obj: 0.003163, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.484854, Class: 1.000000, Obj: 0.076280, No Obj: 0.002886, Avg Recall: 0.563636, count: 110 -Region Avg IOU: 0.526650, Class: 1.000000, Obj: 0.056120, No Obj: 0.002931, Avg Recall: 0.575758, count: 99 -209: 0.519875, 0.593533 avg, 0.001000 rate, 30.107185 seconds, 53504 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.514928, Class: 1.000000, Obj: 0.069915, No Obj: 0.002998, Avg Recall: 0.544715, count: 123 -Region Avg IOU: 0.482324, Class: 1.000000, Obj: 0.076031, No Obj: 0.003112, Avg Recall: 0.520661, count: 121 -Region Avg IOU: 0.531375, Class: 1.000000, Obj: 0.068564, No Obj: 0.003126, Avg Recall: 0.632812, count: 128 -Region Avg IOU: 0.485175, Class: 1.000000, Obj: 0.099716, No Obj: 0.003160, Avg Recall: 0.515152, count: 132 -Region Avg IOU: 0.547741, Class: 1.000000, Obj: 0.086520, No Obj: 0.003080, Avg Recall: 0.701149, count: 87 -Region Avg IOU: 0.530079, Class: 1.000000, Obj: 0.106958, No Obj: 0.003086, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.499261, Class: 1.000000, Obj: 0.069720, No Obj: 0.002793, Avg Recall: 0.568421, count: 95 -Region Avg IOU: 0.533693, Class: 1.000000, Obj: 0.074802, No Obj: 0.003045, Avg Recall: 0.616071, count: 112 -210: 0.561539, 0.590333 avg, 0.001000 rate, 29.911888 seconds, 53760 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 15.632970 seconds -Region Avg IOU: 0.438808, Class: 1.000000, Obj: 0.071325, No Obj: 0.002688, Avg Recall: 0.442478, count: 113 -Region Avg IOU: 0.449655, Class: 1.000000, Obj: 0.068861, No Obj: 0.002772, Avg Recall: 0.469388, count: 98 -Region Avg IOU: 0.458276, Class: 1.000000, Obj: 0.085214, No Obj: 0.002877, Avg Recall: 0.438776, count: 98 -Region Avg IOU: 0.454452, Class: 1.000000, Obj: 0.090889, No Obj: 0.002878, Avg Recall: 0.487395, count: 119 -Region Avg IOU: 0.424539, Class: 1.000000, Obj: 0.099163, No Obj: 0.002834, Avg Recall: 0.400000, count: 100 -Region Avg IOU: 0.411076, Class: 1.000000, Obj: 0.081417, No Obj: 0.002815, Avg Recall: 0.380000, count: 100 -Region Avg IOU: 0.452389, Class: 1.000000, Obj: 0.081385, No Obj: 0.002977, Avg Recall: 0.405941, count: 101 -Region Avg IOU: 0.450884, Class: 1.000000, Obj: 0.085883, No Obj: 0.002879, Avg Recall: 0.469565, count: 115 -211: 0.553191, 0.586619 avg, 0.001000 rate, 24.029421 seconds, 54016 images -Loaded: 0.675906 seconds -Region Avg IOU: 0.470018, Class: 1.000000, Obj: 0.101642, No Obj: 0.003284, Avg Recall: 0.516949, count: 118 -Region Avg IOU: 0.469335, Class: 1.000000, Obj: 0.108071, No Obj: 0.003293, Avg Recall: 0.468085, count: 94 -Region Avg IOU: 0.423670, Class: 1.000000, Obj: 0.103403, No Obj: 0.002947, Avg Recall: 0.402174, count: 92 -Region Avg IOU: 0.421344, Class: 1.000000, Obj: 0.073453, No Obj: 0.002673, Avg Recall: 0.446602, count: 103 -Region Avg IOU: 0.442804, Class: 1.000000, Obj: 0.061752, No Obj: 0.002883, Avg Recall: 0.458333, count: 96 -Region Avg IOU: 0.460598, Class: 1.000000, Obj: 0.088462, No Obj: 0.003061, Avg Recall: 0.436090, count: 133 -Region Avg IOU: 0.454728, Class: 1.000000, Obj: 0.078018, No Obj: 0.002741, Avg Recall: 0.489583, count: 96 -Region Avg IOU: 0.461299, Class: 1.000000, Obj: 0.094228, No Obj: 0.003378, Avg Recall: 0.500000, count: 116 -212: 0.656669, 0.593624 avg, 0.001000 rate, 24.349939 seconds, 54272 images -Loaded: 1.063379 seconds -Region Avg IOU: 0.450447, Class: 1.000000, Obj: 0.120874, No Obj: 0.004132, Avg Recall: 0.466667, count: 165 -Region Avg IOU: 0.465739, Class: 1.000000, Obj: 0.114180, No Obj: 0.003608, Avg Recall: 0.519231, count: 104 -Region Avg IOU: 0.433558, Class: 1.000000, Obj: 0.117006, No Obj: 0.003573, Avg Recall: 0.424528, count: 106 -Region Avg IOU: 0.477521, Class: 1.000000, Obj: 0.111022, No Obj: 0.003417, Avg Recall: 0.527273, count: 110 -Region Avg IOU: 0.443386, Class: 1.000000, Obj: 0.094887, No Obj: 0.003290, Avg Recall: 0.447619, count: 105 -Region Avg IOU: 0.504347, Class: 1.000000, Obj: 0.082166, No Obj: 0.003088, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.485954, Class: 1.000000, Obj: 0.186307, No Obj: 0.003961, Avg Recall: 0.526316, count: 114 -Region Avg IOU: 0.462744, Class: 1.000000, Obj: 0.109554, No Obj: 0.003670, Avg Recall: 0.510000, count: 100 -213: 0.675191, 0.601781 avg, 0.001000 rate, 23.490419 seconds, 54528 images -Loaded: 0.497003 seconds -Region Avg IOU: 0.497175, Class: 1.000000, Obj: 0.159456, No Obj: 0.004070, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.451668, Class: 1.000000, Obj: 0.127727, No Obj: 0.003903, Avg Recall: 0.452830, count: 106 -Region Avg IOU: 0.427478, Class: 1.000000, Obj: 0.126433, No Obj: 0.003935, Avg Recall: 0.428571, count: 133 -Region Avg IOU: 0.452699, Class: 1.000000, Obj: 0.147916, No Obj: 0.004235, Avg Recall: 0.451128, count: 133 -Region Avg IOU: 0.440393, Class: 1.000000, Obj: 0.112547, No Obj: 0.003679, Avg Recall: 0.456311, count: 103 -Region Avg IOU: 0.455159, Class: 1.000000, Obj: 0.113336, No Obj: 0.004033, Avg Recall: 0.488189, count: 127 -Region Avg IOU: 0.451138, Class: 1.000000, Obj: 0.139719, No Obj: 0.003999, Avg Recall: 0.455446, count: 101 -Region Avg IOU: 0.471777, Class: 1.000000, Obj: 0.138352, No Obj: 0.003519, Avg Recall: 0.477778, count: 90 -214: 0.585303, 0.600133 avg, 0.001000 rate, 24.396576 seconds, 54784 images -Loaded: 0.143396 seconds -Region Avg IOU: 0.476928, Class: 1.000000, Obj: 0.134213, No Obj: 0.004287, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.479623, Class: 1.000000, Obj: 0.137425, No Obj: 0.004362, Avg Recall: 0.504274, count: 117 -Region Avg IOU: 0.452817, Class: 1.000000, Obj: 0.151084, No Obj: 0.004069, Avg Recall: 0.443396, count: 106 -Region Avg IOU: 0.441977, Class: 1.000000, Obj: 0.134023, No Obj: 0.004104, Avg Recall: 0.421429, count: 140 -Region Avg IOU: 0.490590, Class: 1.000000, Obj: 0.121974, No Obj: 0.004308, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.461551, Class: 1.000000, Obj: 0.140848, No Obj: 0.004037, Avg Recall: 0.470588, count: 119 -Region Avg IOU: 0.420445, Class: 1.000000, Obj: 0.140183, No Obj: 0.004138, Avg Recall: 0.427184, count: 103 -Region Avg IOU: 0.483868, Class: 1.000000, Obj: 0.168017, No Obj: 0.005187, Avg Recall: 0.548148, count: 135 -215: 0.600176, 0.600137 avg, 0.001000 rate, 22.777185 seconds, 55040 images -Loaded: 4.199807 seconds -Region Avg IOU: 0.450633, Class: 1.000000, Obj: 0.161734, No Obj: 0.004744, Avg Recall: 0.453846, count: 130 -Region Avg IOU: 0.453413, Class: 1.000000, Obj: 0.137250, No Obj: 0.004086, Avg Recall: 0.463158, count: 95 -Region Avg IOU: 0.462047, Class: 1.000000, Obj: 0.178142, No Obj: 0.004601, Avg Recall: 0.471154, count: 104 -Region Avg IOU: 0.468083, Class: 1.000000, Obj: 0.173364, No Obj: 0.005281, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.495859, Class: 1.000000, Obj: 0.174512, No Obj: 0.004446, Avg Recall: 0.552083, count: 96 -Region Avg IOU: 0.514523, Class: 1.000000, Obj: 0.194264, No Obj: 0.004571, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.438068, Class: 1.000000, Obj: 0.136327, No Obj: 0.004323, Avg Recall: 0.450820, count: 122 -Region Avg IOU: 0.464642, Class: 1.000000, Obj: 0.179607, No Obj: 0.004566, Avg Recall: 0.526316, count: 95 -216: 0.541981, 0.594322 avg, 0.001000 rate, 25.141472 seconds, 55296 images -Loaded: 0.227305 seconds -Region Avg IOU: 0.459781, Class: 1.000000, Obj: 0.164797, No Obj: 0.004119, Avg Recall: 0.556818, count: 88 -Region Avg IOU: 0.441993, Class: 1.000000, Obj: 0.159617, No Obj: 0.004649, Avg Recall: 0.441860, count: 129 -Region Avg IOU: 0.432989, Class: 1.000000, Obj: 0.181827, No Obj: 0.004280, Avg Recall: 0.412371, count: 97 -Region Avg IOU: 0.451219, Class: 1.000000, Obj: 0.164091, No Obj: 0.004389, Avg Recall: 0.445545, count: 101 -Region Avg IOU: 0.442598, Class: 1.000000, Obj: 0.124912, No Obj: 0.004328, Avg Recall: 0.445783, count: 83 -Region Avg IOU: 0.436427, Class: 1.000000, Obj: 0.212021, No Obj: 0.005001, Avg Recall: 0.391667, count: 120 -Region Avg IOU: 0.419658, Class: 1.000000, Obj: 0.167272, No Obj: 0.004097, Avg Recall: 0.412371, count: 97 -Region Avg IOU: 0.461657, Class: 1.000000, Obj: 0.178020, No Obj: 0.004665, Avg Recall: 0.500000, count: 110 -217: 0.549717, 0.589861 avg, 0.001000 rate, 24.001478 seconds, 55552 images -Loaded: 0.272413 seconds -Region Avg IOU: 0.411243, Class: 1.000000, Obj: 0.148663, No Obj: 0.004344, Avg Recall: 0.425000, count: 120 -Region Avg IOU: 0.493396, Class: 1.000000, Obj: 0.176955, No Obj: 0.004391, Avg Recall: 0.547368, count: 95 -Region Avg IOU: 0.462740, Class: 1.000000, Obj: 0.155309, No Obj: 0.004501, Avg Recall: 0.504132, count: 121 -Region Avg IOU: 0.448379, Class: 1.000000, Obj: 0.153501, No Obj: 0.004268, Avg Recall: 0.465347, count: 101 -Region Avg IOU: 0.434612, Class: 1.000000, Obj: 0.110131, No Obj: 0.004195, Avg Recall: 0.495652, count: 115 -Region Avg IOU: 0.440500, Class: 1.000000, Obj: 0.160119, No Obj: 0.005003, Avg Recall: 0.482143, count: 112 -Region Avg IOU: 0.483694, Class: 1.000000, Obj: 0.183052, No Obj: 0.004945, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.435979, Class: 1.000000, Obj: 0.144031, No Obj: 0.004274, Avg Recall: 0.449541, count: 109 -218: 0.577353, 0.588610 avg, 0.001000 rate, 23.415640 seconds, 55808 images -Loaded: 0.493226 seconds -Region Avg IOU: 0.404916, Class: 1.000000, Obj: 0.147468, No Obj: 0.004189, Avg Recall: 0.403670, count: 109 -Region Avg IOU: 0.426225, Class: 1.000000, Obj: 0.182975, No Obj: 0.004324, Avg Recall: 0.461538, count: 104 -Region Avg IOU: 0.451370, Class: 1.000000, Obj: 0.188935, No Obj: 0.004559, Avg Recall: 0.385827, count: 127 -Region Avg IOU: 0.434578, Class: 1.000000, Obj: 0.141674, No Obj: 0.004427, Avg Recall: 0.441441, count: 111 -Region Avg IOU: 0.458562, Class: 1.000000, Obj: 0.192810, No Obj: 0.004438, Avg Recall: 0.471264, count: 87 -Region Avg IOU: 0.428535, Class: 1.000000, Obj: 0.138844, No Obj: 0.004557, Avg Recall: 0.420561, count: 107 -Region Avg IOU: 0.465997, Class: 1.000000, Obj: 0.160629, No Obj: 0.004473, Avg Recall: 0.491525, count: 118 -Region Avg IOU: 0.468564, Class: 1.000000, Obj: 0.147865, No Obj: 0.004320, Avg Recall: 0.500000, count: 112 -219: 0.601841, 0.589933 avg, 0.001000 rate, 24.236137 seconds, 56064 images -Loaded: 0.000039 seconds -Region Avg IOU: 0.450297, Class: 1.000000, Obj: 0.148827, No Obj: 0.004143, Avg Recall: 0.462264, count: 106 -Region Avg IOU: 0.466613, Class: 1.000000, Obj: 0.129437, No Obj: 0.004452, Avg Recall: 0.514286, count: 105 -Region Avg IOU: 0.409341, Class: 1.000000, Obj: 0.152408, No Obj: 0.004346, Avg Recall: 0.392857, count: 112 -Region Avg IOU: 0.453031, Class: 1.000000, Obj: 0.158292, No Obj: 0.004355, Avg Recall: 0.413043, count: 92 -Region Avg IOU: 0.428125, Class: 1.000000, Obj: 0.143542, No Obj: 0.004584, Avg Recall: 0.389381, count: 113 -Region Avg IOU: 0.430534, Class: 1.000000, Obj: 0.174558, No Obj: 0.004292, Avg Recall: 0.424242, count: 99 -Region Avg IOU: 0.438531, Class: 1.000000, Obj: 0.160899, No Obj: 0.004053, Avg Recall: 0.426829, count: 82 -Region Avg IOU: 0.397071, Class: 1.000000, Obj: 0.128619, No Obj: 0.004097, Avg Recall: 0.396396, count: 111 -220: 0.493114, 0.580251 avg, 0.001000 rate, 24.714788 seconds, 56320 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000100 seconds -Region Avg IOU: 0.491406, Class: 1.000000, Obj: 0.141752, No Obj: 0.003863, Avg Recall: 0.514019, count: 107 -Region Avg IOU: 0.499504, Class: 1.000000, Obj: 0.173379, No Obj: 0.003884, Avg Recall: 0.554054, count: 74 -Region Avg IOU: 0.519181, Class: 1.000000, Obj: 0.146416, No Obj: 0.004212, Avg Recall: 0.606838, count: 117 -Region Avg IOU: 0.479084, Class: 1.000000, Obj: 0.162492, No Obj: 0.004200, Avg Recall: 0.508333, count: 120 -Region Avg IOU: 0.538090, Class: 1.000000, Obj: 0.143703, No Obj: 0.004118, Avg Recall: 0.702381, count: 84 -Region Avg IOU: 0.465206, Class: 1.000000, Obj: 0.117746, No Obj: 0.004270, Avg Recall: 0.467742, count: 124 -Region Avg IOU: 0.489104, Class: 1.000000, Obj: 0.173149, No Obj: 0.004089, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.481753, Class: 1.000000, Obj: 0.156356, No Obj: 0.004406, Avg Recall: 0.542373, count: 118 -221: 0.561954, 0.578422 avg, 0.001000 rate, 28.821379 seconds, 56576 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.511818, Class: 1.000000, Obj: 0.130141, No Obj: 0.003768, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.502876, Class: 1.000000, Obj: 0.151253, No Obj: 0.003777, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.514036, Class: 1.000000, Obj: 0.154391, No Obj: 0.003991, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.488581, Class: 1.000000, Obj: 0.106573, No Obj: 0.003776, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.463166, Class: 1.000000, Obj: 0.114133, No Obj: 0.003746, Avg Recall: 0.495868, count: 121 -Region Avg IOU: 0.535296, Class: 1.000000, Obj: 0.137589, No Obj: 0.004012, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.506383, Class: 1.000000, Obj: 0.160393, No Obj: 0.003909, Avg Recall: 0.567568, count: 111 -Region Avg IOU: 0.493349, Class: 1.000000, Obj: 0.147002, No Obj: 0.003463, Avg Recall: 0.540000, count: 100 -222: 0.542863, 0.574866 avg, 0.001000 rate, 27.719542 seconds, 56832 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.494147, Class: 1.000000, Obj: 0.153420, No Obj: 0.003384, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.520818, Class: 1.000000, Obj: 0.116111, No Obj: 0.003668, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.514085, Class: 1.000000, Obj: 0.118987, No Obj: 0.003539, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.532984, Class: 1.000000, Obj: 0.142952, No Obj: 0.003821, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.525733, Class: 1.000000, Obj: 0.130664, No Obj: 0.003732, Avg Recall: 0.661538, count: 130 -Region Avg IOU: 0.553488, Class: 1.000000, Obj: 0.135400, No Obj: 0.003584, Avg Recall: 0.689655, count: 87 -Region Avg IOU: 0.487413, Class: 1.000000, Obj: 0.121257, No Obj: 0.003394, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.513757, Class: 1.000000, Obj: 0.123079, No Obj: 0.003760, Avg Recall: 0.556604, count: 106 -223: 0.518062, 0.569185 avg, 0.001000 rate, 30.323820 seconds, 57088 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.492199, Class: 1.000000, Obj: 0.132195, No Obj: 0.003805, Avg Recall: 0.582734, count: 139 -Region Avg IOU: 0.532264, Class: 1.000000, Obj: 0.107417, No Obj: 0.003342, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.492713, Class: 1.000000, Obj: 0.092165, No Obj: 0.003470, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.537374, Class: 1.000000, Obj: 0.124018, No Obj: 0.003391, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.487952, Class: 1.000000, Obj: 0.126193, No Obj: 0.003140, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.497918, Class: 1.000000, Obj: 0.114324, No Obj: 0.003203, Avg Recall: 0.556818, count: 88 -Region Avg IOU: 0.518425, Class: 1.000000, Obj: 0.092502, No Obj: 0.003221, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.492016, Class: 1.000000, Obj: 0.099204, No Obj: 0.003552, Avg Recall: 0.566038, count: 106 -224: 0.533702, 0.565637 avg, 0.001000 rate, 28.831263 seconds, 57344 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.502937, Class: 1.000000, Obj: 0.123871, No Obj: 0.003530, Avg Recall: 0.536364, count: 110 -Region Avg IOU: 0.497793, Class: 1.000000, Obj: 0.097586, No Obj: 0.003512, Avg Recall: 0.521739, count: 138 -Region Avg IOU: 0.491808, Class: 1.000000, Obj: 0.101440, No Obj: 0.002933, Avg Recall: 0.550000, count: 80 -Region Avg IOU: 0.506444, Class: 1.000000, Obj: 0.118387, No Obj: 0.003449, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.512382, Class: 1.000000, Obj: 0.103572, No Obj: 0.003333, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.520737, Class: 1.000000, Obj: 0.128235, No Obj: 0.003475, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.512154, Class: 1.000000, Obj: 0.102464, No Obj: 0.003518, Avg Recall: 0.582677, count: 127 -Region Avg IOU: 0.535000, Class: 1.000000, Obj: 0.113969, No Obj: 0.003339, Avg Recall: 0.641667, count: 120 -225: 0.575244, 0.566598 avg, 0.001000 rate, 25.589418 seconds, 57600 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.496937, Class: 1.000000, Obj: 0.106228, No Obj: 0.003310, Avg Recall: 0.553719, count: 121 -Region Avg IOU: 0.490189, Class: 1.000000, Obj: 0.101492, No Obj: 0.003145, Avg Recall: 0.533333, count: 105 -Region Avg IOU: 0.510321, Class: 1.000000, Obj: 0.089115, No Obj: 0.003315, Avg Recall: 0.556391, count: 133 -Region Avg IOU: 0.515726, Class: 1.000000, Obj: 0.117612, No Obj: 0.003092, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.525591, Class: 1.000000, Obj: 0.094453, No Obj: 0.003413, Avg Recall: 0.625954, count: 131 -Region Avg IOU: 0.511792, Class: 1.000000, Obj: 0.097218, No Obj: 0.003290, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.548298, Class: 1.000000, Obj: 0.130318, No Obj: 0.003156, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.555878, Class: 1.000000, Obj: 0.118678, No Obj: 0.003655, Avg Recall: 0.669291, count: 127 -226: 0.563246, 0.566263 avg, 0.001000 rate, 28.930374 seconds, 57856 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.500241, Class: 1.000000, Obj: 0.102084, No Obj: 0.003570, Avg Recall: 0.571429, count: 126 -Region Avg IOU: 0.464708, Class: 1.000000, Obj: 0.091807, No Obj: 0.003153, Avg Recall: 0.483146, count: 89 -Region Avg IOU: 0.543881, Class: 1.000000, Obj: 0.097293, No Obj: 0.003544, Avg Recall: 0.637037, count: 135 -Region Avg IOU: 0.486557, Class: 1.000000, Obj: 0.102724, No Obj: 0.003390, Avg Recall: 0.532110, count: 109 -Region Avg IOU: 0.525047, Class: 1.000000, Obj: 0.127730, No Obj: 0.003363, Avg Recall: 0.598485, count: 132 -Region Avg IOU: 0.519297, Class: 1.000000, Obj: 0.137069, No Obj: 0.003438, Avg Recall: 0.600000, count: 120 -Region Avg IOU: 0.479869, Class: 1.000000, Obj: 0.066805, No Obj: 0.003244, Avg Recall: 0.584270, count: 89 -Region Avg IOU: 0.540836, Class: 1.000000, Obj: 0.114535, No Obj: 0.003393, Avg Recall: 0.687500, count: 96 -227: 0.494846, 0.559121 avg, 0.001000 rate, 30.026184 seconds, 58112 images -Loaded: 0.016650 seconds -Region Avg IOU: 0.538613, Class: 1.000000, Obj: 0.126064, No Obj: 0.003739, Avg Recall: 0.647059, count: 136 -Region Avg IOU: 0.575809, Class: 1.000000, Obj: 0.133943, No Obj: 0.003513, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.503276, Class: 1.000000, Obj: 0.138072, No Obj: 0.003481, Avg Recall: 0.556522, count: 115 -Region Avg IOU: 0.507631, Class: 1.000000, Obj: 0.094948, No Obj: 0.003591, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.510702, Class: 1.000000, Obj: 0.142216, No Obj: 0.003425, Avg Recall: 0.559524, count: 84 -Region Avg IOU: 0.469410, Class: 1.000000, Obj: 0.099733, No Obj: 0.003281, Avg Recall: 0.460000, count: 100 -Region Avg IOU: 0.457773, Class: 1.000000, Obj: 0.095187, No Obj: 0.003012, Avg Recall: 0.509259, count: 108 -Region Avg IOU: 0.528779, Class: 1.000000, Obj: 0.142009, No Obj: 0.003721, Avg Recall: 0.700935, count: 107 -228: 0.577131, 0.560922 avg, 0.001000 rate, 27.019957 seconds, 58368 images -Loaded: 3.291509 seconds -Region Avg IOU: 0.504774, Class: 1.000000, Obj: 0.136599, No Obj: 0.004089, Avg Recall: 0.551515, count: 165 -Region Avg IOU: 0.558903, Class: 1.000000, Obj: 0.142604, No Obj: 0.003731, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.503195, Class: 1.000000, Obj: 0.140203, No Obj: 0.004433, Avg Recall: 0.512658, count: 158 -Region Avg IOU: 0.526722, Class: 1.000000, Obj: 0.114749, No Obj: 0.003878, Avg Recall: 0.573643, count: 129 -Region Avg IOU: 0.521840, Class: 1.000000, Obj: 0.133535, No Obj: 0.003675, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.533500, Class: 1.000000, Obj: 0.129998, No Obj: 0.003621, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.498519, Class: 1.000000, Obj: 0.129962, No Obj: 0.004003, Avg Recall: 0.608000, count: 125 -Region Avg IOU: 0.502907, Class: 1.000000, Obj: 0.100692, No Obj: 0.003608, Avg Recall: 0.584906, count: 106 -229: 0.626938, 0.567524 avg, 0.001000 rate, 25.963295 seconds, 58624 images -Loaded: 5.005632 seconds -Region Avg IOU: 0.504661, Class: 1.000000, Obj: 0.112452, No Obj: 0.003605, Avg Recall: 0.520661, count: 121 -Region Avg IOU: 0.527254, Class: 1.000000, Obj: 0.109464, No Obj: 0.003756, Avg Recall: 0.625000, count: 120 -Region Avg IOU: 0.466993, Class: 1.000000, Obj: 0.128156, No Obj: 0.004182, Avg Recall: 0.489583, count: 96 -Region Avg IOU: 0.496202, Class: 1.000000, Obj: 0.148329, No Obj: 0.003713, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.514238, Class: 1.000000, Obj: 0.120417, No Obj: 0.003776, Avg Recall: 0.574468, count: 94 -Region Avg IOU: 0.512204, Class: 1.000000, Obj: 0.165456, No Obj: 0.003823, Avg Recall: 0.540816, count: 98 -Region Avg IOU: 0.545068, Class: 1.000000, Obj: 0.141048, No Obj: 0.003534, Avg Recall: 0.593023, count: 86 -Region Avg IOU: 0.541709, Class: 1.000000, Obj: 0.157741, No Obj: 0.003841, Avg Recall: 0.651786, count: 112 -230: 0.541404, 0.564912 avg, 0.001000 rate, 29.801065 seconds, 58880 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 20.692375 seconds -Region Avg IOU: 0.457867, Class: 1.000000, Obj: 0.158475, No Obj: 0.003841, Avg Recall: 0.468750, count: 96 -Region Avg IOU: 0.500468, Class: 1.000000, Obj: 0.185855, No Obj: 0.003549, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.491457, Class: 1.000000, Obj: 0.164692, No Obj: 0.003447, Avg Recall: 0.535714, count: 84 -Region Avg IOU: 0.529665, Class: 1.000000, Obj: 0.152327, No Obj: 0.004105, Avg Recall: 0.607843, count: 102 -Region Avg IOU: 0.478729, Class: 1.000000, Obj: 0.177882, No Obj: 0.003844, Avg Recall: 0.478261, count: 92 -Region Avg IOU: 0.511277, Class: 1.000000, Obj: 0.140815, No Obj: 0.003962, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.487335, Class: 1.000000, Obj: 0.140688, No Obj: 0.003918, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.531911, Class: 1.000000, Obj: 0.130698, No Obj: 0.004072, Avg Recall: 0.623853, count: 109 -231: 0.484143, 0.556835 avg, 0.001000 rate, 27.628366 seconds, 59136 images -Loaded: 0.000119 seconds -Region Avg IOU: 0.495657, Class: 1.000000, Obj: 0.158406, No Obj: 0.003752, Avg Recall: 0.505495, count: 91 -Region Avg IOU: 0.482482, Class: 1.000000, Obj: 0.109205, No Obj: 0.003812, Avg Recall: 0.522727, count: 88 -Region Avg IOU: 0.502984, Class: 1.000000, Obj: 0.146939, No Obj: 0.004308, Avg Recall: 0.564516, count: 124 -Region Avg IOU: 0.505903, Class: 1.000000, Obj: 0.135706, No Obj: 0.004405, Avg Recall: 0.547009, count: 117 -Region Avg IOU: 0.469621, Class: 1.000000, Obj: 0.155626, No Obj: 0.004131, Avg Recall: 0.495413, count: 109 -Region Avg IOU: 0.555268, Class: 1.000000, Obj: 0.177176, No Obj: 0.004209, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.534382, Class: 1.000000, Obj: 0.171910, No Obj: 0.004193, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.464273, Class: 1.000000, Obj: 0.125943, No Obj: 0.004724, Avg Recall: 0.519685, count: 127 -232: 0.600491, 0.561200 avg, 0.001000 rate, 27.244467 seconds, 59392 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.526591, Class: 1.000000, Obj: 0.165243, No Obj: 0.004387, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.519636, Class: 1.000000, Obj: 0.144602, No Obj: 0.004171, Avg Recall: 0.553398, count: 103 -Region Avg IOU: 0.494345, Class: 1.000000, Obj: 0.166032, No Obj: 0.004484, Avg Recall: 0.553030, count: 132 -Region Avg IOU: 0.483396, Class: 1.000000, Obj: 0.130094, No Obj: 0.003851, Avg Recall: 0.535714, count: 84 -Region Avg IOU: 0.511783, Class: 1.000000, Obj: 0.184548, No Obj: 0.004384, Avg Recall: 0.620690, count: 116 -Region Avg IOU: 0.490927, Class: 1.000000, Obj: 0.129946, No Obj: 0.003800, Avg Recall: 0.571429, count: 112 -Region Avg IOU: 0.538166, Class: 1.000000, Obj: 0.135630, No Obj: 0.004582, Avg Recall: 0.600000, count: 125 -Region Avg IOU: 0.514511, Class: 1.000000, Obj: 0.171582, No Obj: 0.004312, Avg Recall: 0.583333, count: 120 -233: 0.559744, 0.561055 avg, 0.001000 rate, 27.620455 seconds, 59648 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.556449, Class: 1.000000, Obj: 0.162827, No Obj: 0.004374, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.505653, Class: 1.000000, Obj: 0.165262, No Obj: 0.003934, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.501597, Class: 1.000000, Obj: 0.161473, No Obj: 0.004421, Avg Recall: 0.576087, count: 92 -Region Avg IOU: 0.558289, Class: 1.000000, Obj: 0.149414, No Obj: 0.004622, Avg Recall: 0.708075, count: 161 -Region Avg IOU: 0.541551, Class: 1.000000, Obj: 0.181486, No Obj: 0.004213, Avg Recall: 0.619565, count: 92 -Region Avg IOU: 0.523418, Class: 1.000000, Obj: 0.166285, No Obj: 0.004739, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.538883, Class: 1.000000, Obj: 0.215107, No Obj: 0.003929, Avg Recall: 0.659341, count: 91 -Region Avg IOU: 0.527389, Class: 1.000000, Obj: 0.167905, No Obj: 0.004596, Avg Recall: 0.585938, count: 128 -234: 0.576007, 0.562550 avg, 0.001000 rate, 26.726202 seconds, 59904 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.540218, Class: 1.000000, Obj: 0.168078, No Obj: 0.004020, Avg Recall: 0.673684, count: 95 -Region Avg IOU: 0.487023, Class: 1.000000, Obj: 0.187277, No Obj: 0.004174, Avg Recall: 0.494949, count: 99 -Region Avg IOU: 0.513389, Class: 1.000000, Obj: 0.190333, No Obj: 0.004430, Avg Recall: 0.581967, count: 122 -Region Avg IOU: 0.513809, Class: 1.000000, Obj: 0.169183, No Obj: 0.004220, Avg Recall: 0.546875, count: 128 -Region Avg IOU: 0.540172, Class: 1.000000, Obj: 0.155928, No Obj: 0.004471, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.486200, Class: 1.000000, Obj: 0.161083, No Obj: 0.004167, Avg Recall: 0.525862, count: 116 -Region Avg IOU: 0.501423, Class: 1.000000, Obj: 0.175093, No Obj: 0.003964, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.542130, Class: 1.000000, Obj: 0.161023, No Obj: 0.004123, Avg Recall: 0.616667, count: 120 -235: 0.545014, 0.560796 avg, 0.001000 rate, 27.614536 seconds, 60160 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.524014, Class: 1.000000, Obj: 0.153123, No Obj: 0.004259, Avg Recall: 0.588235, count: 102 -Region Avg IOU: 0.530625, Class: 1.000000, Obj: 0.171629, No Obj: 0.004442, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.506864, Class: 1.000000, Obj: 0.102472, No Obj: 0.003837, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.540088, Class: 1.000000, Obj: 0.144976, No Obj: 0.004545, Avg Recall: 0.595420, count: 131 -Region Avg IOU: 0.526945, Class: 1.000000, Obj: 0.148264, No Obj: 0.004002, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.456734, Class: 1.000000, Obj: 0.121147, No Obj: 0.003650, Avg Recall: 0.495495, count: 111 -Region Avg IOU: 0.534929, Class: 1.000000, Obj: 0.178465, No Obj: 0.004540, Avg Recall: 0.648855, count: 131 -Region Avg IOU: 0.504903, Class: 1.000000, Obj: 0.148985, No Obj: 0.003627, Avg Recall: 0.608108, count: 74 -236: 0.533944, 0.558111 avg, 0.001000 rate, 28.094582 seconds, 60416 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.488488, Class: 1.000000, Obj: 0.156422, No Obj: 0.003950, Avg Recall: 0.496124, count: 129 -Region Avg IOU: 0.513358, Class: 1.000000, Obj: 0.135868, No Obj: 0.003416, Avg Recall: 0.582418, count: 91 -Region Avg IOU: 0.523744, Class: 1.000000, Obj: 0.140374, No Obj: 0.004106, Avg Recall: 0.559633, count: 109 -Region Avg IOU: 0.504143, Class: 1.000000, Obj: 0.153932, No Obj: 0.003644, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.550685, Class: 1.000000, Obj: 0.145853, No Obj: 0.003877, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.512769, Class: 1.000000, Obj: 0.179776, No Obj: 0.003833, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.532344, Class: 1.000000, Obj: 0.148031, No Obj: 0.004604, Avg Recall: 0.595092, count: 163 -Region Avg IOU: 0.530635, Class: 1.000000, Obj: 0.140600, No Obj: 0.003834, Avg Recall: 0.611650, count: 103 -237: 0.586114, 0.560911 avg, 0.001000 rate, 28.831087 seconds, 60672 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.515021, Class: 1.000000, Obj: 0.142256, No Obj: 0.003869, Avg Recall: 0.571429, count: 140 -Region Avg IOU: 0.506144, Class: 1.000000, Obj: 0.136084, No Obj: 0.003693, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.519304, Class: 1.000000, Obj: 0.124423, No Obj: 0.003788, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.543795, Class: 1.000000, Obj: 0.118134, No Obj: 0.003650, Avg Recall: 0.620690, count: 87 -Region Avg IOU: 0.507499, Class: 1.000000, Obj: 0.160690, No Obj: 0.004114, Avg Recall: 0.535433, count: 127 -Region Avg IOU: 0.512962, Class: 1.000000, Obj: 0.137992, No Obj: 0.003634, Avg Recall: 0.586466, count: 133 -Region Avg IOU: 0.512208, Class: 1.000000, Obj: 0.139609, No Obj: 0.003701, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.531892, Class: 1.000000, Obj: 0.138650, No Obj: 0.003704, Avg Recall: 0.663636, count: 110 -238: 0.546797, 0.559500 avg, 0.001000 rate, 28.229763 seconds, 60928 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.482543, Class: 1.000000, Obj: 0.120511, No Obj: 0.003159, Avg Recall: 0.545455, count: 88 -Region Avg IOU: 0.533288, Class: 1.000000, Obj: 0.156503, No Obj: 0.003641, Avg Recall: 0.593750, count: 128 -Region Avg IOU: 0.530590, Class: 1.000000, Obj: 0.121065, No Obj: 0.003343, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.483501, Class: 1.000000, Obj: 0.125257, No Obj: 0.003639, Avg Recall: 0.567568, count: 111 -Region Avg IOU: 0.521110, Class: 1.000000, Obj: 0.103151, No Obj: 0.003267, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.486819, Class: 1.000000, Obj: 0.139794, No Obj: 0.003485, Avg Recall: 0.533898, count: 118 -Region Avg IOU: 0.517212, Class: 1.000000, Obj: 0.115666, No Obj: 0.003383, Avg Recall: 0.569767, count: 86 -Region Avg IOU: 0.492327, Class: 1.000000, Obj: 0.121006, No Obj: 0.003338, Avg Recall: 0.550847, count: 118 -239: 0.499302, 0.553480 avg, 0.001000 rate, 28.395573 seconds, 61184 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.546684, Class: 1.000000, Obj: 0.121570, No Obj: 0.003250, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.484537, Class: 1.000000, Obj: 0.126820, No Obj: 0.003434, Avg Recall: 0.586466, count: 133 -Region Avg IOU: 0.495868, Class: 1.000000, Obj: 0.144047, No Obj: 0.003540, Avg Recall: 0.541667, count: 120 -Region Avg IOU: 0.525030, Class: 1.000000, Obj: 0.136197, No Obj: 0.003319, Avg Recall: 0.589744, count: 117 -Region Avg IOU: 0.526776, Class: 1.000000, Obj: 0.154040, No Obj: 0.003348, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.521250, Class: 1.000000, Obj: 0.116520, No Obj: 0.003413, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.489677, Class: 1.000000, Obj: 0.161184, No Obj: 0.003541, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.476392, Class: 1.000000, Obj: 0.108481, No Obj: 0.003433, Avg Recall: 0.529915, count: 117 -240: 0.606735, 0.558806 avg, 0.001000 rate, 27.491877 seconds, 61440 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 19.696302 seconds -Region Avg IOU: 0.426789, Class: 1.000000, Obj: 0.101404, No Obj: 0.003167, Avg Recall: 0.415254, count: 118 -Region Avg IOU: 0.479637, Class: 1.000000, Obj: 0.104654, No Obj: 0.003379, Avg Recall: 0.438596, count: 114 -Region Avg IOU: 0.477550, Class: 1.000000, Obj: 0.143147, No Obj: 0.003830, Avg Recall: 0.541667, count: 144 -Region Avg IOU: 0.492180, Class: 1.000000, Obj: 0.132231, No Obj: 0.003229, Avg Recall: 0.500000, count: 106 -Region Avg IOU: 0.460736, Class: 1.000000, Obj: 0.113860, No Obj: 0.003285, Avg Recall: 0.464646, count: 99 -Region Avg IOU: 0.440014, Class: 1.000000, Obj: 0.158869, No Obj: 0.003314, Avg Recall: 0.434783, count: 92 -Region Avg IOU: 0.472478, Class: 1.000000, Obj: 0.127024, No Obj: 0.003132, Avg Recall: 0.474576, count: 118 -Region Avg IOU: 0.490672, Class: 1.000000, Obj: 0.132252, No Obj: 0.003492, Avg Recall: 0.523077, count: 130 -241: 0.560773, 0.559002 avg, 0.001000 rate, 23.911652 seconds, 61696 images -Loaded: 0.917461 seconds -Region Avg IOU: 0.436530, Class: 1.000000, Obj: 0.099892, No Obj: 0.003200, Avg Recall: 0.444444, count: 117 -Region Avg IOU: 0.401000, Class: 1.000000, Obj: 0.080613, No Obj: 0.003027, Avg Recall: 0.440000, count: 100 -Region Avg IOU: 0.497263, Class: 1.000000, Obj: 0.144660, No Obj: 0.003456, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.419146, Class: 1.000000, Obj: 0.091943, No Obj: 0.002922, Avg Recall: 0.434783, count: 115 -Region Avg IOU: 0.458610, Class: 1.000000, Obj: 0.107532, No Obj: 0.003474, Avg Recall: 0.465517, count: 116 -Region Avg IOU: 0.415731, Class: 1.000000, Obj: 0.103167, No Obj: 0.002866, Avg Recall: 0.474227, count: 97 -Region Avg IOU: 0.385218, Class: 1.000000, Obj: 0.087730, No Obj: 0.002887, Avg Recall: 0.310000, count: 100 -Region Avg IOU: 0.441405, Class: 1.000000, Obj: 0.113451, No Obj: 0.003256, Avg Recall: 0.466667, count: 105 -242: 0.576088, 0.560711 avg, 0.001000 rate, 24.329739 seconds, 61952 images -Loaded: 0.206267 seconds -Region Avg IOU: 0.424798, Class: 1.000000, Obj: 0.133346, No Obj: 0.003108, Avg Recall: 0.430233, count: 86 -Region Avg IOU: 0.411038, Class: 1.000000, Obj: 0.089549, No Obj: 0.003196, Avg Recall: 0.398058, count: 103 -Region Avg IOU: 0.465799, Class: 1.000000, Obj: 0.133506, No Obj: 0.003239, Avg Recall: 0.494624, count: 93 -Region Avg IOU: 0.465371, Class: 1.000000, Obj: 0.145260, No Obj: 0.003935, Avg Recall: 0.489209, count: 139 -Region Avg IOU: 0.441353, Class: 1.000000, Obj: 0.115593, No Obj: 0.003289, Avg Recall: 0.441441, count: 111 -Region Avg IOU: 0.449088, Class: 1.000000, Obj: 0.151890, No Obj: 0.003196, Avg Recall: 0.489362, count: 94 -Region Avg IOU: 0.483566, Class: 1.000000, Obj: 0.128991, No Obj: 0.003468, Avg Recall: 0.496124, count: 129 -Region Avg IOU: 0.432018, Class: 1.000000, Obj: 0.146536, No Obj: 0.003363, Avg Recall: 0.417391, count: 115 -243: 0.616800, 0.566320 avg, 0.001000 rate, 23.852543 seconds, 62208 images -Loaded: 0.025839 seconds -Region Avg IOU: 0.485483, Class: 1.000000, Obj: 0.131096, No Obj: 0.003703, Avg Recall: 0.525547, count: 137 -Region Avg IOU: 0.449087, Class: 1.000000, Obj: 0.136919, No Obj: 0.003564, Avg Recall: 0.475248, count: 101 -Region Avg IOU: 0.440892, Class: 1.000000, Obj: 0.120061, No Obj: 0.003195, Avg Recall: 0.500000, count: 112 -Region Avg IOU: 0.426736, Class: 1.000000, Obj: 0.133811, No Obj: 0.003180, Avg Recall: 0.459459, count: 111 -Region Avg IOU: 0.447056, Class: 1.000000, Obj: 0.100600, No Obj: 0.003253, Avg Recall: 0.421053, count: 95 -Region Avg IOU: 0.500592, Class: 1.000000, Obj: 0.127992, No Obj: 0.003799, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.411883, Class: 1.000000, Obj: 0.118706, No Obj: 0.003529, Avg Recall: 0.452174, count: 115 -Region Avg IOU: 0.424016, Class: 1.000000, Obj: 0.098366, No Obj: 0.003351, Avg Recall: 0.385827, count: 127 -244: 0.636455, 0.573333 avg, 0.001000 rate, 22.217524 seconds, 62464 images -Loaded: 2.138626 seconds -Region Avg IOU: 0.493624, Class: 1.000000, Obj: 0.139195, No Obj: 0.003515, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.431127, Class: 1.000000, Obj: 0.147399, No Obj: 0.003555, Avg Recall: 0.440000, count: 125 -Region Avg IOU: 0.435860, Class: 1.000000, Obj: 0.135000, No Obj: 0.003705, Avg Recall: 0.453782, count: 119 -Region Avg IOU: 0.444371, Class: 1.000000, Obj: 0.143404, No Obj: 0.003687, Avg Recall: 0.423729, count: 118 -Region Avg IOU: 0.433395, Class: 1.000000, Obj: 0.143221, No Obj: 0.002933, Avg Recall: 0.381443, count: 97 -Region Avg IOU: 0.487133, Class: 1.000000, Obj: 0.134131, No Obj: 0.003660, Avg Recall: 0.537736, count: 106 -Region Avg IOU: 0.484483, Class: 1.000000, Obj: 0.157778, No Obj: 0.003629, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.420821, Class: 1.000000, Obj: 0.106430, No Obj: 0.003533, Avg Recall: 0.441176, count: 102 -245: 0.631582, 0.579158 avg, 0.001000 rate, 24.151850 seconds, 62720 images -Loaded: 0.000058 seconds -Region Avg IOU: 0.463486, Class: 1.000000, Obj: 0.132199, No Obj: 0.004317, Avg Recall: 0.495727, count: 117 -Region Avg IOU: 0.466372, Class: 1.000000, Obj: 0.153881, No Obj: 0.003665, Avg Recall: 0.455446, count: 101 -Region Avg IOU: 0.495403, Class: 1.000000, Obj: 0.130120, No Obj: 0.003806, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.449483, Class: 1.000000, Obj: 0.176620, No Obj: 0.003723, Avg Recall: 0.505376, count: 93 -Region Avg IOU: 0.451141, Class: 1.000000, Obj: 0.142245, No Obj: 0.004011, Avg Recall: 0.455446, count: 101 -Region Avg IOU: 0.457455, Class: 1.000000, Obj: 0.131434, No Obj: 0.003920, Avg Recall: 0.447154, count: 123 -Region Avg IOU: 0.450835, Class: 1.000000, Obj: 0.152847, No Obj: 0.003769, Avg Recall: 0.411215, count: 107 -Region Avg IOU: 0.401868, Class: 1.000000, Obj: 0.156404, No Obj: 0.003581, Avg Recall: 0.360000, count: 100 -246: 0.534746, 0.574717 avg, 0.001000 rate, 22.640432 seconds, 62976 images -Loaded: 0.956964 seconds -Region Avg IOU: 0.476741, Class: 1.000000, Obj: 0.177008, No Obj: 0.004208, Avg Recall: 0.495798, count: 119 -Region Avg IOU: 0.456402, Class: 1.000000, Obj: 0.140825, No Obj: 0.003879, Avg Recall: 0.457944, count: 107 -Region Avg IOU: 0.397679, Class: 1.000000, Obj: 0.131988, No Obj: 0.003845, Avg Recall: 0.356522, count: 115 -Region Avg IOU: 0.436937, Class: 1.000000, Obj: 0.128014, No Obj: 0.003956, Avg Recall: 0.507812, count: 128 -Region Avg IOU: 0.422592, Class: 1.000000, Obj: 0.146935, No Obj: 0.003835, Avg Recall: 0.450000, count: 100 -Region Avg IOU: 0.461554, Class: 1.000000, Obj: 0.145625, No Obj: 0.003662, Avg Recall: 0.479592, count: 98 -Region Avg IOU: 0.470491, Class: 1.000000, Obj: 0.151598, No Obj: 0.003491, Avg Recall: 0.550562, count: 89 -Region Avg IOU: 0.443632, Class: 1.000000, Obj: 0.143798, No Obj: 0.003877, Avg Recall: 0.505155, count: 97 -247: 0.555976, 0.572843 avg, 0.001000 rate, 24.328617 seconds, 63232 images -Loaded: 0.322144 seconds -Region Avg IOU: 0.498781, Class: 1.000000, Obj: 0.170362, No Obj: 0.004268, Avg Recall: 0.597826, count: 92 -Region Avg IOU: 0.479032, Class: 1.000000, Obj: 0.171366, No Obj: 0.003976, Avg Recall: 0.516129, count: 93 -Region Avg IOU: 0.465985, Class: 1.000000, Obj: 0.151412, No Obj: 0.004054, Avg Recall: 0.490196, count: 102 -Region Avg IOU: 0.480246, Class: 1.000000, Obj: 0.172362, No Obj: 0.004404, Avg Recall: 0.484848, count: 132 -Region Avg IOU: 0.489925, Class: 1.000000, Obj: 0.142871, No Obj: 0.004349, Avg Recall: 0.571429, count: 105 -Region Avg IOU: 0.472634, Class: 1.000000, Obj: 0.161445, No Obj: 0.004044, Avg Recall: 0.513274, count: 113 -Region Avg IOU: 0.477234, Class: 1.000000, Obj: 0.192941, No Obj: 0.003931, Avg Recall: 0.525773, count: 97 -Region Avg IOU: 0.440158, Class: 1.000000, Obj: 0.177453, No Obj: 0.003806, Avg Recall: 0.468750, count: 96 -248: 0.578026, 0.573361 avg, 0.001000 rate, 24.406000 seconds, 63488 images -Loaded: 0.317285 seconds -Region Avg IOU: 0.448424, Class: 1.000000, Obj: 0.172304, No Obj: 0.004237, Avg Recall: 0.500000, count: 126 -Region Avg IOU: 0.454356, Class: 1.000000, Obj: 0.152330, No Obj: 0.004025, Avg Recall: 0.469231, count: 130 -Region Avg IOU: 0.421010, Class: 1.000000, Obj: 0.164511, No Obj: 0.003959, Avg Recall: 0.414414, count: 111 -Region Avg IOU: 0.414512, Class: 1.000000, Obj: 0.130237, No Obj: 0.003538, Avg Recall: 0.447917, count: 96 -Region Avg IOU: 0.484874, Class: 1.000000, Obj: 0.172025, No Obj: 0.004340, Avg Recall: 0.486957, count: 115 -Region Avg IOU: 0.476448, Class: 1.000000, Obj: 0.172332, No Obj: 0.004524, Avg Recall: 0.524752, count: 101 -Region Avg IOU: 0.433625, Class: 1.000000, Obj: 0.159634, No Obj: 0.003912, Avg Recall: 0.409524, count: 105 -Region Avg IOU: 0.420457, Class: 1.000000, Obj: 0.139364, No Obj: 0.004229, Avg Recall: 0.442478, count: 113 -249: 0.558079, 0.571833 avg, 0.001000 rate, 25.009546 seconds, 63744 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.476275, Class: 1.000000, Obj: 0.162670, No Obj: 0.004178, Avg Recall: 0.476635, count: 107 -Region Avg IOU: 0.467740, Class: 1.000000, Obj: 0.127403, No Obj: 0.004040, Avg Recall: 0.524272, count: 103 -Region Avg IOU: 0.500841, Class: 1.000000, Obj: 0.171001, No Obj: 0.004483, Avg Recall: 0.495146, count: 103 -Region Avg IOU: 0.470205, Class: 1.000000, Obj: 0.186819, No Obj: 0.004293, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.495833, Class: 1.000000, Obj: 0.164161, No Obj: 0.004554, Avg Recall: 0.564885, count: 131 -Region Avg IOU: 0.440358, Class: 1.000000, Obj: 0.150787, No Obj: 0.003810, Avg Recall: 0.480769, count: 104 -Region Avg IOU: 0.440657, Class: 1.000000, Obj: 0.149106, No Obj: 0.004034, Avg Recall: 0.425743, count: 101 -Region Avg IOU: 0.433269, Class: 1.000000, Obj: 0.174155, No Obj: 0.004105, Avg Recall: 0.430000, count: 100 -250: 0.603230, 0.574973 avg, 0.001000 rate, 24.939695 seconds, 64000 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 20.815123 seconds -Region Avg IOU: 0.432411, Class: 1.000000, Obj: 0.103288, No Obj: 0.003874, Avg Recall: 0.386364, count: 88 -Region Avg IOU: 0.414217, Class: 1.000000, Obj: 0.156763, No Obj: 0.003700, Avg Recall: 0.431818, count: 88 -Region Avg IOU: 0.352796, Class: 1.000000, Obj: 0.138272, No Obj: 0.003545, Avg Recall: 0.330275, count: 109 -Region Avg IOU: 0.437284, Class: 1.000000, Obj: 0.144759, No Obj: 0.003761, Avg Recall: 0.444444, count: 81 -Region Avg IOU: 0.453780, Class: 1.000000, Obj: 0.165197, No Obj: 0.004357, Avg Recall: 0.507812, count: 128 -Region Avg IOU: 0.421410, Class: 1.000000, Obj: 0.136523, No Obj: 0.004173, Avg Recall: 0.490566, count: 106 -Region Avg IOU: 0.396856, Class: 1.000000, Obj: 0.114201, No Obj: 0.003801, Avg Recall: 0.394495, count: 109 -Region Avg IOU: 0.475793, Class: 1.000000, Obj: 0.150714, No Obj: 0.004419, Avg Recall: 0.507812, count: 128 -251: 0.606060, 0.578081 avg, 0.001000 rate, 20.157421 seconds, 64256 images -Loaded: 1.910781 seconds -Region Avg IOU: 0.380643, Class: 1.000000, Obj: 0.123443, No Obj: 0.003464, Avg Recall: 0.325843, count: 89 -Region Avg IOU: 0.422722, Class: 1.000000, Obj: 0.112896, No Obj: 0.003939, Avg Recall: 0.478633, count: 117 -Region Avg IOU: 0.451727, Class: 1.000000, Obj: 0.116406, No Obj: 0.003828, Avg Recall: 0.418367, count: 98 -Region Avg IOU: 0.391636, Class: 1.000000, Obj: 0.137402, No Obj: 0.004298, Avg Recall: 0.381356, count: 118 -Region Avg IOU: 0.406425, Class: 1.000000, Obj: 0.146726, No Obj: 0.004126, Avg Recall: 0.391304, count: 115 -Region Avg IOU: 0.416222, Class: 1.000000, Obj: 0.138360, No Obj: 0.004367, Avg Recall: 0.440678, count: 118 -Region Avg IOU: 0.387742, Class: 1.000000, Obj: 0.145467, No Obj: 0.003792, Avg Recall: 0.357895, count: 95 -Region Avg IOU: 0.421016, Class: 1.000000, Obj: 0.157145, No Obj: 0.004223, Avg Recall: 0.385321, count: 109 -252: 0.593940, 0.579667 avg, 0.001000 rate, 20.604036 seconds, 64512 images -Loaded: 1.588754 seconds -Region Avg IOU: 0.383075, Class: 1.000000, Obj: 0.133866, No Obj: 0.004104, Avg Recall: 0.390728, count: 151 -Region Avg IOU: 0.420568, Class: 1.000000, Obj: 0.109763, No Obj: 0.003786, Avg Recall: 0.415929, count: 113 -Region Avg IOU: 0.435364, Class: 1.000000, Obj: 0.131527, No Obj: 0.004496, Avg Recall: 0.448000, count: 125 -Region Avg IOU: 0.442654, Class: 1.000000, Obj: 0.160999, No Obj: 0.004131, Avg Recall: 0.486957, count: 115 -Region Avg IOU: 0.389248, Class: 1.000000, Obj: 0.100599, No Obj: 0.003835, Avg Recall: 0.368421, count: 152 -Region Avg IOU: 0.407735, Class: 1.000000, Obj: 0.143267, No Obj: 0.003919, Avg Recall: 0.405660, count: 106 -Region Avg IOU: 0.400474, Class: 1.000000, Obj: 0.152541, No Obj: 0.003842, Avg Recall: 0.409091, count: 88 -Region Avg IOU: 0.378001, Class: 1.000000, Obj: 0.103134, No Obj: 0.003468, Avg Recall: 0.368421, count: 114 -253: 0.602966, 0.581997 avg, 0.001000 rate, 20.808403 seconds, 64768 images -Loaded: 2.107008 seconds -Region Avg IOU: 0.432834, Class: 1.000000, Obj: 0.177451, No Obj: 0.004088, Avg Recall: 0.466667, count: 90 -Region Avg IOU: 0.437119, Class: 1.000000, Obj: 0.139629, No Obj: 0.003929, Avg Recall: 0.471264, count: 87 -Region Avg IOU: 0.380082, Class: 1.000000, Obj: 0.120231, No Obj: 0.004108, Avg Recall: 0.338346, count: 133 -Region Avg IOU: 0.370594, Class: 1.000000, Obj: 0.112244, No Obj: 0.003892, Avg Recall: 0.350427, count: 117 -Region Avg IOU: 0.450749, Class: 1.000000, Obj: 0.162946, No Obj: 0.004808, Avg Recall: 0.443396, count: 106 -Region Avg IOU: 0.389922, Class: 1.000000, Obj: 0.132205, No Obj: 0.003939, Avg Recall: 0.363636, count: 110 -Region Avg IOU: 0.417058, Class: 1.000000, Obj: 0.115183, No Obj: 0.004226, Avg Recall: 0.423423, count: 111 -Region Avg IOU: 0.412010, Class: 1.000000, Obj: 0.122998, No Obj: 0.003475, Avg Recall: 0.395349, count: 86 -254: 0.599872, 0.583785 avg, 0.001000 rate, 19.452364 seconds, 65024 images -Loaded: 3.384858 seconds -Region Avg IOU: 0.425948, Class: 1.000000, Obj: 0.159524, No Obj: 0.004331, Avg Recall: 0.412698, count: 126 -Region Avg IOU: 0.426599, Class: 1.000000, Obj: 0.130241, No Obj: 0.003646, Avg Recall: 0.445783, count: 83 -Region Avg IOU: 0.406826, Class: 1.000000, Obj: 0.143876, No Obj: 0.003724, Avg Recall: 0.393617, count: 94 -Region Avg IOU: 0.453452, Class: 1.000000, Obj: 0.160786, No Obj: 0.004042, Avg Recall: 0.473684, count: 95 -Region Avg IOU: 0.434698, Class: 1.000000, Obj: 0.129945, No Obj: 0.004545, Avg Recall: 0.425000, count: 120 -Region Avg IOU: 0.424411, Class: 1.000000, Obj: 0.126601, No Obj: 0.003817, Avg Recall: 0.414894, count: 94 -Region Avg IOU: 0.364670, Class: 1.000000, Obj: 0.139184, No Obj: 0.003922, Avg Recall: 0.377049, count: 122 -Region Avg IOU: 0.465428, Class: 1.000000, Obj: 0.145769, No Obj: 0.004250, Avg Recall: 0.500000, count: 118 -255: 0.624764, 0.587883 avg, 0.001000 rate, 18.366669 seconds, 65280 images -Loaded: 1.880324 seconds -Region Avg IOU: 0.377092, Class: 1.000000, Obj: 0.123332, No Obj: 0.003867, Avg Recall: 0.376238, count: 101 -Region Avg IOU: 0.414821, Class: 1.000000, Obj: 0.117657, No Obj: 0.004282, Avg Recall: 0.429907, count: 107 -Region Avg IOU: 0.426941, Class: 1.000000, Obj: 0.148811, No Obj: 0.004424, Avg Recall: 0.380531, count: 113 -Region Avg IOU: 0.435032, Class: 1.000000, Obj: 0.171584, No Obj: 0.004111, Avg Recall: 0.450000, count: 100 -Region Avg IOU: 0.399682, Class: 1.000000, Obj: 0.126857, No Obj: 0.004091, Avg Recall: 0.354545, count: 110 -Region Avg IOU: 0.410797, Class: 1.000000, Obj: 0.125322, No Obj: 0.003957, Avg Recall: 0.404762, count: 126 -Region Avg IOU: 0.431230, Class: 1.000000, Obj: 0.122038, No Obj: 0.003243, Avg Recall: 0.465753, count: 73 -Region Avg IOU: 0.453659, Class: 1.000000, Obj: 0.127161, No Obj: 0.003937, Avg Recall: 0.524752, count: 101 -256: 0.543623, 0.583457 avg, 0.001000 rate, 20.318373 seconds, 65536 images -Loaded: 1.769022 seconds -Region Avg IOU: 0.453343, Class: 1.000000, Obj: 0.135777, No Obj: 0.003720, Avg Recall: 0.469880, count: 83 -Region Avg IOU: 0.455458, Class: 1.000000, Obj: 0.182949, No Obj: 0.003825, Avg Recall: 0.463918, count: 97 -Region Avg IOU: 0.374389, Class: 1.000000, Obj: 0.137636, No Obj: 0.003891, Avg Recall: 0.333333, count: 114 -Region Avg IOU: 0.447702, Class: 1.000000, Obj: 0.159202, No Obj: 0.004046, Avg Recall: 0.462264, count: 106 -Region Avg IOU: 0.397986, Class: 1.000000, Obj: 0.148694, No Obj: 0.004589, Avg Recall: 0.338843, count: 121 -Region Avg IOU: 0.433184, Class: 1.000000, Obj: 0.138409, No Obj: 0.003955, Avg Recall: 0.411215, count: 107 -Region Avg IOU: 0.363270, Class: 1.000000, Obj: 0.120872, No Obj: 0.003538, Avg Recall: 0.321101, count: 109 -Region Avg IOU: 0.389582, Class: 1.000000, Obj: 0.121928, No Obj: 0.003768, Avg Recall: 0.303922, count: 102 -257: 0.540290, 0.579140 avg, 0.001000 rate, 21.498356 seconds, 65792 images -Loaded: 0.376759 seconds -Region Avg IOU: 0.457686, Class: 1.000000, Obj: 0.147609, No Obj: 0.004879, Avg Recall: 0.503597, count: 139 -Region Avg IOU: 0.392242, Class: 1.000000, Obj: 0.144703, No Obj: 0.003840, Avg Recall: 0.340206, count: 97 -Region Avg IOU: 0.397065, Class: 1.000000, Obj: 0.122344, No Obj: 0.004151, Avg Recall: 0.403670, count: 109 -Region Avg IOU: 0.383669, Class: 1.000000, Obj: 0.122450, No Obj: 0.003455, Avg Recall: 0.349515, count: 103 -Region Avg IOU: 0.445211, Class: 1.000000, Obj: 0.123028, No Obj: 0.003974, Avg Recall: 0.445545, count: 101 -Region Avg IOU: 0.427400, Class: 1.000000, Obj: 0.166745, No Obj: 0.003840, Avg Recall: 0.378049, count: 82 -Region Avg IOU: 0.421341, Class: 1.000000, Obj: 0.134766, No Obj: 0.004155, Avg Recall: 0.433566, count: 143 -Region Avg IOU: 0.425900, Class: 1.000000, Obj: 0.096226, No Obj: 0.003757, Avg Recall: 0.466019, count: 103 -258: 0.530714, 0.574297 avg, 0.001000 rate, 22.205572 seconds, 66048 images -Loaded: 0.046871 seconds -Region Avg IOU: 0.385902, Class: 1.000000, Obj: 0.115315, No Obj: 0.003842, Avg Recall: 0.340000, count: 100 -Region Avg IOU: 0.419619, Class: 1.000000, Obj: 0.116749, No Obj: 0.004201, Avg Recall: 0.420168, count: 119 -Region Avg IOU: 0.425188, Class: 1.000000, Obj: 0.123765, No Obj: 0.003831, Avg Recall: 0.437500, count: 96 -Region Avg IOU: 0.451944, Class: 1.000000, Obj: 0.144123, No Obj: 0.003674, Avg Recall: 0.477477, count: 111 -Region Avg IOU: 0.447405, Class: 1.000000, Obj: 0.160301, No Obj: 0.004238, Avg Recall: 0.494845, count: 97 -Region Avg IOU: 0.491746, Class: 1.000000, Obj: 0.149488, No Obj: 0.004127, Avg Recall: 0.516484, count: 91 -Region Avg IOU: 0.442425, Class: 1.000000, Obj: 0.144974, No Obj: 0.004338, Avg Recall: 0.460784, count: 102 -Region Avg IOU: 0.434508, Class: 1.000000, Obj: 0.151642, No Obj: 0.004381, Avg Recall: 0.447154, count: 123 -259: 0.551658, 0.572033 avg, 0.001000 rate, 20.784008 seconds, 66304 images -Loaded: 1.594419 seconds -Region Avg IOU: 0.431532, Class: 1.000000, Obj: 0.159893, No Obj: 0.004138, Avg Recall: 0.456790, count: 81 -Region Avg IOU: 0.451249, Class: 1.000000, Obj: 0.163810, No Obj: 0.004737, Avg Recall: 0.539683, count: 126 -Region Avg IOU: 0.480524, Class: 1.000000, Obj: 0.156133, No Obj: 0.004853, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.375978, Class: 1.000000, Obj: 0.111467, No Obj: 0.004161, Avg Recall: 0.320000, count: 125 -Region Avg IOU: 0.367710, Class: 1.000000, Obj: 0.127370, No Obj: 0.004340, Avg Recall: 0.343434, count: 99 -Region Avg IOU: 0.417915, Class: 1.000000, Obj: 0.141435, No Obj: 0.004565, Avg Recall: 0.398438, count: 128 -Region Avg IOU: 0.388231, Class: 1.000000, Obj: 0.108332, No Obj: 0.004146, Avg Recall: 0.324786, count: 117 -Region Avg IOU: 0.454325, Class: 1.000000, Obj: 0.154517, No Obj: 0.004503, Avg Recall: 0.514286, count: 105 -260: 0.620193, 0.576849 avg, 0.001000 rate, 20.440952 seconds, 66560 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000077 seconds -Region Avg IOU: 0.490997, Class: 1.000000, Obj: 0.179936, No Obj: 0.005011, Avg Recall: 0.540323, count: 124 -Region Avg IOU: 0.534612, Class: 1.000000, Obj: 0.150579, No Obj: 0.005244, Avg Recall: 0.640625, count: 128 -Region Avg IOU: 0.542330, Class: 1.000000, Obj: 0.167416, No Obj: 0.004856, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.541402, Class: 1.000000, Obj: 0.199906, No Obj: 0.005043, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.503921, Class: 1.000000, Obj: 0.182407, No Obj: 0.004675, Avg Recall: 0.488372, count: 86 -Region Avg IOU: 0.565647, Class: 1.000000, Obj: 0.155793, No Obj: 0.005010, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.493045, Class: 1.000000, Obj: 0.164191, No Obj: 0.004824, Avg Recall: 0.572727, count: 110 -Region Avg IOU: 0.482842, Class: 1.000000, Obj: 0.163153, No Obj: 0.004916, Avg Recall: 0.526316, count: 114 -261: 0.622420, 0.581406 avg, 0.001000 rate, 27.188875 seconds, 66816 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.474825, Class: 1.000000, Obj: 0.160056, No Obj: 0.004581, Avg Recall: 0.512605, count: 119 -Region Avg IOU: 0.530605, Class: 1.000000, Obj: 0.185172, No Obj: 0.004799, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.572541, Class: 1.000000, Obj: 0.141402, No Obj: 0.004551, Avg Recall: 0.671053, count: 76 -Region Avg IOU: 0.441396, Class: 1.000000, Obj: 0.125967, No Obj: 0.004265, Avg Recall: 0.494624, count: 93 -Region Avg IOU: 0.518090, Class: 1.000000, Obj: 0.188746, No Obj: 0.005229, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.489697, Class: 1.000000, Obj: 0.149114, No Obj: 0.004307, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.486175, Class: 1.000000, Obj: 0.172843, No Obj: 0.004695, Avg Recall: 0.522523, count: 111 -Region Avg IOU: 0.509438, Class: 1.000000, Obj: 0.133904, No Obj: 0.004793, Avg Recall: 0.572917, count: 96 -262: 0.561900, 0.579456 avg, 0.001000 rate, 29.629614 seconds, 67072 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.472078, Class: 1.000000, Obj: 0.153594, No Obj: 0.004947, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.544407, Class: 1.000000, Obj: 0.175288, No Obj: 0.004769, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.509146, Class: 1.000000, Obj: 0.138807, No Obj: 0.004986, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.561989, Class: 1.000000, Obj: 0.153470, No Obj: 0.005162, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.513250, Class: 1.000000, Obj: 0.166082, No Obj: 0.004619, Avg Recall: 0.543307, count: 127 -Region Avg IOU: 0.505296, Class: 1.000000, Obj: 0.169783, No Obj: 0.004371, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.512879, Class: 1.000000, Obj: 0.166110, No Obj: 0.004265, Avg Recall: 0.554348, count: 92 -Region Avg IOU: 0.506423, Class: 1.000000, Obj: 0.147281, No Obj: 0.004776, Avg Recall: 0.596330, count: 109 -263: 0.578909, 0.579401 avg, 0.001000 rate, 26.222876 seconds, 67328 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.513918, Class: 1.000000, Obj: 0.155449, No Obj: 0.004364, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.507906, Class: 1.000000, Obj: 0.149264, No Obj: 0.004562, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.508563, Class: 1.000000, Obj: 0.141301, No Obj: 0.004414, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.508756, Class: 1.000000, Obj: 0.180547, No Obj: 0.004723, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.486129, Class: 1.000000, Obj: 0.125937, No Obj: 0.004796, Avg Recall: 0.587302, count: 126 -Region Avg IOU: 0.516332, Class: 1.000000, Obj: 0.136515, No Obj: 0.004060, Avg Recall: 0.617284, count: 81 -Region Avg IOU: 0.514694, Class: 1.000000, Obj: 0.160936, No Obj: 0.004067, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.514910, Class: 1.000000, Obj: 0.157233, No Obj: 0.004131, Avg Recall: 0.580247, count: 81 -264: 0.571184, 0.578579 avg, 0.001000 rate, 25.590847 seconds, 67584 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.484333, Class: 1.000000, Obj: 0.171310, No Obj: 0.004016, Avg Recall: 0.491525, count: 118 -Region Avg IOU: 0.536892, Class: 1.000000, Obj: 0.147601, No Obj: 0.004191, Avg Recall: 0.630435, count: 92 -Region Avg IOU: 0.504554, Class: 1.000000, Obj: 0.136112, No Obj: 0.004216, Avg Recall: 0.567797, count: 118 -Region Avg IOU: 0.463275, Class: 1.000000, Obj: 0.145383, No Obj: 0.003420, Avg Recall: 0.428571, count: 84 -Region Avg IOU: 0.527100, Class: 1.000000, Obj: 0.155591, No Obj: 0.004747, Avg Recall: 0.596899, count: 129 -Region Avg IOU: 0.517681, Class: 1.000000, Obj: 0.180643, No Obj: 0.004047, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.478282, Class: 1.000000, Obj: 0.129677, No Obj: 0.003876, Avg Recall: 0.524272, count: 103 -Region Avg IOU: 0.502423, Class: 1.000000, Obj: 0.148523, No Obj: 0.003831, Avg Recall: 0.535714, count: 84 -265: 0.539536, 0.574675 avg, 0.001000 rate, 28.883440 seconds, 67840 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.497587, Class: 1.000000, Obj: 0.133997, No Obj: 0.004270, Avg Recall: 0.543307, count: 127 -Region Avg IOU: 0.477373, Class: 1.000000, Obj: 0.116149, No Obj: 0.004109, Avg Recall: 0.536585, count: 123 -Region Avg IOU: 0.512323, Class: 1.000000, Obj: 0.102767, No Obj: 0.004126, Avg Recall: 0.605769, count: 104 -Region Avg IOU: 0.501899, Class: 1.000000, Obj: 0.138671, No Obj: 0.003939, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.524863, Class: 1.000000, Obj: 0.170113, No Obj: 0.004377, Avg Recall: 0.560606, count: 132 -Region Avg IOU: 0.546577, Class: 1.000000, Obj: 0.155943, No Obj: 0.004007, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.555233, Class: 1.000000, Obj: 0.173495, No Obj: 0.004184, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.519240, Class: 1.000000, Obj: 0.137757, No Obj: 0.003800, Avg Recall: 0.556604, count: 106 -266: 0.570223, 0.574230 avg, 0.001000 rate, 27.516895 seconds, 68096 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.520214, Class: 1.000000, Obj: 0.157294, No Obj: 0.004026, Avg Recall: 0.589474, count: 95 -Region Avg IOU: 0.488555, Class: 1.000000, Obj: 0.110290, No Obj: 0.003956, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.493076, Class: 1.000000, Obj: 0.125146, No Obj: 0.003906, Avg Recall: 0.538462, count: 130 -Region Avg IOU: 0.532959, Class: 1.000000, Obj: 0.153774, No Obj: 0.003924, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.533945, Class: 1.000000, Obj: 0.143009, No Obj: 0.004004, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.519068, Class: 1.000000, Obj: 0.141337, No Obj: 0.003787, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.503971, Class: 1.000000, Obj: 0.151099, No Obj: 0.003577, Avg Recall: 0.583333, count: 96 -Region Avg IOU: 0.489117, Class: 1.000000, Obj: 0.130743, No Obj: 0.003860, Avg Recall: 0.529915, count: 117 -267: 0.478634, 0.564670 avg, 0.001000 rate, 29.617647 seconds, 68352 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.487194, Class: 1.000000, Obj: 0.140432, No Obj: 0.003522, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.535949, Class: 1.000000, Obj: 0.180257, No Obj: 0.003640, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.528122, Class: 1.000000, Obj: 0.168570, No Obj: 0.003938, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.509970, Class: 1.000000, Obj: 0.127582, No Obj: 0.003897, Avg Recall: 0.525862, count: 116 -Region Avg IOU: 0.520701, Class: 1.000000, Obj: 0.147641, No Obj: 0.003657, Avg Recall: 0.592920, count: 113 -Region Avg IOU: 0.525653, Class: 1.000000, Obj: 0.146889, No Obj: 0.003905, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.555118, Class: 1.000000, Obj: 0.165277, No Obj: 0.004164, Avg Recall: 0.641026, count: 117 -Region Avg IOU: 0.515182, Class: 1.000000, Obj: 0.125017, No Obj: 0.004175, Avg Recall: 0.625000, count: 128 -268: 0.560904, 0.564294 avg, 0.001000 rate, 25.884581 seconds, 68608 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.535772, Class: 1.000000, Obj: 0.152839, No Obj: 0.003739, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.539732, Class: 1.000000, Obj: 0.158009, No Obj: 0.003949, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.499192, Class: 1.000000, Obj: 0.175316, No Obj: 0.003547, Avg Recall: 0.504348, count: 115 -Region Avg IOU: 0.496857, Class: 1.000000, Obj: 0.148194, No Obj: 0.003925, Avg Recall: 0.595745, count: 94 -Region Avg IOU: 0.556035, Class: 1.000000, Obj: 0.147856, No Obj: 0.004110, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.534128, Class: 1.000000, Obj: 0.151760, No Obj: 0.004060, Avg Recall: 0.637795, count: 127 -Region Avg IOU: 0.483956, Class: 1.000000, Obj: 0.132759, No Obj: 0.003843, Avg Recall: 0.512000, count: 125 -Region Avg IOU: 0.517925, Class: 1.000000, Obj: 0.154374, No Obj: 0.003635, Avg Recall: 0.588785, count: 107 -269: 0.540284, 0.561893 avg, 0.001000 rate, 27.779163 seconds, 68864 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.513977, Class: 1.000000, Obj: 0.165688, No Obj: 0.003718, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.503092, Class: 1.000000, Obj: 0.167893, No Obj: 0.004037, Avg Recall: 0.574713, count: 87 -Region Avg IOU: 0.512764, Class: 1.000000, Obj: 0.167117, No Obj: 0.004173, Avg Recall: 0.533333, count: 150 -Region Avg IOU: 0.530110, Class: 1.000000, Obj: 0.109243, No Obj: 0.003849, Avg Recall: 0.540816, count: 98 -Region Avg IOU: 0.556258, Class: 1.000000, Obj: 0.165849, No Obj: 0.003706, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.537537, Class: 1.000000, Obj: 0.164637, No Obj: 0.003689, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.503103, Class: 1.000000, Obj: 0.126713, No Obj: 0.004003, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.571344, Class: 1.000000, Obj: 0.157520, No Obj: 0.004111, Avg Recall: 0.693069, count: 101 -270: 0.539512, 0.559655 avg, 0.001000 rate, 29.323441 seconds, 69120 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000099 seconds -Region Avg IOU: 0.556865, Class: 1.000000, Obj: 0.198714, No Obj: 0.004136, Avg Recall: 0.720000, count: 125 -Region Avg IOU: 0.539527, Class: 1.000000, Obj: 0.169768, No Obj: 0.004363, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.558330, Class: 1.000000, Obj: 0.118827, No Obj: 0.004136, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.491922, Class: 1.000000, Obj: 0.111143, No Obj: 0.004143, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.558699, Class: 1.000000, Obj: 0.199758, No Obj: 0.003791, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.527429, Class: 1.000000, Obj: 0.143087, No Obj: 0.004262, Avg Recall: 0.598485, count: 132 -Region Avg IOU: 0.527304, Class: 1.000000, Obj: 0.169884, No Obj: 0.004241, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.549875, Class: 1.000000, Obj: 0.150609, No Obj: 0.004098, Avg Recall: 0.661017, count: 118 -271: 0.566103, 0.560300 avg, 0.001000 rate, 28.725151 seconds, 69376 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.547361, Class: 1.000000, Obj: 0.217227, No Obj: 0.003988, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.531966, Class: 1.000000, Obj: 0.134044, No Obj: 0.004386, Avg Recall: 0.622807, count: 114 -Region Avg IOU: 0.522565, Class: 1.000000, Obj: 0.148085, No Obj: 0.004044, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.536624, Class: 1.000000, Obj: 0.162054, No Obj: 0.004481, Avg Recall: 0.634483, count: 145 -Region Avg IOU: 0.544902, Class: 1.000000, Obj: 0.180215, No Obj: 0.003887, Avg Recall: 0.671233, count: 73 -Region Avg IOU: 0.558903, Class: 1.000000, Obj: 0.131482, No Obj: 0.004272, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.571953, Class: 1.000000, Obj: 0.178526, No Obj: 0.004232, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.544071, Class: 1.000000, Obj: 0.169805, No Obj: 0.004200, Avg Recall: 0.663551, count: 107 -272: 0.545062, 0.558776 avg, 0.001000 rate, 30.322599 seconds, 69632 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.557530, Class: 1.000000, Obj: 0.191495, No Obj: 0.004211, Avg Recall: 0.698276, count: 116 -Region Avg IOU: 0.528554, Class: 1.000000, Obj: 0.128528, No Obj: 0.003537, Avg Recall: 0.603306, count: 121 -Region Avg IOU: 0.532974, Class: 1.000000, Obj: 0.127931, No Obj: 0.003910, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.546113, Class: 1.000000, Obj: 0.172018, No Obj: 0.004511, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.524055, Class: 1.000000, Obj: 0.160294, No Obj: 0.003613, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.515613, Class: 1.000000, Obj: 0.117647, No Obj: 0.004231, Avg Recall: 0.646617, count: 133 -Region Avg IOU: 0.573336, Class: 1.000000, Obj: 0.168887, No Obj: 0.003975, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.525965, Class: 1.000000, Obj: 0.172924, No Obj: 0.004355, Avg Recall: 0.617021, count: 141 -273: 0.580059, 0.560904 avg, 0.001000 rate, 29.707067 seconds, 69888 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.534671, Class: 1.000000, Obj: 0.178077, No Obj: 0.004129, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.537834, Class: 1.000000, Obj: 0.161378, No Obj: 0.004024, Avg Recall: 0.596639, count: 119 -Region Avg IOU: 0.527291, Class: 1.000000, Obj: 0.189798, No Obj: 0.003666, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.497768, Class: 1.000000, Obj: 0.125987, No Obj: 0.003649, Avg Recall: 0.546392, count: 97 -Region Avg IOU: 0.563237, Class: 1.000000, Obj: 0.207263, No Obj: 0.003996, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.563397, Class: 1.000000, Obj: 0.216869, No Obj: 0.003636, Avg Recall: 0.662791, count: 86 -Region Avg IOU: 0.507687, Class: 1.000000, Obj: 0.153698, No Obj: 0.003994, Avg Recall: 0.513043, count: 115 -Region Avg IOU: 0.503400, Class: 1.000000, Obj: 0.141626, No Obj: 0.003729, Avg Recall: 0.592593, count: 108 -274: 0.498798, 0.554693 avg, 0.001000 rate, 29.154343 seconds, 70144 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.532230, Class: 1.000000, Obj: 0.206600, No Obj: 0.004048, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.549842, Class: 1.000000, Obj: 0.160902, No Obj: 0.003867, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.502450, Class: 1.000000, Obj: 0.158830, No Obj: 0.003330, Avg Recall: 0.551724, count: 87 -Region Avg IOU: 0.515921, Class: 1.000000, Obj: 0.177322, No Obj: 0.003384, Avg Recall: 0.548780, count: 82 -Region Avg IOU: 0.538891, Class: 1.000000, Obj: 0.156879, No Obj: 0.003349, Avg Recall: 0.658537, count: 82 -Region Avg IOU: 0.559696, Class: 1.000000, Obj: 0.172188, No Obj: 0.003471, Avg Recall: 0.674699, count: 83 -Region Avg IOU: 0.553288, Class: 1.000000, Obj: 0.175104, No Obj: 0.003735, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.535089, Class: 1.000000, Obj: 0.193253, No Obj: 0.003854, Avg Recall: 0.628319, count: 113 -275: 0.428993, 0.542123 avg, 0.001000 rate, 31.072737 seconds, 70400 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.547497, Class: 1.000000, Obj: 0.134351, No Obj: 0.003310, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.537090, Class: 1.000000, Obj: 0.171206, No Obj: 0.003496, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.560179, Class: 1.000000, Obj: 0.167124, No Obj: 0.003700, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.551118, Class: 1.000000, Obj: 0.147506, No Obj: 0.003457, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.541405, Class: 1.000000, Obj: 0.198286, No Obj: 0.003650, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.562261, Class: 1.000000, Obj: 0.160767, No Obj: 0.003418, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.514883, Class: 1.000000, Obj: 0.162738, No Obj: 0.003699, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.564579, Class: 1.000000, Obj: 0.160322, No Obj: 0.003466, Avg Recall: 0.673913, count: 92 -276: 0.531320, 0.541043 avg, 0.001000 rate, 30.081066 seconds, 70656 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.501877, Class: 1.000000, Obj: 0.160268, No Obj: 0.003415, Avg Recall: 0.540984, count: 122 -Region Avg IOU: 0.538307, Class: 1.000000, Obj: 0.141183, No Obj: 0.003277, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.525132, Class: 1.000000, Obj: 0.111736, No Obj: 0.003291, Avg Recall: 0.604651, count: 129 -Region Avg IOU: 0.564166, Class: 1.000000, Obj: 0.156576, No Obj: 0.003299, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.560319, Class: 1.000000, Obj: 0.161355, No Obj: 0.003062, Avg Recall: 0.674419, count: 86 -Region Avg IOU: 0.558155, Class: 1.000000, Obj: 0.126973, No Obj: 0.003160, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.555219, Class: 1.000000, Obj: 0.127922, No Obj: 0.003431, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.531885, Class: 1.000000, Obj: 0.158387, No Obj: 0.003580, Avg Recall: 0.611650, count: 103 -277: 0.520461, 0.538985 avg, 0.001000 rate, 29.525162 seconds, 70912 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.493658, Class: 1.000000, Obj: 0.125242, No Obj: 0.003088, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.518258, Class: 1.000000, Obj: 0.153402, No Obj: 0.003144, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.564787, Class: 1.000000, Obj: 0.164639, No Obj: 0.003071, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.529447, Class: 1.000000, Obj: 0.122042, No Obj: 0.003102, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.581895, Class: 1.000000, Obj: 0.154703, No Obj: 0.003485, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.541827, Class: 1.000000, Obj: 0.166411, No Obj: 0.003128, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.510917, Class: 1.000000, Obj: 0.142517, No Obj: 0.002978, Avg Recall: 0.526316, count: 76 -Region Avg IOU: 0.547528, Class: 1.000000, Obj: 0.160586, No Obj: 0.003257, Avg Recall: 0.648936, count: 94 -278: 0.465086, 0.531595 avg, 0.001000 rate, 28.609327 seconds, 71168 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.525798, Class: 1.000000, Obj: 0.157222, No Obj: 0.003205, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.515554, Class: 1.000000, Obj: 0.117884, No Obj: 0.003208, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.525432, Class: 1.000000, Obj: 0.136249, No Obj: 0.003224, Avg Recall: 0.568421, count: 95 -Region Avg IOU: 0.499159, Class: 1.000000, Obj: 0.164625, No Obj: 0.003281, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.556720, Class: 1.000000, Obj: 0.133243, No Obj: 0.003053, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.533394, Class: 1.000000, Obj: 0.147214, No Obj: 0.003193, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.535160, Class: 1.000000, Obj: 0.149940, No Obj: 0.003199, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.551547, Class: 1.000000, Obj: 0.127073, No Obj: 0.003381, Avg Recall: 0.698413, count: 126 -279: 0.489822, 0.527418 avg, 0.001000 rate, 29.766994 seconds, 71424 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.496627, Class: 1.000000, Obj: 0.143313, No Obj: 0.003295, Avg Recall: 0.553719, count: 121 -Region Avg IOU: 0.533530, Class: 1.000000, Obj: 0.144156, No Obj: 0.003285, Avg Recall: 0.661290, count: 124 -Region Avg IOU: 0.513339, Class: 1.000000, Obj: 0.153573, No Obj: 0.002736, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.547634, Class: 1.000000, Obj: 0.126299, No Obj: 0.003260, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.520991, Class: 1.000000, Obj: 0.142846, No Obj: 0.003181, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.575573, Class: 1.000000, Obj: 0.138166, No Obj: 0.003058, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.499912, Class: 1.000000, Obj: 0.121316, No Obj: 0.003156, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.536420, Class: 1.000000, Obj: 0.173919, No Obj: 0.003130, Avg Recall: 0.643564, count: 101 -280: 0.565859, 0.531262 avg, 0.001000 rate, 31.148880 seconds, 71680 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 15.763371 seconds -Region Avg IOU: 0.497113, Class: 1.000000, Obj: 0.154488, No Obj: 0.003423, Avg Recall: 0.545455, count: 132 -Region Avg IOU: 0.439679, Class: 1.000000, Obj: 0.125938, No Obj: 0.002817, Avg Recall: 0.436975, count: 119 -Region Avg IOU: 0.450362, Class: 1.000000, Obj: 0.127313, No Obj: 0.002887, Avg Recall: 0.477273, count: 132 -Region Avg IOU: 0.551707, Class: 1.000000, Obj: 0.200296, No Obj: 0.003199, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.529219, Class: 1.000000, Obj: 0.136477, No Obj: 0.003102, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.517899, Class: 1.000000, Obj: 0.137226, No Obj: 0.002963, Avg Recall: 0.532110, count: 109 -Region Avg IOU: 0.532707, Class: 1.000000, Obj: 0.191377, No Obj: 0.002908, Avg Recall: 0.617978, count: 89 -Region Avg IOU: 0.492571, Class: 1.000000, Obj: 0.164456, No Obj: 0.002950, Avg Recall: 0.570000, count: 100 -281: 0.549231, 0.533059 avg, 0.001000 rate, 26.347759 seconds, 71936 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.508185, Class: 1.000000, Obj: 0.180690, No Obj: 0.002972, Avg Recall: 0.506667, count: 75 -Region Avg IOU: 0.440098, Class: 1.000000, Obj: 0.147368, No Obj: 0.003161, Avg Recall: 0.473214, count: 112 -Region Avg IOU: 0.497198, Class: 1.000000, Obj: 0.144741, No Obj: 0.002784, Avg Recall: 0.533333, count: 90 -Region Avg IOU: 0.512891, Class: 1.000000, Obj: 0.146540, No Obj: 0.003128, Avg Recall: 0.642857, count: 84 -Region Avg IOU: 0.507115, Class: 1.000000, Obj: 0.173078, No Obj: 0.002846, Avg Recall: 0.550000, count: 80 -Region Avg IOU: 0.509919, Class: 1.000000, Obj: 0.151161, No Obj: 0.003335, Avg Recall: 0.611111, count: 126 -Region Avg IOU: 0.474578, Class: 1.000000, Obj: 0.151438, No Obj: 0.003133, Avg Recall: 0.484375, count: 128 -Region Avg IOU: 0.492049, Class: 1.000000, Obj: 0.156225, No Obj: 0.002920, Avg Recall: 0.581081, count: 74 -282: 0.466565, 0.526409 avg, 0.001000 rate, 28.098928 seconds, 72192 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.463531, Class: 1.000000, Obj: 0.108009, No Obj: 0.003151, Avg Recall: 0.528455, count: 123 -Region Avg IOU: 0.534385, Class: 1.000000, Obj: 0.141358, No Obj: 0.003161, Avg Recall: 0.635659, count: 129 -Region Avg IOU: 0.512448, Class: 1.000000, Obj: 0.172773, No Obj: 0.003112, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.516305, Class: 1.000000, Obj: 0.160945, No Obj: 0.003082, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.501670, Class: 1.000000, Obj: 0.158839, No Obj: 0.003048, Avg Recall: 0.549451, count: 91 -Region Avg IOU: 0.521485, Class: 1.000000, Obj: 0.168198, No Obj: 0.003758, Avg Recall: 0.601562, count: 128 -Region Avg IOU: 0.503000, Class: 1.000000, Obj: 0.138276, No Obj: 0.003130, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.463280, Class: 1.000000, Obj: 0.170086, No Obj: 0.002926, Avg Recall: 0.486957, count: 115 -283: 0.570719, 0.530840 avg, 0.001000 rate, 24.719412 seconds, 72448 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.491161, Class: 1.000000, Obj: 0.158607, No Obj: 0.003445, Avg Recall: 0.524823, count: 141 -Region Avg IOU: 0.545258, Class: 1.000000, Obj: 0.144280, No Obj: 0.003437, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.494637, Class: 1.000000, Obj: 0.175700, No Obj: 0.003512, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.526156, Class: 1.000000, Obj: 0.201319, No Obj: 0.003286, Avg Recall: 0.583333, count: 96 -Region Avg IOU: 0.503317, Class: 1.000000, Obj: 0.121257, No Obj: 0.003761, Avg Recall: 0.566474, count: 173 -Region Avg IOU: 0.493036, Class: 1.000000, Obj: 0.163726, No Obj: 0.003226, Avg Recall: 0.532110, count: 109 -Region Avg IOU: 0.468099, Class: 1.000000, Obj: 0.127082, No Obj: 0.003021, Avg Recall: 0.488095, count: 84 -Region Avg IOU: 0.536664, Class: 1.000000, Obj: 0.165896, No Obj: 0.003142, Avg Recall: 0.628319, count: 113 -284: 0.562883, 0.534045 avg, 0.001000 rate, 27.275564 seconds, 72704 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.508343, Class: 1.000000, Obj: 0.157492, No Obj: 0.003342, Avg Recall: 0.605769, count: 104 -Region Avg IOU: 0.517044, Class: 1.000000, Obj: 0.192589, No Obj: 0.003377, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.505312, Class: 1.000000, Obj: 0.216218, No Obj: 0.003539, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.493211, Class: 1.000000, Obj: 0.198840, No Obj: 0.003310, Avg Recall: 0.483146, count: 89 -Region Avg IOU: 0.480726, Class: 1.000000, Obj: 0.172992, No Obj: 0.003444, Avg Recall: 0.520833, count: 144 -Region Avg IOU: 0.499169, Class: 1.000000, Obj: 0.202020, No Obj: 0.003572, Avg Recall: 0.563025, count: 119 -Region Avg IOU: 0.502193, Class: 1.000000, Obj: 0.167268, No Obj: 0.003309, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.489673, Class: 1.000000, Obj: 0.131340, No Obj: 0.003246, Avg Recall: 0.532710, count: 107 -285: 0.499327, 0.530573 avg, 0.001000 rate, 26.295940 seconds, 72960 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.505693, Class: 1.000000, Obj: 0.195395, No Obj: 0.003509, Avg Recall: 0.554348, count: 92 -Region Avg IOU: 0.510184, Class: 1.000000, Obj: 0.139689, No Obj: 0.003193, Avg Recall: 0.597938, count: 97 -Region Avg IOU: 0.465824, Class: 1.000000, Obj: 0.161049, No Obj: 0.003182, Avg Recall: 0.490000, count: 100 -Region Avg IOU: 0.495354, Class: 1.000000, Obj: 0.210573, No Obj: 0.003883, Avg Recall: 0.557252, count: 131 -Region Avg IOU: 0.548879, Class: 1.000000, Obj: 0.221690, No Obj: 0.003714, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.479980, Class: 1.000000, Obj: 0.179740, No Obj: 0.003750, Avg Recall: 0.537815, count: 119 -Region Avg IOU: 0.525276, Class: 1.000000, Obj: 0.172591, No Obj: 0.003161, Avg Recall: 0.630435, count: 92 -Region Avg IOU: 0.506624, Class: 1.000000, Obj: 0.206545, No Obj: 0.003587, Avg Recall: 0.537037, count: 108 -286: 0.521655, 0.529681 avg, 0.001000 rate, 26.263935 seconds, 73216 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.505857, Class: 1.000000, Obj: 0.189024, No Obj: 0.003326, Avg Recall: 0.521739, count: 92 -Region Avg IOU: 0.525001, Class: 1.000000, Obj: 0.195024, No Obj: 0.003621, Avg Recall: 0.581197, count: 117 -Region Avg IOU: 0.455650, Class: 1.000000, Obj: 0.176121, No Obj: 0.003852, Avg Recall: 0.488000, count: 125 -Region Avg IOU: 0.531358, Class: 1.000000, Obj: 0.214945, No Obj: 0.003466, Avg Recall: 0.597222, count: 72 -Region Avg IOU: 0.490432, Class: 1.000000, Obj: 0.171513, No Obj: 0.003398, Avg Recall: 0.533898, count: 118 -Region Avg IOU: 0.474361, Class: 1.000000, Obj: 0.216652, No Obj: 0.003741, Avg Recall: 0.504132, count: 121 -Region Avg IOU: 0.499633, Class: 1.000000, Obj: 0.153817, No Obj: 0.003630, Avg Recall: 0.570248, count: 121 -Region Avg IOU: 0.527887, Class: 1.000000, Obj: 0.179926, No Obj: 0.003549, Avg Recall: 0.612903, count: 93 -287: 0.507116, 0.527424 avg, 0.001000 rate, 27.391056 seconds, 73472 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.507210, Class: 1.000000, Obj: 0.195270, No Obj: 0.003291, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.522379, Class: 1.000000, Obj: 0.146571, No Obj: 0.003719, Avg Recall: 0.605042, count: 119 -Region Avg IOU: 0.516972, Class: 1.000000, Obj: 0.165693, No Obj: 0.003710, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.545212, Class: 1.000000, Obj: 0.183256, No Obj: 0.003828, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.481203, Class: 1.000000, Obj: 0.169050, No Obj: 0.003349, Avg Recall: 0.540816, count: 98 -Region Avg IOU: 0.537162, Class: 1.000000, Obj: 0.226699, No Obj: 0.003477, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.465243, Class: 1.000000, Obj: 0.176442, No Obj: 0.003695, Avg Recall: 0.471831, count: 142 -Region Avg IOU: 0.480535, Class: 1.000000, Obj: 0.164124, No Obj: 0.003274, Avg Recall: 0.454545, count: 110 -288: 0.546341, 0.529316 avg, 0.001000 rate, 26.459152 seconds, 73728 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.530048, Class: 1.000000, Obj: 0.175387, No Obj: 0.003506, Avg Recall: 0.563636, count: 110 -Region Avg IOU: 0.488920, Class: 1.000000, Obj: 0.178435, No Obj: 0.003527, Avg Recall: 0.520000, count: 100 -Region Avg IOU: 0.481990, Class: 1.000000, Obj: 0.156293, No Obj: 0.003352, Avg Recall: 0.571429, count: 112 -Region Avg IOU: 0.528358, Class: 1.000000, Obj: 0.188570, No Obj: 0.003566, Avg Recall: 0.549451, count: 91 -Region Avg IOU: 0.501715, Class: 1.000000, Obj: 0.157597, No Obj: 0.003348, Avg Recall: 0.510638, count: 94 -Region Avg IOU: 0.463116, Class: 1.000000, Obj: 0.116122, No Obj: 0.003505, Avg Recall: 0.468254, count: 126 -Region Avg IOU: 0.443246, Class: 1.000000, Obj: 0.157954, No Obj: 0.003332, Avg Recall: 0.459854, count: 137 -Region Avg IOU: 0.467086, Class: 1.000000, Obj: 0.143840, No Obj: 0.003399, Avg Recall: 0.495652, count: 115 -289: 0.578986, 0.534283 avg, 0.001000 rate, 27.403969 seconds, 73984 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.539911, Class: 1.000000, Obj: 0.186349, No Obj: 0.003657, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.495877, Class: 1.000000, Obj: 0.197833, No Obj: 0.003453, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.533248, Class: 1.000000, Obj: 0.153460, No Obj: 0.003592, Avg Recall: 0.620438, count: 137 -Region Avg IOU: 0.496252, Class: 1.000000, Obj: 0.175580, No Obj: 0.003304, Avg Recall: 0.522523, count: 111 -Region Avg IOU: 0.529818, Class: 1.000000, Obj: 0.166868, No Obj: 0.003565, Avg Recall: 0.601562, count: 128 -Region Avg IOU: 0.504421, Class: 1.000000, Obj: 0.207175, No Obj: 0.003938, Avg Recall: 0.581560, count: 141 -Region Avg IOU: 0.531886, Class: 1.000000, Obj: 0.146995, No Obj: 0.003248, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.497274, Class: 1.000000, Obj: 0.192064, No Obj: 0.003719, Avg Recall: 0.552632, count: 114 -290: 0.550669, 0.535922 avg, 0.001000 rate, 26.501432 seconds, 74240 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.037636 seconds -Region Avg IOU: 0.363530, Class: 1.000000, Obj: 0.108332, No Obj: 0.003026, Avg Recall: 0.352941, count: 102 -Region Avg IOU: 0.377350, Class: 1.000000, Obj: 0.103561, No Obj: 0.003412, Avg Recall: 0.338983, count: 118 -Region Avg IOU: 0.402586, Class: 1.000000, Obj: 0.104006, No Obj: 0.003494, Avg Recall: 0.384615, count: 117 -Region Avg IOU: 0.402544, Class: 1.000000, Obj: 0.100845, No Obj: 0.003367, Avg Recall: 0.365591, count: 93 -Region Avg IOU: 0.388901, Class: 1.000000, Obj: 0.098254, No Obj: 0.003111, Avg Recall: 0.387755, count: 98 -Region Avg IOU: 0.422567, Class: 1.000000, Obj: 0.105593, No Obj: 0.003337, Avg Recall: 0.408163, count: 98 -Region Avg IOU: 0.403047, Class: 1.000000, Obj: 0.120270, No Obj: 0.003638, Avg Recall: 0.357143, count: 112 -Region Avg IOU: 0.438335, Class: 1.000000, Obj: 0.148965, No Obj: 0.004162, Avg Recall: 0.416667, count: 144 -291: 0.728686, 0.555198 avg, 0.001000 rate, 16.323757 seconds, 74496 images -Loaded: 3.321232 seconds -Region Avg IOU: 0.386224, Class: 1.000000, Obj: 0.116986, No Obj: 0.003886, Avg Recall: 0.338710, count: 124 -Region Avg IOU: 0.428288, Class: 1.000000, Obj: 0.148153, No Obj: 0.003580, Avg Recall: 0.485437, count: 103 -Region Avg IOU: 0.389244, Class: 1.000000, Obj: 0.091946, No Obj: 0.003235, Avg Recall: 0.367647, count: 136 -Region Avg IOU: 0.321713, Class: 1.000000, Obj: 0.096178, No Obj: 0.002963, Avg Recall: 0.316239, count: 117 -Region Avg IOU: 0.390604, Class: 1.000000, Obj: 0.082342, No Obj: 0.002758, Avg Recall: 0.421053, count: 76 -Region Avg IOU: 0.386243, Class: 1.000000, Obj: 0.095708, No Obj: 0.002742, Avg Recall: 0.305556, count: 72 -Region Avg IOU: 0.378506, Class: 1.000000, Obj: 0.144869, No Obj: 0.003637, Avg Recall: 0.387931, count: 116 -Region Avg IOU: 0.384869, Class: 1.000000, Obj: 0.116384, No Obj: 0.003398, Avg Recall: 0.363636, count: 99 -292: 0.612963, 0.560975 avg, 0.001000 rate, 17.697487 seconds, 74752 images -Loaded: 3.863321 seconds -Region Avg IOU: 0.417926, Class: 1.000000, Obj: 0.109358, No Obj: 0.003668, Avg Recall: 0.403361, count: 119 -Region Avg IOU: 0.360041, Class: 1.000000, Obj: 0.148831, No Obj: 0.003214, Avg Recall: 0.292929, count: 99 -Region Avg IOU: 0.384577, Class: 1.000000, Obj: 0.087616, No Obj: 0.002957, Avg Recall: 0.303922, count: 102 -Region Avg IOU: 0.375267, Class: 1.000000, Obj: 0.098436, No Obj: 0.003564, Avg Recall: 0.365217, count: 115 -Region Avg IOU: 0.408460, Class: 1.000000, Obj: 0.110980, No Obj: 0.003350, Avg Recall: 0.445455, count: 110 -Region Avg IOU: 0.396432, Class: 1.000000, Obj: 0.115738, No Obj: 0.003163, Avg Recall: 0.397959, count: 98 -Region Avg IOU: 0.409567, Class: 1.000000, Obj: 0.130972, No Obj: 0.003584, Avg Recall: 0.398374, count: 123 -Region Avg IOU: 0.439747, Class: 1.000000, Obj: 0.114507, No Obj: 0.003416, Avg Recall: 0.477273, count: 88 -293: 0.583911, 0.563268 avg, 0.001000 rate, 14.136419 seconds, 75008 images -Loaded: 7.405248 seconds -Region Avg IOU: 0.379632, Class: 1.000000, Obj: 0.117533, No Obj: 0.004099, Avg Recall: 0.331034, count: 145 -Region Avg IOU: 0.396201, Class: 1.000000, Obj: 0.117617, No Obj: 0.003275, Avg Recall: 0.380435, count: 92 -Region Avg IOU: 0.360303, Class: 1.000000, Obj: 0.110975, No Obj: 0.003495, Avg Recall: 0.337079, count: 89 -Region Avg IOU: 0.334284, Class: 1.000000, Obj: 0.083734, No Obj: 0.003769, Avg Recall: 0.289655, count: 145 -Region Avg IOU: 0.371577, Class: 1.000000, Obj: 0.102057, No Obj: 0.003199, Avg Recall: 0.353535, count: 99 -Region Avg IOU: 0.407480, Class: 1.000000, Obj: 0.098224, No Obj: 0.003738, Avg Recall: 0.423423, count: 111 -Region Avg IOU: 0.371512, Class: 1.000000, Obj: 0.104448, No Obj: 0.003972, Avg Recall: 0.353448, count: 116 -Region Avg IOU: 0.425516, Class: 1.000000, Obj: 0.106350, No Obj: 0.003351, Avg Recall: 0.464646, count: 99 -294: 0.605307, 0.567472 avg, 0.001000 rate, 14.637572 seconds, 75264 images -Loaded: 5.482596 seconds -Region Avg IOU: 0.397949, Class: 1.000000, Obj: 0.111740, No Obj: 0.003712, Avg Recall: 0.315789, count: 95 -Region Avg IOU: 0.386716, Class: 1.000000, Obj: 0.112853, No Obj: 0.004013, Avg Recall: 0.386139, count: 101 -Region Avg IOU: 0.374422, Class: 1.000000, Obj: 0.119406, No Obj: 0.003371, Avg Recall: 0.339623, count: 106 -Region Avg IOU: 0.373847, Class: 1.000000, Obj: 0.081929, No Obj: 0.003305, Avg Recall: 0.313131, count: 99 -Region Avg IOU: 0.339494, Class: 1.000000, Obj: 0.109636, No Obj: 0.003507, Avg Recall: 0.274336, count: 113 -Region Avg IOU: 0.385718, Class: 1.000000, Obj: 0.074307, No Obj: 0.003002, Avg Recall: 0.313953, count: 86 -Region Avg IOU: 0.371824, Class: 1.000000, Obj: 0.105422, No Obj: 0.004195, Avg Recall: 0.326733, count: 101 -Region Avg IOU: 0.425384, Class: 1.000000, Obj: 0.110739, No Obj: 0.003899, Avg Recall: 0.422018, count: 109 -295: 0.634212, 0.574146 avg, 0.001000 rate, 15.394881 seconds, 75520 images -Loaded: 5.833238 seconds -Region Avg IOU: 0.339401, Class: 1.000000, Obj: 0.082772, No Obj: 0.003536, Avg Recall: 0.360656, count: 122 -Region Avg IOU: 0.400198, Class: 1.000000, Obj: 0.130224, No Obj: 0.004325, Avg Recall: 0.434783, count: 115 -Region Avg IOU: 0.359254, Class: 1.000000, Obj: 0.121361, No Obj: 0.003651, Avg Recall: 0.327731, count: 119 -Region Avg IOU: 0.402308, Class: 1.000000, Obj: 0.118612, No Obj: 0.004228, Avg Recall: 0.426087, count: 115 -Region Avg IOU: 0.399291, Class: 1.000000, Obj: 0.122527, No Obj: 0.003683, Avg Recall: 0.369048, count: 84 -Region Avg IOU: 0.394655, Class: 1.000000, Obj: 0.138162, No Obj: 0.003994, Avg Recall: 0.381356, count: 118 -Region Avg IOU: 0.370906, Class: 1.000000, Obj: 0.131018, No Obj: 0.003777, Avg Recall: 0.367925, count: 106 -Region Avg IOU: 0.367370, Class: 1.000000, Obj: 0.103565, No Obj: 0.003505, Avg Recall: 0.340426, count: 94 -296: 0.648444, 0.581576 avg, 0.001000 rate, 18.035612 seconds, 75776 images -Loaded: 4.587497 seconds -Region Avg IOU: 0.376724, Class: 1.000000, Obj: 0.146269, No Obj: 0.005127, Avg Recall: 0.374150, count: 147 -Region Avg IOU: 0.396393, Class: 1.000000, Obj: 0.151720, No Obj: 0.004859, Avg Recall: 0.377049, count: 122 -Region Avg IOU: 0.359584, Class: 1.000000, Obj: 0.128857, No Obj: 0.003866, Avg Recall: 0.333333, count: 105 -Region Avg IOU: 0.371382, Class: 1.000000, Obj: 0.097906, No Obj: 0.003909, Avg Recall: 0.339623, count: 106 -Region Avg IOU: 0.428278, Class: 1.000000, Obj: 0.130996, No Obj: 0.004128, Avg Recall: 0.406977, count: 86 -Region Avg IOU: 0.419502, Class: 1.000000, Obj: 0.167056, No Obj: 0.004622, Avg Recall: 0.408696, count: 115 -Region Avg IOU: 0.384451, Class: 1.000000, Obj: 0.138467, No Obj: 0.003953, Avg Recall: 0.395604, count: 91 -Region Avg IOU: 0.470798, Class: 1.000000, Obj: 0.164183, No Obj: 0.003997, Avg Recall: 0.551724, count: 87 -297: 0.643312, 0.587749 avg, 0.001000 rate, 16.224169 seconds, 76032 images -Loaded: 4.670341 seconds -Region Avg IOU: 0.397600, Class: 1.000000, Obj: 0.152001, No Obj: 0.004335, Avg Recall: 0.425532, count: 94 -Region Avg IOU: 0.324937, Class: 1.000000, Obj: 0.130418, No Obj: 0.004016, Avg Recall: 0.272727, count: 110 -Region Avg IOU: 0.381126, Class: 1.000000, Obj: 0.124024, No Obj: 0.004662, Avg Recall: 0.362832, count: 113 -Region Avg IOU: 0.335682, Class: 1.000000, Obj: 0.088057, No Obj: 0.003818, Avg Recall: 0.271930, count: 114 -Region Avg IOU: 0.432506, Class: 1.000000, Obj: 0.121375, No Obj: 0.003691, Avg Recall: 0.408602, count: 93 -Region Avg IOU: 0.382539, Class: 1.000000, Obj: 0.157377, No Obj: 0.004798, Avg Recall: 0.413793, count: 145 -Region Avg IOU: 0.390254, Class: 1.000000, Obj: 0.150448, No Obj: 0.004475, Avg Recall: 0.385965, count: 114 -Region Avg IOU: 0.373344, Class: 1.000000, Obj: 0.116284, No Obj: 0.004244, Avg Recall: 0.319328, count: 119 -298: 0.640631, 0.593038 avg, 0.001000 rate, 14.190890 seconds, 76288 images -Loaded: 6.242354 seconds -Region Avg IOU: 0.381035, Class: 1.000000, Obj: 0.137190, No Obj: 0.004520, Avg Recall: 0.367925, count: 106 -Region Avg IOU: 0.386963, Class: 1.000000, Obj: 0.164107, No Obj: 0.004192, Avg Recall: 0.364486, count: 107 -Region Avg IOU: 0.331426, Class: 1.000000, Obj: 0.147331, No Obj: 0.004776, Avg Recall: 0.289474, count: 114 -Region Avg IOU: 0.333285, Class: 1.000000, Obj: 0.133263, No Obj: 0.003866, Avg Recall: 0.234234, count: 111 -Region Avg IOU: 0.388259, Class: 1.000000, Obj: 0.097607, No Obj: 0.003945, Avg Recall: 0.400000, count: 90 -Region Avg IOU: 0.395543, Class: 1.000000, Obj: 0.111632, No Obj: 0.004289, Avg Recall: 0.432203, count: 118 -Region Avg IOU: 0.423335, Class: 1.000000, Obj: 0.141776, No Obj: 0.004295, Avg Recall: 0.392405, count: 79 -Region Avg IOU: 0.398461, Class: 1.000000, Obj: 0.140546, No Obj: 0.004625, Avg Recall: 0.366667, count: 90 -299: 0.591405, 0.592874 avg, 0.001000 rate, 18.115835 seconds, 76544 images -Loaded: 1.833616 seconds -Region Avg IOU: 0.418639, Class: 1.000000, Obj: 0.144724, No Obj: 0.004528, Avg Recall: 0.414414, count: 111 -Region Avg IOU: 0.317379, Class: 1.000000, Obj: 0.156287, No Obj: 0.004369, Avg Recall: 0.243478, count: 115 -Region Avg IOU: 0.384810, Class: 1.000000, Obj: 0.176225, No Obj: 0.005125, Avg Recall: 0.347826, count: 138 -Region Avg IOU: 0.396323, Class: 1.000000, Obj: 0.135064, No Obj: 0.004353, Avg Recall: 0.370000, count: 100 -Region Avg IOU: 0.390308, Class: 1.000000, Obj: 0.122252, No Obj: 0.004006, Avg Recall: 0.390476, count: 105 -Region Avg IOU: 0.385748, Class: 1.000000, Obj: 0.099225, No Obj: 0.004284, Avg Recall: 0.342105, count: 114 -Region Avg IOU: 0.421265, Class: 1.000000, Obj: 0.141841, No Obj: 0.004585, Avg Recall: 0.416667, count: 108 -Region Avg IOU: 0.424023, Class: 1.000000, Obj: 0.142129, No Obj: 0.004666, Avg Recall: 0.475728, count: 103 -300: 0.659023, 0.599489 avg, 0.001000 rate, 15.387315 seconds, 76800 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000062 seconds -Region Avg IOU: 0.548404, Class: 1.000000, Obj: 0.202870, No Obj: 0.005112, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.537589, Class: 1.000000, Obj: 0.226848, No Obj: 0.005232, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.546113, Class: 1.000000, Obj: 0.207792, No Obj: 0.005206, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.511432, Class: 1.000000, Obj: 0.196363, No Obj: 0.005129, Avg Recall: 0.563492, count: 126 -Region Avg IOU: 0.514555, Class: 1.000000, Obj: 0.189367, No Obj: 0.004820, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.527953, Class: 1.000000, Obj: 0.155518, No Obj: 0.005232, Avg Recall: 0.650406, count: 123 -Region Avg IOU: 0.489864, Class: 1.000000, Obj: 0.191873, No Obj: 0.004810, Avg Recall: 0.549550, count: 111 -Region Avg IOU: 0.506587, Class: 1.000000, Obj: 0.214377, No Obj: 0.004914, Avg Recall: 0.598361, count: 122 -301: 0.580501, 0.597590 avg, 0.001000 rate, 29.569220 seconds, 77056 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.533861, Class: 1.000000, Obj: 0.193623, No Obj: 0.005127, Avg Recall: 0.666667, count: 120 -Region Avg IOU: 0.445563, Class: 1.000000, Obj: 0.173807, No Obj: 0.004241, Avg Recall: 0.402299, count: 87 -Region Avg IOU: 0.488468, Class: 1.000000, Obj: 0.163196, No Obj: 0.004855, Avg Recall: 0.576471, count: 85 -Region Avg IOU: 0.524298, Class: 1.000000, Obj: 0.199412, No Obj: 0.004801, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.519888, Class: 1.000000, Obj: 0.188389, No Obj: 0.004482, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.561834, Class: 1.000000, Obj: 0.238587, No Obj: 0.005050, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.500167, Class: 1.000000, Obj: 0.185444, No Obj: 0.004186, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.519937, Class: 1.000000, Obj: 0.213025, No Obj: 0.004789, Avg Recall: 0.605263, count: 114 -302: 0.559154, 0.593747 avg, 0.001000 rate, 29.574137 seconds, 77312 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.544526, Class: 1.000000, Obj: 0.178600, No Obj: 0.004248, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.518415, Class: 1.000000, Obj: 0.134127, No Obj: 0.004025, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.573960, Class: 1.000000, Obj: 0.147104, No Obj: 0.004829, Avg Recall: 0.702290, count: 131 -Region Avg IOU: 0.530147, Class: 1.000000, Obj: 0.172196, No Obj: 0.004614, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.518931, Class: 1.000000, Obj: 0.140616, No Obj: 0.004280, Avg Recall: 0.612903, count: 124 -Region Avg IOU: 0.547810, Class: 1.000000, Obj: 0.162853, No Obj: 0.004489, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.557540, Class: 1.000000, Obj: 0.213447, No Obj: 0.004339, Avg Recall: 0.685393, count: 89 -Region Avg IOU: 0.530626, Class: 1.000000, Obj: 0.164691, No Obj: 0.004347, Avg Recall: 0.627451, count: 102 -303: 0.527352, 0.587107 avg, 0.001000 rate, 29.709156 seconds, 77568 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.536554, Class: 1.000000, Obj: 0.150292, No Obj: 0.003994, Avg Recall: 0.669492, count: 118 -Region Avg IOU: 0.528133, Class: 1.000000, Obj: 0.167110, No Obj: 0.004054, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.513021, Class: 1.000000, Obj: 0.138219, No Obj: 0.004139, Avg Recall: 0.608333, count: 120 -Region Avg IOU: 0.592362, Class: 1.000000, Obj: 0.156594, No Obj: 0.004007, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.509315, Class: 1.000000, Obj: 0.144838, No Obj: 0.004179, Avg Recall: 0.547297, count: 148 -Region Avg IOU: 0.508928, Class: 1.000000, Obj: 0.164679, No Obj: 0.004247, Avg Recall: 0.568182, count: 132 -Region Avg IOU: 0.511945, Class: 1.000000, Obj: 0.175316, No Obj: 0.003902, Avg Recall: 0.605042, count: 119 -Region Avg IOU: 0.524942, Class: 1.000000, Obj: 0.161031, No Obj: 0.003933, Avg Recall: 0.596330, count: 109 -304: 0.594926, 0.587889 avg, 0.001000 rate, 29.845188 seconds, 77824 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.488082, Class: 1.000000, Obj: 0.121220, No Obj: 0.003711, Avg Recall: 0.536585, count: 123 -Region Avg IOU: 0.528472, Class: 1.000000, Obj: 0.135998, No Obj: 0.003466, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.517375, Class: 1.000000, Obj: 0.136359, No Obj: 0.003845, Avg Recall: 0.617978, count: 89 -Region Avg IOU: 0.551444, Class: 1.000000, Obj: 0.116949, No Obj: 0.003745, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.498384, Class: 1.000000, Obj: 0.118391, No Obj: 0.003433, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.562495, Class: 1.000000, Obj: 0.145365, No Obj: 0.003881, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.533728, Class: 1.000000, Obj: 0.146247, No Obj: 0.003941, Avg Recall: 0.612903, count: 124 -Region Avg IOU: 0.539798, Class: 1.000000, Obj: 0.124716, No Obj: 0.003684, Avg Recall: 0.596154, count: 104 -305: 0.523844, 0.581485 avg, 0.001000 rate, 30.391308 seconds, 78080 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.553751, Class: 1.000000, Obj: 0.131319, No Obj: 0.003333, Avg Recall: 0.662791, count: 86 -Region Avg IOU: 0.533593, Class: 1.000000, Obj: 0.098253, No Obj: 0.003316, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.525803, Class: 1.000000, Obj: 0.130343, No Obj: 0.003527, Avg Recall: 0.616000, count: 125 -Region Avg IOU: 0.562087, Class: 1.000000, Obj: 0.158234, No Obj: 0.003722, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.554963, Class: 1.000000, Obj: 0.131453, No Obj: 0.003282, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.473912, Class: 1.000000, Obj: 0.113944, No Obj: 0.003409, Avg Recall: 0.508772, count: 114 -Region Avg IOU: 0.514468, Class: 1.000000, Obj: 0.140538, No Obj: 0.003804, Avg Recall: 0.587786, count: 131 -Region Avg IOU: 0.535357, Class: 1.000000, Obj: 0.136877, No Obj: 0.003785, Avg Recall: 0.648936, count: 94 -306: 0.553421, 0.578678 avg, 0.001000 rate, 31.109734 seconds, 78336 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.547341, Class: 1.000000, Obj: 0.145409, No Obj: 0.003695, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.559908, Class: 1.000000, Obj: 0.142139, No Obj: 0.003524, Avg Recall: 0.640449, count: 89 -Region Avg IOU: 0.499659, Class: 1.000000, Obj: 0.104378, No Obj: 0.003799, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.563494, Class: 1.000000, Obj: 0.169825, No Obj: 0.003518, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.566722, Class: 1.000000, Obj: 0.142673, No Obj: 0.003487, Avg Recall: 0.689394, count: 132 -Region Avg IOU: 0.524901, Class: 1.000000, Obj: 0.104744, No Obj: 0.004068, Avg Recall: 0.627737, count: 137 -Region Avg IOU: 0.509778, Class: 1.000000, Obj: 0.123243, No Obj: 0.003584, Avg Recall: 0.574803, count: 127 -Region Avg IOU: 0.570366, Class: 1.000000, Obj: 0.150408, No Obj: 0.003569, Avg Recall: 0.698113, count: 106 -307: 0.531976, 0.574008 avg, 0.001000 rate, 29.814411 seconds, 78592 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.544225, Class: 1.000000, Obj: 0.152492, No Obj: 0.003720, Avg Recall: 0.658824, count: 85 -Region Avg IOU: 0.545981, Class: 1.000000, Obj: 0.132660, No Obj: 0.003777, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.523357, Class: 1.000000, Obj: 0.116019, No Obj: 0.002892, Avg Recall: 0.556701, count: 97 -Region Avg IOU: 0.547196, Class: 1.000000, Obj: 0.163552, No Obj: 0.003450, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.488689, Class: 1.000000, Obj: 0.112850, No Obj: 0.003748, Avg Recall: 0.556452, count: 124 -Region Avg IOU: 0.516300, Class: 1.000000, Obj: 0.114049, No Obj: 0.003415, Avg Recall: 0.585366, count: 123 -Region Avg IOU: 0.516243, Class: 1.000000, Obj: 0.137901, No Obj: 0.003446, Avg Recall: 0.584270, count: 89 -Region Avg IOU: 0.553723, Class: 1.000000, Obj: 0.136526, No Obj: 0.003223, Avg Recall: 0.694737, count: 95 -308: 0.535354, 0.570143 avg, 0.001000 rate, 27.618204 seconds, 78848 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.550989, Class: 1.000000, Obj: 0.108089, No Obj: 0.003476, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.528656, Class: 1.000000, Obj: 0.123171, No Obj: 0.003571, Avg Recall: 0.610169, count: 118 -Region Avg IOU: 0.539911, Class: 1.000000, Obj: 0.124496, No Obj: 0.003514, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.559182, Class: 1.000000, Obj: 0.143992, No Obj: 0.003888, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.572765, Class: 1.000000, Obj: 0.156331, No Obj: 0.003627, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.542027, Class: 1.000000, Obj: 0.147281, No Obj: 0.003495, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.515997, Class: 1.000000, Obj: 0.136193, No Obj: 0.003392, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.521095, Class: 1.000000, Obj: 0.130732, No Obj: 0.003554, Avg Recall: 0.589552, count: 134 -309: 0.484063, 0.561535 avg, 0.001000 rate, 29.277781 seconds, 79104 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.510320, Class: 1.000000, Obj: 0.146318, No Obj: 0.003866, Avg Recall: 0.616667, count: 120 -Region Avg IOU: 0.522636, Class: 1.000000, Obj: 0.158953, No Obj: 0.003622, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.521302, Class: 1.000000, Obj: 0.141484, No Obj: 0.003914, Avg Recall: 0.604839, count: 124 -Region Avg IOU: 0.555675, Class: 1.000000, Obj: 0.130868, No Obj: 0.003948, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.544880, Class: 1.000000, Obj: 0.174660, No Obj: 0.003863, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.545866, Class: 1.000000, Obj: 0.171105, No Obj: 0.003890, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.524502, Class: 1.000000, Obj: 0.179992, No Obj: 0.003643, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.580885, Class: 1.000000, Obj: 0.157831, No Obj: 0.003843, Avg Recall: 0.720930, count: 86 -310: 0.540330, 0.559414 avg, 0.001000 rate, 31.055588 seconds, 79360 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 20.444168 seconds -Region Avg IOU: 0.511585, Class: 1.000000, Obj: 0.158745, No Obj: 0.004020, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.484800, Class: 1.000000, Obj: 0.147569, No Obj: 0.003826, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.514263, Class: 1.000000, Obj: 0.159210, No Obj: 0.004292, Avg Recall: 0.536585, count: 123 -Region Avg IOU: 0.439239, Class: 1.000000, Obj: 0.133149, No Obj: 0.003613, Avg Recall: 0.466667, count: 105 -Region Avg IOU: 0.472253, Class: 1.000000, Obj: 0.172332, No Obj: 0.004168, Avg Recall: 0.492064, count: 126 -Region Avg IOU: 0.463668, Class: 1.000000, Obj: 0.132587, No Obj: 0.003311, Avg Recall: 0.469388, count: 98 -Region Avg IOU: 0.467716, Class: 1.000000, Obj: 0.133710, No Obj: 0.003772, Avg Recall: 0.527273, count: 110 -Region Avg IOU: 0.480999, Class: 1.000000, Obj: 0.186499, No Obj: 0.004267, Avg Recall: 0.500000, count: 102 -311: 0.509322, 0.554405 avg, 0.001000 rate, 25.222940 seconds, 79616 images -Loaded: 0.088820 seconds -Region Avg IOU: 0.447980, Class: 1.000000, Obj: 0.142154, No Obj: 0.003696, Avg Recall: 0.444444, count: 72 -Region Avg IOU: 0.487363, Class: 1.000000, Obj: 0.182963, No Obj: 0.003957, Avg Recall: 0.469388, count: 98 -Region Avg IOU: 0.484505, Class: 1.000000, Obj: 0.172169, No Obj: 0.004237, Avg Recall: 0.524752, count: 101 -Region Avg IOU: 0.459309, Class: 1.000000, Obj: 0.159558, No Obj: 0.004076, Avg Recall: 0.492754, count: 138 -Region Avg IOU: 0.529602, Class: 1.000000, Obj: 0.174474, No Obj: 0.003839, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.463427, Class: 1.000000, Obj: 0.177022, No Obj: 0.003896, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.443079, Class: 1.000000, Obj: 0.185228, No Obj: 0.004186, Avg Recall: 0.488889, count: 135 -Region Avg IOU: 0.462146, Class: 1.000000, Obj: 0.185037, No Obj: 0.004257, Avg Recall: 0.462810, count: 121 -312: 0.620363, 0.561001 avg, 0.001000 rate, 23.768204 seconds, 79872 images -Loaded: 0.184037 seconds -Region Avg IOU: 0.443801, Class: 1.000000, Obj: 0.182646, No Obj: 0.004460, Avg Recall: 0.483051, count: 118 -Region Avg IOU: 0.438638, Class: 1.000000, Obj: 0.221990, No Obj: 0.003881, Avg Recall: 0.413043, count: 92 -Region Avg IOU: 0.440639, Class: 1.000000, Obj: 0.160399, No Obj: 0.004229, Avg Recall: 0.486726, count: 113 -Region Avg IOU: 0.488767, Class: 1.000000, Obj: 0.184569, No Obj: 0.004136, Avg Recall: 0.547368, count: 95 -Region Avg IOU: 0.451117, Class: 1.000000, Obj: 0.171425, No Obj: 0.004185, Avg Recall: 0.510417, count: 96 -Region Avg IOU: 0.442179, Class: 1.000000, Obj: 0.190737, No Obj: 0.003966, Avg Recall: 0.448598, count: 107 -Region Avg IOU: 0.425864, Class: 1.000000, Obj: 0.142439, No Obj: 0.003639, Avg Recall: 0.479592, count: 98 -Region Avg IOU: 0.519180, Class: 1.000000, Obj: 0.205618, No Obj: 0.004456, Avg Recall: 0.594340, count: 106 -313: 0.536126, 0.558513 avg, 0.001000 rate, 25.166218 seconds, 80128 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.465389, Class: 1.000000, Obj: 0.188712, No Obj: 0.003927, Avg Recall: 0.432990, count: 97 -Region Avg IOU: 0.480564, Class: 1.000000, Obj: 0.200790, No Obj: 0.004628, Avg Recall: 0.557377, count: 122 -Region Avg IOU: 0.464449, Class: 1.000000, Obj: 0.190100, No Obj: 0.004364, Avg Recall: 0.495495, count: 111 -Region Avg IOU: 0.470815, Class: 1.000000, Obj: 0.203457, No Obj: 0.004323, Avg Recall: 0.504202, count: 119 -Region Avg IOU: 0.406510, Class: 1.000000, Obj: 0.164925, No Obj: 0.004060, Avg Recall: 0.433628, count: 113 -Region Avg IOU: 0.414565, Class: 1.000000, Obj: 0.131840, No Obj: 0.003798, Avg Recall: 0.415842, count: 101 -Region Avg IOU: 0.465455, Class: 1.000000, Obj: 0.164693, No Obj: 0.004694, Avg Recall: 0.466165, count: 133 -Region Avg IOU: 0.477338, Class: 1.000000, Obj: 0.152261, No Obj: 0.004196, Avg Recall: 0.556604, count: 106 -314: 0.599078, 0.562570 avg, 0.001000 rate, 22.894196 seconds, 80384 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.454099, Class: 1.000000, Obj: 0.210756, No Obj: 0.004628, Avg Recall: 0.528926, count: 121 -Region Avg IOU: 0.486446, Class: 1.000000, Obj: 0.185643, No Obj: 0.004419, Avg Recall: 0.597403, count: 77 -Region Avg IOU: 0.448749, Class: 1.000000, Obj: 0.180656, No Obj: 0.004166, Avg Recall: 0.480000, count: 100 -Region Avg IOU: 0.462792, Class: 1.000000, Obj: 0.197266, No Obj: 0.004909, Avg Recall: 0.486842, count: 152 -Region Avg IOU: 0.469295, Class: 1.000000, Obj: 0.193466, No Obj: 0.004524, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.512587, Class: 1.000000, Obj: 0.244925, No Obj: 0.004199, Avg Recall: 0.558140, count: 86 -Region Avg IOU: 0.443425, Class: 1.000000, Obj: 0.214334, No Obj: 0.004122, Avg Recall: 0.466667, count: 105 -Region Avg IOU: 0.536631, Class: 1.000000, Obj: 0.205784, No Obj: 0.003989, Avg Recall: 0.642857, count: 84 -315: 0.524089, 0.558722 avg, 0.001000 rate, 22.906631 seconds, 80640 images -Loaded: 0.052866 seconds -Region Avg IOU: 0.491089, Class: 1.000000, Obj: 0.169684, No Obj: 0.004624, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.444619, Class: 1.000000, Obj: 0.201420, No Obj: 0.004464, Avg Recall: 0.481203, count: 133 -Region Avg IOU: 0.463195, Class: 1.000000, Obj: 0.184015, No Obj: 0.004291, Avg Recall: 0.472222, count: 108 -Region Avg IOU: 0.451835, Class: 1.000000, Obj: 0.170169, No Obj: 0.004185, Avg Recall: 0.510638, count: 94 -Region Avg IOU: 0.486293, Class: 1.000000, Obj: 0.146718, No Obj: 0.004584, Avg Recall: 0.530303, count: 132 -Region Avg IOU: 0.471597, Class: 1.000000, Obj: 0.178118, No Obj: 0.004706, Avg Recall: 0.530303, count: 132 -Region Avg IOU: 0.417221, Class: 1.000000, Obj: 0.184358, No Obj: 0.004049, Avg Recall: 0.438776, count: 98 -Region Avg IOU: 0.499267, Class: 1.000000, Obj: 0.145456, No Obj: 0.004064, Avg Recall: 0.568421, count: 95 -316: 0.604294, 0.563279 avg, 0.001000 rate, 24.595100 seconds, 80896 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.450626, Class: 1.000000, Obj: 0.154936, No Obj: 0.004234, Avg Recall: 0.481928, count: 83 -Region Avg IOU: 0.474569, Class: 1.000000, Obj: 0.195321, No Obj: 0.004713, Avg Recall: 0.527273, count: 110 -Region Avg IOU: 0.456475, Class: 1.000000, Obj: 0.214260, No Obj: 0.004578, Avg Recall: 0.448000, count: 125 -Region Avg IOU: 0.435463, Class: 1.000000, Obj: 0.177812, No Obj: 0.004439, Avg Recall: 0.464000, count: 125 -Region Avg IOU: 0.452517, Class: 1.000000, Obj: 0.160145, No Obj: 0.004078, Avg Recall: 0.500000, count: 106 -Region Avg IOU: 0.434085, Class: 1.000000, Obj: 0.172152, No Obj: 0.003975, Avg Recall: 0.483871, count: 93 -Region Avg IOU: 0.438923, Class: 1.000000, Obj: 0.179663, No Obj: 0.003841, Avg Recall: 0.467391, count: 92 -Region Avg IOU: 0.481146, Class: 1.000000, Obj: 0.218634, No Obj: 0.004341, Avg Recall: 0.513761, count: 109 -317: 0.557616, 0.562713 avg, 0.001000 rate, 24.332548 seconds, 81152 images -Loaded: 0.455216 seconds -Region Avg IOU: 0.459569, Class: 1.000000, Obj: 0.153667, No Obj: 0.003754, Avg Recall: 0.534653, count: 101 -Region Avg IOU: 0.477481, Class: 1.000000, Obj: 0.194913, No Obj: 0.004313, Avg Recall: 0.477477, count: 111 -Region Avg IOU: 0.446265, Class: 1.000000, Obj: 0.185253, No Obj: 0.004073, Avg Recall: 0.504425, count: 113 -Region Avg IOU: 0.474837, Class: 1.000000, Obj: 0.169358, No Obj: 0.004100, Avg Recall: 0.528090, count: 89 -Region Avg IOU: 0.512909, Class: 1.000000, Obj: 0.204234, No Obj: 0.004472, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.487610, Class: 1.000000, Obj: 0.216211, No Obj: 0.004498, Avg Recall: 0.556701, count: 97 -Region Avg IOU: 0.471104, Class: 1.000000, Obj: 0.170273, No Obj: 0.004131, Avg Recall: 0.559633, count: 109 -Region Avg IOU: 0.462324, Class: 1.000000, Obj: 0.217370, No Obj: 0.004257, Avg Recall: 0.532710, count: 107 -318: 0.485304, 0.554972 avg, 0.001000 rate, 23.582336 seconds, 81408 images -Loaded: 0.000121 seconds -Region Avg IOU: 0.433683, Class: 1.000000, Obj: 0.192329, No Obj: 0.003852, Avg Recall: 0.438596, count: 114 -Region Avg IOU: 0.486582, Class: 1.000000, Obj: 0.153078, No Obj: 0.004362, Avg Recall: 0.521739, count: 115 -Region Avg IOU: 0.487187, Class: 1.000000, Obj: 0.204146, No Obj: 0.003939, Avg Recall: 0.578313, count: 83 -Region Avg IOU: 0.481036, Class: 1.000000, Obj: 0.173623, No Obj: 0.004707, Avg Recall: 0.522388, count: 134 -Region Avg IOU: 0.479353, Class: 1.000000, Obj: 0.170631, No Obj: 0.004002, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.462506, Class: 1.000000, Obj: 0.220026, No Obj: 0.003843, Avg Recall: 0.493333, count: 75 -Region Avg IOU: 0.444051, Class: 1.000000, Obj: 0.216421, No Obj: 0.003866, Avg Recall: 0.455556, count: 90 -Region Avg IOU: 0.489173, Class: 1.000000, Obj: 0.190837, No Obj: 0.003886, Avg Recall: 0.548077, count: 104 -319: 0.497278, 0.549202 avg, 0.001000 rate, 23.806032 seconds, 81664 images -Loaded: 1.834323 seconds -Region Avg IOU: 0.447476, Class: 1.000000, Obj: 0.155733, No Obj: 0.003754, Avg Recall: 0.426829, count: 82 -Region Avg IOU: 0.454940, Class: 1.000000, Obj: 0.152532, No Obj: 0.003962, Avg Recall: 0.486957, count: 115 -Region Avg IOU: 0.440510, Class: 1.000000, Obj: 0.153531, No Obj: 0.004126, Avg Recall: 0.442857, count: 140 -Region Avg IOU: 0.463268, Class: 1.000000, Obj: 0.179400, No Obj: 0.003853, Avg Recall: 0.456311, count: 103 -Region Avg IOU: 0.478905, Class: 1.000000, Obj: 0.167681, No Obj: 0.003585, Avg Recall: 0.495146, count: 103 -Region Avg IOU: 0.484513, Class: 1.000000, Obj: 0.185259, No Obj: 0.004413, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.457912, Class: 1.000000, Obj: 0.177490, No Obj: 0.004070, Avg Recall: 0.476635, count: 107 -Region Avg IOU: 0.445010, Class: 1.000000, Obj: 0.196213, No Obj: 0.004526, Avg Recall: 0.532847, count: 137 -320: 0.606217, 0.554904 avg, 0.001000 rate, 25.378847 seconds, 81920 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000066 seconds -Region Avg IOU: 0.546381, Class: 1.000000, Obj: 0.192829, No Obj: 0.003731, Avg Recall: 0.640449, count: 89 -Region Avg IOU: 0.537662, Class: 1.000000, Obj: 0.241931, No Obj: 0.003905, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.570361, Class: 1.000000, Obj: 0.139486, No Obj: 0.003881, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.528776, Class: 1.000000, Obj: 0.131583, No Obj: 0.004023, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.551126, Class: 1.000000, Obj: 0.159834, No Obj: 0.004433, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.529124, Class: 1.000000, Obj: 0.173658, No Obj: 0.003876, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.567236, Class: 1.000000, Obj: 0.178571, No Obj: 0.003990, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.558011, Class: 1.000000, Obj: 0.171090, No Obj: 0.004104, Avg Recall: 0.690909, count: 110 -321: 0.563396, 0.555753 avg, 0.001000 rate, 32.254215 seconds, 82176 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.563226, Class: 1.000000, Obj: 0.137592, No Obj: 0.003763, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.567228, Class: 1.000000, Obj: 0.148957, No Obj: 0.004133, Avg Recall: 0.710938, count: 128 -Region Avg IOU: 0.560935, Class: 1.000000, Obj: 0.155055, No Obj: 0.004039, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.564557, Class: 1.000000, Obj: 0.154637, No Obj: 0.004031, Avg Recall: 0.664430, count: 149 -Region Avg IOU: 0.531393, Class: 1.000000, Obj: 0.181130, No Obj: 0.003730, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.570547, Class: 1.000000, Obj: 0.177854, No Obj: 0.003736, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.548105, Class: 1.000000, Obj: 0.149809, No Obj: 0.003892, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.519554, Class: 1.000000, Obj: 0.185142, No Obj: 0.003661, Avg Recall: 0.605505, count: 109 -322: 0.546468, 0.554825 avg, 0.001000 rate, 32.062878 seconds, 82432 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.540019, Class: 1.000000, Obj: 0.120994, No Obj: 0.003679, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.547345, Class: 1.000000, Obj: 0.171977, No Obj: 0.003973, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.582921, Class: 1.000000, Obj: 0.168752, No Obj: 0.003839, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.565613, Class: 1.000000, Obj: 0.154045, No Obj: 0.003736, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.529955, Class: 1.000000, Obj: 0.123939, No Obj: 0.003658, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.592035, Class: 1.000000, Obj: 0.197093, No Obj: 0.003869, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.533265, Class: 1.000000, Obj: 0.124732, No Obj: 0.003496, Avg Recall: 0.608247, count: 97 -Region Avg IOU: 0.563638, Class: 1.000000, Obj: 0.165275, No Obj: 0.003861, Avg Recall: 0.684685, count: 111 -323: 0.548466, 0.554189 avg, 0.001000 rate, 31.609983 seconds, 82688 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.530971, Class: 1.000000, Obj: 0.148377, No Obj: 0.003856, Avg Recall: 0.641667, count: 120 -Region Avg IOU: 0.587523, Class: 1.000000, Obj: 0.145488, No Obj: 0.004069, Avg Recall: 0.709924, count: 131 -Region Avg IOU: 0.553490, Class: 1.000000, Obj: 0.174885, No Obj: 0.003703, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.588154, Class: 1.000000, Obj: 0.160006, No Obj: 0.003578, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.604658, Class: 1.000000, Obj: 0.153187, No Obj: 0.004354, Avg Recall: 0.765957, count: 141 -Region Avg IOU: 0.535071, Class: 1.000000, Obj: 0.174985, No Obj: 0.003573, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.552656, Class: 1.000000, Obj: 0.157831, No Obj: 0.003430, Avg Recall: 0.657895, count: 76 -Region Avg IOU: 0.626708, Class: 1.000000, Obj: 0.153186, No Obj: 0.003344, Avg Recall: 0.823529, count: 68 -324: 0.526877, 0.551458 avg, 0.001000 rate, 33.110867 seconds, 82944 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.574850, Class: 1.000000, Obj: 0.122285, No Obj: 0.003426, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.516220, Class: 1.000000, Obj: 0.187529, No Obj: 0.003775, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.550204, Class: 1.000000, Obj: 0.153618, No Obj: 0.003524, Avg Recall: 0.658537, count: 123 -Region Avg IOU: 0.574862, Class: 1.000000, Obj: 0.132722, No Obj: 0.003222, Avg Recall: 0.725000, count: 80 -Region Avg IOU: 0.533175, Class: 1.000000, Obj: 0.142072, No Obj: 0.003833, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.520230, Class: 1.000000, Obj: 0.157771, No Obj: 0.003519, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.519067, Class: 1.000000, Obj: 0.131693, No Obj: 0.003876, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.522363, Class: 1.000000, Obj: 0.109954, No Obj: 0.003417, Avg Recall: 0.642857, count: 126 -325: 0.535898, 0.549902 avg, 0.001000 rate, 33.390762 seconds, 83200 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.553175, Class: 1.000000, Obj: 0.136710, No Obj: 0.003481, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.572937, Class: 1.000000, Obj: 0.158325, No Obj: 0.003667, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.562975, Class: 1.000000, Obj: 0.171697, No Obj: 0.003374, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.570113, Class: 1.000000, Obj: 0.156019, No Obj: 0.003818, Avg Recall: 0.712000, count: 125 -Region Avg IOU: 0.564134, Class: 1.000000, Obj: 0.191951, No Obj: 0.003681, Avg Recall: 0.663866, count: 119 -Region Avg IOU: 0.557821, Class: 1.000000, Obj: 0.162905, No Obj: 0.003671, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.577934, Class: 1.000000, Obj: 0.164080, No Obj: 0.003243, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.497423, Class: 1.000000, Obj: 0.160438, No Obj: 0.003313, Avg Recall: 0.574468, count: 94 -326: 0.493419, 0.544253 avg, 0.001000 rate, 31.710882 seconds, 83456 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.556057, Class: 1.000000, Obj: 0.165476, No Obj: 0.003441, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.532322, Class: 1.000000, Obj: 0.163546, No Obj: 0.003215, Avg Recall: 0.630952, count: 84 -Region Avg IOU: 0.547255, Class: 1.000000, Obj: 0.112491, No Obj: 0.003283, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.549179, Class: 1.000000, Obj: 0.182550, No Obj: 0.003626, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.591747, Class: 1.000000, Obj: 0.144749, No Obj: 0.003389, Avg Recall: 0.735043, count: 117 -Region Avg IOU: 0.588831, Class: 1.000000, Obj: 0.174336, No Obj: 0.003444, Avg Recall: 0.701149, count: 87 -Region Avg IOU: 0.548256, Class: 1.000000, Obj: 0.154172, No Obj: 0.003256, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.557255, Class: 1.000000, Obj: 0.120846, No Obj: 0.003343, Avg Recall: 0.659794, count: 97 -327: 0.494980, 0.539326 avg, 0.001000 rate, 31.564556 seconds, 83712 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.544067, Class: 1.000000, Obj: 0.167996, No Obj: 0.003595, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.547745, Class: 1.000000, Obj: 0.125636, No Obj: 0.003311, Avg Recall: 0.605769, count: 104 -Region Avg IOU: 0.533939, Class: 1.000000, Obj: 0.151369, No Obj: 0.003455, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.558604, Class: 1.000000, Obj: 0.165363, No Obj: 0.003655, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.559617, Class: 1.000000, Obj: 0.180277, No Obj: 0.003263, Avg Recall: 0.646617, count: 133 -Region Avg IOU: 0.533990, Class: 1.000000, Obj: 0.158734, No Obj: 0.003715, Avg Recall: 0.601695, count: 118 -Region Avg IOU: 0.580928, Class: 1.000000, Obj: 0.132713, No Obj: 0.003582, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.574427, Class: 1.000000, Obj: 0.156100, No Obj: 0.003420, Avg Recall: 0.709677, count: 93 -328: 0.497801, 0.535174 avg, 0.001000 rate, 31.316113 seconds, 83968 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.595206, Class: 1.000000, Obj: 0.125638, No Obj: 0.003586, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.574508, Class: 1.000000, Obj: 0.164604, No Obj: 0.003418, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.556629, Class: 1.000000, Obj: 0.154461, No Obj: 0.003660, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.571784, Class: 1.000000, Obj: 0.190255, No Obj: 0.003596, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.568316, Class: 1.000000, Obj: 0.157801, No Obj: 0.004053, Avg Recall: 0.687500, count: 128 -Region Avg IOU: 0.565823, Class: 1.000000, Obj: 0.146952, No Obj: 0.003625, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.555662, Class: 1.000000, Obj: 0.178368, No Obj: 0.003742, Avg Recall: 0.669492, count: 118 -Region Avg IOU: 0.556539, Class: 1.000000, Obj: 0.135502, No Obj: 0.003160, Avg Recall: 0.663551, count: 107 -329: 0.557661, 0.537422 avg, 0.001000 rate, 32.231319 seconds, 84224 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.587600, Class: 1.000000, Obj: 0.190605, No Obj: 0.003815, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.543166, Class: 1.000000, Obj: 0.135879, No Obj: 0.003571, Avg Recall: 0.609756, count: 82 -Region Avg IOU: 0.574184, Class: 1.000000, Obj: 0.170998, No Obj: 0.003445, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.561602, Class: 1.000000, Obj: 0.177331, No Obj: 0.003501, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.557395, Class: 1.000000, Obj: 0.151494, No Obj: 0.003722, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.534254, Class: 1.000000, Obj: 0.175469, No Obj: 0.003439, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.561388, Class: 1.000000, Obj: 0.170393, No Obj: 0.003636, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.556896, Class: 1.000000, Obj: 0.138674, No Obj: 0.003511, Avg Recall: 0.663636, count: 110 -330: 0.541269, 0.537807 avg, 0.001000 rate, 33.085842 seconds, 84480 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 12.745435 seconds -Region Avg IOU: 0.418929, Class: 1.000000, Obj: 0.145769, No Obj: 0.003578, Avg Recall: 0.389381, count: 113 -Region Avg IOU: 0.433885, Class: 1.000000, Obj: 0.111492, No Obj: 0.004008, Avg Recall: 0.471074, count: 121 -Region Avg IOU: 0.418158, Class: 1.000000, Obj: 0.136846, No Obj: 0.003997, Avg Recall: 0.406250, count: 128 -Region Avg IOU: 0.439513, Class: 1.000000, Obj: 0.129848, No Obj: 0.003533, Avg Recall: 0.460674, count: 89 -Region Avg IOU: 0.392414, Class: 1.000000, Obj: 0.144477, No Obj: 0.003759, Avg Recall: 0.363636, count: 121 -Region Avg IOU: 0.403707, Class: 1.000000, Obj: 0.133713, No Obj: 0.003274, Avg Recall: 0.376344, count: 93 -Region Avg IOU: 0.383445, Class: 1.000000, Obj: 0.134959, No Obj: 0.003633, Avg Recall: 0.363636, count: 132 -Region Avg IOU: 0.444019, Class: 1.000000, Obj: 0.166549, No Obj: 0.003886, Avg Recall: 0.480392, count: 102 -331: 0.676613, 0.551688 avg, 0.001000 rate, 16.059305 seconds, 84736 images -Loaded: 5.055703 seconds -Region Avg IOU: 0.423978, Class: 1.000000, Obj: 0.174370, No Obj: 0.004083, Avg Recall: 0.403226, count: 124 -Region Avg IOU: 0.430405, Class: 1.000000, Obj: 0.160837, No Obj: 0.003703, Avg Recall: 0.406593, count: 91 -Region Avg IOU: 0.411249, Class: 1.000000, Obj: 0.151923, No Obj: 0.003644, Avg Recall: 0.380435, count: 92 -Region Avg IOU: 0.441159, Class: 1.000000, Obj: 0.155976, No Obj: 0.004098, Avg Recall: 0.418803, count: 117 -Region Avg IOU: 0.370650, Class: 1.000000, Obj: 0.164244, No Obj: 0.004043, Avg Recall: 0.355072, count: 138 -Region Avg IOU: 0.378913, Class: 1.000000, Obj: 0.119278, No Obj: 0.003608, Avg Recall: 0.390909, count: 110 -Region Avg IOU: 0.394460, Class: 1.000000, Obj: 0.119478, No Obj: 0.003575, Avg Recall: 0.404959, count: 121 -Region Avg IOU: 0.437503, Class: 1.000000, Obj: 0.107020, No Obj: 0.003621, Avg Recall: 0.435185, count: 108 -332: 0.600381, 0.556557 avg, 0.001000 rate, 20.723969 seconds, 84992 images -Loaded: 0.805069 seconds -Region Avg IOU: 0.449061, Class: 1.000000, Obj: 0.154676, No Obj: 0.003925, Avg Recall: 0.471698, count: 106 -Region Avg IOU: 0.444987, Class: 1.000000, Obj: 0.141987, No Obj: 0.003620, Avg Recall: 0.446429, count: 112 -Region Avg IOU: 0.393894, Class: 1.000000, Obj: 0.113121, No Obj: 0.004163, Avg Recall: 0.412371, count: 97 -Region Avg IOU: 0.426831, Class: 1.000000, Obj: 0.122230, No Obj: 0.004015, Avg Recall: 0.393162, count: 117 -Region Avg IOU: 0.391604, Class: 1.000000, Obj: 0.108129, No Obj: 0.003861, Avg Recall: 0.355932, count: 118 -Region Avg IOU: 0.404801, Class: 1.000000, Obj: 0.154755, No Obj: 0.003709, Avg Recall: 0.357143, count: 98 -Region Avg IOU: 0.403167, Class: 1.000000, Obj: 0.111512, No Obj: 0.003314, Avg Recall: 0.454545, count: 88 -Region Avg IOU: 0.381488, Class: 1.000000, Obj: 0.148560, No Obj: 0.003893, Avg Recall: 0.344086, count: 93 -333: 0.535918, 0.554493 avg, 0.001000 rate, 19.716702 seconds, 85248 images -Loaded: 1.039186 seconds -Region Avg IOU: 0.383445, Class: 1.000000, Obj: 0.138207, No Obj: 0.003604, Avg Recall: 0.386364, count: 88 -Region Avg IOU: 0.400103, Class: 1.000000, Obj: 0.141055, No Obj: 0.004043, Avg Recall: 0.425000, count: 120 -Region Avg IOU: 0.404801, Class: 1.000000, Obj: 0.166738, No Obj: 0.004129, Avg Recall: 0.406977, count: 86 -Region Avg IOU: 0.415407, Class: 1.000000, Obj: 0.158702, No Obj: 0.004061, Avg Recall: 0.388889, count: 126 -Region Avg IOU: 0.401043, Class: 1.000000, Obj: 0.131037, No Obj: 0.003888, Avg Recall: 0.453608, count: 97 -Region Avg IOU: 0.432668, Class: 1.000000, Obj: 0.156191, No Obj: 0.004451, Avg Recall: 0.455285, count: 123 -Region Avg IOU: 0.373530, Class: 1.000000, Obj: 0.120857, No Obj: 0.003535, Avg Recall: 0.303922, count: 102 -Region Avg IOU: 0.338162, Class: 1.000000, Obj: 0.130297, No Obj: 0.003807, Avg Recall: 0.333333, count: 111 -334: 0.614792, 0.560523 avg, 0.001000 rate, 16.927803 seconds, 85504 images -Loaded: 3.629100 seconds -Region Avg IOU: 0.404792, Class: 1.000000, Obj: 0.142143, No Obj: 0.003788, Avg Recall: 0.370000, count: 100 -Region Avg IOU: 0.395853, Class: 1.000000, Obj: 0.140693, No Obj: 0.004100, Avg Recall: 0.430769, count: 130 -Region Avg IOU: 0.404553, Class: 1.000000, Obj: 0.144054, No Obj: 0.003843, Avg Recall: 0.402062, count: 97 -Region Avg IOU: 0.385579, Class: 1.000000, Obj: 0.195369, No Obj: 0.004592, Avg Recall: 0.327586, count: 116 -Region Avg IOU: 0.357742, Class: 1.000000, Obj: 0.155151, No Obj: 0.004079, Avg Recall: 0.330579, count: 121 -Region Avg IOU: 0.385424, Class: 1.000000, Obj: 0.149109, No Obj: 0.004024, Avg Recall: 0.348837, count: 129 -Region Avg IOU: 0.431100, Class: 1.000000, Obj: 0.173244, No Obj: 0.004346, Avg Recall: 0.466019, count: 103 -Region Avg IOU: 0.386441, Class: 1.000000, Obj: 0.118066, No Obj: 0.003725, Avg Recall: 0.370370, count: 81 -335: 0.646229, 0.569093 avg, 0.001000 rate, 20.196608 seconds, 85760 images -Loaded: 2.836575 seconds -Region Avg IOU: 0.432039, Class: 1.000000, Obj: 0.192174, No Obj: 0.004752, Avg Recall: 0.450450, count: 111 -Region Avg IOU: 0.405682, Class: 1.000000, Obj: 0.180409, No Obj: 0.004227, Avg Recall: 0.473684, count: 114 -Region Avg IOU: 0.409148, Class: 1.000000, Obj: 0.131005, No Obj: 0.003602, Avg Recall: 0.377778, count: 90 -Region Avg IOU: 0.415048, Class: 1.000000, Obj: 0.163627, No Obj: 0.003929, Avg Recall: 0.415094, count: 106 -Region Avg IOU: 0.424992, Class: 1.000000, Obj: 0.165056, No Obj: 0.004195, Avg Recall: 0.425926, count: 108 -Region Avg IOU: 0.437065, Class: 1.000000, Obj: 0.173522, No Obj: 0.004470, Avg Recall: 0.484127, count: 126 -Region Avg IOU: 0.434527, Class: 1.000000, Obj: 0.186816, No Obj: 0.004405, Avg Recall: 0.456693, count: 127 -Region Avg IOU: 0.364874, Class: 1.000000, Obj: 0.144777, No Obj: 0.003891, Avg Recall: 0.298969, count: 97 -336: 0.642100, 0.576394 avg, 0.001000 rate, 17.229988 seconds, 86016 images -Loaded: 2.239814 seconds -Region Avg IOU: 0.398771, Class: 1.000000, Obj: 0.173588, No Obj: 0.003742, Avg Recall: 0.339806, count: 103 -Region Avg IOU: 0.386026, Class: 1.000000, Obj: 0.163290, No Obj: 0.004398, Avg Recall: 0.354610, count: 141 -Region Avg IOU: 0.375068, Class: 1.000000, Obj: 0.174207, No Obj: 0.004055, Avg Recall: 0.369231, count: 130 -Region Avg IOU: 0.436399, Class: 1.000000, Obj: 0.146464, No Obj: 0.003311, Avg Recall: 0.481013, count: 79 -Region Avg IOU: 0.392271, Class: 1.000000, Obj: 0.169626, No Obj: 0.004204, Avg Recall: 0.444444, count: 117 -Region Avg IOU: 0.413681, Class: 1.000000, Obj: 0.131817, No Obj: 0.004045, Avg Recall: 0.449541, count: 109 -Region Avg IOU: 0.371463, Class: 1.000000, Obj: 0.153100, No Obj: 0.004138, Avg Recall: 0.383929, count: 112 -Region Avg IOU: 0.426180, Class: 1.000000, Obj: 0.160394, No Obj: 0.004422, Avg Recall: 0.419643, count: 112 -337: 0.742603, 0.593015 avg, 0.001000 rate, 14.799929 seconds, 86272 images -Loaded: 5.786982 seconds -Region Avg IOU: 0.427762, Class: 1.000000, Obj: 0.168184, No Obj: 0.004693, Avg Recall: 0.439394, count: 132 -Region Avg IOU: 0.429564, Class: 1.000000, Obj: 0.149923, No Obj: 0.004242, Avg Recall: 0.433628, count: 113 -Region Avg IOU: 0.387617, Class: 1.000000, Obj: 0.151805, No Obj: 0.004347, Avg Recall: 0.352941, count: 85 -Region Avg IOU: 0.353623, Class: 1.000000, Obj: 0.122732, No Obj: 0.003656, Avg Recall: 0.344828, count: 87 -Region Avg IOU: 0.392376, Class: 1.000000, Obj: 0.139205, No Obj: 0.004135, Avg Recall: 0.366071, count: 112 -Region Avg IOU: 0.377938, Class: 1.000000, Obj: 0.143326, No Obj: 0.004130, Avg Recall: 0.346774, count: 124 -Region Avg IOU: 0.367014, Class: 1.000000, Obj: 0.162456, No Obj: 0.004177, Avg Recall: 0.348214, count: 112 -Region Avg IOU: 0.414515, Class: 1.000000, Obj: 0.137364, No Obj: 0.004206, Avg Recall: 0.443609, count: 133 -338: 0.657435, 0.599457 avg, 0.001000 rate, 19.008623 seconds, 86528 images -Loaded: 1.895883 seconds -Region Avg IOU: 0.398165, Class: 1.000000, Obj: 0.151548, No Obj: 0.004202, Avg Recall: 0.403226, count: 124 -Region Avg IOU: 0.434800, Class: 1.000000, Obj: 0.134168, No Obj: 0.004338, Avg Recall: 0.467290, count: 107 -Region Avg IOU: 0.434083, Class: 1.000000, Obj: 0.210699, No Obj: 0.004627, Avg Recall: 0.500000, count: 104 -Region Avg IOU: 0.423721, Class: 1.000000, Obj: 0.176606, No Obj: 0.003678, Avg Recall: 0.500000, count: 88 -Region Avg IOU: 0.396441, Class: 1.000000, Obj: 0.170591, No Obj: 0.004235, Avg Recall: 0.376068, count: 117 -Region Avg IOU: 0.386896, Class: 1.000000, Obj: 0.162967, No Obj: 0.003842, Avg Recall: 0.371429, count: 105 -Region Avg IOU: 0.415771, Class: 1.000000, Obj: 0.173036, No Obj: 0.004070, Avg Recall: 0.391753, count: 97 -Region Avg IOU: 0.371975, Class: 1.000000, Obj: 0.131894, No Obj: 0.003983, Avg Recall: 0.361111, count: 108 -339: 0.597527, 0.599264 avg, 0.001000 rate, 17.864601 seconds, 86784 images -Loaded: 2.551564 seconds -Region Avg IOU: 0.373690, Class: 1.000000, Obj: 0.158678, No Obj: 0.004378, Avg Recall: 0.356061, count: 132 -Region Avg IOU: 0.436067, Class: 1.000000, Obj: 0.205899, No Obj: 0.004497, Avg Recall: 0.483051, count: 118 -Region Avg IOU: 0.395314, Class: 1.000000, Obj: 0.143631, No Obj: 0.003897, Avg Recall: 0.424000, count: 125 -Region Avg IOU: 0.439723, Class: 1.000000, Obj: 0.147647, No Obj: 0.004241, Avg Recall: 0.484211, count: 95 -Region Avg IOU: 0.407682, Class: 1.000000, Obj: 0.175591, No Obj: 0.004554, Avg Recall: 0.425000, count: 120 -Region Avg IOU: 0.414503, Class: 1.000000, Obj: 0.175375, No Obj: 0.004861, Avg Recall: 0.401869, count: 107 -Region Avg IOU: 0.401286, Class: 1.000000, Obj: 0.138962, No Obj: 0.004456, Avg Recall: 0.464286, count: 140 -Region Avg IOU: 0.418699, Class: 1.000000, Obj: 0.185723, No Obj: 0.004199, Avg Recall: 0.431373, count: 102 -340: 0.623400, 0.601678 avg, 0.001000 rate, 16.557232 seconds, 87040 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.127706 seconds -Region Avg IOU: 0.488587, Class: 1.000000, Obj: 0.172589, No Obj: 0.003957, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.521973, Class: 1.000000, Obj: 0.212029, No Obj: 0.004002, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.452739, Class: 1.000000, Obj: 0.177118, No Obj: 0.003942, Avg Recall: 0.451923, count: 104 -Region Avg IOU: 0.512672, Class: 1.000000, Obj: 0.197235, No Obj: 0.004594, Avg Recall: 0.601562, count: 128 -Region Avg IOU: 0.437070, Class: 1.000000, Obj: 0.201740, No Obj: 0.003668, Avg Recall: 0.438095, count: 105 -Region Avg IOU: 0.478313, Class: 1.000000, Obj: 0.182515, No Obj: 0.003652, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.502318, Class: 1.000000, Obj: 0.219148, No Obj: 0.004138, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.484197, Class: 1.000000, Obj: 0.215153, No Obj: 0.003958, Avg Recall: 0.569892, count: 93 -341: 0.526783, 0.594188 avg, 0.001000 rate, 23.282591 seconds, 87296 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.478829, Class: 1.000000, Obj: 0.200266, No Obj: 0.003424, Avg Recall: 0.546667, count: 75 -Region Avg IOU: 0.510779, Class: 1.000000, Obj: 0.177641, No Obj: 0.003759, Avg Recall: 0.544554, count: 101 -Region Avg IOU: 0.484559, Class: 1.000000, Obj: 0.178622, No Obj: 0.003910, Avg Recall: 0.556818, count: 88 -Region Avg IOU: 0.496422, Class: 1.000000, Obj: 0.224860, No Obj: 0.004054, Avg Recall: 0.559055, count: 127 -Region Avg IOU: 0.504419, Class: 1.000000, Obj: 0.184334, No Obj: 0.004366, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.476960, Class: 1.000000, Obj: 0.201352, No Obj: 0.003790, Avg Recall: 0.484848, count: 99 -Region Avg IOU: 0.460808, Class: 1.000000, Obj: 0.199331, No Obj: 0.004260, Avg Recall: 0.485507, count: 138 -Region Avg IOU: 0.464014, Class: 1.000000, Obj: 0.179323, No Obj: 0.003121, Avg Recall: 0.474359, count: 78 -342: 0.489355, 0.583705 avg, 0.001000 rate, 24.468742 seconds, 87552 images -Loaded: 0.004738 seconds -Region Avg IOU: 0.493471, Class: 1.000000, Obj: 0.193596, No Obj: 0.003703, Avg Recall: 0.588235, count: 85 -Region Avg IOU: 0.458693, Class: 1.000000, Obj: 0.200538, No Obj: 0.004322, Avg Recall: 0.500000, count: 130 -Region Avg IOU: 0.496767, Class: 1.000000, Obj: 0.177874, No Obj: 0.004389, Avg Recall: 0.525926, count: 135 -Region Avg IOU: 0.510628, Class: 1.000000, Obj: 0.180752, No Obj: 0.003584, Avg Recall: 0.617284, count: 81 -Region Avg IOU: 0.492947, Class: 1.000000, Obj: 0.162816, No Obj: 0.003705, Avg Recall: 0.515625, count: 128 -Region Avg IOU: 0.466436, Class: 1.000000, Obj: 0.161048, No Obj: 0.003508, Avg Recall: 0.477273, count: 88 -Region Avg IOU: 0.433201, Class: 1.000000, Obj: 0.134847, No Obj: 0.003905, Avg Recall: 0.461538, count: 104 -Region Avg IOU: 0.464814, Class: 1.000000, Obj: 0.191452, No Obj: 0.003565, Avg Recall: 0.470588, count: 102 -343: 0.603484, 0.585683 avg, 0.001000 rate, 24.251469 seconds, 87808 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.468056, Class: 1.000000, Obj: 0.196225, No Obj: 0.003667, Avg Recall: 0.546392, count: 97 -Region Avg IOU: 0.464378, Class: 1.000000, Obj: 0.177084, No Obj: 0.003695, Avg Recall: 0.505050, count: 99 -Region Avg IOU: 0.480400, Class: 1.000000, Obj: 0.147545, No Obj: 0.003966, Avg Recall: 0.522936, count: 109 -Region Avg IOU: 0.488090, Class: 1.000000, Obj: 0.197932, No Obj: 0.003483, Avg Recall: 0.511628, count: 86 -Region Avg IOU: 0.538899, Class: 1.000000, Obj: 0.185474, No Obj: 0.003649, Avg Recall: 0.686046, count: 86 -Region Avg IOU: 0.430897, Class: 1.000000, Obj: 0.182682, No Obj: 0.003956, Avg Recall: 0.408000, count: 125 -Region Avg IOU: 0.526558, Class: 1.000000, Obj: 0.166535, No Obj: 0.003686, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.484008, Class: 1.000000, Obj: 0.169374, No Obj: 0.004031, Avg Recall: 0.580357, count: 112 -344: 0.553608, 0.582475 avg, 0.001000 rate, 24.515047 seconds, 88064 images -Loaded: 1.096108 seconds -Region Avg IOU: 0.503569, Class: 1.000000, Obj: 0.187091, No Obj: 0.004258, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.490723, Class: 1.000000, Obj: 0.191192, No Obj: 0.003618, Avg Recall: 0.551724, count: 87 -Region Avg IOU: 0.472756, Class: 1.000000, Obj: 0.159593, No Obj: 0.003525, Avg Recall: 0.495238, count: 105 -Region Avg IOU: 0.463386, Class: 1.000000, Obj: 0.164041, No Obj: 0.003774, Avg Recall: 0.505155, count: 97 -Region Avg IOU: 0.491130, Class: 1.000000, Obj: 0.158231, No Obj: 0.003681, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.450717, Class: 1.000000, Obj: 0.176758, No Obj: 0.003500, Avg Recall: 0.504132, count: 121 -Region Avg IOU: 0.502824, Class: 1.000000, Obj: 0.172602, No Obj: 0.004632, Avg Recall: 0.530303, count: 132 -Region Avg IOU: 0.455067, Class: 1.000000, Obj: 0.186686, No Obj: 0.004340, Avg Recall: 0.448000, count: 125 -345: 0.591002, 0.583328 avg, 0.001000 rate, 24.841848 seconds, 88320 images -Loaded: 0.013190 seconds -Region Avg IOU: 0.522224, Class: 1.000000, Obj: 0.174137, No Obj: 0.004383, Avg Recall: 0.597122, count: 139 -Region Avg IOU: 0.492866, Class: 1.000000, Obj: 0.211414, No Obj: 0.003691, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.506293, Class: 1.000000, Obj: 0.176719, No Obj: 0.003615, Avg Recall: 0.545455, count: 88 -Region Avg IOU: 0.471042, Class: 1.000000, Obj: 0.162275, No Obj: 0.003752, Avg Recall: 0.491525, count: 118 -Region Avg IOU: 0.493824, Class: 1.000000, Obj: 0.168165, No Obj: 0.003784, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.460939, Class: 1.000000, Obj: 0.209493, No Obj: 0.003807, Avg Recall: 0.489583, count: 96 -Region Avg IOU: 0.522954, Class: 1.000000, Obj: 0.178649, No Obj: 0.003733, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.514022, Class: 1.000000, Obj: 0.208971, No Obj: 0.003625, Avg Recall: 0.590476, count: 105 -346: 0.521539, 0.577149 avg, 0.001000 rate, 25.058077 seconds, 88576 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.517372, Class: 1.000000, Obj: 0.160889, No Obj: 0.003741, Avg Recall: 0.567568, count: 111 -Region Avg IOU: 0.507249, Class: 1.000000, Obj: 0.161156, No Obj: 0.003780, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.472573, Class: 1.000000, Obj: 0.152342, No Obj: 0.003813, Avg Recall: 0.462185, count: 119 -Region Avg IOU: 0.480621, Class: 1.000000, Obj: 0.192928, No Obj: 0.003670, Avg Recall: 0.545455, count: 88 -Region Avg IOU: 0.500236, Class: 1.000000, Obj: 0.178736, No Obj: 0.003544, Avg Recall: 0.557522, count: 113 -Region Avg IOU: 0.472532, Class: 1.000000, Obj: 0.169546, No Obj: 0.003271, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.508098, Class: 1.000000, Obj: 0.160044, No Obj: 0.003754, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.535377, Class: 1.000000, Obj: 0.164065, No Obj: 0.003634, Avg Recall: 0.651685, count: 89 -347: 0.499510, 0.569385 avg, 0.001000 rate, 24.697599 seconds, 88832 images -Loaded: 2.904845 seconds -Region Avg IOU: 0.508382, Class: 1.000000, Obj: 0.188507, No Obj: 0.004009, Avg Recall: 0.604839, count: 124 -Region Avg IOU: 0.480861, Class: 1.000000, Obj: 0.148488, No Obj: 0.003702, Avg Recall: 0.516129, count: 124 -Region Avg IOU: 0.493208, Class: 1.000000, Obj: 0.187074, No Obj: 0.003693, Avg Recall: 0.539326, count: 89 -Region Avg IOU: 0.519631, Class: 1.000000, Obj: 0.169988, No Obj: 0.005088, Avg Recall: 0.625000, count: 160 -Region Avg IOU: 0.455944, Class: 1.000000, Obj: 0.163494, No Obj: 0.003790, Avg Recall: 0.483333, count: 120 -Region Avg IOU: 0.459647, Class: 1.000000, Obj: 0.184153, No Obj: 0.003600, Avg Recall: 0.515790, count: 95 -Region Avg IOU: 0.487953, Class: 1.000000, Obj: 0.207700, No Obj: 0.003909, Avg Recall: 0.552845, count: 123 -Region Avg IOU: 0.457268, Class: 1.000000, Obj: 0.109571, No Obj: 0.003274, Avg Recall: 0.515464, count: 97 -348: 0.590753, 0.571522 avg, 0.001000 rate, 25.327400 seconds, 89088 images -Loaded: 0.677905 seconds -Region Avg IOU: 0.511425, Class: 1.000000, Obj: 0.180955, No Obj: 0.003699, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.485660, Class: 1.000000, Obj: 0.146445, No Obj: 0.003636, Avg Recall: 0.537736, count: 106 -Region Avg IOU: 0.434182, Class: 1.000000, Obj: 0.178479, No Obj: 0.003452, Avg Recall: 0.431818, count: 88 -Region Avg IOU: 0.464883, Class: 1.000000, Obj: 0.180196, No Obj: 0.004241, Avg Recall: 0.479730, count: 148 -Region Avg IOU: 0.476584, Class: 1.000000, Obj: 0.177340, No Obj: 0.004120, Avg Recall: 0.482456, count: 114 -Region Avg IOU: 0.503427, Class: 1.000000, Obj: 0.191466, No Obj: 0.003613, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.471544, Class: 1.000000, Obj: 0.169074, No Obj: 0.003667, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.501212, Class: 1.000000, Obj: 0.168073, No Obj: 0.003589, Avg Recall: 0.516129, count: 93 -349: 0.499380, 0.564308 avg, 0.001000 rate, 25.071562 seconds, 89344 images -Loaded: 0.371238 seconds -Region Avg IOU: 0.469673, Class: 1.000000, Obj: 0.151019, No Obj: 0.003827, Avg Recall: 0.463158, count: 95 -Region Avg IOU: 0.501062, Class: 1.000000, Obj: 0.164902, No Obj: 0.003707, Avg Recall: 0.513514, count: 111 -Region Avg IOU: 0.538477, Class: 1.000000, Obj: 0.227501, No Obj: 0.004168, Avg Recall: 0.607843, count: 102 -Region Avg IOU: 0.454203, Class: 1.000000, Obj: 0.183232, No Obj: 0.004026, Avg Recall: 0.512821, count: 117 -Region Avg IOU: 0.459774, Class: 1.000000, Obj: 0.198003, No Obj: 0.003897, Avg Recall: 0.504587, count: 109 -Region Avg IOU: 0.484938, Class: 1.000000, Obj: 0.175594, No Obj: 0.004149, Avg Recall: 0.514085, count: 142 -Region Avg IOU: 0.432059, Class: 1.000000, Obj: 0.177764, No Obj: 0.003531, Avg Recall: 0.500000, count: 86 -Region Avg IOU: 0.488875, Class: 1.000000, Obj: 0.200296, No Obj: 0.004369, Avg Recall: 0.517857, count: 112 -350: 0.548903, 0.562767 avg, 0.001000 rate, 24.507038 seconds, 89600 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000070 seconds -Region Avg IOU: 0.524889, Class: 1.000000, Obj: 0.193298, No Obj: 0.004074, Avg Recall: 0.606557, count: 122 -Region Avg IOU: 0.510295, Class: 1.000000, Obj: 0.183696, No Obj: 0.003918, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.545347, Class: 1.000000, Obj: 0.172465, No Obj: 0.003881, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.524715, Class: 1.000000, Obj: 0.157501, No Obj: 0.003932, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.533063, Class: 1.000000, Obj: 0.192025, No Obj: 0.003820, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.479959, Class: 1.000000, Obj: 0.182933, No Obj: 0.003947, Avg Recall: 0.528846, count: 104 -Region Avg IOU: 0.553802, Class: 1.000000, Obj: 0.183860, No Obj: 0.004222, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.565405, Class: 1.000000, Obj: 0.212248, No Obj: 0.004338, Avg Recall: 0.728155, count: 103 -351: 0.539969, 0.560487 avg, 0.001000 rate, 30.530773 seconds, 89856 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.566045, Class: 1.000000, Obj: 0.240047, No Obj: 0.004431, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.532188, Class: 1.000000, Obj: 0.183997, No Obj: 0.004115, Avg Recall: 0.584615, count: 130 -Region Avg IOU: 0.504484, Class: 1.000000, Obj: 0.169723, No Obj: 0.004340, Avg Recall: 0.564286, count: 140 -Region Avg IOU: 0.553751, Class: 1.000000, Obj: 0.173666, No Obj: 0.003853, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.525727, Class: 1.000000, Obj: 0.183934, No Obj: 0.004123, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.544299, Class: 1.000000, Obj: 0.195059, No Obj: 0.004309, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.508193, Class: 1.000000, Obj: 0.198743, No Obj: 0.004530, Avg Recall: 0.575163, count: 153 -Region Avg IOU: 0.522623, Class: 1.000000, Obj: 0.163058, No Obj: 0.004262, Avg Recall: 0.613861, count: 101 -352: 0.616494, 0.566088 avg, 0.001000 rate, 30.277102 seconds, 90112 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.526464, Class: 1.000000, Obj: 0.221777, No Obj: 0.004128, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.485326, Class: 1.000000, Obj: 0.166654, No Obj: 0.004102, Avg Recall: 0.581395, count: 129 -Region Avg IOU: 0.472206, Class: 1.000000, Obj: 0.165593, No Obj: 0.003650, Avg Recall: 0.542857, count: 105 -Region Avg IOU: 0.556681, Class: 1.000000, Obj: 0.204842, No Obj: 0.004146, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.501923, Class: 1.000000, Obj: 0.202940, No Obj: 0.003909, Avg Recall: 0.601562, count: 128 -Region Avg IOU: 0.545825, Class: 1.000000, Obj: 0.172269, No Obj: 0.003589, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.542751, Class: 1.000000, Obj: 0.185086, No Obj: 0.003482, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.569678, Class: 1.000000, Obj: 0.214581, No Obj: 0.004132, Avg Recall: 0.743363, count: 113 -353: 0.564881, 0.565967 avg, 0.001000 rate, 28.898460 seconds, 90368 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.585188, Class: 1.000000, Obj: 0.205740, No Obj: 0.004211, Avg Recall: 0.696629, count: 89 -Region Avg IOU: 0.514545, Class: 1.000000, Obj: 0.155319, No Obj: 0.004024, Avg Recall: 0.595745, count: 94 -Region Avg IOU: 0.567490, Class: 1.000000, Obj: 0.178025, No Obj: 0.003743, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.564070, Class: 1.000000, Obj: 0.182240, No Obj: 0.003912, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.533608, Class: 1.000000, Obj: 0.184327, No Obj: 0.004121, Avg Recall: 0.626761, count: 142 -Region Avg IOU: 0.541213, Class: 1.000000, Obj: 0.146087, No Obj: 0.003712, Avg Recall: 0.619048, count: 84 -Region Avg IOU: 0.557913, Class: 1.000000, Obj: 0.204897, No Obj: 0.004106, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.572331, Class: 1.000000, Obj: 0.179816, No Obj: 0.003794, Avg Recall: 0.686869, count: 99 -354: 0.537601, 0.563131 avg, 0.001000 rate, 30.692076 seconds, 90624 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.540094, Class: 1.000000, Obj: 0.185270, No Obj: 0.003620, Avg Recall: 0.609756, count: 82 -Region Avg IOU: 0.536773, Class: 1.000000, Obj: 0.173827, No Obj: 0.004308, Avg Recall: 0.616667, count: 120 -Region Avg IOU: 0.550826, Class: 1.000000, Obj: 0.153449, No Obj: 0.004178, Avg Recall: 0.664474, count: 152 -Region Avg IOU: 0.570835, Class: 1.000000, Obj: 0.194616, No Obj: 0.004160, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.573250, Class: 1.000000, Obj: 0.163024, No Obj: 0.003812, Avg Recall: 0.689655, count: 87 -Region Avg IOU: 0.525307, Class: 1.000000, Obj: 0.183037, No Obj: 0.003818, Avg Recall: 0.603053, count: 131 -Region Avg IOU: 0.492709, Class: 1.000000, Obj: 0.170305, No Obj: 0.003769, Avg Recall: 0.592593, count: 108 -Region Avg IOU: 0.527732, Class: 1.000000, Obj: 0.170342, No Obj: 0.004094, Avg Recall: 0.642202, count: 109 -355: 0.561126, 0.562930 avg, 0.001000 rate, 28.083097 seconds, 90880 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.523575, Class: 1.000000, Obj: 0.145936, No Obj: 0.003977, Avg Recall: 0.641221, count: 131 -Region Avg IOU: 0.524255, Class: 1.000000, Obj: 0.166004, No Obj: 0.003898, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.530266, Class: 1.000000, Obj: 0.176308, No Obj: 0.003731, Avg Recall: 0.596491, count: 114 -Region Avg IOU: 0.506270, Class: 1.000000, Obj: 0.155663, No Obj: 0.003463, Avg Recall: 0.629630, count: 81 -Region Avg IOU: 0.519814, Class: 1.000000, Obj: 0.170112, No Obj: 0.003457, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.537153, Class: 1.000000, Obj: 0.173855, No Obj: 0.003833, Avg Recall: 0.634921, count: 126 -Region Avg IOU: 0.518902, Class: 1.000000, Obj: 0.175612, No Obj: 0.003755, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.541080, Class: 1.000000, Obj: 0.152623, No Obj: 0.003687, Avg Recall: 0.685393, count: 89 -356: 0.518111, 0.558448 avg, 0.001000 rate, 29.326565 seconds, 91136 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.501889, Class: 1.000000, Obj: 0.170068, No Obj: 0.003525, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.562458, Class: 1.000000, Obj: 0.150690, No Obj: 0.003697, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.515222, Class: 1.000000, Obj: 0.172975, No Obj: 0.003660, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.533608, Class: 1.000000, Obj: 0.184066, No Obj: 0.003584, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.527937, Class: 1.000000, Obj: 0.130083, No Obj: 0.003624, Avg Recall: 0.616667, count: 120 -Region Avg IOU: 0.530604, Class: 1.000000, Obj: 0.197730, No Obj: 0.003580, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.545669, Class: 1.000000, Obj: 0.188051, No Obj: 0.003385, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.548054, Class: 1.000000, Obj: 0.134761, No Obj: 0.003183, Avg Recall: 0.641026, count: 78 -357: 0.492683, 0.551872 avg, 0.001000 rate, 28.544144 seconds, 91392 images -Loaded: 2.107909 seconds -Region Avg IOU: 0.524692, Class: 1.000000, Obj: 0.189759, No Obj: 0.003659, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.549781, Class: 1.000000, Obj: 0.166999, No Obj: 0.003512, Avg Recall: 0.684211, count: 76 -Region Avg IOU: 0.556250, Class: 1.000000, Obj: 0.186772, No Obj: 0.003530, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.540112, Class: 1.000000, Obj: 0.185788, No Obj: 0.003625, Avg Recall: 0.623188, count: 138 -Region Avg IOU: 0.550620, Class: 1.000000, Obj: 0.171800, No Obj: 0.003207, Avg Recall: 0.606742, count: 89 -Region Avg IOU: 0.563590, Class: 1.000000, Obj: 0.180757, No Obj: 0.003595, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.565212, Class: 1.000000, Obj: 0.147097, No Obj: 0.003879, Avg Recall: 0.709924, count: 131 -Region Avg IOU: 0.538509, Class: 1.000000, Obj: 0.199854, No Obj: 0.003456, Avg Recall: 0.633663, count: 101 -358: 0.553187, 0.552003 avg, 0.001000 rate, 29.328869 seconds, 91648 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.584616, Class: 1.000000, Obj: 0.196459, No Obj: 0.003508, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.535345, Class: 1.000000, Obj: 0.162440, No Obj: 0.003559, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.553920, Class: 1.000000, Obj: 0.215035, No Obj: 0.003677, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.563894, Class: 1.000000, Obj: 0.180157, No Obj: 0.003645, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.506321, Class: 1.000000, Obj: 0.190081, No Obj: 0.003191, Avg Recall: 0.561798, count: 89 -Region Avg IOU: 0.540635, Class: 1.000000, Obj: 0.143440, No Obj: 0.003315, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.554295, Class: 1.000000, Obj: 0.150831, No Obj: 0.003602, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.550368, Class: 1.000000, Obj: 0.207827, No Obj: 0.003726, Avg Recall: 0.610619, count: 113 -359: 0.534835, 0.550287 avg, 0.001000 rate, 30.440479 seconds, 91904 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.558218, Class: 1.000000, Obj: 0.170302, No Obj: 0.003760, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.548308, Class: 1.000000, Obj: 0.148212, No Obj: 0.003909, Avg Recall: 0.641026, count: 117 -Region Avg IOU: 0.547201, Class: 1.000000, Obj: 0.225541, No Obj: 0.003627, Avg Recall: 0.614173, count: 127 -Region Avg IOU: 0.519648, Class: 1.000000, Obj: 0.171899, No Obj: 0.003432, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.510273, Class: 1.000000, Obj: 0.168210, No Obj: 0.003523, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.529418, Class: 1.000000, Obj: 0.176398, No Obj: 0.003232, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.547835, Class: 1.000000, Obj: 0.160092, No Obj: 0.003997, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.549946, Class: 1.000000, Obj: 0.191321, No Obj: 0.003926, Avg Recall: 0.647059, count: 85 -360: 0.558637, 0.551122 avg, 0.001000 rate, 28.685911 seconds, 92160 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 18.976925 seconds -Region Avg IOU: 0.471607, Class: 1.000000, Obj: 0.167091, No Obj: 0.004363, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.476917, Class: 1.000000, Obj: 0.175470, No Obj: 0.003938, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.424595, Class: 1.000000, Obj: 0.185778, No Obj: 0.003802, Avg Recall: 0.408602, count: 93 -Region Avg IOU: 0.506664, Class: 1.000000, Obj: 0.172905, No Obj: 0.003855, Avg Recall: 0.550000, count: 100 -Region Avg IOU: 0.438209, Class: 1.000000, Obj: 0.175248, No Obj: 0.003930, Avg Recall: 0.420635, count: 126 -Region Avg IOU: 0.472874, Class: 1.000000, Obj: 0.190768, No Obj: 0.004322, Avg Recall: 0.525424, count: 118 -Region Avg IOU: 0.457912, Class: 1.000000, Obj: 0.136805, No Obj: 0.004233, Avg Recall: 0.520325, count: 123 -Region Avg IOU: 0.472359, Class: 1.000000, Obj: 0.172443, No Obj: 0.003424, Avg Recall: 0.534884, count: 86 -361: 0.506366, 0.546646 avg, 0.001000 rate, 24.467823 seconds, 92416 images -Loaded: 0.379731 seconds -Region Avg IOU: 0.442537, Class: 1.000000, Obj: 0.185620, No Obj: 0.004019, Avg Recall: 0.418803, count: 117 -Region Avg IOU: 0.492878, Class: 1.000000, Obj: 0.203427, No Obj: 0.004261, Avg Recall: 0.512605, count: 119 -Region Avg IOU: 0.495162, Class: 1.000000, Obj: 0.159405, No Obj: 0.003735, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.459187, Class: 1.000000, Obj: 0.166754, No Obj: 0.003854, Avg Recall: 0.483516, count: 91 -Region Avg IOU: 0.519994, Class: 1.000000, Obj: 0.165012, No Obj: 0.004185, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.485146, Class: 1.000000, Obj: 0.182672, No Obj: 0.004458, Avg Recall: 0.480392, count: 102 -Region Avg IOU: 0.492651, Class: 1.000000, Obj: 0.214033, No Obj: 0.004337, Avg Recall: 0.525000, count: 120 -Region Avg IOU: 0.442308, Class: 1.000000, Obj: 0.190386, No Obj: 0.003900, Avg Recall: 0.523364, count: 107 -362: 0.571596, 0.549141 avg, 0.001000 rate, 24.535952 seconds, 92672 images -Loaded: 0.125537 seconds -Region Avg IOU: 0.516395, Class: 1.000000, Obj: 0.221497, No Obj: 0.004061, Avg Recall: 0.551282, count: 78 -Region Avg IOU: 0.421547, Class: 1.000000, Obj: 0.179453, No Obj: 0.004079, Avg Recall: 0.398148, count: 108 -Region Avg IOU: 0.469092, Class: 1.000000, Obj: 0.204632, No Obj: 0.003853, Avg Recall: 0.500000, count: 100 -Region Avg IOU: 0.492882, Class: 1.000000, Obj: 0.198648, No Obj: 0.004135, Avg Recall: 0.554622, count: 119 -Region Avg IOU: 0.443447, Class: 1.000000, Obj: 0.186389, No Obj: 0.004171, Avg Recall: 0.440000, count: 100 -Region Avg IOU: 0.470635, Class: 1.000000, Obj: 0.177612, No Obj: 0.004617, Avg Recall: 0.476190, count: 147 -Region Avg IOU: 0.502236, Class: 1.000000, Obj: 0.234084, No Obj: 0.004359, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.460374, Class: 1.000000, Obj: 0.139244, No Obj: 0.003890, Avg Recall: 0.479592, count: 98 -363: 0.566915, 0.550918 avg, 0.001000 rate, 23.844751 seconds, 92928 images -Loaded: 1.073801 seconds -Region Avg IOU: 0.439588, Class: 1.000000, Obj: 0.235636, No Obj: 0.003570, Avg Recall: 0.430233, count: 86 -Region Avg IOU: 0.428860, Class: 1.000000, Obj: 0.175494, No Obj: 0.003701, Avg Recall: 0.433333, count: 90 -Region Avg IOU: 0.469605, Class: 1.000000, Obj: 0.244012, No Obj: 0.004310, Avg Recall: 0.504348, count: 115 -Region Avg IOU: 0.479887, Class: 1.000000, Obj: 0.170528, No Obj: 0.003688, Avg Recall: 0.520833, count: 96 -Region Avg IOU: 0.487095, Class: 1.000000, Obj: 0.192103, No Obj: 0.004130, Avg Recall: 0.528090, count: 89 -Region Avg IOU: 0.457886, Class: 1.000000, Obj: 0.212369, No Obj: 0.003703, Avg Recall: 0.515152, count: 99 -Region Avg IOU: 0.509647, Class: 1.000000, Obj: 0.210453, No Obj: 0.004320, Avg Recall: 0.568965, count: 116 -Region Avg IOU: 0.393766, Class: 1.000000, Obj: 0.199776, No Obj: 0.004248, Avg Recall: 0.320312, count: 128 -364: 0.518090, 0.547636 avg, 0.001000 rate, 22.799038 seconds, 93184 images -Loaded: 1.433579 seconds -Region Avg IOU: 0.499588, Class: 1.000000, Obj: 0.179296, No Obj: 0.003962, Avg Recall: 0.601770, count: 113 -Region Avg IOU: 0.498920, Class: 1.000000, Obj: 0.228708, No Obj: 0.004297, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.485450, Class: 1.000000, Obj: 0.167520, No Obj: 0.003724, Avg Recall: 0.540816, count: 98 -Region Avg IOU: 0.434863, Class: 1.000000, Obj: 0.223448, No Obj: 0.003520, Avg Recall: 0.454545, count: 88 -Region Avg IOU: 0.492415, Class: 1.000000, Obj: 0.218770, No Obj: 0.004224, Avg Recall: 0.489130, count: 92 -Region Avg IOU: 0.492459, Class: 1.000000, Obj: 0.197098, No Obj: 0.004392, Avg Recall: 0.581197, count: 117 -Region Avg IOU: 0.430912, Class: 1.000000, Obj: 0.195677, No Obj: 0.003683, Avg Recall: 0.425532, count: 94 -Region Avg IOU: 0.450473, Class: 1.000000, Obj: 0.187050, No Obj: 0.004156, Avg Recall: 0.489796, count: 98 -365: 0.467880, 0.539660 avg, 0.001000 rate, 24.239340 seconds, 93440 images -Loaded: 0.508627 seconds -Region Avg IOU: 0.436324, Class: 1.000000, Obj: 0.184298, No Obj: 0.004359, Avg Recall: 0.469388, count: 147 -Region Avg IOU: 0.474834, Class: 1.000000, Obj: 0.211356, No Obj: 0.004502, Avg Recall: 0.500000, count: 162 -Region Avg IOU: 0.456788, Class: 1.000000, Obj: 0.201631, No Obj: 0.004372, Avg Recall: 0.535433, count: 127 -Region Avg IOU: 0.497514, Class: 1.000000, Obj: 0.182946, No Obj: 0.004236, Avg Recall: 0.578512, count: 121 -Region Avg IOU: 0.448069, Class: 1.000000, Obj: 0.171381, No Obj: 0.004369, Avg Recall: 0.465409, count: 159 -Region Avg IOU: 0.475950, Class: 1.000000, Obj: 0.187880, No Obj: 0.004136, Avg Recall: 0.508197, count: 122 -Region Avg IOU: 0.435721, Class: 1.000000, Obj: 0.172289, No Obj: 0.004116, Avg Recall: 0.494505, count: 91 -Region Avg IOU: 0.486455, Class: 1.000000, Obj: 0.208360, No Obj: 0.003618, Avg Recall: 0.476190, count: 84 -366: 0.644576, 0.550152 avg, 0.001000 rate, 23.211483 seconds, 93696 images -Loaded: 0.059924 seconds -Region Avg IOU: 0.479722, Class: 1.000000, Obj: 0.175867, No Obj: 0.003973, Avg Recall: 0.514019, count: 107 -Region Avg IOU: 0.494780, Class: 1.000000, Obj: 0.193648, No Obj: 0.004158, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.498668, Class: 1.000000, Obj: 0.196098, No Obj: 0.003854, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.467654, Class: 1.000000, Obj: 0.202129, No Obj: 0.003695, Avg Recall: 0.490909, count: 110 -Region Avg IOU: 0.429105, Class: 1.000000, Obj: 0.158265, No Obj: 0.003906, Avg Recall: 0.496000, count: 125 -Region Avg IOU: 0.458086, Class: 1.000000, Obj: 0.165073, No Obj: 0.003701, Avg Recall: 0.521739, count: 92 -Region Avg IOU: 0.457579, Class: 1.000000, Obj: 0.187524, No Obj: 0.003605, Avg Recall: 0.443396, count: 106 -Region Avg IOU: 0.469505, Class: 1.000000, Obj: 0.200171, No Obj: 0.004108, Avg Recall: 0.491071, count: 112 -367: 0.542770, 0.549414 avg, 0.001000 rate, 23.550203 seconds, 93952 images -Loaded: 0.946465 seconds -Region Avg IOU: 0.494102, Class: 1.000000, Obj: 0.198879, No Obj: 0.003674, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.486869, Class: 1.000000, Obj: 0.190719, No Obj: 0.003498, Avg Recall: 0.483516, count: 91 -Region Avg IOU: 0.479591, Class: 1.000000, Obj: 0.173561, No Obj: 0.003752, Avg Recall: 0.525253, count: 99 -Region Avg IOU: 0.482815, Class: 1.000000, Obj: 0.181814, No Obj: 0.003919, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.449171, Class: 1.000000, Obj: 0.183284, No Obj: 0.003809, Avg Recall: 0.432692, count: 104 -Region Avg IOU: 0.437222, Class: 1.000000, Obj: 0.198665, No Obj: 0.003896, Avg Recall: 0.470000, count: 100 -Region Avg IOU: 0.433251, Class: 1.000000, Obj: 0.184990, No Obj: 0.004377, Avg Recall: 0.432203, count: 118 -Region Avg IOU: 0.497438, Class: 1.000000, Obj: 0.214112, No Obj: 0.004166, Avg Recall: 0.549550, count: 111 -368: 0.595513, 0.554024 avg, 0.001000 rate, 23.460917 seconds, 94208 images -Loaded: 0.557796 seconds -Region Avg IOU: 0.488884, Class: 1.000000, Obj: 0.167554, No Obj: 0.004216, Avg Recall: 0.558621, count: 145 -Region Avg IOU: 0.471388, Class: 1.000000, Obj: 0.149065, No Obj: 0.003556, Avg Recall: 0.517241, count: 116 -Region Avg IOU: 0.490931, Class: 1.000000, Obj: 0.191958, No Obj: 0.003651, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.483099, Class: 1.000000, Obj: 0.205927, No Obj: 0.003816, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.459754, Class: 1.000000, Obj: 0.221740, No Obj: 0.003273, Avg Recall: 0.408163, count: 98 -Region Avg IOU: 0.452261, Class: 1.000000, Obj: 0.206934, No Obj: 0.003860, Avg Recall: 0.477064, count: 109 -Region Avg IOU: 0.484123, Class: 1.000000, Obj: 0.190704, No Obj: 0.003450, Avg Recall: 0.511628, count: 86 -Region Avg IOU: 0.429705, Class: 1.000000, Obj: 0.155160, No Obj: 0.003737, Avg Recall: 0.434783, count: 115 -369: 0.555318, 0.554153 avg, 0.001000 rate, 24.323025 seconds, 94464 images -Loaded: 0.969960 seconds -Region Avg IOU: 0.474323, Class: 1.000000, Obj: 0.199619, No Obj: 0.003971, Avg Recall: 0.526882, count: 93 -Region Avg IOU: 0.441870, Class: 1.000000, Obj: 0.174766, No Obj: 0.003420, Avg Recall: 0.446429, count: 112 -Region Avg IOU: 0.404099, Class: 1.000000, Obj: 0.159967, No Obj: 0.003245, Avg Recall: 0.396040, count: 101 -Region Avg IOU: 0.450328, Class: 1.000000, Obj: 0.187988, No Obj: 0.003732, Avg Recall: 0.440000, count: 125 -Region Avg IOU: 0.507009, Class: 1.000000, Obj: 0.193972, No Obj: 0.003909, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.508409, Class: 1.000000, Obj: 0.182250, No Obj: 0.003595, Avg Recall: 0.606838, count: 117 -Region Avg IOU: 0.480259, Class: 1.000000, Obj: 0.208820, No Obj: 0.003687, Avg Recall: 0.505155, count: 97 -Region Avg IOU: 0.508715, Class: 1.000000, Obj: 0.175339, No Obj: 0.003811, Avg Recall: 0.532110, count: 109 -370: 0.575994, 0.556337 avg, 0.001000 rate, 22.585840 seconds, 94720 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.893963 seconds -Region Avg IOU: 0.346739, Class: 1.000000, Obj: 0.153846, No Obj: 0.003450, Avg Recall: 0.303030, count: 99 -Region Avg IOU: 0.389360, Class: 1.000000, Obj: 0.126841, No Obj: 0.004058, Avg Recall: 0.388430, count: 121 -Region Avg IOU: 0.399385, Class: 1.000000, Obj: 0.122717, No Obj: 0.003436, Avg Recall: 0.408163, count: 98 -Region Avg IOU: 0.374430, Class: 1.000000, Obj: 0.154265, No Obj: 0.003852, Avg Recall: 0.321101, count: 109 -Region Avg IOU: 0.391384, Class: 1.000000, Obj: 0.118460, No Obj: 0.003822, Avg Recall: 0.375000, count: 112 -Region Avg IOU: 0.379326, Class: 1.000000, Obj: 0.104776, No Obj: 0.003367, Avg Recall: 0.372549, count: 102 -Region Avg IOU: 0.396873, Class: 1.000000, Obj: 0.123069, No Obj: 0.003585, Avg Recall: 0.365217, count: 115 -Region Avg IOU: 0.376764, Class: 1.000000, Obj: 0.123964, No Obj: 0.003988, Avg Recall: 0.352941, count: 136 -371: 0.631215, 0.563825 avg, 0.001000 rate, 16.616304 seconds, 94976 images -Loaded: 4.367051 seconds -Region Avg IOU: 0.413466, Class: 1.000000, Obj: 0.130683, No Obj: 0.003693, Avg Recall: 0.377049, count: 122 -Region Avg IOU: 0.371207, Class: 1.000000, Obj: 0.144071, No Obj: 0.003651, Avg Recall: 0.387755, count: 98 -Region Avg IOU: 0.378892, Class: 1.000000, Obj: 0.107509, No Obj: 0.003573, Avg Recall: 0.366972, count: 109 -Region Avg IOU: 0.397022, Class: 1.000000, Obj: 0.146979, No Obj: 0.003631, Avg Recall: 0.356522, count: 115 -Region Avg IOU: 0.330311, Class: 1.000000, Obj: 0.110789, No Obj: 0.003570, Avg Recall: 0.299213, count: 127 -Region Avg IOU: 0.392301, Class: 1.000000, Obj: 0.132356, No Obj: 0.003641, Avg Recall: 0.373737, count: 99 -Region Avg IOU: 0.362853, Class: 1.000000, Obj: 0.105719, No Obj: 0.003280, Avg Recall: 0.349057, count: 106 -Region Avg IOU: 0.418270, Class: 1.000000, Obj: 0.127985, No Obj: 0.003725, Avg Recall: 0.428571, count: 112 -372: 0.704273, 0.577870 avg, 0.001000 rate, 14.695843 seconds, 95232 images -Loaded: 5.639634 seconds -Region Avg IOU: 0.369928, Class: 1.000000, Obj: 0.130937, No Obj: 0.003989, Avg Recall: 0.387324, count: 142 -Region Avg IOU: 0.396586, Class: 1.000000, Obj: 0.152718, No Obj: 0.004486, Avg Recall: 0.360000, count: 125 -Region Avg IOU: 0.387686, Class: 1.000000, Obj: 0.159808, No Obj: 0.003866, Avg Recall: 0.320755, count: 106 -Region Avg IOU: 0.406757, Class: 1.000000, Obj: 0.121725, No Obj: 0.004223, Avg Recall: 0.407143, count: 140 -Region Avg IOU: 0.400498, Class: 1.000000, Obj: 0.135686, No Obj: 0.004276, Avg Recall: 0.402985, count: 134 -Region Avg IOU: 0.388667, Class: 1.000000, Obj: 0.168965, No Obj: 0.004038, Avg Recall: 0.420000, count: 100 -Region Avg IOU: 0.388269, Class: 1.000000, Obj: 0.141697, No Obj: 0.004011, Avg Recall: 0.344828, count: 116 -Region Avg IOU: 0.375953, Class: 1.000000, Obj: 0.143687, No Obj: 0.003840, Avg Recall: 0.336364, count: 110 -373: 0.631209, 0.583204 avg, 0.001000 rate, 16.956181 seconds, 95488 images -Loaded: 3.646846 seconds -Region Avg IOU: 0.384578, Class: 1.000000, Obj: 0.128392, No Obj: 0.003557, Avg Recall: 0.411111, count: 90 -Region Avg IOU: 0.383268, Class: 1.000000, Obj: 0.148077, No Obj: 0.003927, Avg Recall: 0.406250, count: 96 -Region Avg IOU: 0.387506, Class: 1.000000, Obj: 0.132018, No Obj: 0.003789, Avg Recall: 0.400000, count: 100 -Region Avg IOU: 0.370297, Class: 1.000000, Obj: 0.137500, No Obj: 0.004120, Avg Recall: 0.359649, count: 114 -Region Avg IOU: 0.417187, Class: 1.000000, Obj: 0.114235, No Obj: 0.003933, Avg Recall: 0.411111, count: 90 -Region Avg IOU: 0.354956, Class: 1.000000, Obj: 0.124034, No Obj: 0.003999, Avg Recall: 0.333333, count: 129 -Region Avg IOU: 0.376197, Class: 1.000000, Obj: 0.140840, No Obj: 0.004196, Avg Recall: 0.341463, count: 123 -Region Avg IOU: 0.324378, Class: 1.000000, Obj: 0.116872, No Obj: 0.003596, Avg Recall: 0.330508, count: 118 -374: 0.585901, 0.583473 avg, 0.001000 rate, 16.012733 seconds, 95744 images -Loaded: 3.843840 seconds -Region Avg IOU: 0.346820, Class: 1.000000, Obj: 0.125446, No Obj: 0.004064, Avg Recall: 0.371901, count: 121 -Region Avg IOU: 0.400668, Class: 1.000000, Obj: 0.130415, No Obj: 0.004114, Avg Recall: 0.434783, count: 115 -Region Avg IOU: 0.373531, Class: 1.000000, Obj: 0.130762, No Obj: 0.004117, Avg Recall: 0.347826, count: 138 -Region Avg IOU: 0.355899, Class: 1.000000, Obj: 0.160419, No Obj: 0.004253, Avg Recall: 0.375000, count: 120 -Region Avg IOU: 0.311977, Class: 1.000000, Obj: 0.121859, No Obj: 0.003884, Avg Recall: 0.330000, count: 100 -Region Avg IOU: 0.402225, Class: 1.000000, Obj: 0.175198, No Obj: 0.004838, Avg Recall: 0.391304, count: 138 -Region Avg IOU: 0.380162, Class: 1.000000, Obj: 0.127099, No Obj: 0.004701, Avg Recall: 0.371681, count: 113 -Region Avg IOU: 0.364216, Class: 1.000000, Obj: 0.179603, No Obj: 0.004077, Avg Recall: 0.384615, count: 104 -375: 0.632013, 0.588327 avg, 0.001000 rate, 17.467718 seconds, 96000 images -Loaded: 3.319342 seconds -Region Avg IOU: 0.365521, Class: 1.000000, Obj: 0.150516, No Obj: 0.004137, Avg Recall: 0.328125, count: 128 -Region Avg IOU: 0.360518, Class: 1.000000, Obj: 0.163504, No Obj: 0.004420, Avg Recall: 0.336634, count: 101 -Region Avg IOU: 0.403802, Class: 1.000000, Obj: 0.164229, No Obj: 0.004399, Avg Recall: 0.419355, count: 124 -Region Avg IOU: 0.442636, Class: 1.000000, Obj: 0.166366, No Obj: 0.004526, Avg Recall: 0.510638, count: 94 -Region Avg IOU: 0.357947, Class: 1.000000, Obj: 0.162111, No Obj: 0.004336, Avg Recall: 0.345794, count: 107 -Region Avg IOU: 0.356051, Class: 1.000000, Obj: 0.135907, No Obj: 0.003950, Avg Recall: 0.329670, count: 91 -Region Avg IOU: 0.344093, Class: 1.000000, Obj: 0.177092, No Obj: 0.004805, Avg Recall: 0.328244, count: 131 -Region Avg IOU: 0.420510, Class: 1.000000, Obj: 0.167839, No Obj: 0.004048, Avg Recall: 0.419355, count: 93 -376: 0.578940, 0.587389 avg, 0.001000 rate, 17.056015 seconds, 96256 images -Loaded: 2.713691 seconds -Region Avg IOU: 0.418825, Class: 1.000000, Obj: 0.141410, No Obj: 0.004480, Avg Recall: 0.426667, count: 75 -Region Avg IOU: 0.380492, Class: 1.000000, Obj: 0.164550, No Obj: 0.004502, Avg Recall: 0.362903, count: 124 -Region Avg IOU: 0.386908, Class: 1.000000, Obj: 0.183774, No Obj: 0.005322, Avg Recall: 0.357143, count: 98 -Region Avg IOU: 0.408757, Class: 1.000000, Obj: 0.147248, No Obj: 0.004558, Avg Recall: 0.431818, count: 88 -Region Avg IOU: 0.406661, Class: 1.000000, Obj: 0.180141, No Obj: 0.004538, Avg Recall: 0.347107, count: 121 -Region Avg IOU: 0.383072, Class: 1.000000, Obj: 0.185390, No Obj: 0.004315, Avg Recall: 0.343137, count: 102 -Region Avg IOU: 0.413995, Class: 1.000000, Obj: 0.188767, No Obj: 0.004739, Avg Recall: 0.427350, count: 117 -Region Avg IOU: 0.405488, Class: 1.000000, Obj: 0.165255, No Obj: 0.004888, Avg Recall: 0.456897, count: 116 -377: 0.545224, 0.583172 avg, 0.001000 rate, 15.163202 seconds, 96512 images -Loaded: 4.874282 seconds -Region Avg IOU: 0.387639, Class: 1.000000, Obj: 0.149832, No Obj: 0.004426, Avg Recall: 0.396396, count: 111 -Region Avg IOU: 0.378911, Class: 1.000000, Obj: 0.143873, No Obj: 0.004533, Avg Recall: 0.376147, count: 109 -Region Avg IOU: 0.344468, Class: 1.000000, Obj: 0.141569, No Obj: 0.004343, Avg Recall: 0.310345, count: 116 -Region Avg IOU: 0.393954, Class: 1.000000, Obj: 0.138100, No Obj: 0.004522, Avg Recall: 0.371134, count: 97 -Region Avg IOU: 0.402184, Class: 1.000000, Obj: 0.136210, No Obj: 0.004354, Avg Recall: 0.390476, count: 105 -Region Avg IOU: 0.382228, Class: 1.000000, Obj: 0.180285, No Obj: 0.004834, Avg Recall: 0.394231, count: 104 -Region Avg IOU: 0.365912, Class: 1.000000, Obj: 0.182059, No Obj: 0.004885, Avg Recall: 0.338583, count: 127 -Region Avg IOU: 0.395916, Class: 1.000000, Obj: 0.159287, No Obj: 0.004481, Avg Recall: 0.355932, count: 118 -378: 0.649846, 0.589840 avg, 0.001000 rate, 17.973087 seconds, 96768 images -Loaded: 2.367886 seconds -Region Avg IOU: 0.368541, Class: 1.000000, Obj: 0.190512, No Obj: 0.004836, Avg Recall: 0.348214, count: 112 -Region Avg IOU: 0.366958, Class: 1.000000, Obj: 0.173812, No Obj: 0.004566, Avg Recall: 0.333333, count: 102 -Region Avg IOU: 0.368286, Class: 1.000000, Obj: 0.166157, No Obj: 0.005006, Avg Recall: 0.370370, count: 135 -Region Avg IOU: 0.365991, Class: 1.000000, Obj: 0.194669, No Obj: 0.004635, Avg Recall: 0.317757, count: 107 -Region Avg IOU: 0.401550, Class: 1.000000, Obj: 0.175969, No Obj: 0.004292, Avg Recall: 0.375000, count: 88 -Region Avg IOU: 0.362235, Class: 1.000000, Obj: 0.180043, No Obj: 0.004902, Avg Recall: 0.326733, count: 101 -Region Avg IOU: 0.354548, Class: 1.000000, Obj: 0.138734, No Obj: 0.004332, Avg Recall: 0.310345, count: 116 -Region Avg IOU: 0.371289, Class: 1.000000, Obj: 0.133692, No Obj: 0.004356, Avg Recall: 0.383838, count: 99 -379: 0.618574, 0.592713 avg, 0.001000 rate, 16.766676 seconds, 97024 images -Loaded: 5.758061 seconds -Region Avg IOU: 0.384683, Class: 1.000000, Obj: 0.176497, No Obj: 0.004801, Avg Recall: 0.419643, count: 112 -Region Avg IOU: 0.379880, Class: 1.000000, Obj: 0.180558, No Obj: 0.005588, Avg Recall: 0.368852, count: 122 -Region Avg IOU: 0.303915, Class: 1.000000, Obj: 0.158230, No Obj: 0.004633, Avg Recall: 0.267241, count: 116 -Region Avg IOU: 0.434694, Class: 1.000000, Obj: 0.187368, No Obj: 0.006394, Avg Recall: 0.459854, count: 137 -Region Avg IOU: 0.365892, Class: 1.000000, Obj: 0.187869, No Obj: 0.005048, Avg Recall: 0.410256, count: 78 -Region Avg IOU: 0.405816, Class: 1.000000, Obj: 0.179894, No Obj: 0.005424, Avg Recall: 0.412281, count: 114 -Region Avg IOU: 0.344421, Class: 1.000000, Obj: 0.137931, No Obj: 0.004675, Avg Recall: 0.309091, count: 110 -Region Avg IOU: 0.350893, Class: 1.000000, Obj: 0.149874, No Obj: 0.004421, Avg Recall: 0.319588, count: 97 -380: 0.583850, 0.591827 avg, 0.001000 rate, 16.109663 seconds, 97280 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000066 seconds -Region Avg IOU: 0.514441, Class: 1.000000, Obj: 0.217084, No Obj: 0.004605, Avg Recall: 0.544444, count: 90 -Region Avg IOU: 0.539533, Class: 1.000000, Obj: 0.226745, No Obj: 0.004275, Avg Recall: 0.580645, count: 93 -Region Avg IOU: 0.572078, Class: 1.000000, Obj: 0.217582, No Obj: 0.005148, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.576950, Class: 1.000000, Obj: 0.252344, No Obj: 0.004440, Avg Recall: 0.651163, count: 86 -Region Avg IOU: 0.505045, Class: 1.000000, Obj: 0.236053, No Obj: 0.004557, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.557711, Class: 1.000000, Obj: 0.199792, No Obj: 0.004889, Avg Recall: 0.655738, count: 122 -Region Avg IOU: 0.543152, Class: 1.000000, Obj: 0.242450, No Obj: 0.004071, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.524457, Class: 1.000000, Obj: 0.178156, No Obj: 0.004329, Avg Recall: 0.597701, count: 87 -381: 0.508146, 0.583459 avg, 0.001000 rate, 29.701519 seconds, 97536 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.527602, Class: 1.000000, Obj: 0.215932, No Obj: 0.004667, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.516907, Class: 1.000000, Obj: 0.185919, No Obj: 0.004235, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.529204, Class: 1.000000, Obj: 0.194624, No Obj: 0.004473, Avg Recall: 0.601504, count: 133 -Region Avg IOU: 0.565471, Class: 1.000000, Obj: 0.227457, No Obj: 0.004375, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.506152, Class: 1.000000, Obj: 0.197839, No Obj: 0.003945, Avg Recall: 0.559140, count: 93 -Region Avg IOU: 0.546536, Class: 1.000000, Obj: 0.238001, No Obj: 0.004681, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.589733, Class: 1.000000, Obj: 0.170477, No Obj: 0.003841, Avg Recall: 0.710526, count: 76 -Region Avg IOU: 0.509430, Class: 1.000000, Obj: 0.212703, No Obj: 0.004078, Avg Recall: 0.556701, count: 97 -382: 0.539366, 0.579050 avg, 0.001000 rate, 31.792721 seconds, 97792 images -Loaded: 0.172151 seconds -Region Avg IOU: 0.550879, Class: 1.000000, Obj: 0.166657, No Obj: 0.003879, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.535210, Class: 1.000000, Obj: 0.190466, No Obj: 0.004120, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.561936, Class: 1.000000, Obj: 0.149971, No Obj: 0.003989, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.553838, Class: 1.000000, Obj: 0.150390, No Obj: 0.004161, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.575716, Class: 1.000000, Obj: 0.173914, No Obj: 0.003965, Avg Recall: 0.659091, count: 132 -Region Avg IOU: 0.546943, Class: 1.000000, Obj: 0.171967, No Obj: 0.004157, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.562773, Class: 1.000000, Obj: 0.187435, No Obj: 0.004335, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.569690, Class: 1.000000, Obj: 0.166652, No Obj: 0.004110, Avg Recall: 0.760000, count: 100 -383: 0.591518, 0.580296 avg, 0.001000 rate, 28.074734 seconds, 98048 images -Loaded: 3.326346 seconds -Region Avg IOU: 0.576521, Class: 1.000000, Obj: 0.140319, No Obj: 0.003583, Avg Recall: 0.686747, count: 83 -Region Avg IOU: 0.522192, Class: 1.000000, Obj: 0.172341, No Obj: 0.003543, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.516320, Class: 1.000000, Obj: 0.148317, No Obj: 0.003549, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.538222, Class: 1.000000, Obj: 0.156093, No Obj: 0.003817, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.580382, Class: 1.000000, Obj: 0.200434, No Obj: 0.003723, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.565744, Class: 1.000000, Obj: 0.162093, No Obj: 0.003423, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.521648, Class: 1.000000, Obj: 0.154869, No Obj: 0.003645, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.586796, Class: 1.000000, Obj: 0.189298, No Obj: 0.003812, Avg Recall: 0.771739, count: 92 -384: 0.501569, 0.572424 avg, 0.001000 rate, 30.436928 seconds, 98304 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.536941, Class: 1.000000, Obj: 0.128265, No Obj: 0.003485, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.542031, Class: 1.000000, Obj: 0.143839, No Obj: 0.003336, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.533305, Class: 1.000000, Obj: 0.160077, No Obj: 0.003361, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.531536, Class: 1.000000, Obj: 0.166293, No Obj: 0.003094, Avg Recall: 0.630952, count: 84 -Region Avg IOU: 0.554763, Class: 1.000000, Obj: 0.124684, No Obj: 0.003649, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.562828, Class: 1.000000, Obj: 0.117286, No Obj: 0.003308, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.582252, Class: 1.000000, Obj: 0.162064, No Obj: 0.003416, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.559164, Class: 1.000000, Obj: 0.175614, No Obj: 0.003817, Avg Recall: 0.647619, count: 105 -385: 0.538512, 0.569032 avg, 0.001000 rate, 32.691681 seconds, 98560 images -Loaded: 0.087039 seconds -Region Avg IOU: 0.600176, Class: 1.000000, Obj: 0.183454, No Obj: 0.003584, Avg Recall: 0.754098, count: 122 -Region Avg IOU: 0.560257, Class: 1.000000, Obj: 0.146886, No Obj: 0.003434, Avg Recall: 0.647541, count: 122 -Region Avg IOU: 0.571256, Class: 1.000000, Obj: 0.150060, No Obj: 0.003283, Avg Recall: 0.691057, count: 123 -Region Avg IOU: 0.569146, Class: 1.000000, Obj: 0.129714, No Obj: 0.003244, Avg Recall: 0.669565, count: 115 -Region Avg IOU: 0.525291, Class: 1.000000, Obj: 0.135780, No Obj: 0.003427, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.553895, Class: 1.000000, Obj: 0.163551, No Obj: 0.003430, Avg Recall: 0.669565, count: 115 -Region Avg IOU: 0.539321, Class: 1.000000, Obj: 0.147846, No Obj: 0.003265, Avg Recall: 0.640449, count: 89 -Region Avg IOU: 0.542418, Class: 1.000000, Obj: 0.131014, No Obj: 0.003195, Avg Recall: 0.612613, count: 111 -386: 0.534466, 0.565576 avg, 0.001000 rate, 28.287924 seconds, 98816 images -Loaded: 4.595840 seconds -Region Avg IOU: 0.561922, Class: 1.000000, Obj: 0.163096, No Obj: 0.003087, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.508461, Class: 1.000000, Obj: 0.152910, No Obj: 0.003386, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.515588, Class: 1.000000, Obj: 0.133444, No Obj: 0.003424, Avg Recall: 0.593750, count: 128 -Region Avg IOU: 0.566141, Class: 1.000000, Obj: 0.114555, No Obj: 0.003717, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.603473, Class: 1.000000, Obj: 0.171107, No Obj: 0.003296, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.547129, Class: 1.000000, Obj: 0.157496, No Obj: 0.003309, Avg Recall: 0.609375, count: 128 -Region Avg IOU: 0.491126, Class: 1.000000, Obj: 0.138289, No Obj: 0.003385, Avg Recall: 0.535714, count: 140 -Region Avg IOU: 0.521449, Class: 1.000000, Obj: 0.150280, No Obj: 0.003090, Avg Recall: 0.546392, count: 97 -387: 0.560685, 0.565087 avg, 0.001000 rate, 28.068348 seconds, 99072 images -Loaded: 2.388568 seconds -Region Avg IOU: 0.548448, Class: 1.000000, Obj: 0.134159, No Obj: 0.003226, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.533033, Class: 1.000000, Obj: 0.145450, No Obj: 0.003500, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.574353, Class: 1.000000, Obj: 0.163959, No Obj: 0.003453, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.540404, Class: 1.000000, Obj: 0.141647, No Obj: 0.003687, Avg Recall: 0.619718, count: 142 -Region Avg IOU: 0.541215, Class: 1.000000, Obj: 0.155567, No Obj: 0.003290, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.577483, Class: 1.000000, Obj: 0.147986, No Obj: 0.003687, Avg Recall: 0.671756, count: 131 -Region Avg IOU: 0.524480, Class: 1.000000, Obj: 0.132897, No Obj: 0.003038, Avg Recall: 0.579545, count: 88 -Region Avg IOU: 0.528301, Class: 1.000000, Obj: 0.174807, No Obj: 0.003195, Avg Recall: 0.593023, count: 86 -388: 0.606871, 0.569265 avg, 0.001000 rate, 29.996897 seconds, 99328 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.556590, Class: 1.000000, Obj: 0.154670, No Obj: 0.003437, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.564592, Class: 1.000000, Obj: 0.154916, No Obj: 0.003649, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.533428, Class: 1.000000, Obj: 0.169468, No Obj: 0.003502, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.551632, Class: 1.000000, Obj: 0.172612, No Obj: 0.003333, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.553146, Class: 1.000000, Obj: 0.147171, No Obj: 0.003579, Avg Recall: 0.696000, count: 125 -Region Avg IOU: 0.595878, Class: 1.000000, Obj: 0.162954, No Obj: 0.003593, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.541179, Class: 1.000000, Obj: 0.203628, No Obj: 0.003732, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.569095, Class: 1.000000, Obj: 0.185915, No Obj: 0.003996, Avg Recall: 0.658064, count: 155 -389: 0.530098, 0.565349 avg, 0.001000 rate, 32.095497 seconds, 99584 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.575085, Class: 1.000000, Obj: 0.175959, No Obj: 0.003657, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.535970, Class: 1.000000, Obj: 0.181164, No Obj: 0.003149, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.531573, Class: 1.000000, Obj: 0.193189, No Obj: 0.003596, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.579827, Class: 1.000000, Obj: 0.184325, No Obj: 0.003609, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.584212, Class: 1.000000, Obj: 0.200889, No Obj: 0.003687, Avg Recall: 0.708333, count: 120 -Region Avg IOU: 0.532255, Class: 1.000000, Obj: 0.202022, No Obj: 0.003787, Avg Recall: 0.605042, count: 119 -Region Avg IOU: 0.567278, Class: 1.000000, Obj: 0.158825, No Obj: 0.003523, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.553012, Class: 1.000000, Obj: 0.205653, No Obj: 0.003559, Avg Recall: 0.669492, count: 118 -390: 0.567784, 0.565592 avg, 0.001000 rate, 30.492861 seconds, 99840 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.180891 seconds -Region Avg IOU: 0.463279, Class: 1.000000, Obj: 0.178191, No Obj: 0.004455, Avg Recall: 0.468254, count: 126 -Region Avg IOU: 0.492260, Class: 1.000000, Obj: 0.186375, No Obj: 0.004452, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.468320, Class: 1.000000, Obj: 0.180579, No Obj: 0.004229, Avg Recall: 0.500000, count: 96 -Region Avg IOU: 0.424761, Class: 1.000000, Obj: 0.173950, No Obj: 0.004121, Avg Recall: 0.458716, count: 109 -Region Avg IOU: 0.478362, Class: 1.000000, Obj: 0.179074, No Obj: 0.004293, Avg Recall: 0.504065, count: 123 -Region Avg IOU: 0.438553, Class: 1.000000, Obj: 0.192390, No Obj: 0.003826, Avg Recall: 0.474747, count: 99 -Region Avg IOU: 0.411051, Class: 1.000000, Obj: 0.139761, No Obj: 0.003827, Avg Recall: 0.408333, count: 120 -Region Avg IOU: 0.400117, Class: 1.000000, Obj: 0.155165, No Obj: 0.004046, Avg Recall: 0.411765, count: 119 -391: 0.579814, 0.567014 avg, 0.001000 rate, 19.102200 seconds, 100096 images -Loaded: 2.594628 seconds -Region Avg IOU: 0.398262, Class: 1.000000, Obj: 0.158261, No Obj: 0.003954, Avg Recall: 0.352381, count: 105 -Region Avg IOU: 0.447730, Class: 1.000000, Obj: 0.148270, No Obj: 0.003850, Avg Recall: 0.466019, count: 103 -Region Avg IOU: 0.361155, Class: 1.000000, Obj: 0.149268, No Obj: 0.004459, Avg Recall: 0.348148, count: 135 -Region Avg IOU: 0.458056, Class: 1.000000, Obj: 0.203118, No Obj: 0.004362, Avg Recall: 0.495146, count: 103 -Region Avg IOU: 0.461712, Class: 1.000000, Obj: 0.169807, No Obj: 0.004730, Avg Recall: 0.464286, count: 140 -Region Avg IOU: 0.413340, Class: 1.000000, Obj: 0.145712, No Obj: 0.004252, Avg Recall: 0.414414, count: 111 -Region Avg IOU: 0.424140, Class: 1.000000, Obj: 0.193688, No Obj: 0.004110, Avg Recall: 0.434343, count: 99 -Region Avg IOU: 0.408436, Class: 1.000000, Obj: 0.148433, No Obj: 0.003846, Avg Recall: 0.386364, count: 88 -392: 0.596775, 0.569990 avg, 0.001000 rate, 19.854778 seconds, 100352 images -Loaded: 0.739736 seconds -Region Avg IOU: 0.429515, Class: 1.000000, Obj: 0.171840, No Obj: 0.003948, Avg Recall: 0.442105, count: 95 -Region Avg IOU: 0.430079, Class: 1.000000, Obj: 0.175847, No Obj: 0.004097, Avg Recall: 0.417391, count: 115 -Region Avg IOU: 0.450640, Class: 1.000000, Obj: 0.201489, No Obj: 0.003948, Avg Recall: 0.512195, count: 82 -Region Avg IOU: 0.438152, Class: 1.000000, Obj: 0.220299, No Obj: 0.004312, Avg Recall: 0.485437, count: 103 -Region Avg IOU: 0.455588, Class: 1.000000, Obj: 0.204071, No Obj: 0.004412, Avg Recall: 0.490909, count: 110 -Region Avg IOU: 0.435488, Class: 1.000000, Obj: 0.232089, No Obj: 0.003942, Avg Recall: 0.423077, count: 78 -Region Avg IOU: 0.414695, Class: 1.000000, Obj: 0.139825, No Obj: 0.004225, Avg Recall: 0.417910, count: 134 -Region Avg IOU: 0.446596, Class: 1.000000, Obj: 0.178354, No Obj: 0.004030, Avg Recall: 0.446429, count: 112 -393: 0.504627, 0.563454 avg, 0.001000 rate, 22.391413 seconds, 100608 images -Loaded: 1.777093 seconds -Region Avg IOU: 0.415342, Class: 1.000000, Obj: 0.174326, No Obj: 0.004606, Avg Recall: 0.398374, count: 123 -Region Avg IOU: 0.399408, Class: 1.000000, Obj: 0.148054, No Obj: 0.004234, Avg Recall: 0.423423, count: 111 -Region Avg IOU: 0.452690, Class: 1.000000, Obj: 0.147355, No Obj: 0.004078, Avg Recall: 0.500000, count: 114 -Region Avg IOU: 0.439246, Class: 1.000000, Obj: 0.203605, No Obj: 0.004281, Avg Recall: 0.444444, count: 90 -Region Avg IOU: 0.436665, Class: 1.000000, Obj: 0.191146, No Obj: 0.004675, Avg Recall: 0.437500, count: 112 -Region Avg IOU: 0.418737, Class: 1.000000, Obj: 0.224028, No Obj: 0.004275, Avg Recall: 0.409524, count: 105 -Region Avg IOU: 0.421289, Class: 1.000000, Obj: 0.173433, No Obj: 0.004876, Avg Recall: 0.436508, count: 126 -Region Avg IOU: 0.463462, Class: 1.000000, Obj: 0.257669, No Obj: 0.004704, Avg Recall: 0.456311, count: 103 -394: 0.642509, 0.571360 avg, 0.001000 rate, 18.654612 seconds, 100864 images -Loaded: 2.650688 seconds -Region Avg IOU: 0.414741, Class: 1.000000, Obj: 0.183505, No Obj: 0.004317, Avg Recall: 0.428571, count: 105 -Region Avg IOU: 0.471049, Class: 1.000000, Obj: 0.184836, No Obj: 0.004480, Avg Recall: 0.439252, count: 107 -Region Avg IOU: 0.474985, Class: 1.000000, Obj: 0.219096, No Obj: 0.004616, Avg Recall: 0.526882, count: 93 -Region Avg IOU: 0.411965, Class: 1.000000, Obj: 0.184692, No Obj: 0.004474, Avg Recall: 0.387387, count: 111 -Region Avg IOU: 0.436929, Class: 1.000000, Obj: 0.186044, No Obj: 0.004698, Avg Recall: 0.405941, count: 101 -Region Avg IOU: 0.470169, Class: 1.000000, Obj: 0.207831, No Obj: 0.004880, Avg Recall: 0.471698, count: 106 -Region Avg IOU: 0.428444, Class: 1.000000, Obj: 0.181421, No Obj: 0.004095, Avg Recall: 0.431579, count: 95 -Region Avg IOU: 0.465541, Class: 1.000000, Obj: 0.145017, No Obj: 0.004392, Avg Recall: 0.443299, count: 97 -395: 0.518316, 0.566055 avg, 0.001000 rate, 18.423973 seconds, 101120 images -Loaded: 2.540429 seconds -Region Avg IOU: 0.401011, Class: 1.000000, Obj: 0.215336, No Obj: 0.004749, Avg Recall: 0.426471, count: 136 -Region Avg IOU: 0.415472, Class: 1.000000, Obj: 0.195730, No Obj: 0.004962, Avg Recall: 0.416667, count: 120 -Region Avg IOU: 0.487233, Class: 1.000000, Obj: 0.220725, No Obj: 0.004277, Avg Recall: 0.550000, count: 80 -Region Avg IOU: 0.443631, Class: 1.000000, Obj: 0.159048, No Obj: 0.004181, Avg Recall: 0.421569, count: 102 -Region Avg IOU: 0.437526, Class: 1.000000, Obj: 0.175390, No Obj: 0.004736, Avg Recall: 0.466165, count: 133 -Region Avg IOU: 0.398002, Class: 1.000000, Obj: 0.168614, No Obj: 0.003738, Avg Recall: 0.329897, count: 97 -Region Avg IOU: 0.449962, Class: 1.000000, Obj: 0.172254, No Obj: 0.004238, Avg Recall: 0.446429, count: 112 -Region Avg IOU: 0.460242, Class: 1.000000, Obj: 0.201796, No Obj: 0.004786, Avg Recall: 0.508621, count: 116 -396: 0.544332, 0.563883 avg, 0.001000 rate, 20.309635 seconds, 101376 images -Loaded: 2.193764 seconds -Region Avg IOU: 0.440335, Class: 1.000000, Obj: 0.195825, No Obj: 0.003961, Avg Recall: 0.505495, count: 91 -Region Avg IOU: 0.432445, Class: 1.000000, Obj: 0.186042, No Obj: 0.004172, Avg Recall: 0.468085, count: 94 -Region Avg IOU: 0.410885, Class: 1.000000, Obj: 0.178176, No Obj: 0.004540, Avg Recall: 0.405941, count: 101 -Region Avg IOU: 0.470405, Class: 1.000000, Obj: 0.195373, No Obj: 0.004576, Avg Recall: 0.526718, count: 131 -Region Avg IOU: 0.443064, Class: 1.000000, Obj: 0.230436, No Obj: 0.004354, Avg Recall: 0.538462, count: 104 -Region Avg IOU: 0.444519, Class: 1.000000, Obj: 0.166016, No Obj: 0.004025, Avg Recall: 0.480519, count: 77 -Region Avg IOU: 0.413709, Class: 1.000000, Obj: 0.209872, No Obj: 0.003682, Avg Recall: 0.470588, count: 68 -Region Avg IOU: 0.423851, Class: 1.000000, Obj: 0.167111, No Obj: 0.004314, Avg Recall: 0.441667, count: 120 -397: 0.530714, 0.560566 avg, 0.001000 rate, 17.713207 seconds, 101632 images -Loaded: 3.155987 seconds -Region Avg IOU: 0.440896, Class: 1.000000, Obj: 0.210867, No Obj: 0.004990, Avg Recall: 0.454545, count: 99 -Region Avg IOU: 0.453969, Class: 1.000000, Obj: 0.185040, No Obj: 0.004537, Avg Recall: 0.520833, count: 96 -Region Avg IOU: 0.369395, Class: 1.000000, Obj: 0.190006, No Obj: 0.003982, Avg Recall: 0.346535, count: 101 -Region Avg IOU: 0.447091, Class: 1.000000, Obj: 0.198043, No Obj: 0.004342, Avg Recall: 0.484848, count: 132 -Region Avg IOU: 0.384103, Class: 1.000000, Obj: 0.191893, No Obj: 0.003938, Avg Recall: 0.394495, count: 109 -Region Avg IOU: 0.403058, Class: 1.000000, Obj: 0.162965, No Obj: 0.004749, Avg Recall: 0.411348, count: 141 -Region Avg IOU: 0.447863, Class: 1.000000, Obj: 0.177229, No Obj: 0.004722, Avg Recall: 0.485507, count: 138 -Region Avg IOU: 0.458389, Class: 1.000000, Obj: 0.188169, No Obj:Saving weights to ./person9300/backup/tyvp_93_9c512_400.weights - 0.004379, Avg Recall: 0.495727, count: 117 -398: 0.645943, 0.569104 avg, 0.001000 rate, 19.709641 seconds, 101888 images -Loaded: 3.665678 seconds -Region Avg IOU: 0.455972, Class: 1.000000, Obj: 0.193444, No Obj: 0.004796, Avg Recall: 0.508929, count: 112 -Region Avg IOU: 0.390608, Class: 1.000000, Obj: 0.186751, No Obj: 0.004186, Avg Recall: 0.336538, count: 104 -Region Avg IOU: 0.458766, Class: 1.000000, Obj: 0.133007, No Obj: 0.004123, Avg Recall: 0.508475, count: 118 -Region Avg IOU: 0.352928, Class: 1.000000, Obj: 0.154861, No Obj: 0.003481, Avg Recall: 0.315789, count: 114 -Region Avg IOU: 0.433176, Class: 1.000000, Obj: 0.181499, No Obj: 0.005260, Avg Recall: 0.477941, count: 136 -Region Avg IOU: 0.472522, Class: 1.000000, Obj: 0.182406, No Obj: 0.003653, Avg Recall: 0.530864, count: 81 -Region Avg IOU: 0.404534, Class: 1.000000, Obj: 0.176113, No Obj: 0.004518, Avg Recall: 0.408000, count: 125 -Region Avg IOU: 0.410741, Class: 1.000000, Obj: 0.176446, No Obj: 0.004406, Avg Recall: 0.428571, count: 133 -399: 0.623211, 0.574515 avg, 0.001000 rate, 19.157690 seconds, 102144 images -Loaded: 2.829078 seconds -Region Avg IOU: 0.428929, Class: 1.000000, Obj: 0.214496, No Obj: 0.003870, Avg Recall: 0.453488, count: 86 -Region Avg IOU: 0.461685, Class: 1.000000, Obj: 0.211586, No Obj: 0.004667, Avg Recall: 0.523077, count: 130 -Region Avg IOU: 0.448777, Class: 1.000000, Obj: 0.180892, No Obj: 0.003622, Avg Recall: 0.447059, count: 85 -Region Avg IOU: 0.377071, Class: 1.000000, Obj: 0.167394, No Obj: 0.004353, Avg Recall: 0.341085, count: 129 -Region Avg IOU: 0.452729, Class: 1.000000, Obj: 0.177714, No Obj: 0.003893, Avg Recall: 0.450980, count: 102 -Region Avg IOU: 0.385187, Class: 1.000000, Obj: 0.176681, No Obj: 0.004270, Avg Recall: 0.361905, count: 105 -Region Avg IOU: 0.409930, Class: 1.000000, Obj: 0.156061, No Obj: 0.004644, Avg Recall: 0.404959, count: 121 -Region Avg IOU: 0.434298, Class: 1.000000, Obj: 0.145201, No Obj: 0.003760, Avg Recall: 0.446602, count: 103 -400: 0.515453, 0.568608 avg, 0.001000 rate, 22.026884 seconds, 102400 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 17.248417 seconds -Region Avg IOU: 0.444251, Class: 1.000000, Obj: 0.164390, No Obj: 0.004730, Avg Recall: 0.448276, count: 116 -Region Avg IOU: 0.415904, Class: 1.000000, Obj: 0.196706, No Obj: 0.004133, Avg Recall: 0.352941, count: 119 -Region Avg IOU: 0.402495, Class: 1.000000, Obj: 0.178174, No Obj: 0.003971, Avg Recall: 0.440476, count: 84 -Region Avg IOU: 0.388521, Class: 1.000000, Obj: 0.156117, No Obj: 0.004553, Avg Recall: 0.398230, count: 113 -Region Avg IOU: 0.421417, Class: 1.000000, Obj: 0.151070, No Obj: 0.004235, Avg Recall: 0.423077, count: 104 -Region Avg IOU: 0.411200, Class: 1.000000, Obj: 0.155204, No Obj: 0.004384, Avg Recall: 0.400000, count: 125 -Region Avg IOU: 0.423757, Class: 1.000000, Obj: 0.163876, No Obj: 0.004536, Avg Recall: 0.451613, count: 124 -Region Avg IOU: 0.383856, Class: 1.000000, Obj: 0.174198, No Obj: 0.003815, Avg Recall: 0.370370, count: 108 -401: 0.595752, 0.571323 avg, 0.001000 rate, 17.310738 seconds, 102656 images -Loaded: 2.984039 seconds -Region Avg IOU: 0.432226, Class: 1.000000, Obj: 0.171882, No Obj: 0.004291, Avg Recall: 0.433962, count: 106 -Region Avg IOU: 0.387099, Class: 1.000000, Obj: 0.128109, No Obj: 0.003741, Avg Recall: 0.383838, count: 99 -Region Avg IOU: 0.387455, Class: 1.000000, Obj: 0.144407, No Obj: 0.004023, Avg Recall: 0.366337, count: 101 -Region Avg IOU: 0.420362, Class: 1.000000, Obj: 0.149113, No Obj: 0.003894, Avg Recall: 0.414894, count: 94 -Region Avg IOU: 0.410538, Class: 1.000000, Obj: 0.183727, No Obj: 0.003818, Avg Recall: 0.451219, count: 82 -Region Avg IOU: 0.426530, Class: 1.000000, Obj: 0.147493, No Obj: 0.004167, Avg Recall: 0.447619, count: 105 -Region Avg IOU: 0.421369, Class: 1.000000, Obj: 0.169572, No Obj: 0.004082, Avg Recall: 0.436975, count: 119 -Region Avg IOU: 0.374734, Class: 1.000000, Obj: 0.160544, No Obj: 0.003878, Avg Recall: 0.326923, count: 104 -402: 0.492337, 0.563424 avg, 0.001000 rate, 18.272305 seconds, 102912 images -Loaded: 0.869103 seconds -Region Avg IOU: 0.434185, Class: 1.000000, Obj: 0.162544, No Obj: 0.004299, Avg Recall: 0.464646, count: 99 -Region Avg IOU: 0.408257, Class: 1.000000, Obj: 0.142350, No Obj: 0.003975, Avg Recall: 0.404959, count: 121 -Region Avg IOU: 0.401245, Class: 1.000000, Obj: 0.131117, No Obj: 0.003763, Avg Recall: 0.431373, count: 102 -Region Avg IOU: 0.431702, Class: 1.000000, Obj: 0.169814, No Obj: 0.003610, Avg Recall: 0.488889, count: 90 -Region Avg IOU: 0.425031, Class: 1.000000, Obj: 0.156000, No Obj: 0.003727, Avg Recall: 0.422222, count: 90 -Region Avg IOU: 0.441540, Class: 1.000000, Obj: 0.166230, No Obj: 0.004274, Avg Recall: 0.447619, count: 105 -Region Avg IOU: 0.338506, Class: 1.000000, Obj: 0.132809, No Obj: 0.003955, Avg Recall: 0.282828, count: 99 -Region Avg IOU: 0.363712, Class: 1.000000, Obj: 0.109727, No Obj: 0.003634, Avg Recall: 0.379630, count: 108 -403: 0.599277, 0.567009 avg, 0.001000 rate, 17.997007 seconds, 103168 images -Loaded: 3.046514 seconds -Region Avg IOU: 0.413974, Class: 1.000000, Obj: 0.188395, No Obj: 0.004257, Avg Recall: 0.372549, count: 102 -Region Avg IOU: 0.363329, Class: 1.000000, Obj: 0.138530, No Obj: 0.003964, Avg Recall: 0.369048, count: 84 -Region Avg IOU: 0.395674, Class: 1.000000, Obj: 0.152362, No Obj: 0.004194, Avg Recall: 0.403226, count: 124 -Region Avg IOU: 0.435961, Class: 1.000000, Obj: 0.187342, No Obj: 0.004178, Avg Recall: 0.500000, count: 82 -Region Avg IOU: 0.389526, Class: 1.000000, Obj: 0.155458, No Obj: 0.004045, Avg Recall: 0.416667, count: 96 -Region Avg IOU: 0.389723, Class: 1.000000, Obj: 0.157387, No Obj: 0.004158, Avg Recall: 0.367521, count: 117 -Region Avg IOU: 0.391266, Class: 1.000000, Obj: 0.170815, No Obj: 0.004359, Avg Recall: 0.427184, count: 103 -Region Avg IOU: 0.417052, Class: 1.000000, Obj: 0.134377, No Obj: 0.003584, Avg Recall: 0.445783, count: 83 -404: 0.522923, 0.562601 avg, 0.001000 rate, 17.150053 seconds, 103424 images -Loaded: 3.869353 seconds -Region Avg IOU: 0.444703, Class: 1.000000, Obj: 0.182132, No Obj: 0.004268, Avg Recall: 0.450450, count: 111 -Region Avg IOU: 0.374381, Class: 1.000000, Obj: 0.144142, No Obj: 0.004242, Avg Recall: 0.320755, count: 106 -Region Avg IOU: 0.391491, Class: 1.000000, Obj: 0.155415, No Obj: 0.004119, Avg Recall: 0.394958, count: 119 -Region Avg IOU: 0.418636, Class: 1.000000, Obj: 0.159850, No Obj: 0.003687, Avg Recall: 0.450000, count: 100 -Region Avg IOU: 0.435109, Class: 1.000000, Obj: 0.192809, No Obj: 0.004302, Avg Recall: 0.462185, count: 119 -Region Avg IOU: 0.423841, Class: 1.000000, Obj: 0.158852, No Obj: 0.003834, Avg Recall: 0.422222, count: 90 -Region Avg IOU: 0.467608, Class: 1.000000, Obj: 0.160043, No Obj: 0.004230, Avg Recall: 0.500000, count: 112 -Region Avg IOU: 0.436678, Class: 1.000000, Obj: 0.155923, No Obj: 0.004070, Avg Recall: 0.478261, count: 92 -405: 0.488612, 0.555202 avg, 0.001000 rate, 18.441538 seconds, 103680 images -Loaded: 3.035003 seconds -Region Avg IOU: 0.455608, Class: 1.000000, Obj: 0.244178, No Obj: 0.003826, Avg Recall: 0.500000, count: 70 -Region Avg IOU: 0.379271, Class: 1.000000, Obj: 0.167194, No Obj: 0.004441, Avg Recall: 0.378641, count: 103 -Region Avg IOU: 0.428848, Class: 1.000000, Obj: 0.152391, No Obj: 0.004468, Avg Recall: 0.381443, count: 97 -Region Avg IOU: 0.388983, Class: 1.000000, Obj: 0.176655, No Obj: 0.003938, Avg Recall: 0.383178, count: 107 -Region Avg IOU: 0.370970, Class: 1.000000, Obj: 0.194171, No Obj: 0.004201, Avg Recall: 0.357895, count: 95 -Region Avg IOU: 0.426261, Class: 1.000000, Obj: 0.159827, No Obj: 0.004412, Avg Recall: 0.401515, count: 132 -Region Avg IOU: 0.426064, Class: 1.000000, Obj: 0.169615, No Obj: 0.003959, Avg Recall: 0.481928, count: 83 -Region Avg IOU: 0.396150, Class: 1.000000, Obj: 0.182897, No Obj: 0.003979, Avg Recall: 0.438776, count: 98 -406: 0.480474, 0.547729 avg, 0.001000 rate, 18.744547 seconds, 103936 images -Loaded: 2.165586 seconds -Region Avg IOU: 0.426712, Class: 1.000000, Obj: 0.154360, No Obj: 0.004700, Avg Recall: 0.456897, count: 116 -Region Avg IOU: 0.394686, Class: 1.000000, Obj: 0.170968, No Obj: 0.004382, Avg Recall: 0.385321, count: 109 -Region Avg IOU: 0.426681, Class: 1.000000, Obj: 0.174321, No Obj: 0.004440, Avg Recall: 0.430380, count: 79 -Region Avg IOU: 0.425626, Class: 1.000000, Obj: 0.168978, No Obj: 0.004678, Avg Recall: 0.420561, count: 107 -Region Avg IOU: 0.383198, Class: 1.000000, Obj: 0.182026, No Obj: 0.004010, Avg Recall: 0.376344, count: 93 -Region Avg IOU: 0.396393, Class: 1.000000, Obj: 0.155922, No Obj: 0.004997, Avg Recall: 0.371901, count: 121 -Region Avg IOU: 0.461394, Class: 1.000000, Obj: 0.187194, No Obj: 0.004341, Avg Recall: 0.528736, count: 87 -Region Avg IOU: 0.389049, Class: 1.000000, Obj: 0.215453, No Obj: 0.004480, Avg Recall: 0.378947, count: 95 -407: 0.531113, 0.546067 avg, 0.001000 rate, 18.214104 seconds, 104192 images -Loaded: 3.627844 seconds -Region Avg IOU: 0.446931, Class: 1.000000, Obj: 0.201955, No Obj: 0.004967, Avg Recall: 0.508772, count: 114 -Region Avg IOU: 0.389297, Class: 1.000000, Obj: 0.202297, No Obj: 0.004584, Avg Recall: 0.330275, count: 109 -Region Avg IOU: 0.399376, Class: 1.000000, Obj: 0.205589, No Obj: 0.004510, Avg Recall: 0.442308, count: 104 -Region Avg IOU: 0.429205, Class: 1.000000, Obj: 0.175035, No Obj: 0.004974, Avg Recall: 0.455556, count: 90 -Region Avg IOU: 0.424837, Class: 1.000000, Obj: 0.181995, No Obj: 0.004330, Avg Recall: 0.455446, count: 101 -Region Avg IOU: 0.396940, Class: 1.000000, Obj: 0.218277, No Obj: 0.004774, Avg Recall: 0.359649, count: 114 -Region Avg IOU: 0.414918, Class: 1.000000, Obj: 0.186804, No Obj: 0.004532, Avg Recall: 0.442105, count: 95 -Region Avg IOU: 0.358156, Class: 1.000000, Obj: 0.181435, No Obj: 0.004598, Avg Recall: 0.350427, count: 117 -408: 0.556290, 0.547090 avg, 0.001000 rate, 16.987991 seconds, 104448 images -Loaded: 3.935137 seconds -Region Avg IOU: 0.397949, Class: 1.000000, Obj: 0.155256, No Obj: 0.004713, Avg Recall: 0.401709, count: 117 -Region Avg IOU: 0.387695, Class: 1.000000, Obj: 0.162052, No Obj: 0.004966, Avg Recall: 0.352941, count: 102 -Region Avg IOU: 0.419365, Class: 1.000000, Obj: 0.183268, No Obj: 0.004727, Avg Recall: 0.486726, count: 113 -Region Avg IOU: 0.390033, Class: 1.000000, Obj: 0.208828, No Obj: 0.004646, Avg Recall: 0.397959, count: 98 -Region Avg IOU: 0.378590, Class: 1.000000, Obj: 0.169978, No Obj: 0.004640, Avg Recall: 0.385417, count: 96 -Region Avg IOU: 0.420549, Class: 1.000000, Obj: 0.155339, No Obj: 0.004424, Avg Recall: 0.468354, count: 79 -Region Avg IOU: 0.413193, Class: 1.000000, Obj: 0.184970, No Obj: 0.005086, Avg Recall: 0.416667, count: 120 -Region Avg IOU: 0.379673, Class: 1.000000, Obj: 0.181168, No Obj: 0.004725, Avg Recall: 0.382353, count: 102 -409: 0.545872, 0.546968 avg, 0.001000 rate, 16.364742 seconds, 104704 images -Loaded: 2.241073 seconds -Region Avg IOU: 0.404782, Class: 1.000000, Obj: 0.164818, No Obj: 0.004516, Avg Recall: 0.395604, count: 91 -Region Avg IOU: 0.420895, Class: 1.000000, Obj: 0.186627, No Obj: 0.005631, Avg Recall: 0.419847, count: 131 -Region Avg IOU: 0.413198, Class: 1.000000, Obj: 0.197243, No Obj: 0.005150, Avg Recall: 0.457143, count: 105 -Region Avg IOU: 0.401036, Class: 1.000000, Obj: 0.166888, No Obj: 0.004172, Avg Recall: 0.337349, count: 83 -Region Avg IOU: 0.381046, Class: 1.000000, Obj: 0.176541, No Obj: 0.005137, Avg Recall: 0.404110, count: 146 -Region Avg IOU: 0.419761, Class: 1.000000, Obj: 0.210040, No Obj: 0.004686, Avg Recall: 0.440860, count: 93 -Region Avg IOU: 0.431033, Class: 1.000000, Obj: 0.195573, No Obj: 0.004725, Avg Recall: 0.452632, count: 95 -Region Avg IOU: 0.340157, Class: 1.000000, Obj: 0.165046, No Obj: 0.004668, Avg Recall: 0.298077, count: 104 -410: 0.590715, 0.551343 avg, 0.001000 rate, 19.336674 seconds, 104960 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.263784 seconds -Region Avg IOU: 0.429597, Class: 1.000000, Obj: 0.156571, No Obj: 0.005685, Avg Recall: 0.453782, count: 119 -Region Avg IOU: 0.356605, Class: 1.000000, Obj: 0.164102, No Obj: 0.004774, Avg Recall: 0.368421, count: 95 -Region Avg IOU: 0.358989, Class: 1.000000, Obj: 0.171234, No Obj: 0.005045, Avg Recall: 0.375000, count: 144 -Region Avg IOU: 0.398528, Class: 1.000000, Obj: 0.150136, No Obj: 0.004519, Avg Recall: 0.402174, count: 92 -Region Avg IOU: 0.398893, Class: 1.000000, Obj: 0.167693, No Obj: 0.004911, Avg Recall: 0.395833, count: 96 -Region Avg IOU: 0.407616, Class: 1.000000, Obj: 0.177585, No Obj: 0.005198, Avg Recall: 0.428571, count: 91 -Region Avg IOU: 0.388030, Class: 1.000000, Obj: 0.147902, No Obj: 0.004857, Avg Recall: 0.407080, count: 113 -Region Avg IOU: 0.346914, Class: 1.000000, Obj: 0.149463, No Obj: 0.005311, Avg Recall: 0.308176, count: 159 -411: 0.654785, 0.561687 avg, 0.001000 rate, 15.481550 seconds, 105216 images -Loaded: 4.061940 seconds -Region Avg IOU: 0.341085, Class: 1.000000, Obj: 0.143099, No Obj: 0.004390, Avg Recall: 0.336842, count: 95 -Region Avg IOU: 0.324258, Class: 1.000000, Obj: 0.190799, No Obj: 0.004598, Avg Recall: 0.244444, count: 90 -Region Avg IOU: 0.387894, Class: 1.000000, Obj: 0.133119, No Obj: 0.004654, Avg Recall: 0.398148, count: 108 -Region Avg IOU: 0.388696, Class: 1.000000, Obj: 0.199318, No Obj: 0.005275, Avg Recall: 0.366972, count: 109 -Region Avg IOU: 0.425426, Class: 1.000000, Obj: 0.154650, No Obj: 0.004921, Avg Recall: 0.445545, count: 101 -Region Avg IOU: 0.417800, Class: 1.000000, Obj: 0.188775, No Obj: 0.005588, Avg Recall: 0.423423, count: 111 -Region Avg IOU: 0.413998, Class: 1.000000, Obj: 0.180698, No Obj: 0.005671, Avg Recall: 0.394958, count: 119 -Region Avg IOU: 0.375768, Class: 1.000000, Obj: 0.184720, No Obj: 0.004754, Avg Recall: 0.405660, count: 106 -412: 0.581782, 0.563696 avg, 0.001000 rate, 17.920513 seconds, 105472 images -Loaded: 2.316876 seconds -Region Avg IOU: 0.344144, Class: 1.000000, Obj: 0.134787, No Obj: 0.005208, Avg Recall: 0.319149, count: 141 -Region Avg IOU: 0.361681, Class: 1.000000, Obj: 0.175271, No Obj: 0.005163, Avg Recall: 0.360360, count: 111 -Region Avg IOU: 0.352377, Class: 1.000000, Obj: 0.147884, No Obj: 0.004501, Avg Recall: 0.323529, count: 102 -Region Avg IOU: 0.423865, Class: 1.000000, Obj: 0.192637, No Obj: 0.005189, Avg Recall: 0.468468, count: 111 -Region Avg IOU: 0.385855, Class: 1.000000, Obj: 0.174659, No Obj: 0.004694, Avg Recall: 0.361905, count: 105 -Region Avg IOU: 0.407615, Class: 1.000000, Obj: 0.183408, No Obj: 0.004778, Avg Recall: 0.393939, count: 99 -Region Avg IOU: 0.397350, Class: 1.000000, Obj: 0.163365, No Obj: 0.005113, Avg Recall: 0.346154, count: 104 -Region Avg IOU: 0.422606, Class: 1.000000, Obj: 0.201211, No Obj: 0.005419, Avg Recall: 0.415094, count: 106 -413: 0.607178, 0.568044 avg, 0.001000 rate, 14.220146 seconds, 105728 images -Loaded: 5.998165 seconds -Region Avg IOU: 0.369379, Class: 1.000000, Obj: 0.145814, No Obj: 0.005450, Avg Recall: 0.341085, count: 129 -Region Avg IOU: 0.421139, Class: 1.000000, Obj: 0.157867, No Obj: 0.004174, Avg Recall: 0.447368, count: 76 -Region Avg IOU: 0.432069, Class: 1.000000, Obj: 0.170516, No Obj: 0.005093, Avg Recall: 0.453333, count: 75 -Region Avg IOU: 0.375157, Class: 1.000000, Obj: 0.151098, No Obj: 0.005154, Avg Recall: 0.390244, count: 123 -Region Avg IOU: 0.401459, Class: 1.000000, Obj: 0.188605, No Obj: 0.005172, Avg Recall: 0.419355, count: 124 -Region Avg IOU: 0.393336, Class: 1.000000, Obj: 0.154413, No Obj: 0.005055, Avg Recall: 0.400000, count: 110 -Region Avg IOU: 0.354172, Class: 1.000000, Obj: 0.174313, No Obj: 0.004896, Avg Recall: 0.274725, count: 91 -Region Avg IOU: 0.381407, Class: 1.000000, Obj: 0.176077, No Obj: 0.005038, Avg Recall: 0.366667, count: 120 -414: 0.561839, 0.567424 avg, 0.001000 rate, 17.253752 seconds, 105984 images -Loaded: 3.585732 seconds -Region Avg IOU: 0.411470, Class: 1.000000, Obj: 0.195694, No Obj: 0.005320, Avg Recall: 0.404959, count: 121 -Region Avg IOU: 0.330134, Class: 1.000000, Obj: 0.155686, No Obj: 0.004770, Avg Recall: 0.297030, count: 101 -Region Avg IOU: 0.362580, Class: 1.000000, Obj: 0.140711, No Obj: 0.004461, Avg Recall: 0.326531, count: 98 -Region Avg IOU: 0.370687, Class: 1.000000, Obj: 0.159641, No Obj: 0.004548, Avg Recall: 0.385542, count: 83 -Region Avg IOU: 0.434436, Class: 1.000000, Obj: 0.187802, No Obj: 0.006163, Avg Recall: 0.444444, count: 153 -Region Avg IOU: 0.413578, Class: 1.000000, Obj: 0.150954, No Obj: 0.005210, Avg Recall: 0.383838, count: 99 -Region Avg IOU: 0.389122, Class: 1.000000, Obj: 0.167769, No Obj: 0.004292, Avg Recall: 0.352941, count: 68 -Region Avg IOU: 0.395629, Class: 1.000000, Obj: 0.151389, No Obj: 0.005228, Avg Recall: 0.398551, count: 138 -415: 0.629286, 0.573610 avg, 0.001000 rate, 18.025068 seconds, 106240 images -Loaded: 2.815071 seconds -Region Avg IOU: 0.443661, Class: 1.000000, Obj: 0.206452, No Obj: 0.005974, Avg Recall: 0.483444, count: 151 -Region Avg IOU: 0.417551, Class: 1.000000, Obj: 0.189854, No Obj: 0.005228, Avg Recall: 0.415929, count: 113 -Region Avg IOU: 0.355617, Class: 1.000000, Obj: 0.159132, No Obj: 0.004530, Avg Recall: 0.286957, count: 115 -Region Avg IOU: 0.435363, Class: 1.000000, Obj: 0.195194, No Obj: 0.005823, Avg Recall: 0.457143, count: 140 -Region Avg IOU: 0.390580, Class: 1.000000, Obj: 0.181599, No Obj: 0.005709, Avg Recall: 0.357664, count: 137 -Region Avg IOU: 0.378076, Class: 1.000000, Obj: 0.175488, No Obj: 0.005183, Avg Recall: 0.405405, count: 111 -Region Avg IOU: 0.340486, Class: 1.000000, Obj: 0.168743, No Obj: 0.005418, Avg Recall: 0.304000, count: 125 -Region Avg IOU: 0.404064, Class: 1.000000, Obj: 0.200527, No Obj: 0.005379, Avg Recall: 0.437500, count: 96 -416: 0.770936, 0.593343 avg, 0.001000 rate, 18.577831 seconds, 106496 images -Loaded: 1.852002 seconds -Region Avg IOU: 0.387786, Class: 1.000000, Obj: 0.160060, No Obj: 0.004782, Avg Recall: 0.366071, count: 112 -Region Avg IOU: 0.398197, Class: 1.000000, Obj: 0.162282, No Obj: 0.004844, Avg Recall: 0.394958, count: 119 -Region Avg IOU: 0.359832, Class: 1.000000, Obj: 0.168348, No Obj: 0.004508, Avg Recall: 0.367816, count: 87 -Region Avg IOU: 0.434749, Class: 1.000000, Obj: 0.190177, No Obj: 0.004864, Avg Recall: 0.471264, count: 87 -Region Avg IOU: 0.356080, Class: 1.000000, Obj: 0.164733, No Obj: 0.005249, Avg Recall: 0.292453, count: 106 -Region Avg IOU: 0.392986, Class: 1.000000, Obj: 0.149816, No Obj: 0.004451, Avg Recall: 0.428571, count: 98 -Region Avg IOU: 0.414357, Class: 1.000000, Obj: 0.202263, No Obj: 0.005681, Avg Recall: 0.463636, count: 110 -Region Avg IOU: 0.403120, Class: 1.000000, Obj: 0.183357, No Obj: 0.005183, Avg Recall: 0.367925, count: 106 -417: 0.586736, 0.592682 avg, 0.001000 rate, 16.001705 seconds, 106752 images -Loaded: 4.990352 seconds -Region Avg IOU: 0.382659, Class: 1.000000, Obj: 0.185032, No Obj: 0.005092, Avg Recall: 0.343750, count: 96 -Region Avg IOU: 0.397815, Class: 1.000000, Obj: 0.183890, No Obj: 0.005591, Avg Recall: 0.413043, count: 138 -Region Avg IOU: 0.371233, Class: 1.000000, Obj: 0.168473, No Obj: 0.004704, Avg Recall: 0.361905, count: 105 -Region Avg IOU: 0.390005, Class: 1.000000, Obj: 0.179925, No Obj: 0.005792, Avg Recall: 0.364964, count: 137 -Region Avg IOU: 0.440155, Class: 1.000000, Obj: 0.200244, No Obj: 0.005096, Avg Recall: 0.437500, count: 96 -Region Avg IOU: 0.423089, Class: 1.000000, Obj: 0.172765, No Obj: 0.005002, Avg Recall: 0.416667, count: 108 -Region Avg IOU: 0.398904, Class: 1.000000, Obj: 0.199053, No Obj: 0.004936, Avg Recall: 0.369565, count: 92 -Region Avg IOU: 0.352446, Class: 1.000000, Obj: 0.167286, No Obj: 0.005992, Avg Recall: 0.303279, count: 122 -418: 0.583799, 0.591794 avg, 0.001000 rate, 15.095069 seconds, 107008 images -Loaded: 2.943210 seconds -Region Avg IOU: 0.366950, Class: 1.000000, Obj: 0.177719, No Obj: 0.004750, Avg Recall: 0.317757, count: 107 -Region Avg IOU: 0.359223, Class: 1.000000, Obj: 0.146406, No Obj: 0.004629, Avg Recall: 0.319588, count: 97 -Region Avg IOU: 0.401912, Class: 1.000000, Obj: 0.203108, No Obj: 0.005206, Avg Recall: 0.367521, count: 117 -Region Avg IOU: 0.353167, Class: 1.000000, Obj: 0.166211, No Obj: 0.005070, Avg Recall: 0.362903, count: 124 -Region Avg IOU: 0.332825, Class: 1.000000, Obj: 0.149583, No Obj: 0.004265, Avg Recall: 0.291667, count: 96 -Region Avg IOU: 0.388335, Class: 1.000000, Obj: 0.140647, No Obj: 0.004843, Avg Recall: 0.351064, count: 94 -Region Avg IOU: 0.404044, Class: 1.000000, Obj: 0.175017, No Obj: 0.004972, Avg Recall: 0.362637, count: 91 -Region Avg IOU: 0.406095, Class: 1.000000, Obj: 0.169159, No Obj: 0.005154, Avg Recall: 0.445378, count: 119 -419: 0.551359, 0.587750 avg, 0.001000 rate, 16.729977 seconds, 107264 images -Loaded: 4.706629 seconds -Region Avg IOU: 0.392564, Class: 1.000000, Obj: 0.155439, No Obj: 0.004976, Avg Recall: 0.381443, count: 97 -Region Avg IOU: 0.390840, Class: 1.000000, Obj: 0.192833, No Obj: 0.005755, Avg Recall: 0.402174, count: 92 -Region Avg IOU: 0.396183, Class: 1.000000, Obj: 0.180182, No Obj: 0.005635, Avg Recall: 0.415385, count: 130 -Region Avg IOU: 0.380264, Class: 1.000000, Obj: 0.186538, No Obj: 0.005129, Avg Recall: 0.362745, count: 102 -Region Avg IOU: 0.352398, Class: 1.000000, Obj: 0.152114, No Obj: 0.005633, Avg Recall: 0.348684, count: 152 -Region Avg IOU: 0.428303, Class: 1.000000, Obj: 0.187072, No Obj: 0.005571, Avg Recall: 0.422414, count: 116 -Region Avg IOU: 0.439036, Class: 1.000000, Obj: 0.163676, No Obj: 0.005540, Avg Recall: 0.466102, count: 118 -Region Avg IOU: 0.445123, Class: 1.000000, Obj: 0.198462, No Obj: 0.005421, Avg Recall: 0.464912, count: 114 -420: 0.604271, 0.589402 avg, 0.001000 rate, 17.288824 seconds, 107520 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000067 seconds -Region Avg IOU: 0.506880, Class: 1.000000, Obj: 0.202776, No Obj: 0.005094, Avg Recall: 0.526316, count: 95 -Region Avg IOU: 0.531559, Class: 1.000000, Obj: 0.217261, No Obj: 0.004761, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.475874, Class: 1.000000, Obj: 0.160935, No Obj: 0.005044, Avg Recall: 0.592593, count: 108 -Region Avg IOU: 0.500135, Class: 1.000000, Obj: 0.194124, No Obj: 0.004948, Avg Recall: 0.601695, count: 118 -Region Avg IOU: 0.521250, Class: 1.000000, Obj: 0.217592, No Obj: 0.004873, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.544431, Class: 1.000000, Obj: 0.222646, No Obj: 0.005758, Avg Recall: 0.664286, count: 140 -Region Avg IOU: 0.550523, Class: 1.000000, Obj: 0.196630, No Obj: 0.004995, Avg Recall: 0.648855, count: 131 -Region Avg IOU: 0.524177, Class: 1.000000, Obj: 0.228905, No Obj: 0.004739, Avg Recall: 0.596154, count: 104 -421: 0.573219, 0.587784 avg, 0.001000 rate, 27.849884 seconds, 107776 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.527687, Class: 1.000000, Obj: 0.239843, No Obj: 0.004945, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.528811, Class: 1.000000, Obj: 0.154126, No Obj: 0.005037, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.547161, Class: 1.000000, Obj: 0.203502, No Obj: 0.004772, Avg Recall: 0.651163, count: 86 -Region Avg IOU: 0.518902, Class: 1.000000, Obj: 0.179278, No Obj: 0.005141, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.514240, Class: 1.000000, Obj: 0.184887, No Obj: 0.005301, Avg Recall: 0.595588, count: 136 -Region Avg IOU: 0.512650, Class: 1.000000, Obj: 0.209792, No Obj: 0.004580, Avg Recall: 0.546296, count: 108 -Region Avg IOU: 0.550402, Class: 1.000000, Obj: 0.237018, No Obj: 0.004938, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.557445, Class: 1.000000, Obj: 0.209788, No Obj: 0.004783, Avg Recall: 0.633663, count: 101 -422: 0.536876, 0.582693 avg, 0.001000 rate, 29.583117 seconds, 108032 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.512947, Class: 1.000000, Obj: 0.182199, No Obj: 0.004659, Avg Recall: 0.613636, count: 132 -Region Avg IOU: 0.519557, Class: 1.000000, Obj: 0.194285, No Obj: 0.005024, Avg Recall: 0.601399, count: 143 -Region Avg IOU: 0.501048, Class: 1.000000, Obj: 0.193502, No Obj: 0.003955, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.550590, Class: 1.000000, Obj: 0.219117, No Obj: 0.004698, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.562055, Class: 1.000000, Obj: 0.189196, No Obj: 0.004609, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.567263, Class: 1.000000, Obj: 0.187229, No Obj: 0.004035, Avg Recall: 0.732394, count: 71 -Region Avg IOU: 0.572067, Class: 1.000000, Obj: 0.165504, No Obj: 0.004249, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.537530, Class: 1.000000, Obj: 0.169166, No Obj: 0.004702, Avg Recall: 0.606557, count: 122 -423: 0.581181, 0.582542 avg, 0.001000 rate, 29.855198 seconds, 108288 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.505111, Class: 1.000000, Obj: 0.174177, No Obj: 0.003789, Avg Recall: 0.606742, count: 89 -Region Avg IOU: 0.514634, Class: 1.000000, Obj: 0.182457, No Obj: 0.003755, Avg Recall: 0.582418, count: 91 -Region Avg IOU: 0.544168, Class: 1.000000, Obj: 0.145211, No Obj: 0.004067, Avg Recall: 0.652893, count: 121 -Region Avg IOU: 0.526837, Class: 1.000000, Obj: 0.170307, No Obj: 0.004169, Avg Recall: 0.616541, count: 133 -Region Avg IOU: 0.563843, Class: 1.000000, Obj: 0.199079, No Obj: 0.003949, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.552448, Class: 1.000000, Obj: 0.140157, No Obj: 0.004291, Avg Recall: 0.627907, count: 129 -Region Avg IOU: 0.528047, Class: 1.000000, Obj: 0.175573, No Obj: 0.003980, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.553750, Class: 1.000000, Obj: 0.186095, No Obj: 0.004275, Avg Recall: 0.619835, count: 121 -424: 0.580199, 0.582308 avg, 0.001000 rate, 29.859543 seconds, 108544 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.533098, Class: 1.000000, Obj: 0.158539, No Obj: 0.003837, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.555622, Class: 1.000000, Obj: 0.190772, No Obj: 0.003792, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.541176, Class: 1.000000, Obj: 0.170027, No Obj: 0.003761, Avg Recall: 0.651515, count: 132 -Region Avg IOU: 0.556704, Class: 1.000000, Obj: 0.172916, No Obj: 0.003820, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.570905, Class: 1.000000, Obj: 0.163446, No Obj: 0.003591, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.522068, Class: 1.000000, Obj: 0.145199, No Obj: 0.003766, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.508219, Class: 1.000000, Obj: 0.145216, No Obj: 0.004001, Avg Recall: 0.561151, count: 139 -Region Avg IOU: 0.581732, Class: 1.000000, Obj: 0.158874, No Obj: 0.003604, Avg Recall: 0.753086, count: 81 -425: 0.536167, 0.577693 avg, 0.001000 rate, 30.290422 seconds, 108800 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.580111, Class: 1.000000, Obj: 0.168935, No Obj: 0.003660, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.541562, Class: 1.000000, Obj: 0.156477, No Obj: 0.003875, Avg Recall: 0.624000, count: 125 -Region Avg IOU: 0.571903, Class: 1.000000, Obj: 0.123402, No Obj: 0.003416, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.547966, Class: 1.000000, Obj: 0.165810, No Obj: 0.003697, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.539392, Class: 1.000000, Obj: 0.157875, No Obj: 0.003874, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.536515, Class: 1.000000, Obj: 0.161235, No Obj: 0.003495, Avg Recall: 0.619835, count: 121 -Region Avg IOU: 0.568984, Class: 1.000000, Obj: 0.168329, No Obj: 0.003684, Avg Recall: 0.702479, count: 121 -Region Avg IOU: 0.556490, Class: 1.000000, Obj: 0.146212, No Obj: 0.003981, Avg Recall: 0.696429, count: 112 -426: 0.522139, 0.572138 avg, 0.001000 rate, 27.816675 seconds, 109056 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.599612, Class: 1.000000, Obj: 0.157719, No Obj: 0.003683, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.545137, Class: 1.000000, Obj: 0.147268, No Obj: 0.003508, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.547269, Class: 1.000000, Obj: 0.145809, No Obj: 0.003040, Avg Recall: 0.637681, count: 69 -Region Avg IOU: 0.531041, Class: 1.000000, Obj: 0.135797, No Obj: 0.003483, Avg Recall: 0.642857, count: 140 -Region Avg IOU: 0.572598, Class: 1.000000, Obj: 0.137032, No Obj: 0.003585, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.524592, Class: 1.000000, Obj: 0.142926, No Obj: 0.003454, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.534917, Class: 1.000000, Obj: 0.121257, No Obj: 0.003469, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.565861, Class: 1.000000, Obj: 0.171744, No Obj: 0.003259, Avg Recall: 0.750000, count: 80 -427: 0.481771, 0.563101 avg, 0.001000 rate, 27.981655 seconds, 109312 images -Loaded: 0.000115 seconds -Region Avg IOU: 0.560090, Class: 1.000000, Obj: 0.203036, No Obj: 0.003626, Avg Recall: 0.705426, count: 129 -Region Avg IOU: 0.522196, Class: 1.000000, Obj: 0.129206, No Obj: 0.003122, Avg Recall: 0.608108, count: 74 -Region Avg IOU: 0.558549, Class: 1.000000, Obj: 0.157934, No Obj: 0.003536, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.575092, Class: 1.000000, Obj: 0.139192, No Obj: 0.003378, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.590908, Class: 1.000000, Obj: 0.207373, No Obj: 0.003485, Avg Recall: 0.704545, count: 88 -Region Avg IOU: 0.569481, Class: 1.000000, Obj: 0.148819, No Obj: 0.003243, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.540777, Class: 1.000000, Obj: 0.154298, No Obj: 0.003408, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.592311, Class: 1.000000, Obj: 0.154280, No Obj: 0.003481, Avg Recall: 0.741935, count: 93 -428: 0.445478, 0.551339 avg, 0.001000 rate, 29.608088 seconds, 109568 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.565259, Class: 1.000000, Obj: 0.146783, No Obj: 0.003397, Avg Recall: 0.729412, count: 85 -Region Avg IOU: 0.575707, Class: 1.000000, Obj: 0.169868, No Obj: 0.003653, Avg Recall: 0.739437, count: 142 -Region Avg IOU: 0.494893, Class: 1.000000, Obj: 0.158755, No Obj: 0.004002, Avg Recall: 0.506579, count: 152 -Region Avg IOU: 0.525831, Class: 1.000000, Obj: 0.172885, No Obj: 0.003159, Avg Recall: 0.608247, count: 97 -Region Avg IOU: 0.544000, Class: 1.000000, Obj: 0.185485, No Obj: 0.003834, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.527858, Class: 1.000000, Obj: 0.183441, No Obj: 0.003589, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.579039, Class: 1.000000, Obj: 0.160478, No Obj: 0.003708, Avg Recall: 0.732283, count: 127 -Region Avg IOU: 0.531827, Class: 1.000000, Obj: 0.163462, No Obj: 0.003597, Avg Recall: 0.567308, count: 104 -429: 0.550747, 0.551280 avg, 0.001000 rate, 31.440975 seconds, 109824 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.557469, Class: 1.000000, Obj: 0.220267, No Obj: 0.003560, Avg Recall: 0.729412, count: 85 -Region Avg IOU: 0.563082, Class: 1.000000, Obj: 0.189920, No Obj: 0.003653, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.601638, Class: 1.000000, Obj: 0.212352, No Obj: 0.003862, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.557201, Class: 1.000000, Obj: 0.186233, No Obj: 0.003617, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.559258, Class: 1.000000, Obj: 0.208616, No Obj: 0.003764, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.574203, Class: 1.000000, Obj: 0.186433, No Obj: 0.004171, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.506918, Class: 1.000000, Obj: 0.179699, No Obj: 0.003592, Avg Recall: 0.530435, count: 115 -Region Avg IOU: 0.598721, Class: 1.000000, Obj: 0.163128, No Obj: 0.003575, Avg Recall: 0.768519, count: 108 -430: 0.474186, 0.543570 avg, 0.001000 rate, 29.289536 seconds, 110080 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 21.217176 seconds -Region Avg IOU: 0.489162, Class: 1.000000, Obj: 0.171746, No Obj: 0.003383, Avg Recall: 0.573034, count: 89 -Region Avg IOU: 0.503779, Class: 1.000000, Obj: 0.134579, No Obj: 0.004198, Avg Recall: 0.537931, count: 145 -Region Avg IOU: 0.499041, Class: 1.000000, Obj: 0.183833, No Obj: 0.003730, Avg Recall: 0.496063, count: 127 -Region Avg IOU: 0.461067, Class: 1.000000, Obj: 0.196127, No Obj: 0.004202, Avg Recall: 0.458333, count: 120 -Region Avg IOU: 0.477767, Class: 1.000000, Obj: 0.175663, No Obj: 0.003708, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.526536, Class: 1.000000, Obj: 0.204688, No Obj: 0.004454, Avg Recall: 0.613139, count: 137 -Region Avg IOU: 0.512897, Class: 1.000000, Obj: 0.209939, No Obj: 0.004265, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.507210, Class: 1.000000, Obj: 0.209120, No Obj: 0.004031, Avg Recall: 0.565217, count: 115 -431: 0.551774, 0.544391 avg, 0.001000 rate, 25.626360 seconds, 110336 images -Loaded: 0.244986 seconds -Region Avg IOU: 0.501696, Class: 1.000000, Obj: 0.210702, No Obj: 0.004405, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.499656, Class: 1.000000, Obj: 0.194648, No Obj: 0.004226, Avg Recall: 0.520000, count: 100 -Region Avg IOU: 0.498215, Class: 1.000000, Obj: 0.172356, No Obj: 0.004327, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.494764, Class: 1.000000, Obj: 0.196415, No Obj: 0.004367, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.491233, Class: 1.000000, Obj: 0.201782, No Obj: 0.003856, Avg Recall: 0.565217, count: 92 -Region Avg IOU: 0.506306, Class: 1.000000, Obj: 0.208921, No Obj: 0.004342, Avg Recall: 0.568182, count: 132 -Region Avg IOU: 0.518693, Class: 1.000000, Obj: 0.188286, No Obj: 0.004780, Avg Recall: 0.627451, count: 153 -Region Avg IOU: 0.490455, Class: 1.000000, Obj: 0.215784, No Obj: 0.004391, Avg Recall: 0.540323, count: 124 -432: 0.603686, 0.550320 avg, 0.001000 rate, 24.678562 seconds, 110592 images -Loaded: 2.423030 seconds -Region Avg IOU: 0.481849, Class: 1.000000, Obj: 0.188217, No Obj: 0.004203, Avg Recall: 0.518248, count: 137 -Region Avg IOU: 0.464769, Class: 1.000000, Obj: 0.202637, No Obj: 0.004377, Avg Recall: 0.537037, count: 108 -Region Avg IOU: 0.462194, Class: 1.000000, Obj: 0.185652, No Obj: 0.003964, Avg Recall: 0.526786, count: 112 -Region Avg IOU: 0.450469, Class: 1.000000, Obj: 0.213208, No Obj: 0.004323, Avg Recall: 0.455285, count: 123 -Region Avg IOU: 0.510578, Class: 1.000000, Obj: 0.212815, No Obj: 0.004133, Avg Recall: 0.548673, count: 113 -Region Avg IOU: 0.525525, Class: 1.000000, Obj: 0.202612, No Obj: 0.004448, Avg Recall: 0.608247, count: 97 -Region Avg IOU: 0.425369, Class: 1.000000, Obj: 0.207566, No Obj: 0.004323, Avg Recall: 0.429825, count: 114 -Region Avg IOU: 0.477103, Class: 1.000000, Obj: 0.223194, No Obj: 0.004260, Avg Recall: 0.531250, count: 128 -433: 0.573331, 0.552621 avg, 0.001000 rate, 23.808258 seconds, 110848 images -Loaded: 0.526065 seconds -Region Avg IOU: 0.532222, Class: 1.000000, Obj: 0.236166, No Obj: 0.003895, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.473800, Class: 1.000000, Obj: 0.205737, No Obj: 0.004188, Avg Recall: 0.473214, count: 112 -Region Avg IOU: 0.533478, Class: 1.000000, Obj: 0.227556, No Obj: 0.003648, Avg Recall: 0.583333, count: 84 -Region Avg IOU: 0.465378, Class: 1.000000, Obj: 0.240637, No Obj: 0.003833, Avg Recall: 0.500000, count: 96 -Region Avg IOU: 0.546371, Class: 1.000000, Obj: 0.199505, No Obj: 0.004527, Avg Recall: 0.626984, count: 126 -Region Avg IOU: 0.517331, Class: 1.000000, Obj: 0.239166, No Obj: 0.004172, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.500768, Class: 1.000000, Obj: 0.197184, No Obj: 0.004334, Avg Recall: 0.549550, count: 111 -Region Avg IOU: 0.467084, Class: 1.000000, Obj: 0.187039, No Obj: 0.004452, Avg Recall: 0.500000, count: 118 -434: 0.510110, 0.548370 avg, 0.001000 rate, 24.064291 seconds, 111104 images -Loaded: 0.747860 seconds -Region Avg IOU: 0.510404, Class: 1.000000, Obj: 0.222587, No Obj: 0.004407, Avg Recall: 0.563636, count: 110 -Region Avg IOU: 0.509707, Class: 1.000000, Obj: 0.177131, No Obj: 0.004191, Avg Recall: 0.565217, count: 69 -Region Avg IOU: 0.485363, Class: 1.000000, Obj: 0.224629, No Obj: 0.004439, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.489049, Class: 1.000000, Obj: 0.242195, No Obj: 0.004174, Avg Recall: 0.513514, count: 111 -Region Avg IOU: 0.490895, Class: 1.000000, Obj: 0.225612, No Obj: 0.004330, Avg Recall: 0.550000, count: 120 -Region Avg IOU: 0.481120, Class: 1.000000, Obj: 0.217489, No Obj: 0.004391, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.488724, Class: 1.000000, Obj: 0.235393, No Obj: 0.004735, Avg Recall: 0.576389, count: 144 -Region Avg IOU: 0.487497, Class: 1.000000, Obj: 0.236965, No Obj: 0.003699, Avg Recall: 0.553191, count: 94 -435: 0.533540, 0.546887 avg, 0.001000 rate, 25.374470 seconds, 111360 images -Loaded: 0.038680 seconds -Region Avg IOU: 0.495071, Class: 1.000000, Obj: 0.217546, No Obj: 0.004550, Avg Recall: 0.535714, count: 140 -Region Avg IOU: 0.482473, Class: 1.000000, Obj: 0.198213, No Obj: 0.004457, Avg Recall: 0.485915, count: 142 -Region Avg IOU: 0.516737, Class: 1.000000, Obj: 0.204091, No Obj: 0.003926, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.476618, Class: 1.000000, Obj: 0.203215, No Obj: 0.004200, Avg Recall: 0.496000, count: 125 -Region Avg IOU: 0.503218, Class: 1.000000, Obj: 0.206522, No Obj: 0.004280, Avg Recall: 0.575000, count: 120 -Region Avg IOU: 0.526362, Class: 1.000000, Obj: 0.204479, No Obj: 0.004318, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.511943, Class: 1.000000, Obj: 0.209454, No Obj: 0.004294, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.470333, Class: 1.000000, Obj: 0.219363, No Obj: 0.004026, Avg Recall: 0.455285, count: 123 -436: 0.566799, 0.548878 avg, 0.001000 rate, 25.307972 seconds, 111616 images -Loaded: 1.655830 seconds -Region Avg IOU: 0.487174, Class: 1.000000, Obj: 0.236885, No Obj: 0.004325, Avg Recall: 0.546219, count: 119 -Region Avg IOU: 0.498759, Class: 1.000000, Obj: 0.213338, No Obj: 0.003930, Avg Recall: 0.534884, count: 86 -Region Avg IOU: 0.477561, Class: 1.000000, Obj: 0.244695, No Obj: 0.004781, Avg Recall: 0.540984, count: 122 -Region Avg IOU: 0.465684, Class: 1.000000, Obj: 0.211475, No Obj: 0.004084, Avg Recall: 0.525862, count: 116 -Region Avg IOU: 0.423167, Class: 1.000000, Obj: 0.220828, No Obj: 0.004121, Avg Recall: 0.380952, count: 126 -Region Avg IOU: 0.531738, Class: 1.000000, Obj: 0.196583, No Obj: 0.003839, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.415322, Class: 1.000000, Obj: 0.158942, No Obj: 0.003744, Avg Recall: 0.387597, count: 129 -Region Avg IOU: 0.457496, Class: 1.000000, Obj: 0.180475, No Obj: 0.004009, Avg Recall: 0.513761, count: 109 -437: 0.515759, 0.545566 avg, 0.001000 rate, 25.298470 seconds, 111872 images -Loaded: 0.012465 seconds -Region Avg IOU: 0.527623, Class: 1.000000, Obj: 0.205905, No Obj: 0.003712, Avg Recall: 0.609195, count: 87 -Region Avg IOU: 0.509902, Class: 1.000000, Obj: 0.220867, No Obj: 0.003825, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.455575, Class: 1.000000, Obj: 0.174995, No Obj: 0.004097, Avg Recall: 0.503759, count: 133 -Region Avg IOU: 0.511188, Class: 1.000000, Obj: 0.223907, No Obj: 0.003973, Avg Recall: 0.542553, count: 94 -Region Avg IOU: 0.495553, Class: 1.000000, Obj: 0.156429, No Obj: 0.003892, Avg Recall: 0.569343, count: 137 -Region Avg IOU: 0.462806, Class: 1.000000, Obj: 0.200448, No Obj: 0.003736, Avg Recall: 0.524590, count: 122 -Region Avg IOU: 0.486462, Class: 1.000000, Obj: 0.185630, No Obj: 0.004173, Avg Recall: 0.531250, count: 128 -Region Avg IOU: 0.469620, Class: 1.000000, Obj: 0.202589, No Obj: 0.003823, Avg Recall: 0.494505, count: 91 -438: 0.560155, 0.547025 avg, 0.001000 rate, 23.560987 seconds, 112128 images -Loaded: 1.846549 seconds -Region Avg IOU: 0.493537, Class: 1.000000, Obj: 0.185718, No Obj: 0.003537, Avg Recall: 0.580000, count: 100 -Region Avg IOU: 0.477735, Class: 1.000000, Obj: 0.196078, No Obj: 0.004074, Avg Recall: 0.530435, count: 115 -Region Avg IOU: 0.457230, Class: 1.000000, Obj: 0.197800, No Obj: 0.003756, Avg Recall: 0.469231, count: 130 -Region Avg IOU: 0.493990, Class: 1.000000, Obj: 0.177726, No Obj: 0.004144, Avg Recall: 0.550847, count: 118 -Region Avg IOU: 0.559604, Class: 1.000000, Obj: 0.208103, No Obj: 0.004191, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.485044, Class: 1.000000, Obj: 0.223103, No Obj: 0.003612, Avg Recall: 0.486842, count: 76 -Region Avg IOU: 0.500731, Class: 1.000000, Obj: 0.210789, No Obj: 0.004093, Avg Recall: 0.583333, count: 132 -Region Avg IOU: 0.503877, Class: 1.000000, Obj: 0.181508, No Obj: 0.003901, Avg Recall: 0.571429, count: 126 -439: 0.568500, 0.549173 avg, 0.001000 rate, 24.017859 seconds, 112384 images -Loaded: 0.397202 seconds -Region Avg IOU: 0.487813, Class: 1.000000, Obj: 0.224593, No Obj: 0.003837, Avg Recall: 0.582418, count: 91 -Region Avg IOU: 0.501256, Class: 1.000000, Obj: 0.154544, No Obj: 0.003678, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.489222, Class: 1.000000, Obj: 0.207814, No Obj: 0.003702, Avg Recall: 0.489362, count: 94 -Region Avg IOU: 0.482213, Class: 1.000000, Obj: 0.243000, No Obj: 0.003404, Avg Recall: 0.571429, count: 84 -Region Avg IOU: 0.509031, Class: 1.000000, Obj: 0.239532, No Obj: 0.004054, Avg Recall: 0.555556, count: 99 -Region Avg IOU: 0.490556, Class: 1.000000, Obj: 0.195403, No Obj: 0.003538, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.493564, Class: 1.000000, Obj: 0.199131, No Obj: 0.004186, Avg Recall: 0.541985, count: 131 -Region Avg IOU: 0.507112, Class: 1.000000, Obj: 0.193791, No Obj: 0.003462, Avg Recall: 0.565657, count: 99 -440: 0.488323, 0.543088 avg, 0.001000 rate, 24.359968 seconds, 112640 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.894217 seconds -Region Avg IOU: 0.389796, Class: 1.000000, Obj: 0.190543, No Obj: 0.004372, Avg Recall: 0.368421, count: 133 -Region Avg IOU: 0.434480, Class: 1.000000, Obj: 0.157609, No Obj: 0.003449, Avg Recall: 0.455696, count: 79 -Region Avg IOU: 0.436083, Class: 1.000000, Obj: 0.158283, No Obj: 0.004530, Avg Recall: 0.456140, count: 114 -Region Avg IOU: 0.441390, Class: 1.000000, Obj: 0.177798, No Obj: 0.004082, Avg Recall: 0.444444, count: 117 -Region Avg IOU: 0.420795, Class: 1.000000, Obj: 0.137435, No Obj: 0.004079, Avg Recall: 0.450000, count: 120 -Region Avg IOU: 0.419281, Class: 1.000000, Obj: 0.192326, No Obj: 0.003867, Avg Recall: 0.432990, count: 97 -Region Avg IOU: 0.429169, Class: 1.000000, Obj: 0.156132, No Obj: 0.003658, Avg Recall: 0.437500, count: 96 -Region Avg IOU: 0.410359, Class: 1.000000, Obj: 0.171806, No Obj: 0.004017, Avg Recall: 0.415385, count: 130 -441: 0.587951, 0.547574 avg, 0.001000 rate, 18.922646 seconds, 112896 images -Loaded: 2.068073 seconds -Region Avg IOU: 0.364806, Class: 1.000000, Obj: 0.136893, No Obj: 0.003853, Avg Recall: 0.346774, count: 124 -Region Avg IOU: 0.444274, Class: 1.000000, Obj: 0.167865, No Obj: 0.003967, Avg Recall: 0.457627, count: 118 -Region Avg IOU: 0.389197, Class: 1.000000, Obj: 0.172619, No Obj: 0.003569, Avg Recall: 0.410526, count: 95 -Region Avg IOU: 0.417679, Class: 1.000000, Obj: 0.164253, No Obj: 0.003989, Avg Recall: 0.423077, count: 104 -Region Avg IOU: 0.427737, Class: 1.000000, Obj: 0.149162, No Obj: 0.003320, Avg Recall: 0.438776, count: 98 -Region Avg IOU: 0.408284, Class: 1.000000, Obj: 0.181935, No Obj: 0.004204, Avg Recall: 0.380531, count: 113 -Region Avg IOU: 0.377399, Class: 1.000000, Obj: 0.174556, No Obj: 0.003898, Avg Recall: 0.391304, count: 92 -Region Avg IOU: 0.420024, Class: 1.000000, Obj: 0.152122, No Obj: 0.004106, Avg Recall: 0.390977, count: 133 -442: 0.523871, 0.545204 avg, 0.001000 rate, 19.224928 seconds, 113152 images -Loaded: 4.024443 seconds -Region Avg IOU: 0.412831, Class: 1.000000, Obj: 0.145450, No Obj: 0.004303, Avg Recall: 0.428571, count: 112 -Region Avg IOU: 0.444439, Class: 1.000000, Obj: 0.205792, No Obj: 0.004290, Avg Recall: 0.466667, count: 105 -Region Avg IOU: 0.456732, Class: 1.000000, Obj: 0.146695, No Obj: 0.003775, Avg Recall: 0.474227, count: 97 -Region Avg IOU: 0.446591, Class: 1.000000, Obj: 0.210825, No Obj: 0.004010, Avg Recall: 0.452830, count: 106 -Region Avg IOU: 0.414782, Class: 1.000000, Obj: 0.162830, No Obj: 0.004069, Avg Recall: 0.408333, count: 120 -Region Avg IOU: 0.453579, Class: 1.000000, Obj: 0.180606, No Obj: 0.004436, Avg Recall: 0.446602, count: 103 -Region Avg IOU: 0.479403, Class: 1.000000, Obj: 0.208825, No Obj: 0.004345, Avg Recall: 0.524752, count: 101 -Region Avg IOU: 0.400894, Class: 1.000000, Obj: 0.161036, No Obj: 0.004402, Avg Recall: 0.393701, count: 127 -443: 0.587482, 0.549432 avg, 0.001000 rate, 20.562553 seconds, 113408 images -Loaded: 1.509757 seconds -Region Avg IOU: 0.457250, Class: 1.000000, Obj: 0.163111, No Obj: 0.003938, Avg Recall: 0.477064, count: 109 -Region Avg IOU: 0.409531, Class: 1.000000, Obj: 0.156324, No Obj: 0.003861, Avg Recall: 0.377551, count: 98 -Region Avg IOU: 0.432349, Class: 1.000000, Obj: 0.176553, No Obj: 0.004427, Avg Recall: 0.473214, count: 112 -Region Avg IOU: 0.408019, Class: 1.000000, Obj: 0.163348, No Obj: 0.004071, Avg Recall: 0.440476, count: 84 -Region Avg IOU: 0.456877, Class: 1.000000, Obj: 0.193611, No Obj: 0.004368, Avg Recall: 0.521739, count: 92 -Region Avg IOU: 0.416680, Class: 1.000000, Obj: 0.171744, No Obj: 0.004017, Avg Recall: 0.393617, count: 94 -Region Avg IOU: 0.426438, Class: 1.000000, Obj: 0.205295, No Obj: 0.004613, Avg Recall: 0.376923, count: 130 -Region Avg IOU: 0.403480, Class: 1.000000, Obj: 0.178380, No Obj: 0.004626, Avg Recall: 0.356522, count: 115 -444: 0.499378, 0.544426 avg, 0.001000 rate, 19.621626 seconds, 113664 images -Loaded: 3.369051 seconds -Region Avg IOU: 0.384399, Class: 1.000000, Obj: 0.179333, No Obj: 0.003862, Avg Recall: 0.367816, count: 87 -Region Avg IOU: 0.388604, Class: 1.000000, Obj: 0.164130, No Obj: 0.004104, Avg Recall: 0.343434, count: 99 -Region Avg IOU: 0.395695, Class: 1.000000, Obj: 0.202527, No Obj: 0.004509, Avg Recall: 0.396694, count: 121 -Region Avg IOU: 0.448435, Class: 1.000000, Obj: 0.189708, No Obj: 0.004392, Avg Recall: 0.457831, count: 83 -Region Avg IOU: 0.372697, Class: 1.000000, Obj: 0.184881, No Obj: 0.003980, Avg Recall: 0.328947, count: 76 -Region Avg IOU: 0.427902, Class: 1.000000, Obj: 0.200632, No Obj: 0.004498, Avg Recall: 0.429825, count: 114 -Region Avg IOU: 0.425616, Class: 1.000000, Obj: 0.208908, No Obj: 0.004673, Avg Recall: 0.406667, count: 150 -Region Avg IOU: 0.401844, Class: 1.000000, Obj: 0.157680, No Obj: 0.004162, Avg Recall: 0.414894, count: 94 -445: 0.583940, 0.548378 avg, 0.001000 rate, 19.316084 seconds, 113920 images -Loaded: 3.424788 seconds -Region Avg IOU: 0.449675, Class: 1.000000, Obj: 0.197626, No Obj: 0.004730, Avg Recall: 0.483051, count: 118 -Region Avg IOU: 0.415531, Class: 1.000000, Obj: 0.152901, No Obj: 0.004528, Avg Recall: 0.425532, count: 141 -Region Avg IOU: 0.367747, Class: 1.000000, Obj: 0.193796, No Obj: 0.004328, Avg Recall: 0.344086, count: 93 -Region Avg IOU: 0.417864, Class: 1.000000, Obj: 0.180977, No Obj: 0.004436, Avg Recall: 0.417391, count: 115 -Region Avg IOU: 0.399715, Class: 1.000000, Obj: 0.189966, No Obj: 0.005018, Avg Recall: 0.400000, count: 120 -Region Avg IOU: 0.407930, Class: 1.000000, Obj: 0.203473, No Obj: 0.004622, Avg Recall: 0.385321, count: 109 -Region Avg IOU: 0.407927, Class: 1.000000, Obj: 0.160996, No Obj: 0.004374, Avg Recall: 0.452991, count: 117 -Region Avg IOU: 0.387176, Class: 1.000000, Obj: 0.164928, No Obj: 0.004267, Avg Recall: 0.378378, count: 111 -446: 0.613403, 0.554880 avg, 0.001000 rate, 15.904783 seconds, 114176 images -Loaded: 4.749995 seconds -Region Avg IOU: 0.383711, Class: 1.000000, Obj: 0.142539, No Obj: 0.004141, Avg Recall: 0.327103, count: 107 -Region Avg IOU: 0.397209, Class: 1.000000, Obj: 0.172497, No Obj: 0.004500, Avg Recall: 0.387387, count: 111 -Region Avg IOU: 0.371366, Class: 1.000000, Obj: 0.206901, No Obj: 0.004612, Avg Recall: 0.404959, count: 121 -Region Avg IOU: 0.382539, Class: 1.000000, Obj: 0.209132, No Obj: 0.004382, Avg Recall: 0.403846, count: 104 -Region Avg IOU: 0.449064, Class: 1.000000, Obj: 0.170399, No Obj: 0.004790, Avg Recall: 0.466019, count: 103 -Region Avg IOU: 0.377160, Class: 1.000000, Obj: 0.172297, No Obj: 0.004735, Avg Recall: 0.347458, count: 118 -Region Avg IOU: 0.377974, Class: 1.000000, Obj: 0.164941, No Obj: 0.004548, Avg Recall: 0.400000, count: 120 -Region Avg IOU: 0.389801, Class: 1.000000, Obj: 0.206367, No Obj: 0.004489, Avg Recall: 0.380952, count: 105 -447: 0.582591, 0.557651 avg, 0.001000 rate, 19.268351 seconds, 114432 images -Loaded: 3.879600 seconds -Region Avg IOU: 0.382776, Class: 1.000000, Obj: 0.207802, No Obj: 0.004595, Avg Recall: 0.344828, count: 116 -Region Avg IOU: 0.432285, Class: 1.000000, Obj: 0.178884, No Obj: 0.004745, Avg Recall: 0.437500, count: 80 -Region Avg IOU: 0.393673, Class: 1.000000, Obj: 0.222906, No Obj: 0.004896, Avg Recall: 0.396947, count: 131 -Region Avg IOU: 0.385328, Class: 1.000000, Obj: 0.153097, No Obj: 0.004607, Avg Recall: 0.356061, count: 132 -Region Avg IOU: 0.442215, Class: 1.000000, Obj: 0.190920, No Obj: 0.004748, Avg Recall: 0.456897, count: 116 -Region Avg IOU: 0.399587, Class: 1.000000, Obj: 0.178591, No Obj: 0.004495, Avg Recall: 0.383838, count: 99 -Region Avg IOU: 0.401464, Class: 1.000000, Obj: 0.160964, No Obj: 0.004092, Avg Recall: 0.400000, count: 100 -Region Avg IOU: 0.399629, Class: 1.000000, Obj: 0.188933, No Obj: 0.004763, Avg Recall: 0.382609, count: 115 -448: 0.531925, 0.555079 avg, 0.001000 rate, 18.750811 seconds, 114688 images -Loaded: 2.515178 seconds -Region Avg IOU: 0.432709, Class: 1.000000, Obj: 0.208313, No Obj: 0.004458, Avg Recall: 0.479592, count: 98 -Region Avg IOU: 0.418757, Class: 1.000000, Obj: 0.168222, No Obj: 0.004334, Avg Recall: 0.410256, count: 117 -Region Avg IOU: 0.403793, Class: 1.000000, Obj: 0.183028, No Obj: 0.004912, Avg Recall: 0.421875, count: 128 -Region Avg IOU: 0.411375, Class: 1.000000, Obj: 0.187272, No Obj: 0.005490, Avg Recall: 0.403846, count: 156 -Region Avg IOU: 0.422347, Class: 1.000000, Obj: 0.209382, No Obj: 0.004415, Avg Recall: 0.450000, count: 120 -Region Avg IOU: 0.409130, Class: 1.000000, Obj: 0.156834, No Obj: 0.004216, Avg Recall: 0.412844, count: 109 -Region Avg IOU: 0.441857, Class: 1.000000, Obj: 0.190625, No Obj: 0.004212, Avg Recall: 0.460526, count: 76 -Region Avg IOU: 0.433158, Class: 1.000000, Obj: 0.161380, No Obj: 0.004491, Avg Recall: 0.428571, count: 77 -449: 0.555441, 0.555115 avg, 0.001000 rate, 18.713549 seconds, 114944 images -Loaded: 3.876883 seconds -Region Avg IOU: 0.404950, Class: 1.000000, Obj: 0.159849, No Obj: 0.004157, Avg Recall: 0.356436, count: 101 -Region Avg IOU: 0.383138, Class: 1.000000, Obj: 0.194107, No Obj: 0.005131, Avg Recall: 0.388889, count: 126 -Region Avg IOU: 0.448220, Class: 1.000000, Obj: 0.219984, No Obj: 0.005345, Avg Recall: 0.448819, count: 127 -Region Avg IOU: 0.417370, Class: 1.000000, Obj: 0.174190, No Obj: 0.004629, Avg Recall: 0.451327, count: 113 -Region Avg IOU: 0.398137, Class: 1.000000, Obj: 0.181117, No Obj: 0.004507, Avg Recall: 0.425532, count: 94 -Region Avg IOU: 0.333575, Class: 1.000000, Obj: 0.203254, No Obj: 0.004504, Avg Recall: 0.272000, count: 125 -Region Avg IOU: 0.456044, Class: 1.000000, Obj: 0.199258, No Obj: 0.004950, Avg Recall: 0.508621, count: 116 -Region Avg IOU: 0.405296, Class: 1.000000, Obj: 0.203756, No Obj: 0.005063, Avg Recall: 0.462810, count: 121 -450: 0.635420, 0.563145 avg, 0.001000 rate, 16.685768 seconds, 115200 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000099 seconds -Region Avg IOU: 0.524221, Class: 1.000000, Obj: 0.185348, No Obj: 0.004581, Avg Recall: 0.578512, count: 121 -Region Avg IOU: 0.522448, Class: 1.000000, Obj: 0.226329, No Obj: 0.004261, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.535982, Class: 1.000000, Obj: 0.234618, No Obj: 0.003909, Avg Recall: 0.623377, count: 77 -Region Avg IOU: 0.534803, Class: 1.000000, Obj: 0.234063, No Obj: 0.004543, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.491819, Class: 1.000000, Obj: 0.247443, No Obj: 0.004033, Avg Recall: 0.507042, count: 71 -Region Avg IOU: 0.527458, Class: 1.000000, Obj: 0.239281, No Obj: 0.004609, Avg Recall: 0.587719, count: 114 -Region Avg IOU: 0.554427, Class: 1.000000, Obj: 0.192932, No Obj: 0.004284, Avg Recall: 0.685393, count: 89 -Region Avg IOU: 0.561523, Class: 1.000000, Obj: 0.237129, No Obj: 0.004384, Avg Recall: 0.680000, count: 100 -451: 0.497127, 0.556543 avg, 0.001000 rate, 28.259672 seconds, 115456 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.525574, Class: 1.000000, Obj: 0.209812, No Obj: 0.004737, Avg Recall: 0.669355, count: 124 -Region Avg IOU: 0.464830, Class: 1.000000, Obj: 0.182795, No Obj: 0.004278, Avg Recall: 0.549618, count: 131 -Region Avg IOU: 0.562998, Class: 1.000000, Obj: 0.212697, No Obj: 0.004786, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.492275, Class: 1.000000, Obj: 0.197084, No Obj: 0.004010, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.532426, Class: 1.000000, Obj: 0.249681, No Obj: 0.004436, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.540846, Class: 1.000000, Obj: 0.205373, No Obj: 0.004053, Avg Recall: 0.629214, count: 89 -Region Avg IOU: 0.523052, Class: 1.000000, Obj: 0.225108, No Obj: 0.004305, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.529592, Class: 1.000000, Obj: 0.197685, No Obj: 0.003994, Avg Recall: 0.584270, count: 89 -452: 0.588990, 0.559788 avg, 0.001000 rate, 27.014156 seconds, 115712 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.536602, Class: 1.000000, Obj: 0.217522, No Obj: 0.004180, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.544899, Class: 1.000000, Obj: 0.197560, No Obj: 0.003703, Avg Recall: 0.747126, count: 87 -Region Avg IOU: 0.552823, Class: 1.000000, Obj: 0.228101, No Obj: 0.004548, Avg Recall: 0.661417, count: 127 -Region Avg IOU: 0.524660, Class: 1.000000, Obj: 0.226217, No Obj: 0.004157, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.530309, Class: 1.000000, Obj: 0.214965, No Obj: 0.004218, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.569052, Class: 1.000000, Obj: 0.189114, No Obj: 0.004047, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.534174, Class: 1.000000, Obj: 0.196927, No Obj: 0.004151, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.514760, Class: 1.000000, Obj: 0.202356, No Obj: 0.004451, Avg Recall: 0.594828, count: 116 -453: 0.617883, 0.565598 avg, 0.001000 rate, 28.404947 seconds, 115968 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.513298, Class: 1.000000, Obj: 0.176401, No Obj: 0.003782, Avg Recall: 0.588235, count: 102 -Region Avg IOU: 0.492935, Class: 1.000000, Obj: 0.178406, No Obj: 0.003821, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.503340, Class: 1.000000, Obj: 0.200898, No Obj: 0.004328, Avg Recall: 0.571429, count: 105 -Region Avg IOU: 0.541337, Class: 1.000000, Obj: 0.175001, No Obj: 0.004172, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.540150, Class: 1.000000, Obj: 0.227260, No Obj: 0.004079, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.552867, Class: 1.000000, Obj: 0.191078, No Obj: 0.004353, Avg Recall: 0.643411, count: 129 -Region Avg IOU: 0.511337, Class: 1.000000, Obj: 0.193575, No Obj: 0.003944, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.547345, Class: 1.000000, Obj: 0.196771, No Obj: 0.003874, Avg Recall: 0.666667, count: 87 -454: 0.540197, 0.563058 avg, 0.001000 rate, 28.302940 seconds, 116224 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.516299, Class: 1.000000, Obj: 0.199202, No Obj: 0.003799, Avg Recall: 0.593496, count: 123 -Region Avg IOU: 0.532075, Class: 1.000000, Obj: 0.199610, No Obj: 0.003757, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.519646, Class: 1.000000, Obj: 0.192952, No Obj: 0.003666, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.557910, Class: 1.000000, Obj: 0.174484, No Obj: 0.003552, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.541821, Class: 1.000000, Obj: 0.151989, No Obj: 0.003935, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.510492, Class: 1.000000, Obj: 0.235834, No Obj: 0.003646, Avg Recall: 0.549550, count: 111 -Region Avg IOU: 0.545608, Class: 1.000000, Obj: 0.167222, No Obj: 0.003470, Avg Recall: 0.694444, count: 72 -Region Avg IOU: 0.511366, Class: 1.000000, Obj: 0.173498, No Obj: 0.003885, Avg Recall: 0.553571, count: 112 -455: 0.471628, 0.553915 avg, 0.001000 rate, 28.874012 seconds, 116480 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.529354, Class: 1.000000, Obj: 0.175453, No Obj: 0.003774, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.512595, Class: 1.000000, Obj: 0.211345, No Obj: 0.003598, Avg Recall: 0.578947, count: 114 -Region Avg IOU: 0.561410, Class: 1.000000, Obj: 0.167375, No Obj: 0.004077, Avg Recall: 0.641221, count: 131 -Region Avg IOU: 0.510636, Class: 1.000000, Obj: 0.209029, No Obj: 0.003580, Avg Recall: 0.556818, count: 88 -Region Avg IOU: 0.548841, Class: 1.000000, Obj: 0.201932, No Obj: 0.003836, Avg Recall: 0.696000, count: 125 -Region Avg IOU: 0.480768, Class: 1.000000, Obj: 0.160845, No Obj: 0.003791, Avg Recall: 0.539062, count: 128 -Region Avg IOU: 0.555995, Class: 1.000000, Obj: 0.202918, No Obj: 0.003875, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.548066, Class: 1.000000, Obj: 0.202972, No Obj: 0.004184, Avg Recall: 0.585859, count: 99 -456: 0.548553, 0.553378 avg, 0.001000 rate, 28.208250 seconds, 116736 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.592749, Class: 1.000000, Obj: 0.197847, No Obj: 0.004070, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.535424, Class: 1.000000, Obj: 0.202857, No Obj: 0.003607, Avg Recall: 0.606742, count: 89 -Region Avg IOU: 0.570712, Class: 1.000000, Obj: 0.203695, No Obj: 0.003937, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.550384, Class: 1.000000, Obj: 0.208933, No Obj: 0.003783, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.538619, Class: 1.000000, Obj: 0.193162, No Obj: 0.003921, Avg Recall: 0.619048, count: 126 -Region Avg IOU: 0.500421, Class: 1.000000, Obj: 0.207103, No Obj: 0.003631, Avg Recall: 0.549451, count: 91 -Region Avg IOU: 0.519756, Class: 1.000000, Obj: 0.175571, No Obj: 0.003808, Avg Recall: 0.611111, count: 126 -Region Avg IOU: 0.537406, Class: 1.000000, Obj: 0.209996, No Obj: 0.003781, Avg Recall: 0.593750, count: 128 -457: 0.578542, 0.555895 avg, 0.001000 rate, 28.534214 seconds, 116992 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.511785, Class: 1.000000, Obj: 0.220000, No Obj: 0.003416, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.532516, Class: 1.000000, Obj: 0.175453, No Obj: 0.004402, Avg Recall: 0.625806, count: 155 -Region Avg IOU: 0.545998, Class: 1.000000, Obj: 0.163346, No Obj: 0.003701, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.549261, Class: 1.000000, Obj: 0.173001, No Obj: 0.003565, Avg Recall: 0.644737, count: 76 -Region Avg IOU: 0.562918, Class: 1.000000, Obj: 0.178475, No Obj: 0.003277, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.532843, Class: 1.000000, Obj: 0.217842, No Obj: 0.003600, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.533464, Class: 1.000000, Obj: 0.171003, No Obj: 0.003544, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.524749, Class: 1.000000, Obj: 0.146320, No Obj: 0.003410, Avg Recall: 0.580952, count: 105 -458: 0.484750, 0.548780 avg, 0.001000 rate, 28.433208 seconds, 117248 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.532997, Class: 1.000000, Obj: 0.237686, No Obj: 0.003620, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.525730, Class: 1.000000, Obj: 0.162862, No Obj: 0.003866, Avg Recall: 0.608974, count: 156 -Region Avg IOU: 0.497288, Class: 1.000000, Obj: 0.174512, No Obj: 0.003738, Avg Recall: 0.519380, count: 129 -Region Avg IOU: 0.551048, Class: 1.000000, Obj: 0.193599, No Obj: 0.003605, Avg Recall: 0.637500, count: 80 -Region Avg IOU: 0.538781, Class: 1.000000, Obj: 0.186291, No Obj: 0.003867, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.524415, Class: 1.000000, Obj: 0.242050, No Obj: 0.003708, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.506984, Class: 1.000000, Obj: 0.183676, No Obj: 0.003692, Avg Recall: 0.555556, count: 81 -Region Avg IOU: 0.529448, Class: 1.000000, Obj: 0.198774, No Obj: 0.003303, Avg Recall: 0.617284, count: 81 -459: 0.513130, 0.545215 avg, 0.001000 rate, 29.192524 seconds, 117504 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.571559, Class: 1.000000, Obj: 0.187655, No Obj: 0.003583, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.555850, Class: 1.000000, Obj: 0.204459, No Obj: 0.003347, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.558104, Class: 1.000000, Obj: 0.220154, No Obj: 0.003583, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.519377, Class: 1.000000, Obj: 0.189965, No Obj: 0.003701, Avg Recall: 0.574627, count: 134 -Region Avg IOU: 0.536380, Class: 1.000000, Obj: 0.166981, No Obj: 0.003729, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.546807, Class: 1.000000, Obj: 0.170286, No Obj: 0.004266, Avg Recall: 0.631206, count: 141 -Region Avg IOU: 0.493629, Class: 1.000000, Obj: 0.174855, No Obj: 0.003900, Avg Recall: 0.544000, count: 125 -Region Avg IOU: 0.551789, Class: 1.000000, Obj: 0.209582, No Obj: 0.003663, Avg Recall: 0.663551, count: 107 -460: 0.514595, 0.542153 avg, 0.001000 rate, 27.646666 seconds, 117760 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000072 seconds -Region Avg IOU: 0.533567, Class: 1.000000, Obj: 0.207635, No Obj: 0.003998, Avg Recall: 0.625000, count: 144 -Region Avg IOU: 0.513467, Class: 1.000000, Obj: 0.196415, No Obj: 0.003189, Avg Recall: 0.597701, count: 87 -Region Avg IOU: 0.555372, Class: 1.000000, Obj: 0.194844, No Obj: 0.003372, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.535745, Class: 1.000000, Obj: 0.189855, No Obj: 0.003695, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.569606, Class: 1.000000, Obj: 0.192832, No Obj: 0.003461, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.545964, Class: 1.000000, Obj: 0.168403, No Obj: 0.003672, Avg Recall: 0.641221, count: 131 -Region Avg IOU: 0.525174, Class: 1.000000, Obj: 0.208919, No Obj: 0.003381, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.564871, Class: 1.000000, Obj: 0.169699, No Obj: 0.003829, Avg Recall: 0.657407, count: 108 -461: 0.512849, 0.539223 avg, 0.001000 rate, 28.927843 seconds, 118016 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.571828, Class: 1.000000, Obj: 0.235146, No Obj: 0.003521, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.568992, Class: 1.000000, Obj: 0.219617, No Obj: 0.003493, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.509989, Class: 1.000000, Obj: 0.204945, No Obj: 0.003767, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.539623, Class: 1.000000, Obj: 0.199786, No Obj: 0.004018, Avg Recall: 0.629630, count: 135 -Region Avg IOU: 0.549934, Class: 1.000000, Obj: 0.154339, No Obj: 0.003503, Avg Recall: 0.644444, count: 90 -Region Avg IOU: 0.548144, Class: 1.000000, Obj: 0.197533, No Obj: 0.003569, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.514059, Class: 1.000000, Obj: 0.228832, No Obj: 0.003684, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.563289, Class: 1.000000, Obj: 0.218181, No Obj: 0.004007, Avg Recall: 0.654867, count: 113 -462: 0.521321, 0.537433 avg, 0.001000 rate, 28.907127 seconds, 118272 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.580121, Class: 1.000000, Obj: 0.230561, No Obj: 0.003623, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.556220, Class: 1.000000, Obj: 0.215948, No Obj: 0.004164, Avg Recall: 0.674242, count: 132 -Region Avg IOU: 0.568297, Class: 1.000000, Obj: 0.201273, No Obj: 0.003567, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.534764, Class: 1.000000, Obj: 0.188215, No Obj: 0.003536, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.542725, Class: 1.000000, Obj: 0.176705, No Obj: 0.003757, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.583039, Class: 1.000000, Obj: 0.207069, No Obj: 0.003679, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.549048, Class: 1.000000, Obj: 0.207050, No Obj: 0.003771, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.548136, Class: 1.000000, Obj: 0.217755, No Obj: 0.003473, Avg Recall: 0.647059, count: 102 -463: 0.495661, 0.533256 avg, 0.001000 rate, 28.552536 seconds, 118528 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.535528, Class: 1.000000, Obj: 0.212558, No Obj: 0.003624, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.534102, Class: 1.000000, Obj: 0.207355, No Obj: 0.003358, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.550563, Class: 1.000000, Obj: 0.209030, No Obj: 0.003608, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.524771, Class: 1.000000, Obj: 0.272458, No Obj: 0.003548, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.576292, Class: 1.000000, Obj: 0.187651, No Obj: 0.003915, Avg Recall: 0.716312, count: 141 -Region Avg IOU: 0.530418, Class: 1.000000, Obj: 0.207395, No Obj: 0.003355, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.545418, Class: 1.000000, Obj: 0.243169, No Obj: 0.003519, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.559124, Class: 1.000000, Obj: 0.213116, No Obj: 0.003609, Avg Recall: 0.729730, count: 111 -464: 0.513104, 0.531240 avg, 0.001000 rate, 30.684446 seconds, 118784 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.571058, Class: 1.000000, Obj: 0.240425, No Obj: 0.003516, Avg Recall: 0.701149, count: 87 -Region Avg IOU: 0.557147, Class: 1.000000, Obj: 0.216566, No Obj: 0.003409, Avg Recall: 0.703297, count: 91 -Region Avg IOU: 0.561700, Class: 1.000000, Obj: 0.202733, No Obj: 0.003346, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.528623, Class: 1.000000, Obj: 0.183872, No Obj: 0.003360, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.535666, Class: 1.000000, Obj: 0.180919, No Obj: 0.003690, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.577711, Class: 1.000000, Obj: 0.249767, No Obj: 0.003310, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.548984, Class: 1.000000, Obj: 0.200013, No Obj: 0.003673, Avg Recall: 0.629032, count: 124 -Region Avg IOU: 0.545131, Class: 1.000000, Obj: 0.212930, No Obj: 0.003518, Avg Recall: 0.639640, count: 111 -465: 0.479636, 0.526080 avg, 0.001000 rate, 27.498274 seconds, 119040 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.567393, Class: 1.000000, Obj: 0.203328, No Obj: 0.003194, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.562955, Class: 1.000000, Obj: 0.172949, No Obj: 0.003488, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.550330, Class: 1.000000, Obj: 0.198046, No Obj: 0.003416, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.570180, Class: 1.000000, Obj: 0.185184, No Obj: 0.003246, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.528812, Class: 1.000000, Obj: 0.181873, No Obj: 0.003425, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.558879, Class: 1.000000, Obj: 0.171595, No Obj: 0.003651, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.582142, Class: 1.000000, Obj: 0.184704, No Obj: 0.003815, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.554738, Class: 1.000000, Obj: 0.193887, No Obj: 0.003750, Avg Recall: 0.641379, count: 145 -466: 0.563391, 0.529811 avg, 0.001000 rate, 28.033794 seconds, 119296 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.565906, Class: 1.000000, Obj: 0.166993, No Obj: 0.003500, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.542136, Class: 1.000000, Obj: 0.180870, No Obj: 0.003542, Avg Recall: 0.648000, count: 125 -Region Avg IOU: 0.535761, Class: 1.000000, Obj: 0.157211, No Obj: 0.003592, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.543794, Class: 1.000000, Obj: 0.182519, No Obj: 0.003206, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.535271, Class: 1.000000, Obj: 0.181034, No Obj: 0.003152, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.569698, Class: 1.000000, Obj: 0.199456, No Obj: 0.003858, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.594351, Class: 1.000000, Obj: 0.216308, No Obj: 0.003418, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.523661, Class: 1.000000, Obj: 0.158597, No Obj: 0.003277, Avg Recall: 0.553191, count: 94 -467: 0.514563, 0.528286 avg, 0.001000 rate, 30.047991 seconds, 119552 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.575407, Class: 1.000000, Obj: 0.175686, No Obj: 0.003238, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.538743, Class: 1.000000, Obj: 0.197394, No Obj: 0.003429, Avg Recall: 0.688073, count: 109 -Region Avg IOU: 0.525779, Class: 1.000000, Obj: 0.209912, No Obj: 0.003463, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.570466, Class: 1.000000, Obj: 0.206542, No Obj: 0.003433, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.557332, Class: 1.000000, Obj: 0.167742, No Obj: 0.003384, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.565317, Class: 1.000000, Obj: 0.175233, No Obj: 0.003455, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.530946, Class: 1.000000, Obj: 0.204590, No Obj: 0.003149, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.535004, Class: 1.000000, Obj: 0.216302, No Obj: 0.003380, Avg Recall: 0.620370, count: 108 -468: 0.508655, 0.526323 avg, 0.001000 rate, 30.014206 seconds, 119808 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.570960, Class: 1.000000, Obj: 0.208323, No Obj: 0.003706, Avg Recall: 0.697183, count: 142 -Region Avg IOU: 0.567459, Class: 1.000000, Obj: 0.168379, No Obj: 0.003453, Avg Recall: 0.689076, count: 119 -Region Avg IOU: 0.561454, Class: 1.000000, Obj: 0.175500, No Obj: 0.003550, Avg Recall: 0.664179, count: 134 -Region Avg IOU: 0.549931, Class: 1.000000, Obj: 0.219119, No Obj: 0.003875, Avg Recall: 0.654135, count: 133 -Region Avg IOU: 0.533936, Class: 1.000000, Obj: 0.202874, No Obj: 0.003696, Avg Recall: 0.661417, count: 127 -Region Avg IOU: 0.566819, Class: 1.000000, Obj: 0.195764, No Obj: 0.003666, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.515814, Class: 1.000000, Obj: 0.182406, No Obj: 0.003312, Avg Recall: 0.532710, count: 107 -Region Avg IOU: 0.571680, Class: 1.000000, Obj: 0.196613, No Obj: 0.003860, Avg Recall: 0.680851, count: 141 -469: 0.583054, 0.531996 avg, 0.001000 rate, 28.461329 seconds, 120064 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.549263, Class: 1.000000, Obj: 0.188155, No Obj: 0.003674, Avg Recall: 0.643411, count: 129 -Region Avg IOU: 0.551939, Class: 1.000000, Obj: 0.223474, No Obj: 0.003862, Avg Recall: 0.680000, count: 125 -Region Avg IOU: 0.569639, Class: 1.000000, Obj: 0.219897, No Obj: 0.003436, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.549340, Class: 1.000000, Obj: 0.197183, No Obj: 0.003424, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.599374, Class: 1.000000, Obj: 0.204666, No Obj: 0.003255, Avg Recall: 0.809524, count: 84 -Region Avg IOU: 0.521905, Class: 1.000000, Obj: 0.248040, No Obj: 0.003335, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.539209, Class: 1.000000, Obj: 0.228853, No Obj: 0.003523, Avg Recall: 0.651163, count: 129 -Region Avg IOU: 0.542614, Class: 1.000000, Obj: 0.222410, No Obj: 0.003376, Avg Recall: 0.651786, count: 112 -470: 0.552551, 0.534052 avg, 0.001000 rate, 32.107422 seconds, 120320 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000072 seconds -Region Avg IOU: 0.562612, Class: 1.000000, Obj: 0.204785, No Obj: 0.003816, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.558964, Class: 1.000000, Obj: 0.205352, No Obj: 0.003207, Avg Recall: 0.683544, count: 79 -Region Avg IOU: 0.498554, Class: 1.000000, Obj: 0.194719, No Obj: 0.003257, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.556796, Class: 1.000000, Obj: 0.193172, No Obj: 0.003537, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.555927, Class: 1.000000, Obj: 0.175532, No Obj: 0.003779, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.545178, Class: 1.000000, Obj: 0.196203, No Obj: 0.003488, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.576966, Class: 1.000000, Obj: 0.224282, No Obj: 0.003544, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.574696, Class: 1.000000, Obj: 0.211462, No Obj: 0.003519, Avg Recall: 0.662651, count: 83 -471: 0.480398, 0.528686 avg, 0.001000 rate, 29.449856 seconds, 120576 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.590446, Class: 1.000000, Obj: 0.181687, No Obj: 0.003437, Avg Recall: 0.747126, count: 87 -Region Avg IOU: 0.542641, Class: 1.000000, Obj: 0.202991, No Obj: 0.003416, Avg Recall: 0.629032, count: 124 -Region Avg IOU: 0.522272, Class: 1.000000, Obj: 0.234062, No Obj: 0.003746, Avg Recall: 0.609756, count: 123 -Region Avg IOU: 0.558304, Class: 1.000000, Obj: 0.187088, No Obj: 0.003541, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.574916, Class: 1.000000, Obj: 0.187171, No Obj: 0.003667, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.579737, Class: 1.000000, Obj: 0.238220, No Obj: 0.003682, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.566904, Class: 1.000000, Obj: 0.214597, No Obj: 0.003236, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.558183, Class: 1.000000, Obj: 0.228988, No Obj: 0.003623, Avg Recall: 0.652174, count: 115 -472: 0.510412, 0.526859 avg, 0.001000 rate, 30.546370 seconds, 120832 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.543373, Class: 1.000000, Obj: 0.211974, No Obj: 0.003575, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.510183, Class: 1.000000, Obj: 0.189097, No Obj: 0.003427, Avg Recall: 0.568965, count: 116 -Region Avg IOU: 0.601748, Class: 1.000000, Obj: 0.228149, No Obj: 0.003792, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.534158, Class: 1.000000, Obj: 0.185665, No Obj: 0.003021, Avg Recall: 0.645570, count: 79 -Region Avg IOU: 0.509215, Class: 1.000000, Obj: 0.202306, No Obj: 0.003257, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.528873, Class: 1.000000, Obj: 0.193644, No Obj: 0.003466, Avg Recall: 0.627119, count: 118 -Region Avg IOU: 0.519732, Class: 1.000000, Obj: 0.192665, No Obj: 0.003585, Avg Recall: 0.555556, count: 99 -Region Avg IOU: 0.530931, Class: 1.000000, Obj: 0.221731, No Obj: 0.003066, Avg Recall: 0.656863, count: 102 -473: 0.533263, 0.527499 avg, 0.001000 rate, 30.926477 seconds, 121088 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.504098, Class: 1.000000, Obj: 0.189176, No Obj: 0.003189, Avg Recall: 0.580000, count: 100 -Region Avg IOU: 0.597750, Class: 1.000000, Obj: 0.220499, No Obj: 0.003184, Avg Recall: 0.741176, count: 85 -Region Avg IOU: 0.543815, Class: 1.000000, Obj: 0.167821, No Obj: 0.003663, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.578108, Class: 1.000000, Obj: 0.189742, No Obj: 0.003367, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.570253, Class: 1.000000, Obj: 0.196440, No Obj: 0.003043, Avg Recall: 0.725000, count: 80 -Region Avg IOU: 0.525179, Class: 1.000000, Obj: 0.154687, No Obj: 0.003244, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.563189, Class: 1.000000, Obj: 0.175283, No Obj: 0.003548, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.536831, Class: 1.000000, Obj: 0.200511, No Obj: 0.003540, Avg Recall: 0.636364, count: 132 -474: 0.488001, 0.523549 avg, 0.001000 rate, 29.605600 seconds, 121344 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.548385, Class: 1.000000, Obj: 0.193886, No Obj: 0.003346, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.557726, Class: 1.000000, Obj: 0.181396, No Obj: 0.003115, Avg Recall: 0.661654, count: 133 -Region Avg IOU: 0.503844, Class: 1.000000, Obj: 0.209706, No Obj: 0.003013, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.545115, Class: 1.000000, Obj: 0.233689, No Obj: 0.003446, Avg Recall: 0.600000, count: 135 -Region Avg IOU: 0.545341, Class: 1.000000, Obj: 0.215047, No Obj: 0.003086, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.578001, Class: 1.000000, Obj: 0.251141, No Obj: 0.003398, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.542432, Class: 1.000000, Obj: 0.225507, No Obj: 0.003456, Avg Recall: 0.661290, count: 124 -Region Avg IOU: 0.568357, Class: 1.000000, Obj: 0.214438, No Obj: 0.003275, Avg Recall: 0.686956, count: 115 -475: 0.538063, 0.525001 avg, 0.001000 rate, 31.193684 seconds, 121600 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.561459, Class: 1.000000, Obj: 0.196929, No Obj: 0.003075, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.544035, Class: 1.000000, Obj: 0.220892, No Obj: 0.003053, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.530209, Class: 1.000000, Obj: 0.158939, No Obj: 0.002990, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.542294, Class: 1.000000, Obj: 0.182106, No Obj: 0.003640, Avg Recall: 0.704225, count: 142 -Region Avg IOU: 0.593029, Class: 1.000000, Obj: 0.181416, No Obj: 0.003439, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.563724, Class: 1.000000, Obj: 0.201179, No Obj: 0.002982, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.581421, Class: 1.000000, Obj: 0.179427, No Obj: 0.003157, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.535015, Class: 1.000000, Obj: 0.181892, No Obj: 0.003160, Avg Recall: 0.613445, count: 119 -476: 0.548489, 0.527350 avg, 0.001000 rate, 29.043552 seconds, 121856 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.589911, Class: 1.000000, Obj: 0.194429, No Obj: 0.003060, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.554631, Class: 1.000000, Obj: 0.184464, No Obj: 0.003146, Avg Recall: 0.669421, count: 121 -Region Avg IOU: 0.587973, Class: 1.000000, Obj: 0.208086, No Obj: 0.003272, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.538470, Class: 1.000000, Obj: 0.191765, No Obj: 0.003107, Avg Recall: 0.595506, count: 89 -Region Avg IOU: 0.537744, Class: 1.000000, Obj: 0.200954, No Obj: 0.003265, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.563868, Class: 1.000000, Obj: 0.184426, No Obj: 0.003087, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.567395, Class: 1.000000, Obj: 0.186495, No Obj: 0.003100, Avg Recall: 0.691057, count: 123 -Region Avg IOU: 0.569469, Class: 1.000000, Obj: 0.179583, No Obj: 0.003452, Avg Recall: 0.697479, count: 119 -477: 0.511357, 0.525750 avg, 0.001000 rate, 30.330223 seconds, 122112 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.566480, Class: 1.000000, Obj: 0.211177, No Obj: 0.003092, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.540408, Class: 1.000000, Obj: 0.187080, No Obj: 0.003195, Avg Recall: 0.598485, count: 132 -Region Avg IOU: 0.582471, Class: 1.000000, Obj: 0.203574, No Obj: 0.003331, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.595814, Class: 1.000000, Obj: 0.189548, No Obj: 0.003275, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.552967, Class: 1.000000, Obj: 0.224607, No Obj: 0.003247, Avg Recall: 0.655172, count: 145 -Region Avg IOU: 0.544599, Class: 1.000000, Obj: 0.169723, No Obj: 0.003405, Avg Recall: 0.622807, count: 114 -Region Avg IOU: 0.543488, Class: 1.000000, Obj: 0.198046, No Obj: 0.003362, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.569367, Class: 1.000000, Obj: 0.229181, No Obj: 0.003295, Avg Recall: 0.714286, count: 98 -478: 0.576501, 0.530825 avg, 0.001000 rate, 30.939629 seconds, 122368 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.602829, Class: 1.000000, Obj: 0.219986, No Obj: 0.003598, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.531750, Class: 1.000000, Obj: 0.174556, No Obj: 0.003349, Avg Recall: 0.611570, count: 121 -Region Avg IOU: 0.534307, Class: 1.000000, Obj: 0.169356, No Obj: 0.003591, Avg Recall: 0.652482, count: 141 -Region Avg IOU: 0.576678, Class: 1.000000, Obj: 0.219367, No Obj: 0.003237, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.582805, Class: 1.000000, Obj: 0.181406, No Obj: 0.003200, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.583319, Class: 1.000000, Obj: 0.161172, No Obj: 0.003357, Avg Recall: 0.761905, count: 84 -Region Avg IOU: 0.526855, Class: 1.000000, Obj: 0.168349, No Obj: 0.003055, Avg Recall: 0.592920, count: 113 -Region Avg IOU: 0.522985, Class: 1.000000, Obj: 0.189337, No Obj: 0.003164, Avg Recall: 0.577586, count: 116 -479: 0.531717, 0.530915 avg, 0.001000 rate, 30.392302 seconds, 122624 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.592873, Class: 1.000000, Obj: 0.192509, No Obj: 0.002850, Avg Recall: 0.784810, count: 79 -Region Avg IOU: 0.553173, Class: 1.000000, Obj: 0.220105, No Obj: 0.003450, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.525953, Class: 1.000000, Obj: 0.176375, No Obj: 0.003256, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.570031, Class: 1.000000, Obj: 0.221453, No Obj: 0.003588, Avg Recall: 0.666667, count: 135 -Region Avg IOU: 0.554488, Class: 1.000000, Obj: 0.183071, No Obj: 0.003746, Avg Recall: 0.664336, count: 143 -Region Avg IOU: 0.554951, Class: 1.000000, Obj: 0.219164, No Obj: 0.003368, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.535320, Class: 1.000000, Obj: 0.181621, No Obj: 0.003170, Avg Recall: 0.591667, count: 120 -Region Avg IOU: 0.555358, Class: 1.000000, Obj: 0.211289, No Obj: 0.003568, Avg Recall: 0.675000, count: 120 -480: 0.543871, 0.532210 avg, 0.001000 rate, 30.456799 seconds, 122880 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 17.878565 seconds -Region Avg IOU: 0.450125, Class: 1.000000, Obj: 0.156499, No Obj: 0.003787, Avg Recall: 0.515152, count: 99 -Region Avg IOU: 0.428711, Class: 1.000000, Obj: 0.182615, No Obj: 0.003656, Avg Recall: 0.428571, count: 105 -Region Avg IOU: 0.427972, Class: 1.000000, Obj: 0.151907, No Obj: 0.003989, Avg Recall: 0.479339, count: 121 -Region Avg IOU: 0.411582, Class: 1.000000, Obj: 0.128173, No Obj: 0.003225, Avg Recall: 0.431818, count: 88 -Region Avg IOU: 0.450039, Class: 1.000000, Obj: 0.197493, No Obj: 0.003613, Avg Recall: 0.491379, count: 116 -Region Avg IOU: 0.425765, Class: 1.000000, Obj: 0.182288, No Obj: 0.003724, Avg Recall: 0.387387, count: 111 -Region Avg IOU: 0.431550, Class: 1.000000, Obj: 0.175350, No Obj: 0.003953, Avg Recall: 0.427350, count: 117 -Region Avg IOU: 0.447127, Class: 1.000000, Obj: 0.216068, No Obj: 0.003760, Avg Recall: 0.500000, count: 104 -481: 0.507087, 0.529698 avg, 0.001000 rate, 21.723656 seconds, 123136 images -Loaded: 0.723137 seconds -Region Avg IOU: 0.429866, Class: 1.000000, Obj: 0.198737, No Obj: 0.003623, Avg Recall: 0.456311, count: 103 -Region Avg IOU: 0.421845, Class: 1.000000, Obj: 0.155251, No Obj: 0.003567, Avg Recall: 0.489362, count: 94 -Region Avg IOU: 0.446802, Class: 1.000000, Obj: 0.185110, No Obj: 0.003866, Avg Recall: 0.441860, count: 129 -Region Avg IOU: 0.467193, Class: 1.000000, Obj: 0.214378, No Obj: 0.004028, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.443631, Class: 1.000000, Obj: 0.195880, No Obj: 0.003890, Avg Recall: 0.481132, count: 106 -Region Avg IOU: 0.427624, Class: 1.000000, Obj: 0.165209, No Obj: 0.004377, Avg Recall: 0.379310, count: 116 -Region Avg IOU: 0.419934, Class: 1.000000, Obj: 0.176884, No Obj: 0.003508, Avg Recall: 0.443548, count: 124 -Region Avg IOU: 0.407462, Class: 1.000000, Obj: 0.136336, No Obj: 0.003947, Avg Recall: 0.441558, count: 154 -482: 0.619523, 0.538680 avg, 0.001000 rate, 17.710640 seconds, 123392 images -Loaded: 3.007533 seconds -Region Avg IOU: 0.424045, Class: 1.000000, Obj: 0.173803, No Obj: 0.003804, Avg Recall: 0.423913, count: 92 -Region Avg IOU: 0.466335, Class: 1.000000, Obj: 0.187776, No Obj: 0.003565, Avg Recall: 0.452632, count: 95 -Region Avg IOU: 0.385364, Class: 1.000000, Obj: 0.202885, No Obj: 0.003856, Avg Recall: 0.366337, count: 101 -Region Avg IOU: 0.407923, Class: 1.000000, Obj: 0.205422, No Obj: 0.003202, Avg Recall: 0.400000, count: 95 -Region Avg IOU: 0.443759, Class: 1.000000, Obj: 0.182134, No Obj: 0.004347, Avg Recall: 0.452381, count: 126 -Region Avg IOU: 0.444716, Class: 1.000000, Obj: 0.187883, No Obj: 0.004006, Avg Recall: 0.475806, count: 124 -Region Avg IOU: 0.488860, Class: 1.000000, Obj: 0.184476, No Obj: 0.003767, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.447340, Class: 1.000000, Obj: 0.188171, No Obj: 0.003937, Avg Recall: 0.480769, count: 104 -483: 0.581963, 0.543009 avg, 0.001000 rate, 18.397243 seconds, 123648 images -Loaded: 4.762524 seconds -Region Avg IOU: 0.410354, Class: 1.000000, Obj: 0.158098, No Obj: 0.003767, Avg Recall: 0.396694, count: 121 -Region Avg IOU: 0.403889, Class: 1.000000, Obj: 0.182534, No Obj: 0.003610, Avg Recall: 0.368852, count: 122 -Region Avg IOU: 0.432336, Class: 1.000000, Obj: 0.151619, No Obj: 0.003673, Avg Recall: 0.424779, count: 113 -Region Avg IOU: 0.441288, Class: 1.000000, Obj: 0.172763, No Obj: 0.003861, Avg Recall: 0.437500, count: 112 -Region Avg IOU: 0.480146, Class: 1.000000, Obj: 0.208583, No Obj: 0.004136, Avg Recall: 0.525000, count: 80 -Region Avg IOU: 0.395582, Class: 1.000000, Obj: 0.171794, No Obj: 0.003527, Avg Recall: 0.396396, count: 111 -Region Avg IOU: 0.450627, Class: 1.000000, Obj: 0.218563, No Obj: 0.004242, Avg Recall: 0.495327, count: 107 -Region Avg IOU: 0.446685, Class: 1.000000, Obj: 0.138392, No Obj: 0.003494, Avg Recall: 0.489130, count: 92 -484: 0.543039, 0.543012 avg, 0.001000 rate, 21.183191 seconds, 123904 images -Loaded: 0.800991 seconds -Region Avg IOU: 0.471927, Class: 1.000000, Obj: 0.159188, No Obj: 0.004006, Avg Recall: 0.508772, count: 114 -Region Avg IOU: 0.387902, Class: 1.000000, Obj: 0.194403, No Obj: 0.003623, Avg Recall: 0.392857, count: 112 -Region Avg IOU: 0.388023, Class: 1.000000, Obj: 0.208205, No Obj: 0.003891, Avg Recall: 0.329412, count: 85 -Region Avg IOU: 0.387427, Class: 1.000000, Obj: 0.163494, No Obj: 0.003757, Avg Recall: 0.388060, count: 134 -Region Avg IOU: 0.408739, Class: 1.000000, Obj: 0.196411, No Obj: 0.004183, Avg Recall: 0.436090, count: 133 -Region Avg IOU: 0.435437, Class: 1.000000, Obj: 0.173506, No Obj: 0.003827, Avg Recall: 0.467290, count: 107 -Region Avg IOU: 0.456650, Class: 1.000000, Obj: 0.229919, No Obj: 0.004429, Avg Recall: 0.495652, count: 115 -Region Avg IOU: 0.456115, Class: 1.000000, Obj: 0.178733, No Obj: 0.004232, Avg Recall: 0.500000, count: 114 -485: 0.553449, 0.544055 avg, 0.001000 rate, 21.700167 seconds, 124160 images -Loaded: 0.298492 seconds -Region Avg IOU: 0.489801, Class: 1.000000, Obj: 0.216240, No Obj: 0.003798, Avg Recall: 0.578313, count: 83 -Region Avg IOU: 0.439170, Class: 1.000000, Obj: 0.186324, No Obj: 0.004097, Avg Recall: 0.435065, count: 154 -Region Avg IOU: 0.415548, Class: 1.000000, Obj: 0.195997, No Obj: 0.003844, Avg Recall: 0.444444, count: 99 -Region Avg IOU: 0.451819, Class: 1.000000, Obj: 0.213992, No Obj: 0.004410, Avg Recall: 0.477477, count: 111 -Region Avg IOU: 0.425371, Class: 1.000000, Obj: 0.200421, No Obj: 0.004163, Avg Recall: 0.415929, count: 113 -Region Avg IOU: 0.431360, Class: 1.000000, Obj: 0.163974, No Obj: 0.003502, Avg Recall: 0.478261, count: 92 -Region Avg IOU: 0.434968, Class: 1.000000, Obj: 0.182488, No Obj: 0.003797, Avg Recall: 0.433735, count: 83 -Region Avg IOU: 0.445445, Class: 1.000000, Obj: 0.223406, No Obj: 0.004888, Avg Recall: 0.483607, count: 122 -486: 0.490925, 0.538742 avg, 0.001000 rate, 18.861553 seconds, 124416 images -Loaded: 2.362745 seconds -Region Avg IOU: 0.393460, Class: 1.000000, Obj: 0.167189, No Obj: 0.004245, Avg Recall: 0.434426, count: 122 -Region Avg IOU: 0.447538, Class: 1.000000, Obj: 0.216132, No Obj: 0.004208, Avg Recall: 0.456522, count: 92 -Region Avg IOU: 0.422022, Class: 1.000000, Obj: 0.190050, No Obj: 0.003856, Avg Recall: 0.422680, count: 97 -Region Avg IOU: 0.394242, Class: 1.000000, Obj: 0.166692, No Obj: 0.004296, Avg Recall: 0.438710, count: 155 -Region Avg IOU: 0.420767, Class: 1.000000, Obj: 0.207827, No Obj: 0.003727, Avg Recall: 0.460177, count: 113 -Region Avg IOU: 0.480484, Class: 1.000000, Obj: 0.234371, No Obj: 0.004082, Avg Recall: 0.525641, count: 78 -Region Avg IOU: 0.423525, Class: 1.000000, Obj: 0.188726, No Obj: 0.004396, Avg Recall: 0.458333, count: 120 -Region Avg IOU: 0.413447, Class: 1.000000, Obj: 0.180879, No Obj: 0.004038, Avg Recall: 0.439560, count: 91 -487: 0.559413, 0.540809 avg, 0.001000 rate, 19.890305 seconds, 124672 images -Loaded: 2.055703 seconds -Region Avg IOU: 0.460425, Class: 1.000000, Obj: 0.184843, No Obj: 0.003627, Avg Recall: 0.481481, count: 81 -Region Avg IOU: 0.464847, Class: 1.000000, Obj: 0.216588, No Obj: 0.003840, Avg Recall: 0.532609, count: 92 -Region Avg IOU: 0.473577, Class: 1.000000, Obj: 0.224227, No Obj: 0.004432, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.445170, Class: 1.000000, Obj: 0.182448, No Obj: 0.004420, Avg Recall: 0.480000, count: 100 -Region Avg IOU: 0.428080, Class: 1.000000, Obj: 0.200809, No Obj: 0.004202, Avg Recall: 0.461538, count: 104 -Region Avg IOU: 0.408942, Class: 1.000000, Obj: 0.189999, No Obj: 0.003674, Avg Recall: 0.401961, count: 102 -Region Avg IOU: 0.419412, Class: 1.000000, Obj: 0.216204, No Obj: 0.004069, Avg Recall: 0.429825, count: 114 -Region Avg IOU: 0.401749, Class: 1.000000, Obj: 0.151412, No Obj: 0.003578, Avg Recall: 0.337349, count: 83 -488: 0.475920, 0.534320 avg, 0.001000 rate, 20.927078 seconds, 124928 images -Loaded: 1.063309 seconds -Region Avg IOU: 0.423288, Class: 1.000000, Obj: 0.192972, No Obj: 0.004279, Avg Recall: 0.439655, count: 116 -Region Avg IOU: 0.461949, Class: 1.000000, Obj: 0.163687, No Obj: 0.003539, Avg Recall: 0.533333, count: 90 -Region Avg IOU: 0.446479, Class: 1.000000, Obj: 0.188705, No Obj: 0.004319, Avg Recall: 0.438776, count: 98 -Region Avg IOU: 0.467938, Class: 1.000000, Obj: 0.205272, No Obj: 0.004428, Avg Recall: 0.521008, count: 119 -Region Avg IOU: 0.443715, Class: 1.000000, Obj: 0.171475, No Obj: 0.004633, Avg Recall: 0.466165, count: 133 -Region Avg IOU: 0.426153, Class: 1.000000, Obj: 0.174357, No Obj: 0.004074, Avg Recall: 0.388889, count: 108 -Region Avg IOU: 0.451120, Class: 1.000000, Obj: 0.148061, No Obj: 0.003941, Avg Recall: 0.490741, count: 108 -Region Avg IOU: 0.427584, Class: 1.000000, Obj: 0.212246, No Obj: 0.004168, Avg Recall: 0.445783, count: 83 -489: 0.565939, 0.537482 avg, 0.001000 rate, 16.597498 seconds, 125184 images -Loaded: 2.958961 seconds -Region Avg IOU: 0.399671, Class: 1.000000, Obj: 0.199472, No Obj: 0.004233, Avg Recall: 0.428571, count: 112 -Region Avg IOU: 0.448538, Class: 1.000000, Obj: 0.183749, No Obj: 0.004179, Avg Recall: 0.468468, count: 111 -Region Avg IOU: 0.438358, Class: 1.000000, Obj: 0.213935, No Obj: 0.004218, Avg Recall: 0.427273, count: 110 -Region Avg IOU: 0.444247, Class: 1.000000, Obj: 0.189681, No Obj: 0.004019, Avg Recall: 0.500000, count: 90 -Region Avg IOU: 0.483339, Class: 1.000000, Obj: 0.219916, No Obj: 0.004426, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.470427, Class: 1.000000, Obj: 0.186093, No Obj: 0.004066, Avg Recall: 0.510204, count: 98 -Region Avg IOU: 0.406287, Class: 1.000000, Obj: 0.220202, No Obj: 0.004093, Avg Recall: 0.337079, count: 89 -Region Avg IOU: 0.509109, Class: 1.000000, Obj: 0.232385, No Obj: 0.004512, Avg Recall: 0.576577, count: 111 -490: 0.528585, 0.536593 avg, 0.001000 rate, 19.971090 seconds, 125440 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 0.000059 seconds -Region Avg IOU: 0.454252, Class: 1.000000, Obj: 0.205870, No Obj: 0.003572, Avg Recall: 0.510417, count: 96 -Region Avg IOU: 0.475491, Class: 1.000000, Obj: 0.185934, No Obj: 0.003875, Avg Recall: 0.524752, count: 101 -Region Avg IOU: 0.501332, Class: 1.000000, Obj: 0.188773, No Obj: 0.003676, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.465856, Class: 1.000000, Obj: 0.219806, No Obj: 0.003786, Avg Recall: 0.500000, count: 100 -Region Avg IOU: 0.478699, Class: 1.000000, Obj: 0.193899, No Obj: 0.003962, Avg Recall: 0.510000, count: 100 -Region Avg IOU: 0.465171, Class: 1.000000, Obj: 0.208362, No Obj: 0.004164, Avg Recall: 0.508621, count: 116 -Region Avg IOU: 0.494302, Class: 1.000000, Obj: 0.200085, No Obj: 0.003975, Avg Recall: 0.562500, count: 96 -Region Avg IOU: 0.454804, Class: 1.000000, Obj: 0.203754, No Obj: 0.003657, Avg Recall: 0.482353, count: 85 -491: 0.461811, 0.529114 avg, 0.001000 rate, 23.035601 seconds, 125696 images -Loaded: 1.380382 seconds -Region Avg IOU: 0.492404, Class: 1.000000, Obj: 0.217981, No Obj: 0.003879, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.512702, Class: 1.000000, Obj: 0.240135, No Obj: 0.004251, Avg Recall: 0.552845, count: 123 -Region Avg IOU: 0.437823, Class: 1.000000, Obj: 0.211470, No Obj: 0.003813, Avg Recall: 0.448000, count: 125 -Region Avg IOU: 0.501128, Class: 1.000000, Obj: 0.204118, No Obj: 0.003982, Avg Recall: 0.519231, count: 104 -Region Avg IOU: 0.499783, Class: 1.000000, Obj: 0.201905, No Obj: 0.003984, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.443186, Class: 1.000000, Obj: 0.188820, No Obj: 0.004178, Avg Recall: 0.504762, count: 105 -Region Avg IOU: 0.459186, Class: 1.000000, Obj: 0.213408, No Obj: 0.003762, Avg Recall: 0.473118, count: 93 -Region Avg IOU: 0.469019, Class: 1.000000, Obj: 0.192121, No Obj: 0.003833, Avg Recall: 0.522222, count: 90 -492: 0.505699, 0.526773 avg, 0.001000 rate, 23.403519 seconds, 125952 images -Loaded: 0.749364 seconds -Region Avg IOU: 0.476023, Class: 1.000000, Obj: 0.208359, No Obj: 0.003621, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.472497, Class: 1.000000, Obj: 0.209811, No Obj: 0.003837, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.459669, Class: 1.000000, Obj: 0.218326, No Obj: 0.003209, Avg Recall: 0.511905, count: 84 -Region Avg IOU: 0.493785, Class: 1.000000, Obj: 0.224583, No Obj: 0.003984, Avg Recall: 0.555556, count: 99 -Region Avg IOU: 0.497090, Class: 1.000000, Obj: 0.216107, No Obj: 0.004376, Avg Recall: 0.553030, count: 132 -Region Avg IOU: 0.499467, Class: 1.000000, Obj: 0.193355, No Obj: 0.003892, Avg Recall: 0.495050, count: 101 -Region Avg IOU: 0.482885, Class: 1.000000, Obj: 0.217232, No Obj: 0.004031, Avg Recall: 0.533333, count: 120 -Region Avg IOU: 0.445478, Class: 1.000000, Obj: 0.182703, No Obj: 0.004115, Avg Recall: 0.458333, count: 120 -493: 0.511090, 0.525205 avg, 0.001000 rate, 24.092052 seconds, 126208 images -Loaded: 0.105836 seconds -Region Avg IOU: 0.480758, Class: 1.000000, Obj: 0.256209, No Obj: 0.003495, Avg Recall: 0.513514, count: 74 -Region Avg IOU: 0.470132, Class: 1.000000, Obj: 0.227491, No Obj: 0.004078, Avg Recall: 0.532609, count: 92 -Region Avg IOU: 0.482001, Class: 1.000000, Obj: 0.193728, No Obj: 0.003751, Avg Recall: 0.514286, count: 105 -Region Avg IOU: 0.543330, Class: 1.000000, Obj: 0.219979, No Obj: 0.004290, Avg Recall: 0.683761, count: 117 -Region Avg IOU: 0.464759, Class: 1.000000, Obj: 0.161969, No Obj: 0.004042, Avg Recall: 0.556522, count: 115 -Region Avg IOU: 0.466598, Class: 1.000000, Obj: 0.188138, No Obj: 0.003784, Avg Recall: 0.521739, count: 92 -Region Avg IOU: 0.485404, Class: 1.000000, Obj: 0.162109, No Obj: 0.004051, Avg Recall: 0.538462, count: 104 -Region Avg IOU: 0.442966, Class: 1.000000, Obj: 0.205663, No Obj: 0.004289, Avg Recall: 0.464286, count: 140 -494: 0.510087, 0.523693 avg, 0.001000 rate, 24.385653 seconds, 126464 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.440740, Class: 1.000000, Obj: 0.206282, No Obj: 0.003946, Avg Recall: 0.446429, count: 112 -Region Avg IOU: 0.490177, Class: 1.000000, Obj: 0.204307, No Obj: 0.004323, Avg Recall: 0.500000, count: 130 -Region Avg IOU: 0.495971, Class: 1.000000, Obj: 0.188523, No Obj: 0.003912, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.481277, Class: 1.000000, Obj: 0.238808, No Obj: 0.004034, Avg Recall: 0.500000, count: 110 -Region Avg IOU: 0.482627, Class: 1.000000, Obj: 0.210207, No Obj: 0.003971, Avg Recall: 0.538462, count: 91 -Region Avg IOU: 0.533143, Class: 1.000000, Obj: 0.208168, No Obj: 0.004069, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.519920, Class: 1.000000, Obj: 0.222428, No Obj: 0.004183, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.493954, Class: 1.000000, Obj: 0.200456, No Obj: 0.004520, Avg Recall: 0.562963, count: 135 -495: 0.488768, 0.520200 avg, 0.001000 rate, 23.824659 seconds, 126720 images -Loaded: 1.922030 seconds -Region Avg IOU: 0.473175, Class: 1.000000, Obj: 0.233125, No Obj: 0.003458, Avg Recall: 0.500000, count: 88 -Region Avg IOU: 0.442565, Class: 1.000000, Obj: 0.180771, No Obj: 0.003702, Avg Recall: 0.409836, count: 122 -Region Avg IOU: 0.475162, Class: 1.000000, Obj: 0.223076, No Obj: 0.004463, Avg Recall: 0.492537, count: 134 -Region Avg IOU: 0.503260, Class: 1.000000, Obj: 0.183960, No Obj: 0.004489, Avg Recall: 0.556391, count: 133 -Region Avg IOU: 0.508800, Class: 1.000000, Obj: 0.222619, No Obj: 0.004356, Avg Recall: 0.573770, count: 122 -Region Avg IOU: 0.462577, Class: 1.000000, Obj: 0.193540, No Obj: 0.003971, Avg Recall: 0.480000, count: 125 -Region Avg IOU: 0.472437, Class: 1.000000, Obj: 0.215423, No Obj: 0.003857, Avg Recall: 0.508065, count: 124 -Region Avg IOU: 0.464640, Class: 1.000000, Obj: 0.189551, No Obj: 0.003823, Avg Recall: 0.495727, count: 117 -496: 0.627463, 0.530927 avg, 0.001000 rate, 22.237408 seconds, 126976 images -Loaded: 2.144707 seconds -Region Avg IOU: 0.462920, Class: 1.000000, Obj: 0.184537, No Obj: 0.004181, Avg Recall: 0.503937, count: 127 -Region Avg IOU: 0.472272, Class: 1.000000, Obj: 0.197204, No Obj: 0.004358, Avg Recall: 0.517544, count: 114 -Region Avg IOU: 0.543288, Class: 1.000000, Obj: 0.208496, No Obj: 0.003681, Avg Recall: 0.657534, count: 73 -Region Avg IOU: 0.475878, Class: 1.000000, Obj: 0.184193, No Obj: 0.004151, Avg Recall: 0.563492, count: 126 -Region Avg IOU: 0.519519, Class: 1.000000, Obj: 0.217827, No Obj: 0.004327, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.512344, Class: 1.000000, Obj: 0.200274, No Obj: 0.004149, Avg Recall: 0.524272, count: 103 -Region Avg IOU: 0.505693, Class: 1.000000, Obj: 0.231270, No Obj: 0.003670, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.432058, Class: 1.000000, Obj: 0.226211, No Obj: 0.004553, Avg Recall: 0.488189, count: 127 -497: 0.504628, 0.528297 avg, 0.001000 rate, 22.329210 seconds, 127232 images -Loaded: 1.892259 seconds -Region Avg IOU: 0.487971, Class: 1.000000, Obj: 0.240890, No Obj: 0.004196, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.487170, Class: 1.000000, Obj: 0.226812, No Obj: 0.004234, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.487760, Class: 1.000000, Obj: 0.202040, No Obj: 0.003834, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.453176, Class: 1.000000, Obj: 0.218593, No Obj: 0.003805, Avg Recall: 0.505882, count: 85 -Region Avg IOU: 0.448495, Class: 1.000000, Obj: 0.207883, No Obj: 0.004154, Avg Recall: 0.448980, count: 98 -Region Avg IOU: 0.491672, Class: 1.000000, Obj: 0.213256, No Obj: 0.003762, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.481776, Class: 1.000000, Obj: 0.215528, No Obj: 0.004483, Avg Recall: 0.500000, count: 148 -Region Avg IOU: 0.425383, Class: 1.000000, Obj: 0.206118, No Obj: 0.003782, Avg Recall: 0.383178, count: 107 -498: 0.532981, 0.528765 avg, 0.001000 rate, 23.459011 seconds, 127488 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.487969, Class: 1.000000, Obj: 0.217554, No Obj: 0.004502, Avg Recall: 0.504274, count: 117 -Region Avg IOU: 0.459138, Class: 1.000000, Obj: 0.262529, No Obj: 0.003884, Avg Recall: 0.443182, count: 88 -Region Avg IOU: 0.494842, Class: 1.000000, Obj: 0.234469, No Obj: 0.004365, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.490449, Class: 1.000000, Obj: 0.202918, No Obj: 0.004284, Avg Recall: 0.561404, count: 114 -Region Avg IOU: 0.488715, Class: 1.000000, Obj: 0.198926, No Obj: 0.004151, Avg Recall: 0.576000, count: 125 -Region Avg IOU: 0.538467, Class: 1.000000, Obj: 0.221226, No Obj: 0.004136, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.494876, Class: 1.000000, Obj: 0.242567, No Obj: 0.004122, Avg Recall: 0.549020, count: 102 -Region Avg IOU: 0.460655, Class: 1.000000, Obj: 0.203105, No Obj: 0.004289, Avg Recall: 0.504762, count: 105 -499: 0.544069, 0.530296 avg, 0.001000 rate, 22.369823 seconds, 127744 images -Loaded: 1.908323 seconds -Region Avg IOU: 0.474059, Class: 1.000000, Obj: 0.185886, No Obj: 0.004475, Avg Recall: 0.546875, count: 128 -Region Avg IOU: 0.511951, Class: 1.000000, Obj: 0.220616, No Obj: 0.004636, Avg Recall: 0.569231, count: 130 -Region Avg IOU: 0.506686, Class: 1.000000, Obj: 0.250884, No Obj: 0.003930, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.454366, Class: 1.000000, Obj: 0.194768, No Obj: 0.004546, Avg Recall: 0.475410, count: 122 -Region Avg IOU: 0.477074, Class: 1.000000, Obj: 0.217701, No Obj: 0.004220, Avg Recall: 0.586207, count: 116 -Region Avg IOU: 0.507283, Class: 1.000000, Obj: 0.262211, No Obj: 0.004765, Avg Recall: 0.585938, count: 128 -Region Avg IOU: 0.478836, Class: 1.000000, Obj: 0.234278, No Obj: 0.004023, Avg Recall: 0.509615, count: 104 -Region Avg IOU: 0.489594, Class: 1.000000, Obj: 0.230562, No Obj: 0.003644, Avg Recall: 0.530000, count: 100 -500: 0.552481, 0.532514 avg, 0.001000 rate, 21.774511 seconds, 128000 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.078707 seconds -Region Avg IOU: 0.495198, Class: 1.000000, Obj: 0.242342, No Obj: 0.004562, Avg Recall: 0.561404, count: 114 -Region Avg IOU: 0.481805, Class: 1.000000, Obj: 0.221670, No Obj: 0.004439, Avg Recall: 0.531746, count: 126 -Region Avg IOU: 0.493723, Class: 1.000000, Obj: 0.215030, No Obj: 0.004364, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.467630, Class: 1.000000, Obj: 0.190295, No Obj: 0.004014, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.516771, Class: 1.000000, Obj: 0.281885, No Obj: 0.004586, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.491122, Class: 1.000000, Obj: 0.183842, No Obj: 0.004081, Avg Recall: 0.519231, count: 104 -Region Avg IOU: 0.502315, Class: 1.000000, Obj: 0.215911, No Obj: 0.004878, Avg Recall: 0.569106, count: 123 -Region Avg IOU: 0.475660, Class: 1.000000, Obj: 0.210573, No Obj: 0.004313, Avg Recall: 0.513514, count: 111 -501: 0.538532, 0.533116 avg, 0.001000 rate, 25.612368 seconds, 128256 images -Loaded: 0.738825 seconds -Region Avg IOU: 0.486559, Class: 1.000000, Obj: 0.201763, No Obj: 0.004494, Avg Recall: 0.528926, count: 121 -Region Avg IOU: 0.493337, Class: 1.000000, Obj: 0.202919, No Obj: 0.004203, Avg Recall: 0.523810, count: 105 -Region Avg IOU: 0.493325, Class: 1.000000, Obj: 0.226734, No Obj: 0.004658, Avg Recall: 0.540816, count: 98 -Region Avg IOU: 0.527417, Class: 1.000000, Obj: 0.248607, No Obj: 0.004306, Avg Recall: 0.586957, count: 92 -Region Avg IOU: 0.489831, Class: 1.000000, Obj: 0.222507, No Obj: 0.004560, Avg Recall: 0.538462, count: 130 -Region Avg IOU: 0.481528, Class: 1.000000, Obj: 0.200169, No Obj: 0.004061, Avg Recall: 0.508929, count: 112 -Region Avg IOU: 0.500767, Class: 1.000000, Obj: 0.254112, No Obj: 0.004296, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.465765, Class: 1.000000, Obj: 0.261675, No Obj: 0.004234, Avg Recall: 0.505263, count: 95 -502: 0.513303, 0.531135 avg, 0.001000 rate, 23.249424 seconds, 128512 images -Loaded: 0.251441 seconds -Region Avg IOU: 0.535151, Class: 1.000000, Obj: 0.183635, No Obj: 0.004699, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.520000, Class: 1.000000, Obj: 0.210724, No Obj: 0.004844, Avg Recall: 0.598425, count: 127 -Region Avg IOU: 0.477440, Class: 1.000000, Obj: 0.243596, No Obj: 0.004447, Avg Recall: 0.525862, count: 116 -Region Avg IOU: 0.540971, Class: 1.000000, Obj: 0.246945, No Obj: 0.004239, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.505845, Class: 1.000000, Obj: 0.235107, No Obj: 0.004573, Avg Recall: 0.544715, count: 123 -Region Avg IOU: 0.497625, Class: 1.000000, Obj: 0.221203, No Obj: 0.004346, Avg Recall: 0.509259, count: 108 -Region Avg IOU: 0.497784, Class: 1.000000, Obj: 0.183824, No Obj: 0.004377, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.453561, Class: 1.000000, Obj: 0.205969, No Obj: 0.004355, Avg Recall: 0.431579, count: 95 -503: 0.529643, 0.530986 avg, 0.001000 rate, 25.184849 seconds, 128768 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.498892, Class: 1.000000, Obj: 0.243417, No Obj: 0.003915, Avg Recall: 0.534653, count: 101 -Region Avg IOU: 0.500934, Class: 1.000000, Obj: 0.183192, No Obj: 0.004255, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.520430, Class: 1.000000, Obj: 0.209632, No Obj: 0.004229, Avg Recall: 0.585366, count: 123 -Region Avg IOU: 0.465088, Class: 1.000000, Obj: 0.233045, No Obj: 0.004232, Avg Recall: 0.494845, count: 97 -Region Avg IOU: 0.538401, Class: 1.000000, Obj: 0.238081, No Obj: 0.004146, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.469525, Class: 1.000000, Obj: 0.212043, No Obj: 0.004115, Avg Recall: 0.491228, count: 114 -Region Avg IOU: 0.506792, Class: 1.000000, Obj: 0.192706, No Obj: 0.003961, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.455259, Class: 1.000000, Obj: 0.200007, No Obj: 0.003865, Avg Recall: 0.447917, count: 96 -504: 0.493774, 0.527264 avg, 0.001000 rate, 25.822508 seconds, 129024 images -Loaded: 0.022377 seconds -Region Avg IOU: 0.462943, Class: 1.000000, Obj: 0.172356, No Obj: 0.004672, Avg Recall: 0.500000, count: 158 -Region Avg IOU: 0.501097, Class: 1.000000, Obj: 0.241530, No Obj: 0.004382, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.456396, Class: 1.000000, Obj: 0.187781, No Obj: 0.004072, Avg Recall: 0.514286, count: 105 -Region Avg IOU: 0.541580, Class: 1.000000, Obj: 0.183225, No Obj: 0.004170, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.511581, Class: 1.000000, Obj: 0.206197, No Obj: 0.004252, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.517055, Class: 1.000000, Obj: 0.201197, No Obj: 0.003561, Avg Recall: 0.620253, count: 79 -Region Avg IOU: 0.503117, Class: 1.000000, Obj: 0.205541, No Obj: 0.003755, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.504639, Class: 1.000000, Obj: 0.194427, No Obj: 0.004839, Avg Recall: 0.617188, count: 128 -505: 0.530704, 0.527608 avg, 0.001000 rate, 24.907860 seconds, 129280 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.471429, Class: 1.000000, Obj: 0.196753, No Obj: 0.003789, Avg Recall: 0.445455, count: 110 -Region Avg IOU: 0.482728, Class: 1.000000, Obj: 0.206461, No Obj: 0.003879, Avg Recall: 0.514286, count: 105 -Region Avg IOU: 0.518447, Class: 1.000000, Obj: 0.256252, No Obj: 0.004388, Avg Recall: 0.562500, count: 128 -Region Avg IOU: 0.466267, Class: 1.000000, Obj: 0.170211, No Obj: 0.003947, Avg Recall: 0.469388, count: 98 -Region Avg IOU: 0.552754, Class: 1.000000, Obj: 0.223929, No Obj: 0.004337, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.468835, Class: 1.000000, Obj: 0.192497, No Obj: 0.003802, Avg Recall: 0.537037, count: 108 -Region Avg IOU: 0.525223, Class: 1.000000, Obj: 0.238310, No Obj: 0.004295, Avg Recall: 0.591667, count: 120 -Region Avg IOU: 0.546037, Class: 1.000000, Obj: 0.215142, No Obj: 0.004426, Avg Recall: 0.661765, count: 136 -506: 0.500021, 0.524850 avg, 0.001000 rate, 25.149467 seconds, 129536 images -Loaded: 0.454245 seconds -Region Avg IOU: 0.522906, Class: 1.000000, Obj: 0.174506, No Obj: 0.004137, Avg Recall: 0.616541, count: 133 -Region Avg IOU: 0.478608, Class: 1.000000, Obj: 0.191332, No Obj: 0.003786, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.516110, Class: 1.000000, Obj: 0.222268, No Obj: 0.003897, Avg Recall: 0.541667, count: 96 -Region Avg IOU: 0.502885, Class: 1.000000, Obj: 0.202195, No Obj: 0.004087, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.506865, Class: 1.000000, Obj: 0.185875, No Obj: 0.004447, Avg Recall: 0.583333, count: 144 -Region Avg IOU: 0.518096, Class: 1.000000, Obj: 0.185938, No Obj: 0.004109, Avg Recall: 0.571429, count: 105 -Region Avg IOU: 0.506143, Class: 1.000000, Obj: 0.175039, No Obj: 0.004057, Avg Recall: 0.525000, count: 120 -Region Avg IOU: 0.498367, Class: 1.000000, Obj: 0.208004, No Obj: 0.003920, Avg Recall: 0.539216, count: 102 -507: 0.553658, 0.527730 avg, 0.001000 rate, 24.547531 seconds, 129792 images -Loaded: 1.295683 seconds -Region Avg IOU: 0.530176, Class: 1.000000, Obj: 0.230736, No Obj: 0.004284, Avg Recall: 0.609375, count: 128 -Region Avg IOU: 0.468986, Class: 1.000000, Obj: 0.172723, No Obj: 0.004347, Avg Recall: 0.526786, count: 112 -Region Avg IOU: 0.499115, Class: 1.000000, Obj: 0.211593, No Obj: 0.004305, Avg Recall: 0.553719, count: 121 -Region Avg IOU: 0.507771, Class: 1.000000, Obj: 0.201302, No Obj: 0.003924, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.505701, Class: 1.000000, Obj: 0.196604, No Obj: 0.003834, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.524796, Class: 1.000000, Obj: 0.169077, No Obj: 0.003686, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.511210, Class: 1.000000, Obj: 0.226329, No Obj: 0.004401, Avg Recall: 0.584071, count: 113 -Region Avg IOU: 0.505042, Class: 1.000000, Obj: 0.199765, No Obj: 0.004047, Avg Recall: 0.561224, count: 98 -508: 0.553870, 0.530344 avg, 0.001000 rate, 25.442280 seconds, 130048 images -Loaded: 0.025590 seconds -Region Avg IOU: 0.507201, Class: 1.000000, Obj: 0.218101, No Obj: 0.004416, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.508824, Class: 1.000000, Obj: 0.190378, No Obj: 0.003883, Avg Recall: 0.583333, count: 120 -Region Avg IOU: 0.512995, Class: 1.000000, Obj: 0.221689, No Obj: 0.004490, Avg Recall: 0.578947, count: 114 -Region Avg IOU: 0.453229, Class: 1.000000, Obj: 0.189538, No Obj: 0.004102, Avg Recall: 0.432990, count: 97 -Region Avg IOU: 0.548550, Class: 1.000000, Obj: 0.229946, No Obj: 0.004185, Avg Recall: 0.620690, count: 87 -Region Avg IOU: 0.544344, Class: 1.000000, Obj: 0.236306, No Obj: 0.004244, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.514252, Class: 1.000000, Obj: 0.182147, No Obj: 0.003765, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.523373, Class: 1.000000, Obj: 0.185667, No Obj: 0.004413, Avg Recall: 0.630252, count: 119 -509: 0.563423, 0.533652 avg, 0.001000 rate, 25.273865 seconds, 130304 images -Loaded: 0.752943 seconds -Region Avg IOU: 0.459393, Class: 1.000000, Obj: 0.229995, No Obj: 0.004214, Avg Recall: 0.490000, count: 100 -Region Avg IOU: 0.521683, Class: 1.000000, Obj: 0.189876, No Obj: 0.003980, Avg Recall: 0.592920, count: 113 -Region Avg IOU: 0.555557, Class: 1.000000, Obj: 0.218842, No Obj: 0.004712, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.491995, Class: 1.000000, Obj: 0.203539, No Obj: 0.004059, Avg Recall: 0.538462, count: 104 -Region Avg IOU: 0.494344, Class: 1.000000, Obj: 0.196139, No Obj: 0.004020, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.521479, Class: 1.000000, Obj: 0.223615, No Obj: 0.004655, Avg Recall: 0.596899, count: 129 -Region Avg IOU: 0.493821, Class: 1.000000, Obj: 0.186254, No Obj: 0.004080, Avg Recall: 0.540146, count: 137 -Region Avg IOU: 0.523658, Class: 1.000000, Obj: 0.248248, No Obj: 0.004535, Avg Recall: 0.620690, count: 116 -510: 0.637226, 0.544010 avg, 0.001000 rate, 23.823288 seconds, 130560 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000075 seconds -Region Avg IOU: 0.555270, Class: 1.000000, Obj: 0.243027, No Obj: 0.004130, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.531758, Class: 1.000000, Obj: 0.186202, No Obj: 0.004090, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.521039, Class: 1.000000, Obj: 0.191527, No Obj: 0.003923, Avg Recall: 0.564103, count: 117 -Region Avg IOU: 0.572624, Class: 1.000000, Obj: 0.227690, No Obj: 0.004018, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.526490, Class: 1.000000, Obj: 0.204740, No Obj: 0.003793, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.555330, Class: 1.000000, Obj: 0.270521, No Obj: 0.004070, Avg Recall: 0.727273, count: 77 -Region Avg IOU: 0.559427, Class: 1.000000, Obj: 0.182471, No Obj: 0.003921, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.523645, Class: 1.000000, Obj: 0.228282, No Obj: 0.003651, Avg Recall: 0.586207, count: 87 -511: 0.499733, 0.539582 avg, 0.001000 rate, 29.072351 seconds, 130816 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.590833, Class: 1.000000, Obj: 0.227618, No Obj: 0.004446, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.542054, Class: 1.000000, Obj: 0.257187, No Obj: 0.003903, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.560425, Class: 1.000000, Obj: 0.249275, No Obj: 0.003889, Avg Recall: 0.652174, count: 69 -Region Avg IOU: 0.535105, Class: 1.000000, Obj: 0.223968, No Obj: 0.004190, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.535954, Class: 1.000000, Obj: 0.210183, No Obj: 0.004090, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.519286, Class: 1.000000, Obj: 0.216713, No Obj: 0.003774, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.549927, Class: 1.000000, Obj: 0.226321, No Obj: 0.004498, Avg Recall: 0.620438, count: 137 -Region Avg IOU: 0.536531, Class: 1.000000, Obj: 0.193606, No Obj: 0.004215, Avg Recall: 0.606557, count: 122 -512: 0.508986, 0.536522 avg, 0.001000 rate, 29.016684 seconds, 131072 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.575729, Class: 1.000000, Obj: 0.231119, No Obj: 0.004081, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.490474, Class: 1.000000, Obj: 0.209910, No Obj: 0.003872, Avg Recall: 0.487395, count: 119 -Region Avg IOU: 0.521293, Class: 1.000000, Obj: 0.222830, No Obj: 0.003786, Avg Recall: 0.589474, count: 95 -Region Avg IOU: 0.512818, Class: 1.000000, Obj: 0.237070, No Obj: 0.003991, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.584356, Class: 1.000000, Obj: 0.224460, No Obj: 0.004552, Avg Recall: 0.740458, count: 131 -Region Avg IOU: 0.534582, Class: 1.000000, Obj: 0.207652, No Obj: 0.003430, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.569423, Class: 1.000000, Obj: 0.211780, No Obj: 0.004560, Avg Recall: 0.727941, count: 136 -Region Avg IOU: 0.560570, Class: 1.000000, Obj: 0.205491, No Obj: 0.003871, Avg Recall: 0.704545, count: 88 -513: 0.541334, 0.537004 avg, 0.001000 rate, 29.192392 seconds, 131328 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.580631, Class: 1.000000, Obj: 0.231616, No Obj: 0.003413, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.551462, Class: 1.000000, Obj: 0.199922, No Obj: 0.003365, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.516685, Class: 1.000000, Obj: 0.219875, No Obj: 0.003610, Avg Recall: 0.546296, count: 108 -Region Avg IOU: 0.549742, Class: 1.000000, Obj: 0.177135, No Obj: 0.003842, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.548212, Class: 1.000000, Obj: 0.231532, No Obj: 0.003976, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.571166, Class: 1.000000, Obj: 0.198948, No Obj: 0.003900, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.569568, Class: 1.000000, Obj: 0.209257, No Obj: 0.003882, Avg Recall: 0.652893, count: 121 -Region Avg IOU: 0.542134, Class: 1.000000, Obj: 0.246944, No Obj: 0.004156, Avg Recall: 0.641304, count: 92 -514: 0.521719, 0.535475 avg, 0.001000 rate, 26.720301 seconds, 131584 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.519689, Class: 1.000000, Obj: 0.197284, No Obj: 0.003459, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.534616, Class: 1.000000, Obj: 0.215222, No Obj: 0.004053, Avg Recall: 0.583333, count: 144 -Region Avg IOU: 0.530786, Class: 1.000000, Obj: 0.199865, No Obj: 0.003365, Avg Recall: 0.632479, count: 117 -Region Avg IOU: 0.562271, Class: 1.000000, Obj: 0.232119, No Obj: 0.003617, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.553505, Class: 1.000000, Obj: 0.180279, No Obj: 0.003277, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.546660, Class: 1.000000, Obj: 0.170815, No Obj: 0.003474, Avg Recall: 0.648352, count: 91 -Region Avg IOU: 0.538015, Class: 1.000000, Obj: 0.186618, No Obj: 0.003795, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.558534, Class: 1.000000, Obj: 0.155565, No Obj: 0.003637, Avg Recall: 0.682353, count: 85 -515: 0.529822, 0.534910 avg, 0.001000 rate, 29.364849 seconds, 131840 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.531379, Class: 1.000000, Obj: 0.195635, No Obj: 0.003139, Avg Recall: 0.652174, count: 92 -Region Avg IOU: 0.558819, Class: 1.000000, Obj: 0.189586, No Obj: 0.003443, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.498289, Class: 1.000000, Obj: 0.171635, No Obj: 0.003053, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.532177, Class: 1.000000, Obj: 0.181611, No Obj: 0.003291, Avg Recall: 0.630435, count: 92 -Region Avg IOU: 0.553487, Class: 1.000000, Obj: 0.211157, No Obj: 0.003484, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.568906, Class: 1.000000, Obj: 0.222788, No Obj: 0.003667, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.577514, Class: 1.000000, Obj: 0.171329, No Obj: 0.003349, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.537243, Class: 1.000000, Obj: 0.198770, No Obj: 0.003732, Avg Recall: 0.619048, count: 105 -516: 0.531691, 0.534588 avg, 0.001000 rate, 29.244204 seconds, 132096 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.541895, Class: 1.000000, Obj: 0.165252, No Obj: 0.003334, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.518914, Class: 1.000000, Obj: 0.163449, No Obj: 0.003015, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.515424, Class: 1.000000, Obj: 0.165139, No Obj: 0.003030, Avg Recall: 0.568965, count: 116 -Region Avg IOU: 0.592453, Class: 1.000000, Obj: 0.229714, No Obj: 0.003600, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.556187, Class: 1.000000, Obj: 0.183368, No Obj: 0.003415, Avg Recall: 0.695312, count: 128 -Region Avg IOU: 0.579217, Class: 1.000000, Obj: 0.168136, No Obj: 0.003553, Avg Recall: 0.708029, count: 137 -Region Avg IOU: 0.566515, Class: 1.000000, Obj: 0.190263, No Obj: 0.003635, Avg Recall: 0.656489, count: 131 -Region Avg IOU: 0.531428, Class: 1.000000, Obj: 0.195025, No Obj: 0.003111, Avg Recall: 0.615385, count: 104 -517: 0.582925, 0.539422 avg, 0.001000 rate, 30.569363 seconds, 132352 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.530453, Class: 1.000000, Obj: 0.190358, No Obj: 0.003113, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.551243, Class: 1.000000, Obj: 0.172204, No Obj: 0.003347, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.529351, Class: 1.000000, Obj: 0.161862, No Obj: 0.003220, Avg Recall: 0.622807, count: 114 -Region Avg IOU: 0.547851, Class: 1.000000, Obj: 0.212923, No Obj: 0.003076, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.539483, Class: 1.000000, Obj: 0.198482, No Obj: 0.003556, Avg Recall: 0.640000, count: 125 -Region Avg IOU: 0.532007, Class: 1.000000, Obj: 0.152234, No Obj: 0.003292, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.531607, Class: 1.000000, Obj: 0.201817, No Obj: 0.003451, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.539204, Class: 1.000000, Obj: 0.201267, No Obj: 0.003247, Avg Recall: 0.669118, count: 136 -518: 0.522437, 0.537723 avg, 0.001000 rate, 28.763044 seconds, 132608 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.506445, Class: 1.000000, Obj: 0.208678, No Obj: 0.003454, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.548088, Class: 1.000000, Obj: 0.183469, No Obj: 0.003780, Avg Recall: 0.647482, count: 139 -Region Avg IOU: 0.591980, Class: 1.000000, Obj: 0.192865, No Obj: 0.003316, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.554905, Class: 1.000000, Obj: 0.173716, No Obj: 0.003638, Avg Recall: 0.695312, count: 128 -Region Avg IOU: 0.561229, Class: 1.000000, Obj: 0.217226, No Obj: 0.003306, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.542932, Class: 1.000000, Obj: 0.231508, No Obj: 0.003694, Avg Recall: 0.677419, count: 124 -Region Avg IOU: 0.544263, Class: 1.000000, Obj: 0.194839, No Obj: 0.003594, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.546784, Class: 1.000000, Obj: 0.203297, No Obj: 0.003602, Avg Recall: 0.612903, count: 93 -519: 0.578985, 0.541849 avg, 0.001000 rate, 29.054346 seconds, 132864 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.533340, Class: 1.000000, Obj: 0.209536, No Obj: 0.003536, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.546508, Class: 1.000000, Obj: 0.270440, No Obj: 0.003221, Avg Recall: 0.577778, count: 90 -Region Avg IOU: 0.567401, Class: 1.000000, Obj: 0.178445, No Obj: 0.003526, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.530558, Class: 1.000000, Obj: 0.187572, No Obj: 0.003658, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.565419, Class: 1.000000, Obj: 0.220172, No Obj: 0.003312, Avg Recall: 0.659091, count: 88 -Region Avg IOU: 0.527109, Class: 1.000000, Obj: 0.208510, No Obj: 0.003366, Avg Recall: 0.601626, count: 123 -Region Avg IOU: 0.561460, Class: 1.000000, Obj: 0.218806, No Obj: 0.003690, Avg Recall: 0.664000, count: 125 -Region Avg IOU: 0.559791, Class: 1.000000, Obj: 0.226081, No Obj: 0.003580, Avg Recall: 0.682692, count: 104 -520: 0.525376, 0.540202 avg, 0.001000 rate, 29.137495 seconds, 133120 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 13.737551 seconds -Region Avg IOU: 0.454433, Class: 1.000000, Obj: 0.178808, No Obj: 0.003635, Avg Recall: 0.508475, count: 118 -Region Avg IOU: 0.484979, Class: 1.000000, Obj: 0.230323, No Obj: 0.003731, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.466712, Class: 1.000000, Obj: 0.205868, No Obj: 0.003818, Avg Recall: 0.505618, count: 89 -Region Avg IOU: 0.412780, Class: 1.000000, Obj: 0.231150, No Obj: 0.003858, Avg Recall: 0.456522, count: 92 -Region Avg IOU: 0.458368, Class: 1.000000, Obj: 0.200804, No Obj: 0.004322, Avg Recall: 0.516667, count: 120 -Region Avg IOU: 0.471887, Class: 1.000000, Obj: 0.214221, No Obj: 0.004072, Avg Recall: 0.528926, count: 121 -Region Avg IOU: 0.433130, Class: 1.000000, Obj: 0.176075, No Obj: 0.004018, Avg Recall: 0.450000, count: 120 -Region Avg IOU: 0.443028, Class: 1.000000, Obj: 0.215811, No Obj: 0.003861, Avg Recall: 0.458716, count: 109 -521: 0.487443, 0.534926 avg, 0.001000 rate, 23.017759 seconds, 133376 images -Loaded: 1.390644 seconds -Region Avg IOU: 0.423129, Class: 1.000000, Obj: 0.217429, No Obj: 0.004295, Avg Recall: 0.357143, count: 98 -Region Avg IOU: 0.458216, Class: 1.000000, Obj: 0.232841, No Obj: 0.003765, Avg Recall: 0.521739, count: 92 -Region Avg IOU: 0.450826, Class: 1.000000, Obj: 0.234490, No Obj: 0.004131, Avg Recall: 0.495413, count: 109 -Region Avg IOU: 0.493508, Class: 1.000000, Obj: 0.257364, No Obj: 0.003871, Avg Recall: 0.560440, count: 91 -Region Avg IOU: 0.446314, Class: 1.000000, Obj: 0.196875, No Obj: 0.004074, Avg Recall: 0.473214, count: 112 -Region Avg IOU: 0.449345, Class: 1.000000, Obj: 0.220170, No Obj: 0.004096, Avg Recall: 0.480000, count: 100 -Region Avg IOU: 0.468644, Class: 1.000000, Obj: 0.217824, No Obj: 0.004082, Avg Recall: 0.477064, count: 109 -Region Avg IOU: 0.448327, Class: 1.000000, Obj: 0.239938, No Obj: 0.004532, Avg Recall: 0.464567, count: 127 -522: 0.500470, 0.531481 avg, 0.001000 rate, 19.794960 seconds, 133632 images -Loaded: 2.748113 seconds -Region Avg IOU: 0.422494, Class: 1.000000, Obj: 0.216244, No Obj: 0.003835, Avg Recall: 0.391304, count: 92 -Region Avg IOU: 0.504131, Class: 1.000000, Obj: 0.287486, No Obj: 0.003934, Avg Recall: 0.556962, count: 79 -Region Avg IOU: 0.422026, Class: 1.000000, Obj: 0.204077, No Obj: 0.003955, Avg Recall: 0.429825, count: 114 -Region Avg IOU: 0.455052, Class: 1.000000, Obj: 0.178791, No Obj: 0.003586, Avg Recall: 0.472222, count: 108 -Region Avg IOU: 0.481692, Class: 1.000000, Obj: 0.253761, No Obj: 0.004394, Avg Recall: 0.565217, count: 69 -Region Avg IOU: 0.470494, Class: 1.000000, Obj: 0.222797, No Obj: 0.004306, Avg Recall: 0.536232, count: 138 -Region Avg IOU: 0.466259, Class: 1.000000, Obj: 0.220128, No Obj: 0.004031, Avg Recall: 0.526316, count: 95 -Region Avg IOU: 0.527280, Class: 1.000000, Obj: 0.244590, No Obj: 0.004474, Avg Recall: 0.611111, count: 108 -523: 0.506097, 0.528942 avg, 0.001000 rate, 21.932848 seconds, 133888 images -Loaded: 0.377768 seconds -Region Avg IOU: 0.517138, Class: 1.000000, Obj: 0.219581, No Obj: 0.003956, Avg Recall: 0.597701, count: 87 -Region Avg IOU: 0.435600, Class: 1.000000, Obj: 0.215273, No Obj: 0.004458, Avg Recall: 0.431507, count: 146 -Region Avg IOU: 0.443037, Class: 1.000000, Obj: 0.239996, No Obj: 0.004086, Avg Recall: 0.486957, count: 115 -Region Avg IOU: 0.473953, Class: 1.000000, Obj: 0.204118, No Obj: 0.003608, Avg Recall: 0.485981, count: 107 -Region Avg IOU: 0.453679, Class: 1.000000, Obj: 0.221317, No Obj: 0.004105, Avg Recall: 0.521277, count: 94 -Region Avg IOU: 0.468795, Class: 1.000000, Obj: 0.190588, No Obj: 0.004096, Avg Recall: 0.517544, count: 114 -Region Avg IOU: 0.464844, Class: 1.000000, Obj: 0.180762, No Obj: 0.004794, Avg Recall: 0.495935, count: 123 -Region Avg IOU: 0.471271, Class: 1.000000, Obj: 0.211397, No Obj: 0.004709, Avg Recall: 0.541284, count: 109 -524: 0.566184, 0.532666 avg, 0.001000 rate, 22.799540 seconds, 134144 images -Loaded: 0.185182 seconds -Region Avg IOU: 0.488082, Class: 1.000000, Obj: 0.187643, No Obj: 0.004207, Avg Recall: 0.481132, count: 106 -Region Avg IOU: 0.519837, Class: 1.000000, Obj: 0.286464, No Obj: 0.004136, Avg Recall: 0.607595, count: 79 -Region Avg IOU: 0.408095, Class: 1.000000, Obj: 0.188806, No Obj: 0.003601, Avg Recall: 0.415842, count: 101 -Region Avg IOU: 0.459988, Class: 1.000000, Obj: 0.223210, No Obj: 0.004010, Avg Recall: 0.500000, count: 120 -Region Avg IOU: 0.436006, Class: 1.000000, Obj: 0.193107, No Obj: 0.003811, Avg Recall: 0.469388, count: 98 -Region Avg IOU: 0.414675, Class: 1.000000, Obj: 0.198309, No Obj: 0.004170, Avg Recall: 0.401709, count: 117 -Region Avg IOU: 0.491679, Class: 1.000000, Obj: 0.273993, No Obj: 0.004582, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.475333, Class: 1.000000, Obj: 0.200304, No Obj: 0.004167, Avg Recall: 0.547170, count: 106 -525: 0.559619, 0.535362 avg, 0.001000 rate, 23.152241 seconds, 134400 images -Loaded: 0.528409 seconds -Region Avg IOU: 0.435861, Class: 1.000000, Obj: 0.192384, No Obj: 0.004584, Avg Recall: 0.469697, count: 132 -Region Avg IOU: 0.449454, Class: 1.000000, Obj: 0.224373, No Obj: 0.003741, Avg Recall: 0.462366, count: 93 -Region Avg IOU: 0.413511, Class: 1.000000, Obj: 0.199981, No Obj: 0.004029, Avg Recall: 0.400000, count: 100 -Region Avg IOU: 0.453036, Class: 1.000000, Obj: 0.208105, No Obj: 0.004414, Avg Recall: 0.450450, count: 111 -Region Avg IOU: 0.432489, Class: 1.000000, Obj: 0.257308, No Obj: 0.004268, Avg Recall: 0.457447, count: 94 -Region Avg IOU: 0.497163, Class: 1.000000, Obj: 0.229635, No Obj: 0.004405, Avg Recall: 0.535354, count: 99 -Region Avg IOU: 0.490924, Class: 1.000000, Obj: 0.174304, No Obj: 0.003732, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.436639, Class: 1.000000, Obj: 0.201644, No Obj: 0.003679, Avg Recall: 0.473118, count: 93 -526: 0.478481, 0.529674 avg, 0.001000 rate, 23.053801 seconds, 134656 images -Loaded: 0.881791 seconds -Region Avg IOU: 0.455900, Class: 1.000000, Obj: 0.233132, No Obj: 0.003976, Avg Recall: 0.514019, count: 107 -Region Avg IOU: 0.488643, Class: 1.000000, Obj: 0.220876, No Obj: 0.004418, Avg Recall: 0.504274, count: 117 -Region Avg IOU: 0.458485, Class: 1.000000, Obj: 0.225431, No Obj: 0.004360, Avg Recall: 0.466667, count: 105 -Region Avg IOU: 0.455010, Class: 1.000000, Obj: 0.216359, No Obj: 0.004010, Avg Recall: 0.520325, count: 123 -Region Avg IOU: 0.468474, Class: 1.000000, Obj: 0.228934, No Obj: 0.004166, Avg Recall: 0.530973, count: 113 -Region Avg IOU: 0.400173, Class: 1.000000, Obj: 0.172414, No Obj: 0.003902, Avg Recall: 0.391304, count: 115 -Region Avg IOU: 0.475329, Class: 1.000000, Obj: 0.265673, No Obj: 0.004095, Avg Recall: 0.532710, count: 107 -Region Avg IOU: 0.492310, Class: 1.000000, Obj: 0.240209, No Obj: 0.003888, Avg Recall: 0.547368, count: 95 -527: 0.538647, 0.530571 avg, 0.001000 rate, 22.695623 seconds, 134912 images -Loaded: 0.830635 seconds -Region Avg IOU: 0.416727, Class: 1.000000, Obj: 0.181727, No Obj: 0.004034, Avg Recall: 0.413534, count: 133 -Region Avg IOU: 0.470023, Class: 1.000000, Obj: 0.178862, No Obj: 0.003932, Avg Recall: 0.504587, count: 109 -Region Avg IOU: 0.448711, Class: 1.000000, Obj: 0.179417, No Obj: 0.003868, Avg Recall: 0.481203, count: 133 -Region Avg IOU: 0.457676, Class: 1.000000, Obj: 0.206238, No Obj: 0.004290, Avg Recall: 0.468254, count: 126 -Region Avg IOU: 0.495140, Class: 1.000000, Obj: 0.233187, No Obj: 0.004277, Avg Recall: 0.518182, count: 110 -Region Avg IOU: 0.458051, Class: 1.000000, Obj: 0.199731, No Obj: 0.004708, Avg Recall: 0.531746, count: 126 -Region Avg IOU: 0.484188, Class: 1.000000, Obj: 0.223843, No Obj: 0.004048, Avg Recall: 0.546296, count: 108 -Region Avg IOU: 0.441773, Class: 1.000000, Obj: 0.224173, No Obj: 0.004292, Avg Recall: 0.475806, count: 124 -528: 0.547117, 0.532225 avg, 0.001000 rate, 22.821615 seconds, 135168 images -Loaded: 0.141313 seconds -Region Avg IOU: 0.460301, Class: 1.000000, Obj: 0.243018, No Obj: 0.003836, Avg Recall: 0.490566, count: 106 -Region Avg IOU: 0.483567, Class: 1.000000, Obj: 0.210810, No Obj: 0.003950, Avg Recall: 0.549180, count: 122 -Region Avg IOU: 0.469917, Class: 1.000000, Obj: 0.218116, No Obj: 0.004051, Avg Recall: 0.504132, count: 121 -Region Avg IOU: 0.459147, Class: 1.000000, Obj: 0.179606, No Obj: 0.003939, Avg Recall: 0.495327, count: 107 -Region Avg IOU: 0.487628, Class: 1.000000, Obj: 0.197320, No Obj: 0.003753, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.486697, Class: 1.000000, Obj: 0.197278, No Obj: 0.004052, Avg Recall: 0.512195, count: 123 -Region Avg IOU: 0.480097, Class: 1.000000, Obj: 0.177930, No Obj: 0.003859, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.434238, Class: 1.000000, Obj: 0.182491, No Obj: 0.004188, Avg Recall: 0.476190, count: 147 -529: 0.613979, 0.540401 avg, 0.001000 rate, 21.067411 seconds, 135424 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.400883, Class: 1.000000, Obj: 0.211323, No Obj: 0.003945, Avg Recall: 0.396226, count: 106 -Region Avg IOU: 0.531683, Class: 1.000000, Obj: 0.222929, No Obj: 0.004242, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.445609, Class: 1.000000, Obj: 0.218150, No Obj: 0.003370, Avg Recall: 0.509259, count: 108 -Region Avg IOU: 0.474700, Class: 1.000000, Obj: 0.234896, No Obj: 0.003915, Avg Recall: 0.521277, count: 94 -Region Avg IOU: 0.459112, Class: 1.000000, Obj: 0.209604, No Obj: 0.004451, Avg Recall: 0.453846, count: 130 -Region Avg IOU: 0.461264, Class: 1.000000, Obj: 0.212425, No Obj: 0.003911, Avg Recall: 0.517241, count: 116 -Region Avg IOU: 0.441737, Class: 1.000000, Obj: 0.183729, No Obj: 0.004276, Avg Recall: 0.490909, count: 110 -Region Avg IOU: 0.466860, Class: 1.000000, Obj: 0.218319, No Obj: 0.003871, Avg Recall: 0.505495, count: 91 -530: 0.530763, 0.539437 avg, 0.001000 rate, 21.073774 seconds, 135680 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000072 seconds -Region Avg IOU: 0.508408, Class: 1.000000, Obj: 0.254121, No Obj: 0.003812, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.504652, Class: 1.000000, Obj: 0.241682, No Obj: 0.003842, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.528883, Class: 1.000000, Obj: 0.241827, No Obj: 0.004128, Avg Recall: 0.588652, count: 141 -Region Avg IOU: 0.501640, Class: 1.000000, Obj: 0.179443, No Obj: 0.003931, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.509814, Class: 1.000000, Obj: 0.209600, No Obj: 0.004310, Avg Recall: 0.569444, count: 144 -Region Avg IOU: 0.550218, Class: 1.000000, Obj: 0.260409, No Obj: 0.003824, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.479545, Class: 1.000000, Obj: 0.218473, No Obj: 0.003502, Avg Recall: 0.533333, count: 105 -Region Avg IOU: 0.517104, Class: 1.000000, Obj: 0.203160, No Obj: 0.004195, Avg Recall: 0.627273, count: 110 -531: 0.569499, 0.542443 avg, 0.001000 rate, 26.856068 seconds, 135936 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.514069, Class: 1.000000, Obj: 0.210046, No Obj: 0.004001, Avg Recall: 0.561538, count: 130 -Region Avg IOU: 0.498034, Class: 1.000000, Obj: 0.220215, No Obj: 0.004252, Avg Recall: 0.611842, count: 152 -Region Avg IOU: 0.515712, Class: 1.000000, Obj: 0.239751, No Obj: 0.004025, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.547331, Class: 1.000000, Obj: 0.230572, No Obj: 0.003526, Avg Recall: 0.602410, count: 83 -Region Avg IOU: 0.560243, Class: 1.000000, Obj: 0.256870, No Obj: 0.004038, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.552592, Class: 1.000000, Obj: 0.204440, No Obj: 0.004023, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.565187, Class: 1.000000, Obj: 0.237969, No Obj: 0.004006, Avg Recall: 0.682927, count: 123 -Region Avg IOU: 0.479866, Class: 1.000000, Obj: 0.199404, No Obj: 0.003830, Avg Recall: 0.476562, count: 128 -532: 0.533883, 0.541587 avg, 0.001000 rate, 26.538965 seconds, 136192 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.493015, Class: 1.000000, Obj: 0.203523, No Obj: 0.003727, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.492821, Class: 1.000000, Obj: 0.207972, No Obj: 0.003991, Avg Recall: 0.579365, count: 126 -Region Avg IOU: 0.466670, Class: 1.000000, Obj: 0.227732, No Obj: 0.003667, Avg Recall: 0.487395, count: 119 -Region Avg IOU: 0.496926, Class: 1.000000, Obj: 0.234319, No Obj: 0.003951, Avg Recall: 0.561798, count: 89 -Region Avg IOU: 0.532029, Class: 1.000000, Obj: 0.215970, No Obj: 0.004118, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.482337, Class: 1.000000, Obj: 0.250660, No Obj: 0.003951, Avg Recall: 0.535088, count: 114 -Region Avg IOU: 0.507972, Class: 1.000000, Obj: 0.176343, No Obj: 0.003673, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.491077, Class: 1.000000, Obj: 0.225830, No Obj: 0.003727, Avg Recall: 0.500000, count: 126 -533: 0.487050, 0.536133 avg, 0.001000 rate, 25.693752 seconds, 136448 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.488179, Class: 1.000000, Obj: 0.236000, No Obj: 0.003922, Avg Recall: 0.539683, count: 63 -Region Avg IOU: 0.486806, Class: 1.000000, Obj: 0.211362, No Obj: 0.003899, Avg Recall: 0.500000, count: 114 -Region Avg IOU: 0.492778, Class: 1.000000, Obj: 0.173845, No Obj: 0.003852, Avg Recall: 0.575758, count: 132 -Region Avg IOU: 0.511937, Class: 1.000000, Obj: 0.178647, No Obj: 0.003188, Avg Recall: 0.585366, count: 82 -Region Avg IOU: 0.535004, Class: 1.000000, Obj: 0.235146, No Obj: 0.004048, Avg Recall: 0.566667, count: 90 -Region Avg IOU: 0.539540, Class: 1.000000, Obj: 0.219481, No Obj: 0.003647, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.538790, Class: 1.000000, Obj: 0.232893, No Obj: 0.003617, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.525014, Class: 1.000000, Obj: 0.199030, No Obj: 0.003896, Avg Recall: 0.612613, count: 111 -534: 0.527722, 0.535292 avg, 0.001000 rate, 28.910641 seconds, 136704 images -Loaded: 0.210800 seconds -Region Avg IOU: 0.515704, Class: 1.000000, Obj: 0.232592, No Obj: 0.003642, Avg Recall: 0.608247, count: 97 -Region Avg IOU: 0.508636, Class: 1.000000, Obj: 0.208608, No Obj: 0.003868, Avg Recall: 0.572519, count: 131 -Region Avg IOU: 0.500426, Class: 1.000000, Obj: 0.202825, No Obj: 0.004318, Avg Recall: 0.577586, count: 116 -Region Avg IOU: 0.511348, Class: 1.000000, Obj: 0.225575, No Obj: 0.003643, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.531340, Class: 1.000000, Obj: 0.234850, No Obj: 0.003879, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.504308, Class: 1.000000, Obj: 0.192332, No Obj: 0.003619, Avg Recall: 0.585106, count: 94 -Region Avg IOU: 0.549060, Class: 1.000000, Obj: 0.228280, No Obj: 0.003409, Avg Recall: 0.612500, count: 80 -Region Avg IOU: 0.514935, Class: 1.000000, Obj: 0.212357, No Obj: 0.003927, Avg Recall: 0.603604, count: 111 -535: 0.466441, 0.528407 avg, 0.001000 rate, 26.526463 seconds, 136960 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.530541, Class: 1.000000, Obj: 0.200771, No Obj: 0.003422, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.572436, Class: 1.000000, Obj: 0.249311, No Obj: 0.003836, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.539293, Class: 1.000000, Obj: 0.261513, No Obj: 0.003385, Avg Recall: 0.564103, count: 78 -Region Avg IOU: 0.527893, Class: 1.000000, Obj: 0.208405, No Obj: 0.003857, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.475102, Class: 1.000000, Obj: 0.224042, No Obj: 0.003898, Avg Recall: 0.485915, count: 142 -Region Avg IOU: 0.475168, Class: 1.000000, Obj: 0.197379, No Obj: 0.003868, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.525569, Class: 1.000000, Obj: 0.205460, No Obj: 0.003560, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.527776, Class: 1.000000, Obj: 0.210520, No Obj: 0.004386, Avg Recall: 0.633027, count: 109 -536: 0.511301, 0.526697 avg, 0.001000 rate, 25.677443 seconds, 137216 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.479036, Class: 1.000000, Obj: 0.222942, No Obj: 0.003159, Avg Recall: 0.476744, count: 86 -Region Avg IOU: 0.537182, Class: 1.000000, Obj: 0.229994, No Obj: 0.003744, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.527550, Class: 1.000000, Obj: 0.218479, No Obj: 0.003746, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.533809, Class: 1.000000, Obj: 0.215472, No Obj: 0.003926, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.532034, Class: 1.000000, Obj: 0.180854, No Obj: 0.004065, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.501148, Class: 1.000000, Obj: 0.193807, No Obj: 0.003897, Avg Recall: 0.544715, count: 123 -Region Avg IOU: 0.493749, Class: 1.000000, Obj: 0.213566, No Obj: 0.003448, Avg Recall: 0.534483, count: 116 -Region Avg IOU: 0.489788, Class: 1.000000, Obj: 0.192283, No Obj: 0.003581, Avg Recall: 0.544715, count: 123 -537: 0.488858, 0.522913 avg, 0.001000 rate, 25.450378 seconds, 137472 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.525733, Class: 1.000000, Obj: 0.204344, No Obj: 0.004329, Avg Recall: 0.604027, count: 149 -Region Avg IOU: 0.509762, Class: 1.000000, Obj: 0.217444, No Obj: 0.004308, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.484025, Class: 1.000000, Obj: 0.210388, No Obj: 0.003504, Avg Recall: 0.528846, count: 104 -Region Avg IOU: 0.523376, Class: 1.000000, Obj: 0.257684, No Obj: 0.003778, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.510887, Class: 1.000000, Obj: 0.200752, No Obj: 0.004187, Avg Recall: 0.606838, count: 117 -Region Avg IOU: 0.513953, Class: 1.000000, Obj: 0.193191, No Obj: 0.004070, Avg Recall: 0.617188, count: 128 -Region Avg IOU: 0.576691, Class: 1.000000, Obj: 0.195347, No Obj: 0.003905, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.509826, Class: 1.000000, Obj: 0.201338, No Obj: 0.004140, Avg Recall: 0.582090, count: 134 -538: 0.607907, 0.531412 avg, 0.001000 rate, 25.663197 seconds, 137728 images -Loaded: 0.000120 seconds -Region Avg IOU: 0.494001, Class: 1.000000, Obj: 0.195303, No Obj: 0.003453, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.532557, Class: 1.000000, Obj: 0.178829, No Obj: 0.004075, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.497226, Class: 1.000000, Obj: 0.181870, No Obj: 0.003192, Avg Recall: 0.553398, count: 103 -Region Avg IOU: 0.513986, Class: 1.000000, Obj: 0.211916, No Obj: 0.004139, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.520931, Class: 1.000000, Obj: 0.207597, No Obj: 0.003772, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.484309, Class: 1.000000, Obj: 0.186602, No Obj: 0.003892, Avg Recall: 0.553030, count: 132 -Region Avg IOU: 0.533064, Class: 1.000000, Obj: 0.253540, No Obj: 0.003655, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.542141, Class: 1.000000, Obj: 0.205457, No Obj: 0.003619, Avg Recall: 0.650000, count: 100 -539: 0.524357, 0.530707 avg, 0.001000 rate, 26.848221 seconds, 137984 images -Loaded: 0.011600 seconds -Region Avg IOU: 0.531778, Class: 1.000000, Obj: 0.238523, No Obj: 0.004219, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.542921, Class: 1.000000, Obj: 0.234040, No Obj: 0.004269, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.504470, Class: 1.000000, Obj: 0.177619, No Obj: 0.004322, Avg Recall: 0.548780, count: 164 -Region Avg IOU: 0.526883, Class: 1.000000, Obj: 0.226976, No Obj: 0.003984, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.500307, Class: 1.000000, Obj: 0.234739, No Obj: 0.003544, Avg Recall: 0.568421, count: 95 -Region Avg IOU: 0.524035, Class: 1.000000, Obj: 0.260480, No Obj: 0.003907, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.547018, Class: 1.000000, Obj: 0.215907, No Obj: 0.003524, Avg Recall: 0.625000, count: 72 -Region Avg IOU: 0.494826, Class: 1.000000, Obj: 0.213364, No Obj: 0.003983, Avg Recall: 0.537815, count: 119 -540: 0.555891, 0.533225 avg, 0.001000 rate, 26.249876 seconds, 138240 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000091 seconds -Region Avg IOU: 0.563755, Class: 1.000000, Obj: 0.239192, No Obj: 0.003809, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.568452, Class: 1.000000, Obj: 0.202417, No Obj: 0.003755, Avg Recall: 0.700000, count: 120 -Region Avg IOU: 0.535730, Class: 1.000000, Obj: 0.253210, No Obj: 0.003816, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.566864, Class: 1.000000, Obj: 0.221278, No Obj: 0.004100, Avg Recall: 0.692857, count: 140 -Region Avg IOU: 0.569764, Class: 1.000000, Obj: 0.255590, No Obj: 0.003696, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.587249, Class: 1.000000, Obj: 0.247800, No Obj: 0.003637, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.580220, Class: 1.000000, Obj: 0.227878, No Obj: 0.003708, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.511029, Class: 1.000000, Obj: 0.248209, No Obj: 0.003808, Avg Recall: 0.571429, count: 126 -541: 0.517517, 0.531654 avg, 0.001000 rate, 29.460224 seconds, 138496 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.549508, Class: 1.000000, Obj: 0.199718, No Obj: 0.004026, Avg Recall: 0.661765, count: 136 -Region Avg IOU: 0.549784, Class: 1.000000, Obj: 0.244959, No Obj: 0.003764, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.548828, Class: 1.000000, Obj: 0.200707, No Obj: 0.003762, Avg Recall: 0.679104, count: 134 -Region Avg IOU: 0.583934, Class: 1.000000, Obj: 0.246523, No Obj: 0.003640, Avg Recall: 0.710843, count: 83 -Region Avg IOU: 0.564087, Class: 1.000000, Obj: 0.229621, No Obj: 0.003532, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.553396, Class: 1.000000, Obj: 0.203725, No Obj: 0.003790, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.545486, Class: 1.000000, Obj: 0.179650, No Obj: 0.004062, Avg Recall: 0.642857, count: 140 -Region Avg IOU: 0.559383, Class: 1.000000, Obj: 0.196163, No Obj: 0.003911, Avg Recall: 0.641667, count: 120 -542: 0.537775, 0.532266 avg, 0.001000 rate, 30.603024 seconds, 138752 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.566900, Class: 1.000000, Obj: 0.204943, No Obj: 0.003826, Avg Recall: 0.676923, count: 130 -Region Avg IOU: 0.589381, Class: 1.000000, Obj: 0.261560, No Obj: 0.003722, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.514048, Class: 1.000000, Obj: 0.191612, No Obj: 0.003580, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.575010, Class: 1.000000, Obj: 0.193113, No Obj: 0.003402, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.568847, Class: 1.000000, Obj: 0.254701, No Obj: 0.003577, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.555743, Class: 1.000000, Obj: 0.208405, No Obj: 0.003924, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.559588, Class: 1.000000, Obj: 0.225873, No Obj: 0.003638, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.558399, Class: 1.000000, Obj: 0.266748, No Obj: 0.003640, Avg Recall: 0.647619, count: 105 -543: 0.559314, 0.534971 avg, 0.001000 rate, 31.248880 seconds, 139008 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.555890, Class: 1.000000, Obj: 0.210558, No Obj: 0.003251, Avg Recall: 0.639344, count: 122 -Region Avg IOU: 0.599653, Class: 1.000000, Obj: 0.186864, No Obj: 0.003610, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.553975, Class: 1.000000, Obj: 0.200843, No Obj: 0.003437, Avg Recall: 0.680328, count: 122 -Region Avg IOU: 0.580605, Class: 1.000000, Obj: 0.251190, No Obj: 0.003446, Avg Recall: 0.684783, count: 92 -Region Avg IOU: 0.578154, Class: 1.000000, Obj: 0.206689, No Obj: 0.003435, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.583814, Class: 1.000000, Obj: 0.244158, No Obj: 0.003780, Avg Recall: 0.704918, count: 122 -Region Avg IOU: 0.581103, Class: 1.000000, Obj: 0.202943, No Obj: 0.003550, Avg Recall: 0.744186, count: 129 -Region Avg IOU: 0.566554, Class: 1.000000, Obj: 0.211662, No Obj: 0.003743, Avg Recall: 0.657534, count: 146 -544: 0.546348, 0.536109 avg, 0.001000 rate, 29.849329 seconds, 139264 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.555639, Class: 1.000000, Obj: 0.178186, No Obj: 0.003622, Avg Recall: 0.635294, count: 85 -Region Avg IOU: 0.549195, Class: 1.000000, Obj: 0.219419, No Obj: 0.003736, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.572977, Class: 1.000000, Obj: 0.220915, No Obj: 0.003377, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.558997, Class: 1.000000, Obj: 0.240502, No Obj: 0.003695, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.582684, Class: 1.000000, Obj: 0.167090, No Obj: 0.003136, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.555046, Class: 1.000000, Obj: 0.173873, No Obj: 0.003219, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.519469, Class: 1.000000, Obj: 0.191949, No Obj: 0.003472, Avg Recall: 0.614754, count: 122 -Region Avg IOU: 0.558286, Class: 1.000000, Obj: 0.210788, No Obj: 0.003753, Avg Recall: 0.641509, count: 106 -545: 0.491926, 0.531691 avg, 0.001000 rate, 29.162624 seconds, 139520 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.597258, Class: 1.000000, Obj: 0.147528, No Obj: 0.003435, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.567986, Class: 1.000000, Obj: 0.184366, No Obj: 0.003677, Avg Recall: 0.674603, count: 126 -Region Avg IOU: 0.560883, Class: 1.000000, Obj: 0.144928, No Obj: 0.003239, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.556250, Class: 1.000000, Obj: 0.183761, No Obj: 0.003170, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.545422, Class: 1.000000, Obj: 0.189259, No Obj: 0.003688, Avg Recall: 0.644928, count: 138 -Region Avg IOU: 0.549387, Class: 1.000000, Obj: 0.164207, No Obj: 0.003227, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.596970, Class: 1.000000, Obj: 0.242612, No Obj: 0.003479, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.550540, Class: 1.000000, Obj: 0.204715, No Obj: 0.003548, Avg Recall: 0.701754, count: 114 -546: 0.600923, 0.538614 avg, 0.001000 rate, 30.821133 seconds, 139776 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.576105, Class: 1.000000, Obj: 0.225645, No Obj: 0.003430, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.533214, Class: 1.000000, Obj: 0.232800, No Obj: 0.003342, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.578063, Class: 1.000000, Obj: 0.204764, No Obj: 0.003351, Avg Recall: 0.681818, count: 88 -Region Avg IOU: 0.582174, Class: 1.000000, Obj: 0.171656, No Obj: 0.003379, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.547089, Class: 1.000000, Obj: 0.207925, No Obj: 0.003611, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.551208, Class: 1.000000, Obj: 0.214384, No Obj: 0.003527, Avg Recall: 0.632479, count: 117 -Region Avg IOU: 0.546169, Class: 1.000000, Obj: 0.217239, No Obj: 0.003451, Avg Recall: 0.666667, count: 123 -Region Avg IOU: 0.553838, Class: 1.000000, Obj: 0.196025, No Obj: 0.003616, Avg Recall: 0.652542, count: 118 -547: 0.548691, 0.539621 avg, 0.001000 rate, 31.343245 seconds, 140032 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.553821, Class: 1.000000, Obj: 0.220313, No Obj: 0.003327, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.538603, Class: 1.000000, Obj: 0.179642, No Obj: 0.003310, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.589489, Class: 1.000000, Obj: 0.212978, No Obj: 0.003499, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.597641, Class: 1.000000, Obj: 0.221244, No Obj: 0.003713, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.594408, Class: 1.000000, Obj: 0.185561, No Obj: 0.003808, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.563012, Class: 1.000000, Obj: 0.214795, No Obj: 0.003728, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.544047, Class: 1.000000, Obj: 0.213732, No Obj: 0.003359, Avg Recall: 0.601626, count: 123 -Region Avg IOU: 0.565042, Class: 1.000000, Obj: 0.221796, No Obj: 0.003708, Avg Recall: 0.707547, count: 106 -548: 0.576484, 0.543308 avg, 0.001000 rate, 30.824057 seconds, 140288 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.567582, Class: 1.000000, Obj: 0.195732, No Obj: 0.003674, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.541972, Class: 1.000000, Obj: 0.187811, No Obj: 0.003138, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.544291, Class: 1.000000, Obj: 0.200596, No Obj: 0.003522, Avg Recall: 0.670588, count: 85 -Region Avg IOU: 0.563251, Class: 1.000000, Obj: 0.254059, No Obj: 0.003441, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.484499, Class: 1.000000, Obj: 0.222487, No Obj: 0.003325, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.574271, Class: 1.000000, Obj: 0.214469, No Obj: 0.003536, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.536445, Class: 1.000000, Obj: 0.202361, No Obj: 0.003348, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.602127, Class: 1.000000, Obj: 0.226251, No Obj: 0.003746, Avg Recall: 0.720000, count: 100 -549: 0.478713, 0.536848 avg, 0.001000 rate, 30.669477 seconds, 140544 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.529412, Class: 1.000000, Obj: 0.216491, No Obj: 0.003651, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.546672, Class: 1.000000, Obj: 0.209615, No Obj: 0.003498, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.536748, Class: 1.000000, Obj: 0.205706, No Obj: 0.003552, Avg Recall: 0.629921, count: 127 -Region Avg IOU: 0.560391, Class: 1.000000, Obj: 0.246921, No Obj: 0.003485, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.553473, Class: 1.000000, Obj: 0.216258, No Obj: 0.003318, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.587554, Class: 1.000000, Obj: 0.246588, No Obj: 0.003786, Avg Recall: 0.733333, count: 120 -Region Avg IOU: 0.570411, Class: 1.000000, Obj: 0.169931, No Obj: 0.003817, Avg Recall: 0.682759, count: 145 -Region Avg IOU: 0.551309, Class: 1.000000, Obj: 0.218767, No Obj: 0.003437, Avg Recall: 0.689320, count: 103 -550: 0.543639, 0.537527 avg, 0.001000 rate, 28.337654 seconds, 140800 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 20.326736 seconds -Region Avg IOU: 0.525705, Class: 1.000000, Obj: 0.252200, No Obj: 0.003401, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.536395, Class: 1.000000, Obj: 0.262134, No Obj: 0.003418, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.524757, Class: 1.000000, Obj: 0.149603, No Obj: 0.003027, Avg Recall: 0.614458, count: 83 -Region Avg IOU: 0.537248, Class: 1.000000, Obj: 0.234363, No Obj: 0.003478, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.522001, Class: 1.000000, Obj: 0.204706, No Obj: 0.003622, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.561202, Class: 1.000000, Obj: 0.198697, No Obj: 0.003712, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.523125, Class: 1.000000, Obj: 0.217036, No Obj: 0.003366, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.502466, Class: 1.000000, Obj: 0.211313, No Obj: 0.003376, Avg Recall: 0.579545, count: 88 -551: 0.440451, 0.527820 avg, 0.001000 rate, 28.850948 seconds, 141056 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.555852, Class: 1.000000, Obj: 0.180398, No Obj: 0.003501, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.531877, Class: 1.000000, Obj: 0.234419, No Obj: 0.003813, Avg Recall: 0.641892, count: 148 -Region Avg IOU: 0.542266, Class: 1.000000, Obj: 0.246318, No Obj: 0.003318, Avg Recall: 0.621951, count: 82 -Region Avg IOU: 0.543173, Class: 1.000000, Obj: 0.231489, No Obj: 0.003375, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.547808, Class: 1.000000, Obj: 0.236945, No Obj: 0.003373, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.472857, Class: 1.000000, Obj: 0.228895, No Obj: 0.003597, Avg Recall: 0.517007, count: 147 -Region Avg IOU: 0.559626, Class: 1.000000, Obj: 0.249135, No Obj: 0.003455, Avg Recall: 0.626374, count: 91 -Region Avg IOU: 0.521700, Class: 1.000000, Obj: 0.212224, No Obj: 0.003801, Avg Recall: 0.587302, count: 126 -552: 0.555911, 0.530629 avg, 0.001000 rate, 29.364450 seconds, 141312 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.570316, Class: 1.000000, Obj: 0.222346, No Obj: 0.003521, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.527738, Class: 1.000000, Obj: 0.203395, No Obj: 0.003899, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.554465, Class: 1.000000, Obj: 0.246451, No Obj: 0.003917, Avg Recall: 0.658228, count: 158 -Region Avg IOU: 0.524927, Class: 1.000000, Obj: 0.197950, No Obj: 0.003271, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.535039, Class: 1.000000, Obj: 0.204146, No Obj: 0.003360, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.562150, Class: 1.000000, Obj: 0.244858, No Obj: 0.003644, Avg Recall: 0.698413, count: 126 -Region Avg IOU: 0.522827, Class: 1.000000, Obj: 0.233948, No Obj: 0.003293, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.527867, Class: 1.000000, Obj: 0.222911, No Obj: 0.003594, Avg Recall: 0.617647, count: 102 -553: 0.555441, 0.533110 avg, 0.001000 rate, 29.323000 seconds, 141568 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.548282, Class: 1.000000, Obj: 0.276941, No Obj: 0.003064, Avg Recall: 0.645570, count: 79 -Region Avg IOU: 0.526986, Class: 1.000000, Obj: 0.206405, No Obj: 0.003476, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.542410, Class: 1.000000, Obj: 0.229474, No Obj: 0.003103, Avg Recall: 0.620253, count: 79 -Region Avg IOU: 0.503881, Class: 1.000000, Obj: 0.204746, No Obj: 0.003638, Avg Recall: 0.538462, count: 117 -Region Avg IOU: 0.538668, Class: 1.000000, Obj: 0.231789, No Obj: 0.003489, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.518818, Class: 1.000000, Obj: 0.250926, No Obj: 0.003532, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.541151, Class: 1.000000, Obj: 0.218861, No Obj: 0.003504, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.522545, Class: 1.000000, Obj: 0.218355, No Obj: 0.003351, Avg Recall: 0.620690, count: 116 -554: 0.504997, 0.530299 avg, 0.001000 rate, 29.117453 seconds, 141824 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.536624, Class: 1.000000, Obj: 0.219225, No Obj: 0.003545, Avg Recall: 0.642276, count: 123 -Region Avg IOU: 0.584890, Class: 1.000000, Obj: 0.226209, No Obj: 0.003327, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.493179, Class: 1.000000, Obj: 0.218746, No Obj: 0.003384, Avg Recall: 0.516129, count: 93 -Region Avg IOU: 0.547852, Class: 1.000000, Obj: 0.163999, No Obj: 0.003038, Avg Recall: 0.696629, count: 89 -Region Avg IOU: 0.578530, Class: 1.000000, Obj: 0.234704, No Obj: 0.003489, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.493779, Class: 1.000000, Obj: 0.233534, No Obj: 0.003523, Avg Recall: 0.508621, count: 116 -Region Avg IOU: 0.540661, Class: 1.000000, Obj: 0.179947, No Obj: 0.003177, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.549399, Class: 1.000000, Obj: 0.254411, No Obj: 0.003303, Avg Recall: 0.700935, count: 107 -555: 0.464369, 0.523706 avg, 0.001000 rate, 27.531248 seconds, 142080 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.555270, Class: 1.000000, Obj: 0.218725, No Obj: 0.003555, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.528819, Class: 1.000000, Obj: 0.173039, No Obj: 0.003243, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.547690, Class: 1.000000, Obj: 0.265177, No Obj: 0.003125, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.517608, Class: 1.000000, Obj: 0.212051, No Obj: 0.003224, Avg Recall: 0.586207, count: 116 -Region Avg IOU: 0.514263, Class: 1.000000, Obj: 0.234431, No Obj: 0.003350, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.532553, Class: 1.000000, Obj: 0.226216, No Obj: 0.003110, Avg Recall: 0.585106, count: 94 -Region Avg IOU: 0.535338, Class: 1.000000, Obj: 0.205526, No Obj: 0.003325, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.529660, Class: 1.000000, Obj: 0.237780, No Obj: 0.003161, Avg Recall: 0.666667, count: 99 -556: 0.530234, 0.524359 avg, 0.001000 rate, 27.195423 seconds, 142336 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.575693, Class: 1.000000, Obj: 0.207956, No Obj: 0.003567, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.515853, Class: 1.000000, Obj: 0.228713, No Obj: 0.003246, Avg Recall: 0.573770, count: 122 -Region Avg IOU: 0.546870, Class: 1.000000, Obj: 0.251794, No Obj: 0.003286, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.541969, Class: 1.000000, Obj: 0.194963, No Obj: 0.003422, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.548757, Class: 1.000000, Obj: 0.238610, No Obj: 0.003240, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.548827, Class: 1.000000, Obj: 0.207465, No Obj: 0.002924, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.549106, Class: 1.000000, Obj: 0.193765, No Obj: 0.003257, Avg Recall: 0.620968, count: 124 -Region Avg IOU: 0.558355, Class: 1.000000, Obj: 0.180666, No Obj: 0.003371, Avg Recall: 0.660377, count: 106 -557: 0.503537, 0.522277 avg, 0.001000 rate, 27.993727 seconds, 142592 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.497803, Class: 1.000000, Obj: 0.219349, No Obj: 0.003295, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.541598, Class: 1.000000, Obj: 0.170082, No Obj: 0.003428, Avg Recall: 0.658915, count: 129 -Region Avg IOU: 0.544199, Class: 1.000000, Obj: 0.229281, No Obj: 0.003468, Avg Recall: 0.645161, count: 124 -Region Avg IOU: 0.570033, Class: 1.000000, Obj: 0.211761, No Obj: 0.002855, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.564589, Class: 1.000000, Obj: 0.203538, No Obj: 0.003233, Avg Recall: 0.720000, count: 125 -Region Avg IOU: 0.575164, Class: 1.000000, Obj: 0.233712, No Obj: 0.003541, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.506516, Class: 1.000000, Obj: 0.212712, No Obj: 0.003169, Avg Recall: 0.564516, count: 124 -Region Avg IOU: 0.546163, Class: 1.000000, Obj: 0.206461, No Obj: 0.003533, Avg Recall: 0.625954, count: 131 -558: 0.547362, 0.524785 avg, 0.001000 rate, 29.544355 seconds, 142848 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.572612, Class: 1.000000, Obj: 0.199998, No Obj: 0.003476, Avg Recall: 0.716418, count: 134 -Region Avg IOU: 0.522929, Class: 1.000000, Obj: 0.207478, No Obj: 0.003464, Avg Recall: 0.629032, count: 124 -Region Avg IOU: 0.543173, Class: 1.000000, Obj: 0.190473, No Obj: 0.003300, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.532211, Class: 1.000000, Obj: 0.227688, No Obj: 0.003214, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.572447, Class: 1.000000, Obj: 0.226941, No Obj: 0.003451, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.549701, Class: 1.000000, Obj: 0.229649, No Obj: 0.003569, Avg Recall: 0.614173, count: 127 -Region Avg IOU: 0.560357, Class: 1.000000, Obj: 0.256475, No Obj: 0.003240, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.523077, Class: 1.000000, Obj: 0.185153, No Obj: 0.003077, Avg Recall: 0.629630, count: 108 -559: 0.502181, 0.522525 avg, 0.001000 rate, 29.626720 seconds, 143104 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.569464, Class: 1.000000, Obj: 0.224344, No Obj: 0.003406, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.514610, Class: 1.000000, Obj: 0.263877, No Obj: 0.003548, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.531552, Class: 1.000000, Obj: 0.242220, No Obj: 0.003425, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.551526, Class: 1.000000, Obj: 0.207155, No Obj: 0.003303, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.533188, Class: 1.000000, Obj: 0.258858, No Obj: 0.003617, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.559656, Class: 1.000000, Obj: 0.264251, No Obj: 0.003148, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.545779, Class: 1.000000, Obj: 0.197757, No Obj: 0.003648, Avg Recall: 0.660606, count: 165 -Region Avg IOU: 0.546720, Class: 1.000000, Obj: 0.231485, No Obj: 0.003832, Avg Recall: 0.668966, count: 145 -560: 0.562217, 0.526494 avg, 0.001000 rate, 27.149729 seconds, 143360 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 17.041405 seconds -Region Avg IOU: 0.419437, Class: 1.000000, Obj: 0.171387, No Obj: 0.003859, Avg Recall: 0.418182, count: 110 -Region Avg IOU: 0.470356, Class: 1.000000, Obj: 0.174628, No Obj: 0.004222, Avg Recall: 0.460177, count: 113 -Region Avg IOU: 0.380276, Class: 1.000000, Obj: 0.215117, No Obj: 0.003828, Avg Recall: 0.375000, count: 72 -Region Avg IOU: 0.427117, Class: 1.000000, Obj: 0.166447, No Obj: 0.003936, Avg Recall: 0.407767, count: 103 -Region Avg IOU: 0.436296, Class: 1.000000, Obj: 0.216068, No Obj: 0.003950, Avg Recall: 0.500000, count: 84 -Region Avg IOU: 0.433684, Class: 1.000000, Obj: 0.174404, No Obj: 0.003949, Avg Recall: 0.474747, count: 99 -Region Avg IOU: 0.410862, Class: 1.000000, Obj: 0.226272, No Obj: 0.004388, Avg Recall: 0.401869, count: 107 -Region Avg IOU: 0.433015, Class: 1.000000, Obj: 0.213032, No Obj: 0.004458, Avg Recall: 0.413534, count: 133 -561: 0.591644, 0.533009 avg, 0.001000 rate, 16.850044 seconds, 143616 images -Loaded: 3.375533 seconds -Region Avg IOU: 0.466633, Class: 1.000000, Obj: 0.196868, No Obj: 0.003732, Avg Recall: 0.530120, count: 83 -Region Avg IOU: 0.468483, Class: 1.000000, Obj: 0.180947, No Obj: 0.004080, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.434804, Class: 1.000000, Obj: 0.180024, No Obj: 0.004008, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.403714, Class: 1.000000, Obj: 0.211674, No Obj: 0.004394, Avg Recall: 0.353846, count: 130 -Region Avg IOU: 0.445040, Class: 1.000000, Obj: 0.217521, No Obj: 0.004551, Avg Recall: 0.439716, count: 141 -Region Avg IOU: 0.476647, Class: 1.000000, Obj: 0.202364, No Obj: 0.004418, Avg Recall: 0.495327, count: 107 -Region Avg IOU: 0.433216, Class: 1.000000, Obj: 0.195998, No Obj: 0.004411, Avg Recall: 0.456954, count: 151 -Region Avg IOU: 0.450887, Class: 1.000000, Obj: 0.214492, No Obj: 0.004393, Avg Recall: 0.453704, count: 108 -562: 0.549321, 0.534640 avg, 0.001000 rate, 15.682961 seconds, 143872 images -Loaded: 2.103863 seconds -Region Avg IOU: 0.428368, Class: 1.000000, Obj: 0.232254, No Obj: 0.004334, Avg Recall: 0.446429, count: 112 -Region Avg IOU: 0.412116, Class: 1.000000, Obj: 0.202106, No Obj: 0.003938, Avg Recall: 0.437500, count: 112 -Region Avg IOU: 0.460740, Class: 1.000000, Obj: 0.227655, No Obj: 0.004444, Avg Recall: 0.529412, count: 119 -Region Avg IOU: 0.436983, Class: 1.000000, Obj: 0.194426, No Obj: 0.004794, Avg Recall: 0.442748, count: 131 -Region Avg IOU: 0.354978, Class: 1.000000, Obj: 0.178888, No Obj: 0.003628, Avg Recall: 0.351064, count: 94 -Region Avg IOU: 0.400252, Class: 1.000000, Obj: 0.204467, No Obj: 0.004785, Avg Recall: 0.426229, count: 122 -Region Avg IOU: 0.447175, Class: 1.000000, Obj: 0.228187, No Obj: 0.004298, Avg Recall: 0.452174, count: 115 -Region Avg IOU: 0.469903, Class: 1.000000, Obj: 0.197935, No Obj: 0.003872, Avg Recall: 0.465116, count: 86 -563: 0.543539, 0.535530 avg, 0.001000 rate, 19.255505 seconds, 144128 images -Loaded: 1.064674 seconds -Region Avg IOU: 0.416024, Class: 1.000000, Obj: 0.204345, No Obj: 0.004478, Avg Recall: 0.373016, count: 126 -Region Avg IOU: 0.423756, Class: 1.000000, Obj: 0.205901, No Obj: 0.004797, Avg Recall: 0.458333, count: 96 -Region Avg IOU: 0.438062, Class: 1.000000, Obj: 0.252937, No Obj: 0.004642, Avg Recall: 0.467890, count: 109 -Region Avg IOU: 0.438834, Class: 1.000000, Obj: 0.144162, No Obj: 0.004152, Avg Recall: 0.468750, count: 128 -Region Avg IOU: 0.439457, Class: 1.000000, Obj: 0.217975, No Obj: 0.004267, Avg Recall: 0.506024, count: 83 -Region Avg IOU: 0.387636, Class: 1.000000, Obj: 0.215522, No Obj: 0.004286, Avg Recall: 0.371901, count: 121 -Region Avg IOU: 0.432002, Class: 1.000000, Obj: 0.212945, No Obj: 0.004820, Avg Recall: 0.500000, count: 120 -Region Avg IOU: 0.415747, Class: 1.000000, Obj: 0.182469, No Obj: 0.003841, Avg Recall: 0.430000, count: 100 -564: 0.506738, 0.532651 avg, 0.001000 rate, 17.253218 seconds, 144384 images -Loaded: 4.428804 seconds -Region Avg IOU: 0.419942, Class: 1.000000, Obj: 0.182759, No Obj: 0.004170, Avg Recall: 0.378947, count: 95 -Region Avg IOU: 0.401373, Class: 1.000000, Obj: 0.199984, No Obj: 0.004318, Avg Recall: 0.398230, count: 113 -Region Avg IOU: 0.411970, Class: 1.000000, Obj: 0.168617, No Obj: 0.003832, Avg Recall: 0.416667, count: 108 -Region Avg IOU: 0.444144, Class: 1.000000, Obj: 0.204200, No Obj: 0.004743, Avg Recall: 0.445455, count: 110 -Region Avg IOU: 0.432098, Class: 1.000000, Obj: 0.182274, No Obj: 0.004729, Avg Recall: 0.432000, count: 125 -Region Avg IOU: 0.400845, Class: 1.000000, Obj: 0.252769, No Obj: 0.004715, Avg Recall: 0.361905, count: 105 -Region Avg IOU: 0.418733, Class: 1.000000, Obj: 0.169212, No Obj: 0.005004, Avg Recall: 0.398601, count: 143 -Region Avg IOU: 0.382481, Class: 1.000000, Obj: 0.207091, No Obj: 0.004549, Avg Recall: 0.387324, count: 142 -565: 0.709581, 0.550344 avg, 0.001000 rate, 18.532616 seconds, 144640 images -Loaded: 1.307789 seconds -Region Avg IOU: 0.410955, Class: 1.000000, Obj: 0.195961, No Obj: 0.003733, Avg Recall: 0.409091, count: 66 -Region Avg IOU: 0.423962, Class: 1.000000, Obj: 0.187032, No Obj: 0.004321, Avg Recall: 0.419355, count: 93 -Region Avg IOU: 0.447053, Class: 1.000000, Obj: 0.206667, No Obj: 0.004448, Avg Recall: 0.467890, count: 109 -Region Avg IOU: 0.420364, Class: 1.000000, Obj: 0.203507, No Obj: 0.004002, Avg Recall: 0.437500, count: 96 -Region Avg IOU: 0.409396, Class: 1.000000, Obj: 0.163836, No Obj: 0.003907, Avg Recall: 0.381356, count: 118 -Region Avg IOU: 0.444353, Class: 1.000000, Obj: 0.243971, No Obj: 0.004005, Avg Recall: 0.487500, count: 80 -Region Avg IOU: 0.422387, Class: 1.000000, Obj: 0.190314, No Obj: 0.003991, Avg Recall: 0.452381, count: 84 -Region Avg IOU: 0.427814, Class: 1.000000, Obj: 0.215379, No Obj: 0.003998, Avg Recall: 0.437500, count: 80 -566: 0.469445, 0.542254 avg, 0.001000 rate, 14.868147 seconds, 144896 images -Loaded: 6.550717 seconds -Region Avg IOU: 0.379738, Class: 1.000000, Obj: 0.166387, No Obj: 0.003878, Avg Recall: 0.333333, count: 84 -Region Avg IOU: 0.443960, Class: 1.000000, Obj: 0.193898, No Obj: 0.004089, Avg Recall: 0.495050, count: 101 -Region Avg IOU: 0.411187, Class: 1.000000, Obj: 0.198554, No Obj: 0.004269, Avg Recall: 0.441176, count: 102 -Region Avg IOU: 0.373217, Class: 1.000000, Obj: 0.246187, No Obj: 0.003814, Avg Recall: 0.356322, count: 87 -Region Avg IOU: 0.487196, Class: 1.000000, Obj: 0.222023, No Obj: 0.004804, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.441944, Class: 1.000000, Obj: 0.235896, No Obj: 0.004466, Avg Recall: 0.468085, count: 94 -Region Avg IOU: 0.416376, Class: 1.000000, Obj: 0.248007, No Obj: 0.004267, Avg Recall: 0.411111, count: 90 -Region Avg IOU: 0.462894, Class: 1.000000, Obj: 0.216664, No Obj: 0.003950, Avg Recall: 0.537500, count: 80 -567: 0.522138, 0.540242 avg, 0.001000 rate, 16.288980 seconds, 145152 images -Loaded: 5.908975 seconds -Region Avg IOU: 0.417226, Class: 1.000000, Obj: 0.208084, No Obj: 0.004299, Avg Recall: 0.464646, count: 99 -Region Avg IOU: 0.385577, Class: 1.000000, Obj: 0.196505, No Obj: 0.004296, Avg Recall: 0.370968, count: 124 -Region Avg IOU: 0.424193, Class: 1.000000, Obj: 0.183780, No Obj: 0.005373, Avg Recall: 0.451613, count: 124 -Region Avg IOU: 0.400305, Class: 1.000000, Obj: 0.217916, No Obj: 0.004253, Avg Recall: 0.400000, count: 110 -Region Avg IOU: 0.406585, Class: 1.000000, Obj: 0.192854, No Obj: 0.003919, Avg Recall: 0.377551, count: 98 -Region Avg IOU: 0.430799, Class: 1.000000, Obj: 0.168980, No Obj: 0.004273, Avg Recall: 0.440000, count: 100 -Region Avg IOU: 0.449142, Class: 1.000000, Obj: 0.195274, No Obj: 0.004633, Avg Recall: 0.457364, count: 129 -Region Avg IOU: 0.476690, Class: 1.000000, Obj: 0.224811, No Obj: 0.004389, Avg Recall: 0.533333, count: 105 -568: 0.524019, 0.538620 avg, 0.001000 rate, 15.969183 seconds, 145408 images -Loaded: 5.570620 seconds -Region Avg IOU: 0.429910, Class: 1.000000, Obj: 0.212103, No Obj: 0.004678, Avg Recall: 0.452555, count: 137 -Region Avg IOU: 0.399669, Class: 1.000000, Obj: 0.164362, No Obj: 0.004915, Avg Recall: 0.414474, count: 152 -Region Avg IOU: 0.413943, Class: 1.000000, Obj: 0.219797, No Obj: 0.004185, Avg Recall: 0.354331, count: 127 -Region Avg IOU: 0.398790, Class: 1.000000, Obj: 0.164856, No Obj: 0.004144, Avg Recall: 0.386861, count: 137 -Region Avg IOU: 0.390500, Class: 1.000000, Obj: 0.200568, No Obj: 0.004120, Avg Recall: 0.428571, count: 84 -Region Avg IOU: 0.441150, Class: 1.000000, Obj: 0.207418, No Obj: 0.004563, Avg Recall: 0.482143, count: 112 -Region Avg IOU: 0.444169, Class: 1.000000, Obj: 0.223741, No Obj: 0.004629, Avg Recall: 0.469231, count: 130 -Region Avg IOU: 0.466878, Class: 1.000000, Obj: 0.192939, No Obj: 0.004982, Avg Recall: 0.486957, count: 115 -569: 0.638839, 0.548642 avg, 0.001000 rate, 16.545382 seconds, 145664 images -Loaded: 5.338088 seconds -Region Avg IOU: 0.417722, Class: 1.000000, Obj: 0.205502, No Obj: 0.003752, Avg Recall: 0.415842, count: 101 -Region Avg IOU: 0.410837, Class: 1.000000, Obj: 0.216096, No Obj: 0.004026, Avg Recall: 0.406250, count: 96 -Region Avg IOU: 0.428055, Class: 1.000000, Obj: 0.171165, No Obj: 0.003988, Avg Recall: 0.473684, count: 114 -Region Avg IOU: 0.419281, Class: 1.000000, Obj: 0.202507, No Obj: 0.004202, Avg Recall: 0.474359, count: 78 -Region Avg IOU: 0.403781, Class: 1.000000, Obj: 0.171832, No Obj: 0.004105, Avg Recall: 0.356436, count: 101 -Region Avg IOU: 0.360779, Class: 1.000000, Obj: 0.186780, No Obj: 0.003939, Avg Recall: 0.358491, count: 106 -Region Avg IOU: 0.423758, Class: 1.000000, Obj: 0.209866, No Obj: 0.004590, Avg Recall: 0.417476, count: 103 -Region Avg IOU: 0.386755, Class: 1.000000, Obj: 0.193376, No Obj: 0.004212, Avg Recall: 0.394231, count: 104 -570: 0.529810, 0.546759 avg, 0.001000 rate, 15.467164 seconds, 145920 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000096 seconds -Region Avg IOU: 0.529375, Class: 1.000000, Obj: 0.236454, No Obj: 0.003837, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.543789, Class: 1.000000, Obj: 0.259427, No Obj: 0.004050, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.509827, Class: 1.000000, Obj: 0.239890, No Obj: 0.003423, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.513505, Class: 1.000000, Obj: 0.214239, No Obj: 0.003659, Avg Recall: 0.580952, count: 105 -Region Avg IOU: 0.546085, Class: 1.000000, Obj: 0.270514, No Obj: 0.003665, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.502177, Class: 1.000000, Obj: 0.223353, No Obj: 0.003612, Avg Recall: 0.588235, count: 102 -Region Avg IOU: 0.507461, Class: 1.000000, Obj: 0.219713, No Obj: 0.003769, Avg Recall: 0.592920, count: 113 -Region Avg IOU: 0.554886, Class: 1.000000, Obj: 0.216712, No Obj: 0.004129, Avg Recall: 0.690476, count: 126 -571: 0.481105, 0.540193 avg, 0.001000 rate, 28.426157 seconds, 146176 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.570209, Class: 1.000000, Obj: 0.221168, No Obj: 0.003568, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.544116, Class: 1.000000, Obj: 0.220941, No Obj: 0.004328, Avg Recall: 0.617886, count: 123 -Region Avg IOU: 0.536843, Class: 1.000000, Obj: 0.214694, No Obj: 0.003710, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.550462, Class: 1.000000, Obj: 0.182088, No Obj: 0.003778, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.531107, Class: 1.000000, Obj: 0.172206, No Obj: 0.003809, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.583751, Class: 1.000000, Obj: 0.249734, No Obj: 0.004066, Avg Recall: 0.686046, count: 86 -Region Avg IOU: 0.545036, Class: 1.000000, Obj: 0.216859, No Obj: 0.003711, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.563410, Class: 1.000000, Obj: 0.207336, No Obj: 0.003770, Avg Recall: 0.670213, count: 94 -572: 0.498748, 0.536049 avg, 0.001000 rate, 28.522247 seconds, 146432 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.553992, Class: 1.000000, Obj: 0.279553, No Obj: 0.003587, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.554205, Class: 1.000000, Obj: 0.228018, No Obj: 0.003603, Avg Recall: 0.632000, count: 125 -Region Avg IOU: 0.534192, Class: 1.000000, Obj: 0.222902, No Obj: 0.003562, Avg Recall: 0.616667, count: 120 -Region Avg IOU: 0.570500, Class: 1.000000, Obj: 0.230802, No Obj: 0.003769, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.524080, Class: 1.000000, Obj: 0.198540, No Obj: 0.003238, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.551873, Class: 1.000000, Obj: 0.204872, No Obj: 0.003482, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.515444, Class: 1.000000, Obj: 0.209013, No Obj: 0.003695, Avg Recall: 0.593220, count: 118 -Region Avg IOU: 0.539278, Class: 1.000000, Obj: 0.241272, No Obj: 0.003567, Avg Recall: 0.625000, count: 112 -573: 0.571930, 0.539637 avg, 0.001000 rate, 30.618029 seconds, 146688 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.537066, Class: 1.000000, Obj: 0.223305, No Obj: 0.003344, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.558472, Class: 1.000000, Obj: 0.235661, No Obj: 0.003315, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.549883, Class: 1.000000, Obj: 0.213349, No Obj: 0.003163, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.546411, Class: 1.000000, Obj: 0.195244, No Obj: 0.003348, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.552761, Class: 1.000000, Obj: 0.199321, No Obj: 0.003718, Avg Recall: 0.654135, count: 133 -Region Avg IOU: 0.555622, Class: 1.000000, Obj: 0.205460, No Obj: 0.003410, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.567961, Class: 1.000000, Obj: 0.210867, No Obj: 0.003289, Avg Recall: 0.662500, count: 80 -Region Avg IOU: 0.563704, Class: 1.000000, Obj: 0.229203, No Obj: 0.003593, Avg Recall: 0.656863, count: 102 -574: 0.529072, 0.538581 avg, 0.001000 rate, 28.926865 seconds, 146944 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.534038, Class: 1.000000, Obj: 0.160388, No Obj: 0.003804, Avg Recall: 0.661765, count: 136 -Region Avg IOU: 0.587100, Class: 1.000000, Obj: 0.236225, No Obj: 0.003511, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.536223, Class: 1.000000, Obj: 0.205707, No Obj: 0.003475, Avg Recall: 0.608333, count: 120 -Region Avg IOU: 0.554889, Class: 1.000000, Obj: 0.219954, No Obj: 0.003391, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.552222, Class: 1.000000, Obj: 0.228051, No Obj: 0.003515, Avg Recall: 0.650000, count: 140 -Region Avg IOU: 0.582362, Class: 1.000000, Obj: 0.210115, No Obj: 0.003450, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.553887, Class: 1.000000, Obj: 0.199373, No Obj: 0.003195, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.549313, Class: 1.000000, Obj: 0.190469, No Obj: 0.003564, Avg Recall: 0.605634, count: 142 -575: 0.593116, 0.544034 avg, 0.001000 rate, 26.875368 seconds, 147200 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.597636, Class: 1.000000, Obj: 0.206634, No Obj: 0.003354, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.519004, Class: 1.000000, Obj: 0.192368, No Obj: 0.003367, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.535435, Class: 1.000000, Obj: 0.209544, No Obj: 0.003480, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.548297, Class: 1.000000, Obj: 0.187066, No Obj: 0.003043, Avg Recall: 0.671429, count: 70 -Region Avg IOU: 0.561490, Class: 1.000000, Obj: 0.196742, No Obj: 0.003546, Avg Recall: 0.693878, count: 147 -Region Avg IOU: 0.552619, Class: 1.000000, Obj: 0.188515, No Obj: 0.003213, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.527388, Class: 1.000000, Obj: 0.206935, No Obj: 0.003020, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.491801, Class: 1.000000, Obj: 0.193322, No Obj: 0.003022, Avg Recall: 0.547368, count: 95 -576: 0.488351, 0.538466 avg, 0.001000 rate, 29.297323 seconds, 147456 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.566955, Class: 1.000000, Obj: 0.207665, No Obj: 0.003427, Avg Recall: 0.705882, count: 119 -Region Avg IOU: 0.564298, Class: 1.000000, Obj: 0.204309, No Obj: 0.003140, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.581639, Class: 1.000000, Obj: 0.191418, No Obj: 0.003032, Avg Recall: 0.709302, count: 86 -Region Avg IOU: 0.541508, Class: 1.000000, Obj: 0.185580, No Obj: 0.003519, Avg Recall: 0.620253, count: 158 -Region Avg IOU: 0.580562, Class: 1.000000, Obj: 0.201533, No Obj: 0.003112, Avg Recall: 0.758621, count: 87 -Region Avg IOU: 0.582500, Class: 1.000000, Obj: 0.203495, No Obj: 0.003615, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.554481, Class: 1.000000, Obj: 0.215021, No Obj: 0.003242, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.570089, Class: 1.000000, Obj: 0.224171, No Obj: 0.003379, Avg Recall: 0.682692, count: 104 -577: 0.527282, 0.537347 avg, 0.001000 rate, 28.737852 seconds, 147712 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.547789, Class: 1.000000, Obj: 0.208356, No Obj: 0.003006, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.552609, Class: 1.000000, Obj: 0.192599, No Obj: 0.003275, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.522830, Class: 1.000000, Obj: 0.225409, No Obj: 0.003352, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.565699, Class: 1.000000, Obj: 0.203747, No Obj: 0.003508, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.549067, Class: 1.000000, Obj: 0.221723, No Obj: 0.003187, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.535809, Class: 1.000000, Obj: 0.177622, No Obj: 0.003217, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.547106, Class: 1.000000, Obj: 0.210965, No Obj: 0.003078, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.569916, Class: 1.000000, Obj: 0.209325, No Obj: 0.003098, Avg Recall: 0.632075, count: 106 -578: 0.554062, 0.539019 avg, 0.001000 rate, 29.656023 seconds, 147968 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.557060, Class: 1.000000, Obj: 0.223368, No Obj: 0.003625, Avg Recall: 0.698276, count: 116 -Region Avg IOU: 0.540768, Class: 1.000000, Obj: 0.184813, No Obj: 0.003299, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.575521, Class: 1.000000, Obj: 0.203750, No Obj: 0.003319, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.578667, Class: 1.000000, Obj: 0.241921, No Obj: 0.003447, Avg Recall: 0.673684, count: 95 -Region Avg IOU: 0.555317, Class: 1.000000, Obj: 0.227614, No Obj: 0.003655, Avg Recall: 0.661290, count: 124 -Region Avg IOU: 0.568858, Class: 1.000000, Obj: 0.215237, No Obj: 0.003457, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.543968, Class: 1.000000, Obj: 0.181338, No Obj: 0.003701, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.542516, Class: 1.000000, Obj: 0.240058, No Obj: 0.003178, Avg Recall: 0.574257, count: 101 -579: 0.530574, 0.538174 avg, 0.001000 rate, 28.243267 seconds, 148224 images -Loaded: 0.000059 seconds -Region Avg IOU: 0.544537, Class: 1.000000, Obj: 0.232333, No Obj: 0.003384, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.552985, Class: 1.000000, Obj: 0.199452, No Obj: 0.003673, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.568378, Class: 1.000000, Obj: 0.188014, No Obj: 0.003516, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.536627, Class: 1.000000, Obj: 0.215098, No Obj: 0.003315, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.584399, Class: 1.000000, Obj: 0.221858, No Obj: 0.003300, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.506908, Class: 1.000000, Obj: 0.191476, No Obj: 0.003238, Avg Recall: 0.584746, count: 118 -Region Avg IOU: 0.563459, Class: 1.000000, Obj: 0.196899, No Obj: 0.003828, Avg Recall: 0.662069, count: 145 -Region Avg IOU: 0.566065, Class: 1.000000, Obj: 0.205140, No Obj: 0.003459, Avg Recall: 0.657143, count: 105 -580: 0.521509, 0.536508 avg, 0.001000 rate, 30.326923 seconds, 148480 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 19.147530 seconds -Region Avg IOU: 0.560721, Class: 1.000000, Obj: 0.270149, No Obj: 0.003801, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.552889, Class: 1.000000, Obj: 0.208285, No Obj: 0.003501, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.540473, Class: 1.000000, Obj: 0.240666, No Obj: 0.003829, Avg Recall: 0.635659, count: 129 -Region Avg IOU: 0.533028, Class: 1.000000, Obj: 0.206702, No Obj: 0.003194, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.521456, Class: 1.000000, Obj: 0.259892, No Obj: 0.003930, Avg Recall: 0.580882, count: 136 -Region Avg IOU: 0.563928, Class: 1.000000, Obj: 0.236728, No Obj: 0.003920, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.545896, Class: 1.000000, Obj: 0.196432, No Obj: 0.003425, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.559215, Class: 1.000000, Obj: 0.212057, No Obj: 0.003338, Avg Recall: 0.633929, count: 112 -581: 0.530446, 0.535902 avg, 0.001000 rate, 28.158504 seconds, 148736 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.543718, Class: 1.000000, Obj: 0.273953, No Obj: 0.003592, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.495125, Class: 1.000000, Obj: 0.184204, No Obj: 0.003394, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.508865, Class: 1.000000, Obj: 0.236079, No Obj: 0.003330, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.513478, Class: 1.000000, Obj: 0.257671, No Obj: 0.003544, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.527413, Class: 1.000000, Obj: 0.195590, No Obj: 0.003688, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.481925, Class: 1.000000, Obj: 0.212686, No Obj: 0.003871, Avg Recall: 0.565574, count: 122 -Region Avg IOU: 0.546953, Class: 1.000000, Obj: 0.238993, No Obj: 0.004386, Avg Recall: 0.656934, count: 137 -Region Avg IOU: 0.540530, Class: 1.000000, Obj: 0.235854, No Obj: 0.003713, Avg Recall: 0.640351, count: 114 -582: 0.523639, 0.534675 avg, 0.001000 rate, 28.059202 seconds, 148992 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.529802, Class: 1.000000, Obj: 0.214129, No Obj: 0.003889, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.544139, Class: 1.000000, Obj: 0.241844, No Obj: 0.003685, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.562002, Class: 1.000000, Obj: 0.249881, No Obj: 0.004031, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.535663, Class: 1.000000, Obj: 0.235033, No Obj: 0.003582, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.513484, Class: 1.000000, Obj: 0.210638, No Obj: 0.003512, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.473737, Class: 1.000000, Obj: 0.253175, No Obj: 0.003661, Avg Recall: 0.500000, count: 104 -Region Avg IOU: 0.509047, Class: 1.000000, Obj: 0.235239, No Obj: 0.003927, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.565194, Class: 1.000000, Obj: 0.267977, No Obj: 0.003959, Avg Recall: 0.654206, count: 107 -583: 0.506493, 0.531857 avg, 0.001000 rate, 26.980604 seconds, 149248 images -Loaded: 0.012544 seconds -Region Avg IOU: 0.489923, Class: 1.000000, Obj: 0.268471, No Obj: 0.003650, Avg Recall: 0.533835, count: 133 -Region Avg IOU: 0.529758, Class: 1.000000, Obj: 0.251594, No Obj: 0.003832, Avg Recall: 0.640000, count: 125 -Region Avg IOU: 0.520186, Class: 1.000000, Obj: 0.254009, No Obj: 0.004030, Avg Recall: 0.631148, count: 122 -Region Avg IOU: 0.521344, Class: 1.000000, Obj: 0.226099, No Obj: 0.003662, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.498604, Class: 1.000000, Obj: 0.214091, No Obj: 0.004056, Avg Recall: 0.532895, count: 152 -Region Avg IOU: 0.503543, Class: 1.000000, Obj: 0.223176, No Obj: 0.003675, Avg Recall: 0.563910, count: 133 -Region Avg IOU: 0.517275, Class: 1.000000, Obj: 0.241521, No Obj: 0.003199, Avg Recall: 0.604938, count: 81 -Region Avg IOU: 0.522990, Class: 1.000000, Obj: 0.247009, No Obj: 0.003476, Avg Recall: 0.648148, count: 108 -584: 0.585604, 0.537232 avg, 0.001000 rate, 26.260012 seconds, 149504 images -Loaded: 3.460153 seconds -Region Avg IOU: 0.553711, Class: 1.000000, Obj: 0.252015, No Obj: 0.003857, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.532619, Class: 1.000000, Obj: 0.263747, No Obj: 0.003841, Avg Recall: 0.606299, count: 127 -Region Avg IOU: 0.507812, Class: 1.000000, Obj: 0.291931, No Obj: 0.003838, Avg Recall: 0.560748, count: 107 -Region Avg IOU: 0.575832, Class: 1.000000, Obj: 0.281148, No Obj: 0.003692, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.535186, Class: 1.000000, Obj: 0.243543, No Obj: 0.003992, Avg Recall: 0.580645, count: 124 -Region Avg IOU: 0.548851, Class: 1.000000, Obj: 0.271915, No Obj: 0.003604, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.568165, Class: 1.000000, Obj: 0.292618, No Obj: 0.003932, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.539738, Class: 1.000000, Obj: 0.264611, No Obj: 0.003944, Avg Recall: 0.624000, count: 125 -585: 0.537088, 0.537217 avg, 0.001000 rate, 27.128639 seconds, 149760 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.485349, Class: 1.000000, Obj: 0.260415, No Obj: 0.003815, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.544389, Class: 1.000000, Obj: 0.247630, No Obj: 0.003819, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.541741, Class: 1.000000, Obj: 0.251600, No Obj: 0.003668, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.566989, Class: 1.000000, Obj: 0.217508, No Obj: 0.003840, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.513598, Class: 1.000000, Obj: 0.226516, No Obj: 0.003434, Avg Recall: 0.600000, count: 90 -Region Avg IOU: 0.546336, Class: 1.000000, Obj: 0.218963, No Obj: 0.003722, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.503192, Class: 1.000000, Obj: 0.252466, No Obj: 0.003614, Avg Recall: 0.550000, count: 100 -Region Avg IOU: 0.557700, Class: 1.000000, Obj: 0.266816, No Obj: 0.004119, Avg Recall: 0.652632, count: 95 -586: 0.482347, 0.531730 avg, 0.001000 rate, 26.598108 seconds, 150016 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.544986, Class: 1.000000, Obj: 0.286587, No Obj: 0.003183, Avg Recall: 0.662791, count: 86 -Region Avg IOU: 0.498034, Class: 1.000000, Obj: 0.209963, No Obj: 0.003775, Avg Recall: 0.542373, count: 118 -Region Avg IOU: 0.513054, Class: 1.000000, Obj: 0.245425, No Obj: 0.004164, Avg Recall: 0.596899, count: 129 -Region Avg IOU: 0.525387, Class: 1.000000, Obj: 0.251265, No Obj: 0.003915, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.532424, Class: 1.000000, Obj: 0.192049, No Obj: 0.003624, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.512352, Class: 1.000000, Obj: 0.230612, No Obj: 0.003826, Avg Recall: 0.560000, count: 125 -Region Avg IOU: 0.531457, Class: 1.000000, Obj: 0.202312, No Obj: 0.003605, Avg Recall: 0.611570, count: 121 -Region Avg IOU: 0.483954, Class: 1.000000, Obj: 0.189387, No Obj: 0.003676, Avg Recall: 0.530769, count: 130 -587: 0.555698, 0.534127 avg, 0.001000 rate, 26.718122 seconds, 150272 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.535382, Class: 1.000000, Obj: 0.221913, No Obj: 0.003683, Avg Recall: 0.645669, count: 127 -Region Avg IOU: 0.498468, Class: 1.000000, Obj: 0.220670, No Obj: 0.003273, Avg Recall: 0.500000, count: 86 -Region Avg IOU: 0.514812, Class: 1.000000, Obj: 0.202876, No Obj: 0.003606, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.532991, Class: 1.000000, Obj: 0.223452, No Obj: 0.003554, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.580905, Class: 1.000000, Obj: 0.267527, No Obj: 0.003204, Avg Recall: 0.726191, count: 84 -Region Avg IOU: 0.506633, Class: 1.000000, Obj: 0.210083, No Obj: 0.003875, Avg Recall: 0.589744, count: 117 -Region Avg IOU: 0.540754, Class: 1.000000, Obj: 0.209774, No Obj: 0.003543, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.526018, Class: 1.000000, Obj: 0.234183, No Obj: 0.003390, Avg Recall: 0.605769, count: 104 -588: 0.529645, 0.533679 avg, 0.001000 rate, 26.914680 seconds, 150528 images -Loaded: 1.488497 seconds -Region Avg IOU: 0.529655, Class: 1.000000, Obj: 0.254374, No Obj: 0.003377, Avg Recall: 0.605769, count: 104 -Region Avg IOU: 0.507703, Class: 1.000000, Obj: 0.192142, No Obj: 0.003315, Avg Recall: 0.588710, count: 124 -Region Avg IOU: 0.536089, Class: 1.000000, Obj: 0.217304, No Obj: 0.003117, Avg Recall: 0.632184, count: 87 -Region Avg IOU: 0.505383, Class: 1.000000, Obj: 0.195245, No Obj: 0.003496, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.503437, Class: 1.000000, Obj: 0.223581, No Obj: 0.003453, Avg Recall: 0.552000, count: 125 -Region Avg IOU: 0.565577, Class: 1.000000, Obj: 0.215962, No Obj: 0.003752, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.559356, Class: 1.000000, Obj: 0.184808, No Obj: 0.003674, Avg Recall: 0.687023, count: 131 -Region Avg IOU: 0.523951, Class: 1.000000, Obj: 0.226571, No Obj: 0.003402, Avg Recall: 0.626168, count: 107 -589: 0.537381, 0.534049 avg, 0.001000 rate, 27.253780 seconds, 150784 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.433223, Class: 1.000000, Obj: 0.179053, No Obj: 0.003019, Avg Recall: 0.451613, count: 93 -Region Avg IOU: 0.557505, Class: 1.000000, Obj: 0.262964, No Obj: 0.003539, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.507261, Class: 1.000000, Obj: 0.214434, No Obj: 0.003201, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.476615, Class: 1.000000, Obj: 0.233053, No Obj: 0.003408, Avg Recall: 0.520661, count: 121 -Region Avg IOU: 0.502058, Class: 1.000000, Obj: 0.199665, No Obj: 0.003076, Avg Recall: 0.595506, count: 89 -Region Avg IOU: 0.505381, Class: 1.000000, Obj: 0.211056, No Obj: 0.003263, Avg Recall: 0.541667, count: 120 -Region Avg IOU: 0.532251, Class: 1.000000, Obj: 0.208463, No Obj: 0.003333, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.508394, Class: 1.000000, Obj: 0.232603, No Obj: 0.003407, Avg Recall: 0.573913, count: 115 -590: 0.537009, 0.534345 avg, 0.001000 rate, 28.299061 seconds, 151040 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 17.719341 seconds -Region Avg IOU: 0.432424, Class: 1.000000, Obj: 0.185877, No Obj: 0.004040, Avg Recall: 0.491071, count: 112 -Region Avg IOU: 0.400857, Class: 1.000000, Obj: 0.169004, No Obj: 0.003483, Avg Recall: 0.409524, count: 105 -Region Avg IOU: 0.418528, Class: 1.000000, Obj: 0.177918, No Obj: 0.003680, Avg Recall: 0.422222, count: 90 -Region Avg IOU: 0.434290, Class: 1.000000, Obj: 0.187754, No Obj: 0.004175, Avg Recall: 0.429688, count: 128 -Region Avg IOU: 0.412737, Class: 1.000000, Obj: 0.195018, No Obj: 0.004356, Avg Recall: 0.394737, count: 114 -Region Avg IOU: 0.451608, Class: 1.000000, Obj: 0.202749, No Obj: 0.003847, Avg Recall: 0.495146, count: 103 -Region Avg IOU: 0.460436, Class: 1.000000, Obj: 0.177473, No Obj: 0.004067, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.432079, Class: 1.000000, Obj: 0.170985, No Obj: 0.004041, Avg Recall: 0.494949, count: 99 -591: 0.557105, 0.536621 avg, 0.001000 rate, 17.069572 seconds, 151296 images -Loaded: 2.957413 seconds -Region Avg IOU: 0.397455, Class: 1.000000, Obj: 0.187969, No Obj: 0.003937, Avg Recall: 0.430000, count: 100 -Region Avg IOU: 0.433097, Class: 1.000000, Obj: 0.196834, No Obj: 0.004403, Avg Recall: 0.438095, count: 105 -Region Avg IOU: 0.384514, Class: 1.000000, Obj: 0.135619, No Obj: 0.003406, Avg Recall: 0.416667, count: 96 -Region Avg IOU: 0.460782, Class: 1.000000, Obj: 0.191832, No Obj: 0.004232, Avg Recall: 0.526316, count: 114 -Region Avg IOU: 0.430286, Class: 1.000000, Obj: 0.184559, No Obj: 0.004041, Avg Recall: 0.418182, count: 110 -Region Avg IOU: 0.403073, Class: 1.000000, Obj: 0.148693, No Obj: 0.003713, Avg Recall: 0.410714, count: 112 -Region Avg IOU: 0.458422, Class: 1.000000, Obj: 0.196826, No Obj: 0.003798, Avg Recall: 0.519231, count: 104 -Region Avg IOU: 0.439937, Class: 1.000000, Obj: 0.167185, No Obj: 0.004154, Avg Recall: 0.473214, count: 112 -592: 0.659873, 0.548946 avg, 0.001000 rate, 17.265575 seconds, 151552 images -Loaded: 2.144272 seconds -Region Avg IOU: 0.440840, Class: 1.000000, Obj: 0.211719, No Obj: 0.003695, Avg Recall: 0.488095, count: 84 -Region Avg IOU: 0.397058, Class: 1.000000, Obj: 0.212670, No Obj: 0.003994, Avg Recall: 0.363636, count: 99 -Region Avg IOU: 0.466200, Class: 1.000000, Obj: 0.224822, No Obj: 0.004237, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.349909, Class: 1.000000, Obj: 0.147889, No Obj: 0.003561, Avg Recall: 0.300000, count: 100 -Region Avg IOU: 0.419703, Class: 1.000000, Obj: 0.232115, No Obj: 0.004740, Avg Recall: 0.425287, count: 87 -Region Avg IOU: 0.404744, Class: 1.000000, Obj: 0.196458, No Obj: 0.003770, Avg Recall: 0.428571, count: 98 -Region Avg IOU: 0.390281, Class: 1.000000, Obj: 0.146464, No Obj: 0.003482, Avg Recall: 0.393258, count: 89 -Region Avg IOU: 0.410792, Class: 1.000000, Obj: 0.188881, No Obj: 0.003960, Avg Recall: 0.406780, count: 118 -593: 0.501380, 0.544190 avg, 0.001000 rate, 17.238300 seconds, 151808 images -Loaded: 4.308375 seconds -Region Avg IOU: 0.391830, Class: 1.000000, Obj: 0.201436, No Obj: 0.004234, Avg Recall: 0.376000, count: 125 -Region Avg IOU: 0.421889, Class: 1.000000, Obj: 0.186844, No Obj: 0.004689, Avg Recall: 0.433566, count: 143 -Region Avg IOU: 0.416034, Class: 1.000000, Obj: 0.202885, No Obj: 0.004324, Avg Recall: 0.405941, count: 101 -Region Avg IOU: 0.421075, Class: 1.000000, Obj: 0.203908, No Obj: 0.004557, Avg Recall: 0.420168, count: 119 -Region Avg IOU: 0.429392, Class: 1.000000, Obj: 0.186378, No Obj: 0.003839, Avg Recall: 0.443299, count: 97 -Region Avg IOU: 0.409910, Class: 1.000000, Obj: 0.195120, No Obj: 0.003769, Avg Recall: 0.421053, count: 95 -Region Avg IOU: 0.462886, Class: 1.000000, Obj: 0.215771, No Obj: 0.004196, Avg Recall: 0.487395, count: 119 -Region Avg IOU: 0.422043, Class: 1.000000, Obj: 0.181570, No Obj: 0.004595, Avg Recall: 0.440367, count: 109 -594: 0.560070, 0.545778 avg, 0.001000 rate, 18.477200 seconds, 152064 images -Loaded: 2.531684 seconds -Region Avg IOU: 0.405547, Class: 1.000000, Obj: 0.194051, No Obj: 0.004358, Avg Recall: 0.435185, count: 108 -Region Avg IOU: 0.440576, Class: 1.000000, Obj: 0.234200, No Obj: 0.003801, Avg Recall: 0.543210, count: 81 -Region Avg IOU: 0.415015, Class: 1.000000, Obj: 0.215913, No Obj: 0.003780, Avg Recall: 0.453488, count: 86 -Region Avg IOU: 0.429187, Class: 1.000000, Obj: 0.207626, No Obj: 0.004303, Avg Recall: 0.460177, count: 113 -Region Avg IOU: 0.453325, Class: 1.000000, Obj: 0.193423, No Obj: 0.005034, Avg Recall: 0.518519, count: 108 -Region Avg IOU: 0.416417, Class: 1.000000, Obj: 0.192736, No Obj: 0.004629, Avg Recall: 0.409449, count: 127 -Region Avg IOU: 0.474950, Class: 1.000000, Obj: 0.232078, No Obj: 0.004797, Avg Recall: 0.494949, count: 99 -Region Avg IOU: 0.435525, Class: 1.000000, Obj: 0.205784, No Obj: 0.004665, Avg Recall: 0.440559, count: 143 -595: 0.590049, 0.550205 avg, 0.001000 rate, 18.405552 seconds, 152320 images -Loaded: 3.153909 seconds -Region Avg IOU: 0.463028, Class: 1.000000, Obj: 0.215262, No Obj: 0.005004, Avg Recall: 0.496000, count: 125 -Region Avg IOU: 0.432878, Class: 1.000000, Obj: 0.195227, No Obj: 0.004377, Avg Recall: 0.478261, count: 92 -Region Avg IOU: 0.400341, Class: 1.000000, Obj: 0.215333, No Obj: 0.004195, Avg Recall: 0.418605, count: 86 -Region Avg IOU: 0.456810, Class: 1.000000, Obj: 0.160297, No Obj: 0.004675, Avg Recall: 0.495727, count: 117 -Region Avg IOU: 0.419371, Class: 1.000000, Obj: 0.236700, No Obj: 0.004689, Avg Recall: 0.479592, count: 98 -Region Avg IOU: 0.429199, Class: 1.000000, Obj: 0.199775, No Obj: 0.004541, Avg Recall: 0.462963, count: 108 -Region Avg IOU: 0.426379, Class: 1.000000, Obj: 0.197071, No Obj: 0.004090, Avg Recall: 0.395604, count: 91 -Region Avg IOU: 0.362068, Class: 1.000000, Obj: 0.197281, No Obj: 0.004360, Avg Recall: 0.325203, count: 123 -596: 0.497158, 0.544900 avg, 0.001000 rate, 17.798719 seconds, 152576 images -Loaded: 3.116940 seconds -Region Avg IOU: 0.407721, Class: 1.000000, Obj: 0.188006, No Obj: 0.004458, Avg Recall: 0.412844, count: 109 -Region Avg IOU: 0.440225, Class: 1.000000, Obj: 0.258426, No Obj: 0.004944, Avg Recall: 0.495495, count: 111 -Region Avg IOU: 0.405349, Class: 1.000000, Obj: 0.205403, No Obj: 0.004174, Avg Recall: 0.388889, count: 108 -Region Avg IOU: 0.430729, Class: 1.000000, Obj: 0.236991, No Obj: 0.004313, Avg Recall: 0.451923, count: 104 -Region Avg IOU: 0.419531, Class: 1.000000, Obj: 0.207122, No Obj: 0.004010, Avg Recall: 0.428571, count: 91 -Region Avg IOU: 0.378630, Class: 1.000000, Obj: 0.197224, No Obj: 0.004664, Avg Recall: 0.392000, count: Saving weights to ./person9300/backup/tyvp_93_9c512_600.weights -125 -Region Avg IOU: 0.414534, Class: 1.000000, Obj: 0.231189, No Obj: 0.004311, Avg Recall: 0.430233, count: 86 -Region Avg IOU: 0.402977, Class: 1.000000, Obj: 0.201415, No Obj: 0.004709, Avg Recall: 0.427184, count: 103 -597: 0.552703, 0.545680 avg, 0.001000 rate, 19.359343 seconds, 152832 images -Loaded: 2.108231 seconds -Region Avg IOU: 0.411133, Class: 1.000000, Obj: 0.220851, No Obj: 0.004303, Avg Recall: 0.423913, count: 92 -Region Avg IOU: 0.426994, Class: 1.000000, Obj: 0.240155, No Obj: 0.005145, Avg Recall: 0.431034, count: 116 -Region Avg IOU: 0.411376, Class: 1.000000, Obj: 0.213371, No Obj: 0.005078, Avg Recall: 0.440000, count: 125 -Region Avg IOU: 0.424374, Class: 1.000000, Obj: 0.234807, No Obj: 0.004707, Avg Recall: 0.421053, count: 114 -Region Avg IOU: 0.464683, Class: 1.000000, Obj: 0.276826, No Obj: 0.004448, Avg Recall: 0.511111, count: 90 -Region Avg IOU: 0.458563, Class: 1.000000, Obj: 0.202883, No Obj: 0.004642, Avg Recall: 0.504762, count: 105 -Region Avg IOU: 0.443632, Class: 1.000000, Obj: 0.230163, No Obj: 0.004490, Avg Recall: 0.465517, count: 116 -Region Avg IOU: 0.381108, Class: 1.000000, Obj: 0.204370, No Obj: 0.004445, Avg Recall: 0.394737, count: 114 -598: 0.562149, 0.547327 avg, 0.001000 rate, 16.661873 seconds, 153088 images -Loaded: 3.513686 seconds -Region Avg IOU: 0.404023, Class: 1.000000, Obj: 0.220793, No Obj: 0.004580, Avg Recall: 0.409091, count: 88 -Region Avg IOU: 0.448739, Class: 1.000000, Obj: 0.254995, No Obj: 0.004318, Avg Recall: 0.494118, count: 85 -Region Avg IOU: 0.431784, Class: 1.000000, Obj: 0.232469, No Obj: 0.004852, Avg Recall: 0.453704, count: 108 -Region Avg IOU: 0.409619, Class: 1.000000, Obj: 0.227631, No Obj: 0.004749, Avg Recall: 0.432990, count: 97 -Region Avg IOU: 0.393190, Class: 1.000000, Obj: 0.178612, No Obj: 0.005078, Avg Recall: 0.362319, count: 138 -Region Avg IOU: 0.437352, Class: 1.000000, Obj: 0.241190, No Obj: 0.004290, Avg Recall: 0.438202, count: 89 -Region Avg IOU: 0.373473, Class: 1.000000, Obj: 0.252265, No Obj: 0.004523, Avg Recall: 0.370000, count: 100 -Region Avg IOU: 0.440216, Class: 1.000000, Obj: 0.194179, No Obj: 0.005008, Avg Recall: 0.467890, count: 109 -599: 0.560741, 0.548669 avg, 0.001000 rate, 16.540787 seconds, 153344 images -Loaded: 5.310990 seconds -Region Avg IOU: 0.410280, Class: 1.000000, Obj: 0.223984, No Obj: 0.004529, Avg Recall: 0.407080, count: 113 -Region Avg IOU: 0.414390, Class: 1.000000, Obj: 0.212292, No Obj: 0.004890, Avg Recall: 0.447368, count: 114 -Region Avg IOU: 0.396426, Class: 1.000000, Obj: 0.197199, No Obj: 0.004661, Avg Recall: 0.372727, count: 110 -Region Avg IOU: 0.409638, Class: 1.000000, Obj: 0.191533, No Obj: 0.004960, Avg Recall: 0.437956, count: 137 -Region Avg IOU: 0.358104, Class: 1.000000, Obj: 0.224159, No Obj: 0.004258, Avg Recall: 0.283186, count: 113 -Region Avg IOU: 0.427687, Class: 1.000000, Obj: 0.202105, No Obj: 0.004468, Avg Recall: 0.447368, count: 114 -Region Avg IOU: 0.419738, Class: 1.000000, Obj: 0.228456, No Obj: 0.004777, Avg Recall: 0.428571, count: 112 -Region Avg IOU: 0.387031, Class: 1.000000, Obj: 0.170084, No Obj: 0.004080, Avg Recall: 0.376344, count: 93 -600: 0.590024, 0.552804 avg, 0.001000 rate, 15.958832 seconds, 153600 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.362175 seconds -Region Avg IOU: 0.502351, Class: 1.000000, Obj: 0.238726, No Obj: 0.003887, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.513052, Class: 1.000000, Obj: 0.266033, No Obj: 0.004201, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.499122, Class: 1.000000, Obj: 0.244534, No Obj: 0.004821, Avg Recall: 0.606299, count: 127 -Region Avg IOU: 0.482858, Class: 1.000000, Obj: 0.253982, No Obj: 0.004179, Avg Recall: 0.530973, count: 113 -Region Avg IOU: 0.469045, Class: 1.000000, Obj: 0.236981, No Obj: 0.003740, Avg Recall: 0.512821, count: 78 -Region Avg IOU: 0.558448, Class: 1.000000, Obj: 0.255322, No Obj: 0.004386, Avg Recall: 0.697479, count: 119 -Region Avg IOU: 0.516107, Class: 1.000000, Obj: 0.244397, No Obj: 0.004150, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.524382, Class: 1.000000, Obj: 0.237339, No Obj: 0.004380, Avg Recall: 0.625000, count: 96 -601: 0.538947, 0.551418 avg, 0.001000 rate, 23.962744 seconds, 153856 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.492852, Class: 1.000000, Obj: 0.203758, No Obj: 0.003806, Avg Recall: 0.560976, count: 123 -Region Avg IOU: 0.501877, Class: 1.000000, Obj: 0.261723, No Obj: 0.003795, Avg Recall: 0.576923, count: 78 -Region Avg IOU: 0.468637, Class: 1.000000, Obj: 0.226331, No Obj: 0.003948, Avg Recall: 0.466667, count: 90 -Region Avg IOU: 0.466784, Class: 1.000000, Obj: 0.229782, No Obj: 0.004531, Avg Recall: 0.521127, count: 142 -Region Avg IOU: 0.494600, Class: 1.000000, Obj: 0.253024, No Obj: 0.003875, Avg Recall: 0.560440, count: 91 -Region Avg IOU: 0.462551, Class: 1.000000, Obj: 0.226486, No Obj: 0.004089, Avg Recall: 0.477477, count: 111 -Region Avg IOU: 0.489928, Class: 1.000000, Obj: 0.250842, No Obj: 0.003922, Avg Recall: 0.495575, count: 113 -Region Avg IOU: 0.479129, Class: 1.000000, Obj: 0.208014, No Obj: 0.003997, Avg Recall: 0.536000, count: 125 -602: 0.591581, 0.555435 avg, 0.001000 rate, 23.189636 seconds, 154112 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.512124, Class: 1.000000, Obj: 0.224203, No Obj: 0.003811, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.522244, Class: 1.000000, Obj: 0.237144, No Obj: 0.003576, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.502933, Class: 1.000000, Obj: 0.203929, No Obj: 0.003821, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.484707, Class: 1.000000, Obj: 0.229530, No Obj: 0.004138, Avg Recall: 0.524590, count: 122 -Region Avg IOU: 0.497509, Class: 1.000000, Obj: 0.221532, No Obj: 0.003882, Avg Recall: 0.528846, count: 104 -Region Avg IOU: 0.477021, Class: 1.000000, Obj: 0.209045, No Obj: 0.004058, Avg Recall: 0.504202, count: 119 -Region Avg IOU: 0.482990, Class: 1.000000, Obj: 0.256359, No Obj: 0.003702, Avg Recall: 0.521368, count: 117 -Region Avg IOU: 0.538080, Class: 1.000000, Obj: 0.274506, No Obj: 0.004401, Avg Recall: 0.606383, count: 94 -603: 0.501267, 0.550018 avg, 0.001000 rate, 24.530703 seconds, 154368 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.530737, Class: 1.000000, Obj: 0.240577, No Obj: 0.004441, Avg Recall: 0.630252, count: 119 -Region Avg IOU: 0.503768, Class: 1.000000, Obj: 0.154166, No Obj: 0.003738, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.475647, Class: 1.000000, Obj: 0.229402, No Obj: 0.003861, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.468989, Class: 1.000000, Obj: 0.235180, No Obj: 0.004054, Avg Recall: 0.485981, count: 107 -Region Avg IOU: 0.535245, Class: 1.000000, Obj: 0.225982, No Obj: 0.004299, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.479640, Class: 1.000000, Obj: 0.216017, No Obj: 0.003617, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.525553, Class: 1.000000, Obj: 0.230712, No Obj: 0.003644, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.512010, Class: 1.000000, Obj: 0.210877, No Obj: 0.004182, Avg Recall: 0.561983, count: 121 -604: 0.497145, 0.544731 avg, 0.001000 rate, 23.895794 seconds, 154624 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.485356, Class: 1.000000, Obj: 0.189566, No Obj: 0.004323, Avg Recall: 0.525424, count: 118 -Region Avg IOU: 0.477248, Class: 1.000000, Obj: 0.222312, No Obj: 0.003474, Avg Recall: 0.537736, count: 106 -Region Avg IOU: 0.542740, Class: 1.000000, Obj: 0.214182, No Obj: 0.004092, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.486793, Class: 1.000000, Obj: 0.201410, No Obj: 0.004076, Avg Recall: 0.535714, count: 140 -Region Avg IOU: 0.492506, Class: 1.000000, Obj: 0.208352, No Obj: 0.003917, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.510452, Class: 1.000000, Obj: 0.229577, No Obj: 0.003474, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.538409, Class: 1.000000, Obj: 0.219540, No Obj: 0.003851, Avg Recall: 0.615385, count: 91 -Region Avg IOU: 0.496670, Class: 1.000000, Obj: 0.201962, No Obj: 0.003908, Avg Recall: 0.558621, count: 145 -605: 0.579001, 0.548158 avg, 0.001000 rate, 24.675552 seconds, 154880 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.538722, Class: 1.000000, Obj: 0.223321, No Obj: 0.004049, Avg Recall: 0.632000, count: 125 -Region Avg IOU: 0.529533, Class: 1.000000, Obj: 0.200417, No Obj: 0.004257, Avg Recall: 0.589041, count: 146 -Region Avg IOU: 0.521082, Class: 1.000000, Obj: 0.220242, No Obj: 0.003763, Avg Recall: 0.616667, count: 120 -Region Avg IOU: 0.540135, Class: 1.000000, Obj: 0.234691, No Obj: 0.003748, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.490788, Class: 1.000000, Obj: 0.194693, No Obj: 0.003958, Avg Recall: 0.576923, count: 130 -Region Avg IOU: 0.502684, Class: 1.000000, Obj: 0.212022, No Obj: 0.003715, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.489991, Class: 1.000000, Obj: 0.182578, No Obj: 0.003432, Avg Recall: 0.533333, count: 105 -Region Avg IOU: 0.506694, Class: 1.000000, Obj: 0.234993, No Obj: 0.004003, Avg Recall: 0.547170, count: 106 -606: 0.547425, 0.548084 avg, 0.001000 rate, 24.546284 seconds, 155136 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.528990, Class: 1.000000, Obj: 0.250709, No Obj: 0.003676, Avg Recall: 0.646341, count: 82 -Region Avg IOU: 0.554211, Class: 1.000000, Obj: 0.260551, No Obj: 0.003549, Avg Recall: 0.637681, count: 69 -Region Avg IOU: 0.492076, Class: 1.000000, Obj: 0.243097, No Obj: 0.003701, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.521754, Class: 1.000000, Obj: 0.221722, No Obj: 0.004228, Avg Recall: 0.598485, count: 132 -Region Avg IOU: 0.489652, Class: 1.000000, Obj: 0.243226, No Obj: 0.003834, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.506181, Class: 1.000000, Obj: 0.227977, No Obj: 0.004174, Avg Recall: 0.574468, count: 141 -Region Avg IOU: 0.542798, Class: 1.000000, Obj: 0.209787, No Obj: 0.003936, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.503048, Class: 1.000000, Obj: 0.223491, No Obj: 0.004187, Avg Recall: 0.561983, count: 121 -607: 0.516453, 0.544921 avg, 0.001000 rate, 24.893305 seconds, 155392 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.531119, Class: 1.000000, Obj: 0.212414, No Obj: 0.004415, Avg Recall: 0.643478, count: 115 -Region Avg IOU: 0.516473, Class: 1.000000, Obj: 0.243021, No Obj: 0.003737, Avg Recall: 0.566667, count: 90 -Region Avg IOU: 0.493715, Class: 1.000000, Obj: 0.220535, No Obj: 0.004465, Avg Recall: 0.563380, count: 142 -Region Avg IOU: 0.505944, Class: 1.000000, Obj: 0.275003, No Obj: 0.004022, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.572042, Class: 1.000000, Obj: 0.254165, No Obj: 0.003944, Avg Recall: 0.712644, count: 87 -Region Avg IOU: 0.518196, Class: 1.000000, Obj: 0.242026, No Obj: 0.004123, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.458510, Class: 1.000000, Obj: 0.225372, No Obj: 0.004507, Avg Recall: 0.469231, count: 130 -Region Avg IOU: 0.528279, Class: 1.000000, Obj: 0.243900, No Obj: 0.004040, Avg Recall: 0.610000, count: 100 -608: 0.556595, 0.546089 avg, 0.001000 rate, 24.711493 seconds, 155648 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.499449, Class: 1.000000, Obj: 0.277582, No Obj: 0.004542, Avg Recall: 0.561644, count: 146 -Region Avg IOU: 0.520431, Class: 1.000000, Obj: 0.248063, No Obj: 0.004243, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.515555, Class: 1.000000, Obj: 0.182745, No Obj: 0.004488, Avg Recall: 0.593220, count: 118 -Region Avg IOU: 0.495483, Class: 1.000000, Obj: 0.290823, No Obj: 0.004077, Avg Recall: 0.551020, count: 98 -Region Avg IOU: 0.536415, Class: 1.000000, Obj: 0.293126, No Obj: 0.004105, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.530538, Class: 1.000000, Obj: 0.286025, No Obj: 0.003668, Avg Recall: 0.585366, count: 82 -Region Avg IOU: 0.461442, Class: 1.000000, Obj: 0.225172, No Obj: 0.003986, Avg Recall: 0.491935, count: 124 -Region Avg IOU: 0.528479, Class: 1.000000, Obj: 0.227080, No Obj: 0.004152, Avg Recall: 0.592593, count: 108 -609: 0.525162, 0.543996 avg, 0.001000 rate, 24.276447 seconds, 155904 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.451915, Class: 1.000000, Obj: 0.228603, No Obj: 0.003656, Avg Recall: 0.489796, count: 98 -Region Avg IOU: 0.484147, Class: 1.000000, Obj: 0.278423, No Obj: 0.004061, Avg Recall: 0.553398, count: 103 -Region Avg IOU: 0.518210, Class: 1.000000, Obj: 0.262708, No Obj: 0.004209, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.455962, Class: 1.000000, Obj: 0.250241, No Obj: 0.004027, Avg Recall: 0.450820, count: 122 -Region Avg IOU: 0.484447, Class: 1.000000, Obj: 0.252864, No Obj: 0.003848, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.472120, Class: 1.000000, Obj: 0.214230, No Obj: 0.004042, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.512223, Class: 1.000000, Obj: 0.264133, No Obj: 0.004410, Avg Recall: 0.578947, count: 114 -Region Avg IOU: 0.527373, Class: 1.000000, Obj: 0.249123, No Obj: 0.004445, Avg Recall: 0.601770, count: 113 -610: 0.565301, 0.546126 avg, 0.001000 rate, 23.183424 seconds, 156160 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000069 seconds -Region Avg IOU: 0.585928, Class: 1.000000, Obj: 0.296447, No Obj: 0.004046, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.576952, Class: 1.000000, Obj: 0.235446, No Obj: 0.003956, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.529618, Class: 1.000000, Obj: 0.181402, No Obj: 0.004056, Avg Recall: 0.661871, count: 139 -Region Avg IOU: 0.598038, Class: 1.000000, Obj: 0.234844, No Obj: 0.003667, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.567603, Class: 1.000000, Obj: 0.240165, No Obj: 0.003978, Avg Recall: 0.702128, count: 141 -Region Avg IOU: 0.540875, Class: 1.000000, Obj: 0.238433, No Obj: 0.003769, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.507427, Class: 1.000000, Obj: 0.218605, No Obj: 0.003722, Avg Recall: 0.578512, count: 121 -Region Avg IOU: 0.551394, Class: 1.000000, Obj: 0.233546, No Obj: 0.003582, Avg Recall: 0.638298, count: 94 -611: 0.562146, 0.547728 avg, 0.001000 rate, 27.599651 seconds, 156416 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.548962, Class: 1.000000, Obj: 0.202461, No Obj: 0.003734, Avg Recall: 0.669421, count: 121 -Region Avg IOU: 0.527187, Class: 1.000000, Obj: 0.202795, No Obj: 0.003498, Avg Recall: 0.607143, count: 84 -Region Avg IOU: 0.564540, Class: 1.000000, Obj: 0.262828, No Obj: 0.003711, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.553241, Class: 1.000000, Obj: 0.221489, No Obj: 0.003785, Avg Recall: 0.691667, count: 120 -Region Avg IOU: 0.584222, Class: 1.000000, Obj: 0.262221, No Obj: 0.003795, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.542253, Class: 1.000000, Obj: 0.235658, No Obj: 0.003837, Avg Recall: 0.598684, count: 152 -Region Avg IOU: 0.547193, Class: 1.000000, Obj: 0.221390, No Obj: 0.004054, Avg Recall: 0.641221, count: 131 -Region Avg IOU: 0.558038, Class: 1.000000, Obj: 0.213159, No Obj: 0.003717, Avg Recall: 0.663043, count: 92 -612: 0.504240, 0.543380 avg, 0.001000 rate, 27.727636 seconds, 156672 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.539629, Class: 1.000000, Obj: 0.226903, No Obj: 0.003630, Avg Recall: 0.619048, count: 126 -Region Avg IOU: 0.559400, Class: 1.000000, Obj: 0.236033, No Obj: 0.003539, Avg Recall: 0.647482, count: 139 -Region Avg IOU: 0.546858, Class: 1.000000, Obj: 0.225849, No Obj: 0.003405, Avg Recall: 0.662791, count: 86 -Region Avg IOU: 0.556898, Class: 1.000000, Obj: 0.218106, No Obj: 0.004180, Avg Recall: 0.684211, count: 133 -Region Avg IOU: 0.592493, Class: 1.000000, Obj: 0.190690, No Obj: 0.003789, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.568178, Class: 1.000000, Obj: 0.261528, No Obj: 0.003516, Avg Recall: 0.680672, count: 119 -Region Avg IOU: 0.512065, Class: 1.000000, Obj: 0.237636, No Obj: 0.003283, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.565435, Class: 1.000000, Obj: 0.237289, No Obj: 0.003301, Avg Recall: 0.675926, count: 108 -613: 0.546334, 0.543675 avg, 0.001000 rate, 27.410284 seconds, 156928 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.532394, Class: 1.000000, Obj: 0.207719, No Obj: 0.003106, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.567267, Class: 1.000000, Obj: 0.222601, No Obj: 0.003194, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.569081, Class: 1.000000, Obj: 0.240885, No Obj: 0.003360, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.569112, Class: 1.000000, Obj: 0.219338, No Obj: 0.003225, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.509464, Class: 1.000000, Obj: 0.205903, No Obj: 0.003458, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.583163, Class: 1.000000, Obj: 0.178293, No Obj: 0.003617, Avg Recall: 0.664062, count: 128 -Region Avg IOU: 0.543264, Class: 1.000000, Obj: 0.218951, No Obj: 0.003559, Avg Recall: 0.654412, count: 136 -Region Avg IOU: 0.530125, Class: 1.000000, Obj: 0.224861, No Obj: 0.003056, Avg Recall: 0.646341, count: 82 -614: 0.512417, 0.540549 avg, 0.001000 rate, 27.428265 seconds, 157184 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.557768, Class: 1.000000, Obj: 0.233865, No Obj: 0.003268, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.583357, Class: 1.000000, Obj: 0.226633, No Obj: 0.003456, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.575496, Class: 1.000000, Obj: 0.201917, No Obj: 0.003500, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.546436, Class: 1.000000, Obj: 0.183981, No Obj: 0.003052, Avg Recall: 0.653226, count: 124 -Region Avg IOU: 0.540258, Class: 1.000000, Obj: 0.184614, No Obj: 0.003377, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.559168, Class: 1.000000, Obj: 0.201445, No Obj: 0.003410, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.581958, Class: 1.000000, Obj: 0.191538, No Obj: 0.003785, Avg Recall: 0.676923, count: 130 -Region Avg IOU: 0.535334, Class: 1.000000, Obj: 0.171364, No Obj: 0.003182, Avg Recall: 0.619048, count: 105 -615: 0.571195, 0.543614 avg, 0.001000 rate, 27.627840 seconds, 157440 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.500327, Class: 1.000000, Obj: 0.190471, No Obj: 0.003010, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.567991, Class: 1.000000, Obj: 0.221122, No Obj: 0.003130, Avg Recall: 0.686956, count: 115 -Region Avg IOU: 0.571337, Class: 1.000000, Obj: 0.188594, No Obj: 0.003173, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.572484, Class: 1.000000, Obj: 0.247655, No Obj: 0.003235, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.579307, Class: 1.000000, Obj: 0.219066, No Obj: 0.003457, Avg Recall: 0.713115, count: 122 -Region Avg IOU: 0.551999, Class: 1.000000, Obj: 0.183946, No Obj: 0.003506, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.579037, Class: 1.000000, Obj: 0.280532, No Obj: 0.003202, Avg Recall: 0.640449, count: 89 -Region Avg IOU: 0.542047, Class: 1.000000, Obj: 0.195399, No Obj: 0.003222, Avg Recall: 0.626087, count: 115 -616: 0.515360, 0.540788 avg, 0.001000 rate, 27.336540 seconds, 157696 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.541141, Class: 1.000000, Obj: 0.226499, No Obj: 0.003399, Avg Recall: 0.655738, count: 122 -Region Avg IOU: 0.569986, Class: 1.000000, Obj: 0.230293, No Obj: 0.003273, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.580084, Class: 1.000000, Obj: 0.253579, No Obj: 0.003350, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.503932, Class: 1.000000, Obj: 0.228765, No Obj: 0.003191, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.551468, Class: 1.000000, Obj: 0.222184, No Obj: 0.002831, Avg Recall: 0.720930, count: 86 -Region Avg IOU: 0.494425, Class: 1.000000, Obj: 0.212617, No Obj: 0.002754, Avg Recall: 0.581395, count: 86 -Region Avg IOU: 0.579911, Class: 1.000000, Obj: 0.200835, No Obj: 0.003721, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.527643, Class: 1.000000, Obj: 0.220474, No Obj: 0.003655, Avg Recall: 0.606383, count: 94 -617: 0.485757, 0.535285 avg, 0.001000 rate, 27.474974 seconds, 157952 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.603321, Class: 1.000000, Obj: 0.215027, No Obj: 0.003603, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.565179, Class: 1.000000, Obj: 0.226329, No Obj: 0.003381, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.570548, Class: 1.000000, Obj: 0.218623, No Obj: 0.003294, Avg Recall: 0.669565, count: 115 -Region Avg IOU: 0.523237, Class: 1.000000, Obj: 0.187172, No Obj: 0.003418, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.585508, Class: 1.000000, Obj: 0.234798, No Obj: 0.003272, Avg Recall: 0.715909, count: 88 -Region Avg IOU: 0.581022, Class: 1.000000, Obj: 0.219181, No Obj: 0.003558, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.597729, Class: 1.000000, Obj: 0.170499, No Obj: 0.003590, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.576881, Class: 1.000000, Obj: 0.210559, No Obj: 0.003101, Avg Recall: 0.719101, count: 89 -618: 0.503489, 0.532106 avg, 0.001000 rate, 26.750229 seconds, 158208 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.556505, Class: 1.000000, Obj: 0.247386, No Obj: 0.003738, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.551948, Class: 1.000000, Obj: 0.196482, No Obj: 0.003658, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.528121, Class: 1.000000, Obj: 0.244083, No Obj: 0.003356, Avg Recall: 0.613636, count: 88 -Region Avg IOU: 0.556367, Class: 1.000000, Obj: 0.245848, No Obj: 0.003556, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.535558, Class: 1.000000, Obj: 0.217671, No Obj: 0.003620, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.557878, Class: 1.000000, Obj: 0.274728, No Obj: 0.003421, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.560187, Class: 1.000000, Obj: 0.237792, No Obj: 0.003972, Avg Recall: 0.680328, count: 122 -Region Avg IOU: 0.562812, Class: 1.000000, Obj: 0.270569, No Obj: 0.003847, Avg Recall: 0.654135, count: 133 -619: 0.513762, 0.530271 avg, 0.001000 rate, 27.933867 seconds, 158464 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.576065, Class: 1.000000, Obj: 0.261935, No Obj: 0.003540, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.560747, Class: 1.000000, Obj: 0.231466, No Obj: 0.003768, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.489819, Class: 1.000000, Obj: 0.216943, No Obj: 0.003698, Avg Recall: 0.553719, count: 121 -Region Avg IOU: 0.562379, Class: 1.000000, Obj: 0.221366, No Obj: 0.003638, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.531054, Class: 1.000000, Obj: 0.215091, No Obj: 0.003407, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.530168, Class: 1.000000, Obj: 0.222361, No Obj: 0.003606, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.542745, Class: 1.000000, Obj: 0.239874, No Obj: 0.003765, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.562932, Class: 1.000000, Obj: 0.240985, No Obj: 0.003558, Avg Recall: 0.672727, count: 110 -620: 0.522622, 0.529506 avg, 0.001000 rate, 27.758364 seconds, 158720 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 21.040720 seconds -Region Avg IOU: 0.547043, Class: 1.000000, Obj: 0.243748, No Obj: 0.003848, Avg Recall: 0.620968, count: 124 -Region Avg IOU: 0.523386, Class: 1.000000, Obj: 0.251043, No Obj: 0.003508, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.529424, Class: 1.000000, Obj: 0.270674, No Obj: 0.003769, Avg Recall: 0.587719, count: 114 -Region Avg IOU: 0.511506, Class: 1.000000, Obj: 0.247691, No Obj: 0.003563, Avg Recall: 0.549020, count: 102 -Region Avg IOU: 0.520130, Class: 1.000000, Obj: 0.248925, No Obj: 0.003531, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.549254, Class: 1.000000, Obj: 0.219225, No Obj: 0.003806, Avg Recall: 0.632479, count: 117 -Region Avg IOU: 0.594045, Class: 1.000000, Obj: 0.282869, No Obj: 0.004116, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.542869, Class: 1.000000, Obj: 0.245698, No Obj: 0.003718, Avg Recall: 0.644231, count: 104 -621: 0.479515, 0.524507 avg, 0.001000 rate, 25.135376 seconds, 158976 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.562878, Class: 1.000000, Obj: 0.280919, No Obj: 0.003925, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.568903, Class: 1.000000, Obj: 0.233821, No Obj: 0.003667, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.515091, Class: 1.000000, Obj: 0.240639, No Obj: 0.003991, Avg Recall: 0.537190, count: 121 -Region Avg IOU: 0.526112, Class: 1.000000, Obj: 0.246943, No Obj: 0.003540, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.569319, Class: 1.000000, Obj: 0.270297, No Obj: 0.003753, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.530400, Class: 1.000000, Obj: 0.295984, No Obj: 0.003832, Avg Recall: 0.578313, count: 83 -Region Avg IOU: 0.525181, Class: 1.000000, Obj: 0.264254, No Obj: 0.003637, Avg Recall: 0.601770, count: 113 -Region Avg IOU: 0.561507, Class: 1.000000, Obj: 0.249248, No Obj: 0.003578, Avg Recall: 0.704762, count: 105 -622: 0.510257, 0.523082 avg, 0.001000 rate, 26.972288 seconds, 159232 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.542614, Class: 1.000000, Obj: 0.222289, No Obj: 0.004031, Avg Recall: 0.644628, count: 121 -Region Avg IOU: 0.515450, Class: 1.000000, Obj: 0.218272, No Obj: 0.003648, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.504135, Class: 1.000000, Obj: 0.191616, No Obj: 0.003683, Avg Recall: 0.571429, count: 112 -Region Avg IOU: 0.526671, Class: 1.000000, Obj: 0.259714, No Obj: 0.003925, Avg Recall: 0.584071, count: 113 -Region Avg IOU: 0.506611, Class: 1.000000, Obj: 0.245134, No Obj: 0.003851, Avg Recall: 0.555556, count: 99 -Region Avg IOU: 0.479742, Class: 1.000000, Obj: 0.253599, No Obj: 0.003862, Avg Recall: 0.522727, count: 132 -Region Avg IOU: 0.543928, Class: 1.000000, Obj: 0.270380, No Obj: 0.003622, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.529364, Class: 1.000000, Obj: 0.243198, No Obj: 0.003963, Avg Recall: 0.598361, count: 122 -623: 0.543853, 0.525159 avg, 0.001000 rate, 24.972883 seconds, 159488 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.502129, Class: 1.000000, Obj: 0.213629, No Obj: 0.003263, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.527379, Class: 1.000000, Obj: 0.229777, No Obj: 0.004074, Avg Recall: 0.629921, count: 127 -Region Avg IOU: 0.526998, Class: 1.000000, Obj: 0.178988, No Obj: 0.003308, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.518760, Class: 1.000000, Obj: 0.234694, No Obj: 0.003459, Avg Recall: 0.568421, count: 95 -Region Avg IOU: 0.542380, Class: 1.000000, Obj: 0.288567, No Obj: 0.003904, Avg Recall: 0.645669, count: 127 -Region Avg IOU: 0.519742, Class: 1.000000, Obj: 0.217945, No Obj: 0.003993, Avg Recall: 0.553719, count: 121 -Region Avg IOU: 0.568441, Class: 1.000000, Obj: 0.240736, No Obj: 0.004071, Avg Recall: 0.669291, count: 127 -Region Avg IOU: 0.503226, Class: 1.000000, Obj: 0.228529, No Obj: 0.003673, Avg Recall: 0.570093, count: 107 -624: 0.517854, 0.524429 avg, 0.001000 rate, 25.298048 seconds, 159744 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.548726, Class: 1.000000, Obj: 0.250895, No Obj: 0.003451, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.519146, Class: 1.000000, Obj: 0.198248, No Obj: 0.003166, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.521462, Class: 1.000000, Obj: 0.228390, No Obj: 0.003554, Avg Recall: 0.571429, count: 105 -Region Avg IOU: 0.520735, Class: 1.000000, Obj: 0.238556, No Obj: 0.003382, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.520872, Class: 1.000000, Obj: 0.188134, No Obj: 0.003372, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.515133, Class: 1.000000, Obj: 0.194405, No Obj: 0.003114, Avg Recall: 0.626506, count: 83 -Region Avg IOU: 0.565885, Class: 1.000000, Obj: 0.246298, No Obj: 0.003272, Avg Recall: 0.642857, count: 84 -Region Avg IOU: 0.516412, Class: 1.000000, Obj: 0.239669, No Obj: 0.003495, Avg Recall: 0.603774, count: 106 -625: 0.477529, 0.519739 avg, 0.001000 rate, 25.176008 seconds, 160000 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.537066, Class: 1.000000, Obj: 0.227013, No Obj: 0.003500, Avg Recall: 0.586207, count: 116 -Region Avg IOU: 0.540694, Class: 1.000000, Obj: 0.209277, No Obj: 0.003668, Avg Recall: 0.630252, count: 119 -Region Avg IOU: 0.575935, Class: 1.000000, Obj: 0.234134, No Obj: 0.003256, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.528802, Class: 1.000000, Obj: 0.213186, No Obj: 0.003576, Avg Recall: 0.624161, count: 149 -Region Avg IOU: 0.545364, Class: 1.000000, Obj: 0.247134, No Obj: 0.003384, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.522973, Class: 1.000000, Obj: 0.213526, No Obj: 0.003308, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.522056, Class: 1.000000, Obj: 0.227449, No Obj: 0.003770, Avg Recall: 0.576000, count: 125 -Region Avg IOU: 0.495774, Class: 1.000000, Obj: 0.189733, No Obj: 0.003600, Avg Recall: 0.535211, count: 142 -626: 0.568282, 0.524593 avg, 0.001000 rate, 24.017515 seconds, 160256 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.524864, Class: 1.000000, Obj: 0.207919, No Obj: 0.003272, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.484778, Class: 1.000000, Obj: 0.201655, No Obj: 0.003106, Avg Recall: 0.573034, count: 89 -Region Avg IOU: 0.496326, Class: 1.000000, Obj: 0.240644, No Obj: 0.003163, Avg Recall: 0.510204, count: 98 -Region Avg IOU: 0.538135, Class: 1.000000, Obj: 0.193193, No Obj: 0.003288, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.587121, Class: 1.000000, Obj: 0.246562, No Obj: 0.003318, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.573090, Class: 1.000000, Obj: 0.265732, No Obj: 0.003121, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.514143, Class: 1.000000, Obj: 0.219232, No Obj: 0.003051, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.509603, Class: 1.000000, Obj: 0.212344, No Obj: 0.003778, Avg Recall: 0.533333, count: 135 -627: 0.442940, 0.516428 avg, 0.001000 rate, 25.719997 seconds, 160512 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.554709, Class: 1.000000, Obj: 0.207821, No Obj: 0.002974, Avg Recall: 0.670732, count: 82 -Region Avg IOU: 0.515375, Class: 1.000000, Obj: 0.226644, No Obj: 0.003393, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.554847, Class: 1.000000, Obj: 0.224564, No Obj: 0.003429, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.516165, Class: 1.000000, Obj: 0.221744, No Obj: 0.003405, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.498014, Class: 1.000000, Obj: 0.201891, No Obj: 0.002926, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.476785, Class: 1.000000, Obj: 0.210465, No Obj: 0.003238, Avg Recall: 0.535354, count: 99 -Region Avg IOU: 0.549127, Class: 1.000000, Obj: 0.244448, No Obj: 0.003531, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.518717, Class: 1.000000, Obj: 0.251189, No Obj: 0.003058, Avg Recall: 0.583333, count: 84 -628: 0.477665, 0.512552 avg, 0.001000 rate, 25.252848 seconds, 160768 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.482240, Class: 1.000000, Obj: 0.223539, No Obj: 0.003226, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.570899, Class: 1.000000, Obj: 0.240252, No Obj: 0.003086, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.551947, Class: 1.000000, Obj: 0.232510, No Obj: 0.003602, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.510013, Class: 1.000000, Obj: 0.151907, No Obj: 0.003138, Avg Recall: 0.580645, count: 124 -Region Avg IOU: 0.550549, Class: 1.000000, Obj: 0.223861, No Obj: 0.003361, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.553050, Class: 1.000000, Obj: 0.217165, No Obj: 0.003400, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.490054, Class: 1.000000, Obj: 0.198801, No Obj: 0.003053, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.513778, Class: 1.000000, Obj: 0.235963, No Obj: 0.003148, Avg Recall: 0.572917, count: 96 -629: 0.517503, 0.513047 avg, 0.001000 rate, 26.117260 seconds, 161024 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.520968, Class: 1.000000, Obj: 0.184591, No Obj: 0.003531, Avg Recall: 0.612245, count: 147 -Region Avg IOU: 0.539032, Class: 1.000000, Obj: 0.213582, No Obj: 0.003481, Avg Recall: 0.635714, count: 140 -Region Avg IOU: 0.540367, Class: 1.000000, Obj: 0.253548, No Obj: 0.003389, Avg Recall: 0.666667, count: 129 -Region Avg IOU: 0.535094, Class: 1.000000, Obj: 0.232837, No Obj: 0.003508, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.536619, Class: 1.000000, Obj: 0.230064, No Obj: 0.003394, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.577936, Class: 1.000000, Obj: 0.214005, No Obj: 0.003448, Avg Recall: 0.682353, count: 85 -Region Avg IOU: 0.505419, Class: 1.000000, Obj: 0.206088, No Obj: 0.002892, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.580834, Class: 1.000000, Obj: 0.270985, No Obj: 0.003391, Avg Recall: 0.696078, count: 102 -630: 0.521179, 0.513860 avg, 0.001000 rate, 24.623896 seconds, 161280 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.070024 seconds -Region Avg IOU: 0.413929, Class: 1.000000, Obj: 0.162420, No Obj: 0.004138, Avg Recall: 0.437500, count: 112 -Region Avg IOU: 0.387228, Class: 1.000000, Obj: 0.206414, No Obj: 0.004162, Avg Recall: 0.373134, count: 134 -Region Avg IOU: 0.402195, Class: 1.000000, Obj: 0.163591, No Obj: 0.004217, Avg Recall: 0.363636, count: 132 -Region Avg IOU: 0.382138, Class: 1.000000, Obj: 0.162961, No Obj: 0.003924, Avg Recall: 0.321429, count: 112 -Region Avg IOU: 0.412535, Class: 1.000000, Obj: 0.201838, No Obj: 0.003840, Avg Recall: 0.425287, count: 87 -Region Avg IOU: 0.414719, Class: 1.000000, Obj: 0.164250, No Obj: 0.003893, Avg Recall: 0.445545, count: 101 -Region Avg IOU: 0.433551, Class: 1.000000, Obj: 0.152452, No Obj: 0.004010, Avg Recall: 0.480000, count: 100 -Region Avg IOU: 0.406518, Class: 1.000000, Obj: 0.189638, No Obj: 0.004163, Avg Recall: 0.464912, count: 114 -631: 0.610072, 0.523481 avg, 0.001000 rate, 19.170452 seconds, 161536 images -Loaded: 2.934921 seconds -Region Avg IOU: 0.457033, Class: 1.000000, Obj: 0.200288, No Obj: 0.004102, Avg Recall: 0.452632, count: 95 -Region Avg IOU: 0.457921, Class: 1.000000, Obj: 0.185455, No Obj: 0.004198, Avg Recall: 0.505376, count: 93 -Region Avg IOU: 0.446370, Class: 1.000000, Obj: 0.204106, No Obj: 0.004766, Avg Recall: 0.455357, count: 112 -Region Avg IOU: 0.397992, Class: 1.000000, Obj: 0.171490, No Obj: 0.004104, Avg Recall: 0.409524, count: 105 -Region Avg IOU: 0.397361, Class: 1.000000, Obj: 0.170404, No Obj: 0.004315, Avg Recall: 0.371429, count: 105 -Region Avg IOU: 0.356174, Class: 1.000000, Obj: 0.152128, No Obj: 0.004103, Avg Recall: 0.354545, count: 110 -Region Avg IOU: 0.390379, Class: 1.000000, Obj: 0.193558, No Obj: 0.004471, Avg Recall: 0.378641, count: 103 -Region Avg IOU: 0.409138, Class: 1.000000, Obj: 0.180901, No Obj: 0.004307, Avg Recall: 0.405941, count: 101 -632: 0.507393, 0.521872 avg, 0.001000 rate, 15.293104 seconds, 161792 images -Loaded: 4.465282 seconds -Region Avg IOU: 0.353538, Class: 1.000000, Obj: 0.175916, No Obj: 0.004175, Avg Recall: 0.290000, count: 100 -Region Avg IOU: 0.405328, Class: 1.000000, Obj: 0.173735, No Obj: 0.004903, Avg Recall: 0.420382, count: 157 -Region Avg IOU: 0.383340, Class: 1.000000, Obj: 0.166909, No Obj: 0.004694, Avg Recall: 0.420000, count: 150 -Region Avg IOU: 0.466373, Class: 1.000000, Obj: 0.222981, No Obj: 0.004659, Avg Recall: 0.482143, count: 112 -Region Avg IOU: 0.398156, Class: 1.000000, Obj: 0.203340, No Obj: 0.004128, Avg Recall: 0.423913, count: 92 -Region Avg IOU: 0.458250, Class: 1.000000, Obj: 0.205888, No Obj: 0.004335, Avg Recall: 0.475000, count: 80 -Region Avg IOU: 0.359296, Class: 1.000000, Obj: 0.175530, No Obj: 0.004159, Avg Recall: 0.330000, count: 100 -Region Avg IOU: 0.460947, Class: 1.000000, Obj: 0.228668, No Obj: 0.004797, Avg Recall: 0.500000, count: 90 -633: 0.551968, 0.524882 avg, 0.001000 rate, 14.540940 seconds, 162048 images -Loaded: 7.005194 seconds -Region Avg IOU: 0.405253, Class: 1.000000, Obj: 0.194764, No Obj: 0.004913, Avg Recall: 0.478261, count: 115 -Region Avg IOU: 0.380003, Class: 1.000000, Obj: 0.199415, No Obj: 0.004417, Avg Recall: 0.360000, count: 100 -Region Avg IOU: 0.360958, Class: 1.000000, Obj: 0.191356, No Obj: 0.003902, Avg Recall: 0.333333, count: 96 -Region Avg IOU: 0.389111, Class: 1.000000, Obj: 0.185121, No Obj: 0.004269, Avg Recall: 0.444444, count: 108 -Region Avg IOU: 0.385390, Class: 1.000000, Obj: 0.193154, No Obj: 0.004660, Avg Recall: 0.356522, count: 115 -Region Avg IOU: 0.400630, Class: 1.000000, Obj: 0.218275, No Obj: 0.004641, Avg Recall: 0.400000, count: 110 -Region Avg IOU: 0.393145, Class: 1.000000, Obj: 0.229736, No Obj: 0.005267, Avg Recall: 0.436508, count: 126 -Region Avg IOU: 0.351456, Class: 1.000000, Obj: 0.172565, No Obj: 0.004571, Avg Recall: 0.323810, count: 105 -634: 0.617408, 0.534135 avg, 0.001000 rate, 13.901369 seconds, 162304 images -Loaded: 4.305246 seconds -Region Avg IOU: 0.410468, Class: 1.000000, Obj: 0.238647, No Obj: 0.004530, Avg Recall: 0.408163, count: 98 -Region Avg IOU: 0.372881, Class: 1.000000, Obj: 0.224079, No Obj: 0.004722, Avg Recall: 0.377778, count: 90 -Region Avg IOU: 0.406092, Class: 1.000000, Obj: 0.213772, No Obj: 0.004753, Avg Recall: 0.419048, count: 105 -Region Avg IOU: 0.415529, Class: 1.000000, Obj: 0.223840, No Obj: 0.004867, Avg Recall: 0.422680, count: 97 -Region Avg IOU: 0.413479, Class: 1.000000, Obj: 0.222092, No Obj: 0.005434, Avg Recall: 0.411765, count: 119 -Region Avg IOU: 0.400684, Class: 1.000000, Obj: 0.205258, No Obj: 0.005003, Avg Recall: 0.395833, count: 96 -Region Avg IOU: 0.387983, Class: 1.000000, Obj: 0.202934, No Obj: 0.004408, Avg Recall: 0.400000, count: 95 -Region Avg IOU: 0.389544, Class: 1.000000, Obj: 0.185055, No Obj: 0.004268, Avg Recall: 0.375000, count: 96 -635: 0.498525, 0.530574 avg, 0.001000 rate, 18.214808 seconds, 162560 images -Loaded: 3.039369 seconds -Region Avg IOU: 0.342422, Class: 1.000000, Obj: 0.174057, No Obj: 0.004615, Avg Recall: 0.330508, count: 118 -Region Avg IOU: 0.341746, Class: 1.000000, Obj: 0.180427, No Obj: 0.004233, Avg Recall: 0.367816, count: 87 -Region Avg IOU: 0.399994, Class: 1.000000, Obj: 0.190113, No Obj: 0.004573, Avg Recall: 0.428571, count: 105 -Region Avg IOU: 0.420289, Class: 1.000000, Obj: 0.232227, No Obj: 0.004883, Avg Recall: 0.438596, count: 114 -Region Avg IOU: 0.371810, Class: 1.000000, Obj: 0.175714, No Obj: 0.004605, Avg Recall: 0.339450, count: 109 -Region Avg IOU: 0.391832, Class: 1.000000, Obj: 0.230643, No Obj: 0.005378, Avg Recall: 0.367647, count: 136 -Region Avg IOU: 0.437348, Class: 1.000000, Obj: 0.185585, No Obj: 0.003593, Avg Recall: 0.508772, count: 57 -Region Avg IOU: 0.399588, Class: 1.000000, Obj: 0.200271, No Obj: 0.004674, Avg Recall: 0.351064, count: 94 -636: 0.526902, 0.530206 avg, 0.001000 rate, 17.039028 seconds, 162816 images -Loaded: 4.204621 seconds -Region Avg IOU: 0.355276, Class: 1.000000, Obj: 0.180883, No Obj: 0.004636, Avg Recall: 0.317829, count: 129 -Region Avg IOU: 0.396731, Class: 1.000000, Obj: 0.184108, No Obj: 0.004469, Avg Recall: 0.388889, count: 90 -Region Avg IOU: 0.363489, Class: 1.000000, Obj: 0.207677, No Obj: 0.004252, Avg Recall: 0.391304, count: 92 -Region Avg IOU: 0.431998, Class: 1.000000, Obj: 0.244507, No Obj: 0.005461, Avg Recall: 0.447154, count: 123 -Region Avg IOU: 0.413781, Class: 1.000000, Obj: 0.198537, No Obj: 0.005337, Avg Recall: 0.415254, count: 118 -Region Avg IOU: 0.396059, Class: 1.000000, Obj: 0.189026, No Obj: 0.005429, Avg Recall: 0.394161, count: 137 -Region Avg IOU: 0.388916, Class: 1.000000, Obj: 0.172571, No Obj: 0.004620, Avg Recall: 0.395604, count: 91 -Region Avg IOU: 0.357727, Class: 1.000000, Obj: 0.159931, No Obj: 0.004610, Avg Recall: 0.342105, count: 114 -637: 0.597075, 0.536893 avg, 0.001000 rate, 13.572260 seconds, 163072 images -Loaded: 5.401617 seconds -Region Avg IOU: 0.404046, Class: 1.000000, Obj: 0.247839, No Obj: 0.004616, Avg Recall: 0.441176, count: 102 -Region Avg IOU: 0.364248, Class: 1.000000, Obj: 0.213366, No Obj: 0.004869, Avg Recall: 0.361446, count: 83 -Region Avg IOU: 0.366054, Class: 1.000000, Obj: 0.176915, No Obj: 0.004178, Avg Recall: 0.321429, count: 84 -Region Avg IOU: 0.375357, Class: 1.000000, Obj: 0.177120, No Obj: 0.004553, Avg Recall: 0.356061, count: 132 -Region Avg IOU: 0.394716, Class: 1.000000, Obj: 0.172995, No Obj: 0.003973, Avg Recall: 0.378641, count: 103 -Region Avg IOU: 0.371771, Class: 1.000000, Obj: 0.189656, No Obj: 0.004012, Avg Recall: 0.361905, count: 105 -Region Avg IOU: 0.408832, Class: 1.000000, Obj: 0.213349, No Obj: 0.004754, Avg Recall: 0.372727, count: 110 -Region Avg IOU: 0.419667, Class: 1.000000, Obj: 0.201569, No Obj: 0.004989, Avg Recall: 0.412844, count: 109 -638: 0.555429, 0.538747 avg, 0.001000 rate, 16.512857 seconds, 163328 images -Loaded: 2.871505 seconds -Region Avg IOU: 0.387528, Class: 1.000000, Obj: 0.185705, No Obj: 0.004857, Avg Recall: 0.372881, count: 118 -Region Avg IOU: 0.394926, Class: 1.000000, Obj: 0.214438, No Obj: 0.004286, Avg Recall: 0.377778, count: 90 -Region Avg IOU: 0.434181, Class: 1.000000, Obj: 0.183247, No Obj: 0.004700, Avg Recall: 0.483516, count: 91 -Region Avg IOU: 0.384108, Class: 1.000000, Obj: 0.188252, No Obj: 0.004125, Avg Recall: 0.358974, count: 117 -Region Avg IOU: 0.442332, Class: 1.000000, Obj: 0.197177, No Obj: 0.004573, Avg Recall: 0.466667, count: 90 -Region Avg IOU: 0.408502, Class: 1.000000, Obj: 0.163609, No Obj: 0.004402, Avg Recall: 0.390000, count: 100 -Region Avg IOU: 0.410639, Class: 1.000000, Obj: 0.194894, No Obj: 0.004599, Avg Recall: 0.419643, count: 112 -Region Avg IOU: 0.391768, Class: 1.000000, Obj: 0.189950, No Obj: 0.004277, Avg Recall: 0.410000, count: 100 -639: 0.481895, 0.533062 avg, 0.001000 rate, 15.173604 seconds, 163584 images -Loaded: 6.537694 seconds -Region Avg IOU: 0.372334, Class: 1.000000, Obj: 0.216102, No Obj: 0.004975, Avg Recall: 0.351562, count: 128 -Region Avg IOU: 0.363624, Class: 1.000000, Obj: 0.196397, No Obj: 0.004568, Avg Recall: 0.303797, count: 79 -Region Avg IOU: 0.459436, Class: 1.000000, Obj: 0.208877, No Obj: 0.004899, Avg Recall: 0.495327, count: 107 -Region Avg IOU: 0.395619, Class: 1.000000, Obj: 0.192339, No Obj: 0.004225, Avg Recall: 0.363636, count: 110 -Region Avg IOU: 0.437612, Class: 1.000000, Obj: 0.165541, No Obj: 0.004943, Avg Recall: 0.434343, count: 99 -Region Avg IOU: 0.427828, Class: 1.000000, Obj: 0.188144, No Obj: 0.004301, Avg Recall: 0.425743, count: 101 -Region Avg IOU: 0.385845, Class: 1.000000, Obj: 0.184853, No Obj: 0.004994, Avg Recall: 0.356589, count: 129 -Region Avg IOU: 0.408104, Class: 1.000000, Obj: 0.223532, No Obj: 0.003830, Avg Recall: 0.385714, count: 70 -640: 0.571203, 0.536876 avg, 0.001000 rate, 17.492741 seconds, 163840 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000088 seconds -Region Avg IOU: 0.506671, Class: 1.000000, Obj: 0.261159, No Obj: 0.004092, Avg Recall: 0.512821, count: 117 -Region Avg IOU: 0.556785, Class: 1.000000, Obj: 0.243493, No Obj: 0.004031, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.544767, Class: 1.000000, Obj: 0.239143, No Obj: 0.004264, Avg Recall: 0.633588, count: 131 -Region Avg IOU: 0.523740, Class: 1.000000, Obj: 0.265245, No Obj: 0.003952, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.526405, Class: 1.000000, Obj: 0.238884, No Obj: 0.004479, Avg Recall: 0.560811, count: 148 -Region Avg IOU: 0.547416, Class: 1.000000, Obj: 0.265723, No Obj: 0.004681, Avg Recall: 0.607143, count: 140 -Region Avg IOU: 0.516385, Class: 1.000000, Obj: 0.217335, No Obj: 0.004191, Avg Recall: 0.585938, count: 128 -Region Avg IOU: 0.505382, Class: 1.000000, Obj: 0.240268, No Obj: 0.003767, Avg Recall: 0.575758, count: 99 -641: 0.567680, 0.539956 avg, 0.001000 rate, 25.668550 seconds, 164096 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.520734, Class: 1.000000, Obj: 0.204636, No Obj: 0.004094, Avg Recall: 0.541667, count: 120 -Region Avg IOU: 0.548463, Class: 1.000000, Obj: 0.205778, No Obj: 0.004220, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.550199, Class: 1.000000, Obj: 0.278937, No Obj: 0.004304, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.512415, Class: 1.000000, Obj: 0.196042, No Obj: 0.003815, Avg Recall: 0.590909, count: 88 -Region Avg IOU: 0.510033, Class: 1.000000, Obj: 0.252537, No Obj: 0.003640, Avg Recall: 0.597826, count: 92 -Region Avg IOU: 0.569041, Class: 1.000000, Obj: 0.249367, No Obj: 0.003668, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.539029, Class: 1.000000, Obj: 0.235955, No Obj: 0.004203, Avg Recall: 0.641791, count: 134 -Region Avg IOU: 0.543451, Class: 1.000000, Obj: 0.258911, No Obj: 0.004389, Avg Recall: 0.631148, count: 122 -642: 0.495860, 0.535547 avg, 0.001000 rate, 24.048849 seconds, 164352 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.563151, Class: 1.000000, Obj: 0.254006, No Obj: 0.003891, Avg Recall: 0.680328, count: 122 -Region Avg IOU: 0.546269, Class: 1.000000, Obj: 0.192377, No Obj: 0.003643, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.520301, Class: 1.000000, Obj: 0.263421, No Obj: 0.003702, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.551164, Class: 1.000000, Obj: 0.236536, No Obj: 0.003895, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.555796, Class: 1.000000, Obj: 0.270321, No Obj: 0.003541, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.515218, Class: 1.000000, Obj: 0.224092, No Obj: 0.003714, Avg Recall: 0.604478, count: 134 -Region Avg IOU: 0.519190, Class: 1.000000, Obj: 0.289585, No Obj: 0.003642, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.530299, Class: 1.000000, Obj: 0.252021, No Obj: 0.003694, Avg Recall: 0.639535, count: 86 -643: 0.481106, 0.530103 avg, 0.001000 rate, 24.945553 seconds, 164608 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.499778, Class: 1.000000, Obj: 0.213111, No Obj: 0.003561, Avg Recall: 0.627119, count: 118 -Region Avg IOU: 0.532393, Class: 1.000000, Obj: 0.197034, No Obj: 0.003830, Avg Recall: 0.649635, count: 137 -Region Avg IOU: 0.486165, Class: 1.000000, Obj: 0.220011, No Obj: 0.003906, Avg Recall: 0.560976, count: 123 -Region Avg IOU: 0.531130, Class: 1.000000, Obj: 0.211609, No Obj: 0.003181, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.521445, Class: 1.000000, Obj: 0.226746, No Obj: 0.003669, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.498536, Class: 1.000000, Obj: 0.203085, No Obj: 0.003321, Avg Recall: 0.566667, count: 90 -Region Avg IOU: 0.549167, Class: 1.000000, Obj: 0.273080, No Obj: 0.004093, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.520670, Class: 1.000000, Obj: 0.240173, No Obj: 0.003600, Avg Recall: 0.590909, count: 110 -644: 0.571814, 0.534274 avg, 0.001000 rate, 24.749563 seconds, 164864 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.522800, Class: 1.000000, Obj: 0.224680, No Obj: 0.004101, Avg Recall: 0.625000, count: 144 -Region Avg IOU: 0.520519, Class: 1.000000, Obj: 0.196082, No Obj: 0.003841, Avg Recall: 0.589474, count: 95 -Region Avg IOU: 0.526122, Class: 1.000000, Obj: 0.236892, No Obj: 0.003615, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.530445, Class: 1.000000, Obj: 0.215222, No Obj: 0.004017, Avg Recall: 0.625899, count: 139 -Region Avg IOU: 0.529209, Class: 1.000000, Obj: 0.234137, No Obj: 0.003624, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.551078, Class: 1.000000, Obj: 0.225189, No Obj: 0.004079, Avg Recall: 0.619048, count: 126 -Region Avg IOU: 0.538373, Class: 1.000000, Obj: 0.224465, No Obj: 0.003618, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.511446, Class: 1.000000, Obj: 0.216612, No Obj: 0.003324, Avg Recall: 0.563636, count: 110 -645: 0.558009, 0.536647 avg, 0.001000 rate, 24.747921 seconds, 165120 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.602353, Class: 1.000000, Obj: 0.289618, No Obj: 0.003597, Avg Recall: 0.727273, count: 77 -Region Avg IOU: 0.575778, Class: 1.000000, Obj: 0.290132, No Obj: 0.003199, Avg Recall: 0.682927, count: 82 -Region Avg IOU: 0.564695, Class: 1.000000, Obj: 0.246069, No Obj: 0.003357, Avg Recall: 0.666667, count: 87 -Region Avg IOU: 0.522671, Class: 1.000000, Obj: 0.204524, No Obj: 0.004112, Avg Recall: 0.606061, count: 132 -Region Avg IOU: 0.529352, Class: 1.000000, Obj: 0.218409, No Obj: 0.003709, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.554648, Class: 1.000000, Obj: 0.217837, No Obj: 0.003746, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.506092, Class: 1.000000, Obj: 0.238602, No Obj: 0.003724, Avg Recall: 0.570370, count: 135 -Region Avg IOU: 0.552434, Class: 1.000000, Obj: 0.226712, No Obj: 0.003460, Avg Recall: 0.689320, count: 103 -646: 0.473596, 0.530342 avg, 0.001000 rate, 26.615835 seconds, 165376 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.509997, Class: 1.000000, Obj: 0.210096, No Obj: 0.003540, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.543913, Class: 1.000000, Obj: 0.245993, No Obj: 0.003673, Avg Recall: 0.622807, count: 114 -Region Avg IOU: 0.524699, Class: 1.000000, Obj: 0.214098, No Obj: 0.003436, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.521428, Class: 1.000000, Obj: 0.198258, No Obj: 0.003305, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.532563, Class: 1.000000, Obj: 0.196525, No Obj: 0.004281, Avg Recall: 0.604478, count: 134 -Region Avg IOU: 0.571435, Class: 1.000000, Obj: 0.223791, No Obj: 0.003674, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.516093, Class: 1.000000, Obj: 0.189054, No Obj: 0.003570, Avg Recall: 0.563025, count: 119 -Region Avg IOU: 0.509370, Class: 1.000000, Obj: 0.228273, No Obj: 0.003651, Avg Recall: 0.514085, count: 142 -647: 0.577231, 0.535031 avg, 0.001000 rate, 25.768051 seconds, 165632 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.555073, Class: 1.000000, Obj: 0.222382, No Obj: 0.003588, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.523301, Class: 1.000000, Obj: 0.223374, No Obj: 0.003577, Avg Recall: 0.603053, count: 131 -Region Avg IOU: 0.515198, Class: 1.000000, Obj: 0.248537, No Obj: 0.003323, Avg Recall: 0.600000, count: 90 -Region Avg IOU: 0.523698, Class: 1.000000, Obj: 0.192163, No Obj: 0.003683, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.525145, Class: 1.000000, Obj: 0.208051, No Obj: 0.003886, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.547462, Class: 1.000000, Obj: 0.224365, No Obj: 0.003444, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.513359, Class: 1.000000, Obj: 0.222076, No Obj: 0.003268, Avg Recall: 0.600000, count: 90 -Region Avg IOU: 0.506906, Class: 1.000000, Obj: 0.250254, No Obj: 0.003823, Avg Recall: 0.570312, count: 128 -648: 0.514635, 0.532991 avg, 0.001000 rate, 25.743525 seconds, 165888 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.584818, Class: 1.000000, Obj: 0.243101, No Obj: 0.003627, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.586621, Class: 1.000000, Obj: 0.208914, No Obj: 0.003611, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.552171, Class: 1.000000, Obj: 0.243300, No Obj: 0.003673, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.551373, Class: 1.000000, Obj: 0.259029, No Obj: 0.004005, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.567285, Class: 1.000000, Obj: 0.230375, No Obj: 0.004094, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.537325, Class: 1.000000, Obj: 0.225508, No Obj: 0.004017, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.527535, Class: 1.000000, Obj: 0.206978, No Obj: 0.003867, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.518958, Class: 1.000000, Obj: 0.215488, No Obj: 0.003648, Avg Recall: 0.605442, count: 147 -649: 0.500442, 0.529737 avg, 0.001000 rate, 26.016460 seconds, 166144 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.511465, Class: 1.000000, Obj: 0.273306, No Obj: 0.003693, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.466181, Class: 1.000000, Obj: 0.234581, No Obj: 0.003343, Avg Recall: 0.548387, count: 93 -Region Avg IOU: 0.528856, Class: 1.000000, Obj: 0.223351, No Obj: 0.003481, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.564794, Class: 1.000000, Obj: 0.232135, No Obj: 0.003454, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.547868, Class: 1.000000, Obj: 0.244127, No Obj: 0.004133, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.518094, Class: 1.000000, Obj: 0.247107, No Obj: 0.003758, Avg Recall: 0.596774, count: 124 -Region Avg IOU: 0.540358, Class: 1.000000, Obj: 0.222606, No Obj: 0.003805, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.526923, Class: 1.000000, Obj: 0.278695, No Obj: 0.003684, Avg Recall: 0.552381, count: 105 -650: 0.470789, 0.523842 avg, 0.001000 rate, 24.813351 seconds, 166400 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 18.852060 seconds -Region Avg IOU: 0.516577, Class: 1.000000, Obj: 0.237392, No Obj: 0.003867, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.494674, Class: 1.000000, Obj: 0.204932, No Obj: 0.003655, Avg Recall: 0.527473, count: 91 -Region Avg IOU: 0.484216, Class: 1.000000, Obj: 0.243381, No Obj: 0.003870, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.486486, Class: 1.000000, Obj: 0.245168, No Obj: 0.003864, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.484117, Class: 1.000000, Obj: 0.227464, No Obj: 0.004006, Avg Recall: 0.542373, count: 118 -Region Avg IOU: 0.482942, Class: 1.000000, Obj: 0.247900, No Obj: 0.003837, Avg Recall: 0.488636, count: 88 -Region Avg IOU: 0.474838, Class: 1.000000, Obj: 0.220266, No Obj: 0.003695, Avg Recall: 0.484211, count: 95 -Region Avg IOU: 0.499989, Class: 1.000000, Obj: 0.234735, No Obj: 0.003543, Avg Recall: 0.566038, count: 106 -651: 0.481618, 0.519619 avg, 0.001000 rate, 24.809952 seconds, 166656 images -Loaded: 0.036224 seconds -Region Avg IOU: 0.502449, Class: 1.000000, Obj: 0.258155, No Obj: 0.003618, Avg Recall: 0.554348, count: 92 -Region Avg IOU: 0.523891, Class: 1.000000, Obj: 0.276968, No Obj: 0.004132, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.493361, Class: 1.000000, Obj: 0.226176, No Obj: 0.004145, Avg Recall: 0.560606, count: 132 -Region Avg IOU: 0.540650, Class: 1.000000, Obj: 0.238062, No Obj: 0.003780, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.521697, Class: 1.000000, Obj: 0.211770, No Obj: 0.004141, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.478626, Class: 1.000000, Obj: 0.265384, No Obj: 0.003629, Avg Recall: 0.505376, count: 93 -Region Avg IOU: 0.543840, Class: 1.000000, Obj: 0.247150, No Obj: 0.003858, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.489019, Class: 1.000000, Obj: 0.220885, No Obj: 0.004231, Avg Recall: 0.487603, count: 121 -652: 0.512385, 0.518896 avg, 0.001000 rate, 23.785852 seconds, 166912 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.545319, Class: 1.000000, Obj: 0.225374, No Obj: 0.003696, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.503492, Class: 1.000000, Obj: 0.227946, No Obj: 0.003953, Avg Recall: 0.584615, count: 130 -Region Avg IOU: 0.498807, Class: 1.000000, Obj: 0.184675, No Obj: 0.003510, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.510441, Class: 1.000000, Obj: 0.216562, No Obj: 0.004022, Avg Recall: 0.583333, count: 120 -Region Avg IOU: 0.554617, Class: 1.000000, Obj: 0.274021, No Obj: 0.004114, Avg Recall: 0.659341, count: 91 -Region Avg IOU: 0.556038, Class: 1.000000, Obj: 0.280399, No Obj: 0.003628, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.476697, Class: 1.000000, Obj: 0.216404, No Obj: 0.003704, Avg Recall: 0.495238, count: 105 -Region Avg IOU: 0.479987, Class: 1.000000, Obj: 0.181288, No Obj: 0.003860, Avg Recall: 0.550847, count: 118 -653: 0.527441, 0.519751 avg, 0.001000 rate, 23.083883 seconds, 167168 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.495030, Class: 1.000000, Obj: 0.209107, No Obj: 0.003924, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.564490, Class: 1.000000, Obj: 0.279328, No Obj: 0.003683, Avg Recall: 0.688312, count: 77 -Region Avg IOU: 0.531842, Class: 1.000000, Obj: 0.280208, No Obj: 0.003989, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.488949, Class: 1.000000, Obj: 0.250370, No Obj: 0.004158, Avg Recall: 0.530435, count: 115 -Region Avg IOU: 0.518208, Class: 1.000000, Obj: 0.232944, No Obj: 0.003921, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.497568, Class: 1.000000, Obj: 0.233008, No Obj: 0.004039, Avg Recall: 0.530435, count: 115 -Region Avg IOU: 0.519211, Class: 1.000000, Obj: 0.221979, No Obj: 0.003752, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.495767, Class: 1.000000, Obj: 0.216399, No Obj: 0.004052, Avg Recall: 0.581967, count: 122 -654: 0.564799, 0.524255 avg, 0.001000 rate, 22.556080 seconds, 167424 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.522750, Class: 1.000000, Obj: 0.239617, No Obj: 0.003961, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.505874, Class: 1.000000, Obj: 0.265343, No Obj: 0.003762, Avg Recall: 0.615385, count: 91 -Region Avg IOU: 0.523037, Class: 1.000000, Obj: 0.216003, No Obj: 0.004020, Avg Recall: 0.572519, count: 131 -Region Avg IOU: 0.507348, Class: 1.000000, Obj: 0.244703, No Obj: 0.003605, Avg Recall: 0.580000, count: 100 -Region Avg IOU: 0.433338, Class: 1.000000, Obj: 0.212599, No Obj: 0.003839, Avg Recall: 0.440678, count: 118 -Region Avg IOU: 0.501597, Class: 1.000000, Obj: 0.233266, No Obj: 0.003631, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.515200, Class: 1.000000, Obj: 0.253283, No Obj: 0.003950, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.508099, Class: 1.000000, Obj: 0.225740, No Obj: 0.004142, Avg Recall: 0.584906, count: 106 -655: 0.537399, 0.525570 avg, 0.001000 rate, 24.309206 seconds, 167680 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.484501, Class: 1.000000, Obj: 0.190069, No Obj: 0.003799, Avg Recall: 0.515152, count: 99 -Region Avg IOU: 0.468657, Class: 1.000000, Obj: 0.248854, No Obj: 0.003501, Avg Recall: 0.473118, count: 93 -Region Avg IOU: 0.503801, Class: 1.000000, Obj: 0.258995, No Obj: 0.003641, Avg Recall: 0.574713, count: 87 -Region Avg IOU: 0.487300, Class: 1.000000, Obj: 0.203526, No Obj: 0.003818, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.464503, Class: 1.000000, Obj: 0.230589, No Obj: 0.004083, Avg Recall: 0.500000, count: 128 -Region Avg IOU: 0.495156, Class: 1.000000, Obj: 0.173358, No Obj: 0.003780, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.527031, Class: 1.000000, Obj: 0.235357, No Obj: 0.004501, Avg Recall: 0.575540, count: 139 -Region Avg IOU: 0.476300, Class: 1.000000, Obj: 0.242118, No Obj: 0.004178, Avg Recall: 0.544000, count: 125 -656: 0.545960, 0.527609 avg, 0.001000 rate, 24.745131 seconds, 167936 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.535344, Class: 1.000000, Obj: 0.250560, No Obj: 0.003976, Avg Recall: 0.600000, count: 85 -Region Avg IOU: 0.501557, Class: 1.000000, Obj: 0.251989, No Obj: 0.003976, Avg Recall: 0.566372, count: 113 -Region Avg IOU: 0.510591, Class: 1.000000, Obj: 0.239923, No Obj: 0.004063, Avg Recall: 0.571429, count: 126 -Region Avg IOU: 0.514619, Class: 1.000000, Obj: 0.224151, No Obj: 0.003935, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.498934, Class: 1.000000, Obj: 0.225910, No Obj: 0.004096, Avg Recall: 0.516129, count: 124 -Region Avg IOU: 0.505618, Class: 1.000000, Obj: 0.164133, No Obj: 0.004186, Avg Recall: 0.586957, count: 92 -Region Avg IOU: 0.479011, Class: 1.000000, Obj: 0.275454, No Obj: 0.003423, Avg Recall: 0.564103, count: 78 -Region Avg IOU: 0.456641, Class: 1.000000, Obj: 0.226530, No Obj: 0.003390, Avg Recall: 0.448980, count: 98 -657: 0.445476, 0.519396 avg, 0.001000 rate, 25.356892 seconds, 168192 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.495130, Class: 1.000000, Obj: 0.242546, No Obj: 0.004038, Avg Recall: 0.541667, count: 120 -Region Avg IOU: 0.521429, Class: 1.000000, Obj: 0.220635, No Obj: 0.004162, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.479455, Class: 1.000000, Obj: 0.214216, No Obj: 0.004302, Avg Recall: 0.527778, count: 144 -Region Avg IOU: 0.531938, Class: 1.000000, Obj: 0.221257, No Obj: 0.004088, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.497629, Class: 1.000000, Obj: 0.246378, No Obj: 0.004045, Avg Recall: 0.525000, count: 120 -Region Avg IOU: 0.527715, Class: 1.000000, Obj: 0.223138, No Obj: 0.004020, Avg Recall: 0.555556, count: 90 -Region Avg IOU: 0.475878, Class: 1.000000, Obj: 0.253317, No Obj: 0.003922, Avg Recall: 0.513514, count: 111 -Region Avg IOU: 0.497490, Class: 1.000000, Obj: 0.251814, No Obj: 0.003866, Avg Recall: 0.543210, count: 81 -658: 0.519593, 0.519415 avg, 0.001000 rate, 23.924849 seconds, 168448 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.489997, Class: 1.000000, Obj: 0.272049, No Obj: 0.003597, Avg Recall: 0.551020, count: 98 -Region Avg IOU: 0.478915, Class: 1.000000, Obj: 0.172170, No Obj: 0.003585, Avg Recall: 0.531250, count: 128 -Region Avg IOU: 0.521988, Class: 1.000000, Obj: 0.272107, No Obj: 0.003763, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.519626, Class: 1.000000, Obj: 0.236208, No Obj: 0.004031, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.503921, Class: 1.000000, Obj: 0.227845, No Obj: 0.003928, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.448532, Class: 1.000000, Obj: 0.257457, No Obj: 0.003900, Avg Recall: 0.466667, count: 105 -Region Avg IOU: 0.528882, Class: 1.000000, Obj: 0.229302, No Obj: 0.003953, Avg Recall: 0.589744, count: 78 -Region Avg IOU: 0.506514, Class: 1.000000, Obj: 0.253833, No Obj: 0.003932, Avg Recall: 0.596154, count: 104 -659: 0.469644, 0.514438 avg, 0.001000 rate, 25.781307 seconds, 168704 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.499333, Class: 1.000000, Obj: 0.241334, No Obj: 0.004120, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.503853, Class: 1.000000, Obj: 0.260196, No Obj: 0.004511, Avg Recall: 0.570175, count: 114 -Region Avg IOU: 0.546314, Class: 1.000000, Obj: 0.281254, No Obj: 0.004153, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.496094, Class: 1.000000, Obj: 0.248699, No Obj: 0.003744, Avg Recall: 0.575758, count: 99 -Region Avg IOU: 0.496153, Class: 1.000000, Obj: 0.269918, No Obj: 0.003906, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.465047, Class: 1.000000, Obj: 0.242455, No Obj: 0.004114, Avg Recall: 0.517241, count: 116 -Region Avg IOU: 0.532494, Class: 1.000000, Obj: 0.256349, No Obj: 0.003568, Avg Recall: 0.584270, count: 89 -Region Avg IOU: 0.505395, Class: 1.000000, Obj: 0.274602, No Obj: 0.003793, Avg Recall: 0.543478, count: 92 -660: 0.509524, 0.513947 avg, 0.001000 rate, 23.665045 seconds, 168960 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 20.848955 seconds -Region Avg IOU: 0.450498, Class: 1.000000, Obj: 0.251743, No Obj: 0.004203, Avg Recall: 0.471074, count: 121 -Region Avg IOU: 0.468926, Class: 1.000000, Obj: 0.267442, No Obj: 0.003991, Avg Recall: 0.475248, count: 101 -Region Avg IOU: 0.461362, Class: 1.000000, Obj: 0.225261, No Obj: 0.004300, Avg Recall: 0.477477, count: 111 -Region Avg IOU: 0.502127, Class: 1.000000, Obj: 0.240468, No Obj: 0.004262, Avg Recall: 0.580952, count: 105 -Region Avg IOU: 0.506457, Class: 1.000000, Obj: 0.232486, No Obj: 0.004100, Avg Recall: 0.536082, count: 97 -Region Avg IOU: 0.474097, Class: 1.000000, Obj: 0.258819, No Obj: 0.004138, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.469089, Class: 1.000000, Obj: 0.212961, No Obj: 0.004190, Avg Recall: 0.566667, count: 120 -Region Avg IOU: 0.462688, Class: 1.000000, Obj: 0.251140, No Obj: 0.004048, Avg Recall: 0.461538, count: 104 -661: 0.515231, 0.514075 avg, 0.001000 rate, 23.046658 seconds, 169216 images -Loaded: 0.031374 seconds -Region Avg IOU: 0.470423, Class: 1.000000, Obj: 0.214766, No Obj: 0.004260, Avg Recall: 0.482456, count: 114 -Region Avg IOU: 0.433761, Class: 1.000000, Obj: 0.213968, No Obj: 0.004361, Avg Recall: 0.425197, count: 127 -Region Avg IOU: 0.462126, Class: 1.000000, Obj: 0.229558, No Obj: 0.003865, Avg Recall: 0.480769, count: 104 -Region Avg IOU: 0.466995, Class: 1.000000, Obj: 0.226786, No Obj: 0.004740, Avg Recall: 0.484848, count: 132 -Region Avg IOU: 0.478975, Class: 1.000000, Obj: 0.216202, No Obj: 0.003886, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.499882, Class: 1.000000, Obj: 0.236560, No Obj: 0.004850, Avg Recall: 0.534483, count: 116 -Region Avg IOU: 0.450738, Class: 1.000000, Obj: 0.244580, No Obj: 0.004105, Avg Recall: 0.500000, count: 104 -Region Avg IOU: 0.481607, Class: 1.000000, Obj: 0.228868, No Obj: 0.004003, Avg Recall: 0.509259, count: 108 -662: 0.543181, 0.516986 avg, 0.001000 rate, 24.016251 seconds, 169472 images -Loaded: 0.224107 seconds -Region Avg IOU: 0.445806, Class: 1.000000, Obj: 0.196538, No Obj: 0.004270, Avg Recall: 0.495652, count: 115 -Region Avg IOU: 0.467389, Class: 1.000000, Obj: 0.190831, No Obj: 0.004385, Avg Recall: 0.522523, count: 111 -Region Avg IOU: 0.472499, Class: 1.000000, Obj: 0.242465, No Obj: 0.004373, Avg Recall: 0.486957, count: 115 -Region Avg IOU: 0.471492, Class: 1.000000, Obj: 0.215392, No Obj: 0.004898, Avg Recall: 0.496970, count: 165 -Region Avg IOU: 0.464735, Class: 1.000000, Obj: 0.230651, No Obj: 0.004556, Avg Recall: 0.477941, count: 136 -Region Avg IOU: 0.469549, Class: 1.000000, Obj: 0.233222, No Obj: 0.004380, Avg Recall: 0.466102, count: 118 -Region Avg IOU: 0.459590, Class: 1.000000, Obj: 0.248233, No Obj: 0.004387, Avg Recall: 0.443299, count: 97 -Region Avg IOU: 0.509079, Class: 1.000000, Obj: 0.254869, No Obj: 0.004155, Avg Recall: 0.583333, count: 108 -663: 0.570202, 0.522307 avg, 0.001000 rate, 21.456512 seconds, 169728 images -Loaded: 1.266210 seconds -Region Avg IOU: 0.456319, Class: 1.000000, Obj: 0.217404, No Obj: 0.004072, Avg Recall: 0.514286, count: 105 -Region Avg IOU: 0.501350, Class: 1.000000, Obj: 0.267201, No Obj: 0.004402, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.471467, Class: 1.000000, Obj: 0.238916, No Obj: 0.004528, Avg Recall: 0.493333, count: 150 -Region Avg IOU: 0.447153, Class: 1.000000, Obj: 0.222946, No Obj: 0.004220, Avg Recall: 0.483871, count: 124 -Region Avg IOU: 0.480583, Class: 1.000000, Obj: 0.244201, No Obj: 0.003924, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.452759, Class: 1.000000, Obj: 0.256978, No Obj: 0.004539, Avg Recall: 0.441176, count: 102 -Region Avg IOU: 0.499104, Class: 1.000000, Obj: 0.264848, No Obj: 0.004284, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.472819, Class: 1.000000, Obj: 0.203409, No Obj: 0.004239, Avg Recall: 0.504673, count: 107 -664: 0.503130, 0.520390 avg, 0.001000 rate, 22.602772 seconds, 169984 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.454507, Class: 1.000000, Obj: 0.228422, No Obj: 0.004129, Avg Recall: 0.490385, count: 104 -Region Avg IOU: 0.478514, Class: 1.000000, Obj: 0.236444, No Obj: 0.004428, Avg Recall: 0.532374, count: 139 -Region Avg IOU: 0.446664, Class: 1.000000, Obj: 0.212744, No Obj: 0.004042, Avg Recall: 0.489796, count: 98 -Region Avg IOU: 0.539227, Class: 1.000000, Obj: 0.227032, No Obj: 0.004007, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.451486, Class: 1.000000, Obj: 0.232701, No Obj: 0.003665, Avg Recall: 0.477064, count: 109 -Region Avg IOU: 0.484570, Class: 1.000000, Obj: 0.233999, No Obj: 0.003686, Avg Recall: 0.516129, count: 93 -Region Avg IOU: 0.485345, Class: 1.000000, Obj: 0.210963, No Obj: 0.004052, Avg Recall: 0.532710, count: 107 -Region Avg IOU: 0.467845, Class: 1.000000, Obj: 0.239874, No Obj: 0.003755, Avg Recall: 0.468750, count: 96 -665: 0.567660, 0.525117 avg, 0.001000 rate, 21.543411 seconds, 170240 images -Loaded: 1.205487 seconds -Region Avg IOU: 0.429549, Class: 1.000000, Obj: 0.231858, No Obj: 0.004067, Avg Recall: 0.453782, count: 119 -Region Avg IOU: 0.432550, Class: 1.000000, Obj: 0.221740, No Obj: 0.003940, Avg Recall: 0.468085, count: 94 -Region Avg IOU: 0.487848, Class: 1.000000, Obj: 0.210250, No Obj: 0.004417, Avg Recall: 0.504000, count: 125 -Region Avg IOU: 0.461283, Class: 1.000000, Obj: 0.218687, No Obj: 0.003476, Avg Recall: 0.555556, count: 81 -Region Avg IOU: 0.473099, Class: 1.000000, Obj: 0.209633, No Obj: 0.004327, Avg Recall: 0.522124, count: 113 -Region Avg IOU: 0.478309, Class: 1.000000, Obj: 0.242892, No Obj: 0.004087, Avg Recall: 0.521739, count: 92 -Region Avg IOU: 0.401161, Class: 1.000000, Obj: 0.182077, No Obj: 0.003416, Avg Recall: 0.411765, count: 85 -Region Avg IOU: 0.519047, Class: 1.000000, Obj: 0.232655, No Obj: 0.003879, Avg Recall: 0.531250, count: 96 -666: 0.469154, 0.519520 avg, 0.001000 rate, 22.138325 seconds, 170496 images -Loaded: 0.379960 seconds -Region Avg IOU: 0.433323, Class: 1.000000, Obj: 0.218255, No Obj: 0.004073, Avg Recall: 0.478261, count: 115 -Region Avg IOU: 0.478592, Class: 1.000000, Obj: 0.215127, No Obj: 0.004436, Avg Recall: 0.455882, count: 136 -Region Avg IOU: 0.489396, Class: 1.000000, Obj: 0.244343, No Obj: 0.004113, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.444212, Class: 1.000000, Obj: 0.198091, No Obj: 0.004116, Avg Recall: 0.500000, count: 100 -Region Avg IOU: 0.501625, Class: 1.000000, Obj: 0.247976, No Obj: 0.004204, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.459710, Class: 1.000000, Obj: 0.200574, No Obj: 0.003504, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.492199, Class: 1.000000, Obj: 0.230210, No Obj: 0.004301, Avg Recall: 0.560440, count: 91 -Region Avg IOU: 0.451093, Class: 1.000000, Obj: 0.225857, No Obj: 0.003945, Avg Recall: 0.480769, count: 104 -667: 0.518995, 0.519468 avg, 0.001000 rate, 21.262255 seconds, 170752 images -Loaded: 0.000130 seconds -Region Avg IOU: 0.485230, Class: 1.000000, Obj: 0.236476, No Obj: 0.004171, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.460499, Class: 1.000000, Obj: 0.223329, No Obj: 0.004062, Avg Recall: 0.464912, count: 114 -Region Avg IOU: 0.474862, Class: 1.000000, Obj: 0.226281, No Obj: 0.004195, Avg Recall: 0.504587, count: 109 -Region Avg IOU: 0.474927, Class: 1.000000, Obj: 0.250848, No Obj: 0.004397, Avg Recall: 0.568421, count: 95 -Region Avg IOU: 0.461004, Class: 1.000000, Obj: 0.232386, No Obj: 0.004247, Avg Recall: 0.482759, count: 116 -Region Avg IOU: 0.406545, Class: 1.000000, Obj: 0.221379, No Obj: 0.003015, Avg Recall: 0.414286, count: 70 -Region Avg IOU: 0.531973, Class: 1.000000, Obj: 0.279794, No Obj: 0.004508, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.482329, Class: 1.000000, Obj: 0.253258, No Obj: 0.004249, Avg Recall: 0.523810, count: 105 -668: 0.478360, 0.515357 avg, 0.001000 rate, 22.842968 seconds, 171008 images -Loaded: 0.927542 seconds -Region Avg IOU: 0.453536, Class: 1.000000, Obj: 0.227392, No Obj: 0.004176, Avg Recall: 0.500000, count: 108 -Region Avg IOU: 0.462381, Class: 1.000000, Obj: 0.221881, No Obj: 0.004496, Avg Recall: 0.476562, count: 128 -Region Avg IOU: 0.498315, Class: 1.000000, Obj: 0.257755, No Obj: 0.004056, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.476391, Class: 1.000000, Obj: 0.244275, No Obj: 0.003922, Avg Recall: 0.490000, count: 100 -Region Avg IOU: 0.463014, Class: 1.000000, Obj: 0.220914, No Obj: 0.004061, Avg Recall: 0.515873, count: 126 -Region Avg IOU: 0.415554, Class: 1.000000, Obj: 0.197842, No Obj: 0.003999, Avg Recall: 0.440860, count: 93 -Region Avg IOU: 0.462865, Class: 1.000000, Obj: 0.216621, No Obj: 0.004541, Avg Recall: 0.453782, count: 119 -Region Avg IOU: 0.463076, Class: 1.000000, Obj: 0.232359, No Obj: 0.004229, Avg Recall: 0.500000, count: 132 -669: 0.547554, 0.518577 avg, 0.001000 rate, 22.961843 seconds, 171264 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.516499, Class: 1.000000, Obj: 0.220190, No Obj: 0.004652, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.454471, Class: 1.000000, Obj: 0.247755, No Obj: 0.003958, Avg Recall: 0.500000, count: 82 -Region Avg IOU: 0.512168, Class: 1.000000, Obj: 0.252363, No Obj: 0.004671, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.479817, Class: 1.000000, Obj: 0.238022, No Obj: 0.004052, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.486295, Class: 1.000000, Obj: 0.260987, No Obj: 0.004341, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.497210, Class: 1.000000, Obj: 0.265357, No Obj: 0.004023, Avg Recall: 0.492537, count: 67 -Region Avg IOU: 0.415563, Class: 1.000000, Obj: 0.185319, No Obj: 0.003684, Avg Recall: 0.401709, count: 117 -Region Avg IOU: 0.452324, Class: 1.000000, Obj: 0.231624, No Obj: 0.003956, Avg Recall: 0.456522, count: 92 -670: 0.461161, 0.512835 avg, 0.001000 rate, 22.113491 seconds, 171520 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000067 seconds -Region Avg IOU: 0.502710, Class: 1.000000, Obj: 0.277381, No Obj: 0.004307, Avg Recall: 0.578512, count: 121 -Region Avg IOU: 0.545021, Class: 1.000000, Obj: 0.226763, No Obj: 0.004439, Avg Recall: 0.663866, count: 119 -Region Avg IOU: 0.522950, Class: 1.000000, Obj: 0.280489, No Obj: 0.003925, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.537552, Class: 1.000000, Obj: 0.249618, No Obj: 0.004193, Avg Recall: 0.641667, count: 120 -Region Avg IOU: 0.496723, Class: 1.000000, Obj: 0.219331, No Obj: 0.004237, Avg Recall: 0.516949, count: 118 -Region Avg IOU: 0.496644, Class: 1.000000, Obj: 0.271721, No Obj: 0.004086, Avg Recall: 0.615385, count: 91 -Region Avg IOU: 0.521394, Class: 1.000000, Obj: 0.221951, No Obj: 0.003813, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.528672, Class: 1.000000, Obj: 0.252792, No Obj: 0.004658, Avg Recall: 0.673469, count: 98 -671: 0.499165, 0.511468 avg, 0.001000 rate, 23.401844 seconds, 171776 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.497532, Class: 1.000000, Obj: 0.238292, No Obj: 0.003606, Avg Recall: 0.519231, count: 104 -Region Avg IOU: 0.484699, Class: 1.000000, Obj: 0.237077, No Obj: 0.003663, Avg Recall: 0.489796, count: 98 -Region Avg IOU: 0.522181, Class: 1.000000, Obj: 0.256285, No Obj: 0.003927, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.546704, Class: 1.000000, Obj: 0.295543, No Obj: 0.003847, Avg Recall: 0.639535, count: 86 -Region Avg IOU: 0.505867, Class: 1.000000, Obj: 0.211111, No Obj: 0.004114, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.498556, Class: 1.000000, Obj: 0.211336, No Obj: 0.004195, Avg Recall: 0.537313, count: 134 -Region Avg IOU: 0.472479, Class: 1.000000, Obj: 0.224768, No Obj: 0.004064, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.531264, Class: 1.000000, Obj: 0.260833, No Obj: 0.003994, Avg Recall: 0.617391, count: 115 -672: 0.462463, 0.506568 avg, 0.001000 rate, 24.003984 seconds, 172032 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.512114, Class: 1.000000, Obj: 0.217498, No Obj: 0.003994, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.516842, Class: 1.000000, Obj: 0.243132, No Obj: 0.004240, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.533716, Class: 1.000000, Obj: 0.214821, No Obj: 0.003572, Avg Recall: 0.555556, count: 81 -Region Avg IOU: 0.492792, Class: 1.000000, Obj: 0.269688, No Obj: 0.003947, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.503104, Class: 1.000000, Obj: 0.245258, No Obj: 0.004060, Avg Recall: 0.520325, count: 123 -Region Avg IOU: 0.499104, Class: 1.000000, Obj: 0.262080, No Obj: 0.004280, Avg Recall: 0.544000, count: 125 -Region Avg IOU: 0.499117, Class: 1.000000, Obj: 0.245273, No Obj: 0.004281, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.519588, Class: 1.000000, Obj: 0.236933, No Obj: 0.003835, Avg Recall: 0.636364, count: 99 -673: 0.486962, 0.504607 avg, 0.001000 rate, 23.666765 seconds, 172288 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.446825, Class: 1.000000, Obj: 0.244954, No Obj: 0.003162, Avg Recall: 0.466667, count: 90 -Region Avg IOU: 0.511290, Class: 1.000000, Obj: 0.231703, No Obj: 0.003960, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.497800, Class: 1.000000, Obj: 0.242516, No Obj: 0.003883, Avg Recall: 0.509259, count: 108 -Region Avg IOU: 0.534052, Class: 1.000000, Obj: 0.264394, No Obj: 0.004106, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.553473, Class: 1.000000, Obj: 0.229129, No Obj: 0.004375, Avg Recall: 0.666667, count: 126 -Region Avg IOU: 0.522184, Class: 1.000000, Obj: 0.255916, No Obj: 0.003925, Avg Recall: 0.573770, count: 122 -Region Avg IOU: 0.523774, Class: 1.000000, Obj: 0.236898, No Obj: 0.003985, Avg Recall: 0.565657, count: 99 -Region Avg IOU: 0.508841, Class: 1.000000, Obj: 0.226298, No Obj: 0.004140, Avg Recall: 0.553719, count: 121 -674: 0.537319, 0.507878 avg, 0.001000 rate, 24.865421 seconds, 172544 images -Loaded: 0.140571 seconds -Region Avg IOU: 0.540276, Class: 1.000000, Obj: 0.250299, No Obj: 0.004169, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.528350, Class: 1.000000, Obj: 0.257236, No Obj: 0.004094, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.521311, Class: 1.000000, Obj: 0.245299, No Obj: 0.004308, Avg Recall: 0.571429, count: 126 -Region Avg IOU: 0.518759, Class: 1.000000, Obj: 0.303972, No Obj: 0.004229, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.557904, Class: 1.000000, Obj: 0.219638, No Obj: 0.004283, Avg Recall: 0.701613, count: 124 -Region Avg IOU: 0.532674, Class: 1.000000, Obj: 0.257369, No Obj: 0.003927, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.429887, Class: 1.000000, Obj: 0.203733, No Obj: 0.003632, Avg Recall: 0.457364, count: 129 -Region Avg IOU: 0.546068, Class: 1.000000, Obj: 0.242411, No Obj: 0.003690, Avg Recall: 0.642105, count: 95 -675: 0.454800, 0.502571 avg, 0.001000 rate, 23.260796 seconds, 172800 images -Loaded: 0.000114 seconds -Region Avg IOU: 0.542242, Class: 1.000000, Obj: 0.253334, No Obj: 0.004311, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.484242, Class: 1.000000, Obj: 0.243854, No Obj: 0.003474, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.514207, Class: 1.000000, Obj: 0.215670, No Obj: 0.004100, Avg Recall: 0.607143, count: 140 -Region Avg IOU: 0.524336, Class: 1.000000, Obj: 0.231033, No Obj: 0.003865, Avg Recall: 0.596491, count: 114 -Region Avg IOU: 0.491540, Class: 1.000000, Obj: 0.213337, No Obj: 0.004051, Avg Recall: 0.523438, count: 128 -Region Avg IOU: 0.472197, Class: 1.000000, Obj: 0.245331, No Obj: 0.003932, Avg Recall: 0.482143, count: 112 -Region Avg IOU: 0.491887, Class: 1.000000, Obj: 0.285488, No Obj: 0.003461, Avg Recall: 0.553191, count: 94 -Region Avg IOU: 0.469896, Class: 1.000000, Obj: 0.205581, No Obj: 0.004074, Avg Recall: 0.556452, count: 124 -676: 0.557095, 0.508023 avg, 0.001000 rate, 24.645254 seconds, 173056 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.514583, Class: 1.000000, Obj: 0.254555, No Obj: 0.003913, Avg Recall: 0.526718, count: 131 -Region Avg IOU: 0.564703, Class: 1.000000, Obj: 0.215804, No Obj: 0.004228, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.493427, Class: 1.000000, Obj: 0.224534, No Obj: 0.004027, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.520575, Class: 1.000000, Obj: 0.249135, No Obj: 0.003769, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.554567, Class: 1.000000, Obj: 0.299983, No Obj: 0.004235, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.543695, Class: 1.000000, Obj: 0.256958, No Obj: 0.003824, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.533592, Class: 1.000000, Obj: 0.245270, No Obj: 0.004217, Avg Recall: 0.647059, count: 136 -Region Avg IOU: 0.503117, Class: 1.000000, Obj: 0.239200, No Obj: 0.003487, Avg Recall: 0.574713, count: 87 -677: 0.498289, 0.507050 avg, 0.001000 rate, 24.409037 seconds, 173312 images -Loaded: 0.000121 seconds -Region Avg IOU: 0.517036, Class: 1.000000, Obj: 0.249170, No Obj: 0.003654, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.494182, Class: 1.000000, Obj: 0.238587, No Obj: 0.004426, Avg Recall: 0.528169, count: 142 -Region Avg IOU: 0.484468, Class: 1.000000, Obj: 0.225017, No Obj: 0.003841, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.540335, Class: 1.000000, Obj: 0.234541, No Obj: 0.004070, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.502066, Class: 1.000000, Obj: 0.236941, No Obj: 0.004326, Avg Recall: 0.524590, count: 122 -Region Avg IOU: 0.524066, Class: 1.000000, Obj: 0.237715, No Obj: 0.004203, Avg Recall: 0.578125, count: 128 -Region Avg IOU: 0.515400, Class: 1.000000, Obj: 0.259867, No Obj: 0.004704, Avg Recall: 0.569106, count: 123 -Region Avg IOU: 0.484016, Class: 1.000000, Obj: 0.270620, No Obj: 0.004319, Avg Recall: 0.473282, count: 131 -678: 0.567904, 0.513135 avg, 0.001000 rate, 24.648975 seconds, 173568 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.506994, Class: 1.000000, Obj: 0.251892, No Obj: 0.004040, Avg Recall: 0.559140, count: 93 -Region Avg IOU: 0.513963, Class: 1.000000, Obj: 0.250508, No Obj: 0.004136, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.497655, Class: 1.000000, Obj: 0.247232, No Obj: 0.003958, Avg Recall: 0.571429, count: 112 -Region Avg IOU: 0.520424, Class: 1.000000, Obj: 0.258397, No Obj: 0.003731, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.514420, Class: 1.000000, Obj: 0.251557, No Obj: 0.004315, Avg Recall: 0.597015, count: 134 -Region Avg IOU: 0.465807, Class: 1.000000, Obj: 0.223029, No Obj: 0.004028, Avg Recall: 0.471074, count: 121 -Region Avg IOU: 0.513389, Class: 1.000000, Obj: 0.237167, No Obj: 0.003917, Avg Recall: 0.577778, count: 90 -Region Avg IOU: 0.570283, Class: 1.000000, Obj: 0.237070, No Obj: 0.004340, Avg Recall: 0.681319, count: 91 -679: 0.489959, 0.510818 avg, 0.001000 rate, 23.289936 seconds, 173824 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.533074, Class: 1.000000, Obj: 0.222461, No Obj: 0.004101, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.533240, Class: 1.000000, Obj: 0.235101, No Obj: 0.004230, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.480520, Class: 1.000000, Obj: 0.252579, No Obj: 0.004045, Avg Recall: 0.495238, count: 105 -Region Avg IOU: 0.489623, Class: 1.000000, Obj: 0.214974, No Obj: 0.004047, Avg Recall: 0.536000, count: 125 -Region Avg IOU: 0.506377, Class: 1.000000, Obj: 0.215842, No Obj: 0.004107, Avg Recall: 0.595420, count: 131 -Region Avg IOU: 0.474734, Class: 1.000000, Obj: 0.224459, No Obj: 0.003796, Avg Recall: 0.508621, count: 116 -Region Avg IOU: 0.524625, Class: 1.000000, Obj: 0.240679, No Obj: 0.004315, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.514223, Class: 1.000000, Obj: 0.250457, No Obj: 0.003835, Avg Recall: 0.580645, count: 93 -680: 0.516460, 0.511382 avg, 0.001000 rate, 24.222090 seconds, 174080 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 16.828232 seconds -Region Avg IOU: 0.534622, Class: 1.000000, Obj: 0.273675, No Obj: 0.004123, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.470552, Class: 1.000000, Obj: 0.204733, No Obj: 0.003085, Avg Recall: 0.518072, count: 83 -Region Avg IOU: 0.489991, Class: 1.000000, Obj: 0.213373, No Obj: 0.004287, Avg Recall: 0.542373, count: 118 -Region Avg IOU: 0.386281, Class: 1.000000, Obj: 0.223193, No Obj: 0.004033, Avg Recall: 0.396226, count: 106 -Region Avg IOU: 0.473006, Class: 1.000000, Obj: 0.215176, No Obj: 0.003922, Avg Recall: 0.521739, count: 115 -Region Avg IOU: 0.438943, Class: 1.000000, Obj: 0.232779, No Obj: 0.003400, Avg Recall: 0.522936, count: 109 -Region Avg IOU: 0.455587, Class: 1.000000, Obj: 0.292233, No Obj: 0.004275, Avg Recall: 0.489796, count: 98 -Region Avg IOU: 0.473739, Class: 1.000000, Obj: 0.266604, No Obj: 0.003817, Avg Recall: 0.506494, count: 77 -681: 0.469369, 0.507181 avg, 0.001000 rate, 23.146328 seconds, 174336 images -Loaded: 0.000117 seconds -Region Avg IOU: 0.515893, Class: 1.000000, Obj: 0.246295, No Obj: 0.003990, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.405545, Class: 1.000000, Obj: 0.223458, No Obj: 0.003629, Avg Recall: 0.372549, count: 102 -Region Avg IOU: 0.543312, Class: 1.000000, Obj: 0.291561, No Obj: 0.004794, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.485467, Class: 1.000000, Obj: 0.218731, No Obj: 0.004015, Avg Recall: 0.571429, count: 105 -Region Avg IOU: 0.462701, Class: 1.000000, Obj: 0.261008, No Obj: 0.003671, Avg Recall: 0.494505, count: 91 -Region Avg IOU: 0.485811, Class: 1.000000, Obj: 0.251821, No Obj: 0.004330, Avg Recall: 0.563025, count: 119 -Region Avg IOU: 0.499969, Class: 1.000000, Obj: 0.198874, No Obj: 0.004189, Avg Recall: 0.559633, count: 109 -Region Avg IOU: 0.549748, Class: 1.000000, Obj: 0.246365, No Obj: 0.004127, Avg Recall: 0.619048, count: 105 -682: 0.465264, 0.502989 avg, 0.001000 rate, 24.086985 seconds, 174592 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.497119, Class: 1.000000, Obj: 0.262765, No Obj: 0.004165, Avg Recall: 0.512397, count: 121 -Region Avg IOU: 0.525136, Class: 1.000000, Obj: 0.228608, No Obj: 0.004399, Avg Recall: 0.566176, count: 136 -Region Avg IOU: 0.479075, Class: 1.000000, Obj: 0.223495, No Obj: 0.004118, Avg Recall: 0.526316, count: 114 -Region Avg IOU: 0.519310, Class: 1.000000, Obj: 0.280760, No Obj: 0.004337, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.491078, Class: 1.000000, Obj: 0.265020, No Obj: 0.004312, Avg Recall: 0.508065, count: 124 -Region Avg IOU: 0.495620, Class: 1.000000, Obj: 0.248506, No Obj: 0.004152, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.459392, Class: 1.000000, Obj: 0.265912, No Obj: 0.003658, Avg Recall: 0.467391, count: 92 -Region Avg IOU: 0.463868, Class: 1.000000, Obj: 0.232306, No Obj: 0.004072, Avg Recall: 0.530973, count: 113 -683: 0.526129, 0.505303 avg, 0.001000 rate, 21.796400 seconds, 174848 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.467701, Class: 1.000000, Obj: 0.218548, No Obj: 0.004190, Avg Recall: 0.491803, count: 122 -Region Avg IOU: 0.485345, Class: 1.000000, Obj: 0.225157, No Obj: 0.004309, Avg Recall: 0.526316, count: 133 -Region Avg IOU: 0.515677, Class: 1.000000, Obj: 0.254326, No Obj: 0.003769, Avg Recall: 0.541667, count: 96 -Region Avg IOU: 0.438587, Class: 1.000000, Obj: 0.194536, No Obj: 0.003858, Avg Recall: 0.439655, count: 116 -Region Avg IOU: 0.513115, Class: 1.000000, Obj: 0.243975, No Obj: 0.004282, Avg Recall: 0.584746, count: 118 -Region Avg IOU: 0.468698, Class: 1.000000, Obj: 0.216918, No Obj: 0.004012, Avg Recall: 0.503704, count: 135 -Region Avg IOU: 0.523605, Class: 1.000000, Obj: 0.264590, No Obj: 0.004023, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.473773, Class: 1.000000, Obj: 0.247176, No Obj: 0.003422, Avg Recall: 0.505882, count: 85 -684: 0.536221, 0.508395 avg, 0.001000 rate, 22.705339 seconds, 175104 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.511468, Class: 1.000000, Obj: 0.247246, No Obj: 0.004453, Avg Recall: 0.547826, count: 115 -Region Avg IOU: 0.438382, Class: 1.000000, Obj: 0.202454, No Obj: 0.003725, Avg Recall: 0.441441, count: 111 -Region Avg IOU: 0.458759, Class: 1.000000, Obj: 0.239374, No Obj: 0.003864, Avg Recall: 0.471154, count: 104 -Region Avg IOU: 0.517535, Class: 1.000000, Obj: 0.230912, No Obj: 0.004075, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.456719, Class: 1.000000, Obj: 0.257196, No Obj: 0.003729, Avg Recall: 0.457831, count: 83 -Region Avg IOU: 0.468389, Class: 1.000000, Obj: 0.232273, No Obj: 0.004487, Avg Recall: 0.460784, count: 102 -Region Avg IOU: 0.480836, Class: 1.000000, Obj: 0.248000, No Obj: 0.004213, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.501930, Class: 1.000000, Obj: 0.205962, No Obj: 0.003707, Avg Recall: 0.580952, count: 105 -685: 0.566225, 0.514178 avg, 0.001000 rate, 21.791723 seconds, 175360 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.439480, Class: 1.000000, Obj: 0.203733, No Obj: 0.003826, Avg Recall: 0.487603, count: 121 -Region Avg IOU: 0.473535, Class: 1.000000, Obj: 0.244661, No Obj: 0.004132, Avg Recall: 0.500000, count: 112 -Region Avg IOU: 0.531958, Class: 1.000000, Obj: 0.233601, No Obj: 0.003661, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.433431, Class: 1.000000, Obj: 0.205280, No Obj: 0.003843, Avg Recall: 0.500000, count: 108 -Region Avg IOU: 0.509176, Class: 1.000000, Obj: 0.273822, No Obj: 0.003979, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.448315, Class: 1.000000, Obj: 0.205317, No Obj: 0.004053, Avg Recall: 0.508065, count: 124 -Region Avg IOU: 0.478415, Class: 1.000000, Obj: 0.208303, No Obj: 0.004067, Avg Recall: 0.513043, count: 115 -Region Avg IOU: 0.486231, Class: 1.000000, Obj: 0.244190, No Obj: 0.004057, Avg Recall: 0.511811, count: 127 -686: 0.573144, 0.520074 avg, 0.001000 rate, 23.823963 seconds, 175616 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.525099, Class: 1.000000, Obj: 0.272211, No Obj: 0.004597, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.480382, Class: 1.000000, Obj: 0.239656, No Obj: 0.004088, Avg Recall: 0.500000, count: 106 -Region Avg IOU: 0.515473, Class: 1.000000, Obj: 0.238577, No Obj: 0.004204, Avg Recall: 0.593750, count: 96 -Region Avg IOU: 0.498561, Class: 1.000000, Obj: 0.246929, No Obj: 0.003826, Avg Recall: 0.566667, count: 90 -Region Avg IOU: 0.539801, Class: 1.000000, Obj: 0.271284, No Obj: 0.004036, Avg Recall: 0.637500, count: 80 -Region Avg IOU: 0.553399, Class: 1.000000, Obj: 0.263303, No Obj: 0.003735, Avg Recall: 0.653846, count: 78 -Region Avg IOU: 0.466516, Class: 1.000000, Obj: 0.260420, No Obj: 0.004150, Avg Recall: 0.495050, count: 101 -Region Avg IOU: 0.500776, Class: 1.000000, Obj: 0.259094, No Obj: 0.004011, Avg Recall: 0.566372, count: 113 -687: 0.472053, 0.515272 avg, 0.001000 rate, 23.911268 seconds, 175872 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.518845, Class: 1.000000, Obj: 0.242164, No Obj: 0.004017, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.444883, Class: 1.000000, Obj: 0.262029, No Obj: 0.003887, Avg Recall: 0.437500, count: 96 -Region Avg IOU: 0.543603, Class: 1.000000, Obj: 0.238958, No Obj: 0.004571, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.458044, Class: 1.000000, Obj: 0.247119, No Obj: 0.004496, Avg Recall: 0.491803, count: 122 -Region Avg IOU: 0.460084, Class: 1.000000, Obj: 0.194109, No Obj: 0.003902, Avg Recall: 0.500000, count: 100 -Region Avg IOU: 0.476526, Class: 1.000000, Obj: 0.230076, No Obj: 0.003688, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.511805, Class: 1.000000, Obj: 0.256595, No Obj: 0.004832, Avg Recall: 0.540323, count: 124 -Region Avg IOU: 0.464781, Class: 1.000000, Obj: 0.238631, No Obj: 0.003778, Avg Recall: 0.516854, count: 89 -688: 0.531349, 0.516880 avg, 0.001000 rate, 22.797060 seconds, 176128 images -Loaded: 0.029685 seconds -Region Avg IOU: 0.508643, Class: 1.000000, Obj: 0.228456, No Obj: 0.004437, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.520081, Class: 1.000000, Obj: 0.220961, No Obj: 0.003801, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.506856, Class: 1.000000, Obj: 0.293145, No Obj: 0.004131, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.475554, Class: 1.000000, Obj: 0.258503, No Obj: 0.003746, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.498409, Class: 1.000000, Obj: 0.267059, No Obj: 0.004385, Avg Recall: 0.541985, count: 131 -Region Avg IOU: 0.419471, Class: 1.000000, Obj: 0.232245, No Obj: 0.004241, Avg Recall: 0.484848, count: 99 -Region Avg IOU: 0.514934, Class: 1.000000, Obj: 0.221644, No Obj: 0.004167, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.475237, Class: 1.000000, Obj: 0.236390, No Obj: 0.004146, Avg Recall: 0.526316, count: 133 -689: 0.505168, 0.515709 avg, 0.001000 rate, 23.494108 seconds, 176384 images -Loaded: 0.066073 seconds -Region Avg IOU: 0.503716, Class: 1.000000, Obj: 0.225350, No Obj: 0.004039, Avg Recall: 0.549020, count: 102 -Region Avg IOU: 0.499622, Class: 1.000000, Obj: 0.265220, No Obj: 0.003769, Avg Recall: 0.605263, count: 76 -Region Avg IOU: 0.473090, Class: 1.000000, Obj: 0.251379, No Obj: 0.004282, Avg Recall: 0.503759, count: 133 -Region Avg IOU: 0.486021, Class: 1.000000, Obj: 0.238211, No Obj: 0.004298, Avg Recall: 0.589744, count: 117 -Region Avg IOU: 0.448111, Class: 1.000000, Obj: 0.224259, No Obj: 0.003603, Avg Recall: 0.468750, count: 96 -Region Avg IOU: 0.449932, Class: 1.000000, Obj: 0.235098, No Obj: 0.003745, Avg Recall: 0.478261, count: 115 -Region Avg IOU: 0.515450, Class: 1.000000, Obj: 0.244107, No Obj: 0.004171, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.489220, Class: 1.000000, Obj: 0.242179, No Obj: 0.004958, Avg Recall: 0.571429, count: 126 -690: 0.615997, 0.525738 avg, 0.001000 rate, 23.370932 seconds, 176640 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000060 seconds -Region Avg IOU: 0.571030, Class: 1.000000, Obj: 0.214294, No Obj: 0.003616, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.563674, Class: 1.000000, Obj: 0.245059, No Obj: 0.004007, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.536266, Class: 1.000000, Obj: 0.257004, No Obj: 0.003583, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.579636, Class: 1.000000, Obj: 0.298578, No Obj: 0.003421, Avg Recall: 0.727273, count: 88 -Region Avg IOU: 0.581304, Class: 1.000000, Obj: 0.310759, No Obj: 0.003705, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.551085, Class: 1.000000, Obj: 0.244839, No Obj: 0.003978, Avg Recall: 0.621429, count: 140 -Region Avg IOU: 0.564314, Class: 1.000000, Obj: 0.279429, No Obj: 0.004071, Avg Recall: 0.679688, count: 128 -Region Avg IOU: 0.593720, Class: 1.000000, Obj: 0.252309, No Obj: 0.003821, Avg Recall: 0.760870, count: 92 -691: 0.522684, 0.525432 avg, 0.001000 rate, 26.726322 seconds, 176896 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.556701, Class: 1.000000, Obj: 0.268478, No Obj: 0.003715, Avg Recall: 0.691667, count: 120 -Region Avg IOU: 0.489077, Class: 1.000000, Obj: 0.201213, No Obj: 0.003468, Avg Recall: 0.541667, count: 96 -Region Avg IOU: 0.550310, Class: 1.000000, Obj: 0.254206, No Obj: 0.003348, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.574945, Class: 1.000000, Obj: 0.279234, No Obj: 0.003555, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.526041, Class: 1.000000, Obj: 0.201186, No Obj: 0.003831, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.536688, Class: 1.000000, Obj: 0.266787, No Obj: 0.004101, Avg Recall: 0.601504, count: 133 -Region Avg IOU: 0.573692, Class: 1.000000, Obj: 0.272531, No Obj: 0.003511, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.547972, Class: 1.000000, Obj: 0.210681, No Obj: 0.003694, Avg Recall: 0.595506, count: 89 -692: 0.520310, 0.524920 avg, 0.001000 rate, 28.740261 seconds, 177152 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.571854, Class: 1.000000, Obj: 0.243326, No Obj: 0.003704, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.580865, Class: 1.000000, Obj: 0.232316, No Obj: 0.003846, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.560290, Class: 1.000000, Obj: 0.240666, No Obj: 0.003640, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.498364, Class: 1.000000, Obj: 0.237136, No Obj: 0.003318, Avg Recall: 0.607843, count: 102 -Region Avg IOU: 0.551050, Class: 1.000000, Obj: 0.247921, No Obj: 0.003756, Avg Recall: 0.632000, count: 125 -Region Avg IOU: 0.550250, Class: 1.000000, Obj: 0.226869, No Obj: 0.003385, Avg Recall: 0.585106, count: 94 -Region Avg IOU: 0.560951, Class: 1.000000, Obj: 0.230262, No Obj: 0.003758, Avg Recall: 0.661765, count: 136 -Region Avg IOU: 0.539855, Class: 1.000000, Obj: 0.231717, No Obj: 0.003905, Avg Recall: 0.610687, count: 131 -693: 0.509623, 0.523390 avg, 0.001000 rate, 27.957014 seconds, 177408 images -Loaded: 0.000173 seconds -Region Avg IOU: 0.531067, Class: 1.000000, Obj: 0.217583, No Obj: 0.003395, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.569904, Class: 1.000000, Obj: 0.256875, No Obj: 0.003366, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.565701, Class: 1.000000, Obj: 0.234272, No Obj: 0.003440, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.523862, Class: 1.000000, Obj: 0.174880, No Obj: 0.003587, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.524086, Class: 1.000000, Obj: 0.243253, No Obj: 0.003271, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.559088, Class: 1.000000, Obj: 0.256923, No Obj: 0.003443, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.549644, Class: 1.000000, Obj: 0.270246, No Obj: 0.003497, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.569433, Class: 1.000000, Obj: 0.256761, No Obj: 0.003171, Avg Recall: 0.690000, count: 100 -694: 0.474219, 0.518473 avg, 0.001000 rate, 27.420799 seconds, 177664 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.568778, Class: 1.000000, Obj: 0.227137, No Obj: 0.003336, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.591725, Class: 1.000000, Obj: 0.209033, No Obj: 0.003322, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.554198, Class: 1.000000, Obj: 0.205734, No Obj: 0.003238, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.546828, Class: 1.000000, Obj: 0.211357, No Obj: 0.003665, Avg Recall: 0.676259, count: 139 -Region Avg IOU: 0.503639, Class: 1.000000, Obj: 0.228365, No Obj: 0.003087, Avg Recall: 0.589474, count: 95 -Region Avg IOU: 0.497549, Class: 1.000000, Obj: 0.218022, No Obj: 0.002925, Avg Recall: 0.582278, count: 79 -Region Avg IOU: 0.616541, Class: 1.000000, Obj: 0.236834, No Obj: 0.003473, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.554853, Class: 1.000000, Obj: 0.241157, No Obj: 0.003124, Avg Recall: 0.674157, count: 89 -695: 0.532885, 0.519914 avg, 0.001000 rate, 26.625608 seconds, 177920 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.538175, Class: 1.000000, Obj: 0.161931, No Obj: 0.003155, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.585785, Class: 1.000000, Obj: 0.246428, No Obj: 0.003423, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.550463, Class: 1.000000, Obj: 0.187247, No Obj: 0.003208, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.535924, Class: 1.000000, Obj: 0.241288, No Obj: 0.003596, Avg Recall: 0.606838, count: 117 -Region Avg IOU: 0.551490, Class: 1.000000, Obj: 0.185569, No Obj: 0.003521, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.584249, Class: 1.000000, Obj: 0.269644, No Obj: 0.003528, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.579927, Class: 1.000000, Obj: 0.235745, No Obj: 0.003241, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.555099, Class: 1.000000, Obj: 0.206209, No Obj: 0.003255, Avg Recall: 0.628319, count: 113 -696: 0.528732, 0.520796 avg, 0.001000 rate, 27.919952 seconds, 178176 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.530176, Class: 1.000000, Obj: 0.234760, No Obj: 0.003131, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.548013, Class: 1.000000, Obj: 0.218376, No Obj: 0.003078, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.543408, Class: 1.000000, Obj: 0.213811, No Obj: 0.003560, Avg Recall: 0.616000, count: 125 -Region Avg IOU: 0.554488, Class: 1.000000, Obj: 0.239198, No Obj: 0.003287, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.605790, Class: 1.000000, Obj: 0.249939, No Obj: 0.003475, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.594562, Class: 1.000000, Obj: 0.214805, No Obj: 0.003453, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.538708, Class: 1.000000, Obj: 0.251506, No Obj: 0.003362, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.604928, Class: 1.000000, Obj: 0.228675, No Obj: 0.003651, Avg Recall: 0.711712, count: 111 -697: 0.499381, 0.518655 avg, 0.001000 rate, 26.944784 seconds, 178432 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.563956, Class: 1.000000, Obj: 0.264368, No Obj: 0.003008, Avg Recall: 0.635294, count: 85 -Region Avg IOU: 0.567733, Class: 1.000000, Obj: 0.244462, No Obj: 0.003170, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.564193, Class: 1.000000, Obj: 0.243013, No Obj: 0.003765, Avg Recall: 0.686567, count: 134 -Region Avg IOU: 0.548528, Class: 1.000000, Obj: 0.201652, No Obj: 0.003216, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.536618, Class: 1.000000, Obj: 0.221816, No Obj: 0.002947, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.529645, Class: 1.000000, Obj: 0.232008, No Obj: 0.003314, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.567186, Class: 1.000000, Obj: 0.263860, No Obj: 0.003460, Avg Recall: 0.700680, count: 147 -Region Avg IOU: 0.598503, Class: 1.000000, Obj: 0.211452, No Obj: 0.003326, Avg Recall: 0.755102, count: 98 -698: 0.489409, 0.515730 avg, 0.001000 rate, 26.082308 seconds, 178688 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.586676, Class: 1.000000, Obj: 0.250681, No Obj: 0.003167, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.570393, Class: 1.000000, Obj: 0.208467, No Obj: 0.003377, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.558746, Class: 1.000000, Obj: 0.229706, No Obj: 0.003061, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.537902, Class: 1.000000, Obj: 0.195562, No Obj: 0.002835, Avg Recall: 0.657895, count: 76 -Region Avg IOU: 0.570140, Class: 1.000000, Obj: 0.209339, No Obj: 0.003294, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.592824, Class: 1.000000, Obj: 0.264376, No Obj: 0.003468, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.560983, Class: 1.000000, Obj: 0.209145, No Obj: 0.003479, Avg Recall: 0.666667, count: 129 -Region Avg IOU: 0.562589, Class: 1.000000, Obj: 0.204402, No Obj: 0.003471, Avg Recall: 0.643478, count: 115 -699: 0.515464, 0.515703 avg, 0.001000 rate, 29.925625 seconds, 178944 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.566915, Class: 1.000000, Obj: 0.248271, No Obj: 0.003415, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.490154, Class: 1.000000, Obj: 0.241021, No Obj: 0.003011, Avg Recall: 0.512500, count: 80 -Region Avg IOU: 0.573356, Class: 1.000000, Obj: 0.242309, No Obj: 0.003503, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.583585, Class: 1.000000, Obj: 0.245589, No Obj: 0.003504, Avg Recall: 0.718750, count: 128 -Region Avg IOU: 0.574546, Class: 1.000000, Obj: 0.279075, No Obj: 0.003581, Avg Recall: 0.658333, count: 120 -Region Avg IOU: 0.575195, Class: 1.000000, Obj: 0.234989, No Obj: 0.003820, Avg Recall: 0.669565, count: 115 -Region Avg IOU: 0.586431, Class: 1.000000, Obj: 0.233627, No Obj: 0.003514, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.554723, Class: 1.000000, Obj: 0.191149, No Obj: 0.003137, Avg Recall: 0.639175, count: 97 -700: 0.499821, 0.514115 avg, 0.001000 rate, 27.712641 seconds, 179200 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.246144 seconds -Region Avg IOU: 0.423863, Class: 1.000000, Obj: 0.207780, No Obj: 0.003750, Avg Recall: 0.430000, count: 100 -Region Avg IOU: 0.412575, Class: 1.000000, Obj: 0.222313, No Obj: 0.003672, Avg Recall: 0.370370, count: 108 -Region Avg IOU: 0.454382, Class: 1.000000, Obj: 0.232007, No Obj: 0.004027, Avg Recall: 0.429907, count: 107 -Region Avg IOU: 0.462715, Class: 1.000000, Obj: 0.204110, No Obj: 0.003920, Avg Recall: 0.476191, count: 126 -Region Avg IOU: 0.456949, Class: 1.000000, Obj: 0.217344, No Obj: 0.003974, Avg Recall: 0.478633, count: 117 -Region Avg IOU: 0.394819, Class: 1.000000, Obj: 0.218803, No Obj: 0.004147, Avg Recall: 0.401408, count: 142 -Region Avg IOU: 0.446159, Class: 1.000000, Obj: 0.224267, No Obj: 0.004500, Avg Recall: 0.423077, count: 130 -Region Avg IOU: 0.468882, Class: 1.000000, Obj: 0.218674, No Obj: 0.003984, Avg Recall: 0.500000, count: 100 -701: 0.552617, 0.517965 avg, 0.001000 rate, 19.902264 seconds, 179456 images -Loaded: 3.580686 seconds -Region Avg IOU: 0.464085, Class: 1.000000, Obj: 0.217373, No Obj: 0.004224, Avg Recall: 0.522124, count: 113 -Region Avg IOU: 0.486692, Class: 1.000000, Obj: 0.210325, No Obj: 0.004806, Avg Recall: 0.525424, count: 118 -Region Avg IOU: 0.470713, Class: 1.000000, Obj: 0.213426, No Obj: 0.004175, Avg Recall: 0.500000, count: 108 -Region Avg IOU: 0.457087, Class: 1.000000, Obj: 0.203353, No Obj: 0.004008, Avg Recall: 0.456140, count: 114 -Region Avg IOU: 0.435131, Class: 1.000000, Obj: 0.190513, No Obj: 0.004058, Avg Recall: 0.445312, count: 128 -Region Avg IOU: 0.443778, Class: 1.000000, Obj: 0.219571, No Obj: 0.003822, Avg Recall: 0.446808, count: 94 -Region Avg IOU: 0.459921, Class: 1.000000, Obj: 0.231733, No Obj: 0.003701, Avg Recall: 0.505618, count: 89 -Region Avg IOU: 0.447111, Class: 1.000000, Obj: 0.216905, No Obj: 0.004104, Avg Recall: 0.477064, count: 109 -702: 0.550399, 0.521209 avg, 0.001000 rate, 22.219839 seconds, 179712 images -Loaded: 0.954620 seconds -Region Avg IOU: 0.429355, Class: 1.000000, Obj: 0.227221, No Obj: 0.003967, Avg Recall: 0.449612, count: 129 -Region Avg IOU: 0.495022, Class: 1.000000, Obj: 0.237309, No Obj: 0.004099, Avg Recall: 0.602273, count: 88 -Region Avg IOU: 0.444031, Class: 1.000000, Obj: 0.187574, No Obj: 0.003688, Avg Recall: 0.484848, count: 99 -Region Avg IOU: 0.468790, Class: 1.000000, Obj: 0.267820, No Obj: 0.004645, Avg Recall: 0.495652, count: 115 -Region Avg IOU: 0.475677, Class: 1.000000, Obj: 0.252745, No Obj: 0.003862, Avg Recall: 0.529412, count: 102 -Region Avg IOU: 0.442762, Class: 1.000000, Obj: 0.215250, No Obj: 0.004312, Avg Recall: 0.460870, count: 115 -Region Avg IOU: 0.451423, Class: 1.000000, Obj: 0.229945, No Obj: 0.004416, Avg Recall: 0.484848, count: 132 -Region Avg IOU: 0.416732, Class: 1.000000, Obj: 0.224161, No Obj: 0.004358, Avg Recall: 0.413462, count: 104 -703: 0.544731, 0.523561 avg, 0.001000 rate, 19.715424 seconds, 179968 images -Loaded: 2.230408 seconds -Region Avg IOU: 0.513824, Class: 1.000000, Obj: 0.259926, No Obj: 0.004261, Avg Recall: 0.556818, count: 88 -Region Avg IOU: 0.430764, Class: 1.000000, Obj: 0.239057, No Obj: 0.004046, Avg Recall: 0.431034, count: 116 -Region Avg IOU: 0.447441, Class: 1.000000, Obj: 0.265052, No Obj: 0.004069, Avg Recall: 0.450000, count: 100 -Region Avg IOU: 0.460731, Class: 1.000000, Obj: 0.231670, No Obj: 0.004596, Avg Recall: 0.470588, count: 136 -Region Avg IOU: 0.458537, Class: 1.000000, Obj: 0.224810, No Obj: 0.003713, Avg Recall: 0.500000, count: 92 -Region Avg IOU: 0.453920, Class: 1.000000, Obj: 0.207218, No Obj: 0.004583, Avg Recall: 0.467290, count: 107 -Region Avg IOU: 0.465839, Class: 1.000000, Obj: 0.259297, No Obj: 0.003918, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.469471, Class: 1.000000, Obj: 0.249475, No Obj: 0.005054, Avg Recall: 0.503145, count: 159 -704: 0.571466, 0.528351 avg, 0.001000 rate, 21.690617 seconds, 180224 images -Loaded: 0.488781 seconds -Region Avg IOU: 0.513686, Class: 1.000000, Obj: 0.245814, No Obj: 0.003928, Avg Recall: 0.635135, count: 74 -Region Avg IOU: 0.496117, Class: 1.000000, Obj: 0.263029, No Obj: 0.005194, Avg Recall: 0.592308, count: 130 -Region Avg IOU: 0.406131, Class: 1.000000, Obj: 0.203405, No Obj: 0.004464, Avg Recall: 0.463636, count: 110 -Region Avg IOU: 0.402102, Class: 1.000000, Obj: 0.205477, No Obj: 0.003705, Avg Recall: 0.381818, count: 110 -Region Avg IOU: 0.466413, Class: 1.000000, Obj: 0.236614, No Obj: 0.004957, Avg Recall: 0.481481, count: 108 -Region Avg IOU: 0.444191, Class: 1.000000, Obj: 0.246392, No Obj: 0.004873, Avg Recall: 0.472441, count: 127 -Region Avg IOU: 0.446371, Class: 1.000000, Obj: 0.247547, No Obj: 0.004412, Avg Recall: 0.456522, count: 92 -Region Avg IOU: 0.438075, Class: 1.000000, Obj: 0.246628, No Obj: 0.004473, Avg Recall: 0.455285, count: 123 -705: 0.512940, 0.526810 avg, 0.001000 rate, 21.426651 seconds, 180480 images -Loaded: 0.188997 seconds -Region Avg IOU: 0.486306, Class: 1.000000, Obj: 0.302769, No Obj: 0.004453, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.428423, Class: 1.000000, Obj: 0.242068, No Obj: 0.003776, Avg Recall: 0.406593, count: 91 -Region Avg IOU: 0.461887, Class: 1.000000, Obj: 0.249712, No Obj: 0.004826, Avg Recall: 0.476191, count: 126 -Region Avg IOU: 0.438516, Class: 1.000000, Obj: 0.222828, No Obj: 0.004881, Avg Recall: 0.452703, count: 148 -Region Avg IOU: 0.379697, Class: 1.000000, Obj: 0.205074, No Obj: 0.004127, Avg Recall: 0.373913, count: 115 -Region Avg IOU: 0.486687, Class: 1.000000, Obj: 0.236918, No Obj: 0.004140, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.461622, Class: 1.000000, Obj: 0.264744, No Obj: 0.004113, Avg Recall: 0.489796, count: 98 -Region Avg IOU: 0.447551, Class: 1.000000, Obj: 0.226652, No Obj: 0.004782, Avg Recall: 0.458647, count: 133 -706: 0.538952, 0.528024 avg, 0.001000 rate, 21.773403 seconds, 180736 images -Loaded: 0.230875 seconds -Region Avg IOU: 0.460526, Class: 1.000000, Obj: 0.229620, No Obj: 0.004569, Avg Recall: 0.514925, count: 134 -Region Avg IOU: 0.474260, Class: 1.000000, Obj: 0.244128, No Obj: 0.004585, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.482516, Class: 1.000000, Obj: 0.232169, No Obj: 0.004426, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.412385, Class: 1.000000, Obj: 0.222989, No Obj: 0.003854, Avg Recall: 0.412281, count: 114 -Region Avg IOU: 0.450426, Class: 1.000000, Obj: 0.216634, No Obj: 0.004746, Avg Recall: 0.477273, count: 132 -Region Avg IOU: 0.431391, Class: 1.000000, Obj: 0.244803, No Obj: 0.004283, Avg Recall: 0.440000, count: 100 -Region Avg IOU: 0.465872, Class: 1.000000, Obj: 0.246507, No Obj: 0.004338, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.472214, Class: 1.000000, Obj: 0.204938, No Obj: 0.003882, Avg Recall: 0.525000, count: 80 -707: 0.595498, 0.534772 avg, 0.001000 rate, 20.901524 seconds, 180992 images -Loaded: 0.012945 seconds -Region Avg IOU: 0.437412, Class: 1.000000, Obj: 0.246978, No Obj: 0.004260, Avg Recall: 0.475728, count: 103 -Region Avg IOU: 0.447394, Class: 1.000000, Obj: 0.231927, No Obj: 0.004046, Avg Recall: 0.468750, count: 128 -Region Avg IOU: 0.454304, Class: 1.000000, Obj: 0.212459, No Obj: 0.004542, Avg Recall: 0.550847, count: 118 -Region Avg IOU: 0.459351, Class: 1.000000, Obj: 0.228449, No Obj: 0.004708, Avg Recall: 0.484127, count: 126 -Region Avg IOU: 0.495262, Class: 1.000000, Obj: 0.216609, No Obj: 0.004208, Avg Recall: 0.575758, count: 99 -Region Avg IOU: 0.417958, Class: 1.000000, Obj: 0.239610, No Obj: 0.003748, Avg Recall: 0.376238, count: 101 -Region Avg IOU: 0.427338, Class: 1.000000, Obj: 0.222378, No Obj: 0.004060, Avg Recall: 0.458333, count: 120 -Region Avg IOU: 0.453332, Class: 1.000000, Obj: 0.227546, No Obj: 0.004300, Avg Recall: 0.530201, count: 149 -708: 0.587912, 0.540086 avg, 0.001000 rate, 20.035915 seconds, 181248 images -Loaded: 1.915191 seconds -Region Avg IOU: 0.449103, Class: 1.000000, Obj: 0.221589, No Obj: 0.004181, Avg Recall: 0.469027, count: 113 -Region Avg IOU: 0.423288, Class: 1.000000, Obj: 0.238901, No Obj: 0.004378, Avg Recall: 0.475000, count: 120 -Region Avg IOU: 0.437131, Class: 1.000000, Obj: 0.257153, No Obj: 0.004016, Avg Recall: 0.404762, count: 84 -Region Avg IOU: 0.502603, Class: 1.000000, Obj: 0.220470, No Obj: 0.003984, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.507335, Class: 1.000000, Obj: 0.225872, No Obj: 0.004533, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.414906, Class: 1.000000, Obj: 0.207321, No Obj: 0.004158, Avg Recall: 0.403101, count: 129 -Region Avg IOU: 0.482281, Class: 1.000000, Obj: 0.227377, No Obj: 0.004431, Avg Recall: 0.549180, count: 122 -Region Avg IOU: 0.472266, Class: 1.000000, Obj: 0.182280, No Obj: 0.003731, Avg Recall: 0.494382, count: 89 -709: 0.475661, 0.533643 avg, 0.001000 rate, 19.460936 seconds, 181504 images -Loaded: 0.797753 seconds -Region Avg IOU: 0.377925, Class: 1.000000, Obj: 0.231003, No Obj: 0.004014, Avg Recall: 0.360360, count: 111 -Region Avg IOU: 0.471165, Class: 1.000000, Obj: 0.229173, No Obj: 0.004201, Avg Recall: 0.550000, count: 120 -Region Avg IOU: 0.432094, Class: 1.000000, Obj: 0.213741, No Obj: 0.004668, Avg Recall: 0.463415, count: 123 -Region Avg IOU: 0.524426, Class: 1.000000, Obj: 0.250798, No Obj: 0.004297, Avg Recall: 0.622222, count: 90 -Region Avg IOU: 0.458889, Class: 1.000000, Obj: 0.213659, No Obj: 0.003859, Avg Recall: 0.515790, count: 95 -Region Avg IOU: 0.427857, Class: 1.000000, Obj: 0.192063, No Obj: 0.003505, Avg Recall: 0.477778, count: 90 -Region Avg IOU: 0.420865, Class: 1.000000, Obj: 0.192921, No Obj: 0.003865, Avg Recall: 0.432692, count: 104 -Region Avg IOU: 0.417901, Class: 1.000000, Obj: 0.240272, No Obj: 0.004197, Avg Recall: 0.350427, count: 117 -710: 0.510346, 0.531313 avg, 0.001000 rate, 19.326361 seconds, 181760 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000065 seconds -Region Avg IOU: 0.508909, Class: 1.000000, Obj: 0.272736, No Obj: 0.003583, Avg Recall: 0.564103, count: 117 -Region Avg IOU: 0.531239, Class: 1.000000, Obj: 0.258809, No Obj: 0.003252, Avg Recall: 0.547619, count: 84 -Region Avg IOU: 0.526348, Class: 1.000000, Obj: 0.270596, No Obj: 0.003679, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.530515, Class: 1.000000, Obj: 0.223935, No Obj: 0.004199, Avg Recall: 0.611511, count: 139 -Region Avg IOU: 0.585369, Class: 1.000000, Obj: 0.230936, No Obj: 0.004235, Avg Recall: 0.703704, count: 135 -Region Avg IOU: 0.527077, Class: 1.000000, Obj: 0.245071, No Obj: 0.003472, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.547955, Class: 1.000000, Obj: 0.269926, No Obj: 0.003891, Avg Recall: 0.673684, count: 95 -Region Avg IOU: 0.544699, Class: 1.000000, Obj: 0.245067, No Obj: 0.003406, Avg Recall: 0.659341, count: 91 -711: 0.511768, 0.529359 avg, 0.001000 rate, 25.858404 seconds, 182016 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.557980, Class: 1.000000, Obj: 0.283214, No Obj: 0.003247, Avg Recall: 0.698630, count: 73 -Region Avg IOU: 0.537891, Class: 1.000000, Obj: 0.278136, No Obj: 0.003422, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.558201, Class: 1.000000, Obj: 0.231034, No Obj: 0.004289, Avg Recall: 0.625899, count: 139 -Region Avg IOU: 0.537560, Class: 1.000000, Obj: 0.248815, No Obj: 0.003772, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.533287, Class: 1.000000, Obj: 0.261596, No Obj: 0.003185, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.546051, Class: 1.000000, Obj: 0.245688, No Obj: 0.003797, Avg Recall: 0.620968, count: 124 -Region Avg IOU: 0.551059, Class: 1.000000, Obj: 0.200686, No Obj: 0.003589, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.556930, Class: 1.000000, Obj: 0.289728, No Obj: 0.003761, Avg Recall: 0.608696, count: 115 -712: 0.513295, 0.527753 avg, 0.001000 rate, 25.449684 seconds, 182272 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.536711, Class: 1.000000, Obj: 0.234265, No Obj: 0.003592, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.522835, Class: 1.000000, Obj: 0.219173, No Obj: 0.003354, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.575415, Class: 1.000000, Obj: 0.237261, No Obj: 0.004090, Avg Recall: 0.708661, count: 127 -Region Avg IOU: 0.529495, Class: 1.000000, Obj: 0.235134, No Obj: 0.003730, Avg Recall: 0.620690, count: 116 -Region Avg IOU: 0.567898, Class: 1.000000, Obj: 0.263396, No Obj: 0.003384, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.580127, Class: 1.000000, Obj: 0.281470, No Obj: 0.003956, Avg Recall: 0.686567, count: 134 -Region Avg IOU: 0.512687, Class: 1.000000, Obj: 0.242512, No Obj: 0.003215, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.513005, Class: 1.000000, Obj: 0.257148, No Obj: 0.003403, Avg Recall: 0.609756, count: 82 -713: 0.553833, 0.530361 avg, 0.001000 rate, 26.862965 seconds, 182528 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.561214, Class: 1.000000, Obj: 0.241230, No Obj: 0.003292, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.604126, Class: 1.000000, Obj: 0.248107, No Obj: 0.003918, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.568227, Class: 1.000000, Obj: 0.247710, No Obj: 0.003426, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.569030, Class: 1.000000, Obj: 0.240764, No Obj: 0.003398, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.575289, Class: 1.000000, Obj: 0.225630, No Obj: 0.003518, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.533639, Class: 1.000000, Obj: 0.220696, No Obj: 0.003421, Avg Recall: 0.671329, count: 143 -Region Avg IOU: 0.526841, Class: 1.000000, Obj: 0.252224, No Obj: 0.003053, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.557231, Class: 1.000000, Obj: 0.205975, No Obj: 0.003462, Avg Recall: 0.658537, count: 123 -714: 0.548405, 0.532165 avg, 0.001000 rate, 26.978464 seconds, 182784 images -Loaded: 0.000126 seconds -Region Avg IOU: 0.578988, Class: 1.000000, Obj: 0.259128, No Obj: 0.003542, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.493160, Class: 1.000000, Obj: 0.248899, No Obj: 0.003304, Avg Recall: 0.584746, count: 118 -Region Avg IOU: 0.540151, Class: 1.000000, Obj: 0.188042, No Obj: 0.003475, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.533623, Class: 1.000000, Obj: 0.218975, No Obj: 0.003668, Avg Recall: 0.588235, count: 119 -Region Avg IOU: 0.538198, Class: 1.000000, Obj: 0.238646, No Obj: 0.003217, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.584701, Class: 1.000000, Obj: 0.252431, No Obj: 0.003290, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.531875, Class: 1.000000, Obj: 0.245155, No Obj: 0.003367, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.512284, Class: 1.000000, Obj: 0.218044, No Obj: 0.003546, Avg Recall: 0.526316, count: 114 -715: 0.521327, 0.531081 avg, 0.001000 rate, 26.887999 seconds, 183040 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.541525, Class: 1.000000, Obj: 0.238386, No Obj: 0.003497, Avg Recall: 0.630252, count: 119 -Region Avg IOU: 0.552858, Class: 1.000000, Obj: 0.224773, No Obj: 0.003284, Avg Recall: 0.686747, count: 83 -Region Avg IOU: 0.591416, Class: 1.000000, Obj: 0.255902, No Obj: 0.003579, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.552523, Class: 1.000000, Obj: 0.215757, No Obj: 0.003249, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.584999, Class: 1.000000, Obj: 0.247570, No Obj: 0.003466, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.555333, Class: 1.000000, Obj: 0.255104, No Obj: 0.003517, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.524316, Class: 1.000000, Obj: 0.235934, No Obj: 0.003337, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.501678, Class: 1.000000, Obj: 0.285534, No Obj: 0.003289, Avg Recall: 0.576923, count: 104 -716: 0.514632, 0.529436 avg, 0.001000 rate, 26.314981 seconds, 183296 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.566370, Class: 1.000000, Obj: 0.251446, No Obj: 0.003541, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.565425, Class: 1.000000, Obj: 0.256221, No Obj: 0.003694, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.534384, Class: 1.000000, Obj: 0.233574, No Obj: 0.003468, Avg Recall: 0.607595, count: 79 -Region Avg IOU: 0.569840, Class: 1.000000, Obj: 0.243165, No Obj: 0.003485, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.560423, Class: 1.000000, Obj: 0.271180, No Obj: 0.003435, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.586950, Class: 1.000000, Obj: 0.232643, No Obj: 0.003666, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.528750, Class: 1.000000, Obj: 0.259444, No Obj: 0.003160, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.543384, Class: 1.000000, Obj: 0.206967, No Obj: 0.003578, Avg Recall: 0.641667, count: 120 -717: 0.477589, 0.524252 avg, 0.001000 rate, 25.983503 seconds, 183552 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.540846, Class: 1.000000, Obj: 0.245485, No Obj: 0.003674, Avg Recall: 0.650794, count: 126 -Region Avg IOU: 0.502985, Class: 1.000000, Obj: 0.208652, No Obj: 0.002974, Avg Recall: 0.595238, count: 84 -Region Avg IOU: 0.543657, Class: 1.000000, Obj: 0.212538, No Obj: 0.003093, Avg Recall: 0.614458, count: 83 -Region Avg IOU: 0.540169, Class: 1.000000, Obj: 0.225048, No Obj: 0.003218, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.551481, Class: 1.000000, Obj: 0.277945, No Obj: 0.003441, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.545854, Class: 1.000000, Obj: 0.244718, No Obj: 0.003310, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.507362, Class: 1.000000, Obj: 0.208628, No Obj: 0.003183, Avg Recall: 0.613636, count: 88 -Region Avg IOU: 0.530312, Class: 1.000000, Obj: 0.238598, No Obj: 0.003439, Avg Recall: 0.620370, count: 108 -718: 0.467637, 0.518590 avg, 0.001000 rate, 27.045916 seconds, 183808 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.536645, Class: 1.000000, Obj: 0.185279, No Obj: 0.003297, Avg Recall: 0.611765, count: 85 -Region Avg IOU: 0.581223, Class: 1.000000, Obj: 0.250403, No Obj: 0.003551, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.529020, Class: 1.000000, Obj: 0.252183, No Obj: 0.003377, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.540062, Class: 1.000000, Obj: 0.246981, No Obj: 0.003051, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.506158, Class: 1.000000, Obj: 0.206011, No Obj: 0.003230, Avg Recall: 0.575758, count: 132 -Region Avg IOU: 0.502537, Class: 1.000000, Obj: 0.235277, No Obj: 0.003155, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.591368, Class: 1.000000, Obj: 0.271871, No Obj: 0.003491, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.580778, Class: 1.000000, Obj: 0.257663, No Obj: 0.003157, Avg Recall: 0.715789, count: 95 -719: 0.476622, 0.514393 avg, 0.001000 rate, 26.058517 seconds, 184064 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.558257, Class: 1.000000, Obj: 0.235104, No Obj: 0.003274, Avg Recall: 0.651163, count: 86 -Region Avg IOU: 0.519426, Class: 1.000000, Obj: 0.216988, No Obj: 0.003323, Avg Recall: 0.579365, count: 126 -Region Avg IOU: 0.545963, Class: 1.000000, Obj: 0.243768, No Obj: 0.003226, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.558453, Class: 1.000000, Obj: 0.204731, No Obj: 0.002895, Avg Recall: 0.577778, count: 90 -Region Avg IOU: 0.564648, Class: 1.000000, Obj: 0.266626, No Obj: 0.003662, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.551694, Class: 1.000000, Obj: 0.223102, No Obj: 0.003342, Avg Recall: 0.697479, count: 119 -Region Avg IOU: 0.545226, Class: 1.000000, Obj: 0.224587, No Obj: 0.002817, Avg Recall: 0.602941, count: 68 -Region Avg IOU: 0.561032, Class: 1.000000, Obj: 0.232509, No Obj: 0.003416, Avg Recall: 0.666667, count: 102 -720: 0.548082, 0.517762 avg, 0.001000 rate, 27.070868 seconds, 184320 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000069 seconds -Region Avg IOU: 0.562211, Class: 1.000000, Obj: 0.232865, No Obj: 0.003037, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.596870, Class: 1.000000, Obj: 0.251564, No Obj: 0.002954, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.578534, Class: 1.000000, Obj: 0.247971, No Obj: 0.003209, Avg Recall: 0.666667, count: 117 -Region Avg IOU: 0.562710, Class: 1.000000, Obj: 0.227828, No Obj: 0.003363, Avg Recall: 0.681818, count: 132 -Region Avg IOU: 0.569632, Class: 1.000000, Obj: 0.213453, No Obj: 0.003674, Avg Recall: 0.695035, count: 141 -Region Avg IOU: 0.596585, Class: 1.000000, Obj: 0.234141, No Obj: 0.003170, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.575646, Class: 1.000000, Obj: 0.231282, No Obj: 0.003021, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.574394, Class: 1.000000, Obj: 0.223275, No Obj: 0.003256, Avg Recall: 0.693069, count: 101 -721: 0.520316, 0.518018 avg, 0.001000 rate, 28.191797 seconds, 184576 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.571638, Class: 1.000000, Obj: 0.212398, No Obj: 0.003655, Avg Recall: 0.714286, count: 133 -Region Avg IOU: 0.551677, Class: 1.000000, Obj: 0.232843, No Obj: 0.003309, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.547829, Class: 1.000000, Obj: 0.251335, No Obj: 0.002984, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.573283, Class: 1.000000, Obj: 0.222133, No Obj: 0.003306, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.565570, Class: 1.000000, Obj: 0.246704, No Obj: 0.003268, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.581949, Class: 1.000000, Obj: 0.248039, No Obj: 0.003527, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.585450, Class: 1.000000, Obj: 0.230541, No Obj: 0.003061, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.555054, Class: 1.000000, Obj: 0.234753, No Obj: 0.003704, Avg Recall: 0.642336, count: 137 -722: 0.480485, 0.514264 avg, 0.001000 rate, 28.875324 seconds, 184832 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.605398, Class: 1.000000, Obj: 0.268922, No Obj: 0.003313, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.577153, Class: 1.000000, Obj: 0.242012, No Obj: 0.003402, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.541950, Class: 1.000000, Obj: 0.255294, No Obj: 0.003180, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.540144, Class: 1.000000, Obj: 0.218093, No Obj: 0.003022, Avg Recall: 0.630952, count: 84 -Region Avg IOU: 0.568331, Class: 1.000000, Obj: 0.265225, No Obj: 0.003470, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.515154, Class: 1.000000, Obj: 0.207615, No Obj: 0.003118, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.594182, Class: 1.000000, Obj: 0.295527, No Obj: 0.002916, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.573752, Class: 1.000000, Obj: 0.248035, No Obj: 0.003261, Avg Recall: 0.673913, count: 92 -723: 0.488127, 0.511651 avg, 0.001000 rate, 29.145023 seconds, 185088 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.587330, Class: 1.000000, Obj: 0.281220, No Obj: 0.003368, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.582305, Class: 1.000000, Obj: 0.256637, No Obj: 0.002991, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.558478, Class: 1.000000, Obj: 0.244943, No Obj: 0.003239, Avg Recall: 0.652542, count: 118 -Region Avg IOU: 0.562872, Class: 1.000000, Obj: 0.250462, No Obj: 0.003276, Avg Recall: 0.666667, count: 129 -Region Avg IOU: 0.532808, Class: 1.000000, Obj: 0.225856, No Obj: 0.002859, Avg Recall: 0.619565, count: 92 -Region Avg IOU: 0.590718, Class: 1.000000, Obj: 0.245595, No Obj: 0.003421, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.573338, Class: 1.000000, Obj: 0.258341, No Obj: 0.003160, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.632860, Class: 1.000000, Obj: 0.203391, No Obj: 0.003656, Avg Recall: 0.786885, count: 122 -724: 0.532353, 0.513721 avg, 0.001000 rate, 27.899101 seconds, 185344 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.558723, Class: 1.000000, Obj: 0.228785, No Obj: 0.003443, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.568925, Class: 1.000000, Obj: 0.235420, No Obj: 0.003068, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.585799, Class: 1.000000, Obj: 0.218465, No Obj: 0.003256, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.587375, Class: 1.000000, Obj: 0.235431, No Obj: 0.002938, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.561104, Class: 1.000000, Obj: 0.219137, No Obj: 0.002838, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.521877, Class: 1.000000, Obj: 0.219850, No Obj: 0.002873, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.576060, Class: 1.000000, Obj: 0.194878, No Obj: 0.003299, Avg Recall: 0.703704, count: 135 -Region Avg IOU: 0.562916, Class: 1.000000, Obj: 0.220676, No Obj: 0.003303, Avg Recall: 0.683673, count: 98 -725: 0.481540, 0.510503 avg, 0.001000 rate, 28.361206 seconds, 185600 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.580675, Class: 1.000000, Obj: 0.289499, No Obj: 0.003189, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.573152, Class: 1.000000, Obj: 0.229832, No Obj: 0.002920, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.538517, Class: 1.000000, Obj: 0.220767, No Obj: 0.003124, Avg Recall: 0.658537, count: 123 -Region Avg IOU: 0.515802, Class: 1.000000, Obj: 0.200815, No Obj: 0.003252, Avg Recall: 0.586777, count: 121 -Region Avg IOU: 0.543671, Class: 1.000000, Obj: 0.217783, No Obj: 0.003173, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.554288, Class: 1.000000, Obj: 0.220369, No Obj: 0.003212, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.583120, Class: 1.000000, Obj: 0.214873, No Obj: 0.003106, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.579403, Class: 1.000000, Obj: 0.276344, No Obj: 0.003209, Avg Recall: 0.702703, count: 111 -726: 0.552624, 0.514715 avg, 0.001000 rate, 28.244007 seconds, 185856 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.566353, Class: 1.000000, Obj: 0.224911, No Obj: 0.003335, Avg Recall: 0.668966, count: 145 -Region Avg IOU: 0.617518, Class: 1.000000, Obj: 0.250777, No Obj: 0.003372, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.639252, Class: 1.000000, Obj: 0.293793, No Obj: 0.002962, Avg Recall: 0.783132, count: 83 -Region Avg IOU: 0.577924, Class: 1.000000, Obj: 0.231001, No Obj: 0.003304, Avg Recall: 0.697479, count: 119 -Region Avg IOU: 0.588625, Class: 1.000000, Obj: 0.207727, No Obj: 0.002940, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.592122, Class: 1.000000, Obj: 0.237415, No Obj: 0.003350, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.575094, Class: 1.000000, Obj: 0.212641, No Obj: 0.003382, Avg Recall: 0.676692, count: 133 -Region Avg IOU: 0.587853, Class: 1.000000, Obj: 0.245334, No Obj: 0.003194, Avg Recall: 0.728070, count: 114 -727: 0.514063, 0.514650 avg, 0.001000 rate, 28.137138 seconds, 186112 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.566162, Class: 1.000000, Obj: 0.208461, No Obj: 0.003294, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.608387, Class: 1.000000, Obj: 0.237110, No Obj: 0.003429, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.592571, Class: 1.000000, Obj: 0.228909, No Obj: 0.002975, Avg Recall: 0.797101, count: 69 -Region Avg IOU: 0.553661, Class: 1.000000, Obj: 0.222773, No Obj: 0.002946, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.579812, Class: 1.000000, Obj: 0.205753, No Obj: 0.003266, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.531180, Class: 1.000000, Obj: 0.218996, No Obj: 0.003217, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.548131, Class: 1.000000, Obj: 0.224361, No Obj: 0.003362, Avg Recall: 0.671533, count: 137 -Region Avg IOU: 0.576514, Class: 1.000000, Obj: 0.196028, No Obj: 0.003488, Avg Recall: 0.747826, count: 115 -728: 0.499759, 0.513161 avg, 0.001000 rate, 27.834972 seconds, 186368 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.532234, Class: 1.000000, Obj: 0.209078, No Obj: 0.003287, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.568698, Class: 1.000000, Obj: 0.263456, No Obj: 0.003028, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.548629, Class: 1.000000, Obj: 0.247052, No Obj: 0.002871, Avg Recall: 0.602740, count: 73 -Region Avg IOU: 0.572082, Class: 1.000000, Obj: 0.265307, No Obj: 0.003184, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.568310, Class: 1.000000, Obj: 0.233752, No Obj: 0.003174, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.535747, Class: 1.000000, Obj: 0.240471, No Obj: 0.003172, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.537438, Class: 1.000000, Obj: 0.224700, No Obj: 0.002880, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.558568, Class: 1.000000, Obj: 0.222953, No Obj: 0.003313, Avg Recall: 0.660194, count: 103 -729: 0.476635, 0.509508 avg, 0.001000 rate, 27.043531 seconds, 186624 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.570361, Class: 1.000000, Obj: 0.228235, No Obj: 0.003180, Avg Recall: 0.705426, count: 129 -Region Avg IOU: 0.561574, Class: 1.000000, Obj: 0.246733, No Obj: 0.003334, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.594101, Class: 1.000000, Obj: 0.251276, No Obj: 0.003217, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.580993, Class: 1.000000, Obj: 0.259174, No Obj: 0.003080, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.560205, Class: 1.000000, Obj: 0.284179, No Obj: 0.003285, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.539511, Class: 1.000000, Obj: 0.238777, No Obj: 0.003240, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.552907, Class: 1.000000, Obj: 0.254565, No Obj: 0.003272, Avg Recall: 0.651163, count: 129 -Region Avg IOU: 0.567543, Class: 1.000000, Obj: 0.217009, No Obj: 0.003459, Avg Recall: 0.709220, count: 141 -730: 0.497365, 0.508294 avg, 0.001000 rate, 27.670565 seconds, 186880 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 13.969172 seconds -Region Avg IOU: 0.469168, Class: 1.000000, Obj: 0.238493, No Obj: 0.004108, Avg Recall: 0.510204, count: 98 -Region Avg IOU: 0.473851, Class: 1.000000, Obj: 0.194632, No Obj: 0.003988, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.444529, Class: 1.000000, Obj: 0.220133, No Obj: 0.003785, Avg Recall: 0.462500, count: 80 -Region Avg IOU: 0.448292, Class: 1.000000, Obj: 0.219241, No Obj: 0.003796, Avg Recall: 0.444444, count: 90 -Region Avg IOU: 0.425634, Class: 1.000000, Obj: 0.186843, No Obj: 0.003624, Avg Recall: 0.514019, count: 107 -Region Avg IOU: 0.397800, Class: 1.000000, Obj: 0.178193, No Obj: 0.003831, Avg Recall: 0.366337, count: 101 -Region Avg IOU: 0.473374, Class: 1.000000, Obj: 0.190407, No Obj: 0.003992, Avg Recall: 0.548673, count: 113 -Region Avg IOU: 0.443757, Class: 1.000000, Obj: 0.194196, No Obj: 0.004093, Avg Recall: 0.434426, count: 122 -731: 0.478505, 0.505315 avg, 0.001000 rate, 20.189407 seconds, 187136 images -Loaded: 0.411520 seconds -Region Avg IOU: 0.453118, Class: 1.000000, Obj: 0.192394, No Obj: 0.003740, Avg Recall: 0.472727, count: 110 -Region Avg IOU: 0.454189, Class: 1.000000, Obj: 0.202368, No Obj: 0.003640, Avg Recall: 0.455446, count: 101 -Region Avg IOU: 0.433478, Class: 1.000000, Obj: 0.197112, No Obj: 0.003685, Avg Recall: 0.457364, count: 129 -Region Avg IOU: 0.436243, Class: 1.000000, Obj: 0.186884, No Obj: 0.003538, Avg Recall: 0.446808, count: 94 -Region Avg IOU: 0.504200, Class: 1.000000, Obj: 0.214854, No Obj: 0.003584, Avg Recall: 0.622222, count: 90 -Region Avg IOU: 0.452682, Class: 1.000000, Obj: 0.177876, No Obj: 0.003903, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.428468, Class: 1.000000, Obj: 0.184484, No Obj: 0.004176, Avg Recall: 0.450450, count: 111 -Region Avg IOU: 0.482097, Class: 1.000000, Obj: 0.217959, No Obj: 0.003537, Avg Recall: 0.500000, count: 94 -732: 0.476223, 0.502406 avg, 0.001000 rate, 17.654646 seconds, 187392 images -Loaded: 4.063755 seconds -Region Avg IOU: 0.420812, Class: 1.000000, Obj: 0.188452, No Obj: 0.003674, Avg Recall: 0.447059, count: 85 -Region Avg IOU: 0.455202, Class: 1.000000, Obj: 0.210463, No Obj: 0.003768, Avg Recall: 0.526882, count: 93 -Region Avg IOU: 0.466728, Class: 1.000000, Obj: 0.202307, No Obj: 0.003876, Avg Recall: 0.475728, count: 103 -Region Avg IOU: 0.426537, Class: 1.000000, Obj: 0.207035, No Obj: 0.004019, Avg Recall: 0.424528, count: 106 -Region Avg IOU: 0.483644, Class: 1.000000, Obj: 0.184769, No Obj: 0.004271, Avg Recall: 0.540323, count: 124 -Region Avg IOU: 0.484508, Class: 1.000000, Obj: 0.216347, No Obj: 0.004091, Avg Recall: 0.522124, count: 113 -Region Avg IOU: 0.471786, Class: 1.000000, Obj: 0.184808, No Obj: 0.003739, Avg Recall: 0.529412, count: 85 -Region Avg IOU: 0.422306, Class: 1.000000, Obj: 0.219961, No Obj: 0.003970, Avg Recall: 0.439655, count: 116 -733: 0.522351, 0.504400 avg, 0.001000 rate, 20.194799 seconds, 187648 images -Loaded: 0.727016 seconds -Region Avg IOU: 0.418556, Class: 1.000000, Obj: 0.178153, No Obj: 0.004090, Avg Recall: 0.381944, count: 144 -Region Avg IOU: 0.415005, Class: 1.000000, Obj: 0.220969, No Obj: 0.003948, Avg Recall: 0.367347, count: 98 -Region Avg IOU: 0.402832, Class: 1.000000, Obj: 0.209592, No Obj: 0.004071, Avg Recall: 0.418033, count: 122 -Region Avg IOU: 0.445559, Class: 1.000000, Obj: 0.201215, No Obj: 0.004480, Avg Recall: 0.495935, count: 123 -Region Avg IOU: 0.438121, Class: 1.000000, Obj: 0.186223, No Obj: 0.004411, Avg Recall: 0.486239, count: 109 -Region Avg IOU: 0.437198, Class: 1.000000, Obj: 0.206311, No Obj: 0.004207, Avg Recall: 0.452381, count: 126 -Region Avg IOU: 0.427824, Class: 1.000000, Obj: 0.182559, No Obj: 0.004158, Avg Recall: 0.453704, count: 108 -Region Avg IOU: 0.466609, Class: 1.000000, Obj: 0.232093, No Obj: 0.004175, Avg Recall: 0.520408, count: 98 -734: 0.542323, 0.508192 avg, 0.001000 rate, 18.371670 seconds, 187904 images -Loaded: 1.509172 seconds -Region Avg IOU: 0.409929, Class: 1.000000, Obj: 0.169467, No Obj: 0.004136, Avg Recall: 0.408759, count: 137 -Region Avg IOU: 0.408515, Class: 1.000000, Obj: 0.214164, No Obj: 0.003883, Avg Recall: 0.393162, count: 117 -Region Avg IOU: 0.408518, Class: 1.000000, Obj: 0.192719, No Obj: 0.003981, Avg Recall: 0.404580, count: 131 -Region Avg IOU: 0.411775, Class: 1.000000, Obj: 0.216888, No Obj: 0.003545, Avg Recall: 0.432692, count: 104 -Region Avg IOU: 0.466634, Class: 1.000000, Obj: 0.220326, No Obj: 0.004578, Avg Recall: 0.528846, count: 104 -Region Avg IOU: 0.459002, Class: 1.000000, Obj: 0.216238, No Obj: 0.004771, Avg Recall: 0.500000, count: 132 -Region Avg IOU: 0.447787, Class: 1.000000, Obj: 0.228772, No Obj: 0.004911, Avg Recall: 0.442623, count: 122 -Region Avg IOU: 0.418016, Class: 1.000000, Obj: 0.209409, No Obj: 0.003647, Avg Recall: 0.428571, count: 98 -735: 0.607807, 0.518154 avg, 0.001000 rate, 16.129852 seconds, 188160 images -Loaded: 3.021050 seconds -Region Avg IOU: 0.436342, Class: 1.000000, Obj: 0.192686, No Obj: 0.004436, Avg Recall: 0.447761, count: 134 -Region Avg IOU: 0.420580, Class: 1.000000, Obj: 0.217480, No Obj: 0.004562, Avg Recall: 0.444444, count: 117 -Region Avg IOU: 0.434236, Class: 1.000000, Obj: 0.230510, No Obj: 0.004574, Avg Recall: 0.405941, count: 101 -Region Avg IOU: 0.393624, Class: 1.000000, Obj: 0.244800, No Obj: 0.003853, Avg Recall: 0.425000, count: 80 -Region Avg IOU: 0.404455, Class: 1.000000, Obj: 0.207926, No Obj: 0.004112, Avg Recall: 0.389381, count: 113 -Region Avg IOU: 0.459610, Class: 1.000000, Obj: 0.254610, No Obj: 0.005047, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.363238, Class: 1.000000, Obj: 0.173464, No Obj: 0.003744, Avg Recall: 0.360825, count: 97 -Region Avg IOU: 0.420140, Class: 1.000000, Obj: 0.227316, No Obj: 0.003843, Avg Recall: 0.445652, count: 92 -736: 0.526004, 0.518939 avg, 0.001000 rate, 18.675068 seconds, 188416 images -Loaded: 2.726167 seconds -Region Avg IOU: 0.447095, Class: 1.000000, Obj: 0.233210, No Obj: 0.003736, Avg Recall: 0.493333, count: 75 -Region Avg IOU: 0.461143, Class: 1.000000, Obj: 0.234141, No Obj: 0.005145, Avg Recall: 0.522727, count: 132 -Region Avg IOU: 0.375026, Class: 1.000000, Obj: 0.242305, No Obj: 0.004264, Avg Recall: 0.367347, count: 98 -Region Avg IOU: 0.437912, Class: 1.000000, Obj: 0.235505, No Obj: 0.004222, Avg Recall: 0.465116, count: 86 -Region Avg IOU: 0.446665, Class: 1.000000, Obj: 0.248078, No Obj: 0.004921, Avg Recall: 0.445455, count: 110 -Region Avg IOU: 0.444356, Class: 1.000000, Obj: 0.246125, No Obj: 0.004754, Avg Recall: 0.435484, count: 124 -Region Avg IOU: 0.420420, Class: 1.000000, Obj: 0.216631, No Obj: 0.004609, Avg Recall: 0.444444, count: 117 -Region Avg IOU: 0.405518, Class: 1.000000, Obj: 0.194542, No Obj: 0.004339, Avg Recall: 0.387097, count: 124 -737: 0.525380, 0.519583 avg, 0.001000 rate, 18.856588 seconds, 188672 images -Loaded: 2.649892 seconds -Region Avg IOU: 0.408801, Class: 1.000000, Obj: 0.232448, No Obj: 0.004215, Avg Recall: 0.377358, count: 106 -Region Avg IOU: 0.422527, Class: 1.000000, Obj: 0.264093, No Obj: 0.004527, Avg Recall: 0.469388, count: 98 -Region Avg IOU: 0.414019, Class: 1.000000, Obj: 0.224212, No Obj: 0.004720, Avg Recall: 0.398148, count: 108 -Region Avg IOU: 0.446547, Class: 1.000000, Obj: 0.261524, No Obj: 0.004244, Avg Recall: 0.473118, count: 93 -Region Avg IOU: 0.393452, Class: 1.000000, Obj: 0.223862, No Obj: 0.003776, Avg Recall: 0.409639, count: 83 -Region Avg IOU: 0.438244, Class: 1.000000, Obj: 0.222642, No Obj: 0.004871, Avg Recall: 0.461538, count: 91 -Region Avg IOU: 0.460305, Class: 1.000000, Obj: 0.234305, No Obj: 0.004873, Avg Recall: 0.504505, count: 111 -Region Avg IOU: 0.440604, Class: 1.000000, Obj: 0.236679, No Obj: 0.004721, Avg Recall: 0.462185, count: 119 -738: 0.520652, 0.519690 avg, 0.001000 rate, 19.602699 seconds, 188928 images -Loaded: 1.379240 seconds -Region Avg IOU: 0.446490, Class: 1.000000, Obj: 0.240813, No Obj: 0.004706, Avg Recall: 0.469027, count: 113 -Region Avg IOU: 0.402918, Class: 1.000000, Obj: 0.231605, No Obj: 0.003880, Avg Recall: 0.413043, count: 92 -Region Avg IOU: 0.435809, Class: 1.000000, Obj: 0.220226, No Obj: 0.005554, Avg Recall: 0.417808, count: 146 -Region Avg IOU: 0.455608, Class: 1.000000, Obj: 0.277796, No Obj: 0.004711, Avg Recall: 0.440476, count: 84 -Region Avg IOU: 0.468955, Class: 1.000000, Obj: 0.255012, No Obj: 0.004780, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.430571, Class: 1.000000, Obj: 0.269764, No Obj: 0.004428, Avg Recall: 0.409091, count: 110 -Region Avg IOU: 0.447258, Class: 1.000000, Obj: 0.241289, No Obj: 0.005173, Avg Recall: 0.453947, count: 152 -Region Avg IOU: 0.445532, Class: 1.000000, Obj: 0.251705, No Obj: 0.004851, Avg Recall: 0.495727, count: 117 -739: 0.558630, 0.523584 avg, 0.001000 rate, 16.106155 seconds, 189184 images -Loaded: 4.208845 seconds -Region Avg IOU: 0.406537, Class: 1.000000, Obj: 0.252517, No Obj: 0.003971, Avg Recall: 0.395349, count: 86 -Region Avg IOU: 0.411704, Class: 1.000000, Obj: 0.217567, No Obj: 0.004242, Avg Recall: 0.391753, count: 97 -Region Avg IOU: 0.374251, Class: 1.000000, Obj: 0.194426, No Obj: 0.004316, Avg Recall: 0.342593, count: 108 -Region Avg IOU: 0.383399, Class: 1.000000, Obj: 0.218769, No Obj: 0.004557, Avg Recall: 0.355372, count: 121 -Region Avg IOU: 0.441667, Class: 1.000000, Obj: 0.244981, No Obj: 0.004478, Avg Recall: 0.500000, count: 92 -Region Avg IOU: 0.419527, Class: 1.000000, Obj: 0.223277, No Obj: 0.004947, Avg Recall: 0.388889, count: 108 -Region Avg IOU: 0.435532, Class: 1.000000, Obj: 0.254743, No Obj: 0.005304, Avg Recall: 0.439024, count: 123 -Region Avg IOU: 0.449252, Class: 1.000000, Obj: 0.249926, No Obj: 0.004914, Avg Recall: 0.484375, count: 128 -740: 0.532327, 0.524458 avg, 0.001000 rate, 16.723452 seconds, 189440 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000065 seconds -Region Avg IOU: 0.548421, Class: 1.000000, Obj: 0.285621, No Obj: 0.004140, Avg Recall: 0.697183, count: 142 -Region Avg IOU: 0.544277, Class: 1.000000, Obj: 0.283368, No Obj: 0.003522, Avg Recall: 0.614754, count: 122 -Region Avg IOU: 0.560217, Class: 1.000000, Obj: 0.274942, No Obj: 0.003799, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.548648, Class: 1.000000, Obj: 0.274523, No Obj: 0.003784, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.506396, Class: 1.000000, Obj: 0.294655, No Obj: 0.003209, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.520732, Class: 1.000000, Obj: 0.250304, No Obj: 0.003999, Avg Recall: 0.576923, count: 130 -Region Avg IOU: 0.515509, Class: 1.000000, Obj: 0.313498, No Obj: 0.004008, Avg Recall: 0.584615, count: 130 -Region Avg IOU: 0.505816, Class: 1.000000, Obj: 0.243271, No Obj: 0.003519, Avg Recall: 0.504132, count: 121 -741: 0.582281, 0.530240 avg, 0.001000 rate, 26.328739 seconds, 189696 images -Loaded: 0.000123 seconds -Region Avg IOU: 0.507854, Class: 1.000000, Obj: 0.232981, No Obj: 0.003580, Avg Recall: 0.549180, count: 122 -Region Avg IOU: 0.554766, Class: 1.000000, Obj: 0.235988, No Obj: 0.003750, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.488131, Class: 1.000000, Obj: 0.217583, No Obj: 0.003442, Avg Recall: 0.547826, count: 115 -Region Avg IOU: 0.542982, Class: 1.000000, Obj: 0.268036, No Obj: 0.003519, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.519531, Class: 1.000000, Obj: 0.223241, No Obj: 0.003404, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.567339, Class: 1.000000, Obj: 0.268748, No Obj: 0.003109, Avg Recall: 0.726027, count: 73 -Region Avg IOU: 0.569741, Class: 1.000000, Obj: 0.282452, No Obj: 0.003252, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.581558, Class: 1.000000, Obj: 0.248659, No Obj: 0.003910, Avg Recall: 0.684211, count: 152 -742: 0.502885, 0.527505 avg, 0.001000 rate, 26.186708 seconds, 189952 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.529752, Class: 1.000000, Obj: 0.251450, No Obj: 0.003237, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.548142, Class: 1.000000, Obj: 0.255998, No Obj: 0.003154, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.524072, Class: 1.000000, Obj: 0.274501, No Obj: 0.003413, Avg Recall: 0.586207, count: 116 -Region Avg IOU: 0.520637, Class: 1.000000, Obj: 0.276777, No Obj: 0.003010, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.552550, Class: 1.000000, Obj: 0.205085, No Obj: 0.003594, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.584324, Class: 1.000000, Obj: 0.269369, No Obj: 0.003495, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.557581, Class: 1.000000, Obj: 0.219153, No Obj: 0.003376, Avg Recall: 0.608247, count: 97 -Region Avg IOU: 0.513175, Class: 1.000000, Obj: 0.252271, No Obj: 0.003023, Avg Recall: 0.590909, count: 88 -743: 0.506290, 0.525383 avg, 0.001000 rate, 27.022060 seconds, 190208 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.559486, Class: 1.000000, Obj: 0.246061, No Obj: 0.003172, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.580277, Class: 1.000000, Obj: 0.230767, No Obj: 0.002854, Avg Recall: 0.680556, count: 72 -Region Avg IOU: 0.581730, Class: 1.000000, Obj: 0.224051, No Obj: 0.003145, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.524584, Class: 1.000000, Obj: 0.194723, No Obj: 0.002928, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.570002, Class: 1.000000, Obj: 0.265799, No Obj: 0.002983, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.550431, Class: 1.000000, Obj: 0.195709, No Obj: 0.003214, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.549394, Class: 1.000000, Obj: 0.209205, No Obj: 0.003460, Avg Recall: 0.620438, count: 137 -Region Avg IOU: 0.518113, Class: 1.000000, Obj: 0.209509, No Obj: 0.003342, Avg Recall: 0.552000, count: 125 -744: 0.469887, 0.519834 avg, 0.001000 rate, 27.230000 seconds, 190464 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.476082, Class: 1.000000, Obj: 0.177060, No Obj: 0.002929, Avg Recall: 0.529412, count: 102 -Region Avg IOU: 0.482835, Class: 1.000000, Obj: 0.187553, No Obj: 0.002922, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.541096, Class: 1.000000, Obj: 0.192831, No Obj: 0.003038, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.574406, Class: 1.000000, Obj: 0.258200, No Obj: 0.003127, Avg Recall: 0.709677, count: 124 -Region Avg IOU: 0.525912, Class: 1.000000, Obj: 0.201210, No Obj: 0.002949, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.550202, Class: 1.000000, Obj: 0.211717, No Obj: 0.003174, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.500841, Class: 1.000000, Obj: 0.196929, No Obj: 0.002791, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.533630, Class: 1.000000, Obj: 0.201728, No Obj: 0.002952, Avg Recall: 0.636364, count: 110 -745: 0.504668, 0.518317 avg, 0.001000 rate, 27.978125 seconds, 190720 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.557844, Class: 1.000000, Obj: 0.242315, No Obj: 0.003336, Avg Recall: 0.618705, count: 139 -Region Avg IOU: 0.552618, Class: 1.000000, Obj: 0.214516, No Obj: 0.003025, Avg Recall: 0.666667, count: 120 -Region Avg IOU: 0.557409, Class: 1.000000, Obj: 0.214321, No Obj: 0.003004, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.565729, Class: 1.000000, Obj: 0.235495, No Obj: 0.003104, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.598314, Class: 1.000000, Obj: 0.221733, No Obj: 0.002934, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.581561, Class: 1.000000, Obj: 0.231821, No Obj: 0.003094, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.537621, Class: 1.000000, Obj: 0.203461, No Obj: 0.003121, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.530782, Class: 1.000000, Obj: 0.214193, No Obj: 0.002703, Avg Recall: 0.662791, count: 86 -746: 0.533610, 0.519846 avg, 0.001000 rate, 25.971542 seconds, 190976 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.566483, Class: 1.000000, Obj: 0.212502, No Obj: 0.003127, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.591539, Class: 1.000000, Obj: 0.252280, No Obj: 0.003351, Avg Recall: 0.790323, count: 124 -Region Avg IOU: 0.512510, Class: 1.000000, Obj: 0.222692, No Obj: 0.003273, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.543664, Class: 1.000000, Obj: 0.264182, No Obj: 0.002963, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.567116, Class: 1.000000, Obj: 0.228794, No Obj: 0.002858, Avg Recall: 0.707865, count: 89 -Region Avg IOU: 0.533219, Class: 1.000000, Obj: 0.211767, No Obj: 0.003228, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.565246, Class: 1.000000, Obj: 0.228378, No Obj: 0.003083, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.544132, Class: 1.000000, Obj: 0.224872, No Obj: 0.003107, Avg Recall: 0.621053, count: 95 -747: 0.518652, 0.519727 avg, 0.001000 rate, 26.231863 seconds, 191232 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.591042, Class: 1.000000, Obj: 0.238589, No Obj: 0.003325, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.559733, Class: 1.000000, Obj: 0.261391, No Obj: 0.003743, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.517464, Class: 1.000000, Obj: 0.237340, No Obj: 0.003217, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.533878, Class: 1.000000, Obj: 0.194104, No Obj: 0.003052, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.547561, Class: 1.000000, Obj: 0.240687, No Obj: 0.003323, Avg Recall: 0.624000, count: 125 -Region Avg IOU: 0.584435, Class: 1.000000, Obj: 0.230799, No Obj: 0.003599, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.535661, Class: 1.000000, Obj: 0.245952, No Obj: 0.003342, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.543755, Class: 1.000000, Obj: 0.240133, No Obj: 0.003205, Avg Recall: 0.615385, count: 104 -748: 0.503368, 0.518091 avg, 0.001000 rate, 26.748554 seconds, 191488 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.540495, Class: 1.000000, Obj: 0.268745, No Obj: 0.003524, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.526386, Class: 1.000000, Obj: 0.241947, No Obj: 0.003859, Avg Recall: 0.629032, count: 124 -Region Avg IOU: 0.477817, Class: 1.000000, Obj: 0.269292, No Obj: 0.003230, Avg Recall: 0.490385, count: 104 -Region Avg IOU: 0.556015, Class: 1.000000, Obj: 0.249063, No Obj: 0.003325, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.534267, Class: 1.000000, Obj: 0.285312, No Obj: 0.003153, Avg Recall: 0.595506, count: 89 -Region Avg IOU: 0.559368, Class: 1.000000, Obj: 0.257430, No Obj: 0.003576, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.534647, Class: 1.000000, Obj: 0.229166, No Obj: 0.003147, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.568898, Class: 1.000000, Obj: 0.257096, No Obj: 0.003276, Avg Recall: 0.695122, count: 82 -749: 0.438084, 0.510090 avg, 0.001000 rate, 26.818804 seconds, 191744 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.496432, Class: 1.000000, Obj: 0.250057, No Obj: 0.003206, Avg Recall: 0.525862, count: 116 -Region Avg IOU: 0.550954, Class: 1.000000, Obj: 0.265108, No Obj: 0.003447, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.490047, Class: 1.000000, Obj: 0.238714, No Obj: 0.003379, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.575792, Class: 1.000000, Obj: 0.287032, No Obj: 0.003684, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.566779, Class: 1.000000, Obj: 0.276987, No Obj: 0.003736, Avg Recall: 0.676056, count: 142 -Region Avg IOU: 0.534161, Class: 1.000000, Obj: 0.264796, No Obj: 0.003508, Avg Recall: 0.588235, count: 119 -Region Avg IOU: 0.514672, Class: 1.000000, Obj: 0.221296, No Obj: 0.003798, Avg Recall: 0.601626, count: 123 -Region Avg IOU: 0.527533, Class: 1.000000, Obj: 0.219959, No Obj: 0.003503, Avg Recall: 0.595960, count: 99 -750: 0.511178, 0.510199 avg, 0.001000 rate, 27.968395 seconds, 192000 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.557235 seconds -Region Avg IOU: 0.418313, Class: 1.000000, Obj: 0.175751, No Obj: 0.004118, Avg Recall: 0.472727, count: 110 -Region Avg IOU: 0.424008, Class: 1.000000, Obj: 0.206323, No Obj: 0.005004, Avg Recall: 0.463415, count: 123 -Region Avg IOU: 0.417137, Class: 1.000000, Obj: 0.201014, No Obj: 0.004311, Avg Recall: 0.432432, count: 111 -Region Avg IOU: 0.418624, Class: 1.000000, Obj: 0.213163, No Obj: 0.004369, Avg Recall: 0.421053, count: 114 -Region Avg IOU: 0.445017, Class: 1.000000, Obj: 0.188725, No Obj: 0.004422, Avg Recall: 0.445545, count: 101 -Region Avg IOU: 0.408510, Class: 1.000000, Obj: 0.234574, No Obj: 0.004104, Avg Recall: 0.438776, count: 98 -Region Avg IOU: 0.430629, Class: 1.000000, Obj: 0.178291, No Obj: 0.004753, Avg Recall: 0.439024, count: 123 -Region Avg IOU: 0.406477, Class: 1.000000, Obj: 0.200461, No Obj: 0.004541, Avg Recall: 0.411215, count: 107 -751: 0.625921, 0.521771 avg, 0.001000 rate, 13.840022 seconds, 192256 images -Loaded: 7.194917 seconds -Region Avg IOU: 0.418858, Class: 1.000000, Obj: 0.207416, No Obj: 0.004619, Avg Recall: 0.376147, count: 109 -Region Avg IOU: 0.443520, Class: 1.000000, Obj: 0.218111, No Obj: 0.004292, Avg Recall: 0.443299, count: 97 -Region Avg IOU: 0.367990, Class: 1.000000, Obj: 0.180556, No Obj: 0.004710, Avg Recall: 0.349206, count: 126 -Region Avg IOU: 0.414285, Class: 1.000000, Obj: 0.212854, No Obj: 0.004586, Avg Recall: 0.388350, count: 103 -Region Avg IOU: 0.403203, Class: 1.000000, Obj: 0.229580, No Obj: 0.004590, Avg Recall: 0.416667, count: 108 -Region Avg IOU: 0.406913, Class: 1.000000, Obj: 0.194125, No Obj: 0.004108, Avg Recall: 0.384615, count: 104 -Region Avg IOU: 0.411557, Class: 1.000000, Obj: 0.201395, No Obj: 0.004598, Avg Recall: 0.430769, count: 130 -Region Avg IOU: 0.389881, Class: 1.000000, Obj: 0.160966, No Obj: 0.004115, Avg Recall: 0.388889, count: 108 -752: 0.597772, 0.529371 avg, 0.001000 rate, 17.766821 seconds, 192512 images -Loaded: 1.853122 seconds -Region Avg IOU: 0.391390, Class: 1.000000, Obj: 0.225737, No Obj: 0.004417, Avg Recall: 0.389313, count: 131 -Region Avg IOU: 0.415263, Class: 1.000000, Obj: 0.230383, No Obj: 0.004834, Avg Recall: 0.409524, count: 105 -Region Avg IOU: 0.387205, Class: 1.000000, Obj: 0.187763, No Obj: 0.004039, Avg Recall: 0.401639, count: 122 -Region Avg IOU: 0.403714, Class: 1.000000, Obj: 0.219968, No Obj: 0.004347, Avg Recall: 0.411765, count: 102 -Region Avg IOU: 0.378695, Class: 1.000000, Obj: 0.198138, No Obj: 0.004014, Avg Recall: 0.363636, count: 110 -Region Avg IOU: 0.477484, Class: 1.000000, Obj: 0.210793, No Obj: 0.004660, Avg Recall: 0.523810, count: 105 -Region Avg IOU: 0.406141, Class: 1.000000, Obj: 0.192932, No Obj: 0.004448, Avg Recall: 0.423077, count: 104 -Region Avg IOU: 0.424353, Class: 1.000000, Obj: 0.194869, No Obj: 0.005137, Avg Recall: 0.444444, count: 135 -753: 0.588832, 0.535317 avg, 0.001000 rate, 17.121683 seconds, 192768 images -Loaded: 3.415041 seconds -Region Avg IOU: 0.394198, Class: 1.000000, Obj: 0.169715, No Obj: 0.004690, Avg Recall: 0.361111, count: 108 -Region Avg IOU: 0.419416, Class: 1.000000, Obj: 0.184936, No Obj: 0.004019, Avg Recall: 0.449438, count: 89 -Region Avg IOU: 0.381989, Class: 1.000000, Obj: 0.202825, No Obj: 0.004302, Avg Recall: 0.375000, count: 104 -Region Avg IOU: 0.427259, Class: 1.000000, Obj: 0.205762, No Obj: 0.004671, Avg Recall: 0.444444, count: 99 -Region Avg IOU: 0.445012, Class: 1.000000, Obj: 0.214424, No Obj: 0.005079, Avg Recall: 0.488889, count: 135 -Region Avg IOU: 0.394283, Class: 1.000000, Obj: 0.206053, No Obj: 0.004762, Avg Recall: 0.340426, count: 141 -Region Avg IOU: 0.357277, Class: 1.000000, Obj: 0.217240, No Obj: 0.004504, Avg Recall: 0.344262, count: 122 -Region Avg IOU: 0.419723, Class: 1.000000, Obj: 0.206975, No Obj: 0.004286, Avg Recall: 0.419355, count: 93 -754: 0.596686, 0.541454 avg, 0.001000 rate, 13.612040 seconds, 193024 images -Loaded: 7.056134 seconds -Region Avg IOU: 0.400194, Class: 1.000000, Obj: 0.213739, No Obj: 0.004673, Avg Recall: 0.387387, count: 111 -Region Avg IOU: 0.403365, Class: 1.000000, Obj: 0.241412, No Obj: 0.004494, Avg Recall: 0.411765, count: 85 -Region Avg IOU: 0.448000, Class: 1.000000, Obj: 0.239251, No Obj: 0.004773, Avg Recall: 0.447368, count: 114 -Region Avg IOU: 0.389673, Class: 1.000000, Obj: 0.208232, No Obj: 0.004623, Avg Recall: 0.342105, count: 114 -Region Avg IOU: 0.353552, Class: 1.000000, Obj: 0.220648, No Obj: 0.004604, Avg Recall: 0.330275, count: 109 -Region Avg IOU: 0.441231, Class: 1.000000, Obj: 0.207779, No Obj: 0.004821, Avg Recall: 0.476744, count: 86 -Region Avg IOU: 0.370679, Class: 1.000000, Obj: 0.171903, No Obj: 0.004210, Avg Recall: 0.337349, count: 83 -Region Avg IOU: 0.327605, Class: 1.000000, Obj: 0.251911, No Obj: 0.004106, Avg Recall: 0.370370, count: 81 -755: 0.534606, 0.540770 avg, 0.001000 rate, 14.990808 seconds, 193280 images -Loaded: 5.331410 seconds -Region Avg IOU: 0.336920, Class: 1.000000, Obj: 0.198213, No Obj: 0.004146, Avg Recall: 0.317647, count: 85 -Region Avg IOU: 0.385099, Class: 1.000000, Obj: 0.187950, No Obj: 0.004852, Avg Recall: 0.401961, count: 102 -Region Avg IOU: 0.379775, Class: 1.000000, Obj: 0.209868, No Obj: 0.004315, Avg Recall: 0.362745, count: 102 -Region Avg IOU: 0.397972, Class: 1.000000, Obj: 0.206191, No Obj: 0.004830, Avg Recall: 0.394737, count: 114 -Region Avg IOU: 0.348585, Class: 1.000000, Obj: 0.186465, No Obj: 0.004771, Avg Recall: 0.346154, count: 130 -Region Avg IOU: 0.420223, Class: 1.000000, Obj: 0.226624, No Obj: 0.004584, Avg Recall: 0.429907, count: 107 -Region Avg IOU: 0.360837, Class: 1.000000, Obj: 0.220904, No Obj: 0.005052, Avg Recall: 0.307692, count: 143 -Region Avg IOU: 0.370727, Class: 1.000000, Obj: 0.208817, No Obj: 0.004739, Avg Recall: 0.353982, count: 113 -756: 0.703771, 0.557070 avg, 0.001000 rate, 16.804747 seconds, 193536 images -Loaded: 4.194968 seconds -Region Avg IOU: 0.375194, Class: 1.000000, Obj: 0.169522, No Obj: 0.004744, Avg Recall: 0.365217, count: 115 -Region Avg IOU: 0.400329, Class: 1.000000, Obj: 0.205241, No Obj: 0.005172, Avg Recall: 0.394161, count: 137 -Region Avg IOU: 0.391734, Class: 1.000000, Obj: 0.189407, No Obj: 0.004805, Avg Recall: 0.377193, count: 114 -Region Avg IOU: 0.391930, Class: 1.000000, Obj: 0.240283, No Obj: 0.004671, Avg Recall: 0.396040, count: 101 -Region Avg IOU: 0.436331, Class: 1.000000, Obj: 0.280622, No Obj: 0.005303, Avg Recall: 0.489130, count: 92 -Region Avg IOU: 0.398456, Class: 1.000000, Obj: 0.181790, No Obj: 0.004608, Avg Recall: 0.428571, count: 112 -Region Avg IOU: 0.374773, Class: 1.000000, Obj: 0.185888, No Obj: 0.004489, Avg Recall: 0.342593, count: 108 -Region Avg IOU: 0.355815, Class: 1.000000, Obj: 0.189157, No Obj: 0.004785, Avg Recall: 0.338983, count: 118 -757: 0.610991, 0.562462 avg, 0.001000 rate, 16.960686 seconds, 193792 images -Loaded: 5.279841 seconds -Region Avg IOU: 0.430581, Class: 1.000000, Obj: 0.199826, No Obj: 0.004982, Avg Recall: 0.481132, count: 106 -Region Avg IOU: 0.398575, Class: 1.000000, Obj: 0.206460, No Obj: 0.004879, Avg Recall: 0.384615, count: 130 -Region Avg IOU: 0.327343, Class: 1.000000, Obj: 0.152603, No Obj: 0.004693, Avg Recall: 0.268456, count: 149 -Region Avg IOU: 0.374085, Class: 1.000000, Obj: 0.174796, No Obj: 0.004363, Avg Recall: 0.378641, count: 103 -Region Avg IOU: 0.438767, Class: 1.000000, Obj: 0.234583, No Obj: 0.005459, Avg Recall: 0.433962, count: 106 -Region Avg IOU: 0.360212, Class: 1.000000, Obj: 0.205363, No Obj: 0.004618, Avg Recall: 0.397727, count: 88 -Region Avg IOU: 0.409730, Class: 1.000000, Obj: 0.204732, No Obj: 0.004538, Avg Recall: 0.400000, count: 95 -Region Avg IOU: 0.456817, Class: 1.000000, Obj: 0.245475, No Obj: 0.005579, Avg Recall: 0.462687, count: 134 -758: 0.602957, 0.566511 avg, 0.001000 rate, 15.209343 seconds, 194048 images -Loaded: 5.444338 seconds -Region Avg IOU: 0.411130, Class: 1.000000, Obj: 0.212040, No Obj: 0.004066, Avg Recall: 0.450549, count: 91 -Region Avg IOU: 0.359846, Class: 1.000000, Obj: 0.214304, No Obj: 0.005061, Avg Recall: 0.336364, count: 110 -Region Avg IOU: 0.392099, Class: 1.000000, Obj: 0.213268, No Obj: 0.004948, Avg Recall: 0.381679, count: 131 -Region Avg IOU: 0.434213, Class: 1.000000, Obj: 0.203062, No Obj: 0.005308, Avg Recall: 0.450382, count: 131 -Region Avg IOU: 0.412451, Class: 1.000000, Obj: 0.198168, No Obj: 0.005049, Avg Recall: 0.380435, count: 92 -Region Avg IOU: 0.408861, Class: 1.000000, Obj: 0.210620, No Obj: 0.005586, Avg Recall: 0.416058, count: 137 -Region Avg IOU: 0.373797, Class: 1.000000, Obj: 0.268742, No Obj: 0.004644, Avg Recall: 0.394737, count: 76 -Region Avg IOU: 0.364473, Class: 1.000000, Obj: 0.200916, No Obj: 0.005285, Avg Recall: 0.370690, count: 116 -759: 0.552254, 0.565086 avg, 0.001000 rate, 17.866640 seconds, 194304 images -Loaded: 3.142312 seconds -Region Avg IOU: 0.444896, Class: 1.000000, Obj: 0.232476, No Obj: 0.005093, Avg Recall: 0.422018, count: 109 -Region Avg IOU: 0.409179, Class: 1.000000, Obj: 0.210357, No Obj: 0.004672, Avg Recall: 0.361446, count: 83 -Region Avg IOU: 0.399738, Class: 1.000000, Obj: 0.166383, No Obj: 0.004453, Avg Recall: 0.359649, count: 114 -Region Avg IOU: 0.383821, Class: 1.000000, Obj: 0.180256, No Obj: 0.004481, Avg Recall: 0.401709, count: 117 -Region Avg IOU: 0.404173, Class: 1.000000, Obj: 0.262664, No Obj: 0.005315, Avg Recall: 0.383333, count: 120 -Region Avg IOU: 0.407786, Class: 1.000000, Obj: 0.232338, No Obj: 0.005187, Avg Recall: 0.401786, count: 112 -Region Avg IOU: 0.415609, Class: 1.000000, Obj: 0.224702, No Obj: 0.004696, Avg Recall: 0.420168, count: 119 -Region Avg IOU: 0.414965, Class: 1.000000, Obj: 0.222623, No Obj: 0.005260, Avg Recall: 0.421488, count: 121 -760: 0.549855, 0.563562 avg, 0.001000 rate, 16.497208 seconds, 194560 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000066 seconds -Region Avg IOU: 0.540554, Class: 1.000000, Obj: 0.295830, No Obj: 0.003721, Avg Recall: 0.629630, count: 81 -Region Avg IOU: 0.503014, Class: 1.000000, Obj: 0.299423, No Obj: 0.004492, Avg Recall: 0.569106, count: 123 -Region Avg IOU: 0.554782, Class: 1.000000, Obj: 0.283546, No Obj: 0.004125, Avg Recall: 0.686956, count: 115 -Region Avg IOU: 0.515123, Class: 1.000000, Obj: 0.303568, No Obj: 0.003979, Avg Recall: 0.561798, count: 89 -Region Avg IOU: 0.549619, Class: 1.000000, Obj: 0.279460, No Obj: 0.004187, Avg Recall: 0.604839, count: 124 -Region Avg IOU: 0.559334, Class: 1.000000, Obj: 0.248077, No Obj: 0.003890, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.519405, Class: 1.000000, Obj: 0.327203, No Obj: 0.003979, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.516216, Class: 1.000000, Obj: 0.225011, No Obj: 0.004187, Avg Recall: 0.586207, count: 116 -761: 0.519921, 0.559198 avg, 0.001000 rate, 23.711796 seconds, 194816 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.511810, Class: 1.000000, Obj: 0.273270, No Obj: 0.004302, Avg Recall: 0.571429, count: 119 -Region Avg IOU: 0.477190, Class: 1.000000, Obj: 0.239022, No Obj: 0.003855, Avg Recall: 0.514286, count: 105 -Region Avg IOU: 0.500954, Class: 1.000000, Obj: 0.281603, No Obj: 0.004058, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.454854, Class: 1.000000, Obj: 0.253047, No Obj: 0.003631, Avg Recall: 0.513514, count: 74 -Region Avg IOU: 0.522306, Class: 1.000000, Obj: 0.290484, No Obj: 0.004155, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.511562, Class: 1.000000, Obj: 0.227752, No Obj: 0.003876, Avg Recall: 0.627119, count: 118 -Region Avg IOU: 0.529157, Class: 1.000000, Obj: 0.252023, No Obj: 0.004323, Avg Recall: 0.580153, count: 131 -Region Avg IOU: 0.569776, Class: 1.000000, Obj: 0.299634, No Obj: 0.003698, Avg Recall: 0.705128, count: 78 -762: 0.470761, 0.550355 avg, 0.001000 rate, 26.430454 seconds, 195072 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.517052, Class: 1.000000, Obj: 0.227124, No Obj: 0.004029, Avg Recall: 0.569231, count: 130 -Region Avg IOU: 0.492455, Class: 1.000000, Obj: 0.260162, No Obj: 0.003741, Avg Recall: 0.560345, count: 116 -Region Avg IOU: 0.559536, Class: 1.000000, Obj: 0.254172, No Obj: 0.003900, Avg Recall: 0.712644, count: 87 -Region Avg IOU: 0.537297, Class: 1.000000, Obj: 0.232915, No Obj: 0.003374, Avg Recall: 0.619048, count: 63 -Region Avg IOU: 0.519383, Class: 1.000000, Obj: 0.238081, No Obj: 0.003779, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.574843, Class: 1.000000, Obj: 0.238640, No Obj: 0.003611, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.532315, Class: 1.000000, Obj: 0.242584, No Obj: 0.003965, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.490935, Class: 1.000000, Obj: 0.240959, No Obj: 0.003893, Avg Recall: 0.527559, count: 127 -763: 0.467400, 0.542059 avg, 0.001000 rate, 25.174871 seconds, 195328 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.487204, Class: 1.000000, Obj: 0.238885, No Obj: 0.003478, Avg Recall: 0.526882, count: 93 -Region Avg IOU: 0.508847, Class: 1.000000, Obj: 0.274122, No Obj: 0.003616, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.534471, Class: 1.000000, Obj: 0.280079, No Obj: 0.003497, Avg Recall: 0.572727, count: 110 -Region Avg IOU: 0.506920, Class: 1.000000, Obj: 0.235654, No Obj: 0.003656, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.540855, Class: 1.000000, Obj: 0.253278, No Obj: 0.003564, Avg Recall: 0.606742, count: 89 -Region Avg IOU: 0.552754, Class: 1.000000, Obj: 0.219219, No Obj: 0.003755, Avg Recall: 0.648352, count: 91 -Region Avg IOU: 0.544076, Class: 1.000000, Obj: 0.266549, No Obj: 0.003912, Avg Recall: 0.620155, count: 129 -Region Avg IOU: 0.523736, Class: 1.000000, Obj: 0.255090, No Obj: 0.003605, Avg Recall: 0.551402, count: 107 -764: 0.468605, 0.534714 avg, 0.001000 rate, 25.147287 seconds, 195584 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.575765, Class: 1.000000, Obj: 0.206893, No Obj: 0.003268, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.503793, Class: 1.000000, Obj: 0.193938, No Obj: 0.003195, Avg Recall: 0.509615, count: 104 -Region Avg IOU: 0.555192, Class: 1.000000, Obj: 0.219436, No Obj: 0.003214, Avg Recall: 0.658537, count: 82 -Region Avg IOU: 0.579080, Class: 1.000000, Obj: 0.229548, No Obj: 0.003380, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.511854, Class: 1.000000, Obj: 0.230586, No Obj: 0.003442, Avg Recall: 0.580952, count: 105 -Region Avg IOU: 0.581013, Class: 1.000000, Obj: 0.221951, No Obj: 0.003496, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.513060, Class: 1.000000, Obj: 0.198438, No Obj: 0.003569, Avg Recall: 0.608000, count: 125 -Region Avg IOU: 0.511956, Class: 1.000000, Obj: 0.218573, No Obj: 0.003197, Avg Recall: 0.583333, count: 96 -765: 0.454635, 0.526706 avg, 0.001000 rate, 24.876312 seconds, 195840 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.542626, Class: 1.000000, Obj: 0.245505, No Obj: 0.003216, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.532626, Class: 1.000000, Obj: 0.206351, No Obj: 0.003671, Avg Recall: 0.634328, count: 134 -Region Avg IOU: 0.522665, Class: 1.000000, Obj: 0.204245, No Obj: 0.003263, Avg Recall: 0.585106, count: 94 -Region Avg IOU: 0.527391, Class: 1.000000, Obj: 0.256790, No Obj: 0.003167, Avg Recall: 0.593750, count: 96 -Region Avg IOU: 0.520824, Class: 1.000000, Obj: 0.206190, No Obj: 0.003372, Avg Recall: 0.553719, count: 121 -Region Avg IOU: 0.506335, Class: 1.000000, Obj: 0.192877, No Obj: 0.003400, Avg Recall: 0.555556, count: 135 -Region Avg IOU: 0.530547, Class: 1.000000, Obj: 0.219521, No Obj: 0.003415, Avg Recall: 0.596639, count: 119 -Region Avg IOU: 0.591184, Class: 1.000000, Obj: 0.224745, No Obj: 0.003445, Avg Recall: 0.768519, count: 108 -766: 0.562537, 0.530289 avg, 0.001000 rate, 23.726845 seconds, 196096 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.529199, Class: 1.000000, Obj: 0.218250, No Obj: 0.003221, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.553235, Class: 1.000000, Obj: 0.246323, No Obj: 0.003440, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.545570, Class: 1.000000, Obj: 0.227458, No Obj: 0.003621, Avg Recall: 0.661417, count: 127 -Region Avg IOU: 0.533079, Class: 1.000000, Obj: 0.237441, No Obj: 0.003518, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.504599, Class: 1.000000, Obj: 0.226057, No Obj: 0.003544, Avg Recall: 0.584071, count: 113 -Region Avg IOU: 0.539510, Class: 1.000000, Obj: 0.237990, No Obj: 0.003497, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.548307, Class: 1.000000, Obj: 0.246814, No Obj: 0.003369, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.544716, Class: 1.000000, Obj: 0.207854, No Obj: 0.003230, Avg Recall: 0.584071, count: 113 -767: 0.512056, 0.528466 avg, 0.001000 rate, 25.647097 seconds, 196352 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.529964, Class: 1.000000, Obj: 0.246655, No Obj: 0.003625, Avg Recall: 0.596774, count: 124 -Region Avg IOU: 0.569581, Class: 1.000000, Obj: 0.318523, No Obj: 0.003630, Avg Recall: 0.678161, count: 87 -Region Avg IOU: 0.489261, Class: 1.000000, Obj: 0.218701, No Obj: 0.003414, Avg Recall: 0.568000, count: 125 -Region Avg IOU: 0.554120, Class: 1.000000, Obj: 0.229099, No Obj: 0.003652, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.556721, Class: 1.000000, Obj: 0.238272, No Obj: 0.003382, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.548753, Class: 1.000000, Obj: 0.243942, No Obj: 0.003799, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.536650, Class: 1.000000, Obj: 0.225864, No Obj: 0.003539, Avg Recall: 0.589744, count: 117 -Region Avg IOU: 0.523987, Class: 1.000000, Obj: 0.247960, No Obj: 0.003685, Avg Recall: 0.614035, count: 114 -768: 0.519890, 0.527608 avg, 0.001000 rate, 23.805609 seconds, 196608 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.535467, Class: 1.000000, Obj: 0.242279, No Obj: 0.003774, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.497437, Class: 1.000000, Obj: 0.235691, No Obj: 0.003567, Avg Recall: 0.530973, count: 113 -Region Avg IOU: 0.518548, Class: 1.000000, Obj: 0.254458, No Obj: 0.003769, Avg Recall: 0.563025, count: 119 -Region Avg IOU: 0.553109, Class: 1.000000, Obj: 0.252955, No Obj: 0.003862, Avg Recall: 0.680000, count: 125 -Region Avg IOU: 0.534895, Class: 1.000000, Obj: 0.215503, No Obj: 0.004157, Avg Recall: 0.639344, count: 122 -Region Avg IOU: 0.545722, Class: 1.000000, Obj: 0.266455, No Obj: 0.003943, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.512819, Class: 1.000000, Obj: 0.273643, No Obj: 0.003496, Avg Recall: 0.519481, count: 77 -Region Avg IOU: 0.507159, Class: 1.000000, Obj: 0.261318, No Obj: 0.003408, Avg Recall: 0.505618, count: 89 -769: 0.477639, 0.522611 avg, 0.001000 rate, 24.642393 seconds, 196864 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.497537, Class: 1.000000, Obj: 0.285365, No Obj: 0.004086, Avg Recall: 0.518182, count: 110 -Region Avg IOU: 0.498367, Class: 1.000000, Obj: 0.259851, No Obj: 0.004231, Avg Recall: 0.576000, count: 125 -Region Avg IOU: 0.539835, Class: 1.000000, Obj: 0.273005, No Obj: 0.004415, Avg Recall: 0.622222, count: 135 -Region Avg IOU: 0.565246, Class: 1.000000, Obj: 0.235888, No Obj: 0.003983, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.561997, Class: 1.000000, Obj: 0.265775, No Obj: 0.004298, Avg Recall: 0.666667, count: 135 -Region Avg IOU: 0.496366, Class: 1.000000, Obj: 0.261083, No Obj: 0.003996, Avg Recall: 0.549180, count: 122 -Region Avg IOU: 0.493167, Class: 1.000000, Obj: 0.285313, No Obj: 0.003757, Avg Recall: 0.524752, count: 101 -Region Avg IOU: 0.565895, Class: 1.000000, Obj: 0.270755, No Obj: 0.003951, Avg Recall: 0.655462, count: 119 -770: 0.511571, 0.521507 avg, 0.001000 rate, 24.555859 seconds, 197120 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.697552 seconds -Region Avg IOU: 0.451508, Class: 1.000000, Obj: 0.227511, No Obj: 0.004449, Avg Recall: 0.485714, count: 105 -Region Avg IOU: 0.413076, Class: 1.000000, Obj: 0.188399, No Obj: 0.004289, Avg Recall: 0.435185, count: 108 -Region Avg IOU: 0.475998, Class: 1.000000, Obj: 0.241464, No Obj: 0.004665, Avg Recall: 0.490909, count: 110 -Region Avg IOU: 0.446665, Class: 1.000000, Obj: 0.226818, No Obj: 0.004435, Avg Recall: 0.467290, count: 107 -Region Avg IOU: 0.444504, Class: 1.000000, Obj: 0.231074, No Obj: 0.004749, Avg Recall: 0.482014, count: 139 -Region Avg IOU: 0.435283, Class: 1.000000, Obj: 0.226898, No Obj: 0.004078, Avg Recall: 0.415730, count: 89 -Region Avg IOU: 0.507749, Class: 1.000000, Obj: 0.265975, No Obj: 0.004365, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.431676, Class: 1.000000, Obj: 0.215115, No Obj: 0.003936, Avg Recall: 0.441860, count: 86 -771: 0.486171, 0.517974 avg, 0.001000 rate, 20.064173 seconds, 197376 images -Loaded: 0.156543 seconds -Region Avg IOU: 0.444273, Class: 1.000000, Obj: 0.257504, No Obj: 0.004023, Avg Recall: 0.426966, count: 89 -Region Avg IOU: 0.456598, Class: 1.000000, Obj: 0.236179, No Obj: 0.004451, Avg Recall: 0.471698, count: 106 -Region Avg IOU: 0.456108, Class: 1.000000, Obj: 0.218985, No Obj: 0.004463, Avg Recall: 0.444444, count: 99 -Region Avg IOU: 0.482869, Class: 1.000000, Obj: 0.260774, No Obj: 0.003998, Avg Recall: 0.500000, count: 82 -Region Avg IOU: 0.398751, Class: 1.000000, Obj: 0.190101, No Obj: 0.004171, Avg Recall: 0.405660, count: 106 -Region Avg IOU: 0.363182, Class: 1.000000, Obj: 0.213594, No Obj: 0.004073, Avg Recall: 0.324324, count: 111 -Region Avg IOU: 0.437519, Class: 1.000000, Obj: 0.191790, No Obj: 0.004512, Avg Recall: 0.483146, count: 89 -Region Avg IOU: 0.474582, Class: 1.000000, Obj: 0.270251, No Obj: 0.005127, Avg Recall: 0.527273, count: 110 -772: 0.501178, 0.516294 avg, 0.001000 rate, 21.907703 seconds, 197632 images -Loaded: 0.031360 seconds -Region Avg IOU: 0.464107, Class: 1.000000, Obj: 0.266970, No Obj: 0.005275, Avg Recall: 0.518868, count: 106 -Region Avg IOU: 0.480692, Class: 1.000000, Obj: 0.275455, No Obj: 0.004784, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.418743, Class: 1.000000, Obj: 0.212863, No Obj: 0.004386, Avg Recall: 0.411765, count: 102 -Region Avg IOU: 0.430559, Class: 1.000000, Obj: 0.212022, No Obj: 0.003777, Avg Recall: 0.450704, count: 71 -Region Avg IOU: 0.489670, Class: 1.000000, Obj: 0.254445, No Obj: 0.004522, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.477258, Class: 1.000000, Obj: 0.300955, No Obj: 0.005305, Avg Recall: 0.527132, count: 129 -Region Avg IOU: 0.467907, Class: 1.000000, Obj: 0.233224, No Obj: 0.004622, Avg Recall: 0.549451, count: 91 -Region Avg IOU: 0.428968, Class: 1.000000, Obj: 0.217756, No Obj: 0.004508, Avg Recall: 0.405941, count: 101 -773: 0.454782, 0.510143 avg, 0.001000 rate, 20.358034 seconds, 197888 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.487222, Class: 1.000000, Obj: 0.256940, No Obj: 0.004782, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.423907, Class: 1.000000, Obj: 0.230582, No Obj: 0.004014, Avg Recall: 0.408602, count: 93 -Region Avg IOU: 0.445378, Class: 1.000000, Obj: 0.232752, No Obj: 0.004480, Avg Recall: 0.495327, count: 107 -Region Avg IOU: 0.471575, Class: 1.000000, Obj: 0.268662, No Obj: 0.005101, Avg Recall: 0.467290, count: 107 -Region Avg IOU: 0.410950, Class: 1.000000, Obj: 0.233824, No Obj: 0.004422, Avg Recall: 0.440678, count: 118 -Region Avg IOU: 0.450133, Class: 1.000000, Obj: 0.227819, No Obj: 0.004324, Avg Recall: 0.494737, count: 95 -Region Avg IOU: 0.462756, Class: 1.000000, Obj: 0.242516, No Obj: 0.004006, Avg Recall: 0.477273, count: 88 -Region Avg IOU: 0.472731, Class: 1.000000, Obj: 0.229953, No Obj: 0.004245, Avg Recall: 0.519608, count: 102 -774: 0.497225, 0.508851 avg, 0.001000 rate, 20.592684 seconds, 198144 images -Loaded: 1.396620 seconds -Region Avg IOU: 0.495329, Class: 1.000000, Obj: 0.257547, No Obj: 0.004479, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.416590, Class: 1.000000, Obj: 0.223045, No Obj: 0.004362, Avg Recall: 0.460674, count: 89 -Region Avg IOU: 0.463953, Class: 1.000000, Obj: 0.258609, No Obj: 0.004459, Avg Recall: 0.469136, count: 81 -Region Avg IOU: 0.489770, Class: 1.000000, Obj: 0.275394, No Obj: 0.005027, Avg Recall: 0.491071, count: 112 -Region Avg IOU: 0.429426, Class: 1.000000, Obj: 0.237980, No Obj: 0.004542, Avg Recall: 0.439252, count: 107 -Region Avg IOU: 0.469530, Class: 1.000000, Obj: 0.264610, No Obj: 0.004701, Avg Recall: 0.527473, count: 91 -Region Avg IOU: 0.414455, Class: 1.000000, Obj: 0.245033, No Obj: 0.004386, Avg Recall: 0.386555, count: 119 -Region Avg IOU: 0.443460, Class: 1.000000, Obj: 0.228868, No Obj: 0.003910, Avg Recall: 0.466019, count: 103 -775: 0.469617, 0.504928 avg, 0.001000 rate, 20.580154 seconds, 198400 images -Loaded: 0.640779 seconds -Region Avg IOU: 0.454676, Class: 1.000000, Obj: 0.213288, No Obj: 0.004367, Avg Recall: 0.522222, count: 90 -Region Avg IOU: 0.467763, Class: 1.000000, Obj: 0.233340, No Obj: 0.004452, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.471904, Class: 1.000000, Obj: 0.212778, No Obj: 0.004424, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.455220, Class: 1.000000, Obj: 0.229901, No Obj: 0.004327, Avg Recall: 0.518519, count: 108 -Region Avg IOU: 0.454035, Class: 1.000000, Obj: 0.221377, No Obj: 0.004345, Avg Recall: 0.454545, count: 110 -Region Avg IOU: 0.451929, Class: 1.000000, Obj: 0.253346, No Obj: 0.004727, Avg Recall: 0.470588, count: 102 -Region Avg IOU: 0.421250, Class: 1.000000, Obj: 0.214167, No Obj: 0.004127, Avg Recall: 0.440367, count: 109 -Region Avg IOU: 0.427851, Class: 1.000000, Obj: 0.229613, No Obj: 0.004275, Avg Recall: 0.456522, count: 92 -776: 0.538711, 0.508306 avg, 0.001000 rate, 19.443508 seconds, 198656 images -Loaded: 3.682564 seconds -Region Avg IOU: 0.483962, Class: 1.000000, Obj: 0.260677, No Obj: 0.004687, Avg Recall: 0.546219, count: 119 -Region Avg IOU: 0.421698, Class: 1.000000, Obj: 0.215754, No Obj: 0.004825, Avg Recall: 0.431373, count: 102 -Region Avg IOU: 0.486166, Class: 1.000000, Obj: 0.222906, No Obj: 0.004077, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.440475, Class: 1.000000, Obj: 0.215714, No Obj: 0.004379, Avg Recall: 0.490909, count: 110 -Region Avg IOU: 0.417645, Class: 1.000000, Obj: 0.198162, No Obj: 0.004593, Avg Recall: 0.385135, count: 148 -Region Avg IOU: 0.425197, Class: 1.000000, Obj: 0.201790, No Obj: 0.003495, Avg Recall: 0.387755, count: 98 -Region Avg IOU: 0.415439, Class: 1.000000, Obj: 0.223168, No Obj: 0.004538, Avg Recall: 0.427536, count: 138 -Region Avg IOU: 0.428961, Class: 1.000000, Obj: 0.227229, No Obj: 0.004497, Avg Recall: 0.433962, count: 106 -777: 0.616282, 0.519104 avg, 0.001000 rate, 19.834705 seconds, 198912 images -Loaded: 0.856998 seconds -Region Avg IOU: 0.485477, Class: 1.000000, Obj: 0.247864, No Obj: 0.004522, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.451408, Class: 1.000000, Obj: 0.255134, No Obj: 0.004119, Avg Recall: 0.465347, count: 101 -Region Avg IOU: 0.484974, Class: 1.000000, Obj: 0.223773, No Obj: 0.004485, Avg Recall: 0.525000, count: 120 -Region Avg IOU: 0.429102, Class: 1.000000, Obj: 0.220161, No Obj: 0.005004, Avg Recall: 0.438462, count: 130 -Region Avg IOU: 0.456707, Class: 1.000000, Obj: 0.199490, No Obj: 0.004412, Avg Recall: 0.482456, count: 114 -Region Avg IOU: 0.490975, Class: 1.000000, Obj: 0.236362, No Obj: 0.004747, Avg Recall: 0.577586, count: 116 -Region Avg IOU: 0.411225, Class: 1.000000, Obj: 0.211302, No Obj: 0.003690, Avg Recall: 0.434783, count: 92 -Region Avg IOU: 0.441161, Class: 1.000000, Obj: 0.241421, No Obj: 0.004461, Avg Recall: 0.500000, count: 110 -778: 0.591743, 0.526367 avg, 0.001000 rate, 22.533035 seconds, 199168 images -Loaded: 0.309388 seconds -Region Avg IOU: 0.478789, Class: 1.000000, Obj: 0.224524, No Obj: 0.004691, Avg Recall: 0.529915, count: 117 -Region Avg IOU: 0.437992, Class: 1.000000, Obj: 0.249152, No Obj: 0.004781, Avg Recall: 0.462121, count: 132 -Region Avg IOU: 0.446340, Class: 1.000000, Obj: 0.246715, No Obj: 0.004777, Avg Recall: 0.431655, count: 139 -Region Avg IOU: 0.450683, Class: 1.000000, Obj: 0.250924, No Obj: 0.004711, Avg Recall: 0.470086, count: 117 -Region Avg IOU: 0.453808, Class: 1.000000, Obj: 0.235068, No Obj: 0.004230, Avg Recall: 0.516484, count: 91 -Region Avg IOU: 0.419523, Class: 1.000000, Obj: 0.222004, No Obj: 0.004008, Avg Recall: 0.407407, count: 108 -Region Avg IOU: 0.457891, Class: 1.000000, Obj: 0.244631, No Obj: 0.004605, Avg Recall: 0.446602, count: 103 -Region Avg IOU: 0.479722, Class: 1.000000, Obj: 0.260343, No Obj: 0.004535, Avg Recall: 0.480392, count: 102 -779: 0.534216, 0.527152 avg, 0.001000 rate, 19.650314 seconds, 199424 images -Loaded: 1.615314 seconds -Region Avg IOU: 0.473509, Class: 1.000000, Obj: 0.264027, No Obj: 0.004588, Avg Recall: 0.528846, count: 104 -Region Avg IOU: 0.459161, Class: 1.000000, Obj: 0.233465, No Obj: 0.004229, Avg Recall: 0.442308, count: 104 -Region Avg IOU: 0.481521, Class: 1.000000, Obj: 0.204233, No Obj: 0.004305, Avg Recall: 0.500000, count: 100 -Region Avg IOU: 0.492268, Class: 1.000000, Obj: 0.248798, No Obj: 0.004536, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.463331, Class: 1.000000, Obj: 0.236004, No Obj: 0.004543, Avg Recall: 0.495327, count: 107 -Region Avg IOU: 0.484643, Class: 1.000000, Obj: 0.232325, No Obj: 0.004639, Avg Recall: 0.491803, count: 122 -Region Avg IOU: 0.430662, Class: 1.000000, Obj: 0.212355, No Obj: 0.004280, Avg Recall: 0.424242, count: 99 -Region Avg IOU: 0.476766, Class: 1.000000, Obj: 0.265203, No Obj: 0.004408, Avg Recall: 0.539216, count: 102 -780: 0.485786, 0.523016 avg, 0.001000 rate, 21.778816 seconds, 199680 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.549439 seconds -Region Avg IOU: 0.500884, Class: 1.000000, Obj: 0.274217, No Obj: 0.004330, Avg Recall: 0.536585, count: 82 -Region Avg IOU: 0.517215, Class: 1.000000, Obj: 0.268975, No Obj: 0.004646, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.489536, Class: 1.000000, Obj: 0.263901, No Obj: 0.004819, Avg Recall: 0.541667, count: 120 -Region Avg IOU: 0.494649, Class: 1.000000, Obj: 0.271475, No Obj: 0.004390, Avg Recall: 0.540816, count: 98 -Region Avg IOU: 0.491064, Class: 1.000000, Obj: 0.260511, No Obj: 0.004886, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.481356, Class: 1.000000, Obj: 0.265940, No Obj: 0.004468, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.504128, Class: 1.000000, Obj: 0.265773, No Obj: 0.004820, Avg Recall: 0.532710, count: 107 -Region Avg IOU: 0.507388, Class: 1.000000, Obj: 0.274086, No Obj: 0.004615, Avg Recall: 0.562500, count: 80 -781: 0.504341, 0.521148 avg, 0.001000 rate, 22.936960 seconds, 199936 images -Loaded: 0.176662 seconds -Region Avg IOU: 0.484227, Class: 1.000000, Obj: 0.263385, No Obj: 0.004392, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.475113, Class: 1.000000, Obj: 0.232258, No Obj: 0.004751, Avg Recall: 0.505050, count: 99 -Region Avg IOU: 0.461354, Class: 1.000000, Obj: 0.218164, No Obj: 0.004662, Avg Recall: 0.530769, count: 130 -Region Avg IOU: 0.458707, Class: 1.000000, Obj: 0.214598, No Obj: 0.004681, Avg Recall: 0.491525, count: 118 -Region Avg IOU: 0.508138, Class: 1.000000, Obj: 0.230594, No Obj: 0.005376, Avg Recall: 0.608333, count: 120 -Region Avg IOU: 0.473925, Class: 1.000000, Obj: 0.257299, No Obj: 0.004513, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.509074, Class: 1.000000, Obj: 0.263656, No Obj: 0.005281, Avg Recall: 0.588710, count: 124 -Region Avg IOU: 0.487418, Class: 1.000000, Obj: 0.272268, No Obj: 0.004227, Avg Recall: 0.533333, count: 90 -782: 0.553507, 0.524384 avg, 0.001000 rate, 17.794275 seconds, 200192 images -Loaded: 3.903328 seconds -Region Avg IOU: 0.454225, Class: 1.000000, Obj: 0.279842, No Obj: 0.004601, Avg Recall: 0.468750, count: 128 -Region Avg IOU: 0.465075, Class: 1.000000, Obj: 0.248688, No Obj: 0.005001, Avg Recall: 0.520325, count: 123 -Region Avg IOU: 0.509474, Class: 1.000000, Obj: 0.292519, No Obj: 0.004384, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.491430, Class: 1.000000, Obj: 0.259759, No Obj: 0.004990, Avg Recall: 0.564706, count: 85 -Region Avg IOU: 0.479212, Class: 1.000000, Obj: 0.221440, No Obj: 0.004717, Avg Recall: 0.512500, count: 80 -Region Avg IOU: 0.465838, Class: 1.000000, Obj: 0.246746, No Obj: 0.004170, Avg Recall: 0.524390, count: 82 -Region Avg IOU: 0.445799, Class: 1.000000, Obj: 0.244129, No Obj: 0.004310, Avg Recall: 0.460674, count: 89 -Region Avg IOU: 0.482955, Class: 1.000000, Obj: 0.271985, No Obj: 0.005046, Avg Recall: 0.546875, count: 128 -783: 0.562895, 0.528235 avg, 0.001000 rate, 21.850399 seconds, 200448 images -Loaded: 0.858717 seconds -Region Avg IOU: 0.392479, Class: 1.000000, Obj: 0.221666, No Obj: 0.004086, Avg Recall: 0.411215, count: 107 -Region Avg IOU: 0.437731, Class: 1.000000, Obj: 0.246136, No Obj: 0.004383, Avg Recall: 0.469565, count: 115 -Region Avg IOU: 0.444540, Class: 1.000000, Obj: 0.269228, No Obj: 0.004829, Avg Recall: 0.490741, count: 108 -Region Avg IOU: 0.460018, Class: 1.000000, Obj: 0.220674, No Obj: 0.004617, Avg Recall: 0.512000, count: 125 -Region Avg IOU: 0.457924, Class: 1.000000, Obj: 0.260139, No Obj: 0.005112, Avg Recall: 0.531915, count: 141 -Region Avg IOU: 0.467703, Class: 1.000000, Obj: 0.243574, No Obj: 0.004730, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.461816, Class: 1.000000, Obj: 0.232437, No Obj: 0.004985, Avg Recall: 0.466667, count: 135 -Region Avg IOU: 0.479399, Class: 1.000000, Obj: 0.232716, No Obj: 0.004925, Avg Recall: 0.452991, count: 117 -784: 0.513378, 0.526749 avg, 0.001000 rate, 20.008844 seconds, 200704 images -Loaded: 1.331747 seconds -Region Avg IOU: 0.458011, Class: 1.000000, Obj: 0.240320, No Obj: 0.004415, Avg Recall: 0.456790, count: 81 -Region Avg IOU: 0.452998, Class: 1.000000, Obj: 0.250245, No Obj: 0.004569, Avg Recall: 0.500000, count: 118 -Region Avg IOU: 0.455337, Class: 1.000000, Obj: 0.208203, No Obj: 0.004246, Avg Recall: 0.505155, count: 97 -Region Avg IOU: 0.409671, Class: 1.000000, Obj: 0.249510, No Obj: 0.004323, Avg Recall: 0.379032, count: 124 -Region Avg IOU: 0.428412, Class: 1.000000, Obj: 0.233657, No Obj: 0.004376, Avg Recall: 0.447368, count: 114 -Region Avg IOU: 0.430473, Class: 1.000000, Obj: 0.247997, No Obj: 0.004911, Avg Recall: 0.455285, count: 123 -Region Avg IOU: 0.427542, Class: 1.000000, Obj: 0.193492, No Obj: 0.004807, Avg Recall: 0.436975, count: 119 -Region Avg IOU: 0.465130, Class: 1.000000, Obj: 0.234061, No Obj: 0.005105, Avg Recall: 0.553846, count: 130 -785: 0.576345, 0.531709 avg, 0.001000 rate, 20.241615 seconds, 200960 images -Loaded: 1.563167 seconds -Region Avg IOU: 0.468408, Class: 1.000000, Obj: 0.224097, No Obj: 0.004806, Avg Recall: 0.528455, count: 123 -Region Avg IOU: 0.429093, Class: 1.000000, Obj: 0.188543, No Obj: 0.004693, Avg Recall: 0.463087, count: 149 -Region Avg IOU: 0.420896, Class: 1.000000, Obj: 0.242114, No Obj: 0.004120, Avg Recall: 0.431373, count: 102 -Region Avg IOU: 0.444217, Class: 1.000000, Obj: 0.244586, No Obj: 0.004004, Avg Recall: 0.434343, count: 99 -Region Avg IOU: 0.465698, Class: 1.000000, Obj: 0.263646, No Obj: 0.004799, Avg Recall: 0.467153, count: 137 -Region Avg IOU: 0.469668, Class: 1.000000, Obj: 0.224598, No Obj: 0.004808, Avg Recall: 0.524194, count: 124 -Region Avg IOU: 0.447332, Class: 1.000000, Obj: 0.240811, No Obj: 0.004339, Avg Recall: 0.495575, count: 113 -Region Avg IOU: 0.450714, Class: 1.000000, Obj: 0.245604, No Obj: 0.004669, Avg Recall: 0.507576, count: 132 -786: 0.577569, 0.536295 avg, 0.001000 rate, 20.640093 seconds, 201216 images -Loaded: 1.190064 seconds -Region Avg IOU: 0.475677, Class: 1.000000, Obj: 0.225603, No Obj: 0.004622, Avg Recall: 0.512195, count: 123 -Region Avg IOU: 0.505517, Class: 1.000000, Obj: 0.232061, No Obj: 0.004073, Avg Recall: 0.530864, count: 81 -Region Avg IOU: 0.417756, Class: 1.000000, Obj: 0.187042, No Obj: 0.004547, Avg Recall: 0.398374, count: 123 -Region Avg IOU: 0.532439, Class: 1.000000, Obj: 0.214614, No Obj: 0.004900, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.409590, Class: 1.000000, Obj: 0.208814, No Obj: 0.003844, Avg Recall: 0.423913, count: 92 -Region Avg IOU: 0.463573, Class: 1.000000, Obj: 0.220446, No Obj: 0.004657, Avg Recall: 0.500000, count: 122 -Region Avg IOU: 0.462017, Class: 1.000000, Obj: 0.221395, No Obj: 0.003988, Avg Recall: 0.484536, count: 97 -Region Avg IOU: 0.425103, Class: 1.000000, Obj: 0.207577, No Obj: 0.004652, Avg Recall: 0.471429, count: 140 -787: 0.615340, 0.544200 avg, 0.001000 rate, 21.372816 seconds, 201472 images -Loaded: 0.483269 seconds -Region Avg IOU: 0.399734, Class: 1.000000, Obj: 0.225705, No Obj: 0.003794, Avg Recall: 0.380952, count: 105 -Region Avg IOU: 0.433514, Class: 1.000000, Obj: 0.189838, No Obj: 0.004366, Avg Recall: 0.456897, count: 116 -Region Avg IOU: 0.449317, Class: 1.000000, Obj: 0.213384, No Obj: 0.003733, Avg Recall: 0.488636, count: 88 -Region Avg IOU: 0.437840, Class: 1.000000, Obj: 0.202091, No Obj: 0.003841, Avg Recall: 0.476191, count: 105 -Region Avg IOU: 0.454327, Class: 1.000000, Obj: 0.231273, No Obj: 0.004367, Avg Recall: 0.524194, count: 124 -Region Avg IOU: 0.460231, Class: 1.000000, Obj: 0.210610, No Obj: 0.004435, Avg Recall: 0.491379, count: 116 -Region Avg IOU: 0.503635, Class: 1.000000, Obj: 0.216795, No Obj: 0.004624, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.418529, Class: 1.000000, Obj: 0.183742, No Obj: 0.003572, Avg Recall: 0.425926, count: 108 -788: 0.522186, 0.541998 avg, 0.001000 rate, 21.111952 seconds, 201728 images -Loaded: 0.000056 seconds -Region Avg IOU: 0.456700, Class: 1.000000, Obj: 0.257488, No Obj: 0.004925, Avg Recall: 0.471545, count: 123 -Region Avg IOU: 0.469495, Class: 1.000000, Obj: 0.252987, No Obj: 0.004584, Avg Recall: 0.490566, count: 106 -Region Avg IOU: 0.417281, Class: 1.000000, Obj: 0.185359, No Obj: 0.004156, Avg Recall: 0.428571, count: 119 -Region Avg IOU: 0.465385, Class: 1.000000, Obj: 0.239220, No Obj: 0.003521, Avg Recall: 0.521127, count: 71 -Region Avg IOU: 0.513243, Class: 1.000000, Obj: 0.249875, No Obj: 0.004430, Avg Recall: 0.555556, count: 81 -Region Avg IOU: 0.466967, Class: 1.000000, Obj: 0.233919, No Obj: 0.004506, Avg Recall: 0.519685, count: 127 -Region Avg IOU: 0.436940, Class: 1.000000, Obj: 0.213600, No Obj: 0.004708, Avg Recall: 0.406504, count: 123 -Region Avg IOU: 0.470838, Class: 1.000000, Obj: 0.267612, No Obj: 0.004849, Avg Recall: 0.467290, count: 107 -789: 0.492953, 0.537094 avg, 0.001000 rate, 19.639044 seconds, 201984 images -Loaded: 0.916190 seconds -Region Avg IOU: 0.471014, Class: 1.000000, Obj: 0.251318, No Obj: 0.004541, Avg Recall: 0.510000, count: 100 -Region Avg IOU: 0.458117, Class: 1.000000, Obj: 0.217184, No Obj: 0.004234, Avg Recall: 0.495495, count: 111 -Region Avg IOU: 0.479904, Class: 1.000000, Obj: 0.308014, No Obj: 0.004728, Avg Recall: 0.527132, count: 129 -Region Avg IOU: 0.476673, Class: 1.000000, Obj: 0.233745, No Obj: 0.004090, Avg Recall: 0.577586, count: 116 -Region Avg IOU: 0.475507, Class: 1.000000, Obj: 0.246173, No Obj: 0.004130, Avg Recall: 0.473684, count: 95 -Region Avg IOU: 0.447916, Class: 1.000000, Obj: 0.241778, No Obj: 0.004277, Avg Recall: 0.434343, count: 99 -Region Avg IOU: 0.475111, Class: 1.000000, Obj: 0.250904, No Obj: 0.004277, Avg Recall: 0.540816, count: 98 -Region Avg IOU: 0.438583, Class: 1.000000, Obj: 0.227096, No Obj: 0.004878, Avg Recall: 0.469231, count: 130 -790: 0.518049, 0.535189 avg, 0.001000 rate, 21.606808 seconds, 202240 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000081 seconds -Region Avg IOU: 0.576198, Class: 1.000000, Obj: 0.277717, No Obj: 0.003868, Avg Recall: 0.738462, count: 65 -Region Avg IOU: 0.576056, Class: 1.000000, Obj: 0.255695, No Obj: 0.003693, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.557102, Class: 1.000000, Obj: 0.250329, No Obj: 0.003961, Avg Recall: 0.686046, count: 86 -Region Avg IOU: 0.548934, Class: 1.000000, Obj: 0.285571, No Obj: 0.004699, Avg Recall: 0.621795, count: 156 -Region Avg IOU: 0.559031, Class: 1.000000, Obj: 0.290965, No Obj: 0.004158, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.546724, Class: 1.000000, Obj: 0.258112, No Obj: 0.003563, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.518944, Class: 1.000000, Obj: 0.248526, No Obj: 0.003817, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.540073, Class: 1.000000, Obj: 0.261906, No Obj: 0.003640, Avg Recall: 0.617284, count: 81 -791: 0.530535, 0.534724 avg, 0.001000 rate, 27.696383 seconds, 202496 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.524445, Class: 1.000000, Obj: 0.237216, No Obj: 0.004083, Avg Recall: 0.627737, count: 137 -Region Avg IOU: 0.500611, Class: 1.000000, Obj: 0.206334, No Obj: 0.003829, Avg Recall: 0.545455, count: 132 -Region Avg IOU: 0.505566, Class: 1.000000, Obj: 0.261346, No Obj: 0.003686, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.510950, Class: 1.000000, Obj: 0.273561, No Obj: 0.003382, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.558269, Class: 1.000000, Obj: 0.274359, No Obj: 0.003568, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.586963, Class: 1.000000, Obj: 0.243756, No Obj: 0.004026, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.516109, Class: 1.000000, Obj: 0.236406, No Obj: 0.003817, Avg Recall: 0.605042, count: 119 -Region Avg IOU: 0.546060, Class: 1.000000, Obj: 0.287510, No Obj: 0.004078, Avg Recall: 0.640351, count: 114 -792: 0.554241, 0.536675 avg, 0.001000 rate, 26.776588 seconds, 202752 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.545876, Class: 1.000000, Obj: 0.285211, No Obj: 0.003747, Avg Recall: 0.629032, count: 124 -Region Avg IOU: 0.519641, Class: 1.000000, Obj: 0.242872, No Obj: 0.003553, Avg Recall: 0.540000, count: 100 -Region Avg IOU: 0.531104, Class: 1.000000, Obj: 0.222160, No Obj: 0.003851, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.529999, Class: 1.000000, Obj: 0.218774, No Obj: 0.003807, Avg Recall: 0.641026, count: 117 -Region Avg IOU: 0.565315, Class: 1.000000, Obj: 0.282257, No Obj: 0.003849, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.598355, Class: 1.000000, Obj: 0.243413, No Obj: 0.003606, Avg Recall: 0.737500, count: 80 -Region Avg IOU: 0.550374, Class: 1.000000, Obj: 0.266008, No Obj: 0.003589, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.572100, Class: 1.000000, Obj: 0.238472, No Obj: 0.003437, Avg Recall: 0.693694, count: 111 -793: 0.500267, 0.533035 avg, 0.001000 rate, 25.695309 seconds, 203008 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.567353, Class: 1.000000, Obj: 0.253557, No Obj: 0.003506, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.550480, Class: 1.000000, Obj: 0.221141, No Obj: 0.003510, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.606860, Class: 1.000000, Obj: 0.250233, No Obj: 0.003616, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.538968, Class: 1.000000, Obj: 0.207873, No Obj: 0.003259, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.501014, Class: 1.000000, Obj: 0.231666, No Obj: 0.003330, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.548725, Class: 1.000000, Obj: 0.327872, No Obj: 0.003396, Avg Recall: 0.644444, count: 90 -Region Avg IOU: 0.537561, Class: 1.000000, Obj: 0.276219, No Obj: 0.003129, Avg Recall: 0.645570, count: 79 -Region Avg IOU: 0.557108, Class: 1.000000, Obj: 0.224509, No Obj: 0.003624, Avg Recall: 0.689076, count: 119 -794: 0.463086, 0.526040 avg, 0.001000 rate, 27.306900 seconds, 203264 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.615065, Class: 1.000000, Obj: 0.255075, No Obj: 0.003644, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.616870, Class: 1.000000, Obj: 0.268626, No Obj: 0.003267, Avg Recall: 0.782609, count: 69 -Region Avg IOU: 0.550217, Class: 1.000000, Obj: 0.278340, No Obj: 0.003454, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.521409, Class: 1.000000, Obj: 0.230555, No Obj: 0.003410, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.568748, Class: 1.000000, Obj: 0.199935, No Obj: 0.003654, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.551853, Class: 1.000000, Obj: 0.220200, No Obj: 0.003459, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.513441, Class: 1.000000, Obj: 0.238227, No Obj: 0.003536, Avg Recall: 0.587302, count: 126 -Region Avg IOU: 0.605245, Class: 1.000000, Obj: 0.227648, No Obj: 0.003539, Avg Recall: 0.761364, count: 88 -795: 0.450214, 0.518457 avg, 0.001000 rate, 27.435194 seconds, 203520 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.527286, Class: 1.000000, Obj: 0.191752, No Obj: 0.002945, Avg Recall: 0.618421, count: 76 -Region Avg IOU: 0.514839, Class: 1.000000, Obj: 0.211778, No Obj: 0.003440, Avg Recall: 0.590164, count: 122 -Region Avg IOU: 0.542838, Class: 1.000000, Obj: 0.228682, No Obj: 0.003246, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.525255, Class: 1.000000, Obj: 0.176008, No Obj: 0.003252, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.561374, Class: 1.000000, Obj: 0.220449, No Obj: 0.003963, Avg Recall: 0.658915, count: 129 -Region Avg IOU: 0.556435, Class: 1.000000, Obj: 0.203352, No Obj: 0.003519, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.519377, Class: 1.000000, Obj: 0.239664, No Obj: 0.003406, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.538119, Class: 1.000000, Obj: 0.228141, No Obj: 0.003285, Avg Recall: 0.615385, count: 117 -796: 0.538973, 0.520509 avg, 0.001000 rate, 27.168848 seconds, 203776 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.592719, Class: 1.000000, Obj: 0.199780, No Obj: 0.003329, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.507067, Class: 1.000000, Obj: 0.216717, No Obj: 0.003076, Avg Recall: 0.607407, count: 135 -Region Avg IOU: 0.552261, Class: 1.000000, Obj: 0.215785, No Obj: 0.003562, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.558086, Class: 1.000000, Obj: 0.195790, No Obj: 0.003559, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.525660, Class: 1.000000, Obj: 0.220459, No Obj: 0.002971, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.509487, Class: 1.000000, Obj: 0.272001, No Obj: 0.002827, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.557194, Class: 1.000000, Obj: 0.205369, No Obj: 0.003201, Avg Recall: 0.676923, count: 130 -Region Avg IOU: 0.575153, Class: 1.000000, Obj: 0.243232, No Obj: 0.003482, Avg Recall: 0.677686, count: 121 -797: 0.540983, 0.522556 avg, 0.001000 rate, 25.078913 seconds, 204032 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.588113, Class: 1.000000, Obj: 0.260027, No Obj: 0.003157, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.586493, Class: 1.000000, Obj: 0.203543, No Obj: 0.003043, Avg Recall: 0.716049, count: 81 -Region Avg IOU: 0.523181, Class: 1.000000, Obj: 0.243711, No Obj: 0.003037, Avg Recall: 0.595506, count: 89 -Region Avg IOU: 0.533986, Class: 1.000000, Obj: 0.209806, No Obj: 0.003009, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.569804, Class: 1.000000, Obj: 0.239064, No Obj: 0.003581, Avg Recall: 0.680672, count: 119 -Region Avg IOU: 0.549194, Class: 1.000000, Obj: 0.236658, No Obj: 0.002929, Avg Recall: 0.622222, count: 90 -Region Avg IOU: 0.565028, Class: 1.000000, Obj: 0.241362, No Obj: 0.003210, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.575171, Class: 1.000000, Obj: 0.224873, No Obj: 0.003347, Avg Recall: 0.694915, count: 118 -798: 0.444258, 0.514726 avg, 0.001000 rate, 26.351149 seconds, 204288 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.585812, Class: 1.000000, Obj: 0.232609, No Obj: 0.003969, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.615657, Class: 1.000000, Obj: 0.242638, No Obj: 0.003194, Avg Recall: 0.750000, count: 72 -Region Avg IOU: 0.524494, Class: 1.000000, Obj: 0.217980, No Obj: 0.003427, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.560337, Class: 1.000000, Obj: 0.255610, No Obj: 0.003126, Avg Recall: 0.642857, count: 84 -Region Avg IOU: 0.535686, Class: 1.000000, Obj: 0.256991, No Obj: 0.002992, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.551433, Class: 1.000000, Obj: 0.218132, No Obj: 0.003527, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.548160, Class: 1.000000, Obj: 0.297610, No Obj: 0.003400, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.600950, Class: 1.000000, Obj: 0.278489, No Obj: 0.003499, Avg Recall: 0.728261, count: 92 -799: 0.446129, 0.507867 avg, 0.001000 rate, 27.413635 seconds, 204544 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.512354, Class: 1.000000, Obj: 0.254808, No Obj: 0.003218, Avg Recall: 0.538462, count: 104 -Region Avg IOU: 0.562974, Class: 1.000000, Obj: 0.247483, No Obj: 0.003218, Avg Recall: 0.669565, count: 115 -Region Avg IOU: 0.537042, Class: 1.000000, Obj: 0.211134, No Obj: 0.003705, Avg Recall: 0.572727, count: 110 -Region Avg IOU: 0.544834, Class: 1.000000, Obj: 0.251550, No Obj: 0.003593, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.561436, Class: 1.000000, Obj: 0.234971, No Obj: 0.003515, Avg Recall: 0.651852, count: 135 -Region Avg IOU: 0.501117, Class: 1.000000, Obj: 0.214725, No Obj: 0.003736, Avg Recall: 0.546763, count: 139 -Region Avg IOU: Saving weights to ./person9300/backup/tyvp_93_9c512_800.weights -0.583246, Class: 1.000000, Obj: 0.218966, No Obj: 0.003602, Avg Recall: 0.702479, count: 121 -Region Avg IOU: 0.585094, Class: 1.000000, Obj: 0.222934, No Obj: 0.003133, Avg Recall: 0.723214, count: 112 -800: 0.522513, 0.509331 avg, 0.001000 rate, 27.540154 seconds, 204800 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.981582 seconds -Region Avg IOU: 0.492431, Class: 1.000000, Obj: 0.212546, No Obj: 0.004415, Avg Recall: 0.550388, count: 129 -Region Avg IOU: 0.471336, Class: 1.000000, Obj: 0.226573, No Obj: 0.003948, Avg Recall: 0.527273, count: 110 -Region Avg IOU: 0.484895, Class: 1.000000, Obj: 0.271992, No Obj: 0.003759, Avg Recall: 0.480519, count: 77 -Region Avg IOU: 0.480577, Class: 1.000000, Obj: 0.223564, No Obj: 0.003690, Avg Recall: 0.537037, count: 108 -Region Avg IOU: 0.435910, Class: 1.000000, Obj: 0.208416, No Obj: 0.003710, Avg Recall: 0.414414, count: 111 -Region Avg IOU: 0.488177, Class: 1.000000, Obj: 0.251702, No Obj: 0.003292, Avg Recall: 0.506173, count: 81 -Region Avg IOU: 0.463530, Class: 1.000000, Obj: 0.234366, No Obj: 0.003790, Avg Recall: 0.449438, count: 89 -Region Avg IOU: 0.463046, Class: 1.000000, Obj: 0.214679, No Obj: 0.003491, Avg Recall: 0.528302, count: 106 -801: 0.454226, 0.503821 avg, 0.001000 rate, 22.203323 seconds, 205056 images -Loaded: 0.000054 seconds -Region Avg IOU: 0.430376, Class: 1.000000, Obj: 0.203928, No Obj: 0.003991, Avg Recall: 0.440945, count: 127 -Region Avg IOU: 0.473200, Class: 1.000000, Obj: 0.211878, No Obj: 0.004349, Avg Recall: 0.511811, count: 127 -Region Avg IOU: 0.475450, Class: 1.000000, Obj: 0.242990, No Obj: 0.003736, Avg Recall: 0.517241, count: 116 -Region Avg IOU: 0.464362, Class: 1.000000, Obj: 0.249027, No Obj: 0.003780, Avg Recall: 0.528736, count: 87 -Region Avg IOU: 0.459758, Class: 1.000000, Obj: 0.207544, No Obj: 0.003826, Avg Recall: 0.483051, count: 118 -Region Avg IOU: 0.481726, Class: 1.000000, Obj: 0.226794, No Obj: 0.004002, Avg Recall: 0.500000, count: 120 -Region Avg IOU: 0.466819, Class: 1.000000, Obj: 0.251963, No Obj: 0.003788, Avg Recall: 0.518868, count: 106 -Region Avg IOU: 0.485851, Class: 1.000000, Obj: 0.245214, No Obj: 0.003770, Avg Recall: 0.510204, count: 98 -802: 0.559249, 0.509364 avg, 0.001000 rate, 21.788696 seconds, 205312 images -Loaded: 0.738393 seconds -Region Avg IOU: 0.504163, Class: 1.000000, Obj: 0.247274, No Obj: 0.003941, Avg Recall: 0.595745, count: 94 -Region Avg IOU: 0.467469, Class: 1.000000, Obj: 0.247452, No Obj: 0.003981, Avg Recall: 0.465347, count: 101 -Region Avg IOU: 0.474760, Class: 1.000000, Obj: 0.207265, No Obj: 0.004591, Avg Recall: 0.495413, count: 109 -Region Avg IOU: 0.438218, Class: 1.000000, Obj: 0.213931, No Obj: 0.003692, Avg Recall: 0.445545, count: 101 -Region Avg IOU: 0.502896, Class: 1.000000, Obj: 0.269873, No Obj: 0.004586, Avg Recall: 0.551471, count: 136 -Region Avg IOU: 0.442600, Class: 1.000000, Obj: 0.235331, No Obj: 0.004353, Avg Recall: 0.463235, count: 136 -Region Avg IOU: 0.454444, Class: 1.000000, Obj: 0.232557, No Obj: 0.003779, Avg Recall: 0.473684, count: 114 -Region Avg IOU: 0.496809, Class: 1.000000, Obj: 0.278640, No Obj: 0.004485, Avg Recall: 0.550000, count: 120 -803: 0.497559, 0.508183 avg, 0.001000 rate, 21.522182 seconds, 205568 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.478641, Class: 1.000000, Obj: 0.285192, No Obj: 0.004232, Avg Recall: 0.542857, count: 105 -Region Avg IOU: 0.459266, Class: 1.000000, Obj: 0.210482, No Obj: 0.004102, Avg Recall: 0.521008, count: 119 -Region Avg IOU: 0.511009, Class: 1.000000, Obj: 0.238409, No Obj: 0.005263, Avg Recall: 0.580882, count: 136 -Region Avg IOU: 0.498406, Class: 1.000000, Obj: 0.278148, No Obj: 0.003463, Avg Recall: 0.548387, count: 62 -Region Avg IOU: 0.442562, Class: 1.000000, Obj: 0.258539, No Obj: 0.004270, Avg Recall: 0.447619, count: 105 -Region Avg IOU: 0.491813, Class: 1.000000, Obj: 0.251950, No Obj: 0.004069, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.461152, Class: 1.000000, Obj: 0.227944, No Obj: 0.003834, Avg Recall: 0.516129, count: 93 -Region Avg IOU: 0.524682, Class: 1.000000, Obj: 0.272921, No Obj: 0.004546, Avg Recall: 0.586207, count: 116 -804: 0.464023, 0.503767 avg, 0.001000 rate, 22.081146 seconds, 205824 images -Loaded: 1.573079 seconds -Region Avg IOU: 0.498342, Class: 1.000000, Obj: 0.241701, No Obj: 0.004002, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.508503, Class: 1.000000, Obj: 0.268319, No Obj: 0.004156, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.460906, Class: 1.000000, Obj: 0.226962, No Obj: 0.004381, Avg Recall: 0.532258, count: 124 -Region Avg IOU: 0.490856, Class: 1.000000, Obj: 0.245352, No Obj: 0.004219, Avg Recall: 0.520833, count: 96 -Region Avg IOU: 0.425016, Class: 1.000000, Obj: 0.225784, No Obj: 0.003664, Avg Recall: 0.405660, count: 106 -Region Avg IOU: 0.525993, Class: 1.000000, Obj: 0.267693, No Obj: 0.004254, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.457735, Class: 1.000000, Obj: 0.204641, No Obj: 0.004623, Avg Recall: 0.495327, count: 107 -Region Avg IOU: 0.465191, Class: 1.000000, Obj: 0.223652, No Obj: 0.005195, Avg Recall: 0.512500, count: 160 -805: 0.511927, 0.504583 avg, 0.001000 rate, 23.367790 seconds, 206080 images -Loaded: 0.162518 seconds -Region Avg IOU: 0.464826, Class: 1.000000, Obj: 0.261620, No Obj: 0.003529, Avg Recall: 0.545455, count: 88 -Region Avg IOU: 0.486907, Class: 1.000000, Obj: 0.207497, No Obj: 0.004149, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.474848, Class: 1.000000, Obj: 0.252622, No Obj: 0.004452, Avg Recall: 0.500000, count: 110 -Region Avg IOU: 0.474617, Class: 1.000000, Obj: 0.234927, No Obj: 0.004523, Avg Recall: 0.496644, count: 149 -Region Avg IOU: 0.517079, Class: 1.000000, Obj: 0.247579, No Obj: 0.004633, Avg Recall: 0.615942, count: 138 -Region Avg IOU: 0.465967, Class: 1.000000, Obj: 0.272094, No Obj: 0.004259, Avg Recall: 0.524590, count: 122 -Region Avg IOU: 0.484529, Class: 1.000000, Obj: 0.315874, No Obj: 0.003602, Avg Recall: 0.564706, count: 85 -Region Avg IOU: 0.467624, Class: 1.000000, Obj: 0.248978, No Obj: 0.004356, Avg Recall: 0.561905, count: 105 -806: 0.533723, 0.507497 avg, 0.001000 rate, 22.270733 seconds, 206336 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.464259, Class: 1.000000, Obj: 0.278889, No Obj: 0.003942, Avg Recall: 0.466667, count: 105 -Region Avg IOU: 0.461555, Class: 1.000000, Obj: 0.251228, No Obj: 0.003864, Avg Recall: 0.495146, count: 103 -Region Avg IOU: 0.463865, Class: 1.000000, Obj: 0.263521, No Obj: 0.004239, Avg Recall: 0.529412, count: 119 -Region Avg IOU: 0.486808, Class: 1.000000, Obj: 0.272752, No Obj: 0.004142, Avg Recall: 0.537037, count: 108 -Region Avg IOU: 0.525687, Class: 1.000000, Obj: 0.300999, No Obj: 0.003959, Avg Recall: 0.559524, count: 84 -Region Avg IOU: 0.479735, Class: 1.000000, Obj: 0.261494, No Obj: 0.003952, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.503751, Class: 1.000000, Obj: 0.264026, No Obj: 0.004053, Avg Recall: 0.560440, count: 91 -Region Avg IOU: 0.475241, Class: 1.000000, Obj: 0.240003, No Obj: 0.003381, Avg Recall: 0.481928, count: 83 -807: 0.442109, 0.500958 avg, 0.001000 rate, 23.026567 seconds, 206592 images -Loaded: 0.253358 seconds -Region Avg IOU: 0.458123, Class: 1.000000, Obj: 0.220366, No Obj: 0.004127, Avg Recall: 0.490909, count: 110 -Region Avg IOU: 0.486566, Class: 1.000000, Obj: 0.260717, No Obj: 0.004504, Avg Recall: 0.523810, count: 105 -Region Avg IOU: 0.452380, Class: 1.000000, Obj: 0.213405, No Obj: 0.004355, Avg Recall: 0.488372, count: 129 -Region Avg IOU: 0.457306, Class: 1.000000, Obj: 0.195435, No Obj: 0.003814, Avg Recall: 0.482143, count: 112 -Region Avg IOU: 0.504272, Class: 1.000000, Obj: 0.240677, No Obj: 0.004206, Avg Recall: 0.514286, count: 105 -Region Avg IOU: 0.449124, Class: 1.000000, Obj: 0.229970, No Obj: 0.003968, Avg Recall: 0.495495, count: 111 -Region Avg IOU: 0.484029, Class: 1.000000, Obj: 0.260356, No Obj: 0.003856, Avg Recall: 0.546296, count: 108 -Region Avg IOU: 0.514897, Class: 1.000000, Obj: 0.260816, No Obj: 0.004229, Avg Recall: 0.565217, count: 92 -808: 0.527621, 0.503625 avg, 0.001000 rate, 23.016235 seconds, 206848 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.448342, Class: 1.000000, Obj: 0.245749, No Obj: 0.003951, Avg Recall: 0.431193, count: 109 -Region Avg IOU: 0.494890, Class: 1.000000, Obj: 0.261696, No Obj: 0.004574, Avg Recall: 0.566929, count: 127 -Region Avg IOU: 0.494238, Class: 1.000000, Obj: 0.248396, No Obj: 0.004511, Avg Recall: 0.484848, count: 99 -Region Avg IOU: 0.468121, Class: 1.000000, Obj: 0.224713, No Obj: 0.004029, Avg Recall: 0.553571, count: 112 -Region Avg IOU: 0.457625, Class: 1.000000, Obj: 0.241188, No Obj: 0.003983, Avg Recall: 0.494949, count: 99 -Region Avg IOU: 0.494463, Class: 1.000000, Obj: 0.219663, No Obj: 0.004516, Avg Recall: 0.542373, count: 118 -Region Avg IOU: 0.488045, Class: 1.000000, Obj: 0.242311, No Obj: 0.003974, Avg Recall: 0.489130, count: 92 -Region Avg IOU: 0.525923, Class: 1.000000, Obj: 0.259334, No Obj: 0.004223, Avg Recall: 0.612245, count: 98 -809: 0.452345, 0.498497 avg, 0.001000 rate, 21.548752 seconds, 207104 images -Loaded: 0.033791 seconds -Region Avg IOU: 0.499737, Class: 1.000000, Obj: 0.262226, No Obj: 0.004128, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.451373, Class: 1.000000, Obj: 0.256310, No Obj: 0.004043, Avg Recall: 0.446281, count: 121 -Region Avg IOU: 0.447126, Class: 1.000000, Obj: 0.204875, No Obj: 0.003923, Avg Recall: 0.470588, count: 102 -Region Avg IOU: 0.488016, Class: 1.000000, Obj: 0.247444, No Obj: 0.004113, Avg Recall: 0.513043, count: 115 -Region Avg IOU: 0.478525, Class: 1.000000, Obj: 0.248972, No Obj: 0.003486, Avg Recall: 0.535714, count: 84 -Region Avg IOU: 0.471508, Class: 1.000000, Obj: 0.235847, No Obj: 0.004004, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.495250, Class: 1.000000, Obj: 0.275375, No Obj: 0.004449, Avg Recall: 0.547009, count: 117 -Region Avg IOU: 0.491742, Class: 1.000000, Obj: 0.247840, No Obj: 0.004544, Avg Recall: 0.568000, count: 125 -810: 0.525679, 0.501215 avg, 0.001000 rate, 22.351551 seconds, 207360 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.660595 seconds -Region Avg IOU: 0.411530, Class: 1.000000, Obj: 0.244812, No Obj: 0.004893, Avg Recall: 0.407692, count: 130 -Region Avg IOU: 0.442324, Class: 1.000000, Obj: 0.212774, No Obj: 0.004446, Avg Recall: 0.446429, count: 112 -Region Avg IOU: 0.425610, Class: 1.000000, Obj: 0.240080, No Obj: 0.004737, Avg Recall: 0.449153, count: 118 -Region Avg IOU: 0.419526, Class: 1.000000, Obj: 0.222711, No Obj: 0.004173, Avg Recall: 0.419355, count: 124 -Region Avg IOU: 0.428545, Class: 1.000000, Obj: 0.248002, No Obj: 0.004536, Avg Recall: 0.444444, count: 90 -Region Avg IOU: 0.489140, Class: 1.000000, Obj: 0.244042, No Obj: 0.004949, Avg Recall: 0.561404, count: 114 -Region Avg IOU: 0.474596, Class: 1.000000, Obj: 0.241982, No Obj: 0.004493, Avg Recall: 0.565217, count: 92 -Region Avg IOU: 0.420177, Class: 1.000000, Obj: 0.240865, No Obj: 0.004239, Avg Recall: 0.402174, count: 92 -811: 0.564248, 0.507518 avg, 0.001000 rate, 18.535503 seconds, 207616 images -Loaded: 3.079581 seconds -Region Avg IOU: 0.470581, Class: 1.000000, Obj: 0.246978, No Obj: 0.004904, Avg Recall: 0.480000, count: 125 -Region Avg IOU: 0.429538, Class: 1.000000, Obj: 0.243393, No Obj: 0.004249, Avg Recall: 0.490566, count: 106 -Region Avg IOU: 0.463250, Class: 1.000000, Obj: 0.209061, No Obj: 0.004549, Avg Recall: 0.488000, count: 125 -Region Avg IOU: 0.489212, Class: 1.000000, Obj: 0.258226, No Obj: 0.004864, Avg Recall: 0.526316, count: 95 -Region Avg IOU: 0.457085, Class: 1.000000, Obj: 0.275698, No Obj: 0.004327, Avg Recall: 0.460784, count: 102 -Region Avg IOU: 0.401799, Class: 1.000000, Obj: 0.190782, No Obj: 0.004545, Avg Recall: 0.425000, count: 120 -Region Avg IOU: 0.426846, Class: 1.000000, Obj: 0.245701, No Obj: 0.004998, Avg Recall: 0.447368, count: 114 -Region Avg IOU: 0.450729, Class: 1.000000, Obj: 0.215486, No Obj: 0.004767, Avg Recall: 0.480769, count: 104 -812: 0.577512, 0.514518 avg, 0.001000 rate, 19.567249 seconds, 207872 images -Loaded: 2.113684 seconds -Region Avg IOU: 0.491164, Class: 1.000000, Obj: 0.211800, No Obj: 0.005309, Avg Recall: 0.531469, count: 143 -Region Avg IOU: 0.474500, Class: 1.000000, Obj: 0.230003, No Obj: 0.004681, Avg Recall: 0.548387, count: 93 -Region Avg IOU: 0.420162, Class: 1.000000, Obj: 0.280502, No Obj: 0.004695, Avg Recall: 0.405405, count: 111 -Region Avg IOU: 0.421488, Class: 1.000000, Obj: 0.239954, No Obj: 0.004945, Avg Recall: 0.466667, count: 105 -Region Avg IOU: 0.417009, Class: 1.000000, Obj: 0.249926, No Obj: 0.004414, Avg Recall: 0.430380, count: 79 -Region Avg IOU: 0.501389, Class: 1.000000, Obj: 0.238676, No Obj: 0.004099, Avg Recall: 0.556818, count: 88 -Region Avg IOU: 0.452109, Class: 1.000000, Obj: 0.212163, No Obj: 0.004154, Avg Recall: 0.480000, count: 75 -Region Avg IOU: 0.435289, Class: 1.000000, Obj: 0.239192, No Obj: 0.004566, Avg Recall: 0.463158, count: 95 -813: 0.536460, 0.516712 avg, 0.001000 rate, 15.117608 seconds, 208128 images -Loaded: 5.319549 seconds -Region Avg IOU: 0.375454, Class: 1.000000, Obj: 0.228689, No Obj: 0.004161, Avg Recall: 0.380000, count: 100 -Region Avg IOU: 0.421921, Class: 1.000000, Obj: 0.269721, No Obj: 0.004910, Avg Recall: 0.463158, count: 95 -Region Avg IOU: 0.441949, Class: 1.000000, Obj: 0.240918, No Obj: 0.004884, Avg Recall: 0.448276, count: 116 -Region Avg IOU: 0.428056, Class: 1.000000, Obj: 0.217752, No Obj: 0.004564, Avg Recall: 0.462185, count: 119 -Region Avg IOU: 0.411528, Class: 1.000000, Obj: 0.248532, No Obj: 0.004544, Avg Recall: 0.379630, count: 108 -Region Avg IOU: 0.454310, Class: 1.000000, Obj: 0.250713, No Obj: 0.004874, Avg Recall: 0.480000, count: 100 -Region Avg IOU: 0.425465, Class: 1.000000, Obj: 0.218791, No Obj: 0.004986, Avg Recall: 0.446808, count: 94 -Region Avg IOU: 0.479486, Class: 1.000000, Obj: 0.269541, No Obj: 0.004502, Avg Recall: 0.532609, count: 92 -814: 0.475928, 0.512634 avg, 0.001000 rate, 19.850832 seconds, 208384 images -Loaded: 0.704004 seconds -Region Avg IOU: 0.450765, Class: 1.000000, Obj: 0.241913, No Obj: 0.004269, Avg Recall: 0.505882, count: 85 -Region Avg IOU: 0.402396, Class: 1.000000, Obj: 0.259032, No Obj: 0.004554, Avg Recall: 0.403846, count: 104 -Region Avg IOU: 0.439029, Class: 1.000000, Obj: 0.226777, No Obj: 0.004733, Avg Recall: 0.476635, count: 107 -Region Avg IOU: 0.474299, Class: 1.000000, Obj: 0.246159, No Obj: 0.004637, Avg Recall: 0.489130, count: 92 -Region Avg IOU: 0.455912, Class: 1.000000, Obj: 0.251590, No Obj: 0.004495, Avg Recall: 0.464912, count: 114 -Region Avg IOU: 0.444144, Class: 1.000000, Obj: 0.229427, No Obj: 0.005105, Avg Recall: 0.451613, count: 124 -Region Avg IOU: 0.443828, Class: 1.000000, Obj: 0.235710, No Obj: 0.004269, Avg Recall: 0.470000, count: 100 -Region Avg IOU: 0.478164, Class: 1.000000, Obj: 0.235136, No Obj: 0.005312, Avg Recall: 0.485981, count: 107 -815: 0.497233, 0.511094 avg, 0.001000 rate, 18.692921 seconds, 208640 images -Loaded: 1.595670 seconds -Region Avg IOU: 0.391167, Class: 1.000000, Obj: 0.206873, No Obj: 0.005046, Avg Recall: 0.377953, count: 127 -Region Avg IOU: 0.487587, Class: 1.000000, Obj: 0.287777, No Obj: 0.004974, Avg Recall: 0.510870, count: 92 -Region Avg IOU: 0.410666, Class: 1.000000, Obj: 0.195708, No Obj: 0.004468, Avg Recall: 0.418605, count: 129 -Region Avg IOU: 0.399955, Class: 1.000000, Obj: 0.242656, No Obj: 0.004383, Avg Recall: 0.390000, count: 100 -Region Avg IOU: 0.449805, Class: 1.000000, Obj: 0.278892, No Obj: 0.004726, Avg Recall: 0.448276, count: 87 -Region Avg IOU: 0.451084, Class: 1.000000, Obj: 0.265919, No Obj: 0.004870, Avg Recall: 0.455446, count: 101 -Region Avg IOU: 0.484836, Class: 1.000000, Obj: 0.257743, No Obj: 0.004543, Avg Recall: 0.505747, count: 87 -Region Avg IOU: 0.444203, Class: 1.000000, Obj: 0.211702, No Obj: 0.004146, Avg Recall: 0.450549, count: 91 -816: 0.485376, 0.508522 avg, 0.001000 rate, 18.665215 seconds, 208896 images -Loaded: 3.342109 seconds -Region Avg IOU: 0.484648, Class: 1.000000, Obj: 0.255366, No Obj: 0.005011, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.389346, Class: 1.000000, Obj: 0.227386, No Obj: 0.004836, Avg Recall: 0.350746, count: 134 -Region Avg IOU: 0.434190, Class: 1.000000, Obj: 0.231247, No Obj: 0.005087, Avg Recall: 0.439655, count: 116 -Region Avg IOU: 0.495317, Class: 1.000000, Obj: 0.258119, No Obj: 0.005413, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.424633, Class: 1.000000, Obj: 0.259241, No Obj: 0.005027, Avg Recall: 0.440678, count: 118 -Region Avg IOU: 0.442009, Class: 1.000000, Obj: 0.239529, No Obj: 0.004740, Avg Recall: 0.482353, count: 85 -Region Avg IOU: 0.406865, Class: 1.000000, Obj: 0.195430, No Obj: 0.004780, Avg Recall: 0.421053, count: 95 -Region Avg IOU: 0.403752, Class: 1.000000, Obj: 0.265842, No Obj: 0.004479, Avg Recall: 0.371681, count: 113 -817: 0.559080, 0.513578 avg, 0.001000 rate, 16.846804 seconds, 209152 images -Loaded: 4.426498 seconds -Region Avg IOU: 0.430849, Class: 1.000000, Obj: 0.254116, No Obj: 0.004419, Avg Recall: 0.445783, count: 83 -Region Avg IOU: 0.447900, Class: 1.000000, Obj: 0.242465, No Obj: 0.004379, Avg Recall: 0.449438, count: 89 -Region Avg IOU: 0.455642, Class: 1.000000, Obj: 0.226083, No Obj: 0.004386, Avg Recall: 0.477876, count: 113 -Region Avg IOU: 0.477685, Class: 1.000000, Obj: 0.224732, No Obj: 0.004880, Avg Recall: 0.521277, count: 94 -Region Avg IOU: 0.428607, Class: 1.000000, Obj: 0.211808, No Obj: 0.004412, Avg Recall: 0.446429, count: 112 -Region Avg IOU: 0.375391, Class: 1.000000, Obj: 0.203310, No Obj: 0.003896, Avg Recall: 0.397959, count: 98 -Region Avg IOU: 0.391559, Class: 1.000000, Obj: 0.229183, No Obj: 0.004226, Avg Recall: 0.369565, count: 92 -Region Avg IOU: 0.442318, Class: 1.000000, Obj: 0.207735, No Obj: 0.004563, Avg Recall: 0.428571, count: 126 -818: 0.557338, 0.517954 avg, 0.001000 rate, 19.048923 seconds, 209408 images -Loaded: 3.089069 seconds -Region Avg IOU: 0.406093, Class: 1.000000, Obj: 0.194253, No Obj: 0.003989, Avg Recall: 0.431579, count: 95 -Region Avg IOU: 0.420133, Class: 1.000000, Obj: 0.228121, No Obj: 0.004746, Avg Recall: 0.383459, count: 133 -Region Avg IOU: 0.417763, Class: 1.000000, Obj: 0.202205, No Obj: 0.004460, Avg Recall: 0.420000, count: 100 -Region Avg IOU: 0.425808, Class: 1.000000, Obj: 0.242961, No Obj: 0.004395, Avg Recall: 0.450450, count: 111 -Region Avg IOU: 0.471197, Class: 1.000000, Obj: 0.260064, No Obj: 0.004832, Avg Recall: 0.541667, count: 96 -Region Avg IOU: 0.429275, Class: 1.000000, Obj: 0.235093, No Obj: 0.004765, Avg Recall: 0.452555, count: 137 -Region Avg IOU: 0.419136, Class: 1.000000, Obj: 0.241736, No Obj: 0.004819, Avg Recall: 0.438017, count: 121 -Region Avg IOU: 0.417607, Class: 1.000000, Obj: 0.234456, No Obj: 0.004701, Avg Recall: 0.441860, count: 129 -819: 0.610552, 0.527214 avg, 0.001000 rate, 16.445076 seconds, 209664 images -Loaded: 2.674057 seconds -Region Avg IOU: 0.398154, Class: 1.000000, Obj: 0.184869, No Obj: 0.004080, Avg Recall: 0.414141, count: 99 -Region Avg IOU: 0.466950, Class: 1.000000, Obj: 0.228694, No Obj: 0.004967, Avg Recall: 0.500000, count: 114 -Region Avg IOU: 0.424568, Class: 1.000000, Obj: 0.224705, No Obj: 0.004579, Avg Recall: 0.436975, count: 119 -Region Avg IOU: 0.443830, Class: 1.000000, Obj: 0.254477, No Obj: 0.004869, Avg Recall: 0.491803, count: 122 -Region Avg IOU: 0.426863, Class: 1.000000, Obj: 0.229496, No Obj: 0.004405, Avg Recall: 0.444444, count: 108 -Region Avg IOU: 0.418152, Class: 1.000000, Obj: 0.203222, No Obj: 0.004766, Avg Recall: 0.414414, count: 111 -Region Avg IOU: 0.453843, Class: 1.000000, Obj: 0.219864, No Obj: 0.004431, Avg Recall: 0.554217, count: 83 -Region Avg IOU: 0.462561, Class: 1.000000, Obj: 0.231669, No Obj: 0.004326, Avg Recall: 0.512195, count: 82 -820: 0.503422, 0.524834 avg, 0.001000 rate, 20.203672 seconds, 209920 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 0.000061 seconds -Region Avg IOU: 0.507098, Class: 1.000000, Obj: 0.289253, No Obj: 0.003745, Avg Recall: 0.574468, count: 94 -Region Avg IOU: 0.478642, Class: 1.000000, Obj: 0.258769, No Obj: 0.004200, Avg Recall: 0.514925, count: 134 -Region Avg IOU: 0.544030, Class: 1.000000, Obj: 0.268976, No Obj: 0.004832, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.529249, Class: 1.000000, Obj: 0.272842, No Obj: 0.004434, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.454172, Class: 1.000000, Obj: 0.230316, No Obj: 0.004527, Avg Recall: 0.454545, count: 110 -Region Avg IOU: 0.504955, Class: 1.000000, Obj: 0.278342, No Obj: 0.004305, Avg Recall: 0.540816, count: 98 -Region Avg IOU: 0.484876, Class: 1.000000, Obj: 0.285347, No Obj: 0.004156, Avg Recall: 0.553191, count: 94 -Region Avg IOU: 0.481125, Class: 1.000000, Obj: 0.195584, No Obj: 0.004541, Avg Recall: 0.520833, count: 96 -821: 0.508566, 0.523208 avg, 0.001000 rate, 23.551115 seconds, 210176 images -Loaded: 0.000059 seconds -Region Avg IOU: 0.512450, Class: 1.000000, Obj: 0.266739, No Obj: 0.004482, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.441353, Class: 1.000000, Obj: 0.275004, No Obj: 0.004490, Avg Recall: 0.456311, count: 103 -Region Avg IOU: 0.484980, Class: 1.000000, Obj: 0.255606, No Obj: 0.004713, Avg Recall: 0.531746, count: 126 -Region Avg IOU: 0.455799, Class: 1.000000, Obj: 0.231037, No Obj: 0.004728, Avg Recall: 0.472603, count: 146 -Region Avg IOU: 0.541451, Class: 1.000000, Obj: 0.261971, No Obj: 0.004892, Avg Recall: 0.649635, count: 137 -Region Avg IOU: 0.556865, Class: 1.000000, Obj: 0.266670, No Obj: 0.004493, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.494058, Class: 1.000000, Obj: 0.273952, No Obj: 0.003710, Avg Recall: 0.520548, count: 73 -Region Avg IOU: 0.497524, Class: 1.000000, Obj: 0.253956, No Obj: 0.004009, Avg Recall: 0.584270, count: 89 -822: 0.541365, 0.525023 avg, 0.001000 rate, 22.655180 seconds, 210432 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.464633, Class: 1.000000, Obj: 0.247317, No Obj: 0.004283, Avg Recall: 0.482456, count: 114 -Region Avg IOU: 0.484078, Class: 1.000000, Obj: 0.248632, No Obj: 0.003938, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.533901, Class: 1.000000, Obj: 0.250906, No Obj: 0.004410, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.508339, Class: 1.000000, Obj: 0.300978, No Obj: 0.003929, Avg Recall: 0.595506, count: 89 -Region Avg IOU: 0.475366, Class: 1.000000, Obj: 0.226406, No Obj: 0.003964, Avg Recall: 0.509804, count: 102 -Region Avg IOU: 0.507564, Class: 1.000000, Obj: 0.256137, No Obj: 0.004685, Avg Recall: 0.612903, count: 124 -Region Avg IOU: 0.509613, Class: 1.000000, Obj: 0.284877, No Obj: 0.004443, Avg Recall: 0.601626, count: 123 -Region Avg IOU: 0.478032, Class: 1.000000, Obj: 0.248840, No Obj: 0.004456, Avg Recall: 0.546875, count: 128 -823: 0.552073, 0.527728 avg, 0.001000 rate, 21.765556 seconds, 210688 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.512175, Class: 1.000000, Obj: 0.240040, No Obj: 0.003932, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.543551, Class: 1.000000, Obj: 0.272285, No Obj: 0.004988, Avg Recall: 0.634921, count: 126 -Region Avg IOU: 0.514129, Class: 1.000000, Obj: 0.227254, No Obj: 0.003994, Avg Recall: 0.574468, count: 94 -Region Avg IOU: 0.562247, Class: 1.000000, Obj: 0.252344, No Obj: 0.004709, Avg Recall: 0.639098, count: 133 -Region Avg IOU: 0.526972, Class: 1.000000, Obj: 0.327884, No Obj: 0.004019, Avg Recall: 0.594937, count: 79 -Region Avg IOU: 0.514627, Class: 1.000000, Obj: 0.273867, No Obj: 0.003987, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.478351, Class: 1.000000, Obj: 0.249231, No Obj: 0.004144, Avg Recall: 0.500000, count: 112 -Region Avg IOU: 0.518503, Class: 1.000000, Obj: 0.258113, No Obj: 0.004156, Avg Recall: 0.600000, count: 110 -824: 0.525333, 0.527489 avg, 0.001000 rate, 23.563677 seconds, 210944 images -Loaded: 0.000058 seconds -Region Avg IOU: 0.478801, Class: 1.000000, Obj: 0.267773, No Obj: 0.004107, Avg Recall: 0.450980, count: 102 -Region Avg IOU: 0.492218, Class: 1.000000, Obj: 0.266319, No Obj: 0.004027, Avg Recall: 0.540984, count: 122 -Region Avg IOU: 0.481406, Class: 1.000000, Obj: 0.228873, No Obj: 0.003966, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.511909, Class: 1.000000, Obj: 0.271822, No Obj: 0.004250, Avg Recall: 0.571429, count: 105 -Region Avg IOU: 0.523462, Class: 1.000000, Obj: 0.282202, No Obj: 0.004483, Avg Recall: 0.581395, count: 129 -Region Avg IOU: 0.519963, Class: 1.000000, Obj: 0.281611, No Obj: 0.004580, Avg Recall: 0.577586, count: 116 -Region Avg IOU: 0.518754, Class: 1.000000, Obj: 0.266956, No Obj: 0.004378, Avg Recall: 0.607843, count: 102 -Region Avg IOU: 0.514828, Class: 1.000000, Obj: 0.280864, No Obj: 0.003903, Avg Recall: 0.614583, count: 96 -825: 0.513993, 0.526139 avg, 0.001000 rate, 25.301140 seconds, 211200 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.498970, Class: 1.000000, Obj: 0.235285, No Obj: 0.005049, Avg Recall: 0.561290, count: 155 -Region Avg IOU: 0.509267, Class: 1.000000, Obj: 0.244143, No Obj: 0.004526, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.549514, Class: 1.000000, Obj: 0.283395, No Obj: 0.004203, Avg Recall: 0.615385, count: 91 -Region Avg IOU: 0.525066, Class: 1.000000, Obj: 0.241772, No Obj: 0.004952, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.514191, Class: 1.000000, Obj: 0.257597, No Obj: 0.004512, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.477740, Class: 1.000000, Obj: 0.233444, No Obj: 0.004260, Avg Recall: 0.533333, count: 135 -Region Avg IOU: 0.494995, Class: 1.000000, Obj: 0.252353, No Obj: 0.004543, Avg Recall: 0.526718, count: 131 -Region Avg IOU: 0.492102, Class: 1.000000, Obj: 0.295243, No Obj: 0.004394, Avg Recall: 0.523810, count: 84 -826: 0.576353, 0.531161 avg, 0.001000 rate, 22.175030 seconds, 211456 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.490148, Class: 1.000000, Obj: 0.250725, No Obj: 0.003719, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.512498, Class: 1.000000, Obj: 0.246695, No Obj: 0.004540, Avg Recall: 0.520000, count: 125 -Region Avg IOU: 0.506112, Class: 1.000000, Obj: 0.286595, No Obj: 0.003802, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.513497, Class: 1.000000, Obj: 0.280899, No Obj: 0.004400, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.493348, Class: 1.000000, Obj: 0.258225, No Obj: 0.004144, Avg Recall: 0.535088, count: 114 -Region Avg IOU: 0.545591, Class: 1.000000, Obj: 0.274619, No Obj: 0.004263, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.490090, Class: 1.000000, Obj: 0.239735, No Obj: 0.004255, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.503270, Class: 1.000000, Obj: 0.284422, No Obj: 0.004790, Avg Recall: 0.549669, count: 151 -827: 0.526521, 0.530697 avg, 0.001000 rate, 22.657339 seconds, 211712 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.461228, Class: 1.000000, Obj: 0.226774, No Obj: 0.004092, Avg Recall: 0.477064, count: 109 -Region Avg IOU: 0.569924, Class: 1.000000, Obj: 0.247881, No Obj: 0.004545, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.494645, Class: 1.000000, Obj: 0.276305, No Obj: 0.003613, Avg Recall: 0.546512, count: 86 -Region Avg IOU: 0.485890, Class: 1.000000, Obj: 0.216103, No Obj: 0.004095, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.469035, Class: 1.000000, Obj: 0.214128, No Obj: 0.003958, Avg Recall: 0.491228, count: 114 -Region Avg IOU: 0.513576, Class: 1.000000, Obj: 0.258335, No Obj: 0.003844, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.518979, Class: 1.000000, Obj: 0.281374, No Obj: 0.003712, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.493890, Class: 1.000000, Obj: 0.220071, No Obj: 0.004012, Avg Recall: 0.542056, count: 107 -828: 0.526773, 0.530304 avg, 0.001000 rate, 22.960993 seconds, 211968 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.516671, Class: 1.000000, Obj: 0.243384, No Obj: 0.004295, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.481797, Class: 1.000000, Obj: 0.244189, No Obj: 0.004036, Avg Recall: 0.548387, count: 93 -Region Avg IOU: 0.485891, Class: 1.000000, Obj: 0.255113, No Obj: 0.004013, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.503051, Class: 1.000000, Obj: 0.271027, No Obj: 0.003769, Avg Recall: 0.566667, count: 90 -Region Avg IOU: 0.495660, Class: 1.000000, Obj: 0.224608, No Obj: 0.004161, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.528260, Class: 1.000000, Obj: 0.282549, No Obj: 0.004391, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.505554, Class: 1.000000, Obj: 0.251764, No Obj: 0.003840, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.506663, Class: 1.000000, Obj: 0.213889, No Obj: 0.004027, Avg Recall: 0.612903, count: 93 -829: 0.433105, 0.520584 avg, 0.001000 rate, 22.443953 seconds, 212224 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.497714, Class: 1.000000, Obj: 0.243263, No Obj: 0.003849, Avg Recall: 0.565574, count: 122 -Region Avg IOU: 0.530114, Class: 1.000000, Obj: 0.252497, No Obj: 0.003876, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.536651, Class: 1.000000, Obj: 0.265053, No Obj: 0.004174, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.470440, Class: 1.000000, Obj: 0.223696, No Obj: 0.003955, Avg Recall: 0.495575, count: 113 -Region Avg IOU: 0.447336, Class: 1.000000, Obj: 0.204817, No Obj: 0.003720, Avg Recall: 0.485149, count: 101 -Region Avg IOU: 0.481492, Class: 1.000000, Obj: 0.248788, No Obj: 0.003690, Avg Recall: 0.508929, count: 112 -Region Avg IOU: 0.508873, Class: 1.000000, Obj: 0.257108, No Obj: 0.003555, Avg Recall: 0.550000, count: 80 -Region Avg IOU: 0.530765, Class: 1.000000, Obj: 0.245959, No Obj: 0.004001, Avg Recall: 0.627660, count: 94 -830: 0.498941, 0.518420 avg, 0.001000 rate, 23.914164 seconds, 212480 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.560369 seconds -Region Avg IOU: 0.483374, Class: 1.000000, Obj: 0.242076, No Obj: 0.004344, Avg Recall: 0.513514, count: 111 -Region Avg IOU: 0.517248, Class: 1.000000, Obj: 0.246737, No Obj: 0.003835, Avg Recall: 0.595506, count: 89 -Region Avg IOU: 0.495298, Class: 1.000000, Obj: 0.221561, No Obj: 0.004084, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.478986, Class: 1.000000, Obj: 0.230193, No Obj: 0.003810, Avg Recall: 0.516129, count: 93 -Region Avg IOU: 0.519500, Class: 1.000000, Obj: 0.291347, No Obj: 0.004267, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.495090, Class: 1.000000, Obj: 0.227175, No Obj: 0.004637, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.459329, Class: 1.000000, Obj: 0.234513, No Obj: 0.003659, Avg Recall: 0.477273, count: 88 -Region Avg IOU: 0.505285, Class: 1.000000, Obj: 0.293213, No Obj: 0.004369, Avg Recall: 0.586777, count: 121 -831: 0.503237, 0.516902 avg, 0.001000 rate, 21.783617 seconds, 212736 images -Loaded: 0.563537 seconds -Region Avg IOU: 0.497672, Class: 1.000000, Obj: 0.286425, No Obj: 0.004113, Avg Recall: 0.549451, count: 91 -Region Avg IOU: 0.433955, Class: 1.000000, Obj: 0.249796, No Obj: 0.004069, Avg Recall: 0.443478, count: 115 -Region Avg IOU: 0.505234, Class: 1.000000, Obj: 0.254793, No Obj: 0.004119, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.478823, Class: 1.000000, Obj: 0.248813, No Obj: 0.004656, Avg Recall: 0.559322, count: 118 -Region Avg IOU: 0.442702, Class: 1.000000, Obj: 0.205465, No Obj: 0.003880, Avg Recall: 0.440367, count: 109 -Region Avg IOU: 0.475752, Class: 1.000000, Obj: 0.258051, No Obj: 0.004251, Avg Recall: 0.550562, count: 89 -Region Avg IOU: 0.456910, Class: 1.000000, Obj: 0.240397, No Obj: 0.004273, Avg Recall: 0.429825, count: 114 -Region Avg IOU: 0.469090, Class: 1.000000, Obj: 0.269910, No Obj: 0.004169, Avg Recall: 0.504274, count: 117 -832: 0.478635, 0.513075 avg, 0.001000 rate, 21.360525 seconds, 212992 images -Loaded: 1.003291 seconds -Region Avg IOU: 0.440786, Class: 1.000000, Obj: 0.255625, No Obj: 0.003873, Avg Recall: 0.474747, count: 99 -Region Avg IOU: 0.414322, Class: 1.000000, Obj: 0.227589, No Obj: 0.003695, Avg Recall: 0.460784, count: 102 -Region Avg IOU: 0.483900, Class: 1.000000, Obj: 0.282514, No Obj: 0.004425, Avg Recall: 0.544643, count: 112 -Region Avg IOU: 0.461617, Class: 1.000000, Obj: 0.252477, No Obj: 0.004478, Avg Recall: 0.503876, count: 129 -Region Avg IOU: 0.458688, Class: 1.000000, Obj: 0.241596, No Obj: 0.004092, Avg Recall: 0.583333, count: 120 -Region Avg IOU: 0.445670, Class: 1.000000, Obj: 0.234228, No Obj: 0.004226, Avg Recall: 0.495050, count: 101 -Region Avg IOU: 0.490104, Class: 1.000000, Obj: 0.227449, No Obj: 0.004430, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.465628, Class: 1.000000, Obj: 0.222356, No Obj: 0.004202, Avg Recall: 0.489583, count: 96 -833: 0.513663, 0.513134 avg, 0.001000 rate, 22.687330 seconds, 213248 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.383247, Class: 1.000000, Obj: 0.200519, No Obj: 0.003984, Avg Recall: 0.360825, count: 97 -Region Avg IOU: 0.521585, Class: 1.000000, Obj: 0.300628, No Obj: 0.004478, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.478705, Class: 1.000000, Obj: 0.273655, No Obj: 0.004568, Avg Recall: 0.485714, count: 105 -Region Avg IOU: 0.494513, Class: 1.000000, Obj: 0.233436, No Obj: 0.004352, Avg Recall: 0.572650, count: 117 -Region Avg IOU: 0.464599, Class: 1.000000, Obj: 0.266428, No Obj: 0.004496, Avg Recall: 0.511450, count: 131 -Region Avg IOU: 0.500503, Class: 1.000000, Obj: 0.264589, No Obj: 0.003635, Avg Recall: 0.541176, count: 85 -Region Avg IOU: 0.496364, Class: 1.000000, Obj: 0.270708, No Obj: 0.004718, Avg Recall: 0.564516, count: 124 -Region Avg IOU: 0.470358, Class: 1.000000, Obj: 0.259875, No Obj: 0.003720, Avg Recall: 0.514286, count: 105 -834: 0.455875, 0.507408 avg, 0.001000 rate, 21.742691 seconds, 213504 images -Loaded: 1.020484 seconds -Region Avg IOU: 0.489295, Class: 1.000000, Obj: 0.224214, No Obj: 0.003955, Avg Recall: 0.524390, count: 82 -Region Avg IOU: 0.493832, Class: 1.000000, Obj: 0.249597, No Obj: 0.004156, Avg Recall: 0.577778, count: 90 -Region Avg IOU: 0.482933, Class: 1.000000, Obj: 0.256414, No Obj: 0.004280, Avg Recall: 0.529412, count: 119 -Region Avg IOU: 0.447279, Class: 1.000000, Obj: 0.204973, No Obj: 0.003741, Avg Recall: 0.483871, count: 93 -Region Avg IOU: 0.417881, Class: 1.000000, Obj: 0.226470, No Obj: 0.003630, Avg Recall: 0.457447, count: 94 -Region Avg IOU: 0.485631, Class: 1.000000, Obj: 0.258933, No Obj: 0.004021, Avg Recall: 0.518182, count: 110 -Region Avg IOU: 0.500712, Class: 1.000000, Obj: 0.247737, No Obj: 0.004529, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.452371, Class: 1.000000, Obj: 0.232535, No Obj: 0.003786, Avg Recall: 0.483146, count: 89 -835: 0.452825, 0.501950 avg, 0.001000 rate, 21.344881 seconds, 213760 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.472516, Class: 1.000000, Obj: 0.255256, No Obj: 0.004191, Avg Recall: 0.549618, count: 131 -Region Avg IOU: 0.463173, Class: 1.000000, Obj: 0.202827, No Obj: 0.003779, Avg Recall: 0.465909, count: 88 -Region Avg IOU: 0.511870, Class: 1.000000, Obj: 0.240969, No Obj: 0.004096, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.472409, Class: 1.000000, Obj: 0.265983, No Obj: 0.004089, Avg Recall: 0.465347, count: 101 -Region Avg IOU: 0.519669, Class: 1.000000, Obj: 0.260864, No Obj: 0.004092, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.480182, Class: 1.000000, Obj: 0.260569, No Obj: 0.004060, Avg Recall: 0.509259, count: 108 -Region Avg IOU: 0.443297, Class: 1.000000, Obj: 0.215953, No Obj: 0.003687, Avg Recall: 0.493506, count: 77 -Region Avg IOU: 0.523466, Class: 1.000000, Obj: 0.268555, No Obj: 0.004320, Avg Recall: 0.573034, count: 89 -836: 0.482599, 0.500015 avg, 0.001000 rate, 23.417625 seconds, 214016 images -Loaded: 0.103421 seconds -Region Avg IOU: 0.521684, Class: 1.000000, Obj: 0.273643, No Obj: 0.004992, Avg Recall: 0.600000, count: 120 -Region Avg IOU: 0.521098, Class: 1.000000, Obj: 0.262979, No Obj: 0.004266, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.537127, Class: 1.000000, Obj: 0.270769, No Obj: 0.005168, Avg Recall: 0.647887, count: 142 -Region Avg IOU: 0.508852, Class: 1.000000, Obj: 0.272773, No Obj: 0.005193, Avg Recall: 0.535088, count: 114 -Region Avg IOU: 0.449784, Class: 1.000000, Obj: 0.217650, No Obj: 0.004777, Avg Recall: 0.465517, count: 116 -Region Avg IOU: 0.506025, Class: 1.000000, Obj: 0.282362, No Obj: 0.004326, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.457439, Class: 1.000000, Obj: 0.242215, No Obj: 0.004284, Avg Recall: 0.487395, count: 119 -Region Avg IOU: 0.463530, Class: 1.000000, Obj: 0.222398, No Obj: 0.004125, Avg Recall: 0.480000, count: 100 -837: 0.544457, 0.504459 avg, 0.001000 rate, 24.361776 seconds, 214272 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.417551, Class: 1.000000, Obj: 0.228868, No Obj: 0.004276, Avg Recall: 0.405941, count: 101 -Region Avg IOU: 0.503950, Class: 1.000000, Obj: 0.262494, No Obj: 0.003977, Avg Recall: 0.583333, count: 96 -Region Avg IOU: 0.502845, Class: 1.000000, Obj: 0.223759, No Obj: 0.004452, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.469267, Class: 1.000000, Obj: 0.215980, No Obj: 0.004476, Avg Recall: 0.488000, count: 125 -Region Avg IOU: 0.468078, Class: 1.000000, Obj: 0.298081, No Obj: 0.004061, Avg Recall: 0.508772, count: 114 -Region Avg IOU: 0.454086, Class: 1.000000, Obj: 0.233432, No Obj: 0.004250, Avg Recall: 0.468750, count: 96 -Region Avg IOU: 0.501370, Class: 1.000000, Obj: 0.273404, No Obj: 0.004519, Avg Recall: 0.546392, count: 97 -Region Avg IOU: 0.466621, Class: 1.000000, Obj: 0.266723, No Obj: 0.004185, Avg Recall: 0.535714, count: 112 -838: 0.498806, 0.503894 avg, 0.001000 rate, 20.993244 seconds, 214528 images -Loaded: 1.021758 seconds -Region Avg IOU: 0.464483, Class: 1.000000, Obj: 0.262716, No Obj: 0.004135, Avg Recall: 0.454545, count: 121 -Region Avg IOU: 0.531001, Class: 1.000000, Obj: 0.282835, No Obj: 0.004139, Avg Recall: 0.600000, count: 90 -Region Avg IOU: 0.454003, Class: 1.000000, Obj: 0.242642, No Obj: 0.004501, Avg Recall: 0.448529, count: 136 -Region Avg IOU: 0.484587, Class: 1.000000, Obj: 0.272199, No Obj: 0.003966, Avg Recall: 0.550562, count: 89 -Region Avg IOU: 0.478714, Class: 1.000000, Obj: 0.226302, No Obj: 0.004475, Avg Recall: 0.521739, count: 138 -Region Avg IOU: 0.474753, Class: 1.000000, Obj: 0.266936, No Obj: 0.004326, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.491484, Class: 1.000000, Obj: 0.300227, No Obj: 0.004243, Avg Recall: 0.535714, count: 84 -Region Avg IOU: 0.433161, Class: 1.000000, Obj: 0.243847, No Obj: 0.004709, Avg Recall: 0.418919, count: 148 -839: 0.474461, 0.500950 avg, 0.001000 rate, 23.752977 seconds, 214784 images -Loaded: 0.227259 seconds -Region Avg IOU: 0.506300, Class: 1.000000, Obj: 0.287960, No Obj: 0.004468, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.436805, Class: 1.000000, Obj: 0.217954, No Obj: 0.003795, Avg Recall: 0.403361, count: 119 -Region Avg IOU: 0.487182, Class: 1.000000, Obj: 0.211832, No Obj: 0.004244, Avg Recall: 0.528926, count: 121 -Region Avg IOU: 0.426770, Class: 1.000000, Obj: 0.231805, No Obj: 0.003949, Avg Recall: 0.403361, count: 119 -Region Avg IOU: 0.417877, Class: 1.000000, Obj: 0.229786, No Obj: 0.003791, Avg Recall: 0.450000, count: 100 -Region Avg IOU: 0.464148, Class: 1.000000, Obj: 0.235747, No Obj: 0.004476, Avg Recall: 0.504425, count: 113 -Region Avg IOU: 0.506915, Class: 1.000000, Obj: 0.242049, No Obj: 0.003900, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.457958, Class: 1.000000, Obj: 0.235311, No Obj: 0.004094, Avg Recall: 0.475248, count: 101 -840: 0.527852, 0.503640 avg, 0.001000 rate, 20.428923 seconds, 215040 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.332445 seconds -Region Avg IOU: 0.518027, Class: 1.000000, Obj: 0.254028, No Obj: 0.004454, Avg Recall: 0.591667, count: 120 -Region Avg IOU: 0.415867, Class: 1.000000, Obj: 0.208182, No Obj: 0.004071, Avg Recall: 0.447368, count: 114 -Region Avg IOU: 0.509865, Class: 1.000000, Obj: 0.253692, No Obj: 0.004583, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.492193, Class: 1.000000, Obj: 0.260034, No Obj: 0.004360, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.535231, Class: 1.000000, Obj: 0.253914, No Obj: 0.003730, Avg Recall: 0.620253, count: 79 -Region Avg IOU: 0.447894, Class: 1.000000, Obj: 0.226249, No Obj: 0.003771, Avg Recall: 0.464646, count: 99 -Region Avg IOU: 0.470128, Class: 1.000000, Obj: 0.234452, No Obj: 0.004250, Avg Recall: 0.494949, count: 99 -Region Avg IOU: 0.509003, Class: 1.000000, Obj: 0.265778, No Obj: 0.004286, Avg Recall: 0.561224, count: 98 -841: 0.496901, 0.502966 avg, 0.001000 rate, 22.682121 seconds, 215296 images -Loaded: 0.180896 seconds -Region Avg IOU: 0.473963, Class: 1.000000, Obj: 0.261953, No Obj: 0.004130, Avg Recall: 0.456522, count: 92 -Region Avg IOU: 0.499965, Class: 1.000000, Obj: 0.262895, No Obj: 0.004263, Avg Recall: 0.546296, count: 108 -Region Avg IOU: 0.482920, Class: 1.000000, Obj: 0.222873, No Obj: 0.004259, Avg Recall: 0.524194, count: 124 -Region Avg IOU: 0.466990, Class: 1.000000, Obj: 0.246215, No Obj: 0.003548, Avg Recall: 0.483516, count: 91 -Region Avg IOU: 0.499536, Class: 1.000000, Obj: 0.248866, No Obj: 0.003959, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.463505, Class: 1.000000, Obj: 0.262141, No Obj: 0.003919, Avg Recall: 0.488889, count: 90 -Region Avg IOU: 0.470509, Class: 1.000000, Obj: 0.227242, No Obj: 0.004824, Avg Recall: 0.515152, count: 132 -Region Avg IOU: 0.437648, Class: 1.000000, Obj: 0.254010, No Obj: 0.003805, Avg Recall: 0.476191, count: 105 -842: 0.486879, 0.501358 avg, 0.001000 rate, 22.188652 seconds, 215552 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.514608, Class: 1.000000, Obj: 0.271008, No Obj: 0.004254, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.447044, Class: 1.000000, Obj: 0.249845, No Obj: 0.003754, Avg Recall: 0.522222, count: 90 -Region Avg IOU: 0.457917, Class: 1.000000, Obj: 0.229151, No Obj: 0.003892, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.467059, Class: 1.000000, Obj: 0.257166, No Obj: 0.004572, Avg Recall: 0.489051, count: 137 -Region Avg IOU: 0.469995, Class: 1.000000, Obj: 0.215624, No Obj: 0.004008, Avg Recall: 0.504274, count: 117 -Region Avg IOU: 0.479533, Class: 1.000000, Obj: 0.257228, No Obj: 0.003775, Avg Recall: 0.504762, count: 105 -Region Avg IOU: 0.491980, Class: 1.000000, Obj: 0.284286, No Obj: 0.004296, Avg Recall: 0.519231, count: 104 -Region Avg IOU: 0.510163, Class: 1.000000, Obj: 0.240564, No Obj: 0.004140, Avg Recall: 0.625000, count: 88 -843: 0.497394, 0.500961 avg, 0.001000 rate, 21.853191 seconds, 215808 images -Loaded: 0.103659 seconds -Region Avg IOU: 0.551425, Class: 1.000000, Obj: 0.242856, No Obj: 0.004464, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.482255, Class: 1.000000, Obj: 0.195054, No Obj: 0.003817, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.545792, Class: 1.000000, Obj: 0.282716, No Obj: 0.004757, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.492245, Class: 1.000000, Obj: 0.275959, No Obj: 0.004022, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.434462, Class: 1.000000, Obj: 0.213653, No Obj: 0.004100, Avg Recall: 0.443396, count: 106 -Region Avg IOU: 0.485891, Class: 1.000000, Obj: 0.232026, No Obj: 0.004096, Avg Recall: 0.516129, count: 93 -Region Avg IOU: 0.460448, Class: 1.000000, Obj: 0.257452, No Obj: 0.004302, Avg Recall: 0.495868, count: 121 -Region Avg IOU: 0.469928, Class: 1.000000, Obj: 0.227771, No Obj: 0.003851, Avg Recall: 0.559140, count: 93 -844: 0.495265, 0.500392 avg, 0.001000 rate, 22.534077 seconds, 216064 images -Loaded: 1.282982 seconds -Region Avg IOU: 0.490129, Class: 1.000000, Obj: 0.231221, No Obj: 0.003682, Avg Recall: 0.552083, count: 96 -Region Avg IOU: 0.519017, Class: 1.000000, Obj: 0.266650, No Obj: 0.003950, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.483699, Class: 1.000000, Obj: 0.264772, No Obj: 0.003993, Avg Recall: 0.560440, count: 91 -Region Avg IOU: 0.490506, Class: 1.000000, Obj: 0.235130, No Obj: 0.003984, Avg Recall: 0.521739, count: 115 -Region Avg IOU: 0.451262, Class: 1.000000, Obj: 0.259099, No Obj: 0.004153, Avg Recall: 0.435185, count: 108 -Region Avg IOU: 0.430927, Class: 1.000000, Obj: 0.236187, No Obj: 0.003748, Avg Recall: 0.420561, count: 107 -Region Avg IOU: 0.466122, Class: 1.000000, Obj: 0.302125, No Obj: 0.004314, Avg Recall: 0.470086, count: 117 -Region Avg IOU: 0.445392, Class: 1.000000, Obj: 0.247069, No Obj: 0.004113, Avg Recall: 0.490566, count: 106 -845: 0.434720, 0.493825 avg, 0.001000 rate, 22.429485 seconds, 216320 images -Loaded: 0.486447 seconds -Region Avg IOU: 0.480746, Class: 1.000000, Obj: 0.239767, No Obj: 0.004484, Avg Recall: 0.544000, count: 125 -Region Avg IOU: 0.489785, Class: 1.000000, Obj: 0.247311, No Obj: 0.004095, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.448709, Class: 1.000000, Obj: 0.225168, No Obj: 0.004097, Avg Recall: 0.441176, count: 102 -Region Avg IOU: 0.498251, Class: 1.000000, Obj: 0.247688, No Obj: 0.004674, Avg Recall: 0.550388, count: 129 -Region Avg IOU: 0.465760, Class: 1.000000, Obj: 0.236131, No Obj: 0.004601, Avg Recall: 0.462069, count: 145 -Region Avg IOU: 0.461621, Class: 1.000000, Obj: 0.239571, No Obj: 0.004495, Avg Recall: 0.487180, count: 117 -Region Avg IOU: 0.463334, Class: 1.000000, Obj: 0.227552, No Obj: 0.004327, Avg Recall: 0.504202, count: 119 -Region Avg IOU: 0.474030, Class: 1.000000, Obj: 0.289012, No Obj: 0.004012, Avg Recall: 0.515464, count: 97 -846: 0.535928, 0.498035 avg, 0.001000 rate, 20.836399 seconds, 216576 images -Loaded: 0.982632 seconds -Region Avg IOU: 0.459534, Class: 1.000000, Obj: 0.232107, No Obj: 0.004120, Avg Recall: 0.480916, count: 131 -Region Avg IOU: 0.465066, Class: 1.000000, Obj: 0.258528, No Obj: 0.004532, Avg Recall: 0.515152, count: 132 -Region Avg IOU: 0.467105, Class: 1.000000, Obj: 0.272724, No Obj: 0.003907, Avg Recall: 0.485149, count: 101 -Region Avg IOU: 0.483123, Class: 1.000000, Obj: 0.230088, No Obj: 0.003873, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.475155, Class: 1.000000, Obj: 0.267913, No Obj: 0.003845, Avg Recall: 0.488636, count: 88 -Region Avg IOU: 0.495113, Class: 1.000000, Obj: 0.256963, No Obj: 0.004142, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.458752, Class: 1.000000, Obj: 0.231892, No Obj: 0.004054, Avg Recall: 0.522124, count: 113 -Region Avg IOU: 0.475243, Class: 1.000000, Obj: 0.269907, No Obj: 0.003869, Avg Recall: 0.504505, count: 111 -847: 0.463938, 0.494625 avg, 0.001000 rate, 21.900776 seconds, 216832 images -Loaded: 0.678959 seconds -Region Avg IOU: 0.484194, Class: 1.000000, Obj: 0.280101, No Obj: 0.004322, Avg Recall: 0.495868, count: 121 -Region Avg IOU: 0.452244, Class: 1.000000, Obj: 0.226214, No Obj: 0.004319, Avg Recall: 0.479452, count: 146 -Region Avg IOU: 0.427770, Class: 1.000000, Obj: 0.237578, No Obj: 0.003912, Avg Recall: 0.448276, count: 116 -Region Avg IOU: 0.477669, Class: 1.000000, Obj: 0.285538, No Obj: 0.004894, Avg Recall: 0.486957, count: 115 -Region Avg IOU: 0.523165, Class: 1.000000, Obj: 0.277130, No Obj: 0.005209, Avg Recall: 0.598425, count: 127 -Region Avg IOU: 0.451000, Class: 1.000000, Obj: 0.294616, No Obj: 0.004296, Avg Recall: 0.467391, count: 92 -Region Avg IOU: 0.514285, Class: 1.000000, Obj: 0.266456, No Obj: 0.003790, Avg Recall: 0.558140, count: 86 -Region Avg IOU: 0.515223, Class: 1.000000, Obj: 0.286962, No Obj: 0.004802, Avg Recall: 0.608696, count: 115 -848: 0.558211, 0.500984 avg, 0.001000 rate, 20.727289 seconds, 217088 images -Loaded: 1.117893 seconds -Region Avg IOU: 0.462786, Class: 1.000000, Obj: 0.311972, No Obj: 0.004172, Avg Recall: 0.512821, count: 78 -Region Avg IOU: 0.508752, Class: 1.000000, Obj: 0.255717, No Obj: 0.004558, Avg Recall: 0.544776, count: 134 -Region Avg IOU: 0.493606, Class: 1.000000, Obj: 0.208205, No Obj: 0.004108, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.448620, Class: 1.000000, Obj: 0.234540, No Obj: 0.003941, Avg Recall: 0.463918, count: 97 -Region Avg IOU: 0.498888, Class: 1.000000, Obj: 0.270081, No Obj: 0.004878, Avg Recall: 0.557377, count: 122 -Region Avg IOU: 0.501329, Class: 1.000000, Obj: 0.283059, No Obj: 0.004488, Avg Recall: 0.568965, count: 116 -Region Avg IOU: 0.470812, Class: 1.000000, Obj: 0.232929, No Obj: 0.004119, Avg Recall: 0.528846, count: 104 -Region Avg IOU: 0.467310, Class: 1.000000, Obj: 0.262057, No Obj: 0.004275, Avg Recall: 0.553571, count: 112 -849: 0.528641, 0.503750 avg, 0.001000 rate, 20.981064 seconds, 217344 images -Loaded: 0.559765 seconds -Region Avg IOU: 0.462483, Class: 1.000000, Obj: 0.253887, No Obj: 0.004686, Avg Recall: 0.514019, count: 107 -Region Avg IOU: 0.468620, Class: 1.000000, Obj: 0.259277, No Obj: 0.004587, Avg Recall: 0.500000, count: 116 -Region Avg IOU: 0.495984, Class: 1.000000, Obj: 0.274139, No Obj: 0.004774, Avg Recall: 0.557522, count: 113 -Region Avg IOU: 0.477373, Class: 1.000000, Obj: 0.220703, No Obj: 0.004021, Avg Recall: 0.493506, count: 77 -Region Avg IOU: 0.493882, Class: 1.000000, Obj: 0.247015, No Obj: 0.004481, Avg Recall: 0.470149, count: 134 -Region Avg IOU: 0.510104, Class: 1.000000, Obj: 0.268064, No Obj: 0.004645, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.545439, Class: 1.000000, Obj: 0.226158, No Obj: 0.004209, Avg Recall: 0.626374, count: 91 -Region Avg IOU: 0.477386, Class: 1.000000, Obj: 0.251850, No Obj: 0.004446, Avg Recall: 0.509615, count: 104 -850: 0.471375, 0.500512 avg, 0.001000 rate, 21.974833 seconds, 217600 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.166315 seconds -Region Avg IOU: 0.430300, Class: 1.000000, Obj: 0.236592, No Obj: 0.004860, Avg Recall: 0.445255, count: 137 -Region Avg IOU: 0.453874, Class: 1.000000, Obj: 0.275748, No Obj: 0.004399, Avg Recall: 0.473214, count: 112 -Region Avg IOU: 0.432883, Class: 1.000000, Obj: 0.219291, No Obj: 0.004974, Avg Recall: 0.449612, count: 129 -Region Avg IOU: 0.458715, Class: 1.000000, Obj: 0.259876, No Obj: 0.004740, Avg Recall: 0.468085, count: 94 -Region Avg IOU: 0.427779, Class: 1.000000, Obj: 0.243084, No Obj: 0.004871, Avg Recall: 0.476191, count: 126 -Region Avg IOU: 0.463712, Class: 1.000000, Obj: 0.247478, No Obj: 0.004548, Avg Recall: 0.466019, count: 103 -Region Avg IOU: 0.450724, Class: 1.000000, Obj: 0.241031, No Obj: 0.004319, Avg Recall: 0.428571, count: 77 -Region Avg IOU: 0.446285, Class: 1.000000, Obj: 0.234425, No Obj: 0.004760, Avg Recall: 0.480392, count: 102 -851: 0.547500, 0.505211 avg, 0.001000 rate, 19.252678 seconds, 217856 images -Loaded: 1.782593 seconds -Region Avg IOU: 0.468364, Class: 1.000000, Obj: 0.201082, No Obj: 0.004895, Avg Recall: 0.512397, count: 121 -Region Avg IOU: 0.420250, Class: 1.000000, Obj: 0.227117, No Obj: 0.004694, Avg Recall: 0.415254, count: 118 -Region Avg IOU: 0.443886, Class: 1.000000, Obj: 0.204491, No Obj: 0.004473, Avg Recall: 0.461538, count: 130 -Region Avg IOU: 0.425300, Class: 1.000000, Obj: 0.226666, No Obj: 0.004070, Avg Recall: 0.419048, count: 105 -Region Avg IOU: 0.422072, Class: 1.000000, Obj: 0.203373, No Obj: 0.003996, Avg Recall: 0.463158, count: 95 -Region Avg IOU: 0.526735, Class: 1.000000, Obj: 0.264747, No Obj: 0.005009, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.451842, Class: 1.000000, Obj: 0.255931, No Obj: 0.004616, Avg Recall: 0.504587, count: 109 -Region Avg IOU: 0.493336, Class: 1.000000, Obj: 0.274853, No Obj: 0.004552, Avg Recall: 0.555556, count: 108 -852: 0.548377, 0.509528 avg, 0.001000 rate, 19.858217 seconds, 218112 images -Loaded: 0.736108 seconds -Region Avg IOU: 0.391283, Class: 1.000000, Obj: 0.201230, No Obj: 0.004132, Avg Recall: 0.365591, count: 93 -Region Avg IOU: 0.468130, Class: 1.000000, Obj: 0.250068, No Obj: 0.005210, Avg Recall: 0.479675, count: 123 -Region Avg IOU: 0.480509, Class: 1.000000, Obj: 0.244358, No Obj: 0.004993, Avg Recall: 0.535088, count: 114 -Region Avg IOU: 0.440993, Class: 1.000000, Obj: 0.227723, No Obj: 0.005262, Avg Recall: 0.438462, count: 130 -Region Avg IOU: 0.472918, Class: 1.000000, Obj: 0.247251, No Obj: 0.004387, Avg Recall: 0.482353, count: 85 -Region Avg IOU: 0.443733, Class: 1.000000, Obj: 0.242458, No Obj: 0.004462, Avg Recall: 0.480392, count: 102 -Region Avg IOU: 0.431197, Class: 1.000000, Obj: 0.208328, No Obj: 0.004302, Avg Recall: 0.438095, count: 105 -Region Avg IOU: 0.402106, Class: 1.000000, Obj: 0.231735, No Obj: 0.004379, Avg Recall: 0.395833, count: 96 -853: 0.505759, 0.509151 avg, 0.001000 rate, 17.328388 seconds, 218368 images -Loaded: 2.889979 seconds -Region Avg IOU: 0.433472, Class: 1.000000, Obj: 0.225468, No Obj: 0.004580, Avg Recall: 0.403361, count: 119 -Region Avg IOU: 0.455440, Class: 1.000000, Obj: 0.238514, No Obj: 0.004637, Avg Recall: 0.500000, count: 80 -Region Avg IOU: 0.391309, Class: 1.000000, Obj: 0.224578, No Obj: 0.004645, Avg Recall: 0.391667, count: 120 -Region Avg IOU: 0.371850, Class: 1.000000, Obj: 0.198920, No Obj: 0.003912, Avg Recall: 0.384615, count: 78 -Region Avg IOU: 0.445415, Class: 1.000000, Obj: 0.229001, No Obj: 0.005151, Avg Recall: 0.507143, count: 140 -Region Avg IOU: 0.433405, Class: 1.000000, Obj: 0.240468, No Obj: 0.005058, Avg Recall: 0.448000, count: 125 -Region Avg IOU: 0.438037, Class: 1.000000, Obj: 0.263381, No Obj: 0.004451, Avg Recall: 0.473214, count: 112 -Region Avg IOU: 0.418444, Class: 1.000000, Obj: 0.232105, No Obj: 0.004825, Avg Recall: 0.373016, count: 126 -854: 0.551084, 0.513344 avg, 0.001000 rate, 19.423489 seconds, 218624 images -Loaded: 2.384422 seconds -Region Avg IOU: 0.422839, Class: 1.000000, Obj: 0.194245, No Obj: 0.004643, Avg Recall: 0.441379, count: 145 -Region Avg IOU: 0.445835, Class: 1.000000, Obj: 0.214914, No Obj: 0.004640, Avg Recall: 0.516854, count: 89 -Region Avg IOU: 0.431922, Class: 1.000000, Obj: 0.261354, No Obj: 0.005109, Avg Recall: 0.528926, count: 121 -Region Avg IOU: 0.475315, Class: 1.000000, Obj: 0.236410, No Obj: 0.005259, Avg Recall: 0.481818, count: 110 -Region Avg IOU: 0.460359, Class: 1.000000, Obj: 0.250966, No Obj: 0.004773, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.438127, Class: 1.000000, Obj: 0.209238, No Obj: 0.004580, Avg Recall: 0.487805, count: 123 -Region Avg IOU: 0.430818, Class: 1.000000, Obj: 0.219614, No Obj: 0.004952, Avg Recall: 0.418367, count: 98 -Region Avg IOU: 0.412358, Class: 1.000000, Obj: 0.220572, No Obj: 0.004424, Avg Recall: 0.387755, count: 98 -855: 0.554354, 0.517445 avg, 0.001000 rate, 18.297997 seconds, 218880 images -Loaded: 3.048966 seconds -Region Avg IOU: 0.402063, Class: 1.000000, Obj: 0.225446, No Obj: 0.004435, Avg Recall: 0.348214, count: 112 -Region Avg IOU: 0.437784, Class: 1.000000, Obj: 0.221018, No Obj: 0.004903, Avg Recall: 0.481250, count: 160 -Region Avg IOU: 0.434806, Class: 1.000000, Obj: 0.238056, No Obj: 0.004211, Avg Recall: 0.452174, count: 115 -Region Avg IOU: 0.425955, Class: 1.000000, Obj: 0.228101, No Obj: 0.005288, Avg Recall: 0.440298, count: 134 -Region Avg IOU: 0.512381, Class: 1.000000, Obj: 0.265179, No Obj: 0.004888, Avg Recall: 0.565217, count: 69 -Region Avg IOU: 0.393631, Class: 1.000000, Obj: 0.245184, No Obj: 0.004222, Avg Recall: 0.352381, count: 105 -Region Avg IOU: 0.435949, Class: 1.000000, Obj: 0.246439, No Obj: 0.004900, Avg Recall: 0.469388, count: 147 -Region Avg IOU: 0.420938, Class: 1.000000, Obj: 0.257734, No Obj: 0.004861, Avg Recall: 0.428571, count: 126 -856: 0.569962, 0.522697 avg, 0.001000 rate, 21.230553 seconds, 219136 images -Loaded: 1.037189 seconds -Region Avg IOU: 0.428486, Class: 1.000000, Obj: 0.232513, No Obj: 0.004656, Avg Recall: 0.408696, count: 115 -Region Avg IOU: 0.410769, Class: 1.000000, Obj: 0.228703, No Obj: 0.005354, Avg Recall: 0.391667, count: 120 -Region Avg IOU: 0.440807, Class: 1.000000, Obj: 0.230411, No Obj: 0.004602, Avg Recall: 0.461538, count: 104 -Region Avg IOU: 0.452544, Class: 1.000000, Obj: 0.264023, No Obj: 0.004791, Avg Recall: 0.466667, count: 105 -Region Avg IOU: 0.433895, Class: 1.000000, Obj: 0.274095, No Obj: 0.004887, Avg Recall: 0.425743, count: 101 -Region Avg IOU: 0.426574, Class: 1.000000, Obj: 0.236377, No Obj: 0.004754, Avg Recall: 0.438776, count: 98 -Region Avg IOU: 0.458799, Class: 1.000000, Obj: 0.238482, No Obj: 0.004664, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.458192, Class: 1.000000, Obj: 0.239504, No Obj: 0.005011, Avg Recall: 0.500000, count: 106 -857: 0.509649, 0.521392 avg, 0.001000 rate, 19.713745 seconds, 219392 images -Loaded: 3.050775 seconds -Region Avg IOU: 0.427845, Class: 1.000000, Obj: 0.244457, No Obj: 0.004384, Avg Recall: 0.446602, count: 103 -Region Avg IOU: 0.447631, Class: 1.000000, Obj: 0.253314, No Obj: 0.004841, Avg Recall: 0.483051, count: 118 -Region Avg IOU: 0.414201, Class: 1.000000, Obj: 0.256589, No Obj: 0.004531, Avg Recall: 0.452632, count: 95 -Region Avg IOU: 0.435482, Class: 1.000000, Obj: 0.240778, No Obj: 0.005042, Avg Recall: 0.434783, count: 115 -Region Avg IOU: 0.462930, Class: 1.000000, Obj: 0.261381, No Obj: 0.004471, Avg Recall: 0.483146, count: 89 -Region Avg IOU: 0.435629, Class: 1.000000, Obj: 0.248965, No Obj: 0.005269, Avg Recall: 0.457944, count: 107 -Region Avg IOU: 0.444094, Class: 1.000000, Obj: 0.229109, No Obj: 0.005271, Avg Recall: 0.446043, count: 139 -Region Avg IOU: 0.457832, Class: 1.000000, Obj: 0.248447, No Obj: 0.004417, Avg Recall: 0.489362, count: 94 -858: 0.536929, 0.522946 avg, 0.001000 rate, 18.181547 seconds, 219648 images -Loaded: 3.364627 seconds -Region Avg IOU: 0.421903, Class: 1.000000, Obj: 0.241896, No Obj: 0.004493, Avg Recall: 0.460870, count: 115 -Region Avg IOU: 0.447627, Class: 1.000000, Obj: 0.206504, No Obj: 0.004777, Avg Recall: 0.514925, count: 134 -Region Avg IOU: 0.411169, Class: 1.000000, Obj: 0.266952, No Obj: 0.004771, Avg Recall: 0.400000, count: 120 -Region Avg IOU: 0.466696, Class: 1.000000, Obj: 0.224974, No Obj: 0.005640, Avg Recall: 0.481481, count: 135 -Region Avg IOU: 0.437493, Class: 1.000000, Obj: 0.222263, No Obj: 0.004598, Avg Recall: 0.477778, count: 90 -Region Avg IOU: 0.446500, Class: 1.000000, Obj: 0.237747, No Obj: 0.005209, Avg Recall: 0.469027, count: 113 -Region Avg IOU: 0.445842, Class: 1.000000, Obj: 0.217261, No Obj: 0.005023, Avg Recall: 0.480000, count: 125 -Region Avg IOU: 0.467046, Class: 1.000000, Obj: 0.254344, No Obj: 0.005047, Avg Recall: 0.504762, count: 105 -859: 0.574723, 0.528123 avg, 0.001000 rate, 20.896219 seconds, 219904 images -Loaded: 1.850979 seconds -Region Avg IOU: 0.465012, Class: 1.000000, Obj: 0.265531, No Obj: 0.004648, Avg Recall: 0.510638, count: 94 -Region Avg IOU: 0.497898, Class: 1.000000, Obj: 0.238800, No Obj: 0.005027, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.453197, Class: 1.000000, Obj: 0.255964, No Obj: 0.004915, Avg Recall: 0.480392, count: 102 -Region Avg IOU: 0.431272, Class: 1.000000, Obj: 0.229686, No Obj: 0.004497, Avg Recall: 0.471154, count: 104 -Region Avg IOU: 0.428544, Class: 1.000000, Obj: 0.257664, No Obj: 0.004468, Avg Recall: 0.413043, count: 92 -Region Avg IOU: 0.436943, Class: 1.000000, Obj: 0.272018, No Obj: 0.004988, Avg Recall: 0.431034, count: 116 -Region Avg IOU: 0.436554, Class: 1.000000, Obj: 0.216005, No Obj: 0.004535, Avg Recall: 0.425532, count: 94 -Region Avg IOU: 0.444584, Class: 1.000000, Obj: 0.256905, No Obj: 0.004162, Avg Recall: 0.457831, count: 83 -860: 0.488897, 0.524201 avg, 0.001000 rate, 16.841415 seconds, 220160 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 0.189864 seconds -Region Avg IOU: 0.454555, Class: 1.000000, Obj: 0.238815, No Obj: 0.004957, Avg Recall: 0.514706, count: 136 -Region Avg IOU: 0.472107, Class: 1.000000, Obj: 0.263400, No Obj: 0.004588, Avg Recall: 0.516484, count: 91 -Region Avg IOU: 0.520405, Class: 1.000000, Obj: 0.242754, No Obj: 0.004642, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.434569, Class: 1.000000, Obj: 0.238050, No Obj: 0.004247, Avg Recall: 0.485149, count: 101 -Region Avg IOU: 0.446631, Class: 1.000000, Obj: 0.287833, No Obj: 0.005139, Avg Recall: 0.437909, count: 153 -Region Avg IOU: 0.525140, Class: 1.000000, Obj: 0.283450, No Obj: 0.004565, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.490365, Class: 1.000000, Obj: 0.232538, No Obj: 0.005421, Avg Recall: 0.511628, count: 129 -Region Avg IOU: 0.482821, Class: 1.000000, Obj: 0.259456, No Obj: 0.004586, Avg Recall: 0.505155, count: 97 -861: 0.545484, 0.526329 avg, 0.001000 rate, 19.790499 seconds, 220416 images -Loaded: 1.683041 seconds -Region Avg IOU: 0.435826, Class: 1.000000, Obj: 0.259073, No Obj: 0.004659, Avg Recall: 0.462264, count: 106 -Region Avg IOU: 0.448972, Class: 1.000000, Obj: 0.269076, No Obj: 0.004912, Avg Recall: 0.457627, count: 118 -Region Avg IOU: 0.441694, Class: 1.000000, Obj: 0.259507, No Obj: 0.004676, Avg Recall: 0.444444, count: 108 -Region Avg IOU: 0.438157, Class: 1.000000, Obj: 0.269429, No Obj: 0.004633, Avg Recall: 0.516129, count: 93 -Region Avg IOU: 0.439540, Class: 1.000000, Obj: 0.249582, No Obj: 0.004904, Avg Recall: 0.491667, count: 120 -Region Avg IOU: 0.487532, Class: 1.000000, Obj: 0.222724, No Obj: 0.004100, Avg Recall: 0.559140, count: 93 -Region Avg IOU: 0.464233, Class: 1.000000, Obj: 0.281897, No Obj: 0.004859, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.446723, Class: 1.000000, Obj: 0.221206, No Obj: 0.004365, Avg Recall: 0.445455, count: 110 -862: 0.569703, 0.530667 avg, 0.001000 rate, 21.714272 seconds, 220672 images -Loaded: 1.599948 seconds -Region Avg IOU: 0.453601, Class: 1.000000, Obj: 0.251260, No Obj: 0.004976, Avg Recall: 0.483221, count: 149 -Region Avg IOU: 0.464100, Class: 1.000000, Obj: 0.276837, No Obj: 0.004747, Avg Recall: 0.472000, count: 125 -Region Avg IOU: 0.483807, Class: 1.000000, Obj: 0.260289, No Obj: 0.004723, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.467208, Class: 1.000000, Obj: 0.264525, No Obj: 0.004558, Avg Recall: 0.540230, count: 87 -Region Avg IOU: 0.448713, Class: 1.000000, Obj: 0.243136, No Obj: 0.003899, Avg Recall: 0.487805, count: 82 -Region Avg IOU: 0.449304, Class: 1.000000, Obj: 0.271691, No Obj: 0.005568, Avg Recall: 0.442029, count: 138 -Region Avg IOU: 0.446189, Class: 1.000000, Obj: 0.247405, No Obj: 0.005390, Avg Recall: 0.481481, count: 135 -Region Avg IOU: 0.508492, Class: 1.000000, Obj: 0.225916, No Obj: 0.005009, Avg Recall: 0.537879, count: 132 -863: 0.538447, 0.531445 avg, 0.001000 rate, 21.795795 seconds, 220928 images -Loaded: 1.200100 seconds -Region Avg IOU: 0.470539, Class: 1.000000, Obj: 0.273630, No Obj: 0.004669, Avg Recall: 0.487603, count: 121 -Region Avg IOU: 0.484903, Class: 1.000000, Obj: 0.240833, No Obj: 0.004628, Avg Recall: 0.567568, count: 111 -Region Avg IOU: 0.454127, Class: 1.000000, Obj: 0.244177, No Obj: 0.004005, Avg Recall: 0.457143, count: 105 -Region Avg IOU: 0.441211, Class: 1.000000, Obj: 0.237195, No Obj: 0.004269, Avg Recall: 0.494253, count: 87 -Region Avg IOU: 0.480399, Class: 1.000000, Obj: 0.265739, No Obj: 0.005058, Avg Recall: 0.503876, count: 129 -Region Avg IOU: 0.509976, Class: 1.000000, Obj: 0.226018, No Obj: 0.004966, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.469155, Class: 1.000000, Obj: 0.257155, No Obj: 0.004619, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.421894, Class: 1.000000, Obj: 0.249574, No Obj: 0.004623, Avg Recall: 0.419847, count: 131 -864: 0.566424, 0.534943 avg, 0.001000 rate, 20.698673 seconds, 221184 images -Loaded: 0.482577 seconds -Region Avg IOU: 0.497757, Class: 1.000000, Obj: 0.268731, No Obj: 0.004661, Avg Recall: 0.563492, count: 126 -Region Avg IOU: 0.428614, Class: 1.000000, Obj: 0.218030, No Obj: 0.004100, Avg Recall: 0.428571, count: 119 -Region Avg IOU: 0.511988, Class: 1.000000, Obj: 0.270445, No Obj: 0.004874, Avg Recall: 0.585106, count: 94 -Region Avg IOU: 0.440479, Class: 1.000000, Obj: 0.237576, No Obj: 0.004583, Avg Recall: 0.447619, count: 105 -Region Avg IOU: 0.505163, Class: 1.000000, Obj: 0.273861, No Obj: 0.004505, Avg Recall: 0.562500, count: 96 -Region Avg IOU: 0.502056, Class: 1.000000, Obj: 0.282723, No Obj: 0.004643, Avg Recall: 0.566372, count: 113 -Region Avg IOU: 0.495854, Class: 1.000000, Obj: 0.319507, No Obj: 0.004424, Avg Recall: 0.521739, count: 92 -Region Avg IOU: 0.489658, Class: 1.000000, Obj: 0.244823, No Obj: 0.005213, Avg Recall: 0.522936, count: 109 -865: 0.472166, 0.528665 avg, 0.001000 rate, 22.502333 seconds, 221440 images -Loaded: 0.164856 seconds -Region Avg IOU: 0.440044, Class: 1.000000, Obj: 0.228224, No Obj: 0.005255, Avg Recall: 0.441379, count: 145 -Region Avg IOU: 0.491259, Class: 1.000000, Obj: 0.274129, No Obj: 0.004942, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.487398, Class: 1.000000, Obj: 0.268120, No Obj: 0.005031, Avg Recall: 0.558140, count: 129 -Region Avg IOU: 0.444411, Class: 1.000000, Obj: 0.218142, No Obj: 0.004585, Avg Recall: 0.471698, count: 106 -Region Avg IOU: 0.495462, Class: 1.000000, Obj: 0.255077, No Obj: 0.004112, Avg Recall: 0.510870, count: 92 -Region Avg IOU: 0.482327, Class: 1.000000, Obj: 0.253108, No Obj: 0.004845, Avg Recall: 0.533333, count: 135 -Region Avg IOU: 0.452164, Class: 1.000000, Obj: 0.207172, No Obj: 0.004887, Avg Recall: 0.454545, count: 121 -Region Avg IOU: 0.444010, Class: 1.000000, Obj: 0.267368, No Obj: 0.005092, Avg Recall: 0.428571, count: 98 -866: 0.592084, 0.535007 avg, 0.001000 rate, 22.782766 seconds, 221696 images -Loaded: 0.399571 seconds -Region Avg IOU: 0.438736, Class: 1.000000, Obj: 0.246616, No Obj: 0.004165, Avg Recall: 0.412698, count: 63 -Region Avg IOU: 0.476435, Class: 1.000000, Obj: 0.199940, No Obj: 0.004702, Avg Recall: 0.512821, count: 117 -Region Avg IOU: 0.436796, Class: 1.000000, Obj: 0.266418, No Obj: 0.004938, Avg Recall: 0.453846, count: 130 -Region Avg IOU: 0.459171, Class: 1.000000, Obj: 0.220505, No Obj: 0.004898, Avg Recall: 0.453704, count: 108 -Region Avg IOU: 0.459613, Class: 1.000000, Obj: 0.230925, No Obj: 0.004674, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.424266, Class: 1.000000, Obj: 0.243686, No Obj: 0.004798, Avg Recall: 0.396947, count: 131 -Region Avg IOU: 0.463118, Class: 1.000000, Obj: 0.275702, No Obj: 0.004652, Avg Recall: 0.500000, count: 114 -Region Avg IOU: 0.437394, Class: 1.000000, Obj: 0.236319, No Obj: 0.004393, Avg Recall: 0.508333, count: 120 -867: 0.492776, 0.530784 avg, 0.001000 rate, 19.588446 seconds, 221952 images -Loaded: 2.921281 seconds -Region Avg IOU: 0.521983, Class: 1.000000, Obj: 0.265841, No Obj: 0.004871, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.434755, Class: 1.000000, Obj: 0.249952, No Obj: 0.004818, Avg Recall: 0.463636, count: 110 -Region Avg IOU: 0.522609, Class: 1.000000, Obj: 0.275939, No Obj: 0.004617, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.484983, Class: 1.000000, Obj: 0.233898, No Obj: 0.004844, Avg Recall: 0.548387, count: 93 -Region Avg IOU: 0.439000, Class: 1.000000, Obj: 0.218298, No Obj: 0.004487, Avg Recall: 0.438095, count: 105 -Region Avg IOU: 0.450186, Class: 1.000000, Obj: 0.244663, No Obj: 0.004591, Avg Recall: 0.487180, count: 117 -Region Avg IOU: 0.475454, Class: 1.000000, Obj: 0.250698, No Obj: 0.004349, Avg Recall: 0.457447, count: 94 -Region Avg IOU: 0.455725, Class: 1.000000, Obj: 0.216447, No Obj: 0.004683, Avg Recall: 0.492308, count: 130 -868: 0.525668, 0.530272 avg, 0.001000 rate, 21.392202 seconds, 222208 images -Loaded: 1.492547 seconds -Region Avg IOU: 0.443439, Class: 1.000000, Obj: 0.247689, No Obj: 0.004111, Avg Recall: 0.455446, count: 101 -Region Avg IOU: 0.479212, Class: 1.000000, Obj: 0.294141, No Obj: 0.004644, Avg Recall: 0.504762, count: 105 -Region Avg IOU: 0.467309, Class: 1.000000, Obj: 0.221017, No Obj: 0.004383, Avg Recall: 0.495935, count: 123 -Region Avg IOU: 0.490366, Class: 1.000000, Obj: 0.257457, No Obj: 0.005014, Avg Recall: 0.544776, count: 134 -Region Avg IOU: 0.473298, Class: 1.000000, Obj: 0.260880, No Obj: 0.004500, Avg Recall: 0.481481, count: 108 -Region Avg IOU: 0.454038, Class: 1.000000, Obj: 0.242103, No Obj: 0.004415, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.476166, Class: 1.000000, Obj: 0.264865, No Obj: 0.005011, Avg Recall: 0.508065, count: 124 -Region Avg IOU: 0.452989, Class: 1.000000, Obj: 0.261225, No Obj: 0.004095, Avg Recall: 0.428571, count: 98 -869: 0.514800, 0.528725 avg, 0.001000 rate, 21.509384 seconds, 222464 images -Loaded: 0.000048 seconds -Region Avg IOU: 0.475239, Class: 1.000000, Obj: 0.256140, No Obj: 0.004129, Avg Recall: 0.528090, count: 89 -Region Avg IOU: 0.420743, Class: 1.000000, Obj: 0.210084, No Obj: 0.004297, Avg Recall: 0.428571, count: 98 -Region Avg IOU: 0.450612, Class: 1.000000, Obj: 0.233663, No Obj: 0.004545, Avg Recall: 0.485981, count: 107 -Region Avg IOU: 0.499365, Class: 1.000000, Obj: 0.217075, No Obj: 0.004163, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.428160, Class: 1.000000, Obj: 0.252999, No Obj: 0.003801, Avg Recall: 0.455446, count: 101 -Region Avg IOU: 0.501476, Class: 1.000000, Obj: 0.264025, No Obj: 0.004347, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.479236, Class: 1.000000, Obj: 0.228814, No Obj: 0.004286, Avg Recall: 0.478261, count: 92 -Region Avg IOU: 0.441911, Class: 1.000000, Obj: 0.288676, No Obj: 0.003589, Avg Recall: 0.463415, count: 82 -870: 0.446821, 0.520535 avg, 0.001000 rate, 21.601835 seconds, 222720 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000067 seconds -Region Avg IOU: 0.484083, Class: 1.000000, Obj: 0.256459, No Obj: 0.004159, Avg Recall: 0.515385, count: 130 -Region Avg IOU: 0.539265, Class: 1.000000, Obj: 0.268428, No Obj: 0.003860, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.549038, Class: 1.000000, Obj: 0.268266, No Obj: 0.004406, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.499344, Class: 1.000000, Obj: 0.236406, No Obj: 0.003807, Avg Recall: 0.548387, count: 93 -Region Avg IOU: 0.484720, Class: 1.000000, Obj: 0.253042, No Obj: 0.003918, Avg Recall: 0.550847, count: 118 -Region Avg IOU: 0.541098, Class: 1.000000, Obj: 0.277102, No Obj: 0.004084, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.472873, Class: 1.000000, Obj: 0.302896, No Obj: 0.004057, Avg Recall: 0.428571, count: 112 -Region Avg IOU: 0.466105, Class: 1.000000, Obj: 0.213800, No Obj: 0.003806, Avg Recall: 0.494118, count: 85 -871: 0.477133, 0.516194 avg, 0.001000 rate, 24.156281 seconds, 222976 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.532912, Class: 1.000000, Obj: 0.254479, No Obj: 0.004092, Avg Recall: 0.650602, count: 83 -Region Avg IOU: 0.538058, Class: 1.000000, Obj: 0.255841, No Obj: 0.004376, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.548822, Class: 1.000000, Obj: 0.224727, No Obj: 0.004408, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.526287, Class: 1.000000, Obj: 0.280880, No Obj: 0.003802, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.504400, Class: 1.000000, Obj: 0.292159, No Obj: 0.003704, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.497869, Class: 1.000000, Obj: 0.243541, No Obj: 0.004171, Avg Recall: 0.572581, count: 124 -Region Avg IOU: 0.580710, Class: 1.000000, Obj: 0.290715, No Obj: 0.003925, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.524308, Class: 1.000000, Obj: 0.271314, No Obj: 0.004243, Avg Recall: 0.620000, count: 100 -872: 0.510266, 0.515602 avg, 0.001000 rate, 23.836824 seconds, 223232 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.546379, Class: 1.000000, Obj: 0.271729, No Obj: 0.003989, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.485081, Class: 1.000000, Obj: 0.232943, No Obj: 0.003826, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.490304, Class: 1.000000, Obj: 0.246969, No Obj: 0.003984, Avg Recall: 0.525424, count: 118 -Region Avg IOU: 0.517914, Class: 1.000000, Obj: 0.247124, No Obj: 0.003865, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.490291, Class: 1.000000, Obj: 0.275966, No Obj: 0.004106, Avg Recall: 0.515385, count: 130 -Region Avg IOU: 0.508801, Class: 1.000000, Obj: 0.262556, No Obj: 0.003803, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.466644, Class: 1.000000, Obj: 0.218145, No Obj: 0.003606, Avg Recall: 0.483871, count: 93 -Region Avg IOU: 0.486421, Class: 1.000000, Obj: 0.291414, No Obj: 0.003688, Avg Recall: 0.565217, count: 92 -873: 0.484553, 0.512497 avg, 0.001000 rate, 24.277660 seconds, 223488 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.513631, Class: 1.000000, Obj: 0.239082, No Obj: 0.004305, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.521435, Class: 1.000000, Obj: 0.303314, No Obj: 0.003744, Avg Recall: 0.565657, count: 99 -Region Avg IOU: 0.503753, Class: 1.000000, Obj: 0.234229, No Obj: 0.003651, Avg Recall: 0.533333, count: 105 -Region Avg IOU: 0.545917, Class: 1.000000, Obj: 0.242737, No Obj: 0.003824, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.555914, Class: 1.000000, Obj: 0.235115, No Obj: 0.004437, Avg Recall: 0.606299, count: 127 -Region Avg IOU: 0.601090, Class: 1.000000, Obj: 0.287086, No Obj: 0.004301, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.504525, Class: 1.000000, Obj: 0.265142, No Obj: 0.003897, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.463739, Class: 1.000000, Obj: 0.241264, No Obj: 0.003942, Avg Recall: 0.483871, count: 93 -874: 0.461682, 0.507415 avg, 0.001000 rate, 24.728987 seconds, 223744 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.511641, Class: 1.000000, Obj: 0.230695, No Obj: 0.004048, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.519388, Class: 1.000000, Obj: 0.280469, No Obj: 0.003634, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.547879, Class: 1.000000, Obj: 0.234478, No Obj: 0.003835, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.486335, Class: 1.000000, Obj: 0.234682, No Obj: 0.003816, Avg Recall: 0.514851, count: 101 -Region Avg IOU: 0.506626, Class: 1.000000, Obj: 0.254799, No Obj: 0.003523, Avg Recall: 0.553398, count: 103 -Region Avg IOU: 0.552611, Class: 1.000000, Obj: 0.268430, No Obj: 0.003759, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.543291, Class: 1.000000, Obj: 0.264115, No Obj: 0.003772, Avg Recall: 0.652174, count: 92 -Region Avg IOU: 0.506572, Class: 1.000000, Obj: 0.271225, No Obj: 0.003639, Avg Recall: 0.550459, count: 109 -875: 0.519579, 0.508632 avg, 0.001000 rate, 23.995876 seconds, 224000 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.513635, Class: 1.000000, Obj: 0.285667, No Obj: 0.003583, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.555590, Class: 1.000000, Obj: 0.307106, No Obj: 0.003828, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.542846, Class: 1.000000, Obj: 0.290364, No Obj: 0.003839, Avg Recall: 0.627119, count: 118 -Region Avg IOU: 0.503127, Class: 1.000000, Obj: 0.251689, No Obj: 0.003861, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.507016, Class: 1.000000, Obj: 0.271859, No Obj: 0.004052, Avg Recall: 0.605769, count: 104 -Region Avg IOU: 0.516379, Class: 1.000000, Obj: 0.212203, No Obj: 0.003780, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.495252, Class: 1.000000, Obj: 0.253812, No Obj: 0.004614, Avg Recall: 0.557971, count: 138 -Region Avg IOU: 0.511743, Class: 1.000000, Obj: 0.222438, No Obj: 0.003810, Avg Recall: 0.594828, count: 116 -876: 0.541792, 0.511948 avg, 0.001000 rate, 24.607496 seconds, 224256 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.495318, Class: 1.000000, Obj: 0.244736, No Obj: 0.004017, Avg Recall: 0.568000, count: 125 -Region Avg IOU: 0.518612, Class: 1.000000, Obj: 0.263514, No Obj: 0.004199, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.492113, Class: 1.000000, Obj: 0.226735, No Obj: 0.003511, Avg Recall: 0.554217, count: 83 -Region Avg IOU: 0.513024, Class: 1.000000, Obj: 0.250953, No Obj: 0.003786, Avg Recall: 0.578947, count: 114 -Region Avg IOU: 0.552933, Class: 1.000000, Obj: 0.297469, No Obj: 0.004629, Avg Recall: 0.648438, count: 128 -Region Avg IOU: 0.552477, Class: 1.000000, Obj: 0.240369, No Obj: 0.004356, Avg Recall: 0.652174, count: 92 -Region Avg IOU: 0.515648, Class: 1.000000, Obj: 0.259411, No Obj: 0.003866, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.488324, Class: 1.000000, Obj: 0.236073, No Obj: 0.003781, Avg Recall: 0.527273, count: 110 -877: 0.496068, 0.510360 avg, 0.001000 rate, 23.905376 seconds, 224512 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.502539, Class: 1.000000, Obj: 0.229861, No Obj: 0.004247, Avg Recall: 0.574627, count: 134 -Region Avg IOU: 0.537592, Class: 1.000000, Obj: 0.258431, No Obj: 0.004404, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.540130, Class: 1.000000, Obj: 0.245542, No Obj: 0.003981, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.488223, Class: 1.000000, Obj: 0.272605, No Obj: 0.003930, Avg Recall: 0.524194, count: 124 -Region Avg IOU: 0.547220, Class: 1.000000, Obj: 0.266710, No Obj: 0.004129, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.504364, Class: 1.000000, Obj: 0.218953, No Obj: 0.003954, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.548063, Class: 1.000000, Obj: 0.269676, No Obj: 0.004872, Avg Recall: 0.614815, count: 135 -Region Avg IOU: 0.450472, Class: 1.000000, Obj: 0.269645, No Obj: 0.003845, Avg Recall: 0.476635, count: 107 -878: 0.546855, 0.514009 avg, 0.001000 rate, 24.464264 seconds, 224768 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.486516, Class: 1.000000, Obj: 0.283495, No Obj: 0.003786, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.454707, Class: 1.000000, Obj: 0.232448, No Obj: 0.003509, Avg Recall: 0.489583, count: 96 -Region Avg IOU: 0.500214, Class: 1.000000, Obj: 0.246423, No Obj: 0.004320, Avg Recall: 0.573770, count: 122 -Region Avg IOU: 0.484763, Class: 1.000000, Obj: 0.289121, No Obj: 0.003931, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.509359, Class: 1.000000, Obj: 0.265383, No Obj: 0.003519, Avg Recall: 0.536082, count: 97 -Region Avg IOU: 0.547373, Class: 1.000000, Obj: 0.282781, No Obj: 0.004463, Avg Recall: 0.596774, count: 124 -Region Avg IOU: 0.534858, Class: 1.000000, Obj: 0.264059, No Obj: 0.004217, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.538339, Class: 1.000000, Obj: 0.282883, No Obj: 0.003895, Avg Recall: 0.593407, count: 91 -879: 0.505116, 0.513120 avg, 0.001000 rate, 24.254511 seconds, 225024 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.511476, Class: 1.000000, Obj: 0.256594, No Obj: 0.003776, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.467774, Class: 1.000000, Obj: 0.224743, No Obj: 0.003605, Avg Recall: 0.537634, count: 93 -Region Avg IOU: 0.522011, Class: 1.000000, Obj: 0.273245, No Obj: 0.004147, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.531993, Class: 1.000000, Obj: 0.289514, No Obj: 0.003911, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.516117, Class: 1.000000, Obj: 0.261306, No Obj: 0.004143, Avg Recall: 0.552000, count: 125 -Region Avg IOU: 0.547368, Class: 1.000000, Obj: 0.278663, No Obj: 0.004132, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.494589, Class: 1.000000, Obj: 0.251425, No Obj: 0.004208, Avg Recall: 0.590164, count: 122 -Region Avg IOU: 0.506686, Class: 1.000000, Obj: 0.267152, No Obj: 0.004353, Avg Recall: 0.529412, count: 102 -880: 0.510379, 0.512846 avg, 0.001000 rate, 23.823053 seconds, 225280 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.916752 seconds -Region Avg IOU: 0.461329, Class: 1.000000, Obj: 0.253785, No Obj: 0.004110, Avg Recall: 0.506173, count: 81 -Region Avg IOU: 0.484655, Class: 1.000000, Obj: 0.246571, No Obj: 0.004211, Avg Recall: 0.538462, count: 104 -Region Avg IOU: 0.463370, Class: 1.000000, Obj: 0.237579, No Obj: 0.004412, Avg Recall: 0.483871, count: 124 -Region Avg IOU: 0.498259, Class: 1.000000, Obj: 0.282211, No Obj: 0.003972, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.462600, Class: 1.000000, Obj: 0.245741, No Obj: 0.004891, Avg Recall: 0.506944, count: 144 -Region Avg IOU: 0.423985, Class: 1.000000, Obj: 0.265961, No Obj: 0.004442, Avg Recall: 0.433333, count: 120 -Region Avg IOU: 0.436589, Class: 1.000000, Obj: 0.261510, No Obj: 0.004250, Avg Recall: 0.509615, count: 104 -Region Avg IOU: 0.431145, Class: 1.000000, Obj: 0.198603, No Obj: 0.004702, Avg Recall: 0.452229, count: 157 -881: 0.525468, 0.514108 avg, 0.001000 rate, 22.176548 seconds, 225536 images -Loaded: 0.029238 seconds -Region Avg IOU: 0.524398, Class: 1.000000, Obj: 0.276796, No Obj: 0.004402, Avg Recall: 0.585366, count: 82 -Region Avg IOU: 0.503039, Class: 1.000000, Obj: 0.201150, No Obj: 0.004637, Avg Recall: 0.594828, count: 116 -Region Avg IOU: 0.486839, Class: 1.000000, Obj: 0.277500, No Obj: 0.003951, Avg Recall: 0.545455, count: 77 -Region Avg IOU: 0.486235, Class: 1.000000, Obj: 0.240931, No Obj: 0.004084, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.446765, Class: 1.000000, Obj: 0.221815, No Obj: 0.004406, Avg Recall: 0.431034, count: 116 -Region Avg IOU: 0.423080, Class: 1.000000, Obj: 0.222043, No Obj: 0.003748, Avg Recall: 0.467391, count: 92 -Region Avg IOU: 0.471233, Class: 1.000000, Obj: 0.221810, No Obj: 0.004052, Avg Recall: 0.504202, count: 119 -Region Avg IOU: 0.491438, Class: 1.000000, Obj: 0.261391, No Obj: 0.004642, Avg Recall: 0.523364, count: 107 -882: 0.453526, 0.508050 avg, 0.001000 rate, 21.261885 seconds, 225792 images -Loaded: 1.233635 seconds -Region Avg IOU: 0.454259, Class: 1.000000, Obj: 0.250138, No Obj: 0.004369, Avg Recall: 0.468468, count: 111 -Region Avg IOU: 0.431251, Class: 1.000000, Obj: 0.205893, No Obj: 0.003611, Avg Recall: 0.457447, count: 94 -Region Avg IOU: 0.488997, Class: 1.000000, Obj: 0.226648, No Obj: 0.004219, Avg Recall: 0.547009, count: 117 -Region Avg IOU: 0.501126, Class: 1.000000, Obj: 0.255582, No Obj: 0.004813, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.456139, Class: 1.000000, Obj: 0.206703, No Obj: 0.004870, Avg Recall: 0.510949, count: 137 -Region Avg IOU: 0.463587, Class: 1.000000, Obj: 0.242158, No Obj: 0.004135, Avg Recall: 0.481132, count: 106 -Region Avg IOU: 0.492692, Class: 1.000000, Obj: 0.215831, No Obj: 0.004134, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.471311, Class: 1.000000, Obj: 0.218140, No Obj: 0.004280, Avg Recall: 0.513274, count: 113 -883: 0.574661, 0.514711 avg, 0.001000 rate, 17.893810 seconds, 226048 images -Loaded: 3.649591 seconds -Region Avg IOU: 0.451088, Class: 1.000000, Obj: 0.208128, No Obj: 0.004983, Avg Recall: 0.489510, count: 143 -Region Avg IOU: 0.432770, Class: 1.000000, Obj: 0.238708, No Obj: 0.004212, Avg Recall: 0.372549, count: 102 -Region Avg IOU: 0.457861, Class: 1.000000, Obj: 0.214526, No Obj: 0.004334, Avg Recall: 0.500000, count: 108 -Region Avg IOU: 0.440885, Class: 1.000000, Obj: 0.232474, No Obj: 0.004143, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.482623, Class: 1.000000, Obj: 0.224478, No Obj: 0.004783, Avg Recall: 0.526786, count: 112 -Region Avg IOU: 0.481272, Class: 1.000000, Obj: 0.241508, No Obj: 0.005083, Avg Recall: 0.571429, count: 126 -Region Avg IOU: 0.426050, Class: 1.000000, Obj: 0.232759, No Obj: 0.004003, Avg Recall: 0.396226, count: 106 -Region Avg IOU: 0.468072, Class: 1.000000, Obj: 0.240128, No Obj: 0.004001, Avg Recall: 0.480769, count: 104 -884: 0.509453, 0.514185 avg, 0.001000 rate, 21.678520 seconds, 226304 images -Loaded: 0.000055 seconds -Region Avg IOU: 0.468225, Class: 1.000000, Obj: 0.216606, No Obj: 0.004574, Avg Recall: 0.477941, count: 136 -Region Avg IOU: 0.428811, Class: 1.000000, Obj: 0.240053, No Obj: 0.004611, Avg Recall: 0.398374, count: 123 -Region Avg IOU: 0.493712, Class: 1.000000, Obj: 0.275718, No Obj: 0.004217, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.477869, Class: 1.000000, Obj: 0.251095, No Obj: 0.003479, Avg Recall: 0.521739, count: 92 -Region Avg IOU: 0.475193, Class: 1.000000, Obj: 0.272197, No Obj: 0.004229, Avg Recall: 0.458333, count: 96 -Region Avg IOU: 0.420716, Class: 1.000000, Obj: 0.218057, No Obj: 0.004010, Avg Recall: 0.480392, count: 102 -Region Avg IOU: 0.499293, Class: 1.000000, Obj: 0.272177, No Obj: 0.005185, Avg Recall: 0.521739, count: 138 -Region Avg IOU: 0.472286, Class: 1.000000, Obj: 0.257514, No Obj: 0.005030, Avg Recall: 0.500000, count: 150 -885: 0.545803, 0.517347 avg, 0.001000 rate, 20.201944 seconds, 226560 images -Loaded: 1.110783 seconds -Region Avg IOU: 0.470956, Class: 1.000000, Obj: 0.250920, No Obj: 0.004410, Avg Recall: 0.526316, count: 95 -Region Avg IOU: 0.464810, Class: 1.000000, Obj: 0.264937, No Obj: 0.005133, Avg Recall: 0.533333, count: 105 -Region Avg IOU: 0.441932, Class: 1.000000, Obj: 0.228590, No Obj: 0.004249, Avg Recall: 0.454545, count: 110 -Region Avg IOU: 0.472104, Class: 1.000000, Obj: 0.259611, No Obj: 0.004406, Avg Recall: 0.495413, count: 109 -Region Avg IOU: 0.501490, Class: 1.000000, Obj: 0.246013, No Obj: 0.004319, Avg Recall: 0.516484, count: 91 -Region Avg IOU: 0.445178, Class: 1.000000, Obj: 0.248459, No Obj: 0.004448, Avg Recall: 0.458333, count: 120 -Region Avg IOU: 0.496674, Class: 1.000000, Obj: 0.262780, No Obj: 0.004561, Avg Recall: 0.567797, count: 118 -Region Avg IOU: 0.442937, Class: 1.000000, Obj: 0.230402, No Obj: 0.004426, Avg Recall: 0.412698, count: 126 -886: 0.478957, 0.513508 avg, 0.001000 rate, 19.500544 seconds, 226816 images -Loaded: 1.229623 seconds -Region Avg IOU: 0.481002, Class: 1.000000, Obj: 0.259955, No Obj: 0.004477, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.480593, Class: 1.000000, Obj: 0.275373, No Obj: 0.004179, Avg Recall: 0.543210, count: 81 -Region Avg IOU: 0.435332, Class: 1.000000, Obj: 0.258825, No Obj: 0.004168, Avg Recall: 0.457143, count: 105 -Region Avg IOU: 0.470836, Class: 1.000000, Obj: 0.269185, No Obj: 0.004285, Avg Recall: 0.489796, count: 98 -Region Avg IOU: 0.458586, Class: 1.000000, Obj: 0.269439, No Obj: 0.004287, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.480251, Class: 1.000000, Obj: 0.286105, No Obj: 0.004533, Avg Recall: 0.542857, count: 105 -Region Avg IOU: 0.516616, Class: 1.000000, Obj: 0.280947, No Obj: 0.004370, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.454547, Class: 1.000000, Obj: 0.259445, No Obj: 0.004231, Avg Recall: 0.504587, count: 109 -887: 0.495776, 0.511735 avg, 0.001000 rate, 21.037413 seconds, 227072 images -Loaded: 1.186684 seconds -Region Avg IOU: 0.490531, Class: 1.000000, Obj: 0.289935, No Obj: 0.004495, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.457794, Class: 1.000000, Obj: 0.312972, No Obj: 0.004600, Avg Recall: 0.473684, count: 114 -Region Avg IOU: 0.462300, Class: 1.000000, Obj: 0.254544, No Obj: 0.004930, Avg Recall: 0.490566, count: 106 -Region Avg IOU: 0.456617, Class: 1.000000, Obj: 0.255044, No Obj: 0.004468, Avg Recall: 0.457143, count: 105 -Region Avg IOU: 0.496277, Class: 1.000000, Obj: 0.258655, No Obj: 0.004981, Avg Recall: 0.540146, count: 137 -Region Avg IOU: 0.489252, Class: 1.000000, Obj: 0.260911, No Obj: 0.004779, Avg Recall: 0.519231, count: 104 -Region Avg IOU: 0.481830, Class: 1.000000, Obj: 0.263678, No Obj: 0.004292, Avg Recall: 0.533333, count: 105 -Region Avg IOU: 0.445707, Class: 1.000000, Obj: 0.296960, No Obj: 0.004347, Avg Recall: 0.484536, count: 97 -888: 0.468860, 0.507447 avg, 0.001000 rate, 18.400757 seconds, 227328 images -Loaded: 2.645630 seconds -Region Avg IOU: 0.456146, Class: 1.000000, Obj: 0.285186, No Obj: 0.004501, Avg Recall: 0.474227, count: 97 -Region Avg IOU: 0.522362, Class: 1.000000, Obj: 0.298299, No Obj: 0.004118, Avg Recall: 0.597403, count: 77 -Region Avg IOU: 0.488826, Class: 1.000000, Obj: 0.258371, No Obj: 0.004903, Avg Recall: 0.533898, count: 118 -Region Avg IOU: 0.464400, Class: 1.000000, Obj: 0.279900, No Obj: 0.004202, Avg Recall: 0.505263, count: 95 -Region Avg IOU: 0.478110, Class: 1.000000, Obj: 0.283647, No Obj: 0.004838, Avg Recall: 0.524272, count: 103 -Region Avg IOU: 0.454122, Class: 1.000000, Obj: 0.250676, No Obj: 0.004993, Avg Recall: 0.459016, count: 122 -Region Avg IOU: 0.438042, Class: 1.000000, Obj: 0.276053, No Obj: 0.004778, Avg Recall: 0.449153, count: 118 -Region Avg IOU: 0.471864, Class: 1.000000, Obj: 0.287231, No Obj: 0.004102, Avg Recall: 0.483146, count: 89 -889: 0.478665, 0.504569 avg, 0.001000 rate, 19.417980 seconds, 227584 images -Loaded: 2.005959 seconds -Region Avg IOU: 0.476517, Class: 1.000000, Obj: 0.261854, No Obj: 0.003936, Avg Recall: 0.578313, count: 83 -Region Avg IOU: 0.421853, Class: 1.000000, Obj: 0.252925, No Obj: 0.004513, Avg Recall: 0.401786, count: 112 -Region Avg IOU: 0.458797, Class: 1.000000, Obj: 0.266746, No Obj: 0.004270, Avg Recall: 0.483871, count: 93 -Region Avg IOU: 0.406633, Class: 1.000000, Obj: 0.252863, No Obj: 0.004493, Avg Recall: 0.412698, count: 126 -Region Avg IOU: 0.493651, Class: 1.000000, Obj: 0.265687, No Obj: 0.004657, Avg Recall: 0.514851, count: 101 -Region Avg IOU: 0.457183, Class: 1.000000, Obj: 0.212266, No Obj: 0.004644, Avg Recall: 0.486726, count: 113 -Region Avg IOU: 0.482926, Class: 1.000000, Obj: 0.290203, No Obj: 0.004638, Avg Recall: 0.517647, count: 85 -Region Avg IOU: 0.478187, Class: 1.000000, Obj: 0.253083, No Obj: 0.004649, Avg Recall: 0.546296, count: 108 -890: 0.516334, 0.505746 avg, 0.001000 rate, 22.080416 seconds, 227840 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000065 seconds -Region Avg IOU: 0.531501, Class: 1.000000, Obj: 0.246876, No Obj: 0.003670, Avg Recall: 0.623529, count: 85 -Region Avg IOU: 0.569892, Class: 1.000000, Obj: 0.313766, No Obj: 0.004053, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.548110, Class: 1.000000, Obj: 0.301438, No Obj: 0.004194, Avg Recall: 0.652542, count: 118 -Region Avg IOU: 0.560172, Class: 1.000000, Obj: 0.287143, No Obj: 0.004171, Avg Recall: 0.664062, count: 128 -Region Avg IOU: 0.594147, Class: 1.000000, Obj: 0.322939, No Obj: 0.004195, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.537218, Class: 1.000000, Obj: 0.325514, No Obj: 0.003938, Avg Recall: 0.596491, count: 114 -Region Avg IOU: 0.572033, Class: 1.000000, Obj: 0.246450, No Obj: 0.003940, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.531466, Class: 1.000000, Obj: 0.311552, No Obj: 0.003787, Avg Recall: 0.590164, count: 122 -891: 0.499399, 0.505111 avg, 0.001000 rate, 25.656858 seconds, 228096 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.527922, Class: 1.000000, Obj: 0.306187, No Obj: 0.003667, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.523462, Class: 1.000000, Obj: 0.274076, No Obj: 0.003499, Avg Recall: 0.577778, count: 90 -Region Avg IOU: 0.529702, Class: 1.000000, Obj: 0.254087, No Obj: 0.003600, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.558913, Class: 1.000000, Obj: 0.278129, No Obj: 0.003814, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.554011, Class: 1.000000, Obj: 0.305714, No Obj: 0.004081, Avg Recall: 0.643939, count: 132 -Region Avg IOU: 0.558399, Class: 1.000000, Obj: 0.295417, No Obj: 0.003703, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.574753, Class: 1.000000, Obj: 0.296320, No Obj: 0.004152, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.547081, Class: 1.000000, Obj: 0.274190, No Obj: 0.003503, Avg Recall: 0.621622, count: 111 -892: 0.480686, 0.502668 avg, 0.001000 rate, 26.678493 seconds, 228352 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.542254, Class: 1.000000, Obj: 0.226796, No Obj: 0.003472, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.545677, Class: 1.000000, Obj: 0.202208, No Obj: 0.003498, Avg Recall: 0.633333, count: 90 -Region Avg IOU: 0.543885, Class: 1.000000, Obj: 0.227188, No Obj: 0.003624, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.546260, Class: 1.000000, Obj: 0.239698, No Obj: 0.003895, Avg Recall: 0.634328, count: 134 -Region Avg IOU: 0.568554, Class: 1.000000, Obj: 0.222688, No Obj: 0.003599, Avg Recall: 0.683099, count: 142 -Region Avg IOU: 0.592106, Class: 1.000000, Obj: 0.266534, No Obj: 0.003443, Avg Recall: 0.804598, count: 87 -Region Avg IOU: 0.536815, Class: 1.000000, Obj: 0.222296, No Obj: 0.003966, Avg Recall: 0.619403, count: 134 -Region Avg IOU: 0.562212, Class: 1.000000, Obj: 0.287977, No Obj: 0.003705, Avg Recall: 0.660377, count: 106 -893: 0.537540, 0.506156 avg, 0.001000 rate, 26.789375 seconds, 228608 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.556941, Class: 1.000000, Obj: 0.252972, No Obj: 0.003549, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.577994, Class: 1.000000, Obj: 0.231756, No Obj: 0.003329, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.546966, Class: 1.000000, Obj: 0.227120, No Obj: 0.003357, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.533812, Class: 1.000000, Obj: 0.217922, No Obj: 0.003012, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.554817, Class: 1.000000, Obj: 0.251089, No Obj: 0.003423, Avg Recall: 0.666667, count: 129 -Region Avg IOU: 0.563874, Class: 1.000000, Obj: 0.240386, No Obj: 0.003317, Avg Recall: 0.655738, count: 122 -Region Avg IOU: 0.540112, Class: 1.000000, Obj: 0.279741, No Obj: 0.003327, Avg Recall: 0.600000, count: 85 -Region Avg IOU: 0.586980, Class: 1.000000, Obj: 0.190437, No Obj: 0.003397, Avg Recall: 0.711538, count: 104 -894: 0.525076, 0.508048 avg, 0.001000 rate, 26.552511 seconds, 228864 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.554464, Class: 1.000000, Obj: 0.209488, No Obj: 0.003455, Avg Recall: 0.672000, count: 125 -Region Avg IOU: 0.601681, Class: 1.000000, Obj: 0.246369, No Obj: 0.003181, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.558907, Class: 1.000000, Obj: 0.287119, No Obj: 0.003340, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.585139, Class: 1.000000, Obj: 0.228896, No Obj: 0.003255, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.564282, Class: 1.000000, Obj: 0.216120, No Obj: 0.004258, Avg Recall: 0.685315, count: 143 -Region Avg IOU: 0.560453, Class: 1.000000, Obj: 0.235894, No Obj: 0.003170, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.542791, Class: 1.000000, Obj: 0.228849, No Obj: 0.003207, Avg Recall: 0.622807, count: 114 -Region Avg IOU: 0.594087, Class: 1.000000, Obj: 0.230899, No Obj: 0.003468, Avg Recall: 0.706422, count: 109 -895: 0.500533, 0.507296 avg, 0.001000 rate, 26.252182 seconds, 229120 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.572353, Class: 1.000000, Obj: 0.215808, No Obj: 0.003083, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.586834, Class: 1.000000, Obj: 0.219335, No Obj: 0.003003, Avg Recall: 0.703704, count: 81 -Region Avg IOU: 0.560559, Class: 1.000000, Obj: 0.214819, No Obj: 0.003168, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.525169, Class: 1.000000, Obj: 0.206852, No Obj: 0.003134, Avg Recall: 0.590909, count: 132 -Region Avg IOU: 0.565163, Class: 1.000000, Obj: 0.232588, No Obj: 0.003315, Avg Recall: 0.644068, count: 118 -Region Avg IOU: 0.560739, Class: 1.000000, Obj: 0.233283, No Obj: 0.003592, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.550679, Class: 1.000000, Obj: 0.181990, No Obj: 0.003359, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.506990, Class: 1.000000, Obj: 0.201098, No Obj: 0.003139, Avg Recall: 0.560345, count: 116 -896: 0.534902, 0.510057 avg, 0.001000 rate, 26.360695 seconds, 229376 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.555385, Class: 1.000000, Obj: 0.221811, No Obj: 0.003367, Avg Recall: 0.680672, count: 119 -Region Avg IOU: 0.546065, Class: 1.000000, Obj: 0.211684, No Obj: 0.003569, Avg Recall: 0.691176, count: 136 -Region Avg IOU: 0.542418, Class: 1.000000, Obj: 0.262191, No Obj: 0.003174, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.571085, Class: 1.000000, Obj: 0.229495, No Obj: 0.003268, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.568355, Class: 1.000000, Obj: 0.241055, No Obj: 0.003396, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.558877, Class: 1.000000, Obj: 0.239697, No Obj: 0.003099, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.568414, Class: 1.000000, Obj: 0.231550, No Obj: 0.003194, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.528651, Class: 1.000000, Obj: 0.235975, No Obj: 0.003065, Avg Recall: 0.614458, count: 83 -897: 0.510726, 0.510124 avg, 0.001000 rate, 27.426861 seconds, 229632 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.542169, Class: 1.000000, Obj: 0.234060, No Obj: 0.003617, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.546594, Class: 1.000000, Obj: 0.270829, No Obj: 0.003571, Avg Recall: 0.641791, count: 134 -Region Avg IOU: 0.571306, Class: 1.000000, Obj: 0.287925, No Obj: 0.003936, Avg Recall: 0.688000, count: 125 -Region Avg IOU: 0.568949, Class: 1.000000, Obj: 0.281208, No Obj: 0.003647, Avg Recall: 0.697479, count: 119 -Region Avg IOU: 0.565111, Class: 1.000000, Obj: 0.231301, No Obj: 0.003386, Avg Recall: 0.646341, count: 82 -Region Avg IOU: 0.563575, Class: 1.000000, Obj: 0.304650, No Obj: 0.003589, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.573654, Class: 1.000000, Obj: 0.242853, No Obj: 0.003601, Avg Recall: 0.709302, count: 86 -Region Avg IOU: 0.555210, Class: 1.000000, Obj: 0.262997, No Obj: 0.003652, Avg Recall: 0.664122, count: 131 -898: 0.516403, 0.510752 avg, 0.001000 rate, 26.440826 seconds, 229888 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.547729, Class: 1.000000, Obj: 0.251889, No Obj: 0.003668, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.547720, Class: 1.000000, Obj: 0.259774, No Obj: 0.003292, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.538035, Class: 1.000000, Obj: 0.264258, No Obj: 0.003425, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.574906, Class: 1.000000, Obj: 0.244060, No Obj: 0.003612, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.587781, Class: 1.000000, Obj: 0.276165, No Obj: 0.003537, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.552878, Class: 1.000000, Obj: 0.294358, No Obj: 0.003422, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.593612, Class: 1.000000, Obj: 0.262254, No Obj: 0.003873, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.527472, Class: 1.000000, Obj: 0.272799, No Obj: 0.003386, Avg Recall: 0.616071, count: 112 -899: 0.475474, 0.507224 avg, 0.001000 rate, 27.357069 seconds, 230144 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.541113, Class: 1.000000, Obj: 0.270825, No Obj: 0.003780, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.601903, Class: 1.000000, Obj: 0.214411, No Obj: 0.003519, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.543913, Class: 1.000000, Obj: 0.240088, No Obj: 0.003885, Avg Recall: 0.620438, count: 137 -Region Avg IOU: 0.618634, Class: 1.000000, Obj: 0.273564, No Obj: 0.003730, Avg Recall: 0.793478, count: 92 -Region Avg IOU: 0.567772, Class: 1.000000, Obj: 0.268192, No Obj: 0.004373, Avg Recall: 0.705882, count: 136 -Region Avg IOU: 0.582191, Class: 1.000000, Obj: 0.238192, No Obj: 0.003765, Avg Recall: 0.690476, count: 126 -Region Avg IOU: 0.554935, Class: 1.000000, Obj: 0.299037, No Obj: 0.003655, Avg Recall: 0.714286, count: 84 -Region Avg IOU: 0.563587, Class: 1.000000, Obj: 0.259562, No Obj: 0.003989, Avg Recall: 0.636364, count: 132 -900: 0.556895, 0.512191 avg, 0.001000 rate, 26.036716 seconds, 230400 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000089 seconds -Region Avg IOU: 0.611798, Class: 1.000000, Obj: 0.265837, No Obj: 0.003728, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.553193, Class: 1.000000, Obj: 0.271806, No Obj: 0.003171, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.586884, Class: 1.000000, Obj: 0.259756, No Obj: 0.004221, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.548080, Class: 1.000000, Obj: 0.248535, No Obj: 0.003342, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.547383, Class: 1.000000, Obj: 0.278726, No Obj: 0.003439, Avg Recall: 0.666667, count: 126 -Region Avg IOU: 0.596230, Class: 1.000000, Obj: 0.299454, No Obj: 0.003504, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.562363, Class: 1.000000, Obj: 0.257060, No Obj: 0.003531, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.544642, Class: 1.000000, Obj: 0.243787, No Obj: 0.003481, Avg Recall: 0.616667, count: 120 -901: 0.506261, 0.511598 avg, 0.001000 rate, 26.715300 seconds, 230656 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.569705, Class: 1.000000, Obj: 0.213181, No Obj: 0.003466, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.551507, Class: 1.000000, Obj: 0.237160, No Obj: 0.003470, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.566512, Class: 1.000000, Obj: 0.256153, No Obj: 0.003361, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.538850, Class: 1.000000, Obj: 0.258431, No Obj: 0.003486, Avg Recall: 0.611111, count: 126 -Region Avg IOU: 0.573948, Class: 1.000000, Obj: 0.251494, No Obj: 0.003113, Avg Recall: 0.684783, count: 92 -Region Avg IOU: 0.558910, Class: 1.000000, Obj: 0.279858, No Obj: 0.003406, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.575186, Class: 1.000000, Obj: 0.244836, No Obj: 0.003555, Avg Recall: 0.700000, count: 120 -Region Avg IOU: 0.595058, Class: 1.000000, Obj: 0.277270, No Obj: 0.003385, Avg Recall: 0.728155, count: 103 -902: 0.480861, 0.508524 avg, 0.001000 rate, 28.564072 seconds, 230912 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.568386, Class: 1.000000, Obj: 0.241139, No Obj: 0.003315, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.588758, Class: 1.000000, Obj: 0.216922, No Obj: 0.003309, Avg Recall: 0.732283, count: 127 -Region Avg IOU: 0.570468, Class: 1.000000, Obj: 0.226271, No Obj: 0.003838, Avg Recall: 0.711111, count: 135 -Region Avg IOU: 0.575873, Class: 1.000000, Obj: 0.264468, No Obj: 0.003432, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.559095, Class: 1.000000, Obj: 0.208871, No Obj: 0.003631, Avg Recall: 0.676923, count: 130 -Region Avg IOU: 0.579131, Class: 1.000000, Obj: 0.246447, No Obj: 0.003327, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.532247, Class: 1.000000, Obj: 0.300179, No Obj: 0.003052, Avg Recall: 0.641975, count: 81 -Region Avg IOU: 0.539298, Class: 1.000000, Obj: 0.269962, No Obj: 0.003237, Avg Recall: 0.581197, count: 117 -903: 0.522525, 0.509924 avg, 0.001000 rate, 27.138563 seconds, 231168 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.600606, Class: 1.000000, Obj: 0.293122, No Obj: 0.002959, Avg Recall: 0.723684, count: 76 -Region Avg IOU: 0.564203, Class: 1.000000, Obj: 0.281876, No Obj: 0.003628, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.568076, Class: 1.000000, Obj: 0.255976, No Obj: 0.003159, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.547754, Class: 1.000000, Obj: 0.234005, No Obj: 0.003189, Avg Recall: 0.630435, count: 92 -Region Avg IOU: 0.575784, Class: 1.000000, Obj: 0.231541, No Obj: 0.003358, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.576786, Class: 1.000000, Obj: 0.268055, No Obj: 0.003625, Avg Recall: 0.679389, count: 131 -Region Avg IOU: 0.523222, Class: 1.000000, Obj: 0.204264, No Obj: 0.002984, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.561371, Class: 1.000000, Obj: 0.249588, No Obj: 0.002995, Avg Recall: 0.650000, count: 100 -904: 0.503774, 0.509309 avg, 0.001000 rate, 26.851824 seconds, 231424 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.539203, Class: 1.000000, Obj: 0.249679, No Obj: 0.002958, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.570307, Class: 1.000000, Obj: 0.235486, No Obj: 0.003106, Avg Recall: 0.669565, count: 115 -Region Avg IOU: 0.558888, Class: 1.000000, Obj: 0.214478, No Obj: 0.003004, Avg Recall: 0.640449, count: 89 -Region Avg IOU: 0.539461, Class: 1.000000, Obj: 0.217886, No Obj: 0.003142, Avg Recall: 0.596639, count: 119 -Region Avg IOU: 0.602198, Class: 1.000000, Obj: 0.237904, No Obj: 0.003281, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.612258, Class: 1.000000, Obj: 0.248596, No Obj: 0.003093, Avg Recall: 0.744186, count: 86 -Region Avg IOU: 0.577608, Class: 1.000000, Obj: 0.240506, No Obj: 0.003209, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.598302, Class: 1.000000, Obj: 0.222620, No Obj: 0.003203, Avg Recall: 0.732673, count: 101 -905: 0.439137, 0.502292 avg, 0.001000 rate, 27.967024 seconds, 231680 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.599171, Class: 1.000000, Obj: 0.269566, No Obj: 0.003254, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.576055, Class: 1.000000, Obj: 0.241412, No Obj: 0.003028, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.571820, Class: 1.000000, Obj: 0.245849, No Obj: 0.002891, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.594313, Class: 1.000000, Obj: 0.268501, No Obj: 0.003197, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.593460, Class: 1.000000, Obj: 0.259389, No Obj: 0.003048, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.527623, Class: 1.000000, Obj: 0.219690, No Obj: 0.003132, Avg Recall: 0.588889, count: 90 -Region Avg IOU: 0.559522, Class: 1.000000, Obj: 0.221430, No Obj: 0.003193, Avg Recall: 0.669173, count: 133 -Region Avg IOU: 0.578365, Class: 1.000000, Obj: 0.251658, No Obj: 0.003282, Avg Recall: 0.651786, count: 112 -906: 0.527505, 0.504813 avg, 0.001000 rate, 27.179804 seconds, 231936 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.575718, Class: 1.000000, Obj: 0.262547, No Obj: 0.002986, Avg Recall: 0.654762, count: 84 -Region Avg IOU: 0.578728, Class: 1.000000, Obj: 0.243260, No Obj: 0.003259, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.554126, Class: 1.000000, Obj: 0.226236, No Obj: 0.003536, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.537468, Class: 1.000000, Obj: 0.182811, No Obj: 0.002958, Avg Recall: 0.583333, count: 96 -Region Avg IOU: 0.547122, Class: 1.000000, Obj: 0.225092, No Obj: 0.003155, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.573234, Class: 1.000000, Obj: 0.231761, No Obj: 0.002893, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.561818, Class: 1.000000, Obj: 0.206976, No Obj: 0.003308, Avg Recall: 0.700787, count: 127 -Region Avg IOU: 0.559230, Class: 1.000000, Obj: 0.234009, No Obj: 0.003003, Avg Recall: 0.681818, count: 88 -907: 0.489652, 0.503297 avg, 0.001000 rate, 27.565887 seconds, 232192 images -Loaded: 0.000118 seconds -Region Avg IOU: 0.549597, Class: 1.000000, Obj: 0.256388, No Obj: 0.002917, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.564336, Class: 1.000000, Obj: 0.192760, No Obj: 0.003109, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.574885, Class: 1.000000, Obj: 0.230856, No Obj: 0.003725, Avg Recall: 0.705882, count: 136 -Region Avg IOU: 0.575650, Class: 1.000000, Obj: 0.259254, No Obj: 0.003541, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.603711, Class: 1.000000, Obj: 0.279584, No Obj: 0.003315, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.577879, Class: 1.000000, Obj: 0.256671, No Obj: 0.003254, Avg Recall: 0.691057, count: 123 -Region Avg IOU: 0.573322, Class: 1.000000, Obj: 0.249779, No Obj: 0.003160, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.597518, Class: 1.000000, Obj: 0.265207, No Obj: 0.002929, Avg Recall: 0.674157, count: 89 -908: 0.525995, 0.505567 avg, 0.001000 rate, 27.442904 seconds, 232448 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.584483, Class: 1.000000, Obj: 0.273261, No Obj: 0.003607, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.588357, Class: 1.000000, Obj: 0.224532, No Obj: 0.003097, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.575295, Class: 1.000000, Obj: 0.254284, No Obj: 0.003667, Avg Recall: 0.713333, count: 150 -Region Avg IOU: 0.551025, Class: 1.000000, Obj: 0.250267, No Obj: 0.003109, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.599550, Class: 1.000000, Obj: 0.263967, No Obj: 0.003323, Avg Recall: 0.696000, count: 125 -Region Avg IOU: 0.539393, Class: 1.000000, Obj: 0.248633, No Obj: 0.003043, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.583734, Class: 1.000000, Obj: 0.268772, No Obj: 0.003351, Avg Recall: 0.686956, count: 115 -Region Avg IOU: 0.520038, Class: 1.000000, Obj: 0.253426, No Obj: 0.003135, Avg Recall: 0.631068, count: 103 -909: 0.530065, 0.508017 avg, 0.001000 rate, 27.103745 seconds, 232704 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.545192, Class: 1.000000, Obj: 0.249291, No Obj: 0.003098, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.583177, Class: 1.000000, Obj: 0.254490, No Obj: 0.004076, Avg Recall: 0.720280, count: 143 -Region Avg IOU: 0.544055, Class: 1.000000, Obj: 0.253776, No Obj: 0.003174, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.550713, Class: 1.000000, Obj: 0.240290, No Obj: 0.003472, Avg Recall: 0.629032, count: 124 -Region Avg IOU: 0.535919, Class: 1.000000, Obj: 0.254265, No Obj: 0.003299, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.572399, Class: 1.000000, Obj: 0.231610, No Obj: 0.003274, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.587829, Class: 1.000000, Obj: 0.248438, No Obj: 0.003359, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.540590, Class: 1.000000, Obj: 0.240400, No Obj: 0.003565, Avg Recall: 0.608333, count: 120 -910: 0.530189, 0.510234 avg, 0.001000 rate, 27.151020 seconds, 232960 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.066916 seconds -Region Avg IOU: 0.491491, Class: 1.000000, Obj: 0.204230, No Obj: 0.003889, Avg Recall: 0.514706, count: 68 -Region Avg IOU: 0.394855, Class: 1.000000, Obj: 0.167047, No Obj: 0.003666, Avg Recall: 0.392157, count: 102 -Region Avg IOU: 0.405946, Class: 1.000000, Obj: 0.209920, No Obj: 0.004171, Avg Recall: 0.462185, count: 119 -Region Avg IOU: 0.470174, Class: 1.000000, Obj: 0.260102, No Obj: 0.004425, Avg Recall: 0.468468, count: 111 -Region Avg IOU: 0.428863, Class: 1.000000, Obj: 0.220087, No Obj: 0.003479, Avg Recall: 0.493827, count: 81 -Region Avg IOU: 0.400178, Class: 1.000000, Obj: 0.186943, No Obj: 0.003946, Avg Recall: 0.406780, count: 118 -Region Avg IOU: 0.449807, Class: 1.000000, Obj: 0.213229, No Obj: 0.003641, Avg Recall: 0.457944, count: 107 -Region Avg IOU: 0.433185, Class: 1.000000, Obj: 0.206625, No Obj: 0.004285, Avg Recall: 0.478633, count: 117 -911: 0.538418, 0.513052 avg, 0.001000 rate, 20.384943 seconds, 233216 images -Loaded: 0.956171 seconds -Region Avg IOU: 0.421466, Class: 1.000000, Obj: 0.207828, No Obj: 0.004153, Avg Recall: 0.450980, count: 102 -Region Avg IOU: 0.414489, Class: 1.000000, Obj: 0.238162, No Obj: 0.004676, Avg Recall: 0.424460, count: 139 -Region Avg IOU: 0.444962, Class: 1.000000, Obj: 0.201739, No Obj: 0.004077, Avg Recall: 0.439252, count: 107 -Region Avg IOU: 0.402836, Class: 1.000000, Obj: 0.206700, No Obj: 0.003588, Avg Recall: 0.371429, count: 105 -Region Avg IOU: 0.419071, Class: 1.000000, Obj: 0.212329, No Obj: 0.004521, Avg Recall: 0.408759, count: 137 -Region Avg IOU: 0.435363, Class: 1.000000, Obj: 0.223579, No Obj: 0.004535, Avg Recall: 0.448000, count: 125 -Region Avg IOU: 0.430410, Class: 1.000000, Obj: 0.268774, No Obj: 0.004402, Avg Recall: 0.411765, count: 102 -Region Avg IOU: 0.403419, Class: 1.000000, Obj: 0.210835, No Obj: 0.004120, Avg Recall: 0.388430, count: 121 -912: 0.514866, 0.513234 avg, 0.001000 rate, 19.388252 seconds, 233472 images -Loaded: 2.185739 seconds -Region Avg IOU: 0.380122, Class: 1.000000, Obj: 0.237742, No Obj: 0.003814, Avg Recall: 0.400000, count: 100 -Region Avg IOU: 0.462482, Class: 1.000000, Obj: 0.224117, No Obj: 0.004117, Avg Recall: 0.464286, count: 112 -Region Avg IOU: 0.406357, Class: 1.000000, Obj: 0.214206, No Obj: 0.004234, Avg Recall: 0.382609, count: 115 -Region Avg IOU: 0.439014, Class: 1.000000, Obj: 0.252696, No Obj: 0.004421, Avg Recall: 0.400000, count: 115 -Region Avg IOU: 0.479907, Class: 1.000000, Obj: 0.241177, No Obj: 0.004896, Avg Recall: 0.553719, count: 121 -Region Avg IOU: 0.445373, Class: 1.000000, Obj: 0.215192, No Obj: 0.003693, Avg Recall: 0.490741, count: 108 -Region Avg IOU: 0.442000, Class: 1.000000, Obj: 0.238682, No Obj: 0.003983, Avg Recall: 0.469388, count: 98 -Region Avg IOU: 0.431615, Class: 1.000000, Obj: 0.236605, No Obj: 0.004736, Avg Recall: 0.448819, count: 127 -913: 0.506938, 0.512604 avg, 0.001000 rate, 17.207170 seconds, 233728 images -Loaded: 4.212599 seconds -Region Avg IOU: 0.427196, Class: 1.000000, Obj: 0.218261, No Obj: 0.004647, Avg Recall: 0.415929, count: 113 -Region Avg IOU: 0.421357, Class: 1.000000, Obj: 0.235405, No Obj: 0.004122, Avg Recall: 0.445545, count: 101 -Region Avg IOU: 0.438365, Class: 1.000000, Obj: 0.248780, No Obj: 0.004327, Avg Recall: 0.468085, count: 94 -Region Avg IOU: 0.397506, Class: 1.000000, Obj: 0.202085, No Obj: 0.004381, Avg Recall: 0.396694, count: 121 -Region Avg IOU: 0.451584, Class: 1.000000, Obj: 0.249885, No Obj: 0.004556, Avg Recall: 0.515152, count: 99 -Region Avg IOU: 0.429328, Class: 1.000000, Obj: 0.250394, No Obj: 0.004297, Avg Recall: 0.418182, count: 110 -Region Avg IOU: 0.452155, Class: 1.000000, Obj: 0.194075, No Obj: 0.004252, Avg Recall: 0.465517, count: 116 -Region Avg IOU: 0.485561, Class: 1.000000, Obj: 0.266204, No Obj: 0.004865, Avg Recall: 0.520325, count: 123 -914: 0.539557, 0.515299 avg, 0.001000 rate, 18.597427 seconds, 233984 images -Loaded: 2.688551 seconds -Region Avg IOU: 0.428147, Class: 1.000000, Obj: 0.195496, No Obj: 0.004043, Avg Recall: 0.444444, count: 108 -Region Avg IOU: 0.411262, Class: 1.000000, Obj: 0.249743, No Obj: 0.004138, Avg Recall: 0.411111, count: 90 -Region Avg IOU: 0.429533, Class: 1.000000, Obj: 0.245642, No Obj: 0.004409, Avg Recall: 0.455357, count: 112 -Region Avg IOU: 0.445558, Class: 1.000000, Obj: 0.242759, No Obj: 0.004613, Avg Recall: 0.478992, count: 119 -Region Avg IOU: 0.423069, Class: 1.000000, Obj: 0.238740, No Obj: 0.004672, Avg Recall: 0.435115, count: 131 -Region Avg IOU: 0.418003, Class: 1.000000, Obj: 0.227825, No Obj: 0.004312, Avg Recall: 0.397727, count: 88 -Region Avg IOU: 0.457013, Class: 1.000000, Obj: 0.262692, No Obj: 0.004475, Avg Recall: 0.500000, count: 106 -Region Avg IOU: 0.382244, Class: 1.000000, Obj: 0.209609, No Obj: 0.004920, Avg Recall: 0.372093, count: 129 -915: 0.488630, 0.512633 avg, 0.001000 rate, 20.977148 seconds, 234240 images -Loaded: 1.168845 seconds -Region Avg IOU: 0.397996, Class: 1.000000, Obj: 0.229934, No Obj: 0.004475, Avg Recall: 0.404762, count: 126 -Region Avg IOU: 0.425322, Class: 1.000000, Obj: 0.247830, No Obj: 0.004492, Avg Recall: 0.406504, count: 123 -Region Avg IOU: 0.460552, Class: 1.000000, Obj: 0.290361, No Obj: 0.004201, Avg Recall: 0.476744, count: 86 -Region Avg IOU: 0.420122, Class: 1.000000, Obj: 0.241036, No Obj: 0.004732, Avg Recall: 0.418605, count: 129 -Region Avg IOU: 0.399643, Class: 1.000000, Obj: 0.241596, No Obj: 0.004686, Avg Recall: 0.412281, count: 114 -Region Avg IOU: 0.389486, Class: 1.000000, Obj: 0.228481, No Obj: 0.003999, Avg Recall: 0.377358, count: 106 -Region Avg IOU: 0.454659, Class: 1.000000, Obj: 0.234613, No Obj: 0.004331, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.474186, Class: 1.000000, Obj: 0.249655, No Obj: 0.003960, Avg Recall: 0.590361, count: 83 -916: 0.562812, 0.517650 avg, 0.001000 rate, 19.392908 seconds, 234496 images -Loaded: 1.536360 seconds -Region Avg IOU: 0.457827, Class: 1.000000, Obj: 0.231718, No Obj: 0.005038, Avg Recall: 0.488722, count: 133 -Region Avg IOU: 0.350418, Class: 1.000000, Obj: 0.221601, No Obj: 0.004075, Avg Recall: 0.321101, count: 109 -Region Avg IOU: 0.468100, Class: 1.000000, Obj: 0.203030, No Obj: 0.004489, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.443804, Class: 1.000000, Obj: 0.254362, No Obj: 0.004754, Avg Recall: 0.428571, count: 119 -Region Avg IOU: 0.445771, Class: 1.000000, Obj: 0.258475, No Obj: 0.004363, Avg Recall: 0.516484, count: 91 -Region Avg IOU: 0.442200, Class: 1.000000, Obj: 0.230055, No Obj: 0.004860, Avg Recall: 0.434783, count: 115 -Region Avg IOU: 0.364535, Class: 1.000000, Obj: 0.211596, No Obj: 0.004189, Avg Recall: 0.394495, count: 109 -Region Avg IOU: 0.405576, Class: 1.000000, Obj: 0.226845, No Obj: 0.004288, Avg Recall: 0.394495, count: 109 -917: 0.561054, 0.521991 avg, 0.001000 rate, 17.240904 seconds, 234752 images -Loaded: 3.585756 seconds -Region Avg IOU: 0.437154, Class: 1.000000, Obj: 0.238602, No Obj: 0.003952, Avg Recall: 0.430380, count: 79 -Region Avg IOU: 0.466869, Class: 1.000000, Obj: 0.282737, No Obj: 0.005038, Avg Recall: 0.500000, count: 110 -Region Avg IOU: 0.420444, Class: 1.000000, Obj: 0.215872, No Obj: 0.004669, Avg Recall: 0.465116, count: 129 -Region Avg IOU: 0.389862, Class: 1.000000, Obj: 0.211436, No Obj: 0.004758, Avg Recall: 0.344538, count: 119 -Region Avg IOU: 0.492904, Class: 1.000000, Obj: 0.257640, No Obj: 0.004545, Avg Recall: 0.525773, count: 97 -Region Avg IOU: 0.405623, Class: 1.000000, Obj: 0.250930, No Obj: 0.004081, Avg Recall: 0.373626, count: 91 -Region Avg IOU: 0.388482, Class: 1.000000, Obj: 0.227647, No Obj: 0.004304, Avg Recall: 0.346154, count: 130 -Region Avg IOU: 0.414235, Class: 1.000000, Obj: 0.241570, No Obj: 0.004459, Avg Recall: 0.396040, count: 101 -918: 0.503529, 0.520145 avg, 0.001000 rate, 19.318647 seconds, 235008 images -Loaded: 1.382440 seconds -Region Avg IOU: 0.438872, Class: 1.000000, Obj: 0.245757, No Obj: 0.004068, Avg Recall: 0.421053, count: 95 -Region Avg IOU: 0.498303, Class: 1.000000, Obj: 0.280149, No Obj: 0.004885, Avg Recall: 0.556818, count: 88 -Region Avg IOU: 0.450621, Class: 1.000000, Obj: 0.222704, No Obj: 0.004080, Avg Recall: 0.475610, count: 82 -Region Avg IOU: 0.456274, Class: 1.000000, Obj: 0.236495, No Obj: 0.004356, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.465306, Class: 1.000000, Obj: 0.258310, No Obj: 0.004541, Avg Recall: 0.504202, count: 119 -Region Avg IOU: 0.409849, Class: 1.000000, Obj: 0.221804, No Obj: 0.003613, Avg Recall: 0.447368, count: 76 -Region Avg IOU: 0.380898, Class: 1.000000, Obj: 0.220476, No Obj: 0.003800, Avg Recall: 0.375000, count: 80 -Region Avg IOU: 0.460677, Class: 1.000000, Obj: 0.269256, No Obj: 0.004658, Avg Recall: 0.524752, count: 101 -919: 0.418876, 0.510018 avg, 0.001000 rate, 16.785973 seconds, 235264 images -Loaded: 1.948683 seconds -Region Avg IOU: 0.407417, Class: 1.000000, Obj: 0.208140, No Obj: 0.004233, Avg Recall: 0.394495, count: 109 -Region Avg IOU: 0.482992, Class: 1.000000, Obj: 0.255728, No Obj: 0.004559, Avg Recall: 0.509434, count: 106 -Region Avg IOU: 0.478142, Class: 1.000000, Obj: 0.238128, No Obj: 0.004603, Avg Recall: 0.509804, count: 102 -Region Avg IOU: 0.451974, Class: 1.000000, Obj: 0.242338, No Obj: 0.004382, Avg Recall: 0.500000, count: 122 -Region Avg IOU: 0.416955, Class: 1.000000, Obj: 0.228090, No Obj: 0.004729, Avg Recall: 0.433824, count: 136 -Region Avg IOU: 0.448009, Class: 1.000000, Obj: 0.272548, No Obj: 0.004985, Avg Recall: 0.463415, count: 123 -Region Avg IOU: 0.454743, Class: 1.000000, Obj: 0.292513, No Obj: 0.004037, Avg Recall: 0.494253, count: 87 -Region Avg IOU: 0.410967, Class: 1.000000, Obj: 0.300681, No Obj: 0.004161, Avg Recall: 0.428571, count: 91 -920: 0.510117, 0.510028 avg, 0.001000 rate, 18.761816 seconds, 235520 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000068 seconds -Region Avg IOU: 0.594220, Class: 1.000000, Obj: 0.293934, No Obj: 0.003465, Avg Recall: 0.724409, count: 127 -Region Avg IOU: 0.556791, Class: 1.000000, Obj: 0.273065, No Obj: 0.003900, Avg Recall: 0.640845, count: 142 -Region Avg IOU: 0.586904, Class: 1.000000, Obj: 0.277664, No Obj: 0.003620, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.568295, Class: 1.000000, Obj: 0.291534, No Obj: 0.003297, Avg Recall: 0.703704, count: 81 -Region Avg IOU: 0.587355, Class: 1.000000, Obj: 0.270419, No Obj: 0.003690, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.576987, Class: 1.000000, Obj: 0.251069, No Obj: 0.003597, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.539857, Class: 1.000000, Obj: 0.206093, No Obj: 0.003592, Avg Recall: 0.627119, count: 118 -Region Avg IOU: 0.578556, Class: 1.000000, Obj: 0.254527, No Obj: 0.003822, Avg Recall: 0.728571, count: 140 -921: 0.562976, 0.515323 avg, 0.001000 rate, 27.804789 seconds, 235776 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.545367, Class: 1.000000, Obj: 0.269995, No Obj: 0.003303, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.559664, Class: 1.000000, Obj: 0.289598, No Obj: 0.003227, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.581954, Class: 1.000000, Obj: 0.273214, No Obj: 0.003294, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.592146, Class: 1.000000, Obj: 0.204560, No Obj: 0.003640, Avg Recall: 0.740741, count: 135 -Region Avg IOU: 0.548084, Class: 1.000000, Obj: 0.259500, No Obj: 0.003099, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.586708, Class: 1.000000, Obj: 0.260594, No Obj: 0.003094, Avg Recall: 0.695122, count: 82 -Region Avg IOU: 0.553204, Class: 1.000000, Obj: 0.303420, No Obj: 0.003316, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.591722, Class: 1.000000, Obj: 0.261575, No Obj: 0.003364, Avg Recall: 0.744681, count: 94 -922: 0.502266, 0.514017 avg, 0.001000 rate, 28.899460 seconds, 236032 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.558742, Class: 1.000000, Obj: 0.253225, No Obj: 0.003253, Avg Recall: 0.652893, count: 121 -Region Avg IOU: 0.602547, Class: 1.000000, Obj: 0.284970, No Obj: 0.003226, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.557009, Class: 1.000000, Obj: 0.243254, No Obj: 0.003248, Avg Recall: 0.678571, count: 84 -Region Avg IOU: 0.560693, Class: 1.000000, Obj: 0.258829, No Obj: 0.003056, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.574986, Class: 1.000000, Obj: 0.254958, No Obj: 0.003140, Avg Recall: 0.681818, count: 88 -Region Avg IOU: 0.543108, Class: 1.000000, Obj: 0.259369, No Obj: 0.003432, Avg Recall: 0.589928, count: 139 -Region Avg IOU: 0.608308, Class: 1.000000, Obj: 0.244408, No Obj: 0.003303, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.543236, Class: 1.000000, Obj: 0.213773, No Obj: 0.002988, Avg Recall: 0.626374, count: 91 -923: 0.484878, 0.511103 avg, 0.001000 rate, 29.708481 seconds, 236288 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.573005, Class: 1.000000, Obj: 0.205745, No Obj: 0.002967, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.589864, Class: 1.000000, Obj: 0.229914, No Obj: 0.003588, Avg Recall: 0.718518, count: 135 -Region Avg IOU: 0.587112, Class: 1.000000, Obj: 0.229835, No Obj: 0.003140, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.561642, Class: 1.000000, Obj: 0.235981, No Obj: 0.002968, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.571130, Class: 1.000000, Obj: 0.216725, No Obj: 0.002926, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.576509, Class: 1.000000, Obj: 0.207157, No Obj: 0.003358, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.556220, Class: 1.000000, Obj: 0.221207, No Obj: 0.003224, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.586088, Class: 1.000000, Obj: 0.205243, No Obj: 0.003227, Avg Recall: 0.771739, count: 92 -924: 0.539035, 0.513896 avg, 0.001000 rate, 27.667051 seconds, 236544 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.587321, Class: 1.000000, Obj: 0.251751, No Obj: 0.003312, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.558132, Class: 1.000000, Obj: 0.238257, No Obj: 0.002742, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.577327, Class: 1.000000, Obj: 0.248155, No Obj: 0.003159, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.564433, Class: 1.000000, Obj: 0.187837, No Obj: 0.002654, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.599024, Class: 1.000000, Obj: 0.243025, No Obj: 0.003503, Avg Recall: 0.735294, count: 136 -Region Avg IOU: 0.574152, Class: 1.000000, Obj: 0.205460, No Obj: 0.002870, Avg Recall: 0.691667, count: 120 -Region Avg IOU: 0.561573, Class: 1.000000, Obj: 0.218580, No Obj: 0.002801, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.587229, Class: 1.000000, Obj: 0.230897, No Obj: 0.003347, Avg Recall: 0.727273, count: 143 -925: 0.515708, 0.514077 avg, 0.001000 rate, 27.274940 seconds, 236800 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.611954, Class: 1.000000, Obj: 0.262674, No Obj: 0.002818, Avg Recall: 0.772727, count: 88 -Region Avg IOU: 0.482360, Class: 1.000000, Obj: 0.203475, No Obj: 0.002710, Avg Recall: 0.548387, count: 124 -Region Avg IOU: 0.560839, Class: 1.000000, Obj: 0.205465, No Obj: 0.002814, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.537684, Class: 1.000000, Obj: 0.195431, No Obj: 0.003037, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.578042, Class: 1.000000, Obj: 0.197837, No Obj: 0.003143, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.602964, Class: 1.000000, Obj: 0.231466, No Obj: 0.003287, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.609343, Class: 1.000000, Obj: 0.209565, No Obj: 0.003676, Avg Recall: 0.773973, count: 146 -Region Avg IOU: 0.597728, Class: 1.000000, Obj: 0.238777, No Obj: 0.003320, Avg Recall: 0.771930, count: 114 -926: 0.568300, 0.519500 avg, 0.001000 rate, 28.382467 seconds, 237056 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.578181, Class: 1.000000, Obj: 0.222220, No Obj: 0.003202, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.568758, Class: 1.000000, Obj: 0.277091, No Obj: 0.003056, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.589855, Class: 1.000000, Obj: 0.244493, No Obj: 0.003308, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.554081, Class: 1.000000, Obj: 0.224881, No Obj: 0.003099, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.598906, Class: 1.000000, Obj: 0.223423, No Obj: 0.003189, Avg Recall: 0.710526, count: 114 -Region Avg IOU: 0.554313, Class: 1.000000, Obj: 0.209242, No Obj: 0.002981, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.605074, Class: 1.000000, Obj: 0.237656, No Obj: 0.002989, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.608086, Class: 1.000000, Obj: 0.245425, No Obj: 0.003310, Avg Recall: 0.769231, count: 130 -927: 0.498957, 0.517445 avg, 0.001000 rate, 29.341808 seconds, 237312 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.552298, Class: 1.000000, Obj: 0.219491, No Obj: 0.003518, Avg Recall: 0.678832, count: 137 -Region Avg IOU: 0.581800, Class: 1.000000, Obj: 0.261403, No Obj: 0.003180, Avg Recall: 0.756410, count: 78 -Region Avg IOU: 0.562304, Class: 1.000000, Obj: 0.249812, No Obj: 0.003496, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.603893, Class: 1.000000, Obj: 0.275198, No Obj: 0.002936, Avg Recall: 0.736111, count: 72 -Region Avg IOU: 0.590832, Class: 1.000000, Obj: 0.283941, No Obj: 0.003268, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.554481, Class: 1.000000, Obj: 0.295234, No Obj: 0.002953, Avg Recall: 0.630252, count: 119 -Region Avg IOU: 0.569313, Class: 1.000000, Obj: 0.210441, No Obj: 0.003493, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.585411, Class: 1.000000, Obj: 0.278887, No Obj: 0.003573, Avg Recall: 0.732824, count: 131 -928: 0.500156, 0.515717 avg, 0.001000 rate, 28.641390 seconds, 237568 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.588840, Class: 1.000000, Obj: 0.316102, No Obj: 0.003011, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.602854, Class: 1.000000, Obj: 0.275675, No Obj: 0.003040, Avg Recall: 0.731707, count: 82 -Region Avg IOU: 0.575835, Class: 1.000000, Obj: 0.233672, No Obj: 0.003688, Avg Recall: 0.710345, count: 145 -Region Avg IOU: 0.572976, Class: 1.000000, Obj: 0.242210, No Obj: 0.003401, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.592521, Class: 1.000000, Obj: 0.238395, No Obj: 0.003330, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.562716, Class: 1.000000, Obj: 0.297860, No Obj: 0.003329, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.600098, Class: 1.000000, Obj: 0.287432, No Obj: 0.003206, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.586920, Class: 1.000000, Obj: 0.256996, No Obj: 0.003684, Avg Recall: 0.752381, count: 105 -929: 0.493094, 0.513454 avg, 0.001000 rate, 28.114288 seconds, 237824 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.573709, Class: 1.000000, Obj: 0.211273, No Obj: 0.003414, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.604099, Class: 1.000000, Obj: 0.264984, No Obj: 0.003691, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.565946, Class: 1.000000, Obj: 0.301728, No Obj: 0.003120, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.553741, Class: 1.000000, Obj: 0.229007, No Obj: 0.003629, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.581049, Class: 1.000000, Obj: 0.269712, No Obj: 0.002778, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.557642, Class: 1.000000, Obj: 0.322373, No Obj: 0.003029, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.617731, Class: 1.000000, Obj: 0.276973, No Obj: 0.003548, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.534267, Class: 1.000000, Obj: 0.224619, No Obj: 0.003225, Avg Recall: 0.586207, count: 116 -930: 0.513457, 0.513455 avg, 0.001000 rate, 27.470177 seconds, 238080 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 11.750130 seconds -Region Avg IOU: 0.527277, Class: 1.000000, Obj: 0.257474, No Obj: 0.003038, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.563969, Class: 1.000000, Obj: 0.296009, No Obj: 0.003481, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.569773, Class: 1.000000, Obj: 0.268477, No Obj: 0.003334, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.577155, Class: 1.000000, Obj: 0.314681, No Obj: 0.002991, Avg Recall: 0.700000, count: 70 -Region Avg IOU: 0.599397, Class: 1.000000, Obj: 0.295290, No Obj: 0.003049, Avg Recall: 0.736842, count: 76 -Region Avg IOU: 0.578612, Class: 1.000000, Obj: 0.259458, No Obj: 0.003369, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.558452, Class: 1.000000, Obj: 0.277234, No Obj: 0.003304, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.586450, Class: 1.000000, Obj: 0.243184, No Obj: 0.003230, Avg Recall: 0.739130, count: 92 -931: 0.431272, 0.505236 avg, 0.001000 rate, 27.739880 seconds, 238336 images -Loaded: 0.000129 seconds -Region Avg IOU: 0.531158, Class: 1.000000, Obj: 0.241595, No Obj: 0.003182, Avg Recall: 0.630769, count: 130 -Region Avg IOU: 0.532920, Class: 1.000000, Obj: 0.222404, No Obj: 0.002915, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.580922, Class: 1.000000, Obj: 0.242347, No Obj: 0.003051, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.547507, Class: 1.000000, Obj: 0.274816, No Obj: 0.003522, Avg Recall: 0.626761, count: 142 -Region Avg IOU: 0.584948, Class: 1.000000, Obj: 0.261187, No Obj: 0.003441, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.596871, Class: 1.000000, Obj: 0.256704, No Obj: 0.003134, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.581045, Class: 1.000000, Obj: 0.232221, No Obj: 0.003133, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.559873, Class: 1.000000, Obj: 0.252411, No Obj: 0.002835, Avg Recall: 0.642857, count: 98 -932: 0.496714, 0.504384 avg, 0.001000 rate, 27.440001 seconds, 238592 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.540126, Class: 1.000000, Obj: 0.256429, No Obj: 0.003190, Avg Recall: 0.632000, count: 125 -Region Avg IOU: 0.580196, Class: 1.000000, Obj: 0.251602, No Obj: 0.003247, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.595289, Class: 1.000000, Obj: 0.248418, No Obj: 0.002850, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.552241, Class: 1.000000, Obj: 0.222444, No Obj: 0.003138, Avg Recall: 0.653061, count: 147 -Region Avg IOU: 0.514965, Class: 1.000000, Obj: 0.224070, No Obj: 0.002968, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.521518, Class: 1.000000, Obj: 0.264004, No Obj: 0.003125, Avg Recall: 0.584071, count: 113 -Region Avg IOU: 0.546743, Class: 1.000000, Obj: 0.241913, No Obj: 0.003257, Avg Recall: 0.660000, count: 150 -Region Avg IOU: 0.612739, Class: 1.000000, Obj: 0.293476, No Obj: 0.002885, Avg Recall: 0.806122, count: 98 -933: 0.526962, 0.506642 avg, 0.001000 rate, 26.432108 seconds, 238848 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.579070, Class: 1.000000, Obj: 0.272896, No Obj: 0.002895, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.572477, Class: 1.000000, Obj: 0.249851, No Obj: 0.002824, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.560926, Class: 1.000000, Obj: 0.289214, No Obj: 0.002951, Avg Recall: 0.633333, count: 120 -Region Avg IOU: 0.610606, Class: 1.000000, Obj: 0.262397, No Obj: 0.002888, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.516136, Class: 1.000000, Obj: 0.212660, No Obj: 0.003158, Avg Recall: 0.598540, count: 137 -Region Avg IOU: 0.531905, Class: 1.000000, Obj: 0.220900, No Obj: 0.002956, Avg Recall: 0.608333, count: 120 -Region Avg IOU: 0.597538, Class: 1.000000, Obj: 0.262008, No Obj: 0.002793, Avg Recall: 0.784091, count: 88 -Region Avg IOU: 0.589140, Class: 1.000000, Obj: 0.252422, No Obj: 0.002833, Avg Recall: 0.701754, count: 114 -934: 0.533301, 0.509308 avg, 0.001000 rate, 28.467936 seconds, 239104 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.542330, Class: 1.000000, Obj: 0.239085, No Obj: 0.003161, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.564772, Class: 1.000000, Obj: 0.241227, No Obj: 0.003383, Avg Recall: 0.710145, count: 138 -Region Avg IOU: 0.538513, Class: 1.000000, Obj: 0.249181, No Obj: 0.003030, Avg Recall: 0.611111, count: 126 -Region Avg IOU: 0.530751, Class: 1.000000, Obj: 0.230265, No Obj: 0.002846, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.580359, Class: 1.000000, Obj: 0.260467, No Obj: 0.003379, Avg Recall: 0.697183, count: 142 -Region Avg IOU: 0.576109, Class: 1.000000, Obj: 0.242767, No Obj: 0.002740, Avg Recall: 0.736264, count: 91 -Region Avg IOU: 0.571700, Class: 1.000000, Obj: 0.222868, No Obj: 0.002634, Avg Recall: 0.647727, count: 88 -Region Avg IOU: 0.544644, Class: 1.000000, Obj: 0.201261, No Obj: 0.002779, Avg Recall: 0.631579, count: 114 -935: 0.517919, 0.510169 avg, 0.001000 rate, 27.046291 seconds, 239360 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.564361, Class: 1.000000, Obj: 0.225332, No Obj: 0.003090, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.561391, Class: 1.000000, Obj: 0.229761, No Obj: 0.002902, Avg Recall: 0.686956, count: 115 -Region Avg IOU: 0.544325, Class: 1.000000, Obj: 0.224781, No Obj: 0.003226, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.563522, Class: 1.000000, Obj: 0.249134, No Obj: 0.003198, Avg Recall: 0.700787, count: 127 -Region Avg IOU: 0.573285, Class: 1.000000, Obj: 0.256494, No Obj: 0.003037, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.563902, Class: 1.000000, Obj: 0.274668, No Obj: 0.003302, Avg Recall: 0.661290, count: 124 -Region Avg IOU: 0.561879, Class: 1.000000, Obj: 0.291608, No Obj: 0.003139, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.552414, Class: 1.000000, Obj: 0.165994, No Obj: 0.003066, Avg Recall: 0.704348, count: 115 -936: 0.557583, 0.514910 avg, 0.001000 rate, 29.252165 seconds, 239616 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.612761, Class: 1.000000, Obj: 0.315842, No Obj: 0.003130, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.558182, Class: 1.000000, Obj: 0.257489, No Obj: 0.003182, Avg Recall: 0.666667, count: 120 -Region Avg IOU: 0.598809, Class: 1.000000, Obj: 0.256920, No Obj: 0.003197, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.518803, Class: 1.000000, Obj: 0.251500, No Obj: 0.002676, Avg Recall: 0.543210, count: 81 -Region Avg IOU: 0.575590, Class: 1.000000, Obj: 0.248530, No Obj: 0.003519, Avg Recall: 0.743243, count: 148 -Region Avg IOU: 0.564614, Class: 1.000000, Obj: 0.246914, No Obj: 0.003067, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.551473, Class: 1.000000, Obj: 0.251033, No Obj: 0.002964, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.565539, Class: 1.000000, Obj: 0.255195, No Obj: 0.003151, Avg Recall: 0.623853, count: 109 -937: 0.494665, 0.512886 avg, 0.001000 rate, 28.315136 seconds, 239872 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.570679, Class: 1.000000, Obj: 0.259608, No Obj: 0.003636, Avg Recall: 0.669355, count: 124 -Region Avg IOU: 0.545902, Class: 1.000000, Obj: 0.243878, No Obj: 0.003412, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.542036, Class: 1.000000, Obj: 0.281400, No Obj: 0.003159, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.593343, Class: 1.000000, Obj: 0.263432, No Obj: 0.003283, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.571526, Class: 1.000000, Obj: 0.250791, No Obj: 0.003239, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.545889, Class: 1.000000, Obj: 0.265262, No Obj: 0.003365, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.559297, Class: 1.000000, Obj: 0.290163, No Obj: 0.003467, Avg Recall: 0.666667, count: 132 -Region Avg IOU: 0.522677, Class: 1.000000, Obj: 0.290140, No Obj: 0.003022, Avg Recall: 0.609524, count: 105 -938: 0.515071, 0.513104 avg, 0.001000 rate, 26.711103 seconds, 240128 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.546223, Class: 1.000000, Obj: 0.281707, No Obj: 0.003208, Avg Recall: 0.669231, count: 130 -Region Avg IOU: 0.561244, Class: 1.000000, Obj: 0.248764, No Obj: 0.003311, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.571125, Class: 1.000000, Obj: 0.303040, No Obj: 0.003273, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.540854, Class: 1.000000, Obj: 0.270640, No Obj: 0.003263, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.583380, Class: 1.000000, Obj: 0.229489, No Obj: 0.003602, Avg Recall: 0.702290, count: 131 -Region Avg IOU: 0.566552, Class: 1.000000, Obj: 0.267985, No Obj: 0.003253, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.605367, Class: 1.000000, Obj: 0.310189, No Obj: 0.003311, Avg Recall: 0.756098, count: 82 -Region Avg IOU: 0.568263, Class: 1.000000, Obj: 0.214044, No Obj: 0.003419, Avg Recall: 0.664000, count: 125 -939: 0.482801, 0.510074 avg, 0.001000 rate, 27.191160 seconds, 240384 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.573548, Class: 1.000000, Obj: 0.276375, No Obj: 0.003308, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.551877, Class: 1.000000, Obj: 0.249310, No Obj: 0.003126, Avg Recall: 0.632479, count: 117 -Region Avg IOU: 0.561543, Class: 1.000000, Obj: 0.295575, No Obj: 0.003118, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.586572, Class: 1.000000, Obj: 0.255430, No Obj: 0.003414, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.605000, Class: 1.000000, Obj: 0.277463, No Obj: 0.003538, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.535243, Class: 1.000000, Obj: 0.241013, No Obj: 0.003212, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.527185, Class: 1.000000, Obj: 0.291663, No Obj: 0.003530, Avg Recall: 0.602837, count: 141 -Region Avg IOU: 0.562070, Class: 1.000000, Obj: 0.297224, No Obj: 0.003492, Avg Recall: 0.694915, count: 118 -940: 0.509589, 0.510026 avg, 0.001000 rate, 26.891968 seconds, 240640 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.325056 seconds -Region Avg IOU: 0.399730, Class: 1.000000, Obj: 0.208777, No Obj: 0.003918, Avg Recall: 0.389830, count: 118 -Region Avg IOU: 0.431200, Class: 1.000000, Obj: 0.200306, No Obj: 0.004414, Avg Recall: 0.443609, count: 133 -Region Avg IOU: 0.397977, Class: 1.000000, Obj: 0.196241, No Obj: 0.004185, Avg Recall: 0.371901, count: 121 -Region Avg IOU: 0.416464, Class: 1.000000, Obj: 0.206127, No Obj: 0.004132, Avg Recall: 0.420168, count: 119 -Region Avg IOU: 0.464758, Class: 1.000000, Obj: 0.212181, No Obj: 0.004208, Avg Recall: 0.495575, count: 113 -Region Avg IOU: 0.433312, Class: 1.000000, Obj: 0.154594, No Obj: 0.003481, Avg Recall: 0.481132, count: 106 -Region Avg IOU: 0.459037, Class: 1.000000, Obj: 0.214591, No Obj: 0.004254, Avg Recall: 0.486726, count: 113 -Region Avg IOU: 0.413373, Class: 1.000000, Obj: 0.208542, No Obj: 0.004034, Avg Recall: 0.449153, count: 118 -941: 0.682792, 0.527302 avg, 0.001000 rate, 16.023235 seconds, 240896 images -Loaded: 4.583523 seconds -Region Avg IOU: 0.398763, Class: 1.000000, Obj: 0.181929, No Obj: 0.003768, Avg Recall: 0.373832, count: 107 -Region Avg IOU: 0.424385, Class: 1.000000, Obj: 0.196975, No Obj: 0.004277, Avg Recall: 0.400000, count: 115 -Region Avg IOU: 0.440662, Class: 1.000000, Obj: 0.222581, No Obj: 0.003847, Avg Recall: 0.434783, count: 92 -Region Avg IOU: 0.373548, Class: 1.000000, Obj: 0.162475, No Obj: 0.003838, Avg Recall: 0.342105, count: 114 -Region Avg IOU: 0.407651, Class: 1.000000, Obj: 0.226826, No Obj: 0.003842, Avg Recall: 0.385417, count: 96 -Region Avg IOU: 0.442361, Class: 1.000000, Obj: 0.209741, No Obj: 0.004743, Avg Recall: 0.459677, count: 124 -Region Avg IOU: 0.411495, Class: 1.000000, Obj: 0.229671, No Obj: 0.004408, Avg Recall: 0.440678, count: 118 -Region Avg IOU: 0.434221, Class: 1.000000, Obj: 0.187762, No Obj: 0.004753, Avg Recall: 0.473684, count: 114 -942: 0.560342, 0.530606 avg, 0.001000 rate, 15.471921 seconds, 241152 images -Loaded: 3.182618 seconds -Region Avg IOU: 0.398567, Class: 1.000000, Obj: 0.199264, No Obj: 0.004026, Avg Recall: 0.430108, count: 93 -Region Avg IOU: 0.373832, Class: 1.000000, Obj: 0.204041, No Obj: 0.004078, Avg Recall: 0.354839, count: 93 -Region Avg IOU: 0.431109, Class: 1.000000, Obj: 0.202980, No Obj: 0.003841, Avg Recall: 0.423913, count: 92 -Region Avg IOU: 0.375471, Class: 1.000000, Obj: 0.213294, No Obj: 0.003746, Avg Recall: 0.409639, count: 83 -Region Avg IOU: 0.426563, Class: 1.000000, Obj: 0.245498, No Obj: 0.004435, Avg Recall: 0.446602, count: 103 -Region Avg IOU: 0.422954, Class: 1.000000, Obj: 0.229222, No Obj: 0.004029, Avg Recall: 0.395833, count: 96 -Region Avg IOU: 0.428307, Class: 1.000000, Obj: 0.248573, No Obj: 0.004323, Avg Recall: 0.440945, count: 127 -Region Avg IOU: 0.425088, Class: 1.000000, Obj: 0.194538, No Obj: 0.004595, Avg Recall: 0.454545, count: 143 -943: 0.548177, 0.532363 avg, 0.001000 rate, 17.288679 seconds, 241408 images -Loaded: 4.115894 seconds -Region Avg IOU: 0.413133, Class: 1.000000, Obj: 0.226710, No Obj: 0.004460, Avg Recall: 0.456897, count: 116 -Region Avg IOU: 0.419914, Class: 1.000000, Obj: 0.223975, No Obj: 0.004094, Avg Recall: 0.427083, count: 96 -Region Avg IOU: 0.397627, Class: 1.000000, Obj: 0.193271, No Obj: 0.004459, Avg Recall: 0.365079, count: 126 -Region Avg IOU: 0.441449, Class: 1.000000, Obj: 0.221413, No Obj: 0.004553, Avg Recall: 0.459854, count: 137 -Region Avg IOU: 0.432069, Class: 1.000000, Obj: 0.200256, No Obj: 0.004525, Avg Recall: 0.439252, count: 107 -Region Avg IOU: 0.434634, Class: 1.000000, Obj: 0.194201, No Obj: 0.004075, Avg Recall: 0.475610, count: 82 -Region Avg IOU: 0.429738, Class: 1.000000, Obj: 0.265876, No Obj: 0.004181, Avg Recall: 0.481132, count: 106 -Region Avg IOU: 0.358889, Class: 1.000000, Obj: 0.181254, No Obj: 0.004207, Avg Recall: 0.322835, count: 127 -944: 0.540491, 0.533176 avg, 0.001000 rate, 14.967776 seconds, 241664 images -Loaded: 6.328012 seconds -Region Avg IOU: 0.414459, Class: 1.000000, Obj: 0.226458, No Obj: 0.004110, Avg Recall: 0.391753, count: 97 -Region Avg IOU: 0.404942, Class: 1.000000, Obj: 0.226404, No Obj: 0.004058, Avg Recall: 0.428571, count: 84 -Region Avg IOU: 0.400989, Class: 1.000000, Obj: 0.241358, No Obj: 0.003696, Avg Recall: 0.378947, count: 95 -Region Avg IOU: 0.350107, Class: 1.000000, Obj: 0.195310, No Obj: 0.003896, Avg Recall: 0.326316, count: 95 -Region Avg IOU: 0.444651, Class: 1.000000, Obj: 0.240247, No Obj: 0.004422, Avg Recall: 0.463918, count: 97 -Region Avg IOU: 0.403686, Class: 1.000000, Obj: 0.191030, No Obj: 0.004568, Avg Recall: 0.409091, count: 132 -Region Avg IOU: 0.380412, Class: 1.000000, Obj: 0.185555, No Obj: 0.004346, Avg Recall: 0.388889, count: 90 -Region Avg IOU: 0.412588, Class: 1.000000, Obj: 0.213246, No Obj: 0.005135, Avg Recall: 0.436620, count: 142 -945: 0.535725, 0.533431 avg, 0.001000 rate, 15.916986 seconds, 241920 images -Loaded: 4.139446 seconds -Region Avg IOU: 0.381905, Class: 1.000000, Obj: 0.213705, No Obj: 0.004319, Avg Recall: 0.373494, count: 83 -Region Avg IOU: 0.375037, Class: 1.000000, Obj: 0.205873, No Obj: 0.004519, Avg Recall: 0.378151, count: 119 -Region Avg IOU: 0.377348, Class: 1.000000, Obj: 0.235007, No Obj: 0.004161, Avg Recall: 0.367347, count: 98 -Region Avg IOU: 0.403038, Class: 1.000000, Obj: 0.218777, No Obj: 0.004390, Avg Recall: 0.450000, count: 120 -Region Avg IOU: 0.396821, Class: 1.000000, Obj: 0.230035, No Obj: 0.004262, Avg Recall: 0.427083, count: 96 -Region Avg IOU: 0.391092, Class: 1.000000, Obj: 0.248492, No Obj: 0.004467, Avg Recall: 0.372093, count: 86 -Region Avg IOU: 0.390401, Class: 1.000000, Obj: 0.244292, No Obj: 0.004462, Avg Recall: 0.350515, count: 97 -Region Avg IOU: 0.451140, Class: 1.000000, Obj: 0.222433, No Obj: 0.004539, Avg Recall: 0.430233, count: 86 -946: 0.501966, 0.530284 avg, 0.001000 rate, 15.014758 seconds, 242176 images -Loaded: 4.448399 seconds -Region Avg IOU: 0.402157, Class: 1.000000, Obj: 0.215466, No Obj: 0.004398, Avg Recall: 0.425000, count: 120 -Region Avg IOU: 0.378879, Class: 1.000000, Obj: 0.241348, No Obj: 0.004655, Avg Recall: 0.391304, count: 115 -Region Avg IOU: 0.431210, Class: 1.000000, Obj: 0.223230, No Obj: 0.004964, Avg Recall: 0.418440, count: 141 -Region Avg IOU: 0.399718, Class: 1.000000, Obj: 0.228526, No Obj: 0.004196, Avg Recall: 0.398148, count: 108 -Region Avg IOU: 0.414615, Class: 1.000000, Obj: 0.227706, No Obj: 0.005090, Avg Recall: 0.410448, count: 134 -Region Avg IOU: 0.412842, Class: 1.000000, Obj: 0.225020, No Obj: 0.004797, Avg Recall: 0.396694, count: 121 -Region Avg IOU: 0.389783, Class: 1.000000, Obj: 0.201526, No Obj: 0.004815, Avg Recall: 0.392000, count: 125 -Region Avg IOU: 0.434399, Class: 1.000000, Obj: 0.207698, No Obj: 0.004404, Avg Recall: 0.469880, count: 83 -947: 0.619824, 0.539238 avg, 0.001000 rate, 13.866454 seconds, 242432 images -Loaded: 8.650088 seconds -Region Avg IOU: 0.431585, Class: 1.000000, Obj: 0.234195, No Obj: 0.005222, Avg Recall: 0.471014, count: 138 -Region Avg IOU: 0.415308, Class: 1.000000, Obj: 0.215520, No Obj: 0.004827, Avg Recall: 0.421488, count: 121 -Region Avg IOU: 0.391149, Class: 1.000000, Obj: 0.193324, No Obj: 0.004447, Avg Recall: 0.423729, count: 118 -Region Avg IOU: 0.415675, Class: 1.000000, Obj: 0.224859, No Obj: 0.005131, Avg Recall: 0.436620, count: 142 -Region Avg IOU: 0.434112, Class: 1.000000, Obj: 0.218178, No Obj: 0.004961, Avg Recall: 0.443478, count: 115 -Region Avg IOU: 0.402397, Class: 1.000000, Obj: 0.218639, No Obj: 0.004839, Avg Recall: 0.433824, count: 136 -Region Avg IOU: 0.399580, Class: 1.000000, Obj: 0.231121, No Obj: 0.004978, Avg Recall: 0.404412, count: 136 -Region Avg IOU: 0.411136, Class: 1.000000, Obj: 0.178637, No Obj: 0.004915, Avg Recall: 0.460870, count: 115 -948: 0.598860, 0.545201 avg, 0.001000 rate, 18.274073 seconds, 242688 images -Loaded: 4.132889 seconds -Region Avg IOU: 0.432053, Class: 1.000000, Obj: 0.217683, No Obj: 0.004992, Avg Recall: 0.500000, count: 112 -Region Avg IOU: 0.442065, Class: 1.000000, Obj: 0.225011, No Obj: 0.005124, Avg Recall: 0.472222, count: 108 -Region Avg IOU: 0.395519, Class: 1.000000, Obj: 0.197663, No Obj: 0.004925, Avg Recall: 0.360656, count: 122 -Region Avg IOU: 0.435436, Class: 1.000000, Obj: 0.249707, No Obj: 0.004641, Avg Recall: 0.494949, count: 99 -Region Avg IOU: 0.409609, Class: 1.000000, Obj: 0.208832, No Obj: 0.004327, Avg Recall: 0.425926, count: 108 -Region Avg IOU: 0.380514, Class: 1.000000, Obj: 0.205855, No Obj: 0.004678, Avg Recall: 0.390000, count: 100 -Region Avg IOU: 0.408739, Class: 1.000000, Obj: 0.210055, No Obj: 0.004845, Avg Recall: 0.403846, count: 104 -Region Avg IOU: 0.435367, Class: 1.000000, Obj: 0.210496, No Obj: 0.005533, Avg Recall: 0.468531, count: 143 -949: 0.532291, 0.543910 avg, 0.001000 rate, 14.276209 seconds, 242944 images -Loaded: 7.127241 seconds -Region Avg IOU: 0.447509, Class: 1.000000, Obj: 0.262108, No Obj: 0.004556, Avg Recall: 0.512500, count: 80 -Region Avg IOU: 0.373341, Class: 1.000000, Obj: 0.196313, No Obj: 0.004654, Avg Recall: 0.404040, count: 99 -Region Avg IOU: 0.393726, Class: 1.000000, Obj: 0.238120, No Obj: 0.004674, Avg Recall: 0.330097, count: 103 -Region Avg IOU: 0.419939, Class: 1.000000, Obj: 0.246397, No Obj: 0.004600, Avg Recall: 0.445545, count: 101 -Region Avg IOU: 0.411766, Class: 1.000000, Obj: 0.207489, No Obj: 0.005131, Avg Recall: 0.388889, count: 126 -Region Avg IOU: 0.420483, Class: 1.000000, Obj: 0.214300, No Obj: 0.004664, Avg Recall: 0.439252, count: 107 -Region Avg IOU: 0.393807, Class: 1.000000, Obj: 0.244341, No Obj: 0.004594, Avg Recall: 0.423529, count: 85 -Region Avg IOU: 0.404798, Class: 1.000000, Obj: 0.226619, No Obj: 0.004927, Avg Recall: 0.405172, count: 116 -950: 0.594865, 0.549005 avg, 0.001000 rate, 17.428436 seconds, 243200 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 0.000061 seconds -Region Avg IOU: 0.456121, Class: 1.000000, Obj: 0.245827, No Obj: 0.004730, Avg Recall: 0.489933, count: 149 -Region Avg IOU: 0.466093, Class: 1.000000, Obj: 0.256385, No Obj: 0.004973, Avg Recall: 0.556962, count: 158 -Region Avg IOU: 0.497521, Class: 1.000000, Obj: 0.279872, No Obj: 0.004511, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.454957, Class: 1.000000, Obj: 0.264769, No Obj: 0.004821, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.456022, Class: 1.000000, Obj: 0.266500, No Obj: 0.004525, Avg Recall: 0.490909, count: 110 -Region Avg IOU: 0.431021, Class: 1.000000, Obj: 0.236332, No Obj: 0.004846, Avg Recall: 0.475806, count: 124 -Region Avg IOU: 0.470073, Class: 1.000000, Obj: 0.234858, No Obj: 0.004378, Avg Recall: 0.520833, count: 96 -Region Avg IOU: 0.473524, Class: 1.000000, Obj: 0.251959, No Obj: 0.004811, Avg Recall: 0.492188, count: 128 -951: 0.616484, 0.555753 avg, 0.001000 rate, 19.424280 seconds, 243456 images -Loaded: 4.240160 seconds -Region Avg IOU: 0.404021, Class: 1.000000, Obj: 0.220279, No Obj: 0.004996, Avg Recall: 0.414634, count: 123 -Region Avg IOU: 0.435588, Class: 1.000000, Obj: 0.265732, No Obj: 0.004372, Avg Recall: 0.434783, count: 92 -Region Avg IOU: 0.427641, Class: 1.000000, Obj: 0.244624, No Obj: 0.004003, Avg Recall: 0.441667, count: 120 -Region Avg IOU: 0.415811, Class: 1.000000, Obj: 0.230934, No Obj: 0.004315, Avg Recall: 0.463415, count: 123 -Region Avg IOU: 0.491727, Class: 1.000000, Obj: 0.269299, No Obj: 0.004680, Avg Recall: 0.553398, count: 103 -Region Avg IOU: 0.488021, Class: 1.000000, Obj: 0.288735, No Obj: 0.005091, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.449882, Class: 1.000000, Obj: 0.240722, No Obj: 0.004263, Avg Recall: 0.489583, count: 96 -Region Avg IOU: 0.486713, Class: 1.000000, Obj: 0.242094, No Obj: 0.004650, Avg Recall: 0.583333, count: 96 -952: 0.483239, 0.548502 avg, 0.001000 rate, 18.296143 seconds, 243712 images -Loaded: 2.601113 seconds -Region Avg IOU: 0.435351, Class: 1.000000, Obj: 0.277569, No Obj: 0.004274, Avg Recall: 0.430108, count: 93 -Region Avg IOU: 0.486502, Class: 1.000000, Obj: 0.257429, No Obj: 0.004968, Avg Recall: 0.567797, count: 118 -Region Avg IOU: 0.535994, Class: 1.000000, Obj: 0.277968, No Obj: 0.004673, Avg Recall: 0.608247, count: 97 -Region Avg IOU: 0.500431, Class: 1.000000, Obj: 0.253032, No Obj: 0.004813, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.438781, Class: 1.000000, Obj: 0.241521, No Obj: 0.004667, Avg Recall: 0.456522, count: 138 -Region Avg IOU: 0.477425, Class: 1.000000, Obj: 0.285530, No Obj: 0.004465, Avg Recall: 0.482143, count: 112 -Region Avg IOU: 0.448870, Class: 1.000000, Obj: 0.196981, No Obj: 0.003919, Avg Recall: 0.480000, count: 100 -Region Avg IOU: 0.468885, Class: 1.000000, Obj: 0.265468, No Obj: 0.004863, Avg Recall: 0.531532, count: 111 -953: 0.523918, 0.546043 avg, 0.001000 rate, 20.071289 seconds, 243968 images -Loaded: 2.451676 seconds -Region Avg IOU: 0.476183, Class: 1.000000, Obj: 0.230610, No Obj: 0.004750, Avg Recall: 0.525926, count: 135 -Region Avg IOU: 0.473501, Class: 1.000000, Obj: 0.257199, No Obj: 0.004036, Avg Recall: 0.460000, count: 100 -Region Avg IOU: 0.502477, Class: 1.000000, Obj: 0.300477, No Obj: 0.004714, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.479543, Class: 1.000000, Obj: 0.265220, No Obj: 0.004998, Avg Recall: 0.525862, count: 116 -Region Avg IOU: 0.482840, Class: 1.000000, Obj: 0.239703, No Obj: 0.004773, Avg Recall: 0.535088, count: 114 -Region Avg IOU: 0.423074, Class: 1.000000, Obj: 0.224550, No Obj: 0.004310, Avg Recall: 0.467213, count: 122 -Region Avg IOU: 0.471401, Class: 1.000000, Obj: 0.251255, No Obj: 0.004157, Avg Recall: 0.477064, count: 109 -Region Avg IOU: 0.484261, Class: 1.000000, Obj: 0.297831, No Obj: 0.004722, Avg Recall: 0.540984, count: 122 -954: 0.492160, 0.540655 avg, 0.001000 rate, 22.039255 seconds, 244224 images -Loaded: 0.433357 seconds -Region Avg IOU: 0.484529, Class: 1.000000, Obj: 0.234317, No Obj: 0.004372, Avg Recall: 0.559633, count: 109 -Region Avg IOU: 0.500995, Class: 1.000000, Obj: 0.236184, No Obj: 0.004023, Avg Recall: 0.631579, count: 76 -Region Avg IOU: 0.443270, Class: 1.000000, Obj: 0.222710, No Obj: 0.004111, Avg Recall: 0.436170, count: 94 -Region Avg IOU: 0.430210, Class: 1.000000, Obj: 0.232381, No Obj: 0.004247, Avg Recall: 0.481818, count: 110 -Region Avg IOU: 0.462376, Class: 1.000000, Obj: 0.280923, No Obj: 0.004747, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.510307, Class: 1.000000, Obj: 0.282366, No Obj: 0.004729, Avg Recall: 0.600000, count: 95 -Region Avg IOU: 0.482435, Class: 1.000000, Obj: 0.251214, No Obj: 0.004459, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.428165, Class: 1.000000, Obj: 0.201010, No Obj: 0.004382, Avg Recall: 0.440000, count: 150 -955: 0.496337, 0.536223 avg, 0.001000 rate, 21.934410 seconds, 244480 images -Loaded: 0.021761 seconds -Region Avg IOU: 0.452146, Class: 1.000000, Obj: 0.267881, No Obj: 0.004284, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.491112, Class: 1.000000, Obj: 0.264150, No Obj: 0.003877, Avg Recall: 0.545455, count: 77 -Region Avg IOU: 0.503163, Class: 1.000000, Obj: 0.281231, No Obj: 0.004762, Avg Recall: 0.594828, count: 116 -Region Avg IOU: 0.478964, Class: 1.000000, Obj: 0.244189, No Obj: 0.004111, Avg Recall: 0.543103, count: 116 -Region Avg IOU: 0.433690, Class: 1.000000, Obj: 0.212178, No Obj: 0.004606, Avg Recall: 0.440945, count: 127 -Region Avg IOU: 0.494207, Class: 1.000000, Obj: 0.286338, No Obj: 0.004881, Avg Recall: 0.530435, count: 115 -Region Avg IOU: 0.448927, Class: 1.000000, Obj: 0.239888, No Obj: 0.004464, Avg Recall: 0.532787, count: 122 -Region Avg IOU: 0.502622, Class: 1.000000, Obj: 0.240030, No Obj: 0.004429, Avg Recall: 0.572917, count: 96 -956: 0.514343, 0.534035 avg, 0.001000 rate, 19.580393 seconds, 244736 images -Loaded: 3.157937 seconds -Region Avg IOU: 0.444959, Class: 1.000000, Obj: 0.203828, No Obj: 0.004122, Avg Recall: 0.494949, count: 99 -Region Avg IOU: 0.438558, Class: 1.000000, Obj: 0.260717, No Obj: 0.004144, Avg Recall: 0.494118, count: 85 -Region Avg IOU: 0.501617, Class: 1.000000, Obj: 0.270255, No Obj: 0.004725, Avg Recall: 0.571429, count: 112 -Region Avg IOU: 0.487247, Class: 1.000000, Obj: 0.241600, No Obj: 0.004718, Avg Recall: 0.559322, count: 118 -Region Avg IOU: 0.492290, Class: 1.000000, Obj: 0.228442, No Obj: 0.003981, Avg Recall: 0.528090, count: 89 -Region Avg IOU: 0.455740, Class: 1.000000, Obj: 0.195734, No Obj: 0.004264, Avg Recall: 0.482143, count: 112 -Region Avg IOU: 0.443716, Class: 1.000000, Obj: 0.252580, No Obj: 0.004519, Avg Recall: 0.482143, count: 112 -Region Avg IOU: 0.435728, Class: 1.000000, Obj: 0.202867, No Obj: 0.004662, Avg Recall: 0.429630, count: 135 -957: 0.547033, 0.535335 avg, 0.001000 rate, 20.978661 seconds, 244992 images -Loaded: 0.010555 seconds -Region Avg IOU: 0.491941, Class: 1.000000, Obj: 0.253725, No Obj: 0.004583, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.489398, Class: 1.000000, Obj: 0.254616, No Obj: 0.004734, Avg Recall: 0.537190, count: 121 -Region Avg IOU: 0.434283, Class: 1.000000, Obj: 0.207743, No Obj: 0.003606, Avg Recall: 0.473118, count: 93 -Region Avg IOU: 0.460802, Class: 1.000000, Obj: 0.274744, No Obj: 0.004305, Avg Recall: 0.444444, count: 117 -Region Avg IOU: 0.444930, Class: 1.000000, Obj: 0.235800, No Obj: 0.004057, Avg Recall: 0.469027, count: 113 -Region Avg IOU: 0.524709, Class: 1.000000, Obj: 0.220705, No Obj: 0.004844, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.442382, Class: 1.000000, Obj: 0.255402, No Obj: 0.003888, Avg Recall: 0.465909, count: 88 -Region Avg IOU: 0.433112, Class: 1.000000, Obj: 0.231049, No Obj: 0.004480, Avg Recall: 0.398374, count: 123 -958: 0.521391, 0.533941 avg, 0.001000 rate, 19.539299 seconds, 245248 images -Loaded: 1.385518 seconds -Region Avg IOU: 0.512813, Class: 1.000000, Obj: 0.272081, No Obj: 0.004736, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.445360, Class: 1.000000, Obj: 0.213030, No Obj: 0.004284, Avg Recall: 0.504425, count: 113 -Region Avg IOU: 0.442585, Class: 1.000000, Obj: 0.247240, No Obj: 0.004088, Avg Recall: 0.436170, count: 94 -Region Avg IOU: 0.457465, Class: 1.000000, Obj: 0.261484, No Obj: 0.004395, Avg Recall: 0.500000, count: 110 -Region Avg IOU: 0.447392, Class: 1.000000, Obj: 0.235162, No Obj: 0.004648, Avg Recall: 0.447761, count: 134 -Region Avg IOU: 0.482580, Class: 1.000000, Obj: 0.253211, No Obj: 0.004091, Avg Recall: 0.538462, count: 104 -Region Avg IOU: 0.546766, Class: 1.000000, Obj: 0.279316, No Obj: 0.004534, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.479786, Class: 1.000000, Obj: 0.255458, No Obj: 0.004770, Avg Recall: 0.516393, count: 122 -959: 0.522662, 0.532813 avg, 0.001000 rate, 19.579645 seconds, 245504 images -Loaded: 0.870054 seconds -Region Avg IOU: 0.474673, Class: 1.000000, Obj: 0.260095, No Obj: 0.005068, Avg Recall: 0.481481, count: 135 -Region Avg IOU: 0.436107, Class: 1.000000, Obj: 0.281659, No Obj: 0.004068, Avg Recall: 0.387500, count: 80 -Region Avg IOU: 0.482450, Class: 1.000000, Obj: 0.265600, No Obj: 0.004828, Avg Recall: 0.508065, count: 124 -Region Avg IOU: 0.480125, Class: 1.000000, Obj: 0.239902, No Obj: 0.004535, Avg Recall: 0.500000, count: 108 -Region Avg IOU: 0.465315, Class: 1.000000, Obj: 0.264774, No Obj: 0.004857, Avg Recall: 0.491525, count: 118 -Region Avg IOU: 0.498998, Class: 1.000000, Obj: 0.250289, No Obj: 0.004492, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.477636, Class: 1.000000, Obj: 0.248767, No Obj: 0.005011, Avg Recall: 0.515152, count: 132 -Region Avg IOU: 0.456248, Class: 1.000000, Obj: 0.229387, No Obj: 0.004247, Avg Recall: 0.468468, count: 111 -960: 0.515236, 0.531055 avg, 0.001000 rate, 20.405493 seconds, 245760 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 17.060516 seconds -Region Avg IOU: 0.449942, Class: 1.000000, Obj: 0.250097, No Obj: 0.004468, Avg Recall: 0.476744, count: 86 -Region Avg IOU: 0.485537, Class: 1.000000, Obj: 0.284407, No Obj: 0.004814, Avg Recall: 0.546296, count: 108 -Region Avg IOU: 0.436575, Class: 1.000000, Obj: 0.260012, No Obj: 0.004629, Avg Recall: 0.490000, count: 100 -Region Avg IOU: 0.456704, Class: 1.000000, Obj: 0.236488, No Obj: 0.005028, Avg Recall: 0.508333, count: 120 -Region Avg IOU: 0.438613, Class: 1.000000, Obj: 0.229621, No Obj: 0.004542, Avg Recall: 0.472441, count: 127 -Region Avg IOU: 0.438152, Class: 1.000000, Obj: 0.263117, No Obj: 0.005085, Avg Recall: 0.500000, count: 114 -Region Avg IOU: 0.478088, Class: 1.000000, Obj: 0.235929, No Obj: 0.004366, Avg Recall: 0.509615, count: 104 -Region Avg IOU: 0.447816, Class: 1.000000, Obj: 0.250479, No Obj: 0.005081, Avg Recall: 0.436975, count: 119 -961: 0.471626, 0.525112 avg, 0.001000 rate, 14.901933 seconds, 246016 images -Loaded: 5.253531 seconds -Region Avg IOU: 0.406078, Class: 1.000000, Obj: 0.245934, No Obj: 0.004893, Avg Recall: 0.386555, count: 119 -Region Avg IOU: 0.501738, Class: 1.000000, Obj: 0.308861, No Obj: 0.005137, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.420933, Class: 1.000000, Obj: 0.241333, No Obj: 0.004693, Avg Recall: 0.443478, count: 115 -Region Avg IOU: 0.380208, Class: 1.000000, Obj: 0.214924, No Obj: 0.004824, Avg Recall: 0.388350, count: 103 -Region Avg IOU: 0.425609, Class: 1.000000, Obj: 0.268355, No Obj: 0.004520, Avg Recall: 0.450000, count: 100 -Region Avg IOU: 0.411561, Class: 1.000000, Obj: 0.202477, No Obj: 0.004697, Avg Recall: 0.457143, count: 105 -Region Avg IOU: 0.432142, Class: 1.000000, Obj: 0.254737, No Obj: 0.004205, Avg Recall: 0.409091, count: 88 -Region Avg IOU: 0.448817, Class: 1.000000, Obj: 0.217267, No Obj: 0.004141, Avg Recall: 0.478873, count: 71 -962: 0.573271, 0.529928 avg, 0.001000 rate, 18.075689 seconds, 246272 images -Loaded: 2.195744 seconds -Region Avg IOU: 0.464469, Class: 1.000000, Obj: 0.272541, No Obj: 0.004998, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.460433, Class: 1.000000, Obj: 0.279700, No Obj: 0.004345, Avg Recall: 0.583333, count: 96 -Region Avg IOU: 0.418497, Class: 1.000000, Obj: 0.233887, No Obj: 0.004443, Avg Recall: 0.407407, count: 108 -Region Avg IOU: 0.480665, Class: 1.000000, Obj: 0.251935, No Obj: 0.004854, Avg Recall: 0.506024, count: 83 -Region Avg IOU: 0.428693, Class: 1.000000, Obj: 0.241423, No Obj: 0.004848, Avg Recall: 0.467153, count: 137 -Region Avg IOU: 0.418014, Class: 1.000000, Obj: 0.255179, No Obj: 0.004641, Avg Recall: 0.469565, count: 115 -Region Avg IOU: 0.426208, Class: 1.000000, Obj: 0.254534, No Obj: 0.004076, Avg Recall: 0.475000, count: 80 -Region Avg IOU: 0.464061, Class: 1.000000, Obj: 0.249447, No Obj: 0.004469, Avg Recall: 0.530612, count: 98 -963: 0.498971, 0.526832 avg, 0.001000 rate, 20.168148 seconds, 246528 images -Loaded: 1.469512 seconds -Region Avg IOU: 0.475323, Class: 1.000000, Obj: 0.255056, No Obj: 0.005314, Avg Recall: 0.512195, count: 123 -Region Avg IOU: 0.398503, Class: 1.000000, Obj: 0.228343, No Obj: 0.004532, Avg Recall: 0.455556, count: 90 -Region Avg IOU: 0.433223, Class: 1.000000, Obj: 0.241550, No Obj: 0.004430, Avg Recall: 0.477612, count: 67 -Region Avg IOU: 0.424168, Class: 1.000000, Obj: 0.212214, No Obj: 0.004663, Avg Recall: 0.473684, count: 114 -Region Avg IOU: 0.466873, Class: 1.000000, Obj: 0.252191, No Obj: 0.005123, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.445008, Class: 1.000000, Obj: 0.280507, No Obj: 0.004015, Avg Recall: 0.480000, count: 75 -Region Avg IOU: 0.442194, Class: 1.000000, Obj: 0.209926, No Obj: 0.004147, Avg Recall: 0.475728, count: 103 -Region Avg IOU: 0.362656, Class: 1.000000, Obj: 0.244573, No Obj: 0.004302, Avg Recall: 0.330579, count: 121 -964: 0.492347, 0.523384 avg, 0.001000 rate, 16.585276 seconds, 246784 images -Loaded: 2.777570 seconds -Region Avg IOU: 0.458941, Class: 1.000000, Obj: 0.262453, No Obj: 0.004287, Avg Recall: 0.541176, count: 85 -Region Avg IOU: 0.442269, Class: 1.000000, Obj: 0.212746, No Obj: 0.004776, Avg Recall: 0.468468, count: 111 -Region Avg IOU: 0.451226, Class: 1.000000, Obj: 0.251245, No Obj: 0.004307, Avg Recall: 0.468085, count: 94 -Region Avg IOU: 0.407507, Class: 1.000000, Obj: 0.233776, No Obj: 0.004094, Avg Recall: 0.415842, count: 101 -Region Avg IOU: 0.441590, Class: 1.000000, Obj: 0.259076, No Obj: 0.005354, Avg Recall: 0.473214, count: 112 -Region Avg IOU: 0.439450, Class: 1.000000, Obj: 0.249085, No Obj: 0.004317, Avg Recall: 0.444444, count: 99 -Region Avg IOU: 0.457193, Class: 1.000000, Obj: 0.228581, No Obj: 0.004810, Avg Recall: 0.473282, count: 131 -Region Avg IOU: 0.460210, Class: 1.000000, Obj: 0.270479, No Obj: 0.004683, Avg Recall: 0.539823, count: 113 -965: 0.529905, 0.524036 avg, 0.001000 rate, 17.402143 seconds, 247040 images -Loaded: 5.140105 seconds -Region Avg IOU: 0.443444, Class: 1.000000, Obj: 0.228097, No Obj: 0.005190, Avg Recall: 0.475177, count: 141 -Region Avg IOU: 0.455169, Class: 1.000000, Obj: 0.247145, No Obj: 0.004528, Avg Recall: 0.506494, count: 77 -Region Avg IOU: 0.447860, Class: 1.000000, Obj: 0.260879, No Obj: 0.004073, Avg Recall: 0.452632, count: 95 -Region Avg IOU: 0.433397, Class: 1.000000, Obj: 0.221696, No Obj: 0.004617, Avg Recall: 0.454545, count: 99 -Region Avg IOU: 0.433959, Class: 1.000000, Obj: 0.238450, No Obj: 0.004655, Avg Recall: 0.410714, count: 112 -Region Avg IOU: 0.448046, Class: 1.000000, Obj: 0.256832, No Obj: 0.004705, Avg Recall: 0.460784, count: 102 -Region Avg IOU: 0.454567, Class: 1.000000, Obj: 0.230082, No Obj: 0.004941, Avg Recall: 0.465116, count: 129 -Region Avg IOU: 0.440638, Class: 1.000000, Obj: 0.279799, No Obj: 0.005185, Avg Recall: 0.464646, count: 99 -966: 0.456433, 0.517276 avg, 0.001000 rate, 18.222649 seconds, 247296 images -Loaded: 3.791511 seconds -Region Avg IOU: 0.413101, Class: 1.000000, Obj: 0.234543, No Obj: 0.004846, Avg Recall: 0.398148, count: 108 -Region Avg IOU: 0.461614, Class: 1.000000, Obj: 0.296675, No Obj: 0.005156, Avg Recall: 0.494845, count: 97 -Region Avg IOU: 0.455752, Class: 1.000000, Obj: 0.263306, No Obj: 0.004155, Avg Recall: 0.526316, count: 76 -Region Avg IOU: 0.452438, Class: 1.000000, Obj: 0.256483, No Obj: 0.005055, Avg Recall: 0.500000, count: 126 -Region Avg IOU: 0.441166, Class: 1.000000, Obj: 0.248184, No Obj: 0.004586, Avg Recall: 0.500000, count: 90 -Region Avg IOU: 0.409709, Class: 1.000000, Obj: 0.275130, No Obj: 0.005070, Avg Recall: 0.415254, count: 118 -Region Avg IOU: 0.454462, Class: 1.000000, Obj: 0.249452, No Obj: 0.005096, Avg Recall: 0.491525, count: 118 -Region Avg IOU: 0.470736, Class: 1.000000, Obj: 0.266930, No Obj: 0.004222, Avg Recall: 0.531646, count: 79 -967: 0.453807, 0.510929 avg, 0.001000 rate, 19.121214 seconds, 247552 images -Loaded: 1.771002 seconds -Region Avg IOU: 0.443368, Class: 1.000000, Obj: 0.241223, No Obj: 0.004288, Avg Recall: 0.474747, count: 99 -Region Avg IOU: 0.432183, Class: 1.000000, Obj: 0.254228, No Obj: 0.004889, Avg Recall: 0.456311, count: 103 -Region Avg IOU: 0.469275, Class: 1.000000, Obj: 0.237116, No Obj: 0.004699, Avg Recall: 0.490741, count: 108 -Region Avg IOU: 0.480470, Class: 1.000000, Obj: 0.266743, No Obj: 0.004948, Avg Recall: 0.538462, count: 117 -Region Avg IOU: 0.444708, Class: 1.000000, Obj: 0.234270, No Obj: 0.004273, Avg Recall: 0.433628, count: 113 -Region Avg IOU: 0.426630, Class: 1.000000, Obj: 0.277666, No Obj: 0.004906, Avg Recall: 0.483516, count: 91 -Region Avg IOU: 0.439894, Class: 1.000000, Obj: 0.244826, No Obj: 0.004024, Avg Recall: 0.504762, count: 105 -Region Avg IOU: 0.432432, Class: 1.000000, Obj: 0.245448, No Obj: 0.004277, Avg Recall: 0.477273, count: 88 -968: 0.504806, 0.510316 avg, 0.001000 rate, 18.638037 seconds, 247808 images -Loaded: 2.015509 seconds -Region Avg IOU: 0.447731, Class: 1.000000, Obj: 0.231572, No Obj: 0.004557, Avg Recall: 0.484536, count: 97 -Region Avg IOU: 0.446807, Class: 1.000000, Obj: 0.225357, No Obj: 0.005308, Avg Recall: 0.450000, count: 120 -Region Avg IOU: 0.409894, Class: 1.000000, Obj: 0.223288, No Obj: 0.004604, Avg Recall: 0.401869, count: 107 -Region Avg IOU: 0.451959, Class: 1.000000, Obj: 0.241618, No Obj: 0.004660, Avg Recall: 0.465517, count: 116 -Region Avg IOU: 0.490338, Class: 1.000000, Obj: 0.264104, No Obj: 0.004955, Avg Recall: 0.512195, count: 123 -Region Avg IOU: 0.458731, Class: 1.000000, Obj: 0.238377, No Obj: 0.004632, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.464866, Class: 1.000000, Obj: 0.255496, No Obj: 0.004724, Avg Recall: 0.477477, count: 111 -Region Avg IOU: 0.449708, Class: 1.000000, Obj: 0.197187, No Obj: 0.004782, Avg Recall: 0.482759, count: 116 -969: 0.517408, 0.511026 avg, 0.001000 rate, 19.140034 seconds, 248064 images -Loaded: 2.332263 seconds -Region Avg IOU: 0.454533, Class: 1.000000, Obj: 0.255321, No Obj: 0.005355, Avg Recall: 0.464789, count: 142 -Region Avg IOU: 0.510482, Class: 1.000000, Obj: 0.251142, No Obj: 0.004322, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.418865, Class: 1.000000, Obj: 0.263719, No Obj: 0.004677, Avg Recall: 0.436170, count: 94 -Region Avg IOU: 0.426427, Class: 1.000000, Obj: 0.212321, No Obj: 0.004550, Avg Recall: 0.453608, count: 97 -Region Avg IOU: 0.487084, Class: 1.000000, Obj: 0.233708, No Obj: 0.004886, Avg Recall: 0.496296, count: 135 -Region Avg IOU: 0.459491, Class: 1.000000, Obj: 0.249578, No Obj: 0.004037, Avg Recall: 0.460000, count: 100 -Region Avg IOU: 0.460974, Class: 1.000000, Obj: 0.275727, No Obj: 0.004270, Avg Recall: 0.472527, count: 91 -Region Avg IOU: 0.462828, Class: 1.000000, Obj: 0.249869, No Obj: 0.004395, Avg Recall: 0.530000, count: 100 -970: 0.507267, 0.510650 avg, 0.001000 rate, 18.574127 seconds, 248320 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000075 seconds -Region Avg IOU: 0.479584, Class: 1.000000, Obj: 0.222847, No Obj: 0.003678, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.528745, Class: 1.000000, Obj: 0.271633, No Obj: 0.003910, Avg Recall: 0.585938, count: 128 -Region Avg IOU: 0.525456, Class: 1.000000, Obj: 0.293714, No Obj: 0.003464, Avg Recall: 0.640449, count: 89 -Region Avg IOU: 0.539090, Class: 1.000000, Obj: 0.291245, No Obj: 0.003845, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.551222, Class: 1.000000, Obj: 0.285186, No Obj: 0.004004, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.532867, Class: 1.000000, Obj: 0.279216, No Obj: 0.003599, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.537574, Class: 1.000000, Obj: 0.289101, No Obj: 0.003804, Avg Recall: 0.600000, count: 120 -Region Avg IOU: 0.576583, Class: 1.000000, Obj: 0.308515, No Obj: 0.003567, Avg Recall: 0.742268, count: 97 -971: 0.489129, 0.508498 avg, 0.001000 rate, 26.630907 seconds, 248576 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.559593, Class: 1.000000, Obj: 0.253092, No Obj: 0.003940, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.550708, Class: 1.000000, Obj: 0.282643, No Obj: 0.003487, Avg Recall: 0.648352, count: 91 -Region Avg IOU: 0.594626, Class: 1.000000, Obj: 0.285602, No Obj: 0.003989, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.570401, Class: 1.000000, Obj: 0.266256, No Obj: 0.003594, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.547911, Class: 1.000000, Obj: 0.252200, No Obj: 0.003594, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.565432, Class: 1.000000, Obj: 0.259782, No Obj: 0.003554, Avg Recall: 0.662791, count: 86 -Region Avg IOU: 0.538919, Class: 1.000000, Obj: 0.244103, No Obj: 0.003619, Avg Recall: 0.595745, count: 94 -Region Avg IOU: 0.504134, Class: 1.000000, Obj: 0.306357, No Obj: 0.003522, Avg Recall: 0.544554, count: 101 -972: 0.477469, 0.505395 avg, 0.001000 rate, 27.151361 seconds, 248832 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.565165, Class: 1.000000, Obj: 0.247531, No Obj: 0.003775, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.528211, Class: 1.000000, Obj: 0.256553, No Obj: 0.003702, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.527433, Class: 1.000000, Obj: 0.249268, No Obj: 0.003218, Avg Recall: 0.618421, count: 76 -Region Avg IOU: 0.579664, Class: 1.000000, Obj: 0.241640, No Obj: 0.003699, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.595138, Class: 1.000000, Obj: 0.260908, No Obj: 0.003801, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.552504, Class: 1.000000, Obj: 0.242772, No Obj: 0.003722, Avg Recall: 0.653543, count: 127 -Region Avg IOU: 0.579485, Class: 1.000000, Obj: 0.254997, No Obj: 0.003526, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.569820, Class: 1.000000, Obj: 0.229504, No Obj: 0.004093, Avg Recall: 0.699187, count: 123 -973: 0.505474, 0.505403 avg, 0.001000 rate, 26.490154 seconds, 249088 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.569733, Class: 1.000000, Obj: 0.262019, No Obj: 0.003201, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.549543, Class: 1.000000, Obj: 0.250367, No Obj: 0.003224, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.566055, Class: 1.000000, Obj: 0.255900, No Obj: 0.003503, Avg Recall: 0.658915, count: 129 -Region Avg IOU: 0.538041, Class: 1.000000, Obj: 0.217227, No Obj: 0.003495, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.590620, Class: 1.000000, Obj: 0.235897, No Obj: 0.003668, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.600463, Class: 1.000000, Obj: 0.281137, No Obj: 0.003392, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.515131, Class: 1.000000, Obj: 0.194214, No Obj: 0.003418, Avg Recall: 0.592308, count: 130 -Region Avg IOU: 0.501722, Class: 1.000000, Obj: 0.229896, No Obj: 0.003292, Avg Recall: 0.572816, count: 103 -974: 0.515012, 0.506364 avg, 0.001000 rate, 26.634384 seconds, 249344 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.601223, Class: 1.000000, Obj: 0.248001, No Obj: 0.003286, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.574266, Class: 1.000000, Obj: 0.243552, No Obj: 0.003368, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.594617, Class: 1.000000, Obj: 0.246786, No Obj: 0.003550, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.531795, Class: 1.000000, Obj: 0.243031, No Obj: 0.003246, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.579463, Class: 1.000000, Obj: 0.236985, No Obj: 0.003581, Avg Recall: 0.694118, count: 85 -Region Avg IOU: 0.551432, Class: 1.000000, Obj: 0.273614, No Obj: 0.003001, Avg Recall: 0.641975, count: 81 -Region Avg IOU: 0.574720, Class: 1.000000, Obj: 0.276686, No Obj: 0.003502, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.579827, Class: 1.000000, Obj: 0.256325, No Obj: 0.003461, Avg Recall: 0.714286, count: 98 -975: 0.466993, 0.502427 avg, 0.001000 rate, 26.231979 seconds, 249600 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.549203, Class: 1.000000, Obj: 0.241466, No Obj: 0.003447, Avg Recall: 0.632479, count: 117 -Region Avg IOU: 0.547541, Class: 1.000000, Obj: 0.205928, No Obj: 0.002994, Avg Recall: 0.675214, count: 117 -Region Avg IOU: 0.554241, Class: 1.000000, Obj: 0.237091, No Obj: 0.003214, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.556527, Class: 1.000000, Obj: 0.215394, No Obj: 0.003081, Avg Recall: 0.663866, count: 119 -Region Avg IOU: 0.509620, Class: 1.000000, Obj: 0.199465, No Obj: 0.002842, Avg Recall: 0.611765, count: 85 -Region Avg IOU: 0.543971, Class: 1.000000, Obj: 0.235990, No Obj: 0.003118, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.554513, Class: 1.000000, Obj: 0.269184, No Obj: 0.002653, Avg Recall: 0.604938, count: 81 -Region Avg IOU: 0.560975, Class: 1.000000, Obj: 0.220651, No Obj: 0.002872, Avg Recall: 0.696078, count: 102 -976: 0.501429, 0.502327 avg, 0.001000 rate, 26.529133 seconds, 249856 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.514990, Class: 1.000000, Obj: 0.226503, No Obj: 0.002967, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.596279, Class: 1.000000, Obj: 0.229315, No Obj: 0.003172, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.561529, Class: 1.000000, Obj: 0.242078, No Obj: 0.002707, Avg Recall: 0.678161, count: 87 -Region Avg IOU: 0.553180, Class: 1.000000, Obj: 0.252850, No Obj: 0.003114, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.573503, Class: 1.000000, Obj: 0.188333, No Obj: 0.003032, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.568412, Class: 1.000000, Obj: 0.261700, No Obj: 0.003414, Avg Recall: 0.663866, count: 119 -Region Avg IOU: 0.549527, Class: 1.000000, Obj: 0.215251, No Obj: 0.003420, Avg Recall: 0.651852, count: 135 -Region Avg IOU: 0.554870, Class: 1.000000, Obj: 0.222999, No Obj: 0.003230, Avg Recall: 0.596330, count: 109 -977: 0.478935, 0.499988 avg, 0.001000 rate, 25.366220 seconds, 250112 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.541787, Class: 1.000000, Obj: 0.245910, No Obj: 0.003313, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.607401, Class: 1.000000, Obj: 0.238329, No Obj: 0.003225, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.585903, Class: 1.000000, Obj: 0.228489, No Obj: 0.003495, Avg Recall: 0.666667, count: 138 -Region Avg IOU: 0.584538, Class: 1.000000, Obj: 0.246593, No Obj: 0.003315, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.499817, Class: 1.000000, Obj: 0.244758, No Obj: 0.003110, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.528991, Class: 1.000000, Obj: 0.198120, No Obj: 0.003075, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.532797, Class: 1.000000, Obj: 0.274395, No Obj: 0.002942, Avg Recall: 0.600000, count: 80 -Region Avg IOU: 0.542994, Class: 1.000000, Obj: 0.231316, No Obj: 0.003101, Avg Recall: 0.632653, count: 98 -978: 0.488318, 0.498821 avg, 0.001000 rate, 26.366867 seconds, 250368 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.550086, Class: 1.000000, Obj: 0.262963, No Obj: 0.003310, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.584101, Class: 1.000000, Obj: 0.249727, No Obj: 0.003505, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.514009, Class: 1.000000, Obj: 0.236163, No Obj: 0.003429, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.562330, Class: 1.000000, Obj: 0.255328, No Obj: 0.003188, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.533765, Class: 1.000000, Obj: 0.211931, No Obj: 0.003078, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.581151, Class: 1.000000, Obj: 0.244689, No Obj: 0.003359, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.530771, Class: 1.000000, Obj: 0.218397, No Obj: 0.003203, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.574087, Class: 1.000000, Obj: 0.292856, No Obj: 0.003252, Avg Recall: 0.663636, count: 110 -979: 0.521041, 0.501043 avg, 0.001000 rate, 27.164087 seconds, 250624 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.576899, Class: 1.000000, Obj: 0.288303, No Obj: 0.003630, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.523357, Class: 1.000000, Obj: 0.265812, No Obj: 0.003445, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.564768, Class: 1.000000, Obj: 0.289676, No Obj: 0.003713, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.565299, Class: 1.000000, Obj: 0.304680, No Obj: 0.003563, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.563088, Class: 1.000000, Obj: 0.236624, No Obj: 0.003146, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.550285, Class: 1.000000, Obj: 0.277297, No Obj: 0.003235, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.550664, Class: 1.000000, Obj: 0.257565, No Obj: 0.003470, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.535860, Class: 1.000000, Obj: 0.278077, No Obj: 0.003355, Avg Recall: 0.585586, count: 111 -980: 0.528190, 0.503757 avg, 0.001000 rate, 26.661961 seconds, 250880 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.829442 seconds -Region Avg IOU: 0.476935, Class: 1.000000, Obj: 0.253502, No Obj: 0.004508, Avg Recall: 0.540541, count: 148 -Region Avg IOU: 0.493756, Class: 1.000000, Obj: 0.237189, No Obj: 0.004292, Avg Recall: 0.577236, count: 123 -Region Avg IOU: 0.503331, Class: 1.000000, Obj: 0.238859, No Obj: 0.003696, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.500834, Class: 1.000000, Obj: 0.246703, No Obj: 0.003859, Avg Recall: 0.500000, count: 92 -Region Avg IOU: 0.508350, Class: 1.000000, Obj: 0.310888, No Obj: 0.004467, Avg Recall: 0.565891, count: 129 -Region Avg IOU: 0.514636, Class: 1.000000, Obj: 0.290419, No Obj: 0.004196, Avg Recall: 0.584746, count: 118 -Region Avg IOU: 0.519314, Class: 1.000000, Obj: 0.282391, No Obj: 0.004160, Avg Recall: 0.626374, count: 91 -Region Avg IOU: 0.479708, Class: 1.000000, Obj: 0.214189, No Obj: 0.003603, Avg Recall: 0.540000, count: 100 -981: 0.531348, 0.506517 avg, 0.001000 rate, 22.441999 seconds, 251136 images -Loaded: 1.308167 seconds -Region Avg IOU: 0.425406, Class: 1.000000, Obj: 0.202143, No Obj: 0.003937, Avg Recall: 0.420000, count: 100 -Region Avg IOU: 0.433541, Class: 1.000000, Obj: 0.234116, No Obj: 0.004318, Avg Recall: 0.447761, count: 134 -Region Avg IOU: 0.525055, Class: 1.000000, Obj: 0.263348, No Obj: 0.004038, Avg Recall: 0.589041, count: 73 -Region Avg IOU: 0.528643, Class: 1.000000, Obj: 0.262088, No Obj: 0.004659, Avg Recall: 0.581967, count: 122 -Region Avg IOU: 0.479121, Class: 1.000000, Obj: 0.257031, No Obj: 0.004510, Avg Recall: 0.495413, count: 109 -Region Avg IOU: 0.506854, Class: 1.000000, Obj: 0.263775, No Obj: 0.004386, Avg Recall: 0.542553, count: 94 -Region Avg IOU: 0.506476, Class: 1.000000, Obj: 0.277328, No Obj: 0.004462, Avg Recall: 0.560345, count: 116 -Region Avg IOU: 0.495550, Class: 1.000000, Obj: 0.237741, No Obj: 0.004056, Avg Recall: 0.544554, count: 101 -982: 0.535437, 0.509409 avg, 0.001000 rate, 23.813976 seconds, 251392 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.490385, Class: 1.000000, Obj: 0.265907, No Obj: 0.004030, Avg Recall: 0.546512, count: 86 -Region Avg IOU: 0.516602, Class: 1.000000, Obj: 0.241613, No Obj: 0.004511, Avg Recall: 0.588235, count: 119 -Region Avg IOU: 0.481175, Class: 1.000000, Obj: 0.256287, No Obj: 0.005156, Avg Recall: 0.575163, count: 153 -Region Avg IOU: 0.484396, Class: 1.000000, Obj: 0.263014, No Obj: 0.003945, Avg Recall: 0.563636, count: 110 -Region Avg IOU: 0.519826, Class: 1.000000, Obj: 0.284172, No Obj: 0.004425, Avg Recall: 0.520833, count: 96 -Region Avg IOU: 0.447836, Class: 1.000000, Obj: 0.242393, No Obj: 0.004441, Avg Recall: 0.462963, count: 108 -Region Avg IOU: 0.516050, Class: 1.000000, Obj: 0.291858, No Obj: 0.004723, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.508839, Class: 1.000000, Obj: 0.315104, No Obj: 0.004460, Avg Recall: 0.650602, count: 83 -983: 0.523818, 0.510849 avg, 0.001000 rate, 21.466091 seconds, 251648 images -Loaded: 0.601638 seconds -Region Avg IOU: 0.472646, Class: 1.000000, Obj: 0.244846, No Obj: 0.004140, Avg Recall: 0.491228, count: 114 -Region Avg IOU: 0.521028, Class: 1.000000, Obj: 0.295688, No Obj: 0.004331, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.520912, Class: 1.000000, Obj: 0.312565, No Obj: 0.004544, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.470681, Class: 1.000000, Obj: 0.265512, No Obj: 0.004599, Avg Recall: 0.463415, count: 123 -Region Avg IOU: 0.502878, Class: 1.000000, Obj: 0.304550, No Obj: 0.004786, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.487400, Class: 1.000000, Obj: 0.214693, No Obj: 0.004479, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.489957, Class: 1.000000, Obj: 0.308971, No Obj: 0.004278, Avg Recall: 0.538462, count: 104 -Region Avg IOU: 0.409669, Class: 1.000000, Obj: 0.257627, No Obj: 0.004174, Avg Recall: 0.408602, count: 93 -984: 0.478046, 0.507569 avg, 0.001000 rate, 21.024179 seconds, 251904 images -Loaded: 1.353840 seconds -Region Avg IOU: 0.504174, Class: 1.000000, Obj: 0.239653, No Obj: 0.004910, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.490595, Class: 1.000000, Obj: 0.280537, No Obj: 0.004140, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.486897, Class: 1.000000, Obj: 0.256297, No Obj: 0.004004, Avg Recall: 0.520000, count: 100 -Region Avg IOU: 0.453042, Class: 1.000000, Obj: 0.216694, No Obj: 0.003821, Avg Recall: 0.482143, count: 112 -Region Avg IOU: 0.476784, Class: 1.000000, Obj: 0.258147, No Obj: 0.003936, Avg Recall: 0.495575, count: 113 -Region Avg IOU: 0.463972, Class: 1.000000, Obj: 0.278214, No Obj: 0.004363, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.504951, Class: 1.000000, Obj: 0.261316, No Obj: 0.004167, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.523100, Class: 1.000000, Obj: 0.260442, No Obj: 0.004609, Avg Recall: 0.610619, count: 113 -985: 0.487103, 0.505522 avg, 0.001000 rate, 21.585209 seconds, 252160 images -Loaded: 0.963238 seconds -Region Avg IOU: 0.519709, Class: 1.000000, Obj: 0.278400, No Obj: 0.004408, Avg Recall: 0.565657, count: 99 -Region Avg IOU: 0.424371, Class: 1.000000, Obj: 0.211460, No Obj: 0.004399, Avg Recall: 0.447552, count: 143 -Region Avg IOU: 0.459038, Class: 1.000000, Obj: 0.230878, No Obj: 0.004208, Avg Recall: 0.446281, count: 121 -Region Avg IOU: 0.471320, Class: 1.000000, Obj: 0.274718, No Obj: 0.003704, Avg Recall: 0.540230, count: 87 -Region Avg IOU: 0.532931, Class: 1.000000, Obj: 0.313881, No Obj: 0.004255, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.426359, Class: 1.000000, Obj: 0.212144, No Obj: 0.004515, Avg Recall: 0.504065, count: 123 -Region Avg IOU: 0.499150, Class: 1.000000, Obj: 0.288297, No Obj: 0.004423, Avg Recall: 0.571429, count: 119 -Region Avg IOU: 0.476546, Class: 1.000000, Obj: 0.287611, No Obj: 0.003968, Avg Recall: 0.482456, count: 114 -986: 0.524992, 0.507469 avg, 0.001000 rate, 21.312109 seconds, 252416 images -Loaded: 0.954227 seconds -Region Avg IOU: 0.445019, Class: 1.000000, Obj: 0.278136, No Obj: 0.004092, Avg Recall: 0.478261, count: 115 -Region Avg IOU: 0.458618, Class: 1.000000, Obj: 0.248309, No Obj: 0.004067, Avg Recall: 0.478723, count: 94 -Region Avg IOU: 0.475797, Class: 1.000000, Obj: 0.248273, No Obj: 0.003727, Avg Recall: 0.457447, count: 94 -Region Avg IOU: 0.480670, Class: 1.000000, Obj: 0.209286, No Obj: 0.004219, Avg Recall: 0.559322, count: 118 -Region Avg IOU: 0.470603, Class: 1.000000, Obj: 0.245250, No Obj: 0.004341, Avg Recall: 0.500000, count: 136 -Region Avg IOU: 0.471278, Class: 1.000000, Obj: 0.252154, No Obj: 0.004274, Avg Recall: 0.508333, count: 120 -Region Avg IOU: 0.448024, Class: 1.000000, Obj: 0.277999, No Obj: 0.003851, Avg Recall: 0.494382, count: 89 -Region Avg IOU: 0.506904, Class: 1.000000, Obj: 0.258140, No Obj: 0.003974, Avg Recall: 0.589474, count: 95 -987: 0.474364, 0.504159 avg, 0.001000 rate, 22.842560 seconds, 252672 images -Loaded: 0.228981 seconds -Region Avg IOU: 0.441027, Class: 1.000000, Obj: 0.240196, No Obj: 0.004381, Avg Recall: 0.420635, count: 126 -Region Avg IOU: 0.400503, Class: 1.000000, Obj: 0.178264, No Obj: 0.004208, Avg Recall: 0.425373, count: 134 -Region Avg IOU: 0.514728, Class: 1.000000, Obj: 0.229825, No Obj: 0.003933, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.487000, Class: 1.000000, Obj: 0.265488, No Obj: 0.003746, Avg Recall: 0.542857, count: 105 -Region Avg IOU: 0.528737, Class: 1.000000, Obj: 0.219056, No Obj: 0.004087, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.452016, Class: 1.000000, Obj: 0.230737, No Obj: 0.003688, Avg Recall: 0.464912, count: 114 -Region Avg IOU: 0.487374, Class: 1.000000, Obj: 0.238385, No Obj: 0.004405, Avg Recall: 0.560000, count: 125 -Region Avg IOU: 0.463828, Class: 1.000000, Obj: 0.226519, No Obj: 0.003755, Avg Recall: 0.478992, count: 119 -988: 0.519554, 0.505698 avg, 0.001000 rate, 21.929348 seconds, 252928 images -Loaded: 0.431507 seconds -Region Avg IOU: 0.418284, Class: 1.000000, Obj: 0.183908, No Obj: 0.003645, Avg Recall: 0.466667, count: 105 -Region Avg IOU: 0.477203, Class: 1.000000, Obj: 0.241339, No Obj: 0.004223, Avg Recall: 0.566667, count: 120 -Region Avg IOU: 0.463354, Class: 1.000000, Obj: 0.254842, No Obj: 0.003865, Avg Recall: 0.482143, count: 112 -Region Avg IOU: 0.481779, Class: 1.000000, Obj: 0.255502, No Obj: 0.004044, Avg Recall: 0.521739, count: 92 -Region Avg IOU: 0.481267, Class: 1.000000, Obj: 0.265418, No Obj: 0.003838, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.471652, Class: 1.000000, Obj: 0.228403, No Obj: 0.003943, Avg Recall: 0.541667, count: 96 -Region Avg IOU: 0.502986, Class: 1.000000, Obj: 0.249373, No Obj: 0.003712, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.479860, Class: 1.000000, Obj: 0.303461, No Obj: 0.003939, Avg Recall: 0.539216, count: 102 -989: 0.539523, 0.509081 avg, 0.001000 rate, 22.922688 seconds, 253184 images -Loaded: 0.272506 seconds -Region Avg IOU: 0.474280, Class: 1.000000, Obj: 0.250170, No Obj: 0.004428, Avg Recall: 0.512397, count: 121 -Region Avg IOU: 0.504818, Class: 1.000000, Obj: 0.281068, No Obj: 0.003823, Avg Recall: 0.590361, count: 83 -Region Avg IOU: 0.496779, Class: 1.000000, Obj: 0.301467, No Obj: 0.003837, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.487599, Class: 1.000000, Obj: 0.263600, No Obj: 0.004695, Avg Recall: 0.519231, count: 156 -Region Avg IOU: 0.497702, Class: 1.000000, Obj: 0.273256, No Obj: 0.004313, Avg Recall: 0.538462, count: 117 -Region Avg IOU: 0.505594, Class: 1.000000, Obj: 0.268084, No Obj: 0.003846, Avg Recall: 0.560440, count: 91 -Region Avg IOU: 0.466496, Class: 1.000000, Obj: 0.218693, No Obj: 0.004003, Avg Recall: 0.489362, count: 94 -Region Avg IOU: 0.527915, Class: 1.000000, Obj: 0.270459, No Obj: 0.004352, Avg Recall: 0.663793, count: 116 -990: 0.462929, 0.504466 avg, 0.001000 rate, 23.161564 seconds, 253440 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000068 seconds -Region Avg IOU: 0.540563, Class: 1.000000, Obj: 0.249900, No Obj: 0.003719, Avg Recall: 0.606299, count: 127 -Region Avg IOU: 0.551097, Class: 1.000000, Obj: 0.286273, No Obj: 0.003322, Avg Recall: 0.642857, count: 84 -Region Avg IOU: 0.493179, Class: 1.000000, Obj: 0.301653, No Obj: 0.003091, Avg Recall: 0.562500, count: 96 -Region Avg IOU: 0.556087, Class: 1.000000, Obj: 0.311744, No Obj: 0.003860, Avg Recall: 0.650000, count: 140 -Region Avg IOU: 0.579141, Class: 1.000000, Obj: 0.332543, No Obj: 0.003576, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.532428, Class: 1.000000, Obj: 0.223717, No Obj: 0.004080, Avg Recall: 0.610294, count: 136 -Region Avg IOU: 0.590909, Class: 1.000000, Obj: 0.309983, No Obj: 0.003787, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.553900, Class: 1.000000, Obj: 0.265682, No Obj: 0.003661, Avg Recall: 0.606299, count: 127 -991: 0.553958, 0.509415 avg, 0.001000 rate, 26.400612 seconds, 253696 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.566257, Class: 1.000000, Obj: 0.264685, No Obj: 0.003646, Avg Recall: 0.669291, count: 127 -Region Avg IOU: 0.553530, Class: 1.000000, Obj: 0.260323, No Obj: 0.003371, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.556026, Class: 1.000000, Obj: 0.256752, No Obj: 0.003814, Avg Recall: 0.609929, count: 141 -Region Avg IOU: 0.560881, Class: 1.000000, Obj: 0.257091, No Obj: 0.003855, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.586795, Class: 1.000000, Obj: 0.290448, No Obj: 0.003743, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.571687, Class: 1.000000, Obj: 0.269710, No Obj: 0.003179, Avg Recall: 0.705882, count: 85 -Region Avg IOU: 0.531582, Class: 1.000000, Obj: 0.240958, No Obj: 0.003310, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.523228, Class: 1.000000, Obj: 0.228203, No Obj: 0.003549, Avg Recall: 0.610169, count: 118 -992: 0.550700, 0.513543 avg, 0.001000 rate, 26.432478 seconds, 253952 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.555853, Class: 1.000000, Obj: 0.258991, No Obj: 0.004097, Avg Recall: 0.702479, count: 121 -Region Avg IOU: 0.542959, Class: 1.000000, Obj: 0.269466, No Obj: 0.003409, Avg Recall: 0.674419, count: 86 -Region Avg IOU: 0.550004, Class: 1.000000, Obj: 0.266266, No Obj: 0.003765, Avg Recall: 0.638462, count: 130 -Region Avg IOU: 0.567946, Class: 1.000000, Obj: 0.293732, No Obj: 0.003833, Avg Recall: 0.734375, count: 128 -Region Avg IOU: 0.542669, Class: 1.000000, Obj: 0.229742, No Obj: 0.003395, Avg Recall: 0.598425, count: 127 -Region Avg IOU: 0.571983, Class: 1.000000, Obj: 0.269053, No Obj: 0.003757, Avg Recall: 0.674419, count: 129 -Region Avg IOU: 0.560303, Class: 1.000000, Obj: 0.269307, No Obj: 0.003552, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.551630, Class: 1.000000, Obj: 0.280304, No Obj: 0.003470, Avg Recall: 0.669903, count: 103 -993: 0.544553, 0.516644 avg, 0.001000 rate, 26.387896 seconds, 254208 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.513106, Class: 1.000000, Obj: 0.239540, No Obj: 0.003477, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.562448, Class: 1.000000, Obj: 0.300114, No Obj: 0.003351, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.589989, Class: 1.000000, Obj: 0.263320, No Obj: 0.003746, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.581099, Class: 1.000000, Obj: 0.312436, No Obj: 0.003628, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.583146, Class: 1.000000, Obj: 0.232052, No Obj: 0.003225, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.561678, Class: 1.000000, Obj: 0.281226, No Obj: 0.003594, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.583064, Class: 1.000000, Obj: 0.239052, No Obj: 0.003495, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.556097, Class: 1.000000, Obj: 0.230920, No Obj: 0.003465, Avg Recall: 0.644860, count: 107 -994: 0.477411, 0.512721 avg, 0.001000 rate, 25.640747 seconds, 254464 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.551350, Class: 1.000000, Obj: 0.248626, No Obj: 0.003436, Avg Recall: 0.630252, count: 119 -Region Avg IOU: 0.540531, Class: 1.000000, Obj: 0.277435, No Obj: 0.003681, Avg Recall: 0.610169, count: 118 -Region Avg IOU: 0.561872, Class: 1.000000, Obj: 0.290456, No Obj: 0.003453, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.533919, Class: 1.000000, Obj: 0.265136, No Obj: 0.003134, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.607725, Class: 1.000000, Obj: 0.279445, No Obj: 0.003481, Avg Recall: 0.753247, count: 77 -Region Avg IOU: 0.568150, Class: 1.000000, Obj: 0.269097, No Obj: 0.002944, Avg Recall: 0.688889, count: 90 -Region Avg IOU: 0.558799, Class: 1.000000, Obj: 0.267021, No Obj: 0.002963, Avg Recall: 0.652174, count: 92 -Region Avg IOU: 0.524566, Class: 1.000000, Obj: 0.248910, No Obj: 0.003160, Avg Recall: 0.580000, count: 100 -995: 0.482135, 0.509663 avg, 0.001000 rate, 26.799047 seconds, 254720 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.622016, Class: 1.000000, Obj: 0.266279, No Obj: 0.003683, Avg Recall: 0.826446, count: 121 -Region Avg IOU: 0.557296, Class: 1.000000, Obj: 0.264189, No Obj: 0.003329, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.587935, Class: 1.000000, Obj: 0.236493, No Obj: 0.003292, Avg Recall: 0.669173, count: 133 -Region Avg IOU: 0.572187, Class: 1.000000, Obj: 0.290237, No Obj: 0.004031, Avg Recall: 0.673913, count: 138 -Region Avg IOU: 0.556898, Class: 1.000000, Obj: 0.251025, No Obj: 0.003686, Avg Recall: 0.600000, count: 145 -Region Avg IOU: 0.520266, Class: 1.000000, Obj: 0.219137, No Obj: 0.003556, Avg Recall: 0.612903, count: 124 -Region Avg IOU: 0.573698, Class: 1.000000, Obj: 0.313795, No Obj: 0.003862, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.578566, Class: 1.000000, Obj: 0.225812, No Obj: 0.003643, Avg Recall: 0.720000, count: 125 -996: 0.590232, 0.517719 avg, 0.001000 rate, 25.282883 seconds, 254976 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.567047, Class: 1.000000, Obj: 0.255707, No Obj: 0.002958, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.535475, Class: 1.000000, Obj: 0.236374, No Obj: 0.003536, Avg Recall: 0.661765, count: 136 -Region Avg IOU: 0.582639, Class: 1.000000, Obj: 0.270180, No Obj: 0.003285, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.554062, Class: 1.000000, Obj: 0.239042, No Obj: 0.003027, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.579189, Class: 1.000000, Obj: 0.254048, No Obj: 0.003492, Avg Recall: 0.691729, count: 133 -Region Avg IOU: 0.536967, Class: 1.000000, Obj: 0.272643, No Obj: 0.002901, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.564138, Class: 1.000000, Obj: 0.250171, No Obj: 0.003097, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.551222, Class: 1.000000, Obj: 0.233221, No Obj: 0.003172, Avg Recall: 0.632479, count: 117 -997: 0.567753, 0.522723 avg, 0.001000 rate, 27.399834 seconds, 255232 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.532621, Class: 1.000000, Obj: 0.237217, No Obj: 0.003134, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.565148, Class: 1.000000, Obj: 0.250977, No Obj: 0.003048, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.542170, Class: 1.000000, Obj: 0.251144, No Obj: 0.003267, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.567346, Class: 1.000000, Obj: 0.234552, No Obj: 0.003313, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.595672, Class: 1.000000, Obj: 0.250585, No Obj: 0.003150, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.560215, Class: 1.000000, Obj: 0.272774, No Obj: 0.002954, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.514848, Class: 1.000000, Obj: 0.229758, No Obj: 0.003401, Avg Recall: 0.584615, count: 130 -Region Avg IOU: 0.553430, Class: 1.000000, Obj: 0.244262, No Obj: 0.002971, Avg Recall: 0.654545, count: 110 -998: 0.483077, 0.518758 avg, 0.001000 rate, 25.638210 seconds, 255488 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.575087, Class: 1.000000, Obj: 0.220210, No Obj: 0.003137, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.587600, Class: 1.000000, Obj: 0.253621, No Obj: 0.003144, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.544266, Class: 1.000000, Obj: 0.270417, No Obj: 0.003596, Avg Recall: 0.620438, count: 137 -Region Avg IOU: 0.532268, Class: 1.000000, Obj: 0.270779, No Obj: 0.003259, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.529290, Class: 1.000000, Obj: 0.251987, No Obj: 0Saving weights to ./person9300/backup/tyvp_93_9c512_1000.weights -.003163, Avg Recall: 0.634146, count: 123 -Region Avg IOU: 0.602745, Class: 1.000000, Obj: 0.257714, No Obj: 0.003249, Avg Recall: 0.752809, count: 89 -Region Avg IOU: 0.604699, Class: 1.000000, Obj: 0.281362, No Obj: 0.003339, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.570382, Class: 1.000000, Obj: 0.257528, No Obj: 0.003210, Avg Recall: 0.653465, count: 101 -999: 0.487921, 0.515674 avg, 0.001000 rate, 28.066839 seconds, 255744 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.576646, Class: 1.000000, Obj: 0.249532, No Obj: 0.003373, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.596691, Class: 1.000000, Obj: 0.224410, No Obj: 0.003604, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.589367, Class: 1.000000, Obj: 0.277892, No Obj: 0.002878, Avg Recall: 0.736111, count: 72 -Region Avg IOU: 0.565212, Class: 1.000000, Obj: 0.247465, No Obj: 0.002824, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.501969, Class: 1.000000, Obj: 0.224340, No Obj: 0.003403, Avg Recall: 0.540984, count: 122 -Region Avg IOU: 0.594199, Class: 1.000000, Obj: 0.262267, No Obj: 0.002816, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.543691, Class: 1.000000, Obj: 0.252581, No Obj: 0.003357, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.562815, Class: 1.000000, Obj: 0.253884, No Obj: 0.003488, Avg Recall: 0.699115, count: 113 -1000: 0.481439, 0.512251 avg, 0.001000 rate, 28.293348 seconds, 256000 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 19.995964 seconds -Region Avg IOU: 0.521265, Class: 1.000000, Obj: 0.274712, No Obj: 0.004102, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.561341, Class: 1.000000, Obj: 0.251690, No Obj: 0.003614, Avg Recall: 0.647541, count: 122 -Region Avg IOU: 0.526231, Class: 1.000000, Obj: 0.268943, No Obj: 0.003330, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.489649, Class: 1.000000, Obj: 0.262435, No Obj: 0.003406, Avg Recall: 0.566372, count: 113 -Region Avg IOU: 0.548973, Class: 1.000000, Obj: 0.219144, No Obj: 0.003592, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.530718, Class: 1.000000, Obj: 0.261860, No Obj: 0.003295, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.520977, Class: 1.000000, Obj: 0.230611, No Obj: 0.003500, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.543788, Class: 1.000000, Obj: 0.252345, No Obj: 0.003815, Avg Recall: 0.622642, count: 106 -1001: 0.528019, 0.513828 avg, 0.001000 rate, 23.629517 seconds, 256256 images -Loaded: 0.051121 seconds -Region Avg IOU: 0.562221, Class: 1.000000, Obj: 0.270079, No Obj: 0.003859, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.502321, Class: 1.000000, Obj: 0.224562, No Obj: 0.003548, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.545467, Class: 1.000000, Obj: 0.254252, No Obj: 0.003821, Avg Recall: 0.617886, count: 123 -Region Avg IOU: 0.480118, Class: 1.000000, Obj: 0.247766, No Obj: 0.003371, Avg Recall: 0.540000, count: 100 -Region Avg IOU: 0.521567, Class: 1.000000, Obj: 0.282214, No Obj: 0.003723, Avg Recall: 0.592593, count: 108 -Region Avg IOU: 0.497811, Class: 1.000000, Obj: 0.259143, No Obj: 0.003328, Avg Recall: 0.549020, count: 102 -Region Avg IOU: 0.502392, Class: 1.000000, Obj: 0.245154, No Obj: 0.003683, Avg Recall: 0.572727, count: 110 -Region Avg IOU: 0.542007, Class: 1.000000, Obj: 0.278895, No Obj: 0.003626, Avg Recall: 0.678261, count: 115 -1002: 0.497441, 0.512189 avg, 0.001000 rate, 23.934620 seconds, 256512 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.568736, Class: 1.000000, Obj: 0.302850, No Obj: 0.003864, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.508044, Class: 1.000000, Obj: 0.244511, No Obj: 0.003078, Avg Recall: 0.540541, count: 74 -Region Avg IOU: 0.512071, Class: 1.000000, Obj: 0.250122, No Obj: 0.003826, Avg Recall: 0.508475, count: 118 -Region Avg IOU: 0.550928, Class: 1.000000, Obj: 0.301414, No Obj: 0.003097, Avg Recall: 0.623377, count: 77 -Region Avg IOU: 0.536430, Class: 1.000000, Obj: 0.257026, No Obj: 0.003893, Avg Recall: 0.639098, count: 133 -Region Avg IOU: 0.567775, Class: 1.000000, Obj: 0.310864, No Obj: 0.004195, Avg Recall: 0.683761, count: 117 -Region Avg IOU: 0.519411, Class: 1.000000, Obj: 0.262516, No Obj: 0.003724, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.544037, Class: 1.000000, Obj: 0.238313, No Obj: 0.003875, Avg Recall: 0.609524, count: 105 -1003: 0.444844, 0.505455 avg, 0.001000 rate, 23.419537 seconds, 256768 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.504723, Class: 1.000000, Obj: 0.305609, No Obj: 0.003790, Avg Recall: 0.561983, count: 121 -Region Avg IOU: 0.463263, Class: 1.000000, Obj: 0.240137, No Obj: 0.003749, Avg Recall: 0.512000, count: 125 -Region Avg IOU: 0.475202, Class: 1.000000, Obj: 0.241488, No Obj: 0.003613, Avg Recall: 0.525773, count: 97 -Region Avg IOU: 0.505590, Class: 1.000000, Obj: 0.231669, No Obj: 0.003380, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.444738, Class: 1.000000, Obj: 0.271237, No Obj: 0.003567, Avg Recall: 0.457143, count: 105 -Region Avg IOU: 0.482764, Class: 1.000000, Obj: 0.274985, No Obj: 0.003507, Avg Recall: 0.580952, count: 105 -Region Avg IOU: 0.506730, Class: 1.000000, Obj: 0.269105, No Obj: 0.003518, Avg Recall: 0.546392, count: 97 -Region Avg IOU: 0.512702, Class: 1.000000, Obj: 0.261842, No Obj: 0.003912, Avg Recall: 0.598425, count: 127 -1004: 0.495156, 0.504425 avg, 0.001000 rate, 23.473724 seconds, 257024 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.494431, Class: 1.000000, Obj: 0.239058, No Obj: 0.003631, Avg Recall: 0.483607, count: 122 -Region Avg IOU: 0.524042, Class: 1.000000, Obj: 0.253568, No Obj: 0.004197, Avg Recall: 0.605042, count: 119 -Region Avg IOU: 0.554992, Class: 1.000000, Obj: 0.299928, No Obj: 0.004123, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.536094, Class: 1.000000, Obj: 0.239393, No Obj: 0.004436, Avg Recall: 0.625000, count: 144 -Region Avg IOU: 0.508299, Class: 1.000000, Obj: 0.271688, No Obj: 0.004134, Avg Recall: 0.564516, count: 124 -Region Avg IOU: 0.491253, Class: 1.000000, Obj: 0.245573, No Obj: 0.003425, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.584456, Class: 1.000000, Obj: 0.277638, No Obj: 0.003319, Avg Recall: 0.717949, count: 78 -Region Avg IOU: 0.513914, Class: 1.000000, Obj: 0.252934, No Obj: 0.003859, Avg Recall: 0.589474, count: 95 -1005: 0.611557, 0.515138 avg, 0.001000 rate, 24.649128 seconds, 257280 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.494108, Class: 1.000000, Obj: 0.263122, No Obj: 0.003877, Avg Recall: 0.540741, count: 135 -Region Avg IOU: 0.547989, Class: 1.000000, Obj: 0.298778, No Obj: 0.003984, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.477317, Class: 1.000000, Obj: 0.247984, No Obj: 0.003339, Avg Recall: 0.544444, count: 90 -Region Avg IOU: 0.506588, Class: 1.000000, Obj: 0.265750, No Obj: 0.004057, Avg Recall: 0.547619, count: 126 -Region Avg IOU: 0.540413, Class: 1.000000, Obj: 0.267050, No Obj: 0.003895, Avg Recall: 0.641667, count: 120 -Region Avg IOU: 0.494327, Class: 1.000000, Obj: 0.261693, No Obj: 0.003632, Avg Recall: 0.592000, count: 125 -Region Avg IOU: 0.543174, Class: 1.000000, Obj: 0.333286, No Obj: 0.003744, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.528327, Class: 1.000000, Obj: 0.268898, No Obj: 0.004302, Avg Recall: 0.576923, count: 130 -1006: 0.540325, 0.517657 avg, 0.001000 rate, 23.874489 seconds, 257536 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.557224, Class: 1.000000, Obj: 0.272626, No Obj: 0.003711, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.484611, Class: 1.000000, Obj: 0.282906, No Obj: 0.003528, Avg Recall: 0.505050, count: 99 -Region Avg IOU: 0.532565, Class: 1.000000, Obj: 0.260681, No Obj: 0.003965, Avg Recall: 0.596639, count: 119 -Region Avg IOU: 0.539067, Class: 1.000000, Obj: 0.233108, No Obj: 0.004032, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.543882, Class: 1.000000, Obj: 0.252326, No Obj: 0.003498, Avg Recall: 0.623377, count: 77 -Region Avg IOU: 0.501876, Class: 1.000000, Obj: 0.264485, No Obj: 0.003349, Avg Recall: 0.537634, count: 93 -Region Avg IOU: 0.512810, Class: 1.000000, Obj: 0.281610, No Obj: 0.003566, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.531776, Class: 1.000000, Obj: 0.286622, No Obj: 0.003321, Avg Recall: 0.619718, count: 71 -1007: 0.457676, 0.511659 avg, 0.001000 rate, 23.460543 seconds, 257792 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.518340, Class: 1.000000, Obj: 0.223565, No Obj: 0.003535, Avg Recall: 0.615385, count: 91 -Region Avg IOU: 0.506395, Class: 1.000000, Obj: 0.216510, No Obj: 0.003851, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.547963, Class: 1.000000, Obj: 0.266079, No Obj: 0.003418, Avg Recall: 0.651163, count: 86 -Region Avg IOU: 0.512755, Class: 1.000000, Obj: 0.235625, No Obj: 0.004006, Avg Recall: 0.613139, count: 137 -Region Avg IOU: 0.484128, Class: 1.000000, Obj: 0.244241, No Obj: 0.003627, Avg Recall: 0.495495, count: 111 -Region Avg IOU: 0.513113, Class: 1.000000, Obj: 0.232043, No Obj: 0.003580, Avg Recall: 0.561404, count: 114 -Region Avg IOU: 0.477868, Class: 1.000000, Obj: 0.252897, No Obj: 0.003415, Avg Recall: 0.512821, count: 78 -Region Avg IOU: 0.529061, Class: 1.000000, Obj: 0.251350, No Obj: 0.003708, Avg Recall: 0.600000, count: 90 -1008: 0.480739, 0.508567 avg, 0.001000 rate, 23.511969 seconds, 258048 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.492068, Class: 1.000000, Obj: 0.247147, No Obj: 0.003258, Avg Recall: 0.523364, count: 107 -Region Avg IOU: 0.536836, Class: 1.000000, Obj: 0.255322, No Obj: 0.003693, Avg Recall: 0.605769, count: 104 -Region Avg IOU: 0.513945, Class: 1.000000, Obj: 0.266310, No Obj: 0.003573, Avg Recall: 0.556522, count: 115 -Region Avg IOU: 0.524060, Class: 1.000000, Obj: 0.266244, No Obj: 0.003955, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.483594, Class: 1.000000, Obj: 0.247569, No Obj: 0.003047, Avg Recall: 0.513514, count: 111 -Region Avg IOU: 0.498109, Class: 1.000000, Obj: 0.250142, No Obj: 0.003497, Avg Recall: 0.532847, count: 137 -Region Avg IOU: 0.530186, Class: 1.000000, Obj: 0.293536, No Obj: 0.003952, Avg Recall: 0.632000, count: 125 -Region Avg IOU: 0.510089, Class: 1.000000, Obj: 0.255481, No Obj: 0.003776, Avg Recall: 0.580420, count: 143 -1009: 0.531142, 0.510824 avg, 0.001000 rate, 22.753471 seconds, 258304 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.531593, Class: 1.000000, Obj: 0.282543, No Obj: 0.003596, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.516310, Class: 1.000000, Obj: 0.268879, No Obj: 0.003366, Avg Recall: 0.542857, count: 105 -Region Avg IOU: 0.523913, Class: 1.000000, Obj: 0.291221, No Obj: 0.003600, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.543925, Class: 1.000000, Obj: 0.242083, No Obj: 0.003945, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.511593, Class: 1.000000, Obj: 0.262285, No Obj: 0.003761, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.525696, Class: 1.000000, Obj: 0.261465, No Obj: 0.003839, Avg Recall: 0.585106, count: 94 -Region Avg IOU: 0.554747, Class: 1.000000, Obj: 0.256367, No Obj: 0.003711, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.472221, Class: 1.000000, Obj: 0.280765, No Obj: 0.003594, Avg Recall: 0.504202, count: 119 -1010: 0.463185, 0.506060 avg, 0.001000 rate, 22.105740 seconds, 258560 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.211315 seconds -Region Avg IOU: 0.466958, Class: 1.000000, Obj: 0.224620, No Obj: 0.003940, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.487851, Class: 1.000000, Obj: 0.239245, No Obj: 0.004095, Avg Recall: 0.550000, count: 120 -Region Avg IOU: 0.470187, Class: 1.000000, Obj: 0.260169, No Obj: 0.004160, Avg Recall: 0.523364, count: 107 -Region Avg IOU: 0.504064, Class: 1.000000, Obj: 0.258128, No Obj: 0.004119, Avg Recall: 0.585106, count: 94 -Region Avg IOU: 0.497439, Class: 1.000000, Obj: 0.254626, No Obj: 0.003725, Avg Recall: 0.534091, count: 88 -Region Avg IOU: 0.438214, Class: 1.000000, Obj: 0.205304, No Obj: 0.003856, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.424741, Class: 1.000000, Obj: 0.211349, No Obj: 0.003958, Avg Recall: 0.485149, count: 101 -Region Avg IOU: 0.416463, Class: 1.000000, Obj: 0.173594, No Obj: 0.003051, Avg Recall: 0.471264, count: 87 -1011: 0.478132, 0.503267 avg, 0.001000 rate, 22.187380 seconds, 258816 images -Loaded: 0.292497 seconds -Region Avg IOU: 0.472901, Class: 1.000000, Obj: 0.279621, No Obj: 0.004433, Avg Recall: 0.494253, count: 87 -Region Avg IOU: 0.456550, Class: 1.000000, Obj: 0.257881, No Obj: 0.004029, Avg Recall: 0.504425, count: 113 -Region Avg IOU: 0.415227, Class: 1.000000, Obj: 0.196038, No Obj: 0.003744, Avg Recall: 0.412371, count: 97 -Region Avg IOU: 0.524340, Class: 1.000000, Obj: 0.248116, No Obj: 0.004566, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.499600, Class: 1.000000, Obj: 0.267857, No Obj: 0.004254, Avg Recall: 0.576577, count: 111 -Region Avg IOU: 0.464400, Class: 1.000000, Obj: 0.244372, No Obj: 0.004222, Avg Recall: 0.504762, count: 105 -Region Avg IOU: 0.469934, Class: 1.000000, Obj: 0.234789, No Obj: 0.003885, Avg Recall: 0.475610, count: 82 -Region Avg IOU: 0.453481, Class: 1.000000, Obj: 0.254421, No Obj: 0.004311, Avg Recall: 0.427350, count: 117 -1012: 0.507052, 0.503646 avg, 0.001000 rate, 21.330158 seconds, 259072 images -Loaded: 1.528933 seconds -Region Avg IOU: 0.451193, Class: 1.000000, Obj: 0.245495, No Obj: 0.004066, Avg Recall: 0.490000, count: 100 -Region Avg IOU: 0.533153, Class: 1.000000, Obj: 0.266440, No Obj: 0.004207, Avg Recall: 0.565789, count: 76 -Region Avg IOU: 0.478343, Class: 1.000000, Obj: 0.245336, No Obj: 0.004235, Avg Recall: 0.552845, count: 123 -Region Avg IOU: 0.487449, Class: 1.000000, Obj: 0.249386, No Obj: 0.004058, Avg Recall: 0.559140, count: 93 -Region Avg IOU: 0.474603, Class: 1.000000, Obj: 0.267976, No Obj: 0.003986, Avg Recall: 0.532609, count: 92 -Region Avg IOU: 0.472761, Class: 1.000000, Obj: 0.254309, No Obj: 0.004386, Avg Recall: 0.495413, count: 109 -Region Avg IOU: 0.478785, Class: 1.000000, Obj: 0.239733, No Obj: 0.004383, Avg Recall: 0.528926, count: 121 -Region Avg IOU: 0.521544, Class: 1.000000, Obj: 0.275905, No Obj: 0.004481, Avg Recall: 0.619835, count: 121 -1013: 0.544264, 0.507708 avg, 0.001000 rate, 20.091118 seconds, 259328 images -Loaded: 0.925013 seconds -Region Avg IOU: 0.440297, Class: 1.000000, Obj: 0.262599, No Obj: 0.004115, Avg Recall: 0.427184, count: 103 -Region Avg IOU: 0.460456, Class: 1.000000, Obj: 0.269034, No Obj: 0.004289, Avg Recall: 0.500000, count: 104 -Region Avg IOU: 0.514484, Class: 1.000000, Obj: 0.302096, No Obj: 0.004793, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.470479, Class: 1.000000, Obj: 0.258159, No Obj: 0.003991, Avg Recall: 0.510000, count: 100 -Region Avg IOU: 0.475043, Class: 1.000000, Obj: 0.229982, No Obj: 0.003867, Avg Recall: 0.509804, count: 102 -Region Avg IOU: 0.431634, Class: 1.000000, Obj: 0.258527, No Obj: 0.004071, Avg Recall: 0.450000, count: 120 -Region Avg IOU: 0.510303, Class: 1.000000, Obj: 0.271383, No Obj: 0.004466, Avg Recall: 0.559633, count: 109 -Region Avg IOU: 0.463275, Class: 1.000000, Obj: 0.220536, No Obj: 0.004060, Avg Recall: 0.487180, count: 117 -1014: 0.480037, 0.504941 avg, 0.001000 rate, 20.435326 seconds, 259584 images -Loaded: 2.695980 seconds -Region Avg IOU: 0.509488, Class: 1.000000, Obj: 0.278536, No Obj: 0.004588, Avg Recall: 0.566372, count: 113 -Region Avg IOU: 0.482519, Class: 1.000000, Obj: 0.296090, No Obj: 0.005264, Avg Recall: 0.525180, count: 139 -Region Avg IOU: 0.466671, Class: 1.000000, Obj: 0.282010, No Obj: 0.004355, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.451887, Class: 1.000000, Obj: 0.217505, No Obj: 0.004278, Avg Recall: 0.536082, count: 97 -Region Avg IOU: 0.428432, Class: 1.000000, Obj: 0.237074, No Obj: 0.004258, Avg Recall: 0.473684, count: 95 -Region Avg IOU: 0.422769, Class: 1.000000, Obj: 0.250991, No Obj: 0.004390, Avg Recall: 0.390909, count: 110 -Region Avg IOU: 0.453152, Class: 1.000000, Obj: 0.235025, No Obj: 0.003751, Avg Recall: 0.475248, count: 101 -Region Avg IOU: 0.465578, Class: 1.000000, Obj: 0.260237, No Obj: 0.004286, Avg Recall: 0.532110, count: 109 -1015: 0.502192, 0.504666 avg, 0.001000 rate, 20.915709 seconds, 259840 images -Loaded: 0.602881 seconds -Region Avg IOU: 0.432625, Class: 1.000000, Obj: 0.228205, No Obj: 0.004252, Avg Recall: 0.460000, count: 100 -Region Avg IOU: 0.494277, Class: 1.000000, Obj: 0.282405, No Obj: 0.004505, Avg Recall: 0.556818, count: 88 -Region Avg IOU: 0.465782, Class: 1.000000, Obj: 0.300230, No Obj: 0.004128, Avg Recall: 0.476190, count: 84 -Region Avg IOU: 0.474074, Class: 1.000000, Obj: 0.260085, No Obj: 0.003669, Avg Recall: 0.506024, count: 83 -Region Avg IOU: 0.441947, Class: 1.000000, Obj: 0.265942, No Obj: 0.004420, Avg Recall: 0.462810, count: 121 -Region Avg IOU: 0.453826, Class: 1.000000, Obj: 0.252051, No Obj: 0.004565, Avg Recall: 0.477064, count: 109 -Region Avg IOU: 0.519804, Class: 1.000000, Obj: 0.257338, No Obj: 0.004851, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.497218, Class: 1.000000, Obj: 0.261973, No Obj: 0.004497, Avg Recall: 0.515464, count: 97 -1016: 0.487822, 0.502981 avg, 0.001000 rate, 20.930960 seconds, 260096 images -Loaded: 0.289036 seconds -Region Avg IOU: 0.488084, Class: 1.000000, Obj: 0.278691, No Obj: 0.003888, Avg Recall: 0.526882, count: 93 -Region Avg IOU: 0.474994, Class: 1.000000, Obj: 0.248774, No Obj: 0.004449, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.439960, Class: 1.000000, Obj: 0.232245, No Obj: 0.004477, Avg Recall: 0.442029, count: 138 -Region Avg IOU: 0.467546, Class: 1.000000, Obj: 0.249284, No Obj: 0.004280, Avg Recall: 0.574468, count: 94 -Region Avg IOU: 0.482549, Class: 1.000000, Obj: 0.246343, No Obj: 0.004740, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.405609, Class: 1.000000, Obj: 0.231430, No Obj: 0.003932, Avg Recall: 0.423423, count: 111 -Region Avg IOU: 0.393665, Class: 1.000000, Obj: 0.221881, No Obj: 0.003658, Avg Recall: 0.397849, count: 93 -Region Avg IOU: 0.474984, Class: 1.000000, Obj: 0.302046, No Obj: 0.004582, Avg Recall: 0.518519, count: 108 -1017: 0.508629, 0.503546 avg, 0.001000 rate, 21.414379 seconds, 260352 images -Loaded: 0.000126 seconds -Region Avg IOU: 0.433788, Class: 1.000000, Obj: 0.226135, No Obj: 0.004327, Avg Recall: 0.459459, count: 111 -Region Avg IOU: 0.480980, Class: 1.000000, Obj: 0.290542, No Obj: 0.004148, Avg Recall: 0.516667, count: 120 -Region Avg IOU: 0.470606, Class: 1.000000, Obj: 0.279745, No Obj: 0.004431, Avg Recall: 0.495050, count: 101 -Region Avg IOU: 0.439371, Class: 1.000000, Obj: 0.298395, No Obj: 0.004814, Avg Recall: 0.480620, count: 129 -Region Avg IOU: 0.474017, Class: 1.000000, Obj: 0.246349, No Obj: 0.004817, Avg Recall: 0.525862, count: 116 -Region Avg IOU: 0.445367, Class: 1.000000, Obj: 0.294968, No Obj: 0.003692, Avg Recall: 0.487500, count: 80 -Region Avg IOU: 0.485388, Class: 1.000000, Obj: 0.292618, No Obj: 0.004298, Avg Recall: 0.500000, count: 90 -Region Avg IOU: 0.441927, Class: 1.000000, Obj: 0.258122, No Obj: 0.004101, Avg Recall: 0.490000, count: 100 -1018: 0.505239, 0.503715 avg, 0.001000 rate, 21.196939 seconds, 260608 images -Loaded: 1.323255 seconds -Region Avg IOU: 0.495076, Class: 1.000000, Obj: 0.256172, No Obj: 0.004027, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.433490, Class: 1.000000, Obj: 0.233137, No Obj: 0.003969, Avg Recall: 0.417476, count: 103 -Region Avg IOU: 0.458897, Class: 1.000000, Obj: 0.229761, No Obj: 0.004740, Avg Recall: 0.519084, count: 131 -Region Avg IOU: 0.449758, Class: 1.000000, Obj: 0.295229, No Obj: 0.003931, Avg Recall: 0.483146, count: 89 -Region Avg IOU: 0.457910, Class: 1.000000, Obj: 0.251817, No Obj: 0.004027, Avg Recall: 0.494845, count: 97 -Region Avg IOU: 0.465018, Class: 1.000000, Obj: 0.248898, No Obj: 0.004369, Avg Recall: 0.496063, count: 127 -Region Avg IOU: 0.457138, Class: 1.000000, Obj: 0.243998, No Obj: 0.004133, Avg Recall: 0.483516, count: 91 -Region Avg IOU: 0.413457, Class: 1.000000, Obj: 0.217011, No Obj: 0.004250, Avg Recall: 0.420635, count: 126 -1019: 0.489742, 0.502318 avg, 0.001000 rate, 20.144512 seconds, 260864 images -Loaded: 2.034951 seconds -Region Avg IOU: 0.483692, Class: 1.000000, Obj: 0.258872, No Obj: 0.004539, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.470060, Class: 1.000000, Obj: 0.249730, No Obj: 0.004133, Avg Recall: 0.523810, count: 105 -Region Avg IOU: 0.468027, Class: 1.000000, Obj: 0.230220, No Obj: 0.004574, Avg Recall: 0.511628, count: 129 -Region Avg IOU: 0.462140, Class: 1.000000, Obj: 0.250540, No Obj: 0.004105, Avg Recall: 0.489362, count: 94 -Region Avg IOU: 0.424894, Class: 1.000000, Obj: 0.217526, No Obj: 0.004249, Avg Recall: 0.427350, count: 117 -Region Avg IOU: 0.477316, Class: 1.000000, Obj: 0.257818, No Obj: 0.004074, Avg Recall: 0.534483, count: 116 -Region Avg IOU: 0.472121, Class: 1.000000, Obj: 0.257469, No Obj: 0.003785, Avg Recall: 0.551020, count: 98 -Region Avg IOU: 0.479459, Class: 1.000000, Obj: 0.230395, No Obj: 0.004894, Avg Recall: 0.559322, count: 118 -1020: 0.498399, 0.501926 avg, 0.001000 rate, 20.839144 seconds, 261120 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 0.002316 seconds -Region Avg IOU: 0.487089, Class: 1.000000, Obj: 0.275722, No Obj: 0.004142, Avg Recall: 0.504587, count: 109 -Region Avg IOU: 0.477947, Class: 1.000000, Obj: 0.272480, No Obj: 0.003806, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.543617, Class: 1.000000, Obj: 0.287827, No Obj: 0.004432, Avg Recall: 0.650406, count: 123 -Region Avg IOU: 0.504477, Class: 1.000000, Obj: 0.290609, No Obj: 0.003986, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.474533, Class: 1.000000, Obj: 0.258246, No Obj: 0.004038, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.500984, Class: 1.000000, Obj: 0.280347, No Obj: 0.004332, Avg Recall: 0.529412, count: 119 -Region Avg IOU: 0.535801, Class: 1.000000, Obj: 0.286483, No Obj: 0.004871, Avg Recall: 0.616541, count: 133 -Region Avg IOU: 0.503280, Class: 1.000000, Obj: 0.255763, No Obj: 0.004411, Avg Recall: 0.541667, count: 96 -1021: 0.538546, 0.505588 avg, 0.001000 rate, 23.800793 seconds, 261376 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.544388, Class: 1.000000, Obj: 0.313196, No Obj: 0.004388, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.492613, Class: 1.000000, Obj: 0.235753, No Obj: 0.004507, Avg Recall: 0.525000, count: 120 -Region Avg IOU: 0.498811, Class: 1.000000, Obj: 0.266675, No Obj: 0.003948, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.520250, Class: 1.000000, Obj: 0.272747, No Obj: 0.005099, Avg Recall: 0.579710, count: 138 -Region Avg IOU: 0.514173, Class: 1.000000, Obj: 0.270861, No Obj: 0.004483, Avg Recall: 0.601770, count: 113 -Region Avg IOU: 0.494178, Class: 1.000000, Obj: 0.244758, No Obj: 0.004608, Avg Recall: 0.541401, count: 157 -Region Avg IOU: 0.484071, Class: 1.000000, Obj: 0.274211, No Obj: 0.004076, Avg Recall: 0.536082, count: 97 -Region Avg IOU: 0.494481, Class: 1.000000, Obj: 0.296620, No Obj: 0.004240, Avg Recall: 0.587719, count: 114 -1022: 0.592327, 0.514262 avg, 0.001000 rate, 23.868328 seconds, 261632 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.514637, Class: 1.000000, Obj: 0.234780, No Obj: 0.004379, Avg Recall: 0.548673, count: 113 -Region Avg IOU: 0.558644, Class: 1.000000, Obj: 0.254761, No Obj: 0.004294, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.513762, Class: 1.000000, Obj: 0.290955, No Obj: 0.004364, Avg Recall: 0.568182, count: 132 -Region Avg IOU: 0.496141, Class: 1.000000, Obj: 0.266373, No Obj: 0.004705, Avg Recall: 0.539683, count: 126 -Region Avg IOU: 0.519424, Class: 1.000000, Obj: 0.297870, No Obj: 0.004297, Avg Recall: 0.580882, count: 136 -Region Avg IOU: 0.530466, Class: 1.000000, Obj: 0.312225, No Obj: 0.004687, Avg Recall: 0.559633, count: 109 -Region Avg IOU: 0.494374, Class: 1.000000, Obj: 0.301782, No Obj: 0.003636, Avg Recall: 0.539474, count: 76 -Region Avg IOU: 0.552092, Class: 1.000000, Obj: 0.324398, No Obj: 0.004026, Avg Recall: 0.658824, count: 85 -1023: 0.455269, 0.508363 avg, 0.001000 rate, 21.919231 seconds, 261888 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.562845, Class: 1.000000, Obj: 0.302943, No Obj: 0.003471, Avg Recall: 0.611111, count: 72 -Region Avg IOU: 0.449248, Class: 1.000000, Obj: 0.251126, No Obj: 0.003776, Avg Recall: 0.475728, count: 103 -Region Avg IOU: 0.523986, Class: 1.000000, Obj: 0.267616, No Obj: 0.003495, Avg Recall: 0.613636, count: 88 -Region Avg IOU: 0.486493, Class: 1.000000, Obj: 0.277516, No Obj: 0.003610, Avg Recall: 0.500000, count: 92 -Region Avg IOU: 0.506547, Class: 1.000000, Obj: 0.236943, No Obj: 0.004463, Avg Recall: 0.601562, count: 128 -Region Avg IOU: 0.552894, Class: 1.000000, Obj: 0.290039, No Obj: 0.003746, Avg Recall: 0.611765, count: 85 -Region Avg IOU: 0.500541, Class: 1.000000, Obj: 0.263371, No Obj: 0.004067, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.503815, Class: 1.000000, Obj: 0.317587, No Obj: 0.004056, Avg Recall: 0.525773, count: 97 -1024: 0.430669, 0.500593 avg, 0.001000 rate, 22.636131 seconds, 262144 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.520741, Class: 1.000000, Obj: 0.269774, No Obj: 0.003569, Avg Recall: 0.565657, count: 99 -Region Avg IOU: 0.481189, Class: 1.000000, Obj: 0.277505, No Obj: 0.003474, Avg Recall: 0.514706, count: 68 -Region Avg IOU: 0.534706, Class: 1.000000, Obj: 0.258653, No Obj: 0.004437, Avg Recall: 0.601770, count: 113 -Region Avg IOU: 0.482365, Class: 1.000000, Obj: 0.221149, No Obj: 0.003748, Avg Recall: 0.515790, count: 95 -Region Avg IOU: 0.522187, Class: 1.000000, Obj: 0.306356, No Obj: 0.003887, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.478430, Class: 1.000000, Obj: 0.251969, No Obj: 0.003754, Avg Recall: 0.504348, count: 115 -Region Avg IOU: 0.491595, Class: 1.000000, Obj: 0.278989, No Obj: 0.004004, Avg Recall: 0.518248, count: 137 -Region Avg IOU: 0.476771, Class: 1.000000, Obj: 0.286121, No Obj: 0.003487, Avg Recall: 0.505495, count: 91 -1025: 0.445924, 0.495126 avg, 0.001000 rate, 22.751560 seconds, 262400 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.526524, Class: 1.000000, Obj: 0.301674, No Obj: 0.004148, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.524438, Class: 1.000000, Obj: 0.267871, No Obj: 0.003638, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.468832, Class: 1.000000, Obj: 0.246978, No Obj: 0.003937, Avg Recall: 0.480392, count: 102 -Region Avg IOU: 0.500522, Class: 1.000000, Obj: 0.332552, No Obj: 0.004408, Avg Recall: 0.496296, count: 135 -Region Avg IOU: 0.458960, Class: 1.000000, Obj: 0.204139, No Obj: 0.003661, Avg Recall: 0.494845, count: 97 -Region Avg IOU: 0.470253, Class: 1.000000, Obj: 0.256442, No Obj: 0.003850, Avg Recall: 0.536082, count: 97 -Region Avg IOU: 0.505581, Class: 1.000000, Obj: 0.265533, No Obj: 0.004126, Avg Recall: 0.581967, count: 122 -Region Avg IOU: 0.503785, Class: 1.000000, Obj: 0.256717, No Obj: 0.004067, Avg Recall: 0.557895, count: 95 -1026: 0.503785, 0.495992 avg, 0.001000 rate, 22.859703 seconds, 262656 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.548491, Class: 1.000000, Obj: 0.249373, No Obj: 0.003793, Avg Recall: 0.652174, count: 92 -Region Avg IOU: 0.516964, Class: 1.000000, Obj: 0.260496, No Obj: 0.004054, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.489199, Class: 1.000000, Obj: 0.256984, No Obj: 0.003800, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.516408, Class: 1.000000, Obj: 0.298848, No Obj: 0.004323, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.563686, Class: 1.000000, Obj: 0.242544, No Obj: 0.004086, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.525086, Class: 1.000000, Obj: 0.235864, No Obj: 0.003411, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.491926, Class: 1.000000, Obj: 0.234117, No Obj: 0.003819, Avg Recall: 0.545455, count: 88 -Region Avg IOU: 0.512618, Class: 1.000000, Obj: 0.227219, No Obj: 0.004159, Avg Recall: 0.553719, count: 121 -1027: 0.452039, 0.491597 avg, 0.001000 rate, 23.122587 seconds, 262912 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.500455, Class: 1.000000, Obj: 0.269047, No Obj: 0.004069, Avg Recall: 0.563636, count: 110 -Region Avg IOU: 0.527319, Class: 1.000000, Obj: 0.310116, No Obj: 0.003692, Avg Recall: 0.583333, count: 84 -Region Avg IOU: 0.571519, Class: 1.000000, Obj: 0.285127, No Obj: 0.004405, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.522133, Class: 1.000000, Obj: 0.246745, No Obj: 0.003641, Avg Recall: 0.586957, count: 92 -Region Avg IOU: 0.499877, Class: 1.000000, Obj: 0.248973, No Obj: 0.004191, Avg Recall: 0.582090, count: 134 -Region Avg IOU: 0.527983, Class: 1.000000, Obj: 0.256583, No Obj: 0.004020, Avg Recall: 0.605769, count: 104 -Region Avg IOU: 0.505509, Class: 1.000000, Obj: 0.259051, No Obj: 0.004132, Avg Recall: 0.558333, count: 120 -Region Avg IOU: 0.475203, Class: 1.000000, Obj: 0.287549, No Obj: 0.003944, Avg Recall: 0.512195, count: 123 -1028: 0.488251, 0.491262 avg, 0.001000 rate, 22.771891 seconds, 263168 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.495707, Class: 1.000000, Obj: 0.236036, No Obj: 0.004465, Avg Recall: 0.547826, count: 115 -Region Avg IOU: 0.555488, Class: 1.000000, Obj: 0.313679, No Obj: 0.004286, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.518454, Class: 1.000000, Obj: 0.244456, No Obj: 0.004525, Avg Recall: 0.575540, count: 139 -Region Avg IOU: 0.492024, Class: 1.000000, Obj: 0.249822, No Obj: 0.004241, Avg Recall: 0.511278, count: 133 -Region Avg IOU: 0.556672, Class: 1.000000, Obj: 0.295161, No Obj: 0.004589, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.530785, Class: 1.000000, Obj: 0.281983, No Obj: 0.004079, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.443831, Class: 1.000000, Obj: 0.250364, No Obj: 0.003749, Avg Recall: 0.451327, count: 113 -Region Avg IOU: 0.499501, Class: 1.000000, Obj: 0.284678, No Obj: 0.003740, Avg Recall: 0.514286, count: 105 -1029: 0.503089, 0.492445 avg, 0.001000 rate, 22.742311 seconds, 263424 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.487990, Class: 1.000000, Obj: 0.257168, No Obj: 0.004287, Avg Recall: 0.539683, count: 126 -Region Avg IOU: 0.512280, Class: 1.000000, Obj: 0.218511, No Obj: 0.003725, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.565754, Class: 1.000000, Obj: 0.262523, No Obj: 0.004039, Avg Recall: 0.690476, count: 126 -Region Avg IOU: 0.485113, Class: 1.000000, Obj: 0.277333, No Obj: 0.003559, Avg Recall: 0.509615, count: 104 -Region Avg IOU: 0.528769, Class: 1.000000, Obj: 0.256010, No Obj: 0.004118, Avg Recall: 0.546296, count: 108 -Region Avg IOU: 0.515663, Class: 1.000000, Obj: 0.229536, No Obj: 0.003551, Avg Recall: 0.614286, count: 70 -Region Avg IOU: 0.508078, Class: 1.000000, Obj: 0.240151, No Obj: 0.003365, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.502650, Class: 1.000000, Obj: 0.248267, No Obj: 0.003978, Avg Recall: 0.516393, count: 122 -1030: 0.494442, 0.492645 avg, 0.001000 rate, 23.253723 seconds, 263680 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 20.881514 seconds -Region Avg IOU: 0.479227, Class: 1.000000, Obj: 0.258448, No Obj: 0.004021, Avg Recall: 0.547009, count: 117 -Region Avg IOU: 0.517261, Class: 1.000000, Obj: 0.276989, No Obj: 0.004324, Avg Recall: 0.577236, count: 123 -Region Avg IOU: 0.481466, Class: 1.000000, Obj: 0.254569, No Obj: 0.003878, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.483231, Class: 1.000000, Obj: 0.301879, No Obj: 0.003932, Avg Recall: 0.565574, count: 122 -Region Avg IOU: 0.489620, Class: 1.000000, Obj: 0.279206, No Obj: 0.003692, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.558802, Class: 1.000000, Obj: 0.279385, No Obj: 0.003995, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.516261, Class: 1.000000, Obj: 0.254928, No Obj: 0.003965, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.522554, Class: 1.000000, Obj: 0.277687, No Obj: 0.003377, Avg Recall: 0.623529, count: 85 -1031: 0.551526, 0.498533 avg, 0.001000 rate, 22.227476 seconds, 263936 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.552159, Class: 1.000000, Obj: 0.273652, No Obj: 0.003561, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.560887, Class: 1.000000, Obj: 0.312554, No Obj: 0.003736, Avg Recall: 0.675325, count: 77 -Region Avg IOU: 0.562510, Class: 1.000000, Obj: 0.284256, No Obj: 0.003420, Avg Recall: 0.659091, count: 88 -Region Avg IOU: 0.505396, Class: 1.000000, Obj: 0.224175, No Obj: 0.004074, Avg Recall: 0.554688, count: 128 -Region Avg IOU: 0.477143, Class: 1.000000, Obj: 0.263106, No Obj: 0.003728, Avg Recall: 0.495327, count: 107 -Region Avg IOU: 0.476529, Class: 1.000000, Obj: 0.250740, No Obj: 0.004310, Avg Recall: 0.485915, count: 142 -Region Avg IOU: 0.522811, Class: 1.000000, Obj: 0.295591, No Obj: 0.004556, Avg Recall: 0.596639, count: 119 -Region Avg IOU: 0.537567, Class: 1.000000, Obj: 0.259377, No Obj: 0.004085, Avg Recall: 0.623762, count: 101 -1032: 0.531997, 0.501879 avg, 0.001000 rate, 22.779253 seconds, 264192 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.475643, Class: 1.000000, Obj: 0.234978, No Obj: 0.003857, Avg Recall: 0.544554, count: 101 -Region Avg IOU: 0.519559, Class: 1.000000, Obj: 0.323469, No Obj: 0.003576, Avg Recall: 0.565217, count: 92 -Region Avg IOU: 0.488912, Class: 1.000000, Obj: 0.251635, No Obj: 0.003986, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.500374, Class: 1.000000, Obj: 0.277298, No Obj: 0.003902, Avg Recall: 0.593220, count: 118 -Region Avg IOU: 0.505060, Class: 1.000000, Obj: 0.256962, No Obj: 0.003624, Avg Recall: 0.560440, count: 91 -Region Avg IOU: 0.503311, Class: 1.000000, Obj: 0.242050, No Obj: 0.004253, Avg Recall: 0.525424, count: 118 -Region Avg IOU: 0.515530, Class: 1.000000, Obj: 0.312836, No Obj: 0.003886, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.502923, Class: 1.000000, Obj: 0.269417, No Obj: 0.003909, Avg Recall: 0.556604, count: 106 -1033: 0.506931, 0.502385 avg, 0.001000 rate, 22.875536 seconds, 264448 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.488786, Class: 1.000000, Obj: 0.288140, No Obj: 0.003537, Avg Recall: 0.524752, count: 101 -Region Avg IOU: 0.544622, Class: 1.000000, Obj: 0.323199, No Obj: 0.003658, Avg Recall: 0.674699, count: 83 -Region Avg IOU: 0.558027, Class: 1.000000, Obj: 0.299078, No Obj: 0.004285, Avg Recall: 0.604651, count: 86 -Region Avg IOU: 0.520781, Class: 1.000000, Obj: 0.291505, No Obj: 0.003858, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.524500, Class: 1.000000, Obj: 0.286717, No Obj: 0.004405, Avg Recall: 0.586207, count: 116 -Region Avg IOU: 0.479277, Class: 1.000000, Obj: 0.289958, No Obj: 0.004064, Avg Recall: 0.514851, count: 101 -Region Avg IOU: 0.520492, Class: 1.000000, Obj: 0.296074, No Obj: 0.004125, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.497486, Class: 1.000000, Obj: 0.276412, No Obj: 0.004068, Avg Recall: 0.522124, count: 113 -1034: 0.405548, 0.492701 avg, 0.001000 rate, 22.759216 seconds, 264704 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.511965, Class: 1.000000, Obj: 0.242420, No Obj: 0.003463, Avg Recall: 0.558140, count: 86 -Region Avg IOU: 0.500486, Class: 1.000000, Obj: 0.264921, No Obj: 0.004359, Avg Recall: 0.547619, count: 126 -Region Avg IOU: 0.521209, Class: 1.000000, Obj: 0.327970, No Obj: 0.003571, Avg Recall: 0.605263, count: 76 -Region Avg IOU: 0.430910, Class: 1.000000, Obj: 0.241947, No Obj: 0.003772, Avg Recall: 0.436364, count: 110 -Region Avg IOU: 0.502271, Class: 1.000000, Obj: 0.247591, No Obj: 0.004581, Avg Recall: 0.591241, count: 137 -Region Avg IOU: 0.535005, Class: 1.000000, Obj: 0.282196, No Obj: 0.004441, Avg Recall: 0.581967, count: 122 -Region Avg IOU: 0.489046, Class: 1.000000, Obj: 0.255354, No Obj: 0.003732, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.474690, Class: 1.000000, Obj: 0.276236, No Obj: 0.004085, Avg Recall: 0.489051, count: 137 -1035: 0.526212, 0.496052 avg, 0.001000 rate, 22.672832 seconds, 264960 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.523573, Class: 1.000000, Obj: 0.343995, No Obj: 0.003690, Avg Recall: 0.629630, count: 81 -Region Avg IOU: 0.475127, Class: 1.000000, Obj: 0.321845, No Obj: 0.003957, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.537795, Class: 1.000000, Obj: 0.269645, No Obj: 0.004337, Avg Recall: 0.606838, count: 117 -Region Avg IOU: 0.545303, Class: 1.000000, Obj: 0.258605, No Obj: 0.004501, Avg Recall: 0.632812, count: 128 -Region Avg IOU: 0.543506, Class: 1.000000, Obj: 0.279903, No Obj: 0.003997, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.506512, Class: 1.000000, Obj: 0.273302, No Obj: 0.004230, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.500036, Class: 1.000000, Obj: 0.309702, No Obj: 0.003903, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.468768, Class: 1.000000, Obj: 0.235141, No Obj: 0.003287, Avg Recall: 0.512500, count: 80 -1036: 0.461604, 0.492607 avg, 0.001000 rate, 21.891420 seconds, 265216 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.502385, Class: 1.000000, Obj: 0.290186, No Obj: 0.003827, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.485577, Class: 1.000000, Obj: 0.299065, No Obj: 0.003459, Avg Recall: 0.494505, count: 91 -Region Avg IOU: 0.498527, Class: 1.000000, Obj: 0.257112, No Obj: 0.003823, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.506227, Class: 1.000000, Obj: 0.248896, No Obj: 0.003938, Avg Recall: 0.540000, count: 100 -Region Avg IOU: 0.501387, Class: 1.000000, Obj: 0.264991, No Obj: 0.003940, Avg Recall: 0.552000, count: 125 -Region Avg IOU: 0.519602, Class: 1.000000, Obj: 0.277490, No Obj: 0.004312, Avg Recall: 0.620968, count: 124 -Region Avg IOU: 0.442127, Class: 1.000000, Obj: 0.263649, No Obj: 0.003578, Avg Recall: 0.429907, count: 107 -Region Avg IOU: 0.519802, Class: 1.000000, Obj: 0.274291, No Obj: 0.004473, Avg Recall: 0.589286, count: 112 -1037: 0.449276, 0.488274 avg, 0.001000 rate, 23.028097 seconds, 265472 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.490754, Class: 1.000000, Obj: 0.238168, No Obj: 0.003813, Avg Recall: 0.541176, count: 85 -Region Avg IOU: 0.523472, Class: 1.000000, Obj: 0.282075, No Obj: 0.004063, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.501219, Class: 1.000000, Obj: 0.250943, No Obj: 0.004307, Avg Recall: 0.574468, count: 141 -Region Avg IOU: 0.500075, Class: 1.000000, Obj: 0.240267, No Obj: 0.003683, Avg Recall: 0.583333, count: 84 -Region Avg IOU: 0.533560, Class: 1.000000, Obj: 0.280912, No Obj: 0.004060, Avg Recall: 0.588235, count: 85 -Region Avg IOU: 0.455780, Class: 1.000000, Obj: 0.254147, No Obj: 0.003315, Avg Recall: 0.494382, count: 89 -Region Avg IOU: 0.545793, Class: 1.000000, Obj: 0.272071, No Obj: 0.004410, Avg Recall: 0.633588, count: 131 -Region Avg IOU: 0.512377, Class: 1.000000, Obj: 0.235062, No Obj: 0.003913, Avg Recall: 0.596154, count: 104 -1038: 0.438844, 0.483331 avg, 0.001000 rate, 22.719305 seconds, 265728 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.542620, Class: 1.000000, Obj: 0.296622, No Obj: 0.004606, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.515051, Class: 1.000000, Obj: 0.280167, No Obj: 0.004500, Avg Recall: 0.558333, count: 120 -Region Avg IOU: 0.506791, Class: 1.000000, Obj: 0.270007, No Obj: 0.004071, Avg Recall: 0.500000, count: 112 -Region Avg IOU: 0.484591, Class: 1.000000, Obj: 0.269665, No Obj: 0.004115, Avg Recall: 0.492064, count: 126 -Region Avg IOU: 0.572991, Class: 1.000000, Obj: 0.266035, No Obj: 0.003971, Avg Recall: 0.686747, count: 83 -Region Avg IOU: 0.507752, Class: 1.000000, Obj: 0.302672, No Obj: 0.003887, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.516631, Class: 1.000000, Obj: 0.295998, No Obj: 0.004064, Avg Recall: 0.577586, count: 116 -Region Avg IOU: 0.510952, Class: 1.000000, Obj: 0.302786, No Obj: 0.003661, Avg Recall: 0.560440, count: 91 -1039: 0.470638, 0.482062 avg, 0.001000 rate, 22.114231 seconds, 265984 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.521123, Class: 1.000000, Obj: 0.263064, No Obj: 0.004303, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.489216, Class: 1.000000, Obj: 0.247318, No Obj: 0.003744, Avg Recall: 0.563025, count: 119 -Region Avg IOU: 0.521093, Class: 1.000000, Obj: 0.269935, No Obj: 0.003903, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.503306, Class: 1.000000, Obj: 0.249670, No Obj: 0.004207, Avg Recall: 0.549180, count: 122 -Region Avg IOU: 0.454876, Class: 1.000000, Obj: 0.273544, No Obj: 0.003346, Avg Recall: 0.494624, count: 93 -Region Avg IOU: 0.519250, Class: 1.000000, Obj: 0.286583, No Obj: 0.003814, Avg Recall: 0.566372, count: 113 -Region Avg IOU: 0.517083, Class: 1.000000, Obj: 0.256254, No Obj: 0.004213, Avg Recall: 0.600000, count: 150 -Region Avg IOU: 0.492951, Class: 1.000000, Obj: 0.260836, No Obj: 0.003872, Avg Recall: 0.547009, count: 117 -1040: 0.532502, 0.487106 avg, 0.001000 rate, 22.342470 seconds, 266240 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000083 seconds -Region Avg IOU: 0.526782, Class: 1.000000, Obj: 0.264139, No Obj: 0.003384, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.528038, Class: 1.000000, Obj: 0.289110, No Obj: 0.003704, Avg Recall: 0.611111, count: 144 -Region Avg IOU: 0.594239, Class: 1.000000, Obj: 0.277471, No Obj: 0.003459, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.584548, Class: 1.000000, Obj: 0.279910, No Obj: 0.003536, Avg Recall: 0.703704, count: 135 -Region Avg IOU: 0.555340, Class: 1.000000, Obj: 0.269041, No Obj: 0.003084, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.582069, Class: 1.000000, Obj: 0.335310, No Obj: 0.003388, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.538537, Class: 1.000000, Obj: 0.283841, No Obj: 0.002864, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.593369, Class: 1.000000, Obj: 0.239170, No Obj: 0.003297, Avg Recall: 0.717391, count: 92 -1041: 0.490845, 0.487480 avg, 0.001000 rate, 27.418741 seconds, 266496 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.599400, Class: 1.000000, Obj: 0.293687, No Obj: 0.003481, Avg Recall: 0.762295, count: 122 -Region Avg IOU: 0.555482, Class: 1.000000, Obj: 0.259461, No Obj: 0.003164, Avg Recall: 0.673684, count: 95 -Region Avg IOU: 0.583722, Class: 1.000000, Obj: 0.235924, No Obj: 0.003751, Avg Recall: 0.741935, count: 155 -Region Avg IOU: 0.586544, Class: 1.000000, Obj: 0.276045, No Obj: 0.003613, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.545054, Class: 1.000000, Obj: 0.259546, No Obj: 0.003149, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.607289, Class: 1.000000, Obj: 0.300908, No Obj: 0.003413, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.563646, Class: 1.000000, Obj: 0.241494, No Obj: 0.003115, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.535451, Class: 1.000000, Obj: 0.251440, No Obj: 0.003091, Avg Recall: 0.608696, count: 115 -1042: 0.505231, 0.489255 avg, 0.001000 rate, 27.483986 seconds, 266752 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.560920, Class: 1.000000, Obj: 0.249033, No Obj: 0.003045, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.583663, Class: 1.000000, Obj: 0.250323, No Obj: 0.003371, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.592462, Class: 1.000000, Obj: 0.307838, No Obj: 0.003101, Avg Recall: 0.740741, count: 81 -Region Avg IOU: 0.590955, Class: 1.000000, Obj: 0.244476, No Obj: 0.003129, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.559581, Class: 1.000000, Obj: 0.240781, No Obj: 0.003238, Avg Recall: 0.664062, count: 128 -Region Avg IOU: 0.551163, Class: 1.000000, Obj: 0.235678, No Obj: 0.002954, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.612682, Class: 1.000000, Obj: 0.275411, No Obj: 0.003327, Avg Recall: 0.768000, count: 125 -Region Avg IOU: 0.556431, Class: 1.000000, Obj: 0.249664, No Obj: 0.002883, Avg Recall: 0.659341, count: 91 -1043: 0.448297, 0.485159 avg, 0.001000 rate, 27.549828 seconds, 267008 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.520118, Class: 1.000000, Obj: 0.255772, No Obj: 0.002730, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.560990, Class: 1.000000, Obj: 0.278142, No Obj: 0.003351, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.541189, Class: 1.000000, Obj: 0.239456, No Obj: 0.002902, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.586575, Class: 1.000000, Obj: 0.270788, No Obj: 0.003223, Avg Recall: 0.708029, count: 137 -Region Avg IOU: 0.593850, Class: 1.000000, Obj: 0.188616, No Obj: 0.003346, Avg Recall: 0.744186, count: 129 -Region Avg IOU: 0.531750, Class: 1.000000, Obj: 0.238850, No Obj: 0.003091, Avg Recall: 0.631148, count: 122 -Region Avg IOU: 0.582591, Class: 1.000000, Obj: 0.272295, No Obj: 0.003089, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.573818, Class: 1.000000, Obj: 0.251998, No Obj: 0.003209, Avg Recall: 0.682243, count: 107 -1044: 0.533206, 0.489964 avg, 0.001000 rate, 29.147795 seconds, 267264 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.569329, Class: 1.000000, Obj: 0.258889, No Obj: 0.002941, Avg Recall: 0.669492, count: 118 -Region Avg IOU: 0.579341, Class: 1.000000, Obj: 0.267812, No Obj: 0.003020, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.573302, Class: 1.000000, Obj: 0.264007, No Obj: 0.002726, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.535963, Class: 1.000000, Obj: 0.238932, No Obj: 0.002959, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.573979, Class: 1.000000, Obj: 0.242325, No Obj: 0.002737, Avg Recall: 0.734177, count: 79 -Region Avg IOU: 0.562434, Class: 1.000000, Obj: 0.243921, No Obj: 0.003195, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.610777, Class: 1.000000, Obj: 0.223193, No Obj: 0.003506, Avg Recall: 0.798387, count: 124 -Region Avg IOU: 0.555206, Class: 1.000000, Obj: 0.234530, No Obj: 0.002874, Avg Recall: 0.678899, count: 109 -1045: 0.500478, 0.491015 avg, 0.001000 rate, 28.340805 seconds, 267520 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.591081, Class: 1.000000, Obj: 0.257206, No Obj: 0.003450, Avg Recall: 0.772358, count: 123 -Region Avg IOU: 0.581149, Class: 1.000000, Obj: 0.260540, No Obj: 0.003057, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.539548, Class: 1.000000, Obj: 0.234090, No Obj: 0.002977, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.527290, Class: 1.000000, Obj: 0.262554, No Obj: 0.003159, Avg Recall: 0.589928, count: 139 -Region Avg IOU: 0.598817, Class: 1.000000, Obj: 0.243209, No Obj: 0.003130, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.577158, Class: 1.000000, Obj: 0.266738, No Obj: 0.003149, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.574067, Class: 1.000000, Obj: 0.250884, No Obj: 0.002872, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.554627, Class: 1.000000, Obj: 0.252848, No Obj: 0.003253, Avg Recall: 0.632479, count: 117 -1046: 0.592840, 0.501198 avg, 0.001000 rate, 26.939400 seconds, 267776 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.612413, Class: 1.000000, Obj: 0.257103, No Obj: 0.003141, Avg Recall: 0.791209, count: 91 -Region Avg IOU: 0.644327, Class: 1.000000, Obj: 0.273561, No Obj: 0.003157, Avg Recall: 0.858824, count: 85 -Region Avg IOU: 0.561649, Class: 1.000000, Obj: 0.245453, No Obj: 0.002986, Avg Recall: 0.648352, count: 91 -Region Avg IOU: 0.596330, Class: 1.000000, Obj: 0.274684, No Obj: 0.003168, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.585974, Class: 1.000000, Obj: 0.288063, No Obj: 0.003152, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.550214, Class: 1.000000, Obj: 0.264710, No Obj: 0.003013, Avg Recall: 0.654321, count: 81 -Region Avg IOU: 0.535217, Class: 1.000000, Obj: 0.257607, No Obj: 0.003019, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.571201, Class: 1.000000, Obj: 0.277499, No Obj: 0.003237, Avg Recall: 0.684685, count: 111 -1047: 0.450445, 0.496122 avg, 0.001000 rate, 26.312057 seconds, 268032 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.587096, Class: 1.000000, Obj: 0.273938, No Obj: 0.002855, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.577999, Class: 1.000000, Obj: 0.294067, No Obj: 0.003027, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.548911, Class: 1.000000, Obj: 0.254321, No Obj: 0.003630, Avg Recall: 0.639344, count: 122 -Region Avg IOU: 0.549055, Class: 1.000000, Obj: 0.217057, No Obj: 0.003017, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.618151, Class: 1.000000, Obj: 0.288514, No Obj: 0.003214, Avg Recall: 0.758621, count: 87 -Region Avg IOU: 0.614622, Class: 1.000000, Obj: 0.295293, No Obj: 0.003466, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.563491, Class: 1.000000, Obj: 0.256688, No Obj: 0.003183, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.587788, Class: 1.000000, Obj: 0.259495, No Obj: 0.002948, Avg Recall: 0.704082, count: 98 -1048: 0.488396, 0.495350 avg, 0.001000 rate, 28.574699 seconds, 268288 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.566209, Class: 1.000000, Obj: 0.264651, No Obj: 0.003591, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.551233, Class: 1.000000, Obj: 0.225053, No Obj: 0.003137, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.566534, Class: 1.000000, Obj: 0.290796, No Obj: 0.003254, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.551047, Class: 1.000000, Obj: 0.204339, No Obj: 0.003155, Avg Recall: 0.642857, count: 140 -Region Avg IOU: 0.551035, Class: 1.000000, Obj: 0.267802, No Obj: 0.003064, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.546222, Class: 1.000000, Obj: 0.240671, No Obj: 0.003604, Avg Recall: 0.682759, count: 145 -Region Avg IOU: 0.565607, Class: 1.000000, Obj: 0.260700, No Obj: 0.003238, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.560120, Class: 1.000000, Obj: 0.321824, No Obj: 0.003246, Avg Recall: 0.666667, count: 117 -1049: 0.539153, 0.499730 avg, 0.001000 rate, 27.439323 seconds, 268544 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.568823, Class: 1.000000, Obj: 0.276889, No Obj: 0.003286, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.536088, Class: 1.000000, Obj: 0.262775, No Obj: 0.002973, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.571370, Class: 1.000000, Obj: 0.273369, No Obj: 0.003279, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.588731, Class: 1.000000, Obj: 0.254706, No Obj: 0.003592, Avg Recall: 0.732877, count: 146 -Region Avg IOU: 0.543616, Class: 1.000000, Obj: 0.241155, No Obj: 0.003026, Avg Recall: 0.586957, count: 92 -Region Avg IOU: 0.565592, Class: 1.000000, Obj: 0.245420, No Obj: 0.002824, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.598085, Class: 1.000000, Obj: 0.286888, No Obj: 0.003307, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.539936, Class: 1.000000, Obj: 0.220540, No Obj: 0.002816, Avg Recall: 0.614583, count: 96 -1050: 0.474350, 0.497192 avg, 0.001000 rate, 27.800964 seconds, 268800 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 12.465796 seconds -Region Avg IOU: 0.585439, Class: 1.000000, Obj: 0.270169, No Obj: 0.003546, Avg Recall: 0.738095, count: 126 -Region Avg IOU: 0.545868, Class: 1.000000, Obj: 0.226894, No Obj: 0.003558, Avg Recall: 0.616000, count: 125 -Region Avg IOU: 0.548074, Class: 1.000000, Obj: 0.269143, No Obj: 0.003190, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.519092, Class: 1.000000, Obj: 0.218215, No Obj: 0.002923, Avg Recall: 0.602410, count: 83 -Region Avg IOU: 0.550761, Class: 1.000000, Obj: 0.252171, No Obj: 0.003209, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.582641, Class: 1.000000, Obj: 0.248843, No Obj: 0.003094, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.559727, Class: 1.000000, Obj: 0.228582, No Obj: 0.003033, Avg Recall: 0.640449, count: 89 -Region Avg IOU: 0.546746, Class: 1.000000, Obj: 0.267612, No Obj: 0.003054, Avg Recall: 0.653061, count: 98 -1051: 0.508751, 0.498348 avg, 0.001000 rate, 27.252460 seconds, 269056 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.496289, Class: 1.000000, Obj: 0.238348, No Obj: 0.002993, Avg Recall: 0.500000, count: 106 -Region Avg IOU: 0.504750, Class: 1.000000, Obj: 0.220308, No Obj: 0.003097, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.600315, Class: 1.000000, Obj: 0.296478, No Obj: 0.003307, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.539108, Class: 1.000000, Obj: 0.222838, No Obj: 0.003614, Avg Recall: 0.612403, count: 129 -Region Avg IOU: 0.537930, Class: 1.000000, Obj: 0.249712, No Obj: 0.003433, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.578134, Class: 1.000000, Obj: 0.278630, No Obj: 0.003222, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.545969, Class: 1.000000, Obj: 0.263922, No Obj: 0.003146, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.502449, Class: 1.000000, Obj: 0.247716, No Obj: 0.003200, Avg Recall: 0.555556, count: 126 -1052: 0.546297, 0.503143 avg, 0.001000 rate, 26.005106 seconds, 269312 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.572883, Class: 1.000000, Obj: 0.272414, No Obj: 0.003142, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.545663, Class: 1.000000, Obj: 0.237421, No Obj: 0.003060, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.591301, Class: 1.000000, Obj: 0.270804, No Obj: 0.003044, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.608701, Class: 1.000000, Obj: 0.243869, No Obj: 0.003384, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.535022, Class: 1.000000, Obj: 0.272016, No Obj: 0.002772, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.517397, Class: 1.000000, Obj: 0.241302, No Obj: 0.003087, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.561220, Class: 1.000000, Obj: 0.238982, No Obj: 0.003750, Avg Recall: 0.642336, count: 137 -Region Avg IOU: 0.567199, Class: 1.000000, Obj: 0.260090, No Obj: 0.002717, Avg Recall: 0.687500, count: 96 -1053: 0.459209, 0.498749 avg, 0.001000 rate, 26.321871 seconds, 269568 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.557376, Class: 1.000000, Obj: 0.259381, No Obj: 0.003059, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.548129, Class: 1.000000, Obj: 0.258307, No Obj: 0.003005, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.562605, Class: 1.000000, Obj: 0.244868, No Obj: 0.003074, Avg Recall: 0.652542, count: 118 -Region Avg IOU: 0.595454, Class: 1.000000, Obj: 0.284741, No Obj: 0.003127, Avg Recall: 0.727273, count: 88 -Region Avg IOU: 0.618316, Class: 1.000000, Obj: 0.323648, No Obj: 0.003105, Avg Recall: 0.750000, count: 88 -Region Avg IOU: 0.600465, Class: 1.000000, Obj: 0.244072, No Obj: 0.003649, Avg Recall: 0.731884, count: 138 -Region Avg IOU: 0.596861, Class: 1.000000, Obj: 0.286843, No Obj: 0.003255, Avg Recall: 0.736000, count: 125 -Region Avg IOU: 0.582327, Class: 1.000000, Obj: 0.274924, No Obj: 0.002991, Avg Recall: 0.686275, count: 102 -1054: 0.504313, 0.499306 avg, 0.001000 rate, 27.109560 seconds, 269824 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.626420, Class: 1.000000, Obj: 0.298498, No Obj: 0.003248, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.542937, Class: 1.000000, Obj: 0.245672, No Obj: 0.002996, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.494760, Class: 1.000000, Obj: 0.242843, No Obj: 0.003054, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.569832, Class: 1.000000, Obj: 0.253233, No Obj: 0.003070, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.533392, Class: 1.000000, Obj: 0.266641, No Obj: 0.003003, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.580919, Class: 1.000000, Obj: 0.300889, No Obj: 0.003206, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.553467, Class: 1.000000, Obj: 0.241955, No Obj: 0.002850, Avg Recall: 0.659341, count: 91 -Region Avg IOU: 0.582081, Class: 1.000000, Obj: 0.239173, No Obj: 0.003496, Avg Recall: 0.709677, count: 124 -1055: 0.478992, 0.497274 avg, 0.001000 rate, 26.964355 seconds, 270080 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.565944, Class: 1.000000, Obj: 0.314746, No Obj: 0.003364, Avg Recall: 0.666667, count: 126 -Region Avg IOU: 0.562706, Class: 1.000000, Obj: 0.286370, No Obj: 0.003368, Avg Recall: 0.652542, count: 118 -Region Avg IOU: 0.547616, Class: 1.000000, Obj: 0.261915, No Obj: 0.003141, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.528427, Class: 1.000000, Obj: 0.225173, No Obj: 0.002965, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.536274, Class: 1.000000, Obj: 0.274522, No Obj: 0.003053, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.575318, Class: 1.000000, Obj: 0.281819, No Obj: 0.003119, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.525692, Class: 1.000000, Obj: 0.297326, No Obj: 0.002710, Avg Recall: 0.547945, count: 73 -Region Avg IOU: 0.579424, Class: 1.000000, Obj: 0.323785, No Obj: 0.003158, Avg Recall: 0.663043, count: 92 -1056: 0.450135, 0.492560 avg, 0.001000 rate, 26.688105 seconds, 270336 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.525685, Class: 1.000000, Obj: 0.248175, No Obj: 0.003616, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.567133, Class: 1.000000, Obj: 0.250535, No Obj: 0.003483, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.545562, Class: 1.000000, Obj: 0.306436, No Obj: 0.002950, Avg Recall: 0.574468, count: 94 -Region Avg IOU: 0.582084, Class: 1.000000, Obj: 0.243157, No Obj: 0.003254, Avg Recall: 0.687943, count: 141 -Region Avg IOU: 0.544616, Class: 1.000000, Obj: 0.271091, No Obj: 0.003248, Avg Recall: 0.644628, count: 121 -Region Avg IOU: 0.553342, Class: 1.000000, Obj: 0.276238, No Obj: 0.003179, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.562622, Class: 1.000000, Obj: 0.256005, No Obj: 0.003475, Avg Recall: 0.691176, count: 136 -Region Avg IOU: 0.546502, Class: 1.000000, Obj: 0.252711, No Obj: 0.003045, Avg Recall: 0.638095, count: 105 -1057: 0.502230, 0.493527 avg, 0.001000 rate, 25.631472 seconds, 270592 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.606664, Class: 1.000000, Obj: 0.295787, No Obj: 0.003153, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.575960, Class: 1.000000, Obj: 0.268696, No Obj: 0.003075, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.558029, Class: 1.000000, Obj: 0.279847, No Obj: 0.002756, Avg Recall: 0.651163, count: 86 -Region Avg IOU: 0.559008, Class: 1.000000, Obj: 0.248674, No Obj: 0.002913, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.586352, Class: 1.000000, Obj: 0.278334, No Obj: 0.003415, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.557294, Class: 1.000000, Obj: 0.312890, No Obj: 0.003149, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.548309, Class: 1.000000, Obj: 0.273400, No Obj: 0.002948, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.563098, Class: 1.000000, Obj: 0.254526, No Obj: 0.003265, Avg Recall: 0.657658, count: 111 -1058: 0.441310, 0.488306 avg, 0.001000 rate, 27.383474 seconds, 270848 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.611529, Class: 1.000000, Obj: 0.307379, No Obj: 0.003606, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.570954, Class: 1.000000, Obj: 0.259588, No Obj: 0.003387, Avg Recall: 0.685039, count: 127 -Region Avg IOU: 0.570780, Class: 1.000000, Obj: 0.274172, No Obj: 0.003340, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.538010, Class: 1.000000, Obj: 0.290641, No Obj: 0.003043, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.553420, Class: 1.000000, Obj: 0.252224, No Obj: 0.003240, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.561098, Class: 1.000000, Obj: 0.280619, No Obj: 0.003464, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.572923, Class: 1.000000, Obj: 0.305207, No Obj: 0.003203, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.558673, Class: 1.000000, Obj: 0.265491, No Obj: 0.003348, Avg Recall: 0.669725, count: 109 -1059: 0.502206, 0.489696 avg, 0.001000 rate, 26.137764 seconds, 271104 images -Loaded: 0.000115 seconds -Region Avg IOU: 0.568311, Class: 1.000000, Obj: 0.256663, No Obj: 0.003161, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.544845, Class: 1.000000, Obj: 0.244573, No Obj: 0.003754, Avg Recall: 0.642336, count: 137 -Region Avg IOU: 0.561403, Class: 1.000000, Obj: 0.276895, No Obj: 0.003471, Avg Recall: 0.652893, count: 121 -Region Avg IOU: 0.552110, Class: 1.000000, Obj: 0.273466, No Obj: 0.002987, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.551480, Class: 1.000000, Obj: 0.312642, No Obj: 0.003215, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.595343, Class: 1.000000, Obj: 0.265565, No Obj: 0.003130, Avg Recall: 0.725275, count: 91 -Region Avg IOU: 0.547021, Class: 1.000000, Obj: 0.252531, No Obj: 0.003200, Avg Recall: 0.617188, count: 128 -Region Avg IOU: 0.546880, Class: 1.000000, Obj: 0.262341, No Obj: 0.003547, Avg Recall: 0.647059, count: 136 -1060: 0.542762, 0.495002 avg, 0.001000 rate, 26.512629 seconds, 271360 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.212214 seconds -Region Avg IOU: 0.471712, Class: 1.000000, Obj: 0.253644, No Obj: 0.003991, Avg Recall: 0.521368, count: 117 -Region Avg IOU: 0.479226, Class: 1.000000, Obj: 0.225390, No Obj: 0.004251, Avg Recall: 0.537815, count: 119 -Region Avg IOU: 0.455658, Class: 1.000000, Obj: 0.241565, No Obj: 0.003608, Avg Recall: 0.485149, count: 101 -Region Avg IOU: 0.452965, Class: 1.000000, Obj: 0.230374, No Obj: 0.003786, Avg Recall: 0.504065, count: 123 -Region Avg IOU: 0.477124, Class: 1.000000, Obj: 0.249540, No Obj: 0.004018, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.474200, Class: 1.000000, Obj: 0.259447, No Obj: 0.003864, Avg Recall: 0.524272, count: 103 -Region Avg IOU: 0.449443, Class: 1.000000, Obj: 0.216666, No Obj: 0.003300, Avg Recall: 0.443182, count: 88 -Region Avg IOU: 0.479806, Class: 1.000000, Obj: 0.248600, No Obj: 0.003722, Avg Recall: 0.515464, count: 97 -1061: 0.486513, 0.494153 avg, 0.001000 rate, 20.989458 seconds, 271616 images -Loaded: 0.000053 seconds -Region Avg IOU: 0.440474, Class: 1.000000, Obj: 0.217198, No Obj: 0.003970, Avg Recall: 0.460870, count: 115 -Region Avg IOU: 0.470083, Class: 1.000000, Obj: 0.207502, No Obj: 0.003655, Avg Recall: 0.500000, count: 100 -Region Avg IOU: 0.479781, Class: 1.000000, Obj: 0.259199, No Obj: 0.004045, Avg Recall: 0.488550, count: 131 -Region Avg IOU: 0.453294, Class: 1.000000, Obj: 0.255066, No Obj: 0.003634, Avg Recall: 0.446602, count: 103 -Region Avg IOU: 0.475218, Class: 1.000000, Obj: 0.255120, No Obj: 0.003802, Avg Recall: 0.518519, count: 108 -Region Avg IOU: 0.452048, Class: 1.000000, Obj: 0.217731, No Obj: 0.003759, Avg Recall: 0.500000, count: 94 -Region Avg IOU: 0.493105, Class: 1.000000, Obj: 0.303563, No Obj: 0.003820, Avg Recall: 0.528736, count: 87 -Region Avg IOU: 0.481953, Class: 1.000000, Obj: 0.236933, No Obj: 0.003915, Avg Recall: 0.550847, count: 118 -1062: 0.464473, 0.491185 avg, 0.001000 rate, 20.487965 seconds, 271872 images -Loaded: 1.528119 seconds -Region Avg IOU: 0.458925, Class: 1.000000, Obj: 0.259401, No Obj: 0.004651, Avg Recall: 0.495727, count: 117 -Region Avg IOU: 0.495046, Class: 1.000000, Obj: 0.275820, No Obj: 0.004061, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.434562, Class: 1.000000, Obj: 0.267448, No Obj: 0.004010, Avg Recall: 0.435897, count: 117 -Region Avg IOU: 0.455994, Class: 1.000000, Obj: 0.277643, No Obj: 0.004763, Avg Recall: 0.491667, count: 120 -Region Avg IOU: 0.413609, Class: 1.000000, Obj: 0.249535, No Obj: 0.003595, Avg Recall: 0.428571, count: 112 -Region Avg IOU: 0.435442, Class: 1.000000, Obj: 0.216810, No Obj: 0.003586, Avg Recall: 0.437500, count: 96 -Region Avg IOU: 0.475732, Class: 1.000000, Obj: 0.262293, No Obj: 0.004280, Avg Recall: 0.504000, count: 125 -Region Avg IOU: 0.533573, Class: 1.000000, Obj: 0.262728, No Obj: 0.004051, Avg Recall: 0.637500, count: 80 -1063: 0.543881, 0.496455 avg, 0.001000 rate, 20.520647 seconds, 272128 images -Loaded: 0.839125 seconds -Region Avg IOU: 0.484935, Class: 1.000000, Obj: 0.264852, No Obj: 0.004070, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.476341, Class: 1.000000, Obj: 0.254872, No Obj: 0.004804, Avg Recall: 0.566929, count: 127 -Region Avg IOU: 0.499401, Class: 1.000000, Obj: 0.252352, No Obj: 0.004049, Avg Recall: 0.602410, count: 83 -Region Avg IOU: 0.462174, Class: 1.000000, Obj: 0.265502, No Obj: 0.004447, Avg Recall: 0.491228, count: 114 -Region Avg IOU: 0.450145, Class: 1.000000, Obj: 0.234558, No Obj: 0.004166, Avg Recall: 0.459677, count: 124 -Region Avg IOU: 0.436775, Class: 1.000000, Obj: 0.261260, No Obj: 0.004134, Avg Recall: 0.434783, count: 115 -Region Avg IOU: 0.473065, Class: 1.000000, Obj: 0.275360, No Obj: 0.003994, Avg Recall: 0.532609, count: 92 -Region Avg IOU: 0.490484, Class: 1.000000, Obj: 0.277426, No Obj: 0.004459, Avg Recall: 0.552239, count: 134 -1064: 0.478625, 0.494672 avg, 0.001000 rate, 19.635994 seconds, 272384 images -Loaded: 1.965193 seconds -Region Avg IOU: 0.462836, Class: 1.000000, Obj: 0.277818, No Obj: 0.003761, Avg Recall: 0.494624, count: 93 -Region Avg IOU: 0.455183, Class: 1.000000, Obj: 0.241111, No Obj: 0.004420, Avg Recall: 0.463576, count: 151 -Region Avg IOU: 0.409931, Class: 1.000000, Obj: 0.267853, No Obj: 0.003724, Avg Recall: 0.401786, count: 112 -Region Avg IOU: 0.448936, Class: 1.000000, Obj: 0.239477, No Obj: 0.003540, Avg Recall: 0.432099, count: 81 -Region Avg IOU: 0.490191, Class: 1.000000, Obj: 0.326038, No Obj: 0.004058, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.421332, Class: 1.000000, Obj: 0.224611, No Obj: 0.003809, Avg Recall: 0.401961, count: 102 -Region Avg IOU: 0.416828, Class: 1.000000, Obj: 0.234656, No Obj: 0.003620, Avg Recall: 0.361702, count: 94 -Region Avg IOU: 0.458624, Class: 1.000000, Obj: 0.234844, No Obj: 0.004908, Avg Recall: 0.534483, count: 116 -1065: 0.481325, 0.493337 avg, 0.001000 rate, 21.386211 seconds, 272640 images -Loaded: 0.136121 seconds -Region Avg IOU: 0.502358, Class: 1.000000, Obj: 0.305884, No Obj: 0.003947, Avg Recall: 0.518519, count: 81 -Region Avg IOU: 0.433661, Class: 1.000000, Obj: 0.237627, No Obj: 0.004176, Avg Recall: 0.509615, count: 104 -Region Avg IOU: 0.473138, Class: 1.000000, Obj: 0.268958, No Obj: 0.004241, Avg Recall: 0.500000, count: 104 -Region Avg IOU: 0.482277, Class: 1.000000, Obj: 0.258320, No Obj: 0.004263, Avg Recall: 0.578947, count: 114 -Region Avg IOU: 0.445094, Class: 1.000000, Obj: 0.238845, No Obj: 0.004128, Avg Recall: 0.472603, count: 146 -Region Avg IOU: 0.486828, Class: 1.000000, Obj: 0.277490, No Obj: 0.004731, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.483282, Class: 1.000000, Obj: 0.253455, No Obj: 0.003992, Avg Recall: 0.552941, count: 85 -Region Avg IOU: 0.473460, Class: 1.000000, Obj: 0.278910, No Obj: 0.003811, Avg Recall: 0.500000, count: 68 -1066: 0.463298, 0.490333 avg, 0.001000 rate, 21.429628 seconds, 272896 images -Loaded: 0.000052 seconds -Region Avg IOU: 0.479811, Class: 1.000000, Obj: 0.283820, No Obj: 0.004708, Avg Recall: 0.472000, count: 125 -Region Avg IOU: 0.491852, Class: 1.000000, Obj: 0.275261, No Obj: 0.004209, Avg Recall: 0.551020, count: 98 -Region Avg IOU: 0.452511, Class: 1.000000, Obj: 0.252119, No Obj: 0.003735, Avg Recall: 0.494737, count: 95 -Region Avg IOU: 0.437387, Class: 1.000000, Obj: 0.240531, No Obj: 0.003806, Avg Recall: 0.486239, count: 109 -Region Avg IOU: 0.502565, Class: 1.000000, Obj: 0.285830, No Obj: 0.004513, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.453325, Class: 1.000000, Obj: 0.230246, No Obj: 0.004258, Avg Recall: 0.478873, count: 142 -Region Avg IOU: 0.474505, Class: 1.000000, Obj: 0.258660, No Obj: 0.004499, Avg Recall: 0.483607, count: 122 -Region Avg IOU: 0.451716, Class: 1.000000, Obj: 0.248200, No Obj: 0.003821, Avg Recall: 0.412281, count: 114 -1067: 0.542542, 0.495554 avg, 0.001000 rate, 20.385300 seconds, 273152 images -Loaded: 2.662398 seconds -Region Avg IOU: 0.397386, Class: 1.000000, Obj: 0.247578, No Obj: 0.004246, Avg Recall: 0.410256, count: 117 -Region Avg IOU: 0.483350, Class: 1.000000, Obj: 0.275774, No Obj: 0.003729, Avg Recall: 0.550000, count: 80 -Region Avg IOU: 0.476217, Class: 1.000000, Obj: 0.283215, No Obj: 0.004251, Avg Recall: 0.481818, count: 110 -Region Avg IOU: 0.496727, Class: 1.000000, Obj: 0.278057, No Obj: 0.004811, Avg Recall: 0.550847, count: 118 -Region Avg IOU: 0.437261, Class: 1.000000, Obj: 0.248632, No Obj: 0.003573, Avg Recall: 0.494845, count: 97 -Region Avg IOU: 0.453315, Class: 1.000000, Obj: 0.276177, No Obj: 0.004712, Avg Recall: 0.477477, count: 111 -Region Avg IOU: 0.477296, Class: 1.000000, Obj: 0.278568, No Obj: 0.004717, Avg Recall: 0.491667, count: 120 -Region Avg IOU: 0.424660, Class: 1.000000, Obj: 0.228509, No Obj: 0.004132, Avg Recall: 0.420168, count: 119 -1068: 0.532779, 0.499277 avg, 0.001000 rate, 19.914570 seconds, 273408 images -Loaded: 4.115511 seconds -Region Avg IOU: 0.422333, Class: 1.000000, Obj: 0.247434, No Obj: 0.004256, Avg Recall: 0.418803, count: 117 -Region Avg IOU: 0.473647, Class: 1.000000, Obj: 0.281463, No Obj: 0.004422, Avg Recall: 0.494737, count: 95 -Region Avg IOU: 0.424644, Class: 1.000000, Obj: 0.262884, No Obj: 0.003997, Avg Recall: 0.439394, count: 132 -Region Avg IOU: 0.454494, Class: 1.000000, Obj: 0.252805, No Obj: 0.003966, Avg Recall: 0.485981, count: 107 -Region Avg IOU: 0.493134, Class: 1.000000, Obj: 0.247770, No Obj: 0.004816, Avg Recall: 0.526718, count: 131 -Region Avg IOU: 0.462654, Class: 1.000000, Obj: 0.266081, No Obj: 0.003968, Avg Recall: 0.484536, count: 97 -Region Avg IOU: 0.431954, Class: 1.000000, Obj: 0.213115, No Obj: 0.003734, Avg Recall: 0.494737, count: 95 -Region Avg IOU: 0.469849, Class: 1.000000, Obj: 0.243532, No Obj: 0.004213, Avg Recall: 0.477876, count: 113 -1069: 0.507649, 0.500114 avg, 0.001000 rate, 21.418602 seconds, 273664 images -Loaded: 0.294509 seconds -Region Avg IOU: 0.470834, Class: 1.000000, Obj: 0.254669, No Obj: 0.004150, Avg Recall: 0.460784, count: 102 -Region Avg IOU: 0.495433, Class: 1.000000, Obj: 0.257850, No Obj: 0.004380, Avg Recall: 0.600000, count: 120 -Region Avg IOU: 0.432351, Class: 1.000000, Obj: 0.249576, No Obj: 0.004076, Avg Recall: 0.429907, count: 107 -Region Avg IOU: 0.501365, Class: 1.000000, Obj: 0.286663, No Obj: 0.004058, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.465250, Class: 1.000000, Obj: 0.281000, No Obj: 0.004455, Avg Recall: 0.455285, count: 123 -Region Avg IOU: 0.456776, Class: 1.000000, Obj: 0.259334, No Obj: 0.004493, Avg Recall: 0.477876, count: 113 -Region Avg IOU: 0.469967, Class: 1.000000, Obj: 0.289949, No Obj: 0.004385, Avg Recall: 0.508065, count: 124 -Region Avg IOU: 0.441017, Class: 1.000000, Obj: 0.234527, No Obj: 0.004211, Avg Recall: 0.433962, count: 106 -1070: 0.551723, 0.505275 avg, 0.001000 rate, 22.113077 seconds, 273920 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.605659 seconds -Region Avg IOU: 0.483268, Class: 1.000000, Obj: 0.274064, No Obj: 0.004418, Avg Recall: 0.538462, count: 104 -Region Avg IOU: 0.430009, Class: 1.000000, Obj: 0.278797, No Obj: 0.004612, Avg Recall: 0.448598, count: 107 -Region Avg IOU: 0.461761, Class: 1.000000, Obj: 0.271135, No Obj: 0.004151, Avg Recall: 0.483871, count: 93 -Region Avg IOU: 0.425958, Class: 1.000000, Obj: 0.211143, No Obj: 0.005355, Avg Recall: 0.429530, count: 149 -Region Avg IOU: 0.464667, Class: 1.000000, Obj: 0.246280, No Obj: 0.004588, Avg Recall: 0.471698, count: 106 -Region Avg IOU: 0.451990, Class: 1.000000, Obj: 0.254387, No Obj: 0.004377, Avg Recall: 0.489583, count: 96 -Region Avg IOU: 0.422302, Class: 1.000000, Obj: 0.236683, No Obj: 0.004212, Avg Recall: 0.428571, count: 98 -Region Avg IOU: 0.428702, Class: 1.000000, Obj: 0.214477, No Obj: 0.004403, Avg Recall: 0.459677, count: 124 -1071: 0.439222, 0.498670 avg, 0.001000 rate, 20.143785 seconds, 274176 images -Loaded: 0.978965 seconds -Region Avg IOU: 0.418581, Class: 1.000000, Obj: 0.231541, No Obj: 0.004130, Avg Recall: 0.382022, count: 89 -Region Avg IOU: 0.459477, Class: 1.000000, Obj: 0.250020, No Obj: 0.003966, Avg Recall: 0.446808, count: 94 -Region Avg IOU: 0.449011, Class: 1.000000, Obj: 0.192765, No Obj: 0.004630, Avg Recall: 0.478992, count: 119 -Region Avg IOU: 0.442008, Class: 1.000000, Obj: 0.226602, No Obj: 0.004260, Avg Recall: 0.458333, count: 120 -Region Avg IOU: 0.458072, Class: 1.000000, Obj: 0.248543, No Obj: 0.004296, Avg Recall: 0.500000, count: 114 -Region Avg IOU: 0.451465, Class: 1.000000, Obj: 0.293749, No Obj: 0.004887, Avg Recall: 0.466667, count: 105 -Region Avg IOU: 0.479109, Class: 1.000000, Obj: 0.239516, No Obj: 0.004323, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.470275, Class: 1.000000, Obj: 0.277038, No Obj: 0.004694, Avg Recall: 0.504587, count: 109 -1072: 0.485179, 0.497321 avg, 0.001000 rate, 17.089912 seconds, 274432 images -Loaded: 4.187433 seconds -Region Avg IOU: 0.449063, Class: 1.000000, Obj: 0.261669, No Obj: 0.004261, Avg Recall: 0.500000, count: 98 -Region Avg IOU: 0.442371, Class: 1.000000, Obj: 0.278536, No Obj: 0.004589, Avg Recall: 0.473214, count: 112 -Region Avg IOU: 0.458596, Class: 1.000000, Obj: 0.264533, No Obj: 0.004942, Avg Recall: 0.554622, count: 119 -Region Avg IOU: 0.452624, Class: 1.000000, Obj: 0.246418, No Obj: 0.004616, Avg Recall: 0.491379, count: 116 -Region Avg IOU: 0.415920, Class: 1.000000, Obj: 0.228884, No Obj: 0.004341, Avg Recall: 0.425000, count: 120 -Region Avg IOU: 0.386517, Class: 1.000000, Obj: 0.221870, No Obj: 0.004204, Avg Recall: 0.356436, count: 101 -Region Avg IOU: 0.476626, Class: 1.000000, Obj: 0.252461, No Obj: 0.004234, Avg Recall: 0.516854, count: 89 -Region Avg IOU: 0.400477, Class: 1.000000, Obj: 0.244639, No Obj: 0.004401, Avg Recall: 0.452830, count: 106 -1073: 0.511585, 0.498747 avg, 0.001000 rate, 20.458160 seconds, 274688 images -Loaded: 0.801225 seconds -Region Avg IOU: 0.495632, Class: 1.000000, Obj: 0.238439, No Obj: 0.005024, Avg Recall: 0.564286, count: 140 -Region Avg IOU: 0.428820, Class: 1.000000, Obj: 0.232584, No Obj: 0.004260, Avg Recall: 0.428571, count: 98 -Region Avg IOU: 0.491032, Class: 1.000000, Obj: 0.290562, No Obj: 0.004365, Avg Recall: 0.530864, count: 81 -Region Avg IOU: 0.433601, Class: 1.000000, Obj: 0.220753, No Obj: 0.004485, Avg Recall: 0.431193, count: 109 -Region Avg IOU: 0.463664, Class: 1.000000, Obj: 0.317210, No Obj: 0.004555, Avg Recall: 0.458333, count: 96 -Region Avg IOU: 0.447322, Class: 1.000000, Obj: 0.216660, No Obj: 0.004526, Avg Recall: 0.485075, count: 134 -Region Avg IOU: 0.440371, Class: 1.000000, Obj: 0.233310, No Obj: 0.004287, Avg Recall: 0.489583, count: 96 -Region Avg IOU: 0.479222, Class: 1.000000, Obj: 0.256049, No Obj: 0.004570, Avg Recall: 0.535354, count: 99 -1074: 0.531604, 0.502033 avg, 0.001000 rate, 17.194567 seconds, 274944 images -Loaded: 3.207518 seconds -Region Avg IOU: 0.408664, Class: 1.000000, Obj: 0.231106, No Obj: 0.004333, Avg Recall: 0.412698, count: 126 -Region Avg IOU: 0.403397, Class: 1.000000, Obj: 0.220367, No Obj: 0.004094, Avg Recall: 0.390805, count: 87 -Region Avg IOU: 0.433452, Class: 1.000000, Obj: 0.256007, No Obj: 0.005219, Avg Recall: 0.461538, count: 130 -Region Avg IOU: 0.432511, Class: 1.000000, Obj: 0.277202, No Obj: 0.004095, Avg Recall: 0.426667, count: 75 -Region Avg IOU: 0.417709, Class: 1.000000, Obj: 0.260414, No Obj: 0.004195, Avg Recall: 0.431373, count: 102 -Region Avg IOU: 0.461110, Class: 1.000000, Obj: 0.264448, No Obj: 0.004340, Avg Recall: 0.506494, count: 77 -Region Avg IOU: 0.411980, Class: 1.000000, Obj: 0.262551, No Obj: 0.005114, Avg Recall: 0.413793, count: 116 -Region Avg IOU: 0.435973, Class: 1.000000, Obj: 0.217869, No Obj: 0.004780, Avg Recall: 0.443548, count: 124 -1075: 0.519431, 0.503772 avg, 0.001000 rate, 19.860184 seconds, 275200 images -Loaded: 1.926956 seconds -Region Avg IOU: 0.448701, Class: 1.000000, Obj: 0.268756, No Obj: 0.004458, Avg Recall: 0.460674, count: 89 -Region Avg IOU: 0.466831, Class: 1.000000, Obj: 0.253751, No Obj: 0.005046, Avg Recall: 0.536000, count: 125 -Region Avg IOU: 0.440603, Class: 1.000000, Obj: 0.245790, No Obj: 0.004520, Avg Recall: 0.478723, count: 94 -Region Avg IOU: 0.448544, Class: 1.000000, Obj: 0.266106, No Obj: 0.004741, Avg Recall: 0.495575, count: 113 -Region Avg IOU: 0.394110, Class: 1.000000, Obj: 0.215308, No Obj: 0.004315, Avg Recall: 0.406250, count: 128 -Region Avg IOU: 0.426727, Class: 1.000000, Obj: 0.225923, No Obj: 0.004555, Avg Recall: 0.477876, count: 113 -Region Avg IOU: 0.435424, Class: 1.000000, Obj: 0.250429, No Obj: 0.004122, Avg Recall: 0.459770, count: 87 -Region Avg IOU: 0.424128, Class: 1.000000, Obj: 0.243724, No Obj: 0.005128, Avg Recall: 0.449612, count: 129 -1076: 0.595468, 0.512942 avg, 0.001000 rate, 17.075899 seconds, 275456 images -Loaded: 3.342677 seconds -Region Avg IOU: 0.455152, Class: 1.000000, Obj: 0.273987, No Obj: 0.004712, Avg Recall: 0.510000, count: 100 -Region Avg IOU: 0.465807, Class: 1.000000, Obj: 0.278927, No Obj: 0.004259, Avg Recall: 0.473684, count: 95 -Region Avg IOU: 0.413301, Class: 1.000000, Obj: 0.222311, No Obj: 0.004660, Avg Recall: 0.400000, count: 130 -Region Avg IOU: 0.488532, Class: 1.000000, Obj: 0.278558, No Obj: 0.004789, Avg Recall: 0.526786, count: 112 -Region Avg IOU: 0.473659, Class: 1.000000, Obj: 0.246666, No Obj: 0.004986, Avg Recall: 0.504274, count: 117 -Region Avg IOU: 0.468693, Class: 1.000000, Obj: 0.247849, No Obj: 0.004748, Avg Recall: 0.556452, count: 124 -Region Avg IOU: 0.454696, Class: 1.000000, Obj: 0.266660, No Obj: 0.004635, Avg Recall: 0.468468, count: 111 -Region Avg IOU: 0.491062, Class: 1.000000, Obj: 0.283454, No Obj: 0.004288, Avg Recall: 0.561644, count: 73 -1077: 0.563624, 0.518010 avg, 0.001000 rate, 17.060036 seconds, 275712 images -Loaded: 4.442888 seconds -Region Avg IOU: 0.434771, Class: 1.000000, Obj: 0.257828, No Obj: 0.004141, Avg Recall: 0.428571, count: 105 -Region Avg IOU: 0.482500, Class: 1.000000, Obj: 0.285445, No Obj: 0.005892, Avg Recall: 0.553957, count: 139 -Region Avg IOU: 0.413905, Class: 1.000000, Obj: 0.260644, No Obj: 0.004251, Avg Recall: 0.459770, count: 87 -Region Avg IOU: 0.439614, Class: 1.000000, Obj: 0.236028, No Obj: 0.004128, Avg Recall: 0.436782, count: 87 -Region Avg IOU: 0.462935, Class: 1.000000, Obj: 0.248861, No Obj: 0.004655, Avg Recall: 0.500000, count: 92 -Region Avg IOU: 0.456574, Class: 1.000000, Obj: 0.230294, No Obj: 0.004453, Avg Recall: 0.531250, count: 128 -Region Avg IOU: 0.472793, Class: 1.000000, Obj: 0.241617, No Obj: 0.004223, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.421194, Class: 1.000000, Obj: 0.276474, No Obj: 0.004632, Avg Recall: 0.453608, count: 97 -1078: 0.555327, 0.521742 avg, 0.001000 rate, 18.637886 seconds, 275968 images -Loaded: 1.559831 seconds -Region Avg IOU: 0.432439, Class: 1.000000, Obj: 0.260391, No Obj: 0.004320, Avg Recall: 0.451613, count: 93 -Region Avg IOU: 0.460556, Class: 1.000000, Obj: 0.266470, No Obj: 0.005345, Avg Recall: 0.481818, count: 110 -Region Avg IOU: 0.455518, Class: 1.000000, Obj: 0.252585, No Obj: 0.004549, Avg Recall: 0.504348, count: 115 -Region Avg IOU: 0.453154, Class: 1.000000, Obj: 0.227503, No Obj: 0.004522, Avg Recall: 0.490385, count: 104 -Region Avg IOU: 0.446712, Class: 1.000000, Obj: 0.237437, No Obj: 0.005414, Avg Recall: 0.462585, count: 147 -Region Avg IOU: 0.470478, Class: 1.000000, Obj: 0.253737, No Obj: 0.004232, Avg Recall: 0.551282, count: 78 -Region Avg IOU: 0.424526, Class: 1.000000, Obj: 0.250365, No Obj: 0.004954, Avg Recall: 0.465116, count: 129 -Region Avg IOU: 0.396576, Class: 1.000000, Obj: 0.250487, No Obj: 0.004342, Avg Recall: 0.412844, count: 109 -1079: 0.578698, 0.527438 avg, 0.001000 rate, 20.974602 seconds, 276224 images -Loaded: 0.430749 seconds -Region Avg IOU: 0.427121, Class: 1.000000, Obj: 0.253198, No Obj: 0.004711, Avg Recall: 0.442623, count: 122 -Region Avg IOU: 0.493288, Class: 1.000000, Obj: 0.261540, No Obj: 0.004551, Avg Recall: 0.534653, count: 101 -Region Avg IOU: 0.459929, Class: 1.000000, Obj: 0.253917, No Obj: 0.004735, Avg Recall: 0.514286, count: 105 -Region Avg IOU: 0.456764, Class: 1.000000, Obj: 0.242720, No Obj: 0.004872, Avg Recall: 0.455782, count: 147 -Region Avg IOU: 0.471957, Class: 1.000000, Obj: 0.293818, No Obj: 0.004307, Avg Recall: 0.440476, count: 84 -Region Avg IOU: 0.496858, Class: 1.000000, Obj: 0.287393, No Obj: 0.004552, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.456263, Class: 1.000000, Obj: 0.226902, No Obj: 0.004559, Avg Recall: 0.500000, count: 104 -Region Avg IOU: 0.440338, Class: 1.000000, Obj: 0.290373, No Obj: 0.004822, Avg Recall: 0.440367, count: 109 -1080: 0.483614, 0.523055 avg, 0.001000 rate, 19.858643 seconds, 276480 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000092 seconds -Region Avg IOU: 0.506143, Class: 1.000000, Obj: 0.301058, No Obj: 0.004232, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.517940, Class: 1.000000, Obj: 0.271644, No Obj: 0.004068, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.514824, Class: 1.000000, Obj: 0.298314, No Obj: 0.003929, Avg Recall: 0.581395, count: 86 -Region Avg IOU: 0.444958, Class: 1.000000, Obj: 0.251521, No Obj: 0.003764, Avg Recall: 0.440367, count: 109 -Region Avg IOU: 0.509284, Class: 1.000000, Obj: 0.343072, No Obj: 0.004123, Avg Recall: 0.571429, count: 91 -Region Avg IOU: 0.525603, Class: 1.000000, Obj: 0.271680, No Obj: 0.004381, Avg Recall: 0.608000, count: 125 -Region Avg IOU: 0.512108, Class: 1.000000, Obj: 0.279729, No Obj: 0.004081, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.527217, Class: 1.000000, Obj: 0.279123, No Obj: 0.003885, Avg Recall: 0.556818, count: 88 -1081: 0.437618, 0.514512 avg, 0.001000 rate, 22.317936 seconds, 276736 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.477263, Class: 1.000000, Obj: 0.261796, No Obj: 0.003703, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.515450, Class: 1.000000, Obj: 0.304075, No Obj: 0.003387, Avg Recall: 0.569767, count: 86 -Region Avg IOU: 0.515201, Class: 1.000000, Obj: 0.318515, No Obj: 0.003963, Avg Recall: 0.592920, count: 113 -Region Avg IOU: 0.535901, Class: 1.000000, Obj: 0.270208, No Obj: 0.004134, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.497639, Class: 1.000000, Obj: 0.251350, No Obj: 0.003878, Avg Recall: 0.556391, count: 133 -Region Avg IOU: 0.533953, Class: 1.000000, Obj: 0.298651, No Obj: 0.003912, Avg Recall: 0.606838, count: 117 -Region Avg IOU: 0.534527, Class: 1.000000, Obj: 0.297903, No Obj: 0.004186, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.518339, Class: 1.000000, Obj: 0.270746, No Obj: 0.004387, Avg Recall: 0.587719, count: 114 -1082: 0.538568, 0.516917 avg, 0.001000 rate, 23.363581 seconds, 276992 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.552199, Class: 1.000000, Obj: 0.305249, No Obj: 0.004053, Avg Recall: 0.663866, count: 119 -Region Avg IOU: 0.467729, Class: 1.000000, Obj: 0.234411, No Obj: 0.003832, Avg Recall: 0.519380, count: 129 -Region Avg IOU: 0.511085, Class: 1.000000, Obj: 0.239131, No Obj: 0.003825, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.502936, Class: 1.000000, Obj: 0.234936, No Obj: 0.004265, Avg Recall: 0.572650, count: 117 -Region Avg IOU: 0.491231, Class: 1.000000, Obj: 0.286278, No Obj: 0.003805, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.509821, Class: 1.000000, Obj: 0.262446, No Obj: 0.004205, Avg Recall: 0.524138, count: 145 -Region Avg IOU: 0.522035, Class: 1.000000, Obj: 0.286978, No Obj: 0.003562, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.513357, Class: 1.000000, Obj: 0.262666, No Obj: 0.004055, Avg Recall: 0.576000, count: 125 -1083: 0.586111, 0.523837 avg, 0.001000 rate, 22.797588 seconds, 277248 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.533438, Class: 1.000000, Obj: 0.295849, No Obj: 0.004307, Avg Recall: 0.635036, count: 137 -Region Avg IOU: 0.599958, Class: 1.000000, Obj: 0.256435, No Obj: 0.003988, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.528540, Class: 1.000000, Obj: 0.256949, No Obj: 0.003715, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.474718, Class: 1.000000, Obj: 0.217767, No Obj: 0.003261, Avg Recall: 0.494624, count: 93 -Region Avg IOU: 0.493302, Class: 1.000000, Obj: 0.335073, No Obj: 0.003613, Avg Recall: 0.527473, count: 91 -Region Avg IOU: 0.530810, Class: 1.000000, Obj: 0.304749, No Obj: 0.004274, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.564669, Class: 1.000000, Obj: 0.269208, No Obj: 0.003755, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.510168, Class: 1.000000, Obj: 0.268041, No Obj: 0.003379, Avg Recall: 0.620690, count: 87 -1084: 0.494783, 0.520931 avg, 0.001000 rate, 22.619652 seconds, 277504 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.516647, Class: 1.000000, Obj: 0.263369, No Obj: 0.003641, Avg Recall: 0.572519, count: 131 -Region Avg IOU: 0.531246, Class: 1.000000, Obj: 0.285802, No Obj: 0.003704, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.522172, Class: 1.000000, Obj: 0.255781, No Obj: 0.003350, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.502469, Class: 1.000000, Obj: 0.274318, No Obj: 0.003175, Avg Recall: 0.546512, count: 86 -Region Avg IOU: 0.503630, Class: 1.000000, Obj: 0.251853, No Obj: 0.003308, Avg Recall: 0.545455, count: 77 -Region Avg IOU: 0.531481, Class: 1.000000, Obj: 0.244882, No Obj: 0.003338, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.539834, Class: 1.000000, Obj: 0.290019, No Obj: 0.003679, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.506628, Class: 1.000000, Obj: 0.254742, No Obj: 0.003450, Avg Recall: 0.531250, count: 96 -1085: 0.465850, 0.515423 avg, 0.001000 rate, 24.531345 seconds, 277760 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.553612, Class: 1.000000, Obj: 0.259567, No Obj: 0.003517, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.568033, Class: 1.000000, Obj: 0.266415, No Obj: 0.003802, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.495521, Class: 1.000000, Obj: 0.239619, No Obj: 0.003456, Avg Recall: 0.512605, count: 119 -Region Avg IOU: 0.482998, Class: 1.000000, Obj: 0.211192, No Obj: 0.003628, Avg Recall: 0.556291, count: 151 -Region Avg IOU: 0.549601, Class: 1.000000, Obj: 0.279737, No Obj: 0.003865, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.563032, Class: 1.000000, Obj: 0.302395, No Obj: 0.003762, Avg Recall: 0.631579, count: 76 -Region Avg IOU: 0.526054, Class: 1.000000, Obj: 0.297491, No Obj: 0.003885, Avg Recall: 0.580882, count: 136 -Region Avg IOU: 0.530078, Class: 1.000000, Obj: 0.262403, No Obj: 0.003397, Avg Recall: 0.653061, count: 98 -1086: 0.501719, 0.514053 avg, 0.001000 rate, 24.710344 seconds, 278016 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.484508, Class: 1.000000, Obj: 0.252084, No Obj: 0.003315, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.544715, Class: 1.000000, Obj: 0.286159, No Obj: 0.003928, Avg Recall: 0.600000, count: 130 -Region Avg IOU: 0.529993, Class: 1.000000, Obj: 0.285016, No Obj: 0.003403, Avg Recall: 0.562500, count: 80 -Region Avg IOU: 0.543449, Class: 1.000000, Obj: 0.267871, No Obj: 0.004031, Avg Recall: 0.617188, count: 128 -Region Avg IOU: 0.535932, Class: 1.000000, Obj: 0.253356, No Obj: 0.003804, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.498086, Class: 1.000000, Obj: 0.260062, No Obj: 0.003799, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.564357, Class: 1.000000, Obj: 0.241052, No Obj: 0.003752, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.556095, Class: 1.000000, Obj: 0.263379, No Obj: 0.003411, Avg Recall: 0.593023, count: 86 -1087: 0.517094, 0.514357 avg, 0.001000 rate, 23.602755 seconds, 278272 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.535054, Class: 1.000000, Obj: 0.254220, No Obj: 0.004413, Avg Recall: 0.637681, count: 138 -Region Avg IOU: 0.555111, Class: 1.000000, Obj: 0.291322, No Obj: 0.004168, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.503756, Class: 1.000000, Obj: 0.297812, No Obj: 0.003885, Avg Recall: 0.570175, count: 114 -Region Avg IOU: 0.577207, Class: 1.000000, Obj: 0.249693, No Obj: 0.003466, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.531479, Class: 1.000000, Obj: 0.287411, No Obj: 0.003812, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.551177, Class: 1.000000, Obj: 0.277699, No Obj: 0.003671, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.546164, Class: 1.000000, Obj: 0.296968, No Obj: 0.003620, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.528916, Class: 1.000000, Obj: 0.281801, No Obj: 0.004149, Avg Recall: 0.647482, count: 139 -1088: 0.513634, 0.514284 avg, 0.001000 rate, 23.898523 seconds, 278528 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.549333, Class: 1.000000, Obj: 0.304496, No Obj: 0.004094, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.543177, Class: 1.000000, Obj: 0.287252, No Obj: 0.003760, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.541612, Class: 1.000000, Obj: 0.266457, No Obj: 0.003700, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.492115, Class: 1.000000, Obj: 0.250532, No Obj: 0.004047, Avg Recall: 0.529412, count: 119 -Region Avg IOU: 0.489384, Class: 1.000000, Obj: 0.233710, No Obj: 0.003629, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.531328, Class: 1.000000, Obj: 0.289550, No Obj: 0.004268, Avg Recall: 0.563380, count: 142 -Region Avg IOU: 0.494583, Class: 1.000000, Obj: 0.290251, No Obj: 0.003797, Avg Recall: 0.588235, count: 119 -Region Avg IOU: 0.557553, Class: 1.000000, Obj: 0.260352, No Obj: 0.004142, Avg Recall: 0.696429, count: 112 -1089: 0.493747, 0.512231 avg, 0.001000 rate, 23.762087 seconds, 278784 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.501935, Class: 1.000000, Obj: 0.284364, No Obj: 0.003821, Avg Recall: 0.548387, count: 124 -Region Avg IOU: 0.515270, Class: 1.000000, Obj: 0.252394, No Obj: 0.003826, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.564735, Class: 1.000000, Obj: 0.274939, No Obj: 0.003654, Avg Recall: 0.673684, count: 95 -Region Avg IOU: 0.528416, Class: 1.000000, Obj: 0.319231, No Obj: 0.003930, Avg Recall: 0.575758, count: 99 -Region Avg IOU: 0.513372, Class: 1.000000, Obj: 0.287493, No Obj: 0.003814, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.513344, Class: 1.000000, Obj: 0.302150, No Obj: 0.003758, Avg Recall: 0.636364, count: 88 -Region Avg IOU: 0.547538, Class: 1.000000, Obj: 0.272533, No Obj: 0.004460, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.483140, Class: 1.000000, Obj: 0.262172, No Obj: 0.003883, Avg Recall: 0.547826, count: 115 -1090: 0.487564, 0.509764 avg, 0.001000 rate, 23.593620 seconds, 279040 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.359083 seconds -Region Avg IOU: 0.396893, Class: 1.000000, Obj: 0.247990, No Obj: 0.005570, Avg Recall: 0.360902, count: 133 -Region Avg IOU: 0.484295, Class: 1.000000, Obj: 0.289512, No Obj: 0.004896, Avg Recall: 0.510204, count: 98 -Region Avg IOU: 0.466324, Class: 1.000000, Obj: 0.278775, No Obj: 0.005151, Avg Recall: 0.504202, count: 119 -Region Avg IOU: 0.436579, Class: 1.000000, Obj: 0.195925, No Obj: 0.004376, Avg Recall: 0.454545, count: 99 -Region Avg IOU: 0.415332, Class: 1.000000, Obj: 0.230986, No Obj: 0.004186, Avg Recall: 0.426966, count: 89 -Region Avg IOU: 0.436154, Class: 1.000000, Obj: 0.236055, No Obj: 0.004505, Avg Recall: 0.462963, count: 108 -Region Avg IOU: 0.380550, Class: 1.000000, Obj: 0.240385, No Obj: 0.004373, Avg Recall: 0.356164, count: 73 -Region Avg IOU: 0.414341, Class: 1.000000, Obj: 0.225526, No Obj: 0.004829, Avg Recall: 0.385965, count: 114 -1091: 0.461770, 0.504965 avg, 0.001000 rate, 15.360250 seconds, 279296 images -Loaded: 5.016008 seconds -Region Avg IOU: 0.439421, Class: 1.000000, Obj: 0.205145, No Obj: 0.004626, Avg Recall: 0.440000, count: 100 -Region Avg IOU: 0.422351, Class: 1.000000, Obj: 0.242118, No Obj: 0.004862, Avg Recall: 0.456311, count: 103 -Region Avg IOU: 0.426069, Class: 1.000000, Obj: 0.251075, No Obj: 0.005083, Avg Recall: 0.455285, count: 123 -Region Avg IOU: 0.433431, Class: 1.000000, Obj: 0.224758, No Obj: 0.005027, Avg Recall: 0.442953, count: 149 -Region Avg IOU: 0.503926, Class: 1.000000, Obj: 0.250605, No Obj: 0.005287, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.449020, Class: 1.000000, Obj: 0.241518, No Obj: 0.004954, Avg Recall: 0.486486, count: 111 -Region Avg IOU: 0.423686, Class: 1.000000, Obj: 0.223475, No Obj: 0.004949, Avg Recall: 0.448819, count: 127 -Region Avg IOU: 0.407619, Class: 1.000000, Obj: 0.212490, No Obj: 0.004044, Avg Recall: 0.404762, count: 84 -1092: 0.516675, 0.506136 avg, 0.001000 rate, 15.897107 seconds, 279552 images -Loaded: 4.439943 seconds -Region Avg IOU: 0.407283, Class: 1.000000, Obj: 0.225553, No Obj: 0.004092, Avg Recall: 0.413043, count: 92 -Region Avg IOU: 0.424279, Class: 1.000000, Obj: 0.243361, No Obj: 0.005361, Avg Recall: 0.376068, count: 117 -Region Avg IOU: 0.425524, Class: 1.000000, Obj: 0.234100, No Obj: 0.004341, Avg Recall: 0.469388, count: 98 -Region Avg IOU: 0.447037, Class: 1.000000, Obj: 0.206949, No Obj: 0.004517, Avg Recall: 0.470588, count: 85 -Region Avg IOU: 0.409544, Class: 1.000000, Obj: 0.263848, No Obj: 0.004617, Avg Recall: 0.429825, count: 114 -Region Avg IOU: 0.399951, Class: 1.000000, Obj: 0.238459, No Obj: 0.004910, Avg Recall: 0.396694, count: 121 -Region Avg IOU: 0.454575, Class: 1.000000, Obj: 0.238566, No Obj: 0.004242, Avg Recall: 0.488636, count: 88 -Region Avg IOU: 0.440395, Class: 1.000000, Obj: 0.218878, No Obj: 0.005293, Avg Recall: 0.461538, count: 130 -1093: 0.511960, 0.506718 avg, 0.001000 rate, 15.313857 seconds, 279808 images -Loaded: 5.956779 seconds -Region Avg IOU: 0.397564, Class: 1.000000, Obj: 0.244806, No Obj: 0.004857, Avg Recall: 0.419048, count: 105 -Region Avg IOU: 0.421232, Class: 1.000000, Obj: 0.248544, No Obj: 0.004936, Avg Recall: 0.447368, count: 114 -Region Avg IOU: 0.377121, Class: 1.000000, Obj: 0.221113, No Obj: 0.004665, Avg Recall: 0.391667, count: 120 -Region Avg IOU: 0.379177, Class: 1.000000, Obj: 0.242426, No Obj: 0.004066, Avg Recall: 0.329670, count: 91 -Region Avg IOU: 0.428103, Class: 1.000000, Obj: 0.259974, No Obj: 0.004533, Avg Recall: 0.470588, count: 102 -Region Avg IOU: 0.498475, Class: 1.000000, Obj: 0.274309, No Obj: 0.004996, Avg Recall: 0.552941, count: 85 -Region Avg IOU: 0.434117, Class: 1.000000, Obj: 0.264288, No Obj: 0.005049, Avg Recall: 0.443396, count: 106 -Region Avg IOU: 0.393802, Class: 1.000000, Obj: 0.271491, No Obj: 0.004640, Avg Recall: 0.385321, count: 109 -1094: 0.474955, 0.503542 avg, 0.001000 rate, 16.021406 seconds, 280064 images -Loaded: 4.342326 seconds -Region Avg IOU: 0.413183, Class: 1.000000, Obj: 0.261891, No Obj: 0.004492, Avg Recall: 0.416667, count: 108 -Region Avg IOU: 0.441693, Class: 1.000000, Obj: 0.264785, No Obj: 0.004899, Avg Recall: 0.423077, count: 104 -Region Avg IOU: 0.401113, Class: 1.000000, Obj: 0.222417, No Obj: 0.004929, Avg Recall: 0.373134, count: 134 -Region Avg IOU: 0.424067, Class: 1.000000, Obj: 0.232430, No Obj: 0.004882, Avg Recall: 0.398058, count: 103 -Region Avg IOU: 0.367525, Class: 1.000000, Obj: 0.260548, No Obj: 0.004707, Avg Recall: 0.303371, count: 89 -Region Avg IOU: 0.392157, Class: 1.000000, Obj: 0.265473, No Obj: 0.004389, Avg Recall: 0.327103, count: 107 -Region Avg IOU: 0.411964, Class: 1.000000, Obj: 0.244482, No Obj: 0.004700, Avg Recall: 0.422018, count: 109 -Region Avg IOU: 0.433627, Class: 1.000000, Obj: 0.263604, No Obj: 0.004764, Avg Recall: 0.424242, count: 99 -1095: 0.546387, 0.507826 avg, 0.001000 rate, 17.496729 seconds, 280320 images -Loaded: 2.832683 seconds -Region Avg IOU: 0.424312, Class: 1.000000, Obj: 0.287597, No Obj: 0.004964, Avg Recall: 0.438596, count: 114 -Region Avg IOU: 0.431896, Class: 1.000000, Obj: 0.267403, No Obj: 0.005087, Avg Recall: 0.478261, count: 115 -Region Avg IOU: 0.409286, Class: 1.000000, Obj: 0.218769, No Obj: 0.005355, Avg Recall: 0.409938, count: 161 -Region Avg IOU: 0.396123, Class: 1.000000, Obj: 0.264770, No Obj: 0.004874, Avg Recall: 0.409836, count: 122 -Region Avg IOU: 0.378064, Class: 1.000000, Obj: 0.229893, No Obj: 0.004757, Avg Recall: 0.385185, count: 135 -Region Avg IOU: 0.377732, Class: 1.000000, Obj: 0.244491, No Obj: 0.004516, Avg Recall: 0.373737, count: 99 -Region Avg IOU: 0.434402, Class: 1.000000, Obj: 0.253008, No Obj: 0.005395, Avg Recall: 0.472868, count: 129 -Region Avg IOU: 0.427619, Class: 1.000000, Obj: 0.246561, No Obj: 0.004621, Avg Recall: 0.473118, count: 93 -1096: 0.699364, 0.526980 avg, 0.001000 rate, 18.755508 seconds, 280576 images -Loaded: 2.314249 seconds -Region Avg IOU: 0.411975, Class: 1.000000, Obj: 0.227983, No Obj: 0.004434, Avg Recall: 0.415842, count: 101 -Region Avg IOU: 0.429468, Class: 1.000000, Obj: 0.255454, No Obj: 0.005083, Avg Recall: 0.455285, count: 123 -Region Avg IOU: 0.414775, Class: 1.000000, Obj: 0.246780, No Obj: 0.004135, Avg Recall: 0.452830, count: 106 -Region Avg IOU: 0.421592, Class: 1.000000, Obj: 0.255095, No Obj: 0.004333, Avg Recall: 0.451219, count: 82 -Region Avg IOU: 0.370149, Class: 1.000000, Obj: 0.223045, No Obj: 0.004805, Avg Recall: 0.358779, count: 131 -Region Avg IOU: 0.414535, Class: 1.000000, Obj: 0.232007, No Obj: 0.004438, Avg Recall: 0.440678, count: 118 -Region Avg IOU: 0.432821, Class: 1.000000, Obj: 0.240262, No Obj: 0.004572, Avg Recall: 0.479592, count: 98 -Region Avg IOU: 0.359277, Class: 1.000000, Obj: 0.194694, No Obj: 0.004345, Avg Recall: 0.325000, count: 120 -1097: 0.537731, 0.528055 avg, 0.001000 rate, 18.058723 seconds, 280832 images -Loaded: 4.137233 seconds -Region Avg IOU: 0.412920, Class: 1.000000, Obj: 0.247705, No Obj: 0.004413, Avg Recall: 0.443038, count: 79 -Region Avg IOU: 0.430380, Class: 1.000000, Obj: 0.261473, No Obj: 0.004594, Avg Recall: 0.422018, count: 109 -Region Avg IOU: 0.450617, Class: 1.000000, Obj: 0.256491, No Obj: 0.005589, Avg Recall: 0.421875, count: 128 -Region Avg IOU: 0.389276, Class: 1.000000, Obj: 0.222817, No Obj: 0.004656, Avg Recall: 0.347107, count: 121 -Region Avg IOU: 0.441990, Class: 1.000000, Obj: 0.259885, No Obj: 0.004682, Avg Recall: 0.445545, count: 101 -Region Avg IOU: 0.408742, Class: 1.000000, Obj: 0.236672, No Obj: 0.004737, Avg Recall: 0.441379, count: 145 -Region Avg IOU: 0.405559, Class: 1.000000, Obj: 0.231675, No Obj: 0.004711, Avg Recall: 0.408163, count: 98 -Region Avg IOU: 0.372627, Class: 1.000000, Obj: 0.249487, No Obj: 0.004677, Avg Recall: 0.388889, count: 126 -1098: 0.546793, 0.529929 avg, 0.001000 rate, 14.835858 seconds, 281088 images -Loaded: 5.386367 seconds -Region Avg IOU: 0.460153, Class: 1.000000, Obj: 0.242200, No Obj: 0.005068, Avg Recall: 0.507692, count: 130 -Region Avg IOU: 0.384014, Class: 1.000000, Obj: 0.218053, No Obj: 0.004027, Avg Recall: 0.357895, count: 95 -Region Avg IOU: 0.451250, Class: 1.000000, Obj: 0.273866, No Obj: 0.004429, Avg Recall: 0.475248, count: 101 -Region Avg IOU: 0.418241, Class: 1.000000, Obj: 0.232937, No Obj: 0.004694, Avg Recall: 0.419048, count: 105 -Region Avg IOU: 0.452393, Class: 1.000000, Obj: 0.252214, No Obj: 0.004279, Avg Recall: 0.462366, count: 93 -Region Avg IOU: 0.460493, Class: 1.000000, Obj: 0.229781, No Obj: 0.004672, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.405068, Class: 1.000000, Obj: 0.242133, No Obj: 0.004616, Avg Recall: 0.393701, count: 127 -Region Avg IOU: 0.489835, Class: 1.000000, Obj: 0.237463, No Obj: 0.004989, Avg Recall: 0.577586, count: 116 -1099: 0.558245, 0.532761 avg, 0.001000 rate, 17.150223 seconds, 281344 images -Loaded: 2.603346 seconds -Region Avg IOU: 0.452451, Class: 1.000000, Obj: 0.244075, No Obj: 0.005366, Avg Recall: 0.525253, count: 99 -Region Avg IOU: 0.447601, Class: 1.000000, Obj: 0.247110, No Obj: 0.005027, Avg Recall: 0.413043, count: 92 -Region Avg IOU: 0.437151, Class: 1.000000, Obj: 0.236401, No Obj: 0.004897, Avg Recall: 0.464286, count: 112 -Region Avg IOU: 0.435823, Class: 1.000000, Obj: 0.243591, No Obj: 0.004716, Avg Recall: 0.458333, count: 96 -Region Avg IOU: 0.468082, Class: 1.000000, Obj: 0.231914, No Obj: 0.004658, Avg Recall: 0.500000, count: 88 -Region Avg IOU: 0.444543, Class: 1.000000, Obj: 0.251134, No Obj: 0.004432, Avg Recall: 0.484211, count: 95 -Region Avg IOU: 0.382693, Class: 1.000000, Obj: 0.251684, No Obj: 0.004670, Avg Recall: 0.340206, count: 97 -Region Avg IOU: 0.367141, Class: 1.000000, Obj: 0.201956, No Obj: 0.004414, Avg Recall: 0.343434, count: 99 -1100: 0.517414, 0.531226 avg, 0.001000 rate, 13.968702 seconds, 281600 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000069 seconds -Region Avg IOU: 0.518342, Class: 1.000000, Obj: 0.280623, No Obj: 0.003923, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.517328, Class: 1.000000, Obj: 0.281875, No Obj: 0.003969, Avg Recall: 0.622807, count: 114 -Region Avg IOU: 0.502647, Class: 1.000000, Obj: 0.246460, No Obj: 0.003901, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.541356, Class: 1.000000, Obj: 0.264541, No Obj: 0.003809, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.566878, Class: 1.000000, Obj: 0.323751, No Obj: 0.003717, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.525235, Class: 1.000000, Obj: 0.301374, No Obj: 0.004108, Avg Recall: 0.582677, count: 127 -Region Avg IOU: 0.559088, Class: 1.000000, Obj: 0.336979, No Obj: 0.004375, Avg Recall: 0.692913, count: 127 -Region Avg IOU: 0.563067, Class: 1.000000, Obj: 0.301873, No Obj: 0.004126, Avg Recall: 0.701613, count: 124 -1101: 0.529585, 0.531062 avg, 0.001000 rate, 25.303522 seconds, 281856 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.543511, Class: 1.000000, Obj: 0.281909, No Obj: 0.003832, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.530689, Class: 1.000000, Obj: 0.295901, No Obj: 0.003908, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.556110, Class: 1.000000, Obj: 0.259973, No Obj: 0.004238, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.522858, Class: 1.000000, Obj: 0.241686, No Obj: 0.003715, Avg Recall: 0.601562, count: 128 -Region Avg IOU: 0.535806, Class: 1.000000, Obj: 0.273316, No Obj: 0.004251, Avg Recall: 0.610738, count: 149 -Region Avg IOU: 0.537508, Class: 1.000000, Obj: 0.304842, No Obj: 0.004086, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.536135, Class: 1.000000, Obj: 0.288784, No Obj: 0.003901, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.569792, Class: 1.000000, Obj: 0.277351, No Obj: 0.004355, Avg Recall: 0.696203, count: 158 -1102: 0.525080, 0.530464 avg, 0.001000 rate, 25.444187 seconds, 282112 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.530994, Class: 1.000000, Obj: 0.259056, No Obj: 0.003510, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.561575, Class: 1.000000, Obj: 0.235404, No Obj: 0.003798, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.534312, Class: 1.000000, Obj: 0.247088, No Obj: 0.003233, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.524568, Class: 1.000000, Obj: 0.237290, No Obj: 0.003431, Avg Recall: 0.581395, count: 129 -Region Avg IOU: 0.538831, Class: 1.000000, Obj: 0.288575, No Obj: 0.003670, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.576792, Class: 1.000000, Obj: 0.321527, No Obj: 0.003525, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.564424, Class: 1.000000, Obj: 0.324627, No Obj: 0.003924, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.559497, Class: 1.000000, Obj: 0.309793, No Obj: 0.003594, Avg Recall: 0.712963, count: 108 -1103: 0.465995, 0.524017 avg, 0.001000 rate, 26.808109 seconds, 282368 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.543625, Class: 1.000000, Obj: 0.263730, No Obj: 0.003861, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.531082, Class: 1.000000, Obj: 0.237088, No Obj: 0.003493, Avg Recall: 0.661290, count: 124 -Region Avg IOU: 0.553210, Class: 1.000000, Obj: 0.300450, No Obj: 0.003612, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.532935, Class: 1.000000, Obj: 0.263312, No Obj: 0.003472, Avg Recall: 0.610169, count: 118 -Region Avg IOU: 0.564455, Class: 1.000000, Obj: 0.291616, No Obj: 0.003394, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.540914, Class: 1.000000, Obj: 0.283500, No Obj: 0.003487, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.579876, Class: 1.000000, Obj: 0.318539, No Obj: 0.003847, Avg Recall: 0.687500, count: 128 -Region Avg IOU: 0.567285, Class: 1.000000, Obj: 0.279299, No Obj: 0.003721, Avg Recall: 0.666667, count: 102 -1104: 0.498164, 0.521432 avg, 0.001000 rate, 25.686275 seconds, 282624 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.565536, Class: 1.000000, Obj: 0.214795, No Obj: 0.003435, Avg Recall: 0.656250, count: 128 -Region Avg IOU: 0.581635, Class: 1.000000, Obj: 0.245132, No Obj: 0.003518, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.529974, Class: 1.000000, Obj: 0.225640, No Obj: 0.003528, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.561909, Class: 1.000000, Obj: 0.247612, No Obj: 0.003563, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.556594, Class: 1.000000, Obj: 0.268587, No Obj: 0.002948, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.521047, Class: 1.000000, Obj: 0.240921, No Obj: 0.003157, Avg Recall: 0.630435, count: 92 -Region Avg IOU: 0.555740, Class: 1.000000, Obj: 0.256779, No Obj: 0.003049, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.573433, Class: 1.000000, Obj: 0.229522, No Obj: 0.003396, Avg Recall: 0.725490, count: 102 -1105: 0.470102, 0.516299 avg, 0.001000 rate, 26.977112 seconds, 282880 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.488511, Class: 1.000000, Obj: 0.220582, No Obj: 0.002935, Avg Recall: 0.534483, count: 116 -Region Avg IOU: 0.585875, Class: 1.000000, Obj: 0.266720, No Obj: 0.003229, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.550029, Class: 1.000000, Obj: 0.249892, No Obj: 0.003053, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.541655, Class: 1.000000, Obj: 0.251369, No Obj: 0.003264, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.553087, Class: 1.000000, Obj: 0.273210, No Obj: 0.002751, Avg Recall: 0.658228, count: 79 -Region Avg IOU: 0.519673, Class: 1.000000, Obj: 0.255876, No Obj: 0.002952, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.561061, Class: 1.000000, Obj: 0.231694, No Obj: 0.003382, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.535127, Class: 1.000000, Obj: 0.270873, No Obj: 0.003373, Avg Recall: 0.596154, count: 104 -1106: 0.460863, 0.510755 avg, 0.001000 rate, 24.443356 seconds, 283136 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.561920, Class: 1.000000, Obj: 0.236859, No Obj: 0.002945, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.538099, Class: 1.000000, Obj: 0.236288, No Obj: 0.002737, Avg Recall: 0.632911, count: 79 -Region Avg IOU: 0.548246, Class: 1.000000, Obj: 0.222489, No Obj: 0.003023, Avg Recall: 0.646154, count: 130 -Region Avg IOU: 0.584735, Class: 1.000000, Obj: 0.245259, No Obj: 0.002840, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.562592, Class: 1.000000, Obj: 0.224279, No Obj: 0.002966, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.569169, Class: 1.000000, Obj: 0.271068, No Obj: 0.002917, Avg Recall: 0.684783, count: 92 -Region Avg IOU: 0.546829, Class: 1.000000, Obj: 0.241824, No Obj: 0.003047, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.564183, Class: 1.000000, Obj: 0.252405, No Obj: 0.003199, Avg Recall: 0.666667, count: 111 -1107: 0.459757, 0.505655 avg, 0.001000 rate, 25.078558 seconds, 283392 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.555442, Class: 1.000000, Obj: 0.250987, No Obj: 0.003222, Avg Recall: 0.614754, count: 122 -Region Avg IOU: 0.570092, Class: 1.000000, Obj: 0.227779, No Obj: 0.003068, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.532130, Class: 1.000000, Obj: 0.234729, No Obj: 0.003374, Avg Recall: 0.614286, count: 140 -Region Avg IOU: 0.577507, Class: 1.000000, Obj: 0.272344, No Obj: 0.003684, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.572869, Class: 1.000000, Obj: 0.251964, No Obj: 0.003371, Avg Recall: 0.681481, count: 135 -Region Avg IOU: 0.571575, Class: 1.000000, Obj: 0.277949, No Obj: 0.003137, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.582996, Class: 1.000000, Obj: 0.271498, No Obj: 0.003074, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.586482, Class: 1.000000, Obj: 0.312106, No Obj: 0.002704, Avg Recall: 0.729730, count: 74 -1108: 0.488324, 0.503922 avg, 0.001000 rate, 24.907703 seconds, 283648 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.568056, Class: 1.000000, Obj: 0.260053, No Obj: 0.003506, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.553609, Class: 1.000000, Obj: 0.243044, No Obj: 0.003112, Avg Recall: 0.678571, count: 84 -Region Avg IOU: 0.529291, Class: 1.000000, Obj: 0.294760, No Obj: 0.003472, Avg Recall: 0.596491, count: 114 -Region Avg IOU: 0.560373, Class: 1.000000, Obj: 0.248564, No Obj: 0.002997, Avg Recall: 0.654321, count: 81 -Region Avg IOU: 0.552825, Class: 1.000000, Obj: 0.278013, No Obj: 0.003046, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.555019, Class: 1.000000, Obj: 0.268790, No Obj: 0.003337, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.544170, Class: 1.000000, Obj: 0.293937, No Obj: 0.003345, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.553257, Class: 1.000000, Obj: 0.250420, No Obj: 0.003653, Avg Recall: 0.625954, count: 131 -1109: 0.431355, 0.496665 avg, 0.001000 rate, 24.583492 seconds, 283904 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.547174, Class: 1.000000, Obj: 0.258346, No Obj: 0.003859, Avg Recall: 0.631206, count: 141 -Region Avg IOU: 0.536419, Class: 1.000000, Obj: 0.278480, No Obj: 0.003034, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.536865, Class: 1.000000, Obj: 0.267009, No Obj: 0.003271, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.523570, Class: 1.000000, Obj: 0.266643, No Obj: 0.003202, Avg Recall: 0.549296, count: 71 -Region Avg IOU: 0.548524, Class: 1.000000, Obj: 0.283043, No Obj: 0.003708, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.534915, Class: 1.000000, Obj: 0.273713, No Obj: 0.003207, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.551866, Class: 1.000000, Obj: 0.276918, No Obj: 0.003510, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.554376, Class: 1.000000, Obj: 0.272998, No Obj: 0.003424, Avg Recall: 0.681319, count: 91 -1110: 0.467250, 0.493724 avg, 0.001000 rate, 26.668413 seconds, 284160 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.604172 seconds -Region Avg IOU: 0.502536, Class: 1.000000, Obj: 0.271766, No Obj: 0.004200, Avg Recall: 0.532110, count: 109 -Region Avg IOU: 0.496873, Class: 1.000000, Obj: 0.293349, No Obj: 0.004202, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.454498, Class: 1.000000, Obj: 0.200144, No Obj: 0.004076, Avg Recall: 0.487603, count: 121 -Region Avg IOU: 0.466109, Class: 1.000000, Obj: 0.240318, No Obj: 0.003734, Avg Recall: 0.514851, count: 101 -Region Avg IOU: 0.481289, Class: 1.000000, Obj: 0.261933, No Obj: 0.003780, Avg Recall: 0.578313, count: 83 -Region Avg IOU: 0.505968, Class: 1.000000, Obj: 0.245333, No Obj: 0.004282, Avg Recall: 0.572727, count: 110 -Region Avg IOU: 0.494951, Class: 1.000000, Obj: 0.297221, No Obj: 0.004028, Avg Recall: 0.565217, count: 92 -Region Avg IOU: 0.525057, Class: 1.000000, Obj: 0.293425, No Obj: 0.004615, Avg Recall: 0.632075, count: 106 -1111: 0.485722, 0.492924 avg, 0.001000 rate, 21.252218 seconds, 284416 images -Loaded: 1.136737 seconds -Region Avg IOU: 0.452459, Class: 1.000000, Obj: 0.274048, No Obj: 0.004116, Avg Recall: 0.482456, count: 114 -Region Avg IOU: 0.518158, Class: 1.000000, Obj: 0.252787, No Obj: 0.003955, Avg Recall: 0.600000, count: 90 -Region Avg IOU: 0.505798, Class: 1.000000, Obj: 0.272775, No Obj: 0.004213, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.474987, Class: 1.000000, Obj: 0.268425, No Obj: 0.004352, Avg Recall: 0.523810, count: 126 -Region Avg IOU: 0.470115, Class: 1.000000, Obj: 0.260018, No Obj: 0.004202, Avg Recall: 0.512000, count: 125 -Region Avg IOU: 0.435629, Class: 1.000000, Obj: 0.255116, No Obj: 0.003664, Avg Recall: 0.490566, count: 106 -Region Avg IOU: 0.496583, Class: 1.000000, Obj: 0.273696, No Obj: 0.004141, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.496488, Class: 1.000000, Obj: 0.258538, No Obj: 0.004172, Avg Recall: 0.541284, count: 109 -1112: 0.558666, 0.499498 avg, 0.001000 rate, 22.492132 seconds, 284672 images -Loaded: 0.000054 seconds -Region Avg IOU: 0.495026, Class: 1.000000, Obj: 0.288625, No Obj: 0.004498, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.471330, Class: 1.000000, Obj: 0.330118, No Obj: 0.004065, Avg Recall: 0.489583, count: 96 -Region Avg IOU: 0.439103, Class: 1.000000, Obj: 0.276447, No Obj: 0.004124, Avg Recall: 0.412371, count: 97 -Region Avg IOU: 0.435386, Class: 1.000000, Obj: 0.233319, No Obj: 0.004268, Avg Recall: 0.429825, count: 114 -Region Avg IOU: 0.512251, Class: 1.000000, Obj: 0.288110, No Obj: 0.004414, Avg Recall: 0.561404, count: 114 -Region Avg IOU: 0.439885, Class: 1.000000, Obj: 0.266491, No Obj: 0.003976, Avg Recall: 0.442623, count: 122 -Region Avg IOU: 0.432694, Class: 1.000000, Obj: 0.258061, No Obj: 0.004530, Avg Recall: 0.432624, count: 141 -Region Avg IOU: 0.532439, Class: 1.000000, Obj: 0.304728, No Obj: 0.004427, Avg Recall: 0.618644, count: 118 -1113: 0.509757, 0.500524 avg, 0.001000 rate, 21.496616 seconds, 284928 images -Loaded: 0.192319 seconds -Region Avg IOU: 0.476375, Class: 1.000000, Obj: 0.283931, No Obj: 0.004722, Avg Recall: 0.547445, count: 137 -Region Avg IOU: 0.545828, Class: 1.000000, Obj: 0.264164, No Obj: 0.003800, Avg Recall: 0.615385, count: 78 -Region Avg IOU: 0.470266, Class: 1.000000, Obj: 0.266921, No Obj: 0.003929, Avg Recall: 0.508621, count: 116 -Region Avg IOU: 0.514657, Class: 1.000000, Obj: 0.240800, No Obj: 0.004318, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.499313, Class: 1.000000, Obj: 0.272373, No Obj: 0.004016, Avg Recall: 0.547368, count: 95 -Region Avg IOU: 0.468564, Class: 1.000000, Obj: 0.233849, No Obj: 0.004540, Avg Recall: 0.520661, count: 121 -Region Avg IOU: 0.483229, Class: 1.000000, Obj: 0.246368, No Obj: 0.004517, Avg Recall: 0.516129, count: 124 -Region Avg IOU: 0.467437, Class: 1.000000, Obj: 0.253476, No Obj: 0.003813, Avg Recall: 0.490566, count: 106 -1114: 0.525383, 0.503010 avg, 0.001000 rate, 22.541651 seconds, 285184 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.481986, Class: 1.000000, Obj: 0.274087, No Obj: 0.003782, Avg Recall: 0.518519, count: 81 -Region Avg IOU: 0.469638, Class: 1.000000, Obj: 0.281564, No Obj: 0.004030, Avg Recall: 0.500000, count: 114 -Region Avg IOU: 0.473588, Class: 1.000000, Obj: 0.285750, No Obj: 0.003909, Avg Recall: 0.515790, count: 95 -Region Avg IOU: 0.534123, Class: 1.000000, Obj: 0.257635, No Obj: 0.004319, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.482376, Class: 1.000000, Obj: 0.252597, No Obj: 0.003450, Avg Recall: 0.540230, count: 87 -Region Avg IOU: 0.556377, Class: 1.000000, Obj: 0.295928, No Obj: 0.004047, Avg Recall: 0.680556, count: 72 -Region Avg IOU: 0.462515, Class: 1.000000, Obj: 0.242964, No Obj: 0.004262, Avg Recall: 0.519231, count: 104 -Region Avg IOU: 0.501365, Class: 1.000000, Obj: 0.289190, No Obj: 0.004368, Avg Recall: 0.553398, count: 103 -1115: 0.441143, 0.496823 avg, 0.001000 rate, 21.923092 seconds, 285440 images -Loaded: 0.305409 seconds -Region Avg IOU: 0.505983, Class: 1.000000, Obj: 0.267367, No Obj: 0.004509, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.485987, Class: 1.000000, Obj: 0.263726, No Obj: 0.003930, Avg Recall: 0.543210, count: 81 -Region Avg IOU: 0.509081, Class: 1.000000, Obj: 0.270753, No Obj: 0.004288, Avg Recall: 0.569106, count: 123 -Region Avg IOU: 0.470804, Class: 1.000000, Obj: 0.261971, No Obj: 0.004688, Avg Recall: 0.532710, count: 107 -Region Avg IOU: 0.476324, Class: 1.000000, Obj: 0.278945, No Obj: 0.004359, Avg Recall: 0.478992, count: 119 -Region Avg IOU: 0.505087, Class: 1.000000, Obj: 0.277566, No Obj: 0.003288, Avg Recall: 0.585714, count: 70 -Region Avg IOU: 0.514053, Class: 1.000000, Obj: 0.293331, No Obj: 0.004352, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.479606, Class: 1.000000, Obj: 0.287802, No Obj: 0.004148, Avg Recall: 0.521277, count: 94 -1116: 0.527986, 0.499939 avg, 0.001000 rate, 19.866547 seconds, 285696 images -Loaded: 1.225281 seconds -Region Avg IOU: 0.493043, Class: 1.000000, Obj: 0.270910, No Obj: 0.003502, Avg Recall: 0.507042, count: 71 -Region Avg IOU: 0.527487, Class: 1.000000, Obj: 0.289586, No Obj: 0.004131, Avg Recall: 0.597938, count: 97 -Region Avg IOU: 0.459256, Class: 1.000000, Obj: 0.246537, No Obj: 0.004134, Avg Recall: 0.495238, count: 105 -Region Avg IOU: 0.498233, Class: 1.000000, Obj: 0.246198, No Obj: 0.004377, Avg Recall: 0.564516, count: 124 -Region Avg IOU: 0.503965, Class: 1.000000, Obj: 0.277153, No Obj: 0.004290, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.479776, Class: 1.000000, Obj: 0.229814, No Obj: 0.003516, Avg Recall: 0.506173, count: 81 -Region Avg IOU: 0.484051, Class: 1.000000, Obj: 0.261903, No Obj: 0.003945, Avg Recall: 0.528846, count: 104 -Region Avg IOU: 0.444523, Class: 1.000000, Obj: 0.250749, No Obj: 0.004196, Avg Recall: 0.408000, count: 125 -1117: 0.511220, 0.501067 avg, 0.001000 rate, 22.463938 seconds, 285952 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.435292, Class: 1.000000, Obj: 0.261097, No Obj: 0.004171, Avg Recall: 0.464000, count: 125 -Region Avg IOU: 0.488811, Class: 1.000000, Obj: 0.276619, No Obj: 0.004234, Avg Recall: 0.592920, count: 113 -Region Avg IOU: 0.464455, Class: 1.000000, Obj: 0.266154, No Obj: 0.004414, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.494669, Class: 1.000000, Obj: 0.284398, No Obj: 0.004123, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.497089, Class: 1.000000, Obj: 0.274911, No Obj: 0.003766, Avg Recall: 0.574713, count: 87 -Region Avg IOU: 0.542605, Class: 1.000000, Obj: 0.251200, No Obj: 0.003824, Avg Recall: 0.595745, count: 94 -Region Avg IOU: 0.501774, Class: 1.000000, Obj: 0.242345, No Obj: 0.003740, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.419238, Class: 1.000000, Obj: 0.232081, No Obj: 0.004039, Avg Recall: 0.392523, count: 107 -1118: 0.471896, 0.498150 avg, 0.001000 rate, 22.335833 seconds, 286208 images -Loaded: 0.401302 seconds -Region Avg IOU: 0.464184, Class: 1.000000, Obj: 0.256871, No Obj: 0.004219, Avg Recall: 0.507246, count: 138 -Region Avg IOU: 0.513804, Class: 1.000000, Obj: 0.260551, No Obj: 0.004761, Avg Recall: 0.578125, count: 128 -Region Avg IOU: 0.494849, Class: 1.000000, Obj: 0.298308, No Obj: 0.004140, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.495130, Class: 1.000000, Obj: 0.263818, No Obj: 0.004215, Avg Recall: 0.526316, count: 114 -Region Avg IOU: 0.484263, Class: 1.000000, Obj: 0.302083, No Obj: 0.003248, Avg Recall: 0.536585, count: 82 -Region Avg IOU: 0.553597, Class: 1.000000, Obj: 0.255636, No Obj: 0.004251, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.471732, Class: 1.000000, Obj: 0.259791, No Obj: 0.004361, Avg Recall: 0.538462, count: 117 -Region Avg IOU: 0.516618, Class: 1.000000, Obj: 0.299704, No Obj: 0.004102, Avg Recall: 0.525000, count: 80 -1119: 0.516083, 0.499943 avg, 0.001000 rate, 22.482250 seconds, 286464 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.508152, Class: 1.000000, Obj: 0.281104, No Obj: 0.003810, Avg Recall: 0.555556, count: 99 -Region Avg IOU: 0.449520, Class: 1.000000, Obj: 0.234157, No Obj: 0.004253, Avg Recall: 0.470086, count: 117 -Region Avg IOU: 0.471829, Class: 1.000000, Obj: 0.230653, No Obj: 0.003871, Avg Recall: 0.532787, count: 122 -Region Avg IOU: 0.508533, Class: 1.000000, Obj: 0.308828, No Obj: 0.003917, Avg Recall: 0.555556, count: 90 -Region Avg IOU: 0.478133, Class: 1.000000, Obj: 0.227903, No Obj: 0.003358, Avg Recall: 0.574713, count: 87 -Region Avg IOU: 0.486693, Class: 1.000000, Obj: 0.266383, No Obj: 0.004385, Avg Recall: 0.561983, count: 121 -Region Avg IOU: 0.527694, Class: 1.000000, Obj: 0.267578, No Obj: 0.004027, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.483963, Class: 1.000000, Obj: 0.257175, No Obj: 0.003751, Avg Recall: 0.485981, count: 107 -1120: 0.499851, 0.499934 avg, 0.001000 rate, 21.269260 seconds, 286720 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000065 seconds -Region Avg IOU: 0.455812, Class: 1.000000, Obj: 0.257516, No Obj: 0.003630, Avg Recall: 0.481481, count: 81 -Region Avg IOU: 0.512448, Class: 1.000000, Obj: 0.257442, No Obj: 0.003346, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.569151, Class: 1.000000, Obj: 0.284181, No Obj: 0.004337, Avg Recall: 0.627737, count: 137 -Region Avg IOU: 0.530844, Class: 1.000000, Obj: 0.288338, No Obj: 0.003665, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.553833, Class: 1.000000, Obj: 0.235965, No Obj: 0.003561, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.551887, Class: 1.000000, Obj: 0.271002, No Obj: 0.003409, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.553922, Class: 1.000000, Obj: 0.318257, No Obj: 0.004085, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.571086, Class: 1.000000, Obj: 0.298339, No Obj: 0.003542, Avg Recall: 0.725275, count: 91 -1121: 0.439212, 0.493862 avg, 0.001000 rate, 25.002756 seconds, 286976 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.517220, Class: 1.000000, Obj: 0.332760, No Obj: 0.003447, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.551128, Class: 1.000000, Obj: 0.276653, No Obj: 0.003786, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.526984, Class: 1.000000, Obj: 0.243981, No Obj: 0.003900, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.539958, Class: 1.000000, Obj: 0.245050, No Obj: 0.003855, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.508457, Class: 1.000000, Obj: 0.289709, No Obj: 0.003617, Avg Recall: 0.585106, count: 94 -Region Avg IOU: 0.540076, Class: 1.000000, Obj: 0.267716, No Obj: 0.003925, Avg Recall: 0.611570, count: 121 -Region Avg IOU: 0.533380, Class: 1.000000, Obj: 0.284209, No Obj: 0.003776, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.526278, Class: 1.000000, Obj: 0.240184, No Obj: 0.003696, Avg Recall: 0.635514, count: 107 -1122: 0.502325, 0.494708 avg, 0.001000 rate, 24.590384 seconds, 287232 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.585729, Class: 1.000000, Obj: 0.296713, No Obj: 0.003395, Avg Recall: 0.682927, count: 82 -Region Avg IOU: 0.555566, Class: 1.000000, Obj: 0.258457, No Obj: 0.004068, Avg Recall: 0.648855, count: 131 -Region Avg IOU: 0.523512, Class: 1.000000, Obj: 0.284779, No Obj: 0.003807, Avg Recall: 0.571429, count: 140 -Region Avg IOU: 0.528252, Class: 1.000000, Obj: 0.276132, No Obj: 0.003474, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.545085, Class: 1.000000, Obj: 0.269292, No Obj: 0.004385, Avg Recall: 0.632812, count: 128 -Region Avg IOU: 0.553977, Class: 1.000000, Obj: 0.246563, No Obj: 0.003224, Avg Recall: 0.644444, count: 90 -Region Avg IOU: 0.530563, Class: 1.000000, Obj: 0.301882, No Obj: 0.003314, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.573977, Class: 1.000000, Obj: 0.268977, No Obj: 0.003442, Avg Recall: 0.702128, count: 94 -1123: 0.433280, 0.488566 avg, 0.001000 rate, 24.224855 seconds, 287488 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.535803, Class: 1.000000, Obj: 0.284888, No Obj: 0.003393, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.510833, Class: 1.000000, Obj: 0.238310, No Obj: 0.003667, Avg Recall: 0.619835, count: 121 -Region Avg IOU: 0.550033, Class: 1.000000, Obj: 0.273780, No Obj: 0.003709, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.490282, Class: 1.000000, Obj: 0.247734, No Obj: 0.003414, Avg Recall: 0.504202, count: 119 -Region Avg IOU: 0.551780, Class: 1.000000, Obj: 0.277951, No Obj: 0.003496, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.537666, Class: 1.000000, Obj: 0.255351, No Obj: 0.003841, Avg Recall: 0.637795, count: 127 -Region Avg IOU: 0.510661, Class: 1.000000, Obj: 0.209065, No Obj: 0.003457, Avg Recall: 0.556522, count: 115 -Region Avg IOU: 0.541711, Class: 1.000000, Obj: 0.279123, No Obj: 0.003333, Avg Recall: 0.608108, count: 74 -1124: 0.469021, 0.486611 avg, 0.001000 rate, 26.527384 seconds, 287744 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.513157, Class: 1.000000, Obj: 0.233691, No Obj: 0.003178, Avg Recall: 0.554348, count: 92 -Region Avg IOU: 0.568171, Class: 1.000000, Obj: 0.276165, No Obj: 0.003482, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.538953, Class: 1.000000, Obj: 0.262229, No Obj: 0.003683, Avg Recall: 0.604651, count: 129 -Region Avg IOU: 0.600727, Class: 1.000000, Obj: 0.281652, No Obj: 0.003283, Avg Recall: 0.743243, count: 74 -Region Avg IOU: 0.557897, Class: 1.000000, Obj: 0.276700, No Obj: 0.004122, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.541880, Class: 1.000000, Obj: 0.286356, No Obj: 0.003572, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.547380, Class: 1.000000, Obj: 0.267988, No Obj: 0.003408, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.561285, Class: 1.000000, Obj: 0.296668, No Obj: 0.004193, Avg Recall: 0.669118, count: 136 -1125: 0.508323, 0.488782 avg, 0.001000 rate, 25.645229 seconds, 288000 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.569677, Class: 1.000000, Obj: 0.318134, No Obj: 0.003353, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.481264, Class: 1.000000, Obj: 0.212314, No Obj: 0.003253, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.543826, Class: 1.000000, Obj: 0.283242, No Obj: 0.003542, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.544573, Class: 1.000000, Obj: 0.271928, No Obj: 0.003310, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.531492, Class: 1.000000, Obj: 0.219670, No Obj: 0.003776, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.524363, Class: 1.000000, Obj: 0.249109, No Obj: 0.003524, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.535771, Class: 1.000000, Obj: 0.253945, No Obj: 0.003482, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.547717, Class: 1.000000, Obj: 0.280167, No Obj: 0.003302, Avg Recall: 0.654762, count: 84 -1126: 0.462864, 0.486190 avg, 0.001000 rate, 24.958704 seconds, 288256 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.550073, Class: 1.000000, Obj: 0.253001, No Obj: 0.003249, Avg Recall: 0.643411, count: 129 -Region Avg IOU: 0.531333, Class: 1.000000, Obj: 0.272344, No Obj: 0.003300, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.553652, Class: 1.000000, Obj: 0.260261, No Obj: 0.003189, Avg Recall: 0.643678, count: 87 -Region Avg IOU: 0.574569, Class: 1.000000, Obj: 0.283461, No Obj: 0.003438, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.560104, Class: 1.000000, Obj: 0.275039, No Obj: 0.003960, Avg Recall: 0.640000, count: 125 -Region Avg IOU: 0.564940, Class: 1.000000, Obj: 0.289807, No Obj: 0.003687, Avg Recall: 0.647541, count: 122 -Region Avg IOU: 0.510986, Class: 1.000000, Obj: 0.243958, No Obj: 0.003411, Avg Recall: 0.607843, count: 102 -Region Avg IOU: 0.497740, Class: 1.000000, Obj: 0.245695, No Obj: 0.002993, Avg Recall: 0.572727, count: 110 -1127: 0.458655, 0.483437 avg, 0.001000 rate, 24.378607 seconds, 288512 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.527699, Class: 1.000000, Obj: 0.244145, No Obj: 0.003617, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.522288, Class: 1.000000, Obj: 0.225932, No Obj: 0.003812, Avg Recall: 0.644928, count: 138 -Region Avg IOU: 0.577803, Class: 1.000000, Obj: 0.316822, No Obj: 0.003696, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.498251, Class: 1.000000, Obj: 0.244038, No Obj: 0.003436, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.555294, Class: 1.000000, Obj: 0.219056, No Obj: 0.003552, Avg Recall: 0.671756, count: 131 -Region Avg IOU: 0.584394, Class: 1.000000, Obj: 0.267226, No Obj: 0.003598, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.538908, Class: 1.000000, Obj: 0.291670, No Obj: 0.003559, Avg Recall: 0.606557, count: 122 -Region Avg IOU: 0.558961, Class: 1.000000, Obj: 0.299162, No Obj: 0.003400, Avg Recall: 0.673267, count: 101 -1128: 0.538195, 0.488913 avg, 0.001000 rate, 25.776768 seconds, 288768 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.542363, Class: 1.000000, Obj: 0.269500, No Obj: 0.003338, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.535948, Class: 1.000000, Obj: 0.275246, No Obj: 0.003592, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.552788, Class: 1.000000, Obj: 0.253120, No Obj: 0.004079, Avg Recall: 0.647059, count: 136 -Region Avg IOU: 0.520531, Class: 1.000000, Obj: 0.284044, No Obj: 0.003426, Avg Recall: 0.576923, count: 78 -Region Avg IOU: 0.529804, Class: 1.000000, Obj: 0.302372, No Obj: 0.003383, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.578170, Class: 1.000000, Obj: 0.239308, No Obj: 0.003487, Avg Recall: 0.694444, count: 72 -Region Avg IOU: 0.542223, Class: 1.000000, Obj: 0.303676, No Obj: 0.003716, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.538796, Class: 1.000000, Obj: 0.302522, No Obj: 0.003076, Avg Recall: 0.583333, count: 84 -1129: 0.425460, 0.482567 avg, 0.001000 rate, 24.967785 seconds, 289024 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.574218, Class: 1.000000, Obj: 0.290231, No Obj: 0.003298, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.550534, Class: 1.000000, Obj: 0.277795, No Obj: 0.003993, Avg Recall: 0.681481, count: 135 -Region Avg IOU: 0.557404, Class: 1.000000, Obj: 0.296060, No Obj: 0.003881, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.562935, Class: 1.000000, Obj: 0.313394, No Obj: 0.003594, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.546575, Class: 1.000000, Obj: 0.317815, No Obj: 0.003342, Avg Recall: 0.627907, count: 86 -Region Avg IOU: 0.557063, Class: 1.000000, Obj: 0.314815, No Obj: 0.003476, Avg Recall: 0.658537, count: 82 -Region Avg IOU: 0.538500, Class: 1.000000, Obj: 0.253858, No Obj: 0.003859, Avg Recall: 0.609375, count: 128 -Region Avg IOU: 0.538846, Class: 1.000000, Obj: 0.251423, No Obj: 0.003572, Avg Recall: 0.611650, count: 103 -1130: 0.477463, 0.482057 avg, 0.001000 rate, 24.781488 seconds, 289280 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.899799 seconds -Region Avg IOU: 0.449035, Class: 1.000000, Obj: 0.193248, No Obj: 0.004336, Avg Recall: 0.424779, count: 113 -Region Avg IOU: 0.422679, Class: 1.000000, Obj: 0.240757, No Obj: 0.003898, Avg Recall: 0.417476, count: 103 -Region Avg IOU: 0.463397, Class: 1.000000, Obj: 0.272946, No Obj: 0.004278, Avg Recall: 0.538462, count: 78 -Region Avg IOU: 0.476578, Class: 1.000000, Obj: 0.259280, No Obj: 0.003951, Avg Recall: 0.521277, count: 94 -Region Avg IOU: 0.414745, Class: 1.000000, Obj: 0.218593, No Obj: 0.003835, Avg Recall: 0.410526, count: 95 -Region Avg IOU: 0.453544, Class: 1.000000, Obj: 0.220592, No Obj: 0.004536, Avg Recall: 0.459016, count: 122 -Region Avg IOU: 0.434167, Class: 1.000000, Obj: 0.214179, No Obj: 0.004609, Avg Recall: 0.424528, count: 106 -Region Avg IOU: 0.470639, Class: 1.000000, Obj: 0.264879, No Obj: 0.004395, Avg Recall: 0.520408, count: 98 -1131: 0.474727, 0.481324 avg, 0.001000 rate, 20.965509 seconds, 289536 images -Loaded: 1.506878 seconds -Region Avg IOU: 0.512016, Class: 1.000000, Obj: 0.258472, No Obj: 0.005093, Avg Recall: 0.541667, count: 144 -Region Avg IOU: 0.503864, Class: 1.000000, Obj: 0.247543, No Obj: 0.003853, Avg Recall: 0.538462, count: 78 -Region Avg IOU: 0.428559, Class: 1.000000, Obj: 0.239686, No Obj: 0.004300, Avg Recall: 0.455446, count: 101 -Region Avg IOU: 0.501868, Class: 1.000000, Obj: 0.232627, No Obj: 0.005506, Avg Recall: 0.564885, count: 131 -Region Avg IOU: 0.463672, Class: 1.000000, Obj: 0.249861, No Obj: 0.004698, Avg Recall: 0.529412, count: 119 -Region Avg IOU: 0.440251, Class: 1.000000, Obj: 0.267393, No Obj: 0.004289, Avg Recall: 0.455446, count: 101 -Region Avg IOU: 0.422673, Class: 1.000000, Obj: 0.253103, No Obj: 0.003892, Avg Recall: 0.426667, count: 75 -Region Avg IOU: 0.478355, Class: 1.000000, Obj: 0.259389, No Obj: 0.004789, Avg Recall: 0.508772, count: 114 -1132: 0.518833, 0.485075 avg, 0.001000 rate, 16.940844 seconds, 289792 images -Loaded: 3.522567 seconds -Region Avg IOU: 0.446004, Class: 1.000000, Obj: 0.274413, No Obj: 0.004477, Avg Recall: 0.457447, count: 94 -Region Avg IOU: 0.463069, Class: 1.000000, Obj: 0.285102, No Obj: 0.004815, Avg Recall: 0.513761, count: 109 -Region Avg IOU: 0.436520, Class: 1.000000, Obj: 0.260576, No Obj: 0.004623, Avg Recall: 0.471545, count: 123 -Region Avg IOU: 0.473250, Class: 1.000000, Obj: 0.293679, No Obj: 0.004337, Avg Recall: 0.512397, count: 121 -Region Avg IOU: 0.447224, Class: 1.000000, Obj: 0.293722, No Obj: 0.004295, Avg Recall: 0.495146, count: 103 -Region Avg IOU: 0.450482, Class: 1.000000, Obj: 0.252213, No Obj: 0.004157, Avg Recall: 0.433962, count: 106 -Region Avg IOU: 0.405360, Class: 1.000000, Obj: 0.226997, No Obj: 0.004415, Avg Recall: 0.386792, count: 106 -Region Avg IOU: 0.424653, Class: 1.000000, Obj: 0.250679, No Obj: 0.004358, Avg Recall: 0.462963, count: 108 -1133: 0.570314, 0.493599 avg, 0.001000 rate, 18.556459 seconds, 290048 images -Loaded: 1.427257 seconds -Region Avg IOU: 0.437662, Class: 1.000000, Obj: 0.238190, No Obj: 0.005307, Avg Recall: 0.518987, count: 158 -Region Avg IOU: 0.401060, Class: 1.000000, Obj: 0.250209, No Obj: 0.004162, Avg Recall: 0.398305, count: 118 -Region Avg IOU: 0.415192, Class: 1.000000, Obj: 0.242705, No Obj: 0.005190, Avg Recall: 0.396104, count: 154 -Region Avg IOU: 0.437244, Class: 1.000000, Obj: 0.234282, No Obj: 0.003860, Avg Recall: 0.444444, count: 90 -Region Avg IOU: 0.437862, Class: 1.000000, Obj: 0.294383, No Obj: 0.004203, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.457163, Class: 1.000000, Obj: 0.235171, No Obj: 0.004811, Avg Recall: 0.459016, count: 122 -Region Avg IOU: 0.490041, Class: 1.000000, Obj: 0.273927, No Obj: 0.004194, Avg Recall: 0.535714, count: 84 -Region Avg IOU: 0.442982, Class: 1.000000, Obj: 0.223829, No Obj: 0.004521, Avg Recall: 0.479167, count: 96 -1134: 0.550311, 0.499270 avg, 0.001000 rate, 17.271404 seconds, 290304 images -Loaded: 3.874574 seconds -Region Avg IOU: 0.420406, Class: 1.000000, Obj: 0.262841, No Obj: 0.004490, Avg Recall: 0.342593, count: 108 -Region Avg IOU: 0.395412, Class: 1.000000, Obj: 0.259028, No Obj: 0.004024, Avg Recall: 0.352941, count: 102 -Region Avg IOU: 0.418788, Class: 1.000000, Obj: 0.253053, No Obj: 0.004437, Avg Recall: 0.449153, count: 118 -Region Avg IOU: 0.406314, Class: 1.000000, Obj: 0.252614, No Obj: 0.004428, Avg Recall: 0.403509, count: 114 -Region Avg IOU: 0.441450, Class: 1.000000, Obj: 0.274738, No Obj: 0.005203, Avg Recall: 0.441860, count: 129 -Region Avg IOU: 0.411418, Class: 1.000000, Obj: 0.261407, No Obj: 0.004705, Avg Recall: 0.438017, count: 121 -Region Avg IOU: 0.437799, Class: 1.000000, Obj: 0.250071, No Obj: 0.004763, Avg Recall: 0.425926, count: 108 -Region Avg IOU: 0.406945, Class: 1.000000, Obj: 0.231633, No Obj: 0.004524, Avg Recall: 0.439252, count: 107 -1135: 0.527941, 0.502137 avg, 0.001000 rate, 16.493397 seconds, 290560 images -Loaded: 2.990729 seconds -Region Avg IOU: 0.440083, Class: 1.000000, Obj: 0.286288, No Obj: 0.004881, Avg Recall: 0.483871, count: 124 -Region Avg IOU: 0.444088, Class: 1.000000, Obj: 0.273068, No Obj: 0.004897, Avg Recall: 0.472000, count: 125 -Region Avg IOU: 0.454627, Class: 1.000000, Obj: 0.236738, No Obj: 0.004460, Avg Recall: 0.470588, count: 85 -Region Avg IOU: 0.441124, Class: 1.000000, Obj: 0.252579, No Obj: 0.004876, Avg Recall: 0.477477, count: 111 -Region Avg IOU: 0.397677, Class: 1.000000, Obj: 0.220598, No Obj: 0.004096, Avg Recall: 0.377953, count: 127 -Region Avg IOU: 0.426356, Class: 1.000000, Obj: 0.264464, No Obj: 0.004017, Avg Recall: 0.393617, count: 94 -Region Avg IOU: 0.479627, Class: 1.000000, Obj: 0.279676, No Obj: 0.004711, Avg Recall: 0.490385, count: 104 -Region Avg IOU: 0.458850, Class: 1.000000, Obj: 0.301050, No Obj: 0.005026, Avg Recall: 0.486726, count: 113 -1136: 0.641037, 0.516027 avg, 0.001000 rate, 19.271545 seconds, 290816 images -Loaded: 0.925623 seconds -Region Avg IOU: 0.426357, Class: 1.000000, Obj: 0.237513, No Obj: 0.004615, Avg Recall: 0.396825, count: 126 -Region Avg IOU: 0.484677, Class: 1.000000, Obj: 0.273676, No Obj: 0.004934, Avg Recall: 0.490000, count: 100 -Region Avg IOU: 0.472442, Class: 1.000000, Obj: 0.301410, No Obj: 0.004437, Avg Recall: 0.512821, count: 117 -Region Avg IOU: 0.419307, Class: 1.000000, Obj: 0.250535, No Obj: 0.004365, Avg Recall: 0.450000, count: 100 -Region Avg IOU: 0.428614, Class: 1.000000, Obj: 0.260619, No Obj: 0.004284, Avg Recall: 0.457143, count: 105 -Region Avg IOU: 0.425901, Class: 1.000000, Obj: 0.208611, No Obj: 0.004138, Avg Recall: 0.442478, count: 113 -Region Avg IOU: 0.494125, Class: 1.000000, Obj: 0.331407, No Obj: 0.004709, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.406426, Class: 1.000000, Obj: 0.238029, No Obj: 0.004932, Avg Recall: 0.428571, count: 133 -1137: 0.530220, 0.517446 avg, 0.001000 rate, 18.912340 seconds, 291072 images -Loaded: 1.193346 seconds -Region Avg IOU: 0.400670, Class: 1.000000, Obj: 0.271649, No Obj: 0.004455, Avg Recall: 0.420168, count: 119 -Region Avg IOU: 0.415524, Class: 1.000000, Obj: 0.251854, No Obj: 0.004201, Avg Recall: 0.414414, count: 111 -Region Avg IOU: 0.422982, Class: 1.000000, Obj: 0.221126, No Obj: 0.004061, Avg Recall: 0.464646, count: 99 -Region Avg IOU: 0.468712, Class: 1.000000, Obj: 0.301276, No Obj: 0.004595, Avg Recall: 0.514851, count: 101 -Region Avg IOU: 0.409632, Class: 1.000000, Obj: 0.263352, No Obj: 0.003934, Avg Recall: 0.420000, count: 100 -Region Avg IOU: 0.443697, Class: 1.000000, Obj: 0.251042, No Obj: 0.003710, Avg Recall: 0.487805, count: 82 -Region Avg IOU: 0.445087, Class: 1.000000, Obj: 0.261491, No Obj: 0.004620, Avg Recall: 0.465116, count: 129 -Region Avg IOU: 0.456440, Class: 1.000000, Obj: 0.240238, No Obj: 0.004587, Avg Recall: 0.480769, count: 104 -1138: 0.518516, 0.517553 avg, 0.001000 rate, 20.184572 seconds, 291328 images -Loaded: 2.025159 seconds -Region Avg IOU: 0.413571, Class: 1.000000, Obj: 0.247621, No Obj: 0.004333, Avg Recall: 0.382114, count: 123 -Region Avg IOU: 0.413177, Class: 1.000000, Obj: 0.209569, No Obj: 0.003893, Avg Recall: 0.428571, count: 119 -Region Avg IOU: 0.426598, Class: 1.000000, Obj: 0.237742, No Obj: 0.004814, Avg Recall: 0.432000, count: 125 -Region Avg IOU: 0.424462, Class: 1.000000, Obj: 0.244729, No Obj: 0.004611, Avg Recall: 0.452991, count: 117 -Region Avg IOU: 0.467767, Class: 1.000000, Obj: 0.274370, No Obj: 0.004222, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.458352, Class: 1.000000, Obj: 0.248700, No Obj: 0.004763, Avg Recall: 0.471545, count: 123 -Region Avg IOU: 0.442722, Class: 1.000000, Obj: 0.212532, No Obj: 0.004335, Avg Recall: 0.484536, count: 97 -Region Avg IOU: 0.479239, Class: 1.000000, Obj: 0.279672, No Obj: 0.004795, Avg Recall: 0.508772, count: 114 -1139: 0.525859, 0.518384 avg, 0.001000 rate, 17.461632 seconds, 291584 images -Loaded: 2.491908 seconds -Region Avg IOU: 0.468536, Class: 1.000000, Obj: 0.261900, No Obj: 0.004869, Avg Recall: 0.508621, count: 116 -Region Avg IOU: 0.451860, Class: 1.000000, Obj: 0.251618, No Obj: 0.004073, Avg Recall: 0.487805, count: 82 -Region Avg IOU: 0.437706, Class: 1.000000, Obj: 0.250904, No Obj: 0.003726, Avg Recall: 0.489362, count: 94 -Region Avg IOU: 0.451560, Class: 1.000000, Obj: 0.228034, No Obj: 0.004812, Avg Recall: 0.455882, count: 136 -Region Avg IOU: 0.443307, Class: 1.000000, Obj: 0.232867, No Obj: 0.004633, Avg Recall: 0.482269, count: 141 -Region Avg IOU: 0.429301, Class: 1.000000, Obj: 0.218918, No Obj: 0.004446, Avg Recall: 0.450000, count: 100 -Region Avg IOU: 0.427981, Class: 1.000000, Obj: 0.232792, No Obj: 0.004897, Avg Recall: 0.453947, count: 152 -Region Avg IOU: 0.428332, Class: 1.000000, Obj: 0.236449, No Obj: 0.004091, Avg Recall: 0.427273, count: 110 -1140: 0.540366, 0.520582 avg, 0.001000 rate, 15.841950 seconds, 291840 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 0.085860 seconds -Region Avg IOU: 0.458290, Class: 1.000000, Obj: 0.242394, No Obj: 0.004057, Avg Recall: 0.480392, count: 102 -Region Avg IOU: 0.420638, Class: 1.000000, Obj: 0.225060, No Obj: 0.003716, Avg Recall: 0.426087, count: 115 -Region Avg IOU: 0.485914, Class: 1.000000, Obj: 0.291464, No Obj: 0.003956, Avg Recall: 0.583333, count: 96 -Region Avg IOU: 0.519801, Class: 1.000000, Obj: 0.277769, No Obj: 0.003921, Avg Recall: 0.602564, count: 78 -Region Avg IOU: 0.472892, Class: 1.000000, Obj: 0.254590, No Obj: 0.004521, Avg Recall: 0.549020, count: 102 -Region Avg IOU: 0.437214, Class: 1.000000, Obj: 0.219368, No Obj: 0.004377, Avg Recall: 0.484375, count: 128 -Region Avg IOU: 0.444546, Class: 1.000000, Obj: 0.224666, No Obj: 0.003971, Avg Recall: 0.432432, count: 111 -Region Avg IOU: 0.526128, Class: 1.000000, Obj: 0.262791, No Obj: 0.004338, Avg Recall: 0.602151, count: 93 -1141: 0.421576, 0.510682 avg, 0.001000 rate, 21.286009 seconds, 292096 images -Loaded: 0.543818 seconds -Region Avg IOU: 0.513961, Class: 1.000000, Obj: 0.240135, No Obj: 0.003960, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.480703, Class: 1.000000, Obj: 0.246815, No Obj: 0.004451, Avg Recall: 0.513043, count: 115 -Region Avg IOU: 0.504125, Class: 1.000000, Obj: 0.265850, No Obj: 0.004067, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.466888, Class: 1.000000, Obj: 0.254405, No Obj: 0.003845, Avg Recall: 0.477778, count: 90 -Region Avg IOU: 0.500493, Class: 1.000000, Obj: 0.268960, No Obj: 0.004259, Avg Recall: 0.529412, count: 102 -Region Avg IOU: 0.448283, Class: 1.000000, Obj: 0.244917, No Obj: 0.004243, Avg Recall: 0.478992, count: 119 -Region Avg IOU: 0.502721, Class: 1.000000, Obj: 0.253015, No Obj: 0.004707, Avg Recall: 0.549180, count: 122 -Region Avg IOU: 0.464514, Class: 1.000000, Obj: 0.269449, No Obj: 0.004450, Avg Recall: 0.509259, count: 108 -1142: 0.511800, 0.510793 avg, 0.001000 rate, 18.712732 seconds, 292352 images -Loaded: 4.282105 seconds -Region Avg IOU: 0.423245, Class: 1.000000, Obj: 0.208197, No Obj: 0.004011, Avg Recall: 0.473118, count: 93 -Region Avg IOU: 0.443590, Class: 1.000000, Obj: 0.217346, No Obj: 0.003804, Avg Recall: 0.452632, count: 95 -Region Avg IOU: 0.432171, Class: 1.000000, Obj: 0.256288, No Obj: 0.003990, Avg Recall: 0.436364, count: 110 -Region Avg IOU: 0.454876, Class: 1.000000, Obj: 0.253663, No Obj: 0.004800, Avg Recall: 0.456522, count: 138 -Region Avg IOU: 0.459415, Class: 1.000000, Obj: 0.261573, No Obj: 0.003979, Avg Recall: 0.544554, count: 101 -Region Avg IOU: 0.465586, Class: 1.000000, Obj: 0.289673, No Obj: 0.004261, Avg Recall: 0.522124, count: 113 -Region Avg IOU: 0.488470, Class: 1.000000, Obj: 0.291742, No Obj: 0.004532, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.430294, Class: 1.000000, Obj: 0.234403, No Obj: 0.004103, Avg Recall: 0.480392, count: 102 -1143: 0.549878, 0.514702 avg, 0.001000 rate, 19.990358 seconds, 292608 images -Loaded: 1.092453 seconds -Region Avg IOU: 0.470460, Class: 1.000000, Obj: 0.244505, No Obj: 0.004190, Avg Recall: 0.508772, count: 114 -Region Avg IOU: 0.459880, Class: 1.000000, Obj: 0.243663, No Obj: 0.004243, Avg Recall: 0.495413, count: 109 -Region Avg IOU: 0.490037, Class: 1.000000, Obj: 0.256987, No Obj: 0.003680, Avg Recall: 0.500000, count: 94 -Region Avg IOU: 0.452721, Class: 1.000000, Obj: 0.243446, No Obj: 0.003796, Avg Recall: 0.469880, count: 83 -Region Avg IOU: 0.496398, Class: 1.000000, Obj: 0.261821, No Obj: 0.004313, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.454205, Class: 1.000000, Obj: 0.270597, No Obj: 0.003598, Avg Recall: 0.505747, count: 87 -Region Avg IOU: 0.424550, Class: 1.000000, Obj: 0.235777, No Obj: 0.004174, Avg Recall: 0.429907, count: 107 -Region Avg IOU: 0.470465, Class: 1.000000, Obj: 0.266573, No Obj: 0.004051, Avg Recall: 0.495050, count: 101 -1144: 0.507232, 0.513955 avg, 0.001000 rate, 19.700312 seconds, 292864 images -Loaded: 2.331110 seconds -Region Avg IOU: 0.450737, Class: 1.000000, Obj: 0.275991, No Obj: 0.004239, Avg Recall: 0.467290, count: 107 -Region Avg IOU: 0.455052, Class: 1.000000, Obj: 0.234102, No Obj: 0.004360, Avg Recall: 0.488550, count: 131 -Region Avg IOU: 0.455603, Class: 1.000000, Obj: 0.286792, No Obj: 0.003931, Avg Recall: 0.474227, count: 97 -Region Avg IOU: 0.472078, Class: 1.000000, Obj: 0.244795, No Obj: 0.003694, Avg Recall: 0.510204, count: 98 -Region Avg IOU: 0.498109, Class: 1.000000, Obj: 0.260400, No Obj: 0.004711, Avg Recall: 0.521739, count: 138 -Region Avg IOU: 0.482326, Class: 1.000000, Obj: 0.281806, No Obj: 0.004672, Avg Recall: 0.518519, count: 108 -Region Avg IOU: 0.441601, Class: 1.000000, Obj: 0.267420, No Obj: 0.004086, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.437387, Class: 1.000000, Obj: 0.234496, No Obj: 0.004098, Avg Recall: 0.474576, count: 118 -1145: 0.470944, 0.509654 avg, 0.001000 rate, 21.761560 seconds, 293120 images -Loaded: 0.019658 seconds -Region Avg IOU: 0.452807, Class: 1.000000, Obj: 0.280417, No Obj: 0.004029, Avg Recall: 0.444444, count: 99 -Region Avg IOU: 0.464137, Class: 1.000000, Obj: 0.245055, No Obj: 0.004572, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.501776, Class: 1.000000, Obj: 0.250437, No Obj: 0.004563, Avg Recall: 0.572727, count: 110 -Region Avg IOU: 0.507818, Class: 1.000000, Obj: 0.283088, No Obj: 0.004120, Avg Recall: 0.597826, count: 92 -Region Avg IOU: 0.445690, Class: 1.000000, Obj: 0.238717, No Obj: 0.004318, Avg Recall: 0.454545, count: 121 -Region Avg IOU: 0.463654, Class: 1.000000, Obj: 0.253521, No Obj: 0.004516, Avg Recall: 0.495495, count: 111 -Region Avg IOU: 0.461812, Class: 1.000000, Obj: 0.268334, No Obj: 0.004842, Avg Recall: 0.480916, count: 131 -Region Avg IOU: 0.461682, Class: 1.000000, Obj: 0.219816, No Obj: 0.004331, Avg Recall: 0.495327, count: 107 -1146: 0.473336, 0.506022 avg, 0.001000 rate, 20.866915 seconds, 293376 images -Loaded: 1.133977 seconds -Region Avg IOU: 0.522070, Class: 1.000000, Obj: 0.291108, No Obj: 0.004691, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.442105, Class: 1.000000, Obj: 0.248098, No Obj: 0.004127, Avg Recall: 0.483051, count: 118 -Region Avg IOU: 0.477171, Class: 1.000000, Obj: 0.268055, No Obj: 0.004037, Avg Recall: 0.464646, count: 99 -Region Avg IOU: 0.463085, Class: 1.000000, Obj: 0.240094, No Obj: 0.004466, Avg Recall: 0.457627, count: 118 -Region Avg IOU: 0.461571, Class: 1.000000, Obj: 0.259871, No Obj: 0.004134, Avg Recall: 0.494505, count: 91 -Region Avg IOU: 0.473635, Class: 1.000000, Obj: 0.285022, No Obj: 0.003833, Avg Recall: 0.543478, count: 92 -Region Avg IOU: 0.467372, Class: 1.000000, Obj: 0.292868, No Obj: 0.004362, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.481975, Class: 1.000000, Obj: 0.308488, No Obj: 0.004423, Avg Recall: 0.525253, count: 99 -1147: 0.427180, 0.498138 avg, 0.001000 rate, 17.977884 seconds, 293632 images -Loaded: 2.360280 seconds -Region Avg IOU: 0.482775, Class: 1.000000, Obj: 0.304054, No Obj: 0.004712, Avg Recall: 0.491803, count: 122 -Region Avg IOU: 0.511065, Class: 1.000000, Obj: 0.312560, No Obj: 0.004454, Avg Recall: 0.576087, count: 92 -Region Avg IOU: 0.484794, Class: 1.000000, Obj: 0.260656, No Obj: 0.004345, Avg Recall: 0.515790, count: 95 -Region Avg IOU: 0.401102, Class: 1.000000, Obj: 0.249898, No Obj: 0.003772, Avg Recall: 0.423913, count: 92 -Region Avg IOU: 0.468942, Class: 1.000000, Obj: 0.303388, No Obj: 0.004315, Avg Recall: 0.484848, count: 99 -Region Avg IOU: 0.490716, Class: 1.000000, Obj: 0.243794, No Obj: 0.004197, Avg Recall: 0.560976, count: 82 -Region Avg IOU: 0.479980, Class: 1.000000, Obj: 0.293119, No Obj: 0.005262, Avg Recall: 0.522124, count: 113 -Region Avg IOU: 0.460176, Class: 1.000000, Obj: 0.233783, No Obj: 0.004158, Avg Recall: 0.462264, count: 106 -1148: 0.449859, 0.493310 avg, 0.001000 rate, 22.512825 seconds, 293888 images -Loaded: 0.390948 seconds -Region Avg IOU: 0.478168, Class: 1.000000, Obj: 0.243970, No Obj: 0.004719, Avg Recall: 0.542373, count: 118 -Region Avg IOU: 0.450307, Class: 1.000000, Obj: 0.226792, No Obj: 0.004091, Avg Recall: 0.486486, count: 111 -Region Avg IOU: 0.525458, Class: 1.000000, Obj: 0.285463, No Obj: 0.003639, Avg Recall: 0.577465, count: 71 -Region Avg IOU: 0.447418, Class: 1.000000, Obj: 0.242101, No Obj: 0.004264, Avg Recall: 0.494118, count: 85 -Region Avg IOU: 0.468107, Class: 1.000000, Obj: 0.277197, No Obj: 0.004692, Avg Recall: 0.549180, count: 122 -Region Avg IOU: 0.474472, Class: 1.000000, Obj: 0.299206, No Obj: 0.004612, Avg Recall: 0.525253, count: 99 -Region Avg IOU: 0.431354, Class: 1.000000, Obj: 0.273845, No Obj: 0.004447, Avg Recall: 0.434783, count: 115 -Region Avg IOU: 0.447411, Class: 1.000000, Obj: 0.272241, No Obj: 0.004858, Avg Recall: 0.455882, count: 136 -1149: 0.486672, 0.492646 avg, 0.001000 rate, 20.461201 seconds, 294144 images -Loaded: 1.582025 seconds -Region Avg IOU: 0.495399, Class: 1.000000, Obj: 0.284785, No Obj: 0.004470, Avg Recall: 0.534653, count: 101 -Region Avg IOU: 0.476568, Class: 1.000000, Obj: 0.256342, No Obj: 0.004415, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.480808, Class: 1.000000, Obj: 0.223672, No Obj: 0.004271, Avg Recall: 0.537736, count: 106 -Region Avg IOU: 0.449347, Class: 1.000000, Obj: 0.279628, No Obj: 0.004239, Avg Recall: 0.484211, count: 95 -Region Avg IOU: 0.451733, Class: 1.000000, Obj: 0.282147, No Obj: 0.003938, Avg Recall: 0.493671, count: 79 -Region Avg IOU: 0.467491, Class: 1.000000, Obj: 0.240444, No Obj: 0.003845, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.429637, Class: 1.000000, Obj: 0.267623, No Obj: 0.004254, Avg Recall: 0.418605, count: 129 -Region Avg IOU: 0.523679, Class: 1.000000, Obj: 0.310900, No Obj: 0.004826, Avg Recall: 0.557692, count: 104 -1150: 0.475014, 0.490883 avg, 0.001000 rate, 18.235901 seconds, 294400 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.747463 seconds -Region Avg IOU: 0.416321, Class: 1.000000, Obj: 0.220923, No Obj: 0.004743, Avg Recall: 0.425373, count: 134 -Region Avg IOU: 0.410319, Class: 1.000000, Obj: 0.243019, No Obj: 0.004623, Avg Recall: 0.411765, count: 119 -Region Avg IOU: 0.448059, Class: 1.000000, Obj: 0.262965, No Obj: 0.004526, Avg Recall: 0.455357, count: 112 -Region Avg IOU: 0.496038, Class: 1.000000, Obj: 0.273525, No Obj: 0.004478, Avg Recall: 0.590909, count: 88 -Region Avg IOU: 0.422413, Class: 1.000000, Obj: 0.252561, No Obj: 0.004944, Avg Recall: 0.442748, count: 131 -Region Avg IOU: 0.462777, Class: 1.000000, Obj: 0.262786, No Obj: 0.004495, Avg Recall: 0.514019, count: 107 -Region Avg IOU: 0.480932, Class: 1.000000, Obj: 0.259582, No Obj: 0.005275, Avg Recall: 0.531915, count: 141 -Region Avg IOU: 0.469878, Class: 1.000000, Obj: 0.254635, No Obj: 0.004314, Avg Recall: 0.510000, count: 100 -1151: 0.542634, 0.496058 avg, 0.001000 rate, 18.072355 seconds, 294656 images -Loaded: 4.151647 seconds -Region Avg IOU: 0.428551, Class: 1.000000, Obj: 0.223238, No Obj: 0.004512, Avg Recall: 0.448980, count: 98 -Region Avg IOU: 0.484768, Class: 1.000000, Obj: 0.272054, No Obj: 0.004978, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.441052, Class: 1.000000, Obj: 0.238687, No Obj: 0.005874, Avg Recall: 0.462025, count: 158 -Region Avg IOU: 0.468813, Class: 1.000000, Obj: 0.243149, No Obj: 0.004244, Avg Recall: 0.504762, count: 105 -Region Avg IOU: 0.456406, Class: 1.000000, Obj: 0.266628, No Obj: 0.004681, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.456751, Class: 1.000000, Obj: 0.259265, No Obj: 0.004450, Avg Recall: 0.510638, count: 94 -Region Avg IOU: 0.472567, Class: 1.000000, Obj: 0.225881, No Obj: 0.004535, Avg Recall: 0.476191, count: 126 -Region Avg IOU: 0.442783, Class: 1.000000, Obj: 0.259950, No Obj: 0.004888, Avg Recall: 0.490196, count: 102 -1152: 0.565255, 0.502978 avg, 0.001000 rate, 17.209280 seconds, 294912 images -Loaded: 2.773841 seconds -Region Avg IOU: 0.407504, Class: 1.000000, Obj: 0.254893, No Obj: 0.003966, Avg Recall: 0.386364, count: 88 -Region Avg IOU: 0.476925, Class: 1.000000, Obj: 0.256676, No Obj: 0.004114, Avg Recall: 0.527027, count: 74 -Region Avg IOU: 0.486921, Class: 1.000000, Obj: 0.280705, No Obj: 0.003989, Avg Recall: 0.541176, count: 85 -Region Avg IOU: 0.509814, Class: 1.000000, Obj: 0.273637, No Obj: 0.004591, Avg Recall: 0.576577, count: 111 -Region Avg IOU: 0.410271, Class: 1.000000, Obj: 0.213363, No Obj: 0.004617, Avg Recall: 0.413793, count: 116 -Region Avg IOU: 0.445442, Class: 1.000000, Obj: 0.254778, No Obj: 0.003839, Avg Recall: 0.481481, count: 81 -Region Avg IOU: 0.445462, Class: 1.000000, Obj: 0.263868, No Obj: 0.004965, Avg Recall: 0.426471, count: 136 -Region Avg IOU: 0.475671, Class: 1.000000, Obj: 0.312492, No Obj: 0.004520, Avg Recall: 0.531915, count: 94 -1153: 0.468809, 0.499561 avg, 0.001000 rate, 17.373724 seconds, 295168 images -Loaded: 3.049565 seconds -Region Avg IOU: 0.433761, Class: 1.000000, Obj: 0.237661, No Obj: 0.004426, Avg Recall: 0.450450, count: 111 -Region Avg IOU: 0.462487, Class: 1.000000, Obj: 0.283717, No Obj: 0.004544, Avg Recall: 0.426087, count: 115 -Region Avg IOU: 0.429937, Class: 1.000000, Obj: 0.241219, No Obj: 0.004210, Avg Recall: 0.465347, count: 101 -Region Avg IOU: 0.427584, Class: 1.000000, Obj: 0.282050, No Obj: 0.004577, Avg Recall: 0.463636, count: 110 -Region Avg IOU: 0.439843, Class: 1.000000, Obj: 0.263159, No Obj: 0.004090, Avg Recall: 0.418605, count: 86 -Region Avg IOU: 0.428621, Class: 1.000000, Obj: 0.217753, No Obj: 0.004693, Avg Recall: 0.429752, count: 121 -Region Avg IOU: 0.489358, Class: 1.000000, Obj: 0.275777, No Obj: 0.004587, Avg Recall: 0.537634, count: 93 -Region Avg IOU: 0.390618, Class: 1.000000, Obj: 0.248639, No Obj: 0.004641, Avg Recall: 0.392000, count: 125 -1154: 0.514975, 0.501102 avg, 0.001000 rate, 17.317427 seconds, 295424 images -Loaded: 2.738836 seconds -Region Avg IOU: 0.467022, Class: 1.000000, Obj: 0.269519, No Obj: 0.004748, Avg Recall: 0.555556, count: 90 -Region Avg IOU: 0.408312, Class: 1.000000, Obj: 0.235557, No Obj: 0.004547, Avg Recall: 0.376344, count: 93 -Region Avg IOU: 0.503727, Class: 1.000000, Obj: 0.298275, No Obj: 0.005221, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.426020, Class: 1.000000, Obj: 0.260073, No Obj: 0.004355, Avg Recall: 0.438095, count: 105 -Region Avg IOU: 0.419460, Class: 1.000000, Obj: 0.269892, No Obj: 0.004106, Avg Recall: 0.448980, count: 98 -Region Avg IOU: 0.491806, Class: 1.000000, Obj: 0.251044, No Obj: 0.004491, Avg Recall: 0.595238, count: 84 -Region Avg IOU: 0.436297, Class: 1.000000, Obj: 0.258513, No Obj: 0.005163, Avg Recall: 0.458015, count: 131 -Region Avg IOU: 0.467892, Class: 1.000000, Obj: 0.227758, No Obj: 0.004664, Avg Recall: 0.470086, count: 117 -1155: 0.478911, 0.498883 avg, 0.001000 rate, 16.579576 seconds, 295680 images -Loaded: 4.367698 seconds -Region Avg IOU: 0.441266, Class: 1.000000, Obj: 0.235968, No Obj: 0.004280, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.472142, Class: 1.000000, Obj: 0.243897, No Obj: 0.004274, Avg Recall: 0.500000, count: 90 -Region Avg IOU: 0.398064, Class: 1.000000, Obj: 0.243049, No Obj: 0.004465, Avg Recall: 0.412281, count: 114 -Region Avg IOU: 0.453064, Class: 1.000000, Obj: 0.285142, No Obj: 0.004586, Avg Recall: 0.483516, count: 91 -Region Avg IOU: 0.421312, Class: 1.000000, Obj: 0.259054, No Obj: 0.004308, Avg Recall: 0.421053, count: 95 -Region Avg IOU: 0.466520, Class: 1.000000, Obj: 0.241710, No Obj: 0.004572, Avg Recall: 0.469565, count: 115 -Region Avg IOU: 0.423198, Class: 1.000000, Obj: 0.244791, No Obj: 0.004355, Avg Recall: 0.418367, count: 98 -Region Avg IOU: 0.435326, Class: 1.000000, Obj: 0.264672, No Obj: 0.004475, Avg Recall: 0.495652, count: 115 -1156: 0.452501, 0.494245 avg, 0.001000 rate, 16.450274 seconds, 295936 images -Loaded: 3.909307 seconds -Region Avg IOU: 0.460974, Class: 1.000000, Obj: 0.276873, No Obj: 0.004718, Avg Recall: 0.504587, count: 109 -Region Avg IOU: 0.427655, Class: 1.000000, Obj: 0.201820, No Obj: 0.004879, Avg Recall: 0.460870, count: 115 -Region Avg IOU: 0.430592, Class: 1.000000, Obj: 0.252679, No Obj: 0.005107, Avg Recall: 0.451852, count: 135 -Region Avg IOU: 0.420520, Class: 1.000000, Obj: 0.240732, No Obj: 0.004531, Avg Recall: 0.381356, count: 118 -Region Avg IOU: 0.441176, Class: 1.000000, Obj: 0.219992, No Obj: 0.004611, Avg Recall: 0.434426, count: 122 -Region Avg IOU: 0.452116, Class: 1.000000, Obj: 0.224720, No Obj: 0.003960, Avg Recall: 0.436782, count: 87 -Region Avg IOU: 0.411648, Class: 1.000000, Obj: 0.235687, No Obj: 0.004177, Avg Recall: 0.411765, count: 102 -Region Avg IOU: 0.421103, Class: 1.000000, Obj: 0.219480, No Obj: 0.004658, Avg Recall: 0.440678, count: 118 -1157: 0.507699, 0.495590 avg, 0.001000 rate, 18.879240 seconds, 296192 images -Loaded: 2.548308 seconds -Region Avg IOU: 0.480434, Class: 1.000000, Obj: 0.251455, No Obj: 0.004316, Avg Recall: 0.505050, count: 99 -Region Avg IOU: 0.473237, Class: 1.000000, Obj: 0.265548, No Obj: 0.005112, Avg Recall: 0.537815, count: 119 -Region Avg IOU: 0.446951, Class: 1.000000, Obj: 0.272079, No Obj: 0.004016, Avg Recall: 0.486486, count: 74 -Region Avg IOU: 0.428980, Class: 1.000000, Obj: 0.250506, No Obj: 0.004912, Avg Recall: 0.454545, count: 121 -Region Avg IOU: 0.436979, Class: 1.000000, Obj: 0.289694, No Obj: 0.004440, Avg Recall: 0.468468, count: 111 -Region Avg IOU: 0.460400, Class: 1.000000, Obj: 0.281287, No Obj: 0.004777, Avg Recall: 0.500000, count: 126 -Region Avg IOU: 0.469209, Class: 1.000000, Obj: 0.243234, No Obj: 0.004319, Avg Recall: 0.547368, count: 95 -Region Avg IOU: 0.510665, Class: 1.000000, Obj: 0.286561, No Obj: 0.004586, Avg Recall: 0.558824, count: 102 -1158: 0.481605, 0.494192 avg, 0.001000 rate, 21.348068 seconds, 296448 images -Loaded: 1.360430 seconds -Region Avg IOU: 0.470323, Class: 1.000000, Obj: 0.275755, No Obj: 0.004095, Avg Recall: 0.481928, count: 83 -Region Avg IOU: 0.393902, Class: 1.000000, Obj: 0.252273, No Obj: 0.004520, Avg Recall: 0.358491, count: 106 -Region Avg IOU: 0.455848, Class: 1.000000, Obj: 0.229224, No Obj: 0.004241, Avg Recall: 0.571429, count: 84 -Region Avg IOU: 0.450162, Class: 1.000000, Obj: 0.243943, No Obj: 0.005454, Avg Recall: 0.478571, count: 140 -Region Avg IOU: 0.453844, Class: 1.000000, Obj: 0.277626, No Obj: 0.004578, Avg Recall: 0.490000, count: 100 -Region Avg IOU: 0.404365, Class: 1.000000, Obj: 0.230805, No Obj: 0.004829, Avg Recall: 0.387931, count: 116 -Region Avg IOU: 0.459201, Class: 1.000000, Obj: 0.252202, No Obj: 0.004778, Avg Recall: 0.440367, count: 109 -Region Avg IOU: 0.421575, Class: 1.000000, Obj: 0.232419, No Obj: 0.004535, Avg Recall: 0.416667, count: 108 -1159: 0.505908, 0.495363 avg, 0.001000 rate, 19.500813 seconds, 296704 images -Loaded: 0.676312 seconds -Region Avg IOU: 0.405457, Class: 1.000000, Obj: 0.244264, No Obj: 0.004301, Avg Recall: 0.433071, count: 127 -Region Avg IOU: 0.445839, Class: 1.000000, Obj: 0.262906, No Obj: 0.005151, Avg Recall: 0.465517, count: 116 -Region Avg IOU: 0.480237, Class: 1.000000, Obj: 0.247283, No Obj: 0.004821, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.406699, Class: 1.000000, Obj: 0.235977, No Obj: 0.004272, Avg Recall: 0.436364, count: 110 -Region Avg IOU: 0.480048, Class: 1.000000, Obj: 0.243052, No Obj: 0.004677, Avg Recall: 0.485714, count: 105 -Region Avg IOU: 0.439967, Class: 1.000000, Obj: 0.239272, No Obj: 0.004857, Avg Recall: 0.454545, count: 121 -Region Avg IOU: 0.414110, Class: 1.000000, Obj: 0.227594, No Obj: 0.004218, Avg Recall: 0.421569, count: 102 -Region Avg IOU: 0.433808, Class: 1.000000, Obj: 0.270827, No Obj: 0.004611, Avg Recall: 0.463636, count: 110 -1160: 0.489822, 0.494809 avg, 0.001000 rate, 18.783768 seconds, 296960 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000077 seconds -Region Avg IOU: 0.570850, Class: 1.000000, Obj: 0.317726, No Obj: 0.003830, Avg Recall: 0.664122, count: 131 -Region Avg IOU: 0.531174, Class: 1.000000, Obj: 0.242069, No Obj: 0.003571, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.545250, Class: 1.000000, Obj: 0.275297, No Obj: 0.003618, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.544407, Class: 1.000000, Obj: 0.285497, No Obj: 0.003582, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.544609, Class: 1.000000, Obj: 0.304332, No Obj: 0.003659, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.581842, Class: 1.000000, Obj: 0.259346, No Obj: 0.003653, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.545388, Class: 1.000000, Obj: 0.302130, No Obj: 0.003356, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.563291, Class: 1.000000, Obj: 0.271825, No Obj: 0.003863, Avg Recall: 0.691729, count: 133 -1161: 0.520459, 0.497374 avg, 0.001000 rate, 29.233732 seconds, 297216 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.592665, Class: 1.000000, Obj: 0.251990, No Obj: 0.003352, Avg Recall: 0.738462, count: 130 -Region Avg IOU: 0.574517, Class: 1.000000, Obj: 0.295681, No Obj: 0.003752, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.561720, Class: 1.000000, Obj: 0.287060, No Obj: 0.003733, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.591622, Class: 1.000000, Obj: 0.361208, No Obj: 0.003534, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.580063, Class: 1.000000, Obj: 0.295420, No Obj: 0.003597, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.600810, Class: 1.000000, Obj: 0.283342, No Obj: 0.003751, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.530568, Class: 1.000000, Obj: 0.227795, No Obj: 0.003388, Avg Recall: 0.616000, count: 125 -Region Avg IOU: 0.542084, Class: 1.000000, Obj: 0.289250, No Obj: 0.003808, Avg Recall: 0.647482, count: 139 -1162: 0.569244, 0.504561 avg, 0.001000 rate, 29.446728 seconds, 297472 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.595266, Class: 1.000000, Obj: 0.277659, No Obj: 0.003437, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.525153, Class: 1.000000, Obj: 0.253167, No Obj: 0.002953, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.607417, Class: 1.000000, Obj: 0.280013, No Obj: 0.003179, Avg Recall: 0.788889, count: 90 -Region Avg IOU: 0.612753, Class: 1.000000, Obj: 0.327908, No Obj: 0.003095, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.607639, Class: 1.000000, Obj: 0.296501, No Obj: 0.003728, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.524392, Class: 1.000000, Obj: 0.267924, No Obj: 0.002777, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.618943, Class: 1.000000, Obj: 0.299217, No Obj: 0.003170, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.572407, Class: 1.000000, Obj: 0.288834, No Obj: 0.003386, Avg Recall: 0.684685, count: 111 -1163: 0.480454, 0.502150 avg, 0.001000 rate, 28.090044 seconds, 297728 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.608214, Class: 1.000000, Obj: 0.299860, No Obj: 0.003174, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.577458, Class: 1.000000, Obj: 0.267049, No Obj: 0.003065, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.618369, Class: 1.000000, Obj: 0.247737, No Obj: 0.003224, Avg Recall: 0.733333, count: 120 -Region Avg IOU: 0.552672, Class: 1.000000, Obj: 0.206390, No Obj: 0.003341, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.539957, Class: 1.000000, Obj: 0.254646, No Obj: 0.003194, Avg Recall: 0.625000, count: 128 -Region Avg IOU: 0.588100, Class: 1.000000, Obj: 0.242978, No Obj: 0.003261, Avg Recall: 0.669291, count: 127 -Region Avg IOU: 0.541538, Class: 1.000000, Obj: 0.203639, No Obj: 0.002881, Avg Recall: 0.607843, count: 102 -Region Avg IOU: 0.573334, Class: 1.000000, Obj: 0.248274, No Obj: 0.003358, Avg Recall: 0.698113, count: 106 -1164: 0.560351, 0.507970 avg, 0.001000 rate, 28.339012 seconds, 297984 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.607507, Class: 1.000000, Obj: 0.229955, No Obj: 0.002764, Avg Recall: 0.762500, count: 80 -Region Avg IOU: 0.510057, Class: 1.000000, Obj: 0.196322, No Obj: 0.002921, Avg Recall: 0.561538, count: 130 -Region Avg IOU: 0.590763, Class: 1.000000, Obj: 0.236404, No Obj: 0.002952, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.602516, Class: 1.000000, Obj: 0.237953, No Obj: 0.002996, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.597131, Class: 1.000000, Obj: 0.204854, No Obj: 0.002938, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.585077, Class: 1.000000, Obj: 0.264714, No Obj: 0.003011, Avg Recall: 0.774194, count: 124 -Region Avg IOU: 0.572466, Class: 1.000000, Obj: 0.263084, No Obj: 0.002776, Avg Recall: 0.696629, count: 89 -Region Avg IOU: 0.590121, Class: 1.000000, Obj: 0.258742, No Obj: 0.003192, Avg Recall: 0.737864, count: 103 -1165: 0.513064, 0.508480 avg, 0.001000 rate, 27.852133 seconds, 298240 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.566437, Class: 1.000000, Obj: 0.239472, No Obj: 0.002885, Avg Recall: 0.691057, count: 123 -Region Avg IOU: 0.573097, Class: 1.000000, Obj: 0.223360, No Obj: 0.002911, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.626151, Class: 1.000000, Obj: 0.234402, No Obj: 0.003250, Avg Recall: 0.766129, count: 124 -Region Avg IOU: 0.564960, Class: 1.000000, Obj: 0.251422, No Obj: 0.002974, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.580193, Class: 1.000000, Obj: 0.294088, No Obj: 0.002918, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.589741, Class: 1.000000, Obj: 0.220253, No Obj: 0.002962, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.605927, Class: 1.000000, Obj: 0.190096, No Obj: 0.003241, Avg Recall: 0.776923, count: 130 -Region Avg IOU: 0.561737, Class: 1.000000, Obj: 0.211846, No Obj: 0.002772, Avg Recall: 0.670000, count: 100 -1166: 0.527386, 0.510370 avg, 0.001000 rate, 26.924540 seconds, 298496 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.540329, Class: 1.000000, Obj: 0.221483, No Obj: 0.002749, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.544229, Class: 1.000000, Obj: 0.241978, No Obj: 0.003194, Avg Recall: 0.641221, count: 131 -Region Avg IOU: 0.562059, Class: 1.000000, Obj: 0.262233, No Obj: 0.002881, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.551677, Class: 1.000000, Obj: 0.247005, No Obj: 0.002933, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.589253, Class: 1.000000, Obj: 0.224787, No Obj: 0.002872, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.626750, Class: 1.000000, Obj: 0.252232, No Obj: 0.002873, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.597762, Class: 1.000000, Obj: 0.304085, No Obj: 0.002663, Avg Recall: 0.712121, count: 66 -Region Avg IOU: 0.570807, Class: 1.000000, Obj: 0.213668, No Obj: 0.002975, Avg Recall: 0.704918, count: 122 -1167: 0.477927, 0.507126 avg, 0.001000 rate, 28.188879 seconds, 298752 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.604414, Class: 1.000000, Obj: 0.250625, No Obj: 0.003171, Avg Recall: 0.748201, count: 139 -Region Avg IOU: 0.612524, Class: 1.000000, Obj: 0.322645, No Obj: 0.003071, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.594738, Class: 1.000000, Obj: 0.239768, No Obj: 0.003063, Avg Recall: 0.786325, count: 117 -Region Avg IOU: 0.583204, Class: 1.000000, Obj: 0.208812, No Obj: 0.003297, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.581323, Class: 1.000000, Obj: 0.283005, No Obj: 0.003047, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.590114, Class: 1.000000, Obj: 0.264732, No Obj: 0.003411, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.577518, Class: 1.000000, Obj: 0.219552, No Obj: 0.003035, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.601071, Class: 1.000000, Obj: 0.235327, No Obj: 0.003092, Avg Recall: 0.750000, count: 108 -1168: 0.517649, 0.508178 avg, 0.001000 rate, 28.150482 seconds, 299008 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.590457, Class: 1.000000, Obj: 0.233941, No Obj: 0.003115, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.550122, Class: 1.000000, Obj: 0.247877, No Obj: 0.003009, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.576421, Class: 1.000000, Obj: 0.255964, No Obj: 0.002873, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.578367, Class: 1.000000, Obj: 0.260013, No Obj: 0.003304, Avg Recall: 0.685484, count: 124 -Region Avg IOU: 0.596822, Class: 1.000000, Obj: 0.266503, No Obj: 0.003459, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.597224, Class: 1.000000, Obj: 0.277409, No Obj: 0.003197, Avg Recall: 0.746032, count: 126 -Region Avg IOU: 0.593082, Class: 1.000000, Obj: 0.281960, No Obj: 0.003123, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.567983, Class: 1.000000, Obj: 0.269856, No Obj: 0.003113, Avg Recall: 0.653846, count: 104 -1169: 0.537551, 0.511116 avg, 0.001000 rate, 29.006495 seconds, 299264 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.615336, Class: 1.000000, Obj: 0.306468, No Obj: 0.003284, Avg Recall: 0.781250, count: 96 -Region Avg IOU: 0.599054, Class: 1.000000, Obj: 0.281651, No Obj: 0.003091, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.584953, Class: 1.000000, Obj: 0.337728, No Obj: 0.003208, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.611883, Class: 1.000000, Obj: 0.263432, No Obj: 0.003431, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.595427, Class: 1.000000, Obj: 0.302833, No Obj: 0.003307, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.575434, Class: 1.000000, Obj: 0.235331, No Obj: 0.003432, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.593011, Class: 1.000000, Obj: 0.277937, No Obj: 0.003544, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.549146, Class: 1.000000, Obj: 0.268420, No Obj: 0.003199, Avg Recall: 0.628099, count: 121 -1170: 0.512009, 0.511205 avg, 0.001000 rate, 28.516449 seconds, 299520 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 14.878473 seconds -Region Avg IOU: 0.518689, Class: 1.000000, Obj: 0.274451, No Obj: 0.003429, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.539857, Class: 1.000000, Obj: 0.304924, No Obj: 0.003964, Avg Recall: 0.600000, count: 135 -Region Avg IOU: 0.513319, Class: 1.000000, Obj: 0.265621, No Obj: 0.003329, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.454682, Class: 1.000000, Obj: 0.233417, No Obj: 0.003275, Avg Recall: 0.504132, count: 121 -Region Avg IOU: 0.535659, Class: 1.000000, Obj: 0.261580, No Obj: 0.003535, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.537601, Class: 1.000000, Obj: 0.279787, No Obj: 0.003790, Avg Recall: 0.587719, count: 114 -Region Avg IOU: 0.540653, Class: 1.000000, Obj: 0.312775, No Obj: 0.003678, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.533196, Class: 1.000000, Obj: 0.307145, No Obj: 0.003253, Avg Recall: 0.608696, count: 115 -1171: 0.516279, 0.511712 avg, 0.001000 rate, 24.453796 seconds, 299776 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.534106, Class: 1.000000, Obj: 0.297977, No Obj: 0.003438, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.552398, Class: 1.000000, Obj: 0.276494, No Obj: 0.003619, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.569977, Class: 1.000000, Obj: 0.272367, No Obj: 0.003326, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.555506, Class: 1.000000, Obj: 0.257290, No Obj: 0.003084, Avg Recall: 0.640449, count: 89 -Region Avg IOU: 0.555666, Class: 1.000000, Obj: 0.289962, No Obj: 0.003460, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.543679, Class: 1.000000, Obj: 0.284837, No Obj: 0.003552, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.539644, Class: 1.000000, Obj: 0.263211, No Obj: 0.003342, Avg Recall: 0.626374, count: 91 -Region Avg IOU: 0.514701, Class: 1.000000, Obj: 0.271749, No Obj: 0.003559, Avg Recall: 0.574074, count: 108 -1172: 0.484615, 0.509003 avg, 0.001000 rate, 25.301346 seconds, 300032 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.507110, Class: 1.000000, Obj: 0.258366, No Obj: 0.003480, Avg Recall: 0.558333, count: 120 -Region Avg IOU: 0.535872, Class: 1.000000, Obj: 0.274986, No Obj: 0.003521, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.484702, Class: 1.000000, Obj: 0.235903, No Obj: 0.003054, Avg Recall: 0.485149, count: 101 -Region Avg IOU: 0.551884, Class: 1.000000, Obj: 0.287629, No Obj: 0.003594, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.523595, Class: 1.000000, Obj: 0.247750, No Obj: 0.003092, Avg Recall: 0.500000, count: 108 -Region Avg IOU: 0.508422, Class: 1.000000, Obj: 0.235685, No Obj: 0.003344, Avg Recall: 0.606838, count: 117 -Region Avg IOU: 0.523822, Class: 1.000000, Obj: 0.275810, No Obj: 0.003216, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.534142, Class: 1.000000, Obj: 0.310236, No Obj: 0.003839, Avg Recall: 0.593496, count: 123 -1173: 0.524664, 0.510569 avg, 0.001000 rate, 25.744795 seconds, 300288 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.566530, Class: 1.000000, Obj: 0.289776, No Obj: 0.003394, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.471124, Class: 1.000000, Obj: 0.239430, No Obj: 0.003495, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.475472, Class: 1.000000, Obj: 0.278902, No Obj: 0.003338, Avg Recall: 0.516854, count: 89 -Region Avg IOU: 0.571012, Class: 1.000000, Obj: 0.323138, No Obj: 0.002847, Avg Recall: 0.662162, count: 74 -Region Avg IOU: 0.563329, Class: 1.000000, Obj: 0.216792, No Obj: 0.003650, Avg Recall: 0.680000, count: 125 -Region Avg IOU: 0.543434, Class: 1.000000, Obj: 0.287558, No Obj: 0.003486, Avg Recall: 0.613445, count: 119 -Region Avg IOU: 0.499961, Class: 1.000000, Obj: 0.214898, No Obj: 0.003200, Avg Recall: 0.589147, count: 129 -Region Avg IOU: 0.560821, Class: 1.000000, Obj: 0.283498, No Obj: 0.003350, Avg Recall: 0.619048, count: 105 -1174: 0.494859, 0.508998 avg, 0.001000 rate, 24.667128 seconds, 300544 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.503032, Class: 1.000000, Obj: 0.250871, No Obj: 0.003349, Avg Recall: 0.546219, count: 119 -Region Avg IOU: 0.578344, Class: 1.000000, Obj: 0.309210, No Obj: 0.003101, Avg Recall: 0.728571, count: 70 -Region Avg IOU: 0.496551, Class: 1.000000, Obj: 0.237951, No Obj: 0.003104, Avg Recall: 0.522222, count: 90 -Region Avg IOU: 0.572465, Class: 1.000000, Obj: 0.256700, No Obj: 0.003662, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.538420, Class: 1.000000, Obj: 0.256756, No Obj: 0.003323, Avg Recall: 0.632479, count: 117 -Region Avg IOU: 0.579479, Class: 1.000000, Obj: 0.270565, No Obj: 0.003431, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.557858, Class: 1.000000, Obj: 0.286687, No Obj: 0.003060, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.506308, Class: 1.000000, Obj: 0.269381, No Obj: 0.002996, Avg Recall: 0.521739, count: 92 -1175: 0.434952, 0.501593 avg, 0.001000 rate, 25.382223 seconds, 300800 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.542685, Class: 1.000000, Obj: 0.260007, No Obj: 0.003491, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.528329, Class: 1.000000, Obj: 0.270150, No Obj: 0.003245, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.554871, Class: 1.000000, Obj: 0.278325, No Obj: 0.003584, Avg Recall: 0.650794, count: 126 -Region Avg IOU: 0.540313, Class: 1.000000, Obj: 0.267898, No Obj: 0.003040, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.535757, Class: 1.000000, Obj: 0.245662, No Obj: 0.003374, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.492631, Class: 1.000000, Obj: 0.234522, No Obj: 0.003386, Avg Recall: 0.571429, count: 133 -Region Avg IOU: 0.510175, Class: 1.000000, Obj: 0.246475, No Obj: 0.002849, Avg Recall: 0.617647, count: 68 -Region Avg IOU: 0.537331, Class: 1.000000, Obj: 0.279810, No Obj: 0.003379, Avg Recall: 0.647059, count: 85 -1176: 0.501724, 0.501606 avg, 0.001000 rate, 24.274721 seconds, 301056 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.518642, Class: 1.000000, Obj: 0.270320, No Obj: 0.003197, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.535905, Class: 1.000000, Obj: 0.274060, No Obj: 0.003446, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.503565, Class: 1.000000, Obj: 0.229372, No Obj: 0.002980, Avg Recall: 0.576577, count: 111 -Region Avg IOU: 0.564089, Class: 1.000000, Obj: 0.257062, No Obj: 0.003261, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.547018, Class: 1.000000, Obj: 0.267509, No Obj: 0.003465, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.492094, Class: 1.000000, Obj: 0.248546, No Obj: 0.003037, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.540751, Class: 1.000000, Obj: 0.260682, No Obj: 0.002866, Avg Recall: 0.627907, count: 86 -Region Avg IOU: 0.534145, Class: 1.000000, Obj: 0.286539, No Obj: 0.002794, Avg Recall: 0.558442, count: 77 -1177: 0.421764, 0.493622 avg, 0.001000 rate, 24.657349 seconds, 301312 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.562962, Class: 1.000000, Obj: 0.289883, No Obj: 0.003720, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.511562, Class: 1.000000, Obj: 0.249458, No Obj: 0.003523, Avg Recall: 0.601626, count: 123 -Region Avg IOU: 0.549126, Class: 1.000000, Obj: 0.297868, No Obj: 0.003510, Avg Recall: 0.650406, count: 123 -Region Avg IOU: 0.460185, Class: 1.000000, Obj: 0.210298, No Obj: 0.002753, Avg Recall: 0.484536, count: 97 -Region Avg IOU: 0.527200, Class: 1.000000, Obj: 0.292155, No Obj: 0.003521, Avg Recall: 0.592308, count: 130 -Region Avg IOU: 0.561841, Class: 1.000000, Obj: 0.309760, No Obj: 0.003128, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.556574, Class: 1.000000, Obj: 0.300253, No Obj: 0.003328, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.577922, Class: 1.000000, Obj: 0.286911, No Obj: 0.003239, Avg Recall: 0.683673, count: 98 -1178: 0.509574, 0.495217 avg, 0.001000 rate, 23.382860 seconds, 301568 images -Loaded: 0.000129 seconds -Region Avg IOU: 0.547737, Class: 1.000000, Obj: 0.258243, No Obj: 0.003879, Avg Recall: 0.628571, count: 140 -Region Avg IOU: 0.534321, Class: 1.000000, Obj: 0.254883, No Obj: 0.003251, Avg Recall: 0.674699, count: 83 -Region Avg IOU: 0.515573, Class: 1.000000, Obj: 0.253991, No Obj: 0.003448, Avg Recall: 0.589744, count: 117 -Region Avg IOU: 0.515398, Class: 1.000000, Obj: 0.274313, No Obj: 0.002870, Avg Recall: 0.581081, count: 74 -Region Avg IOU: 0.556013, Class: 1.000000, Obj: 0.286111, No Obj: 0.003338, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.544259, Class: 1.000000, Obj: 0.252312, No Obj: 0.003139, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.565266, Class: 1.000000, Obj: 0.264674, No Obj: 0.003220, Avg Recall: 0.673684, count: 95 -Region Avg IOU: 0.531865, Class: 1.000000, Obj: 0.299344, No Obj: 0.003505, Avg Recall: 0.603604, count: 111 -1179: 0.484973, 0.494193 avg, 0.001000 rate, 24.662064 seconds, 301824 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.498072, Class: 1.000000, Obj: 0.248923, No Obj: 0.003712, Avg Recall: 0.571429, count: 126 -Region Avg IOU: 0.534008, Class: 1.000000, Obj: 0.268620, No Obj: 0.003437, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.560135, Class: 1.000000, Obj: 0.273813, No Obj: 0.003932, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.514685, Class: 1.000000, Obj: 0.254364, No Obj: 0.002884, Avg Recall: 0.613636, count: 88 -Region Avg IOU: 0.540152, Class: 1.000000, Obj: 0.282092, No Obj: 0.004029, Avg Recall: 0.655462, count: 119 -Region Avg IOU: 0.555094, Class: 1.000000, Obj: 0.269870, No Obj: 0.003427, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.559669, Class: 1.000000, Obj: 0.276746, No Obj: 0.003665, Avg Recall: 0.693182, count: 88 -Region Avg IOU: 0.544891, Class: 1.000000, Obj: 0.290651, No Obj: 0.003508, Avg Recall: 0.633929, count: 112 -1180: 0.535320, 0.498305 avg, 0.001000 rate, 25.612236 seconds, 302080 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000065 seconds -Region Avg IOU: 0.611141, Class: 1.000000, Obj: 0.301382, No Obj: 0.003195, Avg Recall: 0.761364, count: 88 -Region Avg IOU: 0.555142, Class: 1.000000, Obj: 0.260333, No Obj: 0.003565, Avg Recall: 0.681481, count: 135 -Region Avg IOU: 0.577136, Class: 1.000000, Obj: 0.298945, No Obj: 0.003021, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.580545, Class: 1.000000, Obj: 0.298587, No Obj: 0.003164, Avg Recall: 0.702290, count: 131 -Region Avg IOU: 0.577128, Class: 1.000000, Obj: 0.326340, No Obj: 0.003493, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.563596, Class: 1.000000, Obj: 0.266177, No Obj: 0.003047, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.603559, Class: 1.000000, Obj: 0.325436, No Obj: 0.003455, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.624043, Class: 1.000000, Obj: 0.298715, No Obj: 0.003698, Avg Recall: 0.787037, count: 108 -1181: 0.495739, 0.498049 avg, 0.001000 rate, 26.496161 seconds, 302336 images -Loaded: 0.000117 seconds -Region Avg IOU: 0.576281, Class: 1.000000, Obj: 0.305578, No Obj: 0.003077, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.576817, Class: 1.000000, Obj: 0.272175, No Obj: 0.003181, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.603084, Class: 1.000000, Obj: 0.285553, No Obj: 0.003124, Avg Recall: 0.725275, count: 91 -Region Avg IOU: 0.594789, Class: 1.000000, Obj: 0.299895, No Obj: 0.002960, Avg Recall: 0.722222, count: 90 -Region Avg IOU: 0.567634, Class: 1.000000, Obj: 0.282996, No Obj: 0.003025, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.532427, Class: 1.000000, Obj: 0.283921, No Obj: 0.003273, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.593603, Class: 1.000000, Obj: 0.325757, No Obj: 0.003019, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.560639, Class: 1.000000, Obj: 0.292555, No Obj: 0.002970, Avg Recall: 0.705882, count: 85 -1182: 0.478459, 0.496090 avg, 0.001000 rate, 28.381140 seconds, 302592 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.577880, Class: 1.000000, Obj: 0.298914, No Obj: 0.002915, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.591956, Class: 1.000000, Obj: 0.264379, No Obj: 0.002743, Avg Recall: 0.714286, count: 84 -Region Avg IOU: 0.564644, Class: 1.000000, Obj: 0.283345, No Obj: 0.002558, Avg Recall: 0.740741, count: 81 -Region Avg IOU: 0.618803, Class: 1.000000, Obj: 0.282610, No Obj: 0.003155, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.567966, Class: 1.000000, Obj: 0.227912, No Obj: 0.003208, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.543923, Class: 1.000000, Obj: 0.279250, No Obj: 0.003034, Avg Recall: 0.661871, count: 139 -Region Avg IOU: 0.593305, Class: 1.000000, Obj: 0.256570, No Obj: 0.003325, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.580498, Class: 1.000000, Obj: 0.248335, No Obj: 0.003154, Avg Recall: 0.697987, count: 149 -1183: 0.527098, 0.499191 avg, 0.001000 rate, 28.866058 seconds, 302848 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.561127, Class: 1.000000, Obj: 0.224026, No Obj: 0.003008, Avg Recall: 0.669291, count: 127 -Region Avg IOU: 0.611811, Class: 1.000000, Obj: 0.263613, No Obj: 0.002817, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.545748, Class: 1.000000, Obj: 0.227368, No Obj: 0.002836, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.559665, Class: 1.000000, Obj: 0.259950, No Obj: 0.002648, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.505232, Class: 1.000000, Obj: 0.226430, No Obj: 0.002608, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.569658, Class: 1.000000, Obj: 0.271510, No Obj: 0.002639, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.580752, Class: 1.000000, Obj: 0.274806, No Obj: 0.002683, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.587095, Class: 1.000000, Obj: 0.222780, No Obj: 0.003139, Avg Recall: 0.727273, count: 132 -1184: 0.519426, 0.501214 avg, 0.001000 rate, 27.514206 seconds, 303104 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.567910, Class: 1.000000, Obj: 0.275309, No Obj: 0.002818, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.582986, Class: 1.000000, Obj: 0.294209, No Obj: 0.002836, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.579125, Class: 1.000000, Obj: 0.272189, No Obj: 0.002834, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.585710, Class: 1.000000, Obj: 0.238757, No Obj: 0.002806, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.570266, Class: 1.000000, Obj: 0.236837, No Obj: 0.002708, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.582979, Class: 1.000000, Obj: 0.260651, No Obj: 0.002963, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.546474, Class: 1.000000, Obj: 0.245973, No Obj: 0.002403, Avg Recall: 0.616279, count: 86 -Region Avg IOU: 0.539561, Class: 1.000000, Obj: 0.191533, No Obj: 0.002968, Avg Recall: 0.642336, count: 137 -1185: 0.489841, 0.500077 avg, 0.001000 rate, 28.682844 seconds, 303360 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.585757, Class: 1.000000, Obj: 0.232890, No Obj: 0.002700, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.535894, Class: 1.000000, Obj: 0.235508, No Obj: 0.002699, Avg Recall: 0.600000, count: 125 -Region Avg IOU: 0.548512, Class: 1.000000, Obj: 0.252137, No Obj: 0.002465, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.590679, Class: 1.000000, Obj: 0.260696, No Obj: 0.002737, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.581402, Class: 1.000000, Obj: 0.244126, No Obj: 0.002808, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.622349, Class: 1.000000, Obj: 0.248768, No Obj: 0.002842, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.575722, Class: 1.000000, Obj: 0.246453, No Obj: 0.002656, Avg Recall: 0.630952, count: 84 -Region Avg IOU: 0.609338, Class: 1.000000, Obj: 0.249849, No Obj: 0.002974, Avg Recall: 0.754545, count: 110 -1186: 0.466388, 0.496708 avg, 0.001000 rate, 28.819471 seconds, 303616 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.553176, Class: 1.000000, Obj: 0.224638, No Obj: 0.002664, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.572094, Class: 1.000000, Obj: 0.254779, No Obj: 0.002732, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.626753, Class: 1.000000, Obj: 0.301882, No Obj: 0.003091, Avg Recall: 0.778761, count: 113 -Region Avg IOU: 0.565446, Class: 1.000000, Obj: 0.268434, No Obj: 0.003099, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.579703, Class: 1.000000, Obj: 0.260460, No Obj: 0.002668, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.617496, Class: 1.000000, Obj: 0.249711, No Obj: 0.002898, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.560490, Class: 1.000000, Obj: 0.283043, No Obj: 0.002798, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.588588, Class: 1.000000, Obj: 0.279265, No Obj: 0.003042, Avg Recall: 0.685185, count: 108 -1187: 0.538041, 0.500841 avg, 0.001000 rate, 28.839409 seconds, 303872 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.613242, Class: 1.000000, Obj: 0.278175, No Obj: 0.002776, Avg Recall: 0.733333, count: 75 -Region Avg IOU: 0.572412, Class: 1.000000, Obj: 0.222865, No Obj: 0.003098, Avg Recall: 0.708661, count: 127 -Region Avg IOU: 0.599488, Class: 1.000000, Obj: 0.253019, No Obj: 0.003158, Avg Recall: 0.746154, count: 130 -Region Avg IOU: 0.619266, Class: 1.000000, Obj: 0.310548, No Obj: 0.002784, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.617113, Class: 1.000000, Obj: 0.223149, No Obj: 0.003115, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.629644, Class: 1.000000, Obj: 0.239028, No Obj: 0.003199, Avg Recall: 0.752066, count: 121 -Region Avg IOU: 0.594226, Class: 1.000000, Obj: 0.243207, No Obj: 0.003186, Avg Recall: 0.722222, count: 144 -Region Avg IOU: 0.559739, Class: 1.000000, Obj: 0.237296, No Obj: 0.002556, Avg Recall: 0.649485, count: 97 -1188: 0.595372, 0.510294 avg, 0.001000 rate, 28.042540 seconds, 304128 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.607513, Class: 1.000000, Obj: 0.293740, No Obj: 0.002995, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.586103, Class: 1.000000, Obj: 0.253387, No Obj: 0.003145, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.592395, Class: 1.000000, Obj: 0.269513, No Obj: 0.002986, Avg Recall: 0.733333, count: 120 -Region Avg IOU: 0.600856, Class: 1.000000, Obj: 0.330813, No Obj: 0.002980, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.594622, Class: 1.000000, Obj: 0.270044, No Obj: 0.003231, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.594829, Class: 1.000000, Obj: 0.179287, No Obj: 0.002966, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.611360, Class: 1.000000, Obj: 0.252319, No Obj: 0.002695, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.583685, Class: 1.000000, Obj: 0.274507, No Obj: 0.002811, Avg Recall: 0.690722, count: 97 -1189: 0.478593, 0.507124 avg, 0.001000 rate, 29.519611 seconds, 304384 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.602826, Class: 1.000000, Obj: 0.278217, No Obj: 0.003015, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.606508, Class: 1.000000, Obj: 0.303842, No Obj: 0.003375, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.566754, Class: 1.000000, Obj: 0.250280, No Obj: 0.003066, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.605340, Class: 1.000000, Obj: 0.285241, No Obj: 0.002931, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.540350, Class: 1.000000, Obj: 0.251200, No Obj: 0.003049, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.573359, Class: 1.000000, Obj: 0.270087, No Obj: 0.003246, Avg Recall: 0.688525, count: 122 -Region Avg IOU: 0.624505, Class: 1.000000, Obj: 0.311439, No Obj: 0.002936, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.582406, Class: 1.000000, Obj: 0.293227, No Obj: 0.003198, Avg Recall: 0.721739, count: 115 -1190: 0.528271, 0.509239 avg, 0.001000 rate, 28.120176 seconds, 304640 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 15.164802 seconds -Region Avg IOU: 0.503481, Class: 1.000000, Obj: 0.253108, No Obj: 0.003473, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.602834, Class: 1.000000, Obj: 0.322954, No Obj: 0.003722, Avg Recall: 0.701613, count: 124 -Region Avg IOU: 0.544084, Class: 1.000000, Obj: 0.266806, No Obj: 0.003290, Avg Recall: 0.630952, count: 84 -Region Avg IOU: 0.554248, Class: 1.000000, Obj: 0.269509, No Obj: 0.003955, Avg Recall: 0.625954, count: 131 -Region Avg IOU: 0.533259, Class: 1.000000, Obj: 0.315488, No Obj: 0.003585, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.508091, Class: 1.000000, Obj: 0.313194, No Obj: 0.003508, Avg Recall: 0.560748, count: 107 -Region Avg IOU: 0.557307, Class: 1.000000, Obj: 0.278389, No Obj: 0.003655, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.557451, Class: 1.000000, Obj: 0.344390, No Obj: 0.003432, Avg Recall: 0.657407, count: 108 -1191: 0.484316, 0.506747 avg, 0.001000 rate, 25.003052 seconds, 304896 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.525375, Class: 1.000000, Obj: 0.294399, No Obj: 0.003555, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.585067, Class: 1.000000, Obj: 0.294643, No Obj: 0.004053, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.570760, Class: 1.000000, Obj: 0.332784, No Obj: 0.003269, Avg Recall: 0.647727, count: 88 -Region Avg IOU: 0.526615, Class: 1.000000, Obj: 0.258129, No Obj: 0.003229, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.559556, Class: 1.000000, Obj: 0.311840, No Obj: 0.004184, Avg Recall: 0.666667, count: 147 -Region Avg IOU: 0.544191, Class: 1.000000, Obj: 0.315965, No Obj: 0.003732, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.561112, Class: 1.000000, Obj: 0.280740, No Obj: 0.003507, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.501916, Class: 1.000000, Obj: 0.299864, No Obj: 0.003353, Avg Recall: 0.580645, count: 124 -1192: 0.496230, 0.505695 avg, 0.001000 rate, 24.661470 seconds, 305152 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.539541, Class: 1.000000, Obj: 0.300135, No Obj: 0.003836, Avg Recall: 0.656250, count: 128 -Region Avg IOU: 0.542410, Class: 1.000000, Obj: 0.309944, No Obj: 0.003532, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.547717, Class: 1.000000, Obj: 0.318332, No Obj: 0.003264, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.509767, Class: 1.000000, Obj: 0.298749, No Obj: 0.003590, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.574428, Class: 1.000000, Obj: 0.319298, No Obj: 0.003318, Avg Recall: 0.666667, count: 78 -Region Avg IOU: 0.501191, Class: 1.000000, Obj: 0.223336, No Obj: 0.003226, Avg Recall: 0.620690, count: 87 -Region Avg IOU: 0.564628, Class: 1.000000, Obj: 0.307814, No Obj: 0.003618, Avg Recall: 0.652542, count: 118 -Region Avg IOU: 0.489983, Class: 1.000000, Obj: 0.253100, No Obj: 0.003087, Avg Recall: 0.574713, count: 87 -1193: 0.474010, 0.502526 avg, 0.001000 rate, 25.490925 seconds, 305408 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.568914, Class: 1.000000, Obj: 0.340370, No Obj: 0.003589, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.559160, Class: 1.000000, Obj: 0.311588, No Obj: 0.003634, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.565713, Class: 1.000000, Obj: 0.347845, No Obj: 0.003164, Avg Recall: 0.705128, count: 78 -Region Avg IOU: 0.552940, Class: 1.000000, Obj: 0.334672, No Obj: 0.003263, Avg Recall: 0.630952, count: 84 -Region Avg IOU: 0.494809, Class: 1.000000, Obj: 0.296746, No Obj: 0.003773, Avg Recall: 0.542373, count: 118 -Region Avg IOU: 0.508222, Class: 1.000000, Obj: 0.271020, No Obj: 0.003599, Avg Recall: 0.632812, count: 128 -Region Avg IOU: 0.535644, Class: 1.000000, Obj: 0.325456, No Obj: 0.003965, Avg Recall: 0.591241, count: 137 -Region Avg IOU: 0.514982, Class: 1.000000, Obj: 0.292766, No Obj: 0.003228, Avg Recall: 0.617978, count: 89 -1194: 0.465718, 0.498846 avg, 0.001000 rate, 24.582432 seconds, 305664 images -Loaded: 0.000157 seconds -Region Avg IOU: 0.532313, Class: 1.000000, Obj: 0.314426, No Obj: 0.003192, Avg Recall: 0.639535, count: 86 -Region Avg IOU: 0.521257, Class: 1.000000, Obj: 0.277952, No Obj: 0.003198, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.597424, Class: 1.000000, Obj: 0.285986, No Obj: 0.002948, Avg Recall: 0.728395, count: 81 -Region Avg IOU: 0.573643, Class: 1.000000, Obj: 0.328766, No Obj: 0.003271, Avg Recall: 0.750000, count: 80 -Region Avg IOU: 0.509136, Class: 1.000000, Obj: 0.296917, No Obj: 0.003057, Avg Recall: 0.562500, count: 96 -Region Avg IOU: 0.498232, Class: 1.000000, Obj: 0.245218, No Obj: 0.003470, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.536749, Class: 1.000000, Obj: 0.251042, No Obj: 0.003861, Avg Recall: 0.624113, count: 141 -Region Avg IOU: 0.495769, Class: 1.000000, Obj: 0.282392, No Obj: 0.003419, Avg Recall: 0.540741, count: 135 -1195: 0.478295, 0.496791 avg, 0.001000 rate, 26.200966 seconds, 305920 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.516241, Class: 1.000000, Obj: 0.291746, No Obj: 0.003069, Avg Recall: 0.583333, count: 96 -Region Avg IOU: 0.522848, Class: 1.000000, Obj: 0.269182, No Obj: 0.003102, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.565417, Class: 1.000000, Obj: 0.302617, No Obj: 0.003494, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.500972, Class: 1.000000, Obj: 0.239680, No Obj: 0.003070, Avg Recall: 0.563636, count: 110 -Region Avg IOU: 0.554707, Class: 1.000000, Obj: 0.276148, No Obj: 0.003347, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.530117, Class: 1.000000, Obj: 0.297593, No Obj: 0.003093, Avg Recall: 0.597561, count: 82 -Region Avg IOU: 0.514545, Class: 1.000000, Obj: 0.226983, No Obj: 0.003092, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.525197, Class: 1.000000, Obj: 0.272801, No Obj: 0.003306, Avg Recall: 0.605263, count: 114 -1196: 0.504511, 0.497563 avg, 0.001000 rate, 25.632019 seconds, 306176 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.568985, Class: 1.000000, Obj: 0.259783, No Obj: 0.003315, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.501173, Class: 1.000000, Obj: 0.264557, No Obj: 0.003629, Avg Recall: 0.592593, count: 135 -Region Avg IOU: 0.574505, Class: 1.000000, Obj: 0.287979, No Obj: 0.003068, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.533034, Class: 1.000000, Obj: 0.279021, No Obj: 0.003216, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.547421, Class: 1.000000, Obj: 0.279592, No Obj: 0.003559, Avg Recall: 0.593220, count: 118 -Region Avg IOU: 0.552890, Class: 1.000000, Obj: 0.234384, No Obj: 0.003213, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.550830, Class: 1.000000, Obj: 0.263620, No Obj: 0.003583, Avg Recall: 0.666667, count: 120 -Region Avg IOU: 0.552227, Class: 1.000000, Obj: 0.277193, No Obj: 0.003323, Avg Recall: 0.631579, count: 114 -1197: 0.516997, 0.499506 avg, 0.001000 rate, 25.107828 seconds, 306432 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.510469, Class: 1.000000, Obj: 0.239845, No Obj: 0.003080, Avg Recall: 0.612903, count: 124 -Region Avg IOU: 0.501338, Class: 1.000000, Obj: 0.260320, No Obj: 0.002899, Saving weights to ./person9300/backup/tyvp_93_9c512_1200.weights -Avg Recall: 0.539474, count: 76 -Region Avg IOU: 0.571846, Class: 1.000000, Obj: 0.262528, No Obj: 0.003360, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.557372, Class: 1.000000, Obj: 0.245389, No Obj: 0.003326, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.539734, Class: 1.000000, Obj: 0.271746, No Obj: 0.003442, Avg Recall: 0.650000, count: 120 -Region Avg IOU: 0.558835, Class: 1.000000, Obj: 0.255935, No Obj: 0.003374, Avg Recall: 0.634921, count: 126 -Region Avg IOU: 0.499777, Class: 1.000000, Obj: 0.249729, No Obj: 0.003454, Avg Recall: 0.549180, count: 122 -Region Avg IOU: 0.510092, Class: 1.000000, Obj: 0.266541, No Obj: 0.003021, Avg Recall: 0.603604, count: 111 -1198: 0.529734, 0.502529 avg, 0.001000 rate, 24.680548 seconds, 306688 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.506242, Class: 1.000000, Obj: 0.225257, No Obj: 0.003094, Avg Recall: 0.609023, count: 133 -Region Avg IOU: 0.568551, Class: 1.000000, Obj: 0.263755, No Obj: 0.003468, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.535565, Class: 1.000000, Obj: 0.259833, No Obj: 0.003040, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.568568, Class: 1.000000, Obj: 0.272767, No Obj: 0.003233, Avg Recall: 0.662651, count: 83 -Region Avg IOU: 0.510614, Class: 1.000000, Obj: 0.249962, No Obj: 0.003086, Avg Recall: 0.589474, count: 95 -Region Avg IOU: 0.523013, Class: 1.000000, Obj: 0.284285, No Obj: 0.003082, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.541627, Class: 1.000000, Obj: 0.277230, No Obj: 0.003119, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.516354, Class: 1.000000, Obj: 0.275043, No Obj: 0.003633, Avg Recall: 0.596899, count: 129 -1199: 0.464949, 0.498771 avg, 0.001000 rate, 24.967009 seconds, 306944 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.494319, Class: 1.000000, Obj: 0.236349, No Obj: 0.003255, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.569478, Class: 1.000000, Obj: 0.301708, No Obj: 0.003198, Avg Recall: 0.659091, count: 88 -Region Avg IOU: 0.520483, Class: 1.000000, Obj: 0.262794, No Obj: 0.003590, Avg Recall: 0.576389, count: 144 -Region Avg IOU: 0.553136, Class: 1.000000, Obj: 0.296242, No Obj: 0.003607, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.523765, Class: 1.000000, Obj: 0.225445, No Obj: 0.003578, Avg Recall: 0.594203, count: 138 -Region Avg IOU: 0.522960, Class: 1.000000, Obj: 0.290212, No Obj: 0.003290, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.532351, Class: 1.000000, Obj: 0.287845, No Obj: 0.003452, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.570946, Class: 1.000000, Obj: 0.298135, No Obj: 0.002980, Avg Recall: 0.687500, count: 80 -1200: 0.489025, 0.497796 avg, 0.001000 rate, 25.014973 seconds, 307200 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000065 seconds -Region Avg IOU: 0.587746, Class: 1.000000, Obj: 0.282993, No Obj: 0.003200, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.605150, Class: 1.000000, Obj: 0.224112, No Obj: 0.002978, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.604183, Class: 1.000000, Obj: 0.300197, No Obj: 0.003285, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.582375, Class: 1.000000, Obj: 0.289626, No Obj: 0.003258, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.536456, Class: 1.000000, Obj: 0.298599, No Obj: 0.003300, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.610934, Class: 1.000000, Obj: 0.285670, No Obj: 0.003411, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.598104, Class: 1.000000, Obj: 0.290273, No Obj: 0.003246, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.571159, Class: 1.000000, Obj: 0.272051, No Obj: 0.003400, Avg Recall: 0.644444, count: 135 -1201: 0.525174, 0.500534 avg, 0.001000 rate, 28.415171 seconds, 307456 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.553753, Class: 1.000000, Obj: 0.284736, No Obj: 0.003748, Avg Recall: 0.614865, count: 148 -Region Avg IOU: 0.598718, Class: 1.000000, Obj: 0.319774, No Obj: 0.003325, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.587259, Class: 1.000000, Obj: 0.288201, No Obj: 0.003730, Avg Recall: 0.714286, count: 119 -Region Avg IOU: 0.582652, Class: 1.000000, Obj: 0.290633, No Obj: 0.003613, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.568518, Class: 1.000000, Obj: 0.310454, No Obj: 0.003404, Avg Recall: 0.669421, count: 121 -Region Avg IOU: 0.548466, Class: 1.000000, Obj: 0.344310, No Obj: 0.003407, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.572660, Class: 1.000000, Obj: 0.338214, No Obj: 0.003280, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.568577, Class: 1.000000, Obj: 0.312458, No Obj: 0.003511, Avg Recall: 0.681818, count: 88 -1202: 0.529633, 0.503444 avg, 0.001000 rate, 26.908587 seconds, 307712 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.601970, Class: 1.000000, Obj: 0.319014, No Obj: 0.003322, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.608453, Class: 1.000000, Obj: 0.295976, No Obj: 0.003691, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.561532, Class: 1.000000, Obj: 0.271912, No Obj: 0.003079, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.609986, Class: 1.000000, Obj: 0.303938, No Obj: 0.003345, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.558779, Class: 1.000000, Obj: 0.276002, No Obj: 0.003488, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.568841, Class: 1.000000, Obj: 0.288484, No Obj: 0.003083, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.572858, Class: 1.000000, Obj: 0.319461, No Obj: 0.003204, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.617453, Class: 1.000000, Obj: 0.331019, No Obj: 0.003293, Avg Recall: 0.771739, count: 92 -1203: 0.447801, 0.497880 avg, 0.001000 rate, 26.827412 seconds, 307968 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.555462, Class: 1.000000, Obj: 0.225866, No Obj: 0.003127, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.595511, Class: 1.000000, Obj: 0.310020, No Obj: 0.003148, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.567052, Class: 1.000000, Obj: 0.259882, No Obj: 0.002974, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.578037, Class: 1.000000, Obj: 0.272094, No Obj: 0.003165, Avg Recall: 0.650406, count: 123 -Region Avg IOU: 0.604431, Class: 1.000000, Obj: 0.250586, No Obj: 0.003385, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.572833, Class: 1.000000, Obj: 0.293862, No Obj: 0.002782, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.538236, Class: 1.000000, Obj: 0.303411, No Obj: 0.003274, Avg Recall: 0.595238, count: 126 -Region Avg IOU: 0.588961, Class: 1.000000, Obj: 0.289956, No Obj: 0.003123, Avg Recall: 0.726562, count: 128 -1204: 0.520122, 0.500104 avg, 0.001000 rate, 28.886265 seconds, 308224 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.555968, Class: 1.000000, Obj: 0.209221, No Obj: 0.003019, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.541139, Class: 1.000000, Obj: 0.277164, No Obj: 0.002894, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.586009, Class: 1.000000, Obj: 0.306603, No Obj: 0.003376, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.562635, Class: 1.000000, Obj: 0.281421, No Obj: 0.003277, Avg Recall: 0.659722, count: 144 -Region Avg IOU: 0.562284, Class: 1.000000, Obj: 0.261784, No Obj: 0.003626, Avg Recall: 0.662420, count: 157 -Region Avg IOU: 0.523430, Class: 1.000000, Obj: 0.259083, No Obj: 0.002890, Avg Recall: 0.592000, count: 125 -Region Avg IOU: 0.596635, Class: 1.000000, Obj: 0.285514, No Obj: 0.003434, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.529363, Class: 1.000000, Obj: 0.252656, No Obj: 0.003389, Avg Recall: 0.661157, count: 121 -1205: 0.539008, 0.503994 avg, 0.001000 rate, 28.465076 seconds, 308480 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.557748, Class: 1.000000, Obj: 0.240857, No Obj: 0.003139, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.571533, Class: 1.000000, Obj: 0.209515, No Obj: 0.003124, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.600888, Class: 1.000000, Obj: 0.279027, No Obj: 0.003211, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.564258, Class: 1.000000, Obj: 0.233565, No Obj: 0.002657, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.507743, Class: 1.000000, Obj: 0.260723, No Obj: 0.002973, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.569796, Class: 1.000000, Obj: 0.280657, No Obj: 0.002781, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.568277, Class: 1.000000, Obj: 0.292812, No Obj: 0.003031, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.593915, Class: 1.000000, Obj: 0.304707, No Obj: 0.003049, Avg Recall: 0.721649, count: 97 -1206: 0.503727, 0.503968 avg, 0.001000 rate, 27.122356 seconds, 308736 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.581430, Class: 1.000000, Obj: 0.235302, No Obj: 0.002904, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.618148, Class: 1.000000, Obj: 0.251567, No Obj: 0.003008, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.545377, Class: 1.000000, Obj: 0.207146, No Obj: 0.003079, Avg Recall: 0.630252, count: 119 -Region Avg IOU: 0.615916, Class: 1.000000, Obj: 0.259604, No Obj: 0.002925, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.595902, Class: 1.000000, Obj: 0.271301, No Obj: 0.003058, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.561058, Class: 1.000000, Obj: 0.239801, No Obj: 0.002932, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.570141, Class: 1.000000, Obj: 0.238206, No Obj: 0.003253, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.570246, Class: 1.000000, Obj: 0.267192, No Obj: 0.002923, Avg Recall: 0.648148, count: 108 -1207: 0.505416, 0.504112 avg, 0.001000 rate, 28.193655 seconds, 308992 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.579723, Class: 1.000000, Obj: 0.298300, No Obj: 0.002982, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.536627, Class: 1.000000, Obj: 0.269129, No Obj: 0.003099, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.569857, Class: 1.000000, Obj: 0.269795, No Obj: 0.003104, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.604420, Class: 1.000000, Obj: 0.276508, No Obj: 0.002856, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.561518, Class: 1.000000, Obj: 0.259222, No Obj: 0.003006, Avg Recall: 0.700000, count: 120 -Region Avg IOU: 0.570587, Class: 1.000000, Obj: 0.251408, No Obj: 0.002677, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.584224, Class: 1.000000, Obj: 0.240862, No Obj: 0.002632, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.578123, Class: 1.000000, Obj: 0.215332, No Obj: 0.002783, Avg Recall: 0.712871, count: 101 -1208: 0.468661, 0.500567 avg, 0.001000 rate, 26.182863 seconds, 309248 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.585509, Class: 1.000000, Obj: 0.369065, No Obj: 0.002599, Avg Recall: 0.693333, count: 75 -Region Avg IOU: 0.560537, Class: 1.000000, Obj: 0.264992, No Obj: 0.003172, Avg Recall: 0.656716, count: 134 -Region Avg IOU: 0.624368, Class: 1.000000, Obj: 0.269836, No Obj: 0.002672, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.596733, Class: 1.000000, Obj: 0.275292, No Obj: 0.002631, Avg Recall: 0.686046, count: 86 -Region Avg IOU: 0.574022, Class: 1.000000, Obj: 0.261629, No Obj: 0.002712, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.559734, Class: 1.000000, Obj: 0.293162, No Obj: 0.002530, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.565697, Class: 1.000000, Obj: 0.267564, No Obj: 0.002759, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.597775, Class: 1.000000, Obj: 0.223590, No Obj: 0.003396, Avg Recall: 0.727941, count: 136 -1209: 0.454475, 0.495958 avg, 0.001000 rate, 28.326609 seconds, 309504 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.589232, Class: 1.000000, Obj: 0.266433, No Obj: 0.002963, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.606199, Class: 1.000000, Obj: 0.271614, No Obj: 0.003201, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.516660, Class: 1.000000, Obj: 0.273206, No Obj: 0.002833, Avg Recall: 0.534653, count: 101 -Region Avg IOU: 0.632206, Class: 1.000000, Obj: 0.319351, No Obj: 0.002926, Avg Recall: 0.833333, count: 84 -Region Avg IOU: 0.528594, Class: 1.000000, Obj: 0.301647, No Obj: 0.002771, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.540897, Class: 1.000000, Obj: 0.291603, No Obj: 0.002904, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.569565, Class: 1.000000, Obj: 0.276604, No Obj: 0.003024, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.548586, Class: 1.000000, Obj: 0.293662, No Obj: 0.003048, Avg Recall: 0.625000, count: 112 -1210: 0.469233, 0.493286 avg, 0.001000 rate, 27.456570 seconds, 309760 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 20.143940 seconds -Region Avg IOU: 0.551383, Class: 1.000000, Obj: 0.256615, No Obj: 0.002955, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.552741, Class: 1.000000, Obj: 0.258600, No Obj: 0.002914, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.523725, Class: 1.000000, Obj: 0.249993, No Obj: 0.002876, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.579898, Class: 1.000000, Obj: 0.320224, No Obj: 0.002997, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.556354, Class: 1.000000, Obj: 0.283942, No Obj: 0.003393, Avg Recall: 0.671875, count: 128 -Region Avg IOU: 0.586780, Class: 1.000000, Obj: 0.266531, No Obj: 0.003146, Avg Recall: 0.753086, count: 81 -Region Avg IOU: 0.583885, Class: 1.000000, Obj: 0.249715, No Obj: 0.002989, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.531721, Class: 1.000000, Obj: 0.220595, No Obj: 0.003224, Avg Recall: 0.651852, count: 135 -1211: 0.465850, 0.490542 avg, 0.001000 rate, 25.889936 seconds, 310016 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.521005, Class: 1.000000, Obj: 0.282400, No Obj: 0.003120, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.527803, Class: 1.000000, Obj: 0.226715, No Obj: 0.003244, Avg Recall: 0.607143, count: 140 -Region Avg IOU: 0.531658, Class: 1.000000, Obj: 0.239080, No Obj: 0.003394, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.566039, Class: 1.000000, Obj: 0.244869, No Obj: 0.003518, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.568375, Class: 1.000000, Obj: 0.250772, No Obj: 0.003037, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.547471, Class: 1.000000, Obj: 0.231550, No Obj: 0.003465, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.565559, Class: 1.000000, Obj: 0.277100, No Obj: 0.003529, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.567985, Class: 1.000000, Obj: 0.289442, No Obj: 0.003561, Avg Recall: 0.663793, count: 116 -1212: 0.574204, 0.498908 avg, 0.001000 rate, 26.414400 seconds, 310272 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.592424, Class: 1.000000, Obj: 0.313165, No Obj: 0.004023, Avg Recall: 0.732394, count: 142 -Region Avg IOU: 0.588051, Class: 1.000000, Obj: 0.249638, No Obj: 0.003470, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.520830, Class: 1.000000, Obj: 0.228141, No Obj: 0.002849, Avg Recall: 0.638554, count: 83 -Region Avg IOU: 0.514659, Class: 1.000000, Obj: 0.240400, No Obj: 0.003735, Avg Recall: 0.572464, count: 138 -Region Avg IOU: 0.517861, Class: 1.000000, Obj: 0.253448, No Obj: 0.003720, Avg Recall: 0.625000, count: 136 -Region Avg IOU: 0.617275, Class: 1.000000, Obj: 0.319130, No Obj: 0.003470, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.543072, Class: 1.000000, Obj: 0.270213, No Obj: 0.003353, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.533454, Class: 1.000000, Obj: 0.306361, No Obj: 0.003231, Avg Recall: 0.598214, count: 112 -1213: 0.494857, 0.498503 avg, 0.001000 rate, 25.714127 seconds, 310528 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.550675, Class: 1.000000, Obj: 0.288817, No Obj: 0.003809, Avg Recall: 0.658537, count: 123 -Region Avg IOU: 0.560245, Class: 1.000000, Obj: 0.287849, No Obj: 0.003607, Avg Recall: 0.643478, count: 115 -Region Avg IOU: 0.605814, Class: 1.000000, Obj: 0.288018, No Obj: 0.003359, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.597423, Class: 1.000000, Obj: 0.295825, No Obj: 0.003472, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.572219, Class: 1.000000, Obj: 0.287268, No Obj: 0.003182, Avg Recall: 0.659091, count: 88 -Region Avg IOU: 0.585530, Class: 1.000000, Obj: 0.250274, No Obj: 0.003640, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.554130, Class: 1.000000, Obj: 0.298164, No Obj: 0.003650, Avg Recall: 0.618705, count: 139 -Region Avg IOU: 0.532616, Class: 1.000000, Obj: 0.273289, No Obj: 0.003356, Avg Recall: 0.642857, count: 126 -1214: 0.511121, 0.499765 avg, 0.001000 rate, 26.345867 seconds, 310784 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.518066, Class: 1.000000, Obj: 0.278261, No Obj: 0.003663, Avg Recall: 0.559322, count: 118 -Region Avg IOU: 0.543335, Class: 1.000000, Obj: 0.297854, No Obj: 0.003999, Avg Recall: 0.664000, count: 125 -Region Avg IOU: 0.516298, Class: 1.000000, Obj: 0.267529, No Obj: 0.004106, Avg Recall: 0.552147, count: 163 -Region Avg IOU: 0.541791, Class: 1.000000, Obj: 0.270500, No Obj: 0.003354, Avg Recall: 0.597938, count: 97 -Region Avg IOU: 0.520657, Class: 1.000000, Obj: 0.270419, No Obj: 0.003141, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.509553, Class: 1.000000, Obj: 0.284989, No Obj: 0.003362, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.483582, Class: 1.000000, Obj: 0.245388, No Obj: 0.003266, Avg Recall: 0.495727, count: 117 -Region Avg IOU: 0.566919, Class: 1.000000, Obj: 0.319058, No Obj: 0.003230, Avg Recall: 0.673684, count: 95 -1215: 0.515140, 0.501302 avg, 0.001000 rate, 25.784151 seconds, 311040 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.551219, Class: 1.000000, Obj: 0.300063, No Obj: 0.003221, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.555907, Class: 1.000000, Obj: 0.281136, No Obj: 0.003164, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.489795, Class: 1.000000, Obj: 0.277829, No Obj: 0.003049, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.509332, Class: 1.000000, Obj: 0.282495, No Obj: 0.003056, Avg Recall: 0.580645, count: 93 -Region Avg IOU: 0.595781, Class: 1.000000, Obj: 0.331598, No Obj: 0.003627, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.531377, Class: 1.000000, Obj: 0.313968, No Obj: 0.003141, Avg Recall: 0.622807, count: 114 -Region Avg IOU: 0.594324, Class: 1.000000, Obj: 0.337301, No Obj: 0.003803, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.542755, Class: 1.000000, Obj: 0.307244, No Obj: 0.003104, Avg Recall: 0.593750, count: 96 -1216: 0.477104, 0.498883 avg, 0.001000 rate, 26.011480 seconds, 311296 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.540138, Class: 1.000000, Obj: 0.310934, No Obj: 0.003398, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.528378, Class: 1.000000, Obj: 0.283187, No Obj: 0.003121, Avg Recall: 0.581395, count: 86 -Region Avg IOU: 0.556527, Class: 1.000000, Obj: 0.309268, No Obj: 0.003537, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.558552, Class: 1.000000, Obj: 0.300756, No Obj: 0.003182, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.533534, Class: 1.000000, Obj: 0.271317, No Obj: 0.003478, Avg Recall: 0.616667, count: 120 -Region Avg IOU: 0.549446, Class: 1.000000, Obj: 0.286947, No Obj: 0.003619, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.568813, Class: 1.000000, Obj: 0.261880, No Obj: 0.003133, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.558405, Class: 1.000000, Obj: 0.302464, No Obj: 0.003436, Avg Recall: 0.670103, count: 97 -1217: 0.457632, 0.494758 avg, 0.001000 rate, 25.762388 seconds, 311552 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.512849, Class: 1.000000, Obj: 0.267995, No Obj: 0.002728, Avg Recall: 0.556818, count: 88 -Region Avg IOU: 0.594491, Class: 1.000000, Obj: 0.282735, No Obj: 0.002528, Avg Recall: 0.725806, count: 62 -Region Avg IOU: 0.573205, Class: 1.000000, Obj: 0.265361, No Obj: 0.002955, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.548925, Class: 1.000000, Obj: 0.258206, No Obj: 0.002801, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.560336, Class: 1.000000, Obj: 0.289829, No Obj: 0.003160, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.551036, Class: 1.000000, Obj: 0.246673, No Obj: 0.003301, Avg Recall: 0.645669, count: 127 -Region Avg IOU: 0.545475, Class: 1.000000, Obj: 0.282569, No Obj: 0.003105, Avg Recall: 0.658537, count: 82 -Region Avg IOU: 0.585464, Class: 1.000000, Obj: 0.274339, No Obj: 0.003499, Avg Recall: 0.736842, count: 95 -1218: 0.442716, 0.489553 avg, 0.001000 rate, 25.610720 seconds, 311808 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.530994, Class: 1.000000, Obj: 0.259161, No Obj: 0.002833, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.509782, Class: 1.000000, Obj: 0.283602, No Obj: 0.003016, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.547175, Class: 1.000000, Obj: 0.292362, No Obj: 0.003192, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.602756, Class: 1.000000, Obj: 0.290847, No Obj: 0.003238, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.576169, Class: 1.000000, Obj: 0.283872, No Obj: 0.002924, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.542266, Class: 1.000000, Obj: 0.236789, No Obj: 0.003320, Avg Recall: 0.619835, count: 121 -Region Avg IOU: 0.534079, Class: 1.000000, Obj: 0.264418, No Obj: 0.003120, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.516937, Class: 1.000000, Obj: 0.252174, No Obj: 0.003284, Avg Recall: 0.545455, count: 110 -1219: 0.514356, 0.492034 avg, 0.001000 rate, 24.824743 seconds, 312064 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.528045, Class: 1.000000, Obj: 0.234421, No Obj: 0.002580, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.579221, Class: 1.000000, Obj: 0.243040, No Obj: 0.003509, Avg Recall: 0.705882, count: 119 -Region Avg IOU: 0.603989, Class: 1.000000, Obj: 0.283113, No Obj: 0.002938, Avg Recall: 0.729412, count: 85 -Region Avg IOU: 0.568068, Class: 1.000000, Obj: 0.264350, No Obj: 0.003093, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.563515, Class: 1.000000, Obj: 0.275802, No Obj: 0.003051, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.545142, Class: 1.000000, Obj: 0.282008, No Obj: 0.002794, Avg Recall: 0.619565, count: 92 -Region Avg IOU: 0.553276, Class: 1.000000, Obj: 0.269915, No Obj: 0.002931, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.542036, Class: 1.000000, Obj: 0.248795, No Obj: 0.002913, Avg Recall: 0.637795, count: 127 -1220: 0.489430, 0.491773 avg, 0.001000 rate, 26.592972 seconds, 312320 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000071 seconds -Region Avg IOU: 0.605713, Class: 1.000000, Obj: 0.254395, No Obj: 0.002808, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.588011, Class: 1.000000, Obj: 0.296372, No Obj: 0.002558, Avg Recall: 0.662651, count: 83 -Region Avg IOU: 0.584558, Class: 1.000000, Obj: 0.220542, No Obj: 0.003100, Avg Recall: 0.706349, count: 126 -Region Avg IOU: 0.576158, Class: 1.000000, Obj: 0.235221, No Obj: 0.003183, Avg Recall: 0.689655, count: 145 -Region Avg IOU: 0.631663, Class: 1.000000, Obj: 0.288720, No Obj: 0.002824, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.591333, Class: 1.000000, Obj: 0.255874, No Obj: 0.002808, Avg Recall: 0.707865, count: 89 -Region Avg IOU: 0.617566, Class: 1.000000, Obj: 0.295619, No Obj: 0.002774, Avg Recall: 0.793103, count: 87 -Region Avg IOU: 0.600503, Class: 1.000000, Obj: 0.284147, No Obj: 0.002849, Avg Recall: 0.733945, count: 109 -1221: 0.490066, 0.491602 avg, 0.001000 rate, 29.139000 seconds, 312576 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.567200, Class: 1.000000, Obj: 0.294211, No Obj: 0.002811, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.622703, Class: 1.000000, Obj: 0.280542, No Obj: 0.003111, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.591655, Class: 1.000000, Obj: 0.277090, No Obj: 0.002828, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.565643, Class: 1.000000, Obj: 0.274487, No Obj: 0.002739, Avg Recall: 0.669492, count: 118 -Region Avg IOU: 0.599468, Class: 1.000000, Obj: 0.288161, No Obj: 0.002951, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.592890, Class: 1.000000, Obj: 0.283788, No Obj: 0.003367, Avg Recall: 0.732394, count: 142 -Region Avg IOU: 0.653369, Class: 1.000000, Obj: 0.276005, No Obj: 0.003018, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.593663, Class: 1.000000, Obj: 0.303308, No Obj: 0.003181, Avg Recall: 0.703390, count: 118 -1222: 0.476123, 0.490055 avg, 0.001000 rate, 27.445896 seconds, 312832 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.598352, Class: 1.000000, Obj: 0.267875, No Obj: 0.002987, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.551280, Class: 1.000000, Obj: 0.277551, No Obj: 0.003416, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.597180, Class: 1.000000, Obj: 0.259687, No Obj: 0.003180, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.603733, Class: 1.000000, Obj: 0.292554, No Obj: 0.002940, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.583735, Class: 1.000000, Obj: 0.298931, No Obj: 0.003008, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.592670, Class: 1.000000, Obj: 0.275582, No Obj: 0.003361, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.583192, Class: 1.000000, Obj: 0.237178, No Obj: 0.003228, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.602670, Class: 1.000000, Obj: 0.276033, No Obj: 0.002914, Avg Recall: 0.708738, count: 103 -1223: 0.514673, 0.492516 avg, 0.001000 rate, 28.553848 seconds, 313088 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.616501, Class: 1.000000, Obj: 0.326626, No Obj: 0.003260, Avg Recall: 0.807229, count: 83 -Region Avg IOU: 0.610804, Class: 1.000000, Obj: 0.329226, No Obj: 0.003375, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.577245, Class: 1.000000, Obj: 0.256496, No Obj: 0.002955, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.545143, Class: 1.000000, Obj: 0.274070, No Obj: 0.003231, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.574342, Class: 1.000000, Obj: 0.269384, No Obj: 0.002995, Avg Recall: 0.688073, count: 109 -Region Avg IOU: 0.581407, Class: 1.000000, Obj: 0.320165, No Obj: 0.003249, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.601736, Class: 1.000000, Obj: 0.320956, No Obj: 0.003075, Avg Recall: 0.739583, count: 96 -Region Avg IOU: 0.557070, Class: 1.000000, Obj: 0.327215, No Obj: 0.002908, Avg Recall: 0.637255, count: 102 -1224: 0.486147, 0.491879 avg, 0.001000 rate, 28.580187 seconds, 313344 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.578524, Class: 1.000000, Obj: 0.336227, No Obj: 0.002999, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.594537, Class: 1.000000, Obj: 0.318355, No Obj: 0.003027, Avg Recall: 0.714286, count: 133 -Region Avg IOU: 0.608537, Class: 1.000000, Obj: 0.274466, No Obj: 0.003282, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.625976, Class: 1.000000, Obj: 0.256227, No Obj: 0.003245, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.567251, Class: 1.000000, Obj: 0.287506, No Obj: 0.003037, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.553253, Class: 1.000000, Obj: 0.271765, No Obj: 0.003158, Avg Recall: 0.642857, count: 126 -Region Avg IOU: 0.611387, Class: 1.000000, Obj: 0.262672, No Obj: 0.003350, Avg Recall: 0.779661, count: 118 -Region Avg IOU: 0.576896, Class: 1.000000, Obj: 0.267993, No Obj: 0.003127, Avg Recall: 0.714286, count: 105 -1225: 0.531923, 0.495884 avg, 0.001000 rate, 28.170073 seconds, 313600 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.598549, Class: 1.000000, Obj: 0.260960, No Obj: 0.003264, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.550806, Class: 1.000000, Obj: 0.285979, No Obj: 0.003156, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.630564, Class: 1.000000, Obj: 0.314460, No Obj: 0.003161, Avg Recall: 0.778761, count: 113 -Region Avg IOU: 0.565718, Class: 1.000000, Obj: 0.279225, No Obj: 0.002567, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.582665, Class: 1.000000, Obj: 0.262504, No Obj: 0.003248, Avg Recall: 0.736434, count: 129 -Region Avg IOU: 0.609159, Class: 1.000000, Obj: 0.303324, No Obj: 0.002914, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.596341, Class: 1.000000, Obj: 0.325938, No Obj: 0.002903, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.554437, Class: 1.000000, Obj: 0.254175, No Obj: 0.003269, Avg Recall: 0.647541, count: 122 -1226: 0.512803, 0.497576 avg, 0.001000 rate, 28.951241 seconds, 313856 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.594155, Class: 1.000000, Obj: 0.273229, No Obj: 0.003020, Avg Recall: 0.735043, count: 117 -Region Avg IOU: 0.551943, Class: 1.000000, Obj: 0.259842, No Obj: 0.002924, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.618996, Class: 1.000000, Obj: 0.264234, No Obj: 0.003114, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.616164, Class: 1.000000, Obj: 0.319581, No Obj: 0.003208, Avg Recall: 0.756303, count: 119 -Region Avg IOU: 0.604337, Class: 1.000000, Obj: 0.249565, No Obj: 0.002736, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.601493, Class: 1.000000, Obj: 0.321837, No Obj: 0.003317, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.597554, Class: 1.000000, Obj: 0.297481, No Obj: 0.002985, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.605582, Class: 1.000000, Obj: 0.267928, No Obj: 0.002947, Avg Recall: 0.747899, count: 119 -1227: 0.494169, 0.497235 avg, 0.001000 rate, 29.133728 seconds, 314112 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.598454, Class: 1.000000, Obj: 0.314521, No Obj: 0.002777, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.635706, Class: 1.000000, Obj: 0.276527, No Obj: 0.002943, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.589364, Class: 1.000000, Obj: 0.258862, No Obj: 0.002875, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.545762, Class: 1.000000, Obj: 0.246646, No Obj: 0.002635, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.573264, Class: 1.000000, Obj: 0.262507, No Obj: 0.003058, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.584597, Class: 1.000000, Obj: 0.277400, No Obj: 0.002897, Avg Recall: 0.689076, count: 119 -Region Avg IOU: 0.595890, Class: 1.000000, Obj: 0.278435, No Obj: 0.003005, Avg Recall: 0.728000, count: 125 -Region Avg IOU: 0.574401, Class: 1.000000, Obj: 0.275869, No Obj: 0.003139, Avg Recall: 0.689076, count: 119 -1228: 0.470112, 0.494523 avg, 0.001000 rate, 29.338043 seconds, 314368 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.563520, Class: 1.000000, Obj: 0.250218, No Obj: 0.002707, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.572589, Class: 1.000000, Obj: 0.234018, No Obj: 0.003049, Avg Recall: 0.684615, count: 130 -Region Avg IOU: 0.596395, Class: 1.000000, Obj: 0.272898, No Obj: 0.003061, Avg Recall: 0.750000, count: 128 -Region Avg IOU: 0.586241, Class: 1.000000, Obj: 0.281576, No Obj: 0.002862, Avg Recall: 0.722222, count: 126 -Region Avg IOU: 0.611756, Class: 1.000000, Obj: 0.314998, No Obj: 0.002649, Avg Recall: 0.800000, count: 80 -Region Avg IOU: 0.595970, Class: 1.000000, Obj: 0.247081, No Obj: 0.002837, Avg Recall: 0.717742, count: 124 -Region Avg IOU: 0.616717, Class: 1.000000, Obj: 0.283284, No Obj: 0.002684, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.558421, Class: 1.000000, Obj: 0.251937, No Obj: 0.002544, Avg Recall: 0.638889, count: 108 -1229: 0.508618, 0.495932 avg, 0.001000 rate, 28.736036 seconds, 314624 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.603800, Class: 1.000000, Obj: 0.244341, No Obj: 0.002596, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.600751, Class: 1.000000, Obj: 0.303726, No Obj: 0.002872, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.559055, Class: 1.000000, Obj: 0.257459, No Obj: 0.002657, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.631452, Class: 1.000000, Obj: 0.266919, No Obj: 0.002712, Avg Recall: 0.797872, count: 94 -Region Avg IOU: 0.581823, Class: 1.000000, Obj: 0.271298, No Obj: 0.002877, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.568988, Class: 1.000000, Obj: 0.220623, No Obj: 0.002513, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.588839, Class: 1.000000, Obj: 0.308252, No Obj: 0.002753, Avg Recall: 0.739583, count: 96 -Region Avg IOU: 0.596124, Class: 1.000000, Obj: 0.256646, No Obj: 0.002808, Avg Recall: 0.748148, count: 135 -1230: 0.482697, 0.494609 avg, 0.001000 rate, 28.416624 seconds, 314880 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.607840 seconds -Region Avg IOU: 0.441780, Class: 1.000000, Obj: 0.214717, No Obj: 0.003647, Avg Recall: 0.464646, count: 99 -Region Avg IOU: 0.439819, Class: 1.000000, Obj: 0.221769, No Obj: 0.003971, Avg Recall: 0.460177, count: 113 -Region Avg IOU: 0.470471, Class: 1.000000, Obj: 0.202510, No Obj: 0.003620, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.412554, Class: 1.000000, Obj: 0.196949, No Obj: 0.003273, Avg Recall: 0.410000, count: 100 -Region Avg IOU: 0.463769, Class: 1.000000, Obj: 0.208170, No Obj: 0.003642, Avg Recall: 0.448000, count: 125 -Region Avg IOU: 0.501773, Class: 1.000000, Obj: 0.247787, No Obj: 0.004240, Avg Recall: 0.538462, count: 117 -Region Avg IOU: 0.468590, Class: 1.000000, Obj: 0.241033, No Obj: 0.003897, Avg Recall: 0.500000, count: 108 -Region Avg IOU: 0.458935, Class: 1.000000, Obj: 0.244224, No Obj: 0.003077, Avg Recall: 0.514706, count: 68 -1231: 0.561480, 0.501296 avg, 0.001000 rate, 20.497299 seconds, 315136 images -Loaded: 0.999706 seconds -Region Avg IOU: 0.455284, Class: 1.000000, Obj: 0.224450, No Obj: 0.003722, Avg Recall: 0.489796, count: 98 -Region Avg IOU: 0.453966, Class: 1.000000, Obj: 0.223451, No Obj: 0.004231, Avg Recall: 0.519231, count: 104 -Region Avg IOU: 0.455896, Class: 1.000000, Obj: 0.211795, No Obj: 0.003098, Avg Recall: 0.455556, count: 90 -Region Avg IOU: 0.463675, Class: 1.000000, Obj: 0.235927, No Obj: 0.004009, Avg Recall: 0.500000, count: 134 -Region Avg IOU: 0.405275, Class: 1.000000, Obj: 0.223015, No Obj: 0.003880, Avg Recall: 0.414634, count: 123 -Region Avg IOU: 0.414730, Class: 1.000000, Obj: 0.226699, No Obj: 0.003644, Avg Recall: 0.440000, count: 100 -Region Avg IOU: 0.468309, Class: 1.000000, Obj: 0.249825, No Obj: 0.004024, Avg Recall: 0.514851, count: 101 -Region Avg IOU: 0.457892, Class: 1.000000, Obj: 0.241830, No Obj: 0.003726, Avg Recall: 0.500000, count: 102 -1232: 0.591720, 0.510338 avg, 0.001000 rate, 17.472605 seconds, 315392 images -Loaded: 2.239692 seconds -Region Avg IOU: 0.476355, Class: 1.000000, Obj: 0.279383, No Obj: 0.004084, Avg Recall: 0.473684, count: 95 -Region Avg IOU: 0.489595, Class: 1.000000, Obj: 0.286570, No Obj: 0.004262, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.433453, Class: 1.000000, Obj: 0.214015, No Obj: 0.003615, Avg Recall: 0.412281, count: 114 -Region Avg IOU: 0.467330, Class: 1.000000, Obj: 0.219473, No Obj: 0.003476, Avg Recall: 0.518868, count: 106 -Region Avg IOU: 0.427206, Class: 1.000000, Obj: 0.219744, No Obj: 0.003875, Avg Recall: 0.468254, count: 126 -Region Avg IOU: 0.417136, Class: 1.000000, Obj: 0.209737, No Obj: 0.003687, Avg Recall: 0.437500, count: 128 -Region Avg IOU: 0.438445, Class: 1.000000, Obj: 0.193727, No Obj: 0.003027, Avg Recall: 0.493506, count: 77 -Region Avg IOU: 0.405622, Class: 1.000000, Obj: 0.206061, No Obj: 0.003437, Avg Recall: 0.371134, count: 97 -1233: 0.563828, 0.515687 avg, 0.001000 rate, 16.977043 seconds, 315648 images -Loaded: 4.266137 seconds -Region Avg IOU: 0.463369, Class: 1.000000, Obj: 0.239666, No Obj: 0.004353, Avg Recall: 0.529915, count: 117 -Region Avg IOU: 0.441703, Class: 1.000000, Obj: 0.243748, No Obj: 0.004099, Avg Recall: 0.495652, count: 115 -Region Avg IOU: 0.484253, Class: 1.000000, Obj: 0.284753, No Obj: 0.004424, Avg Recall: 0.556701, count: 97 -Region Avg IOU: 0.475402, Class: 1.000000, Obj: 0.282654, No Obj: 0.003979, Avg Recall: 0.490196, count: 102 -Region Avg IOU: 0.450505, Class: 1.000000, Obj: 0.273870, No Obj: 0.003905, Avg Recall: 0.474227, count: 97 -Region Avg IOU: 0.459939, Class: 1.000000, Obj: 0.240144, No Obj: 0.003974, Avg Recall: 0.473684, count: 114 -Region Avg IOU: 0.454909, Class: 1.000000, Obj: 0.264895, No Obj: 0.004187, Avg Recall: 0.479339, count: 121 -Region Avg IOU: 0.438594, Class: 1.000000, Obj: 0.218693, No Obj: 0.004019, Avg Recall: 0.515873, count: 126 -1234: 0.512850, 0.515404 avg, 0.001000 rate, 18.722267 seconds, 315904 images -Loaded: 2.917973 seconds -Region Avg IOU: 0.478305, Class: 1.000000, Obj: 0.278906, No Obj: 0.004858, Avg Recall: 0.483051, count: 118 -Region Avg IOU: 0.434806, Class: 1.000000, Obj: 0.248184, No Obj: 0.004238, Avg Recall: 0.500000, count: 114 -Region Avg IOU: 0.423237, Class: 1.000000, Obj: 0.239594, No Obj: 0.004342, Avg Recall: 0.449612, count: 129 -Region Avg IOU: 0.473393, Class: 1.000000, Obj: 0.269907, No Obj: 0.004383, Avg Recall: 0.544554, count: 101 -Region Avg IOU: 0.464049, Class: 1.000000, Obj: 0.282675, No Obj: 0.004659, Avg Recall: 0.535088, count: 114 -Region Avg IOU: 0.485650, Class: 1.000000, Obj: 0.242645, No Obj: 0.004265, Avg Recall: 0.520833, count: 96 -Region Avg IOU: 0.410160, Class: 1.000000, Obj: 0.241190, No Obj: 0.003832, Avg Recall: 0.404494, count: 89 -Region Avg IOU: 0.435112, Class: 1.000000, Obj: 0.286133, No Obj: 0.004524, Avg Recall: 0.478992, count: 119 -1235: 0.558564, 0.519720 avg, 0.001000 rate, 19.401426 seconds, 316160 images -Loaded: 0.866465 seconds -Region Avg IOU: 0.402680, Class: 1.000000, Obj: 0.263275, No Obj: 0.004662, Avg Recall: 0.404762, count: 126 -Region Avg IOU: 0.416453, Class: 1.000000, Obj: 0.274629, No Obj: 0.004545, Avg Recall: 0.414634, count: 123 -Region Avg IOU: 0.462810, Class: 1.000000, Obj: 0.315671, No Obj: 0.004523, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.435748, Class: 1.000000, Obj: 0.284242, No Obj: 0.004980, Avg Recall: 0.457627, count: 118 -Region Avg IOU: 0.453967, Class: 1.000000, Obj: 0.302708, No Obj: 0.004368, Avg Recall: 0.476744, count: 86 -Region Avg IOU: 0.421607, Class: 1.000000, Obj: 0.293411, No Obj: 0.004784, Avg Recall: 0.394495, count: 109 -Region Avg IOU: 0.447411, Class: 1.000000, Obj: 0.288902, No Obj: 0.005097, Avg Recall: 0.483333, count: 120 -Region Avg IOU: 0.413049, Class: 1.000000, Obj: 0.254110, No Obj: 0.005082, Avg Recall: 0.403509, count: 114 -1236: 0.538817, 0.521629 avg, 0.001000 rate, 20.724924 seconds, 316416 images -Loaded: 1.868091 seconds -Region Avg IOU: 0.478615, Class: 1.000000, Obj: 0.293517, No Obj: 0.005047, Avg Recall: 0.480769, count: 104 -Region Avg IOU: 0.489131, Class: 1.000000, Obj: 0.307737, No Obj: 0.004715, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.489426, Class: 1.000000, Obj: 0.268382, No Obj: 0.004932, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.439292, Class: 1.000000, Obj: 0.282510, No Obj: 0.005108, Avg Recall: 0.478633, count: 117 -Region Avg IOU: 0.445409, Class: 1.000000, Obj: 0.282113, No Obj: 0.004878, Avg Recall: 0.433628, count: 113 -Region Avg IOU: 0.450564, Class: 1.000000, Obj: 0.279988, No Obj: 0.004563, Avg Recall: 0.491071, count: 112 -Region Avg IOU: 0.446103, Class: 1.000000, Obj: 0.266529, No Obj: 0.004971, Avg Recall: 0.449541, count: 109 -Region Avg IOU: 0.433773, Class: 1.000000, Obj: 0.243320, No Obj: 0.004245, Avg Recall: 0.450000, count: 100 -1237: 0.525379, 0.522004 avg, 0.001000 rate, 18.935320 seconds, 316672 images -Loaded: 1.212952 seconds -Region Avg IOU: 0.470397, Class: 1.000000, Obj: 0.314860, No Obj: 0.004922, Avg Recall: 0.528926, count: 121 -Region Avg IOU: 0.418079, Class: 1.000000, Obj: 0.284520, No Obj: 0.003977, Avg Recall: 0.384615, count: 104 -Region Avg IOU: 0.484048, Class: 1.000000, Obj: 0.300261, No Obj: 0.005193, Avg Recall: 0.553398, count: 103 -Region Avg IOU: 0.486676, Class: 1.000000, Obj: 0.273507, No Obj: 0.004160, Avg Recall: 0.597938, count: 97 -Region Avg IOU: 0.416023, Class: 1.000000, Obj: 0.282998, No Obj: 0.004695, Avg Recall: 0.447917, count: 96 -Region Avg IOU: 0.453714, Class: 1.000000, Obj: 0.293721, No Obj: 0.005094, Avg Recall: 0.478261, count: 115 -Region Avg IOU: 0.424090, Class: 1.000000, Obj: 0.294302, No Obj: 0.004595, Avg Recall: 0.454545, count: 88 -Region Avg IOU: 0.434192, Class: 1.000000, Obj: 0.265189, No Obj: 0.004350, Avg Recall: 0.481481, count: 108 -1238: 0.494936, 0.519297 avg, 0.001000 rate, 19.668846 seconds, 316928 images -Loaded: 1.573736 seconds -Region Avg IOU: 0.458565, Class: 1.000000, Obj: 0.275755, No Obj: 0.004077, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.397792, Class: 1.000000, Obj: 0.242848, No Obj: 0.004420, Avg Recall: 0.429825, count: 114 -Region Avg IOU: 0.421076, Class: 1.000000, Obj: 0.276737, No Obj: 0.003946, Avg Recall: 0.425532, count: 94 -Region Avg IOU: 0.480300, Class: 1.000000, Obj: 0.285281, No Obj: 0.004353, Avg Recall: 0.500000, count: 90 -Region Avg IOU: 0.503749, Class: 1.000000, Obj: 0.249477, No Obj: 0.005029, Avg Recall: 0.552000, count: 125 -Region Avg IOU: 0.467156, Class: 1.000000, Obj: 0.266389, No Obj: 0.004575, Avg Recall: 0.489130, count: 92 -Region Avg IOU: 0.484715, Class: 1.000000, Obj: 0.302076, No Obj: 0.005187, Avg Recall: 0.484211, count: 95 -Region Avg IOU: 0.442996, Class: 1.000000, Obj: 0.261807, No Obj: 0.004708, Avg Recall: 0.472727, count: 110 -1239: 0.481466, 0.515514 avg, 0.001000 rate, 18.656582 seconds, 317184 images -Loaded: 1.705548 seconds -Region Avg IOU: 0.426963, Class: 1.000000, Obj: 0.245069, No Obj: 0.004232, Avg Recall: 0.435644, count: 101 -Region Avg IOU: 0.489931, Class: 1.000000, Obj: 0.248278, No Obj: 0.004355, Avg Recall: 0.534653, count: 101 -Region Avg IOU: 0.446793, Class: 1.000000, Obj: 0.247809, No Obj: 0.004977, Avg Recall: 0.472868, count: 129 -Region Avg IOU: 0.444798, Class: 1.000000, Obj: 0.251332, No Obj: 0.004507, Avg Recall: 0.473684, count: 114 -Region Avg IOU: 0.468735, Class: 1.000000, Obj: 0.270693, No Obj: 0.004666, Avg Recall: 0.517241, count: 87 -Region Avg IOU: 0.497274, Class: 1.000000, Obj: 0.276679, No Obj: 0.004453, Avg Recall: 0.534884, count: 86 -Region Avg IOU: 0.424493, Class: 1.000000, Obj: 0.258514, No Obj: 0.004020, Avg Recall: 0.469388, count: 98 -Region Avg IOU: 0.413565, Class: 1.000000, Obj: 0.239611, No Obj: 0.003838, Avg Recall: 0.475728, count: 103 -1240: 0.452540, 0.509217 avg, 0.001000 rate, 17.146870 seconds, 317440 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000084 seconds -Region Avg IOU: 0.541551, Class: 1.000000, Obj: 0.287296, No Obj: 0.002791, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.600525, Class: 1.000000, Obj: 0.289842, No Obj: 0.003085, Avg Recall: 0.779070, count: 86 -Region Avg IOU: 0.504791, Class: 1.000000, Obj: 0.280604, No Obj: 0.002873, Avg Recall: 0.626374, count: 91 -Region Avg IOU: 0.585372, Class: 1.000000, Obj: 0.254097, No Obj: 0.003264, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.546173, Class: 1.000000, Obj: 0.348962, No Obj: 0.002987, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.552733, Class: 1.000000, Obj: 0.305413, No Obj: 0.003058, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.569130, Class: 1.000000, Obj: 0.339952, No Obj: 0.003210, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.597976, Class: 1.000000, Obj: 0.292691, No Obj: 0.003400, Avg Recall: 0.780952, count: 105 -1241: 0.476429, 0.505938 avg, 0.001000 rate, 27.000973 seconds, 317696 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.549932, Class: 1.000000, Obj: 0.276859, No Obj: 0.003006, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.567797, Class: 1.000000, Obj: 0.296513, No Obj: 0.003085, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.574064, Class: 1.000000, Obj: 0.298046, No Obj: 0.003288, Avg Recall: 0.691176, count: 136 -Region Avg IOU: 0.542776, Class: 1.000000, Obj: 0.263492, No Obj: 0.002549, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.587741, Class: 1.000000, Obj: 0.267142, No Obj: 0.002677, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.565494, Class: 1.000000, Obj: 0.292383, No Obj: 0.003066, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.561947, Class: 1.000000, Obj: 0.260046, No Obj: 0.002855, Avg Recall: 0.650406, count: 123 -Region Avg IOU: 0.538658, Class: 1.000000, Obj: 0.266616, No Obj: 0.002803, Avg Recall: 0.587156, count: 109 -1242: 0.510359, 0.506380 avg, 0.001000 rate, 28.765285 seconds, 317952 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.558375, Class: 1.000000, Obj: 0.238238, No Obj: 0.003028, Avg Recall: 0.669065, count: 139 -Region Avg IOU: 0.591377, Class: 1.000000, Obj: 0.246767, No Obj: 0.002664, Avg Recall: 0.731707, count: 82 -Region Avg IOU: 0.523587, Class: 1.000000, Obj: 0.286825, No Obj: 0.002483, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.577141, Class: 1.000000, Obj: 0.256474, No Obj: 0.002602, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.611308, Class: 1.000000, Obj: 0.270736, No Obj: 0.002935, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.572696, Class: 1.000000, Obj: 0.263994, No Obj: 0.002781, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.541100, Class: 1.000000, Obj: 0.249903, No Obj: 0.002640, Avg Recall: 0.648000, count: 125 -Region Avg IOU: 0.577047, Class: 1.000000, Obj: 0.225196, No Obj: 0.002946, Avg Recall: 0.685714, count: 105 -1243: 0.509190, 0.506661 avg, 0.001000 rate, 27.295284 seconds, 318208 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.604841, Class: 1.000000, Obj: 0.242192, No Obj: 0.002812, Avg Recall: 0.760000, count: 125 -Region Avg IOU: 0.563041, Class: 1.000000, Obj: 0.234202, No Obj: 0.002861, Avg Recall: 0.669565, count: 115 -Region Avg IOU: 0.547293, Class: 1.000000, Obj: 0.268446, No Obj: 0.002828, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.540472, Class: 1.000000, Obj: 0.210316, No Obj: 0.002511, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.502383, Class: 1.000000, Obj: 0.206823, No Obj: 0.002498, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.602472, Class: 1.000000, Obj: 0.234780, No Obj: 0.002495, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.565740, Class: 1.000000, Obj: 0.234492, No Obj: 0.002525, Avg Recall: 0.663866, count: 119 -Region Avg IOU: 0.580490, Class: 1.000000, Obj: 0.258170, No Obj: 0.002793, Avg Recall: 0.683453, count: 139 -1244: 0.488269, 0.504822 avg, 0.001000 rate, 28.587692 seconds, 318464 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.565921, Class: 1.000000, Obj: 0.210073, No Obj: 0.002580, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.592034, Class: 1.000000, Obj: 0.253672, No Obj: 0.002734, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.543086, Class: 1.000000, Obj: 0.204731, No Obj: 0.003163, Avg Recall: 0.635135, count: 148 -Region Avg IOU: 0.596534, Class: 1.000000, Obj: 0.225690, No Obj: 0.002461, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.551422, Class: 1.000000, Obj: 0.275485, No Obj: 0.002414, Avg Recall: 0.565217, count: 92 -Region Avg IOU: 0.587997, Class: 1.000000, Obj: 0.282184, No Obj: 0.002376, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.574280, Class: 1.000000, Obj: 0.227822, No Obj: 0.002707, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.596592, Class: 1.000000, Obj: 0.222867, No Obj: 0.002631, Avg Recall: 0.718182, count: 110 -1245: 0.497146, 0.504054 avg, 0.001000 rate, 27.044981 seconds, 318720 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.562138, Class: 1.000000, Obj: 0.241367, No Obj: 0.002539, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.579610, Class: 1.000000, Obj: 0.261048, No Obj: 0.003032, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.576436, Class: 1.000000, Obj: 0.249152, No Obj: 0.003144, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.569944, Class: 1.000000, Obj: 0.242532, No Obj: 0.002507, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.589783, Class: 1.000000, Obj: 0.287556, No Obj: 0.002924, Avg Recall: 0.714286, count: 126 -Region Avg IOU: 0.626581, Class: 1.000000, Obj: 0.312831, No Obj: 0.002871, Avg Recall: 0.822430, count: 107 -Region Avg IOU: 0.608058, Class: 1.000000, Obj: 0.286206, No Obj: 0.002533, Avg Recall: 0.781250, count: 96 -Region Avg IOU: 0.577447, Class: 1.000000, Obj: 0.271899, No Obj: 0.002963, Avg Recall: 0.722689, count: 119 -1246: 0.508511, 0.504500 avg, 0.001000 rate, 26.553947 seconds, 318976 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.607401, Class: 1.000000, Obj: 0.299989, No Obj: 0.002991, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.619330, Class: 1.000000, Obj: 0.295192, No Obj: 0.003203, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.582900, Class: 1.000000, Obj: 0.272808, No Obj: 0.003257, Avg Recall: 0.700000, count: 120 -Region Avg IOU: 0.585173, Class: 1.000000, Obj: 0.271529, No Obj: 0.003005, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.577804, Class: 1.000000, Obj: 0.293224, No Obj: 0.002883, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.554312, Class: 1.000000, Obj: 0.258185, No Obj: 0.003049, Avg Recall: 0.672000, count: 125 -Region Avg IOU: 0.481774, Class: 1.000000, Obj: 0.265061, No Obj: 0.002980, Avg Recall: 0.556391, count: 133 -Region Avg IOU: 0.613091, Class: 1.000000, Obj: 0.240746, No Obj: 0.002951, Avg Recall: 0.750000, count: 108 -1247: 0.512248, 0.505275 avg, 0.001000 rate, 26.318933 seconds, 319232 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.573308, Class: 1.000000, Obj: 0.267740, No Obj: 0.003487, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.607065, Class: 1.000000, Obj: 0.321865, No Obj: 0.003114, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.572912, Class: 1.000000, Obj: 0.298493, No Obj: 0.003185, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.559737, Class: 1.000000, Obj: 0.324818, No Obj: 0.003457, Avg Recall: 0.656716, count: 134 -Region Avg IOU: 0.569109, Class: 1.000000, Obj: 0.266350, No Obj: 0.003339, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.536280, Class: 1.000000, Obj: 0.288620, No Obj: 0.003244, Avg Recall: 0.595588, count: 136 -Region Avg IOU: 0.583963, Class: 1.000000, Obj: 0.350948, No Obj: 0.003059, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.592730, Class: 1.000000, Obj: 0.289408, No Obj: 0.003294, Avg Recall: 0.739130, count: 115 -1248: 0.568982, 0.511646 avg, 0.001000 rate, 27.074411 seconds, 319488 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.585526, Class: 1.000000, Obj: 0.276853, No Obj: 0.003497, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.558857, Class: 1.000000, Obj: 0.341095, No Obj: 0.003409, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.587759, Class: 1.000000, Obj: 0.324922, No Obj: 0.003564, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.599476, Class: 1.000000, Obj: 0.313488, No Obj: 0.003634, Avg Recall: 0.713178, count: 129 -Region Avg IOU: 0.568858, Class: 1.000000, Obj: 0.324062, No Obj: 0.003188, Avg Recall: 0.686747, count: 83 -Region Avg IOU: 0.590890, Class: 1.000000, Obj: 0.319758, No Obj: 0.003358, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.583502, Class: 1.000000, Obj: 0.318159, No Obj: 0.003485, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.596283, Class: 1.000000, Obj: 0.325765, No Obj: 0.003488, Avg Recall: 0.762376, count: 101 -1249: 0.490951, 0.509576 avg, 0.001000 rate, 28.146259 seconds, 319744 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.550378, Class: 1.000000, Obj: 0.280330, No Obj: 0.003149, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.604074, Class: 1.000000, Obj: 0.352061, No Obj: 0.003851, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.548543, Class: 1.000000, Obj: 0.327245, No Obj: 0.003408, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.549065, Class: 1.000000, Obj: 0.307427, No Obj: 0.003675, Avg Recall: 0.644628, count: 121 -Region Avg IOU: 0.542474, Class: 1.000000, Obj: 0.302430, No Obj: 0.003451, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.597495, Class: 1.000000, Obj: 0.259250, No Obj: 0.003702, Avg Recall: 0.733871, count: 124 -Region Avg IOU: 0.569602, Class: 1.000000, Obj: 0.260548, No Obj: 0.003353, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.547302, Class: 1.000000, Obj: 0.256026, No Obj: 0.003218, Avg Recall: 0.625000, count: 96 -1250: 0.557552, 0.514374 avg, 0.001000 rate, 27.591423 seconds, 320000 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 13.030960 seconds -Region Avg IOU: 0.489872, Class: 1.000000, Obj: 0.283648, No Obj: 0.004590, Avg Recall: 0.521739, count: 115 -Region Avg IOU: 0.435335, Class: 1.000000, Obj: 0.241415, No Obj: 0.004312, Avg Recall: 0.428571, count: 112 -Region Avg IOU: 0.464408, Class: 1.000000, Obj: 0.211772, No Obj: 0.004110, Avg Recall: 0.460000, count: 100 -Region Avg IOU: 0.449829, Class: 1.000000, Obj: 0.266523, No Obj: 0.004018, Avg Recall: 0.440860, count: 93 -Region Avg IOU: 0.397668, Class: 1.000000, Obj: 0.229182, No Obj: 0.003741, Avg Recall: 0.356522, count: 115 -Region Avg IOU: 0.495227, Class: 1.000000, Obj: 0.273609, No Obj: 0.004149, Avg Recall: 0.569620, count: 79 -Region Avg IOU: 0.456839, Class: 1.000000, Obj: 0.241146, No Obj: 0.004549, Avg Recall: 0.496000, count: 125 -Region Avg IOU: 0.470642, Class: 1.000000, Obj: 0.232444, No Obj: 0.004533, Avg Recall: 0.496063, count: 127 -1251: 0.592829, 0.522219 avg, 0.001000 rate, 18.991585 seconds, 320256 images -Loaded: 3.069033 seconds -Region Avg IOU: 0.414209, Class: 1.000000, Obj: 0.197219, No Obj: 0.003912, Avg Recall: 0.367521, count: 117 -Region Avg IOU: 0.455383, Class: 1.000000, Obj: 0.259426, No Obj: 0.004349, Avg Recall: 0.462963, count: 108 -Region Avg IOU: 0.452505, Class: 1.000000, Obj: 0.237092, No Obj: 0.004231, Avg Recall: 0.509615, count: 104 -Region Avg IOU: 0.410852, Class: 1.000000, Obj: 0.172108, No Obj: 0.004154, Avg Recall: 0.450980, count: 102 -Region Avg IOU: 0.458478, Class: 1.000000, Obj: 0.225969, No Obj: 0.004445, Avg Recall: 0.486957, count: 115 -Region Avg IOU: 0.418198, Class: 1.000000, Obj: 0.253979, No Obj: 0.003971, Avg Recall: 0.420561, count: 107 -Region Avg IOU: 0.449918, Class: 1.000000, Obj: 0.221940, No Obj: 0.003725, Avg Recall: 0.425743, count: 101 -Region Avg IOU: 0.462429, Class: 1.000000, Obj: 0.242544, No Obj: 0.004211, Avg Recall: 0.453782, count: 119 -1252: 0.572839, 0.527281 avg, 0.001000 rate, 19.985519 seconds, 320512 images -Loaded: 2.156182 seconds -Region Avg IOU: 0.428696, Class: 1.000000, Obj: 0.242432, No Obj: 0.003799, Avg Recall: 0.459184, count: 98 -Region Avg IOU: 0.454423, Class: 1.000000, Obj: 0.216867, No Obj: 0.003727, Avg Recall: 0.481481, count: 108 -Region Avg IOU: 0.421356, Class: 1.000000, Obj: 0.211603, No Obj: 0.003913, Avg Recall: 0.440860, count: 93 -Region Avg IOU: 0.452127, Class: 1.000000, Obj: 0.209277, No Obj: 0.004000, Avg Recall: 0.472868, count: 129 -Region Avg IOU: 0.395339, Class: 1.000000, Obj: 0.205618, No Obj: 0.003680, Avg Recall: 0.387387, count: 111 -Region Avg IOU: 0.409466, Class: 1.000000, Obj: 0.217156, No Obj: 0.004131, Avg Recall: 0.402778, count: 144 -Region Avg IOU: 0.479689, Class: 1.000000, Obj: 0.242636, No Obj: 0.004176, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.479745, Class: 1.000000, Obj: 0.202103, No Obj: 0.004275, Avg Recall: 0.553571, count: 112 -1253: 0.539100, 0.528463 avg, 0.001000 rate, 14.849948 seconds, 320768 images -Loaded: 4.907868 seconds -Region Avg IOU: 0.455551, Class: 1.000000, Obj: 0.228088, No Obj: 0.004383, Avg Recall: 0.457627, count: 118 -Region Avg IOU: 0.467356, Class: 1.000000, Obj: 0.270640, No Obj: 0.003886, Avg Recall: 0.542553, count: 94 -Region Avg IOU: 0.456724, Class: 1.000000, Obj: 0.247781, No Obj: 0.004320, Avg Recall: 0.514851, count: 101 -Region Avg IOU: 0.356925, Class: 1.000000, Obj: 0.185019, No Obj: 0.003388, Avg Recall: 0.354545, count: 110 -Region Avg IOU: 0.438879, Class: 1.000000, Obj: 0.200289, No Obj: 0.004164, Avg Recall: 0.459259, count: 135 -Region Avg IOU: 0.399462, Class: 1.000000, Obj: 0.230052, No Obj: 0.003552, Avg Recall: 0.392857, count: 112 -Region Avg IOU: 0.465565, Class: 1.000000, Obj: 0.242676, No Obj: 0.004078, Avg Recall: 0.452830, count: 106 -Region Avg IOU: 0.479953, Class: 1.000000, Obj: 0.273550, No Obj: 0.004391, Avg Recall: 0.558559, count: 111 -1254: 0.488288, 0.524446 avg, 0.001000 rate, 19.632460 seconds, 321024 images -Loaded: 2.328433 seconds -Region Avg IOU: 0.465335, Class: 1.000000, Obj: 0.217918, No Obj: 0.004071, Avg Recall: 0.490566, count: 106 -Region Avg IOU: 0.468583, Class: 1.000000, Obj: 0.285105, No Obj: 0.003748, Avg Recall: 0.529412, count: 85 -Region Avg IOU: 0.412235, Class: 1.000000, Obj: 0.217891, No Obj: 0.004038, Avg Recall: 0.375000, count: 112 -Region Avg IOU: 0.463154, Class: 1.000000, Obj: 0.230154, No Obj: 0.004190, Avg Recall: 0.512821, count: 117 -Region Avg IOU: 0.426368, Class: 1.000000, Obj: 0.214251, No Obj: 0.004293, Avg Recall: 0.406015, count: 133 -Region Avg IOU: 0.447267, Class: 1.000000, Obj: 0.261512, No Obj: 0.004296, Avg Recall: 0.455446, count: 101 -Region Avg IOU: 0.378538, Class: 1.000000, Obj: 0.213013, No Obj: 0.004452, Avg Recall: 0.439716, count: 141 -Region Avg IOU: 0.425077, Class: 1.000000, Obj: 0.234740, No Obj: 0.003785, Avg Recall: 0.423529, count: 85 -1255: 0.587768, 0.530778 avg, 0.001000 rate, 19.392580 seconds, 321280 images -Loaded: 2.777607 seconds -Region Avg IOU: 0.441030, Class: 1.000000, Obj: 0.238416, No Obj: 0.005025, Avg Recall: 0.456140, count: 114 -Region Avg IOU: 0.488876, Class: 1.000000, Obj: 0.246086, No Obj: 0.004189, Avg Recall: 0.500000, count: 108 -Region Avg IOU: 0.410355, Class: 1.000000, Obj: 0.227338, No Obj: 0.004459, Avg Recall: 0.414286, count: 140 -Region Avg IOU: 0.406721, Class: 1.000000, Obj: 0.200981, No Obj: 0.004392, Avg Recall: 0.375000, count: 112 -Region Avg IOU: 0.373648, Class: 1.000000, Obj: 0.222836, No Obj: 0.003820, Avg Recall: 0.297030, count: 101 -Region Avg IOU: 0.385368, Class: 1.000000, Obj: 0.216191, No Obj: 0.004109, Avg Recall: 0.389830, count: 118 -Region Avg IOU: 0.418894, Class: 1.000000, Obj: 0.219573, No Obj: 0.004430, Avg Recall: 0.435714, count: 140 -Region Avg IOU: 0.416613, Class: 1.000000, Obj: 0.246545, No Obj: 0.004054, Avg Recall: 0.381818, count: 110 -1256: 0.600379, 0.537738 avg, 0.001000 rate, 19.449047 seconds, 321536 images -Loaded: 1.850073 seconds -Region Avg IOU: 0.402790, Class: 1.000000, Obj: 0.264376, No Obj: 0.003751, Avg Recall: 0.370787, count: 89 -Region Avg IOU: 0.415539, Class: 1.000000, Obj: 0.225986, No Obj: 0.004093, Avg Recall: 0.441441, count: 111 -Region Avg IOU: 0.455482, Class: 1.000000, Obj: 0.228257, No Obj: 0.004324, Avg Recall: 0.440367, count: 109 -Region Avg IOU: 0.453372, Class: 1.000000, Obj: 0.259671, No Obj: 0.004792, Avg Recall: 0.473214, count: 112 -Region Avg IOU: 0.466173, Class: 1.000000, Obj: 0.257212, No Obj: 0.004029, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.479214, Class: 1.000000, Obj: 0.297227, No Obj: 0.004590, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.486934, Class: 1.000000, Obj: 0.262964, No Obj: 0.004340, Avg Recall: 0.577586, count: 116 -Region Avg IOU: 0.473597, Class: 1.000000, Obj: 0.244118, No Obj: 0.004230, Avg Recall: 0.491935, count: 124 -1257: 0.588397, 0.542804 avg, 0.001000 rate, 19.187679 seconds, 321792 images -Loaded: 1.657529 seconds -Region Avg IOU: 0.436002, Class: 1.000000, Obj: 0.280190, No Obj: 0.004186, Avg Recall: 0.457944, count: 107 -Region Avg IOU: 0.394215, Class: 1.000000, Obj: 0.281384, No Obj: 0.004070, Avg Recall: 0.371134, count: 97 -Region Avg IOU: 0.412246, Class: 1.000000, Obj: 0.220136, No Obj: 0.004680, Avg Recall: 0.424460, count: 139 -Region Avg IOU: 0.451779, Class: 1.000000, Obj: 0.242929, No Obj: 0.005342, Avg Recall: 0.478873, count: 142 -Region Avg IOU: 0.455631, Class: 1.000000, Obj: 0.246251, No Obj: 0.003955, Avg Recall: 0.483871, count: 93 -Region Avg IOU: 0.417763, Class: 1.000000, Obj: 0.212917, No Obj: 0.004093, Avg Recall: 0.408163, count: 98 -Region Avg IOU: 0.421363, Class: 1.000000, Obj: 0.249899, No Obj: 0.004249, Avg Recall: 0.460870, count: 115 -Region Avg IOU: 0.435494, Class: 1.000000, Obj: 0.279623, No Obj: 0.004295, Avg Recall: 0.472727, count: 110 -1258: 0.574320, 0.545955 avg, 0.001000 rate, 18.541033 seconds, 322048 images -Loaded: 1.889398 seconds -Region Avg IOU: 0.409516, Class: 1.000000, Obj: 0.236221, No Obj: 0.003798, Avg Recall: 0.434343, count: 99 -Region Avg IOU: 0.400231, Class: 1.000000, Obj: 0.242403, No Obj: 0.004353, Avg Recall: 0.401869, count: 107 -Region Avg IOU: 0.427169, Class: 1.000000, Obj: 0.263423, No Obj: 0.004576, Avg Recall: 0.464286, count: 112 -Region Avg IOU: 0.501787, Class: 1.000000, Obj: 0.296440, No Obj: 0.004592, Avg Recall: 0.552083, count: 96 -Region Avg IOU: 0.404440, Class: 1.000000, Obj: 0.253027, No Obj: 0.004251, Avg Recall: 0.440945, count: 127 -Region Avg IOU: 0.449483, Class: 1.000000, Obj: 0.261862, No Obj: 0.004398, Avg Recall: 0.467391, count: 92 -Region Avg IOU: 0.471277, Class: 1.000000, Obj: 0.264616, No Obj: 0.004822, Avg Recall: 0.471429, count: 140 -Region Avg IOU: 0.428177, Class: 1.000000, Obj: 0.289124, No Obj: 0.004978, Avg Recall: 0.468750, count: 96 -1259: 0.541489, 0.545509 avg, 0.001000 rate, 17.915112 seconds, 322304 images -Loaded: 2.770772 seconds -Region Avg IOU: 0.429953, Class: 1.000000, Obj: 0.294603, No Obj: 0.004489, Avg Recall: 0.404255, count: 94 -Region Avg IOU: 0.468434, Class: 1.000000, Obj: 0.264711, No Obj: 0.004697, Avg Recall: 0.490566, count: 106 -Region Avg IOU: 0.454807, Class: 1.000000, Obj: 0.290670, No Obj: 0.004557, Avg Recall: 0.490566, count: 106 -Region Avg IOU: 0.434564, Class: 1.000000, Obj: 0.258045, No Obj: 0.004366, Avg Recall: 0.480000, count: 100 -Region Avg IOU: 0.483189, Class: 1.000000, Obj: 0.280652, No Obj: 0.004525, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.412249, Class: 1.000000, Obj: 0.306756, No Obj: 0.004269, Avg Recall: 0.414634, count: 123 -Region Avg IOU: 0.474172, Class: 1.000000, Obj: 0.268741, No Obj: 0.005239, Avg Recall: 0.513043, count: 115 -Region Avg IOU: 0.410144, Class: 1.000000, Obj: 0.259342, No Obj: 0.003815, Avg Recall: 0.397590, count: 83 -1260: 0.504523, 0.541410 avg, 0.001000 rate, 18.707315 seconds, 322560 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000068 seconds -Region Avg IOU: 0.579143, Class: 1.000000, Obj: 0.315747, No Obj: 0.002996, Avg Recall: 0.729730, count: 74 -Region Avg IOU: 0.564277, Class: 1.000000, Obj: 0.260510, No Obj: 0.003546, Avg Recall: 0.680328, count: 122 -Region Avg IOU: 0.539343, Class: 1.000000, Obj: 0.312976, No Obj: 0.003666, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.599558, Class: 1.000000, Obj: 0.282071, No Obj: 0.003432, Avg Recall: 0.773810, count: 84 -Region Avg IOU: 0.577678, Class: 1.000000, Obj: 0.292648, No Obj: 0.003685, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.564651, Class: 1.000000, Obj: 0.303060, No Obj: 0.003336, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.588243, Class: 1.000000, Obj: 0.321784, No Obj: 0.003468, Avg Recall: 0.719697, count: 132 -Region Avg IOU: 0.555809, Class: 1.000000, Obj: 0.296318, No Obj: 0.003424, Avg Recall: 0.689076, count: 119 -1261: 0.563002, 0.543569 avg, 0.001000 rate, 28.912519 seconds, 322816 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.634166, Class: 1.000000, Obj: 0.282162, No Obj: 0.003562, Avg Recall: 0.849057, count: 106 -Region Avg IOU: 0.604917, Class: 1.000000, Obj: 0.261923, No Obj: 0.003041, Avg Recall: 0.750000, count: 80 -Region Avg IOU: 0.578020, Class: 1.000000, Obj: 0.306776, No Obj: 0.003109, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.568070, Class: 1.000000, Obj: 0.302931, No Obj: 0.003378, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.545037, Class: 1.000000, Obj: 0.247274, No Obj: 0.002784, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.590563, Class: 1.000000, Obj: 0.337714, No Obj: 0.003063, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.618793, Class: 1.000000, Obj: 0.283148, No Obj: 0.003273, Avg Recall: 0.810345, count: 116 -Region Avg IOU: 0.553847, Class: 1.000000, Obj: 0.219107, No Obj: 0.003082, Avg Recall: 0.661290, count: 124 -1262: 0.537319, 0.542944 avg, 0.001000 rate, 29.144575 seconds, 323072 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.609230, Class: 1.000000, Obj: 0.297937, No Obj: 0.002728, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.637128, Class: 1.000000, Obj: 0.257767, No Obj: 0.002751, Avg Recall: 0.804878, count: 82 -Region Avg IOU: 0.634842, Class: 1.000000, Obj: 0.297192, No Obj: 0.002914, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.587753, Class: 1.000000, Obj: 0.266161, No Obj: 0.002871, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.606546, Class: 1.000000, Obj: 0.274087, No Obj: 0.003003, Avg Recall: 0.772358, count: 123 -Region Avg IOU: 0.581527, Class: 1.000000, Obj: 0.259955, No Obj: 0.003045, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.593359, Class: 1.000000, Obj: 0.230041, No Obj: 0.002807, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.508856, Class: 1.000000, Obj: 0.214102, No Obj: 0.002848, Avg Recall: 0.601852, count: 108 -1263: 0.493743, 0.538024 avg, 0.001000 rate, 27.351618 seconds, 323328 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.612189, Class: 1.000000, Obj: 0.188947, No Obj: 0.002530, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.615968, Class: 1.000000, Obj: 0.293134, No Obj: 0.002729, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.633536, Class: 1.000000, Obj: 0.265646, No Obj: 0.002475, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.541199, Class: 1.000000, Obj: 0.237588, No Obj: 0.002564, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.588093, Class: 1.000000, Obj: 0.225435, No Obj: 0.002754, Avg Recall: 0.719298, count: 114 -Region Avg IOU: 0.573434, Class: 1.000000, Obj: 0.233034, No Obj: 0.002481, Avg Recall: 0.678571, count: 84 -Region Avg IOU: 0.617938, Class: 1.000000, Obj: 0.228546, No Obj: 0.002646, Avg Recall: 0.753968, count: 126 -Region Avg IOU: 0.621818, Class: 1.000000, Obj: 0.269641, No Obj: 0.002486, Avg Recall: 0.790476, count: 105 -1264: 0.486236, 0.532845 avg, 0.001000 rate, 30.742836 seconds, 323584 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.572571, Class: 1.000000, Obj: 0.248259, No Obj: 0.002869, Avg Recall: 0.685714, count: 140 -Region Avg IOU: 0.576746, Class: 1.000000, Obj: 0.260516, No Obj: 0.002244, Avg Recall: 0.707865, count: 89 -Region Avg IOU: 0.625265, Class: 1.000000, Obj: 0.250786, No Obj: 0.002742, Avg Recall: 0.808333, count: 120 -Region Avg IOU: 0.602336, Class: 1.000000, Obj: 0.233543, No Obj: 0.002631, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.583526, Class: 1.000000, Obj: 0.201146, No Obj: 0.002472, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.634001, Class: 1.000000, Obj: 0.235832, No Obj: 0.002595, Avg Recall: 0.823529, count: 102 -Region Avg IOU: 0.607761, Class: 1.000000, Obj: 0.188642, No Obj: 0.003070, Avg Recall: 0.802721, count: 147 -Region Avg IOU: 0.608186, Class: 1.000000, Obj: 0.213720, No Obj: 0.002675, Avg Recall: 0.758621, count: 116 -1265: 0.577127, 0.537274 avg, 0.001000 rate, 30.732183 seconds, 323840 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.585044, Class: 1.000000, Obj: 0.256493, No Obj: 0.002746, Avg Recall: 0.694656, count: 131 -Region Avg IOU: 0.605327, Class: 1.000000, Obj: 0.264059, No Obj: 0.002752, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.595844, Class: 1.000000, Obj: 0.266090, No Obj: 0.002555, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.590685, Class: 1.000000, Obj: 0.229473, No Obj: 0.002419, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.630869, Class: 1.000000, Obj: 0.297560, No Obj: 0.002695, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.563848, Class: 1.000000, Obj: 0.216375, No Obj: 0.002449, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.599448, Class: 1.000000, Obj: 0.248780, No Obj: 0.002432, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.606116, Class: 1.000000, Obj: 0.233747, No Obj: 0.002571, Avg Recall: 0.760000, count: 100 -1266: 0.495165, 0.533063 avg, 0.001000 rate, 28.892481 seconds, 324096 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.625071, Class: 1.000000, Obj: 0.249167, No Obj: 0.002655, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.597486, Class: 1.000000, Obj: 0.278721, No Obj: 0.002736, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.615754, Class: 1.000000, Obj: 0.251896, No Obj: 0.002747, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.598776, Class: 1.000000, Obj: 0.263798, No Obj: 0.002677, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.618067, Class: 1.000000, Obj: 0.225684, No Obj: 0.002485, Avg Recall: 0.783784, count: 74 -Region Avg IOU: 0.573001, Class: 1.000000, Obj: 0.243760, No Obj: 0.002861, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.590449, Class: 1.000000, Obj: 0.219405, No Obj: 0.002525, Avg Recall: 0.708861, count: 79 -Region Avg IOU: 0.609808, Class: 1.000000, Obj: 0.259585, No Obj: 0.002587, Avg Recall: 0.773196, count: 97 -1267: 0.457878, 0.525544 avg, 0.001000 rate, 29.732365 seconds, 324352 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.588404, Class: 1.000000, Obj: 0.249975, No Obj: 0.003136, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.607271, Class: 1.000000, Obj: 0.257138, No Obj: 0.002753, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.615281, Class: 1.000000, Obj: 0.256714, No Obj: 0.002947, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.570351, Class: 1.000000, Obj: 0.255168, No Obj: 0.003044, Avg Recall: 0.675214, count: 117 -Region Avg IOU: 0.583733, Class: 1.000000, Obj: 0.258299, No Obj: 0.002781, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.604178, Class: 1.000000, Obj: 0.311860, No Obj: 0.002951, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.607001, Class: 1.000000, Obj: 0.254572, No Obj: 0.003002, Avg Recall: 0.776000, count: 125 -Region Avg IOU: 0.645829, Class: 1.000000, Obj: 0.294796, No Obj: 0.002874, Avg Recall: 0.831579, count: 95 -1268: 0.547736, 0.527763 avg, 0.001000 rate, 30.162764 seconds, 324608 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.583959, Class: 1.000000, Obj: 0.270146, No Obj: 0.003038, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.577173, Class: 1.000000, Obj: 0.286153, No Obj: 0.003184, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.584864, Class: 1.000000, Obj: 0.246578, No Obj: 0.003065, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.568702, Class: 1.000000, Obj: 0.260199, No Obj: 0.003033, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.623516, Class: 1.000000, Obj: 0.260033, No Obj: 0.003236, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.586195, Class: 1.000000, Obj: 0.301574, No Obj: 0.003035, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.613333, Class: 1.000000, Obj: 0.255525, No Obj: 0.003294, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.590507, Class: 1.000000, Obj: 0.310983, No Obj: 0.002592, Avg Recall: 0.693333, count: 75 -1269: 0.535564, 0.528543 avg, 0.001000 rate, 29.010342 seconds, 324864 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.567065, Class: 1.000000, Obj: 0.272259, No Obj: 0.003479, Avg Recall: 0.642336, count: 137 -Region Avg IOU: 0.584198, Class: 1.000000, Obj: 0.235886, No Obj: 0.003649, Avg Recall: 0.698630, count: 146 -Region Avg IOU: 0.612307, Class: 1.000000, Obj: 0.288607, No Obj: 0.003243, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.598949, Class: 1.000000, Obj: 0.286237, No Obj: 0.003201, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.631205, Class: 1.000000, Obj: 0.308447, No Obj: 0.002919, Avg Recall: 0.777778, count: 90 -Region Avg IOU: 0.630066, Class: 1.000000, Obj: 0.306116, No Obj: 0.002523, Avg Recall: 0.772727, count: 66 -Region Avg IOU: 0.621028, Class: 1.000000, Obj: 0.320011, No Obj: 0.002893, Avg Recall: 0.764706, count: 85 -Region Avg IOU: 0.597997, Class: 1.000000, Obj: 0.286283, No Obj: 0.003387, Avg Recall: 0.762590, count: 139 -1270: 0.526354, 0.528324 avg, 0.001000 rate, 29.326471 seconds, 325120 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 9.895838 seconds -Region Avg IOU: 0.634802, Class: 1.000000, Obj: 0.297866, No Obj: 0.003855, Avg Recall: 0.811189, count: 143 -Region Avg IOU: 0.572489, Class: 1.000000, Obj: 0.262206, No Obj: 0.003445, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.545332, Class: 1.000000, Obj: 0.307972, No Obj: 0.003215, Avg Recall: 0.605769, count: 104 -Region Avg IOU: 0.589648, Class: 1.000000, Obj: 0.303900, No Obj: 0.002902, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.552610, Class: 1.000000, Obj: 0.270027, No Obj: 0.003504, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.613783, Class: 1.000000, Obj: 0.278247, No Obj: 0.003223, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.597989, Class: 1.000000, Obj: 0.349011, No Obj: 0.003454, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.618906, Class: 1.000000, Obj: 0.301367, No Obj: 0.003338, Avg Recall: 0.732673, count: 101 -1271: 0.573943, 0.532886 avg, 0.001000 rate, 28.046400 seconds, 325376 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.555790, Class: 1.000000, Obj: 0.317613, No Obj: 0.003156, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.586410, Class: 1.000000, Obj: 0.285053, No Obj: 0.003556, Avg Recall: 0.702899, count: 138 -Region Avg IOU: 0.530627, Class: 1.000000, Obj: 0.247298, No Obj: 0.002934, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.618203, Class: 1.000000, Obj: 0.368443, No Obj: 0.003314, Avg Recall: 0.804598, count: 87 -Region Avg IOU: 0.557183, Class: 1.000000, Obj: 0.247215, No Obj: 0.003721, Avg Recall: 0.688235, count: 170 -Region Avg IOU: 0.534487, Class: 1.000000, Obj: 0.277720, No Obj: 0.003192, Avg Recall: 0.626984, count: 126 -Region Avg IOU: 0.625370, Class: 1.000000, Obj: 0.298931, No Obj: 0.003166, Avg Recall: 0.788889, count: 90 -Region Avg IOU: 0.587061, Class: 1.000000, Obj: 0.301118, No Obj: 0.003535, Avg Recall: 0.716814, count: 113 -1272: 0.554067, 0.535004 avg, 0.001000 rate, 27.751877 seconds, 325632 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.532909, Class: 1.000000, Obj: 0.354290, No Obj: 0.003012, Avg Recall: 0.646341, count: 82 -Region Avg IOU: 0.607662, Class: 1.000000, Obj: 0.324226, No Obj: 0.003410, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.586068, Class: 1.000000, Obj: 0.298457, No Obj: 0.003100, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.582570, Class: 1.000000, Obj: 0.293867, No Obj: 0.003535, Avg Recall: 0.669231, count: 130 -Region Avg IOU: 0.576914, Class: 1.000000, Obj: 0.296109, No Obj: 0.003230, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.545994, Class: 1.000000, Obj: 0.275250, No Obj: 0.003450, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.560703, Class: 1.000000, Obj: 0.314719, No Obj: 0.003211, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.559414, Class: 1.000000, Obj: 0.264740, No Obj: 0.002820, Avg Recall: 0.711111, count: 90 -1273: 0.520840, 0.533588 avg, 0.001000 rate, 27.505739 seconds, 325888 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.612256, Class: 1.000000, Obj: 0.259417, No Obj: 0.003431, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.603394, Class: 1.000000, Obj: 0.269132, No Obj: 0.003047, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.605887, Class: 1.000000, Obj: 0.269703, No Obj: 0.002959, Avg Recall: 0.747126, count: 87 -Region Avg IOU: 0.563532, Class: 1.000000, Obj: 0.233071, No Obj: 0.003119, Avg Recall: 0.680672, count: 119 -Region Avg IOU: 0.562149, Class: 1.000000, Obj: 0.274223, No Obj: 0.003247, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.570048, Class: 1.000000, Obj: 0.268448, No Obj: 0.003373, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.535149, Class: 1.000000, Obj: 0.273086, No Obj: 0.002901, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.554569, Class: 1.000000, Obj: 0.289532, No Obj: 0.003210, Avg Recall: 0.641509, count: 106 -1274: 0.488006, 0.529030 avg, 0.001000 rate, 27.340788 seconds, 326144 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.564576, Class: 1.000000, Obj: 0.273399, No Obj: 0.003097, Avg Recall: 0.674242, count: 132 -Region Avg IOU: 0.605319, Class: 1.000000, Obj: 0.275845, No Obj: 0.003182, Avg Recall: 0.734375, count: 128 -Region Avg IOU: 0.576368, Class: 1.000000, Obj: 0.263211, No Obj: 0.002976, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.584220, Class: 1.000000, Obj: 0.265802, No Obj: 0.003054, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.582437, Class: 1.000000, Obj: 0.266407, No Obj: 0.003153, Avg Recall: 0.724832, count: 149 -Region Avg IOU: 0.569878, Class: 1.000000, Obj: 0.262775, No Obj: 0.002921, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.554977, Class: 1.000000, Obj: 0.283632, No Obj: 0.002810, Avg Recall: 0.670732, count: 82 -Region Avg IOU: 0.579467, Class: 1.000000, Obj: 0.266660, No Obj: 0.002905, Avg Recall: 0.693069, count: 101 -1275: 0.549046, 0.531031 avg, 0.001000 rate, 27.704887 seconds, 326400 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.535512, Class: 1.000000, Obj: 0.232296, No Obj: 0.003279, Avg Recall: 0.684932, count: 146 -Region Avg IOU: 0.570555, Class: 1.000000, Obj: 0.247073, No Obj: 0.002828, Avg Recall: 0.681818, count: 132 -Region Avg IOU: 0.577435, Class: 1.000000, Obj: 0.277784, No Obj: 0.002809, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.568775, Class: 1.000000, Obj: 0.262817, No Obj: 0.002964, Avg Recall: 0.682171, count: 129 -Region Avg IOU: 0.550242, Class: 1.000000, Obj: 0.274373, No Obj: 0.002985, Avg Recall: 0.631579, count: 133 -Region Avg IOU: 0.584346, Class: 1.000000, Obj: 0.276452, No Obj: 0.002741, Avg Recall: 0.726027, count: 73 -Region Avg IOU: 0.584105, Class: 1.000000, Obj: 0.272293, No Obj: 0.002991, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.561023, Class: 1.000000, Obj: 0.242248, No Obj: 0.002916, Avg Recall: 0.660194, count: 103 -1276: 0.479495, 0.525878 avg, 0.001000 rate, 27.835169 seconds, 326656 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.574318, Class: 1.000000, Obj: 0.307778, No Obj: 0.002625, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.561238, Class: 1.000000, Obj: 0.288054, No Obj: 0.002865, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.564982, Class: 1.000000, Obj: 0.243356, No Obj: 0.003026, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.572366, Class: 1.000000, Obj: 0.310603, No Obj: 0.002651, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.563313, Class: 1.000000, Obj: 0.314164, No Obj: 0.002682, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.580361, Class: 1.000000, Obj: 0.270829, No Obj: 0.002977, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.558093, Class: 1.000000, Obj: 0.235265, No Obj: 0.002638, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.533617, Class: 1.000000, Obj: 0.276184, No Obj: 0.002661, Avg Recall: 0.686956, count: 115 -1277: 0.442769, 0.517567 avg, 0.001000 rate, 27.587204 seconds, 326912 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.502013, Class: 1.000000, Obj: 0.263509, No Obj: 0.002692, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.579759, Class: 1.000000, Obj: 0.257604, No Obj: 0.002817, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.538539, Class: 1.000000, Obj: 0.260050, No Obj: 0.003175, Avg Recall: 0.637795, count: 127 -Region Avg IOU: 0.564469, Class: 1.000000, Obj: 0.295302, No Obj: 0.003028, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.561441, Class: 1.000000, Obj: 0.275270, No Obj: 0.002965, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.534122, Class: 1.000000, Obj: 0.296515, No Obj: 0.003038, Avg Recall: 0.602837, count: 141 -Region Avg IOU: 0.529933, Class: 1.000000, Obj: 0.262800, No Obj: 0.002749, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.595051, Class: 1.000000, Obj: 0.238576, No Obj: 0.003033, Avg Recall: 0.747368, count: 95 -1278: 0.509954, 0.516805 avg, 0.001000 rate, 28.702816 seconds, 327168 images -Loaded: 0.000114 seconds -Region Avg IOU: 0.577991, Class: 1.000000, Obj: 0.268274, No Obj: 0.003107, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.591060, Class: 1.000000, Obj: 0.321628, No Obj: 0.003004, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.617226, Class: 1.000000, Obj: 0.325745, No Obj: 0.003267, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.553138, Class: 1.000000, Obj: 0.248739, No Obj: 0.003361, Avg Recall: 0.664336, count: 143 -Region Avg IOU: 0.565172, Class: 1.000000, Obj: 0.282632, No Obj: 0.003034, Avg Recall: 0.675214, count: 117 -Region Avg IOU: 0.552138, Class: 1.000000, Obj: 0.227621, No Obj: 0.003097, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.540585, Class: 1.000000, Obj: 0.265261, No Obj: 0.003035, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.545901, Class: 1.000000, Obj: 0.259418, No Obj: 0.003138, Avg Recall: 0.650943, count: 106 -1279: 0.528785, 0.518003 avg, 0.001000 rate, 26.211248 seconds, 327424 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.522973, Class: 1.000000, Obj: 0.271249, No Obj: 0.002928, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.563402, Class: 1.000000, Obj: 0.291004, No Obj: 0.003281, Avg Recall: 0.682540, count: 126 -Region Avg IOU: 0.589908, Class: 1.000000, Obj: 0.330930, No Obj: 0.002713, Avg Recall: 0.708861, count: 79 -Region Avg IOU: 0.595302, Class: 1.000000, Obj: 0.339839, No Obj: 0.003372, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.558993, Class: 1.000000, Obj: 0.316498, No Obj: 0.003051, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.567620, Class: 1.000000, Obj: 0.280114, No Obj: 0.003274, Avg Recall: 0.658730, count: 126 -Region Avg IOU: 0.564933, Class: 1.000000, Obj: 0.249570, No Obj: 0.003002, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.550627, Class: 1.000000, Obj: 0.288906, No Obj: 0.003462, Avg Recall: 0.628788, count: 132 -1280: 0.511808, 0.517384 avg, 0.001000 rate, 27.220072 seconds, 327680 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 15.235280 seconds -Region Avg IOU: 0.560096, Class: 1.000000, Obj: 0.271427, No Obj: 0.003610, Avg Recall: 0.689922, count: 129 -Region Avg IOU: 0.552714, Class: 1.000000, Obj: 0.281440, No Obj: 0.003717, Avg Recall: 0.666667, count: 117 -Region Avg IOU: 0.577412, Class: 1.000000, Obj: 0.266100, No Obj: 0.003069, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.616234, Class: 1.000000, Obj: 0.299920, No Obj: 0.003540, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.517328, Class: 1.000000, Obj: 0.265235, No Obj: 0.003450, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.563007, Class: 1.000000, Obj: 0.268097, No Obj: 0.003223, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.559551, Class: 1.000000, Obj: 0.298340, No Obj: 0.003755, Avg Recall: 0.659091, count: 132 -Region Avg IOU: 0.560014, Class: 1.000000, Obj: 0.296836, No Obj: 0.003239, Avg Recall: 0.666667, count: 93 -1281: 0.507598, 0.516405 avg, 0.001000 rate, 25.104853 seconds, 327936 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.503464, Class: 1.000000, Obj: 0.320022, No Obj: 0.002786, Avg Recall: 0.571429, count: 84 -Region Avg IOU: 0.576705, Class: 1.000000, Obj: 0.294736, No Obj: 0.003608, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.539919, Class: 1.000000, Obj: 0.273230, No Obj: 0.003144, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.551193, Class: 1.000000, Obj: 0.247041, No Obj: 0.003461, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.554125, Class: 1.000000, Obj: 0.296911, No Obj: 0.003156, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.548875, Class: 1.000000, Obj: 0.314404, No Obj: 0.003238, Avg Recall: 0.643678, count: 87 -Region Avg IOU: 0.513868, Class: 1.000000, Obj: 0.269221, No Obj: 0.003032, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.543957, Class: 1.000000, Obj: 0.263986, No Obj: 0.003928, Avg Recall: 0.635593, count: 118 -1282: 0.444805, 0.509245 avg, 0.001000 rate, 25.719025 seconds, 328192 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.486790, Class: 1.000000, Obj: 0.279693, No Obj: 0.003115, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.552099, Class: 1.000000, Obj: 0.304586, No Obj: 0.003290, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.568492, Class: 1.000000, Obj: 0.326334, No Obj: 0.003169, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.573657, Class: 1.000000, Obj: 0.318674, No Obj: 0.003535, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.574628, Class: 1.000000, Obj: 0.308569, No Obj: 0.003669, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.601943, Class: 1.000000, Obj: 0.322873, No Obj: 0.003466, Avg Recall: 0.727273, count: 88 -Region Avg IOU: 0.505263, Class: 1.000000, Obj: 0.266359, No Obj: 0.003043, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.570912, Class: 1.000000, Obj: 0.332086, No Obj: 0.003541, Avg Recall: 0.699115, count: 113 -1283: 0.475333, 0.505854 avg, 0.001000 rate, 25.348688 seconds, 328448 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.571149, Class: 1.000000, Obj: 0.286142, No Obj: 0.003353, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.590019, Class: 1.000000, Obj: 0.308098, No Obj: 0.003315, Avg Recall: 0.735043, count: 117 -Region Avg IOU: 0.544302, Class: 1.000000, Obj: 0.300083, No Obj: 0.003890, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.545769, Class: 1.000000, Obj: 0.265380, No Obj: 0.003628, Avg Recall: 0.644628, count: 121 -Region Avg IOU: 0.552530, Class: 1.000000, Obj: 0.289654, No Obj: 0.003141, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.544254, Class: 1.000000, Obj: 0.288302, No Obj: 0.003352, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.543478, Class: 1.000000, Obj: 0.342538, No Obj: 0.003199, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.499025, Class: 1.000000, Obj: 0.239053, No Obj: 0.003188, Avg Recall: 0.550459, count: 109 -1284: 0.472037, 0.502472 avg, 0.001000 rate, 25.916939 seconds, 328704 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.569808, Class: 1.000000, Obj: 0.296666, No Obj: 0.003275, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.568125, Class: 1.000000, Obj: 0.281339, No Obj: 0.003295, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.504033, Class: 1.000000, Obj: 0.298476, No Obj: 0.003302, Avg Recall: 0.518182, count: 110 -Region Avg IOU: 0.583701, Class: 1.000000, Obj: 0.294844, No Obj: 0.003417, Avg Recall: 0.712000, count: 125 -Region Avg IOU: 0.523011, Class: 1.000000, Obj: 0.254075, No Obj: 0.003100, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.543064, Class: 1.000000, Obj: 0.288150, No Obj: 0.003275, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.490064, Class: 1.000000, Obj: 0.248091, No Obj: 0.002879, Avg Recall: 0.550000, count: 100 -Region Avg IOU: 0.564230, Class: 1.000000, Obj: 0.270179, No Obj: 0.003185, Avg Recall: 0.649485, count: 97 -1285: 0.491094, 0.501334 avg, 0.001000 rate, 24.416368 seconds, 328960 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.596954, Class: 1.000000, Obj: 0.306360, No Obj: 0.003269, Avg Recall: 0.781250, count: 96 -Region Avg IOU: 0.521146, Class: 1.000000, Obj: 0.229445, No Obj: 0.002731, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.588442, Class: 1.000000, Obj: 0.252013, No Obj: 0.003209, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.532044, Class: 1.000000, Obj: 0.246431, No Obj: 0.003094, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.530726, Class: 1.000000, Obj: 0.279902, No Obj: 0.003132, Avg Recall: 0.598361, count: 122 -Region Avg IOU: 0.571353, Class: 1.000000, Obj: 0.268606, No Obj: 0.002819, Avg Recall: 0.678571, count: 84 -Region Avg IOU: 0.513216, Class: 1.000000, Obj: 0.275936, No Obj: 0.002881, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.508890, Class: 1.000000, Obj: 0.290347, No Obj: 0.002824, Avg Recall: 0.557895, count: 95 -1286: 0.431488, 0.494350 avg, 0.001000 rate, 27.070044 seconds, 329216 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.553446, Class: 1.000000, Obj: 0.286912, No Obj: 0.003267, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.564234, Class: 1.000000, Obj: 0.260060, No Obj: 0.003213, Avg Recall: 0.626984, count: 126 -Region Avg IOU: 0.532351, Class: 1.000000, Obj: 0.280542, No Obj: 0.002728, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.563732, Class: 1.000000, Obj: 0.273603, No Obj: 0.003635, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.578247, Class: 1.000000, Obj: 0.271090, No Obj: 0.002998, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.525334, Class: 1.000000, Obj: 0.283634, No Obj: 0.003418, Avg Recall: 0.614173, count: 127 -Region Avg IOU: 0.538474, Class: 1.000000, Obj: 0.294280, No Obj: 0.003289, Avg Recall: 0.625000, count: 128 -Region Avg IOU: 0.518730, Class: 1.000000, Obj: 0.287281, No Obj: 0.003255, Avg Recall: 0.598039, count: 102 -1287: 0.547227, 0.499638 avg, 0.001000 rate, 25.529207 seconds, 329472 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.542723, Class: 1.000000, Obj: 0.294907, No Obj: 0.003130, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.616969, Class: 1.000000, Obj: 0.298891, No Obj: 0.003469, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.551576, Class: 1.000000, Obj: 0.299203, No Obj: 0.003390, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.574221, Class: 1.000000, Obj: 0.224104, No Obj: 0.003159, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.495497, Class: 1.000000, Obj: 0.250049, No Obj: 0.003100, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.552981, Class: 1.000000, Obj: 0.262699, No Obj: 0.003477, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.569416, Class: 1.000000, Obj: 0.264653, No Obj: 0.003025, Avg Recall: 0.651163, count: 86 -Region Avg IOU: 0.588929, Class: 1.000000, Obj: 0.360290, No Obj: 0.003345, Avg Recall: 0.715596, count: 109 -1288: 0.460980, 0.495772 avg, 0.001000 rate, 25.995016 seconds, 329728 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.556480, Class: 1.000000, Obj: 0.260476, No Obj: 0.003080, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.566226, Class: 1.000000, Obj: 0.323273, No Obj: 0.003220, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.573932, Class: 1.000000, Obj: 0.301783, No Obj: 0.002890, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.591907, Class: 1.000000, Obj: 0.328297, No Obj: 0.003308, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.541888, Class: 1.000000, Obj: 0.308425, No Obj: 0.003101, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.554493, Class: 1.000000, Obj: 0.252645, No Obj: 0.003331, Avg Recall: 0.619835, count: 121 -Region Avg IOU: 0.552235, Class: 1.000000, Obj: 0.238681, No Obj: 0.003043, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.543692, Class: 1.000000, Obj: 0.273783, No Obj: 0.003402, Avg Recall: 0.633027, count: 109 -1289: 0.441074, 0.490302 avg, 0.001000 rate, 24.917473 seconds, 329984 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.556446, Class: 1.000000, Obj: 0.255597, No Obj: 0.003161, Avg Recall: 0.680672, count: 119 -Region Avg IOU: 0.538603, Class: 1.000000, Obj: 0.291487, No Obj: 0.003224, Avg Recall: 0.640000, count: 125 -Region Avg IOU: 0.551962, Class: 1.000000, Obj: 0.306743, No Obj: 0.002816, Avg Recall: 0.676471, count: 68 -Region Avg IOU: 0.544615, Class: 1.000000, Obj: 0.253426, No Obj: 0.003097, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.575658, Class: 1.000000, Obj: 0.308592, No Obj: 0.002967, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.555691, Class: 1.000000, Obj: 0.236775, No Obj: 0.002607, Avg Recall: 0.638889, count: 72 -Region Avg IOU: 0.533290, Class: 1.000000, Obj: 0.253267, No Obj: 0.002857, Avg Recall: 0.596491, count: 114 -Region Avg IOU: 0.587720, Class: 1.000000, Obj: 0.281009, No Obj: 0.003677, Avg Recall: 0.709677, count: 124 -1290: 0.480303, 0.489302 avg, 0.001000 rate, 24.898142 seconds, 330240 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.532135 seconds -Region Avg IOU: 0.458426, Class: 1.000000, Obj: 0.235380, No Obj: 0.003689, Avg Recall: 0.493333, count: 75 -Region Avg IOU: 0.452555, Class: 1.000000, Obj: 0.248459, No Obj: 0.004063, Avg Recall: 0.460177, count: 113 -Region Avg IOU: 0.450454, Class: 1.000000, Obj: 0.221426, No Obj: 0.004243, Avg Recall: 0.447368, count: 152 -Region Avg IOU: 0.457773, Class: 1.000000, Obj: 0.233909, No Obj: 0.003482, Avg Recall: 0.564103, count: 78 -Region Avg IOU: 0.429423, Class: 1.000000, Obj: 0.242577, No Obj: 0.004029, Avg Recall: 0.410256, count: 117 -Region Avg IOU: 0.473048, Class: 1.000000, Obj: 0.222078, No Obj: 0.004409, Avg Recall: 0.532710, count: 107 -Region Avg IOU: 0.484479, Class: 1.000000, Obj: 0.260215, No Obj: 0.004677, Avg Recall: 0.495935, count: 123 -Region Avg IOU: 0.452422, Class: 1.000000, Obj: 0.246196, No Obj: 0.003876, Avg Recall: 0.441441, count: 111 -1291: 0.556458, 0.496018 avg, 0.001000 rate, 18.994671 seconds, 330496 images -Loaded: 1.438200 seconds -Region Avg IOU: 0.474136, Class: 1.000000, Obj: 0.257659, No Obj: 0.004393, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.467168, Class: 1.000000, Obj: 0.230936, No Obj: 0.003795, Avg Recall: 0.494118, count: 85 -Region Avg IOU: 0.424021, Class: 1.000000, Obj: 0.220064, No Obj: 0.004667, Avg Recall: 0.447154, count: 123 -Region Avg IOU: 0.419672, Class: 1.000000, Obj: 0.222528, No Obj: 0.004470, Avg Recall: 0.466667, count: 105 -Region Avg IOU: 0.477878, Class: 1.000000, Obj: 0.246489, No Obj: 0.004615, Avg Recall: 0.538462, count: 130 -Region Avg IOU: 0.456779, Class: 1.000000, Obj: 0.218001, No Obj: 0.004253, Avg Recall: 0.472727, count: 110 -Region Avg IOU: 0.431656, Class: 1.000000, Obj: 0.236674, No Obj: 0.004098, Avg Recall: 0.495413, count: 109 -Region Avg IOU: 0.479018, Class: 1.000000, Obj: 0.238878, No Obj: 0.004190, Avg Recall: 0.515152, count: 99 -1292: 0.510179, 0.497434 avg, 0.001000 rate, 20.254040 seconds, 330752 images -Loaded: 0.938391 seconds -Region Avg IOU: 0.436327, Class: 1.000000, Obj: 0.229431, No Obj: 0.004452, Avg Recall: 0.455357, count: 112 -Region Avg IOU: 0.486098, Class: 1.000000, Obj: 0.288083, No Obj: 0.003937, Avg Recall: 0.505050, count: 99 -Region Avg IOU: 0.474317, Class: 1.000000, Obj: 0.246731, No Obj: 0.004493, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.527189, Class: 1.000000, Obj: 0.289849, No Obj: 0.004319, Avg Recall: 0.563218, count: 87 -Region Avg IOU: 0.499543, Class: 1.000000, Obj: 0.267986, No Obj: 0.003732, Avg Recall: 0.593023, count: 86 -Region Avg IOU: 0.460070, Class: 1.000000, Obj: 0.245979, No Obj: 0.004494, Avg Recall: 0.514286, count: 105 -Region Avg IOU: 0.465591, Class: 1.000000, Obj: 0.261401, No Obj: 0.004317, Avg Recall: 0.517544, count: 114 -Region Avg IOU: 0.469600, Class: 1.000000, Obj: 0.266670, No Obj: 0.004360, Avg Recall: 0.500000, count: 108 -1293: 0.502798, 0.497970 avg, 0.001000 rate, 18.213469 seconds, 331008 images -Loaded: 3.190208 seconds -Region Avg IOU: 0.462245, Class: 1.000000, Obj: 0.272529, No Obj: 0.004340, Avg Recall: 0.464286, count: 112 -Region Avg IOU: 0.474003, Class: 1.000000, Obj: 0.290940, No Obj: 0.004719, Avg Recall: 0.513274, count: 113 -Region Avg IOU: 0.460708, Class: 1.000000, Obj: 0.270024, No Obj: 0.004485, Avg Recall: 0.485981, count: 107 -Region Avg IOU: 0.397499, Class: 1.000000, Obj: 0.267159, No Obj: 0.004515, Avg Recall: 0.413043, count: 92 -Region Avg IOU: 0.439829, Class: 1.000000, Obj: 0.276776, No Obj: 0.004354, Avg Recall: 0.414894, count: 94 -Region Avg IOU: 0.476955, Class: 1.000000, Obj: 0.254812, No Obj: 0.004133, Avg Recall: 0.540000, count: 100 -Region Avg IOU: 0.480226, Class: 1.000000, Obj: 0.279060, No Obj: 0.004382, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.418171, Class: 1.000000, Obj: 0.235436, No Obj: 0.004491, Avg Recall: 0.427273, count: 110 -1294: 0.477403, 0.495914 avg, 0.001000 rate, 20.277756 seconds, 331264 images -Loaded: 1.491125 seconds -Region Avg IOU: 0.406166, Class: 1.000000, Obj: 0.270870, No Obj: 0.004494, Avg Recall: 0.400000, count: 125 -Region Avg IOU: 0.467344, Class: 1.000000, Obj: 0.261714, No Obj: 0.004361, Avg Recall: 0.540816, count: 98 -Region Avg IOU: 0.470075, Class: 1.000000, Obj: 0.300133, No Obj: 0.004509, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.436410, Class: 1.000000, Obj: 0.281443, No Obj: 0.005087, Avg Recall: 0.470588, count: 102 -Region Avg IOU: 0.444485, Class: 1.000000, Obj: 0.282430, No Obj: 0.004619, Avg Recall: 0.490385, count: 104 -Region Avg IOU: 0.399687, Class: 1.000000, Obj: 0.277570, No Obj: 0.004239, Avg Recall: 0.425743, count: 101 -Region Avg IOU: 0.469542, Class: 1.000000, Obj: 0.281067, No Obj: 0.004344, Avg Recall: 0.523256, count: 86 -Region Avg IOU: 0.417301, Class: 1.000000, Obj: 0.277979, No Obj: 0.004406, Avg Recall: 0.505495, count: 91 -1295: 0.480639, 0.494386 avg, 0.001000 rate, 19.813812 seconds, 331520 images -Loaded: 1.610752 seconds -Region Avg IOU: 0.434238, Class: 1.000000, Obj: 0.269602, No Obj: 0.004945, Avg Recall: 0.430000, count: 100 -Region Avg IOU: 0.408135, Class: 1.000000, Obj: 0.255026, No Obj: 0.004075, Avg Recall: 0.418605, count: 86 -Region Avg IOU: 0.423646, Class: 1.000000, Obj: 0.285641, No Obj: 0.004204, Avg Recall: 0.421687, count: 83 -Region Avg IOU: 0.415325, Class: 1.000000, Obj: 0.200467, No Obj: 0.004516, Avg Recall: 0.424779, count: 113 -Region Avg IOU: 0.390594, Class: 1.000000, Obj: 0.240498, No Obj: 0.004504, Avg Recall: 0.396040, count: 101 -Region Avg IOU: 0.446051, Class: 1.000000, Obj: 0.248491, No Obj: 0.004896, Avg Recall: 0.480263, count: 152 -Region Avg IOU: 0.437476, Class: 1.000000, Obj: 0.287387, No Obj: 0.005444, Avg Recall: 0.428571, count: 112 -Region Avg IOU: 0.446568, Class: 1.000000, Obj: 0.273501, No Obj: 0.004225, Avg Recall: 0.494505, count: 91 -1296: 0.547459, 0.499693 avg, 0.001000 rate, 20.517611 seconds, 331776 images -Loaded: 0.542379 seconds -Region Avg IOU: 0.453046, Class: 1.000000, Obj: 0.282235, No Obj: 0.004790, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.384101, Class: 1.000000, Obj: 0.271833, No Obj: 0.004467, Avg Recall: 0.347826, count: 115 -Region Avg IOU: 0.428726, Class: 1.000000, Obj: 0.275324, No Obj: 0.004327, Avg Recall: 0.438202, count: 89 -Region Avg IOU: 0.425877, Class: 1.000000, Obj: 0.252179, No Obj: 0.004462, Avg Recall: 0.420455, count: 88 -Region Avg IOU: 0.428563, Class: 1.000000, Obj: 0.264631, No Obj: 0.004082, Avg Recall: 0.510204, count: 98 -Region Avg IOU: 0.413836, Class: 1.000000, Obj: 0.268647, No Obj: 0.004681, Avg Recall: 0.467890, count: 109 -Region Avg IOU: 0.465451, Class: 1.000000, Obj: 0.252997, No Obj: 0.005297, Avg Recall: 0.508333, count: 120 -Region Avg IOU: 0.460404, Class: 1.000000, Obj: 0.311369, No Obj: 0.005018, Avg Recall: 0.469027, count: 113 -1297: 0.548568, 0.504581 avg, 0.001000 rate, 16.398411 seconds, 332032 images -Loaded: 6.774095 seconds -Region Avg IOU: 0.437506, Class: 1.000000, Obj: 0.284575, No Obj: 0.004652, Avg Recall: 0.458824, count: 85 -Region Avg IOU: 0.442012, Class: 1.000000, Obj: 0.232342, No Obj: 0.004437, Avg Recall: 0.457143, count: 105 -Region Avg IOU: 0.388128, Class: 1.000000, Obj: 0.233399, No Obj: 0.004786, Avg Recall: 0.386364, count: 132 -Region Avg IOU: 0.394770, Class: 1.000000, Obj: 0.268066, No Obj: 0.004145, Avg Recall: 0.372549, count: 102 -Region Avg IOU: 0.389985, Class: 1.000000, Obj: 0.228954, No Obj: 0.003766, Avg Recall: 0.400000, count: 80 -Region Avg IOU: 0.409698, Class: 1.000000, Obj: 0.276420, No Obj: 0.004356, Avg Recall: 0.422018, count: 109 -Region Avg IOU: 0.446870, Class: 1.000000, Obj: 0.251280, No Obj: 0.004737, Avg Recall: 0.433962, count: 106 -Region Avg IOU: 0.488362, Class: 1.000000, Obj: 0.279930, No Obj: 0.004727, Avg Recall: 0.560000, count: 100 -1298: 0.478372, 0.501960 avg, 0.001000 rate, 19.857224 seconds, 332288 images -Loaded: 2.304877 seconds -Region Avg IOU: 0.460016, Class: 1.000000, Obj: 0.242648, No Obj: 0.005298, Avg Recall: 0.471831, count: 142 -Region Avg IOU: 0.420544, Class: 1.000000, Obj: 0.265308, No Obj: 0.004538, Avg Recall: 0.440678, count: 118 -Region Avg IOU: 0.434115, Class: 1.000000, Obj: 0.258149, No Obj: 0.004375, Avg Recall: 0.480392, count: 102 -Region Avg IOU: 0.433558, Class: 1.000000, Obj: 0.257780, No Obj: 0.003881, Avg Recall: 0.471264, count: 87 -Region Avg IOU: 0.467995, Class: 1.000000, Obj: 0.312985, No Obj: 0.004603, Avg Recall: 0.518182, count: 110 -Region Avg IOU: 0.405031, Class: 1.000000, Obj: 0.235795, No Obj: 0.004329, Avg Recall: 0.411765, count: 119 -Region Avg IOU: 0.474520, Class: 1.000000, Obj: 0.231896, No Obj: 0.004516, Avg Recall: 0.530435, count: 115 -Region Avg IOU: 0.456556, Class: 1.000000, Obj: 0.270653, No Obj: 0.004452, Avg Recall: 0.525773, count: 97 -1299: 0.593836, 0.511147 avg, 0.001000 rate, 19.286627 seconds, 332544 images -Loaded: 1.632628 seconds -Region Avg IOU: 0.437101, Class: 1.000000, Obj: 0.259006, No Obj: 0.004013, Avg Recall: 0.450980, count: 102 -Region Avg IOU: 0.482822, Class: 1.000000, Obj: 0.254752, No Obj: 0.004582, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.453172, Class: 1.000000, Obj: 0.250152, No Obj: 0.004306, Avg Recall: 0.484375, count: 128 -Region Avg IOU: 0.450140, Class: 1.000000, Obj: 0.263525, No Obj: 0.004587, Avg Recall: 0.462121, count: 132 -Region Avg IOU: 0.381022, Class: 1.000000, Obj: 0.224195, No Obj: 0.004460, Avg Recall: 0.427273, count: 110 -Region Avg IOU: 0.468799, Class: 1.000000, Obj: 0.253843, No Obj: 0.003966, Avg Recall: 0.569620, count: 79 -Region Avg IOU: 0.477001, Class: 1.000000, Obj: 0.259558, No Obj: 0.003974, Avg Recall: 0.512821, count: 78 -Region Avg IOU: 0.451359, Class: 1.000000, Obj: 0.248387, No Obj: 0.004520, Avg Recall: 0.454545, count: 121 -1300: 0.496723, 0.509705 avg, 0.001000 rate, 18.049517 seconds, 332800 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000071 seconds -Region Avg IOU: 0.576898, Class: 1.000000, Obj: 0.339392, No Obj: 0.003374, Avg Recall: 0.711111, count: 90 -Region Avg IOU: 0.546183, Class: 1.000000, Obj: 0.330517, No Obj: 0.003096, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.609635, Class: 1.000000, Obj: 0.323509, No Obj: 0.003623, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.567837, Class: 1.000000, Obj: 0.287784, No Obj: 0.003257, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.557088, Class: 1.000000, Obj: 0.298572, No Obj: 0.003298, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.550352, Class: 1.000000, Obj: 0.288456, No Obj: 0.003243, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.559266, Class: 1.000000, Obj: 0.293443, No Obj: 0.003410, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.518764, Class: 1.000000, Obj: 0.258478, No Obj: 0.002949, Avg Recall: 0.623932, count: 117 -1301: 0.518353, 0.510570 avg, 0.001000 rate, 27.464056 seconds, 333056 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.585097, Class: 1.000000, Obj: 0.311219, No Obj: 0.003328, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.585917, Class: 1.000000, Obj: 0.280405, No Obj: 0.003541, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.551118, Class: 1.000000, Obj: 0.256893, No Obj: 0.003100, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.530721, Class: 1.000000, Obj: 0.311965, No Obj: 0.002916, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.530964, Class: 1.000000, Obj: 0.247103, No Obj: 0.003007, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.582231, Class: 1.000000, Obj: 0.299081, No Obj: 0.003338, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.520689, Class: 1.000000, Obj: 0.261466, No Obj: 0.003441, Avg Recall: 0.575758, count: 132 -Region Avg IOU: 0.590372, Class: 1.000000, Obj: 0.290593, No Obj: 0.003502, Avg Recall: 0.729167, count: 96 -1302: 0.527315, 0.512244 avg, 0.001000 rate, 26.433599 seconds, 333312 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.603543, Class: 1.000000, Obj: 0.274848, No Obj: 0.003313, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.586708, Class: 1.000000, Obj: 0.296083, No Obj: 0.003130, Avg Recall: 0.738095, count: 126 -Region Avg IOU: 0.567647, Class: 1.000000, Obj: 0.309611, No Obj: 0.003393, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.603966, Class: 1.000000, Obj: 0.308945, No Obj: 0.003296, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.602477, Class: 1.000000, Obj: 0.262200, No Obj: 0.003310, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.514645, Class: 1.000000, Obj: 0.249070, No Obj: 0.002755, Avg Recall: 0.528846, count: 104 -Region Avg IOU: 0.566615, Class: 1.000000, Obj: 0.242038, No Obj: 0.003357, Avg Recall: 0.680000, count: 125 -Region Avg IOU: 0.576786, Class: 1.000000, Obj: 0.276030, No Obj: 0.003111, Avg Recall: 0.717172, count: 99 -1303: 0.507948, 0.511815 avg, 0.001000 rate, 27.334896 seconds, 333568 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.538372, Class: 1.000000, Obj: 0.299040, No Obj: 0.003057, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.552066, Class: 1.000000, Obj: 0.251416, No Obj: 0.003061, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.562352, Class: 1.000000, Obj: 0.295142, No Obj: 0.003187, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.590738, Class: 1.000000, Obj: 0.246367, No Obj: 0.003198, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.572527, Class: 1.000000, Obj: 0.260499, No Obj: 0.003152, Avg Recall: 0.659574, count: 141 -Region Avg IOU: 0.585994, Class: 1.000000, Obj: 0.265937, No Obj: 0.002926, Avg Recall: 0.675214, count: 117 -Region Avg IOU: 0.585797, Class: 1.000000, Obj: 0.254085, No Obj: 0.003201, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.611758, Class: 1.000000, Obj: 0.270621, No Obj: 0.003379, Avg Recall: 0.785714, count: 112 -1304: 0.532081, 0.513841 avg, 0.001000 rate, 26.375860 seconds, 333824 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.615365, Class: 1.000000, Obj: 0.280504, No Obj: 0.003306, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.618729, Class: 1.000000, Obj: 0.296114, No Obj: 0.002982, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.571096, Class: 1.000000, Obj: 0.270838, No Obj: 0.002941, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.582141, Class: 1.000000, Obj: 0.301052, No Obj: 0.003096, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.569402, Class: 1.000000, Obj: 0.264933, No Obj: 0.003112, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.566357, Class: 1.000000, Obj: 0.261159, No Obj: 0.002885, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.626573, Class: 1.000000, Obj: 0.276007, No Obj: 0.003058, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.558731, Class: 1.000000, Obj: 0.294376, No Obj: 0.002810, Avg Recall: 0.683673, count: 98 -1305: 0.500153, 0.512473 avg, 0.001000 rate, 26.323069 seconds, 334080 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.602034, Class: 1.000000, Obj: 0.287672, No Obj: 0.003430, Avg Recall: 0.716418, count: 134 -Region Avg IOU: 0.546866, Class: 1.000000, Obj: 0.267350, No Obj: 0.003346, Avg Recall: 0.637795, count: 127 -Region Avg IOU: 0.583143, Class: 1.000000, Obj: 0.282154, No Obj: 0.003450, Avg Recall: 0.708333, count: 120 -Region Avg IOU: 0.546038, Class: 1.000000, Obj: 0.333303, No Obj: 0.002913, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.575527, Class: 1.000000, Obj: 0.343413, No Obj: 0.002916, Avg Recall: 0.729412, count: 85 -Region Avg IOU: 0.560758, Class: 1.000000, Obj: 0.290862, No Obj: 0.003048, Avg Recall: 0.648352, count: 91 -Region Avg IOU: 0.576415, Class: 1.000000, Obj: 0.244495, No Obj: 0.003176, Avg Recall: 0.712000, count: 125 -Region Avg IOU: 0.572651, Class: 1.000000, Obj: 0.257648, No Obj: 0.003412, Avg Recall: 0.684211, count: 133 -1306: 0.521112, 0.513337 avg, 0.001000 rate, 26.660927 seconds, 334336 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.546434, Class: 1.000000, Obj: 0.284828, No Obj: 0.003191, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.573806, Class: 1.000000, Obj: 0.247631, No Obj: 0.002794, Avg Recall: 0.706667, count: 75 -Region Avg IOU: 0.552473, Class: 1.000000, Obj: 0.286197, No Obj: 0.003155, Avg Recall: 0.625000, count: 120 -Region Avg IOU: 0.503602, Class: 1.000000, Obj: 0.245407, No Obj: 0.003182, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.572066, Class: 1.000000, Obj: 0.283450, No Obj: 0.003154, Avg Recall: 0.692308, count: 130 -Region Avg IOU: 0.557971, Class: 1.000000, Obj: 0.318967, No Obj: 0.002924, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.589767, Class: 1.000000, Obj: 0.295425, No Obj: 0.003375, Avg Recall: 0.700787, count: 127 -Region Avg IOU: 0.514232, Class: 1.000000, Obj: 0.276357, No Obj: 0.002963, Avg Recall: 0.532468, count: 77 -1307: 0.552453, 0.517248 avg, 0.001000 rate, 25.328112 seconds, 334592 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.585692, Class: 1.000000, Obj: 0.306619, No Obj: 0.003528, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.563995, Class: 1.000000, Obj: 0.311697, No Obj: 0.003068, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.506961, Class: 1.000000, Obj: 0.288740, No Obj: 0.003659, Avg Recall: 0.601504, count: 133 -Region Avg IOU: 0.555363, Class: 1.000000, Obj: 0.247760, No Obj: 0.002850, Avg Recall: 0.684211, count: 76 -Region Avg IOU: 0.607941, Class: 1.000000, Obj: 0.349337, No Obj: 0.003367, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.553936, Class: 1.000000, Obj: 0.325060, No Obj: 0.002962, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.561414, Class: 1.000000, Obj: 0.278376, No Obj: 0.003416, Avg Recall: 0.643478, count: 115 -Region Avg IOU: 0.577531, Class: 1.000000, Obj: 0.299116, No Obj: 0.003162, Avg Recall: 0.731959, count: 97 -1308: 0.508422, 0.516366 avg, 0.001000 rate, 28.480362 seconds, 334848 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.591096, Class: 1.000000, Obj: 0.312806, No Obj: 0.003335, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.596851, Class: 1.000000, Obj: 0.304300, No Obj: 0.003664, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.511901, Class: 1.000000, Obj: 0.266413, No Obj: 0.003123, Avg Recall: 0.572581, count: 124 -Region Avg IOU: 0.548628, Class: 1.000000, Obj: 0.326215, No Obj: 0.003522, Avg Recall: 0.616667, count: 120 -Region Avg IOU: 0.569461, Class: 1.000000, Obj: 0.278599, No Obj: 0.002864, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.592824, Class: 1.000000, Obj: 0.301858, No Obj: 0.003393, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.519396, Class: 1.000000, Obj: 0.303996, No Obj: 0.003271, Avg Recall: 0.544643, count: 112 -Region Avg IOU: 0.542384, Class: 1.000000, Obj: 0.325685, No Obj: 0.003363, Avg Recall: 0.657407, count: 108 -1309: 0.515730, 0.516302 avg, 0.001000 rate, 26.069426 seconds, 335104 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.533339, Class: 1.000000, Obj: 0.271995, No Obj: 0.003588, Avg Recall: 0.572414, count: 145 -Region Avg IOU: 0.553998, Class: 1.000000, Obj: 0.288439, No Obj: 0.003223, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.542171, Class: 1.000000, Obj: 0.268211, No Obj: 0.003147, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.601175, Class: 1.000000, Obj: 0.274480, No Obj: 0.002915, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.571636, Class: 1.000000, Obj: 0.274549, No Obj: 0.003291, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.572539, Class: 1.000000, Obj: 0.331624, No Obj: 0.003358, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.578430, Class: 1.000000, Obj: 0.333410, No Obj: 0.003116, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.568224, Class: 1.000000, Obj: 0.276999, No Obj: 0.003004, Avg Recall: 0.666667, count: 102 -1310: 0.459856, 0.510657 avg, 0.001000 rate, 25.811144 seconds, 335360 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.666081 seconds -Region Avg IOU: 0.486938, Class: 1.000000, Obj: 0.300848, No Obj: 0.003208, Avg Recall: 0.550562, count: 89 -Region Avg IOU: 0.471621, Class: 1.000000, Obj: 0.241121, No Obj: 0.004359, Avg Recall: 0.540373, count: 161 -Region Avg IOU: 0.518595, Class: 1.000000, Obj: 0.265821, No Obj: 0.004124, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.480316, Class: 1.000000, Obj: 0.291134, No Obj: 0.003726, Avg Recall: 0.511111, count: 90 -Region Avg IOU: 0.536138, Class: 1.000000, Obj: 0.246371, No Obj: 0.003550, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.514914, Class: 1.000000, Obj: 0.270132, No Obj: 0.003703, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.433022, Class: 1.000000, Obj: 0.230283, No Obj: 0.003533, Avg Recall: 0.440000, count: 125 -Region Avg IOU: 0.496428, Class: 1.000000, Obj: 0.250558, No Obj: 0.003969, Avg Recall: 0.568493, count: 146 -1311: 0.562717, 0.515863 avg, 0.001000 rate, 22.922655 seconds, 335616 images -Loaded: 0.000055 seconds -Region Avg IOU: 0.462708, Class: 1.000000, Obj: 0.244894, No Obj: 0.003874, Avg Recall: 0.456250, count: 160 -Region Avg IOU: 0.494480, Class: 1.000000, Obj: 0.270020, No Obj: 0.003713, Avg Recall: 0.534483, count: 116 -Region Avg IOU: 0.528575, Class: 1.000000, Obj: 0.255187, No Obj: 0.003484, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.454356, Class: 1.000000, Obj: 0.223015, No Obj: 0.003608, Avg Recall: 0.495798, count: 119 -Region Avg IOU: 0.532973, Class: 1.000000, Obj: 0.206961, No Obj: 0.003499, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.447922, Class: 1.000000, Obj: 0.268568, No Obj: 0.003507, Avg Recall: 0.481481, count: 108 -Region Avg IOU: 0.518916, Class: 1.000000, Obj: 0.274455, No Obj: 0.003851, Avg Recall: 0.546296, count: 108 -Region Avg IOU: 0.532156, Class: 1.000000, Obj: 0.251231, No Obj: 0.003613, Avg Recall: 0.578947, count: 95 -1312: 0.494285, 0.513706 avg, 0.001000 rate, 22.164633 seconds, 335872 images -Loaded: 0.545595 seconds -Region Avg IOU: 0.551158, Class: 1.000000, Obj: 0.293381, No Obj: 0.003745, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.484322, Class: 1.000000, Obj: 0.253319, No Obj: 0.003408, Avg Recall: 0.485981, count: 107 -Region Avg IOU: 0.485684, Class: 1.000000, Obj: 0.251367, No Obj: 0.003502, Avg Recall: 0.540984, count: 122 -Region Avg IOU: 0.495342, Class: 1.000000, Obj: 0.236508, No Obj: 0.003299, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.551660, Class: 1.000000, Obj: 0.276847, No Obj: 0.003370, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.473108, Class: 1.000000, Obj: 0.216122, No Obj: 0.003800, Avg Recall: 0.521739, count: 138 -Region Avg IOU: 0.472544, Class: 1.000000, Obj: 0.228587, No Obj: 0.003501, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.522638, Class: 1.000000, Obj: 0.229700, No Obj: 0.003661, Avg Recall: 0.557377, count: 122 -1313: 0.477845, 0.510119 avg, 0.001000 rate, 22.810513 seconds, 336128 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.478929, Class: 1.000000, Obj: 0.257261, No Obj: 0.003749, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.492239, Class: 1.000000, Obj: 0.237753, No Obj: 0.003812, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.505356, Class: 1.000000, Obj: 0.280077, No Obj: 0.003721, Avg Recall: 0.543103, count: 116 -Region Avg IOU: 0.500414, Class: 1.000000, Obj: 0.235384, No Obj: 0.003633, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.472177, Class: 1.000000, Obj: 0.267116, No Obj: 0.003406, Avg Recall: 0.519231, count: 104 -Region Avg IOU: 0.546097, Class: 1.000000, Obj: 0.262641, No Obj: 0.003797, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.495813, Class: 1.000000, Obj: 0.247050, No Obj: 0.003760, Avg Recall: 0.540000, count: 100 -Region Avg IOU: 0.485269, Class: 1.000000, Obj: 0.243848, No Obj: 0.003870, Avg Recall: 0.530201, count: 149 -1314: 0.516045, 0.510712 avg, 0.001000 rate, 21.264469 seconds, 336384 images -Loaded: 0.695452 seconds -Region Avg IOU: 0.528040, Class: 1.000000, Obj: 0.302338, No Obj: 0.003571, Avg Recall: 0.651163, count: 86 -Region Avg IOU: 0.530051, Class: 1.000000, Obj: 0.278567, No Obj: 0.003966, Avg Recall: 0.622807, count: 114 -Region Avg IOU: 0.489070, Class: 1.000000, Obj: 0.256095, No Obj: 0.004017, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.459881, Class: 1.000000, Obj: 0.244990, No Obj: 0.003901, Avg Recall: 0.500000, count: 126 -Region Avg IOU: 0.456797, Class: 1.000000, Obj: 0.258598, No Obj: 0.003468, Avg Recall: 0.476190, count: 84 -Region Avg IOU: 0.472713, Class: 1.000000, Obj: 0.257007, No Obj: 0.004173, Avg Recall: 0.465649, count: 131 -Region Avg IOU: 0.512057, Class: 1.000000, Obj: 0.285413, No Obj: 0.003293, Avg Recall: 0.594595, count: 74 -Region Avg IOU: 0.518761, Class: 1.000000, Obj: 0.259075, No Obj: 0.004452, Avg Recall: 0.539683, count: 126 -1315: 0.490712, 0.508712 avg, 0.001000 rate, 21.692280 seconds, 336640 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.522021, Class: 1.000000, Obj: 0.286911, No Obj: 0.004134, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.486544, Class: 1.000000, Obj: 0.257660, No Obj: 0.004535, Avg Recall: 0.547826, count: 115 -Region Avg IOU: 0.492834, Class: 1.000000, Obj: 0.316706, No Obj: 0.004259, Avg Recall: 0.575758, count: 99 -Region Avg IOU: 0.512174, Class: 1.000000, Obj: 0.288517, No Obj: 0.004081, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.550935, Class: 1.000000, Obj: 0.283212, No Obj: 0.004228, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.504538, Class: 1.000000, Obj: 0.284156, No Obj: 0.004209, Avg Recall: 0.557252, count: 131 -Region Avg IOU: 0.481662, Class: 1.000000, Obj: 0.274110, No Obj: 0.004019, Avg Recall: 0.508333, count: 120 -Region Avg IOU: 0.519696, Class: 1.000000, Obj: 0.235636, No Obj: 0.004612, Avg Recall: 0.603448, count: 116 -1316: 0.504035, 0.508244 avg, 0.001000 rate, 21.946899 seconds, 336896 images -Loaded: 1.207422 seconds -Region Avg IOU: 0.489217, Class: 1.000000, Obj: 0.279631, No Obj: 0.004821, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.479504, Class: 1.000000, Obj: 0.331821, No Obj: 0.003992, Avg Recall: 0.535354, count: 99 -Region Avg IOU: 0.493895, Class: 1.000000, Obj: 0.305893, No Obj: 0.003686, Avg Recall: 0.562500, count: 80 -Region Avg IOU: 0.490132, Class: 1.000000, Obj: 0.267173, No Obj: 0.004441, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.491452, Class: 1.000000, Obj: 0.337744, No Obj: 0.004495, Avg Recall: 0.534653, count: 101 -Region Avg IOU: 0.505028, Class: 1.000000, Obj: 0.312622, No Obj: 0.004538, Avg Recall: 0.566667, count: 120 -Region Avg IOU: 0.484780, Class: 1.000000, Obj: 0.280526, No Obj: 0.004087, Avg Recall: 0.550000, count: 100 -Region Avg IOU: 0.518876, Class: 1.000000, Obj: 0.313365, No Obj: 0.004335, Avg Recall: 0.584071, count: 113 -1317: 0.496623, 0.507082 avg, 0.001000 rate, 23.317024 seconds, 337152 images -Loaded: 0.000051 seconds -Region Avg IOU: 0.498503, Class: 1.000000, Obj: 0.310315, No Obj: 0.004141, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.477114, Class: 1.000000, Obj: 0.330790, No Obj: 0.004508, Avg Recall: 0.538462, count: 104 -Region Avg IOU: 0.456746, Class: 1.000000, Obj: 0.269473, No Obj: 0.004070, Avg Recall: 0.465347, count: 101 -Region Avg IOU: 0.489309, Class: 1.000000, Obj: 0.319952, No Obj: 0.004543, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.483059, Class: 1.000000, Obj: 0.278279, No Obj: 0.004206, Avg Recall: 0.509434, count: 106 -Region Avg IOU: 0.443329, Class: 1.000000, Obj: 0.256112, No Obj: 0.004406, Avg Recall: 0.455357, count: 112 -Region Avg IOU: 0.486569, Class: 1.000000, Obj: 0.306522, No Obj: 0.005110, Avg Recall: 0.564935, count: 154 -Region Avg IOU: 0.494383, Class: 1.000000, Obj: 0.304572, No Obj: 0.004529, Avg Recall: 0.554622, count: 119 -1318: 0.578443, 0.514218 avg, 0.001000 rate, 21.576008 seconds, 337408 images -Loaded: 1.600885 seconds -Region Avg IOU: 0.487841, Class: 1.000000, Obj: 0.325015, No Obj: 0.004507, Avg Recall: 0.546219, count: 119 -Region Avg IOU: 0.486908, Class: 1.000000, Obj: 0.288707, No Obj: 0.004924, Avg Recall: 0.575000, count: 120 -Region Avg IOU: 0.508329, Class: 1.000000, Obj: 0.287329, No Obj: 0.004743, Avg Recall: 0.561983, count: 121 -Region Avg IOU: 0.457758, Class: 1.000000, Obj: 0.261339, No Obj: 0.004809, Avg Recall: 0.517241, count: 116 -Region Avg IOU: 0.530829, Class: 1.000000, Obj: 0.303913, No Obj: 0.004336, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.482272, Class: 1.000000, Obj: 0.310668, No Obj: 0.004857, Avg Recall: 0.544715, count: 123 -Region Avg IOU: 0.524975, Class: 1.000000, Obj: 0.299166, No Obj: 0.004559, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.470513, Class: 1.000000, Obj: 0.313377, No Obj: 0.004953, Avg Recall: 0.513699, count: 146 -1319: 0.569611, 0.519758 avg, 0.001000 rate, 21.443659 seconds, 337664 images -Loaded: 0.046595 seconds -Region Avg IOU: 0.555287, Class: 1.000000, Obj: 0.355378, No Obj: 0.004712, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.518848, Class: 1.000000, Obj: 0.316362, No Obj: 0.004222, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.404321, Class: 1.000000, Obj: 0.242377, No Obj: 0.004726, Avg Recall: 0.403226, count: 124 -Region Avg IOU: 0.467594, Class: 1.000000, Obj: 0.273232, No Obj: 0.004500, Avg Recall: 0.500000, count: 136 -Region Avg IOU: 0.459416, Class: 1.000000, Obj: 0.286980, No Obj: 0.004535, Avg Recall: 0.508475, count: 118 -Region Avg IOU: 0.531799, Class: 1.000000, Obj: 0.301749, No Obj: 0.004275, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.495584, Class: 1.000000, Obj: 0.329675, No Obj: 0.004241, Avg Recall: 0.542857, count: 105 -Region Avg IOU: 0.489993, Class: 1.000000, Obj: 0.332846, No Obj: 0.004139, Avg Recall: 0.574713, count: 87 -1320: 0.484764, 0.516258 avg, 0.001000 rate, 22.771654 seconds, 337920 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000074 seconds -Region Avg IOU: 0.500023, Class: 1.000000, Obj: 0.294005, No Obj: 0.004192, Avg Recall: 0.611111, count: 126 -Region Avg IOU: 0.571562, Class: 1.000000, Obj: 0.279815, No Obj: 0.004216, Avg Recall: 0.702290, count: 131 -Region Avg IOU: 0.545610, Class: 1.000000, Obj: 0.331146, No Obj: 0.004081, Avg Recall: 0.620690, count: 116 -Region Avg IOU: 0.572411, Class: 1.000000, Obj: 0.300035, No Obj: 0.004670, Avg Recall: 0.653595, count: 153 -Region Avg IOU: 0.523896, Class: 1.000000, Obj: 0.305505, No Obj: 0.003773, Avg Recall: 0.609195, count: 87 -Region Avg IOU: 0.532516, Class: 1.000000, Obj: 0.318769, No Obj: 0.003397, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.562573, Class: 1.000000, Obj: 0.326251, No Obj: 0.003402, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.540739, Class: 1.000000, Obj: 0.340602, No Obj: 0.003716, Avg Recall: 0.617647, count: 102 -1321: 0.517620, 0.516394 avg, 0.001000 rate, 25.404259 seconds, 338176 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.578681, Class: 1.000000, Obj: 0.290713, No Obj: 0.003519, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.574858, Class: 1.000000, Obj: 0.322472, No Obj: 0.003572, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.545090, Class: 1.000000, Obj: 0.264364, No Obj: 0.003405, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.557830, Class: 1.000000, Obj: 0.298081, No Obj: 0.003290, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.515456, Class: 1.000000, Obj: 0.291749, No Obj: 0.003193, Avg Recall: 0.654762, count: 84 -Region Avg IOU: 0.516680, Class: 1.000000, Obj: 0.295948, No Obj: 0.003229, Avg Recall: 0.544444, count: 90 -Region Avg IOU: 0.530396, Class: 1.000000, Obj: 0.287997, No Obj: 0.003947, Avg Recall: 0.603053, count: 131 -Region Avg IOU: 0.522744, Class: 1.000000, Obj: 0.275587, No Obj: 0.003091, Avg Recall: 0.600000, count: 100 -1322: 0.439557, 0.508711 avg, 0.001000 rate, 26.257893 seconds, 338432 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.517236, Class: 1.000000, Obj: 0.294333, No Obj: 0.003114, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.565409, Class: 1.000000, Obj: 0.325625, No Obj: 0.002702, Avg Recall: 0.687500, count: 80 -Region Avg IOU: 0.522138, Class: 1.000000, Obj: 0.253769, No Obj: 0.003457, Avg Recall: 0.606061, count: 132 -Region Avg IOU: 0.587090, Class: 1.000000, Obj: 0.315869, No Obj: 0.003145, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.529577, Class: 1.000000, Obj: 0.272676, No Obj: 0.003104, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.508786, Class: 1.000000, Obj: 0.314835, No Obj: 0.003147, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.542075, Class: 1.000000, Obj: 0.272860, No Obj: 0.003893, Avg Recall: 0.593750, count: 128 -Region Avg IOU: 0.587211, Class: 1.000000, Obj: 0.270153, No Obj: 0.003372, Avg Recall: 0.716814, count: 113 -1323: 0.461622, 0.504002 avg, 0.001000 rate, 24.497091 seconds, 338688 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.584333, Class: 1.000000, Obj: 0.267080, No Obj: 0.002808, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.596173, Class: 1.000000, Obj: 0.251325, No Obj: 0.003307, Avg Recall: 0.700787, count: 127 -Region Avg IOU: 0.512022, Class: 1.000000, Obj: 0.242917, No Obj: 0.002953, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.565296, Class: 1.000000, Obj: 0.283429, No Obj: 0.002725, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.562439, Class: 1.000000, Obj: 0.252573, No Obj: 0.003604, Avg Recall: 0.653846, count: 130 -Region Avg IOU: 0.580456, Class: 1.000000, Obj: 0.239739, No Obj: 0.003018, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.483085, Class: 1.000000, Obj: 0.243485, No Obj: 0.002740, Avg Recall: 0.543478, count: 92 -Region Avg IOU: 0.534844, Class: 1.000000, Obj: 0.244032, No Obj: 0.003337, Avg Recall: 0.608000, count: 125 -1324: 0.484378, 0.502039 avg, 0.001000 rate, 24.684816 seconds, 338944 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.565423, Class: 1.000000, Obj: 0.242039, No Obj: 0.003044, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.570730, Class: 1.000000, Obj: 0.264062, No Obj: 0.003007, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.560868, Class: 1.000000, Obj: 0.268489, No Obj: 0.003403, Avg Recall: 0.641026, count: 117 -Region Avg IOU: 0.554904, Class: 1.000000, Obj: 0.236273, No Obj: 0.002828, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.596315, Class: 1.000000, Obj: 0.259856, No Obj: 0.003024, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.523966, Class: 1.000000, Obj: 0.222530, No Obj: 0.002930, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.596708, Class: 1.000000, Obj: 0.293565, No Obj: 0.002823, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.574201, Class: 1.000000, Obj: 0.268835, No Obj: 0.002937, Avg Recall: 0.684211, count: 114 -1325: 0.490602, 0.500896 avg, 0.001000 rate, 25.954111 seconds, 339200 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.613668, Class: 1.000000, Obj: 0.250830, No Obj: 0.002591, Avg Recall: 0.747126, count: 87 -Region Avg IOU: 0.560833, Class: 1.000000, Obj: 0.250967, No Obj: 0.003189, Avg Recall: 0.650000, count: 120 -Region Avg IOU: 0.582287, Class: 1.000000, Obj: 0.269767, No Obj: 0.003203, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.563927, Class: 1.000000, Obj: 0.284277, No Obj: 0.003134, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.537080, Class: 1.000000, Obj: 0.220362, No Obj: 0.003287, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.596656, Class: 1.000000, Obj: 0.278608, No Obj: 0.003382, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.562671, Class: 1.000000, Obj: 0.268204, No Obj: 0.003107, Avg Recall: 0.681481, count: 135 -Region Avg IOU: 0.561776, Class: 1.000000, Obj: 0.316771, No Obj: 0.002786, Avg Recall: 0.705128, count: 78 -1326: 0.492015, 0.500008 avg, 0.001000 rate, 25.705416 seconds, 339456 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.558563, Class: 1.000000, Obj: 0.254038, No Obj: 0.003134, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.593145, Class: 1.000000, Obj: 0.299782, No Obj: 0.003352, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.568097, Class: 1.000000, Obj: 0.253329, No Obj: 0.003486, Avg Recall: 0.634146, count: 123 -Region Avg IOU: 0.532198, Class: 1.000000, Obj: 0.262465, No Obj: 0.003361, Avg Recall: 0.643939, count: 132 -Region Avg IOU: 0.581882, Class: 1.000000, Obj: 0.239332, No Obj: 0.003124, Avg Recall: 0.726562, count: 128 -Region Avg IOU: 0.577779, Class: 1.000000, Obj: 0.250830, No Obj: 0.003190, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.564216, Class: 1.000000, Obj: 0.304477, No Obj: 0.003136, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.560593, Class: 1.000000, Obj: 0.297066, No Obj: 0.002967, Avg Recall: 0.646018, count: 113 -1327: 0.545439, 0.504551 avg, 0.001000 rate, 26.010241 seconds, 339712 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.576015, Class: 1.000000, Obj: 0.282580, No Obj: 0.003174, Avg Recall: 0.709302, count: 86 -Region Avg IOU: 0.539339, Class: 1.000000, Obj: 0.272132, No Obj: 0.003599, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.589248, Class: 1.000000, Obj: 0.274034, No Obj: 0.003912, Avg Recall: 0.750000, count: 124 -Region Avg IOU: 0.583555, Class: 1.000000, Obj: 0.241258, No Obj: 0.003004, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.559037, Class: 1.000000, Obj: 0.297616, No Obj: 0.003478, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.560167, Class: 1.000000, Obj: 0.271961, No Obj: 0.003608, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.563533, Class: 1.000000, Obj: 0.292356, No Obj: 0.003023, Avg Recall: 0.630435, count: 92 -Region Avg IOU: 0.516930, Class: 1.000000, Obj: 0.300738, No Obj: 0.003442, Avg Recall: 0.554794, count: 146 -1328: 0.519984, 0.506094 avg, 0.001000 rate, 24.947271 seconds, 339968 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.562383, Class: 1.000000, Obj: 0.359629, No Obj: 0.003476, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.570747, Class: 1.000000, Obj: 0.305006, No Obj: 0.003415, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.565144, Class: 1.000000, Obj: 0.316041, No Obj: 0.003750, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.544108, Class: 1.000000, Obj: 0.291900, No Obj: 0.004048, Avg Recall: 0.664234, count: 137 -Region Avg IOU: 0.567109, Class: 1.000000, Obj: 0.300809, No Obj: 0.003336, Avg Recall: 0.651163, count: 86 -Region Avg IOU: 0.548372, Class: 1.000000, Obj: 0.291833, No Obj: 0.003817, Avg Recall: 0.643939, count: 132 -Region Avg IOU: 0.607533, Class: 1.000000, Obj: 0.338638, No Obj: 0.003992, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.575323, Class: 1.000000, Obj: 0.308853, No Obj: 0.003414, Avg Recall: 0.719298, count: 114 -1329: 0.497972, 0.505282 avg, 0.001000 rate, 26.358822 seconds, 340224 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.577727, Class: 1.000000, Obj: 0.322891, No Obj: 0.003629, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.522765, Class: 1.000000, Obj: 0.250143, No Obj: 0.003358, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.542298, Class: 1.000000, Obj: 0.303607, No Obj: 0.003523, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.551560, Class: 1.000000, Obj: 0.300081, No Obj: 0.003717, Avg Recall: 0.644444, count: 90 -Region Avg IOU: 0.590137, Class: 1.000000, Obj: 0.330781, No Obj: 0.003773, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.562724, Class: 1.000000, Obj: 0.337531, No Obj: 0.003846, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.596549, Class: 1.000000, Obj: 0.349434, No Obj: 0.003464, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.570391, Class: 1.000000, Obj: 0.298952, No Obj: 0.004055, Avg Recall: 0.707317, count: 123 -1330: 0.494981, 0.504252 avg, 0.001000 rate, 26.122368 seconds, 340480 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000073 seconds -Region Avg IOU: 0.551547, Class: 1.000000, Obj: 0.331855, No Obj: 0.003081, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.574139, Class: 1.000000, Obj: 0.303169, No Obj: 0.003299, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.581482, Class: 1.000000, Obj: 0.306753, No Obj: 0.003333, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.556327, Class: 1.000000, Obj: 0.298628, No Obj: 0.003526, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.549792, Class: 1.000000, Obj: 0.308306, No Obj: 0.003274, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.599539, Class: 1.000000, Obj: 0.328755, No Obj: 0.004088, Avg Recall: 0.738462, count: 130 -Region Avg IOU: 0.637489, Class: 1.000000, Obj: 0.357676, No Obj: 0.003297, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.576584, Class: 1.000000, Obj: 0.336995, No Obj: 0.003335, Avg Recall: 0.631068, count: 103 -1331: 0.463389, 0.500165 avg, 0.001000 rate, 27.371832 seconds, 340736 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.583729, Class: 1.000000, Obj: 0.255771, No Obj: 0.003578, Avg Recall: 0.714286, count: 126 -Region Avg IOU: 0.592353, Class: 1.000000, Obj: 0.350494, No Obj: 0.003220, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.519544, Class: 1.000000, Obj: 0.265023, No Obj: 0.003075, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.609550, Class: 1.000000, Obj: 0.322322, No Obj: 0.003062, Avg Recall: 0.761905, count: 84 -Region Avg IOU: 0.584430, Class: 1.000000, Obj: 0.332372, No Obj: 0.003731, Avg Recall: 0.714286, count: 126 -Region Avg IOU: 0.541373, Class: 1.000000, Obj: 0.247056, No Obj: 0.003502, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.598824, Class: 1.000000, Obj: 0.301508, No Obj: 0.003613, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.544978, Class: 1.000000, Obj: 0.295291, No Obj: 0.003362, Avg Recall: 0.631148, count: 122 -1332: 0.474829, 0.497632 avg, 0.001000 rate, 26.487207 seconds, 340992 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.599385, Class: 1.000000, Obj: 0.296305, No Obj: 0.002963, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.555903, Class: 1.000000, Obj: 0.251911, No Obj: 0.003268, Avg Recall: 0.664122, count: 131 -Region Avg IOU: 0.577564, Class: 1.000000, Obj: 0.276986, No Obj: 0.003125, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.559546, Class: 1.000000, Obj: 0.242158, No Obj: 0.002949, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.552778, Class: 1.000000, Obj: 0.303567, No Obj: 0.003111, Avg Recall: 0.610169, count: 118 -Region Avg IOU: 0.558161, Class: 1.000000, Obj: 0.278852, No Obj: 0.003240, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.524377, Class: 1.000000, Obj: 0.271792, No Obj: 0.002861, Avg Recall: 0.548387, count: 93 -Region Avg IOU: 0.550278, Class: 1.000000, Obj: 0.273278, No Obj: 0.002767, Avg Recall: 0.705263, count: 95 -1333: 0.476449, 0.495514 avg, 0.001000 rate, 26.222618 seconds, 341248 images -Loaded: 0.000115 seconds -Region Avg IOU: 0.594529, Class: 1.000000, Obj: 0.264363, No Obj: 0.002937, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.579512, Class: 1.000000, Obj: 0.251375, No Obj: 0.002845, Avg Recall: 0.717742, count: 124 -Region Avg IOU: 0.559612, Class: 1.000000, Obj: 0.252676, No Obj: 0.002704, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.506570, Class: 1.000000, Obj: 0.237533, No Obj: 0.002903, Avg Recall: 0.589147, count: 129 -Region Avg IOU: 0.551842, Class: 1.000000, Obj: 0.306353, No Obj: 0.002646, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.589352, Class: 1.000000, Obj: 0.283550, No Obj: 0.003024, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.588094, Class: 1.000000, Obj: 0.269697, No Obj: 0.002766, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.536443, Class: 1.000000, Obj: 0.243619, No Obj: 0.002969, Avg Recall: 0.613861, count: 101 -1334: 0.496501, 0.495612 avg, 0.001000 rate, 27.055868 seconds, 341504 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.583278, Class: 1.000000, Obj: 0.265056, No Obj: 0.002519, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.577563, Class: 1.000000, Obj: 0.287412, No Obj: 0.002752, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.598954, Class: 1.000000, Obj: 0.287316, No Obj: 0.002527, Avg Recall: 0.761364, count: 88 -Region Avg IOU: 0.593813, Class: 1.000000, Obj: 0.280754, No Obj: 0.002726, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.573428, Class: 1.000000, Obj: 0.272780, No Obj: 0.002574, Avg Recall: 0.709302, count: 86 -Region Avg IOU: 0.603327, Class: 1.000000, Obj: 0.264519, No Obj: 0.002748, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.538867, Class: 1.000000, Obj: 0.261621, No Obj: 0.002610, Avg Recall: 0.644444, count: 90 -Region Avg IOU: 0.573534, Class: 1.000000, Obj: 0.264188, No Obj: 0.002961, Avg Recall: 0.691667, count: 120 -1335: 0.429728, 0.489024 avg, 0.001000 rate, 27.911123 seconds, 341760 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.613106, Class: 1.000000, Obj: 0.280870, No Obj: 0.002797, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.587846, Class: 1.000000, Obj: 0.209529, No Obj: 0.003117, Avg Recall: 0.690647, count: 139 -Region Avg IOU: 0.592490, Class: 1.000000, Obj: 0.240657, No Obj: 0.002902, Avg Recall: 0.737931, count: 145 -Region Avg IOU: 0.609171, Class: 1.000000, Obj: 0.297064, No Obj: 0.002767, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.599395, Class: 1.000000, Obj: 0.272593, No Obj: 0.003058, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.582565, Class: 1.000000, Obj: 0.269384, No Obj: 0.002527, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.569855, Class: 1.000000, Obj: 0.255159, No Obj: 0.002996, Avg Recall: 0.705426, count: 129 -Region Avg IOU: 0.597102, Class: 1.000000, Obj: 0.290638, No Obj: 0.002845, Avg Recall: 0.747826, count: 115 -1336: 0.531954, 0.493317 avg, 0.001000 rate, 28.563351 seconds, 342016 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.599961, Class: 1.000000, Obj: 0.272546, No Obj: 0.003119, Avg Recall: 0.733871, count: 124 -Region Avg IOU: 0.605998, Class: 1.000000, Obj: 0.265415, No Obj: 0.002817, Avg Recall: 0.716049, count: 81 -Region Avg IOU: 0.582171, Class: 1.000000, Obj: 0.252108, No Obj: 0.003142, Avg Recall: 0.698529, count: 136 -Region Avg IOU: 0.569195, Class: 1.000000, Obj: 0.262171, No Obj: 0.002793, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.543808, Class: 1.000000, Obj: 0.249153, No Obj: 0.002654, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.572963, Class: 1.000000, Obj: 0.238395, No Obj: 0.002950, Avg Recall: 0.652542, count: 118 -Region Avg IOU: 0.599840, Class: 1.000000, Obj: 0.291599, No Obj: 0.002761, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.594747, Class: 1.000000, Obj: 0.250252, No Obj: 0.002820, Avg Recall: 0.747826, count: 115 -1337: 0.496837, 0.493669 avg, 0.001000 rate, 26.696136 seconds, 342272 images -Loaded: 0.000120 seconds -Region Avg IOU: 0.589112, Class: 1.000000, Obj: 0.304021, No Obj: 0.003073, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.578926, Class: 1.000000, Obj: 0.305696, No Obj: 0.003045, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.570482, Class: 1.000000, Obj: 0.280015, No Obj: 0.002734, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.586477, Class: 1.000000, Obj: 0.276048, No Obj: 0.003051, Avg Recall: 0.705882, count: 119 -Region Avg IOU: 0.573830, Class: 1.000000, Obj: 0.295184, No Obj: 0.002866, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.558095, Class: 1.000000, Obj: 0.254964, No Obj: 0.002970, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.584884, Class: 1.000000, Obj: 0.286896, No Obj: 0.002897, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.552329, Class: 1.000000, Obj: 0.285639, No Obj: 0.002768, Avg Recall: 0.621359, count: 103 -1338: 0.457716, 0.490074 avg, 0.001000 rate, 25.805147 seconds, 342528 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.554529, Class: 1.000000, Obj: 0.269059, No Obj: 0.002774, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.584957, Class: 1.000000, Obj: 0.307380, No Obj: 0.003034, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.579916, Class: 1.000000, Obj: 0.266362, No Obj: 0.002778, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.618071, Class: 1.000000, Obj: 0.329764, No Obj: 0.003054, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.558676, Class: 1.000000, Obj: 0.302463, No Obj: 0.003226, Avg Recall: 0.629371, count: 143 -Region Avg IOU: 0.586888, Class: 1.000000, Obj: 0.309498, No Obj: 0.002939, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.575633, Class: 1.000000, Obj: 0.327030, No Obj: 0.003027, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.612512, Class: 1.000000, Obj: 0.293103, No Obj: 0.003401, Avg Recall: 0.752137, count: 117 -1339: 0.468690, 0.487935 avg, 0.001000 rate, 27.893587 seconds, 342784 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.573965, Class: 1.000000, Obj: 0.272343, No Obj: 0.003546, Avg Recall: 0.719697, count: 132 -Region Avg IOU: 0.595252, Class: 1.000000, Obj: 0.278163, No Obj: 0.003594, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.581207, Class: 1.000000, Obj: 0.312220, No Obj: 0.003092, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.598840, Class: 1.000000, Obj: 0.305650, No Obj: 0.003277, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.576032, Class: 1.000000, Obj: 0.299998, No Obj: 0.002771, Avg Recall: 0.674699, count: 83 -Region Avg IOU: 0.590849, Class: 1.000000, Obj: 0.299906, No Obj: 0.002890, Avg Recall: 0.716049, count: 81 -Region Avg IOU: 0.598569, Class: 1.000000, Obj: 0.303427, No Obj: 0.003112, Avg Recall: 0.730769, count: 78 -Region Avg IOU: 0.570683, Class: 1.000000, Obj: 0.294057, No Obj: 0.003612, Avg Recall: 0.700000, count: 130 -1340: 0.464334, 0.485575 avg, 0.001000 rate, 27.069103 seconds, 343040 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.353827 seconds -Region Avg IOU: 0.456581, Class: 1.000000, Obj: 0.237028, No Obj: 0.004216, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.464189, Class: 1.000000, Obj: 0.238327, No Obj: 0.004358, Avg Recall: 0.517544, count: 114 -Region Avg IOU: 0.389515, Class: 1.000000, Obj: 0.215835, No Obj: 0.004291, Avg Recall: 0.409091, count: 132 -Region Avg IOU: 0.443985, Class: 1.000000, Obj: 0.269988, No Obj: 0.003964, Avg Recall: 0.428571, count: 105 -Region Avg IOU: 0.428824, Class: 1.000000, Obj: 0.236040, No Obj: 0.004516, Avg Recall: 0.436975, count: 119 -Region Avg IOU: 0.477298, Class: 1.000000, Obj: 0.268131, No Obj: 0.004818, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.467726, Class: 1.000000, Obj: 0.281610, No Obj: 0.004746, Avg Recall: 0.476923, count: 130 -Region Avg IOU: 0.471433, Class: 1.000000, Obj: 0.271961, No Obj: 0.004018, Avg Recall: 0.471698, count: 106 -1341: 0.695752, 0.506593 avg, 0.001000 rate, 19.345804 seconds, 343296 images -Loaded: 1.549274 seconds -Region Avg IOU: 0.407135, Class: 1.000000, Obj: 0.228785, No Obj: 0.004279, Avg Recall: 0.439655, count: 116 -Region Avg IOU: 0.419700, Class: 1.000000, Obj: 0.237019, No Obj: 0.003924, Avg Recall: 0.388889, count: 108 -Region Avg IOU: 0.414064, Class: 1.000000, Obj: 0.208536, No Obj: 0.004882, Avg Recall: 0.430894, count: 123 -Region Avg IOU: 0.450088, Class: 1.000000, Obj: 0.260002, No Obj: 0.004504, Avg Recall: 0.500000, count: 100 -Region Avg IOU: 0.471331, Class: 1.000000, Obj: 0.242700, No Obj: 0.003826, Avg Recall: 0.480392, count: 102 -Region Avg IOU: 0.464594, Class: 1.000000, Obj: 0.288786, No Obj: 0.003856, Avg Recall: 0.518072, count: 83 -Region Avg IOU: 0.475484, Class: 1.000000, Obj: 0.298505, No Obj: 0.003733, Avg Recall: 0.476923, count: 65 -Region Avg IOU: 0.478974, Class: 1.000000, Obj: 0.278556, No Obj: 0.004483, Avg Recall: 0.476635, count: 107 -1342: 0.487003, 0.504634 avg, 0.001000 rate, 17.410908 seconds, 343552 images -Loaded: 4.336930 seconds -Region Avg IOU: 0.513858, Class: 1.000000, Obj: 0.250332, No Obj: 0.004322, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.440895, Class: 1.000000, Obj: 0.274440, No Obj: 0.004908, Avg Recall: 0.512000, count: 125 -Region Avg IOU: 0.443229, Class: 1.000000, Obj: 0.266128, No Obj: 0.004252, Avg Recall: 0.467890, count: 109 -Region Avg IOU: 0.436704, Class: 1.000000, Obj: 0.296855, No Obj: 0.004073, Avg Recall: 0.432692, count: 104 -Region Avg IOU: 0.471968, Class: 1.000000, Obj: 0.260871, No Obj: 0.004051, Avg Recall: 0.545455, count: 77 -Region Avg IOU: 0.458516, Class: 1.000000, Obj: 0.288658, No Obj: 0.004973, Avg Recall: 0.480769, count: 104 -Region Avg IOU: 0.478524, Class: 1.000000, Obj: 0.258615, No Obj: 0.004314, Avg Recall: 0.494505, count: 91 -Region Avg IOU: 0.484160, Class: 1.000000, Obj: 0.303724, No Obj: 0.004788, Avg Recall: 0.543689, count: 103 -1343: 0.499747, 0.504145 avg, 0.001000 rate, 15.735064 seconds, 343808 images -Loaded: 4.964861 seconds -Region Avg IOU: 0.447621, Class: 1.000000, Obj: 0.269648, No Obj: 0.004128, Avg Recall: 0.525000, count: 80 -Region Avg IOU: 0.469374, Class: 1.000000, Obj: 0.301121, No Obj: 0.005010, Avg Recall: 0.512397, count: 121 -Region Avg IOU: 0.441631, Class: 1.000000, Obj: 0.280269, No Obj: 0.004196, Avg Recall: 0.485149, count: 101 -Region Avg IOU: 0.438201, Class: 1.000000, Obj: 0.289360, No Obj: 0.003777, Avg Recall: 0.478261, count: 92 -Region Avg IOU: 0.473461, Class: 1.000000, Obj: 0.275712, No Obj: 0.004340, Avg Recall: 0.533333, count: 105 -Region Avg IOU: 0.411511, Class: 1.000000, Obj: 0.261444, No Obj: 0.004980, Avg Recall: 0.423077, count: 156 -Region Avg IOU: 0.435648, Class: 1.000000, Obj: 0.246488, No Obj: 0.004303, Avg Recall: 0.446281, count: 121 -Region Avg IOU: 0.451408, Class: 1.000000, Obj: 0.281866, No Obj: 0.004516, Avg Recall: 0.485149, count: 101 -1344: 0.484156, 0.502146 avg, 0.001000 rate, 20.091721 seconds, 344064 images -Loaded: 3.113944 seconds -Region Avg IOU: 0.429488, Class: 1.000000, Obj: 0.264883, No Obj: 0.004090, Avg Recall: 0.415730, count: 89 -Region Avg IOU: 0.440948, Class: 1.000000, Obj: 0.269559, No Obj: 0.004633, Avg Recall: 0.436893, count: 103 -Region Avg IOU: 0.488844, Class: 1.000000, Obj: 0.226277, No Obj: 0.004711, Avg Recall: 0.514286, count: 105 -Region Avg IOU: 0.488385, Class: 1.000000, Obj: 0.298252, No Obj: 0.004669, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.392770, Class: 1.000000, Obj: 0.223524, No Obj: 0.004190, Avg Recall: 0.393939, count: 132 -Region Avg IOU: 0.433192, Class: 1.000000, Obj: 0.288447, No Obj: 0.004796, Avg Recall: 0.421488, count: 121 -Region Avg IOU: 0.455096, Class: 1.000000, Obj: 0.294843, No Obj: 0.003985, Avg Recall: 0.506494, count: 77 -Region Avg IOU: 0.502923, Class: 1.000000, Obj: 0.291138, No Obj: 0.004840, Avg Recall: 0.543689, count: 103 -1345: 0.424985, 0.494430 avg, 0.001000 rate, 17.686169 seconds, 344320 images -Loaded: 4.988478 seconds -Region Avg IOU: 0.446496, Class: 1.000000, Obj: 0.236648, No Obj: 0.004137, Avg Recall: 0.484536, count: 97 -Region Avg IOU: 0.464370, Class: 1.000000, Obj: 0.300165, No Obj: 0.004328, Avg Recall: 0.530612, count: 98 -Region Avg IOU: 0.488969, Class: 1.000000, Obj: 0.235154, No Obj: 0.004522, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.426792, Class: 1.000000, Obj: 0.226054, No Obj: 0.004289, Avg Recall: 0.433333, count: 120 -Region Avg IOU: 0.426840, Class: 1.000000, Obj: 0.217892, No Obj: 0.004729, Avg Recall: 0.440678, count: 118 -Region Avg IOU: 0.397098, Class: 1.000000, Obj: 0.250684, No Obj: 0.004613, Avg Recall: 0.379845, count: 129 -Region Avg IOU: 0.394250, Class: 1.000000, Obj: 0.214951, No Obj: 0.004192, Avg Recall: 0.425743, count: 101 -Region Avg IOU: 0.383573, Class: 1.000000, Obj: 0.241665, No Obj: 0.003994, Avg Recall: 0.378947, count: 95 -1346: 0.457764, 0.490764 avg, 0.001000 rate, 17.264233 seconds, 344576 images -Loaded: 1.760199 seconds -Region Avg IOU: 0.468552, Class: 1.000000, Obj: 0.251779, No Obj: 0.004280, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.421835, Class: 1.000000, Obj: 0.280222, No Obj: 0.003931, Avg Recall: 0.409639, count: 83 -Region Avg IOU: 0.409763, Class: 1.000000, Obj: 0.237793, No Obj: 0.004832, Avg Recall: 0.428571, count: 140 -Region Avg IOU: 0.432093, Class: 1.000000, Obj: 0.245085, No Obj: 0.003718, Avg Recall: 0.457143, count: 70 -Region Avg IOU: 0.450532, Class: 1.000000, Obj: 0.292459, No Obj: 0.003981, Avg Recall: 0.490000, count: 100 -Region Avg IOU: 0.422730, Class: 1.000000, Obj: 0.253444, No Obj: 0.004022, Avg Recall: 0.466667, count: 90 -Region Avg IOU: 0.430194, Class: 1.000000, Obj: 0.253286, No Obj: 0.004222, Avg Recall: 0.459184, count: 98 -Region Avg IOU: 0.350415, Class: 1.000000, Obj: 0.203997, No Obj: 0.003880, Avg Recall: 0.356436, count: 101 -1347: 0.492198, 0.490907 avg, 0.001000 rate, 17.631536 seconds, 344832 images -Loaded: 4.668089 seconds -Region Avg IOU: 0.444868, Class: 1.000000, Obj: 0.207671, No Obj: 0.004568, Avg Recall: 0.443662, count: 142 -Region Avg IOU: 0.442576, Class: 1.000000, Obj: 0.272147, No Obj: 0.003623, Avg Recall: 0.468750, count: 96 -Region Avg IOU: 0.435409, Class: 1.000000, Obj: 0.263216, No Obj: 0.004666, Avg Recall: 0.457364, count: 129 -Region Avg IOU: 0.454439, Class: 1.000000, Obj: 0.239389, No Obj: 0.004214, Avg Recall: 0.521008, count: 119 -Region Avg IOU: 0.455265, Class: 1.000000, Obj: 0.267837, No Obj: 0.003860, Avg Recall: 0.463918, count: 97 -Region Avg IOU: 0.408623, Class: 1.000000, Obj: 0.236097, No Obj: 0.003880, Avg Recall: 0.425926, count: 108 -Region Avg IOU: 0.452105, Class: 1.000000, Obj: 0.235413, No Obj: 0.004286, Avg Recall: 0.491379, count: 116 -Region Avg IOU: 0.477637, Class: 1.000000, Obj: 0.284227, No Obj: 0.004181, Avg Recall: 0.529412, count: 85 -1348: 0.564485, 0.498265 avg, 0.001000 rate, 15.140586 seconds, 345088 images -Loaded: 5.578088 seconds -Region Avg IOU: 0.446380, Class: 1.000000, Obj: 0.222566, No Obj: 0.004592, Avg Recall: 0.463415, count: 123 -Region Avg IOU: 0.463012, Class: 1.000000, Obj: 0.278578, No Obj: 0.004141, Avg Recall: 0.491379, count: 116 -Region Avg IOU: 0.404067, Class: 1.000000, Obj: 0.226401, No Obj: 0.003638, Avg Recall: 0.394495, count: 109 -Region Avg IOU: 0.485532, Class: 1.000000, Obj: 0.274721, No Obj: 0.004092, Avg Recall: 0.494624, count: 93 -Region Avg IOU: 0.476499, Class: 1.000000, Obj: 0.263381, No Obj: 0.004774, Avg Recall: 0.504202, count: 119 -Region Avg IOU: 0.437996, Class: 1.000000, Obj: 0.257266, No Obj: 0.004298, Avg Recall: 0.470000, count: 100 -Region Avg IOU: 0.429419, Class: 1.000000, Obj: 0.240042, No Obj: 0.004483, Avg Recall: 0.429577, count: 142 -Region Avg IOU: 0.426880, Class: 1.000000, Obj: 0.252004, No Obj: 0.003800, Avg Recall: 0.429825, count: 114 -1349: 0.565858, 0.505024 avg, 0.001000 rate, 19.063210 seconds, 345344 images -Loaded: 3.160948 seconds -Region Avg IOU: 0.447675, Class: 1.000000, Obj: 0.277281, No Obj: 0.003993, Avg Recall: 0.476635, count: 107 -Region Avg IOU: 0.404946, Class: 1.000000, Obj: 0.247255, No Obj: 0.003579, Avg Recall: 0.406977, count: 86 -Region Avg IOU: 0.461629, Class: 1.000000, Obj: 0.247041, No Obj: 0.004462, Avg Recall: 0.472222, count: 144 -Region Avg IOU: 0.462784, Class: 1.000000, Obj: 0.286854, No Obj: 0.004015, Avg Recall: 0.500000, count: 104 -Region Avg IOU: 0.444190, Class: 1.000000, Obj: 0.250580, No Obj: 0.004581, Avg Recall: 0.485714, count: 105 -Region Avg IOU: 0.457637, Class: 1.000000, Obj: 0.247839, No Obj: 0.004065, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.510093, Class: 1.000000, Obj: 0.273784, No Obj: 0.004227, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.456407, Class: 1.000000, Obj: 0.247462, No Obj: 0.003823, Avg Recall: 0.457627, count: 118 -1350: 0.556036, 0.510125 avg, 0.001000 rate, 18.825239 seconds, 345600 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000092 seconds -Region Avg IOU: 0.537762, Class: 1.000000, Obj: 0.323005, No Obj: 0.003714, Avg Recall: 0.609195, count: 174 -Region Avg IOU: 0.550450, Class: 1.000000, Obj: 0.304375, No Obj: 0.002956, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.556395, Class: 1.000000, Obj: 0.260558, No Obj: 0.003189, Avg Recall: 0.679688, count: 128 -Region Avg IOU: 0.590788, Class: 1.000000, Obj: 0.313945, No Obj: 0.003258, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.593355, Class: 1.000000, Obj: 0.324713, No Obj: 0.002904, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.620683, Class: 1.000000, Obj: 0.334031, No Obj: 0.003593, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.610112, Class: 1.000000, Obj: 0.296072, No Obj: 0.003235, Avg Recall: 0.789474, count: 114 -Region Avg IOU: 0.563596, Class: 1.000000, Obj: 0.341023, No Obj: 0.002638, Avg Recall: 0.640449, count: 89 -1351: 0.539625, 0.513075 avg, 0.001000 rate, 28.222063 seconds, 345856 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.584604, Class: 1.000000, Obj: 0.303570, No Obj: 0.003368, Avg Recall: 0.740458, count: 131 -Region Avg IOU: 0.594304, Class: 1.000000, Obj: 0.305517, No Obj: 0.002688, Avg Recall: 0.720930, count: 86 -Region Avg IOU: 0.563112, Class: 1.000000, Obj: 0.269662, No Obj: 0.003191, Avg Recall: 0.648438, count: 128 -Region Avg IOU: 0.522361, Class: 1.000000, Obj: 0.264293, No Obj: 0.002974, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.604559, Class: 1.000000, Obj: 0.324123, No Obj: 0.002968, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.595842, Class: 1.000000, Obj: 0.307178, No Obj: 0.002786, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.645568, Class: 1.000000, Obj: 0.354092, No Obj: 0.002989, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.572041, Class: 1.000000, Obj: 0.284092, No Obj: 0.003267, Avg Recall: 0.652893, count: 121 -1352: 0.475869, 0.509355 avg, 0.001000 rate, 28.614817 seconds, 346112 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.603729, Class: 1.000000, Obj: 0.347878, No Obj: 0.002937, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.563157, Class: 1.000000, Obj: 0.250687, No Obj: 0.003012, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.599339, Class: 1.000000, Obj: 0.253659, No Obj: 0.002848, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.590291, Class: 1.000000, Obj: 0.296124, No Obj: 0.002909, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.574239, Class: 1.000000, Obj: 0.283270, No Obj: 0.002647, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.543918, Class: 1.000000, Obj: 0.221333, No Obj: 0.002976, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.565857, Class: 1.000000, Obj: 0.239753, No Obj: 0.002693, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.652092, Class: 1.000000, Obj: 0.292124, No Obj: 0.003171, Avg Recall: 0.810345, count: 116 -1353: 0.479384, 0.506358 avg, 0.001000 rate, 27.247467 seconds, 346368 images -Loaded: 0.000165 seconds -Region Avg IOU: 0.616048, Class: 1.000000, Obj: 0.274993, No Obj: 0.002787, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.598455, Class: 1.000000, Obj: 0.266973, No Obj: 0.002554, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.569005, Class: 1.000000, Obj: 0.264002, No Obj: 0.002720, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.571187, Class: 1.000000, Obj: 0.233167, No Obj: 0.003136, Avg Recall: 0.671642, count: 134 -Region Avg IOU: 0.596886, Class: 1.000000, Obj: 0.273075, No Obj: 0.002841, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.599275, Class: 1.000000, Obj: 0.268448, No Obj: 0.002924, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.589973, Class: 1.000000, Obj: 0.257272, No Obj: 0.002972, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.552798, Class: 1.000000, Obj: 0.254195, No Obj: 0.002761, Avg Recall: 0.621622, count: 111 -1354: 0.555426, 0.511264 avg, 0.001000 rate, 28.275642 seconds, 346624 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.607991, Class: 1.000000, Obj: 0.274825, No Obj: 0.002921, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.642856, Class: 1.000000, Obj: 0.299369, No Obj: 0.002729, Avg Recall: 0.830000, count: 100 -Region Avg IOU: 0.616111, Class: 1.000000, Obj: 0.253232, No Obj: 0.002966, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.588040, Class: 1.000000, Obj: 0.320554, No Obj: 0.002727, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.588874, Class: 1.000000, Obj: 0.277749, No Obj: 0.003233, Avg Recall: 0.742647, count: 136 -Region Avg IOU: 0.593839, Class: 1.000000, Obj: 0.277525, No Obj: 0.002642, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.554232, Class: 1.000000, Obj: 0.350361, No Obj: 0.002383, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.559415, Class: 1.000000, Obj: 0.283457, No Obj: 0.002460, Avg Recall: 0.649485, count: 97 -1355: 0.509815, 0.511120 avg, 0.001000 rate, 29.169716 seconds, 346880 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.582530, Class: 1.000000, Obj: 0.242927, No Obj: 0.002903, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.587988, Class: 1.000000, Obj: 0.273276, No Obj: 0.002386, Avg Recall: 0.697368, count: 76 -Region Avg IOU: 0.599352, Class: 1.000000, Obj: 0.260092, No Obj: 0.003003, Avg Recall: 0.738806, count: 134 -Region Avg IOU: 0.581608, Class: 1.000000, Obj: 0.271170, No Obj: 0.002639, Avg Recall: 0.701613, count: 124 -Region Avg IOU: 0.567740, Class: 1.000000, Obj: 0.267681, No Obj: 0.002868, Avg Recall: 0.697479, count: 119 -Region Avg IOU: 0.594211, Class: 1.000000, Obj: 0.244597, No Obj: 0.002664, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.588517, Class: 1.000000, Obj: 0.293551, No Obj: 0.002699, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.551628, Class: 1.000000, Obj: 0.269342, No Obj: 0.002706, Avg Recall: 0.631579, count: 95 -1356: 0.515567, 0.511564 avg, 0.001000 rate, 27.436232 seconds, 347136 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.610115, Class: 1.000000, Obj: 0.281193, No Obj: 0.002821, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.565345, Class: 1.000000, Obj: 0.255907, No Obj: 0.002633, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.620601, Class: 1.000000, Obj: 0.268186, No Obj: 0.003041, Avg Recall: 0.768657, count: 134 -Region Avg IOU: 0.580347, Class: 1.000000, Obj: 0.243748, No Obj: 0.002645, Avg Recall: 0.713115, count: 122 -Region Avg IOU: 0.581011, Class: 1.000000, Obj: 0.273671, No Obj: 0.002940, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.558712, Class: 1.000000, Obj: 0.281252, No Obj: 0.002665, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.587985, Class: 1.000000, Obj: 0.276014, No Obj: 0.002751, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.584024, Class: 1.000000, Obj: 0.282231, No Obj: 0.002718, Avg Recall: 0.731481, count: 108 -1357: 0.521933, 0.512601 avg, 0.001000 rate, 27.081369 seconds, 347392 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.611416, Class: 1.000000, Obj: 0.269541, No Obj: 0.002630, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.559850, Class: 1.000000, Obj: 0.274928, No Obj: 0.002871, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.583843, Class: 1.000000, Obj: 0.271264, No Obj: 0.002330, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.589834, Class: 1.000000, Obj: 0.289598, No Obj: 0.003069, Avg Recall: 0.711268, count: 142 -Region Avg IOU: 0.569644, Class: 1.000000, Obj: 0.241125, No Obj: 0.002680, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.570166, Class: 1.000000, Obj: 0.280416, No Obj: 0.002867, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.615129, Class: 1.000000, Obj: 0.250546, No Obj: 0.002951, Avg Recall: 0.786765, count: 136 -Region Avg IOU: 0.612096, Class: 1.000000, Obj: 0.309174, No Obj: 0.002661, Avg Recall: 0.730337, count: 89 -1358: 0.449571, 0.506298 avg, 0.001000 rate, 28.593315 seconds, 347648 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.569186, Class: 1.000000, Obj: 0.276478, No Obj: 0.003240, Avg Recall: 0.674242, count: 132 -Region Avg IOU: 0.600313, Class: 1.000000, Obj: 0.258185, No Obj: 0.002786, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.597066, Class: 1.000000, Obj: 0.339363, No Obj: 0.002665, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.599435, Class: 1.000000, Obj: 0.278262, No Obj: 0.002715, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.631808, Class: 1.000000, Obj: 0.311980, No Obj: 0.003218, Avg Recall: 0.803571, count: 112 -Region Avg IOU: 0.610998, Class: 1.000000, Obj: 0.252923, No Obj: 0.003177, Avg Recall: 0.775862, count: 116 -Region Avg IOU: 0.533502, Class: 1.000000, Obj: 0.244562, No Obj: 0.002932, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.567178, Class: 1.000000, Obj: 0.271101, No Obj: 0.002909, Avg Recall: 0.659091, count: 132 -1359: 0.497975, 0.505466 avg, 0.001000 rate, 28.926479 seconds, 347904 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.548363, Class: 1.000000, Obj: 0.319954, No Obj: 0.002722, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.572522, Class: 1.000000, Obj: 0.228138, No Obj: 0.003656, Avg Recall: 0.685315, count: 143 -Region Avg IOU: 0.578370, Class: 1.000000, Obj: 0.289620, No Obj: 0.003115, Avg Recall: 0.692913, count: 127 -Region Avg IOU: 0.584689, Class: 1.000000, Obj: 0.286797, No Obj: 0.002841, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.583596, Class: 1.000000, Obj: 0.286594, No Obj: 0.003174, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.580389, Class: 1.000000, Obj: 0.289479, No Obj: 0.003206, Avg Recall: 0.687500, count: 144 -Region Avg IOU: 0.564752, Class: 1.000000, Obj: 0.263095, No Obj: 0.002817, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.611216, Class: 1.000000, Obj: 0.293588, No Obj: 0.003151, Avg Recall: 0.767857, count: 112 -1360: 0.543115, 0.509231 avg, 0.001000 rate, 28.514547 seconds, 348160 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.503294 seconds -Region Avg IOU: 0.432379, Class: 1.000000, Obj: 0.249178, No Obj: 0.004030, Avg Recall: 0.456140, count: 114 -Region Avg IOU: 0.439337, Class: 1.000000, Obj: 0.253244, No Obj: 0.003467, Avg Recall: 0.430233, count: 86 -Region Avg IOU: 0.493176, Class: 1.000000, Obj: 0.236710, No Obj: 0.003929, Avg Recall: 0.525253, count: 99 -Region Avg IOU: 0.509363, Class: 1.000000, Obj: 0.238878, No Obj: 0.003898, Avg Recall: 0.548673, count: 113 -Region Avg IOU: 0.471031, Class: 1.000000, Obj: 0.235720, No Obj: 0.004070, Avg Recall: 0.500000, count: 120 -Region Avg IOU: 0.433738, Class: 1.000000, Obj: 0.242255, No Obj: 0.003901, Avg Recall: 0.428571, count: 133 -Region Avg IOU: 0.502083, Class: 1.000000, Obj: 0.246569, No Obj: 0.004361, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.465120, Class: 1.000000, Obj: 0.252779, No Obj: 0.004024, Avg Recall: 0.500000, count: 108 -1361: 0.618591, 0.520167 avg, 0.001000 rate, 19.388599 seconds, 348416 images -Loaded: 3.644243 seconds -Region Avg IOU: 0.440821, Class: 1.000000, Obj: 0.210461, No Obj: 0.004026, Avg Recall: 0.481481, count: 108 -Region Avg IOU: 0.487874, Class: 1.000000, Obj: 0.282138, No Obj: 0.004053, Avg Recall: 0.535088, count: 114 -Region Avg IOU: 0.417197, Class: 1.000000, Obj: 0.253724, No Obj: 0.003733, Avg Recall: 0.415929, count: 113 -Region Avg IOU: 0.465166, Class: 1.000000, Obj: 0.288032, No Obj: 0.003983, Avg Recall: 0.453608, count: 97 -Region Avg IOU: 0.475517, Class: 1.000000, Obj: 0.259399, No Obj: 0.003839, Avg Recall: 0.521277, count: 94 -Region Avg IOU: 0.441872, Class: 1.000000, Obj: 0.252176, No Obj: 0.004065, Avg Recall: 0.476635, count: 107 -Region Avg IOU: 0.475991, Class: 1.000000, Obj: 0.248023, No Obj: 0.004138, Avg Recall: 0.516949, count: 118 -Region Avg IOU: 0.538559, Class: 1.000000, Obj: 0.301958, No Obj: 0.003767, Avg Recall: 0.576471, count: 85 -1362: 0.539687, 0.522119 avg, 0.001000 rate, 22.882664 seconds, 348672 images -Loaded: 1.917038 seconds -Region Avg IOU: 0.545029, Class: 1.000000, Obj: 0.288551, No Obj: 0.004817, Avg Recall: 0.632000, count: 125 -Region Avg IOU: 0.489076, Class: 1.000000, Obj: 0.296528, No Obj: 0.004044, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.456064, Class: 1.000000, Obj: 0.247265, No Obj: 0.003850, Avg Recall: 0.463636, count: 110 -Region Avg IOU: 0.491683, Class: 1.000000, Obj: 0.228709, No Obj: 0.004130, Avg Recall: 0.516949, count: 118 -Region Avg IOU: 0.491567, Class: 1.000000, Obj: 0.257528, No Obj: 0.004554, Avg Recall: 0.560748, count: 107 -Region Avg IOU: 0.491583, Class: 1.000000, Obj: 0.260859, No Obj: 0.004038, Avg Recall: 0.543478, count: 92 -Region Avg IOU: 0.539143, Class: 1.000000, Obj: 0.295391, No Obj: 0.003631, Avg Recall: 0.634146, count: 82 -Region Avg IOU: 0.418686, Class: 1.000000, Obj: 0.233149, No Obj: 0.004100, Avg Recall: 0.392000, count: 125 -1363: 0.545772, 0.524484 avg, 0.001000 rate, 22.032955 seconds, 348928 images -Loaded: 0.201691 seconds -Region Avg IOU: 0.449404, Class: 1.000000, Obj: 0.301902, No Obj: 0.003690, Avg Recall: 0.432990, count: 97 -Region Avg IOU: 0.490904, Class: 1.000000, Obj: 0.270345, No Obj: 0.004198, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.491776, Class: 1.000000, Obj: 0.292296, No Obj: 0.004043, Avg Recall: 0.534884, count: 86 -Region Avg IOU: 0.453353, Class: 1.000000, Obj: 0.262489, No Obj: 0.004233, Avg Recall: 0.455357, count: 112 -Region Avg IOU: 0.428730, Class: 1.000000, Obj: 0.306505, No Obj: 0.003869, Avg Recall: 0.446602, count: 103 -Region Avg IOU: 0.474936, Class: 1.000000, Obj: 0.252357, No Obj: 0.004886, Avg Recall: 0.471545, count: 123 -Region Avg IOU: 0.526869, Class: 1.000000, Obj: 0.288556, No Obj: 0.003827, Avg Recall: 0.564706, count: 85 -Region Avg IOU: 0.456252, Class: 1.000000, Obj: 0.246159, No Obj: 0.004442, Avg Recall: 0.495935, count: 123 -1364: 0.459808, 0.518016 avg, 0.001000 rate, 22.063894 seconds, 349184 images -Loaded: 0.040259 seconds -Region Avg IOU: 0.507816, Class: 1.000000, Obj: 0.269699, No Obj: 0.005115, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.450253, Class: 1.000000, Obj: 0.281392, No Obj: 0.003653, Avg Recall: 0.425532, count: 94 -Region Avg IOU: 0.496355, Class: 1.000000, Obj: 0.297546, No Obj: 0.004843, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.443037, Class: 1.000000, Obj: 0.278511, No Obj: 0.003970, Avg Recall: 0.495146, count: 103 -Region Avg IOU: 0.523666, Class: 1.000000, Obj: 0.249035, No Obj: 0.004150, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.494330, Class: 1.000000, Obj: 0.322354, No Obj: 0.004439, Avg Recall: 0.579545, count: 88 -Region Avg IOU: 0.453098, Class: 1.000000, Obj: 0.277868, No Obj: 0.004512, Avg Recall: 0.516129, count: 124 -Region Avg IOU: 0.496178, Class: 1.000000, Obj: 0.270939, No Obj: 0.004307, Avg Recall: 0.530973, count: 113 -1365: 0.480850, 0.514300 avg, 0.001000 rate, 21.757427 seconds, 349440 images -Loaded: 0.363165 seconds -Region Avg IOU: 0.466445, Class: 1.000000, Obj: 0.296258, No Obj: 0.004170, Avg Recall: 0.552000, count: 125 -Region Avg IOU: 0.417542, Class: 1.000000, Obj: 0.276674, No Obj: 0.003712, Avg Recall: 0.436782, count: 87 -Region Avg IOU: 0.467316, Class: 1.000000, Obj: 0.290933, No Obj: 0.003781, Avg Recall: 0.525253, count: 99 -Region Avg IOU: 0.519702, Class: 1.000000, Obj: 0.306387, No Obj: 0.003779, Avg Recall: 0.600000, count: 80 -Region Avg IOU: 0.461868, Class: 1.000000, Obj: 0.253561, No Obj: 0.004345, Avg Recall: 0.500000, count: 110 -Region Avg IOU: 0.515700, Class: 1.000000, Obj: 0.333633, No Obj: 0.004264, Avg Recall: 0.541176, count: 85 -Region Avg IOU: 0.474364, Class: 1.000000, Obj: 0.288608, No Obj: 0.004676, Avg Recall: 0.451923, count: 104 -Region Avg IOU: 0.530680, Class: 1.000000, Obj: 0.301425, No Obj: 0.004409, Avg Recall: 0.660714, count: 112 -1366: 0.479905, 0.510860 avg, 0.001000 rate, 19.978909 seconds, 349696 images -Loaded: 1.305332 seconds -Region Avg IOU: 0.465569, Class: 1.000000, Obj: 0.263067, No Obj: 0.004249, Avg Recall: 0.492424, count: 132 -Region Avg IOU: 0.496202, Class: 1.000000, Obj: 0.317119, No Obj: 0.004888, Avg Recall: 0.527273, count: 110 -Region Avg IOU: 0.512584, Class: 1.000000, Obj: 0.233636, No Obj: 0.004038, Avg Recall: 0.615385, count: 91 -Region Avg IOU: 0.446896, Class: 1.000000, Obj: 0.248207, No Obj: 0.004305, Avg Recall: 0.451923, count: 104 -Region Avg IOU: 0.458078, Class: 1.000000, Obj: 0.274690, No Obj: 0.004353, Avg Recall: 0.540984, count: 122 -Region Avg IOU: 0.416650, Class: 1.000000, Obj: 0.211112, No Obj: 0.003592, Avg Recall: 0.444444, count: 108 -Region Avg IOU: 0.460364, Class: 1.000000, Obj: 0.273913, No Obj: 0.004012, Avg Recall: 0.485149, count: 101 -Region Avg IOU: 0.428080, Class: 1.000000, Obj: 0.259299, No Obj: 0.003835, Avg Recall: 0.419048, count: 105 -1367: 0.531009, 0.512875 avg, 0.001000 rate, 22.105452 seconds, 349952 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.462958, Class: 1.000000, Obj: 0.279001, No Obj: 0.003425, Avg Recall: 0.500000, count: 88 -Region Avg IOU: 0.459809, Class: 1.000000, Obj: 0.283536, No Obj: 0.004298, Avg Recall: 0.483607, count: 122 -Region Avg IOU: 0.494602, Class: 1.000000, Obj: 0.249530, No Obj: 0.005271, Avg Recall: 0.535211, count: 142 -Region Avg IOU: 0.377239, Class: 1.000000, Obj: 0.232814, No Obj: 0.003888, Avg Recall: 0.343434, count: 99 -Region Avg IOU: 0.494162, Class: 1.000000, Obj: 0.314045, No Obj: 0.004810, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.450069, Class: 1.000000, Obj: 0.285271, No Obj: 0.003952, Avg Recall: 0.480769, count: 104 -Region Avg IOU: 0.452765, Class: 1.000000, Obj: 0.280827, No Obj: 0.004347, Avg Recall: 0.443548, count: 124 -Region Avg IOU: 0.469477, Class: 1.000000, Obj: 0.247447, No Obj: 0.004906, Avg Recall: 0.495868, count: 121 -1368: 0.586025, 0.520190 avg, 0.001000 rate, 20.917603 seconds, 350208 images -Loaded: 1.701542 seconds -Region Avg IOU: 0.503183, Class: 1.000000, Obj: 0.265191, No Obj: 0.004186, Avg Recall: 0.562500, count: 96 -Region Avg IOU: 0.440544, Class: 1.000000, Obj: 0.245984, No Obj: 0.003720, Avg Recall: 0.466667, count: 90 -Region Avg IOU: 0.431280, Class: 1.000000, Obj: 0.257415, No Obj: 0.004088, Avg Recall: 0.450000, count: 120 -Region Avg IOU: 0.453464, Class: 1.000000, Obj: 0.281041, No Obj: 0.003788, Avg Recall: 0.481481, count: 108 -Region Avg IOU: 0.453453, Class: 1.000000, Obj: 0.292605, No Obj: 0.004291, Avg Recall: 0.504348, count: 115 -Region Avg IOU: 0.482349, Class: 1.000000, Obj: 0.288703, No Obj: 0.003841, Avg Recall: 0.521739, count: 69 -Region Avg IOU: 0.411946, Class: 1.000000, Obj: 0.291945, No Obj: 0.004417, Avg Recall: 0.407692, count: 130 -Region Avg IOU: 0.451797, Class: 1.000000, Obj: 0.288914, No Obj: 0.003843, Avg Recall: 0.454545, count: 88 -1369: 0.499102, 0.518081 avg, 0.001000 rate, 22.088030 seconds, 350464 images -Loaded: 0.112369 seconds -Region Avg IOU: 0.485064, Class: 1.000000, Obj: 0.238363, No Obj: 0.004052, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.473427, Class: 1.000000, Obj: 0.271805, No Obj: 0.004367, Avg Recall: 0.500000, count: 130 -Region Avg IOU: 0.490699, Class: 1.000000, Obj: 0.225730, No Obj: 0.004246, Avg Recall: 0.509259, count: 108 -Region Avg IOU: 0.499464, Class: 1.000000, Obj: 0.267939, No Obj: 0.003949, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.482580, Class: 1.000000, Obj: 0.223771, No Obj: 0.003701, Avg Recall: 0.543478, count: 92 -Region Avg IOU: 0.484978, Class: 1.000000, Obj: 0.294393, No Obj: 0.004192, Avg Recall: 0.478723, count: 94 -Region Avg IOU: 0.437699, Class: 1.000000, Obj: 0.227254, No Obj: 0.004202, Avg Recall: 0.428571, count: 140 -Region Avg IOU: 0.447894, Class: 1.000000, Obj: 0.265000, No Obj: 0.004144, Avg Recall: 0.457944, count: 107 -1370: 0.511084, 0.517381 avg, 0.001000 rate, 21.389919 seconds, 350720 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.378984 seconds -Region Avg IOU: 0.442317, Class: 1.000000, Obj: 0.257246, No Obj: 0.004585, Avg Recall: 0.447368, count: 114 -Region Avg IOU: 0.444062, Class: 1.000000, Obj: 0.237630, No Obj: 0.004556, Avg Recall: 0.460000, count: 100 -Region Avg IOU: 0.412840, Class: 1.000000, Obj: 0.270066, No Obj: 0.004024, Avg Recall: 0.401961, count: 102 -Region Avg IOU: 0.409055, Class: 1.000000, Obj: 0.240869, No Obj: 0.004198, Avg Recall: 0.369048, count: 84 -Region Avg IOU: 0.376188, Class: 1.000000, Obj: 0.228473, No Obj: 0.004308, Avg Recall: 0.346535, count: 101 -Region Avg IOU: 0.454598, Class: 1.000000, Obj: 0.247847, No Obj: 0.004471, Avg Recall: 0.486239, count: 109 -Region Avg IOU: 0.429373, Class: 1.000000, Obj: 0.239594, No Obj: 0.004898, Avg Recall: 0.435714, count: 140 -Region Avg IOU: 0.437340, Class: 1.000000, Obj: 0.235709, No Obj: 0.004898, Avg Recall: 0.466667, count: 120 -1371: 0.548762, 0.520519 avg, 0.001000 rate, 15.984353 seconds, 350976 images -Loaded: 5.804183 seconds -Region Avg IOU: 0.406656, Class: 1.000000, Obj: 0.225000, No Obj: 0.004639, Avg Recall: 0.390244, count: 123 -Region Avg IOU: 0.448503, Class: 1.000000, Obj: 0.212690, No Obj: 0.004909, Avg Recall: 0.471074, count: 121 -Region Avg IOU: 0.367342, Class: 1.000000, Obj: 0.199238, No Obj: 0.003956, Avg Recall: 0.393617, count: 94 -Region Avg IOU: 0.394524, Class: 1.000000, Obj: 0.212577, No Obj: 0.004626, Avg Recall: 0.400000, count: 125 -Region Avg IOU: 0.439145, Class: 1.000000, Obj: 0.232171, No Obj: 0.003824, Avg Recall: 0.479167, count: 96 -Region Avg IOU: 0.418593, Class: 1.000000, Obj: 0.253664, No Obj: 0.004376, Avg Recall: 0.457944, count: 107 -Region Avg IOU: 0.391706, Class: 1.000000, Obj: 0.237589, No Obj: 0.004169, Avg Recall: 0.355556, count: 90 -Region Avg IOU: 0.393826, Class: 1.000000, Obj: 0.234232, No Obj: 0.004437, Avg Recall: 0.421429, count: 140 -1372: 0.553480, 0.523815 avg, 0.001000 rate, 16.161922 seconds, 351232 images -Loaded: 4.702908 seconds -Region Avg IOU: 0.432784, Class: 1.000000, Obj: 0.249160, No Obj: 0.004611, Avg Recall: 0.423423, count: 111 -Region Avg IOU: 0.438545, Class: 1.000000, Obj: 0.259263, No Obj: 0.005012, Avg Recall: 0.465517, count: 116 -Region Avg IOU: 0.382796, Class: 1.000000, Obj: 0.217067, No Obj: 0.004449, Avg Recall: 0.379310, count: 116 -Region Avg IOU: 0.460702, Class: 1.000000, Obj: 0.270712, No Obj: 0.004862, Avg Recall: 0.491071, count: 112 -Region Avg IOU: 0.426779, Class: 1.000000, Obj: 0.238473, No Obj: 0.004786, Avg Recall: 0.456311, count: 103 -Region Avg IOU: 0.428601, Class: 1.000000, Obj: 0.237814, No Obj: 0.004496, Avg Recall: 0.484536, count: 97 -Region Avg IOU: 0.458381, Class: 1.000000, Obj: 0.221283, No Obj: 0.004486, Avg Recall: 0.483146, count: 89 -Region Avg IOU: 0.444644, Class: 1.000000, Obj: 0.237306, No Obj: 0.004500, Avg Recall: 0.454545, count: 110 -1373: 0.574372, 0.528871 avg, 0.001000 rate, 15.015227 seconds, 351488 images -Loaded: 6.213581 seconds -Region Avg IOU: 0.395113, Class: 1.000000, Obj: 0.233041, No Obj: 0.004567, Avg Recall: 0.386364, count: 132 -Region Avg IOU: 0.475222, Class: 1.000000, Obj: 0.288547, No Obj: 0.004499, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.466287, Class: 1.000000, Obj: 0.253756, No Obj: 0.004669, Avg Recall: 0.522936, count: 109 -Region Avg IOU: 0.476921, Class: 1.000000, Obj: 0.219994, No Obj: 0.005133, Avg Recall: 0.522059, count: 136 -Region Avg IOU: 0.416552, Class: 1.000000, Obj: 0.248459, No Obj: 0.004378, Avg Recall: 0.425743, count: 101 -Region Avg IOU: 0.404391, Class: 1.000000, Obj: 0.231640, No Obj: 0.005238, Avg Recall: 0.421769, count: 147 -Region Avg IOU: 0.413238, Class: 1.000000, Obj: 0.232881, No Obj: 0.004743, Avg Recall: 0.433071, count: 127 -Region Avg IOU: 0.407225, Class: 1.000000, Obj: 0.205444, No Obj: 0.004271, Avg Recall: 0.430233, count: 86 -1374: 0.607333, 0.536717 avg, 0.001000 rate, 15.780750 seconds, 351744 images -Loaded: 5.985045 seconds -Region Avg IOU: 0.465754, Class: 1.000000, Obj: 0.253210, No Obj: 0.004686, Avg Recall: 0.513043, count: 115 -Region Avg IOU: 0.412503, Class: 1.000000, Obj: 0.215064, No Obj: 0.005461, Avg Recall: 0.422535, count: 142 -Region Avg IOU: 0.415777, Class: 1.000000, Obj: 0.232646, No Obj: 0.004796, Avg Recall: 0.436508, count: 126 -Region Avg IOU: 0.426656, Class: 1.000000, Obj: 0.250209, No Obj: 0.004618, Avg Recall: 0.438596, count: 114 -Region Avg IOU: 0.441162, Class: 1.000000, Obj: 0.231259, No Obj: 0.004329, Avg Recall: 0.474747, count: 99 -Region Avg IOU: 0.410630, Class: 1.000000, Obj: 0.244974, No Obj: 0.004454, Avg Recall: 0.371901, count: 121 -Region Avg IOU: 0.446927, Class: 1.000000, Obj: 0.226391, No Obj: 0.004645, Avg Recall: 0.462366, count: 93 -Region Avg IOU: 0.414376, Class: 1.000000, Obj: 0.251327, No Obj: 0.004410, Avg Recall: 0.459184, count: 98 -1375: 0.587541, 0.541800 avg, 0.001000 rate, 13.397546 seconds, 352000 images -Loaded: 7.980043 seconds -Region Avg IOU: 0.476410, Class: 1.000000, Obj: 0.257552, No Obj: 0.005035, Avg Recall: 0.528926, count: 121 -Region Avg IOU: 0.436419, Class: 1.000000, Obj: 0.273118, No Obj: 0.004901, Avg Recall: 0.481481, count: 108 -Region Avg IOU: 0.443146, Class: 1.000000, Obj: 0.248120, No Obj: 0.005022, Avg Recall: 0.458333, count: 120 -Region Avg IOU: 0.390027, Class: 1.000000, Obj: 0.257991, No Obj: 0.004140, Avg Recall: 0.364583, count: 96 -Region Avg IOU: 0.499373, Class: 1.000000, Obj: 0.291247, No Obj: 0.005248, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.370119, Class: 1.000000, Obj: 0.234159, No Obj: 0.004908, Avg Recall: 0.346535, count: 101 -Region Avg IOU: 0.400261, Class: 1.000000, Obj: 0.237053, No Obj: 0.004620, Avg Recall: 0.379310, count: 116 -Region Avg IOU: 0.423912, Class: 1.000000, Obj: 0.271996, No Obj: 0.004657, Avg Recall: 0.456522, count: 92 -1376: 0.528301, 0.540450 avg, 0.001000 rate, 15.842152 seconds, 352256 images -Loaded: 5.576989 seconds -Region Avg IOU: 0.465132, Class: 1.000000, Obj: 0.290171, No Obj: 0.005199, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.430341, Class: 1.000000, Obj: 0.254653, No Obj: 0.004770, Avg Recall: 0.448980, count: 98 -Region Avg IOU: 0.403213, Class: 1.000000, Obj: 0.251854, No Obj: 0.004346, Avg Recall: 0.402597, count: 77 -Region Avg IOU: 0.428349, Class: 1.000000, Obj: 0.285391, No Obj: 0.004994, Avg Recall: 0.472527, count: 91 -Region Avg IOU: 0.391099, Class: 1.000000, Obj: 0.261637, No Obj: 0.005000, Avg Recall: 0.393617, count: 94 -Region Avg IOU: 0.406243, Class: 1.000000, Obj: 0.257369, No Obj: 0.004932, Avg Recall: 0.432692, count: 104 -Region Avg IOU: 0.424801, Class: 1.000000, Obj: 0.252662, No Obj: 0.004726, Avg Recall: 0.449438, count: 89 -Region Avg IOU: 0.411600, Class: 1.000000, Obj: 0.251949, No Obj: 0.005263, Avg Recall: 0.441379, count: 145 -1377: 0.487880, 0.535193 avg, 0.001000 rate, 16.310740 seconds, 352512 images -Loaded: 3.232870 seconds -Region Avg IOU: 0.391393, Class: 1.000000, Obj: 0.226715, No Obj: 0.005093, Avg Recall: 0.377953, count: 127 -Region Avg IOU: 0.455625, Class: 1.000000, Obj: 0.283517, No Obj: 0.005313, Avg Recall: 0.431818, count: 88 -Region Avg IOU: 0.460974, Class: 1.000000, Obj: 0.250530, No Obj: 0.005594, Avg Recall: 0.530769, count: 130 -Region Avg IOU: 0.376258, Class: 1.000000, Obj: 0.244922, No Obj: 0.003943, Avg Recall: 0.390805, count: 87 -Region Avg IOU: 0.452293, Class: 1.000000, Obj: 0.291868, No Obj: 0.005149, Avg Recall: 0.442105, count: 95 -Region Avg IOU: 0.435487, Class: 1.000000, Obj: 0.275143, No Obj: 0.005307, Avg Recall: 0.504505, count: 111 -Region Avg IOU: 0.432846, Class: 1.000000, Obj: 0.297193, No Obj: 0.005180, Avg Recall: 0.489796, count: 98 -Region Avg IOU: 0.413022, Class: 1.000000, Obj: 0.277855, No Obj: 0.004323, Avg Recall: 0.402597, count: 77 -1378: 0.563333, 0.538007 avg, 0.001000 rate, 17.957144 seconds, 352768 images -Loaded: 2.865277 seconds -Region Avg IOU: 0.376016, Class: 1.000000, Obj: 0.241683, No Obj: 0.005155, Avg Recall: 0.395161, count: 124 -Region Avg IOU: 0.431569, Class: 1.000000, Obj: 0.310925, No Obj: 0.005116, Avg Recall: 0.469388, count: 98 -Region Avg IOU: 0.429088, Class: 1.000000, Obj: 0.242260, No Obj: 0.005425, Avg Recall: 0.438462, count: 130 -Region Avg IOU: 0.387274, Class: 1.000000, Obj: 0.255030, No Obj: 0.005033, Avg Recall: 0.408333, count: 120 -Region Avg IOU: 0.440194, Class: 1.000000, Obj: 0.267339, No Obj: 0.005285, Avg Recall: 0.495868, count: 121 -Region Avg IOU: 0.395416, Class: 1.000000, Obj: 0.285134, No Obj: 0.004520, Avg Recall: 0.364706, count: 85 -Region Avg IOU: 0.415642, Class: 1.000000, Obj: 0.264166, No Obj: 0.004609, Avg Recall: 0.436782, count: 87 -Region Avg IOU: 0.431342, Class: 1.000000, Obj: 0.284510, No Obj: 0.006048, Avg Recall: 0.390977, count: 133 -1379: 0.590138, 0.543220 avg, 0.001000 rate, 16.477301 seconds, 353024 images -Loaded: 2.989939 seconds -Region Avg IOU: 0.417986, Class: 1.000000, Obj: 0.267248, No Obj: 0.005687, Avg Recall: 0.405172, count: 116 -Region Avg IOU: 0.445258, Class: 1.000000, Obj: 0.278830, No Obj: 0.005348, Avg Recall: 0.527132, count: 129 -Region Avg IOU: 0.457116, Class: 1.000000, Obj: 0.268132, No Obj: 0.005020, Avg Recall: 0.511111, count: 90 -Region Avg IOU: 0.409412, Class: 1.000000, Obj: 0.228912, No Obj: 0.005129, Avg Recall: 0.415254, count: 118 -Region Avg IOU: 0.432028, Class: 1.000000, Obj: 0.268319, No Obj: 0.004951, Avg Recall: 0.425743, count: 101 -Region Avg IOU: 0.468226, Class: 1.000000, Obj: 0.284339, No Obj: 0.004572, Avg Recall: 0.488095, count: 84 -Region Avg IOU: 0.442619, Class: 1.000000, Obj: 0.298622, No Obj: 0.005038, Avg Recall: 0.438776, count: 98 -Region Avg IOU: 0.445122, Class: 1.000000, Obj: 0.273661, No Obj: 0.005056, Avg Recall: 0.444444, count: 99 -1380: 0.520689, 0.540967 avg, 0.001000 rate, 18.719908 seconds, 353280 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000076 seconds -Region Avg IOU: 0.541750, Class: 1.000000, Obj: 0.353651, No Obj: 0.003318, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.564155, Class: 1.000000, Obj: 0.307963, No Obj: 0.003811, Avg Recall: 0.686567, count: 134 -Region Avg IOU: 0.566997, Class: 1.000000, Obj: 0.330037, No Obj: 0.003411, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.614185, Class: 1.000000, Obj: 0.349372, No Obj: 0.003613, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.560820, Class: 1.000000, Obj: 0.279868, No Obj: 0.003539, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.572949, Class: 1.000000, Obj: 0.341990, No Obj: 0.003373, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.550001, Class: 1.000000, Obj: 0.360581, No Obj: 0.003505, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.586233, Class: 1.000000, Obj: 0.349772, No Obj: 0.003640, Avg Recall: 0.742574, count: 101 -1381: 0.598120, 0.546682 avg, 0.001000 rate, 28.351959 seconds, 353536 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.590168, Class: 1.000000, Obj: 0.270228, No Obj: 0.003164, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.575568, Class: 1.000000, Obj: 0.292718, No Obj: 0.003150, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.628585, Class: 1.000000, Obj: 0.303077, No Obj: 0.003291, Avg Recall: 0.846154, count: 91 -Region Avg IOU: 0.540795, Class: 1.000000, Obj: 0.346176, No Obj: 0.002780, Avg Recall: 0.616279, count: 86 -Region Avg IOU: 0.598884, Class: 1.000000, Obj: 0.278151, No Obj: 0.003277, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.564134, Class: 1.000000, Obj: 0.312913, No Obj: 0.003679, Avg Recall: 0.658064, count: 155 -Region Avg IOU: 0.504860, Class: 1.000000, Obj: 0.279652, No Obj: 0.003289, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.596064, Class: 1.000000, Obj: 0.338560, No Obj: 0.003169, Avg Recall: 0.771739, count: 92 -1382: 0.495814, 0.541595 avg, 0.001000 rate, 28.019148 seconds, 353792 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.559390, Class: 1.000000, Obj: 0.241980, No Obj: 0.003034, Avg Recall: 0.666667, count: 132 -Region Avg IOU: 0.591882, Class: 1.000000, Obj: 0.251412, No Obj: 0.002724, Avg Recall: 0.764706, count: 85 -Region Avg IOU: 0.628327, Class: 1.000000, Obj: 0.296061, No Obj: 0.002558, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.578193, Class: 1.000000, Obj: 0.301708, No Obj: 0.002475, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.582974, Class: 1.000000, Obj: 0.244762, No Obj: 0.002899, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.576365, Class: 1.000000, Obj: 0.278528, No Obj: 0.002856, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.525657, Class: 1.000000, Obj: 0.239285, No Obj: 0.002719, Avg Recall: 0.600000, count: 130 -Region Avg IOU: 0.581705, Class: 1.000000, Obj: 0.257846, No Obj: 0.002430, Avg Recall: 0.707317, count: 82 -1383: 0.514262, 0.538862 avg, 0.001000 rate, 27.026554 seconds, 354048 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.579212, Class: 1.000000, Obj: 0.201906, No Obj: 0.002510, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.578015, Class: 1.000000, Obj: 0.224696, No Obj: 0.002601, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.587180, Class: 1.000000, Obj: 0.251570, No Obj: 0.002459, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.613530, Class: 1.000000, Obj: 0.261903, No Obj: 0.002507, Avg Recall: 0.785047, count: 107 -Region Avg IOU: 0.619765, Class: 1.000000, Obj: 0.248698, No Obj: 0.002680, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.607339, Class: 1.000000, Obj: 0.249333, No Obj: 0.002435, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.564957, Class: 1.000000, Obj: 0.259716, No Obj: 0.002385, Avg Recall: 0.697368, count: 76 -Region Avg IOU: 0.570616, Class: 1.000000, Obj: 0.213750, No Obj: 0.002734, Avg Recall: 0.672566, count: 113 -1384: 0.499679, 0.534944 avg, 0.001000 rate, 28.174887 seconds, 354304 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.588830, Class: 1.000000, Obj: 0.277682, No Obj: 0.002417, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.598873, Class: 1.000000, Obj: 0.205849, No Obj: 0.002595, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.593118, Class: 1.000000, Obj: 0.233393, No Obj: 0.002310, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.584783, Class: 1.000000, Obj: 0.227180, No Obj: 0.002744, Avg Recall: 0.676923, count: 130 -Region Avg IOU: 0.601662, Class: 1.000000, Obj: 0.200132, No Obj: 0.002465, Avg Recall: 0.785047, count: 107 -Region Avg IOU: 0.632600, Class: 1.000000, Obj: 0.231696, No Obj: 0.002551, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.586719, Class: 1.000000, Obj: 0.229640, No Obj: 0.002652, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.586513, Class: 1.000000, Obj: 0.231907, No Obj: 0.002316, Avg Recall: 0.724138, count: 87 -1385: 0.501719, 0.531621 avg, 0.001000 rate, 28.742722 seconds, 354560 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.599779, Class: 1.000000, Obj: 0.215413, No Obj: 0.002841, Avg Recall: 0.760870, count: 138 -Region Avg IOU: 0.560620, Class: 1.000000, Obj: 0.242803, No Obj: 0.002627, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.566324, Class: 1.000000, Obj: 0.257867, No Obj: 0.002471, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.606216, Class: 1.000000, Obj: 0.239402, No Obj: 0.002606, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.607188, Class: 1.000000, Obj: 0.213035, No Obj: 0.002668, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.564828, Class: 1.000000, Obj: 0.219206, No Obj: 0.002265, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.595484, Class: 1.000000, Obj: 0.187961, No Obj: 0.002722, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.620568, Class: 1.000000, Obj: 0.204184, No Obj: 0.002690, Avg Recall: 0.761905, count: 105 -1386: 0.578447, 0.536304 avg, 0.001000 rate, 29.306200 seconds, 354816 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.610407, Class: 1.000000, Obj: 0.233573, No Obj: 0.002817, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.596779, Class: 1.000000, Obj: 0.250243, No Obj: 0.002746, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.575324, Class: 1.000000, Obj: 0.218409, No Obj: 0.002581, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.596676, Class: 1.000000, Obj: 0.277719, No Obj: 0.002677, Avg Recall: 0.779070, count: 86 -Region Avg IOU: 0.615981, Class: 1.000000, Obj: 0.280675, No Obj: 0.002852, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.600997, Class: 1.000000, Obj: 0.282135, No Obj: 0.002664, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.571833, Class: 1.000000, Obj: 0.226296, No Obj: 0.002720, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.558281, Class: 1.000000, Obj: 0.248429, No Obj: 0.002698, Avg Recall: 0.675676, count: 111 -1387: 0.471675, 0.529841 avg, 0.001000 rate, 28.166553 seconds, 355072 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.617027, Class: 1.000000, Obj: 0.217433, No Obj: 0.002731, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.618449, Class: 1.000000, Obj: 0.260115, No Obj: 0.002876, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.549962, Class: 1.000000, Obj: 0.291190, No Obj: 0.003108, Avg Recall: 0.613636, count: 132 -Region Avg IOU: 0.610476, Class: 1.000000, Obj: 0.254759, No Obj: 0.002862, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.621962, Class: 1.000000, Obj: 0.290065, No Obj: 0.002682, Avg Recall: 0.815217, count: 92 -Region Avg IOU: 0.614126, Class: 1.000000, Obj: 0.241101, No Obj: 0.003328, Avg Recall: 0.784000, count: 125 -Region Avg IOU: 0.574370, Class: 1.000000, Obj: 0.253598, No Obj: 0.002839, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.575732, Class: 1.000000, Obj: 0.263867, No Obj: 0.003091, Avg Recall: 0.673913, count: 138 -1388: 0.503337, 0.527191 avg, 0.001000 rate, 27.247408 seconds, 355328 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.597806, Class: 1.000000, Obj: 0.265546, No Obj: 0.003326, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.566662, Class: 1.000000, Obj: 0.321824, No Obj: 0.003095, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.579166, Class: 1.000000, Obj: 0.289872, No Obj: 0.003189, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.600475, Class: 1.000000, Obj: 0.287226, No Obj: 0.003183, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.551776, Class: 1.000000, Obj: 0.298120, No Obj: 0.003301, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.549659, Class: 1.000000, Obj: 0.276339, No Obj: 0.003143, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.620884, Class: 1.000000, Obj: 0.285059, No Obj: 0.003102, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.600547, Class: 1.000000, Obj: 0.291246, No Obj: 0.003618, Avg Recall: 0.731034, count: 145 -1389: 0.541047, 0.528576 avg, 0.001000 rate, 28.378927 seconds, 355584 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.568059, Class: 1.000000, Obj: 0.278615, No Obj: 0.003519, Avg Recall: 0.685484, count: 124 -Region Avg IOU: 0.554672, Class: 1.000000, Obj: 0.308033, No Obj: 0.003082, Avg Recall: 0.633333, count: 90 -Region Avg IOU: 0.551289, Class: 1.000000, Obj: 0.242364, No Obj: 0.003052, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.569104, Class: 1.000000, Obj: 0.333337, No Obj: 0.003167, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.587351, Class: 1.000000, Obj: 0.315440, No Obj: 0.003568, Avg Recall: 0.715385, count: 130 -Region Avg IOU: 0.632235, Class: 1.000000, Obj: 0.364469, No Obj: 0.003599, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.612753, Class: 1.000000, Obj: 0.301643, No Obj: 0.003791, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.580573, Class: 1.000000, Obj: 0.304806, No Obj: 0.003499, Avg Recall: 0.734043, count: 94 -1390: 0.475142, 0.523233 avg, 0.001000 rate, 29.257229 seconds, 355840 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 14.444525 seconds -Region Avg IOU: 0.543881, Class: 1.000000, Obj: 0.288934, No Obj: 0.004272, Avg Recall: 0.603053, count: 131 -Region Avg IOU: 0.524724, Class: 1.000000, Obj: 0.318873, No Obj: 0.003996, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.522476, Class: 1.000000, Obj: 0.293821, No Obj: 0.004235, Avg Recall: 0.567797, count: 118 -Region Avg IOU: 0.521936, Class: 1.000000, Obj: 0.312575, No Obj: 0.003976, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.558231, Class: 1.000000, Obj: 0.334585, No Obj: 0.003897, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.552925, Class: 1.000000, Obj: 0.302810, No Obj: 0.003416, Avg Recall: 0.666667, count: 78 -Region Avg IOU: 0.536915, Class: 1.000000, Obj: 0.296825, No Obj: 0.003823, Avg Recall: 0.607843, count: 102 -Region Avg IOU: 0.543512, Class: 1.000000, Obj: 0.305512, No Obj: 0.004328, Avg Recall: 0.595420, count: 131 -1391: 0.547837, 0.525693 avg, 0.001000 rate, 23.461515 seconds, 356096 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.522141, Class: 1.000000, Obj: 0.321628, No Obj: 0.003674, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.513920, Class: 1.000000, Obj: 0.312874, No Obj: 0.004272, Avg Recall: 0.573529, count: 136 -Region Avg IOU: 0.492759, Class: 1.000000, Obj: 0.292771, No Obj: 0.003689, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.523707, Class: 1.000000, Obj: 0.314654, No Obj: 0.004474, Avg Recall: 0.632000, count: 125 -Region Avg IOU: 0.482455, Class: 1.000000, Obj: 0.280796, No Obj: 0.003475, Avg Recall: 0.500000, count: 104 -Region Avg IOU: 0.493858, Class: 1.000000, Obj: 0.290898, No Obj: 0.004262, Avg Recall: 0.545455, count: 121 -Region Avg IOU: 0.536007, Class: 1.000000, Obj: 0.299617, No Obj: 0.004319, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.536284, Class: 1.000000, Obj: 0.296255, No Obj: 0.003867, Avg Recall: 0.591667, count: 120 -1392: 0.595301, 0.532654 avg, 0.001000 rate, 25.651440 seconds, 356352 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.542270, Class: 1.000000, Obj: 0.296848, No Obj: 0.003879, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.505195, Class: 1.000000, Obj: 0.265146, No Obj: 0.003666, Avg Recall: 0.565657, count: 99 -Region Avg IOU: 0.514157, Class: 1.000000, Obj: 0.254289, No Obj: 0.003495, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.492563, Class: 1.000000, Obj: 0.279116, No Obj: 0.004390, Avg Recall: 0.537313, count: 134 -Region Avg IOU: 0.524243, Class: 1.000000, Obj: 0.288108, No Obj: 0.004273, Avg Recall: 0.578125, count: 128 -Region Avg IOU: 0.496572, Class: 1.000000, Obj: 0.320195, No Obj: 0.003382, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.496013, Class: 1.000000, Obj: 0.286858, No Obj: 0.003626, Avg Recall: 0.556522, count: 115 -Region Avg IOU: 0.499686, Class: 1.000000, Obj: 0.246765, No Obj: 0.003839, Avg Recall: 0.565217, count: 115 -1393: 0.600759, 0.539464 avg, 0.001000 rate, 23.843140 seconds, 356608 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.519961, Class: 1.000000, Obj: 0.267507, No Obj: 0.004063, Avg Recall: 0.592000, count: 125 -Region Avg IOU: 0.501665, Class: 1.000000, Obj: 0.292631, No Obj: 0.003458, Avg Recall: 0.555556, count: 99 -Region Avg IOU: 0.493023, Class: 1.000000, Obj: 0.267208, No Obj: 0.003653, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.507105, Class: 1.000000, Obj: 0.288413, No Obj: 0.003967, Avg Recall: 0.529412, count: 136 -Region Avg IOU: 0.532470, Class: 1.000000, Obj: 0.287129, No Obj: 0.003374, Avg Recall: 0.630435, count: 92 -Region Avg IOU: 0.495789, Class: 1.000000, Obj: 0.316931, No Obj: 0.003360, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.511563, Class: 1.000000, Obj: 0.287809, No Obj: 0.003562, Avg Recall: 0.548387, count: 124 -Region Avg IOU: 0.538123, Class: 1.000000, Obj: 0.293040, No Obj: 0.004050, Avg Recall: 0.591304, count: 115 -1394: 0.459826, 0.531501 avg, 0.001000 rate, 23.527292 seconds, 356864 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.578215, Class: 1.000000, Obj: 0.256056, No Obj: 0.003769, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.491562, Class: 1.000000, Obj: 0.244913, No Obj: 0.003374, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.505638, Class: 1.000000, Obj: 0.282411, No Obj: 0.003707, Avg Recall: 0.572581, count: 124 -Region Avg IOU: 0.497238, Class: 1.000000, Obj: 0.269352, No Obj: 0.003571, Avg Recall: 0.520661, count: 121 -Region Avg IOU: 0.517775, Class: 1.000000, Obj: 0.293726, No Obj: 0.003623, Avg Recall: 0.586777, count: 121 -Region Avg IOU: 0.522529, Class: 1.000000, Obj: 0.236098, No Obj: 0.003593, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.500764, Class: 1.000000, Obj: 0.282698, No Obj: 0.002978, Avg Recall: 0.549451, count: 91 -Region Avg IOU: 0.575771, Class: 1.000000, Obj: 0.270296, No Obj: 0.003757, Avg Recall: 0.676190, count: 105 -1395: 0.487305, 0.527081 avg, 0.001000 rate, 23.899263 seconds, 357120 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.506337, Class: 1.000000, Obj: 0.242277, No Obj: 0.003680, Avg Recall: 0.560000, count: 125 -Region Avg IOU: 0.540449, Class: 1.000000, Obj: 0.283544, No Obj: 0.003048, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.509128, Class: 1.000000, Obj: 0.273952, No Obj: 0.003644, Avg Recall: 0.561983, count: 121 -Region Avg IOU: 0.557456, Class: 1.000000, Obj: 0.272932, No Obj: 0.003673, Avg Recall: 0.620968, count: 124 -Region Avg IOU: 0.494295, Class: 1.000000, Obj: 0.260327, No Obj: 0.003424, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.514614, Class: 1.000000, Obj: 0.279763, No Obj: 0.003071, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.560634, Class: 1.000000, Obj: 0.296117, No Obj: 0.003291, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.519102, Class: 1.000000, Obj: 0.275068, No Obj: 0.003361, Avg Recall: 0.587156, count: 109 -1396: 0.454330, 0.519806 avg, 0.001000 rate, 25.240253 seconds, 357376 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.541151, Class: 1.000000, Obj: 0.295857, No Obj: 0.003272, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.506131, Class: 1.000000, Obj: 0.284729, No Obj: 0.003213, Avg Recall: 0.539326, count: 89 -Region Avg IOU: 0.563099, Class: 1.000000, Obj: 0.267365, No Obj: 0.003402, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.554426, Class: 1.000000, Obj: 0.314685, No Obj: 0.003113, Avg Recall: 0.602941, count: 68 -Region Avg IOU: 0.547258, Class: 1.000000, Obj: 0.255034, No Obj: 0.003693, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.445415, Class: 1.000000, Obj: 0.227274, No Obj: 0.002828, Avg Recall: 0.437500, count: 96 -Region Avg IOU: 0.530382, Class: 1.000000, Obj: 0.256291, No Obj: 0.003670, Avg Recall: 0.619835, count: 121 -Region Avg IOU: 0.556527, Class: 1.000000, Obj: 0.250896, No Obj: 0.003673, Avg Recall: 0.620690, count: 116 -1397: 0.511122, 0.518938 avg, 0.001000 rate, 24.461510 seconds, 357632 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.467299, Class: 1.000000, Obj: 0.233801, No Obj: 0.003151, Avg Recall: 0.495798, count: 119 -Region Avg IOU: 0.569426, Class: 1.000000, Obj: 0.248577, No Obj: 0.003286, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.542727, Class: 1.000000, Obj: 0.296741, No Obj: 0.003282, Avg Recall: 0.608247, count: 97 -Region Avg IOU: 0.499425, Class: 1.000000, Obj: 0.250827, No Obj: 0.002956, Avg Recall: 0.537634, count: 93 -Region Avg IOU: 0.470024, Class: 1.000000, Obj: 0.228606, No Obj: 0.003298, Avg Recall: 0.519685, count: 127 -Region Avg IOU: 0.544434, Class: 1.000000, Obj: 0.256954, No Obj: 0.003568, Avg Recall: 0.632812, count: 128 -Region Avg IOU: 0.476813, Class: 1.000000, Obj: 0.263727, No Obj: 0.003142, Avg Recall: 0.509615, count: 104 -Region Avg IOU: 0.533150, Class: 1.000000, Obj: 0.288421, No Obj: 0.003564, Avg Recall: 0.616541, count: 133 -1398: 0.501730, 0.517217 avg, 0.001000 rate, 23.652851 seconds, 357888 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.557915, Class: 1.000000, Obj: 0.228804, No Obj: 0.003776, Avg Recall: 0.686046, count: 86 -Region Avg IOU: 0.541667, Class: 1.000000, Obj: 0.267238, No Obj: 0.003700, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.546072, Class: 1.000000, Obj: 0.261995, No Obj: 0.003189, Avg Recall: 0.629214, count: 89 -Region Avg IOU: 0.549880, Class: 1.000000, Obj: 0.306754, No Obj: 0.003289, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.462556, Class: 1.000000, Obj: 0.261079, No Obj: 0.002984, Avg Recall: 0.474747, count: 99 -Region Avg IOU: 0.536784, Class: 1.000000, Obj: 0.269269, No Obj: 0.003234, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.524610, Class: 1.000000, Obj: 0.286483, No Obj: 0.003156, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.530002, Class: 1.000000, Obj: 0.278776, No Obj: 0.003364, Avg Recall: 0.640000, count: 100 -1399: 0.473185, 0.512814 avg, 0.001000 rate, 23.461670 seconds, 358144 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.523932, Class: 1.000000, Obj: 0.263409, No Obj: 0.003259, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.562493, Class: 1.000000, Obj: 0.277630, No Obj: 0.003764, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.586550, Class: 1.000000, Obj: 0.301486, No Obj: 0.003753, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.490816, Class: 1.000000, Obj: 0.263295, No Obj: 0.003549, Avg Recall: 0.516667, count: 120 -Region Avg IOU: 0.553189, Class: 1.000000, Obj: 0.308814, No Obj: 0.003927, Avg Recall: 0.671756, count: 131 -Region Avg IOU: 0.514145, Class: 1.000000, Obj: 0.236431, No Obj: 0.003511, Avg Recall: 0.620690, count: 116 -Region Avg IOU: 0.561579, Class: 1.000000, Obj: 0.355212, No Obj: 0.003571, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.576976, Class: 1.000000, Obj: 0.279028, No Obj: 0.003940, Avg Recall: 0.677686, count: 121 -1400: 0.521695, 0.513702 avg, 0.00Saving weights to ./person9300/backup/tyvp_93_9c512_1400.weights -1000 rate, 23.320915 seconds, 358400 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.360239 seconds -Region Avg IOU: 0.457856, Class: 1.000000, Obj: 0.249541, No Obj: 0.004115, Avg Recall: 0.482456, count: 114 -Region Avg IOU: 0.460190, Class: 1.000000, Obj: 0.206821, No Obj: 0.004033, Avg Recall: 0.543210, count: 81 -Region Avg IOU: 0.449388, Class: 1.000000, Obj: 0.238614, No Obj: 0.004041, Avg Recall: 0.455446, count: 101 -Region Avg IOU: 0.471716, Class: 1.000000, Obj: 0.297198, No Obj: 0.003800, Avg Recall: 0.569444, count: 72 -Region Avg IOU: 0.426003, Class: 1.000000, Obj: 0.200048, No Obj: 0.004076, Avg Recall: 0.508333, count: 120 -Region Avg IOU: 0.434214, Class: 1.000000, Obj: 0.260356, No Obj: 0.004648, Avg Recall: 0.459259, count: 135 -Region Avg IOU: 0.481204, Class: 1.000000, Obj: 0.272797, No Obj: 0.004810, Avg Recall: 0.496124, count: 129 -Region Avg IOU: 0.440381, Class: 1.000000, Obj: 0.262861, No Obj: 0.004431, Avg Recall: 0.449612, count: 129 -1401: 0.472508, 0.509582 avg, 0.001000 rate, 21.087240 seconds, 358656 images -Loaded: 0.165970 seconds -Region Avg IOU: 0.464858, Class: 1.000000, Obj: 0.259553, No Obj: 0.005074, Avg Recall: 0.504202, count: 119 -Region Avg IOU: 0.467748, Class: 1.000000, Obj: 0.278137, No Obj: 0.004491, Avg Recall: 0.523810, count: 84 -Region Avg IOU: 0.451886, Class: 1.000000, Obj: 0.264275, No Obj: 0.005052, Avg Recall: 0.515152, count: 132 -Region Avg IOU: 0.444851, Class: 1.000000, Obj: 0.231103, No Obj: 0.004174, Avg Recall: 0.510417, count: 96 -Region Avg IOU: 0.530723, Class: 1.000000, Obj: 0.296991, No Obj: 0.004894, Avg Recall: 0.607843, count: 102 -Region Avg IOU: 0.483412, Class: 1.000000, Obj: 0.319092, No Obj: 0.004853, Avg Recall: 0.559140, count: 93 -Region Avg IOU: 0.476255, Class: 1.000000, Obj: 0.273601, No Obj: 0.004825, Avg Recall: 0.523077, count: 130 -Region Avg IOU: 0.477435, Class: 1.000000, Obj: 0.299403, No Obj: 0.005038, Avg Recall: 0.491525, count: 118 -1402: 0.531480, 0.511772 avg, 0.001000 rate, 19.435925 seconds, 358912 images -Loaded: 3.113570 seconds -Region Avg IOU: 0.487454, Class: 1.000000, Obj: 0.293109, No Obj: 0.005532, Avg Recall: 0.509434, count: 106 -Region Avg IOU: 0.418929, Class: 1.000000, Obj: 0.281355, No Obj: 0.004634, Avg Recall: 0.443396, count: 106 -Region Avg IOU: 0.445909, Class: 1.000000, Obj: 0.291859, No Obj: 0.004871, Avg Recall: 0.462121, count: 132 -Region Avg IOU: 0.455321, Class: 1.000000, Obj: 0.300567, No Obj: 0.006045, Avg Recall: 0.494186, count: 172 -Region Avg IOU: 0.486290, Class: 1.000000, Obj: 0.288229, No Obj: 0.004904, Avg Recall: 0.490909, count: 110 -Region Avg IOU: 0.398241, Class: 1.000000, Obj: 0.237448, No Obj: 0.004287, Avg Recall: 0.383838, count: 99 -Region Avg IOU: 0.498907, Class: 1.000000, Obj: 0.291890, No Obj: 0.004506, Avg Recall: 0.602410, count: 83 -Region Avg IOU: 0.496189, Class: 1.000000, Obj: 0.283554, No Obj: 0.004417, Avg Recall: 0.553191, count: 94 -1403: 0.534135, 0.514008 avg, 0.001000 rate, 18.034021 seconds, 359168 images -Loaded: 1.850001 seconds -Region Avg IOU: 0.388761, Class: 1.000000, Obj: 0.289750, No Obj: 0.004223, Avg Recall: 0.397849, count: 93 -Region Avg IOU: 0.451195, Class: 1.000000, Obj: 0.292499, No Obj: 0.005098, Avg Recall: 0.491667, count: 120 -Region Avg IOU: 0.458417, Class: 1.000000, Obj: 0.269286, No Obj: 0.005459, Avg Recall: 0.489933, count: 149 -Region Avg IOU: 0.481273, Class: 1.000000, Obj: 0.326860, No Obj: 0.004908, Avg Recall: 0.564103, count: 78 -Region Avg IOU: 0.420077, Class: 1.000000, Obj: 0.254843, No Obj: 0.005014, Avg Recall: 0.419355, count: 93 -Region Avg IOU: 0.429413, Class: 1.000000, Obj: 0.286796, No Obj: 0.004989, Avg Recall: 0.443478, count: 115 -Region Avg IOU: 0.467055, Class: 1.000000, Obj: 0.315894, No Obj: 0.005434, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.378821, Class: 1.000000, Obj: 0.293181, No Obj: 0.004805, Avg Recall: 0.404494, count: 89 -1404: 0.509150, 0.513523 avg, 0.001000 rate, 20.943853 seconds, 359424 images -Loaded: 0.718339 seconds -Region Avg IOU: 0.412023, Class: 1.000000, Obj: 0.271510, No Obj: 0.005262, Avg Recall: 0.398496, count: 133 -Region Avg IOU: 0.433488, Class: 1.000000, Obj: 0.249029, No Obj: 0.004235, Avg Recall: 0.442105, count: 95 -Region Avg IOU: 0.438012, Class: 1.000000, Obj: 0.314599, No Obj: 0.004814, Avg Recall: 0.387097, count: 93 -Region Avg IOU: 0.473265, Class: 1.000000, Obj: 0.305468, No Obj: 0.005294, Avg Recall: 0.486726, count: 113 -Region Avg IOU: 0.429369, Class: 1.000000, Obj: 0.280225, No Obj: 0.005310, Avg Recall: 0.415094, count: 106 -Region Avg IOU: 0.481625, Class: 1.000000, Obj: 0.285637, No Obj: 0.004851, Avg Recall: 0.546512, count: 86 -Region Avg IOU: 0.413707, Class: 1.000000, Obj: 0.245829, No Obj: 0.004953, Avg Recall: 0.403846, count: 104 -Region Avg IOU: 0.471007, Class: 1.000000, Obj: 0.327287, No Obj: 0.004914, Avg Recall: 0.471154, count: 104 -1405: 0.548131, 0.516983 avg, 0.001000 rate, 20.627176 seconds, 359680 images -Loaded: 0.984917 seconds -Region Avg IOU: 0.414020, Class: 1.000000, Obj: 0.285295, No Obj: 0.004936, Avg Recall: 0.390909, count: 110 -Region Avg IOU: 0.366060, Class: 1.000000, Obj: 0.265898, No Obj: 0.004313, Avg Recall: 0.353448, count: 116 -Region Avg IOU: 0.501371, Class: 1.000000, Obj: 0.343495, No Obj: 0.005253, Avg Recall: 0.543478, count: 92 -Region Avg IOU: 0.421434, Class: 1.000000, Obj: 0.326349, No Obj: 0.004723, Avg Recall: 0.413043, count: 92 -Region Avg IOU: 0.451899, Class: 1.000000, Obj: 0.326046, No Obj: 0.004776, Avg Recall: 0.517647, count: 85 -Region Avg IOU: 0.440503, Class: 1.000000, Obj: 0.271056, No Obj: 0.004848, Avg Recall: 0.486486, count: 111 -Region Avg IOU: 0.424443, Class: 1.000000, Obj: 0.244082, No Obj: 0.004703, Avg Recall: 0.489130, count: 92 -Region Avg IOU: 0.421486, Class: 1.000000, Obj: 0.283736, No Obj: 0.004791, Avg Recall: 0.443478, count: 115 -1406: 0.472271, 0.512512 avg, 0.001000 rate, 17.300722 seconds, 359936 images -Loaded: 1.824626 seconds -Region Avg IOU: 0.475919, Class: 1.000000, Obj: 0.303047, No Obj: 0.004666, Avg Recall: 0.554217, count: 83 -Region Avg IOU: 0.462073, Class: 1.000000, Obj: 0.305382, No Obj: 0.003908, Avg Recall: 0.545455, count: 88 -Region Avg IOU: 0.433432, Class: 1.000000, Obj: 0.256020, No Obj: 0.004605, Avg Recall: 0.415929, count: 113 -Region Avg IOU: 0.441951, Class: 1.000000, Obj: 0.267113, No Obj: 0.005049, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.481214, Class: 1.000000, Obj: 0.276063, No Obj: 0.005141, Avg Recall: 0.508475, count: 118 -Region Avg IOU: 0.429263, Class: 1.000000, Obj: 0.272360, No Obj: 0.004476, Avg Recall: 0.424242, count: 99 -Region Avg IOU: 0.427825, Class: 1.000000, Obj: 0.263418, No Obj: 0.004831, Avg Recall: 0.478261, count: 115 -Region Avg IOU: 0.435223, Class: 1.000000, Obj: 0.295493, No Obj: 0.004833, Avg Recall: 0.418367, count: 98 -1407: 0.511714, 0.512432 avg, 0.001000 rate, 20.242968 seconds, 360192 images -Loaded: 0.731763 seconds -Region Avg IOU: 0.473837, Class: 1.000000, Obj: 0.253298, No Obj: 0.004511, Avg Recall: 0.518182, count: 110 -Region Avg IOU: 0.447889, Class: 1.000000, Obj: 0.259747, No Obj: 0.003785, Avg Recall: 0.535714, count: 84 -Region Avg IOU: 0.410365, Class: 1.000000, Obj: 0.255849, No Obj: 0.004136, Avg Recall: 0.401869, count: 107 -Region Avg IOU: 0.457031, Class: 1.000000, Obj: 0.255663, No Obj: 0.003965, Avg Recall: 0.526316, count: 95 -Region Avg IOU: 0.415218, Class: 1.000000, Obj: 0.212623, No Obj: 0.004476, Avg Recall: 0.444444, count: 108 -Region Avg IOU: 0.460075, Class: 1.000000, Obj: 0.280313, No Obj: 0.004629, Avg Recall: 0.526316, count: 114 -Region Avg IOU: 0.433369, Class: 1.000000, Obj: 0.272346, No Obj: 0.004637, Avg Recall: 0.452381, count: 126 -Region Avg IOU: 0.426263, Class: 1.000000, Obj: 0.221748, No Obj: 0.004340, Avg Recall: 0.412281, count: 114 -1408: 0.510278, 0.512217 avg, 0.001000 rate, 19.344564 seconds, 360448 images -Loaded: 2.689154 seconds -Region Avg IOU: 0.383839, Class: 1.000000, Obj: 0.220197, No Obj: 0.003528, Avg Recall: 0.355556, count: 90 -Region Avg IOU: 0.434495, Class: 1.000000, Obj: 0.229550, No Obj: 0.004572, Avg Recall: 0.423423, count: 111 -Region Avg IOU: 0.417967, Class: 1.000000, Obj: 0.233505, No Obj: 0.004380, Avg Recall: 0.469027, count: 113 -Region Avg IOU: 0.507842, Class: 1.000000, Obj: 0.274609, No Obj: 0.005006, Avg Recall: 0.548387, count: 124 -Region Avg IOU: 0.454965, Class: 1.000000, Obj: 0.221586, No Obj: 0.004212, Avg Recall: 0.516949, count: 118 -Region Avg IOU: 0.436177, Class: 1.000000, Obj: 0.249343, No Obj: 0.004674, Avg Recall: 0.487395, count: 119 -Region Avg IOU: 0.432723, Class: 1.000000, Obj: 0.246611, No Obj: 0.004237, Avg Recall: 0.385542, count: 83 -Region Avg IOU: 0.450935, Class: 1.000000, Obj: 0.237362, No Obj: 0.004659, Avg Recall: 0.515152, count: 132 -1409: 0.586458, 0.519641 avg, 0.001000 rate, 19.639791 seconds, 360704 images -Loaded: 1.149965 seconds -Region Avg IOU: 0.443503, Class: 1.000000, Obj: 0.262142, No Obj: 0.004368, Avg Recall: 0.504132, count: 121 -Region Avg IOU: 0.464387, Class: 1.000000, Obj: 0.268206, No Obj: 0.004727, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.484124, Class: 1.000000, Obj: 0.230467, No Obj: 0.003892, Avg Recall: 0.567901, count: 81 -Region Avg IOU: 0.421817, Class: 1.000000, Obj: 0.207323, No Obj: 0.004082, Avg Recall: 0.433628, count: 113 -Region Avg IOU: 0.466685, Class: 1.000000, Obj: 0.219015, No Obj: 0.004450, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.439652, Class: 1.000000, Obj: 0.219552, No Obj: 0.004873, Avg Recall: 0.425676, count: 148 -Region Avg IOU: 0.454977, Class: 1.000000, Obj: 0.252059, No Obj: 0.003591, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.422345, Class: 1.000000, Obj: 0.217721, No Obj: 0.004177, Avg Recall: 0.439252, count: 107 -1410: 0.515509, 0.519228 avg, 0.001000 rate, 16.960546 seconds, 360960 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000097 seconds -Region Avg IOU: 0.611145, Class: 1.000000, Obj: 0.302566, No Obj: 0.003423, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.591866, Class: 1.000000, Obj: 0.282770, No Obj: 0.003377, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.570843, Class: 1.000000, Obj: 0.256095, No Obj: 0.002995, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.565334, Class: 1.000000, Obj: 0.287870, No Obj: 0.003227, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.581238, Class: 1.000000, Obj: 0.324671, No Obj: 0.003231, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.550196, Class: 1.000000, Obj: 0.260090, No Obj: 0.003342, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.575910, Class: 1.000000, Obj: 0.308961, No Obj: 0.003384, Avg Recall: 0.676923, count: 130 -Region Avg IOU: 0.574989, Class: 1.000000, Obj: 0.330535, No Obj: 0.003077, Avg Recall: 0.735632, count: 87 -1411: 0.511269, 0.518432 avg, 0.001000 rate, 28.453249 seconds, 361216 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.530075, Class: 1.000000, Obj: 0.308476, No Obj: 0.002944, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.589011, Class: 1.000000, Obj: 0.242593, No Obj: 0.003421, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.595701, Class: 1.000000, Obj: 0.241255, No Obj: 0.003026, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.588028, Class: 1.000000, Obj: 0.278091, No Obj: 0.003146, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.551635, Class: 1.000000, Obj: 0.255585, No Obj: 0.002812, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.589280, Class: 1.000000, Obj: 0.316661, No Obj: 0.002786, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.577106, Class: 1.000000, Obj: 0.287921, No Obj: 0.003342, Avg Recall: 0.663866, count: 119 -Region Avg IOU: 0.550637, Class: 1.000000, Obj: 0.316068, No Obj: 0.003193, Avg Recall: 0.691589, count: 107 -1412: 0.520170, 0.518606 avg, 0.001000 rate, 28.512815 seconds, 361472 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.560820, Class: 1.000000, Obj: 0.243396, No Obj: 0.002869, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.618460, Class: 1.000000, Obj: 0.300110, No Obj: 0.002815, Avg Recall: 0.800000, count: 95 -Region Avg IOU: 0.583716, Class: 1.000000, Obj: 0.282232, No Obj: 0.002853, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.593178, Class: 1.000000, Obj: 0.263576, No Obj: 0.002923, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.606954, Class: 1.000000, Obj: 0.287689, No Obj: 0.002976, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.572587, Class: 1.000000, Obj: 0.288876, No Obj: 0.002757, Avg Recall: 0.688525, count: 122 -Region Avg IOU: 0.594674, Class: 1.000000, Obj: 0.236078, No Obj: 0.002823, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.590070, Class: 1.000000, Obj: 0.278093, No Obj: 0.002895, Avg Recall: 0.729167, count: 96 -1413: 0.533106, 0.520056 avg, 0.001000 rate, 27.283079 seconds, 361728 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.593734, Class: 1.000000, Obj: 0.244438, No Obj: 0.002669, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.600632, Class: 1.000000, Obj: 0.236862, No Obj: 0.003129, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.580296, Class: 1.000000, Obj: 0.283691, No Obj: 0.002659, Avg Recall: 0.722222, count: 90 -Region Avg IOU: 0.572957, Class: 1.000000, Obj: 0.301075, No Obj: 0.002699, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.583443, Class: 1.000000, Obj: 0.243024, No Obj: 0.002889, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.607496, Class: 1.000000, Obj: 0.268012, No Obj: 0.002744, Avg Recall: 0.762295, count: 122 -Region Avg IOU: 0.594056, Class: 1.000000, Obj: 0.266936, No Obj: 0.002795, Avg Recall: 0.763780, count: 127 -Region Avg IOU: 0.555774, Class: 1.000000, Obj: 0.232588, No Obj: 0.002615, Avg Recall: 0.661017, count: 118 -1414: 0.528115, 0.520862 avg, 0.001000 rate, 27.481184 seconds, 361984 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.577216, Class: 1.000000, Obj: 0.252545, No Obj: 0.002815, Avg Recall: 0.744186, count: 129 -Region Avg IOU: 0.617887, Class: 1.000000, Obj: 0.262162, No Obj: 0.002795, Avg Recall: 0.756303, count: 119 -Region Avg IOU: 0.574486, Class: 1.000000, Obj: 0.292546, No Obj: 0.002494, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.638489, Class: 1.000000, Obj: 0.314713, No Obj: 0.002674, Avg Recall: 0.793103, count: 87 -Region Avg IOU: 0.574841, Class: 1.000000, Obj: 0.301647, No Obj: 0.002601, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.577335, Class: 1.000000, Obj: 0.264202, No Obj: 0.002917, Avg Recall: 0.725191, count: 131 -Region Avg IOU: 0.618216, Class: 1.000000, Obj: 0.288687, No Obj: 0.002933, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.614014, Class: 1.000000, Obj: 0.287210, No Obj: 0.002984, Avg Recall: 0.766423, count: 137 -1415: 0.516639, 0.520439 avg, 0.001000 rate, 27.515400 seconds, 362240 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.592331, Class: 1.000000, Obj: 0.232070, No Obj: 0.003190, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.614043, Class: 1.000000, Obj: 0.286037, No Obj: 0.002758, Avg Recall: 0.826087, count: 92 -Region Avg IOU: 0.578811, Class: 1.000000, Obj: 0.290995, No Obj: 0.002604, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.586566, Class: 1.000000, Obj: 0.276411, No Obj: 0.002909, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.597520, Class: 1.000000, Obj: 0.260212, No Obj: 0.002944, Avg Recall: 0.722222, count: 126 -Region Avg IOU: 0.593661, Class: 1.000000, Obj: 0.279615, No Obj: 0.003023, Avg Recall: 0.768657, count: 134 -Region Avg IOU: 0.592134, Class: 1.000000, Obj: 0.311164, No Obj: 0.002820, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.565143, Class: 1.000000, Obj: 0.325195, No Obj: 0.002754, Avg Recall: 0.641509, count: 106 -1416: 0.530423, 0.521438 avg, 0.001000 rate, 28.598455 seconds, 362496 images -Loaded: 0.000116 seconds -Region Avg IOU: 0.588625, Class: 1.000000, Obj: 0.287343, No Obj: 0.003038, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.614604, Class: 1.000000, Obj: 0.313742, No Obj: 0.002967, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.568080, Class: 1.000000, Obj: 0.336817, No Obj: 0.002676, Avg Recall: 0.679012, count: 81 -Region Avg IOU: 0.579513, Class: 1.000000, Obj: 0.242781, No Obj: 0.003100, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.609993, Class: 1.000000, Obj: 0.262959, No Obj: 0.002848, Avg Recall: 0.739583, count: 96 -Region Avg IOU: 0.644222, Class: 1.000000, Obj: 0.330565, No Obj: 0.003221, Avg Recall: 0.845361, count: 97 -Region Avg IOU: 0.594586, Class: 1.000000, Obj: 0.290030, No Obj: 0.002897, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.606051, Class: 1.000000, Obj: 0.315885, No Obj: 0.002743, Avg Recall: 0.714286, count: 91 -1417: 0.418833, 0.511177 avg, 0.001000 rate, 28.733025 seconds, 362752 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.560241, Class: 1.000000, Obj: 0.261477, No Obj: 0.003207, Avg Recall: 0.684615, count: 130 -Region Avg IOU: 0.579205, Class: 1.000000, Obj: 0.303992, No Obj: 0.002920, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.608653, Class: 1.000000, Obj: 0.294256, No Obj: 0.003259, Avg Recall: 0.757576, count: 132 -Region Avg IOU: 0.543829, Class: 1.000000, Obj: 0.275599, No Obj: 0.002870, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.600236, Class: 1.000000, Obj: 0.263304, No Obj: 0.003478, Avg Recall: 0.747967, count: 123 -Region Avg IOU: 0.574744, Class: 1.000000, Obj: 0.253748, No Obj: 0.002821, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.565816, Class: 1.000000, Obj: 0.263344, No Obj: 0.003185, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.576014, Class: 1.000000, Obj: 0.274712, No Obj: 0.003185, Avg Recall: 0.704000, count: 125 -1418: 0.537089, 0.513768 avg, 0.001000 rate, 27.791000 seconds, 363008 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.567954, Class: 1.000000, Obj: 0.298263, No Obj: 0.002934, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.598431, Class: 1.000000, Obj: 0.304474, No Obj: 0.003195, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.617806, Class: 1.000000, Obj: 0.311832, No Obj: 0.003579, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.602395, Class: 1.000000, Obj: 0.248480, No Obj: 0.002641, Avg Recall: 0.772727, count: 88 -Region Avg IOU: 0.597219, Class: 1.000000, Obj: 0.302773, No Obj: 0.003078, Avg Recall: 0.702479, count: 121 -Region Avg IOU: 0.596847, Class: 1.000000, Obj: 0.324887, No Obj: 0.002997, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.528071, Class: 1.000000, Obj: 0.310949, No Obj: 0.002861, Avg Recall: 0.571429, count: 105 -Region Avg IOU: 0.633713, Class: 1.000000, Obj: 0.330055, No Obj: 0.003280, Avg Recall: 0.776699, count: 103 -1419: 0.470827, 0.509474 avg, 0.001000 rate, 28.140505 seconds, 363264 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.619217, Class: 1.000000, Obj: 0.279336, No Obj: 0.003365, Avg Recall: 0.792308, count: 130 -Region Avg IOU: 0.588230, Class: 1.000000, Obj: 0.281343, No Obj: 0.002906, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.589851, Class: 1.000000, Obj: 0.296357, No Obj: 0.002997, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.550917, Class: 1.000000, Obj: 0.304390, No Obj: 0.002841, Avg Recall: 0.630435, count: 92 -Region Avg IOU: 0.572822, Class: 1.000000, Obj: 0.306034, No Obj: 0.002979, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.579257, Class: 1.000000, Obj: 0.285900, No Obj: 0.003091, Avg Recall: 0.700000, count: 120 -Region Avg IOU: 0.564981, Class: 1.000000, Obj: 0.248519, No Obj: 0.003071, Avg Recall: 0.674419, count: 129 -Region Avg IOU: 0.556777, Class: 1.000000, Obj: 0.278057, No Obj: 0.002937, Avg Recall: 0.651685, count: 89 -1420: 0.476247, 0.506152 avg, 0.001000 rate, 27.340990 seconds, 363520 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.777580 seconds -Region Avg IOU: 0.429888, Class: 1.000000, Obj: 0.237633, No Obj: 0.003543, Avg Recall: 0.462366, count: 93 -Region Avg IOU: 0.491878, Class: 1.000000, Obj: 0.276037, No Obj: 0.004354, Avg Recall: 0.560606, count: 132 -Region Avg IOU: 0.481662, Class: 1.000000, Obj: 0.251440, No Obj: 0.003864, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.478593, Class: 1.000000, Obj: 0.229511, No Obj: 0.004031, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.462320, Class: 1.000000, Obj: 0.255428, No Obj: 0.004169, Avg Recall: 0.500000, count: 108 -Region Avg IOU: 0.482487, Class: 1.000000, Obj: 0.247016, No Obj: 0.003923, Avg Recall: 0.467890, count: 109 -Region Avg IOU: 0.469493, Class: 1.000000, Obj: 0.225581, No Obj: 0.003828, Avg Recall: 0.518519, count: 108 -Region Avg IOU: 0.466524, Class: 1.000000, Obj: 0.209306, No Obj: 0.003855, Avg Recall: 0.503817, count: 131 -1421: 0.666293, 0.522166 avg, 0.001000 rate, 18.669847 seconds, 363776 images -Loaded: 4.725191 seconds -Region Avg IOU: 0.487129, Class: 1.000000, Obj: 0.226494, No Obj: 0.004092, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.499477, Class: 1.000000, Obj: 0.286095, No Obj: 0.004287, Avg Recall: 0.537815, count: 119 -Region Avg IOU: 0.537924, Class: 1.000000, Obj: 0.296769, No Obj: 0.004669, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.456403, Class: 1.000000, Obj: 0.270578, No Obj: 0.004824, Avg Recall: 0.507812, count: 128 -Region Avg IOU: 0.450634, Class: 1.000000, Obj: 0.286878, No Obj: 0.004123, Avg Recall: 0.466019, count: 103 -Region Avg IOU: 0.504428, Class: 1.000000, Obj: 0.242689, No Obj: 0.004649, Avg Recall: 0.577236, count: 123 -Region Avg IOU: 0.480265, Class: 1.000000, Obj: 0.262346, No Obj: 0.004115, Avg Recall: 0.555556, count: 135 -Region Avg IOU: 0.496313, Class: 1.000000, Obj: 0.260306, No Obj: 0.003415, Avg Recall: 0.537500, count: 80 -1422: 0.609506, 0.530900 avg, 0.001000 rate, 20.598156 seconds, 364032 images -Loaded: 0.494323 seconds -Region Avg IOU: 0.511717, Class: 1.000000, Obj: 0.216107, No Obj: 0.004346, Avg Recall: 0.629032, count: 124 -Region Avg IOU: 0.478898, Class: 1.000000, Obj: 0.271039, No Obj: 0.004577, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.483196, Class: 1.000000, Obj: 0.224970, No Obj: 0.004614, Avg Recall: 0.516129, count: 124 -Region Avg IOU: 0.467153, Class: 1.000000, Obj: 0.282127, No Obj: 0.004499, Avg Recall: 0.496000, count: 125 -Region Avg IOU: 0.525712, Class: 1.000000, Obj: 0.263658, No Obj: 0.003822, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.536323, Class: 1.000000, Obj: 0.296391, No Obj: 0.004602, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.432431, Class: 1.000000, Obj: 0.256200, No Obj: 0.004381, Avg Recall: 0.421384, count: 159 -Region Avg IOU: 0.477376, Class: 1.000000, Obj: 0.255649, No Obj: 0.003820, Avg Recall: 0.526316, count: 95 -1423: 0.533310, 0.531141 avg, 0.001000 rate, 20.668800 seconds, 364288 images -Loaded: 0.588392 seconds -Region Avg IOU: 0.455883, Class: 1.000000, Obj: 0.259406, No Obj: 0.003868, Avg Recall: 0.463636, count: 110 -Region Avg IOU: 0.489362, Class: 1.000000, Obj: 0.306127, No Obj: 0.003445, Avg Recall: 0.525641, count: 78 -Region Avg IOU: 0.485459, Class: 1.000000, Obj: 0.302729, No Obj: 0.004712, Avg Recall: 0.534884, count: 129 -Region Avg IOU: 0.426610, Class: 1.000000, Obj: 0.238822, No Obj: 0.003932, Avg Recall: 0.410714, count: 112 -Region Avg IOU: 0.532530, Class: 1.000000, Obj: 0.292979, No Obj: 0.003825, Avg Recall: 0.658824, count: 85 -Region Avg IOU: 0.455982, Class: 1.000000, Obj: 0.254756, No Obj: 0.004598, Avg Recall: 0.478571, count: 140 -Region Avg IOU: 0.469262, Class: 1.000000, Obj: 0.289328, No Obj: 0.003503, Avg Recall: 0.526316, count: 95 -Region Avg IOU: 0.458554, Class: 1.000000, Obj: 0.288643, No Obj: 0.004029, Avg Recall: 0.513274, count: 113 -1424: 0.508294, 0.528856 avg, 0.001000 rate, 20.525341 seconds, 364544 images -Loaded: 0.770840 seconds -Region Avg IOU: 0.484047, Class: 1.000000, Obj: 0.289360, No Obj: 0.004514, Avg Recall: 0.560748, count: 107 -Region Avg IOU: 0.501935, Class: 1.000000, Obj: 0.311538, No Obj: 0.004180, Avg Recall: 0.535354, count: 99 -Region Avg IOU: 0.475041, Class: 1.000000, Obj: 0.305223, No Obj: 0.004084, Avg Recall: 0.494949, count: 99 -Region Avg IOU: 0.465991, Class: 1.000000, Obj: 0.304986, No Obj: 0.004128, Avg Recall: 0.509804, count: 102 -Region Avg IOU: 0.443136, Class: 1.000000, Obj: 0.285215, No Obj: 0.004641, Avg Recall: 0.488550, count: 131 -Region Avg IOU: 0.428370, Class: 1.000000, Obj: 0.252453, No Obj: 0.004265, Avg Recall: 0.407143, count: 140 -Region Avg IOU: 0.486797, Class: 1.000000, Obj: 0.267020, No Obj: 0.004844, Avg Recall: 0.531250, count: 128 -Region Avg IOU: 0.467386, Class: 1.000000, Obj: 0.278515, No Obj: 0.003910, Avg Recall: 0.548077, count: 104 -1425: 0.527471, 0.528718 avg, 0.001000 rate, 20.268730 seconds, 364800 images -Loaded: 0.009263 seconds -Region Avg IOU: 0.481243, Class: 1.000000, Obj: 0.342172, No Obj: 0.004311, Avg Recall: 0.469388, count: 98 -Region Avg IOU: 0.486650, Class: 1.000000, Obj: 0.285633, No Obj: 0.004439, Avg Recall: 0.555556, count: 90 -Region Avg IOU: 0.427264, Class: 1.000000, Obj: 0.231689, No Obj: 0.004531, Avg Recall: 0.450980, count: 102 -Region Avg IOU: 0.461146, Class: 1.000000, Obj: 0.265167, No Obj: 0.004633, Avg Recall: 0.453782, count: 119 -Region Avg IOU: 0.479246, Class: 1.000000, Obj: 0.300709, No Obj: 0.005059, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.533173, Class: 1.000000, Obj: 0.263104, No Obj: 0.005069, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.490831, Class: 1.000000, Obj: 0.283778, No Obj: 0.004055, Avg Recall: 0.489362, count: 94 -Region Avg IOU: 0.459164, Class: 1.000000, Obj: 0.299877, No Obj: 0.004160, Avg Recall: 0.515152, count: 99 -1426: 0.445861, 0.520432 avg, 0.001000 rate, 22.374603 seconds, 365056 images -Loaded: 0.237051 seconds -Region Avg IOU: 0.471521, Class: 1.000000, Obj: 0.302008, No Obj: 0.004230, Avg Recall: 0.523810, count: 84 -Region Avg IOU: 0.433568, Class: 1.000000, Obj: 0.290541, No Obj: 0.004362, Avg Recall: 0.431034, count: 116 -Region Avg IOU: 0.481127, Class: 1.000000, Obj: 0.275443, No Obj: 0.005288, Avg Recall: 0.507143, count: 140 -Region Avg IOU: 0.446688, Class: 1.000000, Obj: 0.287273, No Obj: 0.004067, Avg Recall: 0.427184, count: 103 -Region Avg IOU: 0.480438, Class: 1.000000, Obj: 0.367946, No Obj: 0.003750, Avg Recall: 0.512821, count: 78 -Region Avg IOU: 0.501842, Class: 1.000000, Obj: 0.291075, No Obj: 0.005116, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.508858, Class: 1.000000, Obj: 0.309188, No Obj: 0.004634, Avg Recall: 0.523810, count: 105 -Region Avg IOU: 0.457656, Class: 1.000000, Obj: 0.284042, No Obj: 0.004427, Avg Recall: 0.468468, count: 111 -1427: 0.501387, 0.518527 avg, 0.001000 rate, 21.504398 seconds, 365312 images -Loaded: 0.005046 seconds -Region Avg IOU: 0.449245, Class: 1.000000, Obj: 0.257996, No Obj: 0.004118, Avg Recall: 0.490000, count: 100 -Region Avg IOU: 0.437226, Class: 1.000000, Obj: 0.296063, No Obj: 0.004249, Avg Recall: 0.477778, count: 90 -Region Avg IOU: 0.451045, Class: 1.000000, Obj: 0.269162, No Obj: 0.004436, Avg Recall: 0.504065, count: 123 -Region Avg IOU: 0.523523, Class: 1.000000, Obj: 0.336754, No Obj: 0.005225, Avg Recall: 0.596491, count: 114 -Region Avg IOU: 0.425709, Class: 1.000000, Obj: 0.306646, No Obj: 0.003795, Avg Recall: 0.457447, count: 94 -Region Avg IOU: 0.441891, Class: 1.000000, Obj: 0.289107, No Obj: 0.004391, Avg Recall: 0.459184, count: 98 -Region Avg IOU: 0.431304, Class: 1.000000, Obj: 0.268163, No Obj: 0.004570, Avg Recall: 0.476191, count: 126 -Region Avg IOU: 0.503442, Class: 1.000000, Obj: 0.279188, No Obj: 0.004940, Avg Recall: 0.603448, count: 116 -1428: 0.580776, 0.524752 avg, 0.001000 rate, 21.612673 seconds, 365568 images -Loaded: 0.254982 seconds -Region Avg IOU: 0.424233, Class: 1.000000, Obj: 0.307893, No Obj: 0.003639, Avg Recall: 0.390805, count: 87 -Region Avg IOU: 0.476333, Class: 1.000000, Obj: 0.300765, No Obj: 0.004743, Avg Recall: 0.565217, count: 138 -Region Avg IOU: 0.457958, Class: 1.000000, Obj: 0.236890, No Obj: 0.003935, Avg Recall: 0.512000, count: 125 -Region Avg IOU: 0.485021, Class: 1.000000, Obj: 0.299560, No Obj: 0.003812, Avg Recall: 0.481481, count: 108 -Region Avg IOU: 0.419229, Class: 1.000000, Obj: 0.256408, No Obj: 0.004288, Avg Recall: 0.403670, count: 109 -Region Avg IOU: 0.449631, Class: 1.000000, Obj: 0.266179, No Obj: 0.003979, Avg Recall: 0.470588, count: 102 -Region Avg IOU: 0.450216, Class: 1.000000, Obj: 0.258885, No Obj: 0.004155, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.500179, Class: 1.000000, Obj: 0.297484, No Obj: 0.004866, Avg Recall: 0.584071, count: 113 -1429: 0.542503, 0.526527 avg, 0.001000 rate, 20.850748 seconds, 365824 images -Loaded: 0.027187 seconds -Region Avg IOU: 0.461802, Class: 1.000000, Obj: 0.197404, No Obj: 0.003971, Avg Recall: 0.512397, count: 121 -Region Avg IOU: 0.500712, Class: 1.000000, Obj: 0.299277, No Obj: 0.003819, Avg Recall: 0.527473, count: 91 -Region Avg IOU: 0.453390, Class: 1.000000, Obj: 0.244255, No Obj: 0.003970, Avg Recall: 0.485714, count: 105 -Region Avg IOU: 0.535206, Class: 1.000000, Obj: 0.317624, No Obj: 0.004085, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.420030, Class: 1.000000, Obj: 0.249362, No Obj: 0.003610, Avg Recall: 0.416667, count: 108 -Region Avg IOU: 0.436860, Class: 1.000000, Obj: 0.284318, No Obj: 0.004413, Avg Recall: 0.483607, count: 122 -Region Avg IOU: 0.455740, Class: 1.000000, Obj: 0.285646, No Obj: 0.003850, Avg Recall: 0.511364, count: 88 -Region Avg IOU: 0.440873, Class: 1.000000, Obj: 0.296892, No Obj: 0.004070, Avg Recall: 0.482759, count: 87 -1430: 0.499684, 0.523843 avg, 0.001000 rate, 21.133245 seconds, 366080 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 0.000087 seconds -Region Avg IOU: 0.503291, Class: 1.000000, Obj: 0.246523, No Obj: 0.004294, Avg Recall: 0.568000, count: 125 -Region Avg IOU: 0.477490, Class: 1.000000, Obj: 0.271592, No Obj: 0.003962, Avg Recall: 0.476191, count: 105 -Region Avg IOU: 0.492029, Class: 1.000000, Obj: 0.267632, No Obj: 0.003717, Avg Recall: 0.525641, count: 78 -Region Avg IOU: 0.497620, Class: 1.000000, Obj: 0.283910, No Obj: 0.003595, Avg Recall: 0.525773, count: 97 -Region Avg IOU: 0.480379, Class: 1.000000, Obj: 0.239724, No Obj: 0.004329, Avg Recall: 0.516667, count: 120 -Region Avg IOU: 0.523525, Class: 1.000000, Obj: 0.252432, No Obj: 0.004193, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.479268, Class: 1.000000, Obj: 0.247198, No Obj: 0.003995, Avg Recall: 0.495652, count: 115 -Region Avg IOU: 0.540856, Class: 1.000000, Obj: 0.307235, No Obj: 0.003622, Avg Recall: 0.592105, count: 76 -1431: 0.486998, 0.520159 avg, 0.001000 rate, 20.013731 seconds, 366336 images -Loaded: 1.818484 seconds -Region Avg IOU: 0.505072, Class: 1.000000, Obj: 0.265059, No Obj: 0.003792, Avg Recall: 0.556522, count: 115 -Region Avg IOU: 0.513420, Class: 1.000000, Obj: 0.262663, No Obj: 0.003638, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.514686, Class: 1.000000, Obj: 0.299914, No Obj: 0.004191, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.496661, Class: 1.000000, Obj: 0.287467, No Obj: 0.004200, Avg Recall: 0.521368, count: 117 -Region Avg IOU: 0.501288, Class: 1.000000, Obj: 0.234978, No Obj: 0.004211, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.544049, Class: 1.000000, Obj: 0.296325, No Obj: 0.003382, Avg Recall: 0.540230, count: 87 -Region Avg IOU: 0.502629, Class: 1.000000, Obj: 0.315064, No Obj: 0.004117, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.477714, Class: 1.000000, Obj: 0.233644, No Obj: 0.003776, Avg Recall: 0.540230, count: 87 -1432: 0.475874, 0.515730 avg, 0.001000 rate, 22.899857 seconds, 366592 images -Loaded: 0.908334 seconds -Region Avg IOU: 0.459626, Class: 1.000000, Obj: 0.223703, No Obj: 0.003293, Avg Recall: 0.514286, count: 105 -Region Avg IOU: 0.522305, Class: 1.000000, Obj: 0.279178, No Obj: 0.004045, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.517417, Class: 1.000000, Obj: 0.265681, No Obj: 0.003946, Avg Recall: 0.603053, count: 131 -Region Avg IOU: 0.482577, Class: 1.000000, Obj: 0.299048, No Obj: 0.003936, Avg Recall: 0.512605, count: 119 -Region Avg IOU: 0.482994, Class: 1.000000, Obj: 0.295119, No Obj: 0.003732, Avg Recall: 0.500000, count: 94 -Region Avg IOU: 0.497000, Class: 1.000000, Obj: 0.247914, No Obj: 0.003522, Avg Recall: 0.568421, count: 95 -Region Avg IOU: 0.495975, Class: 1.000000, Obj: 0.271109, No Obj: 0.004109, Avg Recall: 0.563025, count: 119 -Region Avg IOU: 0.472688, Class: 1.000000, Obj: 0.237120, No Obj: 0.003504, Avg Recall: 0.479592, count: 98 -1433: 0.505017, 0.514659 avg, 0.001000 rate, 20.093418 seconds, 366848 images -Loaded: 0.928975 seconds -Region Avg IOU: 0.462661, Class: 1.000000, Obj: 0.263776, No Obj: 0.003560, Avg Recall: 0.521008, count: 119 -Region Avg IOU: 0.562733, Class: 1.000000, Obj: 0.294225, No Obj: 0.003772, Avg Recall: 0.636364, count: 88 -Region Avg IOU: 0.543108, Class: 1.000000, Obj: 0.277494, No Obj: 0.004393, Avg Recall: 0.637795, count: 127 -Region Avg IOU: 0.507414, Class: 1.000000, Obj: 0.277698, No Obj: 0.003686, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.509831, Class: 1.000000, Obj: 0.272555, No Obj: 0.003590, Avg Recall: 0.600000, count: 90 -Region Avg IOU: 0.512932, Class: 1.000000, Obj: 0.283112, No Obj: 0.003982, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.473619, Class: 1.000000, Obj: 0.287097, No Obj: 0.003450, Avg Recall: 0.514851, count: 101 -Region Avg IOU: 0.527385, Class: 1.000000, Obj: 0.324142, No Obj: 0.003570, Avg Recall: 0.595506, count: 89 -1434: 0.552778, 0.518471 avg, 0.001000 rate, 22.252363 seconds, 367104 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.542839, Class: 1.000000, Obj: 0.323824, No Obj: 0.004197, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.531935, Class: 1.000000, Obj: 0.308538, No Obj: 0.003913, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.534794, Class: 1.000000, Obj: 0.280139, No Obj: 0.003938, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.505450, Class: 1.000000, Obj: 0.287328, No Obj: 0.003676, Avg Recall: 0.539326, count: 89 -Region Avg IOU: 0.529073, Class: 1.000000, Obj: 0.277539, No Obj: 0.004021, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.469877, Class: 1.000000, Obj: 0.223625, No Obj: 0.003916, Avg Recall: 0.520325, count: 123 -Region Avg IOU: 0.506995, Class: 1.000000, Obj: 0.272814, No Obj: 0.004117, Avg Recall: 0.549180, count: 122 -Region Avg IOU: 0.509617, Class: 1.000000, Obj: 0.276823, No Obj: 0.003712, Avg Recall: 0.568627, count: 102 -1435: 0.459058, 0.512529 avg, 0.001000 rate, 21.103275 seconds, 367360 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.520155, Class: 1.000000, Obj: 0.249903, No Obj: 0.003473, Avg Recall: 0.597938, count: 97 -Region Avg IOU: 0.510040, Class: 1.000000, Obj: 0.274592, No Obj: 0.004033, Avg Recall: 0.584746, count: 118 -Region Avg IOU: 0.507235, Class: 1.000000, Obj: 0.268749, No Obj: 0.004319, Avg Recall: 0.533898, count: 118 -Region Avg IOU: 0.564569, Class: 1.000000, Obj: 0.339629, No Obj: 0.003640, Avg Recall: 0.662651, count: 83 -Region Avg IOU: 0.472603, Class: 1.000000, Obj: 0.259896, No Obj: 0.003333, Avg Recall: 0.505618, count: 89 -Region Avg IOU: 0.488498, Class: 1.000000, Obj: 0.259516, No Obj: 0.003583, Avg Recall: 0.506667, count: 75 -Region Avg IOU: 0.533344, Class: 1.000000, Obj: 0.319489, No Obj: 0.003870, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.473324, Class: 1.000000, Obj: 0.266758, No Obj: 0.003859, Avg Recall: 0.521739, count: 92 -1436: 0.466875, 0.507964 avg, 0.001000 rate, 21.207876 seconds, 367616 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.449700, Class: 1.000000, Obj: 0.299579, No Obj: 0.003857, Avg Recall: 0.457143, count: 105 -Region Avg IOU: 0.479817, Class: 1.000000, Obj: 0.251598, No Obj: 0.004262, Avg Recall: 0.570248, count: 121 -Region Avg IOU: 0.484787, Class: 1.000000, Obj: 0.300251, No Obj: 0.003819, Avg Recall: 0.516129, count: 93 -Region Avg IOU: 0.471631, Class: 1.000000, Obj: 0.265099, No Obj: 0.003738, Avg Recall: 0.548673, count: 113 -Region Avg IOU: 0.517150, Class: 1.000000, Obj: 0.309723, No Obj: 0.004411, Avg Recall: 0.572581, count: 124 -Region Avg IOU: 0.505505, Class: 1.000000, Obj: 0.306875, No Obj: 0.003837, Avg Recall: 0.510000, count: 100 -Region Avg IOU: 0.459361, Class: 1.000000, Obj: 0.289739, No Obj: 0.004158, Avg Recall: 0.466102, count: 118 -Region Avg IOU: 0.460593, Class: 1.000000, Obj: 0.265330, No Obj: 0.003755, Avg Recall: 0.479167, count: 96 -1437: 0.613764, 0.518544 avg, 0.001000 rate, 22.904907 seconds, 367872 images -Loaded: 0.680968 seconds -Region Avg IOU: 0.500364, Class: 1.000000, Obj: 0.271106, No Obj: 0.003868, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.499786, Class: 1.000000, Obj: 0.312048, No Obj: 0.004442, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.524658, Class: 1.000000, Obj: 0.304952, No Obj: 0.003581, Avg Recall: 0.579545, count: 88 -Region Avg IOU: 0.499604, Class: 1.000000, Obj: 0.333405, No Obj: 0.004122, Avg Recall: 0.550000, count: 100 -Region Avg IOU: 0.502097, Class: 1.000000, Obj: 0.272658, No Obj: 0.003969, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.511008, Class: 1.000000, Obj: 0.332360, No Obj: 0.003829, Avg Recall: 0.583333, count: 84 -Region Avg IOU: 0.538380, Class: 1.000000, Obj: 0.317654, No Obj: 0.004054, Avg Recall: 0.600000, count: 95 -Region Avg IOU: 0.521305, Class: 1.000000, Obj: 0.336400, No Obj: 0.003874, Avg Recall: 0.556818, count: 88 -1438: 0.403850, 0.507075 avg, 0.001000 rate, 22.293526 seconds, 368128 images -Loaded: 0.000056 seconds -Region Avg IOU: 0.495217, Class: 1.000000, Obj: 0.296484, No Obj: 0.003905, Avg Recall: 0.560440, count: 91 -Region Avg IOU: 0.445802, Class: 1.000000, Obj: 0.296004, No Obj: 0.003913, Avg Recall: 0.403670, count: 109 -Region Avg IOU: 0.483874, Class: 1.000000, Obj: 0.279594, No Obj: 0.004219, Avg Recall: 0.521368, count: 117 -Region Avg IOU: 0.493920, Class: 1.000000, Obj: 0.248035, No Obj: 0.004221, Avg Recall: 0.546875, count: 128 -Region Avg IOU: 0.459111, Class: 1.000000, Obj: 0.266162, No Obj: 0.004242, Avg Recall: 0.485437, count: 103 -Region Avg IOU: 0.514473, Class: 1.000000, Obj: 0.263324, No Obj: 0.004320, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.484381, Class: 1.000000, Obj: 0.300508, No Obj: 0.004528, Avg Recall: 0.532258, count: 124 -Region Avg IOU: 0.492205, Class: 1.000000, Obj: 0.280513, No Obj: 0.004316, Avg Recall: 0.555556, count: 108 -1439: 0.532181, 0.509585 avg, 0.001000 rate, 22.406687 seconds, 368384 images -Loaded: 0.138795 seconds -Region Avg IOU: 0.462701, Class: 1.000000, Obj: 0.267560, No Obj: 0.004241, Avg Recall: 0.486726, count: 113 -Region Avg IOU: 0.476954, Class: 1.000000, Obj: 0.309264, No Obj: 0.003918, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.523959, Class: 1.000000, Obj: 0.290127, No Obj: 0.004454, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.502925, Class: 1.000000, Obj: 0.269146, No Obj: 0.005012, Avg Recall: 0.576159, count: 151 -Region Avg IOU: 0.528770, Class: 1.000000, Obj: 0.295068, No Obj: 0.004706, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.445123, Class: 1.000000, Obj: 0.270132, No Obj: 0.003789, Avg Recall: 0.467290, count: 107 -Region Avg IOU: 0.435591, Class: 1.000000, Obj: 0.279657, No Obj: 0.003715, Avg Recall: 0.430000, count: 100 -Region Avg IOU: 0.562153, Class: 1.000000, Obj: 0.318209, No Obj: 0.004878, Avg Recall: 0.704000, count: 125 -1440: 0.458121, 0.504439 avg, 0.001000 rate, 21.828176 seconds, 368640 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000074 seconds -Region Avg IOU: 0.582045, Class: 1.000000, Obj: 0.321104, No Obj: 0.003698, Avg Recall: 0.700000, count: 120 -Region Avg IOU: 0.531336, Class: 1.000000, Obj: 0.322834, No Obj: 0.003178, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.522966, Class: 1.000000, Obj: 0.335556, No Obj: 0.003622, Avg Recall: 0.596774, count: 124 -Region Avg IOU: 0.543376, Class: 1.000000, Obj: 0.333829, No Obj: 0.003179, Avg Recall: 0.647727, count: 88 -Region Avg IOU: 0.559267, Class: 1.000000, Obj: 0.366522, No Obj: 0.003731, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.566924, Class: 1.000000, Obj: 0.282248, No Obj: 0.003513, Avg Recall: 0.699248, count: 133 -Region Avg IOU: 0.571595, Class: 1.000000, Obj: 0.352146, No Obj: 0.003641, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.581135, Class: 1.000000, Obj: 0.363827, No Obj: 0.003632, Avg Recall: 0.677419, count: 93 -1441: 0.471140, 0.501109 avg, 0.001000 rate, 26.794804 seconds, 368896 images -Loaded: 0.000130 seconds -Region Avg IOU: 0.594101, Class: 1.000000, Obj: 0.277401, No Obj: 0.003711, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.529646, Class: 1.000000, Obj: 0.315414, No Obj: 0.003183, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.548285, Class: 1.000000, Obj: 0.286956, No Obj: 0.003393, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.574932, Class: 1.000000, Obj: 0.339552, No Obj: 0.003346, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.511615, Class: 1.000000, Obj: 0.259378, No Obj: 0.003394, Avg Recall: 0.570312, count: 128 -Region Avg IOU: 0.552983, Class: 1.000000, Obj: 0.313628, No Obj: 0.003028, Avg Recall: 0.659091, count: 88 -Region Avg IOU: 0.558815, Class: 1.000000, Obj: 0.333497, No Obj: 0.003499, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.593463, Class: 1.000000, Obj: 0.332433, No Obj: 0.003319, Avg Recall: 0.721154, count: 104 -1442: 0.494862, 0.500484 avg, 0.001000 rate, 28.824900 seconds, 369152 images -Loaded: 0.000115 seconds -Region Avg IOU: 0.583537, Class: 1.000000, Obj: 0.302848, No Obj: 0.003175, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.540695, Class: 1.000000, Obj: 0.299884, No Obj: 0.003085, Avg Recall: 0.669565, count: 115 -Region Avg IOU: 0.533401, Class: 1.000000, Obj: 0.245607, No Obj: 0.002941, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.582968, Class: 1.000000, Obj: 0.297750, No Obj: 0.003380, Avg Recall: 0.725191, count: 131 -Region Avg IOU: 0.546319, Class: 1.000000, Obj: 0.247780, No Obj: 0.003408, Avg Recall: 0.632000, count: 125 -Region Avg IOU: 0.586413, Class: 1.000000, Obj: 0.307429, No Obj: 0.003664, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.541085, Class: 1.000000, Obj: 0.287664, No Obj: 0.003356, Avg Recall: 0.647541, count: 122 -Region Avg IOU: 0.558266, Class: 1.000000, Obj: 0.246635, No Obj: 0.003086, Avg Recall: 0.681035, count: 116 -1443: 0.551796, 0.505615 avg, 0.001000 rate, 26.018051 seconds, 369408 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.574119, Class: 1.000000, Obj: 0.306200, No Obj: 0.002949, Avg Recall: 0.666667, count: 84 -Region Avg IOU: 0.572101, Class: 1.000000, Obj: 0.283907, No Obj: 0.003025, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.577954, Class: 1.000000, Obj: 0.273969, No Obj: 0.003232, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.544975, Class: 1.000000, Obj: 0.279360, No Obj: 0.003386, Avg Recall: 0.641892, count: 148 -Region Avg IOU: 0.586850, Class: 1.000000, Obj: 0.245642, No Obj: 0.003044, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.634976, Class: 1.000000, Obj: 0.287034, No Obj: 0.003069, Avg Recall: 0.836538, count: 104 -Region Avg IOU: 0.538153, Class: 1.000000, Obj: 0.296997, No Obj: 0.002710, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.575227, Class: 1.000000, Obj: 0.261047, No Obj: 0.003174, Avg Recall: 0.715385, count: 130 -1444: 0.475502, 0.502604 avg, 0.001000 rate, 26.892252 seconds, 369664 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.552933, Class: 1.000000, Obj: 0.253306, No Obj: 0.002740, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.600096, Class: 1.000000, Obj: 0.294276, No Obj: 0.002989, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.570461, Class: 1.000000, Obj: 0.260422, No Obj: 0.003365, Avg Recall: 0.708029, count: 137 -Region Avg IOU: 0.528117, Class: 1.000000, Obj: 0.226219, No Obj: 0.002659, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.521917, Class: 1.000000, Obj: 0.232120, No Obj: 0.002628, Avg Recall: 0.589474, count: 95 -Region Avg IOU: 0.587619, Class: 1.000000, Obj: 0.265873, No Obj: 0.003054, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.586939, Class: 1.000000, Obj: 0.267325, No Obj: 0.002894, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.579158, Class: 1.000000, Obj: 0.272422, No Obj: 0.002821, Avg Recall: 0.710280, count: 107 -1445: 0.496218, 0.501965 avg, 0.001000 rate, 26.463806 seconds, 369920 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.550322, Class: 1.000000, Obj: 0.274844, No Obj: 0.002728, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.530575, Class: 1.000000, Obj: 0.217159, No Obj: 0.003006, Avg Recall: 0.626866, count: 134 -Region Avg IOU: 0.582825, Class: 1.000000, Obj: 0.250738, No Obj: 0.003152, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.601451, Class: 1.000000, Obj: 0.240538, No Obj: 0.002839, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.585531, Class: 1.000000, Obj: 0.261806, No Obj: 0.003046, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.571343, Class: 1.000000, Obj: 0.255091, No Obj: 0.003024, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.616738, Class: 1.000000, Obj: 0.292703, No Obj: 0.002855, Avg Recall: 0.784091, count: 88 -Region Avg IOU: 0.541713, Class: 1.000000, Obj: 0.250999, No Obj: 0.002759, Avg Recall: 0.621359, count: 103 -1446: 0.505257, 0.502295 avg, 0.001000 rate, 27.460426 seconds, 370176 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.560632, Class: 1.000000, Obj: 0.248878, No Obj: 0.003121, Avg Recall: 0.686956, count: 115 -Region Avg IOU: 0.575866, Class: 1.000000, Obj: 0.270802, No Obj: 0.003017, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.550464, Class: 1.000000, Obj: 0.204525, No Obj: 0.003023, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.602433, Class: 1.000000, Obj: 0.281910, No Obj: 0.003454, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.587972, Class: 1.000000, Obj: 0.270668, No Obj: 0.002938, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.558466, Class: 1.000000, Obj: 0.219102, No Obj: 0.002726, Avg Recall: 0.684211, count: 76 -Region Avg IOU: 0.582800, Class: 1.000000, Obj: 0.283982, No Obj: 0.002778, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.594814, Class: 1.000000, Obj: 0.297233, No Obj: 0.002799, Avg Recall: 0.705263, count: 95 -1447: 0.479604, 0.500026 avg, 0.001000 rate, 27.748600 seconds, 370432 images -Loaded: 0.000144 seconds -Region Avg IOU: 0.590241, Class: 1.000000, Obj: 0.273978, No Obj: 0.003071, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.589898, Class: 1.000000, Obj: 0.261099, No Obj: 0.003117, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.569112, Class: 1.000000, Obj: 0.257409, No Obj: 0.003075, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.641720, Class: 1.000000, Obj: 0.338397, No Obj: 0.003367, Avg Recall: 0.801653, count: 121 -Region Avg IOU: 0.592020, Class: 1.000000, Obj: 0.277771, No Obj: 0.003496, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.607604, Class: 1.000000, Obj: 0.306837, No Obj: 0.003293, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.574084, Class: 1.000000, Obj: 0.210399, No Obj: 0.002898, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.599997, Class: 1.000000, Obj: 0.272483, No Obj: 0.002863, Avg Recall: 0.780488, count: 82 -1448: 0.493303, 0.499353 avg, 0.001000 rate, 26.631395 seconds, 370688 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.571565, Class: 1.000000, Obj: 0.264226, No Obj: 0.003194, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.594418, Class: 1.000000, Obj: 0.285583, No Obj: 0.003453, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.550851, Class: 1.000000, Obj: 0.296983, No Obj: 0.003020, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.606047, Class: 1.000000, Obj: 0.312806, No Obj: 0.003486, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.560928, Class: 1.000000, Obj: 0.269544, No Obj: 0.003453, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.600591, Class: 1.000000, Obj: 0.289118, No Obj: 0.002991, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.560336, Class: 1.000000, Obj: 0.233806, No Obj: 0.003141, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.585648, Class: 1.000000, Obj: 0.311987, No Obj: 0.003211, Avg Recall: 0.663636, count: 110 -1449: 0.561090, 0.505527 avg, 0.001000 rate, 26.447231 seconds, 370944 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.575386, Class: 1.000000, Obj: 0.290488, No Obj: 0.003243, Avg Recall: 0.706349, count: 126 -Region Avg IOU: 0.594265, Class: 1.000000, Obj: 0.313043, No Obj: 0.003469, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.611447, Class: 1.000000, Obj: 0.312392, No Obj: 0.003597, Avg Recall: 0.737589, count: 141 -Region Avg IOU: 0.565364, Class: 1.000000, Obj: 0.308291, No Obj: 0.003443, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.528142, Class: 1.000000, Obj: 0.305428, No Obj: 0.003569, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.606881, Class: 1.000000, Obj: 0.348473, No Obj: 0.003398, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.547461, Class: 1.000000, Obj: 0.297062, No Obj: 0.003510, Avg Recall: 0.574803, count: 127 -Region Avg IOU: 0.615136, Class: 1.000000, Obj: 0.307926, No Obj: 0.003296, Avg Recall: 0.772727, count: 88 -1450: 0.494553, 0.504430 avg, 0.001000 rate, 27.116522 seconds, 371200 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000073 seconds -Region Avg IOU: 0.602549, Class: 1.000000, Obj: 0.360152, No Obj: 0.003573, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.555074, Class: 1.000000, Obj: 0.276187, No Obj: 0.003973, Avg Recall: 0.664286, count: 140 -Region Avg IOU: 0.572733, Class: 1.000000, Obj: 0.346037, No Obj: 0.003465, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.553503, Class: 1.000000, Obj: 0.359410, No Obj: 0.003334, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.625933, Class: 1.000000, Obj: 0.350335, No Obj: 0.003728, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.563728, Class: 1.000000, Obj: 0.411067, No Obj: 0.003427, Avg Recall: 0.652542, count: 118 -Region Avg IOU: 0.588347, Class: 1.000000, Obj: 0.317561, No Obj: 0.003419, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.605865, Class: 1.000000, Obj: 0.323029, No Obj: 0.003379, Avg Recall: 0.719626, count: 107 -1451: 0.492823, 0.503269 avg, 0.001000 rate, 28.135464 seconds, 371456 images -Loaded: 0.000118 seconds -Region Avg IOU: 0.634537, Class: 1.000000, Obj: 0.328662, No Obj: 0.003752, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.573404, Class: 1.000000, Obj: 0.310145, No Obj: 0.003368, Avg Recall: 0.702381, count: 84 -Region Avg IOU: 0.561807, Class: 1.000000, Obj: 0.333348, No Obj: 0.003454, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.580859, Class: 1.000000, Obj: 0.332521, No Obj: 0.003464, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.568324, Class: 1.000000, Obj: 0.395191, No Obj: 0.003353, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.594451, Class: 1.000000, Obj: 0.303194, No Obj: 0.003386, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.548964, Class: 1.000000, Obj: 0.341232, No Obj: 0.003619, Avg Recall: 0.613139, count: 137 -Region Avg IOU: 0.590146, Class: 1.000000, Obj: 0.289836, No Obj: 0.003705, Avg Recall: 0.682927, count: 123 -1452: 0.461201, 0.499062 avg, 0.001000 rate, 26.792839 seconds, 371712 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.583111, Class: 1.000000, Obj: 0.311395, No Obj: 0.003277, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.589868, Class: 1.000000, Obj: 0.298149, No Obj: 0.003042, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.588000, Class: 1.000000, Obj: 0.304841, No Obj: 0.003409, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.585506, Class: 1.000000, Obj: 0.289018, No Obj: 0.003497, Avg Recall: 0.699248, count: 133 -Region Avg IOU: 0.526838, Class: 1.000000, Obj: 0.331124, No Obj: 0.003067, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.511983, Class: 1.000000, Obj: 0.262585, No Obj: 0.002645, Avg Recall: 0.559524, count: 84 -Region Avg IOU: 0.589329, Class: 1.000000, Obj: 0.314116, No Obj: 0.003218, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.581896, Class: 1.000000, Obj: 0.330726, No Obj: 0.003257, Avg Recall: 0.739583, count: 96 -1453: 0.489707, 0.498127 avg, 0.001000 rate, 27.189680 seconds, 371968 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.568489, Class: 1.000000, Obj: 0.324396, No Obj: 0.002896, Avg Recall: 0.688525, count: 122 -Region Avg IOU: 0.574195, Class: 1.000000, Obj: 0.307168, No Obj: 0.002951, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.590437, Class: 1.000000, Obj: 0.302428, No Obj: 0.003272, Avg Recall: 0.757812, count: 128 -Region Avg IOU: 0.601779, Class: 1.000000, Obj: 0.296945, No Obj: 0.002927, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.594730, Class: 1.000000, Obj: 0.298891, No Obj: 0.002938, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.597868, Class: 1.000000, Obj: 0.310434, No Obj: 0.002940, Avg Recall: 0.736264, count: 91 -Region Avg IOU: 0.586193, Class: 1.000000, Obj: 0.266906, No Obj: 0.003013, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.631668, Class: 1.000000, Obj: 0.279171, No Obj: 0.002891, Avg Recall: 0.810127, count: 79 -1454: 0.464213, 0.494735 avg, 0.001000 rate, 27.500179 seconds, 372224 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.593225, Class: 1.000000, Obj: 0.260622, No Obj: 0.003280, Avg Recall: 0.699248, count: 133 -Region Avg IOU: 0.560308, Class: 1.000000, Obj: 0.276203, No Obj: 0.002878, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.639635, Class: 1.000000, Obj: 0.297107, No Obj: 0.002941, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.600127, Class: 1.000000, Obj: 0.289771, No Obj: 0.002768, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.582799, Class: 1.000000, Obj: 0.242794, No Obj: 0.003274, Avg Recall: 0.755396, count: 139 -Region Avg IOU: 0.557004, Class: 1.000000, Obj: 0.273001, No Obj: 0.002895, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.578431, Class: 1.000000, Obj: 0.292309, No Obj: 0.002750, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.578359, Class: 1.000000, Obj: 0.303387, No Obj: 0.003059, Avg Recall: 0.651786, count: 112 -1455: 0.514997, 0.496761 avg, 0.001000 rate, 29.182819 seconds, 372480 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.540471, Class: 1.000000, Obj: 0.244692, No Obj: 0.002980, Avg Recall: 0.617886, count: 123 -Region Avg IOU: 0.586197, Class: 1.000000, Obj: 0.257607, No Obj: 0.002857, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.591988, Class: 1.000000, Obj: 0.280059, No Obj: 0.002651, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.568924, Class: 1.000000, Obj: 0.257181, No Obj: 0.002903, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.588206, Class: 1.000000, Obj: 0.213649, No Obj: 0.002932, Avg Recall: 0.697183, count: 142 -Region Avg IOU: 0.575079, Class: 1.000000, Obj: 0.271732, No Obj: 0.002443, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.583795, Class: 1.000000, Obj: 0.260567, No Obj: 0.002775, Avg Recall: 0.703704, count: 81 -Region Avg IOU: 0.591121, Class: 1.000000, Obj: 0.208531, No Obj: 0.002750, Avg Recall: 0.754386, count: 114 -1456: 0.498968, 0.496982 avg, 0.001000 rate, 26.162884 seconds, 372736 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.621126, Class: 1.000000, Obj: 0.285274, No Obj: 0.002547, Avg Recall: 0.756410, count: 78 -Region Avg IOU: 0.574275, Class: 1.000000, Obj: 0.228820, No Obj: 0.002717, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.574669, Class: 1.000000, Obj: 0.225203, No Obj: 0.002499, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.585128, Class: 1.000000, Obj: 0.242859, No Obj: 0.002459, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.539722, Class: 1.000000, Obj: 0.274622, No Obj: 0.002496, Avg Recall: 0.589474, count: 95 -Region Avg IOU: 0.595607, Class: 1.000000, Obj: 0.254370, No Obj: 0.002742, Avg Recall: 0.760000, count: 125 -Region Avg IOU: 0.576769, Class: 1.000000, Obj: 0.267908, No Obj: 0.002526, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.596648, Class: 1.000000, Obj: 0.220723, No Obj: 0.002751, Avg Recall: 0.716667, count: 120 -1457: 0.439808, 0.491265 avg, 0.001000 rate, 27.010376 seconds, 372992 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.567365, Class: 1.000000, Obj: 0.296425, No Obj: 0.002411, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.562471, Class: 1.000000, Obj: 0.271523, No Obj: 0.002788, Avg Recall: 0.677686, count: 121 -Region Avg IOU: 0.592023, Class: 1.000000, Obj: 0.259627, No Obj: 0.002523, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.566305, Class: 1.000000, Obj: 0.231931, No Obj: 0.002873, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.592024, Class: 1.000000, Obj: 0.300742, No Obj: 0.002741, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.554938, Class: 1.000000, Obj: 0.207710, No Obj: 0.002809, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.574560, Class: 1.000000, Obj: 0.289724, No Obj: 0.002628, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.603053, Class: 1.000000, Obj: 0.255895, No Obj: 0.002920, Avg Recall: 0.732759, count: 116 -1458: 0.465660, 0.488704 avg, 0.001000 rate, 26.629169 seconds, 373248 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.557069, Class: 1.000000, Obj: 0.257112, No Obj: 0.002683, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.613278, Class: 1.000000, Obj: 0.255374, No Obj: 0.002701, Avg Recall: 0.790698, count: 86 -Region Avg IOU: 0.608436, Class: 1.000000, Obj: 0.294413, No Obj: 0.002858, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.588118, Class: 1.000000, Obj: 0.275618, No Obj: 0.002589, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.609946, Class: 1.000000, Obj: 0.268361, No Obj: 0.002930, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.588539, Class: 1.000000, Obj: 0.294678, No Obj: 0.002714, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.579761, Class: 1.000000, Obj: 0.308123, No Obj: 0.002670, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.548727, Class: 1.000000, Obj: 0.265218, No Obj: 0.002816, Avg Recall: 0.690265, count: 113 -1459: 0.485944, 0.488428 avg, 0.001000 rate, 27.005409 seconds, 373504 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.578968, Class: 1.000000, Obj: 0.282805, No Obj: 0.002858, Avg Recall: 0.683761, count: 117 -Region Avg IOU: 0.613320, Class: 1.000000, Obj: 0.276948, No Obj: 0.002668, Avg Recall: 0.765432, count: 81 -Region Avg IOU: 0.593967, Class: 1.000000, Obj: 0.348072, No Obj: 0.002628, Avg Recall: 0.679012, count: 81 -Region Avg IOU: 0.544583, Class: 1.000000, Obj: 0.255150, No Obj: 0.002720, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.590868, Class: 1.000000, Obj: 0.311574, No Obj: 0.002812, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.621328, Class: 1.000000, Obj: 0.264533, No Obj: 0.002809, Avg Recall: 0.831683, count: 101 -Region Avg IOU: 0.573201, Class: 1.000000, Obj: 0.280020, No Obj: 0.002511, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.583285, Class: 1.000000, Obj: 0.281284, No Obj: 0.002870, Avg Recall: 0.700935, count: 107 -1460: 0.433203, 0.482906 avg, 0.001000 rate, 27.269886 seconds, 373760 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 13.709646 seconds -Region Avg IOU: 0.502521, Class: 1.000000, Obj: 0.265060, No Obj: 0.004207, Avg Recall: 0.552448, count: 143 -Region Avg IOU: 0.530483, Class: 1.000000, Obj: 0.265346, No Obj: 0.003394, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.482265, Class: 1.000000, Obj: 0.236284, No Obj: 0.003884, Avg Recall: 0.556452, count: 124 -Region Avg IOU: 0.501832, Class: 1.000000, Obj: 0.228967, No Obj: 0.003880, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.474045, Class: 1.000000, Obj: 0.267169, No Obj: 0.003564, Avg Recall: 0.484211, count: 95 -Region Avg IOU: 0.501508, Class: 1.000000, Obj: 0.282138, No Obj: 0.003387, Avg Recall: 0.523810, count: 84 -Region Avg IOU: 0.506116, Class: 1.000000, Obj: 0.230350, No Obj: 0.004204, Avg Recall: 0.601504, count: 133 -Region Avg IOU: 0.514923, Class: 1.000000, Obj: 0.243349, No Obj: 0.004003, Avg Recall: 0.558824, count: 136 -1461: 0.574861, 0.492101 avg, 0.001000 rate, 23.037012 seconds, 374016 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.480089, Class: 1.000000, Obj: 0.283742, No Obj: 0.004032, Avg Recall: 0.491071, count: 112 -Region Avg IOU: 0.507043, Class: 1.000000, Obj: 0.266641, No Obj: 0.004314, Avg Recall: 0.560976, count: 123 -Region Avg IOU: 0.579088, Class: 1.000000, Obj: 0.341273, No Obj: 0.003864, Avg Recall: 0.710526, count: 76 -Region Avg IOU: 0.476263, Class: 1.000000, Obj: 0.279757, No Obj: 0.003671, Avg Recall: 0.574468, count: 94 -Region Avg IOU: 0.516153, Class: 1.000000, Obj: 0.309538, No Obj: 0.004229, Avg Recall: 0.567164, count: 134 -Region Avg IOU: 0.529531, Class: 1.000000, Obj: 0.281858, No Obj: 0.004501, Avg Recall: 0.590551, count: 127 -Region Avg IOU: 0.507755, Class: 1.000000, Obj: 0.316882, No Obj: 0.003830, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.522898, Class: 1.000000, Obj: 0.294935, No Obj: 0.004089, Avg Recall: 0.605263, count: 114 -1462: 0.472217, 0.490113 avg, 0.001000 rate, 21.953581 seconds, 374272 images -Loaded: 0.073761 seconds -Region Avg IOU: 0.490886, Class: 1.000000, Obj: 0.308729, No Obj: 0.003820, Avg Recall: 0.554217, count: 83 -Region Avg IOU: 0.462069, Class: 1.000000, Obj: 0.241002, No Obj: 0.004203, Avg Recall: 0.492424, count: 132 -Region Avg IOU: 0.474469, Class: 1.000000, Obj: 0.308096, No Obj: 0.004626, Avg Recall: 0.537190, count: 121 -Region Avg IOU: 0.489873, Class: 1.000000, Obj: 0.293314, No Obj: 0.004394, Avg Recall: 0.555556, count: 144 -Region Avg IOU: 0.462734, Class: 1.000000, Obj: 0.287719, No Obj: 0.004359, Avg Recall: 0.494253, count: 87 -Region Avg IOU: 0.494838, Class: 1.000000, Obj: 0.284983, No Obj: 0.004205, Avg Recall: 0.578947, count: 114 -Region Avg IOU: 0.458071, Class: 1.000000, Obj: 0.267839, No Obj: 0.003979, Avg Recall: 0.526786, count: 112 -Region Avg IOU: 0.489807, Class: 1.000000, Obj: 0.259603, No Obj: 0.004048, Avg Recall: 0.500000, count: 106 -1463: 0.495620, 0.490663 avg, 0.001000 rate, 21.953873 seconds, 374528 images -Loaded: 1.236135 seconds -Region Avg IOU: 0.462687, Class: 1.000000, Obj: 0.309799, No Obj: 0.004871, Avg Recall: 0.493056, count: 144 -Region Avg IOU: 0.540195, Class: 1.000000, Obj: 0.359087, No Obj: 0.004488, Avg Recall: 0.678161, count: 87 -Region Avg IOU: 0.483301, Class: 1.000000, Obj: 0.283779, No Obj: 0.004523, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.516902, Class: 1.000000, Obj: 0.308074, No Obj: 0.004602, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.489026, Class: 1.000000, Obj: 0.311034, No Obj: 0.004194, Avg Recall: 0.549451, count: 91 -Region Avg IOU: 0.544308, Class: 1.000000, Obj: 0.302126, No Obj: 0.004698, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.435789, Class: 1.000000, Obj: 0.276761, No Obj: 0.004708, Avg Recall: 0.463235, count: 136 -Region Avg IOU: 0.506082, Class: 1.000000, Obj: 0.319354, No Obj: 0.004727, Avg Recall: 0.534483, count: 116 -1464: 0.472238, 0.488821 avg, 0.001000 rate, 22.317047 seconds, 374784 images -Loaded: 0.000122 seconds -Region Avg IOU: 0.468131, Class: 1.000000, Obj: 0.275267, No Obj: 0.004127, Avg Recall: 0.487179, count: 78 -Region Avg IOU: 0.448515, Class: 1.000000, Obj: 0.309728, No Obj: 0.004107, Avg Recall: 0.500000, count: 114 -Region Avg IOU: 0.537995, Class: 1.000000, Obj: 0.321700, No Obj: 0.004485, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.413458, Class: 1.000000, Obj: 0.281652, No Obj: 0.004237, Avg Recall: 0.425000, count: 120 -Region Avg IOU: 0.483415, Class: 1.000000, Obj: 0.299153, No Obj: 0.004681, Avg Recall: 0.556522, count: 115 -Region Avg IOU: 0.500573, Class: 1.000000, Obj: 0.318230, No Obj: 0.004762, Avg Recall: 0.592308, count: 130 -Region Avg IOU: 0.515404, Class: 1.000000, Obj: 0.341845, No Obj: 0.004573, Avg Recall: 0.576577, count: 111 -Region Avg IOU: 0.455717, Class: 1.000000, Obj: 0.330407, No Obj: 0.003854, Avg Recall: 0.471910, count: 89 -1465: 0.475211, 0.487460 avg, 0.001000 rate, 22.525215 seconds, 375040 images -Loaded: 0.102974 seconds -Region Avg IOU: 0.464368, Class: 1.000000, Obj: 0.293434, No Obj: 0.004214, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.517807, Class: 1.000000, Obj: 0.291487, No Obj: 0.004887, Avg Recall: 0.606557, count: 122 -Region Avg IOU: 0.466073, Class: 1.000000, Obj: 0.286978, No Obj: 0.004588, Avg Recall: 0.540323, count: 124 -Region Avg IOU: 0.466301, Class: 1.000000, Obj: 0.354796, No Obj: 0.003907, Avg Recall: 0.457831, count: 83 -Region Avg IOU: 0.500194, Class: 1.000000, Obj: 0.312865, No Obj: 0.004981, Avg Recall: 0.531746, count: 126 -Region Avg IOU: 0.481773, Class: 1.000000, Obj: 0.281668, No Obj: 0.003912, Avg Recall: 0.525773, count: 97 -Region Avg IOU: 0.478333, Class: 1.000000, Obj: 0.294985, No Obj: 0.003739, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.457188, Class: 1.000000, Obj: 0.249564, No Obj: 0.004193, Avg Recall: 0.426471, count: 136 -1466: 0.484652, 0.487179 avg, 0.001000 rate, 23.007601 seconds, 375296 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.474569, Class: 1.000000, Obj: 0.279021, No Obj: 0.004061, Avg Recall: 0.522523, count: 111 -Region Avg IOU: 0.456216, Class: 1.000000, Obj: 0.269776, No Obj: 0.004303, Avg Recall: 0.526316, count: 114 -Region Avg IOU: 0.477491, Class: 1.000000, Obj: 0.276871, No Obj: 0.004411, Avg Recall: 0.510345, count: 145 -Region Avg IOU: 0.464070, Class: 1.000000, Obj: 0.316641, No Obj: 0.004501, Avg Recall: 0.508772, count: 114 -Region Avg IOU: 0.501438, Class: 1.000000, Obj: 0.270361, No Obj: 0.003890, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.506031, Class: 1.000000, Obj: 0.281318, No Obj: 0.004272, Avg Recall: 0.610169, count: 118 -Region Avg IOU: 0.487659, Class: 1.000000, Obj: 0.317304, No Obj: 0.004086, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.496349, Class: 1.000000, Obj: 0.279263, No Obj: 0.004356, Avg Recall: 0.557377, count: 122 -1467: 0.522260, 0.490687 avg, 0.001000 rate, 23.271065 seconds, 375552 images -Loaded: 0.524577 seconds -Region Avg IOU: 0.409839, Class: 1.000000, Obj: 0.249515, No Obj: 0.003633, Avg Recall: 0.393162, count: 117 -Region Avg IOU: 0.423891, Class: 1.000000, Obj: 0.279468, No Obj: 0.003573, Avg Recall: 0.435897, count: 117 -Region Avg IOU: 0.486464, Class: 1.000000, Obj: 0.242972, No Obj: 0.004003, Avg Recall: 0.510204, count: 98 -Region Avg IOU: 0.483371, Class: 1.000000, Obj: 0.283980, No Obj: 0.003752, Avg Recall: 0.542857, count: 105 -Region Avg IOU: 0.464507, Class: 1.000000, Obj: 0.254449, No Obj: 0.004033, Avg Recall: 0.503759, count: 133 -Region Avg IOU: 0.477740, Class: 1.000000, Obj: 0.271278, No Obj: 0.004172, Avg Recall: 0.584746, count: 118 -Region Avg IOU: 0.502940, Class: 1.000000, Obj: 0.260366, No Obj: 0.004118, Avg Recall: 0.528926, count: 121 -Region Avg IOU: 0.458200, Class: 1.000000, Obj: 0.284243, No Obj: 0.003919, Avg Recall: 0.515385, count: 130 -1468: 0.517433, 0.493362 avg, 0.001000 rate, 24.008450 seconds, 375808 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.499172, Class: 1.000000, Obj: 0.254942, No Obj: 0.003712, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.507972, Class: 1.000000, Obj: 0.274349, No Obj: 0.004028, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.532598, Class: 1.000000, Obj: 0.254623, No Obj: 0.004225, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.524837, Class: 1.000000, Obj: 0.320836, No Obj: 0.003948, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.490346, Class: 1.000000, Obj: 0.264606, No Obj: 0.003887, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.446506, Class: 1.000000, Obj: 0.217416, No Obj: 0.003657, Avg Recall: 0.484127, count: 126 -Region Avg IOU: 0.491488, Class: 1.000000, Obj: 0.256101, No Obj: 0.003957, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.565846, Class: 1.000000, Obj: 0.289073, No Obj: 0.003628, Avg Recall: 0.638095, count: 105 -1469: 0.455421, 0.489568 avg, 0.001000 rate, 22.370836 seconds, 376064 images -Loaded: 0.157752 seconds -Region Avg IOU: 0.513769, Class: 1.000000, Obj: 0.280101, No Obj: 0.003708, Avg Recall: 0.527473, count: 91 -Region Avg IOU: 0.476939, Class: 1.000000, Obj: 0.226548, No Obj: 0.003771, Avg Recall: 0.547009, count: 117 -Region Avg IOU: 0.516574, Class: 1.000000, Obj: 0.257226, No Obj: 0.003527, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.491338, Class: 1.000000, Obj: 0.248807, No Obj: 0.004050, Avg Recall: 0.560345, count: 116 -Region Avg IOU: 0.528499, Class: 1.000000, Obj: 0.262620, No Obj: 0.003532, Avg Recall: 0.584270, count: 89 -Region Avg IOU: 0.485814, Class: 1.000000, Obj: 0.288323, No Obj: 0.003476, Avg Recall: 0.546392, count: 97 -Region Avg IOU: 0.478349, Class: 1.000000, Obj: 0.267538, No Obj: 0.004054, Avg Recall: 0.504854, count: 103 -Region Avg IOU: 0.544685, Class: 1.000000, Obj: 0.275493, No Obj: 0.004277, Avg Recall: 0.617391, count: 115 -1470: 0.494899, 0.490101 avg, 0.001000 rate, 22.780149 seconds, 376320 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000072 seconds -Region Avg IOU: 0.537899, Class: 1.000000, Obj: 0.239012, No Obj: 0.003033, Avg Recall: 0.632911, count: 79 -Region Avg IOU: 0.552393, Class: 1.000000, Obj: 0.290617, No Obj: 0.003654, Avg Recall: 0.666667, count: 123 -Region Avg IOU: 0.523734, Class: 1.000000, Obj: 0.287341, No Obj: 0.003046, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.572958, Class: 1.000000, Obj: 0.281679, No Obj: 0.003806, Avg Recall: 0.666667, count: 126 -Region Avg IOU: 0.529258, Class: 1.000000, Obj: 0.296899, No Obj: 0.003472, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.573540, Class: 1.000000, Obj: 0.276432, No Obj: 0.003435, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.540065, Class: 1.000000, Obj: 0.277336, No Obj: 0.003928, Avg Recall: 0.658915, count: 129 -Region Avg IOU: 0.501306, Class: 1.000000, Obj: 0.299219, No Obj: 0.003235, Avg Recall: 0.560748, count: 107 -1471: 0.491926, 0.490283 avg, 0.001000 rate, 25.088535 seconds, 376576 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.546094, Class: 1.000000, Obj: 0.280202, No Obj: 0.003082, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.529983, Class: 1.000000, Obj: 0.267626, No Obj: 0.003394, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.567750, Class: 1.000000, Obj: 0.263665, No Obj: 0.003458, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.558920, Class: 1.000000, Obj: 0.303501, No Obj: 0.003266, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.554276, Class: 1.000000, Obj: 0.304372, No Obj: 0.003294, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.552841, Class: 1.000000, Obj: 0.275931, No Obj: 0.003678, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.592271, Class: 1.000000, Obj: 0.310109, No Obj: 0.003679, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.526619, Class: 1.000000, Obj: 0.248886, No Obj: 0.003845, Avg Recall: 0.577640, count: 161 -1472: 0.517914, 0.493046 avg, 0.001000 rate, 24.496864 seconds, 376832 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.587725, Class: 1.000000, Obj: 0.270349, No Obj: 0.003338, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.574833, Class: 1.000000, Obj: 0.301945, No Obj: 0.003057, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.556401, Class: 1.000000, Obj: 0.285303, No Obj: 0.003614, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.592337, Class: 1.000000, Obj: 0.329042, No Obj: 0.003663, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.568866, Class: 1.000000, Obj: 0.276035, No Obj: 0.003576, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.559811, Class: 1.000000, Obj: 0.298392, No Obj: 0.003265, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.508483, Class: 1.000000, Obj: 0.282760, No Obj: 0.003745, Avg Recall: 0.611940, count: 134 -Region Avg IOU: 0.562157, Class: 1.000000, Obj: 0.296490, No Obj: 0.003718, Avg Recall: 0.690909, count: 110 -1473: 0.498143, 0.493556 avg, 0.001000 rate, 25.222580 seconds, 377088 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.547928, Class: 1.000000, Obj: 0.340609, No Obj: 0.003714, Avg Recall: 0.656000, count: 125 -Region Avg IOU: 0.559900, Class: 1.000000, Obj: 0.326147, No Obj: 0.003272, Avg Recall: 0.691358, count: 81 -Region Avg IOU: 0.578500, Class: 1.000000, Obj: 0.335399, No Obj: 0.003590, Avg Recall: 0.679012, count: 81 -Region Avg IOU: 0.559688, Class: 1.000000, Obj: 0.327829, No Obj: 0.003527, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.531188, Class: 1.000000, Obj: 0.306017, No Obj: 0.003739, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.516044, Class: 1.000000, Obj: 0.320757, No Obj: 0.003586, Avg Recall: 0.571429, count: 112 -Region Avg IOU: 0.543067, Class: 1.000000, Obj: 0.289752, No Obj: 0.003573, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.524123, Class: 1.000000, Obj: 0.287775, No Obj: 0.003446, Avg Recall: 0.645161, count: 93 -1474: 0.433042, 0.487505 avg, 0.001000 rate, 25.228161 seconds, 377344 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.556864, Class: 1.000000, Obj: 0.320179, No Obj: 0.003440, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.570779, Class: 1.000000, Obj: 0.331396, No Obj: 0.003491, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.573413, Class: 1.000000, Obj: 0.368229, No Obj: 0.003628, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.606067, Class: 1.000000, Obj: 0.290200, No Obj: 0.003735, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.520441, Class: 1.000000, Obj: 0.323790, No Obj: 0.003742, Avg Recall: 0.590164, count: 122 -Region Avg IOU: 0.565312, Class: 1.000000, Obj: 0.321405, No Obj: 0.003345, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.563933, Class: 1.000000, Obj: 0.275626, No Obj: 0.003719, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.552173, Class: 1.000000, Obj: 0.259299, No Obj: 0.003539, Avg Recall: 0.663717, count: 113 -1475: 0.469508, 0.485705 avg, 0.001000 rate, 25.304148 seconds, 377600 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.560464, Class: 1.000000, Obj: 0.336420, No Obj: 0.003620, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.586013, Class: 1.000000, Obj: 0.260143, No Obj: 0.003549, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.555806, Class: 1.000000, Obj: 0.327248, No Obj: 0.003759, Avg Recall: 0.635294, count: 85 -Region Avg IOU: 0.595590, Class: 1.000000, Obj: 0.289615, No Obj: 0.003846, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.535919, Class: 1.000000, Obj: 0.336438, No Obj: 0.003883, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.526527, Class: 1.000000, Obj: 0.281412, No Obj: 0.003135, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.595909, Class: 1.000000, Obj: 0.306888, No Obj: 0.004114, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.509149, Class: 1.000000, Obj: 0.322991, No Obj: 0.002980, Avg Recall: 0.546512, count: 86 -1476: 0.456364, 0.482771 avg, 0.001000 rate, 25.505503 seconds, 377856 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.566699, Class: 1.000000, Obj: 0.340511, No Obj: 0.003568, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.544799, Class: 1.000000, Obj: 0.319388, No Obj: 0.003847, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.513212, Class: 1.000000, Obj: 0.323007, No Obj: 0.003315, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.565724, Class: 1.000000, Obj: 0.339276, No Obj: 0.003849, Avg Recall: 0.669291, count: 127 -Region Avg IOU: 0.568872, Class: 1.000000, Obj: 0.352628, No Obj: 0.004012, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.595358, Class: 1.000000, Obj: 0.344372, No Obj: 0.003468, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.589298, Class: 1.000000, Obj: 0.292225, No Obj: 0.003735, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.542069, Class: 1.000000, Obj: 0.306592, No Obj: 0.003478, Avg Recall: 0.603774, count: 106 -1477: 0.449814, 0.479475 avg, 0.001000 rate, 24.799816 seconds, 378112 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.549074, Class: 1.000000, Obj: 0.317929, No Obj: 0.003211, Avg Recall: 0.642857, count: 84 -Region Avg IOU: 0.552087, Class: 1.000000, Obj: 0.292887, No Obj: 0.003490, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.580540, Class: 1.000000, Obj: 0.301727, No Obj: 0.002984, Avg Recall: 0.716216, count: 74 -Region Avg IOU: 0.562220, Class: 1.000000, Obj: 0.316002, No Obj: 0.003952, Avg Recall: 0.664122, count: 131 -Region Avg IOU: 0.511810, Class: 1.000000, Obj: 0.302905, No Obj: 0.003579, Avg Recall: 0.562963, count: 135 -Region Avg IOU: 0.560625, Class: 1.000000, Obj: 0.313314, No Obj: 0.003932, Avg Recall: 0.666667, count: 117 -Region Avg IOU: 0.548968, Class: 1.000000, Obj: 0.304892, No Obj: 0.003465, Avg Recall: 0.641221, count: 131 -Region Avg IOU: 0.564881, Class: 1.000000, Obj: 0.278433, No Obj: 0.003451, Avg Recall: 0.669565, count: 115 -1478: 0.451299, 0.476658 avg, 0.001000 rate, 24.768787 seconds, 378368 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.485877, Class: 1.000000, Obj: 0.289641, No Obj: 0.003504, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.501951, Class: 1.000000, Obj: 0.282999, No Obj: 0.003618, Avg Recall: 0.549180, count: 122 -Region Avg IOU: 0.498534, Class: 1.000000, Obj: 0.301756, No Obj: 0.003297, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.575900, Class: 1.000000, Obj: 0.331784, No Obj: 0.002937, Avg Recall: 0.691358, count: 81 -Region Avg IOU: 0.539077, Class: 1.000000, Obj: 0.293596, No Obj: 0.003703, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.573517, Class: 1.000000, Obj: 0.326282, No Obj: 0.003749, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.565851, Class: 1.000000, Obj: 0.249307, No Obj: 0.003357, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.557144, Class: 1.000000, Obj: 0.280578, No Obj: 0.003589, Avg Recall: 0.612613, count: 111 -1479: 0.450401, 0.474032 avg, 0.001000 rate, 25.405537 seconds, 378624 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.525565, Class: 1.000000, Obj: 0.262813, No Obj: 0.003445, Avg Recall: 0.629921, count: 127 -Region Avg IOU: 0.547153, Class: 1.000000, Obj: 0.261797, No Obj: 0.003273, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.520650, Class: 1.000000, Obj: 0.287239, No Obj: 0.003883, Avg Recall: 0.585366, count: 123 -Region Avg IOU: 0.526761, Class: 1.000000, Obj: 0.283071, No Obj: 0.003412, Avg Recall: 0.565891, count: 129 -Region Avg IOU: 0.522060, Class: 1.000000, Obj: 0.265509, No Obj: 0.003446, Avg Recall: 0.587786, count: 131 -Region Avg IOU: 0.551428, Class: 1.000000, Obj: 0.287150, No Obj: 0.003198, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.539350, Class: 1.000000, Obj: 0.291645, No Obj: 0.004036, Avg Recall: 0.634146, count: 123 -Region Avg IOU: 0.530924, Class: 1.000000, Obj: 0.296752, No Obj: 0.003519, Avg Recall: 0.558559, count: 111 -1480: 0.568809, 0.483510 avg, 0.001000 rate, 25.528303 seconds, 378880 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.770355 seconds -Region Avg IOU: 0.479011, Class: 1.000000, Obj: 0.256659, No Obj: 0.003938, Avg Recall: 0.528000, count: 125 -Region Avg IOU: 0.476856, Class: 1.000000, Obj: 0.241831, No Obj: 0.003722, Avg Recall: 0.500000, count: 92 -Region Avg IOU: 0.440840, Class: 1.000000, Obj: 0.230949, No Obj: 0.004027, Avg Recall: 0.452381, count: 126 -Region Avg IOU: 0.489557, Class: 1.000000, Obj: 0.258020, No Obj: 0.004024, Avg Recall: 0.520661, count: 121 -Region Avg IOU: 0.486410, Class: 1.000000, Obj: 0.266968, No Obj: 0.003848, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.592080, Class: 1.000000, Obj: 0.277221, No Obj: 0.003991, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.486897, Class: 1.000000, Obj: 0.254208, No Obj: 0.003622, Avg Recall: 0.509615, count: 104 -Region Avg IOU: 0.472334, Class: 1.000000, Obj: 0.245322, No Obj: 0.003604, Avg Recall: 0.532609, count: 92 -1481: 0.541534, 0.489312 avg, 0.001000 rate, 19.826420 seconds, 379136 images -Loaded: 2.187449 seconds -Region Avg IOU: 0.460513, Class: 1.000000, Obj: 0.243848, No Obj: 0.003296, Avg Recall: 0.505495, count: 91 -Region Avg IOU: 0.485047, Class: 1.000000, Obj: 0.289787, No Obj: 0.003911, Avg Recall: 0.536585, count: 82 -Region Avg IOU: 0.455101, Class: 1.000000, Obj: 0.257193, No Obj: 0.003709, Avg Recall: 0.375000, count: 112 -Region Avg IOU: 0.485057, Class: 1.000000, Obj: 0.246924, No Obj: 0.003541, Avg Recall: 0.526316, count: 95 -Region Avg IOU: 0.520425, Class: 1.000000, Obj: 0.252440, No Obj: 0.003733, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.443669, Class: 1.000000, Obj: 0.227297, No Obj: 0.004314, Avg Recall: 0.514085, count: 142 -Region Avg IOU: 0.487833, Class: 1.000000, Obj: 0.254942, No Obj: 0.003745, Avg Recall: 0.512195, count: 82 -Region Avg IOU: 0.522854, Class: 1.000000, Obj: 0.256966, No Obj: 0.003776, Avg Recall: 0.602151, count: 93 -1482: 0.487874, 0.489168 avg, 0.001000 rate, 18.830748 seconds, 379392 images -Loaded: 3.425722 seconds -Region Avg IOU: 0.461908, Class: 1.000000, Obj: 0.231207, No Obj: 0.004213, Avg Recall: 0.459016, count: 122 -Region Avg IOU: 0.479831, Class: 1.000000, Obj: 0.262236, No Obj: 0.004243, Avg Recall: 0.580000, count: 100 -Region Avg IOU: 0.475670, Class: 1.000000, Obj: 0.253200, No Obj: 0.004388, Avg Recall: 0.504000, count: 125 -Region Avg IOU: 0.477247, Class: 1.000000, Obj: 0.248667, No Obj: 0.003615, Avg Recall: 0.494949, count: 99 -Region Avg IOU: 0.506040, Class: 1.000000, Obj: 0.274017, No Obj: 0.004109, Avg Recall: 0.565657, count: 99 -Region Avg IOU: 0.549039, Class: 1.000000, Obj: 0.269907, No Obj: 0.003567, Avg Recall: 0.698630, count: 73 -Region Avg IOU: 0.458291, Class: 1.000000, Obj: 0.252656, No Obj: 0.003788, Avg Recall: 0.451327, count: 113 -Region Avg IOU: 0.489210, Class: 1.000000, Obj: 0.263689, No Obj: 0.004333, Avg Recall: 0.532110, count: 109 -1483: 0.467407, 0.486992 avg, 0.001000 rate, 20.291540 seconds, 379648 images -Loaded: 1.224758 seconds -Region Avg IOU: 0.494103, Class: 1.000000, Obj: 0.296016, No Obj: 0.004343, Avg Recall: 0.582418, count: 91 -Region Avg IOU: 0.481429, Class: 1.000000, Obj: 0.283402, No Obj: 0.004289, Avg Recall: 0.482143, count: 112 -Region Avg IOU: 0.467974, Class: 1.000000, Obj: 0.307427, No Obj: 0.004085, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.488810, Class: 1.000000, Obj: 0.228730, No Obj: 0.004783, Avg Recall: 0.521127, count: 142 -Region Avg IOU: 0.468256, Class: 1.000000, Obj: 0.276781, No Obj: 0.004592, Avg Recall: 0.479167, count: 144 -Region Avg IOU: 0.496815, Class: 1.000000, Obj: 0.282371, No Obj: 0.004171, Avg Recall: 0.571429, count: 70 -Region Avg IOU: 0.506713, Class: 1.000000, Obj: 0.254730, No Obj: 0.004097, Avg Recall: 0.542169, count: 83 -Region Avg IOU: 0.472588, Class: 1.000000, Obj: 0.285476, No Obj: 0.004253, Avg Recall: 0.512195, count: 123 -1484: 0.487477, 0.487041 avg, 0.001000 rate, 20.229465 seconds, 379904 images -Loaded: 1.196578 seconds -Region Avg IOU: 0.501320, Class: 1.000000, Obj: 0.287101, No Obj: 0.004279, Avg Recall: 0.606838, count: 117 -Region Avg IOU: 0.474517, Class: 1.000000, Obj: 0.323942, No Obj: 0.004394, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.466620, Class: 1.000000, Obj: 0.260967, No Obj: 0.004495, Avg Recall: 0.512605, count: 119 -Region Avg IOU: 0.506977, Class: 1.000000, Obj: 0.282612, No Obj: 0.005081, Avg Recall: 0.568000, count: 125 -Region Avg IOU: 0.512995, Class: 1.000000, Obj: 0.315757, No Obj: 0.004456, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.483339, Class: 1.000000, Obj: 0.274700, No Obj: 0.004476, Avg Recall: 0.540984, count: 122 -Region Avg IOU: 0.460684, Class: 1.000000, Obj: 0.257923, No Obj: 0.004119, Avg Recall: 0.442478, count: 113 -Region Avg IOU: 0.422185, Class: 1.000000, Obj: 0.244597, No Obj: 0.003755, Avg Recall: 0.438596, count: 114 -1485: 0.530896, 0.491426 avg, 0.001000 rate, 21.133606 seconds, 380160 images -Loaded: 0.973780 seconds -Region Avg IOU: 0.459411, Class: 1.000000, Obj: 0.283888, No Obj: 0.004663, Avg Recall: 0.460870, count: 115 -Region Avg IOU: 0.490088, Class: 1.000000, Obj: 0.296170, No Obj: 0.005129, Avg Recall: 0.500000, count: 126 -Region Avg IOU: 0.438679, Class: 1.000000, Obj: 0.224458, No Obj: 0.004864, Avg Recall: 0.466165, count: 133 -Region Avg IOU: 0.484071, Class: 1.000000, Obj: 0.343640, No Obj: 0.004504, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.466066, Class: 1.000000, Obj: 0.284684, No Obj: 0.004409, Avg Recall: 0.514019, count: 107 -Region Avg IOU: 0.504960, Class: 1.000000, Obj: 0.306570, No Obj: 0.004606, Avg Recall: 0.542373, count: 118 -Region Avg IOU: 0.490571, Class: 1.000000, Obj: 0.299318, No Obj: 0.004400, Avg Recall: 0.549020, count: 102 -Region Avg IOU: 0.489186, Class: 1.000000, Obj: 0.286229, No Obj: 0.004754, Avg Recall: 0.542553, count: 94 -1486: 0.545241, 0.496808 avg, 0.001000 rate, 22.402107 seconds, 380416 images -Loaded: 0.142891 seconds -Region Avg IOU: 0.457819, Class: 1.000000, Obj: 0.303207, No Obj: 0.005578, Avg Recall: 0.459119, count: 159 -Region Avg IOU: 0.484520, Class: 1.000000, Obj: 0.261555, No Obj: 0.005080, Avg Recall: 0.538462, count: 104 -Region Avg IOU: 0.447469, Class: 1.000000, Obj: 0.308185, No Obj: 0.005313, Avg Recall: 0.458333, count: 144 -Region Avg IOU: 0.462347, Class: 1.000000, Obj: 0.282655, No Obj: 0.005152, Avg Recall: 0.511628, count: 129 -Region Avg IOU: 0.522140, Class: 1.000000, Obj: 0.389606, No Obj: 0.004758, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.501625, Class: 1.000000, Obj: 0.334866, No Obj: 0.004612, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.452443, Class: 1.000000, Obj: 0.255808, No Obj: 0.004394, Avg Recall: 0.477064, count: 109 -Region Avg IOU: 0.458555, Class: 1.000000, Obj: 0.281448, No Obj: 0.004454, Avg Recall: 0.467290, count: 107 -1487: 0.523534, 0.499480 avg, 0.001000 rate, 22.138224 seconds, 380672 images -Loaded: 0.043462 seconds -Region Avg IOU: 0.469246, Class: 1.000000, Obj: 0.327709, No Obj: 0.003887, Avg Recall: 0.500000, count: 74 -Region Avg IOU: 0.468357, Class: 1.000000, Obj: 0.344347, No Obj: 0.005121, Avg Recall: 0.508333, count: 120 -Region Avg IOU: 0.542129, Class: 1.000000, Obj: 0.340706, No Obj: 0.004676, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.528117, Class: 1.000000, Obj: 0.309359, No Obj: 0.005009, Avg Recall: 0.608247, count: 97 -Region Avg IOU: 0.481659, Class: 1.000000, Obj: 0.291814, No Obj: 0.005026, Avg Recall: 0.539683, count: 126 -Region Avg IOU: 0.522427, Class: 1.000000, Obj: 0.348537, No Obj: 0.004869, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.507301, Class: 1.000000, Obj: 0.328390, No Obj: 0.004531, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.431245, Class: 1.000000, Obj: 0.334596, No Obj: 0.004565, Avg Recall: 0.437500, count: 112 -1488: 0.451810, 0.494713 avg, 0.001000 rate, 21.134020 seconds, 380928 images -Loaded: 0.262596 seconds -Region Avg IOU: 0.451397, Class: 1.000000, Obj: 0.308475, No Obj: 0.004715, Avg Recall: 0.492308, count: 130 -Region Avg IOU: 0.493456, Class: 1.000000, Obj: 0.321691, No Obj: 0.004881, Avg Recall: 0.496000, count: 125 -Region Avg IOU: 0.468669, Class: 1.000000, Obj: 0.240290, No Obj: 0.004528, Avg Recall: 0.496063, count: 127 -Region Avg IOU: 0.514232, Class: 1.000000, Obj: 0.344998, No Obj: 0.004800, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.513372, Class: 1.000000, Obj: 0.339758, No Obj: 0.004911, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.486081, Class: 1.000000, Obj: 0.324767, No Obj: 0.004017, Avg Recall: 0.493506, count: 77 -Region Avg IOU: 0.500918, Class: 1.000000, Obj: 0.314290, No Obj: 0.005096, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.527881, Class: 1.000000, Obj: 0.287324, No Obj: 0.004419, Avg Recall: 0.591398, count: 93 -1489: 0.517340, 0.496976 avg, 0.001000 rate, 20.259037 seconds, 381184 images -Loaded: 2.083993 seconds -Region Avg IOU: 0.490785, Class: 1.000000, Obj: 0.294742, No Obj: 0.005068, Avg Recall: 0.508621, count: 116 -Region Avg IOU: 0.468575, Class: 1.000000, Obj: 0.285135, No Obj: 0.004454, Avg Recall: 0.509615, count: 104 -Region Avg IOU: 0.463955, Class: 1.000000, Obj: 0.314719, No Obj: 0.004925, Avg Recall: 0.468254, count: 126 -Region Avg IOU: 0.490817, Class: 1.000000, Obj: 0.316748, No Obj: 0.003959, Avg Recall: 0.506494, count: 77 -Region Avg IOU: 0.419214, Class: 1.000000, Obj: 0.264450, No Obj: 0.004610, Avg Recall: 0.393939, count: 132 -Region Avg IOU: 0.488693, Class: 1.000000, Obj: 0.269693, No Obj: 0.004727, Avg Recall: 0.529915, count: 117 -Region Avg IOU: 0.437878, Class: 1.000000, Obj: 0.295134, No Obj: 0.003712, Avg Recall: 0.465116, count: 86 -Region Avg IOU: 0.421826, Class: 1.000000, Obj: 0.255600, No Obj: 0.004695, Avg Recall: 0.446808, count: 141 -1490: 0.491749, 0.496453 avg, 0.001000 rate, 21.166737 seconds, 381440 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000068 seconds -Region Avg IOU: 0.548505, Class: 1.000000, Obj: 0.305996, No Obj: 0.003989, Avg Recall: 0.647541, count: 122 -Region Avg IOU: 0.543182, Class: 1.000000, Obj: 0.359515, No Obj: 0.003716, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.530963, Class: 1.000000, Obj: 0.281338, No Obj: 0.003967, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.548842, Class: 1.000000, Obj: 0.290151, No Obj: 0.004053, Avg Recall: 0.650000, count: 120 -Region Avg IOU: 0.506644, Class: 1.000000, Obj: 0.320038, No Obj: 0.003137, Avg Recall: 0.590361, count: 83 -Region Avg IOU: 0.572255, Class: 1.000000, Obj: 0.368212, No Obj: 0.003942, Avg Recall: 0.636364, count: 88 -Region Avg IOU: 0.531063, Class: 1.000000, Obj: 0.321534, No Obj: 0.003704, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.545131, Class: 1.000000, Obj: 0.283020, No Obj: 0.004152, Avg Recall: 0.654206, count: 107 -1491: 0.488859, 0.495694 avg, 0.001000 rate, 25.244768 seconds, 381696 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.558161, Class: 1.000000, Obj: 0.292045, No Obj: 0.003614, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.580448, Class: 1.000000, Obj: 0.311430, No Obj: 0.004225, Avg Recall: 0.738462, count: 130 -Region Avg IOU: 0.532138, Class: 1.000000, Obj: 0.284402, No Obj: 0.003140, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.540440, Class: 1.000000, Obj: 0.265737, No Obj: 0.003485, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.520260, Class: 1.000000, Obj: 0.280543, No Obj: 0.002776, Avg Recall: 0.618421, count: 76 -Region Avg IOU: 0.523321, Class: 1.000000, Obj: 0.297918, No Obj: 0.003530, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.531316, Class: 1.000000, Obj: 0.278353, No Obj: 0.003599, Avg Recall: 0.577236, count: 123 -Region Avg IOU: 0.526093, Class: 1.000000, Obj: 0.259431, No Obj: 0.003360, Avg Recall: 0.622449, count: 98 -1492: 0.506830, 0.496807 avg, 0.001000 rate, 24.688404 seconds, 381952 images -Loaded: 0.000147 seconds -Region Avg IOU: 0.554188, Class: 1.000000, Obj: 0.262729, No Obj: 0.003659, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.548840, Class: 1.000000, Obj: 0.309490, No Obj: 0.003231, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.580439, Class: 1.000000, Obj: 0.217298, No Obj: 0.003816, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.538510, Class: 1.000000, Obj: 0.296235, No Obj: 0.002660, Avg Recall: 0.625000, count: 72 -Region Avg IOU: 0.529522, Class: 1.000000, Obj: 0.257626, No Obj: 0.003036, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.534924, Class: 1.000000, Obj: 0.212049, No Obj: 0.002977, Avg Recall: 0.666667, count: 84 -Region Avg IOU: 0.556223, Class: 1.000000, Obj: 0.270976, No Obj: 0.003028, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.578728, Class: 1.000000, Obj: 0.296960, No Obj: 0.003631, Avg Recall: 0.702128, count: 141 -1493: 0.462064, 0.493333 avg, 0.001000 rate, 26.414312 seconds, 382208 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.536449, Class: 1.000000, Obj: 0.222671, No Obj: 0.003168, Avg Recall: 0.638298, count: 141 -Region Avg IOU: 0.561702, Class: 1.000000, Obj: 0.296460, No Obj: 0.003027, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.495082, Class: 1.000000, Obj: 0.248697, No Obj: 0.002858, Avg Recall: 0.524752, count: 101 -Region Avg IOU: 0.582138, Class: 1.000000, Obj: 0.288644, No Obj: 0.003267, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.542677, Class: 1.000000, Obj: 0.228704, No Obj: 0.002838, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.597185, Class: 1.000000, Obj: 0.290134, No Obj: 0.003210, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.558550, Class: 1.000000, Obj: 0.265103, No Obj: 0.003541, Avg Recall: 0.637037, count: 135 -Region Avg IOU: 0.516223, Class: 1.000000, Obj: 0.264714, No Obj: 0.003384, Avg Recall: 0.606838, count: 117 -1494: 0.493692, 0.493369 avg, 0.001000 rate, 23.539473 seconds, 382464 images -Loaded: 0.000114 seconds -Region Avg IOU: 0.541089, Class: 1.000000, Obj: 0.213907, No Obj: 0.002663, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.582249, Class: 1.000000, Obj: 0.267700, No Obj: 0.003049, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.489792, Class: 1.000000, Obj: 0.222818, No Obj: 0.002638, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.560290, Class: 1.000000, Obj: 0.239187, No Obj: 0.003365, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.554431, Class: 1.000000, Obj: 0.220011, No Obj: 0.003116, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.484345, Class: 1.000000, Obj: 0.248270, No Obj: 0.002576, Avg Recall: 0.523810, count: 84 -Region Avg IOU: 0.528973, Class: 1.000000, Obj: 0.218960, No Obj: 0.002862, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.507691, Class: 1.000000, Obj: 0.226300, No Obj: 0.003336, Avg Recall: 0.550847, count: 118 -1495: 0.479202, 0.491952 avg, 0.001000 rate, 25.578512 seconds, 382720 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.518678, Class: 1.000000, Obj: 0.260041, No Obj: 0.002991, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.588404, Class: 1.000000, Obj: 0.284949, No Obj: 0.002755, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.543754, Class: 1.000000, Obj: 0.215372, No Obj: 0.002797, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.548588, Class: 1.000000, Obj: 0.261068, No Obj: 0.003131, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.580198, Class: 1.000000, Obj: 0.306005, No Obj: 0.002882, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.563324, Class: 1.000000, Obj: 0.273872, No Obj: 0.002660, Avg Recall: 0.686046, count: 86 -Region Avg IOU: 0.582946, Class: 1.000000, Obj: 0.250883, No Obj: 0.003144, Avg Recall: 0.686956, count: 115 -Region Avg IOU: 0.525206, Class: 1.000000, Obj: 0.237372, No Obj: 0.002927, Avg Recall: 0.666667, count: 105 -1496: 0.480184, 0.490775 avg, 0.001000 rate, 25.510107 seconds, 382976 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.498476, Class: 1.000000, Obj: 0.254455, No Obj: 0.003084, Avg Recall: 0.548611, count: 144 -Region Avg IOU: 0.576713, Class: 1.000000, Obj: 0.352140, No Obj: 0.003289, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.574157, Class: 1.000000, Obj: 0.302564, No Obj: 0.003005, Avg Recall: 0.678571, count: 84 -Region Avg IOU: 0.565723, Class: 1.000000, Obj: 0.312497, No Obj: 0.003101, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.554719, Class: 1.000000, Obj: 0.246544, No Obj: 0.002858, Avg Recall: 0.630952, count: 84 -Region Avg IOU: 0.568209, Class: 1.000000, Obj: 0.281444, No Obj: 0.003041, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.538005, Class: 1.000000, Obj: 0.295489, No Obj: 0.002861, Avg Recall: 0.650602, count: 83 -Region Avg IOU: 0.570051, Class: 1.000000, Obj: 0.257991, No Obj: 0.003593, Avg Recall: 0.707692, count: 130 -1497: 0.485798, 0.490278 avg, 0.001000 rate, 24.057808 seconds, 383232 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.596139, Class: 1.000000, Obj: 0.281288, No Obj: 0.003789, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.567532, Class: 1.000000, Obj: 0.296181, No Obj: 0.003698, Avg Recall: 0.656250, count: 128 -Region Avg IOU: 0.579895, Class: 1.000000, Obj: 0.278604, No Obj: 0.003746, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.568338, Class: 1.000000, Obj: 0.281346, No Obj: 0.003743, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.571624, Class: 1.000000, Obj: 0.341661, No Obj: 0.003038, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.534317, Class: 1.000000, Obj: 0.296888, No Obj: 0.003338, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.566436, Class: 1.000000, Obj: 0.292625, No Obj: 0.003239, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.489654, Class: 1.000000, Obj: 0.291097, No Obj: 0.003161, Avg Recall: 0.520000, count: 100 -1498: 0.517364, 0.492986 avg, 0.001000 rate, 25.958622 seconds, 383488 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.562783, Class: 1.000000, Obj: 0.295556, No Obj: 0.003890, Avg Recall: 0.687500, count: 128 -Region Avg IOU: 0.510379, Class: 1.000000, Obj: 0.291653, No Obj: 0.003470, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.602306, Class: 1.000000, Obj: 0.288315, No Obj: 0.003326, Avg Recall: 0.710526, count: 76 -Region Avg IOU: 0.539203, Class: 1.000000, Obj: 0.280774, No Obj: 0.003479, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.577551, Class: 1.000000, Obj: 0.289591, No Obj: 0.004174, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.544284, Class: 1.000000, Obj: 0.322936, No Obj: 0.003311, Avg Recall: 0.659091, count: 88 -Region Avg IOU: 0.499591, Class: 1.000000, Obj: 0.303777, No Obj: 0.003176, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.534764, Class: 1.000000, Obj: 0.320910, No Obj: 0.003576, Avg Recall: 0.600000, count: 105 -1499: 0.466465, 0.490334 avg, 0.001000 rate, 25.218647 seconds, 383744 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.558823, Class: 1.000000, Obj: 0.307783, No Obj: 0.003659, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.569516, Class: 1.000000, Obj: 0.334961, No Obj: 0.003947, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.549719, Class: 1.000000, Obj: 0.325089, No Obj: 0.004054, Avg Recall: 0.597938, count: 97 -Region Avg IOU: 0.581266, Class: 1.000000, Obj: 0.364976, No Obj: 0.003737, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.534287, Class: 1.000000, Obj: 0.326922, No Obj: 0.003548, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.566755, Class: 1.000000, Obj: 0.297037, No Obj: 0.003593, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.553345, Class: 1.000000, Obj: 0.302101, No Obj: 0.003709, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.531718, Class: 1.000000, Obj: 0.331163, No Obj: 0.003600, Avg Recall: 0.645455, count: 110 -1500: 0.458374, 0.487138 avg, 0.001000 rate, 26.469791 seconds, 384000 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 19.378569 seconds -Region Avg IOU: 0.518312, Class: 1.000000, Obj: 0.332923, No Obj: 0.003972, Avg Recall: 0.621951, count: 82 -Region Avg IOU: 0.480980, Class: 1.000000, Obj: 0.298808, No Obj: 0.004521, Avg Recall: 0.527397, count: 146 -Region Avg IOU: 0.518922, Class: 1.000000, Obj: 0.282067, No Obj: 0.003993, Avg Recall: 0.556452, count: 124 -Region Avg IOU: 0.475320, Class: 1.000000, Obj: 0.266118, No Obj: 0.003856, Avg Recall: 0.509434, count: 106 -Region Avg IOU: 0.554316, Class: 1.000000, Obj: 0.312926, No Obj: 0.003969, Avg Recall: 0.640449, count: 89 -Region Avg IOU: 0.500962, Class: 1.000000, Obj: 0.335502, No Obj: 0.004044, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.537110, Class: 1.000000, Obj: 0.351016, No Obj: 0.003919, Avg Recall: 0.588889, count: 90 -Region Avg IOU: 0.541961, Class: 1.000000, Obj: 0.361109, No Obj: 0.003508, Avg Recall: 0.630952, count: 84 -1501: 0.443246, 0.482749 avg, 0.001000 rate, 23.643360 seconds, 384256 images -Loaded: 0.003545 seconds -Region Avg IOU: 0.553249, Class: 1.000000, Obj: 0.314510, No Obj: 0.004153, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.565235, Class: 1.000000, Obj: 0.327900, No Obj: 0.003963, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.529191, Class: 1.000000, Obj: 0.278408, No Obj: 0.004320, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.529242, Class: 1.000000, Obj: 0.309576, No Obj: 0.003890, Avg Recall: 0.580645, count: 93 -Region Avg IOU: 0.523643, Class: 1.000000, Obj: 0.297474, No Obj: 0.003809, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.490520, Class: 1.000000, Obj: 0.288866, No Obj: 0.003891, Avg Recall: 0.566667, count: 120 -Region Avg IOU: 0.509921, Class: 1.000000, Obj: 0.283488, No Obj: 0.003580, Avg Recall: 0.581395, count: 86 -Region Avg IOU: 0.509194, Class: 1.000000, Obj: 0.313130, No Obj: 0.004711, Avg Recall: 0.552000, count: 125 -1502: 0.471289, 0.481603 avg, 0.001000 rate, 24.187952 seconds, 384512 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.490638, Class: 1.000000, Obj: 0.288262, No Obj: 0.003656, Avg Recall: 0.553571, count: 112 -Region Avg IOU: 0.520971, Class: 1.000000, Obj: 0.306176, No Obj: 0.003825, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.487902, Class: 1.000000, Obj: 0.286492, No Obj: 0.003805, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.537915, Class: 1.000000, Obj: 0.294929, No Obj: 0.003974, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.505827, Class: 1.000000, Obj: 0.305534, No Obj: 0.004134, Avg Recall: 0.556522, count: 115 -Region Avg IOU: 0.521211, Class: 1.000000, Obj: 0.303259, No Obj: 0.003854, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.520296, Class: 1.000000, Obj: 0.337193, No Obj: 0.003644, Avg Recall: 0.579545, count: 88 -Region Avg IOU: 0.499833, Class: 1.000000, Obj: 0.272918, No Obj: 0.003860, Avg Recall: 0.576000, count: 125 -1503: 0.484573, 0.481900 avg, 0.001000 rate, 24.704739 seconds, 384768 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.522840, Class: 1.000000, Obj: 0.275387, No Obj: 0.003888, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.467219, Class: 1.000000, Obj: 0.279530, No Obj: 0.003437, Avg Recall: 0.482456, count: 114 -Region Avg IOU: 0.458598, Class: 1.000000, Obj: 0.275209, No Obj: 0.003850, Avg Recall: 0.466667, count: 105 -Region Avg IOU: 0.547360, Class: 1.000000, Obj: 0.285507, No Obj: 0.003888, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.530204, Class: 1.000000, Obj: 0.287665, No Obj: 0.003667, Avg Recall: 0.585106, count: 94 -Region Avg IOU: 0.519196, Class: 1.000000, Obj: 0.271530, No Obj: 0.004164, Avg Recall: 0.600000, count: 130 -Region Avg IOU: 0.498792, Class: 1.000000, Obj: 0.263857, No Obj: 0.004152, Avg Recall: 0.561538, count: 130 -Region Avg IOU: 0.504223, Class: 1.000000, Obj: 0.294550, No Obj: 0.004120, Avg Recall: 0.566929, count: 127 -1504: 0.513414, 0.485051 avg, 0.001000 rate, 23.123919 seconds, 385024 images -Loaded: 0.000054 seconds -Region Avg IOU: 0.493592, Class: 1.000000, Obj: 0.288870, No Obj: 0.003639, Avg Recall: 0.570175, count: 114 -Region Avg IOU: 0.561065, Class: 1.000000, Obj: 0.315665, No Obj: 0.003555, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.510600, Class: 1.000000, Obj: 0.280224, No Obj: 0.003777, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.526796, Class: 1.000000, Obj: 0.301304, No Obj: 0.004241, Avg Recall: 0.555556, count: 135 -Region Avg IOU: 0.487436, Class: 1.000000, Obj: 0.286819, No Obj: 0.003927, Avg Recall: 0.552845, count: 123 -Region Avg IOU: 0.535302, Class: 1.000000, Obj: 0.285832, No Obj: 0.003650, Avg Recall: 0.650406, count: 123 -Region Avg IOU: 0.551852, Class: 1.000000, Obj: 0.307389, No Obj: 0.003769, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.521208, Class: 1.000000, Obj: 0.299198, No Obj: 0.003355, Avg Recall: 0.590000, count: 100 -1505: 0.498500, 0.486396 avg, 0.001000 rate, 23.166910 seconds, 385280 images -Loaded: 0.400955 seconds -Region Avg IOU: 0.499675, Class: 1.000000, Obj: 0.337702, No Obj: 0.003212, Avg Recall: 0.516129, count: 93 -Region Avg IOU: 0.479766, Class: 1.000000, Obj: 0.275650, No Obj: 0.003481, Avg Recall: 0.534483, count: 116 -Region Avg IOU: 0.499078, Class: 1.000000, Obj: 0.295257, No Obj: 0.002807, Avg Recall: 0.574713, count: 87 -Region Avg IOU: 0.468497, Class: 1.000000, Obj: 0.289766, No Obj: 0.003329, Avg Recall: 0.515790, count: 95 -Region Avg IOU: 0.436728, Class: 1.000000, Obj: 0.241279, No Obj: 0.003440, Avg Recall: 0.412371, count: 97 -Region Avg IOU: 0.569807, Class: 1.000000, Obj: 0.281079, No Obj: 0.004247, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.490184, Class: 1.000000, Obj: 0.252608, No Obj: 0.003548, Avg Recall: 0.553398, count: 103 -Region Avg IOU: 0.472603, Class: 1.000000, Obj: 0.244458, No Obj: 0.002909, Avg Recall: 0.460000, count: 100 -1506: 0.470061, 0.484763 avg, 0.001000 rate, 22.581890 seconds, 385536 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.483437, Class: 1.000000, Obj: 0.260213, No Obj: 0.003989, Avg Recall: 0.558824, count: 136 -Region Avg IOU: 0.487668, Class: 1.000000, Obj: 0.272793, No Obj: 0.003447, Avg Recall: 0.500000, count: 104 -Region Avg IOU: 0.527855, Class: 1.000000, Obj: 0.269245, No Obj: 0.003674, Avg Recall: 0.634921, count: 126 -Region Avg IOU: 0.563693, Class: 1.000000, Obj: 0.311333, No Obj: 0.003544, Avg Recall: 0.674419, count: 86 -Region Avg IOU: 0.528119, Class: 1.000000, Obj: 0.270631, No Obj: 0.003894, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.544607, Class: 1.000000, Obj: 0.302547, No Obj: 0.003668, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.539471, Class: 1.000000, Obj: 0.295691, No Obj: 0.003645, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.501187, Class: 1.000000, Obj: 0.303758, No Obj: 0.003459, Avg Recall: 0.525253, count: 99 -1507: 0.500824, 0.486369 avg, 0.001000 rate, 23.238020 seconds, 385792 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.501094, Class: 1.000000, Obj: 0.286003, No Obj: 0.003816, Avg Recall: 0.580952, count: 105 -Region Avg IOU: 0.567482, Class: 1.000000, Obj: 0.310539, No Obj: 0.003766, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.530486, Class: 1.000000, Obj: 0.316767, No Obj: 0.003644, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.492770, Class: 1.000000, Obj: 0.250676, No Obj: 0.003777, Avg Recall: 0.567797, count: 118 -Region Avg IOU: 0.541394, Class: 1.000000, Obj: 0.344564, No Obj: 0.003527, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.506043, Class: 1.000000, Obj: 0.287386, No Obj: 0.003351, Avg Recall: 0.552083, count: 96 -Region Avg IOU: 0.485928, Class: 1.000000, Obj: 0.284304, No Obj: 0.003568, Avg Recall: 0.507576, count: 132 -Region Avg IOU: 0.510272, Class: 1.000000, Obj: 0.290229, No Obj: 0.003821, Avg Recall: 0.580357, count: 112 -1508: 0.475964, 0.485328 avg, 0.001000 rate, 24.563955 seconds, 386048 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.505068, Class: 1.000000, Obj: 0.267923, No Obj: 0.003945, Avg Recall: 0.591667, count: 120 -Region Avg IOU: 0.504908, Class: 1.000000, Obj: 0.267637, No Obj: 0.003255, Avg Recall: 0.586207, count: 87 -Region Avg IOU: 0.560219, Class: 1.000000, Obj: 0.294767, No Obj: 0.003559, Avg Recall: 0.636364, count: 88 -Region Avg IOU: 0.569234, Class: 1.000000, Obj: 0.327822, No Obj: 0.004056, Avg Recall: 0.634146, count: 82 -Region Avg IOU: 0.574744, Class: 1.000000, Obj: 0.333521, No Obj: 0.003736, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.485222, Class: 1.000000, Obj: 0.273250, No Obj: 0.003295, Avg Recall: 0.510204, count: 98 -Region Avg IOU: 0.442207, Class: 1.000000, Obj: 0.281608, No Obj: 0.003652, Avg Recall: 0.459016, count: 122 -Region Avg IOU: 0.560960, Class: 1.000000, Obj: 0.272455, No Obj: 0.003752, Avg Recall: 0.666667, count: 105 -1509: 0.469456, 0.483741 avg, 0.001000 rate, 22.604734 seconds, 386304 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.549662, Class: 1.000000, Obj: 0.323093, No Obj: 0.003795, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.548841, Class: 1.000000, Obj: 0.298562, No Obj: 0.003532, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.501344, Class: 1.000000, Obj: 0.305209, No Obj: 0.003582, Avg Recall: 0.551020, count: 98 -Region Avg IOU: 0.466790, Class: 1.000000, Obj: 0.254214, No Obj: 0.003440, Avg Recall: 0.531746, count: 126 -Region Avg IOU: 0.533508, Class: 1.000000, Obj: 0.310346, No Obj: 0.004111, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.535987, Class: 1.000000, Obj: 0.300897, No Obj: 0.003726, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.484009, Class: 1.000000, Obj: 0.297172, No Obj: 0.003219, Avg Recall: 0.488889, count: 90 -Region Avg IOU: 0.490378, Class: 1.000000, Obj: 0.306444, No Obj: 0.003383, Avg Recall: 0.494624, count: 93 -1510: 0.451879, 0.480555 avg, 0.001000 rate, 22.951641 seconds, 386560 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 20.995264 seconds -Region Avg IOU: 0.485758, Class: 1.000000, Obj: 0.238628, No Obj: 0.004646, Avg Recall: 0.530769, count: 130 -Region Avg IOU: 0.490891, Class: 1.000000, Obj: 0.292122, No Obj: 0.005062, Avg Recall: 0.544715, count: 123 -Region Avg IOU: 0.501027, Class: 1.000000, Obj: 0.265705, No Obj: 0.004906, Avg Recall: 0.532374, count: 139 -Region Avg IOU: 0.505829, Class: 1.000000, Obj: 0.281172, No Obj: 0.004215, Avg Recall: 0.522936, count: 109 -Region Avg IOU: 0.499332, Class: 1.000000, Obj: 0.282936, No Obj: 0.004626, Avg Recall: 0.554622, count: 119 -Region Avg IOU: 0.473426, Class: 1.000000, Obj: 0.281301, No Obj: 0.004581, Avg Recall: 0.516949, count: 118 -Region Avg IOU: 0.467334, Class: 1.000000, Obj: 0.262051, No Obj: 0.004149, Avg Recall: 0.540984, count: 122 -Region Avg IOU: 0.475580, Class: 1.000000, Obj: 0.292762, No Obj: 0.003880, Avg Recall: 0.533981, count: 103 -1511: 0.572161, 0.489716 avg, 0.001000 rate, 19.391800 seconds, 386816 images -Loaded: 1.199843 seconds -Region Avg IOU: 0.524998, Class: 1.000000, Obj: 0.319067, No Obj: 0.005151, Avg Recall: 0.590551, count: 127 -Region Avg IOU: 0.500803, Class: 1.000000, Obj: 0.309512, No Obj: 0.004486, Avg Recall: 0.544643, count: 112 -Region Avg IOU: 0.525952, Class: 1.000000, Obj: 0.266606, No Obj: 0.004935, Avg Recall: 0.582090, count: 134 -Region Avg IOU: 0.508686, Class: 1.000000, Obj: 0.296169, No Obj: 0.004260, Avg Recall: 0.590164, count: 122 -Region Avg IOU: 0.490967, Class: 1.000000, Obj: 0.305254, No Obj: 0.004320, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.462479, Class: 1.000000, Obj: 0.253797, No Obj: 0.004451, Avg Recall: 0.505495, count: 91 -Region Avg IOU: 0.502352, Class: 1.000000, Obj: 0.268380, No Obj: 0.004695, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.488624, Class: 1.000000, Obj: 0.263522, No Obj: 0.004475, Avg Recall: 0.560345, count: 116 -1512: 0.506090, 0.491353 avg, 0.001000 rate, 21.222332 seconds, 387072 images -Loaded: 1.203249 seconds -Region Avg IOU: 0.468577, Class: 1.000000, Obj: 0.312888, No Obj: 0.004794, Avg Recall: 0.547826, count: 115 -Region Avg IOU: 0.476017, Class: 1.000000, Obj: 0.269838, No Obj: 0.004137, Avg Recall: 0.500000, count: 104 -Region Avg IOU: 0.504311, Class: 1.000000, Obj: 0.277906, No Obj: 0.004526, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.442375, Class: 1.000000, Obj: 0.325743, No Obj: 0.004814, Avg Recall: 0.466102, count: 118 -Region Avg IOU: 0.550628, Class: 1.000000, Obj: 0.351810, No Obj: 0.005109, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.482504, Class: 1.000000, Obj: 0.249599, No Obj: 0.004149, Avg Recall: 0.595238, count: 84 -Region Avg IOU: 0.474466, Class: 1.000000, Obj: 0.324098, No Obj: 0.004170, Avg Recall: 0.505155, count: 97 -Region Avg IOU: 0.477551, Class: 1.000000, Obj: 0.324999, No Obj: 0.004525, Avg Recall: 0.526316, count: 114 -1513: 0.468122, 0.489030 avg, 0.001000 rate, 20.680002 seconds, 387328 images -Loaded: 1.596033 seconds -Region Avg IOU: 0.481747, Class: 1.000000, Obj: 0.265219, No Obj: 0.004393, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.514564, Class: 1.000000, Obj: 0.302188, No Obj: 0.004512, Avg Recall: 0.556522, count: 115 -Region Avg IOU: 0.429558, Class: 1.000000, Obj: 0.241811, No Obj: 0.004323, Avg Recall: 0.446281, count: 121 -Region Avg IOU: 0.500537, Class: 1.000000, Obj: 0.329249, No Obj: 0.004657, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.482803, Class: 1.000000, Obj: 0.313923, No Obj: 0.004322, Avg Recall: 0.490385, count: 104 -Region Avg IOU: 0.462932, Class: 1.000000, Obj: 0.281269, No Obj: 0.003917, Avg Recall: 0.494253, count: 87 -Region Avg IOU: 0.500009, Class: 1.000000, Obj: 0.323644, No Obj: 0.004591, Avg Recall: 0.500000, count: 104 -Region Avg IOU: 0.443902, Class: 1.000000, Obj: 0.318335, No Obj: 0.004753, Avg Recall: 0.417391, count: 115 -1514: 0.469157, 0.487043 avg, 0.001000 rate, 20.665064 seconds, 387584 images -Loaded: 0.618359 seconds -Region Avg IOU: 0.554910, Class: 1.000000, Obj: 0.339864, No Obj: 0.004320, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.430001, Class: 1.000000, Obj: 0.319431, No Obj: 0.004444, Avg Recall: 0.478633, count: 117 -Region Avg IOU: 0.488670, Class: 1.000000, Obj: 0.284672, No Obj: 0.003914, Avg Recall: 0.526316, count: 76 -Region Avg IOU: 0.450733, Class: 1.000000, Obj: 0.302878, No Obj: 0.004710, Avg Recall: 0.490909, count: 110 -Region Avg IOU: 0.486478, Class: 1.000000, Obj: 0.260564, No Obj: 0.005175, Avg Recall: 0.555556, count: 153 -Region Avg IOU: 0.501245, Class: 1.000000, Obj: 0.294252, No Obj: 0.005015, Avg Recall: 0.530769, count: 130 -Region Avg IOU: 0.458526, Class: 1.000000, Obj: 0.337703, No Obj: 0.004400, Avg Recall: 0.521739, count: 92 -Region Avg IOU: 0.455800, Class: 1.000000, Obj: 0.313643, No Obj: 0.004238, Avg Recall: 0.495798, count: 119 -1515: 0.468674, 0.485206 avg, 0.001000 rate, 19.645573 seconds, 387840 images -Loaded: 2.861021 seconds -Region Avg IOU: 0.455842, Class: 1.000000, Obj: 0.291611, No Obj: 0.004946, Avg Recall: 0.446667, count: 150 -Region Avg IOU: 0.480085, Class: 1.000000, Obj: 0.281369, No Obj: 0.004161, Avg Recall: 0.508333, count: 120 -Region Avg IOU: 0.481121, Class: 1.000000, Obj: 0.251643, No Obj: 0.005103, Avg Recall: 0.493151, count: 146 -Region Avg IOU: 0.454311, Class: 1.000000, Obj: 0.300259, No Obj: 0.004387, Avg Recall: 0.492308, count: 130 -Region Avg IOU: 0.479865, Class: 1.000000, Obj: 0.292607, No Obj: 0.003859, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.435098, Class: 1.000000, Obj: 0.246729, No Obj: 0.004235, Avg Recall: 0.474227, count: 97 -Region Avg IOU: 0.499559, Class: 1.000000, Obj: 0.294742, No Obj: 0.004841, Avg Recall: 0.560606, count: 132 -Region Avg IOU: 0.480849, Class: 1.000000, Obj: 0.296832, No Obj: 0.003903, Avg Recall: 0.527473, count: 91 -1516: 0.530024, 0.489688 avg, 0.001000 rate, 21.107563 seconds, 388096 images -Loaded: 0.040448 seconds -Region Avg IOU: 0.521132, Class: 1.000000, Obj: 0.281451, No Obj: 0.003943, Avg Recall: 0.585106, count: 94 -Region Avg IOU: 0.460725, Class: 1.000000, Obj: 0.250063, No Obj: 0.003865, Avg Recall: 0.513761, count: 109 -Region Avg IOU: 0.509417, Class: 1.000000, Obj: 0.296529, No Obj: 0.004349, Avg Recall: 0.534653, count: 101 -Region Avg IOU: 0.565553, Class: 1.000000, Obj: 0.299247, No Obj: 0.004338, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.450829, Class: 1.000000, Obj: 0.267215, No Obj: 0.003960, Avg Recall: 0.427350, count: 117 -Region Avg IOU: 0.536353, Class: 1.000000, Obj: 0.324350, No Obj: 0.004238, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.457972, Class: 1.000000, Obj: 0.264838, No Obj: 0.004893, Avg Recall: 0.481752, count: 137 -Region Avg IOU: 0.429862, Class: 1.000000, Obj: 0.270585, No Obj: 0.004935, Avg Recall: 0.444444, count: 126 -1517: 0.525079, 0.493227 avg, 0.001000 rate, 21.460957 seconds, 388352 images -Loaded: 0.639854 seconds -Region Avg IOU: 0.462816, Class: 1.000000, Obj: 0.271123, No Obj: 0.003915, Avg Recall: 0.522124, count: 113 -Region Avg IOU: 0.480966, Class: 1.000000, Obj: 0.322508, No Obj: 0.004323, Avg Recall: 0.514019, count: 107 -Region Avg IOU: 0.470737, Class: 1.000000, Obj: 0.242417, No Obj: 0.003998, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.449121, Class: 1.000000, Obj: 0.260453, No Obj: 0.004244, Avg Recall: 0.448276, count: 116 -Region Avg IOU: 0.463279, Class: 1.000000, Obj: 0.294057, No Obj: 0.004023, Avg Recall: 0.469388, count: 98 -Region Avg IOU: 0.469787, Class: 1.000000, Obj: 0.273330, No Obj: 0.004080, Avg Recall: 0.524752, count: 101 -Region Avg IOU: 0.496146, Class: 1.000000, Obj: 0.261100, No Obj: 0.004343, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.486335, Class: 1.000000, Obj: 0.289413, No Obj: 0.004383, Avg Recall: 0.552083, count: 96 -1518: 0.446425, 0.488546 avg, 0.001000 rate, 22.166124 seconds, 388608 images -Loaded: 1.110470 seconds -Region Avg IOU: 0.503300, Class: 1.000000, Obj: 0.282948, No Obj: 0.004365, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.510287, Class: 1.000000, Obj: 0.305307, No Obj: 0.004654, Avg Recall: 0.582418, count: 91 -Region Avg IOU: 0.458968, Class: 1.000000, Obj: 0.246570, No Obj: 0.004667, Avg Recall: 0.449612, count: 129 -Region Avg IOU: 0.487361, Class: 1.000000, Obj: 0.269707, No Obj: 0.004581, Avg Recall: 0.503817, count: 131 -Region Avg IOU: 0.459089, Class: 1.000000, Obj: 0.253394, No Obj: 0.004770, Avg Recall: 0.506849, count: 146 -Region Avg IOU: 0.520568, Class: 1.000000, Obj: 0.275676, No Obj: 0.004169, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.447103, Class: 1.000000, Obj: 0.246829, No Obj: 0.004730, Avg Recall: 0.480315, count: 127 -Region Avg IOU: 0.470092, Class: 1.000000, Obj: 0.280186, No Obj: 0.004473, Avg Recall: 0.482759, count: 116 -1519: 0.508365, 0.490528 avg, 0.001000 rate, 19.697292 seconds, 388864 images -Loaded: 1.344967 seconds -Region Avg IOU: 0.470776, Class: 1.000000, Obj: 0.264452, No Obj: 0.004140, Avg Recall: 0.474576, count: 118 -Region Avg IOU: 0.439055, Class: 1.000000, Obj: 0.263658, No Obj: 0.004089, Avg Recall: 0.468750, count: 96 -Region Avg IOU: 0.480400, Class: 1.000000, Obj: 0.270488, No Obj: 0.004788, Avg Recall: 0.507042, count: 142 -Region Avg IOU: 0.473831, Class: 1.000000, Obj: 0.276390, No Obj: 0.004677, Avg Recall: 0.517986, count: 139 -Region Avg IOU: 0.506652, Class: 1.000000, Obj: 0.271368, No Obj: 0.004450, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.454949, Class: 1.000000, Obj: 0.235882, No Obj: 0.004115, Avg Recall: 0.464912, count: 114 -Region Avg IOU: 0.501152, Class: 1.000000, Obj: 0.300721, No Obj: 0.004856, Avg Recall: 0.571429, count: 119 -Region Avg IOU: 0.499087, Class: 1.000000, Obj: 0.275481, No Obj: 0.003265, Avg Recall: 0.500000, count: 74 -1520: 0.531266, 0.494602 avg, 0.001000 rate, 20.365160 seconds, 389120 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 0.000082 seconds -Region Avg IOU: 0.518303, Class: 1.000000, Obj: 0.343327, No Obj: 0.003524, Avg Recall: 0.563218, count: 87 -Region Avg IOU: 0.496252, Class: 1.000000, Obj: 0.295442, No Obj: 0.004253, Avg Recall: 0.532258, count: 124 -Region Avg IOU: 0.494721, Class: 1.000000, Obj: 0.328186, No Obj: 0.003549, Avg Recall: 0.545455, count: 77 -Region Avg IOU: 0.498295, Class: 1.000000, Obj: 0.310004, No Obj: 0.004202, Avg Recall: 0.549550, count: 111 -Region Avg IOU: 0.551393, Class: 1.000000, Obj: 0.322653, No Obj: 0.004523, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.500624, Class: 1.000000, Obj: 0.312574, No Obj: 0.003776, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.545342, Class: 1.000000, Obj: 0.320159, No Obj: 0.004362, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.530901, Class: 1.000000, Obj: 0.355268, No Obj: 0.004519, Avg Recall: 0.613208, count: 106 -1521: 0.454902, 0.490632 avg, 0.001000 rate, 23.857592 seconds, 389376 images -Loaded: 0.050706 seconds -Region Avg IOU: 0.535674, Class: 1.000000, Obj: 0.329244, No Obj: 0.004199, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.489543, Class: 1.000000, Obj: 0.254607, No Obj: 0.003560, Avg Recall: 0.525773, count: 97 -Region Avg IOU: 0.492777, Class: 1.000000, Obj: 0.309060, No Obj: 0.003666, Avg Recall: 0.530864, count: 81 -Region Avg IOU: 0.609163, Class: 1.000000, Obj: 0.292848, No Obj: 0.003703, Avg Recall: 0.731707, count: 82 -Region Avg IOU: 0.516859, Class: 1.000000, Obj: 0.344917, No Obj: 0.003869, Avg Recall: 0.586207, count: 87 -Region Avg IOU: 0.524964, Class: 1.000000, Obj: 0.283867, No Obj: 0.004696, Avg Recall: 0.572581, count: 124 -Region Avg IOU: 0.494928, Class: 1.000000, Obj: 0.257851, No Obj: 0.003922, Avg Recall: 0.574468, count: 94 -Region Avg IOU: 0.515073, Class: 1.000000, Obj: 0.310278, No Obj: 0.004187, Avg Recall: 0.594340, count: 106 -1522: 0.422633, 0.483832 avg, 0.001000 rate, 23.707537 seconds, 389632 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.525715, Class: 1.000000, Obj: 0.332643, No Obj: 0.003737, Avg Recall: 0.622807, count: 114 -Region Avg IOU: 0.529724, Class: 1.000000, Obj: 0.310697, No Obj: 0.003609, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.530454, Class: 1.000000, Obj: 0.303771, No Obj: 0.004270, Avg Recall: 0.658915, count: 129 -Region Avg IOU: 0.559427, Class: 1.000000, Obj: 0.328088, No Obj: 0.004440, Avg Recall: 0.677165, count: 127 -Region Avg IOU: 0.505055, Class: 1.000000, Obj: 0.274506, No Obj: 0.004285, Avg Recall: 0.524194, count: 124 -Region Avg IOU: 0.547620, Class: 1.000000, Obj: 0.305208, No Obj: 0.003516, Avg Recall: 0.650602, count: 83 -Region Avg IOU: 0.542102, Class: 1.000000, Obj: 0.308416, No Obj: 0.004551, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.560489, Class: 1.000000, Obj: 0.322177, No Obj: 0.004339, Avg Recall: 0.689320, count: 103 -1523: 0.468980, 0.482347 avg, 0.001000 rate, 24.869524 seconds, 389888 images -Loaded: 0.082510 seconds -Region Avg IOU: 0.493179, Class: 1.000000, Obj: 0.318333, No Obj: 0.003539, Avg Recall: 0.577778, count: 90 -Region Avg IOU: 0.493432, Class: 1.000000, Obj: 0.310332, No Obj: 0.003591, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.498759, Class: 1.000000, Obj: 0.309860, No Obj: 0.003767, Avg Recall: 0.521368, count: 117 -Region Avg IOU: 0.505706, Class: 1.000000, Obj: 0.255768, No Obj: 0.003774, Avg Recall: 0.641026, count: 117 -Region Avg IOU: 0.543966, Class: 1.000000, Obj: 0.316825, No Obj: 0.004484, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.538667, Class: 1.000000, Obj: 0.300386, No Obj: 0.004494, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.555359, Class: 1.000000, Obj: 0.314528, No Obj: 0.003823, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.532544, Class: 1.000000, Obj: 0.268229, No Obj: 0.003619, Avg Recall: 0.600000, count: 100 -1524: 0.464700, 0.480582 avg, 0.001000 rate, 22.788616 seconds, 390144 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.505900, Class: 1.000000, Obj: 0.353414, No Obj: 0.003568, Avg Recall: 0.494737, count: 95 -Region Avg IOU: 0.530785, Class: 1.000000, Obj: 0.276377, No Obj: 0.004195, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.560592, Class: 1.000000, Obj: 0.326399, No Obj: 0.003871, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.534098, Class: 1.000000, Obj: 0.284541, No Obj: 0.003949, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.483133, Class: 1.000000, Obj: 0.300002, No Obj: 0.003460, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.539450, Class: 1.000000, Obj: 0.319717, No Obj: 0.004079, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.514630, Class: 1.000000, Obj: 0.318653, No Obj: 0.004047, Avg Recall: 0.607843, count: 102 -Region Avg IOU: 0.548983, Class: 1.000000, Obj: 0.319976, No Obj: 0.004082, Avg Recall: 0.627119, count: 118 -1525: 0.512170, 0.483741 avg, 0.001000 rate, 21.496464 seconds, 390400 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.474909, Class: 1.000000, Obj: 0.286732, No Obj: 0.003681, Avg Recall: 0.509615, count: 104 -Region Avg IOU: 0.540534, Class: 1.000000, Obj: 0.333667, No Obj: 0.004124, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.528798, Class: 1.000000, Obj: 0.296382, No Obj: 0.003483, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.503357, Class: 1.000000, Obj: 0.303726, No Obj: 0.003849, Avg Recall: 0.543103, count: 116 -Region Avg IOU: 0.514850, Class: 1.000000, Obj: 0.271725, No Obj: 0.003741, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.527872, Class: 1.000000, Obj: 0.339753, No Obj: 0.003850, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.507100, Class: 1.000000, Obj: 0.324327, No Obj: 0.003608, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.494519, Class: 1.000000, Obj: 0.259743, No Obj: 0.003796, Avg Recall: 0.568807, count: 109 -1526: 0.482815, 0.483648 avg, 0.001000 rate, 23.507408 seconds, 390656 images -Loaded: 0.037340 seconds -Region Avg IOU: 0.500189, Class: 1.000000, Obj: 0.286707, No Obj: 0.003430, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.530817, Class: 1.000000, Obj: 0.317434, No Obj: 0.003632, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.480149, Class: 1.000000, Obj: 0.324039, No Obj: 0.004131, Avg Recall: 0.482143, count: 112 -Region Avg IOU: 0.482924, Class: 1.000000, Obj: 0.266140, No Obj: 0.003741, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.496826, Class: 1.000000, Obj: 0.303376, No Obj: 0.004481, Avg Recall: 0.549383, count: 162 -Region Avg IOU: 0.543045, Class: 1.000000, Obj: 0.284000, No Obj: 0.003961, Avg Recall: 0.627119, count: 118 -Region Avg IOU: 0.480013, Class: 1.000000, Obj: 0.275652, No Obj: 0.003981, Avg Recall: 0.560345, count: 116 -Region Avg IOU: 0.515879, Class: 1.000000, Obj: 0.342105, No Obj: 0.003740, Avg Recall: 0.605263, count: 114 -1527: 0.492912, 0.484575 avg, 0.001000 rate, 23.061596 seconds, 390912 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.483401, Class: 1.000000, Obj: 0.293904, No Obj: 0.003383, Avg Recall: 0.494382, count: 89 -Region Avg IOU: 0.552222, Class: 1.000000, Obj: 0.273211, No Obj: 0.003807, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.509219, Class: 1.000000, Obj: 0.342960, No Obj: 0.003489, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.513945, Class: 1.000000, Obj: 0.307916, No Obj: 0.004233, Avg Recall: 0.593496, count: 123 -Region Avg IOU: 0.518382, Class: 1.000000, Obj: 0.316417, No Obj: 0.003835, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.546691, Class: 1.000000, Obj: 0.306165, No Obj: 0.004104, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.557997, Class: 1.000000, Obj: 0.314060, No Obj: 0.004102, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.486510, Class: 1.000000, Obj: 0.268751, No Obj: 0.003522, Avg Recall: 0.570000, count: 100 -1528: 0.440187, 0.480136 avg, 0.001000 rate, 23.069220 seconds, 391168 images -Loaded: 0.000058 seconds -Region Avg IOU: 0.548214, Class: 1.000000, Obj: 0.271420, No Obj: 0.003622, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.550135, Class: 1.000000, Obj: 0.280547, No Obj: 0.003240, Avg Recall: 0.632184, count: 87 -Region Avg IOU: 0.505149, Class: 1.000000, Obj: 0.269862, No Obj: 0.003683, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.567593, Class: 1.000000, Obj: 0.320309, No Obj: 0.003915, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.520137, Class: 1.000000, Obj: 0.304113, No Obj: 0.003274, Avg Recall: 0.581395, count: 86 -Region Avg IOU: 0.551225, Class: 1.000000, Obj: 0.303512, No Obj: 0.003622, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.487779, Class: 1.000000, Obj: 0.283313, No Obj: 0.003652, Avg Recall: 0.532110, count: 109 -Region Avg IOU: 0.486643, Class: 1.000000, Obj: 0.285916, No Obj: 0.004069, Avg Recall: 0.495146, count: 103 -1529: 0.469951, 0.479118 avg, 0.001000 rate, 22.687866 seconds, 391424 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.496188, Class: 1.000000, Obj: 0.274137, No Obj: 0.003527, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.485179, Class: 1.000000, Obj: 0.318050, No Obj: 0.002894, Avg Recall: 0.529412, count: 68 -Region Avg IOU: 0.547276, Class: 1.000000, Obj: 0.307932, No Obj: 0.003529, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.493541, Class: 1.000000, Obj: 0.289017, No Obj: 0.003497, Avg Recall: 0.516129, count: 124 -Region Avg IOU: 0.488195, Class: 1.000000, Obj: 0.255387, No Obj: 0.003705, Avg Recall: 0.578512, count: 121 -Region Avg IOU: 0.566501, Class: 1.000000, Obj: 0.267441, No Obj: 0.003329, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.539970, Class: 1.000000, Obj: 0.298807, No Obj: 0.003555, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.556752, Class: 1.000000, Obj: 0.310433, No Obj: 0.003882, Avg Recall: 0.650943, count: 106 -1530: 0.474671, 0.478673 avg, 0.001000 rate, 24.423838 seconds, 391680 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000098 seconds -Region Avg IOU: 0.580466, Class: 1.000000, Obj: 0.274822, No Obj: 0.003468, Avg Recall: 0.698630, count: 146 -Region Avg IOU: 0.592899, Class: 1.000000, Obj: 0.267500, No Obj: 0.003387, Avg Recall: 0.752000, count: 125 -Region Avg IOU: 0.608036, Class: 1.000000, Obj: 0.319421, No Obj: 0.003559, Avg Recall: 0.784483, count: 116 -Region Avg IOU: 0.583038, Class: 1.000000, Obj: 0.289331, No Obj: 0.002913, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.597090, Class: 1.000000, Obj: 0.269809, No Obj: 0.002992, Avg Recall: 0.730769, count: 78 -Region Avg IOU: 0.560483, Class: 1.000000, Obj: 0.296146, No Obj: 0.002982, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.569769, Class: 1.000000, Obj: 0.331400, No Obj: 0.003079, Avg Recall: 0.633333, count: 120 -Region Avg IOU: 0.600109, Class: 1.000000, Obj: 0.295981, No Obj: 0.003182, Avg Recall: 0.763158, count: 114 -1531: 0.496437, 0.480449 avg, 0.001000 rate, 28.190994 seconds, 391936 images -Loaded: 0.000130 seconds -Region Avg IOU: 0.592465, Class: 1.000000, Obj: 0.311383, No Obj: 0.003398, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.589011, Class: 1.000000, Obj: 0.298289, No Obj: 0.002724, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.570939, Class: 1.000000, Obj: 0.368926, No Obj: 0.003571, Avg Recall: 0.682432, count: 148 -Region Avg IOU: 0.570207, Class: 1.000000, Obj: 0.296158, No Obj: 0.003008, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.571160, Class: 1.000000, Obj: 0.307759, No Obj: 0.003271, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.575295, Class: 1.000000, Obj: 0.354764, No Obj: 0.003475, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.613174, Class: 1.000000, Obj: 0.307283, No Obj: 0.003531, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.534387, Class: 1.000000, Obj: 0.304547, No Obj: 0.003193, Avg Recall: 0.607692, count: 130 -1532: 0.481900, 0.480594 avg, 0.001000 rate, 27.104383 seconds, 392192 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.558620, Class: 1.000000, Obj: 0.276965, No Obj: 0.003235, Avg Recall: 0.634146, count: 123 -Region Avg IOU: 0.588178, Class: 1.000000, Obj: 0.270743, No Obj: 0.003371, Avg Recall: 0.716418, count: 134 -Region Avg IOU: 0.586143, Class: 1.000000, Obj: 0.323123, No Obj: 0.003224, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.574461, Class: 1.000000, Obj: 0.302296, No Obj: 0.002954, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.602875, Class: 1.000000, Obj: 0.345869, No Obj: 0.003253, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.540664, Class: 1.000000, Obj: 0.259093, No Obj: 0.002893, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.576921, Class: 1.000000, Obj: 0.299198, No Obj: 0.002713, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.564444, Class: 1.000000, Obj: 0.317979, No Obj: 0.003304, Avg Recall: 0.649254, count: 134 -1533: 0.464826, 0.479018 avg, 0.001000 rate, 27.118912 seconds, 392448 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.582196, Class: 1.000000, Obj: 0.302546, No Obj: 0.002616, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.581273, Class: 1.000000, Obj: 0.300598, No Obj: 0.003071, Avg Recall: 0.756098, count: 123 -Region Avg IOU: 0.534202, Class: 1.000000, Obj: 0.228970, No Obj: 0.002547, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.599050, Class: 1.000000, Obj: 0.263880, No Obj: 0.002860, Avg Recall: 0.714286, count: 84 -Region Avg IOU: 0.547664, Class: 1.000000, Obj: 0.240081, No Obj: 0.003005, Avg Recall: 0.640000, count: 125 -Region Avg IOU: 0.610238, Class: 1.000000, Obj: 0.326150, No Obj: 0.003402, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.590369, Class: 1.000000, Obj: 0.296512, No Obj: 0.002791, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.559359, Class: 1.000000, Obj: 0.245613, No Obj: 0.002774, Avg Recall: 0.651786, count: 112 -1534: 0.449820, 0.476098 avg, 0.001000 rate, 27.009100 seconds, 392704 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.608851, Class: 1.000000, Obj: 0.260886, No Obj: 0.002750, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.607430, Class: 1.000000, Obj: 0.261284, No Obj: 0.003025, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.540981, Class: 1.000000, Obj: 0.249494, No Obj: 0.002776, Avg Recall: 0.604839, count: 124 -Region Avg IOU: 0.614253, Class: 1.000000, Obj: 0.286474, No Obj: 0.002548, Avg Recall: 0.746835, count: 79 -Region Avg IOU: 0.593921, Class: 1.000000, Obj: 0.252434, No Obj: 0.002881, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.591214, Class: 1.000000, Obj: 0.272063, No Obj: 0.002591, Avg Recall: 0.734177, count: 79 -Region Avg IOU: 0.564253, Class: 1.000000, Obj: 0.348235, No Obj: 0.002593, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.607221, Class: 1.000000, Obj: 0.268084, No Obj: 0.002748, Avg Recall: 0.718750, count: 96 -1535: 0.452811, 0.473769 avg, 0.001000 rate, 28.928976 seconds, 392960 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.586969, Class: 1.000000, Obj: 0.271868, No Obj: 0.002979, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.583232, Class: 1.000000, Obj: 0.258518, No Obj: 0.002605, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.609945, Class: 1.000000, Obj: 0.330251, No Obj: 0.002534, Avg Recall: 0.705882, count: 85 -Region Avg IOU: 0.595936, Class: 1.000000, Obj: 0.298323, No Obj: 0.003082, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.556045, Class: 1.000000, Obj: 0.263655, No Obj: 0.002547, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.589226, Class: 1.000000, Obj: 0.274745, No Obj: 0.003155, Avg Recall: 0.697842, count: 139 -Region Avg IOU: 0.567725, Class: 1.000000, Obj: 0.270012, No Obj: 0.002989, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.564015, Class: 1.000000, Obj: 0.279619, No Obj: 0.002599, Avg Recall: 0.688525, count: 122 -1536: 0.446266, 0.471019 avg, 0.001000 rate, 27.465105 seconds, 393216 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.577296, Class: 1.000000, Obj: 0.288638, No Obj: 0.003013, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.612364, Class: 1.000000, Obj: 0.272797, No Obj: 0.002770, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.610898, Class: 1.000000, Obj: 0.295827, No Obj: 0.002650, Avg Recall: 0.746835, count: 79 -Region Avg IOU: 0.544773, Class: 1.000000, Obj: 0.282753, No Obj: 0.002714, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.558724, Class: 1.000000, Obj: 0.272066, No Obj: 0.003011, Avg Recall: 0.687500, count: 128 -Region Avg IOU: 0.610522, Class: 1.000000, Obj: 0.294841, No Obj: 0.002967, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.609255, Class: 1.000000, Obj: 0.300326, No Obj: 0.003015, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.574400, Class: 1.000000, Obj: 0.275338, No Obj: 0.002721, Avg Recall: 0.683761, count: 117 -1537: 0.532500, 0.477167 avg, 0.001000 rate, 28.548201 seconds, 393472 images -Loaded: 0.000119 seconds -Region Avg IOU: 0.561294, Class: 1.000000, Obj: 0.271577, No Obj: 0.002846, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.619019, Class: 1.000000, Obj: 0.336194, No Obj: 0.002773, Avg Recall: 0.819149, count: 94 -Region Avg IOU: 0.593218, Class: 1.000000, Obj: 0.279279, No Obj: 0.002883, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.567419, Class: 1.000000, Obj: 0.315586, No Obj: 0.002982, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.575967, Class: 1.000000, Obj: 0.275151, No Obj: 0.002386, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.585674, Class: 1.000000, Obj: 0.281125, No Obj: 0.002975, Avg Recall: 0.746032, count: 126 -Region Avg IOU: 0.613916, Class: 1.000000, Obj: 0.290847, No Obj: 0.002670, Avg Recall: 0.744186, count: 86 -Region Avg IOU: 0.587855, Class: 1.000000, Obj: 0.240202, No Obj: 0.002849, Avg Recall: 0.713043, count: 115 -1538: 0.450436, 0.474494 avg, 0.001000 rate, 27.499285 seconds, 393728 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.580570, Class: 1.000000, Obj: 0.231368, No Obj: 0.003242, Avg Recall: 0.664384, count: 146 -Region Avg IOU: 0.582831, Class: 1.000000, Obj: 0.226316, No Obj: 0.002726, Avg Recall: 0.727273, count: 77 -Region Avg IOU: 0.607197, Class: 1.000000, Obj: 0.298709, No Obj: 0.002843, Avg Recall: 0.755556, count: 90 -Region Avg IOU: 0.577353, Class: 1.000000, Obj: 0.304160, No Obj: 0.002820, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.619802, Class: 1.000000, Obj: 0.342331, No Obj: 0.002987, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.580131, Class: 1.000000, Obj: 0.239136, No Obj: 0.003265, Avg Recall: 0.692308, count: 143 -Region Avg IOU: 0.576256, Class: 1.000000, Obj: 0.233572, No Obj: 0.003186, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.565892, Class: 1.000000, Obj: 0.274978, No Obj: 0.002592, Avg Recall: 0.697368, count: 76 -1539: 0.509837, 0.478028 avg, 0.001000 rate, 28.267656 seconds, 393984 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.606771, Class: 1.000000, Obj: 0.286099, No Obj: 0.002944, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.582132, Class: 1.000000, Obj: 0.297690, No Obj: 0.003302, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.588361, Class: 1.000000, Obj: 0.279711, No Obj: 0.003153, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.615361, Class: 1.000000, Obj: 0.321198, No Obj: 0.003378, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.593842, Class: 1.000000, Obj: 0.273634, No Obj: 0.002813, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.596137, Class: 1.000000, Obj: 0.352502, No Obj: 0.003340, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.579875, Class: 1.000000, Obj: 0.317906, No Obj: 0.003254, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.588661, Class: 1.000000, Obj: 0.307607, No Obj: 0.003023, Avg Recall: 0.715596, count: 109 -1540: 0.525496, 0.482775 avg, 0.001000 rate, 27.792053 seconds, 394240 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 16.106628 seconds -Region Avg IOU: 0.519749, Class: 1.000000, Obj: 0.287523, No Obj: 0.004200, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.527011, Class: 1.000000, Obj: 0.305081, No Obj: 0.003780, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.524472, Class: 1.000000, Obj: 0.274408, No Obj: 0.004367, Avg Recall: 0.616541, count: 133 -Region Avg IOU: 0.527223, Class: 1.000000, Obj: 0.342805, No Obj: 0.003702, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.549856, Class: 1.000000, Obj: 0.318930, No Obj: 0.003513, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.502090, Class: 1.000000, Obj: 0.283378, No Obj: 0.003863, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.545273, Class: 1.000000, Obj: 0.294425, No Obj: 0.005011, Avg Recall: 0.666667, count: 153 -Region Avg IOU: 0.508836, Class: 1.000000, Obj: 0.302572, No Obj: 0.003678, Avg Recall: 0.615385, count: 104 -1541: 0.510949, 0.485592 avg, 0.001000 rate, 22.895344 seconds, 394496 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.447911, Class: 1.000000, Obj: 0.279603, No Obj: 0.003329, Avg Recall: 0.469388, count: 98 -Region Avg IOU: 0.513808, Class: 1.000000, Obj: 0.259390, No Obj: 0.003510, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.523815, Class: 1.000000, Obj: 0.302684, No Obj: 0.003801, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.544194, Class: 1.000000, Obj: 0.343834, No Obj: 0.003769, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.467438, Class: 1.000000, Obj: 0.339148, No Obj: 0.003591, Avg Recall: 0.495575, count: 113 -Region Avg IOU: 0.523277, Class: 1.000000, Obj: 0.331070, No Obj: 0.004127, Avg Recall: 0.589744, count: 117 -Region Avg IOU: 0.542142, Class: 1.000000, Obj: 0.322734, No Obj: 0.003544, Avg Recall: 0.597826, count: 92 -Region Avg IOU: 0.518372, Class: 1.000000, Obj: 0.300178, No Obj: 0.003727, Avg Recall: 0.521368, count: 117 -1542: 0.472238, 0.484257 avg, 0.001000 rate, 24.061136 seconds, 394752 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.503817, Class: 1.000000, Obj: 0.303977, No Obj: 0.003755, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.588659, Class: 1.000000, Obj: 0.296584, No Obj: 0.004253, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.523493, Class: 1.000000, Obj: 0.313204, No Obj: 0.003908, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.552631, Class: 1.000000, Obj: 0.270163, No Obj: 0.003966, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.548902, Class: 1.000000, Obj: 0.318867, No Obj: 0.003961, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.495075, Class: 1.000000, Obj: 0.292514, No Obj: 0.004210, Avg Recall: 0.552239, count: 134 -Region Avg IOU: 0.521132, Class: 1.000000, Obj: 0.279627, No Obj: 0.003581, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.519674, Class: 1.000000, Obj: 0.301216, No Obj: 0.003612, Avg Recall: 0.546392, count: 97 -1543: 0.489382, 0.484769 avg, 0.001000 rate, 24.321552 seconds, 395008 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.481167, Class: 1.000000, Obj: 0.315593, No Obj: 0.004225, Avg Recall: 0.531746, count: 126 -Region Avg IOU: 0.524596, Class: 1.000000, Obj: 0.278296, No Obj: 0.004328, Avg Recall: 0.576577, count: 111 -Region Avg IOU: 0.486289, Class: 1.000000, Obj: 0.276181, No Obj: 0.003467, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.517847, Class: 1.000000, Obj: 0.301058, No Obj: 0.003609, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.547132, Class: 1.000000, Obj: 0.350436, No Obj: 0.003404, Avg Recall: 0.628571, count: 70 -Region Avg IOU: 0.581456, Class: 1.000000, Obj: 0.372885, No Obj: 0.004395, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.471352, Class: 1.000000, Obj: 0.265947, No Obj: 0.003575, Avg Recall: 0.571429, count: 105 -Region Avg IOU: 0.476542, Class: 1.000000, Obj: 0.293151, No Obj: 0.003218, Avg Recall: 0.519608, count: 102 -1544: 0.430045, 0.479297 avg, 0.001000 rate, 23.456600 seconds, 395264 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.522105, Class: 1.000000, Obj: 0.270112, No Obj: 0.004572, Avg Recall: 0.593750, count: 128 -Region Avg IOU: 0.451306, Class: 1.000000, Obj: 0.224540, No Obj: 0.003456, Avg Recall: 0.508772, count: 114 -Region Avg IOU: 0.489291, Class: 1.000000, Obj: 0.290349, No Obj: 0.003696, Avg Recall: 0.534483, count: 116 -Region Avg IOU: 0.498017, Class: 1.000000, Obj: 0.274594, No Obj: 0.003714, Avg Recall: 0.559322, count: 118 -Region Avg IOU: 0.484195, Class: 1.000000, Obj: 0.293068, No Obj: 0.003333, Avg Recall: 0.511905, count: 84 -Region Avg IOU: 0.492373, Class: 1.000000, Obj: 0.277057, No Obj: 0.003581, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.485919, Class: 1.000000, Obj: 0.339061, No Obj: 0.003616, Avg Recall: 0.505155, count: 97 -Region Avg IOU: 0.544555, Class: 1.000000, Obj: 0.308786, No Obj: 0.004068, Avg Recall: 0.605042, count: 119 -1545: 0.510044, 0.482372 avg, 0.001000 rate, 23.378489 seconds, 395520 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.493466, Class: 1.000000, Obj: 0.288638, No Obj: 0.003530, Avg Recall: 0.513761, count: 109 -Region Avg IOU: 0.504953, Class: 1.000000, Obj: 0.273490, No Obj: 0.003542, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.505680, Class: 1.000000, Obj: 0.264879, No Obj: 0.003844, Avg Recall: 0.547009, count: 117 -Region Avg IOU: 0.514217, Class: 1.000000, Obj: 0.298737, No Obj: 0.003690, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.527655, Class: 1.000000, Obj: 0.284923, No Obj: 0.003276, Avg Recall: 0.602410, count: 83 -Region Avg IOU: 0.514509, Class: 1.000000, Obj: 0.303892, No Obj: 0.003494, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.534715, Class: 1.000000, Obj: 0.263920, No Obj: 0.004191, Avg Recall: 0.580882, count: 136 -Region Avg IOU: 0.493329, Class: 1.000000, Obj: 0.308212, No Obj: 0.003323, Avg Recall: 0.500000, count: 106 -1546: 0.470841, 0.481219 avg, 0.001000 rate, 23.556513 seconds, 395776 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.515337, Class: 1.000000, Obj: 0.305302, No Obj: 0.004347, Avg Recall: 0.601399, count: 143 -Region Avg IOU: 0.492921, Class: 1.000000, Obj: 0.262079, No Obj: 0.003472, Avg Recall: 0.524590, count: 122 -Region Avg IOU: 0.466507, Class: 1.000000, Obj: 0.285382, No Obj: 0.003268, Avg Recall: 0.470588, count: 102 -Region Avg IOU: 0.478620, Class: 1.000000, Obj: 0.288651, No Obj: 0.003197, Avg Recall: 0.500000, count: 92 -Region Avg IOU: 0.536718, Class: 1.000000, Obj: 0.326896, No Obj: 0.003254, Avg Recall: 0.604938, count: 81 -Region Avg IOU: 0.539100, Class: 1.000000, Obj: 0.295268, No Obj: 0.003803, Avg Recall: 0.623529, count: 85 -Region Avg IOU: 0.527291, Class: 1.000000, Obj: 0.279563, No Obj: 0.003198, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.498447, Class: 1.000000, Obj: 0.313624, No Obj: 0.003153, Avg Recall: 0.571429, count: 77 -1547: 0.414836, 0.474580 avg, 0.001000 rate, 23.174393 seconds, 396032 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.497983, Class: 1.000000, Obj: 0.249532, No Obj: 0.004087, Avg Recall: 0.548387, count: 124 -Region Avg IOU: 0.524056, Class: 1.000000, Obj: 0.274320, No Obj: 0.004232, Avg Recall: 0.588710, count: 124 -Region Avg IOU: 0.481563, Class: 1.000000, Obj: 0.233803, No Obj: 0.003480, Avg Recall: 0.533333, count: 120 -Region Avg IOU: 0.505370, Class: 1.000000, Obj: 0.318261, No Obj: 0.003385, Avg Recall: 0.607143, count: 84 -Region Avg IOU: 0.486652, Class: 1.000000, Obj: 0.265143, No Obj: 0.003437, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.517206, Class: 1.000000, Obj: 0.277795, No Obj: 0.003008, Avg Recall: 0.548780, count: 82 -Region Avg IOU: 0.521639, Class: 1.000000, Obj: 0.259135, No Obj: 0.003746, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.523136, Class: 1.000000, Obj: 0.304663, No Obj: 0.003964, Avg Recall: 0.578947, count: 114 -1548: 0.501249, 0.477247 avg, 0.001000 rate, 23.588057 seconds, 396288 images -Loaded: 0.238930 seconds -Region Avg IOU: 0.516268, Class: 1.000000, Obj: 0.260514, No Obj: 0.003661, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.568489, Class: 1.000000, Obj: 0.311686, No Obj: 0.004026, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.536142, Class: 1.000000, Obj: 0.281167, No Obj: 0.003618, Avg Recall: 0.578125, count: 128 -Region Avg IOU: 0.511654, Class: 1.000000, Obj: 0.324826, No Obj: 0.003177, Avg Recall: 0.559524, count: 84 -Region Avg IOU: 0.518042, Class: 1.000000, Obj: 0.292784, No Obj: 0.003905, Avg Recall: 0.584507, count: 142 -Region Avg IOU: 0.571763, Class: 1.000000, Obj: 0.314963, No Obj: 0.003737, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.544336, Class: 1.000000, Obj: 0.274013, No Obj: 0.003503, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.493180, Class: 1.000000, Obj: 0.270537, No Obj: 0.003246, Avg Recall: 0.589286, count: 112 -1549: 0.500228, 0.479545 avg, 0.001000 rate, 23.728310 seconds, 396544 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.498272, Class: 1.000000, Obj: 0.235691, No Obj: 0.003650, Avg Recall: 0.592593, count: 108 -Region Avg IOU: 0.539321, Class: 1.000000, Obj: 0.294273, No Obj: 0.004156, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.498915, Class: 1.000000, Obj: 0.283564, No Obj: 0.003828, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.496184, Class: 1.000000, Obj: 0.290550, No Obj: 0.003866, Avg Recall: 0.537634, count: 93 -Region Avg IOU: 0.521670, Class: 1.000000, Obj: 0.292664, No Obj: 0.004310, Avg Recall: 0.598425, count: 127 -Region Avg IOU: 0.520840, Class: 1.000000, Obj: 0.251799, No Obj: 0.003858, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.570858, Class: 1.000000, Obj: 0.337293, No Obj: 0.003871, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.484951, Class: 1.000000, Obj: 0.288998, No Obj: 0.003716, Avg Recall: 0.509804, count: 102 -1550: 0.476534, 0.479244 avg, 0.001000 rate, 22.004044 seconds, 396800 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.399328 seconds -Region Avg IOU: 0.466775, Class: 1.000000, Obj: 0.274695, No Obj: 0.004128, Avg Recall: 0.491667, count: 120 -Region Avg IOU: 0.522119, Class: 1.000000, Obj: 0.264438, No Obj: 0.004131, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.492482, Class: 1.000000, Obj: 0.272497, No Obj: 0.004253, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.470133, Class: 1.000000, Obj: 0.252004, No Obj: 0.004737, Avg Recall: 0.490385, count: 104 -Region Avg IOU: 0.510417, Class: 1.000000, Obj: 0.260874, No Obj: 0.004364, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.429041, Class: 1.000000, Obj: 0.226904, No Obj: 0.003874, Avg Recall: 0.478633, count: 117 -Region Avg IOU: 0.462505, Class: 1.000000, Obj: 0.252477, No Obj: 0.004810, Avg Recall: 0.487180, count: 117 -Region Avg IOU: 0.434267, Class: 1.000000, Obj: 0.252838, No Obj: 0.004415, Avg Recall: 0.472000, count: 125 -1551: 0.560114, 0.487331 avg, 0.001000 rate, 16.969765 seconds, 397056 images -Loaded: 4.306155 seconds -Region Avg IOU: 0.400735, Class: 1.000000, Obj: 0.283136, No Obj: 0.003909, Avg Recall: 0.427273, count: 110 -Region Avg IOU: 0.416449, Class: 1.000000, Obj: 0.276762, No Obj: 0.003809, Avg Recall: 0.423423, count: 111 -Region Avg IOU: 0.402167, Class: 1.000000, Obj: 0.228945, No Obj: 0.004165, Avg Recall: 0.404959, count: 121 -Region Avg IOU: 0.415692, Class: 1.000000, Obj: 0.265695, No Obj: 0.004178, Avg Recall: 0.425743, count: 101 -Region Avg IOU: 0.475811, Class: 1.000000, Obj: 0.275409, No Obj: 0.004751, Avg Recall: 0.516949, count: 118 -Region Avg IOU: 0.477283, Class: 1.000000, Obj: 0.287324, No Obj: 0.004465, Avg Recall: 0.508475, count: 118 -Region Avg IOU: 0.508219, Class: 1.000000, Obj: 0.322652, No Obj: 0.003698, Avg Recall: 0.535714, count: 84 -Region Avg IOU: 0.458081, Class: 1.000000, Obj: 0.272701, No Obj: 0.003927, Avg Recall: 0.457447, count: 94 -1552: 0.488761, 0.487474 avg, 0.001000 rate, 17.474562 seconds, 397312 images -Loaded: 4.087337 seconds -Region Avg IOU: 0.495840, Class: 1.000000, Obj: 0.296102, No Obj: 0.004799, Avg Recall: 0.564885, count: 131 -Region Avg IOU: 0.436380, Class: 1.000000, Obj: 0.299089, No Obj: 0.004271, Avg Recall: 0.526316, count: 95 -Region Avg IOU: 0.482338, Class: 1.000000, Obj: 0.267954, No Obj: 0.004292, Avg Recall: 0.504587, count: 109 -Region Avg IOU: 0.468538, Class: 1.000000, Obj: 0.265077, No Obj: 0.004702, Avg Recall: 0.488372, count: 129 -Region Avg IOU: 0.447906, Class: 1.000000, Obj: 0.250866, No Obj: 0.004370, Avg Recall: 0.484211, count: 95 -Region Avg IOU: 0.373141, Class: 1.000000, Obj: 0.287261, No Obj: 0.003778, Avg Recall: 0.316832, count: 101 -Region Avg IOU: 0.485478, Class: 1.000000, Obj: 0.333230, No Obj: 0.005316, Avg Recall: 0.496063, count: 127 -Region Avg IOU: 0.468574, Class: 1.000000, Obj: 0.303490, No Obj: 0.004555, Avg Recall: 0.522523, count: 111 -1553: 0.503442, 0.489071 avg, 0.001000 rate, 19.899134 seconds, 397568 images -Loaded: 1.554800 seconds -Region Avg IOU: 0.453801, Class: 1.000000, Obj: 0.289711, No Obj: 0.004807, Avg Recall: 0.500000, count: 132 -Region Avg IOU: 0.490375, Class: 1.000000, Obj: 0.290947, No Obj: 0.004587, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.474444, Class: 1.000000, Obj: 0.283878, No Obj: 0.004124, Avg Recall: 0.487805, count: 82 -Region Avg IOU: 0.486590, Class: 1.000000, Obj: 0.298173, No Obj: 0.005075, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.520561, Class: 1.000000, Obj: 0.324734, No Obj: 0.004488, Avg Recall: 0.571429, count: 84 -Region Avg IOU: 0.459158, Class: 1.000000, Obj: 0.314897, No Obj: 0.004485, Avg Recall: 0.508929, count: 112 -Region Avg IOU: 0.423620, Class: 1.000000, Obj: 0.294900, No Obj: 0.004584, Avg Recall: 0.453782, count: 119 -Region Avg IOU: 0.470754, Class: 1.000000, Obj: 0.322284, No Obj: 0.004638, Avg Recall: 0.528846, count: 104 -1554: 0.454491, 0.485613 avg, 0.001000 rate, 19.308475 seconds, 397824 images -Loaded: 0.783675 seconds -Region Avg IOU: 0.441189, Class: 1.000000, Obj: 0.269273, No Obj: 0.005552, Avg Recall: 0.443609, count: 133 -Region Avg IOU: 0.510641, Class: 1.000000, Obj: 0.281083, No Obj: 0.005086, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.443503, Class: 1.000000, Obj: 0.304061, No Obj: 0.004614, Avg Recall: 0.466667, count: 105 -Region Avg IOU: 0.483331, Class: 1.000000, Obj: 0.278981, No Obj: 0.003815, Avg Recall: 0.549296, count: 71 -Region Avg IOU: 0.460584, Class: 1.000000, Obj: 0.336009, No Obj: 0.004993, Avg Recall: 0.500000, count: 120 -Region Avg IOU: 0.445569, Class: 1.000000, Obj: 0.328567, No Obj: 0.004698, Avg Recall: 0.484536, count: 97 -Region Avg IOU: 0.428658, Class: 1.000000, Obj: 0.288523, No Obj: 0.005433, Avg Recall: 0.448276, count: 145 -Region Avg IOU: 0.438005, Class: 1.000000, Obj: 0.311262, No Obj: 0.003946, Avg Recall: 0.460674, count: 89 -1555: 0.524622, 0.489514 avg, 0.001000 rate, 19.512527 seconds, 398080 images -Loaded: 1.522429 seconds -Region Avg IOU: 0.430690, Class: 1.000000, Obj: 0.230797, No Obj: 0.004665, Avg Recall: 0.460317, count: 126 -Region Avg IOU: 0.433921, Class: 1.000000, Obj: 0.268840, No Obj: 0.004694, Avg Recall: 0.469027, count: 113 -Region Avg IOU: 0.416290, Class: 1.000000, Obj: 0.290274, No Obj: 0.003937, Avg Recall: 0.425532, count: 94 -Region Avg IOU: 0.410278, Class: 1.000000, Obj: 0.279217, No Obj: 0.003814, Avg Recall: 0.438202, count: 89 -Region Avg IOU: 0.484238, Class: 1.000000, Obj: 0.333148, No Obj: 0.004590, Avg Recall: 0.505155, count: 97 -Region Avg IOU: 0.461839, Class: 1.000000, Obj: 0.303737, No Obj: 0.004775, Avg Recall: 0.514019, count: 107 -Region Avg IOU: 0.418859, Class: 1.000000, Obj: 0.288004, No Obj: 0.004404, Avg Recall: 0.438596, count: 114 -Region Avg IOU: 0.436778, Class: 1.000000, Obj: 0.265087, No Obj: 0.004999, Avg Recall: 0.467742, count: 124 -1556: 0.495001, 0.490063 avg, 0.001000 rate, 20.942348 seconds, 398336 images -Loaded: 0.893037 seconds -Region Avg IOU: 0.466048, Class: 1.000000, Obj: 0.274382, No Obj: 0.005401, Avg Recall: 0.504274, count: 117 -Region Avg IOU: 0.437092, Class: 1.000000, Obj: 0.282009, No Obj: 0.004547, Avg Recall: 0.475000, count: 120 -Region Avg IOU: 0.496119, Class: 1.000000, Obj: 0.293369, No Obj: 0.004821, Avg Recall: 0.557252, count: 131 -Region Avg IOU: 0.431599, Class: 1.000000, Obj: 0.273777, No Obj: 0.004351, Avg Recall: 0.474747, count: 99 -Region Avg IOU: 0.426640, Class: 1.000000, Obj: 0.288512, No Obj: 0.004649, Avg Recall: 0.450980, count: 102 -Region Avg IOU: 0.455960, Class: 1.000000, Obj: 0.301634, No Obj: 0.004760, Avg Recall: 0.496124, count: 129 -Region Avg IOU: 0.480968, Class: 1.000000, Obj: 0.311056, No Obj: 0.004583, Avg Recall: 0.555556, count: 90 -Region Avg IOU: 0.419675, Class: 1.000000, Obj: 0.284340, No Obj: 0.004561, Avg Recall: 0.430108, count: 93 -1557: 0.471917, 0.488248 avg, 0.001000 rate, 17.269491 seconds, 398592 images -Loaded: 4.070803 seconds -Region Avg IOU: 0.436351, Class: 1.000000, Obj: 0.289519, No Obj: 0.004310, Avg Recall: 0.500000, count: 88 -Region Avg IOU: 0.454883, Class: 1.000000, Obj: 0.279314, No Obj: 0.003845, Avg Recall: 0.552941, count: 85 -Region Avg IOU: 0.451564, Class: 1.000000, Obj: 0.301104, No Obj: 0.004533, Avg Recall: 0.500000, count: 98 -Region Avg IOU: 0.434442, Class: 1.000000, Obj: 0.254261, No Obj: 0.004540, Avg Recall: 0.477477, count: 111 -Region Avg IOU: 0.498010, Class: 1.000000, Obj: 0.290539, No Obj: 0.003971, Avg Recall: 0.543210, count: 81 -Region Avg IOU: 0.443574, Class: 1.000000, Obj: 0.267733, No Obj: 0.004626, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.439113, Class: 1.000000, Obj: 0.283771, No Obj: 0.004059, Avg Recall: 0.409639, count: 83 -Region Avg IOU: 0.438958, Class: 1.000000, Obj: 0.278340, No Obj: 0.004290, Avg Recall: 0.483871, count: 93 -1558: 0.414631, 0.480886 avg, 0.001000 rate, 18.168633 seconds, 398848 images -Loaded: 1.938863 seconds -Region Avg IOU: 0.462786, Class: 1.000000, Obj: 0.298356, No Obj: 0.004142, Avg Recall: 0.461538, count: 104 -Region Avg IOU: 0.470018, Class: 1.000000, Obj: 0.304145, No Obj: 0.003815, Avg Recall: 0.511628, count: 86 -Region Avg IOU: 0.462090, Class: 1.000000, Obj: 0.271458, No Obj: 0.005218, Avg Recall: 0.478873, count: 142 -Region Avg IOU: 0.468597, Class: 1.000000, Obj: 0.296321, No Obj: 0.004712, Avg Recall: 0.481481, count: 108 -Region Avg IOU: 0.454437, Class: 1.000000, Obj: 0.309507, No Obj: 0.004448, Avg Recall: 0.489796, count: 98 -Region Avg IOU: 0.540392, Class: 1.000000, Obj: 0.296181, No Obj: 0.004516, Avg Recall: 0.595506, count: 89 -Region Avg IOU: 0.443581, Class: 1.000000, Obj: 0.256047, No Obj: 0.004195, Avg Recall: 0.505155, count: 97 -Region Avg IOU: 0.460202, Class: 1.000000, Obj: 0.254024, No Obj: 0.004915, Avg Recall: 0.490196, count: 102 -1559: 0.437142, 0.476512 avg, 0.001000 rate, 16.216406 seconds, 399104 images -Loaded: 3.356114 seconds -Region Avg IOU: 0.459561, Class: 1.000000, Obj: 0.286464, No Obj: 0.004024, Avg Recall: 0.505050, count: 99 -Region Avg IOU: 0.469523, Class: 1.000000, Obj: 0.235864, No Obj: 0.004480, Avg Recall: 0.490000, count: 100 -Region Avg IOU: 0.460821, Class: 1.000000, Obj: 0.273291, No Obj: 0.003908, Avg Recall: 0.500000, count: 110 -Region Avg IOU: 0.458069, Class: 1.000000, Obj: 0.290765, No Obj: 0.004225, Avg Recall: 0.516949, count: 118 -Region Avg IOU: 0.479189, Class: 1.000000, Obj: 0.250073, No Obj: 0.003568, Avg Recall: 0.550725, count: 69 -Region Avg IOU: 0.454403, Class: 1.000000, Obj: 0.263504, No Obj: 0.003853, Avg Recall: 0.411111, count: 90 -Region Avg IOU: 0.427100, Class: 1.000000, Obj: 0.247238, No Obj: 0.003696, Avg Recall: 0.423529, count: 85 -Region Avg IOU: 0.442441, Class: 1.000000, Obj: 0.258922, No Obj: 0.004287, Avg Recall: 0.482759, count: 116 -1560: 0.452956, 0.474156 avg, 0.001000 rate, 20.159819 seconds, 399360 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000080 seconds -Region Avg IOU: 0.598259, Class: 1.000000, Obj: 0.305147, No Obj: 0.003524, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.577221, Class: 1.000000, Obj: 0.353002, No Obj: 0.003136, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.527930, Class: 1.000000, Obj: 0.245362, No Obj: 0.003140, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.549450, Class: 1.000000, Obj: 0.299323, No Obj: 0.003058, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.581830, Class: 1.000000, Obj: 0.374804, No Obj: 0.003099, Avg Recall: 0.728395, count: 81 -Region Avg IOU: 0.518678, Class: 1.000000, Obj: 0.285115, No Obj: 0.003226, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.561115, Class: 1.000000, Obj: 0.365376, No Obj: 0.003054, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.568095, Class: 1.000000, Obj: 0.303836, No Obj: 0.003362, Avg Recall: 0.704762, count: 105 -1561: 0.475802, 0.474321 avg, 0.001000 rate, 26.642605 seconds, 399616 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.545987, Class: 1.000000, Obj: 0.275112, No Obj: 0.003138, Avg Recall: 0.623077, count: 130 -Region Avg IOU: 0.553768, Class: 1.000000, Obj: 0.314974, No Obj: 0.002949, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.548444, Class: 1.000000, Obj: 0.297281, No Obj: 0.003251, Avg Recall: 0.630252, count: 119 -Region Avg IOU: 0.593813, Class: 1.000000, Obj: 0.300444, No Obj: 0.002827, Avg Recall: 0.740741, count: 81 -Region Avg IOU: 0.539565, Class: 1.000000, Obj: 0.294332, No Obj: 0.003239, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.567047, Class: 1.000000, Obj: 0.301659, No Obj: 0.003410, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.582919, Class: 1.000000, Obj: 0.297180, No Obj: 0.003630, Avg Recall: 0.688000, count: 125 -Region Avg IOU: 0.575758, Class: 1.000000, Obj: 0.304315, No Obj: 0.003412, Avg Recall: 0.651515, count: 132 -1562: 0.513602, 0.478249 avg, 0.001000 rate, 25.881140 seconds, 399872 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.614093, Class: 1.000000, Obj: 0.287489, No Obj: 0.002881, Avg Recall: 0.770270, count: 74 -Region Avg IOU: 0.570406, Class: 1.000000, Obj: 0.276995, No Obj: 0.003048, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.507138, Class: 1.000000, Obj: 0.261271, No Obj: 0.002934, Avg Recall: 0.577586, count: 116 -Region Avg IOU: 0.542204, Class: 1.000000, Obj: 0.266188, No Obj: 0.002814, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.545477, Class: 1.000000, Obj: 0.270343, No Obj: 0.003087, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.590304, Class: 1.000000, Obj: 0.299946, No Obj: 0.002992, Avg Recall: 0.715447, count: 123 -Region Avg IOU: 0.552180, Class: 1.000000, Obj: 0.314874, No Obj: 0.003153, Avg Recall: 0.720000, count: 125 -Region Avg IOU: 0.571919, Class: 1.000000, Obj: 0.250377, No Obj: 0.003195, Avg Recall: 0.704545, count: 132 -1563: 0.518813, 0.482305 avg, 0.001000 rate, 26.768038 seconds, 400128 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.589431, Class: 1.000000, Obj: 0.265512, No Obj: 0.002731, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.590890, Class: 1.000000, Obj: 0.263447, No Obj: 0.003264, Avg Recall: 0.715385, count: 130 -Region Avg IOU: 0.534296, Class: 1.000000, Obj: 0.234836, No Obj: 0.002827, Avg Recall: 0.634146, count: 123 -Region Avg IOU: 0.572358, Class: 1.000000, Obj: 0.273870, No Obj: 0.002698, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.542404, Class: 1.000000, Obj: 0.272791, No Obj: 0.002683, Avg Recall: 0.629921, count: 127 -Region Avg IOU: 0.582938, Class: 1.000000, Obj: 0.270667, No Obj: 0.002954, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.510969, Class: 1.000000, Obj: 0.233564, No Obj: 0.002643, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.585997, Class: 1.000000, Obj: 0.272799, No Obj: 0.003126, Avg Recall: 0.705357, count: 112 -1564: 0.495058, 0.483581 avg, 0.001000 rate, 27.941124 seconds, 400384 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.582104, Class: 1.000000, Obj: 0.231077, No Obj: 0.002710, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.583247, Class: 1.000000, Obj: 0.253367, No Obj: 0.002826, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.577385, Class: 1.000000, Obj: 0.278646, No Obj: 0.002804, Avg Recall: 0.706349, count: 126 -Region Avg IOU: 0.566020, Class: 1.000000, Obj: 0.266362, No Obj: 0.002563, Avg Recall: 0.608247, count: 97 -Region Avg IOU: 0.567459, Class: 1.000000, Obj: 0.253844, No Obj: 0.002957, Avg Recall: 0.670886, count: 158 -Region Avg IOU: 0.609208, Class: 1.000000, Obj: 0.271161, No Obj: 0.002939, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.554023, Class: 1.000000, Obj: 0.274538, No Obj: 0.002619, Avg Recall: 0.650602, count: 83 -Region Avg IOU: 0.601173, Class: 1.000000, Obj: 0.277482, No Obj: 0.002928, Avg Recall: 0.723810, count: 105 -1565: 0.532390, 0.488462 avg, 0.001000 rate, 25.111034 seconds, 400640 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.540251, Class: 1.000000, Obj: 0.254594, No Obj: 0.002727, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.549620, Class: 1.000000, Obj: 0.270135, No Obj: 0.002629, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.590183, Class: 1.000000, Obj: 0.293540, No Obj: 0.002956, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.562118, Class: 1.000000, Obj: 0.278026, No Obj: 0.003073, Avg Recall: 0.687023, count: 131 -Region Avg IOU: 0.553099, Class: 1.000000, Obj: 0.275494, No Obj: 0.002609, Avg Recall: 0.674699, count: 83 -Region Avg IOU: 0.564842, Class: 1.000000, Obj: 0.245891, No Obj: 0.002628, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.554941, Class: 1.000000, Obj: 0.254778, No Obj: 0.002535, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.579098, Class: 1.000000, Obj: 0.276008, No Obj: 0.002765, Avg Recall: 0.714286, count: 91 -1566: 0.513342, 0.490950 avg, 0.001000 rate, 27.856768 seconds, 400896 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.554186, Class: 1.000000, Obj: 0.289325, No Obj: 0.003093, Avg Recall: 0.590164, count: 122 -Region Avg IOU: 0.580657, Class: 1.000000, Obj: 0.298755, No Obj: 0.003293, Avg Recall: 0.700787, count: 127 -Region Avg IOU: 0.568793, Class: 1.000000, Obj: 0.306076, No Obj: 0.003211, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.590558, Class: 1.000000, Obj: 0.283959, No Obj: 0.003383, Avg Recall: 0.728000, count: 125 -Region Avg IOU: 0.582936, Class: 1.000000, Obj: 0.298329, No Obj: 0.003348, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.562800, Class: 1.000000, Obj: 0.280360, No Obj: 0.003217, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.543383, Class: 1.000000, Obj: 0.251849, No Obj: 0.003211, Avg Recall: 0.601504, count: 133 -Region Avg IOU: 0.611236, Class: 1.000000, Obj: 0.222107, No Obj: 0.002843, Avg Recall: 0.788889, count: 90 -1567: 0.561581, 0.498013 avg, 0.001000 rate, 25.098396 seconds, 401152 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.615631, Class: 1.000000, Obj: 0.308253, No Obj: 0.003147, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.578123, Class: 1.000000, Obj: 0.309629, No Obj: 0.002930, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.573382, Class: 1.000000, Obj: 0.304929, No Obj: 0.002930, Avg Recall: 0.704545, count: 88 -Region Avg IOU: 0.551892, Class: 1.000000, Obj: 0.260549, No Obj: 0.002943, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.577459, Class: 1.000000, Obj: 0.282379, No Obj: 0.003069, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.631889, Class: 1.000000, Obj: 0.395568, No Obj: 0.003084, Avg Recall: 0.764706, count: 85 -Region Avg IOU: 0.576767, Class: 1.000000, Obj: 0.293960, No Obj: 0.002896, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.552743, Class: 1.000000, Obj: 0.283588, No Obj: 0.002908, Avg Recall: 0.660550, count: 109 -1568: 0.456660, 0.493877 avg, 0.001000 rate, 27.101372 seconds, 401408 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.619554, Class: 1.000000, Obj: 0.328644, No Obj: 0.003244, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.555909, Class: 1.000000, Obj: 0.322182, No Obj: 0.003229, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.550139, Class: 1.000000, Obj: 0.311658, No Obj: 0.003432, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.561702, Class: 1.000000, Obj: 0.313997, No Obj: 0.003395, Avg Recall: 0.632812, count: 128 -Region Avg IOU: 0.566505, Class: 1.000000, Obj: 0.288452, No Obj: 0.003048, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.588120, Class: 1.000000, Obj: 0.328223, No Obj: 0.003358, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.584811, Class: 1.000000, Obj: 0.318505, No Obj: 0.003685, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.609783, Class: 1.000000, Obj: 0.349319, No Obj: 0.003040, Avg Recall: 0.712644, count: 87 -1569: 0.437586, 0.488248 avg, 0.001000 rate, 26.405205 seconds, 401664 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.600013, Class: 1.000000, Obj: 0.304055, No Obj: 0.003892, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.598350, Class: 1.000000, Obj: 0.348343, No Obj: 0.003136, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.601438, Class: 1.000000, Obj: 0.334090, No Obj: 0.003372, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.584769, Class: 1.000000, Obj: 0.329977, No Obj: 0.003268, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.589389, Class: 1.000000, Obj: 0.309483, No Obj: 0.003208, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.572318, Class: 1.000000, Obj: 0.351732, No Obj: 0.003327, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.546037, Class: 1.000000, Obj: 0.307389, No Obj: 0.003650, Avg Recall: 0.620155, count: 129 -Region Avg IOU: 0.582332, Class: 1.000000, Obj: 0.314397, No Obj: 0.003530, Avg Recall: 0.709677, count: 124 -1570: 0.454220, 0.484846 avg, 0.001000 rate, 25.819019 seconds, 401920 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000085 seconds -Region Avg IOU: 0.583656, Class: 1.000000, Obj: 0.329744, No Obj: 0.003082, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.590531, Class: 1.000000, Obj: 0.290731, No Obj: 0.003222, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.595275, Class: 1.000000, Obj: 0.327777, No Obj: 0.003256, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.557406, Class: 1.000000, Obj: 0.259041, No Obj: 0.002990, Avg Recall: 0.664000, count: 125 -Region Avg IOU: 0.573199, Class: 1.000000, Obj: 0.308525, No Obj: 0.003119, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.592700, Class: 1.000000, Obj: 0.319917, No Obj: 0.003449, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.587688, Class: 1.000000, Obj: 0.328405, No Obj: 0.003086, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.563143, Class: 1.000000, Obj: 0.358337, No Obj: 0.003539, Avg Recall: 0.653846, count: 104 -1571: 0.447821, 0.481143 avg, 0.001000 rate, 28.363379 seconds, 402176 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.594805, Class: 1.000000, Obj: 0.304345, No Obj: 0.003481, Avg Recall: 0.740458, count: 131 -Region Avg IOU: 0.545828, Class: 1.000000, Obj: 0.303388, No Obj: 0.003083, Avg Recall: 0.593985, count: 133 -Region Avg IOU: 0.581903, Class: 1.000000, Obj: 0.319887, No Obj: 0.003152, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.568386, Class: 1.000000, Obj: 0.308489, No Obj: 0.003209, Avg Recall: 0.697674, count: 129 -Region Avg IOU: 0.566804, Class: 1.000000, Obj: 0.321399, No Obj: 0.003220, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.569620, Class: 1.000000, Obj: 0.341998, No Obj: 0.003118, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.584334, Class: 1.000000, Obj: 0.308613, No Obj: 0.003103, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.608989, Class: 1.000000, Obj: 0.355447, No Obj: 0.003248, Avg Recall: 0.754386, count: 114 -1572: 0.489447, 0.481973 avg, 0.001000 rate, 27.890318 seconds, 402432 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.577773, Class: 1.000000, Obj: 0.303040, No Obj: 0.003036, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.564974, Class: 1.000000, Obj: 0.276899, No Obj: 0.002765, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.561437, Class: 1.000000, Obj: 0.298403, No Obj: 0.003076, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.607360, Class: 1.000000, Obj: 0.285333, No Obj: 0.003236, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.581336, Class: 1.000000, Obj: 0.294013, No Obj: 0.003236, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.571385, Class: 1.000000, Obj: 0.283356, No Obj: 0.002789, Avg Recall: 0.626374, count: 91 -Region Avg IOU: 0.557789, Class: 1.000000, Obj: 0.279689, No Obj: 0.003000, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.585332, Class: 1.000000, Obj: 0.341374, No Obj: 0.002709, Avg Recall: 0.710526, count: 76 -1573: 0.424522, 0.476228 avg, 0.001000 rate, 26.593157 seconds, 402688 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.609505, Class: 1.000000, Obj: 0.276444, No Obj: 0.003264, Avg Recall: 0.766667, count: 120 -Region Avg IOU: 0.577341, Class: 1.000000, Obj: 0.250910, No Obj: 0.002690, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.544964, Class: 1.000000, Obj: 0.250701, No Obj: 0.002681, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.586202, Class: 1.000000, Obj: 0.287972, No Obj: 0.003037, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.602481, Class: 1.000000, Obj: 0.277804, No Obj: 0.003027, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.629719, Class: 1.000000, Obj: 0.332923, No Obj: 0.003070, Avg Recall: 0.818182, count: 77 -Region Avg IOU: 0.578447, Class: 1.000000, Obj: 0.244207, No Obj: 0.003177, Avg Recall: 0.698413, count: 126 -Region Avg IOU: 0.587620, Class: 1.000000, Obj: 0.284144, No Obj: 0.003022, Avg Recall: 0.680851, count: 94 -1574: 0.487683, 0.477374 avg, 0.001000 rate, 29.317396 seconds, 402944 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.585222, Class: 1.000000, Obj: 0.261224, No Obj: 0.002975, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.583232, Class: 1.000000, Obj: 0.227265, No Obj: 0.003158, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.583604, Class: 1.000000, Obj: 0.294864, No Obj: 0.002932, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.576894, Class: 1.000000, Obj: 0.228719, No Obj: 0.003488, Avg Recall: 0.706767, count: 133 -Region Avg IOU: 0.571000, Class: 1.000000, Obj: 0.287453, No Obj: 0.002775, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.567633, Class: 1.000000, Obj: 0.237882, No Obj: 0.002555, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.617783, Class: 1.000000, Obj: 0.322020, No Obj: 0.002999, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.551897, Class: 1.000000, Obj: 0.285491, No Obj: 0.002693, Avg Recall: 0.653846, count: 78 -1575: 0.502614, 0.479898 avg, 0.001000 rate, 26.704573 seconds, 403200 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.588626, Class: 1.000000, Obj: 0.285514, No Obj: 0.003009, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.574859, Class: 1.000000, Obj: 0.293455, No Obj: 0.002778, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.598863, Class: 1.000000, Obj: 0.282286, No Obj: 0.002509, Avg Recall: 0.730769, count: 78 -Region Avg IOU: 0.599490, Class: 1.000000, Obj: 0.293709, No Obj: 0.002748, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.564519, Class: 1.000000, Obj: 0.241019, No Obj: 0.002788, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.601072, Class: 1.000000, Obj: 0.259831, No Obj: 0.003029, Avg Recall: 0.728682, count: 129 -Region Avg IOU: 0.556263, Class: 1.000000, Obj: 0.245929, No Obj: 0.003055, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.586959, Class: 1.000000, Obj: 0.311469, No Obj: 0.002981, Avg Recall: 0.703704, count: 108 -1576: 0.528121, 0.484720 avg, 0.001000 rate, 27.655424 seconds, 403456 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.590225, Class: 1.000000, Obj: 0.244404, No Obj: 0.002623, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.599330, Class: 1.000000, Obj: 0.256221, No Obj: 0.003030, Avg Recall: 0.697674, count: 129 -Region Avg IOU: 0.555421, Class: 1.000000, Obj: 0.215957, No Obj: 0.002915, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.591959, Class: 1.000000, Obj: 0.301315, No Obj: 0.002621, Avg Recall: 0.734940, count: 83 -Region Avg IOU: 0.567692, Class: 1.000000, Obj: 0.292431, No Obj: 0.002730, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.606602, Class: 1.000000, Obj: 0.309273, No Obj: 0.002599, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.586452, Class: 1.000000, Obj: 0.303347, No Obj: 0.002975, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.578246, Class: 1.000000, Obj: 0.292977, No Obj: 0.003328, Avg Recall: 0.748252, count: 143 -1577: 0.486059, 0.484854 avg, 0.001000 rate, 27.392351 seconds, 403712 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.622824, Class: 1.000000, Obj: 0.282774, No Obj: 0.002914, Avg Recall: 0.792683, count: 82 -Region Avg IOU: 0.614357, Class: 1.000000, Obj: 0.296788, No Obj: 0.002709, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.580820, Class: 1.000000, Obj: 0.312961, No Obj: 0.002598, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.592534, Class: 1.000000, Obj: 0.305044, No Obj: 0.002862, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.588636, Class: 1.000000, Obj: 0.283727, No Obj: 0.002776, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.594631, Class: 1.000000, Obj: 0.263297, No Obj: 0.002888, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.581552, Class: 1.000000, Obj: 0.241428, No Obj: 0.003087, Avg Recall: 0.711111, count: 135 -Region Avg IOU: 0.565708, Class: 1.000000, Obj: 0.284539, No Obj: 0.002762, Avg Recall: 0.655462, count: 119 -1578: 0.516844, 0.488053 avg, 0.001000 rate, 27.383408 seconds, 403968 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.580069, Class: 1.000000, Obj: 0.286469, No Obj: 0.002976, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.552744, Class: 1.000000, Obj: 0.266312, No Obj: 0.002764, Avg Recall: 0.643678, count: 87 -Region Avg IOU: 0.562308, Class: 1.000000, Obj: 0.259897, No Obj: 0.003007, Avg Recall: 0.690476, count: 126 -Region Avg IOU: 0.603392, Class: 1.000000, Obj: 0.283660, No Obj: 0.003018, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.541438, Class: 1.000000, Obj: 0.322666, No Obj: 0.002379, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.662628, Class: 1.000000, Obj: 0.292564, No Obj: 0.003086, Avg Recall: 0.808696, count: 115 -Region Avg IOU: 0.588453, Class: 1.000000, Obj: 0.330477, No Obj: 0.003070, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.619020, Class: 1.000000, Obj: 0.280386, No Obj: 0.002827, Avg Recall: 0.752688, count: 93 -1579: 0.452238, 0.484472 avg, 0.001000 rate, 28.845076 seconds, 404224 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.572010, Class: 1.000000, Obj: 0.320962, No Obj: 0.002902, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.584252, Class: 1.000000, Obj: 0.282287, No Obj: 0.003258, Avg Recall: 0.702479, count: 121 -Region Avg IOU: 0.578924, Class: 1.000000, Obj: 0.238645, No Obj: 0.002823, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.581416, Class: 1.000000, Obj: 0.306289, No Obj: 0.002862, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.566433, Class: 1.000000, Obj: 0.296219, No Obj: 0.003129, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.568730, Class: 1.000000, Obj: 0.282411, No Obj: 0.003192, Avg Recall: 0.691667, count: 120 -Region Avg IOU: 0.597845, Class: 1.000000, Obj: 0.305729, No Obj: 0.003005, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.585527, Class: 1.000000, Obj: 0.278037, No Obj: 0.003117, Avg Recall: 0.660550, count: 109 -1580: 0.489105, 0.484935 avg, 0.001000 rate, 28.993013 seconds, 404480 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 14.842848 seconds -Region Avg IOU: 0.531057, Class: 1.000000, Obj: 0.293024, No Obj: 0.004116, Avg Recall: 0.617021, count: 141 -Region Avg IOU: 0.528713, Class: 1.000000, Obj: 0.311747, No Obj: 0.003639, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.584136, Class: 1.000000, Obj: 0.300333, No Obj: 0.003266, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.490578, Class: 1.000000, Obj: 0.297766, No Obj: 0.003076, Avg Recall: 0.562500, count: 64 -Region Avg IOU: 0.541856, Class: 1.000000, Obj: 0.280411, No Obj: 0.003558, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.499439, Class: 1.000000, Obj: 0.274004, No Obj: 0.003519, Avg Recall: 0.568965, count: 116 -Region Avg IOU: 0.541776, Class: 1.000000, Obj: 0.300341, No Obj: 0.003330, Avg Recall: 0.620690, count: 87 -Region Avg IOU: 0.501745, Class: 1.000000, Obj: 0.279935, No Obj: 0.003242, Avg Recall: 0.537736, count: 106 -1581: 0.413063, 0.477748 avg, 0.001000 rate, 24.636566 seconds, 404736 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.514426, Class: 1.000000, Obj: 0.287143, No Obj: 0.003352, Avg Recall: 0.573171, count: 82 -Region Avg IOU: 0.488177, Class: 1.000000, Obj: 0.291848, No Obj: 0.004022, Avg Recall: 0.522059, count: 136 -Region Avg IOU: 0.545796, Class: 1.000000, Obj: 0.347918, No Obj: 0.003802, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.467846, Class: 1.000000, Obj: 0.296900, No Obj: 0.003716, Avg Recall: 0.472727, count: 110 -Region Avg IOU: 0.484301, Class: 1.000000, Obj: 0.286565, No Obj: 0.003909, Avg Recall: 0.500000, count: 108 -Region Avg IOU: 0.527965, Class: 1.000000, Obj: 0.315847, No Obj: 0.003425, Avg Recall: 0.607595, count: 79 -Region Avg IOU: 0.532921, Class: 1.000000, Obj: 0.281849, No Obj: 0.004237, Avg Recall: 0.581560, count: 141 -Region Avg IOU: 0.497396, Class: 1.000000, Obj: 0.259479, No Obj: 0.003442, Avg Recall: 0.510204, count: 98 -1582: 0.520376, 0.482011 avg, 0.001000 rate, 22.924671 seconds, 404992 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.508514, Class: 1.000000, Obj: 0.272461, No Obj: 0.003703, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.516889, Class: 1.000000, Obj: 0.322387, No Obj: 0.004414, Avg Recall: 0.612403, count: 129 -Region Avg IOU: 0.549482, Class: 1.000000, Obj: 0.251502, No Obj: 0.003754, Avg Recall: 0.655462, count: 119 -Region Avg IOU: 0.559592, Class: 1.000000, Obj: 0.362836, No Obj: 0.004127, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.465247, Class: 1.000000, Obj: 0.285248, No Obj: 0.004384, Avg Recall: 0.487342, count: 158 -Region Avg IOU: 0.471880, Class: 1.000000, Obj: 0.302231, No Obj: 0.003719, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.489374, Class: 1.000000, Obj: 0.315102, No Obj: 0.003696, Avg Recall: 0.555556, count: 90 -Region Avg IOU: 0.502835, Class: 1.000000, Obj: 0.261393, No Obj: 0.004244, Avg Recall: 0.515385, count: 130 -1583: 0.505437, 0.484353 avg, 0.001000 rate, 24.184713 seconds, 405248 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.512995, Class: 1.000000, Obj: 0.277461, No Obj: 0.003794, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.528484, Class: 1.000000, Obj: 0.347066, No Obj: 0.004137, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.492354, Class: 1.000000, Obj: 0.270980, No Obj: 0.003860, Avg Recall: 0.513761, count: 109 -Region Avg IOU: 0.518681, Class: 1.000000, Obj: 0.306027, No Obj: 0.004119, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.464773, Class: 1.000000, Obj: 0.252061, No Obj: 0.003670, Avg Recall: 0.480620, count: 129 -Region Avg IOU: 0.496715, Class: 1.000000, Obj: 0.269183, No Obj: 0.004108, Avg Recall: 0.561404, count: 114 -Region Avg IOU: 0.472161, Class: 1.000000, Obj: 0.300912, No Obj: 0.003933, Avg Recall: 0.482456, count: 114 -Region Avg IOU: 0.481959, Class: 1.000000, Obj: 0.293823, No Obj: 0.004384, Avg Recall: 0.513043, count: 115 -1584: 0.477734, 0.483691 avg, 0.001000 rate, 22.326565 seconds, 405504 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.521712, Class: 1.000000, Obj: 0.345520, No Obj: 0.003590, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.503750, Class: 1.000000, Obj: 0.311463, No Obj: 0.003924, Avg Recall: 0.495238, count: 105 -Region Avg IOU: 0.512353, Class: 1.000000, Obj: 0.319317, No Obj: 0.003995, Avg Recall: 0.610169, count: 118 -Region Avg IOU: 0.510638, Class: 1.000000, Obj: 0.321902, No Obj: 0.003881, Avg Recall: 0.533333, count: 120 -Region Avg IOU: 0.508356, Class: 1.000000, Obj: 0.347291, No Obj: 0.003964, Avg Recall: 0.557522, count: 113 -Region Avg IOU: 0.532181, Class: 1.000000, Obj: 0.339747, No Obj: 0.003896, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.479140, Class: 1.000000, Obj: 0.294765, No Obj: 0.003964, Avg Recall: 0.553191, count: 94 -Region Avg IOU: 0.549858, Class: 1.000000, Obj: 0.353063, No Obj: 0.003534, Avg Recall: 0.634146, count: 82 -1585: 0.438398, 0.479162 avg, 0.001000 rate, 23.617512 seconds, 405760 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.500129, Class: 1.000000, Obj: 0.308988, No Obj: 0.003768, Avg Recall: 0.547009, count: 117 -Region Avg IOU: 0.513362, Class: 1.000000, Obj: 0.314221, No Obj: 0.003703, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.495100, Class: 1.000000, Obj: 0.286233, No Obj: 0.003723, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.514631, Class: 1.000000, Obj: 0.324081, No Obj: 0.003630, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.572507, Class: 1.000000, Obj: 0.343209, No Obj: 0.004106, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.511508, Class: 1.000000, Obj: 0.273888, No Obj: 0.003730, Avg Recall: 0.609756, count: 123 -Region Avg IOU: 0.546965, Class: 1.000000, Obj: 0.294446, No Obj: 0.004182, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.471975, Class: 1.000000, Obj: 0.291670, No Obj: 0.003589, Avg Recall: 0.529412, count: 119 -1586: 0.469047, 0.478150 avg, 0.001000 rate, 24.057362 seconds, 406016 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.510160, Class: 1.000000, Obj: 0.317342, No Obj: 0.003074, Avg Recall: 0.597403, count: 77 -Region Avg IOU: 0.599216, Class: 1.000000, Obj: 0.327082, No Obj: 0.003425, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.522288, Class: 1.000000, Obj: 0.283541, No Obj: 0.004390, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.515420, Class: 1.000000, Obj: 0.357242, No Obj: 0.003609, Avg Recall: 0.576087, count: 92 -Region Avg IOU: 0.524330, Class: 1.000000, Obj: 0.289539, No Obj: 0.003833, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.512151, Class: 1.000000, Obj: 0.306500, No Obj: 0.003893, Avg Recall: 0.584071, count: 113 -Region Avg IOU: 0.515340, Class: 1.000000, Obj: 0.338038, No Obj: 0.003978, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.540319, Class: 1.000000, Obj: 0.309155, No Obj: 0.003755, Avg Recall: 0.598214, count: 112 -1587: 0.451201, 0.475455 avg, 0.001000 rate, 23.227404 seconds, 406272 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.523183, Class: 1.000000, Obj: 0.318824, No Obj: 0.003538, Avg Recall: 0.568965, count: 116 -Region Avg IOU: 0.537693, Class: 1.000000, Obj: 0.307511, No Obj: 0.003958, Avg Recall: 0.593985, count: 133 -Region Avg IOU: 0.486288, Class: 1.000000, Obj: 0.287244, No Obj: 0.002893, Avg Recall: 0.500000, count: 86 -Region Avg IOU: 0.482338, Class: 1.000000, Obj: 0.251586, No Obj: 0.003526, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.462179, Class: 1.000000, Obj: 0.243192, No Obj: 0.003059, Avg Recall: 0.505882, count: 85 -Region Avg IOU: 0.570903, Class: 1.000000, Obj: 0.328012, No Obj: 0.004100, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.516069, Class: 1.000000, Obj: 0.274036, No Obj: 0.003039, Avg Recall: 0.561798, count: 89 -Region Avg IOU: 0.509315, Class: 1.000000, Obj: 0.234269, No Obj: 0.003793, Avg Recall: 0.564885, count: 131 -1588: 0.501190, 0.478029 avg, 0.001000 rate, 21.991264 seconds, 406528 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.501458, Class: 1.000000, Obj: 0.292476, No Obj: 0.002927, Avg Recall: 0.521277, count: 94 -Region Avg IOU: 0.525339, Class: 1.000000, Obj: 0.306361, No Obj: 0.003120, Avg Recall: 0.594937, count: 79 -Region Avg IOU: 0.551581, Class: 1.000000, Obj: 0.315272, No Obj: 0.003724, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.496099, Class: 1.000000, Obj: 0.309874, No Obj: 0.003093, Avg Recall: 0.526882, count: 93 -Region Avg IOU: 0.488444, Class: 1.000000, Obj: 0.260351, No Obj: 0.003287, Avg Recall: 0.508475, count: 118 -Region Avg IOU: 0.515374, Class: 1.000000, Obj: 0.288235, No Obj: 0.003320, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.513085, Class: 1.000000, Obj: 0.267204, No Obj: 0.003189, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.524325, Class: 1.000000, Obj: 0.276043, No Obj: 0.003843, Avg Recall: 0.609091, count: 110 -1589: 0.431559, 0.473382 avg, 0.001000 rate, 22.313650 seconds, 406784 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.560686, Class: 1.000000, Obj: 0.274743, No Obj: 0.004642, Avg Recall: 0.660000, count: 150 -Region Avg IOU: 0.509670, Class: 1.000000, Obj: 0.297432, No Obj: 0.003531, Avg Recall: 0.540000, count: 100 -Region Avg IOU: 0.498422, Class: 1.000000, Obj: 0.273934, No Obj: 0.003883, Avg Recall: 0.572464, count: 138 -Region Avg IOU: 0.522085, Class: 1.000000, Obj: 0.243236, No Obj: 0.003089, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.536848, Class: 1.000000, Obj: 0.305018, No Obj: 0.003457, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.523116, Class: 1.000000, Obj: 0.315475, No Obj: 0.003752, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.578815, Class: 1.000000, Obj: 0.255209, No Obj: 0.003270, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.518479, Class: 1.000000, Obj: 0.260966, No Obj: 0.003614, Avg Recall: 0.607143, count: 112 -1590: 0.494884, 0.475532 avg, 0.001000 rate, 23.405975 seconds, 407040 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 20.711212 seconds -Region Avg IOU: 0.521488, Class: 1.000000, Obj: 0.291845, No Obj: 0.003743, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.549201, Class: 1.000000, Obj: 0.255730, No Obj: 0.003682, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.483811, Class: 1.000000, Obj: 0.245220, No Obj: 0.003836, Avg Recall: 0.550000, count: 140 -Region Avg IOU: 0.501858, Class: 1.000000, Obj: 0.282949, No Obj: 0.003548, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.545170, Class: 1.000000, Obj: 0.314575, No Obj: 0.003539, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.492941, Class: 1.000000, Obj: 0.270060, No Obj: 0.003603, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.540878, Class: 1.000000, Obj: 0.275529, No Obj: 0.003838, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.450377, Class: 1.000000, Obj: 0.272470, No Obj: 0.003230, Avg Recall: 0.506329, count: 79 -1591: 0.473556, 0.475334 avg, 0.001000 rate, 22.282221 seconds, 407296 images -Loaded: 1.589743 seconds -Region Avg IOU: 0.535764, Class: 1.000000, Obj: 0.273887, No Obj: 0.003920, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.474163, Class: 1.000000, Obj: 0.263573, No Obj: 0.003869, Avg Recall: 0.457516, count: 153 -Region Avg IOU: 0.560324, Class: 1.000000, Obj: 0.284090, No Obj: 0.004395, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.490850, Class: 1.000000, Obj: 0.265826, No Obj: 0.004190, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.449054, Class: 1.000000, Obj: 0.278223, No Obj: 0.004041, Avg Recall: 0.512605, count: 119 -Region Avg IOU: 0.506906, Class: 1.000000, Obj: 0.264931, No Obj: 0.003524, Avg Recall: 0.559524, count: 84 -Region Avg IOU: 0.515766, Class: 1.000000, Obj: 0.257188, No Obj: 0.003557, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.475560, Class: 1.000000, Obj: 0.256418, No Obj: 0.003919, Avg Recall: 0.519084, count: 131 -1592: 0.499771, 0.477778 avg, 0.001000 rate, 22.933378 seconds, 407552 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.501406, Class: 1.000000, Obj: 0.297319, No Obj: 0.003876, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.532235, Class: 1.000000, Obj: 0.318476, No Obj: 0.003662, Avg Recall: 0.593750, count: 96 -Region Avg IOU: 0.508859, Class: 1.000000, Obj: 0.262821, No Obj: 0.004105, Avg Recall: 0.572650, count: 117 -Region Avg IOU: 0.543828, Class: 1.000000, Obj: 0.294471, No Obj: 0.004823, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.508301, Class: 1.000000, Obj: 0.294043, No Obj: 0.003807, Avg Recall: 0.565217, count: 92 -Region Avg IOU: 0.491309, Class: 1.000000, Obj: 0.297006, No Obj: 0.003795, Avg Recall: 0.529412, count: 102 -Region Avg IOU: 0.505445, Class: 1.000000, Obj: 0.329853, No Obj: 0.003852, Avg Recall: 0.522222, count: 90 -Region Avg IOU: 0.460788, Class: 1.000000, Obj: 0.284527, No Obj: 0.003622, Avg Recall: 0.517857, count: 112 -1593: 0.531762, 0.483177 avg, 0.001000 rate, 21.240908 seconds, 407808 images -Loaded: 0.165121 seconds -Region Avg IOU: 0.513254, Class: 1.000000, Obj: 0.316005, No Obj: 0.004615, Avg Recall: 0.573427, count: 143 -Region Avg IOU: 0.520532, Class: 1.000000, Obj: 0.290603, No Obj: 0.004112, Avg Recall: 0.586777, count: 121 -Region Avg IOU: 0.539388, Class: 1.000000, Obj: 0.299315, No Obj: 0.003954, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.521213, Class: 1.000000, Obj: 0.337236, No Obj: 0.004319, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.531449, Class: 1.000000, Obj: 0.320799, No Obj: 0.004323, Avg Recall: 0.614754, count: 122 -Region Avg IOU: 0.504148, Class: 1.000000, Obj: 0.287676, No Obj: 0.004118, Avg Recall: 0.538462, count: 117 -Region Avg IOU: 0.528241, Class: 1.000000, Obj: 0.358083, No Obj: 0.003942, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.474703, Class: 1.000000, Obj: 0.293031, No Obj: 0.004214, Avg Recall: 0.483607, count: 122 -1594: 0.484201, 0.483279 avg, 0.001000 rate, 22.831324 seconds, 408064 images -Loaded: 0.738049 seconds -Region Avg IOU: 0.532256, Class: 1.000000, Obj: 0.286170, No Obj: 0.004773, Avg Recall: 0.566372, count: 113 -Region Avg IOU: 0.474370, Class: 1.000000, Obj: 0.287241, No Obj: 0.004692, Avg Recall: 0.536000, count: 125 -Region Avg IOU: 0.498301, Class: 1.000000, Obj: 0.326083, No Obj: 0.004465, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.520320, Class: 1.000000, Obj: 0.307284, No Obj: 0.003920, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.519476, Class: 1.000000, Obj: 0.349440, No Obj: 0.004449, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.470628, Class: 1.000000, Obj: 0.337413, No Obj: 0.004383, Avg Recall: 0.504762, count: 105 -Region Avg IOU: 0.489049, Class: 1.000000, Obj: 0.281674, No Obj: 0.004389, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.485987, Class: 1.000000, Obj: 0.294906, No Obj: 0.004104, Avg Recall: 0.564356, count: 101 -1595: 0.478414, 0.482793 avg, 0.001000 rate, 22.496897 seconds, 408320 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.496324, Class: 1.000000, Obj: 0.320843, No Obj: 0.005114, Avg Recall: 0.576923, count: 130 -Region Avg IOU: 0.514524, Class: 1.000000, Obj: 0.305206, No Obj: 0.004023, Avg Recall: 0.567568, count: 111 -Region Avg IOU: 0.504791, Class: 1.000000, Obj: 0.325541, No Obj: 0.004281, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.491202, Class: 1.000000, Obj: 0.274041, No Obj: 0.004485, Avg Recall: 0.523077, count: 130 -Region Avg IOU: 0.553942, Class: 1.000000, Obj: 0.327540, No Obj: 0.004280, Avg Recall: 0.666667, count: 75 -Region Avg IOU: 0.508048, Class: 1.000000, Obj: 0.277542, No Obj: 0.003689, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.510701, Class: 1.000000, Obj: 0.343993, No Obj: 0.004550, Avg Recall: 0.513274, count: 113 -Region Avg IOU: 0.446860, Class: 1.000000, Obj: 0.274451, No Obj: 0.004106, Avg Recall: 0.427083, count: 96 -1596: 0.534344, 0.487948 avg, 0.001000 rate, 21.674908 seconds, 408576 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.488370, Class: 1.000000, Obj: 0.296909, No Obj: 0.004794, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.530544, Class: 1.000000, Obj: 0.322681, No Obj: 0.004264, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.498464, Class: 1.000000, Obj: 0.282722, No Obj: 0.004257, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.530352, Class: 1.000000, Obj: 0.321342, No Obj: 0.003402, Avg Recall: 0.561644, count: 73 -Region Avg IOU: 0.486087, Class: 1.000000, Obj: 0.311299, No Obj: 0.004035, Avg Recall: 0.513761, count: 109 -Region Avg IOU: 0.479746, Class: 1.000000, Obj: 0.312647, No Obj: 0.004129, Avg Recall: 0.521739, count: 115 -Region Avg IOU: 0.508338, Class: 1.000000, Obj: 0.301901, No Obj: 0.004454, Avg Recall: 0.559140, count: 93 -Region Avg IOU: 0.497254, Class: 1.000000, Obj: 0.339625, No Obj: 0.004143, Avg Recall: 0.574713, count: 87 -1597: 0.421335, 0.481286 avg, 0.001000 rate, 22.396544 seconds, 408832 images -Loaded: 0.137133 seconds -Region Avg IOU: 0.492246, Class: 1.000000, Obj: 0.293530, No Obj: 0.003949, Avg Recall: 0.577778, count: 90 -Region Avg IOU: 0.467348, Class: 1.000000, Obj: 0.303853, No Obj: 0.003598, Avg Recall: 0.545455, count: 88 -Region Avg IOU: 0.512792, Class: 1.000000, Obj: 0.305312, No Obj: 0.004203, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.470792, Class: 1.000000, Obj: 0.281812, No Obj: 0.004050, Avg Recall: 0.529915, count: 117 -Region Avg IOU: 0.558939, Class: 1.000000, Obj: 0.333835, No Obj: 0.003524, Avg Recall: 0.702703, count: 74 -Region Avg IOU: 0.424696, Class: 1.000000, Obj: 0.262739, No Obj: 0.003419, Avg Recall: 0.474227, count: 97 -Region Avg IOU: 0.519953, Class: 1.000000, Obj: 0.324623, No Obj: 0.004794, Avg Recall: 0.588235, count: 119 -Region Avg IOU: 0.490548, Class: 1.000000, Obj: 0.325155, No Obj: 0.004101, Avg Recall: 0.528846, count: 104 -1598: 0.398691, 0.473027 avg, 0.001000 rate, 21.818119 seconds, 409088 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.448178, Class: 1.000000, Obj: 0.296310, No Obj: 0.003862, Avg Recall: 0.427273, count: 110 -Region Avg IOU: 0.492332, Class: 1.000000, Obj: 0.276250, No Obj: 0.003971, Avg Recall: 0.500000, count: 82 -Region Avg IOU: 0.499775, Class: 1.000000, Obj: 0.299129, No Obj: 0.004073, Avg Recall: 0.555556, count: 90 -Region Avg IOU: 0.526501, Class: 1.000000, Obj: 0.327685, No Obj: 0.004107, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.509172, Class: 1.000000, Obj: 0.277345, No Obj: 0.004544, Avg Recall: 0.591837, count: 147 -Region Avg IOU: 0.507307, Saving weights to ./person9300/backup/tyvp_93_9c512_1600.weights -Class: 1.000000, Obj: 0.321845, No Obj: 0.003788, Avg Recall: 0.539326, count: 89 -Region Avg IOU: 0.476283, Class: 1.000000, Obj: 0.337033, No Obj: 0.003900, Avg Recall: 0.514019, count: 107 -Region Avg IOU: 0.511932, Class: 1.000000, Obj: 0.258486, No Obj: 0.003816, Avg Recall: 0.549020, count: 102 -1599: 0.449610, 0.470685 avg, 0.001000 rate, 22.954538 seconds, 409344 images -Loaded: 1.322957 seconds -Region Avg IOU: 0.477784, Class: 1.000000, Obj: 0.303610, No Obj: 0.004491, Avg Recall: 0.551181, count: 127 -Region Avg IOU: 0.445855, Class: 1.000000, Obj: 0.231640, No Obj: 0.003673, Avg Recall: 0.436364, count: 110 -Region Avg IOU: 0.436274, Class: 1.000000, Obj: 0.290210, No Obj: 0.003562, Avg Recall: 0.451923, count: 104 -Region Avg IOU: 0.538295, Class: 1.000000, Obj: 0.320914, No Obj: 0.003929, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.456869, Class: 1.000000, Obj: 0.258872, No Obj: 0.003796, Avg Recall: 0.505050, count: 99 -Region Avg IOU: 0.553757, Class: 1.000000, Obj: 0.296559, No Obj: 0.004675, Avg Recall: 0.674074, count: 135 -Region Avg IOU: 0.495296, Class: 1.000000, Obj: 0.300558, No Obj: 0.004018, Avg Recall: 0.566929, count: 127 -Region Avg IOU: 0.509432, Class: 1.000000, Obj: 0.289955, No Obj: 0.003774, Avg Recall: 0.571429, count: 98 -1600: 0.516607, 0.475277 avg, 0.001000 rate, 21.953653 seconds, 409600 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000069 seconds -Region Avg IOU: 0.505098, Class: 1.000000, Obj: 0.287492, No Obj: 0.003013, Avg Recall: 0.571429, count: 77 -Region Avg IOU: 0.515483, Class: 1.000000, Obj: 0.275477, No Obj: 0.003591, Avg Recall: 0.608000, count: 125 -Region Avg IOU: 0.569453, Class: 1.000000, Obj: 0.323436, No Obj: 0.003968, Avg Recall: 0.693548, count: 124 -Region Avg IOU: 0.552468, Class: 1.000000, Obj: 0.319117, No Obj: 0.002836, Avg Recall: 0.675000, count: 80 -Region Avg IOU: 0.545237, Class: 1.000000, Obj: 0.298082, No Obj: 0.003435, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.525403, Class: 1.000000, Obj: 0.269605, No Obj: 0.003548, Avg Recall: 0.581967, count: 122 -Region Avg IOU: 0.524883, Class: 1.000000, Obj: 0.337815, No Obj: 0.003282, Avg Recall: 0.542553, count: 94 -Region Avg IOU: 0.524601, Class: 1.000000, Obj: 0.263234, No Obj: 0.003462, Avg Recall: 0.574803, count: 127 -1601: 0.492370, 0.476987 avg, 0.001000 rate, 24.246719 seconds, 409856 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.516778, Class: 1.000000, Obj: 0.296203, No Obj: 0.003412, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.503735, Class: 1.000000, Obj: 0.268455, No Obj: 0.003200, Avg Recall: 0.606742, count: 89 -Region Avg IOU: 0.507804, Class: 1.000000, Obj: 0.296231, No Obj: 0.003453, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.504061, Class: 1.000000, Obj: 0.286680, No Obj: 0.003018, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.540032, Class: 1.000000, Obj: 0.288624, No Obj: 0.003114, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.543073, Class: 1.000000, Obj: 0.286415, No Obj: 0.003778, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.557115, Class: 1.000000, Obj: 0.320051, No Obj: 0.003278, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.537498, Class: 1.000000, Obj: 0.286175, No Obj: 0.003378, Avg Recall: 0.660714, count: 112 -1602: 0.456485, 0.474936 avg, 0.001000 rate, 23.942450 seconds, 410112 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.504150, Class: 1.000000, Obj: 0.230737, No Obj: 0.003207, Avg Recall: 0.571429, count: 112 -Region Avg IOU: 0.518257, Class: 1.000000, Obj: 0.278313, No Obj: 0.003399, Avg Recall: 0.595238, count: 126 -Region Avg IOU: 0.534281, Class: 1.000000, Obj: 0.276820, No Obj: 0.003260, Avg Recall: 0.600000, count: 95 -Region Avg IOU: 0.550120, Class: 1.000000, Obj: 0.318353, No Obj: 0.003715, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.536561, Class: 1.000000, Obj: 0.278067, No Obj: 0.003600, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.549969, Class: 1.000000, Obj: 0.279998, No Obj: 0.004027, Avg Recall: 0.645833, count: 144 -Region Avg IOU: 0.553207, Class: 1.000000, Obj: 0.304960, No Obj: 0.003422, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.562244, Class: 1.000000, Obj: 0.307576, No Obj: 0.003607, Avg Recall: 0.657143, count: 105 -1603: 0.516663, 0.479109 avg, 0.001000 rate, 23.644445 seconds, 410368 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.571818, Class: 1.000000, Obj: 0.327125, No Obj: 0.003256, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.557850, Class: 1.000000, Obj: 0.270384, No Obj: 0.003941, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.542252, Class: 1.000000, Obj: 0.303767, No Obj: 0.003339, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.557142, Class: 1.000000, Obj: 0.296213, No Obj: 0.003565, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.555911, Class: 1.000000, Obj: 0.276756, No Obj: 0.003556, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.544587, Class: 1.000000, Obj: 0.301088, No Obj: 0.003391, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.526660, Class: 1.000000, Obj: 0.251750, No Obj: 0.003020, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.553484, Class: 1.000000, Obj: 0.299441, No Obj: 0.003982, Avg Recall: 0.653846, count: 130 -1604: 0.465235, 0.477722 avg, 0.001000 rate, 24.562525 seconds, 410624 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.516073, Class: 1.000000, Obj: 0.331189, No Obj: 0.003492, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.532987, Class: 1.000000, Obj: 0.284423, No Obj: 0.003472, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.541505, Class: 1.000000, Obj: 0.283301, No Obj: 0.002906, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.543722, Class: 1.000000, Obj: 0.268715, No Obj: 0.004036, Avg Recall: 0.628571, count: 140 -Region Avg IOU: 0.561515, Class: 1.000000, Obj: 0.290028, No Obj: 0.003372, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.569429, Class: 1.000000, Obj: 0.332953, No Obj: 0.003306, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.508566, Class: 1.000000, Obj: 0.279117, No Obj: 0.003608, Avg Recall: 0.572650, count: 117 -Region Avg IOU: 0.542517, Class: 1.000000, Obj: 0.337426, No Obj: 0.003197, Avg Recall: 0.641975, count: 81 -1605: 0.434549, 0.473404 avg, 0.001000 rate, 23.735125 seconds, 410880 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.573912, Class: 1.000000, Obj: 0.296810, No Obj: 0.003604, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.527915, Class: 1.000000, Obj: 0.255312, No Obj: 0.003354, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.557176, Class: 1.000000, Obj: 0.263094, No Obj: 0.003390, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.527928, Class: 1.000000, Obj: 0.352649, No Obj: 0.003667, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.602024, Class: 1.000000, Obj: 0.363501, No Obj: 0.003638, Avg Recall: 0.707317, count: 82 -Region Avg IOU: 0.536733, Class: 1.000000, Obj: 0.343839, No Obj: 0.003284, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.544860, Class: 1.000000, Obj: 0.292216, No Obj: 0.003442, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.504582, Class: 1.000000, Obj: 0.304198, No Obj: 0.003090, Avg Recall: 0.531915, count: 94 -1606: 0.435861, 0.469650 avg, 0.001000 rate, 23.056116 seconds, 411136 images -Loaded: 0.000058 seconds -Region Avg IOU: 0.552808, Class: 1.000000, Obj: 0.289164, No Obj: 0.004471, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.577505, Class: 1.000000, Obj: 0.319575, No Obj: 0.003396, Avg Recall: 0.727273, count: 88 -Region Avg IOU: 0.516476, Class: 1.000000, Obj: 0.318931, No Obj: 0.003844, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.572968, Class: 1.000000, Obj: 0.304369, No Obj: 0.004182, Avg Recall: 0.682927, count: 123 -Region Avg IOU: 0.516919, Class: 1.000000, Obj: 0.278237, No Obj: 0.003772, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.564331, Class: 1.000000, Obj: 0.294370, No Obj: 0.003650, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.522520, Class: 1.000000, Obj: 0.268639, No Obj: 0.003427, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.566773, Class: 1.000000, Obj: 0.300505, No Obj: 0.004540, Avg Recall: 0.656051, count: 157 -1607: 0.539451, 0.476630 avg, 0.001000 rate, 22.854918 seconds, 411392 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.495571, Class: 1.000000, Obj: 0.308256, No Obj: 0.003436, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.532643, Class: 1.000000, Obj: 0.299368, No Obj: 0.003427, Avg Recall: 0.606742, count: 89 -Region Avg IOU: 0.529386, Class: 1.000000, Obj: 0.346672, No Obj: 0.004069, Avg Recall: 0.601449, count: 138 -Region Avg IOU: 0.569485, Class: 1.000000, Obj: 0.319480, No Obj: 0.003821, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.543040, Class: 1.000000, Obj: 0.299358, No Obj: 0.003558, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.548950, Class: 1.000000, Obj: 0.287150, No Obj: 0.003967, Avg Recall: 0.581967, count: 122 -Region Avg IOU: 0.615993, Class: 1.000000, Obj: 0.359196, No Obj: 0.004011, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.515958, Class: 1.000000, Obj: 0.299956, No Obj: 0.003333, Avg Recall: 0.598039, count: 102 -1608: 0.439197, 0.472887 avg, 0.001000 rate, 23.157660 seconds, 411648 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.549769, Class: 1.000000, Obj: 0.359994, No Obj: 0.003551, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.542020, Class: 1.000000, Obj: 0.307118, No Obj: 0.003535, Avg Recall: 0.597938, count: 97 -Region Avg IOU: 0.567561, Class: 1.000000, Obj: 0.308099, No Obj: 0.003935, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.517828, Class: 1.000000, Obj: 0.319817, No Obj: 0.003681, Avg Recall: 0.589474, count: 95 -Region Avg IOU: 0.523193, Class: 1.000000, Obj: 0.337687, No Obj: 0.003718, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.504698, Class: 1.000000, Obj: 0.308277, No Obj: 0.003292, Avg Recall: 0.537736, count: 106 -Region Avg IOU: 0.491371, Class: 1.000000, Obj: 0.299450, No Obj: 0.003766, Avg Recall: 0.568182, count: 88 -Region Avg IOU: 0.521468, Class: 1.000000, Obj: 0.311789, No Obj: 0.003739, Avg Recall: 0.580357, count: 112 -1609: 0.460685, 0.471667 avg, 0.001000 rate, 23.666872 seconds, 411904 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.544095, Class: 1.000000, Obj: 0.306503, No Obj: 0.003588, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.518360, Class: 1.000000, Obj: 0.331111, No Obj: 0.003140, Avg Recall: 0.576471, count: 85 -Region Avg IOU: 0.550155, Class: 1.000000, Obj: 0.308520, No Obj: 0.004135, Avg Recall: 0.640625, count: 128 -Region Avg IOU: 0.557202, Class: 1.000000, Obj: 0.330480, No Obj: 0.003821, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.499728, Class: 1.000000, Obj: 0.280781, No Obj: 0.003581, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.532948, Class: 1.000000, Obj: 0.336865, No Obj: 0.004271, Avg Recall: 0.584746, count: 118 -Region Avg IOU: 0.561715, Class: 1.000000, Obj: 0.353889, No Obj: 0.003671, Avg Recall: 0.673684, count: 95 -Region Avg IOU: 0.518112, Class: 1.000000, Obj: 0.310502, No Obj: 0.004078, Avg Recall: 0.605042, count: 119 -1610: 0.447963, 0.469296 avg, 0.001000 rate, 24.125912 seconds, 412160 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000060 seconds -Region Avg IOU: 0.579108, Class: 1.000000, Obj: 0.353590, No Obj: 0.003981, Avg Recall: 0.715328, count: 137 -Region Avg IOU: 0.542272, Class: 1.000000, Obj: 0.340686, No Obj: 0.004144, Avg Recall: 0.635714, count: 140 -Region Avg IOU: 0.589385, Class: 1.000000, Obj: 0.354011, No Obj: 0.003744, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.571472, Class: 1.000000, Obj: 0.318034, No Obj: 0.003366, Avg Recall: 0.688525, count: 122 -Region Avg IOU: 0.588724, Class: 1.000000, Obj: 0.328977, No Obj: 0.003206, Avg Recall: 0.722222, count: 90 -Region Avg IOU: 0.544066, Class: 1.000000, Obj: 0.354273, No Obj: 0.002871, Avg Recall: 0.658824, count: 85 -Region Avg IOU: 0.583752, Class: 1.000000, Obj: 0.367132, No Obj: 0.003323, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.562440, Class: 1.000000, Obj: 0.310368, No Obj: 0.003159, Avg Recall: 0.723810, count: 105 -1611: 0.483920, 0.470759 avg, 0.001000 rate, 25.568220 seconds, 412416 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.601231, Class: 1.000000, Obj: 0.321389, No Obj: 0.003484, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.529239, Class: 1.000000, Obj: 0.274934, No Obj: 0.003211, Avg Recall: 0.576577, count: 111 -Region Avg IOU: 0.632799, Class: 1.000000, Obj: 0.335053, No Obj: 0.003405, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.594776, Class: 1.000000, Obj: 0.281956, No Obj: 0.003021, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.586980, Class: 1.000000, Obj: 0.306558, No Obj: 0.003018, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.567894, Class: 1.000000, Obj: 0.304073, No Obj: 0.003131, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.540790, Class: 1.000000, Obj: 0.249701, No Obj: 0.002881, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.570206, Class: 1.000000, Obj: 0.297344, No Obj: 0.003408, Avg Recall: 0.731183, count: 93 -1612: 0.436997, 0.467383 avg, 0.001000 rate, 26.723330 seconds, 412672 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.634185, Class: 1.000000, Obj: 0.341512, No Obj: 0.003372, Avg Recall: 0.824742, count: 97 -Region Avg IOU: 0.576203, Class: 1.000000, Obj: 0.297576, No Obj: 0.003025, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.580397, Class: 1.000000, Obj: 0.298658, No Obj: 0.002894, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.538173, Class: 1.000000, Obj: 0.275814, No Obj: 0.003021, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.540030, Class: 1.000000, Obj: 0.314474, No Obj: 0.002947, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.592591, Class: 1.000000, Obj: 0.300379, No Obj: 0.003583, Avg Recall: 0.717557, count: 131 -Region Avg IOU: 0.596773, Class: 1.000000, Obj: 0.290612, No Obj: 0.003209, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.588990, Class: 1.000000, Obj: 0.301007, No Obj: 0.002651, Avg Recall: 0.666667, count: 87 -1613: 0.440562, 0.464700 avg, 0.001000 rate, 25.903860 seconds, 412928 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.564542, Class: 1.000000, Obj: 0.232970, No Obj: 0.002912, Avg Recall: 0.647541, count: 122 -Region Avg IOU: 0.624061, Class: 1.000000, Obj: 0.315539, No Obj: 0.002558, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.523268, Class: 1.000000, Obj: 0.226005, No Obj: 0.002544, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.553306, Class: 1.000000, Obj: 0.286215, No Obj: 0.003083, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.597257, Class: 1.000000, Obj: 0.299246, No Obj: 0.003320, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.591174, Class: 1.000000, Obj: 0.288456, No Obj: 0.002628, Avg Recall: 0.741176, count: 85 -Region Avg IOU: 0.585523, Class: 1.000000, Obj: 0.320625, No Obj: 0.002850, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.568953, Class: 1.000000, Obj: 0.253834, No Obj: 0.002974, Avg Recall: 0.661157, count: 121 -1614: 0.469196, 0.465150 avg, 0.001000 rate, 27.134865 seconds, 413184 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.617326, Class: 1.000000, Obj: 0.287961, No Obj: 0.002857, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.583310, Class: 1.000000, Obj: 0.250985, No Obj: 0.002919, Avg Recall: 0.669565, count: 115 -Region Avg IOU: 0.526726, Class: 1.000000, Obj: 0.253704, No Obj: 0.003112, Avg Recall: 0.600000, count: 130 -Region Avg IOU: 0.560211, Class: 1.000000, Obj: 0.279098, No Obj: 0.002586, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.586447, Class: 1.000000, Obj: 0.260334, No Obj: 0.002876, Avg Recall: 0.666667, count: 117 -Region Avg IOU: 0.581541, Class: 1.000000, Obj: 0.290119, No Obj: 0.002580, Avg Recall: 0.684783, count: 92 -Region Avg IOU: 0.581379, Class: 1.000000, Obj: 0.290181, No Obj: 0.002526, Avg Recall: 0.706667, count: 75 -Region Avg IOU: 0.526144, Class: 1.000000, Obj: 0.220169, No Obj: 0.002407, Avg Recall: 0.629630, count: 108 -1615: 0.454820, 0.464117 avg, 0.001000 rate, 25.552660 seconds, 413440 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.585210, Class: 1.000000, Obj: 0.280185, No Obj: 0.002842, Avg Recall: 0.713115, count: 122 -Region Avg IOU: 0.637616, Class: 1.000000, Obj: 0.250614, No Obj: 0.003094, Avg Recall: 0.836066, count: 122 -Region Avg IOU: 0.590977, Class: 1.000000, Obj: 0.263567, No Obj: 0.002641, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.546809, Class: 1.000000, Obj: 0.284277, No Obj: 0.002713, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.601465, Class: 1.000000, Obj: 0.277821, No Obj: 0.002588, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.572437, Class: 1.000000, Obj: 0.272397, No Obj: 0.002475, Avg Recall: 0.707865, count: 89 -Region Avg IOU: 0.577382, Class: 1.000000, Obj: 0.293636, No Obj: 0.003364, Avg Recall: 0.709677, count: 124 -Region Avg IOU: 0.589399, Class: 1.000000, Obj: 0.300876, No Obj: 0.002654, Avg Recall: 0.726415, count: 106 -1616: 0.509979, 0.468703 avg, 0.001000 rate, 26.026184 seconds, 413696 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.559476, Class: 1.000000, Obj: 0.291168, No Obj: 0.002994, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.593438, Class: 1.000000, Obj: 0.344690, No Obj: 0.002640, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.556218, Class: 1.000000, Obj: 0.286321, No Obj: 0.002986, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.599906, Class: 1.000000, Obj: 0.299575, No Obj: 0.003183, Avg Recall: 0.752066, count: 121 -Region Avg IOU: 0.615394, Class: 1.000000, Obj: 0.279528, No Obj: 0.003157, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.584736, Class: 1.000000, Obj: 0.290666, No Obj: 0.002829, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.608903, Class: 1.000000, Obj: 0.297876, No Obj: 0.002909, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.565241, Class: 1.000000, Obj: 0.312532, No Obj: 0.003071, Avg Recall: 0.700855, count: 117 -1617: 0.487005, 0.470533 avg, 0.001000 rate, 26.398932 seconds, 413952 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.626873, Class: 1.000000, Obj: 0.313777, No Obj: 0.003569, Avg Recall: 0.776000, count: 125 -Region Avg IOU: 0.618057, Class: 1.000000, Obj: 0.287987, No Obj: 0.003336, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.583616, Class: 1.000000, Obj: 0.317105, No Obj: 0.002851, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.606590, Class: 1.000000, Obj: 0.323792, No Obj: 0.003383, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.620051, Class: 1.000000, Obj: 0.299454, No Obj: 0.003137, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.607931, Class: 1.000000, Obj: 0.302292, No Obj: 0.002677, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.635564, Class: 1.000000, Obj: 0.338214, No Obj: 0.003396, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.535090, Class: 1.000000, Obj: 0.277653, No Obj: 0.002929, Avg Recall: 0.562500, count: 96 -1618: 0.464447, 0.469925 avg, 0.001000 rate, 26.965525 seconds, 414208 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.552312, Class: 1.000000, Obj: 0.328019, No Obj: 0.002965, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.631467, Class: 1.000000, Obj: 0.303485, No Obj: 0.003418, Avg Recall: 0.773810, count: 84 -Region Avg IOU: 0.602533, Class: 1.000000, Obj: 0.356157, No Obj: 0.003385, Avg Recall: 0.764228, count: 123 -Region Avg IOU: 0.548937, Class: 1.000000, Obj: 0.318423, No Obj: 0.003344, Avg Recall: 0.643478, count: 115 -Region Avg IOU: 0.564374, Class: 1.000000, Obj: 0.347398, No Obj: 0.003244, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.556298, Class: 1.000000, Obj: 0.314925, No Obj: 0.003146, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.571758, Class: 1.000000, Obj: 0.279054, No Obj: 0.003798, Avg Recall: 0.645390, count: 141 -Region Avg IOU: 0.544051, Class: 1.000000, Obj: 0.304942, No Obj: 0.003097, Avg Recall: 0.625000, count: 120 -1619: 0.551655, 0.478098 avg, 0.001000 rate, 27.156055 seconds, 414464 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.533021, Class: 1.000000, Obj: 0.356649, No Obj: 0.003321, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.558887, Class: 1.000000, Obj: 0.291227, No Obj: 0.003359, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.604772, Class: 1.000000, Obj: 0.373633, No Obj: 0.003529, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.582117, Class: 1.000000, Obj: 0.368074, No Obj: 0.003840, Avg Recall: 0.710938, count: 128 -Region Avg IOU: 0.585815, Class: 1.000000, Obj: 0.359910, No Obj: 0.003660, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.607247, Class: 1.000000, Obj: 0.330731, No Obj: 0.003339, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.602241, Class: 1.000000, Obj: 0.349492, No Obj: 0.003484, Avg Recall: 0.744681, count: 94 -Region Avg IOU: 0.557896, Class: 1.000000, Obj: 0.324654, No Obj: 0.002884, Avg Recall: 0.658228, count: 79 -1620: 0.494629, 0.479751 avg, 0.001000 rate, 26.510813 seconds, 414720 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 16.144472 seconds -Region Avg IOU: 0.588913, Class: 1.000000, Obj: 0.326569, No Obj: 0.004345, Avg Recall: 0.742857, count: 140 -Region Avg IOU: 0.573584, Class: 1.000000, Obj: 0.320137, No Obj: 0.003416, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.540852, Class: 1.000000, Obj: 0.332772, No Obj: 0.003575, Avg Recall: 0.605042, count: 119 -Region Avg IOU: 0.561792, Class: 1.000000, Obj: 0.326245, No Obj: 0.003091, Avg Recall: 0.686046, count: 86 -Region Avg IOU: 0.575280, Class: 1.000000, Obj: 0.345250, No Obj: 0.003470, Avg Recall: 0.658730, count: 126 -Region Avg IOU: 0.551624, Class: 1.000000, Obj: 0.311886, No Obj: 0.003318, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.569865, Class: 1.000000, Obj: 0.353854, No Obj: 0.003311, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.551476, Class: 1.000000, Obj: 0.346048, No Obj: 0.003421, Avg Recall: 0.660000, count: 100 -1621: 0.508604, 0.482636 avg, 0.001000 rate, 26.916998 seconds, 414976 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.595547, Class: 1.000000, Obj: 0.305742, No Obj: 0.003787, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.616689, Class: 1.000000, Obj: 0.361626, No Obj: 0.003400, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.531145, Class: 1.000000, Obj: 0.312934, No Obj: 0.003579, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.569268, Class: 1.000000, Obj: 0.330513, No Obj: 0.003000, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.552715, Class: 1.000000, Obj: 0.341543, No Obj: 0.003432, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.572774, Class: 1.000000, Obj: 0.316062, No Obj: 0.003186, Avg Recall: 0.710843, count: 83 -Region Avg IOU: 0.561104, Class: 1.000000, Obj: 0.308134, No Obj: 0.003203, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.593390, Class: 1.000000, Obj: 0.346009, No Obj: 0.003643, Avg Recall: 0.709402, count: 117 -1622: 0.477541, 0.482127 avg, 0.001000 rate, 26.328960 seconds, 415232 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.602136, Class: 1.000000, Obj: 0.302633, No Obj: 0.003340, Avg Recall: 0.736842, count: 133 -Region Avg IOU: 0.554442, Class: 1.000000, Obj: 0.304178, No Obj: 0.002772, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.519931, Class: 1.000000, Obj: 0.273160, No Obj: 0.002677, Avg Recall: 0.572727, count: 110 -Region Avg IOU: 0.571549, Class: 1.000000, Obj: 0.299621, No Obj: 0.002981, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.593485, Class: 1.000000, Obj: 0.343326, No Obj: 0.003296, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.572795, Class: 1.000000, Obj: 0.317048, No Obj: 0.003413, Avg Recall: 0.673333, count: 150 -Region Avg IOU: 0.555610, Class: 1.000000, Obj: 0.287099, No Obj: 0.003343, Avg Recall: 0.669173, count: 133 -Region Avg IOU: 0.596941, Class: 1.000000, Obj: 0.326053, No Obj: 0.003149, Avg Recall: 0.750000, count: 100 -1623: 0.516032, 0.485517 avg, 0.001000 rate, 26.221401 seconds, 415488 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.574068, Class: 1.000000, Obj: 0.310260, No Obj: 0.003393, Avg Recall: 0.704000, count: 125 -Region Avg IOU: 0.600716, Class: 1.000000, Obj: 0.295670, No Obj: 0.003308, Avg Recall: 0.740157, count: 127 -Region Avg IOU: 0.535471, Class: 1.000000, Obj: 0.214624, No Obj: 0.002723, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.542784, Class: 1.000000, Obj: 0.269512, No Obj: 0.002683, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.583061, Class: 1.000000, Obj: 0.290487, No Obj: 0.002764, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.573366, Class: 1.000000, Obj: 0.292804, No Obj: 0.002831, Avg Recall: 0.743590, count: 78 -Region Avg IOU: 0.537497, Class: 1.000000, Obj: 0.298887, No Obj: 0.003026, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.571223, Class: 1.000000, Obj: 0.283876, No Obj: 0.002763, Avg Recall: 0.658824, count: 85 -1624: 0.465527, 0.483518 avg, 0.001000 rate, 27.162973 seconds, 415744 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.575120, Class: 1.000000, Obj: 0.291708, No Obj: 0.002751, Avg Recall: 0.710526, count: 114 -Region Avg IOU: 0.586088, Class: 1.000000, Obj: 0.296907, No Obj: 0.002888, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.521800, Class: 1.000000, Obj: 0.280033, No Obj: 0.002815, Avg Recall: 0.585106, count: 94 -Region Avg IOU: 0.595517, Class: 1.000000, Obj: 0.254116, No Obj: 0.002748, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.568343, Class: 1.000000, Obj: 0.277337, No Obj: 0.003022, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.564343, Class: 1.000000, Obj: 0.243144, No Obj: 0.003295, Avg Recall: 0.678832, count: 137 -Region Avg IOU: 0.555070, Class: 1.000000, Obj: 0.231042, No Obj: 0.002719, Avg Recall: 0.636364, count: 88 -Region Avg IOU: 0.562355, Class: 1.000000, Obj: 0.272383, No Obj: 0.003006, Avg Recall: 0.621622, count: 148 -1625: 0.504594, 0.485626 avg, 0.001000 rate, 27.089128 seconds, 416000 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.537673, Class: 1.000000, Obj: 0.263910, No Obj: 0.002527, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.553604, Class: 1.000000, Obj: 0.302364, No Obj: 0.002475, Avg Recall: 0.717647, count: 85 -Region Avg IOU: 0.574704, Class: 1.000000, Obj: 0.280794, No Obj: 0.003131, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.583574, Class: 1.000000, Obj: 0.242358, No Obj: 0.002366, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.603617, Class: 1.000000, Obj: 0.305419, No Obj: 0.002906, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.561595, Class: 1.000000, Obj: 0.280461, No Obj: 0.003041, Avg Recall: 0.710526, count: 114 -Region Avg IOU: 0.526183, Class: 1.000000, Obj: 0.234499, No Obj: 0.002818, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.552347, Class: 1.000000, Obj: 0.312948, No Obj: 0.002790, Avg Recall: 0.652632, count: 95 -1626: 0.447518, 0.481815 avg, 0.001000 rate, 26.744335 seconds, 416256 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.622975, Class: 1.000000, Obj: 0.299866, No Obj: 0.003237, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.578220, Class: 1.000000, Obj: 0.275799, No Obj: 0.002577, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.573088, Class: 1.000000, Obj: 0.287124, No Obj: 0.003017, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.607914, Class: 1.000000, Obj: 0.298574, No Obj: 0.003240, Avg Recall: 0.732394, count: 142 -Region Avg IOU: 0.599743, Class: 1.000000, Obj: 0.272588, No Obj: 0.003180, Avg Recall: 0.737705, count: 122 -Region Avg IOU: 0.593782, Class: 1.000000, Obj: 0.295839, No Obj: 0.002713, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.520790, Class: 1.000000, Obj: 0.239715, No Obj: 0.003117, Avg Recall: 0.587838, count: 148 -Region Avg IOU: 0.559600, Class: 1.000000, Obj: 0.328640, No Obj: 0.002718, Avg Recall: 0.656566, count: 99 -1627: 0.513682, 0.485002 avg, 0.001000 rate, 26.298445 seconds, 416512 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.582788, Class: 1.000000, Obj: 0.299534, No Obj: 0.002956, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.576748, Class: 1.000000, Obj: 0.309897, No Obj: 0.002785, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.585449, Class: 1.000000, Obj: 0.320028, No Obj: 0.003186, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.574232, Class: 1.000000, Obj: 0.264671, No Obj: 0.002744, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.588956, Class: 1.000000, Obj: 0.311187, No Obj: 0.002401, Avg Recall: 0.652174, count: 92 -Region Avg IOU: 0.550046, Class: 1.000000, Obj: 0.276255, No Obj: 0.002788, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.588613, Class: 1.000000, Obj: 0.259576, No Obj: 0.002807, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.599440, Class: 1.000000, Obj: 0.283656, No Obj: 0.003040, Avg Recall: 0.727273, count: 121 -1628: 0.458030, 0.482305 avg, 0.001000 rate, 25.553522 seconds, 416768 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.612276, Class: 1.000000, Obj: 0.296914, No Obj: 0.003021, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.555136, Class: 1.000000, Obj: 0.292328, No Obj: 0.003005, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.556352, Class: 1.000000, Obj: 0.329058, No Obj: 0.002936, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.578232, Class: 1.000000, Obj: 0.287333, No Obj: 0.002812, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.597310, Class: 1.000000, Obj: 0.273447, No Obj: 0.002866, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.620886, Class: 1.000000, Obj: 0.283999, No Obj: 0.002823, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.616292, Class: 1.000000, Obj: 0.299125, No Obj: 0.002890, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.631523, Class: 1.000000, Obj: 0.312370, No Obj: 0.003224, Avg Recall: 0.807018, count: 114 -1629: 0.474002, 0.481474 avg, 0.001000 rate, 27.018450 seconds, 417024 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.586995, Class: 1.000000, Obj: 0.278884, No Obj: 0.003498, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.575369, Class: 1.000000, Obj: 0.327370, No Obj: 0.003286, Avg Recall: 0.713178, count: 129 -Region Avg IOU: 0.529049, Class: 1.000000, Obj: 0.280148, No Obj: 0.003143, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.525147, Class: 1.000000, Obj: 0.269678, No Obj: 0.002864, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.574826, Class: 1.000000, Obj: 0.311099, No Obj: 0.003185, Avg Recall: 0.710938, count: 128 -Region Avg IOU: 0.570560, Class: 1.000000, Obj: 0.312654, No Obj: 0.003242, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.591219, Class: 1.000000, Obj: 0.356678, No Obj: 0.002950, Avg Recall: 0.744186, count: 86 -Region Avg IOU: 0.619804, Class: 1.000000, Obj: 0.329526, No Obj: 0.003322, Avg Recall: 0.781818, count: 110 -1630: 0.472095, 0.480536 avg, 0.001000 rate, 26.238544 seconds, 417280 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 19.523949 seconds -Region Avg IOU: 0.593873, Class: 1.000000, Obj: 0.304108, No Obj: 0.003518, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.586595, Class: 1.000000, Obj: 0.312199, No Obj: 0.003400, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.566717, Class: 1.000000, Obj: 0.315281, No Obj: 0.003524, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.570347, Class: 1.000000, Obj: 0.346171, No Obj: 0.003953, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.545192, Class: 1.000000, Obj: 0.260663, No Obj: 0.003209, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.582537, Class: 1.000000, Obj: 0.339937, No Obj: 0.003507, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.557540, Class: 1.000000, Obj: 0.321119, No Obj: 0.004227, Avg Recall: 0.677165, count: 127 -Region Avg IOU: 0.544658, Class: 1.000000, Obj: 0.296984, No Obj: 0.003693, Avg Recall: 0.631068, count: 103 -1631: 0.479791, 0.480462 avg, 0.001000 rate, 26.001629 seconds, 417536 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.560117, Class: 1.000000, Obj: 0.319068, No Obj: 0.003587, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.588503, Class: 1.000000, Obj: 0.331115, No Obj: 0.003569, Avg Recall: 0.712000, count: 125 -Region Avg IOU: 0.560270, Class: 1.000000, Obj: 0.397176, No Obj: 0.003638, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.552261, Class: 1.000000, Obj: 0.259789, No Obj: 0.004356, Avg Recall: 0.623188, count: 138 -Region Avg IOU: 0.566399, Class: 1.000000, Obj: 0.250237, No Obj: 0.003973, Avg Recall: 0.691667, count: 120 -Region Avg IOU: 0.570829, Class: 1.000000, Obj: 0.356858, No Obj: 0.003473, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.549170, Class: 1.000000, Obj: 0.304599, No Obj: 0.003766, Avg Recall: 0.693548, count: 124 -Region Avg IOU: 0.557302, Class: 1.000000, Obj: 0.371135, No Obj: 0.003601, Avg Recall: 0.605042, count: 119 -1632: 0.501149, 0.482530 avg, 0.001000 rate, 26.279987 seconds, 417792 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.574933, Class: 1.000000, Obj: 0.355709, No Obj: 0.003336, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.551615, Class: 1.000000, Obj: 0.280404, No Obj: 0.003868, Avg Recall: 0.648855, count: 131 -Region Avg IOU: 0.570087, Class: 1.000000, Obj: 0.347678, No Obj: 0.003533, Avg Recall: 0.708955, count: 134 -Region Avg IOU: 0.581358, Class: 1.000000, Obj: 0.342407, No Obj: 0.003658, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.580536, Class: 1.000000, Obj: 0.340424, No Obj: 0.003744, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.586294, Class: 1.000000, Obj: 0.368745, No Obj: 0.003488, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.538290, Class: 1.000000, Obj: 0.337916, No Obj: 0.003543, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.536716, Class: 1.000000, Obj: 0.366746, No Obj: 0.003831, Avg Recall: 0.616071, count: 112 -1633: 0.464568, 0.480734 avg, 0.001000 rate, 26.911455 seconds, 418048 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.558419, Class: 1.000000, Obj: 0.291774, No Obj: 0.003247, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.602350, Class: 1.000000, Obj: 0.312795, No Obj: 0.003640, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.570282, Class: 1.000000, Obj: 0.348098, No Obj: 0.003250, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.549175, Class: 1.000000, Obj: 0.348326, No Obj: 0.003398, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.566401, Class: 1.000000, Obj: 0.346647, No Obj: 0.003742, Avg Recall: 0.674603, count: 126 -Region Avg IOU: 0.603673, Class: 1.000000, Obj: 0.370520, No Obj: 0.003269, Avg Recall: 0.725275, count: 91 -Region Avg IOU: 0.531346, Class: 1.000000, Obj: 0.292884, No Obj: 0.003377, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.538762, Class: 1.000000, Obj: 0.298778, No Obj: 0.003436, Avg Recall: 0.623932, count: 117 -1634: 0.487201, 0.481381 avg, 0.001000 rate, 27.653776 seconds, 418304 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.565667, Class: 1.000000, Obj: 0.316099, No Obj: 0.002846, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.580617, Class: 1.000000, Obj: 0.294841, No Obj: 0.003422, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.566721, Class: 1.000000, Obj: 0.343694, No Obj: 0.003738, Avg Recall: 0.693548, count: 124 -Region Avg IOU: 0.556401, Class: 1.000000, Obj: 0.313436, No Obj: 0.002938, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.586953, Class: 1.000000, Obj: 0.330768, No Obj: 0.003333, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.528840, Class: 1.000000, Obj: 0.262218, No Obj: 0.003079, Avg Recall: 0.607407, count: 135 -Region Avg IOU: 0.537303, Class: 1.000000, Obj: 0.269816, No Obj: 0.003431, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.560514, Class: 1.000000, Obj: 0.289115, No Obj: 0.003157, Avg Recall: 0.694444, count: 108 -1635: 0.470051, 0.480248 avg, 0.001000 rate, 25.522739 seconds, 418560 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.542847, Class: 1.000000, Obj: 0.299524, No Obj: 0.002918, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.531365, Class: 1.000000, Obj: 0.254324, No Obj: 0.002926, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.550723, Class: 1.000000, Obj: 0.294888, No Obj: 0.003300, Avg Recall: 0.626866, count: 134 -Region Avg IOU: 0.592849, Class: 1.000000, Obj: 0.291666, No Obj: 0.003113, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.578782, Class: 1.000000, Obj: 0.319875, No Obj: 0.003049, Avg Recall: 0.744186, count: 86 -Region Avg IOU: 0.515126, Class: 1.000000, Obj: 0.301230, No Obj: 0.002983, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.575598, Class: 1.000000, Obj: 0.305397, No Obj: 0.003572, Avg Recall: 0.680000, count: 125 -Region Avg IOU: 0.578424, Class: 1.000000, Obj: 0.336550, No Obj: 0.003239, Avg Recall: 0.688073, count: 109 -1636: 0.480017, 0.480225 avg, 0.001000 rate, 27.683710 seconds, 418816 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.560438, Class: 1.000000, Obj: 0.280398, No Obj: 0.002771, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.566630, Class: 1.000000, Obj: 0.269347, No Obj: 0.003045, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.557371, Class: 1.000000, Obj: 0.279252, No Obj: 0.002743, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.590745, Class: 1.000000, Obj: 0.335286, No Obj: 0.002959, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.563042, Class: 1.000000, Obj: 0.329549, No Obj: 0.003290, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.574219, Class: 1.000000, Obj: 0.273755, No Obj: 0.002931, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.550057, Class: 1.000000, Obj: 0.286512, No Obj: 0.003118, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.542689, Class: 1.000000, Obj: 0.285645, No Obj: 0.002736, Avg Recall: 0.607143, count: 112 -1637: 0.455221, 0.477724 avg, 0.001000 rate, 24.391056 seconds, 419072 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.499756, Class: 1.000000, Obj: 0.270117, No Obj: 0.002805, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.519218, Class: 1.000000, Obj: 0.283060, No Obj: 0.002599, Avg Recall: 0.580000, count: 100 -Region Avg IOU: 0.580482, Class: 1.000000, Obj: 0.280070, No Obj: 0.002829, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.599918, Class: 1.000000, Obj: 0.306089, No Obj: 0.002890, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.534235, Class: 1.000000, Obj: 0.326165, No Obj: 0.002591, Avg Recall: 0.607843, count: 102 -Region Avg IOU: 0.589546, Class: 1.000000, Obj: 0.252154, No Obj: 0.002991, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.587673, Class: 1.000000, Obj: 0.286978, No Obj: 0.002827, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.552658, Class: 1.000000, Obj: 0.263339, No Obj: 0.003185, Avg Recall: 0.646617, count: 133 -1638: 0.444827, 0.474435 avg, 0.001000 rate, 24.903713 seconds, 419328 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.584994, Class: 1.000000, Obj: 0.239875, No Obj: 0.002753, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.567799, Class: 1.000000, Obj: 0.309552, No Obj: 0.002507, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.587397, Class: 1.000000, Obj: 0.296246, No Obj: 0.002995, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.538663, Class: 1.000000, Obj: 0.249783, No Obj: 0.002913, Avg Recall: 0.627586, count: 145 -Region Avg IOU: 0.578394, Class: 1.000000, Obj: 0.323200, No Obj: 0.002627, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.572050, Class: 1.000000, Obj: 0.268550, No Obj: 0.003009, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.588126, Class: 1.000000, Obj: 0.308720, No Obj: 0.002915, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.613990, Class: 1.000000, Obj: 0.270171, No Obj: 0.002842, Avg Recall: 0.754545, count: 110 -1639: 0.446310, 0.471622 avg, 0.001000 rate, 26.570677 seconds, 419584 images -Loaded: 0.000115 seconds -Region Avg IOU: 0.577813, Class: 1.000000, Obj: 0.294554, No Obj: 0.002950, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.581316, Class: 1.000000, Obj: 0.226756, No Obj: 0.003057, Avg Recall: 0.691781, count: 146 -Region Avg IOU: 0.574016, Class: 1.000000, Obj: 0.263317, No Obj: 0.003009, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.593330, Class: 1.000000, Obj: 0.258168, No Obj: 0.003292, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.599713, Class: 1.000000, Obj: 0.320961, No Obj: 0.002719, Avg Recall: 0.724638, count: 69 -Region Avg IOU: 0.558369, Class: 1.000000, Obj: 0.271024, No Obj: 0.003226, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.604365, Class: 1.000000, Obj: 0.305572, No Obj: 0.002950, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.594555, Class: 1.000000, Obj: 0.273163, No Obj: 0.003035, Avg Recall: 0.706422, count: 109 -1640: 0.494045, 0.473864 avg, 0.001000 rate, 25.680990 seconds, 419840 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000068 seconds -Region Avg IOU: 0.583563, Class: 1.000000, Obj: 0.285169, No Obj: 0.003119, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.587200, Class: 1.000000, Obj: 0.300696, No Obj: 0.003130, Avg Recall: 0.710938, count: 128 -Region Avg IOU: 0.581730, Class: 1.000000, Obj: 0.309473, No Obj: 0.002672, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.557048, Class: 1.000000, Obj: 0.274234, No Obj: 0.002986, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.623740, Class: 1.000000, Obj: 0.283035, No Obj: 0.003287, Avg Recall: 0.789474, count: 133 -Region Avg IOU: 0.610694, Class: 1.000000, Obj: 0.293921, No Obj: 0.003143, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.600975, Class: 1.000000, Obj: 0.323947, No Obj: 0.003307, Avg Recall: 0.788321, count: 137 -Region Avg IOU: 0.621923, Class: 1.000000, Obj: 0.323211, No Obj: 0.003470, Avg Recall: 0.787037, count: 108 -1641: 0.531750, 0.479653 avg, 0.001000 rate, 26.305588 seconds, 420096 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.621357, Class: 1.000000, Obj: 0.319397, No Obj: 0.003231, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.601330, Class: 1.000000, Obj: 0.300174, No Obj: 0.002758, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.629205, Class: 1.000000, Obj: 0.314070, No Obj: 0.003308, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.608131, Class: 1.000000, Obj: 0.318008, No Obj: 0.003498, Avg Recall: 0.761905, count: 126 -Region Avg IOU: 0.588606, Class: 1.000000, Obj: 0.291316, No Obj: 0.002893, Avg Recall: 0.696629, count: 89 -Region Avg IOU: 0.555120, Class: 1.000000, Obj: 0.309689, No Obj: 0.003036, Avg Recall: 0.686956, count: 115 -Region Avg IOU: 0.580243, Class: 1.000000, Obj: 0.350186, No Obj: 0.003031, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.538861, Class: 1.000000, Obj: 0.296751, No Obj: 0.003166, Avg Recall: 0.652893, count: 121 -1642: 0.463021, 0.477990 avg, 0.001000 rate, 27.159267 seconds, 420352 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.587675, Class: 1.000000, Obj: 0.291774, No Obj: 0.003056, Avg Recall: 0.687500, count: 128 -Region Avg IOU: 0.594939, Class: 1.000000, Obj: 0.348858, No Obj: 0.003281, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.587704, Class: 1.000000, Obj: 0.312602, No Obj: 0.003394, Avg Recall: 0.721804, count: 133 -Region Avg IOU: 0.590977, Class: 1.000000, Obj: 0.328003, No Obj: 0.003352, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.562119, Class: 1.000000, Obj: 0.330643, No Obj: 0.002951, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.591412, Class: 1.000000, Obj: 0.321134, No Obj: 0.002987, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.598762, Class: 1.000000, Obj: 0.325378, No Obj: 0.003390, Avg Recall: 0.712000, count: 125 -Region Avg IOU: 0.620692, Class: 1.000000, Obj: 0.325671, No Obj: 0.003148, Avg Recall: 0.776786, count: 112 -1643: 0.503371, 0.480528 avg, 0.001000 rate, 27.970160 seconds, 420608 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.609273, Class: 1.000000, Obj: 0.385134, No Obj: 0.002905, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.582601, Class: 1.000000, Obj: 0.300804, No Obj: 0.003301, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.625621, Class: 1.000000, Obj: 0.359808, No Obj: 0.003247, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.540905, Class: 1.000000, Obj: 0.314019, No Obj: 0.003017, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.565949, Class: 1.000000, Obj: 0.288325, No Obj: 0.002806, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.596503, Class: 1.000000, Obj: 0.344873, No Obj: 0.003025, Avg Recall: 0.727273, count: 88 -Region Avg IOU: 0.598971, Class: 1.000000, Obj: 0.342015, No Obj: 0.003475, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.592047, Class: 1.000000, Obj: 0.343218, No Obj: 0.002929, Avg Recall: 0.742574, count: 101 -1644: 0.467221, 0.479197 avg, 0.001000 rate, 28.454386 seconds, 420864 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.586480, Class: 1.000000, Obj: 0.309463, No Obj: 0.003231, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.611695, Class: 1.000000, Obj: 0.272124, No Obj: 0.003360, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.558098, Class: 1.000000, Obj: 0.276063, No Obj: 0.003199, Avg Recall: 0.664234, count: 137 -Region Avg IOU: 0.560426, Class: 1.000000, Obj: 0.294040, No Obj: 0.003035, Avg Recall: 0.681481, count: 135 -Region Avg IOU: 0.584920, Class: 1.000000, Obj: 0.344280, No Obj: 0.002980, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.596133, Class: 1.000000, Obj: 0.289313, No Obj: 0.002789, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.625046, Class: 1.000000, Obj: 0.365328, No Obj: 0.002925, Avg Recall: 0.724138, count: 87 -Region Avg IOU: 0.627290, Class: 1.000000, Obj: 0.331998, No Obj: 0.002836, Avg Recall: 0.795181, count: 83 -1645: 0.499629, 0.481240 avg, 0.001000 rate, 28.041622 seconds, 421120 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.588073, Class: 1.000000, Obj: 0.283160, No Obj: 0.002804, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.588095, Class: 1.000000, Obj: 0.289283, No Obj: 0.002955, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.609885, Class: 1.000000, Obj: 0.336226, No Obj: 0.003231, Avg Recall: 0.700787, count: 127 -Region Avg IOU: 0.597651, Class: 1.000000, Obj: 0.273371, No Obj: 0.002595, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.609040, Class: 1.000000, Obj: 0.293654, No Obj: 0.002845, Avg Recall: 0.744444, count: 90 -Region Avg IOU: 0.510997, Class: 1.000000, Obj: 0.246076, No Obj: 0.002875, Avg Recall: 0.562963, count: 135 -Region Avg IOU: 0.568502, Class: 1.000000, Obj: 0.267810, No Obj: 0.002962, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.618419, Class: 1.000000, Obj: 0.356647, No Obj: 0.003012, Avg Recall: 0.773196, count: 97 -1646: 0.478587, 0.480975 avg, 0.001000 rate, 27.626736 seconds, 421376 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.581183, Class: 1.000000, Obj: 0.268525, No Obj: 0.003076, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.567679, Class: 1.000000, Obj: 0.284949, No Obj: 0.002574, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.617517, Class: 1.000000, Obj: 0.315059, No Obj: 0.003068, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.597128, Class: 1.000000, Obj: 0.289857, No Obj: 0.003146, Avg Recall: 0.736000, count: 125 -Region Avg IOU: 0.600501, Class: 1.000000, Obj: 0.254320, No Obj: 0.003143, Avg Recall: 0.729508, count: 122 -Region Avg IOU: 0.584539, Class: 1.000000, Obj: 0.285652, No Obj: 0.002799, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.567515, Class: 1.000000, Obj: 0.286092, No Obj: 0.002979, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.596376, Class: 1.000000, Obj: 0.315742, No Obj: 0.002724, Avg Recall: 0.750000, count: 104 -1647: 0.524084, 0.485286 avg, 0.001000 rate, 26.027527 seconds, 421632 images -Loaded: 0.000145 seconds -Region Avg IOU: 0.568637, Class: 1.000000, Obj: 0.316087, No Obj: 0.002716, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.581305, Class: 1.000000, Obj: 0.289129, No Obj: 0.002777, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.577615, Class: 1.000000, Obj: 0.293080, No Obj: 0.002917, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.585803, Class: 1.000000, Obj: 0.357057, No Obj: 0.002629, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.590533, Class: 1.000000, Obj: 0.241160, No Obj: 0.002816, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.556615, Class: 1.000000, Obj: 0.329599, No Obj: 0.002782, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.565785, Class: 1.000000, Obj: 0.290630, No Obj: 0.002841, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.620718, Class: 1.000000, Obj: 0.336324, No Obj: 0.002626, Avg Recall: 0.778846, count: 104 -1648: 0.456728, 0.482430 avg, 0.001000 rate, 27.078144 seconds, 421888 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.577906, Class: 1.000000, Obj: 0.313029, No Obj: 0.002752, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.589066, Class: 1.000000, Obj: 0.273946, No Obj: 0.002502, Avg Recall: 0.670588, count: 85 -Region Avg IOU: 0.545772, Class: 1.000000, Obj: 0.306338, No Obj: 0.002613, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.584324, Class: 1.000000, Obj: 0.307040, No Obj: 0.002447, Avg Recall: 0.691358, count: 81 -Region Avg IOU: 0.545031, Class: 1.000000, Obj: 0.295249, No Obj: 0.003038, Avg Recall: 0.641667, count: 120 -Region Avg IOU: 0.547033, Class: 1.000000, Obj: 0.243039, No Obj: 0.002601, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.579978, Class: 1.000000, Obj: 0.263854, No Obj: 0.003089, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.637795, Class: 1.000000, Obj: 0.274952, No Obj: 0.002787, Avg Recall: 0.790698, count: 86 -1649: 0.461836, 0.480371 avg, 0.001000 rate, 26.202160 seconds, 422144 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.598455, Class: 1.000000, Obj: 0.274486, No Obj: 0.003052, Avg Recall: 0.732824, count: 131 -Region Avg IOU: 0.622326, Class: 1.000000, Obj: 0.309026, No Obj: 0.002721, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.611303, Class: 1.000000, Obj: 0.297494, No Obj: 0.002370, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.595581, Class: 1.000000, Obj: 0.277638, No Obj: 0.002711, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.550427, Class: 1.000000, Obj: 0.289718, No Obj: 0.002722, Avg Recall: 0.682927, count: 123 -Region Avg IOU: 0.589254, Class: 1.000000, Obj: 0.241337, No Obj: 0.002981, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.571815, Class: 1.000000, Obj: 0.288139, No Obj: 0.002582, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.575603, Class: 1.000000, Obj: 0.285810, No Obj: 0.002696, Avg Recall: 0.675676, count: 111 -1650: 0.506731, 0.483007 avg, 0.001000 rate, 26.283056 seconds, 422400 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 20.174053 seconds -Region Avg IOU: 0.510363, Class: 1.000000, Obj: 0.293373, No Obj: 0.003553, Avg Recall: 0.589552, count: 134 -Region Avg IOU: 0.526284, Class: 1.000000, Obj: 0.233515, No Obj: 0.003406, Avg Recall: 0.592000, count: 125 -Region Avg IOU: 0.568886, Class: 1.000000, Obj: 0.292964, No Obj: 0.002980, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.483194, Class: 1.000000, Obj: 0.232921, No Obj: 0.003422, Avg Recall: 0.564103, count: 117 -Region Avg IOU: 0.548519, Class: 1.000000, Obj: 0.252038, No Obj: 0.003337, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.480413, Class: 1.000000, Obj: 0.278370, No Obj: 0.002933, Avg Recall: 0.527273, count: 110 -Region Avg IOU: 0.538346, Class: 1.000000, Obj: 0.215141, No Obj: 0.003059, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.518593, Class: 1.000000, Obj: 0.278715, No Obj: 0.002765, Avg Recall: 0.580645, count: 93 -1651: 0.474303, 0.482136 avg, 0.001000 rate, 23.135500 seconds, 422656 images -Loaded: 0.000131 seconds -Region Avg IOU: 0.524541, Class: 1.000000, Obj: 0.259874, No Obj: 0.003497, Avg Recall: 0.572650, count: 117 -Region Avg IOU: 0.544126, Class: 1.000000, Obj: 0.309502, No Obj: 0.003106, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.544106, Class: 1.000000, Obj: 0.275848, No Obj: 0.003083, Avg Recall: 0.626374, count: 91 -Region Avg IOU: 0.522041, Class: 1.000000, Obj: 0.298495, No Obj: 0.003185, Avg Recall: 0.584270, count: 89 -Region Avg IOU: 0.500837, Class: 1.000000, Obj: 0.248451, No Obj: 0.003135, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.494863, Class: 1.000000, Obj: 0.257279, No Obj: 0.003458, Avg Recall: 0.540323, count: 124 -Region Avg IOU: 0.557083, Class: 1.000000, Obj: 0.297834, No Obj: 0.003674, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.536739, Class: 1.000000, Obj: 0.281960, No Obj: 0.003347, Avg Recall: 0.618182, count: 110 -1652: 0.479446, 0.481867 avg, 0.001000 rate, 22.601320 seconds, 422912 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.550591, Class: 1.000000, Obj: 0.281902, No Obj: 0.004188, Avg Recall: 0.631579, count: 133 -Region Avg IOU: 0.545629, Class: 1.000000, Obj: 0.295430, No Obj: 0.003417, Avg Recall: 0.622807, count: 114 -Region Avg IOU: 0.520247, Class: 1.000000, Obj: 0.285812, No Obj: 0.003281, Avg Recall: 0.597403, count: 77 -Region Avg IOU: 0.521704, Class: 1.000000, Obj: 0.291224, No Obj: 0.003065, Avg Recall: 0.608247, count: 97 -Region Avg IOU: 0.525479, Class: 1.000000, Obj: 0.283215, No Obj: 0.003581, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.495155, Class: 1.000000, Obj: 0.306924, No Obj: 0.003295, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.529917, Class: 1.000000, Obj: 0.327822, No Obj: 0.003786, Avg Recall: 0.650406, count: 123 -Region Avg IOU: 0.563268, Class: 1.000000, Obj: 0.276868, No Obj: 0.003268, Avg Recall: 0.675000, count: 80 -1653: 0.452904, 0.478971 avg, 0.001000 rate, 23.502417 seconds, 423168 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.542496, Class: 1.000000, Obj: 0.309265, No Obj: 0.004303, Avg Recall: 0.658824, count: 85 -Region Avg IOU: 0.492154, Class: 1.000000, Obj: 0.303333, No Obj: 0.003743, Avg Recall: 0.490385, count: 104 -Region Avg IOU: 0.504906, Class: 1.000000, Obj: 0.302200, No Obj: 0.003681, Avg Recall: 0.568965, count: 116 -Region Avg IOU: 0.563585, Class: 1.000000, Obj: 0.289734, No Obj: 0.003746, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.481008, Class: 1.000000, Obj: 0.303248, No Obj: 0.003595, Avg Recall: 0.481818, count: 110 -Region Avg IOU: 0.514826, Class: 1.000000, Obj: 0.282919, No Obj: 0.003365, Avg Recall: 0.654762, count: 84 -Region Avg IOU: 0.562702, Class: 1.000000, Obj: 0.324068, No Obj: 0.003834, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.536811, Class: 1.000000, Obj: 0.327177, No Obj: 0.003964, Avg Recall: 0.614583, count: 96 -1654: 0.457193, 0.476793 avg, 0.001000 rate, 22.868452 seconds, 423424 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.550726, Class: 1.000000, Obj: 0.331528, No Obj: 0.004290, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.526455, Class: 1.000000, Obj: 0.320580, No Obj: 0.004187, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.523666, Class: 1.000000, Obj: 0.308293, No Obj: 0.003882, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.533577, Class: 1.000000, Obj: 0.347763, No Obj: 0.004398, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.541687, Class: 1.000000, Obj: 0.346022, No Obj: 0.003734, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.540752, Class: 1.000000, Obj: 0.380986, No Obj: 0.004445, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.496738, Class: 1.000000, Obj: 0.327284, No Obj: 0.003647, Avg Recall: 0.550562, count: 89 -Region Avg IOU: 0.487210, Class: 1.000000, Obj: 0.312405, No Obj: 0.003543, Avg Recall: 0.540230, count: 87 -1655: 0.431212, 0.472235 avg, 0.001000 rate, 22.780441 seconds, 423680 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.472937, Class: 1.000000, Obj: 0.300321, No Obj: 0.004081, Avg Recall: 0.486957, count: 115 -Region Avg IOU: 0.537794, Class: 1.000000, Obj: 0.316637, No Obj: 0.004172, Avg Recall: 0.580000, count: 100 -Region Avg IOU: 0.544713, Class: 1.000000, Obj: 0.317786, No Obj: 0.003724, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.532431, Class: 1.000000, Obj: 0.363470, No Obj: 0.004095, Avg Recall: 0.589744, count: 117 -Region Avg IOU: 0.554319, Class: 1.000000, Obj: 0.356527, No Obj: 0.003875, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.508605, Class: 1.000000, Obj: 0.301894, No Obj: 0.004253, Avg Recall: 0.512821, count: 117 -Region Avg IOU: 0.516089, Class: 1.000000, Obj: 0.300365, No Obj: 0.004035, Avg Recall: 0.600000, count: 95 -Region Avg IOU: 0.534657, Class: 1.000000, Obj: 0.356799, No Obj: 0.004152, Avg Recall: 0.620370, count: 108 -1656: 0.437483, 0.468760 avg, 0.001000 rate, 24.289309 seconds, 423936 images -Loaded: 0.000059 seconds -Region Avg IOU: 0.545847, Class: 1.000000, Obj: 0.307033, No Obj: 0.004502, Avg Recall: 0.617188, count: 128 -Region Avg IOU: 0.496612, Class: 1.000000, Obj: 0.323867, No Obj: 0.004675, Avg Recall: 0.507937, count: 126 -Region Avg IOU: 0.547338, Class: 1.000000, Obj: 0.345839, No Obj: 0.003851, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.498252, Class: 1.000000, Obj: 0.300015, No Obj: 0.004094, Avg Recall: 0.593496, count: 123 -Region Avg IOU: 0.579818, Class: 1.000000, Obj: 0.350712, No Obj: 0.004373, Avg Recall: 0.686567, count: 134 -Region Avg IOU: 0.521698, Class: 1.000000, Obj: 0.338033, No Obj: 0.004492, Avg Recall: 0.582090, count: 134 -Region Avg IOU: 0.541543, Class: 1.000000, Obj: 0.306572, No Obj: 0.003778, Avg Recall: 0.600000, count: 95 -Region Avg IOU: 0.503554, Class: 1.000000, Obj: 0.364666, No Obj: 0.004216, Avg Recall: 0.589744, count: 117 -1657: 0.571441, 0.479028 avg, 0.001000 rate, 23.539688 seconds, 424192 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.510612, Class: 1.000000, Obj: 0.319797, No Obj: 0.004370, Avg Recall: 0.544828, count: 145 -Region Avg IOU: 0.543982, Class: 1.000000, Obj: 0.261953, No Obj: 0.004713, Avg Recall: 0.630872, count: 149 -Region Avg IOU: 0.551875, Class: 1.000000, Obj: 0.323497, No Obj: 0.003809, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.597046, Class: 1.000000, Obj: 0.380777, No Obj: 0.004679, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.539055, Class: 1.000000, Obj: 0.348523, No Obj: 0.004098, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.539518, Class: 1.000000, Obj: 0.314918, No Obj: 0.004025, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.515182, Class: 1.000000, Obj: 0.320257, No Obj: 0.003886, Avg Recall: 0.567797, count: 118 -Region Avg IOU: 0.534211, Class: 1.000000, Obj: 0.312632, No Obj: 0.004338, Avg Recall: 0.607692, count: 130 -1658: 0.544552, 0.485580 avg, 0.001000 rate, 23.317524 seconds, 424448 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.544754, Class: 1.000000, Obj: 0.313871, No Obj: 0.004109, Avg Recall: 0.656000, count: 125 -Region Avg IOU: 0.501665, Class: 1.000000, Obj: 0.325886, No Obj: 0.003320, Avg Recall: 0.533333, count: 105 -Region Avg IOU: 0.540151, Class: 1.000000, Obj: 0.339033, No Obj: 0.003830, Avg Recall: 0.641667, count: 120 -Region Avg IOU: 0.531770, Class: 1.000000, Obj: 0.286004, No Obj: 0.004244, Avg Recall: 0.572519, count: 131 -Region Avg IOU: 0.484948, Class: 1.000000, Obj: 0.285235, No Obj: 0.003743, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.532882, Class: 1.000000, Obj: 0.299919, No Obj: 0.003585, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.510621, Class: 1.000000, Obj: 0.303310, No Obj: 0.003949, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.449223, Class: 1.000000, Obj: 0.265593, No Obj: 0.003249, Avg Recall: 0.465517, count: 116 -1659: 0.482236, 0.485246 avg, 0.001000 rate, 23.549065 seconds, 424704 images -Loaded: 0.000117 seconds -Region Avg IOU: 0.550416, Class: 1.000000, Obj: 0.302485, No Obj: 0.003532, Avg Recall: 0.647059, count: 85 -Region Avg IOU: 0.519388, Class: 1.000000, Obj: 0.291134, No Obj: 0.003537, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.503362, Class: 1.000000, Obj: 0.304410, No Obj: 0.003702, Avg Recall: 0.541667, count: 120 -Region Avg IOU: 0.533712, Class: 1.000000, Obj: 0.284996, No Obj: 0.003797, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.508831, Class: 1.000000, Obj: 0.270521, No Obj: 0.003189, Avg Recall: 0.543478, count: 92 -Region Avg IOU: 0.561040, Class: 1.000000, Obj: 0.352264, No Obj: 0.003788, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.498142, Class: 1.000000, Obj: 0.299200, No Obj: 0.003441, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.465920, Class: 1.000000, Obj: 0.289048, No Obj: 0.003664, Avg Recall: 0.473684, count: 133 -1660: 0.456936, 0.482415 avg, 0.001000 rate, 22.224518 seconds, 424960 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.334782 seconds -Region Avg IOU: 0.474983, Class: 1.000000, Obj: 0.248757, No Obj: 0.004047, Avg Recall: 0.513514, count: 111 -Region Avg IOU: 0.461927, Class: 1.000000, Obj: 0.247178, No Obj: 0.004319, Avg Recall: 0.479592, count: 98 -Region Avg IOU: 0.460747, Class: 1.000000, Obj: 0.257503, No Obj: 0.003788, Avg Recall: 0.465116, count: 86 -Region Avg IOU: 0.468189, Class: 1.000000, Obj: 0.244361, No Obj: 0.004402, Avg Recall: 0.462264, count: 106 -Region Avg IOU: 0.429389, Class: 1.000000, Obj: 0.237044, No Obj: 0.004053, Avg Recall: 0.388889, count: 90 -Region Avg IOU: 0.416939, Class: 1.000000, Obj: 0.225738, No Obj: 0.003884, Avg Recall: 0.426229, count: 122 -Region Avg IOU: 0.405049, Class: 1.000000, Obj: 0.240471, No Obj: 0.004287, Avg Recall: 0.384000, count: 125 -Region Avg IOU: 0.426147, Class: 1.000000, Obj: 0.224924, No Obj: 0.004180, Avg Recall: 0.415254, count: 118 -1661: 0.565906, 0.490764 avg, 0.001000 rate, 18.585745 seconds, 425216 images -Loaded: 3.067722 seconds -Region Avg IOU: 0.419615, Class: 1.000000, Obj: 0.250970, No Obj: 0.003968, Avg Recall: 0.423077, count: 130 -Region Avg IOU: 0.416202, Class: 1.000000, Obj: 0.246994, No Obj: 0.003977, Avg Recall: 0.475728, count: 103 -Region Avg IOU: 0.448230, Class: 1.000000, Obj: 0.210787, No Obj: 0.003594, Avg Recall: 0.518072, count: 83 -Region Avg IOU: 0.436366, Class: 1.000000, Obj: 0.213665, No Obj: 0.004105, Avg Recall: 0.483871, count: 124 -Region Avg IOU: 0.506397, Class: 1.000000, Obj: 0.286965, No Obj: 0.004586, Avg Recall: 0.500000, count: 100 -Region Avg IOU: 0.381650, Class: 1.000000, Obj: 0.219282, No Obj: 0.003857, Avg Recall: 0.335878, count: 131 -Region Avg IOU: 0.446083, Class: 1.000000, Obj: 0.231630, No Obj: 0.003466, Avg Recall: 0.527473, count: 91 -Region Avg IOU: 0.448379, Class: 1.000000, Obj: 0.250981, No Obj: 0.004078, Avg Recall: 0.480769, count: 104 -1662: 0.501709, 0.491859 avg, 0.001000 rate, 13.302386 seconds, 425472 images -Loaded: 7.419634 seconds -Region Avg IOU: 0.455960, Class: 1.000000, Obj: 0.225502, No Obj: 0.003776, Avg Recall: 0.482353, count: 85 -Region Avg IOU: 0.434010, Class: 1.000000, Obj: 0.224695, No Obj: 0.003573, Avg Recall: 0.464646, count: 99 -Region Avg IOU: 0.435635, Class: 1.000000, Obj: 0.241979, No Obj: 0.003964, Avg Recall: 0.457143, count: 70 -Region Avg IOU: 0.445399, Class: 1.000000, Obj: 0.241340, No Obj: 0.004067, Avg Recall: 0.452632, count: 95 -Region Avg IOU: 0.466508, Class: 1.000000, Obj: 0.237249, No Obj: 0.004068, Avg Recall: 0.500000, count: 100 -Region Avg IOU: 0.458295, Class: 1.000000, Obj: 0.255073, No Obj: 0.004890, Avg Recall: 0.526718, count: 131 -Region Avg IOU: 0.479367, Class: 1.000000, Obj: 0.254039, No Obj: 0.004131, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.427995, Class: 1.000000, Obj: 0.260489, No Obj: 0.004034, Avg Recall: 0.429907, count: 107 -1663: 0.534190, 0.496092 avg, 0.001000 rate, 18.093742 seconds, 425728 images -Loaded: 2.639667 seconds -Region Avg IOU: 0.487211, Class: 1.000000, Obj: 0.252861, No Obj: 0.004671, Avg Recall: 0.489796, count: 98 -Region Avg IOU: 0.458037, Class: 1.000000, Obj: 0.270509, No Obj: 0.004289, Avg Recall: 0.520000, count: 100 -Region Avg IOU: 0.504484, Class: 1.000000, Obj: 0.281791, No Obj: 0.004307, Avg Recall: 0.550562, count: 89 -Region Avg IOU: 0.462446, Class: 1.000000, Obj: 0.268339, No Obj: 0.004790, Avg Recall: 0.510417, count: 96 -Region Avg IOU: 0.410579, Class: 1.000000, Obj: 0.245620, No Obj: 0.004504, Avg Recall: 0.422222, count: 135 -Region Avg IOU: 0.449800, Class: 1.000000, Obj: 0.230940, No Obj: 0.004368, Avg Recall: 0.489796, count: 98 -Region Avg IOU: 0.489194, Class: 1.000000, Obj: 0.289281, No Obj: 0.004624, Avg Recall: 0.555556, count: 99 -Region Avg IOU: 0.438865, Class: 1.000000, Obj: 0.250713, No Obj: 0.004727, Avg Recall: 0.434109, count: 129 -1664: 0.510591, 0.497542 avg, 0.001000 rate, 17.172373 seconds, 425984 images -Loaded: 3.004514 seconds -Region Avg IOU: 0.429373, Class: 1.000000, Obj: 0.259128, No Obj: 0.004672, Avg Recall: 0.492064, count: 126 -Region Avg IOU: 0.438153, Class: 1.000000, Obj: 0.238611, No Obj: 0.004941, Avg Recall: 0.423423, count: 111 -Region Avg IOU: 0.397287, Class: 1.000000, Obj: 0.258986, No Obj: 0.004389, Avg Recall: 0.362832, count: 113 -Region Avg IOU: 0.438983, Class: 1.000000, Obj: 0.277243, No Obj: 0.004471, Avg Recall: 0.432692, count: 104 -Region Avg IOU: 0.413468, Class: 1.000000, Obj: 0.265701, No Obj: 0.004476, Avg Recall: 0.411765, count: 119 -Region Avg IOU: 0.447130, Class: 1.000000, Obj: 0.278926, No Obj: 0.005214, Avg Recall: 0.518519, count: 108 -Region Avg IOU: 0.392701, Class: 1.000000, Obj: 0.289088, No Obj: 0.004723, Avg Recall: 0.392523, count: 107 -Region Avg IOU: 0.430212, Class: 1.000000, Obj: 0.248096, No Obj: 0.004751, Avg Recall: 0.452830, count: 106 -1665: 0.527345, 0.500522 avg, 0.001000 rate, 15.752454 seconds, 426240 images -Loaded: 3.459770 seconds -Region Avg IOU: 0.457487, Class: 1.000000, Obj: 0.295159, No Obj: 0.005045, Avg Recall: 0.471698, count: 106 -Region Avg IOU: 0.410320, Class: 1.000000, Obj: 0.239654, No Obj: 0.005363, Avg Recall: 0.429825, count: 114 -Region Avg IOU: 0.426310, Class: 1.000000, Obj: 0.277143, No Obj: 0.004460, Avg Recall: 0.462366, count: 93 -Region Avg IOU: 0.427037, Class: 1.000000, Obj: 0.307913, No Obj: 0.005057, Avg Recall: 0.468354, count: 79 -Region Avg IOU: 0.419777, Class: 1.000000, Obj: 0.277204, No Obj: 0.004819, Avg Recall: 0.403846, count: 104 -Region Avg IOU: 0.430143, Class: 1.000000, Obj: 0.282775, No Obj: 0.004899, Avg Recall: 0.438095, count: 105 -Region Avg IOU: 0.400499, Class: 1.000000, Obj: 0.294279, No Obj: 0.004841, Avg Recall: 0.423423, count: 111 -Region Avg IOU: 0.413244, Class: 1.000000, Obj: 0.251996, No Obj: 0.005087, Avg Recall: 0.433333, count: 120 -1666: 0.470807, 0.497550 avg, 0.001000 rate, 11.982169 seconds, 426496 images -Loaded: 9.227968 seconds -Region Avg IOU: 0.433028, Class: 1.000000, Obj: 0.301827, No Obj: 0.004885, Avg Recall: 0.471698, count: 106 -Region Avg IOU: 0.470823, Class: 1.000000, Obj: 0.324176, No Obj: 0.005040, Avg Recall: 0.536082, count: 97 -Region Avg IOU: 0.435006, Class: 1.000000, Obj: 0.300383, No Obj: 0.005753, Avg Recall: 0.415385, count: 130 -Region Avg IOU: 0.488120, Class: 1.000000, Obj: 0.316164, No Obj: 0.004601, Avg Recall: 0.554054, count: 74 -Region Avg IOU: 0.357596, Class: 1.000000, Obj: 0.242744, No Obj: 0.004801, Avg Recall: 0.324324, count: 111 -Region Avg IOU: 0.388124, Class: 1.000000, Obj: 0.295799, No Obj: 0.005777, Avg Recall: 0.325926, count: 135 -Region Avg IOU: 0.416715, Class: 1.000000, Obj: 0.301719, No Obj: 0.004229, Avg Recall: 0.416667, count: 96 -Region Avg IOU: 0.430524, Class: 1.000000, Obj: 0.293872, No Obj: 0.005103, Avg Recall: 0.383929, count: 112 -1667: 0.488957, 0.496691 avg, 0.001000 rate, 17.792755 seconds, 426752 images -Loaded: 2.325073 seconds -Region Avg IOU: 0.438784, Class: 1.000000, Obj: 0.313091, No Obj: 0.005068, Avg Recall: 0.485714, count: 105 -Region Avg IOU: 0.386350, Class: 1.000000, Obj: 0.285537, No Obj: 0.004646, Avg Recall: 0.426966, count: 89 -Region Avg IOU: 0.443567, Class: 1.000000, Obj: 0.270014, No Obj: 0.005201, Avg Recall: 0.425532, count: 94 -Region Avg IOU: 0.416061, Class: 1.000000, Obj: 0.288314, No Obj: 0.005954, Avg Recall: 0.419643, count: 112 -Region Avg IOU: 0.434830, Class: 1.000000, Obj: 0.289451, No Obj: 0.005049, Avg Recall: 0.429688, count: 128 -Region Avg IOU: 0.412548, Class: 1.000000, Obj: 0.310457, No Obj: 0.004821, Avg Recall: 0.434783, count: 92 -Region Avg IOU: 0.411973, Class: 1.000000, Obj: 0.294432, No Obj: 0.005289, Avg Recall: 0.418182, count: 110 -Region Avg IOU: 0.438064, Class: 1.000000, Obj: 0.277363, No Obj: 0.005074, Avg Recall: 0.417476, count: 103 -1668: 0.520852, 0.499107 avg, 0.001000 rate, 19.521849 seconds, 427008 images -Loaded: 2.207559 seconds -Region Avg IOU: 0.424892, Class: 1.000000, Obj: 0.266405, No Obj: 0.005474, Avg Recall: 0.445783, count: 83 -Region Avg IOU: 0.436240, Class: 1.000000, Obj: 0.303782, No Obj: 0.006208, Avg Recall: 0.469388, count: 147 -Region Avg IOU: 0.412270, Class: 1.000000, Obj: 0.278298, No Obj: 0.005042, Avg Recall: 0.405660, count: 106 -Region Avg IOU: 0.396313, Class: 1.000000, Obj: 0.246419, No Obj: 0.004589, Avg Recall: 0.385417, count: 96 -Region Avg IOU: 0.461853, Class: 1.000000, Obj: 0.251655, No Obj: 0.005473, Avg Recall: 0.523810, count: 126 -Region Avg IOU: 0.431045, Class: 1.000000, Obj: 0.269714, No Obj: 0.004400, Avg Recall: 0.510870, count: 92 -Region Avg IOU: 0.366097, Class: 1.000000, Obj: 0.248349, No Obj: 0.005580, Avg Recall: 0.344156, count: 154 -Region Avg IOU: 0.444362, Class: 1.000000, Obj: 0.308383, No Obj: 0.005599, Avg Recall: 0.476191, count: 126 -1669: 0.572826, 0.506479 avg, 0.001000 rate, 17.892361 seconds, 427264 images -Loaded: 3.549294 seconds -Region Avg IOU: 0.422964, Class: 1.000000, Obj: 0.304568, No Obj: 0.005426, Avg Recall: 0.419643, count: 112 -Region Avg IOU: 0.420351, Class: 1.000000, Obj: 0.297096, No Obj: 0.005132, Avg Recall: 0.433962, count: 106 -Region Avg IOU: 0.474726, Class: 1.000000, Obj: 0.254426, No Obj: 0.004465, Avg Recall: 0.527473, count: 91 -Region Avg IOU: 0.399546, Class: 1.000000, Obj: 0.240788, No Obj: 0.004861, Avg Recall: 0.373984, count: 123 -Region Avg IOU: 0.461971, Class: 1.000000, Obj: 0.291178, No Obj: 0.006233, Avg Recall: 0.541667, count: 144 -Region Avg IOU: 0.410133, Class: 1.000000, Obj: 0.252049, No Obj: 0.005361, Avg Recall: 0.433824, count: 136 -Region Avg IOU: 0.458775, Class: 1.000000, Obj: 0.254931, No Obj: 0.004484, Avg Recall: 0.465909, count: 88 -Region Avg IOU: 0.398151, Class: 1.000000, Obj: 0.254173, No Obj: 0.004625, Avg Recall: 0.422018, count: 109 -1670: 0.521241, 0.507955 avg, 0.001000 rate, 16.185593 seconds, 427520 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 0.000064 seconds -Region Avg IOU: 0.483333, Class: 1.000000, Obj: 0.249326, No Obj: 0.003879, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.466291, Class: 1.000000, Obj: 0.289446, No Obj: 0.004318, Avg Recall: 0.529412, count: 119 -Region Avg IOU: 0.516955, Class: 1.000000, Obj: 0.293548, No Obj: 0.004194, Avg Recall: 0.568000, count: 125 -Region Avg IOU: 0.493104, Class: 1.000000, Obj: 0.325670, No Obj: 0.003763, Avg Recall: 0.566667, count: 90 -Region Avg IOU: 0.502096, Class: 1.000000, Obj: 0.342175, No Obj: 0.004237, Avg Recall: 0.544554, count: 101 -Region Avg IOU: 0.496409, Class: 1.000000, Obj: 0.313322, No Obj: 0.004312, Avg Recall: 0.531250, count: 128 -Region Avg IOU: 0.501304, Class: 1.000000, Obj: 0.303463, No Obj: 0.004538, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.486516, Class: 1.000000, Obj: 0.333182, No Obj: 0.004554, Avg Recall: 0.539823, count: 113 -1671: 0.459789, 0.503139 avg, 0.001000 rate, 22.443027 seconds, 427776 images -Loaded: 0.719739 seconds -Region Avg IOU: 0.461840, Class: 1.000000, Obj: 0.279783, No Obj: 0.004072, Avg Recall: 0.476562, count: 128 -Region Avg IOU: 0.477124, Class: 1.000000, Obj: 0.291696, No Obj: 0.004064, Avg Recall: 0.495495, count: 111 -Region Avg IOU: 0.528438, Class: 1.000000, Obj: 0.316209, No Obj: 0.004062, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.463052, Class: 1.000000, Obj: 0.318166, No Obj: 0.004305, Avg Recall: 0.519231, count: 104 -Region Avg IOU: 0.485496, Class: 1.000000, Obj: 0.280432, No Obj: 0.004096, Avg Recall: 0.525773, count: 97 -Region Avg IOU: 0.551858, Class: 1.000000, Obj: 0.312005, No Obj: 0.003659, Avg Recall: 0.671233, count: 73 -Region Avg IOU: 0.461092, Class: 1.000000, Obj: 0.262899, No Obj: 0.003881, Avg Recall: 0.512397, count: 121 -Region Avg IOU: 0.500479, Class: 1.000000, Obj: 0.295784, No Obj: 0.003979, Avg Recall: 0.537634, count: 93 -1672: 0.464233, 0.499248 avg, 0.001000 rate, 21.380459 seconds, 428032 images -Loaded: 1.001279 seconds -Region Avg IOU: 0.562641, Class: 1.000000, Obj: 0.272263, No Obj: 0.003236, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.535194, Class: 1.000000, Obj: 0.308449, No Obj: 0.003567, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.546245, Class: 1.000000, Obj: 0.302721, No Obj: 0.003465, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.503631, Class: 1.000000, Obj: 0.259432, No Obj: 0.003677, Avg Recall: 0.541667, count: 96 -Region Avg IOU: 0.533679, Class: 1.000000, Obj: 0.311182, No Obj: 0.003717, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.503615, Class: 1.000000, Obj: 0.320712, No Obj: 0.003565, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.496491, Class: 1.000000, Obj: 0.247217, No Obj: 0.003768, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.528645, Class: 1.000000, Obj: 0.297878, No Obj: 0.003952, Avg Recall: 0.601942, count: 103 -1673: 0.424525, 0.491776 avg, 0.001000 rate, 22.628204 seconds, 428288 images -Loaded: 0.089242 seconds -Region Avg IOU: 0.489572, Class: 1.000000, Obj: 0.227994, No Obj: 0.003640, Avg Recall: 0.536585, count: 123 -Region Avg IOU: 0.548514, Class: 1.000000, Obj: 0.287259, No Obj: 0.003718, Avg Recall: 0.659091, count: 88 -Region Avg IOU: 0.477022, Class: 1.000000, Obj: 0.313006, No Obj: 0.003734, Avg Recall: 0.487603, count: 121 -Region Avg IOU: 0.507398, Class: 1.000000, Obj: 0.264119, No Obj: 0.003492, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.521907, Class: 1.000000, Obj: 0.303030, No Obj: 0.003594, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.478657, Class: 1.000000, Obj: 0.252869, No Obj: 0.003892, Avg Recall: 0.477477, count: 111 -Region Avg IOU: 0.518062, Class: 1.000000, Obj: 0.244043, No Obj: 0.003567, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.528979, Class: 1.000000, Obj: 0.271975, No Obj: 0.003950, Avg Recall: 0.555556, count: 108 -1674: 0.483581, 0.490956 avg, 0.001000 rate, 23.275547 seconds, 428544 images -Loaded: 0.031740 seconds -Region Avg IOU: 0.502166, Class: 1.000000, Obj: 0.285299, No Obj: 0.003014, Avg Recall: 0.528736, count: 87 -Region Avg IOU: 0.455015, Class: 1.000000, Obj: 0.263535, No Obj: 0.003436, Avg Recall: 0.448819, count: 127 -Region Avg IOU: 0.466965, Class: 1.000000, Obj: 0.230524, No Obj: 0.003826, Avg Recall: 0.504000, count: 125 -Region Avg IOU: 0.538473, Class: 1.000000, Obj: 0.291897, No Obj: 0.003326, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.489935, Class: 1.000000, Obj: 0.258450, No Obj: 0.003507, Avg Recall: 0.532609, count: 92 -Region Avg IOU: 0.509663, Class: 1.000000, Obj: 0.227420, No Obj: 0.003297, Avg Recall: 0.572650, count: 117 -Region Avg IOU: 0.516462, Class: 1.000000, Obj: 0.263825, No Obj: 0.003714, Avg Recall: 0.613139, count: 137 -Region Avg IOU: 0.500897, Class: 1.000000, Obj: 0.290231, No Obj: 0.003690, Avg Recall: 0.525424, count: 118 -1675: 0.510336, 0.492894 avg, 0.001000 rate, 22.296972 seconds, 428800 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.511954, Class: 1.000000, Obj: 0.258666, No Obj: 0.003741, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.533593, Class: 1.000000, Obj: 0.290553, No Obj: 0.003759, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.544130, Class: 1.000000, Obj: 0.306301, No Obj: 0.003609, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.467286, Class: 1.000000, Obj: 0.244032, No Obj: 0.003573, Avg Recall: 0.523438, count: 128 -Region Avg IOU: 0.503245, Class: 1.000000, Obj: 0.288726, No Obj: 0.003591, Avg Recall: 0.570175, count: 114 -Region Avg IOU: 0.565818, Class: 1.000000, Obj: 0.291045, No Obj: 0.003400, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.535051, Class: 1.000000, Obj: 0.267912, No Obj: 0.003784, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.495093, Class: 1.000000, Obj: 0.231497, No Obj: 0.003721, Avg Recall: 0.570000, count: 100 -1676: 0.521353, 0.495740 avg, 0.001000 rate, 22.887434 seconds, 429056 images -Loaded: 0.012353 seconds -Region Avg IOU: 0.466880, Class: 1.000000, Obj: 0.254532, No Obj: 0.003468, Avg Recall: 0.500000, count: 96 -Region Avg IOU: 0.513537, Class: 1.000000, Obj: 0.303993, No Obj: 0.004357, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.442523, Class: 1.000000, Obj: 0.247662, No Obj: 0.003323, Avg Recall: 0.452991, count: 117 -Region Avg IOU: 0.483356, Class: 1.000000, Obj: 0.280930, No Obj: 0.003793, Avg Recall: 0.539683, count: 126 -Region Avg IOU: 0.473757, Class: 1.000000, Obj: 0.236625, No Obj: 0.004170, Avg Recall: 0.546392, count: 97 -Region Avg IOU: 0.506337, Class: 1.000000, Obj: 0.303045, No Obj: 0.003507, Avg Recall: 0.604651, count: 86 -Region Avg IOU: 0.554381, Class: 1.000000, Obj: 0.302567, No Obj: 0.003924, Avg Recall: 0.651163, count: 86 -Region Avg IOU: 0.499563, Class: 1.000000, Obj: 0.264077, No Obj: 0.003760, Avg Recall: 0.574468, count: 94 -1677: 0.560506, 0.502217 avg, 0.001000 rate, 21.466208 seconds, 429312 images -Loaded: 0.972865 seconds -Region Avg IOU: 0.523311, Class: 1.000000, Obj: 0.288332, No Obj: 0.004078, Avg Recall: 0.586777, count: 121 -Region Avg IOU: 0.559013, Class: 1.000000, Obj: 0.301813, No Obj: 0.003773, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.491597, Class: 1.000000, Obj: 0.260347, No Obj: 0.004018, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.534968, Class: 1.000000, Obj: 0.304021, No Obj: 0.004145, Avg Recall: 0.596774, count: 124 -Region Avg IOU: 0.548211, Class: 1.000000, Obj: 0.273180, No Obj: 0.003943, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.442868, Class: 1.000000, Obj: 0.260152, No Obj: 0.003727, Avg Recall: 0.480392, count: 102 -Region Avg IOU: 0.529116, Class: 1.000000, Obj: 0.296156, No Obj: 0.003921, Avg Recall: 0.594828, count: 116 -Region Avg IOU: 0.449482, Class: 1.000000, Obj: 0.231445, No Obj: 0.003997, Avg Recall: 0.482456, count: 114 -1678: 0.504336, 0.502429 avg, 0.001000 rate, 21.703028 seconds, 429568 images -Loaded: 0.105740 seconds -Region Avg IOU: 0.470455, Class: 1.000000, Obj: 0.300127, No Obj: 0.003997, Avg Recall: 0.521739, count: 115 -Region Avg IOU: 0.514513, Class: 1.000000, Obj: 0.295122, No Obj: 0.004160, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.477592, Class: 1.000000, Obj: 0.291545, No Obj: 0.003673, Avg Recall: 0.534884, count: 86 -Region Avg IOU: 0.522781, Class: 1.000000, Obj: 0.271907, No Obj: 0.004437, Avg Recall: 0.578947, count: 114 -Region Avg IOU: 0.538575, Class: 1.000000, Obj: 0.251623, No Obj: 0.004752, Avg Recall: 0.646154, count: 130 -Region Avg IOU: 0.507840, Class: 1.000000, Obj: 0.317672, No Obj: 0.004035, Avg Recall: 0.549550, count: 111 -Region Avg IOU: 0.523430, Class: 1.000000, Obj: 0.324677, No Obj: 0.003907, Avg Recall: 0.608247, count: 97 -Region Avg IOU: 0.543099, Class: 1.000000, Obj: 0.321524, No Obj: 0.003990, Avg Recall: 0.670732, count: 82 -1679: 0.476497, 0.499835 avg, 0.001000 rate, 20.788408 seconds, 429824 images -Loaded: 2.373010 seconds -Region Avg IOU: 0.556460, Class: 1.000000, Obj: 0.347012, No Obj: 0.004448, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.478031, Class: 1.000000, Obj: 0.333815, No Obj: 0.004587, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.556729, Class: 1.000000, Obj: 0.322920, No Obj: 0.004500, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.488710, Class: 1.000000, Obj: 0.325373, No Obj: 0.004356, Avg Recall: 0.517544, count: 114 -Region Avg IOU: 0.548104, Class: 1.000000, Obj: 0.331364, No Obj: 0.004602, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.532472, Class: 1.000000, Obj: 0.343607, No Obj: 0.004629, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.482043, Class: 1.000000, Obj: 0.285936, No Obj: 0.004466, Avg Recall: 0.539568, count: 139 -Region Avg IOU: 0.498947, Class: 1.000000, Obj: 0.317208, No Obj: 0.004266, Avg Recall: 0.559633, count: 109 -1680: 0.515857, 0.501438 avg, 0.001000 rate, 21.080324 seconds, 430080 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000073 seconds -Region Avg IOU: 0.584378, Class: 1.000000, Obj: 0.395375, No Obj: 0.004107, Avg Recall: 0.700730, count: 137 -Region Avg IOU: 0.544317, Class: 1.000000, Obj: 0.337661, No Obj: 0.004066, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.585447, Class: 1.000000, Obj: 0.358167, No Obj: 0.003986, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.570652, Class: 1.000000, Obj: 0.333325, No Obj: 0.003697, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.610166, Class: 1.000000, Obj: 0.324403, No Obj: 0.004418, Avg Recall: 0.786260, count: 131 -Region Avg IOU: 0.581400, Class: 1.000000, Obj: 0.337212, No Obj: 0.004124, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.591676, Class: 1.000000, Obj: 0.323032, No Obj: 0.003962, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.589003, Class: 1.000000, Obj: 0.382105, No Obj: 0.003572, Avg Recall: 0.739130, count: 92 -1681: 0.514659, 0.502760 avg, 0.001000 rate, 27.174091 seconds, 430336 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.542647, Class: 1.000000, Obj: 0.342080, No Obj: 0.003719, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.623015, Class: 1.000000, Obj: 0.386036, No Obj: 0.003829, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.579601, Class: 1.000000, Obj: 0.346408, No Obj: 0.003539, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.571763, Class: 1.000000, Obj: 0.352685, No Obj: 0.004121, Avg Recall: 0.701613, count: 124 -Region Avg IOU: 0.557278, Class: 1.000000, Obj: 0.305574, No Obj: 0.004123, Avg Recall: 0.685714, count: 140 -Region Avg IOU: 0.571239, Class: 1.000000, Obj: 0.329735, No Obj: 0.003552, Avg Recall: 0.639535, count: 86 -Region Avg IOU: 0.606447, Class: 1.000000, Obj: 0.358463, No Obj: 0.003778, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.565351, Class: 1.000000, Obj: 0.336907, No Obj: 0.003443, Avg Recall: 0.648352, count: 91 -1682: 0.479228, 0.500407 avg, 0.001000 rate, 26.115295 seconds, 430592 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.568787, Class: 1.000000, Obj: 0.273824, No Obj: 0.003845, Avg Recall: 0.669231, count: 130 -Region Avg IOU: 0.609198, Class: 1.000000, Obj: 0.362269, No Obj: 0.003616, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.544365, Class: 1.000000, Obj: 0.360021, No Obj: 0.003621, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.561503, Class: 1.000000, Obj: 0.318531, No Obj: 0.003664, Avg Recall: 0.656250, count: 128 -Region Avg IOU: 0.573733, Class: 1.000000, Obj: 0.301092, No Obj: 0.004067, Avg Recall: 0.669231, count: 130 -Region Avg IOU: 0.550641, Class: 1.000000, Obj: 0.310617, No Obj: 0.003741, Avg Recall: 0.607692, count: 130 -Region Avg IOU: 0.566025, Class: 1.000000, Obj: 0.347899, No Obj: 0.003188, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.537985, Class: 1.000000, Obj: 0.279705, No Obj: 0.003539, Avg Recall: 0.616000, count: 125 -1683: 0.515832, 0.501949 avg, 0.001000 rate, 25.994080 seconds, 430848 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.569188, Class: 1.000000, Obj: 0.314986, No Obj: 0.003340, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.576100, Class: 1.000000, Obj: 0.304286, No Obj: 0.003298, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.550285, Class: 1.000000, Obj: 0.299064, No Obj: 0.003478, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.561563, Class: 1.000000, Obj: 0.299520, No Obj: 0.003074, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.572432, Class: 1.000000, Obj: 0.310153, No Obj: 0.003020, Avg Recall: 0.648352, count: 91 -Region Avg IOU: 0.550549, Class: 1.000000, Obj: 0.305959, No Obj: 0.002964, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.590443, Class: 1.000000, Obj: 0.283330, No Obj: 0.003959, Avg Recall: 0.754491, count: 167 -Region Avg IOU: 0.606315, Class: 1.000000, Obj: 0.369095, No Obj: 0.003025, Avg Recall: 0.651163, count: 86 -1684: 0.466581, 0.498412 avg, 0.001000 rate, 25.568138 seconds, 431104 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.582225, Class: 1.000000, Obj: 0.267688, No Obj: 0.003235, Avg Recall: 0.685315, count: 143 -Region Avg IOU: 0.566760, Class: 1.000000, Obj: 0.296619, No Obj: 0.002961, Avg Recall: 0.710843, count: 83 -Region Avg IOU: 0.570322, Class: 1.000000, Obj: 0.310597, No Obj: 0.003165, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.525173, Class: 1.000000, Obj: 0.267189, No Obj: 0.003061, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.605437, Class: 1.000000, Obj: 0.316228, No Obj: 0.003535, Avg Recall: 0.748031, count: 127 -Region Avg IOU: 0.576644, Class: 1.000000, Obj: 0.258589, No Obj: 0.003331, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.523276, Class: 1.000000, Obj: 0.237576, No Obj: 0.003063, Avg Recall: 0.607407, count: 135 -Region Avg IOU: 0.546214, Class: 1.000000, Obj: 0.289351, No Obj: 0.002923, Avg Recall: 0.596154, count: 104 -1685: 0.519005, 0.500472 avg, 0.001000 rate, 26.841970 seconds, 431360 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.535751, Class: 1.000000, Obj: 0.278993, No Obj: 0.002878, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.601617, Class: 1.000000, Obj: 0.301814, No Obj: 0.003323, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.579369, Class: 1.000000, Obj: 0.271236, No Obj: 0.002856, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.516863, Class: 1.000000, Obj: 0.235250, No Obj: 0.002665, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.597283, Class: 1.000000, Obj: 0.277470, No Obj: 0.003219, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.538745, Class: 1.000000, Obj: 0.227470, No Obj: 0.002950, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.554356, Class: 1.000000, Obj: 0.258463, No Obj: 0.002581, Avg Recall: 0.618421, count: 76 -Region Avg IOU: 0.525577, Class: 1.000000, Obj: 0.250035, No Obj: 0.003209, Avg Recall: 0.604317, count: 139 -1686: 0.486975, 0.499122 avg, 0.001000 rate, 26.443756 seconds, 431616 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.613692, Class: 1.000000, Obj: 0.290477, No Obj: 0.003023, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.565447, Class: 1.000000, Obj: 0.287544, No Obj: 0.003559, Avg Recall: 0.678832, count: 137 -Region Avg IOU: 0.541451, Class: 1.000000, Obj: 0.279670, No Obj: 0.002373, Avg Recall: 0.633333, count: 90 -Region Avg IOU: 0.568022, Class: 1.000000, Obj: 0.264156, No Obj: 0.003299, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.564958, Class: 1.000000, Obj: 0.264734, No Obj: 0.003024, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.573505, Class: 1.000000, Obj: 0.277578, No Obj: 0.002928, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.559202, Class: 1.000000, Obj: 0.296786, No Obj: 0.002746, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.590762, Class: 1.000000, Obj: 0.264885, No Obj: 0.003504, Avg Recall: 0.710145, count: 138 -1687: 0.483415, 0.497551 avg, 0.001000 rate, 25.727455 seconds, 431872 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.555186, Class: 1.000000, Obj: 0.311723, No Obj: 0.002836, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.560580, Class: 1.000000, Obj: 0.286708, No Obj: 0.003037, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.573152, Class: 1.000000, Obj: 0.266365, No Obj: 0.003121, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.594609, Class: 1.000000, Obj: 0.284461, No Obj: 0.003294, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.608667, Class: 1.000000, Obj: 0.312159, No Obj: 0.003040, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.515575, Class: 1.000000, Obj: 0.256597, No Obj: 0.003077, Avg Recall: 0.601695, count: 118 -Region Avg IOU: 0.612037, Class: 1.000000, Obj: 0.336513, No Obj: 0.003085, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.597842, Class: 1.000000, Obj: 0.347288, No Obj: 0.003141, Avg Recall: 0.721649, count: 97 -1688: 0.446135, 0.492410 avg, 0.001000 rate, 25.382883 seconds, 432128 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.594778, Class: 1.000000, Obj: 0.291921, No Obj: 0.003029, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.555028, Class: 1.000000, Obj: 0.261764, No Obj: 0.003219, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.554153, Class: 1.000000, Obj: 0.302855, No Obj: 0.003434, Avg Recall: 0.642276, count: 123 -Region Avg IOU: 0.571260, Class: 1.000000, Obj: 0.353840, No Obj: 0.003158, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.566168, Class: 1.000000, Obj: 0.311951, No Obj: 0.003103, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.525008, Class: 1.000000, Obj: 0.287335, No Obj: 0.003141, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.579155, Class: 1.000000, Obj: 0.313813, No Obj: 0.003452, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.556846, Class: 1.000000, Obj: 0.281450, No Obj: 0.003341, Avg Recall: 0.666667, count: 114 -1689: 0.466154, 0.489784 avg, 0.001000 rate, 25.670933 seconds, 432384 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.596343, Class: 1.000000, Obj: 0.310151, No Obj: 0.003352, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.579931, Class: 1.000000, Obj: 0.351374, No Obj: 0.003328, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.544707, Class: 1.000000, Obj: 0.291582, No Obj: 0.003433, Avg Recall: 0.639706, count: 136 -Region Avg IOU: 0.560794, Class: 1.000000, Obj: 0.317614, No Obj: 0.003059, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.552602, Class: 1.000000, Obj: 0.341370, No Obj: 0.003519, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.582686, Class: 1.000000, Obj: 0.333352, No Obj: 0.003591, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.560459, Class: 1.000000, Obj: 0.300625, No Obj: 0.003580, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.505525, Class: 1.000000, Obj: 0.308994, No Obj: 0.003208, Avg Recall: 0.557377, count: 122 -1690: 0.509224, 0.491728 avg, 0.001000 rate, 27.088633 seconds, 432640 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 22.211382 seconds -Region Avg IOU: 0.537711, Class: 1.000000, Obj: 0.325257, No Obj: 0.004073, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.579087, Class: 1.000000, Obj: 0.291035, No Obj: 0.004231, Avg Recall: 0.677686, count: 121 -Region Avg IOU: 0.569475, Class: 1.000000, Obj: 0.337234, No Obj: 0.004033, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.557267, Class: 1.000000, Obj: 0.323797, No Obj: 0.004279, Avg Recall: 0.712121, count: 132 -Region Avg IOU: 0.488397, Class: 1.000000, Obj: 0.283910, No Obj: 0.003053, Avg Recall: 0.520548, count: 73 -Region Avg IOU: 0.533606, Class: 1.000000, Obj: 0.293283, No Obj: 0.003515, Avg Recall: 0.659341, count: 91 -Region Avg IOU: 0.570057, Class: 1.000000, Obj: 0.366041, No Obj: 0.004032, Avg Recall: 0.666667, count: 123 -Region Avg IOU: 0.540834, Class: 1.000000, Obj: 0.320325, No Obj: 0.003693, Avg Recall: 0.642857, count: 112 -1691: 0.465072, 0.489062 avg, 0.001000 rate, 23.155800 seconds, 432896 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.473408, Class: 1.000000, Obj: 0.285406, No Obj: 0.003678, Avg Recall: 0.500000, count: 128 -Region Avg IOU: 0.548913, Class: 1.000000, Obj: 0.334680, No Obj: 0.003875, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.519082, Class: 1.000000, Obj: 0.319082, No Obj: 0.003529, Avg Recall: 0.593023, count: 86 -Region Avg IOU: 0.550225, Class: 1.000000, Obj: 0.331177, No Obj: 0.003717, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.534625, Class: 1.000000, Obj: 0.338574, No Obj: 0.003488, Avg Recall: 0.621951, count: 82 -Region Avg IOU: 0.516555, Class: 1.000000, Obj: 0.315577, No Obj: 0.003641, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.524533, Class: 1.000000, Obj: 0.310393, No Obj: 0.003821, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.536424, Class: 1.000000, Obj: 0.357486, No Obj: 0.003722, Avg Recall: 0.571429, count: 112 -1692: 0.447047, 0.484861 avg, 0.001000 rate, 23.961493 seconds, 433152 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.553835, Class: 1.000000, Obj: 0.356272, No Obj: 0.003956, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.548498, Class: 1.000000, Obj: 0.355950, No Obj: 0.003790, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.537801, Class: 1.000000, Obj: 0.325029, No Obj: 0.003890, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.558030, Class: 1.000000, Obj: 0.342681, No Obj: 0.003761, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.533424, Class: 1.000000, Obj: 0.346318, No Obj: 0.003524, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.526281, Class: 1.000000, Obj: 0.298464, No Obj: 0.003537, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.537489, Class: 1.000000, Obj: 0.315078, No Obj: 0.003802, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.503828, Class: 1.000000, Obj: 0.319330, No Obj: 0.003939, Avg Recall: 0.555556, count: 144 -1693: 0.512425, 0.487617 avg, 0.001000 rate, 23.510923 seconds, 433408 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.508206, Class: 1.000000, Obj: 0.311501, No Obj: 0.003866, Avg Recall: 0.552083, count: 96 -Region Avg IOU: 0.539586, Class: 1.000000, Obj: 0.364355, No Obj: 0.004091, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.528423, Class: 1.000000, Obj: 0.334479, No Obj: 0.003907, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.566913, Class: 1.000000, Obj: 0.344639, No Obj: 0.004023, Avg Recall: 0.638554, count: 83 -Region Avg IOU: 0.572044, Class: 1.000000, Obj: 0.328900, No Obj: 0.003442, Avg Recall: 0.701149, count: 87 -Region Avg IOU: 0.521025, Class: 1.000000, Obj: 0.270806, No Obj: 0.003602, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.574097, Class: 1.000000, Obj: 0.285785, No Obj: 0.003455, Avg Recall: 0.701149, count: 87 -Region Avg IOU: 0.553744, Class: 1.000000, Obj: 0.327616, No Obj: 0.003528, Avg Recall: 0.639175, count: 97 -1694: 0.464836, 0.485339 avg, 0.001000 rate, 23.641687 seconds, 433664 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.572770, Class: 1.000000, Obj: 0.340972, No Obj: 0.004450, Avg Recall: 0.673611, count: 144 -Region Avg IOU: 0.541856, Class: 1.000000, Obj: 0.314856, No Obj: 0.003684, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.561836, Class: 1.000000, Obj: 0.340521, No Obj: 0.003724, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.514110, Class: 1.000000, Obj: 0.320420, No Obj: 0.003488, Avg Recall: 0.556522, count: 115 -Region Avg IOU: 0.579790, Class: 1.000000, Obj: 0.300134, No Obj: 0.003792, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.544314, Class: 1.000000, Obj: 0.291957, No Obj: 0.003761, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.522730, Class: 1.000000, Obj: 0.323634, No Obj: 0.003793, Avg Recall: 0.547826, count: 115 -Region Avg IOU: 0.537110, Class: 1.000000, Obj: 0.298490, No Obj: 0.003184, Avg Recall: 0.615385, count: 91 -1695: 0.500083, 0.486814 avg, 0.001000 rate, 23.435825 seconds, 433920 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.571651, Class: 1.000000, Obj: 0.319581, No Obj: 0.003368, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.504818, Class: 1.000000, Obj: 0.283146, No Obj: 0.003290, Avg Recall: 0.533333, count: 90 -Region Avg IOU: 0.583005, Class: 1.000000, Obj: 0.330481, No Obj: 0.004214, Avg Recall: 0.702479, count: 121 -Region Avg IOU: 0.554560, Class: 1.000000, Obj: 0.329916, No Obj: 0.003890, Avg Recall: 0.624060, count: 133 -Region Avg IOU: 0.549372, Class: 1.000000, Obj: 0.332049, No Obj: 0.003312, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.562681, Class: 1.000000, Obj: 0.326385, No Obj: 0.003572, Avg Recall: 0.643478, count: 115 -Region Avg IOU: 0.525253, Class: 1.000000, Obj: 0.307828, No Obj: 0.003375, Avg Recall: 0.586207, count: 116 -Region Avg IOU: 0.541584, Class: 1.000000, Obj: 0.343400, No Obj: 0.003829, Avg Recall: 0.614679, count: 109 -1696: 0.453177, 0.483450 avg, 0.001000 rate, 23.919754 seconds, 434176 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.555086, Class: 1.000000, Obj: 0.302890, No Obj: 0.003486, Avg Recall: 0.655462, count: 119 -Region Avg IOU: 0.489490, Class: 1.000000, Obj: 0.279664, No Obj: 0.003189, Avg Recall: 0.528846, count: 104 -Region Avg IOU: 0.548721, Class: 1.000000, Obj: 0.301992, No Obj: 0.003277, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.529270, Class: 1.000000, Obj: 0.291745, No Obj: 0.003867, Avg Recall: 0.594828, count: 116 -Region Avg IOU: 0.520331, Class: 1.000000, Obj: 0.296596, No Obj: 0.003647, Avg Recall: 0.547009, count: 117 -Region Avg IOU: 0.499214, Class: 1.000000, Obj: 0.266263, No Obj: 0.003284, Avg Recall: 0.522523, count: 111 -Region Avg IOU: 0.509080, Class: 1.000000, Obj: 0.244192, No Obj: 0.002893, Avg Recall: 0.608247, count: 97 -Region Avg IOU: 0.560600, Class: 1.000000, Obj: 0.319126, No Obj: 0.003185, Avg Recall: 0.650000, count: 100 -1697: 0.476076, 0.482712 avg, 0.001000 rate, 23.658348 seconds, 434432 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.580056, Class: 1.000000, Obj: 0.292423, No Obj: 0.003410, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.494354, Class: 1.000000, Obj: 0.289201, No Obj: 0.003878, Avg Recall: 0.542253, count: 142 -Region Avg IOU: 0.496233, Class: 1.000000, Obj: 0.260636, No Obj: 0.003436, Avg Recall: 0.571429, count: 112 -Region Avg IOU: 0.516842, Class: 1.000000, Obj: 0.273837, No Obj: 0.002940, Avg Recall: 0.565657, count: 99 -Region Avg IOU: 0.492426, Class: 1.000000, Obj: 0.264527, No Obj: 0.003208, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.504009, Class: 1.000000, Obj: 0.300150, No Obj: 0.003596, Avg Recall: 0.570248, count: 121 -Region Avg IOU: 0.595047, Class: 1.000000, Obj: 0.304469, No Obj: 0.003431, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.519251, Class: 1.000000, Obj: 0.279362, No Obj: 0.003366, Avg Recall: 0.594059, count: 101 -1698: 0.491107, 0.483552 avg, 0.001000 rate, 24.599176 seconds, 434688 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.538224, Class: 1.000000, Obj: 0.277080, No Obj: 0.003132, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.515343, Class: 1.000000, Obj: 0.300905, No Obj: 0.003512, Avg Recall: 0.611111, count: 126 -Region Avg IOU: 0.511754, Class: 1.000000, Obj: 0.277498, No Obj: 0.003301, Avg Recall: 0.583333, count: 120 -Region Avg IOU: 0.576218, Class: 1.000000, Obj: 0.333710, No Obj: 0.003693, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.581268, Class: 1.000000, Obj: 0.337403, No Obj: 0.003055, Avg Recall: 0.666667, count: 81 -Region Avg IOU: 0.514979, Class: 1.000000, Obj: 0.297866, No Obj: 0.003178, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.505307, Class: 1.000000, Obj: 0.274288, No Obj: 0.003214, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.519246, Class: 1.000000, Obj: 0.241005, No Obj: 0.003558, Avg Recall: 0.596899, count: 129 -1699: 0.464014, 0.481598 avg, 0.001000 rate, 23.650871 seconds, 434944 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.490145, Class: 1.000000, Obj: 0.307953, No Obj: 0.003791, Avg Recall: 0.541667, count: 120 -Region Avg IOU: 0.552807, Class: 1.000000, Obj: 0.296087, No Obj: 0.003241, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.515616, Class: 1.000000, Obj: 0.319323, No Obj: 0.003093, Avg Recall: 0.625000, count: 80 -Region Avg IOU: 0.542515, Class: 1.000000, Obj: 0.258973, No Obj: 0.003396, Avg Recall: 0.626374, count: 91 -Region Avg IOU: 0.497994, Class: 1.000000, Obj: 0.294179, No Obj: 0.003606, Avg Recall: 0.524194, count: 124 -Region Avg IOU: 0.532850, Class: 1.000000, Obj: 0.312102, No Obj: 0.003179, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.522260, Class: 1.000000, Obj: 0.316998, No Obj: 0.003294, Avg Recall: 0.594937, count: 79 -Region Avg IOU: 0.573595, Class: 1.000000, Obj: 0.281160, No Obj: 0.003750, Avg Recall: 0.695238, count: 105 -1700: 0.424837, 0.475922 avg, 0.001000 rate, 24.467731 seconds, 435200 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000081 seconds -Region Avg IOU: 0.602459, Class: 1.000000, Obj: 0.301831, No Obj: 0.003044, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.629386, Class: 1.000000, Obj: 0.362613, No Obj: 0.002943, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.601352, Class: 1.000000, Obj: 0.317054, No Obj: 0.002953, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.628172, Class: 1.000000, Obj: 0.304957, No Obj: 0.003636, Avg Recall: 0.773333, count: 150 -Region Avg IOU: 0.579880, Class: 1.000000, Obj: 0.287788, No Obj: 0.003389, Avg Recall: 0.700000, count: 160 -Region Avg IOU: 0.606137, Class: 1.000000, Obj: 0.365578, No Obj: 0.003022, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.616988, Class: 1.000000, Obj: 0.339093, No Obj: 0.002962, Avg Recall: 0.784091, count: 88 -Region Avg IOU: 0.593073, Class: 1.000000, Obj: 0.311859, No Obj: 0.003108, Avg Recall: 0.711712, count: 111 -1701: 0.526610, 0.480991 avg, 0.001000 rate, 28.884567 seconds, 435456 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.646851, Class: 1.000000, Obj: 0.346524, No Obj: 0.003057, Avg Recall: 0.827273, count: 110 -Region Avg IOU: 0.557852, Class: 1.000000, Obj: 0.342514, No Obj: 0.002889, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.618028, Class: 1.000000, Obj: 0.350201, No Obj: 0.003003, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.606376, Class: 1.000000, Obj: 0.292245, No Obj: 0.003357, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.594907, Class: 1.000000, Obj: 0.303479, No Obj: 0.003353, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.617751, Class: 1.000000, Obj: 0.323422, No Obj: 0.002748, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.645206, Class: 1.000000, Obj: 0.372039, No Obj: 0.003274, Avg Recall: 0.823009, count: 113 -Region Avg IOU: 0.647111, Class: 1.000000, Obj: 0.308782, No Obj: 0.003199, Avg Recall: 0.815217, count: 92 -1702: 0.486655, 0.481557 avg, 0.001000 rate, 28.846052 seconds, 435712 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.625256, Class: 1.000000, Obj: 0.317111, No Obj: 0.002742, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.611344, Class: 1.000000, Obj: 0.271991, No Obj: 0.003052, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.605326, Class: 1.000000, Obj: 0.300808, No Obj: 0.002878, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.630898, Class: 1.000000, Obj: 0.346704, No Obj: 0.003033, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.636690, Class: 1.000000, Obj: 0.312479, No Obj: 0.002748, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.635904, Class: 1.000000, Obj: 0.311800, No Obj: 0.003214, Avg Recall: 0.806202, count: 129 -Region Avg IOU: 0.609956, Class: 1.000000, Obj: 0.288644, No Obj: 0.002863, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.608994, Class: 1.000000, Obj: 0.256123, No Obj: 0.002665, Avg Recall: 0.755556, count: 90 -1703: 0.531577, 0.486559 avg, 0.001000 rate, 30.033800 seconds, 435968 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.647410, Class: 1.000000, Obj: 0.305971, No Obj: 0.002741, Avg Recall: 0.833333, count: 96 -Region Avg IOU: 0.610417, Class: 1.000000, Obj: 0.294655, No Obj: 0.002761, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.605293, Class: 1.000000, Obj: 0.305637, No Obj: 0.002986, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.580501, Class: 1.000000, Obj: 0.274296, No Obj: 0.002488, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.569637, Class: 1.000000, Obj: 0.292990, No Obj: 0.002548, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.643469, Class: 1.000000, Obj: 0.314366, No Obj: 0.002739, Avg Recall: 0.814815, count: 108 -Region Avg IOU: 0.606061, Class: 1.000000, Obj: 0.285678, No Obj: 0.003077, Avg Recall: 0.748148, count: 135 -Region Avg IOU: 0.566767, Class: 1.000000, Obj: 0.291409, No Obj: 0.002944, Avg Recall: 0.680272, count: 147 -1704: 0.533568, 0.491260 avg, 0.001000 rate, 29.640053 seconds, 436224 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.630744, Class: 1.000000, Obj: 0.269460, No Obj: 0.002580, Avg Recall: 0.814159, count: 113 -Region Avg IOU: 0.598108, Class: 1.000000, Obj: 0.263300, No Obj: 0.002714, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.608480, Class: 1.000000, Obj: 0.336695, No Obj: 0.002489, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.590355, Class: 1.000000, Obj: 0.251937, No Obj: 0.002513, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.602356, Class: 1.000000, Obj: 0.251160, No Obj: 0.002748, Avg Recall: 0.707692, count: 130 -Region Avg IOU: 0.622814, Class: 1.000000, Obj: 0.318753, No Obj: 0.002867, Avg Recall: 0.800000, count: 115 -Region Avg IOU: 0.604252, Class: 1.000000, Obj: 0.311565, No Obj: 0.002479, Avg Recall: 0.775000, count: 80 -Region Avg IOU: 0.641988, Class: 1.000000, Obj: 0.329846, No Obj: 0.002579, Avg Recall: 0.776596, count: 94 -1705: 0.475174, 0.489651 avg, 0.001000 rate, 29.376396 seconds, 436480 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.639468, Class: 1.000000, Obj: 0.303589, No Obj: 0.002475, Avg Recall: 0.835052, count: 97 -Region Avg IOU: 0.589836, Class: 1.000000, Obj: 0.292736, No Obj: 0.002339, Avg Recall: 0.711111, count: 90 -Region Avg IOU: 0.629795, Class: 1.000000, Obj: 0.250511, No Obj: 0.002547, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.546342, Class: 1.000000, Obj: 0.231710, No Obj: 0.002529, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.575782, Class: 1.000000, Obj: 0.267542, No Obj: 0.002441, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.603208, Class: 1.000000, Obj: 0.295595, No Obj: 0.002540, Avg Recall: 0.781250, count: 96 -Region Avg IOU: 0.622916, Class: 1.000000, Obj: 0.292247, No Obj: 0.002685, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.613841, Class: 1.000000, Obj: 0.311750, No Obj: 0.002914, Avg Recall: 0.792593, count: 135 -1706: 0.474325, 0.488119 avg, 0.001000 rate, 29.269413 seconds, 436736 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.613417, Class: 1.000000, Obj: 0.316483, No Obj: 0.002469, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.651760, Class: 1.000000, Obj: 0.290718, No Obj: 0.002492, Avg Recall: 0.817308, count: 104 -Region Avg IOU: 0.621564, Class: 1.000000, Obj: 0.267839, No Obj: 0.002579, Avg Recall: 0.775000, count: 120 -Region Avg IOU: 0.592575, Class: 1.000000, Obj: 0.284802, No Obj: 0.002650, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.634435, Class: 1.000000, Obj: 0.301537, No Obj: 0.002653, Avg Recall: 0.846154, count: 104 -Region Avg IOU: 0.588472, Class: 1.000000, Obj: 0.287200, No Obj: 0.002379, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.590284, Class: 1.000000, Obj: 0.273170, No Obj: 0.002697, Avg Recall: 0.724638, count: 138 -Region Avg IOU: 0.632963, Class: 1.000000, Obj: 0.292528, No Obj: 0.002642, Avg Recall: 0.786408, count: 103 -1707: 0.504498, 0.489757 avg, 0.001000 rate, 29.463877 seconds, 436992 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.567744, Class: 1.000000, Obj: 0.241118, No Obj: 0.002522, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.591847, Class: 1.000000, Obj: 0.289575, No Obj: 0.002703, Avg Recall: 0.702479, count: 121 -Region Avg IOU: 0.621930, Class: 1.000000, Obj: 0.257457, No Obj: 0.002822, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.629020, Class: 1.000000, Obj: 0.295428, No Obj: 0.002850, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.623795, Class: 1.000000, Obj: 0.282641, No Obj: 0.002819, Avg Recall: 0.792308, count: 130 -Region Avg IOU: 0.609908, Class: 1.000000, Obj: 0.270125, No Obj: 0.002765, Avg Recall: 0.796460, count: 113 -Region Avg IOU: 0.629008, Class: 1.000000, Obj: 0.282736, No Obj: 0.003109, Avg Recall: 0.792857, count: 140 -Region Avg IOU: 0.654468, Class: 1.000000, Obj: 0.337466, No Obj: 0.002716, Avg Recall: 0.822785, count: 79 -1708: 0.571295, 0.497911 avg, 0.001000 rate, 29.052879 seconds, 437248 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.610277, Class: 1.000000, Obj: 0.258312, No Obj: 0.002817, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.626447, Class: 1.000000, Obj: 0.254412, No Obj: 0.003080, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.590361, Class: 1.000000, Obj: 0.336383, No Obj: 0.002449, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.595569, Class: 1.000000, Obj: 0.282222, No Obj: 0.003143, Avg Recall: 0.742857, count: 140 -Region Avg IOU: 0.577117, Class: 1.000000, Obj: 0.356335, No Obj: 0.002988, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.632819, Class: 1.000000, Obj: 0.309496, No Obj: 0.002760, Avg Recall: 0.831776, count: 107 -Region Avg IOU: 0.606331, Class: 1.000000, Obj: 0.332059, No Obj: 0.003055, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.620834, Class: 1.000000, Obj: 0.323007, No Obj: 0.002564, Avg Recall: 0.797980, count: 99 -1709: 0.503935, 0.498513 avg, 0.001000 rate, 28.596228 seconds, 437504 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.600010, Class: 1.000000, Obj: 0.335058, No Obj: 0.002536, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.619521, Class: 1.000000, Obj: 0.328005, No Obj: 0.002650, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.599063, Class: 1.000000, Obj: 0.288761, No Obj: 0.002574, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.560588, Class: 1.000000, Obj: 0.302516, No Obj: 0.002679, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.556931, Class: 1.000000, Obj: 0.266964, No Obj: 0.002741, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.567451, Class: 1.000000, Obj: 0.313404, No Obj: 0.002424, Avg Recall: 0.637500, count: 80 -Region Avg IOU: 0.574043, Class: 1.000000, Obj: 0.312029, No Obj: 0.002722, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.620644, Class: 1.000000, Obj: 0.325933, No Obj: 0.002610, Avg Recall: 0.811881, count: 101 -1710: 0.409152, 0.489577 avg, 0.001000 rate, 28.470659 seconds, 437760 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 12.294998 seconds -Region Avg IOU: 0.546720, Class: 1.000000, Obj: 0.265209, No Obj: 0.003768, Avg Recall: 0.637037, count: 135 -Region Avg IOU: 0.525105, Class: 1.000000, Obj: 0.253290, No Obj: 0.003315, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.588545, Class: 1.000000, Obj: 0.347151, No Obj: 0.002989, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.566540, Class: 1.000000, Obj: 0.256491, No Obj: 0.003275, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.570979, Class: 1.000000, Obj: 0.290977, No Obj: 0.003740, Avg Recall: 0.681818, count: 132 -Region Avg IOU: 0.582652, Class: 1.000000, Obj: 0.274075, No Obj: 0.003108, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.588983, Class: 1.000000, Obj: 0.320747, No Obj: 0.002527, Avg Recall: 0.752941, count: 85 -Region Avg IOU: 0.555600, Class: 1.000000, Obj: 0.270526, No Obj: 0.003978, Avg Recall: 0.662252, count: 151 -1711: 0.547878, 0.495407 avg, 0.001000 rate, 25.802088 seconds, 438016 images -Loaded: 0.000130 seconds -Region Avg IOU: 0.588248, Class: 1.000000, Obj: 0.305771, No Obj: 0.003476, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.582475, Class: 1.000000, Obj: 0.311103, No Obj: 0.003094, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.535740, Class: 1.000000, Obj: 0.267711, No Obj: 0.003172, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.566379, Class: 1.000000, Obj: 0.267707, No Obj: 0.004061, Avg Recall: 0.650350, count: 143 -Region Avg IOU: 0.602414, Class: 1.000000, Obj: 0.378671, No Obj: 0.003038, Avg Recall: 0.744186, count: 86 -Region Avg IOU: 0.588064, Class: 1.000000, Obj: 0.341379, No Obj: 0.003979, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.580251, Class: 1.000000, Obj: 0.336183, No Obj: 0.002865, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.608204, Class: 1.000000, Obj: 0.313684, No Obj: 0.003328, Avg Recall: 0.719626, count: 107 -1712: 0.518926, 0.497759 avg, 0.001000 rate, 26.985792 seconds, 438272 images -Loaded: 0.000121 seconds -Region Avg IOU: 0.595310, Class: 1.000000, Obj: 0.344873, No Obj: 0.003535, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.583565, Class: 1.000000, Obj: 0.285048, No Obj: 0.003544, Avg Recall: 0.705882, count: 119 -Region Avg IOU: 0.591361, Class: 1.000000, Obj: 0.316052, No Obj: 0.003778, Avg Recall: 0.727273, count: 132 -Region Avg IOU: 0.569855, Class: 1.000000, Obj: 0.320855, No Obj: 0.003376, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.564885, Class: 1.000000, Obj: 0.340194, No Obj: 0.003053, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.570558, Class: 1.000000, Obj: 0.323337, No Obj: 0.003827, Avg Recall: 0.674074, count: 135 -Region Avg IOU: 0.565128, Class: 1.000000, Obj: 0.320210, No Obj: 0.003308, Avg Recall: 0.677165, count: 127 -Region Avg IOU: 0.552769, Class: 1.000000, Obj: 0.330833, No Obj: 0.003311, Avg Recall: 0.614173, count: 127 -1713: 0.495458, 0.497529 avg, 0.001000 rate, 26.072348 seconds, 438528 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.558302, Class: 1.000000, Obj: 0.344338, No Obj: 0.003071, Avg Recall: 0.658824, count: 85 -Region Avg IOU: 0.549395, Class: 1.000000, Obj: 0.257264, No Obj: 0.003302, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.544580, Class: 1.000000, Obj: 0.275923, No Obj: 0.003210, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.580436, Class: 1.000000, Obj: 0.305900, No Obj: 0.003884, Avg Recall: 0.697842, count: 139 -Region Avg IOU: 0.534987, Class: 1.000000, Obj: 0.348430, No Obj: 0.003656, Avg Recall: 0.611111, count: 126 -Region Avg IOU: 0.567764, Class: 1.000000, Obj: 0.326219, No Obj: 0.003534, Avg Recall: 0.637795, count: 127 -Region Avg IOU: 0.559109, Class: 1.000000, Obj: 0.298120, No Obj: 0.003192, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.560407, Class: 1.000000, Obj: 0.273722, No Obj: 0.002941, Avg Recall: 0.604938, count: 81 -1714: 0.502463, 0.498022 avg, 0.001000 rate, 26.693033 seconds, 438784 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.565106, Class: 1.000000, Obj: 0.316206, No Obj: 0.003201, Avg Recall: 0.648352, count: 91 -Region Avg IOU: 0.557196, Class: 1.000000, Obj: 0.327886, No Obj: 0.004051, Avg Recall: 0.647541, count: 122 -Region Avg IOU: 0.560093, Class: 1.000000, Obj: 0.337209, No Obj: 0.003057, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.576195, Class: 1.000000, Obj: 0.322354, No Obj: 0.003363, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.570486, Class: 1.000000, Obj: 0.345023, No Obj: 0.003388, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.548539, Class: 1.000000, Obj: 0.335092, No Obj: 0.003379, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.558509, Class: 1.000000, Obj: 0.344775, No Obj: 0.003598, Avg Recall: 0.625899, count: 139 -Region Avg IOU: 0.589675, Class: 1.000000, Obj: 0.334300, No Obj: 0.003070, Avg Recall: 0.685185, count: 108 -1715: 0.469635, 0.495183 avg, 0.001000 rate, 26.113312 seconds, 439040 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.567895, Class: 1.000000, Obj: 0.359719, No Obj: 0.003380, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.562661, Class: 1.000000, Obj: 0.298044, No Obj: 0.003341, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.611694, Class: 1.000000, Obj: 0.321031, No Obj: 0.003613, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.553607, Class: 1.000000, Obj: 0.268660, No Obj: 0.002708, Avg Recall: 0.615385, count: 78 -Region Avg IOU: 0.581149, Class: 1.000000, Obj: 0.342302, No Obj: 0.002832, Avg Recall: 0.721519, count: 79 -Region Avg IOU: 0.590919, Class: 1.000000, Obj: 0.335401, No Obj: 0.003125, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.550200, Class: 1.000000, Obj: 0.317181, No Obj: 0.003071, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.563104, Class: 1.000000, Obj: 0.338791, No Obj: 0.003363, Avg Recall: 0.684685, count: 111 -1716: 0.448349, 0.490500 avg, 0.001000 rate, 26.418818 seconds, 439296 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.576182, Class: 1.000000, Obj: 0.316850, No Obj: 0.003233, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.561841, Class: 1.000000, Obj: 0.302242, No Obj: 0.003219, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.569789, Class: 1.000000, Obj: 0.291799, No Obj: 0.003616, Avg Recall: 0.681159, count: 138 -Region Avg IOU: 0.615179, Class: 1.000000, Obj: 0.282586, No Obj: 0.003976, Avg Recall: 0.744000, count: 125 -Region Avg IOU: 0.552214, Class: 1.000000, Obj: 0.339610, No Obj: 0.002945, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.567600, Class: 1.000000, Obj: 0.284790, No Obj: 0.003473, Avg Recall: 0.684615, count: 130 -Region Avg IOU: 0.603793, Class: 1.000000, Obj: 0.306987, No Obj: 0.002900, Avg Recall: 0.807692, count: 78 -Region Avg IOU: 0.532106, Class: 1.000000, Obj: 0.333181, No Obj: 0.003124, Avg Recall: 0.561983, count: 121 -1717: 0.510386, 0.492489 avg, 0.001000 rate, 26.294199 seconds, 439552 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.635922, Class: 1.000000, Obj: 0.292183, No Obj: 0.003228, Avg Recall: 0.791209, count: 91 -Region Avg IOU: 0.594910, Class: 1.000000, Obj: 0.305769, No Obj: 0.002943, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.521861, Class: 1.000000, Obj: 0.291553, No Obj: 0.003070, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.590660, Class: 1.000000, Obj: 0.294779, No Obj: 0.003192, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.555638, Class: 1.000000, Obj: 0.322293, No Obj: 0.002912, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.628389, Class: 1.000000, Obj: 0.298070, No Obj: 0.002970, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.576390, Class: 1.000000, Obj: 0.289031, No Obj: 0.002937, Avg Recall: 0.708333, count: 120 -Region Avg IOU: 0.575647, Class: 1.000000, Obj: 0.292709, No Obj: 0.002868, Avg Recall: 0.692982, count: 114 -1718: 0.465740, 0.489814 avg, 0.001000 rate, 25.662008 seconds, 439808 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.529396, Class: 1.000000, Obj: 0.296961, No Obj: 0.002900, Avg Recall: 0.573034, count: 89 -Region Avg IOU: 0.542787, Class: 1.000000, Obj: 0.316675, No Obj: 0.002787, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.518037, Class: 1.000000, Obj: 0.240202, No Obj: 0.002304, Avg Recall: 0.595506, count: 89 -Region Avg IOU: 0.578634, Class: 1.000000, Obj: 0.316431, No Obj: 0.003116, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.608873, Class: 1.000000, Obj: 0.319031, No Obj: 0.003120, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.559449, Class: 1.000000, Obj: 0.320669, No Obj: 0.002613, Avg Recall: 0.604651, count: 86 -Region Avg IOU: 0.534363, Class: 1.000000, Obj: 0.290759, No Obj: 0.003535, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.592852, Class: 1.000000, Obj: 0.346045, No Obj: 0.002910, Avg Recall: 0.694444, count: 108 -1719: 0.456312, 0.486464 avg, 0.001000 rate, 25.610628 seconds, 440064 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.556994, Class: 1.000000, Obj: 0.299583, No Obj: 0.002965, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.550298, Class: 1.000000, Obj: 0.271821, No Obj: 0.002881, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.623540, Class: 1.000000, Obj: 0.304198, No Obj: 0.003328, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.588778, Class: 1.000000, Obj: 0.314435, No Obj: 0.002815, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.563441, Class: 1.000000, Obj: 0.291295, No Obj: 0.003537, Avg Recall: 0.655629, count: 151 -Region Avg IOU: 0.566864, Class: 1.000000, Obj: 0.272588, No Obj: 0.003242, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.556088, Class: 1.000000, Obj: 0.300065, No Obj: 0.002887, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.629879, Class: 1.000000, Obj: 0.302404, No Obj: 0.003167, Avg Recall: 0.818182, count: 88 -1720: 0.463492, 0.484167 avg, 0.001000 rate, 26.216801 seconds, 440320 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000070 seconds -Region Avg IOU: 0.570693, Class: 1.000000, Obj: 0.292462, No Obj: 0.002914, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.576545, Class: 1.000000, Obj: 0.288680, No Obj: 0.002886, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.589215, Class: 1.000000, Obj: 0.290240, No Obj: 0.003198, Avg Recall: 0.688889, count: 135 -Region Avg IOU: 0.587054, Class: 1.000000, Obj: 0.314596, No Obj: 0.003438, Avg Recall: 0.671756, count: 131 -Region Avg IOU: 0.609298, Class: 1.000000, Obj: 0.318655, No Obj: 0.003026, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.600915, Class: 1.000000, Obj: 0.269011, No Obj: 0.002983, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.565063, Class: 1.000000, Obj: 0.300030, No Obj: 0.003180, Avg Recall: 0.674074, count: 135 -Region Avg IOU: 0.609435, Class: 1.000000, Obj: 0.309528, No Obj: 0.003116, Avg Recall: 0.694444, count: 108 -1721: 0.507754, 0.486525 avg, 0.001000 rate, 26.324026 seconds, 440576 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.562760, Class: 1.000000, Obj: 0.312060, No Obj: 0.002715, Avg Recall: 0.708333, count: 72 -Region Avg IOU: 0.615903, Class: 1.000000, Obj: 0.310712, No Obj: 0.003577, Avg Recall: 0.771186, count: 118 -Region Avg IOU: 0.645658, Class: 1.000000, Obj: 0.372420, No Obj: 0.002869, Avg Recall: 0.845361, count: 97 -Region Avg IOU: 0.601920, Class: 1.000000, Obj: 0.356376, No Obj: 0.002969, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.582685, Class: 1.000000, Obj: 0.342855, No Obj: 0.003040, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.593709, Class: 1.000000, Obj: 0.295573, No Obj: 0.002953, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.598377, Class: 1.000000, Obj: 0.285737, No Obj: 0.003188, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.538605, Class: 1.000000, Obj: 0.302981, No Obj: 0.003073, Avg Recall: 0.627273, count: 110 -1722: 0.409803, 0.478853 avg, 0.001000 rate, 24.571592 seconds, 440832 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.575693, Class: 1.000000, Obj: 0.279633, No Obj: 0.003443, Avg Recall: 0.666667, count: 117 -Region Avg IOU: 0.600978, Class: 1.000000, Obj: 0.307941, No Obj: 0.003357, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.586347, Class: 1.000000, Obj: 0.322239, No Obj: 0.003169, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.610208, Class: 1.000000, Obj: 0.306549, No Obj: 0.003148, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.528917, Class: 1.000000, Obj: 0.267822, No Obj: 0.003200, Avg Recall: 0.604651, count: 129 -Region Avg IOU: 0.495466, Class: 1.000000, Obj: 0.303062, No Obj: 0.002810, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.570841, Class: 1.000000, Obj: 0.357067, No Obj: 0.002773, Avg Recall: 0.684783, count: 92 -Region Avg IOU: 0.606031, Class: 1.000000, Obj: 0.321149, No Obj: 0.003738, Avg Recall: 0.746269, count: 134 -1723: 0.467300, 0.477698 avg, 0.001000 rate, 26.746620 seconds, 441088 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.572230, Class: 1.000000, Obj: 0.283807, No Obj: 0.003009, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.619372, Class: 1.000000, Obj: 0.354899, No Obj: 0.003351, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.500531, Class: 1.000000, Obj: 0.290626, No Obj: 0.002698, Avg Recall: 0.575342, count: 73 -Region Avg IOU: 0.581537, Class: 1.000000, Obj: 0.314145, No Obj: 0.003647, Avg Recall: 0.699248, count: 133 -Region Avg IOU: 0.575721, Class: 1.000000, Obj: 0.320423, No Obj: 0.003605, Avg Recall: 0.712230, count: 139 -Region Avg IOU: 0.529079, Class: 1.000000, Obj: 0.358076, No Obj: 0.003176, Avg Recall: 0.617886, count: 123 -Region Avg IOU: 0.552583, Class: 1.000000, Obj: 0.250584, No Obj: 0.002764, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.606652, Class: 1.000000, Obj: 0.350480, No Obj: 0.003159, Avg Recall: 0.760417, count: 96 -1724: 0.467841, 0.476712 avg, 0.001000 rate, 26.767193 seconds, 441344 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.564292, Class: 1.000000, Obj: 0.304458, No Obj: 0.003131, Avg Recall: 0.678161, count: 87 -Region Avg IOU: 0.611951, Class: 1.000000, Obj: 0.347521, No Obj: 0.003305, Avg Recall: 0.739583, count: 96 -Region Avg IOU: 0.550395, Class: 1.000000, Obj: 0.288786, No Obj: 0.003403, Avg Recall: 0.640845, count: 142 -Region Avg IOU: 0.611432, Class: 1.000000, Obj: 0.364602, No Obj: 0.003319, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.576335, Class: 1.000000, Obj: 0.336055, No Obj: 0.003151, Avg Recall: 0.680000, count: 125 -Region Avg IOU: 0.551371, Class: 1.000000, Obj: 0.318073, No Obj: 0.003142, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.558355, Class: 1.000000, Obj: 0.351442, No Obj: 0.003142, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.589514, Class: 1.000000, Obj: 0.332639, No Obj: 0.003623, Avg Recall: 0.685484, count: 124 -1725: 0.445733, 0.473614 avg, 0.001000 rate, 25.979364 seconds, 441600 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.598079, Class: 1.000000, Obj: 0.286086, No Obj: 0.003735, Avg Recall: 0.729508, count: 122 -Region Avg IOU: 0.556844, Class: 1.000000, Obj: 0.348645, No Obj: 0.003144, Avg Recall: 0.634921, count: 126 -Region Avg IOU: 0.610265, Class: 1.000000, Obj: 0.358413, No Obj: 0.003033, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.625094, Class: 1.000000, Obj: 0.379451, No Obj: 0.002988, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.608202, Class: 1.000000, Obj: 0.330646, No Obj: 0.003098, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.589638, Class: 1.000000, Obj: 0.360966, No Obj: 0.003242, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.568759, Class: 1.000000, Obj: 0.322901, No Obj: 0.003106, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.516083, Class: 1.000000, Obj: 0.326471, No Obj: 0.003005, Avg Recall: 0.584158, count: 101 -1726: 0.428257, 0.469078 avg, 0.001000 rate, 26.833351 seconds, 441856 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.515862, Class: 1.000000, Obj: 0.255033, No Obj: 0.002667, Avg Recall: 0.629630, count: 81 -Region Avg IOU: 0.577227, Class: 1.000000, Obj: 0.305723, No Obj: 0.003460, Avg Recall: 0.682927, count: 123 -Region Avg IOU: 0.577466, Class: 1.000000, Obj: 0.364533, No Obj: 0.003216, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.556835, Class: 1.000000, Obj: 0.307874, No Obj: 0.003011, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.563806, Class: 1.000000, Obj: 0.320923, No Obj: 0.003419, Avg Recall: 0.687075, count: 147 -Region Avg IOU: 0.620853, Class: 1.000000, Obj: 0.371966, No Obj: 0.003365, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.594537, Class: 1.000000, Obj: 0.335391, No Obj: 0.003147, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.613441, Class: 1.000000, Obj: 0.320163, No Obj: 0.003099, Avg Recall: 0.741573, count: 89 -1727: 0.461550, 0.468326 avg, 0.001000 rate, 27.707209 seconds, 442112 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.564333, Class: 1.000000, Obj: 0.286677, No Obj: 0.002661, Avg Recall: 0.688889, count: 90 -Region Avg IOU: 0.558788, Class: 1.000000, Obj: 0.340462, No Obj: 0.002999, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.582033, Class: 1.000000, Obj: 0.296591, No Obj: 0.003060, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.571462, Class: 1.000000, Obj: 0.313775, No Obj: 0.003196, Avg Recall: 0.691667, count: 120 -Region Avg IOU: 0.590410, Class: 1.000000, Obj: 0.282758, No Obj: 0.003183, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.600178, Class: 1.000000, Obj: 0.295647, No Obj: 0.003480, Avg Recall: 0.703297, count: 91 -Region Avg IOU: 0.563552, Class: 1.000000, Obj: 0.337150, No Obj: 0.002894, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.574928, Class: 1.000000, Obj: 0.329434, No Obj: 0.003150, Avg Recall: 0.691489, count: 94 -1728: 0.470702, 0.468563 avg, 0.001000 rate, 26.047455 seconds, 442368 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.583399, Class: 1.000000, Obj: 0.312870, No Obj: 0.002786, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.580061, Class: 1.000000, Obj: 0.341405, No Obj: 0.002603, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.559509, Class: 1.000000, Obj: 0.277259, No Obj: 0.002946, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.546268, Class: 1.000000, Obj: 0.267072, No Obj: 0.002643, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.628651, Class: 1.000000, Obj: 0.304617, No Obj: 0.003236, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.589692, Class: 1.000000, Obj: 0.315976, No Obj: 0.002535, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.562452, Class: 1.000000, Obj: 0.305416, No Obj: 0.002883, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.548815, Class: 1.000000, Obj: 0.278352, No Obj: 0.002910, Avg Recall: 0.630631, count: 111 -1729: 0.488981, 0.470605 avg, 0.001000 rate, 26.996634 seconds, 442624 images -Loaded: 0.000116 seconds -Region Avg IOU: 0.579813, Class: 1.000000, Obj: 0.285064, No Obj: 0.002814, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.572170, Class: 1.000000, Obj: 0.290352, No Obj: 0.002909, Avg Recall: 0.641791, count: 134 -Region Avg IOU: 0.600723, Class: 1.000000, Obj: 0.286138, No Obj: 0.002926, Avg Recall: 0.735537, count: 121 -Region Avg IOU: 0.627848, Class: 1.000000, Obj: 0.333920, No Obj: 0.002723, Avg Recall: 0.772152, count: 79 -Region Avg IOU: 0.595605, Class: 1.000000, Obj: 0.301473, No Obj: 0.002736, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.543528, Class: 1.000000, Obj: 0.294771, No Obj: 0.002767, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.571815, Class: 1.000000, Obj: 0.262220, No Obj: 0.002802, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.549704, Class: 1.000000, Obj: 0.286711, No Obj: 0.002968, Avg Recall: 0.602151, count: 93 -1730: 0.470431, 0.470588 avg, 0.001000 rate, 27.112499 seconds, 442880 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000073 seconds -Region Avg IOU: 0.622785, Class: 1.000000, Obj: 0.298186, No Obj: 0.002856, Avg Recall: 0.818182, count: 121 -Region Avg IOU: 0.609263, Class: 1.000000, Obj: 0.294755, No Obj: 0.002944, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.616488, Class: 1.000000, Obj: 0.282632, No Obj: 0.002759, Avg Recall: 0.733333, count: 135 -Region Avg IOU: 0.606727, Class: 1.000000, Obj: 0.304063, No Obj: 0.002635, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.566964, Class: 1.000000, Obj: 0.285910, No Obj: 0.002444, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.612286, Class: 1.000000, Obj: 0.338375, No Obj: 0.002848, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.633085, Class: 1.000000, Obj: 0.293804, No Obj: 0.002589, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.608511, Class: 1.000000, Obj: 0.283803, No Obj: 0.002559, Avg Recall: 0.752475, count: 101 -1731: 0.491867, 0.472716 avg, 0.001000 rate, 29.512568 seconds, 443136 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.643568, Class: 1.000000, Obj: 0.282800, No Obj: 0.002411, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.645177, Class: 1.000000, Obj: 0.337970, No Obj: 0.002529, Avg Recall: 0.806818, count: 88 -Region Avg IOU: 0.614808, Class: 1.000000, Obj: 0.296982, No Obj: 0.002816, Avg Recall: 0.756757, count: 148 -Region Avg IOU: 0.629366, Class: 1.000000, Obj: 0.325040, No Obj: 0.002930, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.598531, Class: 1.000000, Obj: 0.300369, No Obj: 0.002680, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.626634, Class: 1.000000, Obj: 0.309535, No Obj: 0.002583, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.618785, Class: 1.000000, Obj: 0.267197, No Obj: 0.002630, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.627931, Class: 1.000000, Obj: 0.262850, No Obj: 0.002796, Avg Recall: 0.807692, count: 104 -1732: 0.513085, 0.476752 avg, 0.001000 rate, 29.291737 seconds, 443392 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.595084, Class: 1.000000, Obj: 0.321113, No Obj: 0.002905, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.589014, Class: 1.000000, Obj: 0.266431, No Obj: 0.002404, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.649305, Class: 1.000000, Obj: 0.306719, No Obj: 0.002461, Avg Recall: 0.815217, count: 92 -Region Avg IOU: 0.617096, Class: 1.000000, Obj: 0.289214, No Obj: 0.002837, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.610364, Class: 1.000000, Obj: 0.293367, No Obj: 0.002935, Avg Recall: 0.739437, count: 142 -Region Avg IOU: 0.562760, Class: 1.000000, Obj: 0.266267, No Obj: 0.002812, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.595880, Class: 1.000000, Obj: 0.298217, No Obj: 0.002523, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.581439, Class: 1.000000, Obj: 0.249008, No Obj: 0.002937, Avg Recall: 0.750000, count: 140 -1733: 0.519814, 0.481059 avg, 0.001000 rate, 30.008659 seconds, 443648 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.653156, Class: 1.000000, Obj: 0.308464, No Obj: 0.002596, Avg Recall: 0.841121, count: 107 -Region Avg IOU: 0.545700, Class: 1.000000, Obj: 0.285555, No Obj: 0.002392, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.588730, Class: 1.000000, Obj: 0.274926, No Obj: 0.002703, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.609854, Class: 1.000000, Obj: 0.302715, No Obj: 0.002652, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.633705, Class: 1.000000, Obj: 0.331830, No Obj: 0.002718, Avg Recall: 0.813084, count: 107 -Region Avg IOU: 0.583204, Class: 1.000000, Obj: 0.292935, No Obj: 0.002657, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.624840, Class: 1.000000, Obj: 0.307890, No Obj: 0.002567, Avg Recall: 0.790698, count: 86 -Region Avg IOU: 0.656789, Class: 1.000000, Obj: 0.336921, No Obj: 0.003071, Avg Recall: 0.820225, count: 89 -1734: 0.468289, 0.479782 avg, 0.001000 rate, 29.615751 seconds, 443904 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.614749, Class: 1.000000, Obj: 0.294860, No Obj: 0.002593, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.587929, Class: 1.000000, Obj: 0.310516, No Obj: 0.002843, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.612804, Class: 1.000000, Obj: 0.329627, No Obj: 0.002577, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.630085, Class: 1.000000, Obj: 0.353730, No Obj: 0.002728, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.615008, Class: 1.000000, Obj: 0.346274, No Obj: 0.002730, Avg Recall: 0.775281, count: 89 -Region Avg IOU: 0.602399, Class: 1.000000, Obj: 0.313813, No Obj: 0.002859, Avg Recall: 0.688525, count: 122 -Region Avg IOU: 0.636914, Class: 1.000000, Obj: 0.314541, No Obj: 0.002742, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.616259, Class: 1.000000, Obj: 0.312455, No Obj: 0.002759, Avg Recall: 0.770115, count: 87 -1735: 0.464678, 0.478271 avg, 0.001000 rate, 29.006683 seconds, 444160 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.626425, Class: 1.000000, Obj: 0.259305, No Obj: 0.002684, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.600173, Class: 1.000000, Obj: 0.268632, No Obj: 0.002986, Avg Recall: 0.729927, count: 137 -Region Avg IOU: 0.604635, Class: 1.000000, Obj: 0.283170, No Obj: 0.002712, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.638052, Class: 1.000000, Obj: 0.350772, No Obj: 0.002890, Avg Recall: 0.840336, count: 119 -Region Avg IOU: 0.599661, Class: 1.000000, Obj: 0.311028, No Obj: 0.002819, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.602767, Class: 1.000000, Obj: 0.311467, No Obj: 0.002761, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.587759, Class: 1.000000, Obj: 0.304077, No Obj: 0.002624, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.581170, Class: 1.000000, Obj: 0.271599, No Obj: 0.002694, Avg Recall: 0.728972, count: 107 -1736: 0.499273, 0.480372 avg, 0.001000 rate, 28.758440 seconds, 444416 images -Loaded: 0.000123 seconds -Region Avg IOU: 0.583585, Class: 1.000000, Obj: 0.312690, No Obj: 0.002901, Avg Recall: 0.676259, count: 139 -Region Avg IOU: 0.594801, Class: 1.000000, Obj: 0.311192, No Obj: 0.002602, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.624066, Class: 1.000000, Obj: 0.296607, No Obj: 0.002923, Avg Recall: 0.782258, count: 124 -Region Avg IOU: 0.602702, Class: 1.000000, Obj: 0.281387, No Obj: 0.002912, Avg Recall: 0.748092, count: 131 -Region Avg IOU: 0.622216, Class: 1.000000, Obj: 0.311315, No Obj: 0.002844, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.609029, Class: 1.000000, Obj: 0.316424, No Obj: 0.002715, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.654255, Class: 1.000000, Obj: 0.326385, No Obj: 0.002896, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.596824, Class: 1.000000, Obj: 0.314955, No Obj: 0.003079, Avg Recall: 0.721311, count: 122 -1737: 0.507154, 0.483050 avg, 0.001000 rate, 29.304993 seconds, 444672 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.638609, Class: 1.000000, Obj: 0.342382, No Obj: 0.002841, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.589756, Class: 1.000000, Obj: 0.262923, No Obj: 0.002543, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.600183, Class: 1.000000, Obj: 0.305289, No Obj: 0.002771, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.604855, Class: 1.000000, Obj: 0.306950, No Obj: 0.002938, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.643313, Class: 1.000000, Obj: 0.276053, No Obj: 0.002863, Avg Recall: 0.817308, count: 104 -Region Avg IOU: 0.601651, Class: 1.000000, Obj: 0.263368, No Obj: 0.002542, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.614313, Class: 1.000000, Obj: 0.330141, No Obj: 0.002754, Avg Recall: 0.750000, count: 84 -Region Avg IOU: 0.610911, Class: 1.000000, Obj: 0.332204, No Obj: 0.002893, Avg Recall: 0.743363, count: 113 -1738: 0.470276, 0.481772 avg, 0.001000 rate, 31.054684 seconds, 444928 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.572547, Class: 1.000000, Obj: 0.264232, No Obj: 0.002523, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.597971, Class: 1.000000, Obj: 0.291228, No Obj: 0.002739, Avg Recall: 0.709924, count: 131 -Region Avg IOU: 0.562907, Class: 1.000000, Obj: 0.281635, No Obj: 0.002865, Avg Recall: 0.661290, count: 124 -Region Avg IOU: 0.583322, Class: 1.000000, Obj: 0.327377, No Obj: 0.002527, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.620830, Class: 1.000000, Obj: 0.342225, No Obj: 0.002542, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.577338, Class: 1.000000, Obj: 0.292444, No Obj: 0.002670, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.593723, Class: 1.000000, Obj: 0.306100, No Obj: 0.002439, Avg Recall: 0.719512, count: 82 -Region Avg IOU: 0.601464, Class: 1.000000, Obj: 0.259329, No Obj: 0.002738, Avg Recall: 0.741667, count: 120 -1739: 0.468772, 0.480472 avg, 0.001000 rate, 28.677067 seconds, 445184 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.601888, Class: 1.000000, Obj: 0.294102, No Obj: 0.002623, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.646533, Class: 1.000000, Obj: 0.307829, No Obj: 0.002741, Avg Recall: 0.857143, count: 126 -Region Avg IOU: 0.571573, Class: 1.000000, Obj: 0.282350, No Obj: 0.002308, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.633572, Class: 1.000000, Obj: 0.273845, No Obj: 0.002669, Avg Recall: 0.823529, count: 102 -Region Avg IOU: 0.572811, Class: 1.000000, Obj: 0.309045, No Obj: 0.002421, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.605850, Class: 1.000000, Obj: 0.307101, No Obj: 0.002394, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.645038, Class: 1.000000, Obj: 0.321612, No Obj: 0.002750, Avg Recall: 0.820000, count: 100 -Region Avg IOU: 0.600129, Class: 1.000000, Obj: 0.274514, No Obj: 0.002783, Avg Recall: 0.669355, count: 124 -1740: 0.503074, 0.482732 avg, 0.001000 rate, 27.010063 seconds, 445440 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 11.263165 seconds -Region Avg IOU: 0.528960, Class: 1.000000, Obj: 0.245438, No Obj: 0.003099, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.546964, Class: 1.000000, Obj: 0.257715, No Obj: 0.003030, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.509710, Class: 1.000000, Obj: 0.261778, No Obj: 0.002881, Avg Recall: 0.589474, count: 95 -Region Avg IOU: 0.534636, Class: 1.000000, Obj: 0.267055, No Obj: 0.002972, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.546124, Class: 1.000000, Obj: 0.289976, No Obj: 0.002688, Avg Recall: 0.632911, count: 79 -Region Avg IOU: 0.534496, Class: 1.000000, Obj: 0.271640, No Obj: 0.003250, Avg Recall: 0.616000, count: 125 -Region Avg IOU: 0.444966, Class: 1.000000, Obj: 0.278712, No Obj: 0.002938, Avg Recall: 0.467290, count: 107 -Region Avg IOU: 0.544228, Class: 1.000000, Obj: 0.261359, No Obj: 0.002901, Avg Recall: 0.647059, count: 102 -1741: 0.472519, 0.481711 avg, 0.001000 rate, 22.864878 seconds, 445696 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.600498, Class: 1.000000, Obj: 0.313264, No Obj: 0.003386, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.549271, Class: 1.000000, Obj: 0.270584, No Obj: 0.003327, Avg Recall: 0.632812, count: 128 -Region Avg IOU: 0.524835, Class: 1.000000, Obj: 0.262697, No Obj: 0.003010, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.558187, Class: 1.000000, Obj: 0.281307, No Obj: 0.003107, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.539729, Class: 1.000000, Obj: 0.300774, No Obj: 0.003132, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.542257, Class: 1.000000, Obj: 0.286892, No Obj: 0.003573, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.549160, Class: 1.000000, Obj: 0.288864, No Obj: 0.003029, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.612032, Class: 1.000000, Obj: 0.306869, No Obj: 0.002743, Avg Recall: 0.765432, count: 81 -1742: 0.463780, 0.479918 avg, 0.001000 rate, 23.019417 seconds, 445952 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.515944, Class: 1.000000, Obj: 0.274098, No Obj: 0.002891, Avg Recall: 0.608247, count: 97 -Region Avg IOU: 0.521763, Class: 1.000000, Obj: 0.268065, No Obj: 0.003546, Avg Recall: 0.603053, count: 131 -Region Avg IOU: 0.601673, Class: 1.000000, Obj: 0.291639, No Obj: 0.003529, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.518227, Class: 1.000000, Obj: 0.327464, No Obj: 0.002651, Avg Recall: 0.589474, count: 95 -Region Avg IOU: 0.584692, Class: 1.000000, Obj: 0.311124, No Obj: 0.002955, Avg Recall: 0.734940, count: 83 -Region Avg IOU: 0.528274, Class: 1.000000, Obj: 0.284582, No Obj: 0.003277, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.531972, Class: 1.000000, Obj: 0.291188, No Obj: 0.003066, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.564819, Class: 1.000000, Obj: 0.317680, No Obj: 0.003245, Avg Recall: 0.648352, count: 91 -1743: 0.450133, 0.476939 avg, 0.001000 rate, 25.233133 seconds, 446208 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.496118, Class: 1.000000, Obj: 0.276167, No Obj: 0.003124, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.530150, Class: 1.000000, Obj: 0.326054, No Obj: 0.003938, Avg Recall: 0.590909, count: 132 -Region Avg IOU: 0.549401, Class: 1.000000, Obj: 0.306680, No Obj: 0.003915, Avg Recall: 0.626984, count: 126 -Region Avg IOU: 0.525052, Class: 1.000000, Obj: 0.306355, No Obj: 0.003077, Avg Recall: 0.619048, count: 84 -Region Avg IOU: 0.548869, Class: 1.000000, Obj: 0.300612, No Obj: 0.003546, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.560010, Class: 1.000000, Obj: 0.282764, No Obj: 0.002868, Avg Recall: 0.683544, count: 79 -Region Avg IOU: 0.532224, Class: 1.000000, Obj: 0.306102, No Obj: 0.003345, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.563514, Class: 1.000000, Obj: 0.388789, No Obj: 0.003470, Avg Recall: 0.660000, count: 100 -1744: 0.435074, 0.472753 avg, 0.001000 rate, 24.215370 seconds, 446464 images -Loaded: 0.000128 seconds -Region Avg IOU: 0.538085, Class: 1.000000, Obj: 0.318130, No Obj: 0.003785, Avg Recall: 0.603306, count: 121 -Region Avg IOU: 0.564288, Class: 1.000000, Obj: 0.332904, No Obj: 0.003757, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.533424, Class: 1.000000, Obj: 0.297091, No Obj: 0.003139, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.544478, Class: 1.000000, Obj: 0.282191, No Obj: 0.004412, Avg Recall: 0.644928, count: 138 -Region Avg IOU: 0.574163, Class: 1.000000, Obj: 0.367767, No Obj: 0.003374, Avg Recall: 0.710526, count: 76 -Region Avg IOU: 0.553014, Class: 1.000000, Obj: 0.284993, No Obj: 0.003299, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.551163, Class: 1.000000, Obj: 0.306437, No Obj: 0.003162, Avg Recall: 0.685393, count: 89 -Region Avg IOU: 0.539707, Class: 1.000000, Obj: 0.316233, No Obj: 0.003221, Avg Recall: 0.611111, count: 90 -1745: 0.439843, 0.469462 avg, 0.001000 rate, 23.528652 seconds, 446720 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.563783, Class: 1.000000, Obj: 0.373155, No Obj: 0.003644, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.557039, Class: 1.000000, Obj: 0.293017, No Obj: 0.003279, Avg Recall: 0.674419, count: 86 -Region Avg IOU: 0.574193, Class: 1.000000, Obj: 0.396447, No Obj: 0.003604, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.538544, Class: 1.000000, Obj: 0.363549, No Obj: 0.004000, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.502515, Class: 1.000000, Obj: 0.317761, No Obj: 0.003956, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.543738, Class: 1.000000, Obj: 0.314779, No Obj: 0.004088, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.502914, Class: 1.000000, Obj: 0.305401, No Obj: 0.003685, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.542584, Class: 1.000000, Obj: 0.393135, No Obj: 0.003567, Avg Recall: 0.634146, count: 82 -1746: 0.475040, 0.470020 avg, 0.001000 rate, 23.819317 seconds, 446976 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.587454, Class: 1.000000, Obj: 0.321925, No Obj: 0.003700, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.554550, Class: 1.000000, Obj: 0.342923, No Obj: 0.003971, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.563045, Class: 1.000000, Obj: 0.350196, No Obj: 0.004065, Avg Recall: 0.644068, count: 118 -Region Avg IOU: 0.511831, Class: 1.000000, Obj: 0.369158, No Obj: 0.003267, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.572651, Class: 1.000000, Obj: 0.258593, No Obj: 0.003841, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.475335, Class: 1.000000, Obj: 0.277834, No Obj: 0.003394, Avg Recall: 0.504762, count: 105 -Region Avg IOU: 0.538000, Class: 1.000000, Obj: 0.378612, No Obj: 0.003275, Avg Recall: 0.615385, count: 91 -Region Avg IOU: 0.528128, Class: 1.000000, Obj: 0.321693, No Obj: 0.003573, Avg Recall: 0.592000, count: 125 -1747: 0.470931, 0.470111 avg, 0.001000 rate, 23.260391 seconds, 447232 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.536387, Class: 1.000000, Obj: 0.289246, No Obj: 0.003509, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.528481, Class: 1.000000, Obj: 0.308549, No Obj: 0.003522, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.572717, Class: 1.000000, Obj: 0.313489, No Obj: 0.003398, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.548868, Class: 1.000000, Obj: 0.308819, No Obj: 0.003784, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.544254, Class: 1.000000, Obj: 0.329706, No Obj: 0.003872, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.494148, Class: 1.000000, Obj: 0.313124, No Obj: 0.003077, Avg Recall: 0.607595, count: 79 -Region Avg IOU: 0.580678, Class: 1.000000, Obj: 0.342399, No Obj: 0.004068, Avg Recall: 0.686956, count: 115 -Region Avg IOU: 0.534988, Class: 1.000000, Obj: 0.335642, No Obj: 0.003794, Avg Recall: 0.607477, count: 107 -1748: 0.432252, 0.466325 avg, 0.001000 rate, 23.432997 seconds, 447488 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.528979, Class: 1.000000, Obj: 0.303909, No Obj: 0.003759, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.529634, Class: 1.000000, Obj: 0.297585, No Obj: 0.003369, Avg Recall: 0.589474, count: 95 -Region Avg IOU: 0.543968, Class: 1.000000, Obj: 0.309349, No Obj: 0.003562, Avg Recall: 0.652542, count: 118 -Region Avg IOU: 0.522426, Class: 1.000000, Obj: 0.298764, No Obj: 0.003303, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.545803, Class: 1.000000, Obj: 0.336104, No Obj: 0.003472, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.481867, Class: 1.000000, Obj: 0.302867, No Obj: 0.003289, Avg Recall: 0.479339, count: 121 -Region Avg IOU: 0.596034, Class: 1.000000, Obj: 0.320048, No Obj: 0.003515, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.511886, Class: 1.000000, Obj: 0.304894, No Obj: 0.003341, Avg Recall: 0.610526, count: 95 -1749: 0.452774, 0.464970 avg, 0.001000 rate, 23.581097 seconds, 447744 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.505883, Class: 1.000000, Obj: 0.251405, No Obj: 0.003134, Avg Recall: 0.551020, count: 98 -Region Avg IOU: 0.518932, Class: 1.000000, Obj: 0.278632, No Obj: 0.003276, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.601329, Class: 1.000000, Obj: 0.277154, No Obj: 0.003166, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.530400, Class: 1.000000, Obj: 0.326181, No Obj: 0.003309, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.518562, Class: 1.000000, Obj: 0.276223, No Obj: 0.003177, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.509935, Class: 1.000000, Obj: 0.289939, No Obj: 0.003122, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.574066, Class: 1.000000, Obj: 0.253178, No Obj: 0.003216, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.531024, Class: 1.000000, Obj: 0.297066, No Obj: 0.002912, Avg Recall: 0.563107, count: 103 -1750: 0.475306, 0.466004 avg, 0.001000 rate, 24.335577 seconds, 448000 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000067 seconds -Region Avg IOU: 0.598679, Class: 1.000000, Obj: 0.252046, No Obj: 0.002749, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.601184, Class: 1.000000, Obj: 0.276531, No Obj: 0.002583, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.551050, Class: 1.000000, Obj: 0.257994, No Obj: 0.002603, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.643974, Class: 1.000000, Obj: 0.292359, No Obj: 0.002723, Avg Recall: 0.831325, count: 83 -Region Avg IOU: 0.588511, Class: 1.000000, Obj: 0.283749, No Obj: 0.002579, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.612673, Class: 1.000000, Obj: 0.284155, No Obj: 0.002982, Avg Recall: 0.746269, count: 134 -Region Avg IOU: 0.558852, Class: 1.000000, Obj: 0.280698, No Obj: 0.002529, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.588205, Class: 1.000000, Obj: 0.308429, No Obj: 0.002457, Avg Recall: 0.732759, count: 116 -1751: 0.502812, 0.469684 avg, 0.001000 rate, 29.105188 seconds, 448256 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.565623, Class: 1.000000, Obj: 0.309151, No Obj: 0.002528, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.635783, Class: 1.000000, Obj: 0.269147, No Obj: 0.002611, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.576216, Class: 1.000000, Obj: 0.291258, No Obj: 0.002222, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.657215, Class: 1.000000, Obj: 0.284962, No Obj: 0.002776, Avg Recall: 0.833333, count: 108 -Region Avg IOU: 0.572603, Class: 1.000000, Obj: 0.250665, No Obj: 0.002623, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.620757, Class: 1.000000, Obj: 0.304892, No Obj: 0.002466, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.608653, Class: 1.000000, Obj: 0.278440, No Obj: 0.002766, Avg Recall: 0.698413, count: 126 -Region Avg IOU: 0.609715, Class: 1.000000, Obj: 0.266846, No Obj: 0.002775, Avg Recall: 0.769231, count: 91 -1752: 0.503503, 0.473066 avg, 0.001000 rate, 29.398336 seconds, 448512 images -Loaded: 0.000126 seconds -Region Avg IOU: 0.625818, Class: 1.000000, Obj: 0.257403, No Obj: 0.002492, Avg Recall: 0.813559, count: 118 -Region Avg IOU: 0.630945, Class: 1.000000, Obj: 0.323212, No Obj: 0.002956, Avg Recall: 0.787402, count: 127 -Region Avg IOU: 0.567898, Class: 1.000000, Obj: 0.269405, No Obj: 0.002552, Avg Recall: 0.683824, count: 136 -Region Avg IOU: 0.607531, Class: 1.000000, Obj: 0.287490, No Obj: 0.002301, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.616004, Class: 1.000000, Obj: 0.301462, No Obj: 0.002700, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.577884, Class: 1.000000, Obj: 0.293349, No Obj: 0.002454, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.606812, Class: 1.000000, Obj: 0.274335, No Obj: 0.002788, Avg Recall: 0.733766, count: 154 -Region Avg IOU: 0.601167, Class: 1.000000, Obj: 0.223948, No Obj: 0.002581, Avg Recall: 0.724138, count: 116 -1753: 0.511149, 0.476875 avg, 0.001000 rate, 28.540419 seconds, 448768 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.586065, Class: 1.000000, Obj: 0.319685, No Obj: 0.002243, Avg Recall: 0.714286, count: 77 -Region Avg IOU: 0.610112, Class: 1.000000, Obj: 0.269527, No Obj: 0.002620, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.604950, Class: 1.000000, Obj: 0.251303, No Obj: 0.002311, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.642163, Class: 1.000000, Obj: 0.313429, No Obj: 0.002656, Avg Recall: 0.835052, count: 97 -Region Avg IOU: 0.623198, Class: 1.000000, Obj: 0.357762, No Obj: 0.002503, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.633747, Class: 1.000000, Obj: 0.307407, No Obj: 0.003022, Avg Recall: 0.791367, count: 139 -Region Avg IOU: 0.607480, Class: 1.000000, Obj: 0.290534, No Obj: 0.002378, Avg Recall: 0.707317, count: 82 -Region Avg IOU: 0.622612, Class: 1.000000, Obj: 0.299879, No Obj: 0.002724, Avg Recall: 0.746032, count: 126 -1754: 0.462692, 0.475456 avg, 0.001000 rate, 30.061541 seconds, 449024 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.620917, Class: 1.000000, Obj: 0.280656, No Obj: 0.002873, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.656692, Class: 1.000000, Obj: 0.372985, No Obj: 0.002962, Avg Recall: 0.848485, count: 99 -Region Avg IOU: 0.617649, Class: 1.000000, Obj: 0.321691, No Obj: 0.002800, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.588497, Class: 1.000000, Obj: 0.320847, No Obj: 0.002719, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.604353, Class: 1.000000, Obj: 0.348584, No Obj: 0.002882, Avg Recall: 0.719298, count: 114 -Region Avg IOU: 0.597149, Class: 1.000000, Obj: 0.286646, No Obj: 0.002723, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.599533, Class: 1.000000, Obj: 0.294007, No Obj: 0.002782, Avg Recall: 0.781250, count: 128 -Region Avg IOU: 0.570090, Class: 1.000000, Obj: 0.300494, No Obj: 0.002827, Avg Recall: 0.671429, count: 140 -1755: 0.493341, 0.477245 avg, 0.001000 rate, 27.501961 seconds, 449280 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.616773, Class: 1.000000, Obj: 0.350447, No Obj: 0.002581, Avg Recall: 0.753086, count: 81 -Region Avg IOU: 0.597918, Class: 1.000000, Obj: 0.319301, No Obj: 0.002586, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.620186, Class: 1.000000, Obj: 0.287625, No Obj: 0.002908, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.615733, Class: 1.000000, Obj: 0.334398, No Obj: 0.002761, Avg Recall: 0.772727, count: 88 -Region Avg IOU: 0.615966, Class: 1.000000, Obj: 0.284704, No Obj: 0.003034, Avg Recall: 0.759690, count: 129 -Region Avg IOU: 0.608306, Class: 1.000000, Obj: 0.280031, No Obj: 0.002986, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.637601, Class: 1.000000, Obj: 0.278971, No Obj: 0.002792, Avg Recall: 0.824176, count: 91 -Region Avg IOU: 0.642456, Class: 1.000000, Obj: 0.297684, No Obj: 0.002753, Avg Recall: 0.792453, count: 106 -1756: 0.487812, 0.478301 avg, 0.001000 rate, 29.885904 seconds, 449536 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.640483, Class: 1.000000, Obj: 0.337215, No Obj: 0.003060, Avg Recall: 0.779661, count: 118 -Region Avg IOU: 0.609462, Class: 1.000000, Obj: 0.323529, No Obj: 0.002918, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.596731, Class: 1.000000, Obj: 0.292443, No Obj: 0.003074, Avg Recall: 0.721429, count: 140 -Region Avg IOU: 0.627224, Class: 1.000000, Obj: 0.339323, No Obj: 0.002607, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.628228, Class: 1.000000, Obj: 0.337456, No Obj: 0.002762, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.615398, Class: 1.000000, Obj: 0.300228, No Obj: 0.002827, Avg Recall: 0.770492, count: 122 -Region Avg IOU: 0.564945, Class: 1.000000, Obj: 0.209570, No Obj: 0.002588, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.588017, Class: 1.000000, Obj: 0.311596, No Obj: 0.002312, Avg Recall: 0.662500, count: 80 -1757: 0.480936, 0.478565 avg, 0.001000 rate, 28.203756 seconds, 449792 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.591004, Class: 1.000000, Obj: 0.287113, No Obj: 0.002507, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.616440, Class: 1.000000, Obj: 0.291669, No Obj: 0.002663, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.606591, Class: 1.000000, Obj: 0.281538, No Obj: 0.002626, Avg Recall: 0.777778, count: 90 -Region Avg IOU: 0.634883, Class: 1.000000, Obj: 0.327711, No Obj: 0.002501, Avg Recall: 0.818182, count: 88 -Region Avg IOU: 0.648536, Class: 1.000000, Obj: 0.326390, No Obj: 0.002666, Avg Recall: 0.831579, count: 95 -Region Avg IOU: 0.571550, Class: 1.000000, Obj: 0.317558, No Obj: 0.002742, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.576546, Class: 1.000000, Obj: 0.308889, No Obj: 0.002930, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.639811, Class: 1.000000, Obj: 0.317886, No Obj: 0.002571, Avg Recall: 0.821429, count: 84 -1758: 0.432081, 0.473917 avg, 0.001000 rate, 29.853367 seconds, 450048 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.647786, Class: 1.000000, Obj: 0.282641, No Obj: 0.002590, Avg Recall: 0.804878, count: 82 -Region Avg IOU: 0.599356, Class: 1.000000, Obj: 0.332645, No Obj: 0.002677, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.601028, Class: 1.000000, Obj: 0.265662, No Obj: 0.002544, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.626024, Class: 1.000000, Obj: 0.305096, No Obj: 0.002471, Avg Recall: 0.773333, count: 75 -Region Avg IOU: 0.602330, Class: 1.000000, Obj: 0.285278, No Obj: 0.002789, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.615135, Class: 1.000000, Obj: 0.313705, No Obj: 0.002679, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.608784, Class: 1.000000, Obj: 0.325581, No Obj: 0.002530, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.565501, Class: 1.000000, Obj: 0.260188, No Obj: 0.002396, Avg Recall: 0.694215, count: 121 -1759: 0.448469, 0.471372 avg, 0.001000 rate, 28.144268 seconds, 450304 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.628070, Class: 1.000000, Obj: 0.306143, No Obj: 0.002490, Avg Recall: 0.791667, count: 72 -Region Avg IOU: 0.586004, Class: 1.000000, Obj: 0.293207, No Obj: 0.002556, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.615140, Class: 1.000000, Obj: 0.334033, No Obj: 0.002598, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.594723, Class: 1.000000, Obj: 0.315910, No Obj: 0.002585, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.619596, Class: 1.000000, Obj: 0.300842, No Obj: 0.002550, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.648904, Class: 1.000000, Obj: 0.279944, No Obj: 0.002207, Avg Recall: 0.847059, count: 85 -Region Avg IOU: 0.594277, Class: 1.000000, Obj: 0.269176, No Obj: 0.002692, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.635122, Class: 1.000000, Obj: 0.280079, No Obj: 0.002505, Avg Recall: 0.795699, count: 93 -1760: 0.441753, 0.468410 avg, 0.001000 rate, 30.600817 seconds, 450560 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 13.507758 seconds -Region Avg IOU: 0.578419, Class: 1.000000, Obj: 0.293332, No Obj: 0.002497, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.581761, Class: 1.000000, Obj: 0.270763, No Obj: 0.002679, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.594985, Class: 1.000000, Obj: 0.259640, No Obj: 0.002514, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.592078, Class: 1.000000, Obj: 0.272749, No Obj: 0.002531, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.561829, Class: 1.000000, Obj: 0.296496, No Obj: 0.002338, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.610034, Class: 1.000000, Obj: 0.279573, No Obj: 0.002839, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.592926, Class: 1.000000, Obj: 0.301851, No Obj: 0.002443, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.606346, Class: 1.000000, Obj: 0.312872, No Obj: 0.002890, Avg Recall: 0.761194, count: 134 -1761: 0.487624, 0.470331 avg, 0.001000 rate, 28.535875 seconds, 450816 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.592321, Class: 1.000000, Obj: 0.239813, No Obj: 0.002463, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.600717, Class: 1.000000, Obj: 0.293181, No Obj: 0.002589, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.595542, Class: 1.000000, Obj: 0.299968, No Obj: 0.002381, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.590925, Class: 1.000000, Obj: 0.282900, No Obj: 0.002401, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.573030, Class: 1.000000, Obj: 0.274330, No Obj: 0.002674, Avg Recall: 0.682927, count: 123 -Region Avg IOU: 0.587674, Class: 1.000000, Obj: 0.257067, No Obj: 0.002626, Avg Recall: 0.714286, count: 119 -Region Avg IOU: 0.545666, Class: 1.000000, Obj: 0.273589, No Obj: 0.002654, Avg Recall: 0.640845, count: 142 -Region Avg IOU: 0.601618, Class: 1.000000, Obj: 0.277349, No Obj: 0.003160, Avg Recall: 0.710692, count: 159 -1762: 0.515171, 0.474815 avg, 0.001000 rate, 27.964788 seconds, 451072 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.597976, Class: 1.000000, Obj: 0.246627, No Obj: 0.002536, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.581488, Class: 1.000000, Obj: 0.290488, No Obj: 0.002810, Avg Recall: 0.711409, count: 149 -Region Avg IOU: 0.615416, Class: 1.000000, Obj: 0.307363, No Obj: 0.002459, Avg Recall: 0.746988, count: 83 -Region Avg IOU: 0.593903, Class: 1.000000, Obj: 0.280597, No Obj: 0.002698, Avg Recall: 0.737705, count: 122 -Region Avg IOU: 0.587515, Class: 1.000000, Obj: 0.339152, No Obj: 0.002702, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.563222, Class: 1.000000, Obj: 0.239645, No Obj: 0.002565, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.626467, Class: 1.000000, Obj: 0.312766, No Obj: 0.002678, Avg Recall: 0.781609, count: 87 -Region Avg IOU: 0.608878, Class: 1.000000, Obj: 0.241431, No Obj: 0.002518, Avg Recall: 0.784314, count: 102 -1763: 0.480899, 0.475424 avg, 0.001000 rate, 28.491764 seconds, 451328 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.598921, Class: 1.000000, Obj: 0.303944, No Obj: 0.002971, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.582837, Class: 1.000000, Obj: 0.263352, No Obj: 0.002665, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.609233, Class: 1.000000, Obj: 0.317436, No Obj: 0.002432, Avg Recall: 0.759036, count: 83 -Region Avg IOU: 0.596877, Class: 1.000000, Obj: 0.300637, No Obj: 0.002762, Avg Recall: 0.715909, count: 88 -Region Avg IOU: 0.565892, Class: 1.000000, Obj: 0.335533, No Obj: 0.002282, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.630879, Class: 1.000000, Obj: 0.308439, No Obj: 0.002776, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.605472, Class: 1.000000, Obj: 0.290096, No Obj: 0.003119, Avg Recall: 0.746154, count: 130 -Region Avg IOU: 0.614387, Class: 1.000000, Obj: 0.339991, No Obj: 0.002546, Avg Recall: 0.758621, count: 87 -1764: 0.403942, 0.468276 avg, 0.001000 rate, 28.870501 seconds, 451584 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.607186, Class: 1.000000, Obj: 0.335378, No Obj: 0.002812, Avg Recall: 0.730159, count: 126 -Region Avg IOU: 0.595568, Class: 1.000000, Obj: 0.307471, No Obj: 0.002328, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.601399, Class: 1.000000, Obj: 0.285659, No Obj: 0.002880, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.623366, Class: 1.000000, Obj: 0.308417, No Obj: 0.002940, Avg Recall: 0.765432, count: 81 -Region Avg IOU: 0.624623, Class: 1.000000, Obj: 0.345253, No Obj: 0.003027, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.570268, Class: 1.000000, Obj: 0.296697, No Obj: 0.003096, Avg Recall: 0.656000, count: 125 -Region Avg IOU: 0.617209, Class: 1.000000, Obj: 0.300873, No Obj: 0.002841, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.588664, Class: 1.000000, Obj: 0.328037, No Obj: 0.002787, Avg Recall: 0.716814, count: 113 -1765: 0.482394, 0.469687 avg, 0.001000 rate, 28.366932 seconds, 451840 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.618253, Class: 1.000000, Obj: 0.268630, No Obj: 0.002962, Avg Recall: 0.779661, count: 118 -Region Avg IOU: 0.641656, Class: 1.000000, Obj: 0.391665, No Obj: 0.003138, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.618265, Class: 1.000000, Obj: 0.266005, No Obj: 0.002574, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.554530, Class: 1.000000, Obj: 0.278010, No Obj: 0.002653, Avg Recall: 0.609756, count: 123 -Region Avg IOU: 0.652529, Class: 1.000000, Obj: 0.311679, No Obj: 0.002721, Avg Recall: 0.826087, count: 92 -Region Avg IOU: 0.592932, Class: 1.000000, Obj: 0.350386, No Obj: 0.002938, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.642726, Class: 1.000000, Obj: 0.343976, No Obj: 0.002853, Avg Recall: 0.790698, count: 86 -Region Avg IOU: 0.634027, Class: 1.000000, Obj: 0.346790, No Obj: 0.002688, Avg Recall: 0.862500, count: 80 -1766: 0.462155, 0.468934 avg, 0.001000 rate, 27.202684 seconds, 452096 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.609805, Class: 1.000000, Obj: 0.276459, No Obj: 0.003072, Avg Recall: 0.763359, count: 131 -Region Avg IOU: 0.637447, Class: 1.000000, Obj: 0.354362, No Obj: 0.002592, Avg Recall: 0.793478, count: 92 -Region Avg IOU: 0.590985, Class: 1.000000, Obj: 0.301974, No Obj: 0.002749, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.601100, Class: 1.000000, Obj: 0.385236, No Obj: 0.002623, Avg Recall: 0.743590, count: 78 -Region Avg IOU: 0.639181, Class: 1.000000, Obj: 0.342342, No Obj: 0.002965, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.571882, Class: 1.000000, Obj: 0.296347, No Obj: 0.002796, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.627452, Class: 1.000000, Obj: 0.300801, No Obj: 0.002790, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.546363, Class: 1.000000, Obj: 0.340573, No Obj: 0.002911, Avg Recall: 0.647059, count: 119 -1767: 0.428330, 0.464874 avg, 0.001000 rate, 28.548281 seconds, 452352 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.597417, Class: 1.000000, Obj: 0.296572, No Obj: 0.002767, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.631847, Class: 1.000000, Obj: 0.327681, No Obj: 0.002889, Avg Recall: 0.782609, count: 115 -Region Avg IOU: 0.561519, Class: 1.000000, Obj: 0.333369, No Obj: 0.002424, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.622527, Class: 1.000000, Obj: 0.264777, No Obj: 0.003198, Avg Recall: 0.804878, count: 123 -Region Avg IOU: 0.594505, Class: 1.000000, Obj: 0.273272, No Obj: 0.003092, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.632453, Class: 1.000000, Obj: 0.295258, No Obj: 0.002558, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.622043, Class: 1.000000, Obj: 0.366789, No Obj: 0.002562, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.612008, Class: 1.000000, Obj: 0.282031, No Obj: 0.002631, Avg Recall: 0.709091, count: 110 -1768: 0.460732, 0.464460 avg, 0.001000 rate, 29.771664 seconds, 452608 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.595213, Class: 1.000000, Obj: 0.406412, No Obj: 0.002784, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.577907, Class: 1.000000, Obj: 0.340798, No Obj: 0.003026, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.598044, Class: 1.000000, Obj: 0.325590, No Obj: 0.002954, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.579077, Class: 1.000000, Obj: 0.293507, No Obj: 0.002835, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.610221, Class: 1.000000, Obj: 0.325335, No Obj: 0.003112, Avg Recall: 0.770492, count: 122 -Region Avg IOU: 0.624121, Class: 1.000000, Obj: 0.284151, No Obj: 0.002799, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.604238, Class: 1.000000, Obj: 0.308844, No Obj: 0.002837, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.630912, Class: 1.000000, Obj: 0.259287, No Obj: 0.002810, Avg Recall: 0.805825, count: 103 -1769: 0.506384, 0.468652 avg, 0.001000 rate, 27.619057 seconds, 452864 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.625543, Class: 1.000000, Obj: 0.327269, No Obj: 0.002726, Avg Recall: 0.772152, count: 79 -Region Avg IOU: 0.548746, Class: 1.000000, Obj: 0.269629, No Obj: 0.002644, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.569600, Class: 1.000000, Obj: 0.287699, No Obj: 0.002673, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.575688, Class: 1.000000, Obj: 0.296850, No Obj: 0.002960, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.598750, Class: 1.000000, Obj: 0.335090, No Obj: 0.002652, Avg Recall: 0.727273, count: 88 -Region Avg IOU: 0.585510, Class: 1.000000, Obj: 0.296524, No Obj: 0.003135, Avg Recall: 0.707143, count: 140 -Region Avg IOU: 0.618271, Class: 1.000000, Obj: 0.318519, No Obj: 0.002512, Avg Recall: 0.793103, count: 87 -Region Avg IOU: 0.581507, Class: 1.000000, Obj: 0.310573, No Obj: 0.002941, Avg Recall: 0.666667, count: 117 -1770: 0.447213, 0.466508 avg, 0.001000 rate, 26.843037 seconds, 453120 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 13.820872 seconds -Region Avg IOU: 0.442917, Class: 1.000000, Obj: 0.226275, No Obj: 0.003935, Avg Recall: 0.457364, count: 129 -Region Avg IOU: 0.450069, Class: 1.000000, Obj: 0.222968, No Obj: 0.003066, Avg Recall: 0.481132, count: 106 -Region Avg IOU: 0.452652, Class: 1.000000, Obj: 0.222580, No Obj: 0.003355, Avg Recall: 0.457627, count: 118 -Region Avg IOU: 0.490617, Class: 1.000000, Obj: 0.229194, No Obj: 0.003630, Avg Recall: 0.550000, count: 120 -Region Avg IOU: 0.437395, Class: 1.000000, Obj: 0.239781, No Obj: 0.003248, Avg Recall: 0.495238, count: 105 -Region Avg IOU: 0.456564, Class: 1.000000, Obj: 0.217243, No Obj: 0.003861, Avg Recall: 0.495798, count: 119 -Region Avg IOU: 0.498089, Class: 1.000000, Obj: 0.185564, No Obj: 0.003756, Avg Recall: 0.568965, count: 116 -Region Avg IOU: 0.430574, Class: 1.000000, Obj: 0.213615, No Obj: 0.003413, Avg Recall: 0.463415, count: 123 -1771: 0.616876, 0.481545 avg, 0.001000 rate, 16.739471 seconds, 453376 images -Loaded: 1.653876 seconds -Region Avg IOU: 0.486261, Class: 1.000000, Obj: 0.256376, No Obj: 0.003767, Avg Recall: 0.450980, count: 102 -Region Avg IOU: 0.433735, Class: 1.000000, Obj: 0.228822, No Obj: 0.003504, Avg Recall: 0.457364, count: 129 -Region Avg IOU: 0.482205, Class: 1.000000, Obj: 0.224610, No Obj: 0.003580, Avg Recall: 0.520661, count: 121 -Region Avg IOU: 0.483638, Class: 1.000000, Obj: 0.231202, No Obj: 0.003241, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.480785, Class: 1.000000, Obj: 0.229642, No Obj: 0.003288, Avg Recall: 0.505050, count: 99 -Region Avg IOU: 0.446076, Class: 1.000000, Obj: 0.224513, No Obj: 0.003690, Avg Recall: 0.488636, count: 88 -Region Avg IOU: 0.488035, Class: 1.000000, Obj: 0.264411, No Obj: 0.003615, Avg Recall: 0.538462, count: 91 -Region Avg IOU: 0.421498, Class: 1.000000, Obj: 0.210452, No Obj: 0.003691, Avg Recall: 0.460317, count: 126 -1772: 0.567557, 0.490146 avg, 0.001000 rate, 16.685200 seconds, 453632 images -Loaded: 3.792813 seconds -Region Avg IOU: 0.463031, Class: 1.000000, Obj: 0.222216, No Obj: 0.003926, Avg Recall: 0.511450, count: 131 -Region Avg IOU: 0.458813, Class: 1.000000, Obj: 0.259983, No Obj: 0.003902, Avg Recall: 0.515873, count: 126 -Region Avg IOU: 0.475827, Class: 1.000000, Obj: 0.236154, No Obj: 0.004247, Avg Recall: 0.507692, count: 130 -Region Avg IOU: 0.472110, Class: 1.000000, Obj: 0.247532, No Obj: 0.003258, Avg Recall: 0.517647, count: 85 -Region Avg IOU: 0.525149, Class: 1.000000, Obj: 0.287001, No Obj: 0.003664, Avg Recall: 0.551282, count: 78 -Region Avg IOU: 0.453286, Class: 1.000000, Obj: 0.264932, No Obj: 0.004049, Avg Recall: 0.427419, count: 124 -Region Avg IOU: 0.466761, Class: 1.000000, Obj: 0.252175, No Obj: 0.004086, Avg Recall: 0.534483, count: 116 -Region Avg IOU: 0.494527, Class: 1.000000, Obj: 0.254456, No Obj: 0.003764, Avg Recall: 0.504673, count: 107 -1773: 0.504084, 0.491540 avg, 0.001000 rate, 18.851440 seconds, 453888 images -Loaded: 1.862772 seconds -Region Avg IOU: 0.419878, Class: 1.000000, Obj: 0.242371, No Obj: 0.004359, Avg Recall: 0.376923, count: 130 -Region Avg IOU: 0.413113, Class: 1.000000, Obj: 0.307573, No Obj: 0.004105, Avg Recall: 0.432203, count: 118 -Region Avg IOU: 0.447166, Class: 1.000000, Obj: 0.226185, No Obj: 0.004326, Avg Recall: 0.454545, count: 132 -Region Avg IOU: 0.436962, Class: 1.000000, Obj: 0.238414, No Obj: 0.004046, Avg Recall: 0.472000, count: 125 -Region Avg IOU: 0.427172, Class: 1.000000, Obj: 0.239958, No Obj: 0.004257, Avg Recall: 0.448529, count: 136 -Region Avg IOU: 0.451136, Class: 1.000000, Obj: 0.278035, No Obj: 0.005015, Avg Recall: 0.448000, count: 125 -Region Avg IOU: 0.419483, Class: 1.000000, Obj: 0.259855, No Obj: 0.003638, Avg Recall: 0.382353, count: 102 -Region Avg IOU: 0.477712, Class: 1.000000, Obj: 0.313421, No Obj: 0.004264, Avg Recall: 0.534091, count: 88 -1774: 0.580294, 0.500415 avg, 0.001000 rate, 19.723171 seconds, 454144 images -Loaded: 0.505178 seconds -Region Avg IOU: 0.503860, Class: 1.000000, Obj: 0.292306, No Obj: 0.004762, Avg Recall: 0.500000, count: 98 -Region Avg IOU: 0.409031, Class: 1.000000, Obj: 0.268865, No Obj: 0.004867, Avg Recall: 0.389381, count: 113 -Region Avg IOU: 0.446309, Class: 1.000000, Obj: 0.329804, No Obj: 0.004338, Avg Recall: 0.448980, count: 98 -Region Avg IOU: 0.455183, Class: 1.000000, Obj: 0.239517, No Obj: 0.003804, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.454189, Class: 1.000000, Obj: 0.227817, No Obj: 0.003851, Avg Recall: 0.460674, count: 89 -Region Avg IOU: 0.432706, Class: 1.000000, Obj: 0.280916, No Obj: 0.004521, Avg Recall: 0.421053, count: 114 -Region Avg IOU: 0.450080, Class: 1.000000, Obj: 0.334188, No Obj: 0.004281, Avg Recall: 0.479167, count: 96 -Region Avg IOU: 0.411322, Class: 1.000000, Obj: 0.288249, No Obj: 0.003890, Avg Recall: 0.410526, count: 95 -1775: 0.462001, 0.496574 avg, 0.001000 rate, 19.871708 seconds, 454400 images -Loaded: 3.371809 seconds -Region Avg IOU: 0.470573, Class: 1.000000, Obj: 0.329545, No Obj: 0.004651, Avg Recall: 0.494737, count: 95 -Region Avg IOU: 0.387841, Class: 1.000000, Obj: 0.287086, No Obj: 0.004435, Avg Recall: 0.421053, count: 114 -Region Avg IOU: 0.442686, Class: 1.000000, Obj: 0.302674, No Obj: 0.004448, Avg Recall: 0.483051, count: 118 -Region Avg IOU: 0.447407, Class: 1.000000, Obj: 0.255899, No Obj: 0.004974, Avg Recall: 0.474820, count: 139 -Region Avg IOU: 0.516622, Class: 1.000000, Obj: 0.316041, No Obj: 0.004681, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.420943, Class: 1.000000, Obj: 0.269295, No Obj: 0.005056, Avg Recall: 0.460993, count: 141 -Region Avg IOU: 0.405991, Class: 1.000000, Obj: 0.293335, No Obj: 0.004449, Avg Recall: 0.438462, count: 130 -Region Avg IOU: 0.398890, Class: 1.000000, Obj: 0.306839, No Obj: 0.004426, Avg Recall: 0.367925, count: 106 -1776: 0.612511, 0.508168 avg, 0.001000 rate, 18.294748 seconds, 454656 images -Loaded: 2.069147 seconds -Region Avg IOU: 0.444376, Class: 1.000000, Obj: 0.314664, No Obj: 0.004510, Avg Recall: 0.485437, count: 103 -Region Avg IOU: 0.436265, Class: 1.000000, Obj: 0.298937, No Obj: 0.004516, Avg Recall: 0.471154, count: 104 -Region Avg IOU: 0.431704, Class: 1.000000, Obj: 0.284292, No Obj: 0.004446, Avg Recall: 0.449541, count: 109 -Region Avg IOU: 0.425799, Class: 1.000000, Obj: 0.289324, No Obj: 0.004321, Avg Recall: 0.454545, count: 121 -Region Avg IOU: 0.426203, Class: 1.000000, Obj: 0.282028, No Obj: 0.004549, Avg Recall: 0.446429, count: 112 -Region Avg IOU: 0.426180, Class: 1.000000, Obj: 0.288971, No Obj: 0.004523, Avg Recall: 0.504425, count: 113 -Region Avg IOU: 0.424958, Class: 1.000000, Obj: 0.312589, No Obj: 0.004465, Avg Recall: 0.410000, count: 100 -Region Avg IOU: 0.465943, Class: 1.000000, Obj: 0.296299, No Obj: 0.004789, Avg Recall: 0.500000, count: 108 -1777: 0.514957, 0.508847 avg, 0.001000 rate, 17.375957 seconds, 454912 images -Loaded: 3.406445 seconds -Region Avg IOU: 0.413130, Class: 1.000000, Obj: 0.288376, No Obj: 0.004509, Avg Recall: 0.415254, count: 118 -Region Avg IOU: 0.443611, Class: 1.000000, Obj: 0.294402, No Obj: 0.004981, Avg Recall: 0.464912, count: 114 -Region Avg IOU: 0.479487, Class: 1.000000, Obj: 0.277767, No Obj: 0.005502, Avg Recall: 0.503817, count: 131 -Region Avg IOU: 0.405715, Class: 1.000000, Obj: 0.298759, No Obj: 0.004267, Avg Recall: 0.452830, count: 106 -Region Avg IOU: 0.434021, Class: 1.000000, Obj: 0.259839, No Obj: 0.004244, Avg Recall: 0.432432, count: 111 -Region Avg IOU: 0.449367, Class: 1.000000, Obj: 0.313473, No Obj: 0.004637, Avg Recall: 0.522936, count: 109 -Region Avg IOU: 0.502164, Class: 1.000000, Obj: 0.298829, No Obj: 0.004536, Avg Recall: 0.530864, count: 81 -Region Avg IOU: 0.407614, Class: 1.000000, Obj: 0.294576, No Obj: 0.004161, Avg Recall: 0.390244, count: 82 -1778: 0.497113, 0.507673 avg, 0.001000 rate, 19.749449 seconds, 455168 images -Loaded: 2.119593 seconds -Region Avg IOU: 0.472239, Class: 1.000000, Obj: 0.336088, No Obj: 0.004002, Avg Recall: 0.543210, count: 81 -Region Avg IOU: 0.446407, Class: 1.000000, Obj: 0.283109, No Obj: 0.004919, Avg Recall: 0.464286, count: 140 -Region Avg IOU: 0.480469, Class: 1.000000, Obj: 0.287439, No Obj: 0.005206, Avg Recall: 0.554745, count: 137 -Region Avg IOU: 0.445618, Class: 1.000000, Obj: 0.292496, No Obj: 0.004556, Avg Recall: 0.440000, count: 100 -Region Avg IOU: 0.488899, Class: 1.000000, Obj: 0.304993, No Obj: 0.004357, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.451392, Class: 1.000000, Obj: 0.260182, No Obj: 0.004437, Avg Recall: 0.500000, count: 96 -Region Avg IOU: 0.474644, Class: 1.000000, Obj: 0.317988, No Obj: 0.004239, Avg Recall: 0.586957, count: 92 -Region Avg IOU: 0.436791, Class: 1.000000, Obj: 0.291803, No Obj: 0.004281, Avg Recall: 0.486239, count: 109 -1779: 0.533852, 0.510291 avg, 0.001000 rate, 20.239651 seconds, 455424 images -Loaded: 1.705978 seconds -Region Avg IOU: 0.460500, Class: 1.000000, Obj: 0.262824, No Obj: 0.004048, Avg Recall: 0.571429, count: 77 -Region Avg IOU: 0.470149, Class: 1.000000, Obj: 0.317244, No Obj: 0.003806, Avg Recall: 0.500000, count: 76 -Region Avg IOU: 0.490307, Class: 1.000000, Obj: 0.287073, No Obj: 0.004425, Avg Recall: 0.559322, count: 118 -Region Avg IOU: 0.482246, Class: 1.000000, Obj: 0.277472, No Obj: 0.004364, Avg Recall: 0.529412, count: 119 -Region Avg IOU: 0.412205, Class: 1.000000, Obj: 0.302358, No Obj: 0.003830, Avg Recall: 0.430000, count: 100 -Region Avg IOU: 0.478573, Class: 1.000000, Obj: 0.265335, No Obj: 0.004883, Avg Recall: 0.572414, count: 145 -Region Avg IOU: 0.453173, Class: 1.000000, Obj: 0.241817, No Obj: 0.004513, Avg Recall: 0.486726, count: 113 -Region Avg IOU: 0.428033, Class: 1.000000, Obj: 0.239113, No Obj: 0.003654, Avg Recall: 0.450450, count: 111 -1780: 0.454050, 0.504667 avg, 0.001000 rate, 18.747868 seconds, 455680 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 0.000072 seconds -Region Avg IOU: 0.489582, Class: 1.000000, Obj: 0.280532, No Obj: 0.004271, Avg Recall: 0.564103, count: 156 -Region Avg IOU: 0.493945, Class: 1.000000, Obj: 0.286491, No Obj: 0.003934, Avg Recall: 0.528000, count: 125 -Region Avg IOU: 0.528270, Class: 1.000000, Obj: 0.276866, No Obj: 0.003931, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.530159, Class: 1.000000, Obj: 0.317972, No Obj: 0.004037, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.536678, Class: 1.000000, Obj: 0.303412, No Obj: 0.004443, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.453164, Class: 1.000000, Obj: 0.277881, No Obj: 0.003919, Avg Recall: 0.474138, count: 116 -Region Avg IOU: 0.508481, Class: 1.000000, Obj: 0.277324, No Obj: 0.003563, Avg Recall: 0.536082, count: 97 -Region Avg IOU: 0.487571, Class: 1.000000, Obj: 0.304448, No Obj: 0.004608, Avg Recall: 0.539568, count: 139 -1781: 0.503332, 0.504533 avg, 0.001000 rate, 21.823013 seconds, 455936 images -Loaded: 1.515925 seconds -Region Avg IOU: 0.479598, Class: 1.000000, Obj: 0.268715, No Obj: 0.003963, Avg Recall: 0.516949, count: 118 -Region Avg IOU: 0.481412, Class: 1.000000, Obj: 0.283187, No Obj: 0.003657, Avg Recall: 0.563636, count: 110 -Region Avg IOU: 0.512467, Class: 1.000000, Obj: 0.283169, No Obj: 0.003811, Avg Recall: 0.545455, count: 121 -Region Avg IOU: 0.515415, Class: 1.000000, Obj: 0.305670, No Obj: 0.004234, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.532553, Class: 1.000000, Obj: 0.277020, No Obj: 0.004218, Avg Recall: 0.571429, count: 112 -Region Avg IOU: 0.561343, Class: 1.000000, Obj: 0.322460, No Obj: 0.004280, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.519710, Class: 1.000000, Obj: 0.295142, No Obj: 0.004372, Avg Recall: 0.640000, count: 125 -Region Avg IOU: 0.502359, Class: 1.000000, Obj: 0.299989, No Obj: 0.003554, Avg Recall: 0.575758, count: 99 -1782: 0.519844, 0.506064 avg, 0.001000 rate, 21.479834 seconds, 456192 images -Loaded: 1.109527 seconds -Region Avg IOU: 0.492376, Class: 1.000000, Obj: 0.262939, No Obj: 0.003798, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.512010, Class: 1.000000, Obj: 0.257928, No Obj: 0.004031, Avg Recall: 0.588710, count: 124 -Region Avg IOU: 0.531519, Class: 1.000000, Obj: 0.291110, No Obj: 0.003466, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.493980, Class: 1.000000, Obj: 0.320924, No Obj: 0.003336, Avg Recall: 0.526882, count: 93 -Region Avg IOU: 0.509698, Class: 1.000000, Obj: 0.301985, No Obj: 0.003737, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.454430, Class: 1.000000, Obj: 0.248211, No Obj: 0.003293, Avg Recall: 0.467290, count: 107 -Region Avg IOU: 0.508194, Class: 1.000000, Obj: 0.356864, No Obj: 0.003485, Avg Recall: 0.571429, count: 91 -Region Avg IOU: 0.552114, Class: 1.000000, Obj: 0.298952, No Obj: 0.003711, Avg Recall: 0.654545, count: 110 -1783: 0.472025, 0.502661 avg, 0.001000 rate, 21.318661 seconds, 456448 images -Loaded: 1.502407 seconds -Region Avg IOU: 0.542869, Class: 1.000000, Obj: 0.320569, No Obj: 0.004394, Avg Recall: 0.650794, count: 126 -Region Avg IOU: 0.570561, Class: 1.000000, Obj: 0.327508, No Obj: 0.004476, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.524517, Class: 1.000000, Obj: 0.297353, No Obj: 0.003711, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.500695, Class: 1.000000, Obj: 0.285266, No Obj: 0.003812, Avg Recall: 0.561151, count: 139 -Region Avg IOU: 0.492127, Class: 1.000000, Obj: 0.288089, No Obj: 0.004401, Avg Recall: 0.538462, count: 130 -Region Avg IOU: 0.478076, Class: 1.000000, Obj: 0.294930, No Obj: 0.003599, Avg Recall: 0.504587, count: 109 -Region Avg IOU: 0.494276, Class: 1.000000, Obj: 0.272274, No Obj: 0.003671, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.527699, Class: 1.000000, Obj: 0.302578, No Obj: 0.004165, Avg Recall: 0.595960, count: 99 -1784: 0.525230, 0.504917 avg, 0.001000 rate, 21.019064 seconds, 456704 images -Loaded: 0.000059 seconds -Region Avg IOU: 0.501515, Class: 1.000000, Obj: 0.294434, No Obj: 0.003993, Avg Recall: 0.570175, count: 114 -Region Avg IOU: 0.540374, Class: 1.000000, Obj: 0.294509, No Obj: 0.003141, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.491814, Class: 1.000000, Obj: 0.269021, No Obj: 0.003814, Avg Recall: 0.515152, count: 99 -Region Avg IOU: 0.528373, Class: 1.000000, Obj: 0.298695, No Obj: 0.004156, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.511096, Class: 1.000000, Obj: 0.315792, No Obj: 0.003992, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.507303, Class: 1.000000, Obj: 0.320187, No Obj: 0.004375, Avg Recall: 0.539683, count: 126 -Region Avg IOU: 0.485860, Class: 1.000000, Obj: 0.253257, No Obj: 0.003807, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.506913, Class: 1.000000, Obj: 0.325985, No Obj: 0.003860, Avg Recall: 0.535088, count: 114 -1785: 0.478196, 0.502245 avg, 0.001000 rate, 21.754736 seconds, 456960 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.517385, Class: 1.000000, Obj: 0.313438, No Obj: 0.004494, Avg Recall: 0.562500, count: 128 -Region Avg IOU: 0.534058, Class: 1.000000, Obj: 0.288510, No Obj: 0.004180, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.490624, Class: 1.000000, Obj: 0.300792, No Obj: 0.004361, Avg Recall: 0.535211, count: 142 -Region Avg IOU: 0.499981, Class: 1.000000, Obj: 0.297113, No Obj: 0.004254, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.451985, Class: 1.000000, Obj: 0.305566, No Obj: 0.003910, Avg Recall: 0.466102, count: 118 -Region Avg IOU: 0.529675, Class: 1.000000, Obj: 0.280975, No Obj: 0.003353, Avg Recall: 0.600000, count: 90 -Region Avg IOU: 0.504664, Class: 1.000000, Obj: 0.265336, No Obj: 0.003961, Avg Recall: 0.592593, count: 108 -Region Avg IOU: 0.505080, Class: 1.000000, Obj: 0.338751, No Obj: 0.004047, Avg Recall: 0.566038, count: 106 -1786: 0.521833, 0.504204 avg, 0.001000 rate, 22.240532 seconds, 457216 images -Loaded: 0.287708 seconds -Region Avg IOU: 0.510854, Class: 1.000000, Obj: 0.346677, No Obj: 0.003467, Avg Recall: 0.547619, count: 84 -Region Avg IOU: 0.489693, Class: 1.000000, Obj: 0.329304, No Obj: 0.003866, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.506519, Class: 1.000000, Obj: 0.320874, No Obj: 0.004017, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.502248, Class: 1.000000, Obj: 0.303464, No Obj: 0.004167, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.490744, Class: 1.000000, Obj: 0.302727, No Obj: 0.004455, Avg Recall: 0.533898, count: 118 -Region Avg IOU: 0.498719, Class: 1.000000, Obj: 0.333191, No Obj: 0.004622, Avg Recall: 0.561404, count: 114 -Region Avg IOU: 0.474608, Class: 1.000000, Obj: 0.330116, No Obj: 0.003860, Avg Recall: 0.495935, count: 123 -Region Avg IOU: 0.539895, Class: 1.000000, Obj: 0.306915, No Obj: 0.004018, Avg Recall: 0.657895, count: 114 -1787: 0.415447, 0.495328 avg, 0.001000 rate, 21.151791 seconds, 457472 images -Loaded: 1.481572 seconds -Region Avg IOU: 0.495416, Class: 1.000000, Obj: 0.311172, No Obj: 0.003984, Avg Recall: 0.534653, count: 101 -Region Avg IOU: 0.562631, Class: 1.000000, Obj: 0.373362, No Obj: 0.004213, Avg Recall: 0.678161, count: 87 -Region Avg IOU: 0.530768, Class: 1.000000, Obj: 0.312865, No Obj: 0.004164, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.519558, Class: 1.000000, Obj: 0.346781, No Obj: 0.004843, Avg Recall: 0.589744, count: 117 -Region Avg IOU: 0.512624, Class: 1.000000, Obj: 0.317922, No Obj: 0.003933, Avg Recall: 0.577586, count: 116 -Region Avg IOU: 0.502881, Class: 1.000000, Obj: 0.325731, No Obj: 0.004114, Avg Recall: 0.561798, count: 89 -Region Avg IOU: 0.456225, Class: 1.000000, Obj: 0.282764, No Obj: 0.003836, Avg Recall: 0.519084, count: 131 -Region Avg IOU: 0.533437, Class: 1.000000, Obj: 0.324853, No Obj: 0.003996, Avg Recall: 0.588785, count: 107 -1788: 0.449959, 0.490791 avg, 0.001000 rate, 22.581577 seconds, 457728 images -Loaded: 0.055559 seconds -Region Avg IOU: 0.518071, Class: 1.000000, Obj: 0.296780, No Obj: 0.004136, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.515290, Class: 1.000000, Obj: 0.315267, No Obj: 0.003982, Avg Recall: 0.583333, count: 120 -Region Avg IOU: 0.539249, Class: 1.000000, Obj: 0.309834, No Obj: 0.003602, Avg Recall: 0.616279, count: 86 -Region Avg IOU: 0.460135, Class: 1.000000, Obj: 0.320689, No Obj: 0.004078, Avg Recall: 0.510204, count: 98 -Region Avg IOU: 0.522924, Class: 1.000000, Obj: 0.352993, No Obj: 0.003810, Avg Recall: 0.600000, count: 95 -Region Avg IOU: 0.518041, Class: 1.000000, Obj: 0.279203, No Obj: 0.004214, Avg Recall: 0.563636, count: 110 -Region Avg IOU: 0.502933, Class: 1.000000, Obj: 0.330939, No Obj: 0.004181, Avg Recall: 0.546099, count: 141 -Region Avg IOU: 0.475292, Class: 1.000000, Obj: 0.302420, No Obj: 0.004316, Avg Recall: 0.542373, count: 118 -1789: 0.506830, 0.492395 avg, 0.001000 rate, 21.285477 seconds, 457984 images -Loaded: 1.131372 seconds -Region Avg IOU: 0.536002, Class: 1.000000, Obj: 0.300300, No Obj: 0.004556, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.463848, Class: 1.000000, Obj: 0.286732, No Obj: 0.003324, Avg Recall: 0.444444, count: 90 -Region Avg IOU: 0.498051, Class: 1.000000, Obj: 0.304925, No Obj: 0.004785, Avg Recall: 0.577236, count: 123 -Region Avg IOU: 0.544000, Class: 1.000000, Obj: 0.332764, No Obj: 0.003758, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.526751, Class: 1.000000, Obj: 0.314255, No Obj: 0.004595, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.506326, Class: 1.000000, Obj: 0.322065, No Obj: 0.003542, Avg Recall: 0.553398, count: 103 -Region Avg IOU: 0.536182, Class: 1.000000, Obj: 0.308844, No Obj: 0.003781, Avg Recall: 0.600000, count: 85 -Region Avg IOU: 0.527749, Class: 1.000000, Obj: 0.318262, No Obj: 0.004122, Avg Recall: 0.621622, count: 111 -1790: 0.481422, 0.491298 avg, 0.001000 rate, 23.465204 seconds, 458240 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 20.535011 seconds -Region Avg IOU: 0.499681, Class: 1.000000, Obj: 0.261162, No Obj: 0.004409, Avg Recall: 0.576471, count: 85 -Region Avg IOU: 0.501446, Class: 1.000000, Obj: 0.266603, No Obj: 0.004883, Avg Recall: 0.564885, count: 131 -Region Avg IOU: 0.460272, Class: 1.000000, Obj: 0.258628, No Obj: 0.004236, Avg Recall: 0.478992, count: 119 -Region Avg IOU: 0.421634, Class: 1.000000, Obj: 0.248894, No Obj: 0.004569, Avg Recall: 0.425373, count: 134 -Region Avg IOU: 0.409788, Class: 1.000000, Obj: 0.252427, No Obj: 0.003536, Avg Recall: 0.415842, count: 101 -Region Avg IOU: 0.416376, Class: 1.000000, Obj: 0.248573, No Obj: 0.004064, Avg Recall: 0.426087, count: 115 -Region Avg IOU: 0.401029, Class: 1.000000, Obj: 0.260983, No Obj: 0.004052, Avg Recall: 0.391753, count: 97 -Region Avg IOU: 0.495638, Class: 1.000000, Obj: 0.279528, No Obj: 0.004055, Avg Recall: 0.554217, count: 83 -1791: 0.489249, 0.491093 avg, 0.001000 rate, 19.269409 seconds, 458496 images -Loaded: 2.785664 seconds -Region Avg IOU: 0.418646, Class: 1.000000, Obj: 0.246506, No Obj: 0.004232, Avg Recall: 0.453608, count: 97 -Region Avg IOU: 0.466625, Class: 1.000000, Obj: 0.266496, No Obj: 0.004493, Avg Recall: 0.514286, count: 105 -Region Avg IOU: 0.462310, Class: 1.000000, Obj: 0.263353, No Obj: 0.004773, Avg Recall: 0.508065, count: 124 -Region Avg IOU: 0.424351, Class: 1.000000, Obj: 0.264615, No Obj: 0.003681, Avg Recall: 0.430000, count: 100 -Region Avg IOU: 0.464398, Class: 1.000000, Obj: 0.259539, No Obj: 0.003917, Avg Recall: 0.526316, count: 95 -Region Avg IOU: 0.469452, Class: 1.000000, Obj: 0.298072, No Obj: 0.004470, Avg Recall: 0.483333, count: 120 -Region Avg IOU: 0.464203, Class: 1.000000, Obj: 0.254192, No Obj: 0.004306, Avg Recall: 0.466667, count: 105 -Region Avg IOU: 0.471950, Class: 1.000000, Obj: 0.284263, No Obj: 0.003668, Avg Recall: 0.500000, count: 86 -1792: 0.461508, 0.488135 avg, 0.001000 rate, 19.995996 seconds, 458752 images -Loaded: 0.975106 seconds -Region Avg IOU: 0.454596, Class: 1.000000, Obj: 0.212067, No Obj: 0.003679, Avg Recall: 0.494845, count: 97 -Region Avg IOU: 0.475569, Class: 1.000000, Obj: 0.287895, No Obj: 0.004540, Avg Recall: 0.464912, count: 114 -Region Avg IOU: 0.494760, Class: 1.000000, Obj: 0.278518, No Obj: 0.004078, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.474746, Class: 1.000000, Obj: 0.307653, No Obj: 0.004531, Avg Recall: 0.530435, count: 115 -Region Avg IOU: 0.483285, Class: 1.000000, Obj: 0.249125, No Obj: 0.004220, Avg Recall: 0.508772, count: 114 -Region Avg IOU: 0.461431, Class: 1.000000, Obj: 0.278132, No Obj: 0.004207, Avg Recall: 0.504762, count: 105 -Region Avg IOU: 0.475049, Class: 1.000000, Obj: 0.272789, No Obj: 0.005091, Avg Recall: 0.551181, count: 127 -Region Avg IOU: 0.459404, Class: 1.000000, Obj: 0.274793, No Obj: 0.004309, Avg Recall: 0.468468, count: 111 -1793: 0.469476, 0.486269 avg, 0.001000 rate, 19.237284 seconds, 459008 images -Loaded: 0.816491 seconds -Region Avg IOU: 0.408051, Class: 1.000000, Obj: 0.241988, No Obj: 0.004188, Avg Recall: 0.394231, count: 104 -Region Avg IOU: 0.470374, Class: 1.000000, Obj: 0.281775, No Obj: 0.004427, Avg Recall: 0.510204, count: 98 -Region Avg IOU: 0.474900, Class: 1.000000, Obj: 0.255588, No Obj: 0.004741, Avg Recall: 0.504274, count: 117 -Region Avg IOU: 0.411984, Class: 1.000000, Obj: 0.232708, No Obj: 0.003641, Avg Recall: 0.390000, count: 100 -Region Avg IOU: 0.408604, Class: 1.000000, Obj: 0.225212, No Obj: 0.004438, Avg Recall: 0.395349, count: 129 -Region Avg IOU: 0.489599, Class: 1.000000, Obj: 0.337245, No Obj: 0.004551, Avg Recall: 0.509804, count: 102 -Region Avg IOU: 0.502828, Class: 1.000000, Obj: 0.290975, No Obj: 0.004310, Avg Recall: 0.566667, count: 90 -Region Avg IOU: 0.468862, Class: 1.000000, Obj: 0.294969, No Obj: 0.004051, Avg Recall: 0.569444, count: 72 -1794: 0.477144, 0.485356 avg, 0.001000 rate, 17.531952 seconds, 459264 images -Loaded: 3.760691 seconds -Region Avg IOU: 0.480322, Class: 1.000000, Obj: 0.263836, No Obj: 0.004471, Avg Recall: 0.553398, count: 103 -Region Avg IOU: 0.456661, Class: 1.000000, Obj: 0.284298, No Obj: 0.004374, Avg Recall: 0.473684, count: 95 -Region Avg IOU: 0.446355, Class: 1.000000, Obj: 0.306166, No Obj: 0.004372, Avg Recall: 0.464286, count: 112 -Region Avg IOU: 0.488606, Class: 1.000000, Obj: 0.271295, No Obj: 0.004328, Avg Recall: 0.524752, count: 101 -Region Avg IOU: 0.438469, Class: 1.000000, Obj: 0.261214, No Obj: 0.003835, Avg Recall: 0.420561, count: 107 -Region Avg IOU: 0.436072, Class: 1.000000, Obj: 0.251610, No Obj: 0.004135, Avg Recall: 0.456311, count: 103 -Region Avg IOU: 0.466758, Class: 1.000000, Obj: 0.287946, No Obj: 0.003995, Avg Recall: 0.528571, count: 70 -Region Avg IOU: 0.475768, Class: 1.000000, Obj: 0.294906, No Obj: 0.004842, Avg Recall: 0.500000, count: 108 -1795: 0.447563, 0.481577 avg, 0.001000 rate, 16.032368 seconds, 459520 images -Loaded: 5.386292 seconds -Region Avg IOU: 0.459968, Class: 1.000000, Obj: 0.266013, No Obj: 0.004927, Avg Recall: 0.493333, count: 150 -Region Avg IOU: 0.478566, Class: 1.000000, Obj: 0.278614, No Obj: 0.004446, Avg Recall: 0.474747, count: 99 -Region Avg IOU: 0.465867, Class: 1.000000, Obj: 0.301576, No Obj: 0.004368, Avg Recall: 0.484536, count: 97 -Region Avg IOU: 0.436012, Class: 1.000000, Obj: 0.287749, No Obj: 0.004842, Avg Recall: 0.426087, count: 115 -Region Avg IOU: 0.455343, Class: 1.000000, Obj: 0.264325, No Obj: 0.004517, Avg Recall: 0.482353, count: 85 -Region Avg IOU: 0.482982, Class: 1.000000, Obj: 0.287801, No Obj: 0.004605, Avg Recall: 0.526786, count: 112 -Region Avg IOU: 0.459607, Class: 1.000000, Obj: 0.229679, No Obj: 0.003875, Avg Recall: 0.465347, count: 101 -Region Avg IOU: 0.437372, Class: 1.000000, Obj: 0.260004, No Obj: 0.004076, Avg Recall: 0.559140, count: 93 -1796: 0.482539, 0.481673 avg, 0.001000 rate, 18.213535 seconds, 459776 images -Loaded: 3.394797 seconds -Region Avg IOU: 0.454974, Class: 1.000000, Obj: 0.270361, No Obj: 0.004917, Avg Recall: 0.486957, count: 115 -Region Avg IOU: 0.507298, Class: 1.000000, Obj: 0.300382, No Obj: 0.004404, Avg Recall: 0.549451, count: 91 -Region Avg IOU: 0.412208, Class: 1.000000, Obj: 0.264762, No Obj: 0.004839, Avg Recall: 0.398305, count: 118 -Region Avg IOU: 0.461470, Class: 1.000000, Obj: 0.254449, No Obj: 0.003856, Avg Recall: 0.475248, count: 101 -Region Avg IOU: 0.428366, Class: 1.000000, Obj: 0.261895, No Obj: 0.004103, Avg Recall: 0.489583, count: 96 -Region Avg IOU: 0.388023, Class: 1.000000, Obj: 0.251159, No Obj: 0.004079, Avg Recall: 0.368932, count: 103 -Region Avg IOU: 0.514701, Class: 1.000000, Obj: 0.285897, No Obj: 0.005158, Avg Recall: 0.572727, count: 110 -Region Avg IOU: 0.455723, Class: 1.000000, Obj: 0.279047, No Obj: 0.004974, Avg Recall: 0.474576, count: 118 -1797: 0.458835, 0.479389 avg, 0.001000 rate, 15.944188 seconds, 460032 images -Loaded: 4.359442 seconds -Region Avg IOU: 0.477719, Class: 1.000000, Obj: 0.275611, No Obj: 0.004338, Avg Recall: 0.530435, count: 115 -Region AvgSaving weights to ./person9300/backup/tyvp_93_9c512_1800.weights - IOU: 0.479076, Class: 1.000000, Obj: 0.262818, No Obj: 0.004134, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.462014, Class: 1.000000, Obj: 0.255067, No Obj: 0.004024, Avg Recall: 0.526316, count: 95 -Region Avg IOU: 0.482453, Class: 1.000000, Obj: 0.259183, No Obj: 0.004864, Avg Recall: 0.521739, count: 115 -Region Avg IOU: 0.442369, Class: 1.000000, Obj: 0.262208, No Obj: 0.004148, Avg Recall: 0.491071, count: 112 -Region Avg IOU: 0.511444, Class: 1.000000, Obj: 0.270466, No Obj: 0.004536, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.429074, Class: 1.000000, Obj: 0.284952, No Obj: 0.004404, Avg Recall: 0.446154, count: 130 -Region Avg IOU: 0.463704, Class: 1.000000, Obj: 0.324741, No Obj: 0.004462, Avg Recall: 0.571429, count: 91 -1798: 0.423454, 0.473796 avg, 0.001000 rate, 16.222187 seconds, 460288 images -Loaded: 5.762913 seconds -Region Avg IOU: 0.460118, Class: 1.000000, Obj: 0.270165, No Obj: 0.004831, Avg Recall: 0.464912, count: 114 -Region Avg IOU: 0.455966, Class: 1.000000, Obj: 0.274385, No Obj: 0.004327, Avg Recall: 0.490909, count: 110 -Region Avg IOU: 0.476441, Class: 1.000000, Obj: 0.284103, No Obj: 0.004722, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.470665, Class: 1.000000, Obj: 0.282969, No Obj: 0.003886, Avg Recall: 0.511364, count: 88 -Region Avg IOU: 0.467582, Class: 1.000000, Obj: 0.260493, No Obj: 0.005045, Avg Recall: 0.542857, count: 105 -Region Avg IOU: 0.456148, Class: 1.000000, Obj: 0.309311, No Obj: 0.004263, Avg Recall: 0.513274, count: 113 -Region Avg IOU: 0.505768, Class: 1.000000, Obj: 0.308906, No Obj: 0.004442, Avg Recall: 0.552083, count: 96 -Region Avg IOU: 0.471036, Class: 1.000000, Obj: 0.298736, No Obj: 0.004193, Avg Recall: 0.500000, count: 86 -1799: 0.464973, 0.472913 avg, 0.001000 rate, 19.354551 seconds, 460544 images -Loaded: 1.589048 seconds -Region Avg IOU: 0.431964, Class: 1.000000, Obj: 0.283658, No Obj: 0.004371, Avg Recall: 0.486239, count: 109 -Region Avg IOU: 0.413642, Class: 1.000000, Obj: 0.244902, No Obj: 0.004535, Avg Recall: 0.405405, count: 111 -Region Avg IOU: 0.460694, Class: 1.000000, Obj: 0.290686, No Obj: 0.004746, Avg Recall: 0.473214, count: 112 -Region Avg IOU: 0.484194, Class: 1.000000, Obj: 0.290042, No Obj: 0.005140, Avg Recall: 0.518519, count: 135 -Region Avg IOU: 0.454967, Class: 1.000000, Obj: 0.260825, No Obj: 0.004670, Avg Recall: 0.418803, count: 117 -Region Avg IOU: 0.460171, Class: 1.000000, Obj: 0.287942, No Obj: 0.004797, Avg Recall: 0.488722, count: 133 -Region Avg IOU: 0.476774, Class: 1.000000, Obj: 0.298787, No Obj: 0.005070, Avg Recall: 0.538462, count: 117 -Region Avg IOU: 0.445523, Class: 1.000000, Obj: 0.251440, No Obj: 0.004695, Avg Recall: 0.500000, count: 112 -1800: 0.554624, 0.481084 avg, 0.001000 rate, 19.991972 seconds, 460800 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000078 seconds -Region Avg IOU: 0.568544, Class: 1.000000, Obj: 0.284406, No Obj: 0.003424, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.529816, Class: 1.000000, Obj: 0.344748, No Obj: 0.003589, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.562730, Class: 1.000000, Obj: 0.350642, No Obj: 0.003927, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.550387, Class: 1.000000, Obj: 0.312544, No Obj: 0.003405, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.533734, Class: 1.000000, Obj: 0.358478, No Obj: 0.003748, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.552276, Class: 1.000000, Obj: 0.332066, No Obj: 0.004218, Avg Recall: 0.655462, count: 119 -Region Avg IOU: 0.567365, Class: 1.000000, Obj: 0.341397, No Obj: 0.003820, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.570382, Class: 1.000000, Obj: 0.350047, No Obj: 0.004038, Avg Recall: 0.700935, count: 107 -1801: 0.481620, 0.481138 avg, 0.001000 rate, 26.290228 seconds, 461056 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.617939, Class: 1.000000, Obj: 0.363141, No Obj: 0.003764, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.566680, Class: 1.000000, Obj: 0.335815, No Obj: 0.003684, Avg Recall: 0.654321, count: 81 -Region Avg IOU: 0.551535, Class: 1.000000, Obj: 0.284549, No Obj: 0.003676, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.541365, Class: 1.000000, Obj: 0.298335, No Obj: 0.004197, Avg Recall: 0.616541, count: 133 -Region Avg IOU: 0.544588, Class: 1.000000, Obj: 0.338936, No Obj: 0.003604, Avg Recall: 0.601626, count: 123 -Region Avg IOU: 0.561711, Class: 1.000000, Obj: 0.292305, No Obj: 0.004495, Avg Recall: 0.661654, count: 133 -Region Avg IOU: 0.593808, Class: 1.000000, Obj: 0.320010, No Obj: 0.004055, Avg Recall: 0.704000, count: 125 -Region Avg IOU: 0.546442, Class: 1.000000, Obj: 0.317772, No Obj: 0.003739, Avg Recall: 0.658730, count: 126 -1802: 0.538997, 0.486924 avg, 0.001000 rate, 24.892347 seconds, 461312 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.561477, Class: 1.000000, Obj: 0.295765, No Obj: 0.003881, Avg Recall: 0.659420, count: 138 -Region Avg IOU: 0.525260, Class: 1.000000, Obj: 0.313559, No Obj: 0.003394, Avg Recall: 0.580952, count: 105 -Region Avg IOU: 0.518444, Class: 1.000000, Obj: 0.318134, No Obj: 0.003416, Avg Recall: 0.606557, count: 122 -Region Avg IOU: 0.568155, Class: 1.000000, Obj: 0.300064, No Obj: 0.003037, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.529314, Class: 1.000000, Obj: 0.289613, No Obj: 0.003414, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.555442, Class: 1.000000, Obj: 0.325426, No Obj: 0.002914, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.576756, Class: 1.000000, Obj: 0.298867, No Obj: 0.003520, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.489700, Class: 1.000000, Obj: 0.278197, No Obj: 0.003149, Avg Recall: 0.534653, count: 101 -1803: 0.507190, 0.488950 avg, 0.001000 rate, 24.338190 seconds, 461568 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.589435, Class: 1.000000, Obj: 0.339990, No Obj: 0.003525, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.581602, Class: 1.000000, Obj: 0.322368, No Obj: 0.003659, Avg Recall: 0.723404, count: 141 -Region Avg IOU: 0.583360, Class: 1.000000, Obj: 0.318578, No Obj: 0.003415, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.546975, Class: 1.000000, Obj: 0.267340, No Obj: 0.003345, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.558401, Class: 1.000000, Obj: 0.298230, No Obj: 0.003517, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.516057, Class: 1.000000, Obj: 0.300268, No Obj: 0.003595, Avg Recall: 0.587838, count: 148 -Region Avg IOU: 0.561717, Class: 1.000000, Obj: 0.300917, No Obj: 0.003520, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.555328, Class: 1.000000, Obj: 0.271632, No Obj: 0.003393, Avg Recall: 0.640288, count: 139 -1804: 0.499534, 0.490009 avg, 0.001000 rate, 23.386919 seconds, 461824 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.548107, Class: 1.000000, Obj: 0.264095, No Obj: 0.003967, Avg Recall: 0.651899, count: 158 -Region Avg IOU: 0.557810, Class: 1.000000, Obj: 0.291011, No Obj: 0.003610, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.565824, Class: 1.000000, Obj: 0.311157, No Obj: 0.003716, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.539276, Class: 1.000000, Obj: 0.284909, No Obj: 0.002907, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.548674, Class: 1.000000, Obj: 0.299119, No Obj: 0.002692, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.549218, Class: 1.000000, Obj: 0.295125, No Obj: 0.002987, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.541130, Class: 1.000000, Obj: 0.305887, No Obj: 0.003188, Avg Recall: 0.633333, count: 120 -Region Avg IOU: 0.593057, Class: 1.000000, Obj: 0.301625, No Obj: 0.002924, Avg Recall: 0.703297, count: 91 -1805: 0.475102, 0.488518 avg, 0.001000 rate, 24.225124 seconds, 462080 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.514527, Class: 1.000000, Obj: 0.301346, No Obj: 0.002847, Avg Recall: 0.605042, count: 119 -Region Avg IOU: 0.534577, Class: 1.000000, Obj: 0.305093, No Obj: 0.002972, Avg Recall: 0.597826, count: 92 -Region Avg IOU: 0.509134, Class: 1.000000, Obj: 0.286474, No Obj: 0.003241, Avg Recall: 0.584507, count: 142 -Region Avg IOU: 0.567391, Class: 1.000000, Obj: 0.266941, No Obj: 0.003090, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.544233, Class: 1.000000, Obj: 0.316300, No Obj: 0.003108, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.565552, Class: 1.000000, Obj: 0.289688, No Obj: 0.003139, Avg Recall: 0.653226, count: 124 -Region Avg IOU: 0.561596, Class: 1.000000, Obj: 0.326356, No Obj: 0.003452, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.579215, Class: 1.000000, Obj: 0.311886, No Obj: 0.003096, Avg Recall: 0.715447, count: 123 -1806: 0.481781, 0.487844 avg, 0.001000 rate, 25.306084 seconds, 462336 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.560985, Class: 1.000000, Obj: 0.310510, No Obj: 0.003311, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.559011, Class: 1.000000, Obj: 0.313873, No Obj: 0.002966, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.574498, Class: 1.000000, Obj: 0.314520, No Obj: 0.003403, Avg Recall: 0.704545, count: 88 -Region Avg IOU: 0.556788, Class: 1.000000, Obj: 0.272389, No Obj: 0.002881, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.554458, Class: 1.000000, Obj: 0.280421, No Obj: 0.003382, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.578338, Class: 1.000000, Obj: 0.273515, No Obj: 0.003119, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.623527, Class: 1.000000, Obj: 0.365661, No Obj: 0.003162, Avg Recall: 0.775000, count: 80 -Region Avg IOU: 0.563820, Class: 1.000000, Obj: 0.274615, No Obj: 0.003125, Avg Recall: 0.666667, count: 102 -1807: 0.455928, 0.484653 avg, 0.001000 rate, 24.970779 seconds, 462592 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.587686, Class: 1.000000, Obj: 0.333462, No Obj: 0.003757, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.561445, Class: 1.000000, Obj: 0.320230, No Obj: 0.003616, Avg Recall: 0.697479, count: 119 -Region Avg IOU: 0.571796, Class: 1.000000, Obj: 0.359471, No Obj: 0.003720, Avg Recall: 0.643411, count: 129 -Region Avg IOU: 0.560130, Class: 1.000000, Obj: 0.324843, No Obj: 0.003313, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.565848, Class: 1.000000, Obj: 0.309284, No Obj: 0.003224, Avg Recall: 0.691667, count: 120 -Region Avg IOU: 0.471167, Class: 1.000000, Obj: 0.265025, No Obj: 0.003340, Avg Recall: 0.456522, count: 138 -Region Avg IOU: 0.598425, Class: 1.000000, Obj: 0.312110, No Obj: 0.003321, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.590060, Class: 1.000000, Obj: 0.305156, No Obj: 0.003775, Avg Recall: 0.755102, count: 147 -1808: 0.480609, 0.484248 avg, 0.001000 rate, 25.286440 seconds, 462848 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.548370, Class: 1.000000, Obj: 0.318409, No Obj: 0.003238, Avg Recall: 0.595506, count: 89 -Region Avg IOU: 0.530018, Class: 1.000000, Obj: 0.283772, No Obj: 0.003255, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.544859, Class: 1.000000, Obj: 0.300562, No Obj: 0.003557, Avg Recall: 0.645161, count: 124 -Region Avg IOU: 0.540014, Class: 1.000000, Obj: 0.311136, No Obj: 0.003660, Avg Recall: 0.563492, count: 126 -Region Avg IOU: 0.529945, Class: 1.000000, Obj: 0.295685, No Obj: 0.003138, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.588499, Class: 1.000000, Obj: 0.359538, No Obj: 0.003595, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.569673, Class: 1.000000, Obj: 0.319753, No Obj: 0.004116, Avg Recall: 0.661765, count: 136 -Region Avg IOU: 0.547732, Class: 1.000000, Obj: 0.346758, No Obj: 0.003352, Avg Recall: 0.628571, count: 105 -1809: 0.506984, 0.486522 avg, 0.001000 rate, 25.845087 seconds, 463104 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.645930, Class: 1.000000, Obj: 0.355140, No Obj: 0.003625, Avg Recall: 0.813187, count: 91 -Region Avg IOU: 0.576490, Class: 1.000000, Obj: 0.352108, No Obj: 0.003908, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.537479, Class: 1.000000, Obj: 0.327345, No Obj: 0.003284, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.563654, Class: 1.000000, Obj: 0.329316, No Obj: 0.003466, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.581265, Class: 1.000000, Obj: 0.321684, No Obj: 0.003425, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.565574, Class: 1.000000, Obj: 0.324351, No Obj: 0.003628, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.532600, Class: 1.000000, Obj: 0.306069, No Obj: 0.003522, Avg Recall: 0.581197, count: 117 -Region Avg IOU: 0.591275, Class: 1.000000, Obj: 0.334645, No Obj: 0.003596, Avg Recall: 0.708661, count: 127 -1810: 0.433888, 0.481259 avg, 0.001000 rate, 25.376863 seconds, 463360 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000069 seconds -Region Avg IOU: 0.628850, Class: 1.000000, Obj: 0.401888, No Obj: 0.003356, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.614533, Class: 1.000000, Obj: 0.356283, No Obj: 0.003596, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.529838, Class: 1.000000, Obj: 0.343718, No Obj: 0.003196, Avg Recall: 0.568421, count: 95 -Region Avg IOU: 0.584458, Class: 1.000000, Obj: 0.328736, No Obj: 0.003649, Avg Recall: 0.705426, count: 129 -Region Avg IOU: 0.537002, Class: 1.000000, Obj: 0.351307, No Obj: 0.003673, Avg Recall: 0.622222, count: 135 -Region Avg IOU: 0.572882, Class: 1.000000, Obj: 0.374012, No Obj: 0.003259, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.557763, Class: 1.000000, Obj: 0.365996, No Obj: 0.003369, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.609596, Class: 1.000000, Obj: 0.319164, No Obj: 0.003417, Avg Recall: 0.747368, count: 95 -1811: 0.448660, 0.477999 avg, 0.001000 rate, 28.007650 seconds, 463616 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.559385, Class: 1.000000, Obj: 0.306509, No Obj: 0.003779, Avg Recall: 0.656934, count: 137 -Region Avg IOU: 0.570790, Class: 1.000000, Obj: 0.358992, No Obj: 0.003595, Avg Recall: 0.704545, count: 132 -Region Avg IOU: 0.545731, Class: 1.000000, Obj: 0.336172, No Obj: 0.003475, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.574344, Class: 1.000000, Obj: 0.334396, No Obj: 0.002864, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.606334, Class: 1.000000, Obj: 0.361319, No Obj: 0.003643, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.586910, Class: 1.000000, Obj: 0.357028, No Obj: 0.003404, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.611824, Class: 1.000000, Obj: 0.366366, No Obj: 0.003038, Avg Recall: 0.728395, count: 81 -Region Avg IOU: 0.588965, Class: 1.000000, Obj: 0.357474, No Obj: 0.003602, Avg Recall: 0.730159, count: 126 -1812: 0.494182, 0.479617 avg, 0.001000 rate, 27.442358 seconds, 463872 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.591847, Class: 1.000000, Obj: 0.337054, No Obj: 0.003223, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.528048, Class: 1.000000, Obj: 0.338226, No Obj: 0.003188, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.618272, Class: 1.000000, Obj: 0.323837, No Obj: 0.003526, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.570074, Class: 1.000000, Obj: 0.303953, No Obj: 0.003626, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.580035, Class: 1.000000, Obj: 0.306249, No Obj: 0.003510, Avg Recall: 0.666667, count: 144 -Region Avg IOU: 0.632375, Class: 1.000000, Obj: 0.367212, No Obj: 0.003624, Avg Recall: 0.820312, count: 128 -Region Avg IOU: 0.585153, Class: 1.000000, Obj: 0.350721, No Obj: 0.003098, Avg Recall: 0.666667, count: 78 -Region Avg IOU: 0.545305, Class: 1.000000, Obj: 0.301982, No Obj: 0.003425, Avg Recall: 0.633987, count: 153 -1813: 0.511245, 0.482780 avg, 0.001000 rate, 26.126535 seconds, 464128 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.571412, Class: 1.000000, Obj: 0.331505, No Obj: 0.002775, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.589699, Class: 1.000000, Obj: 0.304566, No Obj: 0.002776, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.568945, Class: 1.000000, Obj: 0.334504, No Obj: 0.002968, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.611722, Class: 1.000000, Obj: 0.308747, No Obj: 0.002752, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.598504, Class: 1.000000, Obj: 0.364666, No Obj: 0.002669, Avg Recall: 0.766234, count: 77 -Region Avg IOU: 0.563396, Class: 1.000000, Obj: 0.317395, No Obj: 0.003007, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.556052, Class: 1.000000, Obj: 0.310639, No Obj: 0.002708, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.527328, Class: 1.000000, Obj: 0.309833, No Obj: 0.003117, Avg Recall: 0.642202, count: 109 -1814: 0.412979, 0.475800 avg, 0.001000 rate, 25.546476 seconds, 464384 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.587836, Class: 1.000000, Obj: 0.310461, No Obj: 0.002879, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.621972, Class: 1.000000, Obj: 0.300325, No Obj: 0.002763, Avg Recall: 0.773810, count: 84 -Region Avg IOU: 0.592592, Class: 1.000000, Obj: 0.290526, No Obj: 0.002704, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.582886, Class: 1.000000, Obj: 0.344239, No Obj: 0.002799, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.580605, Class: 1.000000, Obj: 0.285650, No Obj: 0.002907, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.608382, Class: 1.000000, Obj: 0.328980, No Obj: 0.003261, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.545139, Class: 1.000000, Obj: 0.288722, No Obj: 0.003088, Avg Recall: 0.643411, count: 129 -Region Avg IOU: 0.575237, Class: 1.000000, Obj: 0.283427, No Obj: 0.003101, Avg Recall: 0.696078, count: 102 -1815: 0.489649, 0.477185 avg, 0.001000 rate, 26.544744 seconds, 464640 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.577807, Class: 1.000000, Obj: 0.281983, No Obj: 0.002775, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.599676, Class: 1.000000, Obj: 0.289527, No Obj: 0.002936, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.603294, Class: 1.000000, Obj: 0.326184, No Obj: 0.003285, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.576317, Class: 1.000000, Obj: 0.285565, No Obj: 0.002556, Avg Recall: 0.682927, count: 123 -Region Avg IOU: 0.634122, Class: 1.000000, Obj: 0.313062, No Obj: 0.002761, Avg Recall: 0.811111, count: 90 -Region Avg IOU: 0.565956, Class: 1.000000, Obj: 0.278217, No Obj: 0.002736, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.559928, Class: 1.000000, Obj: 0.314202, No Obj: 0.002332, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.590739, Class: 1.000000, Obj: 0.308737, No Obj: 0.003228, Avg Recall: 0.710938, count: 128 -1816: 0.458198, 0.475286 avg, 0.001000 rate, 28.721979 seconds, 464896 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.578068, Class: 1.000000, Obj: 0.259266, No Obj: 0.002911, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.571928, Class: 1.000000, Obj: 0.286870, No Obj: 0.002648, Avg Recall: 0.657895, count: 76 -Region Avg IOU: 0.584546, Class: 1.000000, Obj: 0.261641, No Obj: 0.003083, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.592691, Class: 1.000000, Obj: 0.274425, No Obj: 0.002918, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.600455, Class: 1.000000, Obj: 0.275005, No Obj: 0.003210, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.627476, Class: 1.000000, Obj: 0.321921, No Obj: 0.002936, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.542972, Class: 1.000000, Obj: 0.248954, No Obj: 0.002786, Avg Recall: 0.614754, count: 122 -Region Avg IOU: 0.564038, Class: 1.000000, Obj: 0.270560, No Obj: 0.002944, Avg Recall: 0.645390, count: 141 -1817: 0.484643, 0.476222 avg, 0.001000 rate, 26.428944 seconds, 465152 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.592522, Class: 1.000000, Obj: 0.300519, No Obj: 0.002797, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.636363, Class: 1.000000, Obj: 0.362182, No Obj: 0.003270, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.620394, Class: 1.000000, Obj: 0.361648, No Obj: 0.003125, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.576608, Class: 1.000000, Obj: 0.293904, No Obj: 0.003004, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.594857, Class: 1.000000, Obj: 0.260630, No Obj: 0.003143, Avg Recall: 0.742188, count: 128 -Region Avg IOU: 0.610717, Class: 1.000000, Obj: 0.308546, No Obj: 0.003025, Avg Recall: 0.743802, count: 121 -Region Avg IOU: 0.618920, Class: 1.000000, Obj: 0.353746, No Obj: 0.002823, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.572463, Class: 1.000000, Obj: 0.341628, No Obj: 0.002919, Avg Recall: 0.666667, count: 102 -1818: 0.459807, 0.474580 avg, 0.001000 rate, 25.153942 seconds, 465408 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.569253, Class: 1.000000, Obj: 0.296384, No Obj: 0.003187, Avg Recall: 0.681818, count: 132 -Region Avg IOU: 0.579565, Class: 1.000000, Obj: 0.308226, No Obj: 0.002629, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.586156, Class: 1.000000, Obj: 0.332639, No Obj: 0.002813, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.565311, Class: 1.000000, Obj: 0.281909, No Obj: 0.003015, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.616301, Class: 1.000000, Obj: 0.329675, No Obj: 0.002988, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.575695, Class: 1.000000, Obj: 0.295737, No Obj: 0.002868, Avg Recall: 0.714286, count: 119 -Region Avg IOU: 0.554040, Class: 1.000000, Obj: 0.320154, No Obj: 0.002736, Avg Recall: 0.638554, count: 83 -Region Avg IOU: 0.596918, Class: 1.000000, Obj: 0.326485, No Obj: 0.002785, Avg Recall: 0.719512, count: 82 -1819: 0.459145, 0.473037 avg, 0.001000 rate, 27.571056 seconds, 465664 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.584767, Class: 1.000000, Obj: 0.319103, No Obj: 0.003142, Avg Recall: 0.714286, count: 133 -Region Avg IOU: 0.555703, Class: 1.000000, Obj: 0.278407, No Obj: 0.003184, Avg Recall: 0.627119, count: 118 -Region Avg IOU: 0.563495, Class: 1.000000, Obj: 0.318858, No Obj: 0.002964, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.580089, Class: 1.000000, Obj: 0.330488, No Obj: 0.003246, Avg Recall: 0.671875, count: 128 -Region Avg IOU: 0.583678, Class: 1.000000, Obj: 0.289423, No Obj: 0.002928, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.533255, Class: 1.000000, Obj: 0.312167, No Obj: 0.002969, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.545895, Class: 1.000000, Obj: 0.312411, No Obj: 0.002937, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.574186, Class: 1.000000, Obj: 0.301747, No Obj: 0.002930, Avg Recall: 0.685185, count: 108 -1820: 0.503866, 0.476120 avg, 0.001000 rate, 27.571136 seconds, 465920 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 20.738268 seconds -Region Avg IOU: 0.582587, Class: 1.000000, Obj: 0.335795, No Obj: 0.003567, Avg Recall: 0.707692, count: 130 -Region Avg IOU: 0.515746, Class: 1.000000, Obj: 0.355899, No Obj: 0.002814, Avg Recall: 0.576471, count: 85 -Region Avg IOU: 0.549731, Class: 1.000000, Obj: 0.334400, No Obj: 0.003715, Avg Recall: 0.641791, count: 134 -Region Avg IOU: 0.600546, Class: 1.000000, Obj: 0.333799, No Obj: 0.004064, Avg Recall: 0.709220, count: 141 -Region Avg IOU: 0.555870, Class: 1.000000, Obj: 0.334125, No Obj: 0.003435, Avg Recall: 0.648000, count: 125 -Region Avg IOU: 0.513142, Class: 1.000000, Obj: 0.298301, No Obj: 0.003044, Avg Recall: 0.604651, count: 86 -Region Avg IOU: 0.541537, Class: 1.000000, Obj: 0.253742, No Obj: 0.003128, Avg Recall: 0.630435, count: 92 -Region Avg IOU: 0.575439, Class: 1.000000, Obj: 0.334076, No Obj: 0.003797, Avg Recall: 0.671533, count: 137 -1821: 0.495488, 0.478057 avg, 0.001000 rate, 24.000872 seconds, 466176 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.575268, Class: 1.000000, Obj: 0.349754, No Obj: 0.003355, Avg Recall: 0.611570, count: 121 -Region Avg IOU: 0.574421, Class: 1.000000, Obj: 0.305538, No Obj: 0.003711, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.538981, Class: 1.000000, Obj: 0.298714, No Obj: 0.003319, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.522767, Class: 1.000000, Obj: 0.335025, No Obj: 0.003108, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.479530, Class: 1.000000, Obj: 0.292952, No Obj: 0.003055, Avg Recall: 0.523364, count: 107 -Region Avg IOU: 0.553149, Class: 1.000000, Obj: 0.376901, No Obj: 0.003076, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.558537, Class: 1.000000, Obj: 0.361609, No Obj: 0.003734, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.523446, Class: 1.000000, Obj: 0.351641, No Obj: 0.003574, Avg Recall: 0.626087, count: 115 -1822: 0.464889, 0.476740 avg, 0.001000 rate, 23.656036 seconds, 466432 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.508369, Class: 1.000000, Obj: 0.266269, No Obj: 0.003073, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.561238, Class: 1.000000, Obj: 0.327540, No Obj: 0.003823, Avg Recall: 0.666667, count: 132 -Region Avg IOU: 0.544071, Class: 1.000000, Obj: 0.300999, No Obj: 0.003473, Avg Recall: 0.644068, count: 118 -Region Avg IOU: 0.543731, Class: 1.000000, Obj: 0.297993, No Obj: 0.003126, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.567322, Class: 1.000000, Obj: 0.347395, No Obj: 0.003234, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.566762, Class: 1.000000, Obj: 0.329020, No Obj: 0.003749, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.532862, Class: 1.000000, Obj: 0.298222, No Obj: 0.003313, Avg Recall: 0.567797, count: 118 -Region Avg IOU: 0.521249, Class: 1.000000, Obj: 0.315244, No Obj: 0.003917, Avg Recall: 0.588235, count: 102 -1823: 0.483403, 0.477406 avg, 0.001000 rate, 24.777679 seconds, 466688 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.585958, Class: 1.000000, Obj: 0.355448, No Obj: 0.004208, Avg Recall: 0.714286, count: 133 -Region Avg IOU: 0.607577, Class: 1.000000, Obj: 0.321992, No Obj: 0.003220, Avg Recall: 0.764706, count: 85 -Region Avg IOU: 0.536133, Class: 1.000000, Obj: 0.302156, No Obj: 0.003743, Avg Recall: 0.608000, count: 125 -Region Avg IOU: 0.595694, Class: 1.000000, Obj: 0.337233, No Obj: 0.003446, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.649967, Class: 1.000000, Obj: 0.327929, No Obj: 0.003712, Avg Recall: 0.844444, count: 90 -Region Avg IOU: 0.589825, Class: 1.000000, Obj: 0.330228, No Obj: 0.003444, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.578096, Class: 1.000000, Obj: 0.384252, No Obj: 0.003435, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.549639, Class: 1.000000, Obj: 0.330367, No Obj: 0.003289, Avg Recall: 0.652174, count: 92 -1824: 0.483563, 0.478022 avg, 0.001000 rate, 24.434591 seconds, 466944 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.526724, Class: 1.000000, Obj: 0.341728, No Obj: 0.003071, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.556352, Class: 1.000000, Obj: 0.304193, No Obj: 0.003779, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.572324, Class: 1.000000, Obj: 0.306907, No Obj: 0.003446, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.533269, Class: 1.000000, Obj: 0.298460, No Obj: 0.003680, Avg Recall: 0.581395, count: 129 -Region Avg IOU: 0.482350, Class: 1.000000, Obj: 0.319413, No Obj: 0.003221, Avg Recall: 0.509804, count: 102 -Region Avg IOU: 0.537074, Class: 1.000000, Obj: 0.318344, No Obj: 0.003002, Avg Recall: 0.600000, count: 95 -Region Avg IOU: 0.553966, Class: 1.000000, Obj: 0.343178, No Obj: 0.003195, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.570281, Class: 1.000000, Obj: 0.346022, No Obj: 0.003115, Avg Recall: 0.703704, count: 81 -1825: 0.451331, 0.475353 avg, 0.001000 rate, 25.867727 seconds, 467200 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.549595, Class: 1.000000, Obj: 0.292360, No Obj: 0.002923, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.550889, Class: 1.000000, Obj: 0.324797, No Obj: 0.003085, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.567362, Class: 1.000000, Obj: 0.323523, No Obj: 0.003493, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.576714, Class: 1.000000, Obj: 0.355911, No Obj: 0.003634, Avg Recall: 0.670455, count: 88 -Region Avg IOU: 0.563550, Class: 1.000000, Obj: 0.265218, No Obj: 0.003976, Avg Recall: 0.668966, count: 145 -Region Avg IOU: 0.544928, Class: 1.000000, Obj: 0.329260, No Obj: 0.003604, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.562532, Class: 1.000000, Obj: 0.313503, No Obj: 0.003541, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.532170, Class: 1.000000, Obj: 0.300595, No Obj: 0.003254, Avg Recall: 0.645161, count: 93 -1826: 0.463084, 0.474126 avg, 0.001000 rate, 25.504417 seconds, 467456 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.579015, Class: 1.000000, Obj: 0.355719, No Obj: 0.003041, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.549732, Class: 1.000000, Obj: 0.255756, No Obj: 0.003395, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.542409, Class: 1.000000, Obj: 0.319831, No Obj: 0.003192, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.556114, Class: 1.000000, Obj: 0.322507, No Obj: 0.003244, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.595967, Class: 1.000000, Obj: 0.303065, No Obj: 0.003824, Avg Recall: 0.692857, count: 140 -Region Avg IOU: 0.554305, Class: 1.000000, Obj: 0.255527, No Obj: 0.003215, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.596664, Class: 1.000000, Obj: 0.298882, No Obj: 0.003452, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.579898, Class: 1.000000, Obj: 0.323759, No Obj: 0.003403, Avg Recall: 0.660377, count: 106 -1827: 0.456189, 0.472332 avg, 0.001000 rate, 25.502012 seconds, 467712 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.609300, Class: 1.000000, Obj: 0.279397, No Obj: 0.003528, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.637581, Class: 1.000000, Obj: 0.325122, No Obj: 0.003287, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.561482, Class: 1.000000, Obj: 0.325439, No Obj: 0.003015, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.521415, Class: 1.000000, Obj: 0.297183, No Obj: 0.003110, Avg Recall: 0.544554, count: 101 -Region Avg IOU: 0.576666, Class: 1.000000, Obj: 0.326901, No Obj: 0.003353, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.586476, Class: 1.000000, Obj: 0.321103, No Obj: 0.003005, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.587890, Class: 1.000000, Obj: 0.322873, No Obj: 0.003155, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.546855, Class: 1.000000, Obj: 0.278969, No Obj: 0.003288, Avg Recall: 0.615385, count: 117 -1828: 0.422218, 0.467321 avg, 0.001000 rate, 25.820448 seconds, 467968 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.594380, Class: 1.000000, Obj: 0.328733, No Obj: 0.003017, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.540281, Class: 1.000000, Obj: 0.284543, No Obj: 0.003401, Avg Recall: 0.634146, count: 123 -Region Avg IOU: 0.544808, Class: 1.000000, Obj: 0.285340, No Obj: 0.003307, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.580536, Class: 1.000000, Obj: 0.356474, No Obj: 0.003637, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.607741, Class: 1.000000, Obj: 0.338467, No Obj: 0.003394, Avg Recall: 0.797297, count: 74 -Region Avg IOU: 0.554818, Class: 1.000000, Obj: 0.325312, No Obj: 0.003422, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.557010, Class: 1.000000, Obj: 0.309157, No Obj: 0.003394, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.576319, Class: 1.000000, Obj: 0.310750, No Obj: 0.003901, Avg Recall: 0.728000, count: 125 -1829: 0.470151, 0.467604 avg, 0.001000 rate, 24.588402 seconds, 468224 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.557946, Class: 1.000000, Obj: 0.328545, No Obj: 0.003162, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.527668, Class: 1.000000, Obj: 0.330943, No Obj: 0.003290, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.593848, Class: 1.000000, Obj: 0.295366, No Obj: 0.003971, Avg Recall: 0.746032, count: 126 -Region Avg IOU: 0.600262, Class: 1.000000, Obj: 0.337940, No Obj: 0.003582, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.630257, Class: 1.000000, Obj: 0.314360, No Obj: 0.003433, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.532147, Class: 1.000000, Obj: 0.303740, No Obj: 0.003020, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.498957, Class: 1.000000, Obj: 0.269733, No Obj: 0.003522, Avg Recall: 0.575000, count: 120 -Region Avg IOU: 0.586577, Class: 1.000000, Obj: 0.306284, No Obj: 0.003892, Avg Recall: 0.675926, count: 108 -1830: 0.463586, 0.467202 avg, 0.001000 rate, 24.314892 seconds, 468480 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 19.341608 seconds -Region Avg IOU: 0.484697, Class: 1.000000, Obj: 0.313953, No Obj: 0.003621, Avg Recall: 0.534483, count: 116 -Region Avg IOU: 0.537460, Class: 1.000000, Obj: 0.311133, No Obj: 0.004059, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.549522, Class: 1.000000, Obj: 0.296246, No Obj: 0.003730, Avg Recall: 0.619565, count: 92 -Region Avg IOU: 0.509834, Class: 1.000000, Obj: 0.311784, No Obj: 0.003823, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.511218, Class: 1.000000, Obj: 0.302512, No Obj: 0.004143, Avg Recall: 0.569343, count: 137 -Region Avg IOU: 0.513364, Class: 1.000000, Obj: 0.261896, No Obj: 0.004196, Avg Recall: 0.609589, count: 146 -Region Avg IOU: 0.524197, Class: 1.000000, Obj: 0.293640, No Obj: 0.003595, Avg Recall: 0.584270, count: 89 -Region Avg IOU: 0.540750, Class: 1.000000, Obj: 0.306502, No Obj: 0.003391, Avg Recall: 0.604651, count: 86 -1831: 0.509157, 0.471397 avg, 0.001000 rate, 22.909908 seconds, 468736 images -Loaded: 0.024949 seconds -Region Avg IOU: 0.517908, Class: 1.000000, Obj: 0.309526, No Obj: 0.003479, Avg Recall: 0.575758, count: 99 -Region Avg IOU: 0.527988, Class: 1.000000, Obj: 0.291948, No Obj: 0.003713, Avg Recall: 0.592000, count: 125 -Region Avg IOU: 0.515923, Class: 1.000000, Obj: 0.324541, No Obj: 0.003598, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.555293, Class: 1.000000, Obj: 0.298008, No Obj: 0.004047, Avg Recall: 0.625000, count: 128 -Region Avg IOU: 0.521988, Class: 1.000000, Obj: 0.320099, No Obj: 0.004072, Avg Recall: 0.611111, count: 126 -Region Avg IOU: 0.550294, Class: 1.000000, Obj: 0.328393, No Obj: 0.003862, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.524057, Class: 1.000000, Obj: 0.289001, No Obj: 0.003755, Avg Recall: 0.548673, count: 113 -Region Avg IOU: 0.531451, Class: 1.000000, Obj: 0.322307, No Obj: 0.004157, Avg Recall: 0.613445, count: 119 -1832: 0.488801, 0.473138 avg, 0.001000 rate, 22.545868 seconds, 468992 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.455220, Class: 1.000000, Obj: 0.295326, No Obj: 0.003124, Avg Recall: 0.549296, count: 71 -Region Avg IOU: 0.541654, Class: 1.000000, Obj: 0.351978, No Obj: 0.004285, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.552209, Class: 1.000000, Obj: 0.352927, No Obj: 0.003808, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.500656, Class: 1.000000, Obj: 0.312432, No Obj: 0.004122, Avg Recall: 0.562044, count: 137 -Region Avg IOU: 0.557044, Class: 1.000000, Obj: 0.280764, No Obj: 0.003895, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.536658, Class: 1.000000, Obj: 0.423845, No Obj: 0.003713, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.613203, Class: 1.000000, Obj: 0.392039, No Obj: 0.004077, Avg Recall: 0.736264, count: 91 -Region Avg IOU: 0.541564, Class: 1.000000, Obj: 0.340052, No Obj: 0.004344, Avg Recall: 0.614583, count: 96 -1833: 0.397330, 0.465557 avg, 0.001000 rate, 23.623068 seconds, 469248 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.516061, Class: 1.000000, Obj: 0.280464, No Obj: 0.004132, Avg Recall: 0.593496, count: 123 -Region Avg IOU: 0.531285, Class: 1.000000, Obj: 0.318308, No Obj: 0.004362, Avg Recall: 0.618321, count: 131 -Region Avg IOU: 0.495198, Class: 1.000000, Obj: 0.294363, No Obj: 0.003847, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.504677, Class: 1.000000, Obj: 0.323839, No Obj: 0.003667, Avg Recall: 0.577586, count: 116 -Region Avg IOU: 0.521761, Class: 1.000000, Obj: 0.314966, No Obj: 0.003821, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.512731, Class: 1.000000, Obj: 0.324291, No Obj: 0.004232, Avg Recall: 0.534884, count: 129 -Region Avg IOU: 0.493928, Class: 1.000000, Obj: 0.327704, No Obj: 0.004182, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.537228, Class: 1.000000, Obj: 0.310818, No Obj: 0.003969, Avg Recall: 0.622642, count: 106 -1834: 0.496319, 0.468633 avg, 0.001000 rate, 23.959934 seconds, 469504 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.565718, Class: 1.000000, Obj: 0.389492, No Obj: 0.003312, Avg Recall: 0.656716, count: 67 -Region Avg IOU: 0.535927, Class: 1.000000, Obj: 0.401531, No Obj: 0.004112, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.518887, Class: 1.000000, Obj: 0.291911, No Obj: 0.004179, Avg Recall: 0.589552, count: 134 -Region Avg IOU: 0.547138, Class: 1.000000, Obj: 0.328363, No Obj: 0.003889, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.531451, Class: 1.000000, Obj: 0.366370, No Obj: 0.004015, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.534965, Class: 1.000000, Obj: 0.365241, No Obj: 0.003985, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.577586, Class: 1.000000, Obj: 0.364738, No Obj: 0.004253, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.510576, Class: 1.000000, Obj: 0.338172, No Obj: 0.003502, Avg Recall: 0.525253, count: 99 -1835: 0.443118, 0.466082 avg, 0.001000 rate, 21.929899 seconds, 469760 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.531535, Class: 1.000000, Obj: 0.337090, No Obj: 0.003799, Avg Recall: 0.613445, count: 119 -Region Avg IOU: 0.476528, Class: 1.000000, Obj: 0.254066, No Obj: 0.003612, Avg Recall: 0.530769, count: 130 -Region Avg IOU: 0.559673, Class: 1.000000, Obj: 0.277335, No Obj: 0.003667, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.561983, Class: 1.000000, Obj: 0.268902, No Obj: 0.003610, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.549971, Class: 1.000000, Obj: 0.312518, No Obj: 0.003803, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.522063, Class: 1.000000, Obj: 0.272303, No Obj: 0.004123, Avg Recall: 0.575163, count: 153 -Region Avg IOU: 0.558247, Class: 1.000000, Obj: 0.323372, No Obj: 0.004477, Avg Recall: 0.638298, count: 141 -Region Avg IOU: 0.520266, Class: 1.000000, Obj: 0.313725, No Obj: 0.003169, Avg Recall: 0.634409, count: 93 -1836: 0.492912, 0.468765 avg, 0.001000 rate, 23.750751 seconds, 470016 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.534881, Class: 1.000000, Obj: 0.330396, No Obj: 0.003841, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.545661, Class: 1.000000, Obj: 0.319964, No Obj: 0.003674, Avg Recall: 0.652893, count: 121 -Region Avg IOU: 0.551475, Class: 1.000000, Obj: 0.343685, No Obj: 0.003528, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.414991, Class: 1.000000, Obj: 0.220238, No Obj: 0.002955, Avg Recall: 0.441176, count: 102 -Region Avg IOU: 0.536779, Class: 1.000000, Obj: 0.326037, No Obj: 0.003956, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.567187, Class: 1.000000, Obj: 0.306763, No Obj: 0.004013, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.449870, Class: 1.000000, Obj: 0.288116, No Obj: 0.002993, Avg Recall: 0.409524, count: 105 -Region Avg IOU: 0.526862, Class: 1.000000, Obj: 0.270873, No Obj: 0.003583, Avg Recall: 0.622951, count: 122 -1837: 0.474286, 0.469317 avg, 0.001000 rate, 22.104927 seconds, 470272 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.489181, Class: 1.000000, Obj: 0.284369, No Obj: 0.003479, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.522780, Class: 1.000000, Obj: 0.295245, No Obj: 0.003229, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.508810, Class: 1.000000, Obj: 0.282893, No Obj: 0.003660, Avg Recall: 0.549180, count: 122 -Region Avg IOU: 0.560203, Class: 1.000000, Obj: 0.289103, No Obj: 0.003696, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.551405, Class: 1.000000, Obj: 0.314523, No Obj: 0.003831, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.512075, Class: 1.000000, Obj: 0.315206, No Obj: 0.003255, Avg Recall: 0.580645, count: 93 -Region Avg IOU: 0.530588, Class: 1.000000, Obj: 0.285572, No Obj: 0.003409, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.547303, Class: 1.000000, Obj: 0.296798, No Obj: 0.003565, Avg Recall: 0.642105, count: 95 -1838: 0.421550, 0.464540 avg, 0.001000 rate, 22.529753 seconds, 470528 images -Loaded: 0.098097 seconds -Region Avg IOU: 0.510064, Class: 1.000000, Obj: 0.286817, No Obj: 0.003860, Avg Recall: 0.562044, count: 137 -Region Avg IOU: 0.547162, Class: 1.000000, Obj: 0.279557, No Obj: 0.003186, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.550333, Class: 1.000000, Obj: 0.297323, No Obj: 0.004271, Avg Recall: 0.638462, count: 130 -Region Avg IOU: 0.491806, Class: 1.000000, Obj: 0.300312, No Obj: 0.003621, Avg Recall: 0.537815, count: 119 -Region Avg IOU: 0.563514, Class: 1.000000, Obj: 0.300587, No Obj: 0.003514, Avg Recall: 0.701149, count: 87 -Region Avg IOU: 0.513611, Class: 1.000000, Obj: 0.274572, No Obj: 0.003163, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.481399, Class: 1.000000, Obj: 0.273844, No Obj: 0.003239, Avg Recall: 0.480000, count: 100 -Region Avg IOU: 0.506133, Class: 1.000000, Obj: 0.259641, No Obj: 0.003578, Avg Recall: 0.553719, count: 121 -1839: 0.480950, 0.466181 avg, 0.001000 rate, 24.376032 seconds, 470784 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.577751, Class: 1.000000, Obj: 0.351445, No Obj: 0.003370, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.545227, Class: 1.000000, Obj: 0.303928, No Obj: 0.003917, Avg Recall: 0.625000, count: 128 -Region Avg IOU: 0.550397, Class: 1.000000, Obj: 0.320390, No Obj: 0.003852, Avg Recall: 0.634146, count: 123 -Region Avg IOU: 0.543571, Class: 1.000000, Obj: 0.282225, No Obj: 0.003529, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.516372, Class: 1.000000, Obj: 0.283622, No Obj: 0.003409, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.565778, Class: 1.000000, Obj: 0.314716, No Obj: 0.003858, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.542588, Class: 1.000000, Obj: 0.323838, No Obj: 0.003977, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.488597, Class: 1.000000, Obj: 0.252054, No Obj: 0.003348, Avg Recall: 0.619048, count: 105 -1840: 0.525777, 0.472141 avg, 0.001000 rate, 22.939615 seconds, 471040 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 20.350176 seconds -Region Avg IOU: 0.450928, Class: 1.000000, Obj: 0.301880, No Obj: 0.003046, Avg Recall: 0.455556, count: 90 -Region Avg IOU: 0.517512, Class: 1.000000, Obj: 0.282975, No Obj: 0.003894, Avg Recall: 0.592920, count: 113 -Region Avg IOU: 0.549955, Class: 1.000000, Obj: 0.303904, No Obj: 0.003995, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.497432, Class: 1.000000, Obj: 0.297863, No Obj: 0.003660, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.545590, Class: 1.000000, Obj: 0.322152, No Obj: 0.003956, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.468452, Class: 1.000000, Obj: 0.273548, No Obj: 0.003745, Avg Recall: 0.520000, count: 100 -Region Avg IOU: 0.513616, Class: 1.000000, Obj: 0.320689, No Obj: 0.003819, Avg Recall: 0.581395, count: 129 -Region Avg IOU: 0.467614, Class: 1.000000, Obj: 0.222676, No Obj: 0.003752, Avg Recall: 0.516129, count: 124 -1841: 0.489716, 0.473898 avg, 0.001000 rate, 23.183807 seconds, 471296 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.501355, Class: 1.000000, Obj: 0.283352, No Obj: 0.003453, Avg Recall: 0.576087, count: 92 -Region Avg IOU: 0.512632, Class: 1.000000, Obj: 0.322750, No Obj: 0.003780, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.489021, Class: 1.000000, Obj: 0.254765, No Obj: 0.004546, Avg Recall: 0.559702, count: 134 -Region Avg IOU: 0.469031, Class: 1.000000, Obj: 0.290153, No Obj: 0.003549, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.468076, Class: 1.000000, Obj: 0.255071, No Obj: 0.004000, Avg Recall: 0.496124, count: 129 -Region Avg IOU: 0.506525, Class: 1.000000, Obj: 0.297745, No Obj: 0.003871, Avg Recall: 0.580153, count: 131 -Region Avg IOU: 0.509869, Class: 1.000000, Obj: 0.280921, No Obj: 0.003725, Avg Recall: 0.552083, count: 96 -Region Avg IOU: 0.499626, Class: 1.000000, Obj: 0.264577, No Obj: 0.003433, Avg Recall: 0.574257, count: 101 -1842: 0.520167, 0.478525 avg, 0.001000 rate, 20.722048 seconds, 471552 images -Loaded: 0.053129 seconds -Region Avg IOU: 0.524400, Class: 1.000000, Obj: 0.332452, No Obj: 0.003651, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.508909, Class: 1.000000, Obj: 0.298737, No Obj: 0.004229, Avg Recall: 0.589744, count: 117 -Region Avg IOU: 0.523614, Class: 1.000000, Obj: 0.328035, No Obj: 0.003440, Avg Recall: 0.560440, count: 91 -Region Avg IOU: 0.495884, Class: 1.000000, Obj: 0.276144, No Obj: 0.004085, Avg Recall: 0.570248, count: 121 -Region Avg IOU: 0.472147, Class: 1.000000, Obj: 0.261951, No Obj: 0.004178, Avg Recall: 0.496350, count: 137 -Region Avg IOU: 0.532468, Class: 1.000000, Obj: 0.314353, No Obj: 0.004203, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.478760, Class: 1.000000, Obj: 0.315477, No Obj: 0.003584, Avg Recall: 0.539326, count: 89 -Region Avg IOU: 0.485500, Class: 1.000000, Obj: 0.283846, No Obj: 0.003651, Avg Recall: 0.539216, count: 102 -1843: 0.420842, 0.472757 avg, 0.001000 rate, 22.967793 seconds, 471808 images -Loaded: 0.038791 seconds -Region Avg IOU: 0.524007, Class: 1.000000, Obj: 0.359873, No Obj: 0.003965, Avg Recall: 0.597701, count: 87 -Region Avg IOU: 0.505775, Class: 1.000000, Obj: 0.327244, No Obj: 0.004669, Avg Recall: 0.591667, count: 120 -Region Avg IOU: 0.545891, Class: 1.000000, Obj: 0.326449, No Obj: 0.004521, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.481416, Class: 1.000000, Obj: 0.314683, No Obj: 0.003906, Avg Recall: 0.521739, count: 115 -Region Avg IOU: 0.487636, Class: 1.000000, Obj: 0.315905, No Obj: 0.004097, Avg Recall: 0.577778, count: 135 -Region Avg IOU: 0.559981, Class: 1.000000, Obj: 0.313590, No Obj: 0.004322, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.525473, Class: 1.000000, Obj: 0.315217, No Obj: 0.004577, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.526755, Class: 1.000000, Obj: 0.361674, No Obj: 0.003855, Avg Recall: 0.590909, count: 88 -1844: 0.506079, 0.476089 avg, 0.001000 rate, 21.869747 seconds, 472064 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.536167, Class: 1.000000, Obj: 0.296701, No Obj: 0.004257, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.501523, Class: 1.000000, Obj: 0.335935, No Obj: 0.004283, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.532409, Class: 1.000000, Obj: 0.302153, No Obj: 0.003993, Avg Recall: 0.615385, count: 91 -Region Avg IOU: 0.509763, Class: 1.000000, Obj: 0.339675, No Obj: 0.004564, Avg Recall: 0.592593, count: 108 -Region Avg IOU: 0.539870, Class: 1.000000, Obj: 0.296046, No Obj: 0.004464, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.526880, Class: 1.000000, Obj: 0.295431, No Obj: 0.004310, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.471992, Class: 1.000000, Obj: 0.318436, No Obj: 0.004021, Avg Recall: 0.534653, count: 101 -Region Avg IOU: 0.498279, Class: 1.000000, Obj: 0.309561, No Obj: 0.003840, Avg Recall: 0.535714, count: 84 -1845: 0.437009, 0.472181 avg, 0.001000 rate, 21.601051 seconds, 472320 images -Loaded: 1.191206 seconds -Region Avg IOU: 0.509496, Class: 1.000000, Obj: 0.330415, No Obj: 0.004494, Avg Recall: 0.544776, count: 134 -Region Avg IOU: 0.446512, Class: 1.000000, Obj: 0.272247, No Obj: 0.003934, Avg Recall: 0.454545, count: 110 -Region Avg IOU: 0.480477, Class: 1.000000, Obj: 0.316421, No Obj: 0.004388, Avg Recall: 0.545455, count: 143 -Region Avg IOU: 0.452979, Class: 1.000000, Obj: 0.276302, No Obj: 0.003443, Avg Recall: 0.448718, count: 78 -Region Avg IOU: 0.505855, Class: 1.000000, Obj: 0.312833, No Obj: 0.003887, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.516759, Class: 1.000000, Obj: 0.324210, No Obj: 0.004352, Avg Recall: 0.550000, count: 100 -Region Avg IOU: 0.496277, Class: 1.000000, Obj: 0.312091, No Obj: 0.004208, Avg Recall: 0.504505, count: 111 -Region Avg IOU: 0.497269, Class: 1.000000, Obj: 0.331230, No Obj: 0.004516, Avg Recall: 0.570248, count: 121 -1846: 0.461112, 0.471074 avg, 0.001000 rate, 21.088881 seconds, 472576 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.484583, Class: 1.000000, Obj: 0.303037, No Obj: 0.004195, Avg Recall: 0.500000, count: 120 -Region Avg IOU: 0.495091, Class: 1.000000, Obj: 0.309621, No Obj: 0.004637, Avg Recall: 0.549669, count: 151 -Region Avg IOU: 0.502871, Class: 1.000000, Obj: 0.283570, No Obj: 0.004001, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.502456, Class: 1.000000, Obj: 0.277557, No Obj: 0.004234, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.531009, Class: 1.000000, Obj: 0.320073, No Obj: 0.003918, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.508563, Class: 1.000000, Obj: 0.296272, No Obj: 0.004332, Avg Recall: 0.586777, count: 121 -Region Avg IOU: 0.511196, Class: 1.000000, Obj: 0.341968, No Obj: 0.003951, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.470553, Class: 1.000000, Obj: 0.302460, No Obj: 0.003833, Avg Recall: 0.512397, count: 121 -1847: 0.576805, 0.481647 avg, 0.001000 rate, 22.022291 seconds, 472832 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.475234, Class: 1.000000, Obj: 0.274237, No Obj: 0.003999, Avg Recall: 0.521739, count: 115 -Region Avg IOU: 0.426535, Class: 1.000000, Obj: 0.237230, No Obj: 0.004314, Avg Recall: 0.467153, count: 137 -Region Avg IOU: 0.519362, Class: 1.000000, Obj: 0.279972, No Obj: 0.003880, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.546366, Class: 1.000000, Obj: 0.307244, No Obj: 0.004282, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.500693, Class: 1.000000, Obj: 0.319488, No Obj: 0.003551, Avg Recall: 0.568182, count: 88 -Region Avg IOU: 0.518142, Class: 1.000000, Obj: 0.309556, No Obj: 0.003781, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.529929, Class: 1.000000, Obj: 0.347950, No Obj: 0.003483, Avg Recall: 0.619565, count: 92 -Region Avg IOU: 0.524832, Class: 1.000000, Obj: 0.301567, No Obj: 0.003762, Avg Recall: 0.622449, count: 98 -1848: 0.503016, 0.483784 avg, 0.001000 rate, 21.607347 seconds, 473088 images -Loaded: 0.522344 seconds -Region Avg IOU: 0.476336, Class: 1.000000, Obj: 0.262752, No Obj: 0.003883, Avg Recall: 0.504065, count: 123 -Region Avg IOU: 0.483641, Class: 1.000000, Obj: 0.234959, No Obj: 0.003032, Avg Recall: 0.544304, count: 79 -Region Avg IOU: 0.480299, Class: 1.000000, Obj: 0.296139, No Obj: 0.003744, Avg Recall: 0.513761, count: 109 -Region Avg IOU: 0.506678, Class: 1.000000, Obj: 0.312149, No Obj: 0.003528, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.525737, Class: 1.000000, Obj: 0.302337, No Obj: 0.004065, Avg Recall: 0.576577, count: 111 -Region Avg IOU: 0.495816, Class: 1.000000, Obj: 0.275713, No Obj: 0.003678, Avg Recall: 0.522523, count: 111 -Region Avg IOU: 0.535744, Class: 1.000000, Obj: 0.306451, No Obj: 0.004130, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.525467, Class: 1.000000, Obj: 0.319343, No Obj: 0.003471, Avg Recall: 0.597826, count: 92 -1849: 0.496265, 0.485032 avg, 0.001000 rate, 22.529116 seconds, 473344 images -Loaded: 0.632355 seconds -Region Avg IOU: 0.534066, Class: 1.000000, Obj: 0.317331, No Obj: 0.003664, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.522536, Class: 1.000000, Obj: 0.293378, No Obj: 0.003823, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.527508, Class: 1.000000, Obj: 0.328272, No Obj: 0.004398, Avg Recall: 0.603053, count: 131 -Region Avg IOU: 0.545940, Class: 1.000000, Obj: 0.305959, No Obj: 0.004222, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.511864, Class: 1.000000, Obj: 0.303791, No Obj: 0.003713, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.496081, Class: 1.000000, Obj: 0.288545, No Obj: 0.004140, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.539779, Class: 1.000000, Obj: 0.355237, No Obj: 0.003947, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.547096, Class: 1.000000, Obj: 0.276205, No Obj: 0.004401, Avg Recall: 0.615385, count: 117 -1850: 0.497164, 0.486245 avg, 0.001000 rate, 23.336687 seconds, 473600 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.932327 seconds -Region Avg IOU: 0.549099, Class: 1.000000, Obj: 0.293572, No Obj: 0.004102, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.507568, Class: 1.000000, Obj: 0.315310, No Obj: 0.004525, Avg Recall: 0.583333, count: 132 -Region Avg IOU: 0.483683, Class: 1.000000, Obj: 0.293183, No Obj: 0.004214, Avg Recall: 0.470588, count: 136 -Region Avg IOU: 0.464057, Class: 1.000000, Obj: 0.239067, No Obj: 0.003821, Avg Recall: 0.508621, count: 116 -Region Avg IOU: 0.515939, Class: 1.000000, Obj: 0.311240, No Obj: 0.004547, Avg Recall: 0.594406, count: 143 -Region Avg IOU: 0.505179, Class: 1.000000, Obj: 0.291245, No Obj: 0.003686, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.540621, Class: 1.000000, Obj: 0.324366, No Obj: 0.004005, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.488785, Class: 1.000000, Obj: 0.297360, No Obj: 0.003656, Avg Recall: 0.565657, count: 99 -1851: 0.529834, 0.490604 avg, 0.001000 rate, 22.535007 seconds, 473856 images -Loaded: 0.967045 seconds -Region Avg IOU: 0.535329, Class: 1.000000, Obj: 0.249965, No Obj: 0.004071, Avg Recall: 0.644068, count: 118 -Region Avg IOU: 0.486215, Class: 1.000000, Obj: 0.282515, No Obj: 0.004613, Avg Recall: 0.510791, count: 139 -Region Avg IOU: 0.505649, Class: 1.000000, Obj: 0.302317, No Obj: 0.004268, Avg Recall: 0.579710, count: 138 -Region Avg IOU: 0.516938, Class: 1.000000, Obj: 0.285181, No Obj: 0.004256, Avg Recall: 0.572727, count: 110 -Region Avg IOU: 0.524779, Class: 1.000000, Obj: 0.314103, No Obj: 0.004319, Avg Recall: 0.572581, count: 124 -Region Avg IOU: 0.486653, Class: 1.000000, Obj: 0.286120, No Obj: 0.003985, Avg Recall: 0.529412, count: 119 -Region Avg IOU: 0.550684, Class: 1.000000, Obj: 0.342680, No Obj: 0.003749, Avg Recall: 0.687500, count: 80 -Region Avg IOU: 0.469020, Class: 1.000000, Obj: 0.286714, No Obj: 0.003534, Avg Recall: 0.450549, count: 91 -1852: 0.480254, 0.489569 avg, 0.001000 rate, 22.252581 seconds, 474112 images -Loaded: 0.587372 seconds -Region Avg IOU: 0.482124, Class: 1.000000, Obj: 0.279101, No Obj: 0.003829, Avg Recall: 0.518519, count: 108 -Region Avg IOU: 0.498728, Class: 1.000000, Obj: 0.307617, No Obj: 0.004125, Avg Recall: 0.552000, count: 125 -Region Avg IOU: 0.477388, Class: 1.000000, Obj: 0.303759, No Obj: 0.003752, Avg Recall: 0.460177, count: 113 -Region Avg IOU: 0.497907, Class: 1.000000, Obj: 0.330692, No Obj: 0.003689, Avg Recall: 0.568182, count: 88 -Region Avg IOU: 0.447213, Class: 1.000000, Obj: 0.236587, No Obj: 0.003837, Avg Recall: 0.492754, count: 138 -Region Avg IOU: 0.538248, Class: 1.000000, Obj: 0.299986, No Obj: 0.003953, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.510433, Class: 1.000000, Obj: 0.310910, No Obj: 0.004410, Avg Recall: 0.557377, count: 122 -Region Avg IOU: 0.520478, Class: 1.000000, Obj: 0.328810, No Obj: 0.003752, Avg Recall: 0.585859, count: 99 -1853: 0.492936, 0.489906 avg, 0.001000 rate, 21.567871 seconds, 474368 images -Loaded: 0.471264 seconds -Region Avg IOU: 0.531279, Class: 1.000000, Obj: 0.323250, No Obj: 0.003291, Avg Recall: 0.608108, count: 74 -Region Avg IOU: 0.516349, Class: 1.000000, Obj: 0.280791, No Obj: 0.004237, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.550254, Class: 1.000000, Obj: 0.317465, No Obj: 0.004245, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.487677, Class: 1.000000, Obj: 0.311439, No Obj: 0.004155, Avg Recall: 0.580645, count: 124 -Region Avg IOU: 0.469665, Class: 1.000000, Obj: 0.255775, No Obj: 0.004021, Avg Recall: 0.515152, count: 99 -Region Avg IOU: 0.568815, Class: 1.000000, Obj: 0.361464, No Obj: 0.004789, Avg Recall: 0.620968, count: 124 -Region Avg IOU: 0.555414, Class: 1.000000, Obj: 0.321293, No Obj: 0.004217, Avg Recall: 0.619048, count: 84 -Region Avg IOU: 0.457759, Class: 1.000000, Obj: 0.327015, No Obj: 0.003679, Avg Recall: 0.487805, count: 82 -1854: 0.461664, 0.487082 avg, 0.001000 rate, 23.908310 seconds, 474624 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.559826, Class: 1.000000, Obj: 0.280036, No Obj: 0.004156, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.486221, Class: 1.000000, Obj: 0.319062, No Obj: 0.003891, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.528076, Class: 1.000000, Obj: 0.312448, No Obj: 0.004069, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.514150, Class: 1.000000, Obj: 0.319246, No Obj: 0.004170, Avg Recall: 0.557522, count: 113 -Region Avg IOU: 0.500548, Class: 1.000000, Obj: 0.290371, No Obj: 0.004260, Avg Recall: 0.553719, count: 121 -Region Avg IOU: 0.497337, Class: 1.000000, Obj: 0.321672, No Obj: 0.004698, Avg Recall: 0.571429, count: 112 -Region Avg IOU: 0.517477, Class: 1.000000, Obj: 0.378916, No Obj: 0.003639, Avg Recall: 0.605263, count: 76 -Region Avg IOU: 0.509669, Class: 1.000000, Obj: 0.309870, No Obj: 0.004019, Avg Recall: 0.597938, count: 97 -1855: 0.458703, 0.484244 avg, 0.001000 rate, 21.901945 seconds, 474880 images -Loaded: 1.561361 seconds -Region Avg IOU: 0.531500, Class: 1.000000, Obj: 0.323671, No Obj: 0.004607, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.522565, Class: 1.000000, Obj: 0.302859, No Obj: 0.004532, Avg Recall: 0.600000, count: 130 -Region Avg IOU: 0.475296, Class: 1.000000, Obj: 0.312981, No Obj: 0.003769, Avg Recall: 0.512397, count: 121 -Region Avg IOU: 0.484894, Class: 1.000000, Obj: 0.341279, No Obj: 0.003847, Avg Recall: 0.542857, count: 105 -Region Avg IOU: 0.502455, Class: 1.000000, Obj: 0.294507, No Obj: 0.004245, Avg Recall: 0.616667, count: 120 -Region Avg IOU: 0.519878, Class: 1.000000, Obj: 0.324168, No Obj: 0.004185, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.583823, Class: 1.000000, Obj: 0.345272, No Obj: 0.004108, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.497655, Class: 1.000000, Obj: 0.299622, No Obj: 0.003839, Avg Recall: 0.527778, count: 108 -1856: 0.541406, 0.489960 avg, 0.001000 rate, 22.556459 seconds, 475136 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.477156, Class: 1.000000, Obj: 0.328653, No Obj: 0.003784, Avg Recall: 0.465909, count: 88 -Region Avg IOU: 0.491831, Class: 1.000000, Obj: 0.313825, No Obj: 0.004366, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.506994, Class: 1.000000, Obj: 0.299877, No Obj: 0.003645, Avg Recall: 0.538462, count: 91 -Region Avg IOU: 0.512211, Class: 1.000000, Obj: 0.332588, No Obj: 0.004197, Avg Recall: 0.561404, count: 114 -Region Avg IOU: 0.558143, Class: 1.000000, Obj: 0.316884, No Obj: 0.003996, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.501906, Class: 1.000000, Obj: 0.316279, No Obj: 0.004511, Avg Recall: 0.568965, count: 116 -Region Avg IOU: 0.461569, Class: 1.000000, Obj: 0.281692, No Obj: 0.003769, Avg Recall: 0.510638, count: 94 -Region Avg IOU: 0.547746, Class: 1.000000, Obj: 0.333748, No Obj: 0.004423, Avg Recall: 0.648148, count: 108 -1857: 0.423267, 0.483291 avg, 0.001000 rate, 21.631969 seconds, 475392 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.507883, Class: 1.000000, Obj: 0.284111, No Obj: 0.003800, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.523674, Class: 1.000000, Obj: 0.298706, No Obj: 0.004097, Avg Recall: 0.609756, count: 123 -Region Avg IOU: 0.494290, Class: 1.000000, Obj: 0.301566, No Obj: 0.004604, Avg Recall: 0.546296, count: 108 -Region Avg IOU: 0.567619, Class: 1.000000, Obj: 0.359914, No Obj: 0.004166, Avg Recall: 0.712644, count: 87 -Region Avg IOU: 0.523583, Class: 1.000000, Obj: 0.338384, No Obj: 0.004179, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.562662, Class: 1.000000, Obj: 0.336040, No Obj: 0.003947, Avg Recall: 0.686046, count: 86 -Region Avg IOU: 0.444243, Class: 1.000000, Obj: 0.291367, No Obj: 0.004287, Avg Recall: 0.464286, count: 112 -Region Avg IOU: 0.532007, Class: 1.000000, Obj: 0.330656, No Obj: 0.003848, Avg Recall: 0.645455, count: 110 -1858: 0.454732, 0.480435 avg, 0.001000 rate, 22.616196 seconds, 475648 images -Loaded: 0.563206 seconds -Region Avg IOU: 0.511378, Class: 1.000000, Obj: 0.293183, No Obj: 0.003967, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.520098, Class: 1.000000, Obj: 0.313206, No Obj: 0.004347, Avg Recall: 0.583333, count: 120 -Region Avg IOU: 0.547837, Class: 1.000000, Obj: 0.380016, No Obj: 0.004454, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.531981, Class: 1.000000, Obj: 0.346757, No Obj: 0.003941, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.495760, Class: 1.000000, Obj: 0.288942, No Obj: 0.004212, Avg Recall: 0.576000, count: 125 -Region Avg IOU: 0.547180, Class: 1.000000, Obj: 0.315062, No Obj: 0.004211, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.566617, Class: 1.000000, Obj: 0.327307, No Obj: 0.003786, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.577700, Class: 1.000000, Obj: 0.335096, No Obj: 0.004649, Avg Recall: 0.729730, count: 111 -1859: 0.467586, 0.479150 avg, 0.001000 rate, 21.877897 seconds, 475904 images -Loaded: 0.629134 seconds -Region Avg IOU: 0.550593, Class: 1.000000, Obj: 0.350271, No Obj: 0.004550, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.491558, Class: 1.000000, Obj: 0.289973, No Obj: 0.004166, Avg Recall: 0.544643, count: 112 -Region Avg IOU: 0.514353, Class: 1.000000, Obj: 0.349899, No Obj: 0.004057, Avg Recall: 0.545455, count: 88 -Region Avg IOU: 0.538733, Class: 1.000000, Obj: 0.324917, No Obj: 0.003561, Avg Recall: 0.623377, count: 77 -Region Avg IOU: 0.509612, Class: 1.000000, Obj: 0.289929, No Obj: 0.004284, Avg Recall: 0.552000, count: 125 -Region Avg IOU: 0.528590, Class: 1.000000, Obj: 0.346293, No Obj: 0.004182, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.541936, Class: 1.000000, Obj: 0.291394, No Obj: 0.004654, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.532433, Class: 1.000000, Obj: 0.312701, No Obj: 0.003783, Avg Recall: 0.609091, count: 110 -1860: 0.413082, 0.472543 avg, 0.001000 rate, 22.333296 seconds, 476160 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.048282 seconds -Region Avg IOU: 0.571934, Class: 1.000000, Obj: 0.350567, No Obj: 0.003845, Avg Recall: 0.737500, count: 80 -Region Avg IOU: 0.529235, Class: 1.000000, Obj: 0.338563, No Obj: 0.003518, Avg Recall: 0.606838, count: 117 -Region Avg IOU: 0.565118, Class: 1.000000, Obj: 0.311389, No Obj: 0.004169, Avg Recall: 0.700000, count: 120 -Region Avg IOU: 0.515861, Class: 1.000000, Obj: 0.303997, No Obj: 0.003889, Avg Recall: 0.593750, count: 128 -Region Avg IOU: 0.532661, Class: 1.000000, Obj: 0.325631, No Obj: 0.003230, Avg Recall: 0.597701, count: 87 -Region Avg IOU: 0.551200, Class: 1.000000, Obj: 0.356713, No Obj: 0.003348, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.540738, Class: 1.000000, Obj: 0.326541, No Obj: 0.003909, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.536446, Class: 1.000000, Obj: 0.287254, No Obj: 0.004342, Avg Recall: 0.615385, count: 130 -1861: 0.473933, 0.472682 avg, 0.001000 rate, 24.108404 seconds, 476416 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.511868, Class: 1.000000, Obj: 0.293893, No Obj: 0.003352, Avg Recall: 0.584615, count: 130 -Region Avg IOU: 0.486370, Class: 1.000000, Obj: 0.322768, No Obj: 0.003579, Avg Recall: 0.520000, count: 100 -Region Avg IOU: 0.558168, Class: 1.000000, Obj: 0.324728, No Obj: 0.003501, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.531116, Class: 1.000000, Obj: 0.316524, No Obj: 0.003981, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.572517, Class: 1.000000, Obj: 0.337264, No Obj: 0.003452, Avg Recall: 0.632184, count: 87 -Region Avg IOU: 0.557665, Class: 1.000000, Obj: 0.329535, No Obj: 0.003606, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.577137, Class: 1.000000, Obj: 0.384832, No Obj: 0.003101, Avg Recall: 0.615385, count: 78 -Region Avg IOU: 0.527151, Class: 1.000000, Obj: 0.312311, No Obj: 0.003535, Avg Recall: 0.573770, count: 122 -1862: 0.430544, 0.468468 avg, 0.001000 rate, 23.829447 seconds, 476672 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.552208, Class: 1.000000, Obj: 0.318205, No Obj: 0.003029, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.545221, Class: 1.000000, Obj: 0.282628, No Obj: 0.003579, Avg Recall: 0.625000, count: 128 -Region Avg IOU: 0.511579, Class: 1.000000, Obj: 0.296985, No Obj: 0.002795, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.519827, Class: 1.000000, Obj: 0.298199, No Obj: 0.003684, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.531323, Class: 1.000000, Obj: 0.311702, No Obj: 0.003163, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.532295, Class: 1.000000, Obj: 0.327686, No Obj: 0.003215, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.548409, Class: 1.000000, Obj: 0.335216, No Obj: 0.003505, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.543619, Class: 1.000000, Obj: 0.314083, No Obj: 0.003466, Avg Recall: 0.605505, count: 109 -1863: 0.499163, 0.471538 avg, 0.001000 rate, 23.733694 seconds, 476928 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.537918, Class: 1.000000, Obj: 0.285650, No Obj: 0.004086, Avg Recall: 0.615385, count: 143 -Region Avg IOU: 0.515207, Class: 1.000000, Obj: 0.268826, No Obj: 0.003300, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.576301, Class: 1.000000, Obj: 0.286939, No Obj: 0.004175, Avg Recall: 0.701493, count: 134 -Region Avg IOU: 0.569334, Class: 1.000000, Obj: 0.332488, No Obj: 0.003437, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.557014, Class: 1.000000, Obj: 0.315638, No Obj: 0.003222, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.561300, Class: 1.000000, Obj: 0.331708, No Obj: 0.003288, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.536372, Class: 1.000000, Obj: 0.283585, No Obj: 0.003187, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.562228, Class: 1.000000, Obj: 0.339120, No Obj: 0.003559, Avg Recall: 0.628319, count: 113 -1864: 0.465142, 0.470898 avg, 0.001000 rate, 24.315895 seconds, 477184 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.496635, Class: 1.000000, Obj: 0.295599, No Obj: 0.003164, Avg Recall: 0.530973, count: 113 -Region Avg IOU: 0.536393, Class: 1.000000, Obj: 0.325082, No Obj: 0.003285, Avg Recall: 0.615385, count: 91 -Region Avg IOU: 0.535649, Class: 1.000000, Obj: 0.312739, No Obj: 0.003740, Avg Recall: 0.598485, count: 132 -Region Avg IOU: 0.520334, Class: 1.000000, Obj: 0.265327, No Obj: 0.003165, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.596356, Class: 1.000000, Obj: 0.338203, No Obj: 0.003546, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.587233, Class: 1.000000, Obj: 0.294421, No Obj: 0.003287, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.549855, Class: 1.000000, Obj: 0.299657, No Obj: 0.003962, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.534085, Class: 1.000000, Obj: 0.278573, No Obj: 0.003344, Avg Recall: 0.610169, count: 118 -1865: 0.436822, 0.467491 avg, 0.001000 rate, 23.651796 seconds, 477440 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.512694, Class: 1.000000, Obj: 0.249637, No Obj: 0.003855, Avg Recall: 0.549180, count: 122 -Region Avg IOU: 0.518167, Class: 1.000000, Obj: 0.317151, No Obj: 0.003684, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.558890, Class: 1.000000, Obj: 0.310825, No Obj: 0.003412, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.561344, Class: 1.000000, Obj: 0.352508, No Obj: 0.003136, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.532519, Class: 1.000000, Obj: 0.274944, No Obj: 0.003281, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.567024, Class: 1.000000, Obj: 0.266063, No Obj: 0.003008, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.592667, Class: 1.000000, Obj: 0.325404, No Obj: 0.003485, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.566409, Class: 1.000000, Obj: 0.343405, No Obj: 0.003637, Avg Recall: 0.654545, count: 110 -1866: 0.470118, 0.467753 avg, 0.001000 rate, 23.912687 seconds, 477696 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.527700, Class: 1.000000, Obj: 0.303126, No Obj: 0.003212, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.566222, Class: 1.000000, Obj: 0.303840, No Obj: 0.003539, Avg Recall: 0.661290, count: 124 -Region Avg IOU: 0.596216, Class: 1.000000, Obj: 0.317409, No Obj: 0.003530, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.574068, Class: 1.000000, Obj: 0.313961, No Obj: 0.003807, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.566866, Class: 1.000000, Obj: 0.308319, No Obj: 0.003448, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.570020, Class: 1.000000, Obj: 0.281805, No Obj: 0.003528, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.545737, Class: 1.000000, Obj: 0.312762, No Obj: 0.003579, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.534826, Class: 1.000000, Obj: 0.292358, No Obj: 0.003729, Avg Recall: 0.572464, count: 138 -1867: 0.502831, 0.471261 avg, 0.001000 rate, 24.572996 seconds, 477952 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.556188, Class: 1.000000, Obj: 0.349713, No Obj: 0.003591, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.559411, Class: 1.000000, Obj: 0.333324, No Obj: 0.003615, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.563749, Class: 1.000000, Obj: 0.287281, No Obj: 0.003495, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.539675, Class: 1.000000, Obj: 0.307878, No Obj: 0.003813, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.572454, Class: 1.000000, Obj: 0.318312, No Obj: 0.003299, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.550214, Class: 1.000000, Obj: 0.285494, No Obj: 0.003749, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.546637, Class: 1.000000, Obj: 0.318889, No Obj: 0.004236, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.536662, Class: 1.000000, Obj: 0.392541, No Obj: 0.003270, Avg Recall: 0.619048, count: 84 -1868: 0.446682, 0.468803 avg, 0.001000 rate, 24.968475 seconds, 478208 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.541330, Class: 1.000000, Obj: 0.317993, No Obj: 0.003858, Avg Recall: 0.645161, count: 124 -Region Avg IOU: 0.566045, Class: 1.000000, Obj: 0.384798, No Obj: 0.004198, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.524344, Class: 1.000000, Obj: 0.330770, No Obj: 0.003616, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.575990, Class: 1.000000, Obj: 0.336277, No Obj: 0.004116, Avg Recall: 0.705882, count: 119 -Region Avg IOU: 0.599559, Class: 1.000000, Obj: 0.330216, No Obj: 0.003439, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.532386, Class: 1.000000, Obj: 0.286275, No Obj: 0.004039, Avg Recall: 0.620438, count: 137 -Region Avg IOU: 0.549691, Class: 1.000000, Obj: 0.328606, No Obj: 0.003996, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.570221, Class: 1.000000, Obj: 0.316054, No Obj: 0.003285, Avg Recall: 0.698795, count: 83 -1869: 0.464103, 0.468333 avg, 0.001000 rate, 24.462690 seconds, 478464 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.556992, Class: 1.000000, Obj: 0.303076, No Obj: 0.003655, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.575165, Class: 1.000000, Obj: 0.291479, No Obj: 0.003901, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.506837, Class: 1.000000, Obj: 0.299629, No Obj: 0.003547, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.545657, Class: 1.000000, Obj: 0.294858, No Obj: 0.003631, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.599943, Class: 1.000000, Obj: 0.381591, No Obj: 0.004165, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.585223, Class: 1.000000, Obj: 0.321994, No Obj: 0.003534, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.485385, Class: 1.000000, Obj: 0.274597, No Obj: 0.003773, Avg Recall: 0.522581, count: 155 -Region Avg IOU: 0.534323, Class: 1.000000, Obj: 0.322425, No Obj: 0.003438, Avg Recall: 0.561905, count: 105 -1870: 0.486046, 0.470105 avg, 0.001000 rate, 24.587591 seconds, 478720 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000092 seconds -Region Avg IOU: 0.602810, Class: 1.000000, Obj: 0.342971, No Obj: 0.003722, Avg Recall: 0.748031, count: 127 -Region Avg IOU: 0.583591, Class: 1.000000, Obj: 0.345337, No Obj: 0.003769, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.554208, Class: 1.000000, Obj: 0.304498, No Obj: 0.003539, Avg Recall: 0.661417, count: 127 -Region Avg IOU: 0.543794, Class: 1.000000, Obj: 0.343219, No Obj: 0.003722, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.531086, Class: 1.000000, Obj: 0.324014, No Obj: 0.002890, Avg Recall: 0.594937, count: 79 -Region Avg IOU: 0.541566, Class: 1.000000, Obj: 0.309984, No Obj: 0.003710, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.553186, Class: 1.000000, Obj: 0.324906, No Obj: 0.003545, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.557566, Class: 1.000000, Obj: 0.302944, No Obj: 0.003956, Avg Recall: 0.633333, count: 120 -1871: 0.467785, 0.469873 avg, 0.001000 rate, 24.582432 seconds, 478976 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.554338, Class: 1.000000, Obj: 0.311355, No Obj: 0.002826, Avg Recall: 0.685393, count: 89 -Region Avg IOU: 0.573788, Class: 1.000000, Obj: 0.348695, No Obj: 0.003394, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.536670, Class: 1.000000, Obj: 0.343002, No Obj: 0.003262, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.558954, Class: 1.000000, Obj: 0.315201, No Obj: 0.003504, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.569517, Class: 1.000000, Obj: 0.334707, No Obj: 0.003363, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.533196, Class: 1.000000, Obj: 0.304967, No Obj: 0.004256, Avg Recall: 0.654412, count: 136 -Region Avg IOU: 0.561855, Class: 1.000000, Obj: 0.315370, No Obj: 0.004122, Avg Recall: 0.679389, count: 131 -Region Avg IOU: 0.538037, Class: 1.000000, Obj: 0.294929, No Obj: 0.003562, Avg Recall: 0.634615, count: 104 -1872: 0.443463, 0.467232 avg, 0.001000 rate, 24.678608 seconds, 479232 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.589317, Class: 1.000000, Obj: 0.309838, No Obj: 0.003461, Avg Recall: 0.682540, count: 126 -Region Avg IOU: 0.611606, Class: 1.000000, Obj: 0.349202, No Obj: 0.003423, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.527835, Class: 1.000000, Obj: 0.312929, No Obj: 0.003579, Avg Recall: 0.580153, count: 131 -Region Avg IOU: 0.621329, Class: 1.000000, Obj: 0.324266, No Obj: 0.003220, Avg Recall: 0.729412, count: 85 -Region Avg IOU: 0.592976, Class: 1.000000, Obj: 0.341871, No Obj: 0.003320, Avg Recall: 0.721519, count: 79 -Region Avg IOU: 0.569687, Class: 1.000000, Obj: 0.270395, No Obj: 0.003507, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.533815, Class: 1.000000, Obj: 0.288896, No Obj: 0.003697, Avg Recall: 0.636364, count: 132 -Region Avg IOU: 0.546447, Class: 1.000000, Obj: 0.270032, No Obj: 0.003525, Avg Recall: 0.610294, count: 136 -1873: 0.455893, 0.466098 avg, 0.001000 rate, 24.545856 seconds, 479488 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.555844, Class: 1.000000, Obj: 0.317407, No Obj: 0.003177, Avg Recall: 0.650406, count: 123 -Region Avg IOU: 0.537423, Class: 1.000000, Obj: 0.358028, No Obj: 0.003089, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.563145, Class: 1.000000, Obj: 0.320885, No Obj: 0.003514, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.561785, Class: 1.000000, Obj: 0.329516, No Obj: 0.003232, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.517262, Class: 1.000000, Obj: 0.274293, No Obj: 0.003165, Avg Recall: 0.576577, count: 111 -Region Avg IOU: 0.515618, Class: 1.000000, Obj: 0.289406, No Obj: 0.003409, Avg Recall: 0.571429, count: 126 -Region Avg IOU: 0.545967, Class: 1.000000, Obj: 0.262579, No Obj: 0.003597, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.558408, Class: 1.000000, Obj: 0.351654, No Obj: 0.002888, Avg Recall: 0.615385, count: 91 -1874: 0.468357, 0.466324 avg, 0.001000 rate, 26.072714 seconds, 479744 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.523190, Class: 1.000000, Obj: 0.275079, No Obj: 0.003306, Avg Recall: 0.603306, count: 121 -Region Avg IOU: 0.575157, Class: 1.000000, Obj: 0.319064, No Obj: 0.003419, Avg Recall: 0.680672, count: 119 -Region Avg IOU: 0.572572, Class: 1.000000, Obj: 0.282589, No Obj: 0.003595, Avg Recall: 0.740157, count: 127 -Region Avg IOU: 0.572965, Class: 1.000000, Obj: 0.306470, No Obj: 0.003187, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.568489, Class: 1.000000, Obj: 0.307846, No Obj: 0.002896, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.535172, Class: 1.000000, Obj: 0.312716, No Obj: 0.003427, Avg Recall: 0.592857, count: 140 -Region Avg IOU: 0.526188, Class: 1.000000, Obj: 0.277089, No Obj: 0.003097, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.552922, Class: 1.000000, Obj: 0.297092, No Obj: 0.003276, Avg Recall: 0.701754, count: 114 -1875: 0.496616, 0.469353 avg, 0.001000 rate, 25.364302 seconds, 480000 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.577958, Class: 1.000000, Obj: 0.348367, No Obj: 0.003206, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.504275, Class: 1.000000, Obj: 0.312159, No Obj: 0.002914, Avg Recall: 0.546392, count: 97 -Region Avg IOU: 0.554291, Class: 1.000000, Obj: 0.308951, No Obj: 0.002964, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.519981, Class: 1.000000, Obj: 0.319546, No Obj: 0.003280, Avg Recall: 0.584615, count: 130 -Region Avg IOU: 0.568488, Class: 1.000000, Obj: 0.321209, No Obj: 0.003301, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.567585, Class: 1.000000, Obj: 0.323123, No Obj: 0.003403, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.527708, Class: 1.000000, Obj: 0.331020, No Obj: 0.003138, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.531030, Class: 1.000000, Obj: 0.292436, No Obj: 0.002933, Avg Recall: 0.603604, count: 111 -1876: 0.524664, 0.474884 avg, 0.001000 rate, 25.090109 seconds, 480256 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.573545, Class: 1.000000, Obj: 0.316806, No Obj: 0.003234, Avg Recall: 0.673684, count: 95 -Region Avg IOU: 0.575570, Class: 1.000000, Obj: 0.320137, No Obj: 0.003609, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.548671, Class: 1.000000, Obj: 0.323029, No Obj: 0.003492, Avg Recall: 0.643939, count: 132 -Region Avg IOU: 0.604183, Class: 1.000000, Obj: 0.310797, No Obj: 0.004014, Avg Recall: 0.730769, count: 130 -Region Avg IOU: 0.517977, Class: 1.000000, Obj: 0.268139, No Obj: 0.002715, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.547170, Class: 1.000000, Obj: 0.314758, No Obj: 0.002978, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.571739, Class: 1.000000, Obj: 0.331882, No Obj: 0.003413, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.548137, Class: 1.000000, Obj: 0.320715, No Obj: 0.003392, Avg Recall: 0.657407, count: 108 -1877: 0.466607, 0.474056 avg, 0.001000 rate, 24.508591 seconds, 480512 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.574724, Class: 1.000000, Obj: 0.335005, No Obj: 0.003236, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.567675, Class: 1.000000, Obj: 0.297759, No Obj: 0.003502, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.535001, Class: 1.000000, Obj: 0.306057, No Obj: 0.003263, Avg Recall: 0.589147, count: 129 -Region Avg IOU: 0.565247, Class: 1.000000, Obj: 0.310002, No Obj: 0.003437, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.535096, Class: 1.000000, Obj: 0.316083, No Obj: 0.003609, Avg Recall: 0.616541, count: 133 -Region Avg IOU: 0.547385, Class: 1.000000, Obj: 0.306171, No Obj: 0.003354, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.574197, Class: 1.000000, Obj: 0.365150, No Obj: 0.003290, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.532510, Class: 1.000000, Obj: 0.286806, No Obj: 0.003204, Avg Recall: 0.617647, count: 102 -1878: 0.474287, 0.474079 avg, 0.001000 rate, 25.241009 seconds, 480768 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.543835, Class: 1.000000, Obj: 0.328832, No Obj: 0.003359, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.559817, Class: 1.000000, Obj: 0.351893, No Obj: 0.003869, Avg Recall: 0.691729, count: 133 -Region Avg IOU: 0.578061, Class: 1.000000, Obj: 0.306927, No Obj: 0.003938, Avg Recall: 0.681159, count: 138 -Region Avg IOU: 0.550908, Class: 1.000000, Obj: 0.303704, No Obj: 0.003867, Avg Recall: 0.638298, count: 141 -Region Avg IOU: 0.535962, Class: 1.000000, Obj: 0.278404, No Obj: 0.003158, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.554694, Class: 1.000000, Obj: 0.352892, No Obj: 0.003145, Avg Recall: 0.590909, count: 88 -Region Avg IOU: 0.543850, Class: 1.000000, Obj: 0.332237, No Obj: 0.003159, Avg Recall: 0.620690, count: 87 -Region Avg IOU: 0.559141, Class: 1.000000, Obj: 0.328854, No Obj: 0.003530, Avg Recall: 0.677419, count: 124 -1879: 0.494785, 0.476150 avg, 0.001000 rate, 24.454744 seconds, 481024 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.591012, Class: 1.000000, Obj: 0.303842, No Obj: 0.003935, Avg Recall: 0.748092, count: 131 -Region Avg IOU: 0.533586, Class: 1.000000, Obj: 0.293027, No Obj: 0.003076, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.587319, Class: 1.000000, Obj: 0.330651, No Obj: 0.003467, Avg Recall: 0.688525, count: 122 -Region Avg IOU: 0.593568, Class: 1.000000, Obj: 0.333778, No Obj: 0.004328, Avg Recall: 0.702128, count: 141 -Region Avg IOU: 0.538295, Class: 1.000000, Obj: 0.366837, No Obj: 0.003584, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.543775, Class: 1.000000, Obj: 0.343245, No Obj: 0.003003, Avg Recall: 0.617284, count: 81 -Region Avg IOU: 0.561813, Class: 1.000000, Obj: 0.309386, No Obj: 0.004251, Avg Recall: 0.659574, count: 141 -Region Avg IOU: 0.518320, Class: 1.000000, Obj: 0.321411, No Obj: 0.002600, Avg Recall: 0.549296, count: 71 -1880: 0.505274, 0.479062 avg, 0.001000 rate, 25.541706 seconds, 481280 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000073 seconds -Region Avg IOU: 0.630853, Class: 1.000000, Obj: 0.332447, No Obj: 0.003262, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.631248, Class: 1.000000, Obj: 0.366339, No Obj: 0.002826, Avg Recall: 0.761364, count: 88 -Region Avg IOU: 0.581921, Class: 1.000000, Obj: 0.315683, No Obj: 0.003064, Avg Recall: 0.679389, count: 131 -Region Avg IOU: 0.624592, Class: 1.000000, Obj: 0.328806, No Obj: 0.003129, Avg Recall: 0.822430, count: 107 -Region Avg IOU: 0.556481, Class: 1.000000, Obj: 0.331975, No Obj: 0.003048, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.565882, Class: 1.000000, Obj: 0.314142, No Obj: 0.003315, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.593161, Class: 1.000000, Obj: 0.306766, No Obj: 0.002828, Avg Recall: 0.732558, count: 86 -Region Avg IOU: 0.578850, Class: 1.000000, Obj: 0.323748, No Obj: 0.003168, Avg Recall: 0.674419, count: 129 -1881: 0.466532, 0.477809 avg, 0.001000 rate, 27.803877 seconds, 481536 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.608931, Class: 1.000000, Obj: 0.346705, No Obj: 0.003164, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.596951, Class: 1.000000, Obj: 0.353425, No Obj: 0.002995, Avg Recall: 0.746988, count: 83 -Region Avg IOU: 0.568069, Class: 1.000000, Obj: 0.320370, No Obj: 0.003025, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.590948, Class: 1.000000, Obj: 0.310658, No Obj: 0.002814, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.581589, Class: 1.000000, Obj: 0.353073, No Obj: 0.003153, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.596140, Class: 1.000000, Obj: 0.324723, No Obj: 0.003155, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.587322, Class: 1.000000, Obj: 0.390507, No Obj: 0.003277, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.596076, Class: 1.000000, Obj: 0.290038, No Obj: 0.003412, Avg Recall: 0.734375, count: 128 -1882: 0.485042, 0.478533 avg, 0.001000 rate, 27.687109 seconds, 481792 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.628235, Class: 1.000000, Obj: 0.354398, No Obj: 0.002744, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.608368, Class: 1.000000, Obj: 0.276389, No Obj: 0.002932, Avg Recall: 0.781250, count: 96 -Region Avg IOU: 0.597153, Class: 1.000000, Obj: 0.293628, No Obj: 0.002614, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.644421, Class: 1.000000, Obj: 0.343388, No Obj: 0.002754, Avg Recall: 0.797619, count: 84 -Region Avg IOU: 0.628077, Class: 1.000000, Obj: 0.291043, No Obj: 0.002901, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.633568, Class: 1.000000, Obj: 0.339809, No Obj: 0.002790, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.617161, Class: 1.000000, Obj: 0.328809, No Obj: 0.003037, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.584854, Class: 1.000000, Obj: 0.363250, No Obj: 0.002677, Avg Recall: 0.677083, count: 96 -1883: 0.423483, 0.473028 avg, 0.001000 rate, 29.605858 seconds, 482048 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.634476, Class: 1.000000, Obj: 0.286467, No Obj: 0.003286, Avg Recall: 0.798507, count: 134 -Region Avg IOU: 0.608348, Class: 1.000000, Obj: 0.297558, No Obj: 0.002747, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.577525, Class: 1.000000, Obj: 0.279686, No Obj: 0.002954, Avg Recall: 0.669355, count: 124 -Region Avg IOU: 0.603196, Class: 1.000000, Obj: 0.297380, No Obj: 0.002853, Avg Recall: 0.771186, count: 118 -Region Avg IOU: 0.635186, Class: 1.000000, Obj: 0.308470, No Obj: 0.002505, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.607309, Class: 1.000000, Obj: 0.275958, No Obj: 0.003006, Avg Recall: 0.770492, count: 122 -Region Avg IOU: 0.618978, Class: 1.000000, Obj: 0.298470, No Obj: 0.002748, Avg Recall: 0.766129, count: 124 -Region Avg IOU: 0.615927, Class: 1.000000, Obj: 0.278451, No Obj: 0.002599, Avg Recall: 0.754717, count: 106 -1884: 0.498016, 0.475526 avg, 0.001000 rate, 27.701519 seconds, 482304 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.583113, Class: 1.000000, Obj: 0.295156, No Obj: 0.002546, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.563560, Class: 1.000000, Obj: 0.289628, No Obj: 0.002360, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.609752, Class: 1.000000, Obj: 0.292870, No Obj: 0.002355, Avg Recall: 0.777778, count: 90 -Region Avg IOU: 0.614129, Class: 1.000000, Obj: 0.294821, No Obj: 0.002379, Avg Recall: 0.759036, count: 83 -Region Avg IOU: 0.606550, Class: 1.000000, Obj: 0.297792, No Obj: 0.002697, Avg Recall: 0.723077, count: 130 -Region Avg IOU: 0.570549, Class: 1.000000, Obj: 0.285185, No Obj: 0.002596, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.596113, Class: 1.000000, Obj: 0.277977, No Obj: 0.002877, Avg Recall: 0.755906, count: 127 -Region Avg IOU: 0.605316, Class: 1.000000, Obj: 0.250431, No Obj: 0.002362, Avg Recall: 0.776596, count: 94 -1885: 0.480418, 0.476016 avg, 0.001000 rate, 27.598236 seconds, 482560 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.632380, Class: 1.000000, Obj: 0.316663, No Obj: 0.002850, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.582633, Class: 1.000000, Obj: 0.259262, No Obj: 0.002407, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.568749, Class: 1.000000, Obj: 0.263577, No Obj: 0.002421, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.656981, Class: 1.000000, Obj: 0.350227, No Obj: 0.002574, Avg Recall: 0.855556, count: 90 -Region Avg IOU: 0.603832, Class: 1.000000, Obj: 0.288949, No Obj: 0.002435, Avg Recall: 0.746667, count: 75 -Region Avg IOU: 0.652063, Class: 1.000000, Obj: 0.308508, No Obj: 0.002497, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.606738, Class: 1.000000, Obj: 0.266413, No Obj: 0.002940, Avg Recall: 0.739726, count: 146 -Region Avg IOU: 0.585029, Class: 1.000000, Obj: 0.309924, No Obj: 0.002520, Avg Recall: 0.717949, count: 117 -1886: 0.465309, 0.474945 avg, 0.001000 rate, 28.181076 seconds, 482816 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.617585, Class: 1.000000, Obj: 0.294821, No Obj: 0.002583, Avg Recall: 0.768595, count: 121 -Region Avg IOU: 0.597886, Class: 1.000000, Obj: 0.283215, No Obj: 0.002887, Avg Recall: 0.709677, count: 124 -Region Avg IOU: 0.604164, Class: 1.000000, Obj: 0.320694, No Obj: 0.002666, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.591498, Class: 1.000000, Obj: 0.286666, No Obj: 0.002702, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.610589, Class: 1.000000, Obj: 0.314610, No Obj: 0.002398, Avg Recall: 0.762500, count: 80 -Region Avg IOU: 0.616991, Class: 1.000000, Obj: 0.299536, No Obj: 0.002599, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.633744, Class: 1.000000, Obj: 0.293604, No Obj: 0.002977, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.579668, Class: 1.000000, Obj: 0.269000, No Obj: 0.002560, Avg Recall: 0.690265, count: 113 -1887: 0.473165, 0.474767 avg, 0.001000 rate, 28.783464 seconds, 483072 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.595258, Class: 1.000000, Obj: 0.276022, No Obj: 0.002533, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.579996, Class: 1.000000, Obj: 0.266452, No Obj: 0.002558, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.659399, Class: 1.000000, Obj: 0.278146, No Obj: 0.003021, Avg Recall: 0.846154, count: 91 -Region Avg IOU: 0.621395, Class: 1.000000, Obj: 0.268454, No Obj: 0.002983, Avg Recall: 0.793893, count: 131 -Region Avg IOU: 0.565933, Class: 1.000000, Obj: 0.268798, No Obj: 0.002504, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.622306, Class: 1.000000, Obj: 0.295814, No Obj: 0.002909, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.599422, Class: 1.000000, Obj: 0.252620, No Obj: 0.002704, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.572960, Class: 1.000000, Obj: 0.337882, No Obj: 0.002584, Avg Recall: 0.704545, count: 88 -1888: 0.467602, 0.474050 avg, 0.001000 rate, 29.788769 seconds, 483328 images -Loaded: 0.000052 seconds -Region Avg IOU: 0.618419, Class: 1.000000, Obj: 0.316779, No Obj: 0.003012, Avg Recall: 0.784000, count: 125 -Region Avg IOU: 0.630483, Class: 1.000000, Obj: 0.330972, No Obj: 0.003008, Avg Recall: 0.795455, count: 132 -Region Avg IOU: 0.606212, Class: 1.000000, Obj: 0.325543, No Obj: 0.002971, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.615840, Class: 1.000000, Obj: 0.269096, No Obj: 0.002943, Avg Recall: 0.776000, count: 125 -Region Avg IOU: 0.585000, Class: 1.000000, Obj: 0.320672, No Obj: 0.002685, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.589280, Class: 1.000000, Obj: 0.282437, No Obj: 0.002891, Avg Recall: 0.734375, count: 128 -Region Avg IOU: 0.582022, Class: 1.000000, Obj: 0.299496, No Obj: 0.002628, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.646323, Class: 1.000000, Obj: 0.334100, No Obj: 0.003245, Avg Recall: 0.816794, count: 131 -1889: 0.522608, 0.478906 avg, 0.001000 rate, 27.587893 seconds, 483584 images -Loaded: 0.000053 seconds -Region Avg IOU: 0.653376, Class: 1.000000, Obj: 0.356626, No Obj: 0.002651, Avg Recall: 0.839506, count: 81 -Region Avg IOU: 0.609927, Class: 1.000000, Obj: 0.376533, No Obj: 0.002999, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.594760, Class: 1.000000, Obj: 0.360293, No Obj: 0.002969, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.607425, Class: 1.000000, Obj: 0.305863, No Obj: 0.003030, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.625781, Class: 1.000000, Obj: 0.311257, No Obj: 0.002952, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.578438, Class: 1.000000, Obj: 0.355796, No Obj: 0.003054, Avg Recall: 0.698276, count: 116 -Region Avg IOU: 0.620919, Class: 1.000000, Obj: 0.281922, No Obj: 0.002912, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.601907, Class: 1.000000, Obj: 0.347094, No Obj: 0.002470, Avg Recall: 0.764706, count: 68 -1890: 0.397052, 0.470721 avg, 0.001000 rate, 28.732208 seconds, 483840 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.313583 seconds -Region Avg IOU: 0.409484, Class: 1.000000, Obj: 0.222437, No Obj: 0.004069, Avg Recall: 0.368000, count: 125 -Region Avg IOU: 0.459172, Class: 1.000000, Obj: 0.293968, No Obj: 0.004125, Avg Recall: 0.465347, count: 101 -Region Avg IOU: 0.443062, Class: 1.000000, Obj: 0.252982, No Obj: 0.003599, Avg Recall: 0.470588, count: 68 -Region Avg IOU: 0.477720, Class: 1.000000, Obj: 0.266984, No Obj: 0.004067, Avg Recall: 0.506667, count: 75 -Region Avg IOU: 0.446194, Class: 1.000000, Obj: 0.223227, No Obj: 0.004012, Avg Recall: 0.478723, count: 94 -Region Avg IOU: 0.450923, Class: 1.000000, Obj: 0.239167, No Obj: 0.004334, Avg Recall: 0.475000, count: 120 -Region Avg IOU: 0.445642, Class: 1.000000, Obj: 0.227531, No Obj: 0.004504, Avg Recall: 0.495050, count: 101 -Region Avg IOU: 0.432220, Class: 1.000000, Obj: 0.238321, No Obj: 0.004605, Avg Recall: 0.447154, count: 123 -1891: 0.643292, 0.487978 avg, 0.001000 rate, 17.211748 seconds, 484096 images -Loaded: 2.234785 seconds -Region Avg IOU: 0.402289, Class: 1.000000, Obj: 0.210864, No Obj: 0.004097, Avg Recall: 0.421875, count: 128 -Region Avg IOU: 0.450002, Class: 1.000000, Obj: 0.209397, No Obj: 0.004218, Avg Recall: 0.442623, count: 122 -Region Avg IOU: 0.480343, Class: 1.000000, Obj: 0.244467, No Obj: 0.004091, Avg Recall: 0.564103, count: 117 -Region Avg IOU: 0.453432, Class: 1.000000, Obj: 0.288845, No Obj: 0.004085, Avg Recall: 0.462366, count: 93 -Region Avg IOU: 0.473968, Class: 1.000000, Obj: 0.236111, No Obj: 0.003358, Avg Recall: 0.513514, count: 74 -Region Avg IOU: 0.464617, Class: 1.000000, Obj: 0.255003, No Obj: 0.004302, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.454028, Class: 1.000000, Obj: 0.248605, No Obj: 0.004717, Avg Recall: 0.492754, count: 138 -Region Avg IOU: 0.470715, Class: 1.000000, Obj: 0.243012, No Obj: 0.004218, Avg Recall: 0.522124, count: 113 -1892: 0.514630, 0.490643 avg, 0.001000 rate, 15.527469 seconds, 484352 images -Loaded: 3.440974 seconds -Region Avg IOU: 0.365601, Class: 1.000000, Obj: 0.220027, No Obj: 0.004206, Avg Recall: 0.354167, count: 144 -Region Avg IOU: 0.420253, Class: 1.000000, Obj: 0.257267, No Obj: 0.004385, Avg Recall: 0.388889, count: 108 -Region Avg IOU: 0.486241, Class: 1.000000, Obj: 0.280764, No Obj: 0.004730, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.466302, Class: 1.000000, Obj: 0.282275, No Obj: 0.004023, Avg Recall: 0.518519, count: 81 -Region Avg IOU: 0.424076, Class: 1.000000, Obj: 0.222597, No Obj: 0.004492, Avg Recall: 0.441176, count: 136 -Region Avg IOU: 0.430699, Class: 1.000000, Obj: 0.273790, No Obj: 0.004592, Avg Recall: 0.471014, count: 138 -Region Avg IOU: 0.460247, Class: 1.000000, Obj: 0.265369, No Obj: 0.004806, Avg Recall: 0.459677, count: 124 -Region Avg IOU: 0.449597, Class: 1.000000, Obj: 0.264111, No Obj: 0.004282, Avg Recall: 0.490741, count: 108 -1893: 0.548248, 0.496404 avg, 0.001000 rate, 15.706312 seconds, 484608 images -Loaded: 5.059543 seconds -Region Avg IOU: 0.448819, Class: 1.000000, Obj: 0.279784, No Obj: 0.005167, Avg Recall: 0.444444, count: 108 -Region Avg IOU: 0.464399, Class: 1.000000, Obj: 0.288197, No Obj: 0.004474, Avg Recall: 0.515464, count: 97 -Region Avg IOU: 0.428485, Class: 1.000000, Obj: 0.259016, No Obj: 0.004694, Avg Recall: 0.484211, count: 95 -Region Avg IOU: 0.504612, Class: 1.000000, Obj: 0.312359, No Obj: 0.004952, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.401731, Class: 1.000000, Obj: 0.251319, No Obj: 0.003721, Avg Recall: 0.363636, count: 88 -Region Avg IOU: 0.419530, Class: 1.000000, Obj: 0.261995, No Obj: 0.004338, Avg Recall: 0.441667, count: 120 -Region Avg IOU: 0.481924, Class: 1.000000, Obj: 0.282803, No Obj: 0.004038, Avg Recall: 0.586957, count: 92 -Region Avg IOU: 0.446801, Class: 1.000000, Obj: 0.271236, No Obj: 0.004415, Avg Recall: 0.450980, count: 102 -1894: 0.452108, 0.491974 avg, 0.001000 rate, 17.543348 seconds, 484864 images -Loaded: 4.090760 seconds -Region Avg IOU: 0.426544, Class: 1.000000, Obj: 0.276934, No Obj: 0.005025, Avg Recall: 0.462264, count: 106 -Region Avg IOU: 0.391922, Class: 1.000000, Obj: 0.268321, No Obj: 0.004859, Avg Recall: 0.306452, count: 124 -Region Avg IOU: 0.391695, Class: 1.000000, Obj: 0.269725, No Obj: 0.004980, Avg Recall: 0.427536, count: 138 -Region Avg IOU: 0.411940, Class: 1.000000, Obj: 0.290981, No Obj: 0.004648, Avg Recall: 0.406250, count: 96 -Region Avg IOU: 0.349124, Class: 1.000000, Obj: 0.242340, No Obj: 0.004409, Avg Recall: 0.288889, count: 135 -Region Avg IOU: 0.436874, Class: 1.000000, Obj: 0.306519, No Obj: 0.003895, Avg Recall: 0.460674, count: 89 -Region Avg IOU: 0.414855, Class: 1.000000, Obj: 0.266607, No Obj: 0.005176, Avg Recall: 0.404580, count: 131 -Region Avg IOU: 0.440424, Class: 1.000000, Obj: 0.322828, No Obj: 0.004509, Avg Recall: 0.475248, count: 101 -1895: 0.551849, 0.497962 avg, 0.001000 rate, 17.080168 seconds, 485120 images -Loaded: 4.218985 seconds -Region Avg IOU: 0.420609, Class: 1.000000, Obj: 0.284105, No Obj: 0.005286, Avg Recall: 0.452991, count: 117 -Region Avg IOU: 0.385943, Class: 1.000000, Obj: 0.284844, No Obj: 0.005000, Avg Recall: 0.365217, count: 115 -Region Avg IOU: 0.417142, Class: 1.000000, Obj: 0.298171, No Obj: 0.004467, Avg Recall: 0.402062, count: 97 -Region Avg IOU: 0.413409, Class: 1.000000, Obj: 0.309367, No Obj: 0.004585, Avg Recall: 0.436170, count: 94 -Region Avg IOU: 0.479533, Class: 1.000000, Obj: 0.330795, No Obj: 0.004944, Avg Recall: 0.509434, count: 106 -Region Avg IOU: 0.399648, Class: 1.000000, Obj: 0.287205, No Obj: 0.004599, Avg Recall: 0.372881, count: 118 -Region Avg IOU: 0.389534, Class: 1.000000, Obj: 0.282285, No Obj: 0.004693, Avg Recall: 0.386555, count: 119 -Region Avg IOU: 0.428614, Class: 1.000000, Obj: 0.297016, No Obj: 0.005016, Avg Recall: 0.518182, count: 110 -1896: 0.595757, 0.507741 avg, 0.001000 rate, 17.496384 seconds, 485376 images -Loaded: 3.528379 seconds -Region Avg IOU: 0.416049, Class: 1.000000, Obj: 0.290272, No Obj: 0.004834, Avg Recall: 0.400000, count: 100 -Region Avg IOU: 0.435926, Class: 1.000000, Obj: 0.278803, No Obj: 0.004461, Avg Recall: 0.500000, count: 76 -Region Avg IOU: 0.422975, Class: 1.000000, Obj: 0.350990, No Obj: 0.004573, Avg Recall: 0.425287, count: 87 -Region Avg IOU: 0.433216, Class: 1.000000, Obj: 0.270583, No Obj: 0.005219, Avg Recall: 0.455285, count: 123 -Region Avg IOU: 0.430963, Class: 1.000000, Obj: 0.287432, No Obj: 0.005268, Avg Recall: 0.457944, count: 107 -Region Avg IOU: 0.442160, Class: 1.000000, Obj: 0.284069, No Obj: 0.004021, Avg Recall: 0.469880, count: 83 -Region Avg IOU: 0.445557, Class: 1.000000, Obj: 0.281916, No Obj: 0.004904, Avg Recall: 0.427273, count: 110 -Region Avg IOU: 0.474145, Class: 1.000000, Obj: 0.336217, No Obj: 0.004820, Avg Recall: 0.539216, count: 102 -1897: 0.488320, 0.505799 avg, 0.001000 rate, 18.178707 seconds, 485632 images -Loaded: 3.432799 seconds -Region Avg IOU: 0.511091, Class: 1.000000, Obj: 0.295957, No Obj: 0.004793, Avg Recall: 0.580247, count: 81 -Region Avg IOU: 0.455953, Class: 1.000000, Obj: 0.311224, No Obj: 0.006012, Avg Recall: 0.467213, count: 122 -Region Avg IOU: 0.421946, Class: 1.000000, Obj: 0.295521, No Obj: 0.004122, Avg Recall: 0.423077, count: 78 -Region Avg IOU: 0.427295, Class: 1.000000, Obj: 0.275024, No Obj: 0.004943, Avg Recall: 0.456000, count: 125 -Region Avg IOU: 0.430898, Class: 1.000000, Obj: 0.276046, No Obj: 0.004491, Avg Recall: 0.465909, count: 88 -Region Avg IOU: 0.439124, Class: 1.000000, Obj: 0.301479, No Obj: 0.004915, Avg Recall: 0.495798, count: 119 -Region Avg IOU: 0.435579, Class: 1.000000, Obj: 0.297732, No Obj: 0.004679, Avg Recall: 0.490000, count: 100 -Region Avg IOU: 0.415451, Class: 1.000000, Obj: 0.316039, No Obj: 0.004927, Avg Recall: 0.420561, count: 107 -1898: 0.508332, 0.506052 avg, 0.001000 rate, 14.878661 seconds, 485888 images -Loaded: 6.725805 seconds -Region Avg IOU: 0.420498, Class: 1.000000, Obj: 0.268700, No Obj: 0.004678, Avg Recall: 0.450980, count: 102 -Region Avg IOU: 0.392510, Class: 1.000000, Obj: 0.284630, No Obj: 0.004133, Avg Recall: 0.411111, count: 90 -Region Avg IOU: 0.421621, Class: 1.000000, Obj: 0.272699, No Obj: 0.004404, Avg Recall: 0.461538, count: 91 -Region Avg IOU: 0.429949, Class: 1.000000, Obj: 0.255490, No Obj: 0.004725, Avg Recall: 0.398305, count: 118 -Region Avg IOU: 0.429151, Class: 1.000000, Obj: 0.294432, No Obj: 0.005100, Avg Recall: 0.434109, count: 129 -Region Avg IOU: 0.455620, Class: 1.000000, Obj: 0.279985, No Obj: 0.005197, Avg Recall: 0.463415, count: 123 -Region Avg IOU: 0.422350, Class: 1.000000, Obj: 0.265960, No Obj: 0.004244, Avg Recall: 0.391753, count: 97 -Region Avg IOU: 0.414539, Class: 1.000000, Obj: 0.269360, No Obj: 0.004765, Avg Recall: 0.393162, count: 117 -1899: 0.519726, 0.507420 avg, 0.001000 rate, 17.075272 seconds, 486144 images -Loaded: 5.356814 seconds -Region Avg IOU: 0.425289, Class: 1.000000, Obj: 0.271060, No Obj: 0.004154, Avg Recall: 0.505050, count: 99 -Region Avg IOU: 0.409448, Class: 1.000000, Obj: 0.260810, No Obj: 0.004426, Avg Recall: 0.420168, count: 119 -Region Avg IOU: 0.454930, Class: 1.000000, Obj: 0.245850, No Obj: 0.004797, Avg Recall: 0.483516, count: 91 -Region Avg IOU: 0.470719, Class: 1.000000, Obj: 0.239316, No Obj: 0.004726, Avg Recall: 0.513043, count: 115 -Region Avg IOU: 0.457831, Class: 1.000000, Obj: 0.288615, No Obj: 0.003936, Avg Recall: 0.500000, count: 66 -Region Avg IOU: 0.462043, Class: 1.000000, Obj: 0.272415, No Obj: 0.004333, Avg Recall: 0.500000, count: 106 -Region Avg IOU: 0.440280, Class: 1.000000, Obj: 0.262632, No Obj: 0.005099, Avg Recall: 0.485149, count: 101 -Region Avg IOU: 0.430873, Class: 1.000000, Obj: 0.257734, No Obj: 0.004290, Avg Recall: 0.436975, count: 119 -1900: 0.450158, 0.501693 avg, 0.001000 rate, 18.267941 seconds, 486400 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000067 seconds -Region Avg IOU: 0.549792, Class: 1.000000, Obj: 0.266500, No Obj: 0.002710, Avg Recall: 0.623529, count: 85 -Region Avg IOU: 0.566609, Class: 1.000000, Obj: 0.317440, No Obj: 0.003490, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.563577, Class: 1.000000, Obj: 0.380879, No Obj: 0.002872, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.560408, Class: 1.000000, Obj: 0.298006, No Obj: 0.002810, Avg Recall: 0.674419, count: 86 -Region Avg IOU: 0.597123, Class: 1.000000, Obj: 0.344228, No Obj: 0.003418, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.563230, Class: 1.000000, Obj: 0.313695, No Obj: 0.003321, Avg Recall: 0.633333, count: 120 -Region Avg IOU: 0.539627, Class: 1.000000, Obj: 0.333698, No Obj: 0.003155, Avg Recall: 0.632479, count: 117 -Region Avg IOU: 0.559900, Class: 1.000000, Obj: 0.327603, No Obj: 0.003542, Avg Recall: 0.641509, count: 106 -1901: 0.465693, 0.498093 avg, 0.001000 rate, 24.423147 seconds, 486656 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.548694, Class: 1.000000, Obj: 0.328249, No Obj: 0.003155, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.564122, Class: 1.000000, Obj: 0.302429, No Obj: 0.003144, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.572141, Class: 1.000000, Obj: 0.307518, No Obj: 0.003007, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.604774, Class: 1.000000, Obj: 0.295006, No Obj: 0.003532, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.578994, Class: 1.000000, Obj: 0.305935, No Obj: 0.002466, Avg Recall: 0.726027, count: 73 -Region Avg IOU: 0.596201, Class: 1.000000, Obj: 0.324497, No Obj: 0.003368, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.574606, Class: 1.000000, Obj: 0.328171, No Obj: 0.002907, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.582829, Class: 1.000000, Obj: 0.291636, No Obj: 0.003001, Avg Recall: 0.683673, count: 98 -1902: 0.455472, 0.493831 avg, 0.001000 rate, 24.782000 seconds, 486912 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.515231, Class: 1.000000, Obj: 0.236594, No Obj: 0.002395, Avg Recall: 0.617284, count: 81 -Region Avg IOU: 0.593508, Class: 1.000000, Obj: 0.275488, No Obj: 0.002780, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.565368, Class: 1.000000, Obj: 0.291703, No Obj: 0.003283, Avg Recall: 0.684615, count: 130 -Region Avg IOU: 0.564226, Class: 1.000000, Obj: 0.282234, No Obj: 0.002772, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.570573, Class: 1.000000, Obj: 0.345935, No Obj: 0.002795, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.539156, Class: 1.000000, Obj: 0.315589, No Obj: 0.002828, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.534040, Class: 1.000000, Obj: 0.258099, No Obj: 0.002325, Avg Recall: 0.611765, count: 85 -Region Avg IOU: 0.572919, Class: 1.000000, Obj: 0.307745, No Obj: 0.002855, Avg Recall: 0.698113, count: 106 -1903: 0.468486, 0.491297 avg, 0.001000 rate, 25.713064 seconds, 487168 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.595372, Class: 1.000000, Obj: 0.313461, No Obj: 0.003046, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.580885, Class: 1.000000, Obj: 0.287007, No Obj: 0.002954, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.565611, Class: 1.000000, Obj: 0.303570, No Obj: 0.002444, Avg Recall: 0.712644, count: 87 -Region Avg IOU: 0.609387, Class: 1.000000, Obj: 0.301098, No Obj: 0.002857, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.584837, Class: 1.000000, Obj: 0.325328, No Obj: 0.003115, Avg Recall: 0.692308, count: 130 -Region Avg IOU: 0.603714, Class: 1.000000, Obj: 0.299648, No Obj: 0.002857, Avg Recall: 0.696629, count: 89 -Region Avg IOU: 0.558803, Class: 1.000000, Obj: 0.271301, No Obj: 0.002672, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.515058, Class: 1.000000, Obj: 0.254674, No Obj: 0.002628, Avg Recall: 0.541284, count: 109 -1904: 0.495758, 0.491743 avg, 0.001000 rate, 25.906357 seconds, 487424 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.550965, Class: 1.000000, Obj: 0.298656, No Obj: 0.002342, Avg Recall: 0.658824, count: 85 -Region Avg IOU: 0.516855, Class: 1.000000, Obj: 0.214473, No Obj: 0.002516, Avg Recall: 0.567568, count: 74 -Region Avg IOU: 0.594092, Class: 1.000000, Obj: 0.304675, No Obj: 0.002715, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.543798, Class: 1.000000, Obj: 0.271909, No Obj: 0.002997, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.561672, Class: 1.000000, Obj: 0.299366, No Obj: 0.002616, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.584795, Class: 1.000000, Obj: 0.293972, No Obj: 0.002416, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.624657, Class: 1.000000, Obj: 0.299472, No Obj: 0.002443, Avg Recall: 0.766234, count: 77 -Region Avg IOU: 0.576272, Class: 1.000000, Obj: 0.280915, No Obj: 0.003031, Avg Recall: 0.677419, count: 124 -1905: 0.525994, 0.495168 avg, 0.001000 rate, 26.429468 seconds, 487680 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.583834, Class: 1.000000, Obj: 0.264862, No Obj: 0.003194, Avg Recall: 0.713178, count: 129 -Region Avg IOU: 0.561409, Class: 1.000000, Obj: 0.293680, No Obj: 0.003319, Avg Recall: 0.638298, count: 141 -Region Avg IOU: 0.538916, Class: 1.000000, Obj: 0.269813, No Obj: 0.002433, Avg Recall: 0.607595, count: 79 -Region Avg IOU: 0.578177, Class: 1.000000, Obj: 0.283509, No Obj: 0.003126, Avg Recall: 0.672000, count: 125 -Region Avg IOU: 0.614335, Class: 1.000000, Obj: 0.314810, No Obj: 0.003417, Avg Recall: 0.796875, count: 128 -Region Avg IOU: 0.568320, Class: 1.000000, Obj: 0.308062, No Obj: 0.003284, Avg Recall: 0.644928, count: 138 -Region Avg IOU: 0.591922, Class: 1.000000, Obj: 0.323749, No Obj: 0.003244, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.585935, Class: 1.000000, Obj: 0.364194, No Obj: 0.002641, Avg Recall: 0.727273, count: 99 -1906: 0.543849, 0.500036 avg, 0.001000 rate, 25.760780 seconds, 487936 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.528785, Class: 1.000000, Obj: 0.288274, No Obj: 0.003312, Avg Recall: 0.602837, count: 141 -Region Avg IOU: 0.571453, Class: 1.000000, Obj: 0.283658, No Obj: 0.003030, Avg Recall: 0.710526, count: 114 -Region Avg IOU: 0.544093, Class: 1.000000, Obj: 0.295795, No Obj: 0.002974, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.543793, Class: 1.000000, Obj: 0.278099, No Obj: 0.003157, Avg Recall: 0.606557, count: 122 -Region Avg IOU: 0.599427, Class: 1.000000, Obj: 0.307709, No Obj: 0.003347, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.504296, Class: 1.000000, Obj: 0.245529, No Obj: 0.002742, Avg Recall: 0.605769, count: 104 -Region Avg IOU: 0.564588, Class: 1.000000, Obj: 0.296243, No Obj: 0.002949, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.631962, Class: 1.000000, Obj: 0.306529, No Obj: 0.003066, Avg Recall: 0.795455, count: 88 -1907: 0.529915, 0.503024 avg, 0.001000 rate, 25.304667 seconds, 488192 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.621604, Class: 1.000000, Obj: 0.373888, No Obj: 0.003466, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.567383, Class: 1.000000, Obj: 0.330071, No Obj: 0.003272, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.578753, Class: 1.000000, Obj: 0.332738, No Obj: 0.003187, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.556079, Class: 1.000000, Obj: 0.321138, No Obj: 0.003254, Avg Recall: 0.633333, count: 120 -Region Avg IOU: 0.579491, Class: 1.000000, Obj: 0.343576, No Obj: 0.003303, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.593714, Class: 1.000000, Obj: 0.312775, No Obj: 0.003290, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.555453, Class: 1.000000, Obj: 0.326324, No Obj: 0.003462, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.574397, Class: 1.000000, Obj: 0.308603, No Obj: 0.003376, Avg Recall: 0.682692, count: 104 -1908: 0.487048, 0.501426 avg, 0.001000 rate, 25.798929 seconds, 488448 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.589420, Class: 1.000000, Obj: 0.387813, No Obj: 0.003386, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.560051, Class: 1.000000, Obj: 0.302023, No Obj: 0.003911, Avg Recall: 0.645390, count: 141 -Region Avg IOU: 0.582341, Class: 1.000000, Obj: 0.378129, No Obj: 0.003363, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.589279, Class: 1.000000, Obj: 0.327383, No Obj: 0.003244, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.550065, Class: 1.000000, Obj: 0.329955, No Obj: 0.003625, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.536120, Class: 1.000000, Obj: 0.366729, No Obj: 0.002983, Avg Recall: 0.604938, count: 81 -Region Avg IOU: 0.551702, Class: 1.000000, Obj: 0.308279, No Obj: 0.003385, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.555339, Class: 1.000000, Obj: 0.350254, No Obj: 0.003298, Avg Recall: 0.625000, count: 96 -1909: 0.433122, 0.494596 avg, 0.001000 rate, 24.943943 seconds, 488704 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.573652, Class: 1.000000, Obj: 0.369925, No Obj: 0.003407, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.574394, Class: 1.000000, Obj: 0.328830, No Obj: 0.003297, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.512771, Class: 1.000000, Obj: 0.304072, No Obj: 0.003524, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.525276, Class: 1.000000, Obj: 0.379426, No Obj: 0.003690, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.589043, Class: 1.000000, Obj: 0.334727, No Obj: 0.003053, Avg Recall: 0.721519, count: 79 -Region Avg IOU: 0.564915, Class: 1.000000, Obj: 0.328757, No Obj: 0.003411, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.507455, Class: 1.000000, Obj: 0.359116, No Obj: 0.003204, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.548679, Class: 1.000000, Obj: 0.327497, No Obj: 0.003825, Avg Recall: 0.661417, count: 127 -1910: 0.420491, 0.487185 avg, 0.001000 rate, 25.836325 seconds, 488960 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000070 seconds -Region Avg IOU: 0.571040, Class: 1.000000, Obj: 0.289348, No Obj: 0.003649, Avg Recall: 0.659091, count: 132 -Region Avg IOU: 0.542818, Class: 1.000000, Obj: 0.306743, No Obj: 0.003036, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.600089, Class: 1.000000, Obj: 0.349885, No Obj: 0.003477, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.608000, Class: 1.000000, Obj: 0.326817, No Obj: 0.003217, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.609327, Class: 1.000000, Obj: 0.345446, No Obj: 0.003344, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.562093, Class: 1.000000, Obj: 0.344271, No Obj: 0.003148, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.602091, Class: 1.000000, Obj: 0.325455, No Obj: 0.003271, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.635676, Class: 1.000000, Obj: 0.321440, No Obj: 0.003432, Avg Recall: 0.769231, count: 104 -1911: 0.488836, 0.487350 avg, 0.001000 rate, 27.312880 seconds, 489216 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.575806, Class: 1.000000, Obj: 0.316031, No Obj: 0.003252, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.603371, Class: 1.000000, Obj: 0.304225, No Obj: 0.003304, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.550107, Class: 1.000000, Obj: 0.301021, No Obj: 0.003458, Avg Recall: 0.674074, count: 135 -Region Avg IOU: 0.607044, Class: 1.000000, Obj: 0.316692, No Obj: 0.002984, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.550231, Class: 1.000000, Obj: 0.339651, No Obj: 0.003161, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.612999, Class: 1.000000, Obj: 0.306167, No Obj: 0.003416, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.609106, Class: 1.000000, Obj: 0.370293, No Obj: 0.002969, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.603082, Class: 1.000000, Obj: 0.288615, No Obj: 0.003064, Avg Recall: 0.768519, count: 108 -1912: 0.483660, 0.486981 avg, 0.001000 rate, 27.024179 seconds, 489472 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.590966, Class: 1.000000, Obj: 0.355858, No Obj: 0.002777, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.583112, Class: 1.000000, Obj: 0.309774, No Obj: 0.003060, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.578316, Class: 1.000000, Obj: 0.315988, No Obj: 0.002962, Avg Recall: 0.714286, count: 119 -Region Avg IOU: 0.569855, Class: 1.000000, Obj: 0.297815, No Obj: 0.002571, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.572502, Class: 1.000000, Obj: 0.301897, No Obj: 0.002895, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.559186, Class: 1.000000, Obj: 0.292839, No Obj: 0.002827, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.611996, Class: 1.000000, Obj: 0.302864, No Obj: 0.003207, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.606175, Class: 1.000000, Obj: 0.314881, No Obj: 0.003297, Avg Recall: 0.773913, count: 115 -1913: 0.489646, 0.487248 avg, 0.001000 rate, 26.555355 seconds, 489728 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.544212, Class: 1.000000, Obj: 0.269708, No Obj: 0.002432, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.590005, Class: 1.000000, Obj: 0.321952, No Obj: 0.002708, Avg Recall: 0.724138, count: 87 -Region Avg IOU: 0.574885, Class: 1.000000, Obj: 0.270830, No Obj: 0.002892, Avg Recall: 0.708333, count: 120 -Region Avg IOU: 0.592735, Class: 1.000000, Obj: 0.342023, No Obj: 0.003084, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.546295, Class: 1.000000, Obj: 0.253246, No Obj: 0.002639, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.570937, Class: 1.000000, Obj: 0.313331, No Obj: 0.002842, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.572563, Class: 1.000000, Obj: 0.274197, No Obj: 0.002480, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.543127, Class: 1.000000, Obj: 0.269744, No Obj: 0.002640, Avg Recall: 0.669811, count: 106 -1914: 0.438781, 0.482401 avg, 0.001000 rate, 25.800028 seconds, 489984 images -Loaded: 0.000116 seconds -Region Avg IOU: 0.585665, Class: 1.000000, Obj: 0.291486, No Obj: 0.003188, Avg Recall: 0.654412, count: 136 -Region Avg IOU: 0.593889, Class: 1.000000, Obj: 0.271434, No Obj: 0.002970, Avg Recall: 0.720000, count: 125 -Region Avg IOU: 0.568096, Class: 1.000000, Obj: 0.280637, No Obj: 0.002734, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.597363, Class: 1.000000, Obj: 0.302081, No Obj: 0.002832, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.569165, Class: 1.000000, Obj: 0.299714, No Obj: 0.002882, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.548112, Class: 1.000000, Obj: 0.263510, No Obj: 0.002895, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.518882, Class: 1.000000, Obj: 0.293082, No Obj: 0.002746, Avg Recall: 0.639344, count: 122 -Region Avg IOU: 0.591698, Class: 1.000000, Obj: 0.318919, No Obj: 0.002851, Avg Recall: 0.710526, count: 114 -1915: 0.505031, 0.484664 avg, 0.001000 rate, 27.135231 seconds, 490240 images -Loaded: 0.000125 seconds -Region Avg IOU: 0.601835, Class: 1.000000, Obj: 0.288002, No Obj: 0.003409, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.563166, Class: 1.000000, Obj: 0.265473, No Obj: 0.003083, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.597126, Class: 1.000000, Obj: 0.308098, No Obj: 0.002964, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.572983, Class: 1.000000, Obj: 0.276405, No Obj: 0.003335, Avg Recall: 0.690647, count: 139 -Region Avg IOU: 0.595689, Class: 1.000000, Obj: 0.303932, No Obj: 0.003534, Avg Recall: 0.686567, count: 134 -Region Avg IOU: 0.588587, Class: 1.000000, Obj: 0.283086, No Obj: 0.003274, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.583681, Class: 1.000000, Obj: 0.295954, No Obj: 0.002991, Avg Recall: 0.686956, count: 115 -Region Avg IOU: 0.569065, Class: 1.000000, Obj: 0.309407, No Obj: 0.003009, Avg Recall: 0.660870, count: 115 -1916: 0.532271, 0.489425 avg, 0.001000 rate, 27.111534 seconds, 490496 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.605404, Class: 1.000000, Obj: 0.341560, No Obj: 0.003149, Avg Recall: 0.735043, count: 117 -Region Avg IOU: 0.597673, Class: 1.000000, Obj: 0.321070, No Obj: 0.003206, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.577341, Class: 1.000000, Obj: 0.280996, No Obj: 0.003341, Avg Recall: 0.648855, count: 131 -Region Avg IOU: 0.562104, Class: 1.000000, Obj: 0.309683, No Obj: 0.002712, Avg Recall: 0.620690, count: 87 -Region Avg IOU: 0.560114, Class: 1.000000, Obj: 0.309173, No Obj: 0.002889, Avg Recall: 0.628571, count: 70 -Region Avg IOU: 0.587179, Class: 1.000000, Obj: 0.364295, No Obj: 0.002661, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.610863, Class: 1.000000, Obj: 0.372331, No Obj: 0.002752, Avg Recall: 0.790123, count: 81 -Region Avg IOU: 0.634538, Class: 1.000000, Obj: 0.331640, No Obj: 0.002914, Avg Recall: 0.776471, count: 85 -1917: 0.478144, 0.488297 avg, 0.001000 rate, 26.020144 seconds, 490752 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.612219, Class: 1.000000, Obj: 0.337327, No Obj: 0.003445, Avg Recall: 0.744186, count: 129 -Region Avg IOU: 0.581436, Class: 1.000000, Obj: 0.347262, No Obj: 0.003011, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.597438, Class: 1.000000, Obj: 0.355432, No Obj: 0.003003, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.626209, Class: 1.000000, Obj: 0.347630, No Obj: 0.003671, Avg Recall: 0.794118, count: 136 -Region Avg IOU: 0.582080, Class: 1.000000, Obj: 0.317196, No Obj: 0.003599, Avg Recall: 0.686131, count: 137 -Region Avg IOU: 0.530539, Class: 1.000000, Obj: 0.282082, No Obj: 0.002873, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.631615, Class: 1.000000, Obj: 0.375921, No Obj: 0.003372, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.584181, Class: 1.000000, Obj: 0.302919, No Obj: 0.003178, Avg Recall: 0.725664, count: 113 -1918: 0.518531, 0.491320 avg, 0.001000 rate, 26.308861 seconds, 491008 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.547604, Class: 1.000000, Obj: 0.331347, No Obj: 0.002974, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.619643, Class: 1.000000, Obj: 0.350947, No Obj: 0.003392, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.598951, Class: 1.000000, Obj: 0.278879, No Obj: 0.003302, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.582012, Class: 1.000000, Obj: 0.339857, No Obj: 0.003544, Avg Recall: 0.734375, count: 128 -Region Avg IOU: 0.601382, Class: 1.000000, Obj: 0.307941, No Obj: 0.003207, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.547073, Class: 1.000000, Obj: 0.261979, No Obj: 0.003294, Avg Recall: 0.625000, count: 136 -Region Avg IOU: 0.601887, Class: 1.000000, Obj: 0.341847, No Obj: 0.003361, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.605477, Class: 1.000000, Obj: 0.324826, No Obj: 0.003299, Avg Recall: 0.696970, count: 99 -1919: 0.502195, 0.492408 avg, 0.001000 rate, 25.433764 seconds, 491264 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.595493, Class: 1.000000, Obj: 0.351504, No Obj: 0.002824, Avg Recall: 0.698795, count: 83 -Region Avg IOU: 0.579190, Class: 1.000000, Obj: 0.367477, No Obj: 0.003573, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.608667, Class: 1.000000, Obj: 0.359982, No Obj: 0.003006, Avg Recall: 0.764706, count: 85 -Region Avg IOU: 0.614887, Class: 1.000000, Obj: 0.399157, No Obj: 0.003078, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.590522, Class: 1.000000, Obj: 0.327719, No Obj: 0.003122, Avg Recall: 0.694118, count: 85 -Region Avg IOU: 0.568353, Class: 1.000000, Obj: 0.316216, No Obj: 0.002744, Avg Recall: 0.704545, count: 88 -Region Avg IOU: 0.578801, Class: 1.000000, Obj: 0.344067, No Obj: 0.003285, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.608435, Class: 1.000000, Obj: 0.325079, No Obj: 0.003176, Avg Recall: 0.698795, count: 83 -1920: 0.389696, 0.482136 avg, 0.001000 rate, 27.282393 seconds, 491520 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000076 seconds -Region Avg IOU: 0.571741, Class: 1.000000, Obj: 0.336601, No Obj: 0.002860, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.608622, Class: 1.000000, Obj: 0.348838, No Obj: 0.002994, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.573322, Class: 1.000000, Obj: 0.322300, No Obj: 0.003311, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.610369, Class: 1.000000, Obj: 0.365991, No Obj: 0.003605, Avg Recall: 0.778761, count: 113 -Region Avg IOU: 0.592778, Class: 1.000000, Obj: 0.376647, No Obj: 0.003181, Avg Recall: 0.719298, count: 114 -Region Avg IOU: 0.629948, Class: 1.000000, Obj: 0.394577, No Obj: 0.003310, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.608204, Class: 1.000000, Obj: 0.336297, No Obj: 0.002914, Avg Recall: 0.771084, count: 83 -Region Avg IOU: 0.609037, Class: 1.000000, Obj: 0.316813, No Obj: 0.003212, Avg Recall: 0.741667, count: 120 -1921: 0.467616, 0.480684 avg, 0.001000 rate, 27.447739 seconds, 491776 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.612880, Class: 1.000000, Obj: 0.349649, No Obj: 0.002989, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.587663, Class: 1.000000, Obj: 0.314184, No Obj: 0.002897, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.635188, Class: 1.000000, Obj: 0.301910, No Obj: 0.003492, Avg Recall: 0.782609, count: 138 -Region Avg IOU: 0.637230, Class: 1.000000, Obj: 0.335069, No Obj: 0.003231, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.612038, Class: 1.000000, Obj: 0.311370, No Obj: 0.003081, Avg Recall: 0.744000, count: 125 -Region Avg IOU: 0.571210, Class: 1.000000, Obj: 0.301337, No Obj: 0.002815, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.588515, Class: 1.000000, Obj: 0.316567, No Obj: 0.003249, Avg Recall: 0.704545, count: 132 -Region Avg IOU: 0.600185, Class: 1.000000, Obj: 0.343174, No Obj: 0.002863, Avg Recall: 0.747663, count: 107 -1922: 0.512209, 0.483837 avg, 0.001000 rate, 27.452560 seconds, 492032 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.598977, Class: 1.000000, Obj: 0.320210, No Obj: 0.003283, Avg Recall: 0.728000, count: 125 -Region Avg IOU: 0.621983, Class: 1.000000, Obj: 0.343664, No Obj: 0.002924, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.581861, Class: 1.000000, Obj: 0.322549, No Obj: 0.002999, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.576700, Class: 1.000000, Obj: 0.333665, No Obj: 0.002538, Avg Recall: 0.684783, count: 92 -Region Avg IOU: 0.605109, Class: 1.000000, Obj: 0.291744, No Obj: 0.003162, Avg Recall: 0.779528, count: 127 -Region Avg IOU: 0.614148, Class: 1.000000, Obj: 0.300952, No Obj: 0.002618, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.550763, Class: 1.000000, Obj: 0.253844, No Obj: 0.002765, Avg Recall: 0.650000, count: 120 -Region Avg IOU: 0.603988, Class: 1.000000, Obj: 0.355298, No Obj: 0.002632, Avg Recall: 0.740000, count: 100 -1923: 0.471418, 0.482595 avg, 0.001000 rate, 28.352160 seconds, 492288 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.556856, Class: 1.000000, Obj: 0.289688, No Obj: 0.002583, Avg Recall: 0.647059, count: 85 -Region Avg IOU: 0.618244, Class: 1.000000, Obj: 0.318072, No Obj: 0.003031, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.591130, Class: 1.000000, Obj: 0.337356, No Obj: 0.002570, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.596285, Class: 1.000000, Obj: 0.291749, No Obj: 0.003100, Avg Recall: 0.755725, count: 131 -Region Avg IOU: 0.570383, Class: 1.000000, Obj: 0.295335, No Obj: 0.002635, Avg Recall: 0.659091, count: 88 -Region Avg IOU: 0.556001, Class: 1.000000, Obj: 0.302369, No Obj: 0.002531, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.519570, Class: 1.000000, Obj: 0.302950, No Obj: 0.002478, Avg Recall: 0.616279, count: 86 -Region Avg IOU: 0.608476, Class: 1.000000, Obj: 0.322172, No Obj: 0.002694, Avg Recall: 0.801980, count: 101 -1924: 0.407471, 0.475083 avg, 0.001000 rate, 27.425701 seconds, 492544 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.557496, Class: 1.000000, Obj: 0.256762, No Obj: 0.002537, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.635216, Class: 1.000000, Obj: 0.308105, No Obj: 0.002612, Avg Recall: 0.785714, count: 84 -Region Avg IOU: 0.577642, Class: 1.000000, Obj: 0.297148, No Obj: 0.002768, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.593755, Class: 1.000000, Obj: 0.298714, No Obj: 0.002817, Avg Recall: 0.738462, count: 130 -Region Avg IOU: 0.583530, Class: 1.000000, Obj: 0.298970, No Obj: 0.002883, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.583317, Class: 1.000000, Obj: 0.284377, No Obj: 0.002833, Avg Recall: 0.719697, count: 132 -Region Avg IOU: 0.599570, Class: 1.000000, Obj: 0.257750, No Obj: 0.002189, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.625779, Class: 1.000000, Obj: 0.344795, No Obj: 0.002861, Avg Recall: 0.792079, count: 101 -1925: 0.483650, 0.475939 avg, 0.001000 rate, 27.137411 seconds, 492800 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.608840, Class: 1.000000, Obj: 0.290407, No Obj: 0.002931, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.629326, Class: 1.000000, Obj: 0.275410, No Obj: 0.002846, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.596297, Class: 1.000000, Obj: 0.254166, No Obj: 0.002913, Avg Recall: 0.702479, count: 121 -Region Avg IOU: 0.604462, Class: 1.000000, Obj: 0.317900, No Obj: 0.002628, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.595073, Class: 1.000000, Obj: 0.292461, No Obj: 0.003081, Avg Recall: 0.737589, count: 141 -Region Avg IOU: 0.579487, Class: 1.000000, Obj: 0.263231, No Obj: 0.002697, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.541070, Class: 1.000000, Obj: 0.256634, No Obj: 0.002379, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.577473, Class: 1.000000, Obj: 0.279900, No Obj: 0.002327, Avg Recall: 0.734043, count: 94 -1926: 0.489831, 0.477329 avg, 0.001000 rate, 26.675072 seconds, 493056 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.591310, Class: 1.000000, Obj: 0.347297, No Obj: 0.002602, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.561403, Class: 1.000000, Obj: 0.319108, No Obj: 0.002759, Avg Recall: 0.658537, count: 123 -Region Avg IOU: 0.615820, Class: 1.000000, Obj: 0.326063, No Obj: 0.002343, Avg Recall: 0.716049, count: 81 -Region Avg IOU: 0.575279, Class: 1.000000, Obj: 0.325357, No Obj: 0.002646, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.582761, Class: 1.000000, Obj: 0.302953, No Obj: 0.002665, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.615102, Class: 1.000000, Obj: 0.295063, No Obj: 0.002842, Avg Recall: 0.762295, count: 122 -Region Avg IOU: 0.519639, Class: 1.000000, Obj: 0.225032, No Obj: 0.002441, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.570631, Class: 1.000000, Obj: 0.331706, No Obj: 0.002449, Avg Recall: 0.678571, count: 112 -1927: 0.457370, 0.475333 avg, 0.001000 rate, 27.710493 seconds, 493312 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.612468, Class: 1.000000, Obj: 0.326830, No Obj: 0.002701, Avg Recall: 0.752137, count: 117 -Region Avg IOU: 0.560793, Class: 1.000000, Obj: 0.294605, No Obj: 0.002829, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.638905, Class: 1.000000, Obj: 0.317174, No Obj: 0.002946, Avg Recall: 0.800000, count: 80 -Region Avg IOU: 0.587810, Class: 1.000000, Obj: 0.304206, No Obj: 0.002743, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.641987, Class: 1.000000, Obj: 0.303601, No Obj: 0.002952, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.604893, Class: 1.000000, Obj: 0.325258, No Obj: 0.002846, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.620513, Class: 1.000000, Obj: 0.317456, No Obj: 0.003016, Avg Recall: 0.757576, count: 132 -Region Avg IOU: 0.616866, Class: 1.000000, Obj: 0.362357, No Obj: 0.002831, Avg Recall: 0.763441, count: 93 -1928: 0.453134, 0.473113 avg, 0.001000 rate, 27.396788 seconds, 493568 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.649435, Class: 1.000000, Obj: 0.286554, No Obj: 0.003002, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.591259, Class: 1.000000, Obj: 0.301999, No Obj: 0.002748, Avg Recall: 0.712500, count: 80 -Region Avg IOU: 0.625776, Class: 1.000000, Obj: 0.339881, No Obj: 0.002725, Avg Recall: 0.784091, count: 88 -Region Avg IOU: 0.589030, Class: 1.000000, Obj: 0.355857, No Obj: 0.003126, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.604535, Class: 1.000000, Obj: 0.310150, No Obj: 0.003039, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.619619, Class: 1.000000, Obj: 0.293037, No Obj: 0.002849, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.576844, Class: 1.000000, Obj: 0.326296, No Obj: 0.002961, Avg Recall: 0.701613, count: 124 -Region Avg IOU: 0.584535, Class: 1.000000, Obj: 0.330258, No Obj: 0.002937, Avg Recall: 0.673684, count: 95 -1929: 0.441268, 0.469928 avg, 0.001000 rate, 26.890957 seconds, 493824 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.608731, Class: 1.000000, Obj: 0.380380, No Obj: 0.002774, Avg Recall: 0.773810, count: 84 -Region Avg IOU: 0.610028, Class: 1.000000, Obj: 0.328044, No Obj: 0.002945, Avg Recall: 0.728000, count: 125 -Region Avg IOU: 0.586343, Class: 1.000000, Obj: 0.320719, No Obj: 0.002779, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.620014, Class: 1.000000, Obj: 0.320503, No Obj: 0.003023, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.579992, Class: 1.000000, Obj: 0.319873, No Obj: 0.002986, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.608276, Class: 1.000000, Obj: 0.328742, No Obj: 0.002654, Avg Recall: 0.741176, count: 85 -Region Avg IOU: 0.592121, Class: 1.000000, Obj: 0.341265, No Obj: 0.002908, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.584698, Class: 1.000000, Obj: 0.322618, No Obj: 0.003219, Avg Recall: 0.724638, count: 138 -1930: 0.444419, 0.467377 avg, 0.001000 rate, 27.253305 seconds, 494080 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 17.523579 seconds -Region Avg IOU: 0.588124, Class: 1.000000, Obj: 0.340318, No Obj: 0.003098, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.563764, Class: 1.000000, Obj: 0.352746, No Obj: 0.003350, Avg Recall: 0.634146, count: 123 -Region Avg IOU: 0.623943, Class: 1.000000, Obj: 0.345013, No Obj: 0.003428, Avg Recall: 0.802920, count: 137 -Region Avg IOU: 0.561310, Class: 1.000000, Obj: 0.318058, No Obj: 0.002804, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.606445, Class: 1.000000, Obj: 0.307448, No Obj: 0.003109, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.592510, Class: 1.000000, Obj: 0.328116, No Obj: 0.002929, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.566605, Class: 1.000000, Obj: 0.301380, No Obj: 0.002925, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.643081, Class: 1.000000, Obj: 0.395684, No Obj: 0.002790, Avg Recall: 0.767442, count: 86 -1931: 0.472342, 0.467874 avg, 0.001000 rate, 25.260477 seconds, 494336 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.595482, Class: 1.000000, Obj: 0.340178, No Obj: 0.002800, Avg Recall: 0.727273, count: 88 -Region Avg IOU: 0.649274, Class: 1.000000, Obj: 0.373785, No Obj: 0.003015, Avg Recall: 0.836957, count: 92 -Region Avg IOU: 0.554376, Class: 1.000000, Obj: 0.322798, No Obj: 0.002774, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.563011, Class: 1.000000, Obj: 0.283139, No Obj: 0.002736, Avg Recall: 0.670455, count: 88 -Region Avg IOU: 0.605903, Class: 1.000000, Obj: 0.363813, No Obj: 0.003050, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.530467, Class: 1.000000, Obj: 0.292103, No Obj: 0.002984, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.637781, Class: 1.000000, Obj: 0.406407, No Obj: 0.002809, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.591505, Class: 1.000000, Obj: 0.293809, No Obj: 0.003200, Avg Recall: 0.745614, count: 114 -1932: 0.415352, 0.462622 avg, 0.001000 rate, 27.985771 seconds, 494592 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.563810, Class: 1.000000, Obj: 0.302611, No Obj: 0.003339, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.569802, Class: 1.000000, Obj: 0.289340, No Obj: 0.003519, Avg Recall: 0.658537, count: 123 -Region Avg IOU: 0.557000, Class: 1.000000, Obj: 0.303468, No Obj: 0.002969, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.615578, Class: 1.000000, Obj: 0.327172, No Obj: 0.003354, Avg Recall: 0.753846, count: 130 -Region Avg IOU: 0.572596, Class: 1.000000, Obj: 0.277090, No Obj: 0.003582, Avg Recall: 0.698630, count: 146 -Region Avg IOU: 0.633719, Class: 1.000000, Obj: 0.340931, No Obj: 0.002898, Avg Recall: 0.806818, count: 88 -Region Avg IOU: 0.585239, Class: 1.000000, Obj: 0.323490, No Obj: 0.003264, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.538824, Class: 1.000000, Obj: 0.302369, No Obj: 0.003235, Avg Recall: 0.608696, count: 138 -1933: 0.517194, 0.468079 avg, 0.001000 rate, 26.578465 seconds, 494848 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.617715, Class: 1.000000, Obj: 0.365957, No Obj: 0.003377, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.591078, Class: 1.000000, Obj: 0.348140, No Obj: 0.003464, Avg Recall: 0.755725, count: 131 -Region Avg IOU: 0.603228, Class: 1.000000, Obj: 0.286895, No Obj: 0.003187, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.635924, Class: 1.000000, Obj: 0.309645, No Obj: 0.003387, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.571930, Class: 1.000000, Obj: 0.359014, No Obj: 0.003003, Avg Recall: 0.670455, count: 88 -Region Avg IOU: 0.633837, Class: 1.000000, Obj: 0.347220, No Obj: 0.003192, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.555532, Class: 1.000000, Obj: 0.287635, No Obj: 0.003188, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.543520, Class: 1.000000, Obj: 0.327846, No Obj: 0.002634, Avg Recall: 0.614679, count: 109 -1934: 0.532026, 0.474474 avg, 0.001000 rate, 26.266272 seconds, 495104 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.575871, Class: 1.000000, Obj: 0.341099, No Obj: 0.002618, Avg Recall: 0.693182, count: 88 -Region Avg IOU: 0.570679, Class: 1.000000, Obj: 0.337762, No Obj: 0.003197, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.573888, Class: 1.000000, Obj: 0.307218, No Obj: 0.003097, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.625037, Class: 1.000000, Obj: 0.316221, No Obj: 0.002971, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.578058, Class: 1.000000, Obj: 0.356347, No Obj: 0.003292, Avg Recall: 0.656716, count: 134 -Region Avg IOU: 0.549369, Class: 1.000000, Obj: 0.336818, No Obj: 0.002893, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.554183, Class: 1.000000, Obj: 0.371759, No Obj: 0.002467, Avg Recall: 0.634146, count: 82 -Region Avg IOU: 0.595384, Class: 1.000000, Obj: 0.311153, No Obj: 0.003116, Avg Recall: 0.699187, count: 123 -1935: 0.458304, 0.472857 avg, 0.001000 rate, 26.545568 seconds, 495360 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.606367, Class: 1.000000, Obj: 0.313136, No Obj: 0.002789, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.614477, Class: 1.000000, Obj: 0.344686, No Obj: 0.002752, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.616617, Class: 1.000000, Obj: 0.361100, No Obj: 0.002680, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.544847, Class: 1.000000, Obj: 0.268838, No Obj: 0.002711, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.565303, Class: 1.000000, Obj: 0.341002, No Obj: 0.002692, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.566459, Class: 1.000000, Obj: 0.329649, No Obj: 0.002692, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.590952, Class: 1.000000, Obj: 0.310775, No Obj: 0.002808, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.595042, Class: 1.000000, Obj: 0.330214, No Obj: 0.002985, Avg Recall: 0.675439, count: 114 -1936: 0.450711, 0.470642 avg, 0.001000 rate, 26.970228 seconds, 495616 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.637725, Class: 1.000000, Obj: 0.389197, No Obj: 0.002485, Avg Recall: 0.764706, count: 68 -Region Avg IOU: 0.557379, Class: 1.000000, Obj: 0.288481, No Obj: 0.002708, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.581979, Class: 1.000000, Obj: 0.309633, No Obj: 0.003089, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.556046, Class: 1.000000, Obj: 0.266503, No Obj: 0.002646, Avg Recall: 0.670588, count: 85 -Region Avg IOU: 0.594646, Class: 1.000000, Obj: 0.346348, No Obj: 0.003227, Avg Recall: 0.726562, count: 128 -Region Avg IOU: 0.631726, Class: 1.000000, Obj: 0.365750, No Obj: 0.002915, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.566759, Class: 1.000000, Obj: 0.313002, No Obj: 0.003076, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.592282, Class: 1.000000, Obj: 0.284860, No Obj: 0.002859, Avg Recall: 0.688679, count: 106 -1937: 0.438158, 0.467394 avg, 0.001000 rate, 26.289831 seconds, 495872 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.559002, Class: 1.000000, Obj: 0.282846, No Obj: 0.002979, Avg Recall: 0.679688, count: 128 -Region Avg IOU: 0.544571, Class: 1.000000, Obj: 0.328792, No Obj: 0.002723, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.584288, Class: 1.000000, Obj: 0.312117, No Obj: 0.003342, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.575004, Class: 1.000000, Obj: 0.347843, No Obj: 0.002348, Avg Recall: 0.650794, count: 63 -Region Avg IOU: 0.596530, Class: 1.000000, Obj: 0.327091, No Obj: 0.003071, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.593552, Class: 1.000000, Obj: 0.308774, No Obj: 0.002761, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.540948, Class: 1.000000, Obj: 0.345406, No Obj: 0.002746, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.566631, Class: 1.000000, Obj: 0.289105, No Obj: 0.002685, Avg Recall: 0.660870, count: 115 -1938: 0.431803, 0.463835 avg, 0.001000 rate, 26.756779 seconds, 496128 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.516985, Class: 1.000000, Obj: 0.303439, No Obj: 0.002713, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.573244, Class: 1.000000, Obj: 0.284689, No Obj: 0.002704, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.602738, Class: 1.000000, Obj: 0.345525, No Obj: 0.002657, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.593660, Class: 1.000000, Obj: 0.340799, No Obj: 0.002745, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.616071, Class: 1.000000, Obj: 0.307468, No Obj: 0.002899, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.626646, Class: 1.000000, Obj: 0.294921, No Obj: 0.002983, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.551945, Class: 1.000000, Obj: 0.330418, No Obj: 0.002714, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.559399, Class: 1.000000, Obj: 0.281982, No Obj: 0.002733, Avg Recall: 0.688889, count: 90 -1939: 0.410600, 0.458511 avg, 0.001000 rate, 26.462564 seconds, 496384 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.563690, Class: 1.000000, Obj: 0.280235, No Obj: 0.002452, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.571321, Class: 1.000000, Obj: 0.313138, No Obj: 0.003133, Avg Recall: 0.682540, count: 126 -Region Avg IOU: 0.554125, Class: 1.000000, Obj: 0.287506, No Obj: 0.002507, Avg Recall: 0.670588, count: 85 -Region Avg IOU: 0.609674, Class: 1.000000, Obj: 0.359432, No Obj: 0.003006, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.581509, Class: 1.000000, Obj: 0.353342, No Obj: 0.003002, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.556102, Class: 1.000000, Obj: 0.289241, No Obj: 0.002551, Avg Recall: 0.670588, count: 85 -Region Avg IOU: 0.564198, Class: 1.000000, Obj: 0.305905, No Obj: 0.003246, Avg Recall: 0.641221, count: 131 -Region Avg IOU: 0.573217, Class: 1.000000, Obj: 0.321601, No Obj: 0.002927, Avg Recall: 0.651376, count: 109 -1940: 0.468355, 0.459496 avg, 0.001000 rate, 27.742792 seconds, 496640 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 17.327948 seconds -Region Avg IOU: 0.431280, Class: 1.000000, Obj: 0.198753, No Obj: 0.003561, Avg Recall: 0.470086, count: 117 -Region Avg IOU: 0.410160, Class: 1.000000, Obj: 0.215599, No Obj: 0.003661, Avg Recall: 0.403361, count: 119 -Region Avg IOU: 0.424386, Class: 1.000000, Obj: 0.246479, No Obj: 0.003922, Avg Recall: 0.479592, count: 98 -Region Avg IOU: 0.479840, Class: 1.000000, Obj: 0.233956, No Obj: 0.003843, Avg Recall: 0.540323, count: 124 -Region Avg IOU: 0.418819, Class: 1.000000, Obj: 0.178368, No Obj: 0.003635, Avg Recall: 0.395161, count: 124 -Region Avg IOU: 0.450502, Class: 1.000000, Obj: 0.231909, No Obj: 0.003209, Avg Recall: 0.479592, count: 98 -Region Avg IOU: 0.441067, Class: 1.000000, Obj: 0.213360, No Obj: 0.003690, Avg Recall: 0.447368, count: 114 -Region Avg IOU: 0.409608, Class: 1.000000, Obj: 0.182284, No Obj: 0.003698, Avg Recall: 0.414414, count: 111 -1941: 0.584653, 0.472011 avg, 0.001000 rate, 19.566984 seconds, 496896 images -Loaded: 0.920982 seconds -Region Avg IOU: 0.461440, Class: 1.000000, Obj: 0.242021, No Obj: 0.004162, Avg Recall: 0.466667, count: 120 -Region Avg IOU: 0.456901, Class: 1.000000, Obj: 0.243298, No Obj: 0.004022, Avg Recall: 0.522124, count: 113 -Region Avg IOU: 0.457955, Class: 1.000000, Obj: 0.209376, No Obj: 0.003994, Avg Recall: 0.527559, count: 127 -Region Avg IOU: 0.487596, Class: 1.000000, Obj: 0.237667, No Obj: 0.004342, Avg Recall: 0.522936, count: 109 -Region Avg IOU: 0.406759, Class: 1.000000, Obj: 0.236242, No Obj: 0.003761, Avg Recall: 0.387387, count: 111 -Region Avg IOU: 0.442596, Class: 1.000000, Obj: 0.219696, No Obj: 0.004658, Avg Recall: 0.451128, count: 133 -Region Avg IOU: 0.407148, Class: 1.000000, Obj: 0.193592, No Obj: 0.003444, Avg Recall: 0.440000, count: 100 -Region Avg IOU: 0.445081, Class: 1.000000, Obj: 0.221970, No Obj: 0.003379, Avg Recall: 0.457447, count: 94 -1942: 0.536768, 0.478487 avg, 0.001000 rate, 16.958471 seconds, 497152 images -Loaded: 2.548500 seconds -Region Avg IOU: 0.420293, Class: 1.000000, Obj: 0.252501, No Obj: 0.004310, Avg Recall: 0.449612, count: 129 -Region Avg IOU: 0.412589, Class: 1.000000, Obj: 0.251058, No Obj: 0.004384, Avg Recall: 0.421053, count: 95 -Region Avg IOU: 0.372680, Class: 1.000000, Obj: 0.264753, No Obj: 0.003703, Avg Recall: 0.370787, count: 89 -Region Avg IOU: 0.472136, Class: 1.000000, Obj: 0.232971, No Obj: 0.004273, Avg Recall: 0.535088, count: 114 -Region Avg IOU: 0.446022, Class: 1.000000, Obj: 0.253187, No Obj: 0.004059, Avg Recall: 0.514019, count: 107 -Region Avg IOU: 0.513214, Class: 1.000000, Obj: 0.280029, No Obj: 0.004209, Avg Recall: 0.583333, count: 96 -Region Avg IOU: 0.447014, Class: 1.000000, Obj: 0.208420, No Obj: 0.004433, Avg Recall: 0.442029, count: 138 -Region Avg IOU: 0.456951, Class: 1.000000, Obj: 0.233553, No Obj: 0.004164, Avg Recall: 0.480769, count: 104 -1943: 0.546434, 0.485282 avg, 0.001000 rate, 13.483741 seconds, 497408 images -Loaded: 7.267971 seconds -Region Avg IOU: 0.443703, Class: 1.000000, Obj: 0.262605, No Obj: 0.004271, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.484202, Class: 1.000000, Obj: 0.275205, No Obj: 0.004799, Avg Recall: 0.482143, count: 112 -Region Avg IOU: 0.431480, Class: 1.000000, Obj: 0.272167, No Obj: 0.004079, Avg Recall: 0.425287, count: 87 -Region Avg IOU: 0.487000, Class: 1.000000, Obj: 0.289888, No Obj: 0.005440, Avg Recall: 0.544776, count: 134 -Region Avg IOU: 0.403382, Class: 1.000000, Obj: 0.244900, No Obj: 0.004523, Avg Recall: 0.401639, count: 122 -Region Avg IOU: 0.424282, Class: 1.000000, Obj: 0.241872, No Obj: 0.004108, Avg Recall: 0.470000, count: 100 -Region Avg IOU: 0.428610, Class: 1.000000, Obj: 0.230747, No Obj: 0.004152, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.466788, Class: 1.000000, Obj: 0.284231, No Obj: 0.004181, Avg Recall: 0.531250, count: 96 -1944: 0.538211, 0.490575 avg, 0.001000 rate, 19.262274 seconds, 497664 images -Loaded: 2.607625 seconds -Region Avg IOU: 0.467367, Class: 1.000000, Obj: 0.346395, No Obj: 0.005189, Avg Recall: 0.525000, count: 120 -Region Avg IOU: 0.463977, Class: 1.000000, Obj: 0.260951, No Obj: 0.004816, Avg Recall: 0.491071, count: 112 -Region Avg IOU: 0.438756, Class: 1.000000, Obj: 0.294794, No Obj: 0.005212, Avg Recall: 0.485981, count: 107 -Region Avg IOU: 0.496304, Class: 1.000000, Obj: 0.268453, No Obj: 0.005676, Avg Recall: 0.549618, count: 131 -Region Avg IOU: 0.383240, Class: 1.000000, Obj: 0.257034, No Obj: 0.004115, Avg Recall: 0.370000, count: 100 -Region Avg IOU: 0.470786, Class: 1.000000, Obj: 0.269568, No Obj: 0.005327, Avg Recall: 0.519380, count: 129 -Region Avg IOU: 0.444696, Class: 1.000000, Obj: 0.309831, No Obj: 0.003704, Avg Recall: 0.454545, count: 66 -Region Avg IOU: 0.403972, Class: 1.000000, Obj: 0.236018, No Obj: 0.004643, Avg Recall: 0.400000, count: 115 -1945: 0.522899, 0.493807 avg, 0.001000 rate, 16.765991 seconds, 497920 images -Loaded: 2.778401 seconds -Region Avg IOU: 0.434651, Class: 1.000000, Obj: 0.311412, No Obj: 0.004982, Avg Recall: 0.451327, count: 113 -Region Avg IOU: 0.441739, Class: 1.000000, Obj: 0.324040, No Obj: 0.005044, Avg Recall: 0.479592, count: 98 -Region Avg IOU: 0.412467, Class: 1.000000, Obj: 0.249193, No Obj: 0.006420, Avg Recall: 0.405882, count: 170 -Region Avg IOU: 0.447371, Class: 1.000000, Obj: 0.303087, No Obj: 0.005313, Avg Recall: 0.448276, count: 116 -Region Avg IOU: 0.449247, Class: 1.000000, Obj: 0.249597, No Obj: 0.005182, Avg Recall: 0.510000, count: 100 -Region Avg IOU: 0.434779, Class: 1.000000, Obj: 0.326614, No Obj: 0.005137, Avg Recall: 0.438202, count: 89 -Region Avg IOU: 0.448744, Class: 1.000000, Obj: 0.323628, No Obj: 0.005627, Avg Recall: 0.492188, count: 128 -Region Avg IOU: 0.405772, Class: 1.000000, Obj: 0.321243, No Obj: 0.005257, Avg Recall: 0.479167, count: 96 -1946: 0.565174, 0.500944 avg, 0.001000 rate, 18.611376 seconds, 498176 images -Loaded: 1.288074 seconds -Region Avg IOU: 0.441537, Class: 1.000000, Obj: 0.323626, No Obj: 0.005794, Avg Recall: 0.484127, count: 126 -Region Avg IOU: 0.430410, Class: 1.000000, Obj: 0.306953, No Obj: 0.004617, Avg Recall: 0.440860, count: 93 -Region Avg IOU: 0.495646, Class: 1.000000, Obj: 0.340191, No Obj: 0.005512, Avg Recall: 0.543478, count: 92 -Region Avg IOU: 0.440503, Class: 1.000000, Obj: 0.346059, No Obj: 0.004788, Avg Recall: 0.500000, count: 90 -Region Avg IOU: 0.393625, Class: 1.000000, Obj: 0.292154, No Obj: 0.004802, Avg Recall: 0.418367, count: 98 -Region Avg IOU: 0.452375, Class: 1.000000, Obj: 0.281349, No Obj: 0.004951, Avg Recall: 0.495238, count: 105 -Region Avg IOU: 0.419835, Class: 1.000000, Obj: 0.286571, No Obj: 0.005035, Avg Recall: 0.451613, count: 93 -Region Avg IOU: 0.361277, Class: 1.000000, Obj: 0.277201, No Obj: 0.004670, Avg Recall: 0.403361, count: 119 -1947: 0.463526, 0.497202 avg, 0.001000 rate, 14.809776 seconds, 498432 images -Loaded: 6.348134 seconds -Region Avg IOU: 0.456883, Class: 1.000000, Obj: 0.345339, No Obj: 0.005267, Avg Recall: 0.522727, count: 88 -Region Avg IOU: 0.439332, Class: 1.000000, Obj: 0.318642, No Obj: 0.005192, Avg Recall: 0.481481, count: 108 -Region Avg IOU: 0.412609, Class: 1.000000, Obj: 0.338355, No Obj: 0.005237, Avg Recall: 0.441441, count: 111 -Region Avg IOU: 0.454261, Class: 1.000000, Obj: 0.281587, No Obj: 0.005304, Avg Recall: 0.487805, count: 123 -Region Avg IOU: 0.421008, Class: 1.000000, Obj: 0.290969, No Obj: 0.005530, Avg Recall: 0.444444, count: 117 -Region Avg IOU: 0.416717, Class: 1.000000, Obj: 0.302288, No Obj: 0.004827, Avg Recall: 0.417391, count: 115 -Region Avg IOU: 0.466867, Class: 1.000000, Obj: 0.348947, No Obj: 0.005294, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.423143, Class: 1.000000, Obj: 0.354678, No Obj: 0.005480, Avg Recall: 0.424528, count: 106 -1948: 0.510744, 0.498556 avg, 0.001000 rate, 16.891880 seconds, 498688 images -Loaded: 4.558570 seconds -Region Avg IOU: 0.395921, Class: 1.000000, Obj: 0.340725, No Obj: 0.004839, Avg Recall: 0.410000, count: 100 -Region Avg IOU: 0.432329, Class: 1.000000, Obj: 0.282153, No Obj: 0.005322, Avg Recall: 0.471698, count: 106 -Region Avg IOU: 0.421976, Class: 1.000000, Obj: 0.281355, No Obj: 0.004945, Avg Recall: 0.373913, count: 115 -Region Avg IOU: 0.421875, Class: 1.000000, Obj: 0.309864, No Obj: 0.005091, Avg Recall: 0.385417, count: 96 -Region Avg IOU: 0.454745, Class: 1.000000, Obj: 0.276986, No Obj: 0.005998, Avg Recall: 0.521739, count: 138 -Region Avg IOU: 0.412588, Class: 1.000000, Obj: 0.274711, No Obj: 0.004254, Avg Recall: 0.434343, count: 99 -Region Avg IOU: 0.437844, Class: 1.000000, Obj: 0.306485, No Obj: 0.004854, Avg Recall: 0.450980, count: 102 -Region Avg IOU: 0.403384, Class: 1.000000, Obj: 0.263367, No Obj: 0.004095, Avg Recall: 0.413043, count: 92 -1949: 0.520537, 0.500754 avg, 0.001000 rate, 15.974504 seconds, 498944 images -Loaded: 3.403136 seconds -Region Avg IOU: 0.410920, Class: 1.000000, Obj: 0.247442, No Obj: 0.004604, Avg Recall: 0.398374, count: 123 -Region Avg IOU: 0.456277, Class: 1.000000, Obj: 0.287345, No Obj: 0.004675, Avg Recall: 0.480769, count: 104 -Region Avg IOU: 0.506215, Class: 1.000000, Obj: 0.310901, No Obj: 0.005003, Avg Recall: 0.586957, count: 92 -Region Avg IOU: 0.378347, Class: 1.000000, Obj: 0.293460, No Obj: 0.004776, Avg Recall: 0.373134, count: 134 -Region Avg IOU: 0.354914, Class: 1.000000, Obj: 0.245983, No Obj: 0.005097, Avg Recall: 0.331081, count: 148 -Region Avg IOU: 0.459904, Class: 1.000000, Obj: 0.274848, No Obj: 0.005394, Avg Recall: 0.508333, count: 120 -Region Avg IOU: 0.487811, Class: 1.000000, Obj: 0.283013, No Obj: 0.005502, Avg Recall: 0.549020, count: 102 -Region Avg IOU: 0.407291, Class: 1.000000, Obj: 0.271778, No Obj: 0.004536, Avg Recall: 0.446602, count: 103 -1950: 0.514557, 0.502135 avg, 0.001000 rate, 18.278856 seconds, 499200 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000081 seconds -Region Avg IOU: 0.615310, Class: 1.000000, Obj: 0.345416, No Obj: 0.003443, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.609082, Class: 1.000000, Obj: 0.424160, No Obj: 0.003216, Avg Recall: 0.785714, count: 84 -Region Avg IOU: 0.636745, Class: 1.000000, Obj: 0.345593, No Obj: 0.003633, Avg Recall: 0.778626, count: 131 -Region Avg IOU: 0.568816, Class: 1.000000, Obj: 0.337710, No Obj: 0.003172, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.541528, Class: 1.000000, Obj: 0.363339, No Obj: 0.002896, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.577660, Class: 1.000000, Obj: 0.315230, No Obj: 0.003288, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.582869, Class: 1.000000, Obj: 0.338509, No Obj: 0.002980, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.604663, Class: 1.000000, Obj: 0.292342, No Obj: 0.003338, Avg Recall: 0.792079, count: 101 -1951: 0.557671, 0.507688 avg, 0.001000 rate, 28.286533 seconds, 499456 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.589160, Class: 1.000000, Obj: 0.300644, No Obj: 0.002888, Avg Recall: 0.714286, count: 126 -Region Avg IOU: 0.580088, Class: 1.000000, Obj: 0.295903, No Obj: 0.002689, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.593118, Class: 1.000000, Obj: 0.323441, No Obj: 0.002577, Avg Recall: 0.734177, count: 79 -Region Avg IOU: 0.605128, Class: 1.000000, Obj: 0.337944, No Obj: 0.002736, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.616649, Class: 1.000000, Obj: 0.328245, No Obj: 0.002940, Avg Recall: 0.791667, count: 120 -Region Avg IOU: 0.593644, Class: 1.000000, Obj: 0.330016, No Obj: 0.002692, Avg Recall: 0.723684, count: 76 -Region Avg IOU: 0.605624, Class: 1.000000, Obj: 0.313917, No Obj: 0.002674, Avg Recall: 0.775281, count: 89 -Region Avg IOU: 0.599037, Class: 1.000000, Obj: 0.285350, No Obj: 0.002764, Avg Recall: 0.771739, count: 92 -1952: 0.459083, 0.502828 avg, 0.001000 rate, 29.424036 seconds, 499712 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.604715, Class: 1.000000, Obj: 0.273218, No Obj: 0.002575, Avg Recall: 0.734375, count: 128 -Region Avg IOU: 0.597225, Class: 1.000000, Obj: 0.243089, No Obj: 0.002601, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.557119, Class: 1.000000, Obj: 0.293956, No Obj: 0.002103, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.609197, Class: 1.000000, Obj: 0.295865, No Obj: 0.002398, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.597994, Class: 1.000000, Obj: 0.232896, No Obj: 0.002407, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.623803, Class: 1.000000, Obj: 0.262126, No Obj: 0.002331, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.593287, Class: 1.000000, Obj: 0.283575, No Obj: 0.002352, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.604214, Class: 1.000000, Obj: 0.275553, No Obj: 0.002292, Avg Recall: 0.763441, count: 93 -1953: 0.506256, 0.503171 avg, 0.001000 rate, 28.774240 seconds, 499968 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.633012, Class: 1.000000, Obj: 0.271246, No Obj: 0.002011, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.600946, Class: 1.000000, Obj: 0.282440, No Obj: 0.002205, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.638264, Class: 1.000000, Obj: 0.247469, No Obj: 0.002481, Avg Recall: 0.815789, count: 114 -Region Avg IOU: 0.564577, Class: 1.000000, Obj: 0.229691, No Obj: 0.002268, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.637215, Class: 1.000000, Obj: 0.232209, No Obj: 0.002035, Avg Recall: 0.808000, count: 125 -Region Avg IOU: 0.647661, Class: 1.000000, Obj: 0.321642, No Obj: 0.001995, Avg Recall: 0.807692, count: 78 -Region Avg IOU: 0.639758, Class: 1.000000, Obj: 0.285357, No Obj: 0.001830, Avg Recall: 0.787500, count: 80 -Region Avg IOU: 0.630731, Class: 1.000000, Obj: 0.265866, No Obj: 0.001920, Avg Recall: 0.815789, count: 76 -1954: 0.490602, 0.501914 avg, 0.001000 rate, 29.794823 seconds, 500224 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.595885, Class: 1.000000, Obj: 0.255030, No Obj: 0.001997, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.603779, Class: 1.000000, Obj: 0.222523, No Obj: 0.002071, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.656457, Class: 1.000000, Obj: 0.270300, No Obj: 0.001990, Avg Recall: 0.853933, count: 89 -Region Avg IOU: 0.577801, Class: 1.000000, Obj: 0.227421, No Obj: 0.001979, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.646002, Class: 1.000000, Obj: 0.271154, No Obj: 0.002122, Avg Recall: 0.830189, count: 106 -Region Avg IOU: 0.638960, Class: 1.000000, Obj: 0.221968, No Obj: 0.002253, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.632261, Class: 1.000000, Obj: 0.249111, No Obj: 0.002327, Avg Recall: 0.795620, count: 137 -Region Avg IOU: 0.586936, Class: 1.000000, Obj: 0.261641, No Obj: 0.002092, Avg Recall: 0.720000, count: 100 -1955: 0.488595, 0.500582 avg, 0.001000 rate, 29.678984 seconds, 500480 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.638752, Class: 1.000000, Obj: 0.277009, No Obj: 0.002225, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.640831, Class: 1.000000, Obj: 0.249493, No Obj: 0.002343, Avg Recall: 0.824000, count: 125 -Region Avg IOU: 0.627318, Class: 1.000000, Obj: 0.278410, No Obj: 0.002285, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.612513, Class: 1.000000, Obj: 0.261130, No Obj: 0.002031, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.626222, Class: 1.000000, Obj: 0.272698, No Obj: 0.002318, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.624642, Class: 1.000000, Obj: 0.252313, No Obj: 0.002455, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.599080, Class: 1.000000, Obj: 0.250607, No Obj: 0.002336, Avg Recall: 0.757143, count: 140 -Region Avg IOU: 0.636714, Class: 1.000000, Obj: 0.283654, No Obj: 0.002053, Avg Recall: 0.824176, count: 91 -1956: 0.531017, 0.503625 avg, 0.001000 rate, 30.019094 seconds, 500736 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.617656, Class: 1.000000, Obj: 0.276268, No Obj: 0.002359, Avg Recall: 0.815217, count: 92 -Region Avg IOU: 0.613421, Class: 1.000000, Obj: 0.274538, No Obj: 0.002600, Avg Recall: 0.740458, count: 131 -Region Avg IOU: 0.602134, Class: 1.000000, Obj: 0.283605, No Obj: 0.002541, Avg Recall: 0.740157, count: 127 -Region Avg IOU: 0.587106, Class: 1.000000, Obj: 0.257350, No Obj: 0.002325, Avg Recall: 0.710526, count: 114 -Region Avg IOU: 0.608757, Class: 1.000000, Obj: 0.299277, No Obj: 0.002419, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.629961, Class: 1.000000, Obj: 0.264820, No Obj: 0.002290, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.596026, Class: 1.000000, Obj: 0.279632, No Obj: 0.002304, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.601911, Class: 1.000000, Obj: 0.236211, No Obj: 0.002285, Avg Recall: 0.768421, count: 95 -1957: 0.506938, 0.503957 avg, 0.001000 rate, 30.087828 seconds, 500992 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.589559, Class: 1.000000, Obj: 0.288821, No Obj: 0.002785, Avg Recall: 0.703125, count: 128 -Region Avg IOU: 0.599487, Class: 1.000000, Obj: 0.294905, No Obj: 0.002807, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.593650, Class: 1.000000, Obj: 0.295489, No Obj: 0.002729, Avg Recall: 0.764228, count: 123 -Region Avg IOU: 0.604597, Class: 1.000000, Obj: 0.341982, No Obj: 0.002768, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.606721, Class: 1.000000, Obj: 0.315790, No Obj: 0.002763, Avg Recall: 0.759036, count: 83 -Region Avg IOU: 0.619944, Class: 1.000000, Obj: 0.293666, No Obj: 0.002535, Avg Recall: 0.762500, count: 80 -Region Avg IOU: 0.634836, Class: 1.000000, Obj: 0.289987, No Obj: 0.002847, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.607770, Class: 1.000000, Obj: 0.304565, No Obj: 0.002723, Avg Recall: 0.770833, count: 96 -1958: 0.499319, 0.503493 avg, 0.001000 rate, 28.783173 seconds, 501248 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.618718, Class: 1.000000, Obj: 0.309012, No Obj: 0.003073, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.611876, Class: 1.000000, Obj: 0.332416, No Obj: 0.002649, Avg Recall: 0.746988, count: 83 -Region Avg IOU: 0.614596, Class: 1.000000, Obj: 0.436592, No Obj: 0.002943, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.631953, Class: 1.000000, Obj: 0.392495, No Obj: 0.002847, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.644365, Class: 1.000000, Obj: 0.334118, No Obj: 0.003012, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.629339, Class: 1.000000, Obj: 0.352667, No Obj: 0.003390, Avg Recall: 0.800000, count: 125 -Region Avg IOU: 0.628740, Class: 1.000000, Obj: 0.326218, No Obj: 0.002744, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.620105, Class: 1.000000, Obj: 0.335257, No Obj: 0.002809, Avg Recall: 0.773585, count: 106 -1959: 0.486889, 0.501832 avg, 0.001000 rate, 28.956476 seconds, 501504 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.613788, Class: 1.000000, Obj: 0.317636, No Obj: 0.003008, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.647291, Class: 1.000000, Obj: 0.377934, No Obj: 0.002996, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.589743, Class: 1.000000, Obj: 0.341381, No Obj: 0.003113, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.624769, Class: 1.000000, Obj: 0.352011, No Obj: 0.003005, Avg Recall: 0.765432, count: 81 -Region Avg IOU: 0.635895, Class: 1.000000, Obj: 0.366400, No Obj: 0.003200, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.594021, Class: 1.000000, Obj: 0.338755, No Obj: 0.003310, Avg Recall: 0.702128, count: 141 -Region Avg IOU: 0.604603, Class: 1.000000, Obj: 0.372269, No Obj: 0.003135, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.596353, Class: 1.000000, Obj: 0.339679, No Obj: 0.002721, Avg Recall: 0.754717, count: 106 -1960: 0.462292, 0.497878 avg, 0.001000 rate, 28.660856 seconds, 501760 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 11.533235 seconds -Region Avg IOU: 0.610059, Class: 1.000000, Obj: 0.384844, No Obj: 0.003628, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.651525, Class: 1.000000, Obj: 0.352636, No Obj: 0.003391, Avg Recall: 0.858824, count: 85 -Region Avg IOU: 0.567704, Class: 1.000000, Obj: 0.294652, No Obj: 0.003665, Avg Recall: 0.661290, count: 124 -Region Avg IOU: 0.605868, Class: 1.000000, Obj: 0.354351, No Obj: 0.003093, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.612609, Class: 1.000000, Obj: 0.392806, No Obj: 0.003499, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.606888, Class: 1.000000, Obj: 0.373552, No Obj: 0.003471, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.575092, Class: 1.000000, Obj: 0.303149, No Obj: 0.003179, Avg Recall: 0.684783, count: 92 -Region Avg IOU: 0.627460, Class: 1.000000, Obj: 0.368347, No Obj: 0.003726, Avg Recall: 0.781250, count: 96 -1961: 0.475872, 0.495678 avg, 0.001000 rate, 27.647217 seconds, 502016 images -Loaded: 0.000118 seconds -Region Avg IOU: 0.564954, Class: 1.000000, Obj: 0.350204, No Obj: 0.002729, Avg Recall: 0.646341, count: 82 -Region Avg IOU: 0.582327, Class: 1.000000, Obj: 0.378464, No Obj: 0.003161, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.558061, Class: 1.000000, Obj: 0.363713, No Obj: 0.003520, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.544098, Class: 1.000000, Obj: 0.297435, No Obj: 0.003239, Avg Recall: 0.644068, count: 118 -Region Avg IOU: 0.561315, Class: 1.000000, Obj: 0.333108, No Obj: 0.003164, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.578593, Class: 1.000000, Obj: 0.352663, No Obj: 0.003279, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.564707, Class: 1.000000, Obj: 0.350437, No Obj: 0.003353, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.574161, Class: 1.000000, Obj: 0.346932, No Obj: 0.003008, Avg Recall: 0.679245, count: 106 -1962: 0.504601, 0.496570 avg, 0.001000 rate, 25.695755 seconds, 502272 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.574440, Class: 1.000000, Obj: 0.295946, No Obj: 0.003178, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.540753, Class: 1.000000, Obj: 0.292633, No Obj: 0.003148, Avg Recall: 0.614754, count: 122 -Region Avg IOU: 0.583229, Class: 1.000000, Obj: 0.343179, No Obj: 0.002964, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.546443, Class: 1.000000, Obj: 0.283336, No Obj: 0.003034, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.556520, Class: 1.000000, Obj: 0.319966, No Obj: 0.002987, Avg Recall: 0.686956, count: 115 -Region Avg IOU: 0.569431, Class: 1.000000, Obj: 0.271771, No Obj: 0.003268, Avg Recall: 0.658730, count: 126 -Region Avg IOU: 0.554731, Class: 1.000000, Obj: 0.306542, No Obj: 0.003268, Avg Recall: 0.666667, count: 132 -Region Avg IOU: 0.581513, Class: 1.000000, Obj: 0.294127, No Obj: 0.002721, Avg Recall: 0.676768, count: 99 -1963: 0.515377, 0.498451 avg, 0.001000 rate, 26.919294 seconds, 502528 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.638063, Class: 1.000000, Obj: 0.355086, No Obj: 0.003449, Avg Recall: 0.816667, count: 120 -Region Avg IOU: 0.568248, Class: 1.000000, Obj: 0.261056, No Obj: 0.002956, Avg Recall: 0.698413, count: 126 -Region Avg IOU: 0.584523, Class: 1.000000, Obj: 0.347515, No Obj: 0.002874, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.547946, Class: 1.000000, Obj: 0.328333, No Obj: 0.002844, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.581154, Class: 1.000000, Obj: 0.279667, No Obj: 0.003093, Avg Recall: 0.705882, count: 136 -Region Avg IOU: 0.601299, Class: 1.000000, Obj: 0.313327, No Obj: 0.002348, Avg Recall: 0.753086, count: 81 -Region Avg IOU: 0.597698, Class: 1.000000, Obj: 0.292311, No Obj: 0.002948, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.584224, Class: 1.000000, Obj: 0.279688, No Obj: 0.002623, Avg Recall: 0.707965, count: 113 -1964: 0.543653, 0.502971 avg, 0.001000 rate, 25.737720 seconds, 502784 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.585504, Class: 1.000000, Obj: 0.339693, No Obj: 0.002929, Avg Recall: 0.693548, count: 124 -Region Avg IOU: 0.565518, Class: 1.000000, Obj: 0.311140, No Obj: 0.002520, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.570133, Class: 1.000000, Obj: 0.370391, No Obj: 0.002468, Avg Recall: 0.689189, count: 74 -Region Avg IOU: 0.580914, Class: 1.000000, Obj: 0.294864, No Obj: 0.002286, Avg Recall: 0.693182, count: 88 -Region Avg IOU: 0.558692, Class: 1.000000, Obj: 0.292326, No Obj: 0.002560, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.566553, Class: 1.000000, Obj: 0.291064, No Obj: 0.002711, Avg Recall: 0.666667, count: 117 -Region Avg IOU: 0.579995, Class: 1.000000, Obj: 0.317187, No Obj: 0.002884, Avg Recall: 0.682353, count: 85 -Region Avg IOU: 0.577722, Class: 1.000000, Obj: 0.294339, No Obj: 0.002685, Avg Recall: 0.707547, count: 106 -1965: 0.420468, 0.494721 avg, 0.001000 rate, 26.417736 seconds, 503040 images -Loaded: 0.000123 seconds -Region Avg IOU: 0.579518, Class: 1.000000, Obj: 0.294519, No Obj: 0.002288, Avg Recall: 0.675676, count: 74 -Region Avg IOU: 0.622334, Class: 1.000000, Obj: 0.290771, No Obj: 0.002892, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.614973, Class: 1.000000, Obj: 0.300693, No Obj: 0.002750, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.619097, Class: 1.000000, Obj: 0.284382, No Obj: 0.002482, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.638754, Class: 1.000000, Obj: 0.292355, No Obj: 0.003143, Avg Recall: 0.776923, count: 130 -Region Avg IOU: 0.639152, Class: 1.000000, Obj: 0.338231, No Obj: 0.002521, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.574809, Class: 1.000000, Obj: 0.304667, No Obj: 0.002981, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.557739, Class: 1.000000, Obj: 0.297691, No Obj: 0.002639, Avg Recall: 0.617391, count: 115 -1966: 0.503621, 0.495611 avg, 0.001000 rate, 26.841047 seconds, 503296 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.581880, Class: 1.000000, Obj: 0.304944, No Obj: 0.002197, Avg Recall: 0.666667, count: 72 -Region Avg IOU: 0.581482, Class: 1.000000, Obj: 0.260351, No Obj: 0.002655, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.560558, Class: 1.000000, Obj: 0.321944, No Obj: 0.002825, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.528792, Class: 1.000000, Obj: 0.279664, No Obj: 0.002757, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.594827, Class: 1.000000, Obj: 0.292345, No Obj: 0.002899, Avg Recall: 0.722222, count: 126 -Region Avg IOU: 0.582577, Class: 1.000000, Obj: 0.285392, No Obj: 0.002616, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.609744, Class: 1.000000, Obj: 0.285653, No Obj: 0.002679, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.627848, Class: 1.000000, Obj: 0.329704, No Obj: 0.002791, Avg Recall: 0.785714, count: 70 -1967: 0.456477, 0.491697 avg, 0.001000 rate, 26.500841 seconds, 503552 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.538695, Class: 1.000000, Obj: 0.262585, No Obj: 0.002889, Avg Recall: 0.633588, count: 131 -Region Avg IOU: 0.600157, Class: 1.000000, Obj: 0.294469, No Obj: 0.003148, Avg Recall: 0.691729, count: 133 -Region Avg IOU: 0.602486, Class: 1.000000, Obj: 0.301667, No Obj: 0.002639, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.566016, Class: 1.000000, Obj: 0.280094, No Obj: 0.002312, Avg Recall: 0.675325, count: 77 -Region Avg IOU: 0.606857, Class: 1.000000, Obj: 0.356777, No Obj: 0.002703, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.573581, Class: 1.000000, Obj: 0.265516, No Obj: 0.002450, Avg Recall: 0.673684, count: 95 -Region Avg IOU: 0.606385, Class: 1.000000, Obj: 0.252781, No Obj: 0.002609, Avg Recall: 0.776471, count: 85 -Region Avg IOU: 0.596162, Class: 1.000000, Obj: 0.272221, No Obj: 0.002532, Avg Recall: 0.721154, count: 104 -1968: 0.441538, 0.486681 avg, 0.001000 rate, 26.254307 seconds, 503808 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.632910, Class: 1.000000, Obj: 0.312794, No Obj: 0.003230, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.589933, Class: 1.000000, Obj: 0.313748, No Obj: 0.002676, Avg Recall: 0.703297, count: 91 -Region Avg IOU: 0.564904, Class: 1.000000, Obj: 0.315964, No Obj: 0.003204, Avg Recall: 0.652893, count: 121 -Region Avg IOU: 0.571374, Class: 1.000000, Obj: 0.334668, No Obj: 0.002874, Avg Recall: 0.739583, count: 96 -Region Avg IOU: 0.598251, Class: 1.000000, Obj: 0.249157, No Obj: 0.002756, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.561692, Class: 1.000000, Obj: 0.300731, No Obj: 0.003026, Avg Recall: 0.671429, count: 140 -Region Avg IOU: 0.556050, Class: 1.000000, Obj: 0.322970, No Obj: 0.002975, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.604987, Class: 1.000000, Obj: 0.317711, No Obj: 0.002857, Avg Recall: 0.705263, count: 95 -1969: 0.507364, 0.488750 avg, 0.001000 rate, 26.837000 seconds, 504064 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.550175, Class: 1.000000, Obj: 0.317244, No Obj: 0.003008, Avg Recall: 0.613445, count: 119 -Region Avg IOU: 0.594735, Class: 1.000000, Obj: 0.357329, No Obj: 0.003595, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.602369, Class: 1.000000, Obj: 0.368992, No Obj: 0.003160, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.559779, Class: 1.000000, Obj: 0.351316, No Obj: 0.002930, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.617108, Class: 1.000000, Obj: 0.364929, No Obj: 0.003198, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.578565, Class: 1.000000, Obj: 0.320384, No Obj: 0.003116, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.580909, Class: 1.000000, Obj: 0.320024, No Obj: 0.003288, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.562145, Class: 1.000000, Obj: 0.313410, No Obj: 0.003029, Avg Recall: 0.653846, count: 104 -1970: 0.484643, 0.488339 avg, 0.001000 rate, 27.776360 seconds, 504320 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000075 seconds -Region Avg IOU: 0.605701, Class: 1.000000, Obj: 0.316110, No Obj: 0.002986, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.586993, Class: 1.000000, Obj: 0.269882, No Obj: 0.003296, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.615141, Class: 1.000000, Obj: 0.320610, No Obj: 0.003560, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.601263, Class: 1.000000, Obj: 0.364645, No Obj: 0.003192, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.577538, Class: 1.000000, Obj: 0.324754, No Obj: 0.002979, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.564585, Class: 1.000000, Obj: 0.307052, No Obj: 0.003119, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.563157, Class: 1.000000, Obj: 0.312298, No Obj: 0.003274, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.623731, Class: 1.000000, Obj: 0.369083, No Obj: 0.003229, Avg Recall: 0.800000, count: 100 -1971: 0.472839, 0.486789 avg, 0.001000 rate, 25.601692 seconds, 504576 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.607186, Class: 1.000000, Obj: 0.362324, No Obj: 0.003760, Avg Recall: 0.750000, count: 124 -Region Avg IOU: 0.609284, Class: 1.000000, Obj: 0.338264, No Obj: 0.003297, Avg Recall: 0.717742, count: 124 -Region Avg IOU: 0.593324, Class: 1.000000, Obj: 0.355284, No Obj: 0.003196, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.583951, Class: 1.000000, Obj: 0.315372, No Obj: 0.003048, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.562964, Class: 1.000000, Obj: 0.323226, No Obj: 0.002808, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.588929, Class: 1.000000, Obj: 0.337672, No Obj: 0.003142, Avg Recall: 0.700787, count: 127 -Region Avg IOU: 0.556413, Class: 1.000000, Obj: 0.301880, No Obj: 0.003228, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.596122, Class: 1.000000, Obj: 0.339139, No Obj: 0.003083, Avg Recall: 0.738095, count: 84 -1972: 0.542439, 0.492354 avg, 0.001000 rate, 27.268660 seconds, 504832 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.588045, Class: 1.000000, Obj: 0.277749, No Obj: 0.002951, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.600583, Class: 1.000000, Obj: 0.356188, No Obj: 0.003561, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.599500, Class: 1.000000, Obj: 0.365452, No Obj: 0.003269, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.594621, Class: 1.000000, Obj: 0.298866, No Obj: 0.003345, Avg Recall: 0.746154, count: 130 -Region Avg IOU: 0.589804, Class: 1.000000, Obj: 0.371244, No Obj: 0.003069, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.612842, Class: 1.000000, Obj: 0.356642, No Obj: 0.003218, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.616505, Class: 1.000000, Obj: 0.293457, No Obj: 0.003190, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.601746, Class: 1.000000, Obj: 0.334004, No Obj: 0.002862, Avg Recall: 0.747253, count: 91 -1973: 0.532563, 0.496375 avg, 0.001000 rate, 26.999268 seconds, 505088 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.614532, Class: 1.000000, Obj: 0.311838, No Obj: 0.003295, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.662501, Class: 1.000000, Obj: 0.381866, No Obj: 0.003205, Avg Recall: 0.824742, count: 97 -Region Avg IOU: 0.565717, Class: 1.000000, Obj: 0.367317, No Obj: 0.003152, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.528081, Class: 1.000000, Obj: 0.302146, No Obj: 0.003034, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.579731, Class: 1.000000, Obj: 0.296488, No Obj: 0.002755, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.593551, Class: 1.000000, Obj: 0.341178, No Obj: 0.002941, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.632213, Class: 1.000000, Obj: 0.305292, No Obj: 0.003058, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.600571, Class: 1.000000, Obj: 0.402359, No Obj: 0.002909, Avg Recall: 0.770115, count: 87 -1974: 0.486990, 0.495436 avg, 0.001000 rate, 27.462368 seconds, 505344 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.589233, Class: 1.000000, Obj: 0.309734, No Obj: 0.002819, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.594784, Class: 1.000000, Obj: 0.280594, No Obj: 0.003337, Avg Recall: 0.729323, count: 133 -Region Avg IOU: 0.584067, Class: 1.000000, Obj: 0.318684, No Obj: 0.002993, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.574844, Class: 1.000000, Obj: 0.303301, No Obj: 0.002861, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.600586, Class: 1.000000, Obj: 0.273630, No Obj: 0.003080, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.577488, Class: 1.000000, Obj: 0.306081, No Obj: 0.003179, Avg Recall: 0.697674, count: 129 -Region Avg IOU: 0.587442, Class: 1.000000, Obj: 0.298464, No Obj: 0.002743, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.577789, Class: 1.000000, Obj: 0.333530, No Obj: 0.002659, Avg Recall: 0.720721, count: 111 -1975: 0.511304, 0.497023 avg, 0.001000 rate, 28.390787 seconds, 505600 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.608821, Class: 1.000000, Obj: 0.334175, No Obj: 0.002787, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.601390, Class: 1.000000, Obj: 0.317374, No Obj: 0.002944, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.558923, Class: 1.000000, Obj: 0.306813, No Obj: 0.003159, Avg Recall: 0.633333, count: 150 -Region Avg IOU: 0.617467, Class: 1.000000, Obj: 0.306136, No Obj: 0.003010, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.594514, Class: 1.000000, Obj: 0.289560, No Obj: 0.003169, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.565283, Class: 1.000000, Obj: 0.331288, No Obj: 0.002867, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.560531, Class: 1.000000, Obj: 0.275839, No Obj: 0.002771, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.601854, Class: 1.000000, Obj: 0.297663, No Obj: 0.002974, Avg Recall: 0.709402, count: 117 -1976: 0.512663, 0.498587 avg, 0.001000 rate, 27.888037 seconds, 505856 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.595883, Class: 1.000000, Obj: 0.281268, No Obj: 0.002908, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.638048, Class: 1.000000, Obj: 0.308061, No Obj: 0.002903, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.587511, Class: 1.000000, Obj: 0.285980, No Obj: 0.002900, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.572810, Class: 1.000000, Obj: 0.291944, No Obj: 0.002842, Avg Recall: 0.696296, count: 135 -Region Avg IOU: 0.607857, Class: 1.000000, Obj: 0.301258, No Obj: 0.002730, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.554262, Class: 1.000000, Obj: 0.330500, No Obj: 0.002774, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.607527, Class: 1.000000, Obj: 0.243365, No Obj: 0.003155, Avg Recall: 0.776978, count: 139 -Region Avg IOU: 0.580270, Class: 1.000000, Obj: 0.310681, No Obj: 0.002415, Avg Recall: 0.702381, count: 84 -1977: 0.485803, 0.497309 avg, 0.001000 rate, 26.989199 seconds, 506112 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.606138, Class: 1.000000, Obj: 0.314121, No Obj: 0.002815, Avg Recall: 0.736434, count: 129 -Region Avg IOU: 0.605371, Class: 1.000000, Obj: 0.318133, No Obj: 0.002552, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.597743, Class: 1.000000, Obj: 0.311436, No Obj: 0.002818, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.562610, Class: 1.000000, Obj: 0.270201, No Obj: 0.002542, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.587169, Class: 1.000000, Obj: 0.268943, No Obj: 0.002773, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.565022, Class: 1.000000, Obj: 0.337437, No Obj: 0.002630, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.557037, Class: 1.000000, Obj: 0.321793, No Obj: 0.002834, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.619301, Class: 1.000000, Obj: 0.280627, No Obj: 0.002878, Avg Recall: 0.750000, count: 108 -1978: 0.472010, 0.494779 avg, 0.001000 rate, 27.025442 seconds, 506368 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.606447, Class: 1.000000, Obj: 0.270840, No Obj: 0.002612, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.611495, Class: 1.000000, Obj: 0.300447, No Obj: 0.002701, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.564370, Class: 1.000000, Obj: 0.310952, No Obj: 0.002462, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.608922, Class: 1.000000, Obj: 0.359604, No Obj: 0.002469, Avg Recall: 0.750000, count: 88 -Region Avg IOU: 0.588161, Class: 1.000000, Obj: 0.285159, No Obj: 0.002730, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.592365, Class: 1.000000, Obj: 0.308425, No Obj: 0.002561, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.645439, Class: 1.000000, Obj: 0.304022, No Obj: 0.002690, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.573826, Class: 1.000000, Obj: 0.320357, No Obj: 0.002617, Avg Recall: 0.650000, count: 100 -1979: 0.430286, 0.488330 avg, 0.001000 rate, 26.790268 seconds, 506624 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.623252, Class: 1.000000, Obj: 0.340561, No Obj: 0.002711, Avg Recall: 0.768293, count: 82 -Region Avg IOU: 0.618690, Class: 1.000000, Obj: 0.282132, No Obj: 0.002444, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.610455, Class: 1.000000, Obj: 0.303970, No Obj: 0.002694, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.606763, Class: 1.000000, Obj: 0.289489, No Obj: 0.002787, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.541234, Class: 1.000000, Obj: 0.304921, No Obj: 0.002673, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.616861, Class: 1.000000, Obj: 0.300915, No Obj: 0.002860, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.594566, Class: 1.000000, Obj: 0.291945, No Obj: 0.002635, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.676178, Class: 1.000000, Obj: 0.338875, No Obj: 0.002668, Avg Recall: 0.850746, count: 67 -1980: 0.437853, 0.483282 avg, 0.001000 rate, 26.730352 seconds, 506880 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 16.476198 seconds -Region Avg IOU: 0.500444, Class: 1.000000, Obj: 0.287032, No Obj: 0.003515, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.540739, Class: 1.000000, Obj: 0.300069, No Obj: 0.003894, Avg Recall: 0.627907, count: 129 -Region Avg IOU: 0.524470, Class: 1.000000, Obj: 0.259495, No Obj: 0.003403, Avg Recall: 0.584071, count: 113 -Region Avg IOU: 0.538269, Class: 1.000000, Obj: 0.293421, No Obj: 0.003970, Avg Recall: 0.612403, count: 129 -Region Avg IOU: 0.590025, Class: 1.000000, Obj: 0.291892, No Obj: 0.003964, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.497669, Class: 1.000000, Obj: 0.278783, No Obj: 0.004302, Avg Recall: 0.544828, count: 145 -Region Avg IOU: 0.499555, Class: 1.000000, Obj: 0.246868, No Obj: 0.003237, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.530098, Class: 1.000000, Obj: 0.285168, No Obj: 0.003534, Avg Recall: 0.614035, count: 114 -1981: 0.669272, 0.501881 avg, 0.001000 rate, 23.056240 seconds, 507136 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.562494, Class: 1.000000, Obj: 0.293748, No Obj: 0.003443, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.491452, Class: 1.000000, Obj: 0.277469, No Obj: 0.003295, Avg Recall: 0.566372, count: 113 -Region Avg IOU: 0.535583, Class: 1.000000, Obj: 0.303280, No Obj: 0.004149, Avg Recall: 0.619835, count: 121 -Region Avg IOU: 0.529570, Class: 1.000000, Obj: 0.307233, No Obj: 0.003841, Avg Recall: 0.589744, count: 117 -Region Avg IOU: 0.519236, Class: 1.000000, Obj: 0.290877, No Obj: 0.003954, Avg Recall: 0.608000, count: 125 -Region Avg IOU: 0.521044, Class: 1.000000, Obj: 0.332332, No Obj: 0.003676, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.482062, Class: 1.000000, Obj: 0.289610, No Obj: 0.003612, Avg Recall: 0.557522, count: 113 -Region Avg IOU: 0.527169, Class: 1.000000, Obj: 0.317169, No Obj: 0.004238, Avg Recall: 0.657407, count: 108 -1982: 0.579338, 0.509627 avg, 0.001000 rate, 23.689800 seconds, 507392 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.540086, Class: 1.000000, Obj: 0.306190, No Obj: 0.004301, Avg Recall: 0.674419, count: 129 -Region Avg IOU: 0.502804, Class: 1.000000, Obj: 0.272633, No Obj: 0.003623, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.502835, Class: 1.000000, Obj: 0.327337, No Obj: 0.003408, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.540732, Class: 1.000000, Obj: 0.310160, No Obj: 0.003550, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.550245, Class: 1.000000, Obj: 0.313632, No Obj: 0.004261, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.505318, Class: 1.000000, Obj: 0.331022, No Obj: 0.003771, Avg Recall: 0.546392, count: 97 -Region Avg IOU: 0.510860, Class: 1.000000, Obj: 0.320196, No Obj: 0.004140, Avg Recall: 0.576923, count: 130 -Region Avg IOU: 0.513424, Class: 1.000000, Obj: 0.354276, No Obj: 0.003196, Avg Recall: 0.573171, count: 82 -1983: 0.489631, 0.507627 avg, 0.001000 rate, 23.491358 seconds, 507648 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.517209, Class: 1.000000, Obj: 0.302481, No Obj: 0.003418, Avg Recall: 0.551020, count: 98 -Region Avg IOU: 0.539496, Class: 1.000000, Obj: 0.320954, No Obj: 0.003805, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.544760, Class: 1.000000, Obj: 0.321818, No Obj: 0.003974, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.482149, Class: 1.000000, Obj: 0.292477, No Obj: 0.003795, Avg Recall: 0.500000, count: 124 -Region Avg IOU: 0.569514, Class: 1.000000, Obj: 0.332614, No Obj: 0.004134, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.578178, Class: 1.000000, Obj: 0.321135, No Obj: 0.004072, Avg Recall: 0.710526, count: 114 -Region Avg IOU: 0.525752, Class: 1.000000, Obj: 0.329527, No Obj: 0.003931, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.519181, Class: 1.000000, Obj: 0.274326, No Obj: 0.004142, Avg Recall: 0.574627, count: 134 -1984: 0.464748, 0.503339 avg, 0.001000 rate, 23.594423 seconds, 507904 images -Loaded: 0.000053 seconds -Region Avg IOU: 0.515643, Class: 1.000000, Obj: 0.318821, No Obj: 0.004233, Avg Recall: 0.581967, count: 122 -Region Avg IOU: 0.557201, Class: 1.000000, Obj: 0.377198, No Obj: 0.004292, Avg Recall: 0.669565, count: 115 -Region Avg IOU: 0.507975, Class: 1.000000, Obj: 0.337955, No Obj: 0.003769, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.522312, Class: 1.000000, Obj: 0.312235, No Obj: 0.004068, Avg Recall: 0.592308, count: 130 -Region Avg IOU: 0.521759, Class: 1.000000, Obj: 0.300831, No Obj: 0.003622, Avg Recall: 0.639535, count: 86 -Region Avg IOU: 0.548225, Class: 1.000000, Obj: 0.400583, No Obj: 0.003709, Avg Recall: 0.650000, count: 80 -Region Avg IOU: 0.500589, Class: 1.000000, Obj: 0.333088, No Obj: 0.003885, Avg Recall: 0.522124, count: 113 -Region Avg IOU: 0.565692, Class: 1.000000, Obj: 0.345347, No Obj: 0.004144, Avg Recall: 0.629630, count: 108 -1985: 0.446977, 0.497703 avg, 0.001000 rate, 22.400881 seconds, 508160 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.495327, Class: 1.000000, Obj: 0.323274, No Obj: 0.003878, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.570417, Class: 1.000000, Obj: 0.341688, No Obj: 0.003940, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.513831, Class: 1.000000, Obj: 0.329462, No Obj: 0.003995, Avg Recall: 0.558333, count: 120 -Region Avg IOU: 0.495014, Class: 1.000000, Obj: 0.310380, No Obj: 0.003603, Avg Recall: 0.547368, count: 95 -Region Avg IOU: 0.529758, Class: 1.000000, Obj: 0.262993, No Obj: 0.003740, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.524893, Class: 1.000000, Obj: 0.310573, No Obj: 0.004380, Avg Recall: 0.601504, count: 133 -Region Avg IOU: 0.542989, Class: 1.000000, Obj: 0.349309, No Obj: 0.004185, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.541695, Class: 1.000000, Obj: 0.357984, No Obj: 0.004760, Avg Recall: 0.645669, count: 127 -1986: 0.460281, 0.493961 avg, 0.001000 rate, 23.572399 seconds, 508416 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.524364, Class: 1.000000, Obj: 0.302783, No Obj: 0.003641, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.482582, Class: 1.000000, Obj: 0.326611, No Obj: 0.004185, Avg Recall: 0.553191, count: 141 -Region Avg IOU: 0.525239, Class: 1.000000, Obj: 0.318310, No Obj: 0.003751, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.496437, Class: 1.000000, Obj: 0.291869, No Obj: 0.004094, Avg Recall: 0.597015, count: 134 -Region Avg IOU: 0.561610, Class: 1.000000, Obj: 0.343438, No Obj: 0.004023, Avg Recall: 0.674419, count: 86 -Region Avg IOU: 0.475637, Class: 1.000000, Obj: 0.292913, No Obj: 0.003302, Avg Recall: 0.544643, count: 112 -Region Avg IOU: 0.439770, Class: 1.000000, Obj: 0.279663, No Obj: 0.003624, Avg Recall: 0.427350, count: 117 -Region Avg IOU: 0.522695, Class: 1.000000, Obj: 0.297134, No Obj: 0.003948, Avg Recall: 0.633929, count: 112 -1987: 0.516314, 0.496196 avg, 0.001000 rate, 23.804466 seconds, 508672 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.523507, Class: 1.000000, Obj: 0.329037, No Obj: 0.003807, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.525125, Class: 1.000000, Obj: 0.290809, No Obj: 0.003962, Avg Recall: 0.601562, count: 128 -Region Avg IOU: 0.514144, Class: 1.000000, Obj: 0.354696, No Obj: 0.003425, Avg Recall: 0.584416, count: 77 -Region Avg IOU: 0.496595, Class: 1.000000, Obj: 0.321758, No Obj: 0.003841, Avg Recall: 0.557522, count: 113 -Region Avg IOU: 0.581839, Class: 1.000000, Obj: 0.312427, No Obj: 0.003600, Avg Recall: 0.695122, count: 82 -Region Avg IOU: 0.467412, Class: 1.000000, Obj: 0.299148, No Obj: 0.003412, Avg Recall: 0.553398, count: 103 -Region Avg IOU: 0.526188, Class: 1.000000, Obj: 0.284371, No Obj: 0.003650, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.569464, Class: 1.000000, Obj: 0.299062, No Obj: 0.003878, Avg Recall: 0.649485, count: 97 -1988: 0.447810, 0.491358 avg, 0.001000 rate, 24.770615 seconds, 508928 images -Loaded: 0.217872 seconds -Region Avg IOU: 0.512312, Class: 1.000000, Obj: 0.306374, No Obj: 0.003665, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.539021, Class: 1.000000, Obj: 0.306593, No Obj: 0.004148, Avg Recall: 0.632258, count: 155 -Region Avg IOU: 0.533610, Class: 1.000000, Obj: 0.295951, No Obj: 0.004042, Avg Recall: 0.629921, count: 127 -Region Avg IOU: 0.521327, Class: 1.000000, Obj: 0.311886, No Obj: 0.003434, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.530821, Class: 1.000000, Obj: 0.279843, No Obj: 0.003922, Avg Recall: 0.631579, count: 76 -Region Avg IOU: 0.510898, Class: 1.000000, Obj: 0.244203, No Obj: 0.003161, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.537636, Class: 1.000000, Obj: 0.363262, No Obj: 0.003712, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.511368, Class: 1.000000, Obj: 0.309859, No Obj: 0.003983, Avg Recall: 0.623656, count: 93 -1989: 0.509689, 0.493191 avg, 0.001000 rate, 21.928352 seconds, 509184 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.565498, Class: 1.000000, Obj: 0.317141, No Obj: 0.003429, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.489492, Class: 1.000000, Obj: 0.279643, No Obj: 0.003430, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.533813, Class: 1.000000, Obj: 0.325375, No Obj: 0.003677, Avg Recall: 0.640625, count: 128 -Region Avg IOU: 0.544397, Class: 1.000000, Obj: 0.303990, No Obj: 0.003880, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.596222, Class: 1.000000, Obj: 0.360768, No Obj: 0.003053, Avg Recall: 0.722222, count: 72 -Region Avg IOU: 0.525632, Class: 1.000000, Obj: 0.316936, No Obj: 0.003886, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.537816, Class: 1.000000, Obj: 0.275206, No Obj: 0.003781, Avg Recall: 0.595420, count: 131 -Region Avg IOU: 0.518518, Class: 1.000000, Obj: 0.273308, No Obj: 0.003466, Avg Recall: 0.579545, count: 88 -1990: 0.451105, 0.488982 avg, 0.001000 rate, 23.278591 seconds, 509440 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000053 seconds -Region Avg IOU: 0.566621, Class: 1.000000, Obj: 0.335308, No Obj: 0.003724, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.564077, Class: 1.000000, Obj: 0.332709, No Obj: 0.003383, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.580176, Class: 1.000000, Obj: 0.326897, No Obj: 0.003198, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.519814, Class: 1.000000, Obj: 0.303302, No Obj: 0.003756, Avg Recall: 0.605769, count: 104 -Region Avg IOU: 0.543808, Class: 1.000000, Obj: 0.349663, No Obj: 0.003309, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.548120, Class: 1.000000, Obj: 0.301318, No Obj: 0.003197, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.606455, Class: 1.000000, Obj: 0.295488, No Obj: 0.003379, Avg Recall: 0.759494, count: 79 -Region Avg IOU: 0.532606, Class: 1.000000, Obj: 0.326173, No Obj: 0.003180, Avg Recall: 0.617284, count: 81 -1991: 0.435815, 0.483666 avg, 0.001000 rate, 24.190620 seconds, 509696 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.543946, Class: 1.000000, Obj: 0.300860, No Obj: 0.003607, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.515797, Class: 1.000000, Obj: 0.283657, No Obj: 0.003406, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.547259, Class: 1.000000, Obj: 0.288640, No Obj: 0.003212, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.561973, Class: 1.000000, Obj: 0.340369, No Obj: 0.003481, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.547062, Class: 1.000000, Obj: 0.300644, No Obj: 0.003310, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.468411, Class: 1.000000, Obj: 0.292978, No Obj: 0.003707, Avg Recall: 0.518248, count: 137 -Region Avg IOU: 0.557495, Class: 1.000000, Obj: 0.280316, No Obj: 0.003606, Avg Recall: 0.684783, count: 92 -Region Avg IOU: 0.539190, Class: 1.000000, Obj: 0.336754, No Obj: 0.003291, Avg Recall: 0.581633, count: 98 -1992: 0.449946, 0.480294 avg, 0.001000 rate, 23.635056 seconds, 509952 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.555820, Class: 1.000000, Obj: 0.341297, No Obj: 0.003415, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.555625, Class: 1.000000, Obj: 0.347402, No Obj: 0.003212, Avg Recall: 0.630435, count: 92 -Region Avg IOU: 0.519646, Class: 1.000000, Obj: 0.277287, No Obj: 0.003831, Avg Recall: 0.557252, count: 131 -Region Avg IOU: 0.546084, Class: 1.000000, Obj: 0.349590, No Obj: 0.003385, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.517331, Class: 1.000000, Obj: 0.292202, No Obj: 0.003081, Avg Recall: 0.594828, count: 116 -Region Avg IOU: 0.573814, Class: 1.000000, Obj: 0.316478, No Obj: 0.003471, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.541310, Class: 1.000000, Obj: 0.291601, No Obj: 0.003652, Avg Recall: 0.664000, count: 125 -Region Avg IOU: 0.607722, Class: 1.000000, Obj: 0.328575, No Obj: 0.003552, Avg Recall: 0.696970, count: 99 -1993: 0.466059, 0.478870 avg, 0.001000 rate, 25.457134 seconds, 510208 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.545040, Class: 1.000000, Obj: 0.319657, No Obj: 0.003404, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.545329, Class: 1.000000, Obj: 0.284226, No Obj: 0.003406, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.551727, Class: 1.000000, Obj: 0.354306, No Obj: 0.003727, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.500463, Class: 1.000000, Obj: 0.295338, No Obj: 0.003364, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.575656, Class: 1.000000, Obj: 0.350314, No Obj: 0.003399, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.567429, Class: 1.000000, Obj: 0.344736, No Obj: 0.003412, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.540369, Class: 1.000000, Obj: 0.297598, No Obj: 0.003378, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.508151, Class: 1.000000, Obj: 0.287539, No Obj: 0.003408, Avg Recall: 0.583942, count: 137 -1994: 0.527387, 0.483722 avg, 0.001000 rate, 24.269075 seconds, 510464 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.545034, Class: 1.000000, Obj: 0.319750, No Obj: 0.003379, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.513718, Class: 1.000000, Obj: 0.292521, No Obj: 0.003116, Avg Recall: 0.544643, count: 112 -Region Avg IOU: 0.533700, Class: 1.000000, Obj: 0.350475, No Obj: 0.003160, Avg Recall: 0.604651, count: 86 -Region Avg IOU: 0.543992, Class: 1.000000, Obj: 0.346660, No Obj: 0.003481, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.507177, Class: 1.000000, Obj: 0.305282, No Obj: 0.003519, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.521917, Class: 1.000000, Obj: 0.290777, No Obj: 0.003458, Avg Recall: 0.604839, count: 124 -Region Avg IOU: 0.528519, Class: 1.000000, Obj: 0.342407, No Obj: 0.003788, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.560512, Class: 1.000000, Obj: 0.334326, No Obj: 0.003601, Avg Recall: 0.684211, count: 95 -1995: 0.443906, 0.479740 avg, 0.001000 rate, 24.548288 seconds, 510720 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.592333, Class: 1.000000, Obj: 0.354228, No Obj: 0.004028, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.522189, Class: 1.000000, Obj: 0.309819, No Obj: 0.003958, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.530941, Class: 1.000000, Obj: 0.340192, No Obj: 0.004097, Avg Recall: 0.596639, count: 119 -Region Avg IOU: 0.555840, Class: 1.000000, Obj: 0.348282, No Obj: 0.003489, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.582660, Class: 1.000000, Obj: 0.330935, No Obj: 0.003889, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.566333, Class: 1.000000, Obj: 0.356716, No Obj: 0.003786, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.524862, Class: 1.000000, Obj: 0.294634, No Obj: 0.003958, Avg Recall: 0.537815, count: 119 -Region Avg IOU: 0.596865, Class: 1.000000, Obj: 0.383091, No Obj: 0.003968, Avg Recall: 0.679245, count: 106 -1996: 0.453180, 0.477084 avg, 0.001000 rate, 23.675615 seconds, 510976 images -Loaded: 0.000114 seconds -Region Avg IOU: 0.560676, Class: 1.000000, Obj: 0.319614, No Obj: 0.003578, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.581864, Class: 1.000000, Obj: 0.341534, No Obj: 0.003821, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.504616, Class: 1.000000, Obj: 0.365734, No Obj: 0.003621, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.527615, Class: 1.000000, Obj: 0.321863, No Obj: 0.003438, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.531299, Class: 1.000000, Obj: 0.310033, No Obj: 0.003468, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.466455, Class: 1.000000, Obj: 0.341888, No Obj: 0.003408, Avg Recall: 0.500000, count: 116 -Region Avg IOU: 0.558895, Class: 1.000000, Obj: 0.369062, No Obj: 0.003213, Avg Recall: 0.630137, count: 73 -Region Avg IOU: 0.529151, Class: 1.000000, Obj: 0.356211, No Obj: 0.003167, Avg Recall: 0.666667, count: 78 -1997: 0.471943, 0.476570 avg, 0.001000 rate, 22.687872 seconds, 511232 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.553555, Class: 1.000000, Obj: 0.319909, No Obj: 0.003655, Avg Recall: 0.620155, count: 129 -Region Avg IOU: 0.534732, Class: 1.000000, Obj: 0.357506, No Obj: 0.003609, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.600703, Class: 1.000000, Obj: 0.344132, No Obj: 0.003395, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.554458, Class: 1.000000, Obj: 0.349744, No Obj: 0.003381, Avg Recall: 0.607143, count: 84 -Region Avg IOU: 0.570147, Class: 1.000000, Obj: 0.343644, No Obj: 0.004335, Avg Recall: 0.671756, count: 131 -Region Avg IOU: 0.551696, Class: 1.000000, Obj: 0.332287, No Obj: 0.003465, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.512642, Class: 1.000000, Obj: 0.295114, No Obj: 0.003550, Avg Recall: 0.552000, count: 125 -Region Avg IOU: 0.576003, Class: 1.000000, Obj: 0.358326, No Obj: 0.003677, Avg Recall: 0.641026, count: 117 -1998: 0.464794, 0.475393 avg, 0.001000 rate, 24.468458 seconds, 511488 images -Loaded: 0.000119 seconds -Region Avg IOU: 0.513785, Class: 1.000000, Obj: 0.315010, No Obj: 0.003497, Avg Recall: 0.550847, count: 118 -Region Avg IOU: 0.554405, Class: 1.000000, Obj: 0.315799, No Obj: 0.003956, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.565818, Class: 1.000000, Obj: 0.315961, No Obj: 0.003940, Avg Recall: 0.669291, count: 127 -Region Avg IOU: 0.548703, Class: 1.000000, Obj: 0.323952, No Obj: 0.003479, Avg Recall: 0.595745, count: 94 -Region Avg IOU: 0.545263, Class: 1.000000, Obj: 0.328389, No Obj: 0.003777, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.561160, Class: 1.000000, Obj: 0.333207, No Obj: 0.003554, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.506464, Class: 1.000000, Obj: 0.355325, No Obj: 0.003350, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.559810, Class: 1.000000, Obj: 0.383865, No Obj: 0.003445, Avg Recall: 0.626374, count: 91 -1999: 0.440341, 0.471887 avg, 0.001000 rate, 22.792784 seconds, 511744 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.578748, Class: 1.000000, Obj: 0.378234, No Obj: 0.003881, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.597662, Class: 1.000000, Obj: 0.320733, No Obj: 0.003596, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.582340, Class: 1.000000, Obj: 0.322097, No Obj: 0.003540, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.538978, Class: 1.000000, Obj: 0.343153, No Obj: 0.003270, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.573759, Class: 1.000000, Obj: 0.325940, No Obj: 0.003342, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.569468, Class: 1.000000, Obj: 0.336410, No Obj: 0.004023, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.560227, Class: 1.000000, Obj: 0.297355, No Obj: 0.003823, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.615130, Class: 1.000000, Obj: 0.352619, No Obj: 0.003709, Avg Recall: 0.742268, count: 97 -2000: 0.432501, 0.467949 avg, 0.001000 rate, 23.707657 seconds, 512000 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000061 seconds -Region Avg IOU: 0.580430, Class: 1.000000, Obj: 0.348208, No Obj: 0.002920, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.556323, Class: 1.000000, Obj: 0.355536, No Obj: 0.002653, Avg Recall: 0.678161, count: 87 -Region Avg IOU: 0.573332, Class: 1.000000, Obj: 0.369112, No Obj: 0.003029, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.579479, Class: 1.000000, Obj: 0.340583, No Obj: 0.002960, Avg Recall: 0.682927, count: 82 -Region Avg IOU: 0.514179, Class: 1.000000, Obj: 0.317986, No Obj: 0.003169, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.558813, Class: 1.000000, Obj: 0.337936, No Obj: 0.003185, Avg Recall: 0.643478, count: 115 -Region Avg IOU: 0.629224, Class: 1.000000, Obj: 0.317798, No Obj: 0.003327, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.600301, Class: 1.000000, Obj: 0.340144, No Obj: 0.003323, Avg Recall: 0.758929, count: 112 -2001: 0.415661, 0.462720 avg, 0.001000 rate, 26.298208 seconds, 512256 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.591741, Class: 1.000000, Obj: 0.337013, No Obj: 0.002765, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.566909, Class: 1.000000, Obj: 0.322057, No Obj: 0.003007, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.597995, Class: 1.000000, Obj: 0.369067, No Obj: 0.002777, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.640111, Class: 1.000000, Obj: 0.318362, No Obj: 0.003303, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.586674, Class: 1.000000, Obj: 0.292822, No Obj: 0.003282, Avg Recall: 0.719298, count: 114 -Region Avg IOU: 0.554826, Class: 1.000000, Obj: 0.285226, No Obj: 0.002929, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.573381, Class: 1.000000, Obj: 0.333334, No Obj: 0.002836, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.558600, Class: 1.000000, Obj: 0.352531, No Obj: 0.003078, Avg Recall: 0.689320, count: 103 -2002: 0.409705, 0.457419 avg, 0.001000 rate, 26.875803 seconds, 512512 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.584985, Class: 1.000000, Obj: 0.306733, No Obj: 0.003323, Avg Recall: 0.722222, count: 126 -Region Avg IOU: 0.617090, Class: 1.000000, Obj: 0.349077, No Obj: 0.002878, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.554356, Class: 1.000000, Obj: 0.301494, No Obj: 0.002839, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.536298, Class: 1.000000, Obj: 0.283992, No Obj: 0.002921, Avg Recall: 0.594406, count: 143 -Region Avg IOU: 0.553098, Class: 1.000000, Obj: 0.273903, No Obj: 0.002722, Avg Recall: 0.655462, count: 119 -Region Avg IOU: 0.590159, Class: 1.000000, Obj: 0.346787, No Obj: 0.002791, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.618355, Class: 1.000000, Obj: 0.287599, No Obj: 0.003224, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.607573, Class: 1.000000, Obj: 0.347543, No Obj: 0.002975, Avg Recall: 0.726316, count: 95 -2003: 0.455870, 0.457264 avg, 0.001000 rate, 27.470079 seconds, 512768 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.601106, Class: 1.000000, Obj: 0.309300, No Obj: 0.002849, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.608720, Class: 1.000000, Obj: 0.262759, No Obj: 0.003224, Avg Recall: 0.750000, count: 132 -Region Avg IOU: 0.548664, Class: 1.000000, Obj: 0.283451, No Obj: 0.002740, Avg Recall: 0.622047, count: 127 -Region Avg IOU: 0.608571, Class: 1.000000, Obj: 0.291984, No Obj: 0.003154, Avg Recall: 0.732283, count: 127 -Region Avg IOU: 0.613334, Class: 1.000000, Obj: 0.253278, No Obj: 0.002878, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.619549, Class: 1.000000, Obj: 0.325569, No Obj: 0.003082, Avg Recall: 0.796610, count: 118 -Region Avg IOU: 0.609130, Class: 1.000000, Obj: 0.323565, No Obj: 0.002809, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.528937, Class: 1.000000, Obj: 0.250356, No Obj: 0.002708, Avg Recall: 0.626866, count: 134 -2004: 0.511740, 0.462711 avg, 0.001000 rate, 26.318884 seconds, 513024 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.593557, Class: 1.000000, Obj: 0.279267, No Obj: 0.003272, Avg Recall: 0.732283, count: 127 -Region Avg IOU: 0.572439, Class: 1.000000, Obj: 0.254758, No Obj: 0.002574, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.600180, Class: 1.000000, Obj: 0.274180, No Obj: 0.003043, Avg Recall: 0.698276, count: 116 -Region Avg IOU: 0.579955, Class: 1.000000, Obj: 0.264708, No Obj: 0.002411, Avg Recall: 0.720000, count: 75 -Region Avg IOU: 0.608338, Class: 1.000000, Obj: 0.305242, No Obj: 0.002478, Avg Recall: 0.727273, count: 88 -Region Avg IOU: 0.622019, Class: 1.000000, Obj: 0.333628, No Obj: 0.002717, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.580681, Class: 1.000000, Obj: 0.281976, No Obj: 0.003096, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.592646, Class: 1.000000, Obj: 0.296766, No Obj: 0.003097, Avg Recall: 0.702899, count: 138 -2005: 0.476304, 0.464071 avg, 0.001000 rate, 25.454290 seconds, 513280 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.627482, Class: 1.000000, Obj: 0.260739, No Obj: 0.003182, Avg Recall: 0.794643, count: 112 -Region Avg IOU: 0.614066, Class: 1.000000, Obj: 0.357192, No Obj: 0.002708, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.568769, Class: 1.000000, Obj: 0.271600, No Obj: 0.002867, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.599252, Class: 1.000000, Obj: 0.269098, No Obj: 0.002612, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.542360, Class: 1.000000, Obj: 0.315945, No Obj: 0.002655, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.594805, Class: 1.000000, Obj: 0.275096, No Obj: 0.002732, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.618898, Class: 1.000000, Obj: 0.341846, No Obj: 0.002693, Avg Recall: 0.800000, count: 95 -Region Avg IOU: 0.640860, Class: 1.000000, Obj: 0.313758, No Obj: 0.002919, Avg Recall: 0.809524, count: 105 -2006: 0.434745, 0.461138 avg, 0.001000 rate, 25.573164 seconds, 513536 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.580915, Class: 1.000000, Obj: 0.294091, No Obj: 0.002864, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.640104, Class: 1.000000, Obj: 0.355371, No Obj: 0.003191, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.600253, Class: 1.000000, Obj: 0.329016, No Obj: 0.002835, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.589541, Class: 1.000000, Obj: 0.353255, No Obj: 0.002916, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.589528, Class: 1.000000, Obj: 0.301872, No Obj: 0.003016, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.548703, Class: 1.000000, Obj: 0.286258, No Obj: 0.002891, Avg Recall: 0.682540, count: 126 -Region Avg IOU: 0.601635, Class: 1.000000, Obj: 0.311626, No Obj: 0.003285, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.594448, Class: 1.000000, Obj: 0.282113, No Obj: 0.002570, Avg Recall: 0.711111, count: 90 -2007: 0.467751, 0.461799 avg, 0.001000 rate, 26.313602 seconds, 513792 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.606049, Class: 1.000000, Obj: 0.309255, No Obj: 0.003777, Avg Recall: 0.744681, count: 141 -Region Avg IOU: 0.635734, Class: 1.000000, Obj: 0.337246, No Obj: 0.003077, Avg Recall: 0.824742, count: 97 -Region Avg IOU: 0.578273, Class: 1.000000, Obj: 0.319497, No Obj: 0.003479, Avg Recall: 0.686956, count: 115 -Region Avg IOU: 0.582122, Class: 1.000000, Obj: 0.319103, No Obj: 0.002824, Avg Recall: 0.678161, count: 87 -Region Avg IOU: 0.581404, Class: 1.000000, Obj: 0.361345, No Obj: 0.003085, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.593743, Class: 1.000000, Obj: 0.333999, No Obj: 0.002902, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.555811, Class: 1.000000, Obj: 0.268897, No Obj: 0.002696, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.610222, Class: 1.000000, Obj: 0.388568, No Obj: 0.002926, Avg Recall: 0.780000, count: 100 -2008: 0.463536, 0.461973 avg, 0.001000 rate, 27.270548 seconds, 514048 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.588043, Class: 1.000000, Obj: 0.301186, No Obj: 0.003026, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.558777, Class: 1.000000, Obj: 0.320313, No Obj: 0.002768, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.582243, Class: 1.000000, Obj: 0.284805, No Obj: 0.003254, Avg Recall: 0.688073, count: 109 -Region Avg IOU: 0.581417, Class: 1.000000, Obj: 0.371845, No Obj: 0.003332, Avg Recall: 0.704918, count: 122 -Region Avg IOU: 0.637595, Class: 1.000000, Obj: 0.325804, No Obj: 0.003460, Avg Recall: 0.790323, count: 124 -Region Avg IOU: 0.544012, Class: 1.000000, Obj: 0.305787, No Obj: 0.002508, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.590518, Class: 1.000000, Obj: 0.357894, No Obj: 0.003186, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.606827, Class: 1.000000, Obj: 0.302860, No Obj: 0.003495, Avg Recall: 0.741071, count: 112 -2009: 0.486507, 0.464426 avg, 0.001000 rate, 26.176908 seconds, 514304 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.615827, Class: 1.000000, Obj: 0.318855, No Obj: 0.003660, Avg Recall: 0.746154, count: 130 -Region Avg IOU: 0.593584, Class: 1.000000, Obj: 0.366545, No Obj: 0.002740, Avg Recall: 0.703704, count: 81 -Region Avg IOU: 0.566268, Class: 1.000000, Obj: 0.342500, No Obj: 0.003770, Avg Recall: 0.659091, count: 132 -Region Avg IOU: 0.548139, Class: 1.000000, Obj: 0.335176, No Obj: 0.003032, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.606447, Class: 1.000000, Obj: 0.338160, No Obj: 0.003460, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.590214, Class: 1.000000, Obj: 0.392818, No Obj: 0.003176, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.582648, Class: 1.000000, Obj: 0.354055, No Obj: 0.003481, Avg Recall: 0.666667, count: 132 -Region Avg IOU: 0.599474, Class: 1.000000, Obj: 0.367304, No Obj: 0.003525, Avg Recall: 0.687500, count: 128 -2010: 0.500162, 0.468000 avg, 0.001000 rate, 25.638956 seconds, 514560 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.045542 seconds -Region Avg IOU: 0.538775, Class: 1.000000, Obj: 0.304207, No Obj: 0.004170, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.516424, Class: 1.000000, Obj: 0.306453, No Obj: 0.004630, Avg Recall: 0.552845, count: 123 -Region Avg IOU: 0.464170, Class: 1.000000, Obj: 0.276443, No Obj: 0.003349, Avg Recall: 0.484211, count: 95 -Region Avg IOU: 0.520981, Class: 1.000000, Obj: 0.308241, No Obj: 0.003638, Avg Recall: 0.629214, count: 89 -Region Avg IOU: 0.514755, Class: 1.000000, Obj: 0.260820, No Obj: 0.004541, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.502422, Class: 1.000000, Obj: 0.328359, No Obj: 0.003498, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.501507, Class: 1.000000, Obj: 0.277987, No Obj: 0.003478, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.459533, Class: 1.000000, Obj: 0.245713, No Obj: 0.003590, Avg Recall: 0.463636, count: 110 -2011: 0.469008, 0.468101 avg, 0.001000 rate, 20.143436 seconds, 514816 images -Loaded: 1.444972 seconds -Region Avg IOU: 0.466594, Class: 1.000000, Obj: 0.291830, No Obj: 0.003533, Avg Recall: 0.454545, count: 99 -Region Avg IOU: 0.502106, Class: 1.000000, Obj: 0.287299, No Obj: 0.003733, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.518054, Class: 1.000000, Obj: 0.270865, No Obj: 0.004371, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.463095, Class: 1.000000, Obj: 0.288653, No Obj: 0.004158, Avg Recall: 0.463415, count: 123 -Region Avg IOU: 0.527393, Class: 1.000000, Obj: 0.302159, No Obj: 0.004268, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.487844, Class: 1.000000, Obj: 0.252756, No Obj: 0.003567, Avg Recall: 0.543478, count: 92 -Region Avg IOU: 0.483712, Class: 1.000000, Obj: 0.301558, No Obj: 0.003658, Avg Recall: 0.547368, count: 95 -Region Avg IOU: 0.508656, Class: 1.000000, Obj: 0.273004, No Obj: 0.004229, Avg Recall: 0.589286, count: 112 -2012: 0.509476, 0.472238 avg, 0.001000 rate, 19.989187 seconds, 515072 images -Loaded: 0.652829 seconds -Region Avg IOU: 0.497961, Class: 1.000000, Obj: 0.318329, No Obj: 0.003872, Avg Recall: 0.540816, count: 98 -Region Avg IOU: 0.478577, Class: 1.000000, Obj: 0.306559, No Obj: 0.004110, Avg Recall: 0.546154, count: 130 -Region Avg IOU: 0.501116, Class: 1.000000, Obj: 0.303637, No Obj: 0.003469, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.471785, Class: 1.000000, Obj: 0.310236, No Obj: 0.003712, Avg Recall: 0.495146, count: 103 -Region Avg IOU: 0.481257, Class: 1.000000, Obj: 0.314742, No Obj: 0.004357, Avg Recall: 0.535088, count: 114 -Region Avg IOU: 0.469489, Class: 1.000000, Obj: 0.278044, No Obj: 0.003697, Avg Recall: 0.504505, count: 111 -Region Avg IOU: 0.546350, Class: 1.000000, Obj: 0.335357, No Obj: 0.004118, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.536123, Class: 1.000000, Obj: 0.321204, No Obj: 0.004005, Avg Recall: 0.619048, count: 84 -2013: 0.458212, 0.470836 avg, 0.001000 rate, 21.493956 seconds, 515328 images -Loaded: 1.074155 seconds -Region Avg IOU: 0.498431, Class: 1.000000, Obj: 0.296002, No Obj: 0.004148, Avg Recall: 0.584071, count: 113 -Region Avg IOU: 0.433412, Class: 1.000000, Obj: 0.288742, No Obj: 0.003622, Avg Recall: 0.438202, count: 89 -Region Avg IOU: 0.489666, Class: 1.000000, Obj: 0.358811, No Obj: 0.004184, Avg Recall: 0.520000, count: 100 -Region Avg IOU: 0.475275, Class: 1.000000, Obj: 0.280401, No Obj: 0.004942, Avg Recall: 0.544304, count: 158 -Region Avg IOU: 0.502093, Class: 1.000000, Obj: 0.329298, No Obj: 0.004171, Avg Recall: 0.544715, count: 123 -Region Avg IOU: 0.427424, Class: 1.000000, Obj: 0.249521, No Obj: 0.003768, Avg Recall: 0.437500, count: 112 -Region Avg IOU: 0.476085, Class: 1.000000, Obj: 0.327654, No Obj: 0.004403, Avg Recall: 0.483051, count: 118 -Region Avg IOU: 0.464754, Class: 1.000000, Obj: 0.295989, No Obj: 0.004145, Avg Recall: 0.527273, count: 110 -2014: 0.489969, 0.472749 avg, 0.001000 rate, 21.804111 seconds, 515584 images -Loaded: 0.540696 seconds -Region Avg IOU: 0.458536, Class: 1.000000, Obj: 0.356645, No Obj: 0.004294, Avg Recall: 0.472000, count: 125 -Region Avg IOU: 0.516987, Class: 1.000000, Obj: 0.277745, No Obj: 0.005017, Avg Recall: 0.575758, count: 132 -Region Avg IOU: 0.538166, Class: 1.000000, Obj: 0.305981, No Obj: 0.004082, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.517180, Class: 1.000000, Obj: 0.283988, No Obj: 0.004292, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.504788, Class: 1.000000, Obj: 0.297567, No Obj: 0.004200, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.486919, Class: 1.000000, Obj: 0.304392, No Obj: 0.004094, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.462137, Class: 1.000000, Obj: 0.295099, No Obj: 0.004893, Avg Recall: 0.518797, count: 133 -Region Avg IOU: 0.504293, Class: 1.000000, Obj: 0.265394, No Obj: 0.004045, Avg Recall: 0.591304, count: 115 -2015: 0.461755, 0.471650 avg, 0.001000 rate, 20.083599 seconds, 515840 images -Loaded: 2.674620 seconds -Region Avg IOU: 0.493269, Class: 1.000000, Obj: 0.321948, No Obj: 0.004202, Avg Recall: 0.520325, count: 123 -Region Avg IOU: 0.474102, Class: 1.000000, Obj: 0.329387, No Obj: 0.004214, Avg Recall: 0.530435, count: 115 -Region Avg IOU: 0.477431, Class: 1.000000, Obj: 0.312703, No Obj: 0.004537, Avg Recall: 0.503817, count: 131 -Region Avg IOU: 0.459666, Class: 1.000000, Obj: 0.315184, No Obj: 0.003995, Avg Recall: 0.505747, count: 87 -Region Avg IOU: 0.484930, Class: 1.000000, Obj: 0.334725, No Obj: 0.003851, Avg Recall: 0.500000, count: 80 -Region Avg IOU: 0.459443, Class: 1.000000, Obj: 0.272355, No Obj: 0.004219, Avg Recall: 0.485149, count: 101 -Region Avg IOU: 0.553134, Class: 1.000000, Obj: 0.329481, No Obj: 0.004259, Avg Recall: 0.650000, count: 80 -Region Avg IOU: 0.485428, Class: 1.000000, Obj: 0.318560, No Obj: 0.004511, Avg Recall: 0.582677, count: 127 -2016: 0.490893, 0.473574 avg, 0.001000 rate, 21.586975 seconds, 516096 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.444727, Class: 1.000000, Obj: 0.261813, No Obj: 0.004007, Avg Recall: 0.509434, count: 106 -Region Avg IOU: 0.443989, Class: 1.000000, Obj: 0.279775, No Obj: 0.003814, Avg Recall: 0.435644, count: 101 -Region Avg IOU: 0.450384, Class: 1.000000, Obj: 0.277023, No Obj: 0.004545, Avg Recall: 0.508475, count: 118 -Region Avg IOU: 0.544753, Class: 1.000000, Obj: 0.317473, No Obj: 0.003836, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.505132, Class: 1.000000, Obj: 0.339854, No Obj: 0.003831, Avg Recall: 0.583333, count: 96 -Region Avg IOU: 0.455655, Class: 1.000000, Obj: 0.288831, No Obj: 0.004348, Avg Recall: 0.449275, count: 138 -Region Avg IOU: 0.468183, Class: 1.000000, Obj: 0.282391, No Obj: 0.004571, Avg Recall: 0.545455, count: 132 -Region Avg IOU: 0.463961, Class: 1.000000, Obj: 0.282045, No Obj: 0.004529, Avg Recall: 0.484127, count: 126 -2017: 0.518808, 0.478097 avg, 0.001000 rate, 20.669783 seconds, 516352 images -Loaded: 1.142256 seconds -Region Avg IOU: 0.470739, Class: 1.000000, Obj: 0.300512, No Obj: 0.004382, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.495974, Class: 1.000000, Obj: 0.265889, No Obj: 0.004133, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.508799, Class: 1.000000, Obj: 0.313202, No Obj: 0.004548, Avg Recall: 0.601562, count: 128 -Region Avg IOU: 0.514134, Class: 1.000000, Obj: 0.319101, No Obj: 0.004921, Avg Recall: 0.574468, count: 141 -Region Avg IOU: 0.495519, Class: 1.000000, Obj: 0.271345, No Obj: 0.004631, Avg Recall: 0.559702, count: 134 -Region Avg IOU: 0.513152, Class: 1.000000, Obj: 0.304218, No Obj: 0.004159, Avg Recall: 0.593985, count: 133 -Region Avg IOU: 0.506018, Class: 1.000000, Obj: 0.302164, No Obj: 0.004517, Avg Recall: 0.566929, count: 127 -Region Avg IOU: 0.510316, Class: 1.000000, Obj: 0.292272, No Obj: 0.004135, Avg Recall: 0.564516, count: 124 -2018: 0.598439, 0.490131 avg, 0.001000 rate, 22.663544 seconds, 516608 images -Loaded: 0.173746 seconds -Region Avg IOU: 0.506223, Class: 1.000000, Obj: 0.279990, No Obj: 0.004235, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.455876, Class: 1.000000, Obj: 0.272206, No Obj: 0.004351, Avg Recall: 0.500000, count: 142 -Region Avg IOU: 0.499822, Class: 1.000000, Obj: 0.306023, No Obj: 0.004556, Avg Recall: 0.485507, count: 138 -Region Avg IOU: 0.466416, Class: 1.000000, Obj: 0.267952, No Obj: 0.004164, Avg Recall: 0.544643, count: 112 -Region Avg IOU: 0.531172, Class: 1.000000, Obj: 0.309543, No Obj: 0.003983, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.438808, Class: 1.000000, Obj: 0.287135, No Obj: 0.003598, Avg Recall: 0.469880, count: 83 -Region Avg IOU: 0.512570, Class: 1.000000, Obj: 0.296566, No Obj: 0.004909, Avg Recall: 0.625899, count: 139 -Region Avg IOU: 0.522798, Class: 1.000000, Obj: 0.334045, No Obj: 0.004291, Avg Recall: 0.595506, count: 89 -2019: 0.497357, 0.490854 avg, 0.001000 rate, 20.189848 seconds, 516864 images -Loaded: 1.083080 seconds -Region Avg IOU: 0.519627, Class: 1.000000, Obj: 0.291236, No Obj: 0.004543, Avg Recall: 0.583333, count: 96 -Region Avg IOU: 0.533526, Class: 1.000000, Obj: 0.345406, No Obj: 0.004174, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.430860, Class: 1.000000, Obj: 0.275769, No Obj: 0.003450, Avg Recall: 0.471154, count: 104 -Region Avg IOU: 0.491526, Class: 1.000000, Obj: 0.316056, No Obj: 0.004399, Avg Recall: 0.510000, count: 100 -Region Avg IOU: 0.508785, Class: 1.000000, Obj: 0.295565, No Obj: 0.003869, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.501021, Class: 1.000000, Obj: 0.292268, No Obj: 0.004875, Avg Recall: 0.578512, count: 121 -Region Avg IOU: 0.479705, Class: 1.000000, Obj: 0.315166, No Obj: 0.004351, Avg Recall: 0.492424, count: 132 -Region Avg IOU: 0.523697, Class: 1.000000, Obj: 0.326498, No Obj: 0.004801, Avg Recall: 0.611111, count: 90 -2020: 0.471816, 0.488950 avg, 0.001000 rate, 20.290859 seconds, 517120 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000070 seconds -Region Avg IOU: 0.565399, Class: 1.000000, Obj: 0.373032, No Obj: 0.002859, Avg Recall: 0.685393, count: 89 -Region Avg IOU: 0.594222, Class: 1.000000, Obj: 0.396193, No Obj: 0.003514, Avg Recall: 0.695312, count: 128 -Region Avg IOU: 0.637437, Class: 1.000000, Obj: 0.369596, No Obj: 0.003244, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.602031, Class: 1.000000, Obj: 0.384298, No Obj: 0.003448, Avg Recall: 0.724409, count: 127 -Region Avg IOU: 0.562335, Class: 1.000000, Obj: 0.368048, No Obj: 0.002937, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.601380, Class: 1.000000, Obj: 0.369215, No Obj: 0.003204, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.609594, Class: 1.000000, Obj: 0.344667, No Obj: 0.003539, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.604789, Class: 1.000000, Obj: 0.349455, No Obj: 0.003623, Avg Recall: 0.800000, count: 100 -2021: 0.506069, 0.490662 avg, 0.001000 rate, 27.677975 seconds, 517376 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.621477, Class: 1.000000, Obj: 0.337335, No Obj: 0.003331, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.585246, Class: 1.000000, Obj: 0.347816, No Obj: 0.003010, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.612989, Class: 1.000000, Obj: 0.370234, No Obj: 0.002861, Avg Recall: 0.743902, count: 82 -Region Avg IOU: 0.589845, Class: 1.000000, Obj: 0.324445, No Obj: 0.002877, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.590898, Class: 1.000000, Obj: 0.326609, No Obj: 0.003440, Avg Recall: 0.688000, count: 125 -Region Avg IOU: 0.575907, Class: 1.000000, Obj: 0.364156, No Obj: 0.002847, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.590264, Class: 1.000000, Obj: 0.358314, No Obj: 0.003122, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.664358, Class: 1.000000, Obj: 0.354618, No Obj: 0.003241, Avg Recall: 0.833333, count: 84 -2022: 0.464335, 0.488029 avg, 0.001000 rate, 28.374559 seconds, 517632 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.610086, Class: 1.000000, Obj: 0.376498, No Obj: 0.002583, Avg Recall: 0.725000, count: 80 -Region Avg IOU: 0.586283, Class: 1.000000, Obj: 0.313543, No Obj: 0.003003, Avg Recall: 0.728000, count: 125 -Region Avg IOU: 0.643141, Class: 1.000000, Obj: 0.273282, No Obj: 0.003314, Avg Recall: 0.823529, count: 136 -Region Avg IOU: 0.608555, Class: 1.000000, Obj: 0.307146, No Obj: 0.003021, Avg Recall: 0.760000, count: 125 -Region Avg IOU: 0.656214, Class: 1.000000, Obj: 0.332809, No Obj: 0.003294, Avg Recall: 0.868852, count: 122 -Region Avg IOU: 0.593871, Class: 1.000000, Obj: 0.304455, No Obj: 0.003252, Avg Recall: 0.711111, count: 135 -Region Avg IOU: 0.615979, Class: 1.000000, Obj: 0.342022, No Obj: 0.002560, Avg Recall: 0.743590, count: 78 -Region Avg IOU: 0.620644, Class: 1.000000, Obj: 0.282856, No Obj: 0.002722, Avg Recall: 0.783784, count: 111 -2023: 0.549042, 0.494131 avg, 0.001000 rate, 28.809156 seconds, 517888 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.628420, Class: 1.000000, Obj: 0.283884, No Obj: 0.002942, Avg Recall: 0.783333, count: 120 -Region Avg IOU: 0.616915, Class: 1.000000, Obj: 0.260102, No Obj: 0.002840, Avg Recall: 0.784615, count: 130 -Region Avg IOU: 0.613954, Class: 1.000000, Obj: 0.323033, No Obj: 0.002461, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.633817, Class: 1.000000, Obj: 0.319125, No Obj: 0.002937, Avg Recall: 0.829268, count: 123 -Region Avg IOU: 0.587023, Class: 1.000000, Obj: 0.264547, No Obj: 0.002646, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.552380, Class: 1.000000, Obj: 0.260206, No Obj: 0.002708, Avg Recall: 0.679688, count: 128 -Region Avg IOU: 0.588266, Class: 1.000000, Obj: 0.280765, No Obj: 0.002734, Avg Recall: 0.688525, count: 122 -Region Avg IOU: 0.605224, Class: 1.000000, Obj: 0.299640, No Obj: 0.003018, Avg Recall: 0.721739, count: 115 -2024: 0.534330, 0.498151 avg, 0.001000 rate, 28.092140 seconds, 518144 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.576698, Class: 1.000000, Obj: 0.280390, No Obj: 0.002481, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.614867, Class: 1.000000, Obj: 0.283185, No Obj: 0.002474, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.613843, Class: 1.000000, Obj: 0.293041, No Obj: 0.002669, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.584236, Class: 1.000000, Obj: 0.255245, No Obj: 0.002457, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.614566, Class: 1.000000, Obj: 0.257267, No Obj: 0.002695, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.648931, Class: 1.000000, Obj: 0.273246, No Obj: 0.002878, Avg Recall: 0.790323, count: 124 -Region Avg IOU: 0.590264, Class: 1.000000, Obj: 0.296012, No Obj: 0.002333, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.613305, Class: 1.000000, Obj: 0.268522, No Obj: 0.002908, Avg Recall: 0.794118, count: 136 -2025: 0.532148, 0.501550 avg, 0.001000 rate, 28.765593 seconds, 518400 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.629408, Class: 1.000000, Obj: 0.272704, No Obj: 0.002622, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.620142, Class: 1.000000, Obj: 0.289292, No Obj: 0.002495, Avg Recall: 0.781250, count: 96 -Region Avg IOU: 0.620807, Class: 1.000000, Obj: 0.286472, No Obj: 0.002699, Avg Recall: 0.813084, count: 107 -Region Avg IOU: 0.628707, Class: 1.000000, Obj: 0.283536, No Obj: 0.002502, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.597232, Class: 1.000000, Obj: 0.313312, No Obj: 0.002913, Avg Recall: 0.715385, count: 130 -Region Avg IOU: 0.635459, Class: 1.000000, Obj: 0.253868, No Obj: 0.002743, Avg Recall: 0.809524, count: 126 -Region Avg IOU: 0.608896, Class: 1.000000, Obj: 0.262980, No Obj: 0.002649, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.603817, Class: 1.000000, Obj: 0.273198, No Obj: 0.002562, Avg Recall: 0.739130, count: 115 -2026: 0.501548, 0.501550 avg, 0.001000 rate, 28.138866 seconds, 518656 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.628922, Class: 1.000000, Obj: 0.319853, No Obj: 0.002631, Avg Recall: 0.804348, count: 92 -Region Avg IOU: 0.644102, Class: 1.000000, Obj: 0.316187, No Obj: 0.002927, Avg Recall: 0.810219, count: 137 -Region Avg IOU: 0.628473, Class: 1.000000, Obj: 0.304862, No Obj: 0.002524, Avg Recall: 0.806818, count: 88 -Region Avg IOU: 0.601326, Class: 1.000000, Obj: 0.320040, No Obj: 0.002580, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.616973, Class: 1.000000, Obj: 0.309153, No Obj: 0.002752, Avg Recall: 0.766129, count: 124 -Region Avg IOU: 0.610973, Class: 1.000000, Obj: 0.284167, No Obj: 0.002299, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.593762, Class: 1.000000, Obj: 0.281774, No Obj: 0.002577, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.658392, Class: 1.000000, Obj: 0.301492, No Obj: 0.002332, Avg Recall: 0.871429, count: 70 -2027: 0.489196, 0.500315 avg, 0.001000 rate, 27.811056 seconds, 518912 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.634021, Class: 1.000000, Obj: 0.318034, No Obj: 0.002762, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.569311, Class: 1.000000, Obj: 0.290956, No Obj: 0.002735, Avg Recall: 0.647541, count: 122 -Region Avg IOU: 0.586080, Class: 1.000000, Obj: 0.234769, No Obj: 0.002713, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.635701, Class: 1.000000, Obj: 0.359489, No Obj: 0.002490, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.619691, Class: 1.000000, Obj: 0.309842, No Obj: 0.002774, Avg Recall: 0.757576, count: 132 -Region Avg IOU: 0.606302, Class: 1.000000, Obj: 0.293039, No Obj: 0.002796, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.610620, Class: 1.000000, Obj: 0.302340, No Obj: 0.002676, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.628415, Class: 1.000000, Obj: 0.302247, No Obj: 0.002516, Avg Recall: 0.782609, count: 92 -2028: 0.498344, 0.500118 avg, 0.001000 rate, 28.777033 seconds, 519168 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.630341, Class: 1.000000, Obj: 0.335964, No Obj: 0.002487, Avg Recall: 0.802632, count: 76 -Region Avg IOU: 0.653171, Class: 1.000000, Obj: 0.319449, No Obj: 0.002651, Avg Recall: 0.800000, count: 95 -Region Avg IOU: 0.635659, Class: 1.000000, Obj: 0.340018, No Obj: 0.002842, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.607872, Class: 1.000000, Obj: 0.321001, No Obj: 0.002805, Avg Recall: 0.685484, count: 124 -Region Avg IOU: 0.602176, Class: 1.000000, Obj: 0.329191, No Obj: 0.002499, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.610553, Class: 1.000000, Obj: 0.336694, No Obj: 0.003011, Avg Recall: 0.762295, count: 122 -Region Avg IOU: 0.561632, Class: 1.000000, Obj: 0.315371, No Obj: 0.002912, Avg Recall: 0.705426, count: 129 -Region Avg IOU: 0.618850, Class: 1.000000, Obj: 0.269052, No Obj: 0.002744, Avg Recall: 0.782258, count: 124 -2029: 0.483813, 0.498487 avg, 0.001000 rate, 28.342524 seconds, 519424 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.658695, Class: 1.000000, Obj: 0.334693, No Obj: 0.003198, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.615755, Class: 1.000000, Obj: 0.352808, No Obj: 0.003040, Avg Recall: 0.766667, count: 120 -Region Avg IOU: 0.589831, Class: 1.000000, Obj: 0.325853, No Obj: 0.002835, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.580202, Class: 1.000000, Obj: 0.324459, No Obj: 0.002954, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.575549, Class: 1.000000, Obj: 0.321643, No Obj: 0.002879, Avg Recall: 0.682540, count: 126 -Region Avg IOU: 0.599284, Class: 1.000000, Obj: 0.294283, No Obj: 0.002548, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.587174, Class: 1.000000, Obj: 0.321347, No Obj: 0.003168, Avg Recall: 0.700787, count: 127 -Region Avg IOU: 0.636575, Class: 1.000000, Obj: 0.342123, No Obj: 0.003306, Avg Recall: 0.810219, count: 137 -2030: 0.517689, 0.500407 avg, 0.001000 rate, 28.714628 seconds, 519680 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 20.030085 seconds -Region Avg IOU: 0.564771, Class: 1.000000, Obj: 0.285074, No Obj: 0.003123, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.625547, Class: 1.000000, Obj: 0.347965, No Obj: 0.003594, Avg Recall: 0.773438, count: 128 -Region Avg IOU: 0.588585, Class: 1.000000, Obj: 0.363767, No Obj: 0.003471, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.605669, Class: 1.000000, Obj: 0.325137, No Obj: 0.003244, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.570912, Class: 1.000000, Obj: 0.341903, No Obj: 0.003490, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.611439, Class: 1.000000, Obj: 0.343371, No Obj: 0.003294, Avg Recall: 0.752000, count: 125 -Region Avg IOU: 0.602176, Class: 1.000000, Obj: 0.423154, No Obj: 0.003017, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.561777, Class: 1.000000, Obj: 0.296434, No Obj: 0.003681, Avg Recall: 0.680982, count: 163 -2031: 0.523067, 0.502673 avg, 0.001000 rate, 26.889292 seconds, 519936 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.607943, Class: 1.000000, Obj: 0.325044, No Obj: 0.003317, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.586550, Class: 1.000000, Obj: 0.320651, No Obj: 0.003686, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.592700, Class: 1.000000, Obj: 0.375868, No Obj: 0.003319, Avg Recall: 0.725275, count: 91 -Region Avg IOU: 0.594281, Class: 1.000000, Obj: 0.335604, No Obj: 0.003449, Avg Recall: 0.716418, count: 134 -Region Avg IOU: 0.615959, Class: 1.000000, Obj: 0.355499, No Obj: 0.003071, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.601430, Class: 1.000000, Obj: 0.325010, No Obj: 0.003457, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.548589, Class: 1.000000, Obj: 0.340952, No Obj: 0.002811, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.587973, Class: 1.000000, Obj: 0.352986, No Obj: 0.003593, Avg Recall: 0.727273, count: 121 -2032: 0.504533, 0.502859 avg, 0.001000 rate, 27.090168 seconds, 520192 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.589709, Class: 1.000000, Obj: 0.323110, No Obj: 0.003208, Avg Recall: 0.706349, count: 126 -Region Avg IOU: 0.626785, Class: 1.000000, Obj: 0.345966, No Obj: 0.003409, Avg Recall: 0.761905, count: 126 -Region Avg IOU: 0.630684, Class: 1.000000, Obj: 0.393977, No Obj: 0.003073, Avg Recall: 0.782051, count: 78 -Region Avg IOU: 0.607834, Class: 1.000000, Obj: 0.340308, No Obj: 0.003243, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.591042, Class: 1.000000, Obj: 0.315505, No Obj: 0.003242, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.591397, Class: 1.000000, Obj: 0.355462, No Obj: 0.003435, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.628987, Class: 1.000000, Obj: 0.377864, No Obj: 0.003271, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.612906, Class: 1.000000, Obj: 0.335666, No Obj: 0.002881, Avg Recall: 0.723404, count: 94 -2033: 0.454318, 0.498005 avg, 0.001000 rate, 26.575060 seconds, 520448 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.580285, Class: 1.000000, Obj: 0.357002, No Obj: 0.002473, Avg Recall: 0.725275, count: 91 -Region Avg IOU: 0.615892, Class: 1.000000, Obj: 0.311789, No Obj: 0.003364, Avg Recall: 0.782609, count: 115 -Region Avg IOU: 0.567346, Class: 1.000000, Obj: 0.308390, No Obj: 0.003017, Avg Recall: 0.650350, count: 143 -Region Avg IOU: 0.585316, Class: 1.000000, Obj: 0.304453, No Obj: 0.003207, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.595959, Class: 1.000000, Obj: 0.330201, No Obj: 0.002902, Avg Recall: 0.683761, count: 117 -Region Avg IOU: 0.567989, Class: 1.000000, Obj: 0.321591, No Obj: 0.002940, Avg Recall: 0.642276, count: 123 -Region Avg IOU: 0.589458, Class: 1.000000, Obj: 0.317651, No Obj: 0.002975, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.601587, Class: 1.000000, Obj: 0.310555, No Obj: 0.002680, Avg Recall: 0.746667, count: 75 -2034: 0.506602, 0.498865 avg, 0.001000 rate, 28.583708 seconds, 520704 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.549707, Class: 1.000000, Obj: 0.312268, No Obj: 0.003122, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.603259, Class: 1.000000, Obj: 0.385132, No Obj: 0.003334, Avg Recall: 0.713178, count: 129 -Region Avg IOU: 0.531705, Class: 1.000000, Obj: 0.340441, No Obj: 0.002991, Avg Recall: 0.612903, count: 124 -Region Avg IOU: 0.598591, Class: 1.000000, Obj: 0.281716, No Obj: 0.003173, Avg Recall: 0.735537, count: 121 -Region Avg IOU: 0.564053, Class: 1.000000, Obj: 0.274725, No Obj: 0.002710, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.600151, Class: 1.000000, Obj: 0.328543, No Obj: 0.002890, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.576159, Class: 1.000000, Obj: 0.291409, No Obj: 0.002709, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.592573, Class: 1.000000, Obj: 0.264476, No Obj: 0.003554, Avg Recall: 0.704545, count: 132 -2035: 0.528917, 0.501870 avg, 0.001000 rate, 27.369476 seconds, 520960 images -Loaded: 0.000140 seconds -Region Avg IOU: 0.571496, Class: 1.000000, Obj: 0.279680, No Obj: 0.002732, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.572402, Class: 1.000000, Obj: 0.299399, No Obj: 0.002720, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.568456, Class: 1.000000, Obj: 0.329574, No Obj: 0.003073, Avg Recall: 0.666667, count: 126 -Region Avg IOU: 0.608087, Class: 1.000000, Obj: 0.338307, No Obj: 0.003055, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.547434, Class: 1.000000, Obj: 0.292887, No Obj: 0.002593, Avg Recall: 0.600000, count: 95 -Region Avg IOU: 0.589540, Class: 1.000000, Obj: 0.287603, No Obj: 0.002941, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.524748, Class: 1.000000, Obj: 0.301090, No Obj: 0.002779, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.627041, Class: 1.000000, Obj: 0.353037, No Obj: 0.002755, Avg Recall: 0.730769, count: 78 -2036: 0.463815, 0.498064 avg, 0.001000 rate, 27.008080 seconds, 521216 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.579356, Class: 1.000000, Obj: 0.332144, No Obj: 0.002698, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.559112, Class: 1.000000, Obj: 0.271593, No Obj: 0.002956, Avg Recall: 0.623077, count: 130 -Region Avg IOU: 0.558744, Class: 1.000000, Obj: 0.317316, No Obj: 0.003012, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.574681, Class: 1.000000, Obj: 0.287465, No Obj: 0.002994, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.605762, Class: 1.000000, Obj: 0.339248, No Obj: 0.002838, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.591013, Class: 1.000000, Obj: 0.327146, No Obj: 0.002780, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.592687, Class: 1.000000, Obj: 0.339871, No Obj: 0.002804, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.601707, Class: 1.000000, Obj: 0.342443, No Obj: 0.002339, Avg Recall: 0.759494, count: 79 -2037: 0.449618, 0.493220 avg, 0.001000 rate, 26.210114 seconds, 521472 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.559238, Class: 1.000000, Obj: 0.341292, No Obj: 0.002528, Avg Recall: 0.670455, count: 88 -Region Avg IOU: 0.605967, Class: 1.000000, Obj: 0.341505, No Obj: 0.003147, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.580081, Class: 1.000000, Obj: 0.296903, No Obj: 0.003416, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.600639, Class: 1.000000, Obj: 0.306191, No Obj: 0.003332, Avg Recall: 0.711268, count: 142 -Region Avg IOU: 0.584923, Class: 1.000000, Obj: 0.334439, No Obj: 0.002686, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.621769, Class: 1.000000, Obj: 0.324092, No Obj: 0.003119, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.607525, Class: 1.000000, Obj: 0.361784, No Obj: 0.002584, Avg Recall: 0.716049, count: 81 -Region Avg IOU: 0.599981, Class: 1.000000, Obj: 0.291962, No Obj: 0.003193, Avg Recall: 0.705357, count: 112 -2038: 0.443003, 0.488198 avg, 0.001000 rate, 26.430504 seconds, 521728 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.617356, Class: 1.000000, Obj: 0.326060, No Obj: 0.002940, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.613697, Class: 1.000000, Obj: 0.335124, No Obj: 0.003175, Avg Recall: 0.753846, count: 130 -Region Avg IOU: 0.602548, Class: 1.000000, Obj: 0.309259, No Obj: 0.003558, Avg Recall: 0.770370, count: 135 -Region Avg IOU: 0.625733, Class: 1.000000, Obj: 0.338992, No Obj: 0.002616, Avg Recall: 0.808219, count: 73 -Region Avg IOU: 0.592225, Class: 1.000000, Obj: 0.326555, No Obj: 0.002695, Avg Recall: 0.693182, count: 88 -Region Avg IOU: 0.570242, Class: 1.000000, Obj: 0.309940, No Obj: 0.003320, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.577056, Class: 1.000000, Obj: 0.332871, No Obj: 0.002831, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.567806, Class: 1.000000, Obj: 0.339293, No Obj: 0.002927, Avg Recall: 0.672269, count: 119 -2039: 0.459301, 0.485308 avg, 0.001000 rate, 27.370235 seconds, 521984 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.589247, Class: 1.000000, Obj: 0.333531, No Obj: 0.003114, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.589022, Class: 1.000000, Obj: 0.331313, No Obj: 0.002870, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.584324, Class: 1.000000, Obj: 0.305247, No Obj: 0.003256, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.590755, Class: 1.000000, Obj: 0.351589, No Obj: 0.002937, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.550596, Class: 1.000000, Obj: 0.282510, No Obj: 0.002873, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.598633, Class: 1.000000, Obj: 0.355981, No Obj: 0.003013, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.569693, Class: 1.000000, Obj: 0.370280, No Obj: 0.002776, Avg Recall: 0.681818, count: 88 -Region Avg IOU: 0.562952, Class: 1.000000, Obj: 0.283281, No Obj: 0.002708, Avg Recall: 0.675214, count: 117 -2040: 0.465568, 0.483334 avg, 0.001000 rate, 26.789108 seconds, 522240 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.939243 seconds -Region Avg IOU: 0.515018, Class: 1.000000, Obj: 0.273988, No Obj: 0.003740, Avg Recall: 0.589744, count: 117 -Region Avg IOU: 0.487097, Class: 1.000000, Obj: 0.249976, No Obj: 0.003578, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.579148, Class: 1.000000, Obj: 0.301117, No Obj: 0.004237, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.562699, Class: 1.000000, Obj: 0.312292, No Obj: 0.003647, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.515462, Class: 1.000000, Obj: 0.273140, No Obj: 0.002930, Avg Recall: 0.593023, count: 86 -Region Avg IOU: 0.492541, Class: 1.000000, Obj: 0.260805, No Obj: 0.003500, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.515458, Class: 1.000000, Obj: 0.294233, No Obj: 0.003792, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.518843, Class: 1.000000, Obj: 0.297381, No Obj: 0.004273, Avg Recall: 0.595420, count: 131 -2041: 0.498065, 0.484807 avg, 0.001000 rate, 22.567270 seconds, 522496 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.543574, Class: 1.000000, Obj: 0.283429, No Obj: 0.003663, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.512093, Class: 1.000000, Obj: 0.299841, No Obj: 0.003539, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.534970, Class: 1.000000, Obj: 0.316790, No Obj: 0.004371, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.510816, Class: 1.000000, Obj: 0.317156, No Obj: 0.003878, Avg Recall: 0.590551, count: 127 -Region Avg IOU: 0.512087, Class: 1.000000, Obj: 0.291922, No Obj: 0.003680, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.555113, Class: 1.000000, Obj: 0.328820, No Obj: 0.003428, Avg Recall: 0.695122, count: 82 -Region Avg IOU: 0.525063, Class: 1.000000, Obj: 0.294520, No Obj: 0.003718, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.534880, Class: 1.000000, Obj: 0.290990, No Obj: 0.004143, Avg Recall: 0.608696, count: 115 -2042: 0.453060, 0.481633 avg, 0.001000 rate, 22.175350 seconds, 522752 images -Loaded: 0.749238 seconds -Region Avg IOU: 0.506101, Class: 1.000000, Obj: 0.343846, No Obj: 0.003391, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.515443, Class: 1.000000, Obj: 0.331593, No Obj: 0.003759, Avg Recall: 0.573034, count: 89 -Region Avg IOU: 0.515408, Class: 1.000000, Obj: 0.299787, No Obj: 0.004162, Avg Recall: 0.571429, count: 112 -Region Avg IOU: 0.512634, Class: 1.000000, Obj: 0.320739, No Obj: 0.003936, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.504652, Class: 1.000000, Obj: 0.305218, No Obj: 0.004014, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.466163, Class: 1.000000, Obj: 0.248995, No Obj: 0.003391, Avg Recall: 0.483516, count: 91 -Region Avg IOU: 0.475003, Class: 1.000000, Obj: 0.306159, No Obj: 0.003741, Avg Recall: 0.508475, count: 118 -Region Avg IOU: 0.502094, Class: 1.000000, Obj: 0.282467, No Obj: 0.003440, Avg Recall: 0.529412, count: 102 -2043: 0.440096, 0.477479 avg, 0.001000 rate, 20.674887 seconds, 523008 images -Loaded: 1.056422 seconds -Region Avg IOU: 0.523426, Class: 1.000000, Obj: 0.341730, No Obj: 0.004300, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.540148, Class: 1.000000, Obj: 0.279911, No Obj: 0.003410, Avg Recall: 0.630435, count: 92 -Region Avg IOU: 0.565455, Class: 1.000000, Obj: 0.320465, No Obj: 0.004885, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.547937, Class: 1.000000, Obj: 0.304896, No Obj: 0.003811, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.538333, Class: 1.000000, Obj: 0.334789, No Obj: 0.003241, Avg Recall: 0.690141, count: 71 -Region Avg IOU: 0.475610, Class: 1.000000, Obj: 0.326028, No Obj: 0.004179, Avg Recall: 0.523438, count: 128 -Region Avg IOU: 0.529883, Class: 1.000000, Obj: 0.327021, No Obj: 0.004086, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.559886, Class: 1.000000, Obj: 0.344729, No Obj: 0.004342, Avg Recall: 0.706897, count: 116 -2044: 0.477973, 0.477528 avg, 0.001000 rate, 20.446009 seconds, 523264 images -Loaded: 1.005145 seconds -Region Avg IOU: 0.549843, Class: 1.000000, Obj: 0.322735, No Obj: 0.004355, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.508924, Class: 1.000000, Obj: 0.305685, No Obj: 0.003718, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.496996, Class: 1.000000, Obj: 0.336712, No Obj: 0.004616, Avg Recall: 0.577236, count: 123 -Region Avg IOU: 0.508061, Class: 1.000000, Obj: 0.311705, No Obj: 0.004609, Avg Recall: 0.603306, count: 121 -Region Avg IOU: 0.533830, Class: 1.000000, Obj: 0.312081, No Obj: 0.004703, Avg Recall: 0.606838, count: 117 -Region Avg IOU: 0.510556, Class: 1.000000, Obj: 0.325448, No Obj: 0.004124, Avg Recall: 0.600000, count: 90 -Region Avg IOU: 0.497676, Class: 1.000000, Obj: 0.333389, No Obj: 0.004534, Avg Recall: 0.566929, count: 127 -Region Avg IOU: 0.521447, Class: 1.000000, Obj: 0.296383, No Obj: 0.004521, Avg Recall: 0.570175, count: 114 -2045: 0.446456, 0.474421 avg, 0.001000 rate, 21.854239 seconds, 523520 images -Loaded: 0.328286 seconds -Region Avg IOU: 0.560125, Class: 1.000000, Obj: 0.357291, No Obj: 0.004060, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.544126, Class: 1.000000, Obj: 0.329454, No Obj: 0.004077, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.514164, Class: 1.000000, Obj: 0.356025, No Obj: 0.004347, Avg Recall: 0.589474, count: 95 -Region Avg IOU: 0.488762, Class: 1.000000, Obj: 0.351369, No Obj: 0.004141, Avg Recall: 0.515464, count: 97 -Region Avg IOU: 0.457990, Class: 1.000000, Obj: 0.339842, No Obj: 0.004270, Avg Recall: 0.495798, count: 119 -Region Avg IOU: 0.499396, Class: 1.000000, Obj: 0.299639, No Obj: 0.004646, Avg Recall: 0.532374, count: 139 -Region Avg IOU: 0.551226, Class: 1.000000, Obj: 0.371296, No Obj: 0.004626, Avg Recall: 0.620690, count: 116 -Region Avg IOU: 0.503306, Class: 1.000000, Obj: 0.323157, No Obj: 0.004736, Avg Recall: 0.601770, count: 113 -2046: 0.449165, 0.471896 avg, 0.001000 rate, 22.021418 seconds, 523776 images -Loaded: 0.344476 seconds -Region Avg IOU: 0.413775, Class: 1.000000, Obj: 0.269597, No Obj: 0.003964, Avg Recall: 0.416667, count: 108 -Region Avg IOU: 0.496780, Class: 1.000000, Obj: 0.342465, No Obj: 0.003988, Avg Recall: 0.535354, count: 99 -Region Avg IOU: 0.501613, Class: 1.000000, Obj: 0.333969, No Obj: 0.003011, Avg Recall: 0.560000, count: 75 -Region Avg IOU: 0.494172, Class: 1.000000, Obj: 0.283645, No Obj: 0.004170, Avg Recall: 0.533333, count: 105 -Region Avg IOU: 0.499071, Class: 1.000000, Obj: 0.358017, No Obj: 0.003795, Avg Recall: 0.558140, count: 86 -Region Avg IOU: 0.479305, Class: 1.000000, Obj: 0.328144, No Obj: 0.004333, Avg Recall: 0.500000, count: 128 -Region Avg IOU: 0.534971, Class: 1.000000, Obj: 0.376598, No Obj: 0.004510, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.514220, Class: 1.000000, Obj: 0.372969, No Obj: 0.004403, Avg Recall: 0.585366, count: 123 -2047: 0.423669, 0.467073 avg, 0.001000 rate, 21.967436 seconds, 524032 images -Loaded: 0.019915 seconds -Region Avg IOU: 0.497254, Class: 1.000000, Obj: 0.342507, No Obj: 0.003772, Avg Recall: 0.530612, count: 98 -Region Avg IOU: 0.453162, Class: 1.000000, Obj: 0.319313, No Obj: 0.003952, Avg Recall: 0.481818, count: 110 -Region Avg IOU: 0.491554, Class: 1.000000, Obj: 0.305909, No Obj: 0.004039, Avg Recall: 0.538462, count: 117 -Region Avg IOU: 0.510417, Class: 1.000000, Obj: 0.317485, No Obj: 0.004123, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.505285, Class: 1.000000, Obj: 0.353040, No Obj: 0.003672, Avg Recall: 0.565657, count: 99 -Region Avg IOU: 0.508487, Class: 1.000000, Obj: 0.326581, No Obj: 0.004355, Avg Recall: 0.512000, count: 125 -Region Avg IOU: 0.533020, Class: 1.000000, Obj: 0.334713, No Obj: 0.004251, Avg Recall: 0.592920, count: 113 -Region Avg IOU: 0.526666, Class: 1.000000, Obj: 0.313109, No Obj: 0.003707, Avg Recall: 0.580952, count: 105 -2048: 0.465383, 0.466904 avg, 0.001000 rate, 23.551960 seconds, 524288 images -Loaded: 0.055779 seconds -Region Avg IOU: 0.475528, Class: 1.000000, Obj: 0.278690, No Obj: 0.003706, Avg Recall: 0.533898, count: 118 -Region Avg IOU: 0.528213, Class: 1.000000, Obj: 0.330547, No Obj: 0.003923, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.482265, Class: 1.000000, Obj: 0.289382, No Obj: 0.004208, Avg Recall: 0.528000, count: 125 -Region Avg IOU: 0.532191, Class: 1.000000, Obj: 0.331546, No Obj: 0.004195, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.475717, Class: 1.000000, Obj: 0.312401, No Obj: 0.004084, Avg Recall: 0.513514, count: 111 -Region Avg IOU: 0.517522, Class: 1.000000, Obj: 0.310986, No Obj: 0.004057, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.522207, Class: 1.000000, Obj: 0.304894, No Obj: 0.004409, Avg Recall: 0.661290, count: 124 -Region Avg IOU: 0.438480, Class: 1.000000, Obj: 0.309634, No Obj: 0.003806, Avg Recall: 0.495798, count: 119 -2049: 0.483380, 0.468552 avg, 0.001000 rate, 22.791430 seconds, 524544 images -Loaded: 0.056815 seconds -Region Avg IOU: 0.528618, Class: 1.000000, Obj: 0.328438, No Obj: 0.003570, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.485546, Class: 1.000000, Obj: 0.249696, No Obj: 0.003810, Avg Recall: 0.535088, count: 114 -Region Avg IOU: 0.528991, Class: 1.000000, Obj: 0.270612, No Obj: 0.003595, Avg Recall: 0.585106, count: 94 -Region Avg IOU: 0.503934, Class: 1.000000, Obj: 0.295539, No Obj: 0.003712, Avg Recall: 0.569106, count: 123 -Region Avg IOU: 0.452605, Class: 1.000000, Obj: 0.291987, No Obj: 0.003694, Avg Recall: 0.475000, count: 120 -Region Avg IOU: 0.523217, Class: 1.000000, Obj: 0.277019, No Obj: 0.003976, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.508972, Class: 1.000000, Obj: 0.322063, No Obj: 0.004282, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.463791, Class: 1.000000, Obj: 0.291794, No Obj: 0.003937, Avg Recall: 0.496552, count: 145 -2050: 0.486114, 0.470308 avg, 0.001000 rate, 21.202578 seconds, 524800 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000077 seconds -Region Avg IOU: 0.575216, Class: 1.000000, Obj: 0.256739, No Obj: 0.002497, Avg Recall: 0.685393, count: 89 -Region Avg IOU: 0.591213, Class: 1.000000, Obj: 0.298631, No Obj: 0.003045, Avg Recall: 0.700000, count: 120 -Region Avg IOU: 0.592619, Class: 1.000000, Obj: 0.315476, No Obj: 0.003104, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.603912, Class: 1.000000, Obj: 0.328427, No Obj: 0.003119, Avg Recall: 0.764228, count: 123 -Region Avg IOU: 0.579419, Class: 1.000000, Obj: 0.333519, No Obj: 0.002791, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.572303, Class: 1.000000, Obj: 0.310651, No Obj: 0.002641, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.582220, Class: 1.000000, Obj: 0.240626, No Obj: 0.002739, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.592115, Class: 1.000000, Obj: 0.343259, No Obj: 0.002917, Avg Recall: 0.676471, count: 102 -2051: 0.493695, 0.472646 avg, 0.001000 rate, 27.848248 seconds, 525056 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.623676, Class: 1.000000, Obj: 0.329829, No Obj: 0.002480, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.585837, Class: 1.000000, Obj: 0.310637, No Obj: 0.002643, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.585986, Class: 1.000000, Obj: 0.325742, No Obj: 0.002252, Avg Recall: 0.714286, count: 70 -Region Avg IOU: 0.590862, Class: 1.000000, Obj: 0.282252, No Obj: 0.002501, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.605082, Class: 1.000000, Obj: 0.298901, No Obj: 0.002675, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.587438, Class: 1.000000, Obj: 0.309614, No Obj: 0.002699, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.600141, Class: 1.000000, Obj: 0.284486, No Obj: 0.003162, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.599401, Class: 1.000000, Obj: 0.314329, No Obj: 0.002504, Avg Recall: 0.722222, count: 90 -2052: 0.482195, 0.473601 avg, 0.001000 rate, 28.158560 seconds, 525312 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.624540, Class: 1.000000, Obj: 0.268283, No Obj: 0.002446, Avg Recall: 0.784810, count: 79 -Region Avg IOU: 0.570066, Class: 1.000000, Obj: 0.337120, No Obj: 0.002423, Avg Recall: 0.670886, count: 79 -Region Avg IOU: 0.612287, Class: 1.000000, Obj: 0.259158, No Obj: 0.003039, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.596624, Class: 1.000000, Obj: 0.308210, No Obj: 0.002746, Avg Recall: 0.643478, count: 115 -Region Avg IOU: 0.576908, Class: 1.000000, Obj: 0.272577, No Obj: 0.003058, Avg Recall: 0.664122, count: 131 -Region Avg IOU: 0.600703, Class: 1.000000, Obj: 0.319580, No Obj: 0.003219, Avg Recall: 0.700000, count: 130 -Region Avg IOU: 0.577990, Class: 1.000000, Obj: 0.284308, No Obj: 0.002483, Avg Recall: 0.692308, count: 78 -Region Avg IOU: 0.575519, Class: 1.000000, Obj: 0.305862, No Obj: 0.002753, Avg Recall: 0.659341, count: 91 -2053: 0.468944, 0.473136 avg, 0.001000 rate, 27.461216 seconds, 525568 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.602088, Class: 1.000000, Obj: 0.268461, No Obj: 0.002645, Avg Recall: 0.684783, count: 92 -Region Avg IOU: 0.621006, Class: 1.000000, Obj: 0.259160, No Obj: 0.002683, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.583989, Class: 1.000000, Obj: 0.285764, No Obj: 0.002605, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.594092, Class: 1.000000, Obj: 0.312516, No Obj: 0.002875, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.585850, Class: 1.000000, Obj: 0.278155, No Obj: 0.002588, Avg Recall: 0.681818, count: 88 -Region Avg IOU: 0.599237, Class: 1.000000, Obj: 0.326386, No Obj: 0.002652, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.583021, Class: 1.000000, Obj: 0.309690, No Obj: 0.002701, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.622028, Class: 1.000000, Obj: 0.278002, No Obj: 0.002582, Avg Recall: 0.767241, count: 116 -2054: 0.446860, 0.470508 avg, 0.001000 rate, 26.223328 seconds, 525824 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.581668, Class: 1.000000, Obj: 0.271163, No Obj: 0.003165, Avg Recall: 0.709877, count: 162 -Region Avg IOU: 0.621719, Class: 1.000000, Obj: 0.351866, No Obj: 0.002602, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.608050, Class: 1.000000, Obj: 0.279984, No Obj: 0.002688, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.640364, Class: 1.000000, Obj: 0.332946, No Obj: 0.002985, Avg Recall: 0.805310, count: 113 -Region Avg IOU: 0.588670, Class: 1.000000, Obj: 0.282859, No Obj: 0.002914, Avg Recall: 0.706767, count: 133 -Region Avg IOU: 0.610499, Class: 1.000000, Obj: 0.281964, No Obj: 0.002835, Avg Recall: 0.762295, count: 122 -Region Avg IOU: 0.553506, Class: 1.000000, Obj: 0.263626, No Obj: 0.002535, Avg Recall: 0.639098, count: 133 -Region Avg IOU: 0.609427, Class: 1.000000, Obj: 0.359089, No Obj: 0.002751, Avg Recall: 0.721154, count: 104 -2055: 0.556565, 0.479114 avg, 0.001000 rate, 29.519598 seconds, 526080 images -Loaded: 0.000122 seconds -Region Avg IOU: 0.594100, Class: 1.000000, Obj: 0.279519, No Obj: 0.003068, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.603135, Class: 1.000000, Obj: 0.361558, No Obj: 0.003212, Avg Recall: 0.725926, count: 135 -Region Avg IOU: 0.563637, Class: 1.000000, Obj: 0.301084, No Obj: 0.002919, Avg Recall: 0.666667, count: 117 -Region Avg IOU: 0.625126, Class: 1.000000, Obj: 0.339072, No Obj: 0.003031, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.608761, Class: 1.000000, Obj: 0.338051, No Obj: 0.003055, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.600146, Class: 1.000000, Obj: 0.310803, No Obj: 0.003304, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.575396, Class: 1.000000, Obj: 0.301249, No Obj: 0.002697, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.575472, Class: 1.000000, Obj: 0.303948, No Obj: 0.002850, Avg Recall: 0.676768, count: 99 -2056: 0.482112, 0.479414 avg, 0.001000 rate, 26.244019 seconds, 526336 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.651482, Class: 1.000000, Obj: 0.342119, No Obj: 0.003192, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.599029, Class: 1.000000, Obj: 0.329584, No Obj: 0.003170, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.607813, Class: 1.000000, Obj: 0.347496, No Obj: 0.003030, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.586149, Class: 1.000000, Obj: 0.280687, No Obj: 0.002899, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.606448, Class: 1.000000, Obj: 0.301750, No Obj: 0.002806, Avg Recall: 0.802632, count: 76 -Region Avg IOU: 0.598161, Class: 1.000000, Obj: 0.290879, No Obj: 0.003135, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.593936, Class: 1.000000, Obj: 0.388737, No Obj: 0.002829, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.632723, Class: 1.000000, Obj: 0.260682, No Obj: 0.003239, Avg Recall: 0.801887, count: 106 -2057: 0.457603, 0.477233 avg, 0.001000 rate, 26.122036 seconds, 526592 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.630272, Class: 1.000000, Obj: 0.350804, No Obj: 0.002789, Avg Recall: 0.788235, count: 85 -Region Avg IOU: 0.612682, Class: 1.000000, Obj: 0.371204, No Obj: 0.002815, Avg Recall: 0.785714, count: 84 -Region Avg IOU: 0.615117, Class: 1.000000, Obj: 0.367775, No Obj: 0.003399, Avg Recall: 0.789474, count: 114 -Region Avg IOU: 0.577088, Class: 1.000000, Obj: 0.331279, No Obj: 0.003655, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.598046, Class: 1.000000, Obj: 0.350557, No Obj: 0.003145, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.603913, Class: 1.000000, Obj: 0.308620, No Obj: 0.003255, Avg Recall: 0.759399, count: 133 -Region Avg IOU: 0.680098, Class: 1.000000, Obj: 0.407012, No Obj: 0.003254, Avg Recall: 0.864583, count: 96 -Region Avg IOU: 0.602449, Class: 1.000000, Obj: 0.299203, No Obj: 0.002694, Avg Recall: 0.725000, count: 80 -2058: 0.491831, 0.478692 avg, 0.001000 rate, 28.348318 seconds, 526848 images -Loaded: 0.000117 seconds -Region Avg IOU: 0.548739, Class: 1.000000, Obj: 0.343454, No Obj: 0.003112, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.600257, Class: 1.000000, Obj: 0.346631, No Obj: 0.002882, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.580591, Class: 1.000000, Obj: 0.338550, No Obj: 0.003116, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.622841, Class: 1.000000, Obj: 0.346935, No Obj: 0.004029, Avg Recall: 0.770270, count: 148 -Region Avg IOU: 0.592307, Class: 1.000000, Obj: 0.318618, No Obj: 0.003207, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.556681, Class: 1.000000, Obj: 0.296430, No Obj: 0.003267, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.589065, Class: 1.000000, Obj: 0.344143, No Obj: 0.003156, Avg Recall: 0.693548, count: 124 -Region Avg IOU: 0.634585, Class: 1.000000, Obj: 0.339633, No Obj: 0.003268, Avg Recall: 0.796460, count: 113 -2059: 0.481696, 0.478993 avg, 0.001000 rate, 26.687029 seconds, 527104 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.633712, Class: 1.000000, Obj: 0.354715, No Obj: 0.003110, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.588737, Class: 1.000000, Obj: 0.327632, No Obj: 0.003186, Avg Recall: 0.697674, count: 129 -Region Avg IOU: 0.612608, Class: 1.000000, Obj: 0.310333, No Obj: 0.002809, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.619827, Class: 1.000000, Obj: 0.323107, No Obj: 0.002914, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.577153, Class: 1.000000, Obj: 0.297803, No Obj: 0.002954, Avg Recall: 0.659341, count: 91 -Region Avg IOU: 0.579584, Class: 1.000000, Obj: 0.304966, No Obj: 0.002823, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.609875, Class: 1.000000, Obj: 0.340634, No Obj: 0.003331, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.579024, Class: 1.000000, Obj: 0.295552, No Obj: 0.003180, Avg Recall: 0.672566, count: 113 -2060: 0.433870, 0.474480 avg, 0.001000 rate, 26.654156 seconds, 527360 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000071 seconds -Region Avg IOU: 0.603962, Class: 1.000000, Obj: 0.299739, No Obj: 0.002848, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.659452, Class: 1.000000, Obj: 0.349294, No Obj: 0.002758, Avg Recall: 0.825243, count: 103 -Region Avg IOU: 0.596392, Class: 1.000000, Obj: 0.332329, No Obj: 0.002723, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.630688, Class: 1.000000, Obj: 0.325027, No Obj: 0.003002, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.618483, Class: 1.000000, Obj: 0.319054, No Obj: 0.003114, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.596786, Class: 1.000000, Obj: 0.366719, No Obj: 0.002850, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.560135, Class: 1.000000, Obj: 0.360942, No Obj: 0.002935, Avg Recall: 0.632479, count: 117 -Region Avg IOU: 0.565634, Class: 1.000000, Obj: 0.301446, No Obj: 0.002799, Avg Recall: 0.692308, count: 104 -2061: 0.470879, 0.474120 avg, 0.001000 rate, 28.250214 seconds, 527616 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.603556, Class: 1.000000, Obj: 0.300539, No Obj: 0.002405, Avg Recall: 0.738095, count: 84 -Region Avg IOU: 0.580068, Class: 1.000000, Obj: 0.345536, No Obj: 0.002182, Avg Recall: 0.658537, count: 82 -Region Avg IOU: 0.598101, Class: 1.000000, Obj: 0.293277, No Obj: 0.002614, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.604663, Class: 1.000000, Obj: 0.328323, No Obj: 0.003012, Avg Recall: 0.767442, count: 129 -Region Avg IOU: 0.616842, Class: 1.000000, Obj: 0.324547, No Obj: 0.002548, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.642921, Class: 1.000000, Obj: 0.302918, No Obj: 0.002655, Avg Recall: 0.795181, count: 83 -Region Avg IOU: 0.578368, Class: 1.000000, Obj: 0.301426, No Obj: 0.002643, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.622911, Class: 1.000000, Obj: 0.326604, No Obj: 0.002682, Avg Recall: 0.763441, count: 93 -2062: 0.465069, 0.473215 avg, 0.001000 rate, 27.845852 seconds, 527872 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.604652, Class: 1.000000, Obj: 0.390858, No Obj: 0.002464, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.573557, Class: 1.000000, Obj: 0.281050, No Obj: 0.002672, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.605180, Class: 1.000000, Obj: 0.322541, No Obj: 0.002676, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.630511, Class: 1.000000, Obj: 0.315093, No Obj: 0.002689, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.632308, Class: 1.000000, Obj: 0.299746, No Obj: 0.002790, Avg Recall: 0.828125, count: 128 -Region Avg IOU: 0.607744, Class: 1.000000, Obj: 0.309753, No Obj: 0.002458, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.607658, Class: 1.000000, Obj: 0.311099, No Obj: 0.002446, Avg Recall: 0.777778, count: 90 -Region Avg IOU: 0.575228, Class: 1.000000, Obj: 0.314882, No Obj: 0.002768, Avg Recall: 0.680672, count: 119 -2063: 0.487118, 0.474606 avg, 0.001000 rate, 28.801144 seconds, 528128 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.603062, Class: 1.000000, Obj: 0.291435, No Obj: 0.002421, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.620106, Class: 1.000000, Obj: 0.291208, No Obj: 0.002511, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.604380, Class: 1.000000, Obj: 0.297390, No Obj: 0.002404, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.625238, Class: 1.000000, Obj: 0.284825, No Obj: 0.002602, Avg Recall: 0.815789, count: 114 -Region Avg IOU: 0.624868, Class: 1.000000, Obj: 0.291695, No Obj: 0.002784, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.615848, Class: 1.000000, Obj: 0.302244, No Obj: 0.002566, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.631070, Class: 1.000000, Obj: 0.302847, No Obj: 0.002548, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.630357, Class: 1.000000, Obj: 0.364210, No Obj: 0.002528, Avg Recall: 0.788889, count: 90 -2064: 0.491178, 0.476263 avg, 0.001000 rate, 28.094641 seconds, 528384 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.607454, Class: 1.000000, Obj: 0.282686, No Obj: 0.002646, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.592352, Class: 1.000000, Obj: 0.258679, No Obj: 0.002530, Avg Recall: 0.738095, count: 126 -Region Avg IOU: 0.596132, Class: 1.000000, Obj: 0.303385, No Obj: 0.002535, Avg Recall: 0.716418, count: 134 -Region Avg IOU: 0.577780, Class: 1.000000, Obj: 0.291485, No Obj: 0.002858, Avg Recall: 0.692857, count: 140 -Region Avg IOU: 0.585219, Class: 1.000000, Obj: 0.219794, No Obj: 0.002624, Avg Recall: 0.703125, count: 128 -Region Avg IOU: 0.617599, Class: 1.000000, Obj: 0.276795, No Obj: 0.002518, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.594168, Class: 1.000000, Obj: 0.303270, No Obj: 0.002216, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.602922, Class: 1.000000, Obj: 0.266784, No Obj: 0.002628, Avg Recall: 0.721311, count: 122 -2065: 0.535312, 0.482168 avg, 0.001000 rate, 28.631306 seconds, 528640 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.628698, Class: 1.000000, Obj: 0.335882, No Obj: 0.002590, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.634355, Class: 1.000000, Obj: 0.394293, No Obj: 0.002576, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.583404, Class: 1.000000, Obj: 0.295944, No Obj: 0.002387, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.594722, Class: 1.000000, Obj: 0.307225, No Obj: 0.002953, Avg Recall: 0.693431, count: 137 -Region Avg IOU: 0.630780, Class: 1.000000, Obj: 0.299729, No Obj: 0.002572, Avg Recall: 0.797872, count: 94 -Region Avg IOU: 0.612744, Class: 1.000000, Obj: 0.301309, No Obj: 0.002603, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.629613, Class: 1.000000, Obj: 0.301885, No Obj: 0.002887, Avg Recall: 0.805310, count: 113 -Region Avg IOU: 0.628938, Class: 1.000000, Obj: 0.300591, No Obj: 0.003240, Avg Recall: 0.751880, count: 133 -2066: 0.487814, 0.482732 avg, 0.001000 rate, 27.305984 seconds, 528896 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.622954, Class: 1.000000, Obj: 0.290799, No Obj: 0.003030, Avg Recall: 0.806723, count: 119 -Region Avg IOU: 0.635739, Class: 1.000000, Obj: 0.349555, No Obj: 0.003014, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.613209, Class: 1.000000, Obj: 0.299695, No Obj: 0.002912, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.579271, Class: 1.000000, Obj: 0.278619, No Obj: 0.002589, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.610116, Class: 1.000000, Obj: 0.342212, No Obj: 0.002749, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.575237, Class: 1.000000, Obj: 0.290840, No Obj: 0.002835, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.600639, Class: 1.000000, Obj: 0.329752, No Obj: 0.002583, Avg Recall: 0.770115, count: 87 -Region Avg IOU: 0.634814, Class: 1.000000, Obj: 0.371372, No Obj: 0.002868, Avg Recall: 0.759259, count: 108 -2067: 0.479031, 0.482362 avg, 0.001000 rate, 28.566029 seconds, 529152 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.601438, Class: 1.000000, Obj: 0.337937, No Obj: 0.002813, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.680789, Class: 1.000000, Obj: 0.368211, No Obj: 0.003119, Avg Recall: 0.891089, count: 101 -Region Avg IOU: 0.598539, Class: 1.000000, Obj: 0.276014, No Obj: 0.003160, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.615898, Class: 1.000000, Obj: 0.323503, No Obj: 0.002993, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.603371, Class: 1.000000, Obj: 0.358375, No Obj: 0.003025, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.612010, Class: 1.000000, Obj: 0.327422, No Obj: 0.002646, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.591985, Class: 1.000000, Obj: 0.309381, No Obj: 0.003227, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.644363, Class: 1.000000, Obj: 0.304402, No Obj: 0.003388, Avg Recall: 0.852174, count: 115 -2068: 0.494720, 0.483598 avg, 0.001000 rate, 27.204741 seconds, 529408 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.587097, Class: 1.000000, Obj: 0.316993, No Obj: 0.002736, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.631963, Class: 1.000000, Obj: 0.346260, No Obj: 0.003042, Avg Recall: 0.790323, count: 124 -Region Avg IOU: 0.590408, Class: 1.000000, Obj: 0.306282, No Obj: 0.002951, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.584365, Class: 1.000000, Obj: 0.317478, No Obj: 0.003064, Avg Recall: 0.700730, count: 137 -Region Avg IOU: 0.619716, Class: 1.000000, Obj: 0.339996, No Obj: 0.002964, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.599569, Class: 1.000000, Obj: 0.314198, No Obj: 0.003159, Avg Recall: 0.744000, count: 125 -Region Avg IOU: 0.631900, Class: 1.000000, Obj: 0.301331, No Obj: 0.002976, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.611874, Class: 1.000000, Obj: 0.296782, No Obj: 0.002469, Avg Recall: 0.776699, count: 103 -2069: 0.536638, 0.488902 avg, 0.001000 rate, 27.208345 seconds, 529664 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.581257, Class: 1.000000, Obj: 0.359669, No Obj: 0.002795, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.626773, Class: 1.000000, Obj: 0.338039, No Obj: 0.002686, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.646011, Class: 1.000000, Obj: 0.373316, No Obj: 0.002956, Avg Recall: 0.840909, count: 88 -Region Avg IOU: 0.602551, Class: 1.000000, Obj: 0.320094, No Obj: 0.003168, Avg Recall: 0.736842, count: 133 -Region Avg IOU: 0.623466, Class: 1.000000, Obj: 0.376673, No Obj: 0.002676, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.588394, Class: 1.000000, Obj: 0.320125, No Obj: 0.003098, Avg Recall: 0.666667, count: 132 -Region Avg IOU: 0.634732, Class: 1.000000, Obj: 0.376726, No Obj: 0.003369, Avg Recall: 0.798319, count: 119 -Region Avg IOU: 0.591641, Class: 1.000000, Obj: 0.351623, No Obj: 0.002608, Avg Recall: 0.684783, count: 92 -2070: 0.487602, 0.488772 avg, 0.001000 rate, 28.318001 seconds, 529920 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 16.405024 seconds -Region Avg IOU: 0.630956, Class: 1.000000, Obj: 0.304969, No Obj: 0.002714, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.589537, Class: 1.000000, Obj: 0.316296, No Obj: 0.002894, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.603902, Class: 1.000000, Obj: 0.321441, No Obj: 0.003122, Avg Recall: 0.760000, count: 125 -Region Avg IOU: 0.588770, Class: 1.000000, Obj: 0.318630, No Obj: 0.002819, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.626762, Class: 1.000000, Obj: 0.355937, No Obj: 0.002841, Avg Recall: 0.788235, count: 85 -Region Avg IOU: 0.633416, Class: 1.000000, Obj: 0.352951, No Obj: 0.002990, Avg Recall: 0.805310, count: 113 -Region Avg IOU: 0.591635, Class: 1.000000, Obj: 0.350797, No Obj: 0.002972, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.621994, Class: 1.000000, Obj: 0.389594, No Obj: 0.003011, Avg Recall: 0.736364, count: 110 -2071: 0.482377, 0.488132 avg, 0.001000 rate, 27.337923 seconds, 530176 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.591306, Class: 1.000000, Obj: 0.298132, No Obj: 0.002515, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.621952, Class: 1.000000, Obj: 0.319060, No Obj: 0.002914, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.598149, Class: 1.000000, Obj: 0.322397, No Obj: 0.002488, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.607489, Class: 1.000000, Obj: 0.369604, No Obj: 0.002541, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.617570, Class: 1.000000, Obj: 0.301175, No Obj: 0.002540, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.593598, Class: 1.000000, Obj: 0.320810, No Obj: 0.003058, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.555996, Class: 1.000000, Obj: 0.304276, No Obj: 0.002575, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.582881, Class: 1.000000, Obj: 0.326296, No Obj: 0.002843, Avg Recall: 0.713043, count: 115 -2072: 0.487726, 0.488092 avg, 0.001000 rate, 27.600033 seconds, 530432 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.604300, Class: 1.000000, Obj: 0.283528, No Obj: 0.002853, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.623366, Class: 1.000000, Obj: 0.295143, No Obj: 0.002765, Avg Recall: 0.815126, count: 119 -Region Avg IOU: 0.609677, Class: 1.000000, Obj: 0.258281, No Obj: 0.002647, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.651084, Class: 1.000000, Obj: 0.272190, No Obj: 0.002340, Avg Recall: 0.850000, count: 80 -Region Avg IOU: 0.595738, Class: 1.000000, Obj: 0.293343, No Obj: 0.002839, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.635888, Class: 1.000000, Obj: 0.292320, No Obj: 0.002735, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.582165, Class: 1.000000, Obj: 0.338008, No Obj: 0.002593, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.596775, Class: 1.000000, Obj: 0.330177, No Obj: 0.002705, Avg Recall: 0.747573, count: 103 -2073: 0.487494, 0.488032 avg, 0.001000 rate, 27.854984 seconds, 530688 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.599853, Class: 1.000000, Obj: 0.222521, No Obj: 0.002494, Avg Recall: 0.732283, count: 127 -Region Avg IOU: 0.623669, Class: 1.000000, Obj: 0.264180, No Obj: 0.002690, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.620361, Class: 1.000000, Obj: 0.326895, No Obj: 0.002573, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.543731, Class: 1.000000, Obj: 0.280333, No Obj: 0.002418, Avg Recall: 0.650000, count: 80 -Region Avg IOU: 0.610312, Class: 1.000000, Obj: 0.269558, No Obj: 0.002993, Avg Recall: 0.760274, count: 146 -Region Avg IOU: 0.629671, Class: 1.000000, Obj: 0.356536, No Obj: 0.002750, Avg Recall: 0.769231, count: 117 -Region Avg IOU: 0.672618, Class: 1.000000, Obj: 0.322733, No Obj: 0.002579, Avg Recall: 0.872549, count: 102 -Region Avg IOU: 0.573227, Class: 1.000000, Obj: 0.308169, No Obj: 0.002542, Avg Recall: 0.666667, count: 117 -2074: 0.511733, 0.490402 avg, 0.001000 rate, 26.488159 seconds, 530944 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.585424, Class: 1.000000, Obj: 0.300571, No Obj: 0.002640, Avg Recall: 0.677686, count: 121 -Region Avg IOU: 0.603660, Class: 1.000000, Obj: 0.328441, No Obj: 0.002445, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.608688, Class: 1.000000, Obj: 0.312549, No Obj: 0.002506, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.598782, Class: 1.000000, Obj: 0.320900, No Obj: 0.002396, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.592123, Class: 1.000000, Obj: 0.291161, No Obj: 0.002426, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.646597, Class: 1.000000, Obj: 0.304438, No Obj: 0.002354, Avg Recall: 0.845238, count: 84 -Region Avg IOU: 0.583903, Class: 1.000000, Obj: 0.295641, No Obj: 0.002586, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.568917, Class: 1.000000, Obj: 0.230167, No Obj: 0.002442, Avg Recall: 0.656863, count: 102 -2075: 0.433277, 0.484690 avg, 0.001000 rate, 28.949251 seconds, 531200 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.608467, Class: 1.000000, Obj: 0.316489, No Obj: 0.002731, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.610307, Class: 1.000000, Obj: 0.307954, No Obj: 0.002789, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.615859, Class: 1.000000, Obj: 0.298223, No Obj: 0.002649, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.641485, Class: 1.000000, Obj: 0.292237, No Obj: 0.002774, Avg Recall: 0.822034, count: 118 -Region Avg IOU: 0.600596, Class: 1.000000, Obj: 0.310211, No Obj: 0.002666, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.611012, Class: 1.000000, Obj: 0.285468, No Obj: 0.002852, Avg Recall: 0.779412, count: 136 -Region Avg IOU: 0.612668, Class: 1.000000, Obj: 0.335263, No Obj: 0.002451, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.576749, Class: 1.000000, Obj: 0.291920, No Obj: 0.002530, Avg Recall: 0.675676, count: 111 -2076: 0.473861, 0.483607 avg, 0.001000 rate, 28.736000 seconds, 531456 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.593252, Class: 1.000000, Obj: 0.281371, No Obj: 0.002737, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.628774, Class: 1.000000, Obj: 0.349917, No Obj: 0.003047, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.619279, Class: 1.000000, Obj: 0.313224, No Obj: 0.003032, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.602558, Class: 1.000000, Obj: 0.315246, No Obj: 0.002794, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.595015, Class: 1.000000, Obj: 0.268823, No Obj: 0.002749, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.603332, Class: 1.000000, Obj: 0.322407, No Obj: 0.002756, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.620766, Class: 1.000000, Obj: 0.327570, No Obj: 0.002685, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.614916, Class: 1.000000, Obj: 0.318526, No Obj: 0.003121, Avg Recall: 0.736000, count: 125 -2077: 0.457447, 0.480991 avg, 0.001000 rate, 27.201780 seconds, 531712 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.556542, Class: 1.000000, Obj: 0.242948, No Obj: 0.002589, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.625003, Class: 1.000000, Obj: 0.287679, No Obj: 0.003140, Avg Recall: 0.800000, count: 150 -Region Avg IOU: 0.593451, Class: 1.000000, Obj: 0.333701, No Obj: 0.002782, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.611463, Class: 1.000000, Obj: 0.318090, No Obj: 0.002484, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.597678, Class: 1.000000, Obj: 0.340862, No Obj: 0.003255, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.645584, Class: 1.000000, Obj: 0.344398, No Obj: 0.002993, Avg Recall: 0.798387, count: 124 -Region Avg IOU: 0.619715, Class: 1.000000, Obj: 0.339212, No Obj: 0.002996, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.613299, Class: 1.000000, Obj: 0.358649, No Obj: 0.002507, Avg Recall: 0.739583, count: 96 -2078: 0.495601, 0.482452 avg, 0.001000 rate, 28.239115 seconds, 531968 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.629389, Class: 1.000000, Obj: 0.391239, No Obj: 0.002886, Avg Recall: 0.796117, count: 103 -Region Avg IOU: 0.605026, Class: 1.000000, Obj: 0.371457, No Obj: 0.003057, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.611215, Class: 1.000000, Obj: 0.368213, No Obj: 0.002909, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.585007, Class: 1.000000, Obj: 0.321953, No Obj: 0.002756, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.624460, Class: 1.000000, Obj: 0.327478, No Obj: 0.003307, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.595425, Class: 1.000000, Obj: 0.341103, No Obj: 0.003047, Avg Recall: 0.689076, count: 119 -Region Avg IOU: 0.639240, Class: 1.000000, Obj: 0.326687, No Obj: 0.002434, Avg Recall: 0.814815, count: 81 -Region Avg IOU: 0.563214, Class: 1.000000, Obj: 0.310448, No Obj: 0.002915, Avg Recall: 0.645161, count: 124 -2079: 0.469060, 0.481113 avg, 0.001000 rate, 28.536591 seconds, 532224 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.623136, Class: 1.000000, Obj: 0.334180, No Obj: 0.002769, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.590355, Class: 1.000000, Obj: 0.363301, No Obj: 0.003206, Avg Recall: 0.760870, count: 138 -Region Avg IOU: 0.648817, Class: 1.000000, Obj: 0.407878, No Obj: 0.002976, Avg Recall: 0.817204, count: 93 -Region Avg IOU: 0.622793, Class: 1.000000, Obj: 0.307541, No Obj: 0.003148, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.619498, Class: 1.000000, Obj: 0.332218, No Obj: 0.003013, Avg Recall: 0.772358, count: 123 -Region Avg IOU: 0.570162, Class: 1.000000, Obj: 0.303595, No Obj: 0.002655, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.577765, Class: 1.000000, Obj: 0.316771, No Obj: 0.002961, Avg Recall: 0.679389, count: 131 -Region Avg IOU: 0.640371, Class: 1.000000, Obj: 0.348446, No Obj: 0.003010, Avg Recall: 0.819149, count: 94 -2080: 0.486418, 0.481643 avg, 0.001000 rate, 28.187927 seconds, 532480 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.049952 seconds -Region Avg IOU: 0.530375, Class: 1.000000, Obj: 0.254183, No Obj: 0.003178, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.528264, Class: 1.000000, Obj: 0.309507, No Obj: 0.003903, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.558559, Class: 1.000000, Obj: 0.265834, No Obj: 0.004066, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.498747, Class: 1.000000, Obj: 0.215301, No Obj: 0.004217, Avg Recall: 0.553030, count: 132 -Region Avg IOU: 0.467941, Class: 1.000000, Obj: 0.265974, No Obj: 0.003103, Avg Recall: 0.500000, count: 96 -Region Avg IOU: 0.526967, Class: 1.000000, Obj: 0.284232, No Obj: 0.003981, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.501424, Class: 1.000000, Obj: 0.315985, No Obj: 0.003805, Avg Recall: 0.596774, count: 124 -Region Avg IOU: 0.514523, Class: 1.000000, Obj: 0.260332, No Obj: 0.004012, Avg Recall: 0.612903, count: 124 -2081: 0.541675, 0.487646 avg, 0.001000 rate, 21.325232 seconds, 532736 images -Loaded: 1.472810 seconds -Region Avg IOU: 0.539830, Class: 1.000000, Obj: 0.311899, No Obj: 0.003651, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.508231, Class: 1.000000, Obj: 0.265273, No Obj: 0.003833, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.517949, Class: 1.000000, Obj: 0.283829, No Obj: 0.003538, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.552715, Class: 1.000000, Obj: 0.304658, No Obj: 0.004243, Avg Recall: 0.620438, count: 137 -Region Avg IOU: 0.524901, Class: 1.000000, Obj: 0.329186, No Obj: 0.003591, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.537090, Class: 1.000000, Obj: 0.301778, No Obj: 0.003441, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.522586, Class: 1.000000, Obj: 0.286046, No Obj: 0.003885, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.493482, Class: 1.000000, Obj: 0.316684, No Obj: 0.003716, Avg Recall: 0.510000, count: 100 -2082: 0.457277, 0.484610 avg, 0.001000 rate, 21.076771 seconds, 532992 images -Loaded: 0.448478 seconds -Region Avg IOU: 0.502751, Class: 1.000000, Obj: 0.356084, No Obj: 0.003657, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.572508, Class: 1.000000, Obj: 0.325986, No Obj: 0.004128, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.554982, Class: 1.000000, Obj: 0.326192, No Obj: 0.003773, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.529987, Class: 1.000000, Obj: 0.270614, No Obj: 0.004388, Avg Recall: 0.583333, count: 144 -Region Avg IOU: 0.475427, Class: 1.000000, Obj: 0.268589, No Obj: 0.003561, Avg Recall: 0.517241, count: 116 -Region Avg IOU: 0.565656, Class: 1.000000, Obj: 0.288366, No Obj: 0.004102, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.527200, Class: 1.000000, Obj: 0.290177, No Obj: 0.003686, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.532863, Class: 1.000000, Obj: 0.305489, No Obj: 0.003105, Avg Recall: 0.614458, count: 83 -2083: 0.434852, 0.479634 avg, 0.001000 rate, 22.935274 seconds, 533248 images -Loaded: 0.860810 seconds -Region Avg IOU: 0.499731, Class: 1.000000, Obj: 0.293808, No Obj: 0.004602, Avg Recall: 0.550000, count: 120 -Region Avg IOU: 0.537129, Class: 1.000000, Obj: 0.390427, No Obj: 0.003753, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.546485, Class: 1.000000, Obj: 0.344204, No Obj: 0.004511, Avg Recall: 0.661417, count: 127 -Region Avg IOU: 0.519651, Class: 1.000000, Obj: 0.330713, No Obj: 0.003821, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.539815, Class: 1.000000, Obj: 0.353815, No Obj: 0.004279, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.542681, Class: 1.000000, Obj: 0.321052, No Obj: 0.004259, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.473036, Class: 1.000000, Obj: 0.323151, No Obj: 0.003681, Avg Recall: 0.462185, count: 119 -Region Avg IOU: 0.522937, Class: 1.000000, Obj: 0.312420, No Obj: 0.003450, Avg Recall: 0.574257, count: 101 -2084: 0.454312, 0.477102 avg, 0.001000 rate, 21.851532 seconds, 533504 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.525496, Class: 1.000000, Obj: 0.282564, No Obj: 0.004150, Avg Recall: 0.540816, count: 98 -Region Avg IOU: 0.497988, Class: 1.000000, Obj: 0.335101, No Obj: 0.004028, Avg Recall: 0.528846, count: 104 -Region Avg IOU: 0.489975, Class: 1.000000, Obj: 0.360332, No Obj: 0.004185, Avg Recall: 0.529915, count: 117 -Region Avg IOU: 0.499572, Class: 1.000000, Obj: 0.349881, No Obj: 0.004074, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.565190, Class: 1.000000, Obj: 0.356334, No Obj: 0.003878, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.549445, Class: 1.000000, Obj: 0.365168, No Obj: 0.004938, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.455990, Class: 1.000000, Obj: 0.256181, No Obj: 0.003823, Avg Recall: 0.521368, count: 117 -Region Avg IOU: 0.492809, Class: 1.000000, Obj: 0.358946, No Obj: 0.003915, Avg Recall: 0.548387, count: 93 -2085: 0.473044, 0.476696 avg, 0.001000 rate, 22.742887 seconds, 533760 images -Loaded: 0.148457 seconds -Region Avg IOU: 0.500421, Class: 1.000000, Obj: 0.330017, No Obj: 0.004085, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.480128, Class: 1.000000, Obj: 0.275590, No Obj: 0.003498, Avg Recall: 0.523256, count: 86 -Region Avg IOU: 0.509759, Class: 1.000000, Obj: 0.348617, No Obj: 0.004787, Avg Recall: 0.537415, count: 147 -Region Avg IOU: 0.504730, Class: 1.000000, Obj: 0.329702, No Obj: 0.004298, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.512212, Class: 1.000000, Obj: 0.330792, No Obj: 0.004529, Avg Recall: 0.543307, count: 127 -Region Avg IOU: 0.450347, Class: 1.000000, Obj: 0.266997, No Obj: 0.004266, Avg Recall: 0.467626, count: 139 -Region Avg IOU: 0.503410, Class: 1.000000, Obj: 0.285820, No Obj: 0.004439, Avg Recall: 0.577236, count: 123 -Region Avg IOU: 0.478548, Class: 1.000000, Obj: 0.337191, No Obj: 0.004843, Avg Recall: 0.519481, count: 154 -2086: 0.563364, 0.485363 avg, 0.001000 rate, 23.274578 seconds, 534016 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.477137, Class: 1.000000, Obj: 0.355966, No Obj: 0.003735, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.421838, Class: 1.000000, Obj: 0.264961, No Obj: 0.003900, Avg Recall: 0.376068, count: 117 -Region Avg IOU: 0.450546, Class: 1.000000, Obj: 0.320370, No Obj: 0.004034, Avg Recall: 0.508772, count: 114 -Region Avg IOU: 0.521438, Class: 1.000000, Obj: 0.325184, No Obj: 0.004364, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.493531, Class: 1.000000, Obj: 0.362445, No Obj: 0.003636, Avg Recall: 0.532468, count: 77 -Region Avg IOU: 0.515594, Class: 1.000000, Obj: 0.312024, No Obj: 0.004098, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.479898, Class: 1.000000, Obj: 0.316610, No Obj: 0.004870, Avg Recall: 0.514085, count: 142 -Region Avg IOU: 0.470511, Class: 1.000000, Obj: 0.333485, No Obj: 0.004181, Avg Recall: 0.557692, count: 104 -2087: 0.512435, 0.488070 avg, 0.001000 rate, 23.329180 seconds, 534272 images -Loaded: 0.182484 seconds -Region Avg IOU: 0.465108, Class: 1.000000, Obj: 0.335612, No Obj: 0.003403, Avg Recall: 0.466667, count: 90 -Region Avg IOU: 0.451951, Class: 1.000000, Obj: 0.276620, No Obj: 0.003984, Avg Recall: 0.527273, count: 110 -Region Avg IOU: 0.556776, Class: 1.000000, Obj: 0.328188, No Obj: 0.003873, Avg Recall: 0.636364, count: 77 -Region Avg IOU: 0.531989, Class: 1.000000, Obj: 0.345934, No Obj: 0.003638, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.512174, Class: 1.000000, Obj: 0.318184, No Obj: 0.003516, Avg Recall: 0.547368, count: 95 -Region Avg IOU: 0.477033, Class: 1.000000, Obj: 0.285712, No Obj: 0.004289, Avg Recall: 0.507246, count: 138 -Region Avg IOU: 0.515215, Class: 1.000000, Obj: 0.344719, No Obj: 0.004021, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.426177, Class: 1.000000, Obj: 0.278729, No Obj: 0.003485, Avg Recall: 0.418367, count: 98 -2088: 0.443254, 0.483588 avg, 0.001000 rate, 21.835203 seconds, 534528 images -Loaded: 1.108676 seconds -Region Avg IOU: 0.525126, Class: 1.000000, Obj: 0.325445, No Obj: 0.003904, Avg Recall: 0.554348, count: 92 -Region Avg IOU: 0.534717, Class: 1.000000, Obj: 0.318922, No Obj: 0.003667, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.509670, Class: 1.000000, Obj: 0.284914, No Obj: 0.003925, Avg Recall: 0.598485, count: 132 -Region Avg IOU: 0.490662, Class: 1.000000, Obj: 0.296697, No Obj: 0.003583, Avg Recall: 0.456311, count: 103 -Region Avg IOU: 0.502989, Class: 1.000000, Obj: 0.310595, No Obj: 0.003681, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.508547, Class: 1.000000, Obj: 0.317013, No Obj: 0.003811, Avg Recall: 0.549550, count: 111 -Region Avg IOU: 0.533134, Class: 1.000000, Obj: 0.325288, No Obj: 0.003602, Avg Recall: 0.588889, count: 90 -Region Avg IOU: 0.527254, Class: 1.000000, Obj: 0.271114, No Obj: 0.003376, Avg Recall: 0.590909, count: 88 -2089: 0.460929, 0.481322 avg, 0.001000 rate, 20.666821 seconds, 534784 images -Loaded: 0.883592 seconds -Region Avg IOU: 0.477016, Class: 1.000000, Obj: 0.268451, No Obj: 0.003658, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.519915, Class: 1.000000, Obj: 0.275948, No Obj: 0.003728, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.456656, Class: 1.000000, Obj: 0.244157, No Obj: 0.003736, Avg Recall: 0.477064, count: 109 -Region Avg IOU: 0.491223, Class: 1.000000, Obj: 0.286201, No Obj: 0.003712, Avg Recall: 0.526786, count: 112 -Region Avg IOU: 0.487911, Class: 1.000000, Obj: 0.291603, No Obj: 0.003707, Avg Recall: 0.529851, count: 134 -Region Avg IOU: 0.463252, Class: 1.000000, Obj: 0.275172, No Obj: 0.003222, Avg Recall: 0.494949, count: 99 -Region Avg IOU: 0.515123, Class: 1.000000, Obj: 0.287938, No Obj: 0.004139, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.552795, Class: 1.000000, Obj: 0.323127, No Obj: 0.003429, Avg Recall: 0.635417, count: 96 -2090: 0.483010, 0.481491 avg, 0.001000 rate, 22.470123 seconds, 535040 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000065 seconds -Region Avg IOU: 0.625931, Class: 1.000000, Obj: 0.299470, No Obj: 0.003118, Avg Recall: 0.779661, count: 118 -Region Avg IOU: 0.611212, Class: 1.000000, Obj: 0.346413, No Obj: 0.003174, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.599573, Class: 1.000000, Obj: 0.340560, No Obj: 0.002770, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.590417, Class: 1.000000, Obj: 0.321519, No Obj: 0.003135, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.596715, Class: 1.000000, Obj: 0.350111, No Obj: 0.002971, Avg Recall: 0.730159, count: 126 -Region Avg IOU: 0.566035, Class: 1.000000, Obj: 0.274728, No Obj: 0.002730, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.590504, Class: 1.000000, Obj: 0.282872, No Obj: 0.002676, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.621258, Class: 1.000000, Obj: 0.338842, No Obj: 0.003001, Avg Recall: 0.750000, count: 104 -2091: 0.489160, 0.482258 avg, 0.001000 rate, 25.376991 seconds, 535296 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.598253, Class: 1.000000, Obj: 0.295791, No Obj: 0.002953, Avg Recall: 0.752137, count: 117 -Region Avg IOU: 0.611141, Class: 1.000000, Obj: 0.304603, No Obj: 0.003105, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.620357, Class: 1.000000, Obj: 0.333969, No Obj: 0.002768, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.625906, Class: 1.000000, Obj: 0.312377, No Obj: 0.003237, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.612016, Class: 1.000000, Obj: 0.262673, No Obj: 0.003326, Avg Recall: 0.742647, count: 136 -Region Avg IOU: 0.635149, Class: 1.000000, Obj: 0.320212, No Obj: 0.002970, Avg Recall: 0.803419, count: 117 -Region Avg IOU: 0.592453, Class: 1.000000, Obj: 0.328885, No Obj: 0.002869, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.623259, Class: 1.000000, Obj: 0.312423, No Obj: 0.002776, Avg Recall: 0.783019, count: 106 -2092: 0.618812, 0.495913 avg, 0.001000 rate, 26.524565 seconds, 535552 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.595806, Class: 1.000000, Obj: 0.274224, No Obj: 0.002810, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.606575, Class: 1.000000, Obj: 0.322288, No Obj: 0.002450, Avg Recall: 0.709302, count: 86 -Region Avg IOU: 0.642152, Class: 1.000000, Obj: 0.351175, No Obj: 0.002799, Avg Recall: 0.793478, count: 92 -Region Avg IOU: 0.580585, Class: 1.000000, Obj: 0.296396, No Obj: 0.002384, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.550172, Class: 1.000000, Obj: 0.372604, No Obj: 0.002728, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.634603, Class: 1.000000, Obj: 0.320134, No Obj: 0.003036, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.581964, Class: 1.000000, Obj: 0.356345, No Obj: 0.002563, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.587263, Class: 1.000000, Obj: 0.333989, No Obj: 0.002931, Avg Recall: 0.742857, count: 105 -2093: 0.406003, 0.486922 avg, 0.001000 rate, 28.680857 seconds, 535808 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.608821, Class: 1.000000, Obj: 0.362942, No Obj: 0.002924, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.598360, Class: 1.000000, Obj: 0.313315, No Obj: 0.002817, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.584164, Class: 1.000000, Obj: 0.330692, No Obj: 0.002708, Avg Recall: 0.644068, count: 118 -Region Avg IOU: 0.615093, Class: 1.000000, Obj: 0.338828, No Obj: 0.002691, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.546228, Class: 1.000000, Obj: 0.265430, No Obj: 0.002593, Avg Recall: 0.633333, count: 90 -Region Avg IOU: 0.595144, Class: 1.000000, Obj: 0.325135, No Obj: 0.002991, Avg Recall: 0.709924, count: 131 -Region Avg IOU: 0.606347, Class: 1.000000, Obj: 0.284654, No Obj: 0.002935, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.575262, Class: 1.000000, Obj: 0.342557, No Obj: 0.002504, Avg Recall: 0.653846, count: 104 -2094: 0.501074, 0.488337 avg, 0.001000 rate, 27.176638 seconds, 536064 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.610772, Class: 1.000000, Obj: 0.372424, No Obj: 0.002720, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.603255, Class: 1.000000, Obj: 0.309978, No Obj: 0.002682, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.650649, Class: 1.000000, Obj: 0.323582, No Obj: 0.003229, Avg Recall: 0.803150, count: 127 -Region Avg IOU: 0.583438, Class: 1.000000, Obj: 0.333499, No Obj: 0.002750, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.570441, Class: 1.000000, Obj: 0.297338, No Obj: 0.002906, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.626859, Class: 1.000000, Obj: 0.325167, No Obj: 0.003150, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.574037, Class: 1.000000, Obj: 0.301385, No Obj: 0.002905, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.591224, Class: 1.000000, Obj: 0.311932, No Obj: 0.002730, Avg Recall: 0.714286, count: 105 -2095: 0.505293, 0.490033 avg, 0.001000 rate, 27.624895 seconds, 536320 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.599336, Class: 1.000000, Obj: 0.309489, No Obj: 0.002871, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.604726, Class: 1.000000, Obj: 0.290279, No Obj: 0.002908, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.638388, Class: 1.000000, Obj: 0.381122, No Obj: 0.003128, Avg Recall: 0.792683, count: 82 -Region Avg IOU: 0.615661, Class: 1.000000, Obj: 0.339927, No Obj: 0.003157, Avg Recall: 0.735714, count: 140 -Region Avg IOU: 0.617649, Class: 1.000000, Obj: 0.334505, No Obj: 0.002972, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.570963, Class: 1.000000, Obj: 0.307692, No Obj: 0.002659, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.603044, Class: 1.000000, Obj: 0.342744, No Obj: 0.002945, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.639053, Class: 1.000000, Obj: 0.352227, No Obj: 0.002910, Avg Recall: 0.842105, count: 95 -2096: 0.427511, 0.483781 avg, 0.001000 rate, 27.371759 seconds, 536576 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.644898, Class: 1.000000, Obj: 0.377931, No Obj: 0.002742, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.605474, Class: 1.000000, Obj: 0.320557, No Obj: 0.003080, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.577990, Class: 1.000000, Obj: 0.320035, No Obj: 0.002976, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.600164, Class: 1.000000, Obj: 0.290760, No Obj: 0.002968, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.593239, Class: 1.000000, Obj: 0.344495, No Obj: 0.002961, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.598316, Class: 1.000000, Obj: 0.349888, No Obj: 0.002815, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.622183, Class: 1.000000, Obj: 0.372168, No Obj: 0.003087, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.611017, Class: 1.000000, Obj: 0.327101, No Obj: 0.002846, Avg Recall: 0.801887, count: 106 -2097: 0.483161, 0.483719 avg, 0.001000 rate, 26.778852 seconds, 536832 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.576921, Class: 1.000000, Obj: 0.296931, No Obj: 0.002936, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.612219, Class: 1.000000, Obj: 0.311738, No Obj: 0.002828, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.631662, Class: 1.000000, Obj: 0.318064, No Obj: 0.003201, Avg Recall: 0.783333, count: 120 -Region Avg IOU: 0.614804, Class: 1.000000, Obj: 0.345012, No Obj: 0.003065, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.608992, Class: 1.000000, Obj: 0.327053, No Obj: 0.003450, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.624385, Class: 1.000000, Obj: 0.362052, No Obj: 0.002799, Avg Recall: 0.791209, count: 91 -Region Avg IOU: 0.623285, Class: 1.000000, Obj: 0.367807, No Obj: 0.003007, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.573349, Class: 1.000000, Obj: 0.315443, No Obj: 0.002668, Avg Recall: 0.672131, count: 122 -2098: 0.487153, 0.484062 avg, 0.001000 rate, 27.456211 seconds, 537088 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.644618, Class: 1.000000, Obj: 0.325498, No Obj: 0.003395, Avg Recall: 0.821429, count: 140 -Region Avg IOU: 0.597552, Class: 1.000000, Obj: 0.344684, No Obj: 0.003098, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.579561, Class: 1.000000, Obj: 0.308761, No Obj: 0.002962, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.607444, Class: 1.000000, Obj: 0.384153, No Obj: 0.002707, Avg Recall: 0.756098, count: 82 -Region Avg IOU: 0.586429, Class: 1.000000, Obj: 0.371439, No Obj: 0.003380, Avg Recall: 0.691729, count: 133 -Region Avg IOU: 0.586332, Class: 1.000000, Obj: 0.301325, No Obj: 0.002708, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.589671, Class: 1.000000, Obj: 0.328257, No Obj: 0.002653, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.594134, Class: 1.000000, Obj: 0.321965, No Obj: 0.002910, Avg Recall: 0.745283, count: 106 -2099: 0.505406, 0.486197 avg, 0.001000 rate, 27.989010 seconds, 537344 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.596475, Class: 1.000000, Obj: 0.329105, No Obj: 0.003236, Avg Recall: 0.732283, count: 127 -Region Avg IOU: 0.587962, Class: 1.000000, Obj: 0.329934, No Obj: 0.003218, Avg Recall: 0.711111, count: 90 -Region Avg IOU: 0.580572, Class: 1.000000, Obj: 0.327124, No Obj: 0.002721, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.603556, Class: 1.000000, Obj: 0.350262, No Obj: 0.002867, Avg Recall: 0.815789, count: 114 -Region Avg IOU: 0.602793, Class: 1.000000, Obj: 0.366763, No Obj: 0.002674, Avg Recall: 0.753086, count: 81 -Region Avg IOU: 0.597801, Class: 1.000000, Obj: 0.369188, No Obj: 0.003357, Avg Recall: 0.729323, count: 133 -Region Avg IOU: 0.596276, Class: 1.000000, Obj: 0.307474, No Obj: 0.003268, Avg Recall: 0.740506, count: 158 -Region Avg IOU: 0.585138, Class: 1.000000, Obj: 0.394539, No Obj: 0.002946, Avg Recall: 0.691589, count: 107 -2100: 0.514923, 0.489069 avg, 0.001000 rate, 27.859474 seconds, 537600 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 10.504395 seconds -Region Avg IOU: 0.475922, Class: 1.000000, Obj: 0.266955, No Obj: 0.003481, Avg Recall: 0.504000, count: 125 -Region Avg IOU: 0.478050, Class: 1.000000, Obj: 0.242022, No Obj: 0.003800, Avg Recall: 0.480392, count: 102 -Region Avg IOU: 0.420328, Class: 1.000000, Obj: 0.234880, No Obj: 0.003406, Avg Recall: 0.414414, count: 111 -Region Avg IOU: 0.501377, Class: 1.000000, Obj: 0.283532, No Obj: 0.003562, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.444955, Class: 1.000000, Obj: 0.227760, No Obj: 0.003735, Avg Recall: 0.462810, count: 121 -Region Avg IOU: 0.451164, Class: 1.000000, Obj: 0.292194, No Obj: 0.003962, Avg Recall: 0.464000, count: 125 -Region Avg IOU: 0.488963, Class: 1.000000, Obj: 0.255372, No Obj: 0.004204, Avg Recall: 0.545455, count: 121 -Region Avg IOU: 0.494027, Class: 1.000000, Obj: 0.268989, No Obj: 0.003732, Avg Recall: 0.565657, count: 99 -2101: 0.616293, 0.501792 avg, 0.001000 rate, 20.903273 seconds, 537856 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.410383, Class: 1.000000, Obj: 0.248527, No Obj: 0.003439, Avg Recall: 0.380531, count: 113 -Region Avg IOU: 0.522758, Class: 1.000000, Obj: 0.311524, No Obj: 0.004142, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.486241, Class: 1.000000, Obj: 0.265110, No Obj: 0.003520, Avg Recall: 0.468468, count: 111 -Region Avg IOU: 0.449521, Class: 1.000000, Obj: 0.217812, No Obj: 0.003538, Avg Recall: 0.474576, count: 118 -Region Avg IOU: 0.525481, Class: 1.000000, Obj: 0.247129, No Obj: 0.004038, Avg Recall: 0.598425, count: 127 -Region Avg IOU: 0.501231, Class: 1.000000, Obj: 0.271742, No Obj: 0.003244, Avg Recall: 0.597561, count: 82 -Region Avg IOU: 0.519817, Class: 1.000000, Obj: 0.302004, No Obj: 0.004202, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.537271, Class: 1.000000, Obj: 0.302809, No Obj: 0.003923, Avg Recall: 0.637255, count: 102 -2102: 0.521598, 0.503772 avg, 0.001000 rate, 21.633976 seconds, 538112 images -Loaded: 1.229488 seconds -Region Avg IOU: 0.472254, Class: 1.000000, Obj: 0.324961, No Obj: 0.004250, Avg Recall: 0.445378, count: 119 -Region Avg IOU: 0.513208, Class: 1.000000, Obj: 0.291980, No Obj: 0.003344, Avg Recall: 0.621951, count: 82 -Region Avg IOU: 0.484823, Class: 1.000000, Obj: 0.291603, No Obj: 0.004043, Avg Recall: 0.558333, count: 120 -Region Avg IOU: 0.494573, Class: 1.000000, Obj: 0.295747, No Obj: 0.004076, Avg Recall: 0.510204, count: 98 -Region Avg IOU: 0.497314, Class: 1.000000, Obj: 0.251800, No Obj: 0.005018, Avg Recall: 0.566038, count: 159 -Region Avg IOU: 0.519820, Class: 1.000000, Obj: 0.299374, No Obj: 0.004024, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.469344, Class: 1.000000, Obj: 0.230737, No Obj: 0.003753, Avg Recall: 0.520661, count: 121 -Region Avg IOU: 0.502514, Class: 1.000000, Obj: 0.310184, No Obj: 0.004216, Avg Recall: 0.545455, count: 110 -2103: 0.481509, 0.501546 avg, 0.001000 rate, 22.035040 seconds, 538368 images -Loaded: 1.111717 seconds -Region Avg IOU: 0.501516, Class: 1.000000, Obj: 0.305287, No Obj: 0.004520, Avg Recall: 0.556452, count: 124 -Region Avg IOU: 0.482295, Class: 1.000000, Obj: 0.272189, No Obj: 0.004377, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.458852, Class: 1.000000, Obj: 0.236019, No Obj: 0.004516, Avg Recall: 0.529412, count: 119 -Region Avg IOU: 0.479015, Class: 1.000000, Obj: 0.273390, No Obj: 0.004496, Avg Recall: 0.528986, count: 138 -Region Avg IOU: 0.508880, Class: 1.000000, Obj: 0.307471, No Obj: 0.004322, Avg Recall: 0.536364, count: 110 -Region Avg IOU: 0.523237, Class: 1.000000, Obj: 0.304205, No Obj: 0.004077, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.524239, Class: 1.000000, Obj: 0.341499, No Obj: 0.003934, Avg Recall: 0.546512, count: 86 -Region Avg IOU: 0.504504, Class: 1.000000, Obj: 0.307345, No Obj: 0.003941, Avg Recall: 0.555556, count: 90 -2104: 0.508694, 0.502261 avg, 0.001000 rate, 22.018103 seconds, 538624 images -Loaded: 0.139804 seconds -Region Avg IOU: 0.460330, Class: 1.000000, Obj: 0.301214, No Obj: 0.004172, Avg Recall: 0.441176, count: 102 -Region Avg IOU: 0.447341, Class: 1.000000, Obj: 0.302113, No Obj: 0.004082, Avg Recall: 0.469388, count: 98 -Region Avg IOU: 0.466723, Class: 1.000000, Obj: 0.243878, No Obj: 0.004222, Avg Recall: 0.489796, count: 98 -Region Avg IOU: 0.480217, Class: 1.000000, Obj: 0.300913, No Obj: 0.004474, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.489585, Class: 1.000000, Obj: 0.303800, No Obj: 0.003975, Avg Recall: 0.510417, count: 96 -Region Avg IOU: 0.461857, Class: 1.000000, Obj: 0.321229, No Obj: 0.004270, Avg Recall: 0.476191, count: 126 -Region Avg IOU: 0.515720, Class: 1.000000, Obj: 0.332761, No Obj: 0.004419, Avg Recall: 0.568421, count: 95 -Region Avg IOU: 0.424878, Class: 1.000000, Obj: 0.263482, No Obj: 0.003980, Avg Recall: 0.426087, count: 115 -2105: 0.500869, 0.502122 avg, 0.001000 rate, 20.533340 seconds, 538880 images -Loaded: 1.135279 seconds -Region Avg IOU: 0.461271, Class: 1.000000, Obj: 0.313192, No Obj: 0.004431, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.429894, Class: 1.000000, Obj: 0.292315, No Obj: 0.004746, Avg Recall: 0.408696, count: 115 -Region Avg IOU: 0.484177, Class: 1.000000, Obj: 0.331217, No Obj: 0.005163, Avg Recall: 0.504762, count: 105 -Region Avg IOU: 0.491259, Class: 1.000000, Obj: 0.323350, No Obj: 0.004666, Avg Recall: 0.565574, count: 122 -Region Avg IOU: 0.478784, Class: 1.000000, Obj: 0.317765, No Obj: 0.004596, Avg Recall: 0.508197, count: 122 -Region Avg IOU: 0.475953, Class: 1.000000, Obj: 0.278382, No Obj: 0.004872, Avg Recall: 0.533333, count: 135 -Region Avg IOU: 0.507383, Class: 1.000000, Obj: 0.343032, No Obj: 0.004056, Avg Recall: 0.579545, count: 88 -Region Avg IOU: 0.443387, Class: 1.000000, Obj: 0.291087, No Obj: 0.003734, Avg Recall: 0.534884, count: 86 -2106: 0.509351, 0.502845 avg, 0.001000 rate, 19.032900 seconds, 539136 images -Loaded: 1.864597 seconds -Region Avg IOU: 0.499336, Class: 1.000000, Obj: 0.307785, No Obj: 0.004323, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.449260, Class: 1.000000, Obj: 0.336265, No Obj: 0.004602, Avg Recall: 0.485437, count: 103 -Region Avg IOU: 0.447113, Class: 1.000000, Obj: 0.320161, No Obj: 0.004099, Avg Recall: 0.483333, count: 120 -Region Avg IOU: 0.451189, Class: 1.000000, Obj: 0.300619, No Obj: 0.004370, Avg Recall: 0.504065, count: 123 -Region Avg IOU: 0.482615, Class: 1.000000, Obj: 0.318700, No Obj: 0.004301, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.493244, Class: 1.000000, Obj: 0.347001, No Obj: 0.004347, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.518367, Class: 1.000000, Obj: 0.329026, No Obj: 0.004693, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.488771, Class: 1.000000, Obj: 0.336563, No Obj: 0.004332, Avg Recall: 0.544444, count: 90 -2107: 0.519721, 0.504532 avg, 0.001000 rate, 21.415432 seconds, 539392 images -Loaded: 0.110638 seconds -Region Avg IOU: 0.432121, Class: 1.000000, Obj: 0.343094, No Obj: 0.003934, Avg Recall: 0.442105, count: 95 -Region Avg IOU: 0.482725, Class: 1.000000, Obj: 0.275591, No Obj: 0.004087, Avg Recall: 0.513274, count: 113 -Region Avg IOU: 0.463098, Class: 1.000000, Obj: 0.302600, No Obj: 0.003873, Avg Recall: 0.457143, count: 105 -Region Avg IOU: 0.466531, Class: 1.000000, Obj: 0.281880, No Obj: 0.005031, Avg Recall: 0.486301, count: 146 -Region Avg IOU: 0.416706, Class: 1.000000, Obj: 0.244879, No Obj: 0.003526, Avg Recall: 0.463415, count: 82 -Region Avg IOU: 0.496596, Class: 1.000000, Obj: 0.322272, No Obj: 0.004774, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.566192, Class: 1.000000, Obj: 0.365365, No Obj: 0.004068, Avg Recall: 0.697368, count: 76 -Region Avg IOU: 0.475917, Class: 1.000000, Obj: 0.315635, No Obj: 0.003955, Avg Recall: 0.553398, count: 103 -2108: 0.535991, 0.507678 avg, 0.001000 rate, 22.074141 seconds, 539648 images -Loaded: 0.045805 seconds -Region Avg IOU: 0.474284, Class: 1.000000, Obj: 0.341238, No Obj: 0.004627, Avg Recall: 0.464286, count: 112 -Region Avg IOU: 0.471377, Class: 1.000000, Obj: 0.307247, No Obj: 0.004443, Avg Recall: 0.547619, count: 126 -Region Avg IOU: 0.495310, Class: 1.000000, Obj: 0.331281, No Obj: 0.004504, Avg Recall: 0.513761, count: 109 -Region Avg IOU: 0.493171, Class: 1.000000, Obj: 0.317993, No Obj: 0.005125, Avg Recall: 0.524590, count: 122 -Region Avg IOU: 0.508725, Class: 1.000000, Obj: 0.373306, No Obj: 0.003951, Avg Recall: 0.574468, count: 94 -Region Avg IOU: 0.476562, Class: 1.000000, Obj: 0.280377, No Obj: 0.003970, Avg Recall: 0.532609, count: 92 -Region Avg IOU: 0.497201, Class: 1.000000, Obj: 0.317235, No Obj: 0.004736, Avg Recall: 0.471074, count: 121 -Region Avg IOU: 0.462242, Class: 1.000000, Obj: 0.261121, No Obj: 0.003765, Avg Recall: 0.489796, count: 98 -2109: 0.569286, 0.513839 avg, 0.001000 rate, 21.513836 seconds, 539904 images -Loaded: 0.400935 seconds -Region Avg IOU: 0.500459, Class: 1.000000, Obj: 0.347725, No Obj: 0.004374, Avg Recall: 0.550000, count: 120 -Region Avg IOU: 0.499394, Class: 1.000000, Obj: 0.315385, No Obj: 0.004598, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.477998, Class: 1.000000, Obj: 0.299040, No Obj: 0.004594, Avg Recall: 0.500000, count: 118 -Region Avg IOU: 0.481060, Class: 1.000000, Obj: 0.304992, No Obj: 0.004650, Avg Recall: 0.540000, count: 100 -Region Avg IOU: 0.476338, Class: 1.000000, Obj: 0.306091, No Obj: 0.004129, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.481108, Class: 1.000000, Obj: 0.285811, No Obj: 0.004107, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.523113, Class: 1.000000, Obj: 0.289435, No Obj: 0.003584, Avg Recall: 0.600000, count: 75 -Region Avg IOU: 0.514760, Class: 1.000000, Obj: 0.319003, No Obj: 0.004262, Avg Recall: 0.581633, count: 98 -2110: 0.445424, 0.506997 avg, 0.001000 rate, 21.371126 seconds, 540160 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000071 seconds -Region Avg IOU: 0.590468, Class: 1.000000, Obj: 0.381394, No Obj: 0.003371, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.586600, Class: 1.000000, Obj: 0.336454, No Obj: 0.003335, Avg Recall: 0.711111, count: 90 -Region Avg IOU: 0.576180, Class: 1.000000, Obj: 0.347227, No Obj: 0.003392, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.628895, Class: 1.000000, Obj: 0.392534, No Obj: 0.003125, Avg Recall: 0.793103, count: 87 -Region Avg IOU: 0.536391, Class: 1.000000, Obj: 0.345484, No Obj: 0.003368, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.569973, Class: 1.000000, Obj: 0.380460, No Obj: 0.002964, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.567866, Class: 1.000000, Obj: 0.339286, No Obj: 0.003914, Avg Recall: 0.679245, count: 159 -Region Avg IOU: 0.583963, Class: 1.000000, Obj: 0.385983, No Obj: 0.003793, Avg Recall: 0.694215, count: 121 -2111: 0.514739, 0.507771 avg, 0.001000 rate, 25.881767 seconds, 540416 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.649609, Class: 1.000000, Obj: 0.309830, No Obj: 0.003281, Avg Recall: 0.863158, count: 95 -Region Avg IOU: 0.631913, Class: 1.000000, Obj: 0.400977, No Obj: 0.003348, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.586005, Class: 1.000000, Obj: 0.371564, No Obj: 0.002828, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.575049, Class: 1.000000, Obj: 0.294563, No Obj: 0.003602, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.634118, Class: 1.000000, Obj: 0.361132, No Obj: 0.003262, Avg Recall: 0.813559, count: 118 -Region Avg IOU: 0.618063, Class: 1.000000, Obj: 0.421946, No Obj: 0.002763, Avg Recall: 0.764706, count: 85 -Region Avg IOU: 0.641202, Class: 1.000000, Obj: 0.354665, No Obj: 0.003250, Avg Recall: 0.803279, count: 122 -Region Avg IOU: 0.569688, Class: 1.000000, Obj: 0.321350, No Obj: 0.002992, Avg Recall: 0.660377, count: 106 -2112: 0.495977, 0.506592 avg, 0.001000 rate, 27.459978 seconds, 540672 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.591845, Class: 1.000000, Obj: 0.343554, No Obj: 0.002583, Avg Recall: 0.724138, count: 87 -Region Avg IOU: 0.569525, Class: 1.000000, Obj: 0.287311, No Obj: 0.003076, Avg Recall: 0.701613, count: 124 -Region Avg IOU: 0.567583, Class: 1.000000, Obj: 0.312309, No Obj: 0.002898, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.610033, Class: 1.000000, Obj: 0.285527, No Obj: 0.002899, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.581992, Class: 1.000000, Obj: 0.334011, No Obj: 0.002850, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.580689, Class: 1.000000, Obj: 0.308007, No Obj: 0.002848, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.571537, Class: 1.000000, Obj: 0.298000, No Obj: 0.002626, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.599148, Class: 1.000000, Obj: 0.285007, No Obj: 0.003108, Avg Recall: 0.686567, count: 134 -2113: 0.536533, 0.509586 avg, 0.001000 rate, 26.942600 seconds, 540928 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.565980, Class: 1.000000, Obj: 0.300643, No Obj: 0.003196, Avg Recall: 0.643939, count: 132 -Region Avg IOU: 0.592469, Class: 1.000000, Obj: 0.312976, No Obj: 0.003005, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.557987, Class: 1.000000, Obj: 0.302666, No Obj: 0.002604, Avg Recall: 0.664000, count: 125 -Region Avg IOU: 0.575080, Class: 1.000000, Obj: 0.335684, No Obj: 0.002358, Avg Recall: 0.703704, count: 81 -Region Avg IOU: 0.617338, Class: 1.000000, Obj: 0.316560, No Obj: 0.002912, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.524624, Class: 1.000000, Obj: 0.290450, No Obj: 0.002369, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.618849, Class: 1.000000, Obj: 0.312161, No Obj: 0.002664, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.593132, Class: 1.000000, Obj: 0.315243, No Obj: 0.003040, Avg Recall: 0.774775, count: 111 -2114: 0.496526, 0.508280 avg, 0.001000 rate, 26.257109 seconds, 541184 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.570126, Class: 1.000000, Obj: 0.281831, No Obj: 0.002531, Avg Recall: 0.652174, count: 92 -Region Avg IOU: 0.589420, Class: 1.000000, Obj: 0.314055, No Obj: 0.002927, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.620666, Class: 1.000000, Obj: 0.278075, No Obj: 0.002466, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.572264, Class: 1.000000, Obj: 0.299501, No Obj: 0.002298, Avg Recall: 0.650602, count: 83 -Region Avg IOU: 0.594639, Class: 1.000000, Obj: 0.300235, No Obj: 0.002378, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.573112, Class: 1.000000, Obj: 0.304955, No Obj: 0.002514, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.583569, Class: 1.000000, Obj: 0.301086, No Obj: 0.002603, Avg Recall: 0.713115, count: 122 -Region Avg IOU: 0.585293, Class: 1.000000, Obj: 0.244175, No Obj: 0.003107, Avg Recall: 0.699187, count: 123 -2115: 0.457427, 0.503195 avg, 0.001000 rate, 27.431744 seconds, 541440 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.611702, Class: 1.000000, Obj: 0.282355, No Obj: 0.002697, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.611666, Class: 1.000000, Obj: 0.307365, No Obj: 0.002826, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.583243, Class: 1.000000, Obj: 0.281488, No Obj: 0.002745, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.586574, Class: 1.000000, Obj: 0.295528, No Obj: 0.002742, Avg Recall: 0.735043, count: 117 -Region Avg IOU: 0.574009, Class: 1.000000, Obj: 0.297420, No Obj: 0.002702, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.600429, Class: 1.000000, Obj: 0.306714, No Obj: 0.002724, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.604676, Class: 1.000000, Obj: 0.281917, No Obj: 0.002943, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.683039, Class: 1.000000, Obj: 0.349923, No Obj: 0.002632, Avg Recall: 0.849315, count: 73 -2116: 0.451989, 0.498074 avg, 0.001000 rate, 27.282757 seconds, 541696 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.556678, Class: 1.000000, Obj: 0.312056, No Obj: 0.002736, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.612689, Class: 1.000000, Obj: 0.323488, No Obj: 0.003200, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.585657, Class: 1.000000, Obj: 0.323425, No Obj: 0.003127, Avg Recall: 0.744361, count: 133 -Region Avg IOU: 0.616765, Class: 1.000000, Obj: 0.323139, No Obj: 0.003017, Avg Recall: 0.762712, count: 118 -Region Avg IOU: 0.592272, Class: 1.000000, Obj: 0.302281, No Obj: 0.003016, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.606535, Class: 1.000000, Obj: 0.331508, No Obj: 0.002568, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.589862, Class: 1.000000, Obj: 0.336860, No Obj: 0.002545, Avg Recall: 0.726191, count: 84 -Region Avg IOU: 0.582709, Class: 1.000000, Obj: 0.296824, No Obj: 0.002766, Avg Recall: 0.707071, count: 99 -2117: 0.482880, 0.496555 avg, 0.001000 rate, 26.547861 seconds, 541952 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.573516, Class: 1.000000, Obj: 0.353703, No Obj: 0.002891, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.632905, Class: 1.000000, Obj: 0.305657, No Obj: 0.003189, Avg Recall: 0.798319, count: 119 -Region Avg IOU: 0.605407, Class: 1.000000, Obj: 0.340593, No Obj: 0.002893, Avg Recall: 0.755725, count: 131 -Region Avg IOU: 0.577131, Class: 1.000000, Obj: 0.333511, No Obj: 0.003113, Avg Recall: 0.718750, count: 128 -Region Avg IOU: 0.591658, Class: 1.000000, Obj: 0.352444, No Obj: 0.003009, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.573052, Class: 1.000000, Obj: 0.287000, No Obj: 0.003054, Avg Recall: 0.648438, count: 128 -Region Avg IOU: 0.545296, Class: 1.000000, Obj: 0.331456, No Obj: 0.003118, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.637799, Class: 1.000000, Obj: 0.377457, No Obj: 0.002677, Avg Recall: 0.777778, count: 81 -2118: 0.460106, 0.492910 avg, 0.001000 rate, 26.022739 seconds, 542208 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.627365, Class: 1.000000, Obj: 0.350845, No Obj: 0.003293, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.608145, Class: 1.000000, Obj: 0.365457, No Obj: 0.002756, Avg Recall: 0.712329, count: 73 -Region Avg IOU: 0.593656, Class: 1.000000, Obj: 0.327461, No Obj: 0.002981, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.549393, Class: 1.000000, Obj: 0.319461, No Obj: 0.003346, Avg Recall: 0.622222, count: 135 -Region Avg IOU: 0.626183, Class: 1.000000, Obj: 0.360088, No Obj: 0.003216, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.592975, Class: 1.000000, Obj: 0.313022, No Obj: 0.002746, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.593164, Class: 1.000000, Obj: 0.357737, No Obj: 0.003339, Avg Recall: 0.694656, count: 131 -Region Avg IOU: 0.605970, Class: 1.000000, Obj: 0.375047, No Obj: 0.003140, Avg Recall: 0.747368, count: 95 -2119: 0.443410, 0.487960 avg, 0.001000 rate, 26.060938 seconds, 542464 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.583280, Class: 1.000000, Obj: 0.342055, No Obj: 0.003025, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.614582, Class: 1.000000, Obj: 0.368324, No Obj: 0.003154, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.592977, Class: 1.000000, Obj: 0.410298, No Obj: 0.002730, Avg Recall: 0.721519, count: 79 -Region Avg IOU: 0.615942, Class: 1.000000, Obj: 0.413781, No Obj: 0.003272, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.556871, Class: 1.000000, Obj: 0.313286, No Obj: 0.002753, Avg Recall: 0.652174, count: 92 -Region Avg IOU: 0.606624, Class: 1.000000, Obj: 0.352799, No Obj: 0.002761, Avg Recall: 0.712644, count: 87 -Region Avg IOU: 0.538394, Class: 1.000000, Obj: 0.300761, No Obj: 0.003167, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.558359, Class: 1.000000, Obj: 0.365509, No Obj: 0.002796, Avg Recall: 0.674157, count: 89 -2120: 0.390330, 0.478197 avg, 0.001000 rate, 27.226112 seconds, 542720 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 16.952007 seconds -Region Avg IOU: 0.591465, Class: 1.000000, Obj: 0.338120, No Obj: 0.003307, Avg Recall: 0.682540, count: 126 -Region Avg IOU: 0.605530, Class: 1.000000, Obj: 0.342606, No Obj: 0.003042, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.542586, Class: 1.000000, Obj: 0.365241, No Obj: 0.002933, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.618706, Class: 1.000000, Obj: 0.381864, No Obj: 0.003197, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.585919, Class: 1.000000, Obj: 0.325721, No Obj: 0.003038, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.587328, Class: 1.000000, Obj: 0.340202, No Obj: 0.003323, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.539761, Class: 1.000000, Obj: 0.349752, No Obj: 0.002829, Avg Recall: 0.622807, count: 114 -Region Avg IOU: 0.651281, Class: 1.000000, Obj: 0.432178, No Obj: 0.003495, Avg Recall: 0.798165, count: 109 -2121: 0.477271, 0.478104 avg, 0.001000 rate, 26.709600 seconds, 542976 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.594359, Class: 1.000000, Obj: 0.317677, No Obj: 0.003353, Avg Recall: 0.689076, count: 119 -Region Avg IOU: 0.570997, Class: 1.000000, Obj: 0.315786, No Obj: 0.003392, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.550606, Class: 1.000000, Obj: 0.299746, No Obj: 0.002870, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.555227, Class: 1.000000, Obj: 0.360795, No Obj: 0.003283, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.600568, Class: 1.000000, Obj: 0.317399, No Obj: 0.003379, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.579547, Class: 1.000000, Obj: 0.344886, No Obj: 0.003052, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.585136, Class: 1.000000, Obj: 0.365352, No Obj: 0.003693, Avg Recall: 0.696296, count: 135 -Region Avg IOU: 0.613546, Class: 1.000000, Obj: 0.345594, No Obj: 0.003389, Avg Recall: 0.736364, count: 110 -2122: 0.476612, 0.477955 avg, 0.001000 rate, 27.982445 seconds, 543232 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.573368, Class: 1.000000, Obj: 0.354463, No Obj: 0.003083, Avg Recall: 0.680672, count: 119 -Region Avg IOU: 0.554404, Class: 1.000000, Obj: 0.318661, No Obj: 0.003366, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.548009, Class: 1.000000, Obj: 0.320047, No Obj: 0.003193, Avg Recall: 0.638462, count: 130 -Region Avg IOU: 0.585651, Class: 1.000000, Obj: 0.320956, No Obj: 0.003370, Avg Recall: 0.718750, count: 128 -Region Avg IOU: 0.599798, Class: 1.000000, Obj: 0.340480, No Obj: 0.002951, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.555026, Class: 1.000000, Obj: 0.345954, No Obj: 0.003388, Avg Recall: 0.645161, count: 124 -Region Avg IOU: 0.565632, Class: 1.000000, Obj: 0.366883, No Obj: 0.002946, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.586578, Class: 1.000000, Obj: 0.332054, No Obj: 0.002799, Avg Recall: 0.756098, count: 82 -2123: 0.495265, 0.479686 avg, 0.001000 rate, 26.597416 seconds, 543488 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.571365, Class: 1.000000, Obj: 0.275631, No Obj: 0.002978, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.581126, Class: 1.000000, Obj: 0.327141, No Obj: 0.003013, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.614237, Class: 1.000000, Obj: 0.316326, No Obj: 0.003020, Avg Recall: 0.775862, count: 116 -Region Avg IOU: 0.574757, Class: 1.000000, Obj: 0.340261, No Obj: 0.003137, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.581112, Class: 1.000000, Obj: 0.306684, No Obj: 0.003181, Avg Recall: 0.686131, count: 137 -Region Avg IOU: 0.662841, Class: 1.000000, Obj: 0.344521, No Obj: 0.002620, Avg Recall: 0.850575, count: 87 -Region Avg IOU: 0.577800, Class: 1.000000, Obj: 0.314569, No Obj: 0.002966, Avg Recall: 0.691057, count: 123 -Region Avg IOU: 0.635273, Class: 1.000000, Obj: 0.321817, No Obj: 0.002624, Avg Recall: 0.780488, count: 82 -2124: 0.508645, 0.482582 avg, 0.001000 rate, 25.443144 seconds, 543744 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.600833, Class: 1.000000, Obj: 0.336355, No Obj: 0.003097, Avg Recall: 0.679688, count: 128 -Region Avg IOU: 0.590711, Class: 1.000000, Obj: 0.290767, No Obj: 0.002543, Avg Recall: 0.739583, count: 96 -Region Avg IOU: 0.555244, Class: 1.000000, Obj: 0.270588, No Obj: 0.002764, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.624602, Class: 1.000000, Obj: 0.307592, No Obj: 0.002820, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.572438, Class: 1.000000, Obj: 0.286161, No Obj: 0.002548, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.553940, Class: 1.000000, Obj: 0.317492, No Obj: 0.002918, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.589142, Class: 1.000000, Obj: 0.336447, No Obj: 0.002751, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.563271, Class: 1.000000, Obj: 0.307732, No Obj: 0.002706, Avg Recall: 0.715789, count: 95 -2125: 0.452941, 0.479618 avg, 0.001000 rate, 25.540243 seconds, 544000 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.543060, Class: 1.000000, Obj: 0.299002, No Obj: 0.002603, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.617764, Class: 1.000000, Obj: 0.304415, No Obj: 0.003074, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.588325, Class: 1.000000, Obj: 0.305521, No Obj: 0.002825, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.621892, Class: 1.000000, Obj: 0.335663, No Obj: 0.002551, Avg Recall: 0.773810, count: 84 -Region Avg IOU: 0.595126, Class: 1.000000, Obj: 0.337427, No Obj: 0.003044, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.594996, Class: 1.000000, Obj: 0.356580, No Obj: 0.002711, Avg Recall: 0.682927, count: 82 -Region Avg IOU: 0.572439, Class: 1.000000, Obj: 0.281917, No Obj: 0.002886, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.601359, Class: 1.000000, Obj: 0.336562, No Obj: 0.002659, Avg Recall: 0.725275, count: 91 -2126: 0.424948, 0.474151 avg, 0.001000 rate, 26.072950 seconds, 544256 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.616774, Class: 1.000000, Obj: 0.338882, No Obj: 0.002894, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.582172, Class: 1.000000, Obj: 0.317532, No Obj: 0.002627, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.627172, Class: 1.000000, Obj: 0.357722, No Obj: 0.002728, Avg Recall: 0.795455, count: 88 -Region Avg IOU: 0.521318, Class: 1.000000, Obj: 0.344250, No Obj: 0.002590, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.584962, Class: 1.000000, Obj: 0.317451, No Obj: 0.002836, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.622065, Class: 1.000000, Obj: 0.317409, No Obj: 0.003090, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.602497, Class: 1.000000, Obj: 0.340210, No Obj: 0.003156, Avg Recall: 0.726562, count: 128 -Region Avg IOU: 0.589886, Class: 1.000000, Obj: 0.260153, No Obj: 0.002937, Avg Recall: 0.730769, count: 104 -2127: 0.486963, 0.475432 avg, 0.001000 rate, 26.484123 seconds, 544512 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.590892, Class: 1.000000, Obj: 0.341504, No Obj: 0.002815, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.592310, Class: 1.000000, Obj: 0.299111, No Obj: 0.003466, Avg Recall: 0.748092, count: 131 -Region Avg IOU: 0.602829, Class: 1.000000, Obj: 0.323415, No Obj: 0.002904, Avg Recall: 0.740741, count: 81 -Region Avg IOU: 0.610794, Class: 1.000000, Obj: 0.336181, No Obj: 0.002499, Avg Recall: 0.707317, count: 82 -Region Avg IOU: 0.584161, Class: 1.000000, Obj: 0.309413, No Obj: 0.002475, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.632765, Class: 1.000000, Obj: 0.342806, No Obj: 0.002947, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.607912, Class: 1.000000, Obj: 0.345879, No Obj: 0.003531, Avg Recall: 0.756303, count: 119 -Region Avg IOU: 0.599571, Class: 1.000000, Obj: 0.317966, No Obj: 0.002959, Avg Recall: 0.702970, count: 101 -2128: 0.442759, 0.472165 avg, 0.001000 rate, 26.498384 seconds, 544768 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.600205, Class: 1.000000, Obj: 0.344626, No Obj: 0.003287, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.604498, Class: 1.000000, Obj: 0.333990, No Obj: 0.003326, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.585697, Class: 1.000000, Obj: 0.335445, No Obj: 0.002976, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.618788, Class: 1.000000, Obj: 0.339328, No Obj: 0.003106, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.559707, Class: 1.000000, Obj: 0.360786, No Obj: 0.002760, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.616646, Class: 1.000000, Obj: 0.301433, No Obj: 0.002928, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.595874, Class: 1.000000, Obj: 0.350763, No Obj: 0.003158, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.597035, Class: 1.000000, Obj: 0.369974, No Obj: 0.003226, Avg Recall: 0.756303, count: 119 -2129: 0.456628, 0.470611 avg, 0.001000 rate, 26.471806 seconds, 545024 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.622073, Class: 1.000000, Obj: 0.361603, No Obj: 0.003004, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.577671, Class: 1.000000, Obj: 0.328281, No Obj: 0.003592, Avg Recall: 0.681481, count: 135 -Region Avg IOU: 0.623593, Class: 1.000000, Obj: 0.365694, No Obj: 0.003538, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.587185, Class: 1.000000, Obj: 0.316330, No Obj: 0.003177, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.576789, Class: 1.000000, Obj: 0.294647, No Obj: 0.002801, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.566010, Class: 1.000000, Obj: 0.388761, No Obj: 0.003411, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.578083, Class: 1.000000, Obj: 0.361024, No Obj: 0.003030, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.576633, Class: 1.000000, Obj: 0.311121, No Obj: 0.003260, Avg Recall: 0.686275, count: 102 -2130: 0.472905, 0.470840 avg, 0.001000 rate, 27.032104 seconds, 545280 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 17.581375 seconds -Region Avg IOU: 0.437751, Class: 1.000000, Obj: 0.257065, No Obj: 0.004107, Avg Recall: 0.444444, count: 126 -Region Avg IOU: 0.490556, Class: 1.000000, Obj: 0.268911, No Obj: 0.004629, Avg Recall: 0.589744, count: 117 -Region Avg IOU: 0.472082, Class: 1.000000, Obj: 0.244545, No Obj: 0.004165, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.480143, Class: 1.000000, Obj: 0.272676, No Obj: 0.004671, Avg Recall: 0.486957, count: 115 -Region Avg IOU: 0.449834, Class: 1.000000, Obj: 0.268150, No Obj: 0.003723, Avg Recall: 0.464286, count: 84 -Region Avg IOU: 0.428928, Class: 1.000000, Obj: 0.213897, No Obj: 0.004187, Avg Recall: 0.468254, count: 126 -Region Avg IOU: 0.451009, Class: 1.000000, Obj: 0.224201, No Obj: 0.004170, Avg Recall: 0.468750, count: 128 -Region Avg IOU: 0.455947, Class: 1.000000, Obj: 0.235704, No Obj: 0.004257, Avg Recall: 0.483607, count: 122 -2131: 0.589503, 0.482707 avg, 0.001000 rate, 15.462389 seconds, 545536 images -Loaded: 3.862033 seconds -Region Avg IOU: 0.438550, Class: 1.000000, Obj: 0.226138, No Obj: 0.003540, Avg Recall: 0.488636, count: 88 -Region Avg IOU: 0.528426, Class: 1.000000, Obj: 0.289588, No Obj: 0.003999, Avg Recall: 0.597403, count: 77 -Region Avg IOU: 0.459126, Class: 1.000000, Obj: 0.271185, No Obj: 0.004899, Avg Recall: 0.495727, count: 117 -Region Avg IOU: 0.469115, Class: 1.000000, Obj: 0.247361, No Obj: 0.004614, Avg Recall: 0.496599, count: 147 -Region Avg IOU: 0.393060, Class: 1.000000, Obj: 0.242245, No Obj: 0.004089, Avg Recall: 0.390476, count: 105 -Region Avg IOU: 0.440634, Class: 1.000000, Obj: 0.207641, No Obj: 0.003933, Avg Recall: 0.438095, count: 105 -Region Avg IOU: 0.496140, Class: 1.000000, Obj: 0.278060, No Obj: 0.004313, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.455845, Class: 1.000000, Obj: 0.274080, No Obj: 0.004456, Avg Recall: 0.530769, count: 130 -2132: 0.533908, 0.487827 avg, 0.001000 rate, 15.552750 seconds, 545792 images -Loaded: 6.509258 seconds -Region Avg IOU: 0.405187, Class: 1.000000, Obj: 0.246420, No Obj: 0.004316, Avg Recall: 0.387755, count: 98 -Region Avg IOU: 0.454861, Class: 1.000000, Obj: 0.312877, No Obj: 0.004927, Avg Recall: 0.500000, count: 108 -Region Avg IOU: 0.532165, Class: 1.000000, Obj: 0.322603, No Obj: 0.004589, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.433586, Class: 1.000000, Obj: 0.296077, No Obj: 0.004357, Avg Recall: 0.464646, count: 99 -Region Avg IOU: 0.449335, Class: 1.000000, Obj: 0.274128, No Obj: 0.005060, Avg Recall: 0.504274, count: 117 -Region Avg IOU: 0.456809, Class: 1.000000, Obj: 0.285025, No Obj: 0.004969, Avg Recall: 0.500000, count: 122 -Region Avg IOU: 0.479097, Class: 1.000000, Obj: 0.279546, No Obj: 0.004670, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.411215, Class: 1.000000, Obj: 0.247341, No Obj: 0.004068, Avg Recall: 0.429825, count: 114 -2133: 0.488129, 0.487857 avg, 0.001000 rate, 18.383757 seconds, 546048 images -Loaded: 3.635980 seconds -Region Avg IOU: 0.429293, Class: 1.000000, Obj: 0.281819, No Obj: 0.004352, Avg Recall: 0.443038, count: 79 -Region Avg IOU: 0.439018, Class: 1.000000, Obj: 0.286128, No Obj: 0.004734, Avg Recall: 0.465347, count: 101 -Region Avg IOU: 0.467073, Class: 1.000000, Obj: 0.272760, No Obj: 0.004774, Avg Recall: 0.500000, count: 94 -Region Avg IOU: 0.408775, Class: 1.000000, Obj: 0.276719, No Obj: 0.004959, Avg Recall: 0.435115, count: 131 -Region Avg IOU: 0.496985, Class: 1.000000, Obj: 0.338606, No Obj: 0.004922, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.457594, Class: 1.000000, Obj: 0.334558, No Obj: 0.004960, Avg Recall: 0.515464, count: 97 -Region Avg IOU: 0.446068, Class: 1.000000, Obj: 0.305725, No Obj: 0.005533, Avg Recall: 0.473684, count: 133 -Region Avg IOU: 0.488759, Class: 1.000000, Obj: 0.289499, No Obj: 0.004709, Avg Recall: 0.548387, count: 93 -2134: 0.460599, 0.485131 avg, 0.001000 rate, 14.975438 seconds, 546304 images -Loaded: 6.141716 seconds -Region Avg IOU: 0.428590, Class: 1.000000, Obj: 0.296138, No Obj: 0.005098, Avg Recall: 0.456897, count: 116 -Region Avg IOU: 0.425409, Class: 1.000000, Obj: 0.311265, No Obj: 0.005174, Avg Recall: 0.393701, count: 127 -Region Avg IOU: 0.381453, Class: 1.000000, Obj: 0.300985, No Obj: 0.004658, Avg Recall: 0.361905, count: 105 -Region Avg IOU: 0.447646, Class: 1.000000, Obj: 0.279779, No Obj: 0.005458, Avg Recall: 0.469231, count: 130 -Region Avg IOU: 0.449567, Class: 1.000000, Obj: 0.260366, No Obj: 0.004902, Avg Recall: 0.534884, count: 86 -Region Avg IOU: 0.430369, Class: 1.000000, Obj: 0.287897, No Obj: 0.005295, Avg Recall: 0.456000, count: 125 -Region Avg IOU: 0.429854, Class: 1.000000, Obj: 0.287734, No Obj: 0.005174, Avg Recall: 0.465649, count: 131 -Region Avg IOU: 0.420703, Class: 1.000000, Obj: 0.293621, No Obj: 0.005178, Avg Recall: 0.430769, count: 130 -2135: 0.591273, 0.495745 avg, 0.001000 rate, 13.571046 seconds, 546560 images -Loaded: 7.908617 seconds -Region Avg IOU: 0.399250, Class: 1.000000, Obj: 0.293331, No Obj: 0.004875, Avg Recall: 0.404959, count: 121 -Region Avg IOU: 0.395912, Class: 1.000000, Obj: 0.326840, No Obj: 0.004433, Avg Recall: 0.414141, count: 99 -Region Avg IOU: 0.426357, Class: 1.000000, Obj: 0.295238, No Obj: 0.004887, Avg Recall: 0.453488, count: 86 -Region Avg IOU: 0.373387, Class: 1.000000, Obj: 0.255380, No Obj: 0.004732, Avg Recall: 0.363636, count: 99 -Region Avg IOU: 0.431855, Class: 1.000000, Obj: 0.310398, No Obj: 0.004511, Avg Recall: 0.490385, count: 104 -Region Avg IOU: 0.444444, Class: 1.000000, Obj: 0.340760, No Obj: 0.004164, Avg Recall: 0.488095, count: 84 -Region Avg IOU: 0.452315, Class: 1.000000, Obj: 0.319864, No Obj: 0.004954, Avg Recall: 0.436170, count: 94 -Region Avg IOU: 0.435192, Class: 1.000000, Obj: 0.292383, No Obj: 0.005647, Avg Recall: 0.470086, count: 117 -2136: 0.526026, 0.498774 avg, 0.001000 rate, 15.232807 seconds, 546816 images -Loaded: 4.695778 seconds -Region Avg IOU: 0.448128, Class: 1.000000, Obj: 0.332128, No Obj: 0.005189, Avg Recall: 0.500000, count: 106 -Region Avg IOU: 0.437045, Class: 1.000000, Obj: 0.314767, No Obj: 0.005743, Avg Recall: 0.455882, count: 136 -Region Avg IOU: 0.415770, Class: 1.000000, Obj: 0.328265, No Obj: 0.004825, Avg Recall: 0.453488, count: 86 -Region Avg IOU: 0.433363, Class: 1.000000, Obj: 0.266625, No Obj: 0.004573, Avg Recall: 0.435644, count: 101 -Region Avg IOU: 0.406707, Class: 1.000000, Obj: 0.277714, No Obj: 0.004941, Avg Recall: 0.408696, count: 115 -Region Avg IOU: 0.461552, Class: 1.000000, Obj: 0.327781, No Obj: 0.005134, Avg Recall: 0.490909, count: 110 -Region Avg IOU: 0.434189, Class: 1.000000, Obj: 0.318128, No Obj: 0.005008, Avg Recall: 0.444444, count: 99 -Region Avg IOU: 0.370305, Class: 1.000000, Obj: 0.290296, No Obj: 0.004583, Avg Recall: 0.385542, count: 83 -2137: 0.524781, 0.501374 avg, 0.001000 rate, 15.599106 seconds, 547072 images -Loaded: 4.185038 seconds -Region Avg IOU: 0.429906, Class: 1.000000, Obj: 0.249396, No Obj: 0.005116, Avg Recall: 0.464789, count: 142 -Region Avg IOU: 0.361188, Class: 1.000000, Obj: 0.248449, No Obj: 0.004527, Avg Recall: 0.352381, count: 105 -Region Avg IOU: 0.416219, Class: 1.000000, Obj: 0.300652, No Obj: 0.004877, Avg Recall: 0.418803, count: 117 -Region Avg IOU: 0.450878, Class: 1.000000, Obj: 0.294698, No Obj: 0.004548, Avg Recall: 0.495238, count: 105 -Region Avg IOU: 0.411131, Class: 1.000000, Obj: 0.251473, No Obj: 0.004639, Avg Recall: 0.435644, count: 101 -Region Avg IOU: 0.420045, Class: 1.000000, Obj: 0.301749, No Obj: 0.004847, Avg Recall: 0.400000, count: 110 -Region Avg IOU: 0.458290, Class: 1.000000, Obj: 0.291667, No Obj: 0.005139, Avg Recall: 0.494737, count: 95 -Region Avg IOU: 0.405124, Class: 1.000000, Obj: 0.273204, No Obj: 0.004185, Avg Recall: 0.390805, count: 87 -2138: 0.537085, 0.504945 avg, 0.001000 rate, 18.217785 seconds, 547328 images -Loaded: 1.678576 seconds -Region Avg IOU: 0.497211, Class: 1.000000, Obj: 0.311570, No Obj: 0.005133, Avg Recall: 0.593750, count: 96 -Region Avg IOU: 0.404828, Class: 1.000000, Obj: 0.273907, No Obj: 0.004593, Avg Recall: 0.404580, count: 131 -Region Avg IOU: 0.459325, Class: 1.000000, Obj: 0.298323, No Obj: 0.005011, Avg Recall: 0.491071, count: 112 -Region Avg IOU: 0.471852, Class: 1.000000, Obj: 0.270443, No Obj: 0.004730, Avg Recall: 0.474747, count: 99 -Region Avg IOU: 0.395659, Class: 1.000000, Obj: 0.232270, No Obj: 0.003745, Avg Recall: 0.431818, count: 88 -Region Avg IOU: 0.419813, Class: 1.000000, Obj: 0.288861, No Obj: 0.004872, Avg Recall: 0.426471, count: 136 -Region Avg IOU: 0.443705, Class: 1.000000, Obj: 0.273536, No Obj: 0.005188, Avg Recall: 0.459677, count: 124 -Region Avg IOU: 0.398537, Class: 1.000000, Obj: 0.275281, No Obj: 0.004875, Avg Recall: 0.368421, count: 114 -2139: 0.494149, 0.503866 avg, 0.001000 rate, 18.141552 seconds, 547584 images -Loaded: 1.766242 seconds -Region Avg IOU: 0.452713, Class: 1.000000, Obj: 0.299641, No Obj: 0.004330, Avg Recall: 0.475728, count: 103 -Region Avg IOU: 0.499613, Class: 1.000000, Obj: 0.289652, No Obj: 0.004181, Avg Recall: 0.548387, count: 93 -Region Avg IOU: 0.413435, Class: 1.000000, Obj: 0.275684, No Obj: 0.004103, Avg Recall: 0.435294, count: 85 -Region Avg IOU: 0.473236, Class: 1.000000, Obj: 0.266351, No Obj: 0.005034, Avg Recall: 0.483607, count: 122 -Region Avg IOU: 0.452027, Class: 1.000000, Obj: 0.238715, No Obj: 0.004764, Avg Recall: 0.496000, count: 125 -Region Avg IOU: 0.435419, Class: 1.000000, Obj: 0.232716, No Obj: 0.003534, Avg Recall: 0.464286, count: 84 -Region Avg IOU: 0.405713, Class: 1.000000, Obj: 0.234006, No Obj: 0.004272, Avg Recall: 0.408000, count: 125 -Region Avg IOU: 0.437580, Class: 1.000000, Obj: 0.256942, No Obj: 0.004515, Avg Recall: 0.450382, count: 131 -2140: 0.472331, 0.500712 avg, 0.001000 rate, 19.269552 seconds, 547840 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000077 seconds -Region Avg IOU: 0.619110, Class: 1.000000, Obj: 0.314096, No Obj: 0.003495, Avg Recall: 0.809160, count: 131 -Region Avg IOU: 0.577978, Class: 1.000000, Obj: 0.329986, No Obj: 0.002946, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.551247, Class: 1.000000, Obj: 0.368430, No Obj: 0.003078, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.584318, Class: 1.000000, Obj: 0.362451, No Obj: 0.003244, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.563046, Class: 1.000000, Obj: 0.394697, No Obj: 0.002869, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.630444, Class: 1.000000, Obj: 0.287169, No Obj: 0.003147, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.575179, Class: 1.000000, Obj: 0.387986, No Obj: 0.003126, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.536087, Class: 1.000000, Obj: 0.335682, No Obj: 0.002921, Avg Recall: 0.616071, count: 112 -2141: 0.592208, 0.509862 avg, 0.001000 rate, 27.211020 seconds, 548096 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.588977, Class: 1.000000, Obj: 0.321814, No Obj: 0.002853, Avg Recall: 0.735043, count: 117 -Region Avg IOU: 0.569221, Class: 1.000000, Obj: 0.299845, No Obj: 0.002452, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.573290, Class: 1.000000, Obj: 0.312730, No Obj: 0.002979, Avg Recall: 0.713178, count: 129 -Region Avg IOU: 0.539260, Class: 1.000000, Obj: 0.286708, No Obj: 0.002491, Avg Recall: 0.603175, count: 126 -Region Avg IOU: 0.627995, Class: 1.000000, Obj: 0.368978, No Obj: 0.002995, Avg Recall: 0.804348, count: 92 -Region Avg IOU: 0.612340, Class: 1.000000, Obj: 0.309261, No Obj: 0.002787, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.649287, Class: 1.000000, Obj: 0.340779, No Obj: 0.002799, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.610002, Class: 1.000000, Obj: 0.305425, No Obj: 0.002839, Avg Recall: 0.754098, count: 122 -2142: 0.525221, 0.511398 avg, 0.001000 rate, 29.134516 seconds, 548352 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.621327, Class: 1.000000, Obj: 0.260217, No Obj: 0.002825, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.632336, Class: 1.000000, Obj: 0.331308, No Obj: 0.002642, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.605486, Class: 1.000000, Obj: 0.257254, No Obj: 0.002576, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.629941, Class: 1.000000, Obj: 0.287329, No Obj: 0.002702, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.615755, Class: 1.000000, Obj: 0.272732, No Obj: 0.002451, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.595765, Class: 1.000000, Obj: 0.277588, No Obj: 0.002528, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.628101, Class: 1.000000, Obj: 0.307741, No Obj: 0.003072, Avg Recall: 0.807692, count: 130 -Region Avg IOU: 0.598100, Class: 1.000000, Obj: 0.289684, No Obj: 0.002405, Avg Recall: 0.675676, count: 111 -2143: 0.533006, 0.513559 avg, 0.001000 rate, 28.440817 seconds, 548608 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.613159, Class: 1.000000, Obj: 0.270454, No Obj: 0.002756, Avg Recall: 0.743802, count: 121 -Region Avg IOU: 0.585321, Class: 1.000000, Obj: 0.237756, No Obj: 0.002404, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.614724, Class: 1.000000, Obj: 0.260438, No Obj: 0.002389, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.620267, Class: 1.000000, Obj: 0.311297, No Obj: 0.002474, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.647616, Class: 1.000000, Obj: 0.321973, No Obj: 0.002414, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.591740, Class: 1.000000, Obj: 0.230466, No Obj: 0.002526, Avg Recall: 0.720280, count: 143 -Region Avg IOU: 0.584903, Class: 1.000000, Obj: 0.295748, No Obj: 0.002353, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.649912, Class: 1.000000, Obj: 0.291093, No Obj: 0.002512, Avg Recall: 0.790909, count: 110 -2144: 0.529880, 0.515191 avg, 0.001000 rate, 28.213980 seconds, 548864 images -Loaded: 0.000116 seconds -Region Avg IOU: 0.607191, Class: 1.000000, Obj: 0.252126, No Obj: 0.002470, Avg Recall: 0.764706, count: 136 -Region Avg IOU: 0.592959, Class: 1.000000, Obj: 0.305665, No Obj: 0.002262, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.601601, Class: 1.000000, Obj: 0.287797, No Obj: 0.002181, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.583752, Class: 1.000000, Obj: 0.292369, No Obj: 0.002131, Avg Recall: 0.768293, count: 82 -Region Avg IOU: 0.626708, Class: 1.000000, Obj: 0.303404, No Obj: 0.002411, Avg Recall: 0.798319, count: 119 -Region Avg IOU: 0.596970, Class: 1.000000, Obj: 0.240386, No Obj: 0.002710, Avg Recall: 0.752000, count: 125 -Region Avg IOU: 0.582408, Class: 1.000000, Obj: 0.290416, No Obj: 0.002521, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.617425, Class: 1.000000, Obj: 0.269560, No Obj: 0.002392, Avg Recall: 0.754902, count: 102 -2145: 0.552207, 0.518892 avg, 0.001000 rate, 28.715267 seconds, 549120 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.604393, Class: 1.000000, Obj: 0.272509, No Obj: 0.002291, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.609217, Class: 1.000000, Obj: 0.302952, No Obj: 0.002151, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.578037, Class: 1.000000, Obj: 0.239403, No Obj: 0.002550, Avg Recall: 0.720280, count: 143 -Region Avg IOU: 0.622227, Class: 1.000000, Obj: 0.279276, No Obj: 0.002667, Avg Recall: 0.812500, count: 128 -Region Avg IOU: 0.621947, Class: 1.000000, Obj: 0.337079, No Obj: 0.002474, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.622729, Class: 1.000000, Obj: 0.305484, No Obj: 0.002813, Avg Recall: 0.821705, count: 129 -Region Avg IOU: 0.627572, Class: 1.000000, Obj: 0.303741, No Obj: 0.002471, Avg Recall: 0.755814, count: 86 -Region Avg IOU: 0.605371, Class: 1.000000, Obj: 0.285838, No Obj: 0.002676, Avg Recall: 0.765625, count: 128 -2146: 0.545821, 0.521585 avg, 0.001000 rate, 27.527071 seconds, 549376 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.585509, Class: 1.000000, Obj: 0.298080, No Obj: 0.002611, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.618957, Class: 1.000000, Obj: 0.285938, No Obj: 0.002485, Avg Recall: 0.769231, count: 130 -Region Avg IOU: 0.648377, Class: 1.000000, Obj: 0.331773, No Obj: 0.002537, Avg Recall: 0.804878, count: 82 -Region Avg IOU: 0.659940, Class: 1.000000, Obj: 0.345507, No Obj: 0.002807, Avg Recall: 0.843750, count: 96 -Region Avg IOU: 0.589423, Class: 1.000000, Obj: 0.291123, No Obj: 0.002549, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.599593, Class: 1.000000, Obj: 0.271294, No Obj: 0.002353, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.622005, Class: 1.000000, Obj: 0.292532, No Obj: 0.002867, Avg Recall: 0.782609, count: 115 -Region Avg IOU: 0.608912, Class: 1.000000, Obj: 0.318579, No Obj: 0.002997, Avg Recall: 0.776596, count: 94 -2147: 0.496256, 0.519052 avg, 0.001000 rate, 30.311712 seconds, 549632 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.619200, Class: 1.000000, Obj: 0.325348, No Obj: 0.003131, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.611820, Class: 1.000000, Obj: 0.309325, No Obj: 0.002870, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.596076, Class: 1.000000, Obj: 0.310150, No Obj: 0.003002, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.614320, Class: 1.000000, Obj: 0.330947, No Obj: 0.002689, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.607257, Class: 1.000000, Obj: 0.285476, No Obj: 0.002732, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.580049, Class: 1.000000, Obj: 0.323057, No Obj: 0.003047, Avg Recall: 0.708661, count: 127 -Region Avg IOU: 0.576761, Class: 1.000000, Obj: 0.274726, No Obj: 0.002942, Avg Recall: 0.666667, count: 132 -Region Avg IOU: 0.625335, Class: 1.000000, Obj: 0.349495, No Obj: 0.002870, Avg Recall: 0.757576, count: 99 -2148: 0.521652, 0.519312 avg, 0.001000 rate, 28.143436 seconds, 549888 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.597526, Class: 1.000000, Obj: 0.350553, No Obj: 0.003151, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.555149, Class: 1.000000, Obj: 0.293712, No Obj: 0.003103, Avg Recall: 0.653543, count: 127 -Region Avg IOU: 0.621290, Class: 1.000000, Obj: 0.369968, No Obj: 0.003100, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.643300, Class: 1.000000, Obj: 0.326424, No Obj: 0.003340, Avg Recall: 0.796117, count: 103 -Region Avg IOU: 0.619184, Class: 1.000000, Obj: 0.349517, No Obj: 0.003363, Avg Recall: 0.763359, count: 131 -Region Avg IOU: 0.587863, Class: 1.000000, Obj: 0.333481, No Obj: 0.003106, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.628980, Class: 1.000000, Obj: 0.371158, No Obj: 0.003283, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.612319, Class: 1.000000, Obj: 0.333338, No Obj: 0.003657, Avg Recall: 0.778761, count: 113 -2149: 0.505037, 0.517885 avg, 0.001000 rate, 28.677280 seconds, 550144 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.615274, Class: 1.000000, Obj: 0.411254, No Obj: 0.003124, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.578175, Class: 1.000000, Obj: 0.290970, No Obj: 0.003225, Avg Recall: 0.693548, count: 124 -Region Avg IOU: 0.576036, Class: 1.000000, Obj: 0.348856, No Obj: 0.003113, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.624135, Class: 1.000000, Obj: 0.341138, No Obj: 0.003941, Avg Recall: 0.735714, count: 140 -Region Avg IOU: 0.599105, Class: 1.000000, Obj: 0.355979, No Obj: 0.003057, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.586654, Class: 1.000000, Obj: 0.376113, No Obj: 0.003022, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.583242, Class: 1.000000, Obj: 0.363160, No Obj: 0.003320, Avg Recall: 0.700787, count: 127 -Region Avg IOU: 0.612622, Class: 1.000000, Obj: 0.390336, No Obj: 0.003173, Avg Recall: 0.734513, count: 113 -2150: 0.520480, 0.518144 avg, 0.001000 rate, 27.459488 seconds, 550400 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 11.664909 seconds -Region Avg IOU: 0.587805, Class: 1.000000, Obj: 0.337387, No Obj: 0.004008, Avg Recall: 0.771186, count: 118 -Region Avg IOU: 0.546909, Class: 1.000000, Obj: 0.318393, No Obj: 0.004507, Avg Recall: 0.620690, count: 145 -Region Avg IOU: 0.571927, Class: 1.000000, Obj: 0.334039, No Obj: 0.004009, Avg Recall: 0.669355, count: 124 -Region Avg IOU: 0.565171, Class: 1.000000, Obj: 0.340405, No Obj: 0.003702, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.557199, Class: 1.000000, Obj: 0.326726, No Obj: 0.003586, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.538631, Class: 1.000000, Obj: 0.314974, No Obj: 0.003958, Avg Recall: 0.625954, count: 131 -Region Avg IOU: 0.558194, Class: 1.000000, Obj: 0.325651, No Obj: 0.004111, Avg Recall: 0.669492, count: 118 -Region Avg IOU: 0.574388, Class: 1.000000, Obj: 0.349887, No Obj: 0.003868, Avg Recall: 0.689076, count: 119 -2151: 0.595614, 0.525891 avg, 0.001000 rate, 24.629663 seconds, 550656 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.512888, Class: 1.000000, Obj: 0.347489, No Obj: 0.003383, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.512888, Class: 1.000000, Obj: 0.309208, No Obj: 0.003400, Avg Recall: 0.586207, count: 116 -Region Avg IOU: 0.558975, Class: 1.000000, Obj: 0.350062, No Obj: 0.003702, Avg Recall: 0.652542, count: 118 -Region Avg IOU: 0.562861, Class: 1.000000, Obj: 0.324747, No Obj: 0.003719, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.549535, Class: 1.000000, Obj: 0.328714, No Obj: 0.003585, Avg Recall: 0.629921, count: 127 -Region Avg IOU: 0.573696, Class: 1.000000, Obj: 0.361291, No Obj: 0.003488, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.553598, Class: 1.000000, Obj: 0.364629, No Obj: 0.003624, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.567083, Class: 1.000000, Obj: 0.372663, No Obj: 0.003875, Avg Recall: 0.672566, count: 113 -2152: 0.536718, 0.526974 avg, 0.001000 rate, 24.759272 seconds, 550912 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.535150, Class: 1.000000, Obj: 0.320246, No Obj: 0.003036, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.533758, Class: 1.000000, Obj: 0.333182, No Obj: 0.003119, Avg Recall: 0.571429, count: 105 -Region Avg IOU: 0.533490, Class: 1.000000, Obj: 0.344856, No Obj: 0.003691, Avg Recall: 0.625000, count: 136 -Region Avg IOU: 0.596771, Class: 1.000000, Obj: 0.371131, No Obj: 0.003157, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.533892, Class: 1.000000, Obj: 0.293731, No Obj: 0.003744, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.553461, Class: 1.000000, Obj: 0.319419, No Obj: 0.003212, Avg Recall: 0.652174, count: 92 -Region Avg IOU: 0.593469, Class: 1.000000, Obj: 0.344951, No Obj: 0.003956, Avg Recall: 0.717742, count: 124 -Region Avg IOU: 0.575946, Class: 1.000000, Obj: 0.295183, No Obj: 0.002971, Avg Recall: 0.679487, count: 78 -2153: 0.518131, 0.526090 avg, 0.001000 rate, 24.965328 seconds, 551168 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.595906, Class: 1.000000, Obj: 0.311092, No Obj: 0.003195, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.561188, Class: 1.000000, Obj: 0.346902, No Obj: 0.003513, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.508126, Class: 1.000000, Obj: 0.286927, No Obj: 0.003204, Avg Recall: 0.558333, count: 120 -Region Avg IOU: 0.564844, Class: 1.000000, Obj: 0.327171, No Obj: 0.003209, Avg Recall: 0.642857, count: 126 -Region Avg IOU: 0.516261, Class: 1.000000, Obj: 0.292574, No Obj: 0.002965, Avg Recall: 0.576577, count: 111 -Region Avg IOU: 0.554279, Class: 1.000000, Obj: 0.306603, No Obj: 0.003376, Avg Recall: 0.669173, count: 133 -Region Avg IOU: 0.544114, Class: 1.000000, Obj: 0.343979, No Obj: 0.003022, Avg Recall: 0.583333, count: 96 -Region Avg IOU: 0.571928, Class: 1.000000, Obj: 0.328092, No Obj: 0.003734, Avg Recall: 0.658537, count: 123 -2154: 0.464941, 0.519975 avg, 0.001000 rate, 26.789747 seconds, 551424 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.586633, Class: 1.000000, Obj: 0.311495, No Obj: 0.003249, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.603971, Class: 1.000000, Obj: 0.282368, No Obj: 0.003310, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.539256, Class: 1.000000, Obj: 0.309204, No Obj: 0.003101, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.541232, Class: 1.000000, Obj: 0.276554, No Obj: 0.002894, Avg Recall: 0.641667, count: 120 -Region Avg IOU: 0.555129, Class: 1.000000, Obj: 0.316763, No Obj: 0.003199, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.614654, Class: 1.000000, Obj: 0.300764, No Obj: 0.002982, Avg Recall: 0.788889, count: 90 -Region Avg IOU: 0.540741, Class: 1.000000, Obj: 0.335538, No Obj: 0.003594, Avg Recall: 0.627586, count: 145 -Region Avg IOU: 0.569739, Class: 1.000000, Obj: 0.303742, No Obj: 0.003025, Avg Recall: 0.701923, count: 104 -2155: 0.452158, 0.513193 avg, 0.001000 rate, 23.590885 seconds, 551680 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.569365, Class: 1.000000, Obj: 0.267844, No Obj: 0.002897, Avg Recall: 0.689655, count: 87 -Region Avg IOU: 0.592459, Class: 1.000000, Obj: 0.308554, No Obj: 0.003202, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.616205, Class: 1.000000, Obj: 0.361715, No Obj: 0.003229, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.618938, Class: 1.000000, Obj: 0.345946, No Obj: 0.002821, Avg Recall: 0.818182, count: 88 -Region Avg IOU: 0.505987, Class: 1.000000, Obj: 0.294392, No Obj: 0.002971, Avg Recall: 0.540984, count: 122 -Region Avg IOU: 0.590122, Class: 1.000000, Obj: 0.257908, No Obj: 0.003249, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.559594, Class: 1.000000, Obj: 0.309156, No Obj: 0.003354, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.605753, Class: 1.000000, Obj: 0.281401, No Obj: 0.002656, Avg Recall: 0.721649, count: 97 -2156: 0.480427, 0.509917 avg, 0.001000 rate, 25.974031 seconds, 551936 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.586317, Class: 1.000000, Obj: 0.295397, No Obj: 0.003118, Avg Recall: 0.661538, count: 130 -Region Avg IOU: 0.542252, Class: 1.000000, Obj: 0.252634, No Obj: 0.002963, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.572423, Class: 1.000000, Obj: 0.317899, No Obj: 0.003070, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.481029, Class: 1.000000, Obj: 0.248128, No Obj: 0.002864, Avg Recall: 0.542373, count: 118 -Region Avg IOU: 0.543467, Class: 1.000000, Obj: 0.291700, No Obj: 0.002844, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.573400, Class: 1.000000, Obj: 0.346372, No Obj: 0.002808, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.519143, Class: 1.000000, Obj: 0.327126, No Obj: 0.002870, Avg Recall: 0.592920, count: 113 -Region Avg IOU: 0.554698, Class: 1.000000, Obj: 0.343644, No Obj: 0.003027, Avg Recall: 0.643564, count: 101 -2157: 0.495300, 0.508455 avg, 0.001000 rate, 25.301449 seconds, 552192 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.531747, Class: 1.000000, Obj: 0.257922, No Obj: 0.003185, Avg Recall: 0.605839, count: 137 -Region Avg IOU: 0.587111, Class: 1.000000, Obj: 0.303746, No Obj: 0.003299, Avg Recall: 0.714286, count: 119 -Region Avg IOU: 0.568974, Class: 1.000000, Obj: 0.294492, No Obj: 0.002772, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.543347, Class: 1.000000, Obj: 0.285451, No Obj: 0.002909, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.585495, Class: 1.000000, Obj: 0.304627, No Obj: 0.002923, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.590203, Class: 1.000000, Obj: 0.339627, No Obj: 0.003241, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.560377, Class: 1.000000, Obj: 0.314649, No Obj: 0.003303, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.554269, Class: 1.000000, Obj: 0.307843, No Obj: 0.003649, Avg Recall: 0.641667, count: 120 -2158: 0.495567, 0.507166 avg, 0.001000 rate, 25.242472 seconds, 552448 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.536712, Class: 1.000000, Obj: 0.336714, No Obj: 0.003309, Avg Recall: 0.644444, count: 135 -Region Avg IOU: 0.602896, Class: 1.000000, Obj: 0.342269, No Obj: 0.003926, Avg Recall: 0.735714, count: 140 -Region Avg IOU: 0.572478, Class: 1.000000, Obj: 0.371649, No Obj: 0.003145, Avg Recall: 0.669565, count: 115 -Region Avg IOU: 0.525495, Class: 1.000000, Obj: 0.293544, No Obj: 0.003258, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.531286, Class: 1.000000, Obj: 0.353291, No Obj: 0.003043, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.572109, Class: 1.000000, Obj: 0.335072, No Obj: 0.002726, Avg Recall: 0.581081, count: 74 -Region Avg IOU: 0.587830, Class: 1.000000, Obj: 0.350716, No Obj: 0.003349, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.565217, Class: 1.000000, Obj: 0.323395, No Obj: 0.003093, Avg Recall: 0.721649, count: 97 -2159: 0.454417, 0.501891 avg, 0.001000 rate, 25.945616 seconds, 552704 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.607187, Class: 1.000000, Obj: 0.361875, No Obj: 0.003479, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.599416, Class: 1.000000, Obj: 0.309845, No Obj: 0.004305, Avg Recall: 0.705882, count: 153 -Region Avg IOU: 0.555499, Class: 1.000000, Obj: 0.337945, No Obj: 0.003606, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.619904, Class: 1.000000, Obj: 0.380272, No Obj: 0.003584, Avg Recall: 0.779070, count: 86 -Region Avg IOU: 0.552272, Class: 1.000000, Obj: 0.318938, No Obj: 0.003414, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.566822, Class: 1.000000, Obj: 0.313929, No Obj: 0.003544, Avg Recall: 0.655462, count: 119 -Region Avg IOU: 0.562671, Class: 1.000000, Obj: 0.358356, No Obj: 0.003551, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.566121, Class: 1.000000, Obj: 0.334254, No Obj: 0.003744, Avg Recall: 0.675439, count: 114 -2160: 0.489497, 0.500652 avg, 0.001000 rate, 23.922176 seconds, 552960 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000070 seconds -Region Avg IOU: 0.575891, Class: 1.000000, Obj: 0.373697, No Obj: 0.003483, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.568984, Class: 1.000000, Obj: 0.342369, No Obj: 0.003836, Avg Recall: 0.682540, count: 126 -Region Avg IOU: 0.536588, Class: 1.000000, Obj: 0.301881, No Obj: 0.003655, Avg Recall: 0.657143, count: 140 -Region Avg IOU: 0.579436, Class: 1.000000, Obj: 0.347724, No Obj: 0.003749, Avg Recall: 0.679389, count: 131 -Region Avg IOU: 0.558474, Class: 1.000000, Obj: 0.346563, No Obj: 0.002983, Avg Recall: 0.659341, count: 91 -Region Avg IOU: 0.612910, Class: 1.000000, Obj: 0.316355, No Obj: 0.003895, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.602383, Class: 1.000000, Obj: 0.326080, No Obj: 0.003019, Avg Recall: 0.730769, count: 78 -Region Avg IOU: 0.611387, Class: 1.000000, Obj: 0.386450, No Obj: 0.003741, Avg Recall: 0.703125, count: 128 -2161: 0.502297, 0.500816 avg, 0.001000 rate, 24.692747 seconds, 553216 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.650036, Class: 1.000000, Obj: 0.321995, No Obj: 0.003469, Avg Recall: 0.818965, count: 116 -Region Avg IOU: 0.594668, Class: 1.000000, Obj: 0.364410, No Obj: 0.003635, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.539814, Class: 1.000000, Obj: 0.350447, No Obj: 0.003110, Avg Recall: 0.586207, count: 116 -Region Avg IOU: 0.577996, Class: 1.000000, Obj: 0.325786, No Obj: 0.003360, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.618967, Class: 1.000000, Obj: 0.346826, No Obj: 0.003308, Avg Recall: 0.746988, count: 83 -Region Avg IOU: 0.588124, Class: 1.000000, Obj: 0.347290, No Obj: 0.003359, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.620162, Class: 1.000000, Obj: 0.356742, No Obj: 0.004004, Avg Recall: 0.755556, count: 135 -Region Avg IOU: 0.579944, Class: 1.000000, Obj: 0.404131, No Obj: 0.003439, Avg Recall: 0.648352, count: 91 -2162: 0.473792, 0.498114 avg, 0.001000 rate, 24.316477 seconds, 553472 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.534042, Class: 1.000000, Obj: 0.336525, No Obj: 0.003140, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.619516, Class: 1.000000, Obj: 0.325803, No Obj: 0.004305, Avg Recall: 0.773973, count: 146 -Region Avg IOU: 0.586668, Class: 1.000000, Obj: 0.408235, No Obj: 0.003366, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.576496, Class: 1.000000, Obj: 0.335573, No Obj: 0.003737, Avg Recall: 0.678571, count: 140 -Region Avg IOU: 0.588243, Class: 1.000000, Obj: 0.359714, No Obj: 0.003100, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.603989, Class: 1.000000, Obj: 0.379915, No Obj: 0.003413, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.544680, Class: 1.000000, Obj: 0.366681, No Obj: 0.003190, Avg Recall: 0.597826, count: 92 -Region Avg IOU: 0.575726, Class: 1.000000, Obj: 0.340951, No Obj: 0.003903, Avg Recall: 0.710145, count: 138 -2163: 0.514795, 0.499782 avg, 0.001000 rate, 25.342319 seconds, 553728 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.640674, Class: 1.000000, Obj: 0.373211, No Obj: 0.003191, Avg Recall: 0.843750, count: 96 -Region Avg IOU: 0.570629, Class: 1.000000, Obj: 0.336857, No Obj: 0.003092, Avg Recall: 0.719298, count: 114 -Region Avg IOU: 0.607292, Class: 1.000000, Obj: 0.347915, No Obj: 0.003883, Avg Recall: 0.687500, count: 128 -Region Avg IOU: 0.572696, Class: 1.000000, Obj: 0.345426, No Obj: 0.003533, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.570796, Class: 1.000000, Obj: 0.334601, No Obj: 0.003183, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.599000, Class: 1.000000, Obj: 0.403424, No Obj: 0.002958, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.609130, Class: 1.000000, Obj: 0.404673, No Obj: 0.002749, Avg Recall: 0.714286, count: 77 -Region Avg IOU: 0.559038, Class: 1.000000, Obj: 0.328378, No Obj: 0.003009, Avg Recall: 0.711538, count: 104 -2164: 0.452556, 0.495059 avg, 0.001000 rate, 26.348848 seconds, 553984 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.624453, Class: 1.000000, Obj: 0.360849, No Obj: 0.003046, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.578427, Class: 1.000000, Obj: 0.295094, No Obj: 0.002984, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.582479, Class: 1.000000, Obj: 0.344011, No Obj: 0.003224, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.555565, Class: 1.000000, Obj: 0.354588, No Obj: 0.003432, Avg Recall: 0.634146, count: 123 -Region Avg IOU: 0.581691, Class: 1.000000, Obj: 0.372366, No Obj: 0.002932, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.579135, Class: 1.000000, Obj: 0.323874, No Obj: 0.002903, Avg Recall: 0.681818, count: 88 -Region Avg IOU: 0.585895, Class: 1.000000, Obj: 0.355869, No Obj: 0.002745, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.593346, Class: 1.000000, Obj: 0.303631, No Obj: 0.003107, Avg Recall: 0.743119, count: 109 -2165: 0.461000, 0.491654 avg, 0.001000 rate, 27.242140 seconds, 554240 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.622746, Class: 1.000000, Obj: 0.325759, No Obj: 0.002917, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.642549, Class: 1.000000, Obj: 0.343567, No Obj: 0.003347, Avg Recall: 0.798387, count: 124 -Region Avg IOU: 0.607249, Class: 1.000000, Obj: 0.290227, No Obj: 0.002600, Avg Recall: 0.802632, count: 76 -Region Avg IOU: 0.540162, Class: 1.000000, Obj: 0.281967, No Obj: 0.002946, Avg Recall: 0.617188, count: 128 -Region Avg IOU: 0.568697, Class: 1.000000, Obj: 0.311519, No Obj: 0.002812, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.585678, Class: 1.000000, Obj: 0.305614, No Obj: 0.003399, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.589103, Class: 1.000000, Obj: 0.294419, No Obj: 0.003124, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.613333, Class: 1.000000, Obj: 0.303526, No Obj: 0.002461, Avg Recall: 0.753086, count: 81 -2166: 0.465538, 0.489042 avg, 0.001000 rate, 27.570398 seconds, 554496 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.579708, Class: 1.000000, Obj: 0.316366, No Obj: 0.003377, Avg Recall: 0.673333, count: 150 -Region Avg IOU: 0.573117, Class: 1.000000, Obj: 0.320375, No Obj: 0.002887, Avg Recall: 0.713115, count: 122 -Region Avg IOU: 0.639797, Class: 1.000000, Obj: 0.293804, No Obj: 0.003163, Avg Recall: 0.775000, count: 120 -Region Avg IOU: 0.611050, Class: 1.000000, Obj: 0.337608, No Obj: 0.002873, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.559413, Class: 1.000000, Obj: 0.290698, No Obj: 0.002350, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.622785, Class: 1.000000, Obj: 0.340406, No Obj: 0.002942, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.615914, Class: 1.000000, Obj: 0.317505, No Obj: 0.002904, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.583296, Class: 1.000000, Obj: 0.294176, No Obj: 0.002531, Avg Recall: 0.688889, count: 90 -2167: 0.465990, 0.486737 avg, 0.001000 rate, 27.314074 seconds, 554752 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.663847, Class: 1.000000, Obj: 0.295720, No Obj: 0.002813, Avg Recall: 0.843478, count: 115 -Region Avg IOU: 0.563094, Class: 1.000000, Obj: 0.281309, No Obj: 0.002670, Avg Recall: 0.686567, count: 134 -Region Avg IOU: 0.587025, Class: 1.000000, Obj: 0.324337, No Obj: 0.003011, Avg Recall: 0.696000, count: 125 -Region Avg IOU: 0.537255, Class: 1.000000, Obj: 0.284974, No Obj: 0.002711, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.589507, Class: 1.000000, Obj: 0.332777, No Obj: 0.002315, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.579071, Class: 1.000000, Obj: 0.278818, No Obj: 0.002692, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.597525, Class: 1.000000, Obj: 0.315291, No Obj: 0.002243, Avg Recall: 0.720930, count: 86 -Region Avg IOU: 0.578409, Class: 1.000000, Obj: 0.281916, No Obj: 0.002541, Avg Recall: 0.699115, count: 113 -2168: 0.447046, 0.482768 avg, 0.001000 rate, 26.259972 seconds, 555008 images -Loaded: 0.000117 seconds -Region Avg IOU: 0.637168, Class: 1.000000, Obj: 0.330843, No Obj: 0.002564, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.621398, Class: 1.000000, Obj: 0.283000, No Obj: 0.002752, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.529652, Class: 1.000000, Obj: 0.276945, No Obj: 0.002742, Avg Recall: 0.608333, count: 120 -Region Avg IOU: 0.583016, Class: 1.000000, Obj: 0.329081, No Obj: 0.002555, Avg Recall: 0.743902, count: 82 -Region Avg IOU: 0.619288, Class: 1.000000, Obj: 0.334135, No Obj: 0.002719, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.593580, Class: 1.000000, Obj: 0.354999, No Obj: 0.002555, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.617940, Class: 1.000000, Obj: 0.304874, No Obj: 0.003228, Avg Recall: 0.795775, count: 142 -Region Avg IOU: 0.578518, Class: 1.000000, Obj: 0.340263, No Obj: 0.002562, Avg Recall: 0.715909, count: 88 -2169: 0.463655, 0.480856 avg, 0.001000 rate, 27.071833 seconds, 555264 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.574372, Class: 1.000000, Obj: 0.330836, No Obj: 0.002602, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.593292, Class: 1.000000, Obj: 0.318210, No Obj: 0.002650, Avg Recall: 0.666667, count: 126 -Region Avg IOU: 0.586451, Class: 1.000000, Obj: 0.292033, No Obj: 0.003049, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.612886, Class: 1.000000, Obj: 0.361933, No Obj: 0.002763, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.596643, Class: 1.000000, Obj: 0.300544, No Obj: 0.003424, Avg Recall: 0.729508, count: 122 -Region Avg IOU: 0.610148, Class: 1.000000, Obj: 0.360556, No Obj: 0.003186, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.600975, Class: 1.000000, Obj: 0.341144, No Obj: 0.002963, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.603363, Class: 1.000000, Obj: 0.337392, No Obj: 0.003190, Avg Recall: 0.751938, count: 129 -2170: 0.484443, 0.481215 avg, 0.001000 rate, 26.275082 seconds, 555520 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 15.081589 seconds -Region Avg IOU: 0.501503, Class: 1.000000, Obj: 0.326892, No Obj: 0.003663, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.564355, Class: 1.000000, Obj: 0.293748, No Obj: 0.004027, Avg Recall: 0.659420, count: 138 -Region Avg IOU: 0.570729, Class: 1.000000, Obj: 0.336257, No Obj: 0.003306, Avg Recall: 0.681818, count: 88 -Region Avg IOU: 0.546736, Class: 1.000000, Obj: 0.292943, No Obj: 0.003321, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.539336, Class: 1.000000, Obj: 0.338703, No Obj: 0.003162, Avg Recall: 0.587500, count: 80 -Region Avg IOU: 0.527606, Class: 1.000000, Obj: 0.299062, No Obj: 0.003457, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.570689, Class: 1.000000, Obj: 0.327911, No Obj: 0.003506, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.532654, Class: 1.000000, Obj: 0.330231, No Obj: 0.003563, Avg Recall: 0.642857, count: 98 -2171: 0.467936, 0.479887 avg, 0.001000 rate, 23.339087 seconds, 555776 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.522818, Class: 1.000000, Obj: 0.297920, No Obj: 0.003969, Avg Recall: 0.595588, count: 136 -Region Avg IOU: 0.562585, Class: 1.000000, Obj: 0.299847, No Obj: 0.004364, Avg Recall: 0.645161, count: 124 -Region Avg IOU: 0.566469, Class: 1.000000, Obj: 0.329038, No Obj: 0.003555, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.552715, Class: 1.000000, Obj: 0.364760, No Obj: 0.003750, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.519181, Class: 1.000000, Obj: 0.277286, No Obj: 0.003779, Avg Recall: 0.569231, count: 130 -Region Avg IOU: 0.560028, Class: 1.000000, Obj: 0.322053, No Obj: 0.003538, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.499052, Class: 1.000000, Obj: 0.317638, No Obj: 0.003179, Avg Recall: 0.605769, count: 104 -Region Avg IOU: 0.543308, Class: 1.000000, Obj: 0.289926, No Obj: 0.004018, Avg Recall: 0.609091, count: 110 -2172: 0.537373, 0.485636 avg, 0.001000 rate, 23.000547 seconds, 556032 images -Loaded: 0.000115 seconds -Region Avg IOU: 0.488087, Class: 1.000000, Obj: 0.275428, No Obj: 0.003545, Avg Recall: 0.577586, count: 116 -Region Avg IOU: 0.537682, Class: 1.000000, Obj: 0.310663, No Obj: 0.004217, Avg Recall: 0.581395, count: 129 -Region Avg IOU: 0.541795, Class: 1.000000, Obj: 0.391586, No Obj: 0.004230, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.502580, Class: 1.000000, Obj: 0.316314, No Obj: 0.003709, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.507507, Class: 1.000000, Obj: 0.340179, No Obj: 0.003827, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.540719, Class: 1.000000, Obj: 0.346543, No Obj: 0.003453, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.529895, Class: 1.000000, Obj: 0.308870, No Obj: 0.004207, Avg Recall: 0.612245, count: 147 -Region Avg IOU: 0.527489, Class: 1.000000, Obj: 0.346346, No Obj: 0.004146, Avg Recall: 0.583333, count: 120 -2173: 0.570715, 0.494144 avg, 0.001000 rate, 24.836544 seconds, 556288 images -Loaded: 0.056164 seconds -Region Avg IOU: 0.539104, Class: 1.000000, Obj: 0.362105, No Obj: 0.003753, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.573196, Class: 1.000000, Obj: 0.344838, No Obj: 0.003345, Avg Recall: 0.765432, count: 81 -Region Avg IOU: 0.553470, Class: 1.000000, Obj: 0.364624, No Obj: 0.003915, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.494664, Class: 1.000000, Obj: 0.314574, No Obj: 0.003991, Avg Recall: 0.566372, count: 113 -Region Avg IOU: 0.553630, Class: 1.000000, Obj: 0.323484, No Obj: 0.004477, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.579055, Class: 1.000000, Obj: 0.420685, No Obj: 0.003770, Avg Recall: 0.680000, count: 75 -Region Avg IOU: 0.473506, Class: 1.000000, Obj: 0.276897, No Obj: 0.003933, Avg Recall: 0.504000, count: 125 -Region Avg IOU: 0.552043, Class: 1.000000, Obj: 0.375910, No Obj: 0.003717, Avg Recall: 0.645161, count: 93 -2174: 0.444040, 0.489133 avg, 0.001000 rate, 24.469845 seconds, 556544 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.527882, Class: 1.000000, Obj: 0.349767, No Obj: 0.003713, Avg Recall: 0.632184, count: 87 -Region Avg IOU: 0.530382, Class: 1.000000, Obj: 0.338084, No Obj: 0.003994, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.531891, Class: 1.000000, Obj: 0.363882, No Obj: 0.004034, Avg Recall: 0.643478, count: 115 -Region Avg IOU: 0.544621, Class: 1.000000, Obj: 0.360005, No Obj: 0.004358, Avg Recall: 0.610687, count: 131 -Region Avg IOU: 0.562265, Class: 1.000000, Obj: 0.379749, No Obj: 0.004506, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.544230, Class: 1.000000, Obj: 0.399147, No Obj: 0.004422, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.566029, Class: 1.000000, Obj: 0.343768, No Obj: 0.004778, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.536547, Class: 1.000000, Obj: 0.370121, No Obj: 0.003863, Avg Recall: 0.627660, count: 94 -2175: 0.456283, 0.485848 avg, 0.001000 rate, 23.869787 seconds, 556800 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.528939, Class: 1.000000, Obj: 0.331848, No Obj: 0.004354, Avg Recall: 0.677165, count: 127 -Region Avg IOU: 0.512738, Class: 1.000000, Obj: 0.276811, No Obj: 0.003923, Avg Recall: 0.560976, count: 123 -Region Avg IOU: 0.528529, Class: 1.000000, Obj: 0.411848, No Obj: 0.003410, Avg Recall: 0.597222, count: 72 -Region Avg IOU: 0.558067, Class: 1.000000, Obj: 0.339472, No Obj: 0.003594, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.536847, Class: 1.000000, Obj: 0.308140, No Obj: 0.004764, Avg Recall: 0.635135, count: 148 -Region Avg IOU: 0.566426, Class: 1.000000, Obj: 0.342253, No Obj: 0.003850, Avg Recall: 0.726191, count: 84 -Region Avg IOU: 0.565168, Class: 1.000000, Obj: 0.336206, No Obj: 0.004457, Avg Recall: 0.664000, count: 125 -Region Avg IOU: 0.497457, Class: 1.000000, Obj: 0.336613, No Obj: 0.003731, Avg Recall: 0.528090, count: 89 -2176: 0.523608, 0.489624 avg, 0.001000 rate, 23.000263 seconds, 557056 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.545304, Class: 1.000000, Obj: 0.360027, No Obj: 0.004043, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.517875, Class: 1.000000, Obj: 0.318860, No Obj: 0.003608, Avg Recall: 0.568965, count: 116 -Region Avg IOU: 0.495856, Class: 1.000000, Obj: 0.348994, No Obj: 0.003403, Avg Recall: 0.540000, count: 100 -Region Avg IOU: 0.472418, Class: 1.000000, Obj: 0.325182, No Obj: 0.003616, Avg Recall: 0.559633, count: 109 -Region Avg IOU: 0.503964, Class: 1.000000, Obj: 0.314939, No Obj: 0.004020, Avg Recall: 0.568965, count: 116 -Region Avg IOU: 0.524638, Class: 1.000000, Obj: 0.302292, No Obj: 0.003789, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.515110, Class: 1.000000, Obj: 0.329570, No Obj: 0.003758, Avg Recall: 0.620690, count: 116 -Region Avg IOU: 0.495419, Class: 1.000000, Obj: 0.326131, No Obj: 0.003607, Avg Recall: 0.571429, count: 133 -2177: 0.476134, 0.488275 avg, 0.001000 rate, 22.918779 seconds, 557312 images -Loaded: 0.000055 seconds -Region Avg IOU: 0.525325, Class: 1.000000, Obj: 0.296703, No Obj: 0.003691, Avg Recall: 0.610169, count: 118 -Region Avg IOU: 0.548999, Class: 1.000000, Obj: 0.323315, No Obj: 0.003778, Avg Recall: 0.608247, count: 97 -Region Avg IOU: 0.544105, Class: 1.000000, Obj: 0.364772, No Obj: 0.003676, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.540151, Class: 1.000000, Obj: 0.332105, No Obj: 0.003654, Avg Recall: 0.610169, count: 118 -Region Avg IOU: 0.485979, Class: 1.000000, Obj: 0.322505, No Obj: 0.003200, Avg Recall: 0.554348, count: 92 -Region Avg IOU: 0.523155, Class: 1.000000, Obj: 0.321994, No Obj: 0.003577, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.503571, Class: 1.000000, Obj: 0.318526, No Obj: 0.003446, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.503288, Class: 1.000000, Obj: 0.295839, No Obj: 0.003696, Avg Recall: 0.531250, count: 128 -2178: 0.482353, 0.487683 avg, 0.001000 rate, 22.396021 seconds, 557568 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.517786, Class: 1.000000, Obj: 0.286478, No Obj: 0.003217, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.511858, Class: 1.000000, Obj: 0.314597, No Obj: 0.003078, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.512362, Class: 1.000000, Obj: 0.268882, No Obj: 0.003657, Avg Recall: 0.600000, count: 120 -Region Avg IOU: 0.550289, Class: 1.000000, Obj: 0.326424, No Obj: 0.003415, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.508349, Class: 1.000000, Obj: 0.315019, No Obj: 0.003252, Avg Recall: 0.547619, count: 84 -Region Avg IOU: 0.475180, Class: 1.000000, Obj: 0.304110, No Obj: 0.003866, Avg Recall: 0.506579, count: 152 -Region Avg IOU: 0.552607, Class: 1.000000, Obj: 0.306528, No Obj: 0.003418, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.541674, Class: 1.000000, Obj: 0.323350, No Obj: 0.003264, Avg Recall: 0.596774, count: 124 -2179: 0.450817, 0.483996 avg, 0.001000 rate, 21.612820 seconds, 557824 images -Loaded: 1.583904 seconds -Region Avg IOU: 0.513666, Class: 1.000000, Obj: 0.310661, No Obj: 0.003119, Avg Recall: 0.583333, count: 96 -Region Avg IOU: 0.496648, Class: 1.000000, Obj: 0.292438, No Obj: 0.003697, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.516278, Class: 1.000000, Obj: 0.316376, No Obj: 0.002940, Avg Recall: 0.571429, count: 77 -Region Avg IOU: 0.504917, Class: 1.000000, Obj: 0.312854, No Obj: 0.003380, Avg Recall: 0.546219, count: 119 -Region Avg IOU: 0.521917, Class: 1.000000, Obj: 0.329880, No Obj: 0.003275, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.492618, Class: 1.000000, Obj: 0.283597, No Obj: 0.003801, Avg Recall: 0.553957, count: 139 -Region Avg IOU: 0.507892, Class: 1.000000, Obj: 0.297202, No Obj: 0.003172, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.535573, Class: 1.000000, Obj: 0.295316, No Obj: 0.003950, Avg Recall: 0.680000, count: 125 -2180: 0.507088, 0.486306 avg, 0.001000 rate, 22.843920 seconds, 558080 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000065 seconds -Region Avg IOU: 0.582686, Class: 1.000000, Obj: 0.342325, No Obj: 0.002824, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.580878, Class: 1.000000, Obj: 0.296093, No Obj: 0.003028, Avg Recall: 0.750000, count: 128 -Region Avg IOU: 0.622826, Class: 1.000000, Obj: 0.351460, No Obj: 0.002668, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.540214, Class: 1.000000, Obj: 0.308105, No Obj: 0.003049, Avg Recall: 0.656489, count: 131 -Region Avg IOU: 0.588059, Class: 1.000000, Obj: 0.338047, No Obj: 0.002790, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.569159, Class: 1.000000, Obj: 0.323771, No Obj: 0.003242, Avg Recall: 0.676056, count: 142 -Region Avg IOU: 0.593307, Class: 1.000000, Obj: 0.380533, No Obj: 0.002817, Avg Recall: 0.792683, count: 82 -Region Avg IOU: 0.589737, Class: 1.000000, Obj: 0.328594, No Obj: 0.002539, Avg Recall: 0.725275, count: 91 -2181: 0.475743, 0.485249 avg, 0.001000 rate, 27.159027 seconds, 558336 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.601785, Class: 1.000000, Obj: 0.354831, No Obj: 0.002846, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.594134, Class: 1.000000, Obj: 0.310460, No Obj: 0.002401, Avg Recall: 0.703297, count: 91 -Region Avg IOU: 0.601054, Class: 1.000000, Obj: 0.331711, No Obj: 0.003094, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.612424, Class: 1.000000, Obj: 0.318066, No Obj: 0.002700, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.567545, Class: 1.000000, Obj: 0.266010, No Obj: 0.003315, Avg Recall: 0.671429, count: 140 -Region Avg IOU: 0.611714, Class: 1.000000, Obj: 0.356269, No Obj: 0.002968, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.571414, Class: 1.000000, Obj: 0.338257, No Obj: 0.002728, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.583551, Class: 1.000000, Obj: 0.310774, No Obj: 0.002941, Avg Recall: 0.728070, count: 114 -2182: 0.487346, 0.485459 avg, 0.001000 rate, 27.072840 seconds, 558592 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.586135, Class: 1.000000, Obj: 0.353720, No Obj: 0.002610, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.613988, Class: 1.000000, Obj: 0.316700, No Obj: 0.002873, Avg Recall: 0.761538, count: 130 -Region Avg IOU: 0.650325, Class: 1.000000, Obj: 0.357967, No Obj: 0.002590, Avg Recall: 0.839080, count: 87 -Region Avg IOU: 0.595132, Class: 1.000000, Obj: 0.303837, No Obj: 0.002590, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.552491, Class: 1.000000, Obj: 0.312970, No Obj: 0.002949, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.606473, Class: 1.000000, Obj: 0.330410, No Obj: 0.003201, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.589728, Class: 1.000000, Obj: 0.313907, No Obj: 0.002526, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.597305, Class: 1.000000, Obj: 0.286534, No Obj: 0.002799, Avg Recall: 0.740157, count: 127 -2183: 0.474501, 0.484363 avg, 0.001000 rate, 25.990608 seconds, 558848 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.598745, Class: 1.000000, Obj: 0.265425, No Obj: 0.002671, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.574388, Class: 1.000000, Obj: 0.324871, No Obj: 0.002627, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.593843, Class: 1.000000, Obj: 0.311529, No Obj: 0.002711, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.588763, Class: 1.000000, Obj: 0.310665, No Obj: 0.002710, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.566618, Class: 1.000000, Obj: 0.293004, No Obj: 0.002516, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.578712, Class: 1.000000, Obj: 0.289818, No Obj: 0.002572, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.571075, Class: 1.000000, Obj: 0.301130, No Obj: 0.002648, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.599020, Class: 1.000000, Obj: 0.324371, No Obj: 0.002762, Avg Recall: 0.730000, count: 100 -2184: 0.492380, 0.485165 avg, 0.001000 rate, 27.245090 seconds, 559104 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.620647, Class: 1.000000, Obj: 0.295763, No Obj: 0.002755, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.598206, Class: 1.000000, Obj: 0.346177, No Obj: 0.002701, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.630849, Class: 1.000000, Obj: 0.303605, No Obj: 0.002698, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.627531, Class: 1.000000, Obj: 0.359036, No Obj: 0.002611, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.578662, Class: 1.000000, Obj: 0.341418, No Obj: 0.002466, Avg Recall: 0.681818, count: 88 -Region Avg IOU: 0.609576, Class: 1.000000, Obj: 0.285956, No Obj: 0.002942, Avg Recall: 0.721804, count: 133 -Region Avg IOU: 0.602169, Class: 1.000000, Obj: 0.377935, No Obj: 0.002654, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.608579, Class: 1.000000, Obj: 0.324927, No Obj: 0.002740, Avg Recall: 0.724409, count: 127 -2185: 0.466163, 0.483265 avg, 0.001000 rate, 27.256536 seconds, 559360 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.599582, Class: 1.000000, Obj: 0.332410, No Obj: 0.002826, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.575331, Class: 1.000000, Obj: 0.335342, No Obj: 0.002740, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.599254, Class: 1.000000, Obj: 0.308940, No Obj: 0.003116, Avg Recall: 0.717557, count: 131 -Region Avg IOU: 0.595757, Class: 1.000000, Obj: 0.324707, No Obj: 0.002765, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.619408, Class: 1.000000, Obj: 0.316807, No Obj: 0.002835, Avg Recall: 0.817391, count: 115 -Region Avg IOU: 0.572289, Class: 1.000000, Obj: 0.268557, No Obj: 0.002524, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.596141, Class: 1.000000, Obj: 0.281026, No Obj: 0.002640, Avg Recall: 0.693182, count: 88 -Region Avg IOU: 0.614576, Class: 1.000000, Obj: 0.313859, No Obj: 0.002932, Avg Recall: 0.729323, count: 133 -2186: 0.521924, 0.487131 avg, 0.001000 rate, 26.464609 seconds, 559616 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.634631, Class: 1.000000, Obj: 0.346313, No Obj: 0.002661, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.573819, Class: 1.000000, Obj: 0.332174, No Obj: 0.002604, Avg Recall: 0.691057, count: 123 -Region Avg IOU: 0.612791, Class: 1.000000, Obj: 0.323102, No Obj: 0.003273, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.607666, Class: 1.000000, Obj: 0.275431, No Obj: 0.003028, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.617585, Class: 1.000000, Obj: 0.308716, No Obj: 0.002584, Avg Recall: 0.788889, count: 90 -Region Avg IOU: 0.616836, Class: 1.000000, Obj: 0.330608, No Obj: 0.002452, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.617689, Class: 1.000000, Obj: 0.305749, No Obj: 0.002925, Avg Recall: 0.733871, count: 124 -Region Avg IOU: 0.539995, Class: 1.000000, Obj: 0.311652, No Obj: 0.002664, Avg Recall: 0.629921, count: 127 -2187: 0.475524, 0.485970 avg, 0.001000 rate, 27.216665 seconds, 559872 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.608448, Class: 1.000000, Obj: 0.301635, No Obj: 0.003342, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.641014, Class: 1.000000, Obj: 0.322466, No Obj: 0.003624, Avg Recall: 0.809160, count: 131 -Region Avg IOU: 0.625757, Class: 1.000000, Obj: 0.328501, No Obj: 0.002789, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.645217, Class: 1.000000, Obj: 0.336647, No Obj: 0.003258, Avg Recall: 0.876190, count: 105 -Region Avg IOU: 0.510071, Class: 1.000000, Obj: 0.265678, No Obj: 0.002924, Avg Recall: 0.575163, count: 153 -Region Avg IOU: 0.614647, Class: 1.000000, Obj: 0.309633, No Obj: 0.002855, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.628895, Class: 1.000000, Obj: 0.348697, No Obj: 0.003338, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.616815, Class: 1.000000, Obj: 0.313896, No Obj: 0.003388, Avg Recall: 0.787611, count: 113 -2188: 0.509553, 0.488328 avg, 0.001000 rate, 28.023928 seconds, 560128 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.599250, Class: 1.000000, Obj: 0.331886, No Obj: 0.003150, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.606260, Class: 1.000000, Obj: 0.306679, No Obj: 0.003229, Avg Recall: 0.717391, count: 138 -Region Avg IOU: 0.624076, Class: 1.000000, Obj: 0.321782, No Obj: 0.003246, Avg Recall: 0.785047, count: 107 -Region Avg IOU: 0.630095, Class: 1.000000, Obj: 0.383303, No Obj: 0.003051, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.555201, Class: 1.000000, Obj: 0.308595, No Obj: 0.003193, Avg Recall: 0.653846, count: 130 -Region Avg IOU: 0.620843, Class: 1.000000, Obj: 0.341694, No Obj: 0.003264, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.651086, Class: 1.000000, Obj: 0.356841, No Obj: 0.003235, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.628328, Class: 1.000000, Obj: 0.359922, No Obj: 0.003079, Avg Recall: 0.783505, count: 97 -2189: 0.494455, 0.488941 avg, 0.001000 rate, 26.413528 seconds, 560384 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.579976, Class: 1.000000, Obj: 0.315516, No Obj: 0.003027, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.614612, Class: 1.000000, Obj: 0.323183, No Obj: 0.003308, Avg Recall: 0.751938, count: 129 -Region Avg IOU: 0.626514, Class: 1.000000, Obj: 0.295999, No Obj: 0.003199, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.623972, Class: 1.000000, Obj: 0.353776, No Obj: 0.003067, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.571151, Class: 1.000000, Obj: 0.360825, No Obj: 0.003110, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.599854, Class: 1.000000, Obj: 0.302168, No Obj: 0.003139, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.621283, Class: 1.000000, Obj: 0.406475, No Obj: 0.002955, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.623421, Class: 1.000000, Obj: 0.328668, No Obj: 0.002956, Avg Recall: 0.761905, count: 105 -2190: 0.503287, 0.490375 avg, 0.001000 rate, 27.379017 seconds, 560640 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.083451 seconds -Region Avg IOU: 0.523354, Class: 1.000000, Obj: 0.282933, No Obj: 0.003071, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.551431, Class: 1.000000, Obj: 0.295825, No Obj: 0.003764, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.496559, Class: 1.000000, Obj: 0.242390, No Obj: 0.003878, Avg Recall: 0.503759, count: 133 -Region Avg IOU: 0.479626, Class: 1.000000, Obj: 0.289885, No Obj: 0.003690, Avg Recall: 0.516949, count: 118 -Region Avg IOU: 0.530025, Class: 1.000000, Obj: 0.277634, No Obj: 0.004263, Avg Recall: 0.590551, count: 127 -Region Avg IOU: 0.525554, Class: 1.000000, Obj: 0.299463, No Obj: 0.003448, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.541320, Class: 1.000000, Obj: 0.251636, No Obj: 0.003697, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.531277, Class: 1.000000, Obj: 0.285751, No Obj: 0.003524, Avg Recall: 0.579439, count: 107 -2191: 0.550178, 0.496356 avg, 0.001000 rate, 19.836449 seconds, 560896 images -Loaded: 4.132649 seconds -Region Avg IOU: 0.546306, Class: 1.000000, Obj: 0.296708, No Obj: 0.004412, Avg Recall: 0.656716, count: 134 -Region Avg IOU: 0.485087, Class: 1.000000, Obj: 0.268008, No Obj: 0.003730, Avg Recall: 0.522124, count: 113 -Region Avg IOU: 0.525939, Class: 1.000000, Obj: 0.285354, No Obj: 0.004062, Avg Recall: 0.581967, count: 122 -Region Avg IOU: 0.530779, Class: 1.000000, Obj: 0.292110, No Obj: 0.004182, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.542589, Class: 1.000000, Obj: 0.353157, No Obj: 0.004156, Avg Recall: 0.619565, count: 92 -Region Avg IOU: 0.484762, Class: 1.000000, Obj: 0.288315, No Obj: 0.003710, Avg Recall: 0.485981, count: 107 -Region Avg IOU: 0.510268, Class: 1.000000, Obj: 0.293015, No Obj: 0.003352, Avg Recall: 0.530435, count: 115 -Region Avg IOU: 0.499165, Class: 1.000000, Obj: 0.262826, No Obj: 0.003864, Avg Recall: 0.570248, count: 121 -2192: 0.564958, 0.503216 avg, 0.001000 rate, 20.598764 seconds, 561152 images -Loaded: 0.061884 seconds -Region Avg IOU: 0.514600, Class: 1.000000, Obj: 0.284269, No Obj: 0.003681, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.583795, Class: 1.000000, Obj: 0.325733, No Obj: 0.003709, Avg Recall: 0.701299, count: 77 -Region Avg IOU: 0.538093, Class: 1.000000, Obj: 0.319254, No Obj: 0.003925, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.467942, Class: 1.000000, Obj: 0.277509, No Obj: 0.004006, Avg Recall: 0.485981, count: 107 -Region Avg IOU: 0.543012, Class: 1.000000, Obj: 0.305375, No Obj: 0.003422, Avg Recall: 0.613333, count: 75 -Region Avg IOU: 0.526739, Class: 1.000000, Obj: 0.302942, No Obj: 0.004085, Avg Recall: 0.587302, count: 126 -Region Avg IOU: 0.512992, Class: 1.000000, Obj: 0.343435, No Obj: 0.003690, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.496309, Class: 1.000000, Obj: 0.333993, No Obj: 0.003798, Avg Recall: 0.565657, count: 99 -2193: 0.439241, 0.496818 avg, 0.001000 rate, 21.808367 seconds, 561408 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.489975, Class: 1.000000, Obj: 0.276777, No Obj: 0.004620, Avg Recall: 0.529801, count: 151 -Region Avg IOU: 0.484316, Class: 1.000000, Obj: 0.318546, No Obj: 0.003855, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.517794, Class: 1.000000, Obj: 0.345239, No Obj: 0.003695, Avg Recall: 0.555556, count: 99 -Region Avg IOU: 0.535093, Class: 1.000000, Obj: 0.375317, No Obj: 0.004100, Avg Recall: 0.577778, count: 90 -Region Avg IOU: 0.471603, Class: 1.000000, Obj: 0.294799, No Obj: 0.003816, Avg Recall: 0.481818, count: 110 -Region Avg IOU: 0.494331, Class: 1.000000, Obj: 0.313941, No Obj: 0.003716, Avg Recall: 0.571429, count: 105 -Region Avg IOU: 0.508787, Class: 1.000000, Obj: 0.300537, No Obj: 0.003933, Avg Recall: 0.536082, count: 97 -Region Avg IOU: 0.570545, Class: 1.000000, Obj: 0.371447, No Obj: 0.004140, Avg Recall: 0.675926, count: 108 -2194: 0.451479, 0.492284 avg, 0.001000 rate, 19.462364 seconds, 561664 images -Loaded: 1.654573 seconds -Region Avg IOU: 0.579598, Class: 1.000000, Obj: 0.392826, No Obj: 0.004377, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.519222, Class: 1.000000, Obj: 0.339680, No Obj: 0.003890, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.492187, Class: 1.000000, Obj: 0.316166, No Obj: 0.004199, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.505686, Class: 1.000000, Obj: 0.338999, No Obj: 0.004211, Avg Recall: 0.545455, count: 121 -Region Avg IOU: 0.514036, Class: 1.000000, Obj: 0.325326, No Obj: 0.003813, Avg Recall: 0.542169, count: 83 -Region Avg IOU: 0.527283, Class: 1.000000, Obj: 0.305097, No Obj: 0.003846, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.522995, Class: 1.000000, Obj: 0.306253, No Obj: 0.003805, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.510587, Class: 1.000000, Obj: 0.326376, No Obj: 0.003421, Avg Recall: 0.571429, count: 84 -2195: 0.420080, 0.485064 avg, 0.001000 rate, 21.259081 seconds, 561920 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.563850, Class: 1.000000, Obj: 0.336389, No Obj: 0.004254, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.523674, Class: 1.000000, Obj: 0.340940, No Obj: 0.003993, Avg Recall: 0.589474, count: 95 -Region Avg IOU: 0.499950, Class: 1.000000, Obj: 0.339700, No Obj: 0.004163, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.511333, Class: 1.000000, Obj: 0.367193, No Obj: 0.003691, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.528838, Class: 1.000000, Obj: 0.338441, No Obj: 0.004131, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.538980, Class: 1.000000, Obj: 0.325201, No Obj: 0.003848, Avg Recall: 0.669492, count: 118 -Region Avg IOU: 0.525866, Class: 1.000000, Obj: 0.319982, No Obj: 0.004218, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.501388, Class: 1.000000, Obj: 0.287725, No Obj: 0.004112, Avg Recall: 0.615385, count: 104 -2196: 0.468623, 0.483420 avg, 0.001000 rate, 20.727531 seconds, 562176 images -Loaded: 1.594275 seconds -Region Avg IOU: 0.520622, Class: 1.000000, Obj: 0.317302, No Obj: 0.003706, Avg Recall: 0.609195, count: 87 -Region Avg IOU: 0.511007, Class: 1.000000, Obj: 0.296622, No Obj: 0.004261, Avg Recall: 0.552000, count: 125 -Region Avg IOU: 0.533793, Class: 1.000000, Obj: 0.347691, No Obj: 0.004027, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.494936, Class: 1.000000, Obj: 0.369159, No Obj: 0.004057, Avg Recall: 0.517647, count: 85 -Region Avg IOU: 0.522882, Class: 1.000000, Obj: 0.326690, No Obj: 0.004416, Avg Recall: 0.650000, count: 120 -Region Avg IOU: 0.544500, Class: 1.000000, Obj: 0.324847, No Obj: 0.004480, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.485754, Class: 1.000000, Obj: 0.353691, No Obj: 0.003960, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.498244, Class: 1.000000, Obj: 0.333602, No Obj: 0.003717, Avg Recall: 0.571429, count: 91 -2197: 0.483929, 0.483471 avg, 0.001000 rate, 20.164663 seconds, 562432 images -Loaded: 2.486753 seconds -Region Avg IOU: 0.469979, Class: 1.000000, Obj: 0.286839, No Obj: 0.003198, Avg Recall: 0.533333, count: 90 -Region Avg IOU: 0.534541, Class: 1.000000, Obj: 0.330346, No Obj: 0.003700, Avg Recall: 0.588235, count: 85 -Region Avg IOU: 0.491767, Class: 1.000000, Obj: 0.317521, No Obj: 0.004117, Avg Recall: 0.528846, count: 104 -Region Avg IOU: 0.527256, Class: 1.000000, Obj: 0.291030, No Obj: 0.004412, Avg Recall: 0.600000, count: 125 -Region Avg IOU: 0.525376, Class: 1.000000, Obj: 0.383302, No Obj: 0.004317, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.485830, Class: 1.000000, Obj: 0.332848, No Obj: 0.004026, Avg Recall: 0.514286, count: 105 -Region Avg IOU: 0.528398, Class: 1.000000, Obj: 0.314934, No Obj: 0.004184, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.469492, Class: 1.000000, Obj: 0.301596, No Obj: 0.004215, Avg Recall: 0.542484, count: 153 -2198: 0.477502, 0.482874 avg, 0.001000 rate, 21.865461 seconds, 562688 images -Loaded: 0.538834 seconds -Region Avg IOU: 0.540738, Class: 1.000000, Obj: 0.356197, No Obj: 0.004172, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.487282, Class: 1.000000, Obj: 0.317964, No Obj: 0.003937, Avg Recall: 0.490385, count: 104 -Region Avg IOU: 0.559311, Class: 1.000000, Obj: 0.360181, No Obj: 0.004059, Avg Recall: 0.678571, count: 84 -Region Avg IOU: 0.481418, Class: 1.000000, Obj: 0.317623, No Obj: 0.003806, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.521142, Class: 1.000000, Obj: 0.337907, No Obj: 0.004420, Avg RecallSaving weights to ./person9300/backup/tyvp_93_9c512_2200.weights -: 0.574257, count: 101 -Region Avg IOU: 0.536003, Class: 1.000000, Obj: 0.343347, No Obj: 0.004075, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.556372, Class: 1.000000, Obj: 0.335299, No Obj: 0.003811, Avg Recall: 0.670588, count: 85 -Region Avg IOU: 0.509695, Class: 1.000000, Obj: 0.316073, No Obj: 0.003972, Avg Recall: 0.572727, count: 110 -2199: 0.451895, 0.479776 avg, 0.001000 rate, 17.003332 seconds, 562944 images -Loaded: 3.711044 seconds -Region Avg IOU: 0.477399, Class: 1.000000, Obj: 0.280439, No Obj: 0.003531, Avg Recall: 0.549020, count: 102 -Region Avg IOU: 0.510232, Class: 1.000000, Obj: 0.322771, No Obj: 0.003596, Avg Recall: 0.542169, count: 83 -Region Avg IOU: 0.525030, Class: 1.000000, Obj: 0.315914, No Obj: 0.003794, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.506271, Class: 1.000000, Obj: 0.265817, No Obj: 0.003592, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.503163, Class: 1.000000, Obj: 0.312365, No Obj: 0.004020, Avg Recall: 0.544554, count: 101 -Region Avg IOU: 0.501321, Class: 1.000000, Obj: 0.306332, No Obj: 0.003845, Avg Recall: 0.587302, count: 126 -Region Avg IOU: 0.546770, Class: 1.000000, Obj: 0.335633, No Obj: 0.004313, Avg Recall: 0.661538, count: 130 -Region Avg IOU: 0.492219, Class: 1.000000, Obj: 0.273028, No Obj: 0.003742, Avg Recall: 0.556604, count: 106 -2200: 0.550718, 0.486870 avg, 0.001000 rate, 21.423656 seconds, 563200 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000065 seconds -Region Avg IOU: 0.595017, Class: 1.000000, Obj: 0.360850, No Obj: 0.002792, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.581766, Class: 1.000000, Obj: 0.322550, No Obj: 0.002931, Avg Recall: 0.719298, count: 114 -Region Avg IOU: 0.542013, Class: 1.000000, Obj: 0.319568, No Obj: 0.003343, Avg Recall: 0.669355, count: 124 -Region Avg IOU: 0.639189, Class: 1.000000, Obj: 0.353839, No Obj: 0.002842, Avg Recall: 0.776471, count: 85 -Region Avg IOU: 0.573483, Class: 1.000000, Obj: 0.375925, No Obj: 0.002894, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.662885, Class: 1.000000, Obj: 0.346125, No Obj: 0.003160, Avg Recall: 0.830000, count: 100 -Region Avg IOU: 0.592329, Class: 1.000000, Obj: 0.370457, No Obj: 0.003151, Avg Recall: 0.710938, count: 128 -Region Avg IOU: 0.558434, Class: 1.000000, Obj: 0.360975, No Obj: 0.002953, Avg Recall: 0.675214, count: 117 -2201: 0.493993, 0.487583 avg, 0.001000 rate, 27.675232 seconds, 563456 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.586140, Class: 1.000000, Obj: 0.353979, No Obj: 0.003122, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.616603, Class: 1.000000, Obj: 0.346266, No Obj: 0.003049, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.564661, Class: 1.000000, Obj: 0.312025, No Obj: 0.002952, Avg Recall: 0.633333, count: 120 -Region Avg IOU: 0.604522, Class: 1.000000, Obj: 0.364782, No Obj: 0.002782, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.588982, Class: 1.000000, Obj: 0.325957, No Obj: 0.002791, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.606639, Class: 1.000000, Obj: 0.310224, No Obj: 0.002905, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.591334, Class: 1.000000, Obj: 0.364968, No Obj: 0.003073, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.615905, Class: 1.000000, Obj: 0.384130, No Obj: 0.002900, Avg Recall: 0.773585, count: 106 -2202: 0.481740, 0.486998 avg, 0.001000 rate, 26.894840 seconds, 563712 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.585624, Class: 1.000000, Obj: 0.305399, No Obj: 0.002773, Avg Recall: 0.747967, count: 123 -Region Avg IOU: 0.640031, Class: 1.000000, Obj: 0.347829, No Obj: 0.002622, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.612941, Class: 1.000000, Obj: 0.333507, No Obj: 0.002951, Avg Recall: 0.751825, count: 137 -Region Avg IOU: 0.613991, Class: 1.000000, Obj: 0.282912, No Obj: 0.002283, Avg Recall: 0.795181, count: 83 -Region Avg IOU: 0.588867, Class: 1.000000, Obj: 0.328641, No Obj: 0.002268, Avg Recall: 0.752941, count: 85 -Region Avg IOU: 0.652909, Class: 1.000000, Obj: 0.370244, No Obj: 0.002833, Avg Recall: 0.841584, count: 101 -Region Avg IOU: 0.601305, Class: 1.000000, Obj: 0.285766, No Obj: 0.003288, Avg Recall: 0.751825, count: 137 -Region Avg IOU: 0.598015, Class: 1.000000, Obj: 0.288329, No Obj: 0.002869, Avg Recall: 0.713043, count: 115 -2203: 0.514410, 0.489739 avg, 0.001000 rate, 28.688988 seconds, 563968 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.610044, Class: 1.000000, Obj: 0.300537, No Obj: 0.002488, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.608702, Class: 1.000000, Obj: 0.323424, No Obj: 0.002308, Avg Recall: 0.727273, count: 77 -Region Avg IOU: 0.642129, Class: 1.000000, Obj: 0.269720, No Obj: 0.002644, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.602035, Class: 1.000000, Obj: 0.295724, No Obj: 0.002622, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.611988, Class: 1.000000, Obj: 0.325173, No Obj: 0.002326, Avg Recall: 0.772727, count: 88 -Region Avg IOU: 0.609848, Class: 1.000000, Obj: 0.284210, No Obj: 0.002239, Avg Recall: 0.791209, count: 91 -Region Avg IOU: 0.601639, Class: 1.000000, Obj: 0.309390, No Obj: 0.002657, Avg Recall: 0.778761, count: 113 -Region Avg IOU: 0.614607, Class: 1.000000, Obj: 0.314338, No Obj: 0.002379, Avg Recall: 0.788235, count: 85 -2204: 0.458362, 0.486602 avg, 0.001000 rate, 28.023129 seconds, 564224 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.597030, Class: 1.000000, Obj: 0.317028, No Obj: 0.002754, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.610367, Class: 1.000000, Obj: 0.260496, No Obj: 0.002794, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.581418, Class: 1.000000, Obj: 0.293714, No Obj: 0.002565, Avg Recall: 0.680328, count: 122 -Region Avg IOU: 0.609398, Class: 1.000000, Obj: 0.308615, No Obj: 0.002430, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.616741, Class: 1.000000, Obj: 0.274974, No Obj: 0.002638, Avg Recall: 0.757812, count: 128 -Region Avg IOU: 0.618424, Class: 1.000000, Obj: 0.335589, No Obj: 0.002430, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.646527, Class: 1.000000, Obj: 0.355075, No Obj: 0.002549, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.585825, Class: 1.000000, Obj: 0.283816, No Obj: 0.002212, Avg Recall: 0.709402, count: 117 -2205: 0.479277, 0.485869 avg, 0.001000 rate, 29.781113 seconds, 564480 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.615301, Class: 1.000000, Obj: 0.296889, No Obj: 0.003011, Avg Recall: 0.758865, count: 141 -Region Avg IOU: 0.601349, Class: 1.000000, Obj: 0.301393, No Obj: 0.002650, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.614261, Class: 1.000000, Obj: 0.333788, No Obj: 0.002748, Avg Recall: 0.759690, count: 129 -Region Avg IOU: 0.625802, Class: 1.000000, Obj: 0.337848, No Obj: 0.002715, Avg Recall: 0.789474, count: 133 -Region Avg IOU: 0.585012, Class: 1.000000, Obj: 0.311610, No Obj: 0.002220, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.622408, Class: 1.000000, Obj: 0.272789, No Obj: 0.002594, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.612317, Class: 1.000000, Obj: 0.310333, No Obj: 0.002574, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.593100, Class: 1.000000, Obj: 0.310438, No Obj: 0.002506, Avg Recall: 0.745283, count: 106 -2206: 0.513750, 0.488657 avg, 0.001000 rate, 28.418827 seconds, 564736 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.622412, Class: 1.000000, Obj: 0.294728, No Obj: 0.002765, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.604154, Class: 1.000000, Obj: 0.289428, No Obj: 0.002769, Avg Recall: 0.753846, count: 130 -Region Avg IOU: 0.601934, Class: 1.000000, Obj: 0.291356, No Obj: 0.002579, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.592658, Class: 1.000000, Obj: 0.315209, No Obj: 0.002698, Avg Recall: 0.732824, count: 131 -Region Avg IOU: 0.612785, Class: 1.000000, Obj: 0.312543, No Obj: 0.002645, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.592274, Class: 1.000000, Obj: 0.282431, No Obj: 0.002559, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.646222, Class: 1.000000, Obj: 0.268603, No Obj: 0.002951, Avg Recall: 0.848276, count: 145 -Region Avg IOU: 0.598655, Class: 1.000000, Obj: 0.326485, No Obj: 0.002551, Avg Recall: 0.774194, count: 93 -2207: 0.542264, 0.494018 avg, 0.001000 rate, 27.651026 seconds, 564992 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.564282, Class: 1.000000, Obj: 0.292878, No Obj: 0.002846, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.540835, Class: 1.000000, Obj: 0.332986, No Obj: 0.002561, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.636421, Class: 1.000000, Obj: 0.328825, No Obj: 0.002786, Avg Recall: 0.793388, count: 121 -Region Avg IOU: 0.575692, Class: 1.000000, Obj: 0.318938, No Obj: 0.002549, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.600165, Class: 1.000000, Obj: 0.292228, No Obj: 0.002818, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.602863, Class: 1.000000, Obj: 0.271202, No Obj: 0.002961, Avg Recall: 0.695035, count: 141 -Region Avg IOU: 0.596687, Class: 1.000000, Obj: 0.288383, No Obj: 0.003035, Avg Recall: 0.760274, count: 146 -Region Avg IOU: 0.681097, Class: 1.000000, Obj: 0.382113, No Obj: 0.002475, Avg Recall: 0.810345, count: 58 -2208: 0.523026, 0.496919 avg, 0.001000 rate, 27.650932 seconds, 565248 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.652291, Class: 1.000000, Obj: 0.333906, No Obj: 0.002966, Avg Recall: 0.861111, count: 108 -Region Avg IOU: 0.602202, Class: 1.000000, Obj: 0.334059, No Obj: 0.002939, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.605343, Class: 1.000000, Obj: 0.312108, No Obj: 0.002597, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.599822, Class: 1.000000, Obj: 0.337359, No Obj: 0.002662, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.627371, Class: 1.000000, Obj: 0.343090, No Obj: 0.002616, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.600031, Class: 1.000000, Obj: 0.342542, No Obj: 0.002718, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.569326, Class: 1.000000, Obj: 0.323371, No Obj: 0.002377, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.635631, Class: 1.000000, Obj: 0.319006, No Obj: 0.003009, Avg Recall: 0.826923, count: 104 -2209: 0.468719, 0.494099 avg, 0.001000 rate, 29.285578 seconds, 565504 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.572969, Class: 1.000000, Obj: 0.313557, No Obj: 0.002675, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.600659, Class: 1.000000, Obj: 0.337806, No Obj: 0.002968, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.603925, Class: 1.000000, Obj: 0.356462, No Obj: 0.002906, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.615870, Class: 1.000000, Obj: 0.303399, No Obj: 0.003113, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.609525, Class: 1.000000, Obj: 0.335635, No Obj: 0.002880, Avg Recall: 0.740157, count: 127 -Region Avg IOU: 0.606409, Class: 1.000000, Obj: 0.363034, No Obj: 0.002908, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.607384, Class: 1.000000, Obj: 0.297051, No Obj: 0.003085, Avg Recall: 0.737705, count: 122 -Region Avg IOU: 0.579151, Class: 1.000000, Obj: 0.304302, No Obj: 0.002566, Avg Recall: 0.708333, count: 96 -2210: 0.500264, 0.494715 avg, 0.001000 rate, 27.686943 seconds, 565760 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 15.491559 seconds -Region Avg IOU: 0.559284, Class: 1.000000, Obj: 0.285895, No Obj: 0.003779, Avg Recall: 0.666667, count: 138 -Region Avg IOU: 0.542126, Class: 1.000000, Obj: 0.320688, No Obj: 0.003106, Avg Recall: 0.620690, count: 87 -Region Avg IOU: 0.550300, Class: 1.000000, Obj: 0.290386, No Obj: 0.003078, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.552119, Class: 1.000000, Obj: 0.314568, No Obj: 0.003184, Avg Recall: 0.658824, count: 85 -Region Avg IOU: 0.563352, Class: 1.000000, Obj: 0.316643, No Obj: 0.003927, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.567761, Class: 1.000000, Obj: 0.305664, No Obj: 0.003873, Avg Recall: 0.642336, count: 137 -Region Avg IOU: 0.568047, Class: 1.000000, Obj: 0.324365, No Obj: 0.003538, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.565318, Class: 1.000000, Obj: 0.326106, No Obj: 0.003513, Avg Recall: 0.660194, count: 103 -2211: 0.480258, 0.493270 avg, 0.001000 rate, 21.568968 seconds, 566016 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.548241, Class: 1.000000, Obj: 0.309877, No Obj: 0.003783, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.524317, Class: 1.000000, Obj: 0.302273, No Obj: 0.003486, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.524972, Class: 1.000000, Obj: 0.293122, No Obj: 0.003773, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.553036, Class: 1.000000, Obj: 0.300650, No Obj: 0.003664, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.466436, Class: 1.000000, Obj: 0.241106, No Obj: 0.002740, Avg Recall: 0.511111, count: 90 -Region Avg IOU: 0.529314, Class: 1.000000, Obj: 0.287269, No Obj: 0.003753, Avg Recall: 0.585366, count: 123 -Region Avg IOU: 0.558207, Class: 1.000000, Obj: 0.316729, No Obj: 0.003703, Avg Recall: 0.632479, count: 117 -Region Avg IOU: 0.583421, Class: 1.000000, Obj: 0.331234, No Obj: 0.003955, Avg Recall: 0.655172, count: 116 -2212: 0.516707, 0.495613 avg, 0.001000 rate, 22.612305 seconds, 566272 images -Loaded: 0.127401 seconds -Region Avg IOU: 0.565854, Class: 1.000000, Obj: 0.365426, No Obj: 0.003691, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.539909, Class: 1.000000, Obj: 0.347426, No Obj: 0.003884, Avg Recall: 0.625000, count: 120 -Region Avg IOU: 0.600523, Class: 1.000000, Obj: 0.305338, No Obj: 0.003574, Avg Recall: 0.770270, count: 74 -Region Avg IOU: 0.566494, Class: 1.000000, Obj: 0.347331, No Obj: 0.003643, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.553416, Class: 1.000000, Obj: 0.304559, No Obj: 0.003879, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.526458, Class: 1.000000, Obj: 0.333032, No Obj: 0.003830, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.509926, Class: 1.000000, Obj: 0.330205, No Obj: 0.003462, Avg Recall: 0.568182, count: 88 -Region Avg IOU: 0.536678, Class: 1.000000, Obj: 0.341948, No Obj: 0.003496, Avg Recall: 0.609195, count: 87 -2213: 0.463501, 0.492402 avg, 0.001000 rate, 23.650345 seconds, 566528 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.524332, Class: 1.000000, Obj: 0.338384, No Obj: 0.004278, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.539754, Class: 1.000000, Obj: 0.332879, No Obj: 0.004174, Avg Recall: 0.666667, count: 123 -Region Avg IOU: 0.530826, Class: 1.000000, Obj: 0.363819, No Obj: 0.004465, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.534104, Class: 1.000000, Obj: 0.379026, No Obj: 0.003621, Avg Recall: 0.586207, count: 87 -Region Avg IOU: 0.542088, Class: 1.000000, Obj: 0.351091, No Obj: 0.003777, Avg Recall: 0.565217, count: 92 -Region Avg IOU: 0.557349, Class: 1.000000, Obj: 0.370420, No Obj: 0.004143, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.549942, Class: 1.000000, Obj: 0.310563, No Obj: 0.004322, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.590964, Class: 1.000000, Obj: 0.340385, No Obj: 0.004161, Avg Recall: 0.696429, count: 112 -2214: 0.481930, 0.491355 avg, 0.001000 rate, 22.643942 seconds, 566784 images -Loaded: 0.656300 seconds -Region Avg IOU: 0.599439, Class: 1.000000, Obj: 0.411690, No Obj: 0.003990, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.530372, Class: 1.000000, Obj: 0.345892, No Obj: 0.004219, Avg Recall: 0.631579, count: 133 -Region Avg IOU: 0.562763, Class: 1.000000, Obj: 0.360249, No Obj: 0.004219, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.501108, Class: 1.000000, Obj: 0.349881, No Obj: 0.003908, Avg Recall: 0.556522, count: 115 -Region Avg IOU: 0.555792, Class: 1.000000, Obj: 0.362200, No Obj: 0.003770, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.539328, Class: 1.000000, Obj: 0.327900, No Obj: 0.004630, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.552068, Class: 1.000000, Obj: 0.352344, No Obj: 0.003564, Avg Recall: 0.608108, count: 74 -Region Avg IOU: 0.543691, Class: 1.000000, Obj: 0.329338, No Obj: 0.004053, Avg Recall: 0.617284, count: 81 -2215: 0.435597, 0.485779 avg, 0.001000 rate, 22.333248 seconds, 567040 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.518928, Class: 1.000000, Obj: 0.355219, No Obj: 0.004263, Avg Recall: 0.611111, count: 126 -Region Avg IOU: 0.537167, Class: 1.000000, Obj: 0.346469, No Obj: 0.004212, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.522711, Class: 1.000000, Obj: 0.328548, No Obj: 0.003790, Avg Recall: 0.566372, count: 113 -Region Avg IOU: 0.518569, Class: 1.000000, Obj: 0.363608, No Obj: 0.003990, Avg Recall: 0.580153, count: 131 -Region Avg IOU: 0.464951, Class: 1.000000, Obj: 0.325122, No Obj: 0.003451, Avg Recall: 0.495146, count: 103 -Region Avg IOU: 0.533472, Class: 1.000000, Obj: 0.343855, No Obj: 0.003968, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.507955, Class: 1.000000, Obj: 0.308252, No Obj: 0.004094, Avg Recall: 0.567568, count: 111 -Region Avg IOU: 0.543167, Class: 1.000000, Obj: 0.379340, No Obj: 0.003862, Avg Recall: 0.644231, count: 104 -2216: 0.461575, 0.483359 avg, 0.001000 rate, 23.570297 seconds, 567296 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.526412, Class: 1.000000, Obj: 0.336487, No Obj: 0.003845, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.540682, Class: 1.000000, Obj: 0.354272, No Obj: 0.004361, Avg Recall: 0.624060, count: 133 -Region Avg IOU: 0.527838, Class: 1.000000, Obj: 0.353147, No Obj: 0.003973, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.537160, Class: 1.000000, Obj: 0.300620, No Obj: 0.003242, Avg Recall: 0.583333, count: 72 -Region Avg IOU: 0.550671, Class: 1.000000, Obj: 0.281562, No Obj: 0.003990, Avg Recall: 0.625000, count: 120 -Region Avg IOU: 0.578853, Class: 1.000000, Obj: 0.362808, No Obj: 0.004108, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.511576, Class: 1.000000, Obj: 0.324920, No Obj: 0.003027, Avg Recall: 0.574713, count: 87 -Region Avg IOU: 0.526756, Class: 1.000000, Obj: 0.310860, No Obj: 0.003590, Avg Recall: 0.649573, count: 117 -2217: 0.463429, 0.481366 avg, 0.001000 rate, 18.127048 seconds, 567552 images -Loaded: 2.780677 seconds -Region Avg IOU: 0.528442, Class: 1.000000, Obj: 0.293449, No Obj: 0.004191, Avg Recall: 0.596026, count: 151 -Region Avg IOU: 0.493533, Class: 1.000000, Obj: 0.278477, No Obj: 0.003468, Avg Recall: 0.514851, count: 101 -Region Avg IOU: 0.567980, Class: 1.000000, Obj: 0.278209, No Obj: 0.003931, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.552375, Class: 1.000000, Obj: 0.350257, No Obj: 0.003331, Avg Recall: 0.687500, count: 80 -Region Avg IOU: 0.544124, Class: 1.000000, Obj: 0.343468, No Obj: 0.003884, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.563135, Class: 1.000000, Obj: 0.315754, No Obj: 0.003245, Avg Recall: 0.696629, count: 89 -Region Avg IOU: 0.542567, Class: 1.000000, Obj: 0.382123, No Obj: 0.003348, Avg Recall: 0.620253, count: 79 -Region Avg IOU: 0.468127, Class: 1.000000, Obj: 0.285297, No Obj: 0.003428, Avg Recall: 0.536364, count: 110 -2218: 0.515788, 0.484808 avg, 0.001000 rate, 20.944199 seconds, 567808 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.541244, Class: 1.000000, Obj: 0.335376, No Obj: 0.003039, Avg Recall: 0.623188, count: 69 -Region Avg IOU: 0.520681, Class: 1.000000, Obj: 0.304259, No Obj: 0.003380, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.476833, Class: 1.000000, Obj: 0.277882, No Obj: 0.003230, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.548129, Class: 1.000000, Obj: 0.320116, No Obj: 0.003313, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.496859, Class: 1.000000, Obj: 0.325070, No Obj: 0.003432, Avg Recall: 0.517241, count: 87 -Region Avg IOU: 0.592512, Class: 1.000000, Obj: 0.334236, No Obj: 0.003885, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.492270, Class: 1.000000, Obj: 0.327436, No Obj: 0.003204, Avg Recall: 0.555556, count: 90 -Region Avg IOU: 0.478877, Class: 1.000000, Obj: 0.320811, No Obj: 0.003249, Avg Recall: 0.500000, count: 98 -2219: 0.404666, 0.476794 avg, 0.001000 rate, 23.923422 seconds, 568064 images -Loaded: 0.000051 seconds -Region Avg IOU: 0.537970, Class: 1.000000, Obj: 0.325012, No Obj: 0.003399, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.467571, Class: 1.000000, Obj: 0.262333, No Obj: 0.003197, Avg Recall: 0.525862, count: 116 -Region Avg IOU: 0.527077, Class: 1.000000, Obj: 0.286634, No Obj: 0.003211, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.571328, Class: 1.000000, Obj: 0.333497, No Obj: 0.003274, Avg Recall: 0.703704, count: 81 -Region Avg IOU: 0.557286, Class: 1.000000, Obj: 0.284920, No Obj: 0.003632, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.503606, Class: 1.000000, Obj: 0.262713, No Obj: 0.003258, Avg Recall: 0.584615, count: 130 -Region Avg IOU: 0.501204, Class: 1.000000, Obj: 0.282146, No Obj: 0.003732, Avg Recall: 0.538462, count: 130 -Region Avg IOU: 0.486794, Class: 1.000000, Obj: 0.259153, No Obj: 0.002806, Avg Recall: 0.544554, count: 101 -2220: 0.532459, 0.482360 avg, 0.001000 rate, 22.040136 seconds, 568320 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.315971 seconds -Region Avg IOU: 0.493396, Class: 1.000000, Obj: 0.247260, No Obj: 0.003207, Avg Recall: 0.573171, count: 82 -Region Avg IOU: 0.456422, Class: 1.000000, Obj: 0.240135, No Obj: 0.003611, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.447525, Class: 1.000000, Obj: 0.256082, No Obj: 0.003891, Avg Recall: 0.476635, count: 107 -Region Avg IOU: 0.427655, Class: 1.000000, Obj: 0.243107, No Obj: 0.003577, Avg Recall: 0.431579, count: 95 -Region Avg IOU: 0.482427, Class: 1.000000, Obj: 0.240466, No Obj: 0.004427, Avg Recall: 0.517483, count: 143 -Region Avg IOU: 0.430347, Class: 1.000000, Obj: 0.267267, No Obj: 0.003355, Avg Recall: 0.424242, count: 99 -Region Avg IOU: 0.502869, Class: 1.000000, Obj: 0.292330, No Obj: 0.003830, Avg Recall: 0.537736, count: 106 -Region Avg IOU: 0.496900, Class: 1.000000, Obj: 0.249665, No Obj: 0.003986, Avg Recall: 0.540323, count: 124 -2221: 0.500331, 0.484157 avg, 0.001000 rate, 20.239620 seconds, 568576 images -Loaded: 0.958611 seconds -Region Avg IOU: 0.473025, Class: 1.000000, Obj: 0.268750, No Obj: 0.003462, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.494634, Class: 1.000000, Obj: 0.284247, No Obj: 0.004207, Avg Recall: 0.495798, count: 119 -Region Avg IOU: 0.484167, Class: 1.000000, Obj: 0.270999, No Obj: 0.003639, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.482435, Class: 1.000000, Obj: 0.273473, No Obj: 0.004246, Avg Recall: 0.552000, count: 125 -Region Avg IOU: 0.512115, Class: 1.000000, Obj: 0.269547, No Obj: 0.004201, Avg Recall: 0.507812, count: 128 -Region Avg IOU: 0.452615, Class: 1.000000, Obj: 0.242629, No Obj: 0.004412, Avg Recall: 0.474820, count: 139 -Region Avg IOU: 0.408300, Class: 1.000000, Obj: 0.258994, No Obj: 0.003213, Avg Recall: 0.365591, count: 93 -Region Avg IOU: 0.479557, Class: 1.000000, Obj: 0.242106, No Obj: 0.004105, Avg Recall: 0.483051, count: 118 -2222: 0.559320, 0.491674 avg, 0.001000 rate, 18.558348 seconds, 568832 images -Loaded: 2.328736 seconds -Region Avg IOU: 0.468500, Class: 1.000000, Obj: 0.247518, No Obj: 0.003822, Avg Recall: 0.486957, count: 115 -Region Avg IOU: 0.465919, Class: 1.000000, Obj: 0.269500, No Obj: 0.003871, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.491683, Class: 1.000000, Obj: 0.267010, No Obj: 0.004170, Avg Recall: 0.568965, count: 116 -Region Avg IOU: 0.503129, Class: 1.000000, Obj: 0.296644, No Obj: 0.004202, Avg Recall: 0.585106, count: 94 -Region Avg IOU: 0.467036, Class: 1.000000, Obj: 0.264314, No Obj: 0.004059, Avg Recall: 0.477477, count: 111 -Region Avg IOU: 0.466009, Class: 1.000000, Obj: 0.261660, No Obj: 0.003850, Avg Recall: 0.469231, count: 130 -Region Avg IOU: 0.482302, Class: 1.000000, Obj: 0.283390, No Obj: 0.004138, Avg Recall: 0.490741, count: 108 -Region Avg IOU: 0.466336, Class: 1.000000, Obj: 0.273603, No Obj: 0.004167, Avg Recall: 0.453782, count: 119 -2223: 0.516132, 0.494119 avg, 0.001000 rate, 20.207748 seconds, 569088 images -Loaded: 2.445268 seconds -Region Avg IOU: 0.466132, Class: 1.000000, Obj: 0.293328, No Obj: 0.004259, Avg Recall: 0.511278, count: 133 -Region Avg IOU: 0.438183, Class: 1.000000, Obj: 0.266816, No Obj: 0.004036, Avg Recall: 0.486486, count: 111 -Region Avg IOU: 0.464929, Class: 1.000000, Obj: 0.295883, No Obj: 0.004794, Avg Recall: 0.507812, count: 128 -Region Avg IOU: 0.486822, Class: 1.000000, Obj: 0.271271, No Obj: 0.005400, Avg Recall: 0.521739, count: 161 -Region Avg IOU: 0.454767, Class: 1.000000, Obj: 0.260281, No Obj: 0.004648, Avg Recall: 0.486726, count: 113 -Region Avg IOU: 0.435078, Class: 1.000000, Obj: 0.294013, No Obj: 0.004576, Avg Recall: 0.389830, count: 118 -Region Avg IOU: 0.461885, Class: 1.000000, Obj: 0.272633, No Obj: 0.004547, Avg Recall: 0.495495, count: 111 -Region Avg IOU: 0.486021, Class: 1.000000, Obj: 0.291390, No Obj: 0.004831, Avg Recall: 0.528000, count: 125 -2224: 0.629586, 0.507666 avg, 0.001000 rate, 17.201996 seconds, 569344 images -Loaded: 4.143376 seconds -Region Avg IOU: 0.498637, Class: 1.000000, Obj: 0.309149, No Obj: 0.004708, Avg Recall: 0.549451, count: 91 -Region Avg IOU: 0.525223, Class: 1.000000, Obj: 0.311462, No Obj: 0.005132, Avg Recall: 0.595506, count: 89 -Region Avg IOU: 0.486391, Class: 1.000000, Obj: 0.375674, No Obj: 0.005180, Avg Recall: 0.523364, count: 107 -Region Avg IOU: 0.469746, Class: 1.000000, Obj: 0.298302, No Obj: 0.004186, Avg Recall: 0.462500, count: 80 -Region Avg IOU: 0.508379, Class: 1.000000, Obj: 0.344212, No Obj: 0.004892, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.477489, Class: 1.000000, Obj: 0.279062, No Obj: 0.004846, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.502276, Class: 1.000000, Obj: 0.315739, No Obj: 0.005288, Avg Recall: 0.533333, count: 105 -Region Avg IOU: 0.483981, Class: 1.000000, Obj: 0.261992, No Obj: 0.004589, Avg Recall: 0.534653, count: 101 -2225: 0.556165, 0.512516 avg, 0.001000 rate, 14.665950 seconds, 569600 images -Loaded: 4.706718 seconds -Region Avg IOU: 0.476608, Class: 1.000000, Obj: 0.354176, No Obj: 0.004853, Avg Recall: 0.515464, count: 97 -Region Avg IOU: 0.430490, Class: 1.000000, Obj: 0.323751, No Obj: 0.004388, Avg Recall: 0.472973, count: 74 -Region Avg IOU: 0.424899, Class: 1.000000, Obj: 0.279744, No Obj: 0.004435, Avg Recall: 0.460000, count: 100 -Region Avg IOU: 0.424930, Class: 1.000000, Obj: 0.282165, No Obj: 0.004278, Avg Recall: 0.415842, count: 101 -Region Avg IOU: 0.448372, Class: 1.000000, Obj: 0.346657, No Obj: 0.005256, Avg Recall: 0.451852, count: 135 -Region Avg IOU: 0.480717, Class: 1.000000, Obj: 0.335461, No Obj: 0.006017, Avg Recall: 0.456000, count: 125 -Region Avg IOU: 0.443420, Class: 1.000000, Obj: 0.313246, No Obj: 0.005362, Avg Recall: 0.452174, count: 115 -Region Avg IOU: 0.540141, Class: 1.000000, Obj: 0.396215, No Obj: 0.005056, Avg Recall: 0.625000, count: 104 -2226: 0.487766, 0.510041 avg, 0.001000 rate, 20.090050 seconds, 569856 images -Loaded: 1.632662 seconds -Region Avg IOU: 0.465967, Class: 1.000000, Obj: 0.380296, No Obj: 0.005883, Avg Recall: 0.507692, count: 130 -Region Avg IOU: 0.503939, Class: 1.000000, Obj: 0.325732, No Obj: 0.004757, Avg Recall: 0.559633, count: 109 -Region Avg IOU: 0.489069, Class: 1.000000, Obj: 0.348514, No Obj: 0.005904, Avg Recall: 0.522388, count: 134 -Region Avg IOU: 0.484710, Class: 1.000000, Obj: 0.348931, No Obj: 0.005083, Avg Recall: 0.524590, count: 122 -Region Avg IOU: 0.429102, Class: 1.000000, Obj: 0.290200, No Obj: 0.005041, Avg Recall: 0.487180, count: 117 -Region Avg IOU: 0.456561, Class: 1.000000, Obj: 0.332810, No Obj: 0.005247, Avg Recall: 0.520661, count: 121 -Region Avg IOU: 0.507231, Class: 1.000000, Obj: 0.369197, No Obj: 0.005159, Avg Recall: 0.555556, count: 99 -Region Avg IOU: 0.435073, Class: 1.000000, Obj: 0.333311, No Obj: 0.004499, Avg Recall: 0.472527, count: 91 -2227: 0.521449, 0.511182 avg, 0.001000 rate, 17.530216 seconds, 570112 images -Loaded: 2.938688 seconds -Region Avg IOU: 0.466848, Class: 1.000000, Obj: 0.352041, No Obj: 0.005243, Avg Recall: 0.500000, count: 120 -Region Avg IOU: 0.471443, Class: 1.000000, Obj: 0.366175, No Obj: 0.006404, Avg Recall: 0.510490, count: 143 -Region Avg IOU: 0.438387, Class: 1.000000, Obj: 0.347141, No Obj: 0.005529, Avg Recall: 0.448819, count: 127 -Region Avg IOU: 0.424677, Class: 1.000000, Obj: 0.307806, No Obj: 0.004162, Avg Recall: 0.376812, count: 69 -Region Avg IOU: 0.480555, Class: 1.000000, Obj: 0.341040, No Obj: 0.005212, Avg Recall: 0.518797, count: 133 -Region Avg IOU: 0.406363, Class: 1.000000, Obj: 0.350191, No Obj: 0.005017, Avg Recall: 0.423423, count: 111 -Region Avg IOU: 0.437717, Class: 1.000000, Obj: 0.352417, No Obj: 0.005744, Avg Recall: 0.432000, count: 125 -Region Avg IOU: 0.421552, Class: 1.000000, Obj: 0.358518, No Obj: 0.004671, Avg Recall: 0.416667, count: 84 -2228: 0.489930, 0.509057 avg, 0.001000 rate, 17.218464 seconds, 570368 images -Loaded: 2.974841 seconds -Region Avg IOU: 0.491364, Class: 1.000000, Obj: 0.302080, No Obj: 0.004808, Avg Recall: 0.532609, count: 92 -Region Avg IOU: 0.515503, Class: 1.000000, Obj: 0.319099, No Obj: 0.005014, Avg Recall: 0.587719, count: 114 -Region Avg IOU: 0.431991, Class: 1.000000, Obj: 0.303549, No Obj: 0.004687, Avg Recall: 0.464912, count: 114 -Region Avg IOU: 0.473460, Class: 1.000000, Obj: 0.306810, No Obj: 0.006121, Avg Recall: 0.491228, count: 114 -Region Avg IOU: 0.434056, Class: 1.000000, Obj: 0.336947, No Obj: 0.004392, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.442437, Class: 1.000000, Obj: 0.302484, No Obj: 0.004956, Avg Recall: 0.480000, count: 100 -Region Avg IOU: 0.436104, Class: 1.000000, Obj: 0.305101, No Obj: 0.004521, Avg Recall: 0.451327, count: 113 -Region Avg IOU: 0.463946, Class: 1.000000, Obj: 0.309204, No Obj: 0.004121, Avg Recall: 0.483516, count: 91 -2229: 0.495486, 0.507700 avg, 0.001000 rate, 18.924423 seconds, 570624 images -Loaded: 2.536977 seconds -Region Avg IOU: 0.462334, Class: 1.000000, Obj: 0.312318, No Obj: 0.004950, Avg Recall: 0.518868, count: 106 -Region Avg IOU: 0.410602, Class: 1.000000, Obj: 0.271761, No Obj: 0.004735, Avg Recall: 0.415094, count: 106 -Region Avg IOU: 0.477553, Class: 1.000000, Obj: 0.332130, No Obj: 0.003937, Avg Recall: 0.531646, count: 79 -Region Avg IOU: 0.461133, Class: 1.000000, Obj: 0.299554, No Obj: 0.004792, Avg Recall: 0.477477, count: 111 -Region Avg IOU: 0.492489, Class: 1.000000, Obj: 0.313378, No Obj: 0.004245, Avg Recall: 0.554217, count: 83 -Region Avg IOU: 0.433032, Class: 1.000000, Obj: 0.341034, No Obj: 0.004138, Avg Recall: 0.472527, count: 91 -Region Avg IOU: 0.502292, Class: 1.000000, Obj: 0.342186, No Obj: 0.005020, Avg Recall: 0.533333, count: 120 -Region Avg IOU: 0.485467, Class: 1.000000, Obj: 0.316384, No Obj: 0.004315, Avg Recall: 0.553191, count: 94 -2230: 0.448302, 0.501760 avg, 0.001000 rate, 20.283800 seconds, 570880 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000069 seconds -Region Avg IOU: 0.573433, Class: 1.000000, Obj: 0.362946, No Obj: 0.003726, Avg Recall: 0.690789, count: 152 -Region Avg IOU: 0.609390, Class: 1.000000, Obj: 0.446573, No Obj: 0.003132, Avg Recall: 0.746479, count: 71 -Region Avg IOU: 0.598831, Class: 1.000000, Obj: 0.338853, No Obj: 0.003677, Avg Recall: 0.776859, count: 121 -Region Avg IOU: 0.578753, Class: 1.000000, Obj: 0.323943, No Obj: 0.003545, Avg Recall: 0.714286, count: 119 -Region Avg IOU: 0.537167, Class: 1.000000, Obj: 0.353767, No Obj: 0.002734, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.573891, Class: 1.000000, Obj: 0.397939, No Obj: 0.003358, Avg Recall: 0.713178, count: 129 -Region Avg IOU: 0.602822, Class: 1.000000, Obj: 0.345904, No Obj: 0.003627, Avg Recall: 0.758065, count: 124 -Region Avg IOU: 0.572705, Class: 1.000000, Obj: 0.348407, No Obj: 0.003395, Avg Recall: 0.683168, count: 101 -2231: 0.596893, 0.511273 avg, 0.001000 rate, 26.591383 seconds, 571136 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.580920, Class: 1.000000, Obj: 0.306320, No Obj: 0.002503, Avg Recall: 0.698630, count: 73 -Region Avg IOU: 0.591966, Class: 1.000000, Obj: 0.287523, No Obj: 0.002433, Avg Recall: 0.714286, count: 84 -Region Avg IOU: 0.558465, Class: 1.000000, Obj: 0.329501, No Obj: 0.002527, Avg Recall: 0.682540, count: 126 -Region Avg IOU: 0.582715, Class: 1.000000, Obj: 0.339538, No Obj: 0.002606, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.585826, Class: 1.000000, Obj: 0.375413, No Obj: 0.002643, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.594368, Class: 1.000000, Obj: 0.300775, No Obj: 0.002742, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.563335, Class: 1.000000, Obj: 0.335530, No Obj: 0.002845, Avg Recall: 0.651163, count: 129 -Region Avg IOU: 0.622886, Class: 1.000000, Obj: 0.299091, No Obj: 0.003084, Avg Recall: 0.785047, count: 107 -2232: 0.521108, 0.512257 avg, 0.001000 rate, 28.108654 seconds, 571392 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.599799, Class: 1.000000, Obj: 0.270529, No Obj: 0.002809, Avg Recall: 0.722628, count: 137 -Region Avg IOU: 0.581674, Class: 1.000000, Obj: 0.267281, No Obj: 0.002355, Avg Recall: 0.722222, count: 90 -Region Avg IOU: 0.602341, Class: 1.000000, Obj: 0.298018, No Obj: 0.002328, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.592064, Class: 1.000000, Obj: 0.268982, No Obj: 0.002593, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.607888, Class: 1.000000, Obj: 0.328625, No Obj: 0.002526, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.588438, Class: 1.000000, Obj: 0.271985, No Obj: 0.002345, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.633409, Class: 1.000000, Obj: 0.244313, No Obj: 0.002915, Avg Recall: 0.797203, count: 143 -Region Avg IOU: 0.570145, Class: 1.000000, Obj: 0.292220, No Obj: 0.002424, Avg Recall: 0.722628, count: 137 -2233: 0.513375, 0.512369 avg, 0.001000 rate, 28.598898 seconds, 571648 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.561905, Class: 1.000000, Obj: 0.269961, No Obj: 0.002190, Avg Recall: 0.641667, count: 120 -Region Avg IOU: 0.562243, Class: 1.000000, Obj: 0.204065, No Obj: 0.002030, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.583672, Class: 1.000000, Obj: 0.238476, No Obj: 0.002489, Avg Recall: 0.676923, count: 130 -Region Avg IOU: 0.652446, Class: 1.000000, Obj: 0.259654, No Obj: 0.002310, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.608249, Class: 1.000000, Obj: 0.247972, No Obj: 0.002281, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.643678, Class: 1.000000, Obj: 0.257359, No Obj: 0.002414, Avg Recall: 0.818182, count: 110 -Region Avg IOU: 0.627873, Class: 1.000000, Obj: 0.264588, No Obj: 0.002085, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.618789, Class: 1.000000, Obj: 0.254203, No Obj: 0.002164, Avg Recall: 0.777778, count: 126 -2234: 0.508579, 0.511990 avg, 0.001000 rate, 28.723141 seconds, 571904 images -Loaded: 0.000059 seconds -Region Avg IOU: 0.593608, Class: 1.000000, Obj: 0.289403, No Obj: 0.002420, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.587970, Class: 1.000000, Obj: 0.257462, No Obj: 0.002353, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.608340, Class: 1.000000, Obj: 0.239775, No Obj: 0.002096, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.612027, Class: 1.000000, Obj: 0.243354, No Obj: 0.002356, Avg Recall: 0.753521, count: 142 -Region Avg IOU: 0.637107, Class: 1.000000, Obj: 0.266943, No Obj: 0.002021, Avg Recall: 0.792208, count: 77 -Region Avg IOU: 0.603540, Class: 1.000000, Obj: 0.249437, No Obj: 0.001876, Avg Recall: 0.764706, count: 85 -Region Avg IOU: 0.626078, Class: 1.000000, Obj: 0.287338, No Obj: 0.002461, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.629666, Class: 1.000000, Obj: 0.291193, No Obj: 0.002409, Avg Recall: 0.770642, count: 109 -2235: 0.525842, 0.513375 avg, 0.001000 rate, 27.797327 seconds, 572160 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.654368, Class: 1.000000, Obj: 0.254616, No Obj: 0.002767, Avg Recall: 0.824561, count: 114 -Region Avg IOU: 0.612763, Class: 1.000000, Obj: 0.289076, No Obj: 0.002284, Avg Recall: 0.782609, count: 115 -Region Avg IOU: 0.600326, Class: 1.000000, Obj: 0.256586, No Obj: 0.002419, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.605345, Class: 1.000000, Obj: 0.281458, No Obj: 0.002383, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.575730, Class: 1.000000, Obj: 0.253469, No Obj: 0.002734, Avg Recall: 0.657718, count: 149 -Region Avg IOU: 0.630994, Class: 1.000000, Obj: 0.315800, No Obj: 0.002446, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.598982, Class: 1.000000, Obj: 0.229203, No Obj: 0.002479, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.632264, Class: 1.000000, Obj: 0.293288, No Obj: 0.002145, Avg Recall: 0.787879, count: 99 -2236: 0.533395, 0.515377 avg, 0.001000 rate, 28.165861 seconds, 572416 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.613250, Class: 1.000000, Obj: 0.287595, No Obj: 0.002665, Avg Recall: 0.740157, count: 127 -Region Avg IOU: 0.598790, Class: 1.000000, Obj: 0.292011, No Obj: 0.002453, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.547205, Class: 1.000000, Obj: 0.274924, No Obj: 0.002504, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.609139, Class: 1.000000, Obj: 0.282006, No Obj: 0.002422, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.571517, Class: 1.000000, Obj: 0.241920, No Obj: 0.002866, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.596010, Class: 1.000000, Obj: 0.251148, No Obj: 0.002771, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.593981, Class: 1.000000, Obj: 0.262371, No Obj: 0.002423, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.630237, Class: 1.000000, Obj: 0.274406, No Obj: 0.002421, Avg Recall: 0.804598, count: 87 -2237: 0.537790, 0.517618 avg, 0.001000 rate, 27.482904 seconds, 572672 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.550396, Class: 1.000000, Obj: 0.273905, No Obj: 0.003084, Avg Recall: 0.666667, count: 147 -Region Avg IOU: 0.605543, Class: 1.000000, Obj: 0.325878, No Obj: 0.002844, Avg Recall: 0.798387, count: 124 -Region Avg IOU: 0.622869, Class: 1.000000, Obj: 0.378506, No Obj: 0.002630, Avg Recall: 0.802326, count: 86 -Region Avg IOU: 0.638866, Class: 1.000000, Obj: 0.375131, No Obj: 0.002786, Avg Recall: 0.839623, count: 106 -Region Avg IOU: 0.609588, Class: 1.000000, Obj: 0.320542, No Obj: 0.002938, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.641135, Class: 1.000000, Obj: 0.301710, No Obj: 0.003110, Avg Recall: 0.800000, count: 115 -Region Avg IOU: 0.582776, Class: 1.000000, Obj: 0.309208, No Obj: 0.002924, Avg Recall: 0.742857, count: 140 -Region Avg IOU: 0.618298, Class: 1.000000, Obj: 0.344941, No Obj: 0.003052, Avg Recall: 0.798077, count: 104 -2238: 0.567645, 0.522621 avg, 0.001000 rate, 28.488632 seconds, 572928 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.615398, Class: 1.000000, Obj: 0.337372, No Obj: 0.002647, Avg Recall: 0.777778, count: 81 -Region Avg IOU: 0.582934, Class: 1.000000, Obj: 0.363098, No Obj: 0.003106, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.630401, Class: 1.000000, Obj: 0.342664, No Obj: 0.003100, Avg Recall: 0.774436, count: 133 -Region Avg IOU: 0.636416, Class: 1.000000, Obj: 0.361968, No Obj: 0.002692, Avg Recall: 0.827957, count: 93 -Region Avg IOU: 0.613894, Class: 1.000000, Obj: 0.338398, No Obj: 0.003243, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.613576, Class: 1.000000, Obj: 0.335589, No Obj: 0.003435, Avg Recall: 0.775194, count: 129 -Region Avg IOU: 0.626228, Class: 1.000000, Obj: 0.310042, No Obj: 0.003588, Avg Recall: 0.770270, count: 148 -Region Avg IOU: 0.623171, Class: 1.000000, Obj: 0.344396, No Obj: 0.002842, Avg Recall: 0.759259, count: 108 -2239: 0.522079, 0.522567 avg, 0.001000 rate, 26.064972 seconds, 573184 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.588294, Class: 1.000000, Obj: 0.382550, No Obj: 0.002446, Avg Recall: 0.653333, count: 75 -Region Avg IOU: 0.617730, Class: 1.000000, Obj: 0.390907, No Obj: 0.003350, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.601911, Class: 1.000000, Obj: 0.325847, No Obj: 0.003324, Avg Recall: 0.789474, count: 114 -Region Avg IOU: 0.611631, Class: 1.000000, Obj: 0.330040, No Obj: 0.002958, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.627936, Class: 1.000000, Obj: 0.361247, No Obj: 0.002745, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.612064, Class: 1.000000, Obj: 0.386187, No Obj: 0.002940, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.619996, Class: 1.000000, Obj: 0.338257, No Obj: 0.003168, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.601321, Class: 1.000000, Obj: 0.331719, No Obj: 0.003348, Avg Recall: 0.736364, count: 110 -2240: 0.420098, 0.512320 avg, 0.001000 rate, 28.263512 seconds, 573440 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 16.543137 seconds -Region Avg IOU: 0.601554, Class: 1.000000, Obj: 0.301584, No Obj: 0.004030, Avg Recall: 0.753333, count: 150 -Region Avg IOU: 0.605199, Class: 1.000000, Obj: 0.351867, No Obj: 0.003876, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.596091, Class: 1.000000, Obj: 0.363435, No Obj: 0.003969, Avg Recall: 0.719424, count: 139 -Region Avg IOU: 0.598799, Class: 1.000000, Obj: 0.342740, No Obj: 0.003791, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.607940, Class: 1.000000, Obj: 0.349050, No Obj: 0.003846, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.611634, Class: 1.000000, Obj: 0.343573, No Obj: 0.003311, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.599402, Class: 1.000000, Obj: 0.359519, No Obj: 0.003262, Avg Recall: 0.716216, count: 74 -Region Avg IOU: 0.581492, Class: 1.000000, Obj: 0.367658, No Obj: 0.003618, Avg Recall: 0.675000, count: 120 -2241: 0.495208, 0.510609 avg, 0.001000 rate, 27.524200 seconds, 573696 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.603814, Class: 1.000000, Obj: 0.376218, No Obj: 0.003334, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.565901, Class: 1.000000, Obj: 0.377068, No Obj: 0.004142, Avg Recall: 0.639706, count: 136 -Region Avg IOU: 0.592515, Class: 1.000000, Obj: 0.362558, No Obj: 0.003538, Avg Recall: 0.738095, count: 84 -Region Avg IOU: 0.606159, Class: 1.000000, Obj: 0.344640, No Obj: 0.003816, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.598225, Class: 1.000000, Obj: 0.381591, No Obj: 0.003318, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.581833, Class: 1.000000, Obj: 0.371492, No Obj: 0.003225, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.555149, Class: 1.000000, Obj: 0.394556, No Obj: 0.003890, Avg Recall: 0.633094, count: 139 -Region Avg IOU: 0.624031, Class: 1.000000, Obj: 0.327419, No Obj: 0.003868, Avg Recall: 0.793103, count: 116 -2242: 0.507504, 0.510298 avg, 0.001000 rate, 25.265669 seconds, 573952 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.568109, Class: 1.000000, Obj: 0.355329, No Obj: 0.003737, Avg Recall: 0.698529, count: 136 -Region Avg IOU: 0.547320, Class: 1.000000, Obj: 0.354899, No Obj: 0.003087, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.571868, Class: 1.000000, Obj: 0.373552, No Obj: 0.003540, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.589622, Class: 1.000000, Obj: 0.402216, No Obj: 0.003720, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.548521, Class: 1.000000, Obj: 0.370248, No Obj: 0.003511, Avg Recall: 0.620968, count: 124 -Region Avg IOU: 0.561932, Class: 1.000000, Obj: 0.344671, No Obj: 0.003376, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.573237, Class: 1.000000, Obj: 0.352991, No Obj: 0.003214, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.554361, Class: 1.000000, Obj: 0.374940, No Obj: 0.003425, Avg Recall: 0.622807, count: 114 -2243: 0.529633, 0.512232 avg, 0.001000 rate, 25.949608 seconds, 574208 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.586743, Class: 1.000000, Obj: 0.364596, No Obj: 0.003137, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.562068, Class: 1.000000, Obj: 0.405662, No Obj: 0.003564, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.549607, Class: 1.000000, Obj: 0.370955, No Obj: 0.003256, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.554573, Class: 1.000000, Obj: 0.374965, No Obj: 0.002742, Avg Recall: 0.704545, count: 88 -Region Avg IOU: 0.568885, Class: 1.000000, Obj: 0.387823, No Obj: 0.003639, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.560187, Class: 1.000000, Obj: 0.331282, No Obj: 0.003597, Avg Recall: 0.678571, count: 140 -Region Avg IOU: 0.557416, Class: 1.000000, Obj: 0.380434, No Obj: 0.003709, Avg Recall: 0.630252, count: 119 -Region Avg IOU: 0.569066, Class: 1.000000, Obj: 0.297924, No Obj: 0.003386, Avg Recall: 0.688172, count: 93 -2244: 0.497930, 0.510801 avg, 0.001000 rate, 25.774319 seconds, 574464 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.554460, Class: 1.000000, Obj: 0.351150, No Obj: 0.003033, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.585267, Class: 1.000000, Obj: 0.308219, No Obj: 0.002994, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.559935, Class: 1.000000, Obj: 0.308228, No Obj: 0.003218, Avg Recall: 0.682927, count: 123 -Region Avg IOU: 0.532681, Class: 1.000000, Obj: 0.308289, No Obj: 0.002975, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.571425, Class: 1.000000, Obj: 0.312819, No Obj: 0.003353, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.539021, Class: 1.000000, Obj: 0.329510, No Obj: 0.003456, Avg Recall: 0.601626, count: 123 -Region Avg IOU: 0.542980, Class: 1.000000, Obj: 0.325411, No Obj: 0.002964, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.589796, Class: 1.000000, Obj: 0.296707, No Obj: 0.002721, Avg Recall: 0.747368, count: 95 -2245: 0.546527, 0.514374 avg, 0.001000 rate, 25.856449 seconds, 574720 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.602432, Class: 1.000000, Obj: 0.321448, No Obj: 0.002850, Avg Recall: 0.767123, count: 73 -Region Avg IOU: 0.550693, Class: 1.000000, Obj: 0.323485, No Obj: 0.002854, Avg Recall: 0.653226, count: 124 -Region Avg IOU: 0.580276, Class: 1.000000, Obj: 0.328684, No Obj: 0.002800, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.564394, Class: 1.000000, Obj: 0.337133, No Obj: 0.003032, Avg Recall: 0.677686, count: 121 -Region Avg IOU: 0.607243, Class: 1.000000, Obj: 0.300105, No Obj: 0.002790, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.598908, Class: 1.000000, Obj: 0.328173, No Obj: 0.002937, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.554546, Class: 1.000000, Obj: 0.322640, No Obj: 0.002626, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.580811, Class: 1.000000, Obj: 0.368740, No Obj: 0.002805, Avg Recall: 0.722222, count: 90 -2246: 0.467084, 0.509645 avg, 0.001000 rate, 25.116920 seconds, 574976 images -Loaded: 0.000059 seconds -Region Avg IOU: 0.587312, Class: 1.000000, Obj: 0.281820, No Obj: 0.002739, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.574289, Class: 1.000000, Obj: 0.281864, No Obj: 0.002905, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.592805, Class: 1.000000, Obj: 0.337690, No Obj: 0.002646, Avg Recall: 0.710843, count: 83 -Region Avg IOU: 0.591021, Class: 1.000000, Obj: 0.295681, No Obj: 0.002894, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.591979, Class: 1.000000, Obj: 0.311232, No Obj: 0.002671, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.577075, Class: 1.000000, Obj: 0.277039, No Obj: 0.002955, Avg Recall: 0.679389, count: 131 -Region Avg IOU: 0.572390, Class: 1.000000, Obj: 0.254051, No Obj: 0.003098, Avg Recall: 0.643939, count: 132 -Region Avg IOU: 0.581890, Class: 1.000000, Obj: 0.271788, No Obj: 0.003027, Avg Recall: 0.747899, count: 119 -2247: 0.478020, 0.506482 avg, 0.001000 rate, 26.283720 seconds, 575232 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.569452, Class: 1.000000, Obj: 0.309580, No Obj: 0.002641, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.537728, Class: 1.000000, Obj: 0.272859, No Obj: 0.002831, Avg Recall: 0.607407, count: 135 -Region Avg IOU: 0.593345, Class: 1.000000, Obj: 0.269672, No Obj: 0.003033, Avg Recall: 0.750000, count: 136 -Region Avg IOU: 0.539913, Class: 1.000000, Obj: 0.256385, No Obj: 0.003138, Avg Recall: 0.671875, count: 128 -Region Avg IOU: 0.590411, Class: 1.000000, Obj: 0.311368, No Obj: 0.002867, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.604136, Class: 1.000000, Obj: 0.285638, No Obj: 0.002966, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.593500, Class: 1.000000, Obj: 0.298282, No Obj: 0.002930, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.580093, Class: 1.000000, Obj: 0.292632, No Obj: 0.002479, Avg Recall: 0.681319, count: 91 -2248: 0.516436, 0.507478 avg, 0.001000 rate, 25.785826 seconds, 575488 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.560607, Class: 1.000000, Obj: 0.285411, No Obj: 0.003229, Avg Recall: 0.617886, count: 123 -Region Avg IOU: 0.589267, Class: 1.000000, Obj: 0.302279, No Obj: 0.003130, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.549545, Class: 1.000000, Obj: 0.269710, No Obj: 0.003284, Avg Recall: 0.610738, count: 149 -Region Avg IOU: 0.606553, Class: 1.000000, Obj: 0.340960, No Obj: 0.002674, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.575551, Class: 1.000000, Obj: 0.324566, No Obj: 0.002578, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.583503, Class: 1.000000, Obj: 0.273977, No Obj: 0.002790, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.572756, Class: 1.000000, Obj: 0.312442, No Obj: 0.003070, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.601185, Class: 1.000000, Obj: 0.309072, No Obj: 0.002868, Avg Recall: 0.739130, count: 115 -2249: 0.498689, 0.506599 avg, 0.001000 rate, 25.989300 seconds, 575744 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.577766, Class: 1.000000, Obj: 0.322388, No Obj: 0.003008, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.575126, Class: 1.000000, Obj: 0.303183, No Obj: 0.002948, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.529339, Class: 1.000000, Obj: 0.256821, No Obj: 0.003353, Avg Recall: 0.606667, count: 150 -Region Avg IOU: 0.569424, Class: 1.000000, Obj: 0.309397, No Obj: 0.002891, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.581557, Class: 1.000000, Obj: 0.298252, No Obj: 0.002888, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.633448, Class: 1.000000, Obj: 0.345769, No Obj: 0.003289, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.592475, Class: 1.000000, Obj: 0.290356, No Obj: 0.002989, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.611418, Class: 1.000000, Obj: 0.280587, No Obj: 0.003094, Avg Recall: 0.779817, count: 109 -2250: 0.495175, 0.505456 avg, 0.001000 rate, 25.476128 seconds, 576000 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000066 seconds -Region Avg IOU: 0.632023, Class: 1.000000, Obj: 0.399383, No Obj: 0.003003, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.625621, Class: 1.000000, Obj: 0.364554, No Obj: 0.002984, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.667081, Class: 1.000000, Obj: 0.412362, No Obj: 0.003170, Avg Recall: 0.825397, count: 126 -Region Avg IOU: 0.632211, Class: 1.000000, Obj: 0.320965, No Obj: 0.003006, Avg Recall: 0.788136, count: 118 -Region Avg IOU: 0.641350, Class: 1.000000, Obj: 0.356263, No Obj: 0.002963, Avg Recall: 0.823529, count: 102 -Region Avg IOU: 0.657417, Class: 1.000000, Obj: 0.372331, No Obj: 0.002898, Avg Recall: 0.831776, count: 107 -Region Avg IOU: 0.641210, Class: 1.000000, Obj: 0.382233, No Obj: 0.003143, Avg Recall: 0.801653, count: 121 -Region Avg IOU: 0.617421, Class: 1.000000, Obj: 0.318852, No Obj: 0.002750, Avg Recall: 0.760417, count: 96 -2251: 0.504732, 0.505384 avg, 0.001000 rate, 29.165735 seconds, 576256 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.602972, Class: 1.000000, Obj: 0.345863, No Obj: 0.003185, Avg Recall: 0.720000, count: 125 -Region Avg IOU: 0.615979, Class: 1.000000, Obj: 0.344915, No Obj: 0.003279, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.626847, Class: 1.000000, Obj: 0.331720, No Obj: 0.003292, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.611900, Class: 1.000000, Obj: 0.379236, No Obj: 0.002815, Avg Recall: 0.764706, count: 85 -Region Avg IOU: 0.605202, Class: 1.000000, Obj: 0.317772, No Obj: 0.003452, Avg Recall: 0.759399, count: 133 -Region Avg IOU: 0.615817, Class: 1.000000, Obj: 0.376187, No Obj: 0.003361, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.644119, Class: 1.000000, Obj: 0.353115, No Obj: 0.003287, Avg Recall: 0.807229, count: 83 -Region Avg IOU: 0.633538, Class: 1.000000, Obj: 0.357590, No Obj: 0.002971, Avg Recall: 0.804124, count: 97 -2252: 0.475688, 0.502414 avg, 0.001000 rate, 28.660084 seconds, 576512 images -Loaded: 0.000120 seconds -Region Avg IOU: 0.576107, Class: 1.000000, Obj: 0.291807, No Obj: 0.002836, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.608775, Class: 1.000000, Obj: 0.318977, No Obj: 0.003014, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.650033, Class: 1.000000, Obj: 0.380750, No Obj: 0.002862, Avg Recall: 0.838384, count: 99 -Region Avg IOU: 0.581854, Class: 1.000000, Obj: 0.359514, No Obj: 0.002963, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.613803, Class: 1.000000, Obj: 0.364197, No Obj: 0.003255, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.641472, Class: 1.000000, Obj: 0.375812, No Obj: 0.002722, Avg Recall: 0.841584, count: 101 -Region Avg IOU: 0.632911, Class: 1.000000, Obj: 0.343689, No Obj: 0.003292, Avg Recall: 0.813008, count: 123 -Region Avg IOU: 0.635587, Class: 1.000000, Obj: 0.374043, No Obj: 0.003029, Avg Recall: 0.780000, count: 100 -2253: 0.468544, 0.499027 avg, 0.001000 rate, 29.904217 seconds, 576768 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.664647, Class: 1.000000, Obj: 0.351837, No Obj: 0.002921, Avg Recall: 0.865854, count: 82 -Region Avg IOU: 0.606988, Class: 1.000000, Obj: 0.400452, No Obj: 0.003046, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.622461, Class: 1.000000, Obj: 0.338845, No Obj: 0.002795, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.639320, Class: 1.000000, Obj: 0.362622, No Obj: 0.002633, Avg Recall: 0.823529, count: 102 -Region Avg IOU: 0.575239, Class: 1.000000, Obj: 0.346940, No Obj: 0.002717, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.643654, Class: 1.000000, Obj: 0.335226, No Obj: 0.003188, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.617006, Class: 1.000000, Obj: 0.314084, No Obj: 0.002457, Avg Recall: 0.725000, count: 80 -Region Avg IOU: 0.627909, Class: 1.000000, Obj: 0.352306, No Obj: 0.002680, Avg Recall: 0.804348, count: 92 -2254: 0.443181, 0.493443 avg, 0.001000 rate, 28.239052 seconds, 577024 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.594712, Class: 1.000000, Obj: 0.282979, No Obj: 0.002303, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.629378, Class: 1.000000, Obj: 0.378259, No Obj: 0.002493, Avg Recall: 0.819048, count: 105 -Region Avg IOU: 0.633766, Class: 1.000000, Obj: 0.330381, No Obj: 0.002648, Avg Recall: 0.818182, count: 88 -Region Avg IOU: 0.630115, Class: 1.000000, Obj: 0.348703, No Obj: 0.002612, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.604676, Class: 1.000000, Obj: 0.314906, No Obj: 0.002775, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.638048, Class: 1.000000, Obj: 0.322164, No Obj: 0.002537, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.634865, Class: 1.000000, Obj: 0.341195, No Obj: 0.002997, Avg Recall: 0.770492, count: 122 -Region Avg IOU: 0.587183, Class: 1.000000, Obj: 0.334456, No Obj: 0.002593, Avg Recall: 0.704762, count: 105 -2255: 0.432707, 0.487369 avg, 0.001000 rate, 30.486635 seconds, 577280 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.625104, Class: 1.000000, Obj: 0.321514, No Obj: 0.002712, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.608705, Class: 1.000000, Obj: 0.292134, No Obj: 0.002480, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.597650, Class: 1.000000, Obj: 0.310810, No Obj: 0.002590, Avg Recall: 0.748031, count: 127 -Region Avg IOU: 0.611359, Class: 1.000000, Obj: 0.343280, No Obj: 0.002331, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.646326, Class: 1.000000, Obj: 0.302418, No Obj: 0.002472, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.608731, Class: 1.000000, Obj: 0.289332, No Obj: 0.003011, Avg Recall: 0.740157, count: 127 -Region Avg IOU: 0.621232, Class: 1.000000, Obj: 0.348366, No Obj: 0.002691, Avg Recall: 0.796117, count: 103 -Region Avg IOU: 0.640202, Class: 1.000000, Obj: 0.310839, No Obj: 0.002707, Avg Recall: 0.805085, count: 118 -2256: 0.515340, 0.490166 avg, 0.001000 rate, 28.936216 seconds, 577536 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.583596, Class: 1.000000, Obj: 0.305825, No Obj: 0.002822, Avg Recall: 0.753623, count: 138 -Region Avg IOU: 0.631294, Class: 1.000000, Obj: 0.283344, No Obj: 0.002048, Avg Recall: 0.790698, count: 86 -Region Avg IOU: 0.634607, Class: 1.000000, Obj: 0.296674, No Obj: 0.002340, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.570979, Class: 1.000000, Obj: 0.262239, No Obj: 0.002402, Avg Recall: 0.677419, count: 124 -Region Avg IOU: 0.613570, Class: 1.000000, Obj: 0.296592, No Obj: 0.002527, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.615836, Class: 1.000000, Obj: 0.297390, No Obj: 0.002578, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.660164, Class: 1.000000, Obj: 0.325981, No Obj: 0.002338, Avg Recall: 0.864078, count: 103 -Region Avg IOU: 0.647301, Class: 1.000000, Obj: 0.285066, No Obj: 0.002040, Avg Recall: 0.812500, count: 80 -2257: 0.478071, 0.488957 avg, 0.001000 rate, 28.659285 seconds, 577792 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.597120, Class: 1.000000, Obj: 0.295143, No Obj: 0.002369, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.608656, Class: 1.000000, Obj: 0.301835, No Obj: 0.002729, Avg Recall: 0.730769, count: 130 -Region Avg IOU: 0.615222, Class: 1.000000, Obj: 0.266548, No Obj: 0.002503, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.637360, Class: 1.000000, Obj: 0.351107, No Obj: 0.002220, Avg Recall: 0.809524, count: 84 -Region Avg IOU: 0.607533, Class: 1.000000, Obj: 0.263778, No Obj: 0.002366, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.629290, Class: 1.000000, Obj: 0.295350, No Obj: 0.002252, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.581701, Class: 1.000000, Obj: 0.312474, No Obj: 0.002093, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.619694, Class: 1.000000, Obj: 0.257802, No Obj: 0.002494, Avg Recall: 0.770000, count: 100 -2258: 0.451157, 0.485177 avg, 0.001000 rate, 29.208080 seconds, 578048 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.615254, Class: 1.000000, Obj: 0.310630, No Obj: 0.002521, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.626297, Class: 1.000000, Obj: 0.339062, No Obj: 0.002104, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.634297, Class: 1.000000, Obj: 0.302075, No Obj: 0.002270, Avg Recall: 0.829545, count: 88 -Region Avg IOU: 0.650736, Class: 1.000000, Obj: 0.335654, No Obj: 0.002327, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.607440, Class: 1.000000, Obj: 0.309644, No Obj: 0.002369, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.621667, Class: 1.000000, Obj: 0.289609, No Obj: 0.002380, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.633435, Class: 1.000000, Obj: 0.374180, No Obj: 0.001903, Avg Recall: 0.773333, count: 75 -Region Avg IOU: 0.629075, Class: 1.000000, Obj: 0.267158, No Obj: 0.002302, Avg Recall: 0.787611, count: 113 -2259: 0.442410, 0.480900 avg, 0.001000 rate, 29.189720 seconds, 578304 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.595155, Class: 1.000000, Obj: 0.272633, No Obj: 0.002571, Avg Recall: 0.730159, count: 126 -Region Avg IOU: 0.588707, Class: 1.000000, Obj: 0.322076, No Obj: 0.002197, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.623986, Class: 1.000000, Obj: 0.285288, No Obj: 0.002662, Avg Recall: 0.771654, count: 127 -Region Avg IOU: 0.596124, Class: 1.000000, Obj: 0.319305, No Obj: 0.002436, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.651110, Class: 1.000000, Obj: 0.302583, No Obj: 0.002537, Avg Recall: 0.811111, count: 90 -Region Avg IOU: 0.621273, Class: 1.000000, Obj: 0.292421, No Obj: 0.002501, Avg Recall: 0.790698, count: 129 -Region Avg IOU: 0.616634, Class: 1.000000, Obj: 0.263480, No Obj: 0.002300, Avg Recall: 0.743902, count: 82 -Region Avg IOU: 0.619893, Class: 1.000000, Obj: 0.309631, No Obj: 0.002244, Avg Recall: 0.752809, count: 89 -2260: 0.475824, 0.480392 avg, 0.001000 rate, 30.097712 seconds, 578560 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 15.880409 seconds -Region Avg IOU: 0.604160, Class: 1.000000, Obj: 0.317096, No Obj: 0.002463, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.564249, Class: 1.000000, Obj: 0.313860, No Obj: 0.002591, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.633466, Class: 1.000000, Obj: 0.287939, No Obj: 0.002367, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.613725, Class: 1.000000, Obj: 0.285612, No Obj: 0.002811, Avg Recall: 0.730159, count: 126 -Region Avg IOU: 0.608304, Class: 1.000000, Obj: 0.277738, No Obj: 0.002661, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.633987, Class: 1.000000, Obj: 0.303099, No Obj: 0.002711, Avg Recall: 0.773438, count: 128 -Region Avg IOU: 0.611415, Class: 1.000000, Obj: 0.300045, No Obj: 0.002508, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.572433, Class: 1.000000, Obj: 0.262029, No Obj: 0.002941, Avg Recall: 0.727941, count: 136 -2261: 0.532566, 0.485610 avg, 0.001000 rate, 28.774372 seconds, 578816 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.615656, Class: 1.000000, Obj: 0.360323, No Obj: 0.002690, Avg Recall: 0.756303, count: 119 -Region Avg IOU: 0.628913, Class: 1.000000, Obj: 0.361874, No Obj: 0.002906, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.552705, Class: 1.000000, Obj: 0.303588, No Obj: 0.002370, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.596357, Class: 1.000000, Obj: 0.331631, No Obj: 0.002603, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.629424, Class: 1.000000, Obj: 0.327234, No Obj: 0.002641, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.616383, Class: 1.000000, Obj: 0.320166, No Obj: 0.002452, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.626265, Class: 1.000000, Obj: 0.290684, No Obj: 0.003042, Avg Recall: 0.790323, count: 124 -Region Avg IOU: 0.626223, Class: 1.000000, Obj: 0.338070, No Obj: 0.002507, Avg Recall: 0.794643, count: 112 -2262: 0.470227, 0.484072 avg, 0.001000 rate, 29.068949 seconds, 579072 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.597225, Class: 1.000000, Obj: 0.378763, No Obj: 0.002753, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.622403, Class: 1.000000, Obj: 0.351267, No Obj: 0.002562, Avg Recall: 0.727273, count: 88 -Region Avg IOU: 0.655961, Class: 1.000000, Obj: 0.349837, No Obj: 0.002829, Avg Recall: 0.824742, count: 97 -Region Avg IOU: 0.540860, Class: 1.000000, Obj: 0.272930, No Obj: 0.002810, Avg Recall: 0.669014, count: 142 -Region Avg IOU: 0.647145, Class: 1.000000, Obj: 0.357354, No Obj: 0.002880, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.604740, Class: 1.000000, Obj: 0.343162, No Obj: 0.002976, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.581966, Class: 1.000000, Obj: 0.325503, No Obj: 0.002976, Avg Recall: 0.720000, count: 125 -Region Avg IOU: 0.588715, Class: 1.000000, Obj: 0.318430, No Obj: 0.002703, Avg Recall: 0.722689, count: 119 -2263: 0.475784, 0.483243 avg, 0.001000 rate, 27.897352 seconds, 579328 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.668918, Class: 1.000000, Obj: 0.468646, No Obj: 0.002722, Avg Recall: 0.847059, count: 85 -Region Avg IOU: 0.634021, Class: 1.000000, Obj: 0.360492, No Obj: 0.003073, Avg Recall: 0.820755, count: 106 -Region Avg IOU: 0.646147, Class: 1.000000, Obj: 0.337915, No Obj: 0.002724, Avg Recall: 0.791209, count: 91 -Region Avg IOU: 0.616723, Class: 1.000000, Obj: 0.367302, No Obj: 0.002975, Avg Recall: 0.741007, count: 139 -Region Avg IOU: 0.672648, Class: 1.000000, Obj: 0.358958, No Obj: 0.002845, Avg Recall: 0.824176, count: 91 -Region Avg IOU: 0.612078, Class: 1.000000, Obj: 0.334071, No Obj: 0.002528, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.621588, Class: 1.000000, Obj: 0.312891, No Obj: 0.002581, Avg Recall: 0.802326, count: 86 -Region Avg IOU: 0.559060, Class: 1.000000, Obj: 0.330037, No Obj: 0.002360, Avg Recall: 0.662921, count: 89 -2264: 0.447822, 0.479701 avg, 0.001000 rate, 28.677408 seconds, 579584 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.641867, Class: 1.000000, Obj: 0.384187, No Obj: 0.002706, Avg Recall: 0.779221, count: 77 -Region Avg IOU: 0.635119, Class: 1.000000, Obj: 0.320902, No Obj: 0.002925, Avg Recall: 0.801587, count: 126 -Region Avg IOU: 0.645351, Class: 1.000000, Obj: 0.412975, No Obj: 0.002512, Avg Recall: 0.837209, count: 86 -Region Avg IOU: 0.657883, Class: 1.000000, Obj: 0.321598, No Obj: 0.002301, Avg Recall: 0.829545, count: 88 -Region Avg IOU: 0.600281, Class: 1.000000, Obj: 0.297618, No Obj: 0.002731, Avg Recall: 0.753086, count: 81 -Region Avg IOU: 0.626625, Class: 1.000000, Obj: 0.385130, No Obj: 0.002913, Avg Recall: 0.796460, count: 113 -Region Avg IOU: 0.633736, Class: 1.000000, Obj: 0.355736, No Obj: 0.002708, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.617589, Class: 1.000000, Obj: 0.420620, No Obj: 0.003103, Avg Recall: 0.736000, count: 125 -2265: 0.410280, 0.472759 avg, 0.001000 rate, 30.083893 seconds, 579840 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.608477, Class: 1.000000, Obj: 0.318666, No Obj: 0.002473, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.624202, Class: 1.000000, Obj: 0.312960, No Obj: 0.003238, Avg Recall: 0.776316, count: 152 -Region Avg IOU: 0.615040, Class: 1.000000, Obj: 0.359620, No Obj: 0.002794, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.591672, Class: 1.000000, Obj: 0.336899, No Obj: 0.002496, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.579232, Class: 1.000000, Obj: 0.302088, No Obj: 0.002688, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.572072, Class: 1.000000, Obj: 0.346994, No Obj: 0.002573, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.594736, Class: 1.000000, Obj: 0.266985, No Obj: 0.002657, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.641280, Class: 1.000000, Obj: 0.340983, No Obj: 0.002655, Avg Recall: 0.769231, count: 91 -2266: 0.485626, 0.474045 avg, 0.001000 rate, 29.539965 seconds, 580096 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.578324, Class: 1.000000, Obj: 0.305624, No Obj: 0.002349, Avg Recall: 0.707317, count: 82 -Region Avg IOU: 0.589011, Class: 1.000000, Obj: 0.258274, No Obj: 0.002810, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.625864, Class: 1.000000, Obj: 0.332865, No Obj: 0.002646, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.602231, Class: 1.000000, Obj: 0.291622, No Obj: 0.002982, Avg Recall: 0.736434, count: 129 -Region Avg IOU: 0.656834, Class: 1.000000, Obj: 0.365055, No Obj: 0.002642, Avg Recall: 0.870588, count: 85 -Region Avg IOU: 0.608011, Class: 1.000000, Obj: 0.291691, No Obj: 0.002595, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.621592, Class: 1.000000, Obj: 0.336773, No Obj: 0.002554, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.651762, Class: 1.000000, Obj: 0.354368, No Obj: 0.002526, Avg Recall: 0.822785, count: 79 -2267: 0.514236, 0.478065 avg, 0.001000 rate, 28.097401 seconds, 580352 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.633610, Class: 1.000000, Obj: 0.298021, No Obj: 0.002523, Avg Recall: 0.750000, count: 88 -Region Avg IOU: 0.612794, Class: 1.000000, Obj: 0.303511, No Obj: 0.002734, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.572352, Class: 1.000000, Obj: 0.280608, No Obj: 0.002496, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.596641, Class: 1.000000, Obj: 0.296233, No Obj: 0.002521, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.633968, Class: 1.000000, Obj: 0.258205, No Obj: 0.002711, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.639552, Class: 1.000000, Obj: 0.310869, No Obj: 0.002829, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.646562, Class: 1.000000, Obj: 0.320214, No Obj: 0.002535, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.583872, Class: 1.000000, Obj: 0.279162, No Obj: 0.002364, Avg Recall: 0.647059, count: 102 -2268: 0.473931, 0.477651 avg, 0.001000 rate, 28.293396 seconds, 580608 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.626214, Class: 1.000000, Obj: 0.338968, No Obj: 0.002718, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.615815, Class: 1.000000, Obj: 0.304190, No Obj: 0.002590, Avg Recall: 0.729323, count: 133 -Region Avg IOU: 0.640113, Class: 1.000000, Obj: 0.349439, No Obj: 0.002463, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.618997, Class: 1.000000, Obj: 0.337810, No Obj: 0.002241, Avg Recall: 0.746032, count: 126 -Region Avg IOU: 0.619818, Class: 1.000000, Obj: 0.279908, No Obj: 0.002388, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.614263, Class: 1.000000, Obj: 0.292474, No Obj: 0.002670, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.639358, Class: 1.000000, Obj: 0.319633, No Obj: 0.002283, Avg Recall: 0.784091, count: 88 -Region Avg IOU: 0.638789, Class: 1.000000, Obj: 0.266526, No Obj: 0.002274, Avg Recall: 0.838095, count: 105 -2269: 0.477988, 0.477685 avg, 0.001000 rate, 27.719227 seconds, 580864 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.645358, Class: 1.000000, Obj: 0.265842, No Obj: 0.002039, Avg Recall: 0.813333, count: 75 -Region Avg IOU: 0.638180, Class: 1.000000, Obj: 0.350049, No Obj: 0.002288, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.611597, Class: 1.000000, Obj: 0.313918, No Obj: 0.002249, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.653746, Class: 1.000000, Obj: 0.275669, No Obj: 0.002613, Avg Recall: 0.868217, count: 129 -Region Avg IOU: 0.601271, Class: 1.000000, Obj: 0.317918, No Obj: 0.002040, Avg Recall: 0.683544, count: 79 -Region Avg IOU: 0.639761, Class: 1.000000, Obj: 0.252183, No Obj: 0.002257, Avg Recall: 0.797872, count: 94 -Region Avg IOU: 0.623042, Class: 1.000000, Obj: 0.298205, No Obj: 0.002332, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.578552, Class: 1.000000, Obj: 0.268146, No Obj: 0.002513, Avg Recall: 0.685039, count: 127 -2270: 0.465778, 0.476494 avg, 0.001000 rate, 29.809031 seconds, 581120 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 12.695643 seconds -Region Avg IOU: 0.544055, Class: 1.000000, Obj: 0.268496, No Obj: 0.003038, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.594306, Class: 1.000000, Obj: 0.241685, No Obj: 0.002958, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.621790, Class: 1.000000, Obj: 0.313763, No Obj: 0.002809, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.570796, Class: 1.000000, Obj: 0.291305, No Obj: 0.002840, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.619791, Class: 1.000000, Obj: 0.287883, No Obj: 0.002849, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.584108, Class: 1.000000, Obj: 0.287090, No Obj: 0.002813, Avg Recall: 0.737705, count: 122 -Region Avg IOU: 0.583781, Class: 1.000000, Obj: 0.324270, No Obj: 0.002380, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.562864, Class: 1.000000, Obj: 0.299246, No Obj: 0.002691, Avg Recall: 0.606299, count: 127 -2271: 0.490637, 0.477908 avg, 0.001000 rate, 25.196495 seconds, 581376 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.604795, Class: 1.000000, Obj: 0.290588, No Obj: 0.002745, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.611183, Class: 1.000000, Obj: 0.323505, No Obj: 0.003174, Avg Recall: 0.735043, count: 117 -Region Avg IOU: 0.609449, Class: 1.000000, Obj: 0.320532, No Obj: 0.003163, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.607048, Class: 1.000000, Obj: 0.293276, No Obj: 0.002567, Avg Recall: 0.746667, count: 75 -Region Avg IOU: 0.583661, Class: 1.000000, Obj: 0.296558, No Obj: 0.002651, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.569913, Class: 1.000000, Obj: 0.284224, No Obj: 0.002555, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.624093, Class: 1.000000, Obj: 0.309793, No Obj: 0.002735, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.564555, Class: 1.000000, Obj: 0.306358, No Obj: 0.003323, Avg Recall: 0.638462, count: 130 -2272: 0.437171, 0.473835 avg, 0.001000 rate, 25.553061 seconds, 581632 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.574886, Class: 1.000000, Obj: 0.337571, No Obj: 0.003032, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.588655, Class: 1.000000, Obj: 0.325919, No Obj: 0.002637, Avg Recall: 0.707317, count: 82 -Region Avg IOU: 0.597442, Class: 1.000000, Obj: 0.345178, No Obj: 0.003095, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.580994, Class: 1.000000, Obj: 0.324371, No Obj: 0.003492, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.609805, Class: 1.000000, Obj: 0.318206, No Obj: 0.003010, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.564158, Class: 1.000000, Obj: 0.335574, No Obj: 0.003136, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.588769, Class: 1.000000, Obj: 0.305735, No Obj: 0.003485, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.602959, Class: 1.000000, Obj: 0.289462, No Obj: 0.003296, Avg Recall: 0.697479, count: 119 -2273: 0.490970, 0.475548 avg, 0.001000 rate, 26.647676 seconds, 581888 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.602730, Class: 1.000000, Obj: 0.355735, No Obj: 0.003311, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.592926, Class: 1.000000, Obj: 0.329647, No Obj: 0.003144, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.622034, Class: 1.000000, Obj: 0.359387, No Obj: 0.003237, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.585573, Class: 1.000000, Obj: 0.310361, No Obj: 0.003454, Avg Recall: 0.750000, count: 124 -Region Avg IOU: 0.552040, Class: 1.000000, Obj: 0.308992, No Obj: 0.003085, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.580354, Class: 1.000000, Obj: 0.378798, No Obj: 0.003360, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.568512, Class: 1.000000, Obj: 0.351603, No Obj: 0.003585, Avg Recall: 0.620968, count: 124 -Region Avg IOU: 0.560848, Class: 1.000000, Obj: 0.334522, No Obj: 0.003114, Avg Recall: 0.666667, count: 114 -2274: 0.469734, 0.474967 avg, 0.001000 rate, 26.252974 seconds, 582144 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.586542, Class: 1.000000, Obj: 0.391248, No Obj: 0.003103, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.576176, Class: 1.000000, Obj: 0.311421, No Obj: 0.003515, Avg Recall: 0.680672, count: 119 -Region Avg IOU: 0.593205, Class: 1.000000, Obj: 0.381225, No Obj: 0.003584, Avg Recall: 0.691057, count: 123 -Region Avg IOU: 0.610899, Class: 1.000000, Obj: 0.358191, No Obj: 0.003256, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.600338, Class: 1.000000, Obj: 0.353383, No Obj: 0.003451, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.595399, Class: 1.000000, Obj: 0.400065, No Obj: 0.003415, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.625022, Class: 1.000000, Obj: 0.358272, No Obj: 0.003270, Avg Recall: 0.731707, count: 82 -Region Avg IOU: 0.605196, Class: 1.000000, Obj: 0.357743, No Obj: 0.003667, Avg Recall: 0.727273, count: 121 -2275: 0.481490, 0.475619 avg, 0.001000 rate, 25.614820 seconds, 582400 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.509779, Class: 1.000000, Obj: 0.322380, No Obj: 0.003289, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.544132, Class: 1.000000, Obj: 0.391020, No Obj: 0.003459, Avg Recall: 0.613445, count: 119 -Region Avg IOU: 0.584837, Class: 1.000000, Obj: 0.371394, No Obj: 0.003159, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.580892, Class: 1.000000, Obj: 0.316577, No Obj: 0.003638, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.553275, Class: 1.000000, Obj: 0.383570, No Obj: 0.003620, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.601894, Class: 1.000000, Obj: 0.398549, No Obj: 0.003671, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.565655, Class: 1.000000, Obj: 0.360066, No Obj: 0.003436, Avg Recall: 0.682540, count: 126 -Region Avg IOU: 0.575122, Class: 1.000000, Obj: 0.356086, No Obj: 0.003818, Avg Recall: 0.648352, count: 91 -2276: 0.410165, 0.469074 avg, 0.001000 rate, 25.232365 seconds, 582656 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.553429, Class: 1.000000, Obj: 0.358794, No Obj: 0.003315, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.589375, Class: 1.000000, Obj: 0.355695, No Obj: 0.003739, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.586125, Class: 1.000000, Obj: 0.285033, No Obj: 0.003643, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.586216, Class: 1.000000, Obj: 0.322281, No Obj: 0.003784, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.568764, Class: 1.000000, Obj: 0.384324, No Obj: 0.003243, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.586005, Class: 1.000000, Obj: 0.384672, No Obj: 0.003611, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.591699, Class: 1.000000, Obj: 0.379261, No Obj: 0.003541, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.598921, Class: 1.000000, Obj: 0.441099, No Obj: 0.003439, Avg Recall: 0.716216, count: 74 -2277: 0.452828, 0.467449 avg, 0.001000 rate, 25.028252 seconds, 582912 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.608667, Class: 1.000000, Obj: 0.401749, No Obj: 0.003173, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.561342, Class: 1.000000, Obj: 0.372028, No Obj: 0.003172, Avg Recall: 0.647059, count: 85 -Region Avg IOU: 0.579709, Class: 1.000000, Obj: 0.383404, No Obj: 0.003090, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.618214, Class: 1.000000, Obj: 0.346725, No Obj: 0.003088, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.574585, Class: 1.000000, Obj: 0.376698, No Obj: 0.002923, Avg Recall: 0.697368, count: 76 -Region Avg IOU: 0.569539, Class: 1.000000, Obj: 0.383454, No Obj: 0.003168, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.558505, Class: 1.000000, Obj: 0.345003, No Obj: 0.003345, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.558228, Class: 1.000000, Obj: 0.331733, No Obj: 0.003627, Avg Recall: 0.654135, count: 133 -2278: 0.415832, 0.462287 avg, 0.001000 rate, 24.363600 seconds, 583168 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.578548, Class: 1.000000, Obj: 0.330608, No Obj: 0.003054, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.575911, Class: 1.000000, Obj: 0.303625, No Obj: 0.003033, Avg Recall: 0.698276, count: 116 -Region Avg IOU: 0.545956, Class: 1.000000, Obj: 0.331101, No Obj: 0.002881, Avg Recall: 0.619565, count: 92 -Region Avg IOU: 0.544954, Class: 1.000000, Obj: 0.355529, No Obj: 0.002987, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.514422, Class: 1.000000, Obj: 0.287037, No Obj: 0.002651, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.569785, Class: 1.000000, Obj: 0.340040, No Obj: 0.002898, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.580296, Class: 1.000000, Obj: 0.334036, No Obj: 0.002756, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.499740, Class: 1.000000, Obj: 0.335208, No Obj: 0.002722, Avg Recall: 0.543478, count: 92 -2279: 0.392583, 0.455317 avg, 0.001000 rate, 26.270741 seconds, 583424 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.570641, Class: 1.000000, Obj: 0.339858, No Obj: 0.002747, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.591299, Class: 1.000000, Obj: 0.342967, No Obj: 0.002624, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.560614, Class: 1.000000, Obj: 0.301875, No Obj: 0.003399, Avg Recall: 0.658537, count: 123 -Region Avg IOU: 0.562836, Class: 1.000000, Obj: 0.353150, No Obj: 0.003184, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.606271, Class: 1.000000, Obj: 0.337279, No Obj: 0.003239, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.553252, Class: 1.000000, Obj: 0.323344, No Obj: 0.002653, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.562938, Class: 1.000000, Obj: 0.318428, No Obj: 0.003041, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.588090, Class: 1.000000, Obj: 0.347254, No Obj: 0.003329, Avg Recall: 0.694215, count: 121 -2280: 0.476971, 0.457482 avg, 0.001000 rate, 26.177776 seconds, 583680 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 17.823795 seconds -Region Avg IOU: 0.573890, Class: 1.000000, Obj: 0.304437, No Obj: 0.002954, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.548802, Class: 1.000000, Obj: 0.254355, No Obj: 0.002998, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.591751, Class: 1.000000, Obj: 0.319826, No Obj: 0.002714, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.602966, Class: 1.000000, Obj: 0.346601, No Obj: 0.002772, Avg Recall: 0.752809, count: 89 -Region Avg IOU: 0.543919, Class: 1.000000, Obj: 0.296444, No Obj: 0.002900, Avg Recall: 0.641667, count: 120 -Region Avg IOU: 0.615238, Class: 1.000000, Obj: 0.333019, No Obj: 0.002762, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.571770, Class: 1.000000, Obj: 0.307757, No Obj: 0.002914, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.641769, Class: 1.000000, Obj: 0.311476, No Obj: 0.002934, Avg Recall: 0.824074, count: 108 -2281: 0.430089, 0.454743 avg, 0.001000 rate, 25.478714 seconds, 583936 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.554640, Class: 1.000000, Obj: 0.276767, No Obj: 0.002741, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.612860, Class: 1.000000, Obj: 0.290013, No Obj: 0.002478, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.606779, Class: 1.000000, Obj: 0.307182, No Obj: 0.003059, Avg Recall: 0.731707, count: 123 -Region Avg IOU: 0.596083, Class: 1.000000, Obj: 0.301514, No Obj: 0.002805, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.566070, Class: 1.000000, Obj: 0.295305, No Obj: 0.002658, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.583138, Class: 1.000000, Obj: 0.293870, No Obj: 0.003100, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.601992, Class: 1.000000, Obj: 0.300014, No Obj: 0.003101, Avg Recall: 0.722973, count: 148 -Region Avg IOU: 0.574714, Class: 1.000000, Obj: 0.274732, No Obj: 0.002682, Avg Recall: 0.714286, count: 105 -2282: 0.505882, 0.459857 avg, 0.001000 rate, 25.004936 seconds, 584192 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.609852, Class: 1.000000, Obj: 0.299154, No Obj: 0.003154, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.510589, Class: 1.000000, Obj: 0.259670, No Obj: 0.002552, Avg Recall: 0.560748, count: 107 -Region Avg IOU: 0.607800, Class: 1.000000, Obj: 0.352831, No Obj: 0.002964, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.528608, Class: 1.000000, Obj: 0.314494, No Obj: 0.002675, Avg Recall: 0.576577, count: 111 -Region Avg IOU: 0.604070, Class: 1.000000, Obj: 0.342257, No Obj: 0.003031, Avg Recall: 0.721804, count: 133 -Region Avg IOU: 0.499695, Class: 1.000000, Obj: 0.276380, No Obj: 0.002796, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.596947, Class: 1.000000, Obj: 0.342222, No Obj: 0.002803, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.598543, Class: 1.000000, Obj: 0.303503, No Obj: 0.002813, Avg Recall: 0.700935, count: 107 -2283: 0.457759, 0.459647 avg, 0.001000 rate, 24.613127 seconds, 584448 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.605309, Class: 1.000000, Obj: 0.346087, No Obj: 0.002832, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.616287, Class: 1.000000, Obj: 0.349127, No Obj: 0.002934, Avg Recall: 0.762295, count: 122 -Region Avg IOU: 0.624977, Class: 1.000000, Obj: 0.334352, No Obj: 0.002899, Avg Recall: 0.736264, count: 91 -Region Avg IOU: 0.583655, Class: 1.000000, Obj: 0.290285, No Obj: 0.002725, Avg Recall: 0.706349, count: 126 -Region Avg IOU: 0.594500, Class: 1.000000, Obj: 0.294489, No Obj: 0.002629, Avg Recall: 0.722222, count: 90 -Region Avg IOU: 0.564274, Class: 1.000000, Obj: 0.330594, No Obj: 0.002735, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.541533, Class: 1.000000, Obj: 0.317719, No Obj: 0.002745, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.593360, Class: 1.000000, Obj: 0.330182, No Obj: 0.003173, Avg Recall: 0.685714, count: 105 -2284: 0.442114, 0.457894 avg, 0.001000 rate, 26.141396 seconds, 584704 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.591123, Class: 1.000000, Obj: 0.301700, No Obj: 0.003586, Avg Recall: 0.726562, count: 128 -Region Avg IOU: 0.605276, Class: 1.000000, Obj: 0.367550, No Obj: 0.002960, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.542698, Class: 1.000000, Obj: 0.268025, No Obj: 0.003126, Avg Recall: 0.626866, count: 134 -Region Avg IOU: 0.612254, Class: 1.000000, Obj: 0.364349, No Obj: 0.003651, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.496487, Class: 1.000000, Obj: 0.313306, No Obj: 0.002579, Avg Recall: 0.545455, count: 88 -Region Avg IOU: 0.619225, Class: 1.000000, Obj: 0.329702, No Obj: 0.003199, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.601792, Class: 1.000000, Obj: 0.361462, No Obj: 0.003023, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.606777, Class: 1.000000, Obj: 0.361950, No Obj: 0.003565, Avg Recall: 0.727273, count: 132 -2285: 0.474560, 0.459560 avg, 0.001000 rate, 24.884825 seconds, 584960 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.586546, Class: 1.000000, Obj: 0.356271, No Obj: 0.003414, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.593511, Class: 1.000000, Obj: 0.326193, No Obj: 0.003448, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.546838, Class: 1.000000, Obj: 0.284927, No Obj: 0.003259, Avg Recall: 0.632000, count: 125 -Region Avg IOU: 0.556938, Class: 1.000000, Obj: 0.353088, No Obj: 0.003126, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.574832, Class: 1.000000, Obj: 0.341492, No Obj: 0.003407, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.605813, Class: 1.000000, Obj: 0.392402, No Obj: 0.003047, Avg Recall: 0.709302, count: 86 -Region Avg IOU: 0.612837, Class: 1.000000, Obj: 0.317720, No Obj: 0.003906, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.558636, Class: 1.000000, Obj: 0.343400, No Obj: 0.003260, Avg Recall: 0.626168, count: 107 -2286: 0.463357, 0.459940 avg, 0.001000 rate, 25.648279 seconds, 585216 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.532528, Class: 1.000000, Obj: 0.287898, No Obj: 0.004042, Avg Recall: 0.597222, count: 144 -Region Avg IOU: 0.570805, Class: 1.000000, Obj: 0.329415, No Obj: 0.003551, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.547556, Class: 1.000000, Obj: 0.339657, No Obj: 0.003435, Avg Recall: 0.595588, count: 136 -Region Avg IOU: 0.555189, Class: 1.000000, Obj: 0.316543, No Obj: 0.003184, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.548513, Class: 1.000000, Obj: 0.371431, No Obj: 0.003506, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.560002, Class: 1.000000, Obj: 0.342163, No Obj: 0.003331, Avg Recall: 0.644068, count: 118 -Region Avg IOU: 0.602559, Class: 1.000000, Obj: 0.379362, No Obj: 0.003670, Avg Recall: 0.736000, count: 125 -Region Avg IOU: 0.607447, Class: 1.000000, Obj: 0.315217, No Obj: 0.003833, Avg Recall: 0.751938, count: 129 -2287: 0.501879, 0.464134 avg, 0.001000 rate, 24.980385 seconds, 585472 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.618931, Class: 1.000000, Obj: 0.375570, No Obj: 0.003047, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.591950, Class: 1.000000, Obj: 0.353586, No Obj: 0.003709, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.601217, Class: 1.000000, Obj: 0.364379, No Obj: 0.003350, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.579575, Class: 1.000000, Obj: 0.379503, No Obj: 0.003534, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.603213, Class: 1.000000, Obj: 0.414773, No Obj: 0.003753, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.617400, Class: 1.000000, Obj: 0.420506, No Obj: 0.003062, Avg Recall: 0.731707, count: 82 -Region Avg IOU: 0.577030, Class: 1.000000, Obj: 0.391145, No Obj: 0.003419, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.573617, Class: 1.000000, Obj: 0.333869, No Obj: 0.003087, Avg Recall: 0.684211, count: 95 -2288: 0.389779, 0.456698 avg, 0.001000 rate, 24.950422 seconds, 585728 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.627525, Class: 1.000000, Obj: 0.374116, No Obj: 0.003388, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.591185, Class: 1.000000, Obj: 0.359438, No Obj: 0.003763, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.568757, Class: 1.000000, Obj: 0.368979, No Obj: 0.003584, Avg Recall: 0.692913, count: 127 -Region Avg IOU: 0.589377, Class: 1.000000, Obj: 0.316069, No Obj: 0.003327, Avg Recall: 0.731707, count: 123 -Region Avg IOU: 0.642624, Class: 1.000000, Obj: 0.371052, No Obj: 0.003475, Avg Recall: 0.860215, count: 93 -Region Avg IOU: 0.582899, Class: 1.000000, Obj: 0.406164, No Obj: 0.003069, Avg Recall: 0.688312, count: 77 -Region Avg IOU: 0.596049, Class: 1.000000, Obj: 0.338283, No Obj: 0.003304, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.606061, Class: 1.000000, Obj: 0.355119, No Obj: 0.003280, Avg Recall: 0.712500, count: 80 -2289: 0.415547, 0.452583 avg, 0.001000 rate, 25.023239 seconds, 585984 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.571449, Class: 1.000000, Obj: 0.348506, No Obj: 0.003279, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.549469, Class: 1.000000, Obj: 0.364945, No Obj: 0.002957, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.565185, Class: 1.000000, Obj: 0.346418, No Obj: 0.003293, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.576292, Class: 1.000000, Obj: 0.387776, No Obj: 0.002887, Avg Recall: 0.700000, count: 80 -Region Avg IOU: 0.547593, Class: 1.000000, Obj: 0.399474, No Obj: 0.003185, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.589485, Class: 1.000000, Obj: 0.360900, No Obj: 0.002987, Avg Recall: 0.756098, count: 82 -Region Avg IOU: 0.564922, Class: 1.000000, Obj: 0.366484, No Obj: 0.002824, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.607991, Class: 1.000000, Obj: 0.330883, No Obj: 0.003459, Avg Recall: 0.757009, count: 107 -2290: 0.423283, 0.449653 avg, 0.001000 rate, 25.645844 seconds, 586240 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000083 seconds -Region Avg IOU: 0.577225, Class: 1.000000, Obj: 0.346190, No Obj: 0.002936, Avg Recall: 0.720588, count: 136 -Region Avg IOU: 0.634607, Class: 1.000000, Obj: 0.332910, No Obj: 0.002829, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.593941, Class: 1.000000, Obj: 0.350282, No Obj: 0.002895, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.591212, Class: 1.000000, Obj: 0.308074, No Obj: 0.002950, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.632127, Class: 1.000000, Obj: 0.377709, No Obj: 0.002586, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.618831, Class: 1.000000, Obj: 0.318394, No Obj: 0.002622, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.619344, Class: 1.000000, Obj: 0.365084, No Obj: 0.002789, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.613240, Class: 1.000000, Obj: 0.314731, No Obj: 0.003110, Avg Recall: 0.781513, count: 119 -2291: 0.482309, 0.452919 avg, 0.001000 rate, 26.953896 seconds, 586496 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.624227, Class: 1.000000, Obj: 0.387518, No Obj: 0.002652, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.565860, Class: 1.000000, Obj: 0.313578, No Obj: 0.002448, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.616178, Class: 1.000000, Obj: 0.382513, No Obj: 0.002958, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.597746, Class: 1.000000, Obj: 0.360601, No Obj: 0.002897, Avg Recall: 0.713235, count: 136 -Region Avg IOU: 0.607740, Class: 1.000000, Obj: 0.357071, No Obj: 0.002571, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.600410, Class: 1.000000, Obj: 0.332751, No Obj: 0.002573, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.624369, Class: 1.000000, Obj: 0.358602, No Obj: 0.002795, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.574190, Class: 1.000000, Obj: 0.381301, No Obj: 0.002695, Avg Recall: 0.681035, count: 116 -2292: 0.462139, 0.453841 avg, 0.001000 rate, 26.698471 seconds, 586752 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.569270, Class: 1.000000, Obj: 0.332021, No Obj: 0.002565, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.616368, Class: 1.000000, Obj: 0.327071, No Obj: 0.002347, Avg Recall: 0.788235, count: 85 -Region Avg IOU: 0.612092, Class: 1.000000, Obj: 0.340556, No Obj: 0.002516, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.654465, Class: 1.000000, Obj: 0.336046, No Obj: 0.002653, Avg Recall: 0.829787, count: 94 -Region Avg IOU: 0.573672, Class: 1.000000, Obj: 0.318999, No Obj: 0.002035, Avg Recall: 0.712644, count: 87 -Region Avg IOU: 0.638558, Class: 1.000000, Obj: 0.311556, No Obj: 0.002568, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.636776, Class: 1.000000, Obj: 0.332607, No Obj: 0.002446, Avg Recall: 0.753086, count: 81 -Region Avg IOU: 0.618886, Class: 1.000000, Obj: 0.329078, No Obj: 0.002683, Avg Recall: 0.768657, count: 134 -2293: 0.435769, 0.452034 avg, 0.001000 rate, 29.554180 seconds, 587008 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.648025, Class: 1.000000, Obj: 0.293602, No Obj: 0.002409, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.606020, Class: 1.000000, Obj: 0.266983, No Obj: 0.002234, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.645816, Class: 1.000000, Obj: 0.280212, No Obj: 0.002396, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.561410, Class: 1.000000, Obj: 0.299533, No Obj: 0.002198, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.587390, Class: 1.000000, Obj: 0.266455, No Obj: 0.002382, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.631380, Class: 1.000000, Obj: 0.307619, No Obj: 0.002297, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.559916, Class: 1.000000, Obj: 0.279755, No Obj: 0.002241, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.585113, Class: 1.000000, Obj: 0.300323, No Obj: 0.002337, Avg Recall: 0.736364, count: 110 -2294: 0.472928, 0.454123 avg, 0.001000 rate, 27.681480 seconds, 587264 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.641694, Class: 1.000000, Obj: 0.302367, No Obj: 0.002788, Avg Recall: 0.813559, count: 118 -Region Avg IOU: 0.562366, Class: 1.000000, Obj: 0.242867, No Obj: 0.002222, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.630716, Class: 1.000000, Obj: 0.280644, No Obj: 0.002342, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.609289, Class: 1.000000, Obj: 0.299851, No Obj: 0.002366, Avg Recall: 0.735294, count: 136 -Region Avg IOU: 0.628405, Class: 1.000000, Obj: 0.298991, No Obj: 0.002584, Avg Recall: 0.780303, count: 132 -Region Avg IOU: 0.615550, Class: 1.000000, Obj: 0.331073, No Obj: 0.002453, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.685728, Class: 1.000000, Obj: 0.285595, No Obj: 0.002479, Avg Recall: 0.912088, count: 91 -Region Avg IOU: 0.589723, Class: 1.000000, Obj: 0.299759, No Obj: 0.002197, Avg Recall: 0.736842, count: 114 -2295: 0.541624, 0.462873 avg, 0.001000 rate, 27.455864 seconds, 587520 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.573171, Class: 1.000000, Obj: 0.275627, No Obj: 0.002672, Avg Recall: 0.716418, count: 134 -Region Avg IOU: 0.590424, Class: 1.000000, Obj: 0.319172, No Obj: 0.002574, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.561347, Class: 1.000000, Obj: 0.304699, No Obj: 0.002004, Avg Recall: 0.612500, count: 80 -Region Avg IOU: 0.639671, Class: 1.000000, Obj: 0.360693, No Obj: 0.002285, Avg Recall: 0.813953, count: 86 -Region Avg IOU: 0.637776, Class: 1.000000, Obj: 0.300778, No Obj: 0.002605, Avg Recall: 0.806202, count: 129 -Region Avg IOU: 0.642298, Class: 1.000000, Obj: 0.308890, No Obj: 0.002188, Avg Recall: 0.811111, count: 90 -Region Avg IOU: 0.621814, Class: 1.000000, Obj: 0.313871, No Obj: 0.002376, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.647112, Class: 1.000000, Obj: 0.282541, No Obj: 0.002737, Avg Recall: 0.798246, count: 114 -2296: 0.465307, 0.463117 avg, 0.001000 rate, 29.514704 seconds, 587776 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.606821, Class: 1.000000, Obj: 0.322002, No Obj: 0.002803, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.569214, Class: 1.000000, Obj: 0.288635, No Obj: 0.002550, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.602020, Class: 1.000000, Obj: 0.338153, No Obj: 0.002617, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.590019, Class: 1.000000, Obj: 0.353235, No Obj: 0.002661, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.638694, Class: 1.000000, Obj: 0.352570, No Obj: 0.002870, Avg Recall: 0.818182, count: 110 -Region Avg IOU: 0.627207, Class: 1.000000, Obj: 0.346756, No Obj: 0.002700, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.659013, Class: 1.000000, Obj: 0.354935, No Obj: 0.002648, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.603549, Class: 1.000000, Obj: 0.372156, No Obj: 0.002523, Avg Recall: 0.715789, count: 95 -2297: 0.518591, 0.468664 avg, 0.001000 rate, 27.799208 seconds, 588032 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.581856, Class: 1.000000, Obj: 0.307020, No Obj: 0.002706, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.633214, Class: 1.000000, Obj: 0.390764, No Obj: 0.003099, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.638847, Class: 1.000000, Obj: 0.337263, No Obj: 0.002605, Avg Recall: 0.815217, count: 92 -Region Avg IOU: 0.622626, Class: 1.000000, Obj: 0.284217, No Obj: 0.002320, Avg Recall: 0.756757, count: 74 -Region Avg IOU: 0.606547, Class: 1.000000, Obj: 0.347899, No Obj: 0.002971, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.634519, Class: 1.000000, Obj: 0.343897, No Obj: 0.002850, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.621860, Class: 1.000000, Obj: 0.364665, No Obj: 0.003213, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.650249, Class: 1.000000, Obj: 0.390344, No Obj: 0.003165, Avg Recall: 0.834951, count: 103 -2298: 0.454101, 0.467208 avg, 0.001000 rate, 28.569927 seconds, 588288 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.624696, Class: 1.000000, Obj: 0.384013, No Obj: 0.002807, Avg Recall: 0.790698, count: 86 -Region Avg IOU: 0.606020, Class: 1.000000, Obj: 0.396320, No Obj: 0.003519, Avg Recall: 0.768116, count: 138 -Region Avg IOU: 0.590547, Class: 1.000000, Obj: 0.355190, No Obj: 0.002880, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.609727, Class: 1.000000, Obj: 0.309931, No Obj: 0.003344, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.619610, Class: 1.000000, Obj: 0.331838, No Obj: 0.003089, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.625841, Class: 1.000000, Obj: 0.349130, No Obj: 0.003016, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.564852, Class: 1.000000, Obj: 0.378629, No Obj: 0.003191, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.658085, Class: 1.000000, Obj: 0.355280, No Obj: 0.003446, Avg Recall: 0.847328, count: 131 -2299: 0.467418, 0.467229 avg, 0.001000 rate, 26.954552 seconds, 588544 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.632009, Class: 1.000000, Obj: 0.413201, No Obj: 0.002862, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.619351, Class: 1.000000, Obj: 0.324440, No Obj: 0.003284, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.651764, Class: 1.000000, Obj: 0.377924, No Obj: 0.003572, Avg Recall: 0.815068, count: 146 -Region Avg IOU: 0.631526, Class: 1.000000, Obj: 0.351604, No Obj: 0.002975, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.638430, Class: 1.000000, Obj: 0.378581, No Obj: 0.003123, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.635115, Class: 1.000000, Obj: 0.340468, No Obj: 0.003270, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.595733, Class: 1.000000, Obj: 0.390888, No Obj: 0.002491, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.624826, Class: 1.000000, Obj: 0.367004, No Obj: 0.002456, Avg Recall: 0.828571, count: 70 -2300: 0.441743, 0.464680 avg, 0.001000 rate, 29.021685 seconds, 588800 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 15.636246 seconds -Region Avg IOU: 0.519088, Class: 1.000000, Obj: 0.340387, No Obj: 0.003508, Avg Recall: 0.576923, count: 130 -Region Avg IOU: 0.571121, Class: 1.000000, Obj: 0.357773, No Obj: 0.002930, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.596299, Class: 1.000000, Obj: 0.348147, No Obj: 0.003724, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.534921, Class: 1.000000, Obj: 0.309700, No Obj: 0.003173, Avg Recall: 0.633333, count: 90 -Region Avg IOU: 0.515190, Class: 1.000000, Obj: 0.318466, No Obj: 0.003370, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.576958, Class: 1.000000, Obj: 0.347118, No Obj: 0.002956, Avg Recall: 0.703704, count: 81 -Region Avg IOU: 0.582914, Class: 1.000000, Obj: 0.338990, No Obj: 0.003421, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.532271, Class: 1.000000, Obj: 0.323048, No Obj: 0.003197, Avg Recall: 0.583333, count: 108 -2301: 0.415490, 0.459761 avg, 0.001000 rate, 23.846851 seconds, 589056 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.602820, Class: 1.000000, Obj: 0.328962, No Obj: 0.003422, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.561243, Class: 1.000000, Obj: 0.307024, No Obj: 0.003361, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.534092, Class: 1.000000, Obj: 0.333522, No Obj: 0.003005, Avg Recall: 0.666667, count: 87 -Region Avg IOU: 0.537488, Class: 1.000000, Obj: 0.283462, No Obj: 0.003615, Avg Recall: 0.641026, count: 117 -Region Avg IOU: 0.523085, Class: 1.000000, Obj: 0.323930, No Obj: 0.003107, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.562080, Class: 1.000000, Obj: 0.320664, No Obj: 0.003299, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.616360, Class: 1.000000, Obj: 0.337551, No Obj: 0.003731, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.604621, Class: 1.000000, Obj: 0.305409, No Obj: 0.003638, Avg Recall: 0.747748, count: 111 -2302: 0.474706, 0.461256 avg, 0.001000 rate, 24.327837 seconds, 589312 images -Loaded: 0.271784 seconds -Region Avg IOU: 0.595232, Class: 1.000000, Obj: 0.353416, No Obj: 0.002962, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.526229, Class: 1.000000, Obj: 0.289877, No Obj: 0.003277, Avg Recall: 0.604478, count: 134 -Region Avg IOU: 0.556382, Class: 1.000000, Obj: 0.355432, No Obj: 0.003126, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.531388, Class: 1.000000, Obj: 0.336229, No Obj: 0.003534, Avg Recall: 0.561404, count: 114 -Region Avg IOU: 0.543230, Class: 1.000000, Obj: 0.311331, No Obj: 0.003557, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.559577, Class: 1.000000, Obj: 0.316089, No Obj: 0.003207, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.507657, Class: 1.000000, Obj: 0.277438, No Obj: 0.003417, Avg Recall: 0.570312, count: 128 -Region Avg IOU: 0.603976, Class: 1.000000, Obj: 0.299018, No Obj: 0.003302, Avg Recall: 0.737864, count: 103 -2303: 0.506198, 0.465750 avg, 0.001000 rate, 25.285408 seconds, 589568 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.542704, Class: 1.000000, Obj: 0.333131, No Obj: 0.003424, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.582684, Class: 1.000000, Obj: 0.326537, No Obj: 0.004181, Avg Recall: 0.690647, count: 139 -Region Avg IOU: 0.525970, Class: 1.000000, Obj: 0.303631, No Obj: 0.003352, Avg Recall: 0.600000, count: 130 -Region Avg IOU: 0.520403, Class: 1.000000, Obj: 0.283086, No Obj: 0.003231, Avg Recall: 0.596639, count: 119 -Region Avg IOU: 0.559354, Class: 1.000000, Obj: 0.328683, No Obj: 0.003098, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.554487, Class: 1.000000, Obj: 0.337386, No Obj: 0.003124, Avg Recall: 0.648352, count: 91 -Region Avg IOU: 0.542883, Class: 1.000000, Obj: 0.318531, No Obj: 0.003469, Avg Recall: 0.611570, count: 121 -Region Avg IOU: 0.545123, Class: 1.000000, Obj: 0.298423, No Obj: 0.003543, Avg Recall: 0.634783, count: 115 -2304: 0.504363, 0.469611 avg, 0.001000 rate, 24.667772 seconds, 589824 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.553846, Class: 1.000000, Obj: 0.328987, No Obj: 0.003343, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.465448, Class: 1.000000, Obj: 0.294198, No Obj: 0.002771, Avg Recall: 0.483871, count: 93 -Region Avg IOU: 0.509620, Class: 1.000000, Obj: 0.323661, No Obj: 0.002962, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.575375, Class: 1.000000, Obj: 0.300957, No Obj: 0.002939, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.531862, Class: 1.000000, Obj: 0.302696, No Obj: 0.003333, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.509594, Class: 1.000000, Obj: 0.286290, No Obj: 0.003422, Avg Recall: 0.613636, count: 132 -Region Avg IOU: 0.488211, Class: 1.000000, Obj: 0.293945, No Obj: 0.003035, Avg Recall: 0.532710, count: 107 -Region Avg IOU: 0.544574, Class: 1.000000, Obj: 0.259161, No Obj: 0.003862, Avg Recall: 0.583333, count: 144 -2305: 0.475683, 0.470218 avg, 0.001000 rate, 23.107609 seconds, 590080 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.522419, Class: 1.000000, Obj: 0.329829, No Obj: 0.003192, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.542021, Class: 1.000000, Obj: 0.342961, No Obj: 0.003500, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.543588, Class: 1.000000, Obj: 0.302343, No Obj: 0.003115, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.558651, Class: 1.000000, Obj: 0.341477, No Obj: 0.003357, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.546344, Class: 1.000000, Obj: 0.284498, No Obj: 0.003544, Avg Recall: 0.671756, count: 131 -Region Avg IOU: 0.501809, Class: 1.000000, Obj: 0.328753, No Obj: 0.003035, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.550846, Class: 1.000000, Obj: 0.349873, No Obj: 0.003527, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.513354, Class: 1.000000, Obj: 0.286989, No Obj: 0.003473, Avg Recall: 0.626087, count: 115 -2306: 0.532165, 0.476413 avg, 0.001000 rate, 24.027079 seconds, 590336 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.557123, Class: 1.000000, Obj: 0.335670, No Obj: 0.003049, Avg Recall: 0.662791, count: 86 -Region Avg IOU: 0.520291, Class: 1.000000, Obj: 0.367119, No Obj: 0.003152, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.563633, Class: 1.000000, Obj: 0.336869, No Obj: 0.003222, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.581986, Class: 1.000000, Obj: 0.339719, No Obj: 0.003152, Avg Recall: 0.702381, count: 84 -Region Avg IOU: 0.545991, Class: 1.000000, Obj: 0.347207, No Obj: 0.003368, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.530216, Class: 1.000000, Obj: 0.325288, No Obj: 0.003775, Avg Recall: 0.625000, count: 136 -Region Avg IOU: 0.531012, Class: 1.000000, Obj: 0.340200, No Obj: 0.003321, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.527144, Class: 1.000000, Obj: 0.327155, No Obj: 0.003330, Avg Recall: 0.620000, count: 100 -2307: 0.451873, 0.473959 avg, 0.001000 rate, 24.758465 seconds, 590592 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.551447, Class: 1.000000, Obj: 0.308860, No Obj: 0.003328, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.557865, Class: 1.000000, Obj: 0.371728, No Obj: 0.003165, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.499561, Class: 1.000000, Obj: 0.326782, No Obj: 0.003333, Avg Recall: 0.571429, count: 105 -Region Avg IOU: 0.521009, Class: 1.000000, Obj: 0.307887, No Obj: 0.002648, Avg Recall: 0.586207, count: 87 -Region Avg IOU: 0.542216, Class: 1.000000, Obj: 0.341420, No Obj: 0.003206, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.520882, Class: 1.000000, Obj: 0.287178, No Obj: 0.003209, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.554778, Class: 1.000000, Obj: 0.363606, No Obj: 0.003502, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.552129, Class: 1.000000, Obj: 0.336768, No Obj: 0.003631, Avg Recall: 0.675214, count: 117 -2308: 0.449169, 0.471480 avg, 0.001000 rate, 24.342607 seconds, 590848 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.564533, Class: 1.000000, Obj: 0.322089, No Obj: 0.002747, Avg Recall: 0.611765, count: 85 -Region Avg IOU: 0.542237, Class: 1.000000, Obj: 0.309613, No Obj: 0.003744, Avg Recall: 0.614173, count: 127 -Region Avg IOU: 0.520082, Class: 1.000000, Obj: 0.327218, No Obj: 0.003129, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.498469, Class: 1.000000, Obj: 0.278963, No Obj: 0.003796, Avg Recall: 0.584746, count: 118 -Region Avg IOU: 0.588085, Class: 1.000000, Obj: 0.378040, No Obj: 0.003522, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.615265, Class: 1.000000, Obj: 0.362485, No Obj: 0.003445, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.550492, Class: 1.000000, Obj: 0.359603, No Obj: 0.003660, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.495243, Class: 1.000000, Obj: 0.335122, No Obj: 0.003471, Avg Recall: 0.557522, count: 113 -2309: 0.456903, 0.470022 avg, 0.001000 rate, 24.430742 seconds, 591104 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.519047, Class: 1.000000, Obj: 0.341994, No Obj: 0.003622, Avg Recall: 0.608696, count: 138 -Region Avg IOU: 0.549589, Class: 1.000000, Obj: 0.320391, No Obj: 0.003525, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.580652, Class: 1.000000, Obj: 0.351802, No Obj: 0.003773, Avg Recall: 0.693548, count: 124 -Region Avg IOU: 0.505543, Class: 1.000000, Obj: 0.346239, No Obj: 0.003229, Avg Recall: 0.556701, count: 97 -Region Avg IOU: 0.583531, Class: 1.000000, Obj: 0.331887, No Obj: 0.003614, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.506006, Class: 1.000000, Obj: 0.323991, No Obj: 0.003052, Avg Recall: 0.593750, count: 128 -Region Avg IOU: 0.573983, Class: 1.000000, Obj: 0.298925, No Obj: 0.003462, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.549315, Class: 1.000000, Obj: 0.326166, No Obj: 0.003861, Avg Recall: 0.658120, count: 117 -2310: 0.481604, 0.471181 avg, 0.001000 rate, 23.898695 seconds, 591360 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 15.390648 seconds -Region Avg IOU: 0.516021, Class: 1.000000, Obj: 0.306267, No Obj: 0.003523, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.547946, Class: 1.000000, Obj: 0.310839, No Obj: 0.003365, Avg Recall: 0.654762, count: 84 -Region Avg IOU: 0.508276, Class: 1.000000, Obj: 0.305451, No Obj: 0.003633, Avg Recall: 0.521368, count: 117 -Region Avg IOU: 0.529255, Class: 1.000000, Obj: 0.315894, No Obj: 0.003857, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.562411, Class: 1.000000, Obj: 0.330021, No Obj: 0.003531, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.501787, Class: 1.000000, Obj: 0.308434, No Obj: 0.003331, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.588917, Class: 1.000000, Obj: 0.352909, No Obj: 0.003313, Avg Recall: 0.679487, count: 78 -Region Avg IOU: 0.551305, Class: 1.000000, Obj: 0.343913, No Obj: 0.003680, Avg Recall: 0.612245, count: 98 -2311: 0.450833, 0.469146 avg, 0.001000 rate, 21.961821 seconds, 591616 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.548839, Class: 1.000000, Obj: 0.306421, No Obj: 0.003431, Avg Recall: 0.606557, count: 122 -Region Avg IOU: 0.527639, Class: 1.000000, Obj: 0.303463, No Obj: 0.003171, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.565705, Class: 1.000000, Obj: 0.339234, No Obj: 0.003662, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.561994, Class: 1.000000, Obj: 0.337180, No Obj: 0.003169, Avg Recall: 0.662791, count: 86 -Region Avg IOU: 0.519629, Class: 1.000000, Obj: 0.340880, No Obj: 0.003897, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.599080, Class: 1.000000, Obj: 0.348920, No Obj: 0.003719, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.523156, Class: 1.000000, Obj: 0.362188, No Obj: 0.003266, Avg Recall: 0.621951, count: 82 -Region Avg IOU: 0.570446, Class: 1.000000, Obj: 0.343975, No Obj: 0.003430, Avg Recall: 0.635294, count: 85 -2312: 0.425489, 0.464780 avg, 0.001000 rate, 21.322096 seconds, 591872 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.551446, Class: 1.000000, Obj: 0.356590, No Obj: 0.003867, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.541432, Class: 1.000000, Obj: 0.341724, No Obj: 0.003537, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.467199, Class: 1.000000, Obj: 0.290863, No Obj: 0.002973, Avg Recall: 0.500000, count: 106 -Region Avg IOU: 0.504774, Class: 1.000000, Obj: 0.345928, No Obj: 0.003953, Avg Recall: 0.517241, count: 116 -Region Avg IOU: 0.520203, Class: 1.000000, Obj: 0.303545, No Obj: 0.003216, Avg Recall: 0.585106, count: 94 -Region Avg IOU: 0.578435, Class: 1.000000, Obj: 0.365332, No Obj: 0.003590, Avg Recall: 0.693182, count: 88 -Region Avg IOU: 0.554018, Class: 1.000000, Obj: 0.315573, No Obj: 0.003424, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.557227, Class: 1.000000, Obj: 0.326605, No Obj: 0.003692, Avg Recall: 0.696970, count: 99 -2313: 0.412601, 0.459562 avg, 0.001000 rate, 25.261021 seconds, 592128 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.499622, Class: 1.000000, Obj: 0.284306, No Obj: 0.003017, Avg Recall: 0.534884, count: 86 -Region Avg IOU: 0.547512, Class: 1.000000, Obj: 0.304523, No Obj: 0.003917, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.562869, Class: 1.000000, Obj: 0.322116, No Obj: 0.004126, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.558114, Class: 1.000000, Obj: 0.332388, No Obj: 0.003780, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.580586, Class: 1.000000, Obj: 0.331467, No Obj: 0.004010, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.560839, Class: 1.000000, Obj: 0.342512, No Obj: 0.004086, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.568283, Class: 1.000000, Obj: 0.339276, No Obj: 0.004119, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.530895, Class: 1.000000, Obj: 0.336576, No Obj: 0.003855, Avg Recall: 0.619835, count: 121 -2314: 0.465593, 0.460165 avg, 0.001000 rate, 23.740978 seconds, 592384 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.569377, Class: 1.000000, Obj: 0.376346, No Obj: 0.004273, Avg Recall: 0.653846, count: 130 -Region Avg IOU: 0.552688, Class: 1.000000, Obj: 0.348692, No Obj: 0.003939, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.528014, Class: 1.000000, Obj: 0.340491, No Obj: 0.003836, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.514389, Class: 1.000000, Obj: 0.321754, No Obj: 0.003371, Avg Recall: 0.549451, count: 91 -Region Avg IOU: 0.537468, Class: 1.000000, Obj: 0.300313, No Obj: 0.003734, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.563448, Class: 1.000000, Obj: 0.346669, No Obj: 0.003695, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.470878, Class: 1.000000, Obj: 0.286028, No Obj: 0.004009, Avg Recall: 0.483051, count: 118 -Region Avg IOU: 0.521243, Class: 1.000000, Obj: 0.346832, No Obj: 0.004023, Avg Recall: 0.589744, count: 117 -2315: 0.476286, 0.461777 avg, 0.001000 rate, 23.801668 seconds, 592640 images -Loaded: 0.000053 seconds -Region Avg IOU: 0.544413, Class: 1.000000, Obj: 0.373417, No Obj: 0.003882, Avg Recall: 0.627907, count: 86 -Region Avg IOU: 0.548329, Class: 1.000000, Obj: 0.317747, No Obj: 0.003929, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.503207, Class: 1.000000, Obj: 0.318332, No Obj: 0.004342, Avg Recall: 0.545455, count: 165 -Region Avg IOU: 0.527970, Class: 1.000000, Obj: 0.400508, No Obj: 0.003717, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.570357, Class: 1.000000, Obj: 0.337470, No Obj: 0.003378, Avg Recall: 0.678161, count: 87 -Region Avg IOU: 0.530148, Class: 1.000000, Obj: 0.312153, No Obj: 0.004060, Avg Recall: 0.595238, count: 126 -Region Avg IOU: 0.538858, Class: 1.000000, Obj: 0.322376, No Obj: 0.003300, Avg Recall: 0.565217, count: 92 -Region Avg IOU: 0.513813, Class: 1.000000, Obj: 0.306565, No Obj: 0.003626, Avg Recall: 0.544643, count: 112 -2316: 0.482428, 0.463842 avg, 0.001000 rate, 22.678925 seconds, 592896 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.498039, Class: 1.000000, Obj: 0.344100, No Obj: 0.003600, Avg Recall: 0.563636, count: 110 -Region Avg IOU: 0.577157, Class: 1.000000, Obj: 0.330478, No Obj: 0.004458, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.515695, Class: 1.000000, Obj: 0.340130, No Obj: 0.003581, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.530659, Class: 1.000000, Obj: 0.295215, No Obj: 0.003983, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.583041, Class: 1.000000, Obj: 0.391962, No Obj: 0.003392, Avg Recall: 0.670732, count: 82 -Region Avg IOU: 0.496472, Class: 1.000000, Obj: 0.284121, No Obj: 0.004124, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.547996, Class: 1.000000, Obj: 0.303930, No Obj: 0.004129, Avg Recall: 0.613445, count: 119 -Region Avg IOU: 0.529292, Class: 1.000000, Obj: 0.326513, No Obj: 0.003599, Avg Recall: 0.607143, count: 84 -2317: 0.518127, 0.469271 avg, 0.001000 rate, 23.656454 seconds, 593152 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.548048, Class: 1.000000, Obj: 0.374039, No Obj: 0.003297, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.524145, Class: 1.000000, Obj: 0.320739, No Obj: 0.003650, Avg Recall: 0.596639, count: 119 -Region Avg IOU: 0.537115, Class: 1.000000, Obj: 0.292160, No Obj: 0.004577, Avg Recall: 0.614173, count: 127 -Region Avg IOU: 0.508677, Class: 1.000000, Obj: 0.306845, No Obj: 0.002845, Avg Recall: 0.517241, count: 87 -Region Avg IOU: 0.562300, Class: 1.000000, Obj: 0.319930, No Obj: 0.004191, Avg Recall: 0.683761, count: 117 -Region Avg IOU: 0.555228, Class: 1.000000, Obj: 0.314120, No Obj: 0.003766, Avg Recall: 0.614754, count: 122 -Region Avg IOU: 0.589045, Class: 1.000000, Obj: 0.360971, No Obj: 0.003174, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.562879, Class: 1.000000, Obj: 0.326256, No Obj: 0.004015, Avg Recall: 0.623762, count: 101 -2318: 0.487362, 0.471080 avg, 0.001000 rate, 22.777300 seconds, 593408 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.557114, Class: 1.000000, Obj: 0.351573, No Obj: 0.003438, Avg Recall: 0.644444, count: 90 -Region Avg IOU: 0.535377, Class: 1.000000, Obj: 0.302185, No Obj: 0.004096, Avg Recall: 0.605839, count: 137 -Region Avg IOU: 0.558616, Class: 1.000000, Obj: 0.347729, No Obj: 0.003947, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.551382, Class: 1.000000, Obj: 0.372345, No Obj: 0.003855, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.526908, Class: 1.000000, Obj: 0.340877, No Obj: 0.003627, Avg Recall: 0.590551, count: 127 -Region Avg IOU: 0.562231, Class: 1.000000, Obj: 0.346945, No Obj: 0.004194, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.539610, Class: 1.000000, Obj: 0.294409, No Obj: 0.004081, Avg Recall: 0.632000, count: 125 -Region Avg IOU: 0.475483, Class: 1.000000, Obj: 0.279827, No Obj: 0.004140, Avg Recall: 0.529412, count: 136 -2319: 0.496862, 0.473658 avg, 0.001000 rate, 25.131563 seconds, 593664 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.536541, Class: 1.000000, Obj: 0.342242, No Obj: 0.003491, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.520856, Class: 1.000000, Obj: 0.304086, No Obj: 0.003787, Avg Recall: 0.574468, count: 141 -Region Avg IOU: 0.527948, Class: 1.000000, Obj: 0.342983, No Obj: 0.003563, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.553883, Class: 1.000000, Obj: 0.351914, No Obj: 0.003494, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.555592, Class: 1.000000, Obj: 0.327382, No Obj: 0.003290, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.515800, Class: 1.000000, Obj: 0.313119, No Obj: 0.003785, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.556513, Class: 1.000000, Obj: 0.379423, No Obj: 0.003642, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.520400, Class: 1.000000, Obj: 0.335663, No Obj: 0.004026, Avg Recall: 0.583333, count: 132 -2320: 0.453165, 0.471609 avg, 0.001000 rate, 24.221176 seconds, 593920 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.321571 seconds -Region Avg IOU: 0.510396, Class: 1.000000, Obj: 0.312502, No Obj: 0.004004, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.506115, Class: 1.000000, Obj: 0.305149, No Obj: 0.004287, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.508248, Class: 1.000000, Obj: 0.292463, No Obj: 0.004095, Avg Recall: 0.562500, count: 96 -Region Avg IOU: 0.477176, Class: 1.000000, Obj: 0.290878, No Obj: 0.003927, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.517568, Class: 1.000000, Obj: 0.334439, No Obj: 0.003731, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.466383, Class: 1.000000, Obj: 0.311316, No Obj: 0.003411, Avg Recall: 0.453608, count: 97 -Region Avg IOU: 0.530509, Class: 1.000000, Obj: 0.316525, No Obj: 0.003892, Avg Recall: 0.526882, count: 93 -Region Avg IOU: 0.496664, Class: 1.000000, Obj: 0.323764, No Obj: 0.003676, Avg Recall: 0.564356, count: 101 -2321: 0.427827, 0.467231 avg, 0.001000 rate, 19.735357 seconds, 594176 images -Loaded: 1.742358 seconds -Region Avg IOU: 0.546340, Class: 1.000000, Obj: 0.365579, No Obj: 0.004203, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.486444, Class: 1.000000, Obj: 0.295597, No Obj: 0.003944, Avg Recall: 0.536364, count: 110 -Region Avg IOU: 0.457884, Class: 1.000000, Obj: 0.250896, No Obj: 0.003948, Avg Recall: 0.479730, count: 148 -Region Avg IOU: 0.487779, Class: 1.000000, Obj: 0.290886, No Obj: 0.003686, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.504060, Class: 1.000000, Obj: 0.298673, No Obj: 0.003843, Avg Recall: 0.564103, count: 117 -Region Avg IOU: 0.507557, Class: 1.000000, Obj: 0.288097, No Obj: 0.004239, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.484251, Class: 1.000000, Obj: 0.295605, No Obj: 0.003935, Avg Recall: 0.508197, count: 122 -Region Avg IOU: 0.499193, Class: 1.000000, Obj: 0.306476, No Obj: 0.003737, Avg Recall: 0.531532, count: 111 -2322: 0.501304, 0.470638 avg, 0.001000 rate, 18.977669 seconds, 594432 images -Loaded: 5.001166 seconds -Region Avg IOU: 0.499541, Class: 1.000000, Obj: 0.281248, No Obj: 0.004198, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.582549, Class: 1.000000, Obj: 0.332676, No Obj: 0.004068, Avg Recall: 0.687500, count: 80 -Region Avg IOU: 0.507614, Class: 1.000000, Obj: 0.335018, No Obj: 0.004256, Avg Recall: 0.524590, count: 122 -Region Avg IOU: 0.501669, Class: 1.000000, Obj: 0.253226, No Obj: 0.003721, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.468541, Class: 1.000000, Obj: 0.284188, No Obj: 0.004102, Avg Recall: 0.503759, count: 133 -Region Avg IOU: 0.491958, Class: 1.000000, Obj: 0.284335, No Obj: 0.004282, Avg Recall: 0.549618, count: 131 -Region Avg IOU: 0.505654, Class: 1.000000, Obj: 0.308631, No Obj: 0.004387, Avg Recall: 0.571429, count: 140 -Region Avg IOU: 0.524016, Class: 1.000000, Obj: 0.326953, No Obj: 0.004469, Avg Recall: 0.576271, count: 118 -2323: 0.497598, 0.473334 avg, 0.001000 rate, 21.904863 seconds, 594688 images -Loaded: 0.554393 seconds -Region Avg IOU: 0.465373, Class: 1.000000, Obj: 0.296209, No Obj: 0.004120, Avg Recall: 0.555556, count: 81 -Region Avg IOU: 0.471624, Class: 1.000000, Obj: 0.315992, No Obj: 0.003940, Avg Recall: 0.475728, count: 103 -Region Avg IOU: 0.527936, Class: 1.000000, Obj: 0.325680, No Obj: 0.004282, Avg Recall: 0.590164, count: 122 -Region Avg IOU: 0.416861, Class: 1.000000, Obj: 0.279892, No Obj: 0.003402, Avg Recall: 0.417476, count: 103 -Region Avg IOU: 0.475165, Class: 1.000000, Obj: 0.312685, No Obj: 0.004252, Avg Recall: 0.481203, count: 133 -Region Avg IOU: 0.481149, Class: 1.000000, Obj: 0.301137, No Obj: 0.004412, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.498165, Class: 1.000000, Obj: 0.305904, No Obj: 0.004195, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.472272, Class: 1.000000, Obj: 0.260486, No Obj: 0.003957, Avg Recall: 0.548673, count: 113 -2324: 0.481717, 0.474172 avg, 0.001000 rate, 21.954597 seconds, 594944 images -Loaded: 0.077619 seconds -Region Avg IOU: 0.464414, Class: 1.000000, Obj: 0.298555, No Obj: 0.004413, Avg Recall: 0.514706, count: 136 -Region Avg IOU: 0.540729, Class: 1.000000, Obj: 0.335863, No Obj: 0.004781, Avg Recall: 0.633333, count: 120 -Region Avg IOU: 0.508292, Class: 1.000000, Obj: 0.303424, No Obj: 0.004099, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.436983, Class: 1.000000, Obj: 0.294095, No Obj: 0.003949, Avg Recall: 0.451923, count: 104 -Region Avg IOU: 0.511267, Class: 1.000000, Obj: 0.331083, No Obj: 0.004016, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.501469, Class: 1.000000, Obj: 0.340870, No Obj: 0.004251, Avg Recall: 0.525773, count: 97 -Region Avg IOU: 0.462696, Class: 1.000000, Obj: 0.297769, No Obj: 0.003616, Avg Recall: 0.511628, count: 86 -Region Avg IOU: 0.486852, Class: 1.000000, Obj: 0.315317, No Obj: 0.003874, Avg Recall: 0.575472, count: 106 -2325: 0.441235, 0.470879 avg, 0.001000 rate, 18.875360 seconds, 595200 images -Loaded: 3.078280 seconds -Region Avg IOU: 0.450649, Class: 1.000000, Obj: 0.337721, No Obj: 0.003974, Avg Recall: 0.451613, count: 124 -Region Avg IOU: 0.534578, Class: 1.000000, Obj: 0.301027, No Obj: 0.005037, Avg Recall: 0.633333, count: 120 -Region Avg IOU: 0.472772, Class: 1.000000, Obj: 0.301572, No Obj: 0.004236, Avg Recall: 0.526316, count: 114 -Region Avg IOU: 0.459778, Class: 1.000000, Obj: 0.299471, No Obj: 0.004062, Avg Recall: 0.533333, count: 120 -Region Avg IOU: 0.497859, Class: 1.000000, Obj: 0.315685, No Obj: 0.004520, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.542363, Class: 1.000000, Obj: 0.306424, No Obj: 0.004106, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.425354, Class: 1.000000, Obj: 0.293724, No Obj: 0.004406, Avg Recall: 0.424000, count: 125 -Region Avg IOU: 0.493759, Class: 1.000000, Obj: 0.334982, No Obj: 0.004060, Avg Recall: 0.542553, count: 94 -2326: 0.483929, 0.472184 avg, 0.001000 rate, 21.675461 seconds, 595456 images -Loaded: 1.168945 seconds -Region Avg IOU: 0.510922, Class: 1.000000, Obj: 0.355335, No Obj: 0.003939, Avg Recall: 0.571429, count: 91 -Region Avg IOU: 0.485109, Class: 1.000000, Obj: 0.300225, No Obj: 0.004673, Avg Recall: 0.521429, count: 140 -Region Avg IOU: 0.492655, Class: 1.000000, Obj: 0.325841, No Obj: 0.004468, Avg Recall: 0.528000, count: 125 -Region Avg IOU: 0.489951, Class: 1.000000, Obj: 0.337948, No Obj: 0.004901, Avg Recall: 0.500000, count: 134 -Region Avg IOU: 0.454354, Class: 1.000000, Obj: 0.313968, No Obj: 0.004021, Avg Recall: 0.474576, count: 118 -Region Avg IOU: 0.517479, Class: 1.000000, Obj: 0.358446, No Obj: 0.004567, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.510111, Class: 1.000000, Obj: 0.330274, No Obj: 0.004873, Avg Recall: 0.568000, count: 125 -Region Avg IOU: 0.471446, Class: 1.000000, Obj: 0.318441, No Obj: 0.004779, Avg Recall: 0.500000, count: 122 -2327: 0.481092, 0.473074 avg, 0.001000 rate, 19.498772 seconds, 595712 images -Loaded: 1.533464 seconds -Region Avg IOU: 0.461603, Class: 1.000000, Obj: 0.313579, No Obj: 0.004137, Avg Recall: 0.525253, count: 99 -Region Avg IOU: 0.509815, Class: 1.000000, Obj: 0.311271, No Obj: 0.004027, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.604652, Class: 1.000000, Obj: 0.390060, No Obj: 0.003696, Avg Recall: 0.696970, count: 66 -Region Avg IOU: 0.540858, Class: 1.000000, Obj: 0.363611, No Obj: 0.004884, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.502654, Class: 1.000000, Obj: 0.327920, No Obj: 0.003756, Avg Recall: 0.574713, count: 87 -Region Avg IOU: 0.424958, Class: 1.000000, Obj: 0.275685, No Obj: 0.003730, Avg Recall: 0.446808, count: 94 -Region Avg IOU: 0.495149, Class: 1.000000, Obj: 0.330183, No Obj: 0.004371, Avg Recall: 0.565574, count: 122 -Region Avg IOU: 0.480714, Class: 1.000000, Obj: 0.316519, No Obj: 0.004381, Avg Recall: 0.551724, count: 116 -2328: 0.414408, 0.467208 avg, 0.001000 rate, 22.004984 seconds, 595968 images -Loaded: 0.203989 seconds -Region Avg IOU: 0.518959, Class: 1.000000, Obj: 0.358980, No Obj: 0.004710, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.448877, Class: 1.000000, Obj: 0.318011, No Obj: 0.004081, Avg Recall: 0.493976, count: 83 -Region Avg IOU: 0.483451, Class: 1.000000, Obj: 0.289425, No Obj: 0.004756, Avg Recall: 0.525424, count: 118 -Region Avg IOU: 0.438090, Class: 1.000000, Obj: 0.291188, No Obj: 0.004625, Avg Recall: 0.484615, count: 130 -Region Avg IOU: 0.521604, Class: 1.000000, Obj: 0.346822, No Obj: 0.003731, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.470133, Class: 1.000000, Obj: 0.262621, No Obj: 0.004103, Avg Recall: 0.542553, count: 94 -Region Avg IOU: 0.507871, Class: 1.000000, Obj: 0.331004, No Obj: 0.003581, Avg Recall: 0.544304, count: 79 -Region Avg IOU: 0.487830, Class: 1.000000, Obj: 0.334007, No Obj: 0.004482, Avg Recall: 0.556522, count: 115 -2329: 0.447544, 0.465241 avg, 0.001000 rate, 21.368401 seconds, 596224 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.474926, Class: 1.000000, Obj: 0.337121, No Obj: 0.003021, Avg Recall: 0.582090, count: 67 -Region Avg IOU: 0.547635, Class: 1.000000, Obj: 0.327158, No Obj: 0.004249, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.524867, Class: 1.000000, Obj: 0.327471, No Obj: 0.004646, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.536296, Class: 1.000000, Obj: 0.334955, No Obj: 0.004421, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.449623, Class: 1.000000, Obj: 0.312037, No Obj: 0.003966, Avg Recall: 0.467890, count: 109 -Region Avg IOU: 0.537960, Class: 1.000000, Obj: 0.361741, No Obj: 0.004642, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.483383, Class: 1.000000, Obj: 0.323172, No Obj: 0.004740, Avg Recall: 0.500000, count: 126 -Region Avg IOU: 0.462729, Class: 1.000000, Obj: 0.260913, No Obj: 0.004569, Avg Recall: 0.471074, count: 121 -2330: 0.453747, 0.464092 avg, 0.001000 rate, 20.387596 seconds, 596480 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000058 seconds -Region Avg IOU: 0.539733, Class: 1.000000, Obj: 0.327086, No Obj: 0.003981, Avg Recall: 0.608000, count: 125 -Region Avg IOU: 0.562251, Class: 1.000000, Obj: 0.367885, No Obj: 0.003720, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.529120, Class: 1.000000, Obj: 0.318314, No Obj: 0.003897, Avg Recall: 0.642336, count: 137 -Region Avg IOU: 0.632012, Class: 1.000000, Obj: 0.400782, No Obj: 0.003369, Avg Recall: 0.833333, count: 96 -Region Avg IOU: 0.526026, Class: 1.000000, Obj: 0.351795, No Obj: 0.003660, Avg Recall: 0.608696, count: 138 -Region Avg IOU: 0.549868, Class: 1.000000, Obj: 0.346587, No Obj: 0.003539, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.626910, Class: 1.000000, Obj: 0.440510, No Obj: 0.003684, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.555570, Class: 1.000000, Obj: 0.365336, No Obj: 0.003606, Avg Recall: 0.691667, count: 120 -2331: 0.477764, 0.465459 avg, 0.001000 rate, 24.717855 seconds, 596736 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.584649, Class: 1.000000, Obj: 0.340205, No Obj: 0.003909, Avg Recall: 0.728682, count: 129 -Region Avg IOU: 0.548267, Class: 1.000000, Obj: 0.355832, No Obj: 0.003397, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.566566, Class: 1.000000, Obj: 0.420087, No Obj: 0.003853, Avg Recall: 0.640000, count: 125 -Region Avg IOU: 0.548404, Class: 1.000000, Obj: 0.368579, No Obj: 0.003464, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.515225, Class: 1.000000, Obj: 0.367311, No Obj: 0.003165, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.524553, Class: 1.000000, Obj: 0.362478, No Obj: 0.003451, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.611886, Class: 1.000000, Obj: 0.298829, No Obj: 0.003918, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.532330, Class: 1.000000, Obj: 0.354198, No Obj: 0.003601, Avg Recall: 0.632479, count: 117 -2332: 0.495313, 0.468445 avg, 0.001000 rate, 25.609776 seconds, 596992 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.544264, Class: 1.000000, Obj: 0.310961, No Obj: 0.003654, Avg Recall: 0.612903, count: 124 -Region Avg IOU: 0.516721, Class: 1.000000, Obj: 0.283754, No Obj: 0.002653, Avg Recall: 0.587719, count: 114 -Region Avg IOU: 0.544960, Class: 1.000000, Obj: 0.339004, No Obj: 0.003297, Avg Recall: 0.624060, count: 133 -Region Avg IOU: 0.550099, Class: 1.000000, Obj: 0.334953, No Obj: 0.003130, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.608201, Class: 1.000000, Obj: 0.347645, No Obj: 0.003313, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.565759, Class: 1.000000, Obj: 0.346851, No Obj: 0.003083, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.523807, Class: 1.000000, Obj: 0.321281, No Obj: 0.003475, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.582887, Class: 1.000000, Obj: 0.361153, No Obj: 0.003396, Avg Recall: 0.724138, count: 116 -2333: 0.471469, 0.468747 avg, 0.001000 rate, 24.960987 seconds, 597248 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.608607, Class: 1.000000, Obj: 0.357323, No Obj: 0.003021, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.563988, Class: 1.000000, Obj: 0.319608, No Obj: 0.003147, Avg Recall: 0.641026, count: 117 -Region Avg IOU: 0.587090, Class: 1.000000, Obj: 0.333011, No Obj: 0.003683, Avg Recall: 0.669118, count: 136 -Region Avg IOU: 0.617188, Class: 1.000000, Obj: 0.340655, No Obj: 0.002682, Avg Recall: 0.730159, count: 63 -Region Avg IOU: 0.522882, Class: 1.000000, Obj: 0.246811, No Obj: 0.003397, Avg Recall: 0.582734, count: 139 -Region Avg IOU: 0.545427, Class: 1.000000, Obj: 0.302275, No Obj: 0.002968, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.534863, Class: 1.000000, Obj: 0.287336, No Obj: 0.002908, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.587667, Class: 1.000000, Obj: 0.326457, No Obj: 0.003153, Avg Recall: 0.686956, count: 115 -2334: 0.474985, 0.469371 avg, 0.001000 rate, 25.469816 seconds, 597504 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.536762, Class: 1.000000, Obj: 0.358197, No Obj: 0.003059, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.533811, Class: 1.000000, Obj: 0.362406, No Obj: 0.003248, Avg Recall: 0.601770, count: 113 -Region Avg IOU: 0.580062, Class: 1.000000, Obj: 0.332498, No Obj: 0.002840, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.546240, Class: 1.000000, Obj: 0.262681, No Obj: 0.003286, Avg Recall: 0.688000, count: 125 -Region Avg IOU: 0.590462, Class: 1.000000, Obj: 0.322907, No Obj: 0.003315, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.599941, Class: 1.000000, Obj: 0.325557, No Obj: 0.003177, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.592334, Class: 1.000000, Obj: 0.269143, No Obj: 0.003434, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.605429, Class: 1.000000, Obj: 0.300602, No Obj: 0.003622, Avg Recall: 0.712963, count: 108 -2335: 0.458198, 0.468254 avg, 0.001000 rate, 25.076948 seconds, 597760 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.596095, Class: 1.000000, Obj: 0.309646, No Obj: 0.003512, Avg Recall: 0.712000, count: 125 -Region Avg IOU: 0.593729, Class: 1.000000, Obj: 0.339082, No Obj: 0.002936, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.552188, Class: 1.000000, Obj: 0.264096, No Obj: 0.002876, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.553782, Class: 1.000000, Obj: 0.327265, No Obj: 0.002912, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.570736, Class: 1.000000, Obj: 0.318330, No Obj: 0.003529, Avg Recall: 0.648000, count: 125 -Region Avg IOU: 0.589275, Class: 1.000000, Obj: 0.283190, No Obj: 0.003347, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.502949, Class: 1.000000, Obj: 0.250000, No Obj: 0.002624, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.590944, Class: 1.000000, Obj: 0.325561, No Obj: 0.003378, Avg Recall: 0.685484, count: 124 -2336: 0.509968, 0.472425 avg, 0.001000 rate, 24.260656 seconds, 598016 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.569807, Class: 1.000000, Obj: 0.353549, No Obj: 0.002652, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.553307, Class: 1.000000, Obj: 0.320141, No Obj: 0.002943, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.537357, Class: 1.000000, Obj: 0.304546, No Obj: 0.003169, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.542689, Class: 1.000000, Obj: 0.289677, No Obj: 0.003444, Avg Recall: 0.578512, count: 121 -Region Avg IOU: 0.586259, Class: 1.000000, Obj: 0.320768, No Obj: 0.002921, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.569755, Class: 1.000000, Obj: 0.320931, No Obj: 0.003168, Avg Recall: 0.684615, count: 130 -Region Avg IOU: 0.545090, Class: 1.000000, Obj: 0.305179, No Obj: 0.003269, Avg Recall: 0.632258, count: 155 -Region Avg IOU: 0.562416, Class: 1.000000, Obj: 0.305171, No Obj: 0.003087, Avg Recall: 0.632479, count: 117 -2337: 0.516709, 0.476853 avg, 0.001000 rate, 25.751820 seconds, 598272 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.592447, Class: 1.000000, Obj: 0.297181, No Obj: 0.003584, Avg Recall: 0.726562, count: 128 -Region Avg IOU: 0.576670, Class: 1.000000, Obj: 0.329671, No Obj: 0.002854, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.572159, Class: 1.000000, Obj: 0.303996, No Obj: 0.003653, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.584406, Class: 1.000000, Obj: 0.308082, No Obj: 0.003359, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.545629, Class: 1.000000, Obj: 0.315692, No Obj: 0.003241, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.561525, Class: 1.000000, Obj: 0.320159, No Obj: 0.003414, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.575937, Class: 1.000000, Obj: 0.322637, No Obj: 0.003605, Avg Recall: 0.656000, count: 125 -Region Avg IOU: 0.561002, Class: 1.000000, Obj: 0.339475, No Obj: 0.003277, Avg Recall: 0.635659, count: 129 -2338: 0.519298, 0.481098 avg, 0.001000 rate, 26.124191 seconds, 598528 images -Loaded: 0.000177 seconds -Region Avg IOU: 0.584777, Class: 1.000000, Obj: 0.358732, No Obj: 0.003988, Avg Recall: 0.655738, count: 122 -Region Avg IOU: 0.615104, Class: 1.000000, Obj: 0.380350, No Obj: 0.003978, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.583758, Class: 1.000000, Obj: 0.358066, No Obj: 0.003243, Avg Recall: 0.740741, count: 81 -Region Avg IOU: 0.537838, Class: 1.000000, Obj: 0.338896, No Obj: 0.003405, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.534980, Class: 1.000000, Obj: 0.363213, No Obj: 0.003161, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.561024, Class: 1.000000, Obj: 0.355636, No Obj: 0.003680, Avg Recall: 0.674242, count: 132 -Region Avg IOU: 0.589041, Class: 1.000000, Obj: 0.422024, No Obj: 0.003717, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.605228, Class: 1.000000, Obj: 0.387501, No Obj: 0.002748, Avg Recall: 0.741379, count: 58 -2339: 0.436852, 0.476673 avg, 0.001000 rate, 25.107285 seconds, 598784 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.571613, Class: 1.000000, Obj: 0.371476, No Obj: 0.003273, Avg Recall: 0.631148, count: 122 -Region Avg IOU: 0.545779, Class: 1.000000, Obj: 0.328292, No Obj: 0.003799, Avg Recall: 0.648000, count: 125 -Region Avg IOU: 0.560684, Class: 1.000000, Obj: 0.361234, No Obj: 0.003653, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.600553, Class: 1.000000, Obj: 0.361669, No Obj: 0.003242, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.520787, Class: 1.000000, Obj: 0.355483, No Obj: 0.003502, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.564901, Class: 1.000000, Obj: 0.332150, No Obj: 0.003376, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.518177, Class: 1.000000, Obj: 0.350755, No Obj: 0.003431, Avg Recall: 0.573643, count: 129 -Region Avg IOU: 0.559512, Class: 1.000000, Obj: 0.377928, No Obj: 0.003341, Avg Recall: 0.621053, count: 95 -2340: 0.466054, 0.475611 avg, 0.001000 rate, 25.080448 seconds, 599040 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 15.661686 seconds -Region Avg IOU: 0.558649, Class: 1.000000, Obj: 0.356616, No Obj: 0.003149, Avg Recall: 0.642857, count: 84 -Region Avg IOU: 0.515713, Class: 1.000000, Obj: 0.260063, No Obj: 0.003257, Avg Recall: 0.611511, count: 139 -Region Avg IOU: 0.589912, Class: 1.000000, Obj: 0.370737, No Obj: 0.003608, Avg Recall: 0.691057, count: 123 -Region Avg IOU: 0.532438, Class: 1.000000, Obj: 0.308665, No Obj: 0.003422, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.519861, Class: 1.000000, Obj: 0.352404, No Obj: 0.003595, Avg Recall: 0.583333, count: 120 -Region Avg IOU: 0.594503, Class: 1.000000, Obj: 0.435592, No Obj: 0.003861, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.580069, Class: 1.000000, Obj: 0.387059, No Obj: 0.003746, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.611756, Class: 1.000000, Obj: 0.364757, No Obj: 0.003291, Avg Recall: 0.714286, count: 91 -2341: 0.417300, 0.469780 avg, 0.001000 rate, 25.325745 seconds, 599296 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.549469, Class: 1.000000, Obj: 0.366396, No Obj: 0.003550, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.578845, Class: 1.000000, Obj: 0.393754, No Obj: 0.003780, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.544952, Class: 1.000000, Obj: 0.316023, No Obj: 0.003816, Avg Recall: 0.632479, count: 117 -Region Avg IOU: 0.582786, Class: 1.000000, Obj: 0.346420, No Obj: 0.004159, Avg Recall: 0.682540, count: 126 -Region Avg IOU: 0.576228, Class: 1.000000, Obj: 0.342626, No Obj: 0.004137, Avg Recall: 0.657534, count: 146 -Region Avg IOU: 0.538395, Class: 1.000000, Obj: 0.372068, No Obj: 0.003402, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.560077, Class: 1.000000, Obj: 0.374441, No Obj: 0.003305, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.591202, Class: 1.000000, Obj: 0.356048, No Obj: 0.003431, Avg Recall: 0.672566, count: 113 -2342: 0.497951, 0.472597 avg, 0.001000 rate, 24.864420 seconds, 599552 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.529185, Class: 1.000000, Obj: 0.331987, No Obj: 0.003482, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.560046, Class: 1.000000, Obj: 0.337095, No Obj: 0.003599, Avg Recall: 0.683761, count: 117 -Region Avg IOU: 0.564982, Class: 1.000000, Obj: 0.357879, No Obj: 0.003265, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.562313, Class: 1.000000, Obj: 0.385844, No Obj: 0.003226, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.597304, Class: 1.000000, Obj: 0.334866, No Obj: 0.003720, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.588127, Class: 1.000000, Obj: 0.362580, No Obj: 0.003517, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.533499, Class: 1.000000, Obj: 0.349401, No Obj: 0.002900, Avg Recall: 0.652174, count: 92 -Region Avg IOU: 0.539464, Class: 1.000000, Obj: 0.335730, No Obj: 0.003374, Avg Recall: 0.606557, count: 122 -2343: 0.429934, 0.468331 avg, 0.001000 rate, 24.882618 seconds, 599808 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.578727, Class: 1.000000, Obj: 0.337423, No Obj: 0.003343, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.547603, Class: 1.000000, Obj: 0.332144, No Obj: 0.003355, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.564116, Class: 1.000000, Obj: 0.321316, No Obj: 0.003479, Avg Recall: 0.644628, count: 121 -Region Avg IOU: 0.562737, Class: 1.000000, Obj: 0.358791, No Obj: 0.003727, Avg Recall: 0.651852, count: 135 -Region Avg IOU: 0.590927, Class: 1.000000, Obj: 0.319199, No Obj: 0.003627, Avg Recall: 0.713178, count: 129 -Region Avg IOU: 0.584746, Class: 1.000000, Obj: 0.303839, No Obj: 0.003372, Avg Recall: 0.704545, count: 132 -Region Avg IOU: 0.585694, Class: 1.000000, Obj: 0.365036, No Obj: 0.002934, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.600189, Class: 1.000000, Obj: 0.338771, No Obj: 0.003065, Avg Recall: 0.718750, count: 96 -2344: 0.513432, 0.472841 avg, 0.001000 rate, 24.242592 seconds, 600064 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.545889, Class: 1.000000, Obj: 0.330074, No Obj: 0.002806, Avg Recall: 0.602273, count: 88 -Region Avg IOU: 0.541280, Class: 1.000000, Obj: 0.314559, No Obj: 0.003039, Avg Recall: 0.596491, count: 114 -Region Avg IOU: 0.551923, Class: 1.000000, Obj: 0.295189, No Obj: 0.003309, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.583361, Class: 1.000000, Obj: 0.318158, No Obj: 0.003341, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.607670, Class: 1.000000, Obj: 0.361485, No Obj: 0.002506, Avg Recall: 0.736842, count: 76 -Region Avg IOU: 0.561157, Class: 1.000000, Obj: 0.312710, No Obj: 0.003080, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.536822, Class: 1.000000, Obj: 0.324674, No Obj: 0.002976, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.530968, Class: 1.000000, Obj: 0.279137, No Obj: 0.003387, Avg Recall: 0.604027, count: 149 -2345: 0.435659, 0.469123 avg, 0.001000 rate, 24.694033 seconds, 600320 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.596672, Class: 1.000000, Obj: 0.336210, No Obj: 0.003376, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.592203, Class: 1.000000, Obj: 0.348127, No Obj: 0.002754, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.599439, Class: 1.000000, Obj: 0.332321, No Obj: 0.003084, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.582450, Class: 1.000000, Obj: 0.274001, No Obj: 0.002698, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.550364, Class: 1.000000, Obj: 0.333366, No Obj: 0.002802, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.608014, Class: 1.000000, Obj: 0.334123, No Obj: 0.003494, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.546621, Class: 1.000000, Obj: 0.310084, No Obj: 0.003409, Avg Recall: 0.625000, count: 136 -Region Avg IOU: 0.577644, Class: 1.000000, Obj: 0.326496, No Obj: 0.003346, Avg Recall: 0.681818, count: 132 -2346: 0.464963, 0.468707 avg, 0.001000 rate, 26.033817 seconds, 600576 images -Loaded: 0.000125 seconds -Region Avg IOU: 0.607999, Class: 1.000000, Obj: 0.354378, No Obj: 0.002879, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.589352, Class: 1.000000, Obj: 0.336392, No Obj: 0.003195, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.578691, Class: 1.000000, Obj: 0.280769, No Obj: 0.003106, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.558660, Class: 1.000000, Obj: 0.319553, No Obj: 0.003444, Avg Recall: 0.643411, count: 129 -Region Avg IOU: 0.529730, Class: 1.000000, Obj: 0.319194, No Obj: 0.003160, Avg Recall: 0.593750, count: 128 -Region Avg IOU: 0.561777, Class: 1.000000, Obj: 0.301443, No Obj: 0.003021, Avg Recall: 0.666667, count: 84 -Region Avg IOU: 0.564277, Class: 1.000000, Obj: 0.320343, No Obj: 0.003012, Avg Recall: 0.666667, count: 120 -Region Avg IOU: 0.532789, Class: 1.000000, Obj: 0.324930, No Obj: 0.003032, Avg Recall: 0.628319, count: 113 -2347: 0.467984, 0.468635 avg, 0.001000 rate, 25.414875 seconds, 600832 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.548850, Class: 1.000000, Obj: 0.329728, No Obj: 0.003341, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.598541, Class: 1.000000, Obj: 0.348350, No Obj: 0.003481, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.651996, Class: 1.000000, Obj: 0.361982, No Obj: 0.003137, Avg Recall: 0.833333, count: 84 -Region Avg IOU: 0.619792, Class: 1.000000, Obj: 0.355077, No Obj: 0.003670, Avg Recall: 0.779528, count: 127 -Region Avg IOU: 0.542020, Class: 1.000000, Obj: 0.315191, No Obj: 0.003468, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.543221, Class: 1.000000, Obj: 0.289965, No Obj: 0.003327, Avg Recall: 0.656489, count: 131 -Region Avg IOU: 0.578598, Class: 1.000000, Obj: 0.311883, No Obj: 0.003886, Avg Recall: 0.695312, count: 128 -Region Avg IOU: 0.544913, Class: 1.000000, Obj: 0.283012, No Obj: 0.003004, Avg Recall: 0.647059, count: 102 -2348: 0.481677, 0.469939 avg, 0.001000 rate, 24.377157 seconds, 601088 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.597691, Class: 1.000000, Obj: 0.331703, No Obj: 0.003483, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.604179, Class: 1.000000, Obj: 0.331408, No Obj: 0.003278, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.601130, Class: 1.000000, Obj: 0.372556, No Obj: 0.003213, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.560008, Class: 1.000000, Obj: 0.341188, No Obj: 0.003312, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.618938, Class: 1.000000, Obj: 0.302986, No Obj: 0.004330, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.565024, Class: 1.000000, Obj: 0.315744, No Obj: 0.003034, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.605192, Class: 1.000000, Obj: 0.376976, No Obj: 0.003711, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.580235, Class: 1.000000, Obj: 0.313480, No Obj: 0.003524, Avg Recall: 0.714286, count: 119 -2349: 0.480446, 0.470989 avg, 0.001000 rate, 24.365139 seconds, 601344 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.586629, Class: 1.000000, Obj: 0.372413, No Obj: 0.003776, Avg Recall: 0.680328, count: 122 -Region Avg IOU: 0.590036, Class: 1.000000, Obj: 0.360689, No Obj: 0.003100, Avg Recall: 0.743902, count: 82 -Region Avg IOU: 0.592328, Class: 1.000000, Obj: 0.369899, No Obj: 0.003531, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.581049, Class: 1.000000, Obj: 0.421199, No Obj: 0.003460, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.573025, Class: 1.000000, Obj: 0.311426, No Obj: 0.003408, Avg Recall: 0.669565, count: 115 -Region Avg IOU: 0.602813, Class: 1.000000, Obj: 0.371522, No Obj: 0.003137, Avg Recall: 0.676056, count: 71 -Region Avg IOU: 0.518403, Class: 1.000000, Obj: 0.339764, No Obj: 0.003820, Avg Recall: 0.598425, count: 127 -Region Avg IOU: 0.560976, Class: 1.000000, Obj: 0.363902, No Obj: 0.003681, Avg Recall: 0.659864, count: 147 -2350: 0.483458, 0.472236 avg, 0.001000 rate, 24.601984 seconds, 601600 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 20.387671 seconds -Region Avg IOU: 0.594369, Class: 1.000000, Obj: 0.332694, No Obj: 0.004324, Avg Recall: 0.750000, count: 132 -Region Avg IOU: 0.560374, Class: 1.000000, Obj: 0.363881, No Obj: 0.003916, Avg Recall: 0.608696, count: 138 -Region Avg IOU: 0.590269, Class: 1.000000, Obj: 0.395896, No Obj: 0.004385, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.514813, Class: 1.000000, Obj: 0.355989, No Obj: 0.003503, Avg Recall: 0.586207, count: 116 -Region Avg IOU: 0.557362, Class: 1.000000, Obj: 0.390664, No Obj: 0.004023, Avg Recall: 0.666667, count: 126 -Region Avg IOU: 0.557947, Class: 1.000000, Obj: 0.354255, No Obj: 0.003622, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.597012, Class: 1.000000, Obj: 0.363600, No Obj: 0.003567, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.571185, Class: 1.000000, Obj: 0.334528, No Obj: 0.003952, Avg Recall: 0.696721, count: 122 -2351: 0.518961, 0.476909 avg, 0.001000 rate, 24.183077 seconds, 601856 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.482157, Class: 1.000000, Obj: 0.334225, No Obj: 0.003591, Avg Recall: 0.490385, count: 104 -Region Avg IOU: 0.564631, Class: 1.000000, Obj: 0.382475, No Obj: 0.003679, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.565305, Class: 1.000000, Obj: 0.402534, No Obj: 0.003861, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.597104, Class: 1.000000, Obj: 0.345162, No Obj: 0.003538, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.521814, Class: 1.000000, Obj: 0.351072, No Obj: 0.003087, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.560539, Class: 1.000000, Obj: 0.361861, No Obj: 0.003961, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.590499, Class: 1.000000, Obj: 0.337063, No Obj: 0.003647, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.496809, Class: 1.000000, Obj: 0.378949, No Obj: 0.003132, Avg Recall: 0.564356, count: 101 -2352: 0.395859, 0.468804 avg, 0.001000 rate, 25.435497 seconds, 602112 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.532928, Class: 1.000000, Obj: 0.344866, No Obj: 0.003429, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.538046, Class: 1.000000, Obj: 0.325799, No Obj: 0.004010, Avg Recall: 0.644068, count: 118 -Region Avg IOU: 0.537124, Class: 1.000000, Obj: 0.336033, No Obj: 0.003471, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.552412, Class: 1.000000, Obj: 0.358445, No Obj: 0.003330, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.578415, Class: 1.000000, Obj: 0.392379, No Obj: 0.004061, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.576847, Class: 1.000000, Obj: 0.377714, No Obj: 0.004168, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.551913, Class: 1.000000, Obj: 0.317351, No Obj: 0.003785, Avg Recall: 0.645161, count: 124 -Region Avg IOU: 0.528291, Class: 1.000000, Obj: 0.323031, No Obj: 0.004136, Avg Recall: 0.607692, count: 130 -2353: 0.491255, 0.471049 avg, 0.001000 rate, 23.741928 seconds, 602368 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.551258, Class: 1.000000, Obj: 0.363298, No Obj: 0.003458, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.571716, Class: 1.000000, Obj: 0.327549, No Obj: 0.003146, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.574596, Class: 1.000000, Obj: 0.367361, No Obj: 0.003526, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.498339, Class: 1.000000, Obj: 0.342033, No Obj: 0.003477, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.576012, Class: 1.000000, Obj: 0.368465, No Obj: 0.003693, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.553877, Class: 1.000000, Obj: 0.338517, No Obj: 0.003720, Avg Recall: 0.671756, count: 131 -Region Avg IOU: 0.516615, Class: 1.000000, Obj: 0.324561, No Obj: 0.003395, Avg Recall: 0.535354, count: 99 -Region Avg IOU: 0.500751, Class: 1.000000, Obj: 0.332350, No Obj: 0.003551, Avg Recall: 0.545455, count: 121 -2354: 0.466015, 0.470546 avg, 0.001000 rate, 24.583525 seconds, 602624 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.541589, Class: 1.000000, Obj: 0.335773, No Obj: 0.003138, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.510587, Class: 1.000000, Obj: 0.325995, No Obj: 0.003375, Avg Recall: 0.510638, count: 94 -Region Avg IOU: 0.573776, Class: 1.000000, Obj: 0.340791, No Obj: 0.003593, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.571526, Class: 1.000000, Obj: 0.377477, No Obj: 0.003644, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.562620, Class: 1.000000, Obj: 0.389328, No Obj: 0.003060, Avg Recall: 0.662651, count: 83 -Region Avg IOU: 0.571457, Class: 1.000000, Obj: 0.333748, No Obj: 0.003966, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.547597, Class: 1.000000, Obj: 0.319844, No Obj: 0.004118, Avg Recall: 0.658537, count: 123 -Region Avg IOU: 0.557636, Class: 1.000000, Obj: 0.323960, No Obj: 0.003628, Avg Recall: 0.670886, count: 79 -2355: 0.415170, 0.465008 avg, 0.001000 rate, 25.304752 seconds, 602880 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.567622, Class: 1.000000, Obj: 0.328717, No Obj: 0.003291, Avg Recall: 0.630435, count: 92 -Region Avg IOU: 0.512504, Class: 1.000000, Obj: 0.271968, No Obj: 0.003451, Avg Recall: 0.561404, count: 114 -Region Avg IOU: 0.558042, Class: 1.000000, Obj: 0.280187, No Obj: 0.003545, Avg Recall: 0.633333, count: 120 -Region Avg IOU: 0.541641, Class: 1.000000, Obj: 0.319681, No Obj: 0.003063, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.528483, Class: 1.000000, Obj: 0.329323, No Obj: 0.003467, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.583971, Class: 1.000000, Obj: 0.317308, No Obj: 0.003310, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.589771, Class: 1.000000, Obj: 0.366278, No Obj: 0.003376, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.568798, Class: 1.000000, Obj: 0.372678, No Obj: 0.002998, Avg Recall: 0.648649, count: 74 -2356: 0.409274, 0.459435 avg, 0.001000 rate, 24.453320 seconds, 603136 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.528400, Class: 1.000000, Obj: 0.334891, No Obj: 0.003621, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.560667, Class: 1.000000, Obj: 0.316139, No Obj: 0.003327, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.542389, Class: 1.000000, Obj: 0.319798, No Obj: 0.003753, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.588115, Class: 1.000000, Obj: 0.315331, No Obj: 0.003544, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.542048, Class: 1.000000, Obj: 0.266135, No Obj: 0.003224, Avg Recall: 0.595506, count: 89 -Region Avg IOU: 0.565564, Class: 1.000000, Obj: 0.326615, No Obj: 0.003863, Avg Recall: 0.684211, count: 152 -Region Avg IOU: 0.561602, Class: 1.000000, Obj: 0.314573, No Obj: 0.003174, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.563889, Class: 1.000000, Obj: 0.327272, No Obj: 0.003625, Avg Recall: 0.685950, count: 121 -2357: 0.463359, 0.459827 avg, 0.001000 rate, 23.069763 seconds, 603392 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.539366, Class: 1.000000, Obj: 0.362671, No Obj: 0.002770, Avg Recall: 0.662791, count: 86 -Region Avg IOU: 0.541698, Class: 1.000000, Obj: 0.356026, No Obj: 0.002927, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.628754, Class: 1.000000, Obj: 0.348552, No Obj: 0.003120, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.522093, Class: 1.000000, Obj: 0.302596, No Obj: 0.003421, Avg Recall: 0.659259, count: 135 -Region Avg IOU: 0.560111, Class: 1.000000, Obj: 0.332023, No Obj: 0.003220, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.553122, Class: 1.000000, Obj: 0.360325, No Obj: 0.003005, Avg Recall: 0.619048, count: 84 -Region Avg IOU: 0.550519, Class: 1.000000, Obj: 0.309796, No Obj: 0.003242, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.525215, Class: 1.000000, Obj: 0.270279, No Obj: 0.003085, Avg Recall: 0.575758, count: 99 -2358: 0.413942, 0.455239 avg, 0.001000 rate, 23.922815 seconds, 603648 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.563006, Class: 1.000000, Obj: 0.287162, No Obj: 0.003377, Avg Recall: 0.664062, count: 128 -Region Avg IOU: 0.574877, Class: 1.000000, Obj: 0.305883, No Obj: 0.003019, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.564964, Class: 1.000000, Obj: 0.315324, No Obj: 0.003264, Avg Recall: 0.643678, count: 87 -Region Avg IOU: 0.567823, Class: 1.000000, Obj: 0.300654, No Obj: 0.003129, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.544444, Class: 1.000000, Obj: 0.276855, No Obj: 0.003518, Avg Recall: 0.633094, count: 139 -Region Avg IOU: 0.563460, Class: 1.000000, Obj: 0.333501, No Obj: 0.002644, Avg Recall: 0.690141, count: 71 -Region Avg IOU: 0.556188, Class: 1.000000, Obj: 0.283930, No Obj: 0.003232, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.504239, Class: 1.000000, Obj: 0.292463, No Obj: 0.002847, Avg Recall: 0.525862, count: 116 -2359: 0.442814, 0.453996 avg, 0.001000 rate, 24.598482 seconds, 603904 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.613833, Class: 1.000000, Obj: 0.307345, No Obj: 0.003484, Avg Recall: 0.780488, count: 123 -Region Avg IOU: 0.590707, Class: 1.000000, Obj: 0.318624, No Obj: 0.002903, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.571099, Class: 1.000000, Obj: 0.289025, No Obj: 0.003424, Avg Recall: 0.650794, count: 126 -Region Avg IOU: 0.526153, Class: 1.000000, Obj: 0.287469, No Obj: 0.003209, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.533807, Class: 1.000000, Obj: 0.292444, No Obj: 0.003031, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.600066, Class: 1.000000, Obj: 0.339763, No Obj: 0.003635, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.599020, Class: 1.000000, Obj: 0.350745, No Obj: 0.003493, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.604168, Class: 1.000000, Obj: 0.324402, No Obj: 0.003541, Avg Recall: 0.711712, count: 111 -2360: 0.441606, 0.452757 avg, 0.001000 rate, 23.731169 seconds, 604160 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000066 seconds -Region Avg IOU: 0.564713, Class: 1.000000, Obj: 0.325813, No Obj: 0.002985, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.566989, Class: 1.000000, Obj: 0.348282, No Obj: 0.003240, Avg Recall: 0.605042, count: 119 -Region Avg IOU: 0.625738, Class: 1.000000, Obj: 0.351149, No Obj: 0.003590, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.600416, Class: 1.000000, Obj: 0.366933, No Obj: 0.003195, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.588631, Class: 1.000000, Obj: 0.320717, No Obj: 0.003447, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.602862, Class: 1.000000, Obj: 0.363804, No Obj: 0.003324, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.554806, Class: 1.000000, Obj: 0.316994, No Obj: 0.003652, Avg Recall: 0.640000, count: 125 -Region Avg IOU: 0.583021, Class: 1.000000, Obj: 0.324888, No Obj: 0.003055, Avg Recall: 0.688679, count: 106 -2361: 0.489329, 0.456414 avg, 0.001000 rate, 24.655315 seconds, 604416 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.557367, Class: 1.000000, Obj: 0.333954, No Obj: 0.003293, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.587571, Class: 1.000000, Obj: 0.343930, No Obj: 0.003619, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.571774, Class: 1.000000, Obj: 0.363934, No Obj: 0.003499, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.562301, Class: 1.000000, Obj: 0.329732, No Obj: 0.003453, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.532605, Class: 1.000000, Obj: 0.341502, No Obj: 0.003281, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.512098, Class: 1.000000, Obj: 0.317750, No Obj: 0.003467, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.580838, Class: 1.000000, Obj: 0.392261, No Obj: 0.003592, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.573125, Class: 1.000000, Obj: 0.379945, No Obj: 0.003904, Avg Recall: 0.691489, count: 94 -2362: 0.482756, 0.459048 avg, 0.001000 rate, 24.503670 seconds, 604672 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.580569, Class: 1.000000, Obj: 0.346912, No Obj: 0.003924, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.596817, Class: 1.000000, Obj: 0.377277, No Obj: 0.003286, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.591798, Class: 1.000000, Obj: 0.376610, No Obj: 0.003241, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.626023, Class: 1.000000, Obj: 0.371719, No Obj: 0.003100, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.573062, Class: 1.000000, Obj: 0.344590, No Obj: 0.003563, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.584933, Class: 1.000000, Obj: 0.375410, No Obj: 0.003909, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.568305, Class: 1.000000, Obj: 0.339930, No Obj: 0.003415, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.557258, Class: 1.000000, Obj: 0.307207, No Obj: 0.003802, Avg Recall: 0.620000, count: 150 -2363: 0.456449, 0.458788 avg, 0.001000 rate, 26.260656 seconds, 604928 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.598307, Class: 1.000000, Obj: 0.374271, No Obj: 0.003805, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.576310, Class: 1.000000, Obj: 0.356001, No Obj: 0.003606, Avg Recall: 0.685484, count: 124 -Region Avg IOU: 0.560835, Class: 1.000000, Obj: 0.367349, No Obj: 0.003673, Avg Recall: 0.661417, count: 127 -Region Avg IOU: 0.561674, Class: 1.000000, Obj: 0.373560, No Obj: 0.003527, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.536815, Class: 1.000000, Obj: 0.354507, No Obj: 0.003411, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.577721, Class: 1.000000, Obj: 0.388143, No Obj: 0.003317, Avg Recall: 0.685393, count: 89 -Region Avg IOU: 0.590635, Class: 1.000000, Obj: 0.349255, No Obj: 0.003709, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.554059, Class: 1.000000, Obj: 0.319109, No Obj: 0.003746, Avg Recall: 0.641026, count: 117 -2364: 0.464124, 0.459322 avg, 0.001000 rate, 25.448008 seconds, 605184 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.589941, Class: 1.000000, Obj: 0.365523, No Obj: 0.003853, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.562801, Class: 1.000000, Obj: 0.353067, No Obj: 0.003292, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.557431, Class: 1.000000, Obj: 0.331032, No Obj: 0.003299, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.580581, Class: 1.000000, Obj: 0.344718, No Obj: 0.004143, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.575852, Class: 1.000000, Obj: 0.364321, No Obj: 0.003880, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.507813, Class: 1.000000, Obj: 0.313380, No Obj: 0.003486, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.556935, Class: 1.000000, Obj: 0.361195, No Obj: 0.003972, Avg Recall: 0.661871, count: 139 -Region Avg IOU: 0.540409, Class: 1.000000, Obj: 0.351419, No Obj: 0.003425, Avg Recall: 0.616822, count: 107 -2365: 0.481400, 0.461530 avg, 0.001000 rate, 24.696472 seconds, 605440 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.594411, Class: 1.000000, Obj: 0.340599, No Obj: 0.003332, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.565493, Class: 1.000000, Obj: 0.360960, No Obj: 0.003308, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.504609, Class: 1.000000, Obj: 0.390258, No Obj: 0.003411, Avg Recall: 0.522222, count: 90 -Region Avg IOU: 0.528922, Class: 1.000000, Obj: 0.357527, No Obj: 0.003435, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.570362, Class: 1.000000, Obj: 0.370126, No Obj: 0.002901, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.633679, Class: 1.000000, Obj: 0.364863, No Obj: 0.003855, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.637977, Class: 1.000000, Obj: 0.370181, No Obj: 0.003767, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.590869, Class: 1.000000, Obj: 0.346989, No Obj: 0.003941, Avg Recall: 0.725806, count: 124 -2366: 0.441549, 0.459532 avg, 0.001000 rate, 25.082472 seconds, 605696 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.550510, Class: 1.000000, Obj: 0.370145, No Obj: 0.002937, Avg Recall: 0.630435, count: 92 -Region Avg IOU: 0.568307, Class: 1.000000, Obj: 0.318432, No Obj: 0.003480, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.542478, Class: 1.000000, Obj: 0.312000, No Obj: 0.003470, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.568772, Class: 1.000000, Obj: 0.333119, No Obj: 0.003429, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.602307, Class: 1.000000, Obj: 0.356943, No Obj: 0.003541, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.586079, Class: 1.000000, Obj: 0.363407, No Obj: 0.003768, Avg Recall: 0.717742, count: 124 -Region Avg IOU: 0.539851, Class: 1.000000, Obj: 0.318410, No Obj: 0.003461, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.590815, Class: 1.000000, Obj: 0.373457, No Obj: 0.003585, Avg Recall: 0.700000, count: 100 -2367: 0.448402, 0.458419 avg, 0.001000 rate, 26.599987 seconds, 605952 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.572706, Class: 1.000000, Obj: 0.265505, No Obj: 0.003816, Avg Recall: 0.650602, count: 166 -Region Avg IOU: 0.570935, Class: 1.000000, Obj: 0.316030, No Obj: 0.003018, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.554223, Class: 1.000000, Obj: 0.269593, No Obj: 0.003244, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.569069, Class: 1.000000, Obj: 0.379485, No Obj: 0.002924, Avg Recall: 0.682353, count: 85 -Region Avg IOU: 0.535788, Class: 1.000000, Obj: 0.340269, No Obj: 0.003214, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.505318, Class: 1.000000, Obj: 0.309483, No Obj: 0.003082, Avg Recall: 0.543103, count: 116 -Region Avg IOU: 0.533147, Class: 1.000000, Obj: 0.314977, No Obj: 0.003342, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.589220, Class: 1.000000, Obj: 0.341247, No Obj: 0.003170, Avg Recall: 0.692308, count: 91 -2368: 0.442508, 0.456828 avg, 0.001000 rate, 26.638020 seconds, 606208 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.551397, Class: 1.000000, Obj: 0.335773, No Obj: 0.003451, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.578060, Class: 1.000000, Obj: 0.295393, No Obj: 0.003288, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.538192, Class: 1.000000, Obj: 0.339850, No Obj: 0.002728, Avg Recall: 0.576923, count: 78 -Region Avg IOU: 0.542545, Class: 1.000000, Obj: 0.331482, No Obj: 0.003527, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.545400, Class: 1.000000, Obj: 0.297455, No Obj: 0.003427, Avg Recall: 0.625000, count: 136 -Region Avg IOU: 0.561007, Class: 1.000000, Obj: 0.332437, No Obj: 0.003020, Avg Recall: 0.688073, count: 109 -Region Avg IOU: 0.547843, Class: 1.000000, Obj: 0.314659, No Obj: 0.003362, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.542381, Class: 1.000000, Obj: 0.329448, No Obj: 0.003206, Avg Recall: 0.634409, count: 93 -2369: 0.424527, 0.453598 avg, 0.001000 rate, 25.535231 seconds, 606464 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.596643, Class: 1.000000, Obj: 0.318581, No Obj: 0.003012, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.566094, Class: 1.000000, Obj: 0.346660, No Obj: 0.002714, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.529712, Class: 1.000000, Obj: 0.336617, No Obj: 0.002794, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.612480, Class: 1.000000, Obj: 0.310581, No Obj: 0.002913, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.559201, Class: 1.000000, Obj: 0.334931, No Obj: 0.003424, Avg Recall: 0.669421, count: 121 -Region Avg IOU: 0.594934, Class: 1.000000, Obj: 0.350132, No Obj: 0.003228, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.563963, Class: 1.000000, Obj: 0.343225, No Obj: 0.002548, Avg Recall: 0.701299, count: 77 -Region Avg IOU: 0.555833, Class: 1.000000, Obj: 0.339854, No Obj: 0.003555, Avg Recall: 0.605042, count: 119 -2370: 0.436184, 0.451856 avg, 0.001000 rate, 26.219831 seconds, 606720 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000086 seconds -Region Avg IOU: 0.555686, Class: 1.000000, Obj: 0.348059, No Obj: 0.002533, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.541768, Class: 1.000000, Obj: 0.291586, No Obj: 0.002579, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.578847, Class: 1.000000, Obj: 0.323796, No Obj: 0.002734, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.595482, Class: 1.000000, Obj: 0.333519, No Obj: 0.002954, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.644116, Class: 1.000000, Obj: 0.343130, No Obj: 0.002912, Avg Recall: 0.759036, count: 83 -Region Avg IOU: 0.580787, Class: 1.000000, Obj: 0.318883, No Obj: 0.003001, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.646740, Class: 1.000000, Obj: 0.342961, No Obj: 0.003249, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.592653, Class: 1.000000, Obj: 0.291856, No Obj: 0.002903, Avg Recall: 0.711538, count: 104 -2371: 0.449053, 0.451576 avg, 0.001000 rate, 27.103136 seconds, 606976 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.557783, Class: 1.000000, Obj: 0.308485, No Obj: 0.002667, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.636376, Class: 1.000000, Obj: 0.349136, No Obj: 0.003088, Avg Recall: 0.817391, count: 115 -Region Avg IOU: 0.595193, Class: 1.000000, Obj: 0.306741, No Obj: 0.002983, Avg Recall: 0.734375, count: 128 -Region Avg IOU: 0.568246, Class: 1.000000, Obj: 0.294296, No Obj: 0.002855, Avg Recall: 0.669118, count: 136 -Region Avg IOU: 0.568633, Class: 1.000000, Obj: 0.272337, No Obj: 0.002805, Avg Recall: 0.669492, count: 118 -Region Avg IOU: 0.634364, Class: 1.000000, Obj: 0.336074, No Obj: 0.003358, Avg Recall: 0.796875, count: 128 -Region Avg IOU: 0.584103, Class: 1.000000, Obj: 0.334325, No Obj: 0.002676, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.569522, Class: 1.000000, Obj: 0.298791, No Obj: 0.002910, Avg Recall: 0.651007, count: 149 -2372: 0.528724, 0.459291 avg, 0.001000 rate, 26.100927 seconds, 607232 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.606907, Class: 1.000000, Obj: 0.325014, No Obj: 0.002861, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.578471, Class: 1.000000, Obj: 0.309661, No Obj: 0.003009, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.533430, Class: 1.000000, Obj: 0.314081, No Obj: 0.002651, Avg Recall: 0.622222, count: 90 -Region Avg IOU: 0.610709, Class: 1.000000, Obj: 0.342850, No Obj: 0.002673, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.614840, Class: 1.000000, Obj: 0.303666, No Obj: 0.002861, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.568179, Class: 1.000000, Obj: 0.328905, No Obj: 0.003079, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.550822, Class: 1.000000, Obj: 0.326495, No Obj: 0.002549, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.568777, Class: 1.000000, Obj: 0.309618, No Obj: 0.003041, Avg Recall: 0.656250, count: 128 -2373: 0.475367, 0.460898 avg, 0.001000 rate, 26.345470 seconds, 607488 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.549247, Class: 1.000000, Obj: 0.340862, No Obj: 0.003046, Avg Recall: 0.645161, count: 124 -Region Avg IOU: 0.592497, Class: 1.000000, Obj: 0.365463, No Obj: 0.002957, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.567275, Class: 1.000000, Obj: 0.305955, No Obj: 0.002946, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.601577, Class: 1.000000, Obj: 0.376796, No Obj: 0.002751, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.593159, Class: 1.000000, Obj: 0.382967, No Obj: 0.002942, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.576017, Class: 1.000000, Obj: 0.313222, No Obj: 0.003317, Avg Recall: 0.674242, count: 132 -Region Avg IOU: 0.593111, Class: 1.000000, Obj: 0.344609, No Obj: 0.003375, Avg Recall: 0.717391, count: 138 -Region Avg IOU: 0.537304, Class: 1.000000, Obj: 0.335417, No Obj: 0.002879, Avg Recall: 0.594059, count: 101 -2374: 0.471335, 0.461942 avg, 0.001000 rate, 26.976583 seconds, 607744 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.576452, Class: 1.000000, Obj: 0.335311, No Obj: 0.002582, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.585690, Class: 1.000000, Obj: 0.327676, No Obj: 0.002683, Avg Recall: 0.688889, count: 90 -Region Avg IOU: 0.558504, Class: 1.000000, Obj: 0.295881, No Obj: 0.003033, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.618809, Class: 1.000000, Obj: 0.370598, No Obj: 0.003384, Avg Recall: 0.735043, count: 117 -Region Avg IOU: 0.582454, Class: 1.000000, Obj: 0.326431, No Obj: 0.003184, Avg Recall: 0.680000, count: 125 -Region Avg IOU: 0.608770, Class: 1.000000, Obj: 0.341586, No Obj: 0.003515, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.605270, Class: 1.000000, Obj: 0.385946, No Obj: 0.002957, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.610959, Class: 1.000000, Obj: 0.356531, No Obj: 0.003289, Avg Recall: 0.724771, count: 109 -2375: 0.468452, 0.462593 avg, 0.001000 rate, 26.966969 seconds, 608000 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.629006, Class: 1.000000, Obj: 0.355059, No Obj: 0.003234, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.596052, Class: 1.000000, Obj: 0.419878, No Obj: 0.002836, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.572014, Class: 1.000000, Obj: 0.371421, No Obj: 0.002710, Avg Recall: 0.662500, count: 80 -Region Avg IOU: 0.592265, Class: 1.000000, Obj: 0.387227, No Obj: 0.003397, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.565517, Class: 1.000000, Obj: 0.342690, No Obj: 0.002835, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.572458, Class: 1.000000, Obj: 0.338296, No Obj: 0.002945, Avg Recall: 0.688073, count: 109 -Region Avg IOU: 0.610353, Class: 1.000000, Obj: 0.335796, No Obj: 0.002834, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.624594, Class: 1.000000, Obj: 0.367826, No Obj: 0.003358, Avg Recall: 0.733333, count: 105 -2376: 0.446855, 0.461019 avg, 0.001000 rate, 27.121580 seconds, 608256 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.583775, Class: 1.000000, Obj: 0.332060, No Obj: 0.003244, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.596503, Class: 1.000000, Obj: 0.363602, No Obj: 0.003155, Avg Recall: 0.733333, count: 120 -Region Avg IOU: 0.581026, Class: 1.000000, Obj: 0.340421, No Obj: 0.003126, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.583438, Class: 1.000000, Obj: 0.313340, No Obj: 0.003553, Avg Recall: 0.725191, count: 131 -Region Avg IOU: 0.655956, Class: 1.000000, Obj: 0.343823, No Obj: 0.003059, Avg Recall: 0.791209, count: 91 -Region Avg IOU: 0.609444, Class: 1.000000, Obj: 0.379982, No Obj: 0.003181, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.589522, Class: 1.000000, Obj: 0.353112, No Obj: 0.003183, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.568116, Class: 1.000000, Obj: 0.320255, No Obj: 0.003314, Avg Recall: 0.681481, count: 135 -2377: 0.477694, 0.462687 avg, 0.001000 rate, 28.109940 seconds, 608512 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.573451, Class: 1.000000, Obj: 0.323475, No Obj: 0.003365, Avg Recall: 0.705426, count: 129 -Region Avg IOU: 0.583844, Class: 1.000000, Obj: 0.340594, No Obj: 0.003474, Avg Recall: 0.695035, count: 141 -Region Avg IOU: 0.606794, Class: 1.000000, Obj: 0.345076, No Obj: 0.003003, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.592388, Class: 1.000000, Obj: 0.403735, No Obj: 0.003019, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.604652, Class: 1.000000, Obj: 0.349317, No Obj: 0.003129, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.605721, Class: 1.000000, Obj: 0.316987, No Obj: 0.003003, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.577505, Class: 1.000000, Obj: 0.362740, No Obj: 0.003163, Avg Recall: 0.708661, count: 127 -Region Avg IOU: 0.551658, Class: 1.000000, Obj: 0.331291, No Obj: 0.002845, Avg Recall: 0.640000, count: 100 -2378: 0.461796, 0.462598 avg, 0.001000 rate, 25.619440 seconds, 608768 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.599908, Class: 1.000000, Obj: 0.342225, No Obj: 0.002873, Avg Recall: 0.695122, count: 82 -Region Avg IOU: 0.598854, Class: 1.000000, Obj: 0.383289, No Obj: 0.003095, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.574337, Class: 1.000000, Obj: 0.397717, No Obj: 0.002822, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.592311, Class: 1.000000, Obj: 0.335262, No Obj: 0.002862, Avg Recall: 0.703297, count: 91 -Region Avg IOU: 0.584541, Class: 1.000000, Obj: 0.306818, No Obj: 0.003095, Avg Recall: 0.705882, count: 136 -Region Avg IOU: 0.624488, Class: 1.000000, Obj: 0.352289, No Obj: 0.003027, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.618432, Class: 1.000000, Obj: 0.362617, No Obj: 0.003133, Avg Recall: 0.759494, count: 79 -Region Avg IOU: 0.625854, Class: 1.000000, Obj: 0.350267, No Obj: 0.002701, Avg Recall: 0.750000, count: 96 -2379: 0.395401, 0.455878 avg, 0.001000 rate, 26.427481 seconds, 609024 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.596267, Class: 1.000000, Obj: 0.342123, No Obj: 0.003670, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.569564, Class: 1.000000, Obj: 0.306069, No Obj: 0.002921, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.598874, Class: 1.000000, Obj: 0.340490, No Obj: 0.003130, Avg Recall: 0.692913, count: 127 -Region Avg IOU: 0.631253, Class: 1.000000, Obj: 0.400587, No Obj: 0.003114, Avg Recall: 0.789474, count: 114 -Region Avg IOU: 0.643570, Class: 1.000000, Obj: 0.338401, No Obj: 0.003146, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.615817, Class: 1.000000, Obj: 0.367504, No Obj: 0.002762, Avg Recall: 0.710843, count: 83 -Region Avg IOU: 0.581796, Class: 1.000000, Obj: 0.338457, No Obj: 0.003092, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.530375, Class: 1.000000, Obj: 0.352293, No Obj: 0.002662, Avg Recall: 0.581633, count: 98 -2380: 0.466715, 0.456962 avg, 0.001000 rate, 26.827141 seconds, 609280 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000066 seconds -Region Avg IOU: 0.621015, Class: 1.000000, Obj: 0.317149, No Obj: 0.002653, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.607728, Class: 1.000000, Obj: 0.338622, No Obj: 0.002644, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.597092, Class: 1.000000, Obj: 0.314795, No Obj: 0.002815, Avg Recall: 0.700000, count: 120 -Region Avg IOU: 0.640541, Class: 1.000000, Obj: 0.350596, No Obj: 0.002622, Avg Recall: 0.758621, count: 87 -Region Avg IOU: 0.642797, Class: 1.000000, Obj: 0.332446, No Obj: 0.002873, Avg Recall: 0.766667, count: 120 -Region Avg IOU: 0.610325, Class: 1.000000, Obj: 0.332487, No Obj: 0.002580, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.638310, Class: 1.000000, Obj: 0.382164, No Obj: 0.002846, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.628679, Class: 1.000000, Obj: 0.341662, No Obj: 0.002779, Avg Recall: 0.773585, count: 106 -2381: 0.492765, 0.460542 avg, 0.001000 rate, 29.702328 seconds, 609536 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.652807, Class: 1.000000, Obj: 0.358050, No Obj: 0.002850, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.601800, Class: 1.000000, Obj: 0.293552, No Obj: 0.002758, Avg Recall: 0.710938, count: 128 -Region Avg IOU: 0.638638, Class: 1.000000, Obj: 0.386105, No Obj: 0.002353, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.609696, Class: 1.000000, Obj: 0.298099, No Obj: 0.002540, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.619800, Class: 1.000000, Obj: 0.315294, No Obj: 0.002812, Avg Recall: 0.772727, count: 88 -Region Avg IOU: 0.616407, Class: 1.000000, Obj: 0.289693, No Obj: 0.002542, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.625302, Class: 1.000000, Obj: 0.406965, No Obj: 0.002482, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.645457, Class: 1.000000, Obj: 0.321296, No Obj: 0.002514, Avg Recall: 0.757895, count: 95 -2382: 0.451783, 0.459666 avg, 0.001000 rate, 29.451628 seconds, 609792 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.618875, Class: 1.000000, Obj: 0.283569, No Obj: 0.002512, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.617309, Class: 1.000000, Obj: 0.264421, No Obj: 0.002392, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.673030, Class: 1.000000, Obj: 0.388928, No Obj: 0.002285, Avg Recall: 0.840000, count: 100 -Region Avg IOU: 0.627649, Class: 1.000000, Obj: 0.309507, No Obj: 0.002927, Avg Recall: 0.742857, count: 140 -Region Avg IOU: 0.616551, Class: 1.000000, Obj: 0.347166, No Obj: 0.002393, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.647883, Class: 1.000000, Obj: 0.319723, No Obj: 0.002609, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.627113, Class: 1.000000, Obj: 0.319363, No Obj: 0.002601, Avg Recall: 0.771186, count: 118 -Region Avg IOU: 0.619336, Class: 1.000000, Obj: 0.343638, No Obj: 0.002440, Avg Recall: 0.745455, count: 110 -2383: 0.485717, 0.462271 avg, 0.001000 rate, 28.070108 seconds, 610048 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.618521, Class: 1.000000, Obj: 0.325944, No Obj: 0.002279, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.596162, Class: 1.000000, Obj: 0.318366, No Obj: 0.002447, Avg Recall: 0.698413, count: 126 -Region Avg IOU: 0.654464, Class: 1.000000, Obj: 0.331136, No Obj: 0.002447, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.635594, Class: 1.000000, Obj: 0.337427, No Obj: 0.002467, Avg Recall: 0.816092, count: 87 -Region Avg IOU: 0.616742, Class: 1.000000, Obj: 0.356361, No Obj: 0.002548, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.635907, Class: 1.000000, Obj: 0.306761, No Obj: 0.002735, Avg Recall: 0.773438, count: 128 -Region Avg IOU: 0.645448, Class: 1.000000, Obj: 0.341334, No Obj: 0.002497, Avg Recall: 0.833333, count: 108 -Region Avg IOU: 0.587527, Class: 1.000000, Obj: 0.327743, No Obj: 0.002526, Avg Recall: 0.716535, count: 127 -2384: 0.471092, 0.463153 avg, 0.001000 rate, 30.303265 seconds, 610304 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.619079, Class: 1.000000, Obj: 0.342814, No Obj: 0.002554, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.609983, Class: 1.000000, Obj: 0.275427, No Obj: 0.002581, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.630300, Class: 1.000000, Obj: 0.308553, No Obj: 0.002464, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.624297, Class: 1.000000, Obj: 0.292336, No Obj: 0.002315, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.626937, Class: 1.000000, Obj: 0.358185, No Obj: 0.002672, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.632233, Class: 1.000000, Obj: 0.317896, No Obj: 0.002611, Avg Recall: 0.795082, count: 122 -Region Avg IOU: 0.627139, Class: 1.000000, Obj: 0.306883, No Obj: 0.002505, Avg Recall: 0.737705, count: 122 -Region Avg IOU: 0.592351, Class: 1.000000, Obj: 0.300608, No Obj: 0.002533, Avg Recall: 0.717949, count: 117 -2385: 0.477168, 0.464555 avg, 0.001000 rate, 29.948240 seconds, 610560 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.558819, Class: 1.000000, Obj: 0.278785, No Obj: 0.002624, Avg Recall: 0.643478, count: 115 -Region Avg IOU: 0.616549, Class: 1.000000, Obj: 0.357402, No Obj: 0.002344, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.635963, Class: 1.000000, Obj: 0.296293, No Obj: 0.002571, Avg Recall: 0.814433, count: 97 -Region Avg IOU: 0.603325, Class: 1.000000, Obj: 0.373205, No Obj: 0.002510, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.633804, Class: 1.000000, Obj: 0.317307, No Obj: 0.002793, Avg Recall: 0.827586, count: 116 -Region Avg IOU: 0.594988, Class: 1.000000, Obj: 0.326252, No Obj: 0.002408, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.600230, Class: 1.000000, Obj: 0.273843, No Obj: 0.002522, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.615972, Class: 1.000000, Obj: 0.294483, No Obj: 0.002438, Avg Recall: 0.757576, count: 99 -2386: 0.467591, 0.464858 avg, 0.001000 rate, 28.710629 seconds, 610816 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.633009, Class: 1.000000, Obj: 0.351874, No Obj: 0.002244, Avg Recall: 0.827957, count: 93 -Region Avg IOU: 0.642757, Class: 1.000000, Obj: 0.288385, No Obj: 0.002703, Avg Recall: 0.843478, count: 115 -Region Avg IOU: 0.672491, Class: 1.000000, Obj: 0.370617, No Obj: 0.002717, Avg Recall: 0.877193, count: 114 -Region Avg IOU: 0.645682, Class: 1.000000, Obj: 0.306645, No Obj: 0.002759, Avg Recall: 0.824427, count: 131 -Region Avg IOU: 0.658566, Class: 1.000000, Obj: 0.370055, No Obj: 0.002898, Avg Recall: 0.826531, count: 98 -Region Avg IOU: 0.621437, Class: 1.000000, Obj: 0.322318, No Obj: 0.002577, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.620604, Class: 1.000000, Obj: 0.332625, No Obj: 0.002975, Avg Recall: 0.795276, count: 127 -Region Avg IOU: 0.613160, Class: 1.000000, Obj: 0.296436, No Obj: 0.002827, Avg Recall: 0.751773, count: 141 -2387: 0.513849, 0.469757 avg, 0.001000 rate, 30.163357 seconds, 611072 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.601572, Class: 1.000000, Obj: 0.297092, No Obj: 0.002700, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.627151, Class: 1.000000, Obj: 0.370592, No Obj: 0.002565, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.629831, Class: 1.000000, Obj: 0.399393, No Obj: 0.002685, Avg Recall: 0.800000, count: 95 -Region Avg IOU: 0.604467, Class: 1.000000, Obj: 0.340234, No Obj: 0.002582, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.602340, Class: 1.000000, Obj: 0.301757, No Obj: 0.002644, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.604384, Class: 1.000000, Obj: 0.368083, No Obj: 0.002627, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.650506, Class: 1.000000, Obj: 0.352430, No Obj: 0.002942, Avg Recall: 0.840708, count: 113 -Region Avg IOU: 0.641880, Class: 1.000000, Obj: 0.344769, No Obj: 0.002773, Avg Recall: 0.773585, count: 106 -2388: 0.482111, 0.470993 avg, 0.001000 rate, 28.415148 seconds, 611328 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.650625, Class: 1.000000, Obj: 0.360103, No Obj: 0.002724, Avg Recall: 0.851485, count: 101 -Region Avg IOU: 0.546479, Class: 1.000000, Obj: 0.290874, No Obj: 0.002622, Avg Recall: 0.650406, count: 123 -Region Avg IOU: 0.597778, Class: 1.000000, Obj: 0.352543, No Obj: 0.003023, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.606659, Class: 1.000000, Obj: 0.337176, No Obj: 0.002845, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.619173, Class: 1.000000, Obj: 0.326877, No Obj: 0.002934, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.597739, Class: 1.000000, Obj: 0.379618, No Obj: 0.002414, Avg Recall: 0.688889, count: 90 -Region Avg IOU: 0.645057, Class: 1.000000, Obj: 0.367507, No Obj: 0.003002, Avg Recall: 0.809917, count: 121 -Region Avg IOU: 0.631110, Class: 1.000000, Obj: 0.329199, No Obj: 0.003083, Avg Recall: 0.757353, count: 136 -2389: 0.511049, 0.474998 avg, 0.001000 rate, 28.172256 seconds, 611584 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.641648, Class: 1.000000, Obj: 0.364554, No Obj: 0.002879, Avg Recall: 0.814815, count: 108 -Region Avg IOU: 0.648438, Class: 1.000000, Obj: 0.366161, No Obj: 0.002932, Avg Recall: 0.868421, count: 114 -Region Avg IOU: 0.670864, Class: 1.000000, Obj: 0.348526, No Obj: 0.003063, Avg Recall: 0.886792, count: 106 -Region Avg IOU: 0.608892, Class: 1.000000, Obj: 0.341316, No Obj: 0.002700, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.634933, Class: 1.000000, Obj: 0.342347, No Obj: 0.002690, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.573940, Class: 1.000000, Obj: 0.312251, No Obj: 0.002923, Avg Recall: 0.712000, count: 125 -Region Avg IOU: 0.632161, Class: 1.000000, Obj: 0.397057, No Obj: 0.002646, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.638548, Class: 1.000000, Obj: 0.374131, No Obj: 0.003006, Avg Recall: 0.813559, count: 118 -2390: 0.447268, 0.472225 avg, 0.001000 rate, 30.176355 seconds, 611840 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 13.181984 seconds -Region Avg IOU: 0.499979, Class: 1.000000, Obj: 0.237816, No Obj: 0.003804, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.508625, Class: 1.000000, Obj: 0.249680, No Obj: 0.003639, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.494160, Class: 1.000000, Obj: 0.267335, No Obj: 0.004449, Avg Recall: 0.545455, count: 143 -Region Avg IOU: 0.515397, Class: 1.000000, Obj: 0.253681, No Obj: 0.003854, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.545500, Class: 1.000000, Obj: 0.311045, No Obj: 0.003617, Avg Recall: 0.630435, count: 92 -Region Avg IOU: 0.553850, Class: 1.000000, Obj: 0.318940, No Obj: 0.003867, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.557357, Class: 1.000000, Obj: 0.291596, No Obj: 0.003517, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.540195, Class: 1.000000, Obj: 0.255487, No Obj: 0.003411, Avg Recall: 0.644860, count: 107 -2391: 0.560611, 0.481064 avg, 0.001000 rate, 18.584892 seconds, 612096 images -Loaded: 1.440570 seconds -Region Avg IOU: 0.530594, Class: 1.000000, Obj: 0.260904, No Obj: 0.004134, Avg Recall: 0.615385, count: 130 -Region Avg IOU: 0.517718, Class: 1.000000, Obj: 0.292634, No Obj: 0.003903, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.541617, Class: 1.000000, Obj: 0.307024, No Obj: 0.004377, Avg Recall: 0.628788, count: 132 -Region Avg IOU: 0.499736, Class: 1.000000, Obj: 0.285832, No Obj: 0.003542, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.529980, Class: 1.000000, Obj: 0.273190, No Obj: 0.003808, Avg Recall: 0.641667, count: 120 -Region Avg IOU: 0.451067, Class: 1.000000, Obj: 0.252756, No Obj: 0.002892, Avg Recall: 0.505376, count: 93 -Region Avg IOU: 0.497993, Class: 1.000000, Obj: 0.293743, No Obj: 0.003564, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.506031, Class: 1.000000, Obj: 0.269921, No Obj: 0.004240, Avg Recall: 0.570370, count: 135 -2392: 0.567253, 0.489683 avg, 0.001000 rate, 20.542891 seconds, 612352 images -Loaded: 2.011696 seconds -Region Avg IOU: 0.466912, Class: 1.000000, Obj: 0.278153, No Obj: 0.003562, Avg Recall: 0.471698, count: 106 -Region Avg IOU: 0.529834, Class: 1.000000, Obj: 0.265707, No Obj: 0.003905, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.535526, Class: 1.000000, Obj: 0.340972, No Obj: 0.004312, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.520668, Class: 1.000000, Obj: 0.276084, No Obj: 0.003855, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.500441, Class: 1.000000, Obj: 0.310592, No Obj: 0.004119, Avg Recall: 0.564516, count: 124 -Region Avg IOU: 0.490220, Class: 1.000000, Obj: 0.257198, No Obj: 0.003699, Avg Recall: 0.536364, count: 110 -Region Avg IOU: 0.529351, Class: 1.000000, Obj: 0.294934, No Obj: 0.004559, Avg Recall: 0.604839, count: 124 -Region Avg IOU: 0.536076, Class: 1.000000, Obj: 0.329921, No Obj: 0.003956, Avg Recall: 0.594340, count: 106 -2393: 0.478751, 0.488590 avg, 0.001000 rate, 21.483360 seconds, 612608 images -Loaded: 0.515642 seconds -Region Avg IOU: 0.522643, Class: 1.000000, Obj: 0.342539, No Obj: 0.003833, Avg Recall: 0.576471, count: 85 -Region Avg IOU: 0.513443, Class: 1.000000, Obj: 0.340712, No Obj: 0.004100, Avg Recall: 0.601695, count: 118 -Region Avg IOU: 0.492212, Class: 1.000000, Obj: 0.325855, No Obj: 0.003579, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.499084, Class: 1.000000, Obj: 0.350537, No Obj: 0.003944, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.520658, Class: 1.000000, Obj: 0.351522, No Obj: 0.004555, Avg Recall: 0.542553, count: 94 -Region Avg IOU: 0.555601, Class: 1.000000, Obj: 0.307953, No Obj: 0.004351, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.484022, Class: 1.000000, Obj: 0.344977, No Obj: 0.003894, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.507176, Class: 1.000000, Obj: 0.323795, No Obj: 0.004309, Avg Recall: 0.568627, count: 102 -2394: 0.534904, 0.493221 avg, 0.001000 rate, 20.367365 seconds, 612864 images -Loaded: 0.722655 seconds -Region Avg IOU: 0.537781, Class: 1.000000, Obj: 0.339581, No Obj: 0.005046, Avg Recall: 0.637795, count: 127 -Region Avg IOU: 0.513908, Class: 1.000000, Obj: 0.319713, No Obj: 0.004148, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.461252, Class: 1.000000, Obj: 0.333069, No Obj: 0.004719, Avg Recall: 0.466667, count: 120 -Region Avg IOU: 0.478796, Class: 1.000000, Obj: 0.346430, No Obj: 0.004687, Avg Recall: 0.529412, count: 119 -Region Avg IOU: 0.485653, Class: 1.000000, Obj: 0.307591, No Obj: 0.004275, Avg Recall: 0.500000, count: 120 -Region Avg IOU: 0.489643, Class: 1.000000, Obj: 0.328063, No Obj: 0.003680, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.551837, Class: 1.000000, Obj: 0.308782, No Obj: 0.003726, Avg Recall: 0.688889, count: 90 -Region Avg IOU: 0.455979, Class: 1.000000, Obj: 0.276192, No Obj: 0.003814, Avg Recall: 0.494382, count: 89 -2395: 0.452243, 0.489123 avg, 0.001000 rate, 18.122316 seconds, 613120 images -Loaded: 3.569373 seconds -Region Avg IOU: 0.430614, Class: 1.000000, Obj: 0.356694, No Obj: 0.004106, Avg Recall: 0.487180, count: 117 -Region Avg IOU: 0.510530, Class: 1.000000, Obj: 0.321421, No Obj: 0.004243, Avg Recall: 0.546392, count: 97 -Region Avg IOU: 0.450581, Class: 1.000000, Obj: 0.268281, No Obj: 0.003704, Avg Recall: 0.537634, count: 93 -Region Avg IOU: 0.515052, Class: 1.000000, Obj: 0.399647, No Obj: 0.004350, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.473510, Class: 1.000000, Obj: 0.303635, No Obj: 0.004729, Avg Recall: 0.514286, count: 140 -Region Avg IOU: 0.423093, Class: 1.000000, Obj: 0.324345, No Obj: 0.003853, Avg Recall: 0.423077, count: 104 -Region Avg IOU: 0.471666, Class: 1.000000, Obj: 0.319472, No Obj: 0.003338, Avg Recall: 0.512500, count: 80 -Region Avg IOU: 0.431828, Class: 1.000000, Obj: 0.256889, No Obj: 0.004207, Avg Recall: 0.484848, count: 99 -2396: 0.500778, 0.490289 avg, 0.001000 rate, 19.328096 seconds, 613376 images -Loaded: 3.119901 seconds -Region Avg IOU: 0.478177, Class: 1.000000, Obj: 0.346658, No Obj: 0.003617, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.456179, Class: 1.000000, Obj: 0.329377, No Obj: 0.004363, Avg Recall: 0.525424, count: 118 -Region Avg IOU: 0.454619, Class: 1.000000, Obj: 0.337864, No Obj: 0.004073, Avg Recall: 0.490741, count: 108 -Region Avg IOU: 0.467149, Class: 1.000000, Obj: 0.360772, No Obj: 0.004025, Avg Recall: 0.532110, count: 109 -Region Avg IOU: 0.485400, Class: 1.000000, Obj: 0.288383, No Obj: 0.004021, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.510582, Class: 1.000000, Obj: 0.331210, No Obj: 0.005174, Avg Recall: 0.607407, count: 135 -Region Avg IOU: 0.518516, Class: 1.000000, Obj: 0.344117, No Obj: 0.004366, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.485031, Class: 1.000000, Obj: 0.322791, No Obj: 0.003964, Avg Recall: 0.519608, count: 102 -2397: 0.551335, 0.496393 avg, 0.001000 rate, 20.988989 seconds, 613632 images -Loaded: 0.018009 seconds -Region Avg IOU: 0.4Saving weights to ./person9300/backup/tyvp_93_9c512_2400.weights -75906, Class: 1.000000, Obj: 0.293453, No Obj: 0.004522, Avg Recall: 0.572581, count: 124 -Region Avg IOU: 0.465644, Class: 1.000000, Obj: 0.314566, No Obj: 0.003877, Avg Recall: 0.504854, count: 103 -Region Avg IOU: 0.518516, Class: 1.000000, Obj: 0.394202, No Obj: 0.003575, Avg Recall: 0.522388, count: 67 -Region Avg IOU: 0.514287, Class: 1.000000, Obj: 0.333866, No Obj: 0.004442, Avg Recall: 0.601770, count: 113 -Region Avg IOU: 0.467440, Class: 1.000000, Obj: 0.310339, No Obj: 0.004794, Avg Recall: 0.414634, count: 123 -Region Avg IOU: 0.477248, Class: 1.000000, Obj: 0.260406, No Obj: 0.004632, Avg Recall: 0.514493, count: 138 -Region Avg IOU: 0.475334, Class: 1.000000, Obj: 0.378842, No Obj: 0.003905, Avg Recall: 0.560440, count: 91 -Region Avg IOU: 0.495506, Class: 1.000000, Obj: 0.347473, No Obj: 0.003877, Avg Recall: 0.535714, count: 84 -2398: 0.467833, 0.493537 avg, 0.001000 rate, 19.402218 seconds, 613888 images -Loaded: 1.441351 seconds -Region Avg IOU: 0.499958, Class: 1.000000, Obj: 0.317524, No Obj: 0.003807, Avg Recall: 0.550562, count: 89 -Region Avg IOU: 0.476283, Class: 1.000000, Obj: 0.356083, No Obj: 0.003789, Avg Recall: 0.541176, count: 85 -Region Avg IOU: 0.492271, Class: 1.000000, Obj: 0.305461, No Obj: 0.004203, Avg Recall: 0.583333, count: 120 -Region Avg IOU: 0.494704, Class: 1.000000, Obj: 0.289152, No Obj: 0.003989, Avg Recall: 0.526882, count: 93 -Region Avg IOU: 0.461315, Class: 1.000000, Obj: 0.281247, No Obj: 0.003980, Avg Recall: 0.527559, count: 127 -Region Avg IOU: 0.494438, Class: 1.000000, Obj: 0.313413, No Obj: 0.003925, Avg Recall: 0.597701, count: 87 -Region Avg IOU: 0.513757, Class: 1.000000, Obj: 0.342625, No Obj: 0.003956, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.496769, Class: 1.000000, Obj: 0.333868, No Obj: 0.004195, Avg Recall: 0.546296, count: 108 -2399: 0.462979, 0.490482 avg, 0.001000 rate, 19.181101 seconds, 614144 images -Loaded: 3.446969 seconds -Region Avg IOU: 0.518695, Class: 1.000000, Obj: 0.302023, No Obj: 0.004052, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.502906, Class: 1.000000, Obj: 0.325071, No Obj: 0.003932, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.462448, Class: 1.000000, Obj: 0.275597, No Obj: 0.003706, Avg Recall: 0.447619, count: 105 -Region Avg IOU: 0.533678, Class: 1.000000, Obj: 0.302695, No Obj: 0.003916, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.481100, Class: 1.000000, Obj: 0.311126, No Obj: 0.003814, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.526776, Class: 1.000000, Obj: 0.324080, No Obj: 0.003255, Avg Recall: 0.647727, count: 88 -Region Avg IOU: 0.469039, Class: 1.000000, Obj: 0.312993, No Obj: 0.003670, Avg Recall: 0.490385, count: 104 -Region Avg IOU: 0.509701, Class: 1.000000, Obj: 0.321037, No Obj: 0.004046, Avg Recall: 0.557692, count: 104 -2400: 0.467395, 0.488173 avg, 0.001000 rate, 20.998665 seconds, 614400 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 17.714285 seconds -Region Avg IOU: 0.433443, Class: 1.000000, Obj: 0.236325, No Obj: 0.003476, Avg Recall: 0.448980, count: 98 -Region Avg IOU: 0.524027, Class: 1.000000, Obj: 0.297171, No Obj: 0.004074, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.492609, Class: 1.000000, Obj: 0.259823, No Obj: 0.004430, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.454807, Class: 1.000000, Obj: 0.257359, No Obj: 0.003991, Avg Recall: 0.479675, count: 123 -Region Avg IOU: 0.439262, Class: 1.000000, Obj: 0.275118, No Obj: 0.003473, Avg Recall: 0.475610, count: 82 -Region Avg IOU: 0.477387, Class: 1.000000, Obj: 0.260094, No Obj: 0.003535, Avg Recall: 0.470000, count: 100 -Region Avg IOU: 0.477615, Class: 1.000000, Obj: 0.226141, No Obj: 0.004916, Avg Recall: 0.509804, count: 102 -Region Avg IOU: 0.464803, Class: 1.000000, Obj: 0.288978, No Obj: 0.003934, Avg Recall: 0.457831, count: 83 -2401: 0.463666, 0.485722 avg, 0.001000 rate, 15.298305 seconds, 614656 images -Loaded: 6.891126 seconds -Region Avg IOU: 0.457187, Class: 1.000000, Obj: 0.230101, No Obj: 0.003853, Avg Recall: 0.476191, count: 105 -Region Avg IOU: 0.440321, Class: 1.000000, Obj: 0.237699, No Obj: 0.004161, Avg Recall: 0.433333, count: 120 -Region Avg IOU: 0.469949, Class: 1.000000, Obj: 0.251487, No Obj: 0.005083, Avg Recall: 0.517241, count: 145 -Region Avg IOU: 0.435461, Class: 1.000000, Obj: 0.254934, No Obj: 0.004165, Avg Recall: 0.454545, count: 99 -Region Avg IOU: 0.520033, Class: 1.000000, Obj: 0.281441, No Obj: 0.004233, Avg Recall: 0.528846, count: 104 -Region Avg IOU: 0.460317, Class: 1.000000, Obj: 0.266781, No Obj: 0.003983, Avg Recall: 0.514286, count: 105 -Region Avg IOU: 0.443227, Class: 1.000000, Obj: 0.275278, No Obj: 0.003974, Avg Recall: 0.488636, count: 88 -Region Avg IOU: 0.452256, Class: 1.000000, Obj: 0.250177, No Obj: 0.003916, Avg Recall: 0.510417, count: 96 -2402: 0.490402, 0.486190 avg, 0.001000 rate, 16.249641 seconds, 614912 images -Loaded: 4.560449 seconds -Region Avg IOU: 0.461589, Class: 1.000000, Obj: 0.266037, No Obj: 0.003647, Avg Recall: 0.505747, count: 87 -Region Avg IOU: 0.480938, Class: 1.000000, Obj: 0.293030, No Obj: 0.004243, Avg Recall: 0.509434, count: 106 -Region Avg IOU: 0.483450, Class: 1.000000, Obj: 0.269107, No Obj: 0.003847, Avg Recall: 0.520000, count: 100 -Region Avg IOU: 0.432044, Class: 1.000000, Obj: 0.265135, No Obj: 0.003839, Avg Recall: 0.439560, count: 91 -Region Avg IOU: 0.508205, Class: 1.000000, Obj: 0.274066, No Obj: 0.004246, Avg Recall: 0.573034, count: 89 -Region Avg IOU: 0.482192, Class: 1.000000, Obj: 0.235867, No Obj: 0.003959, Avg Recall: 0.500000, count: 74 -Region Avg IOU: 0.456352, Class: 1.000000, Obj: 0.228467, No Obj: 0.004565, Avg Recall: 0.503759, count: 133 -Region Avg IOU: 0.457201, Class: 1.000000, Obj: 0.221329, No Obj: 0.004377, Avg Recall: 0.495727, count: 117 -2403: 0.481991, 0.485770 avg, 0.001000 rate, 14.652708 seconds, 615168 images -Loaded: 3.675152 seconds -Region Avg IOU: 0.468085, Class: 1.000000, Obj: 0.262730, No Obj: 0.004216, Avg Recall: 0.486239, count: 109 -Region Avg IOU: 0.464624, Class: 1.000000, Obj: 0.306317, No Obj: 0.004865, Avg Recall: 0.475806, count: 124 -Region Avg IOU: 0.466028, Class: 1.000000, Obj: 0.309556, No Obj: 0.004521, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.454999, Class: 1.000000, Obj: 0.224013, No Obj: 0.004514, Avg Recall: 0.495868, count: 121 -Region Avg IOU: 0.388803, Class: 1.000000, Obj: 0.232375, No Obj: 0.003808, Avg Recall: 0.369565, count: 92 -Region Avg IOU: 0.476386, Class: 1.000000, Obj: 0.274603, No Obj: 0.004114, Avg Recall: 0.510000, count: 100 -Region Avg IOU: 0.425774, Class: 1.000000, Obj: 0.223493, No Obj: 0.004188, Avg Recall: 0.456311, count: 103 -Region Avg IOU: 0.424695, Class: 1.000000, Obj: 0.256426, No Obj: 0.004467, Avg Recall: 0.472000, count: 125 -2404: 0.510991, 0.488292 avg, 0.001000 rate, 15.531005 seconds, 615424 images -Loaded: 1.996514 seconds -Region Avg IOU: 0.424905, Class: 1.000000, Obj: 0.256502, No Obj: 0.004405, Avg Recall: 0.516484, count: 91 -Region Avg IOU: 0.459360, Class: 1.000000, Obj: 0.273185, No Obj: 0.004244, Avg Recall: 0.515790, count: 95 -Region Avg IOU: 0.468049, Class: 1.000000, Obj: 0.276083, No Obj: 0.005004, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.443059, Class: 1.000000, Obj: 0.295012, No Obj: 0.004765, Avg Recall: 0.474576, count: 118 -Region Avg IOU: 0.425840, Class: 1.000000, Obj: 0.264959, No Obj: 0.004546, Avg Recall: 0.415730, count: 89 -Region Avg IOU: 0.453613, Class: 1.000000, Obj: 0.275401, No Obj: 0.004933, Avg Recall: 0.433071, count: 127 -Region Avg IOU: 0.445536, Class: 1.000000, Obj: 0.256558, No Obj: 0.004648, Avg Recall: 0.455285, count: 123 -Region Avg IOU: 0.457347, Class: 1.000000, Obj: 0.252711, No Obj: 0.004853, Avg Recall: 0.504348, count: 115 -2405: 0.512385, 0.490702 avg, 0.001000 rate, 18.357040 seconds, 615680 images -Loaded: 3.912474 seconds -Region Avg IOU: 0.437479, Class: 1.000000, Obj: 0.283075, No Obj: 0.004939, Avg Recall: 0.436975, count: 119 -Region Avg IOU: 0.452021, Class: 1.000000, Obj: 0.287504, No Obj: 0.004685, Avg Recall: 0.455446, count: 101 -Region Avg IOU: 0.490986, Class: 1.000000, Obj: 0.293333, No Obj: 0.004829, Avg Recall: 0.560748, count: 107 -Region Avg IOU: 0.449345, Class: 1.000000, Obj: 0.253350, No Obj: 0.004664, Avg Recall: 0.459016, count: 122 -Region Avg IOU: 0.424994, Class: 1.000000, Obj: 0.296004, No Obj: 0.004395, Avg Recall: 0.471154, count: 104 -Region Avg IOU: 0.460476, Class: 1.000000, Obj: 0.275600, No Obj: 0.005380, Avg Recall: 0.520000, count: 125 -Region Avg IOU: 0.431689, Class: 1.000000, Obj: 0.290290, No Obj: 0.004695, Avg Recall: 0.469880, count: 83 -Region Avg IOU: 0.494893, Class: 1.000000, Obj: 0.315168, No Obj: 0.005788, Avg Recall: 0.567568, count: 111 -2406: 0.527917, 0.494423 avg, 0.001000 rate, 18.153547 seconds, 615936 images -Loaded: 2.437727 seconds -Region Avg IOU: 0.437531, Class: 1.000000, Obj: 0.299171, No Obj: 0.004701, Avg Recall: 0.449541, count: 109 -Region Avg IOU: 0.398456, Class: 1.000000, Obj: 0.281785, No Obj: 0.005133, Avg Recall: 0.433628, count: 113 -Region Avg IOU: 0.457413, Class: 1.000000, Obj: 0.301352, No Obj: 0.004714, Avg Recall: 0.476191, count: 105 -Region Avg IOU: 0.425169, Class: 1.000000, Obj: 0.274584, No Obj: 0.004836, Avg Recall: 0.434109, count: 129 -Region Avg IOU: 0.424147, Class: 1.000000, Obj: 0.269765, No Obj: 0.005306, Avg Recall: 0.482759, count: 116 -Region Avg IOU: 0.452960, Class: 1.000000, Obj: 0.318386, No Obj: 0.005039, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.391652, Class: 1.000000, Obj: 0.265979, No Obj: 0.004547, Avg Recall: 0.396552, count: 116 -Region Avg IOU: 0.373657, Class: 1.000000, Obj: 0.249990, No Obj: 0.004745, Avg Recall: 0.335938, count: 128 -2407: 0.511865, 0.496167 avg, 0.001000 rate, 18.049410 seconds, 616192 images -Loaded: 2.094787 seconds -Region Avg IOU: 0.468397, Class: 1.000000, Obj: 0.328393, No Obj: 0.006296, Avg Recall: 0.523077, count: 130 -Region Avg IOU: 0.459449, Class: 1.000000, Obj: 0.274059, No Obj: 0.004977, Avg Recall: 0.521277, count: 94 -Region Avg IOU: 0.458621, Class: 1.000000, Obj: 0.337124, No Obj: 0.005448, Avg Recall: 0.490385, count: 104 -Region Avg IOU: 0.396092, Class: 1.000000, Obj: 0.273818, No Obj: 0.005090, Avg Recall: 0.371901, count: 121 -Region Avg IOU: 0.442275, Class: 1.000000, Obj: 0.323523, No Obj: 0.004482, Avg Recall: 0.456790, count: 81 -Region Avg IOU: 0.463902, Class: 1.000000, Obj: 0.282923, No Obj: 0.005526, Avg Recall: 0.503876, count: 129 -Region Avg IOU: 0.374819, Class: 1.000000, Obj: 0.315842, No Obj: 0.004640, Avg Recall: 0.362832, count: 113 -Region Avg IOU: 0.430222, Class: 1.000000, Obj: 0.330239, No Obj: 0.005306, Avg Recall: 0.444444, count: 117 -2408: 0.587557, 0.505306 avg, 0.001000 rate, 18.981064 seconds, 616448 images -Loaded: 2.888164 seconds -Region Avg IOU: 0.433974, Class: 1.000000, Obj: 0.306513, No Obj: 0.005170, Avg Recall: 0.450450, count: 111 -Region Avg IOU: 0.466992, Class: 1.000000, Obj: 0.344947, No Obj: 0.005413, Avg Recall: 0.471545, count: 123 -Region Avg IOU: 0.451360, Class: 1.000000, Obj: 0.284324, No Obj: 0.005300, Avg Recall: 0.486957, count: 115 -Region Avg IOU: 0.464919, Class: 1.000000, Obj: 0.311212, No Obj: 0.006062, Avg Recall: 0.504425, count: 113 -Region Avg IOU: 0.390597, Class: 1.000000, Obj: 0.264261, No Obj: 0.005582, Avg Recall: 0.351145, count: 131 -Region Avg IOU: 0.466110, Class: 1.000000, Obj: 0.340601, No Obj: 0.004973, Avg Recall: 0.479592, count: 98 -Region Avg IOU: 0.416149, Class: 1.000000, Obj: 0.282816, No Obj: 0.005033, Avg Recall: 0.428571, count: 112 -Region Avg IOU: 0.452076, Class: 1.000000, Obj: 0.330051, No Obj: 0.005276, Avg Recall: 0.448598, count: 107 -2409: 0.543185, 0.509094 avg, 0.001000 rate, 18.503023 seconds, 616704 images -Loaded: 2.683711 seconds -Region Avg IOU: 0.427262, Class: 1.000000, Obj: 0.276380, No Obj: 0.005378, Avg Recall: 0.434783, count: 115 -Region Avg IOU: 0.432546, Class: 1.000000, Obj: 0.267685, No Obj: 0.004860, Avg Recall: 0.505882, count: 85 -Region Avg IOU: 0.456038, Class: 1.000000, Obj: 0.338372, No Obj: 0.006093, Avg Recall: 0.474820, count: 139 -Region Avg IOU: 0.462372, Class: 1.000000, Obj: 0.295225, No Obj: 0.004932, Avg Recall: 0.451219, count: 82 -Region Avg IOU: 0.493341, Class: 1.000000, Obj: 0.314225, No Obj: 0.005205, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.415918, Class: 1.000000, Obj: 0.322228, No Obj: 0.004761, Avg Recall: 0.494118, count: 85 -Region Avg IOU: 0.390896, Class: 1.000000, Obj: 0.275274, No Obj: 0.004940, Avg Recall: 0.382114, count: 123 -Region Avg IOU: 0.418829, Class: 1.000000, Obj: 0.319654, No Obj: 0.005733, Avg Recall: 0.422018, count: 109 -2410: 0.494322, 0.507617 avg, 0.001000 rate, 14.943887 seconds, 616960 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000068 seconds -Region Avg IOU: 0.581785, Class: 1.000000, Obj: 0.402310, No Obj: 0.003448, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.576127, Class: 1.000000, Obj: 0.451908, No Obj: 0.003777, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.575545, Class: 1.000000, Obj: 0.379659, No Obj: 0.003775, Avg Recall: 0.684615, count: 130 -Region Avg IOU: 0.559606, Class: 1.000000, Obj: 0.447310, No Obj: 0.003470, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.600350, Class: 1.000000, Obj: 0.392683, No Obj: 0.003931, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.535553, Class: 1.000000, Obj: 0.465242, No Obj: 0.003490, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.592772, Class: 1.000000, Obj: 0.428822, No Obj: 0.003722, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.585176, Class: 1.000000, Obj: 0.450618, No Obj: 0.004354, Avg Recall: 0.676923, count: 130 -2411: 0.560625, 0.512918 avg, 0.001000 rate, 27.243477 seconds, 617216 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.579645, Class: 1.000000, Obj: 0.325489, No Obj: 0.002953, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.572899, Class: 1.000000, Obj: 0.343920, No Obj: 0.003159, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.576472, Class: 1.000000, Obj: 0.388282, No Obj: 0.003012, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.576526, Class: 1.000000, Obj: 0.363624, No Obj: 0.002974, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.563512, Class: 1.000000, Obj: 0.379146, No Obj: 0.002917, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.594631, Class: 1.000000, Obj: 0.333533, No Obj: 0.003074, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.605996, Class: 1.000000, Obj: 0.371134, No Obj: 0.003112, Avg Recall: 0.761364, count: 88 -Region Avg IOU: 0.609044, Class: 1.000000, Obj: 0.455917, No Obj: 0.002920, Avg Recall: 0.804124, count: 97 -2412: 0.536111, 0.515237 avg, 0.001000 rate, 28.002407 seconds, 617472 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.578370, Class: 1.000000, Obj: 0.312341, No Obj: 0.002507, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.577566, Class: 1.000000, Obj: 0.313448, No Obj: 0.002926, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.557365, Class: 1.000000, Obj: 0.275003, No Obj: 0.002459, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.611736, Class: 1.000000, Obj: 0.273066, No Obj: 0.002550, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.607765, Class: 1.000000, Obj: 0.293640, No Obj: 0.002377, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.592833, Class: 1.000000, Obj: 0.279359, No Obj: 0.002731, Avg Recall: 0.731884, count: 138 -Region Avg IOU: 0.596883, Class: 1.000000, Obj: 0.276171, No Obj: 0.002399, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.582156, Class: 1.000000, Obj: 0.249174, No Obj: 0.002517, Avg Recall: 0.701754, count: 114 -2413: 0.588882, 0.522602 avg, 0.001000 rate, 28.789251 seconds, 617728 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.616240, Class: 1.000000, Obj: 0.201619, No Obj: 0.001997, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.613122, Class: 1.000000, Obj: 0.255942, No Obj: 0.002062, Avg Recall: 0.775000, count: 120 -Region Avg IOU: 0.608212, Class: 1.000000, Obj: 0.257984, No Obj: 0.002007, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.572232, Class: 1.000000, Obj: 0.258280, No Obj: 0.002078, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.602136, Class: 1.000000, Obj: 0.268048, No Obj: 0.002421, Avg Recall: 0.719697, count: 132 -Region Avg IOU: 0.617835, Class: 1.000000, Obj: 0.260984, No Obj: 0.002230, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.599936, Class: 1.000000, Obj: 0.232024, No Obj: 0.002097, Avg Recall: 0.774194, count: 124 -Region Avg IOU: 0.633132, Class: 1.000000, Obj: 0.299403, No Obj: 0.002401, Avg Recall: 0.800000, count: 115 -2414: 0.559972, 0.526339 avg, 0.001000 rate, 28.632252 seconds, 617984 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.667827, Class: 1.000000, Obj: 0.238191, No Obj: 0.001869, Avg Recall: 0.829268, count: 82 -Region Avg IOU: 0.569739, Class: 1.000000, Obj: 0.215131, No Obj: 0.002011, Avg Recall: 0.653226, count: 124 -Region Avg IOU: 0.631657, Class: 1.000000, Obj: 0.227156, No Obj: 0.001860, Avg Recall: 0.795455, count: 88 -Region Avg IOU: 0.628573, Class: 1.000000, Obj: 0.208590, No Obj: 0.001840, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.617801, Class: 1.000000, Obj: 0.244820, No Obj: 0.001919, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.562318, Class: 1.000000, Obj: 0.185351, No Obj: 0.001807, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.611027, Class: 1.000000, Obj: 0.233205, No Obj: 0.001951, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.556029, Class: 1.000000, Obj: 0.229459, No Obj: 0.002033, Avg Recall: 0.647541, count: 122 -2415: 0.477330, 0.521438 avg, 0.001000 rate, 28.818048 seconds, 618240 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.619420, Class: 1.000000, Obj: 0.240617, No Obj: 0.001883, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.610780, Class: 1.000000, Obj: 0.233270, No Obj: 0.002141, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.587403, Class: 1.000000, Obj: 0.236220, No Obj: 0.001775, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.619075, Class: 1.000000, Obj: 0.222046, No Obj: 0.001805, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.650999, Class: 1.000000, Obj: 0.223293, No Obj: 0.002086, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.595865, Class: 1.000000, Obj: 0.229877, No Obj: 0.002176, Avg Recall: 0.731544, count: 149 -Region Avg IOU: 0.655450, Class: 1.000000, Obj: 0.245892, No Obj: 0.002023, Avg Recall: 0.823529, count: 102 -Region Avg IOU: 0.602763, Class: 1.000000, Obj: 0.231541, No Obj: 0.001951, Avg Recall: 0.689655, count: 116 -2416: 0.525717, 0.521866 avg, 0.001000 rate, 28.431620 seconds, 618496 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.582914, Class: 1.000000, Obj: 0.229331, No Obj: 0.002209, Avg Recall: 0.765101, count: 149 -Region Avg IOU: 0.645154, Class: 1.000000, Obj: 0.301121, No Obj: 0.002133, Avg Recall: 0.819149, count: 94 -Region Avg IOU: 0.611772, Class: 1.000000, Obj: 0.232891, No Obj: 0.002246, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.580542, Class: 1.000000, Obj: 0.208833, No Obj: 0.002266, Avg Recall: 0.687075, count: 147 -Region Avg IOU: 0.629154, Class: 1.000000, Obj: 0.236481, No Obj: 0.002196, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.634025, Class: 1.000000, Obj: 0.263579, No Obj: 0.002140, Avg Recall: 0.808081, count: 99 -Region Avg IOU: 0.595047, Class: 1.000000, Obj: 0.233055, No Obj: 0.002080, Avg Recall: 0.747967, count: 123 -Region Avg IOU: 0.577450, Class: 1.000000, Obj: 0.240601, No Obj: 0.002201, Avg Recall: 0.666667, count: 105 -2417: 0.545613, 0.524240 avg, 0.001000 rate, 28.065100 seconds, 618752 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.664165, Class: 1.000000, Obj: 0.329577, No Obj: 0.002707, Avg Recall: 0.881188, count: 101 -Region Avg IOU: 0.613164, Class: 1.000000, Obj: 0.290598, No Obj: 0.002465, Avg Recall: 0.748031, count: 127 -Region Avg IOU: 0.586984, Class: 1.000000, Obj: 0.281790, No Obj: 0.002355, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.619614, Class: 1.000000, Obj: 0.368426, No Obj: 0.002560, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.608654, Class: 1.000000, Obj: 0.237535, No Obj: 0.002355, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.586253, Class: 1.000000, Obj: 0.268689, No Obj: 0.002548, Avg Recall: 0.690141, count: 142 -Region Avg IOU: 0.569630, Class: 1.000000, Obj: 0.281308, No Obj: 0.002259, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.589973, Class: 1.000000, Obj: 0.234491, No Obj: 0.002527, Avg Recall: 0.730000, count: 100 -2418: 0.556620, 0.527478 avg, 0.001000 rate, 28.006285 seconds, 619008 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.648365, Class: 1.000000, Obj: 0.398749, No Obj: 0.002528, Avg Recall: 0.808219, count: 73 -Region Avg IOU: 0.602386, Class: 1.000000, Obj: 0.367363, No Obj: 0.002602, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.613205, Class: 1.000000, Obj: 0.338479, No Obj: 0.003053, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.612897, Class: 1.000000, Obj: 0.354568, No Obj: 0.002804, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.585838, Class: 1.000000, Obj: 0.323418, No Obj: 0.002601, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.619393, Class: 1.000000, Obj: 0.347179, No Obj: 0.002644, Avg Recall: 0.794643, count: 112 -Region Avg IOU: 0.626150, Class: 1.000000, Obj: 0.318207, No Obj: 0.003214, Avg Recall: 0.776978, count: 139 -Region Avg IOU: 0.635269, Class: 1.000000, Obj: 0.304363, No Obj: 0.002592, Avg Recall: 0.819149, count: 94 -2419: 0.470371, 0.521768 avg, 0.001000 rate, 28.025517 seconds, 619264 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.583323, Class: 1.000000, Obj: 0.391422, No Obj: 0.003053, Avg Recall: 0.758621, count: 87 -Region Avg IOU: 0.616530, Class: 1.000000, Obj: 0.381164, No Obj: 0.003721, Avg Recall: 0.763514, count: 148 -Region Avg IOU: 0.639850, Class: 1.000000, Obj: 0.332684, No Obj: 0.003315, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.600698, Class: 1.000000, Obj: 0.335919, No Obj: 0.003030, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.645828, Class: 1.000000, Obj: 0.375739, No Obj: 0.002930, Avg Recall: 0.796117, count: 103 -Region Avg IOU: 0.622336, Class: 1.000000, Obj: 0.374115, No Obj: 0.003202, Avg Recall: 0.775000, count: 120 -Region Avg IOU: 0.631913, Class: 1.000000, Obj: 0.372497, No Obj: 0.003333, Avg Recall: 0.809917, count: 121 -Region Avg IOU: 0.615090, Class: 1.000000, Obj: 0.363202, No Obj: 0.003714, Avg Recall: 0.765152, count: 132 -2420: 0.519540, 0.521545 avg, 0.001000 rate, 27.640779 seconds, 619520 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 15.634366 seconds -Region Avg IOU: 0.575235, Class: 1.000000, Obj: 0.338097, No Obj: 0.004133, Avg Recall: 0.705882, count: 119 -Region Avg IOU: 0.587273, Class: 1.000000, Obj: 0.421290, No Obj: 0.003093, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.559404, Class: 1.000000, Obj: 0.396543, No Obj: 0.003685, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.540543, Class: 1.000000, Obj: 0.406842, No Obj: 0.003548, Avg Recall: 0.587500, count: 80 -Region Avg IOU: 0.552649, Class: 1.000000, Obj: 0.322297, No Obj: 0.003350, Avg Recall: 0.705882, count: 85 -Region Avg IOU: 0.559135, Class: 1.000000, Obj: 0.379922, No Obj: 0.003564, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.575634, Class: 1.000000, Obj: 0.392611, No Obj: 0.003873, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.564936, Class: 1.000000, Obj: 0.315380, No Obj: 0.003872, Avg Recall: 0.700855, count: 117 -2421: 0.451087, 0.514499 avg, 0.001000 rate, 25.147192 seconds, 619776 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.585435, Class: 1.000000, Obj: 0.398440, No Obj: 0.004490, Avg Recall: 0.709677, count: 124 -Region Avg IOU: 0.592418, Class: 1.000000, Obj: 0.372983, No Obj: 0.004080, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.570297, Class: 1.000000, Obj: 0.366249, No Obj: 0.003445, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.602231, Class: 1.000000, Obj: 0.421861, No Obj: 0.003579, Avg Recall: 0.728395, count: 81 -Region Avg IOU: 0.587322, Class: 1.000000, Obj: 0.403119, No Obj: 0.004116, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.540199, Class: 1.000000, Obj: 0.375767, No Obj: 0.003849, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.626992, Class: 1.000000, Obj: 0.418015, No Obj: 0.004134, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.539743, Class: 1.000000, Obj: 0.326806, No Obj: 0.003827, Avg Recall: 0.652632, count: 95 -2422: 0.455082, 0.508557 avg, 0.001000 rate, 25.611362 seconds, 620032 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.563082, Class: 1.000000, Obj: 0.411066, No Obj: 0.003873, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.579983, Class: 1.000000, Obj: 0.413454, No Obj: 0.003838, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.547553, Class: 1.000000, Obj: 0.343896, No Obj: 0.003882, Avg Recall: 0.640000, count: 125 -Region Avg IOU: 0.596326, Class: 1.000000, Obj: 0.407901, No Obj: 0.004251, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.551986, Class: 1.000000, Obj: 0.355710, No Obj: 0.004087, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.543627, Class: 1.000000, Obj: 0.355245, No Obj: 0.003544, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.543354, Class: 1.000000, Obj: 0.344581, No Obj: 0.003959, Avg Recall: 0.594828, count: 116 -Region Avg IOU: 0.596653, Class: 1.000000, Obj: 0.407721, No Obj: 0.004158, Avg Recall: 0.709402, count: 117 -2423: 0.503763, 0.508078 avg, 0.001000 rate, 26.423832 seconds, 620288 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.531743, Class: 1.000000, Obj: 0.327110, No Obj: 0.004653, Avg Recall: 0.596154, count: 156 -Region Avg IOU: 0.583650, Class: 1.000000, Obj: 0.379163, No Obj: 0.003550, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.527255, Class: 1.000000, Obj: 0.356485, No Obj: 0.003781, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.512697, Class: 1.000000, Obj: 0.342271, No Obj: 0.003972, Avg Recall: 0.581560, count: 141 -Region Avg IOU: 0.578676, Class: 1.000000, Obj: 0.373562, No Obj: 0.003500, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.605147, Class: 1.000000, Obj: 0.409137, No Obj: 0.003904, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.576813, Class: 1.000000, Obj: 0.360049, No Obj: 0.003784, Avg Recall: 0.652893, count: 121 -Region Avg IOU: 0.514760, Class: 1.000000, Obj: 0.309307, No Obj: 0.003473, Avg Recall: 0.587719, count: 114 -2424: 0.517643, 0.509035 avg, 0.001000 rate, 25.026760 seconds, 620544 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.509030, Class: 1.000000, Obj: 0.328360, No Obj: 0.003406, Avg Recall: 0.516129, count: 124 -Region Avg IOU: 0.522314, Class: 1.000000, Obj: 0.309180, No Obj: 0.003248, Avg Recall: 0.585938, count: 128 -Region Avg IOU: 0.569862, Class: 1.000000, Obj: 0.301828, No Obj: 0.003460, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.562318, Class: 1.000000, Obj: 0.338756, No Obj: 0.003608, Avg Recall: 0.632000, count: 125 -Region Avg IOU: 0.541434, Class: 1.000000, Obj: 0.352951, No Obj: 0.003272, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.584895, Class: 1.000000, Obj: 0.305133, No Obj: 0.003088, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.576887, Class: 1.000000, Obj: 0.307361, No Obj: 0.003886, Avg Recall: 0.710526, count: 114 -Region Avg IOU: 0.543167, Class: 1.000000, Obj: 0.328982, No Obj: 0.003401, Avg Recall: 0.617391, count: 115 -2425: 0.527255, 0.510857 avg, 0.001000 rate, 25.535072 seconds, 620800 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.539074, Class: 1.000000, Obj: 0.263097, No Obj: 0.003039, Avg Recall: 0.664000, count: 125 -Region Avg IOU: 0.595413, Class: 1.000000, Obj: 0.314359, No Obj: 0.003280, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.556319, Class: 1.000000, Obj: 0.308894, No Obj: 0.003098, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.543139, Class: 1.000000, Obj: 0.274589, No Obj: 0.003147, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.537083, Class: 1.000000, Obj: 0.327281, No Obj: 0.003166, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.581402, Class: 1.000000, Obj: 0.354774, No Obj: 0.002987, Avg Recall: 0.726191, count: 84 -Region Avg IOU: 0.527162, Class: 1.000000, Obj: 0.271081, No Obj: 0.003110, Avg Recall: 0.582090, count: 134 -Region Avg IOU: 0.612588, Class: 1.000000, Obj: 0.294796, No Obj: 0.003093, Avg Recall: 0.791209, count: 91 -2426: 0.512098, 0.510981 avg, 0.001000 rate, 25.054216 seconds, 621056 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.610296, Class: 1.000000, Obj: 0.279328, No Obj: 0.002980, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.580045, Class: 1.000000, Obj: 0.265341, No Obj: 0.002872, Avg Recall: 0.696629, count: 89 -Region Avg IOU: 0.559889, Class: 1.000000, Obj: 0.257499, No Obj: 0.003198, Avg Recall: 0.689922, count: 129 -Region Avg IOU: 0.601824, Class: 1.000000, Obj: 0.300745, No Obj: 0.002762, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.551646, Class: 1.000000, Obj: 0.275560, No Obj: 0.002965, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.502700, Class: 1.000000, Obj: 0.258559, No Obj: 0.002914, Avg Recall: 0.566372, count: 113 -Region Avg IOU: 0.594959, Class: 1.000000, Obj: 0.293596, No Obj: 0.003118, Avg Recall: 0.769231, count: 117 -Region Avg IOU: 0.533445, Class: 1.000000, Obj: 0.295531, No Obj: 0.003105, Avg Recall: 0.628571, count: 140 -2427: 0.533781, 0.513261 avg, 0.001000 rate, 25.264822 seconds, 621312 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.535825, Class: 1.000000, Obj: 0.294820, No Obj: 0.002585, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.615324, Class: 1.000000, Obj: 0.284999, No Obj: 0.002776, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.572620, Class: 1.000000, Obj: 0.290071, No Obj: 0.003183, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.580583, Class: 1.000000, Obj: 0.315784, No Obj: 0.002831, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.606801, Class: 1.000000, Obj: 0.330965, No Obj: 0.002796, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.577110, Class: 1.000000, Obj: 0.313619, No Obj: 0.002732, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.575151, Class: 1.000000, Obj: 0.243948, No Obj: 0.002935, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.519237, Class: 1.000000, Obj: 0.305277, No Obj: 0.002550, Avg Recall: 0.673469, count: 98 -2428: 0.448609, 0.506796 avg, 0.001000 rate, 25.262821 seconds, 621568 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.564204, Class: 1.000000, Obj: 0.262531, No Obj: 0.002722, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.544669, Class: 1.000000, Obj: 0.295825, No Obj: 0.002512, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.572282, Class: 1.000000, Obj: 0.307824, No Obj: 0.002938, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.566919, Class: 1.000000, Obj: 0.276874, No Obj: 0.002833, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.538735, Class: 1.000000, Obj: 0.276905, No Obj: 0.002678, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.547237, Class: 1.000000, Obj: 0.299757, No Obj: 0.003030, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.567677, Class: 1.000000, Obj: 0.305633, No Obj: 0.002781, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.538586, Class: 1.000000, Obj: 0.269421, No Obj: 0.003013, Avg Recall: 0.620968, count: 124 -2429: 0.503783, 0.506494 avg, 0.001000 rate, 24.497133 seconds, 621824 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.555575, Class: 1.000000, Obj: 0.243402, No Obj: 0.003072, Avg Recall: 0.653846, count: 130 -Region Avg IOU: 0.568358, Class: 1.000000, Obj: 0.302293, No Obj: 0.003140, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.572405, Class: 1.000000, Obj: 0.353905, No Obj: 0.002787, Avg Recall: 0.659341, count: 91 -Region Avg IOU: 0.576935, Class: 1.000000, Obj: 0.348349, No Obj: 0.002629, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.560448, Class: 1.000000, Obj: 0.284683, No Obj: 0.003140, Avg Recall: 0.664286, count: 140 -Region Avg IOU: 0.584869, Class: 1.000000, Obj: 0.347610, No Obj: 0.003057, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.574524, Class: 1.000000, Obj: 0.332287, No Obj: 0.003025, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.562474, Class: 1.000000, Obj: 0.268660, No Obj: 0.003035, Avg Recall: 0.685950, count: 121 -2430: 0.493329, 0.505178 avg, 0.001000 rate, 26.442131 seconds, 622080 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.111174 seconds -Region Avg IOU: 0.417923, Class: 1.000000, Obj: 0.234089, No Obj: 0.003966, Avg Recall: 0.448000, count: 125 -Region Avg IOU: 0.428744, Class: 1.000000, Obj: 0.219318, No Obj: 0.003661, Avg Recall: 0.449541, count: 109 -Region Avg IOU: 0.452400, Class: 1.000000, Obj: 0.248834, No Obj: 0.003736, Avg Recall: 0.460000, count: 100 -Region Avg IOU: 0.474784, Class: 1.000000, Obj: 0.243474, No Obj: 0.003832, Avg Recall: 0.506329, count: 79 -Region Avg IOU: 0.435578, Class: 1.000000, Obj: 0.197373, No Obj: 0.003974, Avg Recall: 0.464912, count: 114 -Region Avg IOU: 0.491795, Class: 1.000000, Obj: 0.266781, No Obj: 0.004138, Avg Recall: 0.574468, count: 94 -Region Avg IOU: 0.463683, Class: 1.000000, Obj: 0.231550, No Obj: 0.003796, Avg Recall: 0.494845, count: 97 -Region Avg IOU: 0.436865, Class: 1.000000, Obj: 0.194290, No Obj: 0.003678, Avg Recall: 0.401709, count: 117 -2431: 0.534276, 0.508088 avg, 0.001000 rate, 18.600384 seconds, 622336 images -Loaded: 1.631464 seconds -Region Avg IOU: 0.483889, Class: 1.000000, Obj: 0.278217, No Obj: 0.004687, Avg Recall: 0.550562, count: 89 -Region Avg IOU: 0.441063, Class: 1.000000, Obj: 0.232279, No Obj: 0.004329, Avg Recall: 0.496183, count: 131 -Region Avg IOU: 0.416509, Class: 1.000000, Obj: 0.241014, No Obj: 0.004169, Avg Recall: 0.438596, count: 114 -Region Avg IOU: 0.460322, Class: 1.000000, Obj: 0.255511, No Obj: 0.004257, Avg Recall: 0.474227, count: 97 -Region Avg IOU: 0.457848, Class: 1.000000, Obj: 0.230861, No Obj: 0.004540, Avg Recall: 0.492958, count: 142 -Region Avg IOU: 0.460568, Class: 1.000000, Obj: 0.286475, No Obj: 0.004831, Avg Recall: 0.508475, count: 118 -Region Avg IOU: 0.438075, Class: 1.000000, Obj: 0.260979, No Obj: 0.004773, Avg Recall: 0.481481, count: 108 -Region Avg IOU: 0.466230, Class: 1.000000, Obj: 0.248946, No Obj: 0.004051, Avg Recall: 0.536364, count: 110 -2432: 0.623677, 0.519646 avg, 0.001000 rate, 16.628548 seconds, 622592 images -Loaded: 3.449275 seconds -Region Avg IOU: 0.409444, Class: 1.000000, Obj: 0.206052, No Obj: 0.004510, Avg Recall: 0.392857, count: 112 -Region Avg IOU: 0.475375, Class: 1.000000, Obj: 0.292130, No Obj: 0.005009, Avg Recall: 0.515464, count: 97 -Region Avg IOU: 0.452316, Class: 1.000000, Obj: 0.263270, No Obj: 0.004189, Avg Recall: 0.523256, count: 86 -Region Avg IOU: 0.402887, Class: 1.000000, Obj: 0.247504, No Obj: 0.005375, Avg Recall: 0.425373, count: 134 -Region Avg IOU: 0.397972, Class: 1.000000, Obj: 0.257716, No Obj: 0.004347, Avg Recall: 0.425926, count: 108 -Region Avg IOU: 0.422411, Class: 1.000000, Obj: 0.261242, No Obj: 0.005012, Avg Recall: 0.453901, count: 141 -Region Avg IOU: 0.467966, Class: 1.000000, Obj: 0.284608, No Obj: 0.004703, Avg Recall: 0.530612, count: 98 -Region Avg IOU: 0.509668, Class: 1.000000, Obj: 0.310200, No Obj: 0.004405, Avg Recall: 0.567901, count: 81 -2433: 0.555129, 0.523195 avg, 0.001000 rate, 15.732266 seconds, 622848 images -Loaded: 6.228268 seconds -Region Avg IOU: 0.455946, Class: 1.000000, Obj: 0.312546, No Obj: 0.005270, Avg Recall: 0.504274, count: 117 -Region Avg IOU: 0.490329, Class: 1.000000, Obj: 0.332872, No Obj: 0.006204, Avg Recall: 0.503937, count: 127 -Region Avg IOU: 0.470264, Class: 1.000000, Obj: 0.345052, No Obj: 0.005468, Avg Recall: 0.556701, count: 97 -Region Avg IOU: 0.437750, Class: 1.000000, Obj: 0.299822, No Obj: 0.005598, Avg Recall: 0.460177, count: 113 -Region Avg IOU: 0.524270, Class: 1.000000, Obj: 0.314894, No Obj: 0.005199, Avg Recall: 0.575758, count: 99 -Region Avg IOU: 0.419574, Class: 1.000000, Obj: 0.267400, No Obj: 0.005655, Avg Recall: 0.427632, count: 152 -Region Avg IOU: 0.491701, Class: 1.000000, Obj: 0.337474, No Obj: 0.004702, Avg Recall: 0.588889, count: 90 -Region Avg IOU: 0.456663, Class: 1.000000, Obj: 0.300041, No Obj: 0.005216, Avg Recall: 0.469027, count: 113 -2434: 0.560503, 0.526926 avg, 0.001000 rate, 17.815128 seconds, 623104 images -Loaded: 3.320105 seconds -Region Avg IOU: 0.411344, Class: 1.000000, Obj: 0.311373, No Obj: 0.006144, Avg Recall: 0.415254, count: 118 -Region Avg IOU: 0.469005, Class: 1.000000, Obj: 0.303376, No Obj: 0.005486, Avg Recall: 0.462264, count: 106 -Region Avg IOU: 0.474305, Class: 1.000000, Obj: 0.325418, No Obj: 0.005447, Avg Recall: 0.528736, count: 87 -Region Avg IOU: 0.467799, Class: 1.000000, Obj: 0.289190, No Obj: 0.005930, Avg Recall: 0.504854, count: 103 -Region Avg IOU: 0.497965, Class: 1.000000, Obj: 0.315589, No Obj: 0.005497, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.446840, Class: 1.000000, Obj: 0.303654, No Obj: 0.005153, Avg Recall: 0.480000, count: 100 -Region Avg IOU: 0.399052, Class: 1.000000, Obj: 0.304862, No Obj: 0.005076, Avg Recall: 0.419048, count: 105 -Region Avg IOU: 0.404850, Class: 1.000000, Obj: 0.279452, No Obj: 0.005361, Avg Recall: 0.409524, count: 105 -2435: 0.449017, 0.519135 avg, 0.001000 rate, 14.432007 seconds, 623360 images -Loaded: 6.892217 seconds -Region Avg IOU: 0.417217, Class: 1.000000, Obj: 0.289591, No Obj: 0.005404, Avg Recall: 0.408000, count: 125 -Region Avg IOU: 0.467602, Class: 1.000000, Obj: 0.338029, No Obj: 0.005408, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.390840, Class: 1.000000, Obj: 0.305545, No Obj: 0.006001, Avg Recall: 0.352000, count: 125 -Region Avg IOU: 0.424805, Class: 1.000000, Obj: 0.350070, No Obj: 0.005772, Avg Recall: 0.422764, count: 123 -Region Avg IOU: 0.455020, Class: 1.000000, Obj: 0.320343, No Obj: 0.005685, Avg Recall: 0.454545, count: 110 -Region Avg IOU: 0.455155, Class: 1.000000, Obj: 0.322340, No Obj: 0.006299, Avg Recall: 0.525424, count: 118 -Region Avg IOU: 0.395172, Class: 1.000000, Obj: 0.346024, No Obj: 0.005234, Avg Recall: 0.412281, count: 114 -Region Avg IOU: 0.373497, Class: 1.000000, Obj: 0.299328, No Obj: 0.005083, Avg Recall: 0.346154, count: 104 -2436: 0.540929, 0.521314 avg, 0.001000 rate, 19.745312 seconds, 623616 images -Loaded: 1.275908 seconds -Region Avg IOU: 0.458307, Class: 1.000000, Obj: 0.312972, No Obj: 0.006444, Avg Recall: 0.511278, count: 133 -Region Avg IOU: 0.438995, Class: 1.000000, Obj: 0.304718, No Obj: 0.005417, Avg Recall: 0.485437, count: 103 -Region Avg IOU: 0.431704, Class: 1.000000, Obj: 0.301167, No Obj: 0.005652, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.466067, Class: 1.000000, Obj: 0.332856, No Obj: 0.006301, Avg Recall: 0.511811, count: 127 -Region Avg IOU: 0.433100, Class: 1.000000, Obj: 0.321526, No Obj: 0.005503, Avg Recall: 0.446281, count: 121 -Region Avg IOU: 0.481133, Class: 1.000000, Obj: 0.346888, No Obj: 0.005699, Avg Recall: 0.566667, count: 90 -Region Avg IOU: 0.442195, Class: 1.000000, Obj: 0.307908, No Obj: 0.005607, Avg Recall: 0.423077, count: 104 -Region Avg IOU: 0.446598, Class: 1.000000, Obj: 0.283710, No Obj: 0.005482, Avg Recall: 0.477273, count: 88 -2437: 0.549326, 0.524115 avg, 0.001000 rate, 15.240577 seconds, 623872 images -Loaded: 7.022442 seconds -Region Avg IOU: 0.448405, Class: 1.000000, Obj: 0.330274, No Obj: 0.005620, Avg Recall: 0.469565, count: 115 -Region Avg IOU: 0.446971, Class: 1.000000, Obj: 0.311468, No Obj: 0.005419, Avg Recall: 0.474227, count: 97 -Region Avg IOU: 0.465369, Class: 1.000000, Obj: 0.331332, No Obj: 0.005839, Avg Recall: 0.510204, count: 98 -Region Avg IOU: 0.482619, Class: 1.000000, Obj: 0.316156, No Obj: 0.005293, Avg Recall: 0.528090, count: 89 -Region Avg IOU: 0.440302, Class: 1.000000, Obj: 0.316333, No Obj: 0.005632, Avg Recall: 0.456140, count: 114 -Region Avg IOU: 0.399356, Class: 1.000000, Obj: 0.312584, No Obj: 0.005257, Avg Recall: 0.398058, count: 103 -Region Avg IOU: 0.392592, Class: 1.000000, Obj: 0.312937, No Obj: 0.005773, Avg Recall: 0.420561, count: 107 -Region Avg IOU: 0.439794, Class: 1.000000, Obj: 0.322890, No Obj: 0.006334, Avg Recall: 0.382114, count: 123 -2438: 0.510261, 0.522730 avg, 0.001000 rate, 14.565103 seconds, 624128 images -Loaded: 5.312997 seconds -Region Avg IOU: 0.411799, Class: 1.000000, Obj: 0.281101, No Obj: 0.005750, Avg Recall: 0.415254, count: 118 -Region Avg IOU: 0.454724, Class: 1.000000, Obj: 0.312529, No Obj: 0.005087, Avg Recall: 0.488889, count: 90 -Region Avg IOU: 0.404752, Class: 1.000000, Obj: 0.288924, No Obj: 0.004924, Avg Recall: 0.347826, count: 92 -Region Avg IOU: 0.367991, Class: 1.000000, Obj: 0.320782, No Obj: 0.004239, Avg Recall: 0.311688, count: 77 -Region Avg IOU: 0.432615, Class: 1.000000, Obj: 0.296297, No Obj: 0.005064, Avg Recall: 0.475410, count: 122 -Region Avg IOU: 0.416746, Class: 1.000000, Obj: 0.308665, No Obj: 0.004596, Avg Recall: 0.435294, count: 85 -Region Avg IOU: 0.454118, Class: 1.000000, Obj: 0.326556, No Obj: 0.005493, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.401465, Class: 1.000000, Obj: 0.295866, No Obj: 0.005538, Avg Recall: 0.417391, count: 115 -2439: 0.463375, 0.516794 avg, 0.001000 rate, 17.859221 seconds, 624384 images -Loaded: 3.633789 seconds -Region Avg IOU: 0.511209, Class: 1.000000, Obj: 0.319637, No Obj: 0.005499, Avg Recall: 0.583333, count: 96 -Region Avg IOU: 0.501784, Class: 1.000000, Obj: 0.336498, No Obj: 0.004972, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.422563, Class: 1.000000, Obj: 0.237472, No Obj: 0.004211, Avg Recall: 0.449438, count: 89 -Region Avg IOU: 0.443705, Class: 1.000000, Obj: 0.289518, No Obj: 0.005234, Avg Recall: 0.486486, count: 111 -Region Avg IOU: 0.421126, Class: 1.000000, Obj: 0.312615, No Obj: 0.004807, Avg Recall: 0.449541, count: 109 -Region Avg IOU: 0.445191, Class: 1.000000, Obj: 0.285082, No Obj: 0.005039, Avg Recall: 0.451923, count: 104 -Region Avg IOU: 0.403412, Class: 1.000000, Obj: 0.295468, No Obj: 0.004462, Avg Recall: 0.441441, count: 111 -Region Avg IOU: 0.454335, Class: 1.000000, Obj: 0.259985, No Obj: 0.004876, Avg Recall: 0.424528, count: 106 -2440: 0.484211, 0.513536 avg, 0.001000 rate, 14.532921 seconds, 624640 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000058 seconds -Region Avg IOU: 0.558869, Class: 1.000000, Obj: 0.368617, No Obj: 0.003439, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.562519, Class: 1.000000, Obj: 0.381817, No Obj: 0.003859, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.551094, Class: 1.000000, Obj: 0.367691, No Obj: 0.003801, Avg Recall: 0.669291, count: 127 -Region Avg IOU: 0.533703, Class: 1.000000, Obj: 0.381061, No Obj: 0.003696, Avg Recall: 0.620690, count: 116 -Region Avg IOU: 0.564173, Class: 1.000000, Obj: 0.411503, No Obj: 0.003146, Avg Recall: 0.700000, count: 80 -Region Avg IOU: 0.550521, Class: 1.000000, Obj: 0.370544, No Obj: 0.003472, Avg Recall: 0.669492, count: 118 -Region Avg IOU: 0.572051, Class: 1.000000, Obj: 0.403169, No Obj: 0.003605, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.530625, Class: 1.000000, Obj: 0.357839, No Obj: 0.003135, Avg Recall: 0.620000, count: 100 -2441: 0.556028, 0.517785 avg, 0.001000 rate, 26.057228 seconds, 624896 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.546458, Class: 1.000000, Obj: 0.325979, No Obj: 0.003291, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.552216, Class: 1.000000, Obj: 0.354714, No Obj: 0.003423, Avg Recall: 0.671642, count: 134 -Region Avg IOU: 0.580525, Class: 1.000000, Obj: 0.329004, No Obj: 0.003299, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.588169, Class: 1.000000, Obj: 0.333530, No Obj: 0.003845, Avg Recall: 0.753247, count: 154 -Region Avg IOU: 0.537745, Class: 1.000000, Obj: 0.319951, No Obj: 0.003226, Avg Recall: 0.629630, count: 135 -Region Avg IOU: 0.554366, Class: 1.000000, Obj: 0.331740, No Obj: 0.003153, Avg Recall: 0.656000, count: 125 -Region Avg IOU: 0.556800, Class: 1.000000, Obj: 0.334100, No Obj: 0.003250, Avg Recall: 0.647541, count: 122 -Region Avg IOU: 0.581727, Class: 1.000000, Obj: 0.339211, No Obj: 0.003094, Avg Recall: 0.707547, count: 106 -2442: 0.598877, 0.525894 avg, 0.001000 rate, 27.496403 seconds, 625152 images -Loaded: 0.000134 seconds -Region Avg IOU: 0.562977, Class: 1.000000, Obj: 0.290949, No Obj: 0.002896, Avg Recall: 0.688000, count: 125 -Region Avg IOU: 0.554150, Class: 1.000000, Obj: 0.263997, No Obj: 0.002632, Avg Recall: 0.661417, count: 127 -Region Avg IOU: 0.522878, Class: 1.000000, Obj: 0.286067, No Obj: 0.002549, Avg Recall: 0.558333, count: 120 -Region Avg IOU: 0.584520, Class: 1.000000, Obj: 0.314340, No Obj: 0.002575, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.604157, Class: 1.000000, Obj: 0.278011, No Obj: 0.002579, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.551888, Class: 1.000000, Obj: 0.282008, No Obj: 0.002487, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.580103, Class: 1.000000, Obj: 0.240040, No Obj: 0.002921, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.538888, Class: 1.000000, Obj: 0.290884, No Obj: 0.002730, Avg Recall: 0.626168, count: 107 -2443: 0.494614, 0.522766 avg, 0.001000 rate, 25.913940 seconds, 625408 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.601544, Class: 1.000000, Obj: 0.247111, No Obj: 0.002768, Avg Recall: 0.746377, count: 138 -Region Avg IOU: 0.608395, Class: 1.000000, Obj: 0.245896, No Obj: 0.002482, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.619571, Class: 1.000000, Obj: 0.290630, No Obj: 0.002120, Avg Recall: 0.746988, count: 83 -Region Avg IOU: 0.606597, Class: 1.000000, Obj: 0.303701, No Obj: 0.002333, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.629223, Class: 1.000000, Obj: 0.302075, No Obj: 0.002637, Avg Recall: 0.768657, count: 134 -Region Avg IOU: 0.601894, Class: 1.000000, Obj: 0.282689, No Obj: 0.002273, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.602194, Class: 1.000000, Obj: 0.268842, No Obj: 0.002320, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.559180, Class: 1.000000, Obj: 0.249724, No Obj: 0.002438, Avg Recall: 0.637931, count: 116 -2444: 0.505255, 0.521015 avg, 0.001000 rate, 26.983906 seconds, 625664 images -Loaded: 0.000058 seconds -Region Avg IOU: 0.645413, Class: 1.000000, Obj: 0.271347, No Obj: 0.002555, Avg Recall: 0.805085, count: 118 -Region Avg IOU: 0.613874, Class: 1.000000, Obj: 0.273262, No Obj: 0.002249, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.615114, Class: 1.000000, Obj: 0.278139, No Obj: 0.001983, Avg Recall: 0.795181, count: 83 -Region Avg IOU: 0.595841, Class: 1.000000, Obj: 0.238268, No Obj: 0.001834, Avg Recall: 0.743243, count: 74 -Region Avg IOU: 0.626976, Class: 1.000000, Obj: 0.272984, No Obj: 0.002329, Avg Recall: 0.725275, count: 91 -Region Avg IOU: 0.630252, Class: 1.000000, Obj: 0.292616, No Obj: 0.002262, Avg Recall: 0.776471, count: 85 -Region Avg IOU: 0.604397, Class: 1.000000, Obj: 0.281839, No Obj: 0.002349, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.630739, Class: 1.000000, Obj: 0.251323, No Obj: 0.002506, Avg Recall: 0.785124, count: 121 -2445: 0.470502, 0.515964 avg, 0.001000 rate, 28.515680 seconds, 625920 images -Loaded: 0.000058 seconds -Region Avg IOU: 0.660978, Class: 1.000000, Obj: 0.293902, No Obj: 0.002531, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.607844, Class: 1.000000, Obj: 0.262951, No Obj: 0.002535, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.588593, Class: 1.000000, Obj: 0.253140, No Obj: 0.002688, Avg Recall: 0.739130, count: 138 -Region Avg IOU: 0.621401, Class: 1.000000, Obj: 0.308528, No Obj: 0.002423, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.597365, Class: 1.000000, Obj: 0.254445, No Obj: 0.002552, Avg Recall: 0.776000, count: 125 -Region Avg IOU: 0.616921, Class: 1.000000, Obj: 0.267974, No Obj: 0.002655, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.608439, Class: 1.000000, Obj: 0.298666, No Obj: 0.002710, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.607018, Class: 1.000000, Obj: 0.260602, No Obj: 0.002831, Avg Recall: 0.724638, count: 138 -2446: 0.547030, 0.519071 avg, 0.001000 rate, 26.702108 seconds, 626176 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.626720, Class: 1.000000, Obj: 0.291842, No Obj: 0.002917, Avg Recall: 0.809091, count: 110 -Region Avg IOU: 0.650117, Class: 1.000000, Obj: 0.348195, No Obj: 0.002672, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.608622, Class: 1.000000, Obj: 0.257389, No Obj: 0.003039, Avg Recall: 0.782946, count: 129 -Region Avg IOU: 0.591361, Class: 1.000000, Obj: 0.269734, No Obj: 0.002698, Avg Recall: 0.736842, count: 133 -Region Avg IOU: 0.597426, Class: 1.000000, Obj: 0.287687, No Obj: 0.002743, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.617966, Class: 1.000000, Obj: 0.285722, No Obj: 0.002463, Avg Recall: 0.764706, count: 85 -Region Avg IOU: 0.606640, Class: 1.000000, Obj: 0.286130, No Obj: 0.002831, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.579939, Class: 1.000000, Obj: 0.252568, No Obj: 0.002804, Avg Recall: 0.688073, count: 109 -2447: 0.514842, 0.518648 avg, 0.001000 rate, 26.946695 seconds, 626432 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.603587, Class: 1.000000, Obj: 0.331763, No Obj: 0.002703, Avg Recall: 0.777778, count: 90 -Region Avg IOU: 0.610759, Class: 1.000000, Obj: 0.370367, No Obj: 0.002909, Avg Recall: 0.767442, count: 86 -Region Avg IOU: 0.556953, Class: 1.000000, Obj: 0.297708, No Obj: 0.003038, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.634067, Class: 1.000000, Obj: 0.359217, No Obj: 0.002896, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.580765, Class: 1.000000, Obj: 0.317943, No Obj: 0.003315, Avg Recall: 0.669231, count: 130 -Region Avg IOU: 0.579877, Class: 1.000000, Obj: 0.330867, No Obj: 0.003081, Avg Recall: 0.686567, count: 134 -Region Avg IOU: 0.581718, Class: 1.000000, Obj: 0.334797, No Obj: 0.003023, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.579215, Class: 1.000000, Obj: 0.337214, No Obj: 0.002812, Avg Recall: 0.687500, count: 80 -2448: 0.550993, 0.521882 avg, 0.001000 rate, 25.932961 seconds, 626688 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.582012, Class: 1.000000, Obj: 0.350312, No Obj: 0.003219, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.603118, Class: 1.000000, Obj: 0.281606, No Obj: 0.003391, Avg Recall: 0.721429, count: 140 -Region Avg IOU: 0.644339, Class: 1.000000, Obj: 0.406094, No Obj: 0.003655, Avg Recall: 0.824074, count: 108 -Region Avg IOU: 0.538809, Class: 1.000000, Obj: 0.304226, No Obj: 0.002787, Avg Recall: 0.597938, count: 97 -Region Avg IOU: 0.602783, Class: 1.000000, Obj: 0.333806, No Obj: 0.003572, Avg Recall: 0.746032, count: 126 -Region Avg IOU: 0.556595, Class: 1.000000, Obj: 0.370012, No Obj: 0.003458, Avg Recall: 0.678832, count: 137 -Region Avg IOU: 0.582835, Class: 1.000000, Obj: 0.321308, No Obj: 0.003156, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.602764, Class: 1.000000, Obj: 0.366573, No Obj: 0.003753, Avg Recall: 0.790909, count: 110 -2449: 0.515544, 0.521248 avg, 0.001000 rate, 26.447243 seconds, 626944 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.595765, Class: 1.000000, Obj: 0.383003, No Obj: 0.003992, Avg Recall: 0.748148, count: 135 -Region Avg IOU: 0.578051, Class: 1.000000, Obj: 0.373014, No Obj: 0.003609, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.626467, Class: 1.000000, Obj: 0.334478, No Obj: 0.003100, Avg Recall: 0.791209, count: 91 -Region Avg IOU: 0.584860, Class: 1.000000, Obj: 0.432037, No Obj: 0.003216, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.611642, Class: 1.000000, Obj: 0.358667, No Obj: 0.003920, Avg Recall: 0.754098, count: 122 -Region Avg IOU: 0.604739, Class: 1.000000, Obj: 0.396464, No Obj: 0.003348, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.572944, Class: 1.000000, Obj: 0.384260, No Obj: 0.003608, Avg Recall: 0.643478, count: 115 -Region Avg IOU: 0.603688, Class: 1.000000, Obj: 0.384064, No Obj: 0.003306, Avg Recall: 0.750000, count: 104 -2450: 0.521626, 0.521286 avg, 0.001000 rate, 25.850275 seconds, 627200 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000064 seconds -Region Avg IOU: 0.592002, Class: 1.000000, Obj: 0.382408, No Obj: 0.003854, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.588112, Class: 1.000000, Obj: 0.407084, No Obj: 0.003509, Avg Recall: 0.752809, count: 89 -Region Avg IOU: 0.621860, Class: 1.000000, Obj: 0.387496, No Obj: 0.003401, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.538237, Class: 1.000000, Obj: 0.340150, No Obj: 0.003457, Avg Recall: 0.616541, count: 133 -Region Avg IOU: 0.607366, Class: 1.000000, Obj: 0.366617, No Obj: 0.003795, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.582575, Class: 1.000000, Obj: 0.458291, No Obj: 0.003722, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.620221, Class: 1.000000, Obj: 0.402169, No Obj: 0.003422, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.590466, Class: 1.000000, Obj: 0.362644, No Obj: 0.003356, Avg Recall: 0.676471, count: 102 -2451: 0.483378, 0.517495 avg, 0.001000 rate, 26.330875 seconds, 627456 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.636681, Class: 1.000000, Obj: 0.402575, No Obj: 0.002860, Avg Recall: 0.813953, count: 86 -Region Avg IOU: 0.654170, Class: 1.000000, Obj: 0.436490, No Obj: 0.003648, Avg Recall: 0.852941, count: 102 -Region Avg IOU: 0.601345, Class: 1.000000, Obj: 0.368320, No Obj: 0.003457, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.594916, Class: 1.000000, Obj: 0.369518, No Obj: 0.003688, Avg Recall: 0.708333, count: 120 -Region Avg IOU: 0.646514, Class: 1.000000, Obj: 0.362968, No Obj: 0.003677, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.597656, Class: 1.000000, Obj: 0.381081, No Obj: 0.003261, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.605546, Class: 1.000000, Obj: 0.420018, No Obj: 0.002852, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.580774, Class: 1.000000, Obj: 0.380316, No Obj: 0.003110, Avg Recall: 0.690000, count: 100 -2452: 0.431532, 0.508899 avg, 0.001000 rate, 27.705156 seconds, 627712 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.573202, Class: 1.000000, Obj: 0.307597, No Obj: 0.003106, Avg Recall: 0.691667, count: 120 -Region Avg IOU: 0.633131, Class: 1.000000, Obj: 0.367063, No Obj: 0.003196, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.598117, Class: 1.000000, Obj: 0.312122, No Obj: 0.003049, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.606778, Class: 1.000000, Obj: 0.405604, No Obj: 0.003277, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.594449, Class: 1.000000, Obj: 0.407072, No Obj: 0.003086, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.627611, Class: 1.000000, Obj: 0.334475, No Obj: 0.002939, Avg Recall: 0.756410, count: 78 -Region Avg IOU: 0.586746, Class: 1.000000, Obj: 0.318103, No Obj: 0.003201, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.603249, Class: 1.000000, Obj: 0.300950, No Obj: 0.003754, Avg Recall: 0.701493, count: 134 -2453: 0.504188, 0.508428 avg, 0.001000 rate, 27.040066 seconds, 627968 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.580098, Class: 1.000000, Obj: 0.354646, No Obj: 0.002991, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.549595, Class: 1.000000, Obj: 0.322294, No Obj: 0.002929, Avg Recall: 0.603175, count: 126 -Region Avg IOU: 0.567136, Class: 1.000000, Obj: 0.350731, No Obj: 0.002559, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.583498, Class: 1.000000, Obj: 0.333483, No Obj: 0.002592, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.621224, Class: 1.000000, Obj: 0.371894, No Obj: 0.002965, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.589336, Class: 1.000000, Obj: 0.356033, No Obj: 0.002572, Avg Recall: 0.698795, count: 83 -Region Avg IOU: 0.625241, Class: 1.000000, Obj: 0.400931, No Obj: 0.002884, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.581006, Class: 1.000000, Obj: 0.359957, No Obj: 0.002666, Avg Recall: 0.738636, count: 88 -2454: 0.462852, 0.503870 avg, 0.001000 rate, 27.704311 seconds, 628224 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.659702, Class: 1.000000, Obj: 0.384392, No Obj: 0.002459, Avg Recall: 0.822785, count: 79 -Region Avg IOU: 0.566632, Class: 1.000000, Obj: 0.282139, No Obj: 0.002680, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.589803, Class: 1.000000, Obj: 0.267539, No Obj: 0.002593, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.602409, Class: 1.000000, Obj: 0.338854, No Obj: 0.002490, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.579860, Class: 1.000000, Obj: 0.272023, No Obj: 0.002647, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.584270, Class: 1.000000, Obj: 0.337736, No Obj: 0.002552, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.580008, Class: 1.000000, Obj: 0.273255, No Obj: 0.002379, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.612027, Class: 1.000000, Obj: 0.317837, No Obj: 0.002678, Avg Recall: 0.767857, count: 112 -2455: 0.480133, 0.501497 avg, 0.001000 rate, 28.148540 seconds, 628480 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.543305, Class: 1.000000, Obj: 0.265607, No Obj: 0.002217, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.624547, Class: 1.000000, Obj: 0.253499, No Obj: 0.002721, Avg Recall: 0.784173, count: 139 -Region Avg IOU: 0.598716, Class: 1.000000, Obj: 0.240141, No Obj: 0.002583, Avg Recall: 0.701493, count: 134 -Region Avg IOU: 0.605827, Class: 1.000000, Obj: 0.256594, No Obj: 0.002185, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.601520, Class: 1.000000, Obj: 0.288750, No Obj: 0.002422, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.547826, Class: 1.000000, Obj: 0.251251, No Obj: 0.002550, Avg Recall: 0.617188, count: 128 -Region Avg IOU: 0.582370, Class: 1.000000, Obj: 0.304971, No Obj: 0.002609, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.615167, Class: 1.000000, Obj: 0.277436, No Obj: 0.002392, Avg Recall: 0.769231, count: 104 -2456: 0.544015, 0.505749 avg, 0.001000 rate, 29.008202 seconds, 628736 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.557642, Class: 1.000000, Obj: 0.258242, No Obj: 0.002309, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.565384, Class: 1.000000, Obj: 0.262465, No Obj: 0.002259, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.596040, Class: 1.000000, Obj: 0.267138, No Obj: 0.002439, Avg Recall: 0.713115, count: 122 -Region Avg IOU: 0.597468, Class: 1.000000, Obj: 0.286371, No Obj: 0.002406, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.606670, Class: 1.000000, Obj: 0.284165, No Obj: 0.002456, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.569822, Class: 1.000000, Obj: 0.289193, No Obj: 0.002393, Avg Recall: 0.650406, count: 123 -Region Avg IOU: 0.642664, Class: 1.000000, Obj: 0.274771, No Obj: 0.002472, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.595833, Class: 1.000000, Obj: 0.261391, No Obj: 0.002439, Avg Recall: 0.750000, count: 100 -2457: 0.469179, 0.502092 avg, 0.001000 rate, 27.678583 seconds, 628992 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.641505, Class: 1.000000, Obj: 0.274398, No Obj: 0.002901, Avg Recall: 0.797203, count: 143 -Region Avg IOU: 0.594790, Class: 1.000000, Obj: 0.272186, No Obj: 0.002203, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.609148, Class: 1.000000, Obj: 0.314815, No Obj: 0.002474, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.625308, Class: 1.000000, Obj: 0.286094, No Obj: 0.002046, Avg Recall: 0.701299, count: 77 -Region Avg IOU: 0.595553, Class: 1.000000, Obj: 0.289118, No Obj: 0.002161, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.626440, Class: 1.000000, Obj: 0.310652, No Obj: 0.002532, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.589710, Class: 1.000000, Obj: 0.265073, No Obj: 0.002590, Avg Recall: 0.702899, count: 138 -Region Avg IOU: 0.569875, Class: 1.000000, Obj: 0.246663, No Obj: 0.002604, Avg Recall: 0.685714, count: 140 -2458: 0.468816, 0.498764 avg, 0.001000 rate, 27.257723 seconds, 629248 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.577248, Class: 1.000000, Obj: 0.271970, No Obj: 0.002737, Avg Recall: 0.639706, count: 136 -Region Avg IOU: 0.562651, Class: 1.000000, Obj: 0.241721, No Obj: 0.002846, Avg Recall: 0.671756, count: 131 -Region Avg IOU: 0.613481, Class: 1.000000, Obj: 0.270376, No Obj: 0.002527, Avg Recall: 0.769231, count: 117 -Region Avg IOU: 0.587203, Class: 1.000000, Obj: 0.264135, No Obj: 0.002491, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.608265, Class: 1.000000, Obj: 0.319969, No Obj: 0.002426, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.615781, Class: 1.000000, Obj: 0.346912, No Obj: 0.002593, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.591349, Class: 1.000000, Obj: 0.275369, No Obj: 0.002397, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.623219, Class: 1.000000, Obj: 0.337994, No Obj: 0.002138, Avg Recall: 0.760563, count: 71 -2459: 0.485804, 0.497468 avg, 0.001000 rate, 28.102072 seconds, 629504 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.602876, Class: 1.000000, Obj: 0.312518, No Obj: 0.003105, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.589906, Class: 1.000000, Obj: 0.334752, No Obj: 0.003062, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.635094, Class: 1.000000, Obj: 0.330502, No Obj: 0.003073, Avg Recall: 0.811475, count: 122 -Region Avg IOU: 0.581708, Class: 1.000000, Obj: 0.328191, No Obj: 0.002600, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.603358, Class: 1.000000, Obj: 0.273167, No Obj: 0.002820, Avg Recall: 0.717557, count: 131 -Region Avg IOU: 0.537926, Class: 1.000000, Obj: 0.261996, No Obj: 0.002813, Avg Recall: 0.597015, count: 134 -Region Avg IOU: 0.598522, Class: 1.000000, Obj: 0.304223, No Obj: 0.003141, Avg Recall: 0.720280, count: 143 -Region Avg IOU: 0.572955, Class: 1.000000, Obj: 0.367388, No Obj: 0.002364, Avg Recall: 0.666667, count: 81 -2460: 0.569902, 0.504711 avg, 0.001000 rate, 27.341911 seconds, 629760 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 13.481993 seconds -Region Avg IOU: 0.467742, Class: 1.000000, Obj: 0.278246, No Obj: 0.003486, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.504122, Class: 1.000000, Obj: 0.268901, No Obj: 0.002935, Avg Recall: 0.600000, count: 80 -Region Avg IOU: 0.503273, Class: 1.000000, Obj: 0.258607, No Obj: 0.003348, Avg Recall: 0.559140, count: 93 -Region Avg IOU: 0.482796, Class: 1.000000, Obj: 0.248479, No Obj: 0.003241, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.448987, Class: 1.000000, Obj: 0.258308, No Obj: 0.003545, Avg Recall: 0.500000, count: 116 -Region Avg IOU: 0.534817, Class: 1.000000, Obj: 0.286193, No Obj: 0.004017, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.492687, Class: 1.000000, Obj: 0.242213, No Obj: 0.003281, Avg Recall: 0.542553, count: 94 -Region Avg IOU: 0.502221, Class: 1.000000, Obj: 0.255644, No Obj: 0.003717, Avg Recall: 0.541667, count: 120 -2461: 0.505360, 0.504776 avg, 0.001000 rate, 21.757160 seconds, 630016 images -Loaded: 0.255415 seconds -Region Avg IOU: 0.469763, Class: 1.000000, Obj: 0.301320, No Obj: 0.004126, Avg Recall: 0.524194, count: 124 -Region Avg IOU: 0.507133, Class: 1.000000, Obj: 0.304447, No Obj: 0.003406, Avg Recall: 0.634146, count: 82 -Region Avg IOU: 0.537640, Class: 1.000000, Obj: 0.306310, No Obj: 0.003900, Avg Recall: 0.643678, count: 87 -Region Avg IOU: 0.522056, Class: 1.000000, Obj: 0.276087, No Obj: 0.003702, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.505745, Class: 1.000000, Obj: 0.294468, No Obj: 0.003731, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.527207, Class: 1.000000, Obj: 0.261262, No Obj: 0.004694, Avg Recall: 0.600000, count: 120 -Region Avg IOU: 0.502109, Class: 1.000000, Obj: 0.261616, No Obj: 0.004404, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.495538, Class: 1.000000, Obj: 0.295318, No Obj: 0.004006, Avg Recall: 0.547368, count: 95 -2462: 0.494310, 0.503730 avg, 0.001000 rate, 20.368986 seconds, 630272 images -Loaded: 0.005462 seconds -Region Avg IOU: 0.525216, Class: 1.000000, Obj: 0.305254, No Obj: 0.004195, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.468829, Class: 1.000000, Obj: 0.285172, No Obj: 0.004047, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.498884, Class: 1.000000, Obj: 0.340920, No Obj: 0.004118, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.495830, Class: 1.000000, Obj: 0.345448, No Obj: 0.004076, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.496676, Class: 1.000000, Obj: 0.329242, No Obj: 0.004077, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.444449, Class: 1.000000, Obj: 0.277221, No Obj: 0.004196, Avg Recall: 0.448276, count: 116 -Region Avg IOU: 0.529271, Class: 1.000000, Obj: 0.308928, No Obj: 0.004983, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.571899, Class: 1.000000, Obj: 0.291583, No Obj: 0.004002, Avg Recall: 0.710145, count: 69 -2463: 0.521519, 0.505509 avg, 0.001000 rate, 18.796732 seconds, 630528 images -Loaded: 3.204953 seconds -Region Avg IOU: 0.439195, Class: 1.000000, Obj: 0.288501, No Obj: 0.003978, Avg Recall: 0.494845, count: 97 -Region Avg IOU: 0.493086, Class: 1.000000, Obj: 0.304357, No Obj: 0.005026, Avg Recall: 0.535433, count: 127 -Region Avg IOU: 0.516516, Class: 1.000000, Obj: 0.326307, No Obj: 0.005126, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.512416, Class: 1.000000, Obj: 0.326487, No Obj: 0.004480, Avg Recall: 0.595745, count: 94 -Region Avg IOU: 0.471639, Class: 1.000000, Obj: 0.337573, No Obj: 0.004657, Avg Recall: 0.500000, count: 92 -Region Avg IOU: 0.535440, Class: 1.000000, Obj: 0.352259, No Obj: 0.004458, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.508957, Class: 1.000000, Obj: 0.353174, No Obj: 0.005106, Avg Recall: 0.591549, count: 142 -Region Avg IOU: 0.468542, Class: 1.000000, Obj: 0.304248, No Obj: 0.004832, Avg Recall: 0.476191, count: 126 -2464: 0.557810, 0.510739 avg, 0.001000 rate, 18.700636 seconds, 630784 images -Loaded: 3.536251 seconds -Region Avg IOU: 0.472478, Class: 1.000000, Obj: 0.340793, No Obj: 0.004912, Avg Recall: 0.500000, count: 110 -Region Avg IOU: 0.576805, Class: 1.000000, Obj: 0.407214, No Obj: 0.005666, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.492400, Class: 1.000000, Obj: 0.323141, No Obj: 0.004478, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.530167, Class: 1.000000, Obj: 0.331851, No Obj: 0.005771, Avg Recall: 0.598540, count: 137 -Region Avg IOU: 0.525389, Class: 1.000000, Obj: 0.357074, No Obj: 0.004776, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.495721, Class: 1.000000, Obj: 0.301464, No Obj: 0.004967, Avg Recall: 0.527559, count: 127 -Region Avg IOU: 0.477941, Class: 1.000000, Obj: 0.319259, No Obj: 0.004390, Avg Recall: 0.527473, count: 91 -Region Avg IOU: 0.479373, Class: 1.000000, Obj: 0.331260, No Obj: 0.004694, Avg Recall: 0.563830, count: 94 -2465: 0.553883, 0.515053 avg, 0.001000 rate, 19.962776 seconds, 631040 images -Loaded: 1.444534 seconds -Region Avg IOU: 0.459954, Class: 1.000000, Obj: 0.288092, No Obj: 0.004644, Avg Recall: 0.522936, count: 109 -Region Avg IOU: 0.459545, Class: 1.000000, Obj: 0.357295, No Obj: 0.004645, Avg Recall: 0.443396, count: 106 -Region Avg IOU: 0.513451, Class: 1.000000, Obj: 0.374445, No Obj: 0.005730, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.540644, Class: 1.000000, Obj: 0.381224, No Obj: 0.006383, Avg Recall: 0.629032, count: 124 -Region Avg IOU: 0.508288, Class: 1.000000, Obj: 0.371774, No Obj: 0.004913, Avg Recall: 0.551020, count: 98 -Region Avg IOU: 0.505995, Class: 1.000000, Obj: 0.364911, No Obj: 0.004814, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.473127, Class: 1.000000, Obj: 0.339908, No Obj: 0.004901, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.479633, Class: 1.000000, Obj: 0.350992, No Obj: 0.005148, Avg Recall: 0.525000, count: 120 -2466: 0.468587, 0.510406 avg, 0.001000 rate, 19.520741 seconds, 631296 images -Loaded: 2.461608 seconds -Region Avg IOU: 0.456956, Class: 1.000000, Obj: 0.325302, No Obj: 0.004238, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.498667, Class: 1.000000, Obj: 0.374731, No Obj: 0.005302, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.502371, Class: 1.000000, Obj: 0.331074, No Obj: 0.005476, Avg Recall: 0.577586, count: 116 -Region Avg IOU: 0.501102, Class: 1.000000, Obj: 0.367665, No Obj: 0.004955, Avg Recall: 0.581395, count: 86 -Region Avg IOU: 0.570513, Class: 1.000000, Obj: 0.407313, No Obj: 0.004996, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.475683, Class: 1.000000, Obj: 0.321406, No Obj: 0.004753, Avg Recall: 0.543478, count: 92 -Region Avg IOU: 0.497945, Class: 1.000000, Obj: 0.388930, No Obj: 0.004276, Avg Recall: 0.569620, count: 79 -Region Avg IOU: 0.495889, Class: 1.000000, Obj: 0.371845, No Obj: 0.004450, Avg Recall: 0.526882, count: 93 -2467: 0.391953, 0.498561 avg, 0.001000 rate, 22.308924 seconds, 631552 images -Loaded: 0.198354 seconds -Region Avg IOU: 0.488011, Class: 1.000000, Obj: 0.326930, No Obj: 0.004784, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.515430, Class: 1.000000, Obj: 0.345650, No Obj: 0.005312, Avg Recall: 0.588235, count: 102 -Region Avg IOU: 0.499601, Class: 1.000000, Obj: 0.390256, No Obj: 0.005134, Avg Recall: 0.525424, count: 118 -Region Avg IOU: 0.455229, Class: 1.000000, Obj: 0.317604, No Obj: 0.004661, Avg Recall: 0.463636, count: 110 -Region Avg IOU: 0.495274, Class: 1.000000, Obj: 0.373430, No Obj: 0.004850, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.482017, Class: 1.000000, Obj: 0.343828, No Obj: 0.004883, Avg Recall: 0.550000, count: 100 -Region Avg IOU: 0.425016, Class: 1.000000, Obj: 0.304935, No Obj: 0.004676, Avg Recall: 0.460938, count: 128 -Region Avg IOU: 0.526908, Class: 1.000000, Obj: 0.371465, No Obj: 0.004993, Avg Recall: 0.616667, count: 120 -2468: 0.474935, 0.496199 avg, 0.001000 rate, 18.264919 seconds, 631808 images -Loaded: 2.723144 seconds -Region Avg IOU: 0.482608, Class: 1.000000, Obj: 0.325198, No Obj: 0.004961, Avg Recall: 0.546219, count: 119 -Region Avg IOU: 0.517892, Class: 1.000000, Obj: 0.298359, No Obj: 0.004699, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.477644, Class: 1.000000, Obj: 0.290299, No Obj: 0.004478, Avg Recall: 0.554622, count: 119 -Region Avg IOU: 0.439417, Class: 1.000000, Obj: 0.320733, No Obj: 0.004129, Avg Recall: 0.462264, count: 106 -Region Avg IOU: 0.536252, Class: 1.000000, Obj: 0.338375, No Obj: 0.004520, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.489735, Class: 1.000000, Obj: 0.291384, No Obj: 0.004185, Avg Recall: 0.504762, count: 105 -Region Avg IOU: 0.479285, Class: 1.000000, Obj: 0.328115, No Obj: 0.004772, Avg Recall: 0.503759, count: 133 -Region Avg IOU: 0.433084, Class: 1.000000, Obj: 0.317948, No Obj: 0.004316, Avg Recall: 0.455357, count: 112 -2469: 0.573521, 0.503931 avg, 0.001000 rate, 19.697857 seconds, 632064 images -Loaded: 2.605068 seconds -Region Avg IOU: 0.510588, Class: 1.000000, Obj: 0.310828, No Obj: 0.004945, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.440439, Class: 1.000000, Obj: 0.266061, No Obj: 0.004037, Avg Recall: 0.459184, count: 98 -Region Avg IOU: 0.473108, Class: 1.000000, Obj: 0.294312, No Obj: 0.004156, Avg Recall: 0.513514, count: 111 -Region Avg IOU: 0.503739, Class: 1.000000, Obj: 0.279141, No Obj: 0.004556, Avg Recall: 0.556391, count: 133 -Region Avg IOU: 0.454456, Class: 1.000000, Obj: 0.287502, No Obj: 0.004485, Avg Recall: 0.488000, count: 125 -Region Avg IOU: 0.508289, Class: 1.000000, Obj: 0.302982, No Obj: 0.003943, Avg Recall: 0.533333, count: 105 -Region Avg IOU: 0.471354, Class: 1.000000, Obj: 0.287463, No Obj: 0.004809, Avg Recall: 0.492308, count: 130 -Region Avg IOU: 0.460658, Class: 1.000000, Obj: 0.308488, No Obj: 0.003849, Avg Recall: 0.500000, count: 94 -2470: 0.535947, 0.507132 avg, 0.001000 rate, 19.378712 seconds, 632320 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000067 seconds -Region Avg IOU: 0.582933, Class: 1.000000, Obj: 0.399570, No Obj: 0.003141, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.587103, Class: 1.000000, Obj: 0.369270, No Obj: 0.003240, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.615087, Class: 1.000000, Obj: 0.333708, No Obj: 0.003728, Avg Recall: 0.779528, count: 127 -Region Avg IOU: 0.545726, Class: 1.000000, Obj: 0.355782, No Obj: 0.003050, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.606923, Class: 1.000000, Obj: 0.362599, No Obj: 0.003511, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.620744, Class: 1.000000, Obj: 0.381821, No Obj: 0.002840, Avg Recall: 0.740741, count: 81 -Region Avg IOU: 0.578243, Class: 1.000000, Obj: 0.314398, No Obj: 0.003205, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.577264, Class: 1.000000, Obj: 0.344209, No Obj: 0.003473, Avg Recall: 0.705426, count: 129 -2471: 0.531284, 0.509548 avg, 0.001000 rate, 27.549715 seconds, 632576 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.543669, Class: 1.000000, Obj: 0.300616, No Obj: 0.002828, Avg Recall: 0.655738, count: 122 -Region Avg IOU: 0.581471, Class: 1.000000, Obj: 0.319844, No Obj: 0.003104, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.638169, Class: 1.000000, Obj: 0.368143, No Obj: 0.002857, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.608100, Class: 1.000000, Obj: 0.368265, No Obj: 0.003366, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.617201, Class: 1.000000, Obj: 0.372742, No Obj: 0.002916, Avg Recall: 0.796117, count: 103 -Region Avg IOU: 0.573485, Class: 1.000000, Obj: 0.330224, No Obj: 0.002644, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.599864, Class: 1.000000, Obj: 0.348558, No Obj: 0.002998, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.585584, Class: 1.000000, Obj: 0.345383, No Obj: 0.002803, Avg Recall: 0.712871, count: 101 -2472: 0.508894, 0.509482 avg, 0.001000 rate, 28.477200 seconds, 632832 images -Loaded: 0.000055 seconds -Region Avg IOU: 0.608429, Class: 1.000000, Obj: 0.241037, No Obj: 0.002803, Avg Recall: 0.756098, count: 123 -Region Avg IOU: 0.624376, Class: 1.000000, Obj: 0.350044, No Obj: 0.002320, Avg Recall: 0.732558, count: 86 -Region Avg IOU: 0.594349, Class: 1.000000, Obj: 0.283488, No Obj: 0.003105, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.660979, Class: 1.000000, Obj: 0.304639, No Obj: 0.002901, Avg Recall: 0.841121, count: 107 -Region Avg IOU: 0.611765, Class: 1.000000, Obj: 0.277674, No Obj: 0.002509, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.593974, Class: 1.000000, Obj: 0.291539, No Obj: 0.002678, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.594820, Class: 1.000000, Obj: 0.305037, No Obj: 0.002694, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.606054, Class: 1.000000, Obj: 0.314640, No Obj: 0.002554, Avg Recall: 0.711111, count: 90 -2473: 0.484584, 0.506992 avg, 0.001000 rate, 28.974464 seconds, 633088 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.642588, Class: 1.000000, Obj: 0.335603, No Obj: 0.002441, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.640644, Class: 1.000000, Obj: 0.342382, No Obj: 0.002353, Avg Recall: 0.820755, count: 106 -Region Avg IOU: 0.663700, Class: 1.000000, Obj: 0.313394, No Obj: 0.002771, Avg Recall: 0.837838, count: 111 -Region Avg IOU: 0.607379, Class: 1.000000, Obj: 0.289310, No Obj: 0.002199, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.595839, Class: 1.000000, Obj: 0.265078, No Obj: 0.002550, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.606012, Class: 1.000000, Obj: 0.280015, No Obj: 0.002652, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.571125, Class: 1.000000, Obj: 0.265966, No Obj: 0.002323, Avg Recall: 0.664234, count: 137 -Region Avg IOU: 0.644356, Class: 1.000000, Obj: 0.295899, No Obj: 0.002566, Avg Recall: 0.804348, count: 92 -2474: 0.500514, 0.506344 avg, 0.001000 rate, 28.667444 seconds, 633344 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.658478, Class: 1.000000, Obj: 0.240214, No Obj: 0.002291, Avg Recall: 0.845361, count: 97 -Region Avg IOU: 0.678319, Class: 1.000000, Obj: 0.243818, No Obj: 0.002362, Avg Recall: 0.865979, count: 97 -Region Avg IOU: 0.654712, Class: 1.000000, Obj: 0.314811, No Obj: 0.002286, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.606399, Class: 1.000000, Obj: 0.239262, No Obj: 0.002055, Avg Recall: 0.777778, count: 81 -Region Avg IOU: 0.613678, Class: 1.000000, Obj: 0.239974, No Obj: 0.002399, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.648889, Class: 1.000000, Obj: 0.281542, No Obj: 0.002576, Avg Recall: 0.830645, count: 124 -Region Avg IOU: 0.632498, Class: 1.000000, Obj: 0.331088, No Obj: 0.002202, Avg Recall: 0.826531, count: 98 -Region Avg IOU: 0.637853, Class: 1.000000, Obj: 0.287923, No Obj: 0.002222, Avg Recall: 0.789474, count: 95 -2475: 0.473087, 0.503019 avg, 0.001000 rate, 27.651688 seconds, 633600 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.610744, Class: 1.000000, Obj: 0.226201, No Obj: 0.002196, Avg Recall: 0.808000, count: 125 -Region Avg IOU: 0.591483, Class: 1.000000, Obj: 0.261230, No Obj: 0.002126, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.619560, Class: 1.000000, Obj: 0.306430, No Obj: 0.001861, Avg Recall: 0.746667, count: 75 -Region Avg IOU: 0.605232, Class: 1.000000, Obj: 0.223347, No Obj: 0.002324, Avg Recall: 0.762712, count: 118 -Region Avg IOU: 0.641146, Class: 1.000000, Obj: 0.306957, No Obj: 0.002174, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.574058, Class: 1.000000, Obj: 0.232341, No Obj: 0.002237, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.581293, Class: 1.000000, Obj: 0.269861, No Obj: 0.001746, Avg Recall: 0.666667, count: 78 -Region Avg IOU: 0.600852, Class: 1.000000, Obj: 0.205632, No Obj: 0.002360, Avg Recall: 0.745614, count: 114 -2476: 0.442709, 0.496988 avg, 0.001000 rate, 27.618731 seconds, 633856 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.614753, Class: 1.000000, Obj: 0.312896, No Obj: 0.002516, Avg Recall: 0.759124, count: 137 -Region Avg IOU: 0.610352, Class: 1.000000, Obj: 0.220014, No Obj: 0.002136, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.605974, Class: 1.000000, Obj: 0.272395, No Obj: 0.002421, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.604470, Class: 1.000000, Obj: 0.245417, No Obj: 0.002231, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.581827, Class: 1.000000, Obj: 0.279025, No Obj: 0.002239, Avg Recall: 0.732558, count: 86 -Region Avg IOU: 0.661955, Class: 1.000000, Obj: 0.302395, No Obj: 0.002171, Avg Recall: 0.804348, count: 92 -Region Avg IOU: 0.615486, Class: 1.000000, Obj: 0.269745, No Obj: 0.002274, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.612188, Class: 1.000000, Obj: 0.291999, No Obj: 0.002127, Avg Recall: 0.756098, count: 82 -2477: 0.475287, 0.494818 avg, 0.001000 rate, 28.965714 seconds, 634112 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.630476, Class: 1.000000, Obj: 0.342000, No Obj: 0.002532, Avg Recall: 0.802083, count: 96 -Region Avg IOU: 0.622796, Class: 1.000000, Obj: 0.294695, No Obj: 0.002566, Avg Recall: 0.784091, count: 88 -Region Avg IOU: 0.597917, Class: 1.000000, Obj: 0.267730, No Obj: 0.002724, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.595857, Class: 1.000000, Obj: 0.259604, No Obj: 0.003306, Avg Recall: 0.774194, count: 124 -Region Avg IOU: 0.586462, Class: 1.000000, Obj: 0.292882, No Obj: 0.002534, Avg Recall: 0.689922, count: 129 -Region Avg IOU: 0.594171, Class: 1.000000, Obj: 0.279296, No Obj: 0.002851, Avg Recall: 0.666667, count: 138 -Region Avg IOU: 0.621762, Class: 1.000000, Obj: 0.266611, No Obj: 0.002581, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.634222, Class: 1.000000, Obj: 0.281755, No Obj: 0.002819, Avg Recall: 0.790698, count: 129 -2478: 0.595216, 0.504858 avg, 0.001000 rate, 28.541117 seconds, 634368 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.584583, Class: 1.000000, Obj: 0.283157, No Obj: 0.002974, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.675756, Class: 1.000000, Obj: 0.372731, No Obj: 0.002982, Avg Recall: 0.873874, count: 111 -Region Avg IOU: 0.612684, Class: 1.000000, Obj: 0.281818, No Obj: 0.002954, Avg Recall: 0.766423, count: 137 -Region Avg IOU: 0.619606, Class: 1.000000, Obj: 0.322092, No Obj: 0.003048, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.644099, Class: 1.000000, Obj: 0.335717, No Obj: 0.002920, Avg Recall: 0.814159, count: 113 -Region Avg IOU: 0.644510, Class: 1.000000, Obj: 0.357222, No Obj: 0.002763, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.687990, Class: 1.000000, Obj: 0.333056, No Obj: 0.003146, Avg Recall: 0.887097, count: 124 -Region Avg IOU: 0.665110, Class: 1.000000, Obj: 0.382847, No Obj: 0.002590, Avg Recall: 0.815217, count: 92 -2479: 0.524680, 0.506840 avg, 0.001000 rate, 28.526739 seconds, 634624 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.620937, Class: 1.000000, Obj: 0.343417, No Obj: 0.003354, Avg Recall: 0.728000, count: 125 -Region Avg IOU: 0.645262, Class: 1.000000, Obj: 0.343511, No Obj: 0.003192, Avg Recall: 0.800000, count: 115 -Region Avg IOU: 0.580902, Class: 1.000000, Obj: 0.321519, No Obj: 0.003047, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.626474, Class: 1.000000, Obj: 0.261889, No Obj: 0.003083, Avg Recall: 0.796875, count: 128 -Region Avg IOU: 0.568652, Class: 1.000000, Obj: 0.332509, No Obj: 0.002680, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.625869, Class: 1.000000, Obj: 0.356124, No Obj: 0.003401, Avg Recall: 0.735537, count: 121 -Region Avg IOU: 0.632445, Class: 1.000000, Obj: 0.374833, No Obj: 0.003132, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.600051, Class: 1.000000, Obj: 0.297871, No Obj: 0.003205, Avg Recall: 0.730769, count: 104 -2480: 0.520092, 0.508165 avg, 0.001000 rate, 29.551275 seconds, 634880 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 15.028123 seconds -Region Avg IOU: 0.628352, Class: 1.000000, Obj: 0.283066, No Obj: 0.002849, Avg Recall: 0.783132, count: 83 -Region Avg IOU: 0.624543, Class: 1.000000, Obj: 0.409609, No Obj: 0.003437, Avg Recall: 0.800000, count: 120 -Region Avg IOU: 0.587715, Class: 1.000000, Obj: 0.364520, No Obj: 0.003370, Avg Recall: 0.700000, count: 130 -Region Avg IOU: 0.565295, Class: 1.000000, Obj: 0.371352, No Obj: 0.003014, Avg Recall: 0.674419, count: 86 -Region Avg IOU: 0.577742, Class: 1.000000, Obj: 0.337359, No Obj: 0.003080, Avg Recall: 0.697479, count: 119 -Region Avg IOU: 0.639464, Class: 1.000000, Obj: 0.353780, No Obj: 0.003752, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.609247, Class: 1.000000, Obj: 0.381823, No Obj: 0.003165, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.614198, Class: 1.000000, Obj: 0.364128, No Obj: 0.003385, Avg Recall: 0.745455, count: 110 -2481: 0.472519, 0.504600 avg, 0.001000 rate, 26.607077 seconds, 635136 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.647727, Class: 1.000000, Obj: 0.333552, No Obj: 0.003459, Avg Recall: 0.836364, count: 110 -Region Avg IOU: 0.594805, Class: 1.000000, Obj: 0.320945, No Obj: 0.003193, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.588176, Class: 1.000000, Obj: 0.435365, No Obj: 0.003162, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.616338, Class: 1.000000, Obj: 0.393897, No Obj: 0.003803, Avg Recall: 0.738095, count: 126 -Region Avg IOU: 0.602662, Class: 1.000000, Obj: 0.387392, No Obj: 0.003467, Avg Recall: 0.686956, count: 115 -Region Avg IOU: 0.649434, Class: 1.000000, Obj: 0.401921, No Obj: 0.003101, Avg Recall: 0.842105, count: 76 -Region Avg IOU: 0.611060, Class: 1.000000, Obj: 0.371736, No Obj: 0.003646, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.553649, Class: 1.000000, Obj: 0.350281, No Obj: 0.003334, Avg Recall: 0.557895, count: 95 -2482: 0.440391, 0.498179 avg, 0.001000 rate, 26.874125 seconds, 635392 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.634708, Class: 1.000000, Obj: 0.302985, No Obj: 0.003445, Avg Recall: 0.791304, count: 115 -Region Avg IOU: 0.583780, Class: 1.000000, Obj: 0.391544, No Obj: 0.003723, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.581531, Class: 1.000000, Obj: 0.380265, No Obj: 0.002850, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.655481, Class: 1.000000, Obj: 0.402555, No Obj: 0.003554, Avg Recall: 0.821429, count: 84 -Region Avg IOU: 0.621585, Class: 1.000000, Obj: 0.425924, No Obj: 0.003289, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.590361, Class: 1.000000, Obj: 0.417950, No Obj: 0.003163, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.605872, Class: 1.000000, Obj: 0.366087, No Obj: 0.003263, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.556456, Class: 1.000000, Obj: 0.350935, No Obj: 0.002986, Avg Recall: 0.658730, count: 126 -2483: 0.443029, 0.492664 avg, 0.001000 rate, 28.385592 seconds, 635648 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.654929, Class: 1.000000, Obj: 0.347137, No Obj: 0.002990, Avg Recall: 0.860215, count: 93 -Region Avg IOU: 0.596042, Class: 1.000000, Obj: 0.332857, No Obj: 0.003427, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.625063, Class: 1.000000, Obj: 0.372579, No Obj: 0.002897, Avg Recall: 0.779070, count: 86 -Region Avg IOU: 0.585984, Class: 1.000000, Obj: 0.345769, No Obj: 0.003528, Avg Recall: 0.679104, count: 134 -Region Avg IOU: 0.561911, Class: 1.000000, Obj: 0.373714, No Obj: 0.003241, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.625788, Class: 1.000000, Obj: 0.443276, No Obj: 0.003430, Avg Recall: 0.791304, count: 115 -Region Avg IOU: 0.642570, Class: 1.000000, Obj: 0.338098, No Obj: 0.003246, Avg Recall: 0.828829, count: 111 -Region Avg IOU: 0.620615, Class: 1.000000, Obj: 0.347061, No Obj: 0.003821, Avg Recall: 0.771429, count: 140 -2484: 0.528124, 0.496210 avg, 0.001000 rate, 27.936716 seconds, 635904 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.597944, Class: 1.000000, Obj: 0.363117, No Obj: 0.003071, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.600030, Class: 1.000000, Obj: 0.354632, No Obj: 0.003127, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.575421, Class: 1.000000, Obj: 0.341877, No Obj: 0.003065, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.651248, Class: 1.000000, Obj: 0.407984, No Obj: 0.003143, Avg Recall: 0.820000, count: 100 -Region Avg IOU: 0.621411, Class: 1.000000, Obj: 0.369547, No Obj: 0.003389, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.621417, Class: 1.000000, Obj: 0.331356, No Obj: 0.002896, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.638063, Class: 1.000000, Obj: 0.377446, No Obj: 0.002698, Avg Recall: 0.814815, count: 81 -Region Avg IOU: 0.590705, Class: 1.000000, Obj: 0.323621, No Obj: 0.002857, Avg Recall: 0.755814, count: 86 -2485: 0.462830, 0.492872 avg, 0.001000 rate, 28.160604 seconds, 636160 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.621476, Class: 1.000000, Obj: 0.292637, No Obj: 0.002957, Avg Recall: 0.775000, count: 120 -Region Avg IOU: 0.610673, Class: 1.000000, Obj: 0.360640, No Obj: 0.002870, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.631264, Class: 1.000000, Obj: 0.342532, No Obj: 0.003007, Avg Recall: 0.811966, count: 117 -Region Avg IOU: 0.600755, Class: 1.000000, Obj: 0.333736, No Obj: 0.002844, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.624483, Class: 1.000000, Obj: 0.315070, No Obj: 0.003130, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.564251, Class: 1.000000, Obj: 0.313457, No Obj: 0.003065, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.607012, Class: 1.000000, Obj: 0.309548, No Obj: 0.002738, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.552157, Class: 1.000000, Obj: 0.383980, No Obj: 0.002784, Avg Recall: 0.651786, count: 112 -2486: 0.551759, 0.498761 avg, 0.001000 rate, 27.806292 seconds, 636416 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.615468, Class: 1.000000, Obj: 0.314409, No Obj: 0.002890, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.597358, Class: 1.000000, Obj: 0.348252, No Obj: 0.002527, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.614159, Class: 1.000000, Obj: 0.322889, No Obj: 0.003086, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.622302, Class: 1.000000, Obj: 0.316154, No Obj: 0.002869, Avg Recall: 0.765625, count: 128 -Region Avg IOU: 0.573185, Class: 1.000000, Obj: 0.305416, No Obj: 0.002610, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.617499, Class: 1.000000, Obj: 0.342284, No Obj: 0.002743, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.552838, Class: 1.000000, Obj: 0.319250, No Obj: 0.002533, Avg Recall: 0.610169, count: 118 -Region Avg IOU: 0.580520, Class: 1.000000, Obj: 0.317454, No Obj: 0.002342, Avg Recall: 0.705882, count: 68 -2487: 0.474972, 0.496382 avg, 0.001000 rate, 27.875183 seconds, 636672 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.604352, Class: 1.000000, Obj: 0.281859, No Obj: 0.002667, Avg Recall: 0.762712, count: 118 -Region Avg IOU: 0.605163, Class: 1.000000, Obj: 0.318822, No Obj: 0.002727, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.599673, Class: 1.000000, Obj: 0.288571, No Obj: 0.002403, Avg Recall: 0.690476, count: 84 -Region Avg IOU: 0.591450, Class: 1.000000, Obj: 0.313706, No Obj: 0.002434, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.587019, Class: 1.000000, Obj: 0.263017, No Obj: 0.002875, Avg Recall: 0.693431, count: 137 -Region Avg IOU: 0.613865, Class: 1.000000, Obj: 0.269343, No Obj: 0.003025, Avg Recall: 0.795775, count: 142 -Region Avg IOU: 0.635859, Class: 1.000000, Obj: 0.306786, No Obj: 0.002753, Avg Recall: 0.795455, count: 132 -Region Avg IOU: 0.599434, Class: 1.000000, Obj: 0.282860, No Obj: 0.002597, Avg Recall: 0.764228, count: 123 -2488: 0.496641, 0.496408 avg, 0.001000 rate, 26.827541 seconds, 636928 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.603445, Class: 1.000000, Obj: 0.329105, No Obj: 0.002411, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.589420, Class: 1.000000, Obj: 0.296954, No Obj: 0.002412, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.618585, Class: 1.000000, Obj: 0.300965, No Obj: 0.003114, Avg Recall: 0.737705, count: 122 -Region Avg IOU: 0.549931, Class: 1.000000, Obj: 0.266793, No Obj: 0.002335, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.624993, Class: 1.000000, Obj: 0.328506, No Obj: 0.002512, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.600738, Class: 1.000000, Obj: 0.352795, No Obj: 0.002508, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.604742, Class: 1.000000, Obj: 0.266607, No Obj: 0.002621, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.584874, Class: 1.000000, Obj: 0.305940, No Obj: 0.002225, Avg Recall: 0.673684, count: 95 -2489: 0.491201, 0.495887 avg, 0.001000 rate, 27.726900 seconds, 637184 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.570012, Class: 1.000000, Obj: 0.287434, No Obj: 0.002669, Avg Recall: 0.643411, count: 129 -Region Avg IOU: 0.579481, Class: 1.000000, Obj: 0.293397, No Obj: 0.002572, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.593213, Class: 1.000000, Obj: 0.306227, No Obj: 0.002326, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.610136, Class: 1.000000, Obj: 0.278381, No Obj: 0.002572, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.603735, Class: 1.000000, Obj: 0.259979, No Obj: 0.002924, Avg Recall: 0.744361, count: 133 -Region Avg IOU: 0.616092, Class: 1.000000, Obj: 0.334487, No Obj: 0.002332, Avg Recall: 0.759036, count: 83 -Region Avg IOU: 0.621841, Class: 1.000000, Obj: 0.294462, No Obj: 0.002785, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.587049, Class: 1.000000, Obj: 0.291709, No Obj: 0.002029, Avg Recall: 0.730769, count: 78 -2490: 0.435103, 0.489809 avg, 0.001000 rate, 28.004349 seconds, 637440 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 15.706021 seconds -Region Avg IOU: 0.597024, Class: 1.000000, Obj: 0.332177, No Obj: 0.003115, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.613120, Class: 1.000000, Obj: 0.290181, No Obj: 0.003570, Avg Recall: 0.789474, count: 114 -Region Avg IOU: 0.545883, Class: 1.000000, Obj: 0.290545, No Obj: 0.002774, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.602625, Class: 1.000000, Obj: 0.308036, No Obj: 0.002844, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.557979, Class: 1.000000, Obj: 0.263826, No Obj: 0.003678, Avg Recall: 0.672956, count: 159 -Region Avg IOU: 0.549116, Class: 1.000000, Obj: 0.261149, No Obj: 0.002694, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.538137, Class: 1.000000, Obj: 0.297143, No Obj: 0.002897, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.541770, Class: 1.000000, Obj: 0.304110, No Obj: 0.003073, Avg Recall: 0.642857, count: 112 -2491: 0.525270, 0.493355 avg, 0.001000 rate, 24.398026 seconds, 637696 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.573454, Class: 1.000000, Obj: 0.339224, No Obj: 0.003353, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.592774, Class: 1.000000, Obj: 0.318027, No Obj: 0.003311, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.581770, Class: 1.000000, Obj: 0.329485, No Obj: 0.002890, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.574159, Class: 1.000000, Obj: 0.350294, No Obj: 0.003092, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.544532, Class: 1.000000, Obj: 0.333150, No Obj: 0.002901, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.572857, Class: 1.000000, Obj: 0.361380, No Obj: 0.003459, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.564769, Class: 1.000000, Obj: 0.296445, No Obj: 0.003486, Avg Recall: 0.692913, count: 127 -Region Avg IOU: 0.594271, Class: 1.000000, Obj: 0.305540, No Obj: 0.002779, Avg Recall: 0.744444, count: 90 -2492: 0.432810, 0.487301 avg, 0.001000 rate, 25.165155 seconds, 637952 images -Loaded: 0.000121 seconds -Region Avg IOU: 0.602180, Class: 1.000000, Obj: 0.287443, No Obj: 0.003487, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.589383, Class: 1.000000, Obj: 0.313355, No Obj: 0.003069, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.617663, Class: 1.000000, Obj: 0.395968, No Obj: 0.003588, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.543724, Class: 1.000000, Obj: 0.317576, No Obj: 0.003657, Avg Recall: 0.653846, count: 130 -Region Avg IOU: 0.586676, Class: 1.000000, Obj: 0.348462, No Obj: 0.003607, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.581587, Class: 1.000000, Obj: 0.367537, No Obj: 0.003750, Avg Recall: 0.704918, count: 122 -Region Avg IOU: 0.608653, Class: 1.000000, Obj: 0.397006, No Obj: 0.003340, Avg Recall: 0.736111, count: 72 -Region Avg IOU: 0.561673, Class: 1.000000, Obj: 0.368482, No Obj: 0.003979, Avg Recall: 0.671429, count: 140 -2493: 0.454343, 0.484005 avg, 0.001000 rate, 24.544561 seconds, 638208 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.596496, Class: 1.000000, Obj: 0.404489, No Obj: 0.003934, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.580897, Class: 1.000000, Obj: 0.384107, No Obj: 0.003588, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.588395, Class: 1.000000, Obj: 0.340883, No Obj: 0.004344, Avg Recall: 0.712121, count: 132 -Region Avg IOU: 0.546442, Class: 1.000000, Obj: 0.354139, No Obj: 0.003191, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.554263, Class: 1.000000, Obj: 0.376130, No Obj: 0.003938, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.546341, Class: 1.000000, Obj: 0.344259, No Obj: 0.003791, Avg Recall: 0.648438, count: 128 -Region Avg IOU: 0.587882, Class: 1.000000, Obj: 0.334835, No Obj: 0.003839, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.584309, Class: 1.000000, Obj: 0.352443, No Obj: 0.003877, Avg Recall: 0.694118, count: 85 -2494: 0.463119, 0.481916 avg, 0.001000 rate, 25.489813 seconds, 638464 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.563256, Class: 1.000000, Obj: 0.395272, No Obj: 0.003739, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.568343, Class: 1.000000, Obj: 0.391690, No Obj: 0.003388, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.591189, Class: 1.000000, Obj: 0.430143, No Obj: 0.003613, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.522547, Class: 1.000000, Obj: 0.372489, No Obj: 0.003669, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.577468, Class: 1.000000, Obj: 0.319713, No Obj: 0.003588, Avg Recall: 0.714286, count: 133 -Region Avg IOU: 0.595336, Class: 1.000000, Obj: 0.393905, No Obj: 0.004641, Avg Recall: 0.724638, count: 138 -Region Avg IOU: 0.543188, Class: 1.000000, Obj: 0.329299, No Obj: 0.003384, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.593850, Class: 1.000000, Obj: 0.364389, No Obj: 0.004180, Avg Recall: 0.676471, count: 102 -2495: 0.462751, 0.480000 avg, 0.001000 rate, 25.373407 seconds, 638720 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.564057, Class: 1.000000, Obj: 0.388403, No Obj: 0.003637, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.643683, Class: 1.000000, Obj: 0.427284, No Obj: 0.003494, Avg Recall: 0.821429, count: 84 -Region Avg IOU: 0.583482, Class: 1.000000, Obj: 0.354971, No Obj: 0.003396, Avg Recall: 0.686046, count: 86 -Region Avg IOU: 0.594007, Class: 1.000000, Obj: 0.362140, No Obj: 0.003767, Avg Recall: 0.743802, count: 121 -Region Avg IOU: 0.530400, Class: 1.000000, Obj: 0.354284, No Obj: 0.003368, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.607293, Class: 1.000000, Obj: 0.357430, No Obj: 0.003672, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.584172, Class: 1.000000, Obj: 0.375190, No Obj: 0.003672, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.572738, Class: 1.000000, Obj: 0.366807, No Obj: 0.004252, Avg Recall: 0.654676, count: 139 -2496: 0.411776, 0.473177 avg, 0.001000 rate, 24.364307 seconds, 638976 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.587920, Class: 1.000000, Obj: 0.441240, No Obj: 0.003819, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.552239, Class: 1.000000, Obj: 0.360449, No Obj: 0.003642, Avg Recall: 0.588235, count: 119 -Region Avg IOU: 0.546009, Class: 1.000000, Obj: 0.372037, No Obj: 0.003609, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.579418, Class: 1.000000, Obj: 0.357167, No Obj: 0.003397, Avg Recall: 0.701149, count: 87 -Region Avg IOU: 0.581392, Class: 1.000000, Obj: 0.397893, No Obj: 0.003381, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.575083, Class: 1.000000, Obj: 0.335023, No Obj: 0.003536, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.557011, Class: 1.000000, Obj: 0.344460, No Obj: 0.003996, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.642243, Class: 1.000000, Obj: 0.384830, No Obj: 0.003844, Avg Recall: 0.776699, count: 103 -2497: 0.436376, 0.469497 avg, 0.001000 rate, 25.427469 seconds, 639232 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.556508, Class: 1.000000, Obj: 0.348090, No Obj: 0.003994, Avg Recall: 0.631579, count: 152 -Region Avg IOU: 0.580119, Class: 1.000000, Obj: 0.350806, No Obj: 0.003298, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.579488, Class: 1.000000, Obj: 0.351803, No Obj: 0.003716, Avg Recall: 0.697479, count: 119 -Region Avg IOU: 0.563075, Class: 1.000000, Obj: 0.369296, No Obj: 0.003601, Avg Recall: 0.654135, count: 133 -Region Avg IOU: 0.616288, Class: 1.000000, Obj: 0.339083, No Obj: 0.003378, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.543001, Class: 1.000000, Obj: 0.321295, No Obj: 0.003609, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.589803, Class: 1.000000, Obj: 0.344405, No Obj: 0.003507, Avg Recall: 0.679104, count: 134 -Region Avg IOU: 0.506333, Class: 1.000000, Obj: 0.324157, No Obj: 0.003340, Avg Recall: 0.583333, count: 108 -2498: 0.508946, 0.473442 avg, 0.001000 rate, 26.463991 seconds, 639488 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.578792, Class: 1.000000, Obj: 0.351554, No Obj: 0.003454, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.550955, Class: 1.000000, Obj: 0.352953, No Obj: 0.003020, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.582890, Class: 1.000000, Obj: 0.354839, No Obj: 0.002940, Avg Recall: 0.658824, count: 85 -Region Avg IOU: 0.591512, Class: 1.000000, Obj: 0.353664, No Obj: 0.003876, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.513616, Class: 1.000000, Obj: 0.357463, No Obj: 0.002849, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.563180, Class: 1.000000, Obj: 0.363387, No Obj: 0.003390, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.600348, Class: 1.000000, Obj: 0.345093, No Obj: 0.003611, Avg Recall: 0.737705, count: 122 -Region Avg IOU: 0.581527, Class: 1.000000, Obj: 0.330693, No Obj: 0.003734, Avg Recall: 0.694915, count: 118 -2499: 0.450361, 0.471134 avg, 0.001000 rate, 25.503088 seconds, 639744 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.511740, Class: 1.000000, Obj: 0.306871, No Obj: 0.003088, Avg Recall: 0.576087, count: 92 -Region Avg IOU: 0.558828, Class: 1.000000, Obj: 0.360400, No Obj: 0.002895, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.600521, Class: 1.000000, Obj: 0.334834, No Obj: 0.003632, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.567639, Class: 1.000000, Obj: 0.302227, No Obj: 0.002776, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.570308, Class: 1.000000, Obj: 0.298988, No Obj: 0.003220, Avg Recall: 0.688073, count: 109 -Region Avg IOU: 0.543852, Class: 1.000000, Obj: 0.326401, No Obj: 0.003199, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.593937, Class: 1.000000, Obj: 0.344856, No Obj: 0.003241, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.579338, Class: 1.000000, Obj: 0.340487, No Obj: 0.002862, Avg Recall: 0.692308, count: 91 -2500: 0.446102, 0.468631 avg, 0.001000 rate, 24.886047 seconds, 640000 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 21.997339 seconds -Region Avg IOU: 0.567641, Class: 1.000000, Obj: 0.327364, No Obj: 0.003178, Avg Recall: 0.677686, count: 121 -Region Avg IOU: 0.539588, Class: 1.000000, Obj: 0.311361, No Obj: 0.002802, Avg Recall: 0.623529, count: 85 -Region Avg IOU: 0.583554, Class: 1.000000, Obj: 0.287352, No Obj: 0.002938, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.544994, Class: 1.000000, Obj: 0.355432, No Obj: 0.003148, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.568344, Class: 1.000000, Obj: 0.339631, No Obj: 0.003124, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.585042, Class: 1.000000, Obj: 0.319932, No Obj: 0.003296, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.555174, Class: 1.000000, Obj: 0.305895, No Obj: 0.003376, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.574055, Class: 1.000000, Obj: 0.282324, No Obj: 0.003205, Avg Recall: 0.715385, count: 130 -2501: 0.469608, 0.468728 avg, 0.001000 rate, 25.213272 seconds, 640256 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.553647, Class: 1.000000, Obj: 0.359158, No Obj: 0.002743, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.520673, Class: 1.000000, Obj: 0.286378, No Obj: 0.002809, Avg Recall: 0.574468, count: 94 -Region Avg IOU: 0.552482, Class: 1.000000, Obj: 0.313295, No Obj: 0.003036, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.540596, Class: 1.000000, Obj: 0.308485, No Obj: 0.003150, Avg Recall: 0.625000, count: 128 -Region Avg IOU: 0.602303, Class: 1.000000, Obj: 0.303854, No Obj: 0.003327, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.527518, Class: 1.000000, Obj: 0.308988, No Obj: 0.002403, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.500998, Class: 1.000000, Obj: 0.313059, No Obj: 0.003317, Avg Recall: 0.568000, count: 125 -Region Avg IOU: 0.543838, Class: 1.000000, Obj: 0.345913, No Obj: 0.002957, Avg Recall: 0.635514, count: 107 -2502: 0.462783, 0.468134 avg, 0.001000 rate, 25.483009 seconds, 640512 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.525691, Class: 1.000000, Obj: 0.281957, No Obj: 0.003065, Avg Recall: 0.601770, count: 113 -Region Avg IOU: 0.589092, Class: 1.000000, Obj: 0.297691, No Obj: 0.003087, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.565874, Class: 1.000000, Obj: 0.362426, No Obj: 0.003024, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.563104, Class: 1.000000, Obj: 0.309788, No Obj: 0.003653, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.591256, Class: 1.000000, Obj: 0.345042, No Obj: 0.003398, Avg Recall: 0.704000, count: 125 -Region Avg IOU: 0.590670, Class: 1.000000, Obj: 0.301568, No Obj: 0.003291, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.571172, Class: 1.000000, Obj: 0.264974, No Obj: 0.002854, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.565940, Class: 1.000000, Obj: 0.316520, No Obj: 0.003100, Avg Recall: 0.646018, count: 113 -2503: 0.516242, 0.472945 avg, 0.001000 rate, 25.494881 seconds, 640768 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.550778, Class: 1.000000, Obj: 0.316600, No Obj: 0.003268, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.532481, Class: 1.000000, Obj: 0.313165, No Obj: 0.003061, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.549923, Class: 1.000000, Obj: 0.294235, No Obj: 0.003035, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.577660, Class: 1.000000, Obj: 0.335540, No Obj: 0.003028, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.575837, Class: 1.000000, Obj: 0.297904, No Obj: 0.003049, Avg Recall: 0.697479, count: 119 -Region Avg IOU: 0.585126, Class: 1.000000, Obj: 0.285740, No Obj: 0.003447, Avg Recall: 0.705882, count: 136 -Region Avg IOU: 0.586048, Class: 1.000000, Obj: 0.314191, No Obj: 0.003489, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.563470, Class: 1.000000, Obj: 0.293968, No Obj: 0.003339, Avg Recall: 0.669355, count: 124 -2504: 0.497090, 0.475359 avg, 0.001000 rate, 24.319704 seconds, 641024 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.588355, Class: 1.000000, Obj: 0.282899, No Obj: 0.003150, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.566420, Class: 1.000000, Obj: 0.334090, No Obj: 0.003302, Avg Recall: 0.691667, count: 120 -Region Avg IOU: 0.566583, Class: 1.000000, Obj: 0.300599, No Obj: 0.003136, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.594842, Class: 1.000000, Obj: 0.349831, No Obj: 0.003299, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.587870, Class: 1.000000, Obj: 0.303841, No Obj: 0.003443, Avg Recall: 0.664286, count: 140 -Region Avg IOU: 0.554377, Class: 1.000000, Obj: 0.335887, No Obj: 0.003091, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.564113, Class: 1.000000, Obj: 0.310350, No Obj: 0.002977, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.549943, Class: 1.000000, Obj: 0.307221, No Obj: 0.003531, Avg Recall: 0.629921, count: 127 -2505: 0.506329, 0.478456 avg, 0.001000 rate, 24.821648 seconds, 641280 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.527044, Class: 1.000000, Obj: 0.324063, No Obj: 0.003279, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.588394, Class: 1.000000, Obj: 0.382061, No Obj: 0.003308, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.578657, Class: 1.000000, Obj: 0.361095, No Obj: 0.003213, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.586577, Class: 1.000000, Obj: 0.303068, No Obj: 0.003182, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.578324, Class: 1.000000, Obj: 0.366109, No Obj: 0.003565, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.523374, Class: 1.000000, Obj: 0.311792, No Obj: 0.003046, Avg Recall: 0.593750, count: 96 -Region Avg IOU: 0.592602, Class: 1.000000, Obj: 0.340820, No Obj: 0.003591, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.500045, Class: 1.000000, Obj: 0.326112, No Obj: 0.002862, Avg Recall: 0.571429, count: 105 -2506: 0.461226, 0.476733 avg, 0.001000 rate, 24.807611 seconds, 641536 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.535232, Class: 1.000000, Obj: 0.327069, No Obj: 0.003295, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.554660, Class: 1.000000, Obj: 0.372549, No Obj: 0.003379, Avg Recall: 0.659259, count: 135 -Region Avg IOU: 0.548325, Class: 1.000000, Obj: 0.297693, No Obj: 0.003387, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.570485, Class: 1.000000, Obj: 0.332990, No Obj: 0.004174, Avg Recall: 0.690476, count: 126 -Region Avg IOU: 0.563255, Class: 1.000000, Obj: 0.333906, No Obj: 0.003510, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.586748, Class: 1.000000, Obj: 0.321690, No Obj: 0.003801, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.557800, Class: 1.000000, Obj: 0.302302, No Obj: 0.003383, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.625625, Class: 1.000000, Obj: 0.370439, No Obj: 0.004133, Avg Recall: 0.747573, count: 103 -2507: 0.515485, 0.480608 avg, 0.001000 rate, 24.297001 seconds, 641792 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.590723, Class: 1.000000, Obj: 0.412828, No Obj: 0.003402, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.580771, Class: 1.000000, Obj: 0.342093, No Obj: 0.003800, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.555844, Class: 1.000000, Obj: 0.321629, No Obj: 0.003616, Avg Recall: 0.652542, count: 118 -Region Avg IOU: 0.605354, Class: 1.000000, Obj: 0.385132, No Obj: 0.003799, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.604269, Class: 1.000000, Obj: 0.389211, No Obj: 0.003913, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.590257, Class: 1.000000, Obj: 0.379301, No Obj: 0.003494, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.548308, Class: 1.000000, Obj: 0.335182, No Obj: 0.003289, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.581430, Class: 1.000000, Obj: 0.405523, No Obj: 0.003510, Avg Recall: 0.681818, count: 88 -2508: 0.444508, 0.476998 avg, 0.001000 rate, 25.952116 seconds, 642048 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.593835, Class: 1.000000, Obj: 0.357126, No Obj: 0.003629, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.556036, Class: 1.000000, Obj: 0.286591, No Obj: 0.004235, Avg Recall: 0.672956, count: 159 -Region Avg IOU: 0.597007, Class: 1.000000, Obj: 0.368161, No Obj: 0.003957, Avg Recall: 0.676923, count: 130 -Region Avg IOU: 0.575900, Class: 1.000000, Obj: 0.416500, No Obj: 0.003899, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.500314, Class: 1.000000, Obj: 0.326690, No Obj: 0.003432, Avg Recall: 0.544715, count: 123 -Region Avg IOU: 0.563920, Class: 1.000000, Obj: 0.364854, No Obj: 0.003017, Avg Recall: 0.693182, count: 88 -Region Avg IOU: 0.556844, Class: 1.000000, Obj: 0.350273, No Obj: 0.003384, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.570326, Class: 1.000000, Obj: 0.346429, No Obj: 0.003279, Avg Recall: 0.700935, count: 107 -2509: 0.492645, 0.478563 avg, 0.001000 rate, 25.574867 seconds, 642304 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.568728, Class: 1.000000, Obj: 0.374667, No Obj: 0.003754, Avg Recall: 0.622807, count: 114 -Region Avg IOU: 0.583770, Class: 1.000000, Obj: 0.386647, No Obj: 0.003682, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.574077, Class: 1.000000, Obj: 0.333018, No Obj: 0.003817, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.595035, Class: 1.000000, Obj: 0.358172, No Obj: 0.003558, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.558131, Class: 1.000000, Obj: 0.323265, No Obj: 0.003725, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.550059, Class: 1.000000, Obj: 0.361672, No Obj: 0.003470, Avg Recall: 0.658333, count: 120 -Region Avg IOU: 0.577594, Class: 1.000000, Obj: 0.325837, No Obj: 0.003440, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.588269, Class: 1.000000, Obj: 0.320405, No Obj: 0.003799, Avg Recall: 0.720588, count: 136 -2510: 0.502002, 0.480907 avg, 0.001000 rate, 25.303143 seconds, 642560 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 20.084768 seconds -Region Avg IOU: 0.501340, Class: 1.000000, Obj: 0.276580, No Obj: 0.003692, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.465969, Class: 1.000000, Obj: 0.262227, No Obj: 0.004010, Avg Recall: 0.533333, count: 150 -Region Avg IOU: 0.490752, Class: 1.000000, Obj: 0.271381, No Obj: 0.004185, Avg Recall: 0.571429, count: 119 -Region Avg IOU: 0.538038, Class: 1.000000, Obj: 0.303334, No Obj: 0.004112, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.495978, Class: 1.000000, Obj: 0.282653, No Obj: 0.003463, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.460970, Class: 1.000000, Obj: 0.252867, No Obj: 0.003318, Avg Recall: 0.490385, count: 104 -Region Avg IOU: 0.516219, Class: 1.000000, Obj: 0.249554, No Obj: 0.004040, Avg Recall: 0.594828, count: 116 -Region Avg IOU: 0.573604, Class: 1.000000, Obj: 0.350340, No Obj: 0.003400, Avg Recall: 0.689655, count: 87 -2511: 0.582725, 0.491089 avg, 0.001000 rate, 20.192183 seconds, 642816 images -Loaded: 1.719778 seconds -Region Avg IOU: 0.542678, Class: 1.000000, Obj: 0.327923, No Obj: 0.004071, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.483504, Class: 1.000000, Obj: 0.296896, No Obj: 0.003977, Avg Recall: 0.539062, count: 128 -Region Avg IOU: 0.486232, Class: 1.000000, Obj: 0.304805, No Obj: 0.004132, Avg Recall: 0.525862, count: 116 -Region Avg IOU: 0.494677, Class: 1.000000, Obj: 0.273825, No Obj: 0.003646, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.499698, Class: 1.000000, Obj: 0.275525, No Obj: 0.004448, Avg Recall: 0.522388, count: 134 -Region Avg IOU: 0.550009, Class: 1.000000, Obj: 0.275643, No Obj: 0.003771, Avg Recall: 0.620253, count: 79 -Region Avg IOU: 0.463120, Class: 1.000000, Obj: 0.265648, No Obj: 0.003661, Avg Recall: 0.508065, count: 124 -Region Avg IOU: 0.494084, Class: 1.000000, Obj: 0.322120, No Obj: 0.003724, Avg Recall: 0.541284, count: 109 -2512: 0.517156, 0.493695 avg, 0.001000 rate, 19.736773 seconds, 643072 images -Loaded: 2.674901 seconds -Region Avg IOU: 0.511627, Class: 1.000000, Obj: 0.295505, No Obj: 0.004373, Avg Recall: 0.579710, count: 138 -Region Avg IOU: 0.533932, Class: 1.000000, Obj: 0.307247, No Obj: 0.003461, Avg Recall: 0.604651, count: 86 -Region Avg IOU: 0.519622, Class: 1.000000, Obj: 0.342283, No Obj: 0.003897, Avg Recall: 0.566372, count: 113 -Region Avg IOU: 0.460455, Class: 1.000000, Obj: 0.285154, No Obj: 0.003612, Avg Recall: 0.526786, count: 112 -Region Avg IOU: 0.479971, Class: 1.000000, Obj: 0.313202, No Obj: 0.003651, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.491228, Class: 1.000000, Obj: 0.293587, No Obj: 0.003852, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.487276, Class: 1.000000, Obj: 0.283445, No Obj: 0.003646, Avg Recall: 0.538462, count: 104 -Region Avg IOU: 0.534522, Class: 1.000000, Obj: 0.315133, No Obj: 0.003644, Avg Recall: 0.593407, count: 91 -2513: 0.548635, 0.499189 avg, 0.001000 rate, 21.072992 seconds, 643328 images -Loaded: 0.463497 seconds -Region Avg IOU: 0.525307, Class: 1.000000, Obj: 0.295175, No Obj: 0.004530, Avg Recall: 0.617886, count: 123 -Region Avg IOU: 0.507461, Class: 1.000000, Obj: 0.323473, No Obj: 0.004038, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.497070, Class: 1.000000, Obj: 0.323665, No Obj: 0.003480, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.496114, Class: 1.000000, Obj: 0.296957, No Obj: 0.003324, Avg Recall: 0.555556, count: 90 -Region Avg IOU: 0.519714, Class: 1.000000, Obj: 0.340573, No Obj: 0.003944, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.442727, Class: 1.000000, Obj: 0.304473, No Obj: 0.003745, Avg Recall: 0.512195, count: 82 -Region Avg IOU: 0.490169, Class: 1.000000, Obj: 0.328733, No Obj: 0.004150, Avg Recall: 0.554622, count: 119 -Region Avg IOU: 0.471882, Class: 1.000000, Obj: 0.323879, No Obj: 0.004020, Avg Recall: 0.541667, count: 96 -2514: 0.451060, 0.494377 avg, 0.001000 rate, 21.770506 seconds, 643584 images -Loaded: 0.457453 seconds -Region Avg IOU: 0.503492, Class: 1.000000, Obj: 0.288924, No Obj: 0.004568, Avg Recall: 0.573770, count: 122 -Region Avg IOU: 0.559593, Class: 1.000000, Obj: 0.343837, No Obj: 0.004206, Avg Recall: 0.659341, count: 91 -Region Avg IOU: 0.511591, Class: 1.000000, Obj: 0.373717, No Obj: 0.004112, Avg Recall: 0.597561, count: 82 -Region Avg IOU: 0.513817, Class: 1.000000, Obj: 0.323265, No Obj: 0.004084, Avg Recall: 0.555556, count: 81 -Region Avg IOU: 0.494873, Class: 1.000000, Obj: 0.330914, No Obj: 0.005293, Avg Recall: 0.565517, count: 145 -Region Avg IOU: 0.534425, Class: 1.000000, Obj: 0.344596, No Obj: 0.004085, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.504153, Class: 1.000000, Obj: 0.338643, No Obj: 0.004412, Avg Recall: 0.576577, count: 111 -Region Avg IOU: 0.455780, Class: 1.000000, Obj: 0.296447, No Obj: 0.004030, Avg Recall: 0.482759, count: 116 -2515: 0.451077, 0.490047 avg, 0.001000 rate, 21.919632 seconds, 643840 images -Loaded: 0.163672 seconds -Region Avg IOU: 0.529013, Class: 1.000000, Obj: 0.353798, No Obj: 0.004265, Avg Recall: 0.581197, count: 117 -Region Avg IOU: 0.487963, Class: 1.000000, Obj: 0.332516, No Obj: 0.005239, Avg Recall: 0.503268, count: 153 -Region Avg IOU: 0.499233, Class: 1.000000, Obj: 0.312257, No Obj: 0.004200, Avg Recall: 0.569231, count: 130 -Region Avg IOU: 0.473021, Class: 1.000000, Obj: 0.325746, No Obj: 0.004559, Avg Recall: 0.515625, count: 128 -Region Avg IOU: 0.463173, Class: 1.000000, Obj: 0.354979, No Obj: 0.003869, Avg Recall: 0.504762, count: 105 -Region Avg IOU: 0.458598, Class: 1.000000, Obj: 0.316082, No Obj: 0.003608, Avg Recall: 0.505495, count: 91 -Region Avg IOU: 0.499834, Class: 1.000000, Obj: 0.340584, No Obj: 0.004576, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.486357, Class: 1.000000, Obj: 0.354851, No Obj: 0.003939, Avg Recall: 0.525000, count: 80 -2516: 0.486362, 0.489678 avg, 0.001000 rate, 19.007263 seconds, 644096 images -Loaded: 2.436387 seconds -Region Avg IOU: 0.484123, Class: 1.000000, Obj: 0.338134, No Obj: 0.004167, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.495979, Class: 1.000000, Obj: 0.275839, No Obj: 0.004692, Avg Recall: 0.563025, count: 119 -Region Avg IOU: 0.478501, Class: 1.000000, Obj: 0.290683, No Obj: 0.003830, Avg Recall: 0.544554, count: 101 -Region Avg IOU: 0.464625, Class: 1.000000, Obj: 0.343732, No Obj: 0.004007, Avg Recall: 0.542169, count: 83 -Region Avg IOU: 0.507818, Class: 1.000000, Obj: 0.291870, No Obj: 0.004734, Avg Recall: 0.609756, count: 123 -Region Avg IOU: 0.493589, Class: 1.000000, Obj: 0.306601, No Obj: 0.004152, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.477838, Class: 1.000000, Obj: 0.288113, No Obj: 0.004169, Avg Recall: 0.529412, count: 119 -Region Avg IOU: 0.476414, Class: 1.000000, Obj: 0.315422, No Obj: 0.003715, Avg Recall: 0.522727, count: 88 -2517: 0.467368, 0.487447 avg, 0.001000 rate, 20.983992 seconds, 644352 images -Loaded: 0.546078 seconds -Region Avg IOU: 0.474755, Class: 1.000000, Obj: 0.326985, No Obj: 0.004349, Avg Recall: 0.528926, count: 121 -Region Avg IOU: 0.468643, Class: 1.000000, Obj: 0.315237, No Obj: 0.004512, Avg Recall: 0.538462, count: 117 -Region Avg IOU: 0.461797, Class: 1.000000, Obj: 0.330076, No Obj: 0.003976, Avg Recall: 0.464646, count: 99 -Region Avg IOU: 0.490943, Class: 1.000000, Obj: 0.342338, No Obj: 0.003561, Avg Recall: 0.540230, count: 87 -Region Avg IOU: 0.543924, Class: 1.000000, Obj: 0.358874, No Obj: 0.004223, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.444387, Class: 1.000000, Obj: 0.303548, No Obj: 0.004078, Avg Recall: 0.471698, count: 106 -Region Avg IOU: 0.484032, Class: 1.000000, Obj: 0.311371, No Obj: 0.004403, Avg Recall: 0.533333, count: 120 -Region Avg IOU: 0.498647, Class: 1.000000, Obj: 0.322195, No Obj: 0.004305, Avg Recall: 0.510000, count: 100 -2518: 0.480327, 0.486735 avg, 0.001000 rate, 18.192516 seconds, 644608 images -Loaded: 4.495518 seconds -Region Avg IOU: 0.490247, Class: 1.000000, Obj: 0.297119, No Obj: 0.004622, Avg Recall: 0.568965, count: 116 -Region Avg IOU: 0.494662, Class: 1.000000, Obj: 0.283636, No Obj: 0.004020, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.535306, Class: 1.000000, Obj: 0.340072, No Obj: 0.004076, Avg Recall: 0.596491, count: 114 -Region Avg IOU: 0.551985, Class: 1.000000, Obj: 0.324622, No Obj: 0.004014, Avg Recall: 0.712500, count: 80 -Region Avg IOU: 0.529437, Class: 1.000000, Obj: 0.307516, No Obj: 0.004389, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.510582, Class: 1.000000, Obj: 0.331746, No Obj: 0.004446, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.501880, Class: 1.000000, Obj: 0.312262, No Obj: 0.004168, Avg Recall: 0.514925, count: 134 -Region Avg IOU: 0.469301, Class: 1.000000, Obj: 0.332424, No Obj: 0.004056, Avg Recall: 0.518182, count: 110 -2519: 0.503801, 0.488442 avg, 0.001000 rate, 18.879889 seconds, 644864 images -Loaded: 2.619253 seconds -Region Avg IOU: 0.537106, Class: 1.000000, Obj: 0.353522, No Obj: 0.005171, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.450212, Class: 1.000000, Obj: 0.282399, No Obj: 0.003476, Avg Recall: 0.484536, count: 97 -Region Avg IOU: 0.534226, Class: 1.000000, Obj: 0.281591, No Obj: 0.004246, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.498422, Class: 1.000000, Obj: 0.294339, No Obj: 0.004135, Avg Recall: 0.580952, count: 105 -Region Avg IOU: 0.452358, Class: 1.000000, Obj: 0.293646, No Obj: 0.004280, Avg Recall: 0.479167, count: 96 -Region Avg IOU: 0.503930, Class: 1.000000, Obj: 0.346276, No Obj: 0.003618, Avg Recall: 0.559524, count: 84 -Region Avg IOU: 0.493982, Class: 1.000000, Obj: 0.306608, No Obj: 0.004479, Avg Recall: 0.537879, count: 132 -Region Avg IOU: 0.488135, Class: 1.000000, Obj: 0.287512, No Obj: 0.003676, Avg Recall: 0.537037, count: 108 -2520: 0.485375, 0.488135 avg, 0.001000 rate, 19.333395 seconds, 645120 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000070 seconds -Region Avg IOU: 0.521555, Class: 1.000000, Obj: 0.357771, No Obj: 0.004639, Avg Recall: 0.606897, count: 145 -Region Avg IOU: 0.559957, Class: 1.000000, Obj: 0.410333, No Obj: 0.004283, Avg Recall: 0.677686, count: 121 -Region Avg IOU: 0.517734, Class: 1.000000, Obj: 0.330008, No Obj: 0.003097, Avg Recall: 0.592105, count: 76 -Region Avg IOU: 0.553542, Class: 1.000000, Obj: 0.322857, No Obj: 0.004072, Avg Recall: 0.664000, count: 125 -Region Avg IOU: 0.587147, Class: 1.000000, Obj: 0.380262, No Obj: 0.003639, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.535057, Class: 1.000000, Obj: 0.358197, No Obj: 0.003582, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.567082, Class: 1.000000, Obj: 0.355222, No Obj: 0.004203, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.542593, Class: 1.000000, Obj: 0.357524, No Obj: 0.003670, Avg Recall: 0.663366, count: 101 -2521: 0.454628, 0.484784 avg, 0.001000 rate, 25.072441 seconds, 645376 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.543496, Class: 1.000000, Obj: 0.361174, No Obj: 0.004284, Avg Recall: 0.612903, count: 124 -Region Avg IOU: 0.587061, Class: 1.000000, Obj: 0.375372, No Obj: 0.003891, Avg Recall: 0.716049, count: 81 -Region Avg IOU: 0.525532, Class: 1.000000, Obj: 0.305185, No Obj: 0.003556, Avg Recall: 0.583333, count: 72 -Region Avg IOU: 0.545595, Class: 1.000000, Obj: 0.354272, No Obj: 0.004684, Avg Recall: 0.598485, count: 132 -Region Avg IOU: 0.528409, Class: 1.000000, Obj: 0.358408, No Obj: 0.003462, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.551242, Class: 1.000000, Obj: 0.303260, No Obj: 0.004106, Avg Recall: 0.616279, count: 86 -Region Avg IOU: 0.535502, Class: 1.000000, Obj: 0.310444, No Obj: 0.003573, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.513040, Class: 1.000000, Obj: 0.342983, No Obj: 0.003415, Avg Recall: 0.551020, count: 98 -2522: 0.463338, 0.482640 avg, 0.001000 rate, 24.864355 seconds, 645632 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.542677, Class: 1.000000, Obj: 0.273819, No Obj: 0.003601, Avg Recall: 0.585366, count: 123 -Region Avg IOU: 0.520983, Class: 1.000000, Obj: 0.302540, No Obj: 0.003661, Avg Recall: 0.568965, count: 116 -Region Avg IOU: 0.506101, Class: 1.000000, Obj: 0.315414, No Obj: 0.003262, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.554171, Class: 1.000000, Obj: 0.318286, No Obj: 0.003381, Avg Recall: 0.694118, count: 85 -Region Avg IOU: 0.583549, Class: 1.000000, Obj: 0.332049, No Obj: 0.003799, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.572314, Class: 1.000000, Obj: 0.376381, No Obj: 0.003269, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.575601, Class: 1.000000, Obj: 0.282302, No Obj: 0.002971, Avg Recall: 0.686747, count: 83 -Region Avg IOU: 0.564906, Class: 1.000000, Obj: 0.347153, No Obj: 0.003809, Avg Recall: 0.614035, count: 114 -2523: 0.411700, 0.475546 avg, 0.001000 rate, 23.632336 seconds, 645888 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.565483, Class: 1.000000, Obj: 0.335254, No Obj: 0.003884, Avg Recall: 0.664062, count: 128 -Region Avg IOU: 0.541543, Class: 1.000000, Obj: 0.327689, No Obj: 0.003621, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.520209, Class: 1.000000, Obj: 0.324082, No Obj: 0.003537, Avg Recall: 0.575000, count: 120 -Region Avg IOU: 0.571222, Class: 1.000000, Obj: 0.363145, No Obj: 0.003773, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.582594, Class: 1.000000, Obj: 0.349614, No Obj: 0.003167, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.539337, Class: 1.000000, Obj: 0.365305, No Obj: 0.003008, Avg Recall: 0.625000, count: 80 -Region Avg IOU: 0.537848, Class: 1.000000, Obj: 0.281488, No Obj: 0.003804, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.559710, Class: 1.000000, Obj: 0.313848, No Obj: 0.003997, Avg Recall: 0.661157, count: 121 -2524: 0.440056, 0.471997 avg, 0.001000 rate, 23.721741 seconds, 646144 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.547638, Class: 1.000000, Obj: 0.300956, No Obj: 0.003875, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.538268, Class: 1.000000, Obj: 0.311951, No Obj: 0.003575, Avg Recall: 0.576000, count: 125 -Region Avg IOU: 0.627869, Class: 1.000000, Obj: 0.372698, No Obj: 0.003276, Avg Recall: 0.726191, count: 84 -Region Avg IOU: 0.593176, Class: 1.000000, Obj: 0.307742, No Obj: 0.003620, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.609255, Class: 1.000000, Obj: 0.326330, No Obj: 0.003365, Avg Recall: 0.719512, count: 82 -Region Avg IOU: 0.528754, Class: 1.000000, Obj: 0.326393, No Obj: 0.003318, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.563884, Class: 1.000000, Obj: 0.364661, No Obj: 0.003314, Avg Recall: 0.671233, count: 73 -Region Avg IOU: 0.542008, Class: 1.000000, Obj: 0.321775, No Obj: 0.003303, Avg Recall: 0.617886, count: 123 -2525: 0.421174, 0.466914 avg, 0.001000 rate, 23.696836 seconds, 646400 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.569598, Class: 1.000000, Obj: 0.351616, No Obj: 0.003117, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.573537, Class: 1.000000, Obj: 0.317947, No Obj: 0.002921, Avg Recall: 0.605769, count: 104 -Region Avg IOU: 0.555821, Class: 1.000000, Obj: 0.297259, No Obj: 0.003474, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.551360, Class: 1.000000, Obj: 0.300701, No Obj: 0.003524, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.566153, Class: 1.000000, Obj: 0.360140, No Obj: 0.003067, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.543140, Class: 1.000000, Obj: 0.321816, No Obj: 0.002805, Avg Recall: 0.611111, count: 72 -Region Avg IOU: 0.559900, Class: 1.000000, Obj: 0.348928, No Obj: 0.003144, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.488043, Class: 1.000000, Obj: 0.304199, No Obj: 0.003063, Avg Recall: 0.523810, count: 105 -2526: 0.403636, 0.460586 avg, 0.001000 rate, 23.765108 seconds, 646656 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.530881, Class: 1.000000, Obj: 0.282125, No Obj: 0.003376, Avg Recall: 0.625000, count: 128 -Region Avg IOU: 0.552668, Class: 1.000000, Obj: 0.343927, No Obj: 0.003459, Avg Recall: 0.643478, count: 115 -Region Avg IOU: 0.555310, Class: 1.000000, Obj: 0.319774, No Obj: 0.003583, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.597013, Class: 1.000000, Obj: 0.344750, No Obj: 0.004047, Avg Recall: 0.697479, count: 119 -Region Avg IOU: 0.576644, Class: 1.000000, Obj: 0.306727, No Obj: 0.003273, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.543278, Class: 1.000000, Obj: 0.301728, No Obj: 0.002709, Avg Recall: 0.616279, count: 86 -Region Avg IOU: 0.575797, Class: 1.000000, Obj: 0.321692, No Obj: 0.003260, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.566067, Class: 1.000000, Obj: 0.327418, No Obj: 0.003418, Avg Recall: 0.660714, count: 112 -2527: 0.474284, 0.461956 avg, 0.001000 rate, 25.202105 seconds, 646912 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.539439, Class: 1.000000, Obj: 0.286340, No Obj: 0.003618, Avg Recall: 0.597122, count: 139 -Region Avg IOU: 0.545937, Class: 1.000000, Obj: 0.328870, No Obj: 0.003467, Avg Recall: 0.602837, count: 141 -Region Avg IOU: 0.592813, Class: 1.000000, Obj: 0.362550, No Obj: 0.003933, Avg Recall: 0.690647, count: 139 -Region Avg IOU: 0.561376, Class: 1.000000, Obj: 0.340885, No Obj: 0.003563, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.575095, Class: 1.000000, Obj: 0.373622, No Obj: 0.002811, Avg Recall: 0.666667, count: 87 -Region Avg IOU: 0.586694, Class: 1.000000, Obj: 0.313802, No Obj: 0.003803, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.541067, Class: 1.000000, Obj: 0.291128, No Obj: 0.003421, Avg Recall: 0.663866, count: 119 -Region Avg IOU: 0.553706, Class: 1.000000, Obj: 0.307367, No Obj: 0.003496, Avg Recall: 0.659794, count: 97 -2528: 0.476295, 0.463390 avg, 0.001000 rate, 24.665045 seconds, 647168 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.576929, Class: 1.000000, Obj: 0.327173, No Obj: 0.003471, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.564319, Class: 1.000000, Obj: 0.307697, No Obj: 0.003676, Avg Recall: 0.656000, count: 125 -Region Avg IOU: 0.567799, Class: 1.000000, Obj: 0.332272, No Obj: 0.003371, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.589334, Class: 1.000000, Obj: 0.337446, No Obj: 0.003636, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.551154, Class: 1.000000, Obj: 0.325605, No Obj: 0.003821, Avg Recall: 0.619048, count: 126 -Region Avg IOU: 0.570907, Class: 1.000000, Obj: 0.336654, No Obj: 0.003582, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.573478, Class: 1.000000, Obj: 0.306337, No Obj: 0.003729, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.547093, Class: 1.000000, Obj: 0.363748, No Obj: 0.003363, Avg Recall: 0.633929, count: 112 -2529: 0.483207, 0.465372 avg, 0.001000 rate, 22.825050 seconds, 647424 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.559193, Class: 1.000000, Obj: 0.374530, No Obj: 0.003604, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.570624, Class: 1.000000, Obj: 0.355066, No Obj: 0.003968, Avg Recall: 0.666667, count: 123 -Region Avg IOU: 0.545210, Class: 1.000000, Obj: 0.343723, No Obj: 0.003520, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.530574, Class: 1.000000, Obj: 0.346185, No Obj: 0.003749, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.560984, Class: 1.000000, Obj: 0.325841, No Obj: 0.003923, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.581996, Class: 1.000000, Obj: 0.400305, No Obj: 0.003621, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.595706, Class: 1.000000, Obj: 0.396059, No Obj: 0.003624, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.603558, Class: 1.000000, Obj: 0.351079, No Obj: 0.003700, Avg Recall: 0.718750, count: 96 -2530: 0.453872, 0.464222 avg, 0.001000 rate, 23.818359 seconds, 647680 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000059 seconds -Region Avg IOU: 0.609638, Class: 1.000000, Obj: 0.355319, No Obj: 0.003682, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.597834, Class: 1.000000, Obj: 0.374750, No Obj: 0.002621, Avg Recall: 0.726191, count: 84 -Region Avg IOU: 0.629039, Class: 1.000000, Obj: 0.383544, No Obj: 0.003025, Avg Recall: 0.802198, count: 91 -Region Avg IOU: 0.578082, Class: 1.000000, Obj: 0.294279, No Obj: 0.003133, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.597079, Class: 1.000000, Obj: 0.347949, No Obj: 0.003672, Avg Recall: 0.718518, count: 135 -Region Avg IOU: 0.638551, Class: 1.000000, Obj: 0.409307, No Obj: 0.003169, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.610750, Class: 1.000000, Obj: 0.429609, No Obj: 0.002823, Avg Recall: 0.689655, count: 87 -Region Avg IOU: 0.604949, Class: 1.000000, Obj: 0.387315, No Obj: 0.003537, Avg Recall: 0.717391, count: 138 -2531: 0.472638, 0.465063 avg, 0.001000 rate, 28.569437 seconds, 647936 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.604117, Class: 1.000000, Obj: 0.346034, No Obj: 0.003387, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.645674, Class: 1.000000, Obj: 0.395772, No Obj: 0.003572, Avg Recall: 0.847619, count: 105 -Region Avg IOU: 0.589819, Class: 1.000000, Obj: 0.446340, No Obj: 0.002927, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.609412, Class: 1.000000, Obj: 0.391579, No Obj: 0.003233, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.611009, Class: 1.000000, Obj: 0.425347, No Obj: 0.003143, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.560160, Class: 1.000000, Obj: 0.355131, No Obj: 0.003153, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.631289, Class: 1.000000, Obj: 0.417289, No Obj: 0.002987, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.623711, Class: 1.000000, Obj: 0.405224, No Obj: 0.003333, Avg Recall: 0.754717, count: 106 -2532: 0.431046, 0.461662 avg, 0.001000 rate, 28.093601 seconds, 648192 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.626381, Class: 1.000000, Obj: 0.341860, No Obj: 0.002894, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.640587, Class: 1.000000, Obj: 0.382016, No Obj: 0.003103, Avg Recall: 0.814159, count: 113 -Region Avg IOU: 0.569805, Class: 1.000000, Obj: 0.388359, No Obj: 0.003019, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.662190, Class: 1.000000, Obj: 0.387215, No Obj: 0.002994, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.589327, Class: 1.000000, Obj: 0.329889, No Obj: 0.002848, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.612549, Class: 1.000000, Obj: 0.354094, No Obj: 0.002934, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.585469, Class: 1.000000, Obj: 0.348547, No Obj: 0.003100, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.584284, Class: 1.000000, Obj: 0.337707, No Obj: 0.002683, Avg Recall: 0.691489, count: 94 -2533: 0.482089, 0.463704 avg, 0.001000 rate, 29.472216 seconds, 648448 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.598053, Class: 1.000000, Obj: 0.254015, No Obj: 0.002630, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.588076, Class: 1.000000, Obj: 0.312552, No Obj: 0.002316, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.567524, Class: 1.000000, Obj: 0.334306, No Obj: 0.002444, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.613722, Class: 1.000000, Obj: 0.318576, No Obj: 0.002792, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.621966, Class: 1.000000, Obj: 0.308101, No Obj: 0.002511, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.597162, Class: 1.000000, Obj: 0.231820, No Obj: 0.002866, Avg Recall: 0.740458, count: 131 -Region Avg IOU: 0.597739, Class: 1.000000, Obj: 0.342596, No Obj: 0.002570, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.611105, Class: 1.000000, Obj: 0.334790, No Obj: 0.002952, Avg Recall: 0.732283, count: 127 -2534: 0.472928, 0.464627 avg, 0.001000 rate, 27.171623 seconds, 648704 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.621351, Class: 1.000000, Obj: 0.333659, No Obj: 0.002441, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.614198, Class: 1.000000, Obj: 0.287195, No Obj: 0.002319, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.612511, Class: 1.000000, Obj: 0.291718, No Obj: 0.002308, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.605714, Class: 1.000000, Obj: 0.303216, No Obj: 0.002402, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.618901, Class: 1.000000, Obj: 0.328135, No Obj: 0.002382, Avg Recall: 0.797619, count: 84 -Region Avg IOU: 0.630132, Class: 1.000000, Obj: 0.271959, No Obj: 0.002563, Avg Recall: 0.762712, count: 118 -Region Avg IOU: 0.647786, Class: 1.000000, Obj: 0.350614, No Obj: 0.002526, Avg Recall: 0.813084, count: 107 -Region Avg IOU: 0.634075, Class: 1.000000, Obj: 0.330298, No Obj: 0.002682, Avg Recall: 0.738462, count: 130 -2535: 0.482124, 0.466376 avg, 0.001000 rate, 28.805773 seconds, 648960 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.616231, Class: 1.000000, Obj: 0.307945, No Obj: 0.002462, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.587418, Class: 1.000000, Obj: 0.284909, No Obj: 0.002518, Avg Recall: 0.669355, count: 124 -Region Avg IOU: 0.616668, Class: 1.000000, Obj: 0.287257, No Obj: 0.002705, Avg Recall: 0.762238, count: 143 -Region Avg IOU: 0.640122, Class: 1.000000, Obj: 0.271679, No Obj: 0.002679, Avg Recall: 0.787234, count: 141 -Region Avg IOU: 0.617152, Class: 1.000000, Obj: 0.298831, No Obj: 0.002503, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.660479, Class: 1.000000, Obj: 0.302800, No Obj: 0.002479, Avg Recall: 0.870690, count: 116 -Region Avg IOU: 0.649878, Class: 1.000000, Obj: 0.336846, No Obj: 0.002678, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.584883, Class: 1.000000, Obj: 0.278341, No Obj: 0.002688, Avg Recall: 0.740458, count: 131 -2536: 0.536662, 0.473405 avg, 0.001000 rate, 27.419617 seconds, 649216 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.609604, Class: 1.000000, Obj: 0.306406, No Obj: 0.002399, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.625099, Class: 1.000000, Obj: 0.306843, No Obj: 0.002303, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.635995, Class: 1.000000, Obj: 0.326894, No Obj: 0.002506, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.674159, Class: 1.000000, Obj: 0.329026, No Obj: 0.002838, Avg Recall: 0.844660, count: 103 -Region Avg IOU: 0.621445, Class: 1.000000, Obj: 0.364276, No Obj: 0.002394, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.617439, Class: 1.000000, Obj: 0.269677, No Obj: 0.002947, Avg Recall: 0.751880, count: 133 -Region Avg IOU: 0.627900, Class: 1.000000, Obj: 0.303118, No Obj: 0.002960, Avg Recall: 0.720000, count: 125 -Region Avg IOU: 0.626688, Class: 1.000000, Obj: 0.299927, No Obj: 0.002474, Avg Recall: 0.791304, count: 115 -2537: 0.471628, 0.473227 avg, 0.001000 rate, 28.447952 seconds, 649472 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.621444, Class: 1.000000, Obj: 0.292911, No Obj: 0.002458, Avg Recall: 0.790698, count: 86 -Region Avg IOU: 0.620820, Class: 1.000000, Obj: 0.309235, No Obj: 0.002607, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.642159, Class: 1.000000, Obj: 0.317475, No Obj: 0.002801, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.657517, Class: 1.000000, Obj: 0.291690, No Obj: 0.002930, Avg Recall: 0.841667, count: 120 -Region Avg IOU: 0.599384, Class: 1.000000, Obj: 0.313792, No Obj: 0.002798, Avg Recall: 0.702128, count: 141 -Region Avg IOU: 0.598209, Class: 1.000000, Obj: 0.308945, No Obj: 0.002390, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.669052, Class: 1.000000, Obj: 0.301756, No Obj: 0.002385, Avg Recall: 0.833333, count: 84 -Region Avg IOU: 0.634277, Class: 1.000000, Obj: 0.326834, No Obj: 0.003065, Avg Recall: 0.797468, count: 158 -2538: 0.486175, 0.474522 avg, 0.001000 rate, 28.568756 seconds, 649728 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.582780, Class: 1.000000, Obj: 0.327078, No Obj: 0.003112, Avg Recall: 0.685315, count: 143 -Region Avg IOU: 0.578689, Class: 1.000000, Obj: 0.309922, No Obj: 0.002421, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.589316, Class: 1.000000, Obj: 0.331749, No Obj: 0.002780, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.625633, Class: 1.000000, Obj: 0.287946, No Obj: 0.002851, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.595183, Class: 1.000000, Obj: 0.338931, No Obj: 0.003044, Avg Recall: 0.709924, count: 131 -Region Avg IOU: 0.635236, Class: 1.000000, Obj: 0.355488, No Obj: 0.002743, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.617436, Class: 1.000000, Obj: 0.327514, No Obj: 0.003024, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.626850, Class: 1.000000, Obj: 0.368956, No Obj: 0.002582, Avg Recall: 0.766990, count: 103 -2539: 0.514568, 0.478527 avg, 0.001000 rate, 27.335487 seconds, 649984 images -Loaded: 0.000052 seconds -Region Avg IOU: 0.647870, Class: 1.000000, Obj: 0.339337, No Obj: 0.003022, Avg Recall: 0.817204, count: 93 -Region Avg IOU: 0.615253, Class: 1.000000, Obj: 0.334205, No Obj: 0.002898, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.640460, Class: 1.000000, Obj: 0.385541, No Obj: 0.002879, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.626293, Class: 1.000000, Obj: 0.370746, No Obj: 0.002912, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.597421, Class: 1.000000, Obj: 0.341587, No Obj: 0.002546, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.641132, Class: 1.000000, Obj: 0.357686, No Obj: 0.003055, Avg Recall: 0.809524, count: 126 -Region Avg IOU: 0.627706, Class: 1.000000, Obj: 0.403892, No Obj: 0.003062, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.632484, Class: 1.000000, Obj: 0.296148, No Obj: 0.003333, Avg Recall: 0.762712, count: 118 -2540: 0.454337, 0.476108 avg, 0.001000 rate, 28.876427 seconds, 650240 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 17.662069 seconds -Region Avg IOU: 0.565608, Class: 1.000000, Obj: 0.371200, No Obj: 0.003770, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.532882, Class: 1.000000, Obj: 0.281208, No Obj: 0.002999, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.541330, Class: 1.000000, Obj: 0.355277, No Obj: 0.003675, Avg Recall: 0.650794, count: 126 -Region Avg IOU: 0.623000, Class: 1.000000, Obj: 0.375324, No Obj: 0.003248, Avg Recall: 0.802469, count: 81 -Region Avg IOU: 0.563711, Class: 1.000000, Obj: 0.326497, No Obj: 0.003562, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.578864, Class: 1.000000, Obj: 0.333477, No Obj: 0.003875, Avg Recall: 0.706767, count: 133 -Region Avg IOU: 0.597623, Class: 1.000000, Obj: 0.313704, No Obj: 0.003343, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.496024, Class: 1.000000, Obj: 0.307683, No Obj: 0.003514, Avg Recall: 0.574627, count: 134 -2541: 0.489261, 0.477423 avg, 0.001000 rate, 24.562023 seconds, 650496 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.566925, Class: 1.000000, Obj: 0.394664, No Obj: 0.003740, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.616346, Class: 1.000000, Obj: 0.391858, No Obj: 0.003546, Avg Recall: 0.804878, count: 82 -Region Avg IOU: 0.574753, Class: 1.000000, Obj: 0.336035, No Obj: 0.003978, Avg Recall: 0.659259, count: 135 -Region Avg IOU: 0.558806, Class: 1.000000, Obj: 0.332571, No Obj: 0.003821, Avg Recall: 0.648000, count: 125 -Region Avg IOU: 0.592458, Class: 1.000000, Obj: 0.372409, No Obj: 0.003624, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.543372, Class: 1.000000, Obj: 0.294648, No Obj: 0.003234, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.582789, Class: 1.000000, Obj: 0.362889, No Obj: 0.004057, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.599389, Class: 1.000000, Obj: 0.370949, No Obj: 0.003630, Avg Recall: 0.728155, count: 103 -2542: 0.535311, 0.483212 avg, 0.001000 rate, 23.816441 seconds, 650752 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.553744, Class: 1.000000, Obj: 0.363233, No Obj: 0.003590, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.591794, Class: 1.000000, Obj: 0.382206, No Obj: 0.004367, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.619273, Class: 1.000000, Obj: 0.363498, No Obj: 0.004195, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.576832, Class: 1.000000, Obj: 0.332878, No Obj: 0.003644, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.554364, Class: 1.000000, Obj: 0.346049, No Obj: 0.003457, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.570202, Class: 1.000000, Obj: 0.397746, No Obj: 0.003751, Avg Recall: 0.719298, count: 114 -Region Avg IOU: 0.560887, Class: 1.000000, Obj: 0.348049, No Obj: 0.003904, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.544715, Class: 1.000000, Obj: 0.331268, No Obj: 0.003549, Avg Recall: 0.598131, count: 107 -2543: 0.415839, 0.476475 avg, 0.001000 rate, 22.751440 seconds, 651008 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.580238, Class: 1.000000, Obj: 0.406753, No Obj: 0.004033, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.596028, Class: 1.000000, Obj: 0.373307, No Obj: 0.004329, Avg Recall: 0.698413, count: 126 -Region Avg IOU: 0.566287, Class: 1.000000, Obj: 0.376067, No Obj: 0.003556, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.574468, Class: 1.000000, Obj: 0.369432, No Obj: 0.003864, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.557557, Class: 1.000000, Obj: 0.351431, No Obj: 0.004009, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.528355, Class: 1.000000, Obj: 0.361780, No Obj: 0.003380, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.552179, Class: 1.000000, Obj: 0.372861, No Obj: 0.004624, Avg Recall: 0.628788, count: 132 -Region Avg IOU: 0.542925, Class: 1.000000, Obj: 0.343262, No Obj: 0.003485, Avg Recall: 0.600000, count: 90 -2544: 0.458015, 0.474629 avg, 0.001000 rate, 25.053051 seconds, 651264 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.545477, Class: 1.000000, Obj: 0.399175, No Obj: 0.003863, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.573845, Class: 1.000000, Obj: 0.399759, No Obj: 0.003571, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.548546, Class: 1.000000, Obj: 0.336519, No Obj: 0.004208, Avg Recall: 0.644172, count: 163 -Region Avg IOU: 0.623894, Class: 1.000000, Obj: 0.393009, No Obj: 0.003866, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.549163, Class: 1.000000, Obj: 0.372316, No Obj: 0.004238, Avg Recall: 0.657534, count: 146 -Region Avg IOU: 0.580012, Class: 1.000000, Obj: 0.359688, No Obj: 0.004810, Avg Recall: 0.687075, count: 147 -Region Avg IOU: 0.527465, Class: 1.000000, Obj: 0.351187, No Obj: 0.003854, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.543838, Class: 1.000000, Obj: 0.345877, No Obj: 0.004098, Avg Recall: 0.644068, count: 118 -2545: 0.488693, 0.476035 avg, 0.001000 rate, 25.764711 seconds, 651520 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.539970, Class: 1.000000, Obj: 0.332830, No Obj: 0.004048, Avg Recall: 0.616541, count: 133 -Region Avg IOU: 0.569789, Class: 1.000000, Obj: 0.420288, No Obj: 0.003505, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.552521, Class: 1.000000, Obj: 0.328479, No Obj: 0.003803, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.561006, Class: 1.000000, Obj: 0.374166, No Obj: 0.003772, Avg Recall: 0.682927, count: 123 -Region Avg IOU: 0.511188, Class: 1.000000, Obj: 0.350850, No Obj: 0.003536, Avg Recall: 0.565657, count: 99 -Region Avg IOU: 0.533546, Class: 1.000000, Obj: 0.338796, No Obj: 0.003337, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.542114, Class: 1.000000, Obj: 0.399722, No Obj: 0.003601, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.563675, Class: 1.000000, Obj: 0.383748, No Obj: 0.004385, Avg Recall: 0.664286, count: 140 -2546: 0.486705, 0.477102 avg, 0.001000 rate, 24.126467 seconds, 651776 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.556178, Class: 1.000000, Obj: 0.350545, No Obj: 0.003532, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.530340, Class: 1.000000, Obj: 0.333160, No Obj: 0.003358, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.584899, Class: 1.000000, Obj: 0.328778, No Obj: 0.003583, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.565211, Class: 1.000000, Obj: 0.354226, No Obj: 0.003256, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.554912, Class: 1.000000, Obj: 0.321639, No Obj: 0.003620, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.526149, Class: 1.000000, Obj: 0.335322, No Obj: 0.002996, Avg Recall: 0.589041, count: 73 -Region Avg IOU: 0.533863, Class: 1.000000, Obj: 0.386283, No Obj: 0.003642, Avg Recall: 0.632000, count: 125 -Region Avg IOU: 0.533180, Class: 1.000000, Obj: 0.338456, No Obj: 0.003288, Avg Recall: 0.592233, count: 103 -2547: 0.451312, 0.474523 avg, 0.001000 rate, 24.066219 seconds, 652032 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.565036, Class: 1.000000, Obj: 0.309581, No Obj: 0.003800, Avg Recall: 0.678571, count: 140 -Region Avg IOU: 0.565592, Class: 1.000000, Obj: 0.343422, No Obj: 0.003996, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.583592, Class: 1.000000, Obj: 0.371307, No Obj: 0.003345, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.541797, Class: 1.000000, Obj: 0.325800, No Obj: 0.002958, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.583366, Class: 1.000000, Obj: 0.328247, No Obj: 0.003828, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.506950, Class: 1.000000, Obj: 0.326277, No Obj: 0.003685, Avg Recall: 0.606299, count: 127 -Region Avg IOU: 0.532961, Class: 1.000000, Obj: 0.353425, No Obj: 0.003169, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.512952, Class: 1.000000, Obj: 0.345203, No Obj: 0.002972, Avg Recall: 0.596154, count: 104 -2548: 0.483492, 0.475420 avg, 0.001000 rate, 24.052191 seconds, 652288 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.550241, Class: 1.000000, Obj: 0.314866, No Obj: 0.003477, Avg Recall: 0.645161, count: 124 -Region Avg IOU: 0.522497, Class: 1.000000, Obj: 0.305197, No Obj: 0.003420, Avg Recall: 0.597015, count: 134 -Region Avg IOU: 0.603497, Class: 1.000000, Obj: 0.326243, No Obj: 0.003001, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.553650, Class: 1.000000, Obj: 0.359347, No Obj: 0.003370, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.522698, Class: 1.000000, Obj: 0.332057, No Obj: 0.003449, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.545251, Class: 1.000000, Obj: 0.339337, No Obj: 0.003411, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.551672, Class: 1.000000, Obj: 0.302968, No Obj: 0.003410, Avg Recall: 0.639344, count: 122 -Region Avg IOU: 0.539660, Class: 1.000000, Obj: 0.340031, No Obj: 0.002835, Avg Recall: 0.585366, count: 82 -2549: 0.482410, 0.476119 avg, 0.001000 rate, 24.361076 seconds, 652544 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.523437, Class: 1.000000, Obj: 0.340964, No Obj: 0.003096, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.535603, Class: 1.000000, Obj: 0.321830, No Obj: 0.002938, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.561324, Class: 1.000000, Obj: 0.309897, No Obj: 0.003513, Avg Recall: 0.688525, count: 122 -Region Avg IOU: 0.550161, Class: 1.000000, Obj: 0.304237, No Obj: 0.003018, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.513817, Class: 1.000000, Obj: 0.326382, No Obj: 0.003302, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.535327, Class: 1.000000, Obj: 0.295290, No Obj: 0.003289, Avg Recall: 0.591549, count: 142 -Region Avg IOU: 0.530031, Class: 1.000000, Obj: 0.270696, No Obj: 0.003156, Avg Recall: 0.596774, count: 124 -Region Avg IOU: 0.551599, Class: 1.000000, Obj: 0.310128, No Obj: 0.003407, Avg Recall: 0.634921, count: 126 -2550: 0.478464, 0.476353 avg, 0.001000 rate, 24.041059 seconds, 652800 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000069 seconds -Region Avg IOU: 0.620527, Class: 1.000000, Obj: 0.370957, No Obj: 0.002737, Avg Recall: 0.801980, count: 101 -Region Avg IOU: 0.603175, Class: 1.000000, Obj: 0.296064, No Obj: 0.002933, Avg Recall: 0.767442, count: 129 -Region Avg IOU: 0.568711, Class: 1.000000, Obj: 0.367331, No Obj: 0.002447, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.656876, Class: 1.000000, Obj: 0.375492, No Obj: 0.002868, Avg Recall: 0.802469, count: 81 -Region Avg IOU: 0.630904, Class: 1.000000, Obj: 0.317564, No Obj: 0.003001, Avg Recall: 0.807018, count: 114 -Region Avg IOU: 0.631757, Class: 1.000000, Obj: 0.357690, No Obj: 0.003070, Avg Recall: 0.776859, count: 121 -Region Avg IOU: 0.612928, Class: 1.000000, Obj: 0.358175, No Obj: 0.002648, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.580431, Class: 1.000000, Obj: 0.323569, No Obj: 0.002887, Avg Recall: 0.701613, count: 124 -2551: 0.500795, 0.478798 avg, 0.001000 rate, 28.428772 seconds, 653056 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.608390, Class: 1.000000, Obj: 0.302664, No Obj: 0.002945, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.641717, Class: 1.000000, Obj: 0.367994, No Obj: 0.002785, Avg Recall: 0.824561, count: 114 -Region Avg IOU: 0.655338, Class: 1.000000, Obj: 0.384417, No Obj: 0.002476, Avg Recall: 0.836735, count: 98 -Region Avg IOU: 0.617836, Class: 1.000000, Obj: 0.318709, No Obj: 0.002438, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.637881, Class: 1.000000, Obj: 0.337480, No Obj: 0.002980, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.622645, Class: 1.000000, Obj: 0.328474, No Obj: 0.002726, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.590891, Class: 1.000000, Obj: 0.316562, No Obj: 0.002691, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.588123, Class: 1.000000, Obj: 0.321576, No Obj: 0.002713, Avg Recall: 0.730000, count: 100 -2552: 0.447861, 0.475704 avg, 0.001000 rate, 28.163364 seconds, 653312 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.666610, Class: 1.000000, Obj: 0.321088, No Obj: 0.002769, Avg Recall: 0.840000, count: 100 -Region Avg IOU: 0.636778, Class: 1.000000, Obj: 0.273337, No Obj: 0.002537, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.590255, Class: 1.000000, Obj: 0.339109, No Obj: 0.002859, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.629555, Class: 1.000000, Obj: 0.314270, No Obj: 0.002862, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.642546, Class: 1.000000, Obj: 0.369770, No Obj: 0.002828, Avg Recall: 0.772358, count: 123 -Region Avg IOU: 0.574329, Class: 1.000000, Obj: 0.318984, No Obj: 0.002805, Avg Recall: 0.674603, count: 126 -Region Avg IOU: 0.663266, Class: 1.000000, Obj: 0.361018, No Obj: 0.002468, Avg Recall: 0.909091, count: 88 -Region Avg IOU: 0.622494, Class: 1.000000, Obj: 0.332410, No Obj: 0.003405, Avg Recall: 0.753521, count: 142 -2553: 0.499960, 0.478130 avg, 0.001000 rate, 26.861670 seconds, 653568 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.636835, Class: 1.000000, Obj: 0.338099, No Obj: 0.002604, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.634931, Class: 1.000000, Obj: 0.292841, No Obj: 0.002709, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.655556, Class: 1.000000, Obj: 0.343709, No Obj: 0.002870, Avg Recall: 0.847458, count: 118 -Region Avg IOU: 0.588660, Class: 1.000000, Obj: 0.360690, No Obj: 0.002919, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.583298, Class: 1.000000, Obj: 0.351307, No Obj: 0.002889, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.636377, Class: 1.000000, Obj: 0.366294, No Obj: 0.002469, Avg Recall: 0.768293, count: 82 -Region Avg IOU: 0.630942, Class: 1.000000, Obj: 0.327330, No Obj: 0.003086, Avg Recall: 0.798507, count: 134 -Region Avg IOU: 0.637841, Class: 1.000000, Obj: 0.308969, No Obj: 0.002962, Avg Recall: 0.752381, count: 105 -2554: 0.464427, 0.476759 avg, 0.001000 rate, 28.069620 seconds, 653824 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.632447, Class: 1.000000, Obj: 0.317660, No Obj: 0.002430, Avg Recall: 0.777778, count: 81 -Region Avg IOU: 0.637662, Class: 1.000000, Obj: 0.321421, No Obj: 0.003164, Avg Recall: 0.820895, count: 134 -Region Avg IOU: 0.674609, Class: 1.000000, Obj: 0.429086, No Obj: 0.002745, Avg Recall: 0.835165, count: 91 -Region Avg IOU: 0.573520, Class: 1.000000, Obj: 0.298596, No Obj: 0.002951, Avg Recall: 0.681250, count: 160 -Region Avg IOU: 0.592862, Class: 1.000000, Obj: 0.324229, No Obj: 0.002903, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.594564, Class: 1.000000, Obj: 0.346331, No Obj: 0.002983, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.650026, Class: 1.000000, Obj: 0.313484, No Obj: 0.002658, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.611602, Class: 1.000000, Obj: 0.301882, No Obj: 0.003215, Avg Recall: 0.767241, count: 116 -2555: 0.481558, 0.477239 avg, 0.001000 rate, 28.044271 seconds, 654080 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.664241, Class: 1.000000, Obj: 0.366347, No Obj: 0.002527, Avg Recall: 0.820225, count: 89 -Region Avg IOU: 0.666496, Class: 1.000000, Obj: 0.323313, No Obj: 0.003265, Avg Recall: 0.903846, count: 104 -Region Avg IOU: 0.622324, Class: 1.000000, Obj: 0.334388, No Obj: 0.002764, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.651200, Class: 1.000000, Obj: 0.365888, No Obj: 0.002872, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.636366, Class: 1.000000, Obj: 0.384582, No Obj: 0.002949, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.638570, Class: 1.000000, Obj: 0.343010, No Obj: 0.003130, Avg Recall: 0.817391, count: 115 -Region Avg IOU: 0.603556, Class: 1.000000, Obj: 0.320815, No Obj: 0.003064, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.633576, Class: 1.000000, Obj: 0.321264, No Obj: 0.002800, Avg Recall: 0.794118, count: 102 -2556: 0.469793, 0.476495 avg, 0.001000 rate, 27.506344 seconds, 654336 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.590758, Class: 1.000000, Obj: 0.361387, No Obj: 0.002850, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.625467, Class: 1.000000, Obj: 0.358193, No Obj: 0.002710, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.604579, Class: 1.000000, Obj: 0.319866, No Obj: 0.002755, Avg Recall: 0.761364, count: 88 -Region Avg IOU: 0.634423, Class: 1.000000, Obj: 0.364222, No Obj: 0.002881, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.634418, Class: 1.000000, Obj: 0.361198, No Obj: 0.003265, Avg Recall: 0.771186, count: 118 -Region Avg IOU: 0.618906, Class: 1.000000, Obj: 0.309675, No Obj: 0.003155, Avg Recall: 0.759690, count: 129 -Region Avg IOU: 0.624827, Class: 1.000000, Obj: 0.332664, No Obj: 0.003129, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.622425, Class: 1.000000, Obj: 0.341661, No Obj: 0.002974, Avg Recall: 0.761905, count: 105 -2557: 0.467438, 0.475589 avg, 0.001000 rate, 29.062517 seconds, 654592 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.604096, Class: 1.000000, Obj: 0.328149, No Obj: 0.002923, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.643656, Class: 1.000000, Obj: 0.343981, No Obj: 0.002700, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.643943, Class: 1.000000, Obj: 0.318681, No Obj: 0.002629, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.644974, Class: 1.000000, Obj: 0.346231, No Obj: 0.002718, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.628586, Class: 1.000000, Obj: 0.304504, No Obj: 0.002855, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.583470, Class: 1.000000, Obj: 0.284694, No Obj: 0.002731, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.610698, Class: 1.000000, Obj: 0.320315, No Obj: 0.002765, Avg Recall: 0.715447, count: 123 -Region Avg IOU: 0.630070, Class: 1.000000, Obj: 0.298906, No Obj: 0.002818, Avg Recall: 0.786325, count: 117 -2558: 0.505969, 0.478627 avg, 0.001000 rate, 27.109188 seconds, 654848 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.639588, Class: 1.000000, Obj: 0.344920, No Obj: 0.002400, Avg Recall: 0.843750, count: 96 -Region Avg IOU: 0.612353, Class: 1.000000, Obj: 0.383268, No Obj: 0.002415, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.639585, Class: 1.000000, Obj: 0.374395, No Obj: 0.003042, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.601536, Class: 1.000000, Obj: 0.339343, No Obj: 0.002591, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.610229, Class: 1.000000, Obj: 0.372264, No Obj: 0.002274, Avg Recall: 0.720588, count: 68 -Region Avg IOU: 0.557444, Class: 1.000000, Obj: 0.292416, No Obj: 0.002744, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.651609, Class: 1.000000, Obj: 0.350014, No Obj: 0.002604, Avg Recall: 0.819048, count: 105 -Region Avg IOU: 0.614032, Class: 1.000000, Obj: 0.336792, No Obj: 0.002923, Avg Recall: 0.772358, count: 123 -2559: 0.436219, 0.474386 avg, 0.001000 rate, 28.835360 seconds, 655104 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.631466, Class: 1.000000, Obj: 0.347766, No Obj: 0.003008, Avg Recall: 0.772358, count: 123 -Region Avg IOU: 0.639762, Class: 1.000000, Obj: 0.352471, No Obj: 0.002685, Avg Recall: 0.814815, count: 81 -Region Avg IOU: 0.619005, Class: 1.000000, Obj: 0.360772, No Obj: 0.002607, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.644084, Class: 1.000000, Obj: 0.350278, No Obj: 0.003043, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.579847, Class: 1.000000, Obj: 0.344835, No Obj: 0.002289, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.654450, Class: 1.000000, Obj: 0.329622, No Obj: 0.003343, Avg Recall: 0.801527, count: 131 -Region Avg IOU: 0.619167, Class: 1.000000, Obj: 0.323036, No Obj: 0.002669, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.626048, Class: 1.000000, Obj: 0.301065, No Obj: 0.002761, Avg Recall: 0.750000, count: 88 -2560: 0.467583, 0.473706 avg, 0.001000 rate, 27.853788 seconds, 655360 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 15.244210 seconds -Region Avg IOU: 0.592517, Class: 1.000000, Obj: 0.332460, No Obj: 0.002875, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.660644, Class: 1.000000, Obj: 0.321842, No Obj: 0.002924, Avg Recall: 0.820000, count: 100 -Region Avg IOU: 0.619381, Class: 1.000000, Obj: 0.345504, No Obj: 0.003080, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.601587, Class: 1.000000, Obj: 0.329908, No Obj: 0.002764, Avg Recall: 0.711111, count: 135 -Region Avg IOU: 0.574581, Class: 1.000000, Obj: 0.337209, No Obj: 0.002702, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.583406, Class: 1.000000, Obj: 0.346284, No Obj: 0.002694, Avg Recall: 0.671429, count: 140 -Region Avg IOU: 0.611422, Class: 1.000000, Obj: 0.319376, No Obj: 0.002864, Avg Recall: 0.729508, count: 122 -Region Avg IOU: 0.627783, Class: 1.000000, Obj: 0.298944, No Obj: 0.002616, Avg Recall: 0.790909, count: 110 -2561: 0.504009, 0.476736 avg, 0.001000 rate, 27.076590 seconds, 655616 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.609496, Class: 1.000000, Obj: 0.299765, No Obj: 0.002705, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.613482, Class: 1.000000, Obj: 0.333344, No Obj: 0.002933, Avg Recall: 0.763780, count: 127 -Region Avg IOU: 0.613698, Class: 1.000000, Obj: 0.310167, No Obj: 0.002712, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.617374, Class: 1.000000, Obj: 0.343003, No Obj: 0.002784, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.630140, Class: 1.000000, Obj: 0.336951, No Obj: 0.002861, Avg Recall: 0.796748, count: 123 -Region Avg IOU: 0.650750, Class: 1.000000, Obj: 0.371865, No Obj: 0.002549, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.608381, Class: 1.000000, Obj: 0.330331, No Obj: 0.002724, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.605161, Class: 1.000000, Obj: 0.340542, No Obj: 0.002661, Avg Recall: 0.741379, count: 116 -2562: 0.493100, 0.478373 avg, 0.001000 rate, 29.034803 seconds, 655872 images -Loaded: 0.000132 seconds -Region Avg IOU: 0.619616, Class: 1.000000, Obj: 0.336890, No Obj: 0.002654, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.604474, Class: 1.000000, Obj: 0.362938, No Obj: 0.002230, Avg Recall: 0.752809, count: 89 -Region Avg IOU: 0.644014, Class: 1.000000, Obj: 0.300751, No Obj: 0.002692, Avg Recall: 0.838384, count: 99 -Region Avg IOU: 0.622422, Class: 1.000000, Obj: 0.302794, No Obj: 0.002717, Avg Recall: 0.760000, count: 125 -Region Avg IOU: 0.615097, Class: 1.000000, Obj: 0.344131, No Obj: 0.002383, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.595607, Class: 1.000000, Obj: 0.307475, No Obj: 0.002886, Avg Recall: 0.703704, count: 135 -Region Avg IOU: 0.647406, Class: 1.000000, Obj: 0.333453, No Obj: 0.002849, Avg Recall: 0.819048, count: 105 -Region Avg IOU: 0.619228, Class: 1.000000, Obj: 0.359571, No Obj: 0.002506, Avg Recall: 0.750000, count: 112 -2563: 0.478107, 0.478346 avg, 0.001000 rate, 27.719240 seconds, 656128 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.621882, Class: 1.000000, Obj: 0.338742, No Obj: 0.002975, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.640540, Class: 1.000000, Obj: 0.359325, No Obj: 0.002532, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.684311, Class: 1.000000, Obj: 0.405372, No Obj: 0.002764, Avg Recall: 0.840909, count: 88 -Region Avg IOU: 0.616109, Class: 1.000000, Obj: 0.343380, No Obj: 0.002742, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.592267, Class: 1.000000, Obj: 0.331708, No Obj: 0.002822, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.610957, Class: 1.000000, Obj: 0.368175, No Obj: 0.002783, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.625161, Class: 1.000000, Obj: 0.311466, No Obj: 0.002614, Avg Recall: 0.770115, count: 87 -Region Avg IOU: 0.612219, Class: 1.000000, Obj: 0.362635, No Obj: 0.002505, Avg Recall: 0.775510, count: 98 -2564: 0.438210, 0.474332 avg, 0.001000 rate, 29.762634 seconds, 656384 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.607044, Class: 1.000000, Obj: 0.293455, No Obj: 0.002898, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.637984, Class: 1.000000, Obj: 0.315322, No Obj: 0.002754, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.672301, Class: 1.000000, Obj: 0.357144, No Obj: 0.003383, Avg Recall: 0.843537, count: 147 -Region Avg IOU: 0.612869, Class: 1.000000, Obj: 0.371374, No Obj: 0.002795, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.588937, Class: 1.000000, Obj: 0.319649, No Obj: 0.002990, Avg Recall: 0.704225, count: 142 -Region Avg IOU: 0.602234, Class: 1.000000, Obj: 0.342207, No Obj: 0.002824, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.614925, Class: 1.000000, Obj: 0.325895, No Obj: 0.002695, Avg Recall: 0.712000, count: 125 -Region Avg IOU: 0.593777, Class: 1.000000, Obj: 0.322430, No Obj: 0.002546, Avg Recall: 0.685714, count: 105 -2565: 0.507516, 0.477651 avg, 0.001000 rate, 27.503582 seconds, 656640 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.612465, Class: 1.000000, Obj: 0.385834, No Obj: 0.002568, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.624170, Class: 1.000000, Obj: 0.293585, No Obj: 0.002933, Avg Recall: 0.750000, count: 128 -Region Avg IOU: 0.614927, Class: 1.000000, Obj: 0.340080, No Obj: 0.002976, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.620147, Class: 1.000000, Obj: 0.304869, No Obj: 0.002995, Avg Recall: 0.731544, count: 149 -Region Avg IOU: 0.615832, Class: 1.000000, Obj: 0.369017, No Obj: 0.003271, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.616625, Class: 1.000000, Obj: 0.356832, No Obj: 0.002513, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.631808, Class: 1.000000, Obj: 0.401593, No Obj: 0.002939, Avg Recall: 0.752137, count: 117 -Region Avg IOU: 0.612506, Class: 1.000000, Obj: 0.332593, No Obj: 0.002589, Avg Recall: 0.738318, count: 107 -2566: 0.494449, 0.479331 avg, 0.001000 rate, 28.473515 seconds, 656896 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.627907, Class: 1.000000, Obj: 0.365925, No Obj: 0.002788, Avg Recall: 0.786325, count: 117 -Region Avg IOU: 0.659597, Class: 1.000000, Obj: 0.364317, No Obj: 0.002897, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.611481, Class: 1.000000, Obj: 0.309085, No Obj: 0.002780, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.665688, Class: 1.000000, Obj: 0.369121, No Obj: 0.002562, Avg Recall: 0.802469, count: 81 -Region Avg IOU: 0.621847, Class: 1.000000, Obj: 0.377038, No Obj: 0.002939, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.626592, Class: 1.000000, Obj: 0.346838, No Obj: 0.003021, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.631370, Class: 1.000000, Obj: 0.372355, No Obj: 0.002720, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.635777, Class: 1.000000, Obj: 0.339424, No Obj: 0.002658, Avg Recall: 0.790909, count: 110 -2567: 0.435614, 0.474959 avg, 0.001000 rate, 28.579689 seconds, 657152 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.630985, Class: 1.000000, Obj: 0.390482, No Obj: 0.002834, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.642401, Class: 1.000000, Obj: 0.325028, No Obj: 0.002811, Avg Recall: 0.818182, count: 88 -Region Avg IOU: 0.613789, Class: 1.000000, Obj: 0.354185, No Obj: 0.002820, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.624243, Class: 1.000000, Obj: 0.302034, No Obj: 0.002944, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.619919, Class: 1.000000, Obj: 0.350621, No Obj: 0.002702, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.625097, Class: 1.000000, Obj: 0.361925, No Obj: 0.003383, Avg Recall: 0.776119, count: 134 -Region Avg IOU: 0.632288, Class: 1.000000, Obj: 0.387524, No Obj: 0.002649, Avg Recall: 0.772152, count: 79 -Region Avg IOU: 0.627782, Class: 1.000000, Obj: 0.320277, No Obj: 0.002543, Avg Recall: 0.733333, count: 75 -2568: 0.467244, 0.474187 avg, 0.001000 rate, 27.773081 seconds, 657408 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.594807, Class: 1.000000, Obj: 0.307793, No Obj: 0.003155, Avg Recall: 0.732394, count: 142 -Region Avg IOU: 0.651198, Class: 1.000000, Obj: 0.332830, No Obj: 0.003116, Avg Recall: 0.830357, count: 112 -Region Avg IOU: 0.621483, Class: 1.000000, Obj: 0.330043, No Obj: 0.002921, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.611726, Class: 1.000000, Obj: 0.382475, No Obj: 0.002570, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.644638, Class: 1.000000, Obj: 0.335490, No Obj: 0.002741, Avg Recall: 0.824561, count: 114 -Region Avg IOU: 0.574131, Class: 1.000000, Obj: 0.344767, No Obj: 0.002472, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.621458, Class: 1.000000, Obj: 0.344154, No Obj: 0.003078, Avg Recall: 0.735043, count: 117 -Region Avg IOU: 0.632898, Class: 1.000000, Obj: 0.356714, No Obj: 0.002944, Avg Recall: 0.783505, count: 97 -2569: 0.472049, 0.473974 avg, 0.001000 rate, 27.894007 seconds, 657664 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.598419, Class: 1.000000, Obj: 0.323361, No Obj: 0.002493, Avg Recall: 0.711111, count: 90 -Region Avg IOU: 0.649522, Class: 1.000000, Obj: 0.365160, No Obj: 0.003054, Avg Recall: 0.842105, count: 114 -Region Avg IOU: 0.596792, Class: 1.000000, Obj: 0.329264, No Obj: 0.003178, Avg Recall: 0.758065, count: 124 -Region Avg IOU: 0.650225, Class: 1.000000, Obj: 0.300253, No Obj: 0.002972, Avg Recall: 0.858333, count: 120 -Region Avg IOU: 0.599061, Class: 1.000000, Obj: 0.338719, No Obj: 0.002850, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.612743, Class: 1.000000, Obj: 0.418394, No Obj: 0.002356, Avg Recall: 0.775000, count: 80 -Region Avg IOU: 0.587785, Class: 1.000000, Obj: 0.343003, No Obj: 0.002822, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.623376, Class: 1.000000, Obj: 0.332962, No Obj: 0.002621, Avg Recall: 0.755102, count: 98 -2570: 0.449425, 0.471519 avg, 0.001000 rate, 27.508228 seconds, 657920 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 16.318220 seconds -Region Avg IOU: 0.633556, Class: 1.000000, Obj: 0.345344, No Obj: 0.002953, Avg Recall: 0.791304, count: 115 -Region Avg IOU: 0.611006, Class: 1.000000, Obj: 0.345924, No Obj: 0.002819, Avg Recall: 0.744186, count: 129 -Region Avg IOU: 0.626469, Class: 1.000000, Obj: 0.367160, No Obj: 0.002508, Avg Recall: 0.823529, count: 85 -Region Avg IOU: 0.602209, Class: 1.000000, Obj: 0.324556, No Obj: 0.002620, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.640957, Class: 1.000000, Obj: 0.352089, No Obj: 0.002715, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.593679, Class: 1.000000, Obj: 0.331352, No Obj: 0.002980, Avg Recall: 0.691176, count: 136 -Region Avg IOU: 0.620321, Class: 1.000000, Obj: 0.342487, No Obj: 0.002432, Avg Recall: 0.752809, count: 89 -Region Avg IOU: 0.580937, Class: 1.000000, Obj: 0.336504, No Obj: 0.002707, Avg Recall: 0.712766, count: 94 -2571: 0.424497, 0.466817 avg, 0.001000 rate, 28.488224 seconds, 658176 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.620935, Class: 1.000000, Obj: 0.320802, No Obj: 0.002502, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.596518, Class: 1.000000, Obj: 0.329819, No Obj: 0.002536, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.625837, Class: 1.000000, Obj: 0.376587, No Obj: 0.002786, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.650543, Class: 1.000000, Obj: 0.273626, No Obj: 0.002850, Avg Recall: 0.821138, count: 123 -Region Avg IOU: 0.620105, Class: 1.000000, Obj: 0.295867, No Obj: 0.002812, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.604484, Class: 1.000000, Obj: 0.330058, No Obj: 0.002930, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.602748, Class: 1.000000, Obj: 0.324471, No Obj: 0.002540, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.614452, Class: 1.000000, Obj: 0.378121, No Obj: 0.002613, Avg Recall: 0.744898, count: 98 -2572: 0.463579, 0.466493 avg, 0.001000 rate, 27.594664 seconds, 658432 images -Loaded: 0.000142 seconds -Region Avg IOU: 0.634319, Class: 1.000000, Obj: 0.346503, No Obj: 0.002980, Avg Recall: 0.766917, count: 133 -Region Avg IOU: 0.616968, Class: 1.000000, Obj: 0.349743, No Obj: 0.002200, Avg Recall: 0.760563, count: 71 -Region Avg IOU: 0.614400, Class: 1.000000, Obj: 0.303705, No Obj: 0.002886, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.607561, Class: 1.000000, Obj: 0.327316, No Obj: 0.002633, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.613035, Class: 1.000000, Obj: 0.338460, No Obj: 0.002363, Avg Recall: 0.727273, count: 88 -Region Avg IOU: 0.593491, Class: 1.000000, Obj: 0.337936, No Obj: 0.002903, Avg Recall: 0.691176, count: 136 -Region Avg IOU: 0.611817, Class: 1.000000, Obj: 0.348510, No Obj: 0.002624, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.592943, Class: 1.000000, Obj: 0.309953, No Obj: 0.003036, Avg Recall: 0.771930, count: 114 -2573: 0.469141, 0.466758 avg, 0.001000 rate, 28.207140 seconds, 658688 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.617539, Class: 1.000000, Obj: 0.360291, No Obj: 0.002844, Avg Recall: 0.766129, count: 124 -Region Avg IOU: 0.624210, Class: 1.000000, Obj: 0.335198, No Obj: 0.003016, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.627494, Class: 1.000000, Obj: 0.333988, No Obj: 0.002716, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.654398, Class: 1.000000, Obj: 0.431432, No Obj: 0.002566, Avg Recall: 0.814815, count: 81 -Region Avg IOU: 0.612168, Class: 1.000000, Obj: 0.320917, No Obj: 0.002408, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.632041, Class: 1.000000, Obj: 0.334054, No Obj: 0.002770, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.589618, Class: 1.000000, Obj: 0.331391, No Obj: 0.002548, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.640975, Class: 1.000000, Obj: 0.386384, No Obj: 0.002764, Avg Recall: 0.783505, count: 97 -2574: 0.439551, 0.464037 avg, 0.001000 rate, 28.123093 seconds, 658944 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.645011, Class: 1.000000, Obj: 0.351406, No Obj: 0.002682, Avg Recall: 0.804348, count: 92 -Region Avg IOU: 0.584173, Class: 1.000000, Obj: 0.326473, No Obj: 0.002228, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.605798, Class: 1.000000, Obj: 0.329510, No Obj: 0.002809, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.610804, Class: 1.000000, Obj: 0.333906, No Obj: 0.002756, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.572614, Class: 1.000000, Obj: 0.321884, No Obj: 0.002620, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.596103, Class: 1.000000, Obj: 0.294001, No Obj: 0.002720, Avg Recall: 0.697674, count: 129 -Region Avg IOU: 0.622791, Class: 1.000000, Obj: 0.318868, No Obj: 0.002987, Avg Recall: 0.740741, count: 135 -Region Avg IOU: 0.573128, Class: 1.000000, Obj: 0.344758, No Obj: 0.002575, Avg Recall: 0.696429, count: 112 -2575: 0.464688, 0.464102 avg, 0.001000 rate, 28.825996 seconds, 659200 images -Loaded: 0.000115 seconds -Region Avg IOU: 0.626068, Class: 1.000000, Obj: 0.365415, No Obj: 0.003106, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.584444, Class: 1.000000, Obj: 0.395503, No Obj: 0.002371, Avg Recall: 0.702381, count: 84 -Region Avg IOU: 0.641599, Class: 1.000000, Obj: 0.356889, No Obj: 0.002950, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.561007, Class: 1.000000, Obj: 0.291847, No Obj: 0.002828, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.612073, Class: 1.000000, Obj: 0.332438, No Obj: 0.002749, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.566704, Class: 1.000000, Obj: 0.335247, No Obj: 0.002814, Avg Recall: 0.656250, count: 128 -Region Avg IOU: 0.570223, Class: 1.000000, Obj: 0.362608, No Obj: 0.002704, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.601937, Class: 1.000000, Obj: 0.301990, No Obj: 0.002978, Avg Recall: 0.738318, count: 107 -2576: 0.434860, 0.461178 avg, 0.001000 rate, 26.017126 seconds, 659456 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.620004, Class: 1.000000, Obj: 0.364625, No Obj: 0.002615, Avg Recall: 0.752137, count: 117 -Region Avg IOU: 0.644968, Class: 1.000000, Obj: 0.378235, No Obj: 0.002625, Avg Recall: 0.826087, count: 92 -Region Avg IOU: 0.642782, Class: 1.000000, Obj: 0.358595, No Obj: 0.002987, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.635038, Class: 1.000000, Obj: 0.399243, No Obj: 0.002898, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.622519, Class: 1.000000, Obj: 0.380968, No Obj: 0.002482, Avg Recall: 0.780822, count: 73 -Region Avg IOU: 0.545584, Class: 1.000000, Obj: 0.332119, No Obj: 0.002470, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.618582, Class: 1.000000, Obj: 0.335477, No Obj: 0.002957, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.616061, Class: 1.000000, Obj: 0.356224, No Obj: 0.003095, Avg Recall: 0.752137, count: 117 -2577: 0.407948, 0.455855 avg, 0.001000 rate, 26.546602 seconds, 659712 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.654729, Class: 1.000000, Obj: 0.330434, No Obj: 0.002596, Avg Recall: 0.829545, count: 88 -Region Avg IOU: 0.554649, Class: 1.000000, Obj: 0.362199, No Obj: 0.002673, Avg Recall: 0.701149, count: 87 -Region Avg IOU: 0.631660, Class: 1.000000, Obj: 0.392024, No Obj: 0.002764, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.587622, Class: 1.000000, Obj: 0.339131, No Obj: 0.002794, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.582076, Class: 1.000000, Obj: 0.361888, No Obj: 0.002846, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.609470, Class: 1.000000, Obj: 0.368162, No Obj: 0.002812, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.619134, Class: 1.000000, Obj: 0.328303, No Obj: 0.002629, Avg Recall: 0.750000, count: 88 -Region Avg IOU: 0.630298, Class: 1.000000, Obj: 0.385165, No Obj: 0.002994, Avg Recall: 0.796460, count: 113 -2578: 0.450283, 0.455298 avg, 0.001000 rate, 26.801252 seconds, 659968 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.594521, Class: 1.000000, Obj: 0.296203, No Obj: 0.002737, Avg Recall: 0.705882, count: 119 -Region Avg IOU: 0.625393, Class: 1.000000, Obj: 0.318541, No Obj: 0.002994, Avg Recall: 0.772358, count: 123 -Region Avg IOU: 0.672391, Class: 1.000000, Obj: 0.432314, No Obj: 0.002757, Avg Recall: 0.833333, count: 90 -Region Avg IOU: 0.595390, Class: 1.000000, Obj: 0.369063, No Obj: 0.003084, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.589084, Class: 1.000000, Obj: 0.329460, No Obj: 0.002696, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.605550, Class: 1.000000, Obj: 0.372323, No Obj: 0.002961, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.608354, Class: 1.000000, Obj: 0.386234, No Obj: 0.002945, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.610990, Class: 1.000000, Obj: 0.304654, No Obj: 0.002777, Avg Recall: 0.763158, count: 114 -2579: 0.426452, 0.452413 avg, 0.001000 rate, 27.593012 seconds, 660224 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.649928, Class: 1.000000, Obj: 0.344429, No Obj: 0.002838, Avg Recall: 0.865979, count: 97 -Region Avg IOU: 0.613902, Class: 1.000000, Obj: 0.352059, No Obj: 0.002984, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.560387, Class: 1.000000, Obj: 0.320889, No Obj: 0.002951, Avg Recall: 0.634921, count: 126 -Region Avg IOU: 0.658568, Class: 1.000000, Obj: 0.312748, No Obj: 0.002870, Avg Recall: 0.807692, count: 78 -Region Avg IOU: 0.564710, Class: 1.000000, Obj: 0.362141, No Obj: 0.002723, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.609579, Class: 1.000000, Obj: 0.355718, No Obj: 0.002898, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.603110, Class: 1.000000, Obj: 0.344155, No Obj: 0.002756, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.574064, Class: 1.000000, Obj: 0.345666, No Obj: 0.003237, Avg Recall: 0.669291, count: 127 -2580: 0.507937, 0.457966 avg, 0.001000 rate, 27.770952 seconds, 660480 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 13.882177 seconds -Region Avg IOU: 0.479184, Class: 1.000000, Obj: 0.211191, No Obj: 0.003910, Avg Recall: 0.551020, count: 147 -Region Avg IOU: 0.489081, Class: 1.000000, Obj: 0.233867, No Obj: 0.003322, Avg Recall: 0.522523, count: 111 -Region Avg IOU: 0.517446, Class: 1.000000, Obj: 0.285934, No Obj: 0.003605, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.469765, Class: 1.000000, Obj: 0.220803, No Obj: 0.003768, Avg Recall: 0.466102, count: 118 -Region Avg IOU: 0.495342, Class: 1.000000, Obj: 0.256051, No Obj: 0.003608, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.472848, Class: 1.000000, Obj: 0.207493, No Obj: 0.003715, Avg Recall: 0.483871, count: 124 -Region Avg IOU: 0.452408, Class: 1.000000, Obj: 0.204752, No Obj: 0.002812, Avg Recall: 0.487500, count: 80 -Region Avg IOU: 0.489347, Class: 1.000000, Obj: 0.240796, No Obj: 0.003084, Avg Recall: 0.530864, count: 81 -2581: 0.507117, 0.462881 avg, 0.001000 rate, 19.024288 seconds, 660736 images -Loaded: 2.676751 seconds -Region Avg IOU: 0.510508, Class: 1.000000, Obj: 0.243881, No Obj: 0.003380, Avg Recall: 0.552083, count: 96 -Region Avg IOU: 0.511857, Class: 1.000000, Obj: 0.275449, No Obj: 0.003525, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.464860, Class: 1.000000, Obj: 0.221080, No Obj: 0.003060, Avg Recall: 0.475000, count: 80 -Region Avg IOU: 0.474053, Class: 1.000000, Obj: 0.213600, No Obj: 0.003568, Avg Recall: 0.495495, count: 111 -Region Avg IOU: 0.507540, Class: 1.000000, Obj: 0.220844, No Obj: 0.002889, Avg Recall: 0.516129, count: 93 -Region Avg IOU: 0.437346, Class: 1.000000, Obj: 0.250253, No Obj: 0.003687, Avg Recall: 0.450450, count: 111 -Region Avg IOU: 0.517393, Class: 1.000000, Obj: 0.258324, No Obj: 0.003678, Avg Recall: 0.597701, count: 87 -Region Avg IOU: 0.503073, Class: 1.000000, Obj: 0.245284, No Obj: 0.003876, Avg Recall: 0.570175, count: 114 -2582: 0.467213, 0.463314 avg, 0.001000 rate, 19.570778 seconds, 660992 images -Loaded: 1.552040 seconds -Region Avg IOU: 0.473981, Class: 1.000000, Obj: 0.305046, No Obj: 0.003927, Avg Recall: 0.509434, count: 106 -Region Avg IOU: 0.504769, Class: 1.000000, Obj: 0.286254, No Obj: 0.004020, Avg Recall: 0.542857, count: 105 -Region Avg IOU: 0.450596, Class: 1.000000, Obj: 0.245667, No Obj: 0.004058, Avg Recall: 0.456693, count: 127 -Region Avg IOU: 0.505425, Class: 1.000000, Obj: 0.251659, No Obj: 0.003652, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.450524, Class: 1.000000, Obj: 0.270587, No Obj: 0.003872, Avg Recall: 0.500000, count: 98 -Region Avg IOU: 0.446653, Class: 1.000000, Obj: 0.254437, No Obj: 0.003782, Avg Recall: 0.479339, count: 121 -Region Avg IOU: 0.475938, Class: 1.000000, Obj: 0.299312, No Obj: 0.004044, Avg Recall: 0.517241, count: 116 -Region Avg IOU: 0.424847, Class: 1.000000, Obj: 0.234180, No Obj: 0.003451, Avg Recall: 0.448598, count: 107 -2583: 0.585782, 0.475561 avg, 0.001000 rate, 17.467026 seconds, 661248 images -Loaded: 4.045476 seconds -Region Avg IOU: 0.538420, Class: 1.000000, Obj: 0.336514, No Obj: 0.004501, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.442848, Class: 1.000000, Obj: 0.279197, No Obj: 0.004522, Avg Recall: 0.488189, count: 127 -Region Avg IOU: 0.503880, Class: 1.000000, Obj: 0.287912, No Obj: 0.004318, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.494811, Class: 1.000000, Obj: 0.303428, No Obj: 0.004809, Avg Recall: 0.504587, count: 109 -Region Avg IOU: 0.442735, Class: 1.000000, Obj: 0.243761, No Obj: 0.004030, Avg Recall: 0.443478, count: 115 -Region Avg IOU: 0.516701, Class: 1.000000, Obj: 0.286784, No Obj: 0.004188, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.528902, Class: 1.000000, Obj: 0.331531, No Obj: 0.004654, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.542563, Class: 1.000000, Obj: 0.344423, No Obj: 0.003725, Avg Recall: 0.608108, count: 74 -2584: 0.452197, 0.473224 avg, 0.001000 rate, 20.804008 seconds, 661504 images -Loaded: 0.309240 seconds -Region Avg IOU: 0.499659, Class: 1.000000, Obj: 0.347584, No Obj: 0.004469, Avg Recall: 0.546296, count: 108 -Region Avg IOU: 0.435313, Class: 1.000000, Obj: 0.299649, No Obj: 0.005002, Avg Recall: 0.416667, count: 144 -Region Avg IOU: 0.451325, Class: 1.000000, Obj: 0.273606, No Obj: 0.004498, Avg Recall: 0.508065, count: 124 -Region Avg IOU: 0.400886, Class: 1.000000, Obj: 0.269429, No Obj: 0.004150, Avg Recall: 0.340659, count: 91 -Region Avg IOU: 0.433749, Class: 1.000000, Obj: 0.263491, No Obj: 0.004017, Avg Recall: 0.428571, count: 112 -Region Avg IOU: 0.468886, Class: 1.000000, Obj: 0.287215, No Obj: 0.004677, Avg Recall: 0.522727, count: 132 -Region Avg IOU: 0.488436, Class: 1.000000, Obj: 0.312023, No Obj: 0.004706, Avg Recall: 0.585366, count: 82 -Region Avg IOU: 0.446170, Class: 1.000000, Obj: 0.306637, No Obj: 0.004282, Avg Recall: 0.444444, count: 108 -2585: 0.501611, 0.476063 avg, 0.001000 rate, 16.790606 seconds, 661760 images -Loaded: 3.576385 seconds -Region Avg IOU: 0.488186, Class: 1.000000, Obj: 0.359550, No Obj: 0.004792, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.449723, Class: 1.000000, Obj: 0.327471, No Obj: 0.004474, Avg Recall: 0.514851, count: 101 -Region Avg IOU: 0.506757, Class: 1.000000, Obj: 0.348968, No Obj: 0.005295, Avg Recall: 0.556701, count: 97 -Region Avg IOU: 0.455118, Class: 1.000000, Obj: 0.341076, No Obj: 0.004664, Avg Recall: 0.478633, count: 117 -Region Avg IOU: 0.486912, Class: 1.000000, Obj: 0.353462, No Obj: 0.004872, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.479110, Class: 1.000000, Obj: 0.361746, No Obj: 0.004789, Avg Recall: 0.486239, count: 109 -Region Avg IOU: 0.463132, Class: 1.000000, Obj: 0.283391, No Obj: 0.004694, Avg Recall: 0.510204, count: 98 -Region Avg IOU: 0.392737, Class: 1.000000, Obj: 0.285622, No Obj: 0.004005, Avg Recall: 0.386792, count: 106 -2586: 0.488914, 0.477348 avg, 0.001000 rate, 18.726831 seconds, 662016 images -Loaded: 3.080865 seconds -Region Avg IOU: 0.440700, Class: 1.000000, Obj: 0.300662, No Obj: 0.005807, Avg Recall: 0.440945, count: 127 -Region Avg IOU: 0.478011, Class: 1.000000, Obj: 0.315426, No Obj: 0.005546, Avg Recall: 0.532787, count: 122 -Region Avg IOU: 0.528924, Class: 1.000000, Obj: 0.406264, No Obj: 0.005788, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.474771, Class: 1.000000, Obj: 0.351690, No Obj: 0.004919, Avg Recall: 0.515152, count: 99 -Region Avg IOU: 0.452945, Class: 1.000000, Obj: 0.313495, No Obj: 0.004852, Avg Recall: 0.485981, count: 107 -Region Avg IOU: 0.489024, Class: 1.000000, Obj: 0.333798, No Obj: 0.005156, Avg Recall: 0.508621, count: 116 -Region Avg IOU: 0.452700, Class: 1.000000, Obj: 0.334536, No Obj: 0.005788, Avg Recall: 0.495727, count: 117 -Region Avg IOU: 0.449510, Class: 1.000000, Obj: 0.327446, No Obj: 0.005120, Avg Recall: 0.490741, count: 108 -2587: 0.515538, 0.481167 avg, 0.001000 rate, 20.578304 seconds, 662272 images -Loaded: 0.292926 seconds -Region Avg IOU: 0.456244, Class: 1.000000, Obj: 0.321699, No Obj: 0.004975, Avg Recall: 0.504587, count: 109 -Region Avg IOU: 0.462413, Class: 1.000000, Obj: 0.344576, No Obj: 0.005305, Avg Recall: 0.537815, count: 119 -Region Avg IOU: 0.421385, Class: 1.000000, Obj: 0.350652, No Obj: 0.005138, Avg Recall: 0.434783, count: 92 -Region Avg IOU: 0.398732, Class: 1.000000, Obj: 0.348031, No Obj: 0.004755, Avg Recall: 0.357143, count: 98 -Region Avg IOU: 0.446332, Class: 1.000000, Obj: 0.350330, No Obj: 0.004654, Avg Recall: 0.469388, count: 98 -Region Avg IOU: 0.454666, Class: 1.000000, Obj: 0.354136, No Obj: 0.004632, Avg Recall: 0.447059, count: 85 -Region Avg IOU: 0.470069, Class: 1.000000, Obj: 0.356437, No Obj: 0.005058, Avg Recall: 0.461538, count: 91 -Region Avg IOU: 0.476411, Class: 1.000000, Obj: 0.347951, No Obj: 0.005366, Avg Recall: 0.500000, count: 126 -2588: 0.442427, 0.477293 avg, 0.001000 rate, 19.547417 seconds, 662528 images -Loaded: 1.933294 seconds -Region Avg IOU: 0.469229, Class: 1.000000, Obj: 0.351424, No Obj: 0.005132, Avg Recall: 0.511811, count: 127 -Region Avg IOU: 0.463862, Class: 1.000000, Obj: 0.316205, No Obj: 0.005008, Avg Recall: 0.504132, count: 121 -Region Avg IOU: 0.481457, Class: 1.000000, Obj: 0.322890, No Obj: 0.005090, Avg Recall: 0.564286, count: 140 -Region Avg IOU: 0.498902, Class: 1.000000, Obj: 0.348089, No Obj: 0.005515, Avg Recall: 0.517544, count: 114 -Region Avg IOU: 0.438068, Class: 1.000000, Obj: 0.275231, No Obj: 0.005739, Avg Recall: 0.431655, count: 139 -Region Avg IOU: 0.421785, Class: 1.000000, Obj: 0.307846, No Obj: 0.004654, Avg Recall: 0.397959, count: 98 -Region Avg IOU: 0.471775, Class: 1.000000, Obj: 0.327000, No Obj: 0.005146, Avg Recall: 0.491803, count: 122 -Region Avg IOU: 0.470150, Class: 1.000000, Obj: 0.356744, No Obj: 0.005027, Avg Recall: 0.527778, count: 108 -2589: 0.548745, 0.484438 avg, 0.001000 rate, 19.216976 seconds, 662784 images -Loaded: 1.575270 seconds -Region Avg IOU: 0.458855, Class: 1.000000, Obj: 0.296580, No Obj: 0.004101, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.534530, Class: 1.000000, Obj: 0.349019, No Obj: 0.004835, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.504087, Class: 1.000000, Obj: 0.307689, No Obj: 0.004709, Avg Recall: 0.546392, count: 97 -Region Avg IOU: 0.416303, Class: 1.000000, Obj: 0.295425, No Obj: 0.004803, Avg Recall: 0.397163, count: 141 -Region Avg IOU: 0.456560, Class: 1.000000, Obj: 0.338598, No Obj: 0.004045, Avg Recall: 0.494118, count: 85 -Region Avg IOU: 0.470946, Class: 1.000000, Obj: 0.329795, No Obj: 0.004671, Avg Recall: 0.555556, count: 99 -Region Avg IOU: 0.457943, Class: 1.000000, Obj: 0.318916, No Obj: 0.004497, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.399334, Class: 1.000000, Obj: 0.322897, No Obj: 0.004510, Avg Recall: 0.432203, count: 118 -2590: 0.448022, 0.480797 avg, 0.001000 rate, 15.532381 seconds, 663040 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.448896 seconds -Region Avg IOU: 0.451624, Class: 1.000000, Obj: 0.258246, No Obj: 0.004146, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.437932, Class: 1.000000, Obj: 0.245807, No Obj: 0.004189, Avg Recall: 0.495727, count: 117 -Region Avg IOU: 0.483453, Class: 1.000000, Obj: 0.273093, No Obj: 0.005279, Avg Recall: 0.546219, count: 119 -Region Avg IOU: 0.437508, Class: 1.000000, Obj: 0.271670, No Obj: 0.004634, Avg Recall: 0.441860, count: 129 -Region Avg IOU: 0.441533, Class: 1.000000, Obj: 0.247574, No Obj: 0.004238, Avg Recall: 0.439560, count: 91 -Region Avg IOU: 0.479875, Class: 1.000000, Obj: 0.289211, No Obj: 0.004218, Avg Recall: 0.465909, count: 88 -Region Avg IOU: 0.432493, Class: 1.000000, Obj: 0.253312, No Obj: 0.004295, Avg Recall: 0.439655, count: 116 -Region Avg IOU: 0.482599, Class: 1.000000, Obj: 0.298350, No Obj: 0.004841, Avg Recall: 0.516667, count: 120 -2591: 0.499867, 0.482704 avg, 0.001000 rate, 16.110376 seconds, 663296 images -Loaded: 3.308446 seconds -Region Avg IOU: 0.444377, Class: 1.000000, Obj: 0.241687, No Obj: 0.004229, Avg Recall: 0.472441, count: 127 -Region Avg IOU: 0.485285, Class: 1.000000, Obj: 0.282217, No Obj: 0.003900, Avg Recall: 0.552239, count: 67 -Region Avg IOU: 0.467152, Class: 1.000000, Obj: 0.292901, No Obj: 0.004516, Avg Recall: 0.516949, count: 118 -Region Avg IOU: 0.446792, Class: 1.000000, Obj: 0.240118, No Obj: 0.004416, Avg Recall: 0.433628, count: 113 -Region Avg IOU: 0.500147, Class: 1.000000, Obj: 0.293517, No Obj: 0.003940, Avg Recall: 0.576923, count: 78 -Region Avg IOU: 0.454857, Class: 1.000000, Obj: 0.256761, No Obj: 0.004300, Avg Recall: 0.482759, count: 116 -Region Avg IOU: 0.459034, Class: 1.000000, Obj: 0.261585, No Obj: 0.004622, Avg Recall: 0.500000, count: 130 -Region Avg IOU: 0.511079, Class: 1.000000, Obj: 0.304274, No Obj: 0.004118, Avg Recall: 0.575758, count: 99 -2592: 0.522554, 0.486689 avg, 0.001000 rate, 17.617186 seconds, 663552 images -Loaded: 4.320445 seconds -Region Avg IOU: 0.427347, Class: 1.000000, Obj: 0.230840, No Obj: 0.004624, Avg Recall: 0.446970, count: 132 -Region Avg IOU: 0.453714, Class: 1.000000, Obj: 0.250255, No Obj: 0.004103, Avg Recall: 0.458716, count: 109 -Region Avg IOU: 0.447760, Class: 1.000000, Obj: 0.222703, No Obj: 0.004411, Avg Recall: 0.520325, count: 123 -Region Avg IOU: 0.460762, Class: 1.000000, Obj: 0.264645, No Obj: 0.004435, Avg Recall: 0.457364, count: 129 -Region Avg IOU: 0.475118, Class: 1.000000, Obj: 0.260992, No Obj: 0.004538, Avg Recall: 0.570312, count: 128 -Region Avg IOU: 0.469409, Class: 1.000000, Obj: 0.278489, No Obj: 0.004130, Avg Recall: 0.525773, count: 97 -Region Avg IOU: 0.487253, Class: 1.000000, Obj: 0.286738, No Obj: 0.004239, Avg Recall: 0.534653, count: 101 -Region Avg IOU: 0.473586, Class: 1.000000, Obj: 0.245984, No Obj: 0.004604, Avg Recall: 0.491803, count: 122 -2593: 0.552954, 0.493315 avg, 0.001000 rate, 18.269716 seconds, 663808 images -Loaded: 2.610408 seconds -Region Avg IOU: 0.441847, Class: 1.000000, Obj: 0.238862, No Obj: 0.004492, Avg Recall: 0.500000, count: 116 -Region Avg IOU: 0.473265, Class: 1.000000, Obj: 0.292022, No Obj: 0.004456, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.420605, Class: 1.000000, Obj: 0.229665, No Obj: 0.003480, Avg Recall: 0.490741, count: 108 -Region Avg IOU: 0.440456, Class: 1.000000, Obj: 0.268339, No Obj: 0.003695, Avg Recall: 0.431373, count: 102 -Region Avg IOU: 0.458605, Class: 1.000000, Obj: 0.227295, No Obj: 0.003936, Avg Recall: 0.516129, count: 124 -Region Avg IOU: 0.437992, Class: 1.000000, Obj: 0.245262, No Obj: 0.003943, Avg Recall: 0.446808, count: 94 -Region Avg IOU: 0.476963, Class: 1.000000, Obj: 0.296314, No Obj: 0.004217, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.448092, Class: 1.000000, Obj: 0.271033, No Obj: 0.004233, Avg Recall: 0.457944, count: 107 -2594: 0.593748, 0.503358 avg, 0.001000 rate, 17.039833 seconds, 664064 images -Loaded: 1.997018 seconds -Region Avg IOU: 0.467452, Class: 1.000000, Obj: 0.248966, No Obj: 0.004100, Avg Recall: 0.521277, count: 94 -Region Avg IOU: 0.424918, Class: 1.000000, Obj: 0.266963, No Obj: 0.003685, Avg Recall: 0.415584, count: 77 -Region Avg IOU: 0.434403, Class: 1.000000, Obj: 0.271683, No Obj: 0.003760, Avg Recall: 0.458824, count: 85 -Region Avg IOU: 0.466688, Class: 1.000000, Obj: 0.294305, No Obj: 0.004285, Avg Recall: 0.524272, count: 103 -Region Avg IOU: 0.495893, Class: 1.000000, Obj: 0.264212, No Obj: 0.004534, Avg Recall: 0.552083, count: 96 -Region Avg IOU: 0.445291, Class: 1.000000, Obj: 0.232590, No Obj: 0.004438, Avg Recall: 0.462585, count: 147 -Region Avg IOU: 0.443034, Class: 1.000000, Obj: 0.232947, No Obj: 0.003550, Avg Recall: 0.441176, count: 102 -Region Avg IOU: 0.465772, Class: 1.000000, Obj: 0.263278, No Obj: 0.003926, Avg Recall: 0.504950, count: 101 -2595: 0.519793, 0.505002 avg, 0.001000 rate, 16.315193 seconds, 664320 images -Loaded: 2.812716 seconds -Region Avg IOU: 0.442047, Class: 1.000000, Obj: 0.280939, No Obj: 0.004668, Avg Recall: 0.482759, count: 116 -Region Avg IOU: 0.476564, Class: 1.000000, Obj: 0.288343, No Obj: 0.004227, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.442566, Class: 1.000000, Obj: 0.250262, No Obj: 0.004098, Avg Recall: 0.471154, count: 104 -Region Avg IOU: 0.433490, Class: 1.000000, Obj: 0.277194, No Obj: 0.003973, Avg Recall: 0.451613, count: 93 -Region Avg IOU: 0.468652, Class: 1.000000, Obj: 0.268416, No Obj: 0.005121, Avg Recall: 0.508197, count: 122 -Region Avg IOU: 0.458887, Class: 1.000000, Obj: 0.227706, No Obj: 0.004624, Avg Recall: 0.522936, count: 109 -Region Avg IOU: 0.505371, Class: 1.000000, Obj: 0.302421, No Obj: 0.005368, Avg Recall: 0.567797, count: 118 -Region Avg IOU: 0.445742, Class: 1.000000, Obj: 0.286316, No Obj: 0.004059, Avg Recall: 0.478261, count: 92 -2596: 0.475779, 0.502080 avg, 0.001000 rate, 16.224640 seconds, 664576 images -Loaded: 4.490644 seconds -Region Avg IOU: 0.425222, Class: 1.000000, Obj: 0.243108, No Obj: 0.004495, Avg Recall: 0.456140, count: 114 -Region Avg IOU: 0.453212, Class: 1.000000, Obj: 0.306028, No Obj: 0.004997, Avg Recall: 0.508475, count: 118 -Region Avg IOU: 0.438725, Class: 1.000000, Obj: 0.251842, No Obj: 0.004712, Avg Recall: 0.435484, count: 124 -Region Avg IOU: 0.485925, Class: 1.000000, Obj: 0.314614, No Obj: 0.004185, Avg Recall: 0.505882, count: 85 -Region Avg IOU: 0.495407, Class: 1.000000, Obj: 0.292715, No Obj: 0.004239, Avg Recall: 0.575758, count: 99 -Region Avg IOU: 0.470193, Class: 1.000000, Obj: 0.297457, No Obj: 0.004778, Avg Recall: 0.500000, count: 104 -Region Avg IOU: 0.462116, Class: 1.000000, Obj: 0.321132, No Obj: 0.005065, Avg Recall: 0.473214, count: 112 -Region Avg IOU: 0.426706, Class: 1.000000, Obj: 0.290686, No Obj: 0.004486, Avg Recall: 0.448276, count: 87 -2597: 0.457638, 0.497635 avg, 0.001000 rate, 18.302387 seconds, 664832 images -Loaded: 3.122025 seconds -Region Avg IOU: 0.458657, Class: 1.000000, Obj: 0.285585, No Obj: 0.005592, Avg Recall: 0.493151, count: 146 -Region Avg IOU: 0.484477, Class: 1.000000, Obj: 0.345519, No Obj: 0.004462, Avg Recall: 0.535714, count: 84 -Region Avg IOU: 0.441430, Class: 1.000000, Obj: 0.301140, No Obj: 0.004947, Avg Recall: 0.430894, count: 123 -Region Avg IOU: 0.473179, Class: 1.000000, Obj: 0.330383, No Obj: 0.004229, Avg Recall: 0.546512, count: 86 -Region Avg IOU: 0.499416, Class: 1.000000, Obj: 0.300678, No Obj: 0.005001, Avg Recall: 0.604651, count: 86 -Region Avg IOU: 0.472160, Class: 1.000000, Obj: 0.331407, No Obj: 0.004495, Avg Recall: 0.443182, count: 88 -Region Avg IOU: 0.450597, Class: 1.000000, Obj: 0.316756, No Obj: 0.005356, Avg Recall: 0.464646, count: 99 -Region Avg IOU: 0.475864, Class: 1.000000, Obj: 0.325337, No Obj: 0.004568, Avg Recall: 0.505618, count: 89 -2598: 0.428119, 0.490684 avg, 0.001000 rate, 17.477043 seconds, 665088 images -Loaded: 2.984995 seconds -Region Avg IOU: 0.452276, Class: 1.000000, Obj: 0.312750, No Obj: 0.004573, Avg Recall: 0.467391, count: 92 -Region Avg IOU: 0.437581, Class: 1.000000, Obj: 0.285552, No Obj: 0.004964, Avg Recall: 0.486486, count: 111 -Region Avg IOU: 0.509732, Class: 1.000000, Obj: 0.338475, No Obj: 0.004524, Avg Recall: 0.579545, count: 88 -Region Avg IOU: 0.463154, Class: 1.000000, Obj: 0.294809, No Obj: 0.004928, Avg Recall: 0.500000, count: 120 -Region Avg IOU: 0.456334, Class: 1.000000, Obj: 0.289476, No Obj: 0.004446, Avg Recall: 0.533333, count: 90 -Region Avg IOU: 0.474860, Class: 1.000000, Obj: 0.326545, No Obj: 0.005891, Avg Recall: 0.540741, count: 135 -Region Avg IOU: 0.439454, Class: 1.000000, Obj: 0.285339, No Obj: 0.005023, Avg Recall: 0.480916, count: 131 -Region Avg IOU: 0.456729, Class: 1.000000, Obj: 0.307378, No Obj: 0.004976, Avg Recall: 0.477477, count: 111 -2599: 0.457394, 0.487355 avg, 0.001000 rate, 15.260583 seconds, 665344 images -Loaded: 4.550170 seconds -Region Avg IOU: 0.440867, Class: 1.000000, Obj: 0.332113, No Obj: 0.005315, Avg Recall: 0.490196, count: 102 -Region Avg IOU: 0.438729, Class: 1.000000, Obj: 0.291969, No Obj: 0.005301, Avg Recall: 0.443396, count: 106 -Region Avg IOU: 0.456136, Class: 1.000000, Obj: 0.285511, No Obj: 0.005690, Avg Recall: 0.524194, count: 124 -Region Avg IOU: 0.433881, Class: 1.000000, Obj: 0.297665, No Obj: 0.005558, Avg Recall: 0.421875, count: 128 -Region Avg IOU: 0.435965, Class: 1.000000, Obj: 0.274047, No Obj: 0.004454, Avg Recall: 0.473684, count: 95 -Region Avg IOU: 0.489905, Class: 1.000000, Obj: 0.373620, No Obj: 0.005241, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.474439, Class: 1.000000, Obj: 0.302149, No Obj: 0.005394, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.454571, Class: 1.000000,Saving weights to ./person9300/backup/tyvp_93_9c512_2600.weights - Obj: 0.345513, No Obj: 0.005488, Avg Recall: 0.491228, count: 114 -2600: 0.498908, 0.488510 avg, 0.001000 rate, 19.236835 seconds, 665600 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 2.862140 seconds -Region Avg IOU: 0.437340, Class: 1.000000, Obj: 0.350633, No Obj: 0.004355, Avg Recall: 0.431373, count: 102 -Region Avg IOU: 0.480327, Class: 1.000000, Obj: 0.328648, No Obj: 0.004666, Avg Recall: 0.504348, count: 115 -Region Avg IOU: 0.508682, Class: 1.000000, Obj: 0.334635, No Obj: 0.005736, Avg Recall: 0.567164, count: 134 -Region Avg IOU: 0.505061, Class: 1.000000, Obj: 0.387343, No Obj: 0.005626, Avg Recall: 0.574627, count: 134 -Region Avg IOU: 0.510211, Class: 1.000000, Obj: 0.371903, No Obj: 0.004709, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.521300, Class: 1.000000, Obj: 0.347768, No Obj: 0.004799, Avg Recall: 0.597701, count: 87 -Region Avg IOU: 0.513242, Class: 1.000000, Obj: 0.360612, No Obj: 0.004700, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.510486, Class: 1.000000, Obj: 0.362185, No Obj: 0.004456, Avg Recall: 0.581395, count: 86 -2601: 0.543839, 0.494043 avg, 0.001000 rate, 20.314224 seconds, 665856 images -Loaded: 1.438949 seconds -Region Avg IOU: 0.533133, Class: 1.000000, Obj: 0.347495, No Obj: 0.004503, Avg Recall: 0.580000, count: 100 -Region Avg IOU: 0.476110, Class: 1.000000, Obj: 0.316846, No Obj: 0.005127, Avg Recall: 0.507692, count: 130 -Region Avg IOU: 0.498683, Class: 1.000000, Obj: 0.352256, No Obj: 0.005029, Avg Recall: 0.522936, count: 109 -Region Avg IOU: 0.502041, Class: 1.000000, Obj: 0.358410, No Obj: 0.004624, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.485578, Class: 1.000000, Obj: 0.361061, No Obj: 0.005337, Avg Recall: 0.510345, count: 145 -Region Avg IOU: 0.540706, Class: 1.000000, Obj: 0.401142, No Obj: 0.004766, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.503484, Class: 1.000000, Obj: 0.340839, No Obj: 0.005146, Avg Recall: 0.573529, count: 136 -Region Avg IOU: 0.446451, Class: 1.000000, Obj: 0.335240, No Obj: 0.004523, Avg Recall: 0.477778, count: 90 -2602: 0.503621, 0.495001 avg, 0.001000 rate, 20.965933 seconds, 666112 images -Loaded: 1.143769 seconds -Region Avg IOU: 0.487654, Class: 1.000000, Obj: 0.335374, No Obj: 0.003904, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.492135, Class: 1.000000, Obj: 0.326937, No Obj: 0.004372, Avg Recall: 0.532710, count: 107 -Region Avg IOU: 0.467623, Class: 1.000000, Obj: 0.356568, No Obj: 0.004721, Avg Recall: 0.507463, count: 134 -Region Avg IOU: 0.454473, Class: 1.000000, Obj: 0.324618, No Obj: 0.004623, Avg Recall: 0.504762, count: 105 -Region Avg IOU: 0.498196, Class: 1.000000, Obj: 0.342628, No Obj: 0.004860, Avg Recall: 0.536585, count: 123 -Region Avg IOU: 0.464587, Class: 1.000000, Obj: 0.321022, No Obj: 0.004302, Avg Recall: 0.522936, count: 109 -Region Avg IOU: 0.479830, Class: 1.000000, Obj: 0.323524, No Obj: 0.004780, Avg Recall: 0.560000, count: 125 -Region Avg IOU: 0.511171, Class: 1.000000, Obj: 0.354865, No Obj: 0.004730, Avg Recall: 0.548673, count: 113 -2603: 0.427687, 0.488269 avg, 0.001000 rate, 22.744429 seconds, 666368 images -Loaded: 0.017710 seconds -Region Avg IOU: 0.468650, Class: 1.000000, Obj: 0.308409, No Obj: 0.004434, Avg Recall: 0.491228, count: 114 -Region Avg IOU: 0.509275, Class: 1.000000, Obj: 0.304783, No Obj: 0.004393, Avg Recall: 0.564516, count: 124 -Region Avg IOU: 0.534476, Class: 1.000000, Obj: 0.319010, No Obj: 0.005573, Avg Recall: 0.596899, count: 129 -Region Avg IOU: 0.470642, Class: 1.000000, Obj: 0.314679, No Obj: 0.003924, Avg Recall: 0.488636, count: 88 -Region Avg IOU: 0.509743, Class: 1.000000, Obj: 0.320208, No Obj: 0.004387, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.518591, Class: 1.000000, Obj: 0.344106, No Obj: 0.004749, Avg Recall: 0.587302, count: 126 -Region Avg IOU: 0.519762, Class: 1.000000, Obj: 0.355011, No Obj: 0.004488, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.431246, Class: 1.000000, Obj: 0.299301, No Obj: 0.003905, Avg Recall: 0.469388, count: 98 -2604: 0.502010, 0.489643 avg, 0.001000 rate, 20.396961 seconds, 666624 images -Loaded: 1.780230 seconds -Region Avg IOU: 0.552231, Class: 1.000000, Obj: 0.365807, No Obj: 0.004000, Avg Recall: 0.615385, count: 91 -Region Avg IOU: 0.449164, Class: 1.000000, Obj: 0.269600, No Obj: 0.003859, Avg Recall: 0.492958, count: 142 -Region Avg IOU: 0.502437, Class: 1.000000, Obj: 0.315123, No Obj: 0.004182, Avg Recall: 0.520833, count: 96 -Region Avg IOU: 0.568035, Class: 1.000000, Obj: 0.329563, No Obj: 0.004402, Avg Recall: 0.673684, count: 95 -Region Avg IOU: 0.494120, Class: 1.000000, Obj: 0.324672, No Obj: 0.003836, Avg Recall: 0.505155, count: 97 -Region Avg IOU: 0.510055, Class: 1.000000, Obj: 0.304884, No Obj: 0.004437, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.486272, Class: 1.000000, Obj: 0.335685, No Obj: 0.004368, Avg Recall: 0.515152, count: 99 -Region Avg IOU: 0.497985, Class: 1.000000, Obj: 0.306223, No Obj: 0.004849, Avg Recall: 0.556338, count: 142 -2605: 0.451420, 0.485821 avg, 0.001000 rate, 21.070078 seconds, 666880 images -Loaded: 0.131752 seconds -Region Avg IOU: 0.477994, Class: 1.000000, Obj: 0.312435, No Obj: 0.003828, Avg Recall: 0.515464, count: 97 -Region Avg IOU: 0.489124, Class: 1.000000, Obj: 0.299943, No Obj: 0.004162, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.492603, Class: 1.000000, Obj: 0.318247, No Obj: 0.003808, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.522532, Class: 1.000000, Obj: 0.302298, No Obj: 0.003843, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.499529, Class: 1.000000, Obj: 0.292675, No Obj: 0.003491, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.447274, Class: 1.000000, Obj: 0.279585, No Obj: 0.003774, Avg Recall: 0.456897, count: 116 -Region Avg IOU: 0.476943, Class: 1.000000, Obj: 0.292991, No Obj: 0.003589, Avg Recall: 0.535354, count: 99 -Region Avg IOU: 0.473337, Class: 1.000000, Obj: 0.293473, No Obj: 0.004158, Avg Recall: 0.537879, count: 132 -2606: 0.492383, 0.486477 avg, 0.001000 rate, 21.109493 seconds, 667136 images -Loaded: 0.000053 seconds -Region Avg IOU: 0.517448, Class: 1.000000, Obj: 0.284337, No Obj: 0.003986, Avg Recall: 0.588235, count: 119 -Region Avg IOU: 0.521958, Class: 1.000000, Obj: 0.286540, No Obj: 0.003687, Avg Recall: 0.611570, count: 121 -Region Avg IOU: 0.575846, Class: 1.000000, Obj: 0.364998, No Obj: 0.003330, Avg Recall: 0.666667, count: 75 -Region Avg IOU: 0.511559, Class: 1.000000, Obj: 0.301341, No Obj: 0.003270, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.529806, Class: 1.000000, Obj: 0.300048, No Obj: 0.003835, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.483122, Class: 1.000000, Obj: 0.309417, No Obj: 0.003592, Avg Recall: 0.518868, count: 106 -Region Avg IOU: 0.536696, Class: 1.000000, Obj: 0.297544, No Obj: 0.003799, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.504137, Class: 1.000000, Obj: 0.310047, No Obj: 0.004183, Avg Recall: 0.552632, count: 114 -2607: 0.412607, 0.479090 avg, 0.001000 rate, 19.319073 seconds, 667392 images -Loaded: 1.164393 seconds -Region Avg IOU: 0.504783, Class: 1.000000, Obj: 0.290874, No Obj: 0.004005, Avg Recall: 0.589474, count: 95 -Region Avg IOU: 0.471060, Class: 1.000000, Obj: 0.266120, No Obj: 0.003784, Avg Recall: 0.532710, count: 107 -Region Avg IOU: 0.491753, Class: 1.000000, Obj: 0.307850, No Obj: 0.004218, Avg Recall: 0.563636, count: 110 -Region Avg IOU: 0.490151, Class: 1.000000, Obj: 0.319595, No Obj: 0.003553, Avg Recall: 0.539474, count: 76 -Region Avg IOU: 0.532624, Class: 1.000000, Obj: 0.281728, No Obj: 0.003998, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.526599, Class: 1.000000, Obj: 0.313407, No Obj: 0.003743, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.464219, Class: 1.000000, Obj: 0.261178, No Obj: 0.003303, Avg Recall: 0.505747, count: 87 -Region Avg IOU: 0.512463, Class: 1.000000, Obj: 0.294824, No Obj: 0.004290, Avg Recall: 0.586207, count: 116 -2608: 0.421131, 0.473294 avg, 0.001000 rate, 18.142153 seconds, 667648 images -Loaded: 2.655035 seconds -Region Avg IOU: 0.494687, Class: 1.000000, Obj: 0.285919, No Obj: 0.003736, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.510793, Class: 1.000000, Obj: 0.304430, No Obj: 0.004105, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.532817, Class: 1.000000, Obj: 0.299719, No Obj: 0.003564, Avg Recall: 0.613333, count: 75 -Region Avg IOU: 0.484853, Class: 1.000000, Obj: 0.295382, No Obj: 0.003715, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.542674, Class: 1.000000, Obj: 0.300799, No Obj: 0.003687, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.493128, Class: 1.000000, Obj: 0.288783, No Obj: 0.004070, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.518269, Class: 1.000000, Obj: 0.283425, No Obj: 0.003802, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.487174, Class: 1.000000, Obj: 0.310746, No Obj: 0.004019, Avg Recall: 0.500000, count: 120 -2609: 0.434843, 0.469449 avg, 0.001000 rate, 18.521103 seconds, 667904 images -Loaded: 3.465388 seconds -Region Avg IOU: 0.482742, Class: 1.000000, Obj: 0.292121, No Obj: 0.004167, Avg Recall: 0.514706, count: 136 -Region Avg IOU: 0.522839, Class: 1.000000, Obj: 0.268292, No Obj: 0.004099, Avg Recall: 0.592593, count: 108 -Region Avg IOU: 0.503041, Class: 1.000000, Obj: 0.292316, No Obj: 0.004091, Avg Recall: 0.598361, count: 122 -Region Avg IOU: 0.499405, Class: 1.000000, Obj: 0.267494, No Obj: 0.003579, Avg Recall: 0.586207, count: 116 -Region Avg IOU: 0.454771, Class: 1.000000, Obj: 0.254119, No Obj: 0.003632, Avg Recall: 0.491379, count: 116 -Region Avg IOU: 0.502398, Class: 1.000000, Obj: 0.266784, No Obj: 0.003574, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.478765, Class: 1.000000, Obj: 0.283668, No Obj: 0.003555, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.498983, Class: 1.000000, Obj: 0.326506, No Obj: 0.004038, Avg Recall: 0.577982, count: 109 -2610: 0.485725, 0.471077 avg, 0.001000 rate, 19.060905 seconds, 668160 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000061 seconds -Region Avg IOU: 0.601636, Class: 1.000000, Obj: 0.372371, No Obj: 0.003422, Avg Recall: 0.714286, count: 140 -Region Avg IOU: 0.620159, Class: 1.000000, Obj: 0.388694, No Obj: 0.003352, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.605087, Class: 1.000000, Obj: 0.360129, No Obj: 0.002680, Avg Recall: 0.725275, count: 91 -Region Avg IOU: 0.625842, Class: 1.000000, Obj: 0.342296, No Obj: 0.003285, Avg Recall: 0.776859, count: 121 -Region Avg IOU: 0.642546, Class: 1.000000, Obj: 0.399175, No Obj: 0.003151, Avg Recall: 0.826923, count: 104 -Region Avg IOU: 0.588601, Class: 1.000000, Obj: 0.325296, No Obj: 0.003429, Avg Recall: 0.705426, count: 129 -Region Avg IOU: 0.626191, Class: 1.000000, Obj: 0.365851, No Obj: 0.003143, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.651813, Class: 1.000000, Obj: 0.395643, No Obj: 0.003128, Avg Recall: 0.814159, count: 113 -2611: 0.499719, 0.473941 avg, 0.001000 rate, 28.796656 seconds, 668416 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.649747, Class: 1.000000, Obj: 0.444109, No Obj: 0.002605, Avg Recall: 0.848837, count: 86 -Region Avg IOU: 0.609169, Class: 1.000000, Obj: 0.339962, No Obj: 0.003244, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.600854, Class: 1.000000, Obj: 0.330457, No Obj: 0.003254, Avg Recall: 0.738095, count: 126 -Region Avg IOU: 0.613539, Class: 1.000000, Obj: 0.409979, No Obj: 0.003107, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.614361, Class: 1.000000, Obj: 0.346127, No Obj: 0.003631, Avg Recall: 0.776978, count: 139 -Region Avg IOU: 0.610381, Class: 1.000000, Obj: 0.350890, No Obj: 0.002895, Avg Recall: 0.744000, count: 125 -Region Avg IOU: 0.675744, Class: 1.000000, Obj: 0.366951, No Obj: 0.003264, Avg Recall: 0.854545, count: 110 -Region Avg IOU: 0.642067, Class: 1.000000, Obj: 0.345372, No Obj: 0.003143, Avg Recall: 0.831579, count: 95 -2612: 0.526347, 0.479182 avg, 0.001000 rate, 29.214949 seconds, 668672 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.600798, Class: 1.000000, Obj: 0.381481, No Obj: 0.002983, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.635180, Class: 1.000000, Obj: 0.344092, No Obj: 0.003074, Avg Recall: 0.806452, count: 124 -Region Avg IOU: 0.660005, Class: 1.000000, Obj: 0.363559, No Obj: 0.002806, Avg Recall: 0.840425, count: 94 -Region Avg IOU: 0.605264, Class: 1.000000, Obj: 0.283283, No Obj: 0.002733, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.616650, Class: 1.000000, Obj: 0.379223, No Obj: 0.002921, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.589853, Class: 1.000000, Obj: 0.362553, No Obj: 0.002764, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.633552, Class: 1.000000, Obj: 0.385383, No Obj: 0.003184, Avg Recall: 0.790323, count: 124 -Region Avg IOU: 0.660980, Class: 1.000000, Obj: 0.360636, No Obj: 0.002716, Avg Recall: 0.827160, count: 81 -2613: 0.493859, 0.480649 avg, 0.001000 rate, 29.513214 seconds, 668928 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.554211, Class: 1.000000, Obj: 0.337417, No Obj: 0.002468, Avg Recall: 0.637500, count: 80 -Region Avg IOU: 0.617843, Class: 1.000000, Obj: 0.335811, No Obj: 0.003049, Avg Recall: 0.752066, count: 121 -Region Avg IOU: 0.626936, Class: 1.000000, Obj: 0.409159, No Obj: 0.002929, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.649635, Class: 1.000000, Obj: 0.315621, No Obj: 0.002795, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.636983, Class: 1.000000, Obj: 0.368211, No Obj: 0.002937, Avg Recall: 0.779661, count: 118 -Region Avg IOU: 0.608539, Class: 1.000000, Obj: 0.309592, No Obj: 0.002852, Avg Recall: 0.773438, count: 128 -Region Avg IOU: 0.630561, Class: 1.000000, Obj: 0.346781, No Obj: 0.002852, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.624904, Class: 1.000000, Obj: 0.326735, No Obj: 0.002871, Avg Recall: 0.779528, count: 127 -2614: 0.511687, 0.483753 avg, 0.001000 rate, 29.913660 seconds, 669184 images -Loaded: 0.000059 seconds -Region Avg IOU: 0.641231, Class: 1.000000, Obj: 0.301729, No Obj: 0.002444, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.608621, Class: 1.000000, Obj: 0.304535, No Obj: 0.002684, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.615605, Class: 1.000000, Obj: 0.314021, No Obj: 0.002515, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.590230, Class: 1.000000, Obj: 0.307810, No Obj: 0.002667, Avg Recall: 0.656250, count: 128 -Region Avg IOU: 0.586987, Class: 1.000000, Obj: 0.277212, No Obj: 0.002718, Avg Recall: 0.661654, count: 133 -Region Avg IOU: 0.635441, Class: 1.000000, Obj: 0.350975, No Obj: 0.003023, Avg Recall: 0.807692, count: 130 -Region Avg IOU: 0.609763, Class: 1.000000, Obj: 0.316343, No Obj: 0.002259, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.610918, Class: 1.000000, Obj: 0.327044, No Obj: 0.002611, Avg Recall: 0.744361, count: 133 -2615: 0.518080, 0.487186 avg, 0.001000 rate, 28.529184 seconds, 669440 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.622935, Class: 1.000000, Obj: 0.333468, No Obj: 0.002615, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.648781, Class: 1.000000, Obj: 0.305674, No Obj: 0.002223, Avg Recall: 0.841463, count: 82 -Region Avg IOU: 0.677367, Class: 1.000000, Obj: 0.282597, No Obj: 0.002398, Avg Recall: 0.858696, count: 92 -Region Avg IOU: 0.571153, Class: 1.000000, Obj: 0.301777, No Obj: 0.002207, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.597467, Class: 1.000000, Obj: 0.320565, No Obj: 0.002142, Avg Recall: 0.755814, count: 86 -Region Avg IOU: 0.650159, Class: 1.000000, Obj: 0.340136, No Obj: 0.002486, Avg Recall: 0.796460, count: 113 -Region Avg IOU: 0.629496, Class: 1.000000, Obj: 0.314920, No Obj: 0.002103, Avg Recall: 0.780488, count: 82 -Region Avg IOU: 0.603166, Class: 1.000000, Obj: 0.272457, No Obj: 0.002533, Avg Recall: 0.805556, count: 108 -2616: 0.439828, 0.482450 avg, 0.001000 rate, 30.004423 seconds, 669696 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.671031, Class: 1.000000, Obj: 0.310657, No Obj: 0.002429, Avg Recall: 0.810345, count: 116 -Region Avg IOU: 0.657011, Class: 1.000000, Obj: 0.292680, No Obj: 0.002648, Avg Recall: 0.843478, count: 115 -Region Avg IOU: 0.633201, Class: 1.000000, Obj: 0.247619, No Obj: 0.002423, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.599054, Class: 1.000000, Obj: 0.250102, No Obj: 0.002035, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.622606, Class: 1.000000, Obj: 0.281052, No Obj: 0.002056, Avg Recall: 0.819149, count: 94 -Region Avg IOU: 0.606483, Class: 1.000000, Obj: 0.251633, No Obj: 0.002383, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.588339, Class: 1.000000, Obj: 0.251999, No Obj: 0.002146, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.630366, Class: 1.000000, Obj: 0.265571, No Obj: 0.002634, Avg Recall: 0.803030, count: 132 -2617: 0.525303, 0.486735 avg, 0.001000 rate, 29.431192 seconds, 669952 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.684815, Class: 1.000000, Obj: 0.339722, No Obj: 0.002477, Avg Recall: 0.869919, count: 123 -Region Avg IOU: 0.668158, Class: 1.000000, Obj: 0.311959, No Obj: 0.002233, Avg Recall: 0.903226, count: 93 -Region Avg IOU: 0.650479, Class: 1.000000, Obj: 0.284348, No Obj: 0.002465, Avg Recall: 0.825688, count: 109 -Region Avg IOU: 0.610174, Class: 1.000000, Obj: 0.312197, No Obj: 0.002514, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.646713, Class: 1.000000, Obj: 0.311071, No Obj: 0.002240, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.649078, Class: 1.000000, Obj: 0.313374, No Obj: 0.002430, Avg Recall: 0.784091, count: 88 -Region Avg IOU: 0.633048, Class: 1.000000, Obj: 0.344282, No Obj: 0.002454, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.618248, Class: 1.000000, Obj: 0.274958, No Obj: 0.002067, Avg Recall: 0.739130, count: 92 -2618: 0.454564, 0.483518 avg, 0.001000 rate, 29.072187 seconds, 670208 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.570613, Class: 1.000000, Obj: 0.281147, No Obj: 0.002140, Avg Recall: 0.692308, count: 78 -Region Avg IOU: 0.612454, Class: 1.000000, Obj: 0.277091, No Obj: 0.002371, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.620609, Class: 1.000000, Obj: 0.290192, No Obj: 0.002831, Avg Recall: 0.793103, count: 145 -Region Avg IOU: 0.586255, Class: 1.000000, Obj: 0.303991, No Obj: 0.002379, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.593678, Class: 1.000000, Obj: 0.297744, No Obj: 0.002368, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.657087, Class: 1.000000, Obj: 0.332598, No Obj: 0.002342, Avg Recall: 0.812500, count: 80 -Region Avg IOU: 0.669444, Class: 1.000000, Obj: 0.311281, No Obj: 0.002622, Avg Recall: 0.871212, count: 132 -Region Avg IOU: 0.636859, Class: 1.000000, Obj: 0.288559, No Obj: 0.002389, Avg Recall: 0.767677, count: 99 -2619: 0.478132, 0.482980 avg, 0.001000 rate, 29.699244 seconds, 670464 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.635378, Class: 1.000000, Obj: 0.345379, No Obj: 0.002360, Avg Recall: 0.811111, count: 90 -Region Avg IOU: 0.612232, Class: 1.000000, Obj: 0.383563, No Obj: 0.002534, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.590497, Class: 1.000000, Obj: 0.343749, No Obj: 0.002168, Avg Recall: 0.702381, count: 84 -Region Avg IOU: 0.626188, Class: 1.000000, Obj: 0.311197, No Obj: 0.002602, Avg Recall: 0.817460, count: 126 -Region Avg IOU: 0.631778, Class: 1.000000, Obj: 0.333076, No Obj: 0.002534, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.626968, Class: 1.000000, Obj: 0.274102, No Obj: 0.002768, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.669105, Class: 1.000000, Obj: 0.359544, No Obj: 0.002340, Avg Recall: 0.878788, count: 99 -Region Avg IOU: 0.666926, Class: 1.000000, Obj: 0.353876, No Obj: 0.002609, Avg Recall: 0.841584, count: 101 -2620: 0.434527, 0.478134 avg, 0.001000 rate, 28.246241 seconds, 670720 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 12.762362 seconds -Region Avg IOU: 0.593823, Class: 1.000000, Obj: 0.345070, No Obj: 0.002634, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.603004, Class: 1.000000, Obj: 0.298168, No Obj: 0.002777, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.605076, Class: 1.000000, Obj: 0.336970, No Obj: 0.003130, Avg Recall: 0.739130, count: 138 -Region Avg IOU: 0.643174, Class: 1.000000, Obj: 0.362708, No Obj: 0.002602, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.593357, Class: 1.000000, Obj: 0.316824, No Obj: 0.003123, Avg Recall: 0.719424, count: 139 -Region Avg IOU: 0.611171, Class: 1.000000, Obj: 0.312485, No Obj: 0.002888, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.632680, Class: 1.000000, Obj: 0.350883, No Obj: 0.002265, Avg Recall: 0.810127, count: 79 -Region Avg IOU: 0.618271, Class: 1.000000, Obj: 0.307783, No Obj: 0.002834, Avg Recall: 0.745283, count: 106 -2621: 0.507236, 0.481045 avg, 0.001000 rate, 30.562132 seconds, 670976 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.625427, Class: 1.000000, Obj: 0.357675, No Obj: 0.002463, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.647964, Class: 1.000000, Obj: 0.351672, No Obj: 0.002878, Avg Recall: 0.802083, count: 96 -Region Avg IOU: 0.627560, Class: 1.000000, Obj: 0.337388, No Obj: 0.003033, Avg Recall: 0.823009, count: 113 -Region Avg IOU: 0.650046, Class: 1.000000, Obj: 0.360072, No Obj: 0.003241, Avg Recall: 0.841667, count: 120 -Region Avg IOU: 0.643863, Class: 1.000000, Obj: 0.290113, No Obj: 0.002963, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.608051, Class: 1.000000, Obj: 0.356506, No Obj: 0.003188, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.647150, Class: 1.000000, Obj: 0.342549, No Obj: 0.002741, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.632651, Class: 1.000000, Obj: 0.380361, No Obj: 0.002804, Avg Recall: 0.745455, count: 110 -2622: 0.483580, 0.481298 avg, 0.001000 rate, 28.919191 seconds, 671232 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.610820, Class: 1.000000, Obj: 0.327872, No Obj: 0.002899, Avg Recall: 0.734375, count: 128 -Region Avg IOU: 0.647307, Class: 1.000000, Obj: 0.293661, No Obj: 0.002888, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.630735, Class: 1.000000, Obj: 0.332318, No Obj: 0.002583, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.655298, Class: 1.000000, Obj: 0.361951, No Obj: 0.003363, Avg Recall: 0.831933, count: 119 -Region Avg IOU: 0.623619, Class: 1.000000, Obj: 0.381824, No Obj: 0.002532, Avg Recall: 0.801653, count: 121 -Region Avg IOU: 0.688674, Class: 1.000000, Obj: 0.330936, No Obj: 0.002774, Avg Recall: 0.897727, count: 88 -Region Avg IOU: 0.590419, Class: 1.000000, Obj: 0.340443, No Obj: 0.002818, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.646905, Class: 1.000000, Obj: 0.371205, No Obj: 0.002931, Avg Recall: 0.770000, count: 100 -2623: 0.491961, 0.482364 avg, 0.001000 rate, 30.125273 seconds, 671488 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.648181, Class: 1.000000, Obj: 0.315867, No Obj: 0.002876, Avg Recall: 0.823077, count: 130 -Region Avg IOU: 0.641625, Class: 1.000000, Obj: 0.358847, No Obj: 0.002848, Avg Recall: 0.821705, count: 129 -Region Avg IOU: 0.656504, Class: 1.000000, Obj: 0.357932, No Obj: 0.002167, Avg Recall: 0.835616, count: 73 -Region Avg IOU: 0.635445, Class: 1.000000, Obj: 0.357762, No Obj: 0.002686, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.621786, Class: 1.000000, Obj: 0.345057, No Obj: 0.002745, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.619533, Class: 1.000000, Obj: 0.369188, No Obj: 0.002586, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.629063, Class: 1.000000, Obj: 0.317138, No Obj: 0.003169, Avg Recall: 0.785714, count: 140 -Region Avg IOU: 0.629571, Class: 1.000000, Obj: 0.351730, No Obj: 0.002696, Avg Recall: 0.761468, count: 109 -2624: 0.494795, 0.483608 avg, 0.001000 rate, 29.017122 seconds, 671744 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.623526, Class: 1.000000, Obj: 0.353532, No Obj: 0.002530, Avg Recall: 0.739583, count: 96 -Region Avg IOU: 0.600404, Class: 1.000000, Obj: 0.359553, No Obj: 0.002352, Avg Recall: 0.709302, count: 86 -Region Avg IOU: 0.581169, Class: 1.000000, Obj: 0.323781, No Obj: 0.002396, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.631207, Class: 1.000000, Obj: 0.334905, No Obj: 0.002545, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.638354, Class: 1.000000, Obj: 0.319747, No Obj: 0.002696, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.668769, Class: 1.000000, Obj: 0.362029, No Obj: 0.002826, Avg Recall: 0.838710, count: 124 -Region Avg IOU: 0.633048, Class: 1.000000, Obj: 0.331458, No Obj: 0.002656, Avg Recall: 0.756098, count: 123 -Region Avg IOU: 0.627713, Class: 1.000000, Obj: 0.337485, No Obj: 0.002810, Avg Recall: 0.815789, count: 152 -2625: 0.502255, 0.485472 avg, 0.001000 rate, 29.312712 seconds, 672000 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.556463, Class: 1.000000, Obj: 0.292734, No Obj: 0.002443, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.668919, Class: 1.000000, Obj: 0.406398, No Obj: 0.002384, Avg Recall: 0.845238, count: 84 -Region Avg IOU: 0.669290, Class: 1.000000, Obj: 0.345364, No Obj: 0.002474, Avg Recall: 0.851852, count: 108 -Region Avg IOU: 0.624218, Class: 1.000000, Obj: 0.324473, No Obj: 0.002533, Avg Recall: 0.838983, count: 118 -Region Avg IOU: 0.626078, Class: 1.000000, Obj: 0.335764, No Obj: 0.002484, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.619580, Class: 1.000000, Obj: 0.351190, No Obj: 0.002817, Avg Recall: 0.757812, count: 128 -Region Avg IOU: 0.645620, Class: 1.000000, Obj: 0.344192, No Obj: 0.002632, Avg Recall: 0.828571, count: 105 -Region Avg IOU: 0.578660, Class: 1.000000, Obj: 0.357769, No Obj: 0.002401, Avg Recall: 0.726316, count: 95 -2626: 0.451713, 0.482096 avg, 0.001000 rate, 28.911757 seconds, 672256 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.630631, Class: 1.000000, Obj: 0.342369, No Obj: 0.002514, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.600502, Class: 1.000000, Obj: 0.345275, No Obj: 0.002262, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.610279, Class: 1.000000, Obj: 0.276289, No Obj: 0.002277, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.622293, Class: 1.000000, Obj: 0.316481, No Obj: 0.002750, Avg Recall: 0.780488, count: 123 -Region Avg IOU: 0.635314, Class: 1.000000, Obj: 0.329110, No Obj: 0.002520, Avg Recall: 0.826087, count: 92 -Region Avg IOU: 0.649160, Class: 1.000000, Obj: 0.353211, No Obj: 0.002460, Avg Recall: 0.824742, count: 97 -Region Avg IOU: 0.615461, Class: 1.000000, Obj: 0.289526, No Obj: 0.002843, Avg Recall: 0.770270, count: 148 -Region Avg IOU: 0.606923, Class: 1.000000, Obj: 0.357529, No Obj: 0.002178, Avg Recall: 0.728571, count: 70 -2627: 0.454562, 0.479343 avg, 0.001000 rate, 28.901463 seconds, 672512 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.583462, Class: 1.000000, Obj: 0.313943, No Obj: 0.002697, Avg Recall: 0.705882, count: 119 -Region Avg IOU: 0.612266, Class: 1.000000, Obj: 0.293202, No Obj: 0.002332, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.607337, Class: 1.000000, Obj: 0.319980, No Obj: 0.002786, Avg Recall: 0.714286, count: 126 -Region Avg IOU: 0.636832, Class: 1.000000, Obj: 0.360913, No Obj: 0.002374, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.634656, Class: 1.000000, Obj: 0.310592, No Obj: 0.002144, Avg Recall: 0.804348, count: 92 -Region Avg IOU: 0.614643, Class: 1.000000, Obj: 0.369658, No Obj: 0.002459, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.606313, Class: 1.000000, Obj: 0.309071, No Obj: 0.002535, Avg Recall: 0.717557, count: 131 -Region Avg IOU: 0.592348, Class: 1.000000, Obj: 0.352007, No Obj: 0.002520, Avg Recall: 0.669355, count: 124 -2628: 0.481104, 0.479519 avg, 0.001000 rate, 28.754580 seconds, 672768 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.634907, Class: 1.000000, Obj: 0.301392, No Obj: 0.003000, Avg Recall: 0.787234, count: 141 -Region Avg IOU: 0.649818, Class: 1.000000, Obj: 0.348781, No Obj: 0.002609, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.644237, Class: 1.000000, Obj: 0.316928, No Obj: 0.002339, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.653547, Class: 1.000000, Obj: 0.351606, No Obj: 0.002324, Avg Recall: 0.824074, count: 108 -Region Avg IOU: 0.635010, Class: 1.000000, Obj: 0.314512, No Obj: 0.002298, Avg Recall: 0.818182, count: 88 -Region Avg IOU: 0.644115, Class: 1.000000, Obj: 0.329323, No Obj: 0.002538, Avg Recall: 0.795276, count: 127 -Region Avg IOU: 0.609117, Class: 1.000000, Obj: 0.317496, No Obj: 0.002233, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.634691, Class: 1.000000, Obj: 0.333128, No Obj: 0.002955, Avg Recall: 0.798246, count: 114 -2629: 0.479879, 0.479555 avg, 0.001000 rate, 28.967876 seconds, 673024 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.625273, Class: 1.000000, Obj: 0.357905, No Obj: 0.002337, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.551931, Class: 1.000000, Obj: 0.329511, No Obj: 0.002456, Avg Recall: 0.627119, count: 118 -Region Avg IOU: 0.583058, Class: 1.000000, Obj: 0.312673, No Obj: 0.002668, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.647782, Class: 1.000000, Obj: 0.328724, No Obj: 0.002363, Avg Recall: 0.825243, count: 103 -Region Avg IOU: 0.631805, Class: 1.000000, Obj: 0.349113, No Obj: 0.002271, Avg Recall: 0.831325, count: 83 -Region Avg IOU: 0.612136, Class: 1.000000, Obj: 0.335749, No Obj: 0.002491, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.628592, Class: 1.000000, Obj: 0.321414, No Obj: 0.002662, Avg Recall: 0.793388, count: 121 -Region Avg IOU: 0.591495, Class: 1.000000, Obj: 0.288025, No Obj: 0.002687, Avg Recall: 0.694215, count: 121 -2630: 0.501725, 0.481772 avg, 0.001000 rate, 29.119249 seconds, 673280 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 10.444277 seconds -Region Avg IOU: 0.645464, Class: 1.000000, Obj: 0.351765, No Obj: 0.002901, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.639406, Class: 1.000000, Obj: 0.300494, No Obj: 0.002576, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.626252, Class: 1.000000, Obj: 0.297677, No Obj: 0.002742, Avg Recall: 0.771429, count: 140 -Region Avg IOU: 0.666290, Class: 1.000000, Obj: 0.376979, No Obj: 0.002612, Avg Recall: 0.822222, count: 90 -Region Avg IOU: 0.626435, Class: 1.000000, Obj: 0.309911, No Obj: 0.002602, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.577993, Class: 1.000000, Obj: 0.337133, No Obj: 0.002197, Avg Recall: 0.714286, count: 84 -Region Avg IOU: 0.574571, Class: 1.000000, Obj: 0.276565, No Obj: 0.002104, Avg Recall: 0.697368, count: 76 -Region Avg IOU: 0.603845, Class: 1.000000, Obj: 0.314385, No Obj: 0.002387, Avg Recall: 0.696429, count: 112 -2631: 0.433688, 0.476964 avg, 0.001000 rate, 27.437675 seconds, 673536 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.565072, Class: 1.000000, Obj: 0.313830, No Obj: 0.002593, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.614019, Class: 1.000000, Obj: 0.344494, No Obj: 0.002510, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.648384, Class: 1.000000, Obj: 0.334876, No Obj: 0.002709, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.648968, Class: 1.000000, Obj: 0.352826, No Obj: 0.002309, Avg Recall: 0.829268, count: 82 -Region Avg IOU: 0.614395, Class: 1.000000, Obj: 0.311105, No Obj: 0.002840, Avg Recall: 0.771654, count: 127 -Region Avg IOU: 0.636687, Class: 1.000000, Obj: 0.377623, No Obj: 0.003009, Avg Recall: 0.794118, count: 136 -Region Avg IOU: 0.621194, Class: 1.000000, Obj: 0.331185, No Obj: 0.002890, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.569930, Class: 1.000000, Obj: 0.337580, No Obj: 0.002699, Avg Recall: 0.675926, count: 108 -2632: 0.515373, 0.480805 avg, 0.001000 rate, 27.817589 seconds, 673792 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.568799, Class: 1.000000, Obj: 0.319964, No Obj: 0.002409, Avg Recall: 0.659091, count: 88 -Region Avg IOU: 0.650789, Class: 1.000000, Obj: 0.300186, No Obj: 0.002875, Avg Recall: 0.837838, count: 111 -Region Avg IOU: 0.590448, Class: 1.000000, Obj: 0.329013, No Obj: 0.003183, Avg Recall: 0.752066, count: 121 -Region Avg IOU: 0.604567, Class: 1.000000, Obj: 0.325406, No Obj: 0.002790, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.628620, Class: 1.000000, Obj: 0.330227, No Obj: 0.002579, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.573817, Class: 1.000000, Obj: 0.296025, No Obj: 0.002860, Avg Recall: 0.694656, count: 131 -Region Avg IOU: 0.617058, Class: 1.000000, Obj: 0.299126, No Obj: 0.002490, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.617477, Class: 1.000000, Obj: 0.318168, No Obj: 0.002782, Avg Recall: 0.760417, count: 96 -2633: 0.435515, 0.476276 avg, 0.001000 rate, 29.064695 seconds, 674048 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.657731, Class: 1.000000, Obj: 0.331488, No Obj: 0.002874, Avg Recall: 0.851562, count: 128 -Region Avg IOU: 0.611162, Class: 1.000000, Obj: 0.306452, No Obj: 0.003191, Avg Recall: 0.685315, count: 143 -Region Avg IOU: 0.614542, Class: 1.000000, Obj: 0.349927, No Obj: 0.003042, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.575884, Class: 1.000000, Obj: 0.336961, No Obj: 0.002619, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.613270, Class: 1.000000, Obj: 0.307190, No Obj: 0.002549, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.653748, Class: 1.000000, Obj: 0.342257, No Obj: 0.002795, Avg Recall: 0.823529, count: 102 -Region Avg IOU: 0.610054, Class: 1.000000, Obj: 0.372508, No Obj: 0.002681, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.639692, Class: 1.000000, Obj: 0.364203, No Obj: 0.003042, Avg Recall: 0.781250, count: 128 -2634: 0.520076, 0.480656 avg, 0.001000 rate, 27.749559 seconds, 674304 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.616060, Class: 1.000000, Obj: 0.382278, No Obj: 0.002857, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.601097, Class: 1.000000, Obj: 0.366970, No Obj: 0.002629, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.608637, Class: 1.000000, Obj: 0.354878, No Obj: 0.002762, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.637808, Class: 1.000000, Obj: 0.356581, No Obj: 0.002576, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.591917, Class: 1.000000, Obj: 0.357365, No Obj: 0.002414, Avg Recall: 0.686747, count: 83 -Region Avg IOU: 0.666077, Class: 1.000000, Obj: 0.363481, No Obj: 0.002771, Avg Recall: 0.857143, count: 91 -Region Avg IOU: 0.606120, Class: 1.000000, Obj: 0.302119, No Obj: 0.002734, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.630853, Class: 1.000000, Obj: 0.336665, No Obj: 0.003118, Avg Recall: 0.741379, count: 116 -2635: 0.428476, 0.475438 avg, 0.001000 rate, 28.409748 seconds, 674560 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.622635, Class: 1.000000, Obj: 0.375653, No Obj: 0.002718, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.654317, Class: 1.000000, Obj: 0.355682, No Obj: 0.002888, Avg Recall: 0.828829, count: 111 -Region Avg IOU: 0.614795, Class: 1.000000, Obj: 0.359017, No Obj: 0.002466, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.598740, Class: 1.000000, Obj: 0.326813, No Obj: 0.002321, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.609487, Class: 1.000000, Obj: 0.300998, No Obj: 0.002565, Avg Recall: 0.783132, count: 83 -Region Avg IOU: 0.643484, Class: 1.000000, Obj: 0.319122, No Obj: 0.002943, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.636269, Class: 1.000000, Obj: 0.399989, No Obj: 0.002904, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.630275, Class: 1.000000, Obj: 0.354806, No Obj: 0.002696, Avg Recall: 0.764045, count: 89 -2636: 0.481157, 0.476010 avg, 0.001000 rate, 28.461906 seconds, 674816 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.625751, Class: 1.000000, Obj: 0.341789, No Obj: 0.002319, Avg Recall: 0.744186, count: 86 -Region Avg IOU: 0.604445, Class: 1.000000, Obj: 0.347055, No Obj: 0.003151, Avg Recall: 0.752137, count: 117 -Region Avg IOU: 0.586841, Class: 1.000000, Obj: 0.326874, No Obj: 0.002810, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.625005, Class: 1.000000, Obj: 0.352522, No Obj: 0.002873, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.631081, Class: 1.000000, Obj: 0.396189, No Obj: 0.002385, Avg Recall: 0.750000, count: 68 -Region Avg IOU: 0.668502, Class: 1.000000, Obj: 0.329385, No Obj: 0.002420, Avg Recall: 0.850746, count: 67 -Region Avg IOU: 0.625589, Class: 1.000000, Obj: 0.375213, No Obj: 0.003189, Avg Recall: 0.772059, count: 136 -Region Avg IOU: 0.633329, Class: 1.000000, Obj: 0.371323, No Obj: 0.003152, Avg Recall: 0.789855, count: 138 -2637: 0.438518, 0.472261 avg, 0.001000 rate, 27.653118 seconds, 675072 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.601050, Class: 1.000000, Obj: 0.331032, No Obj: 0.003084, Avg Recall: 0.744828, count: 145 -Region Avg IOU: 0.605680, Class: 1.000000, Obj: 0.368096, No Obj: 0.002754, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.653529, Class: 1.000000, Obj: 0.407752, No Obj: 0.002436, Avg Recall: 0.821429, count: 84 -Region Avg IOU: 0.625736, Class: 1.000000, Obj: 0.415839, No Obj: 0.002699, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.605989, Class: 1.000000, Obj: 0.349899, No Obj: 0.003165, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.601041, Class: 1.000000, Obj: 0.359963, No Obj: 0.002810, Avg Recall: 0.727273, count: 132 -Region Avg IOU: 0.602803, Class: 1.000000, Obj: 0.357696, No Obj: 0.002953, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.629696, Class: 1.000000, Obj: 0.366036, No Obj: 0.002637, Avg Recall: 0.750000, count: 100 -2638: 0.451315, 0.470166 avg, 0.001000 rate, 27.824331 seconds, 675328 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.647441, Class: 1.000000, Obj: 0.331456, No Obj: 0.002650, Avg Recall: 0.824074, count: 108 -Region Avg IOU: 0.605262, Class: 1.000000, Obj: 0.320322, No Obj: 0.002603, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.598926, Class: 1.000000, Obj: 0.335415, No Obj: 0.003067, Avg Recall: 0.713287, count: 143 -Region Avg IOU: 0.632643, Class: 1.000000, Obj: 0.383694, No Obj: 0.002247, Avg Recall: 0.756410, count: 78 -Region Avg IOU: 0.589424, Class: 1.000000, Obj: 0.340593, No Obj: 0.002513, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.587131, Class: 1.000000, Obj: 0.297346, No Obj: 0.002476, Avg Recall: 0.654321, count: 81 -Region Avg IOU: 0.579950, Class: 1.000000, Obj: 0.329746, No Obj: 0.002939, Avg Recall: 0.703125, count: 128 -Region Avg IOU: 0.638931, Class: 1.000000, Obj: 0.369872, No Obj: 0.002575, Avg Recall: 0.797980, count: 99 -2639: 0.467728, 0.469922 avg, 0.001000 rate, 27.430920 seconds, 675584 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.638102, Class: 1.000000, Obj: 0.371587, No Obj: 0.002470, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.620155, Class: 1.000000, Obj: 0.353598, No Obj: 0.002590, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.605028, Class: 1.000000, Obj: 0.341271, No Obj: 0.002699, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.569630, Class: 1.000000, Obj: 0.337593, No Obj: 0.002644, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.629018, Class: 1.000000, Obj: 0.322348, No Obj: 0.002524, Avg Recall: 0.773810, count: 84 -Region Avg IOU: 0.617170, Class: 1.000000, Obj: 0.297321, No Obj: 0.002777, Avg Recall: 0.788618, count: 123 -Region Avg IOU: 0.609481, Class: 1.000000, Obj: 0.335595, No Obj: 0.002806, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.648019, Class: 1.000000, Obj: 0.356691, No Obj: 0.002463, Avg Recall: 0.785714, count: 98 -2640: 0.498570, 0.472787 avg, 0.001000 rate, 28.650028 seconds, 675840 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.175291 seconds -Region Avg IOU: 0.434957, Class: 1.000000, Obj: 0.224542, No Obj: 0.003130, Avg Recall: 0.435185, count: 108 -Region Avg IOU: 0.459713, Class: 1.000000, Obj: 0.220999, No Obj: 0.003674, Avg Recall: 0.528455, count: 123 -Region Avg IOU: 0.491083, Class: 1.000000, Obj: 0.228880, No Obj: 0.003699, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.454080, Class: 1.000000, Obj: 0.204211, No Obj: 0.003506, Avg Recall: 0.464646, count: 99 -Region Avg IOU: 0.496828, Class: 1.000000, Obj: 0.239793, No Obj: 0.003621, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.436727, Class: 1.000000, Obj: 0.247465, No Obj: 0.003224, Avg Recall: 0.440860, count: 93 -Region Avg IOU: 0.447966, Class: 1.000000, Obj: 0.188974, No Obj: 0.003366, Avg Recall: 0.495238, count: 105 -Region Avg IOU: 0.448705, Class: 1.000000, Obj: 0.231361, No Obj: 0.003489, Avg Recall: 0.483871, count: 93 -2641: 0.489822, 0.474491 avg, 0.001000 rate, 16.644508 seconds, 676096 images -Loaded: 4.789968 seconds -Region Avg IOU: 0.462413, Class: 1.000000, Obj: 0.238765, No Obj: 0.003772, Avg Recall: 0.518868, count: 106 -Region Avg IOU: 0.471709, Class: 1.000000, Obj: 0.240951, No Obj: 0.003951, Avg Recall: 0.534247, count: 146 -Region Avg IOU: 0.454250, Class: 1.000000, Obj: 0.230842, No Obj: 0.003916, Avg Recall: 0.500000, count: 98 -Region Avg IOU: 0.475199, Class: 1.000000, Obj: 0.208542, No Obj: 0.003461, Avg Recall: 0.514286, count: 105 -Region Avg IOU: 0.457435, Class: 1.000000, Obj: 0.186045, No Obj: 0.003672, Avg Recall: 0.495413, count: 109 -Region Avg IOU: 0.445256, Class: 1.000000, Obj: 0.194071, No Obj: 0.004025, Avg Recall: 0.460000, count: 150 -Region Avg IOU: 0.428255, Class: 1.000000, Obj: 0.198460, No Obj: 0.003389, Avg Recall: 0.474576, count: 118 -Region Avg IOU: 0.400790, Class: 1.000000, Obj: 0.234384, No Obj: 0.003354, Avg Recall: 0.380531, count: 113 -2642: 0.555668, 0.482608 avg, 0.001000 rate, 16.640026 seconds, 676352 images -Loaded: 1.949234 seconds -Region Avg IOU: 0.447968, Class: 1.000000, Obj: 0.261380, No Obj: 0.003896, Avg Recall: 0.452991, count: 117 -Region Avg IOU: 0.434721, Class: 1.000000, Obj: 0.209752, No Obj: 0.003144, Avg Recall: 0.457143, count: 105 -Region Avg IOU: 0.458960, Class: 1.000000, Obj: 0.280473, No Obj: 0.003401, Avg Recall: 0.493671, count: 79 -Region Avg IOU: 0.386303, Class: 1.000000, Obj: 0.202288, No Obj: 0.003504, Avg Recall: 0.403226, count: 124 -Region Avg IOU: 0.473819, Class: 1.000000, Obj: 0.258945, No Obj: 0.004280, Avg Recall: 0.540816, count: 98 -Region Avg IOU: 0.424792, Class: 1.000000, Obj: 0.213511, No Obj: 0.003580, Avg Recall: 0.409091, count: 88 -Region Avg IOU: 0.489699, Class: 1.000000, Obj: 0.216368, No Obj: 0.004270, Avg Recall: 0.569106, count: 123 -Region Avg IOU: 0.485054, Class: 1.000000, Obj: 0.256930, No Obj: 0.004748, Avg Recall: 0.553719, count: 121 -2643: 0.503807, 0.484728 avg, 0.001000 rate, 16.125881 seconds, 676608 images -Loaded: 5.697986 seconds -Region Avg IOU: 0.478308, Class: 1.000000, Obj: 0.243279, No Obj: 0.004609, Avg Recall: 0.552845, count: 123 -Region Avg IOU: 0.414621, Class: 1.000000, Obj: 0.248528, No Obj: 0.004261, Avg Recall: 0.463636, count: 110 -Region Avg IOU: 0.497028, Class: 1.000000, Obj: 0.300502, No Obj: 0.003936, Avg Recall: 0.529412, count: 68 -Region Avg IOU: 0.473901, Class: 1.000000, Obj: 0.218946, No Obj: 0.003763, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.431783, Class: 1.000000, Obj: 0.276832, No Obj: 0.004266, Avg Recall: 0.420168, count: 119 -Region Avg IOU: 0.416891, Class: 1.000000, Obj: 0.275553, No Obj: 0.004787, Avg Recall: 0.390411, count: 146 -Region Avg IOU: 0.460878, Class: 1.000000, Obj: 0.275118, No Obj: 0.003359, Avg Recall: 0.476190, count: 84 -Region Avg IOU: 0.446393, Class: 1.000000, Obj: 0.292342, No Obj: 0.005071, Avg Recall: 0.422078, count: 154 -2644: 0.448201, 0.481075 avg, 0.001000 rate, 13.773871 seconds, 676864 images -Loaded: 7.308231 seconds -Region Avg IOU: 0.422003, Class: 1.000000, Obj: 0.270810, No Obj: 0.004349, Avg Recall: 0.349515, count: 103 -Region Avg IOU: 0.467449, Class: 1.000000, Obj: 0.301015, No Obj: 0.004266, Avg Recall: 0.518072, count: 83 -Region Avg IOU: 0.476557, Class: 1.000000, Obj: 0.311004, No Obj: 0.004729, Avg Recall: 0.522936, count: 109 -Region Avg IOU: 0.477366, Class: 1.000000, Obj: 0.341406, No Obj: 0.004774, Avg Recall: 0.528090, count: 89 -Region Avg IOU: 0.387601, Class: 1.000000, Obj: 0.245618, No Obj: 0.004062, Avg Recall: 0.370000, count: 100 -Region Avg IOU: 0.439415, Class: 1.000000, Obj: 0.300028, No Obj: 0.004506, Avg Recall: 0.509804, count: 102 -Region Avg IOU: 0.424099, Class: 1.000000, Obj: 0.237712, No Obj: 0.004343, Avg Recall: 0.440367, count: 109 -Region Avg IOU: 0.412048, Class: 1.000000, Obj: 0.229982, No Obj: 0.004813, Avg Recall: 0.437500, count: 112 -2645: 0.493077, 0.482276 avg, 0.001000 rate, 15.782009 seconds, 677120 images -Loaded: 4.449764 seconds -Region Avg IOU: 0.387828, Class: 1.000000, Obj: 0.284949, No Obj: 0.004390, Avg Recall: 0.352941, count: 119 -Region Avg IOU: 0.416927, Class: 1.000000, Obj: 0.323559, No Obj: 0.004284, Avg Recall: 0.456311, count: 103 -Region Avg IOU: 0.416353, Class: 1.000000, Obj: 0.306239, No Obj: 0.005242, Avg Recall: 0.428571, count: 119 -Region Avg IOU: 0.451045, Class: 1.000000, Obj: 0.314963, No Obj: 0.004512, Avg Recall: 0.510638, count: 94 -Region Avg IOU: 0.380859, Class: 1.000000, Obj: 0.289935, No Obj: 0.004697, Avg Recall: 0.382609, count: 115 -Region Avg IOU: 0.408851, Class: 1.000000, Obj: 0.274049, No Obj: 0.004451, Avg Recall: 0.420455, count: 88 -Region Avg IOU: 0.408663, Class: 1.000000, Obj: 0.259216, No Obj: 0.004960, Avg Recall: 0.433962, count: 106 -Region Avg IOU: 0.403260, Class: 1.000000, Obj: 0.294961, No Obj: 0.004726, Avg Recall: 0.357724, count: 123 -2646: 0.551976, 0.489246 avg, 0.001000 rate, 15.662325 seconds, 677376 images -Loaded: 5.711022 seconds -Region Avg IOU: 0.433340, Class: 1.000000, Obj: 0.287383, No Obj: 0.004506, Avg Recall: 0.440000, count: 75 -Region Avg IOU: 0.367869, Class: 1.000000, Obj: 0.306418, No Obj: 0.005790, Avg Recall: 0.366013, count: 153 -Region Avg IOU: 0.417653, Class: 1.000000, Obj: 0.319085, No Obj: 0.005478, Avg Recall: 0.446970, count: 132 -Region Avg IOU: 0.414181, Class: 1.000000, Obj: 0.300952, No Obj: 0.005254, Avg Recall: 0.396552, count: 116 -Region Avg IOU: 0.429151, Class: 1.000000, Obj: 0.332700, No Obj: 0.005419, Avg Recall: 0.418033, count: 122 -Region Avg IOU: 0.402573, Class: 1.000000, Obj: 0.312357, No Obj: 0.005265, Avg Recall: 0.456140, count: 114 -Region Avg IOU: 0.409673, Class: 1.000000, Obj: 0.318968, No Obj: 0.005840, Avg Recall: 0.400000, count: 130 -Region Avg IOU: 0.373547, Class: 1.000000, Obj: 0.288785, No Obj: 0.004952, Avg Recall: 0.389313, count: 131 -2647: 0.624395, 0.502761 avg, 0.001000 rate, 16.334057 seconds, 677632 images -Loaded: 4.299449 seconds -Region Avg IOU: 0.405606, Class: 1.000000, Obj: 0.354623, No Obj: 0.004769, Avg Recall: 0.375000, count: 96 -Region Avg IOU: 0.451968, Class: 1.000000, Obj: 0.306467, No Obj: 0.005133, Avg Recall: 0.447059, count: 85 -Region Avg IOU: 0.414795, Class: 1.000000, Obj: 0.307621, No Obj: 0.004341, Avg Recall: 0.451613, count: 93 -Region Avg IOU: 0.384095, Class: 1.000000, Obj: 0.315148, No Obj: 0.004640, Avg Recall: 0.375000, count: 112 -Region Avg IOU: 0.458328, Class: 1.000000, Obj: 0.336873, No Obj: 0.005617, Avg Recall: 0.515464, count: 97 -Region Avg IOU: 0.481639, Class: 1.000000, Obj: 0.367526, No Obj: 0.005692, Avg Recall: 0.520000, count: 125 -Region Avg IOU: 0.428895, Class: 1.000000, Obj: 0.301354, No Obj: 0.005345, Avg Recall: 0.476635, count: 107 -Region Avg IOU: 0.435404, Class: 1.000000, Obj: 0.303319, No Obj: 0.006366, Avg Recall: 0.474576, count: 118 -2648: 0.500048, 0.502489 avg, 0.001000 rate, 18.877071 seconds, 677888 images -Loaded: 1.468387 seconds -Region Avg IOU: 0.402548, Class: 1.000000, Obj: 0.320025, No Obj: 0.004982, Avg Recall: 0.433735, count: 83 -Region Avg IOU: 0.397213, Class: 1.000000, Obj: 0.333126, No Obj: 0.004889, Avg Recall: 0.393939, count: 99 -Region Avg IOU: 0.424711, Class: 1.000000, Obj: 0.284601, No Obj: 0.005201, Avg Recall: 0.495327, count: 107 -Region Avg IOU: 0.421272, Class: 1.000000, Obj: 0.259676, No Obj: 0.004384, Avg Recall: 0.419355, count: 93 -Region Avg IOU: 0.395553, Class: 1.000000, Obj: 0.326916, No Obj: 0.004655, Avg Recall: 0.406593, count: 91 -Region Avg IOU: 0.451089, Class: 1.000000, Obj: 0.340121, No Obj: 0.004816, Avg Recall: 0.455696, count: 79 -Region Avg IOU: 0.451208, Class: 1.000000, Obj: 0.354958, No Obj: 0.005614, Avg Recall: 0.474747, count: 99 -Region Avg IOU: 0.461938, Class: 1.000000, Obj: 0.318902, No Obj: 0.005563, Avg Recall: 0.464000, count: 125 -2649: 0.454760, 0.497716 avg, 0.001000 rate, 18.651093 seconds, 678144 images -Loaded: 1.902294 seconds -Region Avg IOU: 0.424747, Class: 1.000000, Obj: 0.297570, No Obj: 0.004953, Avg Recall: 0.444444, count: 99 -Region Avg IOU: 0.475279, Class: 1.000000, Obj: 0.310421, No Obj: 0.005735, Avg Recall: 0.518519, count: 108 -Region Avg IOU: 0.420315, Class: 1.000000, Obj: 0.287184, No Obj: 0.005347, Avg Recall: 0.390625, count: 128 -Region Avg IOU: 0.454346, Class: 1.000000, Obj: 0.296872, No Obj: 0.005144, Avg Recall: 0.509259, count: 108 -Region Avg IOU: 0.493313, Class: 1.000000, Obj: 0.340808, No Obj: 0.005404, Avg Recall: 0.541667, count: 96 -Region Avg IOU: 0.425503, Class: 1.000000, Obj: 0.342036, No Obj: 0.004728, Avg Recall: 0.442105, count: 95 -Region Avg IOU: 0.446721, Class: 1.000000, Obj: 0.294760, No Obj: 0.005780, Avg Recall: 0.530769, count: 130 -Region Avg IOU: 0.427165, Class: 1.000000, Obj: 0.318298, No Obj: 0.004797, Avg Recall: 0.434783, count: 92 -2650: 0.459112, 0.493856 avg, 0.001000 rate, 18.207653 seconds, 678400 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 0.290131 seconds -Region Avg IOU: 0.505362, Class: 1.000000, Obj: 0.334437, No Obj: 0.004454, Avg Recall: 0.555556, count: 81 -Region Avg IOU: 0.441207, Class: 1.000000, Obj: 0.307370, No Obj: 0.004814, Avg Recall: 0.425743, count: 101 -Region Avg IOU: 0.501285, Class: 1.000000, Obj: 0.320574, No Obj: 0.004401, Avg Recall: 0.511111, count: 90 -Region Avg IOU: 0.460772, Class: 1.000000, Obj: 0.301541, No Obj: 0.004130, Avg Recall: 0.505263, count: 95 -Region Avg IOU: 0.481768, Class: 1.000000, Obj: 0.345084, No Obj: 0.004487, Avg Recall: 0.522523, count: 111 -Region Avg IOU: 0.472996, Class: 1.000000, Obj: 0.364979, No Obj: 0.004550, Avg Recall: 0.504425, count: 113 -Region Avg IOU: 0.456656, Class: 1.000000, Obj: 0.325101, No Obj: 0.004515, Avg Recall: 0.474138, count: 116 -Region Avg IOU: 0.464403, Class: 1.000000, Obj: 0.335650, No Obj: 0.004876, Avg Recall: 0.495575, count: 113 -2651: 0.424512, 0.486922 avg, 0.001000 rate, 19.594870 seconds, 678656 images -Loaded: 1.245239 seconds -Region Avg IOU: 0.458413, Class: 1.000000, Obj: 0.261798, No Obj: 0.003799, Avg Recall: 0.500000, count: 90 -Region Avg IOU: 0.469588, Class: 1.000000, Obj: 0.275952, No Obj: 0.005104, Avg Recall: 0.486957, count: 115 -Region Avg IOU: 0.446827, Class: 1.000000, Obj: 0.266474, No Obj: 0.004338, Avg Recall: 0.487805, count: 123 -Region Avg IOU: 0.459112, Class: 1.000000, Obj: 0.317752, No Obj: 0.004926, Avg Recall: 0.500000, count: 116 -Region Avg IOU: 0.472262, Class: 1.000000, Obj: 0.328206, No Obj: 0.004471, Avg Recall: 0.540000, count: 100 -Region Avg IOU: 0.497934, Class: 1.000000, Obj: 0.241592, No Obj: 0.004109, Avg Recall: 0.553571, count: 112 -Region Avg IOU: 0.451325, Class: 1.000000, Obj: 0.275793, No Obj: 0.004020, Avg Recall: 0.477064, count: 109 -Region Avg IOU: 0.504261, Class: 1.000000, Obj: 0.280701, No Obj: 0.004325, Avg Recall: 0.582524, count: 103 -2652: 0.503171, 0.488547 avg, 0.001000 rate, 17.210100 seconds, 678912 images -Loaded: 3.728863 seconds -Region Avg IOU: 0.462274, Class: 1.000000, Obj: 0.251661, No Obj: 0.004216, Avg Recall: 0.523364, count: 107 -Region Avg IOU: 0.543880, Class: 1.000000, Obj: 0.290853, No Obj: 0.003990, Avg Recall: 0.641026, count: 78 -Region Avg IOU: 0.457894, Class: 1.000000, Obj: 0.254875, No Obj: 0.003558, Avg Recall: 0.488889, count: 90 -Region Avg IOU: 0.506271, Class: 1.000000, Obj: 0.277432, No Obj: 0.005612, Avg Recall: 0.593103, count: 145 -Region Avg IOU: 0.521773, Class: 1.000000, Obj: 0.272166, No Obj: 0.004087, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.466882, Class: 1.000000, Obj: 0.276336, No Obj: 0.004752, Avg Recall: 0.507246, count: 138 -Region Avg IOU: 0.498813, Class: 1.000000, Obj: 0.282401, No Obj: 0.004615, Avg Recall: 0.542373, count: 118 -Region Avg IOU: 0.478335, Class: 1.000000, Obj: 0.282839, No Obj: 0.004379, Avg Recall: 0.504425, count: 113 -2653: 0.498366, 0.489529 avg, 0.001000 rate, 21.204687 seconds, 679168 images -Loaded: 0.585081 seconds -Region Avg IOU: 0.436585, Class: 1.000000, Obj: 0.274160, No Obj: 0.003985, Avg Recall: 0.495798, count: 119 -Region Avg IOU: 0.525413, Class: 1.000000, Obj: 0.257284, No Obj: 0.004343, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.469505, Class: 1.000000, Obj: 0.283936, No Obj: 0.004262, Avg Recall: 0.522523, count: 111 -Region Avg IOU: 0.524520, Class: 1.000000, Obj: 0.273553, No Obj: 0.003755, Avg Recall: 0.574468, count: 94 -Region Avg IOU: 0.470764, Class: 1.000000, Obj: 0.260817, No Obj: 0.003603, Avg Recall: 0.525773, count: 97 -Region Avg IOU: 0.484279, Class: 1.000000, Obj: 0.257963, No Obj: 0.004200, Avg Recall: 0.552239, count: 134 -Region Avg IOU: 0.497876, Class: 1.000000, Obj: 0.262053, No Obj: 0.003913, Avg Recall: 0.523810, count: 126 -Region Avg IOU: 0.486176, Class: 1.000000, Obj: 0.279343, No Obj: 0.004032, Avg Recall: 0.505263, count: 95 -2654: 0.545268, 0.495103 avg, 0.001000 rate, 20.636232 seconds, 679424 images -Loaded: 0.306230 seconds -Region Avg IOU: 0.478110, Class: 1.000000, Obj: 0.282971, No Obj: 0.003671, Avg Recall: 0.528736, count: 87 -Region Avg IOU: 0.479496, Class: 1.000000, Obj: 0.281499, No Obj: 0.004194, Avg Recall: 0.519231, count: 104 -Region Avg IOU: 0.497177, Class: 1.000000, Obj: 0.282754, No Obj: 0.003796, Avg Recall: 0.544554, count: 101 -Region Avg IOU: 0.470340, Class: 1.000000, Obj: 0.270108, No Obj: 0.003904, Avg Recall: 0.492064, count: 126 -Region Avg IOU: 0.546005, Class: 1.000000, Obj: 0.310052, No Obj: 0.005230, Avg Recall: 0.616541, count: 133 -Region Avg IOU: 0.455093, Class: 1.000000, Obj: 0.239179, No Obj: 0.003786, Avg Recall: 0.504587, count: 109 -Region Avg IOU: 0.466171, Class: 1.000000, Obj: 0.285853, No Obj: 0.003635, Avg Recall: 0.510000, count: 100 -Region Avg IOU: 0.487196, Class: 1.000000, Obj: 0.285059, No Obj: 0.004686, Avg Recall: 0.551724, count: 116 -2655: 0.514531, 0.497045 avg, 0.001000 rate, 20.069201 seconds, 679680 images -Loaded: 0.971471 seconds -Region Avg IOU: 0.531192, Class: 1.000000, Obj: 0.314922, No Obj: 0.004192, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.485124, Class: 1.000000, Obj: 0.289433, No Obj: 0.003858, Avg Recall: 0.490000, count: 100 -Region Avg IOU: 0.475404, Class: 1.000000, Obj: 0.253421, No Obj: 0.004239, Avg Recall: 0.512605, count: 119 -Region Avg IOU: 0.476221, Class: 1.000000, Obj: 0.266412, No Obj: 0.004125, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.457241, Class: 1.000000, Obj: 0.289310, No Obj: 0.003863, Avg Recall: 0.523810, count: 105 -Region Avg IOU: 0.487910, Class: 1.000000, Obj: 0.265980, No Obj: 0.004258, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.447587, Class: 1.000000, Obj: 0.271306, No Obj: 0.003729, Avg Recall: 0.464646, count: 99 -Region Avg IOU: 0.484056, Class: 1.000000, Obj: 0.294954, No Obj: 0.004125, Avg Recall: 0.491803, count: 122 -2656: 0.442998, 0.491641 avg, 0.001000 rate, 17.309273 seconds, 679936 images -Loaded: 3.106711 seconds -Region Avg IOU: 0.493520, Class: 1.000000, Obj: 0.275350, No Obj: 0.004231, Avg Recall: 0.550000, count: 100 -Region Avg IOU: 0.481653, Class: 1.000000, Obj: 0.299899, No Obj: 0.004392, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.488802, Class: 1.000000, Obj: 0.323765, No Obj: 0.004196, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.407292, Class: 1.000000, Obj: 0.257203, No Obj: 0.004219, Avg Recall: 0.467742, count: 124 -Region Avg IOU: 0.459025, Class: 1.000000, Obj: 0.271980, No Obj: 0.003953, Avg Recall: 0.500000, count: 98 -Region Avg IOU: 0.510644, Class: 1.000000, Obj: 0.320027, No Obj: 0.004626, Avg Recall: 0.571429, count: 119 -Region Avg IOU: 0.426282, Class: 1.000000, Obj: 0.254658, No Obj: 0.003727, Avg Recall: 0.400000, count: 120 -Region Avg IOU: 0.511108, Class: 1.000000, Obj: 0.326690, No Obj: 0.003934, Avg Recall: 0.547619, count: 84 -2657: 0.417367, 0.484213 avg, 0.001000 rate, 19.709108 seconds, 680192 images -Loaded: 2.742953 seconds -Region Avg IOU: 0.506611, Class: 1.000000, Obj: 0.308130, No Obj: 0.005089, Avg Recall: 0.571429, count: 119 -Region Avg IOU: 0.489773, Class: 1.000000, Obj: 0.304456, No Obj: 0.004724, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.479756, Class: 1.000000, Obj: 0.317142, No Obj: 0.004711, Avg Recall: 0.504348, count: 115 -Region Avg IOU: 0.471495, Class: 1.000000, Obj: 0.262341, No Obj: 0.003795, Avg Recall: 0.505882, count: 85 -Region Avg IOU: 0.508251, Class: 1.000000, Obj: 0.308452, No Obj: 0.004903, Avg Recall: 0.564516, count: 124 -Region Avg IOU: 0.519698, Class: 1.000000, Obj: 0.301117, No Obj: 0.005035, Avg Recall: 0.587302, count: 126 -Region Avg IOU: 0.482765, Class: 1.000000, Obj: 0.280480, No Obj: 0.004991, Avg Recall: 0.572727, count: 110 -Region Avg IOU: 0.454419, Class: 1.000000, Obj: 0.297758, No Obj: 0.004002, Avg Recall: 0.495238, count: 105 -2658: 0.543446, 0.490137 avg, 0.001000 rate, 17.798847 seconds, 680448 images -Loaded: 2.288917 seconds -Region Avg IOU: 0.455951, Class: 1.000000, Obj: 0.276180, No Obj: 0.004989, Avg Recall: 0.500000, count: 124 -Region Avg IOU: 0.438115, Class: 1.000000, Obj: 0.303204, No Obj: 0.004813, Avg Recall: 0.436975, count: 119 -Region Avg IOU: 0.456076, Class: 1.000000, Obj: 0.285459, No Obj: 0.004305, Avg Recall: 0.495146, count: 103 -Region Avg IOU: 0.443499, Class: 1.000000, Obj: 0.312002, No Obj: 0.004058, Avg Recall: 0.447059, count: 85 -Region Avg IOU: 0.467413, Class: 1.000000, Obj: 0.336533, No Obj: 0.003443, Avg Recall: 0.455696, count: 79 -Region Avg IOU: 0.506966, Class: 1.000000, Obj: 0.294937, No Obj: 0.004865, Avg Recall: 0.561404, count: 114 -Region Avg IOU: 0.520734, Class: 1.000000, Obj: 0.361821, No Obj: 0.004213, Avg Recall: 0.600000, count: 90 -Region Avg IOU: 0.504014, Class: 1.000000, Obj: 0.335116, No Obj: 0.004427, Avg Recall: 0.573171, count: 82 -2659: 0.415688, 0.482692 avg, 0.001000 rate, 19.849836 seconds, 680704 images -Loaded: 0.362491 seconds -Region Avg IOU: 0.447659, Class: 1.000000, Obj: 0.300475, No Obj: 0.003983, Avg Recall: 0.478261, count: 92 -Region Avg IOU: 0.459089, Class: 1.000000, Obj: 0.307517, No Obj: 0.004170, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.453321, Class: 1.000000, Obj: 0.287800, No Obj: 0.004224, Avg Recall: 0.483871, count: 93 -Region Avg IOU: 0.483220, Class: 1.000000, Obj: 0.305231, No Obj: 0.005479, Avg Recall: 0.496454, count: 141 -Region Avg IOU: 0.486826, Class: 1.000000, Obj: 0.318870, No Obj: 0.004605, Avg Recall: 0.507692, count: 130 -Region Avg IOU: 0.526672, Class: 1.000000, Obj: 0.352752, No Obj: 0.004547, Avg Recall: 0.597701, count: 87 -Region Avg IOU: 0.451662, Class: 1.000000, Obj: 0.328612, No Obj: 0.004262, Avg Recall: 0.469027, count: 113 -Region Avg IOU: 0.447886, Class: 1.000000, Obj: 0.278782, No Obj: 0.004294, Avg Recall: 0.452991, count: 117 -2660: 0.496800, 0.484103 avg, 0.001000 rate, 21.252563 seconds, 680960 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000065 seconds -Region Avg IOU: 0.549783, Class: 1.000000, Obj: 0.369087, No Obj: 0.003406, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.611895, Class: 1.000000, Obj: 0.355547, No Obj: 0.003521, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.582469, Class: 1.000000, Obj: 0.463320, No Obj: 0.003188, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.559931, Class: 1.000000, Obj: 0.371323, No Obj: 0.004066, Avg Recall: 0.653543, count: 127 -Region Avg IOU: 0.560608, Class: 1.000000, Obj: 0.389198, No Obj: 0.003454, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.601355, Class: 1.000000, Obj: 0.407498, No Obj: 0.003832, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.619178, Class: 1.000000, Obj: 0.394648, No Obj: 0.003874, Avg Recall: 0.766129, count: 124 -Region Avg IOU: 0.581611, Class: 1.000000, Obj: 0.329977, No Obj: 0.003272, Avg Recall: 0.685393, count: 89 -2661: 0.510514, 0.486744 avg, 0.001000 rate, 27.637177 seconds, 681216 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.628551, Class: 1.000000, Obj: 0.410328, No Obj: 0.003172, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.578329, Class: 1.000000, Obj: 0.395751, No Obj: 0.003731, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.553242, Class: 1.000000, Obj: 0.377858, No Obj: 0.003269, Avg Recall: 0.633333, count: 120 -Region Avg IOU: 0.526578, Class: 1.000000, Obj: 0.390166, No Obj: 0.003150, Avg Recall: 0.553398, count: 103 -Region Avg IOU: 0.539754, Class: 1.000000, Obj: 0.390167, No Obj: 0.002894, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.567884, Class: 1.000000, Obj: 0.356902, No Obj: 0.003055, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.589476, Class: 1.000000, Obj: 0.409826, No Obj: 0.002989, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.630756, Class: 1.000000, Obj: 0.418485, No Obj: 0.003320, Avg Recall: 0.766129, count: 124 -2662: 0.477731, 0.485842 avg, 0.001000 rate, 27.770449 seconds, 681472 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.619198, Class: 1.000000, Obj: 0.310923, No Obj: 0.002738, Avg Recall: 0.770270, count: 74 -Region Avg IOU: 0.624363, Class: 1.000000, Obj: 0.384000, No Obj: 0.003242, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.607651, Class: 1.000000, Obj: 0.376773, No Obj: 0.003183, Avg Recall: 0.753846, count: 130 -Region Avg IOU: 0.589868, Class: 1.000000, Obj: 0.386048, No Obj: 0.002974, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.602246, Class: 1.000000, Obj: 0.362281, No Obj: 0.002825, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.608123, Class: 1.000000, Obj: 0.426362, No Obj: 0.002723, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.598648, Class: 1.000000, Obj: 0.398511, No Obj: 0.002987, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.649855, Class: 1.000000, Obj: 0.383626, No Obj: 0.002726, Avg Recall: 0.853658, count: 82 -2663: 0.507772, 0.488035 avg, 0.001000 rate, 28.548264 seconds, 681728 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.620109, Class: 1.000000, Obj: 0.375327, No Obj: 0.002477, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.592911, Class: 1.000000, Obj: 0.288059, No Obj: 0.002106, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.607083, Class: 1.000000, Obj: 0.343917, No Obj: 0.002609, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.579076, Class: 1.000000, Obj: 0.315783, No Obj: 0.002971, Avg Recall: 0.685039, count: 127 -Region Avg IOU: 0.567480, Class: 1.000000, Obj: 0.340226, No Obj: 0.002419, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.611177, Class: 1.000000, Obj: 0.323211, No Obj: 0.003153, Avg Recall: 0.757576, count: 132 -Region Avg IOU: 0.609512, Class: 1.000000, Obj: 0.363871, No Obj: 0.002619, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.599150, Class: 1.000000, Obj: 0.304516, No Obj: 0.003327, Avg Recall: 0.760274, count: 146 -2664: 0.500109, 0.489243 avg, 0.001000 rate, 29.050327 seconds, 681984 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.617676, Class: 1.000000, Obj: 0.329527, No Obj: 0.002247, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.597740, Class: 1.000000, Obj: 0.314405, No Obj: 0.002308, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.640563, Class: 1.000000, Obj: 0.354399, No Obj: 0.002256, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.584328, Class: 1.000000, Obj: 0.300728, No Obj: 0.002414, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.563163, Class: 1.000000, Obj: 0.253011, No Obj: 0.002462, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.565427, Class: 1.000000, Obj: 0.277581, No Obj: 0.002346, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.597802, Class: 1.000000, Obj: 0.259321, No Obj: 0.002623, Avg Recall: 0.697842, count: 139 -Region Avg IOU: 0.599393, Class: 1.000000, Obj: 0.247238, No Obj: 0.002161, Avg Recall: 0.714286, count: 112 -2665: 0.464907, 0.486809 avg, 0.001000 rate, 28.763569 seconds, 682240 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.576029, Class: 1.000000, Obj: 0.301015, No Obj: 0.002539, Avg Recall: 0.708029, count: 137 -Region Avg IOU: 0.624129, Class: 1.000000, Obj: 0.331611, No Obj: 0.002259, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.603203, Class: 1.000000, Obj: 0.286851, No Obj: 0.002408, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.617527, Class: 1.000000, Obj: 0.287812, No Obj: 0.002340, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.588794, Class: 1.000000, Obj: 0.283381, No Obj: 0.002333, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.581687, Class: 1.000000, Obj: 0.296823, No Obj: 0.002138, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.591709, Class: 1.000000, Obj: 0.303772, No Obj: 0.002321, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.596183, Class: 1.000000, Obj: 0.301025, No Obj: 0.002203, Avg Recall: 0.711340, count: 97 -2666: 0.443774, 0.482506 avg, 0.001000 rate, 27.068779 seconds, 682496 images -Loaded: 0.000056 seconds -Region Avg IOU: 0.579409, Class: 1.000000, Obj: 0.234314, No Obj: 0.002253, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.622240, Class: 1.000000, Obj: 0.279772, No Obj: 0.002178, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.665340, Class: 1.000000, Obj: 0.336175, No Obj: 0.002214, Avg Recall: 0.862745, count: 102 -Region Avg IOU: 0.580562, Class: 1.000000, Obj: 0.270492, No Obj: 0.001869, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.620469, Class: 1.000000, Obj: 0.255163, No Obj: 0.002595, Avg Recall: 0.740458, count: 131 -Region Avg IOU: 0.641157, Class: 1.000000, Obj: 0.306083, No Obj: 0.002074, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.625912, Class: 1.000000, Obj: 0.286405, No Obj: 0.002417, Avg Recall: 0.720930, count: 129 -Region Avg IOU: 0.612854, Class: 1.000000, Obj: 0.287546, No Obj: 0.002421, Avg Recall: 0.723214, count: 112 -2667: 0.484212, 0.482676 avg, 0.001000 rate, 29.341757 seconds, 682752 images -Loaded: 0.000053 seconds -Region Avg IOU: 0.575434, Class: 1.000000, Obj: 0.261994, No Obj: 0.002373, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.596103, Class: 1.000000, Obj: 0.340320, No Obj: 0.002522, Avg Recall: 0.706349, count: 126 -Region Avg IOU: 0.607461, Class: 1.000000, Obj: 0.252444, No Obj: 0.002517, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.642736, Class: 1.000000, Obj: 0.309022, No Obj: 0.002534, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.663368, Class: 1.000000, Obj: 0.320117, No Obj: 0.002578, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.618144, Class: 1.000000, Obj: 0.352185, No Obj: 0.002316, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.625901, Class: 1.000000, Obj: 0.303173, No Obj: 0.002434, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.592373, Class: 1.000000, Obj: 0.347211, No Obj: 0.002348, Avg Recall: 0.738636, count: 88 -2668: 0.435368, 0.477945 avg, 0.001000 rate, 28.729393 seconds, 683008 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.636884, Class: 1.000000, Obj: 0.321300, No Obj: 0.002867, Avg Recall: 0.780488, count: 123 -Region Avg IOU: 0.634575, Class: 1.000000, Obj: 0.287905, No Obj: 0.002614, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.587961, Class: 1.000000, Obj: 0.305210, No Obj: 0.002821, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.615987, Class: 1.000000, Obj: 0.327562, No Obj: 0.003067, Avg Recall: 0.742647, count: 136 -Region Avg IOU: 0.635880, Class: 1.000000, Obj: 0.281196, No Obj: 0.003001, Avg Recall: 0.770992, count: 131 -Region Avg IOU: 0.622197, Class: 1.000000, Obj: 0.309474, No Obj: 0.002681, Avg Recall: 0.775862, count: 116 -Region Avg IOU: 0.600171, Class: 1.000000, Obj: 0.314519, No Obj: 0.003009, Avg Recall: 0.748252, count: 143 -Region Avg IOU: 0.601193, Class: 1.000000, Obj: 0.290161, No Obj: 0.003314, Avg Recall: 0.708029, count: 137 -2669: 0.516815, 0.481832 avg, 0.001000 rate, 27.115213 seconds, 683264 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.614877, Class: 1.000000, Obj: 0.319952, No Obj: 0.003508, Avg Recall: 0.768657, count: 134 -Region Avg IOU: 0.576078, Class: 1.000000, Obj: 0.333224, No Obj: 0.002607, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.616098, Class: 1.000000, Obj: 0.349916, No Obj: 0.002984, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.571811, Class: 1.000000, Obj: 0.385475, No Obj: 0.002810, Avg Recall: 0.648352, count: 91 -Region Avg IOU: 0.607926, Class: 1.000000, Obj: 0.363062, No Obj: 0.002330, Avg Recall: 0.777778, count: 81 -Region Avg IOU: 0.619991, Class: 1.000000, Obj: 0.342958, No Obj: 0.002981, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.650526, Class: 1.000000, Obj: 0.375879, No Obj: 0.002955, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.624692, Class: 1.000000, Obj: 0.359401, No Obj: 0.003028, Avg Recall: 0.752294, count: 109 -2670: 0.461210, 0.479770 avg, 0.001000 rate, 28.139956 seconds, 683520 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000070 seconds -Region Avg IOU: 0.624388, Class: 1.000000, Obj: 0.330792, No Obj: 0.003422, Avg Recall: 0.743802, count: 121 -Region Avg IOU: 0.608679, Class: 1.000000, Obj: 0.338782, No Obj: 0.003108, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.616908, Class: 1.000000, Obj: 0.342476, No Obj: 0.003219, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.611823, Class: 1.000000, Obj: 0.431471, No Obj: 0.002857, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.675871, Class: 1.000000, Obj: 0.411535, No Obj: 0.003032, Avg Recall: 0.833333, count: 90 -Region Avg IOU: 0.591794, Class: 1.000000, Obj: 0.352208, No Obj: 0.002828, Avg Recall: 0.755556, count: 90 -Region Avg IOU: 0.632253, Class: 1.000000, Obj: 0.385152, No Obj: 0.002556, Avg Recall: 0.800000, count: 85 -Region Avg IOU: 0.610249, Class: 1.000000, Obj: 0.364200, No Obj: 0.003000, Avg Recall: 0.735294, count: 102 -2671: 0.463489, 0.478142 avg, 0.001000 rate, 27.080732 seconds, 683776 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.659408, Class: 1.000000, Obj: 0.382301, No Obj: 0.003225, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.586030, Class: 1.000000, Obj: 0.394704, No Obj: 0.002566, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.599742, Class: 1.000000, Obj: 0.381381, No Obj: 0.002829, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.567032, Class: 1.000000, Obj: 0.306356, No Obj: 0.002716, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.602531, Class: 1.000000, Obj: 0.367026, No Obj: 0.003138, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.626153, Class: 1.000000, Obj: 0.390330, No Obj: 0.003263, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.606631, Class: 1.000000, Obj: 0.409699, No Obj: 0.003250, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.590005, Class: 1.000000, Obj: 0.372249, No Obj: 0.002938, Avg Recall: 0.737374, count: 99 -2672: 0.435104, 0.473838 avg, 0.001000 rate, 26.775387 seconds, 684032 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.614621, Class: 1.000000, Obj: 0.406736, No Obj: 0.002770, Avg Recall: 0.744681, count: 94 -Region Avg IOU: 0.606266, Class: 1.000000, Obj: 0.384131, No Obj: 0.003566, Avg Recall: 0.727941, count: 136 -Region Avg IOU: 0.599060, Class: 1.000000, Obj: 0.409236, No Obj: 0.003172, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.645887, Class: 1.000000, Obj: 0.372557, No Obj: 0.002811, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.617996, Class: 1.000000, Obj: 0.416715, No Obj: 0.002984, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.583191, Class: 1.000000, Obj: 0.416100, No Obj: 0.003238, Avg Recall: 0.696970, count: 132 -Region Avg IOU: 0.653916, Class: 1.000000, Obj: 0.380349, No Obj: 0.002914, Avg Recall: 0.831683, count: 101 -Region Avg IOU: 0.649641, Class: 1.000000, Obj: 0.367790, No Obj: 0.002926, Avg Recall: 0.847826, count: 92 -2673: 0.463933, 0.472848 avg, 0.001000 rate, 28.726269 seconds, 684288 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.618236, Class: 1.000000, Obj: 0.336179, No Obj: 0.003038, Avg Recall: 0.764228, count: 123 -Region Avg IOU: 0.629479, Class: 1.000000, Obj: 0.350395, No Obj: 0.003174, Avg Recall: 0.789916, count: 119 -Region Avg IOU: 0.617324, Class: 1.000000, Obj: 0.369553, No Obj: 0.002991, Avg Recall: 0.757576, count: 132 -Region Avg IOU: 0.630197, Class: 1.000000, Obj: 0.330433, No Obj: 0.002855, Avg Recall: 0.803419, count: 117 -Region Avg IOU: 0.619314, Class: 1.000000, Obj: 0.356673, No Obj: 0.002912, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.620657, Class: 1.000000, Obj: 0.345246, No Obj: 0.003371, Avg Recall: 0.758865, count: 141 -Region Avg IOU: 0.644863, Class: 1.000000, Obj: 0.389079, No Obj: 0.002939, Avg Recall: 0.786325, count: 117 -Region Avg IOU: 0.573764, Class: 1.000000, Obj: 0.330756, No Obj: 0.002807, Avg Recall: 0.694444, count: 108 -2674: 0.514748, 0.477038 avg, 0.001000 rate, 28.063124 seconds, 684544 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.615944, Class: 1.000000, Obj: 0.366503, No Obj: 0.002714, Avg Recall: 0.746988, count: 83 -Region Avg IOU: 0.627926, Class: 1.000000, Obj: 0.360376, No Obj: 0.002949, Avg Recall: 0.793478, count: 92 -Region Avg IOU: 0.633771, Class: 1.000000, Obj: 0.343417, No Obj: 0.002544, Avg Recall: 0.781250, count: 96 -Region Avg IOU: 0.597693, Class: 1.000000, Obj: 0.314508, No Obj: 0.002786, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.639229, Class: 1.000000, Obj: 0.368759, No Obj: 0.003223, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.575503, Class: 1.000000, Obj: 0.338116, No Obj: 0.002328, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.651928, Class: 1.000000, Obj: 0.365425, No Obj: 0.003051, Avg Recall: 0.804878, count: 123 -Region Avg IOU: 0.623190, Class: 1.000000, Obj: 0.346239, No Obj: 0.002528, Avg Recall: 0.752577, count: 97 -2675: 0.426525, 0.471987 avg, 0.001000 rate, 28.747765 seconds, 684800 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.603439, Class: 1.000000, Obj: 0.363350, No Obj: 0.002527, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.619404, Class: 1.000000, Obj: 0.380038, No Obj: 0.002512, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.610875, Class: 1.000000, Obj: 0.355171, No Obj: 0.002711, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.585229, Class: 1.000000, Obj: 0.401120, No Obj: 0.002746, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.609081, Class: 1.000000, Obj: 0.333245, No Obj: 0.002679, Avg Recall: 0.744000, count: 125 -Region Avg IOU: 0.664891, Class: 1.000000, Obj: 0.345774, No Obj: 0.002968, Avg Recall: 0.849558, count: 113 -Region Avg IOU: 0.612282, Class: 1.000000, Obj: 0.312942, No Obj: 0.002610, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.638241, Class: 1.000000, Obj: 0.322265, No Obj: 0.002661, Avg Recall: 0.752212, count: 113 -2676: 0.485355, 0.473323 avg, 0.001000 rate, 29.255880 seconds, 685056 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.609843, Class: 1.000000, Obj: 0.359450, No Obj: 0.002401, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.631622, Class: 1.000000, Obj: 0.346527, No Obj: 0.002539, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.635441, Class: 1.000000, Obj: 0.384557, No Obj: 0.002772, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.613641, Class: 1.000000, Obj: 0.346093, No Obj: 0.002222, Avg Recall: 0.720930, count: 86 -Region Avg IOU: 0.609615, Class: 1.000000, Obj: 0.322098, No Obj: 0.002638, Avg Recall: 0.722222, count: 126 -Region Avg IOU: 0.636979, Class: 1.000000, Obj: 0.328312, No Obj: 0.002654, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.593025, Class: 1.000000, Obj: 0.298179, No Obj: 0.002725, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.598474, Class: 1.000000, Obj: 0.356437, No Obj: 0.002504, Avg Recall: 0.684211, count: 114 -2677: 0.450701, 0.471061 avg, 0.001000 rate, 27.924843 seconds, 685312 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.609407, Class: 1.000000, Obj: 0.251712, No Obj: 0.002535, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.600460, Class: 1.000000, Obj: 0.321811, No Obj: 0.002296, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.594407, Class: 1.000000, Obj: 0.321992, No Obj: 0.002346, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.590976, Class: 1.000000, Obj: 0.334871, No Obj: 0.002204, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.597973, Class: 1.000000, Obj: 0.304190, No Obj: 0.002472, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.579326, Class: 1.000000, Obj: 0.320617, No Obj: 0.002474, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.619126, Class: 1.000000, Obj: 0.312626, No Obj: 0.002779, Avg Recall: 0.752000, count: 125 -Region Avg IOU: 0.611587, Class: 1.000000, Obj: 0.352145, No Obj: 0.002303, Avg Recall: 0.747253, count: 91 -2678: 0.465898, 0.470545 avg, 0.001000 rate, 27.731972 seconds, 685568 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.574896, Class: 1.000000, Obj: 0.296255, No Obj: 0.002474, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.627328, Class: 1.000000, Obj: 0.298580, No Obj: 0.002399, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.578373, Class: 1.000000, Obj: 0.298533, No Obj: 0.002363, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.618498, Class: 1.000000, Obj: 0.344028, No Obj: 0.002167, Avg Recall: 0.764706, count: 85 -Region Avg IOU: 0.633906, Class: 1.000000, Obj: 0.293710, No Obj: 0.002016, Avg Recall: 0.800000, count: 85 -Region Avg IOU: 0.666017, Class: 1.000000, Obj: 0.311318, No Obj: 0.002422, Avg Recall: 0.829787, count: 94 -Region Avg IOU: 0.665329, Class: 1.000000, Obj: 0.321060, No Obj: 0.002567, Avg Recall: 0.795455, count: 88 -Region Avg IOU: 0.613387, Class: 1.000000, Obj: 0.312254, No Obj: 0.002947, Avg Recall: 0.774775, count: 111 -2679: 0.464749, 0.469965 avg, 0.001000 rate, 29.162569 seconds, 685824 images -Loaded: 0.000144 seconds -Region Avg IOU: 0.615998, Class: 1.000000, Obj: 0.299927, No Obj: 0.002344, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.633501, Class: 1.000000, Obj: 0.321216, No Obj: 0.002544, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.593651, Class: 1.000000, Obj: 0.291899, No Obj: 0.002573, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.592644, Class: 1.000000, Obj: 0.311908, No Obj: 0.002688, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.602872, Class: 1.000000, Obj: 0.340573, No Obj: 0.002280, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.606311, Class: 1.000000, Obj: 0.304965, No Obj: 0.002769, Avg Recall: 0.723077, count: 130 -Region Avg IOU: 0.581156, Class: 1.000000, Obj: 0.304876, No Obj: 0.002685, Avg Recall: 0.682927, count: 123 -Region Avg IOU: 0.657746, Class: 1.000000, Obj: 0.336369, No Obj: 0.002580, Avg Recall: 0.834783, count: 115 -2680: 0.501616, 0.473130 avg, 0.001000 rate, 28.424158 seconds, 686080 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 8.981588 seconds -Region Avg IOU: 0.596019, Class: 1.000000, Obj: 0.320890, No Obj: 0.002666, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.592780, Class: 1.000000, Obj: 0.324066, No Obj: 0.002776, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.588163, Class: 1.000000, Obj: 0.321854, No Obj: 0.003254, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.585356, Class: 1.000000, Obj: 0.314596, No Obj: 0.002852, Avg Recall: 0.712644, count: 87 -Region Avg IOU: 0.586517, Class: 1.000000, Obj: 0.328021, No Obj: 0.003435, Avg Recall: 0.738095, count: 126 -Region Avg IOU: 0.581770, Class: 1.000000, Obj: 0.352130, No Obj: 0.002583, Avg Recall: 0.743902, count: 82 -Region Avg IOU: 0.558637, Class: 1.000000, Obj: 0.296492, No Obj: 0.002907, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.558181, Class: 1.000000, Obj: 0.332824, No Obj: 0.002778, Avg Recall: 0.669903, count: 103 -2681: 0.468812, 0.472698 avg, 0.001000 rate, 23.501328 seconds, 686336 images -Loaded: 0.000117 seconds -Region Avg IOU: 0.559003, Class: 1.000000, Obj: 0.301444, No Obj: 0.002930, Avg Recall: 0.663866, count: 119 -Region Avg IOU: 0.503422, Class: 1.000000, Obj: 0.300207, No Obj: 0.002509, Avg Recall: 0.513043, count: 115 -Region Avg IOU: 0.585121, Class: 1.000000, Obj: 0.345114, No Obj: 0.002913, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.641580, Class: 1.000000, Obj: 0.398833, No Obj: 0.003149, Avg Recall: 0.813953, count: 86 -Region Avg IOU: 0.564975, Class: 1.000000, Obj: 0.358488, No Obj: 0.003295, Avg Recall: 0.666667, count: 132 -Region Avg IOU: 0.577149, Class: 1.000000, Obj: 0.294663, No Obj: 0.003035, Avg Recall: 0.708861, count: 79 -Region Avg IOU: 0.567015, Class: 1.000000, Obj: 0.347184, No Obj: 0.002852, Avg Recall: 0.722222, count: 90 -Region Avg IOU: 0.585373, Class: 1.000000, Obj: 0.348772, No Obj: 0.003128, Avg Recall: 0.712766, count: 94 -2682: 0.417547, 0.467183 avg, 0.001000 rate, 24.917427 seconds, 686592 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.592887, Class: 1.000000, Obj: 0.383605, No Obj: 0.003463, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.579914, Class: 1.000000, Obj: 0.286874, No Obj: 0.003222, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.583974, Class: 1.000000, Obj: 0.311838, No Obj: 0.003982, Avg Recall: 0.674419, count: 129 -Region Avg IOU: 0.589936, Class: 1.000000, Obj: 0.334618, No Obj: 0.003719, Avg Recall: 0.680328, count: 122 -Region Avg IOU: 0.541323, Class: 1.000000, Obj: 0.333790, No Obj: 0.003268, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.583053, Class: 1.000000, Obj: 0.342773, No Obj: 0.003136, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.546830, Class: 1.000000, Obj: 0.288917, No Obj: 0.003148, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.587198, Class: 1.000000, Obj: 0.344661, No Obj: 0.003467, Avg Recall: 0.708333, count: 120 -2683: 0.491702, 0.469635 avg, 0.001000 rate, 25.273342 seconds, 686848 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.555513, Class: 1.000000, Obj: 0.341936, No Obj: 0.002936, Avg Recall: 0.659091, count: 88 -Region Avg IOU: 0.584785, Class: 1.000000, Obj: 0.355066, No Obj: 0.003649, Avg Recall: 0.688525, count: 122 -Region Avg IOU: 0.607215, Class: 1.000000, Obj: 0.326857, No Obj: 0.003763, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.563296, Class: 1.000000, Obj: 0.375817, No Obj: 0.002729, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.575657, Class: 1.000000, Obj: 0.372046, No Obj: 0.003639, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.579211, Class: 1.000000, Obj: 0.329830, No Obj: 0.003717, Avg Recall: 0.732283, count: 127 -Region Avg IOU: 0.596830, Class: 1.000000, Obj: 0.361781, No Obj: 0.003670, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.569558, Class: 1.000000, Obj: 0.313295, No Obj: 0.003193, Avg Recall: 0.660550, count: 109 -2684: 0.471527, 0.469824 avg, 0.001000 rate, 24.311068 seconds, 687104 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.599061, Class: 1.000000, Obj: 0.410563, No Obj: 0.003829, Avg Recall: 0.681818, count: 88 -Region Avg IOU: 0.584593, Class: 1.000000, Obj: 0.340109, No Obj: 0.003677, Avg Recall: 0.715447, count: 123 -Region Avg IOU: 0.610684, Class: 1.000000, Obj: 0.417081, No Obj: 0.003391, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.548397, Class: 1.000000, Obj: 0.342439, No Obj: 0.003905, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.534050, Class: 1.000000, Obj: 0.349988, No Obj: 0.003144, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.553936, Class: 1.000000, Obj: 0.335053, No Obj: 0.003565, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.588012, Class: 1.000000, Obj: 0.373192, No Obj: 0.003788, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.560875, Class: 1.000000, Obj: 0.356198, No Obj: 0.002711, Avg Recall: 0.613333, count: 75 -2685: 0.426545, 0.465496 avg, 0.001000 rate, 23.808479 seconds, 687360 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.565539, Class: 1.000000, Obj: 0.432397, No Obj: 0.003318, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.545345, Class: 1.000000, Obj: 0.351078, No Obj: 0.004287, Avg Recall: 0.588652, count: 141 -Region Avg IOU: 0.592352, Class: 1.000000, Obj: 0.326784, No Obj: 0.003958, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.623989, Class: 1.000000, Obj: 0.393943, No Obj: 0.003274, Avg Recall: 0.764706, count: 68 -Region Avg IOU: 0.561970, Class: 1.000000, Obj: 0.373874, No Obj: 0.003478, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.539425, Class: 1.000000, Obj: 0.353305, No Obj: 0.003374, Avg Recall: 0.632184, count: 87 -Region Avg IOU: 0.586793, Class: 1.000000, Obj: 0.373055, No Obj: 0.003140, Avg Recall: 0.722222, count: 90 -Region Avg IOU: 0.554721, Class: 1.000000, Obj: 0.429208, No Obj: 0.003117, Avg Recall: 0.630952, count: 84 -2686: 0.404749, 0.459422 avg, 0.001000 rate, 25.719099 seconds, 687616 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.622490, Class: 1.000000, Obj: 0.427348, No Obj: 0.003147, Avg Recall: 0.750000, count: 84 -Region Avg IOU: 0.558352, Class: 1.000000, Obj: 0.387142, No Obj: 0.003483, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.568290, Class: 1.000000, Obj: 0.364505, No Obj: 0.003718, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.546734, Class: 1.000000, Obj: 0.380192, No Obj: 0.003221, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.587495, Class: 1.000000, Obj: 0.408416, No Obj: 0.003855, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.566214, Class: 1.000000, Obj: 0.348429, No Obj: 0.003875, Avg Recall: 0.677419, count: 124 -Region Avg IOU: 0.554751, Class: 1.000000, Obj: 0.381470, No Obj: 0.004233, Avg Recall: 0.649635, count: 137 -Region Avg IOU: 0.576003, Class: 1.000000, Obj: 0.292649, No Obj: 0.003457, Avg Recall: 0.675926, count: 108 -2687: 0.457392, 0.459219 avg, 0.001000 rate, 25.491957 seconds, 687872 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.561217, Class: 1.000000, Obj: 0.335030, No Obj: 0.003541, Avg Recall: 0.641026, count: 117 -Region Avg IOU: 0.548667, Class: 1.000000, Obj: 0.379733, No Obj: 0.003434, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.544259, Class: 1.000000, Obj: 0.308731, No Obj: 0.003418, Avg Recall: 0.616000, count: 125 -Region Avg IOU: 0.575576, Class: 1.000000, Obj: 0.390066, No Obj: 0.002966, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.558230, Class: 1.000000, Obj: 0.318586, No Obj: 0.003468, Avg Recall: 0.655738, count: 122 -Region Avg IOU: 0.571433, Class: 1.000000, Obj: 0.344859, No Obj: 0.004003, Avg Recall: 0.684211, count: 133 -Region Avg IOU: 0.605129, Class: 1.000000, Obj: 0.363567, No Obj: 0.004096, Avg Recall: 0.701613, count: 124 -Region Avg IOU: 0.574230, Class: 1.000000, Obj: 0.368845, No Obj: 0.003479, Avg Recall: 0.654867, count: 113 -2688: 0.487932, 0.462090 avg, 0.001000 rate, 24.602077 seconds, 688128 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.591781, Class: 1.000000, Obj: 0.353968, No Obj: 0.003368, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.558410, Class: 1.000000, Obj: 0.372647, No Obj: 0.003002, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.535430, Class: 1.000000, Obj: 0.324897, No Obj: 0.003358, Avg Recall: 0.666667, count: 126 -Region Avg IOU: 0.539846, Class: 1.000000, Obj: 0.395925, No Obj: 0.002965, Avg Recall: 0.577778, count: 90 -Region Avg IOU: 0.574128, Class: 1.000000, Obj: 0.371059, No Obj: 0.002695, Avg Recall: 0.670455, count: 88 -Region Avg IOU: 0.576724, Class: 1.000000, Obj: 0.349110, No Obj: 0.003459, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.590899, Class: 1.000000, Obj: 0.353217, No Obj: 0.002857, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.589396, Class: 1.000000, Obj: 0.332768, No Obj: 0.003220, Avg Recall: 0.696429, count: 112 -2689: 0.479150, 0.463796 avg, 0.001000 rate, 26.056965 seconds, 688384 images -Loaded: 0.000115 seconds -Region Avg IOU: 0.532527, Class: 1.000000, Obj: 0.349324, No Obj: 0.003377, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.577510, Class: 1.000000, Obj: 0.325320, No Obj: 0.003406, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.606184, Class: 1.000000, Obj: 0.321884, No Obj: 0.003361, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.583231, Class: 1.000000, Obj: 0.325908, No Obj: 0.002989, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.579567, Class: 1.000000, Obj: 0.342967, No Obj: 0.003190, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.590124, Class: 1.000000, Obj: 0.362468, No Obj: 0.003433, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.600170, Class: 1.000000, Obj: 0.371788, No Obj: 0.003382, Avg Recall: 0.748092, count: 131 -Region Avg IOU: 0.613531, Class: 1.000000, Obj: 0.374431, No Obj: 0.003183, Avg Recall: 0.786408, count: 103 -2690: 0.495348, 0.466951 avg, 0.001000 rate, 24.812860 seconds, 688640 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000090 seconds -Region Avg IOU: 0.608286, Class: 1.000000, Obj: 0.318316, No Obj: 0.002555, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.599602, Class: 1.000000, Obj: 0.356544, No Obj: 0.002681, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.630157, Class: 1.000000, Obj: 0.365690, No Obj: 0.002852, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.597653, Class: 1.000000, Obj: 0.328847, No Obj: 0.002492, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.630741, Class: 1.000000, Obj: 0.332394, No Obj: 0.002491, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.590599, Class: 1.000000, Obj: 0.353768, No Obj: 0.002377, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.610927, Class: 1.000000, Obj: 0.350601, No Obj: 0.002851, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.612701, Class: 1.000000, Obj: 0.347838, No Obj: 0.002531, Avg Recall: 0.767857, count: 112 -2691: 0.501970, 0.470453 avg, 0.001000 rate, 29.132755 seconds, 688896 images -Loaded: 0.000132 seconds -Region Avg IOU: 0.570755, Class: 1.000000, Obj: 0.348306, No Obj: 0.002430, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.626514, Class: 1.000000, Obj: 0.349329, No Obj: 0.002732, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.620904, Class: 1.000000, Obj: 0.392218, No Obj: 0.002435, Avg Recall: 0.776471, count: 85 -Region Avg IOU: 0.602272, Class: 1.000000, Obj: 0.329423, No Obj: 0.002874, Avg Recall: 0.723881, count: 134 -Region Avg IOU: 0.612025, Class: 1.000000, Obj: 0.327164, No Obj: 0.002851, Avg Recall: 0.744681, count: 141 -Region Avg IOU: 0.596771, Class: 1.000000, Obj: 0.306478, No Obj: 0.002676, Avg Recall: 0.691057, count: 123 -Region Avg IOU: 0.616912, Class: 1.000000, Obj: 0.250492, No Obj: 0.002523, Avg Recall: 0.747126, count: 87 -Region Avg IOU: 0.637073, Class: 1.000000, Obj: 0.380304, No Obj: 0.002654, Avg Recall: 0.815534, count: 103 -2692: 0.533673, 0.476775 avg, 0.001000 rate, 27.673096 seconds, 689152 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.619696, Class: 1.000000, Obj: 0.331189, No Obj: 0.002679, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.620846, Class: 1.000000, Obj: 0.315199, No Obj: 0.002551, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.585456, Class: 1.000000, Obj: 0.314128, No Obj: 0.002333, Avg Recall: 0.662651, count: 83 -Region Avg IOU: 0.624745, Class: 1.000000, Obj: 0.370109, No Obj: 0.002619, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.593624, Class: 1.000000, Obj: 0.320559, No Obj: 0.002554, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.613812, Class: 1.000000, Obj: 0.305314, No Obj: 0.002599, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.628125, Class: 1.000000, Obj: 0.317003, No Obj: 0.002696, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.636314, Class: 1.000000, Obj: 0.304517, No Obj: 0.002643, Avg Recall: 0.761468, count: 109 -2693: 0.439668, 0.473064 avg, 0.001000 rate, 29.117008 seconds, 689408 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.614696, Class: 1.000000, Obj: 0.342145, No Obj: 0.002940, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.616065, Class: 1.000000, Obj: 0.323775, No Obj: 0.002831, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.625534, Class: 1.000000, Obj: 0.330804, No Obj: 0.002711, Avg Recall: 0.793651, count: 126 -Region Avg IOU: 0.607004, Class: 1.000000, Obj: 0.363108, No Obj: 0.002592, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.641105, Class: 1.000000, Obj: 0.363361, No Obj: 0.002645, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.624805, Class: 1.000000, Obj: 0.309541, No Obj: 0.003267, Avg Recall: 0.779310, count: 145 -Region Avg IOU: 0.646078, Class: 1.000000, Obj: 0.381135, No Obj: 0.003052, Avg Recall: 0.801653, count: 121 -Region Avg IOU: 0.622154, Class: 1.000000, Obj: 0.359514, No Obj: 0.002550, Avg Recall: 0.780000, count: 100 -2694: 0.476369, 0.473395 avg, 0.001000 rate, 27.161728 seconds, 689664 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.653531, Class: 1.000000, Obj: 0.365477, No Obj: 0.002915, Avg Recall: 0.805085, count: 118 -Region Avg IOU: 0.602913, Class: 1.000000, Obj: 0.351992, No Obj: 0.002755, Avg Recall: 0.732558, count: 86 -Region Avg IOU: 0.621678, Class: 1.000000, Obj: 0.304889, No Obj: 0.002862, Avg Recall: 0.808824, count: 136 -Region Avg IOU: 0.585774, Class: 1.000000, Obj: 0.384349, No Obj: 0.002344, Avg Recall: 0.693182, count: 88 -Region Avg IOU: 0.614546, Class: 1.000000, Obj: 0.362955, No Obj: 0.002921, Avg Recall: 0.757576, count: 132 -Region Avg IOU: 0.626410, Class: 1.000000, Obj: 0.364811, No Obj: 0.002859, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.598462, Class: 1.000000, Obj: 0.363421, No Obj: 0.002789, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.621186, Class: 1.000000, Obj: 0.289237, No Obj: 0.002783, Avg Recall: 0.801980, count: 101 -2695: 0.467509, 0.472806 avg, 0.001000 rate, 28.914242 seconds, 689920 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.652315, Class: 1.000000, Obj: 0.395771, No Obj: 0.003123, Avg Recall: 0.833333, count: 138 -Region Avg IOU: 0.614385, Class: 1.000000, Obj: 0.295900, No Obj: 0.002962, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.575554, Class: 1.000000, Obj: 0.377521, No Obj: 0.002758, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.597644, Class: 1.000000, Obj: 0.325367, No Obj: 0.002430, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.571792, Class: 1.000000, Obj: 0.339444, No Obj: 0.003151, Avg Recall: 0.698413, count: 126 -Region Avg IOU: 0.590700, Class: 1.000000, Obj: 0.329860, No Obj: 0.002473, Avg Recall: 0.714286, count: 70 -Region Avg IOU: 0.600271, Class: 1.000000, Obj: 0.341182, No Obj: 0.002877, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.623778, Class: 1.000000, Obj: 0.317416, No Obj: 0.002691, Avg Recall: 0.743590, count: 117 -2696: 0.475845, 0.473110 avg, 0.001000 rate, 28.005892 seconds, 690176 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.578740, Class: 1.000000, Obj: 0.392978, No Obj: 0.002671, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.617218, Class: 1.000000, Obj: 0.347508, No Obj: 0.002667, Avg Recall: 0.818182, count: 88 -Region Avg IOU: 0.594059, Class: 1.000000, Obj: 0.329980, No Obj: 0.002964, Avg Recall: 0.720280, count: 143 -Region Avg IOU: 0.632296, Class: 1.000000, Obj: 0.335177, No Obj: 0.002959, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.613869, Class: 1.000000, Obj: 0.350718, No Obj: 0.003390, Avg Recall: 0.761194, count: 134 -Region Avg IOU: 0.631249, Class: 1.000000, Obj: 0.379936, No Obj: 0.002788, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.655375, Class: 1.000000, Obj: 0.351423, No Obj: 0.002872, Avg Recall: 0.845070, count: 71 -Region Avg IOU: 0.610213, Class: 1.000000, Obj: 0.412819, No Obj: 0.003047, Avg Recall: 0.752000, count: 125 -2697: 0.489029, 0.474702 avg, 0.001000 rate, 28.690977 seconds, 690432 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.618567, Class: 1.000000, Obj: 0.321703, No Obj: 0.003118, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.638631, Class: 1.000000, Obj: 0.417084, No Obj: 0.002589, Avg Recall: 0.772727, count: 88 -Region Avg IOU: 0.635514, Class: 1.000000, Obj: 0.388293, No Obj: 0.002877, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.648319, Class: 1.000000, Obj: 0.434859, No Obj: 0.002432, Avg Recall: 0.779221, count: 77 -Region Avg IOU: 0.644783, Class: 1.000000, Obj: 0.372955, No Obj: 0.002707, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.598522, Class: 1.000000, Obj: 0.328141, No Obj: 0.002891, Avg Recall: 0.735537, count: 121 -Region Avg IOU: 0.606264, Class: 1.000000, Obj: 0.333803, No Obj: 0.002616, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.640332, Class: 1.000000, Obj: 0.328546, No Obj: 0.002547, Avg Recall: 0.806122, count: 98 -2698: 0.426815, 0.469913 avg, 0.001000 rate, 29.201948 seconds, 690688 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.620912, Class: 1.000000, Obj: 0.339957, No Obj: 0.002904, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.569566, Class: 1.000000, Obj: 0.283000, No Obj: 0.002622, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.626585, Class: 1.000000, Obj: 0.374164, No Obj: 0.002582, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.641509, Class: 1.000000, Obj: 0.358559, No Obj: 0.002676, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.616502, Class: 1.000000, Obj: 0.359102, No Obj: 0.002542, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.632532, Class: 1.000000, Obj: 0.340164, No Obj: 0.002571, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.590684, Class: 1.000000, Obj: 0.303593, No Obj: 0.002726, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.609813, Class: 1.000000, Obj: 0.366919, No Obj: 0.002606, Avg Recall: 0.729167, count: 96 -2699: 0.430619, 0.465984 avg, 0.001000 rate, 28.673296 seconds, 690944 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.577802, Class: 1.000000, Obj: 0.302237, No Obj: 0.002761, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.601209, Class: 1.000000, Obj: 0.325678, No Obj: 0.002758, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.666627, Class: 1.000000, Obj: 0.358769, No Obj: 0.002548, Avg Recall: 0.872093, count: 86 -Region Avg IOU: 0.615901, Class: 1.000000, Obj: 0.306216, No Obj: 0.002793, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.629867, Class: 1.000000, Obj: 0.380612, No Obj: 0.002775, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.654548, Class: 1.000000, Obj: 0.372887, No Obj: 0.002643, Avg Recall: 0.866667, count: 90 -Region Avg IOU: 0.623743, Class: 1.000000, Obj: 0.300763, No Obj: 0.002673, Avg Recall: 0.825581, count: 86 -Region Avg IOU: 0.650935, Class: 1.000000, Obj: 0.327235, No Obj: 0.003034, Avg Recall: 0.808000, count: 125 -2700: 0.465778, 0.465963 avg, 0.001000 rate, 27.430403 seconds, 691200 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 16.618938 seconds -Region Avg IOU: 0.532228, Class: 1.000000, Obj: 0.315557, No Obj: 0.003243, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.511560, Class: 1.000000, Obj: 0.253409, No Obj: 0.002977, Avg Recall: 0.567568, count: 111 -Region Avg IOU: 0.513510, Class: 1.000000, Obj: 0.254748, No Obj: 0.003502, Avg Recall: 0.562500, count: 128 -Region Avg IOU: 0.549312, Class: 1.000000, Obj: 0.252283, No Obj: 0.003372, Avg Recall: 0.590551, count: 127 -Region Avg IOU: 0.596958, Class: 1.000000, Obj: 0.302003, No Obj: 0.003476, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.513304, Class: 1.000000, Obj: 0.285172, No Obj: 0.002775, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.572200, Class: 1.000000, Obj: 0.297673, No Obj: 0.003497, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.532612, Class: 1.000000, Obj: 0.309758, No Obj: 0.002844, Avg Recall: 0.597701, count: 87 -2701: 0.487247, 0.468092 avg, 0.001000 rate, 23.879093 seconds, 691456 images -Loaded: 0.000057 seconds -Region Avg IOU: 0.564908, Class: 1.000000, Obj: 0.321214, No Obj: 0.003130, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.602725, Class: 1.000000, Obj: 0.321435, No Obj: 0.003127, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.505388, Class: 1.000000, Obj: 0.298415, No Obj: 0.002975, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.535330, Class: 1.000000, Obj: 0.282732, No Obj: 0.002968, Avg Recall: 0.595238, count: 126 -Region Avg IOU: 0.567984, Class: 1.000000, Obj: 0.310770, No Obj: 0.003112, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.535559, Class: 1.000000, Obj: 0.281658, No Obj: 0.002806, Avg Recall: 0.636364, count: 77 -Region Avg IOU: 0.579977, Class: 1.000000, Obj: 0.338691, No Obj: 0.003416, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.582141, Class: 1.000000, Obj: 0.272532, No Obj: 0.003305, Avg Recall: 0.697248, count: 109 -2702: 0.440917, 0.465374 avg, 0.001000 rate, 22.888084 seconds, 691712 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.551282, Class: 1.000000, Obj: 0.333055, No Obj: 0.003610, Avg Recall: 0.627586, count: 145 -Region Avg IOU: 0.557083, Class: 1.000000, Obj: 0.354406, No Obj: 0.003194, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.532552, Class: 1.000000, Obj: 0.316124, No Obj: 0.003227, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.532240, Class: 1.000000, Obj: 0.267666, No Obj: 0.003713, Avg Recall: 0.627907, count: 129 -Region Avg IOU: 0.546100, Class: 1.000000, Obj: 0.324112, No Obj: 0.003710, Avg Recall: 0.659420, count: 138 -Region Avg IOU: 0.581933, Class: 1.000000, Obj: 0.328103, No Obj: 0.003848, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.554216, Class: 1.000000, Obj: 0.299954, No Obj: 0.003214, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.529934, Class: 1.000000, Obj: 0.297146, No Obj: 0.003209, Avg Recall: 0.577982, count: 109 -2703: 0.532755, 0.472112 avg, 0.001000 rate, 23.345861 seconds, 691968 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.542663, Class: 1.000000, Obj: 0.323124, No Obj: 0.003422, Avg Recall: 0.587302, count: 126 -Region Avg IOU: 0.540678, Class: 1.000000, Obj: 0.339572, No Obj: 0.004010, Avg Recall: 0.640000, count: 125 -Region Avg IOU: 0.548643, Class: 1.000000, Obj: 0.343681, No Obj: 0.003707, Avg Recall: 0.652893, count: 121 -Region Avg IOU: 0.505165, Class: 1.000000, Obj: 0.327647, No Obj: 0.003948, Avg Recall: 0.529412, count: 136 -Region Avg IOU: 0.556776, Class: 1.000000, Obj: 0.310299, No Obj: 0.003518, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.537016, Class: 1.000000, Obj: 0.357269, No Obj: 0.003242, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.591220, Class: 1.000000, Obj: 0.379933, No Obj: 0.003469, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.563946, Class: 1.000000, Obj: 0.316718, No Obj: 0.003456, Avg Recall: 0.642857, count: 112 -2704: 0.497917, 0.474693 avg, 0.001000 rate, 22.683125 seconds, 692224 images -Loaded: 0.000122 seconds -Region Avg IOU: 0.571184, Class: 1.000000, Obj: 0.357681, No Obj: 0.003572, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.569398, Class: 1.000000, Obj: 0.320757, No Obj: 0.003755, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.526422, Class: 1.000000, Obj: 0.309927, No Obj: 0.003296, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.546396, Class: 1.000000, Obj: 0.316965, No Obj: 0.003635, Avg Recall: 0.644444, count: 90 -Region Avg IOU: 0.565803, Class: 1.000000, Obj: 0.351140, No Obj: 0.003735, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.474079, Class: 1.000000, Obj: 0.349375, No Obj: 0.003743, Avg Recall: 0.508621, count: 116 -Region Avg IOU: 0.483779, Class: 1.000000, Obj: 0.331238, No Obj: 0.003648, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.558006, Class: 1.000000, Obj: 0.387953, No Obj: 0.004089, Avg Recall: 0.669903, count: 103 -2705: 0.454924, 0.472716 avg, 0.001000 rate, 23.447226 seconds, 692480 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.539205, Class: 1.000000, Obj: 0.361402, No Obj: 0.004336, Avg Recall: 0.586207, count: 145 -Region Avg IOU: 0.528091, Class: 1.000000, Obj: 0.342181, No Obj: 0.003836, Avg Recall: 0.573643, count: 129 -Region Avg IOU: 0.613976, Class: 1.000000, Obj: 0.375627, No Obj: 0.004281, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.553085, Class: 1.000000, Obj: 0.327595, No Obj: 0.004180, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.533231, Class: 1.000000, Obj: 0.386830, No Obj: 0.003795, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.579198, Class: 1.000000, Obj: 0.355632, No Obj: 0.004297, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.518695, Class: 1.000000, Obj: 0.302345, No Obj: 0.004338, Avg Recall: 0.590909, count: 132 -Region Avg IOU: 0.544036, Class: 1.000000, Obj: 0.359450, No Obj: 0.003953, Avg Recall: 0.616541, count: 133 -2706: 0.485001, 0.473944 avg, 0.001000 rate, 23.345499 seconds, 692736 images -Loaded: 0.000124 seconds -Region Avg IOU: 0.551620, Class: 1.000000, Obj: 0.364445, No Obj: 0.004652, Avg Recall: 0.671429, count: 140 -Region Avg IOU: 0.519444, Class: 1.000000, Obj: 0.326826, No Obj: 0.004627, Avg Recall: 0.544000, count: 125 -Region Avg IOU: 0.543984, Class: 1.000000, Obj: 0.373895, No Obj: 0.003935, Avg Recall: 0.703297, count: 91 -Region Avg IOU: 0.491898, Class: 1.000000, Obj: 0.359682, No Obj: 0.003778, Avg Recall: 0.557522, count: 113 -Region Avg IOU: 0.528018, Class: 1.000000, Obj: 0.355570, No Obj: 0.003936, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.507476, Class: 1.000000, Obj: 0.355140, No Obj: 0.003856, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.507308, Class: 1.000000, Obj: 0.344032, No Obj: 0.004307, Avg Recall: 0.585714, count: 140 -Region Avg IOU: 0.612549, Class: 1.000000, Obj: 0.400742, No Obj: 0.004156, Avg Recall: 0.735632, count: 87 -2707: 0.532403, 0.479790 avg, 0.001000 rate, 23.736790 seconds, 692992 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.543934, Class: 1.000000, Obj: 0.381957, No Obj: 0.004212, Avg Recall: 0.615385, count: 91 -Region Avg IOU: 0.518149, Class: 1.000000, Obj: 0.364843, No Obj: 0.003733, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.511885, Class: 1.000000, Obj: 0.350861, No Obj: 0.004329, Avg Recall: 0.558140, count: 129 -Region Avg IOU: 0.581895, Class: 1.000000, Obj: 0.357230, No Obj: 0.004062, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.485580, Class: 1.000000, Obj: 0.322520, No Obj: 0.003920, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.476953, Class: 1.000000, Obj: 0.379830, No Obj: 0.003782, Avg Recall: 0.553571, count: 112 -Region Avg IOU: 0.560317, Class: 1.000000, Obj: 0.354104, No Obj: 0.004152, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.528208, Class: 1.000000, Obj: 0.352743, No Obj: 0.004366, Avg Recall: 0.594828, count: 116 -2708: 0.457894, 0.477601 avg, 0.001000 rate, 23.137144 seconds, 693248 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.543622, Class: 1.000000, Obj: 0.324822, No Obj: 0.004411, Avg Recall: 0.647541, count: 122 -Region Avg IOU: 0.574163, Class: 1.000000, Obj: 0.374608, No Obj: 0.004299, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.545093, Class: 1.000000, Obj: 0.343339, No Obj: 0.004545, Avg Recall: 0.644628, count: 121 -Region Avg IOU: 0.558048, Class: 1.000000, Obj: 0.381129, No Obj: 0.003581, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.573826, Class: 1.000000, Obj: 0.361453, No Obj: 0.004146, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.541852, Class: 1.000000, Obj: 0.416032, No Obj: 0.003976, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.465607, Class: 1.000000, Obj: 0.389224, No Obj: 0.003517, Avg Recall: 0.486486, count: 111 -Region Avg IOU: 0.509469, Class: 1.000000, Obj: 0.392256, No Obj: 0.003315, Avg Recall: 0.619048, count: 84 -2709: 0.464357, 0.476276 avg, 0.001000 rate, 22.061520 seconds, 693504 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.558095, Class: 1.000000, Obj: 0.317792, No Obj: 0.004064, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.501072, Class: 1.000000, Obj: 0.280028, No Obj: 0.003577, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.531481, Class: 1.000000, Obj: 0.337860, No Obj: 0.004037, Avg Recall: 0.613139, count: 137 -Region Avg IOU: 0.507958, Class: 1.000000, Obj: 0.368499, No Obj: 0.003767, Avg Recall: 0.549020, count: 102 -Region Avg IOU: 0.506273, Class: 1.000000, Obj: 0.345407, No Obj: 0.003882, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.515232, Class: 1.000000, Obj: 0.330925, No Obj: 0.003431, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.529203, Class: 1.000000, Obj: 0.353775, No Obj: 0.003803, Avg Recall: 0.630252, count: 119 -Region Avg IOU: 0.521734, Class: 1.000000, Obj: 0.344120, No Obj: 0.003604, Avg Recall: 0.611111, count: 90 -2710: 0.500014, 0.478650 avg, 0.001000 rate, 22.856400 seconds, 693760 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.819304 seconds -Region Avg IOU: 0.488790, Class: 1.000000, Obj: 0.300680, No Obj: 0.004731, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.531214, Class: 1.000000, Obj: 0.334085, No Obj: 0.003702, Avg Recall: 0.600000, count: 95 -Region Avg IOU: 0.501049, Class: 1.000000, Obj: 0.278186, No Obj: 0.003848, Avg Recall: 0.529412, count: 102 -Region Avg IOU: 0.491366, Class: 1.000000, Obj: 0.319623, No Obj: 0.004181, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.488671, Class: 1.000000, Obj: 0.274852, No Obj: 0.003607, Avg Recall: 0.500000, count: 90 -Region Avg IOU: 0.514187, Class: 1.000000, Obj: 0.309124, No Obj: 0.003579, Avg Recall: 0.670588, count: 85 -Region Avg IOU: 0.530268, Class: 1.000000, Obj: 0.359695, No Obj: 0.004139, Avg Recall: 0.600000, count: 90 -Region Avg IOU: 0.465948, Class: 1.000000, Obj: 0.280648, No Obj: 0.003784, Avg Recall: 0.504673, count: 107 -2711: 0.408825, 0.471668 avg, 0.001000 rate, 22.116894 seconds, 694016 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.527357, Class: 1.000000, Obj: 0.332658, No Obj: 0.004047, Avg Recall: 0.614458, count: 83 -Region Avg IOU: 0.533034, Class: 1.000000, Obj: 0.314794, No Obj: 0.003659, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.509350, Class: 1.000000, Obj: 0.304128, No Obj: 0.004237, Avg Recall: 0.590551, count: 127 -Region Avg IOU: 0.473250, Class: 1.000000, Obj: 0.285991, No Obj: 0.003427, Avg Recall: 0.515152, count: 99 -Region Avg IOU: 0.509088, Class: 1.000000, Obj: 0.295356, No Obj: 0.004598, Avg Recall: 0.580153, count: 131 -Region Avg IOU: 0.481542, Class: 1.000000, Obj: 0.271343, No Obj: 0.003683, Avg Recall: 0.504065, count: 123 -Region Avg IOU: 0.499875, Class: 1.000000, Obj: 0.318119, No Obj: 0.003761, Avg Recall: 0.590551, count: 127 -Region Avg IOU: 0.527086, Class: 1.000000, Obj: 0.350809, No Obj: 0.003904, Avg Recall: 0.607477, count: 107 -2712: 0.444410, 0.468942 avg, 0.001000 rate, 18.536144 seconds, 694272 images -Loaded: 2.572652 seconds -Region Avg IOU: 0.504505, Class: 1.000000, Obj: 0.304760, No Obj: 0.003699, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.503371, Class: 1.000000, Obj: 0.314655, No Obj: 0.003755, Avg Recall: 0.546296, count: 108 -Region Avg IOU: 0.530486, Class: 1.000000, Obj: 0.296070, No Obj: 0.003874, Avg Recall: 0.606838, count: 117 -Region Avg IOU: 0.494370, Class: 1.000000, Obj: 0.271622, No Obj: 0.004411, Avg Recall: 0.552000, count: 125 -Region Avg IOU: 0.513980, Class: 1.000000, Obj: 0.269926, No Obj: 0.003742, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.538185, Class: 1.000000, Obj: 0.315732, No Obj: 0.004214, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.562569, Class: 1.000000, Obj: 0.275137, No Obj: 0.003947, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.514700, Class: 1.000000, Obj: 0.288515, No Obj: 0.004395, Avg Recall: 0.566929, count: 127 -2713: 0.494469, 0.471495 avg, 0.001000 rate, 20.078259 seconds, 694528 images -Loaded: 1.205692 seconds -Region Avg IOU: 0.509060, Class: 1.000000, Obj: 0.286720, No Obj: 0.003917, Avg Recall: 0.559633, count: 109 -Region Avg IOU: 0.512730, Class: 1.000000, Obj: 0.275409, No Obj: 0.003375, Avg Recall: 0.592593, count: 81 -Region Avg IOU: 0.463334, Class: 1.000000, Obj: 0.270355, No Obj: 0.003547, Avg Recall: 0.514851, count: 101 -Region Avg IOU: 0.496021, Class: 1.000000, Obj: 0.296291, No Obj: 0.003937, Avg Recall: 0.579365, count: 126 -Region Avg IOU: 0.516227, Class: 1.000000, Obj: 0.316092, No Obj: 0.003779, Avg Recall: 0.597826, count: 92 -Region Avg IOU: 0.544089, Class: 1.000000, Obj: 0.267959, No Obj: 0.004246, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.522600, Class: 1.000000, Obj: 0.282677, No Obj: 0.003469, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.497817, Class: 1.000000, Obj: 0.300150, No Obj: 0.003830, Avg Recall: 0.554688, count: 128 -2714: 0.478801, 0.472225 avg, 0.001000 rate, 23.422310 seconds, 694784 images -Loaded: 0.227690 seconds -Region Avg IOU: 0.515514, Class: 1.000000, Obj: 0.293541, No Obj: 0.003880, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.466336, Class: 1.000000, Obj: 0.343144, No Obj: 0.003883, Avg Recall: 0.505747, count: 87 -Region Avg IOU: 0.521172, Class: 1.000000, Obj: 0.322003, No Obj: 0.003929, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.477204, Class: 1.000000, Obj: 0.283006, No Obj: 0.003895, Avg Recall: 0.532258, count: 124 -Region Avg IOU: 0.487849, Class: 1.000000, Obj: 0.304157, No Obj: 0.003784, Avg Recall: 0.527473, count: 91 -Region Avg IOU: 0.532247, Class: 1.000000, Obj: 0.338475, No Obj: 0.004163, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.512774, Class: 1.000000, Obj: 0.307318, No Obj: 0.004180, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.481128, Class: 1.000000, Obj: 0.307389, No Obj: 0.004123, Avg Recall: 0.571429, count: 133 -2715: 0.482527, 0.473255 avg, 0.001000 rate, 20.637207 seconds, 695040 images -Loaded: 2.137034 seconds -Region Avg IOU: 0.485748, Class: 1.000000, Obj: 0.323978, No Obj: 0.004600, Avg Recall: 0.562500, count: 128 -Region Avg IOU: 0.519976, Class: 1.000000, Obj: 0.343544, No Obj: 0.004055, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.554390, Class: 1.000000, Obj: 0.334861, No Obj: 0.004521, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.503178, Class: 1.000000, Obj: 0.288441, No Obj: 0.003687, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.456868, Class: 1.000000, Obj: 0.286975, No Obj: 0.003469, Avg Recall: 0.460674, count: 89 -Region Avg IOU: 0.478543, Class: 1.000000, Obj: 0.269706, No Obj: 0.004710, Avg Recall: 0.521008, count: 119 -Region Avg IOU: 0.495586, Class: 1.000000, Obj: 0.287528, No Obj: 0.003983, Avg Recall: 0.559633, count: 109 -Region Avg IOU: 0.510886, Class: 1.000000, Obj: 0.315665, No Obj: 0.004116, Avg Recall: 0.560345, count: 116 -2716: 0.424810, 0.468411 avg, 0.001000 rate, 22.559937 seconds, 695296 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.528074, Class: 1.000000, Obj: 0.327660, No Obj: 0.003967, Avg Recall: 0.597701, count: 87 -Region Avg IOU: 0.566587, Class: 1.000000, Obj: 0.345407, No Obj: 0.004795, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.539767, Class: 1.000000, Obj: 0.305698, No Obj: 0.004198, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.513331, Class: 1.000000, Obj: 0.378466, No Obj: 0.004281, Avg Recall: 0.565657, count: 99 -Region Avg IOU: 0.528633, Class: 1.000000, Obj: 0.337726, No Obj: 0.004372, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.513906, Class: 1.000000, Obj: 0.337531, No Obj: 0.004042, Avg Recall: 0.516484, count: 91 -Region Avg IOU: 0.516639, Class: 1.000000, Obj: 0.346842, No Obj: 0.004744, Avg Recall: 0.589147, count: 129 -Region Avg IOU: 0.464222, Class: 1.000000, Obj: 0.312622, No Obj: 0.003935, Avg Recall: 0.525424, count: 118 -2717: 0.414429, 0.463013 avg, 0.001000 rate, 20.112309 seconds, 695552 images -Loaded: 1.656708 seconds -Region Avg IOU: 0.473615, Class: 1.000000, Obj: 0.327033, No Obj: 0.003872, Avg Recall: 0.500000, count: 92 -Region Avg IOU: 0.490209, Class: 1.000000, Obj: 0.314714, No Obj: 0.004891, Avg Recall: 0.533333, count: 150 -Region Avg IOU: 0.529663, Class: 1.000000, Obj: 0.347663, No Obj: 0.005074, Avg Recall: 0.610169, count: 118 -Region Avg IOU: 0.511626, Class: 1.000000, Obj: 0.385694, No Obj: 0.003923, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.543477, Class: 1.000000, Obj: 0.326322, No Obj: 0.005023, Avg Recall: 0.664000, count: 125 -Region Avg IOU: 0.481859, Class: 1.000000, Obj: 0.343217, No Obj: 0.005173, Avg Recall: 0.560000, count: 150 -Region Avg IOU: 0.491835, Class: 1.000000, Obj: 0.325250, No Obj: 0.004044, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.512636, Class: 1.000000, Obj: 0.339292, No Obj: 0.004470, Avg Recall: 0.628866, count: 97 -2718: 0.476064, 0.464318 avg, 0.001000 rate, 19.952286 seconds, 695808 images -Loaded: 4.056558 seconds -Region Avg IOU: 0.473404, Class: 1.000000, Obj: 0.330607, No Obj: 0.004328, Avg Recall: 0.468468, count: 111 -Region Avg IOU: 0.525524, Class: 1.000000, Obj: 0.381286, No Obj: 0.004900, Avg Recall: 0.572650, count: 117 -Region Avg IOU: 0.475415, Class: 1.000000, Obj: 0.342435, No Obj: 0.004554, Avg Recall: 0.547009, count: 117 -Region Avg IOU: 0.447935, Class: 1.000000, Obj: 0.321976, No Obj: 0.004636, Avg Recall: 0.480000, count: 125 -Region Avg IOU: 0.515562, Class: 1.000000, Obj: 0.384562, No Obj: 0.004571, Avg Recall: 0.580000, count: 100 -Region Avg IOU: 0.494386, Class: 1.000000, Obj: 0.340470, No Obj: 0.004807, Avg Recall: 0.536364, count: 110 -Region Avg IOU: 0.464297, Class: 1.000000, Obj: 0.324842, No Obj: 0.004662, Avg Recall: 0.512397, count: 121 -Region Avg IOU: 0.494612, Class: 1.000000, Obj: 0.326968, No Obj: 0.004932, Avg Recall: 0.543478, count: 138 -2719: 0.477246, 0.465611 avg, 0.001000 rate, 21.581305 seconds, 696064 images -Loaded: 1.059533 seconds -Region Avg IOU: 0.522493, Class: 1.000000, Obj: 0.362970, No Obj: 0.004647, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.495261, Class: 1.000000, Obj: 0.341693, No Obj: 0.004324, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.536337, Class: 1.000000, Obj: 0.348355, No Obj: 0.004728, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.545057, Class: 1.000000, Obj: 0.317703, No Obj: 0.004548, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.504891, Class: 1.000000, Obj: 0.368409, No Obj: 0.004819, Avg Recall: 0.541667, count: 120 -Region Avg IOU: 0.494508, Class: 1.000000, Obj: 0.340069, No Obj: 0.004974, Avg Recall: 0.522727, count: 132 -Region Avg IOU: 0.459854, Class: 1.000000, Obj: 0.341412, No Obj: 0.004684, Avg Recall: 0.504505, count: 111 -Region Avg IOU: 0.519679, Class: 1.000000, Obj: 0.359762, No Obj: 0.004645, Avg Recall: 0.577982, count: 109 -2720: 0.458192, 0.464869 avg, 0.001000 rate, 22.210888 seconds, 696320 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000081 seconds -Region Avg IOU: 0.611221, Class: 1.000000, Obj: 0.467815, No Obj: 0.003606, Avg Recall: 0.759036, count: 83 -Region Avg IOU: 0.493415, Class: 1.000000, Obj: 0.363811, No Obj: 0.003647, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.600434, Class: 1.000000, Obj: 0.439384, No Obj: 0.004453, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.530800, Class: 1.000000, Obj: 0.412578, No Obj: 0.003958, Avg Recall: 0.595238, count: 126 -Region Avg IOU: 0.595459, Class: 1.000000, Obj: 0.462783, No Obj: 0.003498, Avg Recall: 0.730769, count: 78 -Region Avg IOU: 0.555406, Class: 1.000000, Obj: 0.415406, No Obj: 0.004338, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.499549, Class: 1.000000, Obj: 0.370842, No Obj: 0.003718, Avg Recall: 0.516949, count: 118 -Region Avg IOU: 0.532571, Class: 1.000000, Obj: 0.369428, No Obj: 0.004068, Avg Recall: 0.650943, count: 106 -2721: 0.479033, 0.466285 avg, 0.001000 rate, 23.565336 seconds, 696576 images -Loaded: 0.000127 seconds -Region Avg IOU: 0.554571, Class: 1.000000, Obj: 0.355003, No Obj: 0.004018, Avg Recall: 0.639344, count: 122 -Region Avg IOU: 0.547987, Class: 1.000000, Obj: 0.422650, No Obj: 0.003254, Avg Recall: 0.641975, count: 81 -Region Avg IOU: 0.562584, Class: 1.000000, Obj: 0.369121, No Obj: 0.003789, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.547958, Class: 1.000000, Obj: 0.381574, No Obj: 0.004635, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.577543, Class: 1.000000, Obj: 0.412840, No Obj: 0.003927, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.566787, Class: 1.000000, Obj: 0.386903, No Obj: 0.004015, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.573509, Class: 1.000000, Obj: 0.401056, No Obj: 0.003875, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.539544, Class: 1.000000, Obj: 0.401684, No Obj: 0.003243, Avg Recall: 0.628571, count: 70 -2722: 0.399000, 0.459557 avg, 0.001000 rate, 24.272696 seconds, 696832 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.529362, Class: 1.000000, Obj: 0.320589, No Obj: 0.003898, Avg Recall: 0.609375, count: 128 -Region Avg IOU: 0.558917, Class: 1.000000, Obj: 0.364124, No Obj: 0.003200, Avg Recall: 0.614458, count: 83 -Region Avg IOU: 0.543900, Class: 1.000000, Obj: 0.343454, No Obj: 0.003495, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.532382, Class: 1.000000, Obj: 0.353714, No Obj: 0.003572, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.522246, Class: 1.000000, Obj: 0.324289, No Obj: 0.003535, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.549531, Class: 1.000000, Obj: 0.301783, No Obj: 0.003875, Avg Recall: 0.682927, count: 123 -Region Avg IOU: 0.543881, Class: 1.000000, Obj: 0.321765, No Obj: 0.003016, Avg Recall: 0.576087, count: 92 -Region Avg IOU: 0.638683, Class: 1.000000, Obj: 0.350480, No Obj: 0.003612, Avg Recall: 0.808511, count: 94 -2723: 0.441178, 0.457719 avg, 0.001000 rate, 23.842621 seconds, 697088 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.519506, Class: 1.000000, Obj: 0.296938, No Obj: 0.002645, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.552054, Class: 1.000000, Obj: 0.309420, No Obj: 0.003612, Avg Recall: 0.610687, count: 131 -Region Avg IOU: 0.510466, Class: 1.000000, Obj: 0.307122, No Obj: 0.002655, Avg Recall: 0.566667, count: 90 -Region Avg IOU: 0.571573, Class: 1.000000, Obj: 0.302714, No Obj: 0.002922, Avg Recall: 0.682353, count: 85 -Region Avg IOU: 0.516569, Class: 1.000000, Obj: 0.332065, No Obj: 0.002568, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.571853, Class: 1.000000, Obj: 0.332321, No Obj: 0.002919, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.553775, Class: 1.000000, Obj: 0.287478, No Obj: 0.003414, Avg Recall: 0.671756, count: 131 -Region Avg IOU: 0.532674, Class: 1.000000, Obj: 0.337401, No Obj: 0.003303, Avg Recall: 0.620968, count: 124 -2724: 0.429607, 0.454908 avg, 0.001000 rate, 23.040922 seconds, 697344 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.495332, Class: 1.000000, Obj: 0.235295, No Obj: 0.002362, Avg Recall: 0.525641, count: 78 -Region Avg IOU: 0.587387, Class: 1.000000, Obj: 0.341949, No Obj: 0.002473, Avg Recall: 0.683544, count: 79 -Region Avg IOU: 0.538679, Class: 1.000000, Obj: 0.266449, No Obj: 0.003355, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.571783, Class: 1.000000, Obj: 0.312949, No Obj: 0.003073, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.573719, Class: 1.000000, Obj: 0.325830, No Obj: 0.003114, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.575219, Class: 1.000000, Obj: 0.290391, No Obj: 0.003016, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.615368, Class: 1.000000, Obj: 0.343407, No Obj: 0.003059, Avg Recall: 0.797468, count: 79 -Region Avg IOU: 0.559980, Class: 1.000000, Obj: 0.361381, No Obj: 0.002963, Avg Recall: 0.647059, count: 102 -2725: 0.496668, 0.459084 avg, 0.001000 rate, 23.674831 seconds, 697600 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.543321, Class: 1.000000, Obj: 0.282614, No Obj: 0.003182, Avg Recall: 0.630769, count: 130 -Region Avg IOU: 0.603591, Class: 1.000000, Obj: 0.332141, No Obj: 0.002532, Avg Recall: 0.674699, count: 83 -Region Avg IOU: 0.585506, Class: 1.000000, Obj: 0.334032, No Obj: 0.003016, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.552878, Class: 1.000000, Obj: 0.289430, No Obj: 0.002874, Avg Recall: 0.655462, count: 119 -Region Avg IOU: 0.587559, Class: 1.000000, Obj: 0.340672, No Obj: 0.003071, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.589407, Class: 1.000000, Obj: 0.279699, No Obj: 0.003345, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.572905, Class: 1.000000, Obj: 0.316778, No Obj: 0.002968, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.593260, Class: 1.000000, Obj: 0.326737, No Obj: 0.002925, Avg Recall: 0.734694, count: 98 -2726: 0.440432, 0.457219 avg, 0.001000 rate, 24.904882 seconds, 697856 images -Loaded: 0.173809 seconds -Region Avg IOU: 0.531238, Class: 1.000000, Obj: 0.309511, No Obj: 0.002601, Avg Recall: 0.588235, count: 102 -Region Avg IOU: 0.508262, Class: 1.000000, Obj: 0.288524, No Obj: 0.003334, Avg Recall: 0.523810, count: 126 -Region Avg IOU: 0.588168, Class: 1.000000, Obj: 0.300768, No Obj: 0.003233, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.586069, Class: 1.000000, Obj: 0.306166, No Obj: 0.003219, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.567890, Class: 1.000000, Obj: 0.303210, No Obj: 0.003078, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.619609, Class: 1.000000, Obj: 0.317071, No Obj: 0.002777, Avg Recall: 0.734940, count: 83 -Region Avg IOU: 0.575154, Class: 1.000000, Obj: 0.292816, No Obj: 0.003009, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.605838, Class: 1.000000, Obj: 0.302391, No Obj: 0.003693, Avg Recall: 0.718518, count: 135 -2727: 0.537792, 0.465276 avg, 0.001000 rate, 25.069555 seconds, 698112 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.593261, Class: 1.000000, Obj: 0.322972, No Obj: 0.003139, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.566531, Class: 1.000000, Obj: 0.315496, No Obj: 0.002803, Avg Recall: 0.682927, count: 82 -Region Avg IOU: 0.555868, Class: 1.000000, Obj: 0.300391, No Obj: 0.003298, Avg Recall: 0.710526, count: 114 -Region Avg IOU: 0.570414, Class: 1.000000, Obj: 0.329965, No Obj: 0.003859, Avg Recall: 0.652174, count: 138 -Region Avg IOU: 0.554301, Class: 1.000000, Obj: 0.320653, No Obj: 0.003212, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.596318, Class: 1.000000, Obj: 0.331288, No Obj: 0.003379, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.541676, Class: 1.000000, Obj: 0.307394, No Obj: 0.003228, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.560299, Class: 1.000000, Obj: 0.290398, No Obj: 0.003138, Avg Recall: 0.687500, count: 128 -2728: 0.493376, 0.468086 avg, 0.001000 rate, 24.569468 seconds, 698368 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.595910, Class: 1.000000, Obj: 0.317950, No Obj: 0.003396, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.537324, Class: 1.000000, Obj: 0.340908, No Obj: 0.003330, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.543364, Class: 1.000000, Obj: 0.335571, No Obj: 0.003241, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.562056, Class: 1.000000, Obj: 0.303017, No Obj: 0.003850, Avg Recall: 0.678322, count: 143 -Region Avg IOU: 0.546734, Class: 1.000000, Obj: 0.294490, No Obj: 0.003722, Avg Recall: 0.623077, count: 130 -Region Avg IOU: 0.553937, Class: 1.000000, Obj: 0.306556, No Obj: 0.002987, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.626515, Class: 1.000000, Obj: 0.427710, No Obj: 0.003100, Avg Recall: 0.788732, count: 71 -Region Avg IOU: 0.544229, Class: 1.000000, Obj: 0.317757, No Obj: 0.003421, Avg Recall: 0.647059, count: 119 -2729: 0.502923, 0.471570 avg, 0.001000 rate, 24.199976 seconds, 698624 images -Loaded: 0.000115 seconds -Region Avg IOU: 0.596649, Class: 1.000000, Obj: 0.356453, No Obj: 0.004287, Avg Recall: 0.735714, count: 140 -Region Avg IOU: 0.610789, Class: 1.000000, Obj: 0.404343, No Obj: 0.004103, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.578273, Class: 1.000000, Obj: 0.379362, No Obj: 0.004062, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.605891, Class: 1.000000, Obj: 0.407644, No Obj: 0.003590, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.519447, Class: 1.000000, Obj: 0.357268, No Obj: 0.003423, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.547607, Class: 1.000000, Obj: 0.351389, No Obj: 0.004021, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.575519, Class: 1.000000, Obj: 0.341522, No Obj: 0.003543, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.528413, Class: 1.000000, Obj: 0.333524, No Obj: 0.003573, Avg Recall: 0.555556, count: 117 -2730: 0.464592, 0.470872 avg, 0.001000 rate, 23.555271 seconds, 698880 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 20.443392 seconds -Region Avg IOU: 0.545169, Class: 1.000000, Obj: 0.338596, No Obj: 0.005204, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.549120, Class: 1.000000, Obj: 0.344494, No Obj: 0.004112, Avg Recall: 0.644444, count: 90 -Region Avg IOU: 0.529171, Class: 1.000000, Obj: 0.313322, No Obj: 0.005518, Avg Recall: 0.609756, count: 123 -Region Avg IOU: 0.519914, Class: 1.000000, Obj: 0.389513, No Obj: 0.004380, Avg Recall: 0.584270, count: 89 -Region Avg IOU: 0.495420, Class: 1.000000, Obj: 0.322557, No Obj: 0.005131, Avg Recall: 0.568000, count: 125 -Region Avg IOU: 0.511192, Class: 1.000000, Obj: 0.343441, No Obj: 0.004645, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.526126, Class: 1.000000, Obj: 0.335874, No Obj: 0.004142, Avg Recall: 0.639535, count: 86 -Region Avg IOU: 0.572522, Class: 1.000000, Obj: 0.322009, No Obj: 0.004952, Avg Recall: 0.648148, count: 108 -2731: 0.459929, 0.469778 avg, 0.001000 rate, 21.685459 seconds, 699136 images -Loaded: 0.409362 seconds -Region Avg IOU: 0.536068, Class: 1.000000, Obj: 0.347019, No Obj: 0.004923, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.480719, Class: 1.000000, Obj: 0.332489, No Obj: 0.004289, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.485292, Class: 1.000000, Obj: 0.331950, No Obj: 0.005220, Avg Recall: 0.526316, count: 133 -Region Avg IOU: 0.525481, Class: 1.000000, Obj: 0.362422, No Obj: 0.004832, Avg Recall: 0.625899, count: 139 -Region Avg IOU: 0.518049, Class: 1.000000, Obj: 0.367522, No Obj: 0.004841, Avg Recall: 0.537037, count: 108 -Region Avg IOU: 0.541977, Class: 1.000000, Obj: 0.389014, No Obj: 0.003973, Avg Recall: 0.609756, count: 82 -Region Avg IOU: 0.512060, Class: 1.000000, Obj: 0.344992, No Obj: 0.004567, Avg Recall: 0.592593, count: 108 -Region Avg IOU: 0.499748, Class: 1.000000, Obj: 0.355287, No Obj: 0.004704, Avg Recall: 0.539823, count: 113 -2732: 0.461603, 0.468960 avg, 0.001000 rate, 21.521420 seconds, 699392 images -Loaded: 1.618481 seconds -Region Avg IOU: 0.484884, Class: 1.000000, Obj: 0.343076, No Obj: 0.004881, Avg Recall: 0.561983, count: 121 -Region Avg IOU: 0.532352, Class: 1.000000, Obj: 0.342704, No Obj: 0.004463, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.522227, Class: 1.000000, Obj: 0.335116, No Obj: 0.005200, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.502439, Class: 1.000000, Obj: 0.401206, No Obj: 0.004843, Avg Recall: 0.540984, count: 122 -Region Avg IOU: 0.550630, Class: 1.000000, Obj: 0.391043, No Obj: 0.005183, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.513898, Class: 1.000000, Obj: 0.403803, No Obj: 0.005515, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.461338, Class: 1.000000, Obj: 0.305147, No Obj: 0.005350, Avg Recall: 0.468531, count: 143 -Region Avg IOU: 0.533254, Class: 1.000000, Obj: 0.376396, No Obj: 0.004732, Avg Recall: 0.609524, count: 105 -2733: 0.467005, 0.468765 avg, 0.001000 rate, 20.472580 seconds, 699648 images -Loaded: 2.109389 seconds -Region Avg IOU: 0.540722, Class: 1.000000, Obj: 0.355201, No Obj: 0.004091, Avg Recall: 0.658228, count: 79 -Region Avg IOU: 0.484476, Class: 1.000000, Obj: 0.328471, No Obj: 0.005252, Avg Recall: 0.507692, count: 130 -Region Avg IOU: 0.470360, Class: 1.000000, Obj: 0.338613, No Obj: 0.004591, Avg Recall: 0.513043, count: 115 -Region Avg IOU: 0.517899, Class: 1.000000, Obj: 0.413832, No Obj: 0.004520, Avg Recall: 0.541176, count: 85 -Region Avg IOU: 0.499279, Class: 1.000000, Obj: 0.351990, No Obj: 0.004946, Avg Recall: 0.524194, count: 124 -Region Avg IOU: 0.513795, Class: 1.000000, Obj: 0.314422, No Obj: 0.005791, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.573354, Class: 1.000000, Obj: 0.373994, No Obj: 0.005578, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.496799, Class: 1.000000, Obj: 0.390075, No Obj: 0.005366, Avg Recall: 0.500000, count: 112 -2734: 0.490134, 0.470902 avg, 0.001000 rate, 21.298168 seconds, 699904 images -Loaded: 0.570103 seconds -Region Avg IOU: 0.493491, Class: 1.000000, Obj: 0.359328, No Obj: 0.004830, Avg Recall: 0.537815, count: 119 -Region Avg IOU: 0.466342, Class: 1.000000, Obj: 0.296355, No Obj: 0.005260, Avg Recall: 0.492188, count: 128 -Region Avg IOU: 0.509265, Class: 1.000000, Obj: 0.370846, No Obj: 0.004797, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.443605, Class: 1.000000, Obj: 0.350704, No Obj: 0.004608, Avg Recall: 0.440000, count: 100 -Region Avg IOU: 0.460678, Class: 1.000000, Obj: 0.314850, No Obj: 0.004437, Avg Recall: 0.546296, count: 108 -Region Avg IOU: 0.510184, Class: 1.000000, Obj: 0.321953, No Obj: 0.004169, Avg Recall: 0.577778, count: 90 -Region Avg IOU: 0.535879, Class: 1.000000, Obj: 0.352178, No Obj: 0.004701, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.498844, Class: 1.000000, Obj: 0.339230, No Obj: 0.004685, Avg Recall: 0.614679, count: 109 -2735: 0.479572, 0.471769 avg, 0.001000 rate, 21.707548 seconds, 700160 images -Loaded: 1.392877 seconds -Region Avg IOU: 0.522430, Class: 1.000000, Obj: 0.332377, No Obj: 0.004481, Avg Recall: 0.639535, count: 86 -Region Avg IOU: 0.485205, Class: 1.000000, Obj: 0.336526, No Obj: 0.005353, Avg Recall: 0.561644, count: 146 -Region Avg IOU: 0.509705, Class: 1.000000, Obj: 0.376288, No Obj: 0.004618, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.493906, Class: 1.000000, Obj: 0.331347, No Obj: 0.004867, Avg Recall: 0.517544, count: 114 -Region Avg IOU: 0.504588, Class: 1.000000, Obj: 0.326764, No Obj: 0.004434, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.469670, Class: 1.000000, Obj: 0.333894, No Obj: 0.004636, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.500808, Class: 1.000000, Obj: 0.328386, No Obj: 0.004361, Avg Recall: 0.553571, count: 112 -Region Avg IOU: 0.460060, Class: 1.000000, Obj: 0.324909, No Obj: 0.004171, Avg Recall: 0.485437, count: 103 -2736: 0.466333, 0.471225 avg, 0.001000 rate, 19.613508 seconds, 700416 images -Loaded: 2.416746 seconds -Region Avg IOU: 0.443737, Class: 1.000000, Obj: 0.263352, No Obj: 0.004144, Avg Recall: 0.457627, count: 118 -Region Avg IOU: 0.511914, Class: 1.000000, Obj: 0.269624, No Obj: 0.004164, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.413143, Class: 1.000000, Obj: 0.284680, No Obj: 0.004453, Avg Recall: 0.420635, count: 126 -Region Avg IOU: 0.465485, Class: 1.000000, Obj: 0.304686, No Obj: 0.003647, Avg Recall: 0.578313, count: 83 -Region Avg IOU: 0.507422, Class: 1.000000, Obj: 0.323369, No Obj: 0.004046, Avg Recall: 0.574468, count: 94 -Region Avg IOU: 0.477392, Class: 1.000000, Obj: 0.299010, No Obj: 0.004024, Avg Recall: 0.485981, count: 107 -Region Avg IOU: 0.530462, Class: 1.000000, Obj: 0.369130, No Obj: 0.004429, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.502292, Class: 1.000000, Obj: 0.313073, No Obj: 0.003806, Avg Recall: 0.638554, count: 83 -2737: 0.508026, 0.474905 avg, 0.001000 rate, 18.785532 seconds, 700672 images -Loaded: 1.967076 seconds -Region Avg IOU: 0.476299, Class: 1.000000, Obj: 0.273014, No Obj: 0.003782, Avg Recall: 0.544643, count: 112 -Region Avg IOU: 0.502511, Class: 1.000000, Obj: 0.268080, No Obj: 0.003157, Avg Recall: 0.579545, count: 88 -Region Avg IOU: 0.535507, Class: 1.000000, Obj: 0.307659, No Obj: 0.004208, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.511595, Class: 1.000000, Obj: 0.292838, No Obj: 0.004094, Avg Recall: 0.571429, count: 112 -Region Avg IOU: 0.515538, Class: 1.000000, Obj: 0.300629, No Obj: 0.004095, Avg Recall: 0.560748, count: 107 -Region Avg IOU: 0.492472, Class: 1.000000, Obj: 0.313567, No Obj: 0.003501, Avg Recall: 0.558824, count: 68 -Region Avg IOU: 0.456337, Class: 1.000000, Obj: 0.295363, No Obj: 0.004535, Avg Recall: 0.496124, count: 129 -Region Avg IOU: 0.484563, Class: 1.000000, Obj: 0.274201, No Obj: 0.003504, Avg Recall: 0.548387, count: 93 -2738: 0.441954, 0.471610 avg, 0.001000 rate, 20.866768 seconds, 700928 images -Loaded: 0.672223 seconds -Region Avg IOU: 0.512421, Class: 1.000000, Obj: 0.290773, No Obj: 0.004535, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.497696, Class: 1.000000, Obj: 0.294229, No Obj: 0.003841, Avg Recall: 0.578512, count: 121 -Region Avg IOU: 0.460266, Class: 1.000000, Obj: 0.263173, No Obj: 0.003726, Avg Recall: 0.500000, count: 100 -Region Avg IOU: 0.513441, Class: 1.000000, Obj: 0.328606, No Obj: 0.004352, Avg Recall: 0.555556, count: 126 -Region Avg IOU: 0.494683, Class: 1.000000, Obj: 0.310486, No Obj: 0.003679, Avg Recall: 0.539326, count: 89 -Region Avg IOU: 0.490136, Class: 1.000000, Obj: 0.299339, No Obj: 0.004501, Avg Recall: 0.559441, count: 143 -Region Avg IOU: 0.482302, Class: 1.000000, Obj: 0.299948, No Obj: 0.003300, Avg Recall: 0.551724, count: 87 -Region Avg IOU: 0.497065, Class: 1.000000, Obj: 0.327267, No Obj: 0.004082, Avg Recall: 0.564815, count: 108 -2739: 0.498042, 0.474253 avg, 0.001000 rate, 19.648508 seconds, 701184 images -Loaded: 1.411853 seconds -Region Avg IOU: 0.535976, Class: 1.000000, Obj: 0.280588, No Obj: 0.004076, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.521945, Class: 1.000000, Obj: 0.309909, No Obj: 0.003854, Avg Recall: 0.566372, count: 113 -Region Avg IOU: 0.506282, Class: 1.000000, Obj: 0.263851, No Obj: 0.004780, Avg Recall: 0.553333, count: 150 -Region Avg IOU: 0.502602, Class: 1.000000, Obj: 0.272913, No Obj: 0.004125, Avg Recall: 0.544828, count: 145 -Region Avg IOU: 0.515305, Class: 1.000000, Obj: 0.317099, No Obj: 0.003716, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.491012, Class: 1.000000, Obj: 0.297883, No Obj: 0.004190, Avg Recall: 0.526316, count: 114 -Region Avg IOU: 0.529152, Class: 1.000000, Obj: 0.293662, No Obj: 0.004355, Avg Recall: 0.619048, count: 126 -Region Avg IOU: 0.528203, Class: 1.000000, Obj: 0.298126, No Obj: 0.003918, Avg Recall: 0.653465, count: 101 -2740: 0.522076, 0.479036 avg, 0.001000 rate, 21.825228 seconds, 701440 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000072 seconds -Region Avg IOU: 0.608270, Class: 1.000000, Obj: 0.369297, No Obj: 0.002928, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.587050, Class: 1.000000, Obj: 0.353622, No Obj: 0.003134, Avg Recall: 0.720000, count: 125 -Region Avg IOU: 0.608827, Class: 1.000000, Obj: 0.382790, No Obj: 0.003471, Avg Recall: 0.751938, count: 129 -Region Avg IOU: 0.592659, Class: 1.000000, Obj: 0.343445, No Obj: 0.002882, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.565053, Class: 1.000000, Obj: 0.367891, No Obj: 0.002901, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.583245, Class: 1.000000, Obj: 0.339711, No Obj: 0.002927, Avg Recall: 0.679012, count: 81 -Region Avg IOU: 0.596427, Class: 1.000000, Obj: 0.340667, No Obj: 0.003585, Avg Recall: 0.750000, count: 124 -Region Avg IOU: 0.586026, Class: 1.000000, Obj: 0.335098, No Obj: 0.002931, Avg Recall: 0.675439, count: 114 -2741: 0.477756, 0.478908 avg, 0.001000 rate, 27.155912 seconds, 701696 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.609483, Class: 1.000000, Obj: 0.395786, No Obj: 0.003259, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.606958, Class: 1.000000, Obj: 0.334063, No Obj: 0.003486, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.627198, Class: 1.000000, Obj: 0.327244, No Obj: 0.003442, Avg Recall: 0.782258, count: 124 -Region Avg IOU: 0.621280, Class: 1.000000, Obj: 0.392827, No Obj: 0.003216, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.616502, Class: 1.000000, Obj: 0.389420, No Obj: 0.003037, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.609828, Class: 1.000000, Obj: 0.412223, No Obj: 0.002691, Avg Recall: 0.744186, count: 86 -Region Avg IOU: 0.636562, Class: 1.000000, Obj: 0.366576, No Obj: 0.003393, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.612763, Class: 1.000000, Obj: 0.365544, No Obj: 0.002674, Avg Recall: 0.747368, count: 95 -2742: 0.468754, 0.477892 avg, 0.001000 rate, 27.089792 seconds, 701952 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.630974, Class: 1.000000, Obj: 0.309511, No Obj: 0.003158, Avg Recall: 0.830645, count: 124 -Region Avg IOU: 0.552510, Class: 1.000000, Obj: 0.340761, No Obj: 0.002616, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.624817, Class: 1.000000, Obj: 0.373018, No Obj: 0.003075, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.590887, Class: 1.000000, Obj: 0.383306, No Obj: 0.002509, Avg Recall: 0.684932, count: 73 -Region Avg IOU: 0.638723, Class: 1.000000, Obj: 0.334294, No Obj: 0.002968, Avg Recall: 0.817204, count: 93 -Region Avg IOU: 0.583628, Class: 1.000000, Obj: 0.331744, No Obj: 0.002956, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.631085, Class: 1.000000, Obj: 0.373734, No Obj: 0.003195, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.617634, Class: 1.000000, Obj: 0.337436, No Obj: 0.003220, Avg Recall: 0.760000, count: 125 -2743: 0.417803, 0.471883 avg, 0.001000 rate, 29.156372 seconds, 702208 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.652040, Class: 1.000000, Obj: 0.403445, No Obj: 0.003360, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.569636, Class: 1.000000, Obj: 0.315248, No Obj: 0.002595, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.619288, Class: 1.000000, Obj: 0.331830, No Obj: 0.002921, Avg Recall: 0.808989, count: 89 -Region Avg IOU: 0.632916, Class: 1.000000, Obj: 0.333058, No Obj: 0.003217, Avg Recall: 0.786667, count: 150 -Region Avg IOU: 0.638783, Class: 1.000000, Obj: 0.329140, No Obj: 0.002976, Avg Recall: 0.796117, count: 103 -Region Avg IOU: 0.617581, Class: 1.000000, Obj: 0.355714, No Obj: 0.002848, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.605192, Class: 1.000000, Obj: 0.327494, No Obj: 0.002947, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.644892, Class: 1.000000, Obj: 0.404569, No Obj: 0.003163, Avg Recall: 0.787037, count: 108 -2744: 0.471567, 0.471852 avg, 0.001000 rate, 27.499432 seconds, 702464 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.642593, Class: 1.000000, Obj: 0.379430, No Obj: 0.003038, Avg Recall: 0.818182, count: 121 -Region Avg IOU: 0.590990, Class: 1.000000, Obj: 0.304249, No Obj: 0.002410, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.609977, Class: 1.000000, Obj: 0.342514, No Obj: 0.002917, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.628007, Class: 1.000000, Obj: 0.377465, No Obj: 0.002408, Avg Recall: 0.808081, count: 99 -Region Avg IOU: 0.598790, Class: 1.000000, Obj: 0.375449, No Obj: 0.002561, Avg Recall: 0.694118, count: 85 -Region Avg IOU: 0.588238, Class: 1.000000, Obj: 0.307947, No Obj: 0.003162, Avg Recall: 0.701613, count: 124 -Region Avg IOU: 0.614784, Class: 1.000000, Obj: 0.349035, No Obj: 0.003426, Avg Recall: 0.744526, count: 137 -Region Avg IOU: 0.628851, Class: 1.000000, Obj: 0.347989, No Obj: 0.002478, Avg Recall: 0.754902, count: 102 -2745: 0.478128, 0.472479 avg, 0.001000 rate, 27.035339 seconds, 702720 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.631423, Class: 1.000000, Obj: 0.329077, No Obj: 0.002723, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.625665, Class: 1.000000, Obj: 0.325632, No Obj: 0.002694, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.572417, Class: 1.000000, Obj: 0.295364, No Obj: 0.002349, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.621314, Class: 1.000000, Obj: 0.306018, No Obj: 0.003095, Avg Recall: 0.775862, count: 116 -Region Avg IOU: 0.627550, Class: 1.000000, Obj: 0.382664, No Obj: 0.002627, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.614462, Class: 1.000000, Obj: 0.352313, No Obj: 0.002513, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.608800, Class: 1.000000, Obj: 0.354515, No Obj: 0.003032, Avg Recall: 0.761905, count: 126 -Region Avg IOU: 0.672536, Class: 1.000000, Obj: 0.386307, No Obj: 0.002471, Avg Recall: 0.872093, count: 86 -2746: 0.448249, 0.470056 avg, 0.001000 rate, 28.433973 seconds, 702976 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.620036, Class: 1.000000, Obj: 0.355633, No Obj: 0.002755, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.609646, Class: 1.000000, Obj: 0.278098, No Obj: 0.003194, Avg Recall: 0.738095, count: 126 -Region Avg IOU: 0.626532, Class: 1.000000, Obj: 0.366760, No Obj: 0.002827, Avg Recall: 0.848485, count: 99 -Region Avg IOU: 0.607219, Class: 1.000000, Obj: 0.346187, No Obj: 0.003253, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.590336, Class: 1.000000, Obj: 0.353135, No Obj: 0.002875, Avg Recall: 0.707692, count: 130 -Region Avg IOU: 0.527265, Class: 1.000000, Obj: 0.302511, No Obj: 0.002697, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.654972, Class: 1.000000, Obj: 0.379807, No Obj: 0.002570, Avg Recall: 0.826531, count: 98 -Region Avg IOU: 0.575372, Class: 1.000000, Obj: 0.318713, No Obj: 0.003026, Avg Recall: 0.690141, count: 142 -2747: 0.492810, 0.472332 avg, 0.001000 rate, 26.428181 seconds, 703232 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.611879, Class: 1.000000, Obj: 0.345040, No Obj: 0.003112, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.623013, Class: 1.000000, Obj: 0.364722, No Obj: 0.002804, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.624577, Class: 1.000000, Obj: 0.339902, No Obj: 0.003648, Avg Recall: 0.758865, count: 141 -Region Avg IOU: 0.587908, Class: 1.000000, Obj: 0.354516, No Obj: 0.002809, Avg Recall: 0.723077, count: 130 -Region Avg IOU: 0.639941, Class: 1.000000, Obj: 0.348411, No Obj: 0.002986, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.601470, Class: 1.000000, Obj: 0.370380, No Obj: 0.002941, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.577651, Class: 1.000000, Obj: 0.310867, No Obj: 0.002879, Avg Recall: 0.677686, count: 121 -Region Avg IOU: 0.634991, Class: 1.000000, Obj: 0.381540, No Obj: 0.002738, Avg Recall: 0.790476, count: 105 -2748: 0.554215, 0.480520 avg, 0.001000 rate, 26.917219 seconds, 703488 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.630377, Class: 1.000000, Obj: 0.371264, No Obj: 0.003028, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.624903, Class: 1.000000, Obj: 0.342264, No Obj: 0.003154, Avg Recall: 0.746032, count: 126 -Region Avg IOU: 0.622219, Class: 1.000000, Obj: 0.340011, No Obj: 0.003370, Avg Recall: 0.782609, count: 138 -Region Avg IOU: 0.590530, Class: 1.000000, Obj: 0.375849, No Obj: 0.003161, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.635753, Class: 1.000000, Obj: 0.347475, No Obj: 0.003043, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.610017, Class: 1.000000, Obj: 0.343690, No Obj: 0.003055, Avg Recall: 0.714286, count: 84 -Region Avg IOU: 0.589897, Class: 1.000000, Obj: 0.390242, No Obj: 0.002834, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.610772, Class: 1.000000, Obj: 0.367616, No Obj: 0.003453, Avg Recall: 0.741667, count: 120 -2749: 0.524882, 0.484956 avg, 0.001000 rate, 27.272295 seconds, 703744 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.587961, Class: 1.000000, Obj: 0.352086, No Obj: 0.003129, Avg Recall: 0.674074, count: 135 -Region Avg IOU: 0.630817, Class: 1.000000, Obj: 0.389180, No Obj: 0.003217, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.623951, Class: 1.000000, Obj: 0.323986, No Obj: 0.003651, Avg Recall: 0.751724, count: 145 -Region Avg IOU: 0.582099, Class: 1.000000, Obj: 0.342040, No Obj: 0.002716, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.640711, Class: 1.000000, Obj: 0.399837, No Obj: 0.003310, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.619122, Class: 1.000000, Obj: 0.420382, No Obj: 0.003010, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.593421, Class: 1.000000, Obj: 0.324182, No Obj: 0.003000, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.632250, Class: 1.000000, Obj: 0.368312, No Obj: 0.003087, Avg Recall: 0.760684, count: 117 -2750: 0.439826, 0.480443 avg, 0.001000 rate, 26.524391 seconds, 704000 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 13.922738 seconds -Region Avg IOU: 0.473354, Class: 1.000000, Obj: 0.228877, No Obj: 0.003877, Avg Recall: 0.491228, count: 114 -Region Avg IOU: 0.522990, Class: 1.000000, Obj: 0.273957, No Obj: 0.004082, Avg Recall: 0.597701, count: 87 -Region Avg IOU: 0.473242, Class: 1.000000, Obj: 0.233047, No Obj: 0.004242, Avg Recall: 0.537037, count: 108 -Region Avg IOU: 0.439291, Class: 1.000000, Obj: 0.225235, No Obj: 0.003974, Avg Recall: 0.450382, count: 131 -Region Avg IOU: 0.385473, Class: 1.000000, Obj: 0.196090, No Obj: 0.003749, Avg Recall: 0.398305, count: 118 -Region Avg IOU: 0.438574, Class: 1.000000, Obj: 0.224775, No Obj: 0.003915, Avg Recall: 0.396040, count: 101 -Region Avg IOU: 0.401412, Class: 1.000000, Obj: 0.220398, No Obj: 0.004037, Avg Recall: 0.412587, count: 143 -Region Avg IOU: 0.488838, Class: 1.000000, Obj: 0.241866, No Obj: 0.003734, Avg Recall: 0.607143, count: 84 -2751: 0.671857, 0.499584 avg, 0.001000 rate, 12.669416 seconds, 704256 images -Loaded: 8.163679 seconds -Region Avg IOU: 0.467690, Class: 1.000000, Obj: 0.258228, No Obj: 0.003991, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.413271, Class: 1.000000, Obj: 0.250984, No Obj: 0.003743, Avg Recall: 0.407767, count: 103 -Region Avg IOU: 0.463293, Class: 1.000000, Obj: 0.239299, No Obj: 0.003830, Avg Recall: 0.509615, count: 104 -Region Avg IOU: 0.481496, Class: 1.000000, Obj: 0.301743, No Obj: 0.003510, Avg Recall: 0.556818, count: 88 -Region Avg IOU: 0.489349, Class: 1.000000, Obj: 0.240648, No Obj: 0.003967, Avg Recall: 0.517241, count: 116 -Region Avg IOU: 0.502983, Class: 1.000000, Obj: 0.248459, No Obj: 0.004194, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.458285, Class: 1.000000, Obj: 0.257746, No Obj: 0.004857, Avg Recall: 0.474820, count: 139 -Region Avg IOU: 0.495527, Class: 1.000000, Obj: 0.253001, No Obj: 0.004150, Avg Recall: 0.536585, count: 82 -2752: 0.533507, 0.502977 avg, 0.001000 rate, 15.181742 seconds, 704512 images -Loaded: 6.514225 seconds -Region Avg IOU: 0.483829, Class: 1.000000, Obj: 0.283644, No Obj: 0.003922, Avg Recall: 0.545455, count: 77 -Region Avg IOU: 0.449000, Class: 1.000000, Obj: 0.261483, No Obj: 0.004238, Avg Recall: 0.491803, count: 122 -Region Avg IOU: 0.419858, Class: 1.000000, Obj: 0.268282, No Obj: 0.004152, Avg Recall: 0.410714, count: 112 -Region Avg IOU: 0.458153, Class: 1.000000, Obj: 0.239446, No Obj: 0.004139, Avg Recall: 0.479339, count: 121 -Region Avg IOU: 0.408359, Class: 1.000000, Obj: 0.259605, No Obj: 0.004036, Avg Recall: 0.384615, count: 130 -Region Avg IOU: 0.418256, Class: 1.000000, Obj: 0.252881, No Obj: 0.003991, Avg Recall: 0.462366, count: 93 -Region Avg IOU: 0.388229, Class: 1.000000, Obj: 0.216638, No Obj: 0.004230, Avg Recall: 0.401515, count: 132 -Region Avg IOU: 0.440728, Class: 1.000000, Obj: 0.239520, No Obj: 0.003844, Avg Recall: 0.441441, count: 111 -2753: 0.493835, 0.502062 avg, 0.001000 rate, 15.076308 seconds, 704768 images -Loaded: 5.647185 seconds -Region Avg IOU: 0.454563, Class: 1.000000, Obj: 0.277641, No Obj: 0.005216, Avg Recall: 0.500000, count: 134 -Region Avg IOU: 0.462117, Class: 1.000000, Obj: 0.294426, No Obj: 0.004526, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.467490, Class: 1.000000, Obj: 0.228490, No Obj: 0.004312, Avg Recall: 0.488889, count: 90 -Region Avg IOU: 0.416047, Class: 1.000000, Obj: 0.270667, No Obj: 0.004388, Avg Recall: 0.406250, count: 128 -Region Avg IOU: 0.436078, Class: 1.000000, Obj: 0.273595, No Obj: 0.004112, Avg Recall: 0.419048, count: 105 -Region Avg IOU: 0.388630, Class: 1.000000, Obj: 0.251802, No Obj: 0.004262, Avg Recall: 0.344000, count: 125 -Region Avg IOU: 0.406926, Class: 1.000000, Obj: 0.294451, No Obj: 0.004324, Avg Recall: 0.411215, count: 107 -Region Avg IOU: 0.425869, Class: 1.000000, Obj: 0.279230, No Obj: 0.004807, Avg Recall: 0.451613, count: 124 -2754: 0.563078, 0.508164 avg, 0.001000 rate, 15.439629 seconds, 705024 images -Loaded: 4.754846 seconds -Region Avg IOU: 0.424132, Class: 1.000000, Obj: 0.283448, No Obj: 0.004509, Avg Recall: 0.430108, count: 93 -Region Avg IOU: 0.436609, Class: 1.000000, Obj: 0.292352, No Obj: 0.004339, Avg Recall: 0.459770, count: 87 -Region Avg IOU: 0.412718, Class: 1.000000, Obj: 0.294524, No Obj: 0.004124, Avg Recall: 0.432432, count: 111 -Region Avg IOU: 0.364784, Class: 1.000000, Obj: 0.266091, No Obj: 0.004137, Avg Recall: 0.372881, count: 118 -Region Avg IOU: 0.501215, Class: 1.000000, Obj: 0.322787, No Obj: 0.004280, Avg Recall: 0.556818, count: 88 -Region Avg IOU: 0.424935, Class: 1.000000, Obj: 0.300111, No Obj: 0.004019, Avg Recall: 0.422222, count: 90 -Region Avg IOU: 0.437182, Class: 1.000000, Obj: 0.304726, No Obj: 0.004809, Avg Recall: 0.505376, count: 93 -Region Avg IOU: 0.469504, Class: 1.000000, Obj: 0.308211, No Obj: 0.004850, Avg Recall: 0.532110, count: 109 -2755: 0.460188, 0.503366 avg, 0.001000 rate, 14.755852 seconds, 705280 images -Loaded: 6.156036 seconds -Region Avg IOU: 0.389065, Class: 1.000000, Obj: 0.284866, No Obj: 0.004399, Avg Recall: 0.390000, count: 100 -Region Avg IOU: 0.353539, Class: 1.000000, Obj: 0.197100, No Obj: 0.003735, Avg Recall: 0.367521, count: 117 -Region Avg IOU: 0.459025, Class: 1.000000, Obj: 0.275046, No Obj: 0.005290, Avg Recall: 0.524272, count: 103 -Region Avg IOU: 0.437516, Class: 1.000000, Obj: 0.334144, No Obj: 0.004311, Avg Recall: 0.475610, count: 82 -Region Avg IOU: 0.437918, Class: 1.000000, Obj: 0.264054, No Obj: 0.004403, Avg Recall: 0.448598, count: 107 -Region Avg IOU: 0.441681, Class: 1.000000, Obj: 0.391616, No Obj: 0.004637, Avg Recall: 0.407895, count: 76 -Region Avg IOU: 0.440805, Class: 1.000000, Obj: 0.326260, No Obj: 0.004575, Avg Recall: 0.490385, count: 104 -Region Avg IOU: 0.434265, Class: 1.000000, Obj: 0.242290, No Obj: 0.005575, Avg Recall: 0.426573, count: 143 -2756: 0.505785, 0.503608 avg, 0.001000 rate, 16.752562 seconds, 705536 images -Loaded: 3.038742 seconds -Region Avg IOU: 0.473952, Class: 1.000000, Obj: 0.316871, No Obj: 0.005110, Avg Recall: 0.529412, count: 119 -Region Avg IOU: 0.440879, Class: 1.000000, Obj: 0.288898, No Obj: 0.004767, Avg Recall: 0.489130, count: 92 -Region Avg IOU: 0.369698, Class: 1.000000, Obj: 0.313866, No Obj: 0.004514, Avg Recall: 0.395349, count: 86 -Region Avg IOU: 0.457083, Class: 1.000000, Obj: 0.305996, No Obj: 0.004916, Avg Recall: 0.500000, count: 90 -Region Avg IOU: 0.441263, Class: 1.000000, Obj: 0.284294, No Obj: 0.004962, Avg Recall: 0.509615, count: 104 -Region Avg IOU: 0.440334, Class: 1.000000, Obj: 0.320175, No Obj: 0.004885, Avg Recall: 0.430108, count: 93 -Region Avg IOU: 0.428329, Class: 1.000000, Obj: 0.283230, No Obj: 0.005402, Avg Recall: 0.462069, count: 145 -Region Avg IOU: 0.422266, Class: 1.000000, Obj: 0.266335, No Obj: 0.005403, Avg Recall: 0.460938, count: 128 -2757: 0.524092, 0.505657 avg, 0.001000 rate, 15.189593 seconds, 705792 images -Loaded: 4.406856 seconds -Region Avg IOU: 0.385564, Class: 1.000000, Obj: 0.310448, No Obj: 0.005085, Avg Recall: 0.365672, count: 134 -Region Avg IOU: 0.452378, Class: 1.000000, Obj: 0.287227, No Obj: 0.005425, Avg Recall: 0.482759, count: 116 -Region Avg IOU: 0.527483, Class: 1.000000, Obj: 0.392064, No Obj: 0.004728, Avg Recall: 0.662338, count: 77 -Region Avg IOU: 0.402018, Class: 1.000000, Obj: 0.304509, No Obj: 0.004440, Avg Recall: 0.451613, count: 93 -Region Avg IOU: 0.458094, Class: 1.000000, Obj: 0.295282, No Obj: 0.004727, Avg Recall: 0.530612, count: 98 -Region Avg IOU: 0.443124, Class: 1.000000, Obj: 0.281342, No Obj: 0.005413, Avg Recall: 0.465649, count: 131 -Region Avg IOU: 0.428686, Class: 1.000000, Obj: 0.286212, No Obj: 0.005385, Avg Recall: 0.464286, count: 140 -Region Avg IOU: 0.394170, Class: 1.000000, Obj: 0.314117, No Obj: 0.004350, Avg Recall: 0.372549, count: 102 -2758: 0.584574, 0.513548 avg, 0.001000 rate, 13.685438 seconds, 706048 images -Loaded: 7.266009 seconds -Region Avg IOU: 0.428312, Class: 1.000000, Obj: 0.290462, No Obj: 0.005044, Avg Recall: 0.433333, count: 120 -Region Avg IOU: 0.445941, Class: 1.000000, Obj: 0.324984, No Obj: 0.005380, Avg Recall: 0.492188, count: 128 -Region Avg IOU: 0.420918, Class: 1.000000, Obj: 0.300000, No Obj: 0.004543, Avg Recall: 0.443396, count: 106 -Region Avg IOU: 0.456188, Class: 1.000000, Obj: 0.301703, No Obj: 0.004968, Avg Recall: 0.479592, count: 98 -Region Avg IOU: 0.442525, Class: 1.000000, Obj: 0.307525, No Obj: 0.004783, Avg Recall: 0.461538, count: 104 -Region Avg IOU: 0.418993, Class: 1.000000, Obj: 0.357372, No Obj: 0.004914, Avg Recall: 0.432990, count: 97 -Region Avg IOU: 0.407632, Class: 1.000000, Obj: 0.293529, No Obj: 0.005052, Avg Recall: 0.389381, count: 113 -Region Avg IOU: 0.402875, Class: 1.000000, Obj: 0.311422, No Obj: 0.004862, Avg Recall: 0.372549, count: 102 -2759: 0.483253, 0.510519 avg, 0.001000 rate, 20.810692 seconds, 706304 images -Loaded: 2.140428 seconds -Region Avg IOU: 0.426409, Class: 1.000000, Obj: 0.292852, No Obj: 0.004676, Avg Recall: 0.446281, count: 121 -Region Avg IOU: 0.448217, Class: 1.000000, Obj: 0.329423, No Obj: 0.005018, Avg Recall: 0.462963, count: 108 -Region Avg IOU: 0.467985, Class: 1.000000, Obj: 0.298042, No Obj: 0.005094, Avg Recall: 0.544643, count: 112 -Region Avg IOU: 0.500289, Class: 1.000000, Obj: 0.340401, No Obj: 0.004079, Avg Recall: 0.553846, count: 65 -Region Avg IOU: 0.437012, Class: 1.000000, Obj: 0.313152, No Obj: 0.005087, Avg Recall: 0.465116, count: 129 -Region Avg IOU: 0.481452, Class: 1.000000, Obj: 0.263767, No Obj: 0.005745, Avg Recall: 0.548387, count: 124 -Region Avg IOU: 0.484831, Class: 1.000000, Obj: 0.336464, No Obj: 0.005145, Avg Recall: 0.532710, count: 107 -Region Avg IOU: 0.481562, Class: 1.000000, Obj: 0.320475, No Obj: 0.005256, Avg Recall: 0.533981, count: 103 -2760: 0.488439, 0.508311 avg, 0.001000 rate, 17.924990 seconds, 706560 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 0.773447 seconds -Region Avg IOU: 0.547478, Class: 1.000000, Obj: 0.332748, No Obj: 0.004704, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.545842, Class: 1.000000, Obj: 0.407970, No Obj: 0.003756, Avg Recall: 0.610390, count: 77 -Region Avg IOU: 0.508812, Class: 1.000000, Obj: 0.350950, No Obj: 0.004374, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.520204, Class: 1.000000, Obj: 0.380850, No Obj: 0.004402, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.519972, Class: 1.000000, Obj: 0.386341, No Obj: 0.004190, Avg Recall: 0.553398, count: 103 -Region Avg IOU: 0.496811, Class: 1.000000, Obj: 0.357546, No Obj: 0.004341, Avg Recall: 0.558333, count: 120 -Region Avg IOU: 0.576834, Class: 1.000000, Obj: 0.398890, No Obj: 0.004665, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.541528, Class: 1.000000, Obj: 0.372333, No Obj: 0.003780, Avg Recall: 0.646341, count: 82 -2761: 0.430499, 0.500530 avg, 0.001000 rate, 21.940605 seconds, 706816 images -Loaded: 1.577523 seconds -Region Avg IOU: 0.548076, Class: 1.000000, Obj: 0.360273, No Obj: 0.004193, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.486607, Class: 1.000000, Obj: 0.316514, No Obj: 0.004439, Avg Recall: 0.530303, count: 132 -Region Avg IOU: 0.498648, Class: 1.000000, Obj: 0.331923, No Obj: 0.003985, Avg Recall: 0.526786, count: 112 -Region Avg IOU: 0.560572, Class: 1.000000, Obj: 0.382117, No Obj: 0.003901, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.495707, Class: 1.000000, Obj: 0.338312, No Obj: 0.003870, Avg Recall: 0.527559, count: 127 -Region Avg IOU: 0.463655, Class: 1.000000, Obj: 0.288503, No Obj: 0.004208, Avg Recall: 0.537815, count: 119 -Region Avg IOU: 0.498964, Class: 1.000000, Obj: 0.315392, No Obj: 0.003625, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.532794, Class: 1.000000, Obj: 0.367922, No Obj: 0.003862, Avg Recall: 0.659574, count: 94 -2762: 0.476824, 0.498159 avg, 0.001000 rate, 21.971844 seconds, 707072 images -Loaded: 1.407716 seconds -Region Avg IOU: 0.509804, Class: 1.000000, Obj: 0.353183, No Obj: 0.004244, Avg Recall: 0.548673, count: 113 -Region Avg IOU: 0.518957, Class: 1.000000, Obj: 0.378415, No Obj: 0.004506, Avg Recall: 0.606061, count: 132 -Region Avg IOU: 0.537111, Class: 1.000000, Obj: 0.330801, No Obj: 0.003837, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.523691, Class: 1.000000, Obj: 0.333829, No Obj: 0.004269, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.547166, Class: 1.000000, Obj: 0.344756, No Obj: 0.004194, Avg Recall: 0.609756, count: 123 -Region Avg IOU: 0.519888, Class: 1.000000, Obj: 0.326790, No Obj: 0.004307, Avg Recall: 0.615385, count: 130 -Region Avg IOU: 0.564003, Class: 1.000000, Obj: 0.342528, No Obj: 0.003860, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.503674, Class: 1.000000, Obj: 0.273833, No Obj: 0.003519, Avg Recall: 0.574257, count: 101 -2763: 0.489312, 0.497274 avg, 0.001000 rate, 21.532942 seconds, 707328 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.481294, Class: 1.000000, Obj: 0.262753, No Obj: 0.003799, Avg Recall: 0.491935, count: 124 -Region Avg IOU: 0.527591, Class: 1.000000, Obj: 0.280763, No Obj: 0.003889, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.550620, Class: 1.000000, Obj: 0.367239, No Obj: 0.003601, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.548945, Class: 1.000000, Obj: 0.355370, No Obj: 0.003573, Avg Recall: 0.670455, count: 88 -Region Avg IOU: 0.512424, Class: 1.000000, Obj: 0.332195, No Obj: 0.003431, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.547199, Class: 1.000000, Obj: 0.363932, No Obj: 0.004831, Avg Recall: 0.641026, count: 156 -Region Avg IOU: 0.529330, Class: 1.000000, Obj: 0.324350, No Obj: 0.003411, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.515295, Class: 1.000000, Obj: 0.311288, No Obj: 0.003379, Avg Recall: 0.621359, count: 103 -2764: 0.435059, 0.491053 avg, 0.001000 rate, 22.952705 seconds, 707584 images -Loaded: 0.909082 seconds -Region Avg IOU: 0.495071, Class: 1.000000, Obj: 0.303871, No Obj: 0.004268, Avg Recall: 0.551948, count: 154 -Region Avg IOU: 0.478340, Class: 1.000000, Obj: 0.268892, No Obj: 0.003467, Avg Recall: 0.510204, count: 98 -Region Avg IOU: 0.547807, Class: 1.000000, Obj: 0.264460, No Obj: 0.003305, Avg Recall: 0.619718, count: 71 -Region Avg IOU: 0.527808, Class: 1.000000, Obj: 0.342204, No Obj: 0.004082, Avg Recall: 0.620968, count: 124 -Region Avg IOU: 0.549280, Class: 1.000000, Obj: 0.332504, No Obj: 0.004163, Avg Recall: 0.623077, count: 130 -Region Avg IOU: 0.582709, Class: 1.000000, Obj: 0.354807, No Obj: 0.004189, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.498189, Class: 1.000000, Obj: 0.302361, No Obj: 0.003762, Avg Recall: 0.570248, count: 121 -Region Avg IOU: 0.530116, Class: 1.000000, Obj: 0.284402, No Obj: 0.003489, Avg Recall: 0.598039, count: 102 -2765: 0.515933, 0.493541 avg, 0.001000 rate, 18.467512 seconds, 707840 images -Loaded: 2.481095 seconds -Region Avg IOU: 0.529156, Class: 1.000000, Obj: 0.332003, No Obj: 0.003711, Avg Recall: 0.644068, count: 118 -Region Avg IOU: 0.573166, Class: 1.000000, Obj: 0.329784, No Obj: 0.003086, Avg Recall: 0.662162, count: 74 -Region Avg IOU: 0.501051, Class: 1.000000, Obj: 0.299514, No Obj: 0.003723, Avg Recall: 0.593220, count: 118 -Region Avg IOU: 0.514779, Class: 1.000000, Obj: 0.317046, No Obj: 0.003989, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.516834, Class: 1.000000, Obj: 0.318164, No Obj: 0.003993, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.519218, Class: 1.000000, Obj: 0.303906, No Obj: 0.003561, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.530173, Class: 1.000000, Obj: 0.323471, No Obj: 0.003684, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.492485, Class: 1.000000, Obj: 0.295453, No Obj: 0.003822, Avg Recall: 0.526718, count: 131 -2766: 0.441679, 0.488355 avg, 0.001000 rate, 21.399000 seconds, 708096 images -Loaded: 1.378131 seconds -Region Avg IOU: 0.535955, Class: 1.000000, Obj: 0.281807, No Obj: 0.003655, Avg Recall: 0.589744, count: 117 -Region Avg IOU: 0.477386, Class: 1.000000, Obj: 0.243054, No Obj: 0.003557, Avg Recall: 0.548387, count: 124 -Region Avg IOU: 0.530826, Class: 1.000000, Obj: 0.311364, No Obj: 0.003564, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.557034, Class: 1.000000, Obj: 0.337640, No Obj: 0.004502, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.545594, Class: 1.000000, Obj: 0.376794, No Obj: 0.003786, Avg Recall: 0.632184, count: 87 -Region Avg IOU: 0.518564, Class: 1.000000, Obj: 0.305097, No Obj: 0.004097, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.591625, Class: 1.000000, Obj: 0.323364, No Obj: 0.003630, Avg Recall: 0.700000, count: 80 -Region Avg IOU: 0.523203, Class: 1.000000, Obj: 0.323562, No Obj: 0.004456, Avg Recall: 0.609756, count: 123 -2767: 0.473288, 0.486848 avg, 0.001000 rate, 23.384398 seconds, 708352 images -Loaded: 0.000176 seconds -Region Avg IOU: 0.504173, Class: 1.000000, Obj: 0.319710, No Obj: 0.004285, Avg Recall: 0.584615, count: 130 -Region Avg IOU: 0.517930, Class: 1.000000, Obj: 0.343357, No Obj: 0.003760, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.557823, Class: 1.000000, Obj: 0.315400, No Obj: 0.004381, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.537420, Class: 1.000000, Obj: 0.330258, No Obj: 0.003228, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.528538, Class: 1.000000, Obj: 0.314144, No Obj: 0.003696, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.553324, Class: 1.000000, Obj: 0.333446, No Obj: 0.003940, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.516113, Class: 1.000000, Obj: 0.294396, No Obj: 0.003806, Avg Recall: 0.631148, count: 122 -Region Avg IOU: 0.503770, Class: 1.000000, Obj: 0.329406, No Obj: 0.003525, Avg Recall: 0.549550, count: 111 -2768: 0.448608, 0.483024 avg, 0.001000 rate, 20.740265 seconds, 708608 images -Loaded: 0.176952 seconds -Region Avg IOU: 0.519208, Class: 1.000000, Obj: 0.285419, No Obj: 0.003680, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.564858, Class: 1.000000, Obj: 0.341148, No Obj: 0.004001, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.539707, Class: 1.000000, Obj: 0.338004, No Obj: 0.003935, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.535536, Class: 1.000000, Obj: 0.313981, No Obj: 0.003409, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.557968, Class: 1.000000, Obj: 0.360136, No Obj: 0.003995, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.538635, Class: 1.000000, Obj: 0.316215, No Obj: 0.003665, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.508111, Class: 1.000000, Obj: 0.339067, No Obj: 0.003467, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.558220, Class: 1.000000, Obj: 0.297609, No Obj: 0.003305, Avg Recall: 0.644444, count: 90 -2769: 0.480185, 0.482740 avg, 0.001000 rate, 22.237200 seconds, 708864 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.560087, Class: 1.000000, Obj: 0.344112, No Obj: 0.003560, Avg Recall: 0.647727, count: 88 -Region Avg IOU: 0.534823, Class: 1.000000, Obj: 0.361971, No Obj: 0.003936, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.555081, Class: 1.000000, Obj: 0.340741, No Obj: 0.004136, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.507302, Class: 1.000000, Obj: 0.332125, No Obj: 0.003683, Avg Recall: 0.516484, count: 91 -Region Avg IOU: 0.530285, Class: 1.000000, Obj: 0.303011, No Obj: 0.003877, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.554660, Class: 1.000000, Obj: 0.351021, No Obj: 0.003903, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.505338, Class: 1.000000, Obj: 0.279887, No Obj: 0.003774, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.528440, Class: 1.000000, Obj: 0.320352, No Obj: 0.004132, Avg Recall: 0.592233, count: 103 -2770: 0.417689, 0.476235 avg, 0.001000 rate, 21.536535 seconds, 709120 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 17.488169 seconds -Region Avg IOU: 0.525091, Class: 1.000000, Obj: 0.349193, No Obj: 0.004260, Avg Recall: 0.547368, count: 95 -Region Avg IOU: 0.476553, Class: 1.000000, Obj: 0.318030, No Obj: 0.003990, Avg Recall: 0.526786, count: 112 -Region Avg IOU: 0.475756, Class: 1.000000, Obj: 0.306016, No Obj: 0.003990, Avg Recall: 0.556452, count: 124 -Region Avg IOU: 0.557513, Class: 1.000000, Obj: 0.306101, No Obj: 0.004554, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.493678, Class: 1.000000, Obj: 0.289746, No Obj: 0.004012, Avg Recall: 0.522936, count: 109 -Region Avg IOU: 0.551479, Class: 1.000000, Obj: 0.359331, No Obj: 0.004108, Avg Recall: 0.654762, count: 84 -Region Avg IOU: 0.549569, Class: 1.000000, Obj: 0.339474, No Obj: 0.004477, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.523813, Class: 1.000000, Obj: 0.333574, No Obj: 0.003906, Avg Recall: 0.620690, count: 87 -2771: 0.476747, 0.476286 avg, 0.001000 rate, 20.851309 seconds, 709376 images -Loaded: 0.556136 seconds -Region Avg IOU: 0.508113, Class: 1.000000, Obj: 0.316617, No Obj: 0.004032, Avg Recall: 0.539326, count: 89 -Region Avg IOU: 0.545492, Class: 1.000000, Obj: 0.338169, No Obj: 0.004418, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.447562, Class: 1.000000, Obj: 0.316689, No Obj: 0.004644, Avg Recall: 0.444444, count: 126 -Region Avg IOU: 0.472891, Class: 1.000000, Obj: 0.328368, No Obj: 0.004198, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.497224, Class: 1.000000, Obj: 0.313707, No Obj: 0.004063, Avg Recall: 0.586957, count: 92 -Region Avg IOU: 0.513583, Class: 1.000000, Obj: 0.308286, No Obj: 0.004823, Avg Recall: 0.550388, count: 129 -Region Avg IOU: 0.472952, Class: 1.000000, Obj: 0.344970, No Obj: 0.003989, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.543346, Class: 1.000000, Obj: 0.354411, No Obj: 0.004379, Avg Recall: 0.608247, count: 97 -2772: 0.471103, 0.475768 avg, 0.001000 rate, 22.075060 seconds, 709632 images -Loaded: 0.479838 seconds -Region Avg IOU: 0.528603, Class: 1.000000, Obj: 0.333704, No Obj: 0.004617, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.481886, Class: 1.000000, Obj: 0.332175, No Obj: 0.004540, Avg Recall: 0.537815, count: 119 -Region Avg IOU: 0.477573, Class: 1.000000, Obj: 0.356284, No Obj: 0.004262, Avg Recall: 0.552083, count: 96 -Region Avg IOU: 0.462353, Class: 1.000000, Obj: 0.323768, No Obj: 0.004600, Avg Recall: 0.488189, count: 127 -Region Avg IOU: 0.511395, Class: 1.000000, Obj: 0.380655, No Obj: 0.003601, Avg Recall: 0.561644, count: 73 -Region Avg IOU: 0.482419, Class: 1.000000, Obj: 0.326666, No Obj: 0.004540, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.556155, Class: 1.000000, Obj: 0.413529, No Obj: 0.004574, Avg Recall: 0.614458, count: 83 -Region Avg IOU: 0.475837, Class: 1.000000, Obj: 0.318454, No Obj: 0.004256, Avg Recall: 0.537037, count: 108 -2773: 0.437373, 0.471928 avg, 0.001000 rate, 19.825657 seconds, 709888 images -Loaded: 2.158117 seconds -Region Avg IOU: 0.552191, Class: 1.000000, Obj: 0.359153, No Obj: 0.005028, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.542703, Class: 1.000000, Obj: 0.351090, No Obj: 0.004620, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.494621, Class: 1.000000, Obj: 0.401192, No Obj: 0.004842, Avg Recall: 0.608247, count: 97 -Region Avg IOU: 0.481075, Class: 1.000000, Obj: 0.295574, No Obj: 0.004192, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.510782, Class: 1.000000, Obj: 0.324220, No Obj: 0.004764, Avg Recall: 0.601695, count: 118 -Region Avg IOU: 0.519645, Class: 1.000000, Obj: 0.359657, No Obj: 0.004880, Avg Recall: 0.581197, count: 117 -Region Avg IOU: 0.510859, Class: 1.000000, Obj: 0.366344, No Obj: 0.004939, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.491185, Class: 1.000000, Obj: 0.369889, No Obj: 0.005047, Avg Recall: 0.551181, count: 127 -2774: 0.487809, 0.473516 avg, 0.001000 rate, 23.926992 seconds, 710144 images -Loaded: 0.016295 seconds -Region Avg IOU: 0.462638, Class: 1.000000, Obj: 0.331122, No Obj: 0.004534, Avg Recall: 0.474747, count: 99 -Region Avg IOU: 0.491062, Class: 1.000000, Obj: 0.331659, No Obj: 0.004991, Avg Recall: 0.511628, count: 129 -Region Avg IOU: 0.477932, Class: 1.000000, Obj: 0.336755, No Obj: 0.004511, Avg Recall: 0.509804, count: 102 -Region Avg IOU: 0.471765, Class: 1.000000, Obj: 0.332264, No Obj: 0.004598, Avg Recall: 0.504132, count: 121 -Region Avg IOU: 0.424151, Class: 1.000000, Obj: 0.316943, No Obj: 0.004412, Avg Recall: 0.447154, count: 123 -Region Avg IOU: 0.544336, Class: 1.000000, Obj: 0.403610, No Obj: 0.003817, Avg Recall: 0.649351, count: 77 -Region Avg IOU: 0.518864, Class: 1.000000, Obj: 0.351075, No Obj: 0.004849, Avg Recall: 0.560000, count: 125 -Region Avg IOU: 0.507098, Class: 1.000000, Obj: 0.309489, No Obj: 0.005070, Avg Recall: 0.571429, count: 105 -2775: 0.461555, 0.472320 avg, 0.001000 rate, 19.336981 seconds, 710400 images -Loaded: 2.029483 seconds -Region Avg IOU: 0.537324, Class: 1.000000, Obj: 0.362078, No Obj: 0.004863, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.469251, Class: 1.000000, Obj: 0.354201, No Obj: 0.004598, Avg Recall: 0.503937, count: 127 -Region Avg IOU: 0.473276, Class: 1.000000, Obj: 0.323812, No Obj: 0.004882, Avg Recall: 0.504065, count: 123 -Region Avg IOU: 0.478727, Class: 1.000000, Obj: 0.348779, No Obj: 0.004620, Avg Recall: 0.539062, count: 128 -Region Avg IOU: 0.496255, Class: 1.000000, Obj: 0.322861, No Obj: 0.003957, Avg Recall: 0.500000, count: 100 -Region Avg IOU: 0.520533, Class: 1.000000, Obj: 0.394710, No Obj: 0.004932, Avg Recall: 0.593220, count: 118 -Region Avg IOU: 0.469335, Class: 1.000000, Obj: 0.323553, No Obj: 0.004474, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.490181, Class: 1.000000, Obj: 0.367143, No Obj: 0.004404, Avg Recall: 0.553191, count: 94 -2776: 0.560165, 0.481105 avg, 0.001000 rate, 19.775547 seconds, 710656 images -Loaded: 1.684199 seconds -Region Avg IOU: 0.507906, Class: 1.000000, Obj: 0.334067, No Obj: 0.004373, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.506318, Class: 1.000000, Obj: 0.323698, No Obj: 0.004638, Avg Recall: 0.559322, count: 118 -Region Avg IOU: 0.482564, Class: 1.000000, Obj: 0.346392, No Obj: 0.004329, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.496173, Class: 1.000000, Obj: 0.314910, No Obj: 0.005520, Avg Recall: 0.519481, count: 154 -Region Avg IOU: 0.486570, Class: 1.000000, Obj: 0.319291, No Obj: 0.003996, Avg Recall: 0.547826, count: 115 -Region Avg IOU: 0.467754, Class: 1.000000, Obj: 0.296298, No Obj: 0.005103, Avg Recall: 0.476190, count: 168 -Region Avg IOU: 0.520013, Class: 1.000000, Obj: 0.346774, No Obj: 0.003619, Avg Recall: 0.609195, count: 87 -Region Avg IOU: 0.529381, Class: 1.000000, Obj: 0.332497, No Obj: 0.004222, Avg Recall: 0.576923, count: 104 -2777: 0.496239, 0.482618 avg, 0.001000 rate, 20.433008 seconds, 710912 images -Loaded: 1.642737 seconds -Region Avg IOU: 0.493455, Class: 1.000000, Obj: 0.301076, No Obj: 0.003504, Avg Recall: 0.518868, count: 106 -Region Avg IOU: 0.515340, Class: 1.000000, Obj: 0.310083, No Obj: 0.004292, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.525059, Class: 1.000000, Obj: 0.369080, No Obj: 0.004691, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.406505, Class: 1.000000, Obj: 0.274349, No Obj: 0.004161, Avg Recall: 0.405797, count: 138 -Region Avg IOU: 0.501485, Class: 1.000000, Obj: 0.323470, No Obj: 0.003700, Avg Recall: 0.517241, count: 87 -Region Avg IOU: 0.508662, Class: 1.000000, Obj: 0.256179, No Obj: 0.004212, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.509777, Class: 1.000000, Obj: 0.323154, No Obj: 0.005350, Avg Recall: 0.559748, count: 159 -Region Avg IOU: 0.492554, Class: 1.000000, Obj: 0.322395, No Obj: 0.004191, Avg Recall: 0.517544, count: 114 -2778: 0.461535, 0.480510 avg, 0.001000 rate, 22.377735 seconds, 711168 images -Loaded: 0.246776 seconds -Region Avg IOU: 0.547415, Class: 1.000000, Obj: 0.322641, No Obj: 0.003660, Avg Recall: 0.679487, count: 78 -Region Avg IOU: 0.486535, Class: 1.000000, Obj: 0.319623, No Obj: 0.003750, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.465319, Class: 1.000000, Obj: 0.293492, No Obj: 0.003963, Avg Recall: 0.547826, count: 115 -Region Avg IOU: 0.523442, Class: 1.000000, Obj: 0.291381, No Obj: 0.004304, Avg Recall: 0.568965, count: 116 -Region Avg IOU: 0.553689, Class: 1.000000, Obj: 0.384336, No Obj: 0.003870, Avg Recall: 0.627907, count: 86 -Region Avg IOU: 0.498019, Class: 1.000000, Obj: 0.293645, No Obj: 0.003693, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.531404, Class: 1.000000, Obj: 0.314797, No Obj: 0.004455, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.539768, Class: 1.000000, Obj: 0.332072, No Obj: 0.004532, Avg Recall: 0.590000, count: 100 -2779: 0.438352, 0.476294 avg, 0.001000 rate, 21.516869 seconds, 711424 images -Loaded: 0.569946 seconds -Region Avg IOU: 0.502980, Class: 1.000000, Obj: 0.337566, No Obj: 0.003749, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.523847, Class: 1.000000, Obj: 0.324423, No Obj: 0.003837, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.515590, Class: 1.000000, Obj: 0.340696, No Obj: 0.003726, Avg Recall: 0.550000, count: 100 -Region Avg IOU: 0.475215, Class: 1.000000, Obj: 0.312115, No Obj: 0.003756, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.462461, Class: 1.000000, Obj: 0.287480, No Obj: 0.004079, Avg Recall: 0.455357, count: 112 -Region Avg IOU: 0.519006, Class: 1.000000, Obj: 0.302123, No Obj: 0.004350, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.449664, Class: 1.000000, Obj: 0.300121, No Obj: 0.004115, Avg Recall: 0.477876, count: 113 -Region Avg IOU: 0.509157, Class: 1.000000, Obj: 0.315319, No Obj: 0.003983, Avg Recall: 0.620370, count: 108 -2780: 0.431490, 0.471814 avg, 0.001000 rate, 20.190355 seconds, 711680 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 20.013464 seconds -Region Avg IOU: 0.480048, Class: 1.000000, Obj: 0.244764, No Obj: 0.003981, Avg Recall: 0.521739, count: 92 -Region Avg IOU: 0.417065, Class: 1.000000, Obj: 0.247859, No Obj: 0.004313, Avg Recall: 0.467742, count: 124 -Region Avg IOU: 0.484079, Class: 1.000000, Obj: 0.276659, No Obj: 0.004370, Avg Recall: 0.522523, count: 111 -Region Avg IOU: 0.489215, Class: 1.000000, Obj: 0.289208, No Obj: 0.004302, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.478879, Class: 1.000000, Obj: 0.247339, No Obj: 0.003962, Avg Recall: 0.510417, count: 96 -Region Avg IOU: 0.430984, Class: 1.000000, Obj: 0.241577, No Obj: 0.003355, Avg Recall: 0.482759, count: 87 -Region Avg IOU: 0.476634, Class: 1.000000, Obj: 0.257056, No Obj: 0.004169, Avg Recall: 0.500000, count: 94 -Region Avg IOU: 0.459236, Class: 1.000000, Obj: 0.268308, No Obj: 0.004207, Avg Recall: 0.508621, count: 116 -2781: 0.498361, 0.474468 avg, 0.001000 rate, 17.761848 seconds, 711936 images -Loaded: 3.615255 seconds -Region Avg IOU: 0.414437, Class: 1.000000, Obj: 0.249875, No Obj: 0.004118, Avg Recall: 0.441176, count: 102 -Region Avg IOU: 0.488465, Class: 1.000000, Obj: 0.299563, No Obj: 0.004377, Avg Recall: 0.522124, count: 113 -Region Avg IOU: 0.500007, Class: 1.000000, Obj: 0.270873, No Obj: 0.004655, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.441514, Class: 1.000000, Obj: 0.261869, No Obj: 0.004277, Avg Recall: 0.467290, count: 107 -Region Avg IOU: 0.479792, Class: 1.000000, Obj: 0.254347, No Obj: 0.004439, Avg Recall: 0.450549, count: 91 -Region Avg IOU: 0.470008, Class: 1.000000, Obj: 0.256160, No Obj: 0.004480, Avg Recall: 0.529915, count: 117 -Region Avg IOU: 0.469721, Class: 1.000000, Obj: 0.237044, No Obj: 0.004371, Avg Recall: 0.516949, count: 118 -Region Avg IOU: 0.418670, Class: 1.000000, Obj: 0.262221, No Obj: 0.003936, Avg Recall: 0.418182, count: 110 -2782: 0.459985, 0.473020 avg, 0.001000 rate, 15.504683 seconds, 712192 images -Loaded: 5.959944 seconds -Region Avg IOU: 0.462815, Class: 1.000000, Obj: 0.258544, No Obj: 0.004208, Avg Recall: 0.489583, count: 96 -Region Avg IOU: 0.476857, Class: 1.000000, Obj: 0.284178, No Obj: 0.004250, Avg Recall: 0.521277, count: 94 -Region Avg IOU: 0.484929, Class: 1.000000, Obj: 0.281667, No Obj: 0.004738, Avg Recall: 0.548387, count: 124 -Region Avg IOU: 0.442442, Class: 1.000000, Obj: 0.296011, No Obj: 0.005126, Avg Recall: 0.456140, count: 114 -Region Avg IOU: 0.472846, Class: 1.000000, Obj: 0.283100, No Obj: 0.004418, Avg Recall: 0.523810, count: 105 -Region Avg IOU: 0.460376, Class: 1.000000, Obj: 0.242787, No Obj: 0.004627, Avg Recall: 0.504425, count: 113 -Region Avg IOU: 0.442578, Class: 1.000000, Obj: 0.288945, No Obj: 0.003866, Avg Recall: 0.425287, count: 87 -Region Avg IOU: 0.491282, Class: 1.000000, Obj: 0.291089, No Obj: 0.004967, Avg Recall: 0.545455, count: 110 -2783: 0.477694, 0.473487 avg, 0.001000 rate, 13.490287 seconds, 712448 images -Loaded: 8.163991 seconds -Region Avg IOU: 0.501678, Class: 1.000000, Obj: 0.303056, No Obj: 0.004720, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.436408, Class: 1.000000, Obj: 0.256959, No Obj: 0.004226, Avg Recall: 0.464286, count: 84 -Region Avg IOU: 0.463133, Class: 1.000000, Obj: 0.317093, No Obj: 0.004845, Avg Recall: 0.522124, count: 113 -Region Avg IOU: 0.495348, Class: 1.000000, Obj: 0.297244, No Obj: 0.005031, Avg Recall: 0.546392, count: 97 -Region Avg IOU: 0.458454, Class: 1.000000, Obj: 0.256035, No Obj: 0.004749, Avg Recall: 0.465347, count: 101 -Region Avg IOU: 0.420677, Class: 1.000000, Obj: 0.270620, No Obj: 0.004536, Avg Recall: 0.467290, count: 107 -Region Avg IOU: 0.474934, Class: 1.000000, Obj: 0.306246, No Obj: 0.005786, Avg Recall: 0.500000, count: 132 -Region Avg IOU: 0.457548, Class: 1.000000, Obj: 0.271076, No Obj: 0.005012, Avg Recall: 0.465116, count: 129 -2784: 0.533934, 0.479532 avg, 0.001000 rate, 18.628305 seconds, 712704 images -Loaded: 2.859071 seconds -Region Avg IOU: 0.446931, Class: 1.000000, Obj: 0.324431, No Obj: 0.005030, Avg Recall: 0.511111, count: 90 -Region Avg IOU: 0.428536, Class: 1.000000, Obj: 0.271980, No Obj: 0.004625, Avg Recall: 0.395604, count: 91 -Region Avg IOU: 0.483502, Class: 1.000000, Obj: 0.309767, No Obj: 0.005156, Avg Recall: 0.534483, count: 116 -Region Avg IOU: 0.465357, Class: 1.000000, Obj: 0.276496, No Obj: 0.004881, Avg Recall: 0.483871, count: 93 -Region Avg IOU: 0.458772, Class: 1.000000, Obj: 0.300804, No Obj: 0.004936, Avg Recall: 0.470588, count: 119 -Region Avg IOU: 0.483921, Class: 1.000000, Obj: 0.312535, No Obj: 0.005419, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.478786, Class: 1.000000, Obj: 0.289834, No Obj: 0.004781, Avg Recall: 0.567797, count: 118 -Region Avg IOU: 0.427561, Class: 1.000000, Obj: 0.270151, No Obj: 0.005071, Avg Recall: 0.469027, count: 113 -2785: 0.437912, 0.475370 avg, 0.001000 rate, 15.705800 seconds, 712960 images -Loaded: 4.919912 seconds -Region Avg IOU: 0.440377, Class: 1.000000, Obj: 0.289510, No Obj: 0.005676, Avg Recall: 0.449153, count: 118 -Region Avg IOU: 0.411568, Class: 1.000000, Obj: 0.308165, No Obj: 0.005421, Avg Recall: 0.489130, count: 92 -Region Avg IOU: 0.522005, Class: 1.000000, Obj: 0.350295, No Obj: 0.005300, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.454755, Class: 1.000000, Obj: 0.342012, No Obj: 0.005562, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.444746, Class: 1.000000, Obj: 0.323195, No Obj: 0.005019, Avg Recall: 0.463918, count: 97 -Region Avg IOU: 0.427774, Class: 1.000000, Obj: 0.287850, No Obj: 0.005021, Avg Recall: 0.447917, count: 96 -Region Avg IOU: 0.416663, Class: 1.000000, Obj: 0.299887, No Obj: 0.005227, Avg Recall: 0.436508, count: 126 -Region Avg IOU: 0.437811, Class: 1.000000, Obj: 0.289541, No Obj: 0.004009, Avg Recall: 0.443182, count: 88 -2786: 0.497139, 0.477547 avg, 0.001000 rate, 16.305222 seconds, 713216 images -Loaded: 3.967123 seconds -Region Avg IOU: 0.392712, Class: 1.000000, Obj: 0.280168, No Obj: 0.004999, Avg Recall: 0.402985, count: 134 -Region Avg IOU: 0.431235, Class: 1.000000, Obj: 0.295379, No Obj: 0.004944, Avg Recall: 0.467890, count: 109 -Region Avg IOU: 0.427528, Class: 1.000000, Obj: 0.308196, No Obj: 0.005290, Avg Recall: 0.442478, count: 113 -Region Avg IOU: 0.491381, Class: 1.000000, Obj: 0.329035, No Obj: 0.005154, Avg Recall: 0.504762, count: 105 -Region Avg IOU: 0.446714, Class: 1.000000, Obj: 0.311815, No Obj: 0.005584, Avg Recall: 0.486957, count: 115 -Region Avg IOU: 0.475960, Class: 1.000000, Obj: 0.347410, No Obj: 0.005502, Avg Recall: 0.518182, count: 110 -Region Avg IOU: 0.374513, Class: 1.000000, Obj: 0.287464, No Obj: 0.004523, Avg Recall: 0.376147, count: 109 -Region Avg IOU: 0.405957, Class: 1.000000, Obj: 0.322892, No Obj: 0.004940, Avg Recall: 0.387755, count: 98 -2787: 0.534307, 0.483223 avg, 0.001000 rate, 16.976645 seconds, 713472 images -Loaded: 4.474142 seconds -Region Avg IOU: 0.438044, Class: 1.000000, Obj: 0.306248, No Obj: 0.004582, Avg Recall: 0.410714, count: 112 -Region Avg IOU: 0.456633, Class: 1.000000, Obj: 0.315534, No Obj: 0.005626, Avg Recall: 0.504132, count: 121 -Region Avg IOU: 0.411730, Class: 1.000000, Obj: 0.325066, No Obj: 0.005490, Avg Recall: 0.384615, count: 117 -Region Avg IOU: 0.461895, Class: 1.000000, Obj: 0.338990, No Obj: 0.005713, Avg Recall: 0.508065, count: 124 -Region Avg IOU: 0.421083, Class: 1.000000, Obj: 0.288072, No Obj: 0.005058, Avg Recall: 0.459184, count: 98 -Region Avg IOU: 0.477124, Class: 1.000000, Obj: 0.298994, No Obj: 0.005002, Avg Recall: 0.521739, count: 92 -Region Avg IOU: 0.460772, Class: 1.000000, Obj: 0.341799, No Obj: 0.005705, Avg Recall: 0.503817, count: 131 -Region Avg IOU: 0.424196, Class: 1.000000, Obj: 0.307804, No Obj: 0.005208, Avg Recall: 0.446281, count: 121 -2788: 0.608193, 0.495720 avg, 0.001000 rate, 19.874283 seconds, 713728 images -Loaded: 1.388888 seconds -Region Avg IOU: 0.439435, Class: 1.000000, Obj: 0.324363, No Obj: 0.005230, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.502259, Class: 1.000000, Obj: 0.346388, No Obj: 0.005115, Avg Recall: 0.583333, count: 96 -Region Avg IOU: 0.485035, Class: 1.000000, Obj: 0.329387, No Obj: 0.005674, Avg Recall: 0.542373, count: 118 -Region Avg IOU: 0.389966, Class: 1.000000, Obj: 0.321900, No Obj: 0.005077, Avg Recall: 0.414141, count: 99 -Region Avg IOU: 0.464069, Class: 1.000000, Obj: 0.359340, No Obj: 0.005469, Avg Recall: 0.504425, count: 113 -Region Avg IOU: 0.489969, Class: 1.000000, Obj: 0.344177, No Obj: 0.006110, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.467220, Class: 1.000000, Obj: 0.353502, No Obj: 0.005289, Avg Recall: 0.477064, count: 109 -Region Avg IOU: 0.407352, Class: 1.000000, Obj: 0.330817, No Obj: 0.004892, Avg Recall: 0.425532, count: 94 -2789: 0.490794, 0.495227 avg, 0.001000 rate, 18.345196 seconds, 713984 images -Loaded: 3.139035 seconds -Region Avg IOU: 0.376455, Class: 1.000000, Obj: 0.259319, No Obj: 0.005268, Avg Recall: 0.407407, count: 108 -Region Avg IOU: 0.409469, Class: 1.000000, Obj: 0.253602, No Obj: 0.004495, Avg Recall: 0.460000, count: 100 -Region Avg IOU: 0.428297, Class: 1.000000, Obj: 0.309938, No Obj: 0.004736, Avg Recall: 0.462264, count: 106 -Region Avg IOU: 0.478121, Class: 1.000000, Obj: 0.385293, No Obj: 0.005263, Avg Recall: 0.500000, count: 86 -Region Avg IOU: 0.412981, Class: 1.000000, Obj: 0.298218, No Obj: 0.005862, Avg Recall: 0.380165, count: 121 -Region Avg IOU: 0.490990, Class: 1.000000, Obj: 0.347757, No Obj: 0.004730, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.411910, Class: 1.000000, Obj: 0.328416, No Obj: 0.005035, Avg Recall: 0.396396, count: 111 -Region Avg IOU: 0.461318, Class: 1.000000, Obj: 0.291354, No Obj: 0.005513, Avg Recall: 0.520661, count: 121 -2790: 0.451629, 0.490868 avg, 0.001000 rate, 13.867722 seconds, 714240 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000067 seconds -Region Avg IOU: 0.551232, Class: 1.000000, Obj: 0.414093, No Obj: 0.003520, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.557131, Class: 1.000000, Obj: 0.375927, No Obj: 0.003370, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.588861, Class: 1.000000, Obj: 0.357894, No Obj: 0.004329, Avg Recall: 0.737589, count: 141 -Region Avg IOU: 0.574424, Class: 1.000000, Obj: 0.380020, No Obj: 0.004057, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.603099, Class: 1.000000, Obj: 0.411840, No Obj: 0.003902, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.569205, Class: 1.000000, Obj: 0.403306, No Obj: 0.003456, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.542224, Class: 1.000000, Obj: 0.427025, No Obj: 0.003026, Avg Recall: 0.568182, count: 88 -Region Avg IOU: 0.624160, Class: 1.000000, Obj: 0.402820, No Obj: 0.003142, Avg Recall: 0.776316, count: 76 -2791: 0.503635, 0.492144 avg, 0.001000 rate, 27.459187 seconds, 714496 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.615239, Class: 1.000000, Obj: 0.354610, No Obj: 0.003754, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.544389, Class: 1.000000, Obj: 0.321104, No Obj: 0.003030, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.582744, Class: 1.000000, Obj: 0.377187, No Obj: 0.003386, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.520998, Class: 1.000000, Obj: 0.337840, No Obj: 0.003004, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.615870, Class: 1.000000, Obj: 0.383059, No Obj: 0.003752, Avg Recall: 0.804878, count: 123 -Region Avg IOU: 0.559095, Class: 1.000000, Obj: 0.411535, No Obj: 0.003032, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.630225, Class: 1.000000, Obj: 0.402545, No Obj: 0.003009, Avg Recall: 0.820513, count: 78 -Region Avg IOU: 0.566847, Class: 1.000000, Obj: 0.382414, No Obj: 0.003112, Avg Recall: 0.633663, count: 101 -2792: 0.475871, 0.490517 avg, 0.001000 rate, 26.193205 seconds, 714752 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.563969, Class: 1.000000, Obj: 0.343012, No Obj: 0.002591, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.590199, Class: 1.000000, Obj: 0.340325, No Obj: 0.002713, Avg Recall: 0.710843, count: 83 -Region Avg IOU: 0.602567, Class: 1.000000, Obj: 0.375232, No Obj: 0.002749, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.613043, Class: 1.000000, Obj: 0.380928, No Obj: 0.002786, Avg Recall: 0.817204, count: 93 -Region Avg IOU: 0.585452, Class: 1.000000, Obj: 0.362479, No Obj: 0.002977, Avg Recall: 0.752066, count: 121 -Region Avg IOU: 0.546324, Class: 1.000000, Obj: 0.331464, No Obj: 0.002698, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.589924, Class: 1.000000, Obj: 0.336226, No Obj: 0.002885, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.580132, Class: 1.000000, Obj: 0.311330, No Obj: 0.002797, Avg Recall: 0.698113, count: 106 -2793: 0.434160, 0.484881 avg, 0.001000 rate, 27.487007 seconds, 715008 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.603537, Class: 1.000000, Obj: 0.304710, No Obj: 0.002368, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.595328, Class: 1.000000, Obj: 0.348479, No Obj: 0.002351, Avg Recall: 0.746988, count: 83 -Region Avg IOU: 0.588765, Class: 1.000000, Obj: 0.271047, No Obj: 0.002921, Avg Recall: 0.677686, count: 121 -Region Avg IOU: 0.556845, Class: 1.000000, Obj: 0.277584, No Obj: 0.002662, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.590420, Class: 1.000000, Obj: 0.298076, No Obj: 0.002682, Avg Recall: 0.682927, count: 123 -Region Avg IOU: 0.547757, Class: 1.000000, Obj: 0.275579, No Obj: 0.002456, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.600427, Class: 1.000000, Obj: 0.335005, No Obj: 0.002742, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.612835, Class: 1.000000, Obj: 0.322690, No Obj: 0.002294, Avg Recall: 0.746667, count: 75 -2794: 0.463942, 0.482787 avg, 0.001000 rate, 26.085173 seconds, 715264 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.620057, Class: 1.000000, Obj: 0.278880, No Obj: 0.002286, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.590133, Class: 1.000000, Obj: 0.282208, No Obj: 0.002310, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.605204, Class: 1.000000, Obj: 0.240606, No Obj: 0.002357, Avg Recall: 0.736434, count: 129 -Region Avg IOU: 0.585631, Class: 1.000000, Obj: 0.266594, No Obj: 0.002319, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.588163, Class: 1.000000, Obj: 0.270904, No Obj: 0.002279, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.588161, Class: 1.000000, Obj: 0.254915, No Obj: 0.002488, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.575781, Class: 1.000000, Obj: 0.256266, No Obj: 0.002533, Avg Recall: 0.687500, count: 128 -Region Avg IOU: 0.613935, Class: 1.000000, Obj: 0.304592, No Obj: 0.002593, Avg Recall: 0.763780, count: 127 -2795: 0.516511, 0.486160 avg, 0.001000 rate, 26.093107 seconds, 715520 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.587287, Class: 1.000000, Obj: 0.235297, No Obj: 0.002172, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.640021, Class: 1.000000, Obj: 0.310209, No Obj: 0.002575, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.598919, Class: 1.000000, Obj: 0.292861, No Obj: 0.002465, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.598020, Class: 1.000000, Obj: 0.286283, No Obj: 0.002341, Avg Recall: 0.728000, count: 125 -Region Avg IOU: 0.564301, Class: 1.000000, Obj: 0.295589, No Obj: 0.002567, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.551461, Class: 1.000000, Obj: 0.278913, No Obj: 0.002288, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.556459, Class: 1.000000, Obj: 0.266897, No Obj: 0.002185, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.592580, Class: 1.000000, Obj: 0.241233, No Obj: 0.001927, Avg Recall: 0.721519, count: 79 -2796: 0.486010, 0.486145 avg, 0.001000 rate, 26.018950 seconds, 715776 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.602071, Class: 1.000000, Obj: 0.298393, No Obj: 0.002376, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.582549, Class: 1.000000, Obj: 0.277679, No Obj: 0.002496, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.590712, Class: 1.000000, Obj: 0.293840, No Obj: 0.002759, Avg Recall: 0.719697, count: 132 -Region Avg IOU: 0.585215, Class: 1.000000, Obj: 0.263257, No Obj: 0.002273, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.620444, Class: 1.000000, Obj: 0.327293, No Obj: 0.002341, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.577315, Class: 1.000000, Obj: 0.281654, No Obj: 0.002390, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.607684, Class: 1.000000, Obj: 0.291907, No Obj: 0.002187, Avg Recall: 0.693182, count: 88 -Region Avg IOU: 0.630155, Class: 1.000000, Obj: 0.291217, No Obj: 0.002644, Avg Recall: 0.740458, count: 131 -2797: 0.486343, 0.486165 avg, 0.001000 rate, 26.616711 seconds, 716032 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.581753, Class: 1.000000, Obj: 0.312692, No Obj: 0.002331, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.637412, Class: 1.000000, Obj: 0.325655, No Obj: 0.002895, Avg Recall: 0.808511, count: 141 -Region Avg IOU: 0.639519, Class: 1.000000, Obj: 0.306432, No Obj: 0.003062, Avg Recall: 0.813559, count: 118 -Region Avg IOU: 0.620444, Class: 1.000000, Obj: 0.297239, No Obj: 0.003085, Avg Recall: 0.750000, count: 136 -Region Avg IOU: 0.572895, Class: 1.000000, Obj: 0.284396, No Obj: 0.002870, Avg Recall: 0.690476, count: 126 -Region Avg IOU: 0.607694, Class: 1.000000, Obj: 0.353373, No Obj: 0.002711, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.590105, Class: 1.000000, Obj: 0.297035, No Obj: 0.002499, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.598155, Class: 1.000000, Obj: 0.345213, No Obj: 0.002277, Avg Recall: 0.691358, count: 81 -2798: 0.483263, 0.485874 avg, 0.001000 rate, 27.104082 seconds, 716288 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.587541, Class: 1.000000, Obj: 0.317668, No Obj: 0.003238, Avg Recall: 0.682927, count: 123 -Region Avg IOU: 0.574889, Class: 1.000000, Obj: 0.354869, No Obj: 0.002616, Avg Recall: 0.702381, count: 84 -Region Avg IOU: 0.629533, Class: 1.000000, Obj: 0.388679, No Obj: 0.002462, Avg Recall: 0.761905, count: 84 -Region Avg IOU: 0.617774, Class: 1.000000, Obj: 0.Saving weights to ./person9300/backup/tyvp_93_9c512_2800.weights -358281, No Obj: 0.003160, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.594657, Class: 1.000000, Obj: 0.388726, No Obj: 0.003310, Avg Recall: 0.739130, count: 138 -Region Avg IOU: 0.622778, Class: 1.000000, Obj: 0.365151, No Obj: 0.002983, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.620152, Class: 1.000000, Obj: 0.386245, No Obj: 0.003151, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.596372, Class: 1.000000, Obj: 0.350245, No Obj: 0.003291, Avg Recall: 0.742647, count: 136 -2799: 0.531805, 0.490467 avg, 0.001000 rate, 26.162931 seconds, 716544 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.595176, Class: 1.000000, Obj: 0.326866, No Obj: 0.003265, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.568297, Class: 1.000000, Obj: 0.392108, No Obj: 0.003029, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.567351, Class: 1.000000, Obj: 0.346276, No Obj: 0.003788, Avg Recall: 0.664122, count: 131 -Region Avg IOU: 0.583691, Class: 1.000000, Obj: 0.322413, No Obj: 0.003401, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.613136, Class: 1.000000, Obj: 0.392216, No Obj: 0.003617, Avg Recall: 0.734375, count: 128 -Region Avg IOU: 0.623291, Class: 1.000000, Obj: 0.419987, No Obj: 0.002995, Avg Recall: 0.811111, count: 90 -Region Avg IOU: 0.589598, Class: 1.000000, Obj: 0.395648, No Obj: 0.003076, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.560125, Class: 1.000000, Obj: 0.351859, No Obj: 0.003016, Avg Recall: 0.704082, count: 98 -2800: 0.467529, 0.488174 avg, 0.001000 rate, 26.754496 seconds, 716800 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 17.656868 seconds -Region Avg IOU: 0.518976, Class: 1.000000, Obj: 0.363464, No Obj: 0.003432, Avg Recall: 0.550562, count: 89 -Region Avg IOU: 0.558552, Class: 1.000000, Obj: 0.337115, No Obj: 0.004712, Avg Recall: 0.640845, count: 142 -Region Avg IOU: 0.590554, Class: 1.000000, Obj: 0.337157, No Obj: 0.003867, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.532729, Class: 1.000000, Obj: 0.354213, No Obj: 0.004066, Avg Recall: 0.601626, count: 123 -Region Avg IOU: 0.565654, Class: 1.000000, Obj: 0.308705, No Obj: 0.003859, Avg Recall: 0.651163, count: 129 -Region Avg IOU: 0.592410, Class: 1.000000, Obj: 0.330467, No Obj: 0.003387, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.521012, Class: 1.000000, Obj: 0.329308, No Obj: 0.003885, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.598038, Class: 1.000000, Obj: 0.367161, No Obj: 0.004240, Avg Recall: 0.750000, count: 100 -2801: 0.526407, 0.491997 avg, 0.001000 rate, 24.265396 seconds, 717056 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.608878, Class: 1.000000, Obj: 0.417028, No Obj: 0.004167, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.587363, Class: 1.000000, Obj: 0.384211, No Obj: 0.004312, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.586845, Class: 1.000000, Obj: 0.390774, No Obj: 0.004580, Avg Recall: 0.691667, count: 120 -Region Avg IOU: 0.551001, Class: 1.000000, Obj: 0.361914, No Obj: 0.004386, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.538391, Class: 1.000000, Obj: 0.389255, No Obj: 0.003603, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.581559, Class: 1.000000, Obj: 0.376835, No Obj: 0.004739, Avg Recall: 0.688525, count: 122 -Region Avg IOU: 0.598013, Class: 1.000000, Obj: 0.389972, No Obj: 0.004176, Avg Recall: 0.691358, count: 81 -Region Avg IOU: 0.523814, Class: 1.000000, Obj: 0.368308, No Obj: 0.003890, Avg Recall: 0.600000, count: 105 -2802: 0.433935, 0.486191 avg, 0.001000 rate, 24.519947 seconds, 717312 images -Loaded: 0.000052 seconds -Region Avg IOU: 0.504733, Class: 1.000000, Obj: 0.347028, No Obj: 0.004375, Avg Recall: 0.541353, count: 133 -Region Avg IOU: 0.573369, Class: 1.000000, Obj: 0.371326, No Obj: 0.004519, Avg Recall: 0.635659, count: 129 -Region Avg IOU: 0.553509, Class: 1.000000, Obj: 0.400562, No Obj: 0.004115, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.505479, Class: 1.000000, Obj: 0.352387, No Obj: 0.004062, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.594277, Class: 1.000000, Obj: 0.426257, No Obj: 0.003999, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.579298, Class: 1.000000, Obj: 0.419039, No Obj: 0.004497, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.593215, Class: 1.000000, Obj: 0.398920, No Obj: 0.005033, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.533370, Class: 1.000000, Obj: 0.359662, No Obj: 0.004838, Avg Recall: 0.631579, count: 114 -2803: 0.445294, 0.482101 avg, 0.001000 rate, 22.597816 seconds, 717568 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.540707, Class: 1.000000, Obj: 0.394636, No Obj: 0.004314, Avg Recall: 0.613445, count: 119 -Region Avg IOU: 0.568291, Class: 1.000000, Obj: 0.397560, No Obj: 0.004873, Avg Recall: 0.648855, count: 131 -Region Avg IOU: 0.541917, Class: 1.000000, Obj: 0.373989, No Obj: 0.004212, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.514465, Class: 1.000000, Obj: 0.345051, No Obj: 0.004304, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.521214, Class: 1.000000, Obj: 0.374782, No Obj: 0.004099, Avg Recall: 0.593750, count: 96 -Region Avg IOU: 0.521965, Class: 1.000000, Obj: 0.336045, No Obj: 0.004802, Avg Recall: 0.580645, count: 124 -Region Avg IOU: 0.495971, Class: 1.000000, Obj: 0.351351, No Obj: 0.003896, Avg Recall: 0.528455, count: 123 -Region Avg IOU: 0.512447, Class: 1.000000, Obj: 0.327156, No Obj: 0.004537, Avg Recall: 0.554688, count: 128 -2804: 0.468034, 0.480694 avg, 0.001000 rate, 23.448896 seconds, 717824 images -Loaded: 0.000057 seconds -Region Avg IOU: 0.521799, Class: 1.000000, Obj: 0.365196, No Obj: 0.004286, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.499792, Class: 1.000000, Obj: 0.360591, No Obj: 0.003582, Avg Recall: 0.584416, count: 77 -Region Avg IOU: 0.484972, Class: 1.000000, Obj: 0.357021, No Obj: 0.003933, Avg Recall: 0.528846, count: 104 -Region Avg IOU: 0.550104, Class: 1.000000, Obj: 0.402418, No Obj: 0.004232, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.510867, Class: 1.000000, Obj: 0.374343, No Obj: 0.004235, Avg Recall: 0.552000, count: 125 -Region Avg IOU: 0.513137, Class: 1.000000, Obj: 0.361750, No Obj: 0.004406, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.546666, Class: 1.000000, Obj: 0.429957, No Obj: 0.004319, Avg Recall: 0.642857, count: 84 -Region Avg IOU: 0.523575, Class: 1.000000, Obj: 0.330445, No Obj: 0.003529, Avg Recall: 0.567308, count: 104 -2805: 0.421963, 0.474821 avg, 0.001000 rate, 22.057585 seconds, 718080 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.538160, Class: 1.000000, Obj: 0.325322, No Obj: 0.003854, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.540098, Class: 1.000000, Obj: 0.331751, No Obj: 0.003826, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.499067, Class: 1.000000, Obj: 0.368185, No Obj: 0.003743, Avg Recall: 0.525424, count: 118 -Region Avg IOU: 0.479047, Class: 1.000000, Obj: 0.325695, No Obj: 0.003365, Avg Recall: 0.562500, count: 80 -Region Avg IOU: 0.537975, Class: 1.000000, Obj: 0.388176, No Obj: 0.003382, Avg Recall: 0.595238, count: 84 -Region Avg IOU: 0.567914, Class: 1.000000, Obj: 0.296101, No Obj: 0.003109, Avg Recall: 0.683544, count: 79 -Region Avg IOU: 0.534981, Class: 1.000000, Obj: 0.337345, No Obj: 0.004142, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.553308, Class: 1.000000, Obj: 0.337572, No Obj: 0.004008, Avg Recall: 0.663462, count: 104 -2806: 0.477268, 0.475066 avg, 0.001000 rate, 23.755541 seconds, 718336 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.502709, Class: 1.000000, Obj: 0.271036, No Obj: 0.003678, Avg Recall: 0.577465, count: 142 -Region Avg IOU: 0.498821, Class: 1.000000, Obj: 0.331877, No Obj: 0.004307, Avg Recall: 0.550725, count: 138 -Region Avg IOU: 0.493574, Class: 1.000000, Obj: 0.311555, No Obj: 0.003494, Avg Recall: 0.563025, count: 119 -Region Avg IOU: 0.521281, Class: 1.000000, Obj: 0.319011, No Obj: 0.003744, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.541880, Class: 1.000000, Obj: 0.301852, No Obj: 0.004147, Avg Recall: 0.630435, count: 138 -Region Avg IOU: 0.475028, Class: 1.000000, Obj: 0.316234, No Obj: 0.002892, Avg Recall: 0.510638, count: 94 -Region Avg IOU: 0.487990, Class: 1.000000, Obj: 0.319128, No Obj: 0.003887, Avg Recall: 0.493333, count: 150 -Region Avg IOU: 0.479864, Class: 1.000000, Obj: 0.303786, No Obj: 0.003735, Avg Recall: 0.480916, count: 131 -2807: 0.551155, 0.482675 avg, 0.001000 rate, 22.584784 seconds, 718592 images -Loaded: 0.000135 seconds -Region Avg IOU: 0.535396, Class: 1.000000, Obj: 0.307004, No Obj: 0.003148, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.526367, Class: 1.000000, Obj: 0.319669, No Obj: 0.002890, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.512925, Class: 1.000000, Obj: 0.288859, No Obj: 0.002695, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.574313, Class: 1.000000, Obj: 0.311404, No Obj: 0.003312, Avg Recall: 0.696629, count: 89 -Region Avg IOU: 0.527689, Class: 1.000000, Obj: 0.318019, No Obj: 0.003177, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.421613, Class: 1.000000, Obj: 0.273744, No Obj: 0.003012, Avg Recall: 0.411215, count: 107 -Region Avg IOU: 0.558726, Class: 1.000000, Obj: 0.333190, No Obj: 0.003472, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.541018, Class: 1.000000, Obj: 0.306121, No Obj: 0.003744, Avg Recall: 0.647059, count: 119 -2808: 0.488774, 0.483285 avg, 0.001000 rate, 22.762043 seconds, 718848 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.538126, Class: 1.000000, Obj: 0.386523, No Obj: 0.002879, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.512945, Class: 1.000000, Obj: 0.292537, No Obj: 0.002847, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.595461, Class: 1.000000, Obj: 0.328542, No Obj: 0.003124, Avg Recall: 0.744186, count: 86 -Region Avg IOU: 0.600045, Class: 1.000000, Obj: 0.328433, No Obj: 0.003604, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.551074, Class: 1.000000, Obj: 0.319003, No Obj: 0.003451, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.511801, Class: 1.000000, Obj: 0.299358, No Obj: 0.003170, Avg Recall: 0.616279, count: 86 -Region Avg IOU: 0.536963, Class: 1.000000, Obj: 0.294210, No Obj: 0.002800, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.517371, Class: 1.000000, Obj: 0.298400, No Obj: 0.003300, Avg Recall: 0.626263, count: 99 -2809: 0.413993, 0.476356 avg, 0.001000 rate, 23.297440 seconds, 719104 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.510557, Class: 1.000000, Obj: 0.289697, No Obj: 0.002762, Avg Recall: 0.582278, count: 79 -Region Avg IOU: 0.543677, Class: 1.000000, Obj: 0.341705, No Obj: 0.002884, Avg Recall: 0.615385, count: 91 -Region Avg IOU: 0.580330, Class: 1.000000, Obj: 0.340640, No Obj: 0.003103, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.576610, Class: 1.000000, Obj: 0.296006, No Obj: 0.003451, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.559553, Class: 1.000000, Obj: 0.346928, No Obj: 0.003090, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.544119, Class: 1.000000, Obj: 0.318136, No Obj: 0.003564, Avg Recall: 0.641026, count: 117 -Region Avg IOU: 0.511054, Class: 1.000000, Obj: 0.285297, No Obj: 0.003230, Avg Recall: 0.575000, count: 120 -Region Avg IOU: 0.539647, Class: 1.000000, Obj: 0.345822, No Obj: 0.003042, Avg Recall: 0.687500, count: 80 -2810: 0.399885, 0.468709 avg, 0.001000 rate, 24.128336 seconds, 719360 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000071 seconds -Region Avg IOU: 0.579966, Class: 1.000000, Obj: 0.315971, No Obj: 0.002969, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.571803, Class: 1.000000, Obj: 0.346417, No Obj: 0.003293, Avg Recall: 0.664122, count: 131 -Region Avg IOU: 0.574546, Class: 1.000000, Obj: 0.330573, No Obj: 0.003220, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.574196, Class: 1.000000, Obj: 0.378838, No Obj: 0.002919, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.547184, Class: 1.000000, Obj: 0.305029, No Obj: 0.003806, Avg Recall: 0.635036, count: 137 -Region Avg IOU: 0.572782, Class: 1.000000, Obj: 0.309969, No Obj: 0.003112, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.534782, Class: 1.000000, Obj: 0.310768, No Obj: 0.002870, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.577884, Class: 1.000000, Obj: 0.298257, No Obj: 0.002933, Avg Recall: 0.653846, count: 104 -2811: 0.502257, 0.472063 avg, 0.001000 rate, 25.617098 seconds, 719616 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.589881, Class: 1.000000, Obj: 0.382279, No Obj: 0.002849, Avg Recall: 0.687500, count: 80 -Region Avg IOU: 0.618421, Class: 1.000000, Obj: 0.366792, No Obj: 0.003392, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.606245, Class: 1.000000, Obj: 0.348362, No Obj: 0.003217, Avg Recall: 0.727273, count: 77 -Region Avg IOU: 0.606934, Class: 1.000000, Obj: 0.380528, No Obj: 0.003729, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.629593, Class: 1.000000, Obj: 0.367073, No Obj: 0.003121, Avg Recall: 0.811765, count: 85 -Region Avg IOU: 0.576157, Class: 1.000000, Obj: 0.343623, No Obj: 0.003356, Avg Recall: 0.685039, count: 127 -Region Avg IOU: 0.572996, Class: 1.000000, Obj: 0.335825, No Obj: 0.003532, Avg Recall: 0.679389, count: 131 -Region Avg IOU: 0.597955, Class: 1.000000, Obj: 0.368177, No Obj: 0.003322, Avg Recall: 0.717742, count: 124 -2812: 0.424170, 0.467274 avg, 0.001000 rate, 24.704184 seconds, 719872 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.574245, Class: 1.000000, Obj: 0.359324, No Obj: 0.003028, Avg Recall: 0.626374, count: 91 -Region Avg IOU: 0.598317, Class: 1.000000, Obj: 0.303873, No Obj: 0.003129, Avg Recall: 0.711111, count: 90 -Region Avg IOU: 0.626195, Class: 1.000000, Obj: 0.387245, No Obj: 0.003759, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.607215, Class: 1.000000, Obj: 0.404823, No Obj: 0.003453, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.597252, Class: 1.000000, Obj: 0.343579, No Obj: 0.003233, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.593876, Class: 1.000000, Obj: 0.344660, No Obj: 0.002930, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.570947, Class: 1.000000, Obj: 0.349715, No Obj: 0.003490, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.557787, Class: 1.000000, Obj: 0.373607, No Obj: 0.003170, Avg Recall: 0.660377, count: 106 -2813: 0.417048, 0.462251 avg, 0.001000 rate, 25.681959 seconds, 720128 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.543928, Class: 1.000000, Obj: 0.324766, No Obj: 0.003298, Avg Recall: 0.641026, count: 117 -Region Avg IOU: 0.591626, Class: 1.000000, Obj: 0.426600, No Obj: 0.003693, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.625516, Class: 1.000000, Obj: 0.361400, No Obj: 0.004125, Avg Recall: 0.748148, count: 135 -Region Avg IOU: 0.620117, Class: 1.000000, Obj: 0.386360, No Obj: 0.003447, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.610394, Class: 1.000000, Obj: 0.370346, No Obj: 0.003743, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.583855, Class: 1.000000, Obj: 0.324414, No Obj: 0.003987, Avg Recall: 0.685714, count: 140 -Region Avg IOU: 0.623470, Class: 1.000000, Obj: 0.431786, No Obj: 0.003148, Avg Recall: 0.722222, count: 90 -Region Avg IOU: 0.598316, Class: 1.000000, Obj: 0.359165, No Obj: 0.003777, Avg Recall: 0.707965, count: 113 -2814: 0.496624, 0.465689 avg, 0.001000 rate, 26.699848 seconds, 720384 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.589758, Class: 1.000000, Obj: 0.353717, No Obj: 0.003985, Avg Recall: 0.733871, count: 124 -Region Avg IOU: 0.589232, Class: 1.000000, Obj: 0.390747, No Obj: 0.004554, Avg Recall: 0.708029, count: 137 -Region Avg IOU: 0.601864, Class: 1.000000, Obj: 0.361544, No Obj: 0.004229, Avg Recall: 0.731884, count: 138 -Region Avg IOU: 0.582346, Class: 1.000000, Obj: 0.380910, No Obj: 0.003815, Avg Recall: 0.658915, count: 129 -Region Avg IOU: 0.586182, Class: 1.000000, Obj: 0.425500, No Obj: 0.003850, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.585648, Class: 1.000000, Obj: 0.352049, No Obj: 0.003858, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.598194, Class: 1.000000, Obj: 0.385173, No Obj: 0.003672, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.576158, Class: 1.000000, Obj: 0.352757, No Obj: 0.003500, Avg Recall: 0.720430, count: 93 -2815: 0.532165, 0.472336 avg, 0.001000 rate, 25.958696 seconds, 720640 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.566175, Class: 1.000000, Obj: 0.378552, No Obj: 0.003482, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.578462, Class: 1.000000, Obj: 0.343830, No Obj: 0.004007, Avg Recall: 0.656250, count: 128 -Region Avg IOU: 0.547955, Class: 1.000000, Obj: 0.325087, No Obj: 0.004310, Avg Recall: 0.642857, count: 126 -Region Avg IOU: 0.607428, Class: 1.000000, Obj: 0.422016, No Obj: 0.003609, Avg Recall: 0.720930, count: 86 -Region Avg IOU: 0.577213, Class: 1.000000, Obj: 0.398134, No Obj: 0.003782, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.577928, Class: 1.000000, Obj: 0.368272, No Obj: 0.003848, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.562302, Class: 1.000000, Obj: 0.330954, No Obj: 0.003426, Avg Recall: 0.669355, count: 124 -Region Avg IOU: 0.626298, Class: 1.000000, Obj: 0.400159, No Obj: 0.003697, Avg Recall: 0.805310, count: 113 -2816: 0.472856, 0.472388 avg, 0.001000 rate, 23.493156 seconds, 720896 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.567149, Class: 1.000000, Obj: 0.365803, No Obj: 0.003487, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.606893, Class: 1.000000, Obj: 0.378672, No Obj: 0.003113, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.584524, Class: 1.000000, Obj: 0.353497, No Obj: 0.003793, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.623144, Class: 1.000000, Obj: 0.382792, No Obj: 0.003147, Avg Recall: 0.802469, count: 81 -Region Avg IOU: 0.612099, Class: 1.000000, Obj: 0.353169, No Obj: 0.004043, Avg Recall: 0.732283, count: 127 -Region Avg IOU: 0.599966, Class: 1.000000, Obj: 0.354386, No Obj: 0.003617, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.500671, Class: 1.000000, Obj: 0.369711, No Obj: 0.003717, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.556450, Class: 1.000000, Obj: 0.304240, No Obj: 0.003576, Avg Recall: 0.612613, count: 111 -2817: 0.423698, 0.467519 avg, 0.001000 rate, 24.686190 seconds, 721152 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.610267, Class: 1.000000, Obj: 0.386906, No Obj: 0.003459, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.590433, Class: 1.000000, Obj: 0.352703, No Obj: 0.003229, Avg Recall: 0.715909, count: 88 -Region Avg IOU: 0.542883, Class: 1.000000, Obj: 0.317602, No Obj: 0.003237, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.570201, Class: 1.000000, Obj: 0.359710, No Obj: 0.003157, Avg Recall: 0.632184, count: 87 -Region Avg IOU: 0.579647, Class: 1.000000, Obj: 0.321511, No Obj: 0.003645, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.547419, Class: 1.000000, Obj: 0.336470, No Obj: 0.003203, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.553865, Class: 1.000000, Obj: 0.327478, No Obj: 0.003551, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.576343, Class: 1.000000, Obj: 0.395787, No Obj: 0.003485, Avg Recall: 0.632075, count: 106 -2818: 0.456958, 0.466463 avg, 0.001000 rate, 24.361988 seconds, 721408 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.579092, Class: 1.000000, Obj: 0.297964, No Obj: 0.003372, Avg Recall: 0.691729, count: 133 -Region Avg IOU: 0.606841, Class: 1.000000, Obj: 0.363108, No Obj: 0.003170, Avg Recall: 0.744444, count: 90 -Region Avg IOU: 0.605476, Class: 1.000000, Obj: 0.371958, No Obj: 0.003155, Avg Recall: 0.724138, count: 87 -Region Avg IOU: 0.589609, Class: 1.000000, Obj: 0.354384, No Obj: 0.003399, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.611151, Class: 1.000000, Obj: 0.371823, No Obj: 0.003759, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.586854, Class: 1.000000, Obj: 0.349315, No Obj: 0.003556, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.544674, Class: 1.000000, Obj: 0.324090, No Obj: 0.003451, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.567059, Class: 1.000000, Obj: 0.351559, No Obj: 0.003338, Avg Recall: 0.651685, count: 89 -2819: 0.421978, 0.462015 avg, 0.001000 rate, 25.906004 seconds, 721664 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.593216, Class: 1.000000, Obj: 0.348846, No Obj: 0.003360, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.561129, Class: 1.000000, Obj: 0.354544, No Obj: 0.003264, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.594813, Class: 1.000000, Obj: 0.317924, No Obj: 0.003480, Avg Recall: 0.664000, count: 125 -Region Avg IOU: 0.564610, Class: 1.000000, Obj: 0.309929, No Obj: 0.003728, Avg Recall: 0.687500, count: 144 -Region Avg IOU: 0.557040, Class: 1.000000, Obj: 0.343353, No Obj: 0.003040, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.591309, Class: 1.000000, Obj: 0.358940, No Obj: 0.002747, Avg Recall: 0.687500, count: 80 -Region Avg IOU: 0.628955, Class: 1.000000, Obj: 0.356512, No Obj: 0.003216, Avg Recall: 0.818182, count: 88 -Region Avg IOU: 0.564866, Class: 1.000000, Obj: 0.330433, No Obj: 0.003368, Avg Recall: 0.661157, count: 121 -2820: 0.473292, 0.463142 avg, 0.001000 rate, 24.844460 seconds, 721920 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.322576 seconds -Region Avg IOU: 0.495185, Class: 1.000000, Obj: 0.307558, No Obj: 0.003126, Avg Recall: 0.580952, count: 105 -Region Avg IOU: 0.527653, Class: 1.000000, Obj: 0.263886, No Obj: 0.003819, Avg Recall: 0.582734, count: 139 -Region Avg IOU: 0.516455, Class: 1.000000, Obj: 0.312250, No Obj: 0.003685, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.483094, Class: 1.000000, Obj: 0.312732, No Obj: 0.003578, Avg Recall: 0.504065, count: 123 -Region Avg IOU: 0.494259, Class: 1.000000, Obj: 0.269750, No Obj: 0.003899, Avg Recall: 0.545455, count: 143 -Region Avg IOU: 0.518268, Class: 1.000000, Obj: 0.340463, No Obj: 0.003253, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.531212, Class: 1.000000, Obj: 0.307270, No Obj: 0.003513, Avg Recall: 0.590361, count: 83 -Region Avg IOU: 0.479205, Class: 1.000000, Obj: 0.286918, No Obj: 0.003159, Avg Recall: 0.516484, count: 91 -2821: 0.505694, 0.467398 avg, 0.001000 rate, 22.876711 seconds, 722176 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.541524, Class: 1.000000, Obj: 0.306190, No Obj: 0.003894, Avg Recall: 0.637795, count: 127 -Region Avg IOU: 0.538410, Class: 1.000000, Obj: 0.303908, No Obj: 0.003635, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.546607, Class: 1.000000, Obj: 0.291301, No Obj: 0.003811, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.524286, Class: 1.000000, Obj: 0.301426, No Obj: 0.003686, Avg Recall: 0.631579, count: 133 -Region Avg IOU: 0.590795, Class: 1.000000, Obj: 0.322932, No Obj: 0.003299, Avg Recall: 0.720930, count: 86 -Region Avg IOU: 0.513962, Class: 1.000000, Obj: 0.304026, No Obj: 0.003527, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.542141, Class: 1.000000, Obj: 0.300191, No Obj: 0.003341, Avg Recall: 0.597938, count: 97 -Region Avg IOU: 0.491228, Class: 1.000000, Obj: 0.291457, No Obj: 0.002977, Avg Recall: 0.524752, count: 101 -2822: 0.547266, 0.475384 avg, 0.001000 rate, 22.786364 seconds, 722432 images -Loaded: 1.471077 seconds -Region Avg IOU: 0.479916, Class: 1.000000, Obj: 0.311207, No Obj: 0.003382, Avg Recall: 0.505050, count: 99 -Region Avg IOU: 0.521564, Class: 1.000000, Obj: 0.259508, No Obj: 0.003235, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.486876, Class: 1.000000, Obj: 0.306899, No Obj: 0.003100, Avg Recall: 0.504505, count: 111 -Region Avg IOU: 0.565367, Class: 1.000000, Obj: 0.297569, No Obj: 0.003824, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.545426, Class: 1.000000, Obj: 0.342108, No Obj: 0.003279, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.495670, Class: 1.000000, Obj: 0.326238, No Obj: 0.003186, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.558007, Class: 1.000000, Obj: 0.327066, No Obj: 0.004104, Avg Recall: 0.609929, count: 141 -Region Avg IOU: 0.521664, Class: 1.000000, Obj: 0.329022, No Obj: 0.004404, Avg Recall: 0.589928, count: 139 -2823: 0.482107, 0.476057 avg, 0.001000 rate, 22.182753 seconds, 722688 images -Loaded: 0.042282 seconds -Region Avg IOU: 0.526862, Class: 1.000000, Obj: 0.304458, No Obj: 0.004151, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.500784, Class: 1.000000, Obj: 0.293970, No Obj: 0.003785, Avg Recall: 0.546875, count: 128 -Region Avg IOU: 0.492072, Class: 1.000000, Obj: 0.293475, No Obj: 0.003674, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.572876, Class: 1.000000, Obj: 0.366195, No Obj: 0.004060, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.582483, Class: 1.000000, Obj: 0.342477, No Obj: 0.003605, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.561682, Class: 1.000000, Obj: 0.336889, No Obj: 0.003757, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.584287, Class: 1.000000, Obj: 0.366780, No Obj: 0.003594, Avg Recall: 0.714286, count: 77 -Region Avg IOU: 0.527042, Class: 1.000000, Obj: 0.309032, No Obj: 0.003398, Avg Recall: 0.601942, count: 103 -2824: 0.447056, 0.473157 avg, 0.001000 rate, 23.017807 seconds, 722944 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.512558, Class: 1.000000, Obj: 0.355099, No Obj: 0.003780, Avg Recall: 0.570248, count: 121 -Region Avg IOU: 0.547457, Class: 1.000000, Obj: 0.318142, No Obj: 0.004004, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.569660, Class: 1.000000, Obj: 0.314528, No Obj: 0.003798, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.524872, Class: 1.000000, Obj: 0.338447, No Obj: 0.004009, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.515913, Class: 1.000000, Obj: 0.321704, No Obj: 0.004225, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.519691, Class: 1.000000, Obj: 0.314075, No Obj: 0.003670, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.517313, Class: 1.000000, Obj: 0.291408, No Obj: 0.003349, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.548381, Class: 1.000000, Obj: 0.352751, No Obj: 0.004192, Avg Recall: 0.669565, count: 115 -2825: 0.466061, 0.472447 avg, 0.001000 rate, 23.251945 seconds, 723200 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.527146, Class: 1.000000, Obj: 0.323159, No Obj: 0.004238, Avg Recall: 0.552000, count: 125 -Region Avg IOU: 0.472969, Class: 1.000000, Obj: 0.281584, No Obj: 0.004227, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.560271, Class: 1.000000, Obj: 0.322298, No Obj: 0.003489, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.539949, Class: 1.000000, Obj: 0.370437, No Obj: 0.004391, Avg Recall: 0.607692, count: 130 -Region Avg IOU: 0.489931, Class: 1.000000, Obj: 0.317326, No Obj: 0.004295, Avg Recall: 0.557971, count: 138 -Region Avg IOU: 0.564667, Class: 1.000000, Obj: 0.384729, No Obj: 0.004430, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.479685, Class: 1.000000, Obj: 0.305751, No Obj: 0.003971, Avg Recall: 0.490741, count: 108 -Region Avg IOU: 0.499912, Class: 1.000000, Obj: 0.328386, No Obj: 0.004111, Avg Recall: 0.554794, count: 146 -2826: 0.570592, 0.482262 avg, 0.001000 rate, 22.735165 seconds, 723456 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.550792, Class: 1.000000, Obj: 0.377663, No Obj: 0.004702, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.532444, Class: 1.000000, Obj: 0.325463, No Obj: 0.003841, Avg Recall: 0.640449, count: 89 -Region Avg IOU: 0.526466, Class: 1.000000, Obj: 0.367422, No Obj: 0.003934, Avg Recall: 0.562500, count: 96 -Region Avg IOU: 0.538244, Class: 1.000000, Obj: 0.412202, No Obj: 0.004273, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.484169, Class: 1.000000, Obj: 0.351716, No Obj: 0.004259, Avg Recall: 0.522124, count: 113 -Region Avg IOU: 0.492193, Class: 1.000000, Obj: 0.331595, No Obj: 0.004295, Avg Recall: 0.559322, count: 118 -Region Avg IOU: 0.538377, Class: 1.000000, Obj: 0.362834, No Obj: 0.004732, Avg Recall: 0.635036, count: 137 -Region Avg IOU: 0.527098, Class: 1.000000, Obj: 0.330914, No Obj: 0.004290, Avg Recall: 0.553191, count: 94 -2827: 0.448636, 0.478899 avg, 0.001000 rate, 23.016731 seconds, 723712 images -Loaded: 0.000053 seconds -Region Avg IOU: 0.500430, Class: 1.000000, Obj: 0.384214, No Obj: 0.004031, Avg Recall: 0.552083, count: 96 -Region Avg IOU: 0.519423, Class: 1.000000, Obj: 0.395170, No Obj: 0.005000, Avg Recall: 0.512605, count: 119 -Region Avg IOU: 0.498310, Class: 1.000000, Obj: 0.328223, No Obj: 0.004081, Avg Recall: 0.494949, count: 99 -Region Avg IOU: 0.564342, Class: 1.000000, Obj: 0.404317, No Obj: 0.004507, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.502794, Class: 1.000000, Obj: 0.352499, No Obj: 0.003848, Avg Recall: 0.565217, count: 92 -Region Avg IOU: 0.512326, Class: 1.000000, Obj: 0.338807, No Obj: 0.003974, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.478871, Class: 1.000000, Obj: 0.354570, No Obj: 0.004423, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.507355, Class: 1.000000, Obj: 0.353814, No Obj: 0.004235, Avg Recall: 0.562500, count: 96 -2828: 0.439137, 0.474923 avg, 0.001000 rate, 22.037403 seconds, 723968 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.485025, Class: 1.000000, Obj: 0.385914, No Obj: 0.004817, Avg Recall: 0.496241, count: 133 -Region Avg IOU: 0.502173, Class: 1.000000, Obj: 0.341596, No Obj: 0.004703, Avg Recall: 0.570370, count: 135 -Region Avg IOU: 0.501594, Class: 1.000000, Obj: 0.385483, No Obj: 0.004687, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.505468, Class: 1.000000, Obj: 0.357734, No Obj: 0.004443, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.551585, Class: 1.000000, Obj: 0.343503, No Obj: 0.004821, Avg Recall: 0.641221, count: 131 -Region Avg IOU: 0.541098, Class: 1.000000, Obj: 0.383051, No Obj: 0.004175, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.549546, Class: 1.000000, Obj: 0.361283, No Obj: 0.004089, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.521957, Class: 1.000000, Obj: 0.321234, No Obj: 0.004498, Avg Recall: 0.596491, count: 114 -2829: 0.518254, 0.479256 avg, 0.001000 rate, 22.475449 seconds, 724224 images -Loaded: 0.721447 seconds -Region Avg IOU: 0.539487, Class: 1.000000, Obj: 0.349334, No Obj: 0.003834, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.489915, Class: 1.000000, Obj: 0.335091, No Obj: 0.003602, Avg Recall: 0.548387, count: 93 -Region Avg IOU: 0.512654, Class: 1.000000, Obj: 0.344447, No Obj: 0.003775, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.526094, Class: 1.000000, Obj: 0.362431, No Obj: 0.004255, Avg Recall: 0.583333, count: 96 -Region Avg IOU: 0.510779, Class: 1.000000, Obj: 0.361937, No Obj: 0.003877, Avg Recall: 0.588235, count: 102 -Region Avg IOU: 0.537494, Class: 1.000000, Obj: 0.350726, No Obj: 0.004962, Avg Recall: 0.631148, count: 122 -Region Avg IOU: 0.514063, Class: 1.000000, Obj: 0.390999, No Obj: 0.003932, Avg Recall: 0.573171, count: 82 -Region Avg IOU: 0.538191, Class: 1.000000, Obj: 0.391946, No Obj: 0.003939, Avg Recall: 0.638554, count: 83 -2830: 0.446620, 0.475992 avg, 0.001000 rate, 23.384939 seconds, 724480 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.692284 seconds -Region Avg IOU: 0.499164, Class: 1.000000, Obj: 0.352624, No Obj: 0.004963, Avg Recall: 0.572650, count: 117 -Region Avg IOU: 0.449453, Class: 1.000000, Obj: 0.305470, No Obj: 0.004780, Avg Recall: 0.488550, count: 131 -Region Avg IOU: 0.486660, Class: 1.000000, Obj: 0.286217, No Obj: 0.004366, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.473354, Class: 1.000000, Obj: 0.320476, No Obj: 0.004187, Avg Recall: 0.550000, count: 100 -Region Avg IOU: 0.427360, Class: 1.000000, Obj: 0.253930, No Obj: 0.004554, Avg Recall: 0.453125, count: 128 -Region Avg IOU: 0.497751, Class: 1.000000, Obj: 0.283618, No Obj: 0.004418, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.443699, Class: 1.000000, Obj: 0.291728, No Obj: 0.003517, Avg Recall: 0.470588, count: 85 -Region Avg IOU: 0.496399, Class: 1.000000, Obj: 0.299994, No Obj: 0.004485, Avg Recall: 0.555556, count: 108 -2831: 0.492241, 0.477617 avg, 0.001000 rate, 17.754391 seconds, 724736 images -Loaded: 3.116053 seconds -Region Avg IOU: 0.485620, Class: 1.000000, Obj: 0.281107, No Obj: 0.004325, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.487949, Class: 1.000000, Obj: 0.267259, No Obj: 0.005315, Avg Recall: 0.568345, count: 139 -Region Avg IOU: 0.505609, Class: 1.000000, Obj: 0.346795, No Obj: 0.004493, Avg Recall: 0.544643, count: 112 -Region Avg IOU: 0.502139, Class: 1.000000, Obj: 0.325512, No Obj: 0.004898, Avg Recall: 0.575758, count: 132 -Region Avg IOU: 0.472706, Class: 1.000000, Obj: 0.316425, No Obj: 0.004298, Avg Recall: 0.504587, count: 109 -Region Avg IOU: 0.506930, Class: 1.000000, Obj: 0.351734, No Obj: 0.004802, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.531191, Class: 1.000000, Obj: 0.345506, No Obj: 0.004763, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.492070, Class: 1.000000, Obj: 0.306830, No Obj: 0.004602, Avg Recall: 0.532258, count: 124 -2832: 0.507963, 0.480652 avg, 0.001000 rate, 17.680897 seconds, 724992 images -Loaded: 3.161671 seconds -Region Avg IOU: 0.501256, Class: 1.000000, Obj: 0.305618, No Obj: 0.003852, Avg Recall: 0.577778, count: 90 -Region Avg IOU: 0.493525, Class: 1.000000, Obj: 0.305028, No Obj: 0.005086, Avg Recall: 0.516129, count: 124 -Region Avg IOU: 0.452572, Class: 1.000000, Obj: 0.263802, No Obj: 0.003972, Avg Recall: 0.478633, count: 117 -Region Avg IOU: 0.545774, Class: 1.000000, Obj: 0.325003, No Obj: 0.004026, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.468546, Class: 1.000000, Obj: 0.284470, No Obj: 0.004919, Avg Recall: 0.515873, count: 126 -Region Avg IOU: 0.480519, Class: 1.000000, Obj: 0.326245, No Obj: 0.004347, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.471617, Class: 1.000000, Obj: 0.277348, No Obj: 0.003977, Avg Recall: 0.526316, count: 114 -Region Avg IOU: 0.448568, Class: 1.000000, Obj: 0.279151, No Obj: 0.004349, Avg Recall: 0.430894, count: 123 -2833: 0.511743, 0.483761 avg, 0.001000 rate, 18.697952 seconds, 725248 images -Loaded: 1.375184 seconds -Region Avg IOU: 0.509590, Class: 1.000000, Obj: 0.335862, No Obj: 0.004633, Avg Recall: 0.490385, count: 104 -Region Avg IOU: 0.508350, Class: 1.000000, Obj: 0.311482, No Obj: 0.004022, Avg Recall: 0.559140, count: 93 -Region Avg IOU: 0.432016, Class: 1.000000, Obj: 0.233219, No Obj: 0.004049, Avg Recall: 0.462810, count: 121 -Region Avg IOU: 0.511774, Class: 1.000000, Obj: 0.337132, No Obj: 0.004287, Avg Recall: 0.552083, count: 96 -Region Avg IOU: 0.479550, Class: 1.000000, Obj: 0.293377, No Obj: 0.004176, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.459655, Class: 1.000000, Obj: 0.267609, No Obj: 0.003793, Avg Recall: 0.536082, count: 97 -Region Avg IOU: 0.482959, Class: 1.000000, Obj: 0.261481, No Obj: 0.004830, Avg Recall: 0.547826, count: 115 -Region Avg IOU: 0.472531, Class: 1.000000, Obj: 0.252595, No Obj: 0.004097, Avg Recall: 0.456140, count: 114 -2834: 0.482188, 0.483604 avg, 0.001000 rate, 17.476072 seconds, 725504 images -Loaded: 3.029770 seconds -Region Avg IOU: 0.477618, Class: 1.000000, Obj: 0.333970, No Obj: 0.004280, Avg Recall: 0.521739, count: 92 -Region Avg IOU: 0.495325, Class: 1.000000, Obj: 0.271556, No Obj: 0.004422, Avg Recall: 0.581197, count: 117 -Region Avg IOU: 0.510313, Class: 1.000000, Obj: 0.291754, No Obj: 0.005001, Avg Recall: 0.596639, count: 119 -Region Avg IOU: 0.463057, Class: 1.000000, Obj: 0.262293, No Obj: 0.004264, Avg Recall: 0.514851, count: 101 -Region Avg IOU: 0.517620, Class: 1.000000, Obj: 0.311459, No Obj: 0.004162, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.493835, Class: 1.000000, Obj: 0.304741, No Obj: 0.004756, Avg Recall: 0.532787, count: 122 -Region Avg IOU: 0.527204, Class: 1.000000, Obj: 0.341786, No Obj: 0.003664, Avg Recall: 0.571429, count: 70 -Region Avg IOU: 0.465917, Class: 1.000000, Obj: 0.286110, No Obj: 0.004369, Avg Recall: 0.541284, count: 109 -2835: 0.444076, 0.479651 avg, 0.001000 rate, 18.989414 seconds, 725760 images -Loaded: 2.935466 seconds -Region Avg IOU: 0.498384, Class: 1.000000, Obj: 0.315263, No Obj: 0.004461, Avg Recall: 0.529915, count: 117 -Region Avg IOU: 0.439792, Class: 1.000000, Obj: 0.285948, No Obj: 0.003867, Avg Recall: 0.445783, count: 83 -Region Avg IOU: 0.453322, Class: 1.000000, Obj: 0.280397, No Obj: 0.004623, Avg Recall: 0.528000, count: 125 -Region Avg IOU: 0.468736, Class: 1.000000, Obj: 0.276962, No Obj: 0.004264, Avg Recall: 0.500000, count: 128 -Region Avg IOU: 0.546312, Class: 1.000000, Obj: 0.349169, No Obj: 0.005012, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.512008, Class: 1.000000, Obj: 0.327369, No Obj: 0.004870, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.477881, Class: 1.000000, Obj: 0.289744, No Obj: 0.004879, Avg Recall: 0.532710, count: 107 -Region Avg IOU: 0.471872, Class: 1.000000, Obj: 0.309545, No Obj: 0.004352, Avg Recall: 0.550847, count: 118 -2836: 0.458955, 0.477581 avg, 0.001000 rate, 20.860161 seconds, 726016 images -Loaded: 0.982341 seconds -Region Avg IOU: 0.466890, Class: 1.000000, Obj: 0.357931, No Obj: 0.004265, Avg Recall: 0.542553, count: 94 -Region Avg IOU: 0.428404, Class: 1.000000, Obj: 0.280426, No Obj: 0.003933, Avg Recall: 0.442308, count: 104 -Region Avg IOU: 0.437922, Class: 1.000000, Obj: 0.319511, No Obj: 0.004685, Avg Recall: 0.459184, count: 98 -Region Avg IOU: 0.497113, Class: 1.000000, Obj: 0.338329, No Obj: 0.004330, Avg Recall: 0.547619, count: 84 -Region Avg IOU: 0.477654, Class: 1.000000, Obj: 0.336687, No Obj: 0.005235, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.473417, Class: 1.000000, Obj: 0.350353, No Obj: 0.004591, Avg Recall: 0.540816, count: 98 -Region Avg IOU: 0.437776, Class: 1.000000, Obj: 0.300845, No Obj: 0.004113, Avg Recall: 0.447761, count: 67 -Region Avg IOU: 0.440142, Class: 1.000000, Obj: 0.318156, No Obj: 0.004877, Avg Recall: 0.449541, count: 109 -2837: 0.440289, 0.473852 avg, 0.001000 rate, 18.550100 seconds, 726272 images -Loaded: 1.515934 seconds -Region Avg IOU: 0.489223, Class: 1.000000, Obj: 0.254789, No Obj: 0.004569, Avg Recall: 0.529412, count: 119 -Region Avg IOU: 0.482165, Class: 1.000000, Obj: 0.348381, No Obj: 0.004481, Avg Recall: 0.500000, count: 112 -Region Avg IOU: 0.451405, Class: 1.000000, Obj: 0.324006, No Obj: 0.004677, Avg Recall: 0.490741, count: 108 -Region Avg IOU: 0.451522, Class: 1.000000, Obj: 0.314103, No Obj: 0.004660, Avg Recall: 0.494845, count: 97 -Region Avg IOU: 0.482691, Class: 1.000000, Obj: 0.340021, No Obj: 0.004924, Avg Recall: 0.504274, count: 117 -Region Avg IOU: 0.500729, Class: 1.000000, Obj: 0.365839, No Obj: 0.004875, Avg Recall: 0.514851, count: 101 -Region Avg IOU: 0.472792, Class: 1.000000, Obj: 0.321488, No Obj: 0.004726, Avg Recall: 0.495652, count: 115 -Region Avg IOU: 0.484970, Class: 1.000000, Obj: 0.318711, No Obj: 0.004688, Avg Recall: 0.504348, count: 115 -2838: 0.481957, 0.474663 avg, 0.001000 rate, 19.540485 seconds, 726528 images -Loaded: 1.224292 seconds -Region Avg IOU: 0.435593, Class: 1.000000, Obj: 0.335464, No Obj: 0.005077, Avg Recall: 0.429825, count: 114 -Region Avg IOU: 0.453633, Class: 1.000000, Obj: 0.334452, No Obj: 0.004615, Avg Recall: 0.529412, count: 119 -Region Avg IOU: 0.448273, Class: 1.000000, Obj: 0.317730, No Obj: 0.004662, Avg Recall: 0.469027, count: 113 -Region Avg IOU: 0.483886, Class: 1.000000, Obj: 0.302123, No Obj: 0.004734, Avg Recall: 0.542373, count: 118 -Region Avg IOU: 0.515887, Class: 1.000000, Obj: 0.332551, No Obj: 0.004616, Avg Recall: 0.602410, count: 83 -Region Avg IOU: 0.492256, Class: 1.000000, Obj: 0.283481, No Obj: 0.004343, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.469930, Class: 1.000000, Obj: 0.313505, No Obj: 0.003464, Avg Recall: 0.525000, count: 80 -Region Avg IOU: 0.490717, Class: 1.000000, Obj: 0.322975, No Obj: 0.004278, Avg Recall: 0.520833, count: 96 -2839: 0.451993, 0.472396 avg, 0.001000 rate, 19.072563 seconds, 726784 images -Loaded: 2.708154 seconds -Region Avg IOU: 0.428595, Class: 1.000000, Obj: 0.289606, No Obj: 0.004742, Avg Recall: 0.484848, count: 132 -Region Avg IOU: 0.489774, Class: 1.000000, Obj: 0.279640, No Obj: 0.004021, Avg Recall: 0.515790, count: 95 -Region Avg IOU: 0.478429, Class: 1.000000, Obj: 0.322408, No Obj: 0.004925, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.488643, Class: 1.000000, Obj: 0.360647, No Obj: 0.004764, Avg Recall: 0.504065, count: 123 -Region Avg IOU: 0.432271, Class: 1.000000, Obj: 0.301639, No Obj: 0.003883, Avg Recall: 0.463918, count: 97 -Region Avg IOU: 0.497869, Class: 1.000000, Obj: 0.341032, No Obj: 0.004356, Avg Recall: 0.509615, count: 104 -Region Avg IOU: 0.478831, Class: 1.000000, Obj: 0.333977, No Obj: 0.004686, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.486574, Class: 1.000000, Obj: 0.305634, No Obj: 0.004224, Avg Recall: 0.555556, count: 108 -2840: 0.489842, 0.474140 avg, 0.001000 rate, 16.824516 seconds, 727040 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 20.610756 seconds -Region Avg IOU: 0.445962, Class: 1.000000, Obj: 0.332005, No Obj: 0.004738, Avg Recall: 0.511811, count: 127 -Region Avg IOU: 0.508849, Class: 1.000000, Obj: 0.349894, No Obj: 0.004730, Avg Recall: 0.621951, count: 82 -Region Avg IOU: 0.479416, Class: 1.000000, Obj: 0.288028, No Obj: 0.004479, Avg Recall: 0.495727, count: 117 -Region Avg IOU: 0.509558, Class: 1.000000, Obj: 0.315856, No Obj: 0.004427, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.497229, Class: 1.000000, Obj: 0.361985, No Obj: 0.004819, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.499447, Class: 1.000000, Obj: 0.332289, No Obj: 0.004669, Avg Recall: 0.581395, count: 86 -Region Avg IOU: 0.478875, Class: 1.000000, Obj: 0.314704, No Obj: 0.005195, Avg Recall: 0.500000, count: 138 -Region Avg IOU: 0.432483, Class: 1.000000, Obj: 0.253720, No Obj: 0.004173, Avg Recall: 0.452174, count: 115 -2841: 0.449920, 0.471718 avg, 0.001000 rate, 19.916504 seconds, 727296 images -Loaded: 1.287457 seconds -Region Avg IOU: 0.454047, Class: 1.000000, Obj: 0.321872, No Obj: 0.004271, Avg Recall: 0.491071, count: 112 -Region Avg IOU: 0.496019, Class: 1.000000, Obj: 0.339872, No Obj: 0.004649, Avg Recall: 0.549020, count: 102 -Region Avg IOU: 0.492297, Class: 1.000000, Obj: 0.305439, No Obj: 0.004655, Avg Recall: 0.466667, count: 120 -Region Avg IOU: 0.446164, Class: 1.000000, Obj: 0.268050, No Obj: 0.004769, Avg Recall: 0.516393, count: 122 -Region Avg IOU: 0.495674, Class: 1.000000, Obj: 0.298140, No Obj: 0.004773, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.485901, Class: 1.000000, Obj: 0.305513, No Obj: 0.004312, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.478909, Class: 1.000000, Obj: 0.307612, No Obj: 0.004165, Avg Recall: 0.500000, count: 98 -Region Avg IOU: 0.494669, Class: 1.000000, Obj: 0.333487, No Obj: 0.005157, Avg Recall: 0.503759, count: 133 -2842: 0.488111, 0.473357 avg, 0.001000 rate, 19.569016 seconds, 727552 images -Loaded: 1.845085 seconds -Region Avg IOU: 0.491564, Class: 1.000000, Obj: 0.303319, No Obj: 0.004630, Avg Recall: 0.580645, count: 124 -Region Avg IOU: 0.463756, Class: 1.000000, Obj: 0.285548, No Obj: 0.004651, Avg Recall: 0.531250, count: 128 -Region Avg IOU: 0.433952, Class: 1.000000, Obj: 0.290333, No Obj: 0.003745, Avg Recall: 0.473118, count: 93 -Region Avg IOU: 0.502106, Class: 1.000000, Obj: 0.319658, No Obj: 0.004919, Avg Recall: 0.547619, count: 126 -Region Avg IOU: 0.480439, Class: 1.000000, Obj: 0.343961, No Obj: 0.004524, Avg Recall: 0.514851, count: 101 -Region Avg IOU: 0.491143, Class: 1.000000, Obj: 0.307652, No Obj: 0.004689, Avg Recall: 0.553571, count: 112 -Region Avg IOU: 0.462214, Class: 1.000000, Obj: 0.310769, No Obj: 0.003954, Avg Recall: 0.524752, count: 101 -Region Avg IOU: 0.494084, Class: 1.000000, Obj: 0.331309, No Obj: 0.004758, Avg Recall: 0.518868, count: 106 -2843: 0.491715, 0.475193 avg, 0.001000 rate, 16.080944 seconds, 727808 images -Loaded: 4.337962 seconds -Region Avg IOU: 0.499974, Class: 1.000000, Obj: 0.305213, No Obj: 0.005059, Avg Recall: 0.547619, count: 126 -Region Avg IOU: 0.470193, Class: 1.000000, Obj: 0.324650, No Obj: 0.004563, Avg Recall: 0.477064, count: 109 -Region Avg IOU: 0.500057, Class: 1.000000, Obj: 0.338902, No Obj: 0.004308, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.492713, Class: 1.000000, Obj: 0.316767, No Obj: 0.004398, Avg Recall: 0.513761, count: 109 -Region Avg IOU: 0.453713, Class: 1.000000, Obj: 0.291977, No Obj: 0.004710, Avg Recall: 0.462185, count: 119 -Region Avg IOU: 0.524805, Class: 1.000000, Obj: 0.340918, No Obj: 0.004415, Avg Recall: 0.583333, count: 96 -Region Avg IOU: 0.567624, Class: 1.000000, Obj: 0.354848, No Obj: 0.004718, Avg Recall: 0.688889, count: 90 -Region Avg IOU: 0.466294, Class: 1.000000, Obj: 0.286595, No Obj: 0.004458, Avg Recall: 0.470086, count: 117 -2844: 0.467292, 0.474403 avg, 0.001000 rate, 19.032265 seconds, 728064 images -Loaded: 1.641697 seconds -Region Avg IOU: 0.503232, Class: 1.000000, Obj: 0.345117, No Obj: 0.005137, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.460696, Class: 1.000000, Obj: 0.321158, No Obj: 0.003882, Avg Recall: 0.500000, count: 84 -Region Avg IOU: 0.487303, Class: 1.000000, Obj: 0.295176, No Obj: 0.004381, Avg Recall: 0.551020, count: 98 -Region Avg IOU: 0.457141, Class: 1.000000, Obj: 0.297671, No Obj: 0.004658, Avg Recall: 0.479339, count: 121 -Region Avg IOU: 0.507331, Class: 1.000000, Obj: 0.304259, No Obj: 0.004887, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.485842, Class: 1.000000, Obj: 0.287495, No Obj: 0.003607, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.430559, Class: 1.000000, Obj: 0.286249, No Obj: 0.004523, Avg Recall: 0.463636, count: 110 -Region Avg IOU: 0.487712, Class: 1.000000, Obj: 0.315713, No Obj: 0.004353, Avg Recall: 0.559140, count: 93 -2845: 0.460585, 0.473021 avg, 0.001000 rate, 16.225693 seconds, 728320 images -Loaded: 3.594441 seconds -Region Avg IOU: 0.514095, Class: 1.000000, Obj: 0.314435, No Obj: 0.005047, Avg Recall: 0.566667, count: 120 -Region Avg IOU: 0.485824, Class: 1.000000, Obj: 0.288572, No Obj: 0.005019, Avg Recall: 0.525926, count: 135 -Region Avg IOU: 0.498363, Class: 1.000000, Obj: 0.312483, No Obj: 0.005042, Avg Recall: 0.541667, count: 120 -Region Avg IOU: 0.463955, Class: 1.000000, Obj: 0.278964, No Obj: 0.003856, Avg Recall: 0.468468, count: 111 -Region Avg IOU: 0.473960, Class: 1.000000, Obj: 0.327410, No Obj: 0.003856, Avg Recall: 0.542553, count: 94 -Region Avg IOU: 0.503031, Class: 1.000000, Obj: 0.299640, No Obj: 0.005059, Avg Recall: 0.609375, count: 128 -Region Avg IOU: 0.494497, Class: 1.000000, Obj: 0.333510, No Obj: 0.004000, Avg Recall: 0.551020, count: 98 -Region Avg IOU: 0.499453, Class: 1.000000, Obj: 0.330945, No Obj: 0.004248, Avg Recall: 0.635593, count: 118 -2846: 0.531245, 0.478844 avg, 0.001000 rate, 16.409063 seconds, 728576 images -Loaded: 4.626974 seconds -Region Avg IOU: 0.482420, Class: 1.000000, Obj: 0.329323, No Obj: 0.004419, Avg Recall: 0.516854, count: 89 -Region Avg IOU: 0.509577, Class: 1.000000, Obj: 0.355857, No Obj: 0.004367, Avg Recall: 0.576471, count: 85 -Region Avg IOU: 0.484922, Class: 1.000000, Obj: 0.329746, No Obj: 0.004191, Avg Recall: 0.530612, count: 98 -Region Avg IOU: 0.458216, Class: 1.000000, Obj: 0.294390, No Obj: 0.004685, Avg Recall: 0.487180, count: 117 -Region Avg IOU: 0.536109, Class: 1.000000, Obj: 0.306647, No Obj: 0.004032, Avg Recall: 0.590909, count: 88 -Region Avg IOU: 0.475200, Class: 1.000000, Obj: 0.288590, No Obj: 0.004452, Avg Recall: 0.456311, count: 103 -Region Avg IOU: 0.506020, Class: 1.000000, Obj: 0.333696, No Obj: 0.004410, Avg Recall: 0.522727, count: 88 -Region Avg IOU: 0.468352, Class: 1.000000, Obj: 0.298461, No Obj: 0.004584, Avg Recall: 0.504505, count: 111 -2847: 0.431279, 0.474087 avg, 0.001000 rate, 18.364679 seconds, 728832 images -Loaded: 3.623034 seconds -Region Avg IOU: 0.446072, Class: 1.000000, Obj: 0.313852, No Obj: 0.004758, Avg Recall: 0.472868, count: 129 -Region Avg IOU: 0.522534, Class: 1.000000, Obj: 0.347124, No Obj: 0.004439, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.510467, Class: 1.000000, Obj: 0.347170, No Obj: 0.004542, Avg Recall: 0.560976, count: 82 -Region Avg IOU: 0.492531, Class: 1.000000, Obj: 0.337875, No Obj: 0.004216, Avg Recall: 0.600000, count: 95 -Region Avg IOU: 0.418297, Class: 1.000000, Obj: 0.270109, No Obj: 0.004193, Avg Recall: 0.412281, count: 114 -Region Avg IOU: 0.445739, Class: 1.000000, Obj: 0.293700, No Obj: 0.005159, Avg Recall: 0.469799, count: 149 -Region Avg IOU: 0.464960, Class: 1.000000, Obj: 0.246563, No Obj: 0.004939, Avg Recall: 0.465116, count: 129 -Region Avg IOU: 0.468873, Class: 1.000000, Obj: 0.320615, No Obj: 0.004058, Avg Recall: 0.537500, count: 80 -2848: 0.494076, 0.476086 avg, 0.001000 rate, 17.412766 seconds, 729088 images -Loaded: 2.044962 seconds -Region Avg IOU: 0.493415, Class: 1.000000, Obj: 0.281778, No Obj: 0.005198, Avg Recall: 0.504274, count: 117 -Region Avg IOU: 0.481875, Class: 1.000000, Obj: 0.310648, No Obj: 0.004688, Avg Recall: 0.543103, count: 116 -Region Avg IOU: 0.536533, Class: 1.000000, Obj: 0.326121, No Obj: 0.004954, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.508984, Class: 1.000000, Obj: 0.300762, No Obj: 0.004326, Avg Recall: 0.609195, count: 87 -Region Avg IOU: 0.472780, Class: 1.000000, Obj: 0.312057, No Obj: 0.005191, Avg Recall: 0.510949, count: 137 -Region Avg IOU: 0.452055, Class: 1.000000, Obj: 0.305379, No Obj: 0.004252, Avg Recall: 0.476635, count: 107 -Region Avg IOU: 0.506783, Class: 1.000000, Obj: 0.327616, No Obj: 0.004950, Avg Recall: 0.583333, count: 120 -Region Avg IOU: 0.495985, Class: 1.000000, Obj: 0.330853, No Obj: 0.004512, Avg Recall: 0.525253, count: 99 -2849: 0.475923, 0.476070 avg, 0.001000 rate, 21.137316 seconds, 729344 images -Loaded: 0.664070 seconds -Region Avg IOU: 0.473855, Class: 1.000000, Obj: 0.313041, No Obj: 0.003926, Avg Recall: 0.511111, count: 90 -Region Avg IOU: 0.518174, Class: 1.000000, Obj: 0.318912, No Obj: 0.004700, Avg Recall: 0.565657, count: 99 -Region Avg IOU: 0.499832, Class: 1.000000, Obj: 0.324681, No Obj: 0.004319, Avg Recall: 0.552941, count: 85 -Region Avg IOU: 0.506037, Class: 1.000000, Obj: 0.303704, No Obj: 0.005177, Avg Recall: 0.534884, count: 129 -Region Avg IOU: 0.443194, Class: 1.000000, Obj: 0.321072, No Obj: 0.004050, Avg Recall: 0.511364, count: 88 -Region Avg IOU: 0.483520, Class: 1.000000, Obj: 0.285317, No Obj: 0.004822, Avg Recall: 0.530303, count: 132 -Region Avg IOU: 0.457592, Class: 1.000000, Obj: 0.298684, No Obj: 0.005208, Avg Recall: 0.464789, count: 142 -Region Avg IOU: 0.518700, Class: 1.000000, Obj: 0.276599, No Obj: 0.004577, Avg Recall: 0.603604, count: 111 -2850: 0.476414, 0.476104 avg, 0.001000 rate, 18.151531 seconds, 729600 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000069 seconds -Region Avg IOU: 0.572268, Class: 1.000000, Obj: 0.353102, No Obj: 0.004069, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.543870, Class: 1.000000, Obj: 0.394196, No Obj: 0.003755, Avg Recall: 0.608247, count: 97 -Region Avg IOU: 0.550315, Class: 1.000000, Obj: 0.348531, No Obj: 0.003497, Avg Recall: 0.696629, count: 89 -Region Avg IOU: 0.564748, Class: 1.000000, Obj: 0.436250, No Obj: 0.003888, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.575954, Class: 1.000000, Obj: 0.405786, No Obj: 0.004478, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.557333, Class: 1.000000, Obj: 0.407334, No Obj: 0.004500, Avg Recall: 0.653543, count: 127 -Region Avg IOU: 0.572559, Class: 1.000000, Obj: 0.371592, No Obj: 0.004661, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.573466, Class: 1.000000, Obj: 0.392619, No Obj: 0.003874, Avg Recall: 0.697248, count: 109 -2851: 0.482679, 0.476762 avg, 0.001000 rate, 23.317291 seconds, 729856 images -Loaded: 0.000117 seconds -Region Avg IOU: 0.552651, Class: 1.000000, Obj: 0.364182, No Obj: 0.004194, Avg Recall: 0.656716, count: 134 -Region Avg IOU: 0.561393, Class: 1.000000, Obj: 0.377819, No Obj: 0.004034, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.557260, Class: 1.000000, Obj: 0.389539, No Obj: 0.004060, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.557728, Class: 1.000000, Obj: 0.374288, No Obj: 0.003481, Avg Recall: 0.666667, count: 87 -Region Avg IOU: 0.578628, Class: 1.000000, Obj: 0.410064, No Obj: 0.004522, Avg Recall: 0.673913, count: 138 -Region Avg IOU: 0.590107, Class: 1.000000, Obj: 0.335054, No Obj: 0.004712, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.534437, Class: 1.000000, Obj: 0.389618, No Obj: 0.003563, Avg Recall: 0.583333, count: 84 -Region Avg IOU: 0.618069, Class: 1.000000, Obj: 0.411415, No Obj: 0.003893, Avg Recall: 0.739130, count: 92 -2852: 0.450954, 0.474181 avg, 0.001000 rate, 24.507317 seconds, 730112 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.561270, Class: 1.000000, Obj: 0.354891, No Obj: 0.004343, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.543059, Class: 1.000000, Obj: 0.357164, No Obj: 0.003826, Avg Recall: 0.644068, count: 118 -Region Avg IOU: 0.570734, Class: 1.000000, Obj: 0.372297, No Obj: 0.003771, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.555518, Class: 1.000000, Obj: 0.346310, No Obj: 0.003790, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.621470, Class: 1.000000, Obj: 0.384917, No Obj: 0.003871, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.520448, Class: 1.000000, Obj: 0.315954, No Obj: 0.004307, Avg Recall: 0.587302, count: 126 -Region Avg IOU: 0.602048, Class: 1.000000, Obj: 0.427531, No Obj: 0.003607, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.567759, Class: 1.000000, Obj: 0.347266, No Obj: 0.003848, Avg Recall: 0.705882, count: 102 -2853: 0.463298, 0.473093 avg, 0.001000 rate, 23.775480 seconds, 730368 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.566045, Class: 1.000000, Obj: 0.332553, No Obj: 0.004243, Avg Recall: 0.690789, count: 152 -Region Avg IOU: 0.546547, Class: 1.000000, Obj: 0.340341, No Obj: 0.003482, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.536669, Class: 1.000000, Obj: 0.351561, No Obj: 0.003873, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.551363, Class: 1.000000, Obj: 0.343849, No Obj: 0.003335, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.576239, Class: 1.000000, Obj: 0.341956, No Obj: 0.003805, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.567550, Class: 1.000000, Obj: 0.347625, No Obj: 0.003146, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.560010, Class: 1.000000, Obj: 0.389436, No Obj: 0.003184, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.539913, Class: 1.000000, Obj: 0.367880, No Obj: 0.002835, Avg Recall: 0.616279, count: 86 -2854: 0.429019, 0.468685 avg, 0.001000 rate, 24.223864 seconds, 730624 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.593817, Class: 1.000000, Obj: 0.366725, No Obj: 0.003515, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.578456, Class: 1.000000, Obj: 0.309237, No Obj: 0.003546, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.582188, Class: 1.000000, Obj: 0.343118, No Obj: 0.003500, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.601753, Class: 1.000000, Obj: 0.381826, No Obj: 0.003918, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.578912, Class: 1.000000, Obj: 0.351550, No Obj: 0.003099, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.578236, Class: 1.000000, Obj: 0.370503, No Obj: 0.004237, Avg Recall: 0.666667, count: 135 -Region Avg IOU: 0.541724, Class: 1.000000, Obj: 0.317856, No Obj: 0.003911, Avg Recall: 0.593496, count: 123 -Region Avg IOU: 0.576123, Class: 1.000000, Obj: 0.313835, No Obj: 0.003545, Avg Recall: 0.704762, count: 105 -2855: 0.489452, 0.470762 avg, 0.001000 rate, 23.688787 seconds, 730880 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.533501, Class: 1.000000, Obj: 0.349155, No Obj: 0.002938, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.562432, Class: 1.000000, Obj: 0.336309, No Obj: 0.003145, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.541735, Class: 1.000000, Obj: 0.352407, No Obj: 0.003746, Avg Recall: 0.639098, count: 133 -Region Avg IOU: 0.561173, Class: 1.000000, Obj: 0.338026, No Obj: 0.003621, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.542765, Class: 1.000000, Obj: 0.336883, No Obj: 0.003533, Avg Recall: 0.633333, count: 120 -Region Avg IOU: 0.556477, Class: 1.000000, Obj: 0.363045, No Obj: 0.003195, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.537258, Class: 1.000000, Obj: 0.312712, No Obj: 0.002925, Avg Recall: 0.619835, count: 121 -Region Avg IOU: 0.571220, Class: 1.000000, Obj: 0.316625, No Obj: 0.003656, Avg Recall: 0.672897, count: 107 -2856: 0.448125, 0.468498 avg, 0.001000 rate, 24.886848 seconds, 731136 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.572429, Class: 1.000000, Obj: 0.337038, No Obj: 0.003337, Avg Recall: 0.680672, count: 119 -Region Avg IOU: 0.557037, Class: 1.000000, Obj: 0.279569, No Obj: 0.003130, Avg Recall: 0.690476, count: 84 -Region Avg IOU: 0.563139, Class: 1.000000, Obj: 0.347463, No Obj: 0.002800, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.568194, Class: 1.000000, Obj: 0.331936, No Obj: 0.003346, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.579896, Class: 1.000000, Obj: 0.314461, No Obj: 0.003421, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.522758, Class: 1.000000, Obj: 0.316864, No Obj: 0.002913, Avg Recall: 0.552083, count: 96 -Region Avg IOU: 0.529990, Class: 1.000000, Obj: 0.295600, No Obj: 0.003750, Avg Recall: 0.634328, count: 134 -Region Avg IOU: 0.558584, Class: 1.000000, Obj: 0.308797, No Obj: 0.003351, Avg Recall: 0.644860, count: 107 -2857: 0.435033, 0.465152 avg, 0.001000 rate, 24.480808 seconds, 731392 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.546294, Class: 1.000000, Obj: 0.320244, No Obj: 0.003107, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.570194, Class: 1.000000, Obj: 0.308612, No Obj: 0.003491, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.587873, Class: 1.000000, Obj: 0.311099, No Obj: 0.004190, Avg Recall: 0.686567, count: 134 -Region Avg IOU: 0.564766, Class: 1.000000, Obj: 0.361065, No Obj: 0.002924, Avg Recall: 0.701149, count: 87 -Region Avg IOU: 0.543872, Class: 1.000000, Obj: 0.346020, No Obj: 0.003097, Avg Recall: 0.622222, count: 90 -Region Avg IOU: 0.606649, Class: 1.000000, Obj: 0.366324, No Obj: 0.003600, Avg Recall: 0.761905, count: 126 -Region Avg IOU: 0.546241, Class: 1.000000, Obj: 0.322071, No Obj: 0.003507, Avg Recall: 0.653846, count: 130 -Region Avg IOU: 0.555375, Class: 1.000000, Obj: 0.294473, No Obj: 0.003393, Avg Recall: 0.635593, count: 118 -2858: 0.537891, 0.472426 avg, 0.001000 rate, 22.149197 seconds, 731648 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.610637, Class: 1.000000, Obj: 0.336451, No Obj: 0.003251, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.554915, Class: 1.000000, Obj: 0.383383, No Obj: 0.003116, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.551060, Class: 1.000000, Obj: 0.326294, No Obj: 0.003211, Avg Recall: 0.627907, count: 86 -Region Avg IOU: 0.579407, Class: 1.000000, Obj: 0.338588, No Obj: 0.003198, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.528073, Class: 1.000000, Obj: 0.307511, No Obj: 0.002867, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.523829, Class: 1.000000, Obj: 0.360174, No Obj: 0.003299, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.578284, Class: 1.000000, Obj: 0.392071, No Obj: 0.003467, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.568225, Class: 1.000000, Obj: 0.335288, No Obj: 0.003239, Avg Recall: 0.673267, count: 101 -2859: 0.421126, 0.467296 avg, 0.001000 rate, 25.411238 seconds, 731904 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.612330, Class: 1.000000, Obj: 0.396295, No Obj: 0.003833, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.520408, Class: 1.000000, Obj: 0.329259, No Obj: 0.003956, Avg Recall: 0.615385, count: 130 -Region Avg IOU: 0.551993, Class: 1.000000, Obj: 0.341809, No Obj: 0.003950, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.534804, Class: 1.000000, Obj: 0.310578, No Obj: 0.003636, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.564510, Class: 1.000000, Obj: 0.324004, No Obj: 0.003542, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.593612, Class: 1.000000, Obj: 0.338526, No Obj: 0.003614, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.584678, Class: 1.000000, Obj: 0.355938, No Obj: 0.003619, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.532814, Class: 1.000000, Obj: 0.334296, No Obj: 0.003338, Avg Recall: 0.617647, count: 102 -2860: 0.444712, 0.465037 avg, 0.001000 rate, 23.162016 seconds, 732160 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.444223 seconds -Region Avg IOU: 0.503484, Class: 1.000000, Obj: 0.275226, No Obj: 0.003676, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.529264, Class: 1.000000, Obj: 0.326618, No Obj: 0.004023, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.486561, Class: 1.000000, Obj: 0.280941, No Obj: 0.004009, Avg Recall: 0.529412, count: 119 -Region Avg IOU: 0.501157, Class: 1.000000, Obj: 0.306740, No Obj: 0.004800, Avg Recall: 0.566176, count: 136 -Region Avg IOU: 0.554385, Class: 1.000000, Obj: 0.373717, No Obj: 0.004241, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.486321, Class: 1.000000, Obj: 0.277610, No Obj: 0.004138, Avg Recall: 0.504505, count: 111 -Region Avg IOU: 0.452852, Class: 1.000000, Obj: 0.305161, No Obj: 0.003952, Avg Recall: 0.521739, count: 115 -Region Avg IOU: 0.491144, Class: 1.000000, Obj: 0.290036, No Obj: 0.003488, Avg Recall: 0.563107, count: 103 -2861: 0.491369, 0.467671 avg, 0.001000 rate, 21.244961 seconds, 732416 images -Loaded: 1.601949 seconds -Region Avg IOU: 0.507546, Class: 1.000000, Obj: 0.335030, No Obj: 0.005055, Avg Recall: 0.561983, count: 121 -Region Avg IOU: 0.503541, Class: 1.000000, Obj: 0.306809, No Obj: 0.004074, Avg Recall: 0.585366, count: 123 -Region Avg IOU: 0.538780, Class: 1.000000, Obj: 0.320885, No Obj: 0.004408, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.504575, Class: 1.000000, Obj: 0.326425, No Obj: 0.004266, Avg Recall: 0.565891, count: 129 -Region Avg IOU: 0.509076, Class: 1.000000, Obj: 0.337522, No Obj: 0.004112, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.521729, Class: 1.000000, Obj: 0.319716, No Obj: 0.004081, Avg Recall: 0.580952, count: 105 -Region Avg IOU: 0.507778, Class: 1.000000, Obj: 0.330092, No Obj: 0.004241, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.506855, Class: 1.000000, Obj: 0.316625, No Obj: 0.005352, Avg Recall: 0.582192, count: 146 -2862: 0.528095, 0.473713 avg, 0.001000 rate, 21.116888 seconds, 732672 images -Loaded: 0.390707 seconds -Region Avg IOU: 0.480812, Class: 1.000000, Obj: 0.343141, No Obj: 0.005040, Avg Recall: 0.520833, count: 144 -Region Avg IOU: 0.483654, Class: 1.000000, Obj: 0.333152, No Obj: 0.004601, Avg Recall: 0.518519, count: 135 -Region Avg IOU: 0.488392, Class: 1.000000, Obj: 0.344784, No Obj: 0.004183, Avg Recall: 0.580952, count: 105 -Region Avg IOU: 0.536873, Class: 1.000000, Obj: 0.349989, No Obj: 0.004859, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.493120, Class: 1.000000, Obj: 0.299919, No Obj: 0.004537, Avg Recall: 0.534483, count: 116 -Region Avg IOU: 0.512693, Class: 1.000000, Obj: 0.361541, No Obj: 0.004237, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.518339, Class: 1.000000, Obj: 0.324727, No Obj: 0.004929, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.494762, Class: 1.000000, Obj: 0.316587, No Obj: 0.004288, Avg Recall: 0.522124, count: 113 -2863: 0.494049, 0.475747 avg, 0.001000 rate, 21.840340 seconds, 732928 images -Loaded: 0.550984 seconds -Region Avg IOU: 0.529537, Class: 1.000000, Obj: 0.345510, No Obj: 0.004788, Avg Recall: 0.581395, count: 129 -Region Avg IOU: 0.463205, Class: 1.000000, Obj: 0.377486, No Obj: 0.003516, Avg Recall: 0.512821, count: 78 -Region Avg IOU: 0.514306, Class: 1.000000, Obj: 0.339514, No Obj: 0.004885, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.470335, Class: 1.000000, Obj: 0.313841, No Obj: 0.004663, Avg Recall: 0.482014, count: 139 -Region Avg IOU: 0.518519, Class: 1.000000, Obj: 0.384694, No Obj: 0.004707, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.486973, Class: 1.000000, Obj: 0.330214, No Obj: 0.004707, Avg Recall: 0.540323, count: 124 -Region Avg IOU: 0.508220, Class: 1.000000, Obj: 0.321341, No Obj: 0.004414, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.579176, Class: 1.000000, Obj: 0.349768, No Obj: 0.005034, Avg Recall: 0.688679, count: 106 -2864: 0.465068, 0.474679 avg, 0.001000 rate, 20.500408 seconds, 733184 images -Loaded: 1.228165 seconds -Region Avg IOU: 0.516382, Class: 1.000000, Obj: 0.329839, No Obj: 0.004964, Avg Recall: 0.636364, count: 132 -Region Avg IOU: 0.470067, Class: 1.000000, Obj: 0.307780, No Obj: 0.004224, Avg Recall: 0.470588, count: 102 -Region Avg IOU: 0.473754, Class: 1.000000, Obj: 0.345513, No Obj: 0.004885, Avg Recall: 0.537879, count: 132 -Region Avg IOU: 0.536890, Class: 1.000000, Obj: 0.399675, No Obj: 0.004365, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.469150, Class: 1.000000, Obj: 0.333651, No Obj: 0.004540, Avg Recall: 0.510000, count: 100 -Region Avg IOU: 0.452746, Class: 1.000000, Obj: 0.330923, No Obj: 0.004784, Avg Recall: 0.475000, count: 120 -Region Avg IOU: 0.507559, Class: 1.000000, Obj: 0.324924, No Obj: 0.005526, Avg Recall: 0.555556, count: 135 -Region Avg IOU: 0.497056, Class: 1.000000, Obj: 0.366516, No Obj: 0.004564, Avg Recall: 0.511364, count: 88 -2865: 0.488045, 0.476015 avg, 0.001000 rate, 21.735548 seconds, 733440 images -Loaded: 0.898886 seconds -Region Avg IOU: 0.480769, Class: 1.000000, Obj: 0.344583, No Obj: 0.005176, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.514225, Class: 1.000000, Obj: 0.383587, No Obj: 0.005476, Avg Recall: 0.581560, count: 141 -Region Avg IOU: 0.498599, Class: 1.000000, Obj: 0.367944, No Obj: 0.004303, Avg Recall: 0.636364, count: 88 -Region Avg IOU: 0.514122, Class: 1.000000, Obj: 0.320928, No Obj: 0.004860, Avg Recall: 0.593750, count: 128 -Region Avg IOU: 0.516179, Class: 1.000000, Obj: 0.346452, No Obj: 0.005054, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.515328, Class: 1.000000, Obj: 0.388313, No Obj: 0.003844, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.483989, Class: 1.000000, Obj: 0.323926, No Obj: 0.004518, Avg Recall: 0.553571, count: 112 -Region Avg IOU: 0.525668, Class: 1.000000, Obj: 0.375645, No Obj: 0.004589, Avg Recall: 0.561224, count: 98 -2866: 0.442735, 0.472687 avg, 0.001000 rate, 19.791897 seconds, 733696 images -Loaded: 2.112535 seconds -Region Avg IOU: 0.532137, Class: 1.000000, Obj: 0.390197, No Obj: 0.004371, Avg Recall: 0.553191, count: 94 -Region Avg IOU: 0.518049, Class: 1.000000, Obj: 0.408627, No Obj: 0.004386, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.475702, Class: 1.000000, Obj: 0.351827, No Obj: 0.004418, Avg Recall: 0.509434, count: 106 -Region Avg IOU: 0.438382, Class: 1.000000, Obj: 0.314782, No Obj: 0.005017, Avg Recall: 0.469231, count: 130 -Region Avg IOU: 0.436668, Class: 1.000000, Obj: 0.320101, No Obj: 0.004105, Avg Recall: 0.440000, count: 100 -Region Avg IOU: 0.481571, Class: 1.000000, Obj: 0.360728, No Obj: 0.005976, Avg Recall: 0.550000, count: 140 -Region Avg IOU: 0.503970, Class: 1.000000, Obj: 0.333809, No Obj: 0.004595, Avg Recall: 0.603175, count: 126 -Region Avg IOU: 0.519927, Class: 1.000000, Obj: 0.343653, No Obj: 0.004565, Avg Recall: 0.580952, count: 105 -2867: 0.462726, 0.471691 avg, 0.001000 rate, 21.854511 seconds, 733952 images -Loaded: 0.628834 seconds -Region Avg IOU: 0.445207, Class: 1.000000, Obj: 0.355009, No Obj: 0.003901, Avg Recall: 0.440860, count: 93 -Region Avg IOU: 0.496130, Class: 1.000000, Obj: 0.308024, No Obj: 0.004782, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.460349, Class: 1.000000, Obj: 0.326643, No Obj: 0.004598, Avg Recall: 0.538462, count: 104 -Region Avg IOU: 0.556746, Class: 1.000000, Obj: 0.341999, No Obj: 0.004543, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.506648, Class: 1.000000, Obj: 0.333650, No Obj: 0.004378, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.502531, Class: 1.000000, Obj: 0.340842, No Obj: 0.005236, Avg Recall: 0.545455, count: 132 -Region Avg IOU: 0.473669, Class: 1.000000, Obj: 0.336024, No Obj: 0.004468, Avg Recall: 0.513043, count: 115 -Region Avg IOU: 0.468336, Class: 1.000000, Obj: 0.323182, No Obj: 0.004284, Avg Recall: 0.475248, count: 101 -2868: 0.517582, 0.476280 avg, 0.001000 rate, 20.452639 seconds, 734208 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.492978, Class: 1.000000, Obj: 0.310309, No Obj: 0.003773, Avg Recall: 0.519231, count: 104 -Region Avg IOU: 0.508567, Class: 1.000000, Obj: 0.368678, No Obj: 0.003521, Avg Recall: 0.614458, count: 83 -Region Avg IOU: 0.500444, Class: 1.000000, Obj: 0.352492, No Obj: 0.003612, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.479636, Class: 1.000000, Obj: 0.334283, No Obj: 0.003568, Avg Recall: 0.546392, count: 97 -Region Avg IOU: 0.518027, Class: 1.000000, Obj: 0.329470, No Obj: 0.004309, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.508895, Class: 1.000000, Obj: 0.351918, No Obj: 0.004606, Avg Recall: 0.579365, count: 126 -Region Avg IOU: 0.534196, Class: 1.000000, Obj: 0.362532, No Obj: 0.003995, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.546607, Class: 1.000000, Obj: 0.351472, No Obj: 0.004786, Avg Recall: 0.645455, count: 110 -2869: 0.419003, 0.470553 avg, 0.001000 rate, 20.262487 seconds, 734464 images -Loaded: 1.421346 seconds -Region Avg IOU: 0.526062, Class: 1.000000, Obj: 0.365220, No Obj: 0.003895, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.502229, Class: 1.000000, Obj: 0.310681, No Obj: 0.004464, Avg Recall: 0.558621, count: 145 -Region Avg IOU: 0.524679, Class: 1.000000, Obj: 0.291105, No Obj: 0.003917, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.501894, Class: 1.000000, Obj: 0.347170, No Obj: 0.004258, Avg Recall: 0.512000, count: 125 -Region Avg IOU: 0.486774, Class: 1.000000, Obj: 0.275678, No Obj: 0.004404, Avg Recall: 0.516129, count: 124 -Region Avg IOU: 0.513916, Class: 1.000000, Obj: 0.293669, No Obj: 0.004642, Avg Recall: 0.575000, count: 120 -Region Avg IOU: 0.499570, Class: 1.000000, Obj: 0.305906, No Obj: 0.003784, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.485176, Class: 1.000000, Obj: 0.328394, No Obj: 0.003734, Avg Recall: 0.500000, count: 102 -2870: 0.481266, 0.471624 avg, 0.001000 rate, 19.973488 seconds, 734720 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000068 seconds -Region Avg IOU: 0.580734, Class: 1.000000, Obj: 0.366455, No Obj: 0.003234, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.588860, Class: 1.000000, Obj: 0.393489, No Obj: 0.003211, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.558415, Class: 1.000000, Obj: 0.353681, No Obj: 0.002901, Avg Recall: 0.659091, count: 88 -Region Avg IOU: 0.605528, Class: 1.000000, Obj: 0.404513, No Obj: 0.003287, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.616112, Class: 1.000000, Obj: 0.346979, No Obj: 0.003023, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.613721, Class: 1.000000, Obj: 0.427209, No Obj: 0.003155, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.593225, Class: 1.000000, Obj: 0.396432, No Obj: 0.003289, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.624804, Class: 1.000000, Obj: 0.390957, No Obj: 0.003662, Avg Recall: 0.763780, count: 127 -2871: 0.503167, 0.474778 avg, 0.001000 rate, 27.126461 seconds, 734976 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.564479, Class: 1.000000, Obj: 0.360130, No Obj: 0.002957, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.578128, Class: 1.000000, Obj: 0.338566, No Obj: 0.003362, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.587012, Class: 1.000000, Obj: 0.346542, No Obj: 0.003404, Avg Recall: 0.701493, count: 134 -Region Avg IOU: 0.607832, Class: 1.000000, Obj: 0.393949, No Obj: 0.003348, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.577230, Class: 1.000000, Obj: 0.351698, No Obj: 0.002980, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.603496, Class: 1.000000, Obj: 0.325330, No Obj: 0.003073, Avg Recall: 0.742424, count: 132 -Region Avg IOU: 0.582285, Class: 1.000000, Obj: 0.380431, No Obj: 0.003199, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.580061, Class: 1.000000, Obj: 0.366531, No Obj: 0.002793, Avg Recall: 0.642105, count: 95 -2872: 0.469178, 0.474218 avg, 0.001000 rate, 26.812162 seconds, 735232 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.600637, Class: 1.000000, Obj: 0.356140, No Obj: 0.003136, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.590243, Class: 1.000000, Obj: 0.387809, No Obj: 0.002546, Avg Recall: 0.684783, count: 92 -Region Avg IOU: 0.617561, Class: 1.000000, Obj: 0.393242, No Obj: 0.002982, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.601232, Class: 1.000000, Obj: 0.350581, No Obj: 0.003242, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.591744, Class: 1.000000, Obj: 0.314075, No Obj: 0.002737, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.604619, Class: 1.000000, Obj: 0.341179, No Obj: 0.003376, Avg Recall: 0.707692, count: 130 -Region Avg IOU: 0.600079, Class: 1.000000, Obj: 0.375021, No Obj: 0.002980, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.623547, Class: 1.000000, Obj: 0.398875, No Obj: 0.002994, Avg Recall: 0.778846, count: 104 -2873: 0.430943, 0.469891 avg, 0.001000 rate, 25.297859 seconds, 735488 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.604929, Class: 1.000000, Obj: 0.334437, No Obj: 0.003069, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.626717, Class: 1.000000, Obj: 0.292483, No Obj: 0.003143, Avg Recall: 0.769231, count: 130 -Region Avg IOU: 0.633631, Class: 1.000000, Obj: 0.389976, No Obj: 0.003089, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.608179, Class: 1.000000, Obj: 0.324181, No Obj: 0.002567, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.605928, Class: 1.000000, Obj: 0.317798, No Obj: 0.002925, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.609836, Class: 1.000000, Obj: 0.368173, No Obj: 0.002710, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.624781, Class: 1.000000, Obj: 0.384334, No Obj: 0.002751, Avg Recall: 0.755556, count: 90 -Region Avg IOU: 0.612950, Class: 1.000000, Obj: 0.292174, No Obj: 0.003376, Avg Recall: 0.744966, count: 149 -2874: 0.488688, 0.471770 avg, 0.001000 rate, 25.727064 seconds, 735744 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.618654, Class: 1.000000, Obj: 0.318352, No Obj: 0.003223, Avg Recall: 0.793651, count: 126 -Region Avg IOU: 0.559775, Class: 1.000000, Obj: 0.297694, No Obj: 0.002701, Avg Recall: 0.651163, count: 129 -Region Avg IOU: 0.570643, Class: 1.000000, Obj: 0.333025, No Obj: 0.002810, Avg Recall: 0.689076, count: 119 -Region Avg IOU: 0.602279, Class: 1.000000, Obj: 0.364935, No Obj: 0.002764, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.616811, Class: 1.000000, Obj: 0.367206, No Obj: 0.003037, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.613184, Class: 1.000000, Obj: 0.356942, No Obj: 0.002788, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.585039, Class: 1.000000, Obj: 0.314488, No Obj: 0.002532, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.607046, Class: 1.000000, Obj: 0.306921, No Obj: 0.002518, Avg Recall: 0.743119, count: 109 -2875: 0.464395, 0.471033 avg, 0.001000 rate, 27.069180 seconds, 736000 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.599981, Class: 1.000000, Obj: 0.315015, No Obj: 0.003078, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.606998, Class: 1.000000, Obj: 0.324119, No Obj: 0.002940, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.604978, Class: 1.000000, Obj: 0.318888, No Obj: 0.003068, Avg Recall: 0.718750, count: 128 -Region Avg IOU: 0.603505, Class: 1.000000, Obj: 0.311761, No Obj: 0.003279, Avg Recall: 0.708333, count: 120 -Region Avg IOU: 0.612343, Class: 1.000000, Obj: 0.342545, No Obj: 0.002985, Avg Recall: 0.768000, count: 125 -Region Avg IOU: 0.604551, Class: 1.000000, Obj: 0.362996, No Obj: 0.003277, Avg Recall: 0.742857, count: 140 -Region Avg IOU: 0.565174, Class: 1.000000, Obj: 0.335097, No Obj: 0.002533, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.631906, Class: 1.000000, Obj: 0.308103, No Obj: 0.002870, Avg Recall: 0.800000, count: 120 -2876: 0.578638, 0.481793 avg, 0.001000 rate, 25.775429 seconds, 736256 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.598997, Class: 1.000000, Obj: 0.287430, No Obj: 0.002634, Avg Recall: 0.795455, count: 88 -Region Avg IOU: 0.602749, Class: 1.000000, Obj: 0.288424, No Obj: 0.002874, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.588442, Class: 1.000000, Obj: 0.329956, No Obj: 0.002920, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.614631, Class: 1.000000, Obj: 0.301183, No Obj: 0.002687, Avg Recall: 0.765432, count: 81 -Region Avg IOU: 0.592233, Class: 1.000000, Obj: 0.297829, No Obj: 0.003070, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.582400, Class: 1.000000, Obj: 0.298635, No Obj: 0.002574, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.633540, Class: 1.000000, Obj: 0.331340, No Obj: 0.003372, Avg Recall: 0.815126, count: 119 -Region Avg IOU: 0.647916, Class: 1.000000, Obj: 0.338457, No Obj: 0.002812, Avg Recall: 0.794118, count: 102 -2877: 0.435514, 0.477165 avg, 0.001000 rate, 27.621956 seconds, 736512 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.588033, Class: 1.000000, Obj: 0.348122, No Obj: 0.002699, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.582298, Class: 1.000000, Obj: 0.342107, No Obj: 0.003045, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.591252, Class: 1.000000, Obj: 0.327506, No Obj: 0.002669, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.601422, Class: 1.000000, Obj: 0.344180, No Obj: 0.002989, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.601114, Class: 1.000000, Obj: 0.342397, No Obj: 0.003243, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.581429, Class: 1.000000, Obj: 0.307494, No Obj: 0.003035, Avg Recall: 0.705882, count: 119 -Region Avg IOU: 0.571586, Class: 1.000000, Obj: 0.332494, No Obj: 0.003081, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.613360, Class: 1.000000, Obj: 0.371727, No Obj: 0.002929, Avg Recall: 0.790476, count: 105 -2878: 0.498380, 0.479287 avg, 0.001000 rate, 27.075787 seconds, 736768 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.599164, Class: 1.000000, Obj: 0.377758, No Obj: 0.003142, Avg Recall: 0.695652, count: 138 -Region Avg IOU: 0.554823, Class: 1.000000, Obj: 0.326027, No Obj: 0.002770, Avg Recall: 0.670455, count: 88 -Region Avg IOU: 0.594359, Class: 1.000000, Obj: 0.351033, No Obj: 0.003521, Avg Recall: 0.728571, count: 140 -Region Avg IOU: 0.618839, Class: 1.000000, Obj: 0.333929, No Obj: 0.003214, Avg Recall: 0.773438, count: 128 -Region Avg IOU: 0.593419, Class: 1.000000, Obj: 0.330740, No Obj: 0.003681, Avg Recall: 0.714286, count: 133 -Region Avg IOU: 0.603807, Class: 1.000000, Obj: 0.375042, No Obj: 0.002957, Avg Recall: 0.756303, count: 119 -Region Avg IOU: 0.576890, Class: 1.000000, Obj: 0.319939, No Obj: 0.002982, Avg Recall: 0.689076, count: 119 -Region Avg IOU: 0.616196, Class: 1.000000, Obj: 0.354181, No Obj: 0.002977, Avg Recall: 0.745902, count: 122 -2879: 0.512273, 0.482585 avg, 0.001000 rate, 26.895851 seconds, 737024 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.599684, Class: 1.000000, Obj: 0.363754, No Obj: 0.002718, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.549604, Class: 1.000000, Obj: 0.341646, No Obj: 0.002813, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.622823, Class: 1.000000, Obj: 0.343550, No Obj: 0.003556, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.600985, Class: 1.000000, Obj: 0.421077, No Obj: 0.003032, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.656509, Class: 1.000000, Obj: 0.434702, No Obj: 0.002769, Avg Recall: 0.800000, count: 80 -Region Avg IOU: 0.607584, Class: 1.000000, Obj: 0.351443, No Obj: 0.002977, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.596028, Class: 1.000000, Obj: 0.321186, No Obj: 0.003432, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.579429, Class: 1.000000, Obj: 0.326872, No Obj: 0.002783, Avg Recall: 0.692308, count: 91 -2880: 0.439562, 0.478283 avg, 0.001000 rate, 27.559790 seconds, 737280 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 18.180908 seconds -Region Avg IOU: 0.568318, Class: 1.000000, Obj: 0.320880, No Obj: 0.002777, Avg Recall: 0.705128, count: 78 -Region Avg IOU: 0.541993, Class: 1.000000, Obj: 0.319219, No Obj: 0.003271, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.627266, Class: 1.000000, Obj: 0.378889, No Obj: 0.003763, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.596100, Class: 1.000000, Obj: 0.303592, No Obj: 0.003149, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.508005, Class: 1.000000, Obj: 0.321393, No Obj: 0.003565, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.580345, Class: 1.000000, Obj: 0.370036, No Obj: 0.003576, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.580597, Class: 1.000000, Obj: 0.340640, No Obj: 0.003509, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.532393, Class: 1.000000, Obj: 0.329892, No Obj: 0.003971, Avg Recall: 0.592857, count: 140 -2881: 0.446859, 0.475141 avg, 0.001000 rate, 22.443655 seconds, 737536 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.603405, Class: 1.000000, Obj: 0.396217, No Obj: 0.002967, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.535290, Class: 1.000000, Obj: 0.340110, No Obj: 0.003412, Avg Recall: 0.601770, count: 113 -Region Avg IOU: 0.577641, Class: 1.000000, Obj: 0.347557, No Obj: 0.003378, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.573654, Class: 1.000000, Obj: 0.339144, No Obj: 0.003190, Avg Recall: 0.666667, count: 87 -Region Avg IOU: 0.581224, Class: 1.000000, Obj: 0.330554, No Obj: 0.003401, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.578363, Class: 1.000000, Obj: 0.378520, No Obj: 0.003290, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.582111, Class: 1.000000, Obj: 0.372423, No Obj: 0.003613, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.546254, Class: 1.000000, Obj: 0.344634, No Obj: 0.002939, Avg Recall: 0.612245, count: 98 -2882: 0.499095, 0.477536 avg, 0.001000 rate, 24.167736 seconds, 737792 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.530977, Class: 1.000000, Obj: 0.389531, No Obj: 0.003488, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.574810, Class: 1.000000, Obj: 0.335723, No Obj: 0.003877, Avg Recall: 0.698529, count: 136 -Region Avg IOU: 0.566646, Class: 1.000000, Obj: 0.360264, No Obj: 0.003824, Avg Recall: 0.650406, count: 123 -Region Avg IOU: 0.556755, Class: 1.000000, Obj: 0.360995, No Obj: 0.003359, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.581743, Class: 1.000000, Obj: 0.355246, No Obj: 0.003796, Avg Recall: 0.703125, count: 128 -Region Avg IOU: 0.542630, Class: 1.000000, Obj: 0.347526, No Obj: 0.003204, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.574655, Class: 1.000000, Obj: 0.366910, No Obj: 0.003573, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.559451, Class: 1.000000, Obj: 0.320721, No Obj: 0.003965, Avg Recall: 0.661290, count: 124 -2883: 0.477014, 0.477484 avg, 0.001000 rate, 24.158466 seconds, 738048 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.583980, Class: 1.000000, Obj: 0.368539, No Obj: 0.003632, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.604729, Class: 1.000000, Obj: 0.379024, No Obj: 0.003680, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.595271, Class: 1.000000, Obj: 0.436986, No Obj: 0.003405, Avg Recall: 0.695122, count: 82 -Region Avg IOU: 0.543511, Class: 1.000000, Obj: 0.367527, No Obj: 0.003790, Avg Recall: 0.608000, count: 125 -Region Avg IOU: 0.513392, Class: 1.000000, Obj: 0.356803, No Obj: 0.003448, Avg Recall: 0.600000, count: 95 -Region Avg IOU: 0.504354, Class: 1.000000, Obj: 0.338950, No Obj: 0.003267, Avg Recall: 0.520000, count: 100 -Region Avg IOU: 0.541800, Class: 1.000000, Obj: 0.359045, No Obj: 0.003058, Avg Recall: 0.627907, count: 86 -Region Avg IOU: 0.531051, Class: 1.000000, Obj: 0.339930, No Obj: 0.003711, Avg Recall: 0.594595, count: 111 -2884: 0.429708, 0.472706 avg, 0.001000 rate, 24.528952 seconds, 738304 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.552897, Class: 1.000000, Obj: 0.327922, No Obj: 0.004735, Avg Recall: 0.630872, count: 149 -Region Avg IOU: 0.534249, Class: 1.000000, Obj: 0.304470, No Obj: 0.003475, Avg Recall: 0.629032, count: 124 -Region Avg IOU: 0.548830, Class: 1.000000, Obj: 0.349024, No Obj: 0.003667, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.541947, Class: 1.000000, Obj: 0.383326, No Obj: 0.003195, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.573669, Class: 1.000000, Obj: 0.378895, No Obj: 0.003902, Avg Recall: 0.653226, count: 124 -Region Avg IOU: 0.579657, Class: 1.000000, Obj: 0.401148, No Obj: 0.003807, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.536923, Class: 1.000000, Obj: 0.325246, No Obj: 0.003731, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.530511, Class: 1.000000, Obj: 0.318617, No Obj: 0.004016, Avg Recall: 0.605042, count: 119 -2885: 0.466980, 0.472134 avg, 0.001000 rate, 23.351820 seconds, 738560 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.539969, Class: 1.000000, Obj: 0.377770, No Obj: 0.003109, Avg Recall: 0.593023, count: 86 -Region Avg IOU: 0.591381, Class: 1.000000, Obj: 0.370978, No Obj: 0.003805, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.530741, Class: 1.000000, Obj: 0.343306, No Obj: 0.003545, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.584532, Class: 1.000000, Obj: 0.367202, No Obj: 0.003481, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.554564, Class: 1.000000, Obj: 0.411972, No Obj: 0.003789, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.542807, Class: 1.000000, Obj: 0.331795, No Obj: 0.003952, Avg Recall: 0.611465, count: 157 -Region Avg IOU: 0.511601, Class: 1.000000, Obj: 0.326822, No Obj: 0.003449, Avg Recall: 0.623077, count: 130 -Region Avg IOU: 0.579933, Class: 1.000000, Obj: 0.371448, No Obj: 0.003824, Avg Recall: 0.694737, count: 95 -2886: 0.447831, 0.469703 avg, 0.001000 rate, 24.379568 seconds, 738816 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.565956, Class: 1.000000, Obj: 0.344281, No Obj: 0.003688, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.545341, Class: 1.000000, Obj: 0.337139, No Obj: 0.003770, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.525456, Class: 1.000000, Obj: 0.330314, No Obj: 0.003767, Avg Recall: 0.614173, count: 127 -Region Avg IOU: 0.578575, Class: 1.000000, Obj: 0.398285, No Obj: 0.003468, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.575296, Class: 1.000000, Obj: 0.395909, No Obj: 0.003175, Avg Recall: 0.654321, count: 81 -Region Avg IOU: 0.560557, Class: 1.000000, Obj: 0.340448, No Obj: 0.003188, Avg Recall: 0.670886, count: 79 -Region Avg IOU: 0.582627, Class: 1.000000, Obj: 0.333896, No Obj: 0.003747, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.586563, Class: 1.000000, Obj: 0.418825, No Obj: 0.003496, Avg Recall: 0.728155, count: 103 -2887: 0.431835, 0.465917 avg, 0.001000 rate, 23.832876 seconds, 739072 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.552586, Class: 1.000000, Obj: 0.358245, No Obj: 0.003338, Avg Recall: 0.617886, count: 123 -Region Avg IOU: 0.572297, Class: 1.000000, Obj: 0.340025, No Obj: 0.003901, Avg Recall: 0.674074, count: 135 -Region Avg IOU: 0.543814, Class: 1.000000, Obj: 0.329296, No Obj: 0.003199, Avg Recall: 0.643678, count: 87 -Region Avg IOU: 0.545738, Class: 1.000000, Obj: 0.340915, No Obj: 0.003359, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.584313, Class: 1.000000, Obj: 0.378688, No Obj: 0.003484, Avg Recall: 0.720930, count: 86 -Region Avg IOU: 0.527337, Class: 1.000000, Obj: 0.338887, No Obj: 0.003365, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.520155, Class: 1.000000, Obj: 0.353424, No Obj: 0.002887, Avg Recall: 0.592593, count: 81 -Region Avg IOU: 0.564953, Class: 1.000000, Obj: 0.335057, No Obj: 0.003552, Avg Recall: 0.677686, count: 121 -2888: 0.437772, 0.463102 avg, 0.001000 rate, 23.645395 seconds, 739328 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.545952, Class: 1.000000, Obj: 0.382963, No Obj: 0.003316, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.551192, Class: 1.000000, Obj: 0.347322, No Obj: 0.003138, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.482397, Class: 1.000000, Obj: 0.333126, No Obj: 0.002797, Avg Recall: 0.575342, count: 73 -Region Avg IOU: 0.578630, Class: 1.000000, Obj: 0.352043, No Obj: 0.003860, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.539986, Class: 1.000000, Obj: 0.373209, No Obj: 0.003352, Avg Recall: 0.611570, count: 121 -Region Avg IOU: 0.566952, Class: 1.000000, Obj: 0.320691, No Obj: 0.003403, Avg Recall: 0.664000, count: 125 -Region Avg IOU: 0.561471, Class: 1.000000, Obj: 0.358913, No Obj: 0.003367, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.578091, Class: 1.000000, Obj: 0.334128, No Obj: 0.003883, Avg Recall: 0.635135, count: 148 -2889: 0.443696, 0.461162 avg, 0.001000 rate, 24.282936 seconds, 739584 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.607829, Class: 1.000000, Obj: 0.350167, No Obj: 0.003102, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.567517, Class: 1.000000, Obj: 0.343329, No Obj: 0.002798, Avg Recall: 0.641026, count: 78 -Region Avg IOU: 0.567957, Class: 1.000000, Obj: 0.295489, No Obj: 0.003063, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.592697, Class: 1.000000, Obj: 0.403891, No Obj: 0.003983, Avg Recall: 0.728000, count: 125 -Region Avg IOU: 0.522352, Class: 1.000000, Obj: 0.406416, No Obj: 0.002912, Avg Recall: 0.625000, count: 88 -Region Avg IOU: 0.561990, Class: 1.000000, Obj: 0.343234, No Obj: 0.003498, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.574580, Class: 1.000000, Obj: 0.340524, No Obj: 0.003251, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.566691, Class: 1.000000, Obj: 0.358273, No Obj: 0.003669, Avg Recall: 0.669725, count: 109 -2890: 0.414756, 0.456521 avg, 0.001000 rate, 24.348549 seconds, 739840 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000070 seconds -Region Avg IOU: 0.572569, Class: 1.000000, Obj: 0.323785, No Obj: 0.002859, Avg Recall: 0.702290, count: 131 -Region Avg IOU: 0.616096, Class: 1.000000, Obj: 0.347281, No Obj: 0.002697, Avg Recall: 0.795455, count: 88 -Region Avg IOU: 0.631599, Class: 1.000000, Obj: 0.297364, No Obj: 0.002781, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.619670, Class: 1.000000, Obj: 0.388945, No Obj: 0.002879, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.566505, Class: 1.000000, Obj: 0.296272, No Obj: 0.003138, Avg Recall: 0.677686, count: 121 -Region Avg IOU: 0.604687, Class: 1.000000, Obj: 0.363330, No Obj: 0.003118, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.621465, Class: 1.000000, Obj: 0.372964, No Obj: 0.003203, Avg Recall: 0.752137, count: 117 -Region Avg IOU: 0.636026, Class: 1.000000, Obj: 0.362571, No Obj: 0.002617, Avg Recall: 0.789474, count: 95 -2891: 0.509764, 0.461845 avg, 0.001000 rate, 28.025063 seconds, 740096 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.561995, Class: 1.000000, Obj: 0.345888, No Obj: 0.002656, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.625372, Class: 1.000000, Obj: 0.308466, No Obj: 0.002725, Avg Recall: 0.826087, count: 115 -Region Avg IOU: 0.641820, Class: 1.000000, Obj: 0.369920, No Obj: 0.002722, Avg Recall: 0.848837, count: 86 -Region Avg IOU: 0.624312, Class: 1.000000, Obj: 0.323581, No Obj: 0.002711, Avg Recall: 0.774194, count: 124 -Region Avg IOU: 0.641150, Class: 1.000000, Obj: 0.337773, No Obj: 0.003209, Avg Recall: 0.776000, count: 125 -Region Avg IOU: 0.588713, Class: 1.000000, Obj: 0.332133, No Obj: 0.002819, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.619809, Class: 1.000000, Obj: 0.410601, No Obj: 0.002487, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.636102, Class: 1.000000, Obj: 0.384113, No Obj: 0.002728, Avg Recall: 0.789474, count: 114 -2892: 0.471952, 0.462856 avg, 0.001000 rate, 27.541981 seconds, 740352 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.623166, Class: 1.000000, Obj: 0.349753, No Obj: 0.002775, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.621441, Class: 1.000000, Obj: 0.357645, No Obj: 0.003101, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.636191, Class: 1.000000, Obj: 0.365355, No Obj: 0.002664, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.634306, Class: 1.000000, Obj: 0.409492, No Obj: 0.002559, Avg Recall: 0.781609, count: 87 -Region Avg IOU: 0.630356, Class: 1.000000, Obj: 0.304706, No Obj: 0.002834, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.629524, Class: 1.000000, Obj: 0.328058, No Obj: 0.002790, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.619027, Class: 1.000000, Obj: 0.348702, No Obj: 0.002252, Avg Recall: 0.782609, count: 69 -Region Avg IOU: 0.684059, Class: 1.000000, Obj: 0.366390, No Obj: 0.003119, Avg Recall: 0.859813, count: 107 -2893: 0.424438, 0.459014 avg, 0.001000 rate, 27.512852 seconds, 740608 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.619397, Class: 1.000000, Obj: 0.339177, No Obj: 0.002973, Avg Recall: 0.776923, count: 130 -Region Avg IOU: 0.588667, Class: 1.000000, Obj: 0.302949, No Obj: 0.002585, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.583725, Class: 1.000000, Obj: 0.323238, No Obj: 0.003099, Avg Recall: 0.706349, count: 126 -Region Avg IOU: 0.569313, Class: 1.000000, Obj: 0.328402, No Obj: 0.002260, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.639861, Class: 1.000000, Obj: 0.356138, No Obj: 0.002706, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.650242, Class: 1.000000, Obj: 0.395023, No Obj: 0.002627, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.646820, Class: 1.000000, Obj: 0.396899, No Obj: 0.002657, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.614946, Class: 1.000000, Obj: 0.341006, No Obj: 0.002956, Avg Recall: 0.745902, count: 122 -2894: 0.500256, 0.463138 avg, 0.001000 rate, 28.528040 seconds, 740864 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.661778, Class: 1.000000, Obj: 0.324486, No Obj: 0.002695, Avg Recall: 0.853658, count: 82 -Region Avg IOU: 0.623171, Class: 1.000000, Obj: 0.318779, No Obj: 0.002620, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.641949, Class: 1.000000, Obj: 0.345442, No Obj: 0.002747, Avg Recall: 0.814159, count: 113 -Region Avg IOU: 0.590358, Class: 1.000000, Obj: 0.323716, No Obj: 0.002627, Avg Recall: 0.666667, count: 123 -Region Avg IOU: 0.643881, Class: 1.000000, Obj: 0.333913, No Obj: 0.002716, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.601821, Class: 1.000000, Obj: 0.327429, No Obj: 0.002378, Avg Recall: 0.698795, count: 83 -Region Avg IOU: 0.663771, Class: 1.000000, Obj: 0.342873, No Obj: 0.003295, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.652704, Class: 1.000000, Obj: 0.337965, No Obj: 0.002960, Avg Recall: 0.850467, count: 107 -2895: 0.470272, 0.463852 avg, 0.001000 rate, 28.206459 seconds, 741120 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.621875, Class: 1.000000, Obj: 0.296613, No Obj: 0.002714, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.574826, Class: 1.000000, Obj: 0.287150, No Obj: 0.002535, Avg Recall: 0.706349, count: 126 -Region Avg IOU: 0.680289, Class: 1.000000, Obj: 0.374449, No Obj: 0.002322, Avg Recall: 0.859155, count: 71 -Region Avg IOU: 0.616839, Class: 1.000000, Obj: 0.352650, No Obj: 0.002500, Avg Recall: 0.750000, count: 84 -Region Avg IOU: 0.625971, Class: 1.000000, Obj: 0.359382, No Obj: 0.002753, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.654751, Class: 1.000000, Obj: 0.352307, No Obj: 0.002917, Avg Recall: 0.783333, count: 120 -Region Avg IOU: 0.620021, Class: 1.000000, Obj: 0.302639, No Obj: 0.002366, Avg Recall: 0.782051, count: 78 -Region Avg IOU: 0.652696, Class: 1.000000, Obj: 0.354034, No Obj: 0.002808, Avg Recall: 0.826087, count: 92 -2896: 0.423669, 0.459834 avg, 0.001000 rate, 29.164112 seconds, 741376 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.596488, Class: 1.000000, Obj: 0.321438, No Obj: 0.002798, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.628426, Class: 1.000000, Obj: 0.350132, No Obj: 0.002570, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.615525, Class: 1.000000, Obj: 0.335806, No Obj: 0.002872, Avg Recall: 0.736000, count: 125 -Region Avg IOU: 0.632234, Class: 1.000000, Obj: 0.305301, No Obj: 0.003473, Avg Recall: 0.768707, count: 147 -Region Avg IOU: 0.593654, Class: 1.000000, Obj: 0.319397, No Obj: 0.002737, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.632282, Class: 1.000000, Obj: 0.307971, No Obj: 0.002709, Avg Recall: 0.769231, count: 117 -Region Avg IOU: 0.633165, Class: 1.000000, Obj: 0.363330, No Obj: 0.002572, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.650903, Class: 1.000000, Obj: 0.347245, No Obj: 0.003069, Avg Recall: 0.837037, count: 135 -2897: 0.543072, 0.468157 avg, 0.001000 rate, 28.514244 seconds, 741632 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.601021, Class: 1.000000, Obj: 0.282530, No Obj: 0.002637, Avg Recall: 0.705882, count: 119 -Region Avg IOU: 0.653202, Class: 1.000000, Obj: 0.369627, No Obj: 0.002787, Avg Recall: 0.826087, count: 115 -Region Avg IOU: 0.647225, Class: 1.000000, Obj: 0.360005, No Obj: 0.002780, Avg Recall: 0.830357, count: 112 -Region Avg IOU: 0.628667, Class: 1.000000, Obj: 0.348719, No Obj: 0.002849, Avg Recall: 0.785047, count: 107 -Region Avg IOU: 0.595895, Class: 1.000000, Obj: 0.307425, No Obj: 0.002525, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.600522, Class: 1.000000, Obj: 0.375861, No Obj: 0.002498, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.641229, Class: 1.000000, Obj: 0.355280, No Obj: 0.002672, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.598848, Class: 1.000000, Obj: 0.314636, No Obj: 0.002232, Avg Recall: 0.745098, count: 102 -2898: 0.471599, 0.468502 avg, 0.001000 rate, 29.719439 seconds, 741888 images -Loaded: 0.000057 seconds -Region Avg IOU: 0.629933, Class: 1.000000, Obj: 0.350707, No Obj: 0.002843, Avg Recall: 0.803279, count: 122 -Region Avg IOU: 0.625455, Class: 1.000000, Obj: 0.348820, No Obj: 0.002992, Avg Recall: 0.759690, count: 129 -Region Avg IOU: 0.624521, Class: 1.000000, Obj: 0.369477, No Obj: 0.002568, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.635111, Class: 1.000000, Obj: 0.334322, No Obj: 0.003112, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.628316, Class: 1.000000, Obj: 0.398939, No Obj: 0.002730, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.598474, Class: 1.000000, Obj: 0.351060, No Obj: 0.002855, Avg Recall: 0.713235, count: 136 -Region Avg IOU: 0.619845, Class: 1.000000, Obj: 0.364756, No Obj: 0.002413, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.648212, Class: 1.000000, Obj: 0.382042, No Obj: 0.002405, Avg Recall: 0.793103, count: 87 -2899: 0.490292, 0.470681 avg, 0.001000 rate, 27.562847 seconds, 742144 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.661447, Class: 1.000000, Obj: 0.349665, No Obj: 0.002640, Avg Recall: 0.838710, count: 93 -Region Avg IOU: 0.588831, Class: 1.000000, Obj: 0.308033, No Obj: 0.002706, Avg Recall: 0.693548, count: 124 -Region Avg IOU: 0.638559, Class: 1.000000, Obj: 0.358419, No Obj: 0.002740, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.629515, Class: 1.000000, Obj: 0.355195, No Obj: 0.002939, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.683106, Class: 1.000000, Obj: 0.362094, No Obj: 0.002454, Avg Recall: 0.831325, count: 83 -Region Avg IOU: 0.598532, Class: 1.000000, Obj: 0.321148, No Obj: 0.002566, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.633901, Class: 1.000000, Obj: 0.340247, No Obj: 0.003553, Avg Recall: 0.819444, count: 144 -Region Avg IOU: 0.608162, Class: 1.000000, Obj: 0.328169, No Obj: 0.002397, Avg Recall: 0.773913, count: 115 -2900: 0.500728, 0.473685 avg, 0.001000 rate, 28.880917 seconds, 742400 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 12.973856 seconds -Region Avg IOU: 0.439397, Class: 1.000000, Obj: 0.233740, No Obj: 0.003531, Avg Recall: 0.503704, count: 135 -Region Avg IOU: 0.512963, Class: 1.000000, Obj: 0.292043, No Obj: 0.003834, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.474123, Class: 1.000000, Obj: 0.294687, No Obj: 0.002989, Avg Recall: 0.523256, count: 86 -Region Avg IOU: 0.474701, Class: 1.000000, Obj: 0.253089, No Obj: 0.003443, Avg Recall: 0.493976, count: 83 -Region Avg IOU: 0.494196, Class: 1.000000, Obj: 0.293158, No Obj: 0.003226, Avg Recall: 0.554217, count: 83 -Region Avg IOU: 0.487647, Class: 1.000000, Obj: 0.252977, No Obj: 0.003010, Avg Recall: 0.488372, count: 86 -Region Avg IOU: 0.508307, Class: 1.000000, Obj: 0.264633, No Obj: 0.003593, Avg Recall: 0.504505, count: 111 -Region Avg IOU: 0.483108, Class: 1.000000, Obj: 0.301100, No Obj: 0.003506, Avg Recall: 0.510000, count: 100 -2901: 0.472382, 0.473555 avg, 0.001000 rate, 19.883980 seconds, 742656 images -Loaded: 0.957314 seconds -Region Avg IOU: 0.534888, Class: 1.000000, Obj: 0.287964, No Obj: 0.003492, Avg Recall: 0.643678, count: 87 -Region Avg IOU: 0.488947, Class: 1.000000, Obj: 0.292705, No Obj: 0.003887, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.468267, Class: 1.000000, Obj: 0.247210, No Obj: 0.003712, Avg Recall: 0.484375, count: 128 -Region Avg IOU: 0.501514, Class: 1.000000, Obj: 0.294300, No Obj: 0.003728, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.493576, Class: 1.000000, Obj: 0.276261, No Obj: 0.003619, Avg Recall: 0.532710, count: 107 -Region Avg IOU: 0.490546, Class: 1.000000, Obj: 0.267134, No Obj: 0.003615, Avg Recall: 0.494949, count: 99 -Region Avg IOU: 0.517789, Class: 1.000000, Obj: 0.302289, No Obj: 0.003588, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.483981, Class: 1.000000, Obj: 0.245439, No Obj: 0.003449, Avg Recall: 0.514563, count: 103 -2902: 0.517347, 0.477934 avg, 0.001000 rate, 17.309164 seconds, 742912 images -Loaded: 1.980860 seconds -Region Avg IOU: 0.444533, Class: 1.000000, Obj: 0.285476, No Obj: 0.003948, Avg Recall: 0.418367, count: 98 -Region Avg IOU: 0.445878, Class: 1.000000, Obj: 0.272949, No Obj: 0.004293, Avg Recall: 0.467890, count: 109 -Region Avg IOU: 0.426491, Class: 1.000000, Obj: 0.260330, No Obj: 0.003655, Avg Recall: 0.442623, count: 122 -Region Avg IOU: 0.458821, Class: 1.000000, Obj: 0.274022, No Obj: 0.004318, Avg Recall: 0.480000, count: 125 -Region Avg IOU: 0.501571, Class: 1.000000, Obj: 0.313477, No Obj: 0.004076, Avg Recall: 0.580952, count: 105 -Region Avg IOU: 0.505841, Class: 1.000000, Obj: 0.298134, No Obj: 0.004278, Avg Recall: 0.529412, count: 119 -Region Avg IOU: 0.529696, Class: 1.000000, Obj: 0.343229, No Obj: 0.004365, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.464908, Class: 1.000000, Obj: 0.288981, No Obj: 0.003652, Avg Recall: 0.494949, count: 99 -2903: 0.474619, 0.477603 avg, 0.001000 rate, 16.870234 seconds, 743168 images -Loaded: 4.493254 seconds -Region Avg IOU: 0.488331, Class: 1.000000, Obj: 0.303032, No Obj: 0.004189, Avg Recall: 0.522727, count: 88 -Region Avg IOU: 0.527403, Class: 1.000000, Obj: 0.352388, No Obj: 0.004482, Avg Recall: 0.597826, count: 92 -Region Avg IOU: 0.515791, Class: 1.000000, Obj: 0.327137, No Obj: 0.003991, Avg Recall: 0.609195, count: 87 -Region Avg IOU: 0.481573, Class: 1.000000, Obj: 0.304804, No Obj: 0.004276, Avg Recall: 0.485714, count: 105 -Region Avg IOU: 0.475162, Class: 1.000000, Obj: 0.323646, No Obj: 0.004012, Avg Recall: 0.568182, count: 88 -Region Avg IOU: 0.496621, Class: 1.000000, Obj: 0.330952, No Obj: 0.004997, Avg Recall: 0.552845, count: 123 -Region Avg IOU: 0.461464, Class: 1.000000, Obj: 0.285424, No Obj: 0.004794, Avg Recall: 0.500000, count: 134 -Region Avg IOU: 0.442023, Class: 1.000000, Obj: 0.278024, No Obj: 0.004489, Avg Recall: 0.507353, count: 136 -2904: 0.471965, 0.477039 avg, 0.001000 rate, 20.965515 seconds, 743424 images -Loaded: 2.024011 seconds -Region Avg IOU: 0.507291, Class: 1.000000, Obj: 0.295384, No Obj: 0.004638, Avg Recall: 0.525424, count: 118 -Region Avg IOU: 0.490917, Class: 1.000000, Obj: 0.356763, No Obj: 0.004098, Avg Recall: 0.510870, count: 92 -Region Avg IOU: 0.477918, Class: 1.000000, Obj: 0.315291, No Obj: 0.004463, Avg Recall: 0.491379, count: 116 -Region Avg IOU: 0.451840, Class: 1.000000, Obj: 0.260488, No Obj: 0.004278, Avg Recall: 0.504132, count: 121 -Region Avg IOU: 0.463727, Class: 1.000000, Obj: 0.314542, No Obj: 0.004981, Avg Recall: 0.478992, count: 119 -Region Avg IOU: 0.452131, Class: 1.000000, Obj: 0.289020, No Obj: 0.004223, Avg Recall: 0.515464, count: 97 -Region Avg IOU: 0.480458, Class: 1.000000, Obj: 0.323391, No Obj: 0.005029, Avg Recall: 0.495935, count: 123 -Region Avg IOU: 0.485967, Class: 1.000000, Obj: 0.317373, No Obj: 0.004968, Avg Recall: 0.544715, count: 123 -2905: 0.454293, 0.474764 avg, 0.001000 rate, 16.134624 seconds, 743680 images -Loaded: 3.593452 seconds -Region Avg IOU: 0.457414, Class: 1.000000, Obj: 0.359438, No Obj: 0.004708, Avg Recall: 0.462366, count: 93 -Region Avg IOU: 0.509678, Class: 1.000000, Obj: 0.316487, No Obj: 0.005422, Avg Recall: 0.576923, count: 130 -Region Avg IOU: 0.537367, Class: 1.000000, Obj: 0.377256, No Obj: 0.004704, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.505403, Class: 1.000000, Obj: 0.370251, No Obj: 0.005189, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.467722, Class: 1.000000, Obj: 0.339072, No Obj: 0.004393, Avg Recall: 0.537500, count: 80 -Region Avg IOU: 0.453181, Class: 1.000000, Obj: 0.314613, No Obj: 0.005127, Avg Recall: 0.523364, count: 107 -Region Avg IOU: 0.439398, Class: 1.000000, Obj: 0.306905, No Obj: 0.004715, Avg Recall: 0.514286, count: 105 -Region Avg IOU: 0.481494, Class: 1.000000, Obj: 0.327468, No Obj: 0.005124, Avg Recall: 0.552381, count: 105 -2906: 0.478682, 0.475156 avg, 0.001000 rate, 19.554655 seconds, 743936 images -Loaded: 1.810183 seconds -Region Avg IOU: 0.488210, Class: 1.000000, Obj: 0.346779, No Obj: 0.005367, Avg Recall: 0.527132, count: 129 -Region Avg IOU: 0.488275, Class: 1.000000, Obj: 0.367783, No Obj: 0.005318, Avg Recall: 0.553398, count: 103 -Region Avg IOU: 0.421985, Class: 1.000000, Obj: 0.334190, No Obj: 0.004434, Avg Recall: 0.412371, count: 97 -Region Avg IOU: 0.505299, Class: 1.000000, Obj: 0.369973, No Obj: 0.005817, Avg Recall: 0.584746, count: 118 -Region Avg IOU: 0.455750, Class: 1.000000, Obj: 0.305317, No Obj: 0.003733, Avg Recall: 0.554217, count: 83 -Region Avg IOU: 0.474811, Class: 1.000000, Obj: 0.364488, No Obj: 0.004510, Avg Recall: 0.475248, count: 101 -Region Avg IOU: 0.494054, Class: 1.000000, Obj: 0.364293, No Obj: 0.003954, Avg Recall: 0.538462, count: 65 -Region Avg IOU: 0.450133, Class: 1.000000, Obj: 0.341502, No Obj: 0.005423, Avg Recall: 0.470199, count: 151 -2907: 0.467155, 0.474356 avg, 0.001000 rate, 18.853895 seconds, 744192 images -Loaded: 2.184151 seconds -Region Avg IOU: 0.439075, Class: 1.000000, Obj: 0.316830, No Obj: 0.004855, Avg Recall: 0.423729, count: 118 -Region Avg IOU: 0.494938, Class: 1.000000, Obj: 0.363093, No Obj: 0.004753, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.476878, Class: 1.000000, Obj: 0.347775, No Obj: 0.004944, Avg Recall: 0.535088, count: 114 -Region Avg IOU: 0.448813, Class: 1.000000, Obj: 0.327148, No Obj: 0.005144, Avg Recall: 0.503937, count: 127 -Region Avg IOU: 0.461215, Class: 1.000000, Obj: 0.369303, No Obj: 0.004595, Avg Recall: 0.500000, count: 90 -Region Avg IOU: 0.470860, Class: 1.000000, Obj: 0.277655, No Obj: 0.005389, Avg Recall: 0.563910, count: 133 -Region Avg IOU: 0.488867, Class: 1.000000, Obj: 0.312173, No Obj: 0.005299, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.467622, Class: 1.000000, Obj: 0.349780, No Obj: 0.005234, Avg Recall: 0.495652, count: 115 -2908: 0.465971, 0.473517 avg, 0.001000 rate, 17.354660 seconds, 744448 images -Loaded: 3.437674 seconds -Region Avg IOU: 0.496068, Class: 1.000000, Obj: 0.342788, No Obj: 0.003869, Avg Recall: 0.513889, count: 72 -Region Avg IOU: 0.517149, Class: 1.000000, Obj: 0.373643, No Obj: 0.005934, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.484843, Class: 1.000000, Obj: 0.332838, No Obj: 0.004526, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.512614, Class: 1.000000, Obj: 0.378492, No Obj: 0.006254, Avg Recall: 0.574803, count: 127 -Region Avg IOU: 0.481493, Class: 1.000000, Obj: 0.340294, No Obj: 0.005826, Avg Recall: 0.504000, count: 125 -Region Avg IOU: 0.510862, Class: 1.000000, Obj: 0.350417, No Obj: 0.005238, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.457280, Class: 1.000000, Obj: 0.320754, No Obj: 0.004532, Avg Recall: 0.530612, count: 98 -Region Avg IOU: 0.506847, Class: 1.000000, Obj: 0.370966, No Obj: 0.004928, Avg Recall: 0.611650, count: 103 -2909: 0.449718, 0.471138 avg, 0.001000 rate, 20.679171 seconds, 744704 images -Loaded: 0.316127 seconds -Region Avg IOU: 0.510792, Class: 1.000000, Obj: 0.346241, No Obj: 0.004433, Avg Recall: 0.595745, count: 94 -Region Avg IOU: 0.460271, Class: 1.000000, Obj: 0.375068, No Obj: 0.004973, Avg Recall: 0.469388, count: 98 -Region Avg IOU: 0.464197, Class: 1.000000, Obj: 0.324338, No Obj: 0.005021, Avg Recall: 0.504348, count: 115 -Region Avg IOU: 0.481218, Class: 1.000000, Obj: 0.354825, No Obj: 0.004897, Avg Recall: 0.540000, count: 100 -Region Avg IOU: 0.460675, Class: 1.000000, Obj: 0.347252, No Obj: 0.004068, Avg Recall: 0.523810, count: 84 -Region Avg IOU: 0.489679, Class: 1.000000, Obj: 0.294819, No Obj: 0.004058, Avg Recall: 0.597938, count: 97 -Region Avg IOU: 0.488812, Class: 1.000000, Obj: 0.347338, No Obj: 0.004218, Avg Recall: 0.565217, count: 92 -Region Avg IOU: 0.492463, Class: 1.000000, Obj: 0.310561, No Obj: 0.004980, Avg Recall: 0.537190, count: 121 -2910: 0.435601, 0.467584 avg, 0.001000 rate, 18.270697 seconds, 744960 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000079 seconds -Region Avg IOU: 0.581325, Class: 1.000000, Obj: 0.400061, No Obj: 0.003798, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.538952, Class: 1.000000, Obj: 0.403063, No Obj: 0.003488, Avg Recall: 0.646341, count: 82 -Region Avg IOU: 0.629212, Class: 1.000000, Obj: 0.450360, No Obj: 0.003276, Avg Recall: 0.829545, count: 88 -Region Avg IOU: 0.566681, Class: 1.000000, Obj: 0.407170, No Obj: 0.003899, Avg Recall: 0.685484, count: 124 -Region Avg IOU: 0.582320, Class: 1.000000, Obj: 0.414315, No Obj: 0.004128, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.537263, Class: 1.000000, Obj: 0.398378, No Obj: 0.003591, Avg Recall: 0.626984, count: 126 -Region Avg IOU: 0.559521, Class: 1.000000, Obj: 0.387920, No Obj: 0.003710, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.545875, Class: 1.000000, Obj: 0.408684, No Obj: 0.003733, Avg Recall: 0.644068, count: 118 -2911: 0.475163, 0.468342 avg, 0.001000 rate, 26.626148 seconds, 745216 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.578604, Class: 1.000000, Obj: 0.387479, No Obj: 0.002964, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.577639, Class: 1.000000, Obj: 0.372090, No Obj: 0.003286, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.604077, Class: 1.000000, Obj: 0.427374, No Obj: 0.002911, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.606756, Class: 1.000000, Obj: 0.368458, No Obj: 0.003547, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.588034, Class: 1.000000, Obj: 0.381009, No Obj: 0.003459, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.639141, Class: 1.000000, Obj: 0.375886, No Obj: 0.004029, Avg Recall: 0.775862, count: 116 -Region Avg IOU: 0.582776, Class: 1.000000, Obj: 0.345855, No Obj: 0.003491, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.566990, Class: 1.000000, Obj: 0.313557, No Obj: 0.003157, Avg Recall: 0.721154, count: 104 -2912: 0.453708, 0.466878 avg, 0.001000 rate, 26.686766 seconds, 745472 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.587735, Class: 1.000000, Obj: 0.284074, No Obj: 0.003356, Avg Recall: 0.714286, count: 140 -Region Avg IOU: 0.609064, Class: 1.000000, Obj: 0.336474, No Obj: 0.003290, Avg Recall: 0.713178, count: 129 -Region Avg IOU: 0.631384, Class: 1.000000, Obj: 0.352064, No Obj: 0.002967, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.560931, Class: 1.000000, Obj: 0.336725, No Obj: 0.003167, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.587162, Class: 1.000000, Obj: 0.310355, No Obj: 0.003111, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.599875, Class: 1.000000, Obj: 0.320549, No Obj: 0.003271, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.605289, Class: 1.000000, Obj: 0.357973, No Obj: 0.002903, Avg Recall: 0.780488, count: 82 -Region Avg IOU: 0.547432, Class: 1.000000, Obj: 0.319901, No Obj: 0.002613, Avg Recall: 0.629214, count: 89 -2913: 0.457181, 0.465909 avg, 0.001000 rate, 25.617208 seconds, 745728 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.585700, Class: 1.000000, Obj: 0.298457, No Obj: 0.002615, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.619240, Class: 1.000000, Obj: 0.332939, No Obj: 0.002485, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.623835, Class: 1.000000, Obj: 0.339246, No Obj: 0.002887, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.572440, Class: 1.000000, Obj: 0.271850, No Obj: 0.002548, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.646641, Class: 1.000000, Obj: 0.302633, No Obj: 0.002812, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.604717, Class: 1.000000, Obj: 0.300370, No Obj: 0.002810, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.614408, Class: 1.000000, Obj: 0.325426, No Obj: 0.002451, Avg Recall: 0.739583, count: 96 -Region Avg IOU: 0.612436, Class: 1.000000, Obj: 0.340608, No Obj: 0.002451, Avg Recall: 0.741935, count: 93 -2914: 0.432235, 0.462541 avg, 0.001000 rate, 24.559914 seconds, 745984 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.601715, Class: 1.000000, Obj: 0.297798, No Obj: 0.002695, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.614656, Class: 1.000000, Obj: 0.307091, No Obj: 0.002567, Avg Recall: 0.755556, count: 90 -Region Avg IOU: 0.543708, Class: 1.000000, Obj: 0.292405, No Obj: 0.002324, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.635647, Class: 1.000000, Obj: 0.302869, No Obj: 0.002543, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.608794, Class: 1.000000, Obj: 0.325519, No Obj: 0.002662, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.535145, Class: 1.000000, Obj: 0.271959, No Obj: 0.001998, Avg Recall: 0.556962, count: 79 -Region Avg IOU: 0.588722, Class: 1.000000, Obj: 0.271803, No Obj: 0.002518, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.603512, Class: 1.000000, Obj: 0.297964, No Obj: 0.002658, Avg Recall: 0.694215, count: 121 -2915: 0.459656, 0.462253 avg, 0.001000 rate, 25.882614 seconds, 746240 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.584550, Class: 1.000000, Obj: 0.284643, No Obj: 0.002529, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.548388, Class: 1.000000, Obj: 0.241113, No Obj: 0.002289, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.577577, Class: 1.000000, Obj: 0.323381, No Obj: 0.003018, Avg Recall: 0.683544, count: 158 -Region Avg IOU: 0.599871, Class: 1.000000, Obj: 0.366773, No Obj: 0.002798, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.616924, Class: 1.000000, Obj: 0.290599, No Obj: 0.002620, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.594437, Class: 1.000000, Obj: 0.291323, No Obj: 0.002871, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.564619, Class: 1.000000, Obj: 0.259671, No Obj: 0.002420, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.606416, Class: 1.000000, Obj: 0.334328, No Obj: 0.002903, Avg Recall: 0.702703, count: 111 -2916: 0.471342, 0.463162 avg, 0.001000 rate, 25.351858 seconds, 746496 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.562246, Class: 1.000000, Obj: 0.278637, No Obj: 0.002767, Avg Recall: 0.642276, count: 123 -Region Avg IOU: 0.602901, Class: 1.000000, Obj: 0.339842, No Obj: 0.002925, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.593153, Class: 1.000000, Obj: 0.262371, No Obj: 0.002518, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.606111, Class: 1.000000, Obj: 0.315258, No Obj: 0.002347, Avg Recall: 0.679487, count: 78 -Region Avg IOU: 0.555745, Class: 1.000000, Obj: 0.297749, No Obj: 0.002691, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.644079, Class: 1.000000, Obj: 0.330314, No Obj: 0.002798, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.564799, Class: 1.000000, Obj: 0.327453, No Obj: 0.002657, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.592128, Class: 1.000000, Obj: 0.294421, No Obj: 0.002568, Avg Recall: 0.669725, count: 109 -2917: 0.474043, 0.464250 avg, 0.001000 rate, 27.044704 seconds, 746752 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.563107, Class: 1.000000, Obj: 0.355680, No Obj: 0.002929, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.597083, Class: 1.000000, Obj: 0.317481, No Obj: 0.002740, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.603162, Class: 1.000000, Obj: 0.362853, No Obj: 0.002759, Avg Recall: 0.684783, count: 92 -Region Avg IOU: 0.619440, Class: 1.000000, Obj: 0.327455, No Obj: 0.003189, Avg Recall: 0.791667, count: 120 -Region Avg IOU: 0.605653, Class: 1.000000, Obj: 0.302109, No Obj: 0.003266, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.627497, Class: 1.000000, Obj: 0.329922, No Obj: 0.003556, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.615085, Class: 1.000000, Obj: 0.359013, No Obj: 0.002834, Avg Recall: 0.744186, count: 86 -Region Avg IOU: 0.620227, Class: 1.000000, Obj: 0.356767, No Obj: 0.003007, Avg Recall: 0.767677, count: 99 -2918: 0.503357, 0.468160 avg, 0.001000 rate, 25.504974 seconds, 747008 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.608151, Class: 1.000000, Obj: 0.346259, No Obj: 0.003404, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.646002, Class: 1.000000, Obj: 0.343512, No Obj: 0.003413, Avg Recall: 0.844828, count: 116 -Region Avg IOU: 0.550692, Class: 1.000000, Obj: 0.300395, No Obj: 0.003060, Avg Recall: 0.639098, count: 133 -Region Avg IOU: 0.584211, Class: 1.000000, Obj: 0.352026, No Obj: 0.002685, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.575623, Class: 1.000000, Obj: 0.351934, No Obj: 0.002803, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.583522, Class: 1.000000, Obj: 0.372463, No Obj: 0.003130, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.636287, Class: 1.000000, Obj: 0.352335, No Obj: 0.002663, Avg Recall: 0.789474, count: 76 -Region Avg IOU: 0.635198, Class: 1.000000, Obj: 0.375152, No Obj: 0.002657, Avg Recall: 0.787500, count: 80 -2919: 0.425470, 0.463891 avg, 0.001000 rate, 26.415401 seconds, 747264 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.600081, Class: 1.000000, Obj: 0.373094, No Obj: 0.003294, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.580960, Class: 1.000000, Obj: 0.386347, No Obj: 0.003231, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.620538, Class: 1.000000, Obj: 0.437072, No Obj: 0.003339, Avg Recall: 0.758621, count: 87 -Region Avg IOU: 0.581430, Class: 1.000000, Obj: 0.367145, No Obj: 0.003608, Avg Recall: 0.712000, count: 125 -Region Avg IOU: 0.641875, Class: 1.000000, Obj: 0.400161, No Obj: 0.003629, Avg Recall: 0.796117, count: 103 -Region Avg IOU: 0.592817, Class: 1.000000, Obj: 0.331533, No Obj: 0.003658, Avg Recall: 0.707143, count: 140 -Region Avg IOU: 0.581930, Class: 1.000000, Obj: 0.374009, No Obj: 0.003446, Avg Recall: 0.705426, count: 129 -Region Avg IOU: 0.626595, Class: 1.000000, Obj: 0.394049, No Obj: 0.003753, Avg Recall: 0.798165, count: 109 -2920: 0.462737, 0.463776 avg, 0.001000 rate, 25.086969 seconds, 747520 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000075 seconds -Region Avg IOU: 0.625527, Class: 1.000000, Obj: 0.379381, No Obj: 0.003642, Avg Recall: 0.765517, count: 145 -Region Avg IOU: 0.639668, Class: 1.000000, Obj: 0.373735, No Obj: 0.003043, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.637714, Class: 1.000000, Obj: 0.418350, No Obj: 0.002591, Avg Recall: 0.744186, count: 86 -Region Avg IOU: 0.607562, Class: 1.000000, Obj: 0.422242, No Obj: 0.003047, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.589439, Class: 1.000000, Obj: 0.367968, No Obj: 0.002810, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.620051, Class: 1.000000, Obj: 0.384438, No Obj: 0.003142, Avg Recall: 0.768595, count: 121 -Region Avg IOU: 0.649966, Class: 1.000000, Obj: 0.425873, No Obj: 0.003070, Avg Recall: 0.868687, count: 99 -Region Avg IOU: 0.599574, Class: 1.000000, Obj: 0.335339, No Obj: 0.003370, Avg Recall: 0.707317, count: 123 -2921: 0.474907, 0.464889 avg, 0.001000 rate, 28.204704 seconds, 747776 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.614957, Class: 1.000000, Obj: 0.380183, No Obj: 0.003025, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.624698, Class: 1.000000, Obj: 0.386498, No Obj: 0.002813, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.638088, Class: 1.000000, Obj: 0.365936, No Obj: 0.003382, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.621515, Class: 1.000000, Obj: 0.382207, No Obj: 0.003008, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.643660, Class: 1.000000, Obj: 0.358903, No Obj: 0.003285, Avg Recall: 0.752066, count: 121 -Region Avg IOU: 0.626706, Class: 1.000000, Obj: 0.454487, No Obj: 0.002894, Avg Recall: 0.775281, count: 89 -Region Avg IOU: 0.608315, Class: 1.000000, Obj: 0.385049, No Obj: 0.002767, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.630735, Class: 1.000000, Obj: 0.430728, No Obj: 0.003159, Avg Recall: 0.750000, count: 124 -2922: 0.487065, 0.467107 avg, 0.001000 rate, 29.935753 seconds, 748032 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.584479, Class: 1.000000, Obj: 0.380227, No Obj: 0.002874, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.630570, Class: 1.000000, Obj: 0.333234, No Obj: 0.003476, Avg Recall: 0.818792, count: 149 -Region Avg IOU: 0.627379, Class: 1.000000, Obj: 0.373170, No Obj: 0.003022, Avg Recall: 0.788732, count: 142 -Region Avg IOU: 0.622521, Class: 1.000000, Obj: 0.338362, No Obj: 0.002859, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.626478, Class: 1.000000, Obj: 0.394105, No Obj: 0.003621, Avg Recall: 0.769737, count: 152 -Region Avg IOU: 0.562856, Class: 1.000000, Obj: 0.359643, No Obj: 0.002934, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.634438, Class: 1.000000, Obj: 0.368292, No Obj: 0.002648, Avg Recall: 0.777778, count: 90 -Region Avg IOU: 0.611014, Class: 1.000000, Obj: 0.372781, No Obj: 0.002708, Avg Recall: 0.705263, count: 95 -2923: 0.527594, 0.473155 avg, 0.001000 rate, 28.904066 seconds, 748288 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.629581, Class: 1.000000, Obj: 0.389625, No Obj: 0.002438, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.654111, Class: 1.000000, Obj: 0.302285, No Obj: 0.002808, Avg Recall: 0.800000, count: 95 -Region Avg IOU: 0.651954, Class: 1.000000, Obj: 0.361314, No Obj: 0.002833, Avg Recall: 0.850877, count: 114 -Region Avg IOU: 0.666931, Class: 1.000000, Obj: 0.356269, No Obj: 0.002536, Avg Recall: 0.870130, count: 77 -Region Avg IOU: 0.677480, Class: 1.000000, Obj: 0.364955, No Obj: 0.003058, Avg Recall: 0.865546, count: 119 -Region Avg IOU: 0.573014, Class: 1.000000, Obj: 0.344844, No Obj: 0.002523, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.620003, Class: 1.000000, Obj: 0.262018, No Obj: 0.002743, Avg Recall: 0.765625, count: 128 -Region Avg IOU: 0.639966, Class: 1.000000, Obj: 0.385194, No Obj: 0.002556, Avg Recall: 0.808511, count: 94 -2924: 0.464610, 0.472301 avg, 0.001000 rate, 30.919586 seconds, 748544 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.615646, Class: 1.000000, Obj: 0.377265, No Obj: 0.002231, Avg Recall: 0.744681, count: 94 -Region Avg IOU: 0.611775, Class: 1.000000, Obj: 0.336134, No Obj: 0.002420, Avg Recall: 0.759399, count: 133 -Region Avg IOU: 0.603388, Class: 1.000000, Obj: 0.299540, No Obj: 0.002729, Avg Recall: 0.710145, count: 138 -Region Avg IOU: 0.624029, Class: 1.000000, Obj: 0.343701, No Obj: 0.002474, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.611853, Class: 1.000000, Obj: 0.354678, No Obj: 0.002427, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.656590, Class: 1.000000, Obj: 0.380168, No Obj: 0.002340, Avg Recall: 0.825581, count: 86 -Region Avg IOU: 0.626446, Class: 1.000000, Obj: 0.362412, No Obj: 0.002484, Avg Recall: 0.753086, count: 81 -Region Avg IOU: 0.630708, Class: 1.000000, Obj: 0.331149, No Obj: 0.002694, Avg Recall: 0.781818, count: 110 -2925: 0.469246, 0.471995 avg, 0.001000 rate, 29.899839 seconds, 748800 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.606228, Class: 1.000000, Obj: 0.294219, No Obj: 0.002570, Avg Recall: 0.748092, count: 131 -Region Avg IOU: 0.598432, Class: 1.000000, Obj: 0.268565, No Obj: 0.002154, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.615129, Class: 1.000000, Obj: 0.351373, No Obj: 0.002358, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.599853, Class: 1.000000, Obj: 0.309289, No Obj: 0.002233, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.611775, Class: 1.000000, Obj: 0.307897, No Obj: 0.002346, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.575408, Class: 1.000000, Obj: 0.339123, No Obj: 0.002352, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.578937, Class: 1.000000, Obj: 0.298529, No Obj: 0.002335, Avg Recall: 0.702479, count: 121 -Region Avg IOU: 0.637288, Class: 1.000000, Obj: 0.311772, No Obj: 0.002448, Avg Recall: 0.801887, count: 106 -2926: 0.455701, 0.470366 avg, 0.001000 rate, 29.985031 seconds, 749056 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.645106, Class: 1.000000, Obj: 0.305141, No Obj: 0.002444, Avg Recall: 0.809524, count: 126 -Region Avg IOU: 0.655985, Class: 1.000000, Obj: 0.345925, No Obj: 0.002298, Avg Recall: 0.824742, count: 97 -Region Avg IOU: 0.624638, Class: 1.000000, Obj: 0.315185, No Obj: 0.002329, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.629826, Class: 1.000000, Obj: 0.305266, No Obj: 0.002247, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.619038, Class: 1.000000, Obj: 0.264210, No Obj: 0.002387, Avg Recall: 0.789474, count: 114 -Region Avg IOU: 0.670112, Class: 1.000000, Obj: 0.327183, No Obj: 0.002143, Avg Recall: 0.831579, count: 95 -Region Avg IOU: 0.642577, Class: 1.000000, Obj: 0.357564, No Obj: 0.002243, Avg Recall: 0.788889, count: 90 -Region Avg IOU: 0.595913, Class: 1.000000, Obj: 0.278291, No Obj: 0.002415, Avg Recall: 0.721311, count: 122 -2927: 0.499586, 0.473288 avg, 0.001000 rate, 29.043715 seconds, 749312 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.579043, Class: 1.000000, Obj: 0.241562, No Obj: 0.002021, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.623931, Class: 1.000000, Obj: 0.257526, No Obj: 0.002546, Avg Recall: 0.780822, count: 146 -Region Avg IOU: 0.621718, Class: 1.000000, Obj: 0.317712, No Obj: 0.002110, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.619956, Class: 1.000000, Obj: 0.278626, No Obj: 0.002295, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.665742, Class: 1.000000, Obj: 0.249172, No Obj: 0.002445, Avg Recall: 0.893805, count: 113 -Region Avg IOU: 0.621318, Class: 1.000000, Obj: 0.271670, No Obj: 0.002517, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.618613, Class: 1.000000, Obj: 0.266384, No Obj: 0.002195, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.638450, Class: 1.000000, Obj: 0.268537, No Obj: 0.002060, Avg Recall: 0.810526, count: 95 -2928: 0.517687, 0.477728 avg, 0.001000 rate, 28.148487 seconds, 749568 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.634851, Class: 1.000000, Obj: 0.273549, No Obj: 0.002524, Avg Recall: 0.788136, count: 118 -Region Avg IOU: 0.622018, Class: 1.000000, Obj: 0.282405, No Obj: 0.002165, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.624004, Class: 1.000000, Obj: 0.319340, No Obj: 0.002305, Avg Recall: 0.788618, count: 123 -Region Avg IOU: 0.621488, Class: 1.000000, Obj: 0.307546, No Obj: 0.001897, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.645202, Class: 1.000000, Obj: 0.294891, No Obj: 0.002363, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.648318, Class: 1.000000, Obj: 0.326141, No Obj: 0.002306, Avg Recall: 0.824176, count: 91 -Region Avg IOU: 0.589915, Class: 1.000000, Obj: 0.287209, No Obj: 0.002320, Avg Recall: 0.705882, count: 136 -Region Avg IOU: 0.617091, Class: 1.000000, Obj: 0.310239, No Obj: 0.002463, Avg Recall: 0.744000, count: 125 -2929: 0.491907, 0.479146 avg, 0.001000 rate, 29.146996 seconds, 749824 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.651912, Class: 1.000000, Obj: 0.366477, No Obj: 0.002520, Avg Recall: 0.831579, count: 95 -Region Avg IOU: 0.586508, Class: 1.000000, Obj: 0.308230, No Obj: 0.002453, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.669533, Class: 1.000000, Obj: 0.327829, No Obj: 0.002503, Avg Recall: 0.802198, count: 91 -Region Avg IOU: 0.616364, Class: 1.000000, Obj: 0.331669, No Obj: 0.002671, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.636885, Class: 1.000000, Obj: 0.336031, No Obj: 0.002937, Avg Recall: 0.814815, count: 135 -Region Avg IOU: 0.614441, Class: 1.000000, Obj: 0.342803, No Obj: 0.002661, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.633871, Class: 1.000000, Obj: 0.305770, No Obj: 0.002586, Avg Recall: 0.820513, count: 117 -Region Avg IOU: 0.646239, Class: 1.000000, Obj: 0.335277, No Obj: 0.002792, Avg Recall: 0.798319, count: 119 -2930: 0.485627, 0.479794 avg, 0.001000 rate, 29.953976 seconds, 750080 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 12.338672 seconds -Region Avg IOU: 0.483540, Class: 1.000000, Obj: 0.244676, No Obj: 0.003953, Avg Recall: 0.532710, count: 107 -Region Avg IOU: 0.489663, Class: 1.000000, Obj: 0.218734, No Obj: 0.003757, Avg Recall: 0.537736, count: 106 -Region Avg IOU: 0.447435, Class: 1.000000, Obj: 0.188560, No Obj: 0.003619, Avg Recall: 0.471154, count: 104 -Region Avg IOU: 0.492965, Class: 1.000000, Obj: 0.247560, No Obj: 0.003681, Avg Recall: 0.580952, count: 105 -Region Avg IOU: 0.440617, Class: 1.000000, Obj: 0.219548, No Obj: 0.003279, Avg Recall: 0.467290, count: 107 -Region Avg IOU: 0.471320, Class: 1.000000, Obj: 0.239542, No Obj: 0.003616, Avg Recall: 0.515152, count: 99 -Region Avg IOU: 0.497700, Class: 1.000000, Obj: 0.261242, No Obj: 0.003707, Avg Recall: 0.555556, count: 90 -Region Avg IOU: 0.434704, Class: 1.000000, Obj: 0.191996, No Obj: 0.003551, Avg Recall: 0.483607, count: 122 -2931: 0.604429, 0.492257 avg, 0.001000 rate, 17.777040 seconds, 750336 images -Loaded: 3.416653 seconds -Region Avg IOU: 0.462188, Class: 1.000000, Obj: 0.257128, No Obj: 0.003367, Avg Recall: 0.505882, count: 85 -Region Avg IOU: 0.452383, Class: 1.000000, Obj: 0.223754, No Obj: 0.003907, Avg Recall: 0.491667, count: 120 -Region Avg IOU: 0.470614, Class: 1.000000, Obj: 0.246310, No Obj: 0.003951, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.494418, Class: 1.000000, Obj: 0.274196, No Obj: 0.004087, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.405222, Class: 1.000000, Obj: 0.228266, No Obj: 0.003313, Avg Recall: 0.471154, count: 104 -Region Avg IOU: 0.480995, Class: 1.000000, Obj: 0.248987, No Obj: 0.003952, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.458333, Class: 1.000000, Obj: 0.254779, No Obj: 0.003736, Avg Recall: 0.515152, count: 99 -Region Avg IOU: 0.485797, Class: 1.000000, Obj: 0.257718, No Obj: 0.004590, Avg Recall: 0.550847, count: 118 -2932: 0.587425, 0.501774 avg, 0.001000 rate, 18.029648 seconds, 750592 images -Loaded: 2.096222 seconds -Region Avg IOU: 0.484552, Class: 1.000000, Obj: 0.287998, No Obj: 0.005521, Avg Recall: 0.535948, count: 153 -Region Avg IOU: 0.470002, Class: 1.000000, Obj: 0.247223, No Obj: 0.005137, Avg Recall: 0.456000, count: 125 -Region Avg IOU: 0.434545, Class: 1.000000, Obj: 0.304306, No Obj: 0.004528, Avg Recall: 0.464567, count: 127 -Region Avg IOU: 0.476333, Class: 1.000000, Obj: 0.302851, No Obj: 0.005206, Avg Recall: 0.516667, count: 120 -Region Avg IOU: 0.494090, Class: 1.000000, Obj: 0.321367, No Obj: 0.004837, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.487546, Class: 1.000000, Obj: 0.285985, No Obj: 0.005152, Avg Recall: 0.550725, count: 138 -Region Avg IOU: 0.465085, Class: 1.000000, Obj: 0.259374, No Obj: 0.004039, Avg Recall: 0.490000, count: 100 -Region Avg IOU: 0.499829, Class: 1.000000, Obj: 0.294190, No Obj: 0.004329, Avg Recall: 0.583333, count: 84 -2933: 0.589916, 0.510588 avg, 0.001000 rate, 17.984848 seconds, 750848 images -Loaded: 3.547826 seconds -Region Avg IOU: 0.469928, Class: 1.000000, Obj: 0.296896, No Obj: 0.005387, Avg Recall: 0.515873, count: 126 -Region Avg IOU: 0.447917, Class: 1.000000, Obj: 0.316645, No Obj: 0.004974, Avg Recall: 0.495327, count: 107 -Region Avg IOU: 0.451469, Class: 1.000000, Obj: 0.296677, No Obj: 0.005063, Avg Recall: 0.514019, count: 107 -Region Avg IOU: 0.462740, Class: 1.000000, Obj: 0.258102, No Obj: 0.005664, Avg Recall: 0.532258, count: 124 -Region Avg IOU: 0.484734, Class: 1.000000, Obj: 0.361226, No Obj: 0.005374, Avg Recall: 0.532609, count: 92 -Region Avg IOU: 0.424092, Class: 1.000000, Obj: 0.326175, No Obj: 0.005543, Avg Recall: 0.469565, count: 115 -Region Avg IOU: 0.456441, Class: 1.000000, Obj: 0.332725, No Obj: 0.004258, Avg Recall: 0.465909, count: 88 -Region Avg IOU: 0.458277, Class: 1.000000, Obj: 0.303026, No Obj: 0.005231, Avg Recall: 0.484127, count: 126 -2934: 0.496517, 0.509181 avg, 0.001000 rate, 15.686548 seconds, 751104 images -Loaded: 4.996861 seconds -Region Avg IOU: 0.457484, Class: 1.000000, Obj: 0.339134, No Obj: 0.004823, Avg Recall: 0.516484, count: 91 -Region Avg IOU: 0.455888, Class: 1.000000, Obj: 0.356636, No Obj: 0.005561, Avg Recall: 0.530864, count: 81 -Region Avg IOU: 0.477232, Class: 1.000000, Obj: 0.315057, No Obj: 0.004995, Avg Recall: 0.473684, count: 95 -Region Avg IOU: 0.422161, Class: 1.000000, Obj: 0.314262, No Obj: 0.005640, Avg Recall: 0.483051, count: 118 -Region Avg IOU: 0.429925, Class: 1.000000, Obj: 0.305365, No Obj: 0.005746, Avg Recall: 0.383333, count: 120 -Region Avg IOU: 0.461333, Class: 1.000000, Obj: 0.374677, No Obj: 0.005350, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.428124, Class: 1.000000, Obj: 0.355154, No Obj: 0.005149, Avg Recall: 0.475248, count: 101 -Region Avg IOU: 0.464494, Class: 1.000000, Obj: 0.360460, No Obj: 0.006396, Avg Recall: 0.511450, count: 131 -2935: 0.476504, 0.505914 avg, 0.001000 rate, 18.120985 seconds, 751360 images -Loaded: 1.676954 seconds -Region Avg IOU: 0.441225, Class: 1.000000, Obj: 0.349010, No Obj: 0.006025, Avg Recall: 0.440000, count: 125 -Region Avg IOU: 0.398291, Class: 1.000000, Obj: 0.363028, No Obj: 0.005639, Avg Recall: 0.438596, count: 114 -Region Avg IOU: 0.486133, Class: 1.000000, Obj: 0.341536, No Obj: 0.006450, Avg Recall: 0.534351, count: 131 -Region Avg IOU: 0.413497, Class: 1.000000, Obj: 0.336743, No Obj: 0.005899, Avg Recall: 0.411765, count: 119 -Region Avg IOU: 0.440609, Class: 1.000000, Obj: 0.363228, No Obj: 0.005750, Avg Recall: 0.490385, count: 104 -Region Avg IOU: 0.496699, Class: 1.000000, Obj: 0.337976, No Obj: 0.006090, Avg Recall: 0.551020, count: 98 -Region Avg IOU: 0.435982, Class: 1.000000, Obj: 0.360053, No Obj: 0.006059, Avg Recall: 0.455446, count: 101 -Region Avg IOU: 0.496886, Class: 1.000000, Obj: 0.365185, No Obj: 0.006381, Avg Recall: 0.571429, count: 91 -2936: 0.478579, 0.503180 avg, 0.001000 rate, 19.207047 seconds, 751616 images -Loaded: 2.377645 seconds -Region Avg IOU: 0.437388, Class: 1.000000, Obj: 0.350028, No Obj: 0.006278, Avg Recall: 0.443396, count: 106 -Region Avg IOU: 0.425455, Class: 1.000000, Obj: 0.298865, No Obj: 0.005848, Avg Recall: 0.455357, count: 112 -Region Avg IOU: 0.472934, Class: 1.000000, Obj: 0.354678, No Obj: 0.004967, Avg Recall: 0.530864, count: 81 -Region Avg IOU: 0.475599, Class: 1.000000, Obj: 0.372924, No Obj: 0.006091, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.398980, Class: 1.000000, Obj: 0.343927, No Obj: 0.005439, Avg Recall: 0.402597, count: 77 -Region Avg IOU: 0.453081, Class: 1.000000, Obj: 0.379791, No Obj: 0.006018, Avg Recall: 0.516484, count: 91 -Region Avg IOU: 0.422222, Class: 1.000000, Obj: 0.359676, No Obj: 0.005221, Avg Recall: 0.466102, count: 118 -Region Avg IOU: 0.413358, Class: 1.000000, Obj: 0.337753, No Obj: 0.006008, Avg Recall: 0.441441, count: 111 -2937: 0.482606, 0.501123 avg, 0.001000 rate, 19.129580 seconds, 751872 images -Loaded: 2.860954 seconds -Region Avg IOU: 0.394845, Class: 1.000000, Obj: 0.344378, No Obj: 0.005418, Avg Recall: 0.336735, count: 98 -Region Avg IOU: 0.446894, Class: 1.000000, Obj: 0.346540, No Obj: 0.006030, Avg Recall: 0.500000, count: 128 -Region Avg IOU: 0.402971, Class: 1.000000, Obj: 0.342123, No Obj: 0.005692, Avg Recall: 0.392523, count: 107 -Region Avg IOU: 0.438752, Class: 1.000000, Obj: 0.365953, No Obj: 0.005934, Avg Recall: 0.442308, count: 104 -Region Avg IOU: 0.389345, Class: 1.000000, Obj: 0.348526, No Obj: 0.005795, Avg Recall: 0.377358, count: 106 -Region Avg IOU: 0.457176, Class: 1.000000, Obj: 0.341238, No Obj: 0.005583, Avg Recall: 0.520833, count: 96 -Region Avg IOU: 0.399620, Class: 1.000000, Obj: 0.330939, No Obj: 0.006412, Avg Recall: 0.421053, count: 114 -Region Avg IOU: 0.391462, Class: 1.000000, Obj: 0.327603, No Obj: 0.004830, Avg Recall: 0.414414, count: 111 -2938: 0.537147, 0.504725 avg, 0.001000 rate, 19.060160 seconds, 752128 images -Loaded: 1.656609 seconds -Region Avg IOU: 0.419104, Class: 1.000000, Obj: 0.318013, No Obj: 0.005512, Avg Recall: 0.411215, count: 107 -Region Avg IOU: 0.357731, Class: 1.000000, Obj: 0.281076, No Obj: 0.004974, Avg Recall: 0.325000, count: 120 -Region Avg IOU: 0.445936, Class: 1.000000, Obj: 0.356106, No Obj: 0.005904, Avg Recall: 0.472727, count: 110 -Region Avg IOU: 0.426272, Class: 1.000000, Obj: 0.277175, No Obj: 0.004676, Avg Recall: 0.447917, count: 96 -Region Avg IOU: 0.398079, Class: 1.000000, Obj: 0.300541, No Obj: 0.005921, Avg Recall: 0.400000, count: 140 -Region Avg IOU: 0.435567, Class: 1.000000, Obj: 0.331884, No Obj: 0.004799, Avg Recall: 0.472527, count: 91 -Region Avg IOU: 0.482824, Class: 1.000000, Obj: 0.349820, No Obj: 0.005859, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.435547, Class: 1.000000, Obj: 0.339921, No Obj: 0.005263, Avg Recall: 0.462366, count: 93 -2939: 0.544122, 0.508665 avg, 0.001000 rate, 15.935261 seconds, 752384 images -Loaded: 4.569376 seconds -Region Avg IOU: 0.459638, Class: 1.000000, Obj: 0.316199, No Obj: 0.004870, Avg Recall: 0.549550, count: 111 -Region Avg IOU: 0.415199, Class: 1.000000, Obj: 0.316989, No Obj: 0.005197, Avg Recall: 0.442623, count: 122 -Region Avg IOU: 0.371009, Class: 1.000000, Obj: 0.298877, No Obj: 0.004327, Avg Recall: 0.368932, count: 103 -Region Avg IOU: 0.435028, Class: 1.000000, Obj: 0.293021, No Obj: 0.005051, Avg Recall: 0.500000, count: 112 -Region Avg IOU: 0.384550, Class: 1.000000, Obj: 0.258500, No Obj: 0.004908, Avg Recall: 0.411765, count: 119 -Region Avg IOU: 0.444301, Class: 1.000000, Obj: 0.282818, No Obj: 0.004440, Avg Recall: 0.448276, count: 87 -Region Avg IOU: 0.408763, Class: 1.000000, Obj: 0.283155, No Obj: 0.005260, Avg Recall: 0.408696, count: 115 -Region Avg IOU: 0.429476, Class: 1.000000, Obj: 0.295914, No Obj: 0.004777, Avg Recall: 0.495727, count: 117 -2940: 0.568619, 0.514660 avg, 0.001000 rate, 15.325801 seconds, 752640 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000064 seconds -Region Avg IOU: 0.571927, Class: 1.000000, Obj: 0.410868, No Obj: 0.004584, Avg Recall: 0.673077, count: 156 -Region Avg IOU: 0.574542, Class: 1.000000, Obj: 0.439647, No Obj: 0.003852, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.509722, Class: 1.000000, Obj: 0.390635, No Obj: 0.003993, Avg Recall: 0.592308, count: 130 -Region Avg IOU: 0.591413, Class: 1.000000, Obj: 0.387464, No Obj: 0.003169, Avg Recall: 0.746479, count: 71 -Region Avg IOU: 0.575798, Class: 1.000000, Obj: 0.445997, No Obj: 0.003631, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.551865, Class: 1.000000, Obj: 0.417426, No Obj: 0.004265, Avg Recall: 0.614173, count: 127 -Region Avg IOU: 0.559191, Class: 1.000000, Obj: 0.336668, No Obj: 0.003999, Avg Recall: 0.653226, count: 124 -Region Avg IOU: 0.574502, Class: 1.000000, Obj: 0.411231, No Obj: 0.003780, Avg Recall: 0.715789, count: 95 -2941: 0.544005, 0.517595 avg, 0.001000 rate, 24.648365 seconds, 752896 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.595432, Class: 1.000000, Obj: 0.357672, No Obj: 0.003100, Avg Recall: 0.771084, count: 83 -Region Avg IOU: 0.562283, Class: 1.000000, Obj: 0.370485, No Obj: 0.003076, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.529416, Class: 1.000000, Obj: 0.333255, No Obj: 0.002591, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.555502, Class: 1.000000, Obj: 0.346834, No Obj: 0.003283, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.570975, Class: 1.000000, Obj: 0.393760, No Obj: 0.003030, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.574840, Class: 1.000000, Obj: 0.394884, No Obj: 0.003242, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.544042, Class: 1.000000, Obj: 0.347675, No Obj: 0.003133, Avg Recall: 0.638554, count: 83 -Region Avg IOU: 0.595815, Class: 1.000000, Obj: 0.349268, No Obj: 0.003379, Avg Recall: 0.754545, count: 110 -2942: 0.449431, 0.510778 avg, 0.001000 rate, 25.423237 seconds, 753152 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.556156, Class: 1.000000, Obj: 0.323702, No Obj: 0.002654, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.566866, Class: 1.000000, Obj: 0.311051, No Obj: 0.002804, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.578901, Class: 1.000000, Obj: 0.322430, No Obj: 0.002449, Avg Recall: 0.657895, count: 76 -Region Avg IOU: 0.610525, Class: 1.000000, Obj: 0.313168, No Obj: 0.002685, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.619384, Class: 1.000000, Obj: 0.341179, No Obj: 0.002818, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.598331, Class: 1.000000, Obj: 0.315834, No Obj: 0.002754, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.613560, Class: 1.000000, Obj: 0.352672, No Obj: 0.002661, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.593136, Class: 1.000000, Obj: 0.306142, No Obj: 0.003033, Avg Recall: 0.717557, count: 131 -2943: 0.446072, 0.504308 avg, 0.001000 rate, 25.628572 seconds, 753408 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.579733, Class: 1.000000, Obj: 0.284380, No Obj: 0.002419, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.634601, Class: 1.000000, Obj: 0.303759, No Obj: 0.002432, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.593628, Class: 1.000000, Obj: 0.283620, No Obj: 0.002216, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.577688, Class: 1.000000, Obj: 0.259333, No Obj: 0.002665, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.581155, Class: 1.000000, Obj: 0.228938, No Obj: 0.002663, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.574675, Class: 1.000000, Obj: 0.296069, No Obj: 0.002331, Avg Recall: 0.696629, count: 89 -Region Avg IOU: 0.598756, Class: 1.000000, Obj: 0.265925, No Obj: 0.002688, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.602494, Class: 1.000000, Obj: 0.303354, No Obj: 0.002611, Avg Recall: 0.737864, count: 103 -2944: 0.502349, 0.504112 avg, 0.001000 rate, 25.412779 seconds, 753664 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.599214, Class: 1.000000, Obj: 0.254530, No Obj: 0.002411, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.625409, Class: 1.000000, Obj: 0.282153, No Obj: 0.002302, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.589876, Class: 1.000000, Obj: 0.259512, No Obj: 0.002212, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.627389, Class: 1.000000, Obj: 0.298745, No Obj: 0.001935, Avg Recall: 0.740741, count: 81 -Region Avg IOU: 0.593220, Class: 1.000000, Obj: 0.266413, No Obj: 0.002897, Avg Recall: 0.700000, count: 150 -Region Avg IOU: 0.624893, Class: 1.000000, Obj: 0.246313, No Obj: 0.002276, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.617652, Class: 1.000000, Obj: 0.267257, No Obj: 0.002215, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.631731, Class: 1.000000, Obj: 0.303088, No Obj: 0.002224, Avg Recall: 0.790000, count: 100 -2945: 0.513546, 0.505055 avg, 0.001000 rate, 26.147367 seconds, 753920 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.614194, Class: 1.000000, Obj: 0.264267, No Obj: 0.002719, Avg Recall: 0.734940, count: 166 -Region Avg IOU: 0.592549, Class: 1.000000, Obj: 0.266480, No Obj: 0.002327, Avg Recall: 0.695312, count: 128 -Region Avg IOU: 0.574318, Class: 1.000000, Obj: 0.261936, No Obj: 0.002467, Avg Recall: 0.681818, count: 132 -Region Avg IOU: 0.577870, Class: 1.000000, Obj: 0.276094, No Obj: 0.002650, Avg Recall: 0.709924, count: 131 -Region Avg IOU: 0.616714, Class: 1.000000, Obj: 0.280644, No Obj: 0.002412, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.588486, Class: 1.000000, Obj: 0.263559, No Obj: 0.002683, Avg Recall: 0.735294, count: 136 -Region Avg IOU: 0.591172, Class: 1.000000, Obj: 0.255080, No Obj: 0.002263, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.633211, Class: 1.000000, Obj: 0.315730, No Obj: 0.002211, Avg Recall: 0.800000, count: 85 -2946: 0.635562, 0.518106 avg, 0.001000 rate, 25.686495 seconds, 754176 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.581762, Class: 1.000000, Obj: 0.271738, No Obj: 0.002743, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.577347, Class: 1.000000, Obj: 0.328970, No Obj: 0.002841, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.552761, Class: 1.000000, Obj: 0.265978, No Obj: 0.002238, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.592038, Class: 1.000000, Obj: 0.278447, No Obj: 0.002273, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.552781, Class: 1.000000, Obj: 0.304628, No Obj: 0.001920, Avg Recall: 0.666667, count: 87 -Region Avg IOU: 0.583465, Class: 1.000000, Obj: 0.308209, No Obj: 0.002542, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.547815, Class: 1.000000, Obj: 0.267016, No Obj: 0.002493, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.598723, Class: 1.000000, Obj: 0.281976, No Obj: 0.002624, Avg Recall: 0.710526, count: 114 -2947: 0.519851, 0.518280 avg, 0.001000 rate, 24.473915 seconds, 754432 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.581642, Class: 1.000000, Obj: 0.317948, No Obj: 0.003101, Avg Recall: 0.689076, count: 119 -Region Avg IOU: 0.585372, Class: 1.000000, Obj: 0.338633, No Obj: 0.003009, Avg Recall: 0.728000, count: 125 -Region Avg IOU: 0.603898, Class: 1.000000, Obj: 0.280395, No Obj: 0.003115, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.588436, Class: 1.000000, Obj: 0.353839, No Obj: 0.002677, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.595055, Class: 1.000000, Obj: 0.344590, No Obj: 0.003191, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.584712, Class: 1.000000, Obj: 0.333085, No Obj: 0.002601, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.612109, Class: 1.000000, Obj: 0.362359, No Obj: 0.003061, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.555084, Class: 1.000000, Obj: 0.322184, No Obj: 0.002716, Avg Recall: 0.650000, count: 100 -2948: 0.509173, 0.517370 avg, 0.001000 rate, 26.621748 seconds, 754688 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.582471, Class: 1.000000, Obj: 0.400319, No Obj: 0.003361, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.609136, Class: 1.000000, Obj: 0.299683, No Obj: 0.003507, Avg Recall: 0.776923, count: 130 -Region Avg IOU: 0.533612, Class: 1.000000, Obj: 0.338118, No Obj: 0.003501, Avg Recall: 0.608696, count: 138 -Region Avg IOU: 0.622514, Class: 1.000000, Obj: 0.403407, No Obj: 0.003612, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.631598, Class: 1.000000, Obj: 0.345691, No Obj: 0.003471, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.581383, Class: 1.000000, Obj: 0.361260, No Obj: 0.003380, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.611877, Class: 1.000000, Obj: 0.366146, No Obj: 0.003879, Avg Recall: 0.738462, count: 130 -Region Avg IOU: 0.527365, Class: 1.000000, Obj: 0.300558, No Obj: 0.003363, Avg Recall: 0.621212, count: 132 -2949: 0.535838, 0.519217 avg, 0.001000 rate, 25.326859 seconds, 754944 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.632675, Class: 1.000000, Obj: 0.377550, No Obj: 0.004105, Avg Recall: 0.786260, count: 131 -Region Avg IOU: 0.606222, Class: 1.000000, Obj: 0.398914, No Obj: 0.003697, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.614040, Class: 1.000000, Obj: 0.410539, No Obj: 0.003989, Avg Recall: 0.730159, count: 126 -Region Avg IOU: 0.565647, Class: 1.000000, Obj: 0.338560, No Obj: 0.004146, Avg Recall: 0.651007, count: 149 -Region Avg IOU: 0.589006, Class: 1.000000, Obj: 0.386234, No Obj: 0.004356, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.598060, Class: 1.000000, Obj: 0.389876, No Obj: 0.003968, Avg Recall: 0.712644, count: 87 -Region Avg IOU: 0.589622, Class: 1.000000, Obj: 0.331775, No Obj: 0.003591, Avg Recall: 0.722222, count: 90 -Region Avg IOU: 0.553804, Class: 1.000000, Obj: 0.319613, No Obj: 0.003748, Avg Recall: 0.608696, count: 115 -2950: 0.552300, 0.522525 avg, 0.001000 rate, 26.911940 seconds, 755200 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000071 seconds -Region Avg IOU: 0.616596, Class: 1.000000, Obj: 0.469865, No Obj: 0.003115, Avg Recall: 0.746988, count: 83 -Region Avg IOU: 0.590199, Class: 1.000000, Obj: 0.410750, No Obj: 0.004327, Avg Recall: 0.664179, count: 134 -Region Avg IOU: 0.622121, Class: 1.000000, Obj: 0.471509, No Obj: 0.004057, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.608594, Class: 1.000000, Obj: 0.443172, No Obj: 0.003511, Avg Recall: 0.755556, count: 90 -Region Avg IOU: 0.600751, Class: 1.000000, Obj: 0.457406, No Obj: 0.003455, Avg Recall: 0.722222, count: 90 -Region Avg IOU: 0.626323, Class: 1.000000, Obj: 0.455149, No Obj: 0.003916, Avg Recall: 0.768000, count: 125 -Region Avg IOU: 0.638886, Class: 1.000000, Obj: 0.456661, No Obj: 0.003712, Avg Recall: 0.782258, count: 124 -Region Avg IOU: 0.622121, Class: 1.000000, Obj: 0.427304, No Obj: 0.003784, Avg Recall: 0.780952, count: 105 -2951: 0.462295, 0.516502 avg, 0.001000 rate, 27.104887 seconds, 755456 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.591960, Class: 1.000000, Obj: 0.419955, No Obj: 0.003878, Avg Recall: 0.704918, count: 122 -Region Avg IOU: 0.603880, Class: 1.000000, Obj: 0.398547, No Obj: 0.003885, Avg Recall: 0.776923, count: 130 -Region Avg IOU: 0.605264, Class: 1.000000, Obj: 0.457352, No Obj: 0.003478, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.594111, Class: 1.000000, Obj: 0.430389, No Obj: 0.003421, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.632089, Class: 1.000000, Obj: 0.437666, No Obj: 0.003421, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.599155, Class: 1.000000, Obj: 0.420630, No Obj: 0.003196, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.655976, Class: 1.000000, Obj: 0.423468, No Obj: 0.003743, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.604901, Class: 1.000000, Obj: 0.411766, No Obj: 0.002999, Avg Recall: 0.821429, count: 84 -2952: 0.484639, 0.513316 avg, 0.001000 rate, 29.097946 seconds, 755712 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.635970, Class: 1.000000, Obj: 0.370299, No Obj: 0.003804, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.559701, Class: 1.000000, Obj: 0.400785, No Obj: 0.002628, Avg Recall: 0.692308, count: 78 -Region Avg IOU: 0.620474, Class: 1.000000, Obj: 0.395877, No Obj: 0.002954, Avg Recall: 0.756098, count: 82 -Region Avg IOU: 0.642253, Class: 1.000000, Obj: 0.451342, No Obj: 0.002962, Avg Recall: 0.824324, count: 74 -Region Avg IOU: 0.627856, Class: 1.000000, Obj: 0.438095, No Obj: 0.002994, Avg Recall: 0.823529, count: 85 -Region Avg IOU: 0.648511, Class: 1.000000, Obj: 0.428491, No Obj: 0.003254, Avg Recall: 0.846939, count: 98 -Region Avg IOU: 0.591959, Class: 1.000000, Obj: 0.407932, No Obj: 0.003054, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.612537, Class: 1.000000, Obj: 0.436444, No Obj: 0.003448, Avg Recall: 0.750000, count: 124 -2953: 0.424037, 0.504388 avg, 0.001000 rate, 28.987356 seconds, 755968 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.586975, Class: 1.000000, Obj: 0.379122, No Obj: 0.002508, Avg Recall: 0.678161, count: 87 -Region Avg IOU: 0.585597, Class: 1.000000, Obj: 0.363870, No Obj: 0.002639, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.641168, Class: 1.000000, Obj: 0.355073, No Obj: 0.002797, Avg Recall: 0.834862, count: 109 -Region Avg IOU: 0.635745, Class: 1.000000, Obj: 0.365137, No Obj: 0.002602, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.616091, Class: 1.000000, Obj: 0.348775, No Obj: 0.002478, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.593987, Class: 1.000000, Obj: 0.354471, No Obj: 0.002762, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.577857, Class: 1.000000, Obj: 0.314177, No Obj: 0.002775, Avg Recall: 0.668874, count: 151 -Region Avg IOU: 0.633333, Class: 1.000000, Obj: 0.319334, No Obj: 0.002509, Avg Recall: 0.768421, count: 95 -2954: 0.504470, 0.504396 avg, 0.001000 rate, 28.226532 seconds, 756224 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.612479, Class: 1.000000, Obj: 0.332782, No Obj: 0.002714, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.574758, Class: 1.000000, Obj: 0.259272, No Obj: 0.002171, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.565749, Class: 1.000000, Obj: 0.243629, No Obj: 0.002162, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.580227, Class: 1.000000, Obj: 0.335304, No Obj: 0.002354, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.627046, Class: 1.000000, Obj: 0.366684, No Obj: 0.002468, Avg Recall: 0.796610, count: 118 -Region Avg IOU: 0.575866, Class: 1.000000, Obj: 0.260789, No Obj: 0.002221, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.602119, Class: 1.000000, Obj: 0.320278, No Obj: 0.002082, Avg Recall: 0.687500, count: 80 -Region Avg IOU: 0.559062, Class: 1.000000, Obj: 0.297337, No Obj: 0.002164, Avg Recall: 0.684685, count: 111 -2955: 0.501354, 0.504092 avg, 0.001000 rate, 28.533913 seconds, 756480 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.663390, Class: 1.000000, Obj: 0.294488, No Obj: 0.002150, Avg Recall: 0.862069, count: 116 -Region Avg IOU: 0.568958, Class: 1.000000, Obj: 0.327501, No Obj: 0.001936, Avg Recall: 0.662791, count: 86 -Region Avg IOU: 0.619820, Class: 1.000000, Obj: 0.250124, No Obj: 0.002064, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.603607, Class: 1.000000, Obj: 0.279033, No Obj: 0.002133, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.585430, Class: 1.000000, Obj: 0.304273, No Obj: 0.001849, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.623899, Class: 1.000000, Obj: 0.288848, No Obj: 0.002009, Avg Recall: 0.818182, count: 110 -Region Avg IOU: 0.624778, Class: 1.000000, Obj: 0.297092, No Obj: 0.001996, Avg Recall: 0.835165, count: 91 -Region Avg IOU: 0.590011, Class: 1.000000, Obj: 0.266698, No Obj: 0.002103, Avg Recall: 0.716814, count: 113 -2956: 0.496916, 0.503374 avg, 0.001000 rate, 28.288067 seconds, 756736 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.629436, Class: 1.000000, Obj: 0.239210, No Obj: 0.001823, Avg Recall: 0.791667, count: 72 -Region Avg IOU: 0.615333, Class: 1.000000, Obj: 0.251784, No Obj: 0.001919, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.628768, Class: 1.000000, Obj: 0.286876, No Obj: 0.001930, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.644141, Class: 1.000000, Obj: 0.283814, No Obj: 0.002329, Avg Recall: 0.826667, count: 150 -Region Avg IOU: 0.593211, Class: 1.000000, Obj: 0.255091, No Obj: 0.001869, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.621993, Class: 1.000000, Obj: 0.255686, No Obj: 0.001980, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.587414, Class: 1.000000, Obj: 0.255054, No Obj: 0.002056, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.601483, Class: 1.000000, Obj: 0.298732, No Obj: 0.001778, Avg Recall: 0.721519, count: 79 -2957: 0.460206, 0.499057 avg, 0.001000 rate, 28.649445 seconds, 756992 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.607313, Class: 1.000000, Obj: 0.254705, No Obj: 0.002154, Avg Recall: 0.780488, count: 123 -Region Avg IOU: 0.568269, Class: 1.000000, Obj: 0.252157, No Obj: 0.001904, Avg Recall: 0.682171, count: 129 -Region Avg IOU: 0.606802, Class: 1.000000, Obj: 0.243167, No Obj: 0.001805, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.647108, Class: 1.000000, Obj: 0.277519, No Obj: 0.001827, Avg Recall: 0.805195, count: 77 -Region Avg IOU: 0.622277, Class: 1.000000, Obj: 0.284535, No Obj: 0.001972, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.587614, Class: 1.000000, Obj: 0.231954, No Obj: 0.001924, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.621873, Class: 1.000000, Obj: 0.259358, No Obj: 0.001807, Avg Recall: 0.800000, count: 80 -Region Avg IOU: 0.625933, Class: 1.000000, Obj: 0.287195, No Obj: 0.002037, Avg Recall: 0.789916, count: 119 -2958: 0.451080, 0.494260 avg, 0.001000 rate, 27.395639 seconds, 757248 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.651081, Class: 1.000000, Obj: 0.283145, No Obj: 0.001939, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.616266, Class: 1.000000, Obj: 0.253770, No Obj: 0.002290, Avg Recall: 0.757576, count: 132 -Region Avg IOU: 0.656805, Class: 1.000000, Obj: 0.258575, No Obj: 0.002017, Avg Recall: 0.842593, count: 108 -Region Avg IOU: 0.631920, Class: 1.000000, Obj: 0.312308, No Obj: 0.001659, Avg Recall: 0.781250, count: 96 -Region Avg IOU: 0.613758, Class: 1.000000, Obj: 0.278227, No Obj: 0.001693, Avg Recall: 0.781609, count: 87 -Region Avg IOU: 0.636026, Class: 1.000000, Obj: 0.296542, No Obj: 0.001899, Avg Recall: 0.759036, count: 83 -Region Avg IOU: 0.600831, Class: 1.000000, Obj: 0.234786, No Obj: 0.002219, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.576376, Class: 1.000000, Obj: 0.304770, No Obj: 0.001940, Avg Recall: 0.699029, count: 103 -2959: 0.477443, 0.492578 avg, 0.001000 rate, 27.134981 seconds, 757504 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.632124, Class: 1.000000, Obj: 0.336996, No Obj: 0.002287, Avg Recall: 0.841584, count: 101 -Region Avg IOU: 0.623647, Class: 1.000000, Obj: 0.308975, No Obj: 0.002327, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.585413, Class: 1.000000, Obj: 0.310458, No Obj: 0.002211, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.641635, Class: 1.000000, Obj: 0.339740, No Obj: 0.002642, Avg Recall: 0.844828, count: 116 -Region Avg IOU: 0.630469, Class: 1.000000, Obj: 0.313350, No Obj: 0.002175, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.640202, Class: 1.000000, Obj: 0.328954, No Obj: 0.002415, Avg Recall: 0.823009, count: 113 -Region Avg IOU: 0.631537, Class: 1.000000, Obj: 0.326492, No Obj: 0.002232, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.607699, Class: 1.000000, Obj: 0.301916, No Obj: 0.002405, Avg Recall: 0.743590, count: 117 -2960: 0.459348, 0.489255 avg, 0.001000 rate, 28.076597 seconds, 757760 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.386018 seconds -Region Avg IOU: 0.575777, Class: 1.000000, Obj: 0.316397, No Obj: 0.003082, Avg Recall: 0.701149, count: 87 -Region Avg IOU: 0.484776, Class: 1.000000, Obj: 0.252323, No Obj: 0.002833, Avg Recall: 0.524194, count: 124 -Region Avg IOU: 0.568754, Class: 1.000000, Obj: 0.304844, No Obj: 0.002512, Avg Recall: 0.671429, count: 70 -Region Avg IOU: 0.540128, Class: 1.000000, Obj: 0.281913, No Obj: 0.003138, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.470178, Class: 1.000000, Obj: 0.223533, No Obj: 0.002550, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.538637, Class: 1.000000, Obj: 0.250019, No Obj: 0.002596, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.489185, Class: 1.000000, Obj: 0.223347, No Obj: 0.002977, Avg Recall: 0.554622, count: 119 -Region Avg IOU: 0.541517, Class: 1.000000, Obj: 0.261493, No Obj: 0.003056, Avg Recall: 0.663366, count: 101 -2961: 0.456746, 0.486004 avg, 0.001000 rate, 21.517624 seconds, 758016 images -Loaded: 0.419793 seconds -Region Avg IOU: 0.496590, Class: 1.000000, Obj: 0.267285, No Obj: 0.003608, Avg Recall: 0.576000, count: 125 -Region Avg IOU: 0.533290, Class: 1.000000, Obj: 0.323913, No Obj: 0.003372, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.576061, Class: 1.000000, Obj: 0.272618, No Obj: 0.003610, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.517001, Class: 1.000000, Obj: 0.264339, No Obj: 0.002907, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.475567, Class: 1.000000, Obj: 0.275862, No Obj: 0.003023, Avg Recall: 0.511905, count: 84 -Region Avg IOU: 0.568923, Class: 1.000000, Obj: 0.315771, No Obj: 0.004040, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.503814, Class: 1.000000, Obj: 0.274703, No Obj: 0.003487, Avg Recall: 0.566372, count: 113 -Region Avg IOU: 0.539789, Class: 1.000000, Obj: 0.306554, No Obj: 0.003654, Avg Recall: 0.635514, count: 107 -2962: 0.561060, 0.493510 avg, 0.001000 rate, 20.495989 seconds, 758272 images -Loaded: 1.479460 seconds -Region Avg IOU: 0.555310, Class: 1.000000, Obj: 0.295357, No Obj: 0.003739, Avg Recall: 0.675676, count: 74 -Region Avg IOU: 0.509710, Class: 1.000000, Obj: 0.328884, No Obj: 0.003543, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.485674, Class: 1.000000, Obj: 0.288983, No Obj: 0.003944, Avg Recall: 0.527473, count: 91 -Region Avg IOU: 0.528492, Class: 1.000000, Obj: 0.291250, No Obj: 0.004268, Avg Recall: 0.569343, count: 137 -Region Avg IOU: 0.556848, Class: 1.000000, Obj: 0.352373, No Obj: 0.003188, Avg Recall: 0.653333, count: 75 -Region Avg IOU: 0.535033, Class: 1.000000, Obj: 0.304693, No Obj: 0.003568, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.512782, Class: 1.000000, Obj: 0.329172, No Obj: 0.004179, Avg Recall: 0.572581, count: 124 -Region Avg IOU: 0.561004, Class: 1.000000, Obj: 0.340785, No Obj: 0.003309, Avg Recall: 0.666667, count: 84 -2963: 0.529112, 0.497070 avg, 0.001000 rate, 20.982916 seconds, 758528 images -Loaded: 0.000121 seconds -Region Avg IOU: 0.479068, Class: 1.000000, Obj: 0.318783, No Obj: 0.004592, Avg Recall: 0.520661, count: 121 -Region Avg IOU: 0.515941, Class: 1.000000, Obj: 0.350335, No Obj: 0.004153, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.529767, Class: 1.000000, Obj: 0.340587, No Obj: 0.004034, Avg Recall: 0.710938, count: 128 -Region Avg IOU: 0.544918, Class: 1.000000, Obj: 0.385777, No Obj: 0.003685, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.564098, Class: 1.000000, Obj: 0.347469, No Obj: 0.004721, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.505194, Class: 1.000000, Obj: 0.300895, No Obj: 0.003980, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.552312, Class: 1.000000, Obj: 0.380380, No Obj: 0.004419, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.523944, Class: 1.000000, Obj: 0.341579, No Obj: 0.004569, Avg Recall: 0.612613, count: 111 -2964: 0.499742, 0.497337 avg, 0.001000 rate, 23.830261 seconds, 758784 images -Loaded: 0.384604 seconds -Region Avg IOU: 0.521513, Class: 1.000000, Obj: 0.328711, No Obj: 0.005446, Avg Recall: 0.567376, count: 141 -Region Avg IOU: 0.551842, Class: 1.000000, Obj: 0.400430, No Obj: 0.004204, Avg Recall: 0.629630, count: 81 -Region Avg IOU: 0.538380, Class: 1.000000, Obj: 0.372343, No Obj: 0.004993, Avg Recall: 0.604839, count: 124 -Region Avg IOU: 0.512812, Class: 1.000000, Obj: 0.354881, No Obj: 0.005001, Avg Recall: 0.596491, count: 114 -Region Avg IOU: 0.561595, Class: 1.000000, Obj: 0.437694, No Obj: 0.004954, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.509180, Class: 1.000000, Obj: 0.408808, No Obj: 0.005270, Avg Recall: 0.591241, count: 137 -Region Avg IOU: 0.509496, Class: 1.000000, Obj: 0.416694, No Obj: 0.005007, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.496776, Class: 1.000000, Obj: 0.408949, No Obj: 0.004449, Avg Recall: 0.576923, count: 104 -2965: 0.490546, 0.496658 avg, 0.001000 rate, 22.145554 seconds, 759040 images -Loaded: 0.141544 seconds -Region Avg IOU: 0.556623, Class: 1.000000, Obj: 0.390977, No Obj: 0.005423, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.553232, Class: 1.000000, Obj: 0.414602, No Obj: 0.005132, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.559465, Class: 1.000000, Obj: 0.437394, No Obj: 0.004917, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.549809, Class: 1.000000, Obj: 0.463876, No Obj: 0.004748, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.504959, Class: 1.000000, Obj: 0.381309, No Obj: 0.004456, Avg Recall: 0.537634, count: 93 -Region Avg IOU: 0.548817, Class: 1.000000, Obj: 0.377482, No Obj: 0.005404, Avg Recall: 0.637795, count: 127 -Region Avg IOU: 0.508671, Class: 1.000000, Obj: 0.420225, No Obj: 0.005227, Avg Recall: 0.521008, count: 119 -Region Avg IOU: 0.506545, Class: 1.000000, Obj: 0.380185, No Obj: 0.004151, Avg Recall: 0.566667, count: 90 -2966: 0.397625, 0.486755 avg, 0.001000 rate, 22.526281 seconds, 759296 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.508278, Class: 1.000000, Obj: 0.365266, No Obj: 0.005521, Avg Recall: 0.507692, count: 130 -Region Avg IOU: 0.493015, Class: 1.000000, Obj: 0.407862, No Obj: 0.005151, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.525481, Class: 1.000000, Obj: 0.423562, No Obj: 0.004614, Avg Recall: 0.648649, count: 74 -Region Avg IOU: 0.535673, Class: 1.000000, Obj: 0.422096, No Obj: 0.004944, Avg Recall: 0.606742, count: 89 -Region Avg IOU: 0.543566, Class: 1.000000, Obj: 0.405794, No Obj: 0.005384, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.455966, Class: 1.000000, Obj: 0.341686, No Obj: 0.004701, Avg Recall: 0.505747, count: 87 -Region Avg IOU: 0.530766, Class: 1.000000, Obj: 0.426826, No Obj: 0.005625, Avg Recall: 0.583333, count: 120 -Region Avg IOU: 0.554007, Class: 1.000000, Obj: 0.426602, No Obj: 0.005447, Avg Recall: 0.615385, count: 117 -2967: 0.512227, 0.489302 avg, 0.001000 rate, 21.465559 seconds, 759552 images -Loaded: 0.683960 seconds -Region Avg IOU: 0.481790, Class: 1.000000, Obj: 0.419982, No Obj: 0.004398, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.495257, Class: 1.000000, Obj: 0.393384, No Obj: 0.005447, Avg Recall: 0.569536, count: 151 -Region Avg IOU: 0.493163, Class: 1.000000, Obj: 0.366334, No Obj: 0.004993, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.473731, Class: 1.000000, Obj: 0.353395, No Obj: 0.004163, Avg Recall: 0.475248, count: 101 -Region Avg IOU: 0.467368, Class: 1.000000, Obj: 0.398966, No Obj: 0.004708, Avg Recall: 0.537190, count: 121 -Region Avg IOU: 0.523385, Class: 1.000000, Obj: 0.424669, No Obj: 0.004246, Avg Recall: 0.609195, count: 87 -Region Avg IOU: 0.523339, Class: 1.000000, Obj: 0.360745, No Obj: 0.004546, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.552730, Class: 1.000000, Obj: 0.434291, No Obj: 0.004387, Avg Recall: 0.654321, count: 81 -2968: 0.487813, 0.489153 avg, 0.001000 rate, 21.645475 seconds, 759808 images -Loaded: 0.512436 seconds -Region Avg IOU: 0.485827, Class: 1.000000, Obj: 0.378660, No Obj: 0.004434, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.494298, Class: 1.000000, Obj: 0.383237, No Obj: 0.004217, Avg Recall: 0.506329, count: 79 -Region Avg IOU: 0.522834, Class: 1.000000, Obj: 0.368407, No Obj: 0.004367, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.527954, Class: 1.000000, Obj: 0.330505, No Obj: 0.005275, Avg Recall: 0.625954, count: 131 -Region Avg IOU: 0.533130, Class: 1.000000, Obj: 0.328651, No Obj: 0.005509, Avg Recall: 0.659420, count: 138 -Region Avg IOU: 0.549934, Class: 1.000000, Obj: 0.390578, No Obj: 0.005102, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.473724, Class: 1.000000, Obj: 0.366638, No Obj: 0.003867, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.490528, Class: 1.000000, Obj: 0.351349, No Obj: 0.004634, Avg Recall: 0.555556, count: 108 -2969: 0.534398, 0.493678 avg, 0.001000 rate, 22.286768 seconds, 760064 images -Loaded: 0.326093 seconds -Region Avg IOU: 0.498811, Class: 1.000000, Obj: 0.293099, No Obj: 0.003671, Avg Recall: 0.556452, count: 124 -Region Avg IOU: 0.535108, Class: 1.000000, Obj: 0.347345, No Obj: 0.003577, Avg Recall: 0.626506, count: 83 -Region Avg IOU: 0.483982, Class: 1.000000, Obj: 0.331487, No Obj: 0.003679, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.506502, Class: 1.000000, Obj: 0.327072, No Obj: 0.003652, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.482213, Class: 1.000000, Obj: 0.304719, No Obj: 0.003853, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.533581, Class: 1.000000, Obj: 0.339216, No Obj: 0.003910, Avg Recall: 0.565217, count: 92 -Region Avg IOU: 0.540339, Class: 1.000000, Obj: 0.353066, No Obj: 0.004103, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.491034, Class: 1.000000, Obj: 0.357166, No Obj: 0.004102, Avg Recall: 0.545455, count: 110 -2970: 0.487727, 0.493082 avg, 0.001000 rate, 21.050043 seconds, 760320 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000061 seconds -Region Avg IOU: 0.530128, Class: 1.000000, Obj: 0.327358, No Obj: 0.003647, Avg Recall: 0.641026, count: 117 -Region Avg IOU: 0.559966, Class: 1.000000, Obj: 0.343392, No Obj: 0.003497, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.590703, Class: 1.000000, Obj: 0.416584, No Obj: 0.003723, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.549369, Class: 1.000000, Obj: 0.311313, No Obj: 0.003881, Avg Recall: 0.657143, count: 140 -Region Avg IOU: 0.549158, Class: 1.000000, Obj: 0.324415, No Obj: 0.003810, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.578483, Class: 1.000000, Obj: 0.332442, No Obj: 0.003378, Avg Recall: 0.694118, count: 85 -Region Avg IOU: 0.538058, Class: 1.000000, Obj: 0.322329, No Obj: 0.003816, Avg Recall: 0.661871, count: 139 -Region Avg IOU: 0.507275, Class: 1.000000, Obj: 0.317579, No Obj: 0.003915, Avg Recall: 0.586207, count: 116 -2971: 0.568072, 0.500581 avg, 0.001000 rate, 24.465609 seconds, 760576 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.586578, Class: 1.000000, Obj: 0.340486, No Obj: 0.003288, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.569228, Class: 1.000000, Obj: 0.351691, No Obj: 0.002890, Avg Recall: 0.696629, count: 89 -Region Avg IOU: 0.497350, Class: 1.000000, Obj: 0.288073, No Obj: 0.003128, Avg Recall: 0.550000, count: 100 -Region Avg IOU: 0.518876, Class: 1.000000, Obj: 0.290359, No Obj: 0.003580, Avg Recall: 0.530435, count: 115 -Region Avg IOU: 0.515480, Class: 1.000000, Obj: 0.341422, No Obj: 0.003240, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.550687, Class: 1.000000, Obj: 0.302052, No Obj: 0.003026, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.527382, Class: 1.000000, Obj: 0.342953, No Obj: 0.003325, Avg Recall: 0.647727, count: 88 -Region Avg IOU: 0.575861, Class: 1.000000, Obj: 0.285519, No Obj: 0.002906, Avg Recall: 0.686046, count: 86 -2972: 0.486695, 0.499193 avg, 0.001000 rate, 25.133837 seconds, 760832 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.561505, Class: 1.000000, Obj: 0.307959, No Obj: 0.003077, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.547697, Class: 1.000000, Obj: 0.268016, No Obj: 0.002411, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.530322, Class: 1.000000, Obj: 0.270684, No Obj: 0.002957, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.567939, Class: 1.000000, Obj: 0.329062, No Obj: 0.003329, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.601476, Class: 1.000000, Obj: 0.319070, No Obj: 0.003301, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.569560, Class: 1.000000, Obj: 0.315580, No Obj: 0.002857, Avg Recall: 0.710843, count: 83 -Region Avg IOU: 0.537975, Class: 1.000000, Obj: 0.298736, No Obj: 0.003408, Avg Recall: 0.644628, count: 121 -Region Avg IOU: 0.552875, Class: 1.000000, Obj: 0.324108, No Obj: 0.002823, Avg Recall: 0.692308, count: 104 -2973: 0.431674, 0.492441 avg, 0.001000 rate, 24.902493 seconds, 761088 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.535563, Class: 1.000000, Obj: 0.300205, No Obj: 0.003359, Avg Recall: 0.600000, count: 135 -Region Avg IOU: 0.590108, Class: 1.000000, Obj: 0.330109, No Obj: 0.002928, Avg Recall: 0.662651, count: 83 -Region Avg IOU: 0.587158, Class: 1.000000, Obj: 0.303202, No Obj: 0.002516, Avg Recall: 0.746269, count: 67 -Region Avg IOU: 0.576127, Class: 1.000000, Obj: 0.279282, No Obj: 0.003208, Avg Recall: 0.684211, count: 133 -Region Avg IOU: 0.549480, Class: 1.000000, Obj: 0.250998, No Obj: 0.003092, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.539960, Class: 1.000000, Obj: 0.232803, No Obj: 0.003258, Avg Recall: 0.633333, count: 120 -Region Avg IOU: 0.628685, Class: 1.000000, Obj: 0.305329, No Obj: 0.002974, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.513719, Class: 1.000000, Obj: 0.266566, No Obj: 0.002835, Avg Recall: 0.580357, count: 112 -2974: 0.504576, 0.493654 avg, 0.001000 rate, 24.957426 seconds, 761344 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.584800, Class: 1.000000, Obj: 0.282326, No Obj: 0.002606, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.567371, Class: 1.000000, Obj: 0.277033, No Obj: 0.002829, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.495825, Class: 1.000000, Obj: 0.264864, No Obj: 0.002821, Avg Recall: 0.544715, count: 123 -Region Avg IOU: 0.517307, Class: 1.000000, Obj: 0.275801, No Obj: 0.002948, Avg Recall: 0.581197, count: 117 -Region Avg IOU: 0.582408, Class: 1.000000, Obj: 0.282746, No Obj: 0.002471, Avg Recall: 0.659341, count: 91 -Region Avg IOU: 0.530431, Class: 1.000000, Obj: 0.274789, No Obj: 0.003047, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.562701, Class: 1.000000, Obj: 0.247364, No Obj: 0.003116, Avg Recall: 0.669231, count: 130 -Region Avg IOU: 0.547795, Class: 1.000000, Obj: 0.283364, No Obj: 0.003050, Avg Recall: 0.587719, count: 114 -2975: 0.501267, 0.494416 avg, 0.001000 rate, 24.472485 seconds, 761600 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.527085, Class: 1.000000, Obj: 0.243101, No Obj: 0.003091, Avg Recall: 0.615385, count: 143 -Region Avg IOU: 0.583953, Class: 1.000000, Obj: 0.286916, No Obj: 0.003496, Avg Recall: 0.701613, count: 124 -Region Avg IOU: 0.515801, Class: 1.000000, Obj: 0.291125, No Obj: 0.002881, Avg Recall: 0.641667, count: 120 -Region Avg IOU: 0.554682, Class: 1.000000, Obj: 0.303553, No Obj: 0.002557, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.583077, Class: 1.000000, Obj: 0.283422, No Obj: 0.003386, Avg Recall: 0.696000, count: 125 -Region Avg IOU: 0.573975, Class: 1.000000, Obj: 0.286976, No Obj: 0.002716, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.551598, Class: 1.000000, Obj: 0.313076, No Obj: 0.003120, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.528290, Class: 1.000000, Obj: 0.265848, No Obj: 0.002963, Avg Recall: 0.606061, count: 99 -2976: 0.519908, 0.496965 avg, 0.001000 rate, 25.105207 seconds, 761856 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.537454, Class: 1.000000, Obj: 0.276678, No Obj: 0.003010, Avg Recall: 0.637795, count: 127 -Region Avg IOU: 0.553816, Class: 1.000000, Obj: 0.329332, No Obj: 0.003421, Avg Recall: 0.669118, count: 136 -Region Avg IOU: 0.540375, Class: 1.000000, Obj: 0.288164, No Obj: 0.003206, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.580313, Class: 1.000000, Obj: 0.305646, No Obj: 0.004293, Avg Recall: 0.698630, count: 146 -Region Avg IOU: 0.622558, Class: 1.000000, Obj: 0.316247, No Obj: 0.003589, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.566010, Class: 1.000000, Obj: 0.305972, No Obj: 0.003221, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.537468, Class: 1.000000, Obj: 0.286450, No Obj: 0.003329, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.561870, Class: 1.000000, Obj: 0.298322, No Obj: 0.003775, Avg Recall: 0.664179, count: 134 -2977: 0.591186, 0.506387 avg, 0.001000 rate, 24.257467 seconds, 762112 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.562472, Class: 1.000000, Obj: 0.342912, No Obj: 0.003395, Avg Recall: 0.673684, count: 95 -Region Avg IOU: 0.572618, Class: 1.000000, Obj: 0.328793, No Obj: 0.003458, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.559707, Class: 1.000000, Obj: 0.362820, No Obj: 0.003544, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.595620, Class: 1.000000, Obj: 0.332571, No Obj: 0.004076, Avg Recall: 0.686131, count: 137 -Region Avg IOU: 0.545963, Class: 1.000000, Obj: 0.340548, No Obj: 0.003708, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.567662, Class: 1.000000, Obj: 0.337697, No Obj: 0.004111, Avg Recall: 0.637795, count: 127 -Region Avg IOU: 0.543900, Class: 1.000000, Obj: 0.290396, No Obj: 0.003564, Avg Recall: 0.631148, count: 122 -Region Avg IOU: 0.583065, Class: 1.000000, Obj: 0.341169, No Obj: 0.003480, Avg Recall: 0.718750, count: 96 -2978: 0.518339, 0.507582 avg, 0.001000 rate, 23.357384 seconds, 762368 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.579643, Class: 1.000000, Obj: 0.404686, No Obj: 0.003748, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.601195, Class: 1.000000, Obj: 0.359032, No Obj: 0.003120, Avg Recall: 0.743243, count: 74 -Region Avg IOU: 0.542557, Class: 1.000000, Obj: 0.386744, No Obj: 0.003530, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.575265, Class: 1.000000, Obj: 0.351288, No Obj: 0.004105, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.561219, Class: 1.000000, Obj: 0.374253, No Obj: 0.004234, Avg Recall: 0.655738, count: 122 -Region Avg IOU: 0.536757, Class: 1.000000, Obj: 0.355999, No Obj: 0.004214, Avg Recall: 0.596774, count: 124 -Region Avg IOU: 0.582923, Class: 1.000000, Obj: 0.372543, No Obj: 0.004116, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.574787, Class: 1.000000, Obj: 0.386383, No Obj: 0.003756, Avg Recall: 0.670455, count: 88 -2979: 0.496527, 0.506477 avg, 0.001000 rate, 22.443457 seconds, 762624 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.555101, Class: 1.000000, Obj: 0.365093, No Obj: 0.004033, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.571574, Class: 1.000000, Obj: 0.461146, No Obj: 0.004501, Avg Recall: 0.642857, count: 126 -Region Avg IOU: 0.504351, Class: 1.000000, Obj: 0.396079, No Obj: 0.003758, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.544710, Class: 1.000000, Obj: 0.355800, No Obj: 0.003937, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.618484, Class: 1.000000, Obj: 0.400664, No Obj: 0.004726, Avg Recall: 0.776000, count: 125 -Region Avg IOU: 0.564639, Class: 1.000000, Obj: 0.423737, No Obj: 0.004025, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.567810, Class: 1.000000, Obj: 0.380373, No Obj: 0.004099, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.568774, Class: 1.000000, Obj: 0.407411, No Obj: 0.003795, Avg Recall: 0.660000, count: 100 -2980: 0.429835, 0.498813 avg, 0.001000 rate, 23.510303 seconds, 762880 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.703428 seconds -Region Avg IOU: 0.475566, Class: 1.000000, Obj: 0.312941, No Obj: 0.003956, Avg Recall: 0.546392, count: 97 -Region Avg IOU: 0.509541, Class: 1.000000, Obj: 0.315788, No Obj: 0.004488, Avg Recall: 0.561404, count: 114 -Region Avg IOU: 0.528763, Class: 1.000000, Obj: 0.379269, No Obj: 0.004129, Avg Recall: 0.530612, count: 98 -Region Avg IOU: 0.458558, Class: 1.000000, Obj: 0.308507, No Obj: 0.004038, Avg Recall: 0.441667, count: 120 -Region Avg IOU: 0.548167, Class: 1.000000, Obj: 0.314414, No Obj: 0.004386, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.469039, Class: 1.000000, Obj: 0.289670, No Obj: 0.003916, Avg Recall: 0.513043, count: 115 -Region Avg IOU: 0.475921, Class: 1.000000, Obj: 0.320128, No Obj: 0.004471, Avg Recall: 0.528000, count: 125 -Region Avg IOU: 0.470438, Class: 1.000000, Obj: 0.290523, No Obj: 0.004774, Avg Recall: 0.539568, count: 139 -2981: 0.487960, 0.497727 avg, 0.001000 rate, 21.842253 seconds, 763136 images -Loaded: 0.237445 seconds -Region Avg IOU: 0.537965, Class: 1.000000, Obj: 0.348597, No Obj: 0.005021, Avg Recall: 0.611570, count: 121 -Region Avg IOU: 0.454849, Class: 1.000000, Obj: 0.336896, No Obj: 0.004364, Avg Recall: 0.523364, count: 107 -Region Avg IOU: 0.552487, Class: 1.000000, Obj: 0.384842, No Obj: 0.004372, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.491200, Class: 1.000000, Obj: 0.307653, No Obj: 0.004211, Avg Recall: 0.500000, count: 112 -Region Avg IOU: 0.500688, Class: 1.000000, Obj: 0.330664, No Obj: 0.003775, Avg Recall: 0.580952, count: 105 -Region Avg IOU: 0.524218, Class: 1.000000, Obj: 0.357629, No Obj: 0.004767, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.464503, Class: 1.000000, Obj: 0.324494, No Obj: 0.003415, Avg Recall: 0.531646, count: 79 -Region Avg IOU: 0.431557, Class: 1.000000, Obj: 0.334960, No Obj: 0.004096, Avg Recall: 0.518519, count: 108 -2982: 0.438129, 0.491768 avg, 0.001000 rate, 20.444633 seconds, 763392 images -Loaded: 0.499717 seconds -Region Avg IOU: 0.482702, Class: 1.000000, Obj: 0.311623, No Obj: 0.004406, Avg Recall: 0.509259, count: 108 -Region Avg IOU: 0.437891, Class: 1.000000, Obj: 0.272966, No Obj: 0.004122, Avg Recall: 0.447154, count: 123 -Region Avg IOU: 0.454563, Class: 1.000000, Obj: 0.332753, No Obj: 0.003796, Avg Recall: 0.464912, count: 114 -Region Avg IOU: 0.475500, Class: 1.000000, Obj: 0.310612, No Obj: 0.005030, Avg Recall: 0.514019, count: 107 -Region Avg IOU: 0.468866, Class: 1.000000, Obj: 0.324682, No Obj: 0.003527, Avg Recall: 0.531646, count: 79 -Region Avg IOU: 0.451642, Class: 1.000000, Obj: 0.299831, No Obj: 0.004065, Avg Recall: 0.480620, count: 129 -Region Avg IOU: 0.479112, Class: 1.000000, Obj: 0.303306, No Obj: 0.004700, Avg Recall: 0.512397, count: 121 -Region Avg IOU: 0.465295, Class: 1.000000, Obj: 0.334978, No Obj: 0.003621, Avg Recall: 0.511364, count: 88 -2983: 0.548876, 0.497478 avg, 0.001000 rate, 20.418962 seconds, 763648 images -Loaded: 0.709638 seconds -Region Avg IOU: 0.506366, Class: 1.000000, Obj: 0.338490, No Obj: 0.005002, Avg Recall: 0.579365, count: 126 -Region Avg IOU: 0.572338, Class: 1.000000, Obj: 0.366641, No Obj: 0.004180, Avg Recall: 0.666667, count: 87 -Region Avg IOU: 0.465141, Class: 1.000000, Obj: 0.305155, No Obj: 0.004632, Avg Recall: 0.472000, count: 125 -Region Avg IOU: 0.493517, Class: 1.000000, Obj: 0.334399, No Obj: 0.004393, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.530781, Class: 1.000000, Obj: 0.354425, No Obj: 0.004675, Avg Recall: 0.631148, count: 122 -Region Avg IOU: 0.527716, Class: 1.000000, Obj: 0.381349, No Obj: 0.004146, Avg Recall: 0.607595, count: 79 -Region Avg IOU: 0.494735, Class: 1.000000, Obj: 0.316476, No Obj: 0.004151, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.492297, Class: 1.000000, Obj: 0.390370, No Obj: 0.004040, Avg Recall: 0.511364, count: 88 -2984: 0.470358, 0.494766 avg, 0.001000 rate, 20.819984 seconds, 763904 images -Loaded: 0.164523 seconds -Region Avg IOU: 0.552742, Class: 1.000000, Obj: 0.378771, No Obj: 0.004224, Avg Recall: 0.623529, count: 85 -Region Avg IOU: 0.489662, Class: 1.000000, Obj: 0.352595, No Obj: 0.004468, Avg Recall: 0.550000, count: 100 -Region Avg IOU: 0.492515, Class: 1.000000, Obj: 0.339602, No Obj: 0.004333, Avg Recall: 0.553398, count: 103 -Region Avg IOU: 0.507793, Class: 1.000000, Obj: 0.325870, No Obj: 0.004392, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.504103, Class: 1.000000, Obj: 0.361716, No Obj: 0.004440, Avg Recall: 0.580952, count: 105 -Region Avg IOU: 0.518538, Class: 1.000000, Obj: 0.341469, No Obj: 0.004910, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.489537, Class: 1.000000, Obj: 0.330276, No Obj: 0.004744, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.454074, Class: 1.000000, Obj: 0.278607, No Obj: 0.004071, Avg Recall: 0.475000, count: 120 -2985: 0.431123, 0.488402 avg, 0.001000 rate, 21.548903 seconds, 764160 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.488443, Class: 1.000000, Obj: 0.325778, No Obj: 0.004070, Avg Recall: 0.509615, count: 104 -Region Avg IOU: 0.486606, Class: 1.000000, Obj: 0.359195, No Obj: 0.004313, Avg Recall: 0.561538, count: 130 -Region Avg IOU: 0.518815, Class: 1.000000, Obj: 0.359454, No Obj: 0.003550, Avg Recall: 0.587500, count: 80 -Region Avg IOU: 0.563162, Class: 1.000000, Obj: 0.343875, No Obj: 0.004347, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.489610, Class: 1.000000, Obj: 0.340812, No Obj: 0.004575, Avg Recall: 0.586207, count: 116 -Region Avg IOU: 0.482741, Class: 1.000000, Obj: 0.322992, No Obj: 0.004028, Avg Recall: 0.545455, count: 77 -Region Avg IOU: 0.475230, Class: 1.000000, Obj: 0.338279, No Obj: 0.004426, Avg Recall: 0.518519, count: 108 -Region Avg IOU: 0.490276, Class: 1.000000, Obj: 0.337660, No Obj: 0.004366, Avg Recall: 0.590000, count: 100 -2986: 0.441181, 0.483680 avg, 0.001000 rate, 20.310675 seconds, 764416 images -Loaded: 0.823617 seconds -Region Avg IOU: 0.591244, Class: 1.000000, Obj: 0.379043, No Obj: 0.004442, Avg Recall: 0.759494, count: 79 -Region Avg IOU: 0.541978, Class: 1.000000, Obj: 0.368265, No Obj: 0.004413, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.496877, Class: 1.000000, Obj: 0.290889, No Obj: 0.004328, Avg Recall: 0.575540, count: 139 -Region Avg IOU: 0.489976, Class: 1.000000, Obj: 0.306096, No Obj: 0.004178, Avg Recall: 0.570175, count: 114 -Region Avg IOU: 0.481985, Class: 1.000000, Obj: 0.291268, No Obj: 0.003940, Avg Recall: 0.547826, count: 115 -Region Avg IOU: 0.493303, Class: 1.000000, Obj: 0.324980, No Obj: 0.004451, Avg Recall: 0.539062, count: 128 -Region Avg IOU: 0.458361, Class: 1.000000, Obj: 0.290492, No Obj: 0.004154, Avg Recall: 0.515464, count: 97 -Region Avg IOU: 0.425098, Class: 1.000000, Obj: 0.275607, No Obj: 0.003793, Avg Recall: 0.406780, count: 118 -2987: 0.472185, 0.482531 avg, 0.001000 rate, 21.565456 seconds, 764672 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.486823, Class: 1.000000, Obj: 0.303853, No Obj: 0.004052, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.483644, Class: 1.000000, Obj: 0.293286, No Obj: 0.004336, Avg Recall: 0.540984, count: 122 -Region Avg IOU: 0.438828, Class: 1.000000, Obj: 0.322754, No Obj: 0.003701, Avg Recall: 0.468085, count: 94 -Region Avg IOU: 0.483528, Class: 1.000000, Obj: 0.322262, No Obj: 0.003862, Avg Recall: 0.526316, count: 114 -Region Avg IOU: 0.470715, Class: 1.000000, Obj: 0.270569, No Obj: 0.003541, Avg Recall: 0.490566, count: 106 -Region Avg IOU: 0.443675, Class: 1.000000, Obj: 0.289884, No Obj: 0.003878, Avg Recall: 0.484848, count: 99 -Region Avg IOU: 0.534318, Class: 1.000000, Obj: 0.334512, No Obj: 0.004420, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.453060, Class: 1.000000, Obj: 0.314903, No Obj: 0.004149, Avg Recall: 0.500000, count: 102 -2988: 0.495146, 0.483792 avg, 0.001000 rate, 20.866749 seconds, 764928 images -Loaded: 1.637411 seconds -Region Avg IOU: 0.501110, Class: 1.000000, Obj: 0.314566, No Obj: 0.003953, Avg Recall: 0.549550, count: 111 -Region Avg IOU: 0.534282, Class: 1.000000, Obj: 0.262357, No Obj: 0.004424, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.497307, Class: 1.000000, Obj: 0.333556, No Obj: 0.003414, Avg Recall: 0.505376, count: 93 -Region Avg IOU: 0.500420, Class: 1.000000, Obj: 0.305242, No Obj: 0.004242, Avg Recall: 0.505495, count: 91 -Region Avg IOU: 0.552865, Class: 1.000000, Obj: 0.378917, No Obj: 0.003770, Avg Recall: 0.632184, count: 87 -Region Avg IOU: 0.462584, Class: 1.000000, Obj: 0.285011, No Obj: 0.003923, Avg Recall: 0.517241, count: 116 -Region Avg IOU: 0.533966, Class: 1.000000, Obj: 0.346141, No Obj: 0.004477, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.535363, Class: 1.000000, Obj: 0.304930, No Obj: 0.004337, Avg Recall: 0.631579, count: 95 -2989: 0.457122, 0.481125 avg, 0.001000 rate, 19.617081 seconds, 765184 images -Loaded: 2.866948 seconds -Region Avg IOU: 0.463673, Class: 1.000000, Obj: 0.290711, No Obj: 0.004382, Avg Recall: 0.507353, count: 136 -Region Avg IOU: 0.554873, Class: 1.000000, Obj: 0.294236, No Obj: 0.004460, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.467753, Class: 1.000000, Obj: 0.311658, No Obj: 0.003623, Avg Recall: 0.526882, count: 93 -Region Avg IOU: 0.519010, Class: 1.000000, Obj: 0.293863, No Obj: 0.003890, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.477037, Class: 1.000000, Obj: 0.264704, No Obj: 0.003623, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.484272, Class: 1.000000, Obj: 0.297238, No Obj: 0.004344, Avg Recall: 0.520325, count: 123 -Region Avg IOU: 0.475268, Class: 1.000000, Obj: 0.278030, No Obj: 0.004683, Avg Recall: 0.517483, count: 143 -Region Avg IOU: 0.434052, Class: 1.000000, Obj: 0.288481, No Obj: 0.003540, Avg Recall: 0.468468, count: 111 -2990: 0.546556, 0.487668 avg, 0.001000 rate, 18.896748 seconds, 765440 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.490583 seconds -Region Avg IOU: 0.445487, Class: 1.000000, Obj: 0.238327, No Obj: 0.004268, Avg Recall: 0.428571, count: 126 -Region Avg IOU: 0.493520, Class: 1.000000, Obj: 0.279715, No Obj: 0.003946, Avg Recall: 0.566265, count: 83 -Region Avg IOU: 0.454385, Class: 1.000000, Obj: 0.259760, No Obj: 0.003890, Avg Recall: 0.463636, count: 110 -Region Avg IOU: 0.442351, Class: 1.000000, Obj: 0.251628, No Obj: 0.004365, Avg Recall: 0.480769, count: 104 -Region Avg IOU: 0.450724, Class: 1.000000, Obj: 0.256637, No Obj: 0.003800, Avg Recall: 0.478261, count: 115 -Region Avg IOU: 0.462845, Class: 1.000000, Obj: 0.236159, No Obj: 0.003757, Avg Recall: 0.535354, count: 99 -Region Avg IOU: 0.433457, Class: 1.000000, Obj: 0.237007, No Obj: 0.003822, Avg Recall: 0.410526, count: 95 -Region Avg IOU: 0.483641, Class: 1.000000, Obj: 0.259909, No Obj: 0.004269, Avg Recall: 0.536082, count: 97 -2991: 0.559556, 0.494857 avg, 0.001000 rate, 17.434456 seconds, 765696 images -Loaded: 3.930812 seconds -Region Avg IOU: 0.505421, Class: 1.000000, Obj: 0.303081, No Obj: 0.004429, Avg Recall: 0.586207, count: 87 -Region Avg IOU: 0.441140, Class: 1.000000, Obj: 0.267657, No Obj: 0.004536, Avg Recall: 0.466667, count: 120 -Region Avg IOU: 0.451584, Class: 1.000000, Obj: 0.266153, No Obj: 0.004008, Avg Recall: 0.500000, count: 110 -Region Avg IOU: 0.490093, Class: 1.000000, Obj: 0.298429, No Obj: 0.004379, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.477672, Class: 1.000000, Obj: 0.261099, No Obj: 0.004397, Avg Recall: 0.535088, count: 114 -Region Avg IOU: 0.464215, Class: 1.000000, Obj: 0.288871, No Obj: 0.004212, Avg Recall: 0.489796, count: 98 -Region Avg IOU: 0.460832, Class: 1.000000, Obj: 0.261505, No Obj: 0.004099, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.403015, Class: 1.000000, Obj: 0.215981, No Obj: 0.004012, Avg Recall: 0.421875, count: 128 -2992: 0.526817, 0.498053 avg, 0.001000 rate, 18.208532 seconds, 765952 images -Loaded: 1.788337 seconds -Region Avg IOU: 0.458315, Class: 1.000000, Obj: 0.274150, No Obj: 0.004113, Avg Recall: 0.494949, count: 99 -Region Avg IOU: 0.460946, Class: 1.000000, Obj: 0.294969, No Obj: 0.004260, Avg Recall: 0.494737, count: 95 -Region Avg IOU: 0.469124, Class: 1.000000, Obj: 0.341530, No Obj: 0.003773, Avg Recall: 0.518072, count: 83 -Region Avg IOU: 0.457594, Class: 1.000000, Obj: 0.291522, No Obj: 0.004589, Avg Recall: 0.534351, count: 131 -Region Avg IOU: 0.517826, Class: 1.000000, Obj: 0.306483, No Obj: 0.004397, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.469173, Class: 1.000000, Obj: 0.275310, No Obj: 0.004082, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.491918, Class: 1.000000, Obj: 0.296148, No Obj: 0.005457, Avg Recall: 0.536232, count: 138 -Region Avg IOU: 0.434126, Class: 1.000000, Obj: 0.258883, No Obj: 0.004596, Avg Recall: 0.426229, count: 122 -2993: 0.537758, 0.502024 avg, 0.001000 rate, 18.578568 seconds, 766208 images -Loaded: 1.120817 seconds -Region Avg IOU: 0.467607, Class: 1.000000, Obj: 0.309813, No Obj: 0.004281, Avg Recall: 0.531646, count: 79 -Region Avg IOU: 0.462150, Class: 1.000000, Obj: 0.278624, No Obj: 0.004823, Avg Recall: 0.470086, count: 117 -Region Avg IOU: 0.474382, Class: 1.000000, Obj: 0.301364, No Obj: 0.004988, Avg Recall: 0.525862, count: 116 -Region Avg IOU: 0.450609, Class: 1.000000, Obj: 0.325894, No Obj: 0.004103, Avg Recall: 0.455556, count: 90 -Region Avg IOU: 0.448561, Class: 1.000000, Obj: 0.257009, No Obj: 0.005503, Avg Recall: 0.562044, count: 137 -Region Avg IOU: 0.405944, Class: 1.000000, Obj: 0.247514, No Obj: 0.004331, Avg Recall: 0.373832, count: 107 -Region Avg IOU: 0.472622, Class: 1.000000, Obj: 0.306701, No Obj: 0.004950, Avg Recall: 0.486957, count: 115 -Region Avg IOU: 0.492856, Class: 1.000000, Obj: 0.292594, No Obj: 0.004621, Avg Recall: 0.591837, count: 98 -2994: 0.472706, 0.499092 avg, 0.001000 rate, 15.221036 seconds, 766464 images -Loaded: 5.404435 seconds -Region Avg IOU: 0.461972, Class: 1.000000, Obj: 0.292224, No Obj: 0.004974, Avg Recall: 0.494845, count: 97 -Region Avg IOU: 0.498798, Class: 1.000000, Obj: 0.336582, No Obj: 0.004758, Avg Recall: 0.577778, count: 90 -Region Avg IOU: 0.469349, Class: 1.000000, Obj: 0.315024, No Obj: 0.005359, Avg Recall: 0.509804, count: 102 -Region Avg IOU: 0.484965, Class: 1.000000, Obj: 0.274713, No Obj: 0.005548, Avg Recall: 0.568965, count: 116 -Region Avg IOU: 0.462702, Class: 1.000000, Obj: 0.328801, No Obj: 0.004351, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.467931, Class: 1.000000, Obj: 0.353295, No Obj: 0.004914, Avg Recall: 0.500000, count: 104 -Region Avg IOU: 0.382504, Class: 1.000000, Obj: 0.295390, No Obj: 0.005176, Avg Recall: 0.401786, count: 112 -Region Avg IOU: 0.456164, Class: 1.000000, Obj: 0.287958, No Obj: 0.005290, Avg Recall: 0.517986, count: 139 -2995: 0.477241, 0.496907 avg, 0.001000 rate, 19.486519 seconds, 766720 images -Loaded: 1.578795 seconds -Region Avg IOU: 0.425041, Class: 1.000000, Obj: 0.299461, No Obj: 0.005415, Avg Recall: 0.459677, count: 124 -Region Avg IOU: 0.499114, Class: 1.000000, Obj: 0.364397, No Obj: 0.005431, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.446386, Class: 1.000000, Obj: 0.322811, No Obj: 0.005075, Avg Recall: 0.495050, count: 101 -Region Avg IOU: 0.460648, Class: 1.000000, Obj: 0.319960, No Obj: 0.005135, Avg Recall: 0.490741, count: 108 -Region Avg IOU: 0.472380, Class: 1.000000, Obj: 0.314414, No Obj: 0.005534, Avg Recall: 0.517544, count: 114 -Region Avg IOU: 0.414672, Class: 1.000000, Obj: 0.315893, No Obj: 0.004936, Avg Recall: 0.391667, count: 120 -Region Avg IOU: 0.428015, Class: 1.000000, Obj: 0.324182, No Obj: 0.004629, Avg Recall: 0.441176, count: 102 -Region Avg IOU: 0.453468, Class: 1.000000, Obj: 0.322644, No Obj: 0.005247, Avg Recall: 0.464000, count: 125 -2996: 0.552806, 0.502497 avg, 0.001000 rate, 17.693325 seconds, 766976 images -Loaded: 3.714261 seconds -Region Avg IOU: 0.475349, Class: 1.000000, Obj: 0.332564, No Obj: 0.006176, Avg Recall: 0.554622, count: 119 -Region Avg IOU: 0.432516, Class: 1.000000, Obj: 0.333379, No Obj: 0.005209, Avg Recall: 0.500000, count: 104 -Region Avg IOU: 0.465607, Class: 1.000000, Obj: 0.317688, No Obj: 0.005150, Avg Recall: 0.462366, count: 93 -Region Avg IOU: 0.432217, Class: 1.000000, Obj: 0.339967, No Obj: 0.005659, Avg Recall: 0.466667, count: 120 -Region Avg IOU: 0.446012, Class: 1.000000, Obj: 0.309061, No Obj: 0.005279, Avg Recall: 0.504348, count: 115 -Region Avg IOU: 0.465150, Class: 1.000000, Obj: 0.312116, No Obj: 0.005528, Avg Recall: 0.464912, count: 114 -Region Avg IOU: 0.431822, Class: 1.000000, Obj: 0.297441, No Obj: 0.005783, Avg Recall: 0.435115, count: 131 -Region Avg IOU: 0.393248, Class: 1.000000, Obj: 0.304701, No Obj: 0.005231, Avg Recall: 0.421053, count: 133 -2997: 0.523749, 0.504622 avg, 0.001000 rate, 12.636092 seconds, 767232 images -Loaded: Saving weights to ./person9300/backup/tyvp_93_9c512_3000.weights -9.297842 seconds -Region Avg IOU: 0.435885, Class: 1.000000, Obj: 0.318220, No Obj: 0.005643, Avg Recall: 0.452174, count: 115 -Region Avg IOU: 0.520803, Class: 1.000000, Obj: 0.361514, No Obj: 0.005494, Avg Recall: 0.632184, count: 87 -Region Avg IOU: 0.411288, Class: 1.000000, Obj: 0.323858, No Obj: 0.004551, Avg Recall: 0.406977, count: 86 -Region Avg IOU: 0.395955, Class: 1.000000, Obj: 0.303685, No Obj: 0.005526, Avg Recall: 0.413534, count: 133 -Region Avg IOU: 0.451458, Class: 1.000000, Obj: 0.312178, No Obj: 0.004811, Avg Recall: 0.462366, count: 93 -Region Avg IOU: 0.430950, Class: 1.000000, Obj: 0.364421, No Obj: 0.004987, Avg Recall: 0.452632, count: 95 -Region Avg IOU: 0.471027, Class: 1.000000, Obj: 0.283554, No Obj: 0.005162, Avg Recall: 0.500000, count: 108 -Region Avg IOU: 0.517040, Class: 1.000000, Obj: 0.343845, No Obj: 0.005816, Avg Recall: 0.621359, count: 103 -2998: 0.490302, 0.503190 avg, 0.001000 rate, 17.708759 seconds, 767488 images -Loaded: 3.831482 seconds -Region Avg IOU: 0.408396, Class: 1.000000, Obj: 0.279997, No Obj: 0.004927, Avg Recall: 0.403846, count: 104 -Region Avg IOU: 0.478825, Class: 1.000000, Obj: 0.317031, No Obj: 0.005624, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.466485, Class: 1.000000, Obj: 0.305333, No Obj: 0.006525, Avg Recall: 0.503226, count: 155 -Region Avg IOU: 0.441401, Class: 1.000000, Obj: 0.332452, No Obj: 0.005405, Avg Recall: 0.474227, count: 97 -Region Avg IOU: 0.509338, Class: 1.000000, Obj: 0.356908, No Obj: 0.004489, Avg Recall: 0.575000, count: 80 -Region Avg IOU: 0.432086, Class: 1.000000, Obj: 0.281476, No Obj: 0.005224, Avg Recall: 0.451923, count: 104 -Region Avg IOU: 0.492077, Class: 1.000000, Obj: 0.388040, No Obj: 0.005960, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.427035, Class: 1.000000, Obj: 0.321375, No Obj: 0.005106, Avg Recall: 0.457627, count: 118 -2999: 0.545911, 0.507462 avg, 0.001000 rate, 16.327747 seconds, 767744 images -Loaded: 3.854283 seconds -Region Avg IOU: 0.460968, Class: 1.000000, Obj: 0.340574, No Obj: 0.005950, Avg Recall: 0.522388, count: 134 -Region Avg IOU: 0.434986, Class: 1.000000, Obj: 0.309471, No Obj: 0.005344, Avg Recall: 0.477273, count: 132 -Region Avg IOU: 0.437574, Class: 1.000000, Obj: 0.306196, No Obj: 0.005423, Avg Recall: 0.475806, count: 124 -Region Avg IOU: 0.469663, Class: 1.000000, Obj: 0.271727, No Obj: 0.004880, Avg Recall: 0.527473, count: 91 -Region Avg IOU: 0.438427, Class: 1.000000, Obj: 0.327922, No Obj: 0.004611, Avg Recall: 0.510870, count: 92 -Region Avg IOU: 0.467473, Class: 1.000000, Obj: 0.324302, No Obj: 0.004740, Avg Recall: 0.473118, count: 93 -Region Avg IOU: 0.454852, Class: 1.000000, Obj: 0.382017, No Obj: 0.005389, Avg Recall: 0.474138, count: 116 -Region Avg IOU: 0.439332, Class: 1.000000, Obj: 0.318763, No Obj: 0.005325, Avg Recall: 0.451923, count: 104 -3000: 0.522005, 0.508916 avg, 0.001000 rate, 17.645084 seconds, 768000 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.016298 seconds -Region Avg IOU: 0.460475, Class: 1.000000, Obj: 0.330553, No Obj: 0.006020, Avg Recall: 0.476562, count: 128 -Region Avg IOU: 0.466449, Class: 1.000000, Obj: 0.316328, No Obj: 0.004720, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.489859, Class: 1.000000, Obj: 0.339623, No Obj: 0.005301, Avg Recall: 0.565217, count: 92 -Region Avg IOU: 0.484825, Class: 1.000000, Obj: 0.283678, No Obj: 0.004954, Avg Recall: 0.554348, count: 92 -Region Avg IOU: 0.450471, Class: 1.000000, Obj: 0.324605, No Obj: 0.005230, Avg Recall: 0.482759, count: 116 -Region Avg IOU: 0.501078, Class: 1.000000, Obj: 0.347309, No Obj: 0.004951, Avg Recall: 0.566667, count: 90 -Region Avg IOU: 0.479253, Class: 1.000000, Obj: 0.318527, No Obj: 0.005791, Avg Recall: 0.528926, count: 121 -Region Avg IOU: 0.442273, Class: 1.000000, Obj: 0.305302, No Obj: 0.004743, Avg Recall: 0.430000, count: 100 -3001: 0.498985, 0.507923 avg, 0.001000 rate, 19.968784 seconds, 768256 images -Loaded: 1.118004 seconds -Region Avg IOU: 0.461908, Class: 1.000000, Obj: 0.297113, No Obj: 0.005011, Avg Recall: 0.457944, count: 107 -Region Avg IOU: 0.436696, Class: 1.000000, Obj: 0.276390, No Obj: 0.004970, Avg Recall: 0.442478, count: 113 -Region Avg IOU: 0.523780, Class: 1.000000, Obj: 0.369866, No Obj: 0.005562, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.473876, Class: 1.000000, Obj: 0.304027, No Obj: 0.005322, Avg Recall: 0.509259, count: 108 -Region Avg IOU: 0.449172, Class: 1.000000, Obj: 0.299294, No Obj: 0.005305, Avg Recall: 0.458015, count: 131 -Region Avg IOU: 0.463886, Class: 1.000000, Obj: 0.339964, No Obj: 0.004796, Avg Recall: 0.500000, count: 98 -Region Avg IOU: 0.436338, Class: 1.000000, Obj: 0.286072, No Obj: 0.005657, Avg Recall: 0.432432, count: 111 -Region Avg IOU: 0.422138, Class: 1.000000, Obj: 0.240587, No Obj: 0.004745, Avg Recall: 0.431579, count: 95 -3002: 0.454555, 0.502586 avg, 0.001000 rate, 18.121738 seconds, 768512 images -Loaded: 1.435024 seconds -Region Avg IOU: 0.461955, Class: 1.000000, Obj: 0.328003, No Obj: 0.004792, Avg Recall: 0.500000, count: 108 -Region Avg IOU: 0.473060, Class: 1.000000, Obj: 0.298996, No Obj: 0.004658, Avg Recall: 0.515790, count: 95 -Region Avg IOU: 0.449913, Class: 1.000000, Obj: 0.269983, No Obj: 0.004084, Avg Recall: 0.472727, count: 110 -Region Avg IOU: 0.437529, Class: 1.000000, Obj: 0.274310, No Obj: 0.004501, Avg Recall: 0.490909, count: 110 -Region Avg IOU: 0.462731, Class: 1.000000, Obj: 0.255036, No Obj: 0.004382, Avg Recall: 0.454545, count: 110 -Region Avg IOU: 0.409194, Class: 1.000000, Obj: 0.280300, No Obj: 0.004512, Avg Recall: 0.384615, count: 104 -Region Avg IOU: 0.437621, Class: 1.000000, Obj: 0.283781, No Obj: 0.004835, Avg Recall: 0.438017, count: 121 -Region Avg IOU: 0.453681, Class: 1.000000, Obj: 0.286248, No Obj: 0.003770, Avg Recall: 0.517241, count: 87 -3003: 0.469106, 0.499238 avg, 0.001000 rate, 14.186691 seconds, 768768 images -Loaded: 6.900508 seconds -Region Avg IOU: 0.476896, Class: 1.000000, Obj: 0.291496, No Obj: 0.004936, Avg Recall: 0.547826, count: 115 -Region Avg IOU: 0.476571, Class: 1.000000, Obj: 0.344612, No Obj: 0.005366, Avg Recall: 0.510204, count: 98 -Region Avg IOU: 0.452605, Class: 1.000000, Obj: 0.294975, No Obj: 0.004467, Avg Recall: 0.455446, count: 101 -Region Avg IOU: 0.448021, Class: 1.000000, Obj: 0.268366, No Obj: 0.004427, Avg Recall: 0.509615, count: 104 -Region Avg IOU: 0.475763, Class: 1.000000, Obj: 0.287610, No Obj: 0.005165, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.443666, Class: 1.000000, Obj: 0.277349, No Obj: 0.004883, Avg Recall: 0.427481, count: 131 -Region Avg IOU: 0.461067, Class: 1.000000, Obj: 0.271138, No Obj: 0.004711, Avg Recall: 0.454545, count: 110 -Region Avg IOU: 0.432933, Class: 1.000000, Obj: 0.265129, No Obj: 0.004401, Avg Recall: 0.470588, count: 102 -3004: 0.476061, 0.496921 avg, 0.001000 rate, 15.003365 seconds, 769024 images -Loaded: 6.249747 seconds -Region Avg IOU: 0.462724, Class: 1.000000, Obj: 0.262115, No Obj: 0.004527, Avg Recall: 0.529412, count: 119 -Region Avg IOU: 0.436071, Class: 1.000000, Obj: 0.265402, No Obj: 0.004066, Avg Recall: 0.414141, count: 99 -Region Avg IOU: 0.468349, Class: 1.000000, Obj: 0.309737, No Obj: 0.004833, Avg Recall: 0.518519, count: 135 -Region Avg IOU: 0.469691, Class: 1.000000, Obj: 0.275300, No Obj: 0.004358, Avg Recall: 0.495238, count: 105 -Region Avg IOU: 0.465707, Class: 1.000000, Obj: 0.264846, No Obj: 0.004881, Avg Recall: 0.518868, count: 106 -Region Avg IOU: 0.453184, Class: 1.000000, Obj: 0.280821, No Obj: 0.004136, Avg Recall: 0.452632, count: 95 -Region Avg IOU: 0.432832, Class: 1.000000, Obj: 0.272201, No Obj: 0.004304, Avg Recall: 0.500000, count: 94 -Region Avg IOU: 0.468567, Class: 1.000000, Obj: 0.273371, No Obj: 0.004479, Avg Recall: 0.491071, count: 112 -3005: 0.485300, 0.495759 avg, 0.001000 rate, 16.072748 seconds, 769280 images -Loaded: 4.297680 seconds -Region Avg IOU: 0.401791, Class: 1.000000, Obj: 0.257905, No Obj: 0.004281, Avg Recall: 0.423729, count: 118 -Region Avg IOU: 0.436750, Class: 1.000000, Obj: 0.290919, No Obj: 0.004601, Avg Recall: 0.489583, count: 96 -Region Avg IOU: 0.504840, Class: 1.000000, Obj: 0.312349, No Obj: 0.004601, Avg Recall: 0.523810, count: 84 -Region Avg IOU: 0.449106, Class: 1.000000, Obj: 0.304867, No Obj: 0.004514, Avg Recall: 0.461538, count: 104 -Region Avg IOU: 0.452616, Class: 1.000000, Obj: 0.274049, No Obj: 0.004393, Avg Recall: 0.456522, count: 92 -Region Avg IOU: 0.426851, Class: 1.000000, Obj: 0.277310, No Obj: 0.004056, Avg Recall: 0.442478, count: 113 -Region Avg IOU: 0.520000, Class: 1.000000, Obj: 0.309465, No Obj: 0.005017, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.477747, Class: 1.000000, Obj: 0.264226, No Obj: 0.005508, Avg Recall: 0.547619, count: 126 -3006: 0.480770, 0.494260 avg, 0.001000 rate, 16.368017 seconds, 769536 images -Loaded: 3.868267 seconds -Region Avg IOU: 0.442737, Class: 1.000000, Obj: 0.276859, No Obj: 0.005166, Avg Recall: 0.451852, count: 135 -Region Avg IOU: 0.399533, Class: 1.000000, Obj: 0.246109, No Obj: 0.003958, Avg Recall: 0.440000, count: 100 -Region Avg IOU: 0.500571, Class: 1.000000, Obj: 0.292296, No Obj: 0.005007, Avg Recall: 0.542373, count: 118 -Region Avg IOU: 0.496125, Class: 1.000000, Obj: 0.265568, No Obj: 0.004656, Avg Recall: 0.576923, count: 78 -Region Avg IOU: 0.492476, Class: 1.000000, Obj: 0.309467, No Obj: 0.004579, Avg Recall: 0.538462, count: 104 -Region Avg IOU: 0.466262, Class: 1.000000, Obj: 0.301151, No Obj: 0.004402, Avg Recall: 0.517544, count: 114 -Region Avg IOU: 0.516277, Class: 1.000000, Obj: 0.299552, No Obj: 0.004796, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.461717, Class: 1.000000, Obj: 0.269129, No Obj: 0.004343, Avg Recall: 0.480620, count: 129 -3007: 0.472456, 0.492079 avg, 0.001000 rate, 15.082600 seconds, 769792 images -Loaded: 6.548613 seconds -Region Avg IOU: 0.430906, Class: 1.000000, Obj: 0.306208, No Obj: 0.004470, Avg Recall: 0.427273, count: 110 -Region Avg IOU: 0.453541, Class: 1.000000, Obj: 0.265480, No Obj: 0.004783, Avg Recall: 0.459677, count: 124 -Region Avg IOU: 0.473251, Class: 1.000000, Obj: 0.271166, No Obj: 0.004298, Avg Recall: 0.517544, count: 114 -Region Avg IOU: 0.409461, Class: 1.000000, Obj: 0.271533, No Obj: 0.004256, Avg Recall: 0.409524, count: 105 -Region Avg IOU: 0.501359, Class: 1.000000, Obj: 0.319913, No Obj: 0.004908, Avg Recall: 0.548673, count: 113 -Region Avg IOU: 0.411524, Class: 1.000000, Obj: 0.250837, No Obj: 0.004454, Avg Recall: 0.432203, count: 118 -Region Avg IOU: 0.462435, Class: 1.000000, Obj: 0.290908, No Obj: 0.004229, Avg Recall: 0.505263, count: 95 -Region Avg IOU: 0.533170, Class: 1.000000, Obj: 0.338182, No Obj: 0.005121, Avg Recall: 0.642202, count: 109 -3008: 0.518935, 0.494765 avg, 0.001000 rate, 17.802099 seconds, 770048 images -Loaded: 2.773514 seconds -Region Avg IOU: 0.491661, Class: 1.000000, Obj: 0.280598, No Obj: 0.004812, Avg Recall: 0.584071, count: 113 -Region Avg IOU: 0.488732, Class: 1.000000, Obj: 0.322080, No Obj: 0.004755, Avg Recall: 0.571429, count: 84 -Region Avg IOU: 0.425030, Class: 1.000000, Obj: 0.309078, No Obj: 0.004281, Avg Recall: 0.500000, count: 88 -Region Avg IOU: 0.489947, Class: 1.000000, Obj: 0.330067, No Obj: 0.004395, Avg Recall: 0.595238, count: 84 -Region Avg IOU: 0.511233, Class: 1.000000, Obj: 0.305397, No Obj: 0.005170, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.460766, Class: 1.000000, Obj: 0.255535, No Obj: 0.005230, Avg Recall: 0.495868, count: 121 -Region Avg IOU: 0.454640, Class: 1.000000, Obj: 0.315673, No Obj: 0.004359, Avg Recall: 0.452381, count: 84 -Region Avg IOU: 0.447538, Class: 1.000000, Obj: 0.267416, No Obj: 0.005025, Avg Recall: 0.463235, count: 136 -3009: 0.490186, 0.494307 avg, 0.001000 rate, 15.815488 seconds, 770304 images -Loaded: 4.165358 seconds -Region Avg IOU: 0.510450, Class: 1.000000, Obj: 0.292842, No Obj: 0.005100, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.472154, Class: 1.000000, Obj: 0.335277, No Obj: 0.005016, Avg Recall: 0.533333, count: 105 -Region Avg IOU: 0.470671, Class: 1.000000, Obj: 0.331280, No Obj: 0.004288, Avg Recall: 0.566667, count: 90 -Region Avg IOU: 0.475926, Class: 1.000000, Obj: 0.334971, No Obj: 0.005266, Avg Recall: 0.547826, count: 115 -Region Avg IOU: 0.446920, Class: 1.000000, Obj: 0.326969, No Obj: 0.005057, Avg Recall: 0.452991, count: 117 -Region Avg IOU: 0.484298, Class: 1.000000, Obj: 0.327636, No Obj: 0.004816, Avg Recall: 0.520325, count: 123 -Region Avg IOU: 0.429166, Class: 1.000000, Obj: 0.313667, No Obj: 0.004454, Avg Recall: 0.489583, count: 96 -Region Avg IOU: 0.436983, Class: 1.000000, Obj: 0.301043, No Obj: 0.004859, Avg Recall: 0.474747, count: 99 -3010: 0.471478, 0.492024 avg, 0.001000 rate, 15.638054 seconds, 770560 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 0.145141 seconds -Region Avg IOU: 0.530200, Class: 1.000000, Obj: 0.438090, No Obj: 0.004798, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.484188, Class: 1.000000, Obj: 0.332922, No Obj: 0.004286, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.489795, Class: 1.000000, Obj: 0.425506, No Obj: 0.004281, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.527199, Class: 1.000000, Obj: 0.404198, No Obj: 0.005290, Avg Recall: 0.586777, count: 121 -Region Avg IOU: 0.526423, Class: 1.000000, Obj: 0.423923, No Obj: 0.005652, Avg Recall: 0.598540, count: 137 -Region Avg IOU: 0.560612, Class: 1.000000, Obj: 0.400281, No Obj: 0.005616, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.565719, Class: 1.000000, Obj: 0.402297, No Obj: 0.005153, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.568951, Class: 1.000000, Obj: 0.415226, No Obj: 0.005178, Avg Recall: 0.721739, count: 115 -3011: 0.489377, 0.491759 avg, 0.001000 rate, 22.111790 seconds, 770816 images -Loaded: 1.212093 seconds -Region Avg IOU: 0.511503, Class: 1.000000, Obj: 0.400704, No Obj: 0.005197, Avg Recall: 0.596774, count: 124 -Region Avg IOU: 0.557869, Class: 1.000000, Obj: 0.390258, No Obj: 0.005181, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.554623, Class: 1.000000, Obj: 0.385711, No Obj: 0.005670, Avg Recall: 0.655738, count: 122 -Region Avg IOU: 0.536604, Class: 1.000000, Obj: 0.387542, No Obj: 0.005074, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.517485, Class: 1.000000, Obj: 0.358876, No Obj: 0.005515, Avg Recall: 0.558824, count: 136 -Region Avg IOU: 0.535170, Class: 1.000000, Obj: 0.384074, No Obj: 0.004998, Avg Recall: 0.634146, count: 123 -Region Avg IOU: 0.503368, Class: 1.000000, Obj: 0.379963, No Obj: 0.004813, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.517471, Class: 1.000000, Obj: 0.357617, No Obj: 0.005105, Avg Recall: 0.593985, count: 133 -3012: 0.481952, 0.490779 avg, 0.001000 rate, 22.411291 seconds, 771072 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.551735, Class: 1.000000, Obj: 0.404999, No Obj: 0.004646, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.528440, Class: 1.000000, Obj: 0.382428, No Obj: 0.004538, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.537133, Class: 1.000000, Obj: 0.401374, No Obj: 0.004869, Avg Recall: 0.613445, count: 119 -Region Avg IOU: 0.539605, Class: 1.000000, Obj: 0.393708, No Obj: 0.004608, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.485344, Class: 1.000000, Obj: 0.378195, No Obj: 0.004384, Avg Recall: 0.575758, count: 99 -Region Avg IOU: 0.544641, Class: 1.000000, Obj: 0.443729, No Obj: 0.004367, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.539314, Class: 1.000000, Obj: 0.360161, No Obj: 0.004678, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.530318, Class: 1.000000, Obj: 0.389509, No Obj: 0.004539, Avg Recall: 0.584906, count: 106 -3013: 0.381574, 0.479858 avg, 0.001000 rate, 22.236828 seconds, 771328 images -Loaded: 0.037072 seconds -Region Avg IOU: 0.545260, Class: 1.000000, Obj: 0.352050, No Obj: 0.004518, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.566179, Class: 1.000000, Obj: 0.376030, No Obj: 0.005227, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.517776, Class: 1.000000, Obj: 0.336607, No Obj: 0.004153, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.506358, Class: 1.000000, Obj: 0.377503, No Obj: 0.003621, Avg Recall: 0.505376, count: 93 -Region Avg IOU: 0.553077, Class: 1.000000, Obj: 0.347443, No Obj: 0.004750, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.567217, Class: 1.000000, Obj: 0.384098, No Obj: 0.004538, Avg Recall: 0.694118, count: 85 -Region Avg IOU: 0.523425, Class: 1.000000, Obj: 0.381790, No Obj: 0.004650, Avg Recall: 0.613445, count: 119 -Region Avg IOU: 0.562421, Class: 1.000000, Obj: 0.402797, No Obj: 0.004215, Avg Recall: 0.701923, count: 104 -3014: 0.413539, 0.473226 avg, 0.001000 rate, 22.183855 seconds, 771584 images -Loaded: 0.280472 seconds -Region Avg IOU: 0.516924, Class: 1.000000, Obj: 0.355446, No Obj: 0.004211, Avg Recall: 0.563492, count: 126 -Region Avg IOU: 0.555284, Class: 1.000000, Obj: 0.402359, No Obj: 0.003584, Avg Recall: 0.640000, count: 75 -Region Avg IOU: 0.554386, Class: 1.000000, Obj: 0.364967, No Obj: 0.003924, Avg Recall: 0.597826, count: 92 -Region Avg IOU: 0.506444, Class: 1.000000, Obj: 0.304535, No Obj: 0.004073, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.500870, Class: 1.000000, Obj: 0.342682, No Obj: 0.004123, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.530645, Class: 1.000000, Obj: 0.341817, No Obj: 0.004519, Avg Recall: 0.642857, count: 126 -Region Avg IOU: 0.462984, Class: 1.000000, Obj: 0.283796, No Obj: 0.003848, Avg Recall: 0.486486, count: 111 -Region Avg IOU: 0.512164, Class: 1.000000, Obj: 0.360177, No Obj: 0.003679, Avg Recall: 0.585106, count: 94 -3015: 0.493026, 0.475206 avg, 0.001000 rate, 22.649817 seconds, 771840 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.503665, Class: 1.000000, Obj: 0.282917, No Obj: 0.003768, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.547668, Class: 1.000000, Obj: 0.353993, No Obj: 0.003811, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.511834, Class: 1.000000, Obj: 0.329672, No Obj: 0.003831, Avg Recall: 0.562500, count: 96 -Region Avg IOU: 0.533512, Class: 1.000000, Obj: 0.329494, No Obj: 0.004286, Avg Recall: 0.596491, count: 114 -Region Avg IOU: 0.517998, Class: 1.000000, Obj: 0.316672, No Obj: 0.003877, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.509463, Class: 1.000000, Obj: 0.301950, No Obj: 0.004006, Avg Recall: 0.551181, count: 127 -Region Avg IOU: 0.566685, Class: 1.000000, Obj: 0.332468, No Obj: 0.003850, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.514787, Class: 1.000000, Obj: 0.352541, No Obj: 0.003240, Avg Recall: 0.617284, count: 81 -3016: 0.425703, 0.470256 avg, 0.001000 rate, 22.050404 seconds, 772096 images -Loaded: 0.342430 seconds -Region Avg IOU: 0.555234, Class: 1.000000, Obj: 0.340296, No Obj: 0.003417, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.532661, Class: 1.000000, Obj: 0.315795, No Obj: 0.003327, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.509095, Class: 1.000000, Obj: 0.284662, No Obj: 0.003590, Avg Recall: 0.589744, count: 117 -Region Avg IOU: 0.530076, Class: 1.000000, Obj: 0.352298, No Obj: 0.004116, Avg Recall: 0.578947, count: 114 -Region Avg IOU: 0.509594, Class: 1.000000, Obj: 0.277527, No Obj: 0.003673, Avg Recall: 0.581967, count: 122 -Region Avg IOU: 0.580504, Class: 1.000000, Obj: 0.354354, No Obj: 0.003674, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.509328, Class: 1.000000, Obj: 0.347131, No Obj: 0.003180, Avg Recall: 0.600000, count: 85 -Region Avg IOU: 0.488118, Class: 1.000000, Obj: 0.301658, No Obj: 0.004193, Avg Recall: 0.538462, count: 143 -3017: 0.441292, 0.467360 avg, 0.001000 rate, 23.031708 seconds, 772352 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.496733, Class: 1.000000, Obj: 0.296889, No Obj: 0.003436, Avg Recall: 0.555556, count: 99 -Region Avg IOU: 0.506780, Class: 1.000000, Obj: 0.272949, No Obj: 0.003341, Avg Recall: 0.564103, count: 117 -Region Avg IOU: 0.523747, Class: 1.000000, Obj: 0.281192, No Obj: 0.003707, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.536664, Class: 1.000000, Obj: 0.325810, No Obj: 0.003448, Avg Recall: 0.602273, count: 88 -Region Avg IOU: 0.538093, Class: 1.000000, Obj: 0.315003, No Obj: 0.003845, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.546809, Class: 1.000000, Obj: 0.377826, No Obj: 0.004073, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.586640, Class: 1.000000, Obj: 0.366395, No Obj: 0.004050, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.522707, Class: 1.000000, Obj: 0.272065, No Obj: 0.002845, Avg Recall: 0.623656, count: 93 -3018: 0.405845, 0.461208 avg, 0.001000 rate, 22.482307 seconds, 772608 images -Loaded: 0.359971 seconds -Region Avg IOU: 0.522937, Class: 1.000000, Obj: 0.282485, No Obj: 0.003404, Avg Recall: 0.578512, count: 121 -Region Avg IOU: 0.515725, Class: 1.000000, Obj: 0.324774, No Obj: 0.003499, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.489729, Class: 1.000000, Obj: 0.264421, No Obj: 0.003205, Avg Recall: 0.519685, count: 127 -Region Avg IOU: 0.568893, Class: 1.000000, Obj: 0.327490, No Obj: 0.003508, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.539752, Class: 1.000000, Obj: 0.358087, No Obj: 0.003240, Avg Recall: 0.628205, count: 78 -Region Avg IOU: 0.505392, Class: 1.000000, Obj: 0.280286, No Obj: 0.003342, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.509899, Class: 1.000000, Obj: 0.292791, No Obj: 0.003541, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.527075, Class: 1.000000, Obj: 0.336393, No Obj: 0.003293, Avg Recall: 0.620253, count: 79 -3019: 0.452578, 0.460345 avg, 0.001000 rate, 21.898684 seconds, 772864 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.535951, Class: 1.000000, Obj: 0.288243, No Obj: 0.003277, Avg Recall: 0.619565, count: 92 -Region Avg IOU: 0.528977, Class: 1.000000, Obj: 0.292563, No Obj: 0.003450, Avg Recall: 0.588235, count: 102 -Region Avg IOU: 0.532988, Class: 1.000000, Obj: 0.332544, No Obj: 0.003666, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.515458, Class: 1.000000, Obj: 0.279489, No Obj: 0.003699, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.545129, Class: 1.000000, Obj: 0.316179, No Obj: 0.003766, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.498099, Class: 1.000000, Obj: 0.312009, No Obj: 0.003872, Avg Recall: 0.523077, count: 130 -Region Avg IOU: 0.553650, Class: 1.000000, Obj: 0.328984, No Obj: 0.003882, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.524085, Class: 1.000000, Obj: 0.262793, No Obj: 0.003604, Avg Recall: 0.660714, count: 112 -3020: 0.498883, 0.464199 avg, 0.001000 rate, 21.576307 seconds, 773120 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.238909 seconds -Region Avg IOU: 0.469294, Class: 1.000000, Obj: 0.274417, No Obj: 0.003203, Avg Recall: 0.482143, count: 112 -Region Avg IOU: 0.476742, Class: 1.000000, Obj: 0.267571, No Obj: 0.003517, Avg Recall: 0.513043, count: 115 -Region Avg IOU: 0.498985, Class: 1.000000, Obj: 0.266969, No Obj: 0.003413, Avg Recall: 0.555556, count: 90 -Region Avg IOU: 0.552949, Class: 1.000000, Obj: 0.322253, No Obj: 0.003810, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.514819, Class: 1.000000, Obj: 0.294854, No Obj: 0.004017, Avg Recall: 0.596491, count: 114 -Region Avg IOU: 0.482178, Class: 1.000000, Obj: 0.273860, No Obj: 0.003554, Avg Recall: 0.578512, count: 121 -Region Avg IOU: 0.547252, Class: 1.000000, Obj: 0.306000, No Obj: 0.004353, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.521875, Class: 1.000000, Obj: 0.295424, No Obj: 0.003877, Avg Recall: 0.600000, count: 110 -3021: 0.457576, 0.463537 avg, 0.001000 rate, 20.951897 seconds, 773376 images -Loaded: 1.353119 seconds -Region Avg IOU: 0.568029, Class: 1.000000, Obj: 0.314901, No Obj: 0.003859, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.470959, Class: 1.000000, Obj: 0.257632, No Obj: 0.003439, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.571815, Class: 1.000000, Obj: 0.364576, No Obj: 0.003680, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.537395, Class: 1.000000, Obj: 0.332130, No Obj: 0.004464, Avg Recall: 0.630252, count: 119 -Region Avg IOU: 0.490412, Class: 1.000000, Obj: 0.316335, No Obj: 0.003813, Avg Recall: 0.494845, count: 97 -Region Avg IOU: 0.515075, Class: 1.000000, Obj: 0.298268, No Obj: 0.003892, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.556826, Class: 1.000000, Obj: 0.327623, No Obj: 0.003611, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.510909, Class: 1.000000, Obj: 0.326458, No Obj: 0.003772, Avg Recall: 0.537736, count: 106 -3022: 0.440824, 0.461265 avg, 0.001000 rate, 18.992168 seconds, 773632 images -Loaded: 2.908578 seconds -Region Avg IOU: 0.547065, Class: 1.000000, Obj: 0.349509, No Obj: 0.004332, Avg Recall: 0.631148, count: 122 -Region Avg IOU: 0.523439, Class: 1.000000, Obj: 0.306803, No Obj: 0.004474, Avg Recall: 0.625000, count: 136 -Region Avg IOU: 0.483584, Class: 1.000000, Obj: 0.354043, No Obj: 0.003953, Avg Recall: 0.504854, count: 103 -Region Avg IOU: 0.490859, Class: 1.000000, Obj: 0.281481, No Obj: 0.004340, Avg Recall: 0.530435, count: 115 -Region Avg IOU: 0.555431, Class: 1.000000, Obj: 0.406447, No Obj: 0.004042, Avg Recall: 0.644444, count: 90 -Region Avg IOU: 0.490705, Class: 1.000000, Obj: 0.279114, No Obj: 0.004501, Avg Recall: 0.562963, count: 135 -Region Avg IOU: 0.532917, Class: 1.000000, Obj: 0.354475, No Obj: 0.004598, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.431195, Class: 1.000000, Obj: 0.301228, No Obj: 0.003879, Avg Recall: 0.448819, count: 127 -3023: 0.518193, 0.466958 avg, 0.001000 rate, 22.031624 seconds, 773888 images -Loaded: 0.104819 seconds -Region Avg IOU: 0.513257, Class: 1.000000, Obj: 0.337212, No Obj: 0.004412, Avg Recall: 0.537037, count: 108 -Region Avg IOU: 0.524437, Class: 1.000000, Obj: 0.371187, No Obj: 0.004090, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.455032, Class: 1.000000, Obj: 0.284189, No Obj: 0.003741, Avg Recall: 0.547619, count: 84 -Region Avg IOU: 0.525457, Class: 1.000000, Obj: 0.382847, No Obj: 0.004510, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.503923, Class: 1.000000, Obj: 0.299495, No Obj: 0.004263, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.511451, Class: 1.000000, Obj: 0.322958, No Obj: 0.004208, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.513919, Class: 1.000000, Obj: 0.336434, No Obj: 0.004111, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.526086, Class: 1.000000, Obj: 0.345527, No Obj: 0.004973, Avg Recall: 0.603960, count: 101 -3024: 0.429160, 0.463178 avg, 0.001000 rate, 20.299925 seconds, 774144 images -Loaded: 2.280949 seconds -Region Avg IOU: 0.504063, Class: 1.000000, Obj: 0.360824, No Obj: 0.004765, Avg Recall: 0.578512, count: 121 -Region Avg IOU: 0.513500, Class: 1.000000, Obj: 0.381247, No Obj: 0.004101, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.478567, Class: 1.000000, Obj: 0.372062, No Obj: 0.004427, Avg Recall: 0.513514, count: 111 -Region Avg IOU: 0.520311, Class: 1.000000, Obj: 0.379128, No Obj: 0.004318, Avg Recall: 0.577778, count: 90 -Region Avg IOU: 0.532389, Class: 1.000000, Obj: 0.373592, No Obj: 0.004369, Avg Recall: 0.602273, count: 88 -Region Avg IOU: 0.498025, Class: 1.000000, Obj: 0.352721, No Obj: 0.003856, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.532121, Class: 1.000000, Obj: 0.355254, No Obj: 0.004368, Avg Recall: 0.609195, count: 87 -Region Avg IOU: 0.532793, Class: 1.000000, Obj: 0.332554, No Obj: 0.004194, Avg Recall: 0.585859, count: 99 -3025: 0.375235, 0.454384 avg, 0.001000 rate, 20.472206 seconds, 774400 images -Loaded: 0.879708 seconds -Region Avg IOU: 0.503963, Class: 1.000000, Obj: 0.383484, No Obj: 0.004793, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.454761, Class: 1.000000, Obj: 0.375560, No Obj: 0.004775, Avg Recall: 0.453901, count: 141 -Region Avg IOU: 0.516403, Class: 1.000000, Obj: 0.400609, No Obj: 0.004333, Avg Recall: 0.600000, count: 95 -Region Avg IOU: 0.534627, Class: 1.000000, Obj: 0.364163, No Obj: 0.005245, Avg Recall: 0.648000, count: 125 -Region Avg IOU: 0.522087, Class: 1.000000, Obj: 0.340488, No Obj: 0.004761, Avg Recall: 0.540816, count: 98 -Region Avg IOU: 0.528584, Class: 1.000000, Obj: 0.324843, No Obj: 0.004608, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.466272, Class: 1.000000, Obj: 0.331876, No Obj: 0.004777, Avg Recall: 0.536082, count: 97 -Region Avg IOU: 0.505886, Class: 1.000000, Obj: 0.330156, No Obj: 0.004447, Avg Recall: 0.586207, count: 87 -3026: 0.433810, 0.452327 avg, 0.001000 rate, 20.946848 seconds, 774656 images -Loaded: 0.748229 seconds -Region Avg IOU: 0.510580, Class: 1.000000, Obj: 0.385214, No Obj: 0.005196, Avg Recall: 0.570312, count: 128 -Region Avg IOU: 0.520616, Class: 1.000000, Obj: 0.378592, No Obj: 0.004481, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.499857, Class: 1.000000, Obj: 0.337651, No Obj: 0.004830, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.502196, Class: 1.000000, Obj: 0.375857, No Obj: 0.005555, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.541871, Class: 1.000000, Obj: 0.364554, No Obj: 0.005078, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.506659, Class: 1.000000, Obj: 0.422467, No Obj: 0.004793, Avg Recall: 0.549020, count: 102 -Region Avg IOU: 0.525716, Class: 1.000000, Obj: 0.342839, No Obj: 0.005262, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.481201, Class: 1.000000, Obj: 0.351729, No Obj: 0.005058, Avg Recall: 0.496000, count: 125 -3027: 0.440858, 0.451180 avg, 0.001000 rate, 22.196203 seconds, 774912 images -Loaded: 0.335642 seconds -Region Avg IOU: 0.452300, Class: 1.000000, Obj: 0.353735, No Obj: 0.004629, Avg Recall: 0.508065, count: 124 -Region Avg IOU: 0.524133, Class: 1.000000, Obj: 0.335517, No Obj: 0.005010, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.484520, Class: 1.000000, Obj: 0.382169, No Obj: 0.004657, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.477274, Class: 1.000000, Obj: 0.363706, No Obj: 0.005072, Avg Recall: 0.523077, count: 130 -Region Avg IOU: 0.526337, Class: 1.000000, Obj: 0.406476, No Obj: 0.004341, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.501758, Class: 1.000000, Obj: 0.346623, No Obj: 0.005289, Avg Recall: 0.584615, count: 130 -Region Avg IOU: 0.560719, Class: 1.000000, Obj: 0.381843, No Obj: 0.004874, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.451582, Class: 1.000000, Obj: 0.352745, No Obj: 0.004115, Avg Recall: 0.534653, count: 101 -3028: 0.552935, 0.461355 avg, 0.001000 rate, 19.212286 seconds, 775168 images -Loaded: 1.199477 seconds -Region Avg IOU: 0.503712, Class: 1.000000, Obj: 0.329017, No Obj: 0.004759, Avg Recall: 0.555556, count: 99 -Region Avg IOU: 0.506682, Class: 1.000000, Obj: 0.345169, No Obj: 0.004818, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.528268, Class: 1.000000, Obj: 0.350398, No Obj: 0.004741, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.520713, Class: 1.000000, Obj: 0.370232, No Obj: 0.005134, Avg Recall: 0.571429, count: 126 -Region Avg IOU: 0.537216, Class: 1.000000, Obj: 0.358091, No Obj: 0.005208, Avg Recall: 0.579310, count: 145 -Region Avg IOU: 0.433045, Class: 1.000000, Obj: 0.307429, No Obj: 0.003760, Avg Recall: 0.477778, count: 90 -Region Avg IOU: 0.493006, Class: 1.000000, Obj: 0.371765, No Obj: 0.004682, Avg Recall: 0.500000, count: 126 -Region Avg IOU: 0.537372, Class: 1.000000, Obj: 0.390850, No Obj: 0.004427, Avg Recall: 0.576087, count: 92 -3029: 0.538399, 0.469060 avg, 0.001000 rate, 19.713093 seconds, 775424 images -Loaded: 0.455485 seconds -Region Avg IOU: 0.519064, Class: 1.000000, Obj: 0.311383, No Obj: 0.004648, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.462573, Class: 1.000000, Obj: 0.300168, No Obj: 0.004415, Avg Recall: 0.484615, count: 130 -Region Avg IOU: 0.512861, Class: 1.000000, Obj: 0.346467, No Obj: 0.005025, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.452726, Class: 1.000000, Obj: 0.297069, No Obj: 0.003984, Avg Recall: 0.516129, count: 93 -Region Avg IOU: 0.540986, Class: 1.000000, Obj: 0.331154, No Obj: 0.005104, Avg Recall: 0.624000, count: 125 -Region Avg IOU: 0.504905, Class: 1.000000, Obj: 0.355323, No Obj: 0.004155, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.498052, Class: 1.000000, Obj: 0.318760, No Obj: 0.004786, Avg Recall: 0.556338, count: 142 -Region Avg IOU: 0.511109, Class: 1.000000, Obj: 0.397823, No Obj: 0.004524, Avg Recall: 0.568965, count: 116 -3030: 0.569142, 0.479068 avg, 0.001000 rate, 21.187700 seconds, 775680 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.754894 seconds -Region Avg IOU: 0.528604, Class: 1.000000, Obj: 0.286672, No Obj: 0.004643, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.474349, Class: 1.000000, Obj: 0.253232, No Obj: 0.004273, Avg Recall: 0.483516, count: 91 -Region Avg IOU: 0.422578, Class: 1.000000, Obj: 0.276943, No Obj: 0.004564, Avg Recall: 0.444444, count: 126 -Region Avg IOU: 0.418487, Class: 1.000000, Obj: 0.282377, No Obj: 0.004226, Avg Recall: 0.433962, count: 106 -Region Avg IOU: 0.458321, Class: 1.000000, Obj: 0.278798, No Obj: 0.004861, Avg Recall: 0.500000, count: 120 -Region Avg IOU: 0.451372, Class: 1.000000, Obj: 0.269792, No Obj: 0.004417, Avg Recall: 0.479167, count: 96 -Region Avg IOU: 0.455309, Class: 1.000000, Obj: 0.296390, No Obj: 0.004741, Avg Recall: 0.480392, count: 102 -Region Avg IOU: 0.479271, Class: 1.000000, Obj: 0.273308, No Obj: 0.004335, Avg Recall: 0.512821, count: 117 -3031: 0.507917, 0.481953 avg, 0.001000 rate, 19.125744 seconds, 775936 images -Loaded: 1.823922 seconds -Region Avg IOU: 0.438768, Class: 1.000000, Obj: 0.250654, No Obj: 0.004425, Avg Recall: 0.441860, count: 129 -Region Avg IOU: 0.458469, Class: 1.000000, Obj: 0.261683, No Obj: 0.004168, Avg Recall: 0.491803, count: 122 -Region Avg IOU: 0.473404, Class: 1.000000, Obj: 0.282440, No Obj: 0.004686, Avg Recall: 0.467391, count: 92 -Region Avg IOU: 0.481970, Class: 1.000000, Obj: 0.285273, No Obj: 0.004305, Avg Recall: 0.525862, count: 116 -Region Avg IOU: 0.493929, Class: 1.000000, Obj: 0.281309, No Obj: 0.004216, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.469892, Class: 1.000000, Obj: 0.280306, No Obj: 0.004842, Avg Recall: 0.519084, count: 131 -Region Avg IOU: 0.412968, Class: 1.000000, Obj: 0.252589, No Obj: 0.003904, Avg Recall: 0.418367, count: 98 -Region Avg IOU: 0.496099, Class: 1.000000, Obj: 0.258854, No Obj: 0.004103, Avg Recall: 0.528736, count: 87 -3032: 0.465512, 0.480309 avg, 0.001000 rate, 14.915421 seconds, 776192 images -Loaded: 4.569754 seconds -Region Avg IOU: 0.473083, Class: 1.000000, Obj: 0.290809, No Obj: 0.004766, Avg Recall: 0.500000, count: 126 -Region Avg IOU: 0.483547, Class: 1.000000, Obj: 0.300507, No Obj: 0.004543, Avg Recall: 0.495238, count: 105 -Region Avg IOU: 0.411037, Class: 1.000000, Obj: 0.221726, No Obj: 0.004904, Avg Recall: 0.443478, count: 115 -Region Avg IOU: 0.458864, Class: 1.000000, Obj: 0.276074, No Obj: 0.004827, Avg Recall: 0.460317, count: 126 -Region Avg IOU: 0.440630, Class: 1.000000, Obj: 0.254387, No Obj: 0.004549, Avg Recall: 0.472000, count: 125 -Region Avg IOU: 0.434324, Class: 1.000000, Obj: 0.252406, No Obj: 0.004734, Avg Recall: 0.458333, count: 120 -Region Avg IOU: 0.488468, Class: 1.000000, Obj: 0.291920, No Obj: 0.004279, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.482929, Class: 1.000000, Obj: 0.282772, No Obj: 0.004438, Avg Recall: 0.518519, count: 108 -3033: 0.597037, 0.491981 avg, 0.001000 rate, 16.988802 seconds, 776448 images -Loaded: 3.896843 seconds -Region Avg IOU: 0.444308, Class: 1.000000, Obj: 0.266936, No Obj: 0.004534, Avg Recall: 0.453704, count: 108 -Region Avg IOU: 0.437628, Class: 1.000000, Obj: 0.252396, No Obj: 0.004514, Avg Recall: 0.486486, count: 111 -Region Avg IOU: 0.435633, Class: 1.000000, Obj: 0.269898, No Obj: 0.004287, Avg Recall: 0.478723, count: 94 -Region Avg IOU: 0.460685, Class: 1.000000, Obj: 0.298303, No Obj: 0.004331, Avg Recall: 0.457944, count: 107 -Region Avg IOU: 0.453992, Class: 1.000000, Obj: 0.304310, No Obj: 0.004575, Avg Recall: 0.491379, count: 116 -Region Avg IOU: 0.451895, Class: 1.000000, Obj: 0.262357, No Obj: 0.004173, Avg Recall: 0.489583, count: 96 -Region Avg IOU: 0.459902, Class: 1.000000, Obj: 0.283562, No Obj: 0.004258, Avg Recall: 0.500000, count: 120 -Region Avg IOU: 0.417050, Class: 1.000000, Obj: 0.253316, No Obj: 0.003910, Avg Recall: 0.400000, count: 110 -3034: 0.477514, 0.490535 avg, 0.001000 rate, 17.910889 seconds, 776704 images -Loaded: 4.864508 seconds -Region Avg IOU: 0.450809, Class: 1.000000, Obj: 0.261589, No Obj: 0.005014, Avg Recall: 0.500000, count: 104 -Region Avg IOU: 0.452092, Class: 1.000000, Obj: 0.262965, No Obj: 0.004424, Avg Recall: 0.517544, count: 114 -Region Avg IOU: 0.436057, Class: 1.000000, Obj: 0.280473, No Obj: 0.004446, Avg Recall: 0.393939, count: 99 -Region Avg IOU: 0.491128, Class: 1.000000, Obj: 0.295942, No Obj: 0.004912, Avg Recall: 0.513514, count: 111 -Region Avg IOU: 0.431429, Class: 1.000000, Obj: 0.310053, No Obj: 0.004433, Avg Recall: 0.468468, count: 111 -Region Avg IOU: 0.453988, Class: 1.000000, Obj: 0.298366, No Obj: 0.004841, Avg Recall: 0.500000, count: 114 -Region Avg IOU: 0.484609, Class: 1.000000, Obj: 0.338522, No Obj: 0.004229, Avg Recall: 0.484848, count: 99 -Region Avg IOU: 0.437085, Class: 1.000000, Obj: 0.274677, No Obj: 0.004877, Avg Recall: 0.460938, count: 128 -3035: 0.472220, 0.488703 avg, 0.001000 rate, 14.433324 seconds, 776960 images -Loaded: 6.576970 seconds -Region Avg IOU: 0.502997, Class: 1.000000, Obj: 0.326108, No Obj: 0.004648, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.460877, Class: 1.000000, Obj: 0.306726, No Obj: 0.004452, Avg Recall: 0.484848, count: 99 -Region Avg IOU: 0.461023, Class: 1.000000, Obj: 0.283504, No Obj: 0.005014, Avg Recall: 0.495798, count: 119 -Region Avg IOU: 0.499498, Class: 1.000000, Obj: 0.296669, No Obj: 0.004991, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.411626, Class: 1.000000, Obj: 0.258190, No Obj: 0.004111, Avg Recall: 0.423077, count: 104 -Region Avg IOU: 0.446467, Class: 1.000000, Obj: 0.302440, No Obj: 0.005337, Avg Recall: 0.449541, count: 109 -Region Avg IOU: 0.428419, Class: 1.000000, Obj: 0.262860, No Obj: 0.004310, Avg Recall: 0.447154, count: 123 -Region Avg IOU: 0.432038, Class: 1.000000, Obj: 0.277016, No Obj: 0.004276, Avg Recall: 0.489583, count: 96 -3036: 0.490368, 0.488870 avg, 0.001000 rate, 19.086063 seconds, 777216 images -Loaded: 1.832441 seconds -Region Avg IOU: 0.462141, Class: 1.000000, Obj: 0.319905, No Obj: 0.004788, Avg Recall: 0.480392, count: 102 -Region Avg IOU: 0.467932, Class: 1.000000, Obj: 0.313938, No Obj: 0.005529, Avg Recall: 0.500000, count: 124 -Region Avg IOU: 0.437359, Class: 1.000000, Obj: 0.291676, No Obj: 0.004302, Avg Recall: 0.446808, count: 94 -Region Avg IOU: 0.466926, Class: 1.000000, Obj: 0.270541, No Obj: 0.004616, Avg Recall: 0.524272, count: 103 -Region Avg IOU: 0.469309, Class: 1.000000, Obj: 0.310680, No Obj: 0.005389, Avg Recall: 0.524194, count: 124 -Region Avg IOU: 0.459175, Class: 1.000000, Obj: 0.306663, No Obj: 0.005099, Avg Recall: 0.471074, count: 121 -Region Avg IOU: 0.433282, Class: 1.000000, Obj: 0.292149, No Obj: 0.004752, Avg Recall: 0.448276, count: 116 -Region Avg IOU: 0.506970, Class: 1.000000, Obj: 0.322815, No Obj: 0.004832, Avg Recall: 0.576471, count: 85 -3037: 0.457000, 0.485683 avg, 0.001000 rate, 15.628262 seconds, 777472 images -Loaded: 5.059911 seconds -Region Avg IOU: 0.441258, Class: 1.000000, Obj: 0.299528, No Obj: 0.004739, Avg Recall: 0.480769, count: 104 -Region Avg IOU: 0.506195, Class: 1.000000, Obj: 0.332884, No Obj: 0.005416, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.471593, Class: 1.000000, Obj: 0.363810, No Obj: 0.005367, Avg Recall: 0.495652, count: 115 -Region Avg IOU: 0.450060, Class: 1.000000, Obj: 0.306945, No Obj: 0.005601, Avg Recall: 0.513889, count: 144 -Region Avg IOU: 0.491829, Class: 1.000000, Obj: 0.337389, No Obj: 0.005206, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.497640, Class: 1.000000, Obj: 0.330021, No Obj: 0.004914, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.425978, Class: 1.000000, Obj: 0.281737, No Obj: 0.005016, Avg Recall: 0.474138, count: 116 -Region Avg IOU: 0.494805, Class: 1.000000, Obj: 0.324904, No Obj: 0.004932, Avg Recall: 0.556701, count: 97 -3038: 0.479991, 0.485114 avg, 0.001000 rate, 16.324148 seconds, 777728 images -Loaded: 4.143210 seconds -Region Avg IOU: 0.474173, Class: 1.000000, Obj: 0.341725, No Obj: 0.004788, Avg Recall: 0.504854, count: 103 -Region Avg IOU: 0.430888, Class: 1.000000, Obj: 0.333558, No Obj: 0.004946, Avg Recall: 0.450980, count: 102 -Region Avg IOU: 0.430601, Class: 1.000000, Obj: 0.299735, No Obj: 0.005153, Avg Recall: 0.470588, count: 102 -Region Avg IOU: 0.508304, Class: 1.000000, Obj: 0.339000, No Obj: 0.005712, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.444276, Class: 1.000000, Obj: 0.319620, No Obj: 0.005153, Avg Recall: 0.504132, count: 121 -Region Avg IOU: 0.464011, Class: 1.000000, Obj: 0.319271, No Obj: 0.004980, Avg Recall: 0.465347, count: 101 -Region Avg IOU: 0.444135, Class: 1.000000, Obj: 0.297908, No Obj: 0.005423, Avg Recall: 0.443548, count: 124 -Region Avg IOU: 0.442157, Class: 1.000000, Obj: 0.270845, No Obj: 0.004707, Avg Recall: 0.420561, count: 107 -3039: 0.485427, 0.485145 avg, 0.001000 rate, 19.252707 seconds, 777984 images -Loaded: 2.440499 seconds -Region Avg IOU: 0.417775, Class: 1.000000, Obj: 0.330907, No Obj: 0.004916, Avg Recall: 0.435644, count: 101 -Region Avg IOU: 0.458846, Class: 1.000000, Obj: 0.350184, No Obj: 0.005785, Avg Recall: 0.483333, count: 120 -Region Avg IOU: 0.422205, Class: 1.000000, Obj: 0.306685, No Obj: 0.004693, Avg Recall: 0.491379, count: 116 -Region Avg IOU: 0.517497, Class: 1.000000, Obj: 0.328105, No Obj: 0.005261, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.434560, Class: 1.000000, Obj: 0.301301, No Obj: 0.004987, Avg Recall: 0.456522, count: 92 -Region Avg IOU: 0.496623, Class: 1.000000, Obj: 0.368048, No Obj: 0.005509, Avg Recall: 0.572727, count: 110 -Region Avg IOU: 0.509889, Class: 1.000000, Obj: 0.322459, No Obj: 0.005162, Avg Recall: 0.595238, count: 84 -Region Avg IOU: 0.463927, Class: 1.000000, Obj: 0.309328, No Obj: 0.006305, Avg Recall: 0.513043, count: 115 -3040: 0.406607, 0.477291 avg, 0.001000 rate, 14.938473 seconds, 778240 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 0.007014 seconds -Region Avg IOU: 0.446655, Class: 1.000000, Obj: 0.368252, No Obj: 0.003719, Avg Recall: 0.436620, count: 71 -Region Avg IOU: 0.463104, Class: 1.000000, Obj: 0.367226, No Obj: 0.004779, Avg Recall: 0.431373, count: 102 -Region Avg IOU: 0.511072, Class: 1.000000, Obj: 0.349757, No Obj: 0.005153, Avg Recall: 0.529412, count: 102 -Region Avg IOU: 0.519288, Class: 1.000000, Obj: 0.340609, No Obj: 0.005541, Avg Recall: 0.589552, count: 134 -Region Avg IOU: 0.452994, Class: 1.000000, Obj: 0.344218, No Obj: 0.004858, Avg Recall: 0.487603, count: 121 -Region Avg IOU: 0.490377, Class: 1.000000, Obj: 0.320021, No Obj: 0.004781, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.453016, Class: 1.000000, Obj: 0.293734, No Obj: 0.004676, Avg Recall: 0.463918, count: 97 -Region Avg IOU: 0.508130, Class: 1.000000, Obj: 0.314896, No Obj: 0.005461, Avg Recall: 0.551020, count: 98 -3041: 0.453613, 0.474923 avg, 0.001000 rate, 18.977692 seconds, 778496 images -Loaded: 1.993452 seconds -Region Avg IOU: 0.477307, Class: 1.000000, Obj: 0.376820, No Obj: 0.005720, Avg Recall: 0.508333, count: 120 -Region Avg IOU: 0.452255, Class: 1.000000, Obj: 0.331491, No Obj: 0.004603, Avg Recall: 0.473214, count: 112 -Region Avg IOU: 0.469178, Class: 1.000000, Obj: 0.340684, No Obj: 0.004553, Avg Recall: 0.530120, count: 83 -Region Avg IOU: 0.475409, Class: 1.000000, Obj: 0.355255, No Obj: 0.004586, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.473244, Class: 1.000000, Obj: 0.359273, No Obj: 0.005077, Avg Recall: 0.490566, count: 106 -Region Avg IOU: 0.492395, Class: 1.000000, Obj: 0.300611, No Obj: 0.005074, Avg Recall: 0.561798, count: 89 -Region Avg IOU: 0.492697, Class: 1.000000, Obj: 0.356037, No Obj: 0.004688, Avg Recall: 0.547826, count: 115 -Region Avg IOU: 0.451505, Class: 1.000000, Obj: 0.320724, No Obj: 0.005009, Avg Recall: 0.439655, count: 116 -3042: 0.457842, 0.473215 avg, 0.001000 rate, 20.009777 seconds, 778752 images -Loaded: 1.401487 seconds -Region Avg IOU: 0.515287, Class: 1.000000, Obj: 0.317450, No Obj: 0.004191, Avg Recall: 0.566667, count: 90 -Region Avg IOU: 0.444448, Class: 1.000000, Obj: 0.359834, No Obj: 0.005032, Avg Recall: 0.427184, count: 103 -Region Avg IOU: 0.445500, Class: 1.000000, Obj: 0.299070, No Obj: 0.004574, Avg Recall: 0.460177, count: 113 -Region Avg IOU: 0.500892, Class: 1.000000, Obj: 0.339442, No Obj: 0.005609, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.481056, Class: 1.000000, Obj: 0.351231, No Obj: 0.005171, Avg Recall: 0.532710, count: 107 -Region Avg IOU: 0.479981, Class: 1.000000, Obj: 0.320583, No Obj: 0.004839, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.471779, Class: 1.000000, Obj: 0.350251, No Obj: 0.005207, Avg Recall: 0.525000, count: 120 -Region Avg IOU: 0.504907, Class: 1.000000, Obj: 0.335980, No Obj: 0.005146, Avg Recall: 0.577982, count: 109 -3043: 0.436602, 0.469554 avg, 0.001000 rate, 16.801523 seconds, 779008 images -Loaded: 4.162562 seconds -Region Avg IOU: 0.498875, Class: 1.000000, Obj: 0.336599, No Obj: 0.004898, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.511779, Class: 1.000000, Obj: 0.352980, No Obj: 0.005633, Avg Recall: 0.561538, count: 130 -Region Avg IOU: 0.501012, Class: 1.000000, Obj: 0.359536, No Obj: 0.004712, Avg Recall: 0.556818, count: 88 -Region Avg IOU: 0.506252, Class: 1.000000, Obj: 0.346030, No Obj: 0.004622, Avg Recall: 0.553398, count: 103 -Region Avg IOU: 0.457856, Class: 1.000000, Obj: 0.300589, No Obj: 0.005178, Avg Recall: 0.491379, count: 116 -Region Avg IOU: 0.484903, Class: 1.000000, Obj: 0.347813, No Obj: 0.004114, Avg Recall: 0.532609, count: 92 -Region Avg IOU: 0.463315, Class: 1.000000, Obj: 0.275965, No Obj: 0.006183, Avg Recall: 0.506667, count: 150 -Region Avg IOU: 0.475632, Class: 1.000000, Obj: 0.356983, No Obj: 0.004946, Avg Recall: 0.525862, count: 116 -3044: 0.566180, 0.479217 avg, 0.001000 rate, 16.909477 seconds, 779264 images -Loaded: 2.980718 seconds -Region Avg IOU: 0.456214, Class: 1.000000, Obj: 0.295473, No Obj: 0.004128, Avg Recall: 0.525773, count: 97 -Region Avg IOU: 0.477232, Class: 1.000000, Obj: 0.302140, No Obj: 0.005951, Avg Recall: 0.533333, count: 135 -Region Avg IOU: 0.517713, Class: 1.000000, Obj: 0.338637, No Obj: 0.004762, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.480213, Class: 1.000000, Obj: 0.297334, No Obj: 0.005524, Avg Recall: 0.554688, count: 128 -Region Avg IOU: 0.496448, Class: 1.000000, Obj: 0.313111, No Obj: 0.004182, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.460421, Class: 1.000000, Obj: 0.311715, No Obj: 0.004344, Avg Recall: 0.482759, count: 87 -Region Avg IOU: 0.473374, Class: 1.000000, Obj: 0.306541, No Obj: 0.004325, Avg Recall: 0.516129, count: 124 -Region Avg IOU: 0.449755, Class: 1.000000, Obj: 0.295956, No Obj: 0.005306, Avg Recall: 0.435115, count: 131 -3045: 0.487619, 0.480057 avg, 0.001000 rate, 19.101259 seconds, 779520 images -Loaded: 2.610603 seconds -Region Avg IOU: 0.486258, Class: 1.000000, Obj: 0.304650, No Obj: 0.004699, Avg Recall: 0.553398, count: 103 -Region Avg IOU: 0.499066, Class: 1.000000, Obj: 0.301923, No Obj: 0.004069, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.479801, Class: 1.000000, Obj: 0.342537, No Obj: 0.004552, Avg Recall: 0.471698, count: 106 -Region Avg IOU: 0.527995, Class: 1.000000, Obj: 0.327501, No Obj: 0.004470, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.505322, Class: 1.000000, Obj: 0.350820, No Obj: 0.004390, Avg Recall: 0.577778, count: 90 -Region Avg IOU: 0.522750, Class: 1.000000, Obj: 0.309948, No Obj: 0.004721, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.508885, Class: 1.000000, Obj: 0.294934, No Obj: 0.004776, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.486286, Class: 1.000000, Obj: 0.295226, No Obj: 0.004546, Avg Recall: 0.558824, count: 102 -3046: 0.476864, 0.479737 avg, 0.001000 rate, 19.719942 seconds, 779776 images -Loaded: 0.527282 seconds -Region Avg IOU: 0.473491, Class: 1.000000, Obj: 0.331944, No Obj: 0.004464, Avg Recall: 0.518182, count: 110 -Region Avg IOU: 0.505564, Class: 1.000000, Obj: 0.352243, No Obj: 0.004527, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.548296, Class: 1.000000, Obj: 0.312312, No Obj: 0.004794, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.486670, Class: 1.000000, Obj: 0.298200, No Obj: 0.004500, Avg Recall: 0.521277, count: 94 -Region Avg IOU: 0.452045, Class: 1.000000, Obj: 0.297673, No Obj: 0.004541, Avg Recall: 0.449541, count: 109 -Region Avg IOU: 0.471977, Class: 1.000000, Obj: 0.296495, No Obj: 0.004255, Avg Recall: 0.495238, count: 105 -Region Avg IOU: 0.470396, Class: 1.000000, Obj: 0.280074, No Obj: 0.004547, Avg Recall: 0.495238, count: 105 -Region Avg IOU: 0.488183, Class: 1.000000, Obj: 0.290200, No Obj: 0.005020, Avg Recall: 0.580153, count: 131 -3047: 0.460829, 0.477847 avg, 0.001000 rate, 17.696642 seconds, 780032 images -Loaded: 4.199041 seconds -Region Avg IOU: 0.479754, Class: 1.000000, Obj: 0.282255, No Obj: 0.004407, Avg Recall: 0.467213, count: 122 -Region Avg IOU: 0.467236, Class: 1.000000, Obj: 0.311822, No Obj: 0.004127, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.497680, Class: 1.000000, Obj: 0.273388, No Obj: 0.004105, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.532868, Class: 1.000000, Obj: 0.303734, No Obj: 0.003963, Avg Recall: 0.589474, count: 95 -Region Avg IOU: 0.478124, Class: 1.000000, Obj: 0.298008, No Obj: 0.003823, Avg Recall: 0.520325, count: 123 -Region Avg IOU: 0.533767, Class: 1.000000, Obj: 0.303672, No Obj: 0.004861, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.523169, Class: 1.000000, Obj: 0.326569, No Obj: 0.004522, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.513800, Class: 1.000000, Obj: 0.335323, No Obj: 0.004074, Avg Recall: 0.552083, count: 96 -3048: 0.489142, 0.478976 avg, 0.001000 rate, 18.705814 seconds, 780288 images -Loaded: 2.477802 seconds -Region Avg IOU: 0.429822, Class: 1.000000, Obj: 0.256175, No Obj: 0.004395, Avg Recall: 0.428571, count: 119 -Region Avg IOU: 0.500782, Class: 1.000000, Obj: 0.322428, No Obj: 0.003916, Avg Recall: 0.568182, count: 88 -Region Avg IOU: 0.495941, Class: 1.000000, Obj: 0.313538, No Obj: 0.004558, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.494432, Class: 1.000000, Obj: 0.308260, No Obj: 0.004562, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.495622, Class: 1.000000, Obj: 0.291813, No Obj: 0.004046, Avg Recall: 0.556701, count: 97 -Region Avg IOU: 0.579570, Class: 1.000000, Obj: 0.311074, No Obj: 0.004556, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.459944, Class: 1.000000, Obj: 0.305231, No Obj: 0.005049, Avg Recall: 0.489655, count: 145 -Region Avg IOU: 0.496811, Class: 1.000000, Obj: 0.321063, No Obj: 0.004623, Avg Recall: 0.504673, count: 107 -3049: 0.447782, 0.475857 avg, 0.001000 rate, 16.962086 seconds, 780544 images -Loaded: 4.334891 seconds -Region Avg IOU: 0.452730, Class: 1.000000, Obj: 0.311638, No Obj: 0.004608, Avg Recall: 0.480769, count: 104 -Region Avg IOU: 0.534305, Class: 1.000000, Obj: 0.331772, No Obj: 0.004687, Avg Recall: 0.619565, count: 92 -Region Avg IOU: 0.493000, Class: 1.000000, Obj: 0.303076, No Obj: 0.004088, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.467577, Class: 1.000000, Obj: 0.301230, No Obj: 0.004304, Avg Recall: 0.512821, count: 117 -Region Avg IOU: 0.446401, Class: 1.000000, Obj: 0.245836, No Obj: 0.003891, Avg Recall: 0.477273, count: 88 -Region Avg IOU: 0.556022, Class: 1.000000, Obj: 0.364709, No Obj: 0.004779, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.490862, Class: 1.000000, Obj: 0.324436, No Obj: 0.004559, Avg Recall: 0.544643, count: 112 -Region Avg IOU: 0.411974, Class: 1.000000, Obj: 0.290516, No Obj: 0.003897, Avg Recall: 0.409449, count: 127 -3050: 0.468177, 0.475089 avg, 0.001000 rate, 20.146900 seconds, 780800 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 20.026209 seconds -Region Avg IOU: 0.439500, Class: 1.000000, Obj: 0.310602, No Obj: 0.004084, Avg Recall: 0.504854, count: 103 -Region Avg IOU: 0.478938, Class: 1.000000, Obj: 0.294277, No Obj: 0.004468, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.447999, Class: 1.000000, Obj: 0.303791, No Obj: 0.004367, Avg Recall: 0.456140, count: 114 -Region Avg IOU: 0.505526, Class: 1.000000, Obj: 0.307110, No Obj: 0.005072, Avg Recall: 0.566667, count: 120 -Region Avg IOU: 0.471567, Class: 1.000000, Obj: 0.301816, No Obj: 0.004719, Avg Recall: 0.500000, count: 138 -Region Avg IOU: 0.453618, Class: 1.000000, Obj: 0.288909, No Obj: 0.004406, Avg Recall: 0.485981, count: 107 -Region Avg IOU: 0.501811, Class: 1.000000, Obj: 0.302046, No Obj: 0.004280, Avg Recall: 0.554348, count: 92 -Region Avg IOU: 0.475579, Class: 1.000000, Obj: 0.306289, No Obj: 0.004092, Avg Recall: 0.504505, count: 111 -3051: 0.541973, 0.481777 avg, 0.001000 rate, 18.292356 seconds, 781056 images -Loaded: 3.938353 seconds -Region Avg IOU: 0.485612, Class: 1.000000, Obj: 0.313254, No Obj: 0.005102, Avg Recall: 0.537815, count: 119 -Region Avg IOU: 0.463202, Class: 1.000000, Obj: 0.327906, No Obj: 0.004822, Avg Recall: 0.503876, count: 129 -Region Avg IOU: 0.518037, Class: 1.000000, Obj: 0.350331, No Obj: 0.003942, Avg Recall: 0.579545, count: 88 -Region Avg IOU: 0.459719, Class: 1.000000, Obj: 0.364579, No Obj: 0.004774, Avg Recall: 0.443548, count: 124 -Region Avg IOU: 0.502978, Class: 1.000000, Obj: 0.372614, No Obj: 0.004892, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.498134, Class: 1.000000, Obj: 0.310307, No Obj: 0.004207, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.494887, Class: 1.000000, Obj: 0.313551, No Obj: 0.004629, Avg Recall: 0.526786, count: 112 -Region Avg IOU: 0.501768, Class: 1.000000, Obj: 0.303786, No Obj: 0.004363, Avg Recall: 0.578947, count: 95 -3052: 0.463850, 0.479984 avg, 0.001000 rate, 21.035547 seconds, 781312 images -Loaded: 0.994101 seconds -Region Avg IOU: 0.510574, Class: 1.000000, Obj: 0.328273, No Obj: 0.004985, Avg Recall: 0.572519, count: 131 -Region Avg IOU: 0.481435, Class: 1.000000, Obj: 0.357797, No Obj: 0.004738, Avg Recall: 0.508929, count: 112 -Region Avg IOU: 0.502142, Class: 1.000000, Obj: 0.316924, No Obj: 0.005189, Avg Recall: 0.541667, count: 96 -Region Avg IOU: 0.458490, Class: 1.000000, Obj: 0.283031, No Obj: 0.004824, Avg Recall: 0.493056, count: 144 -Region Avg IOU: 0.435153, Class: 1.000000, Obj: 0.299695, No Obj: 0.004575, Avg Recall: 0.440298, count: 134 -Region Avg IOU: 0.502134, Class: 1.000000, Obj: 0.305063, No Obj: 0.004047, Avg Recall: 0.556962, count: 79 -Region Avg IOU: 0.495559, Class: 1.000000, Obj: 0.324131, No Obj: 0.005002, Avg Recall: 0.526316, count: 114 -Region Avg IOU: 0.523983, Class: 1.000000, Obj: 0.323816, No Obj: 0.004262, Avg Recall: 0.563830, count: 94 -3053: 0.506199, 0.482606 avg, 0.001000 rate, 19.420292 seconds, 781568 images -Loaded: 2.542807 seconds -Region Avg IOU: 0.480930, Class: 1.000000, Obj: 0.324844, No Obj: 0.005385, Avg Recall: 0.480916, count: 131 -Region Avg IOU: 0.509554, Class: 1.000000, Obj: 0.343284, No Obj: 0.004440, Avg Recall: 0.564103, count: 117 -Region Avg IOU: 0.515288, Class: 1.000000, Obj: 0.333900, No Obj: 0.004614, Avg Recall: 0.596491, count: 114 -Region Avg IOU: 0.525073, Class: 1.000000, Obj: 0.353312, No Obj: 0.004732, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.436584, Class: 1.000000, Obj: 0.311398, No Obj: 0.004608, Avg Recall: 0.508929, count: 112 -Region Avg IOU: 0.501377, Class: 1.000000, Obj: 0.332289, No Obj: 0.005055, Avg Recall: 0.536585, count: 123 -Region Avg IOU: 0.423733, Class: 1.000000, Obj: 0.329076, No Obj: 0.004305, Avg Recall: 0.432203, count: 118 -Region Avg IOU: 0.414214, Class: 1.000000, Obj: 0.281244, No Obj: 0.004392, Avg Recall: 0.395349, count: 129 -3054: 0.439584, 0.478304 avg, 0.001000 rate, 17.195263 seconds, 781824 images -Loaded: 3.974453 seconds -Region Avg IOU: 0.493574, Class: 1.000000, Obj: 0.349491, No Obj: 0.004353, Avg Recall: 0.544554, count: 101 -Region Avg IOU: 0.437088, Class: 1.000000, Obj: 0.322465, No Obj: 0.004313, Avg Recall: 0.509434, count: 106 -Region Avg IOU: 0.479259, Class: 1.000000, Obj: 0.330275, No Obj: 0.005103, Avg Recall: 0.495495, count: 111 -Region Avg IOU: 0.544661, Class: 1.000000, Obj: 0.365134, No Obj: 0.004655, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.496933, Class: 1.000000, Obj: 0.333609, No Obj: 0.005852, Avg Recall: 0.549296, count: 142 -Region Avg IOU: 0.488251, Class: 1.000000, Obj: 0.339654, No Obj: 0.004445, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.482713, Class: 1.000000, Obj: 0.343521, No Obj: 0.004836, Avg Recall: 0.532710, count: 107 -Region Avg IOU: 0.519009, Class: 1.000000, Obj: 0.370227, No Obj: 0.004552, Avg Recall: 0.524390, count: 82 -3055: 0.456878, 0.476161 avg, 0.001000 rate, 19.068151 seconds, 782080 images -Loaded: 1.911258 seconds -Region Avg IOU: 0.473582, Class: 1.000000, Obj: 0.277718, No Obj: 0.004600, Avg Recall: 0.541667, count: 96 -Region Avg IOU: 0.430176, Class: 1.000000, Obj: 0.311196, No Obj: 0.004513, Avg Recall: 0.468254, count: 126 -Region Avg IOU: 0.468999, Class: 1.000000, Obj: 0.361936, No Obj: 0.004499, Avg Recall: 0.490566, count: 106 -Region Avg IOU: 0.487660, Class: 1.000000, Obj: 0.319074, No Obj: 0.005073, Avg Recall: 0.541667, count: 120 -Region Avg IOU: 0.472544, Class: 1.000000, Obj: 0.329541, No Obj: 0.005076, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.501328, Class: 1.000000, Obj: 0.332384, No Obj: 0.005153, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.482678, Class: 1.000000, Obj: 0.316040, No Obj: 0.005368, Avg Recall: 0.516393, count: 122 -Region Avg IOU: 0.520562, Class: 1.000000, Obj: 0.358149, No Obj: 0.004234, Avg Recall: 0.581081, count: 74 -3056: 0.472476, 0.475793 avg, 0.001000 rate, 17.893951 seconds, 782336 images -Loaded: 0.843187 seconds -Region Avg IOU: 0.504498, Class: 1.000000, Obj: 0.387619, No Obj: 0.004259, Avg Recall: 0.517241, count: 87 -Region Avg IOU: 0.506152, Class: 1.000000, Obj: 0.356462, No Obj: 0.004886, Avg Recall: 0.593220, count: 118 -Region Avg IOU: 0.459345, Class: 1.000000, Obj: 0.322409, No Obj: 0.004880, Avg Recall: 0.512605, count: 119 -Region Avg IOU: 0.515548, Class: 1.000000, Obj: 0.324755, No Obj: 0.004584, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.517515, Class: 1.000000, Obj: 0.337909, No Obj: 0.004414, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.527847, Class: 1.000000, Obj: 0.334126, No Obj: 0.005167, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.491975, Class: 1.000000, Obj: 0.335704, No Obj: 0.004945, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.454742, Class: 1.000000, Obj: 0.316137, No Obj: 0.004099, Avg Recall: 0.500000, count: 108 -3057: 0.452657, 0.473479 avg, 0.001000 rate, 20.518225 seconds, 782592 images -Loaded: 0.748775 seconds -Region Avg IOU: 0.484737, Class: 1.000000, Obj: 0.329407, No Obj: 0.004750, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.464199, Class: 1.000000, Obj: 0.324765, No Obj: 0.004388, Avg Recall: 0.472727, count: 110 -Region Avg IOU: 0.490437, Class: 1.000000, Obj: 0.342797, No Obj: 0.005398, Avg Recall: 0.510949, count: 137 -Region Avg IOU: 0.518705, Class: 1.000000, Obj: 0.369904, No Obj: 0.004862, Avg Recall: 0.588235, count: 102 -Region Avg IOU: 0.469282, Class: 1.000000, Obj: 0.333434, No Obj: 0.005038, Avg Recall: 0.496183, count: 131 -Region Avg IOU: 0.485512, Class: 1.000000, Obj: 0.326175, No Obj: 0.005033, Avg Recall: 0.533835, count: 133 -Region Avg IOU: 0.481439, Class: 1.000000, Obj: 0.300563, No Obj: 0.004601, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.452990, Class: 1.000000, Obj: 0.340618, No Obj: 0.004334, Avg Recall: 0.467391, count: 92 -3058: 0.472635, 0.473395 avg, 0.001000 rate, 20.052603 seconds, 782848 images -Loaded: 2.044577 seconds -Region Avg IOU: 0.485283, Class: 1.000000, Obj: 0.355907, No Obj: 0.004293, Avg Recall: 0.559140, count: 93 -Region Avg IOU: 0.457730, Class: 1.000000, Obj: 0.306971, No Obj: 0.005002, Avg Recall: 0.488889, count: 135 -Region Avg IOU: 0.460801, Class: 1.000000, Obj: 0.335346, No Obj: 0.004707, Avg Recall: 0.442623, count: 122 -Region Avg IOU: 0.459864, Class: 1.000000, Obj: 0.323829, No Obj: 0.004542, Avg Recall: 0.483051, count: 118 -Region Avg IOU: 0.408778, Class: 1.000000, Obj: 0.298722, No Obj: 0.003510, Avg Recall: 0.427083, count: 96 -Region Avg IOU: 0.465127, Class: 1.000000, Obj: 0.350972, No Obj: 0.003853, Avg Recall: 0.472973, count: 74 -Region Avg IOU: 0.502625, Class: 1.000000, Obj: 0.320738, No Obj: 0.005817, Avg Recall: 0.571429, count: 147 -Region Avg IOU: 0.549751, Class: 1.000000, Obj: 0.323407, No Obj: 0.005813, Avg Recall: 0.614173, count: 127 -3059: 0.472540, 0.473309 avg, 0.001000 rate, 20.814760 seconds, 783104 images -Loaded: 0.972406 seconds -Region Avg IOU: 0.484230, Class: 1.000000, Obj: 0.302344, No Obj: 0.004208, Avg Recall: 0.505155, count: 97 -Region Avg IOU: 0.493327, Class: 1.000000, Obj: 0.311475, No Obj: 0.004630, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.467386, Class: 1.000000, Obj: 0.311528, No Obj: 0.005087, Avg Recall: 0.511450, count: 131 -Region Avg IOU: 0.535211, Class: 1.000000, Obj: 0.357300, No Obj: 0.004592, Avg Recall: 0.643678, count: 87 -Region Avg IOU: 0.434889, Class: 1.000000, Obj: 0.309848, No Obj: 0.004092, Avg Recall: 0.416667, count: 108 -Region Avg IOU: 0.473253, Class: 1.000000, Obj: 0.305962, No Obj: 0.004444, Avg Recall: 0.549550, count: 111 -Region Avg IOU: 0.513748, Class: 1.000000, Obj: 0.370139, No Obj: 0.004127, Avg Recall: 0.616279, count: 86 -Region Avg IOU: 0.489575, Class: 1.000000, Obj: 0.330810, No Obj: 0.004446, Avg Recall: 0.539823, count: 113 -3060: 0.443436, 0.470322 avg, 0.001000 rate, 21.019974 seconds, 783360 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000054 seconds -Region Avg IOU: 0.582006, Class: 1.000000, Obj: 0.353442, No Obj: 0.003567, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.591238, Class: 1.000000, Obj: 0.373999, No Obj: 0.004101, Avg Recall: 0.714286, count: 119 -Region Avg IOU: 0.563848, Class: 1.000000, Obj: 0.404769, No Obj: 0.003784, Avg Recall: 0.669421, count: 121 -Region Avg IOU: 0.605609, Class: 1.000000, Obj: 0.378123, No Obj: 0.004610, Avg Recall: 0.764706, count: 136 -Region Avg IOU: 0.561340, Class: 1.000000, Obj: 0.414152, No Obj: 0.003826, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.636386, Class: 1.000000, Obj: 0.425297, No Obj: 0.003753, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.619443, Class: 1.000000, Obj: 0.447622, No Obj: 0.003674, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.602984, Class: 1.000000, Obj: 0.418994, No Obj: 0.004217, Avg Recall: 0.694215, count: 121 -3061: 0.537927, 0.477082 avg, 0.001000 rate, 25.523472 seconds, 783616 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.606945, Class: 1.000000, Obj: 0.380732, No Obj: 0.003533, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.589150, Class: 1.000000, Obj: 0.396832, No Obj: 0.003467, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.618931, Class: 1.000000, Obj: 0.383547, No Obj: 0.003756, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.607121, Class: 1.000000, Obj: 0.407160, No Obj: 0.003515, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.604097, Class: 1.000000, Obj: 0.418402, No Obj: 0.003340, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.589303, Class: 1.000000, Obj: 0.369575, No Obj: 0.003591, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.601930, Class: 1.000000, Obj: 0.411086, No Obj: 0.003773, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.615059, Class: 1.000000, Obj: 0.404867, No Obj: 0.003632, Avg Recall: 0.745614, count: 114 -3062: 0.440126, 0.473387 avg, 0.001000 rate, 28.122885 seconds, 783872 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.594643, Class: 1.000000, Obj: 0.378574, No Obj: 0.003124, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.579966, Class: 1.000000, Obj: 0.340709, No Obj: 0.003383, Avg Recall: 0.664122, count: 131 -Region Avg IOU: 0.571979, Class: 1.000000, Obj: 0.380919, No Obj: 0.002935, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.638588, Class: 1.000000, Obj: 0.361772, No Obj: 0.003541, Avg Recall: 0.769231, count: 117 -Region Avg IOU: 0.611743, Class: 1.000000, Obj: 0.382678, No Obj: 0.002957, Avg Recall: 0.744444, count: 90 -Region Avg IOU: 0.598686, Class: 1.000000, Obj: 0.346470, No Obj: 0.003436, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.602037, Class: 1.000000, Obj: 0.360412, No Obj: 0.003665, Avg Recall: 0.687023, count: 131 -Region Avg IOU: 0.549304, Class: 1.000000, Obj: 0.372510, No Obj: 0.002816, Avg Recall: 0.603960, count: 101 -3063: 0.513489, 0.477397 avg, 0.001000 rate, 26.795933 seconds, 784128 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.613855, Class: 1.000000, Obj: 0.302663, No Obj: 0.003240, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.634485, Class: 1.000000, Obj: 0.406982, No Obj: 0.002885, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.610620, Class: 1.000000, Obj: 0.317635, No Obj: 0.002688, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.596524, Class: 1.000000, Obj: 0.369381, No Obj: 0.002862, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.602746, Class: 1.000000, Obj: 0.311618, No Obj: 0.003287, Avg Recall: 0.653846, count: 130 -Region Avg IOU: 0.618117, Class: 1.000000, Obj: 0.334300, No Obj: 0.003193, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.623911, Class: 1.000000, Obj: 0.372054, No Obj: 0.003042, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.546121, Class: 1.000000, Obj: 0.309245, No Obj: 0.003245, Avg Recall: 0.634328, count: 134 -3064: 0.527078, 0.482365 avg, 0.001000 rate, 26.315348 seconds, 784384 images -Loaded: 0.000120 seconds -Region Avg IOU: 0.626727, Class: 1.000000, Obj: 0.328076, No Obj: 0.002716, Avg Recall: 0.789916, count: 119 -Region Avg IOU: 0.614027, Class: 1.000000, Obj: 0.362640, No Obj: 0.002797, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.582059, Class: 1.000000, Obj: 0.258390, No Obj: 0.002409, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.629159, Class: 1.000000, Obj: 0.308507, No Obj: 0.002434, Avg Recall: 0.743902, count: 82 -Region Avg IOU: 0.602305, Class: 1.000000, Obj: 0.340683, No Obj: 0.002780, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.646725, Class: 1.000000, Obj: 0.298740, No Obj: 0.003077, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.591989, Class: 1.000000, Obj: 0.354437, No Obj: 0.002514, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.615758, Class: 1.000000, Obj: 0.344912, No Obj: 0.002624, Avg Recall: 0.738318, count: 107 -3065: 0.434207, 0.477549 avg, 0.001000 rate, 26.801828 seconds, 784640 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.628486, Class: 1.000000, Obj: 0.310576, No Obj: 0.002661, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.618505, Class: 1.000000, Obj: 0.277321, No Obj: 0.002268, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.620327, Class: 1.000000, Obj: 0.304178, No Obj: 0.002509, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.625151, Class: 1.000000, Obj: 0.349492, No Obj: 0.002415, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.616443, Class: 1.000000, Obj: 0.320254, No Obj: 0.002673, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.618516, Class: 1.000000, Obj: 0.335794, No Obj: 0.002664, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.622369, Class: 1.000000, Obj: 0.280137, No Obj: 0.002761, Avg Recall: 0.788136, count: 118 -Region Avg IOU: 0.619897, Class: 1.000000, Obj: 0.323597, No Obj: 0.002663, Avg Recall: 0.750000, count: 112 -3066: 0.459506, 0.475745 avg, 0.001000 rate, 26.376549 seconds, 784896 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.677723, Class: 1.000000, Obj: 0.333457, No Obj: 0.003024, Avg Recall: 0.833333, count: 108 -Region Avg IOU: 0.607652, Class: 1.000000, Obj: 0.280518, No Obj: 0.002865, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.643960, Class: 1.000000, Obj: 0.355578, No Obj: 0.002642, Avg Recall: 0.824742, count: 97 -Region Avg IOU: 0.610815, Class: 1.000000, Obj: 0.326917, No Obj: 0.002138, Avg Recall: 0.685393, count: 89 -Region Avg IOU: 0.645660, Class: 1.000000, Obj: 0.330389, No Obj: 0.002666, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.623316, Class: 1.000000, Obj: 0.280059, No Obj: 0.002634, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.590599, Class: 1.000000, Obj: 0.262692, No Obj: 0.002898, Avg Recall: 0.717557, count: 131 -Region Avg IOU: 0.575356, Class: 1.000000, Obj: 0.320697, No Obj: 0.002282, Avg Recall: 0.688889, count: 90 -3067: 0.478384, 0.476009 avg, 0.001000 rate, 26.872801 seconds, 785152 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.574579, Class: 1.000000, Obj: 0.300626, No Obj: 0.002799, Avg Recall: 0.705426, count: 129 -Region Avg IOU: 0.606002, Class: 1.000000, Obj: 0.287225, No Obj: 0.003065, Avg Recall: 0.731343, count: 134 -Region Avg IOU: 0.621885, Class: 1.000000, Obj: 0.299114, No Obj: 0.002903, Avg Recall: 0.769784, count: 139 -Region Avg IOU: 0.629903, Class: 1.000000, Obj: 0.324774, No Obj: 0.003112, Avg Recall: 0.779528, count: 127 -Region Avg IOU: 0.609369, Class: 1.000000, Obj: 0.269296, No Obj: 0.002639, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.592412, Class: 1.000000, Obj: 0.283757, No Obj: 0.002976, Avg Recall: 0.702128, count: 141 -Region Avg IOU: 0.618138, Class: 1.000000, Obj: 0.327570, No Obj: 0.003051, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.646044, Class: 1.000000, Obj: 0.328739, No Obj: 0.002832, Avg Recall: 0.780702, count: 114 -3068: 0.586306, 0.487039 avg, 0.001000 rate, 26.557669 seconds, 785408 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.633578, Class: 1.000000, Obj: 0.301906, No Obj: 0.002582, Avg Recall: 0.792683, count: 82 -Region Avg IOU: 0.627384, Class: 1.000000, Obj: 0.326035, No Obj: 0.003148, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.622256, Class: 1.000000, Obj: 0.314033, No Obj: 0.002704, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.594646, Class: 1.000000, Obj: 0.312039, No Obj: 0.002629, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.561407, Class: 1.000000, Obj: 0.292264, No Obj: 0.002729, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.614686, Class: 1.000000, Obj: 0.306343, No Obj: 0.002872, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.630603, Class: 1.000000, Obj: 0.382439, No Obj: 0.002898, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.618868, Class: 1.000000, Obj: 0.300721, No Obj: 0.002828, Avg Recall: 0.740000, count: 100 -3069: 0.439059, 0.482241 avg, 0.001000 rate, 25.772421 seconds, 785664 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.584736, Class: 1.000000, Obj: 0.343672, No Obj: 0.002947, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.602839, Class: 1.000000, Obj: 0.332597, No Obj: 0.002917, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.617673, Class: 1.000000, Obj: 0.389551, No Obj: 0.002713, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.620078, Class: 1.000000, Obj: 0.347009, No Obj: 0.003196, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.592513, Class: 1.000000, Obj: 0.353122, No Obj: 0.002765, Avg Recall: 0.712644, count: 87 -Region Avg IOU: 0.589783, Class: 1.000000, Obj: 0.334458, No Obj: 0.003423, Avg Recall: 0.713115, count: 122 -Region Avg IOU: 0.642564, Class: 1.000000, Obj: 0.388409, No Obj: 0.002726, Avg Recall: 0.823529, count: 68 -Region Avg IOU: 0.610829, Class: 1.000000, Obj: 0.374065, No Obj: 0.003147, Avg Recall: 0.741667, count: 120 -3070: 0.442364, 0.478253 avg, 0.001000 rate, 27.482948 seconds, 785920 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 16.598118 seconds -Region Avg IOU: 0.542129, Class: 1.000000, Obj: 0.377320, No Obj: 0.003629, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.583018, Class: 1.000000, Obj: 0.344105, No Obj: 0.003900, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.559150, Class: 1.000000, Obj: 0.330252, No Obj: 0.003720, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.552398, Class: 1.000000, Obj: 0.307990, No Obj: 0.003567, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.536151, Class: 1.000000, Obj: 0.343196, No Obj: 0.003601, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.544743, Class: 1.000000, Obj: 0.309809, No Obj: 0.004342, Avg Recall: 0.655405, count: 148 -Region Avg IOU: 0.614854, Class: 1.000000, Obj: 0.322725, No Obj: 0.003674, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.558181, Class: 1.000000, Obj: 0.321511, No Obj: 0.003907, Avg Recall: 0.669903, count: 103 -3071: 0.464329, 0.476861 avg, 0.001000 rate, 21.877312 seconds, 786176 images -Loaded: 1.716627 seconds -Region Avg IOU: 0.565406, Class: 1.000000, Obj: 0.394929, No Obj: 0.004046, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.557617, Class: 1.000000, Obj: 0.354536, No Obj: 0.004233, Avg Recall: 0.663866, count: 119 -Region Avg IOU: 0.593616, Class: 1.000000, Obj: 0.367913, No Obj: 0.003717, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.551266, Class: 1.000000, Obj: 0.320479, No Obj: 0.003815, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.567338, Class: 1.000000, Obj: 0.343915, No Obj: 0.003211, Avg Recall: 0.645570, count: 79 -Region Avg IOU: 0.564324, Class: 1.000000, Obj: 0.304060, No Obj: 0.003892, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.608963, Class: 1.000000, Obj: 0.381249, No Obj: 0.003864, Avg Recall: 0.725275, count: 91 -Region Avg IOU: 0.556028, Class: 1.000000, Obj: 0.338069, No Obj: 0.004195, Avg Recall: 0.630769, count: 130 -3072: 0.425555, 0.471730 avg, 0.001000 rate, 23.274652 seconds, 786432 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.556126, Class: 1.000000, Obj: 0.379693, No Obj: 0.004154, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.520950, Class: 1.000000, Obj: 0.333262, No Obj: 0.003417, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.558226, Class: 1.000000, Obj: 0.349381, No Obj: 0.004323, Avg Recall: 0.619048, count: 126 -Region Avg IOU: 0.557704, Class: 1.000000, Obj: 0.363597, No Obj: 0.004033, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.476895, Class: 1.000000, Obj: 0.347922, No Obj: 0.003317, Avg Recall: 0.505618, count: 89 -Region Avg IOU: 0.548293, Class: 1.000000, Obj: 0.352164, No Obj: 0.003716, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.583572, Class: 1.000000, Obj: 0.352158, No Obj: 0.004363, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.528289, Class: 1.000000, Obj: 0.353873, No Obj: 0.003836, Avg Recall: 0.576577, count: 111 -3073: 0.422024, 0.466759 avg, 0.001000 rate, 23.433224 seconds, 786688 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.530216, Class: 1.000000, Obj: 0.368553, No Obj: 0.004543, Avg Recall: 0.584000, count: 125 -Region Avg IOU: 0.545027, Class: 1.000000, Obj: 0.326805, No Obj: 0.004228, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.546689, Class: 1.000000, Obj: 0.350875, No Obj: 0.003722, Avg Recall: 0.597826, count: 92 -Region Avg IOU: 0.602388, Class: 1.000000, Obj: 0.394705, No Obj: 0.003802, Avg Recall: 0.706667, count: 75 -Region Avg IOU: 0.562728, Class: 1.000000, Obj: 0.370250, No Obj: 0.004114, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.552024, Class: 1.000000, Obj: 0.401350, No Obj: 0.004554, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.516360, Class: 1.000000, Obj: 0.352670, No Obj: 0.004325, Avg Recall: 0.578125, count: 128 -Region Avg IOU: 0.620735, Class: 1.000000, Obj: 0.420105, No Obj: 0.004174, Avg Recall: 0.717172, count: 99 -3074: 0.409046, 0.460988 avg, 0.001000 rate, 23.286793 seconds, 786944 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.567098, Class: 1.000000, Obj: 0.361928, No Obj: 0.005184, Avg Recall: 0.694030, count: 134 -Region Avg IOU: 0.528468, Class: 1.000000, Obj: 0.372886, No Obj: 0.004209, Avg Recall: 0.572650, count: 117 -Region Avg IOU: 0.550620, Class: 1.000000, Obj: 0.363456, No Obj: 0.004387, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.542384, Class: 1.000000, Obj: 0.391451, No Obj: 0.004514, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.520606, Class: 1.000000, Obj: 0.391015, No Obj: 0.003933, Avg Recall: 0.528571, count: 70 -Region Avg IOU: 0.581450, Class: 1.000000, Obj: 0.393725, No Obj: 0.004635, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.555319, Class: 1.000000, Obj: 0.425787, No Obj: 0.003917, Avg Recall: 0.684783, count: 92 -Region Avg IOU: 0.480360, Class: 1.000000, Obj: 0.319992, No Obj: 0.004480, Avg Recall: 0.530303, count: 132 -3075: 0.438557, 0.458745 avg, 0.001000 rate, 22.770449 seconds, 787200 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.552120, Class: 1.000000, Obj: 0.364260, No Obj: 0.003821, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.575017, Class: 1.000000, Obj: 0.388097, No Obj: 0.003840, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.577114, Class: 1.000000, Obj: 0.365196, No Obj: 0.004471, Avg Recall: 0.703297, count: 91 -Region Avg IOU: 0.546595, Class: 1.000000, Obj: 0.380898, No Obj: 0.003971, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.537218, Class: 1.000000, Obj: 0.361522, No Obj: 0.004227, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.518748, Class: 1.000000, Obj: 0.371783, No Obj: 0.004157, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.512938, Class: 1.000000, Obj: 0.414441, No Obj: 0.003472, Avg Recall: 0.565217, count: 92 -Region Avg IOU: 0.545138, Class: 1.000000, Obj: 0.407178, No Obj: 0.003781, Avg Recall: 0.637255, count: 102 -3076: 0.411083, 0.453979 avg, 0.001000 rate, 23.032061 seconds, 787456 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.561947, Class: 1.000000, Obj: 0.371734, No Obj: 0.004454, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.604851, Class: 1.000000, Obj: 0.364403, No Obj: 0.004484, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.562548, Class: 1.000000, Obj: 0.311438, No Obj: 0.003376, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.519819, Class: 1.000000, Obj: 0.332296, No Obj: 0.002990, Avg Recall: 0.606061, count: 66 -Region Avg IOU: 0.525939, Class: 1.000000, Obj: 0.343582, No Obj: 0.003658, Avg Recall: 0.581967, count: 122 -Region Avg IOU: 0.533482, Class: 1.000000, Obj: 0.388113, No Obj: 0.003534, Avg Recall: 0.597938, count: 97 -Region Avg IOU: 0.486563, Class: 1.000000, Obj: 0.312399, No Obj: 0.003932, Avg Recall: 0.556452, count: 124 -Region Avg IOU: 0.500066, Class: 1.000000, Obj: 0.331753, No Obj: 0.004051, Avg Recall: 0.546099, count: 141 -3077: 0.461234, 0.454704 avg, 0.001000 rate, 24.258490 seconds, 787712 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.568843, Class: 1.000000, Obj: 0.362384, No Obj: 0.004378, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.548254, Class: 1.000000, Obj: 0.333852, No Obj: 0.004485, Avg Recall: 0.636364, count: 143 -Region Avg IOU: 0.546225, Class: 1.000000, Obj: 0.324121, No Obj: 0.003956, Avg Recall: 0.629630, count: 135 -Region Avg IOU: 0.553319, Class: 1.000000, Obj: 0.324564, No Obj: 0.003892, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.550525, Class: 1.000000, Obj: 0.326086, No Obj: 0.003575, Avg Recall: 0.565217, count: 92 -Region Avg IOU: 0.611474, Class: 1.000000, Obj: 0.379064, No Obj: 0.003370, Avg Recall: 0.750000, count: 84 -Region Avg IOU: 0.570783, Class: 1.000000, Obj: 0.353880, No Obj: 0.004618, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.559682, Class: 1.000000, Obj: 0.402676, No Obj: 0.003750, Avg Recall: 0.687500, count: 96 -3078: 0.462903, 0.455524 avg, 0.001000 rate, 23.265476 seconds, 787968 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.528399, Class: 1.000000, Obj: 0.358552, No Obj: 0.003727, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.532744, Class: 1.000000, Obj: 0.332380, No Obj: 0.003698, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.536463, Class: 1.000000, Obj: 0.326906, No Obj: 0.003700, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.537543, Class: 1.000000, Obj: 0.353924, No Obj: 0.003381, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.593296, Class: 1.000000, Obj: 0.369624, No Obj: 0.003730, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.511691, Class: 1.000000, Obj: 0.350598, No Obj: 0.003790, Avg Recall: 0.552845, count: 123 -Region Avg IOU: 0.541287, Class: 1.000000, Obj: 0.285231, No Obj: 0.003392, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.511481, Class: 1.000000, Obj: 0.330853, No Obj: 0.003503, Avg Recall: 0.594595, count: 111 -3079: 0.468394, 0.456811 avg, 0.001000 rate, 23.758345 seconds, 788224 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.552116, Class: 1.000000, Obj: 0.331881, No Obj: 0.003897, Avg Recall: 0.635220, count: 159 -Region Avg IOU: 0.548943, Class: 1.000000, Obj: 0.332606, No Obj: 0.003631, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.583256, Class: 1.000000, Obj: 0.380958, No Obj: 0.003563, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.549004, Class: 1.000000, Obj: 0.314639, No Obj: 0.003758, Avg Recall: 0.612403, count: 129 -Region Avg IOU: 0.542401, Class: 1.000000, Obj: 0.312854, No Obj: 0.003957, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.526723, Class: 1.000000, Obj: 0.317450, No Obj: 0.003980, Avg Recall: 0.596899, count: 129 -Region Avg IOU: 0.513171, Class: 1.000000, Obj: 0.322895, No Obj: 0.003633, Avg Recall: 0.595238, count: 126 -Region Avg IOU: 0.566145, Class: 1.000000, Obj: 0.368648, No Obj: 0.003940, Avg Recall: 0.632812, count: 128 -3080: 0.503659, 0.461496 avg, 0.001000 rate, 22.247120 seconds, 788480 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000068 seconds -Region Avg IOU: 0.592109, Class: 1.000000, Obj: 0.385424, No Obj: 0.002895, Avg Recall: 0.753247, count: 77 -Region Avg IOU: 0.586914, Class: 1.000000, Obj: 0.311021, No Obj: 0.003447, Avg Recall: 0.651515, count: 132 -Region Avg IOU: 0.573154, Class: 1.000000, Obj: 0.336604, No Obj: 0.002801, Avg Recall: 0.688889, count: 90 -Region Avg IOU: 0.581915, Class: 1.000000, Obj: 0.361067, No Obj: 0.003435, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.607404, Class: 1.000000, Obj: 0.349242, No Obj: 0.002971, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.575704, Class: 1.000000, Obj: 0.331273, No Obj: 0.003669, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.631325, Class: 1.000000, Obj: 0.378539, No Obj: 0.003602, Avg Recall: 0.781955, count: 133 -Region Avg IOU: 0.633624, Class: 1.000000, Obj: 0.371072, No Obj: 0.003443, Avg Recall: 0.808081, count: 99 -3081: 0.529297, 0.468276 avg, 0.001000 rate, 27.213087 seconds, 788736 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.589424, Class: 1.000000, Obj: 0.353312, No Obj: 0.003130, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.578013, Class: 1.000000, Obj: 0.354596, No Obj: 0.003089, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.595981, Class: 1.000000, Obj: 0.374438, No Obj: 0.002928, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.601802, Class: 1.000000, Obj: 0.413338, No Obj: 0.002822, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.621792, Class: 1.000000, Obj: 0.367232, No Obj: 0.002828, Avg Recall: 0.790698, count: 86 -Region Avg IOU: 0.634898, Class: 1.000000, Obj: 0.348711, No Obj: 0.003064, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.615054, Class: 1.000000, Obj: 0.372017, No Obj: 0.003268, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.632111, Class: 1.000000, Obj: 0.373515, No Obj: 0.003155, Avg Recall: 0.756757, count: 111 -3082: 0.426645, 0.464113 avg, 0.001000 rate, 26.083996 seconds, 788992 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.648571, Class: 1.000000, Obj: 0.376061, No Obj: 0.003017, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.593070, Class: 1.000000, Obj: 0.362238, No Obj: 0.003447, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.650992, Class: 1.000000, Obj: 0.369441, No Obj: 0.002635, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.602241, Class: 1.000000, Obj: 0.374385, No Obj: 0.003446, Avg Recall: 0.680000, count: 125 -Region Avg IOU: 0.562798, Class: 1.000000, Obj: 0.305840, No Obj: 0.002611, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.581319, Class: 1.000000, Obj: 0.353258, No Obj: 0.002896, Avg Recall: 0.708333, count: 120 -Region Avg IOU: 0.625378, Class: 1.000000, Obj: 0.371133, No Obj: 0.002888, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.572142, Class: 1.000000, Obj: 0.332835, No Obj: 0.002860, Avg Recall: 0.682353, count: 85 -3083: 0.428915, 0.460593 avg, 0.001000 rate, 27.464294 seconds, 789248 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.580302, Class: 1.000000, Obj: 0.330821, No Obj: 0.002691, Avg Recall: 0.692308, count: 78 -Region Avg IOU: 0.599997, Class: 1.000000, Obj: 0.344671, No Obj: 0.003181, Avg Recall: 0.713178, count: 129 -Region Avg IOU: 0.606892, Class: 1.000000, Obj: 0.378592, No Obj: 0.003041, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.633875, Class: 1.000000, Obj: 0.360814, No Obj: 0.003147, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.618763, Class: 1.000000, Obj: 0.321896, No Obj: 0.003061, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.639692, Class: 1.000000, Obj: 0.410356, No Obj: 0.003091, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.609044, Class: 1.000000, Obj: 0.348662, No Obj: 0.003151, Avg Recall: 0.747967, count: 123 -Region Avg IOU: 0.620184, Class: 1.000000, Obj: 0.343689, No Obj: 0.002584, Avg Recall: 0.761905, count: 84 -3084: 0.465568, 0.461091 avg, 0.001000 rate, 27.115332 seconds, 789504 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.621932, Class: 1.000000, Obj: 0.352035, No Obj: 0.003362, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.598784, Class: 1.000000, Obj: 0.353892, No Obj: 0.002985, Avg Recall: 0.704000, count: 125 -Region Avg IOU: 0.595538, Class: 1.000000, Obj: 0.312061, No Obj: 0.003161, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.575403, Class: 1.000000, Obj: 0.321323, No Obj: 0.002652, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.615432, Class: 1.000000, Obj: 0.367194, No Obj: 0.002789, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.618126, Class: 1.000000, Obj: 0.364464, No Obj: 0.003381, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.557901, Class: 1.000000, Obj: 0.318971, No Obj: 0.002749, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.586635, Class: 1.000000, Obj: 0.326439, No Obj: 0.002828, Avg Recall: 0.685484, count: 124 -3085: 0.513975, 0.466379 avg, 0.001000 rate, 27.325136 seconds, 789760 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.651667, Class: 1.000000, Obj: 0.416257, No Obj: 0.003397, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.614143, Class: 1.000000, Obj: 0.352630, No Obj: 0.003002, Avg Recall: 0.754098, count: 122 -Region Avg IOU: 0.604588, Class: 1.000000, Obj: 0.339022, No Obj: 0.003299, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.607993, Class: 1.000000, Obj: 0.396008, No Obj: 0.002849, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.626573, Class: 1.000000, Obj: 0.355763, No Obj: 0.002881, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.644182, Class: 1.000000, Obj: 0.348000, No Obj: 0.003051, Avg Recall: 0.858491, count: 106 -Region Avg IOU: 0.649263, Class: 1.000000, Obj: 0.391428, No Obj: 0.002810, Avg Recall: 0.831325, count: 83 -Region Avg IOU: 0.622111, Class: 1.000000, Obj: 0.358939, No Obj: 0.002796, Avg Recall: 0.752688, count: 93 -3086: 0.424352, 0.462176 avg, 0.001000 rate, 24.649340 seconds, 790016 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.601555, Class: 1.000000, Obj: 0.337412, No Obj: 0.003207, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.644940, Class: 1.000000, Obj: 0.389138, No Obj: 0.002742, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.604447, Class: 1.000000, Obj: 0.377244, No Obj: 0.002826, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.644690, Class: 1.000000, Obj: 0.351267, No Obj: 0.003064, Avg Recall: 0.809091, count: 110 -Region Avg IOU: 0.606791, Class: 1.000000, Obj: 0.380006, No Obj: 0.003085, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.540346, Class: 1.000000, Obj: 0.299901, No Obj: 0.002992, Avg Recall: 0.592920, count: 113 -Region Avg IOU: 0.608533, Class: 1.000000, Obj: 0.365712, No Obj: 0.003071, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.568516, Class: 1.000000, Obj: 0.327838, No Obj: 0.003085, Avg Recall: 0.649123, count: 114 -3087: 0.454108, 0.461370 avg, 0.001000 rate, 26.732685 seconds, 790272 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.580381, Class: 1.000000, Obj: 0.346799, No Obj: 0.002750, Avg Recall: 0.622807, count: 114 -Region Avg IOU: 0.596686, Class: 1.000000, Obj: 0.350221, No Obj: 0.003240, Avg Recall: 0.709924, count: 131 -Region Avg IOU: 0.627105, Class: 1.000000, Obj: 0.326949, No Obj: 0.003115, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.599421, Class: 1.000000, Obj: 0.400565, No Obj: 0.003460, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.629845, Class: 1.000000, Obj: 0.360139, No Obj: 0.002827, Avg Recall: 0.785047, count: 107 -Region Avg IOU: 0.621666, Class: 1.000000, Obj: 0.343507, No Obj: 0.002541, Avg Recall: 0.758621, count: 87 -Region Avg IOU: 0.629950, Class: 1.000000, Obj: 0.392953, No Obj: 0.003350, Avg Recall: 0.784483, count: 116 -Region Avg IOU: 0.626415, Class: 1.000000, Obj: 0.391751, No Obj: 0.003746, Avg Recall: 0.776119, count: 134 -3088: 0.461035, 0.461336 avg, 0.001000 rate, 25.740915 seconds, 790528 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.625465, Class: 1.000000, Obj: 0.362129, No Obj: 0.003116, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.554104, Class: 1.000000, Obj: 0.341141, No Obj: 0.002678, Avg Recall: 0.630252, count: 119 -Region Avg IOU: 0.601426, Class: 1.000000, Obj: 0.399089, No Obj: 0.003223, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.621045, Class: 1.000000, Obj: 0.394960, No Obj: 0.002588, Avg Recall: 0.700000, count: 80 -Region Avg IOU: 0.584028, Class: 1.000000, Obj: 0.328081, No Obj: 0.003003, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.606074, Class: 1.000000, Obj: 0.369510, No Obj: 0.003002, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.606057, Class: 1.000000, Obj: 0.349156, No Obj: 0.003357, Avg Recall: 0.723404, count: 141 -Region Avg IOU: 0.644406, Class: 1.000000, Obj: 0.377955, No Obj: 0.003369, Avg Recall: 0.798246, count: 114 -3089: 0.457000, 0.460903 avg, 0.001000 rate, 26.090626 seconds, 790784 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.613188, Class: 1.000000, Obj: 0.372156, No Obj: 0.003326, Avg Recall: 0.771186, count: 118 -Region Avg IOU: 0.600615, Class: 1.000000, Obj: 0.399691, No Obj: 0.003063, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.572036, Class: 1.000000, Obj: 0.360185, No Obj: 0.003138, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.604628, Class: 1.000000, Obj: 0.349309, No Obj: 0.003036, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.610530, Class: 1.000000, Obj: 0.368702, No Obj: 0.003388, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.608935, Class: 1.000000, Obj: 0.332202, No Obj: 0.003459, Avg Recall: 0.733333, count: 135 -Region Avg IOU: 0.636313, Class: 1.000000, Obj: 0.314977, No Obj: 0.003735, Avg Recall: 0.814815, count: 135 -Region Avg IOU: 0.619597, Class: 1.000000, Obj: 0.360707, No Obj: 0.002760, Avg Recall: 0.696203, count: 79 -3090: 0.456416, 0.460454 avg, 0.001000 rate, 26.611628 seconds, 791040 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000074 seconds -Region Avg IOU: 0.609841, Class: 1.000000, Obj: 0.353765, No Obj: 0.003023, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.574272, Class: 1.000000, Obj: 0.399803, No Obj: 0.002764, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.609853, Class: 1.000000, Obj: 0.356718, No Obj: 0.003160, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.623040, Class: 1.000000, Obj: 0.349949, No Obj: 0.003134, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.595668, Class: 1.000000, Obj: 0.399390, No Obj: 0.002717, Avg Recall: 0.686747, count: 83 -Region Avg IOU: 0.577802, Class: 1.000000, Obj: 0.346558, No Obj: 0.003552, Avg Recall: 0.717742, count: 124 -Region Avg IOU: 0.622122, Class: 1.000000, Obj: 0.415784, No Obj: 0.002722, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.666541, Class: 1.000000, Obj: 0.369029, No Obj: 0.003386, Avg Recall: 0.801802, count: 111 -3091: 0.474638, 0.461872 avg, 0.001000 rate, 27.657867 seconds, 791296 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.592753, Class: 1.000000, Obj: 0.353113, No Obj: 0.003206, Avg Recall: 0.731707, count: 123 -Region Avg IOU: 0.586657, Class: 1.000000, Obj: 0.324817, No Obj: 0.002803, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.574303, Class: 1.000000, Obj: 0.384668, No Obj: 0.003006, Avg Recall: 0.674419, count: 129 -Region Avg IOU: 0.612656, Class: 1.000000, Obj: 0.373578, No Obj: 0.002698, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.607494, Class: 1.000000, Obj: 0.391290, No Obj: 0.003222, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.607287, Class: 1.000000, Obj: 0.336632, No Obj: 0.003749, Avg Recall: 0.726744, count: 172 -Region Avg IOU: 0.641968, Class: 1.000000, Obj: 0.372308, No Obj: 0.002922, Avg Recall: 0.802083, count: 96 -Region Avg IOU: 0.612670, Class: 1.000000, Obj: 0.370874, No Obj: 0.002768, Avg Recall: 0.672727, count: 110 -3092: 0.440466, 0.459732 avg, 0.001000 rate, 28.267729 seconds, 791552 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.587901, Class: 1.000000, Obj: 0.342971, No Obj: 0.003022, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.613858, Class: 1.000000, Obj: 0.374680, No Obj: 0.002981, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.636388, Class: 1.000000, Obj: 0.385948, No Obj: 0.002918, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.631960, Class: 1.000000, Obj: 0.404142, No Obj: 0.002996, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.616509, Class: 1.000000, Obj: 0.317538, No Obj: 0.002724, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.620375, Class: 1.000000, Obj: 0.397703, No Obj: 0.003031, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.551203, Class: 1.000000, Obj: 0.341506, No Obj: 0.002993, Avg Recall: 0.669492, count: 118 -Region Avg IOU: 0.616517, Class: 1.000000, Obj: 0.356331, No Obj: 0.002670, Avg Recall: 0.833333, count: 90 -3093: 0.457164, 0.459475 avg, 0.001000 rate, 28.421600 seconds, 791808 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.614396, Class: 1.000000, Obj: 0.360586, No Obj: 0.003210, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.612934, Class: 1.000000, Obj: 0.329944, No Obj: 0.003017, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.602401, Class: 1.000000, Obj: 0.355406, No Obj: 0.002936, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.636956, Class: 1.000000, Obj: 0.349624, No Obj: 0.003035, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.604963, Class: 1.000000, Obj: 0.360821, No Obj: 0.003325, Avg Recall: 0.724638, count: 138 -Region Avg IOU: 0.586478, Class: 1.000000, Obj: 0.308743, No Obj: 0.002494, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.603684, Class: 1.000000, Obj: 0.344321, No Obj: 0.003214, Avg Recall: 0.734375, count: 128 -Region Avg IOU: 0.563269, Class: 1.000000, Obj: 0.328871, No Obj: 0.002556, Avg Recall: 0.647059, count: 102 -3094: 0.496892, 0.463217 avg, 0.001000 rate, 26.607096 seconds, 792064 images -Loaded: 0.000117 seconds -Region Avg IOU: 0.632129, Class: 1.000000, Obj: 0.340764, No Obj: 0.003160, Avg Recall: 0.813084, count: 107 -Region Avg IOU: 0.551129, Class: 1.000000, Obj: 0.342925, No Obj: 0.002795, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.630218, Class: 1.000000, Obj: 0.383166, No Obj: 0.002860, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.611913, Class: 1.000000, Obj: 0.360244, No Obj: 0.002826, Avg Recall: 0.708333, count: 120 -Region Avg IOU: 0.612542, Class: 1.000000, Obj: 0.366364, No Obj: 0.003130, Avg Recall: 0.750000, count: 124 -Region Avg IOU: 0.634256, Class: 1.000000, Obj: 0.390897, No Obj: 0.003134, Avg Recall: 0.786325, count: 117 -Region Avg IOU: 0.641505, Class: 1.000000, Obj: 0.346267, No Obj: 0.002978, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.608725, Class: 1.000000, Obj: 0.328120, No Obj: 0.002935, Avg Recall: 0.742574, count: 101 -3095: 0.488294, 0.465724 avg, 0.001000 rate, 26.533436 seconds, 792320 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.620037, Class: 1.000000, Obj: 0.382039, No Obj: 0.002395, Avg Recall: 0.773333, count: 75 -Region Avg IOU: 0.577554, Class: 1.000000, Obj: 0.324966, No Obj: 0.002912, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.601346, Class: 1.000000, Obj: 0.329740, No Obj: 0.002618, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.611420, Class: 1.000000, Obj: 0.349038, No Obj: 0.002862, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.610100, Class: 1.000000, Obj: 0.313689, No Obj: 0.002747, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.616358, Class: 1.000000, Obj: 0.318689, No Obj: 0.002616, Avg Recall: 0.809917, count: 121 -Region Avg IOU: 0.594130, Class: 1.000000, Obj: 0.347388, No Obj: 0.002964, Avg Recall: 0.712000, count: 125 -Region Avg IOU: 0.596109, Class: 1.000000, Obj: 0.331716, No Obj: 0.002888, Avg Recall: 0.722222, count: 126 -3096: 0.470833, 0.466235 avg, 0.001000 rate, 26.852884 seconds, 792576 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.582135, Class: 1.000000, Obj: 0.331513, No Obj: 0.002596, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.645116, Class: 1.000000, Obj: 0.340654, No Obj: 0.002778, Avg Recall: 0.825243, count: 103 -Region Avg IOU: 0.575480, Class: 1.000000, Obj: 0.299903, No Obj: 0.002478, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.664015, Class: 1.000000, Obj: 0.349608, No Obj: 0.002969, Avg Recall: 0.862385, count: 109 -Region Avg IOU: 0.657772, Class: 1.000000, Obj: 0.332860, No Obj: 0.002929, Avg Recall: 0.833333, count: 96 -Region Avg IOU: 0.652916, Class: 1.000000, Obj: 0.329835, No Obj: 0.002960, Avg Recall: 0.825758, count: 132 -Region Avg IOU: 0.622542, Class: 1.000000, Obj: 0.345698, No Obj: 0.002921, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.617679, Class: 1.000000, Obj: 0.382940, No Obj: 0.002695, Avg Recall: 0.767677, count: 99 -3097: 0.458409, 0.465453 avg, 0.001000 rate, 26.842552 seconds, 792832 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.624056, Class: 1.000000, Obj: 0.375014, No Obj: 0.002774, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.608408, Class: 1.000000, Obj: 0.347426, No Obj: 0.002653, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.644442, Class: 1.000000, Obj: 0.339527, No Obj: 0.002756, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.644912, Class: 1.000000, Obj: 0.385155, No Obj: 0.002420, Avg Recall: 0.800000, count: 70 -Region Avg IOU: 0.663075, Class: 1.000000, Obj: 0.344862, No Obj: 0.002703, Avg Recall: 0.868687, count: 99 -Region Avg IOU: 0.561908, Class: 1.000000, Obj: 0.377312, No Obj: 0.002567, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.594680, Class: 1.000000, Obj: 0.304717, No Obj: 0.002851, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.607502, Class: 1.000000, Obj: 0.317055, No Obj: 0.002695, Avg Recall: 0.741935, count: 124 -3098: 0.421240, 0.461031 avg, 0.001000 rate, 28.205776 seconds, 793088 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.599651, Class: 1.000000, Obj: 0.305095, No Obj: 0.002868, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.637520, Class: 1.000000, Obj: 0.386471, No Obj: 0.003223, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.628463, Class: 1.000000, Obj: 0.339588, No Obj: 0.003182, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.595429, Class: 1.000000, Obj: 0.375584, No Obj: 0.002866, Avg Recall: 0.724138, count: 87 -Region Avg IOU: 0.584274, Class: 1.000000, Obj: 0.399844, No Obj: 0.002662, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.602972, Class: 1.000000, Obj: 0.371809, No Obj: 0.003236, Avg Recall: 0.722628, count: 137 -Region Avg IOU: 0.572549, Class: 1.000000, Obj: 0.344893, No Obj: 0.002575, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.629705, Class: 1.000000, Obj: 0.351065, No Obj: 0.002718, Avg Recall: 0.796610, count: 118 -3099: 0.466862, 0.461615 avg, 0.001000 rate, 26.808620 seconds, 793344 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.631623, Class: 1.000000, Obj: 0.389169, No Obj: 0.002826, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.635192, Class: 1.000000, Obj: 0.306173, No Obj: 0.002901, Avg Recall: 0.762712, count: 118 -Region Avg IOU: 0.635658, Class: 1.000000, Obj: 0.389697, No Obj: 0.002789, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.611060, Class: 1.000000, Obj: 0.356517, No Obj: 0.002991, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.595346, Class: 1.000000, Obj: 0.321290, No Obj: 0.002857, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.644639, Class: 1.000000, Obj: 0.380642, No Obj: 0.003076, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.596450, Class: 1.000000, Obj: 0.355909, No Obj: 0.003049, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.659503, Class: 1.000000, Obj: 0.380866, No Obj: 0.002711, Avg Recall: 0.820755, count: 106 -3100: 0.442175, 0.459671 avg, 0.001000 rate, 26.695436 seconds, 793600 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 14.291023 seconds -Region Avg IOU: 0.618556, Class: 1.000000, Obj: 0.360502, No Obj: 0.003102, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.607088, Class: 1.000000, Obj: 0.347155, No Obj: 0.003219, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.616623, Class: 1.000000, Obj: 0.397769, No Obj: 0.002782, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.563792, Class: 1.000000, Obj: 0.310909, No Obj: 0.002606, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.626814, Class: 1.000000, Obj: 0.358537, No Obj: 0.003129, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.612088, Class: 1.000000, Obj: 0.341087, No Obj: 0.003119, Avg Recall: 0.746032, count: 126 -Region Avg IOU: 0.612283, Class: 1.000000, Obj: 0.395063, No Obj: 0.002953, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.551548, Class: 1.000000, Obj: 0.332141, No Obj: 0.002918, Avg Recall: 0.663636, count: 110 -3101: 0.442414, 0.457945 avg, 0.001000 rate, 27.865335 seconds, 793856 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.621320, Class: 1.000000, Obj: 0.360519, No Obj: 0.002913, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.623381, Class: 1.000000, Obj: 0.369836, No Obj: 0.002732, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.616093, Class: 1.000000, Obj: 0.378452, No Obj: 0.002618, Avg Recall: 0.772152, count: 79 -Region Avg IOU: 0.664715, Class: 1.000000, Obj: 0.345317, No Obj: 0.003244, Avg Recall: 0.844660, count: 103 -Region Avg IOU: 0.604680, Class: 1.000000, Obj: 0.329768, No Obj: 0.002880, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.606268, Class: 1.000000, Obj: 0.366415, No Obj: 0.002640, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.556031, Class: 1.000000, Obj: 0.318871, No Obj: 0.002720, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.663155, Class: 1.000000, Obj: 0.415879, No Obj: 0.002966, Avg Recall: 0.836538, count: 104 -3102: 0.403444, 0.452495 avg, 0.001000 rate, 26.492056 seconds, 794112 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.610621, Class: 1.000000, Obj: 0.361534, No Obj: 0.002854, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.632579, Class: 1.000000, Obj: 0.395607, No Obj: 0.002885, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.622160, Class: 1.000000, Obj: 0.387459, No Obj: 0.002775, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.593359, Class: 1.000000, Obj: 0.381583, No Obj: 0.002867, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.603835, Class: 1.000000, Obj: 0.338325, No Obj: 0.002910, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.606417, Class: 1.000000, Obj: 0.347810, No Obj: 0.002897, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.651675, Class: 1.000000, Obj: 0.392464, No Obj: 0.002888, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.598663, Class: 1.000000, Obj: 0.301413, No Obj: 0.003148, Avg Recall: 0.730263, count: 152 -3103: 0.460406, 0.453286 avg, 0.001000 rate, 26.710472 seconds, 794368 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.554914, Class: 1.000000, Obj: 0.269077, No Obj: 0.002546, Avg Recall: 0.650406, count: 123 -Region Avg IOU: 0.622190, Class: 1.000000, Obj: 0.324843, No Obj: 0.002424, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.572224, Class: 1.000000, Obj: 0.286560, No Obj: 0.002521, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.568096, Class: 1.000000, Obj: 0.345199, No Obj: 0.002715, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.628862, Class: 1.000000, Obj: 0.360164, No Obj: 0.002683, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.658313, Class: 1.000000, Obj: 0.385180, No Obj: 0.003104, Avg Recall: 0.827273, count: 110 -Region Avg IOU: 0.603829, Class: 1.000000, Obj: 0.352450, No Obj: 0.002937, Avg Recall: 0.717557, count: 131 -Region Avg IOU: 0.615707, Class: 1.000000, Obj: 0.356245, No Obj: 0.002994, Avg Recall: 0.748148, count: 135 -3104: 0.472057, 0.455163 avg, 0.001000 rate, 27.071480 seconds, 794624 images -Loaded: 0.000151 seconds -Region Avg IOU: 0.565467, Class: 1.000000, Obj: 0.347848, No Obj: 0.002322, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.600518, Class: 1.000000, Obj: 0.346843, No Obj: 0.002436, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.621613, Class: 1.000000, Obj: 0.365287, No Obj: 0.002964, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.620727, Class: 1.000000, Obj: 0.384113, No Obj: 0.002771, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.632000, Class: 1.000000, Obj: 0.422805, No Obj: 0.002486, Avg Recall: 0.755556, count: 90 -Region Avg IOU: 0.645167, Class: 1.000000, Obj: 0.347349, No Obj: 0.002491, Avg Recall: 0.840425, count: 94 -Region Avg IOU: 0.619162, Class: 1.000000, Obj: 0.365642, No Obj: 0.002761, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.648534, Class: 1.000000, Obj: 0.346099, No Obj: 0.003122, Avg Recall: 0.826923, count: 104 -3105: 0.407689, 0.450416 avg, 0.001000 rate, 26.983992 seconds, 794880 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.623736, Class: 1.000000, Obj: 0.372658, No Obj: 0.002802, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.616159, Class: 1.000000, Obj: 0.333342, No Obj: 0.003027, Avg Recall: 0.735043, count: 117 -Region Avg IOU: 0.635378, Class: 1.000000, Obj: 0.366729, No Obj: 0.002763, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.643841, Class: 1.000000, Obj: 0.358435, No Obj: 0.003308, Avg Recall: 0.819549, count: 133 -Region Avg IOU: 0.579263, Class: 1.000000, Obj: 0.339771, No Obj: 0.002818, Avg Recall: 0.710938, count: 128 -Region Avg IOU: 0.614788, Class: 1.000000, Obj: 0.392922, No Obj: 0.002835, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.578136, Class: 1.000000, Obj: 0.366508, No Obj: 0.002767, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.613570, Class: 1.000000, Obj: 0.390011, No Obj: 0.002585, Avg Recall: 0.742268, count: 97 -3106: 0.464263, 0.451800 avg, 0.001000 rate, 27.953424 seconds, 795136 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.608682, Class: 1.000000, Obj: 0.389410, No Obj: 0.002585, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.650505, Class: 1.000000, Obj: 0.349758, No Obj: 0.003525, Avg Recall: 0.790323, count: 124 -Region Avg IOU: 0.640250, Class: 1.000000, Obj: 0.379198, No Obj: 0.002925, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.636719, Class: 1.000000, Obj: 0.396197, No Obj: 0.003633, Avg Recall: 0.798611, count: 144 -Region Avg IOU: 0.639208, Class: 1.000000, Obj: 0.353726, No Obj: 0.002833, Avg Recall: 0.784810, count: 79 -Region Avg IOU: 0.603869, Class: 1.000000, Obj: 0.360687, No Obj: 0.003168, Avg Recall: 0.708333, count: 120 -Region Avg IOU: 0.596602, Class: 1.000000, Obj: 0.355224, No Obj: 0.003077, Avg Recall: 0.706349, count: 126 -Region Avg IOU: 0.603195, Class: 1.000000, Obj: 0.351059, No Obj: 0.002909, Avg Recall: 0.741071, count: 112 -3107: 0.516546, 0.458275 avg, 0.001000 rate, 26.797337 seconds, 795392 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.616788, Class: 1.000000, Obj: 0.400629, No Obj: 0.003003, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.564134, Class: 1.000000, Obj: 0.350781, No Obj: 0.002837, Avg Recall: 0.666667, count: 117 -Region Avg IOU: 0.577709, Class: 1.000000, Obj: 0.324902, No Obj: 0.002827, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.591373, Class: 1.000000, Obj: 0.387859, No Obj: 0.002596, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.619118, Class: 1.000000, Obj: 0.357507, No Obj: 0.002835, Avg Recall: 0.776471, count: 85 -Region Avg IOU: 0.633112, Class: 1.000000, Obj: 0.398466, No Obj: 0.003069, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.614582, Class: 1.000000, Obj: 0.359098, No Obj: 0.002806, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.636706, Class: 1.000000, Obj: 0.359948, No Obj: 0.003023, Avg Recall: 0.763441, count: 93 -3108: 0.436406, 0.456088 avg, 0.001000 rate, 29.053598 seconds, 795648 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.599669, Class: 1.000000, Obj: 0.345412, No Obj: 0.003288, Avg Recall: 0.735537, count: 121 -Region Avg IOU: 0.604196, Class: 1.000000, Obj: 0.361330, No Obj: 0.002891, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.580675, Class: 1.000000, Obj: 0.390789, No Obj: 0.002596, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.613587, Class: 1.000000, Obj: 0.365205, No Obj: 0.002837, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.592185, Class: 1.000000, Obj: 0.381326, No Obj: 0.002697, Avg Recall: 0.685484, count: 124 -Region Avg IOU: 0.605146, Class: 1.000000, Obj: 0.369770, No Obj: 0.003017, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.620935, Class: 1.000000, Obj: 0.367556, No Obj: 0.002584, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.606752, Class: 1.000000, Obj: 0.380793, No Obj: 0.002893, Avg Recall: 0.765306, count: 98 -3109: 0.441530, 0.454632 avg, 0.001000 rate, 26.952196 seconds, 795904 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.615251, Class: 1.000000, Obj: 0.360613, No Obj: 0.002840, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.587811, Class: 1.000000, Obj: 0.335325, No Obj: 0.002834, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.595347, Class: 1.000000, Obj: 0.394191, No Obj: 0.002691, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.614966, Class: 1.000000, Obj: 0.369255, No Obj: 0.003346, Avg Recall: 0.702290, count: 131 -Region Avg IOU: 0.625411, Class: 1.000000, Obj: 0.390660, No Obj: 0.003155, Avg Recall: 0.809091, count: 110 -Region Avg IOU: 0.584665, Class: 1.000000, Obj: 0.340848, No Obj: 0.002787, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.612533, Class: 1.000000, Obj: 0.376007, No Obj: 0.003114, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.648652, Class: 1.000000, Obj: 0.356131, No Obj: 0.002957, Avg Recall: 0.784483, count: 116 -3110: 0.489762, 0.458145 avg, 0.001000 rate, 27.108246 seconds, 796160 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 14.459953 seconds -Region Avg IOU: 0.575400, Class: 1.000000, Obj: 0.321849, No Obj: 0.003211, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.619797, Class: 1.000000, Obj: 0.320313, No Obj: 0.003048, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.578258, Class: 1.000000, Obj: 0.335584, No Obj: 0.003224, Avg Recall: 0.645161, count: 124 -Region Avg IOU: 0.615578, Class: 1.000000, Obj: 0.359210, No Obj: 0.003324, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.553633, Class: 1.000000, Obj: 0.336267, No Obj: 0.002976, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.641392, Class: 1.000000, Obj: 0.319977, No Obj: 0.003194, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.558152, Class: 1.000000, Obj: 0.359602, No Obj: 0.002795, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.559452, Class: 1.000000, Obj: 0.326381, No Obj: 0.003351, Avg Recall: 0.663551, count: 107 -3111: 0.432280, 0.455559 avg, 0.001000 rate, 22.311583 seconds, 796416 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.580656, Class: 1.000000, Obj: 0.308866, No Obj: 0.002795, Avg Recall: 0.686567, count: 67 -Region Avg IOU: 0.589128, Class: 1.000000, Obj: 0.344734, No Obj: 0.003173, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.555372, Class: 1.000000, Obj: 0.330505, No Obj: 0.003083, Avg Recall: 0.647727, count: 88 -Region Avg IOU: 0.540716, Class: 1.000000, Obj: 0.313942, No Obj: 0.003041, Avg Recall: 0.610169, count: 118 -Region Avg IOU: 0.589528, Class: 1.000000, Obj: 0.309413, No Obj: 0.003115, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.550179, Class: 1.000000, Obj: 0.314184, No Obj: 0.002843, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.512527, Class: 1.000000, Obj: 0.307361, No Obj: 0.002738, Avg Recall: 0.495146, count: 103 -Region Avg IOU: 0.604810, Class: 1.000000, Obj: 0.382899, No Obj: 0.003169, Avg Recall: 0.741935, count: 93 -3112: 0.388350, 0.448838 avg, 0.001000 rate, 22.876604 seconds, 796672 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.589898, Class: 1.000000, Obj: 0.290045, No Obj: 0.003402, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.539757, Class: 1.000000, Obj: 0.316322, No Obj: 0.002887, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.570469, Class: 1.000000, Obj: 0.314517, No Obj: 0.003364, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.601710, Class: 1.000000, Obj: 0.363920, No Obj: 0.002843, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.586518, Class: 1.000000, Obj: 0.362315, No Obj: 0.003199, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.576460, Class: 1.000000, Obj: 0.297934, No Obj: 0.002984, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.567572, Class: 1.000000, Obj: 0.383505, No Obj: 0.003052, Avg Recall: 0.686046, count: 86 -Region Avg IOU: 0.587451, Class: 1.000000, Obj: 0.365770, No Obj: 0.003028, Avg Recall: 0.705882, count: 85 -3113: 0.403927, 0.444347 avg, 0.001000 rate, 24.254189 seconds, 796928 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.545372, Class: 1.000000, Obj: 0.331244, No Obj: 0.003177, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.578599, Class: 1.000000, Obj: 0.388917, No Obj: 0.003080, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.594451, Class: 1.000000, Obj: 0.332401, No Obj: 0.003652, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.576773, Class: 1.000000, Obj: 0.346158, No Obj: 0.003493, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.615724, Class: 1.000000, Obj: 0.370201, No Obj: 0.002949, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.604253, Class: 1.000000, Obj: 0.326230, No Obj: 0.003773, Avg Recall: 0.752137, count: 117 -Region Avg IOU: 0.519434, Class: 1.000000, Obj: 0.313352, No Obj: 0.003093, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.594528, Class: 1.000000, Obj: 0.368558, No Obj: 0.003567, Avg Recall: 0.700855, count: 117 -3114: 0.438451, 0.443757 avg, 0.001000 rate, 22.160728 seconds, 797184 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.546474, Class: 1.000000, Obj: 0.366993, No Obj: 0.003433, Avg Recall: 0.619048, count: 126 -Region Avg IOU: 0.571372, Class: 1.000000, Obj: 0.329194, No Obj: 0.003553, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.591276, Class: 1.000000, Obj: 0.372179, No Obj: 0.003600, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.554688, Class: 1.000000, Obj: 0.401715, No Obj: 0.003142, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.568999, Class: 1.000000, Obj: 0.369245, No Obj: 0.003311, Avg Recall: 0.694118, count: 85 -Region Avg IOU: 0.546967, Class: 1.000000, Obj: 0.354667, No Obj: 0.003448, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.577530, Class: 1.000000, Obj: 0.351661, No Obj: 0.002954, Avg Recall: 0.714286, count: 56 -Region Avg IOU: 0.590745, Class: 1.000000, Obj: 0.346904, No Obj: 0.004101, Avg Recall: 0.663866, count: 119 -3115: 0.420932, 0.441475 avg, 0.001000 rate, 24.028099 seconds, 797440 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.563961, Class: 1.000000, Obj: 0.386124, No Obj: 0.003319, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.586195, Class: 1.000000, Obj: 0.355017, No Obj: 0.004075, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.567680, Class: 1.000000, Obj: 0.337139, No Obj: 0.003794, Avg Recall: 0.643478, count: 115 -Region Avg IOU: 0.545515, Class: 1.000000, Obj: 0.306093, No Obj: 0.003649, Avg Recall: 0.598361, count: 122 -Region Avg IOU: 0.559923, Class: 1.000000, Obj: 0.380581, No Obj: 0.003676, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.529385, Class: 1.000000, Obj: 0.337466, No Obj: 0.003774, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.584826, Class: 1.000000, Obj: 0.345327, No Obj: 0.003403, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.578900, Class: 1.000000, Obj: 0.372001, No Obj: 0.003530, Avg Recall: 0.700935, count: 107 -3116: 0.453320, 0.442659 avg, 0.001000 rate, 25.671928 seconds, 797696 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.520623, Class: 1.000000, Obj: 0.368545, No Obj: 0.003596, Avg Recall: 0.570248, count: 121 -Region Avg IOU: 0.594531, Class: 1.000000, Obj: 0.344685, No Obj: 0.004141, Avg Recall: 0.709677, count: 124 -Region Avg IOU: 0.576482, Class: 1.000000, Obj: 0.360708, No Obj: 0.004461, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.558786, Class: 1.000000, Obj: 0.367189, No Obj: 0.003759, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.542392, Class: 1.000000, Obj: 0.323038, No Obj: 0.003634, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.560367, Class: 1.000000, Obj: 0.440749, No Obj: 0.003793, Avg Recall: 0.592593, count: 108 -Region Avg IOU: 0.563950, Class: 1.000000, Obj: 0.353163, No Obj: 0.003349, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.627690, Class: 1.000000, Obj: 0.373142, No Obj: 0.003701, Avg Recall: 0.782178, count: 101 -3117: 0.455042, 0.443898 avg, 0.001000 rate, 25.088751 seconds, 797952 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.608719, Class: 1.000000, Obj: 0.406378, No Obj: 0.003932, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.552840, Class: 1.000000, Obj: 0.391576, No Obj: 0.003652, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.525201, Class: 1.000000, Obj: 0.356348, No Obj: 0.003772, Avg Recall: 0.571429, count: 105 -Region Avg IOU: 0.503420, Class: 1.000000, Obj: 0.298261, No Obj: 0.003757, Avg Recall: 0.542636, count: 129 -Region Avg IOU: 0.594720, Class: 1.000000, Obj: 0.432919, No Obj: 0.003604, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.605527, Class: 1.000000, Obj: 0.417181, No Obj: 0.003856, Avg Recall: 0.753247, count: 77 -Region Avg IOU: 0.511841, Class: 1.000000, Obj: 0.374550, No Obj: 0.003193, Avg Recall: 0.556701, count: 97 -Region Avg IOU: 0.559690, Class: 1.000000, Obj: 0.358756, No Obj: 0.003667, Avg Recall: 0.678571, count: 112 -3118: 0.447761, 0.444284 avg, 0.001000 rate, 23.668640 seconds, 798208 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.577820, Class: 1.000000, Obj: 0.364188, No Obj: 0.004369, Avg Recall: 0.710938, count: 128 -Region Avg IOU: 0.566181, Class: 1.000000, Obj: 0.399680, No Obj: 0.003875, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.530730, Class: 1.000000, Obj: 0.371404, No Obj: 0.003990, Avg Recall: 0.578512, count: 121 -Region Avg IOU: 0.576071, Class: 1.000000, Obj: 0.355515, No Obj: 0.003229, Avg Recall: 0.717647, count: 85 -Region Avg IOU: 0.588773, Class: 1.000000, Obj: 0.402797, No Obj: 0.003414, Avg Recall: 0.678571, count: 84 -Region Avg IOU: 0.583718, Class: 1.000000, Obj: 0.404372, No Obj: 0.004290, Avg Recall: 0.651852, count: 135 -Region Avg IOU: 0.601755, Class: 1.000000, Obj: 0.407495, No Obj: 0.003889, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.559520, Class: 1.000000, Obj: 0.407329, No Obj: 0.003869, Avg Recall: 0.600000, count: 120 -3119: 0.448872, 0.444743 avg, 0.001000 rate, 25.270763 seconds, 798464 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.570011, Class: 1.000000, Obj: 0.363017, No Obj: 0.003584, Avg Recall: 0.671429, count: 140 -Region Avg IOU: 0.505842, Class: 1.000000, Obj: 0.359529, No Obj: 0.003492, Avg Recall: 0.559322, count: 118 -Region Avg IOU: 0.612818, Class: 1.000000, Obj: 0.345735, No Obj: 0.003954, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.564805, Class: 1.000000, Obj: 0.371649, No Obj: 0.003740, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.570108, Class: 1.000000, Obj: 0.379015, No Obj: 0.003675, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.530591, Class: 1.000000, Obj: 0.348455, No Obj: 0.003122, Avg Recall: 0.571429, count: 112 -Region Avg IOU: 0.600197, Class: 1.000000, Obj: 0.388894, No Obj: 0.003515, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.613549, Class: 1.000000, Obj: 0.352331, No Obj: 0.003863, Avg Recall: 0.741071, count: 112 -3120: 0.416931, 0.441962 avg, 0.001000 rate, 25.065781 seconds, 798720 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000065 seconds -Region Avg IOU: 0.633505, Class: 1.000000, Obj: 0.389450, No Obj: 0.003139, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.592507, Class: 1.000000, Obj: 0.396688, No Obj: 0.003035, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.604267, Class: 1.000000, Obj: 0.370549, No Obj: 0.002711, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.627221, Class: 1.000000, Obj: 0.410736, No Obj: 0.003755, Avg Recall: 0.801471, count: 136 -Region Avg IOU: 0.654726, Class: 1.000000, Obj: 0.457278, No Obj: 0.003160, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.618975, Class: 1.000000, Obj: 0.357912, No Obj: 0.003075, Avg Recall: 0.766129, count: 124 -Region Avg IOU: 0.639528, Class: 1.000000, Obj: 0.392715, No Obj: 0.002919, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.624762, Class: 1.000000, Obj: 0.352745, No Obj: 0.002887, Avg Recall: 0.766990, count: 103 -3121: 0.488530, 0.446618 avg, 0.001000 rate, 26.679880 seconds, 798976 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.575936, Class: 1.000000, Obj: 0.326402, No Obj: 0.002891, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.592974, Class: 1.000000, Obj: 0.341623, No Obj: 0.002911, Avg Recall: 0.729508, count: 122 -Region Avg IOU: 0.608649, Class: 1.000000, Obj: 0.362660, No Obj: 0.002706, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.611961, Class: 1.000000, Obj: 0.345022, No Obj: 0.002798, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.636531, Class: 1.000000, Obj: 0.387869, No Obj: 0.002601, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.595675, Class: 1.000000, Obj: 0.368214, No Obj: 0.002920, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.622245, Class: 1.000000, Obj: 0.380339, No Obj: 0.002765, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.644291, Class: 1.000000, Obj: 0.414584, No Obj: 0.002941, Avg Recall: 0.775701, count: 107 -3122: 0.441536, 0.446110 avg, 0.001000 rate, 28.808254 seconds, 799232 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.608376, Class: 1.000000, Obj: 0.349569, No Obj: 0.002682, Avg Recall: 0.758065, count: 124 -Region Avg IOU: 0.620864, Class: 1.000000, Obj: 0.381440, No Obj: 0.002617, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.628436, Class: 1.000000, Obj: 0.381648, No Obj: 0.002529, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.591577, Class: 1.000000, Obj: 0.305480, No Obj: 0.002704, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.585200, Class: 1.000000, Obj: 0.372274, No Obj: 0.002542, Avg Recall: 0.678161, count: 87 -Region Avg IOU: 0.633273, Class: 1.000000, Obj: 0.350501, No Obj: 0.002646, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.621138, Class: 1.000000, Obj: 0.341793, No Obj: 0.002539, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.618528, Class: 1.000000, Obj: 0.353211, No Obj: 0.002688, Avg Recall: 0.781955, count: 133 -3123: 0.437342, 0.445233 avg, 0.001000 rate, 26.478537 seconds, 799488 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.655290, Class: 1.000000, Obj: 0.345592, No Obj: 0.002461, Avg Recall: 0.847826, count: 92 -Region Avg IOU: 0.640143, Class: 1.000000, Obj: 0.361721, No Obj: 0.002506, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.624635, Class: 1.000000, Obj: 0.333590, No Obj: 0.002254, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.577797, Class: 1.000000, Obj: 0.293968, No Obj: 0.002278, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.600706, Class: 1.000000, Obj: 0.330745, No Obj: 0.002530, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.635093, Class: 1.000000, Obj: 0.356137, No Obj: 0.002330, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.649586, Class: 1.000000, Obj: 0.407437, No Obj: 0.002540, Avg Recall: 0.808081, count: 99 -Region Avg IOU: 0.621302, Class: 1.000000, Obj: 0.287544, No Obj: 0.002363, Avg Recall: 0.822222, count: 90 -3124: 0.419888, 0.442699 avg, 0.001000 rate, 29.456839 seconds, 799744 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.605309, Class: 1.000000, Obj: 0.312609, No Obj: 0.002419, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.545747, Class: 1.000000, Obj: 0.289511, No Obj: 0.002147, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.623583, Class: 1.000000, Obj: 0.260438, No Obj: 0.002096, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.638006, Class: 1.000000, Obj: 0.284527, No Obj: 0.002269, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.648750, Class: 1.000000, Obj: 0.268064, No Obj: 0.002405, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.615869, Class: 1.000000, Obj: 0.284117, No Obj: 0.002579, Avg Recall: 0.778626, count: 131 -Region Avg IOU: 0.579000, Class: 1.000000, Obj: 0.290651, No Obj: 0.002663, Avg Recall: 0.700000, count: 130 -Region Avg IOU: 0.587714, Class: 1.000000, Obj: 0.274400, No Obj: 0.002144, Avg Recall: 0.727273, count: 99 -3125: 0.465878, 0.445017 avg, 0.001000 rate, 27.378765 seconds, 800000 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.594671, Class: 1.000000, Obj: 0.285351, No Obj: 0.002377, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.655480, Class: 1.000000, Obj: 0.321451, No Obj: 0.002362, Avg Recall: 0.807229, count: 83 -Region Avg IOU: 0.620426, Class: 1.000000, Obj: 0.282172, No Obj: 0.002366, Avg Recall: 0.778571, count: 140 -Region Avg IOU: 0.597333, Class: 1.000000, Obj: 0.283207, No Obj: 0.001976, Avg Recall: 0.705882, count: 85 -Region Avg IOU: 0.673459, Class: 1.000000, Obj: 0.372315, No Obj: 0.002157, Avg Recall: 0.883117, count: 77 -Region Avg IOU: 0.636749, Class: 1.000000, Obj: 0.284583, No Obj: 0.002262, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.626745, Class: 1.000000, Obj: 0.313496, No Obj: 0.002011, Avg Recall: 0.804348, count: 92 -Region Avg IOU: 0.643342, Class: 1.000000, Obj: 0.314489, No Obj: 0.002253, Avg Recall: 0.801887, count: 106 -3126: 0.450055, 0.445521 avg, 0.001000 rate, 28.649000 seconds, 800256 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.649047, Class: 1.000000, Obj: 0.341331, No Obj: 0.002473, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.624918, Class: 1.000000, Obj: 0.321611, No Obj: 0.002242, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.611371, Class: 1.000000, Obj: 0.307005, No Obj: 0.002257, Avg Recall: 0.750000, count: 128 -Region Avg IOU: 0.674062, Class: 1.000000, Obj: 0.314629, No Obj: 0.002218, Avg Recall: 0.891089, count: 101 -Region Avg IOU: 0.652221, Class: 1.000000, Obj: 0.352195, No Obj: 0.002374, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.676594, Class: 1.000000, Obj: 0.306906, No Obj: 0.001907, Avg Recall: 0.851852, count: 81 -Region Avg IOU: 0.624637, Class: 1.000000, Obj: 0.289375, No Obj: 0.002364, Avg Recall: 0.767442, count: 129 -Region Avg IOU: 0.632610, Class: 1.000000, Obj: 0.294383, No Obj: 0.002595, Avg Recall: 0.779817, count: 109 -3127: 0.478596, 0.448828 avg, 0.001000 rate, 27.741579 seconds, 800512 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.594338, Class: 1.000000, Obj: 0.301939, No Obj: 0.002537, Avg Recall: 0.731343, count: 134 -Region Avg IOU: 0.617289, Class: 1.000000, Obj: 0.340467, No Obj: 0.002533, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.600458, Class: 1.000000, Obj: 0.306738, No Obj: 0.002506, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.639795, Class: 1.000000, Obj: 0.298238, No Obj: 0.002527, Avg Recall: 0.794643, count: 112 -Region Avg IOU: 0.649275, Class: 1.000000, Obj: 0.351224, No Obj: 0.002472, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.589183, Class: 1.000000, Obj: 0.315797, No Obj: 0.002275, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.649954, Class: 1.000000, Obj: 0.371918, No Obj: 0.002052, Avg Recall: 0.831325, count: 83 -Region Avg IOU: 0.613220, Class: 1.000000, Obj: 0.325257, No Obj: 0.002481, Avg Recall: 0.762712, count: 118 -3128: 0.451344, 0.449080 avg, 0.001000 rate, 28.370407 seconds, 800768 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.641867, Class: 1.000000, Obj: 0.357868, No Obj: 0.002891, Avg Recall: 0.786885, count: 122 -Region Avg IOU: 0.639711, Class: 1.000000, Obj: 0.384792, No Obj: 0.002469, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.643986, Class: 1.000000, Obj: 0.304515, No Obj: 0.002607, Avg Recall: 0.836538, count: 104 -Region Avg IOU: 0.584498, Class: 1.000000, Obj: 0.364590, No Obj: 0.002332, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.623453, Class: 1.000000, Obj: 0.326010, No Obj: 0.002766, Avg Recall: 0.733813, count: 139 -Region Avg IOU: 0.601293, Class: 1.000000, Obj: 0.320150, No Obj: 0.002720, Avg Recall: 0.685484, count: 124 -Region Avg IOU: 0.635487, Class: 1.000000, Obj: 0.321388, No Obj: 0.002173, Avg Recall: 0.780822, count: 73 -Region Avg IOU: 0.620310, Class: 1.000000, Obj: 0.363417, No Obj: 0.002567, Avg Recall: 0.717391, count: 92 -3129: 0.483536, 0.452525 avg, 0.001000 rate, 28.273705 seconds, 801024 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.647360, Class: 1.000000, Obj: 0.384631, No Obj: 0.002591, Avg Recall: 0.813333, count: 75 -Region Avg IOU: 0.659264, Class: 1.000000, Obj: 0.401492, No Obj: 0.002999, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.630932, Class: 1.000000, Obj: 0.369681, No Obj: 0.002474, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.583836, Class: 1.000000, Obj: 0.364958, No Obj: 0.002817, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.592759, Class: 1.000000, Obj: 0.393324, No Obj: 0.002878, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.595417, Class: 1.000000, Obj: 0.321467, No Obj: 0.002323, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.659687, Class: 1.000000, Obj: 0.367071, No Obj: 0.002849, Avg Recall: 0.828829, count: 111 -Region Avg IOU: 0.648920, Class: 1.000000, Obj: 0.386322, No Obj: 0.003050, Avg Recall: 0.790909, count: 110 -3130: 0.445040, 0.451777 avg, 0.001000 rate, 27.046083 seconds, 801280 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000066 seconds -Region Avg IOU: 0.629330, Class: 1.000000, Obj: 0.394652, No Obj: 0.002777, Avg Recall: 0.789916, count: 119 -Region Avg IOU: 0.612604, Class: 1.000000, Obj: 0.356880, No Obj: 0.003013, Avg Recall: 0.785714, count: 84 -Region Avg IOU: 0.657292, Class: 1.000000, Obj: 0.407284, No Obj: 0.003221, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.640175, Class: 1.000000, Obj: 0.451977, No Obj: 0.002678, Avg Recall: 0.781609, count: 87 -Region Avg IOU: 0.659287, Class: 1.000000, Obj: 0.393285, No Obj: 0.002780, Avg Recall: 0.826923, count: 104 -Region Avg IOU: 0.607196, Class: 1.000000, Obj: 0.360055, No Obj: 0.003093, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.633822, Class: 1.000000, Obj: 0.424456, No Obj: 0.002543, Avg Recall: 0.738095, count: 84 -Region Avg IOU: 0.648421, Class: 1.000000, Obj: 0.376147, No Obj: 0.003037, Avg Recall: 0.820000, count: 100 -3131: 0.414728, 0.448072 avg, 0.001000 rate, 29.777647 seconds, 801536 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.630651, Class: 1.000000, Obj: 0.405547, No Obj: 0.003004, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.641986, Class: 1.000000, Obj: 0.444146, No Obj: 0.002902, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.608833, Class: 1.000000, Obj: 0.340221, No Obj: 0.003492, Avg Recall: 0.700730, count: 137 -Region Avg IOU: 0.622719, Class: 1.000000, Obj: 0.413842, No Obj: 0.002776, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.604738, Class: 1.000000, Obj: 0.356525, No Obj: 0.002448, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.646171, Class: 1.000000, Obj: 0.348526, No Obj: 0.003168, Avg Recall: 0.797872, count: 94 -Region Avg IOU: 0.601563, Class: 1.000000, Obj: 0.383676, No Obj: 0.002638, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.648258, Class: 1.000000, Obj: 0.344781, No Obj: 0.003222, Avg Recall: 0.829457, count: 129 -3132: 0.484407, 0.451705 avg, 0.001000 rate, 29.962889 seconds, 801792 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.638497, Class: 1.000000, Obj: 0.365996, No Obj: 0.002892, Avg Recall: 0.801587, count: 126 -Region Avg IOU: 0.620033, Class: 1.000000, Obj: 0.362747, No Obj: 0.002718, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.635829, Class: 1.000000, Obj: 0.409734, No Obj: 0.002846, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.632139, Class: 1.000000, Obj: 0.403183, No Obj: 0.002886, Avg Recall: 0.781250, count: 96 -Region Avg IOU: 0.606989, Class: 1.000000, Obj: 0.374518, No Obj: 0.002642, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.651138, Class: 1.000000, Obj: 0.395449, No Obj: 0.003072, Avg Recall: 0.800000, count: 115 -Region Avg IOU: 0.652208, Class: 1.000000, Obj: 0.342298, No Obj: 0.003003, Avg Recall: 0.830357, count: 112 -Region Avg IOU: 0.619543, Class: 1.000000, Obj: 0.398161, No Obj: 0.002569, Avg Recall: 0.753247, count: 77 -3133: 0.450825, 0.451617 avg, 0.001000 rate, 30.072548 seconds, 802048 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.627162, Class: 1.000000, Obj: 0.338974, No Obj: 0.002750, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.638259, Class: 1.000000, Obj: 0.348578, No Obj: 0.002861, Avg Recall: 0.778761, count: 113 -Region Avg IOU: 0.648338, Class: 1.000000, Obj: 0.365248, No Obj: 0.002312, Avg Recall: 0.811765, count: 85 -Region Avg IOU: 0.632965, Class: 1.000000, Obj: 0.385126, No Obj: 0.003082, Avg Recall: 0.763359, count: 131 -Region Avg IOU: 0.625671, Class: 1.000000, Obj: 0.379440, No Obj: 0.002893, Avg Recall: 0.783333, count: 120 -Region Avg IOU: 0.660993, Class: 1.000000, Obj: 0.328926, No Obj: 0.003393, Avg Recall: 0.802721, count: 147 -Region Avg IOU: 0.644083, Class: 1.000000, Obj: 0.346058, No Obj: 0.002740, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.612177, Class: 1.000000, Obj: 0.368795, No Obj: 0.003048, Avg Recall: 0.712230, count: 139 -3134: 0.497530, 0.456209 avg, 0.001000 rate, 28.301289 seconds, 802304 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.675816, Class: 1.000000, Obj: 0.322773, No Obj: 0.002919, Avg Recall: 0.840909, count: 132 -Region Avg IOU: 0.649022, Class: 1.000000, Obj: 0.374309, No Obj: 0.002745, Avg Recall: 0.806452, count: 124 -Region Avg IOU: 0.597443, Class: 1.000000, Obj: 0.319050, No Obj: 0.002491, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.637816, Class: 1.000000, Obj: 0.351555, No Obj: 0.002721, Avg Recall: 0.813008, count: 123 -Region Avg IOU: 0.638354, Class: 1.000000, Obj: 0.388346, No Obj: 0.002682, Avg Recall: 0.755556, count: 90 -Region Avg IOU: 0.639708, Class: 1.000000, Obj: 0.386060, No Obj: 0.002338, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.618150, Class: 1.000000, Obj: 0.335271, No Obj: 0.002759, Avg Recall: 0.768595, count: 121 -Region Avg IOU: 0.615250, Class: 1.000000, Obj: 0.298099, No Obj: 0.002763, Avg Recall: 0.768595, count: 121 -3135: 0.507124, 0.461300 avg, 0.001000 rate, 27.643965 seconds, 802560 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.630786, Class: 1.000000, Obj: 0.296106, No Obj: 0.002506, Avg Recall: 0.789474, count: 114 -Region Avg IOU: 0.615300, Class: 1.000000, Obj: 0.323886, No Obj: 0.002534, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.582981, Class: 1.000000, Obj: 0.253132, No Obj: 0.002340, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.644925, Class: 1.000000, Obj: 0.340584, No Obj: 0.002307, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.639189, Class: 1.000000, Obj: 0.336229, No Obj: 0.002487, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.632227, Class: 1.000000, Obj: 0.319744, No Obj: 0.002622, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.679678, Class: 1.000000, Obj: 0.323899, No Obj: 0.002428, Avg Recall: 0.925926, count: 81 -Region Avg IOU: 0.612841, Class: 1.000000, Obj: 0.322274, No Obj: 0.002272, Avg Recall: 0.735849, count: 106 -3136: 0.442129, 0.459383 avg, 0.001000 rate, 29.375801 seconds, 802816 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.595686, Class: 1.000000, Obj: 0.271903, No Obj: 0.002261, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.584495, Class: 1.000000, Obj: 0.283207, No Obj: 0.002106, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.651923, Class: 1.000000, Obj: 0.363903, No Obj: 0.002608, Avg Recall: 0.820755, count: 106 -Region Avg IOU: 0.637600, Class: 1.000000, Obj: 0.291843, No Obj: 0.002558, Avg Recall: 0.805310, count: 113 -Region Avg IOU: 0.583629, Class: 1.000000, Obj: 0.312569, No Obj: 0.002567, Avg Recall: 0.727273, count: 132 -Region Avg IOU: 0.647854, Class: 1.000000, Obj: 0.366140, No Obj: 0.002483, Avg Recall: 0.848485, count: 99 -Region Avg IOU: 0.656966, Class: 1.000000, Obj: 0.305602, No Obj: 0.002404, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.631848, Class: 1.000000, Obj: 0.292800, No Obj: 0.002778, Avg Recall: 0.787402, count: 127 -3137: 0.469484, 0.460393 avg, 0.001000 rate, 30.263153 seconds, 803072 images -Loaded: 0.000057 seconds -Region Avg IOU: 0.639151, Class: 1.000000, Obj: 0.335631, No Obj: 0.002703, Avg Recall: 0.833333, count: 108 -Region Avg IOU: 0.643011, Class: 1.000000, Obj: 0.355059, No Obj: 0.002478, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.623313, Class: 1.000000, Obj: 0.343026, No Obj: 0.002310, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.683764, Class: 1.000000, Obj: 0.335662, No Obj: 0.002512, Avg Recall: 0.871287, count: 101 -Region Avg IOU: 0.600615, Class: 1.000000, Obj: 0.277451, No Obj: 0.002432, Avg Recall: 0.774194, count: 124 -Region Avg IOU: 0.581324, Class: 1.000000, Obj: 0.276532, No Obj: 0.002234, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.683161, Class: 1.000000, Obj: 0.335437, No Obj: 0.002319, Avg Recall: 0.870588, count: 85 -Region Avg IOU: 0.622104, Class: 1.000000, Obj: 0.258126, No Obj: 0.002278, Avg Recall: 0.752066, count: 121 -3138: 0.480024, 0.462356 avg, 0.001000 rate, 28.797813 seconds, 803328 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.618782, Class: 1.000000, Obj: 0.314862, No Obj: 0.002432, Avg Recall: 0.818182, count: 110 -Region Avg IOU: 0.596898, Class: 1.000000, Obj: 0.281375, No Obj: 0.002316, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.618823, Class: 1.000000, Obj: 0.308755, No Obj: 0.002282, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.626535, Class: 1.000000, Obj: 0.337543, No Obj: 0.002441, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.620727, Class: 1.000000, Obj: 0.303417, No Obj: 0.002753, Avg Recall: 0.758389, count: 149 -Region Avg IOU: 0.583957, Class: 1.000000, Obj: 0.271143, No Obj: 0.002316, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.651859, Class: 1.000000, Obj: 0.290687, No Obj: 0.002722, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.607461, Class: 1.000000, Obj: 0.309229, No Obj: 0.002425, Avg Recall: 0.744000, count: 125 -3139: 0.512209, 0.467341 avg, 0.001000 rate, 30.046335 seconds, 803584 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.613382, Class: 1.000000, Obj: 0.253809, No Obj: 0.002349, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.605262, Class: 1.000000, Obj: 0.353662, No Obj: 0.002079, Avg Recall: 0.752941, count: 85 -Region Avg IOU: 0.635073, Class: 1.000000, Obj: 0.337533, No Obj: 0.002158, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.632533, Class: 1.000000, Obj: 0.302445, No Obj: 0.002375, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.624189, Class: 1.000000, Obj: 0.334127, No Obj: 0.002420, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.644525, Class: 1.000000, Obj: 0.350876, No Obj: 0.002615, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.667092, Class: 1.000000, Obj: 0.329553, No Obj: 0.002413, Avg Recall: 0.833333, count: 108 -Region Avg IOU: 0.640954, Class: 1.000000, Obj: 0.314556, No Obj: 0.002315, Avg Recall: 0.759615, count: 104 -3140: 0.434310, 0.464038 avg, 0.001000 rate, 29.632715 seconds, 803840 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.082743 seconds -Region Avg IOU: 0.511227, Class: 1.000000, Obj: 0.255076, No Obj: 0.003504, Avg Recall: 0.614173, count: 127 -Region Avg IOU: 0.549860, Class: 1.000000, Obj: 0.280643, No Obj: 0.002954, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.492925, Class: 1.000000, Obj: 0.260752, No Obj: 0.003070, Avg Recall: 0.548148, count: 135 -Region Avg IOU: 0.584850, Class: 1.000000, Obj: 0.286184, No Obj: 0.003140, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.529042, Class: 1.000000, Obj: 0.251146, No Obj: 0.003304, Avg Recall: 0.598425, count: 127 -Region Avg IOU: 0.473307, Class: 1.000000, Obj: 0.231980, No Obj: 0.003021, Avg Recall: 0.525862, count: 116 -Region Avg IOU: 0.568308, Class: 1.000000, Obj: 0.293715, No Obj: 0.003246, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.564042, Class: 1.000000, Obj: 0.261510, No Obj: 0.003017, Avg Recall: 0.642857, count: 112 -3141: 0.510307, 0.468665 avg, 0.001000 rate, 21.836620 seconds, 804096 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.546107, Class: 1.000000, Obj: 0.303705, No Obj: 0.002973, Avg Recall: 0.651163, count: 86 -Region Avg IOU: 0.561775, Class: 1.000000, Obj: 0.308258, No Obj: 0.003109, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.546566, Class: 1.000000, Obj: 0.298817, No Obj: 0.003755, Avg Recall: 0.606299, count: 127 -Region Avg IOU: 0.579309, Class: 1.000000, Obj: 0.336549, No Obj: 0.003075, Avg Recall: 0.695122, count: 82 -Region Avg IOU: 0.520872, Class: 1.000000, Obj: 0.280921, No Obj: 0.003003, Avg Recall: 0.647059, count: 85 -Region Avg IOU: 0.581831, Class: 1.000000, Obj: 0.314938, No Obj: 0.003137, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.568826, Class: 1.000000, Obj: 0.290738, No Obj: 0.003436, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.579487, Class: 1.000000, Obj: 0.303545, No Obj: 0.003230, Avg Recall: 0.647059, count: 102 -3142: 0.415233, 0.463322 avg, 0.001000 rate, 21.651932 seconds, 804352 images -Loaded: 0.020840 seconds -Region Avg IOU: 0.508043, Class: 1.000000, Obj: 0.288980, No Obj: 0.003566, Avg Recall: 0.559702, count: 134 -Region Avg IOU: 0.516776, Class: 1.000000, Obj: 0.324834, No Obj: 0.003369, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.594402, Class: 1.000000, Obj: 0.344858, No Obj: 0.003835, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.566825, Class: 1.000000, Obj: 0.325905, No Obj: 0.003680, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.594167, Class: 1.000000, Obj: 0.348667, No Obj: 0.003896, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.521875, Class: 1.000000, Obj: 0.311395, No Obj: 0.003596, Avg Recall: 0.587719, count: 114 -Region Avg IOU: 0.558661, Class: 1.000000, Obj: 0.324145, No Obj: 0.003532, Avg Recall: 0.666667, count: 117 -Region Avg IOU: 0.502378, Class: 1.000000, Obj: 0.277677, No Obj: 0.002871, Avg Recall: 0.534884, count: 86 -3143: 0.458318, 0.462821 avg, 0.001000 rate, 23.200899 seconds, 804608 images -Loaded: 0.330441 seconds -Region Avg IOU: 0.534552, Class: 1.000000, Obj: 0.332460, No Obj: 0.003469, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.545737, Class: 1.000000, Obj: 0.332923, No Obj: 0.004416, Avg Recall: 0.641667, count: 120 -Region Avg IOU: 0.526118, Class: 1.000000, Obj: 0.304120, No Obj: 0.003680, Avg Recall: 0.606742, count: 89 -Region Avg IOU: 0.538615, Class: 1.000000, Obj: 0.296082, No Obj: 0.004292, Avg Recall: 0.616000, count: 125 -Region Avg IOU: 0.528534, Class: 1.000000, Obj: 0.357080, No Obj: 0.004178, Avg Recall: 0.553571, count: 112 -Region Avg IOU: 0.541995, Class: 1.000000, Obj: 0.371433, No Obj: 0.004460, Avg Recall: 0.607843, count: 102 -Region Avg IOU: 0.545138, Class: 1.000000, Obj: 0.365785, No Obj: 0.004347, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.487748, Class: 1.000000, Obj: 0.334502, No Obj: 0.003629, Avg Recall: 0.528455, count: 123 -3144: 0.489262, 0.465466 avg, 0.001000 rate, 23.082617 seconds, 804864 images -Loaded: 0.441885 seconds -Region Avg IOU: 0.578829, Class: 1.000000, Obj: 0.421683, No Obj: 0.005083, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.521369, Class: 1.000000, Obj: 0.337189, No Obj: 0.004681, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.585077, Class: 1.000000, Obj: 0.383405, No Obj: 0.004215, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.541509, Class: 1.000000, Obj: 0.421177, No Obj: 0.004421, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.544654, Class: 1.000000, Obj: 0.427138, No Obj: 0.004528, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.528317, Class: 1.000000, Obj: 0.373259, No Obj: 0.005111, Avg Recall: 0.592593, count: 135 -Region Avg IOU: 0.568916, Class: 1.000000, Obj: 0.404359, No Obj: 0.003914, Avg Recall: 0.691358, count: 81 -Region Avg IOU: 0.550206, Class: 1.000000, Obj: 0.360283, No Obj: 0.004524, Avg Recall: 0.674603, count: 126 -3145: 0.446398, 0.463559 avg, 0.001000 rate, 20.294497 seconds, 805120 images -Loaded: 2.308197 seconds -Region Avg IOU: 0.510457, Class: 1.000000, Obj: 0.398711, No Obj: 0.004499, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.521870, Class: 1.000000, Obj: 0.400091, No Obj: 0.004656, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.586034, Class: 1.000000, Obj: 0.411828, No Obj: 0.004661, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.531109, Class: 1.000000, Obj: 0.336753, No Obj: 0.004998, Avg Recall: 0.611940, count: 134 -Region Avg IOU: 0.555551, Class: 1.000000, Obj: 0.380347, No Obj: 0.004800, Avg Recall: 0.643478, count: 115 -Region Avg IOU: 0.550654, Class: 1.000000, Obj: 0.348601, No Obj: 0.004583, Avg Recall: 0.650000, count: 120 -Region Avg IOU: 0.542390, Class: 1.000000, Obj: 0.391901, No Obj: 0.004192, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.527994, Class: 1.000000, Obj: 0.421996, No Obj: 0.004843, Avg Recall: 0.601770, count: 113 -3146: 0.439982, 0.461201 avg, 0.001000 rate, 23.213436 seconds, 805376 images -Loaded: 0.081716 seconds -Region Avg IOU: 0.555049, Class: 1.000000, Obj: 0.394910, No Obj: 0.004602, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.523924, Class: 1.000000, Obj: 0.380896, No Obj: 0.004240, Avg Recall: 0.615385, count: 91 -Region Avg IOU: 0.559476, Class: 1.000000, Obj: 0.404145, No Obj: 0.004365, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.522448, Class: 1.000000, Obj: 0.348779, No Obj: 0.004827, Avg Recall: 0.540323, count: 124 -Region Avg IOU: 0.524728, Class: 1.000000, Obj: 0.376863, No Obj: 0.004458, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.561391, Class: 1.000000, Obj: 0.391357, No Obj: 0.005015, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.545085, Class: 1.000000, Obj: 0.418138, No Obj: 0.004971, Avg Recall: 0.592920, count: 113 -Region Avg IOU: 0.536548, Class: 1.000000, Obj: 0.368414, No Obj: 0.004957, Avg Recall: 0.661157, count: 121 -3147: 0.420441, 0.457125 avg, 0.001000 rate, 22.078754 seconds, 805632 images -Loaded: 0.288274 seconds -Region Avg IOU: 0.546651, Class: 1.000000, Obj: 0.410288, No Obj: 0.005647, Avg Recall: 0.648000, count: 125 -Region Avg IOU: 0.528445, Class: 1.000000, Obj: 0.350010, No Obj: 0.004349, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.529118, Class: 1.000000, Obj: 0.402685, No Obj: 0.004715, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.548268, Class: 1.000000, Obj: 0.430635, No Obj: 0.005083, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.480474, Class: 1.000000, Obj: 0.351796, No Obj: 0.004465, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.509452, Class: 1.000000, Obj: 0.373772, No Obj: 0.004447, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.551386, Class: 1.000000, Obj: 0.395339, No Obj: 0.005364, Avg Recall: 0.653226, count: 124 -Region Avg IOU: 0.560975, Class: 1.000000, Obj: 0.415504, No Obj: 0.004661, Avg Recall: 0.673469, count: 98 -3148: 0.431015, 0.454514 avg, 0.001000 rate, 21.372721 seconds, 805888 images -Loaded: 0.096200 seconds -Region Avg IOU: 0.548478, Class: 1.000000, Obj: 0.368302, No Obj: 0.005169, Avg Recall: 0.629214, count: 89 -Region Avg IOU: 0.551897, Class: 1.000000, Obj: 0.390058, No Obj: 0.004111, Avg Recall: 0.623529, count: 85 -Region Avg IOU: 0.484938, Class: 1.000000, Obj: 0.365965, No Obj: 0.003906, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.542184, Class: 1.000000, Obj: 0.420930, No Obj: 0.005490, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.549783, Class: 1.000000, Obj: 0.420064, No Obj: 0.004272, Avg Recall: 0.600000, count: 90 -Region Avg IOU: 0.599652, Class: 1.000000, Obj: 0.445976, No Obj: 0.004094, Avg Recall: 0.742857, count: 70 -Region Avg IOU: 0.539479, Class: 1.000000, Obj: 0.424214, No Obj: 0.004545, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.523861, Class: 1.000000, Obj: 0.394644, No Obj: 0.005300, Avg Recall: 0.566667, count: 120 -3149: 0.384171, 0.447480 avg, 0.001000 rate, 20.905403 seconds, 806144 images -Loaded: 1.617919 seconds -Region Avg IOU: 0.480238, Class: 1.000000, Obj: 0.343373, No Obj: 0.003427, Avg Recall: 0.571429, count: 105 -Region Avg IOU: 0.523055, Class: 1.000000, Obj: 0.372068, No Obj: 0.004563, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.542872, Class: 1.000000, Obj: 0.311232, No Obj: 0.003892, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.545579, Class: 1.000000, Obj: 0.383262, No Obj: 0.004604, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.480334, Class: 1.000000, Obj: 0.395030, No Obj: 0.003712, Avg Recall: 0.537736, count: 106 -Region Avg IOU: 0.582431, Class: 1.000000, Obj: 0.434776, No Obj: 0.004721, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.536859, Class: 1.000000, Obj: 0.377843, No Obj: 0.004604, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.566786, Class: 1.000000, Obj: 0.381942, No Obj: 0.004173, Avg Recall: 0.719626, count: 107 -3150: 0.444835, 0.447215 avg, 0.001000 rate, 23.766153 seconds, 806400 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000083 seconds -Region Avg IOU: 0.597159, Class: 1.000000, Obj: 0.405294, No Obj: 0.003000, Avg Recall: 0.714286, count: 119 -Region Avg IOU: 0.643585, Class: 1.000000, Obj: 0.520189, No Obj: 0.003210, Avg Recall: 0.840000, count: 100 -Region Avg IOU: 0.602278, Class: 1.000000, Obj: 0.448943, No Obj: 0.003118, Avg Recall: 0.712644, count: 87 -Region Avg IOU: 0.619479, Class: 1.000000, Obj: 0.481112, No Obj: 0.003259, Avg Recall: 0.818182, count: 110 -Region Avg IOU: 0.652534, Class: 1.000000, Obj: 0.427153, No Obj: 0.003297, Avg Recall: 0.822785, count: 79 -Region Avg IOU: 0.617059, Class: 1.000000, Obj: 0.421745, No Obj: 0.003187, Avg Recall: 0.744681, count: 94 -Region Avg IOU: 0.600909, Class: 1.000000, Obj: 0.392654, No Obj: 0.002975, Avg Recall: 0.803571, count: 112 -Region Avg IOU: 0.631200, Class: 1.000000, Obj: 0.410866, No Obj: 0.003632, Avg Recall: 0.794326, count: 141 -3151: 0.474265, 0.449920 avg, 0.001000 rate, 28.182310 seconds, 806656 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.593405, Class: 1.000000, Obj: 0.389394, No Obj: 0.002506, Avg Recall: 0.744681, count: 94 -Region Avg IOU: 0.627974, Class: 1.000000, Obj: 0.335146, No Obj: 0.002738, Avg Recall: 0.803279, count: 122 -Region Avg IOU: 0.623420, Class: 1.000000, Obj: 0.382165, No Obj: 0.002748, Avg Recall: 0.748092, count: 131 -Region Avg IOU: 0.575531, Class: 1.000000, Obj: 0.327201, No Obj: 0.002445, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.563092, Class: 1.000000, Obj: 0.337947, No Obj: 0.002796, Avg Recall: 0.684615, count: 130 -Region Avg IOU: 0.592377, Class: 1.000000, Obj: 0.349740, No Obj: 0.002837, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.615870, Class: 1.000000, Obj: 0.339241, No Obj: 0.003118, Avg Recall: 0.736434, count: 129 -Region Avg IOU: 0.661602, Class: 1.000000, Obj: 0.405269, No Obj: 0.002715, Avg Recall: 0.847059, count: 85 -3152: 0.567266, 0.461655 avg, 0.001000 rate, 29.173347 seconds, 806912 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.636735, Class: 1.000000, Obj: 0.316906, No Obj: 0.002339, Avg Recall: 0.809091, count: 110 -Region Avg IOU: 0.649280, Class: 1.000000, Obj: 0.342321, No Obj: 0.002309, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.615540, Class: 1.000000, Obj: 0.328615, No Obj: 0.002369, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.582031, Class: 1.000000, Obj: 0.351191, No Obj: 0.002099, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.656204, Class: 1.000000, Obj: 0.318118, No Obj: 0.002352, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.625937, Class: 1.000000, Obj: 0.336911, No Obj: 0.002102, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.651967, Class: 1.000000, Obj: 0.327730, No Obj: 0.002333, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.648815, Class: 1.000000, Obj: 0.385852, No Obj: 0.002126, Avg Recall: 0.870588, count: 85 -3153: 0.471408, 0.462630 avg, 0.001000 rate, 29.408716 seconds, 807168 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.611297, Class: 1.000000, Obj: 0.293778, No Obj: 0.002007, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.631420, Class: 1.000000, Obj: 0.258860, No Obj: 0.002261, Avg Recall: 0.792000, count: 125 -Region Avg IOU: 0.633978, Class: 1.000000, Obj: 0.237947, No Obj: 0.001953, Avg Recall: 0.813187, count: 91 -Region Avg IOU: 0.652844, Class: 1.000000, Obj: 0.273672, No Obj: 0.002309, Avg Recall: 0.818182, count: 132 -Region Avg IOU: 0.586001, Class: 1.000000, Obj: 0.258228, No Obj: 0.002127, Avg Recall: 0.679389, count: 131 -Region Avg IOU: 0.607872, Class: 1.000000, Obj: 0.277666, No Obj: 0.002045, Avg Recall: 0.728000, count: 125 -Region Avg IOU: 0.647538, Class: 1.000000, Obj: 0.282153, No Obj: 0.001850, Avg Recall: 0.785714, count: 84 -Region Avg IOU: 0.648223, Class: 1.000000, Obj: 0.296824, No Obj: 0.002090, Avg Recall: 0.767241, count: 116 -3154: 0.498916, 0.466259 avg, 0.001000 rate, 30.820007 seconds, 807424 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.636786, Class: 1.000000, Obj: 0.244241, No Obj: 0.001939, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.669998, Class: 1.000000, Obj: 0.286795, No Obj: 0.001959, Avg Recall: 0.843137, count: 102 -Region Avg IOU: 0.636446, Class: 1.000000, Obj: 0.279182, No Obj: 0.002150, Avg Recall: 0.803279, count: 122 -Region Avg IOU: 0.626505, Class: 1.000000, Obj: 0.236739, No Obj: 0.001924, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.617601, Class: 1.000000, Obj: 0.284399, No Obj: 0.002036, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.642043, Class: 1.000000, Obj: 0.300685, No Obj: 0.001947, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.640056, Class: 1.000000, Obj: 0.293544, No Obj: 0.001841, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.691120, Class: 1.000000, Obj: 0.271529, No Obj: 0.001713, Avg Recall: 0.857143, count: 98 -3155: 0.546285, 0.474261 avg, 0.001000 rate, 29.346169 seconds, 807680 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.625220, Class: 1.000000, Obj: 0.316979, No Obj: 0.001659, Avg Recall: 0.768116, count: 69 -Region Avg IOU: 0.615421, Class: 1.000000, Obj: 0.230839, No Obj: 0.002111, Avg Recall: 0.782609, count: 138 -Region Avg IOU: 0.635838, Class: 1.000000, Obj: 0.242506, No Obj: 0.002102, Avg Recall: 0.776000, count: 125 -Region Avg IOU: 0.632961, Class: 1.000000, Obj: 0.280287, No Obj: 0.002188, Avg Recall: 0.763514, count: 148 -Region Avg IOU: 0.613022, Class: 1.000000, Obj: 0.244936, No Obj: 0.001860, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.611582, Class: 1.000000, Obj: 0.234737, No Obj: 0.002000, Avg Recall: 0.753846, count: 130 -Region Avg IOU: 0.617988, Class: 1.000000, Obj: 0.288430, No Obj: 0.002103, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.612232, Class: 1.000000, Obj: 0.270895, No Obj: 0.001802, Avg Recall: 0.757282, count: 103 -3156: 0.521197, 0.478955 avg, 0.001000 rate, 28.750408 seconds, 807936 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.691102, Class: 1.000000, Obj: 0.299593, No Obj: 0.002126, Avg Recall: 0.866667, count: 90 -Region Avg IOU: 0.644167, Class: 1.000000, Obj: 0.293420, No Obj: 0.002272, Avg Recall: 0.831461, count: 89 -Region Avg IOU: 0.646861, Class: 1.000000, Obj: 0.304448, No Obj: 0.002500, Avg Recall: 0.806452, count: 124 -Region Avg IOU: 0.674295, Class: 1.000000, Obj: 0.285297, No Obj: 0.002280, Avg Recall: 0.877358, count: 106 -Region Avg IOU: 0.613182, Class: 1.000000, Obj: 0.293833, No Obj: 0.002428, Avg Recall: 0.759690, count: 129 -Region Avg IOU: 0.615391, Class: 1.000000, Obj: 0.289215, No Obj: 0.002135, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.647397, Class: 1.000000, Obj: 0.318770, No Obj: 0.002152, Avg Recall: 0.808081, count: 99 -Region Avg IOU: 0.659491, Class: 1.000000, Obj: 0.301944, No Obj: 0.002448, Avg Recall: 0.814516, count: 124 -3157: 0.510835, 0.482143 avg, 0.001000 rate, 29.048964 seconds, 808192 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.641129, Class: 1.000000, Obj: 0.341355, No Obj: 0.002631, Avg Recall: 0.768000, count: 125 -Region Avg IOU: 0.623006, Class: 1.000000, Obj: 0.327603, No Obj: 0.002110, Avg Recall: 0.839080, count: 87 -Region Avg IOU: 0.651381, Class: 1.000000, Obj: 0.307129, No Obj: 0.002326, Avg Recall: 0.806818, count: 88 -Region Avg IOU: 0.622441, Class: 1.000000, Obj: 0.313843, No Obj: 0.002399, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.670408, Class: 1.000000, Obj: 0.303922, No Obj: 0.002680, Avg Recall: 0.886792, count: 106 -Region Avg IOU: 0.622843, Class: 1.000000, Obj: 0.296817, No Obj: 0.002211, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.719485, Class: 1.000000, Obj: 0.360809, No Obj: 0.002329, Avg Recall: 0.920000, count: 75 -Region Avg IOU: 0.636177, Class: 1.000000, Obj: 0.332517, No Obj: 0.002339, Avg Recall: 0.820225, count: 89 -3158: 0.452493, 0.479178 avg, 0.001000 rate, 29.810276 seconds, 808448 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.617009, Class: 1.000000, Obj: 0.302890, No Obj: 0.003235, Avg Recall: 0.736000, count: 125 -Region Avg IOU: 0.640902, Class: 1.000000, Obj: 0.351096, No Obj: 0.002835, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.618058, Class: 1.000000, Obj: 0.322541, No Obj: 0.002914, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.611315, Class: 1.000000, Obj: 0.364526, No Obj: 0.002228, Avg Recall: 0.752809, count: 89 -Region Avg IOU: 0.647899, Class: 1.000000, Obj: 0.376187, No Obj: 0.002735, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.622984, Class: 1.000000, Obj: 0.352813, No Obj: 0.002549, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.608597, Class: 1.000000, Obj: 0.395083, No Obj: 0.002593, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.611056, Class: 1.000000, Obj: 0.350521, No Obj: 0.002976, Avg Recall: 0.776119, count: 134 -3159: 0.475736, 0.478834 avg, 0.001000 rate, 29.738146 seconds, 808704 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.653141, Class: 1.000000, Obj: 0.380494, No Obj: 0.002441, Avg Recall: 0.794872, count: 78 -Region Avg IOU: 0.631979, Class: 1.000000, Obj: 0.362492, No Obj: 0.003245, Avg Recall: 0.801653, count: 121 -Region Avg IOU: 0.657817, Class: 1.000000, Obj: 0.405535, No Obj: 0.003339, Avg Recall: 0.833333, count: 120 -Region Avg IOU: 0.600507, Class: 1.000000, Obj: 0.339332, No Obj: 0.002794, Avg Recall: 0.714286, count: 126 -Region Avg IOU: 0.638843, Class: 1.000000, Obj: 0.365800, No Obj: 0.003150, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.641845, Class: 1.000000, Obj: 0.360782, No Obj: 0.003026, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.675561, Class: 1.000000, Obj: 0.375233, No Obj: 0.002660, Avg Recall: 0.844444, count: 90 -Region Avg IOU: 0.630936, Class: 1.000000, Obj: 0.355094, No Obj: 0.003033, Avg Recall: 0.798450, count: 129 -3160: 0.487406, 0.479691 avg, 0.001000 rate, 28.941875 seconds, 808960 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 13.327366 seconds -Region Avg IOU: 0.439424, Class: 1.000000, Obj: 0.203319, No Obj: 0.003749, Avg Recall: 0.495727, count: 117 -Region Avg IOU: 0.430397, Class: 1.000000, Obj: 0.191899, No Obj: 0.003114, Avg Recall: 0.379845, count: 129 -Region Avg IOU: 0.462478, Class: 1.000000, Obj: 0.231426, No Obj: 0.004007, Avg Recall: 0.515385, count: 130 -Region Avg IOU: 0.417969, Class: 1.000000, Obj: 0.221738, No Obj: 0.003192, Avg Recall: 0.392523, count: 107 -Region Avg IOU: 0.449276, Class: 1.000000, Obj: 0.229125, No Obj: 0.003745, Avg Recall: 0.478261, count: 115 -Region Avg IOU: 0.468688, Class: 1.000000, Obj: 0.258003, No Obj: 0.003620, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.485147, Class: 1.000000, Obj: 0.255415, No Obj: 0.003367, Avg Recall: 0.521277, count: 94 -Region Avg IOU: 0.495476, Class: 1.000000, Obj: 0.244946, No Obj: 0.004112, Avg Recall: 0.590164, count: 122 -3161: 0.623455, 0.494067 avg, 0.001000 rate, 14.588993 seconds, 809216 images -Loaded: 6.672670 seconds -Region Avg IOU: 0.480743, Class: 1.000000, Obj: 0.231962, No Obj: 0.003627, Avg Recall: 0.480000, count: 100 -Region Avg IOU: 0.472688, Class: 1.000000, Obj: 0.272714, No Obj: 0.004138, Avg Recall: 0.539007, count: 141 -Region Avg IOU: 0.466433, Class: 1.000000, Obj: 0.274046, No Obj: 0.003483, Avg Recall: 0.528090, count: 89 -Region Avg IOU: 0.479225, Class: 1.000000, Obj: 0.236463, No Obj: 0.004278, Avg Recall: 0.523438, count: 128 -Region Avg IOU: 0.462331, Class: 1.000000, Obj: 0.224642, No Obj: 0.004196, Avg Recall: 0.470149, count: 134 -Region Avg IOU: 0.432625, Class: 1.000000, Obj: 0.223155, No Obj: 0.003594, Avg Recall: 0.454545, count: 88 -Region Avg IOU: 0.566351, Class: 1.000000, Obj: 0.269047, No Obj: 0.004313, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.510377, Class: 1.000000, Obj: 0.271506, No Obj: 0.004373, Avg Recall: 0.571429, count: 133 -3162: 0.587016, 0.503362 avg, 0.001000 rate, 18.748604 seconds, 809472 images -Loaded: 0.692402 seconds -Region Avg IOU: 0.501512, Class: 1.000000, Obj: 0.296041, No Obj: 0.004966, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.462119, Class: 1.000000, Obj: 0.229282, No Obj: 0.004340, Avg Recall: 0.444444, count: 135 -Region Avg IOU: 0.526912, Class: 1.000000, Obj: 0.266707, No Obj: 0.004306, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.458383, Class: 1.000000, Obj: 0.258231, No Obj: 0.004113, Avg Recall: 0.469231, count: 130 -Region Avg IOU: 0.462906, Class: 1.000000, Obj: 0.275473, No Obj: 0.004731, Avg Recall: 0.480620, count: 129 -Region Avg IOU: 0.457783, Class: 1.000000, Obj: 0.267635, No Obj: 0.004093, Avg Recall: 0.505155, count: 97 -Region Avg IOU: 0.436637, Class: 1.000000, Obj: 0.285807, No Obj: 0.004132, Avg Recall: 0.416667, count: 120 -Region Avg IOU: 0.470009, Class: 1.000000, Obj: 0.250563, No Obj: 0.004302, Avg Recall: 0.452991, count: 117 -3163: 0.557101, 0.508736 avg, 0.001000 rate, 15.635291 seconds, 809728 images -Loaded: 4.969600 seconds -Region Avg IOU: 0.454647, Class: 1.000000, Obj: 0.270787, No Obj: 0.004461, Avg Recall: 0.527273, count: 110 -Region Avg IOU: 0.478511, Class: 1.000000, Obj: 0.276847, No Obj: 0.004932, Avg Recall: 0.557522, count: 113 -Region Avg IOU: 0.454730, Class: 1.000000, Obj: 0.305531, No Obj: 0.005001, Avg Recall: 0.462264, count: 106 -Region Avg IOU: 0.444443, Class: 1.000000, Obj: 0.273180, No Obj: 0.004383, Avg Recall: 0.477064, count: 109 -Region Avg IOU: 0.447874, Class: 1.000000, Obj: 0.295409, No Obj: 0.004426, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.463130, Class: 1.000000, Obj: 0.298472, No Obj: 0.005139, Avg Recall: 0.492064, count: 126 -Region Avg IOU: 0.416606, Class: 1.000000, Obj: 0.276795, No Obj: 0.004443, Avg Recall: 0.428571, count: 119 -Region Avg IOU: 0.427626, Class: 1.000000, Obj: 0.258021, No Obj: 0.004163, Avg Recall: 0.478261, count: 115 -3164: 0.492683, 0.507131 avg, 0.001000 rate, 18.936005 seconds, 809984 images -Loaded: 2.921307 seconds -Region Avg IOU: 0.465914, Class: 1.000000, Obj: 0.305629, No Obj: 0.005426, Avg Recall: 0.491379, count: 116 -Region Avg IOU: 0.494255, Class: 1.000000, Obj: 0.326684, No Obj: 0.006110, Avg Recall: 0.558140, count: 129 -Region Avg IOU: 0.472786, Class: 1.000000, Obj: 0.349499, No Obj: 0.004772, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.473868, Class: 1.000000, Obj: 0.341168, No Obj: 0.005249, Avg Recall: 0.504762, count: 105 -Region Avg IOU: 0.435349, Class: 1.000000, Obj: 0.320299, No Obj: 0.005895, Avg Recall: 0.417323, count: 127 -Region Avg IOU: 0.485834, Class: 1.000000, Obj: 0.343862, No Obj: 0.005134, Avg Recall: 0.528846, count: 104 -Region Avg IOU: 0.483000, Class: 1.000000, Obj: 0.337695, No Obj: 0.005373, Avg Recall: 0.514019, count: 107 -Region Avg IOU: 0.493953, Class: 1.000000, Obj: 0.329512, No Obj: 0.006218, Avg Recall: 0.540741, count: 135 -3165: 0.530117, 0.509429 avg, 0.001000 rate, 14.146744 seconds, 810240 images -Loaded: 5.627797 seconds -Region Avg IOU: 0.434767, Class: 1.000000, Obj: 0.356118, No Obj: 0.005325, Avg Recall: 0.439252, count: 107 -Region Avg IOU: 0.428417, Class: 1.000000, Obj: 0.321017, No Obj: 0.005477, Avg Recall: 0.451327, count: 113 -Region Avg IOU: 0.436686, Class: 1.000000, Obj: 0.324384, No Obj: 0.005462, Avg Recall: 0.438776, count: 98 -Region Avg IOU: 0.401505, Class: 1.000000, Obj: 0.302166, No Obj: 0.005563, Avg Recall: 0.451613, count: 124 -Region Avg IOU: 0.425106, Class: 1.000000, Obj: 0.327061, No Obj: 0.005518, Avg Recall: 0.418803, count: 117 -Region Avg IOU: 0.433609, Class: 1.000000, Obj: 0.317326, No Obj: 0.005317, Avg Recall: 0.469697, count: 132 -Region Avg IOU: 0.520367, Class: 1.000000, Obj: 0.373596, No Obj: 0.005927, Avg Recall: 0.556818, count: 88 -Region Avg IOU: 0.450668, Class: 1.000000, Obj: 0.345058, No Obj: 0.005622, Avg Recall: 0.532110, count: 109 -3166: 0.515775, 0.510064 avg, 0.001000 rate, 15.745386 seconds, 810496 images -Loaded: 5.307461 seconds -Region Avg IOU: 0.470087, Class: 1.000000, Obj: 0.361482, No Obj: 0.005885, Avg Recall: 0.504505, count: 111 -Region Avg IOU: 0.401246, Class: 1.000000, Obj: 0.337617, No Obj: 0.005868, Avg Recall: 0.391667, count: 120 -Region Avg IOU: 0.486367, Class: 1.000000, Obj: 0.346258, No Obj: 0.006604, Avg Recall: 0.540323, count: 124 -Region Avg IOU: 0.455075, Class: 1.000000, Obj: 0.392744, No Obj: 0.005880, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.477422, Class: 1.000000, Obj: 0.340007, No Obj: 0.005786, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.432093, Class: 1.000000, Obj: 0.330348, No Obj: 0.006490, Avg Recall: 0.480916, count: 131 -Region Avg IOU: 0.441676, Class: 1.000000, Obj: 0.372805, No Obj: 0.006257, Avg Recall: 0.455285, count: 123 -Region Avg IOU: 0.433568, Class: 1.000000, Obj: 0.345044, No Obj: 0.006600, Avg Recall: 0.432000, count: 125 -3167: 0.614027, 0.520460 avg, 0.001000 rate, 18.594118 seconds, 810752 images -Loaded: 2.446997 seconds -Region Avg IOU: 0.374456, Class: 1.000000, Obj: 0.345562, No Obj: 0.005615, Avg Recall: 0.414634, count: 123 -Region Avg IOU: 0.421489, Class: 1.000000, Obj: 0.315046, No Obj: 0.006707, Avg Recall: 0.438356, count: 146 -Region Avg IOU: 0.428135, Class: 1.000000, Obj: 0.315837, No Obj: 0.006127, Avg Recall: 0.445205, count: 146 -Region Avg IOU: 0.497586, Class: 1.000000, Obj: 0.396777, No Obj: 0.005785, Avg Recall: 0.580645, count: 93 -Region Avg IOU: 0.410188, Class: 1.000000, Obj: 0.346973, No Obj: 0.005158, Avg Recall: 0.445652, count: 92 -Region Avg IOU: 0.445389, Class: 1.000000, Obj: 0.388020, No Obj: 0.006328, Avg Recall: 0.491525, count: 118 -Region Avg IOU: 0.411002, Class: 1.000000, Obj: 0.327556, No Obj: 0.005614, Avg Recall: 0.398438, count: 128 -Region Avg IOU: 0.414258, Class: 1.000000, Obj: 0.334881, No Obj: 0.005309, Avg Recall: 0.423423, count: 111 -3168: 0.620125, 0.530427 avg, 0.001000 rate, 18.279753 seconds, 811008 images -Loaded: 2.112683 seconds -Region Avg IOU: 0.469252, Class: 1.000000, Obj: 0.324464, No Obj: 0.005951, Avg Recall: 0.487180, count: 117 -Region Avg IOU: 0.475373, Class: 1.000000, Obj: 0.374908, No Obj: 0.006557, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.465667, Class: 1.000000, Obj: 0.347847, No Obj: 0.005730, Avg Recall: 0.538462, count: 117 -Region Avg IOU: 0.475735, Class: 1.000000, Obj: 0.396975, No Obj: 0.006243, Avg Recall: 0.500000, count: 124 -Region Avg IOU: 0.412224, Class: 1.000000, Obj: 0.340900, No Obj: 0.005514, Avg Recall: 0.436893, count: 103 -Region Avg IOU: 0.368030, Class: 1.000000, Obj: 0.303974, No Obj: 0.004139, Avg Recall: 0.368932, count: 103 -Region Avg IOU: 0.425506, Class: 1.000000, Obj: 0.372538, No Obj: 0.005423, Avg Recall: 0.446808, count: 94 -Region Avg IOU: 0.422818, Class: 1.000000, Obj: 0.318449, No Obj: 0.006019, Avg Recall: 0.418182, count: 110 -3169: 0.583241, 0.535708 avg, 0.001000 rate, 17.002441 seconds, 811264 images -Loaded: 1.724902 seconds -Region Avg IOU: 0.398723, Class: 1.000000, Obj: 0.277999, No Obj: 0.004840, Avg Recall: 0.372727, count: 110 -Region Avg IOU: 0.452392, Class: 1.000000, Obj: 0.260164, No Obj: 0.005157, Avg Recall: 0.429825, count: 114 -Region Avg IOU: 0.447564, Class: 1.000000, Obj: 0.317962, No Obj: 0.004389, Avg Recall: 0.476190, count: 84 -Region Avg IOU: 0.421951, Class: 1.000000, Obj: 0.313791, No Obj: 0.004152, Avg Recall: 0.445783, count: 83 -Region Avg IOU: 0.414018, Class: 1.000000, Obj: 0.291480, No Obj: 0.006212, Avg Recall: 0.400000, count: 175 -Region Avg IOU: 0.428118, Class: 1.000000, Obj: 0.358488, No Obj: 0.004947, Avg Recall: 0.406250, count: 96 -Region Avg IOU: 0.460011, Class: 1.000000, Obj: 0.320857, No Obj: 0.005154, Avg Recall: 0.448980, count: 98 -Region Avg IOU: 0.418852, Class: 1.000000, Obj: 0.324324, No Obj: 0.004937, Avg Recall: 0.459184, count: 98 -3170: 0.553668, 0.537504 avg, 0.001000 rate, 18.371927 seconds, 811520 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000069 seconds -Region Avg IOU: 0.565702, Class: 1.000000, Obj: 0.502971, No Obj: 0.003722, Avg Recall: 0.685393, count: 89 -Region Avg IOU: 0.586320, Class: 1.000000, Obj: 0.498011, No Obj: 0.004496, Avg Recall: 0.682353, count: 85 -Region Avg IOU: 0.544516, Class: 1.000000, Obj: 0.358618, No Obj: 0.004112, Avg Recall: 0.643478, count: 115 -Region Avg IOU: 0.592653, Class: 1.000000, Obj: 0.471292, No Obj: 0.004433, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.544050, Class: 1.000000, Obj: 0.439643, No Obj: 0.004412, Avg Recall: 0.604839, count: 124 -Region Avg IOU: 0.546970, Class: 1.000000, Obj: 0.481256, No Obj: 0.003887, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.590193, Class: 1.000000, Obj: 0.436654, No Obj: 0.004966, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.610587, Class: 1.000000, Obj: 0.412287, No Obj: 0.003689, Avg Recall: 0.755814, count: 86 -3171: 0.513122, 0.535066 avg, 0.001000 rate, 26.520187 seconds, 811776 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.598673, Class: 1.000000, Obj: 0.464073, No Obj: 0.003668, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.618837, Class: 1.000000, Obj: 0.402680, No Obj: 0.003870, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.587262, Class: 1.000000, Obj: 0.422956, No Obj: 0.004077, Avg Recall: 0.738095, count: 126 -Region Avg IOU: 0.577590, Class: 1.000000, Obj: 0.455206, No Obj: 0.003833, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.641212, Class: 1.000000, Obj: 0.456091, No Obj: 0.004350, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.591324, Class: 1.000000, Obj: 0.429148, No Obj: 0.004407, Avg Recall: 0.711409, count: 149 -Region Avg IOU: 0.604924, Class: 1.000000, Obj: 0.395047, No Obj: 0.003468, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.615329, Class: 1.000000, Obj: 0.412736, No Obj: 0.004245, Avg Recall: 0.782946, count: 129 -3172: 0.526190, 0.534178 avg, 0.001000 rate, 25.988005 seconds, 812032 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.598206, Class: 1.000000, Obj: 0.356699, No Obj: 0.003070, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.624672, Class: 1.000000, Obj: 0.359722, No Obj: 0.002920, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.555745, Class: 1.000000, Obj: 0.329688, No Obj: 0.003115, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.600617, Class: 1.000000, Obj: 0.367930, No Obj: 0.003481, Avg Recall: 0.738462, count: 130 -Region Avg IOU: 0.600554, Class: 1.000000, Obj: 0.370633, No Obj: 0.003120, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.550750, Class: 1.000000, Obj: 0.378398, No Obj: 0.002764, Avg Recall: 0.644444, count: 90 -Region Avg IOU: 0.581035, Class: 1.000000, Obj: 0.325909, No Obj: 0.002875, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.591957, Class: 1.000000, Obj: 0.335027, No Obj: 0.003119, Avg Recall: 0.723077, count: 130 -3173: 0.487575, 0.529518 avg, 0.001000 rate, 25.875156 seconds, 812288 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.583070, Class: 1.000000, Obj: 0.286011, No Obj: 0.002067, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.604036, Class: 1.000000, Obj: 0.310522, No Obj: 0.002605, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.596110, Class: 1.000000, Obj: 0.306634, No Obj: 0.002602, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.584614, Class: 1.000000, Obj: 0.276065, No Obj: 0.002590, Avg Recall: 0.719697, count: 132 -Region Avg IOU: 0.604660, Class: 1.000000, Obj: 0.279512, No Obj: 0.002311, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.623605, Class: 1.000000, Obj: 0.337261, No Obj: 0.002444, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.583214, Class: 1.000000, Obj: 0.313520, No Obj: 0.002241, Avg Recall: 0.696629, count: 89 -Region Avg IOU: 0.618609, Class: 1.000000, Obj: 0.295040, No Obj: 0.002901, Avg Recall: 0.761062, count: 113 -3174: 0.460841, 0.522650 avg, 0.001000 rate, 26.070473 seconds, 812544 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.600806, Class: 1.000000, Obj: 0.262815, No Obj: 0.002146, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.638406, Class: 1.000000, Obj: 0.234661, No Obj: 0.002053, Avg Recall: 0.806818, count: 88 -Region Avg IOU: 0.620671, Class: 1.000000, Obj: 0.259575, No Obj: 0.002685, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.583518, Class: 1.000000, Obj: 0.269952, No Obj: 0.002114, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.628331, Class: 1.000000, Obj: 0.296883, No Obj: 0.002344, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.613692, Class: 1.000000, Obj: 0.257425, No Obj: 0.002231, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.623611, Class: 1.000000, Obj: 0.271878, No Obj: 0.002242, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.613997, Class: 1.000000, Obj: 0.273000, No Obj: 0.002221, Avg Recall: 0.738739, count: 111 -3175: 0.485320, 0.518917 avg, 0.001000 rate, 26.050880 seconds, 812800 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.638964, Class: 1.000000, Obj: 0.297036, No Obj: 0.002156, Avg Recall: 0.841463, count: 82 -Region Avg IOU: 0.610621, Class: 1.000000, Obj: 0.244143, No Obj: 0.002072, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.611235, Class: 1.000000, Obj: 0.300928, No Obj: 0.001829, Avg Recall: 0.750000, count: 84 -Region Avg IOU: 0.621220, Class: 1.000000, Obj: 0.280930, No Obj: 0.001959, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.654049, Class: 1.000000, Obj: 0.254245, No Obj: 0.002151, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.586738, Class: 1.000000, Obj: 0.219602, No Obj: 0.001883, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.617949, Class: 1.000000, Obj: 0.260198, No Obj: 0.002025, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.618860, Class: 1.000000, Obj: 0.265328, No Obj: 0.002190, Avg Recall: 0.775510, count: 98 -3176: 0.436259, 0.510651 avg, 0.001000 rate, 26.935070 seconds, 813056 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.599607, Class: 1.000000, Obj: 0.242052, No Obj: 0.002240, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.604651, Class: 1.000000, Obj: 0.259795, No Obj: 0.002333, Avg Recall: 0.734375, count: 128 -Region Avg IOU: 0.619326, Class: 1.000000, Obj: 0.262713, No Obj: 0.002160, Avg Recall: 0.707692, count: 130 -Region Avg IOU: 0.591874, Class: 1.000000, Obj: 0.207911, No Obj: 0.002354, Avg Recall: 0.727941, count: 136 -Region Avg IOU: 0.603251, Class: 1.000000, Obj: 0.260437, No Obj: 0.002168, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.656493, Class: 1.000000, Obj: 0.264548, No Obj: 0.002151, Avg Recall: 0.844660, count: 103 -Region Avg IOU: 0.620771, Class: 1.000000, Obj: 0.226970, No Obj: 0.002244, Avg Recall: 0.794643, count: 112 -Region Avg IOU: 0.565651, Class: 1.000000, Obj: 0.242503, No Obj: 0.002057, Avg Recall: 0.636364, count: 121 -3177: 0.528262, 0.512412 avg, 0.001000 rate, 25.720552 seconds, 813312 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.587407, Class: 1.000000, Obj: 0.262983, No Obj: 0.002403, Avg Recall: 0.718750, count: 128 -Region Avg IOU: 0.575797, Class: 1.000000, Obj: 0.257948, No Obj: 0.002498, Avg Recall: 0.661538, count: 130 -Region Avg IOU: 0.633012, Class: 1.000000, Obj: 0.282455, No Obj: 0.002178, Avg Recall: 0.784091, count: 88 -Region Avg IOU: 0.643505, Class: 1.000000, Obj: 0.339832, No Obj: 0.002287, Avg Recall: 0.792683, count: 82 -Region Avg IOU: 0.657765, Class: 1.000000, Obj: 0.294892, No Obj: 0.002019, Avg Recall: 0.828571, count: 70 -Region Avg IOU: 0.582282, Class: 1.000000, Obj: 0.263748, No Obj: 0.002362, Avg Recall: 0.679389, count: 131 -Region Avg IOU: 0.604566, Class: 1.000000, Obj: 0.245294, No Obj: 0.002447, Avg Recall: 0.742647, count: 136 -Region Avg IOU: 0.639621, Class: 1.000000, Obj: 0.274490, No Obj: 0.002678, Avg Recall: 0.803419, count: 117 -3178: 0.502929, 0.511464 avg, 0.001000 rate, 26.208050 seconds, 813568 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.614266, Class: 1.000000, Obj: 0.312445, No Obj: 0.002531, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.590339, Class: 1.000000, Obj: 0.253019, No Obj: 0.002086, Avg Recall: 0.750000, count: 88 -Region Avg IOU: 0.640997, Class: 1.000000, Obj: 0.304396, No Obj: 0.002654, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.635173, Class: 1.000000, Obj: 0.354138, No Obj: 0.002396, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.629752, Class: 1.000000, Obj: 0.285764, No Obj: 0.002919, Avg Recall: 0.790323, count: 124 -Region Avg IOU: 0.592021, Class: 1.000000, Obj: 0.327826, No Obj: 0.002640, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.614791, Class: 1.000000, Obj: 0.303075, No Obj: 0.002456, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.596675, Class: 1.000000, Obj: 0.321872, No Obj: 0.002424, Avg Recall: 0.702128, count: 94 -3179: 0.514946, 0.511812 avg, 0.001000 rate, 27.519655 seconds, 813824 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.584699, Class: 1.000000, Obj: 0.366734, No Obj: 0.003080, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.616348, Class: 1.000000, Obj: 0.369062, No Obj: 0.002972, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.596342, Class: 1.000000, Obj: 0.330626, No Obj: 0.003357, Avg Recall: 0.691176, count: 136 -Region Avg IOU: 0.637450, Class: 1.000000, Obj: 0.330545, No Obj: 0.003054, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.579179, Class: 1.000000, Obj: 0.298093, No Obj: 0.002896, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.666445, Class: 1.000000, Obj: 0.439591, No Obj: 0.002947, Avg Recall: 0.831325, count: 83 -Region Avg IOU: 0.573499, Class: 1.000000, Obj: 0.302781, No Obj: 0.003016, Avg Recall: 0.666667, count: 123 -Region Avg IOU: 0.625219, Class: 1.000000, Obj: 0.384489, No Obj: 0.002985, Avg Recall: 0.734694, count: 98 -3180: 0.497386, 0.510370 avg, 0.001000 rate, 26.786291 seconds, 814080 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000076 seconds -Region Avg IOU: 0.621804, Class: 1.000000, Obj: 0.395821, No Obj: 0.002904, Avg Recall: 0.771084, count: 83 -Region Avg IOU: 0.612031, Class: 1.000000, Obj: 0.386174, No Obj: 0.003013, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.619559, Class: 1.000000, Obj: 0.413947, No Obj: 0.002953, Avg Recall: 0.716049, count: 81 -Region Avg IOU: 0.642408, Class: 1.000000, Obj: 0.441472, No Obj: 0.003127, Avg Recall: 0.794872, count: 78 -Region Avg IOU: 0.664595, Class: 1.000000, Obj: 0.453047, No Obj: 0.003331, Avg Recall: 0.856000, count: 125 -Region Avg IOU: 0.611462, Class: 1.000000, Obj: 0.319476, No Obj: 0.003103, Avg Recall: 0.764228, count: 123 -Region Avg IOU: 0.641425, Class: 1.000000, Obj: 0.433286, No Obj: 0.003195, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.608605, Class: 1.000000, Obj: 0.366299, No Obj: 0.002848, Avg Recall: 0.736842, count: 95 -3181: 0.468621, 0.506195 avg, 0.001000 rate, 29.858944 seconds, 814336 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.626052, Class: 1.000000, Obj: 0.422854, No Obj: 0.003481, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.598893, Class: 1.000000, Obj: 0.417743, No Obj: 0.002893, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.622902, Class: 1.000000, Obj: 0.370793, No Obj: 0.003402, Avg Recall: 0.775862, count: 116 -Region Avg IOU: 0.687641, Class: 1.000000, Obj: 0.445741, No Obj: 0.003650, Avg Recall: 0.854369, count: 103 -Region Avg IOU: 0.678975, Class: 1.000000, Obj: 0.509096, No Obj: 0.003559, Avg Recall: 0.862385, count: 109 -Region Avg IOU: 0.670495, Class: 1.000000, Obj: 0.435687, No Obj: 0.004084, Avg Recall: 0.823944, count: 142 -Region Avg IOU: 0.653823, Class: 1.000000, Obj: 0.472041, No Obj: 0.003169, Avg Recall: 0.840000, count: 100 -Region Avg IOU: 0.639605, Class: 1.000000, Obj: 0.396269, No Obj: 0.003298, Avg Recall: 0.826531, count: 98 -3182: 0.440235, 0.499599 avg, 0.001000 rate, 30.096224 seconds, 814592 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.607025, Class: 1.000000, Obj: 0.390235, No Obj: 0.003453, Avg Recall: 0.716216, count: 148 -Region Avg IOU: 0.631852, Class: 1.000000, Obj: 0.397416, No Obj: 0.003438, Avg Recall: 0.835165, count: 91 -Region Avg IOU: 0.633970, Class: 1.000000, Obj: 0.411865, No Obj: 0.003576, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.575402, Class: 1.000000, Obj: 0.445602, No Obj: 0.002738, Avg Recall: 0.644737, count: 76 -Region Avg IOU: 0.653049, Class: 1.000000, Obj: 0.386397, No Obj: 0.003235, Avg Recall: 0.833333, count: 114 -Region Avg IOU: 0.635573, Class: 1.000000, Obj: 0.453374, No Obj: 0.002990, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.622790, Class: 1.000000, Obj: 0.411600, No Obj: 0.003299, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.607748, Class: 1.000000, Obj: 0.424662, No Obj: 0.002902, Avg Recall: 0.750000, count: 88 -3183: 0.480348, 0.497674 avg, 0.001000 rate, 30.916904 seconds, 814848 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.643231, Class: 1.000000, Obj: 0.424867, No Obj: 0.002821, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.616531, Class: 1.000000, Obj: 0.375017, No Obj: 0.002697, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.652671, Class: 1.000000, Obj: 0.372587, No Obj: 0.002778, Avg Recall: 0.826923, count: 104 -Region Avg IOU: 0.642582, Class: 1.000000, Obj: 0.382455, No Obj: 0.003058, Avg Recall: 0.773438, count: 128 -Region Avg IOU: 0.641855, Class: 1.000000, Obj: 0.395953, No Obj: 0.003501, Avg Recall: 0.809524, count: 126 -Region Avg IOU: 0.656632, Class: 1.000000, Obj: 0.427596, No Obj: 0.003051, Avg Recall: 0.818965, count: 116 -Region Avg IOU: 0.656141, Class: 1.000000, Obj: 0.455037, No Obj: 0.003009, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.608509, Class: 1.000000, Obj: 0.357896, No Obj: 0.002876, Avg Recall: 0.718750, count: 96 -3184: 0.467074, 0.494614 avg, 0.001000 rate, 30.370632 seconds, 815104 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.633869, Class: 1.000000, Obj: 0.367329, No Obj: 0.002742, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.646224, Class: 1.000000, Obj: 0.390948, No Obj: 0.002820, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.636348, Class: 1.000000, Obj: 0.369778, No Obj: 0.002911, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.602329, Class: 1.000000, Obj: 0.332148, No Obj: 0.002894, Avg Recall: 0.688406, count: 138 -Region Avg IOU: 0.617278, Class: 1.000000, Obj: 0.340833, No Obj: 0.002815, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.618860, Class: 1.000000, Obj: 0.360611, No Obj: 0.002748, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.624707, Class: 1.000000, Obj: 0.362927, No Obj: 0.002665, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.639565, Class: 1.000000, Obj: 0.376420, No Obj: 0.002470, Avg Recall: 0.800000, count: 85 -3185: 0.453724, 0.490525 avg, 0.001000 rate, 30.673512 seconds, 815360 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.623072, Class: 1.000000, Obj: 0.309838, No Obj: 0.002468, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.621199, Class: 1.000000, Obj: 0.366963, No Obj: 0.002355, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.673232, Class: 1.000000, Obj: 0.397341, No Obj: 0.002520, Avg Recall: 0.860000, count: 100 -Region Avg IOU: 0.635430, Class: 1.000000, Obj: 0.360839, No Obj: 0.002157, Avg Recall: 0.772727, count: 88 -Region Avg IOU: 0.610638, Class: 1.000000, Obj: 0.349057, No Obj: 0.002365, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.637420, Class: 1.000000, Obj: 0.296962, No Obj: 0.002399, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.611691, Class: 1.000000, Obj: 0.290420, No Obj: 0.002639, Avg Recall: 0.733871, count: 124 -Region Avg IOU: 0.644937, Class: 1.000000, Obj: 0.377179, No Obj: 0.002305, Avg Recall: 0.824742, count: 97 -3186: 0.464001, 0.487872 avg, 0.001000 rate, 30.885616 seconds, 815616 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.621540, Class: 1.000000, Obj: 0.321080, No Obj: 0.002157, Avg Recall: 0.789062, count: 128 -Region Avg IOU: 0.625929, Class: 1.000000, Obj: 0.279900, No Obj: 0.002010, Avg Recall: 0.805195, count: 77 -Region Avg IOU: 0.610481, Class: 1.000000, Obj: 0.302131, No Obj: 0.002385, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.582446, Class: 1.000000, Obj: 0.275900, No Obj: 0.002424, Avg Recall: 0.664384, count: 146 -Region Avg IOU: 0.622146, Class: 1.000000, Obj: 0.297416, No Obj: 0.002546, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.640039, Class: 1.000000, Obj: 0.289057, No Obj: 0.002217, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.638063, Class: 1.000000, Obj: 0.259330, No Obj: 0.002035, Avg Recall: 0.785714, count: 84 -Region Avg IOU: 0.648819, Class: 1.000000, Obj: 0.334020, No Obj: 0.002195, Avg Recall: 0.819277, count: 83 -3187: 0.506452, 0.489730 avg, 0.001000 rate, 29.137108 seconds, 815872 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.651232, Class: 1.000000, Obj: 0.345271, No Obj: 0.001958, Avg Recall: 0.850000, count: 80 -Region Avg IOU: 0.631530, Class: 1.000000, Obj: 0.277413, No Obj: 0.001918, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.599420, Class: 1.000000, Obj: 0.295510, No Obj: 0.002166, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.610825, Class: 1.000000, Obj: 0.333950, No Obj: 0.001902, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.621756, Class: 1.000000, Obj: 0.316835, No Obj: 0.001921, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.641304, Class: 1.000000, Obj: 0.327360, No Obj: 0.002178, Avg Recall: 0.836735, count: 98 -Region Avg IOU: 0.641889, Class: 1.000000, Obj: 0.338015, No Obj: 0.002260, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.575809, Class: 1.000000, Obj: 0.314481, No Obj: 0.002005, Avg Recall: 0.714286, count: 98 -3188: 0.436517, 0.484409 avg, 0.001000 rate, 30.375055 seconds, 816128 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.593769, Class: 1.000000, Obj: 0.291443, No Obj: 0.001947, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.646901, Class: 1.000000, Obj: 0.287076, No Obj: 0.002275, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.630039, Class: 1.000000, Obj: 0.321243, No Obj: 0.002119, Avg Recall: 0.798319, count: 119 -Region Avg IOU: 0.627247, Class: 1.000000, Obj: 0.333846, No Obj: 0.002067, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.664794, Class: 1.000000, Obj: 0.353383, No Obj: 0.001983, Avg Recall: 0.855769, count: 104 -Region Avg IOU: 0.593771, Class: 1.000000, Obj: 0.259292, No Obj: 0.002019, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.645389, Class: 1.000000, Obj: 0.270387, No Obj: 0.002358, Avg Recall: 0.805970, count: 134 -Region Avg IOU: 0.640231, Class: 1.000000, Obj: 0.265987, No Obj: 0.002237, Avg Recall: 0.807692, count: 130 -3189: 0.493664, 0.485334 avg, 0.001000 rate, 30.814335 seconds, 816384 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.633986, Class: 1.000000, Obj: 0.339745, No Obj: 0.002270, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.625761, Class: 1.000000, Obj: 0.293503, No Obj: 0.002392, Avg Recall: 0.796460, count: 113 -Region Avg IOU: 0.584714, Class: 1.000000, Obj: 0.283969, No Obj: 0.002221, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.636624, Class: 1.000000, Obj: 0.326911, No Obj: 0.002268, Avg Recall: 0.800000, count: 120 -Region Avg IOU: 0.639073, Class: 1.000000, Obj: 0.316880, No Obj: 0.002577, Avg Recall: 0.827338, count: 139 -Region Avg IOU: 0.678239, Class: 1.000000, Obj: 0.332641, No Obj: 0.002141, Avg Recall: 0.862745, count: 102 -Region Avg IOU: 0.625854, Class: 1.000000, Obj: 0.309451, No Obj: 0.002175, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.587819, Class: 1.000000, Obj: 0.274377, No Obj: 0.002292, Avg Recall: 0.708661, count: 127 -3190: 0.503934, 0.487194 avg, 0.001000 rate, 30.824924 seconds, 816640 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 12.013975 seconds -Region Avg IOU: 0.615572, Class: 1.000000, Obj: 0.300411, No Obj: 0.002103, Avg Recall: 0.790123, count: 81 -Region Avg IOU: 0.610467, Class: 1.000000, Obj: 0.315613, No Obj: 0.002606, Avg Recall: 0.751825, count: 137 -Region Avg IOU: 0.621675, Class: 1.000000, Obj: 0.299156, No Obj: 0.002330, Avg Recall: 0.778761, count: 113 -Region Avg IOU: 0.650747, Class: 1.000000, Obj: 0.365609, No Obj: 0.002600, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.621733, Class: 1.000000, Obj: 0.344192, No Obj: 0.002626, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.648593, Class: 1.000000, Obj: 0.314097, No Obj: 0.002656, Avg Recall: 0.821429, count: 112 -Region Avg IOU: 0.610730, Class: 1.000000, Obj: 0.299570, No Obj: 0.002500, Avg Recall: 0.727273, count: 143 -Region Avg IOU: 0.672219, Class: 1.000000, Obj: 0.343505, No Obj: 0.002394, Avg Recall: 0.841121, count: 107 -3191: 0.493414, 0.487816 avg, 0.001000 rate, 28.463177 seconds, 816896 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.661575, Class: 1.000000, Obj: 0.404045, No Obj: 0.002577, Avg Recall: 0.800000, count: 85 -Region Avg IOU: 0.594378, Class: 1.000000, Obj: 0.359422, No Obj: 0.002223, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.596942, Class: 1.000000, Obj: 0.318862, No Obj: 0.002899, Avg Recall: 0.680272, count: 147 -Region Avg IOU: 0.591884, Class: 1.000000, Obj: 0.314967, No Obj: 0.002969, Avg Recall: 0.715190, count: 158 -Region Avg IOU: 0.632663, Class: 1.000000, Obj: 0.365424, No Obj: 0.003074, Avg Recall: 0.766423, count: 137 -Region Avg IOU: 0.658009, Class: 1.000000, Obj: 0.306107, No Obj: 0.003147, Avg Recall: 0.803419, count: 117 -Region Avg IOU: 0.662000, Class: 1.000000, Obj: 0.363519, No Obj: 0.002653, Avg Recall: 0.826531, count: 98 -Region Avg IOU: 0.663925, Class: 1.000000, Obj: 0.351366, No Obj: 0.002401, Avg Recall: 0.823529, count: 102 -3192: 0.496192, 0.488654 avg, 0.001000 rate, 28.800352 seconds, 817152 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.642153, Class: 1.000000, Obj: 0.388217, No Obj: 0.002753, Avg Recall: 0.826531, count: 98 -Region Avg IOU: 0.640511, Class: 1.000000, Obj: 0.364319, No Obj: 0.002568, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.603369, Class: 1.000000, Obj: 0.356814, No Obj: 0.002253, Avg Recall: 0.743902, count: 82 -Region Avg IOU: 0.651650, Class: 1.000000, Obj: 0.362914, No Obj: 0.003105, Avg Recall: 0.815789, count: 114 -Region Avg IOU: 0.658530, Class: 1.000000, Obj: 0.361918, No Obj: 0.003018, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.639037, Class: 1.000000, Obj: 0.362655, No Obj: 0.003223, Avg Recall: 0.795082, count: 122 -Region Avg IOU: 0.612405, Class: 1.000000, Obj: 0.370922, No Obj: 0.002636, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.680839, Class: 1.000000, Obj: 0.411360, No Obj: 0.003495, Avg Recall: 0.833333, count: 138 -3193: 0.445033, 0.484292 avg, 0.001000 rate, 29.597277 seconds, 817408 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.628825, Class: 1.000000, Obj: 0.393484, No Obj: 0.002743, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.612793, Class: 1.000000, Obj: 0.313911, No Obj: 0.002932, Avg Recall: 0.754098, count: 122 -Region Avg IOU: 0.629254, Class: 1.000000, Obj: 0.335700, No Obj: 0.002730, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.625408, Class: 1.000000, Obj: 0.323338, No Obj: 0.003118, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.602422, Class: 1.000000, Obj: 0.369623, No Obj: 0.002970, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.678973, Class: 1.000000, Obj: 0.358911, No Obj: 0.002965, Avg Recall: 0.876106, count: 113 -Region Avg IOU: 0.630972, Class: 1.000000, Obj: 0.367025, No Obj: 0.003063, Avg Recall: 0.780488, count: 123 -Region Avg IOU: 0.664033, Class: 1.000000, Obj: 0.367317, No Obj: 0.002992, Avg Recall: 0.855769, count: 104 -3194: 0.488810, 0.484744 avg, 0.001000 rate, 29.750572 seconds, 817664 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.630628, Class: 1.000000, Obj: 0.348872, No Obj: 0.003006, Avg Recall: 0.784000, count: 125 -Region Avg IOU: 0.597284, Class: 1.000000, Obj: 0.355554, No Obj: 0.002581, Avg Recall: 0.744444, count: 90 -Region Avg IOU: 0.597555, Class: 1.000000, Obj: 0.344446, No Obj: 0.002752, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.660497, Class: 1.000000, Obj: 0.429167, No Obj: 0.003523, Avg Recall: 0.829457, count: 129 -Region Avg IOU: 0.622822, Class: 1.000000, Obj: 0.405611, No Obj: 0.002822, Avg Recall: 0.789474, count: 114 -Region Avg IOU: 0.672746, Class: 1.000000, Obj: 0.410643, No Obj: 0.003162, Avg Recall: 0.830000, count: 100 -Region Avg IOU: 0.635646, Class: 1.000000, Obj: 0.391396, No Obj: 0.002938, Avg Recall: 0.763780, count: 127 -Region Avg IOU: 0.696633, Class: 1.000000, Obj: 0.391541, No Obj: 0.003309, Avg Recall: 0.907407, count: 108 -3195: 0.522182, 0.488488 avg, 0.001000 rate, 29.765841 seconds, 817920 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.635135, Class: 1.000000, Obj: 0.322009, No Obj: 0.002644, Avg Recall: 0.784091, count: 88 -Region Avg IOU: 0.642584, Class: 1.000000, Obj: 0.409681, No Obj: 0.002953, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.637849, Class: 1.000000, Obj: 0.388264, No Obj: 0.002722, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.640362, Class: 1.000000, Obj: 0.358983, No Obj: 0.002909, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.654777, Class: 1.000000, Obj: 0.351110, No Obj: 0.003190, Avg Recall: 0.844037, count: 109 -Region Avg IOU: 0.611785, Class: 1.000000, Obj: 0.370035, No Obj: 0.002849, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.655511, Class: 1.000000, Obj: 0.419297, No Obj: 0.002913, Avg Recall: 0.849462, count: 93 -Region Avg IOU: 0.672540, Class: 1.000000, Obj: 0.387433, No Obj: 0.003132, Avg Recall: 0.837607, count: 117 -3196: 0.459713, 0.485610 avg, 0.001000 rate, 29.404634 seconds, 818176 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.633380, Class: 1.000000, Obj: 0.374142, No Obj: 0.002933, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.668662, Class: 1.000000, Obj: 0.379677, No Obj: 0.002795, Avg Recall: 0.822222, count: 90 -Region Avg IOU: 0.653831, Class: 1.000000, Obj: 0.455979, No Obj: 0.002663, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.661303, Class: 1.000000, Obj: 0.349790, No Obj: 0.003087, Avg Recall: 0.823529, count: 136 -Region Avg IOU: 0.677663, Class: 1.000000, Obj: 0.410643, No Obj: 0.002795, Avg Recall: 0.843137, count: 102 -Region Avg IOU: 0.630668, Class: 1.000000, Obj: 0.383400, No Obj: 0.002357, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.631934, Class: 1.000000, Obj: 0.379193, No Obj: 0.002923, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.654053, Class: 1.000000, Obj: 0.376414, No Obj: 0.003290, Avg Recall: 0.844828, count: 116 -3197: 0.441866, 0.481236 avg, 0.001000 rate, 29.005833 seconds, 818432 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.595386, Class: 1.000000, Obj: 0.354034, No Obj: 0.002529, Avg Recall: 0.666667, count: 81 -Region Avg IOU: 0.635479, Class: 1.000000, Obj: 0.344474, No Obj: 0.002657, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.634230, Class: 1.000000, Obj: 0.446149, No Obj: 0.002718, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.642499, Class: 1.000000, Obj: 0.367211, No Obj: 0.003063, Avg Recall: 0.808000, count: 125 -Region Avg IOU: 0.635245, Class: 1.000000, Obj: 0.338057, No Obj: 0.002831, Avg Recall: 0.815126, count: 119 -Region Avg IOU: 0.667452, Class: 1.000000, Obj: 0.419939, No Obj: 0.002420, Avg Recall: 0.797872, count: 94 -Region Avg IOU: 0.681480, Class: 1.000000, Obj: 0.379429, No Obj: 0.002600, Avg Recall: 0.847059, count: 85 -Region Avg IOU: 0.636353, Class: 1.000000, Obj: 0.370322, No Obj: 0.002633, Avg Recall: 0.772727, count: 110 -3198: 0.426953, 0.475807 avg, 0.001000 rate, 29.852039 seconds, 818688 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.613922, Class: 1.000000, Obj: 0.343085, No Obj: 0.002415, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.662661, Class: 1.000000, Obj: 0.315055, No Obj: 0.002472, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.605312, Class: 1.000000, Obj: 0.330029, No Obj: 0.002586, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.621354, Class: 1.000000, Obj: 0.316565, No Obj: 0.002789, Avg Recall: 0.753623, count: 138 -Region Avg IOU: 0.602911, Class: 1.000000, Obj: 0.333579, No Obj: 0.002560, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.621854, Class: 1.000000, Obj: 0.356959, No Obj: 0.002673, Avg Recall: 0.768000, count: 125 -Region Avg IOU: 0.631940, Class: 1.000000, Obj: 0.327770, No Obj: 0.002695, Avg Recall: 0.761194, count: 134 -Region Avg IOU: 0.650485, Class: 1.000000, Obj: 0.347070, No Obj: 0.002384, Avg Recall: 0.837209, count: 86 -3199: 0.495862, 0.477813 avg, 0.001000 rate, 29.404152 seconds, 818944 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.653613, Class: 1.000000, Obj: 0.372855, No Obj: 0.002255, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.647883, Class: 1.000000, Obj: 0.314719, No Obj: 0.002577, Avg Recall: 0.806452, count: 124 -Region Avg IOU: 0.655414, Class: 1.000000, Obj: 0.350536, No Obj: 0.002387, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.623441, Class: 1.000000, Obj: 0.332401, No Obj: 0.002631, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.640958, Class: 1.000000, Obj: 0.368584, No Obj: 0.002278, Avg Recall: 0.787500, count: 80 -Region Avg IOU: 0.663628, Class: 1.000000, Obj: Saving weights to ./person9300/backup/tyvp_93_9c512_3200.weights -0.337051, No Obj: 0.002661, Avg Recall: 0.848214, count: 112 -Region Avg IOU: 0.648302, Class: 1.000000, Obj: 0.301443, No Obj: 0.002536, Avg Recall: 0.828358, count: 134 -Region Avg IOU: 0.630077, Class: 1.000000, Obj: 0.366737, No Obj: 0.002428, Avg Recall: 0.759259, count: 108 -3200: 0.494605, 0.479492 avg, 0.001000 rate, 29.249355 seconds, 819200 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 15.116986 seconds -Region Avg IOU: 0.562457, Class: 1.000000, Obj: 0.307273, No Obj: 0.002589, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.564919, Class: 1.000000, Obj: 0.316242, No Obj: 0.002528, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.565600, Class: 1.000000, Obj: 0.279166, No Obj: 0.002703, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.569621, Class: 1.000000, Obj: 0.291968, No Obj: 0.002655, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.553792, Class: 1.000000, Obj: 0.308616, No Obj: 0.003115, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.571740, Class: 1.000000, Obj: 0.322284, No Obj: 0.002617, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.582110, Class: 1.000000, Obj: 0.332465, No Obj: 0.002933, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.515034, Class: 1.000000, Obj: 0.265634, No Obj: 0.002563, Avg Recall: 0.579365, count: 126 -3201: 0.447386, 0.476281 avg, 0.001000 rate, 24.574892 seconds, 819456 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.626373, Class: 1.000000, Obj: 0.337635, No Obj: 0.003164, Avg Recall: 0.808000, count: 125 -Region Avg IOU: 0.553124, Class: 1.000000, Obj: 0.330270, No Obj: 0.003241, Avg Recall: 0.648276, count: 145 -Region Avg IOU: 0.613570, Class: 1.000000, Obj: 0.348651, No Obj: 0.002900, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.571214, Class: 1.000000, Obj: 0.303682, No Obj: 0.003132, Avg Recall: 0.674074, count: 135 -Region Avg IOU: 0.560450, Class: 1.000000, Obj: 0.325767, No Obj: 0.002442, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.572496, Class: 1.000000, Obj: 0.325049, No Obj: 0.002711, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.524122, Class: 1.000000, Obj: 0.321300, No Obj: 0.002599, Avg Recall: 0.536585, count: 82 -Region Avg IOU: 0.555877, Class: 1.000000, Obj: 0.333074, No Obj: 0.002721, Avg Recall: 0.634615, count: 104 -3202: 0.427103, 0.471364 avg, 0.001000 rate, 25.701853 seconds, 819712 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.564485, Class: 1.000000, Obj: 0.341786, No Obj: 0.003015, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.538217, Class: 1.000000, Obj: 0.364937, No Obj: 0.002587, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.580680, Class: 1.000000, Obj: 0.349037, No Obj: 0.002956, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.606577, Class: 1.000000, Obj: 0.374124, No Obj: 0.002972, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.569878, Class: 1.000000, Obj: 0.306076, No Obj: 0.002624, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.553792, Class: 1.000000, Obj: 0.312557, No Obj: 0.003127, Avg Recall: 0.637795, count: 127 -Region Avg IOU: 0.558384, Class: 1.000000, Obj: 0.344768, No Obj: 0.003301, Avg Recall: 0.648855, count: 131 -Region Avg IOU: 0.635754, Class: 1.000000, Obj: 0.377325, No Obj: 0.002824, Avg Recall: 0.782178, count: 101 -3203: 0.435182, 0.467745 avg, 0.001000 rate, 25.471691 seconds, 819968 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.610566, Class: 1.000000, Obj: 0.400892, No Obj: 0.003138, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.531749, Class: 1.000000, Obj: 0.330332, No Obj: 0.003335, Avg Recall: 0.598540, count: 137 -Region Avg IOU: 0.583287, Class: 1.000000, Obj: 0.397815, No Obj: 0.002888, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.569782, Class: 1.000000, Obj: 0.304451, No Obj: 0.003331, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.571450, Class: 1.000000, Obj: 0.329442, No Obj: 0.003445, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.565872, Class: 1.000000, Obj: 0.334682, No Obj: 0.002777, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.569286, Class: 1.000000, Obj: 0.306085, No Obj: 0.003055, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.601448, Class: 1.000000, Obj: 0.364375, No Obj: 0.003671, Avg Recall: 0.700935, count: 107 -3204: 0.451040, 0.466075 avg, 0.001000 rate, 26.133158 seconds, 820224 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.533873, Class: 1.000000, Obj: 0.318362, No Obj: 0.003547, Avg Recall: 0.632000, count: 125 -Region Avg IOU: 0.607031, Class: 1.000000, Obj: 0.394895, No Obj: 0.003182, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.571327, Class: 1.000000, Obj: 0.330823, No Obj: 0.003020, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.588515, Class: 1.000000, Obj: 0.366591, No Obj: 0.003155, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.592175, Class: 1.000000, Obj: 0.354847, No Obj: 0.003793, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.556647, Class: 1.000000, Obj: 0.352226, No Obj: 0.003521, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.583559, Class: 1.000000, Obj: 0.391768, No Obj: 0.003295, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.568982, Class: 1.000000, Obj: 0.347132, No Obj: 0.003446, Avg Recall: 0.697479, count: 119 -3205: 0.463908, 0.465858 avg, 0.001000 rate, 25.584589 seconds, 820480 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.547655, Class: 1.000000, Obj: 0.344588, No Obj: 0.003157, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.625546, Class: 1.000000, Obj: 0.428893, No Obj: 0.003390, Avg Recall: 0.750000, count: 88 -Region Avg IOU: 0.638955, Class: 1.000000, Obj: 0.394095, No Obj: 0.003478, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.565455, Class: 1.000000, Obj: 0.352791, No Obj: 0.003601, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.527241, Class: 1.000000, Obj: 0.347747, No Obj: 0.003519, Avg Recall: 0.556452, count: 124 -Region Avg IOU: 0.609423, Class: 1.000000, Obj: 0.404662, No Obj: 0.003467, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.568220, Class: 1.000000, Obj: 0.288333, No Obj: 0.003652, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.546650, Class: 1.000000, Obj: 0.351187, No Obj: 0.004057, Avg Recall: 0.650000, count: 140 -3206: 0.467628, 0.466035 avg, 0.001000 rate, 26.017197 seconds, 820736 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.609717, Class: 1.000000, Obj: 0.385537, No Obj: 0.004000, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.613008, Class: 1.000000, Obj: 0.392629, No Obj: 0.003229, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.593006, Class: 1.000000, Obj: 0.394050, No Obj: 0.003908, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.548963, Class: 1.000000, Obj: 0.380831, No Obj: 0.003686, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.561646, Class: 1.000000, Obj: 0.375001, No Obj: 0.003621, Avg Recall: 0.656250, count: 128 -Region Avg IOU: 0.569564, Class: 1.000000, Obj: 0.359365, No Obj: 0.004096, Avg Recall: 0.661538, count: 130 -Region Avg IOU: 0.585189, Class: 1.000000, Obj: 0.342639, No Obj: 0.003464, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.581447, Class: 1.000000, Obj: 0.363325, No Obj: 0.003450, Avg Recall: 0.669421, count: 121 -3207: 0.472780, 0.466710 avg, 0.001000 rate, 25.216969 seconds, 820992 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.571961, Class: 1.000000, Obj: 0.353057, No Obj: 0.003340, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.586563, Class: 1.000000, Obj: 0.362718, No Obj: 0.003428, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.544457, Class: 1.000000, Obj: 0.377826, No Obj: 0.003766, Avg Recall: 0.620438, count: 137 -Region Avg IOU: 0.640712, Class: 1.000000, Obj: 0.370605, No Obj: 0.004896, Avg Recall: 0.779528, count: 127 -Region Avg IOU: 0.551023, Class: 1.000000, Obj: 0.419548, No Obj: 0.003842, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.581668, Class: 1.000000, Obj: 0.421731, No Obj: 0.003861, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.578177, Class: 1.000000, Obj: 0.387324, No Obj: 0.004061, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.594523, Class: 1.000000, Obj: 0.335933, No Obj: 0.003679, Avg Recall: 0.696970, count: 99 -3208: 0.439742, 0.464013 avg, 0.001000 rate, 25.159660 seconds, 821248 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.623868, Class: 1.000000, Obj: 0.377559, No Obj: 0.003617, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.544473, Class: 1.000000, Obj: 0.381674, No Obj: 0.003378, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.625548, Class: 1.000000, Obj: 0.397089, No Obj: 0.003603, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.623764, Class: 1.000000, Obj: 0.389008, No Obj: 0.003704, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.584160, Class: 1.000000, Obj: 0.432392, No Obj: 0.003811, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.580185, Class: 1.000000, Obj: 0.378516, No Obj: 0.003171, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.610391, Class: 1.000000, Obj: 0.457249, No Obj: 0.003120, Avg Recall: 0.730769, count: 78 -Region Avg IOU: 0.624626, Class: 1.000000, Obj: 0.388759, No Obj: 0.004123, Avg Recall: 0.739496, count: 119 -3209: 0.374341, 0.455046 avg, 0.001000 rate, 25.369316 seconds, 821504 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.568743, Class: 1.000000, Obj: 0.353679, No Obj: 0.003473, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.548281, Class: 1.000000, Obj: 0.350997, No Obj: 0.003959, Avg Recall: 0.656000, count: 125 -Region Avg IOU: 0.558732, Class: 1.000000, Obj: 0.385282, No Obj: 0.003831, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.588089, Class: 1.000000, Obj: 0.355004, No Obj: 0.003426, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.597823, Class: 1.000000, Obj: 0.372263, No Obj: 0.004376, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.592090, Class: 1.000000, Obj: 0.369328, No Obj: 0.003372, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.594799, Class: 1.000000, Obj: 0.372102, No Obj: 0.003628, Avg Recall: 0.750000, count: 88 -Region Avg IOU: 0.594386, Class: 1.000000, Obj: 0.376378, No Obj: 0.004272, Avg Recall: 0.669118, count: 136 -3210: 0.487542, 0.458295 avg, 0.001000 rate, 25.353556 seconds, 821760 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.184691 seconds -Region Avg IOU: 0.502790, Class: 1.000000, Obj: 0.279779, No Obj: 0.003643, Avg Recall: 0.556701, count: 97 -Region Avg IOU: 0.494356, Class: 1.000000, Obj: 0.293615, No Obj: 0.003877, Avg Recall: 0.544000, count: 125 -Region Avg IOU: 0.467113, Class: 1.000000, Obj: 0.275329, No Obj: 0.003231, Avg Recall: 0.489796, count: 98 -Region Avg IOU: 0.471318, Class: 1.000000, Obj: 0.285077, No Obj: 0.003286, Avg Recall: 0.462366, count: 93 -Region Avg IOU: 0.526094, Class: 1.000000, Obj: 0.299315, No Obj: 0.004200, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.456468, Class: 1.000000, Obj: 0.276870, No Obj: 0.003207, Avg Recall: 0.471910, count: 89 -Region Avg IOU: 0.522329, Class: 1.000000, Obj: 0.300940, No Obj: 0.003333, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.532066, Class: 1.000000, Obj: 0.251970, No Obj: 0.003423, Avg Recall: 0.612903, count: 93 -3211: 0.469921, 0.459458 avg, 0.001000 rate, 18.740505 seconds, 822016 images -Loaded: 1.559544 seconds -Region Avg IOU: 0.523092, Class: 1.000000, Obj: 0.270502, No Obj: 0.003962, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.503736, Class: 1.000000, Obj: 0.275150, No Obj: 0.003499, Avg Recall: 0.530973, count: 113 -Region Avg IOU: 0.501121, Class: 1.000000, Obj: 0.254715, No Obj: 0.003782, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.554593, Class: 1.000000, Obj: 0.311462, No Obj: 0.003505, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.472160, Class: 1.000000, Obj: 0.277386, No Obj: 0.003976, Avg Recall: 0.520000, count: 125 -Region Avg IOU: 0.539341, Class: 1.000000, Obj: 0.247266, No Obj: 0.004078, Avg Recall: 0.608392, count: 143 -Region Avg IOU: 0.499133, Class: 1.000000, Obj: 0.294609, No Obj: 0.003028, Avg Recall: 0.537500, count: 80 -Region Avg IOU: 0.532113, Class: 1.000000, Obj: 0.300193, No Obj: 0.003874, Avg Recall: 0.625000, count: 120 -3212: 0.478177, 0.461330 avg, 0.001000 rate, 19.408821 seconds, 822272 images -Loaded: 1.958105 seconds -Region Avg IOU: 0.564644, Class: 1.000000, Obj: 0.323079, No Obj: 0.003560, Avg Recall: 0.676056, count: 71 -Region Avg IOU: 0.513358, Class: 1.000000, Obj: 0.297327, No Obj: 0.003620, Avg Recall: 0.577778, count: 90 -Region Avg IOU: 0.522704, Class: 1.000000, Obj: 0.297132, No Obj: 0.003706, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.557560, Class: 1.000000, Obj: 0.322329, No Obj: 0.003820, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.485150, Class: 1.000000, Obj: 0.292671, No Obj: 0.003916, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.526395, Class: 1.000000, Obj: 0.293975, No Obj: 0.003742, Avg Recall: 0.576577, count: 111 -Region Avg IOU: 0.482612, Class: 1.000000, Obj: 0.282868, No Obj: 0.003372, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.528393, Class: 1.000000, Obj: 0.301209, No Obj: 0.003697, Avg Recall: 0.651786, count: 112 -3213: 0.458227, 0.461020 avg, 0.001000 rate, 21.254627 seconds, 822528 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.512310, Class: 1.000000, Obj: 0.355009, No Obj: 0.003768, Avg Recall: 0.534884, count: 86 -Region Avg IOU: 0.510911, Class: 1.000000, Obj: 0.287311, No Obj: 0.003362, Avg Recall: 0.541667, count: 96 -Region Avg IOU: 0.447795, Class: 1.000000, Obj: 0.251028, No Obj: 0.003395, Avg Recall: 0.463636, count: 110 -Region Avg IOU: 0.570494, Class: 1.000000, Obj: 0.393065, No Obj: 0.003710, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.530880, Class: 1.000000, Obj: 0.282998, No Obj: 0.004231, Avg Recall: 0.598361, count: 122 -Region Avg IOU: 0.521022, Class: 1.000000, Obj: 0.284135, No Obj: 0.003575, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.520842, Class: 1.000000, Obj: 0.310678, No Obj: 0.004109, Avg Recall: 0.609375, count: 128 -Region Avg IOU: 0.508737, Class: 1.000000, Obj: 0.301736, No Obj: 0.003430, Avg Recall: 0.575758, count: 99 -3214: 0.454676, 0.460385 avg, 0.001000 rate, 21.242975 seconds, 822784 images -Loaded: 0.558438 seconds -Region Avg IOU: 0.439616, Class: 1.000000, Obj: 0.301174, No Obj: 0.003309, Avg Recall: 0.460674, count: 89 -Region Avg IOU: 0.496222, Class: 1.000000, Obj: 0.349852, No Obj: 0.004044, Avg Recall: 0.563636, count: 110 -Region Avg IOU: 0.497933, Class: 1.000000, Obj: 0.314092, No Obj: 0.004261, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.500537, Class: 1.000000, Obj: 0.306339, No Obj: 0.004435, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.538995, Class: 1.000000, Obj: 0.312294, No Obj: 0.004339, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.468893, Class: 1.000000, Obj: 0.287970, No Obj: 0.003587, Avg Recall: 0.524752, count: 101 -Region Avg IOU: 0.502368, Class: 1.000000, Obj: 0.324627, No Obj: 0.004113, Avg Recall: 0.556452, count: 124 -Region Avg IOU: 0.539115, Class: 1.000000, Obj: 0.292763, No Obj: 0.003911, Avg Recall: 0.627451, count: 102 -3215: 0.495595, 0.463906 avg, 0.001000 rate, 18.266809 seconds, 823040 images -Loaded: 2.260679 seconds -Region Avg IOU: 0.505041, Class: 1.000000, Obj: 0.323291, No Obj: 0.004419, Avg Recall: 0.584746, count: 118 -Region Avg IOU: 0.531674, Class: 1.000000, Obj: 0.333500, No Obj: 0.004579, Avg Recall: 0.613636, count: 88 -Region Avg IOU: 0.499521, Class: 1.000000, Obj: 0.363428, No Obj: 0.004110, Avg Recall: 0.548673, count: 113 -Region Avg IOU: 0.580826, Class: 1.000000, Obj: 0.343940, No Obj: 0.003948, Avg Recall: 0.695122, count: 82 -Region Avg IOU: 0.520400, Class: 1.000000, Obj: 0.379661, No Obj: 0.004038, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.559638, Class: 1.000000, Obj: 0.380910, No Obj: 0.003964, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.494634, Class: 1.000000, Obj: 0.317435, No Obj: 0.004006, Avg Recall: 0.525862, count: 116 -Region Avg IOU: 0.544160, Class: 1.000000, Obj: 0.392720, No Obj: 0.004286, Avg Recall: 0.631068, count: 103 -3216: 0.414321, 0.458948 avg, 0.001000 rate, 20.870926 seconds, 823296 images -Loaded: 1.414442 seconds -Region Avg IOU: 0.500712, Class: 1.000000, Obj: 0.323860, No Obj: 0.005248, Avg Recall: 0.566434, count: 143 -Region Avg IOU: 0.500496, Class: 1.000000, Obj: 0.359249, No Obj: 0.005127, Avg Recall: 0.533835, count: 133 -Region Avg IOU: 0.509951, Class: 1.000000, Obj: 0.322075, No Obj: 0.004996, Avg Recall: 0.569231, count: 130 -Region Avg IOU: 0.520851, Class: 1.000000, Obj: 0.344774, No Obj: 0.005413, Avg Recall: 0.598485, count: 132 -Region Avg IOU: 0.476031, Class: 1.000000, Obj: 0.320791, No Obj: 0.003864, Avg Recall: 0.533333, count: 90 -Region Avg IOU: 0.476415, Class: 1.000000, Obj: 0.343211, No Obj: 0.003570, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.499287, Class: 1.000000, Obj: 0.377393, No Obj: 0.003867, Avg Recall: 0.558442, count: 77 -Region Avg IOU: 0.546408, Class: 1.000000, Obj: 0.353175, No Obj: 0.004217, Avg Recall: 0.609756, count: 82 -3217: 0.418785, 0.454931 avg, 0.001000 rate, 20.572119 seconds, 823552 images -Loaded: 1.005854 seconds -Region Avg IOU: 0.490129, Class: 1.000000, Obj: 0.367272, No Obj: 0.004185, Avg Recall: 0.500000, count: 110 -Region Avg IOU: 0.535617, Class: 1.000000, Obj: 0.392382, No Obj: 0.004323, Avg Recall: 0.549451, count: 91 -Region Avg IOU: 0.518275, Class: 1.000000, Obj: 0.333922, No Obj: 0.004547, Avg Recall: 0.559633, count: 109 -Region Avg IOU: 0.528713, Class: 1.000000, Obj: 0.332711, No Obj: 0.004116, Avg Recall: 0.606742, count: 89 -Region Avg IOU: 0.513524, Class: 1.000000, Obj: 0.361371, No Obj: 0.004947, Avg Recall: 0.587302, count: 126 -Region Avg IOU: 0.477691, Class: 1.000000, Obj: 0.338094, No Obj: 0.004645, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.511822, Class: 1.000000, Obj: 0.329341, No Obj: 0.004733, Avg Recall: 0.600000, count: 135 -Region Avg IOU: 0.518128, Class: 1.000000, Obj: 0.377002, No Obj: 0.004681, Avg Recall: 0.596491, count: 114 -3218: 0.444182, 0.453856 avg, 0.001000 rate, 22.956371 seconds, 823808 images -Loaded: 0.480001 seconds -Region Avg IOU: 0.483485, Class: 1.000000, Obj: 0.352062, No Obj: 0.004486, Avg Recall: 0.559633, count: 109 -Region Avg IOU: 0.480113, Class: 1.000000, Obj: 0.329351, No Obj: 0.004137, Avg Recall: 0.500000, count: 106 -Region Avg IOU: 0.525202, Class: 1.000000, Obj: 0.383693, No Obj: 0.005240, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.459645, Class: 1.000000, Obj: 0.357385, No Obj: 0.004197, Avg Recall: 0.454545, count: 110 -Region Avg IOU: 0.521892, Class: 1.000000, Obj: 0.403175, No Obj: 0.004465, Avg Recall: 0.670732, count: 82 -Region Avg IOU: 0.473149, Class: 1.000000, Obj: 0.364344, No Obj: 0.003954, Avg Recall: 0.515152, count: 99 -Region Avg IOU: 0.459361, Class: 1.000000, Obj: 0.330964, No Obj: 0.004549, Avg Recall: 0.508333, count: 120 -Region Avg IOU: 0.488821, Class: 1.000000, Obj: 0.356458, No Obj: 0.004430, Avg Recall: 0.548387, count: 93 -3219: 0.456302, 0.454101 avg, 0.001000 rate, 21.208260 seconds, 824064 images -Loaded: 0.953197 seconds -Region Avg IOU: 0.514003, Class: 1.000000, Obj: 0.317181, No Obj: 0.005153, Avg Recall: 0.546154, count: 130 -Region Avg IOU: 0.502594, Class: 1.000000, Obj: 0.381915, No Obj: 0.004993, Avg Recall: 0.512821, count: 117 -Region Avg IOU: 0.502595, Class: 1.000000, Obj: 0.342626, No Obj: 0.005001, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.465500, Class: 1.000000, Obj: 0.336793, No Obj: 0.004461, Avg Recall: 0.486957, count: 115 -Region Avg IOU: 0.510569, Class: 1.000000, Obj: 0.384559, No Obj: 0.005124, Avg Recall: 0.619403, count: 134 -Region Avg IOU: 0.491805, Class: 1.000000, Obj: 0.402839, No Obj: 0.004058, Avg Recall: 0.541176, count: 85 -Region Avg IOU: 0.479490, Class: 1.000000, Obj: 0.328186, No Obj: 0.005652, Avg Recall: 0.541353, count: 133 -Region Avg IOU: 0.498673, Class: 1.000000, Obj: 0.373624, No Obj: 0.004883, Avg Recall: 0.588710, count: 124 -3220: 0.568932, 0.465584 avg, 0.001000 rate, 21.293236 seconds, 824320 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.328176 seconds -Region Avg IOU: 0.523524, Class: 1.000000, Obj: 0.316127, No Obj: 0.004765, Avg Recall: 0.587719, count: 114 -Region Avg IOU: 0.536569, Class: 1.000000, Obj: 0.353291, No Obj: 0.005324, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.470097, Class: 1.000000, Obj: 0.299821, No Obj: 0.004264, Avg Recall: 0.481132, count: 106 -Region Avg IOU: 0.498437, Class: 1.000000, Obj: 0.313619, No Obj: 0.004170, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.501633, Class: 1.000000, Obj: 0.329631, No Obj: 0.004779, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.510058, Class: 1.000000, Obj: 0.342946, No Obj: 0.004691, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.540640, Class: 1.000000, Obj: 0.370901, No Obj: 0.004431, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.505576, Class: 1.000000, Obj: 0.316295, No Obj: 0.004412, Avg Recall: 0.601504, count: 133 -3221: 0.483469, 0.467373 avg, 0.001000 rate, 19.636473 seconds, 824576 images -Loaded: 1.012875 seconds -Region Avg IOU: 0.520238, Class: 1.000000, Obj: 0.360439, No Obj: 0.004450, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.481364, Class: 1.000000, Obj: 0.309083, No Obj: 0.004268, Avg Recall: 0.484127, count: 126 -Region Avg IOU: 0.454807, Class: 1.000000, Obj: 0.293809, No Obj: 0.004953, Avg Recall: 0.530769, count: 130 -Region Avg IOU: 0.491417, Class: 1.000000, Obj: 0.386735, No Obj: 0.003737, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.496077, Class: 1.000000, Obj: 0.310645, No Obj: 0.004326, Avg Recall: 0.593220, count: 118 -Region Avg IOU: 0.536881, Class: 1.000000, Obj: 0.341279, No Obj: 0.004754, Avg Recall: 0.620690, count: 87 -Region Avg IOU: 0.505233, Class: 1.000000, Obj: 0.324107, No Obj: 0.004831, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.525557, Class: 1.000000, Obj: 0.328117, No Obj: 0.005090, Avg Recall: 0.612403, count: 129 -3222: 0.476037, 0.468239 avg, 0.001000 rate, 20.145737 seconds, 824832 images -Loaded: 0.841211 seconds -Region Avg IOU: 0.532792, Class: 1.000000, Obj: 0.309512, No Obj: 0.004717, Avg Recall: 0.593220, count: 118 -Region Avg IOU: 0.503367, Class: 1.000000, Obj: 0.326903, No Obj: 0.004453, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.518721, Class: 1.000000, Obj: 0.338014, No Obj: 0.003982, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.468334, Class: 1.000000, Obj: 0.291833, No Obj: 0.004418, Avg Recall: 0.531915, count: 141 -Region Avg IOU: 0.478266, Class: 1.000000, Obj: 0.294765, No Obj: 0.004459, Avg Recall: 0.489655, count: 145 -Region Avg IOU: 0.443913, Class: 1.000000, Obj: 0.302982, No Obj: 0.003406, Avg Recall: 0.500000, count: 92 -Region Avg IOU: 0.522032, Class: 1.000000, Obj: 0.341006, No Obj: 0.004692, Avg Recall: 0.580153, count: 131 -Region Avg IOU: 0.492699, Class: 1.000000, Obj: 0.352651, No Obj: 0.003804, Avg Recall: 0.593750, count: 96 -3223: 0.492944, 0.470710 avg, 0.001000 rate, 22.244764 seconds, 825088 images -Loaded: 0.000051 seconds -Region Avg IOU: 0.577770, Class: 1.000000, Obj: 0.360255, No Obj: 0.004461, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.498582, Class: 1.000000, Obj: 0.342664, No Obj: 0.003865, Avg Recall: 0.550000, count: 100 -Region Avg IOU: 0.478525, Class: 1.000000, Obj: 0.289859, No Obj: 0.003786, Avg Recall: 0.566372, count: 113 -Region Avg IOU: 0.479009, Class: 1.000000, Obj: 0.326237, No Obj: 0.004038, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.510727, Class: 1.000000, Obj: 0.317673, No Obj: 0.003600, Avg Recall: 0.543210, count: 81 -Region Avg IOU: 0.511261, Class: 1.000000, Obj: 0.345051, No Obj: 0.003912, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.535115, Class: 1.000000, Obj: 0.311123, No Obj: 0.004225, Avg Recall: 0.593496, count: 123 -Region Avg IOU: 0.462080, Class: 1.000000, Obj: 0.311803, No Obj: 0.003780, Avg Recall: 0.468750, count: 96 -3224: 0.450740, 0.468713 avg, 0.001000 rate, 20.503588 seconds, 825344 images -Loaded: 1.432397 seconds -Region Avg IOU: 0.561353, Class: 1.000000, Obj: 0.350158, No Obj: 0.004309, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.499446, Class: 1.000000, Obj: 0.297024, No Obj: 0.004075, Avg Recall: 0.561538, count: 130 -Region Avg IOU: 0.476559, Class: 1.000000, Obj: 0.341692, No Obj: 0.003937, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.517936, Class: 1.000000, Obj: 0.320198, No Obj: 0.004239, Avg Recall: 0.571429, count: 112 -Region Avg IOU: 0.529894, Class: 1.000000, Obj: 0.315805, No Obj: 0.004075, Avg Recall: 0.587719, count: 114 -Region Avg IOU: 0.497739, Class: 1.000000, Obj: 0.297904, No Obj: 0.004329, Avg Recall: 0.538462, count: 117 -Region Avg IOU: 0.520977, Class: 1.000000, Obj: 0.309900, No Obj: 0.004103, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.509669, Class: 1.000000, Obj: 0.289425, No Obj: 0.004642, Avg Recall: 0.576577, count: 111 -3225: 0.479533, 0.469795 avg, 0.001000 rate, 17.422853 seconds, 825600 images -Loaded: 3.624132 seconds -Region Avg IOU: 0.513838, Class: 1.000000, Obj: 0.330030, No Obj: 0.004161, Avg Recall: 0.575000, count: 120 -Region Avg IOU: 0.578594, Class: 1.000000, Obj: 0.342864, No Obj: 0.004691, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.478280, Class: 1.000000, Obj: 0.306110, No Obj: 0.003566, Avg Recall: 0.500000, count: 92 -Region Avg IOU: 0.479599, Class: 1.000000, Obj: 0.318526, No Obj: 0.003967, Avg Recall: 0.480769, count: 104 -Region Avg IOU: 0.536718, Class: 1.000000, Obj: 0.288356, No Obj: 0.003840, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.574908, Class: 1.000000, Obj: 0.326375, No Obj: 0.004179, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.466281, Class: 1.000000, Obj: 0.298274, No Obj: 0.003799, Avg Recall: 0.486486, count: 111 -Region Avg IOU: 0.506589, Class: 1.000000, Obj: 0.333720, No Obj: 0.004158, Avg Recall: 0.580247, count: 81 -3226: 0.463324, 0.469148 avg, 0.001000 rate, 21.273460 seconds, 825856 images -Loaded: 0.815748 seconds -Region Avg IOU: 0.542028, Class: 1.000000, Obj: 0.356318, No Obj: 0.003836, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.469796, Class: 1.000000, Obj: 0.294880, No Obj: 0.003572, Avg Recall: 0.516129, count: 93 -Region Avg IOU: 0.548660, Class: 1.000000, Obj: 0.319353, No Obj: 0.004237, Avg Recall: 0.644628, count: 121 -Region Avg IOU: 0.477405, Class: 1.000000, Obj: 0.284501, No Obj: 0.003990, Avg Recall: 0.488889, count: 135 -Region Avg IOU: 0.584773, Class: 1.000000, Obj: 0.378198, No Obj: 0.003901, Avg Recall: 0.681818, count: 66 -Region Avg IOU: 0.530827, Class: 1.000000, Obj: 0.320551, No Obj: 0.003731, Avg Recall: 0.601770, count: 113 -Region Avg IOU: 0.494226, Class: 1.000000, Obj: 0.312649, No Obj: 0.004261, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.435942, Class: 1.000000, Obj: 0.276919, No Obj: 0.003857, Avg Recall: 0.469565, count: 115 -3227: 0.428021, 0.465035 avg, 0.001000 rate, 18.584875 seconds, 826112 images -Loaded: 2.434335 seconds -Region Avg IOU: 0.521686, Class: 1.000000, Obj: 0.330959, No Obj: 0.004154, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.496449, Class: 1.000000, Obj: 0.322367, No Obj: 0.004054, Avg Recall: 0.500000, count: 112 -Region Avg IOU: 0.480310, Class: 1.000000, Obj: 0.316059, No Obj: 0.003742, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.541948, Class: 1.000000, Obj: 0.309056, No Obj: 0.004252, Avg Recall: 0.586777, count: 121 -Region Avg IOU: 0.515639, Class: 1.000000, Obj: 0.293523, No Obj: 0.004499, Avg Recall: 0.597015, count: 134 -Region Avg IOU: 0.525204, Class: 1.000000, Obj: 0.327432, No Obj: 0.004331, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.545488, Class: 1.000000, Obj: 0.284919, No Obj: 0.003622, Avg Recall: 0.658228, count: 79 -Region Avg IOU: 0.546993, Class: 1.000000, Obj: 0.375898, No Obj: 0.004695, Avg Recall: 0.605505, count: 109 -3228: 0.418390, 0.460370 avg, 0.001000 rate, 19.794498 seconds, 826368 images -Loaded: 3.871183 seconds -Region Avg IOU: 0.578945, Class: 1.000000, Obj: 0.336177, No Obj: 0.004392, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.542985, Class: 1.000000, Obj: 0.339027, No Obj: 0.004439, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.523615, Class: 1.000000, Obj: 0.339436, No Obj: 0.004177, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.482860, Class: 1.000000, Obj: 0.266596, No Obj: 0.004143, Avg Recall: 0.519231, count: 104 -Region Avg IOU: 0.528018, Class: 1.000000, Obj: 0.353770, No Obj: 0.004401, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.548955, Class: 1.000000, Obj: 0.377877, No Obj: 0.004590, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.467633, Class: 1.000000, Obj: 0.317474, No Obj: 0.003841, Avg Recall: 0.468750, count: 96 -Region Avg IOU: 0.485609, Class: 1.000000, Obj: 0.317967, No Obj: 0.004420, Avg Recall: 0.512821, count: 117 -3229: 0.466050, 0.460938 avg, 0.001000 rate, 21.531780 seconds, 826624 images -Loaded: 1.179728 seconds -Region Avg IOU: 0.542777, Class: 1.000000, Obj: 0.348146, No Obj: 0.004409, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.536624, Class: 1.000000, Obj: 0.343913, No Obj: 0.004063, Avg Recall: 0.615385, count: 78 -Region Avg IOU: 0.498032, Class: 1.000000, Obj: 0.311837, No Obj: 0.004137, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.418723, Class: 1.000000, Obj: 0.297921, No Obj: 0.004117, Avg Recall: 0.396825, count: 126 -Region Avg IOU: 0.515433, Class: 1.000000, Obj: 0.364871, No Obj: 0.005114, Avg Recall: 0.604839, count: 124 -Region Avg IOU: 0.520373, Class: 1.000000, Obj: 0.361203, No Obj: 0.003816, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.536977, Class: 1.000000, Obj: 0.381760, No Obj: 0.004554, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.512827, Class: 1.000000, Obj: 0.337976, No Obj: 0.004441, Avg Recall: 0.526718, count: 131 -3230: 0.466362, 0.461481 avg, 0.001000 rate, 19.244362 seconds, 826880 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000081 seconds -Region Avg IOU: 0.607136, Class: 1.000000, Obj: 0.457812, No Obj: 0.004081, Avg Recall: 0.763780, count: 127 -Region Avg IOU: 0.592125, Class: 1.000000, Obj: 0.432185, No Obj: 0.003783, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.617276, Class: 1.000000, Obj: 0.386114, No Obj: 0.003250, Avg Recall: 0.804878, count: 82 -Region Avg IOU: 0.602458, Class: 1.000000, Obj: 0.383250, No Obj: 0.003164, Avg Recall: 0.712329, count: 73 -Region Avg IOU: 0.615665, Class: 1.000000, Obj: 0.455499, No Obj: 0.003389, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.626595, Class: 1.000000, Obj: 0.494603, No Obj: 0.003224, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.661949, Class: 1.000000, Obj: 0.493890, No Obj: 0.003336, Avg Recall: 0.876543, count: 81 -Region Avg IOU: 0.640538, Class: 1.000000, Obj: 0.467254, No Obj: 0.003351, Avg Recall: 0.800000, count: 65 -3231: 0.399814, 0.455314 avg, 0.001000 rate, 27.921335 seconds, 827136 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.665891, Class: 1.000000, Obj: 0.438527, No Obj: 0.003511, Avg Recall: 0.825688, count: 109 -Region Avg IOU: 0.606393, Class: 1.000000, Obj: 0.405323, No Obj: 0.003528, Avg Recall: 0.731707, count: 123 -Region Avg IOU: 0.642525, Class: 1.000000, Obj: 0.386029, No Obj: 0.003818, Avg Recall: 0.789474, count: 114 -Region Avg IOU: 0.627867, Class: 1.000000, Obj: 0.423094, No Obj: 0.003644, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.598050, Class: 1.000000, Obj: 0.363557, No Obj: 0.003512, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.594027, Class: 1.000000, Obj: 0.420529, No Obj: 0.003294, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.623215, Class: 1.000000, Obj: 0.431350, No Obj: 0.003218, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.584714, Class: 1.000000, Obj: 0.437502, No Obj: 0.003103, Avg Recall: 0.677083, count: 96 -3232: 0.458388, 0.455621 avg, 0.001000 rate, 29.035837 seconds, 827392 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.648196, Class: 1.000000, Obj: 0.357411, No Obj: 0.002982, Avg Recall: 0.847619, count: 105 -Region Avg IOU: 0.608189, Class: 1.000000, Obj: 0.363234, No Obj: 0.003409, Avg Recall: 0.750000, count: 132 -Region Avg IOU: 0.666333, Class: 1.000000, Obj: 0.407076, No Obj: 0.003300, Avg Recall: 0.882353, count: 102 -Region Avg IOU: 0.595520, Class: 1.000000, Obj: 0.401775, No Obj: 0.002692, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.565571, Class: 1.000000, Obj: 0.408586, No Obj: 0.002825, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.599623, Class: 1.000000, Obj: 0.366857, No Obj: 0.003017, Avg Recall: 0.705426, count: 129 -Region Avg IOU: 0.647279, Class: 1.000000, Obj: 0.371039, No Obj: 0.003245, Avg Recall: 0.824427, count: 131 -Region Avg IOU: 0.637670, Class: 1.000000, Obj: 0.373557, No Obj: 0.003013, Avg Recall: 0.776786, count: 112 -3233: 0.482845, 0.458344 avg, 0.001000 rate, 27.701468 seconds, 827648 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.652916, Class: 1.000000, Obj: 0.416275, No Obj: 0.002599, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.665356, Class: 1.000000, Obj: 0.352551, No Obj: 0.002423, Avg Recall: 0.853933, count: 89 -Region Avg IOU: 0.639719, Class: 1.000000, Obj: 0.329471, No Obj: 0.002636, Avg Recall: 0.842520, count: 127 -Region Avg IOU: 0.643245, Class: 1.000000, Obj: 0.386466, No Obj: 0.002448, Avg Recall: 0.820225, count: 89 -Region Avg IOU: 0.619992, Class: 1.000000, Obj: 0.363833, No Obj: 0.002470, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.612122, Class: 1.000000, Obj: 0.332921, No Obj: 0.002699, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.619691, Class: 1.000000, Obj: 0.364781, No Obj: 0.002559, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.605261, Class: 1.000000, Obj: 0.355616, No Obj: 0.002648, Avg Recall: 0.747826, count: 115 -3234: 0.449159, 0.457425 avg, 0.001000 rate, 28.655371 seconds, 827904 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.648135, Class: 1.000000, Obj: 0.294163, No Obj: 0.002419, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.624459, Class: 1.000000, Obj: 0.329390, No Obj: 0.002458, Avg Recall: 0.744186, count: 129 -Region Avg IOU: 0.608536, Class: 1.000000, Obj: 0.271357, No Obj: 0.002293, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.633764, Class: 1.000000, Obj: 0.334388, No Obj: 0.002266, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.627356, Class: 1.000000, Obj: 0.323560, No Obj: 0.002069, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.634395, Class: 1.000000, Obj: 0.331388, No Obj: 0.002003, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.642799, Class: 1.000000, Obj: 0.324611, No Obj: 0.001951, Avg Recall: 0.783784, count: 74 -Region Avg IOU: 0.650416, Class: 1.000000, Obj: 0.289213, No Obj: 0.002365, Avg Recall: 0.814815, count: 108 -3235: 0.477459, 0.459429 avg, 0.001000 rate, 28.279388 seconds, 828160 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.607713, Class: 1.000000, Obj: 0.315635, No Obj: 0.002202, Avg Recall: 0.735537, count: 121 -Region Avg IOU: 0.639169, Class: 1.000000, Obj: 0.348004, No Obj: 0.002247, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.653249, Class: 1.000000, Obj: 0.288921, No Obj: 0.002276, Avg Recall: 0.836364, count: 110 -Region Avg IOU: 0.647145, Class: 1.000000, Obj: 0.343505, No Obj: 0.002252, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.630870, Class: 1.000000, Obj: 0.330479, No Obj: 0.002173, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.625782, Class: 1.000000, Obj: 0.306568, No Obj: 0.001972, Avg Recall: 0.770115, count: 87 -Region Avg IOU: 0.568586, Class: 1.000000, Obj: 0.243189, No Obj: 0.002196, Avg Recall: 0.685039, count: 127 -Region Avg IOU: 0.613435, Class: 1.000000, Obj: 0.298845, No Obj: 0.002232, Avg Recall: 0.756522, count: 115 -3236: 0.471499, 0.460636 avg, 0.001000 rate, 28.256149 seconds, 828416 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.628049, Class: 1.000000, Obj: 0.284058, No Obj: 0.002162, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.581344, Class: 1.000000, Obj: 0.270682, No Obj: 0.002024, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.647407, Class: 1.000000, Obj: 0.321850, No Obj: 0.002277, Avg Recall: 0.836364, count: 110 -Region Avg IOU: 0.593695, Class: 1.000000, Obj: 0.328690, No Obj: 0.001878, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.618953, Class: 1.000000, Obj: 0.317614, No Obj: 0.002579, Avg Recall: 0.765152, count: 132 -Region Avg IOU: 0.631953, Class: 1.000000, Obj: 0.270159, No Obj: 0.002087, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.606205, Class: 1.000000, Obj: 0.277317, No Obj: 0.002009, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.625992, Class: 1.000000, Obj: 0.280481, No Obj: 0.002395, Avg Recall: 0.742188, count: 128 -3237: 0.526391, 0.467211 avg, 0.001000 rate, 28.625811 seconds, 828672 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.606419, Class: 1.000000, Obj: 0.273899, No Obj: 0.002087, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.641640, Class: 1.000000, Obj: 0.302595, No Obj: 0.002129, Avg Recall: 0.772727, count: 88 -Region Avg IOU: 0.659630, Class: 1.000000, Obj: 0.329231, No Obj: 0.002104, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.671051, Class: 1.000000, Obj: 0.344875, No Obj: 0.002139, Avg Recall: 0.855556, count: 90 -Region Avg IOU: 0.674094, Class: 1.000000, Obj: 0.365979, No Obj: 0.002270, Avg Recall: 0.846154, count: 104 -Region Avg IOU: 0.645371, Class: 1.000000, Obj: 0.307890, No Obj: 0.002457, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.648061, Class: 1.000000, Obj: 0.305233, No Obj: 0.002185, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.595965, Class: 1.000000, Obj: 0.274671, No Obj: 0.002213, Avg Recall: 0.766355, count: 107 -3238: 0.469391, 0.467429 avg, 0.001000 rate, 28.357117 seconds, 828928 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.644604, Class: 1.000000, Obj: 0.413787, No Obj: 0.002131, Avg Recall: 0.822785, count: 79 -Region Avg IOU: 0.629059, Class: 1.000000, Obj: 0.315381, No Obj: 0.002420, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.615114, Class: 1.000000, Obj: 0.314939, No Obj: 0.002561, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.649058, Class: 1.000000, Obj: 0.302976, No Obj: 0.002508, Avg Recall: 0.809160, count: 131 -Region Avg IOU: 0.639658, Class: 1.000000, Obj: 0.348703, No Obj: 0.002475, Avg Recall: 0.791304, count: 115 -Region Avg IOU: 0.613067, Class: 1.000000, Obj: 0.313711, No Obj: 0.002144, Avg Recall: 0.769841, count: 126 -Region Avg IOU: 0.635979, Class: 1.000000, Obj: 0.302474, No Obj: 0.002235, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.611213, Class: 1.000000, Obj: 0.253377, No Obj: 0.002021, Avg Recall: 0.755556, count: 90 -3239: 0.449623, 0.465649 avg, 0.001000 rate, 27.778345 seconds, 829184 images -Loaded: 0.000118 seconds -Region Avg IOU: 0.655735, Class: 1.000000, Obj: 0.365708, No Obj: 0.002887, Avg Recall: 0.841584, count: 101 -Region Avg IOU: 0.622483, Class: 1.000000, Obj: 0.285596, No Obj: 0.002393, Avg Recall: 0.770492, count: 122 -Region Avg IOU: 0.591397, Class: 1.000000, Obj: 0.313347, No Obj: 0.002560, Avg Recall: 0.717742, count: 124 -Region Avg IOU: 0.653024, Class: 1.000000, Obj: 0.399142, No Obj: 0.002933, Avg Recall: 0.817391, count: 115 -Region Avg IOU: 0.655436, Class: 1.000000, Obj: 0.376538, No Obj: 0.002655, Avg Recall: 0.850467, count: 107 -Region Avg IOU: 0.618053, Class: 1.000000, Obj: 0.328102, No Obj: 0.002629, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.681326, Class: 1.000000, Obj: 0.355844, No Obj: 0.002428, Avg Recall: 0.892857, count: 84 -Region Avg IOU: 0.607522, Class: 1.000000, Obj: 0.338951, No Obj: 0.002560, Avg Recall: 0.711864, count: 118 -3240: 0.484180, 0.467502 avg, 0.001000 rate, 28.706907 seconds, 829440 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 16.104460 seconds -Region Avg IOU: 0.616919, Class: 1.000000, Obj: 0.363032, No Obj: 0.002453, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.639103, Class: 1.000000, Obj: 0.366716, No Obj: 0.002835, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.638868, Class: 1.000000, Obj: 0.399731, No Obj: 0.002680, Avg Recall: 0.836957, count: 92 -Region Avg IOU: 0.613348, Class: 1.000000, Obj: 0.369786, No Obj: 0.002632, Avg Recall: 0.761905, count: 84 -Region Avg IOU: 0.640285, Class: 1.000000, Obj: 0.358923, No Obj: 0.002566, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.652219, Class: 1.000000, Obj: 0.399581, No Obj: 0.003073, Avg Recall: 0.808333, count: 120 -Region Avg IOU: 0.673945, Class: 1.000000, Obj: 0.387277, No Obj: 0.003497, Avg Recall: 0.860656, count: 122 -Region Avg IOU: 0.651116, Class: 1.000000, Obj: 0.399164, No Obj: 0.002840, Avg Recall: 0.840708, count: 113 -3241: 0.428373, 0.463589 avg, 0.001000 rate, 28.931314 seconds, 829696 images -Loaded: 0.000125 seconds -Region Avg IOU: 0.633603, Class: 1.000000, Obj: 0.340196, No Obj: 0.002698, Avg Recall: 0.777778, count: 126 -Region Avg IOU: 0.610657, Class: 1.000000, Obj: 0.341809, No Obj: 0.002944, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.611975, Class: 1.000000, Obj: 0.377794, No Obj: 0.003085, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.638348, Class: 1.000000, Obj: 0.359203, No Obj: 0.003228, Avg Recall: 0.780488, count: 123 -Region Avg IOU: 0.651882, Class: 1.000000, Obj: 0.380997, No Obj: 0.003119, Avg Recall: 0.824742, count: 97 -Region Avg IOU: 0.659943, Class: 1.000000, Obj: 0.394148, No Obj: 0.003284, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.667560, Class: 1.000000, Obj: 0.395040, No Obj: 0.002993, Avg Recall: 0.861386, count: 101 -Region Avg IOU: 0.607632, Class: 1.000000, Obj: 0.333206, No Obj: 0.002988, Avg Recall: 0.725664, count: 113 -3242: 0.505453, 0.467775 avg, 0.001000 rate, 29.267702 seconds, 829952 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.625470, Class: 1.000000, Obj: 0.430039, No Obj: 0.003176, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.614372, Class: 1.000000, Obj: 0.348206, No Obj: 0.003227, Avg Recall: 0.759124, count: 137 -Region Avg IOU: 0.619199, Class: 1.000000, Obj: 0.326704, No Obj: 0.003268, Avg Recall: 0.773438, count: 128 -Region Avg IOU: 0.666822, Class: 1.000000, Obj: 0.475401, No Obj: 0.003077, Avg Recall: 0.840425, count: 94 -Region Avg IOU: 0.623928, Class: 1.000000, Obj: 0.393581, No Obj: 0.003117, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.641690, Class: 1.000000, Obj: 0.366579, No Obj: 0.003348, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.626895, Class: 1.000000, Obj: 0.450836, No Obj: 0.003153, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.646600, Class: 1.000000, Obj: 0.378466, No Obj: 0.003338, Avg Recall: 0.778688, count: 122 -3243: 0.485932, 0.469591 avg, 0.001000 rate, 27.463957 seconds, 830208 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.642764, Class: 1.000000, Obj: 0.416952, No Obj: 0.002655, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.609162, Class: 1.000000, Obj: 0.360393, No Obj: 0.002995, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.612971, Class: 1.000000, Obj: 0.406471, No Obj: 0.002987, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.645794, Class: 1.000000, Obj: 0.423745, No Obj: 0.003112, Avg Recall: 0.813187, count: 91 -Region Avg IOU: 0.625062, Class: 1.000000, Obj: 0.361522, No Obj: 0.002957, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.637561, Class: 1.000000, Obj: 0.345813, No Obj: 0.002808, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.632751, Class: 1.000000, Obj: 0.434269, No Obj: 0.002628, Avg Recall: 0.795181, count: 83 -Region Avg IOU: 0.634532, Class: 1.000000, Obj: 0.441660, No Obj: 0.002584, Avg Recall: 0.782178, count: 101 -3244: 0.425651, 0.465197 avg, 0.001000 rate, 27.589220 seconds, 830464 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.618556, Class: 1.000000, Obj: 0.377847, No Obj: 0.002897, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.656170, Class: 1.000000, Obj: 0.355483, No Obj: 0.002919, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.634852, Class: 1.000000, Obj: 0.415036, No Obj: 0.003043, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.640157, Class: 1.000000, Obj: 0.385731, No Obj: 0.003122, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.605981, Class: 1.000000, Obj: 0.389588, No Obj: 0.002915, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.664332, Class: 1.000000, Obj: 0.438661, No Obj: 0.003211, Avg Recall: 0.851485, count: 101 -Region Avg IOU: 0.655833, Class: 1.000000, Obj: 0.411341, No Obj: 0.002651, Avg Recall: 0.827957, count: 93 -Region Avg IOU: 0.625656, Class: 1.000000, Obj: 0.384700, No Obj: 0.003158, Avg Recall: 0.771429, count: 105 -3245: 0.414745, 0.460152 avg, 0.001000 rate, 28.247992 seconds, 830720 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.640565, Class: 1.000000, Obj: 0.377111, No Obj: 0.003160, Avg Recall: 0.776923, count: 130 -Region Avg IOU: 0.605018, Class: 1.000000, Obj: 0.390442, No Obj: 0.003285, Avg Recall: 0.729560, count: 159 -Region Avg IOU: 0.664016, Class: 1.000000, Obj: 0.390430, No Obj: 0.002830, Avg Recall: 0.819048, count: 105 -Region Avg IOU: 0.598438, Class: 1.000000, Obj: 0.383969, No Obj: 0.002504, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.627995, Class: 1.000000, Obj: 0.320884, No Obj: 0.003340, Avg Recall: 0.777070, count: 157 -Region Avg IOU: 0.628959, Class: 1.000000, Obj: 0.373063, No Obj: 0.002865, Avg Recall: 0.811966, count: 117 -Region Avg IOU: 0.617930, Class: 1.000000, Obj: 0.340210, No Obj: 0.002897, Avg Recall: 0.752066, count: 121 -Region Avg IOU: 0.640457, Class: 1.000000, Obj: 0.378438, No Obj: 0.003022, Avg Recall: 0.793651, count: 126 -3246: 0.586298, 0.472766 avg, 0.001000 rate, 27.817442 seconds, 830976 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.607113, Class: 1.000000, Obj: 0.298787, No Obj: 0.002836, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.669052, Class: 1.000000, Obj: 0.387420, No Obj: 0.002470, Avg Recall: 0.873239, count: 71 -Region Avg IOU: 0.650344, Class: 1.000000, Obj: 0.402217, No Obj: 0.002649, Avg Recall: 0.819277, count: 83 -Region Avg IOU: 0.626246, Class: 1.000000, Obj: 0.341408, No Obj: 0.002924, Avg Recall: 0.795455, count: 132 -Region Avg IOU: 0.675355, Class: 1.000000, Obj: 0.400193, No Obj: 0.002822, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.631913, Class: 1.000000, Obj: 0.348146, No Obj: 0.002967, Avg Recall: 0.770992, count: 131 -Region Avg IOU: 0.645833, Class: 1.000000, Obj: 0.406282, No Obj: 0.002628, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.636479, Class: 1.000000, Obj: 0.367512, No Obj: 0.002723, Avg Recall: 0.834951, count: 103 -3247: 0.449390, 0.470429 avg, 0.001000 rate, 29.256021 seconds, 831232 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.602428, Class: 1.000000, Obj: 0.327940, No Obj: 0.002878, Avg Recall: 0.701613, count: 124 -Region Avg IOU: 0.606088, Class: 1.000000, Obj: 0.383905, No Obj: 0.002744, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.660161, Class: 1.000000, Obj: 0.351419, No Obj: 0.002763, Avg Recall: 0.834951, count: 103 -Region Avg IOU: 0.641622, Class: 1.000000, Obj: 0.353313, No Obj: 0.002914, Avg Recall: 0.816176, count: 136 -Region Avg IOU: 0.632283, Class: 1.000000, Obj: 0.372834, No Obj: 0.002771, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.638425, Class: 1.000000, Obj: 0.340887, No Obj: 0.003339, Avg Recall: 0.797101, count: 138 -Region Avg IOU: 0.617168, Class: 1.000000, Obj: 0.415408, No Obj: 0.002562, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.636378, Class: 1.000000, Obj: 0.358184, No Obj: 0.003158, Avg Recall: 0.792308, count: 130 -3248: 0.507672, 0.474153 avg, 0.001000 rate, 27.980909 seconds, 831488 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.645119, Class: 1.000000, Obj: 0.387825, No Obj: 0.002939, Avg Recall: 0.769841, count: 126 -Region Avg IOU: 0.657150, Class: 1.000000, Obj: 0.402952, No Obj: 0.002486, Avg Recall: 0.818182, count: 77 -Region Avg IOU: 0.606327, Class: 1.000000, Obj: 0.325528, No Obj: 0.002772, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.589801, Class: 1.000000, Obj: 0.325344, No Obj: 0.002640, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.612868, Class: 1.000000, Obj: 0.393243, No Obj: 0.002621, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.623662, Class: 1.000000, Obj: 0.308226, No Obj: 0.002712, Avg Recall: 0.773438, count: 128 -Region Avg IOU: 0.624737, Class: 1.000000, Obj: 0.350412, No Obj: 0.002976, Avg Recall: 0.755906, count: 127 -Region Avg IOU: 0.597078, Class: 1.000000, Obj: 0.388303, No Obj: 0.002611, Avg Recall: 0.702479, count: 121 -3249: 0.474890, 0.474227 avg, 0.001000 rate, 29.169674 seconds, 831744 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.622407, Class: 1.000000, Obj: 0.342256, No Obj: 0.002511, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.641684, Class: 1.000000, Obj: 0.383046, No Obj: 0.002817, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.592502, Class: 1.000000, Obj: 0.322763, No Obj: 0.002742, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.646103, Class: 1.000000, Obj: 0.341630, No Obj: 0.002659, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.633628, Class: 1.000000, Obj: 0.373654, No Obj: 0.002420, Avg Recall: 0.816092, count: 87 -Region Avg IOU: 0.601606, Class: 1.000000, Obj: 0.343551, No Obj: 0.002933, Avg Recall: 0.710345, count: 145 -Region Avg IOU: 0.560933, Class: 1.000000, Obj: 0.331978, No Obj: 0.002705, Avg Recall: 0.633588, count: 131 -Region Avg IOU: 0.640902, Class: 1.000000, Obj: 0.352277, No Obj: 0.003001, Avg Recall: 0.776859, count: 121 -3250: 0.476247, 0.474429 avg, 0.001000 rate, 28.519264 seconds, 832000 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 17.092299 seconds -Region Avg IOU: 0.541085, Class: 1.000000, Obj: 0.270173, No Obj: 0.003320, Avg Recall: 0.588710, count: 124 -Region Avg IOU: 0.573046, Class: 1.000000, Obj: 0.325490, No Obj: 0.003583, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.553740, Class: 1.000000, Obj: 0.314464, No Obj: 0.003345, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.509420, Class: 1.000000, Obj: 0.272309, No Obj: 0.003005, Avg Recall: 0.583333, count: 96 -Region Avg IOU: 0.542799, Class: 1.000000, Obj: 0.279196, No Obj: 0.003264, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.491869, Class: 1.000000, Obj: 0.283629, No Obj: 0.002925, Avg Recall: 0.553571, count: 112 -Region Avg IOU: 0.588238, Class: 1.000000, Obj: 0.272839, No Obj: 0.003634, Avg Recall: 0.709924, count: 131 -Region Avg IOU: 0.559642, Class: 1.000000, Obj: 0.323981, No Obj: 0.003178, Avg Recall: 0.633663, count: 101 -3251: 0.457989, 0.472785 avg, 0.001000 rate, 22.187443 seconds, 832256 images -Loaded: 0.680718 seconds -Region Avg IOU: 0.534352, Class: 1.000000, Obj: 0.298271, No Obj: 0.003315, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.551789, Class: 1.000000, Obj: 0.353502, No Obj: 0.003441, Avg Recall: 0.648352, count: 91 -Region Avg IOU: 0.497414, Class: 1.000000, Obj: 0.274588, No Obj: 0.003489, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.526081, Class: 1.000000, Obj: 0.297500, No Obj: 0.003432, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.542163, Class: 1.000000, Obj: 0.311152, No Obj: 0.003153, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.535793, Class: 1.000000, Obj: 0.276676, No Obj: 0.003514, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.557598, Class: 1.000000, Obj: 0.304531, No Obj: 0.003328, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.520350, Class: 1.000000, Obj: 0.246567, No Obj: 0.003356, Avg Recall: 0.590164, count: 122 -3252: 0.410186, 0.466525 avg, 0.001000 rate, 22.224621 seconds, 832512 images -Loaded: 0.264907 seconds -Region Avg IOU: 0.517008, Class: 1.000000, Obj: 0.295032, No Obj: 0.003407, Avg Recall: 0.575000, count: 120 -Region Avg IOU: 0.533493, Class: 1.000000, Obj: 0.297079, No Obj: 0.003677, Avg Recall: 0.596639, count: 119 -Region Avg IOU: 0.582810, Class: 1.000000, Obj: 0.366319, No Obj: 0.003458, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.485867, Class: 1.000000, Obj: 0.270372, No Obj: 0.003429, Avg Recall: 0.476562, count: 128 -Region Avg IOU: 0.514519, Class: 1.000000, Obj: 0.291552, No Obj: 0.003050, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.512558, Class: 1.000000, Obj: 0.264539, No Obj: 0.003517, Avg Recall: 0.608247, count: 97 -Region Avg IOU: 0.531331, Class: 1.000000, Obj: 0.296493, No Obj: 0.003411, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.493055, Class: 1.000000, Obj: 0.294448, No Obj: 0.003951, Avg Recall: 0.487805, count: 123 -3253: 0.445793, 0.464452 avg, 0.001000 rate, 24.204348 seconds, 832768 images -Loaded: 0.069990 seconds -Region Avg IOU: 0.536307, Class: 1.000000, Obj: 0.243874, No Obj: 0.003806, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.554233, Class: 1.000000, Obj: 0.336119, No Obj: 0.004041, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.551555, Class: 1.000000, Obj: 0.333942, No Obj: 0.003684, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.523988, Class: 1.000000, Obj: 0.308456, No Obj: 0.004125, Avg Recall: 0.630435, count: 138 -Region Avg IOU: 0.552250, Class: 1.000000, Obj: 0.296354, No Obj: 0.003315, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.571531, Class: 1.000000, Obj: 0.334620, No Obj: 0.003174, Avg Recall: 0.666667, count: 81 -Region Avg IOU: 0.488300, Class: 1.000000, Obj: 0.271085, No Obj: 0.003787, Avg Recall: 0.554688, count: 128 -Region Avg IOU: 0.532894, Class: 1.000000, Obj: 0.306130, No Obj: 0.003415, Avg Recall: 0.660194, count: 103 -3254: 0.553018, 0.473308 avg, 0.001000 rate, 23.171789 seconds, 833024 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.502693, Class: 1.000000, Obj: 0.325054, No Obj: 0.003086, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.509927, Class: 1.000000, Obj: 0.319477, No Obj: 0.003938, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.537355, Class: 1.000000, Obj: 0.317817, No Obj: 0.003637, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.532219, Class: 1.000000, Obj: 0.307167, No Obj: 0.003486, Avg Recall: 0.595745, count: 94 -Region Avg IOU: 0.486780, Class: 1.000000, Obj: 0.270592, No Obj: 0.003829, Avg Recall: 0.513043, count: 115 -Region Avg IOU: 0.526719, Class: 1.000000, Obj: 0.309595, No Obj: 0.004435, Avg Recall: 0.603306, count: 121 -Region Avg IOU: 0.582797, Class: 1.000000, Obj: 0.365178, No Obj: 0.003649, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.516990, Class: 1.000000, Obj: 0.310410, No Obj: 0.003790, Avg Recall: 0.568807, count: 109 -3255: 0.448909, 0.470868 avg, 0.001000 rate, 22.131992 seconds, 833280 images -Loaded: 0.033187 seconds -Region Avg IOU: 0.469267, Class: 1.000000, Obj: 0.364779, No Obj: 0.003834, Avg Recall: 0.500000, count: 94 -Region Avg IOU: 0.490661, Class: 1.000000, Obj: 0.354245, No Obj: 0.003450, Avg Recall: 0.543478, count: 92 -Region Avg IOU: 0.541895, Class: 1.000000, Obj: 0.400361, No Obj: 0.004494, Avg Recall: 0.633333, count: 120 -Region Avg IOU: 0.472122, Class: 1.000000, Obj: 0.363092, No Obj: 0.003424, Avg Recall: 0.543478, count: 92 -Region Avg IOU: 0.546855, Class: 1.000000, Obj: 0.375477, No Obj: 0.004209, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.540592, Class: 1.000000, Obj: 0.352537, No Obj: 0.003501, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.543647, Class: 1.000000, Obj: 0.353120, No Obj: 0.003805, Avg Recall: 0.648352, count: 91 -Region Avg IOU: 0.549712, Class: 1.000000, Obj: 0.400485, No Obj: 0.004668, Avg Recall: 0.625000, count: 112 -3256: 0.364181, 0.460200 avg, 0.001000 rate, 21.853748 seconds, 833536 images -Loaded: 0.476202 seconds -Region Avg IOU: 0.568650, Class: 1.000000, Obj: 0.415281, No Obj: 0.003844, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.503283, Class: 1.000000, Obj: 0.359872, No Obj: 0.004135, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.472050, Class: 1.000000, Obj: 0.386018, No Obj: 0.003631, Avg Recall: 0.522222, count: 90 -Region Avg IOU: 0.564980, Class: 1.000000, Obj: 0.379179, No Obj: 0.004347, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.533861, Class: 1.000000, Obj: 0.378778, No Obj: 0.004402, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.503001, Class: 1.000000, Obj: 0.381907, No Obj: 0.004134, Avg Recall: 0.565657, count: 99 -Region Avg IOU: 0.524792, Class: 1.000000, Obj: 0.353228, No Obj: 0.004412, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.541533, Class: 1.000000, Obj: 0.335304, No Obj: 0.004777, Avg Recall: 0.613861, count: 101 -3257: 0.419852, 0.456165 avg, 0.001000 rate, 22.093279 seconds, 833792 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.529304, Class: 1.000000, Obj: 0.421859, No Obj: 0.004642, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.543650, Class: 1.000000, Obj: 0.390704, No Obj: 0.004687, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.540389, Class: 1.000000, Obj: 0.388660, No Obj: 0.005254, Avg Recall: 0.584000, count: 125 -Region Avg IOU: 0.574851, Class: 1.000000, Obj: 0.407773, No Obj: 0.004148, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.530013, Class: 1.000000, Obj: 0.333170, No Obj: 0.005382, Avg Recall: 0.601307, count: 153 -Region Avg IOU: 0.504012, Class: 1.000000, Obj: 0.379859, No Obj: 0.004209, Avg Recall: 0.528846, count: 104 -Region Avg IOU: 0.526172, Class: 1.000000, Obj: 0.403208, No Obj: 0.004588, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.535844, Class: 1.000000, Obj: 0.324909, No Obj: 0.004983, Avg Recall: 0.619835, count: 121 -3258: 0.458033, 0.456352 avg, 0.001000 rate, 21.493799 seconds, 834048 images -Loaded: 0.696825 seconds -Region Avg IOU: 0.528111, Class: 1.000000, Obj: 0.330154, No Obj: 0.004778, Avg Recall: 0.608000, count: 125 -Region Avg IOU: 0.563028, Class: 1.000000, Obj: 0.423373, No Obj: 0.005194, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.539231, Class: 1.000000, Obj: 0.430828, No Obj: 0.004395, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.529772, Class: 1.000000, Obj: 0.375929, No Obj: 0.004522, Avg Recall: 0.640625, count: 128 -Region Avg IOU: 0.548170, Class: 1.000000, Obj: 0.366111, No Obj: 0.005028, Avg Recall: 0.604651, count: 129 -Region Avg IOU: 0.528373, Class: 1.000000, Obj: 0.351407, No Obj: 0.004505, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.564454, Class: 1.000000, Obj: 0.412470, No Obj: 0.004974, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.537994, Class: 1.000000, Obj: 0.349363, No Obj: 0.004714, Avg Recall: 0.630000, count: 100 -3259: 0.458842, 0.456601 avg, 0.001000 rate, 20.465252 seconds, 834304 images -Loaded: 1.935161 seconds -Region Avg IOU: 0.527609, Class: 1.000000, Obj: 0.353743, No Obj: 0.004931, Avg Recall: 0.594828, count: 116 -Region Avg IOU: 0.482921, Class: 1.000000, Obj: 0.388904, No Obj: 0.004262, Avg Recall: 0.510417, count: 96 -Region Avg IOU: 0.539728, Class: 1.000000, Obj: 0.385536, No Obj: 0.004160, Avg Recall: 0.619565, count: 92 -Region Avg IOU: 0.490494, Class: 1.000000, Obj: 0.352653, No Obj: 0.004585, Avg Recall: 0.528000, count: 125 -Region Avg IOU: 0.582182, Class: 1.000000, Obj: 0.437492, No Obj: 0.004718, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.507912, Class: 1.000000, Obj: 0.355870, No Obj: 0.004747, Avg Recall: 0.568000, count: 125 -Region Avg IOU: 0.502746, Class: 1.000000, Obj: 0.360232, No Obj: 0.005163, Avg Recall: 0.567568, count: 148 -Region Avg IOU: 0.491133, Class: 1.000000, Obj: 0.372359, No Obj: 0.004269, Avg Recall: 0.574074, count: 108 -3260: 0.492099, 0.460151 avg, 0.001000 rate, 21.862183 seconds, 834560 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000091 seconds -Region Avg IOU: 0.565531, Class: 1.000000, Obj: 0.449179, No Obj: 0.002726, Avg Recall: 0.666667, count: 84 -Region Avg IOU: 0.619736, Class: 1.000000, Obj: 0.370084, No Obj: 0.003284, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.599011, Class: 1.000000, Obj: 0.435354, No Obj: 0.003398, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.586658, Class: 1.000000, Obj: 0.370592, No Obj: 0.003359, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.619070, Class: 1.000000, Obj: 0.413796, No Obj: 0.003719, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.574334, Class: 1.000000, Obj: 0.376523, No Obj: 0.003611, Avg Recall: 0.708955, count: 134 -Region Avg IOU: 0.619745, Class: 1.000000, Obj: 0.380458, No Obj: 0.003931, Avg Recall: 0.779661, count: 118 -Region Avg IOU: 0.613024, Class: 1.000000, Obj: 0.434822, No Obj: 0.003243, Avg Recall: 0.762376, count: 101 -3261: 0.461966, 0.460332 avg, 0.001000 rate, 27.540792 seconds, 834816 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.629067, Class: 1.000000, Obj: 0.399681, No Obj: 0.003344, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.588791, Class: 1.000000, Obj: 0.354534, No Obj: 0.003151, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.633927, Class: 1.000000, Obj: 0.384416, No Obj: 0.003225, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.635069, Class: 1.000000, Obj: 0.344722, No Obj: 0.002898, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.615009, Class: 1.000000, Obj: 0.426594, No Obj: 0.002999, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.591613, Class: 1.000000, Obj: 0.442243, No Obj: 0.003427, Avg Recall: 0.713115, count: 122 -Region Avg IOU: 0.570407, Class: 1.000000, Obj: 0.399271, No Obj: 0.003300, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.604172, Class: 1.000000, Obj: 0.408224, No Obj: 0.003142, Avg Recall: 0.713043, count: 115 -3262: 0.495672, 0.463866 avg, 0.001000 rate, 28.317335 seconds, 835072 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.542987, Class: 1.000000, Obj: 0.359593, No Obj: 0.002682, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.598086, Class: 1.000000, Obj: 0.366841, No Obj: 0.002873, Avg Recall: 0.714286, count: 119 -Region Avg IOU: 0.575177, Class: 1.000000, Obj: 0.412539, No Obj: 0.002752, Avg Recall: 0.643478, count: 115 -Region Avg IOU: 0.578448, Class: 1.000000, Obj: 0.366426, No Obj: 0.002628, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.597893, Class: 1.000000, Obj: 0.340620, No Obj: 0.003374, Avg Recall: 0.709924, count: 131 -Region Avg IOU: 0.597907, Class: 1.000000, Obj: 0.380505, No Obj: 0.003124, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.604307, Class: 1.000000, Obj: 0.304929, No Obj: 0.002814, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.582312, Class: 1.000000, Obj: 0.366025, No Obj: 0.002519, Avg Recall: 0.714286, count: 98 -3263: 0.518941, 0.469374 avg, 0.001000 rate, 27.416887 seconds, 835328 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.588342, Class: 1.000000, Obj: 0.345186, No Obj: 0.002248, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.610139, Class: 1.000000, Obj: 0.317719, No Obj: 0.002649, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.598427, Class: 1.000000, Obj: 0.322018, No Obj: 0.002407, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.605747, Class: 1.000000, Obj: 0.367382, No Obj: 0.002535, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.621249, Class: 1.000000, Obj: 0.290132, No Obj: 0.002570, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.640424, Class: 1.000000, Obj: 0.366426, No Obj: 0.003009, Avg Recall: 0.789062, count: 128 -Region Avg IOU: 0.631520, Class: 1.000000, Obj: 0.345512, No Obj: 0.002659, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.624520, Class: 1.000000, Obj: 0.317499, No Obj: 0.002404, Avg Recall: 0.784483, count: 116 -3264: 0.482353, 0.470672 avg, 0.001000 rate, 28.014160 seconds, 835584 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.614801, Class: 1.000000, Obj: 0.338584, No Obj: 0.002092, Avg Recall: 0.802326, count: 86 -Region Avg IOU: 0.652304, Class: 1.000000, Obj: 0.328109, No Obj: 0.002696, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.648614, Class: 1.000000, Obj: 0.352961, No Obj: 0.002230, Avg Recall: 0.771084, count: 83 -Region Avg IOU: 0.612417, Class: 1.000000, Obj: 0.321363, No Obj: 0.002613, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.611430, Class: 1.000000, Obj: 0.297732, No Obj: 0.002319, Avg Recall: 0.775862, count: 116 -Region Avg IOU: 0.577312, Class: 1.000000, Obj: 0.317093, No Obj: 0.002645, Avg Recall: 0.705882, count: 153 -Region Avg IOU: 0.601191, Class: 1.000000, Obj: 0.294353, No Obj: 0.002702, Avg Recall: 0.698413, count: 126 -Region Avg IOU: 0.621263, Class: 1.000000, Obj: 0.325178, No Obj: 0.002357, Avg Recall: 0.723404, count: 94 -3265: 0.471447, 0.470749 avg, 0.001000 rate, 26.771984 seconds, 835840 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.605319, Class: 1.000000, Obj: 0.321734, No Obj: 0.002285, Avg Recall: 0.733871, count: 124 -Region Avg IOU: 0.609755, Class: 1.000000, Obj: 0.343442, No Obj: 0.002361, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.614162, Class: 1.000000, Obj: 0.314781, No Obj: 0.002435, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.637702, Class: 1.000000, Obj: 0.277862, No Obj: 0.002173, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.548994, Class: 1.000000, Obj: 0.264475, No Obj: 0.002308, Avg Recall: 0.664062, count: 128 -Region Avg IOU: 0.604653, Class: 1.000000, Obj: 0.281504, No Obj: 0.002769, Avg Recall: 0.751825, count: 137 -Region Avg IOU: 0.657181, Class: 1.000000, Obj: 0.320334, No Obj: 0.002646, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.630021, Class: 1.000000, Obj: 0.277650, No Obj: 0.002057, Avg Recall: 0.813187, count: 91 -3266: 0.465436, 0.470218 avg, 0.001000 rate, 26.826309 seconds, 836096 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.616861, Class: 1.000000, Obj: 0.284766, No Obj: 0.002252, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.644825, Class: 1.000000, Obj: 0.305083, No Obj: 0.002899, Avg Recall: 0.792593, count: 135 -Region Avg IOU: 0.622440, Class: 1.000000, Obj: 0.307038, No Obj: 0.002619, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.659920, Class: 1.000000, Obj: 0.312034, No Obj: 0.002545, Avg Recall: 0.850877, count: 114 -Region Avg IOU: 0.612972, Class: 1.000000, Obj: 0.312661, No Obj: 0.002277, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.625322, Class: 1.000000, Obj: 0.279234, No Obj: 0.002293, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.601524, Class: 1.000000, Obj: 0.333251, No Obj: 0.002266, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.604155, Class: 1.000000, Obj: 0.304252, No Obj: 0.002504, Avg Recall: 0.747967, count: 123 -3267: 0.470670, 0.470263 avg, 0.001000 rate, 27.725420 seconds, 836352 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.668632, Class: 1.000000, Obj: 0.328577, No Obj: 0.002882, Avg Recall: 0.882883, count: 111 -Region Avg IOU: 0.625460, Class: 1.000000, Obj: 0.288847, No Obj: 0.002798, Avg Recall: 0.761905, count: 126 -Region Avg IOU: 0.611764, Class: 1.000000, Obj: 0.314344, No Obj: 0.002592, Avg Recall: 0.731707, count: 123 -Region Avg IOU: 0.594171, Class: 1.000000, Obj: 0.291895, No Obj: 0.002517, Avg Recall: 0.720000, count: 125 -Region Avg IOU: 0.627604, Class: 1.000000, Obj: 0.298193, No Obj: 0.002379, Avg Recall: 0.791304, count: 115 -Region Avg IOU: 0.659947, Class: 1.000000, Obj: 0.343726, No Obj: 0.002726, Avg Recall: 0.822222, count: 90 -Region Avg IOU: 0.637299, Class: 1.000000, Obj: 0.311855, No Obj: 0.002080, Avg Recall: 0.813953, count: 86 -Region Avg IOU: 0.609278, Class: 1.000000, Obj: 0.325884, No Obj: 0.002665, Avg Recall: 0.710744, count: 121 -3268: 0.474542, 0.470691 avg, 0.001000 rate, 27.397291 seconds, 836608 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.649033, Class: 1.000000, Obj: 0.357632, No Obj: 0.002740, Avg Recall: 0.836538, count: 104 -Region Avg IOU: 0.598078, Class: 1.000000, Obj: 0.364623, No Obj: 0.002497, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.637976, Class: 1.000000, Obj: 0.347270, No Obj: 0.002803, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.667930, Class: 1.000000, Obj: 0.370147, No Obj: 0.002568, Avg Recall: 0.872093, count: 86 -Region Avg IOU: 0.672029, Class: 1.000000, Obj: 0.345493, No Obj: 0.002870, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.599257, Class: 1.000000, Obj: 0.348615, No Obj: 0.002213, Avg Recall: 0.727273, count: 88 -Region Avg IOU: 0.661318, Class: 1.000000, Obj: 0.354615, No Obj: 0.002751, Avg Recall: 0.831858, count: 113 -Region Avg IOU: 0.630026, Class: 1.000000, Obj: 0.351216, No Obj: 0.002786, Avg Recall: 0.740000, count: 100 -3269: 0.406787, 0.464301 avg, 0.001000 rate, 27.645996 seconds, 836864 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.614431, Class: 1.000000, Obj: 0.367516, No Obj: 0.002972, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.586970, Class: 1.000000, Obj: 0.334513, No Obj: 0.003097, Avg Recall: 0.706349, count: 126 -Region Avg IOU: 0.629072, Class: 1.000000, Obj: 0.375632, No Obj: 0.002895, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.630010, Class: 1.000000, Obj: 0.408630, No Obj: 0.003366, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.635165, Class: 1.000000, Obj: 0.349309, No Obj: 0.003574, Avg Recall: 0.802632, count: 152 -Region Avg IOU: 0.613245, Class: 1.000000, Obj: 0.412859, No Obj: 0.002667, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.634855, Class: 1.000000, Obj: 0.377051, No Obj: 0.002773, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.611240, Class: 1.000000, Obj: 0.346824, No Obj: 0.003131, Avg Recall: 0.726496, count: 117 -3270: 0.516006, 0.469471 avg, 0.001000 rate, 27.109951 seconds, 837120 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 17.373756 seconds -Region Avg IOU: 0.501411, Class: 1.000000, Obj: 0.267130, No Obj: 0.004025, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.461099, Class: 1.000000, Obj: 0.214063, No Obj: 0.003803, Avg Recall: 0.503704, count: 135 -Region Avg IOU: 0.500723, Class: 1.000000, Obj: 0.269531, No Obj: 0.003320, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.501574, Class: 1.000000, Obj: 0.241109, No Obj: 0.003369, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.500152, Class: 1.000000, Obj: 0.295087, No Obj: 0.003966, Avg Recall: 0.510417, count: 96 -Region Avg IOU: 0.511007, Class: 1.000000, Obj: 0.279569, No Obj: 0.003524, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.512131, Class: 1.000000, Obj: 0.300357, No Obj: 0.003712, Avg Recall: 0.534483, count: 116 -Region Avg IOU: 0.503836, Class: 1.000000, Obj: 0.250260, No Obj: 0.003577, Avg Recall: 0.610526, count: 95 -3271: 0.547724, 0.477296 avg, 0.001000 rate, 19.123123 seconds, 837376 images -Loaded: 0.550223 seconds -Region Avg IOU: 0.503285, Class: 1.000000, Obj: 0.269808, No Obj: 0.003930, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.492156, Class: 1.000000, Obj: 0.301421, No Obj: 0.003730, Avg Recall: 0.505263, count: 95 -Region Avg IOU: 0.538049, Class: 1.000000, Obj: 0.257214, No Obj: 0.003747, Avg Recall: 0.619565, count: 92 -Region Avg IOU: 0.532719, Class: 1.000000, Obj: 0.281789, No Obj: 0.004115, Avg Recall: 0.626374, count: 91 -Region Avg IOU: 0.484657, Class: 1.000000, Obj: 0.274131, No Obj: 0.003798, Avg Recall: 0.571429, count: 84 -Region Avg IOU: 0.507848, Class: 1.000000, Obj: 0.298723, No Obj: 0.004188, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.484484, Class: 1.000000, Obj: 0.275735, No Obj: 0.003949, Avg Recall: 0.519231, count: 104 -Region Avg IOU: 0.509973, Class: 1.000000, Obj: 0.299610, No Obj: 0.003926, Avg Recall: 0.585106, count: 94 -3272: 0.467625, 0.476329 avg, 0.001000 rate, 16.882544 seconds, 837632 images -Loaded: 4.820941 seconds -Region Avg IOU: 0.508403, Class: 1.000000, Obj: 0.323448, No Obj: 0.004544, Avg Recall: 0.570175, count: 114 -Region Avg IOU: 0.507431, Class: 1.000000, Obj: 0.326276, No Obj: 0.004311, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.545923, Class: 1.000000, Obj: 0.330931, No Obj: 0.005238, Avg Recall: 0.630435, count: 138 -Region Avg IOU: 0.496960, Class: 1.000000, Obj: 0.290910, No Obj: 0.004118, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.464661, Class: 1.000000, Obj: 0.284162, No Obj: 0.004137, Avg Recall: 0.503937, count: 127 -Region Avg IOU: 0.504892, Class: 1.000000, Obj: 0.334608, No Obj: 0.004376, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.529989, Class: 1.000000, Obj: 0.343341, No Obj: 0.004556, Avg Recall: 0.613636, count: 88 -Region Avg IOU: 0.526421, Class: 1.000000, Obj: 0.308179, No Obj: 0.004598, Avg Recall: 0.606061, count: 99 -3273: 0.516415, 0.480338 avg, 0.001000 rate, 19.393518 seconds, 837888 images -Loaded: 2.020664 seconds -Region Avg IOU: 0.525512, Class: 1.000000, Obj: 0.313669, No Obj: 0.005556, Avg Recall: 0.561983, count: 121 -Region Avg IOU: 0.466354, Class: 1.000000, Obj: 0.313853, No Obj: 0.004783, Avg Recall: 0.455357, count: 112 -Region Avg IOU: 0.508108, Class: 1.000000, Obj: 0.332877, No Obj: 0.004732, Avg Recall: 0.524390, count: 82 -Region Avg IOU: 0.474536, Class: 1.000000, Obj: 0.301265, No Obj: 0.004862, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.493919, Class: 1.000000, Obj: 0.341921, No Obj: 0.005521, Avg Recall: 0.568000, count: 125 -Region Avg IOU: 0.535375, Class: 1.000000, Obj: 0.330252, No Obj: 0.004110, Avg Recall: 0.597561, count: 82 -Region Avg IOU: 0.477405, Class: 1.000000, Obj: 0.303666, No Obj: 0.005023, Avg Recall: 0.490741, count: 108 -Region Avg IOU: 0.562149, Class: 1.000000, Obj: 0.342814, No Obj: 0.004441, Avg Recall: 0.658824, count: 85 -3274: 0.414212, 0.473725 avg, 0.001000 rate, 17.136490 seconds, 838144 images -Loaded: 3.765228 seconds -Region Avg IOU: 0.494535, Class: 1.000000, Obj: 0.343185, No Obj: 0.005807, Avg Recall: 0.582090, count: 134 -Region Avg IOU: 0.515281, Class: 1.000000, Obj: 0.376364, No Obj: 0.005216, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.498606, Class: 1.000000, Obj: 0.363185, No Obj: 0.005531, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.476684, Class: 1.000000, Obj: 0.352560, No Obj: 0.004670, Avg Recall: 0.500000, count: 86 -Region Avg IOU: 0.532018, Class: 1.000000, Obj: 0.369833, No Obj: 0.005288, Avg Recall: 0.593750, count: 96 -Region Avg IOU: 0.500298, Class: 1.000000, Obj: 0.404908, No Obj: 0.004478, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.435559, Class: 1.000000, Obj: 0.322429, No Obj: 0.004507, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.517323, Class: 1.000000, Obj: 0.350150, No Obj: 0.004979, Avg Recall: 0.582524, count: 103 -3275: 0.402023, 0.466555 avg, 0.001000 rate, 16.779043 seconds, 838400 images -Loaded: 2.085805 seconds -Region Avg IOU: 0.474742, Class: 1.000000, Obj: 0.359352, No Obj: 0.005135, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.486041, Class: 1.000000, Obj: 0.357867, No Obj: 0.005535, Avg Recall: 0.529851, count: 134 -Region Avg IOU: 0.463751, Class: 1.000000, Obj: 0.365155, No Obj: 0.005054, Avg Recall: 0.513043, count: 115 -Region Avg IOU: 0.478397, Class: 1.000000, Obj: 0.374809, No Obj: 0.004894, Avg Recall: 0.505376, count: 93 -Region Avg IOU: 0.466318, Class: 1.000000, Obj: 0.374006, No Obj: 0.005369, Avg Recall: 0.495238, count: 105 -Region Avg IOU: 0.496138, Class: 1.000000, Obj: 0.339042, No Obj: 0.005878, Avg Recall: 0.560000, count: 125 -Region Avg IOU: 0.491560, Class: 1.000000, Obj: 0.352801, No Obj: 0.004811, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.478904, Class: 1.000000, Obj: 0.338346, No Obj: 0.005440, Avg Recall: 0.526316, count: 133 -3276: 0.469850, 0.466885 avg, 0.001000 rate, 19.208796 seconds, 838656 images -Loaded: 2.149602 seconds -Region Avg IOU: 0.551785, Class: 1.000000, Obj: 0.408875, No Obj: 0.004959, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.497870, Class: 1.000000, Obj: 0.370103, No Obj: 0.005419, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.470077, Class: 1.000000, Obj: 0.358526, No Obj: 0.004367, Avg Recall: 0.488636, count: 88 -Region Avg IOU: 0.485833, Class: 1.000000, Obj: 0.404186, No Obj: 0.006226, Avg Recall: 0.584746, count: 118 -Region Avg IOU: 0.506880, Class: 1.000000, Obj: 0.394795, No Obj: 0.005804, Avg Recall: 0.527273, count: 110 -Region Avg IOU: 0.502108, Class: 1.000000, Obj: 0.354882, No Obj: 0.006125, Avg Recall: 0.578947, count: 133 -Region Avg IOU: 0.469489, Class: 1.000000, Obj: 0.347097, No Obj: 0.005150, Avg Recall: 0.504132, count: 121 -Region Avg IOU: 0.468842, Class: 1.000000, Obj: 0.356569, No Obj: 0.006077, Avg Recall: 0.508197, count: 122 -3277: 0.482875, 0.468484 avg, 0.001000 rate, 17.226603 seconds, 838912 images -Loaded: 3.994895 seconds -Region Avg IOU: 0.455748, Class: 1.000000, Obj: 0.367699, No Obj: 0.005038, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.480739, Class: 1.000000, Obj: 0.370336, No Obj: 0.005520, Avg Recall: 0.535433, count: 127 -Region Avg IOU: 0.452417, Class: 1.000000, Obj: 0.351099, No Obj: 0.005193, Avg Recall: 0.530973, count: 113 -Region Avg IOU: 0.470494, Class: 1.000000, Obj: 0.383303, No Obj: 0.004743, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.441489, Class: 1.000000, Obj: 0.334600, No Obj: 0.005139, Avg Recall: 0.516949, count: 118 -Region Avg IOU: 0.484725, Class: 1.000000, Obj: 0.402702, No Obj: 0.004808, Avg Recall: 0.512500, count: 80 -Region Avg IOU: 0.496677, Class: 1.000000, Obj: 0.371422, No Obj: 0.005261, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.521270, Class: 1.000000, Obj: 0.360691, No Obj: 0.005272, Avg Recall: 0.611111, count: 108 -3278: 0.471833, 0.468818 avg, 0.001000 rate, 19.410559 seconds, 839168 images -Loaded: 1.679248 seconds -Region Avg IOU: 0.446648, Class: 1.000000, Obj: 0.339660, No Obj: 0.005260, Avg Recall: 0.449367, count: 158 -Region Avg IOU: 0.453563, Class: 1.000000, Obj: 0.303998, No Obj: 0.004923, Avg Recall: 0.441176, count: 102 -Region Avg IOU: 0.527456, Class: 1.000000, Obj: 0.394843, No Obj: 0.005486, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.449687, Class: 1.000000, Obj: 0.347777, No Obj: 0.004946, Avg Recall: 0.508475, count: 118 -Region Avg IOU: 0.476456, Class: 1.000000, Obj: 0.359124, No Obj: 0.004825, Avg Recall: 0.468750, count: 96 -Region Avg IOU: 0.482673, Class: 1.000000, Obj: 0.386453, No Obj: 0.005396, Avg Recall: 0.550000, count: 140 -Region Avg IOU: 0.464233, Class: 1.000000, Obj: 0.323241, No Obj: 0.005133, Avg Recall: 0.504202, count: 119 -Region Avg IOU: 0.476910, Class: 1.000000, Obj: 0.360034, No Obj: 0.004352, Avg Recall: 0.531250, count: 96 -3279: 0.525883, 0.474525 avg, 0.001000 rate, 18.900196 seconds, 839424 images -Loaded: 1.827380 seconds -Region Avg IOU: 0.458940, Class: 1.000000, Obj: 0.284630, No Obj: 0.004588, Avg Recall: 0.500000, count: 118 -Region Avg IOU: 0.432323, Class: 1.000000, Obj: 0.322228, No Obj: 0.004696, Avg Recall: 0.450980, count: 102 -Region Avg IOU: 0.484626, Class: 1.000000, Obj: 0.340169, No Obj: 0.004654, Avg Recall: 0.494253, count: 87 -Region Avg IOU: 0.474915, Class: 1.000000, Obj: 0.369493, No Obj: 0.004544, Avg Recall: 0.506024, count: 83 -Region Avg IOU: 0.457582, Class: 1.000000, Obj: 0.343762, No Obj: 0.005290, Avg Recall: 0.491667, count: 120 -Region Avg IOU: 0.452443, Class: 1.000000, Obj: 0.319409, No Obj: 0.004402, Avg Recall: 0.485149, count: 101 -Region Avg IOU: 0.461550, Class: 1.000000, Obj: 0.322961, No Obj: 0.005014, Avg Recall: 0.500000, count: 108 -Region Avg IOU: 0.434834, Class: 1.000000, Obj: 0.317811, No Obj: 0.004917, Avg Recall: 0.470588, count: 119 -3280: 0.438446, 0.470917 avg, 0.001000 rate, 17.385143 seconds, 839680 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000082 seconds -Region Avg IOU: 0.621752, Class: 1.000000, Obj: 0.496319, No Obj: 0.004336, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.584638, Class: 1.000000, Obj: 0.424610, No Obj: 0.003781, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.595995, Class: 1.000000, Obj: 0.412464, No Obj: 0.004192, Avg Recall: 0.719697, count: 132 -Region Avg IOU: 0.629846, Class: 1.000000, Obj: 0.434228, No Obj: 0.003808, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.585350, Class: 1.000000, Obj: 0.373760, No Obj: 0.003770, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.657111, Class: 1.000000, Obj: 0.482987, No Obj: 0.003929, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.622244, Class: 1.000000, Obj: 0.429289, No Obj: 0.004144, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.673676, Class: 1.000000, Obj: 0.477073, No Obj: 0.003632, Avg Recall: 0.881720, count: 93 -3281: 0.505841, 0.474409 avg, 0.001000 rate, 27.763483 seconds, 839936 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.583270, Class: 1.000000, Obj: 0.352397, No Obj: 0.003265, Avg Recall: 0.683099, count: 142 -Region Avg IOU: 0.605756, Class: 1.000000, Obj: 0.386842, No Obj: 0.002893, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.589757, Class: 1.000000, Obj: 0.443417, No Obj: 0.003273, Avg Recall: 0.661290, count: 124 -Region Avg IOU: 0.628851, Class: 1.000000, Obj: 0.396163, No Obj: 0.003092, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.604394, Class: 1.000000, Obj: 0.414369, No Obj: 0.002994, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.607341, Class: 1.000000, Obj: 0.334752, No Obj: 0.003314, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.621471, Class: 1.000000, Obj: 0.356216, No Obj: 0.003210, Avg Recall: 0.819048, count: 105 -Region Avg IOU: 0.609736, Class: 1.000000, Obj: 0.415498, No Obj: 0.002961, Avg Recall: 0.711340, count: 97 -3282: 0.481634, 0.475132 avg, 0.001000 rate, 31.600594 seconds, 840192 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.612757, Class: 1.000000, Obj: 0.312260, No Obj: 0.002715, Avg Recall: 0.788618, count: 123 -Region Avg IOU: 0.591462, Class: 1.000000, Obj: 0.371395, No Obj: 0.002538, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.668174, Class: 1.000000, Obj: 0.343033, No Obj: 0.002887, Avg Recall: 0.873950, count: 119 -Region Avg IOU: 0.595605, Class: 1.000000, Obj: 0.324003, No Obj: 0.002873, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.561269, Class: 1.000000, Obj: 0.337825, No Obj: 0.002269, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.647168, Class: 1.000000, Obj: 0.271071, No Obj: 0.002587, Avg Recall: 0.821429, count: 112 -Region Avg IOU: 0.652162, Class: 1.000000, Obj: 0.337891, No Obj: 0.002665, Avg Recall: 0.824074, count: 108 -Region Avg IOU: 0.627885, Class: 1.000000, Obj: 0.269898, No Obj: 0.002819, Avg Recall: 0.793651, count: 126 -3283: 0.499358, 0.477555 avg, 0.001000 rate, 29.833529 seconds, 840448 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.672224, Class: 1.000000, Obj: 0.252150, No Obj: 0.002373, Avg Recall: 0.850394, count: 127 -Region Avg IOU: 0.605969, Class: 1.000000, Obj: 0.291479, No Obj: 0.002484, Avg Recall: 0.712121, count: 132 -Region Avg IOU: 0.635007, Class: 1.000000, Obj: 0.255285, No Obj: 0.002166, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.663605, Class: 1.000000, Obj: 0.267765, No Obj: 0.002356, Avg Recall: 0.828829, count: 111 -Region Avg IOU: 0.629381, Class: 1.000000, Obj: 0.268337, No Obj: 0.002171, Avg Recall: 0.803150, count: 127 -Region Avg IOU: 0.601939, Class: 1.000000, Obj: 0.305282, No Obj: 0.002108, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.637999, Class: 1.000000, Obj: 0.275171, No Obj: 0.002214, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.643153, Class: 1.000000, Obj: 0.290874, No Obj: 0.002149, Avg Recall: 0.795918, count: 98 -3284: 0.526811, 0.482480 avg, 0.001000 rate, 30.139332 seconds, 840704 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.615496, Class: 1.000000, Obj: 0.277202, No Obj: 0.001915, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.680963, Class: 1.000000, Obj: 0.253119, No Obj: 0.002003, Avg Recall: 0.869919, count: 123 -Region Avg IOU: 0.615642, Class: 1.000000, Obj: 0.278634, No Obj: 0.001878, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.639865, Class: 1.000000, Obj: 0.244303, No Obj: 0.001683, Avg Recall: 0.797872, count: 94 -Region Avg IOU: 0.660283, Class: 1.000000, Obj: 0.291728, No Obj: 0.002211, Avg Recall: 0.824561, count: 114 -Region Avg IOU: 0.604677, Class: 1.000000, Obj: 0.228967, No Obj: 0.001923, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.623095, Class: 1.000000, Obj: 0.267163, No Obj: 0.002147, Avg Recall: 0.719178, count: 146 -Region Avg IOU: 0.634247, Class: 1.000000, Obj: 0.273387, No Obj: 0.001741, Avg Recall: 0.774510, count: 102 -3285: 0.547476, 0.488980 avg, 0.001000 rate, 28.940125 seconds, 840960 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.584043, Class: 1.000000, Obj: 0.270594, No Obj: 0.001730, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.667749, Class: 1.000000, Obj: 0.275722, No Obj: 0.001786, Avg Recall: 0.842593, count: 108 -Region Avg IOU: 0.656581, Class: 1.000000, Obj: 0.280637, No Obj: 0.001680, Avg Recall: 0.857143, count: 84 -Region Avg IOU: 0.662966, Class: 1.000000, Obj: 0.269317, No Obj: 0.001849, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.626987, Class: 1.000000, Obj: 0.249461, No Obj: 0.001771, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.656878, Class: 1.000000, Obj: 0.232709, No Obj: 0.001706, Avg Recall: 0.836735, count: 98 -Region Avg IOU: 0.645186, Class: 1.000000, Obj: 0.268286, No Obj: 0.001936, Avg Recall: 0.807018, count: 114 -Region Avg IOU: 0.674641, Class: 1.000000, Obj: 0.257122, No Obj: 0.001945, Avg Recall: 0.872727, count: 110 -3286: 0.492148, 0.489297 avg, 0.001000 rate, 30.066099 seconds, 841216 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.616379, Class: 1.000000, Obj: 0.248803, No Obj: 0.002096, Avg Recall: 0.779412, count: 136 -Region Avg IOU: 0.602068, Class: 1.000000, Obj: 0.228732, No Obj: 0.001980, Avg Recall: 0.766667, count: 120 -Region Avg IOU: 0.683066, Class: 1.000000, Obj: 0.349766, No Obj: 0.001930, Avg Recall: 0.865979, count: 97 -Region Avg IOU: 0.658623, Class: 1.000000, Obj: 0.266328, No Obj: 0.001707, Avg Recall: 0.862500, count: 80 -Region Avg IOU: 0.672155, Class: 1.000000, Obj: 0.239892, No Obj: 0.002166, Avg Recall: 0.884956, count: 113 -Region Avg IOU: 0.633433, Class: 1.000000, Obj: 0.280821, No Obj: 0.001794, Avg Recall: 0.752809, count: 89 -Region Avg IOU: 0.667377, Class: 1.000000, Obj: 0.245714, No Obj: 0.001952, Avg Recall: 0.824742, count: 97 -Region Avg IOU: 0.595899, Class: 1.000000, Obj: 0.243637, No Obj: 0.001635, Avg Recall: 0.741176, count: 85 -3287: 0.463871, 0.486754 avg, 0.001000 rate, 28.248152 seconds, 841472 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.673908, Class: 1.000000, Obj: 0.338055, No Obj: 0.002156, Avg Recall: 0.842105, count: 114 -Region Avg IOU: 0.596591, Class: 1.000000, Obj: 0.290954, No Obj: 0.001855, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.668053, Class: 1.000000, Obj: 0.341966, No Obj: 0.002103, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.667252, Class: 1.000000, Obj: 0.319218, No Obj: 0.001913, Avg Recall: 0.855670, count: 97 -Region Avg IOU: 0.597938, Class: 1.000000, Obj: 0.286753, No Obj: 0.001960, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.654846, Class: 1.000000, Obj: 0.348689, No Obj: 0.002023, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.636604, Class: 1.000000, Obj: 0.229029, No Obj: 0.002037, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.614186, Class: 1.000000, Obj: 0.301606, No Obj: 0.001962, Avg Recall: 0.742268, count: 97 -3288: 0.452479, 0.483327 avg, 0.001000 rate, 29.276857 seconds, 841728 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.654381, Class: 1.000000, Obj: 0.328322, No Obj: 0.002351, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.636452, Class: 1.000000, Obj: 0.268820, No Obj: 0.002670, Avg Recall: 0.834532, count: 139 -Region Avg IOU: 0.693569, Class: 1.000000, Obj: 0.350043, No Obj: 0.002267, Avg Recall: 0.909091, count: 110 -Region Avg IOU: 0.618596, Class: 1.000000, Obj: 0.319937, No Obj: 0.002457, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.635397, Class: 1.000000, Obj: 0.323215, No Obj: 0.002292, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.636095, Class: 1.000000, Obj: 0.328950, No Obj: 0.001944, Avg Recall: 0.808989, count: 89 -Region Avg IOU: 0.657436, Class: 1.000000, Obj: 0.294980, No Obj: 0.002555, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.620967, Class: 1.000000, Obj: 0.341735, No Obj: 0.002259, Avg Recall: 0.789474, count: 95 -3289: 0.517379, 0.486732 avg, 0.001000 rate, 30.905563 seconds, 841984 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.651569, Class: 1.000000, Obj: 0.341835, No Obj: 0.002908, Avg Recall: 0.813433, count: 134 -Region Avg IOU: 0.671709, Class: 1.000000, Obj: 0.346266, No Obj: 0.002478, Avg Recall: 0.820755, count: 106 -Region Avg IOU: 0.628053, Class: 1.000000, Obj: 0.327502, No Obj: 0.002737, Avg Recall: 0.735294, count: 136 -Region Avg IOU: 0.624513, Class: 1.000000, Obj: 0.297362, No Obj: 0.002707, Avg Recall: 0.744000, count: 125 -Region Avg IOU: 0.632838, Class: 1.000000, Obj: 0.388135, No Obj: 0.002758, Avg Recall: 0.796610, count: 118 -Region Avg IOU: 0.639106, Class: 1.000000, Obj: 0.366090, No Obj: 0.002797, Avg Recall: 0.831933, count: 119 -Region Avg IOU: 0.609133, Class: 1.000000, Obj: 0.401492, No Obj: 0.002639, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.639105, Class: 1.000000, Obj: 0.392063, No Obj: 0.002688, Avg Recall: 0.781250, count: 96 -3290: 0.498501, 0.487909 avg, 0.001000 rate, 29.352579 seconds, 842240 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 14.606787 seconds -Region Avg IOU: 0.616499, Class: 1.000000, Obj: 0.357436, No Obj: 0.003071, Avg Recall: 0.795455, count: 88 -Region Avg IOU: 0.609235, Class: 1.000000, Obj: 0.372157, No Obj: 0.003314, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.509683, Class: 1.000000, Obj: 0.288678, No Obj: 0.003076, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.509282, Class: 1.000000, Obj: 0.290022, No Obj: 0.003724, Avg Recall: 0.554622, count: 119 -Region Avg IOU: 0.554752, Class: 1.000000, Obj: 0.291444, No Obj: 0.003449, Avg Recall: 0.659091, count: 132 -Region Avg IOU: 0.536492, Class: 1.000000, Obj: 0.287754, No Obj: 0.003068, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.552888, Class: 1.000000, Obj: 0.292955, No Obj: 0.002791, Avg Recall: 0.682353, count: 85 -Region Avg IOU: 0.571684, Class: 1.000000, Obj: 0.286970, No Obj: 0.003838, Avg Recall: 0.681818, count: 132 -3291: 0.490197, 0.488138 avg, 0.001000 rate, 22.255980 seconds, 842496 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.535928, Class: 1.000000, Obj: 0.337336, No Obj: 0.003763, Avg Recall: 0.593496, count: 123 -Region Avg IOU: 0.500578, Class: 1.000000, Obj: 0.279926, No Obj: 0.004035, Avg Recall: 0.528986, count: 138 -Region Avg IOU: 0.535876, Class: 1.000000, Obj: 0.309418, No Obj: 0.003401, Avg Recall: 0.560440, count: 91 -Region Avg IOU: 0.501131, Class: 1.000000, Obj: 0.312304, No Obj: 0.003401, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.585855, Class: 1.000000, Obj: 0.325854, No Obj: 0.003208, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.585673, Class: 1.000000, Obj: 0.386646, No Obj: 0.003931, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.566927, Class: 1.000000, Obj: 0.321017, No Obj: 0.004090, Avg Recall: 0.624060, count: 133 -Region Avg IOU: 0.525182, Class: 1.000000, Obj: 0.317954, No Obj: 0.003486, Avg Recall: 0.625000, count: 80 -3292: 0.478454, 0.487169 avg, 0.001000 rate, 24.233240 seconds, 842752 images -Loaded: 0.006798 seconds -Region Avg IOU: 0.481797, Class: 1.000000, Obj: 0.286712, No Obj: 0.003754, Avg Recall: 0.550847, count: 118 -Region Avg IOU: 0.564772, Class: 1.000000, Obj: 0.363559, No Obj: 0.004224, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.532429, Class: 1.000000, Obj: 0.335635, No Obj: 0.004509, Avg Recall: 0.582090, count: 134 -Region Avg IOU: 0.561308, Class: 1.000000, Obj: 0.383741, No Obj: 0.003918, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.567844, Class: 1.000000, Obj: 0.344331, No Obj: 0.004863, Avg Recall: 0.659864, count: 147 -Region Avg IOU: 0.563601, Class: 1.000000, Obj: 0.345629, No Obj: 0.003860, Avg Recall: 0.674699, count: 83 -Region Avg IOU: 0.527601, Class: 1.000000, Obj: 0.325074, No Obj: 0.003619, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.582505, Class: 1.000000, Obj: 0.397514, No Obj: 0.004341, Avg Recall: 0.600000, count: 90 -3293: 0.461673, 0.484620 avg, 0.001000 rate, 23.624264 seconds, 843008 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.568263, Class: 1.000000, Obj: 0.391488, No Obj: 0.004888, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.532511, Class: 1.000000, Obj: 0.372594, No Obj: 0.004181, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.548987, Class: 1.000000, Obj: 0.381482, No Obj: 0.004189, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.565031, Class: 1.000000, Obj: 0.400981, No Obj: 0.004263, Avg Recall: 0.654321, count: 81 -Region Avg IOU: 0.542726, Class: 1.000000, Obj: 0.361685, No Obj: 0.004152, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.520078, Class: 1.000000, Obj: 0.387800, No Obj: 0.004320, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.585541, Class: 1.000000, Obj: 0.374540, No Obj: 0.004425, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.556229, Class: 1.000000, Obj: 0.415358, No Obj: 0.004368, Avg Recall: 0.678571, count: 84 -3294: 0.399118, 0.476069 avg, 0.001000 rate, 23.184616 seconds, 843264 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.544205, Class: 1.000000, Obj: 0.409490, No Obj: 0.004540, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.573158, Class: 1.000000, Obj: 0.463436, No Obj: 0.004544, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.581631, Class: 1.000000, Obj: 0.367366, No Obj: 0.005129, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.555682, Class: 1.000000, Obj: 0.395158, No Obj: 0.003981, Avg Recall: 0.646341, count: 82 -Region Avg IOU: 0.605610, Class: 1.000000, Obj: 0.445713, No Obj: 0.004344, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.509000, Class: 1.000000, Obj: 0.351939, No Obj: 0.004896, Avg Recall: 0.554688, count: 128 -Region Avg IOU: 0.545035, Class: 1.000000, Obj: 0.385906, No Obj: 0.004105, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.552799, Class: 1.000000, Obj: 0.388672, No Obj: 0.004344, Avg Recall: 0.625000, count: 112 -3295: 0.407491, 0.469212 avg, 0.001000 rate, 21.792480 seconds, 843520 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.542282, Class: 1.000000, Obj: 0.362575, No Obj: 0.004866, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.525769, Class: 1.000000, Obj: 0.336119, No Obj: 0.003829, Avg Recall: 0.626374, count: 91 -Region Avg IOU: 0.507874, Class: 1.000000, Obj: 0.382987, No Obj: 0.005006, Avg Recall: 0.558621, count: 145 -Region Avg IOU: 0.575184, Class: 1.000000, Obj: 0.422014, No Obj: 0.004403, Avg Recall: 0.703704, count: 81 -Region Avg IOU: 0.590072, Class: 1.000000, Obj: 0.460937, No Obj: 0.004141, Avg Recall: 0.693182, count: 88 -Region Avg IOU: 0.526647, Class: 1.000000, Obj: 0.380430, No Obj: 0.004740, Avg Recall: 0.601504, count: 133 -Region Avg IOU: 0.579695, Class: 1.000000, Obj: 0.422472, No Obj: 0.004521, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.521981, Class: 1.000000, Obj: 0.430499, No Obj: 0.003986, Avg Recall: 0.637255, count: 102 -3296: 0.439432, 0.466234 avg, 0.001000 rate, 23.843981 seconds, 843776 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.516017, Class: 1.000000, Obj: 0.398136, No Obj: 0.005010, Avg Recall: 0.568182, count: 132 -Region Avg IOU: 0.528065, Class: 1.000000, Obj: 0.371578, No Obj: 0.004024, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.510050, Class: 1.000000, Obj: 0.397227, No Obj: 0.004810, Avg Recall: 0.565891, count: 129 -Region Avg IOU: 0.549849, Class: 1.000000, Obj: 0.383577, No Obj: 0.004345, Avg Recall: 0.666667, count: 84 -Region Avg IOU: 0.545233, Class: 1.000000, Obj: 0.442677, No Obj: 0.004704, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.527912, Class: 1.000000, Obj: 0.374828, No Obj: 0.005199, Avg Recall: 0.577465, count: 142 -Region Avg IOU: 0.545539, Class: 1.000000, Obj: 0.451880, No Obj: 0.004892, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.530491, Class: 1.000000, Obj: 0.397855, No Obj: 0.004800, Avg Recall: 0.638655, count: 119 -3297: 0.479516, 0.467562 avg, 0.001000 rate, 23.954453 seconds, 844032 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.508797, Class: 1.000000, Obj: 0.369766, No Obj: 0.004926, Avg Recall: 0.563758, count: 149 -Region Avg IOU: 0.498950, Class: 1.000000, Obj: 0.379579, No Obj: 0.003881, Avg Recall: 0.581967, count: 122 -Region Avg IOU: 0.516758, Class: 1.000000, Obj: 0.367111, No Obj: 0.003830, Avg Recall: 0.601770, count: 113 -Region Avg IOU: 0.508404, Class: 1.000000, Obj: 0.357289, No Obj: 0.004328, Avg Recall: 0.519231, count: 104 -Region Avg IOU: 0.534080, Class: 1.000000, Obj: 0.372501, No Obj: 0.004001, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.502896, Class: 1.000000, Obj: 0.323951, No Obj: 0.003940, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.509593, Class: 1.000000, Obj: 0.351473, No Obj: 0.004738, Avg Recall: 0.596774, count: 124 -Region Avg IOU: 0.519812, Class: 1.000000, Obj: 0.348624, No Obj: 0.004317, Avg Recall: 0.642857, count: 140 -3298: 0.495983, 0.470404 avg, 0.001000 rate, 21.229721 seconds, 844288 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.503222, Class: 1.000000, Obj: 0.332267, No Obj: 0.003510, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.474954, Class: 1.000000, Obj: 0.342720, No Obj: 0.003422, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.552627, Class: 1.000000, Obj: 0.375931, No Obj: 0.003720, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.565895, Class: 1.000000, Obj: 0.348635, No Obj: 0.003970, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.569393, Class: 1.000000, Obj: 0.389153, No Obj: 0.003753, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.493806, Class: 1.000000, Obj: 0.381785, No Obj: 0.003626, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.521055, Class: 1.000000, Obj: 0.377939, No Obj: 0.003776, Avg Recall: 0.586207, count: 116 -Region Avg IOU: 0.537886, Class: 1.000000, Obj: 0.372794, No Obj: 0.003539, Avg Recall: 0.616279, count: 86 -3299: 0.432240, 0.466588 avg, 0.001000 rate, 23.980518 seconds, 844544 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.526904, Class: 1.000000, Obj: 0.292624, No Obj: 0.003387, Avg Recall: 0.569231, count: 130 -Region Avg IOU: 0.530701, Class: 1.000000, Obj: 0.367046, No Obj: 0.003629, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.560954, Class: 1.000000, Obj: 0.347996, No Obj: 0.003640, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.526628, Class: 1.000000, Obj: 0.312808, No Obj: 0.003585, Avg Recall: 0.588235, count: 119 -Region Avg IOU: 0.531114, Class: 1.000000, Obj: 0.352361, No Obj: 0.003092, Avg Recall: 0.639535, count: 86 -Region Avg IOU: 0.530669, Class: 1.000000, Obj: 0.337666, No Obj: 0.003312, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.537974, Class: 1.000000, Obj: 0.350506, No Obj: 0.004020, Avg Recall: 0.592593, count: 135 -Region Avg IOU: 0.524313, Class: 1.000000, Obj: 0.360080, No Obj: 0.003459, Avg Recall: 0.568421, count: 95 -3300: 0.478894, 0.467818 avg, 0.001000 rate, 22.318375 seconds, 844800 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000073 seconds -Region Avg IOU: 0.634978, Class: 1.000000, Obj: 0.347179, No Obj: 0.003442, Avg Recall: 0.800000, count: 125 -Region Avg IOU: 0.630553, Class: 1.000000, Obj: 0.395477, No Obj: 0.002926, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.628095, Class: 1.000000, Obj: 0.347418, No Obj: 0.003433, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.646572, Class: 1.000000, Obj: 0.413094, No Obj: 0.002826, Avg Recall: 0.864865, count: 111 -Region Avg IOU: 0.673073, Class: 1.000000, Obj: 0.397360, No Obj: 0.003418, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.613874, Class: 1.000000, Obj: 0.345592, No Obj: 0.003066, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.626112, Class: 1.000000, Obj: 0.407272, No Obj: 0.002748, Avg Recall: 0.725275, count: 91 -Region Avg IOU: 0.621863, Class: 1.000000, Obj: 0.381723, No Obj: 0.003034, Avg Recall: 0.779817, count: 109 -3301: 0.512940, 0.472330 avg, 0.001000 rate, 30.083183 seconds, 845056 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.636501, Class: 1.000000, Obj: 0.357015, No Obj: 0.002883, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.620592, Class: 1.000000, Obj: 0.302772, No Obj: 0.002685, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.658179, Class: 1.000000, Obj: 0.335605, No Obj: 0.002770, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.627940, Class: 1.000000, Obj: 0.411799, No Obj: 0.002728, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.636994, Class: 1.000000, Obj: 0.375453, No Obj: 0.003046, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.640633, Class: 1.000000, Obj: 0.316456, No Obj: 0.002776, Avg Recall: 0.791667, count: 120 -Region Avg IOU: 0.596527, Class: 1.000000, Obj: 0.397987, No Obj: 0.002517, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.611608, Class: 1.000000, Obj: 0.401641, No Obj: 0.002392, Avg Recall: 0.772277, count: 101 -3302: 0.479472, 0.473045 avg, 0.001000 rate, 28.073347 seconds, 845312 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.678901, Class: 1.000000, Obj: 0.334586, No Obj: 0.002928, Avg Recall: 0.895161, count: 124 -Region Avg IOU: 0.640647, Class: 1.000000, Obj: 0.337523, No Obj: 0.002763, Avg Recall: 0.785124, count: 121 -Region Avg IOU: 0.627049, Class: 1.000000, Obj: 0.334980, No Obj: 0.002359, Avg Recall: 0.796117, count: 103 -Region Avg IOU: 0.658566, Class: 1.000000, Obj: 0.383484, No Obj: 0.002106, Avg Recall: 0.817073, count: 82 -Region Avg IOU: 0.615373, Class: 1.000000, Obj: 0.343770, No Obj: 0.002697, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.619165, Class: 1.000000, Obj: 0.344016, No Obj: 0.002478, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.639238, Class: 1.000000, Obj: 0.312638, No Obj: 0.002835, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.639926, Class: 1.000000, Obj: 0.358400, No Obj: 0.002558, Avg Recall: 0.810526, count: 95 -3303: 0.490784, 0.474819 avg, 0.001000 rate, 28.818766 seconds, 845568 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.653222, Class: 1.000000, Obj: 0.366601, No Obj: 0.002735, Avg Recall: 0.804878, count: 123 -Region Avg IOU: 0.643498, Class: 1.000000, Obj: 0.339912, No Obj: 0.002131, Avg Recall: 0.773810, count: 84 -Region Avg IOU: 0.631464, Class: 1.000000, Obj: 0.341187, No Obj: 0.002282, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.668071, Class: 1.000000, Obj: 0.321791, No Obj: 0.002472, Avg Recall: 0.845361, count: 97 -Region Avg IOU: 0.655330, Class: 1.000000, Obj: 0.325909, No Obj: 0.002305, Avg Recall: 0.813953, count: 86 -Region Avg IOU: 0.600776, Class: 1.000000, Obj: 0.348491, No Obj: 0.002470, Avg Recall: 0.706349, count: 126 -Region Avg IOU: 0.655948, Class: 1.000000, Obj: 0.384252, No Obj: 0.002428, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.597292, Class: 1.000000, Obj: 0.314169, No Obj: 0.002512, Avg Recall: 0.730769, count: 130 -3304: 0.513044, 0.478641 avg, 0.001000 rate, 28.448736 seconds, 845824 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.633599, Class: 1.000000, Obj: 0.275740, No Obj: 0.002532, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.657246, Class: 1.000000, Obj: 0.341611, No Obj: 0.002326, Avg Recall: 0.852941, count: 102 -Region Avg IOU: 0.637443, Class: 1.000000, Obj: 0.262702, No Obj: 0.002321, Avg Recall: 0.795455, count: 132 -Region Avg IOU: 0.618531, Class: 1.000000, Obj: 0.307196, No Obj: 0.002131, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.624584, Class: 1.000000, Obj: 0.337584, No Obj: 0.002259, Avg Recall: 0.803571, count: 112 -Region Avg IOU: 0.627329, Class: 1.000000, Obj: 0.294606, No Obj: 0.002562, Avg Recall: 0.768000, count: 125 -Region Avg IOU: 0.593913, Class: 1.000000, Obj: 0.294330, No Obj: 0.002191, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.611249, Class: 1.000000, Obj: 0.270963, No Obj: 0.002163, Avg Recall: 0.743802, count: 121 -3305: 0.518680, 0.482645 avg, 0.001000 rate, 29.056442 seconds, 846080 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.674342, Class: 1.000000, Obj: 0.328129, No Obj: 0.002360, Avg Recall: 0.855670, count: 97 -Region Avg IOU: 0.641358, Class: 1.000000, Obj: 0.315325, No Obj: 0.002571, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.629677, Class: 1.000000, Obj: 0.354994, No Obj: 0.002515, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.638604, Class: 1.000000, Obj: 0.280407, No Obj: 0.002650, Avg Recall: 0.806452, count: 124 -Region Avg IOU: 0.656803, Class: 1.000000, Obj: 0.324654, No Obj: 0.002298, Avg Recall: 0.842105, count: 95 -Region Avg IOU: 0.548966, Class: 1.000000, Obj: 0.269649, No Obj: 0.002005, Avg Recall: 0.636364, count: 88 -Region Avg IOU: 0.669001, Class: 1.000000, Obj: 0.348105, No Obj: 0.002203, Avg Recall: 0.824176, count: 91 -Region Avg IOU: 0.640279, Class: 1.000000, Obj: 0.327666, No Obj: 0.002426, Avg Recall: 0.761538, count: 130 -3306: 0.486205, 0.483001 avg, 0.001000 rate, 30.594198 seconds, 846336 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.649173, Class: 1.000000, Obj: 0.354587, No Obj: 0.002112, Avg Recall: 0.837209, count: 86 -Region Avg IOU: 0.627308, Class: 1.000000, Obj: 0.316835, No Obj: 0.002764, Avg Recall: 0.834711, count: 121 -Region Avg IOU: 0.644084, Class: 1.000000, Obj: 0.347142, No Obj: 0.002633, Avg Recall: 0.755725, count: 131 -Region Avg IOU: 0.592991, Class: 1.000000, Obj: 0.296681, No Obj: 0.002434, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.628778, Class: 1.000000, Obj: 0.310558, No Obj: 0.002144, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.652122, Class: 1.000000, Obj: 0.257778, No Obj: 0.002324, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.621200, Class: 1.000000, Obj: 0.308442, No Obj: 0.002153, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.631666, Class: 1.000000, Obj: 0.319527, No Obj: 0.002042, Avg Recall: 0.817073, count: 82 -3307: 0.508038, 0.485505 avg, 0.001000 rate, 29.337347 seconds, 846592 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.631286, Class: 1.000000, Obj: 0.316810, No Obj: 0.002458, Avg Recall: 0.796748, count: 123 -Region Avg IOU: 0.681686, Class: 1.000000, Obj: 0.395532, No Obj: 0.002818, Avg Recall: 0.871795, count: 117 -Region Avg IOU: 0.677031, Class: 1.000000, Obj: 0.371860, No Obj: 0.002715, Avg Recall: 0.887850, count: 107 -Region Avg IOU: 0.626671, Class: 1.000000, Obj: 0.354253, No Obj: 0.002324, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.634426, Class: 1.000000, Obj: 0.374855, No Obj: 0.002348, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.642972, Class: 1.000000, Obj: 0.395803, No Obj: 0.002452, Avg Recall: 0.822430, count: 107 -Region Avg IOU: 0.680346, Class: 1.000000, Obj: 0.341555, No Obj: 0.002776, Avg Recall: 0.825581, count: 86 -Region Avg IOU: 0.621793, Class: 1.000000, Obj: 0.379670, No Obj: 0.002475, Avg Recall: 0.761062, count: 113 -3308: 0.445297, 0.481484 avg, 0.001000 rate, 29.969732 seconds, 846848 images -Loaded: 0.000119 seconds -Region Avg IOU: 0.603896, Class: 1.000000, Obj: 0.327671, No Obj: 0.002209, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.631058, Class: 1.000000, Obj: 0.349293, No Obj: 0.002470, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.655601, Class: 1.000000, Obj: 0.346132, No Obj: 0.002640, Avg Recall: 0.865979, count: 97 -Region Avg IOU: 0.623888, Class: 1.000000, Obj: 0.339758, No Obj: 0.002514, Avg Recall: 0.826923, count: 104 -Region Avg IOU: 0.667935, Class: 1.000000, Obj: 0.347522, No Obj: 0.002564, Avg Recall: 0.831683, count: 101 -Region Avg IOU: 0.595827, Class: 1.000000, Obj: 0.322337, No Obj: 0.002415, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.628463, Class: 1.000000, Obj: 0.327529, No Obj: 0.002736, Avg Recall: 0.796875, count: 128 -Region Avg IOU: 0.657377, Class: 1.000000, Obj: 0.333653, No Obj: 0.003111, Avg Recall: 0.816176, count: 136 -3309: 0.523885, 0.485724 avg, 0.001000 rate, 29.842516 seconds, 847104 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.656725, Class: 1.000000, Obj: 0.369772, No Obj: 0.002831, Avg Recall: 0.824000, count: 125 -Region Avg IOU: 0.650417, Class: 1.000000, Obj: 0.336689, No Obj: 0.002362, Avg Recall: 0.829787, count: 94 -Region Avg IOU: 0.683274, Class: 1.000000, Obj: 0.389309, No Obj: 0.002600, Avg Recall: 0.904255, count: 94 -Region Avg IOU: 0.659196, Class: 1.000000, Obj: 0.404928, No Obj: 0.002498, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.657892, Class: 1.000000, Obj: 0.420770, No Obj: 0.002843, Avg Recall: 0.841584, count: 101 -Region Avg IOU: 0.646705, Class: 1.000000, Obj: 0.356281, No Obj: 0.002701, Avg Recall: 0.836207, count: 116 -Region Avg IOU: 0.622411, Class: 1.000000, Obj: 0.331118, No Obj: 0.003035, Avg Recall: 0.807143, count: 140 -Region Avg IOU: 0.640043, Class: 1.000000, Obj: 0.345568, No Obj: 0.002729, Avg Recall: 0.798319, count: 119 -3310: 0.473598, 0.484512 avg, 0.001000 rate, 28.623327 seconds, 847360 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 13.006204 seconds -Region Avg IOU: 0.570737, Class: 1.000000, Obj: 0.328930, No Obj: 0.003162, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.601663, Class: 1.000000, Obj: 0.290240, No Obj: 0.002854, Avg Recall: 0.717647, count: 85 -Region Avg IOU: 0.547349, Class: 1.000000, Obj: 0.299509, No Obj: 0.003036, Avg Recall: 0.631206, count: 141 -Region Avg IOU: 0.648309, Class: 1.000000, Obj: 0.343779, No Obj: 0.003132, Avg Recall: 0.824000, count: 125 -Region Avg IOU: 0.537258, Class: 1.000000, Obj: 0.297377, No Obj: 0.002915, Avg Recall: 0.592593, count: 108 -Region Avg IOU: 0.537690, Class: 1.000000, Obj: 0.329593, No Obj: 0.002410, Avg Recall: 0.643678, count: 87 -Region Avg IOU: 0.585691, Class: 1.000000, Obj: 0.310968, No Obj: 0.002666, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.566321, Class: 1.000000, Obj: 0.346282, No Obj: 0.002991, Avg Recall: 0.692308, count: 104 -3311: 0.499032, 0.485964 avg, 0.001000 rate, 24.163719 seconds, 847616 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.599675, Class: 1.000000, Obj: 0.304197, No Obj: 0.002913, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.593823, Class: 1.000000, Obj: 0.341617, No Obj: 0.003253, Avg Recall: 0.688525, count: 122 -Region Avg IOU: 0.560268, Class: 1.000000, Obj: 0.312056, No Obj: 0.003088, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.640908, Class: 1.000000, Obj: 0.326267, No Obj: 0.002903, Avg Recall: 0.804124, count: 97 -Region Avg IOU: 0.590696, Class: 1.000000, Obj: 0.316524, No Obj: 0.003072, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.584664, Class: 1.000000, Obj: 0.352731, No Obj: 0.003173, Avg Recall: 0.663866, count: 119 -Region Avg IOU: 0.581356, Class: 1.000000, Obj: 0.345307, No Obj: 0.003004, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.590974, Class: 1.000000, Obj: 0.321412, No Obj: 0.002819, Avg Recall: 0.725490, count: 102 -3312: 0.435555, 0.480923 avg, 0.001000 rate, 24.937891 seconds, 847872 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.601637, Class: 1.000000, Obj: 0.387837, No Obj: 0.003480, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.580144, Class: 1.000000, Obj: 0.393092, No Obj: 0.003029, Avg Recall: 0.659091, count: 88 -Region Avg IOU: 0.571680, Class: 1.000000, Obj: 0.332102, No Obj: 0.003429, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.614943, Class: 1.000000, Obj: 0.348152, No Obj: 0.003216, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.592227, Class: 1.000000, Obj: 0.391726, No Obj: 0.003518, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.536015, Class: 1.000000, Obj: 0.339927, No Obj: 0.002782, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.630455, Class: 1.000000, Obj: 0.388338, No Obj: 0.003496, Avg Recall: 0.765625, count: 128 -Region Avg IOU: 0.576854, Class: 1.000000, Obj: 0.357416, No Obj: 0.003212, Avg Recall: 0.699029, count: 103 -3313: 0.450905, 0.477921 avg, 0.001000 rate, 25.220692 seconds, 848128 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.576792, Class: 1.000000, Obj: 0.388987, No Obj: 0.003438, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.599458, Class: 1.000000, Obj: 0.389230, No Obj: 0.003520, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.605288, Class: 1.000000, Obj: 0.378448, No Obj: 0.003465, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.615585, Class: 1.000000, Obj: 0.372155, No Obj: 0.003579, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.603204, Class: 1.000000, Obj: 0.365202, No Obj: 0.003632, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.577818, Class: 1.000000, Obj: 0.367474, No Obj: 0.003220, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.587525, Class: 1.000000, Obj: 0.406038, No Obj: 0.003155, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.594872, Class: 1.000000, Obj: 0.327197, No Obj: 0.003605, Avg Recall: 0.721739, count: 115 -3314: 0.447748, 0.474904 avg, 0.001000 rate, 26.060513 seconds, 848384 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.608338, Class: 1.000000, Obj: 0.398005, No Obj: 0.003546, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.524372, Class: 1.000000, Obj: 0.357033, No Obj: 0.003317, Avg Recall: 0.588889, count: 90 -Region Avg IOU: 0.587963, Class: 1.000000, Obj: 0.408795, No Obj: 0.003366, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.559555, Class: 1.000000, Obj: 0.402304, No Obj: 0.003714, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.590533, Class: 1.000000, Obj: 0.298348, No Obj: 0.004086, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.575659, Class: 1.000000, Obj: 0.387778, No Obj: 0.003244, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.618398, Class: 1.000000, Obj: 0.370811, No Obj: 0.003771, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.589418, Class: 1.000000, Obj: 0.369910, No Obj: 0.003846, Avg Recall: 0.700000, count: 130 -3315: 0.459866, 0.473400 avg, 0.001000 rate, 24.048628 seconds, 848640 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.567835, Class: 1.000000, Obj: 0.386248, No Obj: 0.004255, Avg Recall: 0.650000, count: 140 -Region Avg IOU: 0.572266, Class: 1.000000, Obj: 0.380175, No Obj: 0.003374, Avg Recall: 0.682927, count: 82 -Region Avg IOU: 0.605436, Class: 1.000000, Obj: 0.448000, No Obj: 0.004336, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.606977, Class: 1.000000, Obj: 0.395678, No Obj: 0.004465, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.530084, Class: 1.000000, Obj: 0.364296, No Obj: 0.003210, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.629312, Class: 1.000000, Obj: 0.432977, No Obj: 0.003724, Avg Recall: 0.805085, count: 118 -Region Avg IOU: 0.519911, Class: 1.000000, Obj: 0.341118, No Obj: 0.003523, Avg Recall: 0.596639, count: 119 -Region Avg IOU: 0.573067, Class: 1.000000, Obj: 0.413600, No Obj: 0.003655, Avg Recall: 0.699029, count: 103 -3316: 0.483314, 0.474391 avg, 0.001000 rate, 25.077042 seconds, 848896 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.573004, Class: 1.000000, Obj: 0.356207, No Obj: 0.004088, Avg Recall: 0.666667, count: 126 -Region Avg IOU: 0.595862, Class: 1.000000, Obj: 0.418529, No Obj: 0.004256, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.623964, Class: 1.000000, Obj: 0.371014, No Obj: 0.004475, Avg Recall: 0.777778, count: 126 -Region Avg IOU: 0.591689, Class: 1.000000, Obj: 0.428319, No Obj: 0.003667, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.604122, Class: 1.000000, Obj: 0.370740, No Obj: 0.003379, Avg Recall: 0.703704, count: 81 -Region Avg IOU: 0.567497, Class: 1.000000, Obj: 0.383382, No Obj: 0.004188, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.637196, Class: 1.000000, Obj: 0.432453, No Obj: 0.003970, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.553926, Class: 1.000000, Obj: 0.353837, No Obj: 0.003768, Avg Recall: 0.625000, count: 104 -3317: 0.489762, 0.475928 avg, 0.001000 rate, 24.751068 seconds, 849152 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.556226, Class: 1.000000, Obj: 0.354440, No Obj: 0.003776, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.595975, Class: 1.000000, Obj: 0.402285, No Obj: 0.004029, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.578994, Class: 1.000000, Obj: 0.341396, No Obj: 0.003340, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.583139, Class: 1.000000, Obj: 0.395760, No Obj: 0.003857, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.552149, Class: 1.000000, Obj: 0.400129, No Obj: 0.003835, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.592265, Class: 1.000000, Obj: 0.415308, No Obj: 0.003399, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.561840, Class: 1.000000, Obj: 0.340319, No Obj: 0.003754, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.510597, Class: 1.000000, Obj: 0.349606, No Obj: 0.003742, Avg Recall: 0.600000, count: 135 -3318: 0.457085, 0.474044 avg, 0.001000 rate, 25.636372 seconds, 849408 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.573164, Class: 1.000000, Obj: 0.403217, No Obj: 0.003286, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.560395, Class: 1.000000, Obj: 0.347595, No Obj: 0.003747, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.563210, Class: 1.000000, Obj: 0.383899, No Obj: 0.003454, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.642368, Class: 1.000000, Obj: 0.414941, No Obj: 0.003945, Avg Recall: 0.847619, count: 105 -Region Avg IOU: 0.556712, Class: 1.000000, Obj: 0.351311, No Obj: 0.003879, Avg Recall: 0.649007, count: 151 -Region Avg IOU: 0.540163, Class: 1.000000, Obj: 0.355739, No Obj: 0.003407, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.599655, Class: 1.000000, Obj: 0.417365, No Obj: 0.003633, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.591312, Class: 1.000000, Obj: 0.403054, No Obj: 0.003743, Avg Recall: 0.685185, count: 108 -3319: 0.493918, 0.476031 avg, 0.001000 rate, 24.670061 seconds, 849664 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.632028, Class: 1.000000, Obj: 0.373525, No Obj: 0.003850, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.512910, Class: 1.000000, Obj: 0.363019, No Obj: 0.002850, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.557893, Class: 1.000000, Obj: 0.386469, No Obj: 0.003185, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.569377, Class: 1.000000, Obj: 0.339328, No Obj: 0.003982, Avg Recall: 0.657343, count: 143 -Region Avg IOU: 0.599967, Class: 1.000000, Obj: 0.362232, No Obj: 0.003471, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.608271, Class: 1.000000, Obj: 0.377581, No Obj: 0.003949, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.553739, Class: 1.000000, Obj: 0.333621, No Obj: 0.003303, Avg Recall: 0.627907, count: 129 -Region Avg IOU: 0.582272, Class: 1.000000, Obj: 0.364564, No Obj: 0.003481, Avg Recall: 0.707965, count: 113 -3320: 0.487881, 0.477216 avg, 0.001000 rate, 24.196688 seconds, 849920 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 13.867517 seconds -Region Avg IOU: 0.565604, Class: 1.000000, Obj: 0.327446, No Obj: 0.002772, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.548989, Class: 1.000000, Obj: 0.326313, No Obj: 0.003325, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.557844, Class: 1.000000, Obj: 0.299500, No Obj: 0.003261, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.527019, Class: 1.000000, Obj: 0.316436, No Obj: 0.002826, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.618543, Class: 1.000000, Obj: 0.350977, No Obj: 0.003061, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.540236, Class: 1.000000, Obj: 0.317427, No Obj: 0.003482, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.542533, Class: 1.000000, Obj: 0.291393, No Obj: 0.002937, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.606811, Class: 1.000000, Obj: 0.328236, No Obj: 0.003416, Avg Recall: 0.723810, count: 105 -3321: 0.487325, 0.478227 avg, 0.001000 rate, 22.699753 seconds, 850176 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.561377, Class: 1.000000, Obj: 0.297257, No Obj: 0.003381, Avg Recall: 0.682353, count: 85 -Region Avg IOU: 0.505274, Class: 1.000000, Obj: 0.277984, No Obj: 0.003128, Avg Recall: 0.552845, count: 123 -Region Avg IOU: 0.544567, Class: 1.000000, Obj: 0.292863, No Obj: 0.002880, Avg Recall: 0.658824, count: 85 -Region Avg IOU: 0.563430, Class: 1.000000, Obj: 0.297465, No Obj: 0.004447, Avg Recall: 0.654412, count: 136 -Region Avg IOU: 0.476474, Class: 1.000000, Obj: 0.283706, No Obj: 0.003127, Avg Recall: 0.544715, count: 123 -Region Avg IOU: 0.529061, Class: 1.000000, Obj: 0.290293, No Obj: 0.002879, Avg Recall: 0.625000, count: 88 -Region Avg IOU: 0.602509, Class: 1.000000, Obj: 0.328441, No Obj: 0.003403, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.507184, Class: 1.000000, Obj: 0.283383, No Obj: 0.003175, Avg Recall: 0.600000, count: 120 -3322: 0.469363, 0.477341 avg, 0.001000 rate, 23.248140 seconds, 850432 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.559893, Class: 1.000000, Obj: 0.298705, No Obj: 0.003180, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.553405, Class: 1.000000, Obj: 0.312927, No Obj: 0.003233, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.551305, Class: 1.000000, Obj: 0.311539, No Obj: 0.003650, Avg Recall: 0.646154, count: 130 -Region Avg IOU: 0.583246, Class: 1.000000, Obj: 0.323989, No Obj: 0.004499, Avg Recall: 0.643750, count: 160 -Region Avg IOU: 0.541231, Class: 1.000000, Obj: 0.362590, No Obj: 0.002842, Avg Recall: 0.574713, count: 87 -Region Avg IOU: 0.532663, Class: 1.000000, Obj: 0.313220, No Obj: 0.003428, Avg Recall: 0.620155, count: 129 -Region Avg IOU: 0.549195, Class: 1.000000, Obj: 0.320025, No Obj: 0.003077, Avg Recall: 0.619565, count: 92 -Region Avg IOU: 0.535353, Class: 1.000000, Obj: 0.302802, No Obj: 0.003584, Avg Recall: 0.611111, count: 126 -3323: 0.499046, 0.479511 avg, 0.001000 rate, 24.615484 seconds, 850688 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.582355, Class: 1.000000, Obj: 0.354053, No Obj: 0.003202, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.598299, Class: 1.000000, Obj: 0.322859, No Obj: 0.003451, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.573643, Class: 1.000000, Obj: 0.343059, No Obj: 0.003794, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.541287, Class: 1.000000, Obj: 0.307118, No Obj: 0.003754, Avg Recall: 0.619048, count: 126 -Region Avg IOU: 0.543063, Class: 1.000000, Obj: 0.291532, No Obj: 0.003444, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.556122, Class: 1.000000, Obj: 0.317314, No Obj: 0.003323, Avg Recall: 0.626374, count: 91 -Region Avg IOU: 0.582794, Class: 1.000000, Obj: 0.379545, No Obj: 0.003516, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.565763, Class: 1.000000, Obj: 0.322877, No Obj: 0.003846, Avg Recall: 0.705426, count: 129 -3324: 0.543438, 0.485904 avg, 0.001000 rate, 22.730772 seconds, 850944 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.562983, Class: 1.000000, Obj: 0.364654, No Obj: 0.003698, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.613714, Class: 1.000000, Obj: 0.334858, No Obj: 0.003198, Avg Recall: 0.755814, count: 86 -Region Avg IOU: 0.606666, Class: 1.000000, Obj: 0.350033, No Obj: 0.003581, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.536171, Class: 1.000000, Obj: 0.289907, No Obj: 0.003765, Avg Recall: 0.644628, count: 121 -Region Avg IOU: 0.580179, Class: 1.000000, Obj: 0.383234, No Obj: 0.003534, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.538308, Class: 1.000000, Obj: 0.296137, No Obj: 0.003723, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.631705, Class: 1.000000, Obj: 0.434500, No Obj: 0.003360, Avg Recall: 0.729730, count: 74 -Region Avg IOU: 0.550611, Class: 1.000000, Obj: 0.352407, No Obj: 0.003558, Avg Recall: 0.675439, count: 114 -3325: 0.414040, 0.478718 avg, 0.001000 rate, 23.440422 seconds, 851200 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.556711, Class: 1.000000, Obj: 0.336662, No Obj: 0.003594, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.598759, Class: 1.000000, Obj: 0.382968, No Obj: 0.004094, Avg Recall: 0.693548, count: 124 -Region Avg IOU: 0.566277, Class: 1.000000, Obj: 0.343845, No Obj: 0.004079, Avg Recall: 0.664062, count: 128 -Region Avg IOU: 0.540083, Class: 1.000000, Obj: 0.351894, No Obj: 0.003803, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.551712, Class: 1.000000, Obj: 0.369321, No Obj: 0.004240, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.512191, Class: 1.000000, Obj: 0.352223, No Obj: 0.003960, Avg Recall: 0.551181, count: 127 -Region Avg IOU: 0.534350, Class: 1.000000, Obj: 0.360084, No Obj: 0.003966, Avg Recall: 0.600000, count: 125 -Region Avg IOU: 0.463701, Class: 1.000000, Obj: 0.261329, No Obj: 0.003522, Avg Recall: 0.489655, count: 145 -3326: 0.537172, 0.484563 avg, 0.001000 rate, 23.929085 seconds, 851456 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.561617, Class: 1.000000, Obj: 0.386660, No Obj: 0.004122, Avg Recall: 0.654321, count: 81 -Region Avg IOU: 0.553931, Class: 1.000000, Obj: 0.356729, No Obj: 0.004328, Avg Recall: 0.664000, count: 125 -Region Avg IOU: 0.570619, Class: 1.000000, Obj: 0.382695, No Obj: 0.004655, Avg Recall: 0.679389, count: 131 -Region Avg IOU: 0.544072, Class: 1.000000, Obj: 0.401482, No Obj: 0.003850, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.554103, Class: 1.000000, Obj: 0.364509, No Obj: 0.003745, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.577211, Class: 1.000000, Obj: 0.408338, No Obj: 0.004682, Avg Recall: 0.674074, count: 135 -Region Avg IOU: 0.579315, Class: 1.000000, Obj: 0.355642, No Obj: 0.004380, Avg Recall: 0.686567, count: 134 -Region Avg IOU: 0.553916, Class: 1.000000, Obj: 0.404548, No Obj: 0.003325, Avg Recall: 0.646154, count: 65 -3327: 0.450860, 0.481193 avg, 0.001000 rate, 22.924892 seconds, 851712 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.557386, Class: 1.000000, Obj: 0.357674, No Obj: 0.004144, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.583977, Class: 1.000000, Obj: 0.397982, No Obj: 0.005192, Avg Recall: 0.671756, count: 131 -Region Avg IOU: 0.594924, Class: 1.000000, Obj: 0.375640, No Obj: 0.004592, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.519104, Class: 1.000000, Obj: 0.401406, No Obj: 0.003753, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.589240, Class: 1.000000, Obj: 0.399590, No Obj: 0.004128, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.597939, Class: 1.000000, Obj: 0.396745, No Obj: 0.004307, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.567504, Class: 1.000000, Obj: 0.394422, No Obj: 0.004098, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.588461, Class: 1.000000, Obj: 0.372159, No Obj: 0.004826, Avg Recall: 0.692308, count: 117 -3328: 0.462018, 0.479275 avg, 0.001000 rate, 22.824354 seconds, 851968 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.573536, Class: 1.000000, Obj: 0.411948, No Obj: 0.003766, Avg Recall: 0.652174, count: 92 -Region Avg IOU: 0.517513, Class: 1.000000, Obj: 0.338775, No Obj: 0.003811, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.606254, Class: 1.000000, Obj: 0.405806, No Obj: 0.004811, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.581109, Class: 1.000000, Obj: 0.414279, No Obj: 0.004206, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.527981, Class: 1.000000, Obj: 0.403632, No Obj: 0.003850, Avg Recall: 0.588889, count: 90 -Region Avg IOU: 0.581812, Class: 1.000000, Obj: 0.416931, No Obj: 0.004133, Avg Recall: 0.679012, count: 81 -Region Avg IOU: 0.562302, Class: 1.000000, Obj: 0.406474, No Obj: 0.004094, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.558289, Class: 1.000000, Obj: 0.408756, No Obj: 0.004095, Avg Recall: 0.663366, count: 101 -3329: 0.381286, 0.469476 avg, 0.001000 rate, 25.144125 seconds, 852224 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.563305, Class: 1.000000, Obj: 0.392882, No Obj: 0.004972, Avg Recall: 0.653543, count: 127 -Region Avg IOU: 0.552931, Class: 1.000000, Obj: 0.355223, No Obj: 0.004436, Avg Recall: 0.641026, count: 117 -Region Avg IOU: 0.538976, Class: 1.000000, Obj: 0.400883, No Obj: 0.003987, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.537939, Class: 1.000000, Obj: 0.342642, No Obj: 0.004393, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.568375, Class: 1.000000, Obj: 0.420922, No Obj: 0.003767, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.553089, Class: 1.000000, Obj: 0.401774, No Obj: 0.004473, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.511530, Class: 1.000000, Obj: 0.369186, No Obj: 0.004246, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.586238, Class: 1.000000, Obj: 0.403304, No Obj: 0.004465, Avg Recall: 0.701754, count: 114 -3330: 0.478066, 0.470335 avg, 0.001000 rate, 23.771410 seconds, 852480 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000074 seconds -Region Avg IOU: 0.621794, Class: 1.000000, Obj: 0.454135, No Obj: 0.003704, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.605148, Class: 1.000000, Obj: 0.427844, No Obj: 0.003685, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.579217, Class: 1.000000, Obj: 0.376855, No Obj: 0.003706, Avg Recall: 0.689922, count: 129 -Region Avg IOU: 0.646339, Class: 1.000000, Obj: 0.388153, No Obj: 0.003871, Avg Recall: 0.796610, count: 118 -Region Avg IOU: 0.610333, Class: 1.000000, Obj: 0.435809, No Obj: 0.003748, Avg Recall: 0.755725, count: 131 -Region Avg IOU: 0.629386, Class: 1.000000, Obj: 0.466107, No Obj: 0.004185, Avg Recall: 0.773050, count: 141 -Region Avg IOU: 0.600938, Class: 1.000000, Obj: 0.417980, No Obj: 0.003299, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.591248, Class: 1.000000, Obj: 0.458162, No Obj: 0.003500, Avg Recall: 0.673469, count: 98 -3331: 0.471756, 0.470477 avg, 0.001000 rate, 27.938540 seconds, 852736 images -Loaded: 0.000132 seconds -Region Avg IOU: 0.606611, Class: 1.000000, Obj: 0.451732, No Obj: 0.003317, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.588744, Class: 1.000000, Obj: 0.385837, No Obj: 0.003563, Avg Recall: 0.682171, count: 129 -Region Avg IOU: 0.596981, Class: 1.000000, Obj: 0.447635, No Obj: 0.002813, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.565871, Class: 1.000000, Obj: 0.353611, No Obj: 0.003904, Avg Recall: 0.697368, count: 152 -Region Avg IOU: 0.617086, Class: 1.000000, Obj: 0.459686, No Obj: 0.003426, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.603754, Class: 1.000000, Obj: 0.446716, No Obj: 0.003747, Avg Recall: 0.744186, count: 129 -Region Avg IOU: 0.617365, Class: 1.000000, Obj: 0.452106, No Obj: 0.003725, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.583182, Class: 1.000000, Obj: 0.422596, No Obj: 0.003822, Avg Recall: 0.685535, count: 159 -3332: 0.502568, 0.473686 avg, 0.001000 rate, 26.538755 seconds, 852992 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.574546, Class: 1.000000, Obj: 0.396349, No Obj: 0.003074, Avg Recall: 0.677419, count: 124 -Region Avg IOU: 0.638669, Class: 1.000000, Obj: 0.383249, No Obj: 0.003311, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.643217, Class: 1.000000, Obj: 0.410571, No Obj: 0.003279, Avg Recall: 0.789062, count: 128 -Region Avg IOU: 0.616394, Class: 1.000000, Obj: 0.404211, No Obj: 0.003374, Avg Recall: 0.750000, count: 132 -Region Avg IOU: 0.625478, Class: 1.000000, Obj: 0.466073, No Obj: 0.002619, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.627993, Class: 1.000000, Obj: 0.382736, No Obj: 0.003118, Avg Recall: 0.791667, count: 120 -Region Avg IOU: 0.610979, Class: 1.000000, Obj: 0.361134, No Obj: 0.003346, Avg Recall: 0.778761, count: 113 -Region Avg IOU: 0.575899, Class: 1.000000, Obj: 0.337804, No Obj: 0.003020, Avg Recall: 0.705882, count: 119 -3333: 0.490568, 0.475374 avg, 0.001000 rate, 26.998135 seconds, 853248 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.598531, Class: 1.000000, Obj: 0.293774, No Obj: 0.002625, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.634179, Class: 1.000000, Obj: 0.345731, No Obj: 0.002752, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.633281, Class: 1.000000, Obj: 0.365159, No Obj: 0.002703, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.593130, Class: 1.000000, Obj: 0.339518, No Obj: 0.002660, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.618205, Class: 1.000000, Obj: 0.380688, No Obj: 0.002746, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.608608, Class: 1.000000, Obj: 0.371258, No Obj: 0.002729, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.639870, Class: 1.000000, Obj: 0.355232, No Obj: 0.002645, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.580249, Class: 1.000000, Obj: 0.429493, No Obj: 0.002282, Avg Recall: 0.693182, count: 88 -3334: 0.473912, 0.475228 avg, 0.001000 rate, 27.537481 seconds, 853504 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.626423, Class: 1.000000, Obj: 0.328165, No Obj: 0.002930, Avg Recall: 0.769841, count: 126 -Region Avg IOU: 0.593262, Class: 1.000000, Obj: 0.316168, No Obj: 0.002637, Avg Recall: 0.755725, count: 131 -Region Avg IOU: 0.642487, Class: 1.000000, Obj: 0.305428, No Obj: 0.002729, Avg Recall: 0.825688, count: 109 -Region Avg IOU: 0.603962, Class: 1.000000, Obj: 0.310663, No Obj: 0.002609, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.628344, Class: 1.000000, Obj: 0.309307, No Obj: 0.002368, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.599219, Class: 1.000000, Obj: 0.332445, No Obj: 0.002335, Avg Recall: 0.714286, count: 84 -Region Avg IOU: 0.604783, Class: 1.000000, Obj: 0.280357, No Obj: 0.002664, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.662836, Class: 1.000000, Obj: 0.338283, No Obj: 0.002546, Avg Recall: 0.825688, count: 109 -3335: 0.474189, 0.475124 avg, 0.001000 rate, 27.740969 seconds, 853760 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.623041, Class: 1.000000, Obj: 0.288348, No Obj: 0.002156, Avg Recall: 0.758621, count: 87 -Region Avg IOU: 0.605931, Class: 1.000000, Obj: 0.291749, No Obj: 0.002874, Avg Recall: 0.765625, count: 192 -Region Avg IOU: 0.658417, Class: 1.000000, Obj: 0.290022, No Obj: 0.002276, Avg Recall: 0.855856, count: 111 -Region Avg IOU: 0.620495, Class: 1.000000, Obj: 0.325090, No Obj: 0.001941, Avg Recall: 0.784810, count: 79 -Region Avg IOU: 0.613344, Class: 1.000000, Obj: 0.276498, No Obj: 0.002442, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.591873, Class: 1.000000, Obj: 0.300059, No Obj: 0.002295, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.635054, Class: 1.000000, Obj: 0.296604, No Obj: 0.002586, Avg Recall: 0.791045, count: 134 -Region Avg IOU: 0.603876, Class: 1.000000, Obj: 0.281568, No Obj: 0.002419, Avg Recall: 0.725000, count: 120 -3336: 0.521134, 0.479725 avg, 0.001000 rate, 27.767656 seconds, 854016 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.612987, Class: 1.000000, Obj: 0.273502, No Obj: 0.002157, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.647080, Class: 1.000000, Obj: 0.349932, No Obj: 0.002072, Avg Recall: 0.761905, count: 84 -Region Avg IOU: 0.622395, Class: 1.000000, Obj: 0.285034, No Obj: 0.002346, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.649333, Class: 1.000000, Obj: 0.297683, No Obj: 0.002126, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.594137, Class: 1.000000, Obj: 0.277908, No Obj: 0.002168, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.591895, Class: 1.000000, Obj: 0.290485, No Obj: 0.002064, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.609574, Class: 1.000000, Obj: 0.270179, No Obj: 0.002139, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.624059, Class: 1.000000, Obj: 0.294019, No Obj: 0.002360, Avg Recall: 0.790909, count: 110 -3337: 0.437699, 0.475523 avg, 0.001000 rate, 26.876204 seconds, 854272 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.603381, Class: 1.000000, Obj: 0.261350, No Obj: 0.002263, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.624150, Class: 1.000000, Obj: 0.323563, No Obj: 0.002450, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.622984, Class: 1.000000, Obj: 0.297891, No Obj: 0.002157, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.570212, Class: 1.000000, Obj: 0.274566, No Obj: 0.002297, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.605101, Class: 1.000000, Obj: 0.332001, No Obj: 0.002339, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.598272, Class: 1.000000, Obj: 0.301357, No Obj: 0.002208, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.591309, Class: 1.000000, Obj: 0.291911, No Obj: 0.002406, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.673828, Class: 1.000000, Obj: 0.287788, No Obj: 0.002273, Avg Recall: 0.890000, count: 100 -3338: 0.444056, 0.472376 avg, 0.001000 rate, 27.045385 seconds, 854528 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.669459, Class: 1.000000, Obj: 0.377153, No Obj: 0.002356, Avg Recall: 0.820225, count: 89 -Region Avg IOU: 0.683585, Class: 1.000000, Obj: 0.373704, No Obj: 0.002293, Avg Recall: 0.860215, count: 93 -Region Avg IOU: 0.598678, Class: 1.000000, Obj: 0.302468, No Obj: 0.002467, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.640493, Class: 1.000000, Obj: 0.336388, No Obj: 0.002488, Avg Recall: 0.812500, count: 80 -Region Avg IOU: 0.602412, Class: 1.000000, Obj: 0.308391, No Obj: 0.002659, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.638277, Class: 1.000000, Obj: 0.340956, No Obj: 0.002425, Avg Recall: 0.791209, count: 91 -Region Avg IOU: 0.633660, Class: 1.000000, Obj: 0.321269, No Obj: 0.002471, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.574545, Class: 1.000000, Obj: 0.300335, No Obj: 0.002479, Avg Recall: 0.672269, count: 119 -3339: 0.427988, 0.467937 avg, 0.001000 rate, 26.730936 seconds, 854784 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.664321, Class: 1.000000, Obj: 0.376947, No Obj: 0.002634, Avg Recall: 0.868132, count: 91 -Region Avg IOU: 0.632134, Class: 1.000000, Obj: 0.365201, No Obj: 0.002706, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.622030, Class: 1.000000, Obj: 0.365695, No Obj: 0.002426, Avg Recall: 0.733333, count: 75 -Region Avg IOU: 0.599029, Class: 1.000000, Obj: 0.337290, No Obj: 0.002349, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.580212, Class: 1.000000, Obj: 0.328205, No Obj: 0.002632, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.617009, Class: 1.000000, Obj: 0.346650, No Obj: 0.002370, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.654947, Class: 1.000000, Obj: 0.363946, No Obj: 0.002793, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.643032, Class: 1.000000, Obj: 0.357307, No Obj: 0.002375, Avg Recall: 0.752809, count: 89 -3340: 0.399097, 0.461053 avg, 0.001000 rate, 26.928076 seconds, 855040 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000064 seconds -Region Avg IOU: 0.632620, Class: 1.000000, Obj: 0.358181, No Obj: 0.003033, Avg Recall: 0.830508, count: 118 -Region Avg IOU: 0.573949, Class: 1.000000, Obj: 0.347821, No Obj: 0.002929, Avg Recall: 0.686567, count: 134 -Region Avg IOU: 0.626308, Class: 1.000000, Obj: 0.333319, No Obj: 0.003171, Avg Recall: 0.766667, count: 120 -Region Avg IOU: 0.631742, Class: 1.000000, Obj: 0.325178, No Obj: 0.003183, Avg Recall: 0.783333, count: 120 -Region Avg IOU: 0.653764, Class: 1.000000, Obj: 0.426210, No Obj: 0.003134, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.619809, Class: 1.000000, Obj: 0.325337, No Obj: 0.002923, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.643662, Class: 1.000000, Obj: 0.343782, No Obj: 0.002963, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.606206, Class: 1.000000, Obj: 0.345190, No Obj: 0.002664, Avg Recall: 0.725664, count: 113 -3341: 0.527840, 0.467732 avg, 0.001000 rate, 26.682240 seconds, 855296 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.629972, Class: 1.000000, Obj: 0.357660, No Obj: 0.002940, Avg Recall: 0.775000, count: 120 -Region Avg IOU: 0.587279, Class: 1.000000, Obj: 0.324316, No Obj: 0.002717, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.599850, Class: 1.000000, Obj: 0.361321, No Obj: 0.002765, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.668015, Class: 1.000000, Obj: 0.399171, No Obj: 0.003039, Avg Recall: 0.861386, count: 101 -Region Avg IOU: 0.660855, Class: 1.000000, Obj: 0.407808, No Obj: 0.003102, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.608667, Class: 1.000000, Obj: 0.351987, No Obj: 0.003382, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.688554, Class: 1.000000, Obj: 0.407688, No Obj: 0.002828, Avg Recall: 0.877551, count: 98 -Region Avg IOU: 0.659625, Class: 1.000000, Obj: 0.438541, No Obj: 0.003110, Avg Recall: 0.859813, count: 107 -3342: 0.444186, 0.465377 avg, 0.001000 rate, 29.358826 seconds, 855552 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.632266, Class: 1.000000, Obj: 0.398976, No Obj: 0.003040, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.609684, Class: 1.000000, Obj: 0.372602, No Obj: 0.003229, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.656613, Class: 1.000000, Obj: 0.393383, No Obj: 0.003227, Avg Recall: 0.819672, count: 122 -Region Avg IOU: 0.649003, Class: 1.000000, Obj: 0.419542, No Obj: 0.003243, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.612162, Class: 1.000000, Obj: 0.417878, No Obj: 0.002948, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.620442, Class: 1.000000, Obj: 0.423024, No Obj: 0.002654, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.658609, Class: 1.000000, Obj: 0.428239, No Obj: 0.003223, Avg Recall: 0.834862, count: 109 -Region Avg IOU: 0.606671, Class: 1.000000, Obj: 0.373097, No Obj: 0.003591, Avg Recall: 0.746479, count: 142 -3343: 0.443870, 0.463227 avg, 0.001000 rate, 28.391357 seconds, 855808 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.592757, Class: 1.000000, Obj: 0.345177, No Obj: 0.002739, Avg Recall: 0.779070, count: 86 -Region Avg IOU: 0.595246, Class: 1.000000, Obj: 0.380679, No Obj: 0.002808, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.626241, Class: 1.000000, Obj: 0.375755, No Obj: 0.003454, Avg Recall: 0.774194, count: 124 -Region Avg IOU: 0.649749, Class: 1.000000, Obj: 0.334290, No Obj: 0.003413, Avg Recall: 0.842520, count: 127 -Region Avg IOU: 0.659633, Class: 1.000000, Obj: 0.396705, No Obj: 0.003328, Avg Recall: 0.819149, count: 94 -Region Avg IOU: 0.630689, Class: 1.000000, Obj: 0.409612, No Obj: 0.003036, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.598356, Class: 1.000000, Obj: 0.406661, No Obj: 0.003290, Avg Recall: 0.703704, count: 135 -Region Avg IOU: 0.617141, Class: 1.000000, Obj: 0.446092, No Obj: 0.002882, Avg Recall: 0.720930, count: 86 -3344: 0.465165, 0.463420 avg, 0.001000 rate, 28.990070 seconds, 856064 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.628557, Class: 1.000000, Obj: 0.440537, No Obj: 0.002695, Avg Recall: 0.823529, count: 85 -Region Avg IOU: 0.601671, Class: 1.000000, Obj: 0.350754, No Obj: 0.003169, Avg Recall: 0.710526, count: 114 -Region Avg IOU: 0.639379, Class: 1.000000, Obj: 0.386054, No Obj: 0.002896, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.593416, Class: 1.000000, Obj: 0.396157, No Obj: 0.002849, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.600398, Class: 1.000000, Obj: 0.428496, No Obj: 0.002696, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.620830, Class: 1.000000, Obj: 0.446682, No Obj: 0.002889, Avg Recall: 0.802083, count: 96 -Region Avg IOU: 0.610261, Class: 1.000000, Obj: 0.315133, No Obj: 0.003145, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.617046, Class: 1.000000, Obj: 0.374002, No Obj: 0.002797, Avg Recall: 0.752381, count: 105 -3345: 0.446935, 0.461772 avg, 0.001000 rate, 27.950686 seconds, 856320 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.667209, Class: 1.000000, Obj: 0.398186, No Obj: 0.003172, Avg Recall: 0.866667, count: 120 -Region Avg IOU: 0.587314, Class: 1.000000, Obj: 0.366639, No Obj: 0.002676, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.603492, Class: 1.000000, Obj: 0.366147, No Obj: 0.002393, Avg Recall: 0.714286, count: 84 -Region Avg IOU: 0.610838, Class: 1.000000, Obj: 0.375669, No Obj: 0.002874, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.624368, Class: 1.000000, Obj: 0.414079, No Obj: 0.002994, Avg Recall: 0.777778, count: 126 -Region Avg IOU: 0.620414, Class: 1.000000, Obj: 0.370730, No Obj: 0.002837, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.659112, Class: 1.000000, Obj: 0.414270, No Obj: 0.003187, Avg Recall: 0.815789, count: 114 -Region Avg IOU: 0.618383, Class: 1.000000, Obj: 0.361079, No Obj: 0.002995, Avg Recall: 0.743363, count: 113 -3346: 0.480977, 0.463692 avg, 0.001000 rate, 28.289064 seconds, 856576 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.612161, Class: 1.000000, Obj: 0.367124, No Obj: 0.002857, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.621132, Class: 1.000000, Obj: 0.383355, No Obj: 0.002461, Avg Recall: 0.759036, count: 83 -Region Avg IOU: 0.605154, Class: 1.000000, Obj: 0.340685, No Obj: 0.002828, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.640977, Class: 1.000000, Obj: 0.415405, No Obj: 0.002895, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.660605, Class: 1.000000, Obj: 0.397159, No Obj: 0.002782, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.603344, Class: 1.000000, Obj: 0.307095, No Obj: 0.002906, Avg Recall: 0.731544, count: 149 -Region Avg IOU: 0.648704, Class: 1.000000, Obj: 0.326135, No Obj: 0.002922, Avg Recall: 0.834862, count: 109 -Region Avg IOU: 0.611530, Class: 1.000000, Obj: 0.389454, No Obj: 0.002853, Avg Recall: 0.731481, count: 108 -3347: 0.447209, 0.462044 avg, 0.001000 rate, 28.238354 seconds, 856832 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.593146, Class: 1.000000, Obj: 0.366596, No Obj: 0.002365, Avg Recall: 0.721519, count: 79 -Region Avg IOU: 0.630211, Class: 1.000000, Obj: 0.368089, No Obj: 0.002638, Avg Recall: 0.744186, count: 129 -Region Avg IOU: 0.604837, Class: 1.000000, Obj: 0.343909, No Obj: 0.002889, Avg Recall: 0.735537, count: 121 -Region Avg IOU: 0.639341, Class: 1.000000, Obj: 0.352110, No Obj: 0.002875, Avg Recall: 0.789855, count: 138 -Region Avg IOU: 0.591746, Class: 1.000000, Obj: 0.364384, No Obj: 0.002653, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.629273, Class: 1.000000, Obj: 0.372452, No Obj: 0.002771, Avg Recall: 0.815789, count: 114 -Region Avg IOU: 0.610681, Class: 1.000000, Obj: 0.361958, No Obj: 0.002363, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.606982, Class: 1.000000, Obj: 0.325277, No Obj: 0.002578, Avg Recall: 0.736364, count: 110 -3348: 0.497730, 0.465613 avg, 0.001000 rate, 27.419844 seconds, 857088 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.656289, Class: 1.000000, Obj: 0.362336, No Obj: 0.002514, Avg Recall: 0.823529, count: 102 -Region Avg IOU: 0.567026, Class: 1.000000, Obj: 0.348507, No Obj: 0.002302, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.615624, Class: 1.000000, Obj: 0.369679, No Obj: 0.002570, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.624464, Class: 1.000000, Obj: 0.360036, No Obj: 0.002541, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.615883, Class: 1.000000, Obj: 0.301909, No Obj: 0.002465, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.593183, Class: 1.000000, Obj: 0.341475, No Obj: 0.002093, Avg Recall: 0.727273, count: 88 -Region Avg IOU: 0.564104, Class: 1.000000, Obj: 0.302167, No Obj: 0.002487, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.623869, Class: 1.000000, Obj: 0.347830, No Obj: 0.002477, Avg Recall: 0.752137, count: 117 -3349: 0.427474, 0.461799 avg, 0.001000 rate, 27.062824 seconds, 857344 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.644885, Class: 1.000000, Obj: 0.315441, No Obj: 0.002606, Avg Recall: 0.788136, count: 118 -Region Avg IOU: 0.622591, Class: 1.000000, Obj: 0.334690, No Obj: 0.002291, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.621560, Class: 1.000000, Obj: 0.338264, No Obj: 0.002436, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.643979, Class: 1.000000, Obj: 0.340909, No Obj: 0.002711, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.635499, Class: 1.000000, Obj: 0.321191, No Obj: 0.002390, Avg Recall: 0.779661, count: 118 -Region Avg IOU: 0.629744, Class: 1.000000, Obj: 0.324503, No Obj: 0.002396, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.608449, Class: 1.000000, Obj: 0.311050, No Obj: 0.002237, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.638255, Class: 1.000000, Obj: 0.326152, No Obj: 0.002498, Avg Recall: 0.785714, count: 98 -3350: 0.475960, 0.463215 avg, 0.001000 rate, 27.078627 seconds, 857600 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.549055 seconds -Region Avg IOU: 0.505979, Class: 1.000000, Obj: 0.245611, No Obj: 0.002653, Avg Recall: 0.528926, count: 121 -Region Avg IOU: 0.511708, Class: 1.000000, Obj: 0.238092, No Obj: 0.002596, Avg Recall: 0.586957, count: 92 -Region Avg IOU: 0.541275, Class: 1.000000, Obj: 0.252508, No Obj: 0.002940, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.528486, Class: 1.000000, Obj: 0.237599, No Obj: 0.002483, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.552920, Class: 1.000000, Obj: 0.255092, No Obj: 0.002949, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.508957, Class: 1.000000, Obj: 0.239183, No Obj: 0.002557, Avg Recall: 0.597938, count: 97 -Region Avg IOU: 0.526784, Class: 1.000000, Obj: 0.232322, No Obj: 0.003139, Avg Recall: 0.571429, count: 119 -Region Avg IOU: 0.544260, Class: 1.000000, Obj: 0.240993, No Obj: 0.003084, Avg Recall: 0.729167, count: 96 -3351: 0.511641, 0.468058 avg, 0.001000 rate, 23.266714 seconds, 857856 images -Loaded: 0.032797 seconds -Region Avg IOU: 0.497695, Class: 1.000000, Obj: 0.232186, No Obj: 0.002965, Avg Recall: 0.552448, count: 143 -Region Avg IOU: 0.531668, Class: 1.000000, Obj: 0.248483, No Obj: 0.002908, Avg Recall: 0.585366, count: 123 -Region Avg IOU: 0.620566, Class: 1.000000, Obj: 0.334493, No Obj: 0.002911, Avg Recall: 0.766234, count: 77 -Region Avg IOU: 0.560134, Class: 1.000000, Obj: 0.256531, No Obj: 0.003428, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.511799, Class: 1.000000, Obj: 0.259404, No Obj: 0.003142, Avg Recall: 0.567164, count: 134 -Region Avg IOU: 0.560085, Class: 1.000000, Obj: 0.263713, No Obj: 0.002754, Avg Recall: 0.670455, count: 88 -Region Avg IOU: 0.547720, Class: 1.000000, Obj: 0.266999, No Obj: 0.002903, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.519780, Class: 1.000000, Obj: 0.268041, No Obj: 0.002900, Avg Recall: 0.568627, count: 102 -3352: 0.472521, 0.468504 avg, 0.001000 rate, 21.855284 seconds, 858112 images -Loaded: 0.303033 seconds -Region Avg IOU: 0.528006, Class: 1.000000, Obj: 0.302753, No Obj: 0.003583, Avg Recall: 0.582090, count: 134 -Region Avg IOU: 0.507429, Class: 1.000000, Obj: 0.258077, No Obj: 0.003052, Avg Recall: 0.571429, count: 105 -Region Avg IOU: 0.525322, Class: 1.000000, Obj: 0.288694, No Obj: 0.002931, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.604770, Class: 1.000000, Obj: 0.353327, No Obj: 0.003116, Avg Recall: 0.696629, count: 89 -Region Avg IOU: 0.506719, Class: 1.000000, Obj: 0.263728, No Obj: 0.002893, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.541131, Class: 1.000000, Obj: 0.345568, No Obj: 0.003067, Avg Recall: 0.640449, count: 89 -Region Avg IOU: 0.557550, Class: 1.000000, Obj: 0.268172, No Obj: 0.002743, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.522455, Class: 1.000000, Obj: 0.295798, No Obj: 0.003209, Avg Recall: 0.579439, count: 107 -3353: 0.428407, 0.464494 avg, 0.001000 rate, 23.568560 seconds, 858368 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.553048, Class: 1.000000, Obj: 0.336307, No Obj: 0.004104, Avg Recall: 0.609375, count: 128 -Region Avg IOU: 0.551176, Class: 1.000000, Obj: 0.328867, No Obj: 0.003434, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.531006, Class: 1.000000, Obj: 0.305374, No Obj: 0.003163, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.535898, Class: 1.000000, Obj: 0.262129, No Obj: 0.004673, Avg Recall: 0.615894, count: 151 -Region Avg IOU: 0.532646, Class: 1.000000, Obj: 0.268341, No Obj: 0.003532, Avg Recall: 0.620968, count: 124 -Region Avg IOU: 0.495220, Class: 1.000000, Obj: 0.297809, No Obj: 0.003661, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.488479, Class: 1.000000, Obj: 0.259326, No Obj: 0.003519, Avg Recall: 0.524194, count: 124 -Region Avg IOU: 0.519401, Class: 1.000000, Obj: 0.305200, No Obj: 0.003676, Avg Recall: 0.587629, count: 97 -3354: 0.519279, 0.469973 avg, 0.001000 rate, 22.592155 seconds, 858624 images -Loaded: 0.373618 seconds -Region Avg IOU: 0.570562, Class: 1.000000, Obj: 0.377380, No Obj: 0.004638, Avg Recall: 0.674603, count: 126 -Region Avg IOU: 0.542830, Class: 1.000000, Obj: 0.390896, No Obj: 0.003563, Avg Recall: 0.662651, count: 83 -Region Avg IOU: 0.541637, Class: 1.000000, Obj: 0.364412, No Obj: 0.004232, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.524466, Class: 1.000000, Obj: 0.294358, No Obj: 0.004160, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.491790, Class: 1.000000, Obj: 0.340938, No Obj: 0.004070, Avg Recall: 0.508621, count: 116 -Region Avg IOU: 0.442371, Class: 1.000000, Obj: 0.298765, No Obj: 0.003589, Avg Recall: 0.478723, count: 94 -Region Avg IOU: 0.567506, Class: 1.000000, Obj: 0.386387, No Obj: 0.003973, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.537730, Class: 1.000000, Obj: 0.345423, No Obj: 0.004252, Avg Recall: 0.625000, count: 96 -3355: 0.462446, 0.469220 avg, 0.001000 rate, 22.083286 seconds, 858880 images -Loaded: 0.047199 seconds -Region Avg IOU: 0.540624, Class: 1.000000, Obj: 0.438089, No Obj: 0.004424, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.523938, Class: 1.000000, Obj: 0.351658, No Obj: 0.004937, Avg Recall: 0.613139, count: 137 -Region Avg IOU: 0.552137, Class: 1.000000, Obj: 0.404207, No Obj: 0.004522, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.509087, Class: 1.000000, Obj: 0.354789, No Obj: 0.003787, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.566837, Class: 1.000000, Obj: 0.374635, No Obj: 0.004645, Avg Recall: 0.710526, count: 76 -Region Avg IOU: 0.529549, Class: 1.000000, Obj: 0.389711, No Obj: 0.003841, Avg Recall: 0.584270, count: 89 -Region Avg IOU: 0.561698, Class: 1.000000, Obj: 0.383700, No Obj: 0.004464, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.516129, Class: 1.000000, Obj: 0.409739, No Obj: 0.004353, Avg Recall: 0.556818, count: 88 -3356: 0.387734, 0.461071 avg, 0.001000 rate, 21.246422 seconds, 859136 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.548371, Class: 1.000000, Obj: 0.334697, No Obj: 0.004378, Avg Recall: 0.600000, count: 95 -Region Avg IOU: 0.526949, Class: 1.000000, Obj: 0.436249, No Obj: 0.004232, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.522362, Class: 1.000000, Obj: 0.394166, No Obj: 0.004689, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.544817, Class: 1.000000, Obj: 0.375732, No Obj: 0.004140, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.495988, Class: 1.000000, Obj: 0.369879, No Obj: 0.003831, Avg Recall: 0.506667, count: 75 -Region Avg IOU: 0.534374, Class: 1.000000, Obj: 0.390299, No Obj: 0.004526, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.507331, Class: 1.000000, Obj: 0.391294, No Obj: 0.004953, Avg Recall: 0.549180, count: 122 -Region Avg IOU: 0.545632, Class: 1.000000, Obj: 0.356044, No Obj: 0.004110, Avg Recall: 0.591398, count: 93 -3357: 0.395810, 0.454545 avg, 0.001000 rate, 21.233973 seconds, 859392 images -Loaded: 1.483634 seconds -Region Avg IOU: 0.559645, Class: 1.000000, Obj: 0.386173, No Obj: 0.003880, Avg Recall: 0.682927, count: 82 -Region Avg IOU: 0.561129, Class: 1.000000, Obj: 0.369065, No Obj: 0.004669, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.541759, Class: 1.000000, Obj: 0.389064, No Obj: 0.005691, Avg Recall: 0.608333, count: 120 -Region Avg IOU: 0.515983, Class: 1.000000, Obj: 0.410254, No Obj: 0.004542, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.527332, Class: 1.000000, Obj: 0.399448, No Obj: 0.005402, Avg Recall: 0.595238, count: 126 -Region Avg IOU: 0.554905, Class: 1.000000, Obj: 0.441093, No Obj: 0.004962, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.492672, Class: 1.000000, Obj: 0.326002, No Obj: 0.005185, Avg Recall: 0.581197, count: 117 -Region Avg IOU: 0.544018, Class: 1.000000, Obj: 0.377483, No Obj: 0.005262, Avg Recall: 0.646154, count: 130 -3358: 0.516036, 0.460694 avg, 0.001000 rate, 21.769941 seconds, 859648 images -Loaded: 2.866017 seconds -Region Avg IOU: 0.496739, Class: 1.000000, Obj: 0.406208, No Obj: 0.004813, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.518142, Class: 1.000000, Obj: 0.372928, No Obj: 0.005480, Avg Recall: 0.548872, count: 133 -Region Avg IOU: 0.519683, Class: 1.000000, Obj: 0.396991, No Obj: 0.004012, Avg Recall: 0.566265, count: 83 -Region Avg IOU: 0.585579, Class: 1.000000, Obj: 0.470251, No Obj: 0.004610, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.600427, Class: 1.000000, Obj: 0.452547, No Obj: 0.004793, Avg Recall: 0.777778, count: 90 -Region Avg IOU: 0.565383, Class: 1.000000, Obj: 0.447300, No Obj: 0.005125, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.595624, Class: 1.000000, Obj: 0.437006, No Obj: 0.005022, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.540833, Class: 1.000000, Obj: 0.417682, No Obj: 0.004667, Avg Recall: 0.625000, count: 96 -3359: 0.421651, 0.456790 avg, 0.001000 rate, 21.467093 seconds, 859904 images -Loaded: 1.683502 seconds -Region Avg IOU: 0.555980, Class: 1.000000, Obj: 0.435822, No Obj: 0.004812, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.583320, Class: 1.000000, Obj: 0.426275, No Obj: 0.004435, Avg Recall: 0.666667, count: 81 -Region Avg IOU: 0.502585, Class: 1.000000, Obj: 0.352623, No Obj: 0.005304, Avg Recall: 0.531915, count: 141 -Region Avg IOU: 0.521269, Class: 1.000000, Obj: 0.418448, No Obj: 0.005241, Avg Recall: 0.585366, count: 123 -Region Avg IOU: 0.570467, Class: 1.000000, Obj: 0.399603, No Obj: 0.005449, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.518064, Class: 1.000000, Obj: 0.361438, No Obj: 0.005388, Avg Recall: 0.592308, count: 130 -Region Avg IOU: 0.534829, Class: 1.000000, Obj: 0.364979, No Obj: 0.004751, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.541435, Class: 1.000000, Obj: 0.413140, No Obj: 0.004925, Avg Recall: 0.641667, count: 120 -3360: 0.468601, 0.457971 avg, 0.001000 rate, 23.129936 seconds, 860160 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000060 seconds -Region Avg IOU: 0.585203, Class: 1.000000, Obj: 0.464518, No Obj: 0.004706, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.583748, Class: 1.000000, Obj: 0.450501, No Obj: 0.004379, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.593738, Class: 1.000000, Obj: 0.419484, No Obj: 0.004715, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.571381, Class: 1.000000, Obj: 0.455371, No Obj: 0.003405, Avg Recall: 0.716049, count: 81 -Region Avg IOU: 0.541518, Class: 1.000000, Obj: 0.407370, No Obj: 0.003864, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.576239, Class: 1.000000, Obj: 0.418071, No Obj: 0.004556, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.546043, Class: 1.000000, Obj: 0.350669, No Obj: 0.004951, Avg Recall: 0.653061, count: 147 -Region Avg IOU: 0.561271, Class: 1.000000, Obj: 0.437668, No Obj: 0.004249, Avg Recall: 0.629032, count: 124 -3361: 0.486915, 0.460866 avg, 0.001000 rate, 24.439051 seconds, 860416 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.582341, Class: 1.000000, Obj: 0.416341, No Obj: 0.004153, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.563289, Class: 1.000000, Obj: 0.362743, No Obj: 0.003620, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.561630, Class: 1.000000, Obj: 0.377083, No Obj: 0.003890, Avg Recall: 0.680328, count: 122 -Region Avg IOU: 0.557337, Class: 1.000000, Obj: 0.392025, No Obj: 0.004173, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.553123, Class: 1.000000, Obj: 0.387778, No Obj: 0.003600, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.570212, Class: 1.000000, Obj: 0.434384, No Obj: 0.003786, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.592830, Class: 1.000000, Obj: 0.408982, No Obj: 0.004078, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.557361, Class: 1.000000, Obj: 0.415819, No Obj: 0.003655, Avg Recall: 0.642202, count: 109 -3362: 0.438135, 0.458593 avg, 0.001000 rate, 24.291094 seconds, 860672 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.590646, Class: 1.000000, Obj: 0.327849, No Obj: 0.003159, Avg Recall: 0.776471, count: 85 -Region Avg IOU: 0.567502, Class: 1.000000, Obj: 0.353865, No Obj: 0.003707, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.523989, Class: 1.000000, Obj: 0.351886, No Obj: 0.003593, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.619008, Class: 1.000000, Obj: 0.432757, No Obj: 0.003419, Avg Recall: 0.727273, count: 88 -Region Avg IOU: 0.581699, Class: 1.000000, Obj: 0.362117, No Obj: 0.003931, Avg Recall: 0.672000, count: 125 -Region Avg IOU: 0.540255, Class: 1.000000, Obj: 0.385731, No Obj: 0.003847, Avg Recall: 0.616000, count: 125 -Region Avg IOU: 0.578340, Class: 1.000000, Obj: 0.346394, No Obj: 0.003525, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.593107, Class: 1.000000, Obj: 0.382964, No Obj: 0.003897, Avg Recall: 0.695652, count: 115 -3363: 0.451353, 0.457869 avg, 0.001000 rate, 23.393312 seconds, 860928 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.519049, Class: 1.000000, Obj: 0.314856, No Obj: 0.002975, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.621728, Class: 1.000000, Obj: 0.322273, No Obj: 0.003560, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.555793, Class: 1.000000, Obj: 0.311131, No Obj: 0.003487, Avg Recall: 0.603175, count: 126 -Region Avg IOU: 0.533729, Class: 1.000000, Obj: 0.337531, No Obj: 0.003064, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.546653, Class: 1.000000, Obj: 0.383179, No Obj: 0.002663, Avg Recall: 0.659091, count: 88 -Region Avg IOU: 0.510781, Class: 1.000000, Obj: 0.312848, No Obj: 0.002657, Avg Recall: 0.575342, count: 73 -Region Avg IOU: 0.574374, Class: 1.000000, Obj: 0.399655, No Obj: 0.003527, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.579820, Class: 1.000000, Obj: 0.380469, No Obj: 0.003181, Avg Recall: 0.666667, count: 87 -3364: 0.440778, 0.456160 avg, 0.001000 rate, 23.288263 seconds, 861184 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.540268, Class: 1.000000, Obj: 0.330536, No Obj: 0.003315, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.554850, Class: 1.000000, Obj: 0.310252, No Obj: 0.003395, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.565860, Class: 1.000000, Obj: 0.352851, No Obj: 0.003088, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.570994, Class: 1.000000, Obj: 0.329863, No Obj: 0.002872, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.548725, Class: 1.000000, Obj: 0.310502, No Obj: 0.002830, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.556585, Class: 1.000000, Obj: 0.307519, No Obj: 0.002820, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.586353, Class: 1.000000, Obj: 0.281477, No Obj: 0.003618, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.528726, Class: 1.000000, Obj: 0.339531, No Obj: 0.002912, Avg Recall: 0.605263, count: 114 -3365: 0.475723, 0.458116 avg, 0.001000 rate, 24.923216 seconds, 861440 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.536085, Class: 1.000000, Obj: 0.305308, No Obj: 0.002888, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.571909, Class: 1.000000, Obj: 0.308455, No Obj: 0.002807, Avg Recall: 0.642857, count: 84 -Region Avg IOU: 0.544519, Class: 1.000000, Obj: 0.317267, No Obj: 0.003025, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.555564, Class: 1.000000, Obj: 0.246591, No Obj: 0.002698, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.535284, Class: 1.000000, Obj: 0.276099, No Obj: 0.003239, Avg Recall: 0.618321, count: 131 -Region Avg IOU: 0.605093, Class: 1.000000, Obj: 0.301037, No Obj: 0.003577, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.556418, Class: 1.000000, Obj: 0.322199, No Obj: 0.002986, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.581456, Class: 1.000000, Obj: 0.340525, No Obj: 0.003058, Avg Recall: 0.688679, count: 106 -3366: 0.474092, 0.459713 avg, 0.001000 rate, 24.438564 seconds, 861696 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.576591, Class: 1.000000, Obj: 0.304322, No Obj: 0.002876, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.589500, Class: 1.000000, Obj: 0.304034, No Obj: 0.002961, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.566226, Class: 1.000000, Obj: 0.305373, No Obj: 0.003468, Avg Recall: 0.666667, count: 129 -Region Avg IOU: 0.531509, Class: 1.000000, Obj: 0.330634, No Obj: 0.002986, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.571690, Class: 1.000000, Obj: 0.303412, No Obj: 0.002950, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.577046, Class: 1.000000, Obj: 0.322430, No Obj: 0.002771, Avg Recall: 0.744681, count: 94 -Region Avg IOU: 0.553876, Class: 1.000000, Obj: 0.321464, No Obj: 0.002840, Avg Recall: 0.621951, count: 82 -Region Avg IOU: 0.562748, Class: 1.000000, Obj: 0.328509, No Obj: 0.002972, Avg Recall: 0.645455, count: 110 -3367: 0.422946, 0.456037 avg, 0.001000 rate, 24.234785 seconds, 861952 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.534357, Class: 1.000000, Obj: 0.296990, No Obj: 0.003318, Avg Recall: 0.623188, count: 138 -Region Avg IOU: 0.539071, Class: 1.000000, Obj: 0.326871, No Obj: 0.002957, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.582953, Class: 1.000000, Obj: 0.331292, No Obj: 0.003160, Avg Recall: 0.702290, count: 131 -Region Avg IOU: 0.549731, Class: 1.000000, Obj: 0.269052, No Obj: 0.003412, Avg Recall: 0.628571, count: 140 -Region Avg IOU: 0.580481, Class: 1.000000, Obj: 0.327178, No Obj: 0.003452, Avg Recall: 0.696296, count: 135 -Region Avg IOU: 0.573009, Class: 1.000000, Obj: 0.350661, No Obj: 0.003204, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.576857, Class: 1.000000, Obj: 0.335388, No Obj: 0.002980, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.590345, Class: 1.000000, Obj: 0.302155, No Obj: 0.002846, Avg Recall: 0.708738, count: 103 -3368: 0.493316, 0.459765 avg, 0.001000 rate, 25.060955 seconds, 862208 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.631118, Class: 1.000000, Obj: 0.351530, No Obj: 0.003458, Avg Recall: 0.758065, count: 124 -Region Avg IOU: 0.563054, Class: 1.000000, Obj: 0.324777, No Obj: 0.003155, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.602034, Class: 1.000000, Obj: 0.344704, No Obj: 0.003197, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.579568, Class: 1.000000, Obj: 0.313632, No Obj: 0.003362, Avg Recall: 0.672000, count: 125 -Region Avg IOU: 0.583104, Class: 1.000000, Obj: 0.306884, No Obj: 0.003282, Avg Recall: 0.714286, count: 126 -Region Avg IOU: 0.552542, Class: 1.000000, Obj: 0.333643, No Obj: 0.003262, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.562913, Class: 1.000000, Obj: 0.332117, No Obj: 0.002955, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.600855, Class: 1.000000, Obj: 0.342278, No Obj: 0.003007, Avg Recall: 0.734694, count: 98 -3369: 0.479602, 0.461748 avg, 0.001000 rate, 23.693523 seconds, 862464 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.577720, Class: 1.000000, Obj: 0.359341, No Obj: 0.003180, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.564707, Class: 1.000000, Obj: 0.348327, No Obj: 0.003580, Avg Recall: 0.654412, count: 136 -Region Avg IOU: 0.566982, Class: 1.000000, Obj: 0.329111, No Obj: 0.003711, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.603409, Class: 1.000000, Obj: 0.381008, No Obj: 0.003171, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.558047, Class: 1.000000, Obj: 0.350254, No Obj: 0.003636, Avg Recall: 0.619403, count: 134 -Region Avg IOU: 0.607681, Class: 1.000000, Obj: 0.317214, No Obj: 0.003575, Avg Recall: 0.751938, count: 129 -Region Avg IOU: 0.576925, Class: 1.000000, Obj: 0.384316, No Obj: 0.002803, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.532703, Class: 1.000000, Obj: 0.324429, No Obj: 0.003624, Avg Recall: 0.633027, count: 109 -3370: 0.471659, 0.462740 avg, 0.001000 rate, 24.245743 seconds, 862720 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000060 seconds -Region Avg IOU: 0.631109, Class: 1.000000, Obj: 0.423546, No Obj: 0.002866, Avg Recall: 0.817204, count: 93 -Region Avg IOU: 0.645755, Class: 1.000000, Obj: 0.428047, No Obj: 0.003326, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.628062, Class: 1.000000, Obj: 0.407719, No Obj: 0.003336, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.621778, Class: 1.000000, Obj: 0.402915, No Obj: 0.003548, Avg Recall: 0.768657, count: 134 -Region Avg IOU: 0.627070, Class: 1.000000, Obj: 0.414366, No Obj: 0.003329, Avg Recall: 0.833333, count: 90 -Region Avg IOU: 0.672373, Class: 1.000000, Obj: 0.398261, No Obj: 0.003636, Avg Recall: 0.834711, count: 121 -Region Avg IOU: 0.621153, Class: 1.000000, Obj: 0.404954, No Obj: 0.003413, Avg Recall: 0.743802, count: 121 -Region Avg IOU: 0.660584, Class: 1.000000, Obj: 0.435049, No Obj: 0.003310, Avg Recall: 0.795699, count: 93 -3371: 0.450039, 0.461469 avg, 0.001000 rate, 27.890612 seconds, 862976 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.594735, Class: 1.000000, Obj: 0.458988, No Obj: 0.003586, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.640724, Class: 1.000000, Obj: 0.447124, No Obj: 0.003704, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.652491, Class: 1.000000, Obj: 0.406189, No Obj: 0.003606, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.590029, Class: 1.000000, Obj: 0.414600, No Obj: 0.003168, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.653616, Class: 1.000000, Obj: 0.405170, No Obj: 0.003853, Avg Recall: 0.792308, count: 130 -Region Avg IOU: 0.589689, Class: 1.000000, Obj: 0.376589, No Obj: 0.003755, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.613356, Class: 1.000000, Obj: 0.412665, No Obj: 0.003421, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.577367, Class: 1.000000, Obj: 0.335644, No Obj: 0.003633, Avg Recall: 0.701389, count: 144 -3372: 0.502645, 0.465587 avg, 0.001000 rate, 27.459980 seconds, 863232 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.589915, Class: 1.000000, Obj: 0.395684, No Obj: 0.003193, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.577689, Class: 1.000000, Obj: 0.403887, No Obj: 0.003501, Avg Recall: 0.647541, count: 122 -Region Avg IOU: 0.667621, Class: 1.000000, Obj: 0.428967, No Obj: 0.003291, Avg Recall: 0.833333, count: 90 -Region Avg IOU: 0.611726, Class: 1.000000, Obj: 0.388979, No Obj: 0.003743, Avg Recall: 0.776978, count: 139 -Region Avg IOU: 0.622234, Class: 1.000000, Obj: 0.421356, No Obj: 0.003441, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.609772, Class: 1.000000, Obj: 0.452893, No Obj: 0.002868, Avg Recall: 0.747126, count: 87 -Region Avg IOU: 0.645174, Class: 1.000000, Obj: 0.385706, No Obj: 0.003483, Avg Recall: 0.817391, count: 115 -Region Avg IOU: 0.601883, Class: 1.000000, Obj: 0.395678, No Obj: 0.003205, Avg Recall: 0.735849, count: 106 -3373: 0.446088, 0.463637 avg, 0.001000 rate, 28.193512 seconds, 863488 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.642634, Class: 1.000000, Obj: 0.456322, No Obj: 0.003161, Avg Recall: 0.802326, count: 86 -Region Avg IOU: 0.627419, Class: 1.000000, Obj: 0.409093, No Obj: 0.003689, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.633693, Class: 1.000000, Obj: 0.430453, No Obj: 0.003497, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.620037, Class: 1.000000, Obj: 0.390674, No Obj: 0.003141, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.608600, Class: 1.000000, Obj: 0.360833, No Obj: 0.003728, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.614832, Class: 1.000000, Obj: 0.420140, No Obj: 0.003401, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.642436, Class: 1.000000, Obj: 0.407244, No Obj: 0.003507, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.648370, Class: 1.000000, Obj: 0.394531, No Obj: 0.003232, Avg Recall: 0.801802, count: 111 -3374: 0.447429, 0.462016 avg, 0.001000 rate, 28.148336 seconds, 863744 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.618550, Class: 1.000000, Obj: 0.364709, No Obj: 0.003583, Avg Recall: 0.744966, count: 149 -Region Avg IOU: 0.608712, Class: 1.000000, Obj: 0.384007, No Obj: 0.003114, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.656932, Class: 1.000000, Obj: 0.391927, No Obj: 0.002712, Avg Recall: 0.845070, count: 71 -Region Avg IOU: 0.668283, Class: 1.000000, Obj: 0.412773, No Obj: 0.003006, Avg Recall: 0.861111, count: 108 -Region Avg IOU: 0.653451, Class: 1.000000, Obj: 0.463094, No Obj: 0.002807, Avg Recall: 0.813333, count: 75 -Region Avg IOU: 0.608639, Class: 1.000000, Obj: 0.410425, No Obj: 0.003156, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.613884, Class: 1.000000, Obj: 0.445390, No Obj: 0.002983, Avg Recall: 0.767442, count: 86 -Region Avg IOU: 0.602808, Class: 1.000000, Obj: 0.371096, No Obj: 0.002972, Avg Recall: 0.765306, count: 98 -3375: 0.428269, 0.458642 avg, 0.001000 rate, 27.468399 seconds, 864000 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.672841, Class: 1.000000, Obj: 0.376543, No Obj: 0.002900, Avg Recall: 0.876289, count: 97 -Region Avg IOU: 0.627362, Class: 1.000000, Obj: 0.354231, No Obj: 0.003072, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.611673, Class: 1.000000, Obj: 0.327899, No Obj: 0.003143, Avg Recall: 0.786765, count: 136 -Region Avg IOU: 0.608658, Class: 1.000000, Obj: 0.384743, No Obj: 0.002605, Avg Recall: 0.685393, count: 89 -Region Avg IOU: 0.627687, Class: 1.000000, Obj: 0.409382, No Obj: 0.002693, Avg Recall: 0.744444, count: 90 -Region Avg IOU: 0.636764, Class: 1.000000, Obj: 0.335237, No Obj: 0.002829, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.617421, Class: 1.000000, Obj: 0.367214, No Obj: 0.002765, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.637951, Class: 1.000000, Obj: 0.366990, No Obj: 0.002862, Avg Recall: 0.787037, count: 108 -3376: 0.458690, 0.458646 avg, 0.001000 rate, 27.693304 seconds, 864256 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.623300, Class: 1.000000, Obj: 0.358308, No Obj: 0.002875, Avg Recall: 0.750000, count: 144 -Region Avg IOU: 0.615482, Class: 1.000000, Obj: 0.392792, No Obj: 0.002690, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.593684, Class: 1.000000, Obj: 0.320842, No Obj: 0.002760, Avg Recall: 0.768000, count: 125 -Region Avg IOU: 0.656168, Class: 1.000000, Obj: 0.345101, No Obj: 0.002229, Avg Recall: 0.811765, count: 85 -Region Avg IOU: 0.668474, Class: 1.000000, Obj: 0.363495, No Obj: 0.002651, Avg Recall: 0.873786, count: 103 -Region Avg IOU: 0.579564, Class: 1.000000, Obj: 0.354993, No Obj: 0.002828, Avg Recall: 0.669118, count: 136 -Region Avg IOU: 0.635798, Class: 1.000000, Obj: 0.326071, No Obj: 0.002734, Avg Recall: 0.784483, count: 116 -Region Avg IOU: 0.631816, Class: 1.000000, Obj: 0.361422, No Obj: 0.002664, Avg Recall: 0.754386, count: 114 -3377: 0.505725, 0.463354 avg, 0.001000 rate, 28.406696 seconds, 864512 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.663039, Class: 1.000000, Obj: 0.396122, No Obj: 0.002504, Avg Recall: 0.804878, count: 82 -Region Avg IOU: 0.654449, Class: 1.000000, Obj: 0.364662, No Obj: 0.002730, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.631121, Class: 1.000000, Obj: 0.322629, No Obj: 0.002682, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.603710, Class: 1.000000, Obj: 0.330794, No Obj: 0.002802, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.629962, Class: 1.000000, Obj: 0.364322, No Obj: 0.002606, Avg Recall: 0.775000, count: 120 -Region Avg IOU: 0.595916, Class: 1.000000, Obj: 0.308242, No Obj: 0.002831, Avg Recall: 0.755906, count: 127 -Region Avg IOU: 0.593863, Class: 1.000000, Obj: 0.341915, No Obj: 0.002826, Avg Recall: 0.666667, count: 144 -Region Avg IOU: 0.587770, Class: 1.000000, Obj: 0.341476, No Obj: 0.002430, Avg Recall: 0.695238, count: 105 -3378: 0.494709, 0.466490 avg, 0.001000 rate, 26.847635 seconds, 864768 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.608876, Class: 1.000000, Obj: 0.317975, No Obj: 0.002400, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.638715, Class: 1.000000, Obj: 0.369364, No Obj: 0.002436, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.593036, Class: 1.000000, Obj: 0.341705, No Obj: 0.002806, Avg Recall: 0.685535, count: 159 -Region Avg IOU: 0.639949, Class: 1.000000, Obj: 0.397169, No Obj: 0.002345, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.589835, Class: 1.000000, Obj: 0.359502, No Obj: 0.002316, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.651364, Class: 1.000000, Obj: 0.328263, No Obj: 0.002906, Avg Recall: 0.800000, count: 115 -Region Avg IOU: 0.604101, Class: 1.000000, Obj: 0.291698, No Obj: 0.002717, Avg Recall: 0.762963, count: 135 -Region Avg IOU: 0.614934, Class: 1.000000, Obj: 0.340525, No Obj: 0.002417, Avg Recall: 0.732143, count: 112 -3379: 0.460651, 0.465906 avg, 0.001000 rate, 28.115046 seconds, 865024 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.612898, Class: 1.000000, Obj: 0.318212, No Obj: 0.002390, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.640430, Class: 1.000000, Obj: 0.369385, No Obj: 0.002520, Avg Recall: 0.802198, count: 91 -Region Avg IOU: 0.594832, Class: 1.000000, Obj: 0.329086, No Obj: 0.002498, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.647396, Class: 1.000000, Obj: 0.328723, No Obj: 0.002229, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.590691, Class: 1.000000, Obj: 0.329073, No Obj: 0.002503, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.556083, Class: 1.000000, Obj: 0.331521, No Obj: 0.002624, Avg Recall: 0.600000, count: 125 -Region Avg IOU: 0.606792, Class: 1.000000, Obj: 0.346715, No Obj: 0.002201, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.642240, Class: 1.000000, Obj: 0.326216, No Obj: 0.002525, Avg Recall: 0.787234, count: 94 -3380: 0.466236, 0.465939 avg, 0.001000 rate, 28.197870 seconds, 865280 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 15.562413 seconds -Region Avg IOU: 0.560745, Class: 1.000000, Obj: 0.304638, No Obj: 0.002837, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.576116, Class: 1.000000, Obj: 0.292731, No Obj: 0.002785, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.629736, Class: 1.000000, Obj: 0.329627, No Obj: 0.002618, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.594202, Class: 1.000000, Obj: 0.253215, No Obj: 0.003007, Avg Recall: 0.710526, count: 114 -Region Avg IOU: 0.584770, Class: 1.000000, Obj: 0.345130, No Obj: 0.002401, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.630908, Class: 1.000000, Obj: 0.321138, No Obj: 0.002673, Avg Recall: 0.778761, count: 113 -Region Avg IOU: 0.633403, Class: 1.000000, Obj: 0.344716, No Obj: 0.002572, Avg Recall: 0.780488, count: 82 -Region Avg IOU: 0.626286, Class: 1.000000, Obj: 0.341509, No Obj: 0.002314, Avg Recall: 0.747126, count: 87 -3381: 0.438282, 0.463173 avg, 0.001000 rate, 26.089878 seconds, 865536 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.606438, Class: 1.000000, Obj: 0.305341, No Obj: 0.003288, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.637147, Class: 1.000000, Obj: 0.365948, No Obj: 0.002732, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.628725, Class: 1.000000, Obj: 0.349969, No Obj: 0.003006, Avg Recall: 0.772358, count: 123 -Region Avg IOU: 0.591959, Class: 1.000000, Obj: 0.352367, No Obj: 0.002505, Avg Recall: 0.707865, count: 89 -Region Avg IOU: 0.637803, Class: 1.000000, Obj: 0.382617, No Obj: 0.002578, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.612490, Class: 1.000000, Obj: 0.281483, No Obj: 0.002901, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.638310, Class: 1.000000, Obj: 0.365783, No Obj: 0.003192, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.592833, Class: 1.000000, Obj: 0.345548, No Obj: 0.002993, Avg Recall: 0.712871, count: 101 -3382: 0.499226, 0.466778 avg, 0.001000 rate, 25.537533 seconds, 865792 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.609259, Class: 1.000000, Obj: 0.345014, No Obj: 0.003189, Avg Recall: 0.768000, count: 125 -Region Avg IOU: 0.590806, Class: 1.000000, Obj: 0.379402, No Obj: 0.002802, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.617356, Class: 1.000000, Obj: 0.398962, No Obj: 0.002534, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.624673, Class: 1.000000, Obj: 0.351510, No Obj: 0.002847, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.628013, Class: 1.000000, Obj: 0.343309, No Obj: 0.002701, Avg Recall: 0.804598, count: 87 -Region Avg IOU: 0.632330, Class: 1.000000, Obj: 0.365729, No Obj: 0.002867, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.596906, Class: 1.000000, Obj: 0.342319, No Obj: 0.003806, Avg Recall: 0.720588, count: 136 -Region Avg IOU: 0.564929, Class: 1.000000, Obj: 0.342267, No Obj: 0.002640, Avg Recall: 0.652632, count: 95 -3383: 0.417817, 0.461882 avg, 0.001000 rate, 26.757843 seconds, 866048 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.595042, Class: 1.000000, Obj: 0.355274, No Obj: 0.003282, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.625410, Class: 1.000000, Obj: 0.359954, No Obj: 0.003015, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.629492, Class: 1.000000, Obj: 0.338038, No Obj: 0.003485, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.633176, Class: 1.000000, Obj: 0.373026, No Obj: 0.003109, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.595595, Class: 1.000000, Obj: 0.401304, No Obj: 0.002497, Avg Recall: 0.686046, count: 86 -Region Avg IOU: 0.661092, Class: 1.000000, Obj: 0.418443, No Obj: 0.002990, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.630269, Class: 1.000000, Obj: 0.422378, No Obj: 0.002965, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.610702, Class: 1.000000, Obj: 0.372751, No Obj: 0.003598, Avg Recall: 0.769231, count: 117 -3384: 0.414520, 0.457146 avg, 0.001000 rate, 26.928560 seconds, 866304 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.634180, Class: 1.000000, Obj: 0.422786, No Obj: 0.003112, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.602856, Class: 1.000000, Obj: 0.381735, No Obj: 0.003181, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.597909, Class: 1.000000, Obj: 0.373675, No Obj: 0.003054, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.605557, Class: 1.000000, Obj: 0.415312, No Obj: 0.003314, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.614703, Class: 1.000000, Obj: 0.359246, No Obj: 0.003521, Avg Recall: 0.772358, count: 123 -Region Avg IOU: 0.596502, Class: 1.000000, Obj: 0.382778, No Obj: 0.003336, Avg Recall: 0.698276, count: 116 -Region Avg IOU: 0.644812, Class: 1.000000, Obj: 0.387453, No Obj: 0.003410, Avg Recall: 0.769231, count: 117 -Region Avg IOU: 0.561451, Class: 1.000000, Obj: 0.357638, No Obj: 0.002937, Avg Recall: 0.630435, count: 92 -3385: 0.418177, 0.453249 avg, 0.001000 rate, 25.899343 seconds, 866560 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.616700, Class: 1.000000, Obj: 0.372177, No Obj: 0.003853, Avg Recall: 0.791667, count: 120 -Region Avg IOU: 0.636598, Class: 1.000000, Obj: 0.359565, No Obj: 0.004139, Avg Recall: 0.798561, count: 139 -Region Avg IOU: 0.629350, Class: 1.000000, Obj: 0.390537, No Obj: 0.003002, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.584854, Class: 1.000000, Obj: 0.350183, No Obj: 0.003193, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.615047, Class: 1.000000, Obj: 0.374664, No Obj: 0.003343, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.613083, Class: 1.000000, Obj: 0.381175, No Obj: 0.003116, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.608456, Class: 1.000000, Obj: 0.396126, No Obj: 0.003475, Avg Recall: 0.772358, count: 123 -Region Avg IOU: 0.579385, Class: 1.000000, Obj: 0.351750, No Obj: 0.002820, Avg Recall: 0.686275, count: 102 -3386: 0.476032, 0.455528 avg, 0.001000 rate, 26.519928 seconds, 866816 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.577295, Class: 1.000000, Obj: 0.393250, No Obj: 0.003101, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.588510, Class: 1.000000, Obj: 0.347834, No Obj: 0.003803, Avg Recall: 0.714286, count: 140 -Region Avg IOU: 0.594315, Class: 1.000000, Obj: 0.456104, No Obj: 0.003225, Avg Recall: 0.670886, count: 79 -Region Avg IOU: 0.628343, Class: 1.000000, Obj: 0.358262, No Obj: 0.003563, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.619429, Class: 1.000000, Obj: 0.394907, No Obj: 0.003466, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.589583, Class: 1.000000, Obj: 0.363749, No Obj: 0.003238, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.583759, Class: 1.000000, Obj: 0.390673, No Obj: 0.003301, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.603786, Class: 1.000000, Obj: 0.376574, No Obj: 0.003496, Avg Recall: 0.727273, count: 132 -3387: 0.449276, 0.454902 avg, 0.001000 rate, 25.897804 seconds, 867072 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.586437, Class: 1.000000, Obj: 0.410858, No Obj: 0.003488, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.573857, Class: 1.000000, Obj: 0.373636, No Obj: 0.003489, Avg Recall: 0.656000, count: 125 -Region Avg IOU: 0.597121, Class: 1.000000, Obj: 0.448630, No Obj: 0.003116, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.604965, Class: 1.000000, Obj: 0.358681, No Obj: 0.003224, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.615381, Class: 1.000000, Obj: 0.418837, No Obj: 0.003205, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.624369, Class: 1.000000, Obj: 0.390217, No Obj: 0.003351, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.568295, Class: 1.000000, Obj: 0.356997, No Obj: 0.003580, Avg Recall: 0.676056, count: 142 -Region Avg IOU: 0.598798, Class: 1.000000, Obj: 0.420027, No Obj: 0.002866, Avg Recall: 0.687500, count: 96 -3388: 0.417996, 0.451212 avg, 0.001000 rate, 27.042021 seconds, 867328 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.618514, Class: 1.000000, Obj: 0.377543, No Obj: 0.003378, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.617309, Class: 1.000000, Obj: 0.365562, No Obj: 0.002996, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.651068, Class: 1.000000, Obj: 0.402037, No Obj: 0.003679, Avg Recall: 0.819048, count: 105 -Region Avg IOU: 0.632126, Class: 1.000000, Obj: 0.394579, No Obj: 0.003275, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.621957, Class: 1.000000, Obj: 0.402716, No Obj: 0.002829, Avg Recall: 0.704545, count: 88 -Region Avg IOU: 0.619209, Class: 1.000000, Obj: 0.395561, No Obj: 0.003123, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.600526, Class: 1.000000, Obj: 0.369724, No Obj: 0.003516, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.619535, Class: 1.000000, Obj: 0.378451, No Obj: 0.003000, Avg Recall: 0.771739, count: 92 -3389: 0.462399, 0.452330 avg, 0.001000 rate, 27.564896 seconds, 867584 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.636014, Class: 1.000000, Obj: 0.368227, No Obj: 0.003616, Avg Recall: 0.820513, count: 156 -Region Avg IOU: 0.607279, Class: 1.000000, Obj: 0.364560, No Obj: 0.003034, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.587348, Class: 1.000000, Obj: 0.353494, No Obj: 0.002921, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.643616, Class: 1.000000, Obj: 0.336623, No Obj: 0.003397, Avg Recall: 0.806897, count: 145 -Region Avg IOU: 0.609214, Class: 1.000000, Obj: 0.390703, No Obj: 0.003214, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.590495, Class: 1.000000, Obj: 0.400600, No Obj: 0.003210, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.552662, Class: 1.000000, Obj: 0.325724, No Obj: 0.003355, Avg Recall: 0.610390, count: 154 -Region Avg IOU: 0.501099, Class: 1.000000, Obj: 0.309971, No Obj: 0.002598, Avg Recall: 0.574074, count: 108 -3390: 0.517792, 0.458877 avg, 0.001000 rate, 26.538715 seconds, 867840 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 15.920497 seconds -Region Avg IOU: 0.570199, Class: 1.000000, Obj: 0.306489, No Obj: 0.003144, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.544184, Class: 1.000000, Obj: 0.319152, No Obj: 0.003263, Avg Recall: 0.648438, count: 128 -Region Avg IOU: 0.557531, Class: 1.000000, Obj: 0.301971, No Obj: 0.003055, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.595852, Class: 1.000000, Obj: 0.337988, No Obj: 0.003343, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.554562, Class: 1.000000, Obj: 0.346475, No Obj: 0.003379, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.537790, Class: 1.000000, Obj: 0.279824, No Obj: 0.003682, Avg Recall: 0.574074, count: 162 -Region Avg IOU: 0.549204, Class: 1.000000, Obj: 0.296894, No Obj: 0.003012, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.588592, Class: 1.000000, Obj: 0.369320, No Obj: 0.003012, Avg Recall: 0.591837, count: 98 -3391: 0.481594, 0.461148 avg, 0.001000 rate, 21.973804 seconds, 868096 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.536215, Class: 1.000000, Obj: 0.344814, No Obj: 0.003202, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.571044, Class: 1.000000, Obj: 0.304167, No Obj: 0.002741, Avg Recall: 0.695122, count: 82 -Region Avg IOU: 0.613450, Class: 1.000000, Obj: 0.366304, No Obj: 0.002908, Avg Recall: 0.779221, count: 77 -Region Avg IOU: 0.527310, Class: 1.000000, Obj: 0.304560, No Obj: 0.002909, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.584452, Class: 1.000000, Obj: 0.263818, No Obj: 0.002864, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.556285, Class: 1.000000, Obj: 0.288226, No Obj: 0.003237, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.537699, Class: 1.000000, Obj: 0.309341, No Obj: 0.003141, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.562010, Class: 1.000000, Obj: 0.320046, No Obj: 0.003158, Avg Recall: 0.669643, count: 112 -3392: 0.411518, 0.456185 avg, 0.001000 rate, 23.587404 seconds, 868352 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.513031, Class: 1.000000, Obj: 0.299390, No Obj: 0.002827, Avg Recall: 0.536585, count: 82 -Region Avg IOU: 0.524430, Class: 1.000000, Obj: 0.296859, No Obj: 0.003082, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.556562, Class: 1.000000, Obj: 0.248671, No Obj: 0.003453, Avg Recall: 0.624060, count: 133 -Region Avg IOU: 0.561121, Class: 1.000000, Obj: 0.318167, No Obj: 0.003038, Avg Recall: 0.670455, count: 88 -Region Avg IOU: 0.526503, Class: 1.000000, Obj: 0.290220, No Obj: 0.002862, Avg Recall: 0.655738, count: 122 -Region Avg IOU: 0.565763, Class: 1.000000, Obj: 0.347459, No Obj: 0.003055, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.589576, Class: 1.000000, Obj: 0.351386, No Obj: 0.003003, Avg Recall: 0.707865, count: 89 -Region Avg IOU: 0.553671, Class: 1.000000, Obj: 0.360664, No Obj: 0.003322, Avg Recall: 0.605505, count: 109 -3393: 0.462454, 0.456812 avg, 0.001000 rate, 23.761124 seconds, 868608 images -Loaded: 0.000046 seconds -Region Avg IOU: 0.485452, Class: 1.000000, Obj: 0.298851, No Obj: 0.003379, Avg Recall: 0.563025, count: 119 -Region Avg IOU: 0.561041, Class: 1.000000, Obj: 0.330284, No Obj: 0.003942, Avg Recall: 0.647887, count: 142 -Region Avg IOU: 0.553839, Class: 1.000000, Obj: 0.291996, No Obj: 0.003313, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.580903, Class: 1.000000, Obj: 0.352646, No Obj: 0.003229, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.530766, Class: 1.000000, Obj: 0.328550, No Obj: 0.003189, Avg Recall: 0.630252, count: 119 -Region Avg IOU: 0.544354, Class: 1.000000, Obj: 0.316575, No Obj: 0.002737, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.571882, Class: 1.000000, Obj: 0.319578, No Obj: 0.003247, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.565645, Class: 1.000000, Obj: 0.265880, No Obj: 0.003530, Avg Recall: 0.641667, count: 120 -3394: 0.487460, 0.459877 avg, 0.001000 rate, 22.970106 seconds, 868864 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.572761, Class: 1.000000, Obj: 0.381050, No Obj: 0.003747, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.532668, Class: 1.000000, Obj: 0.320583, No Obj: 0.003269, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.560211, Class: 1.000000, Obj: 0.329684, No Obj: 0.003483, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.535899, Class: 1.000000, Obj: 0.362951, No Obj: 0.003362, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.553631, Class: 1.000000, Obj: 0.306989, No Obj: 0.003227, Avg Recall: 0.641975, count: 81 -Region Avg IOU: 0.567096, Class: 1.000000, Obj: 0.359852, No Obj: 0.003718, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.514612, Class: 1.000000, Obj: 0.337932, No Obj: 0.003059, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.568986, Class: 1.000000, Obj: 0.384672, No Obj: 0.003392, Avg Recall: 0.631579, count: 95 -3395: 0.388976, 0.452787 avg, 0.001000 rate, 22.562321 seconds, 869120 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.501642, Class: 1.000000, Obj: 0.364974, No Obj: 0.003720, Avg Recall: 0.517241, count: 116 -Region Avg IOU: 0.484852, Class: 1.000000, Obj: 0.324882, No Obj: 0.003913, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.574613, Class: 1.000000, Obj: 0.346111, No Obj: 0.003936, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.525630, Class: 1.000000, Obj: 0.318739, No Obj: 0.003456, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.512939, Class: 1.000000, Obj: 0.325468, No Obj: 0.003611, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.573807, Class: 1.000000, Obj: 0.367945, No Obj: 0.003525, Avg Recall: 0.675214, count: 117 -Region Avg IOU: 0.579691, Class: 1.000000, Obj: 0.381027, No Obj: 0.003664, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.512715, Class: 1.000000, Obj: 0.324574, No Obj: 0.004028, Avg Recall: 0.573913, count: 115 -3396: 0.474555, 0.454964 avg, 0.001000 rate, 23.219812 seconds, 869376 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.563810, Class: 1.000000, Obj: 0.384962, No Obj: 0.003922, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.558662, Class: 1.000000, Obj: 0.411463, No Obj: 0.003574, Avg Recall: 0.689655, count: 87 -Region Avg IOU: 0.569801, Class: 1.000000, Obj: 0.378703, No Obj: 0.003652, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.545092, Class: 1.000000, Obj: 0.386772, No Obj: 0.003933, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.603532, Class: 1.000000, Obj: 0.375194, No Obj: 0.004129, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.548752, Class: 1.000000, Obj: 0.367109, No Obj: 0.004033, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.584623, Class: 1.000000, Obj: 0.395322, No Obj: 0.004356, Avg Recall: 0.690476, count: 126 -Region Avg IOU: 0.588581, Class: 1.000000, Obj: 0.390316, No Obj: 0.003914, Avg Recall: 0.727273, count: 110 -3397: 0.441684, 0.453636 avg, 0.001000 rate, 24.340906 seconds, 869632 images -Loaded: 0.000159 seconds -Region Avg IOU: 0.554895, Class: 1.000000, Obj: 0.356160, No Obj: 0.004480, Avg Recall: 0.651852, count: 135 -Region Avg IOU: 0.496719, Class: 1.000000, Obj: 0.395930, No Obj: 0.004036, Avg Recall: 0.525424, count: 118 -Region Avg IOU: 0.516880, Class: 1.000000, Obj: 0.356404, No Obj: 0.004014, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.569999, Class: 1.000000, Obj: 0.388995, No Obj: 0.003759, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.519356, Class: 1.000000, Obj: 0.358879, No Obj: 0.003839, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.577082, Class: 1.000000, Obj: 0.386034, No Obj: 0.004008, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.544084, Class: 1.000000, Obj: 0.413214, No Obj: 0.003473, Avg Recall: 0.662791, count: 86 -Region Avg IOU: 0.549155, Class: 1.000000, Obj: 0.406814, No Obj: 0.003785, Avg Recall: 0.611765, count: 85 -3398: 0.431620, 0.451434 avg, 0.001000 rate, 23.709892 seconds, 869888 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.593971, Class: 1.000000, Obj: 0.413934, No Obj: 0.004370, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.519392, Class:Saving weights to ./person9300/backup/tyvp_93_9c512_3400.weights - 1.000000, Obj: 0.364784, No Obj: 0.004499, Avg Recall: 0.566667, count: 150 -Region Avg IOU: 0.542396, Class: 1.000000, Obj: 0.394980, No Obj: 0.004061, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.529015, Class: 1.000000, Obj: 0.376015, No Obj: 0.004019, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.607709, Class: 1.000000, Obj: 0.440967, No Obj: 0.004148, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.585624, Class: 1.000000, Obj: 0.371951, No Obj: 0.004176, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.546375, Class: 1.000000, Obj: 0.340021, No Obj: 0.004362, Avg Recall: 0.669231, count: 130 -Region Avg IOU: 0.544571, Class: 1.000000, Obj: 0.366163, No Obj: 0.004028, Avg Recall: 0.616822, count: 107 -3399: 0.465534, 0.452844 avg, 0.001000 rate, 23.632498 seconds, 870144 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.512827, Class: 1.000000, Obj: 0.343643, No Obj: 0.003623, Avg Recall: 0.560748, count: 107 -Region Avg IOU: 0.510793, Class: 1.000000, Obj: 0.343442, No Obj: 0.004539, Avg Recall: 0.604651, count: 129 -Region Avg IOU: 0.549749, Class: 1.000000, Obj: 0.378250, No Obj: 0.004139, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.549292, Class: 1.000000, Obj: 0.411974, No Obj: 0.004024, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.594451, Class: 1.000000, Obj: 0.397056, No Obj: 0.003672, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.556826, Class: 1.000000, Obj: 0.405754, No Obj: 0.003805, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.518047, Class: 1.000000, Obj: 0.343626, No Obj: 0.004034, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.523155, Class: 1.000000, Obj: 0.380916, No Obj: 0.003151, Avg Recall: 0.551020, count: 98 -3400: 0.466475, 0.454207 avg, 0.001000 rate, 23.192032 seconds, 870400 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 20.867952 seconds -Region Avg IOU: 0.479335, Class: 1.000000, Obj: 0.265327, No Obj: 0.004210, Avg Recall: 0.468468, count: 111 -Region Avg IOU: 0.472347, Class: 1.000000, Obj: 0.287655, No Obj: 0.004130, Avg Recall: 0.518182, count: 110 -Region Avg IOU: 0.473435, Class: 1.000000, Obj: 0.317232, No Obj: 0.003908, Avg Recall: 0.505376, count: 93 -Region Avg IOU: 0.525368, Class: 1.000000, Obj: 0.320959, No Obj: 0.004917, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.462445, Class: 1.000000, Obj: 0.262567, No Obj: 0.003772, Avg Recall: 0.471154, count: 104 -Region Avg IOU: 0.460895, Class: 1.000000, Obj: 0.259232, No Obj: 0.003440, Avg Recall: 0.500000, count: 96 -Region Avg IOU: 0.466893, Class: 1.000000, Obj: 0.289888, No Obj: 0.004663, Avg Recall: 0.476191, count: 105 -Region Avg IOU: 0.441952, Class: 1.000000, Obj: 0.272203, No Obj: 0.004144, Avg Recall: 0.439252, count: 107 -3401: 0.501779, 0.458964 avg, 0.001000 rate, 14.375820 seconds, 870656 images -Loaded: 6.802135 seconds -Region Avg IOU: 0.479725, Class: 1.000000, Obj: 0.290024, No Obj: 0.003831, Avg Recall: 0.540230, count: 87 -Region Avg IOU: 0.473406, Class: 1.000000, Obj: 0.262337, No Obj: 0.005371, Avg Recall: 0.543046, count: 151 -Region Avg IOU: 0.486614, Class: 1.000000, Obj: 0.274076, No Obj: 0.004679, Avg Recall: 0.565891, count: 129 -Region Avg IOU: 0.430772, Class: 1.000000, Obj: 0.267520, No Obj: 0.005265, Avg Recall: 0.440252, count: 159 -Region Avg IOU: 0.446925, Class: 1.000000, Obj: 0.237451, No Obj: 0.003900, Avg Recall: 0.459770, count: 87 -Region Avg IOU: 0.512786, Class: 1.000000, Obj: 0.253327, No Obj: 0.004489, Avg Recall: 0.571429, count: 105 -Region Avg IOU: 0.459241, Class: 1.000000, Obj: 0.274823, No Obj: 0.003975, Avg Recall: 0.473118, count: 93 -Region Avg IOU: 0.487014, Class: 1.000000, Obj: 0.301101, No Obj: 0.004354, Avg Recall: 0.580357, count: 112 -3402: 0.543991, 0.467467 avg, 0.001000 rate, 18.819542 seconds, 870912 images -Loaded: 2.601903 seconds -Region Avg IOU: 0.482279, Class: 1.000000, Obj: 0.282885, No Obj: 0.004179, Avg Recall: 0.546392, count: 97 -Region Avg IOU: 0.527765, Class: 1.000000, Obj: 0.297252, No Obj: 0.005274, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.456665, Class: 1.000000, Obj: 0.269457, No Obj: 0.004072, Avg Recall: 0.490385, count: 104 -Region Avg IOU: 0.460728, Class: 1.000000, Obj: 0.250077, No Obj: 0.003929, Avg Recall: 0.483051, count: 118 -Region Avg IOU: 0.477524, Class: 1.000000, Obj: 0.293653, No Obj: 0.004461, Avg Recall: 0.528000, count: 125 -Region Avg IOU: 0.403599, Class: 1.000000, Obj: 0.218870, No Obj: 0.003828, Avg Recall: 0.420561, count: 107 -Region Avg IOU: 0.439793, Class: 1.000000, Obj: 0.222001, No Obj: 0.003747, Avg Recall: 0.482143, count: 112 -Region Avg IOU: 0.451751, Class: 1.000000, Obj: 0.274642, No Obj: 0.004383, Avg Recall: 0.521739, count: 115 -3403: 0.479723, 0.468693 avg, 0.001000 rate, 17.366287 seconds, 871168 images -Loaded: 2.309926 seconds -Region Avg IOU: 0.416333, Class: 1.000000, Obj: 0.262295, No Obj: 0.004423, Avg Recall: 0.412844, count: 109 -Region Avg IOU: 0.392172, Class: 1.000000, Obj: 0.284759, No Obj: 0.003951, Avg Recall: 0.406593, count: 91 -Region Avg IOU: 0.418093, Class: 1.000000, Obj: 0.288471, No Obj: 0.004594, Avg Recall: 0.485294, count: 136 -Region Avg IOU: 0.429384, Class: 1.000000, Obj: 0.277523, No Obj: 0.004779, Avg Recall: 0.413793, count: 145 -Region Avg IOU: 0.442900, Class: 1.000000, Obj: 0.260477, No Obj: 0.004049, Avg Recall: 0.447619, count: 105 -Region Avg IOU: 0.473579, Class: 1.000000, Obj: 0.290721, No Obj: 0.004257, Avg Recall: 0.500000, count: 126 -Region Avg IOU: 0.491903, Class: 1.000000, Obj: 0.278781, No Obj: 0.004209, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.460024, Class: 1.000000, Obj: 0.278650, No Obj: 0.004606, Avg Recall: 0.519685, count: 127 -3404: 0.523206, 0.474144 avg, 0.001000 rate, 19.284597 seconds, 871424 images -Loaded: 1.304128 seconds -Region Avg IOU: 0.450137, Class: 1.000000, Obj: 0.269493, No Obj: 0.005395, Avg Recall: 0.503497, count: 143 -Region Avg IOU: 0.483938, Class: 1.000000, Obj: 0.311397, No Obj: 0.004510, Avg Recall: 0.537736, count: 106 -Region Avg IOU: 0.454110, Class: 1.000000, Obj: 0.259401, No Obj: 0.004626, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.462589, Class: 1.000000, Obj: 0.272180, No Obj: 0.004208, Avg Recall: 0.482456, count: 114 -Region Avg IOU: 0.524213, Class: 1.000000, Obj: 0.316104, No Obj: 0.004032, Avg Recall: 0.553846, count: 65 -Region Avg IOU: 0.466269, Class: 1.000000, Obj: 0.257663, No Obj: 0.004835, Avg Recall: 0.511278, count: 133 -Region Avg IOU: 0.446650, Class: 1.000000, Obj: 0.292091, No Obj: 0.004231, Avg Recall: 0.547368, count: 95 -Region Avg IOU: 0.421859, Class: 1.000000, Obj: 0.260776, No Obj: 0.004498, Avg Recall: 0.447154, count: 123 -3405: 0.523623, 0.479092 avg, 0.001000 rate, 15.247948 seconds, 871680 images -Loaded: 6.171120 seconds -Region Avg IOU: 0.450033, Class: 1.000000, Obj: 0.325035, No Obj: 0.004993, Avg Recall: 0.482143, count: 112 -Region Avg IOU: 0.451638, Class: 1.000000, Obj: 0.279549, No Obj: 0.004939, Avg Recall: 0.448276, count: 116 -Region Avg IOU: 0.374092, Class: 1.000000, Obj: 0.240948, No Obj: 0.004595, Avg Recall: 0.401869, count: 107 -Region Avg IOU: 0.457401, Class: 1.000000, Obj: 0.343302, No Obj: 0.003476, Avg Recall: 0.473684, count: 57 -Region Avg IOU: 0.500731, Class: 1.000000, Obj: 0.283130, No Obj: 0.004988, Avg Recall: 0.608000, count: 125 -Region Avg IOU: 0.485310, Class: 1.000000, Obj: 0.347075, No Obj: 0.004315, Avg Recall: 0.561798, count: 89 -Region Avg IOU: 0.487826, Class: 1.000000, Obj: 0.304952, No Obj: 0.004562, Avg Recall: 0.522523, count: 111 -Region Avg IOU: 0.459840, Class: 1.000000, Obj: 0.271195, No Obj: 0.004716, Avg Recall: 0.517544, count: 114 -3406: 0.463271, 0.477510 avg, 0.001000 rate, 16.939548 seconds, 871936 images -Loaded: 4.217523 seconds -Region Avg IOU: 0.471480, Class: 1.000000, Obj: 0.323760, No Obj: 0.004261, Avg Recall: 0.537500, count: 80 -Region Avg IOU: 0.429380, Class: 1.000000, Obj: 0.299478, No Obj: 0.005588, Avg Recall: 0.473684, count: 133 -Region Avg IOU: 0.487734, Class: 1.000000, Obj: 0.315550, No Obj: 0.004691, Avg Recall: 0.553191, count: 94 -Region Avg IOU: 0.469872, Class: 1.000000, Obj: 0.316590, No Obj: 0.005247, Avg Recall: 0.487395, count: 119 -Region Avg IOU: 0.438418, Class: 1.000000, Obj: 0.293165, No Obj: 0.004832, Avg Recall: 0.451327, count: 113 -Region Avg IOU: 0.474809, Class: 1.000000, Obj: 0.274912, No Obj: 0.005393, Avg Recall: 0.542857, count: 140 -Region Avg IOU: 0.473723, Class: 1.000000, Obj: 0.336962, No Obj: 0.005303, Avg Recall: 0.532258, count: 124 -Region Avg IOU: 0.492683, Class: 1.000000, Obj: 0.340600, No Obj: 0.005425, Avg Recall: 0.581633, count: 98 -3407: 0.486208, 0.478380 avg, 0.001000 rate, 19.168652 seconds, 872192 images -Loaded: 1.107269 seconds -Region Avg IOU: 0.487012, Class: 1.000000, Obj: 0.363451, No Obj: 0.005026, Avg Recall: 0.487805, count: 82 -Region Avg IOU: 0.427523, Class: 1.000000, Obj: 0.316719, No Obj: 0.004564, Avg Recall: 0.454545, count: 88 -Region Avg IOU: 0.391273, Class: 1.000000, Obj: 0.263351, No Obj: 0.004652, Avg Recall: 0.359375, count: 128 -Region Avg IOU: 0.479637, Class: 1.000000, Obj: 0.366557, No Obj: 0.005057, Avg Recall: 0.537736, count: 106 -Region Avg IOU: 0.414164, Class: 1.000000, Obj: 0.308948, No Obj: 0.004630, Avg Recall: 0.438596, count: 114 -Region Avg IOU: 0.436977, Class: 1.000000, Obj: 0.304708, No Obj: 0.005100, Avg Recall: 0.432203, count: 118 -Region Avg IOU: 0.420315, Class: 1.000000, Obj: 0.330163, No Obj: 0.004461, Avg Recall: 0.423913, count: 92 -Region Avg IOU: 0.477084, Class: 1.000000, Obj: 0.296422, No Obj: 0.005450, Avg Recall: 0.533333, count: 105 -3408: 0.465383, 0.477080 avg, 0.001000 rate, 14.915455 seconds, 872448 images -Loaded: 5.172077 seconds -Region Avg IOU: 0.503241, Class: 1.000000, Obj: 0.340991, No Obj: 0.005283, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.463449, Class: 1.000000, Obj: 0.341509, No Obj: 0.005917, Avg Recall: 0.504000, count: 125 -Region Avg IOU: 0.496627, Class: 1.000000, Obj: 0.321489, No Obj: 0.005475, Avg Recall: 0.549451, count: 91 -Region Avg IOU: 0.500161, Class: 1.000000, Obj: 0.345625, No Obj: 0.005121, Avg Recall: 0.545455, count: 88 -Region Avg IOU: 0.469084, Class: 1.000000, Obj: 0.302389, No Obj: 0.005193, Avg Recall: 0.491525, count: 118 -Region Avg IOU: 0.472559, Class: 1.000000, Obj: 0.395947, No Obj: 0.005541, Avg Recall: 0.500000, count: 86 -Region Avg IOU: 0.456854, Class: 1.000000, Obj: 0.308214, No Obj: 0.005362, Avg Recall: 0.489209, count: 139 -Region Avg IOU: 0.428775, Class: 1.000000, Obj: 0.297325, No Obj: 0.005752, Avg Recall: 0.440678, count: 118 -3409: 0.503043, 0.479676 avg, 0.001000 rate, 14.050165 seconds, 872704 images -Loaded: 7.497014 seconds -Region Avg IOU: 0.438723, Class: 1.000000, Obj: 0.323982, No Obj: 0.004811, Avg Recall: 0.435185, count: 108 -Region Avg IOU: 0.486298, Class: 1.000000, Obj: 0.322755, No Obj: 0.005736, Avg Recall: 0.546875, count: 128 -Region Avg IOU: 0.467880, Class: 1.000000, Obj: 0.327880, No Obj: 0.004576, Avg Recall: 0.541176, count: 85 -Region Avg IOU: 0.510724, Class: 1.000000, Obj: 0.372301, No Obj: 0.005178, Avg Recall: 0.560976, count: 82 -Region Avg IOU: 0.437637, Class: 1.000000, Obj: 0.332831, No Obj: 0.005234, Avg Recall: 0.466667, count: 135 -Region Avg IOU: 0.516531, Class: 1.000000, Obj: 0.361016, No Obj: 0.004986, Avg Recall: 0.534884, count: 86 -Region Avg IOU: 0.476340, Class: 1.000000, Obj: 0.322651, No Obj: 0.004649, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.503883, Class: 1.000000, Obj: 0.336284, No Obj: 0.005147, Avg Recall: 0.570093, count: 107 -3410: 0.564064, 0.488115 avg, 0.001000 rate, 17.915909 seconds, 872960 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000065 seconds -Region Avg IOU: 0.578435, Class: 1.000000, Obj: 0.476833, No Obj: 0.004007, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.599723, Class: 1.000000, Obj: 0.485025, No Obj: 0.003859, Avg Recall: 0.714286, count: 84 -Region Avg IOU: 0.581486, Class: 1.000000, Obj: 0.416845, No Obj: 0.004089, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.593276, Class: 1.000000, Obj: 0.446530, No Obj: 0.004977, Avg Recall: 0.727891, count: 147 -Region Avg IOU: 0.591182, Class: 1.000000, Obj: 0.427671, No Obj: 0.003971, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.594120, Class: 1.000000, Obj: 0.461973, No Obj: 0.004323, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.586145, Class: 1.000000, Obj: 0.495188, No Obj: 0.003728, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.616601, Class: 1.000000, Obj: 0.445735, No Obj: 0.004651, Avg Recall: 0.798319, count: 119 -3411: 0.468334, 0.486137 avg, 0.001000 rate, 25.745115 seconds, 873216 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.555800, Class: 1.000000, Obj: 0.438434, No Obj: 0.003727, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.579634, Class: 1.000000, Obj: 0.411818, No Obj: 0.004073, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.587976, Class: 1.000000, Obj: 0.393178, No Obj: 0.003698, Avg Recall: 0.714286, count: 126 -Region Avg IOU: 0.615019, Class: 1.000000, Obj: 0.394193, No Obj: 0.004346, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.593519, Class: 1.000000, Obj: 0.427206, No Obj: 0.003660, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.602675, Class: 1.000000, Obj: 0.436010, No Obj: 0.003700, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.661068, Class: 1.000000, Obj: 0.472928, No Obj: 0.004050, Avg Recall: 0.824742, count: 97 -Region Avg IOU: 0.609585, Class: 1.000000, Obj: 0.511319, No Obj: 0.004286, Avg Recall: 0.721739, count: 115 -3412: 0.465611, 0.484084 avg, 0.001000 rate, 26.321255 seconds, 873472 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.607861, Class: 1.000000, Obj: 0.395830, No Obj: 0.003620, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.585583, Class: 1.000000, Obj: 0.457686, No Obj: 0.003236, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.590415, Class: 1.000000, Obj: 0.389821, No Obj: 0.003822, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.592649, Class: 1.000000, Obj: 0.377276, No Obj: 0.002845, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.605629, Class: 1.000000, Obj: 0.399359, No Obj: 0.003888, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.614784, Class: 1.000000, Obj: 0.419446, No Obj: 0.002942, Avg Recall: 0.725000, count: 80 -Region Avg IOU: 0.593260, Class: 1.000000, Obj: 0.416600, No Obj: 0.003236, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.598931, Class: 1.000000, Obj: 0.418049, No Obj: 0.003562, Avg Recall: 0.715447, count: 123 -3413: 0.440097, 0.479686 avg, 0.001000 rate, 26.203682 seconds, 873728 images -Loaded: 0.000119 seconds -Region Avg IOU: 0.604515, Class: 1.000000, Obj: 0.353625, No Obj: 0.002971, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.610342, Class: 1.000000, Obj: 0.411566, No Obj: 0.003272, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.621113, Class: 1.000000, Obj: 0.357040, No Obj: 0.002951, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.605625, Class: 1.000000, Obj: 0.364305, No Obj: 0.003134, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.617285, Class: 1.000000, Obj: 0.299505, No Obj: 0.002850, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.627853, Class: 1.000000, Obj: 0.402176, No Obj: 0.002799, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.629538, Class: 1.000000, Obj: 0.389402, No Obj: 0.003129, Avg Recall: 0.750000, count: 88 -Region Avg IOU: 0.582728, Class: 1.000000, Obj: 0.343766, No Obj: 0.002501, Avg Recall: 0.702703, count: 111 -3414: 0.417750, 0.473492 avg, 0.001000 rate, 26.462172 seconds, 873984 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.612041, Class: 1.000000, Obj: 0.313284, No Obj: 0.002700, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.631403, Class: 1.000000, Obj: 0.333379, No Obj: 0.002542, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.580670, Class: 1.000000, Obj: 0.323769, No Obj: 0.002763, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.633930, Class: 1.000000, Obj: 0.310599, No Obj: 0.002474, Avg Recall: 0.813084, count: 107 -Region Avg IOU: 0.663754, Class: 1.000000, Obj: 0.365529, No Obj: 0.002665, Avg Recall: 0.836957, count: 92 -Region Avg IOU: 0.608156, Class: 1.000000, Obj: 0.335773, No Obj: 0.002426, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.602981, Class: 1.000000, Obj: 0.317750, No Obj: 0.002826, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.579912, Class: 1.000000, Obj: 0.313417, No Obj: 0.002330, Avg Recall: 0.715596, count: 109 -3415: 0.450669, 0.471210 avg, 0.001000 rate, 25.857874 seconds, 874240 images -Loaded: 0.000120 seconds -Region Avg IOU: 0.573957, Class: 1.000000, Obj: 0.263139, No Obj: 0.002571, Avg Recall: 0.658333, count: 120 -Region Avg IOU: 0.614812, Class: 1.000000, Obj: 0.309488, No Obj: 0.002439, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.648094, Class: 1.000000, Obj: 0.300746, No Obj: 0.002806, Avg Recall: 0.784483, count: 116 -Region Avg IOU: 0.608098, Class: 1.000000, Obj: 0.256242, No Obj: 0.002578, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.623361, Class: 1.000000, Obj: 0.324609, No Obj: 0.002521, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.595091, Class: 1.000000, Obj: 0.300791, No Obj: 0.002206, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.595942, Class: 1.000000, Obj: 0.314229, No Obj: 0.002281, Avg Recall: 0.701149, count: 87 -Region Avg IOU: 0.655679, Class: 1.000000, Obj: 0.307847, No Obj: 0.002419, Avg Recall: 0.862745, count: 102 -3416: 0.468943, 0.470983 avg, 0.001000 rate, 26.133684 seconds, 874496 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.662911, Class: 1.000000, Obj: 0.290524, No Obj: 0.002299, Avg Recall: 0.857143, count: 98 -Region Avg IOU: 0.651357, Class: 1.000000, Obj: 0.308858, No Obj: 0.002616, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.617230, Class: 1.000000, Obj: 0.291600, No Obj: 0.002291, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.606183, Class: 1.000000, Obj: 0.297629, No Obj: 0.001983, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.597487, Class: 1.000000, Obj: 0.269540, No Obj: 0.002127, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.613409, Class: 1.000000, Obj: 0.289973, No Obj: 0.002325, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.597339, Class: 1.000000, Obj: 0.281930, No Obj: 0.002063, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.606798, Class: 1.000000, Obj: 0.332517, No Obj: 0.002140, Avg Recall: 0.741176, count: 85 -3417: 0.449133, 0.468798 avg, 0.001000 rate, 26.239204 seconds, 874752 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.621420, Class: 1.000000, Obj: 0.259752, No Obj: 0.002136, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.624497, Class: 1.000000, Obj: 0.317450, No Obj: 0.002335, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.603866, Class: 1.000000, Obj: 0.263598, No Obj: 0.002073, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.610043, Class: 1.000000, Obj: 0.302599, No Obj: 0.002366, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.572892, Class: 1.000000, Obj: 0.265078, No Obj: 0.002153, Avg Recall: 0.696000, count: 125 -Region Avg IOU: 0.612006, Class: 1.000000, Obj: 0.232811, No Obj: 0.001924, Avg Recall: 0.750000, count: 84 -Region Avg IOU: 0.614544, Class: 1.000000, Obj: 0.284620, No Obj: 0.002100, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.604997, Class: 1.000000, Obj: 0.301279, No Obj: 0.002102, Avg Recall: 0.743119, count: 109 -3418: 0.451695, 0.467088 avg, 0.001000 rate, 26.895405 seconds, 875008 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.628445, Class: 1.000000, Obj: 0.328806, No Obj: 0.002316, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.632570, Class: 1.000000, Obj: 0.330000, No Obj: 0.002158, Avg Recall: 0.792683, count: 82 -Region Avg IOU: 0.571359, Class: 1.000000, Obj: 0.309318, No Obj: 0.001940, Avg Recall: 0.670455, count: 88 -Region Avg IOU: 0.617553, Class: 1.000000, Obj: 0.316497, No Obj: 0.002107, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.606270, Class: 1.000000, Obj: 0.333766, No Obj: 0.002390, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.583454, Class: 1.000000, Obj: 0.326565, No Obj: 0.002213, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.627063, Class: 1.000000, Obj: 0.273637, No Obj: 0.002368, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.642798, Class: 1.000000, Obj: 0.358763, No Obj: 0.001843, Avg Recall: 0.777778, count: 72 -3419: 0.430080, 0.463387 avg, 0.001000 rate, 27.015701 seconds, 875264 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.599587, Class: 1.000000, Obj: 0.337857, No Obj: 0.002063, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.625760, Class: 1.000000, Obj: 0.315622, No Obj: 0.002206, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.613230, Class: 1.000000, Obj: 0.342093, No Obj: 0.002485, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.588632, Class: 1.000000, Obj: 0.301469, No Obj: 0.002219, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.617174, Class: 1.000000, Obj: 0.309298, No Obj: 0.002368, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.621551, Class: 1.000000, Obj: 0.333760, No Obj: 0.002339, Avg Recall: 0.784483, count: 116 -Region Avg IOU: 0.576294, Class: 1.000000, Obj: 0.318156, No Obj: 0.002347, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.617867, Class: 1.000000, Obj: 0.286504, No Obj: 0.002545, Avg Recall: 0.741667, count: 120 -3420: 0.473118, 0.464360 avg, 0.001000 rate, 26.256161 seconds, 875520 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.419588 seconds -Region Avg IOU: 0.561575, Class: 1.000000, Obj: 0.301177, No Obj: 0.003432, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.540398, Class: 1.000000, Obj: 0.267334, No Obj: 0.003325, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.497552, Class: 1.000000, Obj: 0.228877, No Obj: 0.002834, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.482830, Class: 1.000000, Obj: 0.250916, No Obj: 0.003004, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.509989, Class: 1.000000, Obj: 0.272000, No Obj: 0.003305, Avg Recall: 0.596491, count: 114 -Region Avg IOU: 0.540943, Class: 1.000000, Obj: 0.254322, No Obj: 0.003349, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.522614, Class: 1.000000, Obj: 0.258529, No Obj: 0.003476, Avg Recall: 0.610169, count: 118 -Region Avg IOU: 0.567831, Class: 1.000000, Obj: 0.252839, No Obj: 0.003750, Avg Recall: 0.677686, count: 121 -3421: 0.522107, 0.470135 avg, 0.001000 rate, 19.736420 seconds, 875776 images -Loaded: 1.578178 seconds -Region Avg IOU: 0.530686, Class: 1.000000, Obj: 0.318672, No Obj: 0.003556, Avg Recall: 0.607843, count: 102 -Region Avg IOU: 0.527171, Class: 1.000000, Obj: 0.286106, No Obj: 0.003916, Avg Recall: 0.622222, count: 135 -Region Avg IOU: 0.525732, Class: 1.000000, Obj: 0.302212, No Obj: 0.003973, Avg Recall: 0.606557, count: 122 -Region Avg IOU: 0.532733, Class: 1.000000, Obj: 0.266031, No Obj: 0.003371, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.541791, Class: 1.000000, Obj: 0.324378, No Obj: 0.003709, Avg Recall: 0.612903, count: 124 -Region Avg IOU: 0.520963, Class: 1.000000, Obj: 0.304169, No Obj: 0.003421, Avg Recall: 0.561404, count: 114 -Region Avg IOU: 0.493257, Class: 1.000000, Obj: 0.269873, No Obj: 0.003452, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.490683, Class: 1.000000, Obj: 0.286333, No Obj: 0.003968, Avg Recall: 0.561983, count: 121 -3422: 0.517824, 0.474904 avg, 0.001000 rate, 21.944414 seconds, 876032 images -Loaded: 0.629841 seconds -Region Avg IOU: 0.520891, Class: 1.000000, Obj: 0.269273, No Obj: 0.004225, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.483455, Class: 1.000000, Obj: 0.321508, No Obj: 0.003732, Avg Recall: 0.534091, count: 88 -Region Avg IOU: 0.523194, Class: 1.000000, Obj: 0.342885, No Obj: 0.003869, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.522194, Class: 1.000000, Obj: 0.355854, No Obj: 0.003853, Avg Recall: 0.600000, count: 90 -Region Avg IOU: 0.530259, Class: 1.000000, Obj: 0.344325, No Obj: 0.005025, Avg Recall: 0.571429, count: 112 -Region Avg IOU: 0.466419, Class: 1.000000, Obj: 0.314866, No Obj: 0.003254, Avg Recall: 0.500000, count: 92 -Region Avg IOU: 0.516912, Class: 1.000000, Obj: 0.348479, No Obj: 0.004221, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.531680, Class: 1.000000, Obj: 0.338529, No Obj: 0.003601, Avg Recall: 0.604396, count: 91 -3423: 0.441008, 0.471514 avg, 0.001000 rate, 20.522032 seconds, 876288 images -Loaded: 1.062393 seconds -Region Avg IOU: 0.528528, Class: 1.000000, Obj: 0.382411, No Obj: 0.005271, Avg Recall: 0.592000, count: 125 -Region Avg IOU: 0.495142, Class: 1.000000, Obj: 0.353699, No Obj: 0.004259, Avg Recall: 0.575000, count: 120 -Region Avg IOU: 0.518571, Class: 1.000000, Obj: 0.384014, No Obj: 0.004683, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.507217, Class: 1.000000, Obj: 0.335129, No Obj: 0.004577, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.522054, Class: 1.000000, Obj: 0.362192, No Obj: 0.004951, Avg Recall: 0.645161, count: 124 -Region Avg IOU: 0.515801, Class: 1.000000, Obj: 0.340208, No Obj: 0.004326, Avg Recall: 0.537634, count: 93 -Region Avg IOU: 0.512808, Class: 1.000000, Obj: 0.330031, No Obj: 0.005759, Avg Recall: 0.594118, count: 170 -Region Avg IOU: 0.558925, Class: 1.000000, Obj: 0.374176, No Obj: 0.004917, Avg Recall: 0.625000, count: 104 -3424: 0.509812, 0.475344 avg, 0.001000 rate, 20.559780 seconds, 876544 images -Loaded: 2.319893 seconds -Region Avg IOU: 0.552638, Class: 1.000000, Obj: 0.427768, No Obj: 0.005534, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.485856, Class: 1.000000, Obj: 0.368392, No Obj: 0.005186, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.424258, Class: 1.000000, Obj: 0.342377, No Obj: 0.004637, Avg Recall: 0.409091, count: 110 -Region Avg IOU: 0.464891, Class: 1.000000, Obj: 0.340115, No Obj: 0.005024, Avg Recall: 0.495935, count: 123 -Region Avg IOU: 0.563523, Class: 1.000000, Obj: 0.417818, No Obj: 0.005939, Avg Recall: 0.680672, count: 119 -Region Avg IOU: 0.518583, Class: 1.000000, Obj: 0.377975, No Obj: 0.004951, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.516978, Class: 1.000000, Obj: 0.370167, No Obj: 0.005463, Avg Recall: 0.606557, count: 122 -Region Avg IOU: 0.532700, Class: 1.000000, Obj: 0.418298, No Obj: 0.004642, Avg Recall: 0.644444, count: 90 -3425: 0.424991, 0.470309 avg, 0.001000 rate, 21.380577 seconds, 876800 images -Loaded: 1.002996 seconds -Region Avg IOU: 0.478074, Class: 1.000000, Obj: 0.367929, No Obj: 0.005324, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.474461, Class: 1.000000, Obj: 0.424013, No Obj: 0.005421, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.533669, Class: 1.000000, Obj: 0.416128, No Obj: 0.005320, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.509263, Class: 1.000000, Obj: 0.378456, No Obj: 0.004794, Avg Recall: 0.585106, count: 94 -Region Avg IOU: 0.518294, Class: 1.000000, Obj: 0.399963, No Obj: 0.005207, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.459883, Class: 1.000000, Obj: 0.403038, No Obj: 0.005262, Avg Recall: 0.462963, count: 108 -Region Avg IOU: 0.496209, Class: 1.000000, Obj: 0.372980, No Obj: 0.005292, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.538910, Class: 1.000000, Obj: 0.444576, No Obj: 0.006323, Avg Recall: 0.625954, count: 131 -3426: 0.489044, 0.472182 avg, 0.001000 rate, 20.284346 seconds, 877056 images -Loaded: 2.699428 seconds -Region Avg IOU: 0.472008, Class: 1.000000, Obj: 0.393405, No Obj: 0.004869, Avg Recall: 0.544554, count: 101 -Region Avg IOU: 0.502721, Class: 1.000000, Obj: 0.429467, No Obj: 0.004833, Avg Recall: 0.594828, count: 116 -Region Avg IOU: 0.502150, Class: 1.000000, Obj: 0.401067, No Obj: 0.005285, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.499374, Class: 1.000000, Obj: 0.389427, No Obj: 0.005117, Avg Recall: 0.583333, count: 96 -Region Avg IOU: 0.565844, Class: 1.000000, Obj: 0.493093, No Obj: 0.005827, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.502716, Class: 1.000000, Obj: 0.411294, No Obj: 0.005345, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.487661, Class: 1.000000, Obj: 0.378050, No Obj: 0.005133, Avg Recall: 0.554217, count: 83 -Region Avg IOU: 0.500714, Class: 1.000000, Obj: 0.404229, No Obj: 0.005051, Avg Recall: 0.553398, count: 103 -3427: 0.491780, 0.474142 avg, 0.001000 rate, 21.643204 seconds, 877312 images -Loaded: 0.227129 seconds -Region Avg IOU: 0.501015, Class: 1.000000, Obj: 0.417978, No Obj: 0.005315, Avg Recall: 0.558140, count: 86 -Region Avg IOU: 0.477974, Class: 1.000000, Obj: 0.423004, No Obj: 0.004786, Avg Recall: 0.450000, count: 80 -Region Avg IOU: 0.520227, Class: 1.000000, Obj: 0.382768, No Obj: 0.005608, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.528932, Class: 1.000000, Obj: 0.406912, No Obj: 0.005273, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.534383, Class: 1.000000, Obj: 0.389013, No Obj: 0.005712, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.519123, Class: 1.000000, Obj: 0.393173, No Obj: 0.005074, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.482429, Class: 1.000000, Obj: 0.404015, No Obj: 0.004763, Avg Recall: 0.463636, count: 110 -Region Avg IOU: 0.448942, Class: 1.000000, Obj: 0.375430, No Obj: 0.004378, Avg Recall: 0.488095, count: 84 -3428: 0.452203, 0.471948 avg, 0.001000 rate, 19.401062 seconds, 877568 images -Loaded: 1.065635 seconds -Region Avg IOU: 0.488555, Class: 1.000000, Obj: 0.398289, No Obj: 0.004352, Avg Recall: 0.517647, count: 85 -Region Avg IOU: 0.487537, Class: 1.000000, Obj: 0.377150, No Obj: 0.004705, Avg Recall: 0.525253, count: 99 -Region Avg IOU: 0.537714, Class: 1.000000, Obj: 0.395353, No Obj: 0.004818, Avg Recall: 0.638554, count: 83 -Region Avg IOU: 0.502999, Class: 1.000000, Obj: 0.385069, No Obj: 0.005545, Avg Recall: 0.606838, count: 117 -Region Avg IOU: 0.447482, Class: 1.000000, Obj: 0.363980, No Obj: 0.004966, Avg Recall: 0.427419, count: 124 -Region Avg IOU: 0.519250, Class: 1.000000, Obj: 0.420663, No Obj: 0.004835, Avg Recall: 0.582418, count: 91 -Region Avg IOU: 0.487620, Class: 1.000000, Obj: 0.393459, No Obj: 0.004150, Avg Recall: 0.533333, count: 90 -Region Avg IOU: 0.503435, Class: 1.000000, Obj: 0.384616, No Obj: 0.005248, Avg Recall: 0.581967, count: 122 -3429: 0.464342, 0.471187 avg, 0.001000 rate, 19.356005 seconds, 877824 images -Loaded: 2.096564 seconds -Region Avg IOU: 0.475271, Class: 1.000000, Obj: 0.343531, No Obj: 0.004260, Avg Recall: 0.542553, count: 94 -Region Avg IOU: 0.464270, Class: 1.000000, Obj: 0.301173, No Obj: 0.004689, Avg Recall: 0.468254, count: 126 -Region Avg IOU: 0.457730, Class: 1.000000, Obj: 0.335034, No Obj: 0.003912, Avg Recall: 0.484536, count: 97 -Region Avg IOU: 0.480750, Class: 1.000000, Obj: 0.405939, No Obj: 0.004337, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.467499, Class: 1.000000, Obj: 0.323170, No Obj: 0.004339, Avg Recall: 0.532110, count: 109 -Region Avg IOU: 0.462766, Class: 1.000000, Obj: 0.323185, No Obj: 0.003764, Avg Recall: 0.514019, count: 107 -Region Avg IOU: 0.523010, Class: 1.000000, Obj: 0.400248, No Obj: 0.004684, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.468121, Class: 1.000000, Obj: 0.337560, No Obj: 0.004116, Avg Recall: 0.544444, count: 90 -3430: 0.464638, 0.470533 avg, 0.001000 rate, 22.476845 seconds, 878080 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 0.169555 seconds -Region Avg IOU: 0.522070, Class: 1.000000, Obj: 0.392521, No Obj: 0.003655, Avg Recall: 0.607143, count: 84 -Region Avg IOU: 0.483518, Class: 1.000000, Obj: 0.345286, No Obj: 0.003819, Avg Recall: 0.529412, count: 102 -Region Avg IOU: 0.498307, Class: 1.000000, Obj: 0.312717, No Obj: 0.004185, Avg Recall: 0.571429, count: 105 -Region Avg IOU: 0.510952, Class: 1.000000, Obj: 0.320366, No Obj: 0.004022, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.506011, Class: 1.000000, Obj: 0.315459, No Obj: 0.005228, Avg Recall: 0.568627, count: 153 -Region Avg IOU: 0.517469, Class: 1.000000, Obj: 0.380890, No Obj: 0.003802, Avg Recall: 0.583333, count: 84 -Region Avg IOU: 0.511044, Class: 1.000000, Obj: 0.306605, No Obj: 0.003856, Avg Recall: 0.607143, count: 84 -Region Avg IOU: 0.518258, Class: 1.000000, Obj: 0.333122, No Obj: 0.004281, Avg Recall: 0.537815, count: 119 -3431: 0.464387, 0.469918 avg, 0.001000 rate, 23.415953 seconds, 878336 images -Loaded: 0.021591 seconds -Region Avg IOU: 0.500715, Class: 1.000000, Obj: 0.298870, No Obj: 0.003405, Avg Recall: 0.546392, count: 97 -Region Avg IOU: 0.527589, Class: 1.000000, Obj: 0.280865, No Obj: 0.003865, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.516298, Class: 1.000000, Obj: 0.309580, No Obj: 0.004025, Avg Recall: 0.534884, count: 129 -Region Avg IOU: 0.533808, Class: 1.000000, Obj: 0.313385, No Obj: 0.003351, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.508191, Class: 1.000000, Obj: 0.328384, No Obj: 0.004029, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.538148, Class: 1.000000, Obj: 0.319649, No Obj: 0.004053, Avg Recall: 0.644444, count: 135 -Region Avg IOU: 0.525672, Class: 1.000000, Obj: 0.315026, No Obj: 0.004214, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.466312, Class: 1.000000, Obj: 0.278826, No Obj: 0.003237, Avg Recall: 0.535088, count: 114 -3432: 0.514760, 0.474402 avg, 0.001000 rate, 22.885096 seconds, 878592 images -Loaded: 0.011924 seconds -Region Avg IOU: 0.565890, Class: 1.000000, Obj: 0.333344, No Obj: 0.003949, Avg Recall: 0.683761, count: 117 -Region Avg IOU: 0.576513, Class: 1.000000, Obj: 0.357494, No Obj: 0.004220, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.530190, Class: 1.000000, Obj: 0.320417, No Obj: 0.003849, Avg Recall: 0.596774, count: 124 -Region Avg IOU: 0.483800, Class: 1.000000, Obj: 0.266053, No Obj: 0.003142, Avg Recall: 0.525253, count: 99 -Region Avg IOU: 0.527307, Class: 1.000000, Obj: 0.298784, No Obj: 0.003636, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.505679, Class: 1.000000, Obj: 0.289693, No Obj: 0.003211, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.541608, Class: 1.000000, Obj: 0.339452, No Obj: 0.003762, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.517492, Class: 1.000000, Obj: 0.302266, No Obj: 0.003622, Avg Recall: 0.562500, count: 112 -3433: 0.445906, 0.471553 avg, 0.001000 rate, 21.722525 seconds, 878848 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.516913, Class: 1.000000, Obj: 0.307170, No Obj: 0.003385, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.586736, Class: 1.000000, Obj: 0.301787, No Obj: 0.003275, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.471991, Class: 1.000000, Obj: 0.266929, No Obj: 0.003352, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.550934, Class: 1.000000, Obj: 0.296872, No Obj: 0.003963, Avg Recall: 0.640000, count: 125 -Region Avg IOU: 0.526043, Class: 1.000000, Obj: 0.287175, No Obj: 0.003222, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.509907, Class: 1.000000, Obj: 0.305684, No Obj: 0.003223, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.544946, Class: 1.000000, Obj: 0.306177, No Obj: 0.003263, Avg Recall: 0.682353, count: 85 -Region Avg IOU: 0.485748, Class: 1.000000, Obj: 0.264974, No Obj: 0.003244, Avg Recall: 0.500000, count: 122 -3434: 0.451616, 0.469559 avg, 0.001000 rate, 20.795679 seconds, 879104 images -Loaded: 0.102774 seconds -Region Avg IOU: 0.556036, Class: 1.000000, Obj: 0.294525, No Obj: 0.003381, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.546111, Class: 1.000000, Obj: 0.314039, No Obj: 0.003361, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.565920, Class: 1.000000, Obj: 0.354679, No Obj: 0.003554, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.483341, Class: 1.000000, Obj: 0.267575, No Obj: 0.002999, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.571509, Class: 1.000000, Obj: 0.357158, No Obj: 0.003848, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.556301, Class: 1.000000, Obj: 0.320565, No Obj: 0.003547, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.490090, Class: 1.000000, Obj: 0.260300, No Obj: 0.003466, Avg Recall: 0.495575, count: 113 -Region Avg IOU: 0.613869, Class: 1.000000, Obj: 0.316105, No Obj: 0.003121, Avg Recall: 0.740000, count: 100 -3435: 0.435414, 0.466144 avg, 0.001000 rate, 21.824736 seconds, 879360 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.552591, Class: 1.000000, Obj: 0.317904, No Obj: 0.004028, Avg Recall: 0.652893, count: 121 -Region Avg IOU: 0.598462, Class: 1.000000, Obj: 0.359088, No Obj: 0.002571, Avg Recall: 0.753623, count: 69 -Region Avg IOU: 0.555308, Class: 1.000000, Obj: 0.344434, No Obj: 0.003462, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.538119, Class: 1.000000, Obj: 0.345306, No Obj: 0.003237, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.519786, Class: 1.000000, Obj: 0.308030, No Obj: 0.003134, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.515140, Class: 1.000000, Obj: 0.325175, No Obj: 0.003488, Avg Recall: 0.565891, count: 129 -Region Avg IOU: 0.547994, Class: 1.000000, Obj: 0.291881, No Obj: 0.003475, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.530466, Class: 1.000000, Obj: 0.299960, No Obj: 0.003685, Avg Recall: 0.683453, count: 139 -3436: 0.487968, 0.468327 avg, 0.001000 rate, 22.610275 seconds, 879616 images -Loaded: 0.020183 seconds -Region Avg IOU: 0.598496, Class: 1.000000, Obj: 0.373467, No Obj: 0.003873, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.582320, Class: 1.000000, Obj: 0.352954, No Obj: 0.003240, Avg Recall: 0.681818, count: 66 -Region Avg IOU: 0.551431, Class: 1.000000, Obj: 0.287668, No Obj: 0.003985, Avg Recall: 0.636364, count: 132 -Region Avg IOU: 0.567762, Class: 1.000000, Obj: 0.316337, No Obj: 0.003935, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.559389, Class: 1.000000, Obj: 0.334469, No Obj: 0.003690, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.563168, Class: 1.000000, Obj: 0.339164, No Obj: 0.003095, Avg Recall: 0.703297, count: 91 -Region Avg IOU: 0.559658, Class: 1.000000, Obj: 0.301319, No Obj: 0.003843, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.551744, Class: 1.000000, Obj: 0.303883, No Obj: 0.004204, Avg Recall: 0.658537, count: 123 -3437: 0.494752, 0.470969 avg, 0.001000 rate, 22.743116 seconds, 879872 images -Loaded: 0.112118 seconds -Region Avg IOU: 0.542307, Class: 1.000000, Obj: 0.309759, No Obj: 0.003847, Avg Recall: 0.601562, count: 128 -Region Avg IOU: 0.595658, Class: 1.000000, Obj: 0.340145, No Obj: 0.003672, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.536104, Class: 1.000000, Obj: 0.357111, No Obj: 0.004190, Avg Recall: 0.606838, count: 117 -Region Avg IOU: 0.548563, Class: 1.000000, Obj: 0.350400, No Obj: 0.003318, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.542068, Class: 1.000000, Obj: 0.375535, No Obj: 0.004208, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.538838, Class: 1.000000, Obj: 0.328643, No Obj: 0.003866, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.562098, Class: 1.000000, Obj: 0.360570, No Obj: 0.003610, Avg Recall: 0.688312, count: 77 -Region Avg IOU: 0.537205, Class: 1.000000, Obj: 0.351683, No Obj: 0.004083, Avg Recall: 0.626016, count: 123 -3438: 0.480836, 0.471956 avg, 0.001000 rate, 23.970335 seconds, 880128 images -Loaded: 0.200071 seconds -Region Avg IOU: 0.556071, Class: 1.000000, Obj: 0.367762, No Obj: 0.004249, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.551619, Class: 1.000000, Obj: 0.413716, No Obj: 0.004863, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.539527, Class: 1.000000, Obj: 0.326905, No Obj: 0.003930, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.504727, Class: 1.000000, Obj: 0.323027, No Obj: 0.004253, Avg Recall: 0.566667, count: 120 -Region Avg IOU: 0.551052, Class: 1.000000, Obj: 0.310432, No Obj: 0.004761, Avg Recall: 0.615385, count: 130 -Region Avg IOU: 0.540389, Class: 1.000000, Obj: 0.337207, No Obj: 0.003913, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.512170, Class: 1.000000, Obj: 0.361007, No Obj: 0.003523, Avg Recall: 0.585106, count: 94 -Region Avg IOU: 0.570187, Class: 1.000000, Obj: 0.379271, No Obj: 0.004158, Avg Recall: 0.728070, count: 114 -3439: 0.480222, 0.472783 avg, 0.001000 rate, 21.906927 seconds, 880384 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.572237, Class: 1.000000, Obj: 0.393439, No Obj: 0.004051, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.492524, Class: 1.000000, Obj: 0.361937, No Obj: 0.004248, Avg Recall: 0.569106, count: 123 -Region Avg IOU: 0.548828, Class: 1.000000, Obj: 0.413715, No Obj: 0.004219, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.487539, Class: 1.000000, Obj: 0.327397, No Obj: 0.004333, Avg Recall: 0.543307, count: 127 -Region Avg IOU: 0.521870, Class: 1.000000, Obj: 0.358634, No Obj: 0.005250, Avg Recall: 0.573333, count: 150 -Region Avg IOU: 0.482195, Class: 1.000000, Obj: 0.339136, No Obj: 0.004096, Avg Recall: 0.489362, count: 94 -Region Avg IOU: 0.530602, Class: 1.000000, Obj: 0.333604, No Obj: 0.004359, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.550010, Class: 1.000000, Obj: 0.366288, No Obj: 0.003777, Avg Recall: 0.550562, count: 89 -3440: 0.547885, 0.480293 avg, 0.001000 rate, 23.459087 seconds, 880640 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000067 seconds -Region Avg IOU: 0.605603, Class: 1.000000, Obj: 0.439447, No Obj: 0.004414, Avg Recall: 0.723404, count: 141 -Region Avg IOU: 0.646408, Class: 1.000000, Obj: 0.489358, No Obj: 0.003570, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.587618, Class: 1.000000, Obj: 0.390959, No Obj: 0.003563, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.628782, Class: 1.000000, Obj: 0.462318, No Obj: 0.003560, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.599891, Class: 1.000000, Obj: 0.403577, No Obj: 0.003917, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.622594, Class: 1.000000, Obj: 0.461859, No Obj: 0.004127, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.598003, Class: 1.000000, Obj: 0.427563, No Obj: 0.003780, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.632637, Class: 1.000000, Obj: 0.510832, No Obj: 0.003995, Avg Recall: 0.778846, count: 104 -3441: 0.456354, 0.477899 avg, 0.001000 rate, 27.760231 seconds, 880896 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.571237, Class: 1.000000, Obj: 0.442691, No Obj: 0.003204, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.602517, Class: 1.000000, Obj: 0.446219, No Obj: 0.003959, Avg Recall: 0.748031, count: 127 -Region Avg IOU: 0.592860, Class: 1.000000, Obj: 0.402469, No Obj: 0.003740, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.630134, Class: 1.000000, Obj: 0.438262, No Obj: 0.003979, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.594802, Class: 1.000000, Obj: 0.396937, No Obj: 0.003794, Avg Recall: 0.737226, count: 137 -Region Avg IOU: 0.653904, Class: 1.000000, Obj: 0.483416, No Obj: 0.003259, Avg Recall: 0.846154, count: 78 -Region Avg IOU: 0.623465, Class: 1.000000, Obj: 0.399371, No Obj: 0.003916, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.608736, Class: 1.000000, Obj: 0.439454, No Obj: 0.003563, Avg Recall: 0.706422, count: 109 -3442: 0.492571, 0.479366 avg, 0.001000 rate, 27.880808 seconds, 881152 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.633092, Class: 1.000000, Obj: 0.437977, No Obj: 0.004199, Avg Recall: 0.784615, count: 130 -Region Avg IOU: 0.643684, Class: 1.000000, Obj: 0.409985, No Obj: 0.003003, Avg Recall: 0.776471, count: 85 -Region Avg IOU: 0.614220, Class: 1.000000, Obj: 0.439286, No Obj: 0.003077, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.563964, Class: 1.000000, Obj: 0.394092, No Obj: 0.003534, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.574177, Class: 1.000000, Obj: 0.437334, No Obj: 0.002998, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.577172, Class: 1.000000, Obj: 0.379458, No Obj: 0.003357, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.622059, Class: 1.000000, Obj: 0.430265, No Obj: 0.003565, Avg Recall: 0.775862, count: 116 -Region Avg IOU: 0.630760, Class: 1.000000, Obj: 0.430713, No Obj: 0.003236, Avg Recall: 0.788462, count: 104 -3443: 0.478017, 0.479231 avg, 0.001000 rate, 27.786020 seconds, 881408 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.638366, Class: 1.000000, Obj: 0.424306, No Obj: 0.003066, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.593550, Class: 1.000000, Obj: 0.392658, No Obj: 0.002852, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.664920, Class: 1.000000, Obj: 0.365973, No Obj: 0.003518, Avg Recall: 0.844828, count: 116 -Region Avg IOU: 0.614453, Class: 1.000000, Obj: 0.392700, No Obj: 0.002985, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.591261, Class: 1.000000, Obj: 0.382551, No Obj: 0.002915, Avg Recall: 0.715447, count: 123 -Region Avg IOU: 0.555344, Class: 1.000000, Obj: 0.353154, No Obj: 0.003237, Avg Recall: 0.673077, count: 156 -Region Avg IOU: 0.592958, Class: 1.000000, Obj: 0.331335, No Obj: 0.003171, Avg Recall: 0.768707, count: 147 -Region Avg IOU: 0.585532, Class: 1.000000, Obj: 0.367499, No Obj: 0.002803, Avg Recall: 0.729167, count: 96 -3444: 0.535174, 0.484826 avg, 0.001000 rate, 27.448336 seconds, 881664 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.612862, Class: 1.000000, Obj: 0.389898, No Obj: 0.002495, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.634397, Class: 1.000000, Obj: 0.338670, No Obj: 0.002789, Avg Recall: 0.779661, count: 118 -Region Avg IOU: 0.644260, Class: 1.000000, Obj: 0.403611, No Obj: 0.002715, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.601477, Class: 1.000000, Obj: 0.322746, No Obj: 0.002850, Avg Recall: 0.715328, count: 137 -Region Avg IOU: 0.598304, Class: 1.000000, Obj: 0.345225, No Obj: 0.002958, Avg Recall: 0.726027, count: 146 -Region Avg IOU: 0.578610, Class: 1.000000, Obj: 0.375437, No Obj: 0.002590, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.619715, Class: 1.000000, Obj: 0.337368, No Obj: 0.002376, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.614193, Class: 1.000000, Obj: 0.368008, No Obj: 0.002527, Avg Recall: 0.737374, count: 99 -3445: 0.491279, 0.485471 avg, 0.001000 rate, 26.674551 seconds, 881920 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.574457, Class: 1.000000, Obj: 0.319132, No Obj: 0.002585, Avg Recall: 0.698276, count: 116 -Region Avg IOU: 0.597722, Class: 1.000000, Obj: 0.341282, No Obj: 0.002470, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.609501, Class: 1.000000, Obj: 0.311253, No Obj: 0.002241, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.589935, Class: 1.000000, Obj: 0.306648, No Obj: 0.002513, Avg Recall: 0.680672, count: 119 -Region Avg IOU: 0.657635, Class: 1.000000, Obj: 0.401181, No Obj: 0.002417, Avg Recall: 0.827586, count: 87 -Region Avg IOU: 0.549509, Class: 1.000000, Obj: 0.307664, No Obj: 0.002441, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.599541, Class: 1.000000, Obj: 0.316647, No Obj: 0.002214, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.617033, Class: 1.000000, Obj: 0.297286, No Obj: 0.002353, Avg Recall: 0.757282, count: 103 -3446: 0.455350, 0.482459 avg, 0.001000 rate, 27.931612 seconds, 882176 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.610531, Class: 1.000000, Obj: 0.302107, No Obj: 0.002189, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.552971, Class: 1.000000, Obj: 0.259207, No Obj: 0.002467, Avg Recall: 0.633588, count: 131 -Region Avg IOU: 0.615946, Class: 1.000000, Obj: 0.343535, No Obj: 0.002456, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.619489, Class: 1.000000, Obj: 0.343341, No Obj: 0.002490, Avg Recall: 0.764706, count: 136 -Region Avg IOU: 0.619770, Class: 1.000000, Obj: 0.285365, No Obj: 0.002152, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.605886, Class: 1.000000, Obj: 0.335192, No Obj: 0.002392, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.609783, Class: 1.000000, Obj: 0.355856, No Obj: 0.002423, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.581585, Class: 1.000000, Obj: 0.304491, No Obj: 0.002198, Avg Recall: 0.673267, count: 101 -3447: 0.474022, 0.481615 avg, 0.001000 rate, 26.853657 seconds, 882432 images -Loaded: 0.000059 seconds -Region Avg IOU: 0.627464, Class: 1.000000, Obj: 0.308883, No Obj: 0.002496, Avg Recall: 0.755906, count: 127 -Region Avg IOU: 0.598824, Class: 1.000000, Obj: 0.289910, No Obj: 0.002403, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.643942, Class: 1.000000, Obj: 0.348823, No Obj: 0.002370, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.643486, Class: 1.000000, Obj: 0.345652, No Obj: 0.002406, Avg Recall: 0.775000, count: 120 -Region Avg IOU: 0.608559, Class: 1.000000, Obj: 0.332875, No Obj: 0.002653, Avg Recall: 0.750000, count: 132 -Region Avg IOU: 0.547755, Class: 1.000000, Obj: 0.322590, No Obj: 0.002027, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.648159, Class: 1.000000, Obj: 0.331125, No Obj: 0.002288, Avg Recall: 0.823529, count: 102 -Region Avg IOU: 0.622883, Class: 1.000000, Obj: 0.294548, No Obj: 0.001989, Avg Recall: 0.815385, count: 65 -3448: 0.452671, 0.478721 avg, 0.001000 rate, 27.669832 seconds, 882688 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.619227, Class: 1.000000, Obj: 0.316217, No Obj: 0.002598, Avg Recall: 0.743802, count: 121 -Region Avg IOU: 0.642575, Class: 1.000000, Obj: 0.328483, No Obj: 0.002624, Avg Recall: 0.818965, count: 116 -Region Avg IOU: 0.690793, Class: 1.000000, Obj: 0.348914, No Obj: 0.002530, Avg Recall: 0.870000, count: 100 -Region Avg IOU: 0.642484, Class: 1.000000, Obj: 0.319453, No Obj: 0.002197, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.617915, Class: 1.000000, Obj: 0.325580, No Obj: 0.002552, Avg Recall: 0.727273, count: 132 -Region Avg IOU: 0.653333, Class: 1.000000, Obj: 0.341553, No Obj: 0.002251, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.629756, Class: 1.000000, Obj: 0.290162, No Obj: 0.002536, Avg Recall: 0.776923, count: 130 -Region Avg IOU: 0.608918, Class: 1.000000, Obj: 0.295602, No Obj: 0.002484, Avg Recall: 0.732143, count: 112 -3449: 0.521725, 0.483021 avg, 0.001000 rate, 26.786682 seconds, 882944 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.619041, Class: 1.000000, Obj: 0.334399, No Obj: 0.002169, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.620183, Class: 1.000000, Obj: 0.333498, No Obj: 0.002589, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.633775, Class: 1.000000, Obj: 0.315259, No Obj: 0.002591, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.627599, Class: 1.000000, Obj: 0.334176, No Obj: 0.002492, Avg Recall: 0.754098, count: 122 -Region Avg IOU: 0.596403, Class: 1.000000, Obj: 0.339496, No Obj: 0.002775, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.642824, Class: 1.000000, Obj: 0.314174, No Obj: 0.002576, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.615119, Class: 1.000000, Obj: 0.326158, No Obj: 0.002432, Avg Recall: 0.769231, count: 117 -Region Avg IOU: 0.635024, Class: 1.000000, Obj: 0.350135, No Obj: 0.002301, Avg Recall: 0.775281, count: 89 -3450: 0.427073, 0.477426 avg, 0.001000 rate, 26.600668 seconds, 883200 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.659559 seconds -Region Avg IOU: 0.557516, Class: 1.000000, Obj: 0.282163, No Obj: 0.002897, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.512476, Class: 1.000000, Obj: 0.249590, No Obj: 0.003013, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.561233, Class: 1.000000, Obj: 0.281888, No Obj: 0.002826, Avg Recall: 0.694118, count: 85 -Region Avg IOU: 0.513710, Class: 1.000000, Obj: 0.255699, No Obj: 0.003335, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.490489, Class: 1.000000, Obj: 0.263275, No Obj: 0.002873, Avg Recall: 0.549180, count: 122 -Region Avg IOU: 0.532999, Class: 1.000000, Obj: 0.248431, No Obj: 0.003291, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.540166, Class: 1.000000, Obj: 0.268704, No Obj: 0.003067, Avg Recall: 0.585366, count: 82 -Region Avg IOU: 0.466654, Class: 1.000000, Obj: 0.220377, No Obj: 0.002928, Avg Recall: 0.466667, count: 105 -3451: 0.473706, 0.477054 avg, 0.001000 rate, 21.089420 seconds, 883456 images -Loaded: 0.000052 seconds -Region Avg IOU: 0.527619, Class: 1.000000, Obj: 0.246868, No Obj: 0.003269, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.566011, Class: 1.000000, Obj: 0.288238, No Obj: 0.002980, Avg Recall: 0.684211, count: 76 -Region Avg IOU: 0.555133, Class: 1.000000, Obj: 0.264002, No Obj: 0.003387, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.577069, Class: 1.000000, Obj: 0.261424, No Obj: 0.003545, Avg Recall: 0.648438, count: 128 -Region Avg IOU: 0.479012, Class: 1.000000, Obj: 0.241554, No Obj: 0.003482, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.521691, Class: 1.000000, Obj: 0.297446, No Obj: 0.003389, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.504768, Class: 1.000000, Obj: 0.265824, No Obj: 0.002793, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.533073, Class: 1.000000, Obj: 0.292427, No Obj: 0.003642, Avg Recall: 0.606557, count: 122 -3452: 0.468046, 0.476153 avg, 0.001000 rate, 21.056995 seconds, 883712 images -Loaded: 1.275768 seconds -Region Avg IOU: 0.509170, Class: 1.000000, Obj: 0.228288, No Obj: 0.003132, Avg Recall: 0.533333, count: 90 -Region Avg IOU: 0.560212, Class: 1.000000, Obj: 0.337178, No Obj: 0.003791, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.530828, Class: 1.000000, Obj: 0.323556, No Obj: 0.003625, Avg Recall: 0.575758, count: 99 -Region Avg IOU: 0.535680, Class: 1.000000, Obj: 0.302096, No Obj: 0.003751, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.526716, Class: 1.000000, Obj: 0.282138, No Obj: 0.003913, Avg Recall: 0.575000, count: 120 -Region Avg IOU: 0.499535, Class: 1.000000, Obj: 0.273296, No Obj: 0.003239, Avg Recall: 0.515464, count: 97 -Region Avg IOU: 0.536695, Class: 1.000000, Obj: 0.284004, No Obj: 0.004483, Avg Recall: 0.617886, count: 123 -Region Avg IOU: 0.546622, Class: 1.000000, Obj: 0.342286, No Obj: 0.003803, Avg Recall: 0.628319, count: 113 -3453: 0.458745, 0.474413 avg, 0.001000 rate, 18.927050 seconds, 883968 images -Loaded: 1.862469 seconds -Region Avg IOU: 0.544463, Class: 1.000000, Obj: 0.361201, No Obj: 0.003566, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.506566, Class: 1.000000, Obj: 0.340800, No Obj: 0.004510, Avg Recall: 0.559633, count: 109 -Region Avg IOU: 0.514841, Class: 1.000000, Obj: 0.305901, No Obj: 0.004327, Avg Recall: 0.547826, count: 115 -Region Avg IOU: 0.527512, Class: 1.000000, Obj: 0.296960, No Obj: 0.003782, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.488277, Class: 1.000000, Obj: 0.322496, No Obj: 0.003345, Avg Recall: 0.546512, count: 86 -Region Avg IOU: 0.476807, Class: 1.000000, Obj: 0.310295, No Obj: 0.003904, Avg Recall: 0.552083, count: 96 -Region Avg IOU: 0.555125, Class: 1.000000, Obj: 0.342886, No Obj: 0.003831, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.479628, Class: 1.000000, Obj: 0.334041, No Obj: 0.003951, Avg Recall: 0.580645, count: 93 -3454: 0.487835, 0.475755 avg, 0.001000 rate, 20.257019 seconds, 884224 images -Loaded: 1.406922 seconds -Region Avg IOU: 0.556962, Class: 1.000000, Obj: 0.356228, No Obj: 0.004603, Avg Recall: 0.606742, count: 89 -Region Avg IOU: 0.560507, Class: 1.000000, Obj: 0.339264, No Obj: 0.004774, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.538308, Class: 1.000000, Obj: 0.399346, No Obj: 0.004582, Avg Recall: 0.606557, count: 122 -Region Avg IOU: 0.476596, Class: 1.000000, Obj: 0.316755, No Obj: 0.004705, Avg Recall: 0.482759, count: 116 -Region Avg IOU: 0.511077, Class: 1.000000, Obj: 0.296339, No Obj: 0.004467, Avg Recall: 0.592920, count: 113 -Region Avg IOU: 0.515999, Class: 1.000000, Obj: 0.370461, No Obj: 0.005221, Avg Recall: 0.584615, count: 130 -Region Avg IOU: 0.498124, Class: 1.000000, Obj: 0.328573, No Obj: 0.004678, Avg Recall: 0.556522, count: 115 -Region Avg IOU: 0.462171, Class: 1.000000, Obj: 0.319485, No Obj: 0.004246, Avg Recall: 0.486957, count: 115 -3455: 0.511827, 0.479362 avg, 0.001000 rate, 20.001947 seconds, 884480 images -Loaded: 1.693173 seconds -Region Avg IOU: 0.496456, Class: 1.000000, Obj: 0.389488, No Obj: 0.004240, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.500321, Class: 1.000000, Obj: 0.396501, No Obj: 0.004408, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.490512, Class: 1.000000, Obj: 0.373060, No Obj: 0.004826, Avg Recall: 0.547826, count: 115 -Region Avg IOU: 0.485244, Class: 1.000000, Obj: 0.342388, No Obj: 0.004508, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.517236, Class: 1.000000, Obj: 0.401754, No Obj: 0.004424, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.509386, Class: 1.000000, Obj: 0.396576, No Obj: 0.004863, Avg Recall: 0.596774, count: 124 -Region Avg IOU: 0.537816, Class: 1.000000, Obj: 0.379584, No Obj: 0.004664, Avg Recall: 0.641026, count: 117 -Region Avg IOU: 0.548571, Class: 1.000000, Obj: 0.382074, No Obj: 0.005021, Avg Recall: 0.642202, count: 109 -3456: 0.410661, 0.472492 avg, 0.001000 rate, 19.134527 seconds, 884736 images -Loaded: 2.365247 seconds -Region Avg IOU: 0.498147, Class: 1.000000, Obj: 0.417019, No Obj: 0.004760, Avg Recall: 0.571429, count: 105 -Region Avg IOU: 0.529404, Class: 1.000000, Obj: 0.386342, No Obj: 0.004688, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.549208, Class: 1.000000, Obj: 0.449644, No Obj: 0.004914, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.524690, Class: 1.000000, Obj: 0.391130, No Obj: 0.004726, Avg Recall: 0.640449, count: 89 -Region Avg IOU: 0.515423, Class: 1.000000, Obj: 0.399536, No Obj: 0.004838, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.535521, Class: 1.000000, Obj: 0.442312, No Obj: 0.005366, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.523750, Class: 1.000000, Obj: 0.371865, No Obj: 0.004999, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.587244, Class: 1.000000, Obj: 0.443438, No Obj: 0.004837, Avg Recall: 0.682243, count: 107 -3457: 0.410416, 0.466284 avg, 0.001000 rate, 21.625711 seconds, 884992 images -Loaded: 0.493043 seconds -Region Avg IOU: 0.520375, Class: 1.000000, Obj: 0.429810, No Obj: 0.004998, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.512311, Class: 1.000000, Obj: 0.407528, No Obj: 0.005472, Avg Recall: 0.535354, count: 99 -Region Avg IOU: 0.534555, Class: 1.000000, Obj: 0.401108, No Obj: 0.005368, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.569296, Class: 1.000000, Obj: 0.464440, No Obj: 0.005532, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.529886, Class: 1.000000, Obj: 0.368330, No Obj: 0.005882, Avg Recall: 0.595420, count: 131 -Region Avg IOU: 0.511766, Class: 1.000000, Obj: 0.379872, No Obj: 0.005392, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.550459, Class: 1.000000, Obj: 0.425054, No Obj: 0.006177, Avg Recall: 0.669492, count: 118 -Region Avg IOU: 0.536064, Class: 1.000000, Obj: 0.423629, No Obj: 0.005179, Avg Recall: 0.625000, count: 112 -3458: 0.433411, 0.462997 avg, 0.001000 rate, 20.384035 seconds, 885248 images -Loaded: 1.477337 seconds -Region Avg IOU: 0.486569, Class: 1.000000, Obj: 0.385937, No Obj: 0.005698, Avg Recall: 0.525926, count: 135 -Region Avg IOU: 0.482044, Class: 1.000000, Obj: 0.438311, No Obj: 0.004695, Avg Recall: 0.556962, count: 79 -Region Avg IOU: 0.500041, Class: 1.000000, Obj: 0.370913, No Obj: 0.004714, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.496313, Class: 1.000000, Obj: 0.381434, No Obj: 0.005490, Avg Recall: 0.537815, count: 119 -Region Avg IOU: 0.504995, Class: 1.000000, Obj: 0.401755, No Obj: 0.005960, Avg Recall: 0.542373, count: 118 -Region Avg IOU: 0.512119, Class: 1.000000, Obj: 0.405848, No Obj: 0.005095, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.505672, Class: 1.000000, Obj: 0.338533, No Obj: 0.005256, Avg Recall: 0.512821, count: 117 -Region Avg IOU: 0.519249, Class: 1.000000, Obj: 0.390440, No Obj: 0.005105, Avg Recall: 0.587156, count: 109 -3459: 0.499267, 0.466624 avg, 0.001000 rate, 20.605572 seconds, 885504 images -Loaded: 0.289230 seconds -Region Avg IOU: 0.458157, Class: 1.000000, Obj: 0.387659, No Obj: 0.004194, Avg Recall: 0.484211, count: 95 -Region Avg IOU: 0.511388, Class: 1.000000, Obj: 0.411487, No Obj: 0.004382, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.576231, Class: 1.000000, Obj: 0.380892, No Obj: 0.004670, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.485240, Class: 1.000000, Obj: 0.390512, No Obj: 0.004974, Avg Recall: 0.490385, count: 104 -Region Avg IOU: 0.530357, Class: 1.000000, Obj: 0.437117, No Obj: 0.004583, Avg Recall: 0.633333, count: 90 -Region Avg IOU: 0.500755, Class: 1.000000, Obj: 0.396908, No Obj: 0.005130, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.534751, Class: 1.000000, Obj: 0.397303, No Obj: 0.004416, Avg Recall: 0.650000, count: 80 -Region Avg IOU: 0.508011, Class: 1.000000, Obj: 0.313698, No Obj: 0.004289, Avg Recall: 0.589744, count: 78 -3460: 0.392353, 0.459197 avg, 0.001000 rate, 21.817764 seconds, 885760 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000075 seconds -Region Avg IOU: 0.602947, Class: 1.000000, Obj: 0.387535, No Obj: 0.004656, Avg Recall: 0.722973, count: 148 -Region Avg IOU: 0.610388, Class: 1.000000, Obj: 0.490611, No Obj: 0.004047, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.598358, Class: 1.000000, Obj: 0.427138, No Obj: 0.004060, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.636669, Class: 1.000000, Obj: 0.493313, No Obj: 0.004858, Avg Recall: 0.778761, count: 113 -Region Avg IOU: 0.602325, Class: 1.000000, Obj: 0.452225, No Obj: 0.004146, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.616825, Class: 1.000000, Obj: 0.480982, No Obj: 0.003603, Avg Recall: 0.788889, count: 90 -Region Avg IOU: 0.595253, Class: 1.000000, Obj: 0.452707, No Obj: 0.003696, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.618234, Class: 1.000000, Obj: 0.460077, No Obj: 0.004520, Avg Recall: 0.781513, count: 119 -3461: 0.561372, 0.469415 avg, 0.001000 rate, 26.550285 seconds, 886016 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.571167, Class: 1.000000, Obj: 0.377599, No Obj: 0.003164, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.602573, Class: 1.000000, Obj: 0.382662, No Obj: 0.004311, Avg Recall: 0.728571, count: 140 -Region Avg IOU: 0.578645, Class: 1.000000, Obj: 0.424534, No Obj: 0.003460, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.614795, Class: 1.000000, Obj: 0.362219, No Obj: 0.003321, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.603554, Class: 1.000000, Obj: 0.353507, No Obj: 0.003252, Avg Recall: 0.704545, count: 88 -Region Avg IOU: 0.593477, Class: 1.000000, Obj: 0.450519, No Obj: 0.003224, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.619594, Class: 1.000000, Obj: 0.468029, No Obj: 0.003530, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.616028, Class: 1.000000, Obj: 0.430438, No Obj: 0.003245, Avg Recall: 0.686275, count: 102 -3462: 0.477593, 0.470232 avg, 0.001000 rate, 29.180904 seconds, 886272 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.601696, Class: 1.000000, Obj: 0.407115, No Obj: 0.002788, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.628145, Class: 1.000000, Obj: 0.399381, No Obj: 0.002599, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.601560, Class: 1.000000, Obj: 0.404772, No Obj: 0.003111, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.550758, Class: 1.000000, Obj: 0.293116, No Obj: 0.003057, Avg Recall: 0.679104, count: 134 -Region Avg IOU: 0.634117, Class: 1.000000, Obj: 0.405844, No Obj: 0.002770, Avg Recall: 0.785047, count: 107 -Region Avg IOU: 0.654792, Class: 1.000000, Obj: 0.348255, No Obj: 0.003128, Avg Recall: 0.805310, count: 113 -Region Avg IOU: 0.656513, Class: 1.000000, Obj: 0.389907, No Obj: 0.002717, Avg Recall: 0.797468, count: 79 -Region Avg IOU: 0.646077, Class: 1.000000, Obj: 0.435720, No Obj: 0.003397, Avg Recall: 0.816000, count: 125 -3463: 0.500822, 0.473291 avg, 0.001000 rate, 27.884029 seconds, 886528 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.620415, Class: 1.000000, Obj: 0.320367, No Obj: 0.002273, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.623258, Class: 1.000000, Obj: 0.294383, No Obj: 0.002408, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.628259, Class: 1.000000, Obj: 0.339889, No Obj: 0.002256, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.620673, Class: 1.000000, Obj: 0.289828, No Obj: 0.002982, Avg Recall: 0.759124, count: 137 -Region Avg IOU: 0.657179, Class: 1.000000, Obj: 0.331062, No Obj: 0.002320, Avg Recall: 0.823529, count: 102 -Region Avg IOU: 0.596495, Class: 1.000000, Obj: 0.353214, No Obj: 0.001851, Avg Recall: 0.691358, count: 81 -Region Avg IOU: 0.588285, Class: 1.000000, Obj: 0.307040, No Obj: 0.002467, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.666057, Class: 1.000000, Obj: 0.360041, No Obj: 0.002261, Avg Recall: 0.817073, count: 82 -3464: 0.452632, 0.471225 avg, 0.001000 rate, 29.890736 seconds, 886784 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.637071, Class: 1.000000, Obj: 0.293297, No Obj: 0.002448, Avg Recall: 0.773723, count: 137 -Region Avg IOU: 0.620426, Class: 1.000000, Obj: 0.259650, No Obj: 0.002342, Avg Recall: 0.753968, count: 126 -Region Avg IOU: 0.614833, Class: 1.000000, Obj: 0.277139, No Obj: 0.001861, Avg Recall: 0.746835, count: 79 -Region Avg IOU: 0.602886, Class: 1.000000, Obj: 0.268234, No Obj: 0.001996, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.665990, Class: 1.000000, Obj: 0.283130, No Obj: 0.002497, Avg Recall: 0.842857, count: 140 -Region Avg IOU: 0.631335, Class: 1.000000, Obj: 0.263733, No Obj: 0.002282, Avg Recall: 0.788321, count: 137 -Region Avg IOU: 0.687885, Class: 1.000000, Obj: 0.341357, No Obj: 0.002031, Avg Recall: 0.833333, count: 90 -Region Avg IOU: 0.637912, Class: 1.000000, Obj: 0.285204, No Obj: 0.002319, Avg Recall: 0.766990, count: 103 -3465: 0.527093, 0.476812 avg, 0.001000 rate, 28.228155 seconds, 887040 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.627632, Class: 1.000000, Obj: 0.300678, No Obj: 0.001904, Avg Recall: 0.797753, count: 89 -Region Avg IOU: 0.687415, Class: 1.000000, Obj: 0.278864, No Obj: 0.002114, Avg Recall: 0.902439, count: 82 -Region Avg IOU: 0.634703, Class: 1.000000, Obj: 0.261414, No Obj: 0.001961, Avg Recall: 0.826531, count: 98 -Region Avg IOU: 0.610435, Class: 1.000000, Obj: 0.234441, No Obj: 0.001833, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.667535, Class: 1.000000, Obj: 0.296864, No Obj: 0.002101, Avg Recall: 0.838095, count: 105 -Region Avg IOU: 0.616133, Class: 1.000000, Obj: 0.258932, No Obj: 0.002115, Avg Recall: 0.753968, count: 126 -Region Avg IOU: 0.634011, Class: 1.000000, Obj: 0.304614, No Obj: 0.001971, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.659959, Class: 1.000000, Obj: 0.295078, No Obj: 0.001935, Avg Recall: 0.872340, count: 94 -3466: 0.514345, 0.480565 avg, 0.001000 rate, 27.689381 seconds, 887296 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.605706, Class: 1.000000, Obj: 0.242086, No Obj: 0.001964, Avg Recall: 0.719697, count: 132 -Region Avg IOU: 0.583436, Class: 1.000000, Obj: 0.232255, No Obj: 0.001961, Avg Recall: 0.705426, count: 129 -Region Avg IOU: 0.617961, Class: 1.000000, Obj: 0.234659, No Obj: 0.001903, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.659363, Class: 1.000000, Obj: 0.296040, No Obj: 0.002163, Avg Recall: 0.796610, count: 118 -Region Avg IOU: 0.625404, Class: 1.000000, Obj: 0.283652, No Obj: 0.001984, Avg Recall: 0.775194, count: 129 -Region Avg IOU: 0.616611, Class: 1.000000, Obj: 0.271728, No Obj: 0.001883, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.623130, Class: 1.000000, Obj: 0.296147, No Obj: 0.001716, Avg Recall: 0.777778, count: 81 -Region Avg IOU: 0.617554, Class: 1.000000, Obj: 0.280379, No Obj: 0.002241, Avg Recall: 0.725191, count: 131 -3467: 0.514292, 0.483938 avg, 0.001000 rate, 28.410892 seconds, 887552 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.643601, Class: 1.000000, Obj: 0.355408, No Obj: 0.001848, Avg Recall: 0.808989, count: 89 -Region Avg IOU: 0.617560, Class: 1.000000, Obj: 0.315084, No Obj: 0.001744, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.597258, Class: 1.000000, Obj: 0.299125, No Obj: 0.001950, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.648552, Class: 1.000000, Obj: 0.307495, No Obj: 0.001856, Avg Recall: 0.851852, count: 81 -Region Avg IOU: 0.622917, Class: 1.000000, Obj: 0.265228, No Obj: 0.001969, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.623956, Class: 1.000000, Obj: 0.318318, No Obj: 0.001740, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.615509, Class: 1.000000, Obj: 0.274655, No Obj: 0.002283, Avg Recall: 0.739130, count: 138 -Region Avg IOU: 0.635125, Class: 1.000000, Obj: 0.272130, No Obj: 0.001982, Avg Recall: 0.761468, count: 109 -3468: 0.411683, 0.476713 avg, 0.001000 rate, 26.795408 seconds, 887808 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.654809, Class: 1.000000, Obj: 0.311380, No Obj: 0.002144, Avg Recall: 0.854167, count: 96 -Region Avg IOU: 0.630383, Class: 1.000000, Obj: 0.339330, No Obj: 0.002366, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.670527, Class: 1.000000, Obj: 0.278624, No Obj: 0.002243, Avg Recall: 0.872549, count: 102 -Region Avg IOU: 0.644490, Class: 1.000000, Obj: 0.308269, No Obj: 0.002125, Avg Recall: 0.826087, count: 92 -Region Avg IOU: 0.633462, Class: 1.000000, Obj: 0.291802, No Obj: 0.002332, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.652932, Class: 1.000000, Obj: 0.315536, No Obj: 0.002741, Avg Recall: 0.804511, count: 133 -Region Avg IOU: 0.644619, Class: 1.000000, Obj: 0.327392, No Obj: 0.002225, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.638558, Class: 1.000000, Obj: 0.284502, No Obj: 0.002308, Avg Recall: 0.778761, count: 113 -3469: 0.478932, 0.476935 avg, 0.001000 rate, 27.652468 seconds, 888064 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.647737, Class: 1.000000, Obj: 0.352509, No Obj: 0.002349, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.637862, Class: 1.000000, Obj: 0.369520, No Obj: 0.002484, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.649084, Class: 1.000000, Obj: 0.291131, No Obj: 0.002518, Avg Recall: 0.837209, count: 86 -Region Avg IOU: 0.630659, Class: 1.000000, Obj: 0.320247, No Obj: 0.002763, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.587343, Class: 1.000000, Obj: 0.320506, No Obj: 0.002271, Avg Recall: 0.736264, count: 91 -Region Avg IOU: 0.636653, Class: 1.000000, Obj: 0.293383, No Obj: 0.002671, Avg Recall: 0.822222, count: 135 -Region Avg IOU: 0.631297, Class: 1.000000, Obj: 0.337832, No Obj: 0.002450, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.667981, Class: 1.000000, Obj: 0.350119, No Obj: 0.002943, Avg Recall: 0.852174, count: 115 -3470: 0.502188, 0.479460 avg, 0.001000 rate, 29.127544 seconds, 888320 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 15.197840 seconds -Region Avg IOU: 0.653014, Class: 1.000000, Obj: 0.339142, No Obj: 0.002964, Avg Recall: 0.834951, count: 103 -Region Avg IOU: 0.617107, Class: 1.000000, Obj: 0.390185, No Obj: 0.002801, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.613743, Class: 1.000000, Obj: 0.373572, No Obj: 0.002667, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.644875, Class: 1.000000, Obj: 0.374483, No Obj: 0.003089, Avg Recall: 0.789916, count: 119 -Region Avg IOU: 0.614121, Class: 1.000000, Obj: 0.346965, No Obj: 0.002649, Avg Recall: 0.755814, count: 86 -Region Avg IOU: 0.625694, Class: 1.000000, Obj: 0.392215, No Obj: 0.003214, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.634187, Class: 1.000000, Obj: 0.331547, No Obj: 0.003040, Avg Recall: 0.808824, count: 136 -Region Avg IOU: 0.652876, Class: 1.000000, Obj: 0.432182, No Obj: 0.002901, Avg Recall: 0.806452, count: 93 -3471: 0.460890, 0.477603 avg, 0.001000 rate, 28.035904 seconds, 888576 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.640633, Class: 1.000000, Obj: 0.363622, No Obj: 0.003379, Avg Recall: 0.792308, count: 130 -Region Avg IOU: 0.683066, Class: 1.000000, Obj: 0.395177, No Obj: 0.002501, Avg Recall: 0.881356, count: 59 -Region Avg IOU: 0.627649, Class: 1.000000, Obj: 0.378543, No Obj: 0.002737, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.602465, Class: 1.000000, Obj: 0.347741, No Obj: 0.003374, Avg Recall: 0.751773, count: 141 -Region Avg IOU: 0.681090, Class: 1.000000, Obj: 0.441630, No Obj: 0.002640, Avg Recall: 0.861111, count: 72 -Region Avg IOU: 0.649209, Class: 1.000000, Obj: 0.390312, No Obj: 0.002864, Avg Recall: 0.777778, count: 90 -Region Avg IOU: 0.605834, Class: 1.000000, Obj: 0.354932, No Obj: 0.003264, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.664670, Class: 1.000000, Obj: 0.375829, No Obj: 0.003039, Avg Recall: 0.846154, count: 91 -3472: 0.404393, 0.470282 avg, 0.001000 rate, 27.425713 seconds, 888832 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.640631, Class: 1.000000, Obj: 0.385438, No Obj: 0.003008, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.617038, Class: 1.000000, Obj: 0.349617, No Obj: 0.003015, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.632133, Class: 1.000000, Obj: 0.391431, No Obj: 0.003041, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.606137, Class: 1.000000, Obj: 0.342367, No Obj: 0.003137, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.697494, Class: 1.000000, Obj: 0.418106, No Obj: 0.002900, Avg Recall: 0.913043, count: 69 -Region Avg IOU: 0.646735, Class: 1.000000, Obj: 0.418338, No Obj: 0.003108, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.635448, Class: 1.000000, Obj: 0.398146, No Obj: 0.003114, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.636246, Class: 1.000000, Obj: 0.350891, No Obj: 0.003133, Avg Recall: 0.776859, count: 121 -3473: 0.442295, 0.467483 avg, 0.001000 rate, 28.806513 seconds, 889088 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.628477, Class: 1.000000, Obj: 0.443280, No Obj: 0.003307, Avg Recall: 0.796610, count: 118 -Region Avg IOU: 0.644217, Class: 1.000000, Obj: 0.427135, No Obj: 0.002641, Avg Recall: 0.818182, count: 77 -Region Avg IOU: 0.676628, Class: 1.000000, Obj: 0.442847, No Obj: 0.003481, Avg Recall: 0.881720, count: 93 -Region Avg IOU: 0.638179, Class: 1.000000, Obj: 0.419651, No Obj: 0.003547, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.678168, Class: 1.000000, Obj: 0.474796, No Obj: 0.003222, Avg Recall: 0.872093, count: 86 -Region Avg IOU: 0.620943, Class: 1.000000, Obj: 0.431917, No Obj: 0.003203, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.651295, Class: 1.000000, Obj: 0.362942, No Obj: 0.003224, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.649168, Class: 1.000000, Obj: 0.418893, No Obj: 0.002897, Avg Recall: 0.800000, count: 80 -3474: 0.453679, 0.466103 avg, 0.001000 rate, 27.616974 seconds, 889344 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.650002, Class: 1.000000, Obj: 0.456439, No Obj: 0.002816, Avg Recall: 0.817308, count: 104 -Region Avg IOU: 0.607796, Class: 1.000000, Obj: 0.351230, No Obj: 0.003363, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.628035, Class: 1.000000, Obj: 0.376287, No Obj: 0.002703, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.662135, Class: 1.000000, Obj: 0.398742, No Obj: 0.003082, Avg Recall: 0.793103, count: 87 -Region Avg IOU: 0.620082, Class: 1.000000, Obj: 0.398631, No Obj: 0.002948, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.634513, Class: 1.000000, Obj: 0.379248, No Obj: 0.003125, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.591873, Class: 1.000000, Obj: 0.378216, No Obj: 0.003009, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.645213, Class: 1.000000, Obj: 0.441376, No Obj: 0.003129, Avg Recall: 0.800000, count: 105 -3475: 0.420171, 0.461510 avg, 0.001000 rate, 28.734236 seconds, 889600 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.606329, Class: 1.000000, Obj: 0.339929, No Obj: 0.002950, Avg Recall: 0.733871, count: 124 -Region Avg IOU: 0.612213, Class: 1.000000, Obj: 0.355025, No Obj: 0.002667, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.629503, Class: 1.000000, Obj: 0.365950, No Obj: 0.002905, Avg Recall: 0.797619, count: 84 -Region Avg IOU: 0.623981, Class: 1.000000, Obj: 0.355909, No Obj: 0.002844, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.658065, Class: 1.000000, Obj: 0.456259, No Obj: 0.002958, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.615745, Class: 1.000000, Obj: 0.386297, No Obj: 0.002757, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.608044, Class: 1.000000, Obj: 0.387762, No Obj: 0.003011, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.650664, Class: 1.000000, Obj: 0.394424, No Obj: 0.003123, Avg Recall: 0.803030, count: 132 -3476: 0.457135, 0.461072 avg, 0.001000 rate, 27.072111 seconds, 889856 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.606456, Class: 1.000000, Obj: 0.380392, No Obj: 0.002796, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.644808, Class: 1.000000, Obj: 0.306914, No Obj: 0.003054, Avg Recall: 0.823077, count: 130 -Region Avg IOU: 0.642454, Class: 1.000000, Obj: 0.361345, No Obj: 0.002615, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.599746, Class: 1.000000, Obj: 0.376732, No Obj: 0.002629, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.601195, Class: 1.000000, Obj: 0.361950, No Obj: 0.002477, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.616177, Class: 1.000000, Obj: 0.362580, No Obj: 0.002746, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.632443, Class: 1.000000, Obj: 0.375350, No Obj: 0.002704, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.631064, Class: 1.000000, Obj: 0.383670, No Obj: 0.002427, Avg Recall: 0.798165, count: 109 -3477: 0.480780, 0.463043 avg, 0.001000 rate, 28.876154 seconds, 890112 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.657175, Class: 1.000000, Obj: 0.407054, No Obj: 0.002326, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.585049, Class: 1.000000, Obj: 0.305621, No Obj: 0.002763, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.647281, Class: 1.000000, Obj: 0.333785, No Obj: 0.002735, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.639756, Class: 1.000000, Obj: 0.373885, No Obj: 0.002737, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.636388, Class: 1.000000, Obj: 0.339561, No Obj: 0.002656, Avg Recall: 0.781250, count: 96 -Region Avg IOU: 0.627952, Class: 1.000000, Obj: 0.310057, No Obj: 0.002618, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.616162, Class: 1.000000, Obj: 0.339071, No Obj: 0.002383, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.595993, Class: 1.000000, Obj: 0.331143, No Obj: 0.002410, Avg Recall: 0.723404, count: 94 -3478: 0.471464, 0.463885 avg, 0.001000 rate, 26.751535 seconds, 890368 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.654828, Class: 1.000000, Obj: 0.377922, No Obj: 0.002570, Avg Recall: 0.820000, count: 100 -Region Avg IOU: 0.639594, Class: 1.000000, Obj: 0.360561, No Obj: 0.002345, Avg Recall: 0.814433, count: 97 -Region Avg IOU: 0.598788, Class: 1.000000, Obj: 0.280858, No Obj: 0.002508, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.608501, Class: 1.000000, Obj: 0.330633, No Obj: 0.002523, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.627542, Class: 1.000000, Obj: 0.373826, No Obj: 0.002420, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.582233, Class: 1.000000, Obj: 0.284498, No Obj: 0.002166, Avg Recall: 0.651163, count: 86 -Region Avg IOU: 0.608175, Class: 1.000000, Obj: 0.315245, No Obj: 0.002221, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.651466, Class: 1.000000, Obj: 0.329798, No Obj: 0.002451, Avg Recall: 0.794872, count: 117 -3479: 0.466114, 0.464108 avg, 0.001000 rate, 28.514330 seconds, 890624 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.588284, Class: 1.000000, Obj: 0.299934, No Obj: 0.002487, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.604026, Class: 1.000000, Obj: 0.360457, No Obj: 0.002280, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.616095, Class: 1.000000, Obj: 0.335875, No Obj: 0.002320, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.642651, Class: 1.000000, Obj: 0.348343, No Obj: 0.002744, Avg Recall: 0.753968, count: 126 -Region Avg IOU: 0.613205, Class: 1.000000, Obj: 0.297496, No Obj: 0.002523, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.577988, Class: 1.000000, Obj: 0.283472, No Obj: 0.002311, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.595955, Class: 1.000000, Obj: 0.334778, No Obj: 0.002436, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.606749, Class: 1.000000, Obj: 0.335740, No Obj: 0.002485, Avg Recall: 0.714286, count: 119 -3480: 0.493450, 0.467042 avg, 0.001000 rate, 28.048717 seconds, 890880 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 15.238979 seconds -Region Avg IOU: 0.603059, Class: 1.000000, Obj: 0.330105, No Obj: 0.002480, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.567386, Class: 1.000000, Obj: 0.301985, No Obj: 0.002465, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.609174, Class: 1.000000, Obj: 0.317078, No Obj: 0.002822, Avg Recall: 0.751938, count: 129 -Region Avg IOU: 0.571939, Class: 1.000000, Obj: 0.333921, No Obj: 0.002586, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.591954, Class: 1.000000, Obj: 0.306267, No Obj: 0.002488, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.566385, Class: 1.000000, Obj: 0.318693, No Obj: 0.002363, Avg Recall: 0.675000, count: 80 -Region Avg IOU: 0.634564, Class: 1.000000, Obj: 0.305773, No Obj: 0.002846, Avg Recall: 0.780303, count: 132 -Region Avg IOU: 0.615069, Class: 1.000000, Obj: 0.337224, No Obj: 0.002801, Avg Recall: 0.747967, count: 123 -3481: 0.465200, 0.466858 avg, 0.001000 rate, 26.506207 seconds, 891136 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.623663, Class: 1.000000, Obj: 0.358802, No Obj: 0.002529, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.560192, Class: 1.000000, Obj: 0.339334, No Obj: 0.002753, Avg Recall: 0.629921, count: 127 -Region Avg IOU: 0.587665, Class: 1.000000, Obj: 0.330527, No Obj: 0.002656, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.595260, Class: 1.000000, Obj: 0.330360, No Obj: 0.002537, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.624995, Class: 1.000000, Obj: 0.344399, No Obj: 0.002773, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.615527, Class: 1.000000, Obj: 0.383457, No Obj: 0.002547, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.590442, Class: 1.000000, Obj: 0.290515, No Obj: 0.002998, Avg Recall: 0.687500, count: 144 -Region Avg IOU: 0.618022, Class: 1.000000, Obj: 0.365194, No Obj: 0.002822, Avg Recall: 0.787611, count: 113 -3482: 0.457962, 0.465968 avg, 0.001000 rate, 25.687763 seconds, 891392 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.604026, Class: 1.000000, Obj: 0.318743, No Obj: 0.002915, Avg Recall: 0.738806, count: 134 -Region Avg IOU: 0.600304, Class: 1.000000, Obj: 0.344104, No Obj: 0.002931, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.609360, Class: 1.000000, Obj: 0.339742, No Obj: 0.002690, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.603984, Class: 1.000000, Obj: 0.353035, No Obj: 0.002839, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.651588, Class: 1.000000, Obj: 0.381031, No Obj: 0.002528, Avg Recall: 0.793103, count: 87 -Region Avg IOU: 0.576198, Class: 1.000000, Obj: 0.321290, No Obj: 0.002791, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.630082, Class: 1.000000, Obj: 0.372804, No Obj: 0.002778, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.606344, Class: 1.000000, Obj: 0.383349, No Obj: 0.002510, Avg Recall: 0.712500, count: 80 -3483: 0.447308, 0.464102 avg, 0.001000 rate, 25.580591 seconds, 891648 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.607624, Class: 1.000000, Obj: 0.328006, No Obj: 0.003282, Avg Recall: 0.740741, count: 135 -Region Avg IOU: 0.588032, Class: 1.000000, Obj: 0.358707, No Obj: 0.002634, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.567731, Class: 1.000000, Obj: 0.348630, No Obj: 0.002970, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.635691, Class: 1.000000, Obj: 0.374678, No Obj: 0.003447, Avg Recall: 0.770492, count: 122 -Region Avg IOU: 0.547118, Class: 1.000000, Obj: 0.363771, No Obj: 0.002857, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.634717, Class: 1.000000, Obj: 0.398315, No Obj: 0.003417, Avg Recall: 0.776000, count: 125 -Region Avg IOU: 0.679667, Class: 1.000000, Obj: 0.334800, No Obj: 0.003041, Avg Recall: 0.847619, count: 105 -Region Avg IOU: 0.617914, Class: 1.000000, Obj: 0.421243, No Obj: 0.003174, Avg Recall: 0.717949, count: 117 -3484: 0.466005, 0.464293 avg, 0.001000 rate, 26.807640 seconds, 891904 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.580997, Class: 1.000000, Obj: 0.363717, No Obj: 0.003338, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.642685, Class: 1.000000, Obj: 0.370079, No Obj: 0.002743, Avg Recall: 0.820225, count: 89 -Region Avg IOU: 0.582481, Class: 1.000000, Obj: 0.347144, No Obj: 0.003348, Avg Recall: 0.723404, count: 141 -Region Avg IOU: 0.598793, Class: 1.000000, Obj: 0.334245, No Obj: 0.003179, Avg Recall: 0.689076, count: 119 -Region Avg IOU: 0.639600, Class: 1.000000, Obj: 0.353584, No Obj: 0.003198, Avg Recall: 0.801980, count: 101 -Region Avg IOU: 0.609679, Class: 1.000000, Obj: 0.323845, No Obj: 0.003020, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.597328, Class: 1.000000, Obj: 0.414144, No Obj: 0.003054, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.576466, Class: 1.000000, Obj: 0.369197, No Obj: 0.003477, Avg Recall: 0.710145, count: 138 -3485: 0.468976, 0.464761 avg, 0.001000 rate, 25.891708 seconds, 892160 images -Loaded: 0.000118 seconds -Region Avg IOU: 0.660505, Class: 1.000000, Obj: 0.380822, No Obj: 0.003348, Avg Recall: 0.858824, count: 85 -Region Avg IOU: 0.586846, Class: 1.000000, Obj: 0.324635, No Obj: 0.003537, Avg Recall: 0.731707, count: 123 -Region Avg IOU: 0.614064, Class: 1.000000, Obj: 0.384833, No Obj: 0.002850, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.646696, Class: 1.000000, Obj: 0.383724, No Obj: 0.003333, Avg Recall: 0.840909, count: 88 -Region Avg IOU: 0.652194, Class: 1.000000, Obj: 0.465589, No Obj: 0.003114, Avg Recall: 0.863636, count: 88 -Region Avg IOU: 0.617747, Class: 1.000000, Obj: 0.365751, No Obj: 0.003297, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.625717, Class: 1.000000, Obj: 0.345611, No Obj: 0.003467, Avg Recall: 0.769231, count: 130 -Region Avg IOU: 0.617974, Class: 1.000000, Obj: 0.356629, No Obj: 0.003507, Avg Recall: 0.773585, count: 106 -3486: 0.461544, 0.464439 avg, 0.001000 rate, 26.423861 seconds, 892416 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.625051, Class: 1.000000, Obj: 0.403608, No Obj: 0.003294, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.597281, Class: 1.000000, Obj: 0.376403, No Obj: 0.003234, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.607966, Class: 1.000000, Obj: 0.380236, No Obj: 0.002939, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.636749, Class: 1.000000, Obj: 0.411076, No Obj: 0.003593, Avg Recall: 0.815126, count: 119 -Region Avg IOU: 0.609710, Class: 1.000000, Obj: 0.388922, No Obj: 0.003634, Avg Recall: 0.783333, count: 120 -Region Avg IOU: 0.632900, Class: 1.000000, Obj: 0.374570, No Obj: 0.003724, Avg Recall: 0.748148, count: 135 -Region Avg IOU: 0.596136, Class: 1.000000, Obj: 0.374597, No Obj: 0.002926, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.580402, Class: 1.000000, Obj: 0.384653, No Obj: 0.003413, Avg Recall: 0.626984, count: 126 -3487: 0.464539, 0.464449 avg, 0.001000 rate, 26.720413 seconds, 892672 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.602152, Class: 1.000000, Obj: 0.422928, No Obj: 0.002972, Avg Recall: 0.685393, count: 89 -Region Avg IOU: 0.593246, Class: 1.000000, Obj: 0.361642, No Obj: 0.003215, Avg Recall: 0.701613, count: 124 -Region Avg IOU: 0.592534, Class: 1.000000, Obj: 0.379380, No Obj: 0.003273, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.593074, Class: 1.000000, Obj: 0.362055, No Obj: 0.004035, Avg Recall: 0.715151, count: 165 -Region Avg IOU: 0.612488, Class: 1.000000, Obj: 0.360670, No Obj: 0.003436, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.648976, Class: 1.000000, Obj: 0.432087, No Obj: 0.003285, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.602240, Class: 1.000000, Obj: 0.408642, No Obj: 0.003276, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.606808, Class: 1.000000, Obj: 0.380154, No Obj: 0.003777, Avg Recall: 0.697842, count: 139 -3488: 0.490530, 0.467057 avg, 0.001000 rate, 26.441229 seconds, 892928 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.615364, Class: 1.000000, Obj: 0.374169, No Obj: 0.002901, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.622441, Class: 1.000000, Obj: 0.383531, No Obj: 0.002998, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.647857, Class: 1.000000, Obj: 0.387830, No Obj: 0.003239, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.631730, Class: 1.000000, Obj: 0.422708, No Obj: 0.003344, Avg Recall: 0.771186, count: 118 -Region Avg IOU: 0.654920, Class: 1.000000, Obj: 0.415454, No Obj: 0.003793, Avg Recall: 0.807018, count: 114 -Region Avg IOU: 0.630106, Class: 1.000000, Obj: 0.357378, No Obj: 0.003403, Avg Recall: 0.792308, count: 130 -Region Avg IOU: 0.585250, Class: 1.000000, Obj: 0.336848, No Obj: 0.002984, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.600613, Class: 1.000000, Obj: 0.373799, No Obj: 0.003375, Avg Recall: 0.726415, count: 106 -3489: 0.463525, 0.466704 avg, 0.001000 rate, 24.322369 seconds, 893184 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.584809, Class: 1.000000, Obj: 0.366918, No Obj: 0.002772, Avg Recall: 0.659091, count: 88 -Region Avg IOU: 0.612574, Class: 1.000000, Obj: 0.405718, No Obj: 0.002778, Avg Recall: 0.715909, count: 88 -Region Avg IOU: 0.643638, Class: 1.000000, Obj: 0.442342, No Obj: 0.002834, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.595062, Class: 1.000000, Obj: 0.336549, No Obj: 0.002971, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.582364, Class: 1.000000, Obj: 0.370691, No Obj: 0.003042, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.628644, Class: 1.000000, Obj: 0.408027, No Obj: 0.002892, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.609662, Class: 1.000000, Obj: 0.365009, No Obj: 0.003394, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.645946, Class: 1.000000, Obj: 0.462912, No Obj: 0.003081, Avg Recall: 0.828283, count: 99 -3490: 0.415170, 0.461551 avg, 0.001000 rate, 27.007322 seconds, 893440 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000062 seconds -Region Avg IOU: 0.660363, Class: 1.000000, Obj: 0.397365, No Obj: 0.002798, Avg Recall: 0.806818, count: 88 -Region Avg IOU: 0.615982, Class: 1.000000, Obj: 0.370883, No Obj: 0.003092, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.638143, Class: 1.000000, Obj: 0.367315, No Obj: 0.003129, Avg Recall: 0.792308, count: 130 -Region Avg IOU: 0.625655, Class: 1.000000, Obj: 0.423587, No Obj: 0.002839, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.615248, Class: 1.000000, Obj: 0.345455, No Obj: 0.003181, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.676534, Class: 1.000000, Obj: 0.375684, No Obj: 0.003188, Avg Recall: 0.844828, count: 116 -Region Avg IOU: 0.600836, Class: 1.000000, Obj: 0.422465, No Obj: 0.003022, Avg Recall: 0.733333, count: 120 -Region Avg IOU: 0.598818, Class: 1.000000, Obj: 0.374322, No Obj: 0.003310, Avg Recall: 0.750000, count: 132 -3491: 0.471319, 0.462527 avg, 0.001000 rate, 27.925167 seconds, 893696 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.629073, Class: 1.000000, Obj: 0.356538, No Obj: 0.002626, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.623843, Class: 1.000000, Obj: 0.359648, No Obj: 0.002700, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.594414, Class: 1.000000, Obj: 0.317605, No Obj: 0.002958, Avg Recall: 0.700000, count: 130 -Region Avg IOU: 0.613935, Class: 1.000000, Obj: 0.390731, No Obj: 0.002758, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.646330, Class: 1.000000, Obj: 0.414228, No Obj: 0.002790, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.606863, Class: 1.000000, Obj: 0.378015, No Obj: 0.002685, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.597781, Class: 1.000000, Obj: 0.316485, No Obj: 0.002638, Avg Recall: 0.759036, count: 83 -Region Avg IOU: 0.560260, Class: 1.000000, Obj: 0.345262, No Obj: 0.001978, Avg Recall: 0.662791, count: 86 -3492: 0.465477, 0.462822 avg, 0.001000 rate, 29.661905 seconds, 893952 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.626423, Class: 1.000000, Obj: 0.339620, No Obj: 0.002564, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.636176, Class: 1.000000, Obj: 0.334991, No Obj: 0.002791, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.611624, Class: 1.000000, Obj: 0.319130, No Obj: 0.002796, Avg Recall: 0.703704, count: 135 -Region Avg IOU: 0.645074, Class: 1.000000, Obj: 0.403245, No Obj: 0.002649, Avg Recall: 0.820513, count: 117 -Region Avg IOU: 0.614282, Class: 1.000000, Obj: 0.338619, No Obj: 0.002973, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.610505, Class: 1.000000, Obj: 0.342224, No Obj: 0.002721, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.643577, Class: 1.000000, Obj: 0.358769, No Obj: 0.002462, Avg Recall: 0.833333, count: 96 -Region Avg IOU: 0.603964, Class: 1.000000, Obj: 0.339319, No Obj: 0.002501, Avg Recall: 0.716667, count: 120 -3493: 0.502182, 0.466758 avg, 0.001000 rate, 29.052879 seconds, 894208 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.595469, Class: 1.000000, Obj: 0.348504, No Obj: 0.002519, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.630349, Class: 1.000000, Obj: 0.327779, No Obj: 0.002981, Avg Recall: 0.791045, count: 134 -Region Avg IOU: 0.619588, Class: 1.000000, Obj: 0.357374, No Obj: 0.002214, Avg Recall: 0.756098, count: 82 -Region Avg IOU: 0.640508, Class: 1.000000, Obj: 0.306970, No Obj: 0.002689, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.630496, Class: 1.000000, Obj: 0.352293, No Obj: 0.002376, Avg Recall: 0.825688, count: 109 -Region Avg IOU: 0.628606, Class: 1.000000, Obj: 0.321352, No Obj: 0.002580, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.632137, Class: 1.000000, Obj: 0.362289, No Obj: 0.002654, Avg Recall: 0.752137, count: 117 -Region Avg IOU: 0.641904, Class: 1.000000, Obj: 0.362094, No Obj: 0.002744, Avg Recall: 0.833333, count: 108 -3494: 0.438441, 0.463927 avg, 0.001000 rate, 27.715387 seconds, 894464 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.673216, Class: 1.000000, Obj: 0.347647, No Obj: 0.002456, Avg Recall: 0.867347, count: 98 -Region Avg IOU: 0.631800, Class: 1.000000, Obj: 0.360090, No Obj: 0.002213, Avg Recall: 0.788889, count: 90 -Region Avg IOU: 0.611685, Class: 1.000000, Obj: 0.310201, No Obj: 0.002403, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.622327, Class: 1.000000, Obj: 0.344903, No Obj: 0.002425, Avg Recall: 0.793103, count: 87 -Region Avg IOU: 0.623250, Class: 1.000000, Obj: 0.340011, No Obj: 0.002587, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.633920, Class: 1.000000, Obj: 0.325405, No Obj: 0.002679, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.639837, Class: 1.000000, Obj: 0.399549, No Obj: 0.002257, Avg Recall: 0.764706, count: 85 -Region Avg IOU: 0.624341, Class: 1.000000, Obj: 0.330115, No Obj: 0.002618, Avg Recall: 0.771429, count: 105 -3495: 0.436882, 0.461222 avg, 0.001000 rate, 27.370956 seconds, 894720 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.606723, Class: 1.000000, Obj: 0.350047, No Obj: 0.002283, Avg Recall: 0.758621, count: 87 -Region Avg IOU: 0.633291, Class: 1.000000, Obj: 0.343401, No Obj: 0.002643, Avg Recall: 0.785714, count: 126 -Region Avg IOU: 0.643030, Class: 1.000000, Obj: 0.352037, No Obj: 0.002694, Avg Recall: 0.793388, count: 121 -Region Avg IOU: 0.610011, Class: 1.000000, Obj: 0.314996, No Obj: 0.002432, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.648532, Class: 1.000000, Obj: 0.355090, No Obj: 0.002371, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.652698, Class: 1.000000, Obj: 0.354666, No Obj: 0.002332, Avg Recall: 0.833333, count: 96 -Region Avg IOU: 0.625028, Class: 1.000000, Obj: 0.304437, No Obj: 0.002526, Avg Recall: 0.752000, count: 125 -Region Avg IOU: 0.635003, Class: 1.000000, Obj: 0.290293, No Obj: 0.002323, Avg Recall: 0.791304, count: 115 -3496: 0.487577, 0.463858 avg, 0.001000 rate, 28.665424 seconds, 894976 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.587837, Class: 1.000000, Obj: 0.356366, No Obj: 0.002123, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.631489, Class: 1.000000, Obj: 0.335499, No Obj: 0.002741, Avg Recall: 0.826772, count: 127 -Region Avg IOU: 0.651589, Class: 1.000000, Obj: 0.372446, No Obj: 0.003078, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.677850, Class: 1.000000, Obj: 0.394645, No Obj: 0.002559, Avg Recall: 0.873563, count: 87 -Region Avg IOU: 0.605673, Class: 1.000000, Obj: 0.330123, No Obj: 0.002541, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.641951, Class: 1.000000, Obj: 0.363630, No Obj: 0.002795, Avg Recall: 0.810345, count: 116 -Region Avg IOU: 0.615966, Class: 1.000000, Obj: 0.354621, No Obj: 0.002585, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.627811, Class: 1.000000, Obj: 0.323925, No Obj: 0.002851, Avg Recall: 0.824000, count: 125 -3497: 0.462137, 0.463686 avg, 0.001000 rate, 27.538540 seconds, 895232 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.646262, Class: 1.000000, Obj: 0.365749, No Obj: 0.002494, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.554159, Class: 1.000000, Obj: 0.302992, No Obj: 0.002618, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.621697, Class: 1.000000, Obj: 0.332062, No Obj: 0.002549, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.614268, Class: 1.000000, Obj: 0.359807, No Obj: 0.002601, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.609180, Class: 1.000000, Obj: 0.345339, No Obj: 0.002581, Avg Recall: 0.746835, count: 79 -Region Avg IOU: 0.656280, Class: 1.000000, Obj: 0.342172, No Obj: 0.002772, Avg Recall: 0.800000, count: 120 -Region Avg IOU: 0.621120, Class: 1.000000, Obj: 0.385664, No Obj: 0.002498, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.662951, Class: 1.000000, Obj: 0.398377, No Obj: 0.002391, Avg Recall: 0.853333, count: 75 -3498: 0.416061, 0.458923 avg, 0.001000 rate, 28.207567 seconds, 895488 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.632416, Class: 1.000000, Obj: 0.345356, No Obj: 0.002970, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.620755, Class: 1.000000, Obj: 0.353726, No Obj: 0.002587, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.631433, Class: 1.000000, Obj: 0.431334, No Obj: 0.002694, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.604378, Class: 1.000000, Obj: 0.368796, No Obj: 0.002861, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.635517, Class: 1.000000, Obj: 0.363804, No Obj: 0.002386, Avg Recall: 0.806818, count: 88 -Region Avg IOU: 0.586775, Class: 1.000000, Obj: 0.323518, No Obj: 0.002958, Avg Recall: 0.663866, count: 119 -Region Avg IOU: 0.610852, Class: 1.000000, Obj: 0.302896, No Obj: 0.002737, Avg Recall: 0.740157, count: 127 -Region Avg IOU: 0.643091, Class: 1.000000, Obj: 0.361545, No Obj: 0.002359, Avg Recall: 0.827586, count: 87 -3499: 0.466138, 0.459645 avg, 0.001000 rate, 28.539705 seconds, 895744 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.634670, Class: 1.000000, Obj: 0.382923, No Obj: 0.003386, Avg Recall: 0.795082, count: 122 -Region Avg IOU: 0.614635, Class: 1.000000, Obj: 0.342933, No Obj: 0.002611, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.595528, Class: 1.000000, Obj: 0.355968, No Obj: 0.002597, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.593197, Class: 1.000000, Obj: 0.337648, No Obj: 0.002763, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.629865, Class: 1.000000, Obj: 0.307360, No Obj: 0.002927, Avg Recall: 0.794643, count: 112 -Region Avg IOU: 0.633409, Class: 1.000000, Obj: 0.363746, No Obj: 0.003125, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.639532, Class: 1.000000, Obj: 0.335847, No Obj: 0.003086, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.559352, Class: 1.000000, Obj: 0.365036, No Obj: 0.002652, Avg Recall: 0.666667, count: 108 -3500: 0.463993, 0.460079 avg, 0.001000 rate, 27.683403 seconds, 896000 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 16.317898 seconds -Region Avg IOU: 0.534904, Class: 1.000000, Obj: 0.308765, No Obj: 0.003074, Avg Recall: 0.578947, count: 114 -Region Avg IOU: 0.529942, Class: 1.000000, Obj: 0.301254, No Obj: 0.002837, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.587740, Class: 1.000000, Obj: 0.332320, No Obj: 0.003312, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.574346, Class: 1.000000, Obj: 0.338989, No Obj: 0.003913, Avg Recall: 0.677165, count: 127 -Region Avg IOU: 0.557354, Class: 1.000000, Obj: 0.359076, No Obj: 0.002974, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.578947, Class: 1.000000, Obj: 0.303409, No Obj: 0.003612, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.576444, Class: 1.000000, Obj: 0.338823, No Obj: 0.003423, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.590651, Class: 1.000000, Obj: 0.346404, No Obj: 0.003556, Avg Recall: 0.745902, count: 122 -3501: 0.430507, 0.457122 avg, 0.001000 rate, 24.871700 seconds, 896256 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.566732, Class: 1.000000, Obj: 0.307221, No Obj: 0.003558, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.545593, Class: 1.000000, Obj: 0.386360, No Obj: 0.002671, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.550994, Class: 1.000000, Obj: 0.353649, No Obj: 0.003272, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.580306, Class: 1.000000, Obj: 0.369868, No Obj: 0.003581, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.614591, Class: 1.000000, Obj: 0.337055, No Obj: 0.003198, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.594751, Class: 1.000000, Obj: 0.343236, No Obj: 0.002813, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.552474, Class: 1.000000, Obj: 0.345931, No Obj: 0.003419, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.595645, Class: 1.000000, Obj: 0.363453, No Obj: 0.003521, Avg Recall: 0.714286, count: 91 -3502: 0.473761, 0.458786 avg, 0.001000 rate, 23.902880 seconds, 896512 images -Loaded: 0.000146 seconds -Region Avg IOU: 0.540993, Class: 1.000000, Obj: 0.312216, No Obj: 0.003090, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.542106, Class: 1.000000, Obj: 0.332387, No Obj: 0.002980, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.586051, Class: 1.000000, Obj: 0.366926, No Obj: 0.004259, Avg Recall: 0.685039, count: 127 -Region Avg IOU: 0.590454, Class: 1.000000, Obj: 0.360692, No Obj: 0.003540, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.571361, Class: 1.000000, Obj: 0.322493, No Obj: 0.003264, Avg Recall: 0.658333, count: 120 -Region Avg IOU: 0.593611, Class: 1.000000, Obj: 0.359418, No Obj: 0.003382, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.583705, Class: 1.000000, Obj: 0.361049, No Obj: 0.003418, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.545150, Class: 1.000000, Obj: 0.317454, No Obj: 0.003445, Avg Recall: 0.614173, count: 127 -3503: 0.468464, 0.459754 avg, 0.001000 rate, 23.156628 seconds, 896768 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.590741, Class: 1.000000, Obj: 0.357958, No Obj: 0.003699, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.591779, Class: 1.000000, Obj: 0.362173, No Obj: 0.003927, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.551977, Class: 1.000000, Obj: 0.376887, No Obj: 0.003422, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.572339, Class: 1.000000, Obj: 0.334895, No Obj: 0.004461, Avg Recall: 0.652778, count: 144 -Region Avg IOU: 0.574184, Class: 1.000000, Obj: 0.342324, No Obj: 0.003783, Avg Recall: 0.649254, count: 134 -Region Avg IOU: 0.545134, Class: 1.000000, Obj: 0.381198, No Obj: 0.003744, Avg Recall: 0.593220, count: 118 -Region Avg IOU: 0.619936, Class: 1.000000, Obj: 0.364281, No Obj: 0.003255, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.590318, Class: 1.000000, Obj: 0.364953, No Obj: 0.004072, Avg Recall: 0.700855, count: 117 -3504: 0.444180, 0.458196 avg, 0.001000 rate, 24.019258 seconds, 897024 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.568859, Class: 1.000000, Obj: 0.389655, No Obj: 0.003699, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.596001, Class: 1.000000, Obj: 0.358832, No Obj: 0.004317, Avg Recall: 0.714286, count: 126 -Region Avg IOU: 0.538220, Class: 1.000000, Obj: 0.405339, No Obj: 0.002951, Avg Recall: 0.625000, count: 88 -Region Avg IOU: 0.570924, Class: 1.000000, Obj: 0.368488, No Obj: 0.002956, Avg Recall: 0.671233, count: 73 -Region Avg IOU: 0.582528, Class: 1.000000, Obj: 0.385223, No Obj: 0.003711, Avg Recall: 0.710526, count: 114 -Region Avg IOU: 0.640744, Class: 1.000000, Obj: 0.414405, No Obj: 0.003925, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.557319, Class: 1.000000, Obj: 0.343030, No Obj: 0.003850, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.596548, Class: 1.000000, Obj: 0.391022, No Obj: 0.003231, Avg Recall: 0.673267, count: 101 -3505: 0.413553, 0.453732 avg, 0.001000 rate, 25.119976 seconds, 897280 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.570292, Class: 1.000000, Obj: 0.413000, No Obj: 0.003370, Avg Recall: 0.640000, count: 75 -Region Avg IOU: 0.589081, Class: 1.000000, Obj: 0.352757, No Obj: 0.004294, Avg Recall: 0.735043, count: 117 -Region Avg IOU: 0.621655, Class: 1.000000, Obj: 0.402954, No Obj: 0.003715, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.572908, Class: 1.000000, Obj: 0.424183, No Obj: 0.003438, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.579614, Class: 1.000000, Obj: 0.360835, No Obj: 0.002879, Avg Recall: 0.675676, count: 74 -Region Avg IOU: 0.601059, Class: 1.000000, Obj: 0.428768, No Obj: 0.003384, Avg Recall: 0.716216, count: 74 -Region Avg IOU: 0.611904, Class: 1.000000, Obj: 0.390846, No Obj: 0.004118, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.560882, Class: 1.000000, Obj: 0.412550, No Obj: 0.004581, Avg Recall: 0.637795, count: 127 -3506: 0.352752, 0.443634 avg, 0.001000 rate, 24.484646 seconds, 897536 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.576556, Class: 1.000000, Obj: 0.377348, No Obj: 0.003949, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.597377, Class: 1.000000, Obj: 0.398546, No Obj: 0.003977, Avg Recall: 0.670588, count: 85 -Region Avg IOU: 0.601003, Class: 1.000000, Obj: 0.427900, No Obj: 0.004165, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.576013, Class: 1.000000, Obj: 0.373390, No Obj: 0.003788, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.576706, Class: 1.000000, Obj: 0.378437, No Obj: 0.003681, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.585032, Class: 1.000000, Obj: 0.367670, No Obj: 0.003533, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.557779, Class: 1.000000, Obj: 0.396085, No Obj: 0.004423, Avg Recall: 0.682540, count: 126 -Region Avg IOU: 0.581495, Class: 1.000000, Obj: 0.400374, No Obj: 0.003357, Avg Recall: 0.674157, count: 89 -3507: 0.413441, 0.440615 avg, 0.001000 rate, 23.951792 seconds, 897792 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.540268, Class: 1.000000, Obj: 0.411509, No Obj: 0.003466, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.556103, Class: 1.000000, Obj: 0.395313, No Obj: 0.003715, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.617611, Class: 1.000000, Obj: 0.451367, No Obj: 0.004134, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.585656, Class: 1.000000, Obj: 0.411061, No Obj: 0.003978, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.604478, Class: 1.000000, Obj: 0.368216, No Obj: 0.004451, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.552634, Class: 1.000000, Obj: 0.377529, No Obj: 0.004276, Avg Recall: 0.666667, count: 126 -Region Avg IOU: 0.621491, Class: 1.000000, Obj: 0.446856, No Obj: 0.004042, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.593835, Class: 1.000000, Obj: 0.412755, No Obj: 0.003516, Avg Recall: 0.670330, count: 91 -3508: 0.450552, 0.441608 avg, 0.001000 rate, 22.606192 seconds, 898048 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.627334, Class: 1.000000, Obj: 0.411852, No Obj: 0.003946, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.567790, Class: 1.000000, Obj: 0.382804, No Obj: 0.003509, Avg Recall: 0.633333, count: 90 -Region Avg IOU: 0.567812, Class: 1.000000, Obj: 0.407460, No Obj: 0.004253, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.595171, Class: 1.000000, Obj: 0.391961, No Obj: 0.004280, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.567593, Class: 1.000000, Obj: 0.364689, No Obj: 0.003471, Avg Recall: 0.662651, count: 83 -Region Avg IOU: 0.612373, Class: 1.000000, Obj: 0.420941, No Obj: 0.003948, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.590964, Class: 1.000000, Obj: 0.391829, No Obj: 0.003866, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.540068, Class: 1.000000, Obj: 0.433692, No Obj: 0.003590, Avg Recall: 0.600000, count: 110 -3509: 0.402749, 0.437723 avg, 0.001000 rate, 24.425707 seconds, 898304 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.557753, Class: 1.000000, Obj: 0.382452, No Obj: 0.003686, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.560907, Class: 1.000000, Obj: 0.335376, No Obj: 0.003733, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.595381, Class: 1.000000, Obj: 0.356679, No Obj: 0.004196, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.607378, Class: 1.000000, Obj: 0.382576, No Obj: 0.004134, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.495776, Class: 1.000000, Obj: 0.349244, No Obj: 0.003440, Avg Recall: 0.556522, count: 115 -Region Avg IOU: 0.578628, Class: 1.000000, Obj: 0.340734, No Obj: 0.004264, Avg Recall: 0.681159, count: 138 -Region Avg IOU: 0.611508, Class: 1.000000, Obj: 0.402310, No Obj: 0.003752, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.585442, Class: 1.000000, Obj: 0.439577, No Obj: 0.003606, Avg Recall: 0.732673, count: 101 -3510: 0.466620, 0.440612 avg, 0.001000 rate, 24.631140 seconds, 898560 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000098 seconds -Region Avg IOU: 0.579746, Class: 1.000000, Obj: 0.367244, No Obj: 0.003446, Avg Recall: 0.659341, count: 91 -Region Avg IOU: 0.604882, Class: 1.000000, Obj: 0.367041, No Obj: 0.004448, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.559451, Class: 1.000000, Obj: 0.360967, No Obj: 0.003583, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.607085, Class: 1.000000, Obj: 0.429823, No Obj: 0.003836, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.570512, Class: 1.000000, Obj: 0.373845, No Obj: 0.003159, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.538715, Class: 1.000000, Obj: 0.355213, No Obj: 0.003342, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.568259, Class: 1.000000, Obj: 0.406012, No Obj: 0.003565, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.574650, Class: 1.000000, Obj: 0.343715, No Obj: 0.003919, Avg Recall: 0.669725, count: 109 -3511: 0.483429, 0.444894 avg, 0.001000 rate, 25.242672 seconds, 898816 images -Loaded: 0.000119 seconds -Region Avg IOU: 0.546064, Class: 1.000000, Obj: 0.340261, No Obj: 0.003484, Avg Recall: 0.611511, count: 139 -Region Avg IOU: 0.606505, Class: 1.000000, Obj: 0.395326, No Obj: 0.003605, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.584842, Class: 1.000000, Obj: 0.353455, No Obj: 0.003465, Avg Recall: 0.688073, count: 109 -Region Avg IOU: 0.585564, Class: 1.000000, Obj: 0.374798, No Obj: 0.003137, Avg Recall: 0.714286, count: 77 -Region Avg IOU: 0.535693, Class: 1.000000, Obj: 0.392741, No Obj: 0.003128, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.601555, Class: 1.000000, Obj: 0.377860, No Obj: 0.003320, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.595784, Class: 1.000000, Obj: 0.334643, No Obj: 0.003246, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.537677, Class: 1.000000, Obj: 0.312080, No Obj: 0.003973, Avg Recall: 0.604167, count: 144 -3512: 0.450651, 0.445470 avg, 0.001000 rate, 25.561632 seconds, 899072 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.558662, Class: 1.000000, Obj: 0.349879, No Obj: 0.003282, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.559898, Class: 1.000000, Obj: 0.340039, No Obj: 0.003567, Avg Recall: 0.625000, count: 136 -Region Avg IOU: 0.583243, Class: 1.000000, Obj: 0.363306, No Obj: 0.003479, Avg Recall: 0.726562, count: 128 -Region Avg IOU: 0.569109, Class: 1.000000, Obj: 0.346917, No Obj: 0.003011, Avg Recall: 0.683761, count: 117 -Region Avg IOU: 0.580033, Class: 1.000000, Obj: 0.373668, No Obj: 0.003106, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.526983, Class: 1.000000, Obj: 0.333702, No Obj: 0.003125, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.567014, Class: 1.000000, Obj: 0.351380, No Obj: 0.003608, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.592973, Class: 1.000000, Obj: 0.366785, No Obj: 0.003387, Avg Recall: 0.700935, count: 107 -3513: 0.449247, 0.445847 avg, 0.001000 rate, 24.572536 seconds, 899328 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.563460, Class: 1.000000, Obj: 0.318266, No Obj: 0.002963, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.588964, Class: 1.000000, Obj: 0.356378, No Obj: 0.003665, Avg Recall: 0.703125, count: 128 -Region Avg IOU: 0.540383, Class: 1.000000, Obj: 0.325113, No Obj: 0.002989, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.619427, Class: 1.000000, Obj: 0.353493, No Obj: 0.003354, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.575907, Class: 1.000000, Obj: 0.327881, No Obj: 0.003101, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.587451, Class: 1.000000, Obj: 0.346903, No Obj: 0.003236, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.583264, Class: 1.000000, Obj: 0.351461, No Obj: 0.003063, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.560363, Class: 1.000000, Obj: 0.381367, No Obj: 0.003263, Avg Recall: 0.624000, count: 125 -3514: 0.472037, 0.448466 avg, 0.001000 rate, 25.856508 seconds, 899584 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.572200, Class: 1.000000, Obj: 0.336960, No Obj: 0.003515, Avg Recall: 0.645161, count: 124 -Region Avg IOU: 0.598159, Class: 1.000000, Obj: 0.345375, No Obj: 0.002850, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.584578, Class: 1.000000, Obj: 0.309091, No Obj: 0.003134, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.559917, Class: 1.000000, Obj: 0.330107, No Obj: 0.003006, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.581362, Class: 1.000000, Obj: 0.301554, No Obj: 0.002930, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.607845, Class: 1.000000, Obj: 0.309705, No Obj: 0.003350, Avg Recall: 0.760000, count: 125 -Region Avg IOU: 0.568016, Class: 1.000000, Obj: 0.349081, No Obj: 0.002888, Avg Recall: 0.659341, count: 91 -Region Avg IOU: 0.575285, Class: 1.000000, Obj: 0.320794, No Obj: 0.002936, Avg Recall: 0.684685, count: 111 -3515: 0.449119, 0.448532 avg, 0.001000 rate, 24.560575 seconds, 899840 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.578669, Class: 1.000000, Obj: 0.324946, No Obj: 0.003456, Avg Recall: 0.690141, count: 142 -Region Avg IOU: 0.599131, Class: 1.000000, Obj: 0.350592, No Obj: 0.003150, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.598606, Class: 1.000000, Obj: 0.328660, No Obj: 0.003252, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.558242, Class: 1.000000, Obj: 0.346076, No Obj: 0.003117, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.584709, Class: 1.000000, Obj: 0.365280, No Obj: 0.003225, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.591782, Class: 1.000000, Obj: 0.343900, No Obj: 0.003499, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.587866, Class: 1.000000, Obj: 0.340318, No Obj: 0.003471, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.606499, Class: 1.000000, Obj: 0.344026, No Obj: 0.003272, Avg Recall: 0.747475, count: 99 -3516: 0.445610, 0.448240 avg, 0.001000 rate, 25.290585 seconds, 900096 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.547971, Class: 1.000000, Obj: 0.360564, No Obj: 0.002971, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.501650, Class: 1.000000, Obj: 0.305186, No Obj: 0.003065, Avg Recall: 0.536585, count: 123 -Region Avg IOU: 0.573870, Class: 1.000000, Obj: 0.327018, No Obj: 0.003010, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.600029, Class: 1.000000, Obj: 0.351427, No Obj: 0.002556, Avg Recall: 0.736264, count: 91 -Region Avg IOU: 0.626016, Class: 1.000000, Obj: 0.376874, No Obj: 0.004099, Avg Recall: 0.757812, count: 128 -Region Avg IOU: 0.640133, Class: 1.000000, Obj: 0.387032, No Obj: 0.003099, Avg Recall: 0.813187, count: 91 -Region Avg IOU: 0.614830, Class: 1.000000, Obj: 0.351149, No Obj: 0.003497, Avg Recall: 0.770492, count: 122 -Region Avg IOU: 0.550584, Class: 1.000000, Obj: 0.326213, No Obj: 0.003134, Avg Recall: 0.645455, count: 110 -3517: 0.432809, 0.446696 avg, 0.001000 rate, 24.570837 seconds, 900352 images -Loaded: 0.000127 seconds -Region Avg IOU: 0.586780, Class: 1.000000, Obj: 0.346706, No Obj: 0.003400, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.571116, Class: 1.000000, Obj: 0.336330, No Obj: 0.003528, Avg Recall: 0.683824, count: 136 -Region Avg IOU: 0.599335, Class: 1.000000, Obj: 0.349627, No Obj: 0.003422, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.601258, Class: 1.000000, Obj: 0.331706, No Obj: 0.003075, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.581513, Class: 1.000000, Obj: 0.348350, No Obj: 0.003613, Avg Recall: 0.693431, count: 137 -Region Avg IOU: 0.539354, Class: 1.000000, Obj: 0.328373, No Obj: 0.002796, Avg Recall: 0.637500, count: 80 -Region Avg IOU: 0.564876, Class: 1.000000, Obj: 0.356633, No Obj: 0.003250, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.581821, Class: 1.000000, Obj: 0.360279, No Obj: 0.003207, Avg Recall: 0.702703, count: 111 -3518: 0.494259, 0.451453 avg, 0.001000 rate, 25.913294 seconds, 900608 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.617224, Class: 1.000000, Obj: 0.384060, No Obj: 0.003302, Avg Recall: 0.761364, count: 88 -Region Avg IOU: 0.587527, Class: 1.000000, Obj: 0.370011, No Obj: 0.003391, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.597339, Class: 1.000000, Obj: 0.325970, No Obj: 0.003165, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.555447, Class: 1.000000, Obj: 0.329361, No Obj: 0.003109, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.619128, Class: 1.000000, Obj: 0.385020, No Obj: 0.003555, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.604419, Class: 1.000000, Obj: 0.379358, No Obj: 0.003254, Avg Recall: 0.718310, count: 71 -Region Avg IOU: 0.606134, Class: 1.000000, Obj: 0.377384, No Obj: 0.003787, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.623901, Class: 1.000000, Obj: 0.357834, No Obj: 0.003427, Avg Recall: 0.758242, count: 91 -3519: 0.435208, 0.449828 avg, 0.001000 rate, 25.279575 seconds, 900864 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.614182, Class: 1.000000, Obj: 0.344396, No Obj: 0.003493, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.589631, Class: 1.000000, Obj: 0.388659, No Obj: 0.003175, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.600929, Class: 1.000000, Obj: 0.348424, No Obj: 0.003550, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.587486, Class: 1.000000, Obj: 0.448902, No Obj: 0.002845, Avg Recall: 0.675000, count: 80 -Region Avg IOU: 0.598383, Class: 1.000000, Obj: 0.404183, No Obj: 0.004316, Avg Recall: 0.697987, count: 149 -Region Avg IOU: 0.551063, Class: 1.000000, Obj: 0.383130, No Obj: 0.003604, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.607502, Class: 1.000000, Obj: 0.353619, No Obj: 0.003323, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.582810, Class: 1.000000, Obj: 0.404207, No Obj: 0.003783, Avg Recall: 0.690476, count: 126 -3520: 0.431372, 0.447983 avg, 0.001000 rate, 24.965397 seconds, 901120 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 20.746344 seconds -Region Avg IOU: 0.574497, Class: 1.000000, Obj: 0.396579, No Obj: 0.003770, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.604981, Class: 1.000000, Obj: 0.328233, No Obj: 0.003692, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.606563, Class: 1.000000, Obj: 0.381513, No Obj: 0.003582, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.540490, Class: 1.000000, Obj: 0.382376, No Obj: 0.003365, Avg Recall: 0.686747, count: 83 -Region Avg IOU: 0.593059, Class: 1.000000, Obj: 0.344015, No Obj: 0.003703, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.531145, Class: 1.000000, Obj: 0.320223, No Obj: 0.003365, Avg Recall: 0.639344, count: 122 -Region Avg IOU: 0.645163, Class: 1.000000, Obj: 0.391909, No Obj: 0.003750, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.584883, Class: 1.000000, Obj: 0.457500, No Obj: 0.003543, Avg Recall: 0.670000, count: 100 -3521: 0.450204, 0.448205 avg, 0.001000 rate, 23.372904 seconds, 901376 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.579526, Class: 1.000000, Obj: 0.360812, No Obj: 0.003480, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.544714, Class: 1.000000, Obj: 0.332065, No Obj: 0.003516, Avg Recall: 0.624113, count: 141 -Region Avg IOU: 0.592243, Class: 1.000000, Obj: 0.384978, No Obj: 0.003737, Avg Recall: 0.691057, count: 123 -Region Avg IOU: 0.534234, Class: 1.000000, Obj: 0.349129, No Obj: 0.003545, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.585845, Class: 1.000000, Obj: 0.371277, No Obj: 0.004228, Avg Recall: 0.687023, count: 131 -Region Avg IOU: 0.587574, Class: 1.000000, Obj: 0.380701, No Obj: 0.004308, Avg Recall: 0.666667, count: 117 -Region Avg IOU: 0.595867, Class: 1.000000, Obj: 0.382823, No Obj: 0.003947, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.590881, Class: 1.000000, Obj: 0.372583, No Obj: 0.003672, Avg Recall: 0.710280, count: 107 -3522: 0.491253, 0.452510 avg, 0.001000 rate, 23.882656 seconds, 901632 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.534464, Class: 1.000000, Obj: 0.347075, No Obj: 0.003607, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.514117, Class: 1.000000, Obj: 0.308386, No Obj: 0.003371, Avg Recall: 0.560345, count: 116 -Region Avg IOU: 0.543741, Class: 1.000000, Obj: 0.357994, No Obj: 0.003996, Avg Recall: 0.678571, count: 140 -Region Avg IOU: 0.605456, Class: 1.000000, Obj: 0.409985, No Obj: 0.003819, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.526833, Class: 1.000000, Obj: 0.363056, No Obj: 0.003929, Avg Recall: 0.554622, count: 119 -Region Avg IOU: 0.610532, Class: 1.000000, Obj: 0.396583, No Obj: 0.004104, Avg Recall: 0.736000, count: 125 -Region Avg IOU: 0.600061, Class: 1.000000, Obj: 0.379746, No Obj: 0.003813, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.601106, Class: 1.000000, Obj: 0.374784, No Obj: 0.004529, Avg Recall: 0.720000, count: 125 -3523: 0.503915, 0.457650 avg, 0.001000 rate, 24.558212 seconds, 901888 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.563516, Class: 1.000000, Obj: 0.383757, No Obj: 0.003666, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.561941, Class: 1.000000, Obj: 0.375715, No Obj: 0.003665, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.577058, Class: 1.000000, Obj: 0.396078, No Obj: 0.004098, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.541107, Class: 1.000000, Obj: 0.397330, No Obj: 0.003850, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.600963, Class: 1.000000, Obj: 0.403810, No Obj: 0.004307, Avg Recall: 0.731343, count: 134 -Region Avg IOU: 0.642618, Class: 1.000000, Obj: 0.403285, No Obj: 0.003480, Avg Recall: 0.747126, count: 87 -Region Avg IOU: 0.593310, Class: 1.000000, Obj: 0.450133, No Obj: 0.003897, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.563902, Class: 1.000000, Obj: 0.328532, No Obj: 0.004368, Avg Recall: 0.658064, count: 155 -3524: 0.432109, 0.455096 avg, 0.001000 rate, 23.662851 seconds, 902144 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.582789, Class: 1.000000, Obj: 0.375249, No Obj: 0.004191, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.568002, Class: 1.000000, Obj: 0.412130, No Obj: 0.003417, Avg Recall: 0.643678, count: 87 -Region Avg IOU: 0.604750, Class: 1.000000, Obj: 0.396410, No Obj: 0.003742, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.592809, Class: 1.000000, Obj: 0.387687, No Obj: 0.004782, Avg Recall: 0.709677, count: 124 -Region Avg IOU: 0.563411, Class: 1.000000, Obj: 0.380518, No Obj: 0.003639, Avg Recall: 0.647727, count: 88 -Region Avg IOU: 0.588225, Class: 1.000000, Obj: 0.410960, No Obj: 0.004178, Avg Recall: 0.681818, count: 88 -Region Avg IOU: 0.594832, Class: 1.000000, Obj: 0.413629, No Obj: 0.003984, Avg Recall: 0.686956, count: 115 -Region Avg IOU: 0.633702, Class: 1.000000, Obj: 0.423306, No Obj: 0.004211, Avg Recall: 0.785047, count: 107 -3525: 0.390295, 0.448616 avg, 0.001000 rate, 24.240807 seconds, 902400 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.534058, Class: 1.000000, Obj: 0.356566, No Obj: 0.003965, Avg Recall: 0.600000, count: 135 -Region Avg IOU: 0.566479, Class: 1.000000, Obj: 0.378334, No Obj: 0.003889, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.561353, Class: 1.000000, Obj: 0.401476, No Obj: 0.003435, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.586326, Class: 1.000000, Obj: 0.401390, No Obj: 0.003473, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.630261, Class: 1.000000, Obj: 0.465048, No Obj: 0.003850, Avg Recall: 0.750000, count: 84 -Region Avg IOU: 0.570404, Class: 1.000000, Obj: 0.355754, No Obj: 0.003587, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.569415, Class: 1.000000, Obj: 0.388407, No Obj: 0.004038, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.523361, Class: 1.000000, Obj: 0.389150, No Obj: 0.003620, Avg Recall: 0.616071, count: 112 -3526: 0.440952, 0.447850 avg, 0.001000 rate, 24.960123 seconds, 902656 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.581737, Class: 1.000000, Obj: 0.376267, No Obj: 0.003900, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.560700, Class: 1.000000, Obj: 0.362433, No Obj: 0.003453, Avg Recall: 0.644737, count: 76 -Region Avg IOU: 0.583697, Class: 1.000000, Obj: 0.395240, No Obj: 0.003584, Avg Recall: 0.698795, count: 83 -Region Avg IOU: 0.521233, Class: 1.000000, Obj: 0.405408, No Obj: 0.003461, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.593162, Class: 1.000000, Obj: 0.367215, No Obj: 0.003481, Avg Recall: 0.682927, count: 82 -Region Avg IOU: 0.565983, Class: 1.000000, Obj: 0.365884, No Obj: 0.004296, Avg Recall: 0.651163, count: 129 -Region Avg IOU: 0.594784, Class: 1.000000, Obj: 0.435815, No Obj: 0.003262, Avg Recall: 0.737500, count: 80 -Region Avg IOU: 0.539880, Class: 1.000000, Obj: 0.341973, No Obj: 0.004237, Avg Recall: 0.576159, count: 151 -3527: 0.407422, 0.443807 avg, 0.001000 rate, 22.540487 seconds, 902912 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.585237, Class: 1.000000, Obj: 0.359567, No Obj: 0.003499, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.569186, Class: 1.000000, Obj: 0.347603, No Obj: 0.004111, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.601607, Class: 1.000000, Obj: 0.431598, No Obj: 0.003510, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.584862, Class: 1.000000, Obj: 0.380814, No Obj: 0.003898, Avg Recall: 0.717742, count: 124 -Region Avg IOU: 0.564434, Class: 1.000000, Obj: 0.352669, No Obj: 0.003491, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.551527, Class: 1.000000, Obj: 0.364745, No Obj: 0.003034, Avg Recall: 0.641026, count: 78 -Region Avg IOU: 0.591567, Class: 1.000000, Obj: 0.365887, No Obj: 0.003058, Avg Recall: 0.716049, count: 81 -Region Avg IOU: 0.545455, Class: 1.000000, Obj: 0.355676, No Obj: 0.003738, Avg Recall: 0.654867, count: 113 -3528: 0.398491, 0.439275 avg, 0.001000 rate, 25.457069 seconds, 903168 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.559793, Class: 1.000000, Obj: 0.416020, No Obj: 0.003734, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.596628, Class: 1.000000, Obj: 0.378748, No Obj: 0.003409, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.613307, Class: 1.000000, Obj: 0.386166, No Obj: 0.003709, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.568399, Class: 1.000000, Obj: 0.355756, No Obj: 0.003495, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.568013, Class: 1.000000, Obj: 0.376694, No Obj: 0.003401, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.651313, Class: 1.000000, Obj: 0.464225, No Obj: 0.003641, Avg Recall: 0.800000, count: 75 -Region Avg IOU: 0.575982, Class: 1.000000, Obj: 0.332442, No Obj: 0.003455, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.546578, Class: 1.000000, Obj: 0.309590, No Obj: 0.003465, Avg Recall: 0.666667, count: 102 -3529: 0.459637, 0.441311 avg, 0.001000 rate, 23.787748 seconds, 903424 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.544698, Class: 1.000000, Obj: 0.326747, No Obj: 0.003066, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.582443, Class: 1.000000, Obj: 0.305912, No Obj: 0.003816, Avg Recall: 0.675325, count: 154 -Region Avg IOU: 0.537132, Class: 1.000000, Obj: 0.358826, No Obj: 0.003181, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.593775, Class: 1.000000, Obj: 0.352086, No Obj: 0.003239, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.600023, Class: 1.000000, Obj: 0.419552, No Obj: 0.003598, Avg Recall: 0.732558, count: 86 -Region Avg IOU: 0.613171, Class: 1.000000, Obj: 0.395596, No Obj: 0.003232, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.523150, Class: 1.000000, Obj: 0.344083, No Obj: 0.003278, Avg Recall: 0.580882, count: 136 -Region Avg IOU: 0.540704, Class: 1.000000, Obj: 0.325674, No Obj: 0.003374, Avg Recall: 0.598131, count: 107 -3530: 0.411101, 0.438290 avg, 0.001000 rate, 22.674210 seconds, 903680 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 20.385412 seconds -Region Avg IOU: 0.445968, Class: 1.000000, Obj: 0.245762, No Obj: 0.003553, Avg Recall: 0.479675, count: 123 -Region Avg IOU: 0.426796, Class: 1.000000, Obj: 0.220585, No Obj: 0.003971, Avg Recall: 0.413043, count: 138 -Region Avg IOU: 0.442829, Class: 1.000000, Obj: 0.206391, No Obj: 0.003169, Avg Recall: 0.450450, count: 111 -Region Avg IOU: 0.440354, Class: 1.000000, Obj: 0.200153, No Obj: 0.003268, Avg Recall: 0.514019, count: 107 -Region Avg IOU: 0.458001, Class: 1.000000, Obj: 0.227842, No Obj: 0.003459, Avg Recall: 0.465517, count: 116 -Region Avg IOU: 0.466435, Class: 1.000000, Obj: 0.224060, No Obj: 0.003660, Avg Recall: 0.504274, count: 117 -Region Avg IOU: 0.450740, Class: 1.000000, Obj: 0.237216, No Obj: 0.003537, Avg Recall: 0.484536, count: 97 -Region Avg IOU: 0.446307, Class: 1.000000, Obj: 0.233388, No Obj: 0.003039, Avg Recall: 0.509615, count: 104 -3531: 0.563120, 0.450773 avg, 0.001000 rate, 18.861109 seconds, 903936 images -Loaded: 2.263495 seconds -Region Avg IOU: 0.467060, Class: 1.000000, Obj: 0.189378, No Obj: 0.003079, Avg Recall: 0.484848, count: 99 -Region Avg IOU: 0.418206, Class: 1.000000, Obj: 0.236256, No Obj: 0.003585, Avg Recall: 0.463636, count: 110 -Region Avg IOU: 0.441319, Class: 1.000000, Obj: 0.245614, No Obj: 0.003678, Avg Recall: 0.469565, count: 115 -Region Avg IOU: 0.484492, Class: 1.000000, Obj: 0.230576, No Obj: 0.003998, Avg Recall: 0.552448, count: 143 -Region Avg IOU: 0.486962, Class: 1.000000, Obj: 0.253470, No Obj: 0.003876, Avg Recall: 0.557522, count: 113 -Region Avg IOU: 0.446004, Class: 1.000000, Obj: 0.192722, No Obj: 0.003397, Avg Recall: 0.475000, count: 120 -Region Avg IOU: 0.518616, Class: 1.000000, Obj: 0.249782, No Obj: 0.003615, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.464248, Class: 1.000000, Obj: 0.239947, No Obj: 0.003165, Avg Recall: 0.467391, count: 92 -3532: 0.530021, 0.458698 avg, 0.001000 rate, 14.883480 seconds, 904192 images -Loaded: 5.015111 seconds -Region Avg IOU: 0.474361, Class: 1.000000, Obj: 0.279206, No Obj: 0.003885, Avg Recall: 0.532787, count: 122 -Region Avg IOU: 0.412163, Class: 1.000000, Obj: 0.236285, No Obj: 0.003086, Avg Recall: 0.420000, count: 100 -Region Avg IOU: 0.529983, Class: 1.000000, Obj: 0.255289, No Obj: 0.003757, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.410198, Class: 1.000000, Obj: 0.267849, No Obj: 0.003492, Avg Recall: 0.420000, count: 100 -Region Avg IOU: 0.472164, Class: 1.000000, Obj: 0.268317, No Obj: 0.004443, Avg Recall: 0.462185, count: 119 -Region Avg IOU: 0.507004, Class: 1.000000, Obj: 0.229669, No Obj: 0.003504, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.463672, Class: 1.000000, Obj: 0.274482, No Obj: 0.003271, Avg Recall: 0.506024, count: 83 -Region Avg IOU: 0.493508, Class: 1.000000, Obj: 0.244578, No Obj: 0.003841, Avg Recall: 0.500000, count: 94 -3533: 0.497971, 0.462625 avg, 0.001000 rate, 14.803524 seconds, 904448 images -Loaded: 5.401734 seconds -Region Avg IOU: 0.450150, Class: 1.000000, Obj: 0.288916, No Obj: 0.003574, Avg Recall: 0.456311, count: 103 -Region Avg IOU: 0.493929, Class: 1.000000, Obj: 0.278897, No Obj: 0.003955, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.451274, Class: 1.000000, Obj: 0.320321, No Obj: 0.003903, Avg Recall: 0.434783, count: 92 -Region Avg IOU: 0.427270, Class: 1.000000, Obj: 0.260561, No Obj: 0.004133, Avg Recall: 0.441176, count: 136 -Region Avg IOU: 0.448365, Class: 1.000000, Obj: 0.265382, No Obj: 0.004174, Avg Recall: 0.445378, count: 119 -Region Avg IOU: 0.478598, Class: 1.000000, Obj: 0.286480, No Obj: 0.004009, Avg Recall: 0.532110, count: 109 -Region Avg IOU: 0.439372, Class: 1.000000, Obj: 0.281790, No Obj: 0.003770, Avg Recall: 0.455556, count: 90 -Region Avg IOU: 0.466763, Class: 1.000000, Obj: 0.270654, No Obj: 0.003986, Avg Recall: 0.533333, count: 105 -3534: 0.610341, 0.477397 avg, 0.001000 rate, 19.182529 seconds, 904704 images -Loaded: 0.918512 seconds -Region Avg IOU: 0.449023, Class: 1.000000, Obj: 0.313144, No Obj: 0.004085, Avg Recall: 0.505050, count: 99 -Region Avg IOU: 0.446637, Class: 1.000000, Obj: 0.295409, No Obj: 0.004243, Avg Recall: 0.490566, count: 106 -Region Avg IOU: 0.455389, Class: 1.000000, Obj: 0.276564, No Obj: 0.005611, Avg Recall: 0.483444, count: 151 -Region Avg IOU: 0.420680, Class: 1.000000, Obj: 0.270261, No Obj: 0.004619, Avg Recall: 0.456311, count: 103 -Region Avg IOU: 0.474335, Class: 1.000000, Obj: 0.332170, No Obj: 0.004358, Avg Recall: 0.464646, count: 99 -Region Avg IOU: 0.513523, Class: 1.000000, Obj: 0.321143, No Obj: 0.004685, Avg Recall: 0.597938, count: 97 -Region Avg IOU: 0.435079, Class: 1.000000, Obj: 0.292867, No Obj: 0.003974, Avg Recall: 0.505376, count: 93 -Region Avg IOU: 0.437924, Class: 1.000000, Obj: 0.286560, No Obj: 0.003799, Avg Recall: 0.474227, count: 97 -3535: 0.530014, 0.482659 avg, 0.001000 rate, 17.716583 seconds, 904960 images -Loaded: 3.450668 seconds -Region Avg IOU: 0.498222, Class: 1.000000, Obj: 0.352042, No Obj: 0.004632, Avg Recall: 0.551020, count: 98 -Region Avg IOU: 0.447249, Class: 1.000000, Obj: 0.291924, No Obj: 0.004176, Avg Recall: 0.483871, count: 93 -Region Avg IOU: 0.474483, Class: 1.000000, Obj: 0.327667, No Obj: 0.004856, Avg Recall: 0.517241, count: 87 -Region Avg IOU: 0.507118, Class: 1.000000, Obj: 0.323361, No Obj: 0.004483, Avg Recall: 0.537634, count: 93 -Region Avg IOU: 0.490444, Class: 1.000000, Obj: 0.329941, No Obj: 0.005266, Avg Recall: 0.550000, count: 120 -Region Avg IOU: 0.450122, Class: 1.000000, Obj: 0.286021, No Obj: 0.003818, Avg Recall: 0.476190, count: 84 -Region Avg IOU: 0.542312, Class: 1.000000, Obj: 0.348257, No Obj: 0.004721, Avg Recall: 0.670886, count: 79 -Region Avg IOU: 0.443532, Class: 1.000000, Obj: 0.277375, No Obj: 0.004987, Avg Recall: 0.485981, count: 107 -3536: 0.408051, 0.475198 avg, 0.001000 rate, 18.361744 seconds, 905216 images -Loaded: 0.736782 seconds -Region Avg IOU: 0.471104, Class: 1.000000, Obj: 0.322494, No Obj: 0.004944, Avg Recall: 0.523810, count: 105 -Region Avg IOU: 0.448175, Class: 1.000000, Obj: 0.337384, No Obj: 0.005237, Avg Recall: 0.500000, count: 114 -Region Avg IOU: 0.454667, Class: 1.000000, Obj: 0.341745, No Obj: 0.005060, Avg Recall: 0.544554, count: 101 -Region Avg IOU: 0.472507, Class: 1.000000, Obj: 0.340362, No Obj: 0.004937, Avg Recall: 0.468468, count: 111 -Region Avg IOU: 0.440711, Class: 1.000000, Obj: 0.293038, No Obj: 0.004990, Avg Recall: 0.477477, count: 111 -Region Avg IOU: 0.455105, Class: 1.000000, Obj: 0.315930, No Obj: 0.005009, Avg Recall: 0.433628, count: 113 -Region Avg IOU: 0.439501, Class: 1.000000, Obj: 0.297101, No Obj: 0.004382, Avg Recall: 0.504202, count: 119 -Region Avg IOU: 0.476474, Class: 1.000000, Obj: 0.323219, No Obj: 0.005292, Avg Recall: 0.538462, count: 130 -3537: 0.503742, 0.478052 avg, 0.001000 rate, 14.036054 seconds, 905472 images -Loaded: 6.735510 seconds -Region Avg IOU: 0.481816, Class: 1.000000, Obj: 0.368192, No Obj: 0.005190, Avg Recall: 0.559140, count: 93 -Region Avg IOU: 0.472986, Class: 1.000000, Obj: 0.317822, No Obj: 0.005899, Avg Recall: 0.514925, count: 134 -Region Avg IOU: 0.503924, Class: 1.000000, Obj: 0.351989, No Obj: 0.005354, Avg Recall: 0.522727, count: 88 -Region Avg IOU: 0.452771, Class: 1.000000, Obj: 0.335582, No Obj: 0.005242, Avg Recall: 0.456897, count: 116 -Region Avg IOU: 0.453740, Class: 1.000000, Obj: 0.312345, No Obj: 0.005543, Avg Recall: 0.461538, count: 117 -Region Avg IOU: 0.476672, Class: 1.000000, Obj: 0.340029, No Obj: 0.005187, Avg Recall: 0.543210, count: 81 -Region Avg IOU: 0.444855, Class: 1.000000, Obj: 0.349231, No Obj: 0.004486, Avg Recall: 0.483516, count: 91 -Region Avg IOU: 0.480068, Class: 1.000000, Obj: 0.361599, No Obj: 0.006466, Avg Recall: 0.529412, count: 136 -3538: 0.522452, 0.482492 avg, 0.001000 rate, 17.135395 seconds, 905728 images -Loaded: 3.422262 seconds -Region Avg IOU: 0.419102, Class: 1.000000, Obj: 0.346244, No Obj: 0.005444, Avg Recall: 0.443396, count: 106 -Region Avg IOU: 0.435093, Class: 1.000000, Obj: 0.341669, No Obj: 0.005207, Avg Recall: 0.474747, count: 99 -Region Avg IOU: 0.436831, Class: 1.000000, Obj: 0.328986, No Obj: 0.005472, Avg Recall: 0.495798, count: 119 -Region Avg IOU: 0.476945, Class: 1.000000, Obj: 0.373993, No Obj: 0.005886, Avg Recall: 0.534653, count: 101 -Region Avg IOU: 0.480647, Class: 1.000000, Obj: 0.338304, No Obj: 0.005790, Avg Recall: 0.508333, count: 120 -Region Avg IOU: 0.510580, Class: 1.000000, Obj: 0.378729, No Obj: 0.005885, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.516985, Class: 1.000000, Obj: 0.359547, No Obj: 0.005295, Avg Recall: 0.593023, count: 86 -Region Avg IOU: 0.396490, Class: 1.000000, Obj: 0.325124, No Obj: 0.004717, Avg Recall: 0.390000, count: 100 -3539: 0.458443, 0.480087 avg, 0.001000 rate, 18.855732 seconds, 905984 images -Loaded: 1.611130 seconds -Region Avg IOU: 0.507195, Class: 1.000000, Obj: 0.387689, No Obj: 0.006350, Avg Recall: 0.514286, count: 105 -Region Avg IOU: 0.484781, Class: 1.000000, Obj: 0.369903, No Obj: 0.005580, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.488972, Class: 1.000000, Obj: 0.309088, No Obj: 0.004997, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.492884, Class: 1.000000, Obj: 0.376441, No Obj: 0.006024, Avg Recall: 0.531746, count: 126 -Region Avg IOU: 0.457142, Class: 1.000000, Obj: 0.376519, No Obj: 0.004867, Avg Recall: 0.512500, count: 80 -Region Avg IOU: 0.458675, Class: 1.000000, Obj: 0.369190, No Obj: 0.005583, Avg Recall: 0.471698, count: 106 -Region Avg IOU: 0.406544, Class: 1.000000, Obj: 0.332067, No Obj: 0.005346, Avg Recall: 0.377049, count: 122 -Region Avg IOU: 0.500997, Class: 1.000000, Obj: 0.367763, No Obj: 0.005875, Avg Recall: 0.508333, count: 120 -3540: 0.442059, 0.476284 avg, 0.001000 rate, 17.717436 seconds, 906240 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000062 seconds -Region Avg IOU: 0.617869, Class: 1.000000, Obj: 0.522852, No Obj: 0.004541, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.581093, Class: 1.000000, Obj: 0.471895, No Obj: 0.004489, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.613413, Class: 1.000000, Obj: 0.517363, No Obj: 0.004933, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.587060, Class: 1.000000, Obj: 0.487255, No Obj: 0.004244, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.629384, Class: 1.000000, Obj: 0.515240, No Obj: 0.004194, Avg Recall: 0.815217, count: 92 -Region Avg IOU: 0.603612, Class: 1.000000, Obj: 0.502294, No Obj: 0.004062, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.581723, Class: 1.000000, Obj: 0.511112, No Obj: 0.004778, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.593606, Class: 1.000000, Obj: 0.502579, No Obj: 0.005293, Avg Recall: 0.723077, count: 130 -3541: 0.391268, 0.467783 avg, 0.001000 rate, 26.220976 seconds, 906496 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.571686, Class: 1.000000, Obj: 0.432696, No Obj: 0.003696, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.535381, Class: 1.000000, Obj: 0.409696, No Obj: 0.003568, Avg Recall: 0.568421, count: 95 -Region Avg IOU: 0.628311, Class: 1.000000, Obj: 0.485498, No Obj: 0.003821, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.601337, Class: 1.000000, Obj: 0.450543, No Obj: 0.004352, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.633729, Class: 1.000000, Obj: 0.482295, No Obj: 0.004420, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.585635, Class: 1.000000, Obj: 0.463223, No Obj: 0.003991, Avg Recall: 0.693182, count: 88 -Region Avg IOU: 0.581246, Class: 1.000000, Obj: 0.498731, No Obj: 0.004316, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.619796, Class: 1.000000, Obj: 0.442730, No Obj: 0.005052, Avg Recall: 0.763514, count: 148 -3542: 0.460024, 0.467007 avg, 0.001000 rate, 25.450289 seconds, 906752 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.576895, Class: 1.000000, Obj: 0.425734, No Obj: 0.003039, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.594281, Class: 1.000000, Obj: 0.437431, No Obj: 0.003450, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.555151, Class: 1.000000, Obj: 0.400449, No Obj: 0.003401, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.601271, Class: 1.000000, Obj: 0.406370, No Obj: 0.003706, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.595634, Class: 1.000000, Obj: 0.410135, No Obj: 0.003390, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.585961, Class: 1.000000, Obj: 0.438773, No Obj: 0.003418, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.568668, Class: 1.000000, Obj: 0.402133, No Obj: 0.003417, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.532935, Class: 1.000000, Obj: 0.416656, No Obj: 0.003501, Avg Recall: 0.620968, count: 124 -3543: 0.432446, 0.463551 avg, 0.001000 rate, 25.553949 seconds, 907008 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.605332, Class: 1.000000, Obj: 0.394344, No Obj: 0.003527, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.611649, Class: 1.000000, Obj: 0.373956, No Obj: 0.003082, Avg Recall: 0.784483, count: 116 -Region Avg IOU: 0.564816, Class: 1.000000, Obj: 0.347227, No Obj: 0.003617, Avg Recall: 0.683453, count: 139 -Region Avg IOU: 0.603395, Class: 1.000000, Obj: 0.373064, No Obj: 0.002819, Avg Recall: 0.711111, count: 90 -Region Avg IOU: 0.643614, Class: 1.000000, Obj: 0.423750, No Obj: 0.002881, Avg Recall: 0.795181, count: 83 -Region Avg IOU: 0.570957, Class: 1.000000, Obj: 0.390788, No Obj: 0.002926, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.563464, Class: 1.000000, Obj: 0.343929, No Obj: 0.003110, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.578335, Class: 1.000000, Obj: 0.356527, No Obj: 0.003051, Avg Recall: 0.672727, count: 110 -3544: 0.448990, 0.462095 avg, 0.001000 rate, 26.469940 seconds, 907264 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.579389, Class: 1.000000, Obj: 0.330690, No Obj: 0.002749, Avg Recall: 0.704918, count: 122 -Region Avg IOU: 0.593194, Class: 1.000000, Obj: 0.321972, No Obj: 0.002589, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.572273, Class: 1.000000, Obj: 0.317185, No Obj: 0.002683, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.624718, Class: 1.000000, Obj: 0.383631, No Obj: 0.003442, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.624502, Class: 1.000000, Obj: 0.323576, No Obj: 0.002728, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.593925, Class: 1.000000, Obj: 0.352979, No Obj: 0.002571, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.597888, Class: 1.000000, Obj: 0.356869, No Obj: 0.002786, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.605444, Class: 1.000000, Obj: 0.340436, No Obj: 0.002509, Avg Recall: 0.737374, count: 99 -3545: 0.462332, 0.462118 avg, 0.001000 rate, 25.818457 seconds, 907520 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.631035, Class: 1.000000, Obj: 0.327712, No Obj: 0.002664, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.572585, Class: 1.000000, Obj: 0.281473, No Obj: 0.002469, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.580645, Class: 1.000000, Obj: 0.233250, No Obj: 0.002118, Avg Recall: 0.735043, count: 117 -Region Avg IOU: 0.627581, Class: 1.000000, Obj: 0.293076, No Obj: 0.002532, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.579637, Class: 1.000000, Obj: 0.303149, No Obj: 0.002499, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.606131, Class: 1.000000, Obj: 0.356381, No Obj: 0.002524, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.614015, Class: 1.000000, Obj: 0.291715, No Obj: 0.002612, Avg Recall: 0.776000, count: 125 -Region Avg IOU: 0.548228, Class: 1.000000, Obj: 0.285344, No Obj: 0.002552, Avg Recall: 0.643357, count: 143 -3546: 0.493186, 0.465225 avg, 0.001000 rate, 24.860607 seconds, 907776 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.594902, Class: 1.000000, Obj: 0.290150, No Obj: 0.002449, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.634000, Class: 1.000000, Obj: 0.283960, No Obj: 0.001991, Avg Recall: 0.761364, count: 88 -Region Avg IOU: 0.605299, Class: 1.000000, Obj: 0.283590, No Obj: 0.002061, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.610508, Class: 1.000000, Obj: 0.299758, No Obj: 0.001985, Avg Recall: 0.734940, count: 83 -Region Avg IOU: 0.592903, Class: 1.000000, Obj: 0.240631, No Obj: 0.002022, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.637725, Class: 1.000000, Obj: 0.304865, No Obj: 0.002250, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.582922, Class: 1.000000, Obj: 0.288267, No Obj: 0.002193, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.578427, Class: 1.000000, Obj: 0.257854, No Obj: 0.002495, Avg Recall: 0.692308, count: 117 -3547: 0.422129, 0.460916 avg, 0.001000 rate, 25.510227 seconds, 908032 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.558596, Class: 1.000000, Obj: 0.274107, No Obj: 0.002380, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.616058, Class: 1.000000, Obj: 0.296361, No Obj: 0.002611, Avg Recall: 0.729508, count: 122 -Region Avg IOU: 0.643723, Class: 1.000000, Obj: 0.287541, No Obj: 0.002870, Avg Recall: 0.794118, count: 136 -Region Avg IOU: 0.606620, Class: 1.000000, Obj: 0.295145, No Obj: 0.002580, Avg Recall: 0.704545, count: 88 -Region Avg IOU: 0.578850, Class: 1.000000, Obj: 0.305831, No Obj: 0.002475, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.592120, Class: 1.000000, Obj: 0.252723, No Obj: 0.002502, Avg Recall: 0.696970, count: 132 -Region Avg IOU: 0.573366, Class: 1.000000, Obj: 0.269210, No Obj: 0.002152, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.566991, Class: 1.000000, Obj: 0.259628, No Obj: 0.002450, Avg Recall: 0.646018, count: 113 -3548: 0.490437, 0.463868 avg, 0.001000 rate, 26.092592 seconds, 908288 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.586915, Class: 1.000000, Obj: 0.278525, No Obj: 0.003043, Avg Recall: 0.713287, count: 143 -Region Avg IOU: 0.548014, Class: 1.000000, Obj: 0.256507, No Obj: 0.001957, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.615752, Class: 1.000000, Obj: 0.356314, No Obj: 0.002381, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.600447, Class: 1.000000, Obj: 0.282352, No Obj: 0.002399, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.595329, Class: 1.000000, Obj: 0.286335, No Obj: 0.002703, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.573567, Class: 1.000000, Obj: 0.266350, No Obj: 0.002674, Avg Recall: 0.666667, count: 135 -Region Avg IOU: 0.618808, Class: 1.000000, Obj: 0.337745, No Obj: 0.002041, Avg Recall: 0.784810, count: 79 -Region Avg IOU: 0.621901, Class: 1.000000, Obj: 0.311045, No Obj: 0.002705, Avg Recall: 0.766667, count: 120 -3549: 0.519976, 0.469479 avg, 0.001000 rate, 25.589817 seconds, 908544 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.581804, Class: 1.000000, Obj: 0.317325, No Obj: 0.002549, Avg Recall: 0.666667, count: 123 -Region Avg IOU: 0.581832, Class: 1.000000, Obj: 0.329283, No Obj: 0.002473, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.631422, Class: 1.000000, Obj: 0.301928, No Obj: 0.002781, Avg Recall: 0.788889, count: 90 -Region Avg IOU: 0.630102, Class: 1.000000, Obj: 0.395539, No Obj: 0.002180, Avg Recall: 0.773333, count: 75 -Region Avg IOU: 0.634924, Class: 1.000000, Obj: 0.310910, No Obj: 0.002269, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.571813, Class: 1.000000, Obj: 0.291408, No Obj: 0.002565, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.615345, Class: 1.000000, Obj: 0.299739, No Obj: 0.003067, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.608215, Class: 1.000000, Obj: 0.337666, No Obj: 0.002594, Avg Recall: 0.734694, count: 98 -3550: 0.437118, 0.466243 avg, 0.001000 rate, 25.952871 seconds, 908800 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 14.335711 seconds -Region Avg IOU: 0.563850, Class: 1.000000, Obj: 0.342054, No Obj: 0.002724, Avg Recall: 0.664000, count: 125 -Region Avg IOU: 0.616344, Class: 1.000000, Obj: 0.370632, No Obj: 0.003122, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.608717, Class: 1.000000, Obj: 0.342197, No Obj: 0.002728, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.636678, Class: 1.000000, Obj: 0.340602, No Obj: 0.002973, Avg Recall: 0.794643, count: 112 -Region Avg IOU: 0.642936, Class: 1.000000, Obj: 0.368164, No Obj: 0.002735, Avg Recall: 0.803571, count: 112 -Region Avg IOU: 0.619188, Class: 1.000000, Obj: 0.372656, No Obj: 0.003046, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.579819, Class: 1.000000, Obj: 0.347696, No Obj: 0.002938, Avg Recall: 0.651163, count: 129 -Region Avg IOU: 0.618127, Class: 1.000000, Obj: 0.388968, No Obj: 0.002753, Avg Recall: 0.723810, count: 105 -3551: 0.497268, 0.469345 avg, 0.001000 rate, 25.565676 seconds, 909056 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.594872, Class: 1.000000, Obj: 0.345353, No Obj: 0.003396, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.612469, Class: 1.000000, Obj: 0.386585, No Obj: 0.003426, Avg Recall: 0.711111, count: 90 -Region Avg IOU: 0.606022, Class: 1.000000, Obj: 0.349992, No Obj: 0.003382, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.570847, Class: 1.000000, Obj: 0.347507, No Obj: 0.002797, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.576976, Class: 1.000000, Obj: 0.384331, No Obj: 0.003013, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.598936, Class: 1.000000, Obj: 0.367353, No Obj: 0.002969, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.579858, Class: 1.000000, Obj: 0.325801, No Obj: 0.003197, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.621713, Class: 1.000000, Obj: 0.381080, No Obj: 0.003031, Avg Recall: 0.765957, count: 94 -3552: 0.450316, 0.467442 avg, 0.001000 rate, 27.760738 seconds, 909312 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.622699, Class: 1.000000, Obj: 0.397555, No Obj: 0.003177, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.577769, Class: 1.000000, Obj: 0.362745, No Obj: 0.002755, Avg Recall: 0.715909, count: 88 -Region Avg IOU: 0.611580, Class: 1.000000, Obj: 0.411297, No Obj: 0.003792, Avg Recall: 0.742188, count: 128 -Region Avg IOU: 0.592509, Class: 1.000000, Obj: 0.363077, No Obj: 0.003916, Avg Recall: 0.695312, count: 128 -Region Avg IOU: 0.629302, Class: 1.000000, Obj: 0.356256, No Obj: 0.004068, Avg Recall: 0.776859, count: 121 -Region Avg IOU: 0.578176, Class: 1.000000, Obj: 0.355004, No Obj: 0.003630, Avg Recall: 0.666667, count: 132 -Region Avg IOU: 0.563395, Class: 1.000000, Obj: 0.357528, No Obj: 0.003196, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.634258, Class: 1.000000, Obj: 0.445481, No Obj: 0.003531, Avg Recall: 0.744681, count: 94 -3553: 0.487010, 0.469399 avg, 0.001000 rate, 25.271730 seconds, 909568 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.602946, Class: 1.000000, Obj: 0.415299, No Obj: 0.003291, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.595145, Class: 1.000000, Obj: 0.480026, No Obj: 0.003209, Avg Recall: 0.709302, count: 86 -Region Avg IOU: 0.568696, Class: 1.000000, Obj: 0.359022, No Obj: 0.003849, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.600439, Class: 1.000000, Obj: 0.420037, No Obj: 0.003178, Avg Recall: 0.684783, count: 92 -Region Avg IOU: 0.599848, Class: 1.000000, Obj: 0.413652, No Obj: 0.003632, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.623047, Class: 1.000000, Obj: 0.445304, No Obj: 0.003393, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.606157, Class: 1.000000, Obj: 0.372913, No Obj: 0.003349, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.561822, Class: 1.000000, Obj: 0.365151, No Obj: 0.003126, Avg Recall: 0.633663, count: 101 -3554: 0.396386, 0.462098 avg, 0.001000 rate, 25.341320 seconds, 909824 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.590945, Class: 1.000000, Obj: 0.377803, No Obj: 0.003653, Avg Recall: 0.689076, count: 119 -Region Avg IOU: 0.592514, Class: 1.000000, Obj: 0.407520, No Obj: 0.003479, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.604279, Class: 1.000000, Obj: 0.468801, No Obj: 0.002713, Avg Recall: 0.684932, count: 73 -Region Avg IOU: 0.629660, Class: 1.000000, Obj: 0.368947, No Obj: 0.003562, Avg Recall: 0.797753, count: 89 -Region Avg IOU: 0.596200, Class: 1.000000, Obj: 0.460362, No Obj: 0.003216, Avg Recall: 0.755556, count: 90 -Region Avg IOU: 0.576874, Class: 1.000000, Obj: 0.395970, No Obj: 0.003664, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.614686, Class: 1.000000, Obj: 0.411170, No Obj: 0.003877, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.632019, Class: 1.000000, Obj: 0.407167, No Obj: 0.004107, Avg Recall: 0.739837, count: 123 -3555: 0.453230, 0.461211 avg, 0.001000 rate, 26.211985 seconds, 910080 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.611021, Class: 1.000000, Obj: 0.502912, No Obj: 0.003794, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.572264, Class: 1.000000, Obj: 0.411793, No Obj: 0.003495, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.611060, Class: 1.000000, Obj: 0.423896, No Obj: 0.003608, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.572840, Class: 1.000000, Obj: 0.440304, No Obj: 0.003840, Avg Recall: 0.747967, count: 123 -Region Avg IOU: 0.564319, Class: 1.000000, Obj: 0.397572, No Obj: 0.003418, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.573608, Class: 1.000000, Obj: 0.417443, No Obj: 0.003685, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.612013, Class: 1.000000, Obj: 0.440654, No Obj: 0.003240, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.625893, Class: 1.000000, Obj: 0.438131, No Obj: 0.004071, Avg Recall: 0.783333, count: 120 -3556: 0.410437, 0.456134 avg, 0.001000 rate, 25.724560 seconds, 910336 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.641151, Class: 1.000000, Obj: 0.470806, No Obj: 0.003863, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.610900, Class: 1.000000, Obj: 0.402208, No Obj: 0.004124, Avg Recall: 0.768000, count: 125 -Region Avg IOU: 0.592623, Class: 1.000000, Obj: 0.412884, No Obj: 0.003096, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.605674, Class: 1.000000, Obj: 0.373882, No Obj: 0.003773, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.593810, Class: 1.000000, Obj: 0.400019, No Obj: 0.003703, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.651511, Class: 1.000000, Obj: 0.448228, No Obj: 0.003383, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.576916, Class: 1.000000, Obj: 0.407551, No Obj: 0.003787, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.559851, Class: 1.000000, Obj: 0.389984, No Obj: 0.003582, Avg Recall: 0.649123, count: 114 -3557: 0.486457, 0.459166 avg, 0.001000 rate, 24.929632 seconds, 910592 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.611525, Class: 1.000000, Obj: 0.395401, No Obj: 0.003332, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.597751, Class: 1.000000, Obj: 0.358809, No Obj: 0.003652, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.555906, Class: 1.000000, Obj: 0.393018, No Obj: 0.003073, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.572307, Class: 1.000000, Obj: 0.384692, No Obj: 0.003533, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.588197, Class: 1.000000, Obj: 0.355856, No Obj: 0.003574, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.613430, Class: 1.000000, Obj: 0.408348, No Obj: 0.003527, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.586194, Class: 1.000000, Obj: 0.421908, No Obj: 0.003369, Avg Recall: 0.709302, count: 86 -Region Avg IOU: 0.654847, Class: 1.000000, Obj: 0.421859, No Obj: 0.003721, Avg Recall: 0.828829, count: 111 -3558: 0.475228, 0.460772 avg, 0.001000 rate, 26.048037 seconds, 910848 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.600924, Class: 1.000000, Obj: 0.396866, No Obj: 0.003300, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.616918, Class: 1.000000, Obj: 0.363804, No Obj: 0.003386, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.603720, Class: 1.000000, Obj: 0.405860, No Obj: 0.002908, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.625071, Class: 1.000000, Obj: 0.421591, No Obj: 0.003044, Avg Recall: 0.802326, count: 86 -Region Avg IOU: 0.574895, Class: 1.000000, Obj: 0.402263, No Obj: 0.002801, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.589541, Class: 1.000000, Obj: 0.461142, No Obj: 0.003241, Avg Recall: 0.703297, count: 91 -Region Avg IOU: 0.583437, Class: 1.000000, Obj: 0.388529, No Obj: 0.002959, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.629904, Class: 1.000000, Obj: 0.385415, No Obj: 0.003308, Avg Recall: 0.824742, count: 97 -3559: 0.403988, 0.455094 avg, 0.001000 rate, 26.930244 seconds, 911104 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.617405, Class: 1.000000, Obj: 0.397954, No Obj: 0.002998, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.578263, Class: 1.000000, Obj: 0.370956, No Obj: 0.002723, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.600258, Class: 1.000000, Obj: 0.341845, No Obj: 0.003464, Avg Recall: 0.734375, count: 128 -Region Avg IOU: 0.610956, Class: 1.000000, Obj: 0.341017, No Obj: 0.003248, Avg Recall: 0.755906, count: 127 -Region Avg IOU: 0.577498, Class: 1.000000, Obj: 0.382629, No Obj: 0.002844, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.638438, Class: 1.000000, Obj: 0.385686, No Obj: 0.003000, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.554919, Class: 1.000000, Obj: 0.357923, No Obj: 0.003198, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.598458, Class: 1.000000, Obj: 0.393663, No Obj: 0.003182, Avg Recall: 0.736364, count: 110 -3560: 0.469173, 0.456502 avg, 0.001000 rate, 25.548649 seconds, 911360 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 20.491833 seconds -Region Avg IOU: 0.573427, Class: 1.000000, Obj: 0.323252, No Obj: 0.003423, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.581786, Class: 1.000000, Obj: 0.339918, No Obj: 0.002966, Avg Recall: 0.687500, count: 128 -Region Avg IOU: 0.564744, Class: 1.000000, Obj: 0.366364, No Obj: 0.002866, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.611164, Class: 1.000000, Obj: 0.406762, No Obj: 0.003149, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.628619, Class: 1.000000, Obj: 0.401463, No Obj: 0.002767, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.607675, Class: 1.000000, Obj: 0.313929, No Obj: 0.003276, Avg Recall: 0.723404, count: 141 -Region Avg IOU: 0.581389, Class: 1.000000, Obj: 0.336471, No Obj: 0.002633, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.587285, Class: 1.000000, Obj: 0.340604, No Obj: 0.003418, Avg Recall: 0.705521, count: 163 -3561: 0.535159, 0.464367 avg, 0.001000 rate, 24.945520 seconds, 911616 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.559996, Class: 1.000000, Obj: 0.315312, No Obj: 0.003100, Avg Recall: 0.671429, count: 140 -Region Avg IOU: 0.591405, Class: 1.000000, Obj: 0.362749, No Obj: 0.002478, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.610605, Class: 1.000000, Obj: 0.359011, No Obj: 0.002729, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.614470, Class: 1.000000, Obj: 0.383974, No Obj: 0.002998, Avg Recall: 0.735043, count: 117 -Region Avg IOU: 0.597621, Class: 1.000000, Obj: 0.381482, No Obj: 0.002634, Avg Recall: 0.689655, count: 87 -Region Avg IOU: 0.606500, Class: 1.000000, Obj: 0.367871, No Obj: 0.003065, Avg Recall: 0.702899, count: 138 -Region Avg IOU: 0.561350, Class: 1.000000, Obj: 0.257700, No Obj: 0.003043, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.597894, Class: 1.000000, Obj: 0.364640, No Obj: 0.003288, Avg Recall: 0.736434, count: 129 -3562: 0.485973, 0.466528 avg, 0.001000 rate, 26.141098 seconds, 911872 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.569421, Class: 1.000000, Obj: 0.298214, No Obj: 0.002920, Avg Recall: 0.650794, count: 126 -Region Avg IOU: 0.654354, Class: 1.000000, Obj: 0.342216, No Obj: 0.003006, Avg Recall: 0.826087, count: 92 -Region Avg IOU: 0.609184, Class: 1.000000, Obj: 0.354882, No Obj: 0.002957, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.555037, Class: 1.000000, Obj: 0.305668, No Obj: 0.002999, Avg Recall: 0.661290, count: 124 -Region Avg IOU: 0.594414, Class: 1.000000, Obj: 0.328699, No Obj: 0.003253, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.596090, Class: 1.000000, Obj: 0.365114, No Obj: 0.002587, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.585604, Class: 1.000000, Obj: 0.376790, No Obj: 0.002633, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.571199, Class: 1.000000, Obj: 0.319099, No Obj: 0.002844, Avg Recall: 0.666667, count: 117 -3563: 0.464993, 0.466374 avg, 0.001000 rate, 25.195805 seconds, 912128 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.589385, Class: 1.000000, Obj: 0.383563, No Obj: 0.002676, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.586771, Class: 1.000000, Obj: 0.340590, No Obj: 0.003007, Avg Recall: 0.692308, count: 130 -Region Avg IOU: 0.610685, Class: 1.000000, Obj: 0.315888, No Obj: 0.002558, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.584538, Class: 1.000000, Obj: 0.342595, No Obj: 0.002798, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.590946, Class: 1.000000, Obj: 0.319418, No Obj: 0.002381, Avg Recall: 0.692308, count: 78 -Region Avg IOU: 0.588118, Class: 1.000000, Obj: 0.338745, No Obj: 0.002666, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.631205, Class: 1.000000, Obj: 0.334567, No Obj: 0.002930, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.586932, Class: 1.000000, Obj: 0.366403, No Obj: 0.002479, Avg Recall: 0.714286, count: 98 -3564: 0.425655, 0.462302 avg, 0.001000 rate, 24.658308 seconds, 912384 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.618993, Class: 1.000000, Obj: 0.403167, No Obj: 0.002802, Avg Recall: 0.739583, count: 96 -Region Avg IOU: 0.633011, Class: 1.000000, Obj: 0.349521, No Obj: 0.003086, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.591533, Class: 1.000000, Obj: 0.350507, No Obj: 0.002605, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.593315, Class: 1.000000, Obj: 0.317071, No Obj: 0.002579, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.638557, Class: 1.000000, Obj: 0.405090, No Obj: 0.002491, Avg Recall: 0.800000, count: 80 -Region Avg IOU: 0.617399, Class: 1.000000, Obj: 0.387249, No Obj: 0.002920, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.563171, Class: 1.000000, Obj: 0.312598, No Obj: 0.002934, Avg Recall: 0.680672, count: 119 -Region Avg IOU: 0.638635, Class: 1.000000, Obj: 0.344148, No Obj: 0.002693, Avg Recall: 0.789474, count: 95 -3565: 0.386709, 0.454743 avg, 0.001000 rate, 24.967113 seconds, 912640 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.581227, Class: 1.000000, Obj: 0.346917, No Obj: 0.003571, Avg Recall: 0.697479, count: 119 -Region Avg IOU: 0.620574, Class: 1.000000, Obj: 0.364996, No Obj: 0.003115, Avg Recall: 0.754098, count: 122 -Region Avg IOU: 0.569563, Class: 1.000000, Obj: 0.352412, No Obj: 0.002626, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.596308, Class: 1.000000, Obj: 0.365489, No Obj: 0.002992, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.558478, Class: 1.000000, Obj: 0.360009, No Obj: 0.002650, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.606136, Class: 1.000000, Obj: 0.314021, No Obj: 0.002888, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.618580, Class: 1.000000, Obj: 0.348601, No Obj: 0.002993, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.607674, Class: 1.000000, Obj: 0.365776, No Obj: 0.003626, Avg Recall: 0.694030, count: 134 -3566: 0.462116, 0.455480 avg, 0.001000 rate, 25.903364 seconds, 912896 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.599452, Class: 1.000000, Obj: 0.398756, No Obj: 0.002997, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.648183, Class: 1.000000, Obj: 0.389924, No Obj: 0.002966, Avg Recall: 0.804348, count: 92 -Region Avg IOU: 0.600587, Class: 1.000000, Obj: 0.376826, No Obj: 0.003364, Avg Recall: 0.725275, count: 91 -Region Avg IOU: 0.569968, Class: 1.000000, Obj: 0.353748, No Obj: 0.003221, Avg Recall: 0.689076, count: 119 -Region Avg IOU: 0.590304, Class: 1.000000, Obj: 0.380144, No Obj: 0.002745, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.607041, Class: 1.000000, Obj: 0.362542, No Obj: 0.003387, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.610422, Class: 1.000000, Obj: 0.412753, No Obj: 0.003346, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.622228, Class: 1.000000, Obj: 0.433681, No Obj: 0.003023, Avg Recall: 0.730000, count: 100 -3567: 0.457590, 0.455691 avg, 0.001000 rate, 24.883940 seconds, 913152 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.613109, Class: 1.000000, Obj: 0.397338, No Obj: 0.002986, Avg Recall: 0.726191, count: 84 -Region Avg IOU: 0.585957, Class: 1.000000, Obj: 0.387124, No Obj: 0.003250, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.593273, Class: 1.000000, Obj: 0.387336, No Obj: 0.003085, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.654839, Class: 1.000000, Obj: 0.431782, No Obj: 0.003103, Avg Recall: 0.802198, count: 91 -Region Avg IOU: 0.621227, Class: 1.000000, Obj: 0.341505, No Obj: 0.002992, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.592352, Class: 1.000000, Obj: 0.362991, No Obj: 0.003208, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.661705, Class: 1.000000, Obj: 0.383751, No Obj: 0.003130, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.620889, Class: 1.000000, Obj: 0.384400, No Obj: 0.003367, Avg Recall: 0.763636, count: 110 -3568: 0.423373, 0.452459 avg, 0.001000 rate, 23.836452 seconds, 913408 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.611963, Class: 1.000000, Obj: 0.416170, No Obj: 0.002904, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.601648, Class: 1.000000, Obj: 0.386355, No Obj: 0.003090, Avg Recall: 0.727273, count: 88 -Region Avg IOU: 0.592585, Class: 1.000000, Obj: 0.360894, No Obj: 0.003554, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.645258, Class: 1.000000, Obj: 0.415570, No Obj: 0.003522, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.633786, Class: 1.000000, Obj: 0.358115, No Obj: 0.003382, Avg Recall: 0.814433, count: 97 -Region Avg IOU: 0.621012, Class: 1.000000, Obj: 0.362923, No Obj: 0.002929, Avg Recall: 0.756098, count: 82 -Region Avg IOU: 0.660138, Class: 1.000000, Obj: 0.397555, No Obj: 0.003501, Avg Recall: 0.850467, count: 107 -Region Avg IOU: 0.593420, Class: 1.000000, Obj: 0.396865, No Obj: 0.003205, Avg Recall: 0.689320, count: 103 -3569: 0.386807, 0.445894 avg, 0.001000 rate, 25.773561 seconds, 913664 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.586677, Class: 1.000000, Obj: 0.418171, No Obj: 0.002654, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.629680, Class: 1.000000, Obj: 0.389857, No Obj: 0.003612, Avg Recall: 0.760000, count: 125 -Region Avg IOU: 0.586173, Class: 1.000000, Obj: 0.367677, No Obj: 0.003060, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.615414, Class: 1.000000, Obj: 0.402878, No Obj: 0.002958, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.649999, Class: 1.000000, Obj: 0.454208, No Obj: 0.003797, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.571699, Class: 1.000000, Obj: 0.330471, No Obj: 0.003285, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.599723, Class: 1.000000, Obj: 0.405749, No Obj: 0.003613, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.615068, Class: 1.000000, Obj: 0.407454, No Obj: 0.004234, Avg Recall: 0.750000, count: 124 -3570: 0.430346, 0.444339 avg, 0.001000 rate, 25.803404 seconds, 913920 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000084 seconds -Region Avg IOU: 0.644987, Class: 1.000000, Obj: 0.338272, No Obj: 0.003263, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.664630, Class: 1.000000, Obj: 0.440641, No Obj: 0.003305, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.651464, Class: 1.000000, Obj: 0.404261, No Obj: 0.003371, Avg Recall: 0.819048, count: 105 -Region Avg IOU: 0.659660, Class: 1.000000, Obj: 0.399590, No Obj: 0.003297, Avg Recall: 0.818182, count: 121 -Region Avg IOU: 0.635537, Class: 1.000000, Obj: 0.435432, No Obj: 0.003606, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.629119, Class: 1.000000, Obj: 0.350024, No Obj: 0.003746, Avg Recall: 0.760563, count: 142 -Region Avg IOU: 0.655685, Class: 1.000000, Obj: 0.405021, No Obj: 0.002883, Avg Recall: 0.785714, count: 84 -Region Avg IOU: 0.645609, Class: 1.000000, Obj: 0.409638, No Obj: 0.003292, Avg Recall: 0.786325, count: 117 -3571: 0.487798, 0.448685 avg, 0.001000 rate, 26.739107 seconds, 914176 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.604793, Class: 1.000000, Obj: 0.393963, No Obj: 0.003151, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.618793, Class: 1.000000, Obj: 0.404619, No Obj: 0.003418, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.640435, Class: 1.000000, Obj: 0.408937, No Obj: 0.003218, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.645727, Class: 1.000000, Obj: 0.400761, No Obj: 0.003371, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.599819, Class: 1.000000, Obj: 0.383684, No Obj: 0.002653, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.620550, Class: 1.000000, Obj: 0.392902, No Obj: 0.002988, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.641971, Class: 1.000000, Obj: 0.485546, No Obj: 0.002844, Avg Recall: 0.811111, count: 90 -Region Avg IOU: 0.634315, Class: 1.000000, Obj: 0.397800, No Obj: 0.003531, Avg Recall: 0.774775, count: 111 -3572: 0.444742, 0.448291 avg, 0.001000 rate, 26.162712 seconds, 914432 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.599880, Class: 1.000000, Obj: 0.365721, No Obj: 0.003326, Avg Recall: 0.733333, count: 135 -Region Avg IOU: 0.635076, Class: 1.000000, Obj: 0.353862, No Obj: 0.003133, Avg Recall: 0.781250, count: 128 -Region Avg IOU: 0.684415, Class: 1.000000, Obj: 0.408460, No Obj: 0.003395, Avg Recall: 0.890110, count: 91 -Region Avg IOU: 0.590770, Class: 1.000000, Obj: 0.385645, No Obj: 0.002775, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.624961, Class: 1.000000, Obj: 0.421423, No Obj: 0.002754, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.637774, Class: 1.000000, Obj: 0.366375, No Obj: 0.003456, Avg Recall: 0.790698, count: 129 -Region Avg IOU: 0.596621, Class: 1.000000, Obj: 0.396623, No Obj: 0.003238, Avg Recall: 0.718518, count: 135 -Region Avg IOU: 0.585425, Class: 1.000000, Obj: 0.401493, No Obj: 0.003635, Avg Recall: 0.700855, count: 117 -3573: 0.475223, 0.450984 avg, 0.001000 rate, 27.072723 seconds, 914688 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.597983, Class: 1.000000, Obj: 0.382366, No Obj: 0.002584, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.619836, Class: 1.000000, Obj: 0.379127, No Obj: 0.002907, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.571003, Class: 1.000000, Obj: 0.385891, No Obj: 0.003226, Avg Recall: 0.664286, count: 140 -Region Avg IOU: 0.606892, Class: 1.000000, Obj: 0.369861, No Obj: 0.002843, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.590616, Class: 1.000000, Obj: 0.380075, No Obj: 0.003128, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.602327, Class: 1.000000, Obj: 0.359842, No Obj: 0.003195, Avg Recall: 0.736000, count: 125 -Region Avg IOU: 0.590959, Class: 1.000000, Obj: 0.380816, No Obj: 0.002818, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.616480, Class: 1.000000, Obj: 0.391914, No Obj: 0.003022, Avg Recall: 0.764706, count: 119 -3574: 0.455641, 0.451450 avg, 0.001000 rate, 27.963280 seconds, 914944 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.650494, Class: 1.000000, Obj: 0.382828, No Obj: 0.003283, Avg Recall: 0.798387, count: 124 -Region Avg IOU: 0.589718, Class: 1.000000, Obj: 0.350768, No Obj: 0.002730, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.583242, Class: 1.000000, Obj: 0.349603, No Obj: 0.003036, Avg Recall: 0.683761, count: 117 -Region Avg IOU: 0.629152, Class: 1.000000, Obj: 0.404030, No Obj: 0.002718, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.591078, Class: 1.000000, Obj: 0.366425, No Obj: 0.002682, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.599582, Class: 1.000000, Obj: 0.360675, No Obj: 0.002933, Avg Recall: 0.717742, count: 124 -Region Avg IOU: 0.615723, Class: 1.000000, Obj: 0.421651, No Obj: 0.002307, Avg Recall: 0.715909, count: 88 -Region Avg IOU: 0.601344, Class: 1.000000, Obj: 0.360339, No Obj: 0.002803, Avg Recall: 0.739583, count: 96 -3575: 0.443104, 0.450615 avg, 0.001000 rate, 26.731199 seconds, 915200 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.608681, Class: 1.000000, Obj: 0.326246, No Obj: 0.002813, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.578275, Class: 1.000000, Obj: 0.346942, No Obj: 0.002666, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.612997, Class: 1.000000, Obj: 0.381867, No Obj: 0.002467, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.644553, Class: 1.000000, Obj: 0.322961, No Obj: 0.002769, Avg Recall: 0.827273, count: 110 -Region Avg IOU: 0.671693, Class: 1.000000, Obj: 0.332824, No Obj: 0.002786, Avg Recall: 0.861111, count: 108 -Region Avg IOU: 0.664798, Class: 1.000000, Obj: 0.321404, No Obj: 0.003150, Avg Recall: 0.837607, count: 117 -Region Avg IOU: 0.627717, Class: 1.000000, Obj: 0.350271, No Obj: 0.002663, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.580690, Class: 1.000000, Obj: 0.317857, No Obj: 0.002895, Avg Recall: 0.661538, count: 130 -3576: 0.483325, 0.453886 avg, 0.001000 rate, 27.867168 seconds, 915456 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.641918, Class: 1.000000, Obj: 0.389347, No Obj: 0.002437, Avg Recall: 0.784091, count: 88 -Region Avg IOU: 0.603346, Class: 1.000000, Obj: 0.319119, No Obj: 0.002926, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.644587, Class: 1.000000, Obj: 0.403315, No Obj: 0.002651, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.654891, Class: 1.000000, Obj: 0.340509, No Obj: 0.002576, Avg Recall: 0.796117, count: 103 -Region Avg IOU: 0.655173, Class: 1.000000, Obj: 0.376247, No Obj: 0.002941, Avg Recall: 0.782258, count: 124 -Region Avg IOU: 0.540417, Class: 1.000000, Obj: 0.329771, No Obj: 0.002391, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.642752, Class: 1.000000, Obj: 0.373467, No Obj: 0.002557, Avg Recall: 0.784091, count: 88 -Region Avg IOU: 0.674838, Class: 1.000000, Obj: 0.420202, No Obj: 0.002729, Avg Recall: 0.840000, count: 75 -3577: 0.424460, 0.450944 avg, 0.001000 rate, 27.543898 seconds, 915712 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.619995, Class: 1.000000, Obj: 0.348692, No Obj: 0.002523, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.633329, Class: 1.000000, Obj: 0.359527, No Obj: 0.002780, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.604129, Class: 1.000000, Obj: 0.353482, No Obj: 0.002861, Avg Recall: 0.726619, count: 139 -Region Avg IOU: 0.611517, Class: 1.000000, Obj: 0.312450, No Obj: 0.002451, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.641796, Class: 1.000000, Obj: 0.355252, No Obj: 0.002801, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.652073, Class: 1.000000, Obj: 0.338475, No Obj: 0.002855, Avg Recall: 0.803279, count: 122 -Region Avg IOU: 0.579485, Class: 1.000000, Obj: 0.274783, No Obj: 0.002986, Avg Recall: 0.710145, count: 138 -Region Avg IOU: 0.599206, Class: 1.000000, Obj: 0.330412, No Obj: 0.002201, Avg Recall: 0.711111, count: 90 -3578: 0.498831, 0.455732 avg, 0.001000 rate, 27.460060 seconds, 915968 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.597044, Class: 1.000000, Obj: 0.285348, No Obj: 0.002375, Avg Recall: 0.717647, count: 85 -Region Avg IOU: 0.607729, Class: 1.000000, Obj: 0.358588, No Obj: 0.002777, Avg Recall: 0.746032, count: 126 -Region Avg IOU: 0.634240, Class: 1.000000, Obj: 0.310093, No Obj: 0.002506, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.586556, Class: 1.000000, Obj: 0.389753, No Obj: 0.002285, Avg Recall: 0.690476, count: 84 -Region Avg IOU: 0.588799, Class: 1.000000, Obj: 0.331200, No Obj: 0.002641, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.648129, Class: 1.000000, Obj: 0.404463, No Obj: 0.002783, Avg Recall: 0.831579, count: 95 -Region Avg IOU: 0.603034, Class: 1.000000, Obj: 0.339349, No Obj: 0.002611, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.618586, Class: 1.000000, Obj: 0.336457, No Obj: 0.002828, Avg Recall: 0.760684, count: 117 -3579: 0.459200, 0.456079 avg, 0.001000 rate, 28.584543 seconds, 916224 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.563433, Class: 1.000000, Obj: 0.258639, No Obj: 0.002684, Avg Recall: 0.685484, count: 124 -Region Avg IOU: 0.590927, Class: 1.000000, Obj: 0.350399, No Obj: 0.002471, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.565236, Class: 1.000000, Obj: 0.367748, No Obj: 0.002341, Avg Recall: 0.600000, count: 80 -Region Avg IOU: 0.655094, Class: 1.000000, Obj: 0.377123, No Obj: 0.002700, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.627035, Class: 1.000000, Obj: 0.335179, No Obj: 0.003333, Avg Recall: 0.767123, count: 146 -Region Avg IOU: 0.618363, Class: 1.000000, Obj: 0.405862, No Obj: 0.002591, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.585665, Class: 1.000000, Obj: 0.337192, No Obj: 0.003160, Avg Recall: 0.713235, count: 136 -Region Avg IOU: 0.594472, Class: 1.000000, Obj: 0.352951, No Obj: 0.002529, Avg Recall: 0.711712, count: 111 -3580: 0.452781, 0.455749 avg, 0.001000 rate, 27.806156 seconds, 916480 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 16.224384 seconds -Region Avg IOU: 0.580307, Class: 1.000000, Obj: 0.317026, No Obj: 0.002865, Avg Recall: 0.726191, count: 84 -Region Avg IOU: 0.559177, Class: 1.000000, Obj: 0.333032, No Obj: 0.003638, Avg Recall: 0.642276, count: 123 -Region Avg IOU: 0.547441, Class: 1.000000, Obj: 0.307824, No Obj: 0.003194, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.561762, Class: 1.000000, Obj: 0.296221, No Obj: 0.002843, Avg Recall: 0.673684, count: 95 -Region Avg IOU: 0.533404, Class: 1.000000, Obj: 0.270884, No Obj: 0.003178, Avg Recall: 0.634328, count: 134 -Region Avg IOU: 0.583292, Class: 1.000000, Obj: 0.316574, No Obj: 0.003711, Avg Recall: 0.643939, count: 132 -Region Avg IOU: 0.542110, Class: 1.000000, Obj: 0.289844, No Obj: 0.003619, Avg Recall: 0.622047, count: 127 -Region Avg IOU: 0.560439, Class: 1.000000, Obj: 0.293253, No Obj: 0.003095, Avg Recall: 0.642857, count: 98 -3581: 0.464624, 0.456637 avg, 0.001000 rate, 22.720907 seconds, 916736 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.551976, Class: 1.000000, Obj: 0.338542, No Obj: 0.003723, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.573203, Class: 1.000000, Obj: 0.326598, No Obj: 0.003154, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.557822, Class: 1.000000, Obj: 0.341171, No Obj: 0.003671, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.635996, Class: 1.000000, Obj: 0.420929, No Obj: 0.003440, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.576709, Class: 1.000000, Obj: 0.316675, No Obj: 0.003317, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.556273, Class: 1.000000, Obj: 0.318741, No Obj: 0.003190, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.540548, Class: 1.000000, Obj: 0.341523, No Obj: 0.003545, Avg Recall: 0.642276, count: 123 -Region Avg IOU: 0.565143, Class: 1.000000, Obj: 0.324903, No Obj: 0.003433, Avg Recall: 0.638462, count: 130 -3582: 0.476508, 0.458624 avg, 0.001000 rate, 23.189932 seconds, 916992 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.575821, Class: 1.000000, Obj: 0.318511, No Obj: 0.003703, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.528275, Class: 1.000000, Obj: 0.325706, No Obj: 0.003166, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.562088, Class: 1.000000, Obj: 0.358969, No Obj: 0.003442, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.525626, Class: 1.000000, Obj: 0.332582, No Obj: 0.003378, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.557426, Class: 1.000000, Obj: 0.339209, No Obj: 0.003291, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.512977, Class: 1.000000, Obj: 0.317559, No Obj: 0.003462, Avg Recall: 0.593496, count: 123 -Region Avg IOU: 0.540656, Class: 1.000000, Obj: 0.318065, No Obj: 0.003645, Avg Recall: 0.639344, count: 122 -Region Avg IOU: 0.538066, Class: 1.000000, Obj: 0.344745, No Obj: 0.004449, Avg Recall: 0.606061, count: 132 -3583: 0.446497, 0.457411 avg, 0.001000 rate, 23.368807 seconds, 917248 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.566662, Class: 1.000000, Obj: 0.316270, No Obj: 0.004446, Avg Recall: 0.692810, count: 153 -Region Avg IOU: 0.572547, Class: 1.000000, Obj: 0.391146, No Obj: 0.003188, Avg Recall: 0.650602, count: 83 -Region Avg IOU: 0.578929, Class: 1.000000, Obj: 0.372801, No Obj: 0.003847, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.529449, Class: 1.000000, Obj: 0.365035, No Obj: 0.003974, Avg Recall: 0.562044, count: 137 -Region Avg IOU: 0.549740, Class: 1.000000, Obj: 0.308017, No Obj: 0.004705, Avg Recall: 0.626761, count: 142 -Region Avg IOU: 0.549499, Class: 1.000000, Obj: 0.344408, No Obj: 0.003670, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.542269, Class: 1.000000, Obj: 0.343681, No Obj: 0.004318, Avg Recall: 0.644737, count: 152 -Region Avg IOU: 0.599371, Class: 1.000000, Obj: 0.377052, No Obj: 0.004016, Avg Recall: 0.750000, count: 104 -3584: 0.526696, 0.464340 avg, 0.001000 rate, 23.383295 seconds, 917504 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.577063, Class: 1.000000, Obj: 0.408192, No Obj: 0.004306, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.512940, Class: 1.000000, Obj: 0.327475, No Obj: 0.003927, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.591637, Class: 1.000000, Obj: 0.455988, No Obj: 0.003656, Avg Recall: 0.686046, count: 86 -Region Avg IOU: 0.500638, Class: 1.000000, Obj: 0.344108, No Obj: 0.003770, Avg Recall: 0.555556, count: 90 -Region Avg IOU: 0.588152, Class: 1.000000, Obj: 0.407543, No Obj: 0.004010, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.528013, Class: 1.000000, Obj: 0.343570, No Obj: 0.003773, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.512951, Class: 1.000000, Obj: 0.384695, No Obj: 0.003739, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.491780, Class: 1.000000, Obj: 0.371755, No Obj: 0.003599, Avg Recall: 0.532609, count: 92 -3585: 0.421763, 0.460082 avg, 0.001000 rate, 23.018864 seconds, 917760 images -Loaded: 0.000138 seconds -Region Avg IOU: 0.549591, Class: 1.000000, Obj: 0.361381, No Obj: 0.003766, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.556175, Class: 1.000000, Obj: 0.403510, No Obj: 0.003533, Avg Recall: 0.683544, count: 79 -Region Avg IOU: 0.548759, Class: 1.000000, Obj: 0.404598, No Obj: 0.004192, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.577675, Class: 1.000000, Obj: 0.403373, No Obj: 0.004113, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.576389, Class: 1.000000, Obj: 0.377304, No Obj: 0.004217, Avg Recall: 0.666667, count: 123 -Region Avg IOU: 0.567574, Class: 1.000000, Obj: 0.379702, No Obj: 0.004610, Avg Recall: 0.596491, count: 114 -Region Avg IOU: 0.608648, Class: 1.000000, Obj: 0.367920, No Obj: 0.004485, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.523950, Class: 1.000000, Obj: 0.351893, No Obj: 0.003741, Avg Recall: 0.556604, count: 106 -3586: 0.452271, 0.459301 avg, 0.001000 rate, 21.458801 seconds, 918016 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.503345, Class: 1.000000, Obj: 0.395187, No Obj: 0.003205, Avg Recall: 0.561798, count: 89 -Region Avg IOU: 0.562212, Class: 1.000000, Obj: 0.412477, No Obj: 0.004028, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.552458, Class: 1.000000, Obj: 0.399900, No Obj: 0.004165, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.563163, Class: 1.000000, Obj: 0.389850, No Obj: 0.003838, Avg Recall: 0.632184, count: 87 -Region Avg IOU: 0.591270, Class: 1.000000, Obj: 0.408198, No Obj: 0.004079, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.535560, Class: 1.000000, Obj: 0.375094, No Obj: 0.004147, Avg Recall: 0.622807, count: 114 -Region Avg IOU: 0.579586, Class: 1.000000, Obj: 0.383740, No Obj: 0.004567, Avg Recall: 0.682927, count: 123 -Region Avg IOU: 0.554616, Class: 1.000000, Obj: 0.396767, No Obj: 0.003888, Avg Recall: 0.598131, count: 107 -3587: 0.446248, 0.457996 avg, 0.001000 rate, 23.504192 seconds, 918272 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.511292, Class: 1.000000, Obj: 0.341719, No Obj: 0.003934, Avg Recall: 0.593496, count: 123 -Region Avg IOU: 0.500322, Class: 1.000000, Obj: 0.337771, No Obj: 0.003707, Avg Recall: 0.561798, count: 89 -Region Avg IOU: 0.552299, Class: 1.000000, Obj: 0.385444, No Obj: 0.004172, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.537788, Class: 1.000000, Obj: 0.368146, No Obj: 0.003673, Avg Recall: 0.573034, count: 89 -Region Avg IOU: 0.561727, Class: 1.000000, Obj: 0.390521, No Obj: 0.004430, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.567763, Class: 1.000000, Obj: 0.377403, No Obj: 0.004539, Avg Recall: 0.706767, count: 133 -Region Avg IOU: 0.582807, Class: 1.000000, Obj: 0.421279, No Obj: 0.004551, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.547362, Class: 1.000000, Obj: 0.403942, No Obj: 0.004570, Avg Recall: 0.630252, count: 119 -3588: 0.420403, 0.454236 avg, 0.001000 rate, 23.375975 seconds, 918528 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.571284, Class: 1.000000, Obj: 0.384755, No Obj: 0.004277, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.538376, Class: 1.000000, Obj: 0.381829, No Obj: 0.003998, Avg Recall: 0.594828, count: 116 -Region Avg IOU: 0.554173, Class: 1.000000, Obj: 0.357325, No Obj: 0.004368, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.568195, Class: 1.000000, Obj: 0.366044, No Obj: 0.004156, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.559064, Class: 1.000000, Obj: 0.394491, No Obj: 0.003975, Avg Recall: 0.669421, count: 121 -Region Avg IOU: 0.550445, Class: 1.000000, Obj: 0.352855, No Obj: 0.003939, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.585810, Class: 1.000000, Obj: 0.371008, No Obj: 0.004836, Avg Recall: 0.717742, count: 124 -Region Avg IOU: 0.580082, Class: 1.000000, Obj: 0.380208, No Obj: 0.003976, Avg Recall: 0.729167, count: 96 -3589: 0.463858, 0.455198 avg, 0.001000 rate, 23.178886 seconds, 918784 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.526078, Class: 1.000000, Obj: 0.373171, No Obj: 0.004365, Avg Recall: 0.563025, count: 119 -Region Avg IOU: 0.540569, Class: 1.000000, Obj: 0.377047, No Obj: 0.003458, Avg Recall: 0.620253, count: 79 -Region Avg IOU: 0.550598, Class: 1.000000, Obj: 0.393936, No Obj: 0.003092, Avg Recall: 0.621951, count: 82 -Region Avg IOU: 0.571262, Class: 1.000000, Obj: 0.352775, No Obj: 0.003926, Avg Recall: 0.661538, count: 130 -Region Avg IOU: 0.468167, Class: 1.000000, Obj: 0.354841, No Obj: 0.003629, Avg Recall: 0.500000, count: 98 -Region Avg IOU: 0.542748, Class: 1.000000, Obj: 0.342061, No Obj: 0.003587, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.587619, Class: 1.000000, Obj: 0.365348, No Obj: 0.004394, Avg Recall: 0.669173, count: 133 -Region Avg IOU: 0.558287, Class: 1.000000, Obj: 0.408354, No Obj: 0.003516, Avg Recall: 0.648936, count: 94 -3590: 0.424475, 0.452126 avg, 0.001000 rate, 22.302780 seconds, 919040 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.082773 seconds -Region Avg IOU: 0.533481, Class: 1.000000, Obj: 0.370133, No Obj: 0.003757, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.550235, Class: 1.000000, Obj: 0.360703, No Obj: 0.003701, Avg Recall: 0.633333, count: 120 -Region Avg IOU: 0.526181, Class: 1.000000, Obj: 0.348734, No Obj: 0.003650, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.595019, Class: 1.000000, Obj: 0.408785, No Obj: 0.004031, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.585886, Class: 1.000000, Obj: 0.356651, No Obj: 0.003790, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.561107, Class: 1.000000, Obj: 0.411192, No Obj: 0.003557, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.560003, Class: 1.000000, Obj: 0.370365, No Obj: 0.003686, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.582489, Class: 1.000000, Obj: 0.370605, No Obj: 0.004106, Avg Recall: 0.706897, count: 116 -3591: 0.457085, 0.452622 avg, 0.001000 rate, 24.725628 seconds, 919296 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.532280, Class: 1.000000, Obj: 0.374593, No Obj: 0.004030, Avg Recall: 0.570370, count: 135 -Region Avg IOU: 0.561803, Class: 1.000000, Obj: 0.384015, No Obj: 0.004095, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.541910, Class: 1.000000, Obj: 0.345944, No Obj: 0.003948, Avg Recall: 0.635659, count: 129 -Region Avg IOU: 0.580056, Class: 1.000000, Obj: 0.372496, No Obj: 0.003576, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.538163, Class: 1.000000, Obj: 0.337228, No Obj: 0.003768, Avg Recall: 0.581197, count: 117 -Region Avg IOU: 0.591548, Class: 1.000000, Obj: 0.379420, No Obj: 0.003656, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.570818, Class: 1.000000, Obj: 0.344729, No Obj: 0.003900, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.512596, Class: 1.000000, Obj: 0.381713, No Obj: 0.003291, Avg Recall: 0.589286, count: 112 -3592: 0.464801, 0.453840 avg, 0.001000 rate, 24.224504 seconds, 919552 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.572254, Class: 1.000000, Obj: 0.411817, No Obj: 0.003401, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.592316, Class: 1.000000, Obj: 0.375475, No Obj: 0.003285, Avg Recall: 0.702381, count: 84 -Region Avg IOU: 0.649295, Class: 1.000000, Obj: 0.401097, No Obj: 0.003877, Avg Recall: 0.822222, count: 90 -Region Avg IOU: 0.525692, Class: 1.000000, Obj: 0.340168, No Obj: 0.003261, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.497976, Class: 1.000000, Obj: 0.286230, No Obj: 0.003190, Avg Recall: 0.584071, count: 113 -Region Avg IOU: 0.562218, Class: 1.000000, Obj: 0.376047, No Obj: 0.003493, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.545669, Class: 1.000000, Obj: 0.356810, No Obj: 0.003580, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.546408, Class: 1.000000, Obj: 0.348342, No Obj: 0.003125, Avg Recall: 0.663043, count: 92 -3593: 0.418205, 0.450276 avg, 0.001000 rate, 23.792934 seconds, 919808 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.525046, Class: 1.000000, Obj: 0.331024, No Obj: 0.003290, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.593520, Class: 1.000000, Obj: 0.317344, No Obj: 0.003430, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.587885, Class: 1.000000, Obj: 0.333670, No Obj: 0.003972, Avg Recall: 0.687943, count: 141 -Region Avg IOU: 0.549877, Class: 1.000000, Obj: 0.376150, No Obj: 0.003280, Avg Recall: 0.597701, count: 87 -Region Avg IOU: 0.589661, Class: 1.000000, Obj: 0.383794, No Obj: 0.003609, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.560239, Class: 1.000000, Obj: 0.380746, No Obj: 0.003411, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.591553, Class: 1.000000, Obj: 0.358791, No Obj: 0.003828, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.556733, Class: 1.000000, Obj: 0.397642, No Obj: 0.002962, Avg Recall: 0.647727, count: 88 -3594: 0.442062, 0.449455 avg, 0.001000 rate, 23.318819 seconds, 920064 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.593842, Class: 1.000000, Obj: 0.358228, No Obj: 0.003381, Avg Recall: 0.670455, count: 88 -Region Avg IOU: 0.542744, Class: 1.000000, Obj: 0.315326, No Obj: 0.002795, Avg Recall: 0.648352, count: 91 -Region Avg IOU: 0.544793, Class: 1.000000, Obj: 0.358693, No Obj: 0.003049, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.555900, Class: 1.000000, Obj: 0.304225, No Obj: 0.003489, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.538037, Class: 1.000000, Obj: 0.324761, No Obj: 0.003425, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.569158, Class: 1.000000, Obj: 0.308922, No Obj: 0.002413, Avg Recall: 0.666667, count: 75 -Region Avg IOU: 0.564014, Class: 1.000000, Obj: 0.327662, No Obj: 0.003402, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.556297, Class: 1.000000, Obj: 0.340198, No Obj: 0.003396, Avg Recall: 0.647059, count: 119 -3595: 0.407848, 0.445294 avg, 0.001000 rate, 23.704224 seconds, 920320 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.552053, Class: 1.000000, Obj: 0.318067, No Obj: 0.003832, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.585044, Class: 1.000000, Obj: 0.344828, No Obj: 0.002946, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.581398, Class: 1.000000, Obj: 0.325377, No Obj: 0.003481, Avg Recall: 0.679389, count: 131 -Region Avg IOU: 0.555135, Class: 1.000000, Obj: 0.354612, No Obj: 0.002884, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.579583, Class: 1.000000, Obj: 0.350560, No Obj: 0.003447, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.601025, Class: 1.000000, Obj: 0.362156, No Obj: 0.003018, Avg Recall: 0.726191, count: 84 -Region Avg IOU: 0.577169, Class: 1.000000, Obj: 0.356959, No Obj: 0.003230, Avg Recall: 0.710526, count: 114 -Region Avg IOU: 0.513351, Class: 1.000000, Obj: 0.279210, No Obj: 0.003361, Avg Recall: 0.584615, count: 130 -3596: 0.445862, 0.445351 avg, 0.001000 rate, 24.017632 seconds, 920576 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.555946, Class: 1.000000, Obj: 0.285181, No Obj: 0.003043, Avg Recall: 0.614458, count: 83 -Region Avg IOU: 0.583305, Class: 1.000000, Obj: 0.310457, No Obj: 0.003447, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.578737, Class: 1.000000, Obj: 0.383967, No Obj: 0.003537, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.573358, Class: 1.000000, Obj: 0.304107, No Obj: 0.003248, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.570312, Class: 1.000000, Obj: 0.325170, No Obj: 0.002781, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.577684, Class: 1.000000, Obj: 0.324076, No Obj: 0.003457, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.575966, Class: 1.000000, Obj: 0.368748, No Obj: 0.003122, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.523107, Class: Saving weights to ./person9300/backup/tyvp_93_9c512_3600.weights -1.000000, Obj: 0.288674, No Obj: 0.003249, Avg Recall: 0.590164, count: 122 -3597: 0.437745, 0.444590 avg, 0.001000 rate, 23.553091 seconds, 920832 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.539439, Class: 1.000000, Obj: 0.304496, No Obj: 0.003452, Avg Recall: 0.631148, count: 122 -Region Avg IOU: 0.545763, Class: 1.000000, Obj: 0.353534, No Obj: 0.003420, Avg Recall: 0.617886, count: 123 -Region Avg IOU: 0.580887, Class: 1.000000, Obj: 0.366182, No Obj: 0.003237, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.565578, Class: 1.000000, Obj: 0.352276, No Obj: 0.003991, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.573949, Class: 1.000000, Obj: 0.403804, No Obj: 0.003524, Avg Recall: 0.614458, count: 83 -Region Avg IOU: 0.583936, Class: 1.000000, Obj: 0.362586, No Obj: 0.003419, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.622871, Class: 1.000000, Obj: 0.373121, No Obj: 0.003374, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.566244, Class: 1.000000, Obj: 0.326600, No Obj: 0.003209, Avg Recall: 0.652893, count: 121 -3598: 0.492912, 0.449423 avg, 0.001000 rate, 24.320913 seconds, 921088 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.547009, Class: 1.000000, Obj: 0.343412, No Obj: 0.003297, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.587534, Class: 1.000000, Obj: 0.345924, No Obj: 0.003792, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.591966, Class: 1.000000, Obj: 0.326641, No Obj: 0.003547, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.557436, Class: 1.000000, Obj: 0.350654, No Obj: 0.003265, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.651186, Class: 1.000000, Obj: 0.383027, No Obj: 0.003518, Avg Recall: 0.788235, count: 85 -Region Avg IOU: 0.579697, Class: 1.000000, Obj: 0.339348, No Obj: 0.003659, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.577029, Class: 1.000000, Obj: 0.363194, No Obj: 0.003336, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.549973, Class: 1.000000, Obj: 0.342397, No Obj: 0.003721, Avg Recall: 0.626087, count: 115 -3599: 0.456526, 0.450133 avg, 0.001000 rate, 24.373133 seconds, 921344 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.562939, Class: 1.000000, Obj: 0.339365, No Obj: 0.003921, Avg Recall: 0.624000, count: 125 -Region Avg IOU: 0.551394, Class: 1.000000, Obj: 0.317391, No Obj: 0.003431, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.591646, Class: 1.000000, Obj: 0.365500, No Obj: 0.003957, Avg Recall: 0.701613, count: 124 -Region Avg IOU: 0.596859, Class: 1.000000, Obj: 0.394662, No Obj: 0.003282, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.588442, Class: 1.000000, Obj: 0.325401, No Obj: 0.003344, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.557555, Class: 1.000000, Obj: 0.363267, No Obj: 0.003080, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.562429, Class: 1.000000, Obj: 0.380652, No Obj: 0.003790, Avg Recall: 0.652542, count: 118 -Region Avg IOU: 0.624162, Class: 1.000000, Obj: 0.382978, No Obj: 0.003366, Avg Recall: 0.773585, count: 106 -3600: 0.451513, 0.450271 avg, 0.001000 rate, 22.869015 seconds, 921600 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 21.615139 seconds -Region Avg IOU: 0.520353, Class: 1.000000, Obj: 0.335958, No Obj: 0.003920, Avg Recall: 0.556522, count: 115 -Region Avg IOU: 0.508917, Class: 1.000000, Obj: 0.295233, No Obj: 0.003801, Avg Recall: 0.558824, count: 136 -Region Avg IOU: 0.548934, Class: 1.000000, Obj: 0.342293, No Obj: 0.004074, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.542284, Class: 1.000000, Obj: 0.336045, No Obj: 0.003840, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.533534, Class: 1.000000, Obj: 0.318779, No Obj: 0.003995, Avg Recall: 0.603306, count: 121 -Region Avg IOU: 0.528032, Class: 1.000000, Obj: 0.333873, No Obj: 0.003528, Avg Recall: 0.576087, count: 92 -Region Avg IOU: 0.576271, Class: 1.000000, Obj: 0.355122, No Obj: 0.004045, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.537957, Class: 1.000000, Obj: 0.394172, No Obj: 0.003543, Avg Recall: 0.612903, count: 93 -3601: 0.418886, 0.447132 avg, 0.001000 rate, 21.514521 seconds, 921856 images -Loaded: 1.465770 seconds -Region Avg IOU: 0.530384, Class: 1.000000, Obj: 0.291825, No Obj: 0.003773, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.531958, Class: 1.000000, Obj: 0.334142, No Obj: 0.003983, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.543310, Class: 1.000000, Obj: 0.356199, No Obj: 0.004069, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.548413, Class: 1.000000, Obj: 0.348861, No Obj: 0.004305, Avg Recall: 0.650000, count: 120 -Region Avg IOU: 0.571051, Class: 1.000000, Obj: 0.424008, No Obj: 0.003944, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.549097, Class: 1.000000, Obj: 0.347982, No Obj: 0.004549, Avg Recall: 0.579365, count: 126 -Region Avg IOU: 0.561197, Class: 1.000000, Obj: 0.362731, No Obj: 0.003961, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.581928, Class: 1.000000, Obj: 0.334288, No Obj: 0.003305, Avg Recall: 0.670213, count: 94 -3602: 0.436682, 0.446087 avg, 0.001000 rate, 21.025057 seconds, 922112 images -Loaded: 0.618507 seconds -Region Avg IOU: 0.524328, Class: 1.000000, Obj: 0.350384, No Obj: 0.004339, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.576183, Class: 1.000000, Obj: 0.403639, No Obj: 0.003539, Avg Recall: 0.708861, count: 79 -Region Avg IOU: 0.606749, Class: 1.000000, Obj: 0.425935, No Obj: 0.004011, Avg Recall: 0.744444, count: 90 -Region Avg IOU: 0.532056, Class: 1.000000, Obj: 0.329859, No Obj: 0.004683, Avg Recall: 0.589552, count: 134 -Region Avg IOU: 0.561024, Class: 1.000000, Obj: 0.370265, No Obj: 0.004269, Avg Recall: 0.627907, count: 129 -Region Avg IOU: 0.574633, Class: 1.000000, Obj: 0.373270, No Obj: 0.004046, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.570056, Class: 1.000000, Obj: 0.385492, No Obj: 0.004593, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.575018, Class: 1.000000, Obj: 0.359986, No Obj: 0.003780, Avg Recall: 0.663043, count: 92 -3603: 0.398851, 0.441364 avg, 0.001000 rate, 20.742865 seconds, 922368 images -Loaded: 0.429492 seconds -Region Avg IOU: 0.583180, Class: 1.000000, Obj: 0.383021, No Obj: 0.004210, Avg Recall: 0.717647, count: 85 -Region Avg IOU: 0.546074, Class: 1.000000, Obj: 0.415440, No Obj: 0.003985, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.579296, Class: 1.000000, Obj: 0.376334, No Obj: 0.004597, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.536252, Class: 1.000000, Obj: 0.423878, No Obj: 0.004282, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.610739, Class: 1.000000, Obj: 0.454300, No Obj: 0.005188, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.557276, Class: 1.000000, Obj: 0.415294, No Obj: 0.004414, Avg Recall: 0.601770, count: 113 -Region Avg IOU: 0.566052, Class: 1.000000, Obj: 0.415497, No Obj: 0.004292, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.566241, Class: 1.000000, Obj: 0.413672, No Obj: 0.004023, Avg Recall: 0.659794, count: 97 -3604: 0.373373, 0.434565 avg, 0.001000 rate, 22.266695 seconds, 922624 images -Loaded: 0.195492 seconds -Region Avg IOU: 0.539183, Class: 1.000000, Obj: 0.333786, No Obj: 0.004435, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.540408, Class: 1.000000, Obj: 0.362395, No Obj: 0.004425, Avg Recall: 0.589147, count: 129 -Region Avg IOU: 0.560362, Class: 1.000000, Obj: 0.391449, No Obj: 0.005071, Avg Recall: 0.644928, count: 138 -Region Avg IOU: 0.560794, Class: 1.000000, Obj: 0.437748, No Obj: 0.004147, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.554656, Class: 1.000000, Obj: 0.383491, No Obj: 0.004254, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.516966, Class: 1.000000, Obj: 0.362989, No Obj: 0.004221, Avg Recall: 0.592308, count: 130 -Region Avg IOU: 0.555922, Class: 1.000000, Obj: 0.418026, No Obj: 0.004206, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.489809, Class: 1.000000, Obj: 0.383238, No Obj: 0.004640, Avg Recall: 0.555556, count: 117 -3605: 0.446943, 0.435802 avg, 0.001000 rate, 22.351416 seconds, 922880 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.517953, Class: 1.000000, Obj: 0.346951, No Obj: 0.004617, Avg Recall: 0.601351, count: 148 -Region Avg IOU: 0.534428, Class: 1.000000, Obj: 0.431985, No Obj: 0.004515, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.542519, Class: 1.000000, Obj: 0.440058, No Obj: 0.004287, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.564399, Class: 1.000000, Obj: 0.390013, No Obj: 0.004100, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.551054, Class: 1.000000, Obj: 0.405814, No Obj: 0.004875, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.561591, Class: 1.000000, Obj: 0.388740, No Obj: 0.005046, Avg Recall: 0.643411, count: 129 -Region Avg IOU: 0.539475, Class: 1.000000, Obj: 0.390674, No Obj: 0.004168, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.534847, Class: 1.000000, Obj: 0.378141, No Obj: 0.004568, Avg Recall: 0.625000, count: 104 -3606: 0.443241, 0.436546 avg, 0.001000 rate, 22.267530 seconds, 923136 images -Loaded: 1.016506 seconds -Region Avg IOU: 0.517574, Class: 1.000000, Obj: 0.372342, No Obj: 0.004049, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.486475, Class: 1.000000, Obj: 0.372655, No Obj: 0.003865, Avg Recall: 0.546296, count: 108 -Region Avg IOU: 0.505313, Class: 1.000000, Obj: 0.401622, No Obj: 0.004454, Avg Recall: 0.569106, count: 123 -Region Avg IOU: 0.524530, Class: 1.000000, Obj: 0.352521, No Obj: 0.005375, Avg Recall: 0.622222, count: 135 -Region Avg IOU: 0.563583, Class: 1.000000, Obj: 0.406918, No Obj: 0.004168, Avg Recall: 0.613636, count: 88 -Region Avg IOU: 0.526977, Class: 1.000000, Obj: 0.353837, No Obj: 0.004365, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.513774, Class: 1.000000, Obj: 0.386255, No Obj: 0.004572, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.585817, Class: 1.000000, Obj: 0.421147, No Obj: 0.004610, Avg Recall: 0.688073, count: 109 -3607: 0.424405, 0.435332 avg, 0.001000 rate, 20.989187 seconds, 923392 images -Loaded: 1.513032 seconds -Region Avg IOU: 0.534178, Class: 1.000000, Obj: 0.386543, No Obj: 0.004311, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.508639, Class: 1.000000, Obj: 0.295211, No Obj: 0.004130, Avg Recall: 0.559633, count: 109 -Region Avg IOU: 0.515482, Class: 1.000000, Obj: 0.386203, No Obj: 0.004311, Avg Recall: 0.571429, count: 91 -Region Avg IOU: 0.531279, Class: 1.000000, Obj: 0.365588, No Obj: 0.004479, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.546669, Class: 1.000000, Obj: 0.410699, No Obj: 0.003978, Avg Recall: 0.643678, count: 87 -Region Avg IOU: 0.503072, Class: 1.000000, Obj: 0.313918, No Obj: 0.004251, Avg Recall: 0.572464, count: 138 -Region Avg IOU: 0.519436, Class: 1.000000, Obj: 0.349390, No Obj: 0.004271, Avg Recall: 0.556522, count: 115 -Region Avg IOU: 0.565596, Class: 1.000000, Obj: 0.394964, No Obj: 0.004915, Avg Recall: 0.653226, count: 124 -3608: 0.469226, 0.438722 avg, 0.001000 rate, 21.715576 seconds, 923648 images -Loaded: 1.455560 seconds -Region Avg IOU: 0.517634, Class: 1.000000, Obj: 0.366132, No Obj: 0.004442, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.518945, Class: 1.000000, Obj: 0.358877, No Obj: 0.004093, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.522789, Class: 1.000000, Obj: 0.428057, No Obj: 0.004209, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.531057, Class: 1.000000, Obj: 0.380551, No Obj: 0.004440, Avg Recall: 0.601562, count: 128 -Region Avg IOU: 0.557533, Class: 1.000000, Obj: 0.383620, No Obj: 0.004308, Avg Recall: 0.619835, count: 121 -Region Avg IOU: 0.580712, Class: 1.000000, Obj: 0.409592, No Obj: 0.005039, Avg Recall: 0.685484, count: 124 -Region Avg IOU: 0.501726, Class: 1.000000, Obj: 0.312683, No Obj: 0.003369, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.552391, Class: 1.000000, Obj: 0.397445, No Obj: 0.004085, Avg Recall: 0.611650, count: 103 -3609: 0.431802, 0.438030 avg, 0.001000 rate, 22.552826 seconds, 923904 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.492357, Class: 1.000000, Obj: 0.358904, No Obj: 0.003425, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.513011, Class: 1.000000, Obj: 0.284388, No Obj: 0.004318, Avg Recall: 0.621212, count: 132 -Region Avg IOU: 0.570995, Class: 1.000000, Obj: 0.373198, No Obj: 0.004876, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.545729, Class: 1.000000, Obj: 0.366293, No Obj: 0.004261, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.512641, Class: 1.000000, Obj: 0.356645, No Obj: 0.004235, Avg Recall: 0.550388, count: 129 -Region Avg IOU: 0.528331, Class: 1.000000, Obj: 0.347692, No Obj: 0.003767, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.553862, Class: 1.000000, Obj: 0.354461, No Obj: 0.004061, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.517385, Class: 1.000000, Obj: 0.344092, No Obj: 0.003855, Avg Recall: 0.536585, count: 82 -3610: 0.452694, 0.439496 avg, 0.001000 rate, 22.580959 seconds, 924160 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.571941 seconds -Region Avg IOU: 0.470133, Class: 1.000000, Obj: 0.306422, No Obj: 0.003341, Avg Recall: 0.495146, count: 103 -Region Avg IOU: 0.546031, Class: 1.000000, Obj: 0.355995, No Obj: 0.004428, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.497792, Class: 1.000000, Obj: 0.341664, No Obj: 0.003950, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.486662, Class: 1.000000, Obj: 0.306234, No Obj: 0.003649, Avg Recall: 0.553398, count: 103 -Region Avg IOU: 0.567646, Class: 1.000000, Obj: 0.332315, No Obj: 0.004214, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.481081, Class: 1.000000, Obj: 0.358519, No Obj: 0.003901, Avg Recall: 0.505050, count: 99 -Region Avg IOU: 0.450441, Class: 1.000000, Obj: 0.296722, No Obj: 0.003963, Avg Recall: 0.543103, count: 116 -Region Avg IOU: 0.555064, Class: 1.000000, Obj: 0.339423, No Obj: 0.004548, Avg Recall: 0.650406, count: 123 -3611: 0.452574, 0.440804 avg, 0.001000 rate, 19.622612 seconds, 924416 images -Loaded: 1.003658 seconds -Region Avg IOU: 0.474333, Class: 1.000000, Obj: 0.309835, No Obj: 0.003657, Avg Recall: 0.527273, count: 110 -Region Avg IOU: 0.492943, Class: 1.000000, Obj: 0.310907, No Obj: 0.004084, Avg Recall: 0.533333, count: 105 -Region Avg IOU: 0.514171, Class: 1.000000, Obj: 0.321427, No Obj: 0.004026, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.518506, Class: 1.000000, Obj: 0.329141, No Obj: 0.004241, Avg Recall: 0.623077, count: 130 -Region Avg IOU: 0.529387, Class: 1.000000, Obj: 0.315686, No Obj: 0.004079, Avg Recall: 0.572727, count: 110 -Region Avg IOU: 0.510565, Class: 1.000000, Obj: 0.329281, No Obj: 0.004097, Avg Recall: 0.563492, count: 126 -Region Avg IOU: 0.527163, Class: 1.000000, Obj: 0.332742, No Obj: 0.003916, Avg Recall: 0.581967, count: 122 -Region Avg IOU: 0.527808, Class: 1.000000, Obj: 0.338362, No Obj: 0.004446, Avg Recall: 0.612069, count: 116 -3612: 0.472692, 0.443993 avg, 0.001000 rate, 21.566156 seconds, 924672 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.512809, Class: 1.000000, Obj: 0.314002, No Obj: 0.003570, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.506731, Class: 1.000000, Obj: 0.313908, No Obj: 0.004020, Avg Recall: 0.561538, count: 130 -Region Avg IOU: 0.505066, Class: 1.000000, Obj: 0.260145, No Obj: 0.003419, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.495064, Class: 1.000000, Obj: 0.309298, No Obj: 0.004068, Avg Recall: 0.562963, count: 135 -Region Avg IOU: 0.545319, Class: 1.000000, Obj: 0.292258, No Obj: 0.003986, Avg Recall: 0.642857, count: 126 -Region Avg IOU: 0.540035, Class: 1.000000, Obj: 0.315422, No Obj: 0.003265, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.460299, Class: 1.000000, Obj: 0.277689, No Obj: 0.003675, Avg Recall: 0.484848, count: 99 -Region Avg IOU: 0.489356, Class: 1.000000, Obj: 0.310436, No Obj: 0.003983, Avg Recall: 0.522124, count: 113 -3613: 0.467096, 0.446303 avg, 0.001000 rate, 19.970661 seconds, 924928 images -Loaded: 1.596039 seconds -Region Avg IOU: 0.472535, Class: 1.000000, Obj: 0.341385, No Obj: 0.003920, Avg Recall: 0.532710, count: 107 -Region Avg IOU: 0.494858, Class: 1.000000, Obj: 0.373085, No Obj: 0.003898, Avg Recall: 0.560748, count: 107 -Region Avg IOU: 0.543637, Class: 1.000000, Obj: 0.378478, No Obj: 0.004343, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.434868, Class: 1.000000, Obj: 0.271278, No Obj: 0.004082, Avg Recall: 0.508333, count: 120 -Region Avg IOU: 0.525946, Class: 1.000000, Obj: 0.327657, No Obj: 0.004479, Avg Recall: 0.606838, count: 117 -Region Avg IOU: 0.538090, Class: 1.000000, Obj: 0.331155, No Obj: 0.004389, Avg Recall: 0.664000, count: 125 -Region Avg IOU: 0.515966, Class: 1.000000, Obj: 0.373128, No Obj: 0.003515, Avg Recall: 0.561644, count: 73 -Region Avg IOU: 0.496320, Class: 1.000000, Obj: 0.314037, No Obj: 0.003779, Avg Recall: 0.535433, count: 127 -3614: 0.430327, 0.444705 avg, 0.001000 rate, 19.525925 seconds, 925184 images -Loaded: 2.043129 seconds -Region Avg IOU: 0.492536, Class: 1.000000, Obj: 0.312018, No Obj: 0.003654, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.525511, Class: 1.000000, Obj: 0.306010, No Obj: 0.004038, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.528750, Class: 1.000000, Obj: 0.364457, No Obj: 0.004350, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.534942, Class: 1.000000, Obj: 0.326572, No Obj: 0.004521, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.532247, Class: 1.000000, Obj: 0.338894, No Obj: 0.004227, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.523808, Class: 1.000000, Obj: 0.313424, No Obj: 0.004507, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.476417, Class: 1.000000, Obj: 0.287348, No Obj: 0.003770, Avg Recall: 0.504505, count: 111 -Region Avg IOU: 0.492575, Class: 1.000000, Obj: 0.355247, No Obj: 0.003288, Avg Recall: 0.506849, count: 73 -3615: 0.424609, 0.442696 avg, 0.001000 rate, 20.086945 seconds, 925440 images -Loaded: 1.616538 seconds -Region Avg IOU: 0.504954, Class: 1.000000, Obj: 0.355082, No Obj: 0.004353, Avg Recall: 0.552845, count: 123 -Region Avg IOU: 0.480011, Class: 1.000000, Obj: 0.282431, No Obj: 0.003965, Avg Recall: 0.508929, count: 112 -Region Avg IOU: 0.507203, Class: 1.000000, Obj: 0.317427, No Obj: 0.003879, Avg Recall: 0.526316, count: 114 -Region Avg IOU: 0.489985, Class: 1.000000, Obj: 0.341186, No Obj: 0.003674, Avg Recall: 0.549020, count: 102 -Region Avg IOU: 0.517908, Class: 1.000000, Obj: 0.335989, No Obj: 0.004065, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.531070, Class: 1.000000, Obj: 0.329767, No Obj: 0.004370, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.516620, Class: 1.000000, Obj: 0.362333, No Obj: 0.003706, Avg Recall: 0.615385, count: 91 -Region Avg IOU: 0.519674, Class: 1.000000, Obj: 0.319436, No Obj: 0.004605, Avg Recall: 0.603053, count: 131 -3616: 0.421039, 0.440530 avg, 0.001000 rate, 21.088301 seconds, 925696 images -Loaded: 1.931255 seconds -Region Avg IOU: 0.520876, Class: 1.000000, Obj: 0.356624, No Obj: 0.003767, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.531866, Class: 1.000000, Obj: 0.347404, No Obj: 0.003977, Avg Recall: 0.647059, count: 85 -Region Avg IOU: 0.468016, Class: 1.000000, Obj: 0.314613, No Obj: 0.004095, Avg Recall: 0.524590, count: 122 -Region Avg IOU: 0.506995, Class: 1.000000, Obj: 0.305156, No Obj: 0.004123, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.523411, Class: 1.000000, Obj: 0.347704, No Obj: 0.004878, Avg Recall: 0.613445, count: 119 -Region Avg IOU: 0.496122, Class: 1.000000, Obj: 0.337697, No Obj: 0.004009, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.478376, Class: 1.000000, Obj: 0.307767, No Obj: 0.004158, Avg Recall: 0.507937, count: 126 -Region Avg IOU: 0.486927, Class: 1.000000, Obj: 0.319010, No Obj: 0.004259, Avg Recall: 0.563636, count: 110 -3617: 0.480610, 0.444538 avg, 0.001000 rate, 19.473528 seconds, 925952 images -Loaded: 2.295648 seconds -Region Avg IOU: 0.495885, Class: 1.000000, Obj: 0.354947, No Obj: 0.003815, Avg Recall: 0.516484, count: 91 -Region Avg IOU: 0.527897, Class: 1.000000, Obj: 0.340318, No Obj: 0.004295, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.538627, Class: 1.000000, Obj: 0.359679, No Obj: 0.004653, Avg Recall: 0.638462, count: 130 -Region Avg IOU: 0.524363, Class: 1.000000, Obj: 0.365269, No Obj: 0.004490, Avg Recall: 0.565891, count: 129 -Region Avg IOU: 0.558536, Class: 1.000000, Obj: 0.394973, No Obj: 0.004619, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.514618, Class: 1.000000, Obj: 0.300629, No Obj: 0.003974, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.559720, Class: 1.000000, Obj: 0.390078, No Obj: 0.004861, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.490696, Class: 1.000000, Obj: 0.313833, No Obj: 0.003669, Avg Recall: 0.558559, count: 111 -3618: 0.430595, 0.443144 avg, 0.001000 rate, 21.821005 seconds, 926208 images -Loaded: 0.909892 seconds -Region Avg IOU: 0.475331, Class: 1.000000, Obj: 0.325040, No Obj: 0.004578, Avg Recall: 0.527559, count: 127 -Region Avg IOU: 0.494760, Class: 1.000000, Obj: 0.321892, No Obj: 0.004982, Avg Recall: 0.513889, count: 144 -Region Avg IOU: 0.527823, Class: 1.000000, Obj: 0.384510, No Obj: 0.004930, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.544771, Class: 1.000000, Obj: 0.330605, No Obj: 0.005093, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.562444, Class: 1.000000, Obj: 0.388584, No Obj: 0.005051, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.502594, Class: 1.000000, Obj: 0.393212, No Obj: 0.004336, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.533848, Class: 1.000000, Obj: 0.361161, No Obj: 0.005154, Avg Recall: 0.615385, count: 130 -Region Avg IOU: 0.523751, Class: 1.000000, Obj: 0.333724, No Obj: 0.004860, Avg Recall: 0.613208, count: 106 -3619: 0.447816, 0.443611 avg, 0.001000 rate, 22.218737 seconds, 926464 images -Loaded: 0.243961 seconds -Region Avg IOU: 0.491412, Class: 1.000000, Obj: 0.371386, No Obj: 0.004579, Avg Recall: 0.532710, count: 107 -Region Avg IOU: 0.476618, Class: 1.000000, Obj: 0.314137, No Obj: 0.004249, Avg Recall: 0.515873, count: 126 -Region Avg IOU: 0.557945, Class: 1.000000, Obj: 0.361504, No Obj: 0.004479, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.518938, Class: 1.000000, Obj: 0.373348, No Obj: 0.004671, Avg Recall: 0.561983, count: 121 -Region Avg IOU: 0.516936, Class: 1.000000, Obj: 0.337585, No Obj: 0.004519, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.550253, Class: 1.000000, Obj: 0.379112, No Obj: 0.004307, Avg Recall: 0.632184, count: 87 -Region Avg IOU: 0.518692, Class: 1.000000, Obj: 0.329681, No Obj: 0.003985, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.524306, Class: 1.000000, Obj: 0.390301, No Obj: 0.004903, Avg Recall: 0.565574, count: 122 -3620: 0.448597, 0.444110 avg, 0.001000 rate, 21.703680 seconds, 926720 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000071 seconds -Region Avg IOU: 0.581132, Class: 1.000000, Obj: 0.420993, No Obj: 0.003673, Avg Recall: 0.729508, count: 122 -Region Avg IOU: 0.603222, Class: 1.000000, Obj: 0.468997, No Obj: 0.003677, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.635806, Class: 1.000000, Obj: 0.460930, No Obj: 0.003714, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.654691, Class: 1.000000, Obj: 0.461077, No Obj: 0.003780, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.659233, Class: 1.000000, Obj: 0.476553, No Obj: 0.003473, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.623622, Class: 1.000000, Obj: 0.480146, No Obj: 0.004070, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.598901, Class: 1.000000, Obj: 0.447597, No Obj: 0.003626, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.591515, Class: 1.000000, Obj: 0.424149, No Obj: 0.003919, Avg Recall: 0.692857, count: 140 -3621: 0.446478, 0.444346 avg, 0.001000 rate, 26.867786 seconds, 926976 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.588348, Class: 1.000000, Obj: 0.409824, No Obj: 0.003210, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.676312, Class: 1.000000, Obj: 0.424090, No Obj: 0.003647, Avg Recall: 0.884297, count: 121 -Region Avg IOU: 0.671998, Class: 1.000000, Obj: 0.422161, No Obj: 0.003726, Avg Recall: 0.824561, count: 114 -Region Avg IOU: 0.611181, Class: 1.000000, Obj: 0.383386, No Obj: 0.003575, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.647282, Class: 1.000000, Obj: 0.422782, No Obj: 0.003261, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.639614, Class: 1.000000, Obj: 0.396242, No Obj: 0.003844, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.658641, Class: 1.000000, Obj: 0.414277, No Obj: 0.003889, Avg Recall: 0.869919, count: 123 -Region Avg IOU: 0.653695, Class: 1.000000, Obj: 0.513213, No Obj: 0.003205, Avg Recall: 0.833333, count: 90 -3622: 0.508365, 0.450748 avg, 0.001000 rate, 27.729774 seconds, 927232 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.591550, Class: 1.000000, Obj: 0.356149, No Obj: 0.002951, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.631058, Class: 1.000000, Obj: 0.398305, No Obj: 0.003025, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.618395, Class: 1.000000, Obj: 0.415398, No Obj: 0.003139, Avg Recall: 0.719298, count: 114 -Region Avg IOU: 0.625315, Class: 1.000000, Obj: 0.351258, No Obj: 0.003074, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.619499, Class: 1.000000, Obj: 0.374512, No Obj: 0.003293, Avg Recall: 0.729323, count: 133 -Region Avg IOU: 0.647434, Class: 1.000000, Obj: 0.428486, No Obj: 0.003424, Avg Recall: 0.808696, count: 115 -Region Avg IOU: 0.599835, Class: 1.000000, Obj: 0.373024, No Obj: 0.003395, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.614625, Class: 1.000000, Obj: 0.413106, No Obj: 0.003207, Avg Recall: 0.709677, count: 93 -3623: 0.461537, 0.451827 avg, 0.001000 rate, 29.679762 seconds, 927488 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.579294, Class: 1.000000, Obj: 0.311318, No Obj: 0.002676, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.629209, Class: 1.000000, Obj: 0.330064, No Obj: 0.002750, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.650982, Class: 1.000000, Obj: 0.369631, No Obj: 0.003025, Avg Recall: 0.817391, count: 115 -Region Avg IOU: 0.625208, Class: 1.000000, Obj: 0.315567, No Obj: 0.002619, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.633383, Class: 1.000000, Obj: 0.374382, No Obj: 0.002917, Avg Recall: 0.740157, count: 127 -Region Avg IOU: 0.643731, Class: 1.000000, Obj: 0.342858, No Obj: 0.002849, Avg Recall: 0.808696, count: 115 -Region Avg IOU: 0.601578, Class: 1.000000, Obj: 0.335685, No Obj: 0.002586, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.654262, Class: 1.000000, Obj: 0.412684, No Obj: 0.002543, Avg Recall: 0.806122, count: 98 -3624: 0.505723, 0.457217 avg, 0.001000 rate, 26.408808 seconds, 927744 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.589135, Class: 1.000000, Obj: 0.331160, No Obj: 0.002229, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.643090, Class: 1.000000, Obj: 0.348164, No Obj: 0.002216, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.589463, Class: 1.000000, Obj: 0.305108, No Obj: 0.002727, Avg Recall: 0.730769, count: 130 -Region Avg IOU: 0.651433, Class: 1.000000, Obj: 0.386891, No Obj: 0.002431, Avg Recall: 0.784483, count: 116 -Region Avg IOU: 0.629789, Class: 1.000000, Obj: 0.305714, No Obj: 0.002462, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.634189, Class: 1.000000, Obj: 0.370477, No Obj: 0.002337, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.667983, Class: 1.000000, Obj: 0.320950, No Obj: 0.002366, Avg Recall: 0.816092, count: 87 -Region Avg IOU: 0.623103, Class: 1.000000, Obj: 0.315481, No Obj: 0.002466, Avg Recall: 0.752137, count: 117 -3625: 0.469994, 0.458494 avg, 0.001000 rate, 27.243851 seconds, 928000 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.653397, Class: 1.000000, Obj: 0.316867, No Obj: 0.002200, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.619274, Class: 1.000000, Obj: 0.315265, No Obj: 0.002299, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.595235, Class: 1.000000, Obj: 0.305531, No Obj: 0.002186, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.620692, Class: 1.000000, Obj: 0.319919, No Obj: 0.002053, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.611708, Class: 1.000000, Obj: 0.276836, No Obj: 0.002074, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.647806, Class: 1.000000, Obj: 0.366133, No Obj: 0.002525, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.599566, Class: 1.000000, Obj: 0.308870, No Obj: 0.002189, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.633476, Class: 1.000000, Obj: 0.276840, No Obj: 0.002520, Avg Recall: 0.777778, count: 153 -3626: 0.479314, 0.460576 avg, 0.001000 rate, 27.890812 seconds, 928256 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.610377, Class: 1.000000, Obj: 0.297619, No Obj: 0.002207, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.612039, Class: 1.000000, Obj: 0.319524, No Obj: 0.002374, Avg Recall: 0.737226, count: 137 -Region Avg IOU: 0.636843, Class: 1.000000, Obj: 0.318640, No Obj: 0.002087, Avg Recall: 0.788889, count: 90 -Region Avg IOU: 0.656628, Class: 1.000000, Obj: 0.276311, No Obj: 0.002233, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.589316, Class: 1.000000, Obj: 0.267668, No Obj: 0.001808, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.646351, Class: 1.000000, Obj: 0.275803, No Obj: 0.002656, Avg Recall: 0.806202, count: 129 -Region Avg IOU: 0.676684, Class: 1.000000, Obj: 0.320528, No Obj: 0.002084, Avg Recall: 0.818182, count: 77 -Region Avg IOU: 0.627271, Class: 1.000000, Obj: 0.285450, No Obj: 0.002284, Avg Recall: 0.745283, count: 106 -3627: 0.481213, 0.462640 avg, 0.001000 rate, 27.050283 seconds, 928512 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.626694, Class: 1.000000, Obj: 0.275697, No Obj: 0.002029, Avg Recall: 0.801980, count: 101 -Region Avg IOU: 0.663723, Class: 1.000000, Obj: 0.328566, No Obj: 0.001929, Avg Recall: 0.866667, count: 75 -Region Avg IOU: 0.643486, Class: 1.000000, Obj: 0.301094, No Obj: 0.002051, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.636335, Class: 1.000000, Obj: 0.292624, No Obj: 0.002139, Avg Recall: 0.786325, count: 117 -Region Avg IOU: 0.611524, Class: 1.000000, Obj: 0.334758, No Obj: 0.001968, Avg Recall: 0.746835, count: 79 -Region Avg IOU: 0.654347, Class: 1.000000, Obj: 0.341099, No Obj: 0.002177, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.642589, Class: 1.000000, Obj: 0.297687, No Obj: 0.002357, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.634583, Class: 1.000000, Obj: 0.311762, No Obj: 0.001988, Avg Recall: 0.808989, count: 89 -3628: 0.418417, 0.458218 avg, 0.001000 rate, 28.069946 seconds, 928768 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.632421, Class: 1.000000, Obj: 0.332982, No Obj: 0.001888, Avg Recall: 0.802083, count: 96 -Region Avg IOU: 0.627277, Class: 1.000000, Obj: 0.302672, No Obj: 0.002530, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.627370, Class: 1.000000, Obj: 0.326630, No Obj: 0.002437, Avg Recall: 0.735043, count: 117 -Region Avg IOU: 0.662165, Class: 1.000000, Obj: 0.346632, No Obj: 0.002454, Avg Recall: 0.837607, count: 117 -Region Avg IOU: 0.600304, Class: 1.000000, Obj: 0.249827, No Obj: 0.002309, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.620967, Class: 1.000000, Obj: 0.297668, No Obj: 0.002208, Avg Recall: 0.789062, count: 128 -Region Avg IOU: 0.631401, Class: 1.000000, Obj: 0.316380, No Obj: 0.002266, Avg Recall: 0.791304, count: 115 -Region Avg IOU: 0.640605, Class: 1.000000, Obj: 0.356848, No Obj: 0.002429, Avg Recall: 0.761062, count: 113 -3629: 0.457413, 0.458137 avg, 0.001000 rate, 27.300703 seconds, 929024 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.640428, Class: 1.000000, Obj: 0.352488, No Obj: 0.002632, Avg Recall: 0.816000, count: 125 -Region Avg IOU: 0.632282, Class: 1.000000, Obj: 0.313027, No Obj: 0.002351, Avg Recall: 0.784483, count: 116 -Region Avg IOU: 0.646998, Class: 1.000000, Obj: 0.358347, No Obj: 0.002790, Avg Recall: 0.776859, count: 121 -Region Avg IOU: 0.682193, Class: 1.000000, Obj: 0.336945, No Obj: 0.002323, Avg Recall: 0.858824, count: 85 -Region Avg IOU: 0.598038, Class: 1.000000, Obj: 0.310671, No Obj: 0.001977, Avg Recall: 0.661290, count: 62 -Region Avg IOU: 0.608572, Class: 1.000000, Obj: 0.325009, No Obj: 0.002162, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.702020, Class: 1.000000, Obj: 0.363973, No Obj: 0.002296, Avg Recall: 0.878049, count: 82 -Region Avg IOU: 0.632186, Class: 1.000000, Obj: 0.317775, No Obj: 0.002529, Avg Recall: 0.784000, count: 125 -3630: 0.456097, 0.457933 avg, 0.001000 rate, 27.925224 seconds, 929280 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 15.834228 seconds -Region Avg IOU: 0.577195, Class: 1.000000, Obj: 0.315337, No Obj: 0.003502, Avg Recall: 0.687943, count: 141 -Region Avg IOU: 0.602255, Class: 1.000000, Obj: 0.291087, No Obj: 0.002941, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.588658, Class: 1.000000, Obj: 0.311859, No Obj: 0.003269, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.556932, Class: 1.000000, Obj: 0.314770, No Obj: 0.002756, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.592498, Class: 1.000000, Obj: 0.309577, No Obj: 0.003245, Avg Recall: 0.719298, count: 114 -Region Avg IOU: 0.571820, Class: 1.000000, Obj: 0.302659, No Obj: 0.002874, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.575627, Class: 1.000000, Obj: 0.285765, No Obj: 0.003124, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.596663, Class: 1.000000, Obj: 0.317373, No Obj: 0.003345, Avg Recall: 0.732283, count: 127 -3631: 0.494817, 0.461622 avg, 0.001000 rate, 24.037067 seconds, 929536 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.570369, Class: 1.000000, Obj: 0.337792, No Obj: 0.003052, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.593290, Class: 1.000000, Obj: 0.315730, No Obj: 0.003689, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.599737, Class: 1.000000, Obj: 0.370232, No Obj: 0.003047, Avg Recall: 0.724138, count: 87 -Region Avg IOU: 0.593248, Class: 1.000000, Obj: 0.335740, No Obj: 0.003234, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.562029, Class: 1.000000, Obj: 0.380135, No Obj: 0.003341, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.580381, Class: 1.000000, Obj: 0.344761, No Obj: 0.003113, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.556163, Class: 1.000000, Obj: 0.315764, No Obj: 0.003383, Avg Recall: 0.691729, count: 133 -Region Avg IOU: 0.586761, Class: 1.000000, Obj: 0.325038, No Obj: 0.003158, Avg Recall: 0.752809, count: 89 -3632: 0.425418, 0.458001 avg, 0.001000 rate, 25.364012 seconds, 929792 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.559857, Class: 1.000000, Obj: 0.344441, No Obj: 0.003140, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.563369, Class: 1.000000, Obj: 0.304405, No Obj: 0.003466, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.527168, Class: 1.000000, Obj: 0.315114, No Obj: 0.003593, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.582709, Class: 1.000000, Obj: 0.340919, No Obj: 0.003971, Avg Recall: 0.680328, count: 122 -Region Avg IOU: 0.584771, Class: 1.000000, Obj: 0.370431, No Obj: 0.003178, Avg Recall: 0.707865, count: 89 -Region Avg IOU: 0.541401, Class: 1.000000, Obj: 0.304505, No Obj: 0.003466, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.629197, Class: 1.000000, Obj: 0.413107, No Obj: 0.004274, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.638170, Class: 1.000000, Obj: 0.359686, No Obj: 0.003641, Avg Recall: 0.790698, count: 86 -3633: 0.465369, 0.458738 avg, 0.001000 rate, 24.389303 seconds, 930048 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.593529, Class: 1.000000, Obj: 0.398083, No Obj: 0.005154, Avg Recall: 0.718518, count: 135 -Region Avg IOU: 0.608728, Class: 1.000000, Obj: 0.352179, No Obj: 0.003969, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.613703, Class: 1.000000, Obj: 0.384300, No Obj: 0.004316, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.570524, Class: 1.000000, Obj: 0.356497, No Obj: 0.004040, Avg Recall: 0.639344, count: 122 -Region Avg IOU: 0.546424, Class: 1.000000, Obj: 0.365244, No Obj: 0.003927, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.582166, Class: 1.000000, Obj: 0.347991, No Obj: 0.003705, Avg Recall: 0.689076, count: 119 -Region Avg IOU: 0.557226, Class: 1.000000, Obj: 0.383225, No Obj: 0.003877, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.621749, Class: 1.000000, Obj: 0.380787, No Obj: 0.003853, Avg Recall: 0.721649, count: 97 -3634: 0.472907, 0.460155 avg, 0.001000 rate, 24.374260 seconds, 930304 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.576049, Class: 1.000000, Obj: 0.410894, No Obj: 0.004145, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.587786, Class: 1.000000, Obj: 0.445976, No Obj: 0.003992, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.598716, Class: 1.000000, Obj: 0.413276, No Obj: 0.004710, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.526659, Class: 1.000000, Obj: 0.400649, No Obj: 0.003785, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.614180, Class: 1.000000, Obj: 0.394676, No Obj: 0.004424, Avg Recall: 0.761194, count: 134 -Region Avg IOU: 0.635063, Class: 1.000000, Obj: 0.439129, No Obj: 0.004061, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.580348, Class: 1.000000, Obj: 0.376457, No Obj: 0.003762, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.625709, Class: 1.000000, Obj: 0.466851, No Obj: 0.004250, Avg Recall: 0.731481, count: 108 -3635: 0.425141, 0.456654 avg, 0.001000 rate, 23.779016 seconds, 930560 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.565123, Class: 1.000000, Obj: 0.447671, No Obj: 0.004493, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.567893, Class: 1.000000, Obj: 0.425009, No Obj: 0.004312, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.563306, Class: 1.000000, Obj: 0.420059, No Obj: 0.003941, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.603213, Class: 1.000000, Obj: 0.411069, No Obj: 0.004734, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.553712, Class: 1.000000, Obj: 0.429179, No Obj: 0.004452, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.533099, Class: 1.000000, Obj: 0.375352, No Obj: 0.004494, Avg Recall: 0.564103, count: 117 -Region Avg IOU: 0.542184, Class: 1.000000, Obj: 0.394350, No Obj: 0.004244, Avg Recall: 0.596639, count: 119 -Region Avg IOU: 0.578250, Class: 1.000000, Obj: 0.418814, No Obj: 0.004503, Avg Recall: 0.666667, count: 114 -3636: 0.425196, 0.453508 avg, 0.001000 rate, 22.545464 seconds, 930816 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.547712, Class: 1.000000, Obj: 0.389311, No Obj: 0.003812, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.559736, Class: 1.000000, Obj: 0.395349, No Obj: 0.004050, Avg Recall: 0.644628, count: 121 -Region Avg IOU: 0.566307, Class: 1.000000, Obj: 0.416219, No Obj: 0.004164, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.562298, Class: 1.000000, Obj: 0.422051, No Obj: 0.003979, Avg Recall: 0.637500, count: 80 -Region Avg IOU: 0.578699, Class: 1.000000, Obj: 0.442235, No Obj: 0.003939, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.551343, Class: 1.000000, Obj: 0.448790, No Obj: 0.003995, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.577229, Class: 1.000000, Obj: 0.434921, No Obj: 0.003844, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.575249, Class: 1.000000, Obj: 0.455976, No Obj: 0.004672, Avg Recall: 0.686956, count: 115 -3637: 0.397696, 0.447927 avg, 0.001000 rate, 24.222481 seconds, 931072 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.598806, Class: 1.000000, Obj: 0.381157, No Obj: 0.004704, Avg Recall: 0.682927, count: 123 -Region Avg IOU: 0.592890, Class: 1.000000, Obj: 0.422586, No Obj: 0.004230, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.570669, Class: 1.000000, Obj: 0.382473, No Obj: 0.003811, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.590521, Class: 1.000000, Obj: 0.462007, No Obj: 0.004080, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.582141, Class: 1.000000, Obj: 0.378672, No Obj: 0.003742, Avg Recall: 0.679012, count: 81 -Region Avg IOU: 0.578234, Class: 1.000000, Obj: 0.433671, No Obj: 0.003094, Avg Recall: 0.695652, count: 69 -Region Avg IOU: 0.574543, Class: 1.000000, Obj: 0.431321, No Obj: 0.003886, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.516929, Class: 1.000000, Obj: 0.355091, No Obj: 0.003936, Avg Recall: 0.614679, count: 109 -3638: 0.423442, 0.445478 avg, 0.001000 rate, 23.886860 seconds, 931328 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.558494, Class: 1.000000, Obj: 0.416738, No Obj: 0.003413, Avg Recall: 0.646341, count: 82 -Region Avg IOU: 0.531828, Class: 1.000000, Obj: 0.347889, No Obj: 0.003529, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.595177, Class: 1.000000, Obj: 0.455731, No Obj: 0.003562, Avg Recall: 0.721519, count: 79 -Region Avg IOU: 0.604597, Class: 1.000000, Obj: 0.414055, No Obj: 0.003941, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.544975, Class: 1.000000, Obj: 0.377286, No Obj: 0.004335, Avg Recall: 0.661765, count: 136 -Region Avg IOU: 0.560035, Class: 1.000000, Obj: 0.390807, No Obj: 0.004081, Avg Recall: 0.645161, count: 124 -Region Avg IOU: 0.578564, Class: 1.000000, Obj: 0.391670, No Obj: 0.003461, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.498671, Class: 1.000000, Obj: 0.358791, No Obj: 0.003560, Avg Recall: 0.552632, count: 114 -3639: 0.418960, 0.442826 avg, 0.001000 rate, 23.338968 seconds, 931584 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.574576, Class: 1.000000, Obj: 0.401671, No Obj: 0.003204, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.545855, Class: 1.000000, Obj: 0.350951, No Obj: 0.004333, Avg Recall: 0.640288, count: 139 -Region Avg IOU: 0.581332, Class: 1.000000, Obj: 0.331960, No Obj: 0.003254, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.540628, Class: 1.000000, Obj: 0.398731, No Obj: 0.003539, Avg Recall: 0.603306, count: 121 -Region Avg IOU: 0.591762, Class: 1.000000, Obj: 0.341603, No Obj: 0.003652, Avg Recall: 0.700000, count: 120 -Region Avg IOU: 0.592114, Class: 1.000000, Obj: 0.348128, No Obj: 0.003614, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.616917, Class: 1.000000, Obj: 0.358538, No Obj: 0.004085, Avg Recall: 0.714286, count: 119 -Region Avg IOU: 0.562988, Class: 1.000000, Obj: 0.321006, No Obj: 0.003784, Avg Recall: 0.711712, count: 111 -3640: 0.465943, 0.445138 avg, 0.001000 rate, 23.537319 seconds, 931840 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000067 seconds -Region Avg IOU: 0.559387, Class: 1.000000, Obj: 0.357430, No Obj: 0.003735, Avg Recall: 0.637795, count: 127 -Region Avg IOU: 0.578077, Class: 1.000000, Obj: 0.341716, No Obj: 0.003709, Avg Recall: 0.690476, count: 126 -Region Avg IOU: 0.591967, Class: 1.000000, Obj: 0.355988, No Obj: 0.003265, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.544013, Class: 1.000000, Obj: 0.357315, No Obj: 0.003819, Avg Recall: 0.597122, count: 139 -Region Avg IOU: 0.623445, Class: 1.000000, Obj: 0.389903, No Obj: 0.003283, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.624024, Class: 1.000000, Obj: 0.388274, No Obj: 0.003330, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.615248, Class: 1.000000, Obj: 0.388577, No Obj: 0.003143, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.621046, Class: 1.000000, Obj: 0.414494, No Obj: 0.003096, Avg Recall: 0.783132, count: 83 -3641: 0.485834, 0.449208 avg, 0.001000 rate, 25.813227 seconds, 932096 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.609716, Class: 1.000000, Obj: 0.339798, No Obj: 0.003531, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.602472, Class: 1.000000, Obj: 0.360619, No Obj: 0.003062, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.559636, Class: 1.000000, Obj: 0.353376, No Obj: 0.002650, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.660681, Class: 1.000000, Obj: 0.405288, No Obj: 0.002654, Avg Recall: 0.807692, count: 78 -Region Avg IOU: 0.592444, Class: 1.000000, Obj: 0.389294, No Obj: 0.003090, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.591833, Class: 1.000000, Obj: 0.416336, No Obj: 0.002969, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.616127, Class: 1.000000, Obj: 0.372346, No Obj: 0.003293, Avg Recall: 0.737705, count: 122 -Region Avg IOU: 0.636993, Class: 1.000000, Obj: 0.384761, No Obj: 0.002685, Avg Recall: 0.765432, count: 81 -3642: 0.427732, 0.447060 avg, 0.001000 rate, 25.139179 seconds, 932352 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.584814, Class: 1.000000, Obj: 0.340009, No Obj: 0.002959, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.620412, Class: 1.000000, Obj: 0.388516, No Obj: 0.003122, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.635927, Class: 1.000000, Obj: 0.317944, No Obj: 0.003277, Avg Recall: 0.793651, count: 126 -Region Avg IOU: 0.574846, Class: 1.000000, Obj: 0.401107, No Obj: 0.002460, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.584772, Class: 1.000000, Obj: 0.359955, No Obj: 0.002846, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.659353, Class: 1.000000, Obj: 0.358013, No Obj: 0.003656, Avg Recall: 0.822222, count: 135 -Region Avg IOU: 0.636984, Class: 1.000000, Obj: 0.411680, No Obj: 0.002768, Avg Recall: 0.747126, count: 87 -Region Avg IOU: 0.560666, Class: 1.000000, Obj: 0.393832, No Obj: 0.002856, Avg Recall: 0.641667, count: 120 -3643: 0.437638, 0.446118 avg, 0.001000 rate, 26.002108 seconds, 932608 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.611903, Class: 1.000000, Obj: 0.326753, No Obj: 0.002689, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.620684, Class: 1.000000, Obj: 0.336783, No Obj: 0.002694, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.579420, Class: 1.000000, Obj: 0.361607, No Obj: 0.002920, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.596467, Class: 1.000000, Obj: 0.310048, No Obj: 0.003411, Avg Recall: 0.689189, count: 148 -Region Avg IOU: 0.623346, Class: 1.000000, Obj: 0.333133, No Obj: 0.002853, Avg Recall: 0.746988, count: 83 -Region Avg IOU: 0.613081, Class: 1.000000, Obj: 0.320496, No Obj: 0.003232, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.593931, Class: 1.000000, Obj: 0.331677, No Obj: 0.002924, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.620771, Class: 1.000000, Obj: 0.355450, No Obj: 0.002727, Avg Recall: 0.727273, count: 121 -3644: 0.468168, 0.448323 avg, 0.001000 rate, 26.864676 seconds, 932864 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.628386, Class: 1.000000, Obj: 0.320613, No Obj: 0.003055, Avg Recall: 0.768595, count: 121 -Region Avg IOU: 0.546142, Class: 1.000000, Obj: 0.300874, No Obj: 0.002354, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.592432, Class: 1.000000, Obj: 0.335359, No Obj: 0.002796, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.630266, Class: 1.000000, Obj: 0.358699, No Obj: 0.002676, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.620441, Class: 1.000000, Obj: 0.343296, No Obj: 0.002504, Avg Recall: 0.721519, count: 79 -Region Avg IOU: 0.602727, Class: 1.000000, Obj: 0.372024, No Obj: 0.002991, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.571334, Class: 1.000000, Obj: 0.329142, No Obj: 0.002722, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.587471, Class: 1.000000, Obj: 0.316425, No Obj: 0.003097, Avg Recall: 0.715596, count: 109 -3645: 0.432809, 0.446771 avg, 0.001000 rate, 24.529144 seconds, 933120 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.588147, Class: 1.000000, Obj: 0.319690, No Obj: 0.002969, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.625948, Class: 1.000000, Obj: 0.427029, No Obj: 0.002292, Avg Recall: 0.769231, count: 78 -Region Avg IOU: 0.576830, Class: 1.000000, Obj: 0.356801, No Obj: 0.002879, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.583137, Class: 1.000000, Obj: 0.301894, No Obj: 0.003254, Avg Recall: 0.681818, count: 132 -Region Avg IOU: 0.651149, Class: 1.000000, Obj: 0.355547, No Obj: 0.002551, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.590227, Class: 1.000000, Obj: 0.333795, No Obj: 0.003139, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.606241, Class: 1.000000, Obj: 0.328483, No Obj: 0.002890, Avg Recall: 0.780488, count: 82 -Region Avg IOU: 0.643143, Class: 1.000000, Obj: 0.361515, No Obj: 0.003128, Avg Recall: 0.800000, count: 110 -3646: 0.456338, 0.447728 avg, 0.001000 rate, 26.042700 seconds, 933376 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.601113, Class: 1.000000, Obj: 0.365803, No Obj: 0.003101, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.572519, Class: 1.000000, Obj: 0.328117, No Obj: 0.002744, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.588572, Class: 1.000000, Obj: 0.336356, No Obj: 0.002521, Avg Recall: 0.763889, count: 72 -Region Avg IOU: 0.554433, Class: 1.000000, Obj: 0.364997, No Obj: 0.003047, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.587050, Class: 1.000000, Obj: 0.353766, No Obj: 0.002693, Avg Recall: 0.670588, count: 85 -Region Avg IOU: 0.536573, Class: 1.000000, Obj: 0.314617, No Obj: 0.003104, Avg Recall: 0.616541, count: 133 -Region Avg IOU: 0.606185, Class: 1.000000, Obj: 0.379458, No Obj: 0.002810, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.603941, Class: 1.000000, Obj: 0.329259, No Obj: 0.003161, Avg Recall: 0.731092, count: 119 -3647: 0.432074, 0.446163 avg, 0.001000 rate, 25.598839 seconds, 933632 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.622725, Class: 1.000000, Obj: 0.393136, No Obj: 0.003413, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.555804, Class: 1.000000, Obj: 0.355296, No Obj: 0.003070, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.578071, Class: 1.000000, Obj: 0.358401, No Obj: 0.003319, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.606435, Class: 1.000000, Obj: 0.367141, No Obj: 0.002890, Avg Recall: 0.723881, count: 134 -Region Avg IOU: 0.584001, Class: 1.000000, Obj: 0.398414, No Obj: 0.003320, Avg Recall: 0.656000, count: 125 -Region Avg IOU: 0.603090, Class: 1.000000, Obj: 0.363017, No Obj: 0.003200, Avg Recall: 0.729508, count: 122 -Region Avg IOU: 0.649481, Class: 1.000000, Obj: 0.334775, No Obj: 0.003459, Avg Recall: 0.827586, count: 116 -Region Avg IOU: 0.637542, Class: 1.000000, Obj: 0.359758, No Obj: 0.003274, Avg Recall: 0.793103, count: 116 -3648: 0.473468, 0.448893 avg, 0.001000 rate, 25.733688 seconds, 933888 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.599938, Class: 1.000000, Obj: 0.394148, No Obj: 0.003426, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.589835, Class: 1.000000, Obj: 0.381643, No Obj: 0.003099, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.608861, Class: 1.000000, Obj: 0.400390, No Obj: 0.003119, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.633622, Class: 1.000000, Obj: 0.424669, No Obj: 0.003031, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.608112, Class: 1.000000, Obj: 0.357696, No Obj: 0.002958, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.590949, Class: 1.000000, Obj: 0.402670, No Obj: 0.003307, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.606295, Class: 1.000000, Obj: 0.449122, No Obj: 0.003780, Avg Recall: 0.702290, count: 131 -Region Avg IOU: 0.605466, Class: 1.000000, Obj: 0.352543, No Obj: 0.003274, Avg Recall: 0.728972, count: 107 -3649: 0.404359, 0.444440 avg, 0.001000 rate, 26.401026 seconds, 934144 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.602223, Class: 1.000000, Obj: 0.423224, No Obj: 0.003052, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.615893, Class: 1.000000, Obj: 0.364258, No Obj: 0.003843, Avg Recall: 0.771186, count: 118 -Region Avg IOU: 0.611796, Class: 1.000000, Obj: 0.352474, No Obj: 0.003927, Avg Recall: 0.703704, count: 135 -Region Avg IOU: 0.644332, Class: 1.000000, Obj: 0.448645, No Obj: 0.003362, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.638408, Class: 1.000000, Obj: 0.397991, No Obj: 0.003252, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.594131, Class: 1.000000, Obj: 0.413724, No Obj: 0.003421, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.638893, Class: 1.000000, Obj: 0.359992, No Obj: 0.003536, Avg Recall: 0.769231, count: 130 -Region Avg IOU: 0.598163, Class: 1.000000, Obj: 0.400665, No Obj: 0.003016, Avg Recall: 0.691057, count: 123 -3650: 0.450183, 0.445014 avg, 0.001000 rate, 24.878256 seconds, 934400 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 19.518312 seconds -Region Avg IOU: 0.544297, Class: 1.000000, Obj: 0.308297, No Obj: 0.003695, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.528748, Class: 1.000000, Obj: 0.327284, No Obj: 0.004026, Avg Recall: 0.569106, count: 123 -Region Avg IOU: 0.545279, Class: 1.000000, Obj: 0.327321, No Obj: 0.003350, Avg Recall: 0.576923, count: 78 -Region Avg IOU: 0.580926, Class: 1.000000, Obj: 0.373565, No Obj: 0.003480, Avg Recall: 0.694444, count: 72 -Region Avg IOU: 0.558953, Class: 1.000000, Obj: 0.328538, No Obj: 0.004361, Avg Recall: 0.657143, count: 140 -Region Avg IOU: 0.588127, Class: 1.000000, Obj: 0.354596, No Obj: 0.003632, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.559666, Class: 1.000000, Obj: 0.325019, No Obj: 0.003894, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.566451, Class: 1.000000, Obj: 0.363281, No Obj: 0.003646, Avg Recall: 0.693182, count: 88 -3651: 0.445978, 0.445111 avg, 0.001000 rate, 23.837557 seconds, 934656 images -Loaded: 0.000059 seconds -Region Avg IOU: 0.539370, Class: 1.000000, Obj: 0.361338, No Obj: 0.003642, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.612820, Class: 1.000000, Obj: 0.379977, No Obj: 0.003542, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.540197, Class: 1.000000, Obj: 0.328644, No Obj: 0.003551, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.535453, Class: 1.000000, Obj: 0.369560, No Obj: 0.004085, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.577841, Class: 1.000000, Obj: 0.343927, No Obj: 0.003884, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.521508, Class: 1.000000, Obj: 0.358102, No Obj: 0.003260, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.595794, Class: 1.000000, Obj: 0.383625, No Obj: 0.003427, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.559856, Class: 1.000000, Obj: 0.354661, No Obj: 0.004062, Avg Recall: 0.593496, count: 123 -3652: 0.406407, 0.441240 avg, 0.001000 rate, 23.283144 seconds, 934912 images -Loaded: 0.000149 seconds -Region Avg IOU: 0.593154, Class: 1.000000, Obj: 0.354940, No Obj: 0.003991, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.554023, Class: 1.000000, Obj: 0.332204, No Obj: 0.004447, Avg Recall: 0.656489, count: 131 -Region Avg IOU: 0.547764, Class: 1.000000, Obj: 0.330561, No Obj: 0.003974, Avg Recall: 0.607843, count: 102 -Region Avg IOU: 0.573580, Class: 1.000000, Obj: 0.427610, No Obj: 0.003729, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.522928, Class: 1.000000, Obj: 0.306577, No Obj: 0.003648, Avg Recall: 0.600000, count: 95 -Region Avg IOU: 0.532949, Class: 1.000000, Obj: 0.331712, No Obj: 0.004094, Avg Recall: 0.627119, count: 118 -Region Avg IOU: 0.569953, Class: 1.000000, Obj: 0.396086, No Obj: 0.004202, Avg Recall: 0.686956, count: 115 -Region Avg IOU: 0.578195, Class: 1.000000, Obj: 0.394822, No Obj: 0.003217, Avg Recall: 0.731343, count: 67 -3653: 0.453813, 0.442497 avg, 0.001000 rate, 23.326527 seconds, 935168 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.595624, Class: 1.000000, Obj: 0.425268, No Obj: 0.004249, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.543432, Class: 1.000000, Obj: 0.373766, No Obj: 0.004215, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.569040, Class: 1.000000, Obj: 0.433893, No Obj: 0.003846, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.582763, Class: 1.000000, Obj: 0.432580, No Obj: 0.004458, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.550942, Class: 1.000000, Obj: 0.446639, No Obj: 0.003877, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.515920, Class: 1.000000, Obj: 0.362070, No Obj: 0.004080, Avg Recall: 0.603306, count: 121 -Region Avg IOU: 0.547644, Class: 1.000000, Obj: 0.403028, No Obj: 0.003263, Avg Recall: 0.678161, count: 87 -Region Avg IOU: 0.551340, Class: 1.000000, Obj: 0.411537, No Obj: 0.003955, Avg Recall: 0.617647, count: 102 -3654: 0.420896, 0.440337 avg, 0.001000 rate, 24.309593 seconds, 935424 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.536239, Class: 1.000000, Obj: 0.362394, No Obj: 0.004291, Avg Recall: 0.607692, count: 130 -Region Avg IOU: 0.574988, Class: 1.000000, Obj: 0.396104, No Obj: 0.003688, Avg Recall: 0.670732, count: 82 -Region Avg IOU: 0.551917, Class: 1.000000, Obj: 0.390129, No Obj: 0.003921, Avg Recall: 0.630435, count: 92 -Region Avg IOU: 0.529166, Class: 1.000000, Obj: 0.380571, No Obj: 0.003938, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.584056, Class: 1.000000, Obj: 0.386370, No Obj: 0.004340, Avg Recall: 0.680672, count: 119 -Region Avg IOU: 0.534423, Class: 1.000000, Obj: 0.328967, No Obj: 0.003974, Avg Recall: 0.577236, count: 123 -Region Avg IOU: 0.502697, Class: 1.000000, Obj: 0.311290, No Obj: 0.003803, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.566585, Class: 1.000000, Obj: 0.395799, No Obj: 0.004050, Avg Recall: 0.673077, count: 104 -3655: 0.408794, 0.437183 avg, 0.001000 rate, 23.839714 seconds, 935680 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.563255, Class: 1.000000, Obj: 0.394813, No Obj: 0.003810, Avg Recall: 0.654321, count: 81 -Region Avg IOU: 0.502317, Class: 1.000000, Obj: 0.291378, No Obj: 0.004245, Avg Recall: 0.616000, count: 125 -Region Avg IOU: 0.585795, Class: 1.000000, Obj: 0.394738, No Obj: 0.004042, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.552066, Class: 1.000000, Obj: 0.395980, No Obj: 0.004552, Avg Recall: 0.646617, count: 133 -Region Avg IOU: 0.551192, Class: 1.000000, Obj: 0.337625, No Obj: 0.004379, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.550721, Class: 1.000000, Obj: 0.445544, No Obj: 0.003194, Avg Recall: 0.613333, count: 75 -Region Avg IOU: 0.552827, Class: 1.000000, Obj: 0.359969, No Obj: 0.004296, Avg Recall: 0.697479, count: 119 -Region Avg IOU: 0.556836, Class: 1.000000, Obj: 0.363507, No Obj: 0.004228, Avg Recall: 0.616000, count: 125 -3656: 0.522305, 0.445695 avg, 0.001000 rate, 24.134901 seconds, 935936 images -Loaded: 0.014362 seconds -Region Avg IOU: 0.491776, Class: 1.000000, Obj: 0.302361, No Obj: 0.003550, Avg Recall: 0.557522, count: 113 -Region Avg IOU: 0.595742, Class: 1.000000, Obj: 0.413175, No Obj: 0.004533, Avg Recall: 0.658333, count: 120 -Region Avg IOU: 0.505811, Class: 1.000000, Obj: 0.349696, No Obj: 0.004162, Avg Recall: 0.560748, count: 107 -Region Avg IOU: 0.530011, Class: 1.000000, Obj: 0.389160, No Obj: 0.003745, Avg Recall: 0.586957, count: 92 -Region Avg IOU: 0.528317, Class: 1.000000, Obj: 0.423047, No Obj: 0.004303, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.584925, Class: 1.000000, Obj: 0.417554, No Obj: 0.004451, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.539578, Class: 1.000000, Obj: 0.331657, No Obj: 0.003756, Avg Recall: 0.546219, count: 119 -Region Avg IOU: 0.547640, Class: 1.000000, Obj: 0.352208, No Obj: 0.003453, Avg Recall: 0.618182, count: 110 -3657: 0.427984, 0.443924 avg, 0.001000 rate, 23.440638 seconds, 936192 images -Loaded: 0.364158 seconds -Region Avg IOU: 0.582319, Class: 1.000000, Obj: 0.414837, No Obj: 0.003289, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.563287, Class: 1.000000, Obj: 0.386852, No Obj: 0.003929, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.578658, Class: 1.000000, Obj: 0.389683, No Obj: 0.004170, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.539784, Class: 1.000000, Obj: 0.361346, No Obj: 0.003706, Avg Recall: 0.594828, count: 116 -Region Avg IOU: 0.532301, Class: 1.000000, Obj: 0.365715, No Obj: 0.003823, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.577771, Class: 1.000000, Obj: 0.390306, No Obj: 0.003620, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.565463, Class: 1.000000, Obj: 0.406843, No Obj: 0.003479, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.522247, Class: 1.000000, Obj: 0.341756, No Obj: 0.004160, Avg Recall: 0.595041, count: 121 -3658: 0.406972, 0.440229 avg, 0.001000 rate, 22.419048 seconds, 936448 images -Loaded: 0.652863 seconds -Region Avg IOU: 0.616845, Class: 1.000000, Obj: 0.397744, No Obj: 0.003528, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.543448, Class: 1.000000, Obj: 0.354673, No Obj: 0.003408, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.556530, Class: 1.000000, Obj: 0.358823, No Obj: 0.003989, Avg Recall: 0.617886, count: 123 -Region Avg IOU: 0.564023, Class: 1.000000, Obj: 0.340479, No Obj: 0.003683, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.575395, Class: 1.000000, Obj: 0.394674, No Obj: 0.003949, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.598517, Class: 1.000000, Obj: 0.368077, No Obj: 0.003895, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.573878, Class: 1.000000, Obj: 0.359492, No Obj: 0.004008, Avg Recall: 0.658730, count: 126 -Region Avg IOU: 0.517723, Class: 1.000000, Obj: 0.335175, No Obj: 0.003705, Avg Recall: 0.607692, count: 130 -3659: 0.514111, 0.447617 avg, 0.001000 rate, 23.583744 seconds, 936704 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.593428, Class: 1.000000, Obj: 0.387519, No Obj: 0.003673, Avg Recall: 0.666667, count: 87 -Region Avg IOU: 0.535933, Class: 1.000000, Obj: 0.345320, No Obj: 0.003312, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.517081, Class: 1.000000, Obj: 0.332373, No Obj: 0.003947, Avg Recall: 0.525547, count: 137 -Region Avg IOU: 0.543153, Class: 1.000000, Obj: 0.336031, No Obj: 0.003638, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.561374, Class: 1.000000, Obj: 0.392779, No Obj: 0.003569, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.632054, Class: 1.000000, Obj: 0.379818, No Obj: 0.004206, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.525176, Class: 1.000000, Obj: 0.347871, No Obj: 0.003928, Avg Recall: 0.606838, count: 117 -Region Avg IOU: 0.509585, Class: 1.000000, Obj: 0.308530, No Obj: 0.002966, Avg Recall: 0.569892, count: 93 -3660: 0.460661, 0.448922 avg, 0.001000 rate, 22.118467 seconds, 936960 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000074 seconds -Region Avg IOU: 0.630472, Class: 1.000000, Obj: 0.395185, No Obj: 0.002818, Avg Recall: 0.784810, count: 79 -Region Avg IOU: 0.664658, Class: 1.000000, Obj: 0.401798, No Obj: 0.003220, Avg Recall: 0.854369, count: 103 -Region Avg IOU: 0.624217, Class: 1.000000, Obj: 0.367398, No Obj: 0.003232, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.596796, Class: 1.000000, Obj: 0.392589, No Obj: 0.003122, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.647338, Class: 1.000000, Obj: 0.395334, No Obj: 0.002892, Avg Recall: 0.808081, count: 99 -Region Avg IOU: 0.610934, Class: 1.000000, Obj: 0.397267, No Obj: 0.002792, Avg Recall: 0.696629, count: 89 -Region Avg IOU: 0.650765, Class: 1.000000, Obj: 0.418200, No Obj: 0.003190, Avg Recall: 0.796610, count: 118 -Region Avg IOU: 0.633557, Class: 1.000000, Obj: 0.379451, No Obj: 0.003221, Avg Recall: 0.783784, count: 111 -3661: 0.429244, 0.446954 avg, 0.001000 rate, 28.419949 seconds, 937216 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.636234, Class: 1.000000, Obj: 0.416533, No Obj: 0.003100, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.633099, Class: 1.000000, Obj: 0.381807, No Obj: 0.003057, Avg Recall: 0.809091, count: 110 -Region Avg IOU: 0.653637, Class: 1.000000, Obj: 0.431067, No Obj: 0.002839, Avg Recall: 0.825243, count: 103 -Region Avg IOU: 0.650541, Class: 1.000000, Obj: 0.431548, No Obj: 0.002858, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.636786, Class: 1.000000, Obj: 0.407748, No Obj: 0.003162, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.639493, Class: 1.000000, Obj: 0.394593, No Obj: 0.003155, Avg Recall: 0.808989, count: 89 -Region Avg IOU: 0.628450, Class: 1.000000, Obj: 0.374358, No Obj: 0.003296, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.629437, Class: 1.000000, Obj: 0.335560, No Obj: 0.003050, Avg Recall: 0.782609, count: 92 -3662: 0.461306, 0.448389 avg, 0.001000 rate, 29.738482 seconds, 937472 images -Loaded: 0.000119 seconds -Region Avg IOU: 0.611483, Class: 1.000000, Obj: 0.366954, No Obj: 0.002476, Avg Recall: 0.770492, count: 122 -Region Avg IOU: 0.690682, Class: 1.000000, Obj: 0.458761, No Obj: 0.002539, Avg Recall: 0.879121, count: 91 -Region Avg IOU: 0.646063, Class: 1.000000, Obj: 0.366971, No Obj: 0.002937, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.622751, Class: 1.000000, Obj: 0.368303, No Obj: 0.002754, Avg Recall: 0.795082, count: 122 -Region Avg IOU: 0.595709, Class: 1.000000, Obj: 0.331860, No Obj: 0.002736, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.621614, Class: 1.000000, Obj: 0.380806, No Obj: 0.003152, Avg Recall: 0.783582, count: 134 -Region Avg IOU: 0.638042, Class: 1.000000, Obj: 0.387007, No Obj: 0.002495, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.665273, Class: 1.000000, Obj: 0.438620, No Obj: 0.002638, Avg Recall: 0.860465, count: 86 -3663: 0.483803, 0.451930 avg, 0.001000 rate, 29.475456 seconds, 937728 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.638858, Class: 1.000000, Obj: 0.383956, No Obj: 0.002446, Avg Recall: 0.753968, count: 126 -Region Avg IOU: 0.642424, Class: 1.000000, Obj: 0.371949, No Obj: 0.002333, Avg Recall: 0.862745, count: 102 -Region Avg IOU: 0.640629, Class: 1.000000, Obj: 0.347823, No Obj: 0.003228, Avg Recall: 0.801418, count: 141 -Region Avg IOU: 0.652619, Class: 1.000000, Obj: 0.356827, No Obj: 0.002556, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.605581, Class: 1.000000, Obj: 0.339809, No Obj: 0.002757, Avg Recall: 0.712121, count: 132 -Region Avg IOU: 0.654276, Class: 1.000000, Obj: 0.353112, No Obj: 0.002850, Avg Recall: 0.833333, count: 126 -Region Avg IOU: 0.680134, Class: 1.000000, Obj: 0.400958, No Obj: 0.002627, Avg Recall: 0.843750, count: 96 -Region Avg IOU: 0.649501, Class: 1.000000, Obj: 0.294049, No Obj: 0.002818, Avg Recall: 0.829457, count: 129 -3664: 0.530675, 0.459805 avg, 0.001000 rate, 30.185514 seconds, 937984 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.663411, Class: 1.000000, Obj: 0.354547, No Obj: 0.002448, Avg Recall: 0.835052, count: 97 -Region Avg IOU: 0.687278, Class: 1.000000, Obj: 0.411163, No Obj: 0.002605, Avg Recall: 0.875000, count: 112 -Region Avg IOU: 0.640036, Class: 1.000000, Obj: 0.327388, No Obj: 0.002496, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.636065, Class: 1.000000, Obj: 0.312734, No Obj: 0.002406, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.677876, Class: 1.000000, Obj: 0.345959, No Obj: 0.002235, Avg Recall: 0.842697, count: 89 -Region Avg IOU: 0.653751, Class: 1.000000, Obj: 0.359385, No Obj: 0.003085, Avg Recall: 0.818792, count: 149 -Region Avg IOU: 0.670861, Class: 1.000000, Obj: 0.332383, No Obj: 0.002494, Avg Recall: 0.831579, count: 95 -Region Avg IOU: 0.624017, Class: 1.000000, Obj: 0.360390, No Obj: 0.002399, Avg Recall: 0.773913, count: 115 -3665: 0.467939, 0.460618 avg, 0.001000 rate, 29.759695 seconds, 938240 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.680347, Class: 1.000000, Obj: 0.325689, No Obj: 0.002571, Avg Recall: 0.853448, count: 116 -Region Avg IOU: 0.666582, Class: 1.000000, Obj: 0.406094, No Obj: 0.002341, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.664105, Class: 1.000000, Obj: 0.334530, No Obj: 0.002160, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.645674, Class: 1.000000, Obj: 0.329876, No Obj: 0.002037, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.653232, Class: 1.000000, Obj: 0.347586, No Obj: 0.002087, Avg Recall: 0.831169, count: 77 -Region Avg IOU: 0.680572, Class: 1.000000, Obj: 0.352334, No Obj: 0.002230, Avg Recall: 0.858824, count: 85 -Region Avg IOU: 0.617074, Class: 1.000000, Obj: 0.345907, No Obj: 0.002386, Avg Recall: 0.731343, count: 134 -Region Avg IOU: 0.623170, Class: 1.000000, Obj: 0.310606, No Obj: 0.002048, Avg Recall: 0.763158, count: 76 -3666: 0.451417, 0.459698 avg, 0.001000 rate, 30.511984 seconds, 938496 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.660777, Class: 1.000000, Obj: 0.333143, No Obj: 0.002358, Avg Recall: 0.823009, count: 113 -Region Avg IOU: 0.614835, Class: 1.000000, Obj: 0.282648, No Obj: 0.002521, Avg Recall: 0.752137, count: 117 -Region Avg IOU: 0.709430, Class: 1.000000, Obj: 0.392391, No Obj: 0.002392, Avg Recall: 0.902439, count: 82 -Region Avg IOU: 0.627794, Class: 1.000000, Obj: 0.307137, No Obj: 0.002596, Avg Recall: 0.771186, count: 118 -Region Avg IOU: 0.598906, Class: 1.000000, Obj: 0.350456, No Obj: 0.002204, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.651635, Class: 1.000000, Obj: 0.373809, No Obj: 0.002368, Avg Recall: 0.818182, count: 110 -Region Avg IOU: 0.615851, Class: 1.000000, Obj: 0.296631, No Obj: 0.002267, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.652797, Class: 1.000000, Obj: 0.295906, No Obj: 0.002446, Avg Recall: 0.860656, count: 122 -3667: 0.458013, 0.459529 avg, 0.001000 rate, 28.476185 seconds, 938752 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.647572, Class: 1.000000, Obj: 0.366971, No Obj: 0.002186, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.636740, Class: 1.000000, Obj: 0.365053, No Obj: 0.002692, Avg Recall: 0.780303, count: 132 -Region Avg IOU: 0.651964, Class: 1.000000, Obj: 0.332277, No Obj: 0.002582, Avg Recall: 0.828829, count: 111 -Region Avg IOU: 0.704807, Class: 1.000000, Obj: 0.418107, No Obj: 0.002349, Avg Recall: 0.931035, count: 87 -Region Avg IOU: 0.645726, Class: 1.000000, Obj: 0.315103, No Obj: 0.002065, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.619325, Class: 1.000000, Obj: 0.301518, No Obj: 0.001917, Avg Recall: 0.750000, count: 72 -Region Avg IOU: 0.666177, Class: 1.000000, Obj: 0.378602, No Obj: 0.002563, Avg Recall: 0.818965, count: 116 -Region Avg IOU: 0.672823, Class: 1.000000, Obj: 0.315772, No Obj: 0.002261, Avg Recall: 0.834862, count: 109 -3668: 0.441670, 0.457744 avg, 0.001000 rate, 30.251865 seconds, 939008 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.602816, Class: 1.000000, Obj: 0.352023, No Obj: 0.002490, Avg Recall: 0.703704, count: 135 -Region Avg IOU: 0.620839, Class: 1.000000, Obj: 0.292676, No Obj: 0.001984, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.635539, Class: 1.000000, Obj: 0.285130, No Obj: 0.002288, Avg Recall: 0.800000, count: 95 -Region Avg IOU: 0.663085, Class: 1.000000, Obj: 0.376456, No Obj: 0.002808, Avg Recall: 0.835821, count: 134 -Region Avg IOU: 0.630386, Class: 1.000000, Obj: 0.330039, No Obj: 0.002302, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.661927, Class: 1.000000, Obj: 0.384477, No Obj: 0.002484, Avg Recall: 0.854167, count: 96 -Region Avg IOU: 0.667083, Class: 1.000000, Obj: 0.371296, No Obj: 0.002391, Avg Recall: 0.885714, count: 105 -Region Avg IOU: 0.634828, Class: 1.000000, Obj: 0.338122, No Obj: 0.002284, Avg Recall: 0.786325, count: 117 -3669: 0.466926, 0.458662 avg, 0.001000 rate, 30.226055 seconds, 939264 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.623496, Class: 1.000000, Obj: 0.342174, No Obj: 0.002423, Avg Recall: 0.789474, count: 114 -Region Avg IOU: 0.672861, Class: 1.000000, Obj: 0.379251, No Obj: 0.002454, Avg Recall: 0.855670, count: 97 -Region Avg IOU: 0.642898, Class: 1.000000, Obj: 0.299454, No Obj: 0.002028, Avg Recall: 0.776471, count: 85 -Region Avg IOU: 0.658550, Class: 1.000000, Obj: 0.328066, No Obj: 0.002503, Avg Recall: 0.870588, count: 85 -Region Avg IOU: 0.630292, Class: 1.000000, Obj: 0.321588, No Obj: 0.002610, Avg Recall: 0.818898, count: 127 -Region Avg IOU: 0.646735, Class: 1.000000, Obj: 0.362257, No Obj: 0.002632, Avg Recall: 0.807018, count: 114 -Region Avg IOU: 0.647658, Class: 1.000000, Obj: 0.391927, No Obj: 0.002307, Avg Recall: 0.797619, count: 84 -Region Avg IOU: 0.669616, Class: 1.000000, Obj: 0.415708, No Obj: 0.002158, Avg Recall: 0.800000, count: 75 -3670: 0.424530, 0.455249 avg, 0.001000 rate, 29.235165 seconds, 939520 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 11.784248 seconds -Region Avg IOU: 0.664770, Class: 1.000000, Obj: 0.326932, No Obj: 0.002864, Avg Recall: 0.840000, count: 125 -Region Avg IOU: 0.613292, Class: 1.000000, Obj: 0.409291, No Obj: 0.002377, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.630781, Class: 1.000000, Obj: 0.359516, No Obj: 0.002417, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.682272, Class: 1.000000, Obj: 0.450176, No Obj: 0.002315, Avg Recall: 0.885057, count: 87 -Region Avg IOU: 0.642197, Class: 1.000000, Obj: 0.356229, No Obj: 0.002973, Avg Recall: 0.842975, count: 121 -Region Avg IOU: 0.624912, Class: 1.000000, Obj: 0.304776, No Obj: 0.002721, Avg Recall: 0.796875, count: 128 -Region Avg IOU: 0.632805, Class: 1.000000, Obj: 0.370980, No Obj: 0.002358, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.681764, Class: 1.000000, Obj: 0.393329, No Obj: 0.002699, Avg Recall: 0.869159, count: 107 -3671: 0.447453, 0.454469 avg, 0.001000 rate, 27.349661 seconds, 939776 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.628261, Class: 1.000000, Obj: 0.372513, No Obj: 0.002570, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.633355, Class: 1.000000, Obj: 0.413794, No Obj: 0.003208, Avg Recall: 0.729167, count: 144 -Region Avg IOU: 0.638805, Class: 1.000000, Obj: 0.368997, No Obj: 0.002415, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.628318, Class: 1.000000, Obj: 0.394658, No Obj: 0.002752, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.651136, Class: 1.000000, Obj: 0.420619, No Obj: 0.002615, Avg Recall: 0.788618, count: 123 -Region Avg IOU: 0.630230, Class: 1.000000, Obj: 0.328954, No Obj: 0.002826, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.590417, Class: 1.000000, Obj: 0.304976, No Obj: 0.002610, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.618979, Class: 1.000000, Obj: 0.394020, No Obj: 0.002190, Avg Recall: 0.722772, count: 101 -3672: 0.438932, 0.452915 avg, 0.001000 rate, 28.643316 seconds, 940032 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.632741, Class: 1.000000, Obj: 0.401904, No Obj: 0.003192, Avg Recall: 0.786885, count: 122 -Region Avg IOU: 0.631969, Class: 1.000000, Obj: 0.359631, No Obj: 0.002755, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.595043, Class: 1.000000, Obj: 0.378046, No Obj: 0.002764, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.653512, Class: 1.000000, Obj: 0.379419, No Obj: 0.002542, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.649322, Class: 1.000000, Obj: 0.428408, No Obj: 0.002595, Avg Recall: 0.811111, count: 90 -Region Avg IOU: 0.567327, Class: 1.000000, Obj: 0.353148, No Obj: 0.002706, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.622650, Class: 1.000000, Obj: 0.405190, No Obj: 0.002909, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.631811, Class: 1.000000, Obj: 0.349604, No Obj: 0.003126, Avg Recall: 0.791667, count: 144 -3673: 0.467478, 0.454372 avg, 0.001000 rate, 28.567101 seconds, 940288 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.676106, Class: 1.000000, Obj: 0.441346, No Obj: 0.003217, Avg Recall: 0.836066, count: 122 -Region Avg IOU: 0.655057, Class: 1.000000, Obj: 0.405641, No Obj: 0.002495, Avg Recall: 0.802469, count: 81 -Region Avg IOU: 0.666175, Class: 1.000000, Obj: 0.391749, No Obj: 0.002993, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.604802, Class: 1.000000, Obj: 0.357741, No Obj: 0.003314, Avg Recall: 0.723881, count: 134 -Region Avg IOU: 0.629565, Class: 1.000000, Obj: 0.374952, No Obj: 0.002812, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.592845, Class: 1.000000, Obj: 0.428996, No Obj: 0.002873, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.621696, Class: 1.000000, Obj: 0.380998, No Obj: 0.003028, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.640898, Class: 1.000000, Obj: 0.360525, No Obj: 0.003305, Avg Recall: 0.769231, count: 117 -3674: 0.471674, 0.456102 avg, 0.001000 rate, 28.418203 seconds, 940544 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.603393, Class: 1.000000, Obj: 0.409539, No Obj: 0.003111, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.649728, Class: 1.000000, Obj: 0.413069, No Obj: 0.002824, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.653357, Class: 1.000000, Obj: 0.365736, No Obj: 0.003144, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.656548, Class: 1.000000, Obj: 0.463051, No Obj: 0.003059, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.676958, Class: 1.000000, Obj: 0.426507, No Obj: 0.002927, Avg Recall: 0.844444, count: 90 -Region Avg IOU: 0.650196, Class: 1.000000, Obj: 0.438163, No Obj: 0.003084, Avg Recall: 0.822430, count: 107 -Region Avg IOU: 0.581286, Class: 1.000000, Obj: 0.413058, No Obj: 0.002957, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.611632, Class: 1.000000, Obj: 0.333697, No Obj: 0.003399, Avg Recall: 0.755245, count: 143 -3675: 0.474045, 0.457896 avg, 0.001000 rate, 28.150194 seconds, 940800 images -Loaded: 0.000059 seconds -Region Avg IOU: 0.662426, Class: 1.000000, Obj: 0.388378, No Obj: 0.003377, Avg Recall: 0.834711, count: 121 -Region Avg IOU: 0.624553, Class: 1.000000, Obj: 0.358211, No Obj: 0.002863, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.652186, Class: 1.000000, Obj: 0.453821, No Obj: 0.002879, Avg Recall: 0.819444, count: 72 -Region Avg IOU: 0.613636, Class: 1.000000, Obj: 0.399678, No Obj: 0.002470, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.621973, Class: 1.000000, Obj: 0.390454, No Obj: 0.002974, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.589793, Class: 1.000000, Obj: 0.381747, No Obj: 0.003079, Avg Recall: 0.718750, count: 128 -Region Avg IOU: 0.622520, Class: 1.000000, Obj: 0.359766, No Obj: 0.002744, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.621096, Class: 1.000000, Obj: 0.366695, No Obj: 0.002501, Avg Recall: 0.734940, count: 83 -3676: 0.421804, 0.454287 avg, 0.001000 rate, 26.959885 seconds, 941056 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.598441, Class: 1.000000, Obj: 0.386267, No Obj: 0.002742, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.599303, Class: 1.000000, Obj: 0.431574, No Obj: 0.002285, Avg Recall: 0.739726, count: 73 -Region Avg IOU: 0.621897, Class: 1.000000, Obj: 0.426264, No Obj: 0.002658, Avg Recall: 0.747126, count: 87 -Region Avg IOU: 0.608453, Class: 1.000000, Obj: 0.432337, No Obj: 0.002473, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.691159, Class: 1.000000, Obj: 0.454102, No Obj: 0.002917, Avg Recall: 0.903846, count: 104 -Region Avg IOU: 0.619750, Class: 1.000000, Obj: 0.410900, No Obj: 0.002997, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.606935, Class: 1.000000, Obj: 0.369702, No Obj: 0.003004, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.615757, Class: 1.000000, Obj: 0.358881, No Obj: 0.002591, Avg Recall: 0.767677, count: 99 -3677: 0.398088, 0.448667 avg, 0.001000 rate, 28.808245 seconds, 941312 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.645603, Class: 1.000000, Obj: 0.403184, No Obj: 0.002723, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.612264, Class: 1.000000, Obj: 0.362212, No Obj: 0.002679, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.679630, Class: 1.000000, Obj: 0.367548, No Obj: 0.002507, Avg Recall: 0.857143, count: 98 -Region Avg IOU: 0.651297, Class: 1.000000, Obj: 0.450622, No Obj: 0.003123, Avg Recall: 0.825000, count: 120 -Region Avg IOU: 0.597613, Class: 1.000000, Obj: 0.338867, No Obj: 0.002577, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.599184, Class: 1.000000, Obj: 0.389258, No Obj: 0.003003, Avg Recall: 0.713178, count: 129 -Region Avg IOU: 0.638191, Class: 1.000000, Obj: 0.419813, No Obj: 0.002792, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.631824, Class: 1.000000, Obj: 0.371343, No Obj: 0.002668, Avg Recall: 0.762712, count: 118 -3678: 0.461553, 0.449956 avg, 0.001000 rate, 27.455040 seconds, 941568 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.651610, Class: 1.000000, Obj: 0.409389, No Obj: 0.002662, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.640806, Class: 1.000000, Obj: 0.385715, No Obj: 0.002844, Avg Recall: 0.779528, count: 127 -Region Avg IOU: 0.601300, Class: 1.000000, Obj: 0.356667, No Obj: 0.002610, Avg Recall: 0.701149, count: 87 -Region Avg IOU: 0.656537, Class: 1.000000, Obj: 0.384564, No Obj: 0.002821, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.599218, Class: 1.000000, Obj: 0.352372, No Obj: 0.002294, Avg Recall: 0.662651, count: 83 -Region Avg IOU: 0.628499, Class: 1.000000, Obj: 0.390379, No Obj: 0.002699, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.629504, Class: 1.000000, Obj: 0.344432, No Obj: 0.002626, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.584002, Class: 1.000000, Obj: 0.329400, No Obj: 0.002662, Avg Recall: 0.719298, count: 114 -3679: 0.438602, 0.448820 avg, 0.001000 rate, 28.207676 seconds, 941824 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.632481, Class: 1.000000, Obj: 0.298360, No Obj: 0.002186, Avg Recall: 0.732558, count: 86 -Region Avg IOU: 0.650123, Class: 1.000000, Obj: 0.375944, No Obj: 0.002427, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.608455, Class: 1.000000, Obj: 0.283777, No Obj: 0.002321, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.641464, Class: 1.000000, Obj: 0.339716, No Obj: 0.003212, Avg Recall: 0.828358, count: 134 -Region Avg IOU: 0.653962, Class: 1.000000, Obj: 0.342093, No Obj: 0.002405, Avg Recall: 0.817204, count: 93 -Region Avg IOU: 0.627344, Class: 1.000000, Obj: 0.345283, No Obj: 0.002400, Avg Recall: 0.793651, count: 126 -Region Avg IOU: 0.682801, Class: 1.000000, Obj: 0.401896, No Obj: 0.002859, Avg Recall: 0.894737, count: 95 -Region Avg IOU: 0.615589, Class: 1.000000, Obj: 0.355850, No Obj: 0.002629, Avg Recall: 0.750000, count: 108 -3680: 0.460136, 0.449952 avg, 0.001000 rate, 27.981552 seconds, 942080 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.493495 seconds -Region Avg IOU: 0.467541, Class: 1.000000, Obj: 0.215273, No Obj: 0.002909, Avg Recall: 0.522523, count: 111 -Region Avg IOU: 0.473836, Class: 1.000000, Obj: 0.212971, No Obj: 0.002515, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.480866, Class: 1.000000, Obj: 0.233568, No Obj: 0.002970, Avg Recall: 0.514286, count: 105 -Region Avg IOU: 0.479091, Class: 1.000000, Obj: 0.235443, No Obj: 0.002820, Avg Recall: 0.541667, count: 120 -Region Avg IOU: 0.430107, Class: 1.000000, Obj: 0.196035, No Obj: 0.003187, Avg Recall: 0.447552, count: 143 -Region Avg IOU: 0.514581, Class: 1.000000, Obj: 0.246530, No Obj: 0.002973, Avg Recall: 0.596491, count: 114 -Region Avg IOU: 0.521033, Class: 1.000000, Obj: 0.215799, No Obj: 0.002478, Avg Recall: 0.561798, count: 89 -Region Avg IOU: 0.513707, Class: 1.000000, Obj: 0.224887, No Obj: 0.003455, Avg Recall: 0.638710, count: 155 -3681: 0.560530, 0.461010 avg, 0.001000 rate, 20.061052 seconds, 942336 images -Loaded: 1.302616 seconds -Region Avg IOU: 0.496391, Class: 1.000000, Obj: 0.242877, No Obj: 0.003042, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.466648, Class: 1.000000, Obj: 0.217433, No Obj: 0.002794, Avg Recall: 0.464912, count: 114 -Region Avg IOU: 0.508747, Class: 1.000000, Obj: 0.214824, No Obj: 0.002682, Avg Recall: 0.568182, count: 88 -Region Avg IOU: 0.478847, Class: 1.000000, Obj: 0.233317, No Obj: 0.002977, Avg Recall: 0.546219, count: 119 -Region Avg IOU: 0.486152, Class: 1.000000, Obj: 0.215990, No Obj: 0.003347, Avg Recall: 0.554745, count: 137 -Region Avg IOU: 0.505937, Class: 1.000000, Obj: 0.262195, No Obj: 0.003070, Avg Recall: 0.525253, count: 99 -Region Avg IOU: 0.547822, Class: 1.000000, Obj: 0.266124, No Obj: 0.003262, Avg Recall: 0.655738, count: 122 -Region Avg IOU: 0.510413, Class: 1.000000, Obj: 0.227659, No Obj: 0.003486, Avg Recall: 0.571429, count: 112 -3682: 0.513056, 0.466214 avg, 0.001000 rate, 19.131205 seconds, 942592 images -Loaded: 1.260655 seconds -Region Avg IOU: 0.484499, Class: 1.000000, Obj: 0.247399, No Obj: 0.003081, Avg Recall: 0.523810, count: 105 -Region Avg IOU: 0.513452, Class: 1.000000, Obj: 0.250572, No Obj: 0.003282, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.478653, Class: 1.000000, Obj: 0.244504, No Obj: 0.003286, Avg Recall: 0.525773, count: 97 -Region Avg IOU: 0.492249, Class: 1.000000, Obj: 0.287242, No Obj: 0.003096, Avg Recall: 0.529412, count: 119 -Region Avg IOU: 0.492631, Class: 1.000000, Obj: 0.251302, No Obj: 0.003715, Avg Recall: 0.579710, count: 138 -Region Avg IOU: 0.471402, Class: 1.000000, Obj: 0.241307, No Obj: 0.003233, Avg Recall: 0.523810, count: 105 -Region Avg IOU: 0.446025, Class: 1.000000, Obj: 0.263248, No Obj: 0.002898, Avg Recall: 0.424528, count: 106 -Region Avg IOU: 0.505783, Class: 1.000000, Obj: 0.243595, No Obj: 0.003292, Avg Recall: 0.574074, count: 108 -3683: 0.535604, 0.473153 avg, 0.001000 rate, 17.950945 seconds, 942848 images -Loaded: 2.040558 seconds -Region Avg IOU: 0.485291, Class: 1.000000, Obj: 0.318767, No Obj: 0.003726, Avg Recall: 0.525862, count: 116 -Region Avg IOU: 0.478651, Class: 1.000000, Obj: 0.236910, No Obj: 0.003302, Avg Recall: 0.593750, count: 96 -Region Avg IOU: 0.528340, Class: 1.000000, Obj: 0.299868, No Obj: 0.003427, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.475594, Class: 1.000000, Obj: 0.291670, No Obj: 0.003264, Avg Recall: 0.520000, count: 100 -Region Avg IOU: 0.542496, Class: 1.000000, Obj: 0.281734, No Obj: 0.003507, Avg Recall: 0.626506, count: 83 -Region Avg IOU: 0.492694, Class: 1.000000, Obj: 0.277899, No Obj: 0.003309, Avg Recall: 0.543478, count: 92 -Region Avg IOU: 0.493262, Class: 1.000000, Obj: 0.311109, No Obj: 0.003240, Avg Recall: 0.534884, count: 86 -Region Avg IOU: 0.441896, Class: 1.000000, Obj: 0.253925, No Obj: 0.003672, Avg Recall: 0.412698, count: 126 -3684: 0.436808, 0.469519 avg, 0.001000 rate, 21.078300 seconds, 943104 images -Loaded: 0.858593 seconds -Region Avg IOU: 0.436492, Class: 1.000000, Obj: 0.245104, No Obj: 0.003670, Avg Recall: 0.471545, count: 123 -Region Avg IOU: 0.481025, Class: 1.000000, Obj: 0.325206, No Obj: 0.003684, Avg Recall: 0.525253, count: 99 -Region Avg IOU: 0.474026, Class: 1.000000, Obj: 0.327858, No Obj: 0.003867, Avg Recall: 0.474576, count: 118 -Region Avg IOU: 0.491919, Class: 1.000000, Obj: 0.327965, No Obj: 0.003831, Avg Recall: 0.539326, count: 89 -Region Avg IOU: 0.535378, Class: 1.000000, Obj: 0.371618, No Obj: 0.004144, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.496305, Class: 1.000000, Obj: 0.306636, No Obj: 0.003620, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.528973, Class: 1.000000, Obj: 0.284634, No Obj: 0.003985, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.496358, Class: 1.000000, Obj: 0.263026, No Obj: 0.004167, Avg Recall: 0.530435, count: 115 -3685: 0.487920, 0.471359 avg, 0.001000 rate, 17.762985 seconds, 943360 images -Loaded: 1.919424 seconds -Region Avg IOU: 0.484020, Class: 1.000000, Obj: 0.332255, No Obj: 0.004076, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.553550, Class: 1.000000, Obj: 0.351244, No Obj: 0.004747, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.429636, Class: 1.000000, Obj: 0.277241, No Obj: 0.004244, Avg Recall: 0.396947, count: 131 -Region Avg IOU: 0.527356, Class: 1.000000, Obj: 0.326228, No Obj: 0.004457, Avg Recall: 0.587719, count: 114 -Region Avg IOU: 0.553450, Class: 1.000000, Obj: 0.365289, No Obj: 0.004273, Avg Recall: 0.609756, count: 82 -Region Avg IOU: 0.502321, Class: 1.000000, Obj: 0.329550, No Obj: 0.004361, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.499828, Class: 1.000000, Obj: 0.311035, No Obj: 0.004480, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.496566, Class: 1.000000, Obj: 0.362043, No Obj: 0.003707, Avg Recall: 0.556962, count: 79 -3686: 0.444771, 0.468700 avg, 0.001000 rate, 21.122652 seconds, 943616 images -Loaded: 1.536475 seconds -Region Avg IOU: 0.486445, Class: 1.000000, Obj: 0.321571, No Obj: 0.005159, Avg Recall: 0.563025, count: 119 -Region Avg IOU: 0.476436, Class: 1.000000, Obj: 0.298177, No Obj: 0.005124, Avg Recall: 0.541353, count: 133 -Region Avg IOU: 0.533236, Class: 1.000000, Obj: 0.369174, No Obj: 0.004785, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.508641, Class: 1.000000, Obj: 0.321307, No Obj: 0.005122, Avg Recall: 0.589147, count: 129 -Region Avg IOU: 0.518669, Class: 1.000000, Obj: 0.374599, No Obj: 0.005214, Avg Recall: 0.609023, count: 133 -Region Avg IOU: 0.455457, Class: 1.000000, Obj: 0.319371, No Obj: 0.005067, Avg Recall: 0.462687, count: 134 -Region Avg IOU: 0.529884, Class: 1.000000, Obj: 0.395018, No Obj: 0.005278, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.471146, Class: 1.000000, Obj: 0.369733, No Obj: 0.004869, Avg Recall: 0.467742, count: 124 -3687: 0.575895, 0.479420 avg, 0.001000 rate, 17.784245 seconds, 943872 images -Loaded: 3.876940 seconds -Region Avg IOU: 0.501007, Class: 1.000000, Obj: 0.396789, No Obj: 0.005070, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.456021, Class: 1.000000, Obj: 0.328265, No Obj: 0.004977, Avg Recall: 0.494949, count: 99 -Region Avg IOU: 0.514221, Class: 1.000000, Obj: 0.427358, No Obj: 0.004541, Avg Recall: 0.556962, count: 79 -Region Avg IOU: 0.509180, Class: 1.000000, Obj: 0.367799, No Obj: 0.005384, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.494499, Class: 1.000000, Obj: 0.372298, No Obj: 0.005174, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.450953, Class: 1.000000, Obj: 0.338863, No Obj: 0.005337, Avg Recall: 0.454545, count: 121 -Region Avg IOU: 0.506497, Class: 1.000000, Obj: 0.348968, No Obj: 0.004933, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.455161, Class: 1.000000, Obj: 0.338211, No Obj: 0.004840, Avg Recall: 0.504673, count: 107 -3688: 0.448267, 0.476304 avg, 0.001000 rate, 19.581263 seconds, 944128 images -Loaded: 1.446523 seconds -Region Avg IOU: 0.492791, Class: 1.000000, Obj: 0.382842, No Obj: 0.005799, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.490191, Class: 1.000000, Obj: 0.368773, No Obj: 0.005154, Avg Recall: 0.556701, count: 97 -Region Avg IOU: 0.484405, Class: 1.000000, Obj: 0.385066, No Obj: 0.005232, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.501151, Class: 1.000000, Obj: 0.399408, No Obj: 0.005969, Avg Recall: 0.582734, count: 139 -Region Avg IOU: 0.428739, Class: 1.000000, Obj: 0.341206, No Obj: 0.004960, Avg Recall: 0.415929, count: 113 -Region Avg IOU: 0.461916, Class: 1.000000, Obj: 0.366514, No Obj: 0.005222, Avg Recall: 0.522124, count: 113 -Region Avg IOU: 0.486695, Class: 1.000000, Obj: 0.359232, No Obj: 0.005317, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.462765, Class: 1.000000, Obj: 0.402901, No Obj: 0.005160, Avg Recall: 0.504505, count: 111 -3689: 0.497638, 0.478438 avg, 0.001000 rate, 15.641436 seconds, 944384 images -Loaded: 7.596829 seconds -Region Avg IOU: 0.456973, Class: 1.000000, Obj: 0.355779, No Obj: 0.005655, Avg Recall: 0.495727, count: 117 -Region Avg IOU: 0.495142, Class: 1.000000, Obj: 0.385075, No Obj: 0.005351, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.523333, Class: 1.000000, Obj: 0.390623, No Obj: 0.006907, Avg Recall: 0.643357, count: 143 -Region Avg IOU: 0.551870, Class: 1.000000, Obj: 0.461391, No Obj: 0.005005, Avg Recall: 0.620690, count: 87 -Region Avg IOU: 0.522076, Class: 1.000000, Obj: 0.425582, No Obj: 0.005491, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.525047, Class: 1.000000, Obj: 0.364798, No Obj: 0.006227, Avg Recall: 0.563492, count: 126 -Region Avg IOU: 0.513234, Class: 1.000000, Obj: 0.355334, No Obj: 0.006181, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.460688, Class: 1.000000, Obj: 0.361948, No Obj: 0.005217, Avg Recall: 0.485981, count: 107 -3690: 0.501187, 0.480713 avg, 0.001000 rate, 18.986307 seconds, 944640 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000070 seconds -Region Avg IOU: 0.592635, Class: 1.000000, Obj: 0.499101, No Obj: 0.004528, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.622127, Class: 1.000000, Obj: 0.445699, No Obj: 0.004759, Avg Recall: 0.750000, count: 132 -Region Avg IOU: 0.624188, Class: 1.000000, Obj: 0.549512, No Obj: 0.004581, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.613980, Class: 1.000000, Obj: 0.514870, No Obj: 0.004971, Avg Recall: 0.755725, count: 131 -Region Avg IOU: 0.576650, Class: 1.000000, Obj: 0.451543, No Obj: 0.004405, Avg Recall: 0.706349, count: 126 -Region Avg IOU: 0.605654, Class: 1.000000, Obj: 0.503021, No Obj: 0.004623, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.591161, Class: 1.000000, Obj: 0.477391, No Obj: 0.004305, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.617290, Class: 1.000000, Obj: 0.497951, No Obj: 0.003639, Avg Recall: 0.727273, count: 99 -3691: 0.477896, 0.480431 avg, 0.001000 rate, 27.761332 seconds, 944896 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.609932, Class: 1.000000, Obj: 0.422209, No Obj: 0.004024, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.638826, Class: 1.000000, Obj: 0.432818, No Obj: 0.003779, Avg Recall: 0.801980, count: 101 -Region Avg IOU: 0.596855, Class: 1.000000, Obj: 0.443799, No Obj: 0.004227, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.646814, Class: 1.000000, Obj: 0.494602, No Obj: 0.004199, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.596898, Class: 1.000000, Obj: 0.507602, No Obj: 0.003974, Avg Recall: 0.733333, count: 135 -Region Avg IOU: 0.616294, Class: 1.000000, Obj: 0.387349, No Obj: 0.003758, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.641743, Class: 1.000000, Obj: 0.463327, No Obj: 0.003789, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.655819, Class: 1.000000, Obj: 0.493093, No Obj: 0.004258, Avg Recall: 0.830508, count: 118 -3692: 0.478838, 0.480272 avg, 0.001000 rate, 26.399015 seconds, 945152 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.629364, Class: 1.000000, Obj: 0.432993, No Obj: 0.003438, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.585464, Class: 1.000000, Obj: 0.405828, No Obj: 0.003362, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.612878, Class: 1.000000, Obj: 0.431994, No Obj: 0.003402, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.634995, Class: 1.000000, Obj: 0.400296, No Obj: 0.003481, Avg Recall: 0.816794, count: 131 -Region Avg IOU: 0.628660, Class: 1.000000, Obj: 0.447671, No Obj: 0.002675, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.653756, Class: 1.000000, Obj: 0.436439, No Obj: 0.003710, Avg Recall: 0.840708, count: 113 -Region Avg IOU: 0.607355, Class: 1.000000, Obj: 0.383798, No Obj: 0.003468, Avg Recall: 0.722222, count: 126 -Region Avg IOU: 0.620269, Class: 1.000000, Obj: 0.391091, No Obj: 0.002953, Avg Recall: 0.757009, count: 107 -3693: 0.455078, 0.477752 avg, 0.001000 rate, 28.004442 seconds, 945408 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.602833, Class: 1.000000, Obj: 0.342152, No Obj: 0.002530, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.622311, Class: 1.000000, Obj: 0.371732, No Obj: 0.002545, Avg Recall: 0.852632, count: 95 -Region Avg IOU: 0.591247, Class: 1.000000, Obj: 0.422758, No Obj: 0.002517, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.588838, Class: 1.000000, Obj: 0.358940, No Obj: 0.002491, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.608299, Class: 1.000000, Obj: 0.335493, No Obj: 0.002685, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.645109, Class: 1.000000, Obj: 0.434532, No Obj: 0.002391, Avg Recall: 0.790123, count: 81 -Region Avg IOU: 0.622267, Class: 1.000000, Obj: 0.381533, No Obj: 0.002621, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.629025, Class: 1.000000, Obj: 0.373323, No Obj: 0.002485, Avg Recall: 0.761062, count: 113 -3694: 0.439273, 0.473904 avg, 0.001000 rate, 27.323959 seconds, 945664 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.585923, Class: 1.000000, Obj: 0.303204, No Obj: 0.002050, Avg Recall: 0.694118, count: 85 -Region Avg IOU: 0.640195, Class: 1.000000, Obj: 0.335972, No Obj: 0.002315, Avg Recall: 0.811111, count: 90 -Region Avg IOU: 0.633808, Class: 1.000000, Obj: 0.334575, No Obj: 0.002501, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.583386, Class: 1.000000, Obj: 0.332864, No Obj: 0.002533, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.609197, Class: 1.000000, Obj: 0.340124, No Obj: 0.002211, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.623988, Class: 1.000000, Obj: 0.333957, No Obj: 0.002476, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.621493, Class: 1.000000, Obj: 0.345528, No Obj: 0.002555, Avg Recall: 0.725191, count: 131 -Region Avg IOU: 0.581140, Class: 1.000000, Obj: 0.314032, No Obj: 0.002390, Avg Recall: 0.707547, count: 106 -3695: 0.463606, 0.472875 avg, 0.001000 rate, 27.592913 seconds, 945920 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.584029, Class: 1.000000, Obj: 0.288803, No Obj: 0.001996, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.594387, Class: 1.000000, Obj: 0.256442, No Obj: 0.002296, Avg Recall: 0.761194, count: 134 -Region Avg IOU: 0.674617, Class: 1.000000, Obj: 0.280807, No Obj: 0.002050, Avg Recall: 0.833333, count: 96 -Region Avg IOU: 0.616169, Class: 1.000000, Obj: 0.292655, No Obj: 0.001991, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.602081, Class: 1.000000, Obj: 0.303854, No Obj: 0.002359, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.621561, Class: 1.000000, Obj: 0.299751, No Obj: 0.002193, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.623564, Class: 1.000000, Obj: 0.268464, No Obj: 0.002094, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.680233, Class: 1.000000, Obj: 0.353415, No Obj: 0.002205, Avg Recall: 0.804598, count: 87 -3696: 0.499183, 0.475505 avg, 0.001000 rate, 27.340052 seconds, 946176 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.625886, Class: 1.000000, Obj: 0.215966, No Obj: 0.002351, Avg Recall: 0.788321, count: 137 -Region Avg IOU: 0.608661, Class: 1.000000, Obj: 0.292037, No Obj: 0.001989, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.638140, Class: 1.000000, Obj: 0.315606, No Obj: 0.002065, Avg Recall: 0.808989, count: 89 -Region Avg IOU: 0.619995, Class: 1.000000, Obj: 0.254626, No Obj: 0.002451, Avg Recall: 0.780000, count: 150 -Region Avg IOU: 0.615189, Class: 1.000000, Obj: 0.290309, No Obj: 0.001907, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.634749, Class: 1.000000, Obj: 0.297043, No Obj: 0.001940, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.627627, Class: 1.000000, Obj: 0.280660, No Obj: 0.002103, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.621923, Class: 1.000000, Obj: 0.269613, No Obj: 0.001995, Avg Recall: 0.750000, count: 112 -3697: 0.476756, 0.475630 avg, 0.001000 rate, 27.345903 seconds, 946432 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.648136, Class: 1.000000, Obj: 0.303032, No Obj: 0.001926, Avg Recall: 0.826087, count: 92 -Region Avg IOU: 0.621680, Class: 1.000000, Obj: 0.284200, No Obj: 0.002010, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.678979, Class: 1.000000, Obj: 0.316883, No Obj: 0.001858, Avg Recall: 0.842105, count: 76 -Region Avg IOU: 0.610548, Class: 1.000000, Obj: 0.295912, No Obj: 0.001927, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.636524, Class: 1.000000, Obj: 0.274058, No Obj: 0.002101, Avg Recall: 0.791667, count: 120 -Region Avg IOU: 0.630611, Class: 1.000000, Obj: 0.315825, No Obj: 0.001997, Avg Recall: 0.781609, count: 87 -Region Avg IOU: 0.621646, Class: 1.000000, Obj: 0.282006, No Obj: 0.001856, Avg Recall: 0.758621, count: 87 -Region Avg IOU: 0.654246, Class: 1.000000, Obj: 0.258320, No Obj: 0.002240, Avg Recall: 0.842105, count: 114 -3698: 0.440827, 0.472150 avg, 0.001000 rate, 27.230431 seconds, 946688 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.632206, Class: 1.000000, Obj: 0.291970, No Obj: 0.002099, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.647985, Class: 1.000000, Obj: 0.336959, No Obj: 0.002232, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.621020, Class: 1.000000, Obj: 0.302854, No Obj: 0.002144, Avg Recall: 0.756098, count: 123 -Region Avg IOU: 0.616425, Class: 1.000000, Obj: 0.304202, No Obj: 0.002209, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.649890, Class: 1.000000, Obj: 0.327127, No Obj: 0.002015, Avg Recall: 0.800000, count: 95 -Region Avg IOU: 0.661933, Class: 1.000000, Obj: 0.350575, No Obj: 0.001969, Avg Recall: 0.867470, count: 83 -Region Avg IOU: 0.597469, Class: 1.000000, Obj: 0.287086, No Obj: 0.002150, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.626276, Class: 1.000000, Obj: 0.312045, No Obj: 0.001897, Avg Recall: 0.765957, count: 94 -3699: 0.420339, 0.466969 avg, 0.001000 rate, 27.493736 seconds, 946944 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.654544, Class: 1.000000, Obj: 0.331194, No Obj: 0.002551, Avg Recall: 0.792000, count: 125 -Region Avg IOU: 0.622830, Class: 1.000000, Obj: 0.312215, No Obj: 0.002247, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.609520, Class: 1.000000, Obj: 0.289014, No Obj: 0.002516, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.626815, Class: 1.000000, Obj: 0.298517, No Obj: 0.002265, Avg Recall: 0.733333, count: 120 -Region Avg IOU: 0.620614, Class: 1.000000, Obj: 0.305840, No Obj: 0.002309, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.649499, Class: 1.000000, Obj: 0.302140, No Obj: 0.002680, Avg Recall: 0.819549, count: 133 -Region Avg IOU: 0.643788, Class: 1.000000, Obj: 0.323393, No Obj: 0.002455, Avg Recall: 0.803419, count: 117 -Region Avg IOU: 0.613502, Class: 1.000000, Obj: 0.365084, No Obj: 0.002224, Avg Recall: 0.780220, count: 91 -3700: 0.481275, 0.468400 avg, 0.001000 rate, 27.089788 seconds, 947200 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 20.392452 seconds -Region Avg IOU: 0.621303, Class: 1.000000, Obj: 0.348013, No Obj: 0.003129, Avg Recall: 0.772727, count: 132 -Region Avg IOU: 0.600387, Class: 1.000000, Obj: 0.331971, No Obj: 0.002363, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.602552, Class: 1.000000, Obj: 0.320797, No Obj: 0.002658, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.643812, Class: 1.000000, Obj: 0.388371, No Obj: 0.002417, Avg Recall: 0.842593, count: 108 -Region Avg IOU: 0.657246, Class: 1.000000, Obj: 0.391131, No Obj: 0.002524, Avg Recall: 0.841463, count: 82 -Region Avg IOU: 0.662961, Class: 1.000000, Obj: 0.343425, No Obj: 0.002537, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.614593, Class: 1.000000, Obj: 0.320324, No Obj: 0.002615, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.626602, Class: 1.000000, Obj: 0.339349, No Obj: 0.002157, Avg Recall: 0.780220, count: 91 -3701: 0.424646, 0.464024 avg, 0.001000 rate, 27.553461 seconds, 947456 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.627282, Class: 1.000000, Obj: 0.393157, No Obj: 0.002747, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.652950, Class: 1.000000, Obj: 0.341078, No Obj: 0.002665, Avg Recall: 0.788732, count: 71 -Region Avg IOU: 0.634375, Class: 1.000000, Obj: 0.355697, No Obj: 0.003249, Avg Recall: 0.784615, count: 130 -Region Avg IOU: 0.632254, Class: 1.000000, Obj: 0.399887, No Obj: 0.002901, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.620699, Class: 1.000000, Obj: 0.391830, No Obj: 0.002607, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.676150, Class: 1.000000, Obj: 0.355548, No Obj: 0.002975, Avg Recall: 0.857143, count: 91 -Region Avg IOU: 0.633020, Class: 1.000000, Obj: 0.369676, No Obj: 0.002816, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.612370, Class: 1.000000, Obj: 0.345812, No Obj: 0.002935, Avg Recall: 0.731034, count: 145 -3702: 0.455810, 0.463203 avg, 0.001000 rate, 27.338064 seconds, 947712 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.605981, Class: 1.000000, Obj: 0.377367, No Obj: 0.003185, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.605516, Class: 1.000000, Obj: 0.405134, No Obj: 0.003049, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.623918, Class: 1.000000, Obj: 0.400993, No Obj: 0.003156, Avg Recall: 0.793388, count: 121 -Region Avg IOU: 0.626468, Class: 1.000000, Obj: 0.427892, No Obj: 0.003315, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.671360, Class: 1.000000, Obj: 0.490050, No Obj: 0.002991, Avg Recall: 0.797297, count: 74 -Region Avg IOU: 0.584430, Class: 1.000000, Obj: 0.396208, No Obj: 0.002704, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.562946, Class: 1.000000, Obj: 0.368685, No Obj: 0.003062, Avg Recall: 0.674603, count: 126 -Region Avg IOU: 0.616105, Class: 1.000000, Obj: 0.416388, No Obj: 0.003023, Avg Recall: 0.790909, count: 110 -3703: 0.413422, 0.458225 avg, 0.001000 rate, 28.227757 seconds, 947968 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.657076, Class: 1.000000, Obj: 0.451614, No Obj: 0.003202, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.629036, Class: 1.000000, Obj: 0.417229, No Obj: 0.003401, Avg Recall: 0.781955, count: 133 -Region Avg IOU: 0.621475, Class: 1.000000, Obj: 0.462662, No Obj: 0.002958, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.578781, Class: 1.000000, Obj: 0.328715, No Obj: 0.003274, Avg Recall: 0.688525, count: 122 -Region Avg IOU: 0.624327, Class: 1.000000, Obj: 0.457921, No Obj: 0.003098, Avg Recall: 0.711111, count: 90 -Region Avg IOU: 0.601837, Class: 1.000000, Obj: 0.351038, No Obj: 0.003110, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.587835, Class: 1.000000, Obj: 0.353442, No Obj: 0.002925, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.606968, Class: 1.000000, Obj: 0.415199, No Obj: 0.003144, Avg Recall: 0.714286, count: 91 -3704: 0.446532, 0.457055 avg, 0.001000 rate, 27.488136 seconds, 948224 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.559167, Class: 1.000000, Obj: 0.320117, No Obj: 0.002926, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.658496, Class: 1.000000, Obj: 0.440454, No Obj: 0.002937, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.580788, Class: 1.000000, Obj: 0.416574, No Obj: 0.003234, Avg Recall: 0.669565, count: 115 -Region Avg IOU: 0.606411, Class: 1.000000, Obj: 0.397208, No Obj: 0.003312, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.644018, Class: 1.000000, Obj: 0.413716, No Obj: 0.003008, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.629870, Class: 1.000000, Obj: 0.404890, No Obj: 0.003369, Avg Recall: 0.782609, count: 115 -Region Avg IOU: 0.606247, Class: 1.000000, Obj: 0.384610, No Obj: 0.002911, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.670305, Class: 1.000000, Obj: 0.462854, No Obj: 0.003350, Avg Recall: 0.847458, count: 118 -3705: 0.446983, 0.456048 avg, 0.001000 rate, 27.277952 seconds, 948480 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.646329, Class: 1.000000, Obj: 0.431950, No Obj: 0.003239, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.636465, Class: 1.000000, Obj: 0.409734, No Obj: 0.003299, Avg Recall: 0.823529, count: 102 -Region Avg IOU: 0.642522, Class: 1.000000, Obj: 0.460103, No Obj: 0.003383, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.614845, Class: 1.000000, Obj: 0.415559, No Obj: 0.002593, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.600878, Class: 1.000000, Obj: 0.374064, No Obj: 0.003530, Avg Recall: 0.773438, count: 128 -Region Avg IOU: 0.639383, Class: 1.000000, Obj: 0.423751, No Obj: 0.003403, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.645801, Class: 1.000000, Obj: 0.410447, No Obj: 0.002731, Avg Recall: 0.782609, count: 69 -Region Avg IOU: 0.607693, Class: 1.000000, Obj: 0.419890, No Obj: 0.003376, Avg Recall: 0.732283, count: 127 -3706: 0.469513, 0.457395 avg, 0.001000 rate, 28.272320 seconds, 948736 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.633005, Class: 1.000000, Obj: 0.386739, No Obj: 0.003166, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.612994, Class: 1.000000, Obj: 0.380184, No Obj: 0.002880, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.623955, Class: 1.000000, Obj: 0.429818, No Obj: 0.003472, Avg Recall: 0.793388, count: 121 -Region Avg IOU: 0.604156, Class: 1.000000, Obj: 0.412749, No Obj: 0.002962, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.640179, Class: 1.000000, Obj: 0.426480, No Obj: 0.003404, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.592081, Class: 1.000000, Obj: 0.361659, No Obj: 0.003120, Avg Recall: 0.706349, count: 126 -Region Avg IOU: 0.637135, Class: 1.000000, Obj: 0.495524, No Obj: 0.003118, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.610579, Class: 1.000000, Obj: 0.367446, No Obj: 0.003508, Avg Recall: 0.733333, count: 120 -3707: 0.484352, 0.460090 avg, 0.001000 rate, 27.692188 seconds, 948992 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.630459, Class: 1.000000, Obj: 0.406258, No Obj: 0.003139, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.533092, Class: 1.000000, Obj: 0.362188, No Obj: 0.002830, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.650842, Class: 1.000000, Obj: 0.349034, No Obj: 0.003293, Avg Recall: 0.831858, count: 113 -Region Avg IOU: 0.591988, Class: 1.000000, Obj: 0.347936, No Obj: 0.002814, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.619745, Class: 1.000000, Obj: 0.352811, No Obj: 0.002970, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.639813, Class: 1.000000, Obj: 0.413905, No Obj: 0.002885, Avg Recall: 0.770115, count: 87 -Region Avg IOU: 0.647917, Class: 1.000000, Obj: 0.380337, No Obj: 0.002942, Avg Recall: 0.781250, count: 96 -Region Avg IOU: 0.633130, Class: 1.000000, Obj: 0.386187, No Obj: 0.003331, Avg Recall: 0.800000, count: 125 -3708: 0.449506, 0.459032 avg, 0.001000 rate, 27.972752 seconds, 949248 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.560577, Class: 1.000000, Obj: 0.333229, No Obj: 0.002574, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.608748, Class: 1.000000, Obj: 0.417871, No Obj: 0.002403, Avg Recall: 0.737500, count: 80 -Region Avg IOU: 0.597564, Class: 1.000000, Obj: 0.374916, No Obj: 0.002739, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.610482, Class: 1.000000, Obj: 0.383151, No Obj: 0.003258, Avg Recall: 0.771812, count: 149 -Region Avg IOU: 0.626323, Class: 1.000000, Obj: 0.378333, No Obj: 0.002767, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.613425, Class: 1.000000, Obj: 0.389859, No Obj: 0.002793, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.560338, Class: 1.000000, Obj: 0.339413, No Obj: 0.003136, Avg Recall: 0.653595, count: 153 -Region Avg IOU: 0.632766, Class: 1.000000, Obj: 0.383579, No Obj: 0.003007, Avg Recall: 0.794872, count: 117 -3709: 0.466122, 0.459741 avg, 0.001000 rate, 28.329544 seconds, 949504 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.664206, Class: 1.000000, Obj: 0.431773, No Obj: 0.002508, Avg Recall: 0.872340, count: 94 -Region Avg IOU: 0.642686, Class: 1.000000, Obj: 0.363619, No Obj: 0.002651, Avg Recall: 0.824742, count: 97 -Region Avg IOU: 0.571232, Class: 1.000000, Obj: 0.376708, No Obj: 0.002840, Avg Recall: 0.658333, count: 120 -Region Avg IOU: 0.642165, Class: 1.000000, Obj: 0.359923, No Obj: 0.002879, Avg Recall: 0.777027, count: 148 -Region Avg IOU: 0.587289, Class: 1.000000, Obj: 0.387073, No Obj: 0.002501, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.616303, Class: 1.000000, Obj: 0.305016, No Obj: 0.002512, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.621184, Class: 1.000000, Obj: 0.297223, No Obj: 0.002846, Avg Recall: 0.776000, count: 125 -Region Avg IOU: 0.585676, Class: 1.000000, Obj: 0.333654, No Obj: 0.002870, Avg Recall: 0.710145, count: 138 -3710: 0.478278, 0.461595 avg, 0.001000 rate, 26.603741 seconds, 949760 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 12.565588 seconds -Region Avg IOU: 0.543153, Class: 1.000000, Obj: 0.300883, No Obj: 0.002389, Avg Recall: 0.602410, count: 83 -Region Avg IOU: 0.518706, Class: 1.000000, Obj: 0.297516, No Obj: 0.003203, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.540602, Class: 1.000000, Obj: 0.297299, No Obj: 0.003075, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.577074, Class: 1.000000, Obj: 0.295032, No Obj: 0.003048, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.532616, Class: 1.000000, Obj: 0.330614, No Obj: 0.002971, Avg Recall: 0.575758, count: 99 -Region Avg IOU: 0.557311, Class: 1.000000, Obj: 0.281782, No Obj: 0.003231, Avg Recall: 0.656250, count: 128 -Region Avg IOU: 0.571878, Class: 1.000000, Obj: 0.288047, No Obj: 0.002933, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.586316, Class: 1.000000, Obj: 0.301936, No Obj: 0.003654, Avg Recall: 0.719008, count: 121 -3711: 0.469796, 0.462415 avg, 0.001000 rate, 23.279148 seconds, 950016 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.534860, Class: 1.000000, Obj: 0.299789, No Obj: 0.002741, Avg Recall: 0.644444, count: 90 -Region Avg IOU: 0.525920, Class: 1.000000, Obj: 0.298994, No Obj: 0.002937, Avg Recall: 0.569231, count: 130 -Region Avg IOU: 0.545115, Class: 1.000000, Obj: 0.303889, No Obj: 0.003268, Avg Recall: 0.655462, count: 119 -Region Avg IOU: 0.577185, Class: 1.000000, Obj: 0.312673, No Obj: 0.003085, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.606825, Class: 1.000000, Obj: 0.290402, No Obj: 0.003676, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.568679, Class: 1.000000, Obj: 0.346391, No Obj: 0.002787, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.558128, Class: 1.000000, Obj: 0.324081, No Obj: 0.002864, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.608918, Class: 1.000000, Obj: 0.301982, No Obj: 0.003453, Avg Recall: 0.711864, count: 118 -3712: 0.504845, 0.466658 avg, 0.001000 rate, 24.017365 seconds, 950272 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.512399, Class: 1.000000, Obj: 0.268535, No Obj: 0.002947, Avg Recall: 0.567568, count: 111 -Region Avg IOU: 0.574763, Class: 1.000000, Obj: 0.353971, No Obj: 0.002917, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.543539, Class: 1.000000, Obj: 0.319986, No Obj: 0.003125, Avg Recall: 0.674419, count: 86 -Region Avg IOU: 0.505422, Class: 1.000000, Obj: 0.248024, No Obj: 0.003046, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.520731, Class: 1.000000, Obj: 0.298816, No Obj: 0.003148, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.558684, Class: 1.000000, Obj: 0.318571, No Obj: 0.002914, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.534934, Class: 1.000000, Obj: 0.334399, No Obj: 0.002843, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.578210, Class: 1.000000, Obj: 0.318893, No Obj: 0.003302, Avg Recall: 0.672269, count: 119 -3713: 0.438003, 0.463792 avg, 0.001000 rate, 24.852888 seconds, 950528 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.564705, Class: 1.000000, Obj: 0.302503, No Obj: 0.003350, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.554818, Class: 1.000000, Obj: 0.267134, No Obj: 0.003407, Avg Recall: 0.593750, count: 128 -Region Avg IOU: 0.531395, Class: 1.000000, Obj: 0.312180, No Obj: 0.003217, Avg Recall: 0.559055, count: 127 -Region Avg IOU: 0.515147, Class: 1.000000, Obj: 0.294124, No Obj: 0.003224, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.544186, Class: 1.000000, Obj: 0.314276, No Obj: 0.003351, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.586507, Class: 1.000000, Obj: 0.346016, No Obj: 0.004115, Avg Recall: 0.699301, count: 143 -Region Avg IOU: 0.591823, Class: 1.000000, Obj: 0.315351, No Obj: 0.003246, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.541662, Class: 1.000000, Obj: 0.313952, No Obj: 0.003298, Avg Recall: 0.621622, count: 111 -3714: 0.515116, 0.468925 avg, 0.001000 rate, 24.767738 seconds, 950784 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.510294, Class: 1.000000, Obj: 0.308807, No Obj: 0.002981, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.561747, Class: 1.000000, Obj: 0.353690, No Obj: 0.003769, Avg Recall: 0.691729, count: 133 -Region Avg IOU: 0.497162, Class: 1.000000, Obj: 0.335087, No Obj: 0.003208, Avg Recall: 0.583333, count: 96 -Region Avg IOU: 0.527464, Class: 1.000000, Obj: 0.297407, No Obj: 0.003561, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.540370, Class: 1.000000, Obj: 0.333731, No Obj: 0.003049, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.564969, Class: 1.000000, Obj: 0.339820, No Obj: 0.003775, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.535345, Class: 1.000000, Obj: 0.290246, No Obj: 0.003445, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.539766, Class: 1.000000, Obj: 0.352350, No Obj: 0.003333, Avg Recall: 0.623932, count: 117 -3715: 0.461602, 0.468192 avg, 0.001000 rate, 23.953636 seconds, 951040 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.531498, Class: 1.000000, Obj: 0.338186, No Obj: 0.003905, Avg Recall: 0.601504, count: 133 -Region Avg IOU: 0.528004, Class: 1.000000, Obj: 0.331182, No Obj: 0.003505, Avg Recall: 0.632000, count: 125 -Region Avg IOU: 0.572074, Class: 1.000000, Obj: 0.348168, No Obj: 0.003123, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.579701, Class: 1.000000, Obj: 0.388943, No Obj: 0.004431, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.565286, Class: 1.000000, Obj: 0.367820, No Obj: 0.003282, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.571996, Class: 1.000000, Obj: 0.333895, No Obj: 0.003924, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.565942, Class: 1.000000, Obj: 0.371940, No Obj: 0.004242, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.546130, Class: 1.000000, Obj: 0.378029, No Obj: 0.003356, Avg Recall: 0.613636, count: 88 -3716: 0.433479, 0.464721 avg, 0.001000 rate, 23.488808 seconds, 951296 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.568494, Class: 1.000000, Obj: 0.377544, No Obj: 0.003576, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.546464, Class: 1.000000, Obj: 0.348130, No Obj: 0.004246, Avg Recall: 0.650794, count: 126 -Region Avg IOU: 0.574446, Class: 1.000000, Obj: 0.416582, No Obj: 0.004148, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.533188, Class: 1.000000, Obj: 0.387759, No Obj: 0.003842, Avg Recall: 0.607843, count: 102 -Region Avg IOU: 0.571958, Class: 1.000000, Obj: 0.394309, No Obj: 0.004219, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.594283, Class: 1.000000, Obj: 0.362406, No Obj: 0.003878, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.573078, Class: 1.000000, Obj: 0.398330, No Obj: 0.004022, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.556331, Class: 1.000000, Obj: 0.383547, No Obj: 0.004102, Avg Recall: 0.653465, count: 101 -3717: 0.501506, 0.468400 avg, 0.001000 rate, 22.700777 seconds, 951552 images -Loaded: 0.000123 seconds -Region Avg IOU: 0.601710, Class: 1.000000, Obj: 0.366625, No Obj: 0.005136, Avg Recall: 0.679389, count: 131 -Region Avg IOU: 0.520877, Class: 1.000000, Obj: 0.369660, No Obj: 0.003604, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.541470, Class: 1.000000, Obj: 0.399827, No Obj: 0.004357, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.546622, Class: 1.000000, Obj: 0.428612, No Obj: 0.003764, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.569685, Class: 1.000000, Obj: 0.361079, No Obj: 0.003985, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.527918, Class: 1.000000, Obj: 0.344530, No Obj: 0.004250, Avg Recall: 0.583333, count: 120 -Region Avg IOU: 0.540742, Class: 1.000000, Obj: 0.375725, No Obj: 0.004487, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.562993, Class: 1.000000, Obj: 0.394944, No Obj: 0.004160, Avg Recall: 0.614035, count: 114 -3718: 0.540704, 0.475630 avg, 0.001000 rate, 24.123259 seconds, 951808 images -Loaded: 0.000045 seconds -Region Avg IOU: 0.534918, Class: 1.000000, Obj: 0.384134, No Obj: 0.004013, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.545947, Class: 1.000000, Obj: 0.372502, No Obj: 0.004282, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.516706, Class: 1.000000, Obj: 0.328066, No Obj: 0.004381, Avg Recall: 0.537931, count: 145 -Region Avg IOU: 0.557907, Class: 1.000000, Obj: 0.425854, No Obj: 0.004257, Avg Recall: 0.632479, count: 117 -Region Avg IOU: 0.607939, Class: 1.000000, Obj: 0.417552, No Obj: 0.005230, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.564391, Class: 1.000000, Obj: 0.417330, No Obj: 0.004391, Avg Recall: 0.645161, count: 124 -Region Avg IOU: 0.547146, Class: 1.000000, Obj: 0.399246, No Obj: 0.003745, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.541640, Class: 1.000000, Obj: 0.423105, No Obj: 0.003988, Avg Recall: 0.650000, count: 80 -3719: 0.436505, 0.471718 avg, 0.001000 rate, 23.650202 seconds, 952064 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.542428, Class: 1.000000, Obj: 0.416637, No Obj: 0.003743, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.573530, Class: 1.000000, Obj: 0.397237, No Obj: 0.004160, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.565260, Class: 1.000000, Obj: 0.377188, No Obj: 0.005113, Avg Recall: 0.678322, count: 143 -Region Avg IOU: 0.588762, Class: 1.000000, Obj: 0.404304, No Obj: 0.004517, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.582227, Class: 1.000000, Obj: 0.436762, No Obj: 0.003747, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.564754, Class: 1.000000, Obj: 0.440269, No Obj: 0.004299, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.503607, Class: 1.000000, Obj: 0.370642, No Obj: 0.004048, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.594438, Class: 1.000000, Obj: 0.383120, No Obj: 0.004820, Avg Recall: 0.710145, count: 138 -3720: 0.445500, 0.469096 avg, 0.001000 rate, 22.233004 seconds, 952320 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000070 seconds -Region Avg IOU: 0.632667, Class: 1.000000, Obj: 0.404100, No Obj: 0.003498, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.612797, Class: 1.000000, Obj: 0.431067, No Obj: 0.004271, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.635997, Class: 1.000000, Obj: 0.449183, No Obj: 0.004032, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.617335, Class: 1.000000, Obj: 0.417965, No Obj: 0.004527, Avg Recall: 0.772358, count: 123 -Region Avg IOU: 0.595127, Class: 1.000000, Obj: 0.446770, No Obj: 0.003912, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.615193, Class: 1.000000, Obj: 0.454018, No Obj: 0.004237, Avg Recall: 0.740458, count: 131 -Region Avg IOU: 0.648528, Class: 1.000000, Obj: 0.424958, No Obj: 0.004389, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.644174, Class: 1.000000, Obj: 0.458333, No Obj: 0.003715, Avg Recall: 0.757895, count: 95 -3721: 0.462497, 0.468436 avg, 0.001000 rate, 25.537945 seconds, 952576 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.647550, Class: 1.000000, Obj: 0.421288, No Obj: 0.003764, Avg Recall: 0.804124, count: 97 -Region Avg IOU: 0.625984, Class: 1.000000, Obj: 0.473028, No Obj: 0.003738, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.614909, Class: 1.000000, Obj: 0.430611, No Obj: 0.003687, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.614626, Class: 1.000000, Obj: 0.481186, No Obj: 0.003719, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.591233, Class: 1.000000, Obj: 0.377143, No Obj: 0.003132, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.589156, Class: 1.000000, Obj: 0.419470, No Obj: 0.003592, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.582084, Class: 1.000000, Obj: 0.426229, No Obj: 0.003842, Avg Recall: 0.679389, count: 131 -Region Avg IOU: 0.633345, Class: 1.000000, Obj: 0.401484, No Obj: 0.003955, Avg Recall: 0.780702, count: 114 -3722: 0.423427, 0.463935 avg, 0.001000 rate, 26.546051 seconds, 952832 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.639820, Class: 1.000000, Obj: 0.424590, No Obj: 0.003253, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.650327, Class: 1.000000, Obj: 0.381056, No Obj: 0.003093, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.548770, Class: 1.000000, Obj: 0.393893, No Obj: 0.002966, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.628693, Class: 1.000000, Obj: 0.432714, No Obj: 0.003511, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.582823, Class: 1.000000, Obj: 0.400311, No Obj: 0.003068, Avg Recall: 0.705882, count: 119 -Region Avg IOU: 0.568611, Class: 1.000000, Obj: 0.423822, No Obj: 0.002946, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.632475, Class: 1.000000, Obj: 0.404138, No Obj: 0.003452, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.635876, Class: 1.000000, Obj: 0.427422, No Obj: 0.003312, Avg Recall: 0.762295, count: 122 -3723: 0.442173, 0.461759 avg, 0.001000 rate, 26.093227 seconds, 953088 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.607850, Class: 1.000000, Obj: 0.375181, No Obj: 0.003184, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.598274, Class: 1.000000, Obj: 0.388013, No Obj: 0.002785, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.621591, Class: 1.000000, Obj: 0.400516, No Obj: 0.003283, Avg Recall: 0.720280, count: 143 -Region Avg IOU: 0.601586, Class: 1.000000, Obj: 0.331274, No Obj: 0.002781, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.629625, Class: 1.000000, Obj: 0.366257, No Obj: 0.003016, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.611872, Class: 1.000000, Obj: 0.395441, No Obj: 0.002885, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.587775, Class: 1.000000, Obj: 0.392121, No Obj: 0.002368, Avg Recall: 0.674419, count: 86 -Region Avg IOU: 0.594964, Class: 1.000000, Obj: 0.388054, No Obj: 0.002806, Avg Recall: 0.701031, count: 97 -3724: 0.415023, 0.457085 avg, 0.001000 rate, 26.790842 seconds, 953344 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.559350, Class: 1.000000, Obj: 0.299914, No Obj: 0.002853, Avg Recall: 0.641379, count: 145 -Region Avg IOU: 0.635657, Class: 1.000000, Obj: 0.368638, No Obj: 0.002620, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.595122, Class: 1.000000, Obj: 0.303869, No Obj: 0.003030, Avg Recall: 0.724409, count: 127 -Region Avg IOU: 0.579503, Class: 1.000000, Obj: 0.338086, No Obj: 0.002911, Avg Recall: 0.693431, count: 137 -Region Avg IOU: 0.626786, Class: 1.000000, Obj: 0.387910, No Obj: 0.002834, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.642003, Class: 1.000000, Obj: 0.358815, No Obj: 0.003021, Avg Recall: 0.795082, count: 122 -Region Avg IOU: 0.611618, Class: 1.000000, Obj: 0.295572, No Obj: 0.002685, Avg Recall: 0.752137, count: 117 -Region Avg IOU: 0.598722, Class: 1.000000, Obj: 0.320930, No Obj: 0.002982, Avg Recall: 0.761468, count: 109 -3725: 0.500284, 0.461405 avg, 0.001000 rate, 28.988316 seconds, 953600 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.620465, Class: 1.000000, Obj: 0.327410, No Obj: 0.003094, Avg Recall: 0.768212, count: 151 -Region Avg IOU: 0.601283, Class: 1.000000, Obj: 0.345660, No Obj: 0.002345, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.596151, Class: 1.000000, Obj: 0.332584, No Obj: 0.002468, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.613481, Class: 1.000000, Obj: 0.299792, No Obj: 0.002630, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.635849, Class: 1.000000, Obj: 0.370704, No Obj: 0.002773, Avg Recall: 0.779661, count: 118 -Region Avg IOU: 0.646352, Class: 1.000000, Obj: 0.352793, No Obj: 0.002525, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.637573, Class: 1.000000, Obj: 0.349985, No Obj: 0.002336, Avg Recall: 0.829268, count: 82 -Region Avg IOU: 0.649603, Class: 1.000000, Obj: 0.325166, No Obj: 0.003302, Avg Recall: 0.808000, count: 125 -3726: 0.457807, 0.461045 avg, 0.001000 rate, 27.866268 seconds, 953856 images -Loaded: 0.000055 seconds -Region Avg IOU: 0.609923, Class: 1.000000, Obj: 0.317876, No Obj: 0.002280, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.595501, Class: 1.000000, Obj: 0.316866, No Obj: 0.002786, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.612221, Class: 1.000000, Obj: 0.306934, No Obj: 0.002708, Avg Recall: 0.768000, count: 125 -Region Avg IOU: 0.604397, Class: 1.000000, Obj: 0.327192, No Obj: 0.002503, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.586459, Class: 1.000000, Obj: 0.325725, No Obj: 0.002671, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.600231, Class: 1.000000, Obj: 0.344359, No Obj: 0.002585, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.597282, Class: 1.000000, Obj: 0.305119, No Obj: 0.002780, Avg Recall: 0.707692, count: 130 -Region Avg IOU: 0.588826, Class: 1.000000, Obj: 0.299530, No Obj: 0.002537, Avg Recall: 0.691176, count: 136 -3727: 0.547339, 0.469675 avg, 0.001000 rate, 25.972677 seconds, 954112 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.613737, Class: 1.000000, Obj: 0.348013, No Obj: 0.002360, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.647117, Class: 1.000000, Obj: 0.362898, No Obj: 0.002293, Avg Recall: 0.781250, count: 96 -Region Avg IOU: 0.562342, Class: 1.000000, Obj: 0.291843, No Obj: 0.002264, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.605321, Class: 1.000000, Obj: 0.308589, No Obj: 0.002640, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.586835, Class: 1.000000, Obj: 0.339269, No Obj: 0.002586, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.614939, Class: 1.000000, Obj: 0.297670, No Obj: 0.002607, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.624278, Class: 1.000000, Obj: 0.333753, No Obj: 0.002507, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.628357, Class: 1.000000, Obj: 0.341000, No Obj: 0.002891, Avg Recall: 0.756098, count: 123 -3728: 0.477371, 0.470444 avg, 0.001000 rate, 26.514919 seconds, 954368 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.652169, Class: 1.000000, Obj: 0.348363, No Obj: 0.002396, Avg Recall: 0.837209, count: 86 -Region Avg IOU: 0.589928, Class: 1.000000, Obj: 0.352456, No Obj: 0.002464, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.605453, Class: 1.000000, Obj: 0.372209, No Obj: 0.002646, Avg Recall: 0.761905, count: 126 -Region Avg IOU: 0.592003, Class: 1.000000, Obj: 0.329902, No Obj: 0.002663, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.617939, Class: 1.000000, Obj: 0.350029, No Obj: 0.002299, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.672681, Class: 1.000000, Obj: 0.415807, No Obj: 0.002280, Avg Recall: 0.855072, count: 69 -Region Avg IOU: 0.600628, Class: 1.000000, Obj: 0.332998, No Obj: 0.002670, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.626953, Class: 1.000000, Obj: 0.377727, No Obj: 0.002433, Avg Recall: 0.762376, count: 101 -3729: 0.416569, 0.465057 avg, 0.001000 rate, 27.634346 seconds, 954624 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.603755, Class: 1.000000, Obj: 0.323834, No Obj: 0.002328, Avg Recall: 0.717949, count: 78 -Region Avg IOU: 0.622770, Class: 1.000000, Obj: 0.357776, No Obj: 0.003018, Avg Recall: 0.772358, count: 123 -Region Avg IOU: 0.611312, Class: 1.000000, Obj: 0.325501, No Obj: 0.002732, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.670607, Class: 1.000000, Obj: 0.409018, No Obj: 0.002641, Avg Recall: 0.845238, count: 84 -Region Avg IOU: 0.597881, Class: 1.000000, Obj: 0.335284, No Obj: 0.002255, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.573740, Class: 1.000000, Obj: 0.308568, No Obj: 0.002436, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.591833, Class: 1.000000, Obj: 0.323008, No Obj: 0.002303, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.590654, Class: 1.000000, Obj: 0.295581, No Obj: 0.002611, Avg Recall: 0.709091, count: 110 -3730: 0.463103, 0.464862 avg, 0.001000 rate, 25.876320 seconds, 954880 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000067 seconds -Region Avg IOU: 0.601651, Class: 1.000000, Obj: 0.382715, No Obj: 0.002629, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.592194, Class: 1.000000, Obj: 0.341287, No Obj: 0.002745, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.610475, Class: 1.000000, Obj: 0.392035, No Obj: 0.002670, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.625253, Class: 1.000000, Obj: 0.351432, No Obj: 0.002717, Avg Recall: 0.800000, count: 120 -Region Avg IOU: 0.590382, Class: 1.000000, Obj: 0.302309, No Obj: 0.002302, Avg Recall: 0.700000, count: 80 -Region Avg IOU: 0.661272, Class: 1.000000, Obj: 0.317337, No Obj: 0.002671, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.648465, Class: 1.000000, Obj: 0.389539, No Obj: 0.002721, Avg Recall: 0.850000, count: 100 -Region Avg IOU: 0.627812, Class: 1.000000, Obj: 0.354847, No Obj: 0.002380, Avg Recall: 0.774510, count: 102 -3731: 0.453343, 0.463710 avg, 0.001000 rate, 27.966940 seconds, 955136 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.578116, Class: 1.000000, Obj: 0.306039, No Obj: 0.002850, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.623407, Class: 1.000000, Obj: 0.379050, No Obj: 0.002837, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.634825, Class: 1.000000, Obj: 0.358654, No Obj: 0.003017, Avg Recall: 0.779661, count: 118 -Region Avg IOU: 0.655514, Class: 1.000000, Obj: 0.405406, No Obj: 0.002778, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.635665, Class: 1.000000, Obj: 0.399650, No Obj: 0.002736, Avg Recall: 0.800000, count: 75 -Region Avg IOU: 0.647326, Class: 1.000000, Obj: 0.363954, No Obj: 0.002734, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.595658, Class: 1.000000, Obj: 0.344499, No Obj: 0.002804, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.587066, Class: 1.000000, Obj: 0.326932, No Obj: 0.002330, Avg Recall: 0.716049, count: 81 -3732: 0.422732, 0.459612 avg, 0.001000 rate, 30.512709 seconds, 955392 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.645411, Class: 1.000000, Obj: 0.359412, No Obj: 0.003311, Avg Recall: 0.848000, count: 125 -Region Avg IOU: 0.619724, Class: 1.000000, Obj: 0.379082, No Obj: 0.003093, Avg Recall: 0.756303, count: 119 -Region Avg IOU: 0.675895, Class: 1.000000, Obj: 0.391188, No Obj: 0.003785, Avg Recall: 0.859259, count: 135 -Region Avg IOU: 0.661125, Class: 1.000000, Obj: 0.450602, No Obj: 0.002754, Avg Recall: 0.840000, count: 100 -Region Avg IOU: 0.622106, Class: 1.000000, Obj: 0.397825, No Obj: 0.003142, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.620648, Class: 1.000000, Obj: 0.370426, No Obj: 0.003369, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.664928, Class: 1.000000, Obj: 0.400914, No Obj: 0.002786, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.628146, Class: 1.000000, Obj: 0.330122, No Obj: 0.003079, Avg Recall: 0.756303, count: 119 -3733: 0.459015, 0.459552 avg, 0.001000 rate, 28.962954 seconds, 955648 images -Loaded: 0.000133 seconds -Region Avg IOU: 0.646878, Class: 1.000000, Obj: 0.417026, No Obj: 0.003312, Avg Recall: 0.779528, count: 127 -Region Avg IOU: 0.658277, Class: 1.000000, Obj: 0.416013, No Obj: 0.003386, Avg Recall: 0.822581, count: 124 -Region Avg IOU: 0.646838, Class: 1.000000, Obj: 0.444759, No Obj: 0.002889, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.687886, Class: 1.000000, Obj: 0.453061, No Obj: 0.002773, Avg Recall: 0.853933, count: 89 -Region Avg IOU: 0.636298, Class: 1.000000, Obj: 0.415498, No Obj: 0.002758, Avg Recall: 0.808081, count: 99 -Region Avg IOU: 0.647164, Class: 1.000000, Obj: 0.386402, No Obj: 0.002838, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.628388, Class: 1.000000, Obj: 0.405037, No Obj: 0.002937, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.608802, Class: 1.000000, Obj: 0.363762, No Obj: 0.002654, Avg Recall: 0.757576, count: 99 -3734: 0.427363, 0.456333 avg, 0.001000 rate, 28.204754 seconds, 955904 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.626642, Class: 1.000000, Obj: 0.438529, No Obj: 0.003052, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.615814, Class: 1.000000, Obj: 0.428070, No Obj: 0.002751, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.652102, Class: 1.000000, Obj: 0.390843, No Obj: 0.003185, Avg Recall: 0.820755, count: 106 -Region Avg IOU: 0.643376, Class: 1.000000, Obj: 0.386106, No Obj: 0.003045, Avg Recall: 0.790698, count: 86 -Region Avg IOU: 0.596734, Class: 1.000000, Obj: 0.348705, No Obj: 0.003187, Avg Recall: 0.703125, count: 128 -Region Avg IOU: 0.641631, Class: 1.000000, Obj: 0.384673, No Obj: 0.002817, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.640018, Class: 1.000000, Obj: 0.389035, No Obj: 0.002995, Avg Recall: 0.789916, count: 119 -Region Avg IOU: 0.673032, Class: 1.000000, Obj: 0.419920, No Obj: 0.003266, Avg Recall: 0.840336, count: 119 -3735: 0.476547, 0.458355 avg, 0.001000 rate, 28.958609 seconds, 956160 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.611551, Class: 1.000000, Obj: 0.354004, No Obj: 0.003125, Avg Recall: 0.748092, count: 131 -Region Avg IOU: 0.663363, Class: 1.000000, Obj: 0.449155, No Obj: 0.002772, Avg Recall: 0.817204, count: 93 -Region Avg IOU: 0.654413, Class: 1.000000, Obj: 0.384339, No Obj: 0.003277, Avg Recall: 0.789916, count: 119 -Region Avg IOU: 0.654839, Class: 1.000000, Obj: 0.307347, No Obj: 0.003270, Avg Recall: 0.842975, count: 121 -Region Avg IOU: 0.602121, Class: 1.000000, Obj: 0.390135, No Obj: 0.002698, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.638318, Class: 1.000000, Obj: 0.355841, No Obj: 0.003325, Avg Recall: 0.794521, count: 146 -Region Avg IOU: 0.690691, Class: 1.000000, Obj: 0.418971, No Obj: 0.002859, Avg Recall: 0.894737, count: 95 -Region Avg IOU: 0.644266, Class: 1.000000, Obj: 0.383278, No Obj: 0.002986, Avg Recall: 0.787611, count: 113 -3736: 0.458814, 0.458401 avg, 0.001000 rate, 29.764944 seconds, 956416 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.664873, Class: 1.000000, Obj: 0.383291, No Obj: 0.003014, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.624509, Class: 1.000000, Obj: 0.384383, No Obj: 0.002660, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.611847, Class: 1.000000, Obj: 0.372567, No Obj: 0.002983, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.590495, Class: 1.000000, Obj: 0.357137, No Obj: 0.003133, Avg Recall: 0.708029, count: 137 -Region Avg IOU: 0.646182, Class: 1.000000, Obj: 0.447208, No Obj: 0.002848, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.620746, Class: 1.000000, Obj: 0.351036, No Obj: 0.003048, Avg Recall: 0.760870, count: 138 -Region Avg IOU: 0.619498, Class: 1.000000, Obj: 0.356731, No Obj: 0.002954, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.681787, Class: 1.000000, Obj: 0.369001, No Obj: 0.003009, Avg Recall: 0.909091, count: 121 -3737: 0.508901, 0.463451 avg, 0.001000 rate, 28.357355 seconds, 956672 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.608628, Class: 1.000000, Obj: 0.364739, No Obj: 0.002758, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.633081, Class: 1.000000, Obj: 0.354498, No Obj: 0.002777, Avg Recall: 0.775862, count: 116 -Region Avg IOU: 0.653251, Class: 1.000000, Obj: 0.386455, No Obj: 0.002929, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.650760, Class: 1.000000, Obj: 0.394087, No Obj: 0.002531, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.619262, Class: 1.000000, Obj: 0.409818, No Obj: 0.002281, Avg Recall: 0.771084, count: 83 -Region Avg IOU: 0.648112, Class: 1.000000, Obj: 0.374965, No Obj: 0.002638, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.611136, Class: 1.000000, Obj: 0.338559, No Obj: 0.002358, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.635568, Class: 1.000000, Obj: 0.346468, No Obj: 0.002519, Avg Recall: 0.822222, count: 90 -3738: 0.427773, 0.459883 avg, 0.001000 rate, 26.466532 seconds, 956928 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.583698, Class: 1.000000, Obj: 0.324472, No Obj: 0.002842, Avg Recall: 0.735537, count: 121 -Region Avg IOU: 0.645872, Class: 1.000000, Obj: 0.356595, No Obj: 0.002603, Avg Recall: 0.814815, count: 108 -Region Avg IOU: 0.630924, Class: 1.000000, Obj: 0.405009, No Obj: 0.002662, Avg Recall: 0.804598, count: 87 -Region Avg IOU: 0.654859, Class: 1.000000, Obj: 0.401932, No Obj: 0.002911, Avg Recall: 0.836735, count: 98 -Region Avg IOU: 0.660795, Class: 1.000000, Obj: 0.381870, No Obj: 0.002521, Avg Recall: 0.831579, count: 95 -Region Avg IOU: 0.630075, Class: 1.000000, Obj: 0.341269, No Obj: 0.002866, Avg Recall: 0.773723, count: 137 -Region Avg IOU: 0.627926, Class: 1.000000, Obj: 0.348363, No Obj: 0.002572, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.627662, Class: 1.000000, Obj: 0.376957, No Obj: 0.002387, Avg Recall: 0.807339, count: 109 -3739: 0.451980, 0.459092 avg, 0.001000 rate, 28.802729 seconds, 957184 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.633909, Class: 1.000000, Obj: 0.407689, No Obj: 0.002469, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.639203, Class: 1.000000, Obj: 0.368880, No Obj: 0.002945, Avg Recall: 0.787234, count: 141 -Region Avg IOU: 0.612489, Class: 1.000000, Obj: 0.317235, No Obj: 0.002550, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.645539, Class: 1.000000, Obj: 0.377857, No Obj: 0.002421, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.634925, Class: 1.000000, Obj: 0.384974, No Obj: 0.002601, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.646820, Class: 1.000000, Obj: 0.432286, No Obj: 0.002589, Avg Recall: 0.795181, count: 83 -Region Avg IOU: 0.648649, Class: 1.000000, Obj: 0.345884, No Obj: 0.002962, Avg Recall: 0.801653, count: 121 -Region Avg IOU: 0.615372, Class: 1.000000, Obj: 0.330981, No Obj: 0.002318, Avg Recall: 0.752809, count: 89 -3740: 0.476508, 0.460834 avg, 0.001000 rate, 28.295561 seconds, 957440 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 15.019404 seconds -Region Avg IOU: 0.603414, Class: 1.000000, Obj: 0.329113, No Obj: 0.003217, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.543364, Class: 1.000000, Obj: 0.335148, No Obj: 0.002692, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.551420, Class: 1.000000, Obj: 0.325432, No Obj: 0.003268, Avg Recall: 0.619048, count: 126 -Region Avg IOU: 0.569951, Class: 1.000000, Obj: 0.314252, No Obj: 0.003196, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.524893, Class: 1.000000, Obj: 0.316953, No Obj: 0.002565, Avg Recall: 0.630952, count: 84 -Region Avg IOU: 0.573351, Class: 1.000000, Obj: 0.342982, No Obj: 0.002940, Avg Recall: 0.698276, count: 116 -Region Avg IOU: 0.554400, Class: 1.000000, Obj: 0.316142, No Obj: 0.003137, Avg Recall: 0.627119, count: 118 -Region Avg IOU: 0.574466, Class: 1.000000, Obj: 0.320511, No Obj: 0.003232, Avg Recall: 0.654545, count: 110 -3741: 0.442636, 0.459014 avg, 0.001000 rate, 23.208860 seconds, 957696 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.496802, Class: 1.000000, Obj: 0.308169, No Obj: 0.002854, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.491497, Class: 1.000000, Obj: 0.308936, No Obj: 0.002972, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.534351, Class: 1.000000, Obj: 0.299964, No Obj: 0.003327, Avg Recall: 0.600000, count: 125 -Region Avg IOU: 0.525621, Class: 1.000000, Obj: 0.315109, No Obj: 0.003323, Avg Recall: 0.579710, count: 138 -Region Avg IOU: 0.507722, Class: 1.000000, Obj: 0.335254, No Obj: 0.002501, Avg Recall: 0.568182, count: 88 -Region Avg IOU: 0.591211, Class: 1.000000, Obj: 0.320167, No Obj: 0.003049, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.540456, Class: 1.000000, Obj: 0.316599, No Obj: 0.003034, Avg Recall: 0.613445, count: 119 -Region Avg IOU: 0.541082, Class: 1.000000, Obj: 0.305655, No Obj: 0.003602, Avg Recall: 0.601942, count: 103 -3742: 0.418973, 0.455010 avg, 0.001000 rate, 23.380260 seconds, 957952 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.561744, Class: 1.000000, Obj: 0.306195, No Obj: 0.002889, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.546973, Class: 1.000000, Obj: 0.350267, No Obj: 0.003116, Avg Recall: 0.647059, count: 85 -Region Avg IOU: 0.514246, Class: 1.000000, Obj: 0.330367, No Obj: 0.003035, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.597437, Class: 1.000000, Obj: 0.365826, No Obj: 0.003853, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.580937, Class: 1.000000, Obj: 0.326716, No Obj: 0.003391, Avg Recall: 0.686956, count: 115 -Region Avg IOU: 0.582775, Class: 1.000000, Obj: 0.339699, No Obj: 0.003204, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.582588, Class: 1.000000, Obj: 0.336485, No Obj: 0.003355, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.570647, Class: 1.000000, Obj: 0.314059, No Obj: 0.003612, Avg Recall: 0.660377, count: 106 -3743: 0.475267, 0.457036 avg, 0.001000 rate, 23.582661 seconds, 958208 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.563843, Class: 1.000000, Obj: 0.320411, No Obj: 0.003650, Avg Recall: 0.641221, count: 131 -Region Avg IOU: 0.582025, Class: 1.000000, Obj: 0.382526, No Obj: 0.004060, Avg Recall: 0.653226, count: 124 -Region Avg IOU: 0.553587, Class: 1.000000, Obj: 0.324073, No Obj: 0.003792, Avg Recall: 0.629921, count: 127 -Region Avg IOU: 0.581914, Class: 1.000000, Obj: 0.336029, No Obj: 0.003246, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.515879, Class: 1.000000, Obj: 0.382438, No Obj: 0.003027, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.542752, Class: 1.000000, Obj: 0.358020, No Obj: 0.003430, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.599284, Class: 1.000000, Obj: 0.371834, No Obj: 0.003195, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.531528, Class: 1.000000, Obj: 0.350178, No Obj: 0.003557, Avg Recall: 0.601626, count: 123 -3744: 0.445494, 0.455882 avg, 0.001000 rate, 22.160234 seconds, 958464 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.523094, Class: 1.000000, Obj: 0.335654, No Obj: 0.003706, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.588842, Class: 1.000000, Obj: 0.370652, No Obj: 0.004010, Avg Recall: 0.708661, count: 127 -Region Avg IOU: 0.548022, Class: 1.000000, Obj: 0.352753, No Obj: 0.003354, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.540673, Class: 1.000000, Obj: 0.346948, No Obj: 0.003255, Avg Recall: 0.652174, count: 92 -Region Avg IOU: 0.552676, Class: 1.000000, Obj: 0.406252, No Obj: 0.003907, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.557071, Class: 1.000000, Obj: 0.367368, No Obj: 0.003582, Avg Recall: 0.616000, count: 125 -Region Avg IOU: 0.565764, Class: 1.000000, Obj: 0.376532, No Obj: 0.003775, Avg Recall: 0.659420, count: 138 -Region Avg IOU: 0.615648, Class: 1.000000, Obj: 0.371803, No Obj: 0.003482, Avg Recall: 0.704545, count: 88 -3745: 0.456403, 0.455934 avg, 0.001000 rate, 24.211540 seconds, 958720 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.568226, Class: 1.000000, Obj: 0.407885, No Obj: 0.003755, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.582880, Class: 1.000000, Obj: 0.393650, No Obj: 0.003861, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.536245, Class: 1.000000, Obj: 0.345838, No Obj: 0.003451, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.578303, Class: 1.000000, Obj: 0.378517, No Obj: 0.003970, Avg Recall: 0.709677, count: 124 -Region Avg IOU: 0.581960, Class: 1.000000, Obj: 0.360711, No Obj: 0.003766, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.603701, Class: 1.000000, Obj: 0.438476, No Obj: 0.004264, Avg Recall: 0.726191, count: 84 -Region Avg IOU: 0.504573, Class: 1.000000, Obj: 0.370945, No Obj: 0.004014, Avg Recall: 0.549550, count: 111 -Region Avg IOU: 0.583714, Class: 1.000000, Obj: 0.375589, No Obj: 0.004539, Avg Recall: 0.717557, count: 131 -3746: 0.452161, 0.455556 avg, 0.001000 rate, 23.968096 seconds, 958976 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.529398, Class: 1.000000, Obj: 0.338638, No Obj: 0.003552, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.524022, Class: 1.000000, Obj: 0.343663, No Obj: 0.003094, Avg Recall: 0.556701, count: 97 -Region Avg IOU: 0.511534, Class: 1.000000, Obj: 0.362114, No Obj: 0.004464, Avg Recall: 0.553030, count: 132 -Region Avg IOU: 0.534438, Class: 1.000000, Obj: 0.348328, No Obj: 0.004017, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.569071, Class: 1.000000, Obj: 0.410757, No Obj: 0.004665, Avg Recall: 0.686567, count: 134 -Region Avg IOU: 0.507658, Class: 1.000000, Obj: 0.355143, No Obj: 0.004044, Avg Recall: 0.522523, count: 111 -Region Avg IOU: 0.582840, Class: 1.000000, Obj: 0.367094, No Obj: 0.003993, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.526299, Class: 1.000000, Obj: 0.367375, No Obj: 0.004115, Avg Recall: 0.600000, count: 115 -3747: 0.435043, 0.453505 avg, 0.001000 rate, 22.638519 seconds, 959232 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.542106, Class: 1.000000, Obj: 0.369355, No Obj: 0.004306, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.506542, Class: 1.000000, Obj: 0.373332, No Obj: 0.004231, Avg Recall: 0.508929, count: 112 -Region Avg IOU: 0.561358, Class: 1.000000, Obj: 0.388850, No Obj: 0.003585, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.563426, Class: 1.000000, Obj: 0.456542, No Obj: 0.004205, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.544246, Class: 1.000000, Obj: 0.376730, No Obj: 0.004387, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.536129, Class: 1.000000, Obj: 0.349199, No Obj: 0.004032, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.506586, Class: 1.000000, Obj: 0.365787, No Obj: 0.003537, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.555755, Class: 1.000000, Obj: 0.364064, No Obj: 0.004224, Avg Recall: 0.645669, count: 127 -3748: 0.436377, 0.451792 avg, 0.001000 rate, 23.263222 seconds, 959488 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.577549, Class: 1.000000, Obj: 0.367697, No Obj: 0.004559, Avg Recall: 0.694030, count: 134 -Region Avg IOU: 0.576176, Class: 1.000000, Obj: 0.401833, No Obj: 0.003897, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.564276, Class: 1.000000, Obj: 0.386680, No Obj: 0.003893, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.517761, Class: 1.000000, Obj: 0.377688, No Obj: 0.003939, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.574536, Class: 1.000000, Obj: 0.346019, No Obj: 0.004666, Avg Recall: 0.678082, count: 146 -Region Avg IOU: 0.593646, Class: 1.000000, Obj: 0.424846, No Obj: 0.003713, Avg Recall: 0.695122, count: 82 -Region Avg IOU: 0.601522, Class: 1.000000, Obj: 0.388409, No Obj: 0.004177, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.581219, Class: 1.000000, Obj: 0.404124, No Obj: 0.005008, Avg Recall: 0.646617, count: 133 -3749: 0.445477, 0.451161 avg, 0.001000 rate, 23.926489 seconds, 959744 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.596005, Class: 1.000000, Obj: 0.392311, No Obj: 0.004395, Avg Recall: 0.695312, count: 128 -Region Avg IOU: 0.562517, Class: 1.000000, Obj: 0.337177, No Obj: 0.004879, Avg Recall: 0.659091, count: 132 -Region Avg IOU: 0.581673, Class: 1.000000, Obj: 0.414518, No Obj: 0.004505, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.571557, Class: 1.000000, Obj: 0.405921, No Obj: 0.004059, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.532141, Class: 1.000000, Obj: 0.370558, No Obj: 0.003782, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.583107, Class: 1.000000, Obj: 0.351316, No Obj: 0.003963, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.579793, Class: 1.000000, Obj: 0.427240, No Obj: 0.004088, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.468979, Class: 1.000000, Obj: 0.338279, No Obj: 0.003715, Avg Recall: 0.519231, count: 104 -3750: 0.440007, 0.450045 avg, 0.001000 rate, 23.513020 seconds, 960000 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.076103 seconds -Region Avg IOU: 0.511340, Class: 1.000000, Obj: 0.276320, No Obj: 0.004361, Avg Recall: 0.555556, count: 99 -Region Avg IOU: 0.496170, Class: 1.000000, Obj: 0.301311, No Obj: 0.005007, Avg Recall: 0.540146, count: 137 -Region Avg IOU: 0.477791, Class: 1.000000, Obj: 0.293133, No Obj: 0.004269, Avg Recall: 0.521368, count: 117 -Region Avg IOU: 0.443976, Class: 1.000000, Obj: 0.269010, No Obj: 0.004288, Avg Recall: 0.481818, count: 110 -Region Avg IOU: 0.470037, Class: 1.000000, Obj: 0.314525, No Obj: 0.004342, Avg Recall: 0.529915, count: 117 -Region Avg IOU: 0.437828, Class: 1.000000, Obj: 0.267131, No Obj: 0.004017, Avg Recall: 0.461538, count: 104 -Region Avg IOU: 0.498846, Class: 1.000000, Obj: 0.308595, No Obj: 0.003976, Avg Recall: 0.553191, count: 94 -Region Avg IOU: 0.474340, Class: 1.000000, Obj: 0.296730, No Obj: 0.004524, Avg Recall: 0.528846, count: 104 -3751: 0.450338, 0.450075 avg, 0.001000 rate, 14.991176 seconds, 960256 images -Loaded: 7.177496 seconds -Region Avg IOU: 0.486122, Class: 1.000000, Obj: 0.275302, No Obj: 0.004139, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.442557, Class: 1.000000, Obj: 0.291035, No Obj: 0.004396, Avg Recall: 0.450450, count: 111 -Region Avg IOU: 0.515336, Class: 1.000000, Obj: 0.340279, No Obj: 0.004513, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.450859, Class: 1.000000, Obj: 0.270858, No Obj: 0.004337, Avg Recall: 0.482759, count: 116 -Region Avg IOU: 0.477987, Class: 1.000000, Obj: 0.286457, No Obj: 0.004337, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.479752, Class: 1.000000, Obj: 0.306075, No Obj: 0.003993, Avg Recall: 0.565217, count: 92 -Region Avg IOU: 0.484469, Class: 1.000000, Obj: 0.310968, No Obj: 0.004427, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.509179, Class: 1.000000, Obj: 0.286912, No Obj: 0.003877, Avg Recall: 0.568627, count: 102 -3752: 0.530189, 0.458086 avg, 0.001000 rate, 18.894188 seconds, 960512 images -Loaded: 1.766543 seconds -Region Avg IOU: 0.473827, Class: 1.000000, Obj: 0.290929, No Obj: 0.004823, Avg Recall: 0.522936, count: 109 -Region Avg IOU: 0.449561, Class: 1.000000, Obj: 0.302238, No Obj: 0.004970, Avg Recall: 0.503650, count: 137 -Region Avg IOU: 0.503766, Class: 1.000000, Obj: 0.296958, No Obj: 0.004701, Avg Recall: 0.560748, count: 107 -Region Avg IOU: 0.434256, Class: 1.000000, Obj: 0.279168, No Obj: 0.004561, Avg Recall: 0.448819, count: 127 -Region Avg IOU: 0.447913, Class: 1.000000, Obj: 0.291839, No Obj: 0.004314, Avg Recall: 0.464912, count: 114 -Region Avg IOU: 0.454278, Class: 1.000000, Obj: 0.266091, No Obj: 0.004156, Avg Recall: 0.505376, count: 93 -Region Avg IOU: 0.451913, Class: 1.000000, Obj: 0.328483, No Obj: 0.004339, Avg Recall: 0.521277, count: 94 -Region Avg IOU: 0.451180, Class: 1.000000, Obj: 0.303824, No Obj: 0.004249, Avg Recall: 0.481481, count: 108 -3753: 0.475556, 0.459833 avg, 0.001000 rate, 16.171907 seconds, 960768 images -Loaded: 5.538179 seconds -Region Avg IOU: 0.458201, Class: 1.000000, Obj: 0.312230, No Obj: 0.005219, Avg Recall: 0.476191, count: 126 -Region Avg IOU: 0.514233, Class: 1.000000, Obj: 0.296231, No Obj: 0.004213, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.522197, Class: 1.000000, Obj: 0.317127, No Obj: 0.005021, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.455210, Class: 1.000000, Obj: 0.335479, No Obj: 0.004766, Avg Recall: 0.494737, count: 95 -Region Avg IOU: 0.461862, Class: 1.000000, Obj: 0.313602, No Obj: 0.004618, Avg Recall: 0.522124, count: 113 -Region Avg IOU: 0.448031, Class: 1.000000, Obj: 0.248634, No Obj: 0.004846, Avg Recall: 0.447368, count: 114 -Region Avg IOU: 0.441784, Class: 1.000000, Obj: 0.284658, No Obj: 0.004123, Avg Recall: 0.474227, count: 97 -Region Avg IOU: 0.475060, Class: 1.000000, Obj: 0.310068, No Obj: 0.004720, Avg Recall: 0.507812, count: 128 -3754: 0.442494, 0.458099 avg, 0.001000 rate, 16.123272 seconds, 961024 images -Loaded: 4.749818 seconds -Region Avg IOU: 0.506825, Class: 1.000000, Obj: 0.347471, No Obj: 0.004635, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.441535, Class: 1.000000, Obj: 0.285983, No Obj: 0.004190, Avg Recall: 0.435185, count: 108 -Region Avg IOU: 0.440982, Class: 1.000000, Obj: 0.281070, No Obj: 0.004533, Avg Recall: 0.431034, count: 116 -Region Avg IOU: 0.508015, Class: 1.000000, Obj: 0.317853, No Obj: 0.004679, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.484419, Class: 1.000000, Obj: 0.350857, No Obj: 0.004097, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.476729, Class: 1.000000, Obj: 0.300020, No Obj: 0.005223, Avg Recall: 0.517986, count: 139 -Region Avg IOU: 0.491332, Class: 1.000000, Obj: 0.331566, No Obj: 0.004654, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.486582, Class: 1.000000, Obj: 0.356937, No Obj: 0.004901, Avg Recall: 0.512821, count: 117 -3755: 0.441411, 0.456430 avg, 0.001000 rate, 19.632875 seconds, 961280 images -Loaded: 1.456208 seconds -Region Avg IOU: 0.437352, Class: 1.000000, Obj: 0.334526, No Obj: 0.004693, Avg Recall: 0.409524, count: 105 -Region Avg IOU: 0.451906, Class: 1.000000, Obj: 0.320972, No Obj: 0.004774, Avg Recall: 0.471154, count: 104 -Region Avg IOU: 0.480805, Class: 1.000000, Obj: 0.296490, No Obj: 0.005518, Avg Recall: 0.554688, count: 128 -Region Avg IOU: 0.380696, Class: 1.000000, Obj: 0.265405, No Obj: 0.004136, Avg Recall: 0.373984, count: 123 -Region Avg IOU: 0.504495, Class: 1.000000, Obj: 0.327805, No Obj: 0.004909, Avg Recall: 0.592593, count: 108 -Region Avg IOU: 0.434567, Class: 1.000000, Obj: 0.289730, No Obj: 0.004775, Avg Recall: 0.420690, count: 145 -Region Avg IOU: 0.469896, Class: 1.000000, Obj: 0.296949, No Obj: 0.005066, Avg Recall: 0.500000, count: 126 -Region Avg IOU: 0.429672, Class: 1.000000, Obj: 0.314280, No Obj: 0.004319, Avg Recall: 0.401961, count: 102 -3756: 0.557713, 0.466559 avg, 0.001000 rate, 17.267866 seconds, 961536 images -Loaded: 3.483092 seconds -Region Avg IOU: 0.455420, Class: 1.000000, Obj: 0.365454, No Obj: 0.006058, Avg Recall: 0.493243, count: 148 -Region Avg IOU: 0.484260, Class: 1.000000, Obj: 0.349634, No Obj: 0.004886, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.499934, Class: 1.000000, Obj: 0.358213, No Obj: 0.005106, Avg Recall: 0.575758, count: 99 -Region Avg IOU: 0.462967, Class: 1.000000, Obj: 0.349592, No Obj: 0.005131, Avg Recall: 0.486486, count: 111 -Region Avg IOU: 0.450251, Class: 1.000000, Obj: 0.281031, No Obj: 0.004784, Avg Recall: 0.500000, count: 116 -Region Avg IOU: 0.445530, Class: 1.000000, Obj: 0.342827, No Obj: 0.004637, Avg Recall: 0.460784, count: 102 -Region Avg IOU: 0.433693, Class: 1.000000, Obj: 0.344225, No Obj: 0.004918, Avg Recall: 0.419048, count: 105 -Region Avg IOU: 0.460755, Class: 1.000000, Obj: 0.323503, No Obj: 0.004782, Avg Recall: 0.504854, count: 103 -3757: 0.505200, 0.470423 avg, 0.001000 rate, 15.425011 seconds, 961792 images -Loaded: 5.666360 seconds -Region Avg IOU: 0.465976, Class: 1.000000, Obj: 0.359336, No Obj: 0.004718, Avg Recall: 0.511111, count: 90 -Region Avg IOU: 0.464628, Class: 1.000000, Obj: 0.305237, No Obj: 0.004937, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.515964, Class: 1.000000, Obj: 0.371125, No Obj: 0.005528, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.455079, Class: 1.000000, Obj: 0.336695, No Obj: 0.004068, Avg Recall: 0.506173, count: 81 -Region Avg IOU: 0.448444, Class: 1.000000, Obj: 0.346333, No Obj: 0.004240, Avg Recall: 0.490000, count: 100 -Region Avg IOU: 0.405760, Class: 1.000000, Obj: 0.326958, No Obj: 0.004798, Avg Recall: 0.378947, count: 95 -Region Avg IOU: 0.466371, Class: 1.000000, Obj: 0.372820, No Obj: 0.005257, Avg Recall: 0.521739, count: 115 -Region Avg IOU: 0.505868, Class: 1.000000, Obj: 0.391837, No Obj: 0.004847, Avg Recall: 0.531250, count: 96 -3758: 0.440047, 0.467385 avg, 0.001000 rate, 17.539955 seconds, 962048 images -Loaded: 3.435697 seconds -Region Avg IOU: 0.496304, Class: 1.000000, Obj: 0.351973, No Obj: 0.004979, Avg Recall: 0.550000, count: 100 -Region Avg IOU: 0.509201, Class: 1.000000, Obj: 0.365525, No Obj: 0.005171, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.450451, Class: 1.000000, Obj: 0.315928, No Obj: 0.004551, Avg Recall: 0.460177, count: 113 -Region Avg IOU: 0.512547, Class: 1.000000, Obj: 0.343822, No Obj: 0.004779, Avg Recall: 0.597701, count: 87 -Region Avg IOU: 0.469425, Class: 1.000000, Obj: 0.325536, No Obj: 0.004659, Avg Recall: 0.505050, count: 99 -Region Avg IOU: 0.426632, Class: 1.000000, Obj: 0.299383, No Obj: 0.004792, Avg Recall: 0.412698, count: 126 -Region Avg IOU: 0.404931, Class: 1.000000, Obj: 0.303763, No Obj: 0.004833, Avg Recall: 0.400000, count: 110 -Region Avg IOU: 0.475701, Class: 1.000000, Obj: 0.334078, No Obj: 0.005068, Avg Recall: 0.530973, count: 113 -3759: 0.490885, 0.469735 avg, 0.001000 rate, 16.266569 seconds, 962304 images -Loaded: 4.633718 seconds -Region Avg IOU: 0.412869, Class: 1.000000, Obj: 0.339192, No Obj: 0.004621, Avg Recall: 0.396694, count: 121 -Region Avg IOU: 0.422203, Class: 1.000000, Obj: 0.321784, No Obj: 0.004580, Avg Recall: 0.453608, count: 97 -Region Avg IOU: 0.411774, Class: 1.000000, Obj: 0.323771, No Obj: 0.004515, Avg Recall: 0.388889, count: 108 -Region Avg IOU: 0.457871, Class: 1.000000, Obj: 0.315262, No Obj: 0.005095, Avg Recall: 0.514286, count: 140 -Region Avg IOU: 0.458726, Class: 1.000000, Obj: 0.297026, No Obj: 0.005485, Avg Recall: 0.507937, count: 126 -Region Avg IOU: 0.464848, Class: 1.000000, Obj: 0.362563, No Obj: 0.004528, Avg Recall: 0.500000, count: 88 -Region Avg IOU: 0.478944, Class: 1.000000, Obj: 0.353622, No Obj: 0.005058, Avg Recall: 0.513514, count: 111 -Region Avg IOU: 0.445936, Class: 1.000000, Obj: 0.293726, No Obj: 0.004823, Avg Recall: 0.458333, count: 120 -3760: 0.478453, 0.470607 avg, 0.001000 rate, 16.804327 seconds, 962560 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 0.077253 seconds -Region Avg IOU: 0.439516, Class: 1.000000, Obj: 0.349083, No Obj: 0.003908, Avg Recall: 0.452055, count: 73 -Region Avg IOU: 0.559418, Class: 1.000000, Obj: 0.345393, No Obj: 0.005292, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.467939, Class: 1.000000, Obj: 0.291166, No Obj: 0.004534, Avg Recall: 0.528846, count: 104 -Region Avg IOU: 0.541473, Class: 1.000000, Obj: 0.381674, No Obj: 0.005033, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.462361, Class: 1.000000, Obj: 0.348934, No Obj: 0.004488, Avg Recall: 0.468468, count: 111 -Region Avg IOU: 0.492573, Class: 1.000000, Obj: 0.353383, No Obj: 0.004462, Avg Recall: 0.509259, count: 108 -Region Avg IOU: 0.416812, Class: 1.000000, Obj: 0.317972, No Obj: 0.003771, Avg Recall: 0.450000, count: 100 -Region Avg IOU: 0.465709, Class: 1.000000, Obj: 0.329487, No Obj: 0.005178, Avg Recall: 0.525926, count: 135 -3761: 0.479634, 0.471510 avg, 0.001000 rate, 20.270052 seconds, 962816 images -Loaded: 0.930098 seconds -Region Avg IOU: 0.463093, Class: 1.000000, Obj: 0.287501, No Obj: 0.004503, Avg Recall: 0.518182, count: 110 -Region Avg IOU: 0.494716, Class: 1.000000, Obj: 0.281366, No Obj: 0.005944, Avg Recall: 0.544828, count: 145 -Region Avg IOU: 0.535840, Class: 1.000000, Obj: 0.332211, No Obj: 0.004813, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.526487, Class: 1.000000, Obj: 0.364359, No Obj: 0.004830, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.459165, Class: 1.000000, Obj: 0.311658, No Obj: 0.004738, Avg Recall: 0.536000, count: 125 -Region Avg IOU: 0.503040, Class: 1.000000, Obj: 0.351659, No Obj: 0.005102, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.483918, Class: 1.000000, Obj: 0.346704, No Obj: 0.004837, Avg Recall: 0.537313, count: 134 -Region Avg IOU: 0.465637, Class: 1.000000, Obj: 0.350974, No Obj: 0.004825, Avg Recall: 0.500000, count: 118 -3762: 0.535091, 0.477868 avg, 0.001000 rate, 18.707420 seconds, 963072 images -Loaded: 2.871273 seconds -Region Avg IOU: 0.450488, Class: 1.000000, Obj: 0.271565, No Obj: 0.004961, Avg Recall: 0.478873, count: 142 -Region Avg IOU: 0.490211, Class: 1.000000, Obj: 0.319616, No Obj: 0.003556, Avg Recall: 0.518519, count: 81 -Region Avg IOU: 0.508799, Class: 1.000000, Obj: 0.372531, No Obj: 0.005141, Avg Recall: 0.537736, count: 106 -Region Avg IOU: 0.554596, Class: 1.000000, Obj: 0.355706, No Obj: 0.004522, Avg Recall: 0.654321, count: 81 -Region Avg IOU: 0.488756, Class: 1.000000, Obj: 0.325212, No Obj: 0.005115, Avg Recall: 0.511450, count: 131 -Region Avg IOU: 0.492504, Class: 1.000000, Obj: 0.321968, No Obj: 0.004384, Avg Recall: 0.578512, count: 121 -Region Avg IOU: 0.467781, Class: 1.000000, Obj: 0.365585, No Obj: 0.004143, Avg Recall: 0.484211, count: 95 -Region Avg IOU: 0.508986, Class: 1.000000, Obj: 0.317080, No Obj: 0.003889, Avg Recall: 0.567010, count: 97 -3763: 0.439030, 0.473984 avg, 0.001000 rate, 21.549244 seconds, 963328 images -Loaded: 0.742616 seconds -Region Avg IOU: 0.476547, Class: 1.000000, Obj: 0.321694, No Obj: 0.004127, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.549659, Class: 1.000000, Obj: 0.389711, No Obj: 0.004691, Avg Recall: 0.627907, count: 86 -Region Avg IOU: 0.510431, Class: 1.000000, Obj: 0.325893, No Obj: 0.004779, Avg Recall: 0.561983, count: 121 -Region Avg IOU: 0.453421, Class: 1.000000, Obj: 0.317640, No Obj: 0.004548, Avg Recall: 0.513043, count: 115 -Region Avg IOU: 0.497619, Class: 1.000000, Obj: 0.336630, No Obj: 0.004595, Avg Recall: 0.542857, count: 105 -Region Avg IOU: 0.495141, Class: 1.000000, Obj: 0.307458, No Obj: 0.005489, Avg Recall: 0.525547, count: 137 -Region Avg IOU: 0.493812, Class: 1.000000, Obj: 0.339896, No Obj: 0.004725, Avg Recall: 0.521739, count: 115 -Region Avg IOU: 0.480009, Class: 1.000000, Obj: 0.329738, No Obj: 0.004497, Avg Recall: 0.490196, count: 102 -3764: 0.461487, 0.472734 avg, 0.001000 rate, 18.352880 seconds, 963584 images -Loaded: 3.327420 seconds -Region Avg IOU: 0.481947, Class: 1.000000, Obj: 0.311148, No Obj: 0.004212, Avg Recall: 0.540000, count: 100 -Region Avg IOU: 0.501217, Class: 1.000000, Obj: 0.328745, No Obj: 0.004838, Avg Recall: 0.537037, count: 108 -Region Avg IOU: 0.496631, Class: 1.000000, Obj: 0.288871, No Obj: 0.005197, Avg Recall: 0.562963, count: 135 -Region Avg IOU: 0.479801, Class: 1.000000, Obj: 0.306875, No Obj: 0.004160, Avg Recall: 0.533333, count: 90 -Region Avg IOU: 0.520739, Class: 1.000000, Obj: 0.327017, No Obj: 0.004852, Avg Recall: 0.583333, count: 96 -Region Avg IOU: 0.487640, Class: 1.000000, Obj: 0.302665, No Obj: 0.004331, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.473535, Class: 1.000000, Obj: 0.269415, No Obj: 0.004483, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.499838, Class: 1.000000, Obj: 0.334801, No Obj: 0.003914, Avg Recall: 0.593407, count: 91 -3765: 0.422226, 0.467683 avg, 0.001000 rate, 18.561340 seconds, 963840 images -Loaded: 3.003395 seconds -Region Avg IOU: 0.489600, Class: 1.000000, Obj: 0.358357, No Obj: 0.004786, Avg Recall: 0.574713, count: 87 -Region Avg IOU: 0.471491, Class: 1.000000, Obj: 0.319244, No Obj: 0.003699, Avg Recall: 0.500000, count: 90 -Region Avg IOU: 0.471545, Class: 1.000000, Obj: 0.301183, No Obj: 0.004541, Avg Recall: 0.509259, count: 108 -Region Avg IOU: 0.515716, Class: 1.000000, Obj: 0.348997, No Obj: 0.004594, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.493843, Class: 1.000000, Obj: 0.333451, No Obj: 0.004036, Avg Recall: 0.500000, count: 82 -Region Avg IOU: 0.498877, Class: 1.000000, Obj: 0.333324, No Obj: 0.003918, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.530243, Class: 1.000000, Obj: 0.374900, No Obj: 0.005191, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.519400, Class: 1.000000, Obj: 0.344419, No Obj: 0.005443, Avg Recall: 0.620155, count: 129 -3766: 0.373028, 0.458218 avg, 0.001000 rate, 20.438772 seconds, 964096 images -Loaded: 2.252881 seconds -Region Avg IOU: 0.484686, Class: 1.000000, Obj: 0.315610, No Obj: 0.004730, Avg Recall: 0.512397, count: 121 -Region Avg IOU: 0.472699, Class: 1.000000, Obj: 0.298919, No Obj: 0.004882, Avg Recall: 0.519084, count: 131 -Region Avg IOU: 0.477176, Class: 1.000000, Obj: 0.320841, No Obj: 0.004863, Avg Recall: 0.544715, count: 123 -Region Avg IOU: 0.489001, Class: 1.000000, Obj: 0.292780, No Obj: 0.004571, Avg Recall: 0.520661, count: 121 -Region Avg IOU: 0.468878, Class: 1.000000, Obj: 0.302252, No Obj: 0.004238, Avg Recall: 0.534091, count: 88 -Region Avg IOU: 0.472745, Class: 1.000000, Obj: 0.277178, No Obj: 0.004461, Avg Recall: 0.510870, count: 92 -Region Avg IOU: 0.491613, Class: 1.000000, Obj: 0.323676, No Obj: 0.004573, Avg Recall: 0.536364, count: 110 -Region Avg IOU: 0.476806, Class: 1.000000, Obj: 0.308752, No Obj: 0.004745, Avg Recall: 0.537931, count: 145 -3767: 0.484013, 0.460797 avg, 0.001000 rate, 18.635012 seconds, 964352 images -Loaded: 3.463674 seconds -Region Avg IOU: 0.522533, Class: 1.000000, Obj: 0.324290, No Obj: 0.004878, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.528447, Class: 1.000000, Obj: 0.333008, No Obj: 0.004720, Avg Recall: 0.613445, count: 119 -Region Avg IOU: 0.507512, Class: 1.000000, Obj: 0.331926, No Obj: 0.004805, Avg Recall: 0.577778, count: 90 -Region Avg IOU: 0.479341, Class: 1.000000, Obj: 0.299886, No Obj: 0.004510, Avg Recall: 0.466019, count: 103 -Region Avg IOU: 0.493621, Class: 1.000000, Obj: 0.326252, No Obj: 0.004969, Avg Recall: 0.534884, count: 129 -Region Avg IOU: 0.538047, Class: 1.000000, Obj: 0.308723, No Obj: 0.004607, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.481892, Class: 1.000000, Obj: 0.315267, No Obj: 0.004767, Avg Recall: 0.504202, count: 119 -Region Avg IOU: 0.488277, Class: 1.000000, Obj: 0.333882, No Obj: 0.004455, Avg Recall: 0.573034, count: 89 -3768: 0.418100, 0.456528 avg, 0.001000 rate, 18.812408 seconds, 964608 images -Loaded: 3.024117 seconds -Region Avg IOU: 0.517007, Class: 1.000000, Obj: 0.317773, No Obj: 0.004627, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.477457, Class: 1.000000, Obj: 0.320953, No Obj: 0.005652, Avg Recall: 0.541401, count: 157 -Region Avg IOU: 0.487120, Class: 1.000000, Obj: 0.312040, No Obj: 0.004810, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.472330, Class: 1.000000, Obj: 0.277764, No Obj: 0.004558, Avg Recall: 0.516667, count: 120 -Region Avg IOU: 0.537693, Class: 1.000000, Obj: 0.337050, No Obj: 0.004670, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.504759, Class: 1.000000, Obj: 0.351971, No Obj: 0.004633, Avg Recall: 0.535354, count: 99 -Region Avg IOU: 0.498139, Class: 1.000000, Obj: 0.349708, No Obj: 0.004612, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.513878, Class: 1.000000, Obj: 0.328122, No Obj: 0.004897, Avg Recall: 0.591667, count: 120 -3769: 0.556865, 0.466561 avg, 0.001000 rate, 17.179111 seconds, 964864 images -Loaded: 5.246722 seconds -Region Avg IOU: 0.444008, Class: 1.000000, Obj: 0.298834, No Obj: 0.004696, Avg Recall: 0.518868, count: 106 -Region Avg IOU: 0.476157, Class: 1.000000, Obj: 0.306386, No Obj: 0.005554, Avg Recall: 0.472603, count: 146 -Region Avg IOU: 0.487684, Class: 1.000000, Obj: 0.332578, No Obj: 0.005963, Avg Recall: 0.506410, count: 156 -Region Avg IOU: 0.502216, Class: 1.000000, Obj: 0.331318, No Obj: 0.005958, Avg Recall: 0.535032, count: 157 -Region Avg IOU: 0.483171, Class: 1.000000, Obj: 0.312537, No Obj: 0.004236, Avg Recall: 0.487805, count: 82 -Region Avg IOU: 0.496584, Class: 1.000000, Obj: 0.328811, No Obj: 0.004822, Avg Recall: 0.561983, count: 121 -Region Avg IOU: 0.547661, Class: 1.000000, Obj: 0.351835, No Obj: 0.005611, Avg Recall: 0.625000, count: 120 -Region Avg IOU: 0.521862, Class: 1.000000, Obj: 0.324869, No Obj: 0.005056, Avg Recall: 0.580357, count: 112 -3770: 0.496320, 0.469537 avg, 0.001000 rate, 15.515066 seconds, 965120 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000069 seconds -Region Avg IOU: 0.671265, Class: 1.000000, Obj: 0.460347, No Obj: 0.003317, Avg Recall: 0.861702, count: 94 -Region Avg IOU: 0.611404, Class: 1.000000, Obj: 0.417191, No Obj: 0.003220, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.618152, Class: 1.000000, Obj: 0.401800, No Obj: 0.003507, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.653919, Class: 1.000000, Obj: 0.411681, No Obj: 0.003397, Avg Recall: 0.821429, count: 84 -Region Avg IOU: 0.614250, Class: 1.000000, Obj: 0.413775, No Obj: 0.003612, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.639663, Class: 1.000000, Obj: 0.448801, No Obj: 0.003624, Avg Recall: 0.785047, count: 107 -Region Avg IOU: 0.642233, Class: 1.000000, Obj: 0.467377, No Obj: 0.002887, Avg Recall: 0.811765, count: 85 -Region Avg IOU: 0.634466, Class: 1.000000, Obj: 0.507866, No Obj: 0.003497, Avg Recall: 0.770000, count: 100 -3771: 0.431762, 0.465760 avg, 0.001000 rate, 30.478771 seconds, 965376 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.649878, Class: 1.000000, Obj: 0.404791, No Obj: 0.003384, Avg Recall: 0.761364, count: 88 -Region Avg IOU: 0.645636, Class: 1.000000, Obj: 0.412251, No Obj: 0.002543, Avg Recall: 0.815789, count: 76 -Region Avg IOU: 0.586482, Class: 1.000000, Obj: 0.348022, No Obj: 0.002915, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.632073, Class: 1.000000, Obj: 0.426768, No Obj: 0.002982, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.688994, Class: 1.000000, Obj: 0.494571, No Obj: 0.003467, Avg Recall: 0.823529, count: 102 -Region Avg IOU: 0.632848, Class: 1.000000, Obj: 0.436818, No Obj: 0.003334, Avg Recall: 0.778761, count: 113 -Region Avg IOU: 0.634460, Class: 1.000000, Obj: 0.379658, No Obj: 0.003481, Avg Recall: 0.812030, count: 133 -Region Avg IOU: 0.624628, Class: 1.000000, Obj: 0.432692, No Obj: 0.003597, Avg Recall: 0.745763, count: 118 -3772: 0.495538, 0.468738 avg, 0.001000 rate, 29.758753 seconds, 965632 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.607290, Class: 1.000000, Obj: 0.350078, No Obj: 0.002764, Avg Recall: 0.746988, count: 83 -Region Avg IOU: 0.640378, Class: 1.000000, Obj: 0.374570, No Obj: 0.002783, Avg Recall: 0.793478, count: 92 -Region Avg IOU: 0.586254, Class: 1.000000, Obj: 0.408489, No Obj: 0.002928, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.598575, Class: 1.000000, Obj: 0.388539, No Obj: 0.002927, Avg Recall: 0.705882, count: 136 -Region Avg IOU: 0.637468, Class: 1.000000, Obj: 0.422277, No Obj: 0.002838, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.657063, Class: 1.000000, Obj: 0.411884, No Obj: 0.002789, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.637945, Class: 1.000000, Obj: 0.444007, No Obj: 0.002966, Avg Recall: 0.849462, count: 93 -Region Avg IOU: 0.645499, Class: 1.000000, Obj: 0.348018, No Obj: 0.003154, Avg Recall: 0.821138, count: 123 -3773: 0.500012, 0.471865 avg, 0.001000 rate, 28.826456 seconds, 965888 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.627926, Class: 1.000000, Obj: 0.419011, No Obj: 0.002537, Avg Recall: 0.777778, count: 90 -Region Avg IOU: 0.650277, Class: 1.000000, Obj: 0.342489, No Obj: 0.003175, Avg Recall: 0.767296, count: 159 -Region Avg IOU: 0.612044, Class: 1.000000, Obj: 0.408153, No Obj: 0.002571, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.662661, Class: 1.000000, Obj: 0.361854, No Obj: 0.002764, Avg Recall: 0.862069, count: 116 -Region Avg IOU: 0.635478, Class: 1.000000, Obj: 0.347576, No Obj: 0.002607, Avg Recall: 0.775862, count: 116 -Region Avg IOU: 0.650039, Class: 1.000000, Obj: 0.386678, No Obj: 0.002280, Avg Recall: 0.772727, count: 88 -Region Avg IOU: 0.626878, Class: 1.000000, Obj: 0.380812, No Obj: 0.002225, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.593780, Class: 1.000000, Obj: 0.353350, No Obj: 0.002657, Avg Recall: 0.696970, count: 132 -3774: 0.483775, 0.473056 avg, 0.001000 rate, 30.003456 seconds, 966144 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.633927, Class: 1.000000, Obj: 0.304353, No Obj: 0.002198, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.641680, Class: 1.000000, Obj: 0.266617, No Obj: 0.002123, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.612151, Class: 1.000000, Obj: 0.322229, No Obj: 0.002211, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.625375, Class: 1.000000, Obj: 0.376490, No Obj: 0.002105, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.663574, Class: 1.000000, Obj: 0.329347, No Obj: 0.002370, Avg Recall: 0.793893, count: 131 -Region Avg IOU: 0.665185, Class: 1.000000, Obj: 0.383170, No Obj: 0.002766, Avg Recall: 0.814516, count: 124 -Region Avg IOU: 0.627087, Class: 1.000000, Obj: 0.311839, No Obj: 0.002375, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.656101, Class: 1.000000, Obj: 0.296979, No Obj: 0.002258, Avg Recall: 0.839623, count: 106 -3775: 0.481214, 0.473872 avg, 0.001000 rate, 28.122643 seconds, 966400 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.607363, Class: 1.000000, Obj: 0.284865, No Obj: 0.002084, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.686992, Class: 1.000000, Obj: 0.234222, No Obj: 0.002298, Avg Recall: 0.873786, count: 103 -Region Avg IOU: 0.642005, Class: 1.000000, Obj: 0.343284, No Obj: 0.002106, Avg Recall: 0.809917, count: 121 -Region Avg IOU: 0.653235, Class: 1.000000, Obj: 0.360412, No Obj: 0.002112, Avg Recall: 0.783132, count: 83 -Region Avg IOU: 0.619672, Class: 1.000000, Obj: 0.307101, No Obj: 0.002032, Avg Recall: 0.744681, count: 94 -Region Avg IOU: 0.649445, Class: 1.000000, Obj: 0.304171, No Obj: 0.001973, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.650424, Class: 1.000000, Obj: 0.303836, No Obj: 0.002258, Avg Recall: 0.784000, count: 125 -Region Avg IOU: 0.620696, Class: 1.000000, Obj: 0.273249, No Obj: 0.001974, Avg Recall: 0.781609, count: 87 -3776: 0.476101, 0.474095 avg, 0.001000 rate, 29.455343 seconds, 966656 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.622095, Class: 1.000000, Obj: 0.272993, No Obj: 0.001983, Avg Recall: 0.806202, count: 129 -Region Avg IOU: 0.595187, Class: 1.000000, Obj: 0.262446, No Obj: 0.002165, Avg Recall: 0.702899, count: 138 -Region Avg IOU: 0.648171, Class: 1.000000, Obj: 0.265284, No Obj: 0.002020, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.648318, Class: 1.000000, Obj: 0.250676, No Obj: 0.002073, Avg Recall: 0.797101, count: 138 -Region Avg IOU: 0.573640, Class: 1.000000, Obj: 0.257669, No Obj: 0.001921, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.615774, Class: 1.000000, Obj: 0.248783, No Obj: 0.002371, Avg Recall: 0.763780, count: 127 -Region Avg IOU: 0.652873, Class: 1.000000, Obj: 0.289588, No Obj: 0.002198, Avg Recall: 0.814433, count: 97 -Region Avg IOU: 0.617724, Class: 1.000000, Obj: 0.289987, No Obj: 0.002129, Avg Recall: 0.767677, count: 99 -3777: 0.555904, 0.482276 avg, 0.001000 rate, 30.667747 seconds, 966912 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.649366, Class: 1.000000, Obj: 0.342404, No Obj: 0.001976, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.598109, Class: 1.000000, Obj: 0.293888, No Obj: 0.002036, Avg Recall: 0.719298, count: 114 -Region Avg IOU: 0.656958, Class: 1.000000, Obj: 0.325471, No Obj: 0.002348, Avg Recall: 0.822430, count: 107 -Region Avg IOU: 0.627194, Class: 1.000000, Obj: 0.306830, No Obj: 0.001936, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.628032, Class: 1.000000, Obj: 0.349617, No Obj: 0.002135, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.619615, Class: 1.000000, Obj: 0.275764, No Obj: 0.002140, Avg Recall: 0.766423, count: 137 -Region Avg IOU: 0.550530, Class: 1.000000, Obj: 0.319491, No Obj: 0.001890, Avg Recall: 0.634146, count: 82 -Region Avg IOU: 0.650935, Class: 1.000000, Obj: 0.282457, No Obj: 0.002427, Avg Recall: 0.832000, count: 125 -3778: 0.547657, 0.488814 avg, 0.001000 rate, 30.102535 seconds, 967168 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.655440, Class: 1.000000, Obj: 0.326522, No Obj: 0.002318, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.669802, Class: 1.000000, Obj: 0.326513, No Obj: 0.002405, Avg Recall: 0.842697, count: 89 -Region Avg IOU: 0.642305, Class: 1.000000, Obj: 0.358729, No Obj: 0.002085, Avg Recall: 0.813187, count: 91 -Region Avg IOU: 0.645089, Class: 1.000000, Obj: 0.362536, No Obj: 0.002720, Avg Recall: 0.812030, count: 133 -Region Avg IOU: 0.601449, Class: 1.000000, Obj: 0.303419, No Obj: 0.002000, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.626839, Class: 1.000000, Obj: 0.290598, No Obj: 0.002466, Avg Recall: 0.785185, count: 135 -Region Avg IOU: 0.645528, Class: 1.000000, Obj: 0.313094, No Obj: 0.002216, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.687199, Class: 1.000000, Obj: 0.351280, No Obj: 0.002254, Avg Recall: 0.876289, count: 97 -3779: 0.482340, 0.488166 avg, 0.001000 rate, 30.186924 seconds, 967424 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.621759, Class: 1.000000, Obj: 0.352604, No Obj: 0.002808, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.626386, Class: 1.000000, Obj: 0.307179, No Obj: 0.002290, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.647281, Class: 1.000000, Obj: 0.345298, No Obj: 0.002340, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.608025, Class: 1.000000, Obj: 0.361838, No Obj: 0.002773, Avg Recall: 0.714286, count: 119 -Region Avg IOU: 0.652263, Class: 1.000000, Obj: 0.338682, No Obj: 0.002773, Avg Recall: 0.818965, count: 116 -Region Avg IOU: 0.664489, Class: 1.000000, Obj: 0.344353, No Obj: 0.002520, Avg Recall: 0.846154, count: 104 -Region Avg IOU: 0.630841, Class: 1.000000, Obj: 0.355380, No Obj: 0.002411, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.640134, Class: 1.000000, Obj: 0.325942, No Obj: 0.002927, Avg Recall: 0.792857, count: 140 -3780: 0.517648, 0.491115 avg, 0.001000 rate, 28.800835 seconds, 967680 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 12.558990 seconds -Region Avg IOU: 0.628380, Class: 1.000000, Obj: 0.353550, No Obj: 0.002964, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.633340, Class: 1.000000, Obj: 0.394127, No Obj: 0.003013, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.587290, Class: 1.000000, Obj: 0.346116, No Obj: 0.003276, Avg Recall: 0.664122, count: 131 -Region Avg IOU: 0.597331, Class: 1.000000, Obj: 0.355366, No Obj: 0.002956, Avg Recall: 0.739583, count: 96 -Region Avg IOU: 0.638838, Class: 1.000000, Obj: 0.359542, No Obj: 0.002955, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.617252, Class: 1.000000, Obj: 0.406271, No Obj: 0.003418, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.614036, Class: 1.000000, Obj: 0.351546, No Obj: 0.003233, Avg Recall: 0.764228, count: 123 -Region Avg IOU: 0.609292, Class: 1.000000, Obj: 0.341507, No Obj: 0.003494, Avg Recall: 0.737705, count: 122 -3781: 0.486931, 0.490696 avg, 0.001000 rate, 27.228939 seconds, 967936 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.670885, Class: 1.000000, Obj: 0.447589, No Obj: 0.003051, Avg Recall: 0.833333, count: 78 -Region Avg IOU: 0.579228, Class: 1.000000, Obj: 0.380276, No Obj: 0.003832, Avg Recall: 0.697183, count: 142 -Region Avg IOU: 0.615362, Class: 1.000000, Obj: 0.386868, No Obj: 0.003522, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.583094, Class: 1.000000, Obj: 0.373165, No Obj: 0.003375, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.595487, Class: 1.000000, Obj: 0.394134, No Obj: 0.003127, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.623651, Class: 1.000000, Obj: 0.452087, No Obj: 0.002879, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.646642, Class: 1.000000, Obj: 0.415566, No Obj: 0.003176, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.627197, Class: 1.000000, Obj: 0.383560, No Obj: 0.003108, Avg Recall: 0.699115, count: 113 -3782: 0.429335, 0.484560 avg, 0.001000 rate, 25.583836 seconds, 968192 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.587237, Class: 1.000000, Obj: 0.347388, No Obj: 0.003091, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.574680, Class: 1.000000, Obj: 0.372498, No Obj: 0.002794, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.593911, Class: 1.000000, Obj: 0.343453, No Obj: 0.002766, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.652713, Class: 1.000000, Obj: 0.403703, No Obj: 0.003383, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.593511, Class: 1.000000, Obj: 0.373718, No Obj: 0.003306, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.650065, Class: 1.000000, Obj: 0.432775, No Obj: 0.003534, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.671585, Class: 1.000000, Obj: 0.444942, No Obj: 0.003230, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.582307, Class: 1.000000, Obj: 0.389337, No Obj: 0.003232, Avg Recall: 0.724771, count: 109 -3783: 0.416003, 0.477704 avg, 0.001000 rate, 26.470892 seconds, 968448 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.626842, Class: 1.000000, Obj: 0.419407, No Obj: 0.003748, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.634188, Class: 1.000000, Obj: 0.412133, No Obj: 0.004035, Avg Recall: 0.780488, count: 123 -Region Avg IOU: 0.622457, Class: 1.000000, Obj: 0.424243, No Obj: 0.003656, Avg Recall: 0.727273, count: 132 -Region Avg IOU: 0.608402, Class: 1.000000, Obj: 0.384245, No Obj: 0.003991, Avg Recall: 0.741722, count: 151 -Region Avg IOU: 0.619047, Class: 1.000000, Obj: 0.397382, No Obj: 0.003448, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.589812, Class: 1.000000, Obj: 0.407843, No Obj: 0.003497, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.610541, Class: 1.000000, Obj: 0.449329, No Obj: 0.003138, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.639774, Class: 1.000000, Obj: 0.467988, No Obj: 0.003940, Avg Recall: 0.814815, count: 108 -3784: 0.447957, 0.474730 avg, 0.001000 rate, 27.206696 seconds, 968704 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.585217, Class: 1.000000, Obj: 0.408770, No Obj: 0.003304, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.610454, Class: 1.000000, Obj: 0.383654, No Obj: 0.003809, Avg Recall: 0.766423, count: 137 -Region Avg IOU: 0.596963, Class: 1.000000, Obj: 0.370025, No Obj: 0.003008, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.653868, Class: 1.000000, Obj: 0.426087, No Obj: 0.003006, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.643409, Class: 1.000000, Obj: 0.426513, No Obj: 0.002903, Avg Recall: 0.771084, count: 83 -Region Avg IOU: 0.605739, Class: 1.000000, Obj: 0.415290, No Obj: 0.003026, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.639329, Class: 1.000000, Obj: 0.413570, No Obj: 0.003682, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.602384, Class: 1.000000, Obj: 0.411650, No Obj: 0.003646, Avg Recall: 0.694215, count: 121 -3785: 0.423869, 0.469644 avg, 0.001000 rate, 26.145643 seconds, 968960 images -Loaded: 0.000114 seconds -Region Avg IOU: 0.610491, Class: 1.000000, Obj: 0.394086, No Obj: 0.003340, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.574571, Class: 1.000000, Obj: 0.400117, No Obj: 0.003129, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.628912, Class: 1.000000, Obj: 0.416471, No Obj: 0.002822, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.655309, Class: 1.000000, Obj: 0.434060, No Obj: 0.003384, Avg Recall: 0.833333, count: 108 -Region Avg IOU: 0.639687, Class: 1.000000, Obj: 0.450548, No Obj: 0.003020, Avg Recall: 0.837209, count: 86 -Region Avg IOU: 0.641104, Class: 1.000000, Obj: 0.426282, No Obj: 0.003140, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.657221, Class: 1.000000, Obj: 0.396493, No Obj: 0.002901, Avg Recall: 0.807692, count: 78 -Region Avg IOU: 0.609107, Class: 1.000000, Obj: 0.395991, No Obj: 0.002987, Avg Recall: 0.723214, count: 112 -3786: 0.405577, 0.463237 avg, 0.001000 rate, 26.291916 seconds, 969216 images -Loaded: 0.000129 seconds -Region Avg IOU: 0.587666, Class: 1.000000, Obj: 0.357693, No Obj: 0.003271, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.646090, Class: 1.000000, Obj: 0.478341, No Obj: 0.003145, Avg Recall: 0.826923, count: 104 -Region Avg IOU: 0.586497, Class: 1.000000, Obj: 0.365990, No Obj: 0.002778, Avg Recall: 0.654762, count: 84 -Region Avg IOU: 0.611048, Class: 1.000000, Obj: 0.397826, No Obj: 0.002994, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.615425, Class: 1.000000, Obj: 0.440369, No Obj: 0.003082, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.589169, Class: 1.000000, Obj: 0.366983, No Obj: 0.002525, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.593530, Class: 1.000000, Obj: 0.450979, No Obj: 0.003059, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.552389, Class: 1.000000, Obj: 0.378406, No Obj: 0.002693, Avg Recall: 0.644444, count: 90 -3787: 0.453034, 0.462217 avg, 0.001000 rate, 27.170303 seconds, 969472 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.595539, Class: 1.000000, Obj: 0.338888, No Obj: 0.002736, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.585369, Class: 1.000000, Obj: 0.335758, No Obj: 0.002930, Avg Recall: 0.713115, count: 122 -Region Avg IOU: 0.662458, Class: 1.000000, Obj: 0.344559, No Obj: 0.002521, Avg Recall: 0.810811, count: 74 -Region Avg IOU: 0.649791, Class: 1.000000, Obj: 0.402180, No Obj: 0.003403, Avg Recall: 0.824561, count: 114 -Region Avg IOU: 0.620506, Class: 1.000000, Obj: 0.350540, No Obj: 0.003137, Avg Recall: 0.791304, count: 115 -Region Avg IOU: 0.605632, Class: 1.000000, Obj: 0.368962, No Obj: 0.003352, Avg Recall: 0.702290, count: 131 -Region Avg IOU: 0.603518, Class: 1.000000, Obj: 0.416231, No Obj: 0.002826, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.603363, Class: 1.000000, Obj: 0.371041, No Obj: 0.002368, Avg Recall: 0.707317, count: 82 -3788: 0.426318, 0.458627 avg, 0.001000 rate, 26.579157 seconds, 969728 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.613359, Class: 1.000000, Obj: 0.374895, No Obj: 0.002626, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.623877, Class: 1.000000, Obj: 0.384550, No Obj: 0.002521, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.605102, Class: 1.000000, Obj: 0.388228, No Obj: 0.002881, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.609244, Class: 1.000000, Obj: 0.402817, No Obj: 0.002722, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.664185, Class: 1.000000, Obj: 0.422755, No Obj: 0.002746, Avg Recall: 0.838384, count: 99 -Region Avg IOU: 0.596295, Class: 1.000000, Obj: 0.335620, No Obj: 0.003285, Avg Recall: 0.732824, count: 131 -Region Avg IOU: 0.646474, Class: 1.000000, Obj: 0.395854, No Obj: 0.002949, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.611806, Class: 1.000000, Obj: 0.335439, No Obj: 0.002987, Avg Recall: 0.739837, count: 123 -3789: 0.437332, 0.456497 avg, 0.001000 rate, 27.028151 seconds, 969984 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.629463, Class: 1.000000, Obj: 0.365112, No Obj: 0.002894, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.627181, Class: 1.000000, Obj: 0.414261, No Obj: 0.002442, Avg Recall: 0.771084, count: 83 -Region Avg IOU: 0.596835, Class: 1.000000, Obj: 0.362058, No Obj: 0.002458, Avg Recall: 0.708861, count: 79 -Region Avg IOU: 0.594669, Class: 1.000000, Obj: 0.349308, No Obj: 0.002832, Avg Recall: 0.709677, count: 124 -Region Avg IOU: 0.569589, Class: 1.000000, Obj: 0.330074, No Obj: 0.002891, Avg Recall: 0.678571, count: 140 -Region Avg IOU: 0.627754, Class: 1.000000, Obj: 0.389055, No Obj: 0.002720, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.650796, Class: 1.000000, Obj: 0.384586, No Obj: 0.002802, Avg Recall: 0.831579, count: 95 -Region Avg IOU: 0.629432, Class: 1.000000, Obj: 0.370550, No Obj: 0.002911, Avg Recall: 0.779661, count: 118 -3790: 0.428474, 0.453695 avg, 0.001000 rate, 26.583595 seconds, 970240 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 16.345217 seconds -Region Avg IOU: 0.593977, Class: 1.000000, Obj: 0.304647, No Obj: 0.003066, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.592193, Class: 1.000000, Obj: 0.324891, No Obj: 0.002632, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.608572, Class: 1.000000, Obj: 0.356824, No Obj: 0.002915, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.652706, Class: 1.000000, Obj: 0.386904, No Obj: 0.002471, Avg Recall: 0.810811, count: 74 -Region Avg IOU: 0.622142, Class: 1.000000, Obj: 0.330559, No Obj: 0.002981, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.632211, Class: 1.000000, Obj: 0.379293, No Obj: 0.002751, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.593779, Class: 1.000000, Obj: 0.350261, No Obj: 0.002689, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.561750, Class: 1.000000, Obj: 0.330429, No Obj: 0.002683, Avg Recall: 0.647727, count: 88 -3791: 0.406880, 0.449013 avg, 0.001000 rate, 23.680744 seconds, 970496 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.603293, Class: 1.000000, Obj: 0.336627, No Obj: 0.002571, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.607466, Class: 1.000000, Obj: 0.352295, No Obj: 0.003220, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.592107, Class: 1.000000, Obj: 0.310723, No Obj: 0.002897, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.597961, Class: 1.000000, Obj: 0.329075, No Obj: 0.002686, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.589576, Class: 1.000000, Obj: 0.346562, No Obj: 0.002779, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.509850, Class: 1.000000, Obj: 0.344613, No Obj: 0.002719, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.560309, Class: 1.000000, Obj: 0.331888, No Obj: 0.003199, Avg Recall: 0.617188, count: 128 -Region Avg IOU: 0.610906, Class: 1.000000, Obj: 0.339545, No Obj: 0.003398, Avg Recall: 0.709924, count: 131 -3792: 0.460229, 0.450135 avg, 0.001000 rate, 26.175243 seconds, 970752 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.537773, Class: 1.000000, Obj: 0.323947, No Obj: 0.002448, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.590643, Class: 1.000000, Obj: 0.330516, No Obj: 0.002825, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.605369, Class: 1.000000, Obj: 0.351496, No Obj: 0.003336, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.636545, Class: 1.000000, Obj: 0.422051, No Obj: 0.002841, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.559168, Class: 1.000000, Obj: 0.346606, No Obj: 0.003446, Avg Recall: 0.647887, count: 142 -Region Avg IOU: 0.574420, Class: 1.000000, Obj: 0.352430, No Obj: 0.002813, Avg Recall: 0.680328, count: 122 -Region Avg IOU: 0.610714, Class: 1.000000, Obj: 0.361977, No Obj: 0.003168, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.576756, Class: 1.000000, Obj: 0.314596, No Obj: 0.002968, Avg Recall: 0.733945, count: 109 -3793: 0.494178, 0.454539 avg, 0.001000 rate, 24.513803 seconds, 971008 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.593714, Class: 1.000000, Obj: 0.344275, No Obj: 0.003548, Avg Recall: 0.752066, count: 121 -Region Avg IOU: 0.602927, Class: 1.000000, Obj: 0.373398, No Obj: 0.003660, Avg Recall: 0.727273, count: 132 -Region Avg IOU: 0.600457, Class: 1.000000, Obj: 0.387035, No Obj: 0.002827, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.548850, Class: 1.000000, Obj: 0.362654, No Obj: 0.002903, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.592899, Class: 1.000000, Obj: 0.371307, No Obj: 0.003468, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.537253, Class: 1.000000, Obj: 0.326129, No Obj: 0.002794, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.587716, Class: 1.000000, Obj: 0.391604, No Obj: 0.003129, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.565340, Class: 1.000000, Obj: 0.372121, No Obj: 0.002813, Avg Recall: 0.649485, count: 97 -3794: 0.431896, 0.452275 avg, 0.001000 rate, 24.707945 seconds, 971264 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.603439, Class: 1.000000, Obj: 0.354453, No Obj: 0.003792, Avg Recall: 0.741259, count: 143 -Region Avg IOU: 0.567781, Class: 1.000000, Obj: 0.345551, No Obj: 0.003198, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.613759, Class: 1.000000, Obj: 0.399450, No Obj: 0.003060, Avg Recall: 0.755814, count: 86 -Region Avg IOU: 0.621322, Class: 1.000000, Obj: 0.422833, No Obj: 0.003135, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.603131, Class: 1.000000, Obj: 0.422918, No Obj: 0.003488, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.599900, Class: 1.000000, Obj: 0.346455, No Obj: 0.003088, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.595975, Class: 1.000000, Obj: 0.395028, No Obj: 0.003452, Avg Recall: 0.731707, count: 123 -Region Avg IOU: 0.586226, Class: 1.000000, Obj: 0.350947, No Obj: 0.002776, Avg Recall: 0.673913, count: 92 -3795: 0.451555, 0.452203 avg, 0.001000 rate, 24.289499 seconds, 971520 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.566723, Class: 1.000000, Obj: 0.367851, No Obj: 0.004073, Avg Recall: 0.648855, count: 131 -Region Avg IOU: 0.634306, Class: 1.000000, Obj: 0.392652, No Obj: 0.003267, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.616700, Class: 1.000000, Obj: 0.390688, No Obj: 0.003243, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.628899, Class: 1.000000, Obj: 0.414314, No Obj: 0.003542, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.571434, Class: 1.000000, Obj: 0.389777, No Obj: 0.003689, Avg Recall: 0.654676, count: 139 -Region Avg IOU: 0.624533, Class: 1.000000, Obj: 0.376690, No Obj: 0.003544, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.564292, Class: 1.000000, Obj: 0.341348, No Obj: 0.003642, Avg Recall: 0.714286, count: 119 -Region Avg IOU: 0.578628, Class: 1.000000, Obj: 0.421039, No Obj: 0.003343, Avg Recall: 0.696970, count: 99 -3796: 0.437221, 0.450705 avg, 0.001000 rate, 25.040739 seconds, 971776 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.590915, Class: 1.000000, Obj: 0.398037, No Obj: 0.003564, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.558204, Class: 1.000000, Obj: 0.382954, No Obj: 0.003253, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.604753, Class: 1.000000, Obj: 0.362498, No Obj: 0.003496, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.579951, Class: 1.000000, Obj: 0.377653, No Obj: 0.003775, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.655996, Class: 1.000000, Obj: 0.431092, No Obj: 0.003248, Avg Recall: 0.819444, count: 72 -Region Avg IOU: 0.571195, Class: 1.000000, Obj: 0.385386, No Obj: 0.002869, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.610470, Class: 1.000000, Obj: 0.407882, No Obj: 0.004349, Avg Recall: 0.731343, count: 134 -Region Avg IOU: 0.578159, Class: 1.000000, Obj: 0.412651, No Obj: 0.003410, Avg Recall: 0.750000, count: 104 -3797: 0.403916, 0.446026 avg, 0.001000 rate, 25.500957 seconds, 972032 images -Loaded: 0.000115 seconds -Region Avg IOU: 0.554280, Class: 1.000000, Obj: 0.404036, No Obj: 0.003521, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.621712, Class: 1.000000, Obj: 0.410408, No Obj: 0.003534, Avg Recall: 0.755556, count: 90 -Region Avg IOU: 0.603969, Class: 1.000000, Obj: 0.364323, No Obj: 0.004300, Avg Recall: 0.766667, count: 120 -Region Avg IOU: 0.565175, Class: 1.000000, Obj: 0.403262, No Obj: 0.003571, Avg Recall: 0.680672, count: 119 -Region Avg IOU: 0.571246, Class: 1.000000, Obj: 0.388683, No Obj: 0.003254, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.617519, Class: 1.000000, Obj: 0.376216, No Obj: 0.003729, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.575322, Class: 1.000000, Obj: 0.391175, No Obj: 0.004154, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.572084, Class: 1.000000, Obj: 0.422066, No Obj: 0.003257, Avg Recall: 0.670455, count: 88 -3798: 0.416545, 0.443078 avg, 0.001000 rate, 25.627184 seconds, 972288 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.564799, Class: 1.000000, Obj: 0.424141, No Obj: 0.004060, Avg Recall: 0.619048, count: 126 -Region Avg IOU: 0.570438, Class: 1.000000, Obj: 0.356189, No Obj: 0.003486, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.613375, Class: 1.000000, Obj: 0.481663, No Obj: 0.003117, Avg Recall: 0.693333, count: 75 -Region Avg IOU: 0.589227, Class: 1.000000, Obj: 0.398163, No Obj: 0.003499, Avg Recall: 0.714286, count: 119 -Region Avg IOU: 0.590100, Class: 1.000000, Obj: 0.419466, No Obj: 0.003631, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.600410, Class: 1.000000, Obj: 0.412044, No Obj: 0.003592, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.605634, Class: 1.000000, Obj: 0.394930, No Obj: 0.004127, Avg Recall: 0.708333, count: 120 -Region Avg IOU: 0.532250, Class: 1.000000, Obj: 0.370928, No Obj: 0.003582, Avg Recall: 0.617188, count: 128 -3799: 0.420167, 0.440787 avg, 0.001000 rate, 26.338634 seconds, 972544 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.576174, Class: 1.000000, Obj: 0.393217, No Obj: 0.003526, Avg Recall: 0.691057, count: 123 -Region Avg IOU: 0.570716, Class: 1.000000, Obj: 0.410717, No Obj: 0.003471, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.549086, Class: 1.000000, Obj: 0.348740, No Obj: 0.003396, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.599256, Class: 1.000000, Obj: 0.419377, No Obj: 0.004060, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.626809, Class: 1.000000, Obj: 0.399212, No Obj: 0.003846, Avg ReSaving weights to ./person9300/backup/tyvp_93_9c512_3800.weights -call: 0.771186, count: 118 -Region Avg IOU: 0.582361, Class: 1.000000, Obj: 0.374518, No Obj: 0.003777, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.637280, Class: 1.000000, Obj: 0.475407, No Obj: 0.003939, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.567273, Class: 1.000000, Obj: 0.384070, No Obj: 0.003667, Avg Recall: 0.658915, count: 129 -3800: 0.506325, 0.447341 avg, 0.001000 rate, 25.888596 seconds, 972800 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 16.897888 seconds -Region Avg IOU: 0.629382, Class: 1.000000, Obj: 0.399918, No Obj: 0.003910, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.562316, Class: 1.000000, Obj: 0.387433, No Obj: 0.003713, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.587180, Class: 1.000000, Obj: 0.368360, No Obj: 0.003534, Avg Recall: 0.676923, count: 130 -Region Avg IOU: 0.612370, Class: 1.000000, Obj: 0.412907, No Obj: 0.003710, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.565209, Class: 1.000000, Obj: 0.370885, No Obj: 0.002792, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.597629, Class: 1.000000, Obj: 0.446553, No Obj: 0.003425, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.616456, Class: 1.000000, Obj: 0.400751, No Obj: 0.003484, Avg Recall: 0.775000, count: 120 -Region Avg IOU: 0.592282, Class: 1.000000, Obj: 0.333461, No Obj: 0.003675, Avg Recall: 0.725926, count: 135 -3801: 0.444279, 0.447035 avg, 0.001000 rate, 25.007059 seconds, 973056 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.570109, Class: 1.000000, Obj: 0.420431, No Obj: 0.002710, Avg Recall: 0.716216, count: 74 -Region Avg IOU: 0.618922, Class: 1.000000, Obj: 0.375338, No Obj: 0.003608, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.627786, Class: 1.000000, Obj: 0.424983, No Obj: 0.003313, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.573416, Class: 1.000000, Obj: 0.428095, No Obj: 0.002891, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.627324, Class: 1.000000, Obj: 0.392518, No Obj: 0.003547, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.590823, Class: 1.000000, Obj: 0.369671, No Obj: 0.003210, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.638098, Class: 1.000000, Obj: 0.389988, No Obj: 0.003006, Avg Recall: 0.842105, count: 76 -Region Avg IOU: 0.609579, Class: 1.000000, Obj: 0.354787, No Obj: 0.003634, Avg Recall: 0.736842, count: 114 -3802: 0.397327, 0.442064 avg, 0.001000 rate, 25.574219 seconds, 973312 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.617200, Class: 1.000000, Obj: 0.389932, No Obj: 0.002756, Avg Recall: 0.779070, count: 86 -Region Avg IOU: 0.613131, Class: 1.000000, Obj: 0.363721, No Obj: 0.003324, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.618261, Class: 1.000000, Obj: 0.379105, No Obj: 0.003110, Avg Recall: 0.770115, count: 87 -Region Avg IOU: 0.583300, Class: 1.000000, Obj: 0.333023, No Obj: 0.002814, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.607470, Class: 1.000000, Obj: 0.364228, No Obj: 0.004260, Avg Recall: 0.703947, count: 152 -Region Avg IOU: 0.594281, Class: 1.000000, Obj: 0.359884, No Obj: 0.003618, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.574017, Class: 1.000000, Obj: 0.347093, No Obj: 0.002759, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.593673, Class: 1.000000, Obj: 0.394239, No Obj: 0.003181, Avg Recall: 0.681416, count: 113 -3803: 0.422172, 0.440075 avg, 0.001000 rate, 24.606337 seconds, 973568 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.603936, Class: 1.000000, Obj: 0.362878, No Obj: 0.002466, Avg Recall: 0.767123, count: 73 -Region Avg IOU: 0.565275, Class: 1.000000, Obj: 0.398143, No Obj: 0.003006, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.589909, Class: 1.000000, Obj: 0.386011, No Obj: 0.003297, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.596337, Class: 1.000000, Obj: 0.367986, No Obj: 0.003635, Avg Recall: 0.682540, count: 126 -Region Avg IOU: 0.592767, Class: 1.000000, Obj: 0.345762, No Obj: 0.003186, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.638961, Class: 1.000000, Obj: 0.344458, No Obj: 0.003214, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.582678, Class: 1.000000, Obj: 0.389123, No Obj: 0.002989, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.598408, Class: 1.000000, Obj: 0.373900, No Obj: 0.003317, Avg Recall: 0.718182, count: 110 -3804: 0.452569, 0.441324 avg, 0.001000 rate, 23.857710 seconds, 973824 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.622462, Class: 1.000000, Obj: 0.367345, No Obj: 0.003042, Avg Recall: 0.808989, count: 89 -Region Avg IOU: 0.589405, Class: 1.000000, Obj: 0.345849, No Obj: 0.003518, Avg Recall: 0.730769, count: 130 -Region Avg IOU: 0.603072, Class: 1.000000, Obj: 0.357766, No Obj: 0.002606, Avg Recall: 0.743902, count: 82 -Region Avg IOU: 0.578401, Class: 1.000000, Obj: 0.373986, No Obj: 0.003511, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.562465, Class: 1.000000, Obj: 0.378815, No Obj: 0.003017, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.562890, Class: 1.000000, Obj: 0.381764, No Obj: 0.002824, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.583252, Class: 1.000000, Obj: 0.369780, No Obj: 0.002738, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.585310, Class: 1.000000, Obj: 0.381594, No Obj: 0.002880, Avg Recall: 0.715596, count: 109 -3805: 0.403306, 0.437522 avg, 0.001000 rate, 25.522869 seconds, 974080 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.595994, Class: 1.000000, Obj: 0.346979, No Obj: 0.003482, Avg Recall: 0.683099, count: 142 -Region Avg IOU: 0.616999, Class: 1.000000, Obj: 0.373908, No Obj: 0.003395, Avg Recall: 0.728000, count: 125 -Region Avg IOU: 0.629549, Class: 1.000000, Obj: 0.354687, No Obj: 0.003347, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.614974, Class: 1.000000, Obj: 0.368745, No Obj: 0.003459, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.544701, Class: 1.000000, Obj: 0.345489, No Obj: 0.003258, Avg Recall: 0.671756, count: 131 -Region Avg IOU: 0.598697, Class: 1.000000, Obj: 0.365338, No Obj: 0.003616, Avg Recall: 0.729323, count: 133 -Region Avg IOU: 0.597858, Class: 1.000000, Obj: 0.374908, No Obj: 0.003086, Avg Recall: 0.747967, count: 123 -Region Avg IOU: 0.607213, Class: 1.000000, Obj: 0.406973, No Obj: 0.003069, Avg Recall: 0.720930, count: 86 -3806: 0.491627, 0.442933 avg, 0.001000 rate, 23.966640 seconds, 974336 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.622767, Class: 1.000000, Obj: 0.407306, No Obj: 0.003038, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.554289, Class: 1.000000, Obj: 0.372212, No Obj: 0.003185, Avg Recall: 0.656000, count: 125 -Region Avg IOU: 0.618121, Class: 1.000000, Obj: 0.400942, No Obj: 0.003320, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.616688, Class: 1.000000, Obj: 0.395661, No Obj: 0.003531, Avg Recall: 0.746032, count: 126 -Region Avg IOU: 0.610247, Class: 1.000000, Obj: 0.343414, No Obj: 0.003091, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.536131, Class: 1.000000, Obj: 0.293712, No Obj: 0.003251, Avg Recall: 0.596899, count: 129 -Region Avg IOU: 0.578657, Class: 1.000000, Obj: 0.333417, No Obj: 0.003660, Avg Recall: 0.688742, count: 151 -Region Avg IOU: 0.620189, Class: 1.000000, Obj: 0.368417, No Obj: 0.003197, Avg Recall: 0.745098, count: 102 -3807: 0.521184, 0.450758 avg, 0.001000 rate, 24.951096 seconds, 974592 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.583098, Class: 1.000000, Obj: 0.394210, No Obj: 0.003298, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.567920, Class: 1.000000, Obj: 0.367746, No Obj: 0.003236, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.650845, Class: 1.000000, Obj: 0.416301, No Obj: 0.003255, Avg Recall: 0.826087, count: 92 -Region Avg IOU: 0.627811, Class: 1.000000, Obj: 0.401551, No Obj: 0.002985, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.584935, Class: 1.000000, Obj: 0.320235, No Obj: 0.003459, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.629188, Class: 1.000000, Obj: 0.402785, No Obj: 0.002810, Avg Recall: 0.804598, count: 87 -Region Avg IOU: 0.590148, Class: 1.000000, Obj: 0.385780, No Obj: 0.003055, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.551865, Class: 1.000000, Obj: 0.341848, No Obj: 0.003417, Avg Recall: 0.634921, count: 126 -3808: 0.424820, 0.448164 avg, 0.001000 rate, 24.648516 seconds, 974848 images -Loaded: 0.000133 seconds -Region Avg IOU: 0.559015, Class: 1.000000, Obj: 0.385273, No Obj: 0.002885, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.578857, Class: 1.000000, Obj: 0.346111, No Obj: 0.003337, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.603386, Class: 1.000000, Obj: 0.379530, No Obj: 0.003482, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.598468, Class: 1.000000, Obj: 0.363646, No Obj: 0.003295, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.596819, Class: 1.000000, Obj: 0.392913, No Obj: 0.003040, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.608517, Class: 1.000000, Obj: 0.368877, No Obj: 0.002981, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.631170, Class: 1.000000, Obj: 0.370614, No Obj: 0.003825, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.597228, Class: 1.000000, Obj: 0.344072, No Obj: 0.003746, Avg Recall: 0.750000, count: 112 -3809: 0.431101, 0.446458 avg, 0.001000 rate, 26.464916 seconds, 975104 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.639580, Class: 1.000000, Obj: 0.400455, No Obj: 0.003172, Avg Recall: 0.793478, count: 92 -Region Avg IOU: 0.595080, Class: 1.000000, Obj: 0.405557, No Obj: 0.003579, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.589035, Class: 1.000000, Obj: 0.346913, No Obj: 0.004201, Avg Recall: 0.697674, count: 129 -Region Avg IOU: 0.629612, Class: 1.000000, Obj: 0.394425, No Obj: 0.003924, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.591508, Class: 1.000000, Obj: 0.401686, No Obj: 0.004117, Avg Recall: 0.666667, count: 132 -Region Avg IOU: 0.623070, Class: 1.000000, Obj: 0.395905, No Obj: 0.003765, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.581409, Class: 1.000000, Obj: 0.383110, No Obj: 0.003360, Avg Recall: 0.728395, count: 81 -Region Avg IOU: 0.616226, Class: 1.000000, Obj: 0.373340, No Obj: 0.004105, Avg Recall: 0.742424, count: 132 -3810: 0.493134, 0.451126 avg, 0.001000 rate, 24.556444 seconds, 975360 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000085 seconds -Region Avg IOU: 0.587992, Class: 1.000000, Obj: 0.421276, No Obj: 0.003372, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.573293, Class: 1.000000, Obj: 0.389727, No Obj: 0.003410, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.640347, Class: 1.000000, Obj: 0.412344, No Obj: 0.003449, Avg Recall: 0.793103, count: 87 -Region Avg IOU: 0.611936, Class: 1.000000, Obj: 0.395118, No Obj: 0.003696, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.618809, Class: 1.000000, Obj: 0.405352, No Obj: 0.003616, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.586712, Class: 1.000000, Obj: 0.410676, No Obj: 0.003346, Avg Recall: 0.659341, count: 91 -Region Avg IOU: 0.607410, Class: 1.000000, Obj: 0.406896, No Obj: 0.003618, Avg Recall: 0.715328, count: 137 -Region Avg IOU: 0.597927, Class: 1.000000, Obj: 0.433488, No Obj: 0.003639, Avg Recall: 0.715596, count: 109 -3811: 0.404223, 0.446435 avg, 0.001000 rate, 25.648407 seconds, 975616 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.588477, Class: 1.000000, Obj: 0.387217, No Obj: 0.003474, Avg Recall: 0.683761, count: 117 -Region Avg IOU: 0.594358, Class: 1.000000, Obj: 0.409800, No Obj: 0.003580, Avg Recall: 0.705426, count: 129 -Region Avg IOU: 0.622487, Class: 1.000000, Obj: 0.411981, No Obj: 0.003209, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.622389, Class: 1.000000, Obj: 0.400366, No Obj: 0.003097, Avg Recall: 0.752941, count: 85 -Region Avg IOU: 0.593632, Class: 1.000000, Obj: 0.387576, No Obj: 0.003166, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.631189, Class: 1.000000, Obj: 0.428125, No Obj: 0.003522, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.599213, Class: 1.000000, Obj: 0.374333, No Obj: 0.003595, Avg Recall: 0.708955, count: 134 -Region Avg IOU: 0.611981, Class: 1.000000, Obj: 0.386744, No Obj: 0.003593, Avg Recall: 0.745763, count: 118 -3812: 0.504588, 0.452251 avg, 0.001000 rate, 25.825897 seconds, 975872 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.664628, Class: 1.000000, Obj: 0.403010, No Obj: 0.002883, Avg Recall: 0.810811, count: 74 -Region Avg IOU: 0.640427, Class: 1.000000, Obj: 0.392540, No Obj: 0.003634, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.622247, Class: 1.000000, Obj: 0.390693, No Obj: 0.003713, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.605438, Class: 1.000000, Obj: 0.406076, No Obj: 0.003610, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.640516, Class: 1.000000, Obj: 0.430611, No Obj: 0.003245, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.665177, Class: 1.000000, Obj: 0.479259, No Obj: 0.003014, Avg Recall: 0.802817, count: 71 -Region Avg IOU: 0.597669, Class: 1.000000, Obj: 0.350428, No Obj: 0.003843, Avg Recall: 0.727941, count: 136 -Region Avg IOU: 0.622127, Class: 1.000000, Obj: 0.433909, No Obj: 0.004081, Avg Recall: 0.742424, count: 132 -3813: 0.429743, 0.450000 avg, 0.001000 rate, 25.092005 seconds, 976128 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.601214, Class: 1.000000, Obj: 0.415490, No Obj: 0.003499, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.632233, Class: 1.000000, Obj: 0.456473, No Obj: 0.003422, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.594437, Class: 1.000000, Obj: 0.423491, No Obj: 0.003595, Avg Recall: 0.680328, count: 122 -Region Avg IOU: 0.568478, Class: 1.000000, Obj: 0.381530, No Obj: 0.002751, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.568658, Class: 1.000000, Obj: 0.374424, No Obj: 0.003651, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.639679, Class: 1.000000, Obj: 0.389497, No Obj: 0.004106, Avg Recall: 0.810219, count: 137 -Region Avg IOU: 0.627162, Class: 1.000000, Obj: 0.383186, No Obj: 0.003108, Avg Recall: 0.763158, count: 76 -Region Avg IOU: 0.622619, Class: 1.000000, Obj: 0.375762, No Obj: 0.002757, Avg Recall: 0.763889, count: 72 -3814: 0.400950, 0.445095 avg, 0.001000 rate, 24.455238 seconds, 976384 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.619319, Class: 1.000000, Obj: 0.415822, No Obj: 0.003469, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.605679, Class: 1.000000, Obj: 0.404391, No Obj: 0.003280, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.593658, Class: 1.000000, Obj: 0.377144, No Obj: 0.003682, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.574569, Class: 1.000000, Obj: 0.389611, No Obj: 0.002720, Avg Recall: 0.688889, count: 90 -Region Avg IOU: 0.592352, Class: 1.000000, Obj: 0.398910, No Obj: 0.002805, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.588021, Class: 1.000000, Obj: 0.415351, No Obj: 0.002949, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.597585, Class: 1.000000, Obj: 0.388671, No Obj: 0.002853, Avg Recall: 0.707865, count: 89 -Region Avg IOU: 0.582785, Class: 1.000000, Obj: 0.387244, No Obj: 0.003144, Avg Recall: 0.708661, count: 127 -3815: 0.391367, 0.439722 avg, 0.001000 rate, 24.922045 seconds, 976640 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.608704, Class: 1.000000, Obj: 0.376701, No Obj: 0.003500, Avg Recall: 0.738462, count: 130 -Region Avg IOU: 0.650418, Class: 1.000000, Obj: 0.413864, No Obj: 0.004121, Avg Recall: 0.810345, count: 116 -Region Avg IOU: 0.592444, Class: 1.000000, Obj: 0.386171, No Obj: 0.003158, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.615927, Class: 1.000000, Obj: 0.364886, No Obj: 0.003004, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.588278, Class: 1.000000, Obj: 0.375161, No Obj: 0.002922, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.588715, Class: 1.000000, Obj: 0.427623, No Obj: 0.003204, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.616156, Class: 1.000000, Obj: 0.405730, No Obj: 0.003162, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.601564, Class: 1.000000, Obj: 0.370533, No Obj: 0.003344, Avg Recall: 0.700000, count: 120 -3816: 0.410656, 0.436815 avg, 0.001000 rate, 25.104908 seconds, 976896 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.630419, Class: 1.000000, Obj: 0.371545, No Obj: 0.003432, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.576317, Class: 1.000000, Obj: 0.358071, No Obj: 0.003020, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.622689, Class: 1.000000, Obj: 0.450488, No Obj: 0.002889, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.628053, Class: 1.000000, Obj: 0.375874, No Obj: 0.003435, Avg Recall: 0.756098, count: 123 -Region Avg IOU: 0.617689, Class: 1.000000, Obj: 0.433214, No Obj: 0.003292, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.628072, Class: 1.000000, Obj: 0.348166, No Obj: 0.003050, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.614757, Class: 1.000000, Obj: 0.399188, No Obj: 0.003314, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.555016, Class: 1.000000, Obj: 0.366223, No Obj: 0.002842, Avg Recall: 0.670455, count: 88 -3817: 0.423736, 0.435507 avg, 0.001000 rate, 24.894953 seconds, 977152 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.630707, Class: 1.000000, Obj: 0.384751, No Obj: 0.002799, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.592141, Class: 1.000000, Obj: 0.407744, No Obj: 0.002583, Avg Recall: 0.715909, count: 88 -Region Avg IOU: 0.586859, Class: 1.000000, Obj: 0.368587, No Obj: 0.003567, Avg Recall: 0.682759, count: 145 -Region Avg IOU: 0.600843, Class: 1.000000, Obj: 0.349371, No Obj: 0.003702, Avg Recall: 0.700787, count: 127 -Region Avg IOU: 0.551213, Class: 1.000000, Obj: 0.366027, No Obj: 0.002932, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.614615, Class: 1.000000, Obj: 0.422716, No Obj: 0.002758, Avg Recall: 0.755556, count: 90 -Region Avg IOU: 0.577227, Class: 1.000000, Obj: 0.393646, No Obj: 0.002911, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.585835, Class: 1.000000, Obj: 0.331476, No Obj: 0.002740, Avg Recall: 0.663551, count: 107 -3818: 0.428625, 0.434819 avg, 0.001000 rate, 24.643930 seconds, 977408 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.618210, Class: 1.000000, Obj: 0.389243, No Obj: 0.002349, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.659895, Class: 1.000000, Obj: 0.452050, No Obj: 0.002598, Avg Recall: 0.831461, count: 89 -Region Avg IOU: 0.597355, Class: 1.000000, Obj: 0.349312, No Obj: 0.003386, Avg Recall: 0.726027, count: 146 -Region Avg IOU: 0.634446, Class: 1.000000, Obj: 0.369388, No Obj: 0.003160, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.593077, Class: 1.000000, Obj: 0.375207, No Obj: 0.003363, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.570646, Class: 1.000000, Obj: 0.371139, No Obj: 0.003009, Avg Recall: 0.642276, count: 123 -Region Avg IOU: 0.574193, Class: 1.000000, Obj: 0.392704, No Obj: 0.002984, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.632383, Class: 1.000000, Obj: 0.386306, No Obj: 0.002929, Avg Recall: 0.766990, count: 103 -3819: 0.429098, 0.434247 avg, 0.001000 rate, 26.062622 seconds, 977664 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.591215, Class: 1.000000, Obj: 0.402324, No Obj: 0.002582, Avg Recall: 0.675676, count: 74 -Region Avg IOU: 0.605926, Class: 1.000000, Obj: 0.326815, No Obj: 0.002479, Avg Recall: 0.756098, count: 82 -Region Avg IOU: 0.581207, Class: 1.000000, Obj: 0.347046, No Obj: 0.002875, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.610239, Class: 1.000000, Obj: 0.391537, No Obj: 0.002706, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.626731, Class: 1.000000, Obj: 0.426596, No Obj: 0.002907, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.590346, Class: 1.000000, Obj: 0.383474, No Obj: 0.002967, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.588465, Class: 1.000000, Obj: 0.360977, No Obj: 0.003031, Avg Recall: 0.702479, count: 121 -Region Avg IOU: 0.523168, Class: 1.000000, Obj: 0.329357, No Obj: 0.003063, Avg Recall: 0.570370, count: 135 -3820: 0.401135, 0.430936 avg, 0.001000 rate, 24.802820 seconds, 977920 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 20.017944 seconds -Region Avg IOU: 0.600103, Class: 1.000000, Obj: 0.385568, No Obj: 0.002425, Avg Recall: 0.701299, count: 77 -Region Avg IOU: 0.686447, Class: 1.000000, Obj: 0.400292, No Obj: 0.002751, Avg Recall: 0.862500, count: 80 -Region Avg IOU: 0.632747, Class: 1.000000, Obj: 0.389491, No Obj: 0.002861, Avg Recall: 0.764706, count: 85 -Region Avg IOU: 0.651317, Class: 1.000000, Obj: 0.389448, No Obj: 0.002549, Avg Recall: 0.795455, count: 88 -Region Avg IOU: 0.611295, Class: 1.000000, Obj: 0.345465, No Obj: 0.003223, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.593612, Class: 1.000000, Obj: 0.401262, No Obj: 0.003019, Avg Recall: 0.762712, count: 118 -Region Avg IOU: 0.595572, Class: 1.000000, Obj: 0.371808, No Obj: 0.002795, Avg Recall: 0.739130, count: 69 -Region Avg IOU: 0.647021, Class: 1.000000, Obj: 0.383179, No Obj: 0.002763, Avg Recall: 0.816092, count: 87 -3821: 0.383491, 0.426191 avg, 0.001000 rate, 25.717873 seconds, 978176 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.569377, Class: 1.000000, Obj: 0.355312, No Obj: 0.002756, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.637936, Class: 1.000000, Obj: 0.426640, No Obj: 0.002796, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.612721, Class: 1.000000, Obj: 0.372527, No Obj: 0.003163, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.602788, Class: 1.000000, Obj: 0.345684, No Obj: 0.002994, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.581885, Class: 1.000000, Obj: 0.314786, No Obj: 0.003056, Avg Recall: 0.747967, count: 123 -Region Avg IOU: 0.562951, Class: 1.000000, Obj: 0.329539, No Obj: 0.002994, Avg Recall: 0.642857, count: 126 -Region Avg IOU: 0.606462, Class: 1.000000, Obj: 0.374270, No Obj: 0.003238, Avg Recall: 0.704000, count: 125 -Region Avg IOU: 0.564672, Class: 1.000000, Obj: 0.366427, No Obj: 0.002761, Avg Recall: 0.682692, count: 104 -3822: 0.479306, 0.431503 avg, 0.001000 rate, 27.622252 seconds, 978432 images -Loaded: 0.000116 seconds -Region Avg IOU: 0.602775, Class: 1.000000, Obj: 0.355759, No Obj: 0.003298, Avg Recall: 0.723077, count: 130 -Region Avg IOU: 0.660095, Class: 1.000000, Obj: 0.410991, No Obj: 0.003132, Avg Recall: 0.828571, count: 105 -Region Avg IOU: 0.623509, Class: 1.000000, Obj: 0.371590, No Obj: 0.003002, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.553473, Class: 1.000000, Obj: 0.365411, No Obj: 0.002842, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.635077, Class: 1.000000, Obj: 0.371960, No Obj: 0.003368, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.610635, Class: 1.000000, Obj: 0.343448, No Obj: 0.003137, Avg Recall: 0.774436, count: 133 -Region Avg IOU: 0.675255, Class: 1.000000, Obj: 0.373718, No Obj: 0.002722, Avg Recall: 0.822222, count: 90 -Region Avg IOU: 0.554040, Class: 1.000000, Obj: 0.345852, No Obj: 0.002891, Avg Recall: 0.654545, count: 110 -3823: 0.403857, 0.428738 avg, 0.001000 rate, 25.807217 seconds, 978688 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.594058, Class: 1.000000, Obj: 0.351062, No Obj: 0.003022, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.648827, Class: 1.000000, Obj: 0.397332, No Obj: 0.003118, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.572745, Class: 1.000000, Obj: 0.353189, No Obj: 0.003407, Avg Recall: 0.664062, count: 128 -Region Avg IOU: 0.552253, Class: 1.000000, Obj: 0.387583, No Obj: 0.002660, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.633942, Class: 1.000000, Obj: 0.396480, No Obj: 0.003010, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.579085, Class: 1.000000, Obj: 0.356823, No Obj: 0.003210, Avg Recall: 0.705882, count: 136 -Region Avg IOU: 0.612033, Class: 1.000000, Obj: 0.391519, No Obj: 0.002674, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.605806, Class: 1.000000, Obj: 0.392496, No Obj: 0.003077, Avg Recall: 0.740157, count: 127 -3824: 0.425591, 0.428423 avg, 0.001000 rate, 24.997437 seconds, 978944 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.626490, Class: 1.000000, Obj: 0.400156, No Obj: 0.003194, Avg Recall: 0.688000, count: 125 -Region Avg IOU: 0.579941, Class: 1.000000, Obj: 0.423538, No Obj: 0.002784, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.601865, Class: 1.000000, Obj: 0.378691, No Obj: 0.003348, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.603438, Class: 1.000000, Obj: 0.400010, No Obj: 0.003078, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.560299, Class: 1.000000, Obj: 0.350757, No Obj: 0.003354, Avg Recall: 0.676056, count: 142 -Region Avg IOU: 0.578856, Class: 1.000000, Obj: 0.431548, No Obj: 0.002595, Avg Recall: 0.640449, count: 89 -Region Avg IOU: 0.599691, Class: 1.000000, Obj: 0.372794, No Obj: 0.003123, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.626881, Class: 1.000000, Obj: 0.382897, No Obj: 0.003119, Avg Recall: 0.784483, count: 116 -3825: 0.451933, 0.430774 avg, 0.001000 rate, 25.123058 seconds, 979200 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.657978, Class: 1.000000, Obj: 0.429363, No Obj: 0.003719, Avg Recall: 0.830000, count: 100 -Region Avg IOU: 0.639530, Class: 1.000000, Obj: 0.397673, No Obj: 0.003500, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.645249, Class: 1.000000, Obj: 0.452091, No Obj: 0.003159, Avg Recall: 0.826923, count: 104 -Region Avg IOU: 0.565129, Class: 1.000000, Obj: 0.337494, No Obj: 0.002798, Avg Recall: 0.679688, count: 128 -Region Avg IOU: 0.599751, Class: 1.000000, Obj: 0.381663, No Obj: 0.003015, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.594768, Class: 1.000000, Obj: 0.404943, No Obj: 0.003167, Avg Recall: 0.670455, count: 88 -Region Avg IOU: 0.617038, Class: 1.000000, Obj: 0.428674, No Obj: 0.003195, Avg Recall: 0.743902, count: 82 -Region Avg IOU: 0.594322, Class: 1.000000, Obj: 0.347313, No Obj: 0.002885, Avg Recall: 0.714286, count: 112 -3826: 0.391697, 0.426867 avg, 0.001000 rate, 24.994186 seconds, 979456 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.551926, Class: 1.000000, Obj: 0.386124, No Obj: 0.003415, Avg Recall: 0.658537, count: 123 -Region Avg IOU: 0.627339, Class: 1.000000, Obj: 0.393129, No Obj: 0.002681, Avg Recall: 0.804598, count: 87 -Region Avg IOU: 0.637752, Class: 1.000000, Obj: 0.392606, No Obj: 0.003079, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.610308, Class: 1.000000, Obj: 0.412151, No Obj: 0.003078, Avg Recall: 0.739583, count: 96 -Region Avg IOU: 0.616980, Class: 1.000000, Obj: 0.385422, No Obj: 0.003007, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.568182, Class: 1.000000, Obj: 0.377596, No Obj: 0.003133, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.601376, Class: 1.000000, Obj: 0.391007, No Obj: 0.003030, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.653680, Class: 1.000000, Obj: 0.428552, No Obj: 0.003312, Avg Recall: 0.834862, count: 109 -3827: 0.432816, 0.427462 avg, 0.001000 rate, 26.051355 seconds, 979712 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.575454, Class: 1.000000, Obj: 0.404656, No Obj: 0.002964, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.603283, Class: 1.000000, Obj: 0.384373, No Obj: 0.003577, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.650053, Class: 1.000000, Obj: 0.434899, No Obj: 0.003075, Avg Recall: 0.841584, count: 101 -Region Avg IOU: 0.585252, Class: 1.000000, Obj: 0.435700, No Obj: 0.003330, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.563150, Class: 1.000000, Obj: 0.402849, No Obj: 0.003156, Avg Recall: 0.644068, count: 118 -Region Avg IOU: 0.594758, Class: 1.000000, Obj: 0.368558, No Obj: 0.003212, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.672705, Class: 1.000000, Obj: 0.426836, No Obj: 0.002849, Avg Recall: 0.819277, count: 83 -Region Avg IOU: 0.643999, Class: 1.000000, Obj: 0.349979, No Obj: 0.003648, Avg Recall: 0.838235, count: 136 -3828: 0.412291, 0.425945 avg, 0.001000 rate, 26.217363 seconds, 979968 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.644970, Class: 1.000000, Obj: 0.387908, No Obj: 0.003623, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.636282, Class: 1.000000, Obj: 0.386522, No Obj: 0.003133, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.625107, Class: 1.000000, Obj: 0.405495, No Obj: 0.003921, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.639038, Class: 1.000000, Obj: 0.414435, No Obj: 0.003364, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.603313, Class: 1.000000, Obj: 0.407420, No Obj: 0.003047, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.649594, Class: 1.000000, Obj: 0.376167, No Obj: 0.002963, Avg Recall: 0.834951, count: 103 -Region Avg IOU: 0.662110, Class: 1.000000, Obj: 0.450108, No Obj: 0.003283, Avg Recall: 0.830000, count: 100 -Region Avg IOU: 0.647932, Class: 1.000000, Obj: 0.407039, No Obj: 0.003492, Avg Recall: 0.822581, count: 124 -3829: 0.426761, 0.426026 avg, 0.001000 rate, 26.447435 seconds, 980224 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.594103, Class: 1.000000, Obj: 0.381732, No Obj: 0.003941, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.634326, Class: 1.000000, Obj: 0.474374, No Obj: 0.004084, Avg Recall: 0.753846, count: 130 -Region Avg IOU: 0.570386, Class: 1.000000, Obj: 0.420113, No Obj: 0.003416, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.612047, Class: 1.000000, Obj: 0.406930, No Obj: 0.003657, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.609723, Class: 1.000000, Obj: 0.398602, No Obj: 0.003448, Avg Recall: 0.769231, count: 130 -Region Avg IOU: 0.587058, Class: 1.000000, Obj: 0.406088, No Obj: 0.002934, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.613922, Class: 1.000000, Obj: 0.341010, No Obj: 0.003172, Avg Recall: 0.779070, count: 86 -Region Avg IOU: 0.581452, Class: 1.000000, Obj: 0.368552, No Obj: 0.003668, Avg Recall: 0.671642, count: 134 -3830: 0.476556, 0.431079 avg, 0.001000 rate, 25.165655 seconds, 980480 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 19.914804 seconds -Region Avg IOU: 0.601700, Class: 1.000000, Obj: 0.399824, No Obj: 0.003784, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.570490, Class: 1.000000, Obj: 0.370687, No Obj: 0.004302, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.561748, Class: 1.000000, Obj: 0.381838, No Obj: 0.003789, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.598071, Class: 1.000000, Obj: 0.418457, No Obj: 0.003203, Avg Recall: 0.750000, count: 84 -Region Avg IOU: 0.565202, Class: 1.000000, Obj: 0.374390, No Obj: 0.003990, Avg Recall: 0.624000, count: 125 -Region Avg IOU: 0.583590, Class: 1.000000, Obj: 0.388365, No Obj: 0.003631, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.518374, Class: 1.000000, Obj: 0.322232, No Obj: 0.003934, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.579009, Class: 1.000000, Obj: 0.338648, No Obj: 0.003337, Avg Recall: 0.717172, count: 99 -3831: 0.409162, 0.428887 avg, 0.001000 rate, 22.937578 seconds, 980736 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.535757, Class: 1.000000, Obj: 0.396839, No Obj: 0.003710, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.559782, Class: 1.000000, Obj: 0.348114, No Obj: 0.004079, Avg Recall: 0.688000, count: 125 -Region Avg IOU: 0.574832, Class: 1.000000, Obj: 0.361197, No Obj: 0.004036, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.585365, Class: 1.000000, Obj: 0.367716, No Obj: 0.003611, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.576423, Class: 1.000000, Obj: 0.342136, No Obj: 0.004182, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.543812, Class: 1.000000, Obj: 0.353672, No Obj: 0.003358, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.589175, Class: 1.000000, Obj: 0.368767, No Obj: 0.003138, Avg Recall: 0.715909, count: 88 -Region Avg IOU: 0.570196, Class: 1.000000, Obj: 0.407779, No Obj: 0.003976, Avg Recall: 0.672269, count: 119 -3832: 0.449131, 0.430912 avg, 0.001000 rate, 23.283024 seconds, 980992 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.508303, Class: 1.000000, Obj: 0.335109, No Obj: 0.003677, Avg Recall: 0.582677, count: 127 -Region Avg IOU: 0.604243, Class: 1.000000, Obj: 0.388691, No Obj: 0.003564, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.580496, Class: 1.000000, Obj: 0.388983, No Obj: 0.003776, Avg Recall: 0.700787, count: 127 -Region Avg IOU: 0.544298, Class: 1.000000, Obj: 0.345651, No Obj: 0.002636, Avg Recall: 0.613333, count: 75 -Region Avg IOU: 0.621503, Class: 1.000000, Obj: 0.377738, No Obj: 0.003678, Avg Recall: 0.746988, count: 83 -Region Avg IOU: 0.549259, Class: 1.000000, Obj: 0.371746, No Obj: 0.004108, Avg Recall: 0.591549, count: 142 -Region Avg IOU: 0.534431, Class: 1.000000, Obj: 0.355433, No Obj: 0.003043, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.591429, Class: 1.000000, Obj: 0.361846, No Obj: 0.003355, Avg Recall: 0.730000, count: 100 -3833: 0.474239, 0.435244 avg, 0.001000 rate, 24.541958 seconds, 981248 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.540976, Class: 1.000000, Obj: 0.366141, No Obj: 0.003427, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.549576, Class: 1.000000, Obj: 0.335190, No Obj: 0.003720, Avg Recall: 0.603306, count: 121 -Region Avg IOU: 0.550995, Class: 1.000000, Obj: 0.342447, No Obj: 0.003679, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.591649, Class: 1.000000, Obj: 0.362235, No Obj: 0.003566, Avg Recall: 0.688889, count: 90 -Region Avg IOU: 0.559141, Class: 1.000000, Obj: 0.383992, No Obj: 0.003743, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.550524, Class: 1.000000, Obj: 0.377801, No Obj: 0.004026, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.591062, Class: 1.000000, Obj: 0.379660, No Obj: 0.003834, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.564932, Class: 1.000000, Obj: 0.342497, No Obj: 0.003838, Avg Recall: 0.648148, count: 108 -3834: 0.398482, 0.431568 avg, 0.001000 rate, 23.347248 seconds, 981504 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.559108, Class: 1.000000, Obj: 0.367313, No Obj: 0.003630, Avg Recall: 0.627119, count: 118 -Region Avg IOU: 0.547948, Class: 1.000000, Obj: 0.389791, No Obj: 0.004020, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.557242, Class: 1.000000, Obj: 0.363701, No Obj: 0.004423, Avg Recall: 0.645669, count: 127 -Region Avg IOU: 0.601856, Class: 1.000000, Obj: 0.402316, No Obj: 0.003655, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.518188, Class: 1.000000, Obj: 0.319195, No Obj: 0.003208, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.550031, Class: 1.000000, Obj: 0.374931, No Obj: 0.003740, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.558823, Class: 1.000000, Obj: 0.396066, No Obj: 0.004129, Avg Recall: 0.629921, count: 127 -Region Avg IOU: 0.545802, Class: 1.000000, Obj: 0.363226, No Obj: 0.003528, Avg Recall: 0.597938, count: 97 -3835: 0.431047, 0.431516 avg, 0.001000 rate, 23.362986 seconds, 981760 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.554918, Class: 1.000000, Obj: 0.330791, No Obj: 0.003315, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.538521, Class: 1.000000, Obj: 0.416001, No Obj: 0.003110, Avg Recall: 0.586207, count: 87 -Region Avg IOU: 0.524909, Class: 1.000000, Obj: 0.336388, No Obj: 0.003927, Avg Recall: 0.600000, count: 125 -Region Avg IOU: 0.554708, Class: 1.000000, Obj: 0.351643, No Obj: 0.003671, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.584007, Class: 1.000000, Obj: 0.443186, No Obj: 0.004242, Avg Recall: 0.686956, count: 115 -Region Avg IOU: 0.508490, Class: 1.000000, Obj: 0.314699, No Obj: 0.003881, Avg Recall: 0.543046, count: 151 -Region Avg IOU: 0.553340, Class: 1.000000, Obj: 0.403595, No Obj: 0.003421, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.482230, Class: 1.000000, Obj: 0.323567, No Obj: 0.003519, Avg Recall: 0.546875, count: 128 -3836: 0.484837, 0.436848 avg, 0.001000 rate, 22.552835 seconds, 982016 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.568196, Class: 1.000000, Obj: 0.390477, No Obj: 0.003561, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.550866, Class: 1.000000, Obj: 0.406549, No Obj: 0.003541, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.532261, Class: 1.000000, Obj: 0.372731, No Obj: 0.004227, Avg Recall: 0.601449, count: 138 -Region Avg IOU: 0.555598, Class: 1.000000, Obj: 0.376150, No Obj: 0.003365, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.598884, Class: 1.000000, Obj: 0.390471, No Obj: 0.004048, Avg Recall: 0.719298, count: 114 -Region Avg IOU: 0.535539, Class: 1.000000, Obj: 0.358484, No Obj: 0.003556, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.504386, Class: 1.000000, Obj: 0.363006, No Obj: 0.003672, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.557866, Class: 1.000000, Obj: 0.402052, No Obj: 0.003711, Avg Recall: 0.643478, count: 115 -3837: 0.446300, 0.437793 avg, 0.001000 rate, 24.938742 seconds, 982272 images -Loaded: 0.001682 seconds -Region Avg IOU: 0.573654, Class: 1.000000, Obj: 0.367459, No Obj: 0.003824, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.563641, Class: 1.000000, Obj: 0.338463, No Obj: 0.004038, Avg Recall: 0.674242, count: 132 -Region Avg IOU: 0.564315, Class: 1.000000, Obj: 0.419093, No Obj: 0.003630, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.582205, Class: 1.000000, Obj: 0.386669, No Obj: 0.003797, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.584400, Class: 1.000000, Obj: 0.393876, No Obj: 0.004030, Avg Recall: 0.701613, count: 124 -Region Avg IOU: 0.512512, Class: 1.000000, Obj: 0.344403, No Obj: 0.003959, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.521915, Class: 1.000000, Obj: 0.390375, No Obj: 0.003779, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.600033, Class: 1.000000, Obj: 0.374336, No Obj: 0.003889, Avg Recall: 0.724490, count: 98 -3838: 0.436209, 0.437635 avg, 0.001000 rate, 24.414623 seconds, 982528 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.562867, Class: 1.000000, Obj: 0.405541, No Obj: 0.003620, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.566117, Class: 1.000000, Obj: 0.441643, No Obj: 0.004001, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.527447, Class: 1.000000, Obj: 0.371596, No Obj: 0.003774, Avg Recall: 0.653226, count: 124 -Region Avg IOU: 0.550072, Class: 1.000000, Obj: 0.382104, No Obj: 0.002862, Avg Recall: 0.651515, count: 66 -Region Avg IOU: 0.556400, Class: 1.000000, Obj: 0.379094, No Obj: 0.003609, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.569249, Class: 1.000000, Obj: 0.344744, No Obj: 0.003885, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.552999, Class: 1.000000, Obj: 0.359025, No Obj: 0.003764, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.528755, Class: 1.000000, Obj: 0.349233, No Obj: 0.003580, Avg Recall: 0.627451, count: 102 -3839: 0.414069, 0.435278 avg, 0.001000 rate, 22.636776 seconds, 982784 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.535944, Class: 1.000000, Obj: 0.381697, No Obj: 0.003694, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.578472, Class: 1.000000, Obj: 0.346650, No Obj: 0.004181, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.552905, Class: 1.000000, Obj: 0.369112, No Obj: 0.003722, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.557557, Class: 1.000000, Obj: 0.388338, No Obj: 0.002862, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.616904, Class: 1.000000, Obj: 0.368634, No Obj: 0.003926, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.571177, Class: 1.000000, Obj: 0.348747, No Obj: 0.004012, Avg Recall: 0.693548, count: 124 -Region Avg IOU: 0.535857, Class: 1.000000, Obj: 0.325259, No Obj: 0.003669, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.551646, Class: 1.000000, Obj: 0.355099, No Obj: 0.003739, Avg Recall: 0.618321, count: 131 -3840: 0.451780, 0.436929 avg, 0.001000 rate, 23.310024 seconds, 983040 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.541391 seconds -Region Avg IOU: 0.490984, Class: 1.000000, Obj: 0.369045, No Obj: 0.003446, Avg Recall: 0.545455, count: 88 -Region Avg IOU: 0.482169, Class: 1.000000, Obj: 0.272822, No Obj: 0.003634, Avg Recall: 0.517647, count: 85 -Region Avg IOU: 0.493715, Class: 1.000000, Obj: 0.322758, No Obj: 0.004411, Avg Recall: 0.538462, count: 117 -Region Avg IOU: 0.468308, Class: 1.000000, Obj: 0.260086, No Obj: 0.003695, Avg Recall: 0.552083, count: 96 -Region Avg IOU: 0.531038, Class: 1.000000, Obj: 0.314775, No Obj: 0.003982, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.471799, Class: 1.000000, Obj: 0.275424, No Obj: 0.003918, Avg Recall: 0.522523, count: 111 -Region Avg IOU: 0.464982, Class: 1.000000, Obj: 0.269777, No Obj: 0.003705, Avg Recall: 0.539683, count: 126 -Region Avg IOU: 0.485169, Class: 1.000000, Obj: 0.316264, No Obj: 0.004086, Avg Recall: 0.539216, count: 102 -3841: 0.520741, 0.445310 avg, 0.001000 rate, 20.229071 seconds, 983296 images -Loaded: 0.779573 seconds -Region Avg IOU: 0.519927, Class: 1.000000, Obj: 0.304542, No Obj: 0.004670, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.519080, Class: 1.000000, Obj: 0.325460, No Obj: 0.004490, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.507870, Class: 1.000000, Obj: 0.322640, No Obj: 0.003533, Avg Recall: 0.582278, count: 79 -Region Avg IOU: 0.483817, Class: 1.000000, Obj: 0.299926, No Obj: 0.003635, Avg Recall: 0.542857, count: 105 -Region Avg IOU: 0.547127, Class: 1.000000, Obj: 0.357867, No Obj: 0.004251, Avg Recall: 0.597826, count: 92 -Region Avg IOU: 0.477763, Class: 1.000000, Obj: 0.281105, No Obj: 0.003781, Avg Recall: 0.458716, count: 109 -Region Avg IOU: 0.540166, Class: 1.000000, Obj: 0.317056, No Obj: 0.004891, Avg Recall: 0.596491, count: 114 -Region Avg IOU: 0.501262, Class: 1.000000, Obj: 0.299214, No Obj: 0.004112, Avg Recall: 0.561404, count: 114 -3842: 0.481065, 0.448885 avg, 0.001000 rate, 18.561932 seconds, 983552 images -Loaded: 1.616082 seconds -Region Avg IOU: 0.458464, Class: 1.000000, Obj: 0.298409, No Obj: 0.004119, Avg Recall: 0.476923, count: 130 -Region Avg IOU: 0.460699, Class: 1.000000, Obj: 0.284713, No Obj: 0.003988, Avg Recall: 0.476191, count: 105 -Region Avg IOU: 0.493051, Class: 1.000000, Obj: 0.291375, No Obj: 0.003953, Avg Recall: 0.559633, count: 109 -Region Avg IOU: 0.497717, Class: 1.000000, Obj: 0.327602, No Obj: 0.003926, Avg Recall: 0.573034, count: 89 -Region Avg IOU: 0.454959, Class: 1.000000, Obj: 0.302771, No Obj: 0.003627, Avg Recall: 0.476635, count: 107 -Region Avg IOU: 0.569400, Class: 1.000000, Obj: 0.338901, No Obj: 0.004435, Avg Recall: 0.682353, count: 85 -Region Avg IOU: 0.512676, Class: 1.000000, Obj: 0.335629, No Obj: 0.004126, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.452417, Class: 1.000000, Obj: 0.282013, No Obj: 0.004228, Avg Recall: 0.439024, count: 123 -3843: 0.458542, 0.449851 avg, 0.001000 rate, 19.165504 seconds, 983808 images -Loaded: 2.654716 seconds -Region Avg IOU: 0.470374, Class: 1.000000, Obj: 0.311246, No Obj: 0.003765, Avg Recall: 0.486726, count: 113 -Region Avg IOU: 0.469769, Class: 1.000000, Obj: 0.311530, No Obj: 0.004731, Avg Recall: 0.529851, count: 134 -Region Avg IOU: 0.473305, Class: 1.000000, Obj: 0.323347, No Obj: 0.004569, Avg Recall: 0.537415, count: 147 -Region Avg IOU: 0.522683, Class: 1.000000, Obj: 0.341874, No Obj: 0.004337, Avg Recall: 0.608247, count: 97 -Region Avg IOU: 0.524101, Class: 1.000000, Obj: 0.336925, No Obj: 0.004250, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.484264, Class: 1.000000, Obj: 0.345515, No Obj: 0.004026, Avg Recall: 0.484211, count: 95 -Region Avg IOU: 0.416083, Class: 1.000000, Obj: 0.320125, No Obj: 0.004061, Avg Recall: 0.419643, count: 112 -Region Avg IOU: 0.520679, Class: 1.000000, Obj: 0.319070, No Obj: 0.004904, Avg Recall: 0.617886, count: 123 -3844: 0.523993, 0.457265 avg, 0.001000 rate, 17.315935 seconds, 984064 images -Loaded: 4.619597 seconds -Region Avg IOU: 0.496277, Class: 1.000000, Obj: 0.286300, No Obj: 0.004708, Avg Recall: 0.552000, count: 125 -Region Avg IOU: 0.516762, Class: 1.000000, Obj: 0.366370, No Obj: 0.004993, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.522990, Class: 1.000000, Obj: 0.328317, No Obj: 0.004307, Avg Recall: 0.600000, count: 90 -Region Avg IOU: 0.570868, Class: 1.000000, Obj: 0.370156, No Obj: 0.004534, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.481615, Class: 1.000000, Obj: 0.319579, No Obj: 0.004800, Avg Recall: 0.464567, count: 127 -Region Avg IOU: 0.520212, Class: 1.000000, Obj: 0.364872, No Obj: 0.005148, Avg Recall: 0.603306, count: 121 -Region Avg IOU: 0.519317, Class: 1.000000, Obj: 0.319032, No Obj: 0.004539, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.522901, Class: 1.000000, Obj: 0.312638, No Obj: 0.004929, Avg Recall: 0.568965, count: 116 -3845: 0.468515, 0.458390 avg, 0.001000 rate, 19.922171 seconds, 984320 images -Loaded: 0.233287 seconds -Region Avg IOU: 0.482968, Class: 1.000000, Obj: 0.345949, No Obj: 0.004559, Avg Recall: 0.521008, count: 119 -Region Avg IOU: 0.491690, Class: 1.000000, Obj: 0.329681, No Obj: 0.004991, Avg Recall: 0.579710, count: 138 -Region Avg IOU: 0.508176, Class: 1.000000, Obj: 0.359770, No Obj: 0.004114, Avg Recall: 0.566265, count: 83 -Region Avg IOU: 0.506548, Class: 1.000000, Obj: 0.355918, No Obj: 0.005134, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.509670, Class: 1.000000, Obj: 0.362868, No Obj: 0.004725, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.511492, Class: 1.000000, Obj: 0.301407, No Obj: 0.004704, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.526502, Class: 1.000000, Obj: 0.308150, No Obj: 0.004464, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.529937, Class: 1.000000, Obj: 0.343032, No Obj: 0.004563, Avg Recall: 0.631068, count: 103 -3846: 0.435541, 0.456105 avg, 0.001000 rate, 18.604342 seconds, 984576 images -Loaded: 1.469328 seconds -Region Avg IOU: 0.466989, Class: 1.000000, Obj: 0.322578, No Obj: 0.004371, Avg Recall: 0.485714, count: 105 -Region Avg IOU: 0.458761, Class: 1.000000, Obj: 0.311108, No Obj: 0.005042, Avg Recall: 0.486957, count: 115 -Region Avg IOU: 0.496647, Class: 1.000000, Obj: 0.370169, No Obj: 0.005602, Avg Recall: 0.552448, count: 143 -Region Avg IOU: 0.466235, Class: 1.000000, Obj: 0.333908, No Obj: 0.004339, Avg Recall: 0.530612, count: 98 -Region Avg IOU: 0.482199, Class: 1.000000, Obj: 0.328957, No Obj: 0.004330, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.478776, Class: 1.000000, Obj: 0.332607, No Obj: 0.005038, Avg Recall: 0.487805, count: 123 -Region Avg IOU: 0.479463, Class: 1.000000, Obj: 0.314320, No Obj: 0.004479, Avg Recall: 0.467890, count: 109 -Region Avg IOU: 0.569068, Class: 1.000000, Obj: 0.340999, No Obj: 0.004991, Avg Recall: 0.683333, count: 120 -3847: 0.533030, 0.463798 avg, 0.001000 rate, 18.994707 seconds, 984832 images -Loaded: 2.623766 seconds -Region Avg IOU: 0.498742, Class: 1.000000, Obj: 0.386477, No Obj: 0.004901, Avg Recall: 0.528000, count: 125 -Region Avg IOU: 0.499545, Class: 1.000000, Obj: 0.310867, No Obj: 0.004749, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.508683, Class: 1.000000, Obj: 0.369300, No Obj: 0.005105, Avg Recall: 0.602941, count: 136 -Region Avg IOU: 0.435648, Class: 1.000000, Obj: 0.295057, No Obj: 0.004738, Avg Recall: 0.424658, count: 146 -Region Avg IOU: 0.433973, Class: 1.000000, Obj: 0.294177, No Obj: 0.004878, Avg Recall: 0.439024, count: 123 -Region Avg IOU: 0.476278, Class: 1.000000, Obj: 0.341325, No Obj: 0.004001, Avg Recall: 0.522727, count: 88 -Region Avg IOU: 0.503686, Class: 1.000000, Obj: 0.353690, No Obj: 0.004741, Avg Recall: 0.598485, count: 132 -Region Avg IOU: 0.547060, Class: 1.000000, Obj: 0.383444, No Obj: 0.005283, Avg Recall: 0.644231, count: 104 -3848: 0.524202, 0.469838 avg, 0.001000 rate, 15.819777 seconds, 985088 images -Loaded: 4.345567 seconds -Region Avg IOU: 0.493546, Class: 1.000000, Obj: 0.359503, No Obj: 0.005133, Avg Recall: 0.523810, count: 126 -Region Avg IOU: 0.495693, Class: 1.000000, Obj: 0.360151, No Obj: 0.005083, Avg Recall: 0.523364, count: 107 -Region Avg IOU: 0.548699, Class: 1.000000, Obj: 0.446676, No Obj: 0.004445, Avg Recall: 0.685714, count: 70 -Region Avg IOU: 0.501354, Class: 1.000000, Obj: 0.352116, No Obj: 0.004636, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.448947, Class: 1.000000, Obj: 0.389165, No Obj: 0.004225, Avg Recall: 0.468085, count: 94 -Region Avg IOU: 0.483231, Class: 1.000000, Obj: 0.318275, No Obj: 0.005149, Avg Recall: 0.535433, count: 127 -Region Avg IOU: 0.514543, Class: 1.000000, Obj: 0.382817, No Obj: 0.005212, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.527942, Class: 1.000000, Obj: 0.358526, No Obj: 0.004987, Avg Recall: 0.572917, count: 96 -3849: 0.400816, 0.462936 avg, 0.001000 rate, 20.807060 seconds, 985344 images -Loaded: 1.595196 seconds -Region Avg IOU: 0.502332, Class: 1.000000, Obj: 0.397276, No Obj: 0.004817, Avg Recall: 0.568182, count: 88 -Region Avg IOU: 0.556278, Class: 1.000000, Obj: 0.408540, No Obj: 0.004755, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.491734, Class: 1.000000, Obj: 0.377155, No Obj: 0.004911, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.549975, Class: 1.000000, Obj: 0.414169, No Obj: 0.004480, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.510337, Class: 1.000000, Obj: 0.388739, No Obj: 0.004902, Avg Recall: 0.593750, count: 96 -Region Avg IOU: 0.488619, Class: 1.000000, Obj: 0.360486, No Obj: 0.005247, Avg Recall: 0.532787, count: 122 -Region Avg IOU: 0.447041, Class: 1.000000, Obj: 0.326953, No Obj: 0.004816, Avg Recall: 0.447154, count: 123 -Region Avg IOU: 0.507515, Class: 1.000000, Obj: 0.370711, No Obj: 0.005077, Avg Recall: 0.591304, count: 115 -3850: 0.445875, 0.461230 avg, 0.001000 rate, 18.384373 seconds, 985600 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 0.020764 seconds -Region Avg IOU: 0.537332, Class: 1.000000, Obj: 0.376061, No Obj: 0.004159, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.473632, Class: 1.000000, Obj: 0.349103, No Obj: 0.004124, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.524914, Class: 1.000000, Obj: 0.373700, No Obj: 0.005038, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.516094, Class: 1.000000, Obj: 0.366012, No Obj: 0.004261, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.540397, Class: 1.000000, Obj: 0.432598, No Obj: 0.004281, Avg Recall: 0.612500, count: 80 -Region Avg IOU: 0.500500, Class: 1.000000, Obj: 0.378373, No Obj: 0.005104, Avg Recall: 0.542373, count: 118 -Region Avg IOU: 0.488281, Class: 1.000000, Obj: 0.364154, No Obj: 0.005233, Avg Recall: 0.544304, count: 158 -Region Avg IOU: 0.520336, Class: 1.000000, Obj: 0.363807, No Obj: 0.005610, Avg Recall: 0.587838, count: 148 -3851: 0.538402, 0.468947 avg, 0.001000 rate, 19.140072 seconds, 985856 images -Loaded: 2.865054 seconds -Region Avg IOU: 0.553167, Class: 1.000000, Obj: 0.376392, No Obj: 0.004668, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.535823, Class: 1.000000, Obj: 0.366909, No Obj: 0.004670, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.479235, Class: 1.000000, Obj: 0.346916, No Obj: 0.004906, Avg Recall: 0.487805, count: 123 -Region Avg IOU: 0.555832, Class: 1.000000, Obj: 0.407166, No Obj: 0.004898, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.521201, Class: 1.000000, Obj: 0.390903, No Obj: 0.004106, Avg Recall: 0.581395, count: 86 -Region Avg IOU: 0.519518, Class: 1.000000, Obj: 0.392346, No Obj: 0.005151, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.512340, Class: 1.000000, Obj: 0.359838, No Obj: 0.004139, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.500475, Class: 1.000000, Obj: 0.352071, No Obj: 0.005532, Avg Recall: 0.543307, count: 127 -3852: 0.438412, 0.465894 avg, 0.001000 rate, 20.017647 seconds, 986112 images -Loaded: 2.792419 seconds -Region Avg IOU: 0.519924, Class: 1.000000, Obj: 0.393056, No Obj: 0.003636, Avg Recall: 0.560000, count: 75 -Region Avg IOU: 0.522300, Class: 1.000000, Obj: 0.350024, No Obj: 0.004636, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.457366, Class: 1.000000, Obj: 0.342359, No Obj: 0.003974, Avg Recall: 0.462264, count: 106 -Region Avg IOU: 0.542683, Class: 1.000000, Obj: 0.402137, No Obj: 0.004789, Avg Recall: 0.619565, count: 92 -Region Avg IOU: 0.529433, Class: 1.000000, Obj: 0.355345, No Obj: 0.005231, Avg Recall: 0.629032, count: 124 -Region Avg IOU: 0.522639, Class: 1.000000, Obj: 0.353721, No Obj: 0.004419, Avg Recall: 0.588235, count: 85 -Region Avg IOU: 0.482511, Class: 1.000000, Obj: 0.330630, No Obj: 0.004024, Avg Recall: 0.485437, count: 103 -Region Avg IOU: 0.547333, Class: 1.000000, Obj: 0.371599, No Obj: 0.004819, Avg Recall: 0.636364, count: 121 -3853: 0.452073, 0.464512 avg, 0.001000 rate, 18.913376 seconds, 986368 images -Loaded: 2.860685 seconds -Region Avg IOU: 0.525301, Class: 1.000000, Obj: 0.335209, No Obj: 0.005144, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.492805, Class: 1.000000, Obj: 0.340157, No Obj: 0.004076, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.526202, Class: 1.000000, Obj: 0.373940, No Obj: 0.004257, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.555924, Class: 1.000000, Obj: 0.362980, No Obj: 0.004733, Avg Recall: 0.563492, count: 126 -Region Avg IOU: 0.557528, Class: 1.000000, Obj: 0.348324, No Obj: 0.004557, Avg Recall: 0.705882, count: 85 -Region Avg IOU: 0.530517, Class: 1.000000, Obj: 0.317710, No Obj: 0.004588, Avg Recall: 0.560000, count: 125 -Region Avg IOU: 0.491975, Class: 1.000000, Obj: 0.348235, No Obj: 0.004015, Avg Recall: 0.562500, count: 96 -Region Avg IOU: 0.553667, Class: 1.000000, Obj: 0.300842, No Obj: 0.004482, Avg Recall: 0.627273, count: 110 -3854: 0.442047, 0.462265 avg, 0.001000 rate, 22.067276 seconds, 986624 images -Loaded: 0.088957 seconds -Region Avg IOU: 0.502110, Class: 1.000000, Obj: 0.335168, No Obj: 0.003778, Avg Recall: 0.541176, count: 85 -Region Avg IOU: 0.518306, Class: 1.000000, Obj: 0.338268, No Obj: 0.003877, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.501130, Class: 1.000000, Obj: 0.342267, No Obj: 0.003411, Avg Recall: 0.554348, count: 92 -Region Avg IOU: 0.515875, Class: 1.000000, Obj: 0.332847, No Obj: 0.003908, Avg Recall: 0.617188, count: 128 -Region Avg IOU: 0.499862, Class: 1.000000, Obj: 0.355150, No Obj: 0.003694, Avg Recall: 0.577778, count: 90 -Region Avg IOU: 0.520618, Class: 1.000000, Obj: 0.328317, No Obj: 0.004089, Avg Recall: 0.577586, count: 116 -Region Avg IOU: 0.546223, Class: 1.000000, Obj: 0.327439, No Obj: 0.004632, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.460556, Class: 1.000000, Obj: 0.321002, No Obj: 0.004102, Avg Recall: 0.484211, count: 95 -3855: 0.459316, 0.461970 avg, 0.001000 rate, 20.975351 seconds, 986880 images -Loaded: 0.394192 seconds -Region Avg IOU: 0.525466, Class: 1.000000, Obj: 0.309936, No Obj: 0.003821, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.490939, Class: 1.000000, Obj: 0.297989, No Obj: 0.004258, Avg Recall: 0.571429, count: 119 -Region Avg IOU: 0.544528, Class: 1.000000, Obj: 0.304695, No Obj: 0.003825, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.491796, Class: 1.000000, Obj: 0.299550, No Obj: 0.003529, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.528000, Class: 1.000000, Obj: 0.334263, No Obj: 0.004005, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.515695, Class: 1.000000, Obj: 0.317476, No Obj: 0.004809, Avg Recall: 0.589147, count: 129 -Region Avg IOU: 0.477282, Class: 1.000000, Obj: 0.283150, No Obj: 0.003939, Avg Recall: 0.527273, count: 110 -Region Avg IOU: 0.522860, Class: 1.000000, Obj: 0.313773, No Obj: 0.003416, Avg Recall: 0.586538, count: 104 -3856: 0.447319, 0.460505 avg, 0.001000 rate, 18.594616 seconds, 987136 images -Loaded: 1.812258 seconds -Region Avg IOU: 0.490981, Class: 1.000000, Obj: 0.331583, No Obj: 0.003810, Avg Recall: 0.574713, count: 87 -Region Avg IOU: 0.604826, Class: 1.000000, Obj: 0.359932, No Obj: 0.003744, Avg Recall: 0.701149, count: 87 -Region Avg IOU: 0.535222, Class: 1.000000, Obj: 0.367486, No Obj: 0.003497, Avg Recall: 0.597561, count: 82 -Region Avg IOU: 0.571783, Class: 1.000000, Obj: 0.330783, No Obj: 0.004496, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.513936, Class: 1.000000, Obj: 0.298376, No Obj: 0.003749, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.539465, Class: 1.000000, Obj: 0.324798, No Obj: 0.004000, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.511994, Class: 1.000000, Obj: 0.340771, No Obj: 0.003703, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.483521, Class: 1.000000, Obj: 0.319714, No Obj: 0.004171, Avg Recall: 0.516667, count: 120 -3857: 0.426676, 0.457122 avg, 0.001000 rate, 21.064682 seconds, 987392 images -Loaded: 1.008491 seconds -Region Avg IOU: 0.515828, Class: 1.000000, Obj: 0.350913, No Obj: 0.004770, Avg Recall: 0.541985, count: 131 -Region Avg IOU: 0.503194, Class: 1.000000, Obj: 0.310146, No Obj: 0.003978, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.539487, Class: 1.000000, Obj: 0.338701, No Obj: 0.003500, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.525700, Class: 1.000000, Obj: 0.343832, No Obj: 0.003442, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.490853, Class: 1.000000, Obj: 0.351530, No Obj: 0.004119, Avg Recall: 0.521008, count: 119 -Region Avg IOU: 0.524631, Class: 1.000000, Obj: 0.353161, No Obj: 0.003795, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.529209, Class: 1.000000, Obj: 0.336029, No Obj: 0.004419, Avg Recall: 0.595238, count: 126 -Region Avg IOU: 0.510707, Class: 1.000000, Obj: 0.320042, No Obj: 0.004122, Avg Recall: 0.546296, count: 108 -3858: 0.461704, 0.457580 avg, 0.001000 rate, 21.138620 seconds, 987648 images -Loaded: 1.114524 seconds -Region Avg IOU: 0.514271, Class: 1.000000, Obj: 0.351614, No Obj: 0.004048, Avg Recall: 0.563636, count: 110 -Region Avg IOU: 0.554794, Class: 1.000000, Obj: 0.341291, No Obj: 0.004672, Avg Recall: 0.609756, count: 123 -Region Avg IOU: 0.530532, Class: 1.000000, Obj: 0.386976, No Obj: 0.003942, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.516206, Class: 1.000000, Obj: 0.331359, No Obj: 0.004559, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.521499, Class: 1.000000, Obj: 0.346433, No Obj: 0.004832, Avg Recall: 0.539568, count: 139 -Region Avg IOU: 0.582367, Class: 1.000000, Obj: 0.373184, No Obj: 0.003984, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.539666, Class: 1.000000, Obj: 0.343434, No Obj: 0.003879, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.512175, Class: 1.000000, Obj: 0.301025, No Obj: 0.004322, Avg Recall: 0.546296, count: 108 -3859: 0.454472, 0.457270 avg, 0.001000 rate, 19.604166 seconds, 987904 images -Loaded: 2.533086 seconds -Region Avg IOU: 0.466963, Class: 1.000000, Obj: 0.293558, No Obj: 0.003784, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.531937, Class: 1.000000, Obj: 0.322470, No Obj: 0.004238, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.477748, Class: 1.000000, Obj: 0.315039, No Obj: 0.003544, Avg Recall: 0.550000, count: 100 -Region Avg IOU: 0.503249, Class: 1.000000, Obj: 0.302398, No Obj: 0.004755, Avg Recall: 0.569444, count: 144 -Region Avg IOU: 0.503960, Class: 1.000000, Obj: 0.320407, No Obj: 0.003718, Avg Recall: 0.564103, count: 117 -Region Avg IOU: 0.533751, Class: 1.000000, Obj: 0.371470, No Obj: 0.004806, Avg Recall: 0.616000, count: 125 -Region Avg IOU: 0.542165, Class: 1.000000, Obj: 0.328247, No Obj: 0.004636, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.545334, Class: 1.000000, Obj: 0.388332, No Obj: 0.004358, Avg Recall: 0.628319, count: 113 -3860: 0.487080, 0.460251 avg, 0.001000 rate, 20.732485 seconds, 988160 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 0.000047 seconds -Region Avg IOU: 0.567806, Class: 1.000000, Obj: 0.348892, No Obj: 0.004261, Avg Recall: 0.650406, count: 123 -Region Avg IOU: 0.551207, Class: 1.000000, Obj: 0.343809, No Obj: 0.004224, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.558480, Class: 1.000000, Obj: 0.374298, No Obj: 0.004166, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.549490, Class: 1.000000, Obj: 0.384810, No Obj: 0.004366, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.557045, Class: 1.000000, Obj: 0.397991, No Obj: 0.003893, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.552259, Class: 1.000000, Obj: 0.381018, No Obj: 0.003748, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.528037, Class: 1.000000, Obj: 0.359617, No Obj: 0.003870, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.588323, Class: 1.000000, Obj: 0.343835, No Obj: 0.004158, Avg Recall: 0.690000, count: 100 -3861: 0.411638, 0.455389 avg, 0.001000 rate, 22.426517 seconds, 988416 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.531376, Class: 1.000000, Obj: 0.416805, No Obj: 0.003841, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.565976, Class: 1.000000, Obj: 0.385964, No Obj: 0.003875, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.540242, Class: 1.000000, Obj: 0.345467, No Obj: 0.004168, Avg Recall: 0.634146, count: 123 -Region Avg IOU: 0.574413, Class: 1.000000, Obj: 0.445660, No Obj: 0.003788, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.576101, Class: 1.000000, Obj: 0.433289, No Obj: 0.003992, Avg Recall: 0.710526, count: 76 -Region Avg IOU: 0.568852, Class: 1.000000, Obj: 0.370379, No Obj: 0.004476, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.489862, Class: 1.000000, Obj: 0.378748, No Obj: 0.003473, Avg Recall: 0.480769, count: 104 -Region Avg IOU: 0.576116, Class: 1.000000, Obj: 0.374187, No Obj: 0.004034, Avg Recall: 0.684211, count: 95 -3862: 0.444979, 0.454348 avg, 0.001000 rate, 22.629700 seconds, 988672 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.578210, Class: 1.000000, Obj: 0.370631, No Obj: 0.003529, Avg Recall: 0.678161, count: 87 -Region Avg IOU: 0.569796, Class: 1.000000, Obj: 0.401884, No Obj: 0.003418, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.559919, Class: 1.000000, Obj: 0.397571, No Obj: 0.003740, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.561204, Class: 1.000000, Obj: 0.412970, No Obj: 0.004713, Avg Recall: 0.652174, count: 138 -Region Avg IOU: 0.547596, Class: 1.000000, Obj: 0.356202, No Obj: 0.003526, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.518464, Class: 1.000000, Obj: 0.346355, No Obj: 0.004349, Avg Recall: 0.597315, count: 149 -Region Avg IOU: 0.563692, Class: 1.000000, Obj: 0.420559, No Obj: 0.003927, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.498977, Class: 1.000000, Obj: 0.384971, No Obj: 0.003183, Avg Recall: 0.540816, count: 98 -3863: 0.400498, 0.448963 avg, 0.001000 rate, 24.247736 seconds, 988928 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.541925, Class: 1.000000, Obj: 0.365425, No Obj: 0.004057, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.583569, Class: 1.000000, Obj: 0.420004, No Obj: 0.004583, Avg Recall: 0.681159, count: 138 -Region Avg IOU: 0.544392, Class: 1.000000, Obj: 0.359621, No Obj: 0.003633, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.583495, Class: 1.000000, Obj: 0.395242, No Obj: 0.003821, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.542604, Class: 1.000000, Obj: 0.377499, No Obj: 0.003306, Avg Recall: 0.641975, count: 81 -Region Avg IOU: 0.564080, Class: 1.000000, Obj: 0.399088, No Obj: 0.003699, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.558091, Class: 1.000000, Obj: 0.329555, No Obj: 0.003965, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.572673, Class: 1.000000, Obj: 0.393155, No Obj: 0.003768, Avg Recall: 0.662651, count: 83 -3864: 0.455228, 0.449590 avg, 0.001000 rate, 22.602139 seconds, 989184 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.528223, Class: 1.000000, Obj: 0.370898, No Obj: 0.003484, Avg Recall: 0.598361, count: 122 -Region Avg IOU: 0.539141, Class: 1.000000, Obj: 0.384722, No Obj: 0.003625, Avg Recall: 0.641026, count: 117 -Region Avg IOU: 0.529385, Class: 1.000000, Obj: 0.388509, No Obj: 0.003435, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.524300, Class: 1.000000, Obj: 0.354010, No Obj: 0.003326, Avg Recall: 0.565657, count: 99 -Region Avg IOU: 0.597111, Class: 1.000000, Obj: 0.399846, No Obj: 0.003889, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.528595, Class: 1.000000, Obj: 0.374513, No Obj: 0.003183, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.480967, Class: 1.000000, Obj: 0.297697, No Obj: 0.003405, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.557811, Class: 1.000000, Obj: 0.363333, No Obj: 0.003574, Avg Recall: 0.678261, count: 115 -3865: 0.440104, 0.448641 avg, 0.001000 rate, 22.684200 seconds, 989440 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.495126, Class: 1.000000, Obj: 0.348022, No Obj: 0.003246, Avg Recall: 0.552083, count: 96 -Region Avg IOU: 0.557787, Class: 1.000000, Obj: 0.330378, No Obj: 0.003419, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.560388, Class: 1.000000, Obj: 0.338142, No Obj: 0.004168, Avg Recall: 0.702899, count: 138 -Region Avg IOU: 0.554354, Class: 1.000000, Obj: 0.333801, No Obj: 0.003871, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.607979, Class: 1.000000, Obj: 0.414821, No Obj: 0.003942, Avg Recall: 0.683761, count: 117 -Region Avg IOU: 0.567139, Class: 1.000000, Obj: 0.396931, No Obj: 0.003381, Avg Recall: 0.638554, count: 83 -Region Avg IOU: 0.552835, Class: 1.000000, Obj: 0.386972, No Obj: 0.003322, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.524145, Class: 1.000000, Obj: 0.358785, No Obj: 0.003674, Avg Recall: 0.607143, count: 112 -3866: 0.410885, 0.444866 avg, 0.001000 rate, 23.447987 seconds, 989696 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.532279, Class: 1.000000, Obj: 0.361762, No Obj: 0.002859, Avg Recall: 0.642857, count: 84 -Region Avg IOU: 0.512332, Class: 1.000000, Obj: 0.326971, No Obj: 0.003577, Avg Recall: 0.605769, count: 104 -Region Avg IOU: 0.545957, Class: 1.000000, Obj: 0.360733, No Obj: 0.003331, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.562502, Class: 1.000000, Obj: 0.317056, No Obj: 0.003862, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.533217, Class: 1.000000, Obj: 0.328771, No Obj: 0.003794, Avg Recall: 0.598361, count: 122 -Region Avg IOU: 0.525791, Class: 1.000000, Obj: 0.363082, No Obj: 0.003967, Avg Recall: 0.603306, count: 121 -Region Avg IOU: 0.552084, Class: 1.000000, Obj: 0.386504, No Obj: 0.003086, Avg Recall: 0.646341, count: 82 -Region Avg IOU: 0.523727, Class: 1.000000, Obj: 0.342570, No Obj: 0.003549, Avg Recall: 0.576923, count: 130 -3867: 0.424262, 0.442805 avg, 0.001000 rate, 23.967648 seconds, 989952 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.525875, Class: 1.000000, Obj: 0.350001, No Obj: 0.003156, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.576584, Class: 1.000000, Obj: 0.359099, No Obj: 0.003390, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.516255, Class: 1.000000, Obj: 0.303980, No Obj: 0.003370, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.503622, Class: 1.000000, Obj: 0.330732, No Obj: 0.003667, Avg Recall: 0.547009, count: 117 -Region Avg IOU: 0.569946, Class: 1.000000, Obj: 0.325857, No Obj: 0.003411, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.510212, Class: 1.000000, Obj: 0.337428, No Obj: 0.003207, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.541740, Class: 1.000000, Obj: 0.361012, No Obj: 0.003234, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.566245, Class: 1.000000, Obj: 0.337299, No Obj: 0.003693, Avg Recall: 0.670330, count: 91 -3868: 0.405621, 0.439087 avg, 0.001000 rate, 22.588079 seconds, 990208 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.544896, Class: 1.000000, Obj: 0.331885, No Obj: 0.003449, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.567949, Class: 1.000000, Obj: 0.349004, No Obj: 0.003603, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.559750, Class: 1.000000, Obj: 0.349121, No Obj: 0.002915, Avg Recall: 0.658824, count: 85 -Region Avg IOU: 0.560957, Class: 1.000000, Obj: 0.355170, No Obj: 0.003201, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.557499, Class: 1.000000, Obj: 0.305631, No Obj: 0.003553, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.550055, Class: 1.000000, Obj: 0.347618, No Obj: 0.003391, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.582609, Class: 1.000000, Obj: 0.384491, No Obj: 0.003813, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.502119, Class: 1.000000, Obj: 0.275829, No Obj: 0.003134, Avg Recall: 0.548673, count: 113 -3869: 0.474187, 0.442597 avg, 0.001000 rate, 22.658880 seconds, 990464 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.529781, Class: 1.000000, Obj: 0.315315, No Obj: 0.003249, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.558882, Class: 1.000000, Obj: 0.330421, No Obj: 0.003108, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.514521, Class: 1.000000, Obj: 0.328541, No Obj: 0.002871, Avg Recall: 0.565657, count: 99 -Region Avg IOU: 0.554623, Class: 1.000000, Obj: 0.321581, No Obj: 0.004005, Avg Recall: 0.619048, count: 147 -Region Avg IOU: 0.590977, Class: 1.000000, Obj: 0.391838, No Obj: 0.003700, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.587144, Class: 1.000000, Obj: 0.365572, No Obj: 0.003457, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.574574, Class: 1.000000, Obj: 0.376250, No Obj: 0.003633, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.573486, Class: 1.000000, Obj: 0.328160, No Obj: 0.003302, Avg Recall: 0.695238, count: 105 -3870: 0.441106, 0.442448 avg, 0.001000 rate, 24.576437 seconds, 990720 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.658051 seconds -Region Avg IOU: 0.502119, Class: 1.000000, Obj: 0.282910, No Obj: 0.003750, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.530640, Class: 1.000000, Obj: 0.292455, No Obj: 0.003833, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.474909, Class: 1.000000, Obj: 0.286944, No Obj: 0.003343, Avg Recall: 0.510000, count: 100 -Region Avg IOU: 0.487091, Class: 1.000000, Obj: 0.269905, No Obj: 0.004144, Avg Recall: 0.543103, count: 116 -Region Avg IOU: 0.480620, Class: 1.000000, Obj: 0.294076, No Obj: 0.003754, Avg Recall: 0.536000, count: 125 -Region Avg IOU: 0.492765, Class: 1.000000, Obj: 0.309282, No Obj: 0.003712, Avg Recall: 0.548673, count: 113 -Region Avg IOU: 0.553771, Class: 1.000000, Obj: 0.315279, No Obj: 0.003745, Avg Recall: 0.597826, count: 92 -Region Avg IOU: 0.488812, Class: 1.000000, Obj: 0.286411, No Obj: 0.004139, Avg Recall: 0.507937, count: 126 -3871: 0.473042, 0.445507 avg, 0.001000 rate, 20.702703 seconds, 990976 images -Loaded: 0.916304 seconds -Region Avg IOU: 0.517654, Class: 1.000000, Obj: 0.332000, No Obj: 0.004314, Avg Recall: 0.596639, count: 119 -Region Avg IOU: 0.478887, Class: 1.000000, Obj: 0.318132, No Obj: 0.003961, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.554165, Class: 1.000000, Obj: 0.347043, No Obj: 0.004579, Avg Recall: 0.646154, count: 130 -Region Avg IOU: 0.508846, Class: 1.000000, Obj: 0.295563, No Obj: 0.004384, Avg Recall: 0.559322, count: 118 -Region Avg IOU: 0.457037, Class: 1.000000, Obj: 0.283340, No Obj: 0.003541, Avg Recall: 0.484211, count: 95 -Region Avg IOU: 0.492857, Class: 1.000000, Obj: 0.286889, No Obj: 0.003885, Avg Recall: 0.521368, count: 117 -Region Avg IOU: 0.488661, Class: 1.000000, Obj: 0.277408, No Obj: 0.003576, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.487767, Class: 1.000000, Obj: 0.325121, No Obj: 0.004168, Avg Recall: 0.504505, count: 111 -3872: 0.486132, 0.449570 avg, 0.001000 rate, 17.319180 seconds, 991232 images -Loaded: 4.604710 seconds -Region Avg IOU: 0.477448, Class: 1.000000, Obj: 0.307453, No Obj: 0.003756, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.513038, Class: 1.000000, Obj: 0.355853, No Obj: 0.004713, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.548081, Class: 1.000000, Obj: 0.351255, No Obj: 0.004492, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.489102, Class: 1.000000, Obj: 0.305557, No Obj: 0.005291, Avg Recall: 0.589041, count: 146 -Region Avg IOU: 0.519033, Class: 1.000000, Obj: 0.332374, No Obj: 0.004392, Avg Recall: 0.586207, count: 116 -Region Avg IOU: 0.558912, Class: 1.000000, Obj: 0.308382, No Obj: 0.004038, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.498294, Class: 1.000000, Obj: 0.315406, No Obj: 0.004203, Avg Recall: 0.585938, count: 128 -Region Avg IOU: 0.513105, Class: 1.000000, Obj: 0.313636, No Obj: 0.004228, Avg Recall: 0.598131, count: 107 -3873: 0.450158, 0.449628 avg, 0.001000 rate, 16.838615 seconds, 991488 images -Loaded: 1.910046 seconds -Region Avg IOU: 0.460130, Class: 1.000000, Obj: 0.314671, No Obj: 0.004244, Avg Recall: 0.487805, count: 123 -Region Avg IOU: 0.493400, Class: 1.000000, Obj: 0.327226, No Obj: 0.004492, Avg Recall: 0.557522, count: 113 -Region Avg IOU: 0.480428, Class: 1.000000, Obj: 0.356315, No Obj: 0.003705, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.482148, Class: 1.000000, Obj: 0.356759, No Obj: 0.004656, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.491578, Class: 1.000000, Obj: 0.296956, No Obj: 0.004572, Avg Recall: 0.541667, count: 120 -Region Avg IOU: 0.488386, Class: 1.000000, Obj: 0.334823, No Obj: 0.004943, Avg Recall: 0.496599, count: 147 -Region Avg IOU: 0.524003, Class: 1.000000, Obj: 0.346136, No Obj: 0.004736, Avg Recall: 0.571429, count: 112 -Region Avg IOU: 0.518179, Class: 1.000000, Obj: 0.361977, No Obj: 0.004386, Avg Recall: 0.601770, count: 113 -3874: 0.524243, 0.457090 avg, 0.001000 rate, 17.651333 seconds, 991744 images -Loaded: 2.393086 seconds -Region Avg IOU: 0.523712, Class: 1.000000, Obj: 0.370713, No Obj: 0.004726, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.504922, Class: 1.000000, Obj: 0.348184, No Obj: 0.004454, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.514404, Class: 1.000000, Obj: 0.407015, No Obj: 0.003827, Avg Recall: 0.552632, count: 76 -Region Avg IOU: 0.498853, Class: 1.000000, Obj: 0.316881, No Obj: 0.004733, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.487385, Class: 1.000000, Obj: 0.342741, No Obj: 0.004565, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.470135, Class: 1.000000, Obj: 0.385225, No Obj: 0.004242, Avg Recall: 0.471698, count: 106 -Region Avg IOU: 0.485979, Class: 1.000000, Obj: 0.306842, No Obj: 0.004448, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.482902, Class: 1.000000, Obj: 0.298919, No Obj: 0.005199, Avg Recall: 0.516129, count: 124 -3875: 0.415351, 0.452916 avg, 0.001000 rate, 17.993675 seconds, 992000 images -Loaded: 4.073866 seconds -Region Avg IOU: 0.441138, Class: 1.000000, Obj: 0.368012, No Obj: 0.004635, Avg Recall: 0.489362, count: 94 -Region Avg IOU: 0.451421, Class: 1.000000, Obj: 0.325409, No Obj: 0.004672, Avg Recall: 0.519231, count: 104 -Region Avg IOU: 0.492067, Class: 1.000000, Obj: 0.334415, No Obj: 0.004606, Avg Recall: 0.516129, count: 93 -Region Avg IOU: 0.428085, Class: 1.000000, Obj: 0.329544, No Obj: 0.004685, Avg Recall: 0.461538, count: 117 -Region Avg IOU: 0.501129, Class: 1.000000, Obj: 0.372718, No Obj: 0.005022, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.509513, Class: 1.000000, Obj: 0.366762, No Obj: 0.004531, Avg Recall: 0.568182, count: 88 -Region Avg IOU: 0.478484, Class: 1.000000, Obj: 0.344489, No Obj: 0.004930, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.465268, Class: 1.000000, Obj: 0.390540, No Obj: 0.004231, Avg Recall: 0.500000, count: 96 -3876: 0.385395, 0.446164 avg, 0.001000 rate, 20.416555 seconds, 992256 images -Loaded: 1.300129 seconds -Region Avg IOU: 0.495863, Class: 1.000000, Obj: 0.365062, No Obj: 0.004788, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.462550, Class: 1.000000, Obj: 0.366145, No Obj: 0.004787, Avg Recall: 0.500000, count: 92 -Region Avg IOU: 0.501956, Class: 1.000000, Obj: 0.371029, No Obj: 0.005670, Avg Recall: 0.544643, count: 112 -Region Avg IOU: 0.484507, Class: 1.000000, Obj: 0.358673, No Obj: 0.005239, Avg Recall: 0.528926, count: 121 -Region Avg IOU: 0.529505, Class: 1.000000, Obj: 0.374375, No Obj: 0.004514, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.487311, Class: 1.000000, Obj: 0.387853, No Obj: 0.005210, Avg Recall: 0.503597, count: 139 -Region Avg IOU: 0.512722, Class: 1.000000, Obj: 0.387644, No Obj: 0.004847, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.425977, Class: 1.000000, Obj: 0.337119, No Obj: 0.004423, Avg Recall: 0.418803, count: 117 -3877: 0.462188, 0.447766 avg, 0.001000 rate, 19.239803 seconds, 992512 images -Loaded: 2.893485 seconds -Region Avg IOU: 0.517032, Class: 1.000000, Obj: 0.364481, No Obj: 0.005245, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.491315, Class: 1.000000, Obj: 0.383611, No Obj: 0.005013, Avg Recall: 0.515152, count: 99 -Region Avg IOU: 0.542841, Class: 1.000000, Obj: 0.368464, No Obj: 0.005040, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.522757, Class: 1.000000, Obj: 0.321590, No Obj: 0.005617, Avg Recall: 0.598485, count: 132 -Region Avg IOU: 0.497893, Class: 1.000000, Obj: 0.347061, No Obj: 0.005585, Avg Recall: 0.584000, count: 125 -Region Avg IOU: 0.507570, Class: 1.000000, Obj: 0.364569, No Obj: 0.005137, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.442505, Class: 1.000000, Obj: 0.337685, No Obj: 0.005072, Avg Recall: 0.459854, count: 137 -Region Avg IOU: 0.478944, Class: 1.000000, Obj: 0.332660, No Obj: 0.005159, Avg Recall: 0.504065, count: 123 -3878: 0.463105, 0.449300 avg, 0.001000 rate, 19.666300 seconds, 992768 images -Loaded: 2.233608 seconds -Region Avg IOU: 0.467555, Class: 1.000000, Obj: 0.331056, No Obj: 0.005146, Avg Recall: 0.503876, count: 129 -Region Avg IOU: 0.531045, Class: 1.000000, Obj: 0.385720, No Obj: 0.005380, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.481991, Class: 1.000000, Obj: 0.368372, No Obj: 0.005373, Avg Recall: 0.485981, count: 107 -Region Avg IOU: 0.471143, Class: 1.000000, Obj: 0.354189, No Obj: 0.005160, Avg Recall: 0.496063, count: 127 -Region Avg IOU: 0.514248, Class: 1.000000, Obj: 0.388443, No Obj: 0.005357, Avg Recall: 0.600000, count: 120 -Region Avg IOU: 0.524955, Class: 1.000000, Obj: 0.401321, No Obj: 0.005053, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.518094, Class: 1.000000, Obj: 0.379166, No Obj: 0.004717, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.532619, Class: 1.000000, Obj: 0.377619, No Obj: 0.004666, Avg Recall: 0.595960, count: 99 -3879: 0.457511, 0.450121 avg, 0.001000 rate, 18.514009 seconds, 993024 images -Loaded: 3.297187 seconds -Region Avg IOU: 0.485597, Class: 1.000000, Obj: 0.347798, No Obj: 0.004917, Avg Recall: 0.530769, count: 130 -Region Avg IOU: 0.453714, Class: 1.000000, Obj: 0.312276, No Obj: 0.004597, Avg Recall: 0.495413, count: 109 -Region Avg IOU: 0.454103, Class: 1.000000, Obj: 0.365106, No Obj: 0.004119, Avg Recall: 0.466667, count: 105 -Region Avg IOU: 0.462724, Class: 1.000000, Obj: 0.338182, No Obj: 0.004279, Avg Recall: 0.450980, count: 102 -Region Avg IOU: 0.458108, Class: 1.000000, Obj: 0.317391, No Obj: 0.005242, Avg Recall: 0.507812, count: 128 -Region Avg IOU: 0.516513, Class: 1.000000, Obj: 0.365719, No Obj: 0.004895, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.483328, Class: 1.000000, Obj: 0.387593, No Obj: 0.004841, Avg Recall: 0.573171, count: 82 -Region Avg IOU: 0.484043, Class: 1.000000, Obj: 0.320504, No Obj: 0.005057, Avg Recall: 0.551020, count: 98 -3880: 0.463089, 0.451418 avg, 0.001000 rate, 20.476944 seconds, 993280 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 0.015253 seconds -Region Avg IOU: 0.456796, Class: 1.000000, Obj: 0.340504, No Obj: 0.004092, Avg Recall: 0.477876, count: 113 -Region Avg IOU: 0.508154, Class: 1.000000, Obj: 0.330847, No Obj: 0.004584, Avg Recall: 0.568182, count: 132 -Region Avg IOU: 0.576091, Class: 1.000000, Obj: 0.401592, No Obj: 0.004668, Avg Recall: 0.683544, count: 79 -Region Avg IOU: 0.580667, Class: 1.000000, Obj: 0.381607, No Obj: 0.004487, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.533474, Class: 1.000000, Obj: 0.391813, No Obj: 0.004195, Avg Recall: 0.589474, count: 95 -Region Avg IOU: 0.517892, Class: 1.000000, Obj: 0.364741, No Obj: 0.004488, Avg Recall: 0.543103, count: 116 -Region Avg IOU: 0.543777, Class: 1.000000, Obj: 0.381115, No Obj: 0.004124, Avg Recall: 0.607843, count: 102 -Region Avg IOU: 0.522148, Class: 1.000000, Obj: 0.373296, No Obj: 0.004246, Avg Recall: 0.583333, count: 108 -3881: 0.411780, 0.447454 avg, 0.001000 rate, 22.867374 seconds, 993536 images -Loaded: 0.350864 seconds -Region Avg IOU: 0.521424, Class: 1.000000, Obj: 0.338737, No Obj: 0.004167, Avg Recall: 0.595420, count: 131 -Region Avg IOU: 0.550275, Class: 1.000000, Obj: 0.342671, No Obj: 0.004296, Avg Recall: 0.669492, count: 118 -Region Avg IOU: 0.514226, Class: 1.000000, Obj: 0.330644, No Obj: 0.004302, Avg Recall: 0.587719, count: 114 -Region Avg IOU: 0.582752, Class: 1.000000, Obj: 0.383128, No Obj: 0.003883, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.540083, Class: 1.000000, Obj: 0.385952, No Obj: 0.004022, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.528658, Class: 1.000000, Obj: 0.360195, No Obj: 0.003481, Avg Recall: 0.577778, count: 90 -Region Avg IOU: 0.485201, Class: 1.000000, Obj: 0.349731, No Obj: 0.004420, Avg Recall: 0.572581, count: 124 -Region Avg IOU: 0.497929, Class: 1.000000, Obj: 0.387763, No Obj: 0.004157, Avg Recall: 0.545455, count: 132 -3882: 0.448747, 0.447583 avg, 0.001000 rate, 19.464045 seconds, 993792 images -Loaded: 2.947399 seconds -Region Avg IOU: 0.558567, Class: 1.000000, Obj: 0.339006, No Obj: 0.004135, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.499660, Class: 1.000000, Obj: 0.323903, No Obj: 0.003927, Avg Recall: 0.590909, count: 88 -Region Avg IOU: 0.518544, Class: 1.000000, Obj: 0.371579, No Obj: 0.004030, Avg Recall: 0.589744, count: 117 -Region Avg IOU: 0.553834, Class: 1.000000, Obj: 0.372847, No Obj: 0.003901, Avg Recall: 0.666667, count: 87 -Region Avg IOU: 0.532479, Class: 1.000000, Obj: 0.329061, No Obj: 0.004046, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.521271, Class: 1.000000, Obj: 0.383679, No Obj: 0.004022, Avg Recall: 0.550847, count: 118 -Region Avg IOU: 0.531669, Class: 1.000000, Obj: 0.361762, No Obj: 0.004550, Avg Recall: 0.616000, count: 125 -Region Avg IOU: 0.586711, Class: 1.000000, Obj: 0.396969, No Obj: 0.003886, Avg Recall: 0.689189, count: 74 -3883: 0.404569, 0.443282 avg, 0.001000 rate, 21.233700 seconds, 994048 images -Loaded: 0.343985 seconds -Region Avg IOU: 0.602884, Class: 1.000000, Obj: 0.416083, No Obj: 0.004326, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.541571, Class: 1.000000, Obj: 0.353200, No Obj: 0.004090, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.562113, Class: 1.000000, Obj: 0.335207, No Obj: 0.003737, Avg Recall: 0.597826, count: 92 -Region Avg IOU: 0.547831, Class: 1.000000, Obj: 0.359648, No Obj: 0.003703, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.601259, Class: 1.000000, Obj: 0.391945, No Obj: 0.003992, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.498707, Class: 1.000000, Obj: 0.343397, No Obj: 0.002864, Avg Recall: 0.588235, count: 85 -Region Avg IOU: 0.548877, Class: 1.000000, Obj: 0.349334, No Obj: 0.003253, Avg Recall: 0.619565, count: 92 -Region Avg IOU: 0.504324, Class: 1.000000, Obj: 0.336650, No Obj: 0.003273, Avg Recall: 0.551020, count: 98 -3884: 0.394490, 0.438403 avg, 0.001000 rate, 20.382193 seconds, 994304 images -Loaded: 1.298421 seconds -Region Avg IOU: 0.542546, Class: 1.000000, Obj: 0.345747, No Obj: 0.003561, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.563268, Class: 1.000000, Obj: 0.326845, No Obj: 0.003226, Avg Recall: 0.652174, count: 92 -Region Avg IOU: 0.511692, Class: 1.000000, Obj: 0.285615, No Obj: 0.003874, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.558362, Class: 1.000000, Obj: 0.303374, No Obj: 0.004075, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.577103, Class: 1.000000, Obj: 0.322064, No Obj: 0.003159, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.570615, Class: 1.000000, Obj: 0.349909, No Obj: 0.004195, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.551343, Class: 1.000000, Obj: 0.340499, No Obj: 0.003147, Avg Recall: 0.623529, count: 85 -Region Avg IOU: 0.507364, Class: 1.000000, Obj: 0.333454, No Obj: 0.003528, Avg Recall: 0.552381, count: 105 -3885: 0.416202, 0.436183 avg, 0.001000 rate, 22.404816 seconds, 994560 images -Loaded: 0.102179 seconds -Region Avg IOU: 0.551247, Class: 1.000000, Obj: 0.362900, No Obj: 0.003597, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.536705, Class: 1.000000, Obj: 0.357200, No Obj: 0.003305, Avg Recall: 0.579710, count: 69 -Region Avg IOU: 0.572473, Class: 1.000000, Obj: 0.302868, No Obj: 0.003715, Avg Recall: 0.625000, count: 120 -Region Avg IOU: 0.518221, Class: 1.000000, Obj: 0.270240, No Obj: 0.002936, Avg Recall: 0.564706, count: 85 -Region Avg IOU: 0.510331, Class: 1.000000, Obj: 0.302524, No Obj: 0.004027, Avg Recall: 0.570248, count: 121 -Region Avg IOU: 0.536300, Class: 1.000000, Obj: 0.307437, No Obj: 0.003995, Avg Recall: 0.614754, count: 122 -Region Avg IOU: 0.470722, Class: 1.000000, Obj: 0.279514, No Obj: 0.003689, Avg Recall: 0.472222, count: 108 -Region Avg IOU: 0.523511, Class: 1.000000, Obj: 0.300177, No Obj: 0.003593, Avg Recall: 0.581818, count: 110 -3886: 0.457680, 0.438333 avg, 0.001000 rate, 21.815275 seconds, 994816 images -Loaded: 0.472574 seconds -Region Avg IOU: 0.563588, Class: 1.000000, Obj: 0.320532, No Obj: 0.004280, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.565596, Class: 1.000000, Obj: 0.348525, No Obj: 0.003785, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.533224, Class: 1.000000, Obj: 0.317573, No Obj: 0.003649, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.601625, Class: 1.000000, Obj: 0.344223, No Obj: 0.004331, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.545447, Class: 1.000000, Obj: 0.347516, No Obj: 0.003914, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.543025, Class: 1.000000, Obj: 0.313998, No Obj: 0.003455, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.557325, Class: 1.000000, Obj: 0.363570, No Obj: 0.003941, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.584318, Class: 1.000000, Obj: 0.380145, No Obj: 0.003901, Avg Recall: 0.721649, count: 97 -3887: 0.435949, 0.438094 avg, 0.001000 rate, 21.425024 seconds, 995072 images -Loaded: 0.572316 seconds -Region Avg IOU: 0.488838, Class: 1.000000, Obj: 0.303528, No Obj: 0.003837, Avg Recall: 0.521368, count: 117 -Region Avg IOU: 0.590104, Class: 1.000000, Obj: 0.414777, No Obj: 0.003922, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.525645, Class: 1.000000, Obj: 0.314343, No Obj: 0.003601, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.536432, Class: 1.000000, Obj: 0.318263, No Obj: 0.003880, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.535141, Class: 1.000000, Obj: 0.360635, No Obj: 0.003455, Avg Recall: 0.549451, count: 91 -Region Avg IOU: 0.521934, Class: 1.000000, Obj: 0.337445, No Obj: 0.003192, Avg Recall: 0.643678, count: 87 -Region Avg IOU: 0.594876, Class: 1.000000, Obj: 0.373409, No Obj: 0.003248, Avg Recall: 0.731707, count: 82 -Region Avg IOU: 0.519334, Class: 1.000000, Obj: 0.375721, No Obj: 0.003787, Avg Recall: 0.568807, count: 109 -3888: 0.389448, 0.433230 avg, 0.001000 rate, 21.372080 seconds, 995328 images -Loaded: 1.191744 seconds -Region Avg IOU: 0.550043, Class: 1.000000, Obj: 0.335308, No Obj: 0.003824, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.593330, Class: 1.000000, Obj: 0.349984, No Obj: 0.003947, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.534486, Class: 1.000000, Obj: 0.362292, No Obj: 0.003731, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.565624, Class: 1.000000, Obj: 0.352017, No Obj: 0.004008, Avg Recall: 0.689076, count: 119 -Region Avg IOU: 0.504063, Class: 1.000000, Obj: 0.314685, No Obj: 0.003658, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.597667, Class: 1.000000, Obj: 0.304944, No Obj: 0.004151, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.593147, Class: 1.000000, Obj: 0.349796, No Obj: 0.004008, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.554124, Class: 1.000000, Obj: 0.370218, No Obj: 0.003750, Avg Recall: 0.639344, count: 122 -3889: 0.451788, 0.435085 avg, 0.001000 rate, 21.317892 seconds, 995584 images -Loaded: 0.971338 seconds -Region Avg IOU: 0.524945, Class: 1.000000, Obj: 0.330377, No Obj: 0.003387, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.524381, Class: 1.000000, Obj: 0.367859, No Obj: 0.003998, Avg Recall: 0.571429, count: 105 -Region Avg IOU: 0.566036, Class: 1.000000, Obj: 0.407745, No Obj: 0.003580, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.536255, Class: 1.000000, Obj: 0.376685, No Obj: 0.003707, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.559384, Class: 1.000000, Obj: 0.338191, No Obj: 0.003971, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.531818, Class: 1.000000, Obj: 0.312044, No Obj: 0.003549, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.593642, Class: 1.000000, Obj: 0.397085, No Obj: 0.003648, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.558221, Class: 1.000000, Obj: 0.377603, No Obj: 0.003963, Avg Recall: 0.616000, count: 125 -3890: 0.398029, 0.431380 avg, 0.001000 rate, 22.077200 seconds, 995840 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.181538 seconds -Region Avg IOU: 0.456757, Class: 1.000000, Obj: 0.321544, No Obj: 0.004340, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.461873, Class: 1.000000, Obj: 0.306189, No Obj: 0.003878, Avg Recall: 0.476744, count: 86 -Region Avg IOU: 0.486858, Class: 1.000000, Obj: 0.313989, No Obj: 0.004673, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.472157, Class: 1.000000, Obj: 0.325864, No Obj: 0.004279, Avg Recall: 0.516129, count: 93 -Region Avg IOU: 0.464201, Class: 1.000000, Obj: 0.312343, No Obj: 0.003940, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.444417, Class: 1.000000, Obj: 0.292721, No Obj: 0.003872, Avg Recall: 0.462264, count: 106 -Region Avg IOU: 0.516268, Class: 1.000000, Obj: 0.324826, No Obj: 0.004409, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.513114, Class: 1.000000, Obj: 0.320795, No Obj: 0.004246, Avg Recall: 0.582524, count: 103 -3891: 0.421681, 0.430410 avg, 0.001000 rate, 16.933184 seconds, 996096 images -Loaded: 5.220196 seconds -Region Avg IOU: 0.493509, Class: 1.000000, Obj: 0.316397, No Obj: 0.005189, Avg Recall: 0.517986, count: 139 -Region Avg IOU: 0.455463, Class: 1.000000, Obj: 0.310732, No Obj: 0.004560, Avg Recall: 0.478992, count: 119 -Region Avg IOU: 0.467449, Class: 1.000000, Obj: 0.303278, No Obj: 0.004613, Avg Recall: 0.504425, count: 113 -Region Avg IOU: 0.431526, Class: 1.000000, Obj: 0.299046, No Obj: 0.003916, Avg Recall: 0.419048, count: 105 -Region Avg IOU: 0.533531, Class: 1.000000, Obj: 0.353550, No Obj: 0.004800, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.507626, Class: 1.000000, Obj: 0.333833, No Obj: 0.004304, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.494897, Class: 1.000000, Obj: 0.306207, No Obj: 0.004889, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.460146, Class: 1.000000, Obj: 0.271155, No Obj: 0.004844, Avg Recall: 0.511628, count: 129 -3892: 0.497735, 0.437142 avg, 0.001000 rate, 14.327270 seconds, 996352 images -Loaded: 7.856962 seconds -Region Avg IOU: 0.488465, Class: 1.000000, Obj: 0.332130, No Obj: 0.004752, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.475288, Class: 1.000000, Obj: 0.376152, No Obj: 0.004536, Avg Recall: 0.500000, count: 98 -Region Avg IOU: 0.468818, Class: 1.000000, Obj: 0.282786, No Obj: 0.003943, Avg Recall: 0.494949, count: 99 -Region Avg IOU: 0.492665, Class: 1.000000, Obj: 0.322820, No Obj: 0.004874, Avg Recall: 0.533333, count: 105 -Region Avg IOU: 0.474027, Class: 1.000000, Obj: 0.331766, No Obj: 0.005019, Avg Recall: 0.500000, count: 114 -Region Avg IOU: 0.504840, Class: 1.000000, Obj: 0.317930, No Obj: 0.004011, Avg Recall: 0.592593, count: 81 -Region Avg IOU: 0.519755, Class: 1.000000, Obj: 0.355573, No Obj: 0.005310, Avg Recall: 0.587302, count: 126 -Region Avg IOU: 0.492492, Class: 1.000000, Obj: 0.328942, No Obj: 0.005045, Avg Recall: 0.544000, count: 125 -3893: 0.443210, 0.437749 avg, 0.001000 rate, 17.355314 seconds, 996608 images -Loaded: 3.193892 seconds -Region Avg IOU: 0.511464, Class: 1.000000, Obj: 0.360722, No Obj: 0.005552, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.453532, Class: 1.000000, Obj: 0.345862, No Obj: 0.004255, Avg Recall: 0.488889, count: 90 -Region Avg IOU: 0.509524, Class: 1.000000, Obj: 0.304396, No Obj: 0.004804, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.451214, Class: 1.000000, Obj: 0.307102, No Obj: 0.004747, Avg Recall: 0.527273, count: 110 -Region Avg IOU: 0.460549, Class: 1.000000, Obj: 0.339254, No Obj: 0.004522, Avg Recall: 0.568421, count: 95 -Region Avg IOU: 0.427716, Class: 1.000000, Obj: 0.277237, No Obj: 0.004403, Avg Recall: 0.440000, count: 100 -Region Avg IOU: 0.482106, Class: 1.000000, Obj: 0.287810, No Obj: 0.005613, Avg Recall: 0.564885, count: 131 -Region Avg IOU: 0.477560, Class: 1.000000, Obj: 0.317622, No Obj: 0.004996, Avg Recall: 0.474747, count: 99 -3894: 0.512244, 0.445199 avg, 0.001000 rate, 17.938168 seconds, 996864 images -Loaded: 2.513857 seconds -Region Avg IOU: 0.497079, Class: 1.000000, Obj: 0.362342, No Obj: 0.005743, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.497052, Class: 1.000000, Obj: 0.350909, No Obj: 0.005266, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.428484, Class: 1.000000, Obj: 0.335388, No Obj: 0.005163, Avg Recall: 0.416667, count: 108 -Region Avg IOU: 0.495306, Class: 1.000000, Obj: 0.356937, No Obj: 0.005022, Avg Recall: 0.537634, count: 93 -Region Avg IOU: 0.443806, Class: 1.000000, Obj: 0.352946, No Obj: 0.004263, Avg Recall: 0.489796, count: 98 -Region Avg IOU: 0.433131, Class: 1.000000, Obj: 0.332596, No Obj: 0.004990, Avg Recall: 0.424242, count: 132 -Region Avg IOU: 0.438137, Class: 1.000000, Obj: 0.339533, No Obj: 0.005142, Avg Recall: 0.450000, count: 120 -Region Avg IOU: 0.450538, Class: 1.000000, Obj: 0.355450, No Obj: 0.004370, Avg Recall: 0.448276, count: 87 -3895: 0.419986, 0.442677 avg, 0.001000 rate, 14.183564 seconds, 997120 images -Loaded: 8.197221 seconds -Region Avg IOU: 0.475068, Class: 1.000000, Obj: 0.341344, No Obj: 0.006228, Avg Recall: 0.464000, count: 125 -Region Avg IOU: 0.449854, Class: 1.000000, Obj: 0.351524, No Obj: 0.005071, Avg Recall: 0.473684, count: 114 -Region Avg IOU: 0.468128, Class: 1.000000, Obj: 0.369813, No Obj: 0.004978, Avg Recall: 0.546667, count: 75 -Region Avg IOU: 0.431322, Class: 1.000000, Obj: 0.324272, No Obj: 0.004522, Avg Recall: 0.445652, count: 92 -Region Avg IOU: 0.466528, Class: 1.000000, Obj: 0.364472, No Obj: 0.005525, Avg Recall: 0.500000, count: 120 -Region Avg IOU: 0.497149, Class: 1.000000, Obj: 0.363022, No Obj: 0.005165, Avg Recall: 0.540816, count: 98 -Region Avg IOU: 0.484365, Class: 1.000000, Obj: 0.363152, No Obj: 0.005164, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.450498, Class: 1.000000, Obj: 0.286898, No Obj: 0.005172, Avg Recall: 0.422018, count: 109 -3896: 0.493882, 0.447798 avg, 0.001000 rate, 17.342258 seconds, 997376 images -Loaded: 3.791981 seconds -Region Avg IOU: 0.443767, Class: 1.000000, Obj: 0.325538, No Obj: 0.004666, Avg Recall: 0.489796, count: 98 -Region Avg IOU: 0.477903, Class: 1.000000, Obj: 0.338038, No Obj: 0.005740, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.461554, Class: 1.000000, Obj: 0.340265, No Obj: 0.005129, Avg Recall: 0.504854, count: 103 -Region Avg IOU: 0.510049, Class: 1.000000, Obj: 0.393911, No Obj: 0.005198, Avg Recall: 0.554348, count: 92 -Region Avg IOU: 0.505123, Class: 1.000000, Obj: 0.399772, No Obj: 0.005267, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.457357, Class: 1.000000, Obj: 0.347389, No Obj: 0.005914, Avg Recall: 0.521368, count: 117 -Region Avg IOU: 0.421963, Class: 1.000000, Obj: 0.332693, No Obj: 0.004739, Avg Recall: 0.428571, count: 105 -Region Avg IOU: 0.427309, Class: 1.000000, Obj: 0.294018, No Obj: 0.004420, Avg Recall: 0.450000, count: 100 -3897: 0.462453, 0.449263 avg, 0.001000 rate, 16.917168 seconds, 997632 images -Loaded: 4.980127 seconds -Region Avg IOU: 0.428093, Class: 1.000000, Obj: 0.301484, No Obj: 0.005337, Avg Recall: 0.437037, count: 135 -Region Avg IOU: 0.459087, Class: 1.000000, Obj: 0.307729, No Obj: 0.005550, Avg Recall: 0.542857, count: 140 -Region Avg IOU: 0.443571, Class: 1.000000, Obj: 0.305195, No Obj: 0.004981, Avg Recall: 0.410448, count: 134 -Region Avg IOU: 0.454839, Class: 1.000000, Obj: 0.301121, No Obj: 0.005372, Avg Recall: 0.491935, count: 124 -Region Avg IOU: 0.494264, Class: 1.000000, Obj: 0.319267, No Obj: 0.004987, Avg Recall: 0.550000, count: 100 -Region Avg IOU: 0.461296, Class: 1.000000, Obj: 0.316494, No Obj: 0.004858, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.470962, Class: 1.000000, Obj: 0.312548, No Obj: 0.004755, Avg Recall: 0.515152, count: 99 -Region Avg IOU: 0.446399, Class: 1.000000, Obj: 0.302287, No Obj: 0.005611, Avg Recall: 0.458064, count: 155 -3898: 0.537408, 0.458078 avg, 0.001000 rate, 19.244673 seconds, 997888 images -Loaded: 1.799499 seconds -Region Avg IOU: 0.517430, Class: 1.000000, Obj: 0.381091, No Obj: 0.005746, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.479134, Class: 1.000000, Obj: 0.345366, No Obj: 0.005378, Avg Recall: 0.522936, count: 109 -Region Avg IOU: 0.462165, Class: 1.000000, Obj: 0.346574, No Obj: 0.005211, Avg Recall: 0.510204, count: 98 -Region Avg IOU: 0.498918, Class: 1.000000, Obj: 0.383086, No Obj: 0.004869, Avg Recall: 0.527473, count: 91 -Region Avg IOU: 0.523719, Class: 1.000000, Obj: 0.357679, No Obj: 0.005049, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.448331, Class: 1.000000, Obj: 0.323743, No Obj: 0.004864, Avg Recall: 0.475000, count: 120 -Region Avg IOU: 0.422896, Class: 1.000000, Obj: 0.347742, No Obj: 0.004730, Avg Recall: 0.435644, count: 101 -Region Avg IOU: 0.411938, Class: 1.000000, Obj: 0.323876, No Obj: 0.004501, Avg Recall: 0.468254, count: 126 -3899: 0.451169, 0.457387 avg, 0.001000 rate, 18.878525 seconds, 998144 images -Loaded: 2.827817 seconds -Region Avg IOU: 0.478472, Class: 1.000000, Obj: 0.304838, No Obj: 0.004497, Avg Recall: 0.560976, count: 82 -Region Avg IOU: 0.477551, Class: 1.000000, Obj: 0.336101, No Obj: 0.005502, Avg Recall: 0.548387, count: 93 -Region Avg IOU: 0.526466, Class: 1.000000, Obj: 0.359379, No Obj: 0.005028, Avg Recall: 0.654321, count: 81 -Region Avg IOU: 0.518757, Class: 1.000000, Obj: 0.305398, No Obj: 0.005252, Avg Recall: 0.580645, count: 93 -Region Avg IOU: 0.486920, Class: 1.000000, Obj: 0.366695, No Obj: 0.005522, Avg Recall: 0.567797, count: 118 -Region Avg IOU: 0.487685, Class: 1.000000, Obj: 0.373925, No Obj: 0.004335, Avg Recall: 0.556818, count: 88 -Region Avg IOU: 0.434630, Class: 1.000000, Obj: 0.305552, No Obj: 0.005214, Avg Recall: 0.440000, count: 125 -Region Avg IOU: 0.464209, Class: 1.000000, Obj: 0.313194, No Obj: 0.004484, Avg Recall: 0.531915, count: 94 -3900: 0.418056, 0.453454 avg, 0.001000 rate, 19.380352 seconds, 998400 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.716980 seconds -Region Avg IOU: 0.464389, Class: 1.000000, Obj: 0.314055, No Obj: 0.004135, Avg Recall: 0.457447, count: 94 -Region Avg IOU: 0.411774, Class: 1.000000, Obj: 0.310034, No Obj: 0.004621, Avg Recall: 0.387387, count: 111 -Region Avg IOU: 0.472725, Class: 1.000000, Obj: 0.303993, No Obj: 0.004236, Avg Recall: 0.493827, count: 81 -Region Avg IOU: 0.442878, Class: 1.000000, Obj: 0.316603, No Obj: 0.004016, Avg Recall: 0.413793, count: 87 -Region Avg IOU: 0.445565, Class: 1.000000, Obj: 0.307474, No Obj: 0.004438, Avg Recall: 0.476635, count: 107 -Region Avg IOU: 0.468232, Class: 1.000000, Obj: 0.293400, No Obj: 0.004776, Avg Recall: 0.471154, count: 104 -Region Avg IOU: 0.438070, Class: 1.000000, Obj: 0.289554, No Obj: 0.004229, Avg Recall: 0.479167, count: 96 -Region Avg IOU: 0.453104, Class: 1.000000, Obj: 0.265266, No Obj: 0.004916, Avg Recall: 0.508065, count: 124 -3901: 0.541073, 0.462216 avg, 0.001000 rate, 17.252975 seconds, 998656 images -Loaded: 3.995550 seconds -Region Avg IOU: 0.473617, Class: 1.000000, Obj: 0.321493, No Obj: 0.004462, Avg Recall: 0.532609, count: 92 -Region Avg IOU: 0.469985, Class: 1.000000, Obj: 0.299555, No Obj: 0.004798, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.456051, Class: 1.000000, Obj: 0.313013, No Obj: 0.004877, Avg Recall: 0.452381, count: 126 -Region Avg IOU: 0.477160, Class: 1.000000, Obj: 0.277351, No Obj: 0.004572, Avg Recall: 0.518182, count: 110 -Region Avg IOU: 0.486112, Class: 1.000000, Obj: 0.303511, No Obj: 0.004452, Avg Recall: 0.556701, count: 97 -Region Avg IOU: 0.463283, Class: 1.000000, Obj: 0.289706, No Obj: 0.004103, Avg Recall: 0.528090, count: 89 -Region Avg IOU: 0.451015, Class: 1.000000, Obj: 0.274133, No Obj: 0.004441, Avg Recall: 0.514493, count: 138 -Region Avg IOU: 0.418287, Class: 1.000000, Obj: 0.288360, No Obj: 0.004432, Avg Recall: 0.445378, count: 119 -3902: 0.472361, 0.463230 avg, 0.001000 rate, 18.167397 seconds, 998912 images -Loaded: 2.163931 seconds -Region Avg IOU: 0.485941, Class: 1.000000, Obj: 0.308528, No Obj: 0.004211, Avg Recall: 0.574468, count: 94 -Region Avg IOU: 0.544588, Class: 1.000000, Obj: 0.390962, No Obj: 0.004269, Avg Recall: 0.654321, count: 81 -Region Avg IOU: 0.426632, Class: 1.000000, Obj: 0.288292, No Obj: 0.004035, Avg Recall: 0.436170, count: 94 -Region Avg IOU: 0.400828, Class: 1.000000, Obj: 0.259860, No Obj: 0.003796, Avg Recall: 0.371681, count: 113 -Region Avg IOU: 0.500634, Class: 1.000000, Obj: 0.375703, No Obj: 0.004879, Avg Recall: 0.525862, count: 116 -Region Avg IOU: 0.438048, Class: 1.000000, Obj: 0.287829, No Obj: 0.003932, Avg Recall: 0.488095, count: 84 -Region Avg IOU: 0.505624, Class: 1.000000, Obj: 0.296248, No Obj: 0.004916, Avg Recall: 0.524590, count: 122 -Region Avg IOU: 0.444299, Class: 1.000000, Obj: 0.304797, No Obj: 0.004268, Avg Recall: 0.476635, count: 107 -3903: 0.446180, 0.461525 avg, 0.001000 rate, 16.249735 seconds, 999168 images -Loaded: 4.511854 seconds -Region Avg IOU: 0.496711, Class: 1.000000, Obj: 0.284966, No Obj: 0.004566, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.496075, Class: 1.000000, Obj: 0.320582, No Obj: 0.004958, Avg Recall: 0.532110, count: 109 -Region Avg IOU: 0.463089, Class: 1.000000, Obj: 0.293502, No Obj: 0.003883, Avg Recall: 0.551020, count: 98 -Region Avg IOU: 0.466913, Class: 1.000000, Obj: 0.312134, No Obj: 0.004692, Avg Recall: 0.504505, count: 111 -Region Avg IOU: 0.523118, Class: 1.000000, Obj: 0.337480, No Obj: 0.004693, Avg Recall: 0.633333, count: 90 -Region Avg IOU: 0.483996, Class: 1.000000, Obj: 0.302397, No Obj: 0.004558, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.465342, Class: 1.000000, Obj: 0.255905, No Obj: 0.004842, Avg Recall: 0.537879, count: 132 -Region Avg IOU: 0.460985, Class: 1.000000, Obj: 0.303456, No Obj: 0.005042, Avg Recall: 0.492064, count: 126 -3904: 0.458792, 0.461252 avg, 0.001000 rate, 18.366226 seconds, 999424 images -Loaded: 1.748454 seconds -Region Avg IOU: 0.475077, Class: 1.000000, Obj: 0.285428, No Obj: 0.004333, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.480333, Class: 1.000000, Obj: 0.300544, No Obj: 0.003952, Avg Recall: 0.518072, count: 83 -Region Avg IOU: 0.485359, Class: 1.000000, Obj: 0.324118, No Obj: 0.004517, Avg Recall: 0.520000, count: 100 -Region Avg IOU: 0.458929, Class: 1.000000, Obj: 0.291373, No Obj: 0.004407, Avg Recall: 0.500000, count: 116 -Region Avg IOU: 0.466880, Class: 1.000000, Obj: 0.316627, No Obj: 0.004152, Avg Recall: 0.509259, count: 108 -Region Avg IOU: 0.432014, Class: 1.000000, Obj: 0.293141, No Obj: 0.004518, Avg Recall: 0.394366, count: 142 -Region Avg IOU: 0.461481, Class: 1.000000, Obj: 0.274523, No Obj: 0.004484, Avg Recall: 0.535354, count: 99 -Region Avg IOU: 0.481693, Class: 1.000000, Obj: 0.315062, No Obj: 0.004637, Avg Recall: 0.520661, count: 121 -3905: 0.495467, 0.464673 avg, 0.001000 rate, 18.202772 seconds, 999680 images -Loaded: 3.374370 seconds -Region Avg IOU: 0.473982, Class: 1.000000, Obj: 0.305073, No Obj: 0.005135, Avg Recall: 0.537313, count: 134 -Region Avg IOU: 0.465504, Class: 1.000000, Obj: 0.276997, No Obj: 0.004530, Avg Recall: 0.469388, count: 98 -Region Avg IOU: 0.469398, Class: 1.000000, Obj: 0.325854, No Obj: 0.004070, Avg Recall: 0.480392, count: 102 -Region Avg IOU: 0.441963, Class: 1.000000, Obj: 0.319118, No Obj: 0.004390, Avg Recall: 0.470086, count: 117 -Region Avg IOU: 0.458461, Class: 1.000000, Obj: 0.261647, No Obj: 0.004646, Avg Recall: 0.488372, count: 129 -Region Avg IOU: 0.487930, Class: 1.000000, Obj: 0.308202, No Obj: 0.005226, Avg Recall: 0.471074, count: 121 -Region Avg IOU: 0.523832, Class: 1.000000, Obj: 0.317084, No Obj: 0.005555, Avg Recall: 0.604651, count: 129 -Region Avg IOU: 0.484180, Class: 1.000000, Obj: 0.327377, No Obj: 0.004880, Avg Recall: 0.507576, count: 132 -3906: 0.550534, 0.473260 avg, 0.001000 rate, 19.456911 seconds, 999936 images -Loaded: 2.724529 seconds -Region Avg IOU: 0.539921, Class: 1.000000, Obj: 0.360819, No Obj: 0.005170, Avg Recall: 0.597938, count: 97 -Region Avg IOU: 0.485317, Class: 1.000000, Obj: 0.352128, No Obj: 0.004887, Avg Recall: 0.516129, count: 93 -Region Avg IOU: 0.498272, Class: 1.000000, Obj: 0.321071, No Obj: 0.004680, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.502136, Class: 1.000000, Obj: 0.306885, No Obj: 0.005064, Avg Recall: 0.528926, count: 121 -Region Avg IOU: 0.456169, Class: 1.000000, Obj: 0.298267, No Obj: 0.005016, Avg Recall: 0.473214, count: 112 -Region Avg IOU: 0.448355, Class: 1.000000, Obj: 0.269868, No Obj: 0.004813, Avg Recall: 0.491379, count: 116 -Region Avg IOU: 0.508404, Class: 1.000000, Obj: 0.300283, No Obj: 0.005688, Avg Recall: 0.556338, count: 142 -Region Avg IOU: 0.449996, Class: 1.000000, Obj: 0.295771, No Obj: 0.005549, Avg Recall: 0.478571, count: 140 -3907: 0.531958, 0.479129 avg, 0.001000 rate, 19.506748 seconds, 1000192 images -Loaded: 0.980224 seconds -Region Avg IOU: 0.445641, Class: 1.000000, Obj: 0.297419, No Obj: 0.005568, Avg Recall: 0.448718, count: 156 -Region Avg IOU: 0.442452, Class: 1.000000, Obj: 0.319876, No Obj: 0.004584, Avg Recall: 0.524272, count: 103 -Region Avg IOU: 0.460214, Class: 1.000000, Obj: 0.300487, No Obj: 0.004666, Avg Recall: 0.509804, count: 102 -Region Avg IOU: 0.533136, Class: 1.000000, Obj: 0.374984, No Obj: 0.003929, Avg Recall: 0.586667, count: 75 -Region Avg IOU: 0.501895, Class: 1.000000, Obj: 0.323591, No Obj: 0.004469, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.496948, Class: 1.000000, Obj: 0.322652, No Obj: 0.005409, Avg Recall: 0.581967, count: 122 -Region Avg IOU: 0.431478, Class: 1.000000, Obj: 0.363798, No Obj: 0.004541, Avg Recall: 0.414141, count: 99 -Region Avg IOU: 0.504000, Class: 1.000000, Obj: 0.375281, No Obj: 0.004369, Avg Recall: 0.546667, count: 75 -3908: 0.435973, 0.474814 avg, 0.001000 rate, 14.206922 seconds, 1000448 images -Loaded: 5.454382 seconds -Region Avg IOU: 0.468266, Class: 1.000000, Obj: 0.296522, No Obj: 0.005032, Avg Recall: 0.500000, count: 116 -Region Avg IOU: 0.458923, Class: 1.000000, Obj: 0.327198, No Obj: 0.004834, Avg Recall: 0.461538, count: 91 -Region Avg IOU: 0.453441, Class: 1.000000, Obj: 0.345446, No Obj: 0.005350, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.445588, Class: 1.000000, Obj: 0.336963, No Obj: 0.005534, Avg Recall: 0.435185, count: 108 -Region Avg IOU: 0.434109, Class: 1.000000, Obj: 0.287512, No Obj: 0.005165, Avg Recall: 0.486486, count: 111 -Region Avg IOU: 0.468284, Class: 1.000000, Obj: 0.329286, No Obj: 0.005403, Avg Recall: 0.508475, count: 118 -Region Avg IOU: 0.431014, Class: 1.000000, Obj: 0.314559, No Obj: 0.004750, Avg Recall: 0.432203, count: 118 -Region Avg IOU: 0.482752, Class: 1.000000, Obj: 0.339091, No Obj: 0.005052, Avg Recall: 0.559633, count: 109 -3909: 0.588281, 0.486160 avg, 0.001000 rate, 18.472219 seconds, 1000704 images -Loaded: 2.315381 seconds -Region Avg IOU: 0.417255, Class: 1.000000, Obj: 0.337993, No Obj: 0.005160, Avg Recall: 0.455446, count: 101 -Region Avg IOU: 0.475983, Class: 1.000000, Obj: 0.337762, No Obj: 0.004731, Avg Recall: 0.538462, count: 104 -Region Avg IOU: 0.473486, Class: 1.000000, Obj: 0.343002, No Obj: 0.005418, Avg Recall: 0.533898, count: 118 -Region Avg IOU: 0.507745, Class: 1.000000, Obj: 0.347504, No Obj: 0.005033, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.493472, Class: 1.000000, Obj: 0.407098, No Obj: 0.004563, Avg Recall: 0.513514, count: 74 -Region Avg IOU: 0.492465, Class: 1.000000, Obj: 0.340498, No Obj: 0.005147, Avg Recall: 0.515385, count: 130 -Region Avg IOU: 0.440782, Class: 1.000000, Obj: 0.327686, No Obj: 0.005157, Avg Recall: 0.429907, count: 107 -Region Avg IOU: 0.446340, Class: 1.000000, Obj: 0.339919, No Obj: 0.005209, Avg Recall: 0.490741, count: 108 -3910: 0.433234, 0.480868 avg, 0.001000 rate, 15.241149 seconds, 1000960 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000061 seconds -Region Avg IOU: 0.640573, Class: 1.000000, Obj: 0.512498, No Obj: 0.004162, Avg Recall: 0.776000, count: 125 -Region Avg IOU: 0.617938, Class: 1.000000, Obj: 0.487909, No Obj: 0.003522, Avg Recall: 0.752941, count: 85 -Region Avg IOU: 0.641692, Class: 1.000000, Obj: 0.476045, No Obj: 0.004104, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.635476, Class: 1.000000, Obj: 0.455076, No Obj: 0.004205, Avg Recall: 0.834951, count: 103 -Region Avg IOU: 0.618083, Class: 1.000000, Obj: 0.464288, No Obj: 0.004351, Avg Recall: 0.762295, count: 122 -Region Avg IOU: 0.603878, Class: 1.000000, Obj: 0.514574, No Obj: 0.004130, Avg Recall: 0.719298, count: 114 -Region Avg IOU: 0.588501, Class: 1.000000, Obj: 0.452621, No Obj: 0.004272, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.631488, Class: 1.000000, Obj: 0.515559, No Obj: 0.004197, Avg Recall: 0.789916, count: 119 -3911: 0.476060, 0.480387 avg, 0.001000 rate, 26.067991 seconds, 1001216 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.591348, Class: 1.000000, Obj: 0.423158, No Obj: 0.003592, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.581739, Class: 1.000000, Obj: 0.448400, No Obj: 0.003302, Avg Recall: 0.662500, count: 80 -Region Avg IOU: 0.600435, Class: 1.000000, Obj: 0.438722, No Obj: 0.003458, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.626553, Class: 1.000000, Obj: 0.451137, No Obj: 0.003232, Avg Recall: 0.777778, count: 81 -Region Avg IOU: 0.630576, Class: 1.000000, Obj: 0.474790, No Obj: 0.004086, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.596853, Class: 1.000000, Obj: 0.433731, No Obj: 0.003298, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.626039, Class: 1.000000, Obj: 0.476363, No Obj: 0.003689, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.602728, Class: 1.000000, Obj: 0.415012, No Obj: 0.004389, Avg Recall: 0.739837, count: 123 -3912: 0.448215, 0.477170 avg, 0.001000 rate, 26.221924 seconds, 1001472 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.600489, Class: 1.000000, Obj: 0.362530, No Obj: 0.003180, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.613487, Class: 1.000000, Obj: 0.413482, No Obj: 0.003386, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.581676, Class: 1.000000, Obj: 0.373288, No Obj: 0.003697, Avg Recall: 0.698529, count: 136 -Region Avg IOU: 0.629380, Class: 1.000000, Obj: 0.401854, No Obj: 0.003103, Avg Recall: 0.761905, count: 84 -Region Avg IOU: 0.600085, Class: 1.000000, Obj: 0.433359, No Obj: 0.003571, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.655459, Class: 1.000000, Obj: 0.401260, No Obj: 0.003053, Avg Recall: 0.841463, count: 82 -Region Avg IOU: 0.662649, Class: 1.000000, Obj: 0.405794, No Obj: 0.003272, Avg Recall: 0.834951, count: 103 -Region Avg IOU: 0.608897, Class: 1.000000, Obj: 0.427339, No Obj: 0.003503, Avg Recall: 0.733945, count: 109 -3913: 0.414213, 0.470874 avg, 0.001000 rate, 26.300802 seconds, 1001728 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.570600, Class: 1.000000, Obj: 0.376512, No Obj: 0.003253, Avg Recall: 0.636364, count: 132 -Region Avg IOU: 0.595849, Class: 1.000000, Obj: 0.379094, No Obj: 0.002736, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.658254, Class: 1.000000, Obj: 0.396107, No Obj: 0.003275, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.578471, Class: 1.000000, Obj: 0.399599, No Obj: 0.003079, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.672099, Class: 1.000000, Obj: 0.472487, No Obj: 0.003055, Avg Recall: 0.840425, count: 94 -Region Avg IOU: 0.597428, Class: 1.000000, Obj: 0.396299, No Obj: 0.003226, Avg Recall: 0.728682, count: 129 -Region Avg IOU: 0.631432, Class: 1.000000, Obj: 0.367172, No Obj: 0.002993, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.606986, Class: 1.000000, Obj: 0.319635, No Obj: 0.003123, Avg Recall: 0.711712, count: 111 -3914: 0.421888, 0.465975 avg, 0.001000 rate, 25.875196 seconds, 1001984 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.641550, Class: 1.000000, Obj: 0.389161, No Obj: 0.003069, Avg Recall: 0.779528, count: 127 -Region Avg IOU: 0.658279, Class: 1.000000, Obj: 0.364594, No Obj: 0.002723, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.578813, Class: 1.000000, Obj: 0.330112, No Obj: 0.002766, Avg Recall: 0.685484, count: 124 -Region Avg IOU: 0.608260, Class: 1.000000, Obj: 0.334652, No Obj: 0.002811, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.586569, Class: 1.000000, Obj: 0.313507, No Obj: 0.002540, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.621709, Class: 1.000000, Obj: 0.396670, No Obj: 0.002486, Avg Recall: 0.765432, count: 81 -Region Avg IOU: 0.564671, Class: 1.000000, Obj: 0.333125, No Obj: 0.002956, Avg Recall: 0.695312, count: 128 -Region Avg IOU: 0.560600, Class: 1.000000, Obj: 0.278684, No Obj: 0.003037, Avg Recall: 0.669065, count: 139 -3915: 0.474083, 0.466786 avg, 0.001000 rate, 27.055967 seconds, 1002240 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.630885, Class: 1.000000, Obj: 0.295439, No Obj: 0.002588, Avg Recall: 0.763780, count: 127 -Region Avg IOU: 0.625734, Class: 1.000000, Obj: 0.348787, No Obj: 0.002841, Avg Recall: 0.766667, count: 120 -Region Avg IOU: 0.621414, Class: 1.000000, Obj: 0.305855, No Obj: 0.002462, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.601125, Class: 1.000000, Obj: 0.292089, No Obj: 0.002597, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.614536, Class: 1.000000, Obj: 0.305008, No Obj: 0.002839, Avg Recall: 0.720000, count: 125 -Region Avg IOU: 0.631759, Class: 1.000000, Obj: 0.322450, No Obj: 0.002352, Avg Recall: 0.805556, count: 72 -Region Avg IOU: 0.600062, Class: 1.000000, Obj: 0.323768, No Obj: 0.002243, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.577525, Class: 1.000000, Obj: 0.310610, No Obj: 0.002856, Avg Recall: 0.704225, count: 142 -3916: 0.478978, 0.468005 avg, 0.001000 rate, 26.926336 seconds, 1002496 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.661950, Class: 1.000000, Obj: 0.335005, No Obj: 0.002388, Avg Recall: 0.842105, count: 95 -Region Avg IOU: 0.602627, Class: 1.000000, Obj: 0.358499, No Obj: 0.002250, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.607298, Class: 1.000000, Obj: 0.297292, No Obj: 0.002507, Avg Recall: 0.738806, count: 134 -Region Avg IOU: 0.598596, Class: 1.000000, Obj: 0.356435, No Obj: 0.002416, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.648681, Class: 1.000000, Obj: 0.361125, No Obj: 0.002016, Avg Recall: 0.794118, count: 68 -Region Avg IOU: 0.629790, Class: 1.000000, Obj: 0.300557, No Obj: 0.002505, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.589527, Class: 1.000000, Obj: 0.299816, No Obj: 0.002398, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.609696, Class: 1.000000, Obj: 0.330881, No Obj: 0.002270, Avg Recall: 0.727273, count: 121 -3917: 0.434986, 0.464703 avg, 0.001000 rate, 26.483130 seconds, 1002752 images -Loaded: 0.000116 seconds -Region Avg IOU: 0.592051, Class: 1.000000, Obj: 0.328497, No Obj: 0.002157, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.636199, Class: 1.000000, Obj: 0.327703, No Obj: 0.002113, Avg Recall: 0.797872, count: 94 -Region Avg IOU: 0.602824, Class: 1.000000, Obj: 0.304992, No Obj: 0.002217, Avg Recall: 0.697479, count: 119 -Region Avg IOU: 0.627805, Class: 1.000000, Obj: 0.333836, No Obj: 0.003098, Avg Recall: 0.753333, count: 150 -Region Avg IOU: 0.611222, Class: 1.000000, Obj: 0.343681, No Obj: 0.002306, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.628588, Class: 1.000000, Obj: 0.306695, No Obj: 0.002513, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.680051, Class: 1.000000, Obj: 0.390477, No Obj: 0.002306, Avg Recall: 0.845238, count: 84 -Region Avg IOU: 0.591145, Class: 1.000000, Obj: 0.269260, No Obj: 0.002717, Avg Recall: 0.704698, count: 149 -3918: 0.491787, 0.467412 avg, 0.001000 rate, 26.980766 seconds, 1003008 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.636847, Class: 1.000000, Obj: 0.317060, No Obj: 0.002605, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.612655, Class: 1.000000, Obj: 0.323291, No Obj: 0.002557, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.650836, Class: 1.000000, Obj: 0.394812, No Obj: 0.002699, Avg Recall: 0.823529, count: 102 -Region Avg IOU: 0.597293, Class: 1.000000, Obj: 0.324192, No Obj: 0.002304, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.606602, Class: 1.000000, Obj: 0.344238, No Obj: 0.002282, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.591212, Class: 1.000000, Obj: 0.330070, No Obj: 0.002911, Avg Recall: 0.717557, count: 131 -Region Avg IOU: 0.578279, Class: 1.000000, Obj: 0.270538, No Obj: 0.002547, Avg Recall: 0.669291, count: 127 -Region Avg IOU: 0.615399, Class: 1.000000, Obj: 0.361783, No Obj: 0.002468, Avg Recall: 0.718182, count: 110 -3919: 0.465192, 0.467190 avg, 0.001000 rate, 26.156008 seconds, 1003264 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.642832, Class: 1.000000, Obj: 0.329603, No Obj: 0.002606, Avg Recall: 0.820225, count: 89 -Region Avg IOU: 0.599387, Class: 1.000000, Obj: 0.338346, No Obj: 0.002683, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.631333, Class: 1.000000, Obj: 0.315700, No Obj: 0.002914, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.622889, Class: 1.000000, Obj: 0.398977, No Obj: 0.002658, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.602193, Class: 1.000000, Obj: 0.392120, No Obj: 0.002726, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.606746, Class: 1.000000, Obj: 0.347724, No Obj: 0.002874, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.656634, Class: 1.000000, Obj: 0.350638, No Obj: 0.002450, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.599062, Class: 1.000000, Obj: 0.358489, No Obj: 0.002614, Avg Recall: 0.694444, count: 108 -3920: 0.416105, 0.462081 avg, 0.001000 rate, 25.885548 seconds, 1003520 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 18.772476 seconds -Region Avg IOU: 0.557540, Class: 1.000000, Obj: 0.349937, No Obj: 0.003110, Avg Recall: 0.629214, count: 89 -Region Avg IOU: 0.580044, Class: 1.000000, Obj: 0.356810, No Obj: 0.003308, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.553614, Class: 1.000000, Obj: 0.337984, No Obj: 0.002648, Avg Recall: 0.600000, count: 90 -Region Avg IOU: 0.569916, Class: 1.000000, Obj: 0.354034, No Obj: 0.003432, Avg Recall: 0.609756, count: 82 -Region Avg IOU: 0.572308, Class: 1.000000, Obj: 0.368225, No Obj: 0.003700, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.601997, Class: 1.000000, Obj: 0.365847, No Obj: 0.003362, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.559751, Class: 1.000000, Obj: 0.283197, No Obj: 0.002907, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.644370, Class: 1.000000, Obj: 0.432980, No Obj: 0.003373, Avg Recall: 0.792079, count: 101 -3921: 0.379946, 0.453868 avg, 0.001000 rate, 24.536383 seconds, 1003776 images -Loaded: 0.013094 seconds -Region Avg IOU: 0.578975, Class: 1.000000, Obj: 0.361927, No Obj: 0.003627, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.580840, Class: 1.000000, Obj: 0.381296, No Obj: 0.004075, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.543672, Class: 1.000000, Obj: 0.366743, No Obj: 0.003242, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.534674, Class: 1.000000, Obj: 0.313241, No Obj: 0.003265, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.553003, Class: 1.000000, Obj: 0.338322, No Obj: 0.003536, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.569113, Class: 1.000000, Obj: 0.367653, No Obj: 0.003661, Avg Recall: 0.684783, count: 92 -Region Avg IOU: 0.627630, Class: 1.000000, Obj: 0.381970, No Obj: 0.004134, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.570950, Class: 1.000000, Obj: 0.374448, No Obj: 0.004606, Avg Recall: 0.704918, count: 122 -3922: 0.427158, 0.451197 avg, 0.001000 rate, 23.388008 seconds, 1004032 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.542293, Class: 1.000000, Obj: 0.383662, No Obj: 0.003922, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.545102, Class: 1.000000, Obj: 0.345306, No Obj: 0.004323, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.515926, Class: 1.000000, Obj: 0.354633, No Obj: 0.003798, Avg Recall: 0.583333, count: 132 -Region Avg IOU: 0.515637, Class: 1.000000, Obj: 0.345760, No Obj: 0.003723, Avg Recall: 0.570175, count: 114 -Region Avg IOU: 0.610064, Class: 1.000000, Obj: 0.413440, No Obj: 0.004160, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.543930, Class: 1.000000, Obj: 0.392932, No Obj: 0.004010, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.516750, Class: 1.000000, Obj: 0.390465, No Obj: 0.003250, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.622004, Class: 1.000000, Obj: 0.421945, No Obj: 0.004155, Avg Recall: 0.735632, count: 87 -3923: 0.414777, 0.447555 avg, 0.001000 rate, 23.397324 seconds, 1004288 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.589565, Class: 1.000000, Obj: 0.408132, No Obj: 0.004547, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.576467, Class: 1.000000, Obj: 0.378263, No Obj: 0.004233, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.575631, Class: 1.000000, Obj: 0.411468, No Obj: 0.004177, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.609485, Class: 1.000000, Obj: 0.417106, No Obj: 0.003674, Avg Recall: 0.719512, count: 82 -Region Avg IOU: 0.542525, Class: 1.000000, Obj: 0.365767, No Obj: 0.004446, Avg Recall: 0.642336, count: 137 -Region Avg IOU: 0.563061, Class: 1.000000, Obj: 0.389698, No Obj: 0.005096, Avg Recall: 0.645390, count: 141 -Region Avg IOU: 0.582777, Class: 1.000000, Obj: 0.400884, No Obj: 0.003781, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.535765, Class: 1.000000, Obj: 0.404150, No Obj: 0.004562, Avg Recall: 0.626761, count: 142 -3924: 0.482809, 0.451080 avg, 0.001000 rate, 22.909365 seconds, 1004544 images -Loaded: 0.000117 seconds -Region Avg IOU: 0.581141, Class: 1.000000, Obj: 0.428924, No Obj: 0.003988, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.566422, Class: 1.000000, Obj: 0.408631, No Obj: 0.003578, Avg Recall: 0.689189, count: 74 -Region Avg IOU: 0.552785, Class: 1.000000, Obj: 0.386837, No Obj: 0.004446, Avg Recall: 0.652893, count: 121 -Region Avg IOU: 0.546097, Class: 1.000000, Obj: 0.412500, No Obj: 0.004621, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.581521, Class: 1.000000, Obj: 0.377246, No Obj: 0.004693, Avg Recall: 0.684211, count: 133 -Region Avg IOU: 0.527449, Class: 1.000000, Obj: 0.395508, No Obj: 0.004616, Avg Recall: 0.613445, count: 119 -Region Avg IOU: 0.526518, Class: 1.000000, Obj: 0.389153, No Obj: 0.004282, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.578124, Class: 1.000000, Obj: 0.411142, No Obj: 0.004097, Avg Recall: 0.696429, count: 112 -3925: 0.455935, 0.451566 avg, 0.001000 rate, 23.269035 seconds, 1004800 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.525503, Class: 1.000000, Obj: 0.413234, No Obj: 0.003676, Avg Recall: 0.578947, count: 76 -Region Avg IOU: 0.575518, Class: 1.000000, Obj: 0.426367, No Obj: 0.003986, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.506036, Class: 1.000000, Obj: 0.360599, No Obj: 0.003815, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.566657, Class: 1.000000, Obj: 0.381429, No Obj: 0.003573, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.549582, Class: 1.000000, Obj: 0.377564, No Obj: 0.004741, Avg Recall: 0.596899, count: 129 -Region Avg IOU: 0.558547, Class: 1.000000, Obj: 0.410874, No Obj: 0.004500, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.529264, Class: 1.000000, Obj: 0.399594, No Obj: 0.004331, Avg Recall: 0.592920, count: 113 -Region Avg IOU: 0.577453, Class: 1.000000, Obj: 0.411618, No Obj: 0.004074, Avg Recall: 0.694444, count: 108 -3926: 0.439288, 0.450338 avg, 0.001000 rate, 23.355295 seconds, 1005056 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.579089, Class: 1.000000, Obj: 0.398846, No Obj: 0.004225, Avg Recall: 0.651163, count: 86 -Region Avg IOU: 0.615395, Class: 1.000000, Obj: 0.416870, No Obj: 0.004344, Avg Recall: 0.709302, count: 86 -Region Avg IOU: 0.542156, Class: 1.000000, Obj: 0.391822, No Obj: 0.003690, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.581442, Class: 1.000000, Obj: 0.428901, No Obj: 0.004089, Avg Recall: 0.697479, count: 119 -Region Avg IOU: 0.566889, Class: 1.000000, Obj: 0.409291, No Obj: 0.004300, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.600940, Class: 1.000000, Obj: 0.483493, No Obj: 0.004350, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.605341, Class: 1.000000, Obj: 0.424976, No Obj: 0.004648, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.552231, Class: 1.000000, Obj: 0.423845, No Obj: 0.004540, Avg Recall: 0.658537, count: 123 -3927: 0.389870, 0.444291 avg, 0.001000 rate, 23.487728 seconds, 1005312 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.588841, Class: 1.000000, Obj: 0.402765, No Obj: 0.003555, Avg Recall: 0.732394, count: 71 -Region Avg IOU: 0.599065, Class: 1.000000, Obj: 0.435655, No Obj: 0.003941, Avg Recall: 0.722222, count: 90 -Region Avg IOU: 0.573835, Class: 1.000000, Obj: 0.390062, No Obj: 0.004541, Avg Recall: 0.714286, count: 119 -Region Avg IOU: 0.535388, Class: 1.000000, Obj: 0.369307, No Obj: 0.004018, Avg Recall: 0.549451, count: 91 -Region Avg IOU: 0.526762, Class: 1.000000, Obj: 0.401723, No Obj: 0.004423, Avg Recall: 0.603175, count: 126 -Region Avg IOU: 0.484947, Class: 1.000000, Obj: 0.345113, No Obj: 0.003959, Avg Recall: 0.500000, count: 118 -Region Avg IOU: 0.516694, Class: 1.000000, Obj: 0.374936, No Obj: 0.004143, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.551429, Class: 1.000000, Obj: 0.389989, No Obj: 0.003908, Avg Recall: 0.656250, count: 96 -3928: 0.418990, 0.441761 avg, 0.001000 rate, 23.628061 seconds, 1005568 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.579235, Class: 1.000000, Obj: 0.391295, No Obj: 0.004085, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.541367, Class: 1.000000, Obj: 0.355063, No Obj: 0.004238, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.519615, Class: 1.000000, Obj: 0.363918, No Obj: 0.003936, Avg Recall: 0.557377, count: 122 -Region Avg IOU: 0.543375, Class: 1.000000, Obj: 0.385454, No Obj: 0.003394, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.601947, Class: 1.000000, Obj: 0.447800, No Obj: 0.004254, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.582047, Class: 1.000000, Obj: 0.366728, No Obj: 0.004407, Avg Recall: 0.664000, count: 125 -Region Avg IOU: 0.570164, Class: 1.000000, Obj: 0.351877, No Obj: 0.004225, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.585225, Class: 1.000000, Obj: 0.409682, No Obj: 0.003767, Avg Recall: 0.676768, count: 99 -3929: 0.431729, 0.440758 avg, 0.001000 rate, 23.617622 seconds, 1005824 images -Loaded: 0.000047 seconds -Region Avg IOU: 0.583764, Class: 1.000000, Obj: 0.378684, No Obj: 0.003736, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.562622, Class: 1.000000, Obj: 0.356196, No Obj: 0.003758, Avg Recall: 0.648438, count: 128 -Region Avg IOU: 0.491543, Class: 1.000000, Obj: 0.354782, No Obj: 0.003769, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.569744, Class: 1.000000, Obj: 0.407501, No Obj: 0.003616, Avg Recall: 0.653846, count: 78 -Region Avg IOU: 0.507491, Class: 1.000000, Obj: 0.372510, No Obj: 0.003745, Avg Recall: 0.559055, count: 127 -Region Avg IOU: 0.568610, Class: 1.000000, Obj: 0.390142, No Obj: 0.003619, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.583293, Class: 1.000000, Obj: 0.353378, No Obj: 0.004475, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.577984, Class: 1.000000, Obj: 0.336562, No Obj: 0.003883, Avg Recall: 0.663636, count: 110 -3930: 0.447172, 0.441399 avg, 0.001000 rate, 23.951536 seconds, 1006080 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000064 seconds -Region Avg IOU: 0.671928, Class: 1.000000, Obj: 0.411750, No Obj: 0.003369, Avg Recall: 0.826087, count: 115 -Region Avg IOU: 0.590722, Class: 1.000000, Obj: 0.377871, No Obj: 0.002796, Avg Recall: 0.690476, count: 126 -Region Avg IOU: 0.656924, Class: 1.000000, Obj: 0.341292, No Obj: 0.002892, Avg Recall: 0.859649, count: 114 -Region Avg IOU: 0.640273, Class: 1.000000, Obj: 0.419419, No Obj: 0.002769, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.605320, Class: 1.000000, Obj: 0.408550, No Obj: 0.002500, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.634026, Class: 1.000000, Obj: 0.395985, No Obj: 0.002879, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.627985, Class: 1.000000, Obj: 0.381801, No Obj: 0.003139, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.665583, Class: 1.000000, Obj: 0.392526, No Obj: 0.002933, Avg Recall: 0.840336, count: 119 -3931: 0.466370, 0.443896 avg, 0.001000 rate, 28.287949 seconds, 1006336 images -Loaded: 0.000119 seconds -Region Avg IOU: 0.625740, Class: 1.000000, Obj: 0.334809, No Obj: 0.002354, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.656718, Class: 1.000000, Obj: 0.347597, No Obj: 0.002582, Avg Recall: 0.834862, count: 109 -Region Avg IOU: 0.657764, Class: 1.000000, Obj: 0.371476, No Obj: 0.002671, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.644235, Class: 1.000000, Obj: 0.356156, No Obj: 0.002872, Avg Recall: 0.825758, count: 132 -Region Avg IOU: 0.651081, Class: 1.000000, Obj: 0.331509, No Obj: 0.002586, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.649962, Class: 1.000000, Obj: 0.368073, No Obj: 0.002486, Avg Recall: 0.824176, count: 91 -Region Avg IOU: 0.643925, Class: 1.000000, Obj: 0.382508, No Obj: 0.002409, Avg Recall: 0.790123, count: 81 -Region Avg IOU: 0.695070, Class: 1.000000, Obj: 0.421271, No Obj: 0.002997, Avg Recall: 0.853933, count: 89 -3932: 0.446483, 0.444155 avg, 0.001000 rate, 30.296936 seconds, 1006592 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.616810, Class: 1.000000, Obj: 0.409314, No Obj: 0.002203, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.667699, Class: 1.000000, Obj: 0.371864, No Obj: 0.002520, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.674353, Class: 1.000000, Obj: 0.372356, No Obj: 0.002693, Avg Recall: 0.885714, count: 105 -Region Avg IOU: 0.612884, Class: 1.000000, Obj: 0.273638, No Obj: 0.002815, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.629271, Class: 1.000000, Obj: 0.301776, No Obj: 0.002654, Avg Recall: 0.815603, count: 141 -Region Avg IOU: 0.635650, Class: 1.000000, Obj: 0.329631, No Obj: 0.002493, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.625357, Class: 1.000000, Obj: 0.356416, No Obj: 0.002261, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.627634, Class: 1.000000, Obj: 0.340076, No Obj: 0.002508, Avg Recall: 0.787234, count: 94 -3933: 0.505774, 0.450317 avg, 0.001000 rate, 28.906338 seconds, 1006848 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.618694, Class: 1.000000, Obj: 0.321480, No Obj: 0.002875, Avg Recall: 0.735484, count: 155 -Region Avg IOU: 0.640320, Class: 1.000000, Obj: 0.278968, No Obj: 0.002482, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.634683, Class: 1.000000, Obj: 0.354781, No Obj: 0.001899, Avg Recall: 0.767123, count: 73 -Region Avg IOU: 0.619016, Class: 1.000000, Obj: 0.315937, No Obj: 0.002452, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.648564, Class: 1.000000, Obj: 0.352431, No Obj: 0.002310, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.608691, Class: 1.000000, Obj: 0.296666, No Obj: 0.002398, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.654972, Class: 1.000000, Obj: 0.349740, No Obj: 0.002384, Avg Recall: 0.820755, count: 106 -Region Avg IOU: 0.678250, Class: 1.000000, Obj: 0.315947, No Obj: 0.002220, Avg Recall: 0.848485, count: 99 -3934: 0.498321, 0.455117 avg, 0.001000 rate, 28.914162 seconds, 1007104 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.644417, Class: 1.000000, Obj: 0.323782, No Obj: 0.002368, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.579175, Class: 1.000000, Obj: 0.327910, No Obj: 0.002357, Avg Recall: 0.691057, count: 123 -Region Avg IOU: 0.631702, Class: 1.000000, Obj: 0.338064, No Obj: 0.002428, Avg Recall: 0.717742, count: 124 -Region Avg IOU: 0.656573, Class: 1.000000, Obj: 0.326086, No Obj: 0.002398, Avg Recall: 0.798319, count: 119 -Region Avg IOU: 0.639279, Class: 1.000000, Obj: 0.338548, No Obj: 0.002364, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.615420, Class: 1.000000, Obj: 0.333868, No Obj: 0.002375, Avg Recall: 0.778761, count: 113 -Region Avg IOU: 0.617192, Class: 1.000000, Obj: 0.283636, No Obj: 0.002647, Avg Recall: 0.758065, count: 124 -Region Avg IOU: 0.625875, Class: 1.000000, Obj: 0.273195, No Obj: 0.002302, Avg Recall: 0.770642, count: 109 -3935: 0.551494, 0.464755 avg, 0.001000 rate, 29.932196 seconds, 1007360 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.658512, Class: 1.000000, Obj: 0.362857, No Obj: 0.002128, Avg Recall: 0.862745, count: 102 -Region Avg IOU: 0.634515, Class: 1.000000, Obj: 0.304586, No Obj: 0.002354, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.619229, Class: 1.000000, Obj: 0.325428, No Obj: 0.002323, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.641164, Class: 1.000000, Obj: 0.351794, No Obj: 0.002304, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.654147, Class: 1.000000, Obj: 0.310864, No Obj: 0.002354, Avg Recall: 0.820755, count: 106 -Region Avg IOU: 0.633578, Class: 1.000000, Obj: 0.335249, No Obj: 0.002265, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.645918, Class: 1.000000, Obj: 0.282385, No Obj: 0.002462, Avg Recall: 0.781250, count: 128 -Region Avg IOU: 0.659478, Class: 1.000000, Obj: 0.306884, No Obj: 0.002426, Avg Recall: 0.864583, count: 96 -3936: 0.457022, 0.463982 avg, 0.001000 rate, 29.202744 seconds, 1007616 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.635687, Class: 1.000000, Obj: 0.314783, No Obj: 0.002524, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.673293, Class: 1.000000, Obj: 0.326566, No Obj: 0.002806, Avg Recall: 0.838095, count: 105 -Region Avg IOU: 0.617845, Class: 1.000000, Obj: 0.339163, No Obj: 0.002146, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.592611, Class: 1.000000, Obj: 0.326274, No Obj: 0.002564, Avg Recall: 0.709677, count: 124 -Region Avg IOU: 0.611494, Class: 1.000000, Obj: 0.290326, No Obj: 0.002666, Avg Recall: 0.781022, count: 137 -Region Avg IOU: 0.714560, Class: 1.000000, Obj: 0.393408, No Obj: 0.002184, Avg Recall: 0.894737, count: 95 -Region Avg IOU: 0.635471, Class: 1.000000, Obj: 0.338521, No Obj: 0.002400, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.628319, Class: 1.000000, Obj: 0.284544, No Obj: 0.002631, Avg Recall: 0.833333, count: 120 -3937: 0.528705, 0.470454 avg, 0.001000 rate, 30.789303 seconds, 1007872 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.596414, Class: 1.000000, Obj: 0.317130, No Obj: 0.002383, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.648089, Class: 1.000000, Obj: 0.321703, No Obj: 0.002387, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.619953, Class: 1.000000, Obj: 0.324935, No Obj: 0.002439, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.640803, Class: 1.000000, Obj: 0.397029, No Obj: 0.002444, Avg Recall: 0.795455, count: 88 -Region Avg IOU: 0.631818, Class: 1.000000, Obj: 0.380239, No Obj: 0.002487, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.650169, Class: 1.000000, Obj: 0.381534, No Obj: 0.002383, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.667689, Class: 1.000000, Obj: 0.358342, No Obj: 0.002964, Avg Recall: 0.844961, count: 129 -Region Avg IOU: 0.661470, Class: 1.000000, Obj: 0.327848, No Obj: 0.002731, Avg Recall: 0.871560, count: 109 -3938: 0.504159, 0.473825 avg, 0.001000 rate, 29.248716 seconds, 1008128 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.587230, Class: 1.000000, Obj: 0.305026, No Obj: 0.002544, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.633678, Class: 1.000000, Obj: 0.387138, No Obj: 0.002915, Avg Recall: 0.795455, count: 132 -Region Avg IOU: 0.651953, Class: 1.000000, Obj: 0.381112, No Obj: 0.002570, Avg Recall: 0.802198, count: 91 -Region Avg IOU: 0.635860, Class: 1.000000, Obj: 0.374314, No Obj: 0.002850, Avg Recall: 0.787402, count: 127 -Region Avg IOU: 0.666005, Class: 1.000000, Obj: 0.374242, No Obj: 0.002597, Avg Recall: 0.860215, count: 93 -Region Avg IOU: 0.618005, Class: 1.000000, Obj: 0.419530, No Obj: 0.002382, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.626039, Class: 1.000000, Obj: 0.412916, No Obj: 0.002761, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.678590, Class: 1.000000, Obj: 0.410915, No Obj: 0.002707, Avg Recall: 0.854369, count: 103 -3939: 0.471221, 0.473564 avg, 0.001000 rate, 29.080854 seconds, 1008384 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.686316, Class: 1.000000, Obj: 0.427222, No Obj: 0.002680, Avg Recall: 0.897959, count: 98 -Region Avg IOU: 0.639346, Class: 1.000000, Obj: 0.338072, No Obj: 0.002874, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.656502, Class: 1.000000, Obj: 0.414256, No Obj: 0.002682, Avg Recall: 0.828829, count: 111 -Region Avg IOU: 0.637640, Class: 1.000000, Obj: 0.404614, No Obj: 0.002932, Avg Recall: 0.801653, count: 121 -Region Avg IOU: 0.601426, Class: 1.000000, Obj: 0.345065, No Obj: 0.002787, Avg Recall: 0.728682, count: 129 -Region Avg IOU: 0.664877, Class: 1.000000, Obj: 0.387873, No Obj: 0.002983, Avg Recall: 0.831683, count: 101 -Region Avg IOU: 0.652227, Class: 1.000000, Obj: 0.411756, No Obj: 0.003117, Avg Recall: 0.858407, count: 113 -Region Avg IOU: 0.633050, Class: 1.000000, Obj: 0.427701, No Obj: 0.002975, Avg Recall: 0.752137, count: 117 -3940: 0.462889, 0.472497 avg, 0.001000 rate, 28.696840 seconds, 1008640 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 10.700354 seconds -Region Avg IOU: 0.560713, Class: 1.000000, Obj: 0.368315, No Obj: 0.002904, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.571136, Class: 1.000000, Obj: 0.353798, No Obj: 0.002775, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.544338, Class: 1.000000, Obj: 0.330021, No Obj: 0.003358, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.579496, Class: 1.000000, Obj: 0.328553, No Obj: 0.003429, Avg Recall: 0.692308, count: 130 -Region Avg IOU: 0.588898, Class: 1.000000, Obj: 0.347453, No Obj: 0.003246, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.628624, Class: 1.000000, Obj: 0.356025, No Obj: 0.003096, Avg Recall: 0.793478, count: 92 -Region Avg IOU: 0.590625, Class: 1.000000, Obj: 0.411518, No Obj: 0.002912, Avg Recall: 0.662500, count: 80 -Region Avg IOU: 0.566592, Class: 1.000000, Obj: 0.330434, No Obj: 0.002809, Avg Recall: 0.620690, count: 87 -3941: 0.423932, 0.467640 avg, 0.001000 rate, 24.507971 seconds, 1008896 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.584264, Class: 1.000000, Obj: 0.418222, No Obj: 0.003222, Avg Recall: 0.678571, count: 84 -Region Avg IOU: 0.591871, Class: 1.000000, Obj: 0.369640, No Obj: 0.003565, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.570208, Class: 1.000000, Obj: 0.375622, No Obj: 0.003399, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.560805, Class: 1.000000, Obj: 0.385870, No Obj: 0.003476, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.586515, Class: 1.000000, Obj: 0.370863, No Obj: 0.003710, Avg Recall: 0.688525, count: 122 -Region Avg IOU: 0.536038, Class: 1.000000, Obj: 0.300899, No Obj: 0.003161, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.550030, Class: 1.000000, Obj: 0.372094, No Obj: 0.003554, Avg Recall: 0.610169, count: 118 -Region Avg IOU: 0.643000, Class: 1.000000, Obj: 0.382946, No Obj: 0.003746, Avg Recall: 0.773196, count: 97 -3942: 0.435076, 0.464384 avg, 0.001000 rate, 23.737398 seconds, 1009152 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.602626, Class: 1.000000, Obj: 0.396558, No Obj: 0.004275, Avg Recall: 0.711268, count: 142 -Region Avg IOU: 0.573127, Class: 1.000000, Obj: 0.388682, No Obj: 0.003603, Avg Recall: 0.714286, count: 126 -Region Avg IOU: 0.596075, Class: 1.000000, Obj: 0.353040, No Obj: 0.003479, Avg Recall: 0.700787, count: 127 -Region Avg IOU: 0.585082, Class: 1.000000, Obj: 0.377691, No Obj: 0.003740, Avg Recall: 0.697674, count: 129 -Region Avg IOU: 0.606064, Class: 1.000000, Obj: 0.444211, No Obj: 0.003465, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.589756, Class: 1.000000, Obj: 0.406415, No Obj: 0.003386, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.589938, Class: 1.000000, Obj: 0.413817, No Obj: 0.003623, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.532554, Class: 1.000000, Obj: 0.363719, No Obj: 0.003275, Avg Recall: 0.554455, count: 101 -3943: 0.492873, 0.467233 avg, 0.001000 rate, 24.609688 seconds, 1009408 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.567422, Class: 1.000000, Obj: 0.408890, No Obj: 0.004041, Avg Recall: 0.669173, count: 133 -Region Avg IOU: 0.552645, Class: 1.000000, Obj: 0.371840, No Obj: 0.003534, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.584239, Class: 1.000000, Obj: 0.412535, No Obj: 0.003790, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.551209, Class: 1.000000, Obj: 0.363826, No Obj: 0.003437, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.560022, Class: 1.000000, Obj: 0.360924, No Obj: 0.003170, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.622127, Class: 1.000000, Obj: 0.373049, No Obj: 0.003851, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.595590, Class: 1.000000, Obj: 0.361739, No Obj: 0.003245, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.557181, Class: 1.000000, Obj: 0.412094, No Obj: 0.004225, Avg Recall: 0.652893, count: 121 -3944: 0.467447, 0.467254 avg, 0.001000 rate, 23.754068 seconds, 1009664 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.558537, Class: 1.000000, Obj: 0.402868, No Obj: 0.003417, Avg Recall: 0.647059, count: 85 -Region Avg IOU: 0.581869, Class: 1.000000, Obj: 0.402779, No Obj: 0.004029, Avg Recall: 0.683761, count: 117 -Region Avg IOU: 0.564506, Class: 1.000000, Obj: 0.408198, No Obj: 0.003343, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.612765, Class: 1.000000, Obj: 0.401562, No Obj: 0.003582, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.572596, Class: 1.000000, Obj: 0.383301, No Obj: 0.003431, Avg Recall: 0.688312, count: 77 -Region Avg IOU: 0.613387, Class: 1.000000, Obj: 0.413512, No Obj: 0.003363, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.565706, Class: 1.000000, Obj: 0.384123, No Obj: 0.004583, Avg Recall: 0.679739, count: 153 -Region Avg IOU: 0.510914, Class: 1.000000, Obj: 0.344449, No Obj: 0.003252, Avg Recall: 0.613861, count: 101 -3945: 0.397804, 0.460309 avg, 0.001000 rate, 23.830572 seconds, 1009920 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.552697, Class: 1.000000, Obj: 0.358715, No Obj: 0.004153, Avg Recall: 0.625954, count: 131 -Region Avg IOU: 0.567438, Class: 1.000000, Obj: 0.384679, No Obj: 0.004071, Avg Recall: 0.669014, count: 142 -Region Avg IOU: 0.604044, Class: 1.000000, Obj: 0.417022, No Obj: 0.003828, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.579576, Class: 1.000000, Obj: 0.397444, No Obj: 0.003457, Avg Recall: 0.673684, count: 95 -Region Avg IOU: 0.557675, Class: 1.000000, Obj: 0.365994, No Obj: 0.004017, Avg Recall: 0.661290, count: 124 -Region Avg IOU: 0.571902, Class: 1.000000, Obj: 0.410458, No Obj: 0.003384, Avg Recall: 0.620690, count: 87 -Region Avg IOU: 0.600290, Class: 1.000000, Obj: 0.429330, No Obj: 0.003197, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.537915, Class: 1.000000, Obj: 0.362203, No Obj: 0.003570, Avg Recall: 0.581633, count: 98 -3946: 0.437366, 0.458015 avg, 0.001000 rate, 23.746208 seconds, 1010176 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.609106, Class: 1.000000, Obj: 0.421330, No Obj: 0.003893, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.548144, Class: 1.000000, Obj: 0.376242, No Obj: 0.003660, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.561065, Class: 1.000000, Obj: 0.376570, No Obj: 0.003587, Avg Recall: 0.642857, count: 126 -Region Avg IOU: 0.606966, Class: 1.000000, Obj: 0.341162, No Obj: 0.003935, Avg Recall: 0.747967, count: 123 -Region Avg IOU: 0.541705, Class: 1.000000, Obj: 0.391115, No Obj: 0.003339, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.592639, Class: 1.000000, Obj: 0.417416, No Obj: 0.003923, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.545298, Class: 1.000000, Obj: 0.348421, No Obj: 0.003741, Avg Recall: 0.619835, count: 121 -Region Avg IOU: 0.596419, Class: 1.000000, Obj: 0.392894, No Obj: 0.003879, Avg Recall: 0.724409, count: 127 -3947: 0.467594, 0.458973 avg, 0.001000 rate, 23.428892 seconds, 1010432 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.567724, Class: 1.000000, Obj: 0.374441, No Obj: 0.003488, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.571640, Class: 1.000000, Obj: 0.384748, No Obj: 0.003337, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.577592, Class: 1.000000, Obj: 0.412567, No Obj: 0.003274, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.598751, Class: 1.000000, Obj: 0.358733, No Obj: 0.003574, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.612045, Class: 1.000000, Obj: 0.371100, No Obj: 0.004373, Avg Recall: 0.755245, count: 143 -Region Avg IOU: 0.538363, Class: 1.000000, Obj: 0.357566, No Obj: 0.003216, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.563042, Class: 1.000000, Obj: 0.428585, No Obj: 0.003748, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.589162, Class: 1.000000, Obj: 0.367195, No Obj: 0.003803, Avg Recall: 0.694915, count: 118 -3948: 0.436557, 0.456731 avg, 0.001000 rate, 23.029724 seconds, 1010688 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.596784, Class: 1.000000, Obj: 0.377145, No Obj: 0.003363, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.547377, Class: 1.000000, Obj: 0.372819, No Obj: 0.003172, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.590823, Class: 1.000000, Obj: 0.477051, No Obj: 0.003410, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.613812, Class: 1.000000, Obj: 0.392819, No Obj: 0.003409, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.574204, Class: 1.000000, Obj: 0.358167, No Obj: 0.003589, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.577505, Class: 1.000000, Obj: 0.393208, No Obj: 0.003864, Avg Recall: 0.693548, count: 124 -Region Avg IOU: 0.604895, Class: 1.000000, Obj: 0.377293, No Obj: 0.003662, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.588427, Class: 1.000000, Obj: 0.358298, No Obj: 0.003707, Avg Recall: 0.694915, count: 118 -3949: 0.437178, 0.454776 avg, 0.001000 rate, 22.524376 seconds, 1010944 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.548356, Class: 1.000000, Obj: 0.334301, No Obj: 0.003815, Avg Recall: 0.643411, count: 129 -Region Avg IOU: 0.630779, Class: 1.000000, Obj: 0.409788, No Obj: 0.003830, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.558701, Class: 1.000000, Obj: 0.326993, No Obj: 0.003404, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.618390, Class: 1.000000, Obj: 0.381089, No Obj: 0.003094, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.501402, Class: 1.000000, Obj: 0.383029, No Obj: 0.003136, Avg Recall: 0.549451, count: 91 -Region Avg IOU: 0.560392, Class: 1.000000, Obj: 0.374546, No Obj: 0.003520, Avg Recall: 0.658537, count: 123 -Region Avg IOU: 0.563179, Class: 1.000000, Obj: 0.360320, No Obj: 0.003896, Avg Recall: 0.631206, count: 141 -Region Avg IOU: 0.588692, Class: 1.000000, Obj: 0.386947, No Obj: 0.003271, Avg Recall: 0.745455, count: 110 -3950: 0.439708, 0.453269 avg, 0.001000 rate, 23.291107 seconds, 1011200 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000097 seconds -Region Avg IOU: 0.670861, Class: 1.000000, Obj: 0.420424, No Obj: 0.003083, Avg Recall: 0.828571, count: 105 -Region Avg IOU: 0.592016, Class: 1.000000, Obj: 0.390370, No Obj: 0.003506, Avg Recall: 0.703448, count: 145 -Region Avg IOU: 0.622811, Class: 1.000000, Obj: 0.430938, No Obj: 0.003306, Avg Recall: 0.788136, count: 118 -Region Avg IOU: 0.658536, Class: 1.000000, Obj: 0.437528, No Obj: 0.002902, Avg Recall: 0.829545, count: 88 -Region Avg IOU: 0.633783, Class: 1.000000, Obj: 0.405217, No Obj: 0.003015, Avg Recall: 0.804348, count: 92 -Region Avg IOU: 0.656639, Class: 1.000000, Obj: 0.386832, No Obj: 0.003302, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.669375, Class: 1.000000, Obj: 0.379111, No Obj: 0.003476, Avg Recall: 0.816667, count: 120 -Region Avg IOU: 0.627774, Class: 1.000000, Obj: 0.411885, No Obj: 0.003014, Avg Recall: 0.762376, count: 101 -3951: 0.443738, 0.452316 avg, 0.001000 rate, 27.099178 seconds, 1011456 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.654653, Class: 1.000000, Obj: 0.404144, No Obj: 0.003054, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.631045, Class: 1.000000, Obj: 0.401891, No Obj: 0.003173, Avg Recall: 0.769231, count: 117 -Region Avg IOU: 0.597431, Class: 1.000000, Obj: 0.412252, No Obj: 0.002966, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.616193, Class: 1.000000, Obj: 0.402361, No Obj: 0.002853, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.641917, Class: 1.000000, Obj: 0.417504, No Obj: 0.003277, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.614731, Class: 1.000000, Obj: 0.389455, No Obj: 0.002947, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.633843, Class: 1.000000, Obj: 0.369387, No Obj: 0.003200, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.638527, Class: 1.000000, Obj: 0.420039, No Obj: 0.003298, Avg Recall: 0.798319, count: 119 -3952: 0.428508, 0.449935 avg, 0.001000 rate, 27.780842 seconds, 1011712 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.633649, Class: 1.000000, Obj: 0.377667, No Obj: 0.002699, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.603622, Class: 1.000000, Obj: 0.350248, No Obj: 0.002804, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.624721, Class: 1.000000, Obj: 0.372444, No Obj: 0.003171, Avg Recall: 0.731343, count: 134 -Region Avg IOU: 0.603623, Class: 1.000000, Obj: 0.371074, No Obj: 0.003120, Avg Recall: 0.688889, count: 135 -Region Avg IOU: 0.662286, Class: 1.000000, Obj: 0.394537, No Obj: 0.003389, Avg Recall: 0.834587, count: 133 -Region Avg IOU: 0.680908, Class: 1.000000, Obj: 0.430802, No Obj: 0.002737, Avg Recall: 0.850575, count: 87 -Region Avg IOU: 0.638094, Class: 1.000000, Obj: 0.354771, No Obj: 0.003019, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.612711, Class: 1.000000, Obj: 0.382649, No Obj: 0.002709, Avg Recall: 0.707865, count: 89 -3953: 0.488332, 0.453775 avg, 0.001000 rate, 27.661640 seconds, 1011968 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.610843, Class: 1.000000, Obj: 0.379242, No Obj: 0.002950, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.650398, Class: 1.000000, Obj: 0.412211, No Obj: 0.002859, Avg Recall: 0.841463, count: 82 -Region Avg IOU: 0.640646, Class: 1.000000, Obj: 0.349942, No Obj: 0.003121, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.638382, Class: 1.000000, Obj: 0.374595, No Obj: 0.002823, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.629168, Class: 1.000000, Obj: 0.398554, No Obj: 0.002768, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.593957, Class: 1.000000, Obj: 0.363232, No Obj: 0.003071, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.616868, Class: 1.000000, Obj: 0.393112, No Obj: 0.003215, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.630440, Class: 1.000000, Obj: 0.355277, No Obj: 0.002479, Avg Recall: 0.735294, count: 102 -3954: 0.438304, 0.452228 avg, 0.001000 rate, 26.744207 seconds, 1012224 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.632622, Class: 1.000000, Obj: 0.351134, No Obj: 0.002894, Avg Recall: 0.762712, count: 118 -Region Avg IOU: 0.658863, Class: 1.000000, Obj: 0.344666, No Obj: 0.002981, Avg Recall: 0.818182, count: 121 -Region Avg IOU: 0.622096, Class: 1.000000, Obj: 0.334216, No Obj: 0.003272, Avg Recall: 0.754717, count: 159 -Region Avg IOU: 0.644378, Class: 1.000000, Obj: 0.360339, No Obj: 0.003297, Avg Recall: 0.831933, count: 119 -Region Avg IOU: 0.643473, Class: 1.000000, Obj: 0.376555, No Obj: 0.002671, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.600311, Class: 1.000000, Obj: 0.333765, No Obj: 0.002914, Avg Recall: 0.738462, count: 130 -Region Avg IOU: 0.667082, Class: 1.000000, Obj: 0.342818, No Obj: 0.003034, Avg Recall: 0.852632, count: 95 -Region Avg IOU: 0.609976, Class: 1.000000, Obj: 0.358877, No Obj: 0.002786, Avg Recall: 0.774510, count: 102 -3955: 0.496424, 0.456647 avg, 0.001000 rate, 27.124538 seconds, 1012480 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.630571, Class: 1.000000, Obj: 0.375139, No Obj: 0.002715, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.658732, Class: 1.000000, Obj: 0.369595, No Obj: 0.002685, Avg Recall: 0.837607, count: 117 -Region Avg IOU: 0.607280, Class: 1.000000, Obj: 0.361951, No Obj: 0.002854, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.570548, Class: 1.000000, Obj: 0.328793, No Obj: 0.002469, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.657127, Class: 1.000000, Obj: 0.386591, No Obj: 0.002540, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.623075, Class: 1.000000, Obj: 0.368693, No Obj: 0.002956, Avg Recall: 0.805970, count: 134 -Region Avg IOU: 0.595555, Class: 1.000000, Obj: 0.348381, No Obj: 0.002659, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.665013, Class: 1.000000, Obj: 0.378222, No Obj: 0.002657, Avg Recall: 0.882353, count: 102 -3956: 0.441941, 0.455177 avg, 0.001000 rate, 27.123432 seconds, 1012736 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.628426, Class: 1.000000, Obj: 0.355430, No Obj: 0.002947, Avg Recall: 0.738095, count: 126 -Region Avg IOU: 0.655420, Class: 1.000000, Obj: 0.313487, No Obj: 0.002779, Avg Recall: 0.827586, count: 116 -Region Avg IOU: 0.666451, Class: 1.000000, Obj: 0.390504, No Obj: 0.002820, Avg Recall: 0.862069, count: 116 -Region Avg IOU: 0.673863, Class: 1.000000, Obj: 0.428501, No Obj: 0.002740, Avg Recall: 0.843373, count: 83 -Region Avg IOU: 0.633878, Class: 1.000000, Obj: 0.359965, No Obj: 0.002590, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.617316, Class: 1.000000, Obj: 0.372002, No Obj: 0.002323, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.661122, Class: 1.000000, Obj: 0.364199, No Obj: 0.002513, Avg Recall: 0.793103, count: 87 -Region Avg IOU: 0.665438, Class: 1.000000, Obj: 0.366069, No Obj: 0.002876, Avg Recall: 0.828283, count: 99 -3957: 0.419980, 0.451657 avg, 0.001000 rate, 27.175779 seconds, 1012992 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.583378, Class: 1.000000, Obj: 0.304372, No Obj: 0.002989, Avg Recall: 0.727273, count: 143 -Region Avg IOU: 0.730468, Class: 1.000000, Obj: 0.450336, No Obj: 0.002659, Avg Recall: 0.884615, count: 78 -Region Avg IOU: 0.608423, Class: 1.000000, Obj: 0.349733, No Obj: 0.002867, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.601552, Class: 1.000000, Obj: 0.368714, No Obj: 0.002830, Avg Recall: 0.721804, count: 133 -Region Avg IOU: 0.656431, Class: 1.000000, Obj: 0.368876, No Obj: 0.002673, Avg Recall: 0.859813, count: 107 -Region Avg IOU: 0.662108, Class: 1.000000, Obj: 0.372806, No Obj: 0.002871, Avg Recall: 0.825000, count: 120 -Region Avg IOU: 0.603579, Class: 1.000000, Obj: 0.377375, No Obj: 0.002721, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.613498, Class: 1.000000, Obj: 0.373853, No Obj: 0.002811, Avg Recall: 0.704000, count: 125 -3958: 0.480638, 0.454555 avg, 0.001000 rate, 28.020201 seconds, 1013248 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.647186, Class: 1.000000, Obj: 0.339136, No Obj: 0.002820, Avg Recall: 0.804878, count: 123 -Region Avg IOU: 0.654205, Class: 1.000000, Obj: 0.389905, No Obj: 0.003150, Avg Recall: 0.826087, count: 115 -Region Avg IOU: 0.626356, Class: 1.000000, Obj: 0.385231, No Obj: 0.002457, Avg Recall: 0.790123, count: 81 -Region Avg IOU: 0.596290, Class: 1.000000, Obj: 0.319184, No Obj: 0.002848, Avg Recall: 0.733333, count: 135 -Region Avg IOU: 0.674255, Class: 1.000000, Obj: 0.409588, No Obj: 0.002386, Avg Recall: 0.835443, count: 79 -Region Avg IOU: 0.607007, Class: 1.000000, Obj: 0.395471, No Obj: 0.002230, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.629933, Class: 1.000000, Obj: 0.366581, No Obj: 0.002501, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.612932, Class: 1.000000, Obj: 0.376153, No Obj: 0.002312, Avg Recall: 0.788889, count: 90 -3959: 0.434579, 0.452558 avg, 0.001000 rate, 26.161684 seconds, 1013504 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.625222, Class: 1.000000, Obj: 0.309631, No Obj: 0.002662, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.599536, Class: 1.000000, Obj: 0.379443, No Obj: 0.002728, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.655636, Class: 1.000000, Obj: 0.366986, No Obj: 0.002695, Avg Recall: 0.744186, count: 86 -Region Avg IOU: 0.624458, Class: 1.000000, Obj: 0.384484, No Obj: 0.002920, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.588750, Class: 1.000000, Obj: 0.326393, No Obj: 0.002882, Avg Recall: 0.682927, count: 123 -Region Avg IOU: 0.607723, Class: 1.000000, Obj: 0.379220, No Obj: 0.002784, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.644493, Class: 1.000000, Obj: 0.351899, No Obj: 0.002735, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.630511, Class: 1.000000, Obj: 0.365558, No Obj: 0.002588, Avg Recall: 0.747573, count: 103 -3960: 0.431231, 0.450425 avg, 0.001000 rate, 26.615936 seconds, 1013760 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.349350 seconds -Region Avg IOU: 0.481934, Class: 1.000000, Obj: 0.264421, No Obj: 0.003179, Avg Recall: 0.500000, count: 94 -Region Avg IOU: 0.542844, Class: 1.000000, Obj: 0.330621, No Obj: 0.003833, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.577985, Class: 1.000000, Obj: 0.321739, No Obj: 0.003553, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.494140, Class: 1.000000, Obj: 0.251511, No Obj: 0.003510, Avg Recall: 0.527132, count: 129 -Region Avg IOU: 0.561867, Class: 1.000000, Obj: 0.348970, No Obj: 0.003251, Avg Recall: 0.682927, count: 82 -Region Avg IOU: 0.535201, Class: 1.000000, Obj: 0.303888, No Obj: 0.003628, Avg Recall: 0.626374, count: 91 -Region Avg IOU: 0.533367, Class: 1.000000, Obj: 0.287080, No Obj: 0.003621, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.525701, Class: 1.000000, Obj: 0.282146, No Obj: 0.004115, Avg Recall: 0.593750, count: 128 -3961: 0.446643, 0.450047 avg, 0.001000 rate, 19.576172 seconds, 1014016 images -Loaded: 2.466636 seconds -Region Avg IOU: 0.529686, Class: 1.000000, Obj: 0.328362, No Obj: 0.003566, Avg Recall: 0.626374, count: 91 -Region Avg IOU: 0.522996, Class: 1.000000, Obj: 0.317536, No Obj: 0.003253, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.571120, Class: 1.000000, Obj: 0.314411, No Obj: 0.003196, Avg Recall: 0.694118, count: 85 -Region Avg IOU: 0.500713, Class: 1.000000, Obj: 0.295423, No Obj: 0.003840, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.533076, Class: 1.000000, Obj: 0.295236, No Obj: 0.004141, Avg Recall: 0.631148, count: 122 -Region Avg IOU: 0.558332, Class: 1.000000, Obj: 0.320711, No Obj: 0.003530, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.495286, Class: 1.000000, Obj: 0.294092, No Obj: 0.003364, Avg Recall: 0.549020, count: 102 -Region Avg IOU: 0.544441, Class: 1.000000, Obj: 0.334208, No Obj: 0.003174, Avg Recall: 0.602410, count: 83 -3962: 0.406795, 0.445722 avg, 0.001000 rate, 19.762873 seconds, 1014272 images -Loaded: 0.475136 seconds -Region Avg IOU: 0.563996, Class: 1.000000, Obj: 0.354265, No Obj: 0.003506, Avg Recall: 0.697368, count: 76 -Region Avg IOU: 0.535636, Class: 1.000000, Obj: 0.326465, No Obj: 0.004231, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.502895, Class: 1.000000, Obj: 0.285554, No Obj: 0.003368, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.543711, Class: 1.000000, Obj: 0.330362, No Obj: 0.003954, Avg Recall: 0.620690, count: 87 -Region Avg IOU: 0.501764, Class: 1.000000, Obj: 0.320174, No Obj: 0.004036, Avg Recall: 0.574468, count: 94 -Region Avg IOU: 0.472182, Class: 1.000000, Obj: 0.322579, No Obj: 0.003685, Avg Recall: 0.491525, count: 118 -Region Avg IOU: 0.543624, Class: 1.000000, Obj: 0.292178, No Obj: 0.003527, Avg Recall: 0.653846, count: 78 -Region Avg IOU: 0.514719, Class: 1.000000, Obj: 0.323708, No Obj: 0.004252, Avg Recall: 0.573529, count: 136 -3963: 0.399292, 0.441079 avg, 0.001000 rate, 21.820736 seconds, 1014528 images -Loaded: 0.020194 seconds -Region Avg IOU: 0.525659, Class: 1.000000, Obj: 0.349903, No Obj: 0.003903, Avg Recall: 0.547368, count: 95 -Region Avg IOU: 0.450942, Class: 1.000000, Obj: 0.310382, No Obj: 0.003735, Avg Recall: 0.469388, count: 98 -Region Avg IOU: 0.537553, Class: 1.000000, Obj: 0.325231, No Obj: 0.005151, Avg Recall: 0.595420, count: 131 -Region Avg IOU: 0.566536, Class: 1.000000, Obj: 0.358412, No Obj: 0.003907, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.531610, Class: 1.000000, Obj: 0.366482, No Obj: 0.003824, Avg Recall: 0.582418, count: 91 -Region Avg IOU: 0.560133, Class: 1.000000, Obj: 0.386909, No Obj: 0.004251, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.513799, Class: 1.000000, Obj: 0.316983, No Obj: 0.004089, Avg Recall: 0.584746, count: 118 -Region Avg IOU: 0.549171, Class: 1.000000, Obj: 0.333176, No Obj: 0.004576, Avg Recall: 0.637168, count: 113 -3964: 0.426248, 0.439595 avg, 0.001000 rate, 19.011753 seconds, 1014784 images -Loaded: 1.971619 seconds -Region Avg IOU: 0.584941, Class: 1.000000, Obj: 0.391950, No Obj: 0.003649, Avg Recall: 0.704225, count: 71 -Region Avg IOU: 0.569191, Class: 1.000000, Obj: 0.373282, No Obj: 0.003927, Avg Recall: 0.662500, count: 80 -Region Avg IOU: 0.522587, Class: 1.000000, Obj: 0.356697, No Obj: 0.004264, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.497934, Class: 1.000000, Obj: 0.344102, No Obj: 0.003923, Avg Recall: 0.571429, count: 91 -Region Avg IOU: 0.513208, Class: 1.000000, Obj: 0.328554, No Obj: 0.004602, Avg Recall: 0.562963, count: 135 -Region Avg IOU: 0.461309, Class: 1.000000, Obj: 0.354434, No Obj: 0.003675, Avg Recall: 0.493333, count: 75 -Region Avg IOU: 0.510457, Class: 1.000000, Obj: 0.371143, No Obj: 0.003505, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.497985, Class: 1.000000, Obj: 0.375988, No Obj: 0.003911, Avg Recall: 0.515464, count: 97 -3965: 0.489551, 0.444591 avg, 0.001000 rate, 20.577984 seconds, 1015040 images -Loaded: 1.461446 seconds -Region Avg IOU: 0.579234, Class: 1.000000, Obj: 0.386549, No Obj: 0.004645, Avg Recall: 0.693182, count: 88 -Region Avg IOU: 0.507473, Class: 1.000000, Obj: 0.397694, No Obj: 0.003694, Avg Recall: 0.565789, count: 76 -Region Avg IOU: 0.543695, Class: 1.000000, Obj: 0.392148, No Obj: 0.004609, Avg Recall: 0.650000, count: 120 -Region Avg IOU: 0.553950, Class: 1.000000, Obj: 0.364515, No Obj: 0.004412, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.589200, Class: 1.000000, Obj: 0.421869, No Obj: 0.004499, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.530212, Class: 1.000000, Obj: 0.384670, No Obj: 0.004352, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.472073, Class: 1.000000, Obj: 0.388588, No Obj: 0.004238, Avg Recall: 0.482143, count: 112 -Region Avg IOU: 0.486425, Class: 1.000000, Obj: 0.308900, No Obj: 0.004265, Avg Recall: 0.589286, count: 112 -3966: 0.357002, 0.435832 avg, 0.001000 rate, 21.504156 seconds, 1015296 images -Loaded: 0.921461 seconds -Region Avg IOU: 0.581722, Class: 1.000000, Obj: 0.380559, No Obj: 0.004373, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.466314, Class: 1.000000, Obj: 0.330199, No Obj: 0.004590, Avg Recall: 0.530973, count: 113 -Region Avg IOU: 0.471999, Class: 1.000000, Obj: 0.344357, No Obj: 0.004697, Avg Recall: 0.521368, count: 117 -Region Avg IOU: 0.529831, Class: 1.000000, Obj: 0.326140, No Obj: 0.005253, Avg Recall: 0.617886, count: 123 -Region Avg IOU: 0.473986, Class: 1.000000, Obj: 0.361372, No Obj: 0.004224, Avg Recall: 0.478633, count: 117 -Region Avg IOU: 0.548171, Class: 1.000000, Obj: 0.423617, No Obj: 0.003992, Avg Recall: 0.666667, count: 72 -Region Avg IOU: 0.523903, Class: 1.000000, Obj: 0.407929, No Obj: 0.004423, Avg Recall: 0.560440, count: 91 -Region Avg IOU: 0.453266, Class: 1.000000, Obj: 0.360758, No Obj: 0.004212, Avg Recall: 0.428571, count: 133 -3967: 0.453542, 0.437603 avg, 0.001000 rate, 21.090467 seconds, 1015552 images -Loaded: 0.793338 seconds -Region Avg IOU: 0.507214, Class: 1.000000, Obj: 0.358325, No Obj: 0.004550, Avg Recall: 0.577586, count: 116 -Region Avg IOU: 0.479084, Class: 1.000000, Obj: 0.333970, No Obj: 0.005327, Avg Recall: 0.546154, count: 130 -Region Avg IOU: 0.515438, Class: 1.000000, Obj: 0.338562, No Obj: 0.004973, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.517669, Class: 1.000000, Obj: 0.389888, No Obj: 0.005279, Avg Recall: 0.573643, count: 129 -Region Avg IOU: 0.496880, Class: 1.000000, Obj: 0.359087, No Obj: 0.003840, Avg Recall: 0.525641, count: 78 -Region Avg IOU: 0.544263, Class: 1.000000, Obj: 0.383238, No Obj: 0.005669, Avg Recall: 0.641221, count: 131 -Region Avg IOU: 0.494925, Class: 1.000000, Obj: 0.379833, No Obj: 0.004468, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.514004, Class: 1.000000, Obj: 0.371510, No Obj: 0.005078, Avg Recall: 0.567797, count: 118 -3968: 0.466390, 0.440482 avg, 0.001000 rate, 20.853939 seconds, 1015808 images -Loaded: 1.587109 seconds -Region Avg IOU: 0.527781, Class: 1.000000, Obj: 0.411450, No Obj: 0.004305, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.543077, Class: 1.000000, Obj: 0.371099, No Obj: 0.005001, Avg Recall: 0.632000, count: 125 -Region Avg IOU: 0.526079, Class: 1.000000, Obj: 0.338282, No Obj: 0.004635, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.468074, Class: 1.000000, Obj: 0.379288, No Obj: 0.004204, Avg Recall: 0.524272, count: 103 -Region Avg IOU: 0.510385, Class: 1.000000, Obj: 0.364153, No Obj: 0.004369, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.548064, Class: 1.000000, Obj: 0.388135, No Obj: 0.004748, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.515991, Class: 1.000000, Obj: 0.392074, No Obj: 0.004752, Avg Recall: 0.592920, count: 113 -Region Avg IOU: 0.538646, Class: 1.000000, Obj: 0.391331, No Obj: 0.005938, Avg Recall: 0.605442, count: 147 -3969: 0.415530, 0.437987 avg, 0.001000 rate, 17.909346 seconds, 1016064 images -Loaded: 3.750346 seconds -Region Avg IOU: 0.465042, Class: 1.000000, Obj: 0.362733, No Obj: 0.003791, Avg Recall: 0.520661, count: 121 -Region Avg IOU: 0.526847, Class: 1.000000, Obj: 0.337784, No Obj: 0.004589, Avg Recall: 0.617886, count: 123 -Region Avg IOU: 0.483098, Class: 1.000000, Obj: 0.320264, No Obj: 0.004665, Avg Recall: 0.576923, count: 130 -Region Avg IOU: 0.487079, Class: 1.000000, Obj: 0.340191, No Obj: 0.004788, Avg Recall: 0.541985, count: 131 -Region Avg IOU: 0.554066, Class: 1.000000, Obj: 0.374299, No Obj: 0.004653, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.468612, Class: 1.000000, Obj: 0.327973, No Obj: 0.004793, Avg Recall: 0.483051, count: 118 -Region Avg IOU: 0.546129, Class: 1.000000, Obj: 0.403469, No Obj: 0.004190, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.483102, Class: 1.000000, Obj: 0.371364, No Obj: 0.004408, Avg Recall: 0.508333, count: 120 -3970: 0.472708, 0.441459 avg, 0.001000 rate, 22.633841 seconds, 1016320 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000083 seconds -Region Avg IOU: 0.595077, Class: 1.000000, Obj: 0.391801, No Obj: 0.004473, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.639201, Class: 1.000000, Obj: 0.427630, No Obj: 0.004253, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.611418, Class: 1.000000, Obj: 0.398995, No Obj: 0.005121, Avg Recall: 0.740000, count: 150 -Region Avg IOU: 0.610030, Class: 1.000000, Obj: 0.435225, No Obj: 0.003510, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.613254, Class: 1.000000, Obj: 0.461685, No Obj: 0.004262, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.601083, Class: 1.000000, Obj: 0.452106, No Obj: 0.004585, Avg Recall: 0.685484, count: 124 -Region Avg IOU: 0.548229, Class: 1.000000, Obj: 0.397714, No Obj: 0.003914, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.620680, Class: 1.000000, Obj: 0.464459, No Obj: 0.003611, Avg Recall: 0.733333, count: 90 -3971: 0.455352, 0.442848 avg, 0.001000 rate, 23.740364 seconds, 1016576 images -Loaded: 0.000144 seconds -Region Avg IOU: 0.603403, Class: 1.000000, Obj: 0.446549, No Obj: 0.003467, Avg Recall: 0.743902, count: 82 -Region Avg IOU: 0.597351, Class: 1.000000, Obj: 0.417455, No Obj: 0.004075, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.580207, Class: 1.000000, Obj: 0.390074, No Obj: 0.003541, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.553083, Class: 1.000000, Obj: 0.354354, No Obj: 0.003377, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.547078, Class: 1.000000, Obj: 0.366456, No Obj: 0.003474, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.548604, Class: 1.000000, Obj: 0.390819, No Obj: 0.003608, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.540024, Class: 1.000000, Obj: 0.410126, No Obj: 0.003388, Avg Recall: 0.647541, count: 122 -Region Avg IOU: 0.585396, Class: 1.000000, Obj: 0.392577, No Obj: 0.004007, Avg Recall: 0.723810, count: 105 -3972: 0.419195, 0.440483 avg, 0.001000 rate, 24.683119 seconds, 1016832 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.618004, Class: 1.000000, Obj: 0.404087, No Obj: 0.004228, Avg Recall: 0.740157, count: 127 -Region Avg IOU: 0.540991, Class: 1.000000, Obj: 0.328317, No Obj: 0.003596, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.587564, Class: 1.000000, Obj: 0.412445, No Obj: 0.003725, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.619422, Class: 1.000000, Obj: 0.409508, No Obj: 0.002909, Avg Recall: 0.717647, count: 85 -Region Avg IOU: 0.602212, Class: 1.000000, Obj: 0.371629, No Obj: 0.003698, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.589946, Class: 1.000000, Obj: 0.398036, No Obj: 0.002716, Avg Recall: 0.732394, count: 71 -Region Avg IOU: 0.605260, Class: 1.000000, Obj: 0.421683, No Obj: 0.003657, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.613700, Class: 1.000000, Obj: 0.366167, No Obj: 0.003847, Avg Recall: 0.766129, count: 124 -3973: 0.420369, 0.438471 avg, 0.001000 rate, 25.050991 seconds, 1017088 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.534851, Class: 1.000000, Obj: 0.319882, No Obj: 0.003590, Avg Recall: 0.601770, count: 113 -Region Avg IOU: 0.609277, Class: 1.000000, Obj: 0.338529, No Obj: 0.002659, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.582216, Class: 1.000000, Obj: 0.385577, No Obj: 0.002643, Avg Recall: 0.682927, count: 82 -Region Avg IOU: 0.526266, Class: 1.000000, Obj: 0.317879, No Obj: 0.002976, Avg Recall: 0.615385, count: 91 -Region Avg IOU: 0.609786, Class: 1.000000, Obj: 0.397810, No Obj: 0.003297, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.579755, Class: 1.000000, Obj: 0.347578, No Obj: 0.003375, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.665571, Class: 1.000000, Obj: 0.366623, No Obj: 0.003506, Avg Recall: 0.846154, count: 104 -Region Avg IOU: 0.642549, Class: 1.000000, Obj: 0.372683, No Obj: 0.003337, Avg Recall: 0.786408, count: 103 -3974: 0.412876, 0.435912 avg, 0.001000 rate, 25.457745 seconds, 1017344 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.611625, Class: 1.000000, Obj: 0.376015, No Obj: 0.002912, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.634089, Class: 1.000000, Obj: 0.390859, No Obj: 0.003122, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.554278, Class: 1.000000, Obj: 0.353103, No Obj: 0.003207, Avg Recall: 0.630252, count: 119 -Region Avg IOU: 0.584174, Class: 1.000000, Obj: 0.318085, No Obj: 0.003031, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.552326, Class: 1.000000, Obj: 0.342685, No Obj: 0.002934, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.552193, Class: 1.000000, Obj: 0.320167, No Obj: 0.003087, Avg Recall: 0.632184, count: 87 -Region Avg IOU: 0.631336, Class: 1.000000, Obj: 0.350866, No Obj: 0.003169, Avg Recall: 0.825243, count: 103 -Region Avg IOU: 0.530248, Class: 1.000000, Obj: 0.329841, No Obj: 0.002748, Avg Recall: 0.603448, count: 116 -3975: 0.445401, 0.436861 avg, 0.001000 rate, 24.661261 seconds, 1017600 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.600822, Class: 1.000000, Obj: 0.351314, No Obj: 0.002877, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.638572, Class: 1.000000, Obj: 0.366500, No Obj: 0.002727, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.566915, Class: 1.000000, Obj: 0.308848, No Obj: 0.002937, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.610047, Class: 1.000000, Obj: 0.325903, No Obj: 0.002577, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.615789, Class: 1.000000, Obj: 0.348753, No Obj: 0.002748, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.624718, Class: 1.000000, Obj: 0.360924, No Obj: 0.003144, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.599567, Class: 1.000000, Obj: 0.343594, No Obj: 0.003137, Avg Recall: 0.688000, count: 125 -Region Avg IOU: 0.579812, Class: 1.000000, Obj: 0.338640, No Obj: 0.002312, Avg Recall: 0.602410, count: 83 -3976: 0.425498, 0.435724 avg, 0.001000 rate, 24.649839 seconds, 1017856 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.599524, Class: 1.000000, Obj: 0.395838, No Obj: 0.002611, Avg Recall: 0.716216, count: 74 -Region Avg IOU: 0.580929, Class: 1.000000, Obj: 0.340434, No Obj: 0.002784, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.562895, Class: 1.000000, Obj: 0.334234, No Obj: 0.002719, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.598443, Class: 1.000000, Obj: 0.322440, No Obj: 0.002887, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.559866, Class: 1.000000, Obj: 0.300251, No Obj: 0.002543, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.553646, Class: 1.000000, Obj: 0.325243, No Obj: 0.002959, Avg Recall: 0.642276, count: 123 -Region Avg IOU: 0.616852, Class: 1.000000, Obj: 0.312159, No Obj: 0.003145, Avg Recall: 0.779661, count: 118 -Region Avg IOU: 0.599452, Class: 1.000000, Obj: 0.347021, No Obj: 0.002777, Avg Recall: 0.686046, count: 86 -3977: 0.416936, 0.433846 avg, 0.001000 rate, 25.007500 seconds, 1018112 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.634982, Class: 1.000000, Obj: 0.355921, No Obj: 0.002999, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.636293, Class: 1.000000, Obj: 0.374750, No Obj: 0.002882, Avg Recall: 0.771186, count: 118 -Region Avg IOU: 0.571589, Class: 1.000000, Obj: 0.353700, No Obj: 0.003088, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.598060, Class: 1.000000, Obj: 0.346630, No Obj: 0.002767, Avg Recall: 0.746988, count: 83 -Region Avg IOU: 0.599157, Class: 1.000000, Obj: 0.336445, No Obj: 0.003057, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.642023, Class: 1.000000, Obj: 0.385563, No Obj: 0.002721, Avg Recall: 0.777778, count: 90 -Region Avg IOU: 0.616984, Class: 1.000000, Obj: 0.346241, No Obj: 0.002899, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.564468, Class: 1.000000, Obj: 0.297553, No Obj: 0.002703, Avg Recall: 0.659574, count: 94 -3978: 0.463431, 0.436804 avg, 0.001000 rate, 24.577940 seconds, 1018368 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.596418, Class: 1.000000, Obj: 0.344718, No Obj: 0.003071, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.603682, Class: 1.000000, Obj: 0.336647, No Obj: 0.003102, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.672001, Class: 1.000000, Obj: 0.339180, No Obj: 0.003356, Avg Recall: 0.820000, count: 100 -Region Avg IOU: 0.582619, Class: 1.000000, Obj: 0.363604, No Obj: 0.002918, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.559988, Class: 1.000000, Obj: 0.325637, No Obj: 0.003228, Avg Recall: 0.651316, count: 152 -Region Avg IOU: 0.603027, Class: 1.000000, Obj: 0.369388, No Obj: 0.003204, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.605788, Class: 1.000000, Obj: 0.391380, No Obj: 0.002928, Avg Recall: 0.729508, count: 122 -Region Avg IOU: 0.590795, Class: 1.000000, Obj: 0.323075, No Obj: 0.003067, Avg Recall: 0.713115, count: 122 -3979: 0.511910, 0.444315 avg, 0.001000 rate, 25.305555 seconds, 1018624 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.616474, Class: 1.000000, Obj: 0.343541, No Obj: 0.003688, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.564723, Class: 1.000000, Obj: 0.365116, No Obj: 0.003395, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.582292, Class: 1.000000, Obj: 0.376190, No Obj: 0.003163, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.576567, Class: 1.000000, Obj: 0.358084, No Obj: 0.003369, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.624302, Class: 1.000000, Obj: 0.405480, No Obj: 0.003266, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.648143, Class: 1.000000, Obj: 0.399458, No Obj: 0.003286, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.616918, Class: 1.000000, Obj: 0.361204, No Obj: 0.003093, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.613273, Class: 1.000000, Obj: 0.353802, No Obj: 0.003145, Avg Recall: 0.734043, count: 94 -3980: 0.459432, 0.445827 avg, 0.001000 rate, 25.733145 seconds, 1018880 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 13.106303 seconds -Region Avg IOU: 0.578900, Class: 1.000000, Obj: 0.340334, No Obj: 0.003843, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.552612, Class: 1.000000, Obj: 0.357789, No Obj: 0.003428, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.579413, Class: 1.000000, Obj: 0.374275, No Obj: 0.003430, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.556091, Class: 1.000000, Obj: 0.334043, No Obj: 0.003485, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.578998, Class: 1.000000, Obj: 0.419583, No Obj: 0.003448, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.574392, Class: 1.000000, Obj: 0.349646, No Obj: 0.003476, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.601071, Class: 1.000000, Obj: 0.372545, No Obj: 0.003352, Avg Recall: 0.726191, count: 84 -Region Avg IOU: 0.600535, Class: 1.000000, Obj: 0.331034, No Obj: 0.003621, Avg Recall: 0.693069, count: 101 -3981: 0.419695, 0.443213 avg, 0.001000 rate, 22.798864 seconds, 1019136 images -Loaded: 0.000128 seconds -Region Avg IOU: 0.592961, Class: 1.000000, Obj: 0.339371, No Obj: 0.003765, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.589282, Class: 1.000000, Obj: 0.403933, No Obj: 0.003781, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.563880, Class: 1.000000, Obj: 0.404494, No Obj: 0.004239, Avg Recall: 0.655738, count: 122 -Region Avg IOU: 0.576843, Class: 1.000000, Obj: 0.395390, No Obj: 0.004296, Avg Recall: 0.679688, count: 128 -Region Avg IOU: 0.578519, Class: 1.000000, Obj: 0.370747, No Obj: 0.004093, Avg Recall: 0.644068, count: 118 -Region Avg IOU: 0.583910, Class: 1.000000, Obj: 0.412568, No Obj: 0.003804, Avg Recall: 0.727273, count: 88 -Region Avg IOU: 0.567615, Class: 1.000000, Obj: 0.370331, No Obj: 0.003895, Avg Recall: 0.644628, count: 121 -Region Avg IOU: 0.566169, Class: 1.000000, Obj: 0.403068, No Obj: 0.003328, Avg Recall: 0.620690, count: 87 -3982: 0.412040, 0.440096 avg, 0.001000 rate, 23.306845 seconds, 1019392 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.557924, Class: 1.000000, Obj: 0.304255, No Obj: 0.003723, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.534130, Class: 1.000000, Obj: 0.381938, No Obj: 0.003609, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.550375, Class: 1.000000, Obj: 0.348384, No Obj: 0.004232, Avg Recall: 0.646154, count: 130 -Region Avg IOU: 0.548790, Class: 1.000000, Obj: 0.376042, No Obj: 0.004009, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.591275, Class: 1.000000, Obj: 0.405810, No Obj: 0.004373, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.517356, Class: 1.000000, Obj: 0.377218, No Obj: 0.004032, Avg Recall: 0.577586, count: 116 -Region Avg IOU: 0.586996, Class: 1.000000, Obj: 0.457610, No Obj: 0.003783, Avg Recall: 0.662651, count: 83 -Region Avg IOU: 0.571757, Class: 1.000000, Obj: 0.376799, No Obj: 0.003740, Avg Recall: 0.663717, count: 113 -3983: 0.416974, 0.437784 avg, 0.001000 rate, 24.694717 seconds, 1019648 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.592553, Class: 1.000000, Obj: 0.430671, No Obj: 0.004383, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.497235, Class: 1.000000, Obj: 0.360495, No Obj: 0.003773, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.571246, Class: 1.000000, Obj: 0.412142, No Obj: 0.003689, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.544805, Class: 1.000000, Obj: 0.359304, No Obj: 0.003913, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.520465, Class: 1.000000, Obj: 0.413584, No Obj: 0.003291, Avg Recall: 0.565217, count: 92 -Region Avg IOU: 0.590205, Class: 1.000000, Obj: 0.407595, No Obj: 0.003999, Avg Recall: 0.725000, count: 80 -Region Avg IOU: 0.552368, Class: 1.000000, Obj: 0.430162, No Obj: 0.004228, Avg Recall: 0.593220, count: 118 -Region Avg IOU: 0.561790, Class: 1.000000, Obj: 0.419907, No Obj: 0.003923, Avg Recall: 0.656863, count: 102 -3984: 0.439237, 0.437929 avg, 0.001000 rate, 23.323019 seconds, 1019904 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.535783, Class: 1.000000, Obj: 0.415561, No Obj: 0.004579, Avg Recall: 0.627907, count: 129 -Region Avg IOU: 0.572644, Class: 1.000000, Obj: 0.398562, No Obj: 0.003745, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.574139, Class: 1.000000, Obj: 0.430091, No Obj: 0.004469, Avg Recall: 0.622047, count: 127 -Region Avg IOU: 0.571068, Class: 1.000000, Obj: 0.383171, No Obj: 0.003525, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.550669, Class: 1.000000, Obj: 0.388728, No Obj: 0.004261, Avg Recall: 0.656489, count: 131 -Region Avg IOU: 0.612527, Class: 1.000000, Obj: 0.445078, No Obj: 0.004320, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.633627, Class: 1.000000, Obj: 0.423288, No Obj: 0.003812, Avg Recall: 0.776316, count: 76 -Region Avg IOU: 0.542052, Class: 1.000000, Obj: 0.418443, No Obj: 0.004356, Avg Recall: 0.640777, count: 103 -3985: 0.442825, 0.438419 avg, 0.001000 rate, 22.612356 seconds, 1020160 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.581384, Class: 1.000000, Obj: 0.456031, No Obj: 0.003556, Avg Recall: 0.678161, count: 87 -Region Avg IOU: 0.595983, Class: 1.000000, Obj: 0.403445, No Obj: 0.004235, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.564899, Class: 1.000000, Obj: 0.426086, No Obj: 0.004527, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.512164, Class: 1.000000, Obj: 0.368969, No Obj: 0.004283, Avg Recall: 0.611570, count: 121 -Region Avg IOU: 0.552952, Class: 1.000000, Obj: 0.387712, No Obj: 0.003595, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.571832, Class: 1.000000, Obj: 0.390140, No Obj: 0.004269, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.535113, Class: 1.000000, Obj: 0.355892, No Obj: 0.003996, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.528873, Class: 1.000000, Obj: 0.423957, No Obj: 0.004098, Avg Recall: 0.616071, count: 112 -3986: 0.426793, 0.437256 avg, 0.001000 rate, 22.175091 seconds, 1020416 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.542269, Class: 1.000000, Obj: 0.382010, No Obj: 0.003198, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.556921, Class: 1.000000, Obj: 0.437653, No Obj: 0.004280, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.527278, Class: 1.000000, Obj: 0.412828, No Obj: 0.003643, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.623624, Class: 1.000000, Obj: 0.363108, No Obj: 0.003268, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.578701, Class: 1.000000, Obj: 0.417697, No Obj: 0.003633, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.590163, Class: 1.000000, Obj: 0.442639, No Obj: 0.004380, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.564452, Class: 1.000000, Obj: 0.416532, No Obj: 0.003858, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.515604, Class: 1.000000, Obj: 0.375726, No Obj: 0.003702, Avg Recall: 0.546296, count: 108 -3987: 0.386919, 0.432222 avg, 0.001000 rate, 22.317789 seconds, 1020672 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.575425, Class: 1.000000, Obj: 0.329316, No Obj: 0.004681, Avg Recall: 0.691176, count: 136 -Region Avg IOU: 0.538723, Class: 1.000000, Obj: 0.388502, No Obj: 0.003235, Avg Recall: 0.591549, count: 71 -Region Avg IOU: 0.602212, Class: 1.000000, Obj: 0.414738, No Obj: 0.003687, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.552705, Class: 1.000000, Obj: 0.401994, No Obj: 0.003512, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.494935, Class: 1.000000, Obj: 0.413195, No Obj: 0.003309, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.587162, Class: 1.000000, Obj: 0.398630, No Obj: 0.003354, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.525465, Class: 1.000000, Obj: 0.372165, No Obj: 0.003205, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.556944, Class: 1.000000, Obj: 0.431457, No Obj: 0.003464, Avg Recall: 0.630435, count: 92 -3988: 0.373283, 0.426328 avg, 0.001000 rate, 25.818710 seconds, 1020928 images -Loaded: 0.040833 seconds -Region Avg IOU: 0.484087, Class: 1.000000, Obj: 0.337044, No Obj: 0.003817, Avg Recall: 0.517241, count: 116 -Region Avg IOU: 0.553143, Class: 1.000000, Obj: 0.368291, No Obj: 0.003690, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.543393, Class: 1.000000, Obj: 0.340573, No Obj: 0.003836, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.561255, Class: 1.000000, Obj: 0.363245, No Obj: 0.003819, Avg Recall: 0.633333, count: 120 -Region Avg IOU: 0.551045, Class: 1.000000, Obj: 0.389021, No Obj: 0.003839, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.554872, Class: 1.000000, Obj: 0.392684, No Obj: 0.004172, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.581086, Class: 1.000000, Obj: 0.342270, No Obj: 0.004164, Avg Recall: 0.681818, count: 132 -Region Avg IOU: 0.505646, Class: 1.000000, Obj: 0.341865, No Obj: 0.003177, Avg Recall: 0.601770, count: 113 -3989: 0.441529, 0.427848 avg, 0.001000 rate, 23.870157 seconds, 1021184 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.597245, Class: 1.000000, Obj: 0.368277, No Obj: 0.003894, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.571224, Class: 1.000000, Obj: 0.374252, No Obj: 0.003181, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.620410, Class: 1.000000, Obj: 0.350145, No Obj: 0.003257, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.587497, Class: 1.000000, Obj: 0.382325, No Obj: 0.003710, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.582483, Class: 1.000000, Obj: 0.385390, No Obj: 0.003396, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.514495, Class: 1.000000, Obj: 0.352765, No Obj: 0.003502, Avg Recall: 0.600000, count: 125 -Region Avg IOU: 0.591477, Class: 1.000000, Obj: 0.367365, No Obj: 0.004080, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.503081, Class: 1.000000, Obj: 0.349832, No Obj: 0.003380, Avg Recall: 0.548077, count: 104 -3990: 0.435577, 0.428621 avg, 0.001000 rate, 24.793896 seconds, 1021440 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000074 seconds -Region Avg IOU: 0.596176, Class: 1.000000, Obj: 0.399594, No Obj: 0.003000, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.573531, Class: 1.000000, Obj: 0.362362, No Obj: 0.002797, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.586405, Class: 1.000000, Obj: 0.355929, No Obj: 0.002872, Avg Recall: 0.692308, count: 130 -Region Avg IOU: 0.623731, Class: 1.000000, Obj: 0.375318, No Obj: 0.003434, Avg Recall: 0.750000, count: 132 -Region Avg IOU: 0.602071, Class: 1.000000, Obj: 0.385485, No Obj: 0.002946, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.591446, Class: 1.000000, Obj: 0.348405, No Obj: 0.003394, Avg Recall: 0.738095, count: 126 -Region Avg IOU: 0.650072, Class: 1.000000, Obj: 0.486477, No Obj: 0.002966, Avg Recall: 0.789474, count: 76 -Region Avg IOU: 0.640221, Class: 1.000000, Obj: 0.367421, No Obj: 0.003378, Avg Recall: 0.781250, count: 128 -3991: 0.448184, 0.430578 avg, 0.001000 rate, 26.604836 seconds, 1021696 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.645597, Class: 1.000000, Obj: 0.373013, No Obj: 0.003055, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.653055, Class: 1.000000, Obj: 0.438651, No Obj: 0.002501, Avg Recall: 0.819277, count: 83 -Region Avg IOU: 0.671734, Class: 1.000000, Obj: 0.470817, No Obj: 0.003013, Avg Recall: 0.857143, count: 91 -Region Avg IOU: 0.619613, Class: 1.000000, Obj: 0.389808, No Obj: 0.003261, Avg Recall: 0.769231, count: 117 -Region Avg IOU: 0.644203, Class: 1.000000, Obj: 0.435598, No Obj: 0.002800, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.566379, Class: 1.000000, Obj: 0.313683, No Obj: 0.003307, Avg Recall: 0.638298, count: 141 -Region Avg IOU: 0.645820, Class: 1.000000, Obj: 0.365468, No Obj: 0.002971, Avg Recall: 0.802198, count: 91 -Region Avg IOU: 0.631584, Class: 1.000000, Obj: 0.383673, No Obj: 0.002687, Avg Recall: 0.776596, count: 94 -3992: 0.414792, 0.428999 avg, 0.001000 rate, 26.867359 seconds, 1021952 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.623284, Class: 1.000000, Obj: 0.391295, No Obj: 0.002784, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.665251, Class: 1.000000, Obj: 0.340307, No Obj: 0.002933, Avg Recall: 0.862745, count: 102 -Region Avg IOU: 0.644210, Class: 1.000000, Obj: 0.390293, No Obj: 0.002987, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.583248, Class: 1.000000, Obj: 0.401018, No Obj: 0.002734, Avg Recall: 0.731707, count: 123 -Region Avg IOU: 0.630759, Class: 1.000000, Obj: 0.327297, No Obj: 0.002993, Avg Recall: 0.756303, count: 119 -Region Avg IOU: 0.625680, Class: 1.000000, Obj: 0.387108, No Obj: 0.002741, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.618977, Class: 1.000000, Obj: 0.396266, No Obj: 0.003080, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.607849, Class: 1.000000, Obj: 0.365500, No Obj: 0.002980, Avg Recall: 0.692913, count: 127 -3993: 0.454973, 0.431596 avg, 0.001000 rate, 25.399620 seconds, 1022208 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.618087, Class: 1.000000, Obj: 0.386005, No Obj: 0.002711, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.639989, Class: 1.000000, Obj: 0.329636, No Obj: 0.002509, Avg Recall: 0.785714, count: 84 -Region Avg IOU: 0.641585, Class: 1.000000, Obj: 0.384624, No Obj: 0.002849, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.611461, Class: 1.000000, Obj: 0.387404, No Obj: 0.002356, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.660949, Class: 1.000000, Obj: 0.366837, No Obj: 0.003072, Avg Recall: 0.840425, count: 94 -Region Avg IOU: 0.587379, Class: 1.000000, Obj: 0.408512, No Obj: 0.002492, Avg Recall: 0.693182, count: 88 -Region Avg IOU: 0.612187, Class: 1.000000, Obj: 0.377519, No Obj: 0.002784, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.661095, Class: 1.000000, Obj: 0.405195, No Obj: 0.003357, Avg Recall: 0.824561, count: 114 -3994: 0.404854, 0.428922 avg, 0.001000 rate, 26.633181 seconds, 1022464 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.673210, Class: 1.000000, Obj: 0.406840, No Obj: 0.002932, Avg Recall: 0.852941, count: 102 -Region Avg IOU: 0.605725, Class: 1.000000, Obj: 0.374795, No Obj: 0.002444, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.635913, Class: 1.000000, Obj: 0.377923, No Obj: 0.003217, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.595803, Class: 1.000000, Obj: 0.388013, No Obj: 0.002729, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.631702, Class: 1.000000, Obj: 0.378846, No Obj: 0.003221, Avg Recall: 0.756098, count: 123 -Region Avg IOU: 0.626250, Class: 1.000000, Obj: 0.384171, No Obj: 0.002936, Avg Recall: 0.768595, count: 121 -Region Avg IOU: 0.546207, Class: 1.000000, Obj: 0.359697, No Obj: 0.002663, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.596818, Class: 1.000000, Obj: 0.311470, No Obj: 0.002895, Avg Recall: 0.685714, count: 105 -3995: 0.468245, 0.432854 avg, 0.001000 rate, 26.249985 seconds, 1022720 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.637990, Class: 1.000000, Obj: 0.393245, No Obj: 0.002986, Avg Recall: 0.775000, count: 80 -Region Avg IOU: 0.609811, Class: 1.000000, Obj: 0.363775, No Obj: 0.003167, Avg Recall: 0.761538, count: 130 -Region Avg IOU: 0.577734, Class: 1.000000, Obj: 0.328380, No Obj: 0.002752, Avg Recall: 0.700000, count: 120 -Region Avg IOU: 0.609412, Class: 1.000000, Obj: 0.397349, No Obj: 0.002766, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.655794, Class: 1.000000, Obj: 0.469473, No Obj: 0.002773, Avg Recall: 0.804598, count: 87 -Region Avg IOU: 0.620294, Class: 1.000000, Obj: 0.409905, No Obj: 0.002892, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.602322, Class: 1.000000, Obj: 0.347885, No Obj: 0.002825, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.581335, Class: 1.000000, Obj: 0.334523, No Obj: 0.002796, Avg Recall: 0.700000, count: 150 -3996: 0.451484, 0.434717 avg, 0.001000 rate, 25.510330 seconds, 1022976 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.637687, Class: 1.000000, Obj: 0.376115, No Obj: 0.002875, Avg Recall: 0.788889, count: 90 -Region Avg IOU: 0.568348, Class: 1.000000, Obj: 0.379397, No Obj: 0.002957, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.618736, Class: 1.000000, Obj: 0.427956, No Obj: 0.002715, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.593621, Class: 1.000000, Obj: 0.388074, No Obj: 0.002943, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.577128, Class: 1.000000, Obj: 0.389908, No Obj: 0.003120, Avg Recall: 0.669173, count: 133 -Region Avg IOU: 0.600970, Class: 1.000000, Obj: 0.381238, No Obj: 0.002844, Avg Recall: 0.717391, count: 138 -Region Avg IOU: 0.563906, Class: 1.000000, Obj: 0.319132, No Obj: 0.002797, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.635004, Class: 1.000000, Obj: 0.392489, No Obj: 0.002568, Avg Recall: 0.776471, count: 85 -3997: 0.430767, 0.434322 avg, 0.001000 rate, 25.701630 seconds, 1023232 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.609678, Class: 1.000000, Obj: 0.387376, No Obj: 0.003285, Avg Recall: 0.730159, count: 126 -Region Avg IOU: 0.621130, Class: 1.000000, Obj: 0.372613, No Obj: 0.002844, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.598133, Class: 1.000000, Obj: 0.335521, No Obj: 0.002780, Avg Recall: 0.688889, count: 90 -Region Avg IOU: 0.629838, Class: 1.000000, Obj: 0.389298, No Obj: 0.002506, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.607227, Class: 1.000000, Obj: 0.352323, No Obj: 0.003205, Avg Recall: 0.751880, count: 133 -Region Avg IOU: 0.682479, Class: 1.000000, Obj: 0.360510, No Obj: 0.003215, Avg Recall: 0.862069, count: 116 -Region Avg IOU: 0.594341, Class: 1.000000, Obj: 0.404927, No Obj: 0.002673, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.638614, Class: 1.000000, Obj: 0.397191, No Obj: 0.003117, Avg Recall: 0.776699, count: 103 -3998: 0.427433, 0.433633 avg, 0.001000 rate, 26.483416 seconds, 1023488 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.627432, Class: 1.000000, Obj: 0.416674, NSaving weights to ./person9300/backup/tyvp_93_9c512_4000.weights -o Obj: 0.002989, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.628366, Class: 1.000000, Obj: 0.366369, No Obj: 0.003007, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.630401, Class: 1.000000, Obj: 0.381476, No Obj: 0.003083, Avg Recall: 0.810345, count: 116 -Region Avg IOU: 0.613269, Class: 1.000000, Obj: 0.398168, No Obj: 0.002691, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.572926, Class: 1.000000, Obj: 0.334076, No Obj: 0.002684, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.623402, Class: 1.000000, Obj: 0.380744, No Obj: 0.003220, Avg Recall: 0.786765, count: 136 -Region Avg IOU: 0.608121, Class: 1.000000, Obj: 0.403096, No Obj: 0.002865, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.661868, Class: 1.000000, Obj: 0.406929, No Obj: 0.002886, Avg Recall: 0.791304, count: 115 -3999: 0.432608, 0.433531 avg, 0.001000 rate, 25.857363 seconds, 1023744 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.630675, Class: 1.000000, Obj: 0.354596, No Obj: 0.002881, Avg Recall: 0.827957, count: 93 -Region Avg IOU: 0.613727, Class: 1.000000, Obj: 0.340084, No Obj: 0.003127, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.615157, Class: 1.000000, Obj: 0.394259, No Obj: 0.003001, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.623069, Class: 1.000000, Obj: 0.380518, No Obj: 0.002851, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.612240, Class: 1.000000, Obj: 0.364370, No Obj: 0.002827, Avg Recall: 0.775000, count: 80 -Region Avg IOU: 0.614970, Class: 1.000000, Obj: 0.395715, No Obj: 0.002912, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.626896, Class: 1.000000, Obj: 0.356139, No Obj: 0.003478, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.583986, Class: 1.000000, Obj: 0.376767, No Obj: 0.002863, Avg Recall: 0.702703, count: 111 -4000: 0.407718, 0.430950 avg, 0.001000 rate, 27.054909 seconds, 1024000 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000067 seconds -Region Avg IOU: 0.649978, Class: 1.000000, Obj: 0.426978, No Obj: 0.002583, Avg Recall: 0.835443, count: 79 -Region Avg IOU: 0.698582, Class: 1.000000, Obj: 0.468084, No Obj: 0.002787, Avg Recall: 0.910112, count: 89 -Region Avg IOU: 0.642739, Class: 1.000000, Obj: 0.343806, No Obj: 0.003016, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.610154, Class: 1.000000, Obj: 0.418279, No Obj: 0.002928, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.601608, Class: 1.000000, Obj: 0.368835, No Obj: 0.002647, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.651599, Class: 1.000000, Obj: 0.388909, No Obj: 0.002552, Avg Recall: 0.793103, count: 87 -Region Avg IOU: 0.620438, Class: 1.000000, Obj: 0.403965, No Obj: 0.002627, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.651762, Class: 1.000000, Obj: 0.413914, No Obj: 0.003250, Avg Recall: 0.794872, count: 117 -4001: 0.415402, 0.429395 avg, 0.001000 rate, 26.720100 seconds, 1024256 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.660512, Class: 1.000000, Obj: 0.422246, No Obj: 0.002523, Avg Recall: 0.852273, count: 88 -Region Avg IOU: 0.625691, Class: 1.000000, Obj: 0.390641, No Obj: 0.003103, Avg Recall: 0.783333, count: 120 -Region Avg IOU: 0.629729, Class: 1.000000, Obj: 0.403474, No Obj: 0.003055, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.641727, Class: 1.000000, Obj: 0.474019, No Obj: 0.002679, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.615179, Class: 1.000000, Obj: 0.393075, No Obj: 0.003063, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.661541, Class: 1.000000, Obj: 0.394239, No Obj: 0.002876, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.650679, Class: 1.000000, Obj: 0.394956, No Obj: 0.002744, Avg Recall: 0.863158, count: 95 -Region Avg IOU: 0.631845, Class: 1.000000, Obj: 0.405286, No Obj: 0.003074, Avg Recall: 0.796875, count: 128 -4002: 0.403405, 0.426796 avg, 0.001000 rate, 28.432064 seconds, 1024512 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.620800, Class: 1.000000, Obj: 0.428077, No Obj: 0.002605, Avg Recall: 0.666667, count: 72 -Region Avg IOU: 0.659139, Class: 1.000000, Obj: 0.383630, No Obj: 0.003064, Avg Recall: 0.833333, count: 114 -Region Avg IOU: 0.655527, Class: 1.000000, Obj: 0.373592, No Obj: 0.003007, Avg Recall: 0.821429, count: 112 -Region Avg IOU: 0.650014, Class: 1.000000, Obj: 0.328469, No Obj: 0.003430, Avg Recall: 0.791367, count: 139 -Region Avg IOU: 0.631004, Class: 1.000000, Obj: 0.371195, No Obj: 0.003218, Avg Recall: 0.809917, count: 121 -Region Avg IOU: 0.626311, Class: 1.000000, Obj: 0.372181, No Obj: 0.003199, Avg Recall: 0.769784, count: 139 -Region Avg IOU: 0.653031, Class: 1.000000, Obj: 0.429722, No Obj: 0.003070, Avg Recall: 0.825243, count: 103 -Region Avg IOU: 0.652566, Class: 1.000000, Obj: 0.389246, No Obj: 0.002955, Avg Recall: 0.801980, count: 101 -4003: 0.482888, 0.432405 avg, 0.001000 rate, 28.485722 seconds, 1024768 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.649468, Class: 1.000000, Obj: 0.417176, No Obj: 0.002891, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.648625, Class: 1.000000, Obj: 0.383491, No Obj: 0.002402, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.651691, Class: 1.000000, Obj: 0.395640, No Obj: 0.002679, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.615561, Class: 1.000000, Obj: 0.384633, No Obj: 0.002900, Avg Recall: 0.730769, count: 130 -Region Avg IOU: 0.621142, Class: 1.000000, Obj: 0.348350, No Obj: 0.002508, Avg Recall: 0.781250, count: 96 -Region Avg IOU: 0.647910, Class: 1.000000, Obj: 0.378855, No Obj: 0.002740, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.600389, Class: 1.000000, Obj: 0.385711, No Obj: 0.002333, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.631883, Class: 1.000000, Obj: 0.381457, No Obj: 0.002916, Avg Recall: 0.773585, count: 106 -4004: 0.404164, 0.429581 avg, 0.001000 rate, 27.207012 seconds, 1025024 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.659331, Class: 1.000000, Obj: 0.361162, No Obj: 0.003009, Avg Recall: 0.840336, count: 119 -Region Avg IOU: 0.667506, Class: 1.000000, Obj: 0.356967, No Obj: 0.003018, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.626289, Class: 1.000000, Obj: 0.370397, No Obj: 0.002945, Avg Recall: 0.779661, count: 118 -Region Avg IOU: 0.646153, Class: 1.000000, Obj: 0.322608, No Obj: 0.002734, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.641681, Class: 1.000000, Obj: 0.322440, No Obj: 0.002987, Avg Recall: 0.848921, count: 139 -Region Avg IOU: 0.631226, Class: 1.000000, Obj: 0.394337, No Obj: 0.002554, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.619084, Class: 1.000000, Obj: 0.398115, No Obj: 0.002182, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.683283, Class: 1.000000, Obj: 0.386933, No Obj: 0.002780, Avg Recall: 0.851485, count: 101 -4005: 0.474724, 0.434095 avg, 0.001000 rate, 28.288963 seconds, 1025280 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.653621, Class: 1.000000, Obj: 0.379232, No Obj: 0.002501, Avg Recall: 0.823529, count: 85 -Region Avg IOU: 0.640078, Class: 1.000000, Obj: 0.350633, No Obj: 0.002676, Avg Recall: 0.796748, count: 123 -Region Avg IOU: 0.615364, Class: 1.000000, Obj: 0.297719, No Obj: 0.002301, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.627581, Class: 1.000000, Obj: 0.301831, No Obj: 0.002738, Avg Recall: 0.808000, count: 125 -Region Avg IOU: 0.652636, Class: 1.000000, Obj: 0.363204, No Obj: 0.002354, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.642391, Class: 1.000000, Obj: 0.309506, No Obj: 0.002510, Avg Recall: 0.813084, count: 107 -Region Avg IOU: 0.655528, Class: 1.000000, Obj: 0.333126, No Obj: 0.002555, Avg Recall: 0.834711, count: 121 -Region Avg IOU: 0.667077, Class: 1.000000, Obj: 0.384893, No Obj: 0.002920, Avg Recall: 0.821705, count: 129 -4006: 0.464944, 0.437180 avg, 0.001000 rate, 28.883396 seconds, 1025536 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.663459, Class: 1.000000, Obj: 0.365412, No Obj: 0.002261, Avg Recall: 0.816092, count: 87 -Region Avg IOU: 0.637400, Class: 1.000000, Obj: 0.344983, No Obj: 0.003000, Avg Recall: 0.798507, count: 134 -Region Avg IOU: 0.634416, Class: 1.000000, Obj: 0.366450, No Obj: 0.002312, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.629855, Class: 1.000000, Obj: 0.331861, No Obj: 0.002924, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.657624, Class: 1.000000, Obj: 0.374958, No Obj: 0.002299, Avg Recall: 0.801980, count: 101 -Region Avg IOU: 0.657674, Class: 1.000000, Obj: 0.312352, No Obj: 0.002851, Avg Recall: 0.857143, count: 119 -Region Avg IOU: 0.646518, Class: 1.000000, Obj: 0.349300, No Obj: 0.002566, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.643874, Class: 1.000000, Obj: 0.371956, No Obj: 0.002473, Avg Recall: 0.772277, count: 101 -4007: 0.451465, 0.438609 avg, 0.001000 rate, 28.355627 seconds, 1025792 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.611966, Class: 1.000000, Obj: 0.323129, No Obj: 0.002654, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.658446, Class: 1.000000, Obj: 0.339676, No Obj: 0.002273, Avg Recall: 0.824561, count: 114 -Region Avg IOU: 0.674079, Class: 1.000000, Obj: 0.369091, No Obj: 0.002571, Avg Recall: 0.808989, count: 89 -Region Avg IOU: 0.692481, Class: 1.000000, Obj: 0.357911, No Obj: 0.002939, Avg Recall: 0.905983, count: 117 -Region Avg IOU: 0.645592, Class: 1.000000, Obj: 0.352754, No Obj: 0.002560, Avg Recall: 0.805085, count: 118 -Region Avg IOU: 0.639174, Class: 1.000000, Obj: 0.356291, No Obj: 0.002965, Avg Recall: 0.805556, count: 144 -Region Avg IOU: 0.618484, Class: 1.000000, Obj: 0.326390, No Obj: 0.003027, Avg Recall: 0.754967, count: 151 -Region Avg IOU: 0.632068, Class: 1.000000, Obj: 0.355486, No Obj: 0.002287, Avg Recall: 0.736842, count: 76 -4008: 0.499466, 0.444694 avg, 0.001000 rate, 28.521339 seconds, 1026048 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.610656, Class: 1.000000, Obj: 0.337663, No Obj: 0.002480, Avg Recall: 0.762712, count: 118 -Region Avg IOU: 0.678926, Class: 1.000000, Obj: 0.450092, No Obj: 0.002724, Avg Recall: 0.844444, count: 90 -Region Avg IOU: 0.616655, Class: 1.000000, Obj: 0.394411, No Obj: 0.002709, Avg Recall: 0.758065, count: 124 -Region Avg IOU: 0.638076, Class: 1.000000, Obj: 0.320424, No Obj: 0.002492, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.608849, Class: 1.000000, Obj: 0.354897, No Obj: 0.002281, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.668215, Class: 1.000000, Obj: 0.400400, No Obj: 0.002236, Avg Recall: 0.811765, count: 85 -Region Avg IOU: 0.651593, Class: 1.000000, Obj: 0.372008, No Obj: 0.002505, Avg Recall: 0.795082, count: 122 -Region Avg IOU: 0.613983, Class: 1.000000, Obj: 0.348750, No Obj: 0.002510, Avg Recall: 0.740385, count: 104 -4009: 0.447246, 0.444950 avg, 0.001000 rate, 29.622904 seconds, 1026304 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.639567, Class: 1.000000, Obj: 0.409256, No Obj: 0.002660, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.616680, Class: 1.000000, Obj: 0.354542, No Obj: 0.002421, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.606808, Class: 1.000000, Obj: 0.376724, No Obj: 0.002622, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.613546, Class: 1.000000, Obj: 0.337466, No Obj: 0.002614, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.596452, Class: 1.000000, Obj: 0.316182, No Obj: 0.002317, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.630386, Class: 1.000000, Obj: 0.395242, No Obj: 0.002288, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.644168, Class: 1.000000, Obj: 0.350893, No Obj: 0.003051, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.650952, Class: 1.000000, Obj: 0.380062, No Obj: 0.002741, Avg Recall: 0.825000, count: 120 -4010: 0.442524, 0.444707 avg, 0.001000 rate, 28.499071 seconds, 1026560 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 13.356056 seconds -Region Avg IOU: 0.553254, Class: 1.000000, Obj: 0.317552, No Obj: 0.002758, Avg Recall: 0.616279, count: 86 -Region Avg IOU: 0.540239, Class: 1.000000, Obj: 0.323882, No Obj: 0.002761, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.559437, Class: 1.000000, Obj: 0.335268, No Obj: 0.002844, Avg Recall: 0.685393, count: 89 -Region Avg IOU: 0.531453, Class: 1.000000, Obj: 0.293442, No Obj: 0.003275, Avg Recall: 0.572519, count: 131 -Region Avg IOU: 0.587180, Class: 1.000000, Obj: 0.353065, No Obj: 0.002666, Avg Recall: 0.720000, count: 75 -Region Avg IOU: 0.568769, Class: 1.000000, Obj: 0.300322, No Obj: 0.002886, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.630330, Class: 1.000000, Obj: 0.354604, No Obj: 0.003179, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.546100, Class: 1.000000, Obj: 0.313455, No Obj: 0.003689, Avg Recall: 0.649351, count: 154 -4011: 0.432468, 0.443483 avg, 0.001000 rate, 22.274288 seconds, 1026816 images -Loaded: 1.099015 seconds -Region Avg IOU: 0.614060, Class: 1.000000, Obj: 0.373710, No Obj: 0.002949, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.538373, Class: 1.000000, Obj: 0.333467, No Obj: 0.003555, Avg Recall: 0.605769, count: 104 -Region Avg IOU: 0.565899, Class: 1.000000, Obj: 0.327169, No Obj: 0.003059, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.548743, Class: 1.000000, Obj: 0.312225, No Obj: 0.003650, Avg Recall: 0.582090, count: 134 -Region Avg IOU: 0.558693, Class: 1.000000, Obj: 0.320942, No Obj: 0.003666, Avg Recall: 0.625954, count: 131 -Region Avg IOU: 0.526230, Class: 1.000000, Obj: 0.308251, No Obj: 0.002801, Avg Recall: 0.602740, count: 73 -Region Avg IOU: 0.578033, Class: 1.000000, Obj: 0.373511, No Obj: 0.003242, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.568880, Class: 1.000000, Obj: 0.353303, No Obj: 0.003747, Avg Recall: 0.666667, count: 120 -4012: 0.439652, 0.443100 avg, 0.001000 rate, 22.151518 seconds, 1027072 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.511854, Class: 1.000000, Obj: 0.319689, No Obj: 0.003718, Avg Recall: 0.654135, count: 133 -Region Avg IOU: 0.566490, Class: 1.000000, Obj: 0.334583, No Obj: 0.004107, Avg Recall: 0.664286, count: 140 -Region Avg IOU: 0.552452, Class: 1.000000, Obj: 0.337478, No Obj: 0.003223, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.540307, Class: 1.000000, Obj: 0.306965, No Obj: 0.003995, Avg Recall: 0.626582, count: 158 -Region Avg IOU: 0.596013, Class: 1.000000, Obj: 0.381147, No Obj: 0.003266, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.559681, Class: 1.000000, Obj: 0.350526, No Obj: 0.003409, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.578811, Class: 1.000000, Obj: 0.338399, No Obj: 0.003105, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.531157, Class: 1.000000, Obj: 0.332427, No Obj: 0.003315, Avg Recall: 0.629310, count: 116 -4013: 0.506118, 0.449402 avg, 0.001000 rate, 23.666399 seconds, 1027328 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.576143, Class: 1.000000, Obj: 0.379577, No Obj: 0.003808, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.552454, Class: 1.000000, Obj: 0.330743, No Obj: 0.003112, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.566969, Class: 1.000000, Obj: 0.350357, No Obj: 0.003497, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.554610, Class: 1.000000, Obj: 0.378129, No Obj: 0.003551, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.574752, Class: 1.000000, Obj: 0.372925, No Obj: 0.004005, Avg Recall: 0.680672, count: 119 -Region Avg IOU: 0.587825, Class: 1.000000, Obj: 0.412091, No Obj: 0.003941, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.493704, Class: 1.000000, Obj: 0.349212, No Obj: 0.003943, Avg Recall: 0.483333, count: 120 -Region Avg IOU: 0.562778, Class: 1.000000, Obj: 0.376118, No Obj: 0.004010, Avg Recall: 0.655172, count: 116 -4014: 0.461480, 0.450610 avg, 0.001000 rate, 23.864798 seconds, 1027584 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.553295, Class: 1.000000, Obj: 0.337803, No Obj: 0.004083, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.539113, Class: 1.000000, Obj: 0.410900, No Obj: 0.003676, Avg Recall: 0.592593, count: 108 -Region Avg IOU: 0.517490, Class: 1.000000, Obj: 0.371369, No Obj: 0.003997, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.583395, Class: 1.000000, Obj: 0.377717, No Obj: 0.003355, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.568439, Class: 1.000000, Obj: 0.398594, No Obj: 0.004377, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.536649, Class: 1.000000, Obj: 0.347948, No Obj: 0.004275, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.521431, Class: 1.000000, Obj: 0.353947, No Obj: 0.004166, Avg Recall: 0.594406, count: 143 -Region Avg IOU: 0.550895, Class: 1.000000, Obj: 0.390142, No Obj: 0.004385, Avg Recall: 0.680000, count: 125 -4015: 0.463733, 0.451922 avg, 0.001000 rate, 24.922462 seconds, 1027840 images -Loaded: 0.057252 seconds -Region Avg IOU: 0.569229, Class: 1.000000, Obj: 0.381502, No Obj: 0.004045, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.558281, Class: 1.000000, Obj: 0.377784, No Obj: 0.003819, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.629065, Class: 1.000000, Obj: 0.436819, No Obj: 0.004643, Avg Recall: 0.752066, count: 121 -Region Avg IOU: 0.543355, Class: 1.000000, Obj: 0.438748, No Obj: 0.003683, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.526895, Class: 1.000000, Obj: 0.391650, No Obj: 0.004225, Avg Recall: 0.595238, count: 126 -Region Avg IOU: 0.597157, Class: 1.000000, Obj: 0.407650, No Obj: 0.004684, Avg Recall: 0.707692, count: 130 -Region Avg IOU: 0.570856, Class: 1.000000, Obj: 0.427965, No Obj: 0.003758, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.566824, Class: 1.000000, Obj: 0.407008, No Obj: 0.004520, Avg Recall: 0.640000, count: 125 -4016: 0.474536, 0.454183 avg, 0.001000 rate, 21.465384 seconds, 1028096 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.556683, Class: 1.000000, Obj: 0.418819, No Obj: 0.003860, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.563718, Class: 1.000000, Obj: 0.438459, No Obj: 0.003925, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.604164, Class: 1.000000, Obj: 0.439133, No Obj: 0.004805, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.545475, Class: 1.000000, Obj: 0.361763, No Obj: 0.003246, Avg Recall: 0.595238, count: 84 -Region Avg IOU: 0.577137, Class: 1.000000, Obj: 0.425579, No Obj: 0.004380, Avg Recall: 0.725352, count: 142 -Region Avg IOU: 0.485084, Class: 1.000000, Obj: 0.356301, No Obj: 0.003324, Avg Recall: 0.544715, count: 123 -Region Avg IOU: 0.571184, Class: 1.000000, Obj: 0.441855, No Obj: 0.003859, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.503837, Class: 1.000000, Obj: 0.427533, No Obj: 0.003541, Avg Recall: 0.553191, count: 94 -4017: 0.433034, 0.452068 avg, 0.001000 rate, 23.801367 seconds, 1028352 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.565476, Class: 1.000000, Obj: 0.422613, No Obj: 0.004057, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.558700, Class: 1.000000, Obj: 0.404630, No Obj: 0.004160, Avg Recall: 0.630435, count: 138 -Region Avg IOU: 0.554494, Class: 1.000000, Obj: 0.386727, No Obj: 0.004556, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.547970, Class: 1.000000, Obj: 0.431037, No Obj: 0.004034, Avg Recall: 0.619048, count: 84 -Region Avg IOU: 0.522232, Class: 1.000000, Obj: 0.388217, No Obj: 0.004369, Avg Recall: 0.555556, count: 126 -Region Avg IOU: 0.554639, Class: 1.000000, Obj: 0.388571, No Obj: 0.004375, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.543691, Class: 1.000000, Obj: 0.434310, No Obj: 0.003895, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.553614, Class: 1.000000, Obj: 0.389889, No Obj: 0.004140, Avg Recall: 0.644068, count: 118 -4018: 0.479455, 0.454807 avg, 0.001000 rate, 23.767084 seconds, 1028608 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.565001, Class: 1.000000, Obj: 0.406928, No Obj: 0.004174, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.485403, Class: 1.000000, Obj: 0.364090, No Obj: 0.003865, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.624051, Class: 1.000000, Obj: 0.406413, No Obj: 0.003505, Avg Recall: 0.761905, count: 84 -Region Avg IOU: 0.565685, Class: 1.000000, Obj: 0.448083, No Obj: 0.004084, Avg Recall: 0.636364, count: 88 -Region Avg IOU: 0.515486, Class: 1.000000, Obj: 0.344950, No Obj: 0.004115, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.508119, Class: 1.000000, Obj: 0.364172, No Obj: 0.003929, Avg Recall: 0.556522, count: 115 -Region Avg IOU: 0.570311, Class: 1.000000, Obj: 0.404245, No Obj: 0.004668, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.600843, Class: 1.000000, Obj: 0.413924, No Obj: 0.003884, Avg Recall: 0.730337, count: 89 -4019: 0.406137, 0.449940 avg, 0.001000 rate, 23.869215 seconds, 1028864 images -Loaded: 0.018322 seconds -Region Avg IOU: 0.551191, Class: 1.000000, Obj: 0.391528, No Obj: 0.004553, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.547624, Class: 1.000000, Obj: 0.375222, No Obj: 0.003599, Avg Recall: 0.625000, count: 80 -Region Avg IOU: 0.588307, Class: 1.000000, Obj: 0.375477, No Obj: 0.003685, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.526282, Class: 1.000000, Obj: 0.361909, No Obj: 0.004034, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.545867, Class: 1.000000, Obj: 0.375305, No Obj: 0.004319, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.537763, Class: 1.000000, Obj: 0.344406, No Obj: 0.003305, Avg Recall: 0.600000, count: 90 -Region Avg IOU: 0.562379, Class: 1.000000, Obj: 0.402142, No Obj: 0.004475, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.536108, Class: 1.000000, Obj: 0.375236, No Obj: 0.003999, Avg Recall: 0.593496, count: 123 -4020: 0.439110, 0.448857 avg, 0.001000 rate, 23.719259 seconds, 1029120 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000074 seconds -Region Avg IOU: 0.647192, Class: 1.000000, Obj: 0.458598, No Obj: 0.002934, Avg Recall: 0.823009, count: 113 -Region Avg IOU: 0.658167, Class: 1.000000, Obj: 0.483291, No Obj: 0.003387, Avg Recall: 0.818182, count: 121 -Region Avg IOU: 0.654501, Class: 1.000000, Obj: 0.411660, No Obj: 0.003088, Avg Recall: 0.837607, count: 117 -Region Avg IOU: 0.655287, Class: 1.000000, Obj: 0.426702, No Obj: 0.002924, Avg Recall: 0.831683, count: 101 -Region Avg IOU: 0.616982, Class: 1.000000, Obj: 0.408596, No Obj: 0.003107, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.642187, Class: 1.000000, Obj: 0.425242, No Obj: 0.003675, Avg Recall: 0.796748, count: 123 -Region Avg IOU: 0.664090, Class: 1.000000, Obj: 0.452174, No Obj: 0.002808, Avg Recall: 0.818182, count: 88 -Region Avg IOU: 0.675535, Class: 1.000000, Obj: 0.433952, No Obj: 0.002987, Avg Recall: 0.829787, count: 94 -4021: 0.464462, 0.450418 avg, 0.001000 rate, 28.362518 seconds, 1029376 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.664309, Class: 1.000000, Obj: 0.387675, No Obj: 0.002658, Avg Recall: 0.822785, count: 79 -Region Avg IOU: 0.626279, Class: 1.000000, Obj: 0.407174, No Obj: 0.003298, Avg Recall: 0.731884, count: 138 -Region Avg IOU: 0.620206, Class: 1.000000, Obj: 0.392754, No Obj: 0.002503, Avg Recall: 0.729412, count: 85 -Region Avg IOU: 0.625523, Class: 1.000000, Obj: 0.382287, No Obj: 0.002665, Avg Recall: 0.820225, count: 89 -Region Avg IOU: 0.673689, Class: 1.000000, Obj: 0.403144, No Obj: 0.002939, Avg Recall: 0.859813, count: 107 -Region Avg IOU: 0.659648, Class: 1.000000, Obj: 0.438976, No Obj: 0.002951, Avg Recall: 0.803419, count: 117 -Region Avg IOU: 0.626524, Class: 1.000000, Obj: 0.375046, No Obj: 0.003000, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.608361, Class: 1.000000, Obj: 0.392657, No Obj: 0.002680, Avg Recall: 0.713115, count: 122 -4022: 0.473905, 0.452766 avg, 0.001000 rate, 29.647257 seconds, 1029632 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.644376, Class: 1.000000, Obj: 0.389697, No Obj: 0.002237, Avg Recall: 0.740741, count: 81 -Region Avg IOU: 0.662348, Class: 1.000000, Obj: 0.390823, No Obj: 0.002587, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.667546, Class: 1.000000, Obj: 0.401270, No Obj: 0.002883, Avg Recall: 0.824000, count: 125 -Region Avg IOU: 0.671341, Class: 1.000000, Obj: 0.396890, No Obj: 0.003191, Avg Recall: 0.865079, count: 126 -Region Avg IOU: 0.613990, Class: 1.000000, Obj: 0.353813, No Obj: 0.002955, Avg Recall: 0.765101, count: 149 -Region Avg IOU: 0.650685, Class: 1.000000, Obj: 0.371758, No Obj: 0.002929, Avg Recall: 0.813008, count: 123 -Region Avg IOU: 0.670467, Class: 1.000000, Obj: 0.412196, No Obj: 0.002642, Avg Recall: 0.844660, count: 103 -Region Avg IOU: 0.633899, Class: 1.000000, Obj: 0.333846, No Obj: 0.002785, Avg Recall: 0.787879, count: 132 -4023: 0.485775, 0.456067 avg, 0.001000 rate, 30.859381 seconds, 1029888 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.644510, Class: 1.000000, Obj: 0.338552, No Obj: 0.002699, Avg Recall: 0.819549, count: 133 -Region Avg IOU: 0.655475, Class: 1.000000, Obj: 0.338415, No Obj: 0.002750, Avg Recall: 0.802817, count: 142 -Region Avg IOU: 0.655927, Class: 1.000000, Obj: 0.325915, No Obj: 0.002637, Avg Recall: 0.845528, count: 123 -Region Avg IOU: 0.618975, Class: 1.000000, Obj: 0.347147, No Obj: 0.002651, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.622961, Class: 1.000000, Obj: 0.361252, No Obj: 0.002441, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.654411, Class: 1.000000, Obj: 0.365930, No Obj: 0.002863, Avg Recall: 0.812500, count: 128 -Region Avg IOU: 0.682775, Class: 1.000000, Obj: 0.373502, No Obj: 0.002325, Avg Recall: 0.860000, count: 100 -Region Avg IOU: 0.642528, Class: 1.000000, Obj: 0.399900, No Obj: 0.002502, Avg Recall: 0.772727, count: 88 -4024: 0.506634, 0.461124 avg, 0.001000 rate, 29.321297 seconds, 1030144 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.658445, Class: 1.000000, Obj: 0.333166, No Obj: 0.002067, Avg Recall: 0.807229, count: 83 -Region Avg IOU: 0.652837, Class: 1.000000, Obj: 0.333412, No Obj: 0.002349, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.620804, Class: 1.000000, Obj: 0.343894, No Obj: 0.002363, Avg Recall: 0.769231, count: 117 -Region Avg IOU: 0.653470, Class: 1.000000, Obj: 0.344875, No Obj: 0.002402, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.643796, Class: 1.000000, Obj: 0.336921, No Obj: 0.002567, Avg Recall: 0.768595, count: 121 -Region Avg IOU: 0.651750, Class: 1.000000, Obj: 0.310790, No Obj: 0.002613, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.653636, Class: 1.000000, Obj: 0.357140, No Obj: 0.002193, Avg Recall: 0.829787, count: 94 -Region Avg IOU: 0.605240, Class: 1.000000, Obj: 0.301431, No Obj: 0.002043, Avg Recall: 0.754902, count: 102 -4025: 0.471223, 0.462134 avg, 0.001000 rate, 29.922430 seconds, 1030400 images -Loaded: 0.000130 seconds -Region Avg IOU: 0.641326, Class: 1.000000, Obj: 0.289363, No Obj: 0.002594, Avg Recall: 0.830508, count: 118 -Region Avg IOU: 0.623075, Class: 1.000000, Obj: 0.324327, No Obj: 0.002155, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.675112, Class: 1.000000, Obj: 0.339745, No Obj: 0.002402, Avg Recall: 0.836538, count: 104 -Region Avg IOU: 0.670418, Class: 1.000000, Obj: 0.355855, No Obj: 0.002300, Avg Recall: 0.871287, count: 101 -Region Avg IOU: 0.651704, Class: 1.000000, Obj: 0.352237, No Obj: 0.002075, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.618738, Class: 1.000000, Obj: 0.356764, No Obj: 0.002667, Avg Recall: 0.748252, count: 143 -Region Avg IOU: 0.646280, Class: 1.000000, Obj: 0.337656, No Obj: 0.002757, Avg Recall: 0.801653, count: 121 -Region Avg IOU: 0.667765, Class: 1.000000, Obj: 0.337168, No Obj: 0.002281, Avg Recall: 0.812500, count: 96 -4026: 0.466987, 0.462619 avg, 0.001000 rate, 30.494535 seconds, 1030656 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.645927, Class: 1.000000, Obj: 0.353695, No Obj: 0.002364, Avg Recall: 0.821429, count: 112 -Region Avg IOU: 0.615026, Class: 1.000000, Obj: 0.349014, No Obj: 0.002245, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.639202, Class: 1.000000, Obj: 0.349702, No Obj: 0.002391, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.660553, Class: 1.000000, Obj: 0.379893, No Obj: 0.002546, Avg Recall: 0.826087, count: 115 -Region Avg IOU: 0.654172, Class: 1.000000, Obj: 0.339203, No Obj: 0.002261, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.622877, Class: 1.000000, Obj: 0.304037, No Obj: 0.002477, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.651895, Class: 1.000000, Obj: 0.334667, No Obj: 0.002406, Avg Recall: 0.800000, count: 125 -Region Avg IOU: 0.621564, Class: 1.000000, Obj: 0.268959, No Obj: 0.002306, Avg Recall: 0.785047, count: 107 -4027: 0.503401, 0.466697 avg, 0.001000 rate, 29.268118 seconds, 1030912 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.632069, Class: 1.000000, Obj: 0.303131, No Obj: 0.002623, Avg Recall: 0.771429, count: 140 -Region Avg IOU: 0.617378, Class: 1.000000, Obj: 0.355497, No Obj: 0.002394, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.632920, Class: 1.000000, Obj: 0.347610, No Obj: 0.002359, Avg Recall: 0.735537, count: 121 -Region Avg IOU: 0.672996, Class: 1.000000, Obj: 0.392375, No Obj: 0.002634, Avg Recall: 0.830645, count: 124 -Region Avg IOU: 0.631763, Class: 1.000000, Obj: 0.308289, No Obj: 0.002295, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.645979, Class: 1.000000, Obj: 0.397566, No Obj: 0.002462, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.681073, Class: 1.000000, Obj: 0.398209, No Obj: 0.002253, Avg Recall: 0.862500, count: 80 -Region Avg IOU: 0.664311, Class: 1.000000, Obj: 0.362411, No Obj: 0.002499, Avg Recall: 0.846154, count: 117 -4028: 0.519867, 0.472014 avg, 0.001000 rate, 28.645128 seconds, 1031168 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.627170, Class: 1.000000, Obj: 0.376682, No Obj: 0.002096, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.590442, Class: 1.000000, Obj: 0.293960, No Obj: 0.002261, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.638015, Class: 1.000000, Obj: 0.355546, No Obj: 0.002094, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.656596, Class: 1.000000, Obj: 0.351778, No Obj: 0.002635, Avg Recall: 0.814815, count: 135 -Region Avg IOU: 0.602077, Class: 1.000000, Obj: 0.330868, No Obj: 0.002492, Avg Recall: 0.715278, count: 144 -Region Avg IOU: 0.649321, Class: 1.000000, Obj: 0.355844, No Obj: 0.002509, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.595678, Class: 1.000000, Obj: 0.355998, No Obj: 0.002294, Avg Recall: 0.696629, count: 89 -Region Avg IOU: 0.627467, Class: 1.000000, Obj: 0.301081, No Obj: 0.003010, Avg Recall: 0.744681, count: 141 -4029: 0.485620, 0.473375 avg, 0.001000 rate, 29.693419 seconds, 1031424 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.646057, Class: 1.000000, Obj: 0.325527, No Obj: 0.002685, Avg Recall: 0.827273, count: 110 -Region Avg IOU: 0.663935, Class: 1.000000, Obj: 0.336415, No Obj: 0.003029, Avg Recall: 0.869565, count: 115 -Region Avg IOU: 0.646543, Class: 1.000000, Obj: 0.381743, No Obj: 0.002280, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.661896, Class: 1.000000, Obj: 0.371030, No Obj: 0.002658, Avg Recall: 0.847826, count: 92 -Region Avg IOU: 0.587406, Class: 1.000000, Obj: 0.295198, No Obj: 0.002584, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.649666, Class: 1.000000, Obj: 0.354776, No Obj: 0.002330, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.639235, Class: 1.000000, Obj: 0.323370, No Obj: 0.002695, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.618455, Class: 1.000000, Obj: 0.316280, No Obj: 0.002365, Avg Recall: 0.741071, count: 112 -4030: 0.468089, 0.472846 avg, 0.001000 rate, 29.470331 seconds, 1031680 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 13.604906 seconds -Region Avg IOU: 0.537824, Class: 1.000000, Obj: 0.314546, No Obj: 0.003130, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.521925, Class: 1.000000, Obj: 0.298357, No Obj: 0.003171, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.548941, Class: 1.000000, Obj: 0.340948, No Obj: 0.003074, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.562393, Class: 1.000000, Obj: 0.306568, No Obj: 0.003478, Avg Recall: 0.664062, count: 128 -Region Avg IOU: 0.527370, Class: 1.000000, Obj: 0.295760, No Obj: 0.003151, Avg Recall: 0.616667, count: 120 -Region Avg IOU: 0.541545, Class: 1.000000, Obj: 0.295145, No Obj: 0.003104, Avg Recall: 0.624060, count: 133 -Region Avg IOU: 0.490872, Class: 1.000000, Obj: 0.311305, No Obj: 0.002966, Avg Recall: 0.515152, count: 99 -Region Avg IOU: 0.549133, Class: 1.000000, Obj: 0.265873, No Obj: 0.003819, Avg Recall: 0.690141, count: 142 -4031: 0.520216, 0.477583 avg, 0.001000 rate, 21.632412 seconds, 1031936 images -Loaded: 0.924540 seconds -Region Avg IOU: 0.564991, Class: 1.000000, Obj: 0.350262, No Obj: 0.003548, Avg Recall: 0.648438, count: 128 -Region Avg IOU: 0.535405, Class: 1.000000, Obj: 0.284802, No Obj: 0.003282, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.557007, Class: 1.000000, Obj: 0.336644, No Obj: 0.003438, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.583593, Class: 1.000000, Obj: 0.352326, No Obj: 0.003384, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.551258, Class: 1.000000, Obj: 0.314596, No Obj: 0.003714, Avg Recall: 0.629630, count: 135 -Region Avg IOU: 0.570386, Class: 1.000000, Obj: 0.346754, No Obj: 0.003802, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.492088, Class: 1.000000, Obj: 0.266968, No Obj: 0.003293, Avg Recall: 0.552239, count: 134 -Region Avg IOU: 0.532757, Class: 1.000000, Obj: 0.310273, No Obj: 0.003522, Avg Recall: 0.605769, count: 104 -4032: 0.554458, 0.485271 avg, 0.001000 rate, 22.722460 seconds, 1032192 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.561980, Class: 1.000000, Obj: 0.368810, No Obj: 0.003750, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.568435, Class: 1.000000, Obj: 0.387101, No Obj: 0.003688, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.559330, Class: 1.000000, Obj: 0.377825, No Obj: 0.003731, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.474161, Class: 1.000000, Obj: 0.308263, No Obj: 0.003234, Avg Recall: 0.522222, count: 90 -Region Avg IOU: 0.515203, Class: 1.000000, Obj: 0.327574, No Obj: 0.004281, Avg Recall: 0.564885, count: 131 -Region Avg IOU: 0.543557, Class: 1.000000, Obj: 0.307907, No Obj: 0.003265, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.554600, Class: 1.000000, Obj: 0.391010, No Obj: 0.003715, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.512356, Class: 1.000000, Obj: 0.337053, No Obj: 0.003940, Avg Recall: 0.561404, count: 114 -4033: 0.392738, 0.476017 avg, 0.001000 rate, 21.548428 seconds, 1032448 images -Loaded: 0.469905 seconds -Region Avg IOU: 0.554058, Class: 1.000000, Obj: 0.332961, No Obj: 0.003645, Avg Recall: 0.650000, count: 80 -Region Avg IOU: 0.527648, Class: 1.000000, Obj: 0.366176, No Obj: 0.003174, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.574699, Class: 1.000000, Obj: 0.365379, No Obj: 0.004420, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.520142, Class: 1.000000, Obj: 0.348273, No Obj: 0.003833, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.517091, Class: 1.000000, Obj: 0.317345, No Obj: 0.003937, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.538970, Class: 1.000000, Obj: 0.410858, No Obj: 0.003902, Avg Recall: 0.593023, count: 86 -Region Avg IOU: 0.494128, Class: 1.000000, Obj: 0.335093, No Obj: 0.003942, Avg Recall: 0.528455, count: 123 -Region Avg IOU: 0.515743, Class: 1.000000, Obj: 0.363791, No Obj: 0.003457, Avg Recall: 0.570000, count: 100 -4034: 0.399244, 0.468340 avg, 0.001000 rate, 21.175863 seconds, 1032704 images -Loaded: 1.280056 seconds -Region Avg IOU: 0.550527, Class: 1.000000, Obj: 0.390772, No Obj: 0.003916, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.510471, Class: 1.000000, Obj: 0.344708, No Obj: 0.003535, Avg Recall: 0.566265, count: 83 -Region Avg IOU: 0.547599, Class: 1.000000, Obj: 0.352334, No Obj: 0.004329, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.548493, Class: 1.000000, Obj: 0.392214, No Obj: 0.004664, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.519249, Class: 1.000000, Obj: 0.402820, No Obj: 0.004114, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.552901, Class: 1.000000, Obj: 0.386144, No Obj: 0.003829, Avg Recall: 0.617284, count: 81 -Region Avg IOU: 0.598376, Class: 1.000000, Obj: 0.377461, No Obj: 0.004147, Avg Recall: 0.758621, count: 87 -Region Avg IOU: 0.529819, Class: 1.000000, Obj: 0.429207, No Obj: 0.004059, Avg Recall: 0.615385, count: 91 -4035: 0.414967, 0.463003 avg, 0.001000 rate, 21.315905 seconds, 1032960 images -Loaded: 2.009274 seconds -Region Avg IOU: 0.521953, Class: 1.000000, Obj: 0.399810, No Obj: 0.004524, Avg Recall: 0.596491, count: 114 -Region Avg IOU: 0.537882, Class: 1.000000, Obj: 0.393020, No Obj: 0.004630, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.536716, Class: 1.000000, Obj: 0.384725, No Obj: 0.004826, Avg Recall: 0.563492, count: 126 -Region Avg IOU: 0.572459, Class: 1.000000, Obj: 0.417129, No Obj: 0.004233, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.516424, Class: 1.000000, Obj: 0.399468, No Obj: 0.004683, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.557729, Class: 1.000000, Obj: 0.401839, No Obj: 0.004849, Avg Recall: 0.622807, count: 114 -Region Avg IOU: 0.526347, Class: 1.000000, Obj: 0.399743, No Obj: 0.004510, Avg Recall: 0.588710, count: 124 -Region Avg IOU: 0.552923, Class: 1.000000, Obj: 0.401998, No Obj: 0.004262, Avg Recall: 0.623762, count: 101 -4036: 0.440935, 0.460796 avg, 0.001000 rate, 21.480381 seconds, 1033216 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.479825, Class: 1.000000, Obj: 0.413498, No Obj: 0.004344, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.555847, Class: 1.000000, Obj: 0.408740, No Obj: 0.004609, Avg Recall: 0.659341, count: 91 -Region Avg IOU: 0.552177, Class: 1.000000, Obj: 0.379438, No Obj: 0.004962, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.532632, Class: 1.000000, Obj: 0.445209, No Obj: 0.004497, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.499213, Class: 1.000000, Obj: 0.426198, No Obj: 0.003792, Avg Recall: 0.571429, count: 91 -Region Avg IOU: 0.555184, Class: 1.000000, Obj: 0.437095, No Obj: 0.004777, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.551959, Class: 1.000000, Obj: 0.403708, No Obj: 0.004839, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.560844, Class: 1.000000, Obj: 0.427835, No Obj: 0.004862, Avg Recall: 0.666667, count: 114 -4037: 0.402258, 0.454942 avg, 0.001000 rate, 21.514088 seconds, 1033472 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.530189, Class: 1.000000, Obj: 0.407967, No Obj: 0.004924, Avg Recall: 0.606061, count: 132 -Region Avg IOU: 0.519683, Class: 1.000000, Obj: 0.388669, No Obj: 0.004176, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.524183, Class: 1.000000, Obj: 0.432588, No Obj: 0.004308, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.512575, Class: 1.000000, Obj: 0.435666, No Obj: 0.004024, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.535947, Class: 1.000000, Obj: 0.411779, No Obj: 0.005079, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.515261, Class: 1.000000, Obj: 0.363960, No Obj: 0.004242, Avg Recall: 0.580000, count: 100 -Region Avg IOU: 0.528752, Class: 1.000000, Obj: 0.348519, No Obj: 0.004342, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.552329, Class: 1.000000, Obj: 0.389904, No Obj: 0.005133, Avg Recall: 0.624000, count: 125 -4038: 0.445193, 0.453967 avg, 0.001000 rate, 21.663097 seconds, 1033728 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.541420, Class: 1.000000, Obj: 0.403804, No Obj: 0.004461, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.529193, Class: 1.000000, Obj: 0.363276, No Obj: 0.004533, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.571027, Class: 1.000000, Obj: 0.424871, No Obj: 0.004284, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.523416, Class: 1.000000, Obj: 0.389690, No Obj: 0.004232, Avg Recall: 0.565574, count: 122 -Region Avg IOU: 0.547809, Class: 1.000000, Obj: 0.414589, No Obj: 0.003979, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.530816, Class: 1.000000, Obj: 0.383042, No Obj: 0.004210, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.526762, Class: 1.000000, Obj: 0.343220, No Obj: 0.003813, Avg Recall: 0.585106, count: 94 -Region Avg IOU: 0.492305, Class: 1.000000, Obj: 0.348366, No Obj: 0.004594, Avg Recall: 0.527132, count: 129 -4039: 0.447162, 0.453287 avg, 0.001000 rate, 23.348892 seconds, 1033984 images -Loaded: 0.456733 seconds -Region Avg IOU: 0.544065, Class: 1.000000, Obj: 0.370720, No Obj: 0.003995, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.494098, Class: 1.000000, Obj: 0.351703, No Obj: 0.004529, Avg Recall: 0.521739, count: 138 -Region Avg IOU: 0.526649, Class: 1.000000, Obj: 0.380698, No Obj: 0.003972, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.495114, Class: 1.000000, Obj: 0.338605, No Obj: 0.004307, Avg Recall: 0.553957, count: 139 -Region Avg IOU: 0.537169, Class: 1.000000, Obj: 0.334004, No Obj: 0.004347, Avg Recall: 0.619835, count: 121 -Region Avg IOU: 0.530412, Class: 1.000000, Obj: 0.368215, No Obj: 0.003903, Avg Recall: 0.610169, count: 118 -Region Avg IOU: 0.500874, Class: 1.000000, Obj: 0.355453, No Obj: 0.004106, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.520628, Class: 1.000000, Obj: 0.379574, No Obj: 0.004247, Avg Recall: 0.575000, count: 120 -4040: 0.481699, 0.456128 avg, 0.001000 rate, 20.813232 seconds, 1034240 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000071 seconds -Region Avg IOU: 0.553236, Class: 1.000000, Obj: 0.394918, No Obj: 0.003122, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.628020, Class: 1.000000, Obj: 0.411986, No Obj: 0.003393, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.597417, Class: 1.000000, Obj: 0.462326, No Obj: 0.003457, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.645190, Class: 1.000000, Obj: 0.424800, No Obj: 0.003529, Avg Recall: 0.807018, count: 114 -Region Avg IOU: 0.563948, Class: 1.000000, Obj: 0.378707, No Obj: 0.003456, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.580052, Class: 1.000000, Obj: 0.452772, No Obj: 0.003239, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.601667, Class: 1.000000, Obj: 0.409087, No Obj: 0.003419, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.586583, Class: 1.000000, Obj: 0.430910, No Obj: 0.003434, Avg Recall: 0.712000, count: 125 -4041: 0.431990, 0.453714 avg, 0.001000 rate, 26.189230 seconds, 1034496 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.634551, Class: 1.000000, Obj: 0.384148, No Obj: 0.002879, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.563082, Class: 1.000000, Obj: 0.385997, No Obj: 0.003441, Avg Recall: 0.661290, count: 124 -Region Avg IOU: 0.635745, Class: 1.000000, Obj: 0.399682, No Obj: 0.003305, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.648523, Class: 1.000000, Obj: 0.440855, No Obj: 0.003052, Avg Recall: 0.851064, count: 94 -Region Avg IOU: 0.644522, Class: 1.000000, Obj: 0.458772, No Obj: 0.002981, Avg Recall: 0.800000, count: 95 -Region Avg IOU: 0.628719, Class: 1.000000, Obj: 0.395377, No Obj: 0.003226, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.672461, Class: 1.000000, Obj: 0.421989, No Obj: 0.003385, Avg Recall: 0.857143, count: 91 -Region Avg IOU: 0.620915, Class: 1.000000, Obj: 0.390567, No Obj: 0.003275, Avg Recall: 0.738739, count: 111 -4042: 0.400220, 0.448365 avg, 0.001000 rate, 26.906136 seconds, 1034752 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.634526, Class: 1.000000, Obj: 0.367371, No Obj: 0.003302, Avg Recall: 0.773723, count: 137 -Region Avg IOU: 0.663579, Class: 1.000000, Obj: 0.404649, No Obj: 0.003086, Avg Recall: 0.825243, count: 103 -Region Avg IOU: 0.635103, Class: 1.000000, Obj: 0.340139, No Obj: 0.003389, Avg Recall: 0.768657, count: 134 -Region Avg IOU: 0.595145, Class: 1.000000, Obj: 0.379575, No Obj: 0.002606, Avg Recall: 0.685393, count: 89 -Region Avg IOU: 0.591060, Class: 1.000000, Obj: 0.361526, No Obj: 0.002658, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.558258, Class: 1.000000, Obj: 0.334937, No Obj: 0.003260, Avg Recall: 0.637500, count: 160 -Region Avg IOU: 0.606197, Class: 1.000000, Obj: 0.366836, No Obj: 0.002844, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.605743, Class: 1.000000, Obj: 0.383707, No Obj: 0.003363, Avg Recall: 0.765217, count: 115 -4043: 0.464446, 0.449973 avg, 0.001000 rate, 25.781948 seconds, 1035008 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.608391, Class: 1.000000, Obj: 0.352636, No Obj: 0.003008, Avg Recall: 0.696000, count: 125 -Region Avg IOU: 0.631943, Class: 1.000000, Obj: 0.367941, No Obj: 0.002358, Avg Recall: 0.788889, count: 90 -Region Avg IOU: 0.605567, Class: 1.000000, Obj: 0.341670, No Obj: 0.002633, Avg Recall: 0.752000, count: 125 -Region Avg IOU: 0.609338, Class: 1.000000, Obj: 0.357587, No Obj: 0.002189, Avg Recall: 0.697368, count: 76 -Region Avg IOU: 0.599979, Class: 1.000000, Obj: 0.341539, No Obj: 0.002678, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.641981, Class: 1.000000, Obj: 0.389716, No Obj: 0.002740, Avg Recall: 0.814433, count: 97 -Region Avg IOU: 0.607966, Class: 1.000000, Obj: 0.298531, No Obj: 0.002417, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.645076, Class: 1.000000, Obj: 0.380206, No Obj: 0.002720, Avg Recall: 0.788462, count: 104 -4044: 0.444775, 0.449453 avg, 0.001000 rate, 25.961391 seconds, 1035264 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.583981, Class: 1.000000, Obj: 0.274167, No Obj: 0.002723, Avg Recall: 0.684615, count: 130 -Region Avg IOU: 0.640655, Class: 1.000000, Obj: 0.365813, No Obj: 0.002295, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.649553, Class: 1.000000, Obj: 0.332744, No Obj: 0.002842, Avg Recall: 0.786885, count: 122 -Region Avg IOU: 0.600246, Class: 1.000000, Obj: 0.349711, No Obj: 0.002448, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.618033, Class: 1.000000, Obj: 0.300634, No Obj: 0.002597, Avg Recall: 0.754098, count: 122 -Region Avg IOU: 0.644068, Class: 1.000000, Obj: 0.352958, No Obj: 0.002517, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.612574, Class: 1.000000, Obj: 0.297402, No Obj: 0.001978, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.602208, Class: 1.000000, Obj: 0.326159, No Obj: 0.002648, Avg Recall: 0.756303, count: 119 -4045: 0.494923, 0.454000 avg, 0.001000 rate, 26.365427 seconds, 1035520 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.609070, Class: 1.000000, Obj: 0.314317, No Obj: 0.002936, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.635679, Class: 1.000000, Obj: 0.353461, No Obj: 0.002645, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.591529, Class: 1.000000, Obj: 0.344761, No Obj: 0.002748, Avg Recall: 0.671329, count: 143 -Region Avg IOU: 0.569568, Class: 1.000000, Obj: 0.307620, No Obj: 0.002494, Avg Recall: 0.669565, count: 115 -Region Avg IOU: 0.607982, Class: 1.000000, Obj: 0.320807, No Obj: 0.002637, Avg Recall: 0.715278, count: 144 -Region Avg IOU: 0.629468, Class: 1.000000, Obj: 0.304273, No Obj: 0.002415, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.623676, Class: 1.000000, Obj: 0.365802, No Obj: 0.002260, Avg Recall: 0.701149, count: 87 -Region Avg IOU: 0.600074, Class: 1.000000, Obj: 0.311422, No Obj: 0.002479, Avg Recall: 0.733333, count: 120 -4046: 0.515472, 0.460147 avg, 0.001000 rate, 28.484144 seconds, 1035776 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.633858, Class: 1.000000, Obj: 0.334591, No Obj: 0.002972, Avg Recall: 0.747967, count: 123 -Region Avg IOU: 0.639316, Class: 1.000000, Obj: 0.313726, No Obj: 0.002767, Avg Recall: 0.816794, count: 131 -Region Avg IOU: 0.624154, Class: 1.000000, Obj: 0.383199, No Obj: 0.002254, Avg Recall: 0.764706, count: 85 -Region Avg IOU: 0.655426, Class: 1.000000, Obj: 0.337800, No Obj: 0.002779, Avg Recall: 0.818182, count: 121 -Region Avg IOU: 0.630214, Class: 1.000000, Obj: 0.303492, No Obj: 0.002813, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.630403, Class: 1.000000, Obj: 0.357464, No Obj: 0.002825, Avg Recall: 0.744000, count: 125 -Region Avg IOU: 0.622593, Class: 1.000000, Obj: 0.335591, No Obj: 0.002724, Avg Recall: 0.782609, count: 115 -Region Avg IOU: 0.621562, Class: 1.000000, Obj: 0.368596, No Obj: 0.002484, Avg Recall: 0.758929, count: 112 -4047: 0.497980, 0.463931 avg, 0.001000 rate, 26.702408 seconds, 1036032 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.666061, Class: 1.000000, Obj: 0.418159, No Obj: 0.003142, Avg Recall: 0.818182, count: 88 -Region Avg IOU: 0.620856, Class: 1.000000, Obj: 0.358919, No Obj: 0.003062, Avg Recall: 0.750000, count: 132 -Region Avg IOU: 0.603262, Class: 1.000000, Obj: 0.349092, No Obj: 0.003045, Avg Recall: 0.708333, count: 120 -Region Avg IOU: 0.597268, Class: 1.000000, Obj: 0.381645, No Obj: 0.002664, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.678525, Class: 1.000000, Obj: 0.433733, No Obj: 0.002717, Avg Recall: 0.851852, count: 81 -Region Avg IOU: 0.578084, Class: 1.000000, Obj: 0.395113, No Obj: 0.002785, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.674000, Class: 1.000000, Obj: 0.404856, No Obj: 0.002900, Avg Recall: 0.824176, count: 91 -Region Avg IOU: 0.551704, Class: 1.000000, Obj: 0.309845, No Obj: 0.002576, Avg Recall: 0.686275, count: 102 -4048: 0.412438, 0.458781 avg, 0.001000 rate, 27.459904 seconds, 1036288 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.637544, Class: 1.000000, Obj: 0.420162, No Obj: 0.003088, Avg Recall: 0.789474, count: 114 -Region Avg IOU: 0.641124, Class: 1.000000, Obj: 0.359770, No Obj: 0.003180, Avg Recall: 0.801653, count: 121 -Region Avg IOU: 0.636977, Class: 1.000000, Obj: 0.380494, No Obj: 0.003204, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.617402, Class: 1.000000, Obj: 0.384143, No Obj: 0.002758, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.632533, Class: 1.000000, Obj: 0.331066, No Obj: 0.003366, Avg Recall: 0.785124, count: 121 -Region Avg IOU: 0.600599, Class: 1.000000, Obj: 0.342022, No Obj: 0.003263, Avg Recall: 0.742424, count: 132 -Region Avg IOU: 0.606976, Class: 1.000000, Obj: 0.356863, No Obj: 0.002902, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.653002, Class: 1.000000, Obj: 0.370539, No Obj: 0.002754, Avg Recall: 0.802198, count: 91 -4049: 0.468499, 0.459753 avg, 0.001000 rate, 25.977303 seconds, 1036544 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.628768, Class: 1.000000, Obj: 0.398151, No Obj: 0.003617, Avg Recall: 0.775362, count: 138 -Region Avg IOU: 0.619904, Class: 1.000000, Obj: 0.454383, No Obj: 0.003028, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.590953, Class: 1.000000, Obj: 0.381645, No Obj: 0.002993, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.647543, Class: 1.000000, Obj: 0.449743, No Obj: 0.003151, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.605283, Class: 1.000000, Obj: 0.402391, No Obj: 0.003481, Avg Recall: 0.734375, count: 128 -Region Avg IOU: 0.618911, Class: 1.000000, Obj: 0.378859, No Obj: 0.003694, Avg Recall: 0.760274, count: 146 -Region Avg IOU: 0.627797, Class: 1.000000, Obj: 0.409868, No Obj: 0.003267, Avg Recall: 0.784483, count: 116 -Region Avg IOU: 0.687948, Class: 1.000000, Obj: 0.423376, No Obj: 0.003542, Avg Recall: 0.892473, count: 93 -4050: 0.467564, 0.460534 avg, 0.001000 rate, 25.557840 seconds, 1036800 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 17.087444 seconds -Region Avg IOU: 0.635814, Class: 1.000000, Obj: 0.435789, No Obj: 0.003365, Avg Recall: 0.772727, count: 88 -Region Avg IOU: 0.596495, Class: 1.000000, Obj: 0.386852, No Obj: 0.003250, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.623495, Class: 1.000000, Obj: 0.388181, No Obj: 0.003353, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.642038, Class: 1.000000, Obj: 0.455964, No Obj: 0.003533, Avg Recall: 0.797872, count: 94 -Region Avg IOU: 0.627593, Class: 1.000000, Obj: 0.393467, No Obj: 0.003544, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.571059, Class: 1.000000, Obj: 0.355715, No Obj: 0.003287, Avg Recall: 0.632000, count: 125 -Region Avg IOU: 0.575355, Class: 1.000000, Obj: 0.382964, No Obj: 0.003240, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.535193, Class: 1.000000, Obj: 0.371129, No Obj: 0.003554, Avg Recall: 0.651376, count: 109 -4051: 0.393987, 0.453879 avg, 0.001000 rate, 26.296736 seconds, 1037056 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.560902, Class: 1.000000, Obj: 0.370241, No Obj: 0.004034, Avg Recall: 0.699248, count: 133 -Region Avg IOU: 0.630516, Class: 1.000000, Obj: 0.415217, No Obj: 0.003868, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.595228, Class: 1.000000, Obj: 0.373960, No Obj: 0.003560, Avg Recall: 0.714286, count: 84 -Region Avg IOU: 0.586886, Class: 1.000000, Obj: 0.435232, No Obj: 0.003950, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.593639, Class: 1.000000, Obj: 0.382807, No Obj: 0.003727, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.579314, Class: 1.000000, Obj: 0.389446, No Obj: 0.003783, Avg Recall: 0.644628, count: 121 -Region Avg IOU: 0.557586, Class: 1.000000, Obj: 0.376288, No Obj: 0.003111, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.621524, Class: 1.000000, Obj: 0.377244, No Obj: 0.004347, Avg Recall: 0.705882, count: 119 -4052: 0.447538, 0.453245 avg, 0.001000 rate, 24.448126 seconds, 1037312 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.570935, Class: 1.000000, Obj: 0.385742, No Obj: 0.003655, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.581567, Class: 1.000000, Obj: 0.426084, No Obj: 0.003479, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.647243, Class: 1.000000, Obj: 0.435253, No Obj: 0.003996, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.604656, Class: 1.000000, Obj: 0.410476, No Obj: 0.004073, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.622273, Class: 1.000000, Obj: 0.455437, No Obj: 0.003749, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.592259, Class: 1.000000, Obj: 0.419737, No Obj: 0.003729, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.584897, Class: 1.000000, Obj: 0.427937, No Obj: 0.003535, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.580643, Class: 1.000000, Obj: 0.350156, No Obj: 0.003892, Avg Recall: 0.679612, count: 103 -4053: 0.464594, 0.454380 avg, 0.001000 rate, 25.223433 seconds, 1037568 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.582750, Class: 1.000000, Obj: 0.335636, No Obj: 0.004343, Avg Recall: 0.682119, count: 151 -Region Avg IOU: 0.597079, Class: 1.000000, Obj: 0.399721, No Obj: 0.003725, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.596420, Class: 1.000000, Obj: 0.430618, No Obj: 0.003963, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.622960, Class: 1.000000, Obj: 0.418684, No Obj: 0.003565, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.594336, Class: 1.000000, Obj: 0.414688, No Obj: 0.003789, Avg Recall: 0.709677, count: 124 -Region Avg IOU: 0.589279, Class: 1.000000, Obj: 0.377485, No Obj: 0.004204, Avg Recall: 0.686131, count: 137 -Region Avg IOU: 0.627148, Class: 1.000000, Obj: 0.331116, No Obj: 0.003492, Avg Recall: 0.796460, count: 113 -Region Avg IOU: 0.534297, Class: 1.000000, Obj: 0.369135, No Obj: 0.003381, Avg Recall: 0.633333, count: 90 -4054: 0.472119, 0.456154 avg, 0.001000 rate, 24.600220 seconds, 1037824 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.590371, Class: 1.000000, Obj: 0.386211, No Obj: 0.003617, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.588597, Class: 1.000000, Obj: 0.414510, No Obj: 0.003503, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.598956, Class: 1.000000, Obj: 0.407185, No Obj: 0.003785, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.646055, Class: 1.000000, Obj: 0.464835, No Obj: 0.003154, Avg Recall: 0.822785, count: 79 -Region Avg IOU: 0.633480, Class: 1.000000, Obj: 0.418277, No Obj: 0.003555, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.607370, Class: 1.000000, Obj: 0.385673, No Obj: 0.003574, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.588967, Class: 1.000000, Obj: 0.365670, No Obj: 0.003497, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.620639, Class: 1.000000, Obj: 0.415965, No Obj: 0.003579, Avg Recall: 0.747967, count: 123 -4055: 0.396716, 0.450210 avg, 0.001000 rate, 25.228790 seconds, 1038080 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.564643, Class: 1.000000, Obj: 0.382247, No Obj: 0.003377, Avg Recall: 0.584000, count: 125 -Region Avg IOU: 0.606337, Class: 1.000000, Obj: 0.390347, No Obj: 0.003499, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.592433, Class: 1.000000, Obj: 0.389109, No Obj: 0.003699, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.621266, Class: 1.000000, Obj: 0.433871, No Obj: 0.004123, Avg Recall: 0.791045, count: 134 -Region Avg IOU: 0.606233, Class: 1.000000, Obj: 0.365311, No Obj: 0.003926, Avg Recall: 0.726619, count: 139 -Region Avg IOU: 0.616337, Class: 1.000000, Obj: 0.416926, No Obj: 0.003996, Avg Recall: 0.724409, count: 127 -Region Avg IOU: 0.628018, Class: 1.000000, Obj: 0.440688, No Obj: 0.003507, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.635235, Class: 1.000000, Obj: 0.447382, No Obj: 0.002944, Avg Recall: 0.804598, count: 87 -4056: 0.454721, 0.450661 avg, 0.001000 rate, 25.440918 seconds, 1038336 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.570976, Class: 1.000000, Obj: 0.408273, No Obj: 0.003268, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.552525, Class: 1.000000, Obj: 0.353352, No Obj: 0.003496, Avg Recall: 0.640625, count: 128 -Region Avg IOU: 0.549452, Class: 1.000000, Obj: 0.372246, No Obj: 0.003350, Avg Recall: 0.590909, count: 132 -Region Avg IOU: 0.579024, Class: 1.000000, Obj: 0.380557, No Obj: 0.002977, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.527856, Class: 1.000000, Obj: 0.343437, No Obj: 0.003547, Avg Recall: 0.636364, count: 143 -Region Avg IOU: 0.578714, Class: 1.000000, Obj: 0.370525, No Obj: 0.003176, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.584211, Class: 1.000000, Obj: 0.395079, No Obj: 0.002940, Avg Recall: 0.685393, count: 89 -Region Avg IOU: 0.576517, Class: 1.000000, Obj: 0.392039, No Obj: 0.003287, Avg Recall: 0.683168, count: 101 -4057: 0.430873, 0.448683 avg, 0.001000 rate, 25.389717 seconds, 1038592 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.588739, Class: 1.000000, Obj: 0.399617, No Obj: 0.002971, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.559109, Class: 1.000000, Obj: 0.336295, No Obj: 0.002928, Avg Recall: 0.675000, count: 80 -Region Avg IOU: 0.602846, Class: 1.000000, Obj: 0.368462, No Obj: 0.003271, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.573630, Class: 1.000000, Obj: 0.373749, No Obj: 0.002820, Avg Recall: 0.688073, count: 109 -Region Avg IOU: 0.588694, Class: 1.000000, Obj: 0.333310, No Obj: 0.003308, Avg Recall: 0.717557, count: 131 -Region Avg IOU: 0.561027, Class: 1.000000, Obj: 0.350445, No Obj: 0.003310, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.642027, Class: 1.000000, Obj: 0.346070, No Obj: 0.003215, Avg Recall: 0.846154, count: 104 -Region Avg IOU: 0.546816, Class: 1.000000, Obj: 0.301381, No Obj: 0.003239, Avg Recall: 0.666667, count: 117 -4058: 0.441255, 0.447940 avg, 0.001000 rate, 25.942167 seconds, 1038848 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.581510, Class: 1.000000, Obj: 0.355867, No Obj: 0.002801, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.638889, Class: 1.000000, Obj: 0.352503, No Obj: 0.003547, Avg Recall: 0.783217, count: 143 -Region Avg IOU: 0.603967, Class: 1.000000, Obj: 0.351474, No Obj: 0.003028, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.610413, Class: 1.000000, Obj: 0.322627, No Obj: 0.003300, Avg Recall: 0.752000, count: 125 -Region Avg IOU: 0.612509, Class: 1.000000, Obj: 0.318257, No Obj: 0.002431, Avg Recall: 0.773333, count: 75 -Region Avg IOU: 0.555342, Class: 1.000000, Obj: 0.361264, No Obj: 0.002578, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.646147, Class: 1.000000, Obj: 0.463833, No Obj: 0.003000, Avg Recall: 0.790123, count: 81 -Region Avg IOU: 0.565959, Class: 1.000000, Obj: 0.350745, No Obj: 0.002871, Avg Recall: 0.659794, count: 97 -4059: 0.421051, 0.445251 avg, 0.001000 rate, 25.716417 seconds, 1039104 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.626811, Class: 1.000000, Obj: 0.387173, No Obj: 0.003212, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.609548, Class: 1.000000, Obj: 0.361475, No Obj: 0.003322, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.533814, Class: 1.000000, Obj: 0.262433, No Obj: 0.003222, Avg Recall: 0.588652, count: 141 -Region Avg IOU: 0.552593, Class: 1.000000, Obj: 0.311893, No Obj: 0.002985, Avg Recall: 0.671875, count: 128 -Region Avg IOU: 0.588797, Class: 1.000000, Obj: 0.353695, No Obj: 0.002834, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.546172, Class: 1.000000, Obj: 0.324774, No Obj: 0.003018, Avg Recall: 0.624060, count: 133 -Region Avg IOU: 0.622272, Class: 1.000000, Obj: 0.373805, No Obj: 0.002951, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.582342, Class: 1.000000, Obj: 0.346673, No Obj: 0.003035, Avg Recall: 0.702479, count: 121 -4060: 0.518075, 0.452533 avg, 0.001000 rate, 24.719418 seconds, 1039360 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000060 seconds -Region Avg IOU: 0.594529, Class: 1.000000, Obj: 0.321545, No Obj: 0.002610, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.592018, Class: 1.000000, Obj: 0.355111, No Obj: 0.002849, Avg Recall: 0.736111, count: 144 -Region Avg IOU: 0.633636, Class: 1.000000, Obj: 0.390614, No Obj: 0.002471, Avg Recall: 0.834951, count: 103 -Region Avg IOU: 0.671117, Class: 1.000000, Obj: 0.441997, No Obj: 0.002681, Avg Recall: 0.876289, count: 97 -Region Avg IOU: 0.610205, Class: 1.000000, Obj: 0.360693, No Obj: 0.002384, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.586707, Class: 1.000000, Obj: 0.378291, No Obj: 0.002289, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.656572, Class: 1.000000, Obj: 0.388646, No Obj: 0.002859, Avg Recall: 0.842520, count: 127 -Region Avg IOU: 0.607456, Class: 1.000000, Obj: 0.413241, No Obj: 0.002408, Avg Recall: 0.750000, count: 108 -4061: 0.483348, 0.455615 avg, 0.001000 rate, 28.913973 seconds, 1039616 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.651750, Class: 1.000000, Obj: 0.414107, No Obj: 0.002598, Avg Recall: 0.809091, count: 110 -Region Avg IOU: 0.670422, Class: 1.000000, Obj: 0.392316, No Obj: 0.002967, Avg Recall: 0.855769, count: 104 -Region Avg IOU: 0.655240, Class: 1.000000, Obj: 0.353302, No Obj: 0.002673, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.639457, Class: 1.000000, Obj: 0.350648, No Obj: 0.002455, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.643866, Class: 1.000000, Obj: 0.379371, No Obj: 0.003029, Avg Recall: 0.815789, count: 114 -Region Avg IOU: 0.636026, Class: 1.000000, Obj: 0.372213, No Obj: 0.002585, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.663833, Class: 1.000000, Obj: 0.394049, No Obj: 0.002623, Avg Recall: 0.814433, count: 97 -Region Avg IOU: 0.674677, Class: 1.000000, Obj: 0.388750, No Obj: 0.002980, Avg Recall: 0.899083, count: 109 -4062: 0.452183, 0.455272 avg, 0.001000 rate, 29.794619 seconds, 1039872 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.636247, Class: 1.000000, Obj: 0.383754, No Obj: 0.003195, Avg Recall: 0.807407, count: 135 -Region Avg IOU: 0.679868, Class: 1.000000, Obj: 0.371991, No Obj: 0.002552, Avg Recall: 0.836735, count: 98 -Region Avg IOU: 0.624700, Class: 1.000000, Obj: 0.396554, No Obj: 0.002629, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.653242, Class: 1.000000, Obj: 0.342093, No Obj: 0.002969, Avg Recall: 0.838235, count: 136 -Region Avg IOU: 0.625315, Class: 1.000000, Obj: 0.387470, No Obj: 0.002331, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.681860, Class: 1.000000, Obj: 0.435707, No Obj: 0.002204, Avg Recall: 0.870130, count: 77 -Region Avg IOU: 0.651797, Class: 1.000000, Obj: 0.425394, No Obj: 0.002303, Avg Recall: 0.851064, count: 94 -Region Avg IOU: 0.659779, Class: 1.000000, Obj: 0.345033, No Obj: 0.002888, Avg Recall: 0.837607, count: 117 -4063: 0.450753, 0.454820 avg, 0.001000 rate, 29.370943 seconds, 1040128 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.665106, Class: 1.000000, Obj: 0.347548, No Obj: 0.002779, Avg Recall: 0.851485, count: 101 -Region Avg IOU: 0.628367, Class: 1.000000, Obj: 0.368895, No Obj: 0.002780, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.632668, Class: 1.000000, Obj: 0.375706, No Obj: 0.002284, Avg Recall: 0.835165, count: 91 -Region Avg IOU: 0.651880, Class: 1.000000, Obj: 0.334064, No Obj: 0.002578, Avg Recall: 0.808219, count: 73 -Region Avg IOU: 0.626026, Class: 1.000000, Obj: 0.335508, No Obj: 0.002674, Avg Recall: 0.787879, count: 132 -Region Avg IOU: 0.661240, Class: 1.000000, Obj: 0.376158, No Obj: 0.002729, Avg Recall: 0.848214, count: 112 -Region Avg IOU: 0.654037, Class: 1.000000, Obj: 0.421487, No Obj: 0.002568, Avg Recall: 0.790698, count: 86 -Region Avg IOU: 0.654322, Class: 1.000000, Obj: 0.364663, No Obj: 0.002850, Avg Recall: 0.807339, count: 109 -4064: 0.419300, 0.451268 avg, 0.001000 rate, 28.652708 seconds, 1040384 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.611825, Class: 1.000000, Obj: 0.401540, No Obj: 0.002589, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.669807, Class: 1.000000, Obj: 0.386761, No Obj: 0.002818, Avg Recall: 0.864078, count: 103 -Region Avg IOU: 0.642837, Class: 1.000000, Obj: 0.370415, No Obj: 0.002660, Avg Recall: 0.810345, count: 116 -Region Avg IOU: 0.636716, Class: 1.000000, Obj: 0.409827, No Obj: 0.002479, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.619425, Class: 1.000000, Obj: 0.351921, No Obj: 0.002738, Avg Recall: 0.758065, count: 124 -Region Avg IOU: 0.668826, Class: 1.000000, Obj: 0.433867, No Obj: 0.002642, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.604671, Class: 1.000000, Obj: 0.382352, No Obj: 0.002630, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.655834, Class: 1.000000, Obj: 0.349214, No Obj: 0.002790, Avg Recall: 0.836538, count: 104 -4065: 0.456662, 0.451807 avg, 0.001000 rate, 28.498837 seconds, 1040640 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.636253, Class: 1.000000, Obj: 0.361538, No Obj: 0.002492, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.633625, Class: 1.000000, Obj: 0.398307, No Obj: 0.002268, Avg Recall: 0.800000, count: 95 -Region Avg IOU: 0.624081, Class: 1.000000, Obj: 0.362043, No Obj: 0.002439, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.644162, Class: 1.000000, Obj: 0.359871, No Obj: 0.002678, Avg Recall: 0.802083, count: 96 -Region Avg IOU: 0.639521, Class: 1.000000, Obj: 0.322652, No Obj: 0.002475, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.609747, Class: 1.000000, Obj: 0.308479, No Obj: 0.002807, Avg Recall: 0.756579, count: 152 -Region Avg IOU: 0.669210, Class: 1.000000, Obj: 0.384607, No Obj: 0.002223, Avg Recall: 0.813953, count: 86 -Region Avg IOU: 0.684380, Class: 1.000000, Obj: 0.430014, No Obj: 0.002424, Avg Recall: 0.879518, count: 83 -4066: 0.430228, 0.449649 avg, 0.001000 rate, 29.292433 seconds, 1040896 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.664870, Class: 1.000000, Obj: 0.418310, No Obj: 0.002565, Avg Recall: 0.830000, count: 100 -Region Avg IOU: 0.667305, Class: 1.000000, Obj: 0.364726, No Obj: 0.002800, Avg Recall: 0.836207, count: 116 -Region Avg IOU: 0.637187, Class: 1.000000, Obj: 0.349398, No Obj: 0.002557, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.647470, Class: 1.000000, Obj: 0.419225, No Obj: 0.002465, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.662010, Class: 1.000000, Obj: 0.347429, No Obj: 0.002440, Avg Recall: 0.837500, count: 80 -Region Avg IOU: 0.611263, Class: 1.000000, Obj: 0.347933, No Obj: 0.002263, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.597671, Class: 1.000000, Obj: 0.343760, No Obj: 0.002504, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.638887, Class: 1.000000, Obj: 0.390963, No Obj: 0.002690, Avg Recall: 0.758065, count: 124 -4067: 0.470797, 0.451764 avg, 0.001000 rate, 30.253338 seconds, 1041152 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.642228, Class: 1.000000, Obj: 0.349841, No Obj: 0.002595, Avg Recall: 0.784091, count: 88 -Region Avg IOU: 0.646423, Class: 1.000000, Obj: 0.360165, No Obj: 0.002943, Avg Recall: 0.807018, count: 114 -Region Avg IOU: 0.648112, Class: 1.000000, Obj: 0.359172, No Obj: 0.002485, Avg Recall: 0.844660, count: 103 -Region Avg IOU: 0.659640, Class: 1.000000, Obj: 0.357417, No Obj: 0.002575, Avg Recall: 0.820000, count: 100 -Region Avg IOU: 0.625619, Class: 1.000000, Obj: 0.376238, No Obj: 0.002681, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.626882, Class: 1.000000, Obj: 0.387335, No Obj: 0.002644, Avg Recall: 0.755906, count: 127 -Region Avg IOU: 0.626418, Class: 1.000000, Obj: 0.325959, No Obj: 0.002619, Avg Recall: 0.746269, count: 134 -Region Avg IOU: 0.626406, Class: 1.000000, Obj: 0.331307, No Obj: 0.002889, Avg Recall: 0.760870, count: 138 -4068: 0.478512, 0.454439 avg, 0.001000 rate, 28.243261 seconds, 1041408 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.658909, Class: 1.000000, Obj: 0.344690, No Obj: 0.002401, Avg Recall: 0.843137, count: 102 -Region Avg IOU: 0.616794, Class: 1.000000, Obj: 0.369292, No Obj: 0.002276, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.641288, Class: 1.000000, Obj: 0.410111, No Obj: 0.001937, Avg Recall: 0.802632, count: 76 -Region Avg IOU: 0.654074, Class: 1.000000, Obj: 0.364250, No Obj: 0.001999, Avg Recall: 0.793103, count: 87 -Region Avg IOU: 0.632320, Class: 1.000000, Obj: 0.316246, No Obj: 0.002368, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.665026, Class: 1.000000, Obj: 0.372532, No Obj: 0.002675, Avg Recall: 0.811966, count: 117 -Region Avg IOU: 0.629683, Class: 1.000000, Obj: 0.339301, No Obj: 0.002176, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.679594, Class: 1.000000, Obj: 0.384362, No Obj: 0.002598, Avg Recall: 0.844037, count: 109 -4069: 0.419782, 0.450973 avg, 0.001000 rate, 30.699841 seconds, 1041664 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.676150, Class: 1.000000, Obj: 0.368250, No Obj: 0.002454, Avg Recall: 0.852273, count: 88 -Region Avg IOU: 0.634314, Class: 1.000000, Obj: 0.324009, No Obj: 0.002858, Avg Recall: 0.792308, count: 130 -Region Avg IOU: 0.651555, Class: 1.000000, Obj: 0.343258, No Obj: 0.002250, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.635493, Class: 1.000000, Obj: 0.331010, No Obj: 0.002454, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.659789, Class: 1.000000, Obj: 0.438922, No Obj: 0.002334, Avg Recall: 0.824742, count: 97 -Region Avg IOU: 0.627060, Class: 1.000000, Obj: 0.344075, No Obj: 0.002057, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.654457, Class: 1.000000, Obj: 0.305951, No Obj: 0.003150, Avg Recall: 0.821429, count: 140 -Region Avg IOU: 0.639026, Class: 1.000000, Obj: 0.350063, No Obj: 0.002194, Avg Recall: 0.800000, count: 90 -4070: 0.469030, 0.452779 avg, 0.001000 rate, 29.261953 seconds, 1041920 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 12.098126 seconds -Region Avg IOU: 0.627316, Class: 1.000000, Obj: 0.342567, No Obj: 0.002459, Avg Recall: 0.775194, count: 129 -Region Avg IOU: 0.676834, Class: 1.000000, Obj: 0.396068, No Obj: 0.002317, Avg Recall: 0.895833, count: 96 -Region Avg IOU: 0.614859, Class: 1.000000, Obj: 0.324170, No Obj: 0.002801, Avg Recall: 0.735294, count: 136 -Region Avg IOU: 0.640814, Class: 1.000000, Obj: 0.336060, No Obj: 0.002936, Avg Recall: 0.813433, count: 134 -Region Avg IOU: 0.657575, Class: 1.000000, Obj: 0.377782, No Obj: 0.002274, Avg Recall: 0.836957, count: 92 -Region Avg IOU: 0.674584, Class: 1.000000, Obj: 0.363208, No Obj: 0.002647, Avg Recall: 0.867257, count: 113 -Region Avg IOU: 0.666971, Class: 1.000000, Obj: 0.388891, No Obj: 0.002411, Avg Recall: 0.850000, count: 100 -Region Avg IOU: 0.669649, Class: 1.000000, Obj: 0.359043, No Obj: 0.003076, Avg Recall: 0.856115, count: 139 -4071: 0.498040, 0.457305 avg, 0.001000 rate, 28.044218 seconds, 1042176 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.664658, Class: 1.000000, Obj: 0.393034, No Obj: 0.002728, Avg Recall: 0.813559, count: 118 -Region Avg IOU: 0.666560, Class: 1.000000, Obj: 0.375121, No Obj: 0.002163, Avg Recall: 0.850575, count: 87 -Region Avg IOU: 0.695604, Class: 1.000000, Obj: 0.408816, No Obj: 0.002648, Avg Recall: 0.907216, count: 97 -Region Avg IOU: 0.677379, Class: 1.000000, Obj: 0.403929, No Obj: 0.002278, Avg Recall: 0.851351, count: 74 -Region Avg IOU: 0.664652, Class: 1.000000, Obj: 0.406681, No Obj: 0.002675, Avg Recall: 0.837398, count: 123 -Region Avg IOU: 0.648551, Class: 1.000000, Obj: 0.370065, No Obj: 0.002699, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.658286, Class: 1.000000, Obj: 0.398931, No Obj: 0.002659, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.574467, Class: 1.000000, Obj: 0.326035, No Obj: 0.002284, Avg Recall: 0.642276, count: 123 -4072: 0.443533, 0.455928 avg, 0.001000 rate, 29.855955 seconds, 1042432 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.650097, Class: 1.000000, Obj: 0.380185, No Obj: 0.002627, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.637043, Class: 1.000000, Obj: 0.373963, No Obj: 0.002603, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.629556, Class: 1.000000, Obj: 0.334321, No Obj: 0.002543, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.602608, Class: 1.000000, Obj: 0.359779, No Obj: 0.002439, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.650058, Class: 1.000000, Obj: 0.370467, No Obj: 0.002700, Avg Recall: 0.808000, count: 125 -Region Avg IOU: 0.647830, Class: 1.000000, Obj: 0.364982, No Obj: 0.002254, Avg Recall: 0.853333, count: 75 -Region Avg IOU: 0.623048, Class: 1.000000, Obj: 0.370228, No Obj: 0.002619, Avg Recall: 0.783333, count: 120 -Region Avg IOU: 0.666213, Class: 1.000000, Obj: 0.393700, No Obj: 0.002320, Avg Recall: 0.810127, count: 79 -4073: 0.428053, 0.453140 avg, 0.001000 rate, 28.788719 seconds, 1042688 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.666580, Class: 1.000000, Obj: 0.421386, No Obj: 0.002469, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.669400, Class: 1.000000, Obj: 0.381686, No Obj: 0.002890, Avg Recall: 0.862385, count: 109 -Region Avg IOU: 0.610529, Class: 1.000000, Obj: 0.349166, No Obj: 0.002497, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.680815, Class: 1.000000, Obj: 0.405433, No Obj: 0.002964, Avg Recall: 0.877049, count: 122 -Region Avg IOU: 0.655932, Class: 1.000000, Obj: 0.361830, No Obj: 0.002633, Avg Recall: 0.828829, count: 111 -Region Avg IOU: 0.672100, Class: 1.000000, Obj: 0.356245, No Obj: 0.003001, Avg Recall: 0.876033, count: 121 -Region Avg IOU: 0.654615, Class: 1.000000, Obj: 0.363162, No Obj: 0.002586, Avg Recall: 0.791304, count: 115 -Region Avg IOU: 0.626343, Class: 1.000000, Obj: 0.337196, No Obj: 0.002970, Avg Recall: 0.781022, count: 137 -4074: 0.499833, 0.457809 avg, 0.001000 rate, 30.387808 seconds, 1042944 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.645770, Class: 1.000000, Obj: 0.386746, No Obj: 0.002553, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.621492, Class: 1.000000, Obj: 0.345104, No Obj: 0.002761, Avg Recall: 0.738095, count: 126 -Region Avg IOU: 0.670714, Class: 1.000000, Obj: 0.395679, No Obj: 0.002756, Avg Recall: 0.803571, count: 112 -Region Avg IOU: 0.630032, Class: 1.000000, Obj: 0.387095, No Obj: 0.002432, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.649206, Class: 1.000000, Obj: 0.372549, No Obj: 0.002618, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.646689, Class: 1.000000, Obj: 0.331863, No Obj: 0.002702, Avg Recall: 0.818965, count: 116 -Region Avg IOU: 0.654777, Class: 1.000000, Obj: 0.383560, No Obj: 0.002962, Avg Recall: 0.803279, count: 122 -Region Avg IOU: 0.642251, Class: 1.000000, Obj: 0.385613, No Obj: 0.002558, Avg Recall: 0.817308, count: 104 -4075: 0.483308, 0.460359 avg, 0.001000 rate, 29.144802 seconds, 1043200 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.623103, Class: 1.000000, Obj: 0.379663, No Obj: 0.002784, Avg Recall: 0.758065, count: 124 -Region Avg IOU: 0.635960, Class: 1.000000, Obj: 0.393421, No Obj: 0.002833, Avg Recall: 0.805085, count: 118 -Region Avg IOU: 0.646893, Class: 1.000000, Obj: 0.364045, No Obj: 0.002683, Avg Recall: 0.787879, count: 132 -Region Avg IOU: 0.643440, Class: 1.000000, Obj: 0.396104, No Obj: 0.002670, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.633809, Class: 1.000000, Obj: 0.408306, No Obj: 0.002501, Avg Recall: 0.783333, count: 120 -Region Avg IOU: 0.653604, Class: 1.000000, Obj: 0.438846, No Obj: 0.002647, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.674787, Class: 1.000000, Obj: 0.416168, No Obj: 0.002477, Avg Recall: 0.829268, count: 82 -Region Avg IOU: 0.629614, Class: 1.000000, Obj: 0.357195, No Obj: 0.002518, Avg Recall: 0.780952, count: 105 -4076: 0.430410, 0.457364 avg, 0.001000 rate, 28.134979 seconds, 1043456 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.680042, Class: 1.000000, Obj: 0.441780, No Obj: 0.002308, Avg Recall: 0.828947, count: 76 -Region Avg IOU: 0.668396, Class: 1.000000, Obj: 0.418739, No Obj: 0.002852, Avg Recall: 0.843750, count: 96 -Region Avg IOU: 0.641170, Class: 1.000000, Obj: 0.399306, No Obj: 0.002441, Avg Recall: 0.804878, count: 82 -Region Avg IOU: 0.640108, Class: 1.000000, Obj: 0.404081, No Obj: 0.002676, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.646134, Class: 1.000000, Obj: 0.385905, No Obj: 0.002747, Avg Recall: 0.827586, count: 116 -Region Avg IOU: 0.622039, Class: 1.000000, Obj: 0.346891, No Obj: 0.002715, Avg Recall: 0.746269, count: 134 -Region Avg IOU: 0.653223, Class: 1.000000, Obj: 0.383584, No Obj: 0.002487, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.606160, Class: 1.000000, Obj: 0.399900, No Obj: 0.002739, Avg Recall: 0.723810, count: 105 -4077: 0.426541, 0.454282 avg, 0.001000 rate, 29.228125 seconds, 1043712 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.624645, Class: 1.000000, Obj: 0.420074, No Obj: 0.002717, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.660385, Class: 1.000000, Obj: 0.387609, No Obj: 0.002610, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.671351, Class: 1.000000, Obj: 0.403439, No Obj: 0.002610, Avg Recall: 0.831461, count: 89 -Region Avg IOU: 0.672976, Class: 1.000000, Obj: 0.461033, No Obj: 0.002116, Avg Recall: 0.848101, count: 79 -Region Avg IOU: 0.649777, Class: 1.000000, Obj: 0.383057, No Obj: 0.002530, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.633398, Class: 1.000000, Obj: 0.389133, No Obj: 0.002754, Avg Recall: 0.756098, count: 123 -Region Avg IOU: 0.627577, Class: 1.000000, Obj: 0.364565, No Obj: 0.002945, Avg Recall: 0.765152, count: 132 -Region Avg IOU: 0.671428, Class: 1.000000, Obj: 0.382090, No Obj: 0.002499, Avg Recall: 0.818182, count: 88 -4078: 0.426690, 0.451523 avg, 0.001000 rate, 30.448896 seconds, 1043968 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.671172, Class: 1.000000, Obj: 0.403791, No Obj: 0.002729, Avg Recall: 0.901961, count: 102 -Region Avg IOU: 0.661790, Class: 1.000000, Obj: 0.383633, No Obj: 0.002818, Avg Recall: 0.822222, count: 135 -Region Avg IOU: 0.597386, Class: 1.000000, Obj: 0.325061, No Obj: 0.002641, Avg Recall: 0.669231, count: 130 -Region Avg IOU: 0.644707, Class: 1.000000, Obj: 0.356002, No Obj: 0.002809, Avg Recall: 0.816000, count: 125 -Region Avg IOU: 0.664834, Class: 1.000000, Obj: 0.391124, No Obj: 0.002984, Avg Recall: 0.879032, count: 124 -Region Avg IOU: 0.640501, Class: 1.000000, Obj: 0.429896, No Obj: 0.002504, Avg Recall: 0.803571, count: 112 -Region Avg IOU: 0.616884, Class: 1.000000, Obj: 0.372395, No Obj: 0.002437, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.639838, Class: 1.000000, Obj: 0.372832, No Obj: 0.002630, Avg Recall: 0.750000, count: 120 -4079: 0.480591, 0.454430 avg, 0.001000 rate, 30.024988 seconds, 1044224 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.647910, Class: 1.000000, Obj: 0.380242, No Obj: 0.002382, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.627063, Class: 1.000000, Obj: 0.357171, No Obj: 0.002717, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.653084, Class: 1.000000, Obj: 0.372236, No Obj: 0.002517, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.612217, Class: 1.000000, Obj: 0.364992, No Obj: 0.002395, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.648299, Class: 1.000000, Obj: 0.333548, No Obj: 0.002585, Avg Recall: 0.830189, count: 106 -Region Avg IOU: 0.651303, Class: 1.000000, Obj: 0.400409, No Obj: 0.002531, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.608265, Class: 1.000000, Obj: 0.335727, No Obj: 0.002391, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.666662, Class: 1.000000, Obj: 0.347383, No Obj: 0.002779, Avg Recall: 0.829060, count: 117 -4080: 0.431398, 0.452127 avg, 0.001000 rate, 30.307676 seconds, 1044480 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 8.942314 seconds -Region Avg IOU: 0.438906, Class: 1.000000, Obj: 0.215250, No Obj: 0.002954, Avg Recall: 0.405941, count: 101 -Region Avg IOU: 0.472290, Class: 1.000000, Obj: 0.227784, No Obj: 0.002876, Avg Recall: 0.546154, count: 130 -Region Avg IOU: 0.450734, Class: 1.000000, Obj: 0.211336, No Obj: 0.002812, Avg Recall: 0.464286, count: 112 -Region Avg IOU: 0.513333, Class: 1.000000, Obj: 0.257824, No Obj: 0.003076, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.486597, Class: 1.000000, Obj: 0.223551, No Obj: 0.003205, Avg Recall: 0.515873, count: 126 -Region Avg IOU: 0.482188, Class: 1.000000, Obj: 0.262157, No Obj: 0.003053, Avg Recall: 0.517544, count: 114 -Region Avg IOU: 0.551331, Class: 1.000000, Obj: 0.242457, No Obj: 0.002914, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.481333, Class: 1.000000, Obj: 0.250773, No Obj: 0.003342, Avg Recall: 0.525000, count: 120 -4081: 0.550282, 0.461942 avg, 0.001000 rate, 17.889641 seconds, 1044736 images -Loaded: 2.614287 seconds -Region Avg IOU: 0.503675, Class: 1.000000, Obj: 0.231996, No Obj: 0.003296, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.502390, Class: 1.000000, Obj: 0.259889, No Obj: 0.003069, Avg Recall: 0.566372, count: 113 -Region Avg IOU: 0.519571, Class: 1.000000, Obj: 0.212929, No Obj: 0.002853, Avg Recall: 0.615385, count: 91 -Region Avg IOU: 0.476065, Class: 1.000000, Obj: 0.249027, No Obj: 0.002957, Avg Recall: 0.479675, count: 123 -Region Avg IOU: 0.505841, Class: 1.000000, Obj: 0.251325, No Obj: 0.003350, Avg Recall: 0.546392, count: 97 -Region Avg IOU: 0.480133, Class: 1.000000, Obj: 0.229247, No Obj: 0.002932, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.509353, Class: 1.000000, Obj: 0.207826, No Obj: 0.003208, Avg Recall: 0.578947, count: 114 -Region Avg IOU: 0.535368, Class: 1.000000, Obj: 0.273114, No Obj: 0.003117, Avg Recall: 0.571429, count: 98 -4082: 0.495637, 0.465312 avg, 0.001000 rate, 16.961836 seconds, 1044992 images -Loaded: 3.954949 seconds -Region Avg IOU: 0.495902, Class: 1.000000, Obj: 0.265731, No Obj: 0.003063, Avg Recall: 0.538462, count: 117 -Region Avg IOU: 0.509372, Class: 1.000000, Obj: 0.287850, No Obj: 0.002813, Avg Recall: 0.602410, count: 83 -Region Avg IOU: 0.461346, Class: 1.000000, Obj: 0.239135, No Obj: 0.003640, Avg Recall: 0.485714, count: 140 -Region Avg IOU: 0.478058, Class: 1.000000, Obj: 0.259731, No Obj: 0.003425, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.545330, Class: 1.000000, Obj: 0.270694, No Obj: 0.003024, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.497133, Class: 1.000000, Obj: 0.257139, No Obj: 0.003545, Avg Recall: 0.544000, count: 125 -Region Avg IOU: 0.478869, Class: 1.000000, Obj: 0.270193, No Obj: 0.003086, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.528171, Class: 1.000000, Obj: 0.261172, No Obj: 0.003681, Avg Recall: 0.624000, count: 125 -4083: 0.491324, 0.467913 avg, 0.001000 rate, 20.418337 seconds, 1045248 images -Loaded: 1.123893 seconds -Region Avg IOU: 0.515518, Class: 1.000000, Obj: 0.266971, No Obj: 0.003553, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.510426, Class: 1.000000, Obj: 0.257495, No Obj: 0.003913, Avg Recall: 0.559055, count: 127 -Region Avg IOU: 0.497720, Class: 1.000000, Obj: 0.260279, No Obj: 0.004084, Avg Recall: 0.482014, count: 139 -Region Avg IOU: 0.464019, Class: 1.000000, Obj: 0.276842, No Obj: 0.003943, Avg Recall: 0.560976, count: 123 -Region Avg IOU: 0.494467, Class: 1.000000, Obj: 0.288754, No Obj: 0.004041, Avg Recall: 0.528926, count: 121 -Region Avg IOU: 0.501031, Class: 1.000000, Obj: 0.295426, No Obj: 0.004007, Avg Recall: 0.541667, count: 120 -Region Avg IOU: 0.497366, Class: 1.000000, Obj: 0.279185, No Obj: 0.004056, Avg Recall: 0.488189, count: 127 -Region Avg IOU: 0.486460, Class: 1.000000, Obj: 0.302574, No Obj: 0.003315, Avg Recall: 0.500000, count: 86 -4084: 0.507581, 0.471880 avg, 0.001000 rate, 16.595016 seconds, 1045504 images -Loaded: 2.770749 seconds -Region Avg IOU: 0.484804, Class: 1.000000, Obj: 0.334703, No Obj: 0.004568, Avg Recall: 0.504065, count: 123 -Region Avg IOU: 0.494753, Class: 1.000000, Obj: 0.267402, No Obj: 0.004571, Avg Recall: 0.533898, count: 118 -Region Avg IOU: 0.512582, Class: 1.000000, Obj: 0.327784, No Obj: 0.004164, Avg Recall: 0.600000, count: 120 -Region Avg IOU: 0.439991, Class: 1.000000, Obj: 0.274812, No Obj: 0.003730, Avg Recall: 0.445545, count: 101 -Region Avg IOU: 0.531499, Class: 1.000000, Obj: 0.336730, No Obj: 0.004092, Avg Recall: 0.538462, count: 91 -Region Avg IOU: 0.546521, Class: 1.000000, Obj: 0.358105, No Obj: 0.004477, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.499801, Class: 1.000000, Obj: 0.308138, No Obj: 0.003315, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.473509, Class: 1.000000, Obj: 0.299256, No Obj: 0.004006, Avg Recall: 0.484211, count: 95 -4085: 0.428254, 0.467517 avg, 0.001000 rate, 19.499952 seconds, 1045760 images -Loaded: 1.548409 seconds -Region Avg IOU: 0.436973, Class: 1.000000, Obj: 0.302457, No Obj: 0.004316, Avg Recall: 0.423077, count: 104 -Region Avg IOU: 0.479927, Class: 1.000000, Obj: 0.336191, No Obj: 0.004250, Avg Recall: 0.540000, count: 100 -Region Avg IOU: 0.463197, Class: 1.000000, Obj: 0.348382, No Obj: 0.004626, Avg Recall: 0.490909, count: 110 -Region Avg IOU: 0.500073, Class: 1.000000, Obj: 0.377567, No Obj: 0.004663, Avg Recall: 0.559633, count: 109 -Region Avg IOU: 0.501972, Class: 1.000000, Obj: 0.340030, No Obj: 0.003986, Avg Recall: 0.576471, count: 85 -Region Avg IOU: 0.436520, Class: 1.000000, Obj: 0.297319, No Obj: 0.004397, Avg Recall: 0.464912, count: 114 -Region Avg IOU: 0.506372, Class: 1.000000, Obj: 0.335029, No Obj: 0.004184, Avg Recall: 0.541176, count: 85 -Region Avg IOU: 0.492920, Class: 1.000000, Obj: 0.341431, No Obj: 0.004312, Avg Recall: 0.580952, count: 105 -4086: 0.384423, 0.459208 avg, 0.001000 rate, 17.776072 seconds, 1046016 images -Loaded: 3.378623 seconds -Region Avg IOU: 0.454493, Class: 1.000000, Obj: 0.347103, No Obj: 0.004597, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.434343, Class: 1.000000, Obj: 0.401375, No Obj: 0.004707, Avg Recall: 0.443478, count: 115 -Region Avg IOU: 0.480822, Class: 1.000000, Obj: 0.330024, No Obj: 0.004794, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.513263, Class: 1.000000, Obj: 0.360796, No Obj: 0.004596, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.518321, Class: 1.000000, Obj: 0.367588, No Obj: 0.004700, Avg Recall: 0.552083, count: 96 -Region Avg IOU: 0.515518, Class: 1.000000, Obj: 0.370577, No Obj: 0.005284, Avg Recall: 0.571429, count: 112 -Region Avg IOU: 0.451865, Class: 1.000000, Obj: 0.355012, No Obj: 0.005248, Avg Recall: 0.487603, count: 121 -Region Avg IOU: 0.502614, Class: 1.000000, Obj: 0.365421, No Obj: 0.004721, Avg Recall: 0.561905, count: 105 -4087: 0.459044, 0.459191 avg, 0.001000 rate, 17.226898 seconds, 1046272 images -Loaded: 3.932064 seconds -Region Avg IOU: 0.504528, Class: 1.000000, Obj: 0.375211, No Obj: 0.005660, Avg Recall: 0.557377, count: 122 -Region Avg IOU: 0.475518, Class: 1.000000, Obj: 0.402904, No Obj: 0.004314, Avg Recall: 0.540541, count: 74 -Region Avg IOU: 0.497876, Class: 1.000000, Obj: 0.340712, No Obj: 0.004977, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.574422, Class: 1.000000, Obj: 0.413951, No Obj: 0.005234, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.466899, Class: 1.000000, Obj: 0.355373, No Obj: 0.004456, Avg Recall: 0.504505, count: 111 -Region Avg IOU: 0.481649, Class: 1.000000, Obj: 0.382816, No Obj: 0.005181, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.448605, Class: 1.000000, Obj: 0.382785, No Obj: 0.005409, Avg Recall: 0.452991, count: 117 -Region Avg IOU: 0.465295, Class: 1.000000, Obj: 0.355092, No Obj: 0.005542, Avg Recall: 0.491803, count: 122 -4088: 0.473223, 0.460594 avg, 0.001000 rate, 19.561253 seconds, 1046528 images -Loaded: 0.856063 seconds -Region Avg IOU: 0.509622, Class: 1.000000, Obj: 0.381578, No Obj: 0.005572, Avg Recall: 0.608333, count: 120 -Region Avg IOU: 0.452500, Class: 1.000000, Obj: 0.397686, No Obj: 0.004273, Avg Recall: 0.461538, count: 91 -Region Avg IOU: 0.493964, Class: 1.000000, Obj: 0.398387, No Obj: 0.004395, Avg Recall: 0.630952, count: 84 -Region Avg IOU: 0.506933, Class: 1.000000, Obj: 0.377707, No Obj: 0.005452, Avg Recall: 0.563492, count: 126 -Region Avg IOU: 0.514340, Class: 1.000000, Obj: 0.406983, No Obj: 0.005156, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.555989, Class: 1.000000, Obj: 0.416370, No Obj: 0.005734, Avg Recall: 0.647727, count: 88 -Region Avg IOU: 0.503376, Class: 1.000000, Obj: 0.404243, No Obj: 0.004901, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.430187, Class: 1.000000, Obj: 0.358279, No Obj: 0.005254, Avg Recall: 0.470588, count: 136 -4089: 0.445117, 0.459047 avg, 0.001000 rate, 19.796686 seconds, 1046784 images -Loaded: 2.009203 seconds -Region Avg IOU: 0.493783, Class: 1.000000, Obj: 0.376012, No Obj: 0.005045, Avg Recall: 0.541667, count: 96 -Region Avg IOU: 0.512719, Class: 1.000000, Obj: 0.416673, No Obj: 0.005125, Avg Recall: 0.595745, count: 94 -Region Avg IOU: 0.462621, Class: 1.000000, Obj: 0.355770, No Obj: 0.004865, Avg Recall: 0.500000, count: 100 -Region Avg IOU: 0.523766, Class: 1.000000, Obj: 0.425008, No Obj: 0.005313, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.544204, Class: 1.000000, Obj: 0.404918, No Obj: 0.005995, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.454100, Class: 1.000000, Obj: 0.337765, No Obj: 0.005051, Avg Recall: 0.470086, count: 117 -Region Avg IOU: 0.529240, Class: 1.000000, Obj: 0.383217, No Obj: 0.006323, Avg Recall: 0.574627, count: 134 -Region Avg IOU: 0.496439, Class: 1.000000, Obj: 0.352797, No Obj: 0.004786, Avg Recall: 0.539326, count: 89 -4090: 0.457911, 0.458933 avg, 0.001000 rate, 21.108892 seconds, 1047040 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000065 seconds -Region Avg IOU: 0.624336, Class: 1.000000, Obj: 0.507558, No Obj: 0.004754, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.563144, Class: 1.000000, Obj: 0.474556, No Obj: 0.004560, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.603087, Class: 1.000000, Obj: 0.506675, No Obj: 0.004605, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.590859, Class: 1.000000, Obj: 0.502929, No Obj: 0.004342, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.550462, Class: 1.000000, Obj: 0.449247, No Obj: 0.005392, Avg Recall: 0.607143, count: 140 -Region Avg IOU: 0.614041, Class: 1.000000, Obj: 0.511805, No Obj: 0.003790, Avg Recall: 0.750000, count: 88 -Region Avg IOU: 0.593381, Class: 1.000000, Obj: 0.491945, No Obj: 0.004376, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.565590, Class: 1.000000, Obj: 0.461700, No Obj: 0.003716, Avg Recall: 0.653465, count: 101 -4091: 0.422490, 0.455289 avg, 0.001000 rate, 24.968964 seconds, 1047296 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.587881, Class: 1.000000, Obj: 0.470022, No Obj: 0.003927, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.605096, Class: 1.000000, Obj: 0.438341, No Obj: 0.004280, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.619106, Class: 1.000000, Obj: 0.455374, No Obj: 0.003884, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.607733, Class: 1.000000, Obj: 0.452065, No Obj: 0.004105, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.576199, Class: 1.000000, Obj: 0.468795, No Obj: 0.004193, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.577307, Class: 1.000000, Obj: 0.482619, No Obj: 0.004394, Avg Recall: 0.689394, count: 132 -Region Avg IOU: 0.599306, Class: 1.000000, Obj: 0.463382, No Obj: 0.003928, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.610135, Class: 1.000000, Obj: 0.519229, No Obj: 0.003910, Avg Recall: 0.744444, count: 90 -4092: 0.422105, 0.451970 avg, 0.001000 rate, 25.857162 seconds, 1047552 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.627254, Class: 1.000000, Obj: 0.431719, No Obj: 0.003916, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.630415, Class: 1.000000, Obj: 0.408763, No Obj: 0.003167, Avg Recall: 0.783132, count: 83 -Region Avg IOU: 0.581607, Class: 1.000000, Obj: 0.407608, No Obj: 0.003963, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.581372, Class: 1.000000, Obj: 0.421819, No Obj: 0.003723, Avg Recall: 0.708333, count: 120 -Region Avg IOU: 0.589989, Class: 1.000000, Obj: 0.411400, No Obj: 0.003602, Avg Recall: 0.706767, count: 133 -Region Avg IOU: 0.574227, Class: 1.000000, Obj: 0.420400, No Obj: 0.003611, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.573867, Class: 1.000000, Obj: 0.451904, No Obj: 0.003447, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.585388, Class: 1.000000, Obj: 0.400479, No Obj: 0.003548, Avg Recall: 0.752475, count: 101 -4093: 0.411491, 0.447922 avg, 0.001000 rate, 24.967035 seconds, 1047808 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.642543, Class: 1.000000, Obj: 0.416421, No Obj: 0.003067, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.583342, Class: 1.000000, Obj: 0.360411, No Obj: 0.003537, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.630674, Class: 1.000000, Obj: 0.388691, No Obj: 0.003318, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.568774, Class: 1.000000, Obj: 0.355475, No Obj: 0.003505, Avg Recall: 0.662338, count: 154 -Region Avg IOU: 0.615378, Class: 1.000000, Obj: 0.395484, No Obj: 0.003480, Avg Recall: 0.736000, count: 125 -Region Avg IOU: 0.576295, Class: 1.000000, Obj: 0.388859, No Obj: 0.002631, Avg Recall: 0.710843, count: 83 -Region Avg IOU: 0.574008, Class: 1.000000, Obj: 0.378746, No Obj: 0.002845, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.571472, Class: 1.000000, Obj: 0.303448, No Obj: 0.003182, Avg Recall: 0.672414, count: 116 -4094: 0.427877, 0.445918 avg, 0.001000 rate, 25.462336 seconds, 1048064 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.582387, Class: 1.000000, Obj: 0.307416, No Obj: 0.003174, Avg Recall: 0.722222, count: 126 -Region Avg IOU: 0.585673, Class: 1.000000, Obj: 0.331770, No Obj: 0.003398, Avg Recall: 0.671756, count: 131 -Region Avg IOU: 0.543316, Class: 1.000000, Obj: 0.297496, No Obj: 0.002497, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.613549, Class: 1.000000, Obj: 0.326299, No Obj: 0.002953, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.597948, Class: 1.000000, Obj: 0.314646, No Obj: 0.002950, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.581843, Class: 1.000000, Obj: 0.340723, No Obj: 0.002844, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.627705, Class: 1.000000, Obj: 0.387107, No Obj: 0.003273, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.567458, Class: 1.000000, Obj: 0.374077, No Obj: 0.002703, Avg Recall: 0.643478, count: 115 -4095: 0.426384, 0.443964 avg, 0.001000 rate, 23.360567 seconds, 1048320 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.617516, Class: 1.000000, Obj: 0.367314, No Obj: 0.002151, Avg Recall: 0.750000, count: 88 -Region Avg IOU: 0.603096, Class: 1.000000, Obj: 0.344419, No Obj: 0.002690, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.567122, Class: 1.000000, Obj: 0.263966, No Obj: 0.002678, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.596613, Class: 1.000000, Obj: 0.349345, No Obj: 0.002578, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.554889, Class: 1.000000, Obj: 0.299272, No Obj: 0.002484, Avg Recall: 0.654135, count: 133 -Region Avg IOU: 0.563256, Class: 1.000000, Obj: 0.307003, No Obj: 0.002492, Avg Recall: 0.650000, count: 120 -Region Avg IOU: 0.592882, Class: 1.000000, Obj: 0.331073, No Obj: 0.002719, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.564832, Class: 1.000000, Obj: 0.294706, No Obj: 0.002405, Avg Recall: 0.682692, count: 104 -4096: 0.450168, 0.444585 avg, 0.001000 rate, 24.431871 seconds, 1048576 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.586538, Class: 1.000000, Obj: 0.274586, No Obj: 0.002751, Avg Recall: 0.698413, count: 126 -Region Avg IOU: 0.627679, Class: 1.000000, Obj: 0.329817, No Obj: 0.002532, Avg Recall: 0.788889, count: 90 -Region Avg IOU: 0.671473, Class: 1.000000, Obj: 0.381423, No Obj: 0.002873, Avg Recall: 0.819149, count: 94 -Region Avg IOU: 0.640891, Class: 1.000000, Obj: 0.327712, No Obj: 0.002622, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.589787, Class: 1.000000, Obj: 0.269739, No Obj: 0.002230, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.561103, Class: 1.000000, Obj: 0.288093, No Obj: 0.002220, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.597590, Class: 1.000000, Obj: 0.307980, No Obj: 0.002602, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.560690, Class: 1.000000, Obj: 0.292706, No Obj: 0.002210, Avg Recall: 0.679487, count: 78 -4097: 0.396519, 0.439778 avg, 0.001000 rate, 25.027990 seconds, 1048832 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.630986, Class: 1.000000, Obj: 0.325991, No Obj: 0.003103, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.595319, Class: 1.000000, Obj: 0.311860, No Obj: 0.002685, Avg Recall: 0.695312, count: 128 -Region Avg IOU: 0.558344, Class: 1.000000, Obj: 0.282692, No Obj: 0.002342, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.614184, Class: 1.000000, Obj: 0.311367, No Obj: 0.002695, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.573765, Class: 1.000000, Obj: 0.281695, No Obj: 0.002307, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.564340, Class: 1.000000, Obj: 0.297411, No Obj: 0.002223, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.582210, Class: 1.000000, Obj: 0.300112, No Obj: 0.002392, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.634859, Class: 1.000000, Obj: 0.330707, No Obj: 0.002569, Avg Recall: 0.747663, count: 107 -4098: 0.442631, 0.440064 avg, 0.001000 rate, 24.596869 seconds, 1049088 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.620990, Class: 1.000000, Obj: 0.347745, No Obj: 0.002807, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.578824, Class: 1.000000, Obj: 0.299594, No Obj: 0.002480, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.588183, Class: 1.000000, Obj: 0.357276, No Obj: 0.002520, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.572088, Class: 1.000000, Obj: 0.243592, No Obj: 0.002609, Avg Recall: 0.666667, count: 129 -Region Avg IOU: 0.575814, Class: 1.000000, Obj: 0.280486, No Obj: 0.002608, Avg Recall: 0.674603, count: 126 -Region Avg IOU: 0.618992, Class: 1.000000, Obj: 0.365701, No Obj: 0.002399, Avg Recall: 0.740741, count: 81 -Region Avg IOU: 0.603094, Class: 1.000000, Obj: 0.340086, No Obj: 0.002482, Avg Recall: 0.707865, count: 89 -Region Avg IOU: 0.600143, Class: 1.000000, Obj: 0.335051, No Obj: 0.003071, Avg Recall: 0.714286, count: 126 -4099: 0.499923, 0.446050 avg, 0.001000 rate, 25.505907 seconds, 1049344 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.624003, Class: 1.000000, Obj: 0.384352, No Obj: 0.002988, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.611066, Class: 1.000000, Obj: 0.334407, No Obj: 0.002892, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.571265, Class: 1.000000, Obj: 0.294493, No Obj: 0.003053, Avg Recall: 0.656000, count: 125 -Region Avg IOU: 0.548270, Class: 1.000000, Obj: 0.319788, No Obj: 0.002783, Avg Recall: 0.639706, count: 136 -Region Avg IOU: 0.638327, Class: 1.000000, Obj: 0.455134, No Obj: 0.002488, Avg Recall: 0.816901, count: 71 -Region Avg IOU: 0.604752, Class: 1.000000, Obj: 0.334044, No Obj: 0.002649, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.629445, Class: 1.000000, Obj: 0.340960, No Obj: 0.003030, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.639552, Class: 1.000000, Obj: 0.384029, No Obj: 0.003168, Avg Recall: 0.744898, count: 98 -4100: 0.459564, 0.447401 avg, 0.001000 rate, 25.767883 seconds, 1049600 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.760346 seconds -Region Avg IOU: 0.449576, Class: 1.000000, Obj: 0.226810, No Obj: 0.004428, Avg Recall: 0.493243, count: 148 -Region Avg IOU: 0.469827, Class: 1.000000, Obj: 0.261929, No Obj: 0.003080, Avg Recall: 0.487500, count: 80 -Region Avg IOU: 0.526748, Class: 1.000000, Obj: 0.292556, No Obj: 0.003556, Avg Recall: 0.597561, count: 82 -Region Avg IOU: 0.471868, Class: 1.000000, Obj: 0.247276, No Obj: 0.003790, Avg Recall: 0.533898, count: 118 -Region Avg IOU: 0.495275, Class: 1.000000, Obj: 0.273383, No Obj: 0.003681, Avg Recall: 0.546392, count: 97 -Region Avg IOU: 0.467828, Class: 1.000000, Obj: 0.214529, No Obj: 0.003851, Avg Recall: 0.511811, count: 127 -Region Avg IOU: 0.435642, Class: 1.000000, Obj: 0.256338, No Obj: 0.003751, Avg Recall: 0.455285, count: 123 -Region Avg IOU: 0.448728, Class: 1.000000, Obj: 0.239175, No Obj: 0.003690, Avg Recall: 0.512605, count: 119 -4101: 0.536267, 0.456288 avg, 0.001000 rate, 18.389139 seconds, 1049856 images -Loaded: 1.860384 seconds -Region Avg IOU: 0.433757, Class: 1.000000, Obj: 0.272591, No Obj: 0.003999, Avg Recall: 0.425743, count: 101 -Region Avg IOU: 0.501291, Class: 1.000000, Obj: 0.304243, No Obj: 0.004730, Avg Recall: 0.571429, count: 119 -Region Avg IOU: 0.449559, Class: 1.000000, Obj: 0.249912, No Obj: 0.003457, Avg Recall: 0.439560, count: 91 -Region Avg IOU: 0.460384, Class: 1.000000, Obj: 0.238168, No Obj: 0.004055, Avg Recall: 0.495868, count: 121 -Region Avg IOU: 0.487054, Class: 1.000000, Obj: 0.292987, No Obj: 0.003945, Avg Recall: 0.509434, count: 106 -Region Avg IOU: 0.458303, Class: 1.000000, Obj: 0.257246, No Obj: 0.003806, Avg Recall: 0.489130, count: 92 -Region Avg IOU: 0.518216, Class: 1.000000, Obj: 0.311066, No Obj: 0.004044, Avg Recall: 0.590909, count: 88 -Region Avg IOU: 0.494425, Class: 1.000000, Obj: 0.316501, No Obj: 0.004205, Avg Recall: 0.550000, count: 100 -4102: 0.471085, 0.457767 avg, 0.001000 rate, 16.796474 seconds, 1050112 images -Loaded: 3.587027 seconds -Region Avg IOU: 0.473190, Class: 1.000000, Obj: 0.277193, No Obj: 0.004454, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.474001, Class: 1.000000, Obj: 0.319505, No Obj: 0.003994, Avg Recall: 0.511111, count: 90 -Region Avg IOU: 0.493341, Class: 1.000000, Obj: 0.320951, No Obj: 0.004294, Avg Recall: 0.562500, count: 80 -Region Avg IOU: 0.491515, Class: 1.000000, Obj: 0.334250, No Obj: 0.005143, Avg Recall: 0.538462, count: 117 -Region Avg IOU: 0.472289, Class: 1.000000, Obj: 0.326621, No Obj: 0.004183, Avg Recall: 0.478723, count: 94 -Region Avg IOU: 0.466622, Class: 1.000000, Obj: 0.282818, No Obj: 0.003831, Avg Recall: 0.478723, count: 94 -Region Avg IOU: 0.437242, Class: 1.000000, Obj: 0.299971, No Obj: 0.003630, Avg Recall: 0.445652, count: 92 -Region Avg IOU: 0.498068, Class: 1.000000, Obj: 0.304103, No Obj: 0.003961, Avg Recall: 0.580645, count: 93 -4103: 0.407711, 0.452762 avg, 0.001000 rate, 13.352690 seconds, 1050368 images -Loaded: 7.353079 seconds -Region Avg IOU: 0.473041, Class: 1.000000, Obj: 0.319296, No Obj: 0.005261, Avg Recall: 0.495413, count: 109 -Region Avg IOU: 0.460833, Class: 1.000000, Obj: 0.343746, No Obj: 0.004983, Avg Recall: 0.491379, count: 116 -Region Avg IOU: 0.510898, Class: 1.000000, Obj: 0.354186, No Obj: 0.005611, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.531431, Class: 1.000000, Obj: 0.372524, No Obj: 0.004409, Avg Recall: 0.623529, count: 85 -Region Avg IOU: 0.486599, Class: 1.000000, Obj: 0.326052, No Obj: 0.005393, Avg Recall: 0.510949, count: 137 -Region Avg IOU: 0.469552, Class: 1.000000, Obj: 0.312345, No Obj: 0.005779, Avg Recall: 0.470588, count: 102 -Region Avg IOU: 0.439745, Class: 1.000000, Obj: 0.295401, No Obj: 0.004661, Avg Recall: 0.442478, count: 113 -Region Avg IOU: 0.441574, Class: 1.000000, Obj: 0.290528, No Obj: 0.004632, Avg Recall: 0.480392, count: 102 -4104: 0.474078, 0.454893 avg, 0.001000 rate, 15.326308 seconds, 1050624 images -Loaded: 5.577391 seconds -Region Avg IOU: 0.420854, Class: 1.000000, Obj: 0.364775, No Obj: 0.004323, Avg Recall: 0.459770, count: 87 -Region Avg IOU: 0.486427, Class: 1.000000, Obj: 0.351161, No Obj: 0.005362, Avg Recall: 0.543478, count: 92 -Region Avg IOU: 0.439316, Class: 1.000000, Obj: 0.336411, No Obj: 0.005593, Avg Recall: 0.471698, count: 106 -Region Avg IOU: 0.448839, Class: 1.000000, Obj: 0.303570, No Obj: 0.005521, Avg Recall: 0.532258, count: 124 -Region Avg IOU: 0.455408, Class: 1.000000, Obj: 0.395000, No Obj: 0.005442, Avg Recall: 0.471698, count: 106 -Region Avg IOU: 0.489722, Class: 1.000000, Obj: 0.339585, No Obj: 0.005059, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.472222, Class: 1.000000, Obj: 0.337802, No Obj: 0.004899, Avg Recall: 0.547619, count: 84 -Region Avg IOU: 0.502702, Class: 1.000000, Obj: 0.395819, No Obj: 0.005359, Avg Recall: 0.561798, count: 89 -4105: 0.470807, 0.456485 avg, 0.001000 rate, 15.917323 seconds, 1050880 images -Loaded: 3.918603 seconds -Region Avg IOU: 0.464550, Class: 1.000000, Obj: 0.339031, No Obj: 0.005994, Avg Recall: 0.513514, count: 111 -Region Avg IOU: 0.474161, Class: 1.000000, Obj: 0.349444, No Obj: 0.004584, Avg Recall: 0.500000, count: 80 -Region Avg IOU: 0.436535, Class: 1.000000, Obj: 0.372935, No Obj: 0.005317, Avg Recall: 0.380952, count: 105 -Region Avg IOU: 0.484753, Class: 1.000000, Obj: 0.338456, No Obj: 0.006298, Avg Recall: 0.512605, count: 119 -Region Avg IOU: 0.435862, Class: 1.000000, Obj: 0.318550, No Obj: 0.005476, Avg Recall: 0.467290, count: 107 -Region Avg IOU: 0.436159, Class: 1.000000, Obj: 0.331452, No Obj: 0.005419, Avg Recall: 0.509259, count: 108 -Region Avg IOU: 0.451815, Class: 1.000000, Obj: 0.396092, No Obj: 0.006360, Avg Recall: 0.479675, count: 123 -Region Avg IOU: 0.454332, Class: 1.000000, Obj: 0.327465, No Obj: 0.005580, Avg Recall: 0.504132, count: 121 -4106: 0.448315, 0.455668 avg, 0.001000 rate, 16.131523 seconds, 1051136 images -Loaded: 4.481701 seconds -Region Avg IOU: 0.456899, Class: 1.000000, Obj: 0.378978, No Obj: 0.006982, Avg Recall: 0.496454, count: 141 -Region Avg IOU: 0.454505, Class: 1.000000, Obj: 0.391628, No Obj: 0.006511, Avg Recall: 0.504000, count: 125 -Region Avg IOU: 0.403789, Class: 1.000000, Obj: 0.338313, No Obj: 0.005060, Avg Recall: 0.432990, count: 97 -Region Avg IOU: 0.502497, Class: 1.000000, Obj: 0.418604, No Obj: 0.006207, Avg Recall: 0.539326, count: 89 -Region Avg IOU: 0.396657, Class: 1.000000, Obj: 0.346286, No Obj: 0.005443, Avg Recall: 0.361345, count: 119 -Region Avg IOU: 0.473119, Class: 1.000000, Obj: 0.363853, No Obj: 0.006103, Avg Recall: 0.525000, count: 120 -Region Avg IOU: 0.432871, Class: 1.000000, Obj: 0.354457, No Obj: 0.005290, Avg Recall: 0.453608, count: 97 -Region Avg IOU: 0.501002, Class: 1.000000, Obj: 0.387116, No Obj: 0.006212, Avg Recall: 0.513043, count: 115 -4107: 0.545552, 0.464656 avg, 0.001000 rate, 16.382626 seconds, 1051392 images -Loaded: 4.696634 seconds -Region Avg IOU: 0.449570, Class: 1.000000, Obj: 0.378879, No Obj: 0.005937, Avg Recall: 0.500000, count: 132 -Region Avg IOU: 0.441310, Class: 1.000000, Obj: 0.368516, No Obj: 0.005279, Avg Recall: 0.500000, count: 110 -Region Avg IOU: 0.444308, Class: 1.000000, Obj: 0.354786, No Obj: 0.005627, Avg Recall: 0.509615, count: 104 -Region Avg IOU: 0.464520, Class: 1.000000, Obj: 0.369163, No Obj: 0.006216, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.440651, Class: 1.000000, Obj: 0.371399, No Obj: 0.006223, Avg Recall: 0.445312, count: 128 -Region Avg IOU: 0.496221, Class: 1.000000, Obj: 0.397556, No Obj: 0.006295, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.461730, Class: 1.000000, Obj: 0.367136, No Obj: 0.005538, Avg Recall: 0.483871, count: 93 -Region Avg IOU: 0.439175, Class: 1.000000, Obj: 0.398750, No Obj: 0.005531, Avg Recall: 0.465517, count: 116 -4108: 0.544200, 0.472611 avg, 0.001000 rate, 17.491772 seconds, 1051648 images -Loaded: 4.586105 seconds -Region Avg IOU: 0.483487, Class: 1.000000, Obj: 0.371679, No Obj: 0.005983, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.473049, Class: 1.000000, Obj: 0.351703, No Obj: 0.005503, Avg Recall: 0.509434, count: 106 -Region Avg IOU: 0.481724, Class: 1.000000, Obj: 0.417955, No Obj: 0.005954, Avg Recall: 0.546296, count: 108 -Region Avg IOU: 0.480630, Class: 1.000000, Obj: 0.344523, No Obj: 0.006422, Avg Recall: 0.520325, count: 123 -Region Avg IOU: 0.445138, Class: 1.000000, Obj: 0.312577, No Obj: 0.005719, Avg Recall: 0.474227, count: 97 -Region Avg IOU: 0.437960, Class: 1.000000, Obj: 0.347432, No Obj: 0.005969, Avg Recall: 0.472000, count: 125 -Region Avg IOU: 0.425366, Class: 1.000000, Obj: 0.329477, No Obj: 0.004870, Avg Recall: 0.415094, count: 106 -Region Avg IOU: 0.451295, Class: 1.000000, Obj: 0.361090, No Obj: 0.005511, Avg Recall: 0.488636, count: 88 -4109: 0.488234, 0.474173 avg, 0.001000 rate, 15.464671 seconds, 1051904 images -Loaded: 5.826694 seconds -Region Avg IOU: 0.490573, Class: 1.000000, Obj: 0.397130, No Obj: 0.005439, Avg Recall: 0.529412, count: 102 -Region Avg IOU: 0.458093, Class: 1.000000, Obj: 0.319910, No Obj: 0.005135, Avg Recall: 0.500000, count: 104 -Region Avg IOU: 0.461025, Class: 1.000000, Obj: 0.384927, No Obj: 0.005677, Avg Recall: 0.500000, count: 112 -Region Avg IOU: 0.399494, Class: 1.000000, Obj: 0.282117, No Obj: 0.005509, Avg Recall: 0.414286, count: 140 -Region Avg IOU: 0.449439, Class: 1.000000, Obj: 0.348402, No Obj: 0.005547, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.468637, Class: 1.000000, Obj: 0.331119, No Obj: 0.005521, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.462926, Class: 1.000000, Obj: 0.316734, No Obj: 0.005287, Avg Recall: 0.508333, count: 120 -Region Avg IOU: 0.434618, Class: 1.000000, Obj: 0.325780, No Obj: 0.004688, Avg Recall: 0.454545, count: 110 -4110: 0.528505, 0.479606 avg, 0.001000 rate, 18.364027 seconds, 1052160 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000076 seconds -Region Avg IOU: 0.578062, Class: 1.000000, Obj: 0.509354, No Obj: 0.004077, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.593280, Class: 1.000000, Obj: 0.413616, No Obj: 0.003910, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.600421, Class: 1.000000, Obj: 0.466458, No Obj: 0.003855, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.634416, Class: 1.000000, Obj: 0.504345, No Obj: 0.004269, Avg Recall: 0.778761, count: 113 -Region Avg IOU: 0.616583, Class: 1.000000, Obj: 0.456969, No Obj: 0.004068, Avg Recall: 0.846154, count: 117 -Region Avg IOU: 0.587018, Class: 1.000000, Obj: 0.459592, No Obj: 0.004192, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.584145, Class: 1.000000, Obj: 0.503085, No Obj: 0.004060, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.613097, Class: 1.000000, Obj: 0.505268, No Obj: 0.004727, Avg Recall: 0.713235, count: 136 -4111: 0.517838, 0.483429 avg, 0.001000 rate, 28.604059 seconds, 1052416 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.626607, Class: 1.000000, Obj: 0.424278, No Obj: 0.003403, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.621689, Class: 1.000000, Obj: 0.434379, No Obj: 0.003545, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.653939, Class: 1.000000, Obj: 0.481365, No Obj: 0.003648, Avg Recall: 0.819149, count: 94 -Region Avg IOU: 0.593249, Class: 1.000000, Obj: 0.410115, No Obj: 0.003752, Avg Recall: 0.714286, count: 119 -Region Avg IOU: 0.633890, Class: 1.000000, Obj: 0.429324, No Obj: 0.003117, Avg Recall: 0.756098, count: 82 -Region Avg IOU: 0.611530, Class: 1.000000, Obj: 0.446119, No Obj: 0.003570, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.603419, Class: 1.000000, Obj: 0.491181, No Obj: 0.003406, Avg Recall: 0.764228, count: 123 -Region Avg IOU: 0.682268, Class: 1.000000, Obj: 0.449897, No Obj: 0.003373, Avg Recall: 0.866667, count: 90 -4112: 0.464567, 0.481543 avg, 0.001000 rate, 28.285564 seconds, 1052672 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.578452, Class: 1.000000, Obj: 0.402761, No Obj: 0.002756, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.636369, Class: 1.000000, Obj: 0.400688, No Obj: 0.002605, Avg Recall: 0.759036, count: 83 -Region Avg IOU: 0.618144, Class: 1.000000, Obj: 0.403495, No Obj: 0.003017, Avg Recall: 0.734375, count: 128 -Region Avg IOU: 0.642720, Class: 1.000000, Obj: 0.382925, No Obj: 0.002999, Avg Recall: 0.786260, count: 131 -Region Avg IOU: 0.648598, Class: 1.000000, Obj: 0.462075, No Obj: 0.002840, Avg Recall: 0.797872, count: 94 -Region Avg IOU: 0.612941, Class: 1.000000, Obj: 0.348014, No Obj: 0.003090, Avg Recall: 0.777778, count: 135 -Region Avg IOU: 0.642353, Class: 1.000000, Obj: 0.414447, No Obj: 0.002605, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.619424, Class: 1.000000, Obj: 0.377006, No Obj: 0.002856, Avg Recall: 0.756098, count: 123 -4113: 0.485783, 0.481967 avg, 0.001000 rate, 27.501451 seconds, 1052928 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.631368, Class: 1.000000, Obj: 0.291931, No Obj: 0.002430, Avg Recall: 0.775281, count: 89 -Region Avg IOU: 0.633614, Class: 1.000000, Obj: 0.330014, No Obj: 0.002343, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.640604, Class: 1.000000, Obj: 0.340593, No Obj: 0.002184, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.633897, Class: 1.000000, Obj: 0.382363, No Obj: 0.002520, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.636743, Class: 1.000000, Obj: 0.341251, No Obj: 0.002437, Avg Recall: 0.756098, count: 123 -Region Avg IOU: 0.629463, Class: 1.000000, Obj: 0.321485, No Obj: 0.002188, Avg Recall: 0.810345, count: 116 -Region Avg IOU: 0.638361, Class: 1.000000, Obj: 0.341869, No Obj: 0.002180, Avg Recall: 0.822430, count: 107 -Region Avg IOU: 0.597058, Class: 1.000000, Obj: 0.297211, No Obj: 0.002125, Avg Recall: 0.694915, count: 118 -4114: 0.470053, 0.480776 avg, 0.001000 rate, 28.351704 seconds, 1053184 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.634054, Class: 1.000000, Obj: 0.309102, No Obj: 0.002024, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.645874, Class: 1.000000, Obj: 0.302021, No Obj: 0.002147, Avg Recall: 0.795455, count: 88 -Region Avg IOU: 0.632359, Class: 1.000000, Obj: 0.267567, No Obj: 0.002059, Avg Recall: 0.813559, count: 118 -Region Avg IOU: 0.601239, Class: 1.000000, Obj: 0.291297, No Obj: 0.001632, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.633012, Class: 1.000000, Obj: 0.300883, No Obj: 0.002082, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.679561, Class: 1.000000, Obj: 0.332840, No Obj: 0.002108, Avg Recall: 0.866667, count: 105 -Region Avg IOU: 0.651179, Class: 1.000000, Obj: 0.303188, No Obj: 0.002211, Avg Recall: 0.836957, count: 92 -Region Avg IOU: 0.608242, Class: 1.000000, Obj: 0.288165, No Obj: 0.001757, Avg Recall: 0.717391, count: 92 -4115: 0.460825, 0.478781 avg, 0.001000 rate, 27.571342 seconds, 1053440 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.635623, Class: 1.000000, Obj: 0.259226, No Obj: 0.001999, Avg Recall: 0.786325, count: 117 -Region Avg IOU: 0.639634, Class: 1.000000, Obj: 0.253119, No Obj: 0.002278, Avg Recall: 0.776119, count: 134 -Region Avg IOU: 0.615639, Class: 1.000000, Obj: 0.259835, No Obj: 0.001658, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.649134, Class: 1.000000, Obj: 0.260622, No Obj: 0.001894, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.668941, Class: 1.000000, Obj: 0.287941, No Obj: 0.001898, Avg Recall: 0.836538, count: 104 -Region Avg IOU: 0.621216, Class: 1.000000, Obj: 0.243064, No Obj: 0.001738, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.677948, Class: 1.000000, Obj: 0.289947, No Obj: 0.001956, Avg Recall: 0.882979, count: 94 -Region Avg IOU: 0.634391, Class: 1.000000, Obj: 0.273588, No Obj: 0.002004, Avg Recall: 0.744000, count: 125 -4116: 0.527013, 0.483604 avg, 0.001000 rate, 29.449400 seconds, 1053696 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.610216, Class: 1.000000, Obj: 0.258596, No Obj: 0.001780, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.587411, Class: 1.000000, Obj: 0.258096, No Obj: 0.001862, Avg Recall: 0.710938, count: 128 -Region Avg IOU: 0.630750, Class: 1.000000, Obj: 0.255448, No Obj: 0.001830, Avg Recall: 0.800000, count: 125 -Region Avg IOU: 0.685441, Class: 1.000000, Obj: 0.299129, No Obj: 0.002062, Avg Recall: 0.833333, count: 108 -Region Avg IOU: 0.651307, Class: 1.000000, Obj: 0.230323, No Obj: 0.002042, Avg Recall: 0.798561, count: 139 -Region Avg IOU: 0.642227, Class: 1.000000, Obj: 0.247455, No Obj: 0.001836, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.633100, Class: 1.000000, Obj: 0.237566, No Obj: 0.001948, Avg Recall: 0.793893, count: 131 -Region Avg IOU: 0.628018, Class: 1.000000, Obj: 0.250623, No Obj: 0.001708, Avg Recall: 0.800000, count: 110 -4117: 0.533300, 0.488574 avg, 0.001000 rate, 28.769901 seconds, 1053952 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.630462, Class: 1.000000, Obj: 0.339556, No Obj: 0.001582, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.620900, Class: 1.000000, Obj: 0.237679, No Obj: 0.002115, Avg Recall: 0.752000, count: 125 -Region Avg IOU: 0.596070, Class: 1.000000, Obj: 0.271803, No Obj: 0.002084, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.581471, Class: 1.000000, Obj: 0.291606, No Obj: 0.001778, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.640823, Class: 1.000000, Obj: 0.274902, No Obj: 0.002097, Avg Recall: 0.789474, count: 133 -Region Avg IOU: 0.654939, Class: 1.000000, Obj: 0.270591, No Obj: 0.002181, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.679454, Class: 1.000000, Obj: 0.308279, No Obj: 0.001890, Avg Recall: 0.871287, count: 101 -Region Avg IOU: 0.661990, Class: 1.000000, Obj: 0.294784, No Obj: 0.001798, Avg Recall: 0.842105, count: 95 -4118: 0.496460, 0.489362 avg, 0.001000 rate, 26.504673 seconds, 1054208 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.643382, Class: 1.000000, Obj: 0.319262, No Obj: 0.001990, Avg Recall: 0.838384, count: 99 -Region Avg IOU: 0.609008, Class: 1.000000, Obj: 0.266556, No Obj: 0.002069, Avg Recall: 0.759399, count: 133 -Region Avg IOU: 0.634613, Class: 1.000000, Obj: 0.318630, No Obj: 0.002286, Avg Recall: 0.806202, count: 129 -Region Avg IOU: 0.632857, Class: 1.000000, Obj: 0.278005, No Obj: 0.001985, Avg Recall: 0.807018, count: 114 -Region Avg IOU: 0.649805, Class: 1.000000, Obj: 0.312631, No Obj: 0.001691, Avg Recall: 0.839506, count: 81 -Region Avg IOU: 0.638588, Class: 1.000000, Obj: 0.323410, No Obj: 0.002296, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.604334, Class: 1.000000, Obj: 0.308789, No Obj: 0.002131, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.643699, Class: 1.000000, Obj: 0.304863, No Obj: 0.002058, Avg Recall: 0.791209, count: 91 -4119: 0.468861, 0.487312 avg, 0.001000 rate, 28.169596 seconds, 1054464 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.638114, Class: 1.000000, Obj: 0.374763, No Obj: 0.002672, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.670202, Class: 1.000000, Obj: 0.387872, No Obj: 0.002452, Avg Recall: 0.857143, count: 98 -Region Avg IOU: 0.627595, Class: 1.000000, Obj: 0.325075, No Obj: 0.002663, Avg Recall: 0.758621, count: 145 -Region Avg IOU: 0.639294, Class: 1.000000, Obj: 0.345617, No Obj: 0.002234, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.665768, Class: 1.000000, Obj: 0.352261, No Obj: 0.002585, Avg Recall: 0.878788, count: 99 -Region Avg IOU: 0.616917, Class: 1.000000, Obj: 0.346322, No Obj: 0.002738, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.661856, Class: 1.000000, Obj: 0.374055, No Obj: 0.002960, Avg Recall: 0.842593, count: 108 -Region Avg IOU: 0.685210, Class: 1.000000, Obj: 0.366320, No Obj: 0.002708, Avg Recall: 0.859813, count: 107 -4120: 0.514319, 0.490013 avg, 0.001000 rate, 27.799393 seconds, 1054720 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 15.140060 seconds -Region Avg IOU: 0.535699, Class: 1.000000, Obj: 0.305437, No Obj: 0.002948, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.547640, Class: 1.000000, Obj: 0.304032, No Obj: 0.003069, Avg Recall: 0.696000, count: 125 -Region Avg IOU: 0.539802, Class: 1.000000, Obj: 0.297177, No Obj: 0.003186, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.554148, Class: 1.000000, Obj: 0.317517, No Obj: 0.003169, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.585716, Class: 1.000000, Obj: 0.306337, No Obj: 0.003310, Avg Recall: 0.698276, count: 116 -Region Avg IOU: 0.545290, Class: 1.000000, Obj: 0.295530, No Obj: 0.002712, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.597089, Class: 1.000000, Obj: 0.330635, No Obj: 0.003386, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.552702, Class: 1.000000, Obj: 0.282701, No Obj: 0.002960, Avg Recall: 0.635514, count: 107 -4121: 0.507857, 0.491797 avg, 0.001000 rate, 23.362221 seconds, 1054976 images -Loaded: 0.015001 seconds -Region Avg IOU: 0.542437, Class: 1.000000, Obj: 0.320893, No Obj: 0.003346, Avg Recall: 0.630252, count: 119 -Region Avg IOU: 0.533166, Class: 1.000000, Obj: 0.344519, No Obj: 0.003420, Avg Recall: 0.598425, count: 127 -Region Avg IOU: 0.596471, Class: 1.000000, Obj: 0.356699, No Obj: 0.003718, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.598001, Class: 1.000000, Obj: 0.351937, No Obj: 0.003887, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.607124, Class: 1.000000, Obj: 0.337412, No Obj: 0.003978, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.608083, Class: 1.000000, Obj: 0.323791, No Obj: 0.003104, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.575183, Class: 1.000000, Obj: 0.345666, No Obj: 0.003294, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.596567, Class: 1.000000, Obj: 0.330474, No Obj: 0.004017, Avg Recall: 0.725000, count: 120 -4122: 0.484388, 0.491056 avg, 0.001000 rate, 23.222704 seconds, 1055232 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.612724, Class: 1.000000, Obj: 0.401825, No Obj: 0.004699, Avg Recall: 0.712230, count: 139 -Region Avg IOU: 0.602506, Class: 1.000000, Obj: 0.389601, No Obj: 0.003837, Avg Recall: 0.679012, count: 81 -Region Avg IOU: 0.600291, Class: 1.000000, Obj: 0.381914, No Obj: 0.005211, Avg Recall: 0.714286, count: 133 -Region Avg IOU: 0.583082, Class: 1.000000, Obj: 0.379955, No Obj: 0.004270, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.498518, Class: 1.000000, Obj: 0.313636, No Obj: 0.003336, Avg Recall: 0.515464, count: 97 -Region Avg IOU: 0.537280, Class: 1.000000, Obj: 0.351644, No Obj: 0.004017, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.559772, Class: 1.000000, Obj: 0.342863, No Obj: 0.004509, Avg Recall: 0.694030, count: 134 -Region Avg IOU: 0.599069, Class: 1.000000, Obj: 0.402449, No Obj: 0.003801, Avg Recall: 0.717391, count: 92 -4123: 0.431615, 0.485112 avg, 0.001000 rate, 22.481718 seconds, 1055488 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.568356, Class: 1.000000, Obj: 0.427743, No Obj: 0.004037, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.573223, Class: 1.000000, Obj: 0.391467, No Obj: 0.004731, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.563527, Class: 1.000000, Obj: 0.432623, No Obj: 0.004439, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.562588, Class: 1.000000, Obj: 0.455513, No Obj: 0.004064, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.563951, Class: 1.000000, Obj: 0.445950, No Obj: 0.004101, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.515524, Class: 1.000000, Obj: 0.371251, No Obj: 0.004514, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.608886, Class: 1.000000, Obj: 0.426132, No Obj: 0.004624, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.519952, Class: 1.000000, Obj: 0.392498, No Obj: 0.003910, Avg Recall: 0.596330, count: 109 -4124: 0.386161, 0.475217 avg, 0.001000 rate, 23.674456 seconds, 1055744 images -Loaded: 0.234198 seconds -Region Avg IOU: 0.621387, Class: 1.000000, Obj: 0.477655, No Obj: 0.004938, Avg Recall: 0.784091, count: 88 -Region Avg IOU: 0.556423, Class: 1.000000, Obj: 0.465017, No Obj: 0.004753, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.550613, Class: 1.000000, Obj: 0.418207, No Obj: 0.005351, Avg Recall: 0.641667, count: 120 -Region Avg IOU: 0.593456, Class: 1.000000, Obj: 0.450803, No Obj: 0.004616, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.602302, Class: 1.000000, Obj: 0.497826, No Obj: 0.004581, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.545359, Class: 1.000000, Obj: 0.412474, No Obj: 0.005114, Avg Recall: 0.650000, count: 120 -Region Avg IOU: 0.648724, Class: 1.000000, Obj: 0.541535, No Obj: 0.004564, Avg Recall: 0.753247, count: 77 -Region Avg IOU: 0.571643, Class: 1.000000, Obj: 0.471423, No Obj: 0.005089, Avg Recall: 0.666667, count: 111 -4125: 0.433516, 0.471047 avg, 0.001000 rate, 23.590731 seconds, 1056000 images -Loaded: 0.000119 seconds -Region Avg IOU: 0.559743, Class: 1.000000, Obj: 0.466311, No Obj: 0.005141, Avg Recall: 0.643939, count: 132 -Region Avg IOU: 0.502785, Class: 1.000000, Obj: 0.460863, No Obj: 0.004538, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.560202, Class: 1.000000, Obj: 0.470010, No Obj: 0.004356, Avg Recall: 0.662651, count: 83 -Region Avg IOU: 0.544712, Class: 1.000000, Obj: 0.441792, No Obj: 0.004824, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.502043, Class: 1.000000, Obj: 0.436041, No Obj: 0.004406, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.516797, Class: 1.000000, Obj: 0.466842, No Obj: 0.005735, Avg Recall: 0.569106, count: 123 -Region Avg IOU: 0.561360, Class: 1.000000, Obj: 0.427718, No Obj: 0.005162, Avg Recall: 0.624060, count: 133 -Region Avg IOU: 0.595639, Class: 1.000000, Obj: 0.451005, No Obj: 0.004362, Avg Recall: 0.760000, count: 100 -4126: 0.499816, 0.473924 avg, 0.001000 rate, 22.271963 seconds, 1056256 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.584803, Class: 1.000000, Obj: 0.491949, No Obj: 0.005087, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.570044, Class: 1.000000, Obj: 0.439350, No Obj: 0.004622, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.570837, Class: 1.000000, Obj: 0.437181, No Obj: 0.005261, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.596296, Class: 1.000000, Obj: 0.517270, No Obj: 0.004530, Avg Recall: 0.704225, count: 71 -Region Avg IOU: 0.546963, Class: 1.000000, Obj: 0.464097, No Obj: 0.004475, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.547982, Class: 1.000000, Obj: 0.424366, No Obj: 0.005456, Avg Recall: 0.664179, count: 134 -Region Avg IOU: 0.523178, Class: 1.000000, Obj: 0.427001, No Obj: 0.004987, Avg Recall: 0.591667, count: 120 -Region Avg IOU: 0.606265, Class: 1.000000, Obj: 0.471377, No Obj: 0.004950, Avg Recall: 0.717172, count: 99 -4127: 0.444640, 0.470995 avg, 0.001000 rate, 24.389080 seconds, 1056512 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.530308, Class: 1.000000, Obj: 0.466258, No Obj: 0.004466, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.514713, Class: 1.000000, Obj: 0.412457, No Obj: 0.004818, Avg Recall: 0.576923, count: 130 -Region Avg IOU: 0.549656, Class: 1.000000, Obj: 0.475540, No Obj: 0.004539, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.546345, Class: 1.000000, Obj: 0.466733, No Obj: 0.004780, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.592376, Class: 1.000000, Obj: 0.474269, No Obj: 0.005067, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.563639, Class: 1.000000, Obj: 0.450562, No Obj: 0.004824, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.526452, Class: 1.000000, Obj: 0.403579, No Obj: 0.004771, Avg Recall: 0.595420, count: 131 -Region Avg IOU: 0.484515, Class: 1.000000, Obj: 0.349536, No Obj: 0.004706, Avg Recall: 0.531532, count: 111 -4128: 0.441653, 0.468061 avg, 0.001000 rate, 23.647940 seconds, 1056768 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.541547, Class: 1.000000, Obj: 0.435045, No Obj: 0.004015, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.523648, Class: 1.000000, Obj: 0.386129, No Obj: 0.004190, Avg Recall: 0.575000, count: 120 -Region Avg IOU: 0.492013, Class: 1.000000, Obj: 0.389330, No Obj: 0.004200, Avg Recall: 0.584615, count: 130 -Region Avg IOU: 0.521908, Class: 1.000000, Obj: 0.447222, No Obj: 0.004533, Avg Recall: 0.581395, count: 129 -Region Avg IOU: 0.522311, Class: 1.000000, Obj: 0.381261, No Obj: 0.003757, Avg Recall: 0.605769, count: 104 -Region Avg IOU: 0.509435, Class: 1.000000, Obj: 0.392588, No Obj: 0.004989, Avg Recall: 0.554839, count: 155 -Region Avg IOU: 0.558228, Class: 1.000000, Obj: 0.396221, No Obj: 0.004259, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.534114, Class: 1.000000, Obj: 0.406408, No Obj: 0.004605, Avg Recall: 0.601562, count: 128 -4129: 0.507422, 0.471997 avg, 0.001000 rate, 23.217852 seconds, 1057024 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.544992, Class: 1.000000, Obj: 0.389792, No Obj: 0.003785, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.540945, Class: 1.000000, Obj: 0.363706, No Obj: 0.004168, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.539504, Class: 1.000000, Obj: 0.375214, No Obj: 0.003695, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.588602, Class: 1.000000, Obj: 0.372360, No Obj: 0.003701, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.541426, Class: 1.000000, Obj: 0.329021, No Obj: 0.003548, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.559310, Class: 1.000000, Obj: 0.403717, No Obj: 0.003740, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.552590, Class: 1.000000, Obj: 0.362059, No Obj: 0.003380, Avg Recall: 0.625000, count: 88 -Region Avg IOU: 0.540928, Class: 1.000000, Obj: 0.383889, No Obj: 0.003484, Avg Recall: 0.595238, count: 84 -4130: 0.424997, 0.467297 avg, 0.001000 rate, 24.258928 seconds, 1057280 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.547417 seconds -Region Avg IOU: 0.479664, Class: 1.000000, Obj: 0.257847, No Obj: 0.003295, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.501200, Class: 1.000000, Obj: 0.262765, No Obj: 0.003319, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.521838, Class: 1.000000, Obj: 0.278498, No Obj: 0.003600, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.436069, Class: 1.000000, Obj: 0.227963, No Obj: 0.003099, Avg Recall: 0.456140, count: 114 -Region Avg IOU: 0.465572, Class: 1.000000, Obj: 0.236251, No Obj: 0.003310, Avg Recall: 0.491525, count: 118 -Region Avg IOU: 0.515486, Class: 1.000000, Obj: 0.225316, No Obj: 0.003705, Avg Recall: 0.551181, count: 127 -Region Avg IOU: 0.466627, Class: 1.000000, Obj: 0.262495, No Obj: 0.003121, Avg Recall: 0.471698, count: 106 -Region Avg IOU: 0.467839, Class: 1.000000, Obj: 0.261642, No Obj: 0.003140, Avg Recall: 0.485981, count: 107 -4131: 0.578461, 0.478414 avg, 0.001000 rate, 17.804674 seconds, 1057536 images -Loaded: 2.699057 seconds -Region Avg IOU: 0.499191, Class: 1.000000, Obj: 0.250438, No Obj: 0.003391, Avg Recall: 0.559633, count: 109 -Region Avg IOU: 0.461342, Class: 1.000000, Obj: 0.227410, No Obj: 0.003292, Avg Recall: 0.491935, count: 124 -Region Avg IOU: 0.418911, Class: 1.000000, Obj: 0.203134, No Obj: 0.003430, Avg Recall: 0.450450, count: 111 -Region Avg IOU: 0.479463, Class: 1.000000, Obj: 0.204392, No Obj: 0.002994, Avg Recall: 0.548673, count: 113 -Region Avg IOU: 0.557146, Class: 1.000000, Obj: 0.263555, No Obj: 0.002875, Avg Recall: 0.690476, count: 84 -Region Avg IOU: 0.501697, Class: 1.000000, Obj: 0.262536, No Obj: 0.003082, Avg Recall: 0.544554, count: 101 -Region Avg IOU: 0.447639, Class: 1.000000, Obj: 0.224521, No Obj: 0.003068, Avg Recall: 0.448276, count: 116 -Region Avg IOU: 0.493044, Class: 1.000000, Obj: 0.260241, No Obj: 0.003534, Avg Recall: 0.566667, count: 120 -4132: 0.498361, 0.480408 avg, 0.001000 rate, 16.553936 seconds, 1057792 images -Loaded: 3.804754 seconds -Region Avg IOU: 0.444400, Class: 1.000000, Obj: 0.214112, No Obj: 0.002916, Avg Recall: 0.472441, count: 127 -Region Avg IOU: 0.465485, Class: 1.000000, Obj: 0.234431, No Obj: 0.002914, Avg Recall: 0.485981, count: 107 -Region Avg IOU: 0.573509, Class: 1.000000, Obj: 0.251291, No Obj: 0.002866, Avg Recall: 0.705128, count: 78 -Region Avg IOU: 0.499444, Class: 1.000000, Obj: 0.233848, No Obj: 0.002856, Avg Recall: 0.600000, count: 90 -Region Avg IOU: 0.420074, Class: 1.000000, Obj: 0.220393, No Obj: 0.002523, Avg Recall: 0.471698, count: 106 -Region Avg IOU: 0.477373, Class: 1.000000, Obj: 0.255783, No Obj: 0.002585, Avg Recall: 0.551724, count: 87 -Region Avg IOU: 0.536438, Class: 1.000000, Obj: 0.221426, No Obj: 0.003166, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.516434, Class: 1.000000, Obj: 0.256305, No Obj: 0.003105, Avg Recall: 0.576577, count: 111 -4133: 0.489455, 0.481313 avg, 0.001000 rate, 20.202520 seconds, 1058048 images -Loaded: 1.795407 seconds -Region Avg IOU: 0.490202, Class: 1.000000, Obj: 0.242954, No Obj: 0.003307, Avg Recall: 0.524590, count: 122 -Region Avg IOU: 0.481568, Class: 1.000000, Obj: 0.225796, No Obj: 0.003603, Avg Recall: 0.481752, count: 137 -Region Avg IOU: 0.503775, Class: 1.000000, Obj: 0.268130, No Obj: 0.002799, Avg Recall: 0.553191, count: 94 -Region Avg IOU: 0.525267, Class: 1.000000, Obj: 0.236378, No Obj: 0.003174, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.457307, Class: 1.000000, Obj: 0.230525, No Obj: 0.003625, Avg Recall: 0.482014, count: 139 -Region Avg IOU: 0.432226, Class: 1.000000, Obj: 0.255262, No Obj: 0.003064, Avg Recall: 0.468468, count: 111 -Region Avg IOU: 0.513414, Class: 1.000000, Obj: 0.235390, No Obj: 0.003866, Avg Recall: 0.608108, count: 148 -Region Avg IOU: 0.501777, Class: 1.000000, Obj: 0.252356, No Obj: 0.002726, Avg Recall: 0.588889, count: 90 -4134: 0.556615, 0.488843 avg, 0.001000 rate, 20.568272 seconds, 1058304 images -Loaded: 0.029491 seconds -Region Avg IOU: 0.514919, Class: 1.000000, Obj: 0.259977, No Obj: 0.003665, Avg Recall: 0.611111, count: 126 -Region Avg IOU: 0.453117, Class: 1.000000, Obj: 0.244401, No Obj: 0.002875, Avg Recall: 0.495413, count: 109 -Region Avg IOU: 0.541486, Class: 1.000000, Obj: 0.277992, No Obj: 0.003801, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.481050, Class: 1.000000, Obj: 0.263224, No Obj: 0.003474, Avg Recall: 0.500000, count: 120 -Region Avg IOU: 0.493065, Class: 1.000000, Obj: 0.260084, No Obj: 0.003173, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.545456, Class: 1.000000, Obj: 0.276573, No Obj: 0.003209, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.462321, Class: 1.000000, Obj: 0.246074, No Obj: 0.003033, Avg Recall: 0.512821, count: 78 -Region Avg IOU: 0.539853, Class: 1.000000, Obj: 0.283981, No Obj: 0.003502, Avg Recall: 0.654545, count: 110 -4135: 0.491619, 0.489121 avg, 0.001000 rate, 20.730389 seconds, 1058560 images -Loaded: 0.520176 seconds -Region Avg IOU: 0.504342, Class: 1.000000, Obj: 0.279115, No Obj: 0.003940, Avg Recall: 0.528846, count: 104 -Region Avg IOU: 0.513838, Class: 1.000000, Obj: 0.287841, No Obj: 0.003875, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.519734, Class: 1.000000, Obj: 0.282522, No Obj: 0.003125, Avg Recall: 0.609756, count: 82 -Region Avg IOU: 0.519682, Class: 1.000000, Obj: 0.291793, No Obj: 0.003710, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.486307, Class: 1.000000, Obj: 0.276004, No Obj: 0.003705, Avg Recall: 0.500000, count: 126 -Region Avg IOU: 0.514330, Class: 1.000000, Obj: 0.267229, No Obj: 0.003926, Avg Recall: 0.571429, count: 119 -Region Avg IOU: 0.533982, Class: 1.000000, Obj: 0.280231, No Obj: 0.004160, Avg Recall: 0.589928, count: 139 -Region Avg IOU: 0.508291, Class: 1.000000, Obj: 0.290140, No Obj: 0.003607, Avg Recall: 0.607477, count: 107 -4136: 0.558384, 0.496047 avg, 0.001000 rate, 17.292725 seconds, 1058816 images -Loaded: 4.297040 seconds -Region Avg IOU: 0.524702, Class: 1.000000, Obj: 0.363076, No Obj: 0.003710, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.527835, Class: 1.000000, Obj: 0.314456, No Obj: 0.004345, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.500708, Class: 1.000000, Obj: 0.335096, No Obj: 0.003306, Avg Recall: 0.597561, count: 82 -Region Avg IOU: 0.446529, Class: 1.000000, Obj: 0.281523, No Obj: 0.004271, Avg Recall: 0.521739, count: 138 -Region Avg IOU: 0.504133, Class: 1.000000, Obj: 0.320021, No Obj: 0.004394, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.475760, Class: 1.000000, Obj: 0.262887, No Obj: 0.003858, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.521045, Class: 1.000000, Obj: 0.305710, No Obj: 0.004322, Avg Recall: 0.596491, count: 114 -Region Avg IOU: 0.433344, Class: 1.000000, Obj: 0.295246, No Obj: 0.003611, Avg Recall: 0.457143, count: 105 -4137: 0.444838, 0.490926 avg, 0.001000 rate, 16.960112 seconds, 1059072 images -Loaded: 3.171286 seconds -Region Avg IOU: 0.470394, Class: 1.000000, Obj: 0.351718, No Obj: 0.004210, Avg Recall: 0.496000, count: 125 -Region Avg IOU: 0.511860, Class: 1.000000, Obj: 0.350168, No Obj: 0.004604, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.504728, Class: 1.000000, Obj: 0.351082, No Obj: 0.004544, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.459066, Class: 1.000000, Obj: 0.287507, No Obj: 0.004689, Avg Recall: 0.538462, count: 130 -Region Avg IOU: 0.504744, Class: 1.000000, Obj: 0.314422, No Obj: 0.005378, Avg Recall: 0.528571, count: 140 -Region Avg IOU: 0.441928, Class: 1.000000, Obj: 0.318516, No Obj: 0.004356, Avg Recall: 0.478633, count: 117 -Region Avg IOU: 0.475970, Class: 1.000000, Obj: 0.379828, No Obj: 0.003947, Avg Recall: 0.510000, count: 100 -Region Avg IOU: 0.508552, Class: 1.000000, Obj: 0.299558, No Obj: 0.003909, Avg Recall: 0.582418, count: 91 -4138: 0.538261, 0.495660 avg, 0.001000 rate, 19.133320 seconds, 1059328 images -Loaded: 0.860831 seconds -Region Avg IOU: 0.514402, Class: 1.000000, Obj: 0.383366, No Obj: 0.005121, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.488586, Class: 1.000000, Obj: 0.352455, No Obj: 0.004801, Avg Recall: 0.525773, count: 97 -Region Avg IOU: 0.504610, Class: 1.000000, Obj: 0.340426, No Obj: 0.004999, Avg Recall: 0.505376, count: 93 -Region Avg IOU: 0.462916, Class: 1.000000, Obj: 0.331811, No Obj: 0.004878, Avg Recall: 0.490741, count: 108 -Region Avg IOU: 0.475338, Class: 1.000000, Obj: 0.317945, No Obj: 0.004337, Avg Recall: 0.509434, count: 106 -Region Avg IOU: 0.550893, Class: 1.000000, Obj: 0.403735, No Obj: 0.004763, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.463266, Class: 1.000000, Obj: 0.325848, No Obj: 0.005835, Avg Recall: 0.496063, count: 127 -Region Avg IOU: 0.490004, Class: 1.000000, Obj: 0.378144, No Obj: 0.005188, Avg Recall: 0.513514, count: 111 -4139: 0.492010, 0.495295 avg, 0.001000 rate, 20.159704 seconds, 1059584 images -Loaded: 1.074072 seconds -Region Avg IOU: 0.530226, Class: 1.000000, Obj: 0.377445, No Obj: 0.004995, Avg Recall: 0.595745, count: 94 -Region Avg IOU: 0.511553, Class: 1.000000, Obj: 0.352990, No Obj: 0.005716, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.489677, Class: 1.000000, Obj: 0.379561, No Obj: 0.004877, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.521352, Class: 1.000000, Obj: 0.404890, No Obj: 0.005941, Avg Recall: 0.620690, count: 116 -Region Avg IOU: 0.524733, Class: 1.000000, Obj: 0.372090, No Obj: 0.005765, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.520380, Class: 1.000000, Obj: 0.403806, No Obj: 0.004772, Avg Recall: 0.562500, count: 96 -Region Avg IOU: 0.480901, Class: 1.000000, Obj: 0.400387, No Obj: 0.005507, Avg Recall: 0.525424, count: 118 -Region Avg IOU: 0.498518, Class: 1.000000, Obj: 0.399324, No Obj: 0.004963, Avg Recall: 0.569767, count: 86 -4140: 0.438775, 0.489643 avg, 0.001000 rate, 17.199911 seconds, 1059840 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000074 seconds -Region Avg IOU: 0.644969, Class: 1.000000, Obj: 0.511369, No Obj: 0.004668, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.638801, Class: 1.000000, Obj: 0.519091, No Obj: 0.004600, Avg Recall: 0.806723, count: 119 -Region Avg IOU: 0.668809, Class: 1.000000, Obj: 0.518390, No Obj: 0.004664, Avg Recall: 0.840336, count: 119 -Region Avg IOU: 0.594305, Class: 1.000000, Obj: 0.476423, No Obj: 0.004320, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.626244, Class: 1.000000, Obj: 0.456584, No Obj: 0.004965, Avg Recall: 0.816794, count: 131 -Region Avg IOU: 0.642534, Class: 1.000000, Obj: 0.577446, No Obj: 0.005013, Avg Recall: 0.762295, count: 122 -Region Avg IOU: 0.674754, Class: 1.000000, Obj: 0.504527, No Obj: 0.004796, Avg Recall: 0.863636, count: 110 -Region Avg IOU: 0.648768, Class: 1.000000, Obj: 0.515217, No Obj: 0.004850, Avg Recall: 0.814815, count: 108 -4141: 0.466710, 0.487350 avg, 0.001000 rate, 27.921148 seconds, 1060096 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.641024, Class: 1.000000, Obj: 0.480210, No Obj: 0.004866, Avg Recall: 0.748299, count: 147 -Region Avg IOU: 0.626780, Class: 1.000000, Obj: 0.479023, No Obj: 0.003450, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.608461, Class: 1.000000, Obj: 0.494614, No Obj: 0.004452, Avg Recall: 0.742188, count: 128 -Region Avg IOU: 0.659097, Class: 1.000000, Obj: 0.468745, No Obj: 0.004337, Avg Recall: 0.830189, count: 106 -Region Avg IOU: 0.626768, Class: 1.000000, Obj: 0.527602, No Obj: 0.004306, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.654478, Class: 1.000000, Obj: 0.443463, No Obj: 0.004439, Avg Recall: 0.796610, count: 118 -Region Avg IOU: 0.600316, Class: 1.000000, Obj: 0.464783, No Obj: 0.004201, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.620616, Class: 1.000000, Obj: 0.498153, No Obj: 0.004386, Avg Recall: 0.705036, count: 139 -4142: 0.513401, 0.489955 avg, 0.001000 rate, 27.011045 seconds, 1060352 images -Loaded: 0.000120 seconds -Region Avg IOU: 0.625930, Class: 1.000000, Obj: 0.476524, No Obj: 0.003406, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.644570, Class: 1.000000, Obj: 0.450066, No Obj: 0.003770, Avg Recall: 0.817460, count: 126 -Region Avg IOU: 0.612706, Class: 1.000000, Obj: 0.483419, No Obj: 0.003162, Avg Recall: 0.741176, count: 85 -Region Avg IOU: 0.577789, Class: 1.000000, Obj: 0.449291, No Obj: 0.003227, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.652413, Class: 1.000000, Obj: 0.531197, No Obj: 0.003411, Avg Recall: 0.820225, count: 89 -Region Avg IOU: 0.673511, Class: 1.000000, Obj: 0.501691, No Obj: 0.003660, Avg Recall: 0.870000, count: 100 -Region Avg IOU: 0.608198, Class: 1.000000, Obj: 0.500849, No Obj: 0.003336, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.610144, Class: 1.000000, Obj: 0.427060, No Obj: 0.003586, Avg Recall: 0.723404, count: 94 -4143: 0.428951, 0.483854 avg, 0.001000 rate, 27.658796 seconds, 1060608 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.564658, Class: 1.000000, Obj: 0.405961, No Obj: 0.002783, Avg Recall: 0.682927, count: 82 -Region Avg IOU: 0.583234, Class: 1.000000, Obj: 0.420972, No Obj: 0.003251, Avg Recall: 0.719298, count: 114 -Region Avg IOU: 0.613006, Class: 1.000000, Obj: 0.420999, No Obj: 0.003216, Avg Recall: 0.723881, count: 134 -Region Avg IOU: 0.652551, Class: 1.000000, Obj: 0.460843, No Obj: 0.002974, Avg Recall: 0.833333, count: 96 -Region Avg IOU: 0.610576, Class: 1.000000, Obj: 0.381700, No Obj: 0.003131, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.596757, Class: 1.000000, Obj: 0.404589, No Obj: 0.003310, Avg Recall: 0.714286, count: 119 -Region Avg IOU: 0.618948, Class: 1.000000, Obj: 0.429117, No Obj: 0.002683, Avg Recall: 0.741176, count: 85 -Region Avg IOU: 0.607686, Class: 1.000000, Obj: 0.427698, No Obj: 0.003118, Avg Recall: 0.788991, count: 109 -4144: 0.455745, 0.481043 avg, 0.001000 rate, 28.596548 seconds, 1060864 images -Loaded: 0.000115 seconds -Region Avg IOU: 0.601558, Class: 1.000000, Obj: 0.331864, No Obj: 0.002802, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.570469, Class: 1.000000, Obj: 0.347795, No Obj: 0.002708, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.618678, Class: 1.000000, Obj: 0.364376, No Obj: 0.002952, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.612911, Class: 1.000000, Obj: 0.335981, No Obj: 0.002549, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.602619, Class: 1.000000, Obj: 0.411581, No Obj: 0.002547, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.657624, Class: 1.000000, Obj: 0.374889, No Obj: 0.002891, Avg Recall: 0.820000, count: 100 -Region Avg IOU: 0.613685, Class: 1.000000, Obj: 0.391851, No Obj: 0.002878, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.611798, Class: 1.000000, Obj: 0.363299, No Obj: 0.003039, Avg Recall: 0.742188, count: 128 -4145: 0.457433, 0.478682 avg, 0.001000 rate, 27.055576 seconds, 1061120 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.587241, Class: 1.000000, Obj: 0.300773, No Obj: 0.002345, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.571984, Class: 1.000000, Obj: 0.325344, No Obj: 0.002591, Avg Recall: 0.648855, count: 131 -Region Avg IOU: 0.599344, Class: 1.000000, Obj: 0.346440, No Obj: 0.002358, Avg Recall: 0.720588, count: 136 -Region Avg IOU: 0.635896, Class: 1.000000, Obj: 0.304380, No Obj: 0.002313, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.605449, Class: 1.000000, Obj: 0.316285, No Obj: 0.002239, Avg Recall: 0.734940, count: 83 -Region Avg IOU: 0.592017, Class: 1.000000, Obj: 0.334602, No Obj: 0.002243, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.594453, Class: 1.000000, Obj: 0.381410, No Obj: 0.002220, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.638208, Class: 1.000000, Obj: 0.283653, No Obj: 0.002244, Avg Recall: 0.769231, count: 104 -4146: 0.481166, 0.478931 avg, 0.001000 rate, 27.553553 seconds, 1061376 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.632694, Class: 1.000000, Obj: 0.350722, No Obj: 0.002039, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.639468, Class: 1.000000, Obj: 0.336430, No Obj: 0.001895, Avg Recall: 0.802469, count: 81 -Region Avg IOU: 0.606246, Class: 1.000000, Obj: 0.291877, No Obj: 0.001978, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.582528, Class: 1.000000, Obj: 0.285609, No Obj: 0.002428, Avg Recall: 0.683544, count: 158 -Region Avg IOU: 0.659651, Class: 1.000000, Obj: 0.353007, No Obj: 0.002499, Avg Recall: 0.840336, count: 119 -Region Avg IOU: 0.620099, Class: 1.000000, Obj: 0.385693, No Obj: 0.002575, Avg Recall: 0.766129, count: 124 -Region Avg IOU: 0.632161, Class: 1.000000, Obj: 0.317011, No Obj: 0.002219, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.632818, Class: 1.000000, Obj: 0.324704, No Obj: 0.002037, Avg Recall: 0.798077, count: 104 -4147: 0.486469, 0.479684 avg, 0.001000 rate, 27.883192 seconds, 1061632 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.609179, Class: 1.000000, Obj: 0.295751, No Obj: 0.001843, Avg Recall: 0.704545, count: 88 -Region Avg IOU: 0.664766, Class: 1.000000, Obj: 0.359681, No Obj: 0.002041, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.632147, Class: 1.000000, Obj: 0.307441, No Obj: 0.002162, Avg Recall: 0.791667, count: 120 -Region Avg IOU: 0.590141, Class: 1.000000, Obj: 0.305932, No Obj: 0.002076, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.625761, Class: 1.000000, Obj: 0.277028, No Obj: 0.002063, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.655841, Class: 1.000000, Obj: 0.281470, No Obj: 0.002171, Avg Recall: 0.817308, count: 104 -Region Avg IOU: 0.622234, Class: 1.000000, Obj: 0.267491, No Obj: 0.001914, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.588471, Class: 1.000000, Obj: 0.266307, No Obj: 0.002025, Avg Recall: 0.708333, count: 96 -4148: 0.462688, 0.477985 avg, 0.001000 rate, 28.207600 seconds, 1061888 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.603342, Class: 1.000000, Obj: 0.289692, No Obj: 0.001663, Avg Recall: 0.698795, count: 83 -Region Avg IOU: 0.628786, Class: 1.000000, Obj: 0.299632, No Obj: 0.002165, Avg Recall: 0.782258, count: 124 -Region Avg IOU: 0.616682, Class: 1.000000, Obj: 0.301904, No Obj: 0.001947, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.681590, Class: 1.000000, Obj: 0.347976, No Obj: 0.001691, Avg Recall: 0.850746, count: 67 -Region Avg IOU: 0.664560, Class: 1.000000, Obj: 0.353651, No Obj: 0.001974, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.650585, Class: 1.000000, Obj: 0.324082, No Obj: 0.001923, Avg Recall: 0.784091, count: 88 -Region Avg IOU: 0.651472, Class: 1.000000, Obj: 0.323425, No Obj: 0.002237, Avg Recall: 0.836957, count: 92 -Region Avg IOU: 0.591678, Class: 1.000000, Obj: 0.282576, No Obj: 0.002329, Avg Recall: 0.707483, count: 147 -4149: 0.448897, 0.475076 avg, 0.001000 rate, 28.363674 seconds, 1062144 images -Loaded: 0.000116 seconds -Region Avg IOU: 0.595843, Class: 1.000000, Obj: 0.256374, No Obj: 0.002181, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.634461, Class: 1.000000, Obj: 0.283023, No Obj: 0.002289, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.631735, Class: 1.000000, Obj: 0.306890, No Obj: 0.002305, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.585396, Class: 1.000000, Obj: 0.271061, No Obj: 0.002214, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.653981, Class: 1.000000, Obj: 0.319005, No Obj: 0.002142, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.628427, Class: 1.000000, Obj: 0.300839, No Obj: 0.002053, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.665235, Class: 1.000000, Obj: 0.350777, No Obj: 0.001757, Avg Recall: 0.850000, count: 80 -Region Avg IOU: 0.576769, Class: 1.000000, Obj: 0.254581, No Obj: 0.001961, Avg Recall: 0.638655, count: 119 -4150: 0.438348, 0.471403 avg, 0.001000 rate, 28.110161 seconds, 1062400 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 16.429401 seconds -Region Avg IOU: 0.627818, Class: 1.000000, Obj: 0.345716, No Obj: 0.002461, Avg Recall: 0.773438, count: 128 -Region Avg IOU: 0.639423, Class: 1.000000, Obj: 0.326350, No Obj: 0.002514, Avg Recall: 0.771186, count: 118 -Region Avg IOU: 0.640141, Class: 1.000000, Obj: 0.317793, No Obj: 0.002540, Avg Recall: 0.807018, count: 114 -Region Avg IOU: 0.656928, Class: 1.000000, Obj: 0.315540, No Obj: 0.002302, Avg Recall: 0.825243, count: 103 -Region Avg IOU: 0.671458, Class: 1.000000, Obj: 0.348693, No Obj: 0.002388, Avg Recall: 0.813953, count: 86 -Region Avg IOU: 0.623802, Class: 1.000000, Obj: 0.343188, No Obj: 0.002179, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.653021, Class: 1.000000, Obj: 0.321863, No Obj: 0.002082, Avg Recall: 0.802198, count: 91 -Region Avg IOU: 0.645211, Class: 1.000000, Obj: 0.328844, No Obj: 0.002405, Avg Recall: 0.782178, count: 101 -4151: 0.450762, 0.469339 avg, 0.001000 rate, 26.406446 seconds, 1062656 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.682499, Class: 1.000000, Obj: 0.417821, No Obj: 0.002433, Avg Recall: 0.869565, count: 92 -Region Avg IOU: 0.632697, Class: 1.000000, Obj: 0.354766, No Obj: 0.002584, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.665920, Class: 1.000000, Obj: 0.337686, No Obj: 0.002749, Avg Recall: 0.864407, count: 118 -Region Avg IOU: 0.613980, Class: 1.000000, Obj: 0.348029, No Obj: 0.002232, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.657429, Class: 1.000000, Obj: 0.420526, No Obj: 0.002570, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.648750, Class: 1.000000, Obj: 0.368239, No Obj: 0.002551, Avg Recall: 0.793478, count: 92 -Region Avg IOU: 0.598458, Class: 1.000000, Obj: 0.327681, No Obj: 0.002343, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.671130, Class: 1.000000, Obj: 0.358559, No Obj: 0.002578, Avg Recall: 0.800000, count: 100 -4152: 0.419229, 0.464328 avg, 0.001000 rate, 27.582527 seconds, 1062912 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.617795, Class: 1.000000, Obj: 0.384195, No Obj: 0.003146, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.597797, Class: 1.000000, Obj: 0.388831, No Obj: 0.003226, Avg Recall: 0.687500, count: 144 -Region Avg IOU: 0.665212, Class: 1.000000, Obj: 0.464212, No Obj: 0.002712, Avg Recall: 0.843750, count: 96 -Region Avg IOU: 0.685048, Class: 1.000000, Obj: 0.445616, No Obj: 0.002809, Avg Recall: 0.901099, count: 91 -Region Avg IOU: 0.634235, Class: 1.000000, Obj: 0.375238, No Obj: 0.002991, Avg Recall: 0.805085, count: 118 -Region Avg IOU: 0.649825, Class: 1.000000, Obj: 0.353768, No Obj: 0.002803, Avg Recall: 0.817391, count: 115 -Region Avg IOU: 0.611517, Class: 1.000000, Obj: 0.378926, No Obj: 0.002605, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.610931, Class: 1.000000, Obj: 0.364016, No Obj: 0.002715, Avg Recall: 0.736842, count: 95 -4153: 0.452374, 0.463133 avg, 0.001000 rate, 26.774069 seconds, 1063168 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.684054, Class: 1.000000, Obj: 0.480381, No Obj: 0.003251, Avg Recall: 0.829545, count: 88 -Region Avg IOU: 0.637552, Class: 1.000000, Obj: 0.442503, No Obj: 0.002515, Avg Recall: 0.740260, count: 77 -Region Avg IOU: 0.659135, Class: 1.000000, Obj: 0.486272, No Obj: 0.002927, Avg Recall: 0.849462, count: 93 -Region Avg IOU: 0.624437, Class: 1.000000, Obj: 0.384099, No Obj: 0.002821, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.661873, Class: 1.000000, Obj: 0.428503, No Obj: 0.003003, Avg Recall: 0.830000, count: 100 -Region Avg IOU: 0.625796, Class: 1.000000, Obj: 0.389754, No Obj: 0.002815, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.601569, Class: 1.000000, Obj: 0.406629, No Obj: 0.002861, Avg Recall: 0.746032, count: 126 -Region Avg IOU: 0.603474, Class: 1.000000, Obj: 0.374791, No Obj: 0.003379, Avg Recall: 0.740157, count: 127 -4154: 0.394356, 0.456255 avg, 0.001000 rate, 27.010546 seconds, 1063424 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.609755, Class: 1.000000, Obj: 0.423424, No Obj: 0.003048, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.656373, Class: 1.000000, Obj: 0.440631, No Obj: 0.003288, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.616519, Class: 1.000000, Obj: 0.367132, No Obj: 0.003130, Avg Recall: 0.770992, count: 131 -Region Avg IOU: 0.671556, Class: 1.000000, Obj: 0.479428, No Obj: 0.003181, Avg Recall: 0.862500, count: 80 -Region Avg IOU: 0.662919, Class: 1.000000, Obj: 0.412402, No Obj: 0.002821, Avg Recall: 0.842857, count: 70 -Region Avg IOU: 0.680320, Class: 1.000000, Obj: 0.406661, No Obj: 0.003410, Avg Recall: 0.841667, count: 120 -Region Avg IOU: 0.631671, Class: 1.000000, Obj: 0.439960, No Obj: 0.002915, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.622836, Class: 1.000000, Obj: 0.424956, No Obj: 0.003387, Avg Recall: 0.763780, count: 127 -4155: 0.462410, 0.456870 avg, 0.001000 rate, 27.968801 seconds, 1063680 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.625514, Class: 1.000000, Obj: 0.438265, No Obj: 0.002980, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.617922, Class: 1.000000, Obj: 0.419679, No Obj: 0.002980, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.664630, Class: 1.000000, Obj: 0.394842, No Obj: 0.003349, Avg Recall: 0.839623, count: 106 -Region Avg IOU: 0.557700, Class: 1.000000, Obj: 0.285798, No Obj: 0.003316, Avg Recall: 0.688000, count: 125 -Region Avg IOU: 0.632106, Class: 1.000000, Obj: 0.428309, No Obj: 0.002659, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.644246, Class: 1.000000, Obj: 0.386038, No Obj: 0.003188, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.569126, Class: 1.000000, Obj: 0.355594, No Obj: 0.002918, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.623061, Class: 1.000000, Obj: 0.384876, No Obj: 0.003349, Avg Recall: 0.754098, count: 122 -4156: 0.479676, 0.459151 avg, 0.001000 rate, 27.022808 seconds, 1063936 images -Loaded: 0.000141 seconds -Region Avg IOU: 0.647482, Class: 1.000000, Obj: 0.471337, No Obj: 0.002924, Avg Recall: 0.808989, count: 89 -Region Avg IOU: 0.629660, Class: 1.000000, Obj: 0.401865, No Obj: 0.003189, Avg Recall: 0.693548, count: 124 -Region Avg IOU: 0.620838, Class: 1.000000, Obj: 0.393704, No Obj: 0.003121, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.642275, Class: 1.000000, Obj: 0.391062, No Obj: 0.003129, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.619817, Class: 1.000000, Obj: 0.408914, No Obj: 0.003223, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.575772, Class: 1.000000, Obj: 0.370248, No Obj: 0.003211, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.628996, Class: 1.000000, Obj: 0.425217, No Obj: 0.003181, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.619684, Class: 1.000000, Obj: 0.369107, No Obj: 0.003001, Avg Recall: 0.777778, count: 99 -4157: 0.494604, 0.462696 avg, 0.001000 rate, 26.281973 seconds, 1064192 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.619128, Class: 1.000000, Obj: 0.387836, No Obj: 0.003132, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.646837, Class: 1.000000, Obj: 0.427055, No Obj: 0.003147, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.620057, Class: 1.000000, Obj: 0.398276, No Obj: 0.002912, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.639666, Class: 1.000000, Obj: 0.380643, No Obj: 0.003420, Avg Recall: 0.800000, count: 120 -Region Avg IOU: 0.619692, Class: 1.000000, Obj: 0.414115, No Obj: 0.003374, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.654401, Class: 1.000000, Obj: 0.423039, No Obj: 0.002991, Avg Recall: 0.814815, count: 108 -Region Avg IOU: 0.674207, Class: 1.000000, Obj: 0.431475, No Obj: 0.002631, Avg Recall: 0.816901, count: 71 -Region Avg IOU: 0.661568, Class: 1.000000, Obj: 0.407056, No Obj: 0.003071, Avg Recall: 0.867769, count: 121 -4158: 0.425952, 0.459022 avg, 0.001000 rate, 26.910795 seconds, 1064448 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.652672, Class: 1.000000, Obj: 0.412694, No Obj: 0.002843, Avg Recall: 0.800000, count: 95 -Region Avg IOU: 0.640048, Class: 1.000000, Obj: 0.424405, No Obj: 0.003447, Avg Recall: 0.830882, count: 136 -Region Avg IOU: 0.681528, Class: 1.000000, Obj: 0.392649, No Obj: 0.003156, Avg Recall: 0.884615, count: 104 -Region Avg IOU: 0.672183, Class: 1.000000, Obj: 0.445975, No Obj: 0.003021, Avg Recall: 0.843137, count: 102 -Region Avg IOU: 0.605521, Class: 1.000000, Obj: 0.405288, No Obj: 0.003087, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.628898, Class: 1.000000, Obj: 0.425155, No Obj: 0.003219, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.611686, Class: 1.000000, Obj: 0.354272, No Obj: 0.002843, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.624143, Class: 1.000000, Obj: 0.370963, No Obj: 0.002646, Avg Recall: 0.758621, count: 87 -4159: 0.429147, 0.456034 avg, 0.001000 rate, 27.816032 seconds, 1064704 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.611947, Class: 1.000000, Obj: 0.424101, No Obj: 0.002988, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.615069, Class: 1.000000, Obj: 0.348638, No Obj: 0.003300, Avg Recall: 0.750000, count: 128 -Region Avg IOU: 0.590881, Class: 1.000000, Obj: 0.381556, No Obj: 0.003175, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.618939, Class: 1.000000, Obj: 0.380493, No Obj: 0.002729, Avg Recall: 0.775862, count: 116 -Region Avg IOU: 0.643139, Class: 1.000000, Obj: 0.393952, No Obj: 0.002783, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.645654, Class: 1.000000, Obj: 0.404241, No Obj: 0.003139, Avg Recall: 0.803419, count: 117 -Region Avg IOU: 0.634042, Class: 1.000000, Obj: 0.447276, No Obj: 0.002851, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.665268, Class: 1.000000, Obj: 0.441556, No Obj: 0.002509, Avg Recall: 0.808081, count: 99 -4160: 0.447828, 0.455214 avg, 0.001000 rate, 27.496391 seconds, 1064960 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 14.474218 seconds -Region Avg IOU: 0.620844, Class: 1.000000, Obj: 0.364830, No Obj: 0.002736, Avg Recall: 0.744681, count: 94 -Region Avg IOU: 0.591510, Class: 1.000000, Obj: 0.390796, No Obj: 0.002879, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.607770, Class: 1.000000, Obj: 0.415148, No Obj: 0.002881, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.602599, Class: 1.000000, Obj: 0.399857, No Obj: 0.003016, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.635068, Class: 1.000000, Obj: 0.404338, No Obj: 0.002939, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.578651, Class: 1.000000, Obj: 0.349888, No Obj: 0.003500, Avg Recall: 0.735294, count: 136 -Region Avg IOU: 0.582576, Class: 1.000000, Obj: 0.333880, No Obj: 0.003087, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.585770, Class: 1.000000, Obj: 0.330341, No Obj: 0.003329, Avg Recall: 0.692982, count: 114 -4161: 0.435669, 0.453259 avg, 0.001000 rate, 25.340532 seconds, 1065216 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.608941, Class: 1.000000, Obj: 0.317912, No Obj: 0.002973, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.607039, Class: 1.000000, Obj: 0.352656, No Obj: 0.002754, Avg Recall: 0.679487, count: 78 -Region Avg IOU: 0.597090, Class: 1.000000, Obj: 0.371009, No Obj: 0.002929, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.611484, Class: 1.000000, Obj: 0.376381, No Obj: 0.003481, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.597403, Class: 1.000000, Obj: 0.364828, No Obj: 0.003135, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.605390, Class: 1.000000, Obj: 0.347938, No Obj: 0.003473, Avg Recall: 0.721429, count: 140 -Region Avg IOU: 0.606289, Class: 1.000000, Obj: 0.355782, No Obj: 0.003349, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.576701, Class: 1.000000, Obj: 0.366687, No Obj: 0.003106, Avg Recall: 0.678899, count: 109 -4162: 0.498323, 0.457766 avg, 0.001000 rate, 22.812298 seconds, 1065472 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.604807, Class: 1.000000, Obj: 0.341091, No Obj: 0.003421, Avg Recall: 0.744186, count: 129 -Region Avg IOU: 0.622220, Class: 1.000000, Obj: 0.407302, No Obj: 0.003114, Avg Recall: 0.734940, count: 83 -Region Avg IOU: 0.603532, Class: 1.000000, Obj: 0.375944, No Obj: 0.002745, Avg Recall: 0.724138, count: 87 -Region Avg IOU: 0.592312, Class: 1.000000, Obj: 0.328700, No Obj: 0.002738, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.620071, Class: 1.000000, Obj: 0.370919, No Obj: 0.002976, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.562078, Class: 1.000000, Obj: 0.350895, No Obj: 0.002680, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.610792, Class: 1.000000, Obj: 0.376313, No Obj: 0.003046, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.643484, Class: 1.000000, Obj: 0.393007, No Obj: 0.003374, Avg Recall: 0.781818, count: 110 -4163: 0.409040, 0.452893 avg, 0.001000 rate, 25.282591 seconds, 1065728 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.583095, Class: 1.000000, Obj: 0.344041, No Obj: 0.003207, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.584250, Class: 1.000000, Obj: 0.355879, No Obj: 0.003550, Avg Recall: 0.661538, count: 130 -Region Avg IOU: 0.499595, Class: 1.000000, Obj: 0.339702, No Obj: 0.002713, Avg Recall: 0.518868, count: 106 -Region Avg IOU: 0.616177, Class: 1.000000, Obj: 0.414047, No Obj: 0.003041, Avg Recall: 0.694118, count: 85 -Region Avg IOU: 0.608484, Class: 1.000000, Obj: 0.358160, No Obj: 0.003302, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.637049, Class: 1.000000, Obj: 0.426531, No Obj: 0.002667, Avg Recall: 0.782051, count: 78 -Region Avg IOU: 0.601698, Class: 1.000000, Obj: 0.332132, No Obj: 0.003177, Avg Recall: 0.692913, count: 127 -Region Avg IOU: 0.594811, Class: 1.000000, Obj: 0.406017, No Obj: 0.003058, Avg Recall: 0.706522, count: 92 -4164: 0.479063, 0.455510 avg, 0.001000 rate, 24.325497 seconds, 1065984 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.622435, Class: 1.000000, Obj: 0.404963, No Obj: 0.002937, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.496998, Class: 1.000000, Obj: 0.343090, No Obj: 0.002557, Avg Recall: 0.600000, count: 95 -Region Avg IOU: 0.539643, Class: 1.000000, Obj: 0.301498, No Obj: 0.003362, Avg Recall: 0.658730, count: 126 -Region Avg IOU: 0.560762, Class: 1.000000, Obj: 0.356209, No Obj: 0.003412, Avg Recall: 0.658333, count: 120 -Region Avg IOU: 0.586170, Class: 1.000000, Obj: 0.326163, No Obj: 0.003439, Avg Recall: 0.728000, count: 125 -Region Avg IOU: 0.604016, Class: 1.000000, Obj: 0.346861, No Obj: 0.003278, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.612182, Class: 1.000000, Obj: 0.362953, No Obj: 0.002903, Avg Recall: 0.734940, count: 83 -Region Avg IOU: 0.555375, Class: 1.000000, Obj: 0.361663, No Obj: 0.002890, Avg Recall: 0.626374, count: 91 -4165: 0.462468, 0.456206 avg, 0.001000 rate, 25.285259 seconds, 1066240 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.529343, Class: 1.000000, Obj: 0.362351, No Obj: 0.003132, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.553883, Class: 1.000000, Obj: 0.356688, No Obj: 0.003077, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.617112, Class: 1.000000, Obj: 0.327501, No Obj: 0.003630, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.597089, Class: 1.000000, Obj: 0.366725, No Obj: 0.003782, Avg Recall: 0.754098, count: 122 -Region Avg IOU: 0.577631, Class: 1.000000, Obj: 0.343650, No Obj: 0.003675, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.596464, Class: 1.000000, Obj: 0.408936, No Obj: 0.003363, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.585629, Class: 1.000000, Obj: 0.390189, No Obj: 0.003113, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.589604, Class: 1.000000, Obj: 0.398930, No Obj: 0.003373, Avg Recall: 0.695652, count: 92 -4166: 0.402557, 0.450841 avg, 0.001000 rate, 26.084421 seconds, 1066496 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.569904, Class: 1.000000, Obj: 0.346194, No Obj: 0.003604, Avg Recall: 0.679389, count: 131 -Region Avg IOU: 0.571221, Class: 1.000000, Obj: 0.392721, No Obj: 0.003545, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.622088, Class: 1.000000, Obj: 0.432260, No Obj: 0.004066, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.578513, Class: 1.000000, Obj: 0.355424, No Obj: 0.003944, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.596422, Class: 1.000000, Obj: 0.362464, No Obj: 0.003969, Avg Recall: 0.713235, count: 136 -Region Avg IOU: 0.580385, Class: 1.000000, Obj: 0.388441, No Obj: 0.003808, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.573864, Class: 1.000000, Obj: 0.364663, No Obj: 0.003259, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.600246, Class: 1.000000, Obj: 0.394751, No Obj: 0.003935, Avg Recall: 0.700000, count: 130 -4167: 0.472226, 0.452980 avg, 0.001000 rate, 26.446983 seconds, 1066752 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.621957, Class: 1.000000, Obj: 0.397913, No Obj: 0.003320, Avg Recall: 0.773810, count: 84 -Region Avg IOU: 0.563911, Class: 1.000000, Obj: 0.349783, No Obj: 0.003200, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.631667, Class: 1.000000, Obj: 0.383529, No Obj: 0.003842, Avg Recall: 0.803279, count: 122 -Region Avg IOU: 0.598022, Class: 1.000000, Obj: 0.402540, No Obj: 0.003323, Avg Recall: 0.689655, count: 87 -Region Avg IOU: 0.562162, Class: 1.000000, Obj: 0.363924, No Obj: 0.003437, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.584027, Class: 1.000000, Obj: 0.417456, No Obj: 0.003525, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.577015, Class: 1.000000, Obj: 0.387280, No Obj: 0.003852, Avg Recall: 0.641221, count: 131 -Region Avg IOU: 0.576559, Class: 1.000000, Obj: 0.355950, No Obj: 0.003621, Avg Recall: 0.696721, count: 122 -4168: 0.463612, 0.454043 avg, 0.001000 rate, 24.697935 seconds, 1067008 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.595134, Class: 1.000000, Obj: 0.390887, No Obj: 0.003517, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.575225, Class: 1.000000, Obj: 0.339937, No Obj: 0.003033, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.568670, Class: 1.000000, Obj: 0.332258, No Obj: 0.003450, Avg Recall: 0.601770, count: 113 -Region Avg IOU: 0.619365, Class: 1.000000, Obj: 0.365539, No Obj: 0.004112, Avg Recall: 0.764228, count: 123 -Region Avg IOU: 0.590240, Class: 1.000000, Obj: 0.359458, No Obj: 0.003870, Avg Recall: 0.707692, count: 130 -Region Avg IOU: 0.626733, Class: 1.000000, Obj: 0.432171, No Obj: 0.003216, Avg Recall: 0.707865, count: 89 -Region Avg IOU: 0.556811, Class: 1.000000, Obj: 0.353070, No Obj: 0.003883, Avg Recall: 0.642857, count: 126 -Region Avg IOU: 0.571347, Class: 1.000000, Obj: 0.407448, No Obj: 0.003239, Avg Recall: 0.695238, count: 105 -4169: 0.483897, 0.457028 avg, 0.001000 rate, 23.719690 seconds, 1067264 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.608301, Class: 1.000000, Obj: 0.394076, No Obj: 0.004322, Avg Recall: 0.723077, count: 130 -Region Avg IOU: 0.623226, Class: 1.000000, Obj: 0.415711, No Obj: 0.003308, Avg Recall: 0.797619, count: 84 -Region Avg IOU: 0.542471, Class: 1.000000, Obj: 0.358141, No Obj: 0.003528, Avg Recall: 0.626984, count: 126 -Region Avg IOU: 0.599857, Class: 1.000000, Obj: 0.357856, No Obj: 0.003189, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.593893, Class: 1.000000, Obj: 0.349048, No Obj: 0.003645, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.573254, Class: 1.000000, Obj: 0.367665, No Obj: 0.003248, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.615708, Class: 1.000000, Obj: 0.350869, No Obj: 0.003980, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.613139, Class: 1.000000, Obj: 0.427391, No Obj: 0.003742, Avg Recall: 0.728070, count: 114 -4170: 0.476803, 0.459006 avg, 0.001000 rate, 23.909464 seconds, 1067520 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000080 seconds -Region Avg IOU: 0.669183, Class: 1.000000, Obj: 0.380091, No Obj: 0.003122, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.647402, Class: 1.000000, Obj: 0.448946, No Obj: 0.002989, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.650128, Class: 1.000000, Obj: 0.428126, No Obj: 0.002850, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.640269, Class: 1.000000, Obj: 0.416634, No Obj: 0.002783, Avg Recall: 0.804124, count: 97 -Region Avg IOU: 0.612690, Class: 1.000000, Obj: 0.402546, No Obj: 0.003251, Avg Recall: 0.753846, count: 130 -Region Avg IOU: 0.654271, Class: 1.000000, Obj: 0.427982, No Obj: 0.003206, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.667672, Class: 1.000000, Obj: 0.407639, No Obj: 0.003196, Avg Recall: 0.853211, count: 109 -Region Avg IOU: 0.636679, Class: 1.000000, Obj: 0.388909, No Obj: 0.003224, Avg Recall: 0.800000, count: 125 -4171: 0.456605, 0.458766 avg, 0.001000 rate, 30.639641 seconds, 1067776 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.648191, Class: 1.000000, Obj: 0.433536, No Obj: 0.002820, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.614271, Class: 1.000000, Obj: 0.431041, No Obj: 0.002999, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.661926, Class: 1.000000, Obj: 0.444362, No Obj: 0.002807, Avg Recall: 0.821429, count: 84 -Region Avg IOU: 0.592270, Class: 1.000000, Obj: 0.360337, No Obj: 0.002979, Avg Recall: 0.714286, count: 119 -Region Avg IOU: 0.666633, Class: 1.000000, Obj: 0.441963, No Obj: 0.002630, Avg Recall: 0.876405, count: 89 -Region Avg IOU: 0.645532, Class: 1.000000, Obj: 0.377717, No Obj: 0.003180, Avg Recall: 0.798319, count: 119 -Region Avg IOU: 0.648750, Class: 1.000000, Obj: 0.449905, No Obj: 0.003355, Avg Recall: 0.811475, count: 122 -Region Avg IOU: 0.671810, Class: 1.000000, Obj: 0.412505, No Obj: 0.002924, Avg Recall: 0.850000, count: 100 -4172: 0.434200, 0.456309 avg, 0.001000 rate, 28.135365 seconds, 1068032 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.632612, Class: 1.000000, Obj: 0.400645, No Obj: 0.002952, Avg Recall: 0.752066, count: 121 -Region Avg IOU: 0.626164, Class: 1.000000, Obj: 0.387446, No Obj: 0.002750, Avg Recall: 0.795082, count: 122 -Region Avg IOU: 0.637145, Class: 1.000000, Obj: 0.336335, No Obj: 0.002376, Avg Recall: 0.792208, count: 77 -Region Avg IOU: 0.645295, Class: 1.000000, Obj: 0.383607, No Obj: 0.002730, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.614665, Class: 1.000000, Obj: 0.367341, No Obj: 0.003100, Avg Recall: 0.776316, count: 152 -Region Avg IOU: 0.635617, Class: 1.000000, Obj: 0.432561, No Obj: 0.002524, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.693745, Class: 1.000000, Obj: 0.427717, No Obj: 0.002745, Avg Recall: 0.910000, count: 100 -Region Avg IOU: 0.634082, Class: 1.000000, Obj: 0.352500, No Obj: 0.002683, Avg Recall: 0.792079, count: 101 -4173: 0.484467, 0.459125 avg, 0.001000 rate, 29.934481 seconds, 1068288 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.654567, Class: 1.000000, Obj: 0.361259, No Obj: 0.003458, Avg Recall: 0.827068, count: 133 -Region Avg IOU: 0.635904, Class: 1.000000, Obj: 0.358747, No Obj: 0.002512, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.659079, Class: 1.000000, Obj: 0.383910, No Obj: 0.002969, Avg Recall: 0.833333, count: 120 -Region Avg IOU: 0.635935, Class: 1.000000, Obj: 0.370551, No Obj: 0.002777, Avg Recall: 0.794326, count: 141 -Region Avg IOU: 0.640047, Class: 1.000000, Obj: 0.400227, No Obj: 0.002751, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.678018, Class: 1.000000, Obj: 0.401770, No Obj: 0.002735, Avg Recall: 0.855670, count: 97 -Region Avg IOU: 0.661364, Class: 1.000000, Obj: 0.391959, No Obj: 0.002398, Avg Recall: 0.811111, count: 90 -Region Avg IOU: 0.651423, Class: 1.000000, Obj: 0.397992, No Obj: 0.002718, Avg Recall: 0.842975, count: 121 -4174: 0.470318, 0.460244 avg, 0.001000 rate, 27.989656 seconds, 1068544 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.658690, Class: 1.000000, Obj: 0.373838, No Obj: 0.003077, Avg Recall: 0.843478, count: 115 -Region Avg IOU: 0.684264, Class: 1.000000, Obj: 0.406328, No Obj: 0.002533, Avg Recall: 0.854167, count: 96 -Region Avg IOU: 0.632782, Class: 1.000000, Obj: 0.422796, No Obj: 0.002616, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.642295, Class: 1.000000, Obj: 0.379037, No Obj: 0.002548, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.655873, Class: 1.000000, Obj: 0.338286, No Obj: 0.002461, Avg Recall: 0.831776, count: 107 -Region Avg IOU: 0.642908, Class: 1.000000, Obj: 0.306732, No Obj: 0.002276, Avg Recall: 0.793103, count: 87 -Region Avg IOU: 0.622931, Class: 1.000000, Obj: 0.366409, No Obj: 0.002556, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.624964, Class: 1.000000, Obj: 0.353691, No Obj: 0.002489, Avg Recall: 0.760684, count: 117 -4175: 0.444192, 0.458639 avg, 0.001000 rate, 28.752047 seconds, 1068800 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.659960, Class: 1.000000, Obj: 0.342675, No Obj: 0.003037, Avg Recall: 0.821429, count: 140 -Region Avg IOU: 0.699873, Class: 1.000000, Obj: 0.394320, No Obj: 0.002455, Avg Recall: 0.862069, count: 87 -Region Avg IOU: 0.640230, Class: 1.000000, Obj: 0.346587, No Obj: 0.002344, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.613552, Class: 1.000000, Obj: 0.356131, No Obj: 0.002436, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.652600, Class: 1.000000, Obj: 0.366080, No Obj: 0.002680, Avg Recall: 0.825000, count: 120 -Region Avg IOU: 0.641780, Class: 1.000000, Obj: 0.353366, No Obj: 0.002176, Avg Recall: 0.804878, count: 82 -Region Avg IOU: 0.653437, Class: 1.000000, Obj: 0.354086, No Obj: 0.002300, Avg Recall: 0.851485, count: 101 -Region Avg IOU: 0.629951, Class: 1.000000, Obj: 0.343660, No Obj: 0.002447, Avg Recall: 0.778846, count: 104 -4176: 0.452705, 0.458045 avg, 0.001000 rate, 28.600796 seconds, 1069056 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.629603, Class: 1.000000, Obj: 0.358924, No Obj: 0.002468, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.661273, Class: 1.000000, Obj: 0.402997, No Obj: 0.002793, Avg Recall: 0.842975, count: 121 -Region Avg IOU: 0.634567, Class: 1.000000, Obj: 0.340233, No Obj: 0.002573, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.620673, Class: 1.000000, Obj: 0.391661, No Obj: 0.002255, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.645477, Class: 1.000000, Obj: 0.381743, No Obj: 0.002305, Avg Recall: 0.815217, count: 92 -Region Avg IOU: 0.646206, Class: 1.000000, Obj: 0.304615, No Obj: 0.002406, Avg Recall: 0.836364, count: 110 -Region Avg IOU: 0.644460, Class: 1.000000, Obj: 0.358423, No Obj: 0.002331, Avg Recall: 0.836735, count: 98 -Region Avg IOU: 0.615702, Class: 1.000000, Obj: 0.389641, No Obj: 0.002482, Avg Recall: 0.763636, count: 110 -4177: 0.456860, 0.457927 avg, 0.001000 rate, 29.667110 seconds, 1069312 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.647859, Class: 1.000000, Obj: 0.362692, No Obj: 0.002617, Avg Recall: 0.819048, count: 105 -Region Avg IOU: 0.642652, Class: 1.000000, Obj: 0.336191, No Obj: 0.002613, Avg Recall: 0.792000, count: 125 -Region Avg IOU: 0.614707, Class: 1.000000, Obj: 0.367876, No Obj: 0.002365, Avg Recall: 0.722222, count: 90 -Region Avg IOU: 0.622046, Class: 1.000000, Obj: 0.380686, No Obj: 0.002011, Avg Recall: 0.800000, count: 70 -Region Avg IOU: 0.675853, Class: 1.000000, Obj: 0.387398, No Obj: 0.002514, Avg Recall: 0.876289, count: 97 -Region Avg IOU: 0.670589, Class: 1.000000, Obj: 0.360565, No Obj: 0.002363, Avg Recall: 0.846154, count: 91 -Region Avg IOU: 0.660956, Class: 1.000000, Obj: 0.389720, No Obj: 0.002697, Avg Recall: 0.805970, count: 134 -Region Avg IOU: 0.660990, Class: 1.000000, Obj: 0.377650, No Obj: 0.002562, Avg Recall: 0.839623, count: 106 -4178: 0.424189, 0.454553 avg, 0.001000 rate, 29.519476 seconds, 1069568 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.645538, Class: 1.000000, Obj: 0.402703, No Obj: 0.002457, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.620036, Class: 1.000000, Obj: 0.338482, No Obj: 0.002670, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.622426, Class: 1.000000, Obj: 0.293510, No Obj: 0.002274, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.673333, Class: 1.000000, Obj: 0.361967, No Obj: 0.002549, Avg Recall: 0.829060, count: 117 -Region Avg IOU: 0.651626, Class: 1.000000, Obj: 0.337007, No Obj: 0.002496, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.676626, Class: 1.000000, Obj: 0.367533, No Obj: 0.002708, Avg Recall: 0.857143, count: 126 -Region Avg IOU: 0.661514, Class: 1.000000, Obj: 0.402184, No Obj: 0.002230, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.674485, Class: 1.000000, Obj: 0.386560, No Obj: 0.002475, Avg Recall: 0.839080, count: 87 -4179: 0.448361, 0.453934 avg, 0.001000 rate, 29.880159 seconds, 1069824 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.661231, Class: 1.000000, Obj: 0.379068, No Obj: 0.002545, Avg Recall: 0.862385, count: 109 -Region Avg IOU: 0.630506, Class: 1.000000, Obj: 0.367509, No Obj: 0.002758, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.660777, Class: 1.000000, Obj: 0.325977, No Obj: 0.002100, Avg Recall: 0.800000, count: 80 -Region Avg IOU: 0.616220, Class: 1.000000, Obj: 0.338829, No Obj: 0.002623, Avg Recall: 0.742188, count: 128 -Region Avg IOU: 0.642794, Class: 1.000000, Obj: 0.335741, No Obj: 0.002475, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.639713, Class: 1.000000, Obj: 0.336550, No Obj: 0.002675, Avg Recall: 0.822430, count: 107 -Region Avg IOU: 0.618398, Class: 1.000000, Obj: 0.364189, No Obj: 0.002419, Avg Recall: 0.805310, count: 113 -Region Avg IOU: 0.611687, Class: 1.000000, Obj: 0.308175, No Obj: 0.002439, Avg Recall: 0.741379, count: 116 -4180: 0.511247, 0.459665 avg, 0.001000 rate, 29.001053 seconds, 1070080 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 13.564191 seconds -Region Avg IOU: 0.550001, Class: 1.000000, Obj: 0.314739, No Obj: 0.002647, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.559981, Class: 1.000000, Obj: 0.310526, No Obj: 0.003204, Avg Recall: 0.644628, count: 121 -Region Avg IOU: 0.616242, Class: 1.000000, Obj: 0.333724, No Obj: 0.002672, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.603767, Class: 1.000000, Obj: 0.318007, No Obj: 0.003154, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.567859, Class: 1.000000, Obj: 0.292631, No Obj: 0.002710, Avg Recall: 0.693182, count: 88 -Region Avg IOU: 0.569508, Class: 1.000000, Obj: 0.359927, No Obj: 0.002826, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.579383, Class: 1.000000, Obj: 0.326733, No Obj: 0.002977, Avg Recall: 0.701493, count: 134 -Region Avg IOU: 0.559988, Class: 1.000000, Obj: 0.333359, No Obj: 0.002754, Avg Recall: 0.634783, count: 115 -4181: 0.463082, 0.460007 avg, 0.001000 rate, 24.798573 seconds, 1070336 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.561443, Class: 1.000000, Obj: 0.341663, No Obj: 0.003289, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.596959, Class: 1.000000, Obj: 0.372978, No Obj: 0.003207, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.659712, Class: 1.000000, Obj: 0.386524, No Obj: 0.003228, Avg Recall: 0.815217, count: 92 -Region Avg IOU: 0.574347, Class: 1.000000, Obj: 0.354135, No Obj: 0.002955, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.579388, Class: 1.000000, Obj: 0.335064, No Obj: 0.003564, Avg Recall: 0.682432, count: 148 -Region Avg IOU: 0.585934, Class: 1.000000, Obj: 0.360908, No Obj: 0.003242, Avg Recall: 0.644068, count: 118 -Region Avg IOU: 0.599814, Class: 1.000000, Obj: 0.372918, No Obj: 0.002786, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.618262, Class: 1.000000, Obj: 0.377422, No Obj: 0.002889, Avg Recall: 0.775281, count: 89 -4182: 0.417894, 0.455796 avg, 0.001000 rate, 27.056704 seconds, 1070592 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.618855, Class: 1.000000, Obj: 0.382833, No Obj: 0.003516, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.600415, Class: 1.000000, Obj: 0.364994, No Obj: 0.003070, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.576549, Class: 1.000000, Obj: 0.366925, No Obj: 0.003353, Avg Recall: 0.682171, count: 129 -Region Avg IOU: 0.569530, Class: 1.000000, Obj: 0.292651, No Obj: 0.003144, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.567393, Class: 1.000000, Obj: 0.319159, No Obj: 0.003158, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.571120, Class: 1.000000, Obj: 0.336791, No Obj: 0.003276, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.599496, Class: 1.000000, Obj: 0.371274, No Obj: 0.003902, Avg Recall: 0.735714, count: 140 -Region Avg IOU: 0.551615, Class: 1.000000, Obj: 0.347607, No Obj: 0.003539, Avg Recall: 0.641221, count: 131 -4183: 0.489033, 0.459119 avg, 0.001000 rate, 24.657747 seconds, 1070848 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.641720, Class: 1.000000, Obj: 0.395928, No Obj: 0.002617, Avg Recall: 0.787500, count: 80 -Region Avg IOU: 0.545952, Class: 1.000000, Obj: 0.308927, No Obj: 0.003517, Avg Recall: 0.624060, count: 133 -Region Avg IOU: 0.603622, Class: 1.000000, Obj: 0.386030, No Obj: 0.002699, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.604422, Class: 1.000000, Obj: 0.411957, No Obj: 0.002651, Avg Recall: 0.729730, count: 74 -Region Avg IOU: 0.596443, Class: 1.000000, Obj: 0.362198, No Obj: 0.004098, Avg Recall: 0.724832, count: 149 -Region Avg IOU: 0.578264, Class: 1.000000, Obj: 0.395445, No Obj: 0.002945, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.601957, Class: 1.000000, Obj: 0.360612, No Obj: 0.003767, Avg Recall: 0.740458, count: 131 -Region Avg IOU: 0.585150, Class: 1.000000, Obj: 0.386395, No Obj: 0.003284, Avg Recall: 0.693694, count: 111 -4184: 0.465327, 0.459740 avg, 0.001000 rate, 23.675352 seconds, 1071104 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.581041, Class: 1.000000, Obj: 0.372392, No Obj: 0.003789, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.542208, Class: 1.000000, Obj: 0.348137, No Obj: 0.003176, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.569840, Class: 1.000000, Obj: 0.408402, No Obj: 0.003103, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.583883, Class: 1.000000, Obj: 0.384647, No Obj: 0.003310, Avg Recall: 0.655462, count: 119 -Region Avg IOU: 0.567298, Class: 1.000000, Obj: 0.419044, No Obj: 0.003817, Avg Recall: 0.686567, count: 134 -Region Avg IOU: 0.549403, Class: 1.000000, Obj: 0.324682, No Obj: 0.003819, Avg Recall: 0.601399, count: 143 -Region Avg IOU: 0.589575, Class: 1.000000, Obj: 0.427571, No Obj: 0.002549, Avg Recall: 0.714286, count: 77 -Region Avg IOU: 0.586121, Class: 1.000000, Obj: 0.384295, No Obj: 0.003292, Avg Recall: 0.700935, count: 107 -4185: 0.433038, 0.457070 avg, 0.001000 rate, 26.332588 seconds, 1071360 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.576044, Class: 1.000000, Obj: 0.376910, No Obj: 0.003388, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.632052, Class: 1.000000, Obj: 0.415324, No Obj: 0.004131, Avg Recall: 0.716418, count: 134 -Region Avg IOU: 0.590583, Class: 1.000000, Obj: 0.434796, No Obj: 0.003278, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.631921, Class: 1.000000, Obj: 0.426613, No Obj: 0.003212, Avg Recall: 0.777778, count: 90 -Region Avg IOU: 0.591258, Class: 1.000000, Obj: 0.397822, No Obj: 0.003492, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.602775, Class: 1.000000, Obj: 0.379667, No Obj: 0.003440, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.612007, Class: 1.000000, Obj: 0.398526, No Obj: 0.003675, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.596247, Class: 1.000000, Obj: 0.386287, No Obj: 0.003500, Avg Recall: 0.693548, count: 124 -4186: 0.437457, 0.455109 avg, 0.001000 rate, 24.721436 seconds, 1071616 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.577507, Class: 1.000000, Obj: 0.401709, No Obj: 0.003413, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.598292, Class: 1.000000, Obj: 0.406530, No Obj: 0.003441, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.610737, Class: 1.000000, Obj: 0.450523, No Obj: 0.003842, Avg Recall: 0.713115, count: 122 -Region Avg IOU: 0.611145, Class: 1.000000, Obj: 0.417066, No Obj: 0.003475, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.578038, Class: 1.000000, Obj: 0.400393, No Obj: 0.003740, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.610434, Class: 1.000000, Obj: 0.434918, No Obj: 0.003095, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.608333, Class: 1.000000, Obj: 0.383398, No Obj: 0.003725, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.548759, Class: 1.000000, Obj: 0.338822, No Obj: 0.003135, Avg Recall: 0.620000, count: 100 -4187: 0.432073, 0.452805 avg, 0.001000 rate, 23.693916 seconds, 1071872 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.543073, Class: 1.000000, Obj: 0.361133, No Obj: 0.003797, Avg Recall: 0.613445, count: 119 -Region Avg IOU: 0.557702, Class: 1.000000, Obj: 0.416702, No Obj: 0.003356, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.574896, Class: 1.000000, Obj: 0.370804, No Obj: 0.003751, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.619773, Class: 1.000000, Obj: 0.395216, No Obj: 0.003359, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.599178, Class: 1.000000, Obj: 0.403738, No Obj: 0.004299, Avg Recall: 0.726115, count: 157 -Region Avg IOU: 0.593012, Class: 1.000000, Obj: 0.375176, No Obj: 0.003931, Avg Recall: 0.687500, count: 128 -Region Avg IOU: 0.637084, Class: 1.000000, Obj: 0.414431, No Obj: 0.003372, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.603215, Class: 1.000000, Obj: 0.422987, No Obj: 0.003316, Avg Recall: 0.659574, count: 94 -4188: 0.477241, 0.455249 avg, 0.001000 rate, 25.076160 seconds, 1072128 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.551189, Class: 1.000000, Obj: 0.424413, No Obj: 0.003550, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.593362, Class: 1.000000, Obj: 0.405855, No Obj: 0.003617, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.599351, Class: 1.000000, Obj: 0.408433, No Obj: 0.004256, Avg Recall: 0.674242, count: 132 -Region Avg IOU: 0.583253, Class: 1.000000, Obj: 0.396849, No Obj: 0.003341, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.582479, Class: 1.000000, Obj: 0.369510, No Obj: 0.003406, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.595578, Class: 1.000000, Obj: 0.363737, No Obj: 0.003844, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.582399, Class: 1.000000, Obj: 0.423422, No Obj: 0.003643, Avg Recall: 0.671756, count: 131 -Region Avg IOU: 0.598321, Class: 1.000000, Obj: 0.430082, No Obj: 0.004044, Avg Recall: 0.706897, count: 116 -4189: 0.437595, 0.453483 avg, 0.001000 rate, 24.589027 seconds, 1072384 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.574265, Class: 1.000000, Obj: 0.370783, No Obj: 0.003731, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.638041, Class: 1.000000, Obj: 0.432407, No Obj: 0.003794, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.571859, Class: 1.000000, Obj: 0.387205, No Obj: 0.003273, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.559605, Class: 1.000000, Obj: 0.424655, No Obj: 0.003313, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.559977, Class: 1.000000, Obj: 0.383404, No Obj: 0.003481, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.580286, Class: 1.000000, Obj: 0.403314, No Obj: 0.003935, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.588582, Class: 1.000000, Obj: 0.408826, No Obj: 0.003718, Avg Recall: 0.688000, count: 125 -Region Avg IOU: 0.618889, Class: 1.000000, Obj: 0.393071, No Obj: 0.003704, Avg Recall: 0.774194, count: 93 -4190: 0.458385, 0.453973 avg, 0.001000 rate, 23.548464 seconds, 1072640 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 21.228664 seconds -Region Avg IOU: 0.564520, Class: 1.000000, Obj: 0.373907, No Obj: 0.003775, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.571299, Class: 1.000000, Obj: 0.403760, No Obj: 0.003427, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.543938, Class: 1.000000, Obj: 0.356363, No Obj: 0.003284, Avg Recall: 0.642857, count: 84 -Region Avg IOU: 0.513252, Class: 1.000000, Obj: 0.337640, No Obj: 0.003253, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.597352, Class: 1.000000, Obj: 0.341339, No Obj: 0.004015, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.556787, Class: 1.000000, Obj: 0.397167, No Obj: 0.003597, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.557850, Class: 1.000000, Obj: 0.383863, No Obj: 0.004145, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.567916, Class: 1.000000, Obj: 0.372308, No Obj: 0.003498, Avg Recall: 0.685714, count: 105 -4191: 0.416130, 0.450189 avg, 0.001000 rate, 22.446589 seconds, 1072896 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.574530, Class: 1.000000, Obj: 0.376140, No Obj: 0.003711, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.547565, Class: 1.000000, Obj: 0.336189, No Obj: 0.003255, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.582497, Class: 1.000000, Obj: 0.360423, No Obj: 0.003237, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.557220, Class: 1.000000, Obj: 0.360764, No Obj: 0.003699, Avg Recall: 0.658537, count: 123 -Region Avg IOU: 0.560176, Class: 1.000000, Obj: 0.337825, No Obj: 0.003530, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.519014, Class: 1.000000, Obj: 0.349368, No Obj: 0.003393, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.572688, Class: 1.000000, Obj: 0.367403, No Obj: 0.003509, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.543260, Class: 1.000000, Obj: 0.299024, No Obj: 0.003751, Avg Recall: 0.634783, count: 115 -4192: 0.428105, 0.447981 avg, 0.001000 rate, 23.567707 seconds, 1073152 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.562783, Class: 1.000000, Obj: 0.332737, No Obj: 0.003626, Avg Recall: 0.634146, count: 123 -Region Avg IOU: 0.565036, Class: 1.000000, Obj: 0.339776, No Obj: 0.003376, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.600188, Class: 1.000000, Obj: 0.398241, No Obj: 0.003237, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.542823, Class: 1.000000, Obj: 0.293244, No Obj: 0.003368, Avg Recall: 0.604478, count: 134 -Region Avg IOU: 0.567834, Class: 1.000000, Obj: 0.312202, No Obj: 0.003704, Avg Recall: 0.685484, count: 124 -Region Avg IOU: 0.611944, Class: 1.000000, Obj: 0.385442, No Obj: 0.003282, Avg Recall: 0.791209, count: 91 -Region Avg IOU: 0.540996, Class: 1.000000, Obj: 0.336701, No Obj: 0.003907, Avg Recall: 0.621795, count: 156 -Region Avg IOU: 0.604508, Class: 1.000000, Obj: 0.331264, No Obj: 0.003601, Avg Recall: 0.752212, count: 113 -4193: 0.494568, 0.452639 avg, 0.001000 rate, 22.421036 seconds, 1073408 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.558011, Class: 1.000000, Obj: 0.362187, No Obj: 0.003380, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.565291, Class: 1.000000, Obj: 0.408287, No Obj: 0.003908, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.550204, Class: 1.000000, Obj: 0.335250, No Obj: 0.003257, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.546895, Class: 1.000000, Obj: 0.349557, No Obj: 0.003851, Avg Recall: 0.600000, count: 125 -Region Avg IOU: 0.582314, Class: 1.000000, Obj: 0.343951, No Obj: 0.003909, Avg Recall: 0.694444, count: 144 -Region Avg IOU: 0.584621, Class: 1.000000, Obj: 0.375572, No Obj: 0.003982, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.596649, Class: 1.000000, Obj: 0.378206, No Obj: 0.003549, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.533060, Class: 1.000000, Obj: 0.333346, No Obj: 0.003649, Avg Recall: 0.631148, count: 122 -4194: 0.456163, 0.452992 avg, 0.001000 rate, 22.638676 seconds, 1073664 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.551433, Class: 1.000000, Obj: 0.352390, No Obj: 0.004600, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.533116, Class: 1.000000, Obj: 0.337886, No Obj: 0.003717, Avg Recall: 0.603896, count: 154 -Region Avg IOU: 0.543254, Class: 1.000000, Obj: 0.365951, No Obj: 0.002782, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.543036, Class: 1.000000, Obj: 0.345278, No Obj: 0.003341, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.577105, Class: 1.000000, Obj: 0.351397, No Obj: 0.003539, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.568166, Class: 1.000000, Obj: 0.384034, No Obj: 0.003706, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.579162, Class: 1.000000, Obj: 0.334446, No Obj: 0.003849, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.532350, Class: 1.000000, Obj: 0.342862, No Obj: 0.003497, Avg Recall: 0.618182, count: 110 -4195: 0.474063, 0.455099 avg, 0.001000 rate, 22.315798 seconds, 1073920 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.601187, Class: 1.000000, Obj: 0.412471, No Obj: 0.003207, Avg Recall: 0.682353, count: 85 -Region Avg IOU: 0.579237, Class: 1.000000, Obj: 0.400671, No Obj: 0.003864, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.568342, Class: 1.000000, Obj: 0.389026, No Obj: 0.003365, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.520745, Class: 1.000000, Obj: 0.354167, No Obj: 0.004105, Avg Recall: 0.574627, count: 134 -Region Avg IOU: 0.601309, Class: 1.000000, Obj: 0.383972, No Obj: 0.004286, Avg Recall: 0.721804, count: 133 -Region Avg IOU: 0.547712, Class: 1.000000, Obj: 0.330068, No Obj: 0.003742, Avg Recall: 0.620968, count: 124 -Region Avg IOU: 0.589598, Class: 1.000000, Obj: 0.338843, No Obj: 0.003560, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.557677, Class: 1.000000, Obj: 0.388089, No Obj: 0.003605, Avg Recall: 0.656250, count: 96 -4196: 0.416669, 0.451256 avg, 0.001000 rate, 23.907642 seconds, 1074176 images -Loaded: 0.224490 seconds -Region Avg IOU: 0.549365, Class: 1.000000, Obj: 0.379578, No Obj: 0.004179, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.577576, Class: 1.000000, Obj: 0.322566, No Obj: 0.004300, Avg Recall: 0.661765, count: 136 -Region Avg IOU: 0.553157, Class: 1.000000, Obj: 0.394460, No Obj: 0.003679, Avg RSaving weights to ./person9300/backup/tyvp_93_9c512_4200.weights -ecall: 0.605769, count: 104 -Region Avg IOU: 0.546325, Class: 1.000000, Obj: 0.347867, No Obj: 0.003814, Avg Recall: 0.633588, count: 131 -Region Avg IOU: 0.573626, Class: 1.000000, Obj: 0.367141, No Obj: 0.004645, Avg Recall: 0.693431, count: 137 -Region Avg IOU: 0.625632, Class: 1.000000, Obj: 0.365595, No Obj: 0.003571, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.611813, Class: 1.000000, Obj: 0.426256, No Obj: 0.003389, Avg Recall: 0.752941, count: 85 -Region Avg IOU: 0.579498, Class: 1.000000, Obj: 0.389134, No Obj: 0.003780, Avg Recall: 0.691489, count: 94 -4197: 0.472750, 0.453405 avg, 0.001000 rate, 23.589142 seconds, 1074432 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.566738, Class: 1.000000, Obj: 0.383586, No Obj: 0.003381, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.590543, Class: 1.000000, Obj: 0.358371, No Obj: 0.004566, Avg Recall: 0.712121, count: 132 -Region Avg IOU: 0.476397, Class: 1.000000, Obj: 0.388004, No Obj: 0.003143, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.607819, Class: 1.000000, Obj: 0.389965, No Obj: 0.003567, Avg Recall: 0.705882, count: 85 -Region Avg IOU: 0.575292, Class: 1.000000, Obj: 0.407397, No Obj: 0.003682, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.540038, Class: 1.000000, Obj: 0.373494, No Obj: 0.003721, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.648951, Class: 1.000000, Obj: 0.407678, No Obj: 0.004251, Avg Recall: 0.830000, count: 100 -Region Avg IOU: 0.492398, Class: 1.000000, Obj: 0.337776, No Obj: 0.004007, Avg Recall: 0.488000, count: 125 -4198: 0.463228, 0.454388 avg, 0.001000 rate, 22.864536 seconds, 1074688 images -Loaded: 0.000127 seconds -Region Avg IOU: 0.523346, Class: 1.000000, Obj: 0.415682, No Obj: 0.004214, Avg Recall: 0.587302, count: 126 -Region Avg IOU: 0.535221, Class: 1.000000, Obj: 0.412222, No Obj: 0.003982, Avg Recall: 0.604651, count: 129 -Region Avg IOU: 0.546265, Class: 1.000000, Obj: 0.389558, No Obj: 0.003792, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.561766, Class: 1.000000, Obj: 0.384949, No Obj: 0.004335, Avg Recall: 0.648000, count: 125 -Region Avg IOU: 0.585879, Class: 1.000000, Obj: 0.397797, No Obj: 0.003836, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.553475, Class: 1.000000, Obj: 0.368481, No Obj: 0.004022, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.564418, Class: 1.000000, Obj: 0.412452, No Obj: 0.003977, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.553219, Class: 1.000000, Obj: 0.410301, No Obj: 0.003995, Avg Recall: 0.672566, count: 113 -4199: 0.479570, 0.456906 avg, 0.001000 rate, 23.584517 seconds, 1074944 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.615122, Class: 1.000000, Obj: 0.368924, No Obj: 0.003293, Avg Recall: 0.753086, count: 81 -Region Avg IOU: 0.591945, Class: 1.000000, Obj: 0.409113, No Obj: 0.003580, Avg Recall: 0.710843, count: 83 -Region Avg IOU: 0.533066, Class: 1.000000, Obj: 0.389741, No Obj: 0.003636, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.539155, Class: 1.000000, Obj: 0.405225, No Obj: 0.004051, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.560342, Class: 1.000000, Obj: 0.379506, No Obj: 0.004135, Avg Recall: 0.676923, count: 130 -Region Avg IOU: 0.581368, Class: 1.000000, Obj: 0.436392, No Obj: 0.004390, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.578564, Class: 1.000000, Obj: 0.412039, No Obj: 0.004220, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.535825, Class: 1.000000, Obj: 0.397423, No Obj: 0.003754, Avg Recall: 0.628866, count: 97 -4200: 0.416499, 0.452865 avg, 0.001000 rate, 23.199272 seconds, 1075200 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000073 seconds -Region Avg IOU: 0.604652, Class: 1.000000, Obj: 0.451290, No Obj: 0.004627, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.602029, Class: 1.000000, Obj: 0.412021, No Obj: 0.004390, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.502419, Class: 1.000000, Obj: 0.406217, No Obj: 0.003295, Avg Recall: 0.619048, count: 84 -Region Avg IOU: 0.505539, Class: 1.000000, Obj: 0.381418, No Obj: 0.003752, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.576185, Class: 1.000000, Obj: 0.412568, No Obj: 0.003009, Avg Recall: 0.702381, count: 84 -Region Avg IOU: 0.590699, Class: 1.000000, Obj: 0.370831, No Obj: 0.003904, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.569031, Class: 1.000000, Obj: 0.446246, No Obj: 0.003485, Avg Recall: 0.705128, count: 78 -Region Avg IOU: 0.593281, Class: 1.000000, Obj: 0.417994, No Obj: 0.004033, Avg Recall: 0.704762, count: 105 -4201: 0.380778, 0.445656 avg, 0.001000 rate, 23.900583 seconds, 1075456 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.498542, Class: 1.000000, Obj: 0.373100, No Obj: 0.003458, Avg Recall: 0.548673, count: 113 -Region Avg IOU: 0.573136, Class: 1.000000, Obj: 0.461488, No Obj: 0.003783, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.539795, Class: 1.000000, Obj: 0.339924, No Obj: 0.004052, Avg Recall: 0.624000, count: 125 -Region Avg IOU: 0.575010, Class: 1.000000, Obj: 0.417826, No Obj: 0.004220, Avg Recall: 0.680000, count: 125 -Region Avg IOU: 0.599365, Class: 1.000000, Obj: 0.411095, No Obj: 0.003772, Avg Recall: 0.686046, count: 86 -Region Avg IOU: 0.594742, Class: 1.000000, Obj: 0.427603, No Obj: 0.003932, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.592539, Class: 1.000000, Obj: 0.421356, No Obj: 0.004359, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.565380, Class: 1.000000, Obj: 0.376489, No Obj: 0.004163, Avg Recall: 0.674419, count: 129 -4202: 0.427232, 0.443814 avg, 0.001000 rate, 24.604332 seconds, 1075712 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.590699, Class: 1.000000, Obj: 0.410689, No Obj: 0.004329, Avg Recall: 0.707692, count: 130 -Region Avg IOU: 0.575697, Class: 1.000000, Obj: 0.408954, No Obj: 0.004175, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.581364, Class: 1.000000, Obj: 0.378066, No Obj: 0.004545, Avg Recall: 0.659574, count: 141 -Region Avg IOU: 0.537164, Class: 1.000000, Obj: 0.370222, No Obj: 0.003896, Avg Recall: 0.610169, count: 118 -Region Avg IOU: 0.567314, Class: 1.000000, Obj: 0.393365, No Obj: 0.003773, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.529548, Class: 1.000000, Obj: 0.364141, No Obj: 0.003238, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.610033, Class: 1.000000, Obj: 0.419865, No Obj: 0.004152, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.590949, Class: 1.000000, Obj: 0.446219, No Obj: 0.003601, Avg Recall: 0.656863, count: 102 -4203: 0.424775, 0.441910 avg, 0.001000 rate, 22.608908 seconds, 1075968 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.585629, Class: 1.000000, Obj: 0.463897, No Obj: 0.003322, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.533571, Class: 1.000000, Obj: 0.383108, No Obj: 0.003288, Avg Recall: 0.621622, count: 74 -Region Avg IOU: 0.642475, Class: 1.000000, Obj: 0.403826, No Obj: 0.004629, Avg Recall: 0.776859, count: 121 -Region Avg IOU: 0.572966, Class: 1.000000, Obj: 0.348106, No Obj: 0.004543, Avg Recall: 0.685484, count: 124 -Region Avg IOU: 0.558999, Class: 1.000000, Obj: 0.343449, No Obj: 0.003496, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.579083, Class: 1.000000, Obj: 0.404257, No Obj: 0.003510, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.584493, Class: 1.000000, Obj: 0.390280, No Obj: 0.003376, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.639008, Class: 1.000000, Obj: 0.419820, No Obj: 0.003971, Avg Recall: 0.778761, count: 113 -4204: 0.383719, 0.436091 avg, 0.001000 rate, 25.054173 seconds, 1076224 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.610328, Class: 1.000000, Obj: 0.353540, No Obj: 0.003174, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.541607, Class: 1.000000, Obj: 0.338335, No Obj: 0.003613, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.579986, Class: 1.000000, Obj: 0.364217, No Obj: 0.003155, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.541132, Class: 1.000000, Obj: 0.367978, No Obj: 0.002845, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.573770, Class: 1.000000, Obj: 0.347567, No Obj: 0.003636, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.542174, Class: 1.000000, Obj: 0.391533, No Obj: 0.003868, Avg Recall: 0.580420, count: 143 -Region Avg IOU: 0.585712, Class: 1.000000, Obj: 0.359539, No Obj: 0.003205, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.636404, Class: 1.000000, Obj: 0.428213, No Obj: 0.003874, Avg Recall: 0.740385, count: 104 -4205: 0.469912, 0.439473 avg, 0.001000 rate, 23.993153 seconds, 1076480 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.602395, Class: 1.000000, Obj: 0.400052, No Obj: 0.003464, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.565782, Class: 1.000000, Obj: 0.400219, No Obj: 0.002942, Avg Recall: 0.578313, count: 83 -Region Avg IOU: 0.586104, Class: 1.000000, Obj: 0.348731, No Obj: 0.003355, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.606048, Class: 1.000000, Obj: 0.356467, No Obj: 0.003961, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.567553, Class: 1.000000, Obj: 0.401145, No Obj: 0.003350, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.583111, Class: 1.000000, Obj: 0.364599, No Obj: 0.003109, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.546303, Class: 1.000000, Obj: 0.409885, No Obj: 0.002993, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.600288, Class: 1.000000, Obj: 0.392947, No Obj: 0.003329, Avg Recall: 0.725275, count: 91 -4206: 0.375713, 0.433097 avg, 0.001000 rate, 23.209412 seconds, 1076736 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.581496, Class: 1.000000, Obj: 0.377103, No Obj: 0.003614, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.623288, Class: 1.000000, Obj: 0.386254, No Obj: 0.003977, Avg Recall: 0.742857, count: 140 -Region Avg IOU: 0.517746, Class: 1.000000, Obj: 0.297225, No Obj: 0.003210, Avg Recall: 0.577236, count: 123 -Region Avg IOU: 0.590671, Class: 1.000000, Obj: 0.378557, No Obj: 0.003128, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.614625, Class: 1.000000, Obj: 0.373599, No Obj: 0.003187, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.595476, Class: 1.000000, Obj: 0.384528, No Obj: 0.003517, Avg Recall: 0.707692, count: 130 -Region Avg IOU: 0.561387, Class: 1.000000, Obj: 0.404000, No Obj: 0.003313, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.610822, Class: 1.000000, Obj: 0.373304, No Obj: 0.002685, Avg Recall: 0.714286, count: 84 -4207: 0.456670, 0.435454 avg, 0.001000 rate, 23.512928 seconds, 1076992 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.596561, Class: 1.000000, Obj: 0.380979, No Obj: 0.004375, Avg Recall: 0.713235, count: 136 -Region Avg IOU: 0.565705, Class: 1.000000, Obj: 0.353433, No Obj: 0.003429, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.607598, Class: 1.000000, Obj: 0.401127, No Obj: 0.002911, Avg Recall: 0.725000, count: 80 -Region Avg IOU: 0.561367, Class: 1.000000, Obj: 0.365336, No Obj: 0.002867, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.576672, Class: 1.000000, Obj: 0.359472, No Obj: 0.002918, Avg Recall: 0.674699, count: 83 -Region Avg IOU: 0.575575, Class: 1.000000, Obj: 0.312206, No Obj: 0.003241, Avg Recall: 0.644628, count: 121 -Region Avg IOU: 0.585960, Class: 1.000000, Obj: 0.351844, No Obj: 0.003371, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.622623, Class: 1.000000, Obj: 0.384173, No Obj: 0.003672, Avg Recall: 0.731707, count: 123 -4208: 0.448925, 0.436801 avg, 0.001000 rate, 25.592375 seconds, 1077248 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.597566, Class: 1.000000, Obj: 0.378983, No Obj: 0.003291, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.623866, Class: 1.000000, Obj: 0.416358, No Obj: 0.003463, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.572387, Class: 1.000000, Obj: 0.355237, No Obj: 0.003087, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.530272, Class: 1.000000, Obj: 0.306751, No Obj: 0.003412, Avg Recall: 0.615942, count: 138 -Region Avg IOU: 0.593498, Class: 1.000000, Obj: 0.351268, No Obj: 0.003163, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.533333, Class: 1.000000, Obj: 0.335072, No Obj: 0.003570, Avg Recall: 0.566667, count: 120 -Region Avg IOU: 0.528885, Class: 1.000000, Obj: 0.388871, No Obj: 0.003026, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.545574, Class: 1.000000, Obj: 0.351643, No Obj: 0.003954, Avg Recall: 0.644444, count: 135 -4209: 0.476980, 0.440819 avg, 0.001000 rate, 25.193687 seconds, 1077504 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.566975, Class: 1.000000, Obj: 0.359706, No Obj: 0.003354, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.595433, Class: 1.000000, Obj: 0.380843, No Obj: 0.002868, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.544935, Class: 1.000000, Obj: 0.338869, No Obj: 0.003291, Avg Recall: 0.611940, count: 134 -Region Avg IOU: 0.520694, Class: 1.000000, Obj: 0.330079, No Obj: 0.003247, Avg Recall: 0.559055, count: 127 -Region Avg IOU: 0.581587, Class: 1.000000, Obj: 0.335220, No Obj: 0.002951, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.624545, Class: 1.000000, Obj: 0.400903, No Obj: 0.003142, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.538802, Class: 1.000000, Obj: 0.302969, No Obj: 0.003497, Avg Recall: 0.629032, count: 124 -Region Avg IOU: 0.558233, Class: 1.000000, Obj: 0.326162, No Obj: 0.003805, Avg Recall: 0.642857, count: 140 -4210: 0.441477, 0.440885 avg, 0.001000 rate, 23.643488 seconds, 1077760 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000068 seconds -Region Avg IOU: 0.630110, Class: 1.000000, Obj: 0.429950, No Obj: 0.002772, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.636459, Class: 1.000000, Obj: 0.416963, No Obj: 0.003197, Avg Recall: 0.806202, count: 129 -Region Avg IOU: 0.657131, Class: 1.000000, Obj: 0.388880, No Obj: 0.002759, Avg Recall: 0.827273, count: 110 -Region Avg IOU: 0.634910, Class: 1.000000, Obj: 0.406325, No Obj: 0.002844, Avg Recall: 0.804348, count: 92 -Region Avg IOU: 0.607862, Class: 1.000000, Obj: 0.419291, No Obj: 0.002694, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.634596, Class: 1.000000, Obj: 0.355235, No Obj: 0.003117, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.594640, Class: 1.000000, Obj: 0.417953, No Obj: 0.002623, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.638190, Class: 1.000000, Obj: 0.432249, No Obj: 0.002684, Avg Recall: 0.770115, count: 87 -4211: 0.421213, 0.438918 avg, 0.001000 rate, 27.049000 seconds, 1078016 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.642483, Class: 1.000000, Obj: 0.432997, No Obj: 0.002847, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.622137, Class: 1.000000, Obj: 0.384145, No Obj: 0.003143, Avg Recall: 0.786325, count: 117 -Region Avg IOU: 0.651472, Class: 1.000000, Obj: 0.377379, No Obj: 0.003102, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.632969, Class: 1.000000, Obj: 0.404385, No Obj: 0.002955, Avg Recall: 0.805310, count: 113 -Region Avg IOU: 0.649674, Class: 1.000000, Obj: 0.424222, No Obj: 0.003151, Avg Recall: 0.802198, count: 91 -Region Avg IOU: 0.659386, Class: 1.000000, Obj: 0.456947, No Obj: 0.002578, Avg Recall: 0.790123, count: 81 -Region Avg IOU: 0.679230, Class: 1.000000, Obj: 0.422203, No Obj: 0.003004, Avg Recall: 0.888889, count: 90 -Region Avg IOU: 0.625652, Class: 1.000000, Obj: 0.372611, No Obj: 0.003086, Avg Recall: 0.790323, count: 124 -4212: 0.443057, 0.439332 avg, 0.001000 rate, 28.203659 seconds, 1078272 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.622403, Class: 1.000000, Obj: 0.406886, No Obj: 0.003041, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.630871, Class: 1.000000, Obj: 0.386069, No Obj: 0.003280, Avg Recall: 0.776923, count: 130 -Region Avg IOU: 0.616295, Class: 1.000000, Obj: 0.439378, No Obj: 0.002953, Avg Recall: 0.761905, count: 126 -Region Avg IOU: 0.624758, Class: 1.000000, Obj: 0.433238, No Obj: 0.002735, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.615353, Class: 1.000000, Obj: 0.409365, No Obj: 0.002432, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.625451, Class: 1.000000, Obj: 0.384799, No Obj: 0.003021, Avg Recall: 0.770492, count: 122 -Region Avg IOU: 0.673285, Class: 1.000000, Obj: 0.423197, No Obj: 0.003284, Avg Recall: 0.886792, count: 106 -Region Avg IOU: 0.628108, Class: 1.000000, Obj: 0.402808, No Obj: 0.002888, Avg Recall: 0.758929, count: 112 -4213: 0.462606, 0.441659 avg, 0.001000 rate, 27.765011 seconds, 1078528 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.656862, Class: 1.000000, Obj: 0.383841, No Obj: 0.002662, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.612638, Class: 1.000000, Obj: 0.436320, No Obj: 0.002664, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.655259, Class: 1.000000, Obj: 0.396929, No Obj: 0.002720, Avg Recall: 0.802326, count: 86 -Region Avg IOU: 0.650573, Class: 1.000000, Obj: 0.403188, No Obj: 0.003217, Avg Recall: 0.819549, count: 133 -Region Avg IOU: 0.617031, Class: 1.000000, Obj: 0.361245, No Obj: 0.002711, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.678941, Class: 1.000000, Obj: 0.417646, No Obj: 0.002991, Avg Recall: 0.834862, count: 109 -Region Avg IOU: 0.626492, Class: 1.000000, Obj: 0.412566, No Obj: 0.003194, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.664173, Class: 1.000000, Obj: 0.428632, No Obj: 0.003155, Avg Recall: 0.793103, count: 116 -4214: 0.456054, 0.443099 avg, 0.001000 rate, 28.669979 seconds, 1078784 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.651027, Class: 1.000000, Obj: 0.415729, No Obj: 0.003047, Avg Recall: 0.813008, count: 123 -Region Avg IOU: 0.637443, Class: 1.000000, Obj: 0.385491, No Obj: 0.002798, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.611123, Class: 1.000000, Obj: 0.388301, No Obj: 0.002747, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.659484, Class: 1.000000, Obj: 0.388339, No Obj: 0.002464, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.635134, Class: 1.000000, Obj: 0.382485, No Obj: 0.002920, Avg Recall: 0.803571, count: 112 -Region Avg IOU: 0.680039, Class: 1.000000, Obj: 0.395975, No Obj: 0.002935, Avg Recall: 0.860000, count: 100 -Region Avg IOU: 0.685134, Class: 1.000000, Obj: 0.424829, No Obj: 0.003025, Avg Recall: 0.849057, count: 106 -Region Avg IOU: 0.655060, Class: 1.000000, Obj: 0.399351, No Obj: 0.003006, Avg Recall: 0.809917, count: 121 -4215: 0.435837, 0.442372 avg, 0.001000 rate, 28.218857 seconds, 1079040 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.647113, Class: 1.000000, Obj: 0.412267, No Obj: 0.002804, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.601529, Class: 1.000000, Obj: 0.340614, No Obj: 0.002662, Avg Recall: 0.751938, count: 129 -Region Avg IOU: 0.630806, Class: 1.000000, Obj: 0.350103, No Obj: 0.003091, Avg Recall: 0.731343, count: 134 -Region Avg IOU: 0.679025, Class: 1.000000, Obj: 0.390407, No Obj: 0.002721, Avg Recall: 0.854369, count: 103 -Region Avg IOU: 0.642139, Class: 1.000000, Obj: 0.360535, No Obj: 0.002646, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.659888, Class: 1.000000, Obj: 0.351540, No Obj: 0.002795, Avg Recall: 0.861111, count: 108 -Region Avg IOU: 0.638747, Class: 1.000000, Obj: 0.376652, No Obj: 0.002407, Avg Recall: 0.764706, count: 85 -Region Avg IOU: 0.647666, Class: 1.000000, Obj: 0.404769, No Obj: 0.002597, Avg Recall: 0.771429, count: 105 -4216: 0.467678, 0.444903 avg, 0.001000 rate, 28.504820 seconds, 1079296 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.607695, Class: 1.000000, Obj: 0.370139, No Obj: 0.002009, Avg Recall: 0.696203, count: 79 -Region Avg IOU: 0.649101, Class: 1.000000, Obj: 0.368240, No Obj: 0.002516, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.632790, Class: 1.000000, Obj: 0.359086, No Obj: 0.002330, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.620641, Class: 1.000000, Obj: 0.328184, No Obj: 0.003117, Avg Recall: 0.755396, count: 139 -Region Avg IOU: 0.663515, Class: 1.000000, Obj: 0.369723, No Obj: 0.002679, Avg Recall: 0.836207, count: 116 -Region Avg IOU: 0.617558, Class: 1.000000, Obj: 0.355816, No Obj: 0.002872, Avg Recall: 0.737226, count: 137 -Region Avg IOU: 0.679491, Class: 1.000000, Obj: 0.392318, No Obj: 0.002442, Avg Recall: 0.852632, count: 95 -Region Avg IOU: 0.599655, Class: 1.000000, Obj: 0.379286, No Obj: 0.002874, Avg Recall: 0.727273, count: 110 -4217: 0.476357, 0.448048 avg, 0.001000 rate, 29.166563 seconds, 1079552 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.642815, Class: 1.000000, Obj: 0.328718, No Obj: 0.002799, Avg Recall: 0.757812, count: 128 -Region Avg IOU: 0.644601, Class: 1.000000, Obj: 0.338022, No Obj: 0.002674, Avg Recall: 0.826087, count: 115 -Region Avg IOU: 0.591912, Class: 1.000000, Obj: 0.373562, No Obj: 0.002394, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.621660, Class: 1.000000, Obj: 0.385173, No Obj: 0.002659, Avg Recall: 0.786885, count: 122 -Region Avg IOU: 0.634038, Class: 1.000000, Obj: 0.389410, No Obj: 0.002588, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.659476, Class: 1.000000, Obj: 0.387027, No Obj: 0.002580, Avg Recall: 0.838710, count: 93 -Region Avg IOU: 0.586999, Class: 1.000000, Obj: 0.349091, No Obj: 0.002297, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.633533, Class: 1.000000, Obj: 0.373616, No Obj: 0.002373, Avg Recall: 0.752688, count: 93 -4218: 0.472991, 0.450543 avg, 0.001000 rate, 28.356775 seconds, 1079808 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.643483, Class: 1.000000, Obj: 0.331639, No Obj: 0.002429, Avg Recall: 0.783132, count: 83 -Region Avg IOU: 0.649501, Class: 1.000000, Obj: 0.346216, No Obj: 0.002591, Avg Recall: 0.813953, count: 129 -Region Avg IOU: 0.640321, Class: 1.000000, Obj: 0.381069, No Obj: 0.002625, Avg Recall: 0.796610, count: 118 -Region Avg IOU: 0.650150, Class: 1.000000, Obj: 0.313637, No Obj: 0.002713, Avg Recall: 0.816176, count: 136 -Region Avg IOU: 0.615534, Class: 1.000000, Obj: 0.348873, No Obj: 0.002758, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.608405, Class: 1.000000, Obj: 0.356938, No Obj: 0.002719, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.698062, Class: 1.000000, Obj: 0.434080, No Obj: 0.002411, Avg Recall: 0.920000, count: 100 -Region Avg IOU: 0.634997, Class: 1.000000, Obj: 0.339256, No Obj: 0.002285, Avg Recall: 0.792079, count: 101 -4219: 0.481511, 0.453639 avg, 0.001000 rate, 29.323219 seconds, 1080064 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.660950, Class: 1.000000, Obj: 0.381349, No Obj: 0.002538, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.661722, Class: 1.000000, Obj: 0.364793, No Obj: 0.002536, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.648262, Class: 1.000000, Obj: 0.340494, No Obj: 0.002451, Avg Recall: 0.806452, count: 124 -Region Avg IOU: 0.678480, Class: 1.000000, Obj: 0.399284, No Obj: 0.002242, Avg Recall: 0.875000, count: 88 -Region Avg IOU: 0.686037, Class: 1.000000, Obj: 0.356956, No Obj: 0.002853, Avg Recall: 0.896226, count: 106 -Region Avg IOU: 0.664327, Class: 1.000000, Obj: 0.377918, No Obj: 0.002541, Avg Recall: 0.822785, count: 79 -Region Avg IOU: 0.642918, Class: 1.000000, Obj: 0.393403, No Obj: 0.002721, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.655731, Class: 1.000000, Obj: 0.441020, No Obj: 0.002440, Avg Recall: 0.792453, count: 106 -4220: 0.409293, 0.449205 avg, 0.001000 rate, 26.955015 seconds, 1080320 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.922441 seconds -Region Avg IOU: 0.554275, Class: 1.000000, Obj: 0.309699, No Obj: 0.002716, Avg Recall: 0.626506, count: 83 -Region Avg IOU: 0.521686, Class: 1.000000, Obj: 0.246782, No Obj: 0.003166, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.513106, Class: 1.000000, Obj: 0.263441, No Obj: 0.003435, Avg Recall: 0.562044, count: 137 -Region Avg IOU: 0.486075, Class: 1.000000, Obj: 0.272659, No Obj: 0.003159, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.498576, Class: 1.000000, Obj: 0.259160, No Obj: 0.003142, Avg Recall: 0.556452, count: 124 -Region Avg IOU: 0.546732, Class: 1.000000, Obj: 0.318898, No Obj: 0.003255, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.530869, Class: 1.000000, Obj: 0.303762, No Obj: 0.002908, Avg Recall: 0.602273, count: 88 -Region Avg IOU: 0.512666, Class: 1.000000, Obj: 0.315267, No Obj: 0.003376, Avg Recall: 0.546392, count: 97 -4221: 0.459323, 0.450217 avg, 0.001000 rate, 20.746311 seconds, 1080576 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.551824, Class: 1.000000, Obj: 0.326926, No Obj: 0.003498, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.444727, Class: 1.000000, Obj: 0.262375, No Obj: 0.003332, Avg Recall: 0.472527, count: 91 -Region Avg IOU: 0.512802, Class: 1.000000, Obj: 0.303638, No Obj: 0.002939, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.491775, Class: 1.000000, Obj: 0.275931, No Obj: 0.003690, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.492930, Class: 1.000000, Obj: 0.273647, No Obj: 0.003253, Avg Recall: 0.544715, count: 123 -Region Avg IOU: 0.530441, Class: 1.000000, Obj: 0.342131, No Obj: 0.003290, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.525567, Class: 1.000000, Obj: 0.272328, No Obj: 0.003303, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.549564, Class: 1.000000, Obj: 0.317438, No Obj: 0.003228, Avg Recall: 0.659794, count: 97 -4222: 0.450319, 0.450227 avg, 0.001000 rate, 20.538103 seconds, 1080832 images -Loaded: 1.163993 seconds -Region Avg IOU: 0.570724, Class: 1.000000, Obj: 0.304814, No Obj: 0.003946, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.533530, Class: 1.000000, Obj: 0.320998, No Obj: 0.003538, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.543831, Class: 1.000000, Obj: 0.291775, No Obj: 0.003912, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.517117, Class: 1.000000, Obj: 0.351162, No Obj: 0.003429, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.526914, Class: 1.000000, Obj: 0.298817, No Obj: 0.003617, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.535532, Class: 1.000000, Obj: 0.326991, No Obj: 0.003707, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.527172, Class: 1.000000, Obj: 0.328960, No Obj: 0.003756, Avg Recall: 0.580645, count: 93 -Region Avg IOU: 0.557035, Class: 1.000000, Obj: 0.390223, No Obj: 0.003510, Avg Recall: 0.626506, count: 83 -4223: 0.435623, 0.448767 avg, 0.001000 rate, 21.050261 seconds, 1081088 images -Loaded: 1.047859 seconds -Region Avg IOU: 0.553489, Class: 1.000000, Obj: 0.344485, No Obj: 0.004283, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.542682, Class: 1.000000, Obj: 0.317448, No Obj: 0.003705, Avg Recall: 0.622222, count: 90 -Region Avg IOU: 0.517571, Class: 1.000000, Obj: 0.318431, No Obj: 0.004276, Avg Recall: 0.602941, count: 136 -Region Avg IOU: 0.505646, Class: 1.000000, Obj: 0.288007, No Obj: 0.003676, Avg Recall: 0.567568, count: 111 -Region Avg IOU: 0.507708, Class: 1.000000, Obj: 0.287348, No Obj: 0.003987, Avg Recall: 0.589147, count: 129 -Region Avg IOU: 0.527236, Class: 1.000000, Obj: 0.322594, No Obj: 0.003835, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.489084, Class: 1.000000, Obj: 0.370822, No Obj: 0.003188, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.581052, Class: 1.000000, Obj: 0.389812, No Obj: 0.004122, Avg Recall: 0.674699, count: 83 -4224: 0.408711, 0.444761 avg, 0.001000 rate, 18.446129 seconds, 1081344 images -Loaded: 3.887824 seconds -Region Avg IOU: 0.543310, Class: 1.000000, Obj: 0.382275, No Obj: 0.003763, Avg Recall: 0.576471, count: 85 -Region Avg IOU: 0.524495, Class: 1.000000, Obj: 0.358778, No Obj: 0.004186, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.452569, Class: 1.000000, Obj: 0.310701, No Obj: 0.003497, Avg Recall: 0.509259, count: 108 -Region Avg IOU: 0.546296, Class: 1.000000, Obj: 0.332129, No Obj: 0.004898, Avg Recall: 0.652174, count: 138 -Region Avg IOU: 0.546620, Class: 1.000000, Obj: 0.379616, No Obj: 0.004206, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.483317, Class: 1.000000, Obj: 0.346857, No Obj: 0.004165, Avg Recall: 0.529412, count: 119 -Region Avg IOU: 0.580083, Class: 1.000000, Obj: 0.385443, No Obj: 0.003924, Avg Recall: 0.666667, count: 69 -Region Avg IOU: 0.522321, Class: 1.000000, Obj: 0.371779, No Obj: 0.004222, Avg Recall: 0.562500, count: 112 -4225: 0.422340, 0.442519 avg, 0.001000 rate, 21.491177 seconds, 1081600 images -Loaded: 0.219305 seconds -Region Avg IOU: 0.486008, Class: 1.000000, Obj: 0.348214, No Obj: 0.004237, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.533053, Class: 1.000000, Obj: 0.373777, No Obj: 0.004082, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.519582, Class: 1.000000, Obj: 0.352618, No Obj: 0.005133, Avg Recall: 0.606557, count: 122 -Region Avg IOU: 0.486910, Class: 1.000000, Obj: 0.353906, No Obj: 0.004958, Avg Recall: 0.539007, count: 141 -Region Avg IOU: 0.542867, Class: 1.000000, Obj: 0.393104, No Obj: 0.004518, Avg Recall: 0.617978, count: 89 -Region Avg IOU: 0.504610, Class: 1.000000, Obj: 0.356531, No Obj: 0.004233, Avg Recall: 0.589744, count: 117 -Region Avg IOU: 0.513941, Class: 1.000000, Obj: 0.371841, No Obj: 0.004423, Avg Recall: 0.568965, count: 116 -Region Avg IOU: 0.533333, Class: 1.000000, Obj: 0.339691, No Obj: 0.004406, Avg Recall: 0.623853, count: 109 -4226: 0.460222, 0.444289 avg, 0.001000 rate, 21.821913 seconds, 1081856 images -Loaded: 0.005533 seconds -Region Avg IOU: 0.553707, Class: 1.000000, Obj: 0.363973, No Obj: 0.005155, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.520316, Class: 1.000000, Obj: 0.392575, No Obj: 0.004527, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.521114, Class: 1.000000, Obj: 0.388200, No Obj: 0.004667, Avg Recall: 0.645161, count: 124 -Region Avg IOU: 0.512345, Class: 1.000000, Obj: 0.334385, No Obj: 0.004267, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.572882, Class: 1.000000, Obj: 0.370088, No Obj: 0.004495, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.556740, Class: 1.000000, Obj: 0.415100, No Obj: 0.004186, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.566534, Class: 1.000000, Obj: 0.426715, No Obj: 0.004665, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.494204, Class: 1.000000, Obj: 0.370309, No Obj: 0.004594, Avg Recall: 0.584071, count: 113 -4227: 0.479767, 0.447837 avg, 0.001000 rate, 22.499825 seconds, 1082112 images -Loaded: 0.422046 seconds -Region Avg IOU: 0.611680, Class: 1.000000, Obj: 0.448422, No Obj: 0.005590, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.469417, Class: 1.000000, Obj: 0.357486, No Obj: 0.004089, Avg Recall: 0.485437, count: 103 -Region Avg IOU: 0.545560, Class: 1.000000, Obj: 0.398208, No Obj: 0.005358, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.499594, Class: 1.000000, Obj: 0.379437, No Obj: 0.004554, Avg Recall: 0.517544, count: 114 -Region Avg IOU: 0.498738, Class: 1.000000, Obj: 0.377597, No Obj: 0.005173, Avg Recall: 0.546154, count: 130 -Region Avg IOU: 0.503001, Class: 1.000000, Obj: 0.427824, No Obj: 0.004604, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.465842, Class: 1.000000, Obj: 0.378675, No Obj: 0.004558, Avg Recall: 0.479339, count: 121 -Region Avg IOU: 0.509962, Class: 1.000000, Obj: 0.382448, No Obj: 0.004942, Avg Recall: 0.560000, count: 125 -4228: 0.431875, 0.446241 avg, 0.001000 rate, 20.777960 seconds, 1082368 images -Loaded: 1.078718 seconds -Region Avg IOU: 0.520627, Class: 1.000000, Obj: 0.404399, No Obj: 0.005472, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.512233, Class: 1.000000, Obj: 0.388465, No Obj: 0.004523, Avg Recall: 0.545455, count: 88 -Region Avg IOU: 0.494902, Class: 1.000000, Obj: 0.412364, No Obj: 0.005087, Avg Recall: 0.578512, count: 121 -Region Avg IOU: 0.577585, Class: 1.000000, Obj: 0.403414, No Obj: 0.004991, Avg Recall: 0.683761, count: 117 -Region Avg IOU: 0.478611, Class: 1.000000, Obj: 0.370524, No Obj: 0.004917, Avg Recall: 0.512605, count: 119 -Region Avg IOU: 0.527030, Class: 1.000000, Obj: 0.393847, No Obj: 0.005589, Avg Recall: 0.616667, count: 120 -Region Avg IOU: 0.541770, Class: 1.000000, Obj: 0.391993, No Obj: 0.004842, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.606159, Class: 1.000000, Obj: 0.452761, No Obj: 0.006098, Avg Recall: 0.694915, count: 118 -4229: 0.435388, 0.445156 avg, 0.001000 rate, 22.018448 seconds, 1082624 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.544150, Class: 1.000000, Obj: 0.381587, No Obj: 0.005122, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.496633, Class: 1.000000, Obj: 0.341046, No Obj: 0.004842, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.506806, Class: 1.000000, Obj: 0.409543, No Obj: 0.004014, Avg Recall: 0.611765, count: 85 -Region Avg IOU: 0.529194, Class: 1.000000, Obj: 0.417171, No Obj: 0.004893, Avg Recall: 0.572727, count: 110 -Region Avg IOU: 0.499874, Class: 1.000000, Obj: 0.384615, No Obj: 0.004317, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.542428, Class: 1.000000, Obj: 0.351511, No Obj: 0.005635, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.530935, Class: 1.000000, Obj: 0.416517, No Obj: 0.005224, Avg Recall: 0.578947, count: 114 -Region Avg IOU: 0.534150, Class: 1.000000, Obj: 0.392718, No Obj: 0.005432, Avg Recall: 0.617391, count: 115 -4230: 0.426951, 0.443335 avg, 0.001000 rate, 20.706736 seconds, 1082880 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.274588 seconds -Region Avg IOU: 0.519346, Class: 1.000000, Obj: 0.388263, No Obj: 0.005354, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.525136, Class: 1.000000, Obj: 0.399930, No Obj: 0.004545, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.438819, Class: 1.000000, Obj: 0.368201, No Obj: 0.004378, Avg Recall: 0.400000, count: 105 -Region Avg IOU: 0.524963, Class: 1.000000, Obj: 0.372075, No Obj: 0.005010, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.483952, Class: 1.000000, Obj: 0.393195, No Obj: 0.004986, Avg Recall: 0.500000, count: 124 -Region Avg IOU: 0.542095, Class: 1.000000, Obj: 0.381244, No Obj: 0.004634, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.541453, Class: 1.000000, Obj: 0.404773, No Obj: 0.004033, Avg Recall: 0.597701, count: 87 -Region Avg IOU: 0.489982, Class: 1.000000, Obj: 0.401309, No Obj: 0.004756, Avg Recall: 0.547826, count: 115 -4231: 0.444768, 0.443478 avg, 0.001000 rate, 20.011208 seconds, 1083136 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.507238, Class: 1.000000, Obj: 0.387854, No Obj: 0.004469, Avg Recall: 0.567568, count: 111 -Region Avg IOU: 0.541773, Class: 1.000000, Obj: 0.391724, No Obj: 0.004836, Avg Recall: 0.634146, count: 82 -Region Avg IOU: 0.542069, Class: 1.000000, Obj: 0.382425, No Obj: 0.004350, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.483067, Class: 1.000000, Obj: 0.341148, No Obj: 0.004472, Avg Recall: 0.516393, count: 122 -Region Avg IOU: 0.566440, Class: 1.000000, Obj: 0.419677, No Obj: 0.004802, Avg Recall: 0.670588, count: 85 -Region Avg IOU: 0.529015, Class: 1.000000, Obj: 0.385259, No Obj: 0.004750, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.581129, Class: 1.000000, Obj: 0.417408, No Obj: 0.004502, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.508719, Class: 1.000000, Obj: 0.383053, No Obj: 0.004353, Avg Recall: 0.561905, count: 105 -4232: 0.481674, 0.447298 avg, 0.001000 rate, 19.783092 seconds, 1083392 images -Loaded: 1.151253 seconds -Region Avg IOU: 0.600176, Class: 1.000000, Obj: 0.437441, No Obj: 0.004872, Avg Recall: 0.688889, count: 90 -Region Avg IOU: 0.546709, Class: 1.000000, Obj: 0.404993, No Obj: 0.004901, Avg Recall: 0.627119, count: 118 -Region Avg IOU: 0.547136, Class: 1.000000, Obj: 0.392641, No Obj: 0.004885, Avg Recall: 0.612403, count: 129 -Region Avg IOU: 0.557229, Class: 1.000000, Obj: 0.357806, No Obj: 0.004476, Avg Recall: 0.640449, count: 89 -Region Avg IOU: 0.502180, Class: 1.000000, Obj: 0.383824, No Obj: 0.003976, Avg Recall: 0.537634, count: 93 -Region Avg IOU: 0.544658, Class: 1.000000, Obj: 0.385779, No Obj: 0.004772, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.532725, Class: 1.000000, Obj: 0.375901, No Obj: 0.003824, Avg Recall: 0.595745, count: 94 -Region Avg IOU: 0.439077, Class: 1.000000, Obj: 0.293700, No Obj: 0.003502, Avg Recall: 0.481132, count: 106 -4233: 0.464598, 0.449028 avg, 0.001000 rate, 21.364876 seconds, 1083648 images -Loaded: 2.331407 seconds -Region Avg IOU: 0.548187, Class: 1.000000, Obj: 0.360283, No Obj: 0.004274, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.514000, Class: 1.000000, Obj: 0.339566, No Obj: 0.004140, Avg Recall: 0.572581, count: 124 -Region Avg IOU: 0.574876, Class: 1.000000, Obj: 0.393967, No Obj: 0.004715, Avg Recall: 0.675214, count: 117 -Region Avg IOU: 0.528848, Class: 1.000000, Obj: 0.345053, No Obj: 0.003583, Avg Recall: 0.626374, count: 91 -Region Avg IOU: 0.475686, Class: 1.000000, Obj: 0.335958, No Obj: 0.004601, Avg Recall: 0.540984, count: 122 -Region Avg IOU: 0.468973, Class: 1.000000, Obj: 0.317902, No Obj: 0.004052, Avg Recall: 0.508929, count: 112 -Region Avg IOU: 0.501224, Class: 1.000000, Obj: 0.389163, No Obj: 0.003697, Avg Recall: 0.514019, count: 107 -Region Avg IOU: 0.550733, Class: 1.000000, Obj: 0.365813, No Obj: 0.004061, Avg Recall: 0.625000, count: 104 -4234: 0.427401, 0.446865 avg, 0.001000 rate, 22.085554 seconds, 1083904 images -Loaded: 0.081099 seconds -Region Avg IOU: 0.545583, Class: 1.000000, Obj: 0.353998, No Obj: 0.004197, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.522499, Class: 1.000000, Obj: 0.299713, No Obj: 0.003974, Avg Recall: 0.611570, count: 121 -Region Avg IOU: 0.499624, Class: 1.000000, Obj: 0.315055, No Obj: 0.004135, Avg Recall: 0.563636, count: 110 -Region Avg IOU: 0.474634, Class: 1.000000, Obj: 0.274087, No Obj: 0.004246, Avg Recall: 0.525000, count: 120 -Region Avg IOU: 0.511952, Class: 1.000000, Obj: 0.328475, No Obj: 0.004081, Avg Recall: 0.590164, count: 122 -Region Avg IOU: 0.585280, Class: 1.000000, Obj: 0.344430, No Obj: 0.003715, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.517856, Class: 1.000000, Obj: 0.314413, No Obj: 0.003391, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.509847, Class: 1.000000, Obj: 0.329419, No Obj: 0.004635, Avg Recall: 0.579832, count: 119 -4235: 0.495068, 0.451685 avg, 0.001000 rate, 19.737366 seconds, 1084160 images -Loaded: 3.403536 seconds -Region Avg IOU: 0.463141, Class: 1.000000, Obj: 0.299117, No Obj: 0.003257, Avg Recall: 0.474227, count: 97 -Region Avg IOU: 0.515918, Class: 1.000000, Obj: 0.316915, No Obj: 0.004426, Avg Recall: 0.609756, count: 123 -Region Avg IOU: 0.529222, Class: 1.000000, Obj: 0.270895, No Obj: 0.003804, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.509930, Class: 1.000000, Obj: 0.326818, No Obj: 0.003745, Avg Recall: 0.596774, count: 124 -Region Avg IOU: 0.468723, Class: 1.000000, Obj: 0.326356, No Obj: 0.003299, Avg Recall: 0.490566, count: 106 -Region Avg IOU: 0.556153, Class: 1.000000, Obj: 0.342521, No Obj: 0.004214, Avg Recall: 0.659341, count: 91 -Region Avg IOU: 0.508893, Class: 1.000000, Obj: 0.325463, No Obj: 0.003746, Avg Recall: 0.560748, count: 107 -Region Avg IOU: 0.495024, Class: 1.000000, Obj: 0.299290, No Obj: 0.003745, Avg Recall: 0.563910, count: 133 -4236: 0.464910, 0.453008 avg, 0.001000 rate, 20.378599 seconds, 1084416 images -Loaded: 0.375835 seconds -Region Avg IOU: 0.568153, Class: 1.000000, Obj: 0.345148, No Obj: 0.003810, Avg Recall: 0.632911, count: 79 -Region Avg IOU: 0.540998, Class: 1.000000, Obj: 0.365776, No Obj: 0.003590, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.573200, Class: 1.000000, Obj: 0.336492, No Obj: 0.003828, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.516287, Class: 1.000000, Obj: 0.313786, No Obj: 0.003319, Avg Recall: 0.544444, count: 90 -Region Avg IOU: 0.506250, Class: 1.000000, Obj: 0.305563, No Obj: 0.003736, Avg Recall: 0.580645, count: 93 -Region Avg IOU: 0.525657, Class: 1.000000, Obj: 0.332417, No Obj: 0.004650, Avg Recall: 0.591241, count: 137 -Region Avg IOU: 0.556128, Class: 1.000000, Obj: 0.307045, No Obj: 0.003931, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.504064, Class: 1.000000, Obj: 0.333945, No Obj: 0.003450, Avg Recall: 0.567308, count: 104 -4237: 0.484501, 0.456157 avg, 0.001000 rate, 20.654768 seconds, 1084672 images -Loaded: 1.398677 seconds -Region Avg IOU: 0.546693, Class: 1.000000, Obj: 0.349811, No Obj: 0.003773, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.552512, Class: 1.000000, Obj: 0.300414, No Obj: 0.003603, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.489335, Class: 1.000000, Obj: 0.318677, No Obj: 0.004079, Avg Recall: 0.528571, count: 140 -Region Avg IOU: 0.474508, Class: 1.000000, Obj: 0.333933, No Obj: 0.003754, Avg Recall: 0.534653, count: 101 -Region Avg IOU: 0.536722, Class: 1.000000, Obj: 0.321719, No Obj: 0.003721, Avg Recall: 0.622807, count: 114 -Region Avg IOU: 0.560845, Class: 1.000000, Obj: 0.341794, No Obj: 0.003676, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.529216, Class: 1.000000, Obj: 0.319787, No Obj: 0.003972, Avg Recall: 0.592593, count: 108 -Region Avg IOU: 0.519225, Class: 1.000000, Obj: 0.366098, No Obj: 0.003882, Avg Recall: 0.600000, count: 105 -4238: 0.446901, 0.455232 avg, 0.001000 rate, 22.152128 seconds, 1084928 images -Loaded: 0.394562 seconds -Region Avg IOU: 0.506625, Class: 1.000000, Obj: 0.339890, No Obj: 0.004049, Avg Recall: 0.586777, count: 121 -Region Avg IOU: 0.543043, Class: 1.000000, Obj: 0.340708, No Obj: 0.004147, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.553891, Class: 1.000000, Obj: 0.326129, No Obj: 0.004253, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.482832, Class: 1.000000, Obj: 0.338916, No Obj: 0.004726, Avg Recall: 0.506944, count: 144 -Region Avg IOU: 0.542386, Class: 1.000000, Obj: 0.352071, No Obj: 0.004543, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.507895, Class: 1.000000, Obj: 0.322037, No Obj: 0.003854, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.474266, Class: 1.000000, Obj: 0.326651, No Obj: 0.004018, Avg Recall: 0.504000, count: 125 -Region Avg IOU: 0.578515, Class: 1.000000, Obj: 0.346380, No Obj: 0.003939, Avg Recall: 0.700935, count: 107 -4239: 0.517290, 0.461437 avg, 0.001000 rate, 19.614697 seconds, 1085184 images -Loaded: 1.331504 seconds -Region Avg IOU: 0.535490, Class: 1.000000, Obj: 0.371004, No Obj: 0.003927, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.546869, Class: 1.000000, Obj: 0.370311, No Obj: 0.004558, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.530153, Class: 1.000000, Obj: 0.311894, No Obj: 0.003975, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.501104, Class: 1.000000, Obj: 0.313553, No Obj: 0.004501, Avg Recall: 0.571429, count: 112 -Region Avg IOU: 0.624947, Class: 1.000000, Obj: 0.418821, No Obj: 0.004281, Avg Recall: 0.761905, count: 84 -Region Avg IOU: 0.555553, Class: 1.000000, Obj: 0.342718, No Obj: 0.003503, Avg Recall: 0.631579, count: 76 -Region Avg IOU: 0.509132, Class: 1.000000, Obj: 0.345887, No Obj: 0.004073, Avg Recall: 0.552845, count: 123 -Region Avg IOU: 0.546525, Class: 1.000000, Obj: 0.340237, No Obj: 0.003767, Avg Recall: 0.623762, count: 101 -4240: 0.397560, 0.455050 avg, 0.001000 rate, 18.752352 seconds, 1085440 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000068 seconds -Region Avg IOU: 0.622884, Class: 1.000000, Obj: 0.435443, No Obj: 0.003328, Avg Recall: 0.764228, count: 123 -Region Avg IOU: 0.672639, Class: 1.000000, Obj: 0.452402, No Obj: 0.003249, Avg Recall: 0.850000, count: 100 -Region Avg IOU: 0.633735, Class: 1.000000, Obj: 0.411239, No Obj: 0.003281, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.683919, Class: 1.000000, Obj: 0.502199, No Obj: 0.003277, Avg Recall: 0.819149, count: 94 -Region Avg IOU: 0.607195, Class: 1.000000, Obj: 0.425885, No Obj: 0.003059, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.634318, Class: 1.000000, Obj: 0.398373, No Obj: 0.003368, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.634674, Class: 1.000000, Obj: 0.387779, No Obj: 0.003072, Avg Recall: 0.821429, count: 112 -Region Avg IOU: 0.649870, Class: 1.000000, Obj: 0.399150, No Obj: 0.003302, Avg Recall: 0.852174, count: 115 -4241: 0.451813, 0.454726 avg, 0.001000 rate, 27.912176 seconds, 1085696 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.630887, Class: 1.000000, Obj: 0.421600, No Obj: 0.003297, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.593454, Class: 1.000000, Obj: 0.423237, No Obj: 0.002937, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.647956, Class: 1.000000, Obj: 0.436148, No Obj: 0.002769, Avg Recall: 0.800000, count: 95 -Region Avg IOU: 0.693696, Class: 1.000000, Obj: 0.442163, No Obj: 0.002898, Avg Recall: 0.883117, count: 77 -Region Avg IOU: 0.704469, Class: 1.000000, Obj: 0.462630, No Obj: 0.003202, Avg Recall: 0.886364, count: 88 -Region Avg IOU: 0.644065, Class: 1.000000, Obj: 0.425839, No Obj: 0.003407, Avg Recall: 0.781250, count: 128 -Region Avg IOU: 0.590116, Class: 1.000000, Obj: 0.442091, No Obj: 0.002659, Avg Recall: 0.707865, count: 89 -Region Avg IOU: 0.627793, Class: 1.000000, Obj: 0.406724, No Obj: 0.003255, Avg Recall: 0.738318, count: 107 -4242: 0.413868, 0.450640 avg, 0.001000 rate, 27.546621 seconds, 1085952 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.637523, Class: 1.000000, Obj: 0.415742, No Obj: 0.003223, Avg Recall: 0.808333, count: 120 -Region Avg IOU: 0.649469, Class: 1.000000, Obj: 0.448075, No Obj: 0.003211, Avg Recall: 0.821138, count: 123 -Region Avg IOU: 0.648203, Class: 1.000000, Obj: 0.425435, No Obj: 0.002852, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.626146, Class: 1.000000, Obj: 0.376684, No Obj: 0.003003, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.627344, Class: 1.000000, Obj: 0.427013, No Obj: 0.002411, Avg Recall: 0.791209, count: 91 -Region Avg IOU: 0.620428, Class: 1.000000, Obj: 0.406047, No Obj: 0.002907, Avg Recall: 0.752066, count: 121 -Region Avg IOU: 0.603548, Class: 1.000000, Obj: 0.362089, No Obj: 0.003267, Avg Recall: 0.719697, count: 132 -Region Avg IOU: 0.629574, Class: 1.000000, Obj: 0.450436, No Obj: 0.002967, Avg Recall: 0.796460, count: 113 -4243: 0.478689, 0.453445 avg, 0.001000 rate, 29.267736 seconds, 1086208 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.632285, Class: 1.000000, Obj: 0.350283, No Obj: 0.002831, Avg Recall: 0.787879, count: 132 -Region Avg IOU: 0.644493, Class: 1.000000, Obj: 0.395950, No Obj: 0.002857, Avg Recall: 0.841584, count: 101 -Region Avg IOU: 0.625057, Class: 1.000000, Obj: 0.377617, No Obj: 0.002611, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.621488, Class: 1.000000, Obj: 0.392097, No Obj: 0.002517, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.594662, Class: 1.000000, Obj: 0.368501, No Obj: 0.002798, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.633634, Class: 1.000000, Obj: 0.374134, No Obj: 0.002602, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.578190, Class: 1.000000, Obj: 0.365876, No Obj: 0.002426, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.636460, Class: 1.000000, Obj: 0.441501, No Obj: 0.002486, Avg Recall: 0.787879, count: 99 -4244: 0.426342, 0.450735 avg, 0.001000 rate, 28.358166 seconds, 1086464 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.608908, Class: 1.000000, Obj: 0.373722, No Obj: 0.002567, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.647292, Class: 1.000000, Obj: 0.413646, No Obj: 0.002729, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.651738, Class: 1.000000, Obj: 0.381438, No Obj: 0.003453, Avg Recall: 0.812500, count: 144 -Region Avg IOU: 0.658165, Class: 1.000000, Obj: 0.380399, No Obj: 0.002906, Avg Recall: 0.807692, count: 130 -Region Avg IOU: 0.662939, Class: 1.000000, Obj: 0.420393, No Obj: 0.002390, Avg Recall: 0.802198, count: 91 -Region Avg IOU: 0.643714, Class: 1.000000, Obj: 0.356429, No Obj: 0.002749, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.633195, Class: 1.000000, Obj: 0.332890, No Obj: 0.002899, Avg Recall: 0.786325, count: 117 -Region Avg IOU: 0.641528, Class: 1.000000, Obj: 0.403468, No Obj: 0.002862, Avg Recall: 0.785714, count: 126 -4245: 0.492533, 0.454915 avg, 0.001000 rate, 30.399693 seconds, 1086720 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.629326, Class: 1.000000, Obj: 0.356096, No Obj: 0.002202, Avg Recall: 0.775000, count: 80 -Region Avg IOU: 0.655266, Class: 1.000000, Obj: 0.362842, No Obj: 0.002845, Avg Recall: 0.853448, count: 116 -Region Avg IOU: 0.630341, Class: 1.000000, Obj: 0.393273, No Obj: 0.002904, Avg Recall: 0.747967, count: 123 -Region Avg IOU: 0.680570, Class: 1.000000, Obj: 0.399446, No Obj: 0.002802, Avg Recall: 0.864407, count: 118 -Region Avg IOU: 0.670828, Class: 1.000000, Obj: 0.380785, No Obj: 0.002621, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.654899, Class: 1.000000, Obj: 0.393209, No Obj: 0.002448, Avg Recall: 0.837209, count: 86 -Region Avg IOU: 0.642742, Class: 1.000000, Obj: 0.397930, No Obj: 0.002700, Avg Recall: 0.796460, count: 113 -Region Avg IOU: 0.637884, Class: 1.000000, Obj: 0.376019, No Obj: 0.002603, Avg Recall: 0.761905, count: 105 -4246: 0.447189, 0.454142 avg, 0.001000 rate, 27.756012 seconds, 1086976 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.628518, Class: 1.000000, Obj: 0.389656, No Obj: 0.002926, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.661886, Class: 1.000000, Obj: 0.391061, No Obj: 0.002422, Avg Recall: 0.835165, count: 91 -Region Avg IOU: 0.631353, Class: 1.000000, Obj: 0.317192, No Obj: 0.002914, Avg Recall: 0.779528, count: 127 -Region Avg IOU: 0.572191, Class: 1.000000, Obj: 0.373725, No Obj: 0.002495, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.621158, Class: 1.000000, Obj: 0.350043, No Obj: 0.002499, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.632063, Class: 1.000000, Obj: 0.411608, No Obj: 0.002352, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.626948, Class: 1.000000, Obj: 0.305477, No Obj: 0.002764, Avg Recall: 0.770492, count: 122 -Region Avg IOU: 0.615141, Class: 1.000000, Obj: 0.382609, No Obj: 0.002501, Avg Recall: 0.699029, count: 103 -4247: 0.460435, 0.454771 avg, 0.001000 rate, 28.455025 seconds, 1087232 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.621134, Class: 1.000000, Obj: 0.368736, No Obj: 0.002986, Avg Recall: 0.756944, count: 144 -Region Avg IOU: 0.653597, Class: 1.000000, Obj: 0.392276, No Obj: 0.002636, Avg Recall: 0.838095, count: 105 -Region Avg IOU: 0.682841, Class: 1.000000, Obj: 0.345571, No Obj: 0.002887, Avg Recall: 0.862745, count: 102 -Region Avg IOU: 0.632154, Class: 1.000000, Obj: 0.360571, No Obj: 0.002527, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.660484, Class: 1.000000, Obj: 0.406360, No Obj: 0.002524, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.622419, Class: 1.000000, Obj: 0.398624, No Obj: 0.002577, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.637949, Class: 1.000000, Obj: 0.389372, No Obj: 0.002484, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.613463, Class: 1.000000, Obj: 0.335731, No Obj: 0.002624, Avg Recall: 0.719626, count: 107 -4248: 0.455235, 0.454818 avg, 0.001000 rate, 28.426239 seconds, 1087488 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.622806, Class: 1.000000, Obj: 0.339534, No Obj: 0.002485, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.662921, Class: 1.000000, Obj: 0.351044, No Obj: 0.002643, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.628337, Class: 1.000000, Obj: 0.391706, No Obj: 0.002269, Avg Recall: 0.770115, count: 87 -Region Avg IOU: 0.659031, Class: 1.000000, Obj: 0.391422, No Obj: 0.002456, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.674812, Class: 1.000000, Obj: 0.372942, No Obj: 0.002688, Avg Recall: 0.865979, count: 97 -Region Avg IOU: 0.641470, Class: 1.000000, Obj: 0.366237, No Obj: 0.002667, Avg Recall: 0.796610, count: 118 -Region Avg IOU: 0.642402, Class: 1.000000, Obj: 0.392746, No Obj: 0.002616, Avg Recall: 0.796610, count: 118 -Region Avg IOU: 0.623966, Class: 1.000000, Obj: 0.330611, No Obj: 0.002513, Avg Recall: 0.769912, count: 113 -4249: 0.432800, 0.452616 avg, 0.001000 rate, 29.891115 seconds, 1087744 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.660505, Class: 1.000000, Obj: 0.315872, No Obj: 0.003114, Avg Recall: 0.817308, count: 104 -Region Avg IOU: 0.610688, Class: 1.000000, Obj: 0.330049, No Obj: 0.002807, Avg Recall: 0.767442, count: 129 -Region Avg IOU: 0.607779, Class: 1.000000, Obj: 0.272954, No Obj: 0.002257, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.664715, Class: 1.000000, Obj: 0.368235, No Obj: 0.002706, Avg Recall: 0.841584, count: 101 -Region Avg IOU: 0.572102, Class: 1.000000, Obj: 0.350810, No Obj: 0.002658, Avg Recall: 0.664384, count: 146 -Region Avg IOU: 0.631878, Class: 1.000000, Obj: 0.344965, No Obj: 0.002548, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.668425, Class: 1.000000, Obj: 0.373750, No Obj: 0.002499, Avg Recall: 0.840909, count: 88 -Region Avg IOU: 0.629756, Class: 1.000000, Obj: 0.332166, No Obj: 0.002642, Avg Recall: 0.789062, count: 128 -4250: 0.467362, 0.454091 avg, 0.001000 rate, 27.418144 seconds, 1088000 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.043777 seconds -Region Avg IOU: 0.554863, Class: 1.000000, Obj: 0.284710, No Obj: 0.002803, Avg Recall: 0.659341, count: 91 -Region Avg IOU: 0.481606, Class: 1.000000, Obj: 0.275525, No Obj: 0.003012, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.561191, Class: 1.000000, Obj: 0.311479, No Obj: 0.003439, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.507586, Class: 1.000000, Obj: 0.295242, No Obj: 0.003399, Avg Recall: 0.570312, count: 128 -Region Avg IOU: 0.545550, Class: 1.000000, Obj: 0.293940, No Obj: 0.002723, Avg Recall: 0.625000, count: 88 -Region Avg IOU: 0.551062, Class: 1.000000, Obj: 0.260866, No Obj: 0.003575, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.497778, Class: 1.000000, Obj: 0.275859, No Obj: 0.003635, Avg Recall: 0.560510, count: 157 -Region Avg IOU: 0.526645, Class: 1.000000, Obj: 0.266893, No Obj: 0.003463, Avg Recall: 0.587719, count: 114 -4251: 0.472232, 0.455905 avg, 0.001000 rate, 19.976830 seconds, 1088256 images -Loaded: 2.633905 seconds -Region Avg IOU: 0.498354, Class: 1.000000, Obj: 0.280548, No Obj: 0.003350, Avg Recall: 0.576577, count: 111 -Region Avg IOU: 0.507971, Class: 1.000000, Obj: 0.244219, No Obj: 0.003355, Avg Recall: 0.561151, count: 139 -Region Avg IOU: 0.467296, Class: 1.000000, Obj: 0.306653, No Obj: 0.003086, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.524119, Class: 1.000000, Obj: 0.283392, No Obj: 0.003275, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.522298, Class: 1.000000, Obj: 0.253905, No Obj: 0.003234, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.445723, Class: 1.000000, Obj: 0.233275, No Obj: 0.002821, Avg Recall: 0.500000, count: 114 -Region Avg IOU: 0.495237, Class: 1.000000, Obj: 0.251648, No Obj: 0.003888, Avg Recall: 0.510490, count: 143 -Region Avg IOU: 0.499558, Class: 1.000000, Obj: 0.272539, No Obj: 0.003032, Avg Recall: 0.570093, count: 107 -4252: 0.493500, 0.459664 avg, 0.001000 rate, 20.904976 seconds, 1088512 images -Loaded: 1.701847 seconds -Region Avg IOU: 0.514391, Class: 1.000000, Obj: 0.271887, No Obj: 0.003661, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.527171, Class: 1.000000, Obj: 0.316478, No Obj: 0.003297, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.455276, Class: 1.000000, Obj: 0.301750, No Obj: 0.003013, Avg Recall: 0.481132, count: 106 -Region Avg IOU: 0.502938, Class: 1.000000, Obj: 0.306009, No Obj: 0.003327, Avg Recall: 0.584000, count: 125 -Region Avg IOU: 0.514226, Class: 1.000000, Obj: 0.290158, No Obj: 0.003664, Avg Recall: 0.560606, count: 132 -Region Avg IOU: 0.479541, Class: 1.000000, Obj: 0.265314, No Obj: 0.003197, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.496149, Class: 1.000000, Obj: 0.279641, No Obj: 0.004107, Avg Recall: 0.528571, count: 140 -Region Avg IOU: 0.506023, Class: 1.000000, Obj: 0.273500, No Obj: 0.003357, Avg Recall: 0.536842, count: 95 -4253: 0.496550, 0.463353 avg, 0.001000 rate, 19.660484 seconds, 1088768 images -Loaded: 2.138334 seconds -Region Avg IOU: 0.511921, Class: 1.000000, Obj: 0.330662, No Obj: 0.003674, Avg Recall: 0.533333, count: 105 -Region Avg IOU: 0.490253, Class: 1.000000, Obj: 0.270750, No Obj: 0.004336, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.518822, Class: 1.000000, Obj: 0.338398, No Obj: 0.003706, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.538795, Class: 1.000000, Obj: 0.375772, No Obj: 0.003443, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.527077, Class: 1.000000, Obj: 0.350685, No Obj: 0.003667, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.523946, Class: 1.000000, Obj: 0.364507, No Obj: 0.003719, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.530210, Class: 1.000000, Obj: 0.355258, No Obj: 0.003482, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.467062, Class: 1.000000, Obj: 0.289761, No Obj: 0.003687, Avg Recall: 0.503759, count: 133 -4254: 0.391926, 0.456210 avg, 0.001000 rate, 20.754223 seconds, 1089024 images -Loaded: 0.286308 seconds -Region Avg IOU: 0.546663, Class: 1.000000, Obj: 0.385082, No Obj: 0.004311, Avg Recall: 0.693182, count: 88 -Region Avg IOU: 0.525099, Class: 1.000000, Obj: 0.321680, No Obj: 0.004289, Avg Recall: 0.576000, count: 125 -Region Avg IOU: 0.523010, Class: 1.000000, Obj: 0.382292, No Obj: 0.004057, Avg Recall: 0.553191, count: 94 -Region Avg IOU: 0.530330, Class: 1.000000, Obj: 0.369699, No Obj: 0.003835, Avg Recall: 0.621951, count: 82 -Region Avg IOU: 0.537782, Class: 1.000000, Obj: 0.331097, No Obj: 0.003850, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.515783, Class: 1.000000, Obj: 0.342059, No Obj: 0.003881, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.495679, Class: 1.000000, Obj: 0.346059, No Obj: 0.004169, Avg Recall: 0.516949, count: 118 -Region Avg IOU: 0.572899, Class: 1.000000, Obj: 0.378422, No Obj: 0.003781, Avg Recall: 0.673913, count: 92 -4255: 0.453336, 0.455923 avg, 0.001000 rate, 18.261631 seconds, 1089280 images -Loaded: 2.790251 seconds -Region Avg IOU: 0.546969, Class: 1.000000, Obj: 0.395992, No Obj: 0.004919, Avg Recall: 0.625000, count: 120 -Region Avg IOU: 0.536630, Class: 1.000000, Obj: 0.408563, No Obj: 0.004811, Avg Recall: 0.571429, count: 119 -Region Avg IOU: 0.508408, Class: 1.000000, Obj: 0.361148, No Obj: 0.004345, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.538518, Class: 1.000000, Obj: 0.329698, No Obj: 0.004491, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.565653, Class: 1.000000, Obj: 0.402956, No Obj: 0.004136, Avg Recall: 0.648352, count: 91 -Region Avg IOU: 0.532253, Class: 1.000000, Obj: 0.391090, No Obj: 0.004957, Avg Recall: 0.641791, count: 134 -Region Avg IOU: 0.525195, Class: 1.000000, Obj: 0.368811, No Obj: 0.005195, Avg Recall: 0.607843, count: 153 -Region Avg IOU: 0.592610, Class: 1.000000, Obj: 0.325104, No Obj: 0.004025, Avg Recall: 0.728261, count: 92 -4256: 0.445545, 0.454885 avg, 0.001000 rate, 18.414612 seconds, 1089536 images -Loaded: 2.691096 seconds -Region Avg IOU: 0.496016, Class: 1.000000, Obj: 0.393333, No Obj: 0.004565, Avg Recall: 0.552083, count: 96 -Region Avg IOU: 0.578458, Class: 1.000000, Obj: 0.450304, No Obj: 0.004119, Avg Recall: 0.620690, count: 87 -Region Avg IOU: 0.517536, Class: 1.000000, Obj: 0.416234, No Obj: 0.004608, Avg Recall: 0.596491, count: 114 -Region Avg IOU: 0.454597, Class: 1.000000, Obj: 0.326954, No Obj: 0.004435, Avg Recall: 0.481132, count: 106 -Region Avg IOU: 0.500406, Class: 1.000000, Obj: 0.389773, No Obj: 0.003408, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.457220, Class: 1.000000, Obj: 0.374782, No Obj: 0.004774, Avg Recall: 0.482014, count: 139 -Region Avg IOU: 0.483187, Class: 1.000000, Obj: 0.377189, No Obj: 0.004699, Avg Recall: 0.528000, count: 125 -Region Avg IOU: 0.514724, Class: 1.000000, Obj: 0.385877, No Obj: 0.004620, Avg Recall: 0.534653, count: 101 -4257: 0.421197, 0.451516 avg, 0.001000 rate, 22.183245 seconds, 1089792 images -Loaded: 0.520318 seconds -Region Avg IOU: 0.550111, Class: 1.000000, Obj: 0.397721, No Obj: 0.005849, Avg Recall: 0.631148, count: 122 -Region Avg IOU: 0.501511, Class: 1.000000, Obj: 0.388265, No Obj: 0.004982, Avg Recall: 0.536364, count: 110 -Region Avg IOU: 0.521028, Class: 1.000000, Obj: 0.398995, No Obj: 0.005686, Avg Recall: 0.621212, count: 132 -Region Avg IOU: 0.461540, Class: 1.000000, Obj: 0.365814, No Obj: 0.004616, Avg Recall: 0.507937, count: 126 -Region Avg IOU: 0.544798, Class: 1.000000, Obj: 0.375426, No Obj: 0.004928, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.572308, Class: 1.000000, Obj: 0.406613, No Obj: 0.005048, Avg Recall: 0.669355, count: 124 -Region Avg IOU: 0.520201, Class: 1.000000, Obj: 0.394350, No Obj: 0.004402, Avg Recall: 0.597826, count: 92 -Region Avg IOU: 0.518652, Class: 1.000000, Obj: 0.381195, No Obj: 0.005187, Avg Recall: 0.619403, count: 134 -4258: 0.497946, 0.456159 avg, 0.001000 rate, 20.858025 seconds, 1090048 images -Loaded: 0.908240 seconds -Region Avg IOU: 0.442111, Class: 1.000000, Obj: 0.359432, No Obj: 0.004533, Avg Recall: 0.475000, count: 120 -Region Avg IOU: 0.571084, Class: 1.000000, Obj: 0.436182, No Obj: 0.004712, Avg Recall: 0.666667, count: 87 -Region Avg IOU: 0.532112, Class: 1.000000, Obj: 0.396891, No Obj: 0.004538, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.537721, Class: 1.000000, Obj: 0.391991, No Obj: 0.004668, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.544818, Class: 1.000000, Obj: 0.412341, No Obj: 0.004897, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.531384, Class: 1.000000, Obj: 0.446564, No Obj: 0.005540, Avg Recall: 0.595890, count: 146 -Region Avg IOU: 0.513131, Class: 1.000000, Obj: 0.416189, No Obj: 0.004931, Avg Recall: 0.505050, count: 99 -Region Avg IOU: 0.517387, Class: 1.000000, Obj: 0.434279, No Obj: 0.005139, Avg Recall: 0.640351, count: 114 -4259: 0.501674, 0.460711 avg, 0.001000 rate, 19.047184 seconds, 1090304 images -Loaded: 3.303234 seconds -Region Avg IOU: 0.512530, Class: 1.000000, Obj: 0.376294, No Obj: 0.005170, Avg Recall: 0.580882, count: 136 -Region Avg IOU: 0.576633, Class: 1.000000, Obj: 0.430254, No Obj: 0.004832, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.517197, Class: 1.000000, Obj: 0.400835, No Obj: 0.005081, Avg Recall: 0.532110, count: 109 -Region Avg IOU: 0.552095, Class: 1.000000, Obj: 0.443004, No Obj: 0.004783, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.497617, Class: 1.000000, Obj: 0.361324, No Obj: 0.004446, Avg Recall: 0.494382, count: 89 -Region Avg IOU: 0.513292, Class: 1.000000, Obj: 0.429973, No Obj: 0.005708, Avg Recall: 0.563025, count: 119 -Region Avg IOU: 0.528788, Class: 1.000000, Obj: 0.343196, No Obj: 0.004713, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.437196, Class: 1.000000, Obj: 0.370094, No Obj: 0.004959, Avg Recall: 0.466102, count: 118 -4260: 0.448557, 0.459495 avg, 0.001000 rate, 21.891478 seconds, 1090560 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000072 seconds -Region Avg IOU: 0.543786, Class: 1.000000, Obj: 0.420236, No Obj: 0.003874, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.623536, Class: 1.000000, Obj: 0.408614, No Obj: 0.004114, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.567107, Class: 1.000000, Obj: 0.433373, No Obj: 0.003973, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.620186, Class: 1.000000, Obj: 0.473762, No Obj: 0.003517, Avg Recall: 0.791209, count: 91 -Region Avg IOU: 0.613296, Class: 1.000000, Obj: 0.473504, No Obj: 0.003725, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.546529, Class: 1.000000, Obj: 0.366588, No Obj: 0.003721, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.580802, Class: 1.000000, Obj: 0.377701, No Obj: 0.004296, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.638038, Class: 1.000000, Obj: 0.454200, No Obj: 0.004392, Avg Recall: 0.764706, count: 119 -4261: 0.444976, 0.458043 avg, 0.001000 rate, 24.793535 seconds, 1090816 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.590193, Class: 1.000000, Obj: 0.437023, No Obj: 0.003507, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.597085, Class: 1.000000, Obj: 0.381826, No Obj: 0.004069, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.575908, Class: 1.000000, Obj: 0.417261, No Obj: 0.004522, Avg Recall: 0.664557, count: 158 -Region Avg IOU: 0.592395, Class: 1.000000, Obj: 0.403828, No Obj: 0.003730, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.632188, Class: 1.000000, Obj: 0.442187, No Obj: 0.003485, Avg Recall: 0.814815, count: 81 -Region Avg IOU: 0.590967, Class: 1.000000, Obj: 0.397781, No Obj: 0.003936, Avg Recall: 0.680328, count: 122 -Region Avg IOU: 0.567922, Class: 1.000000, Obj: 0.368311, No Obj: 0.004372, Avg Recall: 0.702479, count: 121 -Region Avg IOU: 0.564654, Class: 1.000000, Obj: 0.383664, No Obj: 0.003825, Avg Recall: 0.663934, count: 122 -4262: 0.488141, 0.461053 avg, 0.001000 rate, 24.995224 seconds, 1091072 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.582565, Class: 1.000000, Obj: 0.405681, No Obj: 0.004125, Avg Recall: 0.694805, count: 154 -Region Avg IOU: 0.590938, Class: 1.000000, Obj: 0.374529, No Obj: 0.003545, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.593473, Class: 1.000000, Obj: 0.384178, No Obj: 0.003854, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.568298, Class: 1.000000, Obj: 0.381714, No Obj: 0.003470, Avg Recall: 0.655738, count: 122 -Region Avg IOU: 0.634229, Class: 1.000000, Obj: 0.379197, No Obj: 0.003467, Avg Recall: 0.779070, count: 86 -Region Avg IOU: 0.614281, Class: 1.000000, Obj: 0.448355, No Obj: 0.003262, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.577173, Class: 1.000000, Obj: 0.355804, No Obj: 0.003222, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.582081, Class: 1.000000, Obj: 0.387147, No Obj: 0.003321, Avg Recall: 0.705882, count: 85 -4263: 0.436966, 0.458644 avg, 0.001000 rate, 25.156679 seconds, 1091328 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.578783, Class: 1.000000, Obj: 0.387265, No Obj: 0.002895, Avg Recall: 0.674699, count: 83 -Region Avg IOU: 0.591107, Class: 1.000000, Obj: 0.371231, No Obj: 0.003250, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.552453, Class: 1.000000, Obj: 0.375608, No Obj: 0.003298, Avg Recall: 0.623077, count: 130 -Region Avg IOU: 0.548360, Class: 1.000000, Obj: 0.371012, No Obj: 0.003359, Avg Recall: 0.633333, count: 120 -Region Avg IOU: 0.578804, Class: 1.000000, Obj: 0.380286, No Obj: 0.003268, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.611356, Class: 1.000000, Obj: 0.424428, No Obj: 0.003583, Avg Recall: 0.750000, count: 128 -Region Avg IOU: 0.615213, Class: 1.000000, Obj: 0.423314, No Obj: 0.002889, Avg Recall: 0.744444, count: 90 -Region Avg IOU: 0.612708, Class: 1.000000, Obj: 0.414778, No Obj: 0.003406, Avg Recall: 0.764151, count: 106 -4264: 0.470869, 0.459867 avg, 0.001000 rate, 24.115580 seconds, 1091584 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.569860, Class: 1.000000, Obj: 0.365838, No Obj: 0.003188, Avg Recall: 0.675214, count: 117 -Region Avg IOU: 0.599288, Class: 1.000000, Obj: 0.364729, No Obj: 0.002663, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.613335, Class: 1.000000, Obj: 0.413565, No Obj: 0.002717, Avg Recall: 0.734177, count: 79 -Region Avg IOU: 0.577271, Class: 1.000000, Obj: 0.340334, No Obj: 0.003241, Avg Recall: 0.680672, count: 119 -Region Avg IOU: 0.588042, Class: 1.000000, Obj: 0.355991, No Obj: 0.002957, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.579599, Class: 1.000000, Obj: 0.332331, No Obj: 0.002952, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.521308, Class: 1.000000, Obj: 0.359549, No Obj: 0.002736, Avg Recall: 0.568421, count: 95 -Region Avg IOU: 0.576316, Class: 1.000000, Obj: 0.325266, No Obj: 0.002995, Avg Recall: 0.694444, count: 108 -4265: 0.430337, 0.456914 avg, 0.001000 rate, 26.173532 seconds, 1091840 images -Loaded: 0.000140 seconds -Region Avg IOU: 0.570246, Class: 1.000000, Obj: 0.362440, No Obj: 0.003019, Avg Recall: 0.655462, count: 119 -Region Avg IOU: 0.597974, Class: 1.000000, Obj: 0.381805, No Obj: 0.003245, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.608299, Class: 1.000000, Obj: 0.352695, No Obj: 0.002846, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.572757, Class: 1.000000, Obj: 0.322241, No Obj: 0.002477, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.604400, Class: 1.000000, Obj: 0.346010, No Obj: 0.002731, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.608684, Class: 1.000000, Obj: 0.331089, No Obj: 0.003457, Avg Recall: 0.694444, count: 144 -Region Avg IOU: 0.583837, Class: 1.000000, Obj: 0.336443, No Obj: 0.003660, Avg Recall: 0.664706, count: 170 -Region Avg IOU: 0.589976, Class: 1.000000, Obj: 0.348108, No Obj: 0.003057, Avg Recall: 0.720000, count: 125 -4266: 0.469690, 0.458191 avg, 0.001000 rate, 23.903557 seconds, 1092096 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.562160, Class: 1.000000, Obj: 0.305167, No Obj: 0.003019, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.623445, Class: 1.000000, Obj: 0.382677, No Obj: 0.003275, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.574854, Class: 1.000000, Obj: 0.325894, No Obj: 0.003206, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.619468, Class: 1.000000, Obj: 0.343612, No Obj: 0.002861, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.630807, Class: 1.000000, Obj: 0.359273, No Obj: 0.003635, Avg Recall: 0.820312, count: 128 -Region Avg IOU: 0.613979, Class: 1.000000, Obj: 0.326173, No Obj: 0.002808, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.603451, Class: 1.000000, Obj: 0.336364, No Obj: 0.002636, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.621120, Class: 1.000000, Obj: 0.339478, No Obj: 0.002913, Avg Recall: 0.737864, count: 103 -4267: 0.460228, 0.458395 avg, 0.001000 rate, 24.563976 seconds, 1092352 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.595415, Class: 1.000000, Obj: 0.328812, No Obj: 0.003287, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.550501, Class: 1.000000, Obj: 0.316596, No Obj: 0.002670, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.602078, Class: 1.000000, Obj: 0.323958, No Obj: 0.003099, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.563304, Class: 1.000000, Obj: 0.334979, No Obj: 0.002951, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.585623, Class: 1.000000, Obj: 0.328277, No Obj: 0.002838, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.600132, Class: 1.000000, Obj: 0.402119, No Obj: 0.003128, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.611808, Class: 1.000000, Obj: 0.372206, No Obj: 0.003364, Avg Recall: 0.744000, count: 125 -Region Avg IOU: 0.601152, Class: 1.000000, Obj: 0.314421, No Obj: 0.003046, Avg Recall: 0.711864, count: 118 -4268: 0.451801, 0.457736 avg, 0.001000 rate, 26.944708 seconds, 1092608 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.611433, Class: 1.000000, Obj: 0.382072, No Obj: 0.004029, Avg Recall: 0.722222, count: 144 -Region Avg IOU: 0.598304, Class: 1.000000, Obj: 0.341481, No Obj: 0.002789, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.634845, Class: 1.000000, Obj: 0.351037, No Obj: 0.002934, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.595567, Class: 1.000000, Obj: 0.313517, No Obj: 0.002864, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.628949, Class: 1.000000, Obj: 0.368448, No Obj: 0.002760, Avg Recall: 0.755814, count: 86 -Region Avg IOU: 0.570261, Class: 1.000000, Obj: 0.295776, No Obj: 0.003028, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.541288, Class: 1.000000, Obj: 0.337892, No Obj: 0.002584, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.599965, Class: 1.000000, Obj: 0.335580, No Obj: 0.003168, Avg Recall: 0.699187, count: 123 -4269: 0.453818, 0.457344 avg, 0.001000 rate, 23.801878 seconds, 1092864 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.623041, Class: 1.000000, Obj: 0.388743, No Obj: 0.003510, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.641036, Class: 1.000000, Obj: 0.342442, No Obj: 0.002990, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.601972, Class: 1.000000, Obj: 0.369304, No Obj: 0.003310, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.599680, Class: 1.000000, Obj: 0.362114, No Obj: 0.002913, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.588948, Class: 1.000000, Obj: 0.357453, No Obj: 0.003164, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.629360, Class: 1.000000, Obj: 0.385450, No Obj: 0.003215, Avg Recall: 0.808989, count: 89 -Region Avg IOU: 0.616699, Class: 1.000000, Obj: 0.422932, No Obj: 0.002909, Avg Recall: 0.744186, count: 86 -Region Avg IOU: 0.590744, Class: 1.000000, Obj: 0.339095, No Obj: 0.003044, Avg Recall: 0.706422, count: 109 -4270: 0.430541, 0.454664 avg, 0.001000 rate, 24.510471 seconds, 1093120 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.383192 seconds -Region Avg IOU: 0.449777, Class: 1.000000, Obj: 0.236961, No Obj: 0.004171, Avg Recall: 0.480263, count: 152 -Region Avg IOU: 0.473970, Class: 1.000000, Obj: 0.223640, No Obj: 0.004064, Avg Recall: 0.517241, count: 116 -Region Avg IOU: 0.509162, Class: 1.000000, Obj: 0.290036, No Obj: 0.003790, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.469472, Class: 1.000000, Obj: 0.277467, No Obj: 0.003789, Avg Recall: 0.508929, count: 112 -Region Avg IOU: 0.478571, Class: 1.000000, Obj: 0.306662, No Obj: 0.003992, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.459710, Class: 1.000000, Obj: 0.268336, No Obj: 0.003969, Avg Recall: 0.508475, count: 118 -Region Avg IOU: 0.490478, Class: 1.000000, Obj: 0.260807, No Obj: 0.004366, Avg Recall: 0.537815, count: 119 -Region Avg IOU: 0.488459, Class: 1.000000, Obj: 0.258614, No Obj: 0.003569, Avg Recall: 0.555556, count: 90 -4271: 0.578261, 0.467023 avg, 0.001000 rate, 17.672829 seconds, 1093376 images -Loaded: 2.888910 seconds -Region Avg IOU: 0.453981, Class: 1.000000, Obj: 0.249474, No Obj: 0.004411, Avg Recall: 0.457364, count: 129 -Region Avg IOU: 0.521681, Class: 1.000000, Obj: 0.297081, No Obj: 0.004068, Avg Recall: 0.521277, count: 94 -Region Avg IOU: 0.466847, Class: 1.000000, Obj: 0.272343, No Obj: 0.004726, Avg Recall: 0.496063, count: 127 -Region Avg IOU: 0.476434, Class: 1.000000, Obj: 0.308016, No Obj: 0.003753, Avg Recall: 0.584270, count: 89 -Region Avg IOU: 0.487272, Class: 1.000000, Obj: 0.272209, No Obj: 0.004580, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.417780, Class: 1.000000, Obj: 0.224542, No Obj: 0.003379, Avg Recall: 0.382979, count: 94 -Region Avg IOU: 0.522021, Class: 1.000000, Obj: 0.309292, No Obj: 0.004144, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.471090, Class: 1.000000, Obj: 0.282855, No Obj: 0.004289, Avg Recall: 0.516667, count: 120 -4272: 0.539148, 0.474236 avg, 0.001000 rate, 15.854087 seconds, 1093632 images -Loaded: 4.163203 seconds -Region Avg IOU: 0.458632, Class: 1.000000, Obj: 0.319008, No Obj: 0.004192, Avg Recall: 0.505495, count: 91 -Region Avg IOU: 0.408269, Class: 1.000000, Obj: 0.286532, No Obj: 0.003932, Avg Recall: 0.427184, count: 103 -Region Avg IOU: 0.452278, Class: 1.000000, Obj: 0.277191, No Obj: 0.004332, Avg Recall: 0.464912, count: 114 -Region Avg IOU: 0.508338, Class: 1.000000, Obj: 0.297339, No Obj: 0.004089, Avg Recall: 0.578313, count: 83 -Region Avg IOU: 0.469144, Class: 1.000000, Obj: 0.275335, No Obj: 0.004422, Avg Recall: 0.532787, count: 122 -Region Avg IOU: 0.430779, Class: 1.000000, Obj: 0.267418, No Obj: 0.003683, Avg Recall: 0.424242, count: 99 -Region Avg IOU: 0.543102, Class: 1.000000, Obj: 0.318767, No Obj: 0.004155, Avg Recall: 0.632184, count: 87 -Region Avg IOU: 0.480114, Class: 1.000000, Obj: 0.260189, No Obj: 0.003683, Avg Recall: 0.541176, count: 85 -4273: 0.462103, 0.473023 avg, 0.001000 rate, 16.478392 seconds, 1093888 images -Loaded: 3.508821 seconds -Region Avg IOU: 0.441793, Class: 1.000000, Obj: 0.258937, No Obj: 0.004102, Avg Recall: 0.477778, count: 90 -Region Avg IOU: 0.524429, Class: 1.000000, Obj: 0.336978, No Obj: 0.004456, Avg Recall: 0.626506, count: 83 -Region Avg IOU: 0.476107, Class: 1.000000, Obj: 0.304904, No Obj: 0.005485, Avg Recall: 0.512195, count: 123 -Region Avg IOU: 0.488899, Class: 1.000000, Obj: 0.333390, No Obj: 0.004753, Avg Recall: 0.525253, count: 99 -Region Avg IOU: 0.461648, Class: 1.000000, Obj: 0.317873, No Obj: 0.004509, Avg Recall: 0.490196, count: 102 -Region Avg IOU: 0.526840, Class: 1.000000, Obj: 0.335077, No Obj: 0.004895, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.473374, Class: 1.000000, Obj: 0.339372, No Obj: 0.005638, Avg Recall: 0.517241, count: 116 -Region Avg IOU: 0.477495, Class: 1.000000, Obj: 0.288055, No Obj: 0.005046, Avg Recall: 0.541667, count: 120 -4274: 0.466639, 0.472384 avg, 0.001000 rate, 15.001098 seconds, 1094144 images -Loaded: 5.909801 seconds -Region Avg IOU: 0.426139, Class: 1.000000, Obj: 0.305641, No Obj: 0.005239, Avg Recall: 0.446281, count: 121 -Region Avg IOU: 0.438098, Class: 1.000000, Obj: 0.314051, No Obj: 0.004298, Avg Recall: 0.458716, count: 109 -Region Avg IOU: 0.429347, Class: 1.000000, Obj: 0.303610, No Obj: 0.004371, Avg Recall: 0.443396, count: 106 -Region Avg IOU: 0.491779, Class: 1.000000, Obj: 0.373839, No Obj: 0.004259, Avg Recall: 0.532468, count: 77 -Region Avg IOU: 0.477471, Class: 1.000000, Obj: 0.329620, No Obj: 0.005284, Avg Recall: 0.508333, count: 120 -Region Avg IOU: 0.462477, Class: 1.000000, Obj: 0.376240, No Obj: 0.004987, Avg Recall: 0.500000, count: 104 -Region Avg IOU: 0.480381, Class: 1.000000, Obj: 0.361847, No Obj: 0.005261, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.437979, Class: 1.000000, Obj: 0.337677, No Obj: 0.004493, Avg Recall: 0.509615, count: 104 -4275: 0.470291, 0.472175 avg, 0.001000 rate, 17.226316 seconds, 1094400 images -Loaded: 3.438845 seconds -Region Avg IOU: 0.487420, Class: 1.000000, Obj: 0.341359, No Obj: 0.005134, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.455882, Class: 1.000000, Obj: 0.345993, No Obj: 0.005446, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.445317, Class: 1.000000, Obj: 0.316545, No Obj: 0.005078, Avg Recall: 0.509804, count: 102 -Region Avg IOU: 0.524130, Class: 1.000000, Obj: 0.388431, No Obj: 0.005597, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.454717, Class: 1.000000, Obj: 0.319149, No Obj: 0.004935, Avg Recall: 0.485149, count: 101 -Region Avg IOU: 0.479904, Class: 1.000000, Obj: 0.359919, No Obj: 0.005963, Avg Recall: 0.474576, count: 118 -Region Avg IOU: 0.470856, Class: 1.000000, Obj: 0.358689, No Obj: 0.005428, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.508590, Class: 1.000000, Obj: 0.387630, No Obj: 0.005785, Avg Recall: 0.580000, count: 100 -4276: 0.418962, 0.466854 avg, 0.001000 rate, 17.999559 seconds, 1094656 images -Loaded: 2.613297 seconds -Region Avg IOU: 0.485786, Class: 1.000000, Obj: 0.350290, No Obj: 0.006011, Avg Recall: 0.556522, count: 115 -Region Avg IOU: 0.450083, Class: 1.000000, Obj: 0.339558, No Obj: 0.005671, Avg Recall: 0.485981, count: 107 -Region Avg IOU: 0.405926, Class: 1.000000, Obj: 0.324616, No Obj: 0.005918, Avg Recall: 0.430769, count: 130 -Region Avg IOU: 0.448398, Class: 1.000000, Obj: 0.393904, No Obj: 0.004951, Avg Recall: 0.490741, count: 108 -Region Avg IOU: 0.504717, Class: 1.000000, Obj: 0.367172, No Obj: 0.005915, Avg Recall: 0.547826, count: 115 -Region Avg IOU: 0.460509, Class: 1.000000, Obj: 0.375865, No Obj: 0.005123, Avg Recall: 0.472527, count: 91 -Region Avg IOU: 0.450038, Class: 1.000000, Obj: 0.343744, No Obj: 0.005968, Avg Recall: 0.452830, count: 106 -Region Avg IOU: 0.486508, Class: 1.000000, Obj: 0.379887, No Obj: 0.004990, Avg Recall: 0.543478, count: 92 -4277: 0.511542, 0.471322 avg, 0.001000 rate, 13.765435 seconds, 1094912 images -Loaded: 7.241955 seconds -Region Avg IOU: 0.485468, Class: 1.000000, Obj: 0.338313, No Obj: 0.005684, Avg Recall: 0.554348, count: 92 -Region Avg IOU: 0.472012, Class: 1.000000, Obj: 0.361794, No Obj: 0.005977, Avg Recall: 0.523438, count: 128 -Region Avg IOU: 0.460842, Class: 1.000000, Obj: 0.372782, No Obj: 0.005839, Avg Recall: 0.495327, count: 107 -Region Avg IOU: 0.465694, Class: 1.000000, Obj: 0.328851, No Obj: 0.005833, Avg Recall: 0.482759, count: 116 -Region Avg IOU: 0.455678, Class: 1.000000, Obj: 0.352004, No Obj: 0.005434, Avg Recall: 0.468085, count: 94 -Region Avg IOU: 0.473389, Class: 1.000000, Obj: 0.353543, No Obj: 0.005011, Avg Recall: 0.523256, count: 86 -Region Avg IOU: 0.500902, Class: 1.000000, Obj: 0.439300, No Obj: 0.005528, Avg Recall: 0.543210, count: 81 -Region Avg IOU: 0.462422, Class: 1.000000, Obj: 0.329448, No Obj: 0.006095, Avg Recall: 0.503937, count: 127 -4278: 0.501362, 0.474326 avg, 0.001000 rate, 17.860468 seconds, 1095168 images -Loaded: 2.870040 seconds -Region Avg IOU: 0.467114, Class: 1.000000, Obj: 0.356502, No Obj: 0.005838, Avg Recall: 0.524590, count: 122 -Region Avg IOU: 0.522290, Class: 1.000000, Obj: 0.405919, No Obj: 0.005376, Avg Recall: 0.574468, count: 94 -Region Avg IOU: 0.431537, Class: 1.000000, Obj: 0.364535, No Obj: 0.005194, Avg Recall: 0.474138, count: 116 -Region Avg IOU: 0.397202, Class: 1.000000, Obj: 0.329396, No Obj: 0.004944, Avg Recall: 0.414414, count: 111 -Region Avg IOU: 0.441361, Class: 1.000000, Obj: 0.322285, No Obj: 0.005164, Avg Recall: 0.448598, count: 107 -Region Avg IOU: 0.457962, Class: 1.000000, Obj: 0.388494, No Obj: 0.004913, Avg Recall: 0.463918, count: 97 -Region Avg IOU: 0.484118, Class: 1.000000, Obj: 0.345944, No Obj: 0.005358, Avg Recall: 0.549020, count: 102 -Region Avg IOU: 0.471053, Class: 1.000000, Obj: 0.345832, No Obj: 0.005982, Avg Recall: 0.509615, count: 104 -4279: 0.582970, 0.485191 avg, 0.001000 rate, 16.143051 seconds, 1095424 images -Loaded: 4.557883 seconds -Region Avg IOU: 0.499480, Class: 1.000000, Obj: 0.371306, No Obj: 0.005684, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.446990, Class: 1.000000, Obj: 0.357232, No Obj: 0.005081, Avg Recall: 0.466019, count: 103 -Region Avg IOU: 0.487244, Class: 1.000000, Obj: 0.330181, No Obj: 0.005315, Avg Recall: 0.500000, count: 96 -Region Avg IOU: 0.476863, Class: 1.000000, Obj: 0.347100, No Obj: 0.005568, Avg Recall: 0.527273, count: 110 -Region Avg IOU: 0.421981, Class: 1.000000, Obj: 0.334195, No Obj: 0.005298, Avg Recall: 0.428571, count: 126 -Region Avg IOU: 0.483826, Class: 1.000000, Obj: 0.353944, No Obj: 0.005830, Avg Recall: 0.481818, count: 110 -Region Avg IOU: 0.463302, Class: 1.000000, Obj: 0.374328, No Obj: 0.006354, Avg Recall: 0.481132, count: 106 -Region Avg IOU: 0.507215, Class: 1.000000, Obj: 0.410621, No Obj: 0.006403, Avg Recall: 0.542857, count: 105 -4280: 0.477048, 0.484376 avg, 0.001000 rate, 18.143623 seconds, 1095680 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 0.028037 seconds -Region Avg IOU: 0.496764, Class: 1.000000, Obj: 0.398938, No Obj: 0.005473, Avg Recall: 0.580000, count: 100 -Region Avg IOU: 0.517215, Class: 1.000000, Obj: 0.436997, No Obj: 0.005534, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.501705, Class: 1.000000, Obj: 0.377714, No Obj: 0.004974, Avg Recall: 0.561404, count: 114 -Region Avg IOU: 0.448975, Class: 1.000000, Obj: 0.402430, No Obj: 0.004603, Avg Recall: 0.473684, count: 114 -Region Avg IOU: 0.518318, Class: 1.000000, Obj: 0.373101, No Obj: 0.004387, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.540594, Class: 1.000000, Obj: 0.343939, No Obj: 0.004476, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.433765, Class: 1.000000, Obj: 0.354520, No Obj: 0.004729, Avg Recall: 0.456693, count: 127 -Region Avg IOU: 0.457784, Class: 1.000000, Obj: 0.333282, No Obj: 0.004821, Avg Recall: 0.513761, count: 109 -4281: 0.544946, 0.490433 avg, 0.001000 rate, 20.036556 seconds, 1095936 images -Loaded: 2.119746 seconds -Region Avg IOU: 0.553924, Class: 1.000000, Obj: 0.374931, No Obj: 0.004826, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.536798, Class: 1.000000, Obj: 0.387855, No Obj: 0.004691, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.571962, Class: 1.000000, Obj: 0.443195, No Obj: 0.005049, Avg Recall: 0.648352, count: 91 -Region Avg IOU: 0.514048, Class: 1.000000, Obj: 0.397063, No Obj: 0.004469, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.491530, Class: 1.000000, Obj: 0.373344, No Obj: 0.004398, Avg Recall: 0.483051, count: 118 -Region Avg IOU: 0.524441, Class: 1.000000, Obj: 0.412296, No Obj: 0.004301, Avg Recall: 0.588235, count: 102 -Region Avg IOU: 0.490585, Class: 1.000000, Obj: 0.373154, No Obj: 0.004137, Avg Recall: 0.523256, count: 86 -Region Avg IOU: 0.520917, Class: 1.000000, Obj: 0.397795, No Obj: 0.004109, Avg Recall: 0.579545, count: 88 -4282: 0.395841, 0.480974 avg, 0.001000 rate, 19.203939 seconds, 1096192 images -Loaded: 1.217879 seconds -Region Avg IOU: 0.522353, Class: 1.000000, Obj: 0.330396, No Obj: 0.004247, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.503001, Class: 1.000000, Obj: 0.328996, No Obj: 0.004222, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.529787, Class: 1.000000, Obj: 0.383229, No Obj: 0.004546, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.590764, Class: 1.000000, Obj: 0.413151, No Obj: 0.004749, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.491757, Class: 1.000000, Obj: 0.319942, No Obj: 0.004628, Avg Recall: 0.572581, count: 124 -Region Avg IOU: 0.484833, Class: 1.000000, Obj: 0.310563, No Obj: 0.004901, Avg Recall: 0.534722, count: 144 -Region Avg IOU: 0.533568, Class: 1.000000, Obj: 0.373457, No Obj: 0.004153, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.503106, Class: 1.000000, Obj: 0.371740, No Obj: 0.004955, Avg Recall: 0.547009, count: 117 -4283: 0.464466, 0.479323 avg, 0.001000 rate, 20.896152 seconds, 1096448 images -Loaded: 0.787257 seconds -Region Avg IOU: 0.465220, Class: 1.000000, Obj: 0.336240, No Obj: 0.003011, Avg Recall: 0.536082, count: 97 -Region Avg IOU: 0.483593, Class: 1.000000, Obj: 0.329298, No Obj: 0.004653, Avg Recall: 0.537037, count: 108 -Region Avg IOU: 0.544010, Class: 1.000000, Obj: 0.323847, No Obj: 0.004458, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.529929, Class: 1.000000, Obj: 0.328111, No Obj: 0.003741, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.540940, Class: 1.000000, Obj: 0.365071, No Obj: 0.004360, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.494087, Class: 1.000000, Obj: 0.329472, No Obj: 0.004205, Avg Recall: 0.584000, count: 125 -Region Avg IOU: 0.472155, Class: 1.000000, Obj: 0.311231, No Obj: 0.004392, Avg Recall: 0.500000, count: 118 -Region Avg IOU: 0.559961, Class: 1.000000, Obj: 0.370281, No Obj: 0.004322, Avg Recall: 0.648936, count: 94 -4284: 0.433353, 0.474726 avg, 0.001000 rate, 21.750664 seconds, 1096704 images -Loaded: 0.351726 seconds -Region Avg IOU: 0.477021, Class: 1.000000, Obj: 0.284743, No Obj: 0.003821, Avg Recall: 0.510000, count: 100 -Region Avg IOU: 0.464374, Class: 1.000000, Obj: 0.293649, No Obj: 0.003853, Avg Recall: 0.491803, count: 122 -Region Avg IOU: 0.509184, Class: 1.000000, Obj: 0.332018, No Obj: 0.004110, Avg Recall: 0.541985, count: 131 -Region Avg IOU: 0.539476, Class: 1.000000, Obj: 0.308253, No Obj: 0.003648, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.507803, Class: 1.000000, Obj: 0.316647, No Obj: 0.003993, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.560341, Class: 1.000000, Obj: 0.368015, No Obj: 0.004047, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.502824, Class: 1.000000, Obj: 0.330492, No Obj: 0.003968, Avg Recall: 0.571429, count: 133 -Region Avg IOU: 0.546084, Class: 1.000000, Obj: 0.336186, No Obj: 0.004127, Avg Recall: 0.656863, count: 102 -4285: 0.492510, 0.476505 avg, 0.001000 rate, 20.848921 seconds, 1096960 images -Loaded: 0.726823 seconds -Region Avg IOU: 0.556466, Class: 1.000000, Obj: 0.314110, No Obj: 0.003837, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.516827, Class: 1.000000, Obj: 0.308979, No Obj: 0.003440, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.471884, Class: 1.000000, Obj: 0.304733, No Obj: 0.003451, Avg Recall: 0.518182, count: 110 -Region Avg IOU: 0.521001, Class: 1.000000, Obj: 0.319940, No Obj: 0.004643, Avg Recall: 0.582781, count: 151 -Region Avg IOU: 0.530419, Class: 1.000000, Obj: 0.326815, No Obj: 0.004117, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.586909, Class: 1.000000, Obj: 0.365154, No Obj: 0.004113, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.528703, Class: 1.000000, Obj: 0.351278, No Obj: 0.003234, Avg Recall: 0.554217, count: 83 -Region Avg IOU: 0.545718, Class: 1.000000, Obj: 0.355101, No Obj: 0.003808, Avg Recall: 0.602151, count: 93 -4286: 0.448079, 0.473662 avg, 0.001000 rate, 20.669701 seconds, 1097216 images -Loaded: 0.923564 seconds -Region Avg IOU: 0.490233, Class: 1.000000, Obj: 0.282475, No Obj: 0.003517, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.476448, Class: 1.000000, Obj: 0.296276, No Obj: 0.003473, Avg Recall: 0.520000, count: 100 -Region Avg IOU: 0.499173, Class: 1.000000, Obj: 0.256307, No Obj: 0.003323, Avg Recall: 0.580952, count: 105 -Region Avg IOU: 0.554712, Class: 1.000000, Obj: 0.325219, No Obj: 0.003796, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.567096, Class: 1.000000, Obj: 0.336553, No Obj: 0.003734, Avg Recall: 0.674419, count: 86 -Region Avg IOU: 0.520644, Class: 1.000000, Obj: 0.295275, No Obj: 0.003713, Avg Recall: 0.578947, count: 114 -Region Avg IOU: 0.513417, Class: 1.000000, Obj: 0.321650, No Obj: 0.003483, Avg Recall: 0.518519, count: 81 -Region Avg IOU: 0.538263, Class: 1.000000, Obj: 0.327015, No Obj: 0.003825, Avg Recall: 0.603774, count: 106 -4287: 0.580350, 0.484331 avg, 0.001000 rate, 17.931969 seconds, 1097472 images -Loaded: 1.918218 seconds -Region Avg IOU: 0.538638, Class: 1.000000, Obj: 0.309543, No Obj: 0.004157, Avg Recall: 0.581560, count: 141 -Region Avg IOU: 0.561276, Class: 1.000000, Obj: 0.323075, No Obj: 0.004172, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.515974, Class: 1.000000, Obj: 0.305935, No Obj: 0.004010, Avg Recall: 0.559055, count: 127 -Region Avg IOU: 0.539967, Class: 1.000000, Obj: 0.310944, No Obj: 0.003922, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.499899, Class: 1.000000, Obj: 0.307264, No Obj: 0.003754, Avg Recall: 0.536082, count: 97 -Region Avg IOU: 0.521441, Class: 1.000000, Obj: 0.339102, No Obj: 0.004012, Avg Recall: 0.612903, count: 124 -Region Avg IOU: 0.543068, Class: 1.000000, Obj: 0.347333, No Obj: 0.003423, Avg Recall: 0.565657, count: 99 -Region Avg IOU: 0.499049, Class: 1.000000, Obj: 0.325434, No Obj: 0.003517, Avg Recall: 0.533981, count: 103 -4288: 0.435201, 0.479418 avg, 0.001000 rate, 21.383886 seconds, 1097728 images -Loaded: 0.668458 seconds -Region Avg IOU: 0.509072, Class: 1.000000, Obj: 0.309348, No Obj: 0.004233, Avg Recall: 0.595420, count: 131 -Region Avg IOU: 0.513575, Class: 1.000000, Obj: 0.290647, No Obj: 0.003659, Avg Recall: 0.551020, count: 98 -Region Avg IOU: 0.487290, Class: 1.000000, Obj: 0.333644, No Obj: 0.003749, Avg Recall: 0.489130, count: 92 -Region Avg IOU: 0.594896, Class: 1.000000, Obj: 0.372707, No Obj: 0.003981, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.592673, Class: 1.000000, Obj: 0.387566, No Obj: 0.003943, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.559512, Class: 1.000000, Obj: 0.329472, No Obj: 0.004007, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.467617, Class: 1.000000, Obj: 0.320774, No Obj: 0.003362, Avg Recall: 0.477778, count: 90 -Region Avg IOU: 0.528224, Class: 1.000000, Obj: 0.336823, No Obj: 0.003884, Avg Recall: 0.605505, count: 109 -4289: 0.432349, 0.474711 avg, 0.001000 rate, 21.330666 seconds, 1097984 images -Loaded: 0.967453 seconds -Region Avg IOU: 0.514356, Class: 1.000000, Obj: 0.311840, No Obj: 0.003944, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.492294, Class: 1.000000, Obj: 0.289845, No Obj: 0.004516, Avg Recall: 0.564516, count: 124 -Region Avg IOU: 0.541566, Class: 1.000000, Obj: 0.342295, No Obj: 0.004594, Avg Recall: 0.603053, count: 131 -Region Avg IOU: 0.533226, Class: 1.000000, Obj: 0.349196, No Obj: 0.004389, Avg Recall: 0.614754, count: 122 -Region Avg IOU: 0.537142, Class: 1.000000, Obj: 0.334997, No Obj: 0.003999, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.506796, Class: 1.000000, Obj: 0.336736, No Obj: 0.004545, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.557258, Class: 1.000000, Obj: 0.373495, No Obj: 0.004359, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.538794, Class: 1.000000, Obj: 0.336924, No Obj: 0.004006, Avg Recall: 0.623762, count: 101 -4290: 0.529642, 0.480204 avg, 0.001000 rate, 19.474691 seconds, 1098240 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000064 seconds -Region Avg IOU: 0.651210, Class: 1.000000, Obj: 0.407579, No Obj: 0.002633, Avg Recall: 0.842697, count: 89 -Region Avg IOU: 0.619983, Class: 1.000000, Obj: 0.432840, No Obj: 0.003302, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.650524, Class: 1.000000, Obj: 0.367286, No Obj: 0.003667, Avg Recall: 0.846154, count: 117 -Region Avg IOU: 0.620627, Class: 1.000000, Obj: 0.416916, No Obj: 0.003081, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.673525, Class: 1.000000, Obj: 0.459894, No Obj: 0.003325, Avg Recall: 0.855769, count: 104 -Region Avg IOU: 0.654931, Class: 1.000000, Obj: 0.429340, No Obj: 0.003387, Avg Recall: 0.802198, count: 91 -Region Avg IOU: 0.655799, Class: 1.000000, Obj: 0.390826, No Obj: 0.003340, Avg Recall: 0.852713, count: 129 -Region Avg IOU: 0.695823, Class: 1.000000, Obj: 0.436021, No Obj: 0.003810, Avg Recall: 0.846154, count: 130 -4291: 0.465560, 0.478740 avg, 0.001000 rate, 29.257668 seconds, 1098496 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.653998, Class: 1.000000, Obj: 0.419323, No Obj: 0.003059, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.637564, Class: 1.000000, Obj: 0.342175, No Obj: 0.002639, Avg Recall: 0.756098, count: 82 -Region Avg IOU: 0.617438, Class: 1.000000, Obj: 0.430196, No Obj: 0.002911, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.633855, Class: 1.000000, Obj: 0.406000, No Obj: 0.003356, Avg Recall: 0.813953, count: 129 -Region Avg IOU: 0.674857, Class: 1.000000, Obj: 0.467684, No Obj: 0.002951, Avg Recall: 0.840000, count: 100 -Region Avg IOU: 0.667780, Class: 1.000000, Obj: 0.403479, No Obj: 0.003351, Avg Recall: 0.845528, count: 123 -Region Avg IOU: 0.662564, Class: 1.000000, Obj: 0.408157, No Obj: 0.002973, Avg Recall: 0.828571, count: 105 -Region Avg IOU: 0.656000, Class: 1.000000, Obj: 0.403451, No Obj: 0.003205, Avg Recall: 0.832000, count: 125 -4292: 0.452730, 0.476139 avg, 0.001000 rate, 29.105986 seconds, 1098752 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.643506, Class: 1.000000, Obj: 0.381234, No Obj: 0.003177, Avg Recall: 0.824074, count: 108 -Region Avg IOU: 0.617565, Class: 1.000000, Obj: 0.379186, No Obj: 0.003149, Avg Recall: 0.769231, count: 117 -Region Avg IOU: 0.643269, Class: 1.000000, Obj: 0.461474, No Obj: 0.002961, Avg Recall: 0.761905, count: 84 -Region Avg IOU: 0.625260, Class: 1.000000, Obj: 0.379132, No Obj: 0.003155, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.643872, Class: 1.000000, Obj: 0.384373, No Obj: 0.003176, Avg Recall: 0.809091, count: 110 -Region Avg IOU: 0.644123, Class: 1.000000, Obj: 0.381701, No Obj: 0.002798, Avg Recall: 0.817204, count: 93 -Region Avg IOU: 0.648499, Class: 1.000000, Obj: 0.408100, No Obj: 0.003163, Avg Recall: 0.805310, count: 113 -Region Avg IOU: 0.641439, Class: 1.000000, Obj: 0.352926, No Obj: 0.003198, Avg Recall: 0.816794, count: 131 -4293: 0.480352, 0.476560 avg, 0.001000 rate, 29.917673 seconds, 1099008 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.668452, Class: 1.000000, Obj: 0.423530, No Obj: 0.002620, Avg Recall: 0.829787, count: 94 -Region Avg IOU: 0.624050, Class: 1.000000, Obj: 0.404321, No Obj: 0.002599, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.635866, Class: 1.000000, Obj: 0.452518, No Obj: 0.002513, Avg Recall: 0.797872, count: 94 -Region Avg IOU: 0.652881, Class: 1.000000, Obj: 0.364666, No Obj: 0.002611, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.660403, Class: 1.000000, Obj: 0.396784, No Obj: 0.002914, Avg Recall: 0.822581, count: 124 -Region Avg IOU: 0.651836, Class: 1.000000, Obj: 0.431815, No Obj: 0.002951, Avg Recall: 0.849558, count: 113 -Region Avg IOU: 0.685160, Class: 1.000000, Obj: 0.367578, No Obj: 0.003034, Avg Recall: 0.853448, count: 116 -Region Avg IOU: 0.651530, Class: 1.000000, Obj: 0.358607, No Obj: 0.002817, Avg Recall: 0.793103, count: 116 -4294: 0.447929, 0.473697 avg, 0.001000 rate, 29.612482 seconds, 1099264 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.645648, Class: 1.000000, Obj: 0.396602, No Obj: 0.002569, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.597866, Class: 1.000000, Obj: 0.366920, No Obj: 0.002363, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.650284, Class: 1.000000, Obj: 0.379276, No Obj: 0.002937, Avg Recall: 0.782946, count: 129 -Region Avg IOU: 0.641042, Class: 1.000000, Obj: 0.343423, No Obj: 0.002574, Avg Recall: 0.797619, count: 84 -Region Avg IOU: 0.607913, Class: 1.000000, Obj: 0.348982, No Obj: 0.002517, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.635165, Class: 1.000000, Obj: 0.418171, No Obj: 0.002862, Avg Recall: 0.775862, count: 116 -Region Avg IOU: 0.643673, Class: 1.000000, Obj: 0.348869, No Obj: 0.002609, Avg Recall: 0.825688, count: 109 -Region Avg IOU: 0.636867, Class: 1.000000, Obj: 0.338071, No Obj: 0.003085, Avg Recall: 0.786885, count: 122 -4295: 0.473102, 0.473637 avg, 0.001000 rate, 28.936878 seconds, 1099520 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.633924, Class: 1.000000, Obj: 0.419693, No Obj: 0.002659, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.601748, Class: 1.000000, Obj: 0.356573, No Obj: 0.002687, Avg Recall: 0.696970, count: 132 -Region Avg IOU: 0.685662, Class: 1.000000, Obj: 0.337581, No Obj: 0.002681, Avg Recall: 0.857143, count: 119 -Region Avg IOU: 0.642762, Class: 1.000000, Obj: 0.299788, No Obj: 0.002952, Avg Recall: 0.775194, count: 129 -Region Avg IOU: 0.610514, Class: 1.000000, Obj: 0.401789, No Obj: 0.002420, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.640215, Class: 1.000000, Obj: 0.380793, No Obj: 0.002710, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.626027, Class: 1.000000, Obj: 0.369800, No Obj: 0.002619, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.638997, Class: 1.000000, Obj: 0.381856, No Obj: 0.002752, Avg Recall: 0.801724, count: 116 -4296: 0.475677, 0.473841 avg, 0.001000 rate, 30.135799 seconds, 1099776 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.653602, Class: 1.000000, Obj: 0.375461, No Obj: 0.002493, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.645079, Class: 1.000000, Obj: 0.388943, No Obj: 0.002572, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.661056, Class: 1.000000, Obj: 0.367525, No Obj: 0.002826, Avg Recall: 0.825000, count: 120 -Region Avg IOU: 0.674082, Class: 1.000000, Obj: 0.373024, No Obj: 0.002374, Avg Recall: 0.840708, count: 113 -Region Avg IOU: 0.632333, Class: 1.000000, Obj: 0.377364, No Obj: 0.002617, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.640707, Class: 1.000000, Obj: 0.345720, No Obj: 0.002423, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.633628, Class: 1.000000, Obj: 0.370052, No Obj: 0.002333, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.634327, Class: 1.000000, Obj: 0.380989, No Obj: 0.002409, Avg Recall: 0.808081, count: 99 -4297: 0.459114, 0.472369 avg, 0.001000 rate, 31.157164 seconds, 1100032 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.682589, Class: 1.000000, Obj: 0.410524, No Obj: 0.002671, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.628597, Class: 1.000000, Obj: 0.354743, No Obj: 0.002879, Avg Recall: 0.805970, count: 134 -Region Avg IOU: 0.608431, Class: 1.000000, Obj: 0.314184, No Obj: 0.002933, Avg Recall: 0.718310, count: 142 -Region Avg IOU: 0.629114, Class: 1.000000, Obj: 0.384994, No Obj: 0.002444, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.663452, Class: 1.000000, Obj: 0.377917, No Obj: 0.002608, Avg Recall: 0.833333, count: 114 -Region Avg IOU: 0.649318, Class: 1.000000, Obj: 0.380363, No Obj: 0.002670, Avg Recall: 0.809917, count: 121 -Region Avg IOU: 0.588459, Class: 1.000000, Obj: 0.279255, No Obj: 0.002270, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.666758, Class: 1.000000, Obj: 0.311726, No Obj: 0.002414, Avg Recall: 0.801887, count: 106 -4298: 0.497423, 0.474874 avg, 0.001000 rate, 30.030577 seconds, 1100288 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.633654, Class: 1.000000, Obj: 0.367298, No Obj: 0.002028, Avg Recall: 0.777778, count: 72 -Region Avg IOU: 0.630713, Class: 1.000000, Obj: 0.332515, No Obj: 0.002315, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.624108, Class: 1.000000, Obj: 0.306498, No Obj: 0.002573, Avg Recall: 0.708661, count: 127 -Region Avg IOU: 0.624146, Class: 1.000000, Obj: 0.271666, No Obj: 0.002136, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.587762, Class: 1.000000, Obj: 0.279113, No Obj: 0.002054, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.663468, Class: 1.000000, Obj: 0.334947, No Obj: 0.002613, Avg Recall: 0.844660, count: 103 -Region Avg IOU: 0.657995, Class: 1.000000, Obj: 0.382788, No Obj: 0.002696, Avg Recall: 0.836207, count: 116 -Region Avg IOU: 0.621072, Class: 1.000000, Obj: 0.289386, No Obj: 0.002502, Avg Recall: 0.772727, count: 132 -4299: 0.498741, 0.477261 avg, 0.001000 rate, 28.619041 seconds, 1100544 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.617846, Class: 1.000000, Obj: 0.297679, No Obj: 0.002325, Avg Recall: 0.744000, count: 125 -Region Avg IOU: 0.650935, Class: 1.000000, Obj: 0.381596, No Obj: 0.002270, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.661578, Class: 1.000000, Obj: 0.342020, No Obj: 0.002280, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.616701, Class: 1.000000, Obj: 0.350205, No Obj: 0.002643, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.648068, Class: 1.000000, Obj: 0.339837, No Obj: 0.002518, Avg Recall: 0.831858, count: 113 -Region Avg IOU: 0.640328, Class: 1.000000, Obj: 0.336148, No Obj: 0.002523, Avg Recall: 0.801980, count: 101 -Region Avg IOU: 0.669772, Class: 1.000000, Obj: 0.345885, No Obj: 0.002355, Avg Recall: 0.839286, count: 112 -Region Avg IOU: 0.663811, Class: 1.000000, Obj: 0.355612, No Obj: 0.002651, Avg Recall: 0.848740, count: 119 -4300: 0.493701, 0.478905 avg, 0.001000 rate, 29.511803 seconds, 1100800 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 12.713728 seconds -Region Avg IOU: 0.567983, Class: 1.000000, Obj: 0.342089, No Obj: 0.002704, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.646394, Class: 1.000000, Obj: 0.380483, No Obj: 0.002572, Avg Recall: 0.795181, count: 83 -Region Avg IOU: 0.596836, Class: 1.000000, Obj: 0.336448, No Obj: 0.002999, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.568585, Class: 1.000000, Obj: 0.349829, No Obj: 0.002829, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.608941, Class: 1.000000, Obj: 0.345300, No Obj: 0.003002, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.620574, Class: 1.000000, Obj: 0.385928, No Obj: 0.002747, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.550424, Class: 1.000000, Obj: 0.381918, No Obj: 0.002620, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.616914, Class: 1.000000, Obj: 0.370186, No Obj: 0.002812, Avg Recall: 0.708333, count: 96 -4301: 0.394781, 0.470492 avg, 0.001000 rate, 24.487404 seconds, 1101056 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.599421, Class: 1.000000, Obj: 0.374048, No Obj: 0.002564, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.593511, Class: 1.000000, Obj: 0.306755, No Obj: 0.002684, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.587880, Class: 1.000000, Obj: 0.324055, No Obj: 0.003072, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.588741, Class: 1.000000, Obj: 0.358833, No Obj: 0.003187, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.629008, Class: 1.000000, Obj: 0.347611, No Obj: 0.002660, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.604713, Class: 1.000000, Obj: 0.348223, No Obj: 0.002908, Avg Recall: 0.709302, count: 86 -Region Avg IOU: 0.623149, Class: 1.000000, Obj: 0.334238, No Obj: 0.003093, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.627293, Class: 1.000000, Obj: 0.349173, No Obj: 0.002582, Avg Recall: 0.755319, count: 94 -4302: 0.439825, 0.467426 avg, 0.001000 rate, 24.396297 seconds, 1101312 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.613435, Class: 1.000000, Obj: 0.357279, No Obj: 0.002834, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.624134, Class: 1.000000, Obj: 0.332828, No Obj: 0.002770, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.577414, Class: 1.000000, Obj: 0.341667, No Obj: 0.002720, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.619249, Class: 1.000000, Obj: 0.366768, No Obj: 0.002935, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.540135, Class: 1.000000, Obj: 0.289653, No Obj: 0.002813, Avg Recall: 0.579365, count: 126 -Region Avg IOU: 0.583107, Class: 1.000000, Obj: 0.339504, No Obj: 0.003208, Avg Recall: 0.679389, count: 131 -Region Avg IOU: 0.610728, Class: 1.000000, Obj: 0.369493, No Obj: 0.003259, Avg Recall: 0.746154, count: 130 -Region Avg IOU: 0.579611, Class: 1.000000, Obj: 0.361310, No Obj: 0.002812, Avg Recall: 0.685714, count: 105 -4303: 0.425306, 0.463214 avg, 0.001000 rate, 26.445347 seconds, 1101568 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.617785, Class: 1.000000, Obj: 0.360719, No Obj: 0.003247, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.589268, Class: 1.000000, Obj: 0.386723, No Obj: 0.003294, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.586266, Class: 1.000000, Obj: 0.383981, No Obj: 0.003086, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.618325, Class: 1.000000, Obj: 0.371883, No Obj: 0.003853, Avg Recall: 0.746032, count: 126 -Region Avg IOU: 0.648866, Class: 1.000000, Obj: 0.435593, No Obj: 0.002864, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.604143, Class: 1.000000, Obj: 0.349277, No Obj: 0.003233, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.589995, Class: 1.000000, Obj: 0.391290, No Obj: 0.003277, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.592688, Class: 1.000000, Obj: 0.389082, No Obj: 0.003536, Avg Recall: 0.716667, count: 120 -4304: 0.407624, 0.457655 avg, 0.001000 rate, 25.997650 seconds, 1101824 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.598578, Class: 1.000000, Obj: 0.380294, No Obj: 0.003336, Avg Recall: 0.714286, count: 119 -Region Avg IOU: 0.572703, Class: 1.000000, Obj: 0.359423, No Obj: 0.003438, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.610186, Class: 1.000000, Obj: 0.406336, No Obj: 0.003458, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.597717, Class: 1.000000, Obj: 0.401547, No Obj: 0.003224, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.619530, Class: 1.000000, Obj: 0.359941, No Obj: 0.003433, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.605033, Class: 1.000000, Obj: 0.400282, No Obj: 0.003758, Avg Recall: 0.702290, count: 131 -Region Avg IOU: 0.593433, Class: 1.000000, Obj: 0.428266, No Obj: 0.003315, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.581605, Class: 1.000000, Obj: 0.424807, No Obj: 0.003337, Avg Recall: 0.689320, count: 103 -4305: 0.417817, 0.453671 avg, 0.001000 rate, 25.036797 seconds, 1102080 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.622619, Class: 1.000000, Obj: 0.434906, No Obj: 0.003198, Avg Recall: 0.777778, count: 81 -Region Avg IOU: 0.654059, Class: 1.000000, Obj: 0.406947, No Obj: 0.003532, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.615378, Class: 1.000000, Obj: 0.444055, No Obj: 0.003359, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.593194, Class: 1.000000, Obj: 0.388055, No Obj: 0.002994, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.572349, Class: 1.000000, Obj: 0.385643, No Obj: 0.003496, Avg Recall: 0.687023, count: 131 -Region Avg IOU: 0.605779, Class: 1.000000, Obj: 0.353791, No Obj: 0.003155, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.614689, Class: 1.000000, Obj: 0.409983, No Obj: 0.003881, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.567433, Class: 1.000000, Obj: 0.408134, No Obj: 0.002971, Avg Recall: 0.696970, count: 99 -4306: 0.453069, 0.453611 avg, 0.001000 rate, 25.568483 seconds, 1102336 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.616025, Class: 1.000000, Obj: 0.362200, No Obj: 0.003881, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.583497, Class: 1.000000, Obj: 0.411862, No Obj: 0.002872, Avg Recall: 0.722222, count: 72 -Region Avg IOU: 0.559868, Class: 1.000000, Obj: 0.366909, No Obj: 0.003301, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.597876, Class: 1.000000, Obj: 0.417736, No Obj: 0.003569, Avg Recall: 0.688000, count: 125 -Region Avg IOU: 0.584924, Class: 1.000000, Obj: 0.409278, No Obj: 0.003660, Avg Recall: 0.701493, count: 134 -Region Avg IOU: 0.576064, Class: 1.000000, Obj: 0.409658, No Obj: 0.003464, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.635867, Class: 1.000000, Obj: 0.423986, No Obj: 0.003873, Avg Recall: 0.777778, count: 126 -Region Avg IOU: 0.619553, Class: 1.000000, Obj: 0.419077, No Obj: 0.003959, Avg Recall: 0.753623, count: 138 -4307: 0.456672, 0.453917 avg, 0.001000 rate, 25.065081 seconds, 1102592 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.550268, Class: 1.000000, Obj: 0.363709, No Obj: 0.003736, Avg Recall: 0.641026, count: 117 -Region Avg IOU: 0.620394, Class: 1.000000, Obj: 0.459996, No Obj: 0.003473, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.598258, Class: 1.000000, Obj: 0.386081, No Obj: 0.003518, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.565728, Class: 1.000000, Obj: 0.415872, No Obj: 0.003308, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.615252, Class: 1.000000, Obj: 0.458968, No Obj: 0.004111, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.634312, Class: 1.000000, Obj: 0.446016, No Obj: 0.003693, Avg Recall: 0.815126, count: 119 -Region Avg IOU: 0.539189, Class: 1.000000, Obj: 0.407152, No Obj: 0.003357, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.541798, Class: 1.000000, Obj: 0.390040, No Obj: 0.002924, Avg Recall: 0.614583, count: 96 -4308: 0.429461, 0.451471 avg, 0.001000 rate, 25.197571 seconds, 1102848 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.577734, Class: 1.000000, Obj: 0.354970, No Obj: 0.003572, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.550401, Class: 1.000000, Obj: 0.372035, No Obj: 0.003322, Avg Recall: 0.581197, count: 117 -Region Avg IOU: 0.589121, Class: 1.000000, Obj: 0.369329, No Obj: 0.003347, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.631799, Class: 1.000000, Obj: 0.475167, No Obj: 0.004029, Avg Recall: 0.728000, count: 125 -Region Avg IOU: 0.602914, Class: 1.000000, Obj: 0.365916, No Obj: 0.003639, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.645894, Class: 1.000000, Obj: 0.453889, No Obj: 0.004193, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.577009, Class: 1.000000, Obj: 0.408464, No Obj: 0.003641, Avg Recall: 0.656250, count: 128 -Region Avg IOU: 0.553863, Class: 1.000000, Obj: 0.405518, No Obj: 0.003882, Avg Recall: 0.620690, count: 116 -4309: 0.442769, 0.450601 avg, 0.001000 rate, 25.226456 seconds, 1103104 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.588170, Class: 1.000000, Obj: 0.373962, No Obj: 0.003979, Avg Recall: 0.686131, count: 137 -Region Avg IOU: 0.619336, Class: 1.000000, Obj: 0.467448, No Obj: 0.003502, Avg Recall: 0.736264, count: 91 -Region Avg IOU: 0.555397, Class: 1.000000, Obj: 0.394875, No Obj: 0.003180, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.596705, Class: 1.000000, Obj: 0.376425, No Obj: 0.003608, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.627224, Class: 1.000000, Obj: 0.432480, No Obj: 0.003447, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.563129, Class: 1.000000, Obj: 0.433753, No Obj: 0.003339, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.584483, Class: 1.000000, Obj: 0.434497, No Obj: 0.003469, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.594762, Class: 1.000000, Obj: 0.417568, No Obj: 0.003243, Avg Recall: 0.693878, count: 98 -4310: 0.464935, 0.452035 avg, 0.001000 rate, 24.927332 seconds, 1103360 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 17.032206 seconds -Region Avg IOU: 0.484695, Class: 1.000000, Obj: 0.305644, No Obj: 0.003773, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.486260, Class: 1.000000, Obj: 0.300829, No Obj: 0.003907, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.463705, Class: 1.000000, Obj: 0.330304, No Obj: 0.003907, Avg Recall: 0.469565, count: 115 -Region Avg IOU: 0.433069, Class: 1.000000, Obj: 0.299377, No Obj: 0.003668, Avg Recall: 0.413462, count: 104 -Region Avg IOU: 0.503347, Class: 1.000000, Obj: 0.347375, No Obj: 0.003580, Avg Recall: 0.549451, count: 91 -Region Avg IOU: 0.542495, Class: 1.000000, Obj: 0.360934, No Obj: 0.004476, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.506265, Class: 1.000000, Obj: 0.317379, No Obj: 0.004355, Avg Recall: 0.536585, count: 123 -Region Avg IOU: 0.528904, Class: 1.000000, Obj: 0.334239, No Obj: 0.004007, Avg Recall: 0.588889, count: 90 -4311: 0.411450, 0.447976 avg, 0.001000 rate, 17.453552 seconds, 1103616 images -Loaded: 4.169914 seconds -Region Avg IOU: 0.493328, Class: 1.000000, Obj: 0.293023, No Obj: 0.004526, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.531204, Class: 1.000000, Obj: 0.332580, No Obj: 0.003499, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.478995, Class: 1.000000, Obj: 0.305946, No Obj: 0.003585, Avg Recall: 0.528736, count: 87 -Region Avg IOU: 0.484196, Class: 1.000000, Obj: 0.313614, No Obj: 0.004110, Avg Recall: 0.504854, count: 103 -Region Avg IOU: 0.525980, Class: 1.000000, Obj: 0.345609, No Obj: 0.004039, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.494991, Class: 1.000000, Obj: 0.304926, No Obj: 0.004564, Avg Recall: 0.548872, count: 133 -Region Avg IOU: 0.502411, Class: 1.000000, Obj: 0.322229, No Obj: 0.004226, Avg Recall: 0.590164, count: 122 -Region Avg IOU: 0.486321, Class: 1.000000, Obj: 0.283965, No Obj: 0.004463, Avg Recall: 0.550388, count: 129 -4312: 0.530573, 0.456236 avg, 0.001000 rate, 18.136641 seconds, 1103872 images -Loaded: 2.096734 seconds -Region Avg IOU: 0.505047, Class: 1.000000, Obj: 0.305361, No Obj: 0.004990, Avg Recall: 0.546763, count: 139 -Region Avg IOU: 0.460294, Class: 1.000000, Obj: 0.305438, No Obj: 0.003645, Avg Recall: 0.485149, count: 101 -Region Avg IOU: 0.489968, Class: 1.000000, Obj: 0.312440, No Obj: 0.003724, Avg Recall: 0.548780, count: 82 -Region Avg IOU: 0.458351, Class: 1.000000, Obj: 0.282902, No Obj: 0.003616, Avg Recall: 0.463158, count: 95 -Region Avg IOU: 0.544064, Class: 1.000000, Obj: 0.332611, No Obj: 0.004473, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.509311, Class: 1.000000, Obj: 0.337171, No Obj: 0.004332, Avg Recall: 0.548673, count: 113 -Region Avg IOU: 0.503141, Class: 1.000000, Obj: 0.294043, No Obj: 0.003821, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.530435, Class: 1.000000, Obj: 0.345736, No Obj: 0.004524, Avg Recall: 0.631579, count: 114 -4313: 0.473581, 0.457970 avg, 0.001000 rate, 19.146265 seconds, 1104128 images -Loaded: 1.832357 seconds -Region Avg IOU: 0.506107, Class: 1.000000, Obj: 0.310966, No Obj: 0.004196, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.504439, Class: 1.000000, Obj: 0.317768, No Obj: 0.003595, Avg Recall: 0.517647, count: 85 -Region Avg IOU: 0.514364, Class: 1.000000, Obj: 0.311052, No Obj: 0.003483, Avg Recall: 0.600000, count: 80 -Region Avg IOU: 0.521818, Class: 1.000000, Obj: 0.284364, No Obj: 0.004530, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.488979, Class: 1.000000, Obj: 0.336313, No Obj: 0.004038, Avg Recall: 0.571429, count: 77 -Region Avg IOU: 0.574923, Class: 1.000000, Obj: 0.390540, No Obj: 0.004966, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.503123, Class: 1.000000, Obj: 0.368694, No Obj: 0.003745, Avg Recall: 0.565657, count: 99 -Region Avg IOU: 0.489912, Class: 1.000000, Obj: 0.312848, No Obj: 0.004043, Avg Recall: 0.555556, count: 126 -4314: 0.476652, 0.459838 avg, 0.001000 rate, 19.473663 seconds, 1104384 images -Loaded: 1.006652 seconds -Region Avg IOU: 0.552209, Class: 1.000000, Obj: 0.333448, No Obj: 0.004051, Avg Recall: 0.640449, count: 89 -Region Avg IOU: 0.499195, Class: 1.000000, Obj: 0.308870, No Obj: 0.003340, Avg Recall: 0.541667, count: 96 -Region Avg IOU: 0.509502, Class: 1.000000, Obj: 0.311617, No Obj: 0.004434, Avg Recall: 0.521008, count: 119 -Region Avg IOU: 0.517680, Class: 1.000000, Obj: 0.326845, No Obj: 0.004429, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.504699, Class: 1.000000, Obj: 0.350093, No Obj: 0.004069, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.527249, Class: 1.000000, Obj: 0.316535, No Obj: 0.003818, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.501353, Class: 1.000000, Obj: 0.297414, No Obj: 0.004389, Avg Recall: 0.566929, count: 127 -Region Avg IOU: 0.487887, Class: 1.000000, Obj: 0.326553, No Obj: 0.003786, Avg Recall: 0.552083, count: 96 -4315: 0.446252, 0.458480 avg, 0.001000 rate, 20.890812 seconds, 1104640 images -Loaded: 0.457434 seconds -Region Avg IOU: 0.517333, Class: 1.000000, Obj: 0.331677, No Obj: 0.004299, Avg Recall: 0.588235, count: 102 -Region Avg IOU: 0.513842, Class: 1.000000, Obj: 0.337170, No Obj: 0.003996, Avg Recall: 0.526316, count: 95 -Region Avg IOU: 0.571515, Class: 1.000000, Obj: 0.395691, No Obj: 0.004489, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.499819, Class: 1.000000, Obj: 0.359550, No Obj: 0.004294, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.482475, Class: 1.000000, Obj: 0.351299, No Obj: 0.004148, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.477311, Class: 1.000000, Obj: 0.359394, No Obj: 0.004526, Avg Recall: 0.546296, count: 108 -Region Avg IOU: 0.464173, Class: 1.000000, Obj: 0.325217, No Obj: 0.003914, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.470970, Class: 1.000000, Obj: 0.309110, No Obj: 0.004610, Avg Recall: 0.552845, count: 123 -4316: 0.434693, 0.456101 avg, 0.001000 rate, 20.362247 seconds, 1104896 images -Loaded: 0.391892 seconds -Region Avg IOU: 0.485028, Class: 1.000000, Obj: 0.368760, No Obj: 0.003954, Avg Recall: 0.555556, count: 81 -Region Avg IOU: 0.522781, Class: 1.000000, Obj: 0.362002, No Obj: 0.004433, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.483156, Class: 1.000000, Obj: 0.319045, No Obj: 0.004036, Avg Recall: 0.509259, count: 108 -Region Avg IOU: 0.449929, Class: 1.000000, Obj: 0.296046, No Obj: 0.003584, Avg Recall: 0.500000, count: 96 -Region Avg IOU: 0.460434, Class: 1.000000, Obj: 0.338947, No Obj: 0.004276, Avg Recall: 0.495238, count: 105 -Region Avg IOU: 0.487697, Class: 1.000000, Obj: 0.332096, No Obj: 0.004838, Avg Recall: 0.536364, count: 110 -Region Avg IOU: 0.475960, Class: 1.000000, Obj: 0.331395, No Obj: 0.004536, Avg Recall: 0.500000, count: 122 -Region Avg IOU: 0.480462, Class: 1.000000, Obj: 0.288632, No Obj: 0.004109, Avg Recall: 0.569767, count: 86 -4317: 0.464313, 0.456922 avg, 0.001000 rate, 20.991938 seconds, 1105152 images -Loaded: 0.517574 seconds -Region Avg IOU: 0.522983, Class: 1.000000, Obj: 0.334067, No Obj: 0.004686, Avg Recall: 0.591667, count: 120 -Region Avg IOU: 0.406815, Class: 1.000000, Obj: 0.272201, No Obj: 0.003756, Avg Recall: 0.429907, count: 107 -Region Avg IOU: 0.499842, Class: 1.000000, Obj: 0.324153, No Obj: 0.004706, Avg Recall: 0.571429, count: 119 -Region Avg IOU: 0.509572, Class: 1.000000, Obj: 0.348950, No Obj: 0.004082, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.458872, Class: 1.000000, Obj: 0.328240, No Obj: 0.005000, Avg Recall: 0.488889, count: 135 -Region Avg IOU: 0.537606, Class: 1.000000, Obj: 0.378578, No Obj: 0.005055, Avg Recall: 0.618321, count: 131 -Region Avg IOU: 0.437424, Class: 1.000000, Obj: 0.314399, No Obj: 0.004477, Avg Recall: 0.442623, count: 122 -Region Avg IOU: 0.458842, Class: 1.000000, Obj: 0.325815, No Obj: 0.004590, Avg Recall: 0.492064, count: 126 -4318: 0.503952, 0.461625 avg, 0.001000 rate, 17.309307 seconds, 1105408 images -Loaded: 2.373100 seconds -Region Avg IOU: 0.513883, Class: 1.000000, Obj: 0.430362, No Obj: 0.005427, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.498598, Class: 1.000000, Obj: 0.364402, No Obj: 0.004729, Avg Recall: 0.549451, count: 91 -Region Avg IOU: 0.539067, Class: 1.000000, Obj: 0.354628, No Obj: 0.005411, Avg Recall: 0.581967, count: 122 -Region Avg IOU: 0.463157, Class: 1.000000, Obj: 0.334983, No Obj: 0.004087, Avg Recall: 0.516854, count: 89 -Region Avg IOU: 0.531765, Class: 1.000000, Obj: 0.349290, No Obj: 0.005613, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.513586, Class: 1.000000, Obj: 0.366105, No Obj: 0.004891, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.468273, Class: 1.000000, Obj: 0.330074, No Obj: 0.004467, Avg Recall: 0.500000, count: 94 -Region Avg IOU: 0.462410, Class: 1.000000, Obj: 0.340914, No Obj: 0.005048, Avg Recall: 0.471698, count: 106 -4319: 0.438152, 0.459278 avg, 0.001000 rate, 20.130222 seconds, 1105664 images -Loaded: 0.892312 seconds -Region Avg IOU: 0.534755, Class: 1.000000, Obj: 0.404923, No Obj: 0.004944, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.469575, Class: 1.000000, Obj: 0.345950, No Obj: 0.004295, Avg Recall: 0.524272, count: 103 -Region Avg IOU: 0.489596, Class: 1.000000, Obj: 0.344364, No Obj: 0.005557, Avg Recall: 0.538462, count: 143 -Region Avg IOU: 0.508480, Class: 1.000000, Obj: 0.357913, No Obj: 0.004570, Avg Recall: 0.596491, count: 114 -Region Avg IOU: 0.509804, Class: 1.000000, Obj: 0.369519, No Obj: 0.005028, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.533880, Class: 1.000000, Obj: 0.338032, No Obj: 0.004837, Avg Recall: 0.571429, count: 105 -Region Avg IOU: 0.540250, Class: 1.000000, Obj: 0.370438, No Obj: 0.004927, Avg Recall: 0.639535, count: 86 -Region Avg IOU: 0.505833, Class: 1.000000, Obj: 0.371848, No Obj: 0.004429, Avg Recall: 0.526316, count: 95 -4320: 0.442014, 0.457551 avg, 0.001000 rate, 19.800844 seconds, 1105920 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000073 seconds -Region Avg IOU: 0.601928, Class: 1.000000, Obj: 0.403572, No Obj: 0.004119, Avg Recall: 0.698276, count: 116 -Region Avg IOU: 0.604954, Class: 1.000000, Obj: 0.427697, No Obj: 0.003887, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.482955, Class: 1.000000, Obj: 0.382463, No Obj: 0.003793, Avg Recall: 0.514493, count: 138 -Region Avg IOU: 0.598584, Class: 1.000000, Obj: 0.472158, No Obj: 0.003984, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.578623, Class: 1.000000, Obj: 0.442267, No Obj: 0.003922, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.601113, Class: 1.000000, Obj: 0.427525, No Obj: 0.003991, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.593472, Class: 1.000000, Obj: 0.489627, No Obj: 0.003416, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.643479, Class: 1.000000, Obj: 0.440683, No Obj: 0.004487, Avg Recall: 0.803279, count: 122 -4321: 0.474138, 0.459210 avg, 0.001000 rate, 25.664572 seconds, 1106176 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.599051, Class: 1.000000, Obj: 0.419447, No Obj: 0.004010, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.599540, Class: 1.000000, Obj: 0.440363, No Obj: 0.003659, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.582034, Class: 1.000000, Obj: 0.454371, No Obj: 0.004124, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.614902, Class: 1.000000, Obj: 0.448405, No Obj: 0.003548, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.617082, Class: 1.000000, Obj: 0.442558, No Obj: 0.003405, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.614816, Class: 1.000000, Obj: 0.433412, No Obj: 0.003441, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.624780, Class: 1.000000, Obj: 0.460501, No Obj: 0.003950, Avg Recall: 0.718310, count: 142 -Region Avg IOU: 0.596826, Class: 1.000000, Obj: 0.427982, No Obj: 0.003936, Avg Recall: 0.711712, count: 111 -4322: 0.425080, 0.455797 avg, 0.001000 rate, 25.749834 seconds, 1106432 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.601558, Class: 1.000000, Obj: 0.441493, No Obj: 0.003728, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.614625, Class: 1.000000, Obj: 0.420716, No Obj: 0.003294, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.602530, Class: 1.000000, Obj: 0.396700, No Obj: 0.003593, Avg Recall: 0.726562, count: 128 -Region Avg IOU: 0.601504, Class: 1.000000, Obj: 0.437941, No Obj: 0.003490, Avg Recall: 0.729508, count: 122 -Region Avg IOU: 0.595749, Class: 1.000000, Obj: 0.425443, No Obj: 0.003943, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.676327, Class: 1.000000, Obj: 0.527326, No Obj: 0.003271, Avg Recall: 0.831325, count: 83 -Region Avg IOU: 0.566999, Class: 1.000000, Obj: 0.380195, No Obj: 0.003772, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.633981, Class: 1.000000, Obj: 0.387944, No Obj: 0.003621, Avg Recall: 0.762712, count: 118 -4323: 0.417298, 0.451947 avg, 0.001000 rate, 25.103825 seconds, 1106688 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.573602, Class: 1.000000, Obj: 0.419457, No Obj: 0.003710, Avg Recall: 0.689394, count: 132 -Region Avg IOU: 0.591544, Class: 1.000000, Obj: 0.402982, No Obj: 0.003822, Avg Recall: 0.696296, count: 135 -Region Avg IOU: 0.640810, Class: 1.000000, Obj: 0.391095, No Obj: 0.003510, Avg Recall: 0.788618, count: 123 -Region Avg IOU: 0.625064, Class: 1.000000, Obj: 0.439619, No Obj: 0.003193, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.619176, Class: 1.000000, Obj: 0.404174, No Obj: 0.003774, Avg Recall: 0.767442, count: 129 -Region Avg IOU: 0.628996, Class: 1.000000, Obj: 0.422027, No Obj: 0.003316, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.612284, Class: 1.000000, Obj: 0.430812, No Obj: 0.003084, Avg Recall: 0.722222, count: 90 -Region Avg IOU: 0.638958, Class: 1.000000, Obj: 0.433716, No Obj: 0.003526, Avg Recall: 0.775701, count: 107 -4324: 0.456130, 0.452365 avg, 0.001000 rate, 26.220657 seconds, 1106944 images -Loaded: 0.000123 seconds -Region Avg IOU: 0.656016, Class: 1.000000, Obj: 0.428899, No Obj: 0.002794, Avg Recall: 0.827160, count: 81 -Region Avg IOU: 0.612454, Class: 1.000000, Obj: 0.407373, No Obj: 0.003129, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.662475, Class: 1.000000, Obj: 0.426458, No Obj: 0.003646, Avg Recall: 0.823009, count: 113 -Region Avg IOU: 0.644437, Class: 1.000000, Obj: 0.379877, No Obj: 0.002920, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.634841, Class: 1.000000, Obj: 0.405429, No Obj: 0.003065, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.602635, Class: 1.000000, Obj: 0.362017, No Obj: 0.002869, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.626156, Class: 1.000000, Obj: 0.395844, No Obj: 0.003214, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.613961, Class: 1.000000, Obj: 0.406150, No Obj: 0.003351, Avg Recall: 0.760684, count: 117 -4325: 0.415247, 0.448654 avg, 0.001000 rate, 26.103483 seconds, 1107200 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.629868, Class: 1.000000, Obj: 0.382785, No Obj: 0.003519, Avg Recall: 0.801527, count: 131 -Region Avg IOU: 0.626296, Class: 1.000000, Obj: 0.377943, No Obj: 0.002729, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.613914, Class: 1.000000, Obj: 0.358925, No Obj: 0.002890, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.551581, Class: 1.000000, Obj: 0.347712, No Obj: 0.002911, Avg Recall: 0.642276, count: 123 -Region Avg IOU: 0.617180, Class: 1.000000, Obj: 0.393649, No Obj: 0.003129, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.567464, Class: 1.000000, Obj: 0.324840, No Obj: 0.002668, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.607567, Class: 1.000000, Obj: 0.375924, No Obj: 0.002594, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.582775, Class: 1.000000, Obj: 0.357591, No Obj: 0.002843, Avg Recall: 0.699115, count: 113 -4326: 0.423083, 0.446097 avg, 0.001000 rate, 26.108873 seconds, 1107456 images -Loaded: 0.000132 seconds -Region Avg IOU: 0.595053, Class: 1.000000, Obj: 0.389653, No Obj: 0.002574, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.630982, Class: 1.000000, Obj: 0.351349, No Obj: 0.003245, Avg Recall: 0.769231, count: 130 -Region Avg IOU: 0.631738, Class: 1.000000, Obj: 0.344208, No Obj: 0.002619, Avg Recall: 0.797297, count: 74 -Region Avg IOU: 0.607505, Class: 1.000000, Obj: 0.401579, No Obj: 0.003229, Avg Recall: 0.692857, count: 140 -Region Avg IOU: 0.586280, Class: 1.000000, Obj: 0.348076, No Obj: 0.003101, Avg Recall: 0.680000, count: 125 -Region Avg IOU: 0.582396, Class: 1.000000, Obj: 0.364788, No Obj: 0.002601, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.592871, Class: 1.000000, Obj: 0.373872, No Obj: 0.002713, Avg Recall: 0.711111, count: 90 -Region Avg IOU: 0.606394, Class: 1.000000, Obj: 0.354403, No Obj: 0.002784, Avg Recall: 0.715789, count: 95 -4327: 0.401511, 0.441638 avg, 0.001000 rate, 25.278021 seconds, 1107712 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.615721, Class: 1.000000, Obj: 0.348523, No Obj: 0.002531, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.633249, Class: 1.000000, Obj: 0.372684, No Obj: 0.002967, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.653180, Class: 1.000000, Obj: 0.392780, No Obj: 0.003086, Avg Recall: 0.822222, count: 90 -Region Avg IOU: 0.595698, Class: 1.000000, Obj: 0.320540, No Obj: 0.002900, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.617974, Class: 1.000000, Obj: 0.350632, No Obj: 0.003081, Avg Recall: 0.738095, count: 126 -Region Avg IOU: 0.610083, Class: 1.000000, Obj: 0.367689, No Obj: 0.002949, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.597822, Class: 1.000000, Obj: 0.324584, No Obj: 0.002906, Avg Recall: 0.698413, count: 126 -Region Avg IOU: 0.591979, Class: 1.000000, Obj: 0.347422, No Obj: 0.002817, Avg Recall: 0.721739, count: 115 -4328: 0.450666, 0.442541 avg, 0.001000 rate, 24.310930 seconds, 1107968 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.552326, Class: 1.000000, Obj: 0.312094, No Obj: 0.002557, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.580170, Class: 1.000000, Obj: 0.314640, No Obj: 0.002791, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.605256, Class: 1.000000, Obj: 0.326166, No Obj: 0.002741, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.611758, Class: 1.000000, Obj: 0.313375, No Obj: 0.003134, Avg Recall: 0.687023, count: 131 -Region Avg IOU: 0.648850, Class: 1.000000, Obj: 0.346122, No Obj: 0.002938, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.626419, Class: 1.000000, Obj: 0.364995, No Obj: 0.002572, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.646994, Class: 1.000000, Obj: 0.327719, No Obj: 0.002586, Avg Recall: 0.781250, count: 96 -Region Avg IOU: 0.672714, Class: 1.000000, Obj: 0.337203, No Obj: 0.002885, Avg Recall: 0.797468, count: 79 -4329: 0.450787, 0.443365 avg, 0.001000 rate, 27.523075 seconds, 1108224 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.606570, Class: 1.000000, Obj: 0.357852, No Obj: 0.002905, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.593002, Class: 1.000000, Obj: 0.352344, No Obj: 0.002672, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.622524, Class: 1.000000, Obj: 0.335547, No Obj: 0.003078, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.569462, Class: 1.000000, Obj: 0.344439, No Obj: 0.002768, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.624499, Class: 1.000000, Obj: 0.340281, No Obj: 0.002923, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.594376, Class: 1.000000, Obj: 0.344082, No Obj: 0.003081, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.564047, Class: 1.000000, Obj: 0.367587, No Obj: 0.002720, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.656581, Class: 1.000000, Obj: 0.390944, No Obj: 0.003073, Avg Recall: 0.785714, count: 112 -4330: 0.438857, 0.442915 avg, 0.001000 rate, 23.401224 seconds, 1108480 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.783203 seconds -Region Avg IOU: 0.526995, Class: 1.000000, Obj: 0.267001, No Obj: 0.003895, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.492877, Class: 1.000000, Obj: 0.282350, No Obj: 0.003500, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.588690, Class: 1.000000, Obj: 0.338967, No Obj: 0.003986, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.545958, Class: 1.000000, Obj: 0.343774, No Obj: 0.002945, Avg Recall: 0.627907, count: 86 -Region Avg IOU: 0.477295, Class: 1.000000, Obj: 0.284690, No Obj: 0.003082, Avg Recall: 0.518182, count: 110 -Region Avg IOU: 0.550373, Class: 1.000000, Obj: 0.301489, No Obj: 0.003500, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.554751, Class: 1.000000, Obj: 0.326565, No Obj: 0.003619, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.504789, Class: 1.000000, Obj: 0.323140, No Obj: 0.003404, Avg Recall: 0.529412, count: 102 -4331: 0.498067, 0.448430 avg, 0.001000 rate, 20.566648 seconds, 1108736 images -Loaded: 1.464388 seconds -Region Avg IOU: 0.529536, Class: 1.000000, Obj: 0.311944, No Obj: 0.003930, Avg Recall: 0.560440, count: 91 -Region Avg IOU: 0.517955, Class: 1.000000, Obj: 0.305116, No Obj: 0.003614, Avg Recall: 0.570248, count: 121 -Region Avg IOU: 0.528590, Class: 1.000000, Obj: 0.333845, No Obj: 0.004069, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.504798, Class: 1.000000, Obj: 0.294538, No Obj: 0.004194, Avg Recall: 0.573770, count: 122 -Region Avg IOU: 0.551366, Class: 1.000000, Obj: 0.312977, No Obj: 0.004303, Avg Recall: 0.625000, count: 120 -Region Avg IOU: 0.590034, Class: 1.000000, Obj: 0.344118, No Obj: 0.004467, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.527982, Class: 1.000000, Obj: 0.302883, No Obj: 0.004193, Avg Recall: 0.573529, count: 136 -Region Avg IOU: 0.536235, Class: 1.000000, Obj: 0.281827, No Obj: 0.003963, Avg Recall: 0.618557, count: 97 -4332: 0.514119, 0.454999 avg, 0.001000 rate, 20.488756 seconds, 1108992 images -Loaded: 1.435288 seconds -Region Avg IOU: 0.490579, Class: 1.000000, Obj: 0.354045, No Obj: 0.003603, Avg Recall: 0.544444, count: 90 -Region Avg IOU: 0.505720, Class: 1.000000, Obj: 0.381963, No Obj: 0.003815, Avg Recall: 0.510870, count: 92 -Region Avg IOU: 0.545730, Class: 1.000000, Obj: 0.369780, No Obj: 0.004246, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.522229, Class: 1.000000, Obj: 0.348474, No Obj: 0.004343, Avg Recall: 0.589744, count: 117 -Region Avg IOU: 0.559620, Class: 1.000000, Obj: 0.331994, No Obj: 0.004467, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.522709, Class: 1.000000, Obj: 0.324230, No Obj: 0.003858, Avg Recall: 0.588235, count: 102 -Region Avg IOU: 0.546045, Class: 1.000000, Obj: 0.316169, No Obj: 0.004179, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.555620, Class: 1.000000, Obj: 0.372970, No Obj: 0.004263, Avg Recall: 0.642105, count: 95 -4333: 0.453881, 0.454887 avg, 0.001000 rate, 23.355688 seconds, 1109248 images -Loaded: 0.792148 seconds -Region Avg IOU: 0.535507, Class: 1.000000, Obj: 0.380212, No Obj: 0.004058, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.569399, Class: 1.000000, Obj: 0.377890, No Obj: 0.004445, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.514197, Class: 1.000000, Obj: 0.386980, No Obj: 0.004594, Avg Recall: 0.573529, count: 136 -Region Avg IOU: 0.542239, Class: 1.000000, Obj: 0.351750, No Obj: 0.004750, Avg Recall: 0.623188, count: 138 -Region Avg IOU: 0.507439, Class: 1.000000, Obj: 0.316803, No Obj: 0.004607, Avg Recall: 0.533333, count: 120 -Region Avg IOU: 0.538588, Class: 1.000000, Obj: 0.349144, No Obj: 0.004506, Avg Recall: 0.614286, count: 140 -Region Avg IOU: 0.469086, Class: 1.000000, Obj: 0.343652, No Obj: 0.004121, Avg Recall: 0.510949, count: 137 -Region Avg IOU: 0.529333, Class: 1.000000, Obj: 0.339222, No Obj: 0.004752, Avg Recall: 0.571429, count: 119 -4334: 0.530511, 0.462449 avg, 0.001000 rate, 20.971872 seconds, 1109504 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.513887, Class: 1.000000, Obj: 0.347258, No Obj: 0.004697, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.533910, Class: 1.000000, Obj: 0.383469, No Obj: 0.004858, Avg Recall: 0.586777, count: 121 -Region Avg IOU: 0.527549, Class: 1.000000, Obj: 0.377035, No Obj: 0.004594, Avg Recall: 0.593220, count: 118 -Region Avg IOU: 0.633476, Class: 1.000000, Obj: 0.392827, No Obj: 0.004839, Avg Recall: 0.744681, count: 94 -Region Avg IOU: 0.536253, Class: 1.000000, Obj: 0.359590, No Obj: 0.004832, Avg Recall: 0.596639, count: 119 -Region Avg IOU: 0.511885, Class: 1.000000, Obj: 0.350649, No Obj: 0.004112, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.512794, Class: 1.000000, Obj: 0.363363, No Obj: 0.004736, Avg Recall: 0.584746, count: 118 -Region Avg IOU: 0.469065, Class: 1.000000, Obj: 0.379905, No Obj: 0.003952, Avg Recall: 0.558140, count: 86 -4335: 0.434027, 0.459607 avg, 0.001000 rate, 19.220720 seconds, 1109760 images -Loaded: 1.643509 seconds -Region Avg IOU: 0.517247, Class: 1.000000, Obj: 0.372539, No Obj: 0.004541, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.531697, Class: 1.000000, Obj: 0.363119, No Obj: 0.004003, Avg Recall: 0.597403, count: 77 -Region Avg IOU: 0.516369, Class: 1.000000, Obj: 0.406211, No Obj: 0.005605, Avg Recall: 0.557252, count: 131 -Region Avg IOU: 0.493740, Class: 1.000000, Obj: 0.345337, No Obj: 0.004752, Avg Recall: 0.517241, count: 116 -Region Avg IOU: 0.486134, Class: 1.000000, Obj: 0.355737, No Obj: 0.004575, Avg Recall: 0.473214, count: 112 -Region Avg IOU: 0.468318, Class: 1.000000, Obj: 0.352274, No Obj: 0.004369, Avg Recall: 0.522222, count: 90 -Region Avg IOU: 0.519832, Class: 1.000000, Obj: 0.402128, No Obj: 0.005164, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.522941, Class: 1.000000, Obj: 0.386680, No Obj: 0.004873, Avg Recall: 0.598214, count: 112 -4336: 0.428113, 0.456458 avg, 0.001000 rate, 21.997648 seconds, 1110016 images -Loaded: 0.021711 seconds -Region Avg IOU: 0.539128, Class: 1.000000, Obj: 0.419085, No Obj: 0.004992, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.528038, Class: 1.000000, Obj: 0.380382, No Obj: 0.005575, Avg Recall: 0.610687, count: 131 -Region Avg IOU: 0.576786, Class: 1.000000, Obj: 0.393009, No Obj: 0.004658, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.558409, Class: 1.000000, Obj: 0.472540, No Obj: 0.004466, Avg Recall: 0.636364, count: 88 -Region Avg IOU: 0.496648, Class: 1.000000, Obj: 0.393322, No Obj: 0.004013, Avg Recall: 0.540816, count: 98 -Region Avg IOU: 0.587781, Class: 1.000000, Obj: 0.425743, No Obj: 0.004605, Avg Recall: 0.684211, count: 76 -Region Avg IOU: 0.522759, Class: 1.000000, Obj: 0.387340, No Obj: 0.004936, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.531863, Class: 1.000000, Obj: 0.420583, No Obj: 0.004733, Avg Recall: 0.586777, count: 121 -4337: 0.403790, 0.451191 avg, 0.001000 rate, 20.907543 seconds, 1110272 images -Loaded: 1.113769 seconds -Region Avg IOU: 0.548110, Class: 1.000000, Obj: 0.443843, No Obj: 0.004874, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.546588, Class: 1.000000, Obj: 0.444367, No Obj: 0.004353, Avg Recall: 0.643678, count: 87 -Region Avg IOU: 0.509171, Class: 1.000000, Obj: 0.408220, No Obj: 0.004845, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.479664, Class: 1.000000, Obj: 0.427634, No Obj: 0.004485, Avg Recall: 0.530612, count: 98 -Region Avg IOU: 0.486733, Class: 1.000000, Obj: 0.324311, No Obj: 0.004325, Avg Recall: 0.520833, count: 96 -Region Avg IOU: 0.498858, Class: 1.000000, Obj: 0.366060, No Obj: 0.004498, Avg Recall: 0.588235, count: 102 -Region Avg IOU: 0.514134, Class: 1.000000, Obj: 0.388074, No Obj: 0.004845, Avg Recall: 0.561404, count: 114 -Region Avg IOU: 0.551123, Class: 1.000000, Obj: 0.443297, No Obj: 0.005228, Avg Recall: 0.637255, count: 102 -4338: 0.382038, 0.444276 avg, 0.001000 rate, 18.715855 seconds, 1110528 images -Loaded: 2.382434 seconds -Region Avg IOU: 0.515013, Class: 1.000000, Obj: 0.414294, No Obj: 0.004418, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.515951, Class: 1.000000, Obj: 0.392319, No Obj: 0.004484, Avg Recall: 0.571429, count: 112 -Region Avg IOU: 0.493610, Class: 1.000000, Obj: 0.368830, No Obj: 0.004651, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.539864, Class: 1.000000, Obj: 0.455979, No Obj: 0.004801, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.530124, Class: 1.000000, Obj: 0.360261, No Obj: 0.004644, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.561419, Class: 1.000000, Obj: 0.412150, No Obj: 0.004948, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.538878, Class: 1.000000, Obj: 0.400303, No Obj: 0.004360, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.485104, Class: 1.000000, Obj: 0.357917, No Obj: 0.004265, Avg Recall: 0.509615, count: 104 -4339: 0.452561, 0.445104 avg, 0.001000 rate, 18.962572 seconds, 1110784 images -Loaded: 2.300742 seconds -Region Avg IOU: 0.507348, Class: 1.000000, Obj: 0.398714, No Obj: 0.005339, Avg Recall: 0.552239, count: 134 -Region Avg IOU: 0.550404, Class: 1.000000, Obj: 0.427788, No Obj: 0.004162, Avg Recall: 0.615385, count: 91 -Region Avg IOU: 0.545618, Class: 1.000000, Obj: 0.422004, No Obj: 0.004629, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.571608, Class: 1.000000, Obj: 0.447348, No Obj: 0.003897, Avg Recall: 0.635135, count: 74 -Region Avg IOU: 0.477527, Class: 1.000000, Obj: 0.371385, No Obj: 0.004059, Avg Recall: 0.538462, count: 104 -Region Avg IOU: 0.521253, Class: 1.000000, Obj: 0.405465, No Obj: 0.004458, Avg Recall: 0.588235, count: 102 -Region Avg IOU: 0.511937, Class: 1.000000, Obj: 0.380286, No Obj: 0.004159, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.561005, Class: 1.000000, Obj: 0.395556, No Obj: 0.005720, Avg Recall: 0.657895, count: 152 -4340: 0.407281, 0.441322 avg, 0.001000 rate, 20.255661 seconds, 1111040 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000076 seconds -Region Avg IOU: 0.607662, Class: 1.000000, Obj: 0.446254, No Obj: 0.003520, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.626150, Class: 1.000000, Obj: 0.428938, No Obj: 0.003198, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.595785, Class: 1.000000, Obj: 0.432149, No Obj: 0.003124, Avg Recall: 0.783333, count: 120 -Region Avg IOU: 0.621083, Class: 1.000000, Obj: 0.428761, No Obj: 0.003454, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.621850, Class: 1.000000, Obj: 0.459719, No Obj: 0.003126, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.629404, Class: 1.000000, Obj: 0.501032, No Obj: 0.003184, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.604716, Class: 1.000000, Obj: 0.444624, No Obj: 0.003287, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.649448, Class: 1.000000, Obj: 0.449548, No Obj: 0.003024, Avg Recall: 0.862069, count: 87 -4341: 0.411162, 0.438306 avg, 0.001000 rate, 28.284510 seconds, 1111296 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.647071, Class: 1.000000, Obj: 0.441961, No Obj: 0.003026, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.669679, Class: 1.000000, Obj: 0.461866, No Obj: 0.003379, Avg Recall: 0.854369, count: 103 -Region Avg IOU: 0.636725, Class: 1.000000, Obj: 0.372976, No Obj: 0.002933, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.601234, Class: 1.000000, Obj: 0.394326, No Obj: 0.003424, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.650249, Class: 1.000000, Obj: 0.414160, No Obj: 0.003736, Avg Recall: 0.833333, count: 132 -Region Avg IOU: 0.661450, Class: 1.000000, Obj: 0.423610, No Obj: 0.002754, Avg Recall: 0.827957, count: 93 -Region Avg IOU: 0.615885, Class: 1.000000, Obj: 0.398648, No Obj: 0.002889, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.600365, Class: 1.000000, Obj: 0.385315, No Obj: 0.003135, Avg Recall: 0.758333, count: 120 -4342: 0.475769, 0.442052 avg, 0.001000 rate, 28.940332 seconds, 1111552 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.645499, Class: 1.000000, Obj: 0.402123, No Obj: 0.003163, Avg Recall: 0.827068, count: 133 -Region Avg IOU: 0.625734, Class: 1.000000, Obj: 0.373006, No Obj: 0.002558, Avg Recall: 0.742857, count: 70 -Region Avg IOU: 0.651807, Class: 1.000000, Obj: 0.376492, No Obj: 0.002679, Avg Recall: 0.850467, count: 107 -Region Avg IOU: 0.659710, Class: 1.000000, Obj: 0.394613, No Obj: 0.002907, Avg Recall: 0.817308, count: 104 -Region Avg IOU: 0.606860, Class: 1.000000, Obj: 0.393507, No Obj: 0.002768, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.635410, Class: 1.000000, Obj: 0.373523, No Obj: 0.002953, Avg Recall: 0.774436, count: 133 -Region Avg IOU: 0.627631, Class: 1.000000, Obj: 0.394775, No Obj: 0.002716, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.642334, Class: 1.000000, Obj: 0.399910, No Obj: 0.002800, Avg Recall: 0.787234, count: 94 -4343: 0.441201, 0.441967 avg, 0.001000 rate, 28.647650 seconds, 1111808 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.598717, Class: 1.000000, Obj: 0.360185, No Obj: 0.002475, Avg Recall: 0.717557, count: 131 -Region Avg IOU: 0.672712, Class: 1.000000, Obj: 0.400696, No Obj: 0.002860, Avg Recall: 0.870690, count: 116 -Region Avg IOU: 0.653832, Class: 1.000000, Obj: 0.367274, No Obj: 0.002466, Avg Recall: 0.770270, count: 74 -Region Avg IOU: 0.625239, Class: 1.000000, Obj: 0.368897, No Obj: 0.002604, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.641770, Class: 1.000000, Obj: 0.399429, No Obj: 0.002656, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.629724, Class: 1.000000, Obj: 0.340098, No Obj: 0.002505, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.625897, Class: 1.000000, Obj: 0.364200, No Obj: 0.002477, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.660600, Class: 1.000000, Obj: 0.367734, No Obj: 0.002573, Avg Recall: 0.797980, count: 99 -4344: 0.450085, 0.442779 avg, 0.001000 rate, 28.045464 seconds, 1112064 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.598682, Class: 1.000000, Obj: 0.314291, No Obj: 0.002334, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.659256, Class: 1.000000, Obj: 0.341743, No Obj: 0.002538, Avg Recall: 0.822222, count: 90 -Region Avg IOU: 0.651136, Class: 1.000000, Obj: 0.376186, No Obj: 0.002523, Avg Recall: 0.787402, count: 127 -Region Avg IOU: 0.609972, Class: 1.000000, Obj: 0.355948, No Obj: 0.002308, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.618605, Class: 1.000000, Obj: 0.354423, No Obj: 0.001998, Avg Recall: 0.765432, count: 81 -Region Avg IOU: 0.620371, Class: 1.000000, Obj: 0.315192, No Obj: 0.002068, Avg Recall: 0.775281, count: 89 -Region Avg IOU: 0.630433, Class: 1.000000, Obj: 0.396605, No Obj: 0.002421, Avg Recall: 0.775281, count: 89 -Region Avg IOU: 0.658917, Class: 1.000000, Obj: 0.341713, No Obj: 0.002296, Avg Recall: 0.801887, count: 106 -4345: 0.446045, 0.443105 avg, 0.001000 rate, 29.190002 seconds, 1112320 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.595061, Class: 1.000000, Obj: 0.278065, No Obj: 0.002375, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.651859, Class: 1.000000, Obj: 0.343890, No Obj: 0.002424, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.632142, Class: 1.000000, Obj: 0.339869, No Obj: 0.002450, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.647770, Class: 1.000000, Obj: 0.308804, No Obj: 0.002343, Avg Recall: 0.837209, count: 129 -Region Avg IOU: 0.645295, Class: 1.000000, Obj: 0.303064, No Obj: 0.002595, Avg Recall: 0.830645, count: 124 -Region Avg IOU: 0.637391, Class: 1.000000, Obj: 0.336905, No Obj: 0.002451, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.597750, Class: 1.000000, Obj: 0.319116, No Obj: 0.002433, Avg Recall: 0.700000, count: 120 -Region Avg IOU: 0.635375, Class: 1.000000, Obj: 0.323713, No Obj: 0.002279, Avg Recall: 0.740385, count: 104 -4346: 0.517261, 0.450521 avg, 0.001000 rate, 27.804262 seconds, 1112576 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.629965, Class: 1.000000, Obj: 0.309842, No Obj: 0.002614, Avg Recall: 0.773723, count: 137 -Region Avg IOU: 0.637734, Class: 1.000000, Obj: 0.344580, No Obj: 0.002324, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.631283, Class: 1.000000, Obj: 0.303984, No Obj: 0.002494, Avg Recall: 0.765625, count: 128 -Region Avg IOU: 0.618847, Class: 1.000000, Obj: 0.337732, No Obj: 0.002261, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.636452, Class: 1.000000, Obj: 0.316740, No Obj: 0.002247, Avg Recall: 0.843137, count: 102 -Region Avg IOU: 0.601470, Class: 1.000000, Obj: 0.291950, No Obj: 0.002306, Avg Recall: 0.764706, count: 136 -Region Avg IOU: 0.609018, Class: 1.000000, Obj: 0.281754, No Obj: 0.002074, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.625699, Class: 1.000000, Obj: 0.337511, No Obj: 0.002300, Avg Recall: 0.801802, count: 111 -4347: 0.488402, 0.454309 avg, 0.001000 rate, 27.440037 seconds, 1112832 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.649826, Class: 1.000000, Obj: 0.343404, No Obj: 0.002393, Avg Recall: 0.800000, count: 120 -Region Avg IOU: 0.642414, Class: 1.000000, Obj: 0.409390, No Obj: 0.002639, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.597336, Class: 1.000000, Obj: 0.329320, No Obj: 0.002283, Avg Recall: 0.688000, count: 125 -Region Avg IOU: 0.631513, Class: 1.000000, Obj: 0.344819, No Obj: 0.002449, Avg Recall: 0.785124, count: 121 -Region Avg IOU: 0.680053, Class: 1.000000, Obj: 0.353380, No Obj: 0.002320, Avg Recall: 0.853658, count: 82 -Region Avg IOU: 0.620336, Class: 1.000000, Obj: 0.388859, No Obj: 0.002057, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.604612, Class: 1.000000, Obj: 0.315790, No Obj: 0.002058, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.662151, Class: 1.000000, Obj: 0.357443, No Obj: 0.002596, Avg Recall: 0.888889, count: 117 -4348: 0.430592, 0.451937 avg, 0.001000 rate, 28.388109 seconds, 1113088 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.649932, Class: 1.000000, Obj: 0.358992, No Obj: 0.002473, Avg Recall: 0.775862, count: 116 -Region Avg IOU: 0.654664, Class: 1.000000, Obj: 0.316642, No Obj: 0.002681, Avg Recall: 0.829060, count: 117 -Region Avg IOU: 0.689473, Class: 1.000000, Obj: 0.420844, No Obj: 0.002565, Avg Recall: 0.887755, count: 98 -Region Avg IOU: 0.637832, Class: 1.000000, Obj: 0.356036, No Obj: 0.002559, Avg Recall: 0.779661, count: 118 -Region Avg IOU: 0.656440, Class: 1.000000, Obj: 0.372622, No Obj: 0.002185, Avg Recall: 0.802632, count: 76 -Region Avg IOU: 0.614442, Class: 1.000000, Obj: 0.349768, No Obj: 0.002096, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.650010, Class: 1.000000, Obj: 0.354830, No Obj: 0.003015, Avg Recall: 0.789855, count: 138 -Region Avg IOU: 0.623701, Class: 1.000000, Obj: 0.333093, No Obj: 0.002343, Avg Recall: 0.759615, count: 104 -4349: 0.453167, 0.452060 avg, 0.001000 rate, 28.562540 seconds, 1113344 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.612818, Class: 1.000000, Obj: 0.340868, No Obj: 0.002671, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.643022, Class: 1.000000, Obj: 0.372437, No Obj: 0.002460, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.627553, Class: 1.000000, Obj: 0.387552, No Obj: 0.002768, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.610773, Class: 1.000000, Obj: 0.365935, No Obj: 0.002713, Avg Recall: 0.735043, count: 117 -Region Avg IOU: 0.650913, Class: 1.000000, Obj: 0.343863, No Obj: 0.002433, Avg Recall: 0.796610, count: 118 -Region Avg IOU: 0.662132, Class: 1.000000, Obj: 0.348966, No Obj: 0.003066, Avg Recall: 0.831776, count: 107 -Region Avg IOU: 0.641342, Class: 1.000000, Obj: 0.348554, No Obj: 0.002411, Avg Recall: 0.836207, count: 116 -Region Avg IOU: 0.686463, Class: 1.000000, Obj: 0.351698, No Obj: 0.002813, Avg Recall: 0.850000, count: 120 -4350: 0.453678, 0.452222 avg, 0.001000 rate, 28.595644 seconds, 1113600 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.468610 seconds -Region Avg IOU: 0.529358, Class: 1.000000, Obj: 0.326703, No Obj: 0.003251, Avg Recall: 0.604938, count: 81 -Region Avg IOU: 0.504566, Class: 1.000000, Obj: 0.275088, No Obj: 0.003444, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.514575, Class: 1.000000, Obj: 0.284540, No Obj: 0.003813, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.522046, Class: 1.000000, Obj: 0.292058, No Obj: 0.003391, Avg Recall: 0.617886, count: 123 -Region Avg IOU: 0.506875, Class: 1.000000, Obj: 0.274471, No Obj: 0.003451, Avg Recall: 0.600000, count: 90 -Region Avg IOU: 0.552060, Class: 1.000000, Obj: 0.272109, No Obj: 0.003697, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.549945, Class: 1.000000, Obj: 0.330337, No Obj: 0.003987, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.564913, Class: 1.000000, Obj: 0.298313, No Obj: 0.004222, Avg Recall: 0.655462, count: 119 -4351: 0.463111, 0.453311 avg, 0.001000 rate, 20.233507 seconds, 1113856 images -Loaded: 1.027597 seconds -Region Avg IOU: 0.536119, Class: 1.000000, Obj: 0.307446, No Obj: 0.004770, Avg Recall: 0.610738, count: 149 -Region Avg IOU: 0.495280, Class: 1.000000, Obj: 0.298785, No Obj: 0.003745, Avg Recall: 0.536082, count: 97 -Region Avg IOU: 0.546425, Class: 1.000000, Obj: 0.315796, No Obj: 0.003981, Avg Recall: 0.644068, count: 118 -Region Avg IOU: 0.559825, Class: 1.000000, Obj: 0.308278, No Obj: 0.003950, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.544605, Class: 1.000000, Obj: 0.308616, No Obj: 0.004338, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.487930, Class: 1.000000, Obj: 0.256467, No Obj: 0.003387, Avg Recall: 0.576577, count: 111 -Region Avg IOU: 0.533499, Class: 1.000000, Obj: 0.332297, No Obj: 0.003467, Avg Recall: 0.613636, count: 88 -Region Avg IOU: 0.530841, Class: 1.000000, Obj: 0.292086, No Obj: 0.004135, Avg Recall: 0.638655, count: 119 -4352: 0.515641, 0.459544 avg, 0.001000 rate, 20.370249 seconds, 1114112 images -Loaded: 0.842234 seconds -Region Avg IOU: 0.541181, Class: 1.000000, Obj: 0.356778, No Obj: 0.003405, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.498166, Class: 1.000000, Obj: 0.366927, No Obj: 0.003687, Avg Recall: 0.548673, count: 113 -Region Avg IOU: 0.548970, Class: 1.000000, Obj: 0.334096, No Obj: 0.004467, Avg Recall: 0.598485, count: 132 -Region Avg IOU: 0.516945, Class: 1.000000, Obj: 0.317285, No Obj: 0.003425, Avg Recall: 0.629214, count: 89 -Region Avg IOU: 0.531987, Class: 1.000000, Obj: 0.309395, No Obj: 0.004222, Avg Recall: 0.632000, count: 125 -Region Avg IOU: 0.519182, Class: 1.000000, Obj: 0.322660, No Obj: 0.003650, Avg Recall: 0.625000, count: 88 -Region Avg IOU: 0.492804, Class: 1.000000, Obj: 0.333832, No Obj: 0.003560, Avg Recall: 0.556701, count: 97 -Region Avg IOU: 0.522108, Class: 1.000000, Obj: 0.303606, No Obj: 0.003866, Avg Recall: 0.629630, count: 108 -4353: 0.457276, 0.459317 avg, 0.001000 rate, 21.579996 seconds, 1114368 images -Loaded: 0.641787 seconds -Region Avg IOU: 0.555915, Class: 1.000000, Obj: 0.350245, No Obj: 0.004585, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.476252, Class: 1.000000, Obj: 0.338476, No Obj: 0.003692, Avg Recall: 0.539326, count: 89 -Region Avg IOU: 0.531224, Class: 1.000000, Obj: 0.336714, No Obj: 0.004511, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.523583, Class: 1.000000, Obj: 0.387131, No Obj: 0.003819, Avg Recall: 0.543210, count: 81 -Region Avg IOU: 0.525883, Class: 1.000000, Obj: 0.305364, No Obj: 0.004395, Avg Recall: 0.600000, count: 120 -Region Avg IOU: 0.494895, Class: 1.000000, Obj: 0.346096, No Obj: 0.004007, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.551164, Class: 1.000000, Obj: 0.357965, No Obj: 0.005475, Avg Recall: 0.672000, count: 125 -Region Avg IOU: 0.531513, Class: 1.000000, Obj: 0.369291, No Obj: 0.004539, Avg Recall: 0.621212, count: 132 -4354: 0.430225, 0.456408 avg, 0.001000 rate, 22.407368 seconds, 1114624 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.547896, Class: 1.000000, Obj: 0.391741, No Obj: 0.004585, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.556618, Class: 1.000000, Obj: 0.386585, No Obj: 0.004030, Avg Recall: 0.670732, count: 82 -Region Avg IOU: 0.566390, Class: 1.000000, Obj: 0.372625, No Obj: 0.004773, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.514702, Class: 1.000000, Obj: 0.364942, No Obj: 0.004156, Avg Recall: 0.561404, count: 114 -Region Avg IOU: 0.526813, Class: 1.000000, Obj: 0.366644, No Obj: 0.004875, Avg Recall: 0.600000, count: 95 -Region Avg IOU: 0.472915, Class: 1.000000, Obj: 0.320670, No Obj: 0.003237, Avg Recall: 0.517241, count: 87 -Region Avg IOU: 0.505318, Class: 1.000000, Obj: 0.336490, No Obj: 0.004820, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.584758, Class: 1.000000, Obj: 0.412213, No Obj: 0.004508, Avg Recall: 0.693878, count: 98 -4355: 0.397308, 0.450498 avg, 0.001000 rate, 20.987720 seconds, 1114880 images -Loaded: 1.407503 seconds -Region Avg IOU: 0.491871, Class: 1.000000, Obj: 0.388434, No Obj: 0.004762, Avg Recall: 0.553719, count: 121 -Region Avg IOU: 0.491531, Class: 1.000000, Obj: 0.377163, No Obj: 0.004617, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.498472, Class: 1.000000, Obj: 0.380527, No Obj: 0.005144, Avg Recall: 0.579365, count: 126 -Region Avg IOU: 0.550304, Class: 1.000000, Obj: 0.400074, No Obj: 0.005745, Avg Recall: 0.656716, count: 134 -Region Avg IOU: 0.529191, Class: 1.000000, Obj: 0.403370, No Obj: 0.005439, Avg Recall: 0.619835, count: 121 -Region Avg IOU: 0.527875, Class: 1.000000, Obj: 0.401523, No Obj: 0.004850, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.538221, Class: 1.000000, Obj: 0.395337, No Obj: 0.004803, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.508958, Class: 1.000000, Obj: 0.347322, No Obj: 0.004822, Avg Recall: 0.583333, count: 108 -4356: 0.511571, 0.456605 avg, 0.001000 rate, 21.411997 seconds, 1115136 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.512889, Class: 1.000000, Obj: 0.386611, No Obj: 0.005242, Avg Recall: 0.552845, count: 123 -Region Avg IOU: 0.526456, Class: 1.000000, Obj: 0.393835, No Obj: 0.005300, Avg Recall: 0.612903, count: 124 -Region Avg IOU: 0.566578, Class: 1.000000, Obj: 0.417095, No Obj: 0.005445, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.485021, Class: 1.000000, Obj: 0.339385, No Obj: 0.004981, Avg Recall: 0.496124, count: 129 -Region Avg IOU: 0.516601, Class: 1.000000, Obj: 0.399944, No Obj: 0.004937, Avg Recall: 0.540984, count: 122 -Region Avg IOU: 0.538810, Class: 1.000000, Obj: 0.429472, No Obj: 0.004659, Avg Recall: 0.613636, count: 88 -Region Avg IOU: 0.533650, Class: 1.000000, Obj: 0.402224, No Obj: 0.004529, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.522750, Class: 1.000000, Obj: 0.428407, No Obj: 0.005197, Avg Recall: 0.606061, count: 99 -4357: 0.434001, 0.454345 avg, 0.001000 rate, 19.248144 seconds, 1115392 images -Loaded: 2.939667 seconds -Region Avg IOU: 0.528538, Class: 1.000000, Obj: 0.455301, No Obj: 0.005152, Avg Recall: 0.561798, count: 89 -Region Avg IOU: 0.478727, Class: 1.000000, Obj: 0.364776, No Obj: 0.004748, Avg Recall: 0.541667, count: 96 -Region Avg IOU: 0.521094, Class: 1.000000, Obj: 0.401714, No Obj: 0.005061, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.527584, Class: 1.000000, Obj: 0.408401, No Obj: 0.004969, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.541967, Class: 1.000000, Obj: 0.426707, No Obj: 0.005192, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.510200, Class: 1.000000, Obj: 0.404414, No Obj: 0.004571, Avg Recall: 0.547619, count: 84 -Region Avg IOU: 0.510621, Class: 1.000000, Obj: 0.426489, No Obj: 0.005423, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.539857, Class: 1.000000, Obj: 0.370534, No Obj: 0.004773, Avg Recall: 0.654206, count: 107 -4358: 0.480008, 0.456911 avg, 0.001000 rate, 22.669456 seconds, 1115648 images -Loaded: 0.150333 seconds -Region Avg IOU: 0.579560, Class: 1.000000, Obj: 0.452566, No Obj: 0.005697, Avg Recall: 0.704918, count: 122 -Region Avg IOU: 0.565709, Class: 1.000000, Obj: 0.416816, No Obj: 0.004652, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.513199, Class: 1.000000, Obj: 0.373037, No Obj: 0.004931, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.487621, Class: 1.000000, Obj: 0.371201, No Obj: 0.004359, Avg Recall: 0.532710, count: 107 -Region Avg IOU: 0.555398, Class: 1.000000, Obj: 0.415428, No Obj: 0.005003, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.554098, Class: 1.000000, Obj: 0.437419, No Obj: 0.004696, Avg Recall: 0.674699, count: 83 -Region Avg IOU: 0.524436, Class: 1.000000, Obj: 0.403162, No Obj: 0.004418, Avg Recall: 0.597701, count: 87 -Region Avg IOU: 0.498675, Class: 1.000000, Obj: 0.338703, No Obj: 0.005182, Avg Recall: 0.564103, count: 117 -4359: 0.440734, 0.455293 avg, 0.001000 rate, 21.264381 seconds, 1115904 images -Loaded: 0.056083 seconds -Region Avg IOU: 0.529666, Class: 1.000000, Obj: 0.396845, No Obj: 0.004437, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.519924, Class: 1.000000, Obj: 0.368968, No Obj: 0.004919, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.534938, Class: 1.000000, Obj: 0.393305, No Obj: 0.004699, Avg Recall: 0.607843, count: 102 -Region Avg IOU: 0.513843, Class: 1.000000, Obj: 0.382592, No Obj: 0.005100, Avg Recall: 0.625954, count: 131 -Region Avg IOU: 0.547998, Class: 1.000000, Obj: 0.382085, No Obj: 0.004457, Avg Recall: 0.636364, count: 88 -Region Avg IOU: 0.512140, Class: 1.000000, Obj: 0.395632, No Obj: 0.004427, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.557436, Class: 1.000000, Obj: 0.419833, No Obj: 0.004517, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.552531, Class: 1.000000, Obj: 0.390847, No Obj: 0.004925, Avg Recall: 0.597561, count: 82 -4360: 0.414062, 0.451170 avg, 0.001000 rate, 20.963495 seconds, 1116160 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 20.008753 seconds -Region Avg IOU: 0.513499, Class: 1.000000, Obj: 0.381599, No Obj: 0.004934, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.484978, Class: 1.000000, Obj: 0.338429, No Obj: 0.005295, Avg Recall: 0.519481, count: 154 -Region Avg IOU: 0.473566, Class: 1.000000, Obj: 0.343738, No Obj: 0.004899, Avg Recall: 0.520000, count: 125 -Region Avg IOU: 0.491985, Class: 1.000000, Obj: 0.387539, No Obj: 0.004703, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.472612, Class: 1.000000, Obj: 0.346754, No Obj: 0.004852, Avg Recall: 0.456140, count: 114 -Region Avg IOU: 0.541140, Class: 1.000000, Obj: 0.385461, No Obj: 0.005401, Avg Recall: 0.610687, count: 131 -Region Avg IOU: 0.512501, Class: 1.000000, Obj: 0.409458, No Obj: 0.004976, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.510888, Class: 1.000000, Obj: 0.380429, No Obj: 0.004638, Avg Recall: 0.549550, count: 111 -4361: 0.468841, 0.452937 avg, 0.001000 rate, 18.375883 seconds, 1116416 images -Loaded: 2.973098 seconds -Region Avg IOU: 0.476913, Class: 1.000000, Obj: 0.324101, No Obj: 0.004371, Avg Recall: 0.537815, count: 119 -Region Avg IOU: 0.572951, Class: 1.000000, Obj: 0.343843, No Obj: 0.004084, Avg Recall: 0.674699, count: 83 -Region Avg IOU: 0.456507, Class: 1.000000, Obj: 0.324202, No Obj: 0.004393, Avg Recall: 0.482014, count: 139 -Region Avg IOU: 0.533828, Class: 1.000000, Obj: 0.387443, No Obj: 0.004907, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.510328, Class: 1.000000, Obj: 0.341149, No Obj: 0.004619, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.473777, Class: 1.000000, Obj: 0.355706, No Obj: 0.004864, Avg Recall: 0.482143, count: 112 -Region Avg IOU: 0.455560, Class: 1.000000, Obj: 0.329916, No Obj: 0.004003, Avg Recall: 0.489362, count: 94 -Region Avg IOU: 0.521365, Class: 1.000000, Obj: 0.372802, No Obj: 0.004050, Avg Recall: 0.610526, count: 95 -4362: 0.449156, 0.452559 avg, 0.001000 rate, 18.025097 seconds, 1116672 images -Loaded: 3.217289 seconds -Region Avg IOU: 0.441865, Class: 1.000000, Obj: 0.322731, No Obj: 0.004062, Avg Recall: 0.457143, count: 105 -Region Avg IOU: 0.512438, Class: 1.000000, Obj: 0.322622, No Obj: 0.004642, Avg Recall: 0.553846, count: 130 -Region Avg IOU: 0.543621, Class: 1.000000, Obj: 0.315093, No Obj: 0.004825, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.531045, Class: 1.000000, Obj: 0.333864, No Obj: 0.004565, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.493859, Class: 1.000000, Obj: 0.302181, No Obj: 0.003981, Avg Recall: 0.524272, count: 103 -Region Avg IOU: 0.522485, Class: 1.000000, Obj: 0.348987, No Obj: 0.004510, Avg Recall: 0.563025, count: 119 -Region Avg IOU: 0.501621, Class: 1.000000, Obj: 0.343041, No Obj: 0.003731, Avg Recall: 0.543478, count: 92 -Region Avg IOU: 0.488055, Class: 1.000000, Obj: 0.292021, No Obj: 0.004324, Avg Recall: 0.516393, count: 122 -4363: 0.452135, 0.452517 avg, 0.001000 rate, 20.122219 seconds, 1116928 images -Loaded: 1.771704 seconds -Region Avg IOU: 0.515177, Class: 1.000000, Obj: 0.328342, No Obj: 0.004682, Avg Recall: 0.592000, count: 125 -Region Avg IOU: 0.393556, Class: 1.000000, Obj: 0.284764, No Obj: 0.003326, Avg Recall: 0.370370, count: 108 -Region Avg IOU: 0.482530, Class: 1.000000, Obj: 0.302331, No Obj: 0.003962, Avg Recall: 0.549550, count: 111 -Region Avg IOU: 0.484687, Class: 1.000000, Obj: 0.281309, No Obj: 0.003852, Avg Recall: 0.517241, count: 116 -Region Avg IOU: 0.424252, Class: 1.000000, Obj: 0.246323, No Obj: 0.004239, Avg Recall: 0.462687, count: 134 -Region Avg IOU: 0.491748, Class: 1.000000, Obj: 0.293481, No Obj: 0.004236, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.485757, Class: 1.000000, Obj: 0.313856, No Obj: 0.004221, Avg Recall: 0.536364, count: 110 -Region Avg IOU: 0.508742, Class: 1.000000, Obj: 0.342312, No Obj: 0.004378, Avg Recall: 0.592593, count: 108 -4364: 0.536221, 0.460887 avg, 0.001000 rate, 19.777069 seconds, 1117184 images -Loaded: 0.722565 seconds -Region Avg IOU: 0.522937, Class: 1.000000, Obj: 0.316855, No Obj: 0.004377, Avg Recall: 0.564103, count: 117 -Region Avg IOU: 0.494867, Class: 1.000000, Obj: 0.301995, No Obj: 0.003601, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.512511, Class: 1.000000, Obj: 0.305945, No Obj: 0.004011, Avg Recall: 0.596491, count: 114 -Region Avg IOU: 0.499625, Class: 1.000000, Obj: 0.290603, No Obj: 0.003604, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.447846, Class: 1.000000, Obj: 0.346712, No Obj: 0.003166, Avg Recall: 0.460674, count: 89 -Region Avg IOU: 0.462013, Class: 1.000000, Obj: 0.312234, No Obj: 0.004233, Avg Recall: 0.521368, count: 117 -Region Avg IOU: 0.468123, Class: 1.000000, Obj: 0.318954, No Obj: 0.004122, Avg Recall: 0.507937, count: 126 -Region Avg IOU: 0.493410, Class: 1.000000, Obj: 0.333905, No Obj: 0.003439, Avg Recall: 0.530000, count: 100 -4365: 0.487600, 0.463559 avg, 0.001000 rate, 18.635571 seconds, 1117440 images -Loaded: 2.013387 seconds -Region Avg IOU: 0.569403, Class: 1.000000, Obj: 0.340150, No Obj: 0.004299, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.502365, Class: 1.000000, Obj: 0.333571, No Obj: 0.004290, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.527062, Class: 1.000000, Obj: 0.326914, No Obj: 0.004367, Avg Recall: 0.632479, count: 117 -Region Avg IOU: 0.483137, Class: 1.000000, Obj: 0.284575, No Obj: 0.004038, Avg Recall: 0.518868, count: 106 -Region Avg IOU: 0.446116, Class: 1.000000, Obj: 0.308304, No Obj: 0.003920, Avg Recall: 0.466667, count: 105 -Region Avg IOU: 0.463234, Class: 1.000000, Obj: 0.281026, No Obj: 0.003609, Avg Recall: 0.471698, count: 106 -Region Avg IOU: 0.491048, Class: 1.000000, Obj: 0.335884, No Obj: 0.003732, Avg Recall: 0.543210, count: 81 -Region Avg IOU: 0.508844, Class: 1.000000, Obj: 0.331980, No Obj: 0.003853, Avg Recall: 0.590909, count: 110 -4366: 0.435002, 0.460703 avg, 0.001000 rate, 20.651527 seconds, 1117696 images -Loaded: 1.416098 seconds -Region Avg IOU: 0.478239, Class: 1.000000, Obj: 0.325020, No Obj: 0.004105, Avg Recall: 0.548387, count: 124 -Region Avg IOU: 0.644468, Class: 1.000000, Obj: 0.371128, No Obj: 0.003595, Avg Recall: 0.797297, count: 74 -Region Avg IOU: 0.513835, Class: 1.000000, Obj: 0.330630, No Obj: 0.004695, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.467881, Class: 1.000000, Obj: 0.288698, No Obj: 0.003964, Avg Recall: 0.500000, count: 100 -Region Avg IOU: 0.527055, Class: 1.000000, Obj: 0.302538, No Obj: 0.004404, Avg Recall: 0.565891, count: 129 -Region Avg IOU: 0.507467, Class: 1.000000, Obj: 0.349939, No Obj: 0.004238, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.467840, Class: 1.000000, Obj: 0.284825, No Obj: 0.004157, Avg Recall: 0.491379, count: 116 -Region Avg IOU: 0.440078, Class: 1.000000, Obj: 0.314890, No Obj: 0.003650, Avg Recall: 0.491071, count: 112 -4367: 0.456100, 0.460243 avg, 0.001000 rate, 21.126532 seconds, 1117952 images -Loaded: 0.678597 seconds -Region Avg IOU: 0.522299, Class: 1.000000, Obj: 0.325001, No Obj: 0.004060, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.508287, Class: 1.000000, Obj: 0.349265, No Obj: 0.005252, Avg Recall: 0.559702, count: 134 -Region Avg IOU: 0.499721, Class: 1.000000, Obj: 0.314082, No Obj: 0.004169, Avg Recall: 0.513514, count: 111 -Region Avg IOU: 0.487717, Class: 1.000000, Obj: 0.320268, No Obj: 0.004283, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.537134, Class: 1.000000, Obj: 0.314544, No Obj: 0.004634, Avg Recall: 0.622047, count: 127 -Region Avg IOU: 0.531156, Class: 1.000000, Obj: 0.305948, No Obj: 0.004083, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.541126, Class: 1.000000, Obj: 0.364519, No Obj: 0.004766, Avg Recall: 0.630435, count: 92 -Region Avg IOU: 0.469441, Class: 1.000000, Obj: 0.322480, No Obj: 0.003483, Avg Recall: 0.523256, count: 86 -4368: 0.482342, 0.462453 avg, 0.001000 rate, 18.388372 seconds, 1118208 images -Loaded: 3.210486 seconds -Region Avg IOU: 0.493791, Class: 1.000000, Obj: 0.345687, No Obj: 0.004812, Avg Recall: 0.531250, count: 128 -Region Avg IOU: 0.522701, Class: 1.000000, Obj: 0.358701, No Obj: 0.005116, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.518400, Class: 1.000000, Obj: 0.356415, No Obj: 0.004270, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.477502, Class: 1.000000, Obj: 0.303135, No Obj: 0.004692, Avg Recall: 0.518182, count: 110 -Region Avg IOU: 0.522184, Class: 1.000000, Obj: 0.286133, No Obj: 0.004552, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.461743, Class: 1.000000, Obj: 0.337307, No Obj: 0.004052, Avg Recall: 0.521739, count: 115 -Region Avg IOU: 0.486917, Class: 1.000000, Obj: 0.350619, No Obj: 0.004307, Avg Recall: 0.546296, count: 108 -Region Avg IOU: 0.523125, Class: 1.000000, Obj: 0.388302, No Obj: 0.005031, Avg Recall: 0.593750, count: 128 -4369: 0.472231, 0.463430 avg, 0.001000 rate, 17.852917 seconds, 1118464 images -Loaded: 3.709648 seconds -Region Avg IOU: 0.497627, Class: 1.000000, Obj: 0.351150, No Obj: 0.005045, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.487550, Class: 1.000000, Obj: 0.361187, No Obj: 0.004776, Avg Recall: 0.530769, count: 130 -Region Avg IOU: 0.465331, Class: 1.000000, Obj: 0.311610, No Obj: 0.004289, Avg Recall: 0.460177, count: 113 -Region Avg IOU: 0.471208, Class: 1.000000, Obj: 0.353436, No Obj: 0.004942, Avg Recall: 0.492064, count: 126 -Region Avg IOU: 0.498413, Class: 1.000000, Obj: 0.347362, No Obj: 0.004549, Avg Recall: 0.580952, count: 105 -Region Avg IOU: 0.502871, Class: 1.000000, Obj: 0.386556, No Obj: 0.005431, Avg Recall: 0.566667, count: 120 -Region Avg IOU: 0.465633, Class: 1.000000, Obj: 0.334229, No Obj: 0.004749, Avg Recall: 0.487180, count: 117 -Region Avg IOU: 0.509101, Class: 1.000000, Obj: 0.379658, No Obj: 0.004465, Avg Recall: 0.593220, count: 118 -4370: 0.447339, 0.461821 avg, 0.001000 rate, 20.100798 seconds, 1118720 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000066 seconds -Region Avg IOU: 0.583727, Class: 1.000000, Obj: 0.452846, No Obj: 0.004438, Avg Recall: 0.707692, count: 130 -Region Avg IOU: 0.557295, Class: 1.000000, Obj: 0.429616, No Obj: 0.004221, Avg Recall: 0.626866, count: 134 -Region Avg IOU: 0.563689, Class: 1.000000, Obj: 0.396308, No Obj: 0.003612, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.592239, Class: 1.000000, Obj: 0.433817, No Obj: 0.004960, Avg Recall: 0.707692, count: 130 -Region Avg IOU: 0.601171, Class: 1.000000, Obj: 0.396547, No Obj: 0.005118, Avg Recall: 0.680851, count: 141 -Region Avg IOU: 0.610359, Class: 1.000000, Obj: 0.415270, No Obj: 0.004962, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.550259, Class: 1.000000, Obj: 0.408241, No Obj: 0.004064, Avg Recall: 0.617886, count: 123 -Region Avg IOU: 0.605968, Class: 1.000000, Obj: 0.488674, No Obj: 0.004484, Avg Recall: 0.692308, count: 104 -4371: 0.493075, 0.464947 avg, 0.001000 rate, 25.508465 seconds, 1118976 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.625979, Class: 1.000000, Obj: 0.428363, No Obj: 0.004884, Avg Recall: 0.752000, count: 125 -Region Avg IOU: 0.591489, Class: 1.000000, Obj: 0.422902, No Obj: 0.003847, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.647920, Class: 1.000000, Obj: 0.500582, No Obj: 0.004268, Avg Recall: 0.813084, count: 107 -Region Avg IOU: 0.619831, Class: 1.000000, Obj: 0.450388, No Obj: 0.004136, Avg Recall: 0.756098, count: 82 -Region Avg IOU: 0.602066, Class: 1.000000, Obj: 0.380656, No Obj: 0.004327, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.594532, Class: 1.000000, Obj: 0.432788, No Obj: 0.003556, Avg Recall: 0.703297, count: 91 -Region Avg IOU: 0.625549, Class: 1.000000, Obj: 0.460072, No Obj: 0.005023, Avg Recall: 0.733813, count: 139 -Region Avg IOU: 0.627137, Class: 1.000000, Obj: 0.480643, No Obj: 0.004351, Avg Recall: 0.760684, count: 117 -4372: 0.419245, 0.460377 avg, 0.001000 rate, 24.199265 seconds, 1119232 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.612526, Class: 1.000000, Obj: 0.452207, No Obj: 0.003959, Avg Recall: 0.733333, count: 120 -Region Avg IOU: 0.589318, Class: 1.000000, Obj: 0.423996, No Obj: 0.003442, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.572135, Class: 1.000000, Obj: 0.394912, No Obj: 0.003536, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.592614, Class: 1.000000, Obj: 0.451363, No Obj: 0.003518, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.602674, Class: 1.000000, Obj: 0.444050, No Obj: 0.004683, Avg Recall: 0.739726, count: 146 -Region Avg IOU: 0.581179, Class: 1.000000, Obj: 0.448888, No Obj: 0.003798, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.607707, Class: 1.000000, Obj: 0.451964, No Obj: 0.004119, Avg Recall: 0.732283, count: 127 -Region Avg IOU: 0.576795, Class: 1.000000, Obj: 0.367442, No Obj: 0.003768, Avg Recall: 0.646465, count: 99 -4373: 0.448034, 0.459142 avg, 0.001000 rate, 24.694366 seconds, 1119488 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.600289, Class: 1.000000, Obj: 0.451344, No Obj: 0.003478, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.605884, Class: 1.000000, Obj: 0.416759, No Obj: 0.003700, Avg Recall: 0.708333, count: 120 -Region Avg IOU: 0.629166, Class: 1.000000, Obj: 0.457113, No Obj: 0.003727, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.624314, Class: 1.000000, Obj: 0.426809, No Obj: 0.003452, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.576263, Class: 1.000000, Obj: 0.392199, No Obj: 0.003368, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.587807, Class: 1.000000, Obj: 0.406091, No Obj: 0.003457, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.585283, Class: 1.000000, Obj: 0.429845, No Obj: 0.003622, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.598634, Class: 1.000000, Obj: 0.414156, No Obj: 0.003601, Avg Recall: 0.715909, count: 88 -4374: 0.433060, 0.456534 avg, 0.001000 rate, 25.264904 seconds, 1119744 images -Loaded: 0.000114 seconds -Region Avg IOU: 0.566400, Class: 1.000000, Obj: 0.390083, No Obj: 0.003217, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.558401, Class: 1.000000, Obj: 0.402978, No Obj: 0.003502, Avg Recall: 0.629032, count: 124 -Region Avg IOU: 0.600184, Class: 1.000000, Obj: 0.403863, No Obj: 0.003658, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.598387, Class: 1.000000, Obj: 0.362655, No Obj: 0.003670, Avg Recall: 0.720588, count: 136 -Region Avg IOU: 0.591577, Class: 1.000000, Obj: 0.415807, No Obj: 0.003645, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.587368, Class: 1.000000, Obj: 0.360070, No Obj: 0.004194, Avg Recall: 0.679389, count: 131 -Region Avg IOU: 0.601954, Class: 1.000000, Obj: 0.427297, No Obj: 0.004092, Avg Recall: 0.708661, count: 127 -Region Avg IOU: 0.585412, Class: 1.000000, Obj: 0.387017, No Obj: 0.003041, Avg Recall: 0.696629, count: 89 -4375: 0.458794, 0.456760 avg, 0.001000 rate, 26.105780 seconds, 1120000 images -Loaded: 0.000140 seconds -Region Avg IOU: 0.597264, Class: 1.000000, Obj: 0.406251, No Obj: 0.003058, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.596227, Class: 1.000000, Obj: 0.391590, No Obj: 0.003594, Avg Recall: 0.744186, count: 129 -Region Avg IOU: 0.566234, Class: 1.000000, Obj: 0.402354, No Obj: 0.003148, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.578367, Class: 1.000000, Obj: 0.339294, No Obj: 0.003515, Avg Recall: 0.684615, count: 130 -Region Avg IOU: 0.609569, Class: 1.000000, Obj: 0.369985, No Obj: 0.003136, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.594292, Class: 1.000000, Obj: 0.357659, No Obj: 0.003263, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.615646, Class: 1.000000, Obj: 0.379595, No Obj: 0.003468, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.591343, Class: 1.000000, Obj: 0.394142, No Obj: 0.003156, Avg Recall: 0.711111, count: 90 -4376: 0.415345, 0.452619 avg, 0.001000 rate, 24.381763 seconds, 1120256 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.601976, Class: 1.000000, Obj: 0.385429, No Obj: 0.002894, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.566545, Class: 1.000000, Obj: 0.372496, No Obj: 0.002713, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.602545, Class: 1.000000, Obj: 0.418939, No Obj: 0.002834, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.574275, Class: 1.000000, Obj: 0.370361, No Obj: 0.002973, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.589030, Class: 1.000000, Obj: 0.370443, No Obj: 0.003422, Avg Recall: 0.712121, count: 132 -Region Avg IOU: 0.575375, Class: 1.000000, Obj: 0.338692, No Obj: 0.003208, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.583550, Class: 1.000000, Obj: 0.321958, No Obj: 0.002870, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.563317, Class: 1.000000, Obj: 0.349911, No Obj: 0.002518, Avg Recall: 0.647727, count: 88 -4377: 0.413399, 0.448697 avg, 0.001000 rate, 25.508299 seconds, 1120512 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.605813, Class: 1.000000, Obj: 0.344475, No Obj: 0.002863, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.585444, Class: 1.000000, Obj: 0.343959, No Obj: 0.003561, Avg Recall: 0.671141, count: 149 -Region Avg IOU: 0.656810, Class: 1.000000, Obj: 0.345680, No Obj: 0.003292, Avg Recall: 0.861111, count: 108 -Region Avg IOU: 0.638412, Class: 1.000000, Obj: 0.375482, No Obj: 0.003035, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.626423, Class: 1.000000, Obj: 0.381599, No Obj: 0.002973, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.548104, Class: 1.000000, Obj: 0.346753, No Obj: 0.002755, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.617253, Class: 1.000000, Obj: 0.352696, No Obj: 0.002937, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.561684, Class: 1.000000, Obj: 0.347439, No Obj: 0.003057, Avg Recall: 0.617391, count: 115 -4378: 0.425296, 0.446357 avg, 0.001000 rate, 25.105200 seconds, 1120768 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.609675, Class: 1.000000, Obj: 0.369252, No Obj: 0.003193, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.617169, Class: 1.000000, Obj: 0.313312, No Obj: 0.002699, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.596449, Class: 1.000000, Obj: 0.376317, No Obj: 0.003227, Avg Recall: 0.710938, count: 128 -Region Avg IOU: 0.602357, Class: 1.000000, Obj: 0.346633, No Obj: 0.002994, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.638739, Class: 1.000000, Obj: 0.383866, No Obj: 0.003099, Avg Recall: 0.800000, count: 95 -Region Avg IOU: 0.610825, Class: 1.000000, Obj: 0.383654, No Obj: 0.002861, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.598637, Class: 1.000000, Obj: 0.348427, No Obj: 0.002875, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.610609, Class: 1.000000, Obj: 0.370736, No Obj: 0.002715, Avg Recall: 0.737864, count: 103 -4379: 0.412774, 0.442998 avg, 0.001000 rate, 24.685625 seconds, 1121024 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.612255, Class: 1.000000, Obj: 0.369426, No Obj: 0.002869, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.604992, Class: 1.000000, Obj: 0.326178, No Obj: 0.003337, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.596085, Class: 1.000000, Obj: 0.303116, No Obj: 0.003363, Avg Recall: 0.717949, count: 156 -Region Avg IOU: 0.584974, Class: 1.000000, Obj: 0.326058, No Obj: 0.003069, Avg Recall: 0.723881, count: 134 -Region Avg IOU: 0.599872, Class: 1.000000, Obj: 0.365066, No Obj: 0.003040, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.618344, Class: 1.000000, Obj: 0.374128, No Obj: 0.002559, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.668016, Class: 1.000000, Obj: 0.349709, No Obj: 0.002758, Avg Recall: 0.844156, count: 77 -Region Avg IOU: 0.611556, Class: 1.000000, Obj: 0.379938, No Obj: 0.003033, Avg Recall: 0.702128, count: 94 -4380: 0.443696, 0.443068 avg, 0.001000 rate, 25.241512 seconds, 1121280 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.621405 seconds -Region Avg IOU: 0.577613, Class: 1.000000, Obj: 0.316738, No Obj: 0.003796, Avg Recall: 0.666667, count: 129 -Region Avg IOU: 0.538701, Class: 1.000000, Obj: 0.295166, No Obj: 0.003251, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.506884, Class: 1.000000, Obj: 0.255201, No Obj: 0.003120, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.510837, Class: 1.000000, Obj: 0.307794, No Obj: 0.003264, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.557119, Class: 1.000000, Obj: 0.306086, No Obj: 0.003199, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.514241, Class: 1.000000, Obj: 0.296974, No Obj: 0.003347, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.551968, Class: 1.000000, Obj: 0.345348, No Obj: 0.003268, Avg Recall: 0.674419, count: 86 -Region Avg IOU: 0.511921, Class: 1.000000, Obj: 0.280223, No Obj: 0.002827, Avg Recall: 0.582524, count: 103 -4381: 0.403501, 0.439111 avg, 0.001000 rate, 19.791780 seconds, 1121536 images -Loaded: 0.361467 seconds -Region Avg IOU: 0.529885, Class: 1.000000, Obj: 0.313181, No Obj: 0.003575, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.515120, Class: 1.000000, Obj: 0.289008, No Obj: 0.003715, Avg Recall: 0.562500, count: 96 -Region Avg IOU: 0.479053, Class: 1.000000, Obj: 0.286251, No Obj: 0.003252, Avg Recall: 0.500000, count: 104 -Region Avg IOU: 0.481014, Class: 1.000000, Obj: 0.300021, No Obj: 0.003388, Avg Recall: 0.584000, count: 125 -Region Avg IOU: 0.521216, Class: 1.000000, Obj: 0.325929, No Obj: 0.003608, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.539988, Class: 1.000000, Obj: 0.332096, No Obj: 0.003936, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.554942, Class: 1.000000, Obj: 0.360725, No Obj: 0.003950, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.570468, Class: 1.000000, Obj: 0.363442, No Obj: 0.003438, Avg Recall: 0.659794, count: 97 -4382: 0.411048, 0.436305 avg, 0.001000 rate, 20.409424 seconds, 1121792 images -Loaded: 1.321570 seconds -Region Avg IOU: 0.535880, Class: 1.000000, Obj: 0.312371, No Obj: 0.004193, Avg Recall: 0.610169, count: 118 -Region Avg IOU: 0.526100, Class: 1.000000, Obj: 0.295389, No Obj: 0.004689, Avg Recall: 0.611570, count: 121 -Region Avg IOU: 0.540862, Class: 1.000000, Obj: 0.288693, No Obj: 0.003874, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.500962, Class: 1.000000, Obj: 0.320762, No Obj: 0.003529, Avg Recall: 0.568182, count: 88 -Region Avg IOU: 0.522552, Class: 1.000000, Obj: 0.305211, No Obj: 0.004203, Avg Recall: 0.602941, count: 136 -Region Avg IOU: 0.497548, Class: 1.000000, Obj: 0.330775, No Obj: 0.004361, Avg Recall: 0.521739, count: 115 -Region Avg IOU: 0.526828, Class: 1.000000, Obj: 0.333608, No Obj: 0.003864, Avg Recall: 0.617886, count: 123 -Region Avg IOU: 0.543063, Class: 1.000000, Obj: 0.333599, No Obj: 0.004725, Avg Recall: 0.631579, count: 133 -4383: 0.490937, 0.441768 avg, 0.001000 rate, 21.877028 seconds, 1122048 images -Loaded: 0.136773 seconds -Region Avg IOU: 0.515357, Class: 1.000000, Obj: 0.381873, No Obj: 0.003470, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.546940, Class: 1.000000, Obj: 0.367930, No Obj: 0.004349, Avg Recall: 0.593220, count: 118 -Region Avg IOU: 0.563633, Class: 1.000000, Obj: 0.350213, No Obj: 0.004607, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.502519, Class: 1.000000, Obj: 0.365488, No Obj: 0.004303, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.616986, Class: 1.000000, Obj: 0.404772, No Obj: 0.004129, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.503761, Class: 1.000000, Obj: 0.312269, No Obj: 0.004295, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.521388, Class: 1.000000, Obj: 0.347850, No Obj: 0.004488, Avg Recall: 0.589744, count: 117 -Region Avg IOU: 0.535116, Class: 1.000000, Obj: 0.345000, No Obj: 0.004399, Avg Recall: 0.595041, count: 121 -4384: 0.442566, 0.441848 avg, 0.001000 rate, 19.469757 seconds, 1122304 images -Loaded: 2.280633 seconds -Region Avg IOU: 0.487947, Class: 1.000000, Obj: 0.336886, No Obj: 0.003988, Avg Recall: 0.524590, count: 122 -Region Avg IOU: 0.575940, Class: 1.000000, Obj: 0.396424, No Obj: 0.004715, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.560946, Class: 1.000000, Obj: 0.397297, No Obj: 0.004663, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.449472, Class: 1.000000, Obj: 0.348395, No Obj: 0.004026, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.492643, Class: 1.000000, Obj: 0.321480, No Obj: 0.004443, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.552883, Class: 1.000000, Obj: 0.392662, No Obj: 0.004665, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.505980, Class: 1.000000, Obj: 0.360975, No Obj: 0.004356, Avg Recall: 0.532787, count: 122 -Region Avg IOU: 0.485020, Class: 1.000000, Obj: 0.387526, No Obj: 0.004455, Avg Recall: 0.572917, count: 96 -4385: 0.421356, 0.439799 avg, 0.001000 rate, 20.655500 seconds, 1122560 images -Loaded: 2.643717 seconds -Region Avg IOU: 0.510149, Class: 1.000000, Obj: 0.310160, No Obj: 0.004731, Avg Recall: 0.565891, count: 129 -Region Avg IOU: 0.497121, Class: 1.000000, Obj: 0.328759, No Obj: 0.004920, Avg Recall: 0.571429, count: 140 -Region Avg IOU: 0.552841, Class: 1.000000, Obj: 0.371558, No Obj: 0.004953, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.492989, Class: 1.000000, Obj: 0.360235, No Obj: 0.005050, Avg Recall: 0.512000, count: 125 -Region Avg IOU: 0.548711, Class: 1.000000, Obj: 0.361413, No Obj: 0.005345, Avg Recall: 0.601562, count: 128 -Region Avg IOU: 0.479479, Class: 1.000000, Obj: 0.391593, No Obj: 0.004338, Avg Recall: 0.520833, count: 96 -Region Avg IOU: 0.550605, Class: 1.000000, Obj: 0.366612, No Obj: 0.004409, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.550269, Class: 1.000000, Obj: 0.451023, No Obj: 0.004290, Avg Recall: 0.611765, count: 85 -4386: 0.540068, 0.449826 avg, 0.001000 rate, 21.301731 seconds, 1122816 images -Loaded: 0.063100 seconds -Region Avg IOU: 0.553228, Class: 1.000000, Obj: 0.403759, No Obj: 0.005855, Avg Recall: 0.666667, count: 123 -Region Avg IOU: 0.555878, Class: 1.000000, Obj: 0.393260, No Obj: 0.005059, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.523203, Class: 1.000000, Obj: 0.421942, No Obj: 0.004134, Avg Recall: 0.619718, count: 71 -Region Avg IOU: 0.513183, Class: 1.000000, Obj: 0.395526, No Obj: 0.004611, Avg Recall: 0.554348, count: 92 -Region Avg IOU: 0.549011, Class: 1.000000, Obj: 0.407665, No Obj: 0.004644, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.526142, Class: 1.000000, Obj: 0.380104, No Obj: 0.005693, Avg Recall: 0.604167, count: 144 -Region Avg IOU: 0.495470, Class: 1.000000, Obj: 0.420235, No Obj: 0.005036, Avg Recall: 0.570175, count: 114 -Region Avg IOU: 0.516737, Class: 1.000000, Obj: 0.423166, No Obj: 0.004946, Avg Recall: 0.584071, count: 113 -4387: 0.410584, 0.445901 avg, 0.001000 rate, 18.206415 seconds, 1123072 images -Loaded: 1.437853 seconds -Region Avg IOU: 0.536056, Class: 1.000000, Obj: 0.374095, No Obj: 0.004832, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.532721, Class: 1.000000, Obj: 0.386783, No Obj: 0.004051, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.511151, Class: 1.000000, Obj: 0.415758, No Obj: 0.004901, Avg Recall: 0.528090, count: 89 -Region Avg IOU: 0.551055, Class: 1.000000, Obj: 0.459399, No Obj: 0.006241, Avg Recall: 0.619048, count: 126 -Region Avg IOU: 0.544586, Class: 1.000000, Obj: 0.443379, No Obj: 0.004448, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.540743, Class: 1.000000, Obj: 0.398465, No Obj: 0.004674, Avg Recall: 0.552083, count: 96 -Region Avg IOU: 0.567237, Class: 1.000000, Obj: 0.456600, No Obj: 0.005513, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.504016, Class: 1.000000, Obj: 0.402433, No Obj: 0.003984, Avg Recall: 0.578313, count: 83 -4388: 0.379735, 0.439285 avg, 0.001000 rate, 19.941742 seconds, 1123328 images -Loaded: 1.853205 seconds -Region Avg IOU: 0.541766, Class: 1.000000, Obj: 0.425833, No Obj: 0.004653, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.483477, Class: 1.000000, Obj: 0.380071, No Obj: 0.004228, Avg Recall: 0.544643, count: 112 -Region Avg IOU: 0.551747, Class: 1.000000, Obj: 0.446432, No Obj: 0.005292, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.516883, Class: 1.000000, Obj: 0.401679, No Obj: 0.004413, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.527363, Class: 1.000000, Obj: 0.423745, No Obj: 0.004686, Avg Recall: 0.605769, count: 104 -Region Avg IOU: 0.531979, Class: 1.000000, Obj: 0.392040, No Obj: 0.004723, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.483545, Class: 1.000000, Obj: 0.398518, No Obj: 0.004152, Avg Recall: 0.557522, count: 113 -Region Avg IOU: 0.500786, Class: 1.000000, Obj: 0.395021, No Obj: 0.004712, Avg Recall: 0.563830, count: 94 -4389: 0.434538, 0.438810 avg, 0.001000 rate, 19.533007 seconds, 1123584 images -Loaded: 0.621445 seconds -Region Avg IOU: 0.542444, Class: 1.000000, Obj: 0.390812, No Obj: 0.004599, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.523972, Class: 1.000000, Obj: 0.364101, No Obj: 0.004955, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.537256, Class: 1.000000, Obj: 0.384596, No Obj: 0.003920, Avg Recall: 0.585366, count: 82 -Region Avg IOU: 0.529188, Class: 1.000000, Obj: 0.410295, No Obj: 0.004622, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.493109, Class: 1.000000, Obj: 0.409241, No Obj: 0.004369, Avg Recall: 0.538462, count: 104 -Region Avg IOU: 0.492631, Class: 1.000000, Obj: 0.367480, No Obj: 0.003900, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.492011, Class: 1.000000, Obj: 0.328551, No Obj: 0.003598, Avg Recall: 0.541176, count: 85 -Region Avg IOU: 0.529713, Class: 1.000000, Obj: 0.409211, No Obj: 0.004603, Avg Recall: 0.590000, count: 100 -4390: 0.381972, 0.433126 avg, 0.001000 rate, 17.757671 seconds, 1123840 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000080 seconds -Region Avg IOU: 0.577664, Class: 1.000000, Obj: 0.424826, No Obj: 0.003907, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.580952, Class: 1.000000, Obj: 0.460752, No Obj: 0.004129, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.522525, Class: 1.000000, Obj: 0.387435, No Obj: 0.003863, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.552932, Class: 1.000000, Obj: 0.450082, No Obj: 0.003574, Avg Recall: 0.654762, count: 84 -Region Avg IOU: 0.601954, Class: 1.000000, Obj: 0.461439, No Obj: 0.004535, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.571357, Class: 1.000000, Obj: 0.480495, No Obj: 0.003712, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.557651, Class: 1.000000, Obj: 0.466271, No Obj: 0.004026, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.547540, Class: 1.000000, Obj: 0.396798, No Obj: 0.004777, Avg Recall: 0.637681, count: 138 -4391: 0.449833, 0.434797 avg, 0.001000 rate, 23.963276 seconds, 1124096 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.549764, Class: 1.000000, Obj: 0.407171, No Obj: 0.003887, Avg Recall: 0.655738, count: 122 -Region Avg IOU: 0.605628, Class: 1.000000, Obj: 0.432068, No Obj: 0.004142, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.582337, Class: 1.000000, Obj: 0.470847, No Obj: 0.004061, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.537792, Class: 1.000000, Obj: 0.407337, No Obj: 0.004123, Avg Recall: 0.623188, count: 138 -Region Avg IOU: 0.564082, Class: 1.000000, Obj: 0.428996, No Obj: 0.004157, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.607080, Class: 1.000000, Obj: 0.383223, No Obj: 0.004061, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.594275, Class: 1.000000, Obj: 0.407053, No Obj: 0.004205, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.492743, Class: 1.000000, Obj: 0.371188, No Obj: 0.003722, Avg Recall: 0.537879, count: 132 -4392: 0.449109, 0.436228 avg, 0.001000 rate, 24.116890 seconds, 1124352 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.559230, Class: 1.000000, Obj: 0.381893, No Obj: 0.004095, Avg Recall: 0.640288, count: 139 -Region Avg IOU: 0.560506, Class: 1.000000, Obj: 0.405661, No Obj: 0.003660, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.597702, Class: 1.000000, Obj: 0.389694, No Obj: 0.003419, Avg Recall: 0.703297, count: 91 -Region Avg IOU: 0.568706, Class: 1.000000, Obj: 0.408399, No Obj: 0.004061, Avg Recall: 0.679688, count: 128 -Region Avg IOU: 0.595528, Class: 1.000000, Obj: 0.426528, No Obj: 0.004246, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.519337, Class: 1.000000, Obj: 0.411466, No Obj: 0.003334, Avg Recall: 0.586777, count: 121 -Region Avg IOU: 0.573166, Class: 1.000000, Obj: 0.387812, No Obj: 0.003313, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.531477, Class: 1.000000, Obj: 0.329087, No Obj: 0.004099, Avg Recall: 0.627586, count: 145 -4393: 0.467125, 0.439318 avg, 0.001000 rate, 24.684996 seconds, 1124608 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.547139, Class: 1.000000, Obj: 0.368526, No Obj: 0.003497, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.607268, Class: 1.000000, Obj: 0.379921, No Obj: 0.004252, Avg Recall: 0.736434, count: 129 -Region Avg IOU: 0.625522, Class: 1.000000, Obj: 0.405144, No Obj: 0.003736, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.590287, Class: 1.000000, Obj: 0.399349, No Obj: 0.003713, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.611314, Class: 1.000000, Obj: 0.381368, No Obj: 0.003808, Avg Recall: 0.698276, count: 116 -Region Avg IOU: 0.562767, Class: 1.000000, Obj: 0.388567, No Obj: 0.003474, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.590414, Class: 1.000000, Obj: 0.397510, No Obj: 0.003247, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.566245, Class: 1.000000, Obj: 0.353477, No Obj: 0.003171, Avg Recall: 0.661417, count: 127 -4394: 0.434811, 0.438867 avg, 0.001000 rate, 23.976849 seconds, 1124864 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.527458, Class: 1.000000, Obj: 0.315578, No Obj: 0.003551, Avg Recall: 0.616541, count: 133 -Region Avg IOU: 0.514365, Class: 1.000000, Obj: 0.316332, No Obj: 0.002837, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.553786, Class: 1.000000, Obj: 0.346242, No Obj: 0.003253, Avg Recall: 0.608333, count: 120 -Region Avg IOU: 0.588157, Class: 1.000000, Obj: 0.355050, No Obj: 0.003470, Avg Recall: 0.695312, count: 128 -Region Avg IOU: 0.625560, Class: 1.000000, Obj: 0.354724, No Obj: 0.003412, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.580594, Class: 1.000000, Obj: 0.367084, No Obj: 0.003802, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.517797, Class: 1.000000, Obj: 0.292935, No Obj: 0.003218, Avg Recall: 0.583942, count: 137 -Region Avg IOU: 0.569648, Class: 1.000000, Obj: 0.378452, No Obj: 0.003052, Avg Recall: 0.666667, count: 126 -4395: 0.568071, 0.451788 avg, 0.001000 rate, 24.961296 seconds, 1125120 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.568173, Class: 1.000000, Obj: 0.316632, No Obj: 0.003072, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.602947, Class: 1.000000, Obj: 0.325696, No Obj: 0.003074, Avg Recall: 0.743802, count: 121 -Region Avg IOU: 0.599272, Class: 1.000000, Obj: 0.368623, No Obj: 0.003256, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.598085, Class: 1.000000, Obj: 0.367356, No Obj: 0.002815, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.601683, Class: 1.000000, Obj: 0.343778, No Obj: 0.002873, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.603641, Class: 1.000000, Obj: 0.359899, No Obj: 0.003431, Avg Recall: 0.720000, count: 125 -Region Avg IOU: 0.539807, Class: 1.000000, Obj: 0.305465, No Obj: 0.003499, Avg Recall: 0.615942, count: 138 -Region Avg IOU: 0.619935, Class: 1.000000, Obj: 0.344930, No Obj: 0.003272, Avg Recall: 0.769231, count: 91 -4396: 0.447900, 0.451399 avg, 0.001000 rate, 24.807577 seconds, 1125376 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.589582, Class: 1.000000, Obj: 0.377988, No Obj: 0.002831, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.529539, Class: 1.000000, Obj: 0.310105, No Obj: 0.003031, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.589377, Class: 1.000000, Obj: 0.367723, No Obj: 0.002845, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.583321, Class: 1.000000, Obj: 0.317377, No Obj: 0.002870, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.594203, Class: 1.000000, Obj: 0.372866, No Obj: 0.003364, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.507710, Class: 1.000000, Obj: 0.304125, No Obj: 0.002593, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.580247, Class: 1.000000, Obj: 0.282883, No Obj: 0.002515, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.572624, Class: 1.000000, Obj: 0.324187, No Obj: 0.003159, Avg Recall: 0.661654, count: 133 -4397: 0.433060, 0.449565 avg, 0.001000 rate, 23.652956 seconds, 1125632 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.621487, Class: 1.000000, Obj: 0.371890, No Obj: 0.003161, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.548214, Class: 1.000000, Obj: 0.329161, No Obj: 0.002841, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.588549, Class: 1.000000, Obj: 0.368552, No Obj: 0.003060, Avg Recall: 0.662651, count: 83 -Region Avg IOU: 0.612332, Class: 1.000000, Obj: 0.396926, No Obj: 0.002500, Avg Recall: 0.762500, count: 80 -Region Avg IOU: 0.570437, Class: 1.000000, Obj: 0.292793, No Obj: 0.002937, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.573577, Class: 1.000000, Obj: 0.347676, No Obj: 0.002800, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.550506, Class: 1.000000, Obj: 0.346713, No Obj: 0.003167, Avg Recall: 0.648352, count: 91 -Region Avg IOU: 0.602135, Class: 1.000000, Obj: 0.316071, No Obj: 0.002702, Avg Recall: 0.738095, count: 84 -4398: 0.398520, 0.444460 avg, 0.001000 rate, 24.446268 seconds, 1125888 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.657614, Class: 1.000000, Obj: 0.398973, No Obj: 0.002971, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.575478, Class: 1.000000, Obj: 0.350466, No Obj: 0.002875, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.578566, Class: 1.000000, Obj: 0.347035, No Obj: 0.003001, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.575317, Class: 1.000000, Obj: 0.309587, No Obj: 0.003198, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.605380, Class: 1.000000, Obj: 0.390912, No Obj: 0.002843, Avg Recall: 0.705882, count: 85 -Region Avg IOU: 0.570362, Class: 1.000000, Obj: 0.326607, No Obj: 0.003068, Avg Recall: 0.689076, count: 119 -Region Avg IOU: 0.573784, Class: 1.000000, Obj: 0.299969, No Obj: 0.003355, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.540946, Class: 1.000000, Obj: 0.320497, No Obj: 0.00Saving weights to ./person9300/backup/tyvp_93_9c512_4400.weights -2530, Avg Recall: 0.620690, count: 87 -4399: 0.421868, 0.442201 avg, 0.001000 rate, 24.199497 seconds, 1126144 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.614514, Class: 1.000000, Obj: 0.382269, No Obj: 0.003419, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.560868, Class: 1.000000, Obj: 0.354167, No Obj: 0.003149, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.576650, Class: 1.000000, Obj: 0.374284, No Obj: 0.003364, Avg Recall: 0.652542, count: 118 -Region Avg IOU: 0.583351, Class: 1.000000, Obj: 0.396383, No Obj: 0.003114, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.548923, Class: 1.000000, Obj: 0.302596, No Obj: 0.003104, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.600213, Class: 1.000000, Obj: 0.368470, No Obj: 0.003191, Avg Recall: 0.707865, count: 89 -Region Avg IOU: 0.615733, Class: 1.000000, Obj: 0.354986, No Obj: 0.003112, Avg Recall: 0.764706, count: 85 -Region Avg IOU: 0.595594, Class: 1.000000, Obj: 0.341763, No Obj: 0.003440, Avg Recall: 0.720000, count: 125 -4400: 0.437999, 0.441781 avg, 0.001000 rate, 23.985834 seconds, 1126400 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 20.521852 seconds -Region Avg IOU: 0.573267, Class: 1.000000, Obj: 0.326017, No Obj: 0.003992, Avg Recall: 0.700000, count: 130 -Region Avg IOU: 0.564829, Class: 1.000000, Obj: 0.347746, No Obj: 0.003803, Avg Recall: 0.627907, count: 86 -Region Avg IOU: 0.493789, Class: 1.000000, Obj: 0.302148, No Obj: 0.003383, Avg Recall: 0.576000, count: 125 -Region Avg IOU: 0.536595, Class: 1.000000, Obj: 0.304757, No Obj: 0.004335, Avg Recall: 0.605839, count: 137 -Region Avg IOU: 0.560784, Class: 1.000000, Obj: 0.384292, No Obj: 0.003282, Avg Recall: 0.645570, count: 79 -Region Avg IOU: 0.577290, Class: 1.000000, Obj: 0.356612, No Obj: 0.003606, Avg Recall: 0.632911, count: 79 -Region Avg IOU: 0.532825, Class: 1.000000, Obj: 0.323602, No Obj: 0.004156, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.562948, Class: 1.000000, Obj: 0.333041, No Obj: 0.004632, Avg Recall: 0.689655, count: 116 -4401: 0.463288, 0.443932 avg, 0.001000 rate, 20.773165 seconds, 1126656 images -Loaded: 2.259130 seconds -Region Avg IOU: 0.512271, Class: 1.000000, Obj: 0.340178, No Obj: 0.003554, Avg Recall: 0.557522, count: 113 -Region Avg IOU: 0.504451, Class: 1.000000, Obj: 0.315341, No Obj: 0.003060, Avg Recall: 0.571429, count: 112 -Region Avg IOU: 0.529315, Class: 1.000000, Obj: 0.297046, No Obj: 0.003665, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.544259, Class: 1.000000, Obj: 0.360531, No Obj: 0.004272, Avg Recall: 0.630252, count: 119 -Region Avg IOU: 0.510799, Class: 1.000000, Obj: 0.322780, No Obj: 0.004070, Avg Recall: 0.566929, count: 127 -Region Avg IOU: 0.515462, Class: 1.000000, Obj: 0.268265, No Obj: 0.004139, Avg Recall: 0.601626, count: 123 -Region Avg IOU: 0.492624, Class: 1.000000, Obj: 0.317439, No Obj: 0.003907, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.561002, Class: 1.000000, Obj: 0.355889, No Obj: 0.004243, Avg Recall: 0.655462, count: 119 -4402: 0.551450, 0.454683 avg, 0.001000 rate, 19.930819 seconds, 1126912 images -Loaded: 1.267716 seconds -Region Avg IOU: 0.472703, Class: 1.000000, Obj: 0.303355, No Obj: 0.003951, Avg Recall: 0.533333, count: 120 -Region Avg IOU: 0.491524, Class: 1.000000, Obj: 0.351253, No Obj: 0.003945, Avg Recall: 0.508621, count: 116 -Region Avg IOU: 0.518014, Class: 1.000000, Obj: 0.352756, No Obj: 0.004000, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.509015, Class: 1.000000, Obj: 0.344648, No Obj: 0.003950, Avg Recall: 0.553571, count: 112 -Region Avg IOU: 0.560392, Class: 1.000000, Obj: 0.376997, No Obj: 0.004251, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.546426, Class: 1.000000, Obj: 0.386968, No Obj: 0.004062, Avg Recall: 0.540816, count: 98 -Region Avg IOU: 0.490064, Class: 1.000000, Obj: 0.346560, No Obj: 0.004444, Avg Recall: 0.511628, count: 129 -Region Avg IOU: 0.524034, Class: 1.000000, Obj: 0.360984, No Obj: 0.004291, Avg Recall: 0.550847, count: 118 -4403: 0.488272, 0.458042 avg, 0.001000 rate, 22.245836 seconds, 1127168 images -Loaded: 0.573705 seconds -Region Avg IOU: 0.555045, Class: 1.000000, Obj: 0.380375, No Obj: 0.004676, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.582772, Class: 1.000000, Obj: 0.393161, No Obj: 0.004904, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.500376, Class: 1.000000, Obj: 0.371819, No Obj: 0.004569, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.549560, Class: 1.000000, Obj: 0.381729, No Obj: 0.005064, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.555578, Class: 1.000000, Obj: 0.403430, No Obj: 0.004944, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.564512, Class: 1.000000, Obj: 0.395151, No Obj: 0.005351, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.495916, Class: 1.000000, Obj: 0.357123, No Obj: 0.004710, Avg Recall: 0.558824, count: 136 -Region Avg IOU: 0.484827, Class: 1.000000, Obj: 0.300563, No Obj: 0.004527, Avg Recall: 0.551181, count: 127 -4404: 0.480592, 0.460297 avg, 0.001000 rate, 20.670732 seconds, 1127424 images -Loaded: 1.654688 seconds -Region Avg IOU: 0.513600, Class: 1.000000, Obj: 0.395283, No Obj: 0.004983, Avg Recall: 0.573770, count: 122 -Region Avg IOU: 0.519396, Class: 1.000000, Obj: 0.357766, No Obj: 0.005047, Avg Recall: 0.567164, count: 134 -Region Avg IOU: 0.469045, Class: 1.000000, Obj: 0.354046, No Obj: 0.004655, Avg Recall: 0.557522, count: 113 -Region Avg IOU: 0.509640, Class: 1.000000, Obj: 0.365603, No Obj: 0.004186, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.528040, Class: 1.000000, Obj: 0.341822, No Obj: 0.004935, Avg Recall: 0.575000, count: 120 -Region Avg IOU: 0.571621, Class: 1.000000, Obj: 0.436165, No Obj: 0.004306, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.495750, Class: 1.000000, Obj: 0.365870, No Obj: 0.004340, Avg Recall: 0.538462, count: 104 -Region Avg IOU: 0.487586, Class: 1.000000, Obj: 0.363515, No Obj: 0.004369, Avg Recall: 0.543689, count: 103 -4405: 0.438211, 0.458089 avg, 0.001000 rate, 22.556454 seconds, 1127680 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.515271, Class: 1.000000, Obj: 0.385455, No Obj: 0.005237, Avg Recall: 0.569231, count: 130 -Region Avg IOU: 0.532948, Class: 1.000000, Obj: 0.392408, No Obj: 0.005047, Avg Recall: 0.612903, count: 124 -Region Avg IOU: 0.529952, Class: 1.000000, Obj: 0.417407, No Obj: 0.005197, Avg Recall: 0.641221, count: 131 -Region Avg IOU: 0.527520, Class: 1.000000, Obj: 0.399670, No Obj: 0.004649, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.514167, Class: 1.000000, Obj: 0.364310, No Obj: 0.005868, Avg Recall: 0.572519, count: 131 -Region Avg IOU: 0.539945, Class: 1.000000, Obj: 0.427392, No Obj: 0.005038, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.541818, Class: 1.000000, Obj: 0.412448, No Obj: 0.005003, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.491402, Class: 1.000000, Obj: 0.372949, No Obj: 0.004727, Avg Recall: 0.536000, count: 125 -4406: 0.447495, 0.457029 avg, 0.001000 rate, 21.936617 seconds, 1127936 images -Loaded: 0.670378 seconds -Region Avg IOU: 0.500394, Class: 1.000000, Obj: 0.412446, No Obj: 0.004719, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.533300, Class: 1.000000, Obj: 0.398061, No Obj: 0.005543, Avg Recall: 0.585366, count: 123 -Region Avg IOU: 0.504944, Class: 1.000000, Obj: 0.398991, No Obj: 0.004602, Avg Recall: 0.578512, count: 121 -Region Avg IOU: 0.434356, Class: 1.000000, Obj: 0.358171, No Obj: 0.004505, Avg Recall: 0.525180, count: 139 -Region Avg IOU: 0.602187, Class: 1.000000, Obj: 0.463897, No Obj: 0.004897, Avg Recall: 0.780488, count: 82 -Region Avg IOU: 0.484016, Class: 1.000000, Obj: 0.388916, No Obj: 0.005296, Avg Recall: 0.536232, count: 138 -Region Avg IOU: 0.502098, Class: 1.000000, Obj: 0.436251, No Obj: 0.005066, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.562438, Class: 1.000000, Obj: 0.378914, No Obj: 0.005245, Avg Recall: 0.657407, count: 108 -4407: 0.442762, 0.455603 avg, 0.001000 rate, 21.398853 seconds, 1128192 images -Loaded: 0.849264 seconds -Region Avg IOU: 0.517088, Class: 1.000000, Obj: 0.420815, No Obj: 0.004223, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.537434, Class: 1.000000, Obj: 0.394549, No Obj: 0.005241, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.492956, Class: 1.000000, Obj: 0.383366, No Obj: 0.004937, Avg Recall: 0.525424, count: 118 -Region Avg IOU: 0.528282, Class: 1.000000, Obj: 0.398015, No Obj: 0.004954, Avg Recall: 0.592593, count: 108 -Region Avg IOU: 0.524491, Class: 1.000000, Obj: 0.378806, No Obj: 0.004440, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.512714, Class: 1.000000, Obj: 0.440930, No Obj: 0.004522, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.548584, Class: 1.000000, Obj: 0.388584, No Obj: 0.006105, Avg Recall: 0.607692, count: 130 -Region Avg IOU: 0.534172, Class: 1.000000, Obj: 0.397065, No Obj: 0.005409, Avg Recall: 0.595238, count: 126 -4408: 0.462857, 0.456328 avg, 0.001000 rate, 20.880596 seconds, 1128448 images -Loaded: 0.387609 seconds -Region Avg IOU: 0.537297, Class: 1.000000, Obj: 0.441355, No Obj: 0.005524, Avg Recall: 0.636364, count: 132 -Region Avg IOU: 0.485259, Class: 1.000000, Obj: 0.333111, No Obj: 0.004627, Avg Recall: 0.508197, count: 122 -Region Avg IOU: 0.541931, Class: 1.000000, Obj: 0.410163, No Obj: 0.004804, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.538734, Class: 1.000000, Obj: 0.382057, No Obj: 0.003989, Avg Recall: 0.654762, count: 84 -Region Avg IOU: 0.490125, Class: 1.000000, Obj: 0.411193, No Obj: 0.004492, Avg Recall: 0.549550, count: 111 -Region Avg IOU: 0.533684, Class: 1.000000, Obj: 0.349902, No Obj: 0.005369, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.515891, Class: 1.000000, Obj: 0.364284, No Obj: 0.004139, Avg Recall: 0.597938, count: 97 -Region Avg IOU: 0.524547, Class: 1.000000, Obj: 0.441376, No Obj: 0.004152, Avg Recall: 0.597826, count: 92 -4409: 0.425713, 0.453267 avg, 0.001000 rate, 19.563622 seconds, 1128704 images -Loaded: 1.364967 seconds -Region Avg IOU: 0.579628, Class: 1.000000, Obj: 0.376718, No Obj: 0.004588, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.466600, Class: 1.000000, Obj: 0.364570, No Obj: 0.005044, Avg Recall: 0.511111, count: 135 -Region Avg IOU: 0.541632, Class: 1.000000, Obj: 0.367782, No Obj: 0.005399, Avg Recall: 0.629032, count: 124 -Region Avg IOU: 0.513347, Class: 1.000000, Obj: 0.377463, No Obj: 0.004425, Avg Recall: 0.586777, count: 121 -Region Avg IOU: 0.490419, Class: 1.000000, Obj: 0.345140, No Obj: 0.004326, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.516836, Class: 1.000000, Obj: 0.326928, No Obj: 0.004638, Avg Recall: 0.597015, count: 134 -Region Avg IOU: 0.526465, Class: 1.000000, Obj: 0.400937, No Obj: 0.004678, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.572988, Class: 1.000000, Obj: 0.401929, No Obj: 0.004630, Avg Recall: 0.730769, count: 104 -4410: 0.451952, 0.453135 avg, 0.001000 rate, 22.379820 seconds, 1128960 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000073 seconds -Region Avg IOU: 0.669158, Class: 1.000000, Obj: 0.418231, No Obj: 0.003412, Avg Recall: 0.836538, count: 104 -Region Avg IOU: 0.643402, Class: 1.000000, Obj: 0.412733, No Obj: 0.003142, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.645891, Class: 1.000000, Obj: 0.489650, No Obj: 0.003603, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.619882, Class: 1.000000, Obj: 0.441201, No Obj: 0.002921, Avg Recall: 0.739583, count: 96 -Region Avg IOU: 0.631674, Class: 1.000000, Obj: 0.429140, No Obj: 0.003784, Avg Recall: 0.762712, count: 118 -Region Avg IOU: 0.611650, Class: 1.000000, Obj: 0.454129, No Obj: 0.003162, Avg Recall: 0.758621, count: 87 -Region Avg IOU: 0.610687, Class: 1.000000, Obj: 0.405354, No Obj: 0.003737, Avg Recall: 0.730496, count: 141 -Region Avg IOU: 0.642497, Class: 1.000000, Obj: 0.432337, No Obj: 0.002997, Avg Recall: 0.811111, count: 90 -4411: 0.440014, 0.451823 avg, 0.001000 rate, 26.815844 seconds, 1129216 images -Loaded: 0.000126 seconds -Region Avg IOU: 0.666987, Class: 1.000000, Obj: 0.428682, No Obj: 0.003244, Avg Recall: 0.826923, count: 104 -Region Avg IOU: 0.721840, Class: 1.000000, Obj: 0.468272, No Obj: 0.003106, Avg Recall: 0.905405, count: 74 -Region Avg IOU: 0.605477, Class: 1.000000, Obj: 0.387466, No Obj: 0.003179, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.640220, Class: 1.000000, Obj: 0.344739, No Obj: 0.002889, Avg Recall: 0.817073, count: 82 -Region Avg IOU: 0.653571, Class: 1.000000, Obj: 0.463901, No Obj: 0.003089, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.591045, Class: 1.000000, Obj: 0.411080, No Obj: 0.002946, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.600677, Class: 1.000000, Obj: 0.362536, No Obj: 0.002424, Avg Recall: 0.700000, count: 70 -Region Avg IOU: 0.691955, Class: 1.000000, Obj: 0.513556, No Obj: 0.003355, Avg Recall: 0.871560, count: 109 -4412: 0.449219, 0.451563 avg, 0.001000 rate, 29.274427 seconds, 1129472 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.640167, Class: 1.000000, Obj: 0.409421, No Obj: 0.002686, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.633377, Class: 1.000000, Obj: 0.392536, No Obj: 0.003088, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.646076, Class: 1.000000, Obj: 0.391693, No Obj: 0.003113, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.658746, Class: 1.000000, Obj: 0.420480, No Obj: 0.002997, Avg Recall: 0.824561, count: 114 -Region Avg IOU: 0.642197, Class: 1.000000, Obj: 0.404779, No Obj: 0.003132, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.605546, Class: 1.000000, Obj: 0.328222, No Obj: 0.003011, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.619844, Class: 1.000000, Obj: 0.373117, No Obj: 0.003144, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.658883, Class: 1.000000, Obj: 0.397138, No Obj: 0.003348, Avg Recall: 0.818898, count: 127 -4413: 0.489122, 0.455319 avg, 0.001000 rate, 28.029251 seconds, 1129728 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.634056, Class: 1.000000, Obj: 0.347400, No Obj: 0.002828, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.653164, Class: 1.000000, Obj: 0.328815, No Obj: 0.002657, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.625371, Class: 1.000000, Obj: 0.389098, No Obj: 0.002418, Avg Recall: 0.738095, count: 84 -Region Avg IOU: 0.640860, Class: 1.000000, Obj: 0.333098, No Obj: 0.002966, Avg Recall: 0.789474, count: 133 -Region Avg IOU: 0.614866, Class: 1.000000, Obj: 0.389170, No Obj: 0.002561, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.649508, Class: 1.000000, Obj: 0.348818, No Obj: 0.002909, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.652458, Class: 1.000000, Obj: 0.343220, No Obj: 0.002838, Avg Recall: 0.841121, count: 107 -Region Avg IOU: 0.656028, Class: 1.000000, Obj: 0.392509, No Obj: 0.002587, Avg Recall: 0.813726, count: 102 -4414: 0.433077, 0.453094 avg, 0.001000 rate, 28.381563 seconds, 1129984 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.609485, Class: 1.000000, Obj: 0.347931, No Obj: 0.002430, Avg Recall: 0.752137, count: 117 -Region Avg IOU: 0.664601, Class: 1.000000, Obj: 0.363945, No Obj: 0.002545, Avg Recall: 0.823009, count: 113 -Region Avg IOU: 0.618104, Class: 1.000000, Obj: 0.383543, No Obj: 0.002492, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.604208, Class: 1.000000, Obj: 0.325116, No Obj: 0.002177, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.635115, Class: 1.000000, Obj: 0.351476, No Obj: 0.002441, Avg Recall: 0.794643, count: 112 -Region Avg IOU: 0.639458, Class: 1.000000, Obj: 0.327413, No Obj: 0.002565, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.686816, Class: 1.000000, Obj: 0.304106, No Obj: 0.002586, Avg Recall: 0.865079, count: 126 -Region Avg IOU: 0.679413, Class: 1.000000, Obj: 0.351748, No Obj: 0.002339, Avg Recall: 0.873786, count: 103 -4415: 0.473121, 0.455097 avg, 0.001000 rate, 28.822182 seconds, 1130240 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.691948, Class: 1.000000, Obj: 0.340129, No Obj: 0.002349, Avg Recall: 0.870000, count: 100 -Region Avg IOU: 0.642492, Class: 1.000000, Obj: 0.347341, No Obj: 0.002001, Avg Recall: 0.788235, count: 85 -Region Avg IOU: 0.669091, Class: 1.000000, Obj: 0.359732, No Obj: 0.002145, Avg Recall: 0.831776, count: 107 -Region Avg IOU: 0.630261, Class: 1.000000, Obj: 0.348892, No Obj: 0.002219, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.640915, Class: 1.000000, Obj: 0.283251, No Obj: 0.002495, Avg Recall: 0.804878, count: 123 -Region Avg IOU: 0.669675, Class: 1.000000, Obj: 0.342921, No Obj: 0.002226, Avg Recall: 0.826531, count: 98 -Region Avg IOU: 0.628810, Class: 1.000000, Obj: 0.316271, No Obj: 0.002418, Avg Recall: 0.743802, count: 121 -Region Avg IOU: 0.644774, Class: 1.000000, Obj: 0.303647, No Obj: 0.002115, Avg Recall: 0.790476, count: 105 -4416: 0.470480, 0.456635 avg, 0.001000 rate, 29.542305 seconds, 1130496 images -Loaded: 0.000120 seconds -Region Avg IOU: 0.615587, Class: 1.000000, Obj: 0.313406, No Obj: 0.002265, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.643533, Class: 1.000000, Obj: 0.313275, No Obj: 0.002341, Avg Recall: 0.788618, count: 123 -Region Avg IOU: 0.676807, Class: 1.000000, Obj: 0.402166, No Obj: 0.002117, Avg Recall: 0.862069, count: 87 -Region Avg IOU: 0.617929, Class: 1.000000, Obj: 0.307604, No Obj: 0.002617, Avg Recall: 0.756579, count: 152 -Region Avg IOU: 0.615932, Class: 1.000000, Obj: 0.354590, No Obj: 0.002041, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.674337, Class: 1.000000, Obj: 0.381585, No Obj: 0.002453, Avg Recall: 0.858586, count: 99 -Region Avg IOU: 0.641057, Class: 1.000000, Obj: 0.266216, No Obj: 0.002035, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.614902, Class: 1.000000, Obj: 0.327561, No Obj: 0.002241, Avg Recall: 0.769912, count: 113 -4417: 0.481496, 0.459121 avg, 0.001000 rate, 28.967869 seconds, 1130752 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.630099, Class: 1.000000, Obj: 0.292020, No Obj: 0.002404, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.662153, Class: 1.000000, Obj: 0.318014, No Obj: 0.002446, Avg Recall: 0.808989, count: 89 -Region Avg IOU: 0.635466, Class: 1.000000, Obj: 0.330549, No Obj: 0.002361, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.598702, Class: 1.000000, Obj: 0.292485, No Obj: 0.002029, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.623480, Class: 1.000000, Obj: 0.343306, No Obj: 0.002160, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.632765, Class: 1.000000, Obj: 0.372220, No Obj: 0.002218, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.647831, Class: 1.000000, Obj: 0.343187, No Obj: 0.002492, Avg Recall: 0.800000, count: 120 -Region Avg IOU: 0.656017, Class: 1.000000, Obj: 0.315326, No Obj: 0.002645, Avg Recall: 0.827586, count: 116 -4418: 0.515687, 0.464778 avg, 0.001000 rate, 28.838804 seconds, 1131008 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.651912, Class: 1.000000, Obj: 0.374816, No Obj: 0.002212, Avg Recall: 0.795455, count: 88 -Region Avg IOU: 0.625389, Class: 1.000000, Obj: 0.333034, No Obj: 0.002711, Avg Recall: 0.772059, count: 136 -Region Avg IOU: 0.664391, Class: 1.000000, Obj: 0.377233, No Obj: 0.002548, Avg Recall: 0.860870, count: 115 -Region Avg IOU: 0.594478, Class: 1.000000, Obj: 0.296029, No Obj: 0.002277, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.678388, Class: 1.000000, Obj: 0.410506, No Obj: 0.002414, Avg Recall: 0.830189, count: 106 -Region Avg IOU: 0.649931, Class: 1.000000, Obj: 0.353004, No Obj: 0.002294, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.663597, Class: 1.000000, Obj: 0.345639, No Obj: 0.002630, Avg Recall: 0.827869, count: 122 -Region Avg IOU: 0.657563, Class: 1.000000, Obj: 0.350295, No Obj: 0.002452, Avg Recall: 0.884211, count: 95 -4419: 0.475872, 0.465887 avg, 0.001000 rate, 28.301273 seconds, 1131264 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.638997, Class: 1.000000, Obj: 0.384761, No Obj: 0.002565, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.661862, Class: 1.000000, Obj: 0.371609, No Obj: 0.002454, Avg Recall: 0.891304, count: 92 -Region Avg IOU: 0.666541, Class: 1.000000, Obj: 0.366702, No Obj: 0.002555, Avg Recall: 0.876190, count: 105 -Region Avg IOU: 0.619804, Class: 1.000000, Obj: 0.353310, No Obj: 0.002434, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.623685, Class: 1.000000, Obj: 0.369998, No Obj: 0.002443, Avg Recall: 0.752137, count: 117 -Region Avg IOU: 0.650449, Class: 1.000000, Obj: 0.363890, No Obj: 0.002253, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.610822, Class: 1.000000, Obj: 0.369157, No Obj: 0.002306, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.679568, Class: 1.000000, Obj: 0.379856, No Obj: 0.003133, Avg Recall: 0.858333, count: 120 -4420: 0.459045, 0.465203 avg, 0.001000 rate, 27.691568 seconds, 1131520 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 20.585384 seconds -Region Avg IOU: 0.645777, Class: 1.000000, Obj: 0.382670, No Obj: 0.002970, Avg Recall: 0.785124, count: 121 -Region Avg IOU: 0.643553, Class: 1.000000, Obj: 0.381732, No Obj: 0.003237, Avg Recall: 0.782609, count: 115 -Region Avg IOU: 0.646587, Class: 1.000000, Obj: 0.386755, No Obj: 0.002898, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.621622, Class: 1.000000, Obj: 0.336862, No Obj: 0.002809, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.656409, Class: 1.000000, Obj: 0.442783, No Obj: 0.002694, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.618612, Class: 1.000000, Obj: 0.353255, No Obj: 0.003271, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.645565, Class: 1.000000, Obj: 0.358441, No Obj: 0.003462, Avg Recall: 0.742857, count: 140 -Region Avg IOU: 0.621271, Class: 1.000000, Obj: 0.351580, No Obj: 0.003103, Avg Recall: 0.778761, count: 113 -4421: 0.484886, 0.467171 avg, 0.001000 rate, 27.105936 seconds, 1131776 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.621243, Class: 1.000000, Obj: 0.369775, No Obj: 0.002898, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.608980, Class: 1.000000, Obj: 0.354180, No Obj: 0.003102, Avg Recall: 0.732283, count: 127 -Region Avg IOU: 0.646658, Class: 1.000000, Obj: 0.435886, No Obj: 0.003065, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.655003, Class: 1.000000, Obj: 0.429684, No Obj: 0.003241, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.665705, Class: 1.000000, Obj: 0.398359, No Obj: 0.003984, Avg Recall: 0.832117, count: 137 -Region Avg IOU: 0.642199, Class: 1.000000, Obj: 0.393361, No Obj: 0.002976, Avg Recall: 0.782609, count: 115 -Region Avg IOU: 0.649911, Class: 1.000000, Obj: 0.380556, No Obj: 0.003041, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.626227, Class: 1.000000, Obj: 0.367156, No Obj: 0.003714, Avg Recall: 0.740741, count: 135 -4422: 0.487756, 0.469230 avg, 0.001000 rate, 28.345823 seconds, 1132032 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.671236, Class: 1.000000, Obj: 0.449505, No Obj: 0.003356, Avg Recall: 0.836364, count: 110 -Region Avg IOU: 0.616697, Class: 1.000000, Obj: 0.374132, No Obj: 0.002556, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.591817, Class: 1.000000, Obj: 0.389315, No Obj: 0.003010, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.609919, Class: 1.000000, Obj: 0.421754, No Obj: 0.002899, Avg Recall: 0.734177, count: 79 -Region Avg IOU: 0.642352, Class: 1.000000, Obj: 0.485595, No Obj: 0.003116, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.667795, Class: 1.000000, Obj: 0.405541, No Obj: 0.003304, Avg Recall: 0.805085, count: 118 -Region Avg IOU: 0.626421, Class: 1.000000, Obj: 0.406441, No Obj: 0.003253, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.633916, Class: 1.000000, Obj: 0.406571, No Obj: 0.003106, Avg Recall: 0.803571, count: 112 -4423: 0.410589, 0.463366 avg, 0.001000 rate, 27.556339 seconds, 1132288 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.674607, Class: 1.000000, Obj: 0.503559, No Obj: 0.002635, Avg Recall: 0.838235, count: 68 -Region Avg IOU: 0.594462, Class: 1.000000, Obj: 0.411671, No Obj: 0.003147, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.603755, Class: 1.000000, Obj: 0.382881, No Obj: 0.003216, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.618129, Class: 1.000000, Obj: 0.406787, No Obj: 0.003277, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.597697, Class: 1.000000, Obj: 0.428890, No Obj: 0.003133, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.634425, Class: 1.000000, Obj: 0.368908, No Obj: 0.003203, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.605546, Class: 1.000000, Obj: 0.405557, No Obj: 0.002800, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.591476, Class: 1.000000, Obj: 0.372197, No Obj: 0.003336, Avg Recall: 0.686441, count: 118 -4424: 0.436647, 0.460694 avg, 0.001000 rate, 27.113688 seconds, 1132544 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.607627, Class: 1.000000, Obj: 0.432181, No Obj: 0.002845, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.613902, Class: 1.000000, Obj: 0.385988, No Obj: 0.003250, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.607750, Class: 1.000000, Obj: 0.368134, No Obj: 0.003056, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.638245, Class: 1.000000, Obj: 0.421538, No Obj: 0.003235, Avg Recall: 0.791667, count: 120 -Region Avg IOU: 0.679532, Class: 1.000000, Obj: 0.468964, No Obj: 0.003572, Avg Recall: 0.830000, count: 100 -Region Avg IOU: 0.583207, Class: 1.000000, Obj: 0.412056, No Obj: 0.002791, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.679834, Class: 1.000000, Obj: 0.410660, No Obj: 0.003428, Avg Recall: 0.830357, count: 112 -Region Avg IOU: 0.615916, Class: 1.000000, Obj: 0.414057, No Obj: 0.003020, Avg Recall: 0.729730, count: 111 -4425: 0.431220, 0.457747 avg, 0.001000 rate, 27.132769 seconds, 1132800 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.638582, Class: 1.000000, Obj: 0.405259, No Obj: 0.003267, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.625851, Class: 1.000000, Obj: 0.424795, No Obj: 0.003105, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.624994, Class: 1.000000, Obj: 0.455315, No Obj: 0.002707, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.620269, Class: 1.000000, Obj: 0.421511, No Obj: 0.002725, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.617621, Class: 1.000000, Obj: 0.412751, No Obj: 0.002816, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.644209, Class: 1.000000, Obj: 0.425584, No Obj: 0.002876, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.674710, Class: 1.000000, Obj: 0.502220, No Obj: 0.003149, Avg Recall: 0.858491, count: 106 -Region Avg IOU: 0.649315, Class: 1.000000, Obj: 0.419319, No Obj: 0.003091, Avg Recall: 0.811321, count: 106 -4426: 0.416720, 0.453644 avg, 0.001000 rate, 27.798704 seconds, 1133056 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.647487, Class: 1.000000, Obj: 0.361303, No Obj: 0.002937, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.598626, Class: 1.000000, Obj: 0.416903, No Obj: 0.002474, Avg Recall: 0.761905, count: 84 -Region Avg IOU: 0.610756, Class: 1.000000, Obj: 0.386814, No Obj: 0.003322, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.610965, Class: 1.000000, Obj: 0.393858, No Obj: 0.002930, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.608686, Class: 1.000000, Obj: 0.403499, No Obj: 0.002913, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.638136, Class: 1.000000, Obj: 0.361820, No Obj: 0.003695, Avg Recall: 0.766423, count: 137 -Region Avg IOU: 0.649646, Class: 1.000000, Obj: 0.385245, No Obj: 0.002838, Avg Recall: 0.846154, count: 104 -Region Avg IOU: 0.624257, Class: 1.000000, Obj: 0.339175, No Obj: 0.002714, Avg Recall: 0.772727, count: 88 -4427: 0.452636, 0.453543 avg, 0.001000 rate, 28.593052 seconds, 1133312 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.645567, Class: 1.000000, Obj: 0.363324, No Obj: 0.002849, Avg Recall: 0.789916, count: 119 -Region Avg IOU: 0.594064, Class: 1.000000, Obj: 0.360468, No Obj: 0.002851, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.606151, Class: 1.000000, Obj: 0.388853, No Obj: 0.002904, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.670828, Class: 1.000000, Obj: 0.405066, No Obj: 0.002479, Avg Recall: 0.839080, count: 87 -Region Avg IOU: 0.636450, Class: 1.000000, Obj: 0.395354, No Obj: 0.003053, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.630923, Class: 1.000000, Obj: 0.382307, No Obj: 0.002781, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.618011, Class: 1.000000, Obj: 0.338629, No Obj: 0.002651, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.584731, Class: 1.000000, Obj: 0.371235, No Obj: 0.003436, Avg Recall: 0.712329, count: 146 -4428: 0.463106, 0.454499 avg, 0.001000 rate, 28.106144 seconds, 1133568 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.600044, Class: 1.000000, Obj: 0.410881, No Obj: 0.002602, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.663138, Class: 1.000000, Obj: 0.360880, No Obj: 0.003311, Avg Recall: 0.856061, count: 132 -Region Avg IOU: 0.592252, Class: 1.000000, Obj: 0.317331, No Obj: 0.002777, Avg Recall: 0.719298, count: 114 -Region Avg IOU: 0.591778, Class: 1.000000, Obj: 0.361201, No Obj: 0.002358, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.640783, Class: 1.000000, Obj: 0.426053, No Obj: 0.002322, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.629553, Class: 1.000000, Obj: 0.381538, No Obj: 0.002501, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.663199, Class: 1.000000, Obj: 0.375941, No Obj: 0.002433, Avg Recall: 0.762500, count: 80 -Region Avg IOU: 0.627234, Class: 1.000000, Obj: 0.364322, No Obj: 0.002486, Avg Recall: 0.750000, count: 104 -4429: 0.419502, 0.451000 avg, 0.001000 rate, 27.964993 seconds, 1133824 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.668016, Class: 1.000000, Obj: 0.402106, No Obj: 0.002823, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.641182, Class: 1.000000, Obj: 0.409500, No Obj: 0.002546, Avg Recall: 0.802083, count: 96 -Region Avg IOU: 0.673913, Class: 1.000000, Obj: 0.387312, No Obj: 0.002657, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.556680, Class: 1.000000, Obj: 0.344462, No Obj: 0.002417, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.622696, Class: 1.000000, Obj: 0.368428, No Obj: 0.002816, Avg Recall: 0.761905, count: 126 -Region Avg IOU: 0.627819, Class: 1.000000, Obj: 0.338339, No Obj: 0.002624, Avg Recall: 0.796460, count: 113 -Region Avg IOU: 0.615812, Class: 1.000000, Obj: 0.352668, No Obj: 0.002584, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.618915, Class: 1.000000, Obj: 0.382068, No Obj: 0.002541, Avg Recall: 0.734043, count: 94 -4430: 0.432433, 0.449143 avg, 0.001000 rate, 27.570969 seconds, 1134080 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.706209 seconds -Region Avg IOU: 0.500968, Class: 1.000000, Obj: 0.305723, No Obj: 0.003377, Avg Recall: 0.584270, count: 89 -Region Avg IOU: 0.519758, Class: 1.000000, Obj: 0.261044, No Obj: 0.003519, Avg Recall: 0.586207, count: 87 -Region Avg IOU: 0.524370, Class: 1.000000, Obj: 0.265709, No Obj: 0.003238, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.470763, Class: 1.000000, Obj: 0.236305, No Obj: 0.003195, Avg Recall: 0.526786, count: 112 -Region Avg IOU: 0.495473, Class: 1.000000, Obj: 0.248538, No Obj: 0.003910, Avg Recall: 0.519685, count: 127 -Region Avg IOU: 0.515540, Class: 1.000000, Obj: 0.277219, No Obj: 0.003494, Avg Recall: 0.597938, count: 97 -Region Avg IOU: 0.502189, Class: 1.000000, Obj: 0.251297, No Obj: 0.003775, Avg Recall: 0.520833, count: 144 -Region Avg IOU: 0.499218, Class: 1.000000, Obj: 0.268930, No Obj: 0.003429, Avg Recall: 0.570175, count: 114 -4431: 0.481893, 0.452418 avg, 0.001000 rate, 18.530674 seconds, 1134336 images -Loaded: 2.760144 seconds -Region Avg IOU: 0.498043, Class: 1.000000, Obj: 0.259921, No Obj: 0.003157, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.521283, Class: 1.000000, Obj: 0.260565, No Obj: 0.003479, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.509962, Class: 1.000000, Obj: 0.284364, No Obj: 0.003711, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.444824, Class: 1.000000, Obj: 0.245572, No Obj: 0.003359, Avg Recall: 0.436090, count: 133 -Region Avg IOU: 0.490205, Class: 1.000000, Obj: 0.259475, No Obj: 0.003024, Avg Recall: 0.519231, count: 104 -Region Avg IOU: 0.484072, Class: 1.000000, Obj: 0.268502, No Obj: 0.003380, Avg Recall: 0.550000, count: 120 -Region Avg IOU: 0.582120, Class: 1.000000, Obj: 0.299290, No Obj: 0.003164, Avg Recall: 0.710843, count: 83 -Region Avg IOU: 0.552080, Class: 1.000000, Obj: 0.292328, No Obj: 0.003462, Avg Recall: 0.611111, count: 90 -4432: 0.497390, 0.456915 avg, 0.001000 rate, 16.894384 seconds, 1134592 images -Loaded: 2.847693 seconds -Region Avg IOU: 0.493302, Class: 1.000000, Obj: 0.263979, No Obj: 0.003701, Avg Recall: 0.533333, count: 120 -Region Avg IOU: 0.469228, Class: 1.000000, Obj: 0.269982, No Obj: 0.004075, Avg Recall: 0.510204, count: 98 -Region Avg IOU: 0.516074, Class: 1.000000, Obj: 0.318980, No Obj: 0.003045, Avg Recall: 0.586207, count: 87 -Region Avg IOU: 0.510577, Class: 1.000000, Obj: 0.269293, No Obj: 0.003304, Avg Recall: 0.583333, count: 96 -Region Avg IOU: 0.479872, Class: 1.000000, Obj: 0.311531, No Obj: 0.003934, Avg Recall: 0.520325, count: 123 -Region Avg IOU: 0.513424, Class: 1.000000, Obj: 0.300681, No Obj: 0.003500, Avg Recall: 0.586207, count: 87 -Region Avg IOU: 0.473397, Class: 1.000000, Obj: 0.271660, No Obj: 0.003565, Avg Recall: 0.549020, count: 102 -Region Avg IOU: 0.515123, Class: 1.000000, Obj: 0.258753, No Obj: 0.003701, Avg Recall: 0.600000, count: 100 -4433: 0.436659, 0.454890 avg, 0.001000 rate, 19.352661 seconds, 1134848 images -Loaded: 1.281104 seconds -Region Avg IOU: 0.481461, Class: 1.000000, Obj: 0.244860, No Obj: 0.003572, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.508457, Class: 1.000000, Obj: 0.299084, No Obj: 0.003643, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.525043, Class: 1.000000, Obj: 0.290649, No Obj: 0.004566, Avg Recall: 0.609023, count: 133 -Region Avg IOU: 0.507150, Class: 1.000000, Obj: 0.316360, No Obj: 0.004252, Avg Recall: 0.586466, count: 133 -Region Avg IOU: 0.512770, Class: 1.000000, Obj: 0.343872, No Obj: 0.003469, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.541504, Class: 1.000000, Obj: 0.318377, No Obj: 0.004371, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.506024, Class: 1.000000, Obj: 0.303834, No Obj: 0.003924, Avg Recall: 0.567797, count: 118 -Region Avg IOU: 0.548024, Class: 1.000000, Obj: 0.334387, No Obj: 0.003424, Avg Recall: 0.600000, count: 85 -4434: 0.476026, 0.457003 avg, 0.001000 rate, 19.152863 seconds, 1135104 images -Loaded: 2.460923 seconds -Region Avg IOU: 0.486762, Class: 1.000000, Obj: 0.292396, No Obj: 0.003649, Avg Recall: 0.508929, count: 112 -Region Avg IOU: 0.553878, Class: 1.000000, Obj: 0.368609, No Obj: 0.004314, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.473971, Class: 1.000000, Obj: 0.296615, No Obj: 0.003868, Avg Recall: 0.532258, count: 124 -Region Avg IOU: 0.489972, Class: 1.000000, Obj: 0.312980, No Obj: 0.003842, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.557325, Class: 1.000000, Obj: 0.302915, No Obj: 0.004355, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.490746, Class: 1.000000, Obj: 0.300364, No Obj: 0.004624, Avg Recall: 0.508621, count: 116 -Region Avg IOU: 0.553131, Class: 1.000000, Obj: 0.330170, No Obj: 0.003554, Avg Recall: 0.605263, count: 76 -Region Avg IOU: 0.486416, Class: 1.000000, Obj: 0.318762, No Obj: 0.003754, Avg Recall: 0.567010, count: 97 -4435: 0.437184, 0.455021 avg, 0.001000 rate, 15.464805 seconds, 1135360 images -Loaded: 5.807237 seconds -Region Avg IOU: 0.472050, Class: 1.000000, Obj: 0.366484, No Obj: 0.003770, Avg Recall: 0.487805, count: 82 -Region Avg IOU: 0.454764, Class: 1.000000, Obj: 0.310683, No Obj: 0.004500, Avg Recall: 0.461538, count: 91 -Region Avg IOU: 0.496767, Class: 1.000000, Obj: 0.329307, No Obj: 0.005010, Avg Recall: 0.575000, count: 120 -Region Avg IOU: 0.494377, Class: 1.000000, Obj: 0.306068, No Obj: 0.004150, Avg Recall: 0.542857, count: 105 -Region Avg IOU: 0.491136, Class: 1.000000, Obj: 0.340643, No Obj: 0.004396, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.499315, Class: 1.000000, Obj: 0.388942, No Obj: 0.004150, Avg Recall: 0.569620, count: 79 -Region Avg IOU: 0.518319, Class: 1.000000, Obj: 0.375276, No Obj: 0.004535, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.512481, Class: 1.000000, Obj: 0.371742, No Obj: 0.004857, Avg Recall: 0.557377, count: 122 -4436: 0.439220, 0.453441 avg, 0.001000 rate, 20.952185 seconds, 1135616 images -Loaded: 0.805463 seconds -Region Avg IOU: 0.495891, Class: 1.000000, Obj: 0.340411, No Obj: 0.004984, Avg Recall: 0.533835, count: 133 -Region Avg IOU: 0.505912, Class: 1.000000, Obj: 0.359534, No Obj: 0.004101, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.443637, Class: 1.000000, Obj: 0.334484, No Obj: 0.004922, Avg Recall: 0.449612, count: 129 -Region Avg IOU: 0.529410, Class: 1.000000, Obj: 0.361151, No Obj: 0.004530, Avg Recall: 0.619565, count: 92 -Region Avg IOU: 0.464693, Class: 1.000000, Obj: 0.347537, No Obj: 0.004267, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.523511, Class: 1.000000, Obj: 0.362155, No Obj: 0.004710, Avg Recall: 0.594828, count: 116 -Region Avg IOU: 0.505543, Class: 1.000000, Obj: 0.366037, No Obj: 0.004444, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.553473, Class: 1.000000, Obj: 0.361880, No Obj: 0.004794, Avg Recall: 0.631579, count: 95 -4437: 0.429153, 0.451012 avg, 0.001000 rate, 19.936935 seconds, 1135872 images -Loaded: 0.220040 seconds -Region Avg IOU: 0.529585, Class: 1.000000, Obj: 0.385840, No Obj: 0.005078, Avg Recall: 0.591667, count: 120 -Region Avg IOU: 0.525735, Class: 1.000000, Obj: 0.355963, No Obj: 0.005719, Avg Recall: 0.601695, count: 118 -Region Avg IOU: 0.501018, Class: 1.000000, Obj: 0.367907, No Obj: 0.005135, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.512644, Class: 1.000000, Obj: 0.389044, No Obj: 0.004956, Avg Recall: 0.526316, count: 95 -Region Avg IOU: 0.423897, Class: 1.000000, Obj: 0.315294, No Obj: 0.004417, Avg Recall: 0.488000, count: 125 -Region Avg IOU: 0.489257, Class: 1.000000, Obj: 0.373752, No Obj: 0.003993, Avg Recall: 0.551020, count: 98 -Region Avg IOU: 0.513140, Class: 1.000000, Obj: 0.365993, No Obj: 0.004369, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.533301, Class: 1.000000, Obj: 0.423101, No Obj: 0.004505, Avg Recall: 0.605769, count: 104 -4438: 0.464699, 0.452381 avg, 0.001000 rate, 19.074911 seconds, 1136128 images -Loaded: 1.267935 seconds -Region Avg IOU: 0.461021, Class: 1.000000, Obj: 0.419576, No Obj: 0.004513, Avg Recall: 0.550000, count: 80 -Region Avg IOU: 0.518092, Class: 1.000000, Obj: 0.372849, No Obj: 0.004876, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.469275, Class: 1.000000, Obj: 0.377728, No Obj: 0.004196, Avg Recall: 0.494624, count: 93 -Region Avg IOU: 0.528695, Class: 1.000000, Obj: 0.409329, No Obj: 0.004403, Avg Recall: 0.610390, count: 77 -Region Avg IOU: 0.450993, Class: 1.000000, Obj: 0.351606, No Obj: 0.004807, Avg Recall: 0.513043, count: 115 -Region Avg IOU: 0.558655, Class: 1.000000, Obj: 0.423022, No Obj: 0.005238, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.451625, Class: 1.000000, Obj: 0.364916, No Obj: 0.005760, Avg Recall: 0.443662, count: 142 -Region Avg IOU: 0.492797, Class: 1.000000, Obj: 0.370219, No Obj: 0.004727, Avg Recall: 0.548387, count: 93 -4439: 0.420253, 0.449168 avg, 0.001000 rate, 18.371532 seconds, 1136384 images -Loaded: 2.995887 seconds -Region Avg IOU: 0.498568, Class: 1.000000, Obj: 0.395193, No Obj: 0.005625, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.517781, Class: 1.000000, Obj: 0.418297, No Obj: 0.005004, Avg Recall: 0.537037, count: 108 -Region Avg IOU: 0.513023, Class: 1.000000, Obj: 0.405136, No Obj: 0.005049, Avg Recall: 0.580952, count: 105 -Region Avg IOU: 0.555875, Class: 1.000000, Obj: 0.427598, No Obj: 0.006067, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.482402, Class: 1.000000, Obj: 0.357542, No Obj: 0.005112, Avg Recall: 0.556701, count: 97 -Region Avg IOU: 0.459831, Class: 1.000000, Obj: 0.393821, No Obj: 0.005012, Avg Recall: 0.475728, count: 103 -Region Avg IOU: 0.493054, Class: 1.000000, Obj: 0.413116, No Obj: 0.005228, Avg Recall: 0.534653, count: 101 -Region Avg IOU: 0.557262, Class: 1.000000, Obj: 0.426895, No Obj: 0.004805, Avg Recall: 0.675325, count: 77 -4440: 0.401985, 0.444450 avg, 0.001000 rate, 17.541712 seconds, 1136640 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000068 seconds -Region Avg IOU: 0.557281, Class: 1.000000, Obj: 0.516411, No Obj: 0.004582, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.572839, Class: 1.000000, Obj: 0.492913, No Obj: 0.004505, Avg Recall: 0.679389, count: 131 -Region Avg IOU: 0.590109, Class: 1.000000, Obj: 0.473223, No Obj: 0.003959, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.542659, Class: 1.000000, Obj: 0.456083, No Obj: 0.004516, Avg Recall: 0.601770, count: 113 -Region Avg IOU: 0.585866, Class: 1.000000, Obj: 0.496584, No Obj: 0.004596, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.628005, Class: 1.000000, Obj: 0.485967, No Obj: 0.004386, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.512534, Class: 1.000000, Obj: 0.437943, No Obj: 0.003893, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.593784, Class: 1.000000, Obj: 0.449106, No Obj: 0.004352, Avg Recall: 0.712963, count: 108 -4441: 0.431755, 0.443180 avg, 0.001000 rate, 25.019133 seconds, 1136896 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.584317, Class: 1.000000, Obj: 0.500516, No Obj: 0.003947, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.638413, Class: 1.000000, Obj: 0.458411, No Obj: 0.004464, Avg Recall: 0.786885, count: 122 -Region Avg IOU: 0.630257, Class: 1.000000, Obj: 0.533646, No Obj: 0.003498, Avg Recall: 0.777778, count: 81 -Region Avg IOU: 0.598827, Class: 1.000000, Obj: 0.453697, No Obj: 0.004671, Avg Recall: 0.695312, count: 128 -Region Avg IOU: 0.558605, Class: 1.000000, Obj: 0.409779, No Obj: 0.004191, Avg Recall: 0.645161, count: 124 -Region Avg IOU: 0.609500, Class: 1.000000, Obj: 0.476054, No Obj: 0.004422, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.570209, Class: 1.000000, Obj: 0.462691, No Obj: 0.004258, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.636461, Class: 1.000000, Obj: 0.406108, No Obj: 0.004021, Avg Recall: 0.785047, count: 107 -4442: 0.418146, 0.440677 avg, 0.001000 rate, 25.346664 seconds, 1137152 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.612648, Class: 1.000000, Obj: 0.407974, No Obj: 0.003285, Avg Recall: 0.777778, count: 81 -Region Avg IOU: 0.601920, Class: 1.000000, Obj: 0.468392, No Obj: 0.003871, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.633350, Class: 1.000000, Obj: 0.494554, No Obj: 0.004221, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.593597, Class: 1.000000, Obj: 0.394889, No Obj: 0.003492, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.573254, Class: 1.000000, Obj: 0.442575, No Obj: 0.003291, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.584365, Class: 1.000000, Obj: 0.390927, No Obj: 0.003762, Avg Recall: 0.686131, count: 137 -Region Avg IOU: 0.549466, Class: 1.000000, Obj: 0.459365, No Obj: 0.003472, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.593348, Class: 1.000000, Obj: 0.449990, No Obj: 0.003826, Avg Recall: 0.730769, count: 104 -4443: 0.410977, 0.437707 avg, 0.001000 rate, 25.255033 seconds, 1137408 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.563829, Class: 1.000000, Obj: 0.412671, No Obj: 0.003108, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.612311, Class: 1.000000, Obj: 0.369532, No Obj: 0.003171, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.571611, Class: 1.000000, Obj: 0.392208, No Obj: 0.003051, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.561710, Class: 1.000000, Obj: 0.373005, No Obj: 0.003475, Avg Recall: 0.677419, count: 124 -Region Avg IOU: 0.607284, Class: 1.000000, Obj: 0.424453, No Obj: 0.003725, Avg Recall: 0.744000, count: 125 -Region Avg IOU: 0.575649, Class: 1.000000, Obj: 0.396475, No Obj: 0.003304, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.566112, Class: 1.000000, Obj: 0.391578, No Obj: 0.003264, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.572676, Class: 1.000000, Obj: 0.415695, No Obj: 0.003057, Avg Recall: 0.649485, count: 97 -4444: 0.426641, 0.436600 avg, 0.001000 rate, 24.637276 seconds, 1137664 images -Loaded: 0.000140 seconds -Region Avg IOU: 0.598283, Class: 1.000000, Obj: 0.371919, No Obj: 0.003390, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.595321, Class: 1.000000, Obj: 0.360971, No Obj: 0.003056, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.610981, Class: 1.000000, Obj: 0.353113, No Obj: 0.003900, Avg Recall: 0.761589, count: 151 -Region Avg IOU: 0.605598, Class: 1.000000, Obj: 0.383551, No Obj: 0.003228, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.615264, Class: 1.000000, Obj: 0.392952, No Obj: 0.003380, Avg Recall: 0.698276, count: 116 -Region Avg IOU: 0.605107, Class: 1.000000, Obj: 0.364948, No Obj: 0.003150, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.595292, Class: 1.000000, Obj: 0.415693, No Obj: 0.003151, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.593183, Class: 1.000000, Obj: 0.370630, No Obj: 0.003084, Avg Recall: 0.701031, count: 97 -4445: 0.467822, 0.439723 avg, 0.001000 rate, 26.067476 seconds, 1137920 images -Loaded: 0.000146 seconds -Region Avg IOU: 0.573138, Class: 1.000000, Obj: 0.307611, No Obj: 0.002965, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.609665, Class: 1.000000, Obj: 0.370522, No Obj: 0.002736, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.575191, Class: 1.000000, Obj: 0.342586, No Obj: 0.002804, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.609309, Class: 1.000000, Obj: 0.354396, No Obj: 0.002896, Avg Recall: 0.737705, count: 122 -Region Avg IOU: 0.634009, Class: 1.000000, Obj: 0.342419, No Obj: 0.002861, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.573715, Class: 1.000000, Obj: 0.347539, No Obj: 0.002375, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.594575, Class: 1.000000, Obj: 0.365333, No Obj: 0.002767, Avg Recall: 0.755814, count: 86 -Region Avg IOU: 0.580114, Class: 1.000000, Obj: 0.335923, No Obj: 0.003022, Avg Recall: 0.724138, count: 116 -4446: 0.447070, 0.440457 avg, 0.001000 rate, 25.107172 seconds, 1138176 images -Loaded: 0.000148 seconds -Region Avg IOU: 0.582964, Class: 1.000000, Obj: 0.314731, No Obj: 0.002587, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.650680, Class: 1.000000, Obj: 0.428909, No Obj: 0.002688, Avg Recall: 0.756098, count: 82 -Region Avg IOU: 0.611195, Class: 1.000000, Obj: 0.343959, No Obj: 0.002619, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.570896, Class: 1.000000, Obj: 0.318220, No Obj: 0.002587, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.593331, Class: 1.000000, Obj: 0.383553, No Obj: 0.002385, Avg Recall: 0.678161, count: 87 -Region Avg IOU: 0.621849, Class: 1.000000, Obj: 0.323152, No Obj: 0.002967, Avg Recall: 0.770492, count: 122 -Region Avg IOU: 0.574494, Class: 1.000000, Obj: 0.341244, No Obj: 0.002896, Avg Recall: 0.690476, count: 126 -Region Avg IOU: 0.553595, Class: 1.000000, Obj: 0.287922, No Obj: 0.002851, Avg Recall: 0.650406, count: 123 -4447: 0.418745, 0.438286 avg, 0.001000 rate, 24.982752 seconds, 1138432 images -Loaded: 0.000120 seconds -Region Avg IOU: 0.607629, Class: 1.000000, Obj: 0.376723, No Obj: 0.002588, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.548671, Class: 1.000000, Obj: 0.299040, No Obj: 0.002761, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.594560, Class: 1.000000, Obj: 0.290370, No Obj: 0.003114, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.654304, Class: 1.000000, Obj: 0.384560, No Obj: 0.002896, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.633317, Class: 1.000000, Obj: 0.335851, No Obj: 0.002462, Avg Recall: 0.807229, count: 83 -Region Avg IOU: 0.598235, Class: 1.000000, Obj: 0.310497, No Obj: 0.002455, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.638803, Class: 1.000000, Obj: 0.378471, No Obj: 0.002727, Avg Recall: 0.728395, count: 81 -Region Avg IOU: 0.601811, Class: 1.000000, Obj: 0.352155, No Obj: 0.002539, Avg Recall: 0.700000, count: 90 -4448: 0.410658, 0.435523 avg, 0.001000 rate, 24.617807 seconds, 1138688 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.603116, Class: 1.000000, Obj: 0.306310, No Obj: 0.002728, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.583973, Class: 1.000000, Obj: 0.359341, No Obj: 0.002801, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.566477, Class: 1.000000, Obj: 0.279435, No Obj: 0.002803, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.629504, Class: 1.000000, Obj: 0.380596, No Obj: 0.002965, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.600162, Class: 1.000000, Obj: 0.359954, No Obj: 0.002769, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.584716, Class: 1.000000, Obj: 0.348368, No Obj: 0.003086, Avg Recall: 0.637795, count: 127 -Region Avg IOU: 0.626687, Class: 1.000000, Obj: 0.339739, No Obj: 0.002676, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.669909, Class: 1.000000, Obj: 0.401131, No Obj: 0.002850, Avg Recall: 0.831461, count: 89 -4449: 0.420704, 0.434041 avg, 0.001000 rate, 25.936571 seconds, 1138944 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.589691, Class: 1.000000, Obj: 0.354545, No Obj: 0.002865, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.565831, Class: 1.000000, Obj: 0.320401, No Obj: 0.002869, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.603696, Class: 1.000000, Obj: 0.336884, No Obj: 0.002944, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.618969, Class: 1.000000, Obj: 0.327953, No Obj: 0.002772, Avg Recall: 0.752941, count: 85 -Region Avg IOU: 0.561204, Class: 1.000000, Obj: 0.288562, No Obj: 0.002828, Avg Recall: 0.611511, count: 139 -Region Avg IOU: 0.609340, Class: 1.000000, Obj: 0.326587, No Obj: 0.002747, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.595154, Class: 1.000000, Obj: 0.338865, No Obj: 0.003129, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.631814, Class: 1.000000, Obj: 0.315402, No Obj: 0.003020, Avg Recall: 0.771429, count: 105 -4450: 0.473110, 0.437948 avg, 0.001000 rate, 25.753843 seconds, 1139200 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 20.411739 seconds -Region Avg IOU: 0.569297, Class: 1.000000, Obj: 0.340021, No Obj: 0.002957, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.623446, Class: 1.000000, Obj: 0.322213, No Obj: 0.003593, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.589303, Class: 1.000000, Obj: 0.359471, No Obj: 0.003072, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.566307, Class: 1.000000, Obj: 0.366269, No Obj: 0.003984, Avg Recall: 0.624204, count: 157 -Region Avg IOU: 0.625711, Class: 1.000000, Obj: 0.354659, No Obj: 0.003211, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.587992, Class: 1.000000, Obj: 0.346462, No Obj: 0.003523, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.591273, Class: 1.000000, Obj: 0.320174, No Obj: 0.003316, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.570429, Class: 1.000000, Obj: 0.338902, No Obj: 0.002877, Avg Recall: 0.657658, count: 111 -4451: 0.477866, 0.441940 avg, 0.001000 rate, 25.588238 seconds, 1139456 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.587723, Class: 1.000000, Obj: 0.396706, No Obj: 0.003561, Avg Recall: 0.713115, count: 122 -Region Avg IOU: 0.605910, Class: 1.000000, Obj: 0.373552, No Obj: 0.003936, Avg Recall: 0.726562, count: 128 -Region Avg IOU: 0.619098, Class: 1.000000, Obj: 0.399385, No Obj: 0.003200, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.590104, Class: 1.000000, Obj: 0.327777, No Obj: 0.003736, Avg Recall: 0.725191, count: 131 -Region Avg IOU: 0.567410, Class: 1.000000, Obj: 0.350358, No Obj: 0.004208, Avg Recall: 0.628049, count: 164 -Region Avg IOU: 0.581747, Class: 1.000000, Obj: 0.365105, No Obj: 0.003205, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.558820, Class: 1.000000, Obj: 0.405669, No Obj: 0.003500, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.556077, Class: 1.000000, Obj: 0.345373, No Obj: 0.003322, Avg Recall: 0.633929, count: 112 -4452: 0.471276, 0.444874 avg, 0.001000 rate, 24.248661 seconds, 1139712 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.615851, Class: 1.000000, Obj: 0.396715, No Obj: 0.003313, Avg Recall: 0.781609, count: 87 -Region Avg IOU: 0.578209, Class: 1.000000, Obj: 0.404270, No Obj: 0.004109, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.627047, Class: 1.000000, Obj: 0.453251, No Obj: 0.003476, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.575883, Class: 1.000000, Obj: 0.350316, No Obj: 0.003194, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.559379, Class: 1.000000, Obj: 0.364697, No Obj: 0.004071, Avg Recall: 0.583333, count: 144 -Region Avg IOU: 0.625232, Class: 1.000000, Obj: 0.433197, No Obj: 0.003622, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.584042, Class: 1.000000, Obj: 0.356857, No Obj: 0.004060, Avg Recall: 0.700000, count: 130 -Region Avg IOU: 0.560559, Class: 1.000000, Obj: 0.347420, No Obj: 0.003462, Avg Recall: 0.655738, count: 122 -4453: 0.416162, 0.442002 avg, 0.001000 rate, 24.064428 seconds, 1139968 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.603781, Class: 1.000000, Obj: 0.441665, No Obj: 0.003792, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.597879, Class: 1.000000, Obj: 0.404968, No Obj: 0.003952, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.592360, Class: 1.000000, Obj: 0.367919, No Obj: 0.003968, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.583910, Class: 1.000000, Obj: 0.386231, No Obj: 0.003887, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.558169, Class: 1.000000, Obj: 0.418606, No Obj: 0.003385, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.536736, Class: 1.000000, Obj: 0.412370, No Obj: 0.003477, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.614738, Class: 1.000000, Obj: 0.378303, No Obj: 0.004511, Avg Recall: 0.761905, count: 126 -Region Avg IOU: 0.636630, Class: 1.000000, Obj: 0.393514, No Obj: 0.004714, Avg Recall: 0.791304, count: 115 -4454: 0.460175, 0.443820 avg, 0.001000 rate, 24.937376 seconds, 1140224 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.556830, Class: 1.000000, Obj: 0.385319, No Obj: 0.003683, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.611838, Class: 1.000000, Obj: 0.421564, No Obj: 0.004408, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.600029, Class: 1.000000, Obj: 0.422617, No Obj: 0.003899, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.516699, Class: 1.000000, Obj: 0.376161, No Obj: 0.003854, Avg Recall: 0.557252, count: 131 -Region Avg IOU: 0.620335, Class: 1.000000, Obj: 0.469025, No Obj: 0.004251, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.555954, Class: 1.000000, Obj: 0.395911, No Obj: 0.004522, Avg Recall: 0.630769, count: 130 -Region Avg IOU: 0.565229, Class: 1.000000, Obj: 0.397109, No Obj: 0.004042, Avg Recall: 0.700000, count: 130 -Region Avg IOU: 0.588391, Class: 1.000000, Obj: 0.419707, No Obj: 0.004055, Avg Recall: 0.725490, count: 102 -4455: 0.502293, 0.449667 avg, 0.001000 rate, 23.319387 seconds, 1140480 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.553573, Class: 1.000000, Obj: 0.412991, No Obj: 0.004174, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.581592, Class: 1.000000, Obj: 0.449944, No Obj: 0.004320, Avg Recall: 0.631148, count: 122 -Region Avg IOU: 0.589799, Class: 1.000000, Obj: 0.412685, No Obj: 0.004329, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.610148, Class: 1.000000, Obj: 0.458100, No Obj: 0.004236, Avg Recall: 0.705882, count: 136 -Region Avg IOU: 0.556248, Class: 1.000000, Obj: 0.415201, No Obj: 0.004310, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.577056, Class: 1.000000, Obj: 0.439159, No Obj: 0.004037, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.588954, Class: 1.000000, Obj: 0.461813, No Obj: 0.004122, Avg Recall: 0.669565, count: 115 -Region Avg IOU: 0.593806, Class: 1.000000, Obj: 0.435334, No Obj: 0.004064, Avg Recall: 0.700000, count: 100 -4456: 0.424677, 0.447168 avg, 0.001000 rate, 24.225920 seconds, 1140736 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.545440, Class: 1.000000, Obj: 0.391266, No Obj: 0.003812, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.563452, Class: 1.000000, Obj: 0.448633, No Obj: 0.004028, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.593056, Class: 1.000000, Obj: 0.449120, No Obj: 0.004008, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.586991, Class: 1.000000, Obj: 0.431414, No Obj: 0.003881, Avg Recall: 0.711111, count: 90 -Region Avg IOU: 0.592906, Class: 1.000000, Obj: 0.457327, No Obj: 0.003870, Avg Recall: 0.678571, count: 84 -Region Avg IOU: 0.608018, Class: 1.000000, Obj: 0.452059, No Obj: 0.004780, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.591567, Class: 1.000000, Obj: 0.435065, No Obj: 0.004204, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.544147, Class: 1.000000, Obj: 0.385282, No Obj: 0.003963, Avg Recall: 0.625000, count: 96 -4457: 0.365113, 0.438963 avg, 0.001000 rate, 24.353945 seconds, 1140992 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.586480, Class: 1.000000, Obj: 0.443031, No Obj: 0.003525, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.579632, Class: 1.000000, Obj: 0.418569, No Obj: 0.004388, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.549347, Class: 1.000000, Obj: 0.380657, No Obj: 0.003686, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.571245, Class: 1.000000, Obj: 0.423534, No Obj: 0.003777, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.596723, Class: 1.000000, Obj: 0.406656, No Obj: 0.004438, Avg Recall: 0.697479, count: 119 -Region Avg IOU: 0.590242, Class: 1.000000, Obj: 0.437676, No Obj: 0.003782, Avg Recall: 0.695122, count: 82 -Region Avg IOU: 0.546786, Class: 1.000000, Obj: 0.422746, No Obj: 0.003277, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.564912, Class: 1.000000, Obj: 0.394640, No Obj: 0.003933, Avg Recall: 0.714286, count: 112 -4458: 0.408079, 0.435874 avg, 0.001000 rate, 23.784781 seconds, 1141248 images -Loaded: 0.000114 seconds -Region Avg IOU: 0.554418, Class: 1.000000, Obj: 0.402982, No Obj: 0.003568, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.604933, Class: 1.000000, Obj: 0.412715, No Obj: 0.004085, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.618400, Class: 1.000000, Obj: 0.403662, No Obj: 0.004336, Avg Recall: 0.764228, count: 123 -Region Avg IOU: 0.609063, Class: 1.000000, Obj: 0.391012, No Obj: 0.003616, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.569240, Class: 1.000000, Obj: 0.385220, No Obj: 0.003535, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.539997, Class: 1.000000, Obj: 0.380622, No Obj: 0.003600, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.553052, Class: 1.000000, Obj: 0.419771, No Obj: 0.004232, Avg Recall: 0.613793, count: 145 -Region Avg IOU: 0.547472, Class: 1.000000, Obj: 0.384558, No Obj: 0.003332, Avg Recall: 0.598131, count: 107 -4459: 0.418506, 0.434137 avg, 0.001000 rate, 23.140076 seconds, 1141504 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.572745, Class: 1.000000, Obj: 0.369733, No Obj: 0.004140, Avg Recall: 0.686567, count: 134 -Region Avg IOU: 0.584260, Class: 1.000000, Obj: 0.385143, No Obj: 0.003617, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.523068, Class: 1.000000, Obj: 0.368824, No Obj: 0.003111, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.628373, Class: 1.000000, Obj: 0.431025, No Obj: 0.003512, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.613200, Class: 1.000000, Obj: 0.388855, No Obj: 0.003973, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.577070, Class: 1.000000, Obj: 0.391832, No Obj: 0.003619, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.625457, Class: 1.000000, Obj: 0.379818, No Obj: 0.003461, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.589207, Class: 1.000000, Obj: 0.372448, No Obj: 0.003977, Avg Recall: 0.727273, count: 110 -4460: 0.469040, 0.437628 avg, 0.001000 rate, 24.284225 seconds, 1141760 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000085 seconds -Region Avg IOU: 0.606291, Class: 1.000000, Obj: 0.336703, No Obj: 0.003179, Avg Recall: 0.734375, count: 128 -Region Avg IOU: 0.620246, Class: 1.000000, Obj: 0.350072, No Obj: 0.003136, Avg Recall: 0.800000, count: 115 -Region Avg IOU: 0.602911, Class: 1.000000, Obj: 0.371778, No Obj: 0.002812, Avg Recall: 0.724409, count: 127 -Region Avg IOU: 0.636196, Class: 1.000000, Obj: 0.467879, No Obj: 0.002639, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.657035, Class: 1.000000, Obj: 0.391409, No Obj: 0.003201, Avg Recall: 0.853658, count: 123 -Region Avg IOU: 0.629701, Class: 1.000000, Obj: 0.413003, No Obj: 0.002670, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.611139, Class: 1.000000, Obj: 0.359009, No Obj: 0.002847, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.631546, Class: 1.000000, Obj: 0.441467, No Obj: 0.002532, Avg Recall: 0.755556, count: 90 -4461: 0.479837, 0.441849 avg, 0.001000 rate, 29.152521 seconds, 1142016 images -Loaded: 0.000165 seconds -Region Avg IOU: 0.626593, Class: 1.000000, Obj: 0.375168, No Obj: 0.003233, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.623882, Class: 1.000000, Obj: 0.383715, No Obj: 0.002919, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.638391, Class: 1.000000, Obj: 0.372100, No Obj: 0.002623, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.678384, Class: 1.000000, Obj: 0.387901, No Obj: 0.002733, Avg Recall: 0.855856, count: 111 -Region Avg IOU: 0.591031, Class: 1.000000, Obj: 0.421619, No Obj: 0.002593, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.640687, Class: 1.000000, Obj: 0.389058, No Obj: 0.002536, Avg Recall: 0.776471, count: 85 -Region Avg IOU: 0.616300, Class: 1.000000, Obj: 0.372353, No Obj: 0.003042, Avg Recall: 0.764228, count: 123 -Region Avg IOU: 0.657673, Class: 1.000000, Obj: 0.366629, No Obj: 0.003228, Avg Recall: 0.800000, count: 120 -4462: 0.455267, 0.443190 avg, 0.001000 rate, 28.686468 seconds, 1142272 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.630581, Class: 1.000000, Obj: 0.321038, No Obj: 0.003101, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.584917, Class: 1.000000, Obj: 0.331405, No Obj: 0.002350, Avg Recall: 0.712644, count: 87 -Region Avg IOU: 0.635826, Class: 1.000000, Obj: 0.411484, No Obj: 0.002745, Avg Recall: 0.784483, count: 116 -Region Avg IOU: 0.662368, Class: 1.000000, Obj: 0.368320, No Obj: 0.002617, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.651526, Class: 1.000000, Obj: 0.368153, No Obj: 0.002699, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.603563, Class: 1.000000, Obj: 0.391444, No Obj: 0.002843, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.633930, Class: 1.000000, Obj: 0.419045, No Obj: 0.002427, Avg Recall: 0.807692, count: 78 -Region Avg IOU: 0.626846, Class: 1.000000, Obj: 0.374186, No Obj: 0.002512, Avg Recall: 0.747748, count: 111 -4463: 0.414955, 0.440367 avg, 0.001000 rate, 29.119673 seconds, 1142528 images -Loaded: 0.000145 seconds -Region Avg IOU: 0.638947, Class: 1.000000, Obj: 0.377442, No Obj: 0.002426, Avg Recall: 0.776471, count: 85 -Region Avg IOU: 0.612543, Class: 1.000000, Obj: 0.379581, No Obj: 0.002045, Avg Recall: 0.744444, count: 90 -Region Avg IOU: 0.605865, Class: 1.000000, Obj: 0.355494, No Obj: 0.002590, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.635644, Class: 1.000000, Obj: 0.335825, No Obj: 0.002652, Avg Recall: 0.768595, count: 121 -Region Avg IOU: 0.638104, Class: 1.000000, Obj: 0.393142, No Obj: 0.002403, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.636654, Class: 1.000000, Obj: 0.379603, No Obj: 0.002440, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.663486, Class: 1.000000, Obj: 0.397631, No Obj: 0.002277, Avg Recall: 0.791209, count: 91 -Region Avg IOU: 0.650653, Class: 1.000000, Obj: 0.385567, No Obj: 0.002749, Avg Recall: 0.841584, count: 101 -4464: 0.399517, 0.436282 avg, 0.001000 rate, 28.332874 seconds, 1142784 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.609741, Class: 1.000000, Obj: 0.335693, No Obj: 0.002382, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.651364, Class: 1.000000, Obj: 0.336712, No Obj: 0.002740, Avg Recall: 0.823009, count: 113 -Region Avg IOU: 0.666006, Class: 1.000000, Obj: 0.403218, No Obj: 0.002454, Avg Recall: 0.808081, count: 99 -Region Avg IOU: 0.648002, Class: 1.000000, Obj: 0.372206, No Obj: 0.002151, Avg Recall: 0.775281, count: 89 -Region Avg IOU: 0.674463, Class: 1.000000, Obj: 0.401584, No Obj: 0.002588, Avg Recall: 0.861111, count: 108 -Region Avg IOU: 0.624633, Class: 1.000000, Obj: 0.338292, No Obj: 0.002464, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.622364, Class: 1.000000, Obj: 0.347658, No Obj: 0.002690, Avg Recall: 0.782258, count: 124 -Region Avg IOU: 0.627862, Class: 1.000000, Obj: 0.313587, No Obj: 0.003225, Avg Recall: 0.765101, count: 149 -4465: 0.465901, 0.439244 avg, 0.001000 rate, 28.145395 seconds, 1143040 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.616696, Class: 1.000000, Obj: 0.341827, No Obj: 0.002353, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.671549, Class: 1.000000, Obj: 0.343248, No Obj: 0.002400, Avg Recall: 0.829787, count: 94 -Region Avg IOU: 0.567025, Class: 1.000000, Obj: 0.324603, No Obj: 0.002257, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.611571, Class: 1.000000, Obj: 0.337295, No Obj: 0.002417, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.650553, Class: 1.000000, Obj: 0.364609, No Obj: 0.002380, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.686553, Class: 1.000000, Obj: 0.377402, No Obj: 0.002518, Avg Recall: 0.871560, count: 109 -Region Avg IOU: 0.628984, Class: 1.000000, Obj: 0.340909, No Obj: 0.002624, Avg Recall: 0.747967, count: 123 -Region Avg IOU: 0.666548, Class: 1.000000, Obj: 0.377540, No Obj: 0.002417, Avg Recall: 0.826531, count: 98 -4466: 0.481500, 0.443469 avg, 0.001000 rate, 27.890615 seconds, 1143296 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.617659, Class: 1.000000, Obj: 0.378566, No Obj: 0.002592, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.600605, Class: 1.000000, Obj: 0.333838, No Obj: 0.002076, Avg Recall: 0.710843, count: 83 -Region Avg IOU: 0.663440, Class: 1.000000, Obj: 0.379169, No Obj: 0.002409, Avg Recall: 0.838384, count: 99 -Region Avg IOU: 0.648977, Class: 1.000000, Obj: 0.372027, No Obj: 0.002438, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.666779, Class: 1.000000, Obj: 0.354531, No Obj: 0.003319, Avg Recall: 0.818182, count: 143 -Region Avg IOU: 0.658618, Class: 1.000000, Obj: 0.370285, No Obj: 0.002469, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.641625, Class: 1.000000, Obj: 0.376838, No Obj: 0.002537, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.624607, Class: 1.000000, Obj: 0.343766, No Obj: 0.002402, Avg Recall: 0.800000, count: 115 -4467: 0.439651, 0.443087 avg, 0.001000 rate, 28.112652 seconds, 1143552 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.652428, Class: 1.000000, Obj: 0.375019, No Obj: 0.003044, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.631486, Class: 1.000000, Obj: 0.365254, No Obj: 0.002214, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.682948, Class: 1.000000, Obj: 0.399356, No Obj: 0.002855, Avg Recall: 0.888889, count: 108 -Region Avg IOU: 0.674426, Class: 1.000000, Obj: 0.433640, No Obj: 0.002600, Avg Recall: 0.866667, count: 105 -Region Avg IOU: 0.656464, Class: 1.000000, Obj: 0.386017, No Obj: 0.002572, Avg Recall: 0.829787, count: 94 -Region Avg IOU: 0.661304, Class: 1.000000, Obj: 0.290427, No Obj: 0.002494, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.571321, Class: 1.000000, Obj: 0.328660, No Obj: 0.002393, Avg Recall: 0.682927, count: 123 -Region Avg IOU: 0.612027, Class: 1.000000, Obj: 0.319446, No Obj: 0.002605, Avg Recall: 0.759259, count: 108 -4468: 0.448654, 0.443644 avg, 0.001000 rate, 29.591616 seconds, 1143808 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.603868, Class: 1.000000, Obj: 0.282665, No Obj: 0.002878, Avg Recall: 0.723404, count: 141 -Region Avg IOU: 0.649887, Class: 1.000000, Obj: 0.342912, No Obj: 0.002536, Avg Recall: 0.794643, count: 112 -Region Avg IOU: 0.626172, Class: 1.000000, Obj: 0.391670, No Obj: 0.002650, Avg Recall: 0.757812, count: 128 -Region Avg IOU: 0.629760, Class: 1.000000, Obj: 0.312217, No Obj: 0.002570, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.601246, Class: 1.000000, Obj: 0.341245, No Obj: 0.002986, Avg Recall: 0.776224, count: 143 -Region Avg IOU: 0.624304, Class: 1.000000, Obj: 0.319426, No Obj: 0.002552, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.656035, Class: 1.000000, Obj: 0.375181, No Obj: 0.002709, Avg Recall: 0.829060, count: 117 -Region Avg IOU: 0.613852, Class: 1.000000, Obj: 0.314624, No Obj: 0.002681, Avg Recall: 0.753623, count: 138 -4469: 0.547127, 0.453992 avg, 0.001000 rate, 28.560724 seconds, 1144064 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.646478, Class: 1.000000, Obj: 0.432281, No Obj: 0.002445, Avg Recall: 0.802083, count: 96 -Region Avg IOU: 0.615331, Class: 1.000000, Obj: 0.398342, No Obj: 0.002544, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.633837, Class: 1.000000, Obj: 0.361397, No Obj: 0.002648, Avg Recall: 0.756303, count: 119 -Region Avg IOU: 0.649139, Class: 1.000000, Obj: 0.345276, No Obj: 0.002953, Avg Recall: 0.835714, count: 140 -Region Avg IOU: 0.659156, Class: 1.000000, Obj: 0.415124, No Obj: 0.002580, Avg Recall: 0.813187, count: 91 -Region Avg IOU: 0.615664, Class: 1.000000, Obj: 0.360040, No Obj: 0.002694, Avg Recall: 0.770492, count: 122 -Region Avg IOU: 0.693527, Class: 1.000000, Obj: 0.426009, No Obj: 0.003034, Avg Recall: 0.869565, count: 115 -Region Avg IOU: 0.702309, Class: 1.000000, Obj: 0.330029, No Obj: 0.002884, Avg Recall: 0.939759, count: 83 -4470: 0.449761, 0.453569 avg, 0.001000 rate, 27.387375 seconds, 1144320 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 13.978723 seconds -Region Avg IOU: 0.599803, Class: 1.000000, Obj: 0.344184, No Obj: 0.003257, Avg Recall: 0.736264, count: 91 -Region Avg IOU: 0.584857, Class: 1.000000, Obj: 0.338575, No Obj: 0.003380, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.562898, Class: 1.000000, Obj: 0.332102, No Obj: 0.002484, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.585758, Class: 1.000000, Obj: 0.363688, No Obj: 0.003407, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.596337, Class: 1.000000, Obj: 0.375811, No Obj: 0.003254, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.573388, Class: 1.000000, Obj: 0.330817, No Obj: 0.003625, Avg Recall: 0.666667, count: 120 -Region Avg IOU: 0.488326, Class: 1.000000, Obj: 0.254051, No Obj: 0.002571, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.596941, Class: 1.000000, Obj: 0.320663, No Obj: 0.003207, Avg Recall: 0.724138, count: 116 -4471: 0.440696, 0.452282 avg, 0.001000 rate, 23.682283 seconds, 1144576 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.595196, Class: 1.000000, Obj: 0.343056, No Obj: 0.003594, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.630295, Class: 1.000000, Obj: 0.361699, No Obj: 0.003955, Avg Recall: 0.733871, count: 124 -Region Avg IOU: 0.569120, Class: 1.000000, Obj: 0.330184, No Obj: 0.003512, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.539629, Class: 1.000000, Obj: 0.344630, No Obj: 0.002944, Avg Recall: 0.638554, count: 83 -Region Avg IOU: 0.533670, Class: 1.000000, Obj: 0.351505, No Obj: 0.003541, Avg Recall: 0.593750, count: 96 -Region Avg IOU: 0.605518, Class: 1.000000, Obj: 0.388587, No Obj: 0.003440, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.618674, Class: 1.000000, Obj: 0.366931, No Obj: 0.003977, Avg Recall: 0.736434, count: 129 -Region Avg IOU: 0.556275, Class: 1.000000, Obj: 0.393091, No Obj: 0.003349, Avg Recall: 0.660000, count: 100 -4472: 0.409774, 0.448031 avg, 0.001000 rate, 23.674091 seconds, 1144832 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.575315, Class: 1.000000, Obj: 0.361910, No Obj: 0.003586, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.558895, Class: 1.000000, Obj: 0.366416, No Obj: 0.003398, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.593252, Class: 1.000000, Obj: 0.382481, No Obj: 0.004001, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.546063, Class: 1.000000, Obj: 0.374356, No Obj: 0.003191, Avg Recall: 0.589744, count: 78 -Region Avg IOU: 0.584979, Class: 1.000000, Obj: 0.384191, No Obj: 0.003618, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.585195, Class: 1.000000, Obj: 0.377313, No Obj: 0.004076, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.588394, Class: 1.000000, Obj: 0.380176, No Obj: 0.004176, Avg Recall: 0.704000, count: 125 -Region Avg IOU: 0.551107, Class: 1.000000, Obj: 0.408737, No Obj: 0.003836, Avg Recall: 0.638298, count: 94 -4473: 0.416607, 0.444889 avg, 0.001000 rate, 23.311123 seconds, 1145088 images -Loaded: 0.000162 seconds -Region Avg IOU: 0.600213, Class: 1.000000, Obj: 0.412194, No Obj: 0.003225, Avg Recall: 0.716418, count: 67 -Region Avg IOU: 0.567067, Class: 1.000000, Obj: 0.418808, No Obj: 0.003384, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.627736, Class: 1.000000, Obj: 0.461799, No Obj: 0.004258, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.538373, Class: 1.000000, Obj: 0.379265, No Obj: 0.003778, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.643525, Class: 1.000000, Obj: 0.427846, No Obj: 0.003339, Avg Recall: 0.766234, count: 77 -Region Avg IOU: 0.598482, Class: 1.000000, Obj: 0.325706, No Obj: 0.004449, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.592399, Class: 1.000000, Obj: 0.399289, No Obj: 0.004687, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.521740, Class: 1.000000, Obj: 0.339229, No Obj: 0.004024, Avg Recall: 0.617886, count: 123 -4474: 0.431412, 0.443541 avg, 0.001000 rate, 22.963112 seconds, 1145344 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.560535, Class: 1.000000, Obj: 0.345750, No Obj: 0.004414, Avg Recall: 0.655738, count: 122 -Region Avg IOU: 0.575495, Class: 1.000000, Obj: 0.395822, No Obj: 0.004096, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.584225, Class: 1.000000, Obj: 0.421120, No Obj: 0.004590, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.559633, Class: 1.000000, Obj: 0.425911, No Obj: 0.003417, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.543914, Class: 1.000000, Obj: 0.372482, No Obj: 0.004139, Avg Recall: 0.603306, count: 121 -Region Avg IOU: 0.571411, Class: 1.000000, Obj: 0.410645, No Obj: 0.004348, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.553650, Class: 1.000000, Obj: 0.409353, No Obj: 0.004495, Avg Recall: 0.631148, count: 122 -Region Avg IOU: 0.573314, Class: 1.000000, Obj: 0.407011, No Obj: 0.003963, Avg Recall: 0.703704, count: 108 -4475: 0.443205, 0.443507 avg, 0.001000 rate, 24.090193 seconds, 1145600 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.636298, Class: 1.000000, Obj: 0.469893, No Obj: 0.004657, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.612655, Class: 1.000000, Obj: 0.425425, No Obj: 0.004467, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.624175, Class: 1.000000, Obj: 0.460620, No Obj: 0.004122, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.536671, Class: 1.000000, Obj: 0.418290, No Obj: 0.004559, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.560843, Class: 1.000000, Obj: 0.408755, No Obj: 0.004232, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.527772, Class: 1.000000, Obj: 0.408496, No Obj: 0.004372, Avg Recall: 0.614286, count: 140 -Region Avg IOU: 0.566027, Class: 1.000000, Obj: 0.394467, No Obj: 0.004332, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.583680, Class: 1.000000, Obj: 0.416437, No Obj: 0.004210, Avg Recall: 0.657143, count: 105 -4476: 0.384600, 0.437617 avg, 0.001000 rate, 22.424488 seconds, 1145856 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.572576, Class: 1.000000, Obj: 0.451753, No Obj: 0.003743, Avg Recall: 0.682927, count: 82 -Region Avg IOU: 0.608573, Class: 1.000000, Obj: 0.448200, No Obj: 0.004758, Avg Recall: 0.696000, count: 125 -Region Avg IOU: 0.605026, Class: 1.000000, Obj: 0.409467, No Obj: 0.003890, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.558715, Class: 1.000000, Obj: 0.419459, No Obj: 0.003904, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.553854, Class: 1.000000, Obj: 0.398661, No Obj: 0.004083, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.587279, Class: 1.000000, Obj: 0.390532, No Obj: 0.003720, Avg Recall: 0.689655, count: 87 -Region Avg IOU: 0.528164, Class: 1.000000, Obj: 0.382076, No Obj: 0.004588, Avg Recall: 0.609195, count: 87 -Region Avg IOU: 0.522776, Class: 1.000000, Obj: 0.351294, No Obj: 0.004233, Avg Recall: 0.597015, count: 134 -4477: 0.445981, 0.438453 avg, 0.001000 rate, 23.579058 seconds, 1146112 images -Loaded: 0.201269 seconds -Region Avg IOU: 0.483523, Class: 1.000000, Obj: 0.423430, No Obj: 0.003948, Avg Recall: 0.525862, count: 116 -Region Avg IOU: 0.591183, Class: 1.000000, Obj: 0.426229, No Obj: 0.003888, Avg Recall: 0.709302, count: 86 -Region Avg IOU: 0.560728, Class: 1.000000, Obj: 0.445972, No Obj: 0.004246, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.584337, Class: 1.000000, Obj: 0.427297, No Obj: 0.004267, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.529097, Class: 1.000000, Obj: 0.384587, No Obj: 0.004628, Avg Recall: 0.631579, count: 133 -Region Avg IOU: 0.542038, Class: 1.000000, Obj: 0.371044, No Obj: 0.004266, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.586926, Class: 1.000000, Obj: 0.428999, No Obj: 0.004157, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.551027, Class: 1.000000, Obj: 0.361874, No Obj: 0.004208, Avg Recall: 0.588235, count: 102 -4478: 0.449356, 0.439543 avg, 0.001000 rate, 23.381420 seconds, 1146368 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.553187, Class: 1.000000, Obj: 0.381944, No Obj: 0.003813, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.536549, Class: 1.000000, Obj: 0.416329, No Obj: 0.003654, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.542602, Class: 1.000000, Obj: 0.459626, No Obj: 0.003695, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.546825, Class: 1.000000, Obj: 0.364165, No Obj: 0.004260, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.560946, Class: 1.000000, Obj: 0.370009, No Obj: 0.004658, Avg Recall: 0.653226, count: 124 -Region Avg IOU: 0.592355, Class: 1.000000, Obj: 0.401864, No Obj: 0.003701, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.596710, Class: 1.000000, Obj: 0.409321, No Obj: 0.003689, Avg Recall: 0.746988, count: 83 -Region Avg IOU: 0.518074, Class: 1.000000, Obj: 0.411720, No Obj: 0.003726, Avg Recall: 0.603774, count: 106 -4479: 0.418710, 0.437460 avg, 0.001000 rate, 23.212751 seconds, 1146624 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.559951, Class: 1.000000, Obj: 0.375908, No Obj: 0.003468, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.547675, Class: 1.000000, Obj: 0.315620, No Obj: 0.003338, Avg Recall: 0.619048, count: 84 -Region Avg IOU: 0.587336, Class: 1.000000, Obj: 0.378786, No Obj: 0.004124, Avg Recall: 0.714286, count: 119 -Region Avg IOU: 0.554002, Class: 1.000000, Obj: 0.363571, No Obj: 0.003910, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.560226, Class: 1.000000, Obj: 0.392742, No Obj: 0.003960, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.538566, Class: 1.000000, Obj: 0.389416, No Obj: 0.003143, Avg Recall: 0.626374, count: 91 -Region Avg IOU: 0.557864, Class: 1.000000, Obj: 0.379605, No Obj: 0.003972, Avg Recall: 0.658537, count: 123 -Region Avg IOU: 0.559418, Class: 1.000000, Obj: 0.354614, No Obj: 0.004346, Avg Recall: 0.685185, count: 108 -4480: 0.481770, 0.441891 avg, 0.001000 rate, 23.499683 seconds, 1146880 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.006477 seconds -Region Avg IOU: 0.511410, Class: 1.000000, Obj: 0.300486, No Obj: 0.003871, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.499250, Class: 1.000000, Obj: 0.322632, No Obj: 0.004369, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.465209, Class: 1.000000, Obj: 0.296759, No Obj: 0.003682, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.487435, Class: 1.000000, Obj: 0.322791, No Obj: 0.004208, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.535499, Class: 1.000000, Obj: 0.301306, No Obj: 0.004127, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.481586, Class: 1.000000, Obj: 0.321408, No Obj: 0.004245, Avg Recall: 0.504132, count: 121 -Region Avg IOU: 0.487109, Class: 1.000000, Obj: 0.285067, No Obj: 0.004187, Avg Recall: 0.542373, count: 118 -Region Avg IOU: 0.517407, Class: 1.000000, Obj: 0.359639, No Obj: 0.003903, Avg Recall: 0.626667, count: 75 -4481: 0.427213, 0.440423 avg, 0.001000 rate, 20.717613 seconds, 1147136 images -Loaded: 1.179454 seconds -Region Avg IOU: 0.537632, Class: 1.000000, Obj: 0.341511, No Obj: 0.004066, Avg Recall: 0.592920, count: 113 -Region Avg IOU: 0.531519, Class: 1.000000, Obj: 0.329323, No Obj: 0.003839, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.478303, Class: 1.000000, Obj: 0.271675, No Obj: 0.004228, Avg Recall: 0.563910, count: 133 -Region Avg IOU: 0.544641, Class: 1.000000, Obj: 0.290394, No Obj: 0.004294, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.504942, Class: 1.000000, Obj: 0.278336, No Obj: 0.003570, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.495086, Class: 1.000000, Obj: 0.304036, No Obj: 0.003362, Avg Recall: 0.538462, count: 91 -Region Avg IOU: 0.499343, Class: 1.000000, Obj: 0.294611, No Obj: 0.003775, Avg Recall: 0.550562, count: 89 -Region Avg IOU: 0.549901, Class: 1.000000, Obj: 0.313730, No Obj: 0.003373, Avg Recall: 0.673684, count: 95 -4482: 0.476920, 0.444073 avg, 0.001000 rate, 16.587601 seconds, 1147392 images -Loaded: 5.002629 seconds -Region Avg IOU: 0.485540, Class: 1.000000, Obj: 0.283319, No Obj: 0.004229, Avg Recall: 0.539568, count: 139 -Region Avg IOU: 0.480796, Class: 1.000000, Obj: 0.305548, No Obj: 0.004369, Avg Recall: 0.511450, count: 131 -Region Avg IOU: 0.482338, Class: 1.000000, Obj: 0.338411, No Obj: 0.004035, Avg Recall: 0.554348, count: 92 -Region Avg IOU: 0.443939, Class: 1.000000, Obj: 0.282115, No Obj: 0.003517, Avg Recall: 0.461538, count: 104 -Region Avg IOU: 0.520734, Class: 1.000000, Obj: 0.354620, No Obj: 0.003428, Avg Recall: 0.549451, count: 91 -Region Avg IOU: 0.469346, Class: 1.000000, Obj: 0.259004, No Obj: 0.004124, Avg Recall: 0.522124, count: 113 -Region Avg IOU: 0.530662, Class: 1.000000, Obj: 0.324853, No Obj: 0.004224, Avg Recall: 0.559322, count: 118 -Region Avg IOU: 0.471547, Class: 1.000000, Obj: 0.278349, No Obj: 0.003647, Avg Recall: 0.544715, count: 123 -4483: 0.517115, 0.451377 avg, 0.001000 rate, 17.387047 seconds, 1147648 images -Loaded: 3.176304 seconds -Region Avg IOU: 0.513942, Class: 1.000000, Obj: 0.325666, No Obj: 0.004149, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.458786, Class: 1.000000, Obj: 0.291600, No Obj: 0.004291, Avg Recall: 0.492958, count: 142 -Region Avg IOU: 0.529693, Class: 1.000000, Obj: 0.312595, No Obj: 0.003777, Avg Recall: 0.626506, count: 83 -Region Avg IOU: 0.521611, Class: 1.000000, Obj: 0.319835, No Obj: 0.003776, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.438966, Class: 1.000000, Obj: 0.283093, No Obj: 0.003619, Avg Recall: 0.469027, count: 113 -Region Avg IOU: 0.506637, Class: 1.000000, Obj: 0.294553, No Obj: 0.003663, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.440024, Class: 1.000000, Obj: 0.290534, No Obj: 0.003487, Avg Recall: 0.459184, count: 98 -Region Avg IOU: 0.484213, Class: 1.000000, Obj: 0.340286, No Obj: 0.004166, Avg Recall: 0.520833, count: 96 -4484: 0.419386, 0.448178 avg, 0.001000 rate, 17.799276 seconds, 1147904 images -Loaded: 3.195473 seconds -Region Avg IOU: 0.510355, Class: 1.000000, Obj: 0.337093, No Obj: 0.004446, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.545747, Class: 1.000000, Obj: 0.337685, No Obj: 0.004333, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.510793, Class: 1.000000, Obj: 0.265595, No Obj: 0.004298, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.465042, Class: 1.000000, Obj: 0.309507, No Obj: 0.003596, Avg Recall: 0.477064, count: 109 -Region Avg IOU: 0.519628, Class: 1.000000, Obj: 0.320903, No Obj: 0.004464, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.494102, Class: 1.000000, Obj: 0.297207, No Obj: 0.003827, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.431610, Class: 1.000000, Obj: 0.285796, No Obj: 0.003935, Avg Recall: 0.410714, count: 112 -Region Avg IOU: 0.525188, Class: 1.000000, Obj: 0.357926, No Obj: 0.004564, Avg Recall: 0.527473, count: 91 -4485: 0.463384, 0.449699 avg, 0.001000 rate, 21.875923 seconds, 1148160 images -Loaded: 0.260710 seconds -Region Avg IOU: 0.559846, Class: 1.000000, Obj: 0.303845, No Obj: 0.004651, Avg Recall: 0.655738, count: 122 -Region Avg IOU: 0.514015, Class: 1.000000, Obj: 0.301618, No Obj: 0.005040, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.537368, Class: 1.000000, Obj: 0.291202, No Obj: 0.004639, Avg Recall: 0.591667, count: 120 -Region Avg IOU: 0.487678, Class: 1.000000, Obj: 0.327348, No Obj: 0.004371, Avg Recall: 0.541667, count: 120 -Region Avg IOU: 0.533616, Class: 1.000000, Obj: 0.307564, No Obj: 0.004336, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.538211, Class: 1.000000, Obj: 0.341178, No Obj: 0.005366, Avg Recall: 0.601449, count: 138 -Region Avg IOU: 0.526605, Class: 1.000000, Obj: 0.334956, No Obj: 0.004191, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.481221, Class: 1.000000, Obj: 0.305347, No Obj: 0.004388, Avg Recall: 0.507937, count: 126 -4486: 0.525383, 0.457267 avg, 0.001000 rate, 17.560488 seconds, 1148416 images -Loaded: 2.984304 seconds -Region Avg IOU: 0.523060, Class: 1.000000, Obj: 0.373321, No Obj: 0.004692, Avg Recall: 0.529412, count: 119 -Region Avg IOU: 0.508289, Class: 1.000000, Obj: 0.363776, No Obj: 0.004312, Avg Recall: 0.528090, count: 89 -Region Avg IOU: 0.502066, Class: 1.000000, Obj: 0.381642, No Obj: 0.004270, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.502369, Class: 1.000000, Obj: 0.337823, No Obj: 0.004988, Avg Recall: 0.572519, count: 131 -Region Avg IOU: 0.535602, Class: 1.000000, Obj: 0.348651, No Obj: 0.004888, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.513985, Class: 1.000000, Obj: 0.355361, No Obj: 0.004175, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.492582, Class: 1.000000, Obj: 0.328506, No Obj: 0.003876, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.506369, Class: 1.000000, Obj: 0.340320, No Obj: 0.004848, Avg Recall: 0.589552, count: 134 -4487: 0.444267, 0.455967 avg, 0.001000 rate, 17.168133 seconds, 1148672 images -Loaded: 3.844305 seconds -Region Avg IOU: 0.521713, Class: 1.000000, Obj: 0.380672, No Obj: 0.005198, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.512162, Class: 1.000000, Obj: 0.348706, No Obj: 0.004476, Avg Recall: 0.586207, count: 87 -Region Avg IOU: 0.476642, Class: 1.000000, Obj: 0.326948, No Obj: 0.003683, Avg Recall: 0.493506, count: 77 -Region Avg IOU: 0.515935, Class: 1.000000, Obj: 0.361676, No Obj: 0.004577, Avg Recall: 0.625000, count: 88 -Region Avg IOU: 0.484790, Class: 1.000000, Obj: 0.300302, No Obj: 0.004853, Avg Recall: 0.546219, count: 119 -Region Avg IOU: 0.495026, Class: 1.000000, Obj: 0.321150, No Obj: 0.004023, Avg Recall: 0.543478, count: 92 -Region Avg IOU: 0.496581, Class: 1.000000, Obj: 0.320116, No Obj: 0.003828, Avg Recall: 0.528090, count: 89 -Region Avg IOU: 0.483280, Class: 1.000000, Obj: 0.363077, No Obj: 0.004515, Avg Recall: 0.543478, count: 92 -4488: 0.390299, 0.449400 avg, 0.001000 rate, 20.473915 seconds, 1148928 images -Loaded: 1.333229 seconds -Region Avg IOU: 0.498715, Class: 1.000000, Obj: 0.355285, No Obj: 0.004832, Avg Recall: 0.559322, count: 118 -Region Avg IOU: 0.461836, Class: 1.000000, Obj: 0.318842, No Obj: 0.004001, Avg Recall: 0.493151, count: 73 -Region Avg IOU: 0.509960, Class: 1.000000, Obj: 0.390348, No Obj: 0.004917, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.450837, Class: 1.000000, Obj: 0.313467, No Obj: 0.005375, Avg Recall: 0.486486, count: 148 -Region Avg IOU: 0.469348, Class: 1.000000, Obj: 0.325980, No Obj: 0.005442, Avg Recall: 0.510791, count: 139 -Region Avg IOU: 0.494893, Class: 1.000000, Obj: 0.376073, No Obj: 0.005816, Avg Recall: 0.558140, count: 129 -Region Avg IOU: 0.478976, Class: 1.000000, Obj: 0.338942, No Obj: 0.005026, Avg Recall: 0.509434, count: 106 -Region Avg IOU: 0.477178, Class: 1.000000, Obj: 0.348090, No Obj: 0.005029, Avg Recall: 0.516129, count: 124 -4489: 0.495642, 0.454024 avg, 0.001000 rate, 20.022009 seconds, 1149184 images -Loaded: 0.970792 seconds -Region Avg IOU: 0.506720, Class: 1.000000, Obj: 0.373494, No Obj: 0.005127, Avg Recall: 0.592593, count: 108 -Region Avg IOU: 0.506119, Class: 1.000000, Obj: 0.371896, No Obj: 0.005152, Avg Recall: 0.555556, count: 90 -Region Avg IOU: 0.502181, Class: 1.000000, Obj: 0.389774, No Obj: 0.004386, Avg Recall: 0.550562, count: 89 -Region Avg IOU: 0.509743, Class: 1.000000, Obj: 0.347293, No Obj: 0.005363, Avg Recall: 0.575758, count: 132 -Region Avg IOU: 0.469385, Class: 1.000000, Obj: 0.388391, No Obj: 0.004943, Avg Recall: 0.495868, count: 121 -Region Avg IOU: 0.499997, Class: 1.000000, Obj: 0.409772, No Obj: 0.004682, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.486682, Class: 1.000000, Obj: 0.363241, No Obj: 0.005650, Avg Recall: 0.529412, count: 136 -Region Avg IOU: 0.554328, Class: 1.000000, Obj: 0.383681, No Obj: 0.005410, Avg Recall: 0.663934, count: 122 -4490: 0.488259, 0.457448 avg, 0.001000 rate, 18.582460 seconds, 1149440 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 0.540659 seconds -Region Avg IOU: 0.597234, Class: 1.000000, Obj: 0.498670, No Obj: 0.004595, Avg Recall: 0.707317, count: 82 -Region Avg IOU: 0.569818, Class: 1.000000, Obj: 0.427895, No Obj: 0.004936, Avg Recall: 0.617188, count: 128 -Region Avg IOU: 0.575143, Class: 1.000000, Obj: 0.476930, No Obj: 0.004194, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.601042, Class: 1.000000, Obj: 0.441382, No Obj: 0.005628, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.571491, Class: 1.000000, Obj: 0.434284, No Obj: 0.004266, Avg Recall: 0.711111, count: 90 -Region Avg IOU: 0.551088, Class: 1.000000, Obj: 0.425267, No Obj: 0.005112, Avg Recall: 0.611111, count: 126 -Region Avg IOU: 0.548661, Class: 1.000000, Obj: 0.427760, No Obj: 0.004818, Avg Recall: 0.647541, count: 122 -Region Avg IOU: 0.599220, Class: 1.000000, Obj: 0.447922, No Obj: 0.005697, Avg Recall: 0.722628, count: 137 -4491: 0.439727, 0.455676 avg, 0.001000 rate, 23.714296 seconds, 1149696 images -Loaded: 0.000116 seconds -Region Avg IOU: 0.529745, Class: 1.000000, Obj: 0.423753, No Obj: 0.004551, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.617829, Class: 1.000000, Obj: 0.529767, No Obj: 0.004217, Avg Recall: 0.759036, count: 83 -Region Avg IOU: 0.577502, Class: 1.000000, Obj: 0.423257, No Obj: 0.004622, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.518868, Class: 1.000000, Obj: 0.403518, No Obj: 0.004775, Avg Recall: 0.609375, count: 128 -Region Avg IOU: 0.555113, Class: 1.000000, Obj: 0.425546, No Obj: 0.004710, Avg Recall: 0.656489, count: 131 -Region Avg IOU: 0.615725, Class: 1.000000, Obj: 0.429277, No Obj: 0.004931, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.536987, Class: 1.000000, Obj: 0.413362, No Obj: 0.005016, Avg Recall: 0.628788, count: 132 -Region Avg IOU: 0.538099, Class: 1.000000, Obj: 0.397987, No Obj: 0.004424, Avg Recall: 0.637255, count: 102 -4492: 0.468480, 0.456956 avg, 0.001000 rate, 23.928265 seconds, 1149952 images -Loaded: 0.000052 seconds -Region Avg IOU: 0.538950, Class: 1.000000, Obj: 0.416286, No Obj: 0.004073, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.605603, Class: 1.000000, Obj: 0.453442, No Obj: 0.004735, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.477475, Class: 1.000000, Obj: 0.381176, No Obj: 0.003999, Avg Recall: 0.537190, count: 121 -Region Avg IOU: 0.543347, Class: 1.000000, Obj: 0.397738, No Obj: 0.005098, Avg Recall: 0.625954, count: 131 -Region Avg IOU: 0.561508, Class: 1.000000, Obj: 0.434675, No Obj: 0.004795, Avg Recall: 0.666667, count: 120 -Region Avg IOU: 0.551064, Class: 1.000000, Obj: 0.412335, No Obj: 0.005208, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.568913, Class: 1.000000, Obj: 0.406880, No Obj: 0.004309, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.474731, Class: 1.000000, Obj: 0.372327, No Obj: 0.003968, Avg Recall: 0.532110, count: 109 -4493: 0.434633, 0.454724 avg, 0.001000 rate, 22.274000 seconds, 1150208 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.527332, Class: 1.000000, Obj: 0.374238, No Obj: 0.003629, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.557480, Class: 1.000000, Obj: 0.393152, No Obj: 0.004118, Avg Recall: 0.616667, count: 120 -Region Avg IOU: 0.547044, Class: 1.000000, Obj: 0.377624, No Obj: 0.003626, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.566452, Class: 1.000000, Obj: 0.457938, No Obj: 0.003563, Avg Recall: 0.642857, count: 84 -Region Avg IOU: 0.578092, Class: 1.000000, Obj: 0.380972, No Obj: 0.003919, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.530573, Class: 1.000000, Obj: 0.383579, No Obj: 0.004095, Avg Recall: 0.625000, count: 128 -Region Avg IOU: 0.566691, Class: 1.000000, Obj: 0.444697, No Obj: 0.004318, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.559224, Class: 1.000000, Obj: 0.408968, No Obj: 0.003968, Avg Recall: 0.632479, count: 117 -4494: 0.442105, 0.453462 avg, 0.001000 rate, 22.977148 seconds, 1150464 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.632783, Class: 1.000000, Obj: 0.426752, No Obj: 0.003856, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.523128, Class: 1.000000, Obj: 0.361305, No Obj: 0.003728, Avg Recall: 0.564516, count: 124 -Region Avg IOU: 0.562117, Class: 1.000000, Obj: 0.348532, No Obj: 0.003807, Avg Recall: 0.648000, count: 125 -Region Avg IOU: 0.590121, Class: 1.000000, Obj: 0.393192, No Obj: 0.003337, Avg Recall: 0.750000, count: 80 -Region Avg IOU: 0.599210, Class: 1.000000, Obj: 0.344601, No Obj: 0.003818, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.603006, Class: 1.000000, Obj: 0.336749, No Obj: 0.004071, Avg Recall: 0.737705, count: 122 -Region Avg IOU: 0.581619, Class: 1.000000, Obj: 0.403870, No Obj: 0.004168, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.512262, Class: 1.000000, Obj: 0.337834, No Obj: 0.004011, Avg Recall: 0.573770, count: 122 -4495: 0.431754, 0.451291 avg, 0.001000 rate, 23.268600 seconds, 1150720 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.613890, Class: 1.000000, Obj: 0.388759, No Obj: 0.003977, Avg Recall: 0.738095, count: 126 -Region Avg IOU: 0.605390, Class: 1.000000, Obj: 0.374002, No Obj: 0.003360, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.585306, Class: 1.000000, Obj: 0.385518, No Obj: 0.003449, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.540483, Class: 1.000000, Obj: 0.357006, No Obj: 0.003900, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.545637, Class: 1.000000, Obj: 0.336813, No Obj: 0.004017, Avg Recall: 0.604839, count: 124 -Region Avg IOU: 0.587250, Class: 1.000000, Obj: 0.382670, No Obj: 0.003290, Avg Recall: 0.686956, count: 115 -Region Avg IOU: 0.595194, Class: 1.000000, Obj: 0.412495, No Obj: 0.003749, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.523387, Class: 1.000000, Obj: 0.296851, No Obj: 0.003426, Avg Recall: 0.584746, count: 118 -4496: 0.484548, 0.454617 avg, 0.001000 rate, 22.756371 seconds, 1150976 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.570366, Class: 1.000000, Obj: 0.321831, No Obj: 0.003905, Avg Recall: 0.668919, count: 148 -Region Avg IOU: 0.532270, Class: 1.000000, Obj: 0.343086, No Obj: 0.003980, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.532903, Class: 1.000000, Obj: 0.332663, No Obj: 0.003017, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.548005, Class: 1.000000, Obj: 0.339894, No Obj: 0.003149, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.557859, Class: 1.000000, Obj: 0.370443, No Obj: 0.003732, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.556199, Class: 1.000000, Obj: 0.359988, No Obj: 0.003009, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.587674, Class: 1.000000, Obj: 0.359030, No Obj: 0.003667, Avg Recall: 0.677419, count: 124 -Region Avg IOU: 0.564812, Class: 1.000000, Obj: 0.344042, No Obj: 0.003189, Avg Recall: 0.657143, count: 105 -4497: 0.442588, 0.453414 avg, 0.001000 rate, 23.536549 seconds, 1151232 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.538421, Class: 1.000000, Obj: 0.312693, No Obj: 0.003793, Avg Recall: 0.659722, count: 144 -Region Avg IOU: 0.596514, Class: 1.000000, Obj: 0.359453, No Obj: 0.003090, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.559149, Class: 1.000000, Obj: 0.318313, No Obj: 0.003277, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.609741, Class: 1.000000, Obj: 0.349865, No Obj: 0.003331, Avg Recall: 0.744444, count: 90 -Region Avg IOU: 0.599824, Class: 1.000000, Obj: 0.408712, No Obj: 0.002565, Avg Recall: 0.780822, count: 73 -Region Avg IOU: 0.567355, Class: 1.000000, Obj: 0.329924, No Obj: 0.003602, Avg Recall: 0.640000, count: 125 -Region Avg IOU: 0.560190, Class: 1.000000, Obj: 0.325345, No Obj: 0.003274, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.599068, Class: 1.000000, Obj: 0.358369, No Obj: 0.002838, Avg Recall: 0.703704, count: 81 -4498: 0.408343, 0.448907 avg, 0.001000 rate, 23.179380 seconds, 1151488 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.552765, Class: 1.000000, Obj: 0.303042, No Obj: 0.003208, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.542257, Class: 1.000000, Obj: 0.308147, No Obj: 0.003578, Avg Recall: 0.622222, count: 135 -Region Avg IOU: 0.577077, Class: 1.000000, Obj: 0.334042, No Obj: 0.002793, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.613160, Class: 1.000000, Obj: 0.342838, No Obj: 0.003387, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.540651, Class: 1.000000, Obj: 0.280116, No Obj: 0.003386, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.563596, Class: 1.000000, Obj: 0.339077, No Obj: 0.003341, Avg Recall: 0.587719, count: 114 -Region Avg IOU: 0.626447, Class: 1.000000, Obj: 0.354773, No Obj: 0.003201, Avg Recall: 0.781609, count: 87 -Region Avg IOU: 0.618157, Class: 1.000000, Obj: 0.359567, No Obj: 0.003351, Avg Recall: 0.725275, count: 91 -4499: 0.426814, 0.446698 avg, 0.001000 rate, 23.667330 seconds, 1151744 images -Loaded: 0.000051 seconds -Region Avg IOU: 0.542303, Class: 1.000000, Obj: 0.301149, No Obj: 0.003614, Avg Recall: 0.625806, count: 155 -Region Avg IOU: 0.611683, Class: 1.000000, Obj: 0.374663, No Obj: 0.003253, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.572081, Class: 1.000000, Obj: 0.339096, No Obj: 0.003060, Avg Recall: 0.647059, count: 85 -Region Avg IOU: 0.604677, Class: 1.000000, Obj: 0.315552, No Obj: 0.003167, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.565157, Class: 1.000000, Obj: 0.337792, No Obj: 0.003411, Avg Recall: 0.653543, count: 127 -Region Avg IOU: 0.581351, Class: 1.000000, Obj: 0.349785, No Obj: 0.002880, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.605044, Class: 1.000000, Obj: 0.381046, No Obj: 0.003072, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.577008, Class: 1.000000, Obj: 0.333203, No Obj: 0.003296, Avg Recall: 0.663551, count: 107 -4500: 0.447574, 0.446785 avg, 0.001000 rate, 22.850672 seconds, 1152000 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.948303 seconds -Region Avg IOU: 0.482565, Class: 1.000000, Obj: 0.246596, No Obj: 0.003525, Avg Recall: 0.482456, count: 114 -Region Avg IOU: 0.466780, Class: 1.000000, Obj: 0.272833, No Obj: 0.003807, Avg Recall: 0.457364, count: 129 -Region Avg IOU: 0.483753, Class: 1.000000, Obj: 0.277053, No Obj: 0.003469, Avg Recall: 0.526786, count: 112 -Region Avg IOU: 0.537726, Class: 1.000000, Obj: 0.288145, No Obj: 0.003827, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.475643, Class: 1.000000, Obj: 0.267782, No Obj: 0.003703, Avg Recall: 0.553719, count: 121 -Region Avg IOU: 0.464033, Class: 1.000000, Obj: 0.296991, No Obj: 0.003440, Avg Recall: 0.467391, count: 92 -Region Avg IOU: 0.536519, Class: 1.000000, Obj: 0.315654, No Obj: 0.003972, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.532824, Class: 1.000000, Obj: 0.307454, No Obj: 0.004217, Avg Recall: 0.627273, count: 110 -4501: 0.468524, 0.448959 avg, 0.001000 rate, 16.369745 seconds, 1152256 images -Loaded: 5.048438 seconds -Region Avg IOU: 0.478398, Class: 1.000000, Obj: 0.310568, No Obj: 0.003567, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.539343, Class: 1.000000, Obj: 0.339437, No Obj: 0.004026, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.480230, Class: 1.000000, Obj: 0.315642, No Obj: 0.003680, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.488323, Class: 1.000000, Obj: 0.298423, No Obj: 0.004116, Avg Recall: 0.559322, count: 118 -Region Avg IOU: 0.462290, Class: 1.000000, Obj: 0.297114, No Obj: 0.003831, Avg Recall: 0.500000, count: 112 -Region Avg IOU: 0.505587, Class: 1.000000, Obj: 0.318704, No Obj: 0.003626, Avg Recall: 0.559140, count: 93 -Region Avg IOU: 0.497344, Class: 1.000000, Obj: 0.294828, No Obj: 0.004559, Avg Recall: 0.542253, count: 142 -Region Avg IOU: 0.536652, Class: 1.000000, Obj: 0.328024, No Obj: 0.004223, Avg Recall: 0.641509, count: 106 -4502: 0.471260, 0.451189 avg, 0.001000 rate, 18.621944 seconds, 1152512 images -Loaded: 2.894566 seconds -Region Avg IOU: 0.555428, Class: 1.000000, Obj: 0.337632, No Obj: 0.004762, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.562556, Class: 1.000000, Obj: 0.341059, No Obj: 0.004288, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.515074, Class: 1.000000, Obj: 0.364019, No Obj: 0.004034, Avg Recall: 0.595745, count: 94 -Region Avg IOU: 0.485908, Class: 1.000000, Obj: 0.318149, No Obj: 0.004132, Avg Recall: 0.545455, count: 121 -Region Avg IOU: 0.535146, Class: 1.000000, Obj: 0.335916, No Obj: 0.004461, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.484608, Class: 1.000000, Obj: 0.331856, No Obj: 0.004177, Avg Recall: 0.537815, count: 119 -Region Avg IOU: 0.546023, Class: 1.000000, Obj: 0.351171, No Obj: 0.004497, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.478466, Class: 1.000000, Obj: 0.298179, No Obj: 0.003616, Avg Recall: 0.552083, count: 96 -4503: 0.422042, 0.448275 avg, 0.001000 rate, 21.410799 seconds, 1152768 images -Loaded: 2.021587 seconds -Region Avg IOU: 0.527710, Class: 1.000000, Obj: 0.336435, No Obj: 0.004634, Avg Recall: 0.581197, count: 117 -Region Avg IOU: 0.500979, Class: 1.000000, Obj: 0.361822, No Obj: 0.004060, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.448537, Class: 1.000000, Obj: 0.333364, No Obj: 0.004472, Avg Recall: 0.483333, count: 120 -Region Avg IOU: 0.529639, Class: 1.000000, Obj: 0.325596, No Obj: 0.004779, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.490296, Class: 1.000000, Obj: 0.372596, No Obj: 0.003902, Avg Recall: 0.534653, count: 101 -Region Avg IOU: 0.483319, Class: 1.000000, Obj: 0.326969, No Obj: 0.004944, Avg Recall: 0.520325, count: 123 -Region Avg IOU: 0.456835, Class: 1.000000, Obj: 0.343198, No Obj: 0.004549, Avg Recall: 0.456311, count: 103 -Region Avg IOU: 0.500397, Class: 1.000000, Obj: 0.322628, No Obj: 0.004391, Avg Recall: 0.564516, count: 124 -4504: 0.443131, 0.447760 avg, 0.001000 rate, 21.204424 seconds, 1153024 images -Loaded: 0.935946 seconds -Region Avg IOU: 0.460509, Class: 1.000000, Obj: 0.364773, No Obj: 0.004779, Avg Recall: 0.533333, count: 105 -Region Avg IOU: 0.538407, Class: 1.000000, Obj: 0.417130, No Obj: 0.005515, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.498074, Class: 1.000000, Obj: 0.332764, No Obj: 0.005101, Avg Recall: 0.537037, count: 108 -Region Avg IOU: 0.502390, Class: 1.000000, Obj: 0.396256, No Obj: 0.004999, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.494748, Class: 1.000000, Obj: 0.346400, No Obj: 0.004705, Avg Recall: 0.529412, count: 102 -Region Avg IOU: 0.506724, Class: 1.000000, Obj: 0.370102, No Obj: 0.004481, Avg Recall: 0.545455, count: 88 -Region Avg IOU: 0.414807, Class: 1.000000, Obj: 0.278047, No Obj: 0.004552, Avg Recall: 0.464567, count: 127 -Region Avg IOU: 0.466709, Class: 1.000000, Obj: 0.332139, No Obj: 0.004792, Avg Recall: 0.461538, count: 117 -4505: 0.446213, 0.447606 avg, 0.001000 rate, 17.770689 seconds, 1153280 images -Loaded: 2.205476 seconds -Region Avg IOU: 0.473188, Class: 1.000000, Obj: 0.316284, No Obj: 0.005090, Avg Recall: 0.560000, count: 125 -Region Avg IOU: 0.462764, Class: 1.000000, Obj: 0.372886, No Obj: 0.004958, Avg Recall: 0.522124, count: 113 -Region Avg IOU: 0.438615, Class: 1.000000, Obj: 0.333340, No Obj: 0.005723, Avg Recall: 0.470588, count: 136 -Region Avg IOU: 0.528663, Class: 1.000000, Obj: 0.357154, No Obj: 0.005062, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.475106, Class: 1.000000, Obj: 0.370503, No Obj: 0.005932, Avg Recall: 0.529412, count: 136 -Region Avg IOU: 0.458855, Class: 1.000000, Obj: 0.346118, No Obj: 0.004759, Avg Recall: 0.517544, count: 114 -Region Avg IOU: 0.435785, Class: 1.000000, Obj: 0.354862, No Obj: 0.004475, Avg Recall: 0.440000, count: 100 -Region Avg IOU: 0.497709, Class: 1.000000, Obj: 0.364350, No Obj: 0.004659, Avg Recall: 0.537634, count: 93 -4506: 0.462717, 0.449117 avg, 0.001000 rate, 17.602312 seconds, 1153536 images -Loaded: 2.114529 seconds -Region Avg IOU: 0.493903, Class: 1.000000, Obj: 0.379632, No Obj: 0.004841, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.448152, Class: 1.000000, Obj: 0.379686, No Obj: 0.004522, Avg Recall: 0.467391, count: 92 -Region Avg IOU: 0.506115, Class: 1.000000, Obj: 0.370761, No Obj: 0.005495, Avg Recall: 0.568000, count: 125 -Region Avg IOU: 0.478966, Class: 1.000000, Obj: 0.362872, No Obj: 0.005226, Avg Recall: 0.538462, count: 117 -Region Avg IOU: 0.494815, Class: 1.000000, Obj: 0.373337, No Obj: 0.005278, Avg Recall: 0.541667, count: 120 -Region Avg IOU: 0.457206, Class: 1.000000, Obj: 0.373053, No Obj: 0.004355, Avg Recall: 0.485714, count: 105 -Region Avg IOU: 0.490682, Class: 1.000000, Obj: 0.399831, No Obj: 0.004676, Avg Recall: 0.538462, count: 91 -Region Avg IOU: 0.536884, Class: 1.000000, Obj: 0.418038, No Obj: 0.004489, Avg Recall: 0.623377, count: 77 -4507: 0.419623, 0.446167 avg, 0.001000 rate, 19.585505 seconds, 1153792 images -Loaded: 0.637187 seconds -Region Avg IOU: 0.540567, Class: 1.000000, Obj: 0.398322, No Obj: 0.005750, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.499650, Class: 1.000000, Obj: 0.373409, No Obj: 0.005421, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.499278, Class: 1.000000, Obj: 0.348809, No Obj: 0.005182, Avg Recall: 0.592920, count: 113 -Region Avg IOU: 0.538296, Class: 1.000000, Obj: 0.403556, No Obj: 0.005685, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.515511, Class: 1.000000, Obj: 0.355696, No Obj: 0.005080, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.594588, Class: 1.000000, Obj: 0.456144, No Obj: 0.005014, Avg Recall: 0.716216, count: 74 -Region Avg IOU: 0.509285, Class: 1.000000, Obj: 0.396672, No Obj: 0.005011, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.479102, Class: 1.000000, Obj: 0.364797, No Obj: 0.004510, Avg Recall: 0.536082, count: 97 -4508: 0.418259, 0.443376 avg, 0.001000 rate, 17.260212 seconds, 1154048 images -Loaded: 3.809855 seconds -Region Avg IOU: 0.470478, Class: 1.000000, Obj: 0.415756, No Obj: 0.005252, Avg Recall: 0.515873, count: 126 -Region Avg IOU: 0.540188, Class: 1.000000, Obj: 0.415837, No Obj: 0.005502, Avg Recall: 0.629921, count: 127 -Region Avg IOU: 0.487903, Class: 1.000000, Obj: 0.419219, No Obj: 0.005404, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.485707, Class: 1.000000, Obj: 0.362365, No Obj: 0.004932, Avg Recall: 0.508621, count: 116 -Region Avg IOU: 0.466673, Class: 1.000000, Obj: 0.348013, No Obj: 0.004767, Avg Recall: 0.514019, count: 107 -Region Avg IOU: 0.475273, Class: 1.000000, Obj: 0.367951, No Obj: 0.005490, Avg Recall: 0.510067, count: 149 -Region Avg IOU: 0.547870, Class: 1.000000, Obj: 0.403731, No Obj: 0.004701, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.510345, Class: 1.000000, Obj: 0.407998, No Obj: 0.004815, Avg Recall: 0.566667, count: 90 -4509: 0.439588, 0.442998 avg, 0.001000 rate, 17.738890 seconds, 1154304 images -Loaded: 1.272592 seconds -Region Avg IOU: 0.496139, Class: 1.000000, Obj: 0.362091, No Obj: 0.004704, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.517472, Class: 1.000000, Obj: 0.402031, No Obj: 0.004754, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.533185, Class: 1.000000, Obj: 0.431159, No Obj: 0.005114, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.491518, Class: 1.000000, Obj: 0.347569, No Obj: 0.005274, Avg Recall: 0.545455, count: 121 -Region Avg IOU: 0.520106, Class: 1.000000, Obj: 0.377234, No Obj: 0.005032, Avg Recall: 0.578512, count: 121 -Region Avg IOU: 0.470378, Class: 1.000000, Obj: 0.341905, No Obj: 0.004054, Avg Recall: 0.443182, count: 88 -Region Avg IOU: 0.467808, Class: 1.000000, Obj: 0.356009, No Obj: 0.005047, Avg Recall: 0.543478, count: 138 -Region Avg IOU: 0.491980, Class: 1.000000, Obj: 0.363781, No Obj: 0.004775, Avg Recall: 0.554545, count: 110 -4510: 0.445490, 0.443247 avg, 0.001000 rate, 17.391369 seconds, 1154560 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 0.000060 seconds -Region Avg IOU: 0.597376, Class: 1.000000, Obj: 0.471591, No Obj: 0.003723, Avg Recall: 0.679487, count: 78 -Region Avg IOU: 0.504215, Class: 1.000000, Obj: 0.364773, No Obj: 0.004586, Avg Recall: 0.566372, count: 113 -Region Avg IOU: 0.598681, Class: 1.000000, Obj: 0.422811, No Obj: 0.004556, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.549860, Class: 1.000000, Obj: 0.460371, No Obj: 0.004890, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.614593, Class: 1.000000, Obj: 0.412626, No Obj: 0.004922, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.582732, Class: 1.000000, Obj: 0.461313, No Obj: 0.004414, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.534697, Class: 1.000000, Obj: 0.432477, No Obj: 0.003908, Avg Recall: 0.648352, count: 91 -Region Avg IOU: 0.510431, Class: 1.000000, Obj: 0.410924, No Obj: 0.004775, Avg Recall: 0.553191, count: 141 -4511: 0.406514, 0.439574 avg, 0.001000 rate, 23.528969 seconds, 1154816 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.559140, Class: 1.000000, Obj: 0.399154, No Obj: 0.004442, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.615765, Class: 1.000000, Obj: 0.429152, No Obj: 0.003864, Avg Recall: 0.738095, count: 84 -Region Avg IOU: 0.526729, Class: 1.000000, Obj: 0.371150, No Obj: 0.005334, Avg Recall: 0.576923, count: 156 -Region Avg IOU: 0.571086, Class: 1.000000, Obj: 0.413631, No Obj: 0.003879, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.519577, Class: 1.000000, Obj: 0.394450, No Obj: 0.004719, Avg Recall: 0.561538, count: 130 -Region Avg IOU: 0.536454, Class: 1.000000, Obj: 0.403355, No Obj: 0.004128, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.550010, Class: 1.000000, Obj: 0.413118, No Obj: 0.004047, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.543598, Class: 1.000000, Obj: 0.424479, No Obj: 0.004281, Avg Recall: 0.616822, count: 107 -4512: 0.493651, 0.444981 avg, 0.001000 rate, 23.553242 seconds, 1155072 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.591660, Class: 1.000000, Obj: 0.405966, No Obj: 0.004013, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.499050, Class: 1.000000, Obj: 0.372899, No Obj: 0.003181, Avg Recall: 0.579545, count: 88 -Region Avg IOU: 0.614559, Class: 1.000000, Obj: 0.451590, No Obj: 0.004096, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.522475, Class: 1.000000, Obj: 0.334799, No Obj: 0.003676, Avg Recall: 0.571429, count: 126 -Region Avg IOU: 0.555164, Class: 1.000000, Obj: 0.383524, No Obj: 0.003914, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.578836, Class: 1.000000, Obj: 0.370594, No Obj: 0.004797, Avg Recall: 0.686956, count: 115 -Region Avg IOU: 0.628050, Class: 1.000000, Obj: 0.411159, No Obj: 0.003728, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.512228, Class: 1.000000, Obj: 0.342856, No Obj: 0.003979, Avg Recall: 0.595238, count: 126 -4513: 0.421204, 0.442604 avg, 0.001000 rate, 23.384636 seconds, 1155328 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.509362, Class: 1.000000, Obj: 0.308941, No Obj: 0.003303, Avg Recall: 0.626374, count: 91 -Region Avg IOU: 0.531703, Class: 1.000000, Obj: 0.346859, No Obj: 0.003613, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.567870, Class: 1.000000, Obj: 0.398264, No Obj: 0.004375, Avg Recall: 0.666667, count: 126 -Region Avg IOU: 0.594410, Class: 1.000000, Obj: 0.368304, No Obj: 0.004216, Avg Recall: 0.753968, count: 126 -Region Avg IOU: 0.576606, Class: 1.000000, Obj: 0.372084, No Obj: 0.003767, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.558177, Class: 1.000000, Obj: 0.365654, No Obj: 0.003931, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.594024, Class: 1.000000, Obj: 0.364524, No Obj: 0.003587, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.565166, Class: 1.000000, Obj: 0.372923, No Obj: 0.004414, Avg Recall: 0.666667, count: 126 -4514: 0.408507, 0.439194 avg, 0.001000 rate, 23.894297 seconds, 1155584 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.495484, Class: 1.000000, Obj: 0.301655, No Obj: 0.003352, Avg Recall: 0.558333, count: 120 -Region Avg IOU: 0.535793, Class: 1.000000, Obj: 0.341642, No Obj: 0.003578, Avg Recall: 0.585938, count: 128 -Region Avg IOU: 0.516860, Class: 1.000000, Obj: 0.318126, No Obj: 0.003362, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.549106, Class: 1.000000, Obj: 0.366900, No Obj: 0.003452, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.577279, Class: 1.000000, Obj: 0.341540, No Obj: 0.003030, Avg Recall: 0.681818, count: 88 -Region Avg IOU: 0.611461, Class: 1.000000, Obj: 0.353097, No Obj: 0.003805, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.556278, Class: 1.000000, Obj: 0.341717, No Obj: 0.003027, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.591397, Class: 1.000000, Obj: 0.354355, No Obj: 0.003340, Avg Recall: 0.690722, count: 97 -4515: 0.430876, 0.438362 avg, 0.001000 rate, 22.634464 seconds, 1155840 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.575527, Class: 1.000000, Obj: 0.340162, No Obj: 0.003434, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.583611, Class: 1.000000, Obj: 0.328960, No Obj: 0.003622, Avg Recall: 0.674242, count: 132 -Region Avg IOU: 0.563644, Class: 1.000000, Obj: 0.303068, No Obj: 0.003380, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.585650, Class: 1.000000, Obj: 0.351616, No Obj: 0.003057, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.595457, Class: 1.000000, Obj: 0.359198, No Obj: 0.003321, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.596540, Class: 1.000000, Obj: 0.381398, No Obj: 0.003039, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.600595, Class: 1.000000, Obj: 0.360167, No Obj: 0.003219, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.543265, Class: 1.000000, Obj: 0.363014, No Obj: 0.003058, Avg Recall: 0.618421, count: 76 -4516: 0.417864, 0.436312 avg, 0.001000 rate, 23.066179 seconds, 1156096 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.581172, Class: 1.000000, Obj: 0.346319, No Obj: 0.003430, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.587455, Class: 1.000000, Obj: 0.354177, No Obj: 0.003703, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.591717, Class: 1.000000, Obj: 0.362948, No Obj: 0.003384, Avg Recall: 0.710526, count: 114 -Region Avg IOU: 0.592976, Class: 1.000000, Obj: 0.353381, No Obj: 0.003433, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.593623, Class: 1.000000, Obj: 0.366345, No Obj: 0.003569, Avg Recall: 0.744681, count: 94 -Region Avg IOU: 0.531191, Class: 1.000000, Obj: 0.300546, No Obj: 0.003049, Avg Recall: 0.643478, count: 115 -Region Avg IOU: 0.565114, Class: 1.000000, Obj: 0.310672, No Obj: 0.003262, Avg Recall: 0.624000, count: 125 -Region Avg IOU: 0.591549, Class: 1.000000, Obj: 0.323555, No Obj: 0.003348, Avg Recall: 0.710000, count: 100 -4517: 0.460129, 0.438694 avg, 0.001000 rate, 23.835756 seconds, 1156352 images -Loaded: 0.107751 seconds -Region Avg IOU: 0.547497, Class: 1.000000, Obj: 0.311096, No Obj: 0.003729, Avg Recall: 0.608108, count: 148 -Region Avg IOU: 0.535918, Class: 1.000000, Obj: 0.339292, No Obj: 0.003142, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.614002, Class: 1.000000, Obj: 0.349642, No Obj: 0.003739, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.552424, Class: 1.000000, Obj: 0.332626, No Obj: 0.003441, Avg Recall: 0.648438, count: 128 -Region Avg IOU: 0.581011, Class: 1.000000, Obj: 0.351252, No Obj: 0.003682, Avg Recall: 0.659091, count: 132 -Region Avg IOU: 0.544483, Class: 1.000000, Obj: 0.331568, No Obj: 0.003062, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.556693, Class: 1.000000, Obj: 0.336835, No Obj: 0.003163, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.567994, Class: 1.000000, Obj: 0.359645, No Obj: 0.003258, Avg Recall: 0.660000, count: 100 -4518: 0.490022, 0.443827 avg, 0.001000 rate, 24.250652 seconds, 1156608 images -Loaded: 0.010900 seconds -Region Avg IOU: 0.573418, Class: 1.000000, Obj: 0.334675, No Obj: 0.003309, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.560378, Class: 1.000000, Obj: 0.371919, No Obj: 0.003124, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.606530, Class: 1.000000, Obj: 0.429991, No Obj: 0.003944, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.586688, Class: 1.000000, Obj: 0.370131, No Obj: 0.003982, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.563515, Class: 1.000000, Obj: 0.370597, No Obj: 0.003735, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.567422, Class: 1.000000, Obj: 0.365577, No Obj: 0.003163, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.546915, Class: 1.000000, Obj: 0.349453, No Obj: 0.003516, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.561168, Class: 1.000000, Obj: 0.331003, No Obj: 0.003765, Avg Recall: 0.626016, count: 123 -4519: 0.474322, 0.446876 avg, 0.001000 rate, 23.399883 seconds, 1156864 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.593914, Class: 1.000000, Obj: 0.366774, No Obj: 0.003914, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.560318, Class: 1.000000, Obj: 0.335463, No Obj: 0.003688, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.608101, Class: 1.000000, Obj: 0.363263, No Obj: 0.003634, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.571345, Class: 1.000000, Obj: 0.402723, No Obj: 0.003344, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.563878, Class: 1.000000, Obj: 0.348959, No Obj: 0.003555, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.590978, Class: 1.000000, Obj: 0.382482, No Obj: 0.003714, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.610816, Class: 1.000000, Obj: 0.339732, No Obj: 0.003739, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.598530, Class: 1.000000, Obj: 0.375778, No Obj: 0.003593, Avg Recall: 0.683761, count: 117 -4520: 0.417017, 0.443890 avg, 0.001000 rate, 23.128132 seconds, 1157120 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000089 seconds -Region Avg IOU: 0.673433, Class: 1.000000, Obj: 0.435469, No Obj: 0.002772, Avg Recall: 0.851485, count: 101 -Region Avg IOU: 0.633390, Class: 1.000000, Obj: 0.446418, No Obj: 0.003044, Avg Recall: 0.800000, count: 85 -Region Avg IOU: 0.674984, Class: 1.000000, Obj: 0.409862, No Obj: 0.003236, Avg Recall: 0.865546, count: 119 -Region Avg IOU: 0.577274, Class: 1.000000, Obj: 0.392549, No Obj: 0.002957, Avg Recall: 0.652174, count: 138 -Region Avg IOU: 0.652531, Class: 1.000000, Obj: 0.380149, No Obj: 0.003087, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.624338, Class: 1.000000, Obj: 0.399606, No Obj: 0.002881, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.647096, Class: 1.000000, Obj: 0.409103, No Obj: 0.002932, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.643782, Class: 1.000000, Obj: 0.388721, No Obj: 0.002772, Avg Recall: 0.825243, count: 103 -4521: 0.471259, 0.446627 avg, 0.001000 rate, 30.201332 seconds, 1157376 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.622249, Class: 1.000000, Obj: 0.394858, No Obj: 0.002671, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.643833, Class: 1.000000, Obj: 0.423178, No Obj: 0.003095, Avg Recall: 0.798319, count: 119 -Region Avg IOU: 0.631806, Class: 1.000000, Obj: 0.360553, No Obj: 0.003176, Avg Recall: 0.765152, count: 132 -Region Avg IOU: 0.676537, Class: 1.000000, Obj: 0.457917, No Obj: 0.003109, Avg Recall: 0.851485, count: 101 -Region Avg IOU: 0.598056, Class: 1.000000, Obj: 0.394783, No Obj: 0.003184, Avg Recall: 0.730496, count: 141 -Region Avg IOU: 0.613823, Class: 1.000000, Obj: 0.454234, No Obj: 0.002823, Avg Recall: 0.756098, count: 123 -Region Avg IOU: 0.648421, Class: 1.000000, Obj: 0.458337, No Obj: 0.003119, Avg Recall: 0.821429, count: 112 -Region Avg IOU: 0.628018, Class: 1.000000, Obj: 0.391474, No Obj: 0.003118, Avg Recall: 0.752000, count: 125 -4522: 0.491810, 0.451145 avg, 0.001000 rate, 29.529951 seconds, 1157632 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.633406, Class: 1.000000, Obj: 0.405491, No Obj: 0.003089, Avg Recall: 0.759399, count: 133 -Region Avg IOU: 0.648310, Class: 1.000000, Obj: 0.390390, No Obj: 0.003234, Avg Recall: 0.817460, count: 126 -Region Avg IOU: 0.579793, Class: 1.000000, Obj: 0.395992, No Obj: 0.002676, Avg Recall: 0.701493, count: 134 -Region Avg IOU: 0.648753, Class: 1.000000, Obj: 0.374263, No Obj: 0.002724, Avg Recall: 0.855670, count: 97 -Region Avg IOU: 0.625254, Class: 1.000000, Obj: 0.399008, No Obj: 0.003181, Avg Recall: 0.788618, count: 123 -Region Avg IOU: 0.663694, Class: 1.000000, Obj: 0.441395, No Obj: 0.002964, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.620319, Class: 1.000000, Obj: 0.379753, No Obj: 0.002741, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.649343, Class: 1.000000, Obj: 0.446870, No Obj: 0.003157, Avg Recall: 0.849558, count: 113 -4523: 0.478110, 0.453842 avg, 0.001000 rate, 29.565868 seconds, 1157888 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.648859, Class: 1.000000, Obj: 0.385591, No Obj: 0.002881, Avg Recall: 0.836364, count: 110 -Region Avg IOU: 0.591894, Class: 1.000000, Obj: 0.324288, No Obj: 0.002974, Avg Recall: 0.692913, count: 127 -Region Avg IOU: 0.662933, Class: 1.000000, Obj: 0.465066, No Obj: 0.002448, Avg Recall: 0.825581, count: 86 -Region Avg IOU: 0.634524, Class: 1.000000, Obj: 0.402553, No Obj: 0.003250, Avg Recall: 0.736434, count: 129 -Region Avg IOU: 0.640562, Class: 1.000000, Obj: 0.376487, No Obj: 0.003441, Avg Recall: 0.815789, count: 114 -Region Avg IOU: 0.656929, Class: 1.000000, Obj: 0.394883, No Obj: 0.002730, Avg Recall: 0.848485, count: 99 -Region Avg IOU: 0.650852, Class: 1.000000, Obj: 0.439134, No Obj: 0.002621, Avg Recall: 0.847826, count: 92 -Region Avg IOU: 0.645303, Class: 1.000000, Obj: 0.396329, No Obj: 0.002553, Avg Recall: 0.817073, count: 82 -4524: 0.428191, 0.451277 avg, 0.001000 rate, 30.739697 seconds, 1158144 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.645494, Class: 1.000000, Obj: 0.386241, No Obj: 0.002462, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.643891, Class: 1.000000, Obj: 0.393546, No Obj: 0.002673, Avg Recall: 0.834862, count: 109 -Region Avg IOU: 0.644002, Class: 1.000000, Obj: 0.351924, No Obj: 0.002661, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.623206, Class: 1.000000, Obj: 0.392500, No Obj: 0.002537, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.637139, Class: 1.000000, Obj: 0.344369, No Obj: 0.002551, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.634573, Class: 1.000000, Obj: 0.377300, No Obj: 0.002790, Avg Recall: 0.793388, count: 121 -Region Avg IOU: 0.638638, Class: 1.000000, Obj: 0.347999, No Obj: 0.002733, Avg Recall: 0.824074, count: 108 -Region Avg IOU: 0.599553, Class: 1.000000, Obj: 0.372049, No Obj: 0.002692, Avg Recall: 0.734375, count: 128 -4525: 0.474525, 0.453602 avg, 0.001000 rate, 28.866833 seconds, 1158400 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.648082, Class: 1.000000, Obj: 0.317901, No Obj: 0.002416, Avg Recall: 0.833333, count: 114 -Region Avg IOU: 0.627326, Class: 1.000000, Obj: 0.345317, No Obj: 0.002766, Avg Recall: 0.733333, count: 120 -Region Avg IOU: 0.587546, Class: 1.000000, Obj: 0.320656, No Obj: 0.002234, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.638409, Class: 1.000000, Obj: 0.371097, No Obj: 0.002600, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.647180, Class: 1.000000, Obj: 0.347470, No Obj: 0.002733, Avg Recall: 0.824427, count: 131 -Region Avg IOU: 0.664587, Class: 1.000000, Obj: 0.372803, No Obj: 0.002535, Avg Recall: 0.836735, count: 98 -Region Avg IOU: 0.579142, Class: 1.000000, Obj: 0.314689, No Obj: 0.002182, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.601319, Class: 1.000000, Obj: 0.346844, No Obj: 0.002103, Avg Recall: 0.750000, count: 76 -4526: 0.446451, 0.452886 avg, 0.001000 rate, 29.359886 seconds, 1158656 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.595021, Class: 1.000000, Obj: 0.342400, No Obj: 0.002391, Avg Recall: 0.672000, count: 125 -Region Avg IOU: 0.633134, Class: 1.000000, Obj: 0.292530, No Obj: 0.002069, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.629012, Class: 1.000000, Obj: 0.316133, No Obj: 0.002324, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.637232, Class: 1.000000, Obj: 0.295961, No Obj: 0.002515, Avg Recall: 0.753731, count: 134 -Region Avg IOU: 0.580751, Class: 1.000000, Obj: 0.347425, No Obj: 0.002003, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.672349, Class: 1.000000, Obj: 0.373137, No Obj: 0.002361, Avg Recall: 0.857143, count: 119 -Region Avg IOU: 0.631559, Class: 1.000000, Obj: 0.348665, No Obj: 0.002805, Avg Recall: 0.792308, count: 130 -Region Avg IOU: 0.662975, Class: 1.000000, Obj: 0.415377, No Obj: 0.002322, Avg Recall: 0.838384, count: 99 -4527: 0.469808, 0.454579 avg, 0.001000 rate, 29.326715 seconds, 1158912 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.630973, Class: 1.000000, Obj: 0.338348, No Obj: 0.002365, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.683161, Class: 1.000000, Obj: 0.318896, No Obj: 0.002014, Avg Recall: 0.890244, count: 82 -Region Avg IOU: 0.638515, Class: 1.000000, Obj: 0.381959, No Obj: 0.002285, Avg Recall: 0.772152, count: 79 -Region Avg IOU: 0.644663, Class: 1.000000, Obj: 0.273530, No Obj: 0.002157, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.597686, Class: 1.000000, Obj: 0.315413, No Obj: 0.002391, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.640007, Class: 1.000000, Obj: 0.377976, No Obj: 0.002296, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.629415, Class: 1.000000, Obj: 0.312908, No Obj: 0.002720, Avg Recall: 0.787671, count: 146 -Region Avg IOU: 0.645080, Class: 1.000000, Obj: 0.327418, No Obj: 0.002255, Avg Recall: 0.793388, count: 121 -4528: 0.422363, 0.451357 avg, 0.001000 rate, 29.186550 seconds, 1159168 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.631230, Class: 1.000000, Obj: 0.345109, No Obj: 0.002092, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.657984, Class: 1.000000, Obj: 0.382366, No Obj: 0.002279, Avg Recall: 0.845361, count: 97 -Region Avg IOU: 0.616863, Class: 1.000000, Obj: 0.407956, No Obj: 0.002428, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.636601, Class: 1.000000, Obj: 0.333374, No Obj: 0.002220, Avg Recall: 0.764706, count: 85 -Region Avg IOU: 0.647973, Class: 1.000000, Obj: 0.340319, No Obj: 0.002175, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.639213, Class: 1.000000, Obj: 0.345015, No Obj: 0.002156, Avg Recall: 0.803571, count: 112 -Region Avg IOU: 0.619852, Class: 1.000000, Obj: 0.369751, No Obj: 0.002380, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.657811, Class: 1.000000, Obj: 0.357600, No Obj: 0.002271, Avg Recall: 0.821782, count: 101 -4529: 0.423414, 0.448563 avg, 0.001000 rate, 29.309423 seconds, 1159424 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.639386, Class: 1.000000, Obj: 0.328366, No Obj: 0.002625, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.636699, Class: 1.000000, Obj: 0.382210, No Obj: 0.002595, Avg Recall: 0.757812, count: 128 -Region Avg IOU: 0.643608, Class: 1.000000, Obj: 0.321097, No Obj: 0.002363, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.643210, Class: 1.000000, Obj: 0.331703, No Obj: 0.002460, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.617855, Class: 1.000000, Obj: 0.376919, No Obj: 0.002321, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.657595, Class: 1.000000, Obj: 0.344884, No Obj: 0.002417, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.653368, Class: 1.000000, Obj: 0.362706, No Obj: 0.002006, Avg Recall: 0.810127, count: 79 -Region Avg IOU: 0.670677, Class: 1.000000, Obj: 0.407826, No Obj: 0.002263, Avg Recall: 0.828283, count: 99 -4530: 0.456818, 0.449388 avg, 0.001000 rate, 28.177134 seconds, 1159680 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 11.884587 seconds -Region Avg IOU: 0.659248, Class: 1.000000, Obj: 0.371819, No Obj: 0.002632, Avg Recall: 0.848837, count: 86 -Region Avg IOU: 0.623781, Class: 1.000000, Obj: 0.345942, No Obj: 0.002786, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.601912, Class: 1.000000, Obj: 0.339938, No Obj: 0.002613, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.621164, Class: 1.000000, Obj: 0.349474, No Obj: 0.002785, Avg Recall: 0.752000, count: 125 -Region Avg IOU: 0.624191, Class: 1.000000, Obj: 0.366500, No Obj: 0.003044, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.610932, Class: 1.000000, Obj: 0.368098, No Obj: 0.002879, Avg Recall: 0.724409, count: 127 -Region Avg IOU: 0.607646, Class: 1.000000, Obj: 0.340155, No Obj: 0.002544, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.596816, Class: 1.000000, Obj: 0.331441, No Obj: 0.002721, Avg Recall: 0.675000, count: 120 -4531: 0.485075, 0.452957 avg, 0.001000 rate, 25.968628 seconds, 1159936 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.621642, Class: 1.000000, Obj: 0.363890, No Obj: 0.002730, Avg Recall: 0.733871, count: 124 -Region Avg IOU: 0.636373, Class: 1.000000, Obj: 0.340639, No Obj: 0.003096, Avg Recall: 0.764228, count: 123 -Region Avg IOU: 0.672639, Class: 1.000000, Obj: 0.375410, No Obj: 0.003260, Avg Recall: 0.845455, count: 110 -Region Avg IOU: 0.639473, Class: 1.000000, Obj: 0.388075, No Obj: 0.002515, Avg Recall: 0.781609, count: 87 -Region Avg IOU: 0.655192, Class: 1.000000, Obj: 0.389193, No Obj: 0.002433, Avg Recall: 0.734940, count: 83 -Region Avg IOU: 0.643610, Class: 1.000000, Obj: 0.383758, No Obj: 0.002939, Avg Recall: 0.842593, count: 108 -Region Avg IOU: 0.582651, Class: 1.000000, Obj: 0.326072, No Obj: 0.002936, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.644780, Class: 1.000000, Obj: 0.400195, No Obj: 0.002717, Avg Recall: 0.783019, count: 106 -4532: 0.446793, 0.452341 avg, 0.001000 rate, 27.414757 seconds, 1160192 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.609148, Class: 1.000000, Obj: 0.350309, No Obj: 0.003096, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.618310, Class: 1.000000, Obj: 0.409589, No Obj: 0.003058, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.635937, Class: 1.000000, Obj: 0.426690, No Obj: 0.002850, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.581012, Class: 1.000000, Obj: 0.369488, No Obj: 0.002681, Avg Recall: 0.720930, count: 86 -Region Avg IOU: 0.631067, Class: 1.000000, Obj: 0.436933, No Obj: 0.002919, Avg Recall: 0.744186, count: 86 -Region Avg IOU: 0.624075, Class: 1.000000, Obj: 0.406661, No Obj: 0.003211, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.617486, Class: 1.000000, Obj: 0.391975, No Obj: 0.003270, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.625302, Class: 1.000000, Obj: 0.335531, No Obj: 0.002664, Avg Recall: 0.779070, count: 86 -4533: 0.427138, 0.449820 avg, 0.001000 rate, 25.176279 seconds, 1160448 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.645799, Class: 1.000000, Obj: 0.424489, No Obj: 0.003455, Avg Recall: 0.808333, count: 120 -Region Avg IOU: 0.656165, Class: 1.000000, Obj: 0.470016, No Obj: 0.002702, Avg Recall: 0.788889, count: 90 -Region Avg IOU: 0.587889, Class: 1.000000, Obj: 0.383969, No Obj: 0.002940, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.637927, Class: 1.000000, Obj: 0.403980, No Obj: 0.003385, Avg Recall: 0.731707, count: 123 -Region Avg IOU: 0.593295, Class: 1.000000, Obj: 0.381113, No Obj: 0.003293, Avg Recall: 0.671875, count: 128 -Region Avg IOU: 0.580892, Class: 1.000000, Obj: 0.372562, No Obj: 0.002933, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.623172, Class: 1.000000, Obj: 0.392921, No Obj: 0.003199, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.628030, Class: 1.000000, Obj: 0.414669, No Obj: 0.003376, Avg Recall: 0.747967, count: 123 -4534: 0.430469, 0.447885 avg, 0.001000 rate, 27.006784 seconds, 1160704 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.605492, Class: 1.000000, Obj: 0.411675, No Obj: 0.003391, Avg Recall: 0.730496, count: 141 -Region Avg IOU: 0.613266, Class: 1.000000, Obj: 0.367841, No Obj: 0.003263, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.604239, Class: 1.000000, Obj: 0.391500, No Obj: 0.003071, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.604122, Class: 1.000000, Obj: 0.394133, No Obj: 0.003378, Avg Recall: 0.713178, count: 129 -Region Avg IOU: 0.621205, Class: 1.000000, Obj: 0.390527, No Obj: 0.003492, Avg Recall: 0.727941, count: 136 -Region Avg IOU: 0.662915, Class: 1.000000, Obj: 0.427461, No Obj: 0.003461, Avg Recall: 0.852941, count: 102 -Region Avg IOU: 0.633404, Class: 1.000000, Obj: 0.443888, No Obj: 0.003644, Avg Recall: 0.764706, count: 136 -Region Avg IOU: 0.615135, Class: 1.000000, Obj: 0.383853, No Obj: 0.003023, Avg Recall: 0.731481, count: 108 -4535: 0.473488, 0.450445 avg, 0.001000 rate, 25.717230 seconds, 1160960 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.626470, Class: 1.000000, Obj: 0.435689, No Obj: 0.003192, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.642422, Class: 1.000000, Obj: 0.421440, No Obj: 0.003497, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.640004, Class: 1.000000, Obj: 0.454559, No Obj: 0.002776, Avg Recall: 0.776316, count: 76 -Region Avg IOU: 0.582035, Class: 1.000000, Obj: 0.369430, No Obj: 0.003351, Avg Recall: 0.685484, count: 124 -Region Avg IOU: 0.631814, Class: 1.000000, Obj: 0.416873, No Obj: 0.003273, Avg Recall: 0.760000, count: 125 -Region Avg IOU: 0.592467, Class: 1.000000, Obj: 0.401810, No Obj: 0.003080, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.617786, Class: 1.000000, Obj: 0.399989, No Obj: 0.002958, Avg Recall: 0.761905, count: 84 -Region Avg IOU: 0.661620, Class: 1.000000, Obj: 0.440508, No Obj: 0.003304, Avg Recall: 0.867347, count: 98 -4536: 0.401586, 0.445559 avg, 0.001000 rate, 26.282032 seconds, 1161216 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.610531, Class: 1.000000, Obj: 0.433632, No Obj: 0.003313, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.644011, Class: 1.000000, Obj: 0.434671, No Obj: 0.003847, Avg Recall: 0.800000, count: 130 -Region Avg IOU: 0.615304, Class: 1.000000, Obj: 0.404158, No Obj: 0.002936, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.618578, Class: 1.000000, Obj: 0.369495, No Obj: 0.003657, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.602384, Class: 1.000000, Obj: 0.429011, No Obj: 0.003178, Avg Recall: 0.735043, count: 117 -Region Avg IOU: 0.657352, Class: 1.000000, Obj: 0.447046, No Obj: 0.003095, Avg Recall: 0.793478, count: 92 -Region Avg IOU: 0.601759, Class: 1.000000, Obj: 0.418567, No Obj: 0.003102, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.666550, Class: 1.000000, Obj: 0.426435, No Obj: 0.003825, Avg Recall: 0.808333, count: 120 -4537: 0.457758, 0.446779 avg, 0.001000 rate, 26.393763 seconds, 1161472 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.589023, Class: 1.000000, Obj: 0.378428, No Obj: 0.003206, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.666742, Class: 1.000000, Obj: 0.447256, No Obj: 0.003006, Avg Recall: 0.822430, count: 107 -Region Avg IOU: 0.625557, Class: 1.000000, Obj: 0.409061, No Obj: 0.003186, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.584798, Class: 1.000000, Obj: 0.407946, No Obj: 0.003387, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.603199, Class: 1.000000, Obj: 0.429543, No Obj: 0.002790, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.630387, Class: 1.000000, Obj: 0.362899, No Obj: 0.003334, Avg Recall: 0.762963, count: 135 -Region Avg IOU: 0.591621, Class: 1.000000, Obj: 0.413331, No Obj: 0.003163, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.615601, Class: 1.000000, Obj: 0.414338, No Obj: 0.003108, Avg Recall: 0.758929, count: 112 -4538: 0.475687, 0.449670 avg, 0.001000 rate, 26.090736 seconds, 1161728 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.611079, Class: 1.000000, Obj: 0.435228, No Obj: 0.003355, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.617972, Class: 1.000000, Obj: 0.467036, No Obj: 0.002643, Avg Recall: 0.708333, count: 72 -Region Avg IOU: 0.619155, Class: 1.000000, Obj: 0.445378, No Obj: 0.002698, Avg Recall: 0.772727, count: 88 -Region Avg IOU: 0.619327, Class: 1.000000, Obj: 0.423141, No Obj: 0.003048, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.608116, Class: 1.000000, Obj: 0.429871, No Obj: 0.003141, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.573503, Class: 1.000000, Obj: 0.425530, No Obj: 0.002675, Avg Recall: 0.666667, count: 87 -Region Avg IOU: 0.615548, Class: 1.000000, Obj: 0.420724, No Obj: 0.003520, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.625674, Class: 1.000000, Obj: 0.374443, No Obj: 0.003167, Avg Recall: 0.769231, count: 104 -4539: 0.389771, 0.443680 avg, 0.001000 rate, 26.071615 seconds, 1161984 images -Loaded: 0.000123 seconds -Region Avg IOU: 0.641702, Class: 1.000000, Obj: 0.428523, No Obj: 0.003560, Avg Recall: 0.805755, count: 139 -Region Avg IOU: 0.628438, Class: 1.000000, Obj: 0.399605, No Obj: 0.002872, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.672623, Class: 1.000000, Obj: 0.381959, No Obj: 0.003642, Avg Recall: 0.860215, count: 93 -Region Avg IOU: 0.622011, Class: 1.000000, Obj: 0.382139, No Obj: 0.002798, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.615465, Class: 1.000000, Obj: 0.411896, No Obj: 0.002793, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.593963, Class: 1.000000, Obj: 0.392812, No Obj: 0.003148, Avg Recall: 0.715447, count: 123 -Region Avg IOU: 0.611561, Class: 1.000000, Obj: 0.379365, No Obj: 0.003209, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.644867, Class: 1.000000, Obj: 0.411976, No Obj: 0.002628, Avg Recall: 0.760563, count: 71 -4540: 0.414020, 0.440714 avg, 0.001000 rate, 27.623184 seconds, 1162240 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.524977 seconds -Region Avg IOU: 0.571802, Class: 1.000000, Obj: 0.343653, No Obj: 0.003589, Avg Recall: 0.622222, count: 90 -Region Avg IOU: 0.503392, Class: 1.000000, Obj: 0.317823, No Obj: 0.003274, Avg Recall: 0.559633, count: 109 -Region Avg IOU: 0.558293, Class: 1.000000, Obj: 0.363156, No Obj: 0.003679, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.594691, Class: 1.000000, Obj: 0.375699, No Obj: 0.003374, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.559026, Class: 1.000000, Obj: 0.336160, No Obj: 0.003729, Avg Recall: 0.647059, count: 136 -Region Avg IOU: 0.566843, Class: 1.000000, Obj: 0.352721, No Obj: 0.003564, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.562237, Class: 1.000000, Obj: 0.357276, No Obj: 0.003351, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.559501, Class: 1.000000, Obj: 0.330980, No Obj: 0.003684, Avg Recall: 0.639640, count: 111 -4541: 0.417525, 0.438395 avg, 0.001000 rate, 22.769913 seconds, 1162496 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.556594, Class: 1.000000, Obj: 0.323335, No Obj: 0.003797, Avg Recall: 0.645161, count: 124 -Region Avg IOU: 0.547165, Class: 1.000000, Obj: 0.349374, No Obj: 0.003330, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.568295, Class: 1.000000, Obj: 0.337647, No Obj: 0.003249, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.531874, Class: 1.000000, Obj: 0.318189, No Obj: 0.003324, Avg Recall: 0.613445, count: 119 -Region Avg IOU: 0.538976, Class: 1.000000, Obj: 0.351951, No Obj: 0.003122, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.616328, Class: 1.000000, Obj: 0.411103, No Obj: 0.003156, Avg Recall: 0.803279, count: 61 -Region Avg IOU: 0.577302, Class: 1.000000, Obj: 0.336757, No Obj: 0.003699, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.490168, Class: 1.000000, Obj: 0.300590, No Obj: 0.003312, Avg Recall: 0.579439, count: 107 -4542: 0.448588, 0.439414 avg, 0.001000 rate, 21.292418 seconds, 1162752 images -Loaded: 0.920580 seconds -Region Avg IOU: 0.524218, Class: 1.000000, Obj: 0.325027, No Obj: 0.003906, Avg Recall: 0.569106, count: 123 -Region Avg IOU: 0.599811, Class: 1.000000, Obj: 0.358172, No Obj: 0.003544, Avg Recall: 0.722222, count: 90 -Region Avg IOU: 0.550212, Class: 1.000000, Obj: 0.348559, No Obj: 0.003305, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.565966, Class: 1.000000, Obj: 0.357397, No Obj: 0.003767, Avg Recall: 0.669492, count: 118 -Region Avg IOU: 0.592017, Class: 1.000000, Obj: 0.371784, No Obj: 0.003233, Avg Recall: 0.705882, count: 68 -Region Avg IOU: 0.515493, Class: 1.000000, Obj: 0.351150, No Obj: 0.003541, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.570701, Class: 1.000000, Obj: 0.323008, No Obj: 0.003517, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.540404, Class: 1.000000, Obj: 0.318029, No Obj: 0.003378, Avg Recall: 0.602041, count: 98 -4543: 0.409188, 0.436392 avg, 0.001000 rate, 20.686916 seconds, 1163008 images -Loaded: 1.935752 seconds -Region Avg IOU: 0.508615, Class: 1.000000, Obj: 0.273225, No Obj: 0.003503, Avg Recall: 0.575758, count: 132 -Region Avg IOU: 0.584351, Class: 1.000000, Obj: 0.346583, No Obj: 0.003567, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.539940, Class: 1.000000, Obj: 0.308958, No Obj: 0.003480, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.519098, Class: 1.000000, Obj: 0.317955, No Obj: 0.003740, Avg Recall: 0.591667, count: 120 -Region Avg IOU: 0.582901, Class: 1.000000, Obj: 0.335777, No Obj: 0.003427, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.494568, Class: 1.000000, Obj: 0.301451, No Obj: 0.003304, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.535969, Class: 1.000000, Obj: 0.299103, No Obj: 0.003730, Avg Recall: 0.619835, count: 121 -Region Avg IOU: 0.567677, Class: 1.000000, Obj: 0.349006, No Obj: 0.003427, Avg Recall: 0.673684, count: 95 -4544: 0.448270, 0.437580 avg, 0.001000 rate, 21.909842 seconds, 1163264 images -Loaded: 1.020295 seconds -Region Avg IOU: 0.553749, Class: 1.000000, Obj: 0.352770, No Obj: 0.003609, Avg Recall: 0.620690, count: 87 -Region Avg IOU: 0.541252, Class: 1.000000, Obj: 0.320653, No Obj: 0.003831, Avg Recall: 0.629032, count: 124 -Region Avg IOU: 0.527265, Class: 1.000000, Obj: 0.334408, No Obj: 0.003206, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.499217, Class: 1.000000, Obj: 0.319188, No Obj: 0.003982, Avg Recall: 0.563291, count: 158 -Region Avg IOU: 0.595970, Class: 1.000000, Obj: 0.383454, No Obj: 0.003504, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.545555, Class: 1.000000, Obj: 0.364364, No Obj: 0.003468, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.588074, Class: 1.000000, Obj: 0.332495, No Obj: 0.003704, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.511820, Class: 1.000000, Obj: 0.316815, No Obj: 0.003484, Avg Recall: 0.580357, count: 112 -4545: 0.479556, 0.441777 avg, 0.001000 rate, 22.574741 seconds, 1163520 images -Loaded: 0.005739 seconds -Region Avg IOU: 0.526421, Class: 1.000000, Obj: 0.359388, No Obj: 0.004029, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.545478, Class: 1.000000, Obj: 0.323633, No Obj: 0.004174, Avg Recall: 0.651515, count: 132 -Region Avg IOU: 0.587523, Class: 1.000000, Obj: 0.362036, No Obj: 0.003663, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.558644, Class: 1.000000, Obj: 0.342692, No Obj: 0.004029, Avg Recall: 0.653543, count: 127 -Region Avg IOU: 0.505064, Class: 1.000000, Obj: 0.369458, No Obj: 0.003190, Avg Recall: 0.520000, count: 100 -Region Avg IOU: 0.587743, Class: 1.000000, Obj: 0.410096, No Obj: 0.003819, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.551735, Class: 1.000000, Obj: 0.346398, No Obj: 0.003422, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.577255, Class: 1.000000, Obj: 0.387023, No Obj: 0.004555, Avg Recall: 0.695312, count: 128 -4546: 0.439615, 0.441561 avg, 0.001000 rate, 22.824844 seconds, 1163776 images -Loaded: 0.041300 seconds -Region Avg IOU: 0.521521, Class: 1.000000, Obj: 0.362949, No Obj: 0.004266, Avg Recall: 0.587302, count: 126 -Region Avg IOU: 0.568531, Class: 1.000000, Obj: 0.403504, No Obj: 0.004417, Avg Recall: 0.683761, count: 117 -Region Avg IOU: 0.520276, Class: 1.000000, Obj: 0.332948, No Obj: 0.004246, Avg Recall: 0.594828, count: 116 -Region Avg IOU: 0.541059, Class: 1.000000, Obj: 0.376530, No Obj: 0.004270, Avg Recall: 0.614173, count: 127 -Region Avg IOU: 0.594353, Class: 1.000000, Obj: 0.427253, No Obj: 0.003484, Avg Recall: 0.671053, count: 76 -Region Avg IOU: 0.526414, Class: 1.000000, Obj: 0.351645, No Obj: 0.003379, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.555938, Class: 1.000000, Obj: 0.389776, No Obj: 0.004413, Avg Recall: 0.669492, count: 118 -Region Avg IOU: 0.568237, Class: 1.000000, Obj: 0.420300, No Obj: 0.003866, Avg Recall: 0.679612, count: 103 -4547: 0.430694, 0.440474 avg, 0.001000 rate, 21.647112 seconds, 1164032 images -Loaded: 0.530057 seconds -Region Avg IOU: 0.538248, Class: 1.000000, Obj: 0.324979, No Obj: 0.004285, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.516624, Class: 1.000000, Obj: 0.336452, No Obj: 0.003877, Avg Recall: 0.556701, count: 97 -Region Avg IOU: 0.536524, Class: 1.000000, Obj: 0.400755, No Obj: 0.004083, Avg Recall: 0.600000, count: 120 -Region Avg IOU: 0.565762, Class: 1.000000, Obj: 0.407664, No Obj: 0.004558, Avg Recall: 0.612903, count: 124 -Region Avg IOU: 0.582993, Class: 1.000000, Obj: 0.377247, No Obj: 0.004328, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.547078, Class: 1.000000, Obj: 0.369271, No Obj: 0.004093, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.532876, Class: 1.000000, Obj: 0.381156, No Obj: 0.003959, Avg Recall: 0.555556, count: 90 -Region Avg IOU: 0.549803, Class: 1.000000, Obj: 0.404273, No Obj: 0.004310, Avg Recall: 0.625000, count: 112 -4548: 0.462379, 0.442665 avg, 0.001000 rate, 22.141958 seconds, 1164288 images -Loaded: 0.237546 seconds -Region Avg IOU: 0.571470, Class: 1.000000, Obj: 0.392301, No Obj: 0.004294, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.585145, Class: 1.000000, Obj: 0.397486, No Obj: 0.004814, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.616841, Class: 1.000000, Obj: 0.412921, No Obj: 0.005516, Avg Recall: 0.741259, count: 143 -Region Avg IOU: 0.541979, Class: 1.000000, Obj: 0.372538, No Obj: 0.004416, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.565858, Class: 1.000000, Obj: 0.411439, No Obj: 0.004430, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.528764, Class: 1.000000, Obj: 0.355556, No Obj: 0.003657, Avg Recall: 0.562500, count: 96 -Region Avg IOU: 0.543818, Class: 1.000000, Obj: 0.427984, No Obj: 0.004467, Avg Recall: 0.580153, count: 131 -Region Avg IOU: 0.543465, Class: 1.000000, Obj: 0.408027, No Obj: 0.003705, Avg Recall: 0.649485, count: 97 -4549: 0.421571, 0.440555 avg, 0.001000 rate, 21.596636 seconds, 1164544 images -Loaded: 1.338793 seconds -Region Avg IOU: 0.539706, Class: 1.000000, Obj: 0.421529, No Obj: 0.004290, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.480059, Class: 1.000000, Obj: 0.367149, No Obj: 0.003652, Avg Recall: 0.509434, count: 106 -Region Avg IOU: 0.556455, Class: 1.000000, Obj: 0.394331, No Obj: 0.004702, Avg Recall: 0.663866, count: 119 -Region Avg IOU: 0.550133, Class: 1.000000, Obj: 0.403158, No Obj: 0.004446, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.544988, Class: 1.000000, Obj: 0.380471, No Obj: 0.004461, Avg Recall: 0.620690, count: 116 -Region Avg IOU: 0.556539, Class: 1.000000, Obj: 0.388030, No Obj: 0.005390, Avg Recall: 0.641892, count: 148 -Region Avg IOU: 0.507758, Class: 1.000000, Obj: 0.366216, No Obj: 0.004178, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.580867, Class: 1.000000, Obj: 0.360718, No Obj: 0.005241, Avg Recall: 0.728070, count: 114 -4550: 0.471260, 0.443626 avg, 0.001000 rate, 22.814495 seconds, 1164800 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.367883 seconds -Region Avg IOU: 0.551493, Class: 1.000000, Obj: 0.440353, No Obj: 0.003699, Avg Recall: 0.679487, count: 78 -Region Avg IOU: 0.514328, Class: 1.000000, Obj: 0.390174, No Obj: 0.003786, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.543856, Class: 1.000000, Obj: 0.381408, No Obj: 0.004332, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.543624, Class: 1.000000, Obj: 0.444159, No Obj: 0.004210, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.546566, Class: 1.000000, Obj: 0.399660, No Obj: 0.004752, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.584333, Class: 1.000000, Obj: 0.396645, No Obj: 0.004571, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.544713, Class: 1.000000, Obj: 0.404768, No Obj: 0.004515, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.496187, Class: 1.000000, Obj: 0.335346, No Obj: 0.004456, Avg Recall: 0.566929, count: 127 -4551: 0.458066, 0.445070 avg, 0.001000 rate, 20.518871 seconds, 1165056 images -Loaded: 1.194613 seconds -Region Avg IOU: 0.523694, Class: 1.000000, Obj: 0.411213, No Obj: 0.005075, Avg Recall: 0.584000, count: 125 -Region Avg IOU: 0.555503, Class: 1.000000, Obj: 0.402396, No Obj: 0.004280, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.525567, Class: 1.000000, Obj: 0.377966, No Obj: 0.004609, Avg Recall: 0.611570, count: 121 -Region Avg IOU: 0.544821, Class: 1.000000, Obj: 0.391617, No Obj: 0.003738, Avg Recall: 0.648352, count: 91 -Region Avg IOU: 0.541870, Class: 1.000000, Obj: 0.389778, No Obj: 0.003761, Avg Recall: 0.666667, count: 75 -Region Avg IOU: 0.523746, Class: 1.000000, Obj: 0.372769, No Obj: 0.003950, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.537183, Class: 1.000000, Obj: 0.379851, No Obj: 0.004215, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.534634, Class: 1.000000, Obj: 0.395420, No Obj: 0.004613, Avg Recall: 0.642857, count: 98 -4552: 0.370019, 0.437565 avg, 0.001000 rate, 20.715229 seconds, 1165312 images -Loaded: 1.328642 seconds -Region Avg IOU: 0.558734, Class: 1.000000, Obj: 0.378420, No Obj: 0.004036, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.537515, Class: 1.000000, Obj: 0.394039, No Obj: 0.004125, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.555273, Class: 1.000000, Obj: 0.399214, No Obj: 0.004490, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.488802, Class: 1.000000, Obj: 0.346842, No Obj: 0.004031, Avg Recall: 0.529851, count: 134 -Region Avg IOU: 0.559010, Class: 1.000000, Obj: 0.393439, No Obj: 0.004329, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.548428, Class: 1.000000, Obj: 0.354294, No Obj: 0.004630, Avg Recall: 0.613636, count: 132 -Region Avg IOU: 0.536405, Class: 1.000000, Obj: 0.339267, No Obj: 0.004975, Avg Recall: 0.626866, count: 134 -Region Avg IOU: 0.551035, Class: 1.000000, Obj: 0.386387, No Obj: 0.003920, Avg Recall: 0.619565, count: 92 -4553: 0.435430, 0.437351 avg, 0.001000 rate, 22.785784 seconds, 1165568 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.542234, Class: 1.000000, Obj: 0.364489, No Obj: 0.004374, Avg Recall: 0.586207, count: 87 -Region Avg IOU: 0.486138, Class: 1.000000, Obj: 0.366920, No Obj: 0.003910, Avg Recall: 0.509434, count: 106 -Region Avg IOU: 0.529757, Class: 1.000000, Obj: 0.344412, No Obj: 0.004653, Avg Recall: 0.605263, count: 152 -Region Avg IOU: 0.551235, Class: 1.000000, Obj: 0.382857, No Obj: 0.004588, Avg Recall: 0.624000, count: 125 -Region Avg IOU: 0.502334, Class: 1.000000, Obj: 0.380551, No Obj: 0.004103, Avg Recall: 0.546392, count: 97 -Region Avg IOU: 0.512606, Class: 1.000000, Obj: 0.329975, No Obj: 0.004198, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.534734, Class: 1.000000, Obj: 0.339037, No Obj: 0.004070, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.561153, Class: 1.000000, Obj: 0.410658, No Obj: 0.004372, Avg Recall: 0.620968, count: 124 -4554: 0.484761, 0.442092 avg, 0.001000 rate, 20.679363 seconds, 1165824 images -Loaded: 1.206773 seconds -Region Avg IOU: 0.555103, Class: 1.000000, Obj: 0.369032, No Obj: 0.004578, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.557974, Class: 1.000000, Obj: 0.353662, No Obj: 0.004181, Avg Recall: 0.658915, count: 129 -Region Avg IOU: 0.553256, Class: 1.000000, Obj: 0.381821, No Obj: 0.003908, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.555831, Class: 1.000000, Obj: 0.369557, No Obj: 0.003654, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.542433, Class: 1.000000, Obj: 0.349999, No Obj: 0.004469, Avg Recall: 0.614865, count: 148 -Region Avg IOU: 0.558715, Class: 1.000000, Obj: 0.418969, No Obj: 0.004032, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.535564, Class: 1.000000, Obj: 0.409447, No Obj: 0.003721, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.545087, Class: 1.000000, Obj: 0.401850, No Obj: 0.003779, Avg Recall: 0.607143, count: 84 -4555: 0.414543, 0.439337 avg, 0.001000 rate, 22.505766 seconds, 1166080 images -Loaded: 0.188805 seconds -Region Avg IOU: 0.552949, Class: 1.000000, Obj: 0.423445, No Obj: 0.003730, Avg Recall: 0.635294, count: 85 -Region Avg IOU: 0.527607, Class: 1.000000, Obj: 0.382967, No Obj: 0.003518, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.520555, Class: 1.000000, Obj: 0.327579, No Obj: 0.004499, Avg Recall: 0.609023, count: 133 -Region Avg IOU: 0.532209, Class: 1.000000, Obj: 0.367998, No Obj: 0.003842, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.578385, Class: 1.000000, Obj: 0.424233, No Obj: 0.004227, Avg Recall: 0.700000, count: 120 -Region Avg IOU: 0.527894, Class: 1.000000, Obj: 0.403380, No Obj: 0.004531, Avg Recall: 0.586777, count: 121 -Region Avg IOU: 0.543681, Class: 1.000000, Obj: 0.311190, No Obj: 0.005499, Avg Recall: 0.662791, count: 172 -Region Avg IOU: 0.531683, Class: 1.000000, Obj: 0.335573, No Obj: 0.004074, Avg Recall: 0.608000, count: 125 -4556: 0.445261, 0.439930 avg, 0.001000 rate, 22.687588 seconds, 1166336 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.506611, Class: 1.000000, Obj: 0.349547, No Obj: 0.003355, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.578338, Class: 1.000000, Obj: 0.362795, No Obj: 0.004144, Avg Recall: 0.677686, count: 121 -Region Avg IOU: 0.525079, Class: 1.000000, Obj: 0.321418, No Obj: 0.003505, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.531051, Class: 1.000000, Obj: 0.382960, No Obj: 0.004357, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.582211, Class: 1.000000, Obj: 0.376863, No Obj: 0.004654, Avg Recall: 0.646154, count: 130 -Region Avg IOU: 0.548456, Class: 1.000000, Obj: 0.357528, No Obj: 0.003780, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.551661, Class: 1.000000, Obj: 0.360860, No Obj: 0.004507, Avg Recall: 0.616667, count: 120 -Region Avg IOU: 0.542563, Class: 1.000000, Obj: 0.374976, No Obj: 0.003550, Avg Recall: 0.637363, count: 91 -4557: 0.449691, 0.440906 avg, 0.001000 rate, 20.379959 seconds, 1166592 images -Loaded: 1.500670 seconds -Region Avg IOU: 0.572266, Class: 1.000000, Obj: 0.371970, No Obj: 0.003855, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.576554, Class: 1.000000, Obj: 0.381886, No Obj: 0.004147, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.526607, Class: 1.000000, Obj: 0.341418, No Obj: 0.003758, Avg Recall: 0.613445, count: 119 -Region Avg IOU: 0.576168, Class: 1.000000, Obj: 0.369484, No Obj: 0.004304, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.577145, Class: 1.000000, Obj: 0.393412, No Obj: 0.003520, Avg Recall: 0.693182, count: 88 -Region Avg IOU: 0.573392, Class: 1.000000, Obj: 0.347863, No Obj: 0.004423, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.506542, Class: 1.000000, Obj: 0.365272, No Obj: 0.003474, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.628732, Class: 1.000000, Obj: 0.434354, No Obj: 0.003435, Avg Recall: 0.779221, count: 77 -4558: 0.429833, 0.439798 avg, 0.001000 rate, 22.433056 seconds, 1166848 images -Loaded: 1.881842 seconds -Region Avg IOU: 0.614709, Class: 1.000000, Obj: 0.437979, No Obj: 0.003857, Avg Recall: 0.780822, count: 73 -Region Avg IOU: 0.571356, Class: 1.000000, Obj: 0.368691, No Obj: 0.004834, Avg Recall: 0.634328, count: 134 -Region Avg IOU: 0.518952, Class: 1.000000, Obj: 0.360986, No Obj: 0.004070, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.552672, Class: 1.000000, Obj: 0.331841, No Obj: 0.003530, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.552249, Class: 1.000000, Obj: 0.442129, No Obj: 0.003775, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.557608, Class: 1.000000, Obj: 0.349544, No Obj: 0.003692, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.583742, Class: 1.000000, Obj: 0.337403, No Obj: 0.004370, Avg Recall: 0.673469, count: 147 -Region Avg IOU: 0.559443, Class: 1.000000, Obj: 0.387773, No Obj: 0.004169, Avg Recall: 0.657895, count: 114 -4559: 0.427474, 0.438566 avg, 0.001000 rate, 22.384453 seconds, 1167104 images -Loaded: 0.000128 seconds -Region Avg IOU: 0.524545, Class: 1.000000, Obj: 0.359866, No Obj: 0.003964, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.548754, Class: 1.000000, Obj: 0.428145, No Obj: 0.004196, Avg Recall: 0.572650, count: 117 -Region Avg IOU: 0.597590, Class: 1.000000, Obj: 0.358581, No Obj: 0.004172, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.488539, Class: 1.000000, Obj: 0.386870, No Obj: 0.004455, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.544880, Class: 1.000000, Obj: 0.397293, No Obj: 0.003825, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.566000, Class: 1.000000, Obj: 0.360062, No Obj: 0.004148, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.533351, Class: 1.000000, Obj: 0.346454, No Obj: 0.003844, Avg Recall: 0.605042, count: 119 -Region Avg IOU: 0.524429, Class: 1.000000, Obj: 0.361385, No Obj: 0.003690, Avg Recall: 0.613861, count: 101 -4560: 0.399296, 0.434639 avg, 0.001000 rate, 23.166672 seconds, 1167360 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000067 seconds -Region Avg IOU: 0.625017, Class: 1.000000, Obj: 0.457933, No Obj: 0.003485, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.611909, Class: 1.000000, Obj: 0.400984, No Obj: 0.003360, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.691186, Class: 1.000000, Obj: 0.428396, No Obj: 0.003880, Avg Recall: 0.896552, count: 116 -Region Avg IOU: 0.609944, Class: 1.000000, Obj: 0.351844, No Obj: 0.003848, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.611700, Class: 1.000000, Obj: 0.433789, No Obj: 0.002833, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.635116, Class: 1.000000, Obj: 0.470319, No Obj: 0.003089, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.616777, Class: 1.000000, Obj: 0.384882, No Obj: 0.003734, Avg Recall: 0.748201, count: 139 -Region Avg IOU: 0.638304, Class: 1.000000, Obj: 0.420950, No Obj: 0.003248, Avg Recall: 0.793103, count: 87 -4561: 0.432569, 0.434432 avg, 0.001000 rate, 26.805897 seconds, 1167616 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.582774, Class: 1.000000, Obj: 0.377065, No Obj: 0.003799, Avg Recall: 0.700000, count: 120 -Region Avg IOU: 0.614306, Class: 1.000000, Obj: 0.420295, No Obj: 0.003235, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.585443, Class: 1.000000, Obj: 0.437455, No Obj: 0.003178, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.613715, Class: 1.000000, Obj: 0.403554, No Obj: 0.002992, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.617228, Class: 1.000000, Obj: 0.409342, No Obj: 0.003470, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.624316, Class: 1.000000, Obj: 0.445332, No Obj: 0.003254, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.613702, Class: 1.000000, Obj: 0.374884, No Obj: 0.003143, Avg Recall: 0.768595, count: 121 -Region Avg IOU: 0.606169, Class: 1.000000, Obj: 0.414296, No Obj: 0.003639, Avg Recall: 0.712000, count: 125 -4562: 0.460663, 0.437055 avg, 0.001000 rate, 26.768724 seconds, 1167872 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.608926, Class: 1.000000, Obj: 0.392681, No Obj: 0.002794, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.605231, Class: 1.000000, Obj: 0.398351, No Obj: 0.003270, Avg Recall: 0.744681, count: 94 -Region Avg IOU: 0.648557, Class: 1.000000, Obj: 0.408524, No Obj: 0.003232, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.631609, Class: 1.000000, Obj: 0.451312, No Obj: 0.003063, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.654131, Class: 1.000000, Obj: 0.428730, No Obj: 0.003013, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.654687, Class: 1.000000, Obj: 0.404694, No Obj: 0.003472, Avg Recall: 0.826923, count: 104 -Region Avg IOU: 0.600250, Class: 1.000000, Obj: 0.366103, No Obj: 0.003351, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.593488, Class: 1.000000, Obj: 0.421164, No Obj: 0.003269, Avg Recall: 0.705882, count: 119 -4563: 0.417690, 0.435119 avg, 0.001000 rate, 27.165144 seconds, 1168128 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.633339, Class: 1.000000, Obj: 0.373932, No Obj: 0.003097, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.593017, Class: 1.000000, Obj: 0.379059, No Obj: 0.003118, Avg Recall: 0.708661, count: 127 -Region Avg IOU: 0.629099, Class: 1.000000, Obj: 0.408505, No Obj: 0.003144, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.627276, Class: 1.000000, Obj: 0.387195, No Obj: 0.003133, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.590717, Class: 1.000000, Obj: 0.407009, No Obj: 0.003093, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.628476, Class: 1.000000, Obj: 0.352526, No Obj: 0.003237, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.601790, Class: 1.000000, Obj: 0.378790, No Obj: 0.002606, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.581539, Class: 1.000000, Obj: 0.380610, No Obj: 0.003053, Avg Recall: 0.690265, count: 113 -4564: 0.460997, 0.437706 avg, 0.001000 rate, 25.862057 seconds, 1168384 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.654261, Class: 1.000000, Obj: 0.415137, No Obj: 0.003124, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.650696, Class: 1.000000, Obj: 0.403507, No Obj: 0.002848, Avg Recall: 0.842697, count: 89 -Region Avg IOU: 0.630085, Class: 1.000000, Obj: 0.343087, No Obj: 0.003053, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.615961, Class: 1.000000, Obj: 0.381489, No Obj: 0.002747, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.548640, Class: 1.000000, Obj: 0.390944, No Obj: 0.002690, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.618104, Class: 1.000000, Obj: 0.368915, No Obj: 0.002675, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.621003, Class: 1.000000, Obj: 0.374547, No Obj: 0.003589, Avg Recall: 0.760870, count: 138 -Region Avg IOU: 0.650940, Class: 1.000000, Obj: 0.365324, No Obj: 0.002920, Avg Recall: 0.831461, count: 89 -4565: 0.432842, 0.437220 avg, 0.001000 rate, 26.216812 seconds, 1168640 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.635179, Class: 1.000000, Obj: 0.370609, No Obj: 0.002792, Avg Recall: 0.826531, count: 98 -Region Avg IOU: 0.598352, Class: 1.000000, Obj: 0.339890, No Obj: 0.002443, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.600836, Class: 1.000000, Obj: 0.394491, No Obj: 0.002877, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.657022, Class: 1.000000, Obj: 0.425048, No Obj: 0.002996, Avg Recall: 0.838095, count: 105 -Region Avg IOU: 0.644538, Class: 1.000000, Obj: 0.362806, No Obj: 0.002831, Avg Recall: 0.819149, count: 94 -Region Avg IOU: 0.646717, Class: 1.000000, Obj: 0.386449, No Obj: 0.002395, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.622825, Class: 1.000000, Obj: 0.366277, No Obj: 0.002850, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.646609, Class: 1.000000, Obj: 0.405780, No Obj: 0.003045, Avg Recall: 0.804878, count: 123 -4566: 0.411448, 0.434643 avg, 0.001000 rate, 27.118212 seconds, 1168896 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.640337, Class: 1.000000, Obj: 0.385273, No Obj: 0.003069, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.655303, Class: 1.000000, Obj: 0.387848, No Obj: 0.002592, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.626711, Class: 1.000000, Obj: 0.354567, No Obj: 0.002529, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.660542, Class: 1.000000, Obj: 0.363568, No Obj: 0.003057, Avg Recall: 0.838384, count: 99 -Region Avg IOU: 0.633578, Class: 1.000000, Obj: 0.381592, No Obj: 0.002620, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.636039, Class: 1.000000, Obj: 0.369613, No Obj: 0.002775, Avg Recall: 0.810345, count: 116 -Region Avg IOU: 0.594631, Class: 1.000000, Obj: 0.366513, No Obj: 0.002729, Avg Recall: 0.700000, count: 120 -Region Avg IOU: 0.587047, Class: 1.000000, Obj: 0.382376, No Obj: 0.002752, Avg Recall: 0.700000, count: 110 -4567: 0.442179, 0.435396 avg, 0.001000 rate, 28.018126 seconds, 1169152 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.648951, Class: 1.000000, Obj: 0.408459, No Obj: 0.002920, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.577363, Class: 1.000000, Obj: 0.296454, No Obj: 0.003259, Avg Recall: 0.659574, count: 141 -Region Avg IOU: 0.599315, Class: 1.000000, Obj: 0.345859, No Obj: 0.003278, Avg Recall: 0.736842, count: 152 -Region Avg IOU: 0.686616, Class: 1.000000, Obj: 0.406480, No Obj: 0.003334, Avg Recall: 0.822581, count: 124 -Region Avg IOU: 0.621516, Class: 1.000000, Obj: 0.347946, No Obj: 0.003293, Avg Recall: 0.757353, count: 136 -Region Avg IOU: 0.611069, Class: 1.000000, Obj: 0.373070, No Obj: 0.002968, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.605732, Class: 1.000000, Obj: 0.349299, No Obj: 0.003011, Avg Recall: 0.700000, count: 130 -Region Avg IOU: 0.563106, Class: 1.000000, Obj: 0.308911, No Obj: 0.002737, Avg Recall: 0.619048, count: 105 -4568: 0.503771, 0.442234 avg, 0.001000 rate, 27.109961 seconds, 1169408 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.669193, Class: 1.000000, Obj: 0.361295, No Obj: 0.002648, Avg Recall: 0.811111, count: 90 -Region Avg IOU: 0.635182, Class: 1.000000, Obj: 0.395878, No Obj: 0.003149, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.600052, Class: 1.000000, Obj: 0.382397, No Obj: 0.003124, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.648556, Class: 1.000000, Obj: 0.400328, No Obj: 0.002954, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.615828, Class: 1.000000, Obj: 0.383339, No Obj: 0.003270, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.625829, Class: 1.000000, Obj: 0.338592, No Obj: 0.002951, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.648169, Class: 1.000000, Obj: 0.416672, No Obj: 0.003084, Avg Recall: 0.805310, count: 113 -Region Avg IOU: 0.602357, Class: 1.000000, Obj: 0.404935, No Obj: 0.002723, Avg Recall: 0.701149, count: 87 -4569: 0.406543, 0.438665 avg, 0.001000 rate, 24.542252 seconds, 1169664 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.644115, Class: 1.000000, Obj: 0.380417, No Obj: 0.002873, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.633124, Class: 1.000000, Obj: 0.428666, No Obj: 0.002630, Avg Recall: 0.817308, count: 104 -Region Avg IOU: 0.604249, Class: 1.000000, Obj: 0.375323, No Obj: 0.002607, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.629093, Class: 1.000000, Obj: 0.366226, No Obj: 0.003077, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.636598, Class: 1.000000, Obj: 0.404411, No Obj: 0.002985, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.660028, Class: 1.000000, Obj: 0.372203, No Obj: 0.003058, Avg Recall: 0.808081, count: 99 -Region Avg IOU: 0.642845, Class: 1.000000, Obj: 0.362396, No Obj: 0.003267, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.632789, Class: 1.000000, Obj: 0.418279, No Obj: 0.002710, Avg Recall: 0.782178, count: 101 -4570: 0.424346, 0.437233 avg, 0.001000 rate, 26.529284 seconds, 1169920 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 19.095709 seconds -Region Avg IOU: 0.592083, Class: 1.000000, Obj: 0.328329, No Obj: 0.003153, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.541834, Class: 1.000000, Obj: 0.347846, No Obj: 0.003175, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.609296, Class: 1.000000, Obj: 0.380482, No Obj: 0.003380, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.618083, Class: 1.000000, Obj: 0.387528, No Obj: 0.003102, Avg Recall: 0.736842, count: 76 -Region Avg IOU: 0.587157, Class: 1.000000, Obj: 0.378210, No Obj: 0.003810, Avg Recall: 0.698276, count: 116 -Region Avg IOU: 0.621934, Class: 1.000000, Obj: 0.378277, No Obj: 0.003824, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.527530, Class: 1.000000, Obj: 0.321897, No Obj: 0.003116, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.542699, Class: 1.000000, Obj: 0.374737, No Obj: 0.002968, Avg Recall: 0.591837, count: 98 -4571: 0.386038, 0.432113 avg, 0.001000 rate, 23.112556 seconds, 1170176 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.542407, Class: 1.000000, Obj: 0.340979, No Obj: 0.003797, Avg Recall: 0.601695, count: 118 -Region Avg IOU: 0.613748, Class: 1.000000, Obj: 0.392115, No Obj: 0.003561, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.490435, Class: 1.000000, Obj: 0.293548, No Obj: 0.003342, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.544498, Class: 1.000000, Obj: 0.374224, No Obj: 0.003098, Avg Recall: 0.686747, count: 83 -Region Avg IOU: 0.620126, Class: 1.000000, Obj: 0.408644, No Obj: 0.003578, Avg Recall: 0.725275, count: 91 -Region Avg IOU: 0.576041, Class: 1.000000, Obj: 0.386563, No Obj: 0.003868, Avg Recall: 0.688525, count: 122 -Region Avg IOU: 0.528574, Class: 1.000000, Obj: 0.341735, No Obj: 0.003380, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.554004, Class: 1.000000, Obj: 0.358502, No Obj: 0.003256, Avg Recall: 0.644444, count: 90 -4572: 0.419679, 0.430870 avg, 0.001000 rate, 23.330551 seconds, 1170432 images -Loaded: 0.031194 seconds -Region Avg IOU: 0.583203, Class: 1.000000, Obj: 0.388015, No Obj: 0.003524, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.589284, Class: 1.000000, Obj: 0.356397, No Obj: 0.003259, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.546679, Class: 1.000000, Obj: 0.366580, No Obj: 0.003778, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.555371, Class: 1.000000, Obj: 0.334719, No Obj: 0.003701, Avg Recall: 0.632479, count: 117 -Region Avg IOU: 0.565740, Class: 1.000000, Obj: 0.376986, No Obj: 0.003694, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.575922, Class: 1.000000, Obj: 0.365060, No Obj: 0.004296, Avg Recall: 0.664474, count: 152 -Region Avg IOU: 0.548152, Class: 1.000000, Obj: 0.356488, No Obj: 0.003929, Avg Recall: 0.601626, count: 123 -Region Avg IOU: 0.537895, Class: 1.000000, Obj: 0.395615, No Obj: 0.003547, Avg Recall: 0.579439, count: 107 -4573: 0.426316, 0.430415 avg, 0.001000 rate, 24.863159 seconds, 1170688 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.630073, Class: 1.000000, Obj: 0.445263, No Obj: 0.004183, Avg Recall: 0.776471, count: 85 -Region Avg IOU: 0.553067, Class: 1.000000, Obj: 0.357568, No Obj: 0.003940, Avg Recall: 0.650406, count: 123 -Region Avg IOU: 0.588545, Class: 1.000000, Obj: 0.399897, No Obj: 0.003359, Avg Recall: 0.727273, count: 88 -Region Avg IOU: 0.569373, Class: 1.000000, Obj: 0.430147, No Obj: 0.003086, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.580651, Class: 1.000000, Obj: 0.420838, No Obj: 0.004237, Avg Recall: 0.672000, count: 125 -Region Avg IOU: 0.563185, Class: 1.000000, Obj: 0.392061, No Obj: 0.003838, Avg Recall: 0.666667, count: 123 -Region Avg IOU: 0.552168, Class: 1.000000, Obj: 0.390934, No Obj: 0.003973, Avg Recall: 0.653226, count: 124 -Region Avg IOU: 0.536996, Class: 1.000000, Obj: 0.371300, No Obj: 0.003817, Avg Recall: 0.606299, count: 127 -4574: 0.438408, 0.431214 avg, 0.001000 rate, 23.330795 seconds, 1170944 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.579658, Class: 1.000000, Obj: 0.467170, No Obj: 0.003913, Avg Recall: 0.692913, count: 127 -Region Avg IOU: 0.562990, Class: 1.000000, Obj: 0.381654, No Obj: 0.004494, Avg Recall: 0.678571, count: 140 -Region Avg IOU: 0.527224, Class: 1.000000, Obj: 0.345192, No Obj: 0.003560, Avg Recall: 0.601695, count: 118 -Region Avg IOU: 0.593590, Class: 1.000000, Obj: 0.437560, No Obj: 0.004066, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.546945, Class: 1.000000, Obj: 0.426549, No Obj: 0.003265, Avg Recall: 0.623377, count: 77 -Region Avg IOU: 0.544587, Class: 1.000000, Obj: 0.347498, No Obj: 0.003719, Avg Recall: 0.645570, count: 79 -Region Avg IOU: 0.586604, Class: 1.000000, Obj: 0.393898, No Obj: 0.003747, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.586346, Class: 1.000000, Obj: 0.417776, No Obj: 0.004050, Avg Recall: 0.703704, count: 108 -4575: 0.414212, 0.429514 avg, 0.001000 rate, 24.115929 seconds, 1171200 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.512169, Class: 1.000000, Obj: 0.386038, No Obj: 0.004171, Avg Recall: 0.527273, count: 110 -Region Avg IOU: 0.589999, Class: 1.000000, Obj: 0.448311, No Obj: 0.004263, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.599251, Class: 1.000000, Obj: 0.432795, No Obj: 0.003519, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.568476, Class: 1.000000, Obj: 0.385992, No Obj: 0.004089, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.593684, Class: 1.000000, Obj: 0.419317, No Obj: 0.003696, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.557686, Class: 1.000000, Obj: 0.400817, No Obj: 0.003079, Avg Recall: 0.600000, count: 85 -Region Avg IOU: 0.580093, Class: 1.000000, Obj: 0.379475, No Obj: 0.004852, Avg Recall: 0.644068, count: 118 -Region Avg IOU: 0.599937, Class: 1.000000, Obj: 0.469298, No Obj: 0.003573, Avg Recall: 0.686747, count: 83 -4576: 0.376741, 0.424236 avg, 0.001000 rate, 23.606564 seconds, 1171456 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.564343, Class: 1.000000, Obj: 0.365696, No Obj: 0.003507, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.549346, Class: 1.000000, Obj: 0.388481, No Obj: 0.004320, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.601664, Class: 1.000000, Obj: 0.410106, No Obj: 0.004543, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.579998, Class: 1.000000, Obj: 0.408311, No Obj: 0.004196, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.627160, Class: 1.000000, Obj: 0.448303, No Obj: 0.004267, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.543962, Class: 1.000000, Obj: 0.375504, No Obj: 0.003756, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.573491, Class: 1.000000, Obj: 0.412384, No Obj: 0.004651, Avg Recall: 0.658730, count: 126 -Region Avg IOU: 0.568569, Class: 1.000000, Obj: 0.386351, No Obj: 0.003573, Avg Recall: 0.651685, count: 89 -4577: 0.424805, 0.424293 avg, 0.001000 rate, 21.756413 seconds, 1171712 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.525848, Class: 1.000000, Obj: 0.379240, No Obj: 0.003874, Avg Recall: 0.581395, count: 86 -Region Avg IOU: 0.575582, Class: 1.000000, Obj: 0.371792, No Obj: 0.004078, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.605953, Class: 1.000000, Obj: 0.436651, No Obj: 0.004077, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.536818, Class: 1.000000, Obj: 0.388565, No Obj: 0.003971, Avg Recall: 0.609756, count: 123 -Region Avg IOU: 0.551086, Class: 1.000000, Obj: 0.399921, No Obj: 0.003987, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.567450, Class: 1.000000, Obj: 0.397863, No Obj: 0.004215, Avg Recall: 0.646617, count: 133 -Region Avg IOU: 0.580395, Class: 1.000000, Obj: 0.412498, No Obj: 0.004736, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.603997, Class: 1.000000, Obj: 0.434625, No Obj: 0.004407, Avg Recall: 0.694915, count: 118 -4578: 0.481807, 0.430045 avg, 0.001000 rate, 23.934072 seconds, 1171968 images -Loaded: 0.048625 seconds -Region Avg IOU: 0.626609, Class: 1.000000, Obj: 0.393922, No Obj: 0.003614, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.598508, Class: 1.000000, Obj: 0.431227, No Obj: 0.003990, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.564912, Class: 1.000000, Obj: 0.408640, No Obj: 0.003524, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.509183, Class: 1.000000, Obj: 0.391301, No Obj: 0.003493, Avg Recall: 0.551020, count: 98 -Region Avg IOU: 0.577791, Class: 1.000000, Obj: 0.411432, No Obj: 0.004179, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.597754, Class: 1.000000, Obj: 0.444905, No Obj: 0.004004, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.526467, Class: 1.000000, Obj: 0.381694, No Obj: 0.003157, Avg Recall: 0.543478, count: 92 -Region Avg IOU: 0.550158, Class: 1.000000, Obj: 0.347192, No Obj: 0.004463, Avg Recall: 0.593496, count: 123 -4579: 0.402758, 0.427316 avg, 0.001000 rate, 22.356760 seconds, 1172224 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.542054, Class: 1.000000, Obj: 0.376225, No Obj: 0.003868, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.564045, Class: 1.000000, Obj: 0.399211, No Obj: 0.004091, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.546365, Class: 1.000000, Obj: 0.407830, No Obj: 0.003160, Avg Recall: 0.622222, count: 90 -Region Avg IOU: 0.607401, Class: 1.000000, Obj: 0.419830, No Obj: 0.003652, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.612527, Class: 1.000000, Obj: 0.466527, No Obj: 0.004332, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.594334, Class: 1.000000, Obj: 0.429017, No Obj: 0.003638, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.555450, Class: 1.000000, Obj: 0.400589, No Obj: 0.003652, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.604833, Class: 1.000000, Obj: 0.344486, No Obj: 0.003781, Avg Recall: 0.731183, count: 93 -4580: 0.404677, 0.425052 avg, 0.001000 rate, 24.383820 seconds, 1172480 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000067 seconds -Region Avg IOU: 0.590880, Class: 1.000000, Obj: 0.429480, No Obj: 0.003475, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.624845, Class: 1.000000, Obj: 0.522623, No Obj: 0.003860, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.609916, Class: 1.000000, Obj: 0.424449, No Obj: 0.003568, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.629559, Class: 1.000000, Obj: 0.468756, No Obj: 0.003350, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.577792, Class: 1.000000, Obj: 0.404599, No Obj: 0.004167, Avg Recall: 0.652174, count: 138 -Region Avg IOU: 0.598244, Class: 1.000000, Obj: 0.406309, No Obj: 0.003983, Avg Recall: 0.694656, count: 131 -Region Avg IOU: 0.595681, Class: 1.000000, Obj: 0.404579, No Obj: 0.004035, Avg Recall: 0.710145, count: 138 -Region Avg IOU: 0.614166, Class: 1.000000, Obj: 0.440383, No Obj: 0.003478, Avg Recall: 0.756757, count: 111 -4581: 0.426902, 0.425237 avg, 0.001000 rate, 25.005981 seconds, 1172736 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.623961, Class: 1.000000, Obj: 0.435040, No Obj: 0.003471, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.571061, Class: 1.000000, Obj: 0.381927, No Obj: 0.003579, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.588642, Class: 1.000000, Obj: 0.356798, No Obj: 0.004283, Avg Recall: 0.699301, count: 143 -Region Avg IOU: 0.572984, Class: 1.000000, Obj: 0.413740, No Obj: 0.003486, Avg Recall: 0.641667, count: 120 -Region Avg IOU: 0.617059, Class: 1.000000, Obj: 0.408740, No Obj: 0.003512, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.563430, Class: 1.000000, Obj: 0.368409, No Obj: 0.003369, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.606511, Class: 1.000000, Obj: 0.404659, No Obj: 0.003650, Avg Recall: 0.762295, count: 122 -Region Avg IOU: 0.586079, Class: 1.000000, Obj: 0.366033, No Obj: 0.003349, Avg Recall: 0.724138, count: 116 -4582: 0.456418, 0.428355 avg, 0.001000 rate, 25.352484 seconds, 1172992 images -Loaded: 0.000133 seconds -Region Avg IOU: 0.623872, Class: 1.000000, Obj: 0.389624, No Obj: 0.004229, Avg Recall: 0.777778, count: 135 -Region Avg IOU: 0.611030, Class: 1.000000, Obj: 0.422710, No Obj: 0.003091, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.608637, Class: 1.000000, Obj: 0.410367, No Obj: 0.003617, Avg Recall: 0.772358, count: 123 -Region Avg IOU: 0.633584, Class: 1.000000, Obj: 0.410871, No Obj: 0.003520, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.587451, Class: 1.000000, Obj: 0.405948, No Obj: 0.003369, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.582055, Class: 1.000000, Obj: 0.375885, No Obj: 0.004014, Avg Recall: 0.666667, count: 138 -Region Avg IOU: 0.602568, Class: 1.000000, Obj: 0.373633, No Obj: 0.003790, Avg Recall: 0.762295, count: 122 -Region Avg IOU: 0.601027, Class: 1.000000, Obj: 0.427519, No Obj: 0.003135, Avg Recall: 0.710526, count: 114 -4583: 0.471024, 0.432622 avg, 0.001000 rate, 24.375992 seconds, 1173248 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.594116, Class: 1.000000, Obj: 0.407314, No Obj: 0.003747, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.568004, Class: 1.000000, Obj: 0.369212, No Obj: 0.003074, Avg Recall: 0.698276, count: 116 -Region Avg IOU: 0.630720, Class: 1.000000, Obj: 0.380965, No Obj: 0.003538, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.612137, Class: 1.000000, Obj: 0.401118, No Obj: 0.003151, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.555313, Class: 1.000000, Obj: 0.371364, No Obj: 0.003234, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.594043, Class: 1.000000, Obj: 0.343840, No Obj: 0.003947, Avg Recall: 0.698718, count: 156 -Region Avg IOU: 0.624581, Class: 1.000000, Obj: 0.408983, No Obj: 0.003672, Avg Recall: 0.725275, count: 91 -Region Avg IOU: 0.568953, Class: 1.000000, Obj: 0.399620, No Obj: 0.003153, Avg Recall: 0.649123, count: 114 -4584: 0.440251, 0.433385 avg, 0.001000 rate, 24.632153 seconds, 1173504 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.577836, Class: 1.000000, Obj: 0.354550, No Obj: 0.003122, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.603788, Class: 1.000000, Obj: 0.380647, No Obj: 0.003869, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.624374, Class: 1.000000, Obj: 0.382361, No Obj: 0.003049, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.616667, Class: 1.000000, Obj: 0.396997, No Obj: 0.003737, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.589059, Class: 1.000000, Obj: 0.405692, No Obj: 0.003721, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.630646, Class: 1.000000, Obj: 0.390015, No Obj: 0.003053, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.608390, Class: 1.000000, Obj: 0.361210, No Obj: 0.002770, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.576731, Class: 1.000000, Obj: 0.403932, No Obj: 0.003147, Avg Recall: 0.653061, count: 98 -4585: 0.410128, 0.431059 avg, 0.001000 rate, 24.869421 seconds, 1173760 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.583669, Class: 1.000000, Obj: 0.396166, No Obj: 0.002911, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.539309, Class: 1.000000, Obj: 0.331681, No Obj: 0.003095, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.552950, Class: 1.000000, Obj: 0.358164, No Obj: 0.003365, Avg Recall: 0.669291, count: 127 -Region Avg IOU: 0.598067, Class: 1.000000, Obj: 0.392878, No Obj: 0.003745, Avg Recall: 0.713178, count: 129 -Region Avg IOU: 0.612930, Class: 1.000000, Obj: 0.398315, No Obj: 0.002945, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.631635, Class: 1.000000, Obj: 0.416177, No Obj: 0.003624, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.649146, Class: 1.000000, Obj: 0.430463, No Obj: 0.003705, Avg Recall: 0.776316, count: 76 -Region Avg IOU: 0.603205, Class: 1.000000, Obj: 0.363529, No Obj: 0.003216, Avg Recall: 0.757812, count: 128 -4586: 0.418153, 0.429769 avg, 0.001000 rate, 26.089058 seconds, 1174016 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.594398, Class: 1.000000, Obj: 0.373909, No Obj: 0.003922, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.596004, Class: 1.000000, Obj: 0.397780, No Obj: 0.003340, Avg Recall: 0.669355, count: 124 -Region Avg IOU: 0.622663, Class: 1.000000, Obj: 0.391972, No Obj: 0.003412, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.613109, Class: 1.000000, Obj: 0.363333, No Obj: 0.003390, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.598501, Class: 1.000000, Obj: 0.391265, No Obj: 0.003291, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.616284, Class: 1.000000, Obj: 0.405447, No Obj: 0.003199, Avg Recall: 0.738095, count: 84 -Region Avg IOU: 0.566286, Class: 1.000000, Obj: 0.383331, No Obj: 0.003561, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.547710, Class: 1.000000, Obj: 0.362655, No Obj: 0.003265, Avg Recall: 0.617188, count: 128 -4587: 0.428966, 0.429688 avg, 0.001000 rate, 25.049736 seconds, 1174272 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.589438, Class: 1.000000, Obj: 0.406464, No Obj: 0.003008, Avg Recall: 0.703704, count: 81 -Region Avg IOU: 0.616382, Class: 1.000000, Obj: 0.371884, No Obj: 0.003649, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.575405, Class: 1.000000, Obj: 0.384125, No Obj: 0.002993, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.585100, Class: 1.000000, Obj: 0.406697, No Obj: 0.003159, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.604888, Class: 1.000000, Obj: 0.369606, No Obj: 0.003631, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.583527, Class: 1.000000, Obj: 0.377438, No Obj: 0.002779, Avg Recall: 0.625000, count: 72 -Region Avg IOU: 0.622376, Class: 1.000000, Obj: 0.409465, No Obj: 0.003181, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.597143, Class: 1.000000, Obj: 0.408543, No Obj: 0.003352, Avg Recall: 0.669903, count: 103 -4588: 0.407559, 0.427476 avg, 0.001000 rate, 24.780275 seconds, 1174528 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.579128, Class: 1.000000, Obj: 0.368759, No Obj: 0.003045, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.585782, Class: 1.000000, Obj: 0.369434, No Obj: 0.003375, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.584533, Class: 1.000000, Obj: 0.342971, No Obj: 0.003367, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.617971, Class: 1.000000, Obj: 0.421337, No Obj: 0.003470, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.623416, Class: 1.000000, Obj: 0.427693, No Obj: 0.003397, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.631502, Class: 1.000000, Obj: 0.380440, No Obj: 0.003457, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.598614, Class: 1.000000, Obj: 0.409940, No Obj: 0.002957, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.591516, Class: 1.000000, Obj: 0.388789, No Obj: 0.002649, Avg Recall: 0.771084, count: 83 -4589: 0.417248, 0.426453 avg, 0.001000 rate, 24.890116 seconds, 1174784 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.582072, Class: 1.000000, Obj: 0.399939, No Obj: 0.002870, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.585559, Class: 1.000000, Obj: 0.399846, No Obj: 0.003013, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.617862, Class: 1.000000, Obj: 0.431231, No Obj: 0.003384, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.611242, Class: 1.000000, Obj: 0.413187, No Obj: 0.002999, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.575842, Class: 1.000000, Obj: 0.382888, No Obj: 0.003243, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.607842, Class: 1.000000, Obj: 0.415227, No Obj: 0.003936, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.645102, Class: 1.000000, Obj: 0.367116, No Obj: 0.002959, Avg Recall: 0.858696, count: 92 -Region Avg IOU: 0.616303, Class: 1.000000, Obj: 0.379632, No Obj: 0.003527, Avg Recall: 0.737288, count: 118 -4590: 0.404680, 0.424276 avg, 0.001000 rate, 24.302296 seconds, 1175040 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.110655 seconds -Region Avg IOU: 0.435451, Class: 1.000000, Obj: 0.267152, No Obj: 0.003838, Avg Recall: 0.432203, count: 118 -Region Avg IOU: 0.462908, Class: 1.000000, Obj: 0.266596, No Obj: 0.004072, Avg Recall: 0.468750, count: 128 -Region Avg IOU: 0.505444, Class: 1.000000, Obj: 0.257649, No Obj: 0.004217, Avg Recall: 0.559441, count: 143 -Region Avg IOU: 0.443104, Class: 1.000000, Obj: 0.247591, No Obj: 0.004054, Avg Recall: 0.422414, count: 116 -Region Avg IOU: 0.468918, Class: 1.000000, Obj: 0.279925, No Obj: 0.004214, Avg Recall: 0.483051, count: 118 -Region Avg IOU: 0.432316, Class: 1.000000, Obj: 0.251642, No Obj: 0.003534, Avg Recall: 0.463158, count: 95 -Region Avg IOU: 0.441498, Class: 1.000000, Obj: 0.261815, No Obj: 0.003879, Avg Recall: 0.480315, count: 127 -Region Avg IOU: 0.471855, Class: 1.000000, Obj: 0.278609, No Obj: 0.003678, Avg Recall: 0.530000, count: 100 -4591: 0.564231, 0.438271 avg, 0.001000 rate, 16.903044 seconds, 1175296 images -Loaded: 5.070676 seconds -Region Avg IOU: 0.497114, Class: 1.000000, Obj: 0.289735, No Obj: 0.004636, Avg Recall: 0.521739, count: 115 -Region Avg IOU: 0.517360, Class: 1.000000, Obj: 0.269631, No Obj: 0.004252, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.475650, Class: 1.000000, Obj: 0.304471, No Obj: 0.003969, Avg Recall: 0.511364, count: 88 -Region Avg IOU: 0.485808, Class: 1.000000, Obj: 0.282738, No Obj: 0.004212, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.479938, Class: 1.000000, Obj: 0.272307, No Obj: 0.004230, Avg Recall: 0.528455, count: 123 -Region Avg IOU: 0.427877, Class: 1.000000, Obj: 0.264021, No Obj: 0.003802, Avg Recall: 0.478992, count: 119 -Region Avg IOU: 0.437105, Class: 1.000000, Obj: 0.283183, No Obj: 0.003561, Avg Recall: 0.475248, count: 101 -Region Avg IOU: 0.443587, Class: 1.000000, Obj: 0.264980, No Obj: 0.004407, Avg Recall: 0.466165, count: 133 -4592: 0.487379, 0.443182 avg, 0.001000 rate, 14.204018 seconds, 1175552 images -Loaded: 6.324516 seconds -Region Avg IOU: 0.464047, Class: 1.000000, Obj: 0.268253, No Obj: 0.003895, Avg Recall: 0.474138, count: 116 -Region Avg IOU: 0.475781, Class: 1.000000, Obj: 0.258156, No Obj: 0.004138, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.498716, Class: 1.000000, Obj: 0.269836, No Obj: 0.004430, Avg Recall: 0.557377, count: 122 -Region Avg IOU: 0.468720, Class: 1.000000, Obj: 0.280521, No Obj: 0.003894, Avg Recall: 0.510638, count: 94 -Region Avg IOU: 0.455431, Class: 1.000000, Obj: 0.310621, No Obj: 0.003925, Avg Recall: 0.517241, count: 87 -Region Avg IOU: 0.450708, Class: 1.000000, Obj: 0.286042, No Obj: 0.004240, Avg Recall: 0.446281, count: 121 -Region Avg IOU: 0.427342, Class: 1.000000, Obj: 0.269218, No Obj: 0.003629, Avg Recall: 0.432432, count: 111 -Region Avg IOU: 0.470857, Class: 1.000000, Obj: 0.282585, No Obj: 0.003845, Avg Recall: 0.526316, count: 76 -4593: 0.451728, 0.444037 avg, 0.001000 rate, 18.159369 seconds, 1175808 images -Loaded: 3.397166 seconds -Region Avg IOU: 0.484516, Class: 1.000000, Obj: 0.282396, No Obj: 0.004328, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.489786, Class: 1.000000, Obj: 0.303348, No Obj: 0.004894, Avg Recall: 0.566176, count: 136 -Region Avg IOU: 0.489438, Class: 1.000000, Obj: 0.332765, No Obj: 0.004025, Avg Recall: 0.523256, count: 86 -Region Avg IOU: 0.502185, Class: 1.000000, Obj: 0.302748, No Obj: 0.004236, Avg Recall: 0.522936, count: 109 -Region Avg IOU: 0.511141, Class: 1.000000, Obj: 0.327942, No Obj: 0.004836, Avg Recall: 0.558333, count: 120 -Region Avg IOU: 0.506447, Class: 1.000000, Obj: 0.336982, No Obj: 0.004734, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.508077, Class: 1.000000, Obj: 0.324619, No Obj: 0.003999, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.511357, Class: 1.000000, Obj: 0.287962, No Obj: 0.005084, Avg Recall: 0.620155, count: 129 -4594: 0.477122, 0.447345 avg, 0.001000 rate, 18.379408 seconds, 1176064 images -Loaded: 2.216831 seconds -Region Avg IOU: 0.483576, Class: 1.000000, Obj: 0.306178, No Obj: 0.004889, Avg Recall: 0.550388, count: 129 -Region Avg IOU: 0.461455, Class: 1.000000, Obj: 0.355226, No Obj: 0.004104, Avg Recall: 0.521739, count: 92 -Region Avg IOU: 0.573194, Class: 1.000000, Obj: 0.350437, No Obj: 0.004540, Avg Recall: 0.682927, count: 82 -Region Avg IOU: 0.450688, Class: 1.000000, Obj: 0.289907, No Obj: 0.004556, Avg Recall: 0.441176, count: 102 -Region Avg IOU: 0.506082, Class: 1.000000, Obj: 0.332722, No Obj: 0.004588, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.487057, Class: 1.000000, Obj: 0.319850, No Obj: 0.003953, Avg Recall: 0.555556, count: 90 -Region Avg IOU: 0.417427, Class: 1.000000, Obj: 0.316426, No Obj: 0.004250, Avg Recall: 0.404255, count: 94 -Region Avg IOU: 0.475877, Class: 1.000000, Obj: 0.332539, No Obj: 0.004532, Avg Recall: 0.479592, count: 98 -4595: 0.456461, 0.448257 avg, 0.001000 rate, 19.053688 seconds, 1176320 images -Loaded: 1.795601 seconds -Region Avg IOU: 0.518933, Class: 1.000000, Obj: 0.371865, No Obj: 0.004587, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.477896, Class: 1.000000, Obj: 0.396990, No Obj: 0.004530, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.492617, Class: 1.000000, Obj: 0.350396, No Obj: 0.004521, Avg Recall: 0.593750, count: 96 -Region Avg IOU: 0.431851, Class: 1.000000, Obj: 0.326733, No Obj: 0.004646, Avg Recall: 0.467290, count: 107 -Region Avg IOU: 0.546319, Class: 1.000000, Obj: 0.356608, No Obj: 0.004770, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.523987, Class: 1.000000, Obj: 0.327863, No Obj: 0.004527, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.455726, Class: 1.000000, Obj: 0.347116, No Obj: 0.005090, Avg Recall: 0.528926, count: 121 -Region Avg IOU: 0.467000, Class: 1.000000, Obj: 0.301954, No Obj: 0.004938, Avg Recall: 0.463918, count: 97 -4596: 0.490914, 0.452522 avg, 0.001000 rate, 15.681288 seconds, 1176576 images -Loaded: 5.875057 seconds -Region Avg IOU: 0.474267, Class: 1.000000, Obj: 0.337191, No Obj: 0.005073, Avg Recall: 0.521739, count: 92 -Region Avg IOU: 0.457982, Class: 1.000000, Obj: 0.353772, No Obj: 0.005185, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.448294, Class: 1.000000, Obj: 0.335635, No Obj: 0.004794, Avg Recall: 0.477477, count: 111 -Region Avg IOU: 0.413274, Class: 1.000000, Obj: 0.311955, No Obj: 0.004142, Avg Recall: 0.416667, count: 84 -Region Avg IOU: 0.484434, Class: 1.000000, Obj: 0.345104, No Obj: 0.005790, Avg Recall: 0.531915, count: 141 -Region Avg IOU: 0.478593, Class: 1.000000, Obj: 0.323047, No Obj: 0.005290, Avg Recall: 0.531746, count: 126 -Region Avg IOU: 0.479947, Class: 1.000000, Obj: 0.320328, No Obj: 0.004744, Avg Recall: 0.500000, count: 92 -Region Avg IOU: 0.472709, Class: 1.000000, Obj: 0.355005, No Obj: 0.004886, Avg Recall: 0.537736, count: 106 -4597: 0.558466, 0.463117 avg, 0.001000 rate, 15.205619 seconds, 1176832 images -Loaded: 3.960702 seconds -Region Avg IOU: 0.486125, Class: 1.000000, Obj: 0.328030, No Obj: 0.004683, Avg Recall: 0.532710, count: 107 -Region Avg IOU: 0.483599, Class: 1.000000, Obj: 0.383087, No Obj: 0.005058, Avg Recall: 0.535088,Saving weights to ./person9300/backup/tyvp_93_9c512_4600.weights - count: 114 -Region Avg IOU: 0.472616, Class: 1.000000, Obj: 0.324050, No Obj: 0.005020, Avg Recall: 0.525862, count: 116 -Region Avg IOU: 0.465240, Class: 1.000000, Obj: 0.376672, No Obj: 0.005236, Avg Recall: 0.508929, count: 112 -Region Avg IOU: 0.485586, Class: 1.000000, Obj: 0.377019, No Obj: 0.005287, Avg Recall: 0.514851, count: 101 -Region Avg IOU: 0.512660, Class: 1.000000, Obj: 0.381774, No Obj: 0.004734, Avg Recall: 0.546512, count: 86 -Region Avg IOU: 0.501980, Class: 1.000000, Obj: 0.379336, No Obj: 0.005200, Avg Recall: 0.553571, count: 112 -Region Avg IOU: 0.489142, Class: 1.000000, Obj: 0.358447, No Obj: 0.005147, Avg Recall: 0.505376, count: 93 -4598: 0.446827, 0.461488 avg, 0.001000 rate, 15.859589 seconds, 1177088 images -Loaded: 4.510179 seconds -Region Avg IOU: 0.480526, Class: 1.000000, Obj: 0.396477, No Obj: 0.005617, Avg Recall: 0.474138, count: 116 -Region Avg IOU: 0.444127, Class: 1.000000, Obj: 0.343905, No Obj: 0.004975, Avg Recall: 0.472441, count: 127 -Region Avg IOU: 0.474176, Class: 1.000000, Obj: 0.376687, No Obj: 0.004850, Avg Recall: 0.485437, count: 103 -Region Avg IOU: 0.460023, Class: 1.000000, Obj: 0.349100, No Obj: 0.004431, Avg Recall: 0.441860, count: 86 -Region Avg IOU: 0.521667, Class: 1.000000, Obj: 0.345724, No Obj: 0.005001, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.514496, Class: 1.000000, Obj: 0.387431, No Obj: 0.004980, Avg Recall: 0.588889, count: 90 -Region Avg IOU: 0.477167, Class: 1.000000, Obj: 0.356837, No Obj: 0.005879, Avg Recall: 0.523810, count: 126 -Region Avg IOU: 0.468475, Class: 1.000000, Obj: 0.387194, No Obj: 0.004908, Avg Recall: 0.520000, count: 100 -4599: 0.413697, 0.456709 avg, 0.001000 rate, 16.670574 seconds, 1177344 images -Loaded: 5.204624 seconds -Region Avg IOU: 0.407170, Class: 1.000000, Obj: 0.312215, No Obj: 0.005415, Avg Recall: 0.445946, count: 148 -Region Avg IOU: 0.451776, Class: 1.000000, Obj: 0.409615, No Obj: 0.004764, Avg Recall: 0.488095, count: 84 -Region Avg IOU: 0.497614, Class: 1.000000, Obj: 0.385398, No Obj: 0.005223, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.509802, Class: 1.000000, Obj: 0.354417, No Obj: 0.005415, Avg Recall: 0.541667, count: 120 -Region Avg IOU: 0.462604, Class: 1.000000, Obj: 0.338430, No Obj: 0.005429, Avg Recall: 0.483051, count: 118 -Region Avg IOU: 0.527465, Class: 1.000000, Obj: 0.330527, No Obj: 0.004807, Avg Recall: 0.639535, count: 86 -Region Avg IOU: 0.517807, Class: 1.000000, Obj: 0.355757, No Obj: 0.004956, Avg Recall: 0.565657, count: 99 -Region Avg IOU: 0.476276, Class: 1.000000, Obj: 0.361818, No Obj: 0.005422, Avg Recall: 0.563830, count: 94 -4600: 0.454967, 0.456535 avg, 0.001000 rate, 18.693396 seconds, 1177600 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000067 seconds -Region Avg IOU: 0.635243, Class: 1.000000, Obj: 0.447843, No Obj: 0.004126, Avg Recall: 0.792000, count: 125 -Region Avg IOU: 0.644315, Class: 1.000000, Obj: 0.462173, No Obj: 0.003597, Avg Recall: 0.814516, count: 124 -Region Avg IOU: 0.645944, Class: 1.000000, Obj: 0.442959, No Obj: 0.003837, Avg Recall: 0.810345, count: 116 -Region Avg IOU: 0.645538, Class: 1.000000, Obj: 0.455465, No Obj: 0.003083, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.611217, Class: 1.000000, Obj: 0.487324, No Obj: 0.003208, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.677441, Class: 1.000000, Obj: 0.512894, No Obj: 0.003350, Avg Recall: 0.885417, count: 96 -Region Avg IOU: 0.637594, Class: 1.000000, Obj: 0.444016, No Obj: 0.003284, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.643124, Class: 1.000000, Obj: 0.418187, No Obj: 0.003476, Avg Recall: 0.812500, count: 128 -4601: 0.475607, 0.458442 avg, 0.001000 rate, 30.194984 seconds, 1177856 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.620142, Class: 1.000000, Obj: 0.399077, No Obj: 0.003704, Avg Recall: 0.748252, count: 143 -Region Avg IOU: 0.620381, Class: 1.000000, Obj: 0.388692, No Obj: 0.003495, Avg Recall: 0.740000, count: 150 -Region Avg IOU: 0.628675, Class: 1.000000, Obj: 0.429088, No Obj: 0.003137, Avg Recall: 0.782946, count: 129 -Region Avg IOU: 0.666389, Class: 1.000000, Obj: 0.452500, No Obj: 0.002857, Avg Recall: 0.804598, count: 87 -Region Avg IOU: 0.661856, Class: 1.000000, Obj: 0.455306, No Obj: 0.003816, Avg Recall: 0.843284, count: 134 -Region Avg IOU: 0.615477, Class: 1.000000, Obj: 0.401420, No Obj: 0.003093, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.632218, Class: 1.000000, Obj: 0.469247, No Obj: 0.003317, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.674979, Class: 1.000000, Obj: 0.535325, No Obj: 0.003391, Avg Recall: 0.849057, count: 106 -4602: 0.492135, 0.461811 avg, 0.001000 rate, 27.668840 seconds, 1178112 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.617144, Class: 1.000000, Obj: 0.352810, No Obj: 0.002734, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.681571, Class: 1.000000, Obj: 0.457757, No Obj: 0.002633, Avg Recall: 0.857143, count: 77 -Region Avg IOU: 0.665755, Class: 1.000000, Obj: 0.422343, No Obj: 0.002761, Avg Recall: 0.821429, count: 112 -Region Avg IOU: 0.618805, Class: 1.000000, Obj: 0.392567, No Obj: 0.002994, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.631322, Class: 1.000000, Obj: 0.448037, No Obj: 0.002956, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.639452, Class: 1.000000, Obj: 0.375260, No Obj: 0.002667, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.678376, Class: 1.000000, Obj: 0.378746, No Obj: 0.002632, Avg Recall: 0.835165, count: 91 -Region Avg IOU: 0.650791, Class: 1.000000, Obj: 0.382147, No Obj: 0.002869, Avg Recall: 0.815789, count: 114 -4603: 0.450998, 0.460730 avg, 0.001000 rate, 29.385496 seconds, 1178368 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.631655, Class: 1.000000, Obj: 0.381238, No Obj: 0.002371, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.634869, Class: 1.000000, Obj: 0.391033, No Obj: 0.002522, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.657719, Class: 1.000000, Obj: 0.376115, No Obj: 0.002716, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.650485, Class: 1.000000, Obj: 0.417981, No Obj: 0.002952, Avg Recall: 0.794643, count: 112 -Region Avg IOU: 0.655709, Class: 1.000000, Obj: 0.418339, No Obj: 0.002587, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.634239, Class: 1.000000, Obj: 0.364305, No Obj: 0.002640, Avg Recall: 0.795181, count: 83 -Region Avg IOU: 0.591573, Class: 1.000000, Obj: 0.343336, No Obj: 0.002613, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.661429, Class: 1.000000, Obj: 0.420453, No Obj: 0.002609, Avg Recall: 0.817204, count: 93 -4604: 0.431267, 0.457783 avg, 0.001000 rate, 28.947088 seconds, 1178624 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.648058, Class: 1.000000, Obj: 0.336406, No Obj: 0.002381, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.662910, Class: 1.000000, Obj: 0.341212, No Obj: 0.002397, Avg Recall: 0.829060, count: 117 -Region Avg IOU: 0.678622, Class: 1.000000, Obj: 0.346377, No Obj: 0.002303, Avg Recall: 0.861702, count: 94 -Region Avg IOU: 0.654373, Class: 1.000000, Obj: 0.313980, No Obj: 0.002162, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.638955, Class: 1.000000, Obj: 0.345638, No Obj: 0.002324, Avg Recall: 0.820755, count: 106 -Region Avg IOU: 0.629104, Class: 1.000000, Obj: 0.348012, No Obj: 0.002072, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.636537, Class: 1.000000, Obj: 0.317114, No Obj: 0.002300, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.699086, Class: 1.000000, Obj: 0.385415, No Obj: 0.002586, Avg Recall: 0.866667, count: 90 -4605: 0.441085, 0.456114 avg, 0.001000 rate, 30.431965 seconds, 1178880 images -Loaded: 0.000148 seconds -Region Avg IOU: 0.632511, Class: 1.000000, Obj: 0.336135, No Obj: 0.002112, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.615578, Class: 1.000000, Obj: 0.337746, No Obj: 0.002622, Avg Recall: 0.734177, count: 158 -Region Avg IOU: 0.668808, Class: 1.000000, Obj: 0.315687, No Obj: 0.001971, Avg Recall: 0.852632, count: 95 -Region Avg IOU: 0.644891, Class: 1.000000, Obj: 0.305822, No Obj: 0.002229, Avg Recall: 0.798387, count: 124 -Region Avg IOU: 0.696465, Class: 1.000000, Obj: 0.335374, No Obj: 0.002302, Avg Recall: 0.882353, count: 102 -Region Avg IOU: 0.660392, Class: 1.000000, Obj: 0.338178, No Obj: 0.002374, Avg Recall: 0.803419, count: 117 -Region Avg IOU: 0.622205, Class: 1.000000, Obj: 0.276340, No Obj: 0.002317, Avg Recall: 0.755102, count: 147 -Region Avg IOU: 0.670688, Class: 1.000000, Obj: 0.338271, No Obj: 0.002054, Avg Recall: 0.833333, count: 108 -4606: 0.521077, 0.462610 avg, 0.001000 rate, 29.018787 seconds, 1179136 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.575787, Class: 1.000000, Obj: 0.280986, No Obj: 0.001869, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.696366, Class: 1.000000, Obj: 0.344037, No Obj: 0.002446, Avg Recall: 0.893130, count: 131 -Region Avg IOU: 0.621740, Class: 1.000000, Obj: 0.278744, No Obj: 0.002149, Avg Recall: 0.746269, count: 134 -Region Avg IOU: 0.689525, Class: 1.000000, Obj: 0.358677, No Obj: 0.002372, Avg Recall: 0.938775, count: 98 -Region Avg IOU: 0.624391, Class: 1.000000, Obj: 0.262460, No Obj: 0.002108, Avg Recall: 0.771186, count: 118 -Region Avg IOU: 0.667750, Class: 1.000000, Obj: 0.346957, No Obj: 0.002058, Avg Recall: 0.922078, count: 77 -Region Avg IOU: 0.661326, Class: 1.000000, Obj: 0.356536, No Obj: 0.002039, Avg Recall: 0.835052, count: 97 -Region Avg IOU: 0.656232, Class: 1.000000, Obj: 0.317280, No Obj: 0.002165, Avg Recall: 0.782609, count: 115 -4607: 0.459027, 0.462252 avg, 0.001000 rate, 28.660919 seconds, 1179392 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.634040, Class: 1.000000, Obj: 0.328250, No Obj: 0.002386, Avg Recall: 0.780488, count: 123 -Region Avg IOU: 0.692399, Class: 1.000000, Obj: 0.369685, No Obj: 0.002305, Avg Recall: 0.872340, count: 94 -Region Avg IOU: 0.629584, Class: 1.000000, Obj: 0.274645, No Obj: 0.001815, Avg Recall: 0.765432, count: 81 -Region Avg IOU: 0.655643, Class: 1.000000, Obj: 0.299654, No Obj: 0.001934, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.643307, Class: 1.000000, Obj: 0.317255, No Obj: 0.002028, Avg Recall: 0.831579, count: 95 -Region Avg IOU: 0.673118, Class: 1.000000, Obj: 0.285025, No Obj: 0.002377, Avg Recall: 0.880000, count: 125 -Region Avg IOU: 0.651610, Class: 1.000000, Obj: 0.309715, No Obj: 0.002070, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.670510, Class: 1.000000, Obj: 0.326606, No Obj: 0.002328, Avg Recall: 0.836735, count: 98 -4608: 0.479305, 0.463957 avg, 0.001000 rate, 29.417995 seconds, 1179648 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.619541, Class: 1.000000, Obj: 0.278861, No Obj: 0.002250, Avg Recall: 0.807407, count: 135 -Region Avg IOU: 0.666440, Class: 1.000000, Obj: 0.324166, No Obj: 0.002613, Avg Recall: 0.866667, count: 120 -Region Avg IOU: 0.663648, Class: 1.000000, Obj: 0.327039, No Obj: 0.002428, Avg Recall: 0.827586, count: 116 -Region Avg IOU: 0.643652, Class: 1.000000, Obj: 0.326315, No Obj: 0.002140, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.641646, Class: 1.000000, Obj: 0.278423, No Obj: 0.002320, Avg Recall: 0.795276, count: 127 -Region Avg IOU: 0.638336, Class: 1.000000, Obj: 0.300155, No Obj: 0.002361, Avg Recall: 0.789855, count: 138 -Region Avg IOU: 0.652843, Class: 1.000000, Obj: 0.339061, No Obj: 0.002138, Avg Recall: 0.823529, count: 102 -Region Avg IOU: 0.643255, Class: 1.000000, Obj: 0.331185, No Obj: 0.002342, Avg Recall: 0.821429, count: 112 -4609: 0.550938, 0.472655 avg, 0.001000 rate, 27.682976 seconds, 1179904 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.681119, Class: 1.000000, Obj: 0.430314, No Obj: 0.002548, Avg Recall: 0.844444, count: 90 -Region Avg IOU: 0.648722, Class: 1.000000, Obj: 0.385648, No Obj: 0.002242, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.639244, Class: 1.000000, Obj: 0.320922, No Obj: 0.002673, Avg Recall: 0.707143, count: 140 -Region Avg IOU: 0.653984, Class: 1.000000, Obj: 0.363467, No Obj: 0.002330, Avg Recall: 0.846939, count: 98 -Region Avg IOU: 0.681057, Class: 1.000000, Obj: 0.374682, No Obj: 0.002047, Avg Recall: 0.839506, count: 81 -Region Avg IOU: 0.670309, Class: 1.000000, Obj: 0.366719, No Obj: 0.002378, Avg Recall: 0.849462, count: 93 -Region Avg IOU: 0.632503, Class: 1.000000, Obj: 0.321746, No Obj: 0.002491, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.655762, Class: 1.000000, Obj: 0.358616, No Obj: 0.002536, Avg Recall: 0.814815, count: 108 -4610: 0.441683, 0.469558 avg, 0.001000 rate, 29.435261 seconds, 1180160 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 13.698357 seconds -Region Avg IOU: 0.621852, Class: 1.000000, Obj: 0.344288, No Obj: 0.003023, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.615716, Class: 1.000000, Obj: 0.398986, No Obj: 0.002895, Avg Recall: 0.756098, count: 82 -Region Avg IOU: 0.638758, Class: 1.000000, Obj: 0.347747, No Obj: 0.002852, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.560493, Class: 1.000000, Obj: 0.342264, No Obj: 0.002581, Avg Recall: 0.630137, count: 73 -Region Avg IOU: 0.550016, Class: 1.000000, Obj: 0.326404, No Obj: 0.003052, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.596061, Class: 1.000000, Obj: 0.348124, No Obj: 0.003309, Avg Recall: 0.704545, count: 132 -Region Avg IOU: 0.593499, Class: 1.000000, Obj: 0.318502, No Obj: 0.002675, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.621536, Class: 1.000000, Obj: 0.339300, No Obj: 0.003285, Avg Recall: 0.744898, count: 98 -4611: 0.430418, 0.465644 avg, 0.001000 rate, 25.422661 seconds, 1180416 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.592938, Class: 1.000000, Obj: 0.374807, No Obj: 0.003387, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.640303, Class: 1.000000, Obj: 0.419712, No Obj: 0.002527, Avg Recall: 0.753623, count: 69 -Region Avg IOU: 0.611258, Class: 1.000000, Obj: 0.396151, No Obj: 0.003467, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.568227, Class: 1.000000, Obj: 0.360762, No Obj: 0.002952, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.617352, Class: 1.000000, Obj: 0.408522, No Obj: 0.003191, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.598960, Class: 1.000000, Obj: 0.380592, No Obj: 0.003552, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.615752, Class: 1.000000, Obj: 0.380753, No Obj: 0.002794, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.600712, Class: 1.000000, Obj: 0.348236, No Obj: 0.003661, Avg Recall: 0.738318, count: 107 -4612: 0.435636, 0.462643 avg, 0.001000 rate, 26.063429 seconds, 1180672 images -Loaded: 0.000124 seconds -Region Avg IOU: 0.589396, Class: 1.000000, Obj: 0.360356, No Obj: 0.003055, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.617531, Class: 1.000000, Obj: 0.384515, No Obj: 0.003314, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.699687, Class: 1.000000, Obj: 0.439288, No Obj: 0.003568, Avg Recall: 0.924051, count: 79 -Region Avg IOU: 0.667734, Class: 1.000000, Obj: 0.375922, No Obj: 0.003809, Avg Recall: 0.810345, count: 116 -Region Avg IOU: 0.583628, Class: 1.000000, Obj: 0.387818, No Obj: 0.003568, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.596393, Class: 1.000000, Obj: 0.416330, No Obj: 0.003221, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.629001, Class: 1.000000, Obj: 0.392801, No Obj: 0.003460, Avg Recall: 0.713178, count: 129 -Region Avg IOU: 0.597563, Class: 1.000000, Obj: 0.364683, No Obj: 0.003638, Avg Recall: 0.711864, count: 118 -4613: 0.461477, 0.462527 avg, 0.001000 rate, 24.518072 seconds, 1180928 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.574792, Class: 1.000000, Obj: 0.406849, No Obj: 0.003402, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.612982, Class: 1.000000, Obj: 0.435211, No Obj: 0.003170, Avg Recall: 0.775281, count: 89 -Region Avg IOU: 0.597829, Class: 1.000000, Obj: 0.370653, No Obj: 0.003999, Avg Recall: 0.723404, count: 141 -Region Avg IOU: 0.643124, Class: 1.000000, Obj: 0.444226, No Obj: 0.003560, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.623209, Class: 1.000000, Obj: 0.439484, No Obj: 0.003260, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.615208, Class: 1.000000, Obj: 0.430333, No Obj: 0.003981, Avg Recall: 0.775194, count: 129 -Region Avg IOU: 0.624084, Class: 1.000000, Obj: 0.401174, No Obj: 0.003461, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.603203, Class: 1.000000, Obj: 0.429324, No Obj: 0.003372, Avg Recall: 0.722222, count: 108 -4614: 0.414182, 0.457692 avg, 0.001000 rate, 24.674747 seconds, 1181184 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.626976, Class: 1.000000, Obj: 0.414845, No Obj: 0.003543, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.582910, Class: 1.000000, Obj: 0.410031, No Obj: 0.003478, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.568711, Class: 1.000000, Obj: 0.405884, No Obj: 0.004067, Avg Recall: 0.669291, count: 127 -Region Avg IOU: 0.615575, Class: 1.000000, Obj: 0.423338, No Obj: 0.003996, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.615557, Class: 1.000000, Obj: 0.444615, No Obj: 0.003724, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.633373, Class: 1.000000, Obj: 0.416046, No Obj: 0.003849, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.637224, Class: 1.000000, Obj: 0.404248, No Obj: 0.003896, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.603755, Class: 1.000000, Obj: 0.439431, No Obj: 0.003477, Avg Recall: 0.686869, count: 99 -4615: 0.434016, 0.455324 avg, 0.001000 rate, 24.437778 seconds, 1181440 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.627938, Class: 1.000000, Obj: 0.420874, No Obj: 0.004254, Avg Recall: 0.761905, count: 126 -Region Avg IOU: 0.570264, Class: 1.000000, Obj: 0.406139, No Obj: 0.003710, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.612592, Class: 1.000000, Obj: 0.426008, No Obj: 0.004829, Avg Recall: 0.741497, count: 147 -Region Avg IOU: 0.598700, Class: 1.000000, Obj: 0.456862, No Obj: 0.004264, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.618117, Class: 1.000000, Obj: 0.431256, No Obj: 0.003880, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.588630, Class: 1.000000, Obj: 0.386718, No Obj: 0.003444, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.600494, Class: 1.000000, Obj: 0.418783, No Obj: 0.003448, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.588661, Class: 1.000000, Obj: 0.391076, No Obj: 0.003463, Avg Recall: 0.750000, count: 96 -4616: 0.467758, 0.456568 avg, 0.001000 rate, 24.236334 seconds, 1181696 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.581489, Class: 1.000000, Obj: 0.399592, No Obj: 0.003886, Avg Recall: 0.666667, count: 135 -Region Avg IOU: 0.613450, Class: 1.000000, Obj: 0.418208, No Obj: 0.003350, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.626099, Class: 1.000000, Obj: 0.469780, No Obj: 0.003287, Avg Recall: 0.777778, count: 90 -Region Avg IOU: 0.583797, Class: 1.000000, Obj: 0.453948, No Obj: 0.003292, Avg Recall: 0.682353, count: 85 -Region Avg IOU: 0.598348, Class: 1.000000, Obj: 0.405836, No Obj: 0.003289, Avg Recall: 0.722222, count: 90 -Region Avg IOU: 0.618513, Class: 1.000000, Obj: 0.483491, No Obj: 0.003899, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.594162, Class: 1.000000, Obj: 0.439609, No Obj: 0.003580, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.593768, Class: 1.000000, Obj: 0.389111, No Obj: 0.004143, Avg Recall: 0.733813, count: 139 -4617: 0.400327, 0.450944 avg, 0.001000 rate, 25.275517 seconds, 1181952 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.573153, Class: 1.000000, Obj: 0.405159, No Obj: 0.003348, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.618418, Class: 1.000000, Obj: 0.444272, No Obj: 0.004110, Avg Recall: 0.746479, count: 142 -Region Avg IOU: 0.631756, Class: 1.000000, Obj: 0.430157, No Obj: 0.003729, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.554143, Class: 1.000000, Obj: 0.378163, No Obj: 0.003403, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.603667, Class: 1.000000, Obj: 0.413577, No Obj: 0.003862, Avg Recall: 0.704918, count: 122 -Region Avg IOU: 0.570378, Class: 1.000000, Obj: 0.438427, No Obj: 0.003647, Avg Recall: 0.650000, count: 120 -Region Avg IOU: 0.568519, Class: 1.000000, Obj: 0.355743, No Obj: 0.004144, Avg Recall: 0.673611, count: 144 -Region Avg IOU: 0.614416, Class: 1.000000, Obj: 0.460480, No Obj: 0.003474, Avg Recall: 0.742857, count: 105 -4618: 0.459241, 0.451773 avg, 0.001000 rate, 24.500408 seconds, 1182208 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.638361, Class: 1.000000, Obj: 0.387292, No Obj: 0.004330, Avg Recall: 0.770370, count: 135 -Region Avg IOU: 0.620430, Class: 1.000000, Obj: 0.449095, No Obj: 0.003713, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.644939, Class: 1.000000, Obj: 0.419904, No Obj: 0.003324, Avg Recall: 0.804598, count: 87 -Region Avg IOU: 0.572613, Class: 1.000000, Obj: 0.370147, No Obj: 0.003355, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.598035, Class: 1.000000, Obj: 0.444326, No Obj: 0.003617, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.604507, Class: 1.000000, Obj: 0.402169, No Obj: 0.003660, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.602933, Class: 1.000000, Obj: 0.424365, No Obj: 0.003522, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.598591, Class: 1.000000, Obj: 0.407280, No Obj: 0.003673, Avg Recall: 0.756098, count: 123 -4619: 0.460819, 0.452678 avg, 0.001000 rate, 24.650740 seconds, 1182464 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.565614, Class: 1.000000, Obj: 0.348257, No Obj: 0.003800, Avg Recall: 0.640625, count: 128 -Region Avg IOU: 0.568637, Class: 1.000000, Obj: 0.374719, No Obj: 0.003227, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.560949, Class: 1.000000, Obj: 0.402660, No Obj: 0.003208, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.599147, Class: 1.000000, Obj: 0.382092, No Obj: 0.003455, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.600568, Class: 1.000000, Obj: 0.360332, No Obj: 0.003770, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.564276, Class: 1.000000, Obj: 0.393295, No Obj: 0.003500, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.610192, Class: 1.000000, Obj: 0.374245, No Obj: 0.004245, Avg Recall: 0.739726, count: 146 -Region Avg IOU: 0.615210, Class: 1.000000, Obj: 0.377717, No Obj: 0.003177, Avg Recall: 0.764045, count: 89 -4620: 0.450302, 0.452440 avg, 0.001000 rate, 24.813610 seconds, 1182720 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000075 seconds -Region Avg IOU: 0.649887, Class: 1.000000, Obj: 0.410912, No Obj: 0.003061, Avg Recall: 0.838384, count: 99 -Region Avg IOU: 0.653539, Class: 1.000000, Obj: 0.398601, No Obj: 0.002975, Avg Recall: 0.808989, count: 89 -Region Avg IOU: 0.594118, Class: 1.000000, Obj: 0.398807, No Obj: 0.002888, Avg Recall: 0.663866, count: 119 -Region Avg IOU: 0.624104, Class: 1.000000, Obj: 0.411883, No Obj: 0.003168, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.616574, Class: 1.000000, Obj: 0.429141, No Obj: 0.003040, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.652888, Class: 1.000000, Obj: 0.430009, No Obj: 0.002880, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.665589, Class: 1.000000, Obj: 0.420978, No Obj: 0.003151, Avg Recall: 0.809091, count: 110 -Region Avg IOU: 0.639445, Class: 1.000000, Obj: 0.408822, No Obj: 0.003054, Avg Recall: 0.826923, count: 104 -4621: 0.457722, 0.452969 avg, 0.001000 rate, 28.010279 seconds, 1182976 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.680023, Class: 1.000000, Obj: 0.393436, No Obj: 0.003124, Avg Recall: 0.870370, count: 108 -Region Avg IOU: 0.618462, Class: 1.000000, Obj: 0.395827, No Obj: 0.003077, Avg Recall: 0.761905, count: 126 -Region Avg IOU: 0.627258, Class: 1.000000, Obj: 0.399761, No Obj: 0.002841, Avg Recall: 0.775000, count: 120 -Region Avg IOU: 0.616091, Class: 1.000000, Obj: 0.373298, No Obj: 0.002897, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.643875, Class: 1.000000, Obj: 0.409136, No Obj: 0.003106, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.634281, Class: 1.000000, Obj: 0.384802, No Obj: 0.002777, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.591481, Class: 1.000000, Obj: 0.351385, No Obj: 0.002770, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.621580, Class: 1.000000, Obj: 0.384171, No Obj: 0.002747, Avg Recall: 0.717742, count: 124 -4622: 0.504822, 0.458154 avg, 0.001000 rate, 27.198477 seconds, 1183232 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.612160, Class: 1.000000, Obj: 0.363076, No Obj: 0.002719, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.654193, Class: 1.000000, Obj: 0.337278, No Obj: 0.002890, Avg Recall: 0.829630, count: 135 -Region Avg IOU: 0.639762, Class: 1.000000, Obj: 0.365934, No Obj: 0.002763, Avg Recall: 0.790323, count: 124 -Region Avg IOU: 0.654934, Class: 1.000000, Obj: 0.386135, No Obj: 0.002656, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.635032, Class: 1.000000, Obj: 0.417971, No Obj: 0.002735, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.633147, Class: 1.000000, Obj: 0.407774, No Obj: 0.002652, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.646484, Class: 1.000000, Obj: 0.347064, No Obj: 0.002632, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.657403, Class: 1.000000, Obj: 0.348155, No Obj: 0.002595, Avg Recall: 0.797980, count: 99 -4623: 0.460245, 0.458363 avg, 0.001000 rate, 28.471949 seconds, 1183488 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.582732, Class: 1.000000, Obj: 0.321740, No Obj: 0.002403, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.651130, Class: 1.000000, Obj: 0.336540, No Obj: 0.002865, Avg Recall: 0.789474, count: 133 -Region Avg IOU: 0.626854, Class: 1.000000, Obj: 0.385990, No Obj: 0.002929, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.587874, Class: 1.000000, Obj: 0.364127, No Obj: 0.002309, Avg Recall: 0.678571, count: 84 -Region Avg IOU: 0.656239, Class: 1.000000, Obj: 0.420098, No Obj: 0.002387, Avg Recall: 0.862500, count: 80 -Region Avg IOU: 0.669864, Class: 1.000000, Obj: 0.363612, No Obj: 0.003016, Avg Recall: 0.876106, count: 113 -Region Avg IOU: 0.637336, Class: 1.000000, Obj: 0.350693, No Obj: 0.002493, Avg Recall: 0.815789, count: 114 -Region Avg IOU: 0.596904, Class: 1.000000, Obj: 0.330482, No Obj: 0.002132, Avg Recall: 0.745098, count: 102 -4624: 0.496528, 0.462179 avg, 0.001000 rate, 28.447220 seconds, 1183744 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.657471, Class: 1.000000, Obj: 0.346924, No Obj: 0.002931, Avg Recall: 0.792593, count: 135 -Region Avg IOU: 0.594469, Class: 1.000000, Obj: 0.286654, No Obj: 0.002852, Avg Recall: 0.687075, count: 147 -Region Avg IOU: 0.637753, Class: 1.000000, Obj: 0.374724, No Obj: 0.002531, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.680668, Class: 1.000000, Obj: 0.381822, No Obj: 0.002827, Avg Recall: 0.840708, count: 113 -Region Avg IOU: 0.612568, Class: 1.000000, Obj: 0.338343, No Obj: 0.002568, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.629170, Class: 1.000000, Obj: 0.329276, No Obj: 0.002958, Avg Recall: 0.805369, count: 149 -Region Avg IOU: 0.634214, Class: 1.000000, Obj: 0.348866, No Obj: 0.002780, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.617989, Class: 1.000000, Obj: 0.390170, No Obj: 0.002132, Avg Recall: 0.709302, count: 86 -4625: 0.467723, 0.462734 avg, 0.001000 rate, 28.625874 seconds, 1184000 images -Loaded: 0.000138 seconds -Region Avg IOU: 0.609576, Class: 1.000000, Obj: 0.316286, No Obj: 0.002854, Avg Recall: 0.751938, count: 129 -Region Avg IOU: 0.676789, Class: 1.000000, Obj: 0.374560, No Obj: 0.002496, Avg Recall: 0.860759, count: 79 -Region Avg IOU: 0.632903, Class: 1.000000, Obj: 0.368374, No Obj: 0.002848, Avg Recall: 0.786885, count: 122 -Region Avg IOU: 0.605882, Class: 1.000000, Obj: 0.293991, No Obj: 0.002661, Avg Recall: 0.748092, count: 131 -Region Avg IOU: 0.669326, Class: 1.000000, Obj: 0.372235, No Obj: 0.002361, Avg Recall: 0.825581, count: 86 -Region Avg IOU: 0.638724, Class: 1.000000, Obj: 0.390139, No Obj: 0.002203, Avg Recall: 0.819444, count: 72 -Region Avg IOU: 0.667545, Class: 1.000000, Obj: 0.402282, No Obj: 0.003207, Avg Recall: 0.813008, count: 123 -Region Avg IOU: 0.696247, Class: 1.000000, Obj: 0.412480, No Obj: 0.002492, Avg Recall: 0.911392, count: 79 -4626: 0.440284, 0.460489 avg, 0.001000 rate, 28.815823 seconds, 1184256 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.640127, Class: 1.000000, Obj: 0.392985, No Obj: 0.002752, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.659446, Class: 1.000000, Obj: 0.392651, No Obj: 0.002617, Avg Recall: 0.840909, count: 88 -Region Avg IOU: 0.645258, Class: 1.000000, Obj: 0.347640, No Obj: 0.002657, Avg Recall: 0.827586, count: 116 -Region Avg IOU: 0.644297, Class: 1.000000, Obj: 0.349239, No Obj: 0.002442, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.677576, Class: 1.000000, Obj: 0.403656, No Obj: 0.003043, Avg Recall: 0.854545, count: 110 -Region Avg IOU: 0.627310, Class: 1.000000, Obj: 0.390927, No Obj: 0.002563, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.641229, Class: 1.000000, Obj: 0.359209, No Obj: 0.002605, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.651894, Class: 1.000000, Obj: 0.386678, No Obj: 0.002529, Avg Recall: 0.810526, count: 95 -4627: 0.441208, 0.458561 avg, 0.001000 rate, 28.395721 seconds, 1184512 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.640926, Class: 1.000000, Obj: 0.391111, No Obj: 0.002833, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.637937, Class: 1.000000, Obj: 0.371050, No Obj: 0.002445, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.637236, Class: 1.000000, Obj: 0.405790, No Obj: 0.002735, Avg Recall: 0.756303, count: 119 -Region Avg IOU: 0.595770, Class: 1.000000, Obj: 0.346556, No Obj: 0.002872, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.713888, Class: 1.000000, Obj: 0.466945, No Obj: 0.002902, Avg Recall: 0.914286, count: 105 -Region Avg IOU: 0.650722, Class: 1.000000, Obj: 0.376583, No Obj: 0.002837, Avg Recall: 0.814159, count: 113 -Region Avg IOU: 0.634366, Class: 1.000000, Obj: 0.334369, No Obj: 0.002299, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.636998, Class: 1.000000, Obj: 0.382087, No Obj: 0.002859, Avg Recall: 0.783333, count: 120 -4628: 0.455455, 0.458250 avg, 0.001000 rate, 28.141644 seconds, 1184768 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.638641, Class: 1.000000, Obj: 0.360896, No Obj: 0.002608, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.679232, Class: 1.000000, Obj: 0.393230, No Obj: 0.002523, Avg Recall: 0.885417, count: 96 -Region Avg IOU: 0.669091, Class: 1.000000, Obj: 0.382168, No Obj: 0.002831, Avg Recall: 0.830645, count: 124 -Region Avg IOU: 0.658296, Class: 1.000000, Obj: 0.409604, No Obj: 0.002699, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.672821, Class: 1.000000, Obj: 0.372782, No Obj: 0.002164, Avg Recall: 0.844156, count: 77 -Region Avg IOU: 0.646134, Class: 1.000000, Obj: 0.397976, No Obj: 0.002631, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.637233, Class: 1.000000, Obj: 0.347177, No Obj: 0.002970, Avg Recall: 0.766423, count: 137 -Region Avg IOU: 0.602222, Class: 1.000000, Obj: 0.391069, No Obj: 0.002668, Avg Recall: 0.709924, count: 131 -4629: 0.473953, 0.459820 avg, 0.001000 rate, 29.609650 seconds, 1185024 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.634201, Class: 1.000000, Obj: 0.343435, No Obj: 0.003112, Avg Recall: 0.770492, count: 122 -Region Avg IOU: 0.621772, Class: 1.000000, Obj: 0.391558, No Obj: 0.002438, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.598123, Class: 1.000000, Obj: 0.356362, No Obj: 0.003162, Avg Recall: 0.720588, count: 136 -Region Avg IOU: 0.674336, Class: 1.000000, Obj: 0.415702, No Obj: 0.002854, Avg Recall: 0.853448, count: 116 -Region Avg IOU: 0.623841, Class: 1.000000, Obj: 0.371497, No Obj: 0.002617, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.665854, Class: 1.000000, Obj: 0.354006, No Obj: 0.002709, Avg Recall: 0.802198, count: 91 -Region Avg IOU: 0.667426, Class: 1.000000, Obj: 0.393519, No Obj: 0.002865, Avg Recall: 0.857143, count: 112 -Region Avg IOU: 0.656909, Class: 1.000000, Obj: 0.404191, No Obj: 0.003352, Avg Recall: 0.802721, count: 147 -4630: 0.496965, 0.463535 avg, 0.001000 rate, 28.807323 seconds, 1185280 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 13.721474 seconds -Region Avg IOU: 0.568932, Class: 1.000000, Obj: 0.374093, No Obj: 0.002932, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.614918, Class: 1.000000, Obj: 0.368064, No Obj: 0.003466, Avg Recall: 0.724409, count: 127 -Region Avg IOU: 0.560809, Class: 1.000000, Obj: 0.363112, No Obj: 0.002847, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.609908, Class: 1.000000, Obj: 0.362558, No Obj: 0.003157, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.626494, Class: 1.000000, Obj: 0.376044, No Obj: 0.002977, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.626909, Class: 1.000000, Obj: 0.349311, No Obj: 0.003402, Avg Recall: 0.751880, count: 133 -Region Avg IOU: 0.626978, Class: 1.000000, Obj: 0.369567, No Obj: 0.003128, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.607415, Class: 1.000000, Obj: 0.404933, No Obj: 0.003144, Avg Recall: 0.722222, count: 90 -4631: 0.443365, 0.461518 avg, 0.001000 rate, 25.319733 seconds, 1185536 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.642129, Class: 1.000000, Obj: 0.382318, No Obj: 0.002664, Avg Recall: 0.820513, count: 78 -Region Avg IOU: 0.595749, Class: 1.000000, Obj: 0.332276, No Obj: 0.003170, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.629566, Class: 1.000000, Obj: 0.400677, No Obj: 0.002818, Avg Recall: 0.771429, count: 70 -Region Avg IOU: 0.635453, Class: 1.000000, Obj: 0.368508, No Obj: 0.003132, Avg Recall: 0.762295, count: 122 -Region Avg IOU: 0.569528, Class: 1.000000, Obj: 0.365400, No Obj: 0.003289, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.617342, Class: 1.000000, Obj: 0.411371, No Obj: 0.003664, Avg Recall: 0.755906, count: 127 -Region Avg IOU: 0.608677, Class: 1.000000, Obj: 0.361548, No Obj: 0.003428, Avg Recall: 0.747967, count: 123 -Region Avg IOU: 0.595553, Class: 1.000000, Obj: 0.345449, No Obj: 0.004055, Avg Recall: 0.708955, count: 134 -4632: 0.445975, 0.459964 avg, 0.001000 rate, 25.918205 seconds, 1185792 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.548430, Class: 1.000000, Obj: 0.384226, No Obj: 0.003726, Avg Recall: 0.598485, count: 132 -Region Avg IOU: 0.602761, Class: 1.000000, Obj: 0.375691, No Obj: 0.003161, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.613078, Class: 1.000000, Obj: 0.431399, No Obj: 0.003228, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.638826, Class: 1.000000, Obj: 0.401590, No Obj: 0.003602, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.595142, Class: 1.000000, Obj: 0.414655, No Obj: 0.003073, Avg Recall: 0.695122, count: 82 -Region Avg IOU: 0.611430, Class: 1.000000, Obj: 0.422063, No Obj: 0.003089, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.621366, Class: 1.000000, Obj: 0.421571, No Obj: 0.003255, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.609555, Class: 1.000000, Obj: 0.396986, No Obj: 0.003118, Avg Recall: 0.717172, count: 99 -4633: 0.394306, 0.453398 avg, 0.001000 rate, 24.979900 seconds, 1186048 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.558018, Class: 1.000000, Obj: 0.401439, No Obj: 0.002814, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.599642, Class: 1.000000, Obj: 0.399874, No Obj: 0.003103, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.573886, Class: 1.000000, Obj: 0.375576, No Obj: 0.002812, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.584993, Class: 1.000000, Obj: 0.421034, No Obj: 0.003330, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.627224, Class: 1.000000, Obj: 0.424438, No Obj: 0.003550, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.619754, Class: 1.000000, Obj: 0.391648, No Obj: 0.003199, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.649417, Class: 1.000000, Obj: 0.404718, No Obj: 0.003189, Avg Recall: 0.755814, count: 86 -Region Avg IOU: 0.543485, Class: 1.000000, Obj: 0.322564, No Obj: 0.003249, Avg Recall: 0.641667, count: 120 -4634: 0.430777, 0.451136 avg, 0.001000 rate, 24.101799 seconds, 1186304 images -Loaded: 0.000057 seconds -Region Avg IOU: 0.571133, Class: 1.000000, Obj: 0.373563, No Obj: 0.003639, Avg Recall: 0.652482, count: 141 -Region Avg IOU: 0.613275, Class: 1.000000, Obj: 0.429045, No Obj: 0.003100, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.579648, Class: 1.000000, Obj: 0.381201, No Obj: 0.003096, Avg Recall: 0.688073, count: 109 -Region Avg IOU: 0.620771, Class: 1.000000, Obj: 0.440287, No Obj: 0.003547, Avg Recall: 0.738095, count: 84 -Region Avg IOU: 0.600471, Class: 1.000000, Obj: 0.435907, No Obj: 0.003265, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.604178, Class: 1.000000, Obj: 0.471853, No Obj: 0.003498, Avg Recall: 0.727273, count: 88 -Region Avg IOU: 0.619254, Class: 1.000000, Obj: 0.424918, No Obj: 0.003367, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.621907, Class: 1.000000, Obj: 0.395558, No Obj: 0.003507, Avg Recall: 0.755556, count: 90 -4635: 0.433199, 0.449342 avg, 0.001000 rate, 25.780245 seconds, 1186560 images -Loaded: 0.000141 seconds -Region Avg IOU: 0.593193, Class: 1.000000, Obj: 0.417432, No Obj: 0.003311, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.621808, Class: 1.000000, Obj: 0.426461, No Obj: 0.003522, Avg Recall: 0.776859, count: 121 -Region Avg IOU: 0.608813, Class: 1.000000, Obj: 0.397005, No Obj: 0.003551, Avg Recall: 0.739583, count: 96 -Region Avg IOU: 0.560948, Class: 1.000000, Obj: 0.388318, No Obj: 0.003378, Avg Recall: 0.625000, count: 120 -Region Avg IOU: 0.628314, Class: 1.000000, Obj: 0.454219, No Obj: 0.004284, Avg Recall: 0.796610, count: 118 -Region Avg IOU: 0.642161, Class: 1.000000, Obj: 0.471717, No Obj: 0.003230, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.630940, Class: 1.000000, Obj: 0.424495, No Obj: 0.003129, Avg Recall: 0.756098, count: 82 -Region Avg IOU: 0.599466, Class: 1.000000, Obj: 0.416381, No Obj: 0.003239, Avg Recall: 0.734043, count: 94 -4636: 0.419170, 0.446325 avg, 0.001000 rate, 26.768974 seconds, 1186816 images -Loaded: 0.000058 seconds -Region Avg IOU: 0.636462, Class: 1.000000, Obj: 0.416611, No Obj: 0.003694, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.574760, Class: 1.000000, Obj: 0.359165, No Obj: 0.003596, Avg Recall: 0.732824, count: 131 -Region Avg IOU: 0.633664, Class: 1.000000, Obj: 0.457325, No Obj: 0.003974, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.610352, Class: 1.000000, Obj: 0.461482, No Obj: 0.003482, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.590938, Class: 1.000000, Obj: 0.398023, No Obj: 0.003590, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.608147, Class: 1.000000, Obj: 0.422439, No Obj: 0.003393, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.603487, Class: 1.000000, Obj: 0.396733, No Obj: 0.003113, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.578937, Class: 1.000000, Obj: 0.428848, No Obj: 0.003280, Avg Recall: 0.689320, count: 103 -4637: 0.437341, 0.445426 avg, 0.001000 rate, 25.535206 seconds, 1187072 images -Loaded: 0.000059 seconds -Region Avg IOU: 0.559284, Class: 1.000000, Obj: 0.375273, No Obj: 0.003508, Avg Recall: 0.609023, count: 133 -Region Avg IOU: 0.599305, Class: 1.000000, Obj: 0.467162, No Obj: 0.003120, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.590512, Class: 1.000000, Obj: 0.389993, No Obj: 0.003665, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.592115, Class: 1.000000, Obj: 0.384951, No Obj: 0.003719, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.602735, Class: 1.000000, Obj: 0.436695, No Obj: 0.003698, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.582734, Class: 1.000000, Obj: 0.445464, No Obj: 0.003244, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.597645, Class: 1.000000, Obj: 0.410207, No Obj: 0.003809, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.613980, Class: 1.000000, Obj: 0.439816, No Obj: 0.003114, Avg Recall: 0.744681, count: 94 -4638: 0.486197, 0.449503 avg, 0.001000 rate, 24.804188 seconds, 1187328 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.575480, Class: 1.000000, Obj: 0.369322, No Obj: 0.003404, Avg Recall: 0.689076, count: 119 -Region Avg IOU: 0.646557, Class: 1.000000, Obj: 0.432859, No Obj: 0.003423, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.569175, Class: 1.000000, Obj: 0.380341, No Obj: 0.003271, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.584229, Class: 1.000000, Obj: 0.387558, No Obj: 0.003417, Avg Recall: 0.674603, count: 126 -Region Avg IOU: 0.589369, Class: 1.000000, Obj: 0.446118, No Obj: 0.003239, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.625993, Class: 1.000000, Obj: 0.412990, No Obj: 0.003512, Avg Recall: 0.758065, count: 124 -Region Avg IOU: 0.616894, Class: 1.000000, Obj: 0.366664, No Obj: 0.003928, Avg Recall: 0.750000, count: 136 -Region Avg IOU: 0.637550, Class: 1.000000, Obj: 0.424709, No Obj: 0.003566, Avg Recall: 0.775701, count: 107 -4639: 0.501314, 0.454684 avg, 0.001000 rate, 25.237434 seconds, 1187584 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.619479, Class: 1.000000, Obj: 0.394757, No Obj: 0.003727, Avg Recall: 0.766129, count: 124 -Region Avg IOU: 0.595043, Class: 1.000000, Obj: 0.377262, No Obj: 0.003145, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.588568, Class: 1.000000, Obj: 0.390467, No Obj: 0.003366, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.576346, Class: 1.000000, Obj: 0.402243, No Obj: 0.003347, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.609883, Class: 1.000000, Obj: 0.439367, No Obj: 0.003520, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.590344, Class: 1.000000, Obj: 0.378474, No Obj: 0.003463, Avg Recall: 0.679688, count: 128 -Region Avg IOU: 0.589576, Class: 1.000000, Obj: 0.344716, No Obj: 0.002988, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.521733, Class: 1.000000, Obj: 0.416106, No Obj: 0.003112, Avg Recall: 0.615385, count: 104 -4640: 0.435876, 0.452804 avg, 0.001000 rate, 25.581249 seconds, 1187840 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000085 seconds -Region Avg IOU: 0.659911, Class: 1.000000, Obj: 0.425181, No Obj: 0.002940, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.654066, Class: 1.000000, Obj: 0.421939, No Obj: 0.002951, Avg Recall: 0.821429, count: 84 -Region Avg IOU: 0.632282, Class: 1.000000, Obj: 0.435540, No Obj: 0.003098, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.616261, Class: 1.000000, Obj: 0.356253, No Obj: 0.003375, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.652477, Class: 1.000000, Obj: 0.456222, No Obj: 0.002791, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.656650, Class: 1.000000, Obj: 0.363336, No Obj: 0.002928, Avg Recall: 0.824742, count: 97 -Region Avg IOU: 0.629215, Class: 1.000000, Obj: 0.437078, No Obj: 0.002874, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.586421, Class: 1.000000, Obj: 0.383336, No Obj: 0.002993, Avg Recall: 0.689076, count: 119 -4641: 0.401464, 0.447670 avg, 0.001000 rate, 26.923067 seconds, 1188096 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.599583, Class: 1.000000, Obj: 0.396888, No Obj: 0.002904, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.627324, Class: 1.000000, Obj: 0.419178, No Obj: 0.002714, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.609292, Class: 1.000000, Obj: 0.380091, No Obj: 0.002614, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.653238, Class: 1.000000, Obj: 0.442305, No Obj: 0.002606, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.622385, Class: 1.000000, Obj: 0.393965, No Obj: 0.002944, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.619191, Class: 1.000000, Obj: 0.371967, No Obj: 0.003020, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.651175, Class: 1.000000, Obj: 0.373911, No Obj: 0.003056, Avg Recall: 0.803279, count: 122 -Region Avg IOU: 0.621437, Class: 1.000000, Obj: 0.427394, No Obj: 0.002487, Avg Recall: 0.750000, count: 108 -4642: 0.430321, 0.445935 avg, 0.001000 rate, 26.188736 seconds, 1188352 images -Loaded: 0.000123 seconds -Region Avg IOU: 0.654242, Class: 1.000000, Obj: 0.425326, No Obj: 0.002827, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.655674, Class: 1.000000, Obj: 0.363882, No Obj: 0.003000, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.605172, Class: 1.000000, Obj: 0.354526, No Obj: 0.002919, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.602229, Class: 1.000000, Obj: 0.422610, No Obj: 0.002347, Avg Recall: 0.716216, count: 74 -Region Avg IOU: 0.650119, Class: 1.000000, Obj: 0.357751, No Obj: 0.002530, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.651866, Class: 1.000000, Obj: 0.398604, No Obj: 0.002449, Avg Recall: 0.825000, count: 80 -Region Avg IOU: 0.623856, Class: 1.000000, Obj: 0.416699, No Obj: 0.002691, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.639304, Class: 1.000000, Obj: 0.392689, No Obj: 0.002702, Avg Recall: 0.761468, count: 109 -4643: 0.394211, 0.440762 avg, 0.001000 rate, 28.962332 seconds, 1188608 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.606625, Class: 1.000000, Obj: 0.347804, No Obj: 0.002687, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.603077, Class: 1.000000, Obj: 0.384246, No Obj: 0.002681, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.632586, Class: 1.000000, Obj: 0.393240, No Obj: 0.002911, Avg Recall: 0.798387, count: 124 -Region Avg IOU: 0.634944, Class: 1.000000, Obj: 0.386203, No Obj: 0.002546, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.630809, Class: 1.000000, Obj: 0.377432, No Obj: 0.002845, Avg Recall: 0.772358, count: 123 -Region Avg IOU: 0.651988, Class: 1.000000, Obj: 0.387732, No Obj: 0.002965, Avg Recall: 0.810345, count: 116 -Region Avg IOU: 0.626208, Class: 1.000000, Obj: 0.350329, No Obj: 0.002609, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.667406, Class: 1.000000, Obj: 0.364673, No Obj: 0.002883, Avg Recall: 0.803150, count: 127 -4644: 0.484455, 0.445132 avg, 0.001000 rate, 27.463682 seconds, 1188864 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.678966, Class: 1.000000, Obj: 0.402402, No Obj: 0.002896, Avg Recall: 0.848000, count: 125 -Region Avg IOU: 0.625653, Class: 1.000000, Obj: 0.360941, No Obj: 0.003316, Avg Recall: 0.772059, count: 136 -Region Avg IOU: 0.646274, Class: 1.000000, Obj: 0.409244, No Obj: 0.002837, Avg Recall: 0.789474, count: 114 -Region Avg IOU: 0.615676, Class: 1.000000, Obj: 0.384415, No Obj: 0.002866, Avg Recall: 0.738095, count: 126 -Region Avg IOU: 0.667061, Class: 1.000000, Obj: 0.385317, No Obj: 0.002687, Avg Recall: 0.821429, count: 84 -Region Avg IOU: 0.651554, Class: 1.000000, Obj: 0.405502, No Obj: 0.002714, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.649419, Class: 1.000000, Obj: 0.396953, No Obj: 0.002926, Avg Recall: 0.744000, count: 125 -Region Avg IOU: 0.634305, Class: 1.000000, Obj: 0.363304, No Obj: 0.002832, Avg Recall: 0.776000, count: 125 -4645: 0.480132, 0.448632 avg, 0.001000 rate, 27.833780 seconds, 1189120 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.639078, Class: 1.000000, Obj: 0.379375, No Obj: 0.003100, Avg Recall: 0.766129, count: 124 -Region Avg IOU: 0.626659, Class: 1.000000, Obj: 0.329393, No Obj: 0.002774, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.608964, Class: 1.000000, Obj: 0.344516, No Obj: 0.002936, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.612077, Class: 1.000000, Obj: 0.367827, No Obj: 0.002379, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.662426, Class: 1.000000, Obj: 0.369513, No Obj: 0.002471, Avg Recall: 0.838710, count: 93 -Region Avg IOU: 0.622688, Class: 1.000000, Obj: 0.361474, No Obj: 0.002501, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.616677, Class: 1.000000, Obj: 0.373566, No Obj: 0.002520, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.677354, Class: 1.000000, Obj: 0.384635, No Obj: 0.003047, Avg Recall: 0.818965, count: 116 -4646: 0.457731, 0.449542 avg, 0.001000 rate, 28.585081 seconds, 1189376 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.627099, Class: 1.000000, Obj: 0.318235, No Obj: 0.003191, Avg Recall: 0.743750, count: 160 -Region Avg IOU: 0.671301, Class: 1.000000, Obj: 0.407377, No Obj: 0.003129, Avg Recall: 0.824561, count: 114 -Region Avg IOU: 0.656375, Class: 1.000000, Obj: 0.406307, No Obj: 0.002505, Avg Recall: 0.779070, count: 86 -Region Avg IOU: 0.624974, Class: 1.000000, Obj: 0.394797, No Obj: 0.002791, Avg Recall: 0.743802, count: 121 -Region Avg IOU: 0.602266, Class: 1.000000, Obj: 0.376695, No Obj: 0.002064, Avg Recall: 0.741176, count: 85 -Region Avg IOU: 0.629756, Class: 1.000000, Obj: 0.333373, No Obj: 0.003090, Avg Recall: 0.804054, count: 148 -Region Avg IOU: 0.636113, Class: 1.000000, Obj: 0.343415, No Obj: 0.003056, Avg Recall: 0.790210, count: 143 -Region Avg IOU: 0.651652, Class: 1.000000, Obj: 0.380639, No Obj: 0.003082, Avg Recall: 0.782609, count: 115 -4647: 0.474982, 0.452086 avg, 0.001000 rate, 25.795412 seconds, 1189632 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.651866, Class: 1.000000, Obj: 0.375543, No Obj: 0.002971, Avg Recall: 0.864078, count: 103 -Region Avg IOU: 0.642042, Class: 1.000000, Obj: 0.350146, No Obj: 0.002770, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.640054, Class: 1.000000, Obj: 0.380006, No Obj: 0.003069, Avg Recall: 0.825000, count: 120 -Region Avg IOU: 0.627319, Class: 1.000000, Obj: 0.365196, No Obj: 0.002790, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.607758, Class: 1.000000, Obj: 0.386832, No Obj: 0.002721, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.673528, Class: 1.000000, Obj: 0.436691, No Obj: 0.002916, Avg Recall: 0.854545, count: 110 -Region Avg IOU: 0.637475, Class: 1.000000, Obj: 0.411531, No Obj: 0.002715, Avg Recall: 0.800000, count: 75 -Region Avg IOU: 0.627363, Class: 1.000000, Obj: 0.392249, No Obj: 0.002649, Avg Recall: 0.816514, count: 109 -4648: 0.440666, 0.450944 avg, 0.001000 rate, 29.088863 seconds, 1189888 images -Loaded: 0.000057 seconds -Region Avg IOU: 0.584177, Class: 1.000000, Obj: 0.367809, No Obj: 0.002437, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.646649, Class: 1.000000, Obj: 0.402426, No Obj: 0.002538, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.650978, Class: 1.000000, Obj: 0.373330, No Obj: 0.003353, Avg Recall: 0.821429, count: 112 -Region Avg IOU: 0.645608, Class: 1.000000, Obj: 0.407961, No Obj: 0.003092, Avg Recall: 0.809524, count: 126 -Region Avg IOU: 0.680097, Class: 1.000000, Obj: 0.417321, No Obj: 0.003416, Avg Recall: 0.833333, count: 120 -Region Avg IOU: 0.655757, Class: 1.000000, Obj: 0.456826, No Obj: 0.002795, Avg Recall: 0.810127, count: 79 -Region Avg IOU: 0.628144, Class: 1.000000, Obj: 0.414604, No Obj: 0.002884, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.629602, Class: 1.000000, Obj: 0.385307, No Obj: 0.003128, Avg Recall: 0.754902, count: 102 -4649: 0.484913, 0.454341 avg, 0.001000 rate, 26.719955 seconds, 1190144 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.613756, Class: 1.000000, Obj: 0.338069, No Obj: 0.002862, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.668278, Class: 1.000000, Obj: 0.432189, No Obj: 0.002737, Avg Recall: 0.804878, count: 82 -Region Avg IOU: 0.644427, Class: 1.000000, Obj: 0.393564, No Obj: 0.003249, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.630363, Class: 1.000000, Obj: 0.427975, No Obj: 0.002683, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.616092, Class: 1.000000, Obj: 0.339938, No Obj: 0.003148, Avg Recall: 0.773438, count: 128 -Region Avg IOU: 0.664519, Class: 1.000000, Obj: 0.456811, No Obj: 0.002786, Avg Recall: 0.849462, count: 93 -Region Avg IOU: 0.617081, Class: 1.000000, Obj: 0.415736, No Obj: 0.003035, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.679102, Class: 1.000000, Obj: 0.421982, No Obj: 0.002742, Avg Recall: 0.885057, count: 87 -4650: 0.415372, 0.450444 avg, 0.001000 rate, 28.556229 seconds, 1190400 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 18.795212 seconds -Region Avg IOU: 0.625265, Class: 1.000000, Obj: 0.382317, No Obj: 0.003806, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.594325, Class: 1.000000, Obj: 0.352830, No Obj: 0.003067, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.615801, Class: 1.000000, Obj: 0.364038, No Obj: 0.003527, Avg Recall: 0.757812, count: 128 -Region Avg IOU: 0.559850, Class: 1.000000, Obj: 0.339708, No Obj: 0.003548, Avg Recall: 0.619403, count: 134 -Region Avg IOU: 0.645076, Class: 1.000000, Obj: 0.415563, No Obj: 0.003130, Avg Recall: 0.773810, count: 84 -Region Avg IOU: 0.614154, Class: 1.000000, Obj: 0.416611, No Obj: 0.003573, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.621260, Class: 1.000000, Obj: 0.427485, No Obj: 0.002818, Avg Recall: 0.700000, count: 70 -Region Avg IOU: 0.537396, Class: 1.000000, Obj: 0.345018, No Obj: 0.003371, Avg Recall: 0.629032, count: 124 -4651: 0.411772, 0.446577 avg, 0.001000 rate, 25.229515 seconds, 1190656 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.566718, Class: 1.000000, Obj: 0.368911, No Obj: 0.002894, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.581014, Class: 1.000000, Obj: 0.347027, No Obj: 0.003762, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.621591, Class: 1.000000, Obj: 0.393052, No Obj: 0.003932, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.592816, Class: 1.000000, Obj: 0.376793, No Obj: 0.003457, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.548555, Class: 1.000000, Obj: 0.387180, No Obj: 0.003403, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.638325, Class: 1.000000, Obj: 0.410933, No Obj: 0.003275, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.614730, Class: 1.000000, Obj: 0.359653, No Obj: 0.003414, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.591277, Class: 1.000000, Obj: 0.361483, No Obj: 0.002869, Avg Recall: 0.694444, count: 108 -4652: 0.423835, 0.444302 avg, 0.001000 rate, 25.911448 seconds, 1190912 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.567880, Class: 1.000000, Obj: 0.358578, No Obj: 0.003094, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.619260, Class: 1.000000, Obj: 0.419785, No Obj: 0.003425, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.640832, Class: 1.000000, Obj: 0.472428, No Obj: 0.003357, Avg Recall: 0.802632, count: 76 -Region Avg IOU: 0.569624, Class: 1.000000, Obj: 0.398069, No Obj: 0.003436, Avg Recall: 0.656000, count: 125 -Region Avg IOU: 0.605257, Class: 1.000000, Obj: 0.400271, No Obj: 0.002835, Avg Recall: 0.728395, count: 81 -Region Avg IOU: 0.597017, Class: 1.000000, Obj: 0.440011, No Obj: 0.003723, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.579094, Class: 1.000000, Obj: 0.341516, No Obj: 0.003376, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.620738, Class: 1.000000, Obj: 0.397315, No Obj: 0.003879, Avg Recall: 0.759399, count: 133 -4653: 0.452482, 0.445120 avg, 0.001000 rate, 25.076529 seconds, 1191168 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.637275, Class: 1.000000, Obj: 0.464946, No Obj: 0.003232, Avg Recall: 0.790698, count: 86 -Region Avg IOU: 0.662498, Class: 1.000000, Obj: 0.450133, No Obj: 0.003763, Avg Recall: 0.848740, count: 119 -Region Avg IOU: 0.581458, Class: 1.000000, Obj: 0.423893, No Obj: 0.003001, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.598130, Class: 1.000000, Obj: 0.420294, No Obj: 0.003687, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.606896, Class: 1.000000, Obj: 0.382808, No Obj: 0.003234, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.564699, Class: 1.000000, Obj: 0.355849, No Obj: 0.003672, Avg Recall: 0.650000, count: 120 -Region Avg IOU: 0.562406, Class: 1.000000, Obj: 0.383189, No Obj: 0.003098, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.614490, Class: 1.000000, Obj: 0.406594, No Obj: 0.003627, Avg Recall: 0.769912, count: 113 -4654: 0.427070, 0.443315 avg, 0.001000 rate, 24.818932 seconds, 1191424 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.570002, Class: 1.000000, Obj: 0.373314, No Obj: 0.003516, Avg Recall: 0.645669, count: 127 -Region Avg IOU: 0.592141, Class: 1.000000, Obj: 0.421851, No Obj: 0.003799, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.580394, Class: 1.000000, Obj: 0.373867, No Obj: 0.003262, Avg Recall: 0.674242, count: 132 -Region Avg IOU: 0.592972, Class: 1.000000, Obj: 0.426852, No Obj: 0.003177, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.629942, Class: 1.000000, Obj: 0.415111, No Obj: 0.003519, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.591778, Class: 1.000000, Obj: 0.365458, No Obj: 0.003577, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.609109, Class: 1.000000, Obj: 0.369225, No Obj: 0.003744, Avg Recall: 0.734375, count: 128 -Region Avg IOU: 0.605108, Class: 1.000000, Obj: 0.374893, No Obj: 0.003903, Avg Recall: 0.707965, count: 113 -4655: 0.491143, 0.448098 avg, 0.001000 rate, 25.162409 seconds, 1191680 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.606824, Class: 1.000000, Obj: 0.382218, No Obj: 0.003806, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.609523, Class: 1.000000, Obj: 0.392221, No Obj: 0.003580, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.604121, Class: 1.000000, Obj: 0.426534, No Obj: 0.003117, Avg Recall: 0.722222, count: 90 -Region Avg IOU: 0.586163, Class: 1.000000, Obj: 0.423648, No Obj: 0.002865, Avg Recall: 0.662791, count: 86 -Region Avg IOU: 0.577405, Class: 1.000000, Obj: 0.417989, No Obj: 0.003089, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.606241, Class: 1.000000, Obj: 0.425061, No Obj: 0.003639, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.551544, Class: 1.000000, Obj: 0.359469, No Obj: 0.003529, Avg Recall: 0.614286, count: 140 -Region Avg IOU: 0.617424, Class: 1.000000, Obj: 0.397058, No Obj: 0.003797, Avg Recall: 0.732143, count: 112 -4656: 0.413471, 0.444635 avg, 0.001000 rate, 24.757257 seconds, 1191936 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.596453, Class: 1.000000, Obj: 0.380250, No Obj: 0.003268, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.616973, Class: 1.000000, Obj: 0.433898, No Obj: 0.003523, Avg Recall: 0.752137, count: 117 -Region Avg IOU: 0.611269, Class: 1.000000, Obj: 0.406659, No Obj: 0.003587, Avg Recall: 0.709302, count: 86 -Region Avg IOU: 0.616768, Class: 1.000000, Obj: 0.446430, No Obj: 0.003133, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.617953, Class: 1.000000, Obj: 0.377393, No Obj: 0.003286, Avg Recall: 0.740741, count: 81 -Region Avg IOU: 0.629652, Class: 1.000000, Obj: 0.415217, No Obj: 0.003084, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.612799, Class: 1.000000, Obj: 0.409764, No Obj: 0.003212, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.614382, Class: 1.000000, Obj: 0.429817, No Obj: 0.003560, Avg Recall: 0.756522, count: 115 -4657: 0.370274, 0.437199 avg, 0.001000 rate, 24.381746 seconds, 1192192 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.557390, Class: 1.000000, Obj: 0.368060, No Obj: 0.002487, Avg Recall: 0.617284, count: 81 -Region Avg IOU: 0.574397, Class: 1.000000, Obj: 0.397125, No Obj: 0.003142, Avg Recall: 0.684783, count: 92 -Region Avg IOU: 0.607420, Class: 1.000000, Obj: 0.432206, No Obj: 0.002874, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.600101, Class: 1.000000, Obj: 0.432888, No Obj: 0.003553, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.598191, Class: 1.000000, Obj: 0.415539, No Obj: 0.003296, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.598638, Class: 1.000000, Obj: 0.438361, No Obj: 0.003734, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.644777, Class: 1.000000, Obj: 0.423684, No Obj: 0.003623, Avg Recall: 0.805085, count: 118 -Region Avg IOU: 0.599247, Class: 1.000000, Obj: 0.407960, No Obj: 0.003002, Avg Recall: 0.728972, count: 107 -4658: 0.367152, 0.430194 avg, 0.001000 rate, 24.082266 seconds, 1192448 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.610930, Class: 1.000000, Obj: 0.398434, No Obj: 0.003401, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.603098, Class: 1.000000, Obj: 0.412990, No Obj: 0.003551, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.606652, Class: 1.000000, Obj: 0.372459, No Obj: 0.003416, Avg Recall: 0.735537, count: 121 -Region Avg IOU: 0.612557, Class: 1.000000, Obj: 0.409697, No Obj: 0.003059, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.569752, Class: 1.000000, Obj: 0.375797, No Obj: 0.003459, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.626020, Class: 1.000000, Obj: 0.473300, No Obj: 0.003928, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.548837, Class: 1.000000, Obj: 0.376095, No Obj: 0.002925, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.623652, Class: 1.000000, Obj: 0.430523, No Obj: 0.003663, Avg Recall: 0.781250, count: 96 -4659: 0.472178, 0.434393 avg, 0.001000 rate, 25.554079 seconds, 1192704 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.595623, Class: 1.000000, Obj: 0.396310, No Obj: 0.003256, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.591222, Class: 1.000000, Obj: 0.404259, No Obj: 0.003160, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.624243, Class: 1.000000, Obj: 0.375637, No Obj: 0.003417, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.605427, Class: 1.000000, Obj: 0.377245, No Obj: 0.003318, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.619059, Class: 1.000000, Obj: 0.413953, No Obj: 0.003506, Avg Recall: 0.786325, count: 117 -Region Avg IOU: 0.590347, Class: 1.000000, Obj: 0.380040, No Obj: 0.003719, Avg Recall: 0.686131, count: 137 -Region Avg IOU: 0.605304, Class: 1.000000, Obj: 0.364628, No Obj: 0.003091, Avg Recall: 0.728395, count: 81 -Region Avg IOU: 0.630641, Class: 1.000000, Obj: 0.434025, No Obj: 0.003113, Avg Recall: 0.752688, count: 93 -4660: 0.445731, 0.435527 avg, 0.001000 rate, 25.171207 seconds, 1192960 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000067 seconds -Region Avg IOU: 0.646535, Class: 1.000000, Obj: 0.459697, No Obj: 0.002763, Avg Recall: 0.747126, count: 87 -Region Avg IOU: 0.643617, Class: 1.000000, Obj: 0.425676, No Obj: 0.003278, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.615586, Class: 1.000000, Obj: 0.390958, No Obj: 0.003289, Avg Recall: 0.702479, count: 121 -Region Avg IOU: 0.597218, Class: 1.000000, Obj: 0.341758, No Obj: 0.003630, Avg Recall: 0.673469, count: 147 -Region Avg IOU: 0.536695, Class: 1.000000, Obj: 0.369297, No Obj: 0.003085, Avg Recall: 0.608333, count: 120 -Region Avg IOU: 0.633576, Class: 1.000000, Obj: 0.409315, No Obj: 0.003244, Avg Recall: 0.804124, count: 97 -Region Avg IOU: 0.605814, Class: 1.000000, Obj: 0.384068, No Obj: 0.003840, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.620222, Class: 1.000000, Obj: 0.386514, No Obj: 0.003078, Avg Recall: 0.752475, count: 101 -4661: 0.446333, 0.436607 avg, 0.001000 rate, 25.596617 seconds, 1193216 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.623976, Class: 1.000000, Obj: 0.398428, No Obj: 0.003160, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.635303, Class: 1.000000, Obj: 0.417431, No Obj: 0.004018, Avg Recall: 0.759399, count: 133 -Region Avg IOU: 0.617190, Class: 1.000000, Obj: 0.428941, No Obj: 0.003321, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.609222, Class: 1.000000, Obj: 0.384437, No Obj: 0.003104, Avg Recall: 0.737705, count: 122 -Region Avg IOU: 0.560078, Class: 1.000000, Obj: 0.340046, No Obj: 0.003256, Avg Recall: 0.658333, count: 120 -Region Avg IOU: 0.628348, Class: 1.000000, Obj: 0.408453, No Obj: 0.003644, Avg Recall: 0.776859, count: 121 -Region Avg IOU: 0.589936, Class: 1.000000, Obj: 0.357481, No Obj: 0.003210, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.591482, Class: 1.000000, Obj: 0.373835, No Obj: 0.003692, Avg Recall: 0.739130, count: 115 -4662: 0.497366, 0.442683 avg, 0.001000 rate, 26.022980 seconds, 1193472 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.630947, Class: 1.000000, Obj: 0.384258, No Obj: 0.003096, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.646888, Class: 1.000000, Obj: 0.437420, No Obj: 0.003369, Avg Recall: 0.771186, count: 118 -Region Avg IOU: 0.626806, Class: 1.000000, Obj: 0.395987, No Obj: 0.003496, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.557262, Class: 1.000000, Obj: 0.395026, No Obj: 0.002617, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.596171, Class: 1.000000, Obj: 0.451346, No Obj: 0.003407, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.624242, Class: 1.000000, Obj: 0.424302, No Obj: 0.003133, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.626121, Class: 1.000000, Obj: 0.430782, No Obj: 0.003339, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.599538, Class: 1.000000, Obj: 0.380410, No Obj: 0.003527, Avg Recall: 0.736000, count: 125 -4663: 0.432585, 0.441673 avg, 0.001000 rate, 27.354048 seconds, 1193728 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.604319, Class: 1.000000, Obj: 0.381788, No Obj: 0.002944, Avg Recall: 0.736842, count: 76 -Region Avg IOU: 0.639346, Class: 1.000000, Obj: 0.428077, No Obj: 0.003489, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.596534, Class: 1.000000, Obj: 0.405239, No Obj: 0.002865, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.588762, Class: 1.000000, Obj: 0.401151, No Obj: 0.003056, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.602005, Class: 1.000000, Obj: 0.405964, No Obj: 0.002896, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.585259, Class: 1.000000, Obj: 0.369175, No Obj: 0.003056, Avg Recall: 0.675214, count: 117 -Region Avg IOU: 0.618648, Class: 1.000000, Obj: 0.412681, No Obj: 0.003326, Avg Recall: 0.769231, count: 130 -Region Avg IOU: 0.558920, Class: 1.000000, Obj: 0.368458, No Obj: 0.003138, Avg Recall: 0.654545, count: 110 -4664: 0.400412, 0.437547 avg, 0.001000 rate, 25.846800 seconds, 1193984 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.654536, Class: 1.000000, Obj: 0.356333, No Obj: 0.002974, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.646026, Class: 1.000000, Obj: 0.443860, No Obj: 0.002935, Avg Recall: 0.808989, count: 89 -Region Avg IOU: 0.626360, Class: 1.000000, Obj: 0.404918, No Obj: 0.003812, Avg Recall: 0.732283, count: 127 -Region Avg IOU: 0.624949, Class: 1.000000, Obj: 0.379841, No Obj: 0.003174, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.622210, Class: 1.000000, Obj: 0.452228, No Obj: 0.003530, Avg Recall: 0.769841, count: 126 -Region Avg IOU: 0.658609, Class: 1.000000, Obj: 0.418075, No Obj: 0.003425, Avg Recall: 0.822034, count: 118 -Region Avg IOU: 0.610082, Class: 1.000000, Obj: 0.402751, No Obj: 0.003380, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.610237, Class: 1.000000, Obj: 0.370720, No Obj: 0.003489, Avg Recall: 0.728682, count: 129 -4665: 0.501838, 0.443976 avg, 0.001000 rate, 24.696136 seconds, 1194240 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.622721, Class: 1.000000, Obj: 0.380094, No Obj: 0.003093, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.580638, Class: 1.000000, Obj: 0.362881, No Obj: 0.003046, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.564894, Class: 1.000000, Obj: 0.384097, No Obj: 0.003226, Avg Recall: 0.691667, count: 120 -Region Avg IOU: 0.585825, Class: 1.000000, Obj: 0.380364, No Obj: 0.002967, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.660065, Class: 1.000000, Obj: 0.400591, No Obj: 0.002845, Avg Recall: 0.830769, count: 65 -Region Avg IOU: 0.629198, Class: 1.000000, Obj: 0.391373, No Obj: 0.003131, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.581655, Class: 1.000000, Obj: 0.343127, No Obj: 0.002845, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.618355, Class: 1.000000, Obj: 0.352249, No Obj: 0.002767, Avg Recall: 0.718447, count: 103 -4666: 0.442699, 0.443848 avg, 0.001000 rate, 25.871153 seconds, 1194496 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.612780, Class: 1.000000, Obj: 0.404848, No Obj: 0.003018, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.649457, Class: 1.000000, Obj: 0.397849, No Obj: 0.003332, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.648096, Class: 1.000000, Obj: 0.397260, No Obj: 0.003363, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.611683, Class: 1.000000, Obj: 0.411209, No Obj: 0.003295, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.617845, Class: 1.000000, Obj: 0.399997, No Obj: 0.003340, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.624294, Class: 1.000000, Obj: 0.360504, No Obj: 0.002981, Avg Recall: 0.800000, count: 115 -Region Avg IOU: 0.618577, Class: 1.000000, Obj: 0.357518, No Obj: 0.002971, Avg Recall: 0.797872, count: 94 -Region Avg IOU: 0.618757, Class: 1.000000, Obj: 0.421983, No Obj: 0.003153, Avg Recall: 0.728155, count: 103 -4667: 0.424104, 0.441874 avg, 0.001000 rate, 24.789003 seconds, 1194752 images -Loaded: 0.000124 seconds -Region Avg IOU: 0.591297, Class: 1.000000, Obj: 0.389554, No Obj: 0.003050, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.620543, Class: 1.000000, Obj: 0.384306, No Obj: 0.003623, Avg Recall: 0.751773, count: 141 -Region Avg IOU: 0.672947, Class: 1.000000, Obj: 0.402475, No Obj: 0.003222, Avg Recall: 0.861386, count: 101 -Region Avg IOU: 0.619968, Class: 1.000000, Obj: 0.418949, No Obj: 0.002972, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.577627, Class: 1.000000, Obj: 0.411612, No Obj: 0.002907, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.583115, Class: 1.000000, Obj: 0.411172, No Obj: 0.002552, Avg Recall: 0.641975, count: 81 -Region Avg IOU: 0.631978, Class: 1.000000, Obj: 0.313854, No Obj: 0.002867, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.572963, Class: 1.000000, Obj: 0.356639, No Obj: 0.002876, Avg Recall: 0.673469, count: 98 -4668: 0.415366, 0.439223 avg, 0.001000 rate, 23.327499 seconds, 1195008 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.617885, Class: 1.000000, Obj: 0.401676, No Obj: 0.002829, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.620316, Class: 1.000000, Obj: 0.408349, No Obj: 0.002675, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.646439, Class: 1.000000, Obj: 0.469277, No Obj: 0.002841, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.598948, Class: 1.000000, Obj: 0.380354, No Obj: 0.002616, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.619823, Class: 1.000000, Obj: 0.383602, No Obj: 0.003129, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.628092, Class: 1.000000, Obj: 0.375598, No Obj: 0.003126, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.605876, Class: 1.000000, Obj: 0.381296, No Obj: 0.002747, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.640405, Class: 1.000000, Obj: 0.391686, No Obj: 0.002797, Avg Recall: 0.795699, count: 93 -4669: 0.396684, 0.434969 avg, 0.001000 rate, 26.292425 seconds, 1195264 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.632460, Class: 1.000000, Obj: 0.384143, No Obj: 0.003375, Avg Recall: 0.759399, count: 133 -Region Avg IOU: 0.606881, Class: 1.000000, Obj: 0.389880, No Obj: 0.003523, Avg Recall: 0.735294, count: 136 -Region Avg IOU: 0.640152, Class: 1.000000, Obj: 0.452017, No Obj: 0.002965, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.628847, Class: 1.000000, Obj: 0.435359, No Obj: 0.002523, Avg Recall: 0.807692, count: 78 -Region Avg IOU: 0.621749, Class: 1.000000, Obj: 0.367217, No Obj: 0.003342, Avg Recall: 0.755906, count: 127 -Region Avg IOU: 0.597351, Class: 1.000000, Obj: 0.366202, No Obj: 0.002771, Avg Recall: 0.758621, count: 87 -Region Avg IOU: 0.631579, Class: 1.000000, Obj: 0.378692, No Obj: 0.003177, Avg Recall: 0.769231, count: 130 -Region Avg IOU: 0.606508, Class: 1.000000, Obj: 0.325079, No Obj: 0.003302, Avg Recall: 0.752137, count: 117 -4670: 0.461203, 0.437593 avg, 0.001000 rate, 25.357780 seconds, 1195520 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000078 seconds -Region Avg IOU: 0.644094, Class: 1.000000, Obj: 0.335773, No Obj: 0.002709, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.629704, Class: 1.000000, Obj: 0.403912, No Obj: 0.002414, Avg Recall: 0.758621, count: 87 -Region Avg IOU: 0.595905, Class: 1.000000, Obj: 0.336205, No Obj: 0.002727, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.628250, Class: 1.000000, Obj: 0.415856, No Obj: 0.002720, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.639949, Class: 1.000000, Obj: 0.437218, No Obj: 0.002576, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.672438, Class: 1.000000, Obj: 0.407115, No Obj: 0.002656, Avg Recall: 0.819048, count: 105 -Region Avg IOU: 0.660842, Class: 1.000000, Obj: 0.421270, No Obj: 0.002445, Avg Recall: 0.808989, count: 89 -Region Avg IOU: 0.654244, Class: 1.000000, Obj: 0.412319, No Obj: 0.002418, Avg Recall: 0.791667, count: 96 -4671: 0.411288, 0.434962 avg, 0.001000 rate, 30.245573 seconds, 1195776 images -Loaded: 0.000117 seconds -Region Avg IOU: 0.648045, Class: 1.000000, Obj: 0.410619, No Obj: 0.002790, Avg Recall: 0.800000, count: 115 -Region Avg IOU: 0.656188, Class: 1.000000, Obj: 0.421840, No Obj: 0.002174, Avg Recall: 0.784810, count: 79 -Region Avg IOU: 0.674648, Class: 1.000000, Obj: 0.366991, No Obj: 0.002636, Avg Recall: 0.837607, count: 117 -Region Avg IOU: 0.671630, Class: 1.000000, Obj: 0.429278, No Obj: 0.002373, Avg Recall: 0.842697, count: 89 -Region Avg IOU: 0.625740, Class: 1.000000, Obj: 0.396510, No Obj: 0.002371, Avg Recall: 0.764706, count: 85 -Region Avg IOU: 0.682887, Class: 1.000000, Obj: 0.402051, No Obj: 0.003015, Avg Recall: 0.827586, count: 116 -Region Avg IOU: 0.634837, Class: 1.000000, Obj: 0.406211, No Obj: 0.002313, Avg Recall: 0.776316, count: 76 -Region Avg IOU: 0.662064, Class: 1.000000, Obj: 0.370682, No Obj: 0.002767, Avg Recall: 0.821138, count: 123 -4672: 0.434622, 0.434928 avg, 0.001000 rate, 28.227167 seconds, 1196032 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.624084, Class: 1.000000, Obj: 0.384514, No Obj: 0.002447, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.628097, Class: 1.000000, Obj: 0.385069, No Obj: 0.002577, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.689931, Class: 1.000000, Obj: 0.471016, No Obj: 0.002151, Avg Recall: 0.848101, count: 79 -Region Avg IOU: 0.628777, Class: 1.000000, Obj: 0.399794, No Obj: 0.002761, Avg Recall: 0.784483, count: 116 -Region Avg IOU: 0.651601, Class: 1.000000, Obj: 0.422603, No Obj: 0.002360, Avg Recall: 0.836957, count: 92 -Region Avg IOU: 0.656962, Class: 1.000000, Obj: 0.410380, No Obj: 0.002613, Avg Recall: 0.807018, count: 114 -Region Avg IOU: 0.661373, Class: 1.000000, Obj: 0.416141, No Obj: 0.002443, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.651655, Class: 1.000000, Obj: 0.466900, No Obj: 0.002157, Avg Recall: 0.843137, count: 102 -4673: 0.393488, 0.430784 avg, 0.001000 rate, 29.838919 seconds, 1196288 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.659526, Class: 1.000000, Obj: 0.401481, No Obj: 0.002534, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.632973, Class: 1.000000, Obj: 0.375349, No Obj: 0.002420, Avg Recall: 0.804598, count: 87 -Region Avg IOU: 0.659855, Class: 1.000000, Obj: 0.386483, No Obj: 0.002600, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.649673, Class: 1.000000, Obj: 0.359666, No Obj: 0.002648, Avg Recall: 0.837838, count: 111 -Region Avg IOU: 0.584132, Class: 1.000000, Obj: 0.326195, No Obj: 0.002472, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.649852, Class: 1.000000, Obj: 0.380302, No Obj: 0.002551, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.629204, Class: 1.000000, Obj: 0.369632, No Obj: 0.002594, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.618030, Class: 1.000000, Obj: 0.358059, No Obj: 0.002633, Avg Recall: 0.760000, count: 100 -4674: 0.424761, 0.430182 avg, 0.001000 rate, 29.783909 seconds, 1196544 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.668500, Class: 1.000000, Obj: 0.416594, No Obj: 0.002588, Avg Recall: 0.813084, count: 107 -Region Avg IOU: 0.644626, Class: 1.000000, Obj: 0.464848, No Obj: 0.002292, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.660574, Class: 1.000000, Obj: 0.414812, No Obj: 0.002549, Avg Recall: 0.840708, count: 113 -Region Avg IOU: 0.617570, Class: 1.000000, Obj: 0.391506, No Obj: 0.002334, Avg Recall: 0.737705, count: 122 -Region Avg IOU: 0.630043, Class: 1.000000, Obj: 0.349697, No Obj: 0.002567, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.633758, Class: 1.000000, Obj: 0.389688, No Obj: 0.002452, Avg Recall: 0.787402, count: 127 -Region Avg IOU: 0.647419, Class: 1.000000, Obj: 0.386532, No Obj: 0.002808, Avg Recall: 0.806452, count: 124 -Region Avg IOU: 0.663825, Class: 1.000000, Obj: 0.419091, No Obj: 0.002263, Avg Recall: 0.800000, count: 90 -4675: 0.434105, 0.430574 avg, 0.001000 rate, 29.965282 seconds, 1196800 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.687597, Class: 1.000000, Obj: 0.392305, No Obj: 0.002500, Avg Recall: 0.913043, count: 92 -Region Avg IOU: 0.696315, Class: 1.000000, Obj: 0.388901, No Obj: 0.002823, Avg Recall: 0.930693, count: 101 -Region Avg IOU: 0.646079, Class: 1.000000, Obj: 0.433329, No Obj: 0.002910, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.645807, Class: 1.000000, Obj: 0.391081, No Obj: 0.002620, Avg Recall: 0.781609, count: 87 -Region Avg IOU: 0.673881, Class: 1.000000, Obj: 0.390119, No Obj: 0.002327, Avg Recall: 0.857143, count: 91 -Region Avg IOU: 0.626893, Class: 1.000000, Obj: 0.314100, No Obj: 0.002549, Avg Recall: 0.771186, count: 118 -Region Avg IOU: 0.656722, Class: 1.000000, Obj: 0.419677, No Obj: 0.002715, Avg Recall: 0.833333, count: 108 -Region Avg IOU: 0.631088, Class: 1.000000, Obj: 0.394409, No Obj: 0.002714, Avg Recall: 0.778626, count: 131 -4676: 0.448005, 0.432317 avg, 0.001000 rate, 29.796106 seconds, 1197056 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.648584, Class: 1.000000, Obj: 0.378685, No Obj: 0.002408, Avg Recall: 0.824742, count: 97 -Region Avg IOU: 0.652802, Class: 1.000000, Obj: 0.373015, No Obj: 0.003116, Avg Recall: 0.833333, count: 138 -Region Avg IOU: 0.655181, Class: 1.000000, Obj: 0.429604, No Obj: 0.002623, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.621261, Class: 1.000000, Obj: 0.434060, No Obj: 0.002589, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.650310, Class: 1.000000, Obj: 0.376539, No Obj: 0.002283, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.638491, Class: 1.000000, Obj: 0.353503, No Obj: 0.002487, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.614995, Class: 1.000000, Obj: 0.316648, No Obj: 0.002651, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.668519, Class: 1.000000, Obj: 0.394792, No Obj: 0.002639, Avg Recall: 0.838983, count: 118 -4677: 0.456381, 0.434724 avg, 0.001000 rate, 28.406202 seconds, 1197312 images -Loaded: 0.000136 seconds -Region Avg IOU: 0.624113, Class: 1.000000, Obj: 0.367001, No Obj: 0.003028, Avg Recall: 0.759740, count: 154 -Region Avg IOU: 0.665464, Class: 1.000000, Obj: 0.359523, No Obj: 0.002391, Avg Recall: 0.836364, count: 110 -Region Avg IOU: 0.651884, Class: 1.000000, Obj: 0.374271, No Obj: 0.002886, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.627733, Class: 1.000000, Obj: 0.400698, No Obj: 0.002636, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.654824, Class: 1.000000, Obj: 0.372702, No Obj: 0.002739, Avg Recall: 0.791667, count: 144 -Region Avg IOU: 0.660356, Class: 1.000000, Obj: 0.350029, No Obj: 0.002319, Avg Recall: 0.833333, count: 114 -Region Avg IOU: 0.666379, Class: 1.000000, Obj: 0.386496, No Obj: 0.002936, Avg Recall: 0.804054, count: 148 -Region Avg IOU: 0.653997, Class: 1.000000, Obj: 0.377426, No Obj: 0.002536, Avg Recall: 0.813726, count: 102 -4678: 0.516833, 0.442935 avg, 0.001000 rate, 30.061804 seconds, 1197568 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.646370, Class: 1.000000, Obj: 0.429942, No Obj: 0.002610, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.672233, Class: 1.000000, Obj: 0.395453, No Obj: 0.002817, Avg Recall: 0.843137, count: 102 -Region Avg IOU: 0.674414, Class: 1.000000, Obj: 0.339331, No Obj: 0.002727, Avg Recall: 0.862903, count: 124 -Region Avg IOU: 0.660912, Class: 1.000000, Obj: 0.402590, No Obj: 0.002829, Avg Recall: 0.793388, count: 121 -Region Avg IOU: 0.622324, Class: 1.000000, Obj: 0.365068, No Obj: 0.002840, Avg Recall: 0.738806, count: 134 -Region Avg IOU: 0.671370, Class: 1.000000, Obj: 0.406461, No Obj: 0.002599, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.670186, Class: 1.000000, Obj: 0.487992, No Obj: 0.002362, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.692846, Class: 1.000000, Obj: 0.406856, No Obj: 0.002500, Avg Recall: 0.924731, count: 93 -4679: 0.433104, 0.441952 avg, 0.001000 rate, 29.901485 seconds, 1197824 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.658878, Class: 1.000000, Obj: 0.353143, No Obj: 0.002960, Avg Recall: 0.836066, count: 122 -Region Avg IOU: 0.611365, Class: 1.000000, Obj: 0.376781, No Obj: 0.002497, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.668459, Class: 1.000000, Obj: 0.354429, No Obj: 0.002381, Avg Recall: 0.823529, count: 102 -Region Avg IOU: 0.642798, Class: 1.000000, Obj: 0.364733, No Obj: 0.002721, Avg Recall: 0.808000, count: 125 -Region Avg IOU: 0.655624, Class: 1.000000, Obj: 0.375629, No Obj: 0.002588, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.643723, Class: 1.000000, Obj: 0.398308, No Obj: 0.002413, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.660450, Class: 1.000000, Obj: 0.384833, No Obj: 0.003048, Avg Recall: 0.813793, count: 145 -Region Avg IOU: 0.650607, Class: 1.000000, Obj: 0.360304, No Obj: 0.002670, Avg Recall: 0.798387, count: 124 -4680: 0.476038, 0.445360 avg, 0.001000 rate, 31.336121 seconds, 1198080 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 13.069094 seconds -Region Avg IOU: 0.533068, Class: 1.000000, Obj: 0.274317, No Obj: 0.003231, Avg Recall: 0.619835, count: 121 -Region Avg IOU: 0.534462, Class: 1.000000, Obj: 0.283399, No Obj: 0.003042, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.494737, Class: 1.000000, Obj: 0.291337, No Obj: 0.003116, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.486797, Class: 1.000000, Obj: 0.283894, No Obj: 0.003370, Avg Recall: 0.536000, count: 125 -Region Avg IOU: 0.548564, Class: 1.000000, Obj: 0.266318, No Obj: 0.003347, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.573455, Class: 1.000000, Obj: 0.312112, No Obj: 0.002815, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.514254, Class: 1.000000, Obj: 0.283390, No Obj: 0.003769, Avg Recall: 0.558140, count: 129 -Region Avg IOU: 0.543340, Class: 1.000000, Obj: 0.285990, No Obj: 0.002950, Avg Recall: 0.610390, count: 77 -4681: 0.409022, 0.441726 avg, 0.001000 rate, 21.144800 seconds, 1198336 images -Loaded: 1.358222 seconds -Region Avg IOU: 0.491162, Class: 1.000000, Obj: 0.264334, No Obj: 0.002999, Avg Recall: 0.470588, count: 102 -Region Avg IOU: 0.454384, Class: 1.000000, Obj: 0.308113, No Obj: 0.003605, Avg Recall: 0.450000, count: 120 -Region Avg IOU: 0.595826, Class: 1.000000, Obj: 0.321303, No Obj: 0.003324, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.541913, Class: 1.000000, Obj: 0.260799, No Obj: 0.003406, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.549647, Class: 1.000000, Obj: 0.325511, No Obj: 0.003486, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.547317, Class: 1.000000, Obj: 0.346246, No Obj: 0.003411, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.538246, Class: 1.000000, Obj: 0.298738, No Obj: 0.003924, Avg Recall: 0.617188, count: 128 -Region Avg IOU: 0.527001, Class: 1.000000, Obj: 0.285835, No Obj: 0.003401, Avg Recall: 0.607143, count: 112 -4682: 0.455970, 0.443151 avg, 0.001000 rate, 22.967377 seconds, 1198592 images -Loaded: 0.061683 seconds -Region Avg IOU: 0.533886, Class: 1.000000, Obj: 0.328191, No Obj: 0.003725, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.524581, Class: 1.000000, Obj: 0.328118, No Obj: 0.003754, Avg Recall: 0.583333, count: 120 -Region Avg IOU: 0.573306, Class: 1.000000, Obj: 0.348079, No Obj: 0.003775, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.544626, Class: 1.000000, Obj: 0.330708, No Obj: 0.003475, Avg Recall: 0.620690, count: 116 -Region Avg IOU: 0.570180, Class: 1.000000, Obj: 0.349954, No Obj: 0.004327, Avg Recall: 0.627907, count: 129 -Region Avg IOU: 0.530684, Class: 1.000000, Obj: 0.291181, No Obj: 0.003689, Avg Recall: 0.627907, count: 129 -Region Avg IOU: 0.479311, Class: 1.000000, Obj: 0.297803, No Obj: 0.003385, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.591504, Class: 1.000000, Obj: 0.332507, No Obj: 0.003998, Avg Recall: 0.652893, count: 121 -4683: 0.455208, 0.444356 avg, 0.001000 rate, 19.593851 seconds, 1198848 images -Loaded: 1.979291 seconds -Region Avg IOU: 0.495109, Class: 1.000000, Obj: 0.347902, No Obj: 0.003354, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.574486, Class: 1.000000, Obj: 0.372683, No Obj: 0.004114, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.509521, Class: 1.000000, Obj: 0.299964, No Obj: 0.003864, Avg Recall: 0.550847, count: 118 -Region Avg IOU: 0.457882, Class: 1.000000, Obj: 0.295406, No Obj: 0.003462, Avg Recall: 0.512195, count: 123 -Region Avg IOU: 0.517938, Class: 1.000000, Obj: 0.368786, No Obj: 0.003862, Avg Recall: 0.557377, count: 122 -Region Avg IOU: 0.565557, Class: 1.000000, Obj: 0.346246, No Obj: 0.004746, Avg Recall: 0.616541, count: 133 -Region Avg IOU: 0.575182, Class: 1.000000, Obj: 0.411427, No Obj: 0.004194, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.498716, Class: 1.000000, Obj: 0.316576, No Obj: 0.003715, Avg Recall: 0.549618, count: 131 -4684: 0.534177, 0.453339 avg, 0.001000 rate, 18.926456 seconds, 1199104 images -Loaded: 1.992261 seconds -Region Avg IOU: 0.549794, Class: 1.000000, Obj: 0.394583, No Obj: 0.004225, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.553860, Class: 1.000000, Obj: 0.370653, No Obj: 0.004554, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.488422, Class: 1.000000, Obj: 0.367406, No Obj: 0.003875, Avg Recall: 0.522936, count: 109 -Region Avg IOU: 0.479471, Class: 1.000000, Obj: 0.347575, No Obj: 0.003848, Avg Recall: 0.508929, count: 112 -Region Avg IOU: 0.523744, Class: 1.000000, Obj: 0.360584, No Obj: 0.004638, Avg Recall: 0.586777, count: 121 -Region Avg IOU: 0.524948, Class: 1.000000, Obj: 0.330465, No Obj: 0.004167, Avg Recall: 0.577586, count: 116 -Region Avg IOU: 0.467520, Class: 1.000000, Obj: 0.368842, No Obj: 0.003711, Avg Recall: 0.471154, count: 104 -Region Avg IOU: 0.542156, Class: 1.000000, Obj: 0.387718, No Obj: 0.004043, Avg Recall: 0.653846, count: 104 -4685: 0.471506, 0.455155 avg, 0.001000 rate, 20.144402 seconds, 1199360 images -Loaded: 1.976317 seconds -Region Avg IOU: 0.491166, Class: 1.000000, Obj: 0.337205, No Obj: 0.004655, Avg Recall: 0.561538, count: 130 -Region Avg IOU: 0.559233, Class: 1.000000, Obj: 0.392601, No Obj: 0.005400, Avg Recall: 0.666667, count: 129 -Region Avg IOU: 0.532069, Class: 1.000000, Obj: 0.389011, No Obj: 0.004790, Avg Recall: 0.627119, count: 118 -Region Avg IOU: 0.543232, Class: 1.000000, Obj: 0.358307, No Obj: 0.004653, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.557924, Class: 1.000000, Obj: 0.442944, No Obj: 0.004587, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.521846, Class: 1.000000, Obj: 0.355421, No Obj: 0.004717, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.494643, Class: 1.000000, Obj: 0.347563, No Obj: 0.004688, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.540808, Class: 1.000000, Obj: 0.362750, No Obj: 0.004600, Avg Recall: 0.618644, count: 118 -4686: 0.471171, 0.456757 avg, 0.001000 rate, 19.189009 seconds, 1199616 images -Loaded: 2.345926 seconds -Region Avg IOU: 0.568383, Class: 1.000000, Obj: 0.450600, No Obj: 0.004557, Avg Recall: 0.626506, count: 83 -Region Avg IOU: 0.550174, Class: 1.000000, Obj: 0.362325, No Obj: 0.004500, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.562176, Class: 1.000000, Obj: 0.407453, No Obj: 0.004929, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.530356, Class: 1.000000, Obj: 0.428626, No Obj: 0.005143, Avg Recall: 0.609756, count: 123 -Region Avg IOU: 0.578727, Class: 1.000000, Obj: 0.447177, No Obj: 0.005037, Avg Recall: 0.666667, count: 78 -Region Avg IOU: 0.504314, Class: 1.000000, Obj: 0.362932, No Obj: 0.005116, Avg Recall: 0.577236, count: 123 -Region Avg IOU: 0.506336, Class: 1.000000, Obj: 0.403262, No Obj: 0.004287, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.547868, Class: 1.000000, Obj: 0.418462, No Obj: 0.005305, Avg Recall: 0.644068, count: 118 -4687: 0.434310, 0.454512 avg, 0.001000 rate, 20.551533 seconds, 1199872 images -Loaded: 0.926070 seconds -Region Avg IOU: 0.480563, Class: 1.000000, Obj: 0.391644, No Obj: 0.004833, Avg Recall: 0.542373, count: 118 -Region Avg IOU: 0.492396, Class: 1.000000, Obj: 0.428439, No Obj: 0.005901, Avg Recall: 0.540323, count: 124 -Region Avg IOU: 0.542175, Class: 1.000000, Obj: 0.454834, No Obj: 0.004375, Avg Recall: 0.612500, count: 80 -Region Avg IOU: 0.531864, Class: 1.000000, Obj: 0.445099, No Obj: 0.005193, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.501203, Class: 1.000000, Obj: 0.394798, No Obj: 0.004179, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.543767, Class: 1.000000, Obj: 0.445487, No Obj: 0.004866, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.487613, Class: 1.000000, Obj: 0.413190, No Obj: 0.005350, Avg Recall: 0.538462, count: 143 -Region Avg IOU: 0.518772, Class: 1.000000, Obj: 0.405575, No Obj: 0.004999, Avg Recall: 0.591667, count: 120 -4688: 0.444861, 0.453547 avg, 0.001000 rate, 19.337412 seconds, 1200128 images -Loaded: 3.161329 seconds -Region Avg IOU: 0.540395, Class: 1.000000, Obj: 0.408899, No Obj: 0.004962, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.504240, Class: 1.000000, Obj: 0.457455, No Obj: 0.004967, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.528982, Class: 1.000000, Obj: 0.403618, No Obj: 0.005972, Avg Recall: 0.622378, count: 143 -Region Avg IOU: 0.569453, Class: 1.000000, Obj: 0.452237, No Obj: 0.005217, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.522178, Class: 1.000000, Obj: 0.424005, No Obj: 0.005162, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.487799, Class: 1.000000, Obj: 0.388134, No Obj: 0.004780, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.564376, Class: 1.000000, Obj: 0.461211, No Obj: 0.004675, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.517974, Class: 1.000000, Obj: 0.443521, No Obj: 0.005494, Avg Recall: 0.570093, count: 107 -4689: 0.446613, 0.452854 avg, 0.001000 rate, 20.035164 seconds, 1200384 images -Loaded: 1.720344 seconds -Region Avg IOU: 0.538479, Class: 1.000000, Obj: 0.413063, No Obj: 0.004723, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.522290, Class: 1.000000, Obj: 0.372095, No Obj: 0.005142, Avg Recall: 0.616541, count: 133 -Region Avg IOU: 0.557128, Class: 1.000000, Obj: 0.489967, No Obj: 0.005116, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.496768, Class: 1.000000, Obj: 0.385744, No Obj: 0.004520, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.469390, Class: 1.000000, Obj: 0.360788, No Obj: 0.004862, Avg Recall: 0.529851, count: 134 -Region Avg IOU: 0.539732, Class: 1.000000, Obj: 0.404541, No Obj: 0.004633, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.557011, Class: 1.000000, Obj: 0.415689, No Obj: 0.005092, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.523846, Class: 1.000000, Obj: 0.408107, No Obj: 0.004259, Avg Recall: 0.615385, count: 104 -4690: 0.430118, 0.450580 avg, 0.001000 rate, 21.716885 seconds, 1200640 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 0.197477 seconds -Region Avg IOU: 0.517991, Class: 1.000000, Obj: 0.425847, No Obj: 0.004211, Avg Recall: 0.590551, count: 127 -Region Avg IOU: 0.540154, Class: 1.000000, Obj: 0.432705, No Obj: 0.004056, Avg Recall: 0.592920, count: 113 -Region Avg IOU: 0.602274, Class: 1.000000, Obj: 0.458577, No Obj: 0.004297, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.510309, Class: 1.000000, Obj: 0.385066, No Obj: 0.004118, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.523950, Class: 1.000000, Obj: 0.423746, No Obj: 0.003943, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.545654, Class: 1.000000, Obj: 0.419663, No Obj: 0.003533, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.535079, Class: 1.000000, Obj: 0.406784, No Obj: 0.004350, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.567033, Class: 1.000000, Obj: 0.386944, No Obj: 0.004111, Avg Recall: 0.706522, count: 92 -4691: 0.410096, 0.446532 avg, 0.001000 rate, 22.996464 seconds, 1200896 images -Loaded: 0.037530 seconds -Region Avg IOU: 0.541441, Class: 1.000000, Obj: 0.457594, No Obj: 0.003672, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.583580, Class: 1.000000, Obj: 0.464989, No Obj: 0.003471, Avg Recall: 0.694118, count: 85 -Region Avg IOU: 0.601168, Class: 1.000000, Obj: 0.396586, No Obj: 0.004584, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.557731, Class: 1.000000, Obj: 0.388483, No Obj: 0.003894, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.548487, Class: 1.000000, Obj: 0.400058, No Obj: 0.004200, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.532614, Class: 1.000000, Obj: 0.400765, No Obj: 0.003175, Avg Recall: 0.632184, count: 87 -Region Avg IOU: 0.527644, Class: 1.000000, Obj: 0.360873, No Obj: 0.003641, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.582963, Class: 1.000000, Obj: 0.456633, No Obj: 0.003729, Avg Recall: 0.728155, count: 103 -4692: 0.378146, 0.439693 avg, 0.001000 rate, 23.431068 seconds, 1201152 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.569898, Class: 1.000000, Obj: 0.360713, No Obj: 0.004024, Avg Recall: 0.675214, count: 117 -Region Avg IOU: 0.560037, Class: 1.000000, Obj: 0.357139, No Obj: 0.003570, Avg Recall: 0.645669, count: 127 -Region Avg IOU: 0.596397, Class: 1.000000, Obj: 0.396705, No Obj: 0.003318, Avg Recall: 0.696203, count: 79 -Region Avg IOU: 0.539745, Class: 1.000000, Obj: 0.357452, No Obj: 0.003740, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.510915, Class: 1.000000, Obj: 0.350183, No Obj: 0.003558, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.526079, Class: 1.000000, Obj: 0.365217, No Obj: 0.003916, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.616571, Class: 1.000000, Obj: 0.441841, No Obj: 0.003774, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.596465, Class: 1.000000, Obj: 0.413053, No Obj: 0.003614, Avg Recall: 0.724138, count: 116 -4693: 0.452427, 0.440966 avg, 0.001000 rate, 22.639734 seconds, 1201408 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.520149, Class: 1.000000, Obj: 0.368118, No Obj: 0.002925, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.540282, Class: 1.000000, Obj: 0.337146, No Obj: 0.003044, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.525804, Class: 1.000000, Obj: 0.320460, No Obj: 0.003761, Avg Recall: 0.563025, count: 119 -Region Avg IOU: 0.571036, Class: 1.000000, Obj: 0.342214, No Obj: 0.003519, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.553335, Class: 1.000000, Obj: 0.366974, No Obj: 0.003591, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.583536, Class: 1.000000, Obj: 0.395924, No Obj: 0.003750, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.510363, Class: 1.000000, Obj: 0.317265, No Obj: 0.003039, Avg Recall: 0.588235, count: 119 -Region Avg IOU: 0.543918, Class: 1.000000, Obj: 0.343165, No Obj: 0.003443, Avg Recall: 0.666667, count: 120 -4694: 0.472671, 0.444137 avg, 0.001000 rate, 22.746059 seconds, 1201664 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.557801, Class: 1.000000, Obj: 0.300886, No Obj: 0.003209, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.535403, Class: 1.000000, Obj: 0.348010, No Obj: 0.002776, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.568481, Class: 1.000000, Obj: 0.311852, No Obj: 0.003218, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.614243, Class: 1.000000, Obj: 0.373902, No Obj: 0.003494, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.589567, Class: 1.000000, Obj: 0.351730, No Obj: 0.003234, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.592878, Class: 1.000000, Obj: 0.358054, No Obj: 0.003637, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.552577, Class: 1.000000, Obj: 0.328315, No Obj: 0.003092, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.592823, Class: 1.000000, Obj: 0.378566, No Obj: 0.003388, Avg Recall: 0.681416, count: 113 -4695: 0.425525, 0.442276 avg, 0.001000 rate, 23.780436 seconds, 1201920 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.587725, Class: 1.000000, Obj: 0.358517, No Obj: 0.003202, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.541677, Class: 1.000000, Obj: 0.300550, No Obj: 0.003207, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.556483, Class: 1.000000, Obj: 0.305555, No Obj: 0.003271, Avg Recall: 0.669421, count: 121 -Region Avg IOU: 0.553928, Class: 1.000000, Obj: 0.361631, No Obj: 0.002953, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.570386, Class: 1.000000, Obj: 0.343749, No Obj: 0.002886, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.593021, Class: 1.000000, Obj: 0.338298, No Obj: 0.003946, Avg Recall: 0.715328, count: 137 -Region Avg IOU: 0.618364, Class: 1.000000, Obj: 0.332697, No Obj: 0.003315, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.543072, Class: 1.000000, Obj: 0.294484, No Obj: 0.002801, Avg Recall: 0.601770, count: 113 -4696: 0.524159, 0.450464 avg, 0.001000 rate, 23.784653 seconds, 1202176 images -Loaded: 0.007146 seconds -Region Avg IOU: 0.541425, Class: 1.000000, Obj: 0.305808, No Obj: 0.003466, Avg Recall: 0.609023, count: 133 -Region Avg IOU: 0.550618, Class: 1.000000, Obj: 0.289371, No Obj: 0.003662, Avg Recall: 0.634146, count: 123 -Region Avg IOU: 0.541787, Class: 1.000000, Obj: 0.259146, No Obj: 0.002869, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.595679, Class: 1.000000, Obj: 0.346095, No Obj: 0.003467, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.580882, Class: 1.000000, Obj: 0.338524, No Obj: 0.003712, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.538246, Class: 1.000000, Obj: 0.310965, No Obj: 0.003390, Avg Recall: 0.592593, count: 108 -Region Avg IOU: 0.537127, Class: 1.000000, Obj: 0.341970, No Obj: 0.003096, Avg Recall: 0.644628, count: 121 -Region Avg IOU: 0.569092, Class: 1.000000, Obj: 0.308216, No Obj: 0.004153, Avg Recall: 0.659091, count: 132 -4697: 0.513630, 0.456781 avg, 0.001000 rate, 22.482084 seconds, 1202432 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.536495, Class: 1.000000, Obj: 0.318890, No Obj: 0.003365, Avg Recall: 0.589147, count: 129 -Region Avg IOU: 0.581128, Class: 1.000000, Obj: 0.352081, No Obj: 0.002984, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.580527, Class: 1.000000, Obj: 0.364917, No Obj: 0.002934, Avg Recall: 0.666667, count: 87 -Region Avg IOU: 0.573922, Class: 1.000000, Obj: 0.304950, No Obj: 0.003383, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.532966, Class: 1.000000, Obj: 0.335229, No Obj: 0.002881, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.559464, Class: 1.000000, Obj: 0.336649, No Obj: 0.003001, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.540173, Class: 1.000000, Obj: 0.285971, No Obj: 0.003212, Avg Recall: 0.627737, count: 137 -Region Avg IOU: 0.618596, Class: 1.000000, Obj: 0.397332, No Obj: 0.002926, Avg Recall: 0.739726, count: 73 -4698: 0.454616, 0.456564 avg, 0.001000 rate, 22.816429 seconds, 1202688 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.586011, Class: 1.000000, Obj: 0.389494, No Obj: 0.004150, Avg Recall: 0.642857, count: 126 -Region Avg IOU: 0.527841, Class: 1.000000, Obj: 0.366745, No Obj: 0.003395, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.599244, Class: 1.000000, Obj: 0.327488, No Obj: 0.003866, Avg Recall: 0.748031, count: 127 -Region Avg IOU: 0.515634, Class: 1.000000, Obj: 0.321074, No Obj: 0.003441, Avg Recall: 0.568965, count: 116 -Region Avg IOU: 0.551391, Class: 1.000000, Obj: 0.361120, No Obj: 0.002989, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.618479, Class: 1.000000, Obj: 0.364267, No Obj: 0.002882, Avg Recall: 0.805195, count: 77 -Region Avg IOU: 0.547464, Class: 1.000000, Obj: 0.359692, No Obj: 0.003368, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.587182, Class: 1.000000, Obj: 0.393696, No Obj: 0.003346, Avg Recall: 0.683673, count: 98 -4699: 0.438251, 0.454733 avg, 0.001000 rate, 22.211294 seconds, 1202944 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.545711, Class: 1.000000, Obj: 0.341300, No Obj: 0.003925, Avg Recall: 0.635659, count: 129 -Region Avg IOU: 0.559291, Class: 1.000000, Obj: 0.389001, No Obj: 0.003583, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.542281, Class: 1.000000, Obj: 0.330484, No Obj: 0.003683, Avg Recall: 0.572650, count: 117 -Region Avg IOU: 0.563910, Class: 1.000000, Obj: 0.374876, No Obj: 0.004090, Avg Recall: 0.644444, count: 135 -Region Avg IOU: 0.537317, Class: 1.000000, Obj: 0.371622, No Obj: 0.003074, Avg Recall: 0.643836, count: 73 -Region Avg IOU: 0.608799, Class: 1.000000, Obj: 0.405275, No Obj: 0.004013, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.559808, Class: 1.000000, Obj: 0.379464, No Obj: 0.003917, Avg Recall: 0.656000, count: 125 -Region Avg IOU: 0.578342, Class: 1.000000, Obj: 0.366073, No Obj: 0.003920, Avg Recall: 0.703125, count: 128 -4700: 0.441380, 0.453398 avg, 0.001000 rate, 23.066975 seconds, 1203200 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000071 seconds -Region Avg IOU: 0.628888, Class: 1.000000, Obj: 0.431491, No Obj: 0.002894, Avg Recall: 0.788136, count: 118 -Region Avg IOU: 0.688973, Class: 1.000000, Obj: 0.408208, No Obj: 0.003228, Avg Recall: 0.897196, count: 107 -Region Avg IOU: 0.602556, Class: 1.000000, Obj: 0.379786, No Obj: 0.003089, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.667460, Class: 1.000000, Obj: 0.452552, No Obj: 0.002975, Avg Recall: 0.831683, count: 101 -Region Avg IOU: 0.686085, Class: 1.000000, Obj: 0.438793, No Obj: 0.003247, Avg Recall: 0.827586, count: 116 -Region Avg IOU: 0.650196, Class: 1.000000, Obj: 0.430158, No Obj: 0.003478, Avg Recall: 0.825503, count: 149 -Region Avg IOU: 0.672661, Class: 1.000000, Obj: 0.465296, No Obj: 0.002919, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.660916, Class: 1.000000, Obj: 0.356528, No Obj: 0.003375, Avg Recall: 0.828571, count: 105 -4701: 0.498631, 0.457921 avg, 0.001000 rate, 29.132904 seconds, 1203456 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.653482, Class: 1.000000, Obj: 0.426401, No Obj: 0.003539, Avg Recall: 0.826087, count: 138 -Region Avg IOU: 0.656892, Class: 1.000000, Obj: 0.437038, No Obj: 0.002742, Avg Recall: 0.779070, count: 86 -Region Avg IOU: 0.625274, Class: 1.000000, Obj: 0.395719, No Obj: 0.003509, Avg Recall: 0.754967, count: 151 -Region Avg IOU: 0.650529, Class: 1.000000, Obj: 0.422569, No Obj: 0.002755, Avg Recall: 0.813084, count: 107 -Region Avg IOU: 0.671663, Class: 1.000000, Obj: 0.424460, No Obj: 0.002858, Avg Recall: 0.842697, count: 89 -Region Avg IOU: 0.667957, Class: 1.000000, Obj: 0.395985, No Obj: 0.002962, Avg Recall: 0.860000, count: 100 -Region Avg IOU: 0.688800, Class: 1.000000, Obj: 0.409549, No Obj: 0.003132, Avg Recall: 0.897196, count: 107 -Region Avg IOU: 0.678315, Class: 1.000000, Obj: 0.468809, No Obj: 0.002916, Avg Recall: 0.846154, count: 78 -4702: 0.425284, 0.454657 avg, 0.001000 rate, 28.539999 seconds, 1203712 images -Loaded: 0.000131 seconds -Region Avg IOU: 0.658607, Class: 1.000000, Obj: 0.390039, No Obj: 0.002465, Avg Recall: 0.836735, count: 98 -Region Avg IOU: 0.661646, Class: 1.000000, Obj: 0.429667, No Obj: 0.002739, Avg Recall: 0.825243, count: 103 -Region Avg IOU: 0.651520, Class: 1.000000, Obj: 0.390863, No Obj: 0.002638, Avg Recall: 0.847826, count: 92 -Region Avg IOU: 0.639720, Class: 1.000000, Obj: 0.358489, No Obj: 0.003229, Avg Recall: 0.813953, count: 129 -Region Avg IOU: 0.661310, Class: 1.000000, Obj: 0.437008, No Obj: 0.003117, Avg Recall: 0.841121, count: 107 -Region Avg IOU: 0.650823, Class: 1.000000, Obj: 0.431270, No Obj: 0.003063, Avg Recall: 0.789474, count: 114 -Region Avg IOU: 0.660317, Class: 1.000000, Obj: 0.422155, No Obj: 0.002698, Avg Recall: 0.829787, count: 94 -Region Avg IOU: 0.693501, Class: 1.000000, Obj: 0.465036, No Obj: 0.002452, Avg Recall: 0.868421, count: 76 -4703: 0.424392, 0.451631 avg, 0.001000 rate, 28.401169 seconds, 1203968 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.648855, Class: 1.000000, Obj: 0.395373, No Obj: 0.002729, Avg Recall: 0.820755, count: 106 -Region Avg IOU: 0.657451, Class: 1.000000, Obj: 0.406226, No Obj: 0.003048, Avg Recall: 0.824818, count: 137 -Region Avg IOU: 0.630670, Class: 1.000000, Obj: 0.386385, No Obj: 0.003001, Avg Recall: 0.754098, count: 122 -Region Avg IOU: 0.642153, Class: 1.000000, Obj: 0.374204, No Obj: 0.002685, Avg Recall: 0.860465, count: 129 -Region Avg IOU: 0.665138, Class: 1.000000, Obj: 0.424410, No Obj: 0.002696, Avg Recall: 0.843750, count: 96 -Region Avg IOU: 0.659849, Class: 1.000000, Obj: 0.351978, No Obj: 0.002664, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.646541, Class: 1.000000, Obj: 0.368913, No Obj: 0.003159, Avg Recall: 0.818182, count: 121 -Region Avg IOU: 0.648491, Class: 1.000000, Obj: 0.417750, No Obj: 0.002493, Avg Recall: 0.756098, count: 82 -4704: 0.451950, 0.451663 avg, 0.001000 rate, 30.166931 seconds, 1204224 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.611283, Class: 1.000000, Obj: 0.404290, No Obj: 0.002186, Avg Recall: 0.746988, count: 83 -Region Avg IOU: 0.663448, Class: 1.000000, Obj: 0.402033, No Obj: 0.003004, Avg Recall: 0.801587, count: 126 -Region Avg IOU: 0.626402, Class: 1.000000, Obj: 0.408486, No Obj: 0.002648, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.652608, Class: 1.000000, Obj: 0.377062, No Obj: 0.002864, Avg Recall: 0.804878, count: 123 -Region Avg IOU: 0.631729, Class: 1.000000, Obj: 0.355970, No Obj: 0.002766, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.583568, Class: 1.000000, Obj: 0.376836, No Obj: 0.002694, Avg Recall: 0.704918, count: 122 -Region Avg IOU: 0.646402, Class: 1.000000, Obj: 0.336298, No Obj: 0.002855, Avg Recall: 0.833333, count: 96 -Region Avg IOU: 0.643157, Class: 1.000000, Obj: 0.353955, No Obj: 0.002785, Avg Recall: 0.806452, count: 124 -4705: 0.495132, 0.456010 avg, 0.001000 rate, 30.587610 seconds, 1204480 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.661579, Class: 1.000000, Obj: 0.405030, No Obj: 0.002717, Avg Recall: 0.825758, count: 132 -Region Avg IOU: 0.625505, Class: 1.000000, Obj: 0.391376, No Obj: 0.002647, Avg Recall: 0.792308, count: 130 -Region Avg IOU: 0.630022, Class: 1.000000, Obj: 0.319923, No Obj: 0.002709, Avg Recall: 0.771429, count: 140 -Region Avg IOU: 0.677334, Class: 1.000000, Obj: 0.338813, No Obj: 0.002671, Avg Recall: 0.875000, count: 120 -Region Avg IOU: 0.646253, Class: 1.000000, Obj: 0.366057, No Obj: 0.002634, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.664141, Class: 1.000000, Obj: 0.401053, No Obj: 0.002678, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.622694, Class: 1.000000, Obj: 0.330789, No Obj: 0.002444, Avg Recall: 0.809091, count: 110 -Region Avg IOU: 0.643473, Class: 1.000000, Obj: 0.363678, No Obj: 0.002622, Avg Recall: 0.759399, count: 133 -4706: 0.522312, 0.462640 avg, 0.001000 rate, 28.948488 seconds, 1204736 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.677879, Class: 1.000000, Obj: 0.390507, No Obj: 0.002518, Avg Recall: 0.823009, count: 113 -Region Avg IOU: 0.633470, Class: 1.000000, Obj: 0.374961, No Obj: 0.002520, Avg Recall: 0.780488, count: 123 -Region Avg IOU: 0.631262, Class: 1.000000, Obj: 0.383630, No Obj: 0.002650, Avg Recall: 0.780822, count: 146 -Region Avg IOU: 0.673599, Class: 1.000000, Obj: 0.395819, No Obj: 0.002534, Avg Recall: 0.815126, count: 119 -Region Avg IOU: 0.636978, Class: 1.000000, Obj: 0.341392, No Obj: 0.002672, Avg Recall: 0.764286, count: 140 -Region Avg IOU: 0.623136, Class: 1.000000, Obj: 0.324800, No Obj: 0.002460, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.630096, Class: 1.000000, Obj: 0.373914, No Obj: 0.002438, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.678425, Class: 1.000000, Obj: 0.378404, No Obj: 0.002299, Avg Recall: 0.835165, count: 91 -4707: 0.526210, 0.468997 avg, 0.001000 rate, 28.886585 seconds, 1204992 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.656967, Class: 1.000000, Obj: 0.399561, No Obj: 0.002225, Avg Recall: 0.869565, count: 92 -Region Avg IOU: 0.638770, Class: 1.000000, Obj: 0.359074, No Obj: 0.002235, Avg Recall: 0.800000, count: 120 -Region Avg IOU: 0.666639, Class: 1.000000, Obj: 0.347322, No Obj: 0.002080, Avg Recall: 0.837500, count: 80 -Region Avg IOU: 0.603674, Class: 1.000000, Obj: 0.364512, No Obj: 0.002160, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.650749, Class: 1.000000, Obj: 0.335338, No Obj: 0.002438, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.649318, Class: 1.000000, Obj: 0.362027, No Obj: 0.002478, Avg Recall: 0.805310, count: 113 -Region Avg IOU: 0.639394, Class: 1.000000, Obj: 0.344489, No Obj: 0.002435, Avg Recall: 0.808696, count: 115 -Region Avg IOU: 0.654866, Class: 1.000000, Obj: 0.333283, No Obj: 0.002356, Avg Recall: 0.809091, count: 110 -4708: 0.491575, 0.471255 avg, 0.001000 rate, 28.815865 seconds, 1205248 images -Loaded: 0.000118 seconds -Region Avg IOU: 0.628470, Class: 1.000000, Obj: 0.359486, No Obj: 0.002514, Avg Recall: 0.750000, count: 124 -Region Avg IOU: 0.646980, Class: 1.000000, Obj: 0.330593, No Obj: 0.002335, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.652929, Class: 1.000000, Obj: 0.361530, No Obj: 0.002591, Avg Recall: 0.801653, count: 121 -Region Avg IOU: 0.629727, Class: 1.000000, Obj: 0.343862, No Obj: 0.002836, Avg Recall: 0.802817, count: 142 -Region Avg IOU: 0.617957, Class: 1.000000, Obj: 0.310183, No Obj: 0.002669, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.657071, Class: 1.000000, Obj: 0.339068, No Obj: 0.002041, Avg Recall: 0.835052, count: 97 -Region Avg IOU: 0.651616, Class: 1.000000, Obj: 0.410961, No Obj: 0.002794, Avg Recall: 0.826446, count: 121 -Region Avg IOU: 0.652362, Class: 1.000000, Obj: 0.344403, No Obj: 0.002490, Avg Recall: 0.777778, count: 108 -4709: 0.524394, 0.476569 avg, 0.001000 rate, 28.972511 seconds, 1205504 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.618438, Class: 1.000000, Obj: 0.345714, No Obj: 0.002786, Avg Recall: 0.708029, count: 137 -Region Avg IOU: 0.622505, Class: 1.000000, Obj: 0.359452, No Obj: 0.002235, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.594545, Class: 1.000000, Obj: 0.330630, No Obj: 0.002520, Avg Recall: 0.737931, count: 145 -Region Avg IOU: 0.666317, Class: 1.000000, Obj: 0.386185, No Obj: 0.002239, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.675531, Class: 1.000000, Obj: 0.369108, No Obj: 0.002700, Avg Recall: 0.854701, count: 117 -Region Avg IOU: 0.618165, Class: 1.000000, Obj: 0.403640, No Obj: 0.002534, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.605416, Class: 1.000000, Obj: 0.351205, No Obj: 0.002719, Avg Recall: 0.736000, count: 125 -Region Avg IOU: 0.634189, Class: 1.000000, Obj: 0.344973, No Obj: 0.002144, Avg Recall: 0.762500, count: 80 -4710: 0.491568, 0.478069 avg, 0.001000 rate, 29.890789 seconds, 1205760 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 10.887677 seconds -Region Avg IOU: 0.550339, Class: 1.000000, Obj: 0.346063, No Obj: 0.003385, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.537721, Class: 1.000000, Obj: 0.298145, No Obj: 0.003545, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.512372, Class: 1.000000, Obj: 0.290772, No Obj: 0.003017, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.577939, Class: 1.000000, Obj: 0.329743, No Obj: 0.003338, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.461381, Class: 1.000000, Obj: 0.274386, No Obj: 0.003165, Avg Recall: 0.500000, count: 116 -Region Avg IOU: 0.485913, Class: 1.000000, Obj: 0.274009, No Obj: 0.003555, Avg Recall: 0.536585, count: 123 -Region Avg IOU: 0.505862, Class: 1.000000, Obj: 0.285434, No Obj: 0.003856, Avg Recall: 0.564516, count: 124 -Region Avg IOU: 0.509738, Class: 1.000000, Obj: 0.276183, No Obj: 0.002882, Avg Recall: 0.581081, count: 74 -4711: 0.440874, 0.474349 avg, 0.001000 rate, 19.906052 seconds, 1206016 images -Loaded: 2.004952 seconds -Region Avg IOU: 0.503139, Class: 1.000000, Obj: 0.310076, No Obj: 0.003906, Avg Recall: 0.566667, count: 120 -Region Avg IOU: 0.570984, Class: 1.000000, Obj: 0.358164, No Obj: 0.003513, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.487307, Class: 1.000000, Obj: 0.253069, No Obj: 0.003468, Avg Recall: 0.529851, count: 134 -Region Avg IOU: 0.523246, Class: 1.000000, Obj: 0.291599, No Obj: 0.004099, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.560646, Class: 1.000000, Obj: 0.315269, No Obj: 0.003651, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.534656, Class: 1.000000, Obj: 0.330534, No Obj: 0.004336, Avg Recall: 0.598361, count: 122 -Region Avg IOU: 0.487462, Class: 1.000000, Obj: 0.280012, No Obj: 0.002749, Avg Recall: 0.541176, count: 85 -Region Avg IOU: 0.522543, Class: 1.000000, Obj: 0.287191, No Obj: 0.004101, Avg Recall: 0.572519, count: 131 -4712: 0.502971, 0.477211 avg, 0.001000 rate, 19.213472 seconds, 1206272 images -Loaded: 0.691376 seconds -Region Avg IOU: 0.533894, Class: 1.000000, Obj: 0.306946, No Obj: 0.004081, Avg Recall: 0.617188, count: 128 -Region Avg IOU: 0.542710, Class: 1.000000, Obj: 0.332826, No Obj: 0.003871, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.495672, Class: 1.000000, Obj: 0.318512, No Obj: 0.003793, Avg Recall: 0.524272, count: 103 -Region Avg IOU: 0.516302, Class: 1.000000, Obj: 0.288974, No Obj: 0.003207, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.563665, Class: 1.000000, Obj: 0.336432, No Obj: 0.003594, Avg Recall: 0.613636, count: 88 -Region Avg IOU: 0.505259, Class: 1.000000, Obj: 0.312235, No Obj: 0.003357, Avg Recall: 0.595506, count: 89 -Region Avg IOU: 0.517232, Class: 1.000000, Obj: 0.341523, No Obj: 0.003928, Avg Recall: 0.611570, count: 121 -Region Avg IOU: 0.541179, Class: 1.000000, Obj: 0.324453, No Obj: 0.003783, Avg Recall: 0.623762, count: 101 -4713: 0.420450, 0.471535 avg, 0.001000 rate, 20.481195 seconds, 1206528 images -Loaded: 0.648465 seconds -Region Avg IOU: 0.535630, Class: 1.000000, Obj: 0.367713, No Obj: 0.003917, Avg Recall: 0.607843, count: 102 -Region Avg IOU: 0.499543, Class: 1.000000, Obj: 0.370203, No Obj: 0.003856, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.517610, Class: 1.000000, Obj: 0.310065, No Obj: 0.004136, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.549249, Class: 1.000000, Obj: 0.366017, No Obj: 0.003961, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.535509, Class: 1.000000, Obj: 0.311433, No Obj: 0.004919, Avg Recall: 0.620968, count: 124 -Region Avg IOU: 0.510549, Class: 1.000000, Obj: 0.366508, No Obj: 0.004043, Avg Recall: 0.567568, count: 111 -Region Avg IOU: 0.510789, Class: 1.000000, Obj: 0.308864, No Obj: 0.004263, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.556468, Class: 1.000000, Obj: 0.369581, No Obj: 0.003982, Avg Recall: 0.690000, count: 100 -4714: 0.447866, 0.469168 avg, 0.001000 rate, 22.092056 seconds, 1206784 images -Loaded: 0.035672 seconds -Region Avg IOU: 0.489317, Class: 1.000000, Obj: 0.362848, No Obj: 0.003997, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.491012, Class: 1.000000, Obj: 0.322688, No Obj: 0.003888, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.541446, Class: 1.000000, Obj: 0.403607, No Obj: 0.004316, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.470105, Class: 1.000000, Obj: 0.350937, No Obj: 0.003374, Avg Recall: 0.489583, count: 96 -Region Avg IOU: 0.563446, Class: 1.000000, Obj: 0.385618, No Obj: 0.005007, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.552788, Class: 1.000000, Obj: 0.396549, No Obj: 0.003684, Avg Recall: 0.674419, count: 86 -Region Avg IOU: 0.503939, Class: 1.000000, Obj: 0.329180, No Obj: 0.004476, Avg Recall: 0.544118, count: 136 -Region Avg IOU: 0.542352, Class: 1.000000, Obj: 0.374931, No Obj: 0.004582, Avg Recall: 0.608247, count: 97 -4715: 0.398393, 0.462091 avg, 0.001000 rate, 22.017420 seconds, 1207040 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.466457, Class: 1.000000, Obj: 0.370106, No Obj: 0.003686, Avg Recall: 0.504762, count: 105 -Region Avg IOU: 0.591028, Class: 1.000000, Obj: 0.393415, No Obj: 0.004827, Avg Recall: 0.715909, count: 88 -Region Avg IOU: 0.495376, Class: 1.000000, Obj: 0.342999, No Obj: 0.004237, Avg Recall: 0.540000, count: 100 -Region Avg IOU: 0.504556, Class: 1.000000, Obj: 0.414977, No Obj: 0.004268, Avg Recall: 0.590909, count: 88 -Region Avg IOU: 0.527149, Class: 1.000000, Obj: 0.376107, No Obj: 0.004971, Avg Recall: 0.594828, count: 116 -Region Avg IOU: 0.542234, Class: 1.000000, Obj: 0.400538, No Obj: 0.005145, Avg Recall: 0.596491, count: 114 -Region Avg IOU: 0.509700, Class: 1.000000, Obj: 0.393241, No Obj: 0.004321, Avg Recall: 0.593750, count: 96 -Region Avg IOU: 0.561141, Class: 1.000000, Obj: 0.427766, No Obj: 0.004457, Avg Recall: 0.625000, count: 96 -4716: 0.481699, 0.464051 avg, 0.001000 rate, 22.358280 seconds, 1207296 images -Loaded: 0.044121 seconds -Region Avg IOU: 0.502797, Class: 1.000000, Obj: 0.424832, No Obj: 0.005022, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.484828, Class: 1.000000, Obj: 0.399330, No Obj: 0.005784, Avg Recall: 0.507143, count: 140 -Region Avg IOU: 0.526669, Class: 1.000000, Obj: 0.374270, No Obj: 0.004472, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.550997, Class: 1.000000, Obj: 0.368302, No Obj: 0.004776, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.470114, Class: 1.000000, Obj: 0.389926, No Obj: 0.004999, Avg Recall: 0.491667, count: 120 -Region Avg IOU: 0.520965, Class: 1.000000, Obj: 0.372427, No Obj: 0.004753, Avg Recall: 0.575758, count: 99 -Region Avg IOU: 0.503999, Class: 1.000000, Obj: 0.426015, No Obj: 0.004414, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.530420, Class: 1.000000, Obj: 0.402847, No Obj: 0.004873, Avg Recall: 0.637931, count: 116 -4717: 0.485896, 0.466236 avg, 0.001000 rate, 18.478037 seconds, 1207552 images -Loaded: 1.782635 seconds -Region Avg IOU: 0.533031, Class: 1.000000, Obj: 0.420371, No Obj: 0.004674, Avg Recall: 0.617284, count: 81 -Region Avg IOU: 0.564374, Class: 1.000000, Obj: 0.405244, No Obj: 0.004578, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.496788, Class: 1.000000, Obj: 0.383205, No Obj: 0.005067, Avg Recall: 0.551181, count: 127 -Region Avg IOU: 0.553922, Class: 1.000000, Obj: 0.406875, No Obj: 0.004332, Avg Recall: 0.600000, count: 85 -Region Avg IOU: 0.525805, Class: 1.000000, Obj: 0.394035, No Obj: 0.005188, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.525632, Class: 1.000000, Obj: 0.391995, No Obj: 0.004908, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.502994, Class: 1.000000, Obj: 0.394207, No Obj: 0.004202, Avg Recall: 0.511364, count: 88 -Region Avg IOU: 0.559722, Class: 1.000000, Obj: 0.460782, No Obj: 0.005978, Avg Recall: 0.669565, count: 115 -4718: 0.455301, 0.465142 avg, 0.001000 rate, 20.352787 seconds, 1207808 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.537925, Class: 1.000000, Obj: 0.409099, No Obj: 0.004759, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.539727, Class: 1.000000, Obj: 0.429952, No Obj: 0.005113, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.505104, Class: 1.000000, Obj: 0.400178, No Obj: 0.004373, Avg Recall: 0.572581, count: 124 -Region Avg IOU: 0.567685, Class: 1.000000, Obj: 0.407329, No Obj: 0.004937, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.581156, Class: 1.000000, Obj: 0.441850, No Obj: 0.004976, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.503935, Class: 1.000000, Obj: 0.422375, No Obj: 0.004206, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.493307, Class: 1.000000, Obj: 0.405003, No Obj: 0.004288, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.511810, Class: 1.000000, Obj: 0.400196, No Obj: 0.004406, Avg Recall: 0.571429, count: 105 -4719: 0.443154, 0.462943 avg, 0.001000 rate, 20.111320 seconds, 1208064 images -Loaded: 1.282983 seconds -Region Avg IOU: 0.549520, Class: 1.000000, Obj: 0.422005, No Obj: 0.004228, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.532850, Class: 1.000000, Obj: 0.388339, No Obj: 0.004673, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.507086, Class: 1.000000, Obj: 0.376567, No Obj: 0.004656, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.527719, Class: 1.000000, Obj: 0.421938, No Obj: 0.005090, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.472759, Class: 1.000000, Obj: 0.379171, No Obj: 0.004377, Avg Recall: 0.479167, count: 96 -Region Avg IOU: 0.507315, Class: 1.000000, Obj: 0.395768, No Obj: 0.005678, Avg Recall: 0.519380, count: 129 -Region Avg IOU: 0.523596, Class: 1.000000, Obj: 0.444000, No Obj: 0.005255, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.536886, Class: 1.000000, Obj: 0.413847, No Obj: 0.004966, Avg Recall: 0.607477, count: 107 -4720: 0.443175, 0.460967 avg, 0.001000 rate, 21.904631 seconds, 1208320 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000090 seconds -Region Avg IOU: 0.632407, Class: 1.000000, Obj: 0.457588, No Obj: 0.004183, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.570357, Class: 1.000000, Obj: 0.468465, No Obj: 0.003058, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.617655, Class: 1.000000, Obj: 0.448285, No Obj: 0.003609, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.628969, Class: 1.000000, Obj: 0.422338, No Obj: 0.004321, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.587337, Class: 1.000000, Obj: 0.439309, No Obj: 0.003779, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.599210, Class: 1.000000, Obj: 0.476355, No Obj: 0.003841, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.572935, Class: 1.000000, Obj: 0.434020, No Obj: 0.003144, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.564460, Class: 1.000000, Obj: 0.471258, No Obj: 0.003867, Avg Recall: 0.625000, count: 112 -4721: 0.397636, 0.454634 avg, 0.001000 rate, 25.902624 seconds, 1208576 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.629280, Class: 1.000000, Obj: 0.446955, No Obj: 0.002962, Avg Recall: 0.781609, count: 87 -Region Avg IOU: 0.597616, Class: 1.000000, Obj: 0.441051, No Obj: 0.003416, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.616646, Class: 1.000000, Obj: 0.340085, No Obj: 0.003183, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.569033, Class: 1.000000, Obj: 0.394441, No Obj: 0.003001, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.567626, Class: 1.000000, Obj: 0.393669, No Obj: 0.003577, Avg Recall: 0.658537, count: 123 -Region Avg IOU: 0.578939, Class: 1.000000, Obj: 0.392404, No Obj: 0.003691, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.610340, Class: 1.000000, Obj: 0.418790, No Obj: 0.003788, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.635901, Class: 1.000000, Obj: 0.443402, No Obj: 0.003277, Avg Recall: 0.785714, count: 98 -4722: 0.444325, 0.453603 avg, 0.001000 rate, 25.680437 seconds, 1208832 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.578530, Class: 1.000000, Obj: 0.496824, No Obj: 0.002904, Avg Recall: 0.704545, count: 88 -Region Avg IOU: 0.598502, Class: 1.000000, Obj: 0.399874, No Obj: 0.003135, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.603466, Class: 1.000000, Obj: 0.428977, No Obj: 0.003134, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.617173, Class: 1.000000, Obj: 0.390434, No Obj: 0.002904, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.594778, Class: 1.000000, Obj: 0.368288, No Obj: 0.003160, Avg Recall: 0.686956, count: 115 -Region Avg IOU: 0.604409, Class: 1.000000, Obj: 0.403730, No Obj: 0.003452, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.615305, Class: 1.000000, Obj: 0.376893, No Obj: 0.002797, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.570003, Class: 1.000000, Obj: 0.367122, No Obj: 0.002658, Avg Recall: 0.683168, count: 101 -4723: 0.407098, 0.448952 avg, 0.001000 rate, 25.944181 seconds, 1209088 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.589423, Class: 1.000000, Obj: 0.403087, No Obj: 0.002899, Avg Recall: 0.712329, count: 73 -Region Avg IOU: 0.605570, Class: 1.000000, Obj: 0.387465, No Obj: 0.003054, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.563978, Class: 1.000000, Obj: 0.366864, No Obj: 0.002798, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.627463, Class: 1.000000, Obj: 0.375828, No Obj: 0.002999, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.612482, Class: 1.000000, Obj: 0.396952, No Obj: 0.002884, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.606651, Class: 1.000000, Obj: 0.411329, No Obj: 0.002942, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.582903, Class: 1.000000, Obj: 0.388539, No Obj: 0.002739, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.629275, Class: 1.000000, Obj: 0.422885, No Obj: 0.003186, Avg Recall: 0.777778, count: 108 -4724: 0.414244, 0.445481 avg, 0.001000 rate, 25.683619 seconds, 1209344 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.642187, Class: 1.000000, Obj: 0.404115, No Obj: 0.003462, Avg Recall: 0.773438, count: 128 -Region Avg IOU: 0.612736, Class: 1.000000, Obj: 0.365055, No Obj: 0.003032, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.622084, Class: 1.000000, Obj: 0.354863, No Obj: 0.002906, Avg Recall: 0.776000, count: 125 -Region Avg IOU: 0.596112, Class: 1.000000, Obj: 0.339938, No Obj: 0.002825, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.606761, Class: 1.000000, Obj: 0.354721, No Obj: 0.003020, Avg Recall: 0.768000, count: 125 -Region Avg IOU: 0.579714, Class: 1.000000, Obj: 0.330298, No Obj: 0.002441, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.590489, Class: 1.000000, Obj: 0.379059, No Obj: 0.002649, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.587588, Class: 1.000000, Obj: 0.360939, No Obj: 0.002477, Avg Recall: 0.702703, count: 111 -4725: 0.465636, 0.447497 avg, 0.001000 rate, 26.223444 seconds, 1209600 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.599205, Class: 1.000000, Obj: 0.328234, No Obj: 0.002749, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.574252, Class: 1.000000, Obj: 0.315673, No Obj: 0.002514, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.598849, Class: 1.000000, Obj: 0.374152, No Obj: 0.002272, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.607398, Class: 1.000000, Obj: 0.373017, No Obj: 0.003109, Avg Recall: 0.706667, count: 150 -Region Avg IOU: 0.617236, Class: 1.000000, Obj: 0.366064, No Obj: 0.002823, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.609500, Class: 1.000000, Obj: 0.345917, No Obj: 0.002540, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.620499, Class: 1.000000, Obj: 0.341737, No Obj: 0.002392, Avg Recall: 0.734940, count: 83 -Region Avg IOU: 0.604748, Class: 1.000000, Obj: 0.344799, No Obj: 0.002807, Avg Recall: 0.730435, count: 115 -4726: 0.493379, 0.452085 avg, 0.001000 rate, 26.586861 seconds, 1209856 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.623776, Class: 1.000000, Obj: 0.352806, No Obj: 0.002790, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.609752, Class: 1.000000, Obj: 0.337906, No Obj: 0.002648, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.592228, Class: 1.000000, Obj: 0.309601, No Obj: 0.002601, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.648046, Class: 1.000000, Obj: 0.326804, No Obj: 0.002661, Avg Recall: 0.796117, count: 103 -Region Avg IOU: 0.613404, Class: 1.000000, Obj: 0.341106, No Obj: 0.002583, Avg Recall: 0.768595, count: 121 -Region Avg IOU: 0.641915, Class: 1.000000, Obj: 0.334958, No Obj: 0.002655, Avg Recall: 0.805310, count: 113 -Region Avg IOU: 0.639263, Class: 1.000000, Obj: 0.359254, No Obj: 0.002711, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.653681, Class: 1.000000, Obj: 0.363361, No Obj: 0.002908, Avg Recall: 0.798319, count: 119 -4727: 0.473729, 0.454249 avg, 0.001000 rate, 25.957092 seconds, 1210112 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.603141, Class: 1.000000, Obj: 0.348681, No Obj: 0.002441, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.617387, Class: 1.000000, Obj: 0.390154, No Obj: 0.002688, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.625188, Class: 1.000000, Obj: 0.352384, No Obj: 0.002764, Avg Recall: 0.735537, count: 121 -Region Avg IOU: 0.589370, Class: 1.000000, Obj: 0.335846, No Obj: 0.003004, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.599763, Class: 1.000000, Obj: 0.332868, No Obj: 0.002670, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.610174, Class: 1.000000, Obj: 0.335110, No Obj: 0.002936, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.640854, Class: 1.000000, Obj: 0.345181, No Obj: 0.002680, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.612559, Class: 1.000000, Obj: 0.376912, No Obj: 0.002773, Avg Recall: 0.716981, count: 106 -4728: 0.437086, 0.452533 avg, 0.001000 rate, 26.792828 seconds, 1210368 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.609750, Class: 1.000000, Obj: 0.358869, No Obj: 0.003427, Avg Recall: 0.762590, count: 139 -Region Avg IOU: 0.616680, Class: 1.000000, Obj: 0.387857, No Obj: 0.002979, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.606663, Class: 1.000000, Obj: 0.386084, No Obj: 0.003064, Avg Recall: 0.689076, count: 119 -Region Avg IOU: 0.580658, Class: 1.000000, Obj: 0.385352, No Obj: 0.002701, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.583864, Class: 1.000000, Obj: 0.358851, No Obj: 0.003198, Avg Recall: 0.689922, count: 129 -Region Avg IOU: 0.605559, Class: 1.000000, Obj: 0.367361, No Obj: 0.002682, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.603479, Class: 1.000000, Obj: 0.338254, No Obj: 0.002582, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.622204, Class: 1.000000, Obj: 0.355986, No Obj: 0.003166, Avg Recall: 0.744361, count: 133 -4729: 0.449350, 0.452215 avg, 0.001000 rate, 26.347441 seconds, 1210624 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.622860, Class: 1.000000, Obj: 0.393795, No Obj: 0.002959, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.612772, Class: 1.000000, Obj: 0.391688, No Obj: 0.002823, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.648903, Class: 1.000000, Obj: 0.408198, No Obj: 0.002510, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.571495, Class: 1.000000, Obj: 0.324088, No Obj: 0.002767, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.623103, Class: 1.000000, Obj: 0.358044, No Obj: 0.002763, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.598132, Class: 1.000000, Obj: 0.329582, No Obj: 0.003268, Avg Recall: 0.727273, count: 132 -Region Avg IOU: 0.616382, Class: 1.000000, Obj: 0.365930, No Obj: 0.002719, Avg Recall: 0.779221, count: 77 -Region Avg IOU: 0.610023, Class: 1.000000, Obj: 0.391859, No Obj: 0.002651, Avg Recall: 0.701923, count: 104 -4730: 0.388107, 0.445804 avg, 0.001000 rate, 24.750744 seconds, 1210880 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000074 seconds -Region Avg IOU: 0.627419, Class: 1.000000, Obj: 0.409046, No Obj: 0.003015, Avg Recall: 0.789062, count: 128 -Region Avg IOU: 0.637837, Class: 1.000000, Obj: 0.430561, No Obj: 0.002666, Avg Recall: 0.805195, count: 77 -Region Avg IOU: 0.628144, Class: 1.000000, Obj: 0.432011, No Obj: 0.002796, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.609012, Class: 1.000000, Obj: 0.400330, No Obj: 0.002710, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.577467, Class: 1.000000, Obj: 0.381615, No Obj: 0.002365, Avg Recall: 0.716049, count: 81 -Region Avg IOU: 0.632190, Class: 1.000000, Obj: 0.398996, No Obj: 0.003000, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.622751, Class: 1.000000, Obj: 0.386521, No Obj: 0.002873, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.620053, Class: 1.000000, Obj: 0.391529, No Obj: 0.002867, Avg Recall: 0.761905, count: 105 -4731: 0.365374, 0.437761 avg, 0.001000 rate, 26.425232 seconds, 1211136 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.648787, Class: 1.000000, Obj: 0.394287, No Obj: 0.003150, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.626881, Class: 1.000000, Obj: 0.396615, No Obj: 0.003163, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.634452, Class: 1.000000, Obj: 0.435333, No Obj: 0.003659, Avg Recall: 0.770992, count: 131 -Region Avg IOU: 0.599852, Class: 1.000000, Obj: 0.403298, No Obj: 0.002874, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.636340, Class: 1.000000, Obj: 0.441061, No Obj: 0.003584, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.629612, Class: 1.000000, Obj: 0.451176, No Obj: 0.002728, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.598697, Class: 1.000000, Obj: 0.374232, No Obj: 0.003098, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.673453, Class: 1.000000, Obj: 0.473662, No Obj: 0.003375, Avg Recall: 0.864865, count: 111 -4732: 0.435903, 0.437575 avg, 0.001000 rate, 27.028320 seconds, 1211392 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.578860, Class: 1.000000, Obj: 0.394169, No Obj: 0.003679, Avg Recall: 0.686131, count: 137 -Region Avg IOU: 0.623031, Class: 1.000000, Obj: 0.416328, No Obj: 0.003329, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.639656, Class: 1.000000, Obj: 0.424614, No Obj: 0.003490, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.601263, Class: 1.000000, Obj: 0.447894, No Obj: 0.002974, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.629292, Class: 1.000000, Obj: 0.433433, No Obj: 0.003842, Avg Recall: 0.724638, count: 138 -Region Avg IOU: 0.650231, Class: 1.000000, Obj: 0.394654, No Obj: 0.003431, Avg Recall: 0.824074, count: 108 -Region Avg IOU: 0.664110, Class: 1.000000, Obj: 0.424993, No Obj: 0.003046, Avg Recall: 0.838710, count: 93 -Region Avg IOU: 0.547728, Class: 1.000000, Obj: 0.396450, No Obj: 0.002621, Avg Recall: 0.584906, count: 106 -4733: 0.442768, 0.438095 avg, 0.001000 rate, 27.026577 seconds, 1211648 images -Loaded: 0.000117 seconds -Region Avg IOU: 0.638343, Class: 1.000000, Obj: 0.434696, No Obj: 0.003514, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.618161, Class: 1.000000, Obj: 0.425413, No Obj: 0.002735, Avg Recall: 0.723684, count: 76 -Region Avg IOU: 0.620631, Class: 1.000000, Obj: 0.426851, No Obj: 0.003322, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.611774, Class: 1.000000, Obj: 0.435989, No Obj: 0.003099, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.609270, Class: 1.000000, Obj: 0.428480, No Obj: 0.003844, Avg Recall: 0.703125, count: 128 -Region Avg IOU: 0.592065, Class: 1.000000, Obj: 0.406389, No Obj: 0.003507, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.640841, Class: 1.000000, Obj: 0.451105, No Obj: 0.003728, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.561585, Class: 1.000000, Obj: 0.401684, No Obj: 0.003643, Avg Recall: 0.647887, count: 142 -4734: 0.432806, 0.437566 avg, 0.001000 rate, 25.661123 seconds, 1211904 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.640417, Class: 1.000000, Obj: 0.422362, No Obj: 0.002809, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.595721, Class: 1.000000, Obj: 0.442901, No Obj: 0.003422, Avg Recall: 0.714286, count: 126 -Region Avg IOU: 0.602007, Class: 1.000000, Obj: 0.424032, No Obj: 0.003499, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.580942, Class: 1.000000, Obj: 0.372697, No Obj: 0.003000, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.668367, Class: 1.000000, Obj: 0.477927, No Obj: 0.003245, Avg Recall: 0.855422, count: 83 -Region Avg IOU: 0.634463, Class: 1.000000, Obj: 0.438231, No Obj: 0.002539, Avg Recall: 0.743243, count: 74 -Region Avg IOU: 0.608303, Class: 1.000000, Obj: 0.463911, No Obj: 0.003126, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.632677, Class: 1.000000, Obj: 0.417338, No Obj: 0.003317, Avg Recall: 0.780488, count: 123 -4735: 0.422446, 0.436054 avg, 0.001000 rate, 26.453341 seconds, 1212160 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.650101, Class: 1.000000, Obj: 0.460255, No Obj: 0.003390, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.620505, Class: 1.000000, Obj: 0.445095, No Obj: 0.003006, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.599054, Class: 1.000000, Obj: 0.403971, No Obj: 0.003008, Avg Recall: 0.678571, count: 84 -Region Avg IOU: 0.646899, Class: 1.000000, Obj: 0.447814, No Obj: 0.003494, Avg Recall: 0.805310, count: 113 -Region Avg IOU: 0.625838, Class: 1.000000, Obj: 0.433594, No Obj: 0.003668, Avg Recall: 0.804878, count: 123 -Region Avg IOU: 0.628422, Class: 1.000000, Obj: 0.445757, No Obj: 0.003154, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.678098, Class: 1.000000, Obj: 0.423257, No Obj: 0.003614, Avg Recall: 0.845528, count: 123 -Region Avg IOU: 0.621799, Class: 1.000000, Obj: 0.381120, No Obj: 0.003259, Avg Recall: 0.736264, count: 91 -4736: 0.419709, 0.434419 avg, 0.001000 rate, 27.874723 seconds, 1212416 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.622515, Class: 1.000000, Obj: 0.351760, No Obj: 0.002905, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.657294, Class: 1.000000, Obj: 0.453069, No Obj: 0.002770, Avg Recall: 0.804598, count: 87 -Region Avg IOU: 0.634544, Class: 1.000000, Obj: 0.414132, No Obj: 0.003171, Avg Recall: 0.746154, count: 130 -Region Avg IOU: 0.651587, Class: 1.000000, Obj: 0.461908, No Obj: 0.003190, Avg Recall: 0.830000, count: 100 -Region Avg IOU: 0.646391, Class: 1.000000, Obj: 0.441616, No Obj: 0.003769, Avg Recall: 0.800000, count: 140 -Region Avg IOU: 0.630698, Class: 1.000000, Obj: 0.356555, No Obj: 0.002744, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.620866, Class: 1.000000, Obj: 0.394567, No Obj: 0.003578, Avg Recall: 0.767606, count: 142 -Region Avg IOU: 0.645564, Class: 1.000000, Obj: 0.380920, No Obj: 0.003218, Avg Recall: 0.788991, count: 109 -4737: 0.460814, 0.437059 avg, 0.001000 rate, 26.447233 seconds, 1212672 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.639508, Class: 1.000000, Obj: 0.391781, No Obj: 0.002831, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.624406, Class: 1.000000, Obj: 0.409657, No Obj: 0.003088, Avg Recall: 0.762295, count: 122 -Region Avg IOU: 0.640896, Class: 1.000000, Obj: 0.411459, No Obj: 0.003159, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.618121, Class: 1.000000, Obj: 0.379222, No Obj: 0.003186, Avg Recall: 0.729508, count: 122 -Region Avg IOU: 0.622409, Class: 1.000000, Obj: 0.348467, No Obj: 0.003200, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.624844, Class: 1.000000, Obj: 0.370213, No Obj: 0.002925, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.625154, Class: 1.000000, Obj: 0.379783, No Obj: 0.003185, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.612900, Class: 1.000000, Obj: 0.378617, No Obj: 0.003304, Avg Recall: 0.729508, count: 122 -4738: 0.475109, 0.440864 avg, 0.001000 rate, 27.052923 seconds, 1212928 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.560630, Class: 1.000000, Obj: 0.337254, No Obj: 0.002739, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.643338, Class: 1.000000, Obj: 0.355657, No Obj: 0.003232, Avg Recall: 0.785714, count: 126 -Region Avg IOU: 0.588799, Class: 1.000000, Obj: 0.331330, No Obj: 0.003224, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.613733, Class: 1.000000, Obj: 0.387629, No Obj: 0.003057, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.618823, Class: 1.000000, Obj: 0.448064, No Obj: 0.002977, Avg Recall: 0.736000, count: 125 -Region Avg IOU: 0.611709, Class: 1.000000, Obj: 0.380791, No Obj: 0.002704, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.634036, Class: 1.000000, Obj: 0.450995, No Obj: 0.002874, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.634362, Class: 1.000000, Obj: 0.381197, No Obj: 0.003427, Avg Recall: 0.776000, count: 125 -4739: 0.455945, 0.442372 avg, 0.001000 rate, 27.018229 seconds, 1213184 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.598399, Class: 1.000000, Obj: 0.342820, No Obj: 0.002481, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.633235, Class: 1.000000, Obj: 0.337087, No Obj: 0.003230, Avg Recall: 0.772358, count: 123 -Region Avg IOU: 0.623523, Class: 1.000000, Obj: 0.363874, No Obj: 0.003273, Avg Recall: 0.733333, count: 120 -Region Avg IOU: 0.675511, Class: 1.000000, Obj: 0.451226, No Obj: 0.003119, Avg Recall: 0.854545, count: 110 -Region Avg IOU: 0.645952, Class: 1.000000, Obj: 0.426619, No Obj: 0.002589, Avg Recall: 0.790698, count: 86 -Region Avg IOU: 0.617058, Class: 1.000000, Obj: 0.387782, No Obj: 0.002994, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.641394, Class: 1.000000, Obj: 0.395728, No Obj: 0.003611, Avg Recall: 0.774648, count: 142 -Region Avg IOU: 0.637777, Class: 1.000000, Obj: 0.315058, No Obj: 0.002934, Avg Recall: 0.801802, count: 111 -4740: 0.456141, 0.443749 avg, 0.001000 rate, 26.533491 seconds, 1213440 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000069 seconds -Region Avg IOU: 0.626716, Class: 1.000000, Obj: 0.356203, No Obj: 0.002916, Avg Recall: 0.775000, count: 120 -Region Avg IOU: 0.629868, Class: 1.000000, Obj: 0.388936, No Obj: 0.002690, Avg Recall: 0.797753, count: 89 -Region Avg IOU: 0.622324, Class: 1.000000, Obj: 0.391195, No Obj: 0.002472, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.617954, Class: 1.000000, Obj: 0.434570, No Obj: 0.002381, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.651127, Class: 1.000000, Obj: 0.401752, No Obj: 0.002598, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.663960, Class: 1.000000, Obj: 0.380742, No Obj: 0.002781, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.622371, Class: 1.000000, Obj: 0.395468, No Obj: 0.002625, Avg Recall: 0.754098, count: 122 -Region Avg IOU: 0.668272, Class: 1.000000, Obj: 0.360194, No Obj: 0.002434, Avg Recall: 0.843373, count: 83 -4741: 0.426092, 0.441983 avg, 0.001000 rate, 27.120697 seconds, 1213696 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.684453, Class: 1.000000, Obj: 0.462557, No Obj: 0.002590, Avg Recall: 0.880000, count: 100 -Region Avg IOU: 0.684763, Class: 1.000000, Obj: 0.384390, No Obj: 0.002765, Avg Recall: 0.861702, count: 94 -Region Avg IOU: 0.620907, Class: 1.000000, Obj: 0.379733, No Obj: 0.002640, Avg Recall: 0.750000, count: 132 -Region Avg IOU: 0.645264, Class: 1.000000, Obj: 0.431291, No Obj: 0.002585, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.632881, Class: 1.000000, Obj: 0.344784, No Obj: 0.002896, Avg Recall: 0.789916, count: 119 -Region Avg IOU: 0.646495, Class: 1.000000, Obj: 0.404498, No Obj: 0.002392, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.627937, Class: 1.000000, Obj: 0.398724, No Obj: 0.002755, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.656007, Class: 1.000000, Obj: 0.370352, No Obj: 0.002679, Avg Recall: 0.808696, count: 115 -4742: 0.446872, 0.442472 avg, 0.001000 rate, 28.109724 seconds, 1213952 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.621333, Class: 1.000000, Obj: 0.343257, No Obj: 0.002552, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.658702, Class: 1.000000, Obj: 0.406655, No Obj: 0.002599, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.670153, Class: 1.000000, Obj: 0.411289, No Obj: 0.002941, Avg Recall: 0.821705, count: 129 -Region Avg IOU: 0.658445, Class: 1.000000, Obj: 0.392881, No Obj: 0.003062, Avg Recall: 0.830189, count: 106 -Region Avg IOU: 0.632120, Class: 1.000000, Obj: 0.326507, No Obj: 0.002694, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.653911, Class: 1.000000, Obj: 0.370080, No Obj: 0.002358, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.678401, Class: 1.000000, Obj: 0.423876, No Obj: 0.002493, Avg Recall: 0.819277, count: 83 -Region Avg IOU: 0.615025, Class: 1.000000, Obj: 0.381274, No Obj: 0.002561, Avg Recall: 0.743363, count: 113 -4743: 0.470903, 0.445315 avg, 0.001000 rate, 27.684988 seconds, 1214208 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.628134, Class: 1.000000, Obj: 0.375222, No Obj: 0.002896, Avg Recall: 0.785185, count: 135 -Region Avg IOU: 0.680560, Class: 1.000000, Obj: 0.414348, No Obj: 0.003105, Avg Recall: 0.828571, count: 105 -Region Avg IOU: 0.705247, Class: 1.000000, Obj: 0.486127, No Obj: 0.002931, Avg Recall: 0.888889, count: 99 -Region Avg IOU: 0.586417, Class: 1.000000, Obj: 0.346192, No Obj: 0.002480, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.650117, Class: 1.000000, Obj: 0.405696, No Obj: 0.002631, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.649905, Class: 1.000000, Obj: 0.354688, No Obj: 0.002282, Avg Recall: 0.804598, count: 87 -Region Avg IOU: 0.667213, Class: 1.000000, Obj: 0.433582, No Obj: 0.002365, Avg Recall: 0.807692, count: 78 -Region Avg IOU: 0.626600, Class: 1.000000, Obj: 0.414607, No Obj: 0.002807, Avg Recall: 0.774775, count: 111 -4744: 0.401215, 0.440905 avg, 0.001000 rate, 27.089100 seconds, 1214464 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.659466, Class: 1.000000, Obj: 0.470716, No Obj: 0.002719, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.652837, Class: 1.000000, Obj: 0.421436, No Obj: 0.002798, Avg Recall: 0.818182, count: 110 -Region Avg IOU: 0.682847, Class: 1.000000, Obj: 0.460820, No Obj: 0.002596, Avg Recall: 0.846939, count: 98 -Region Avg IOU: 0.665308, Class: 1.000000, Obj: 0.390271, No Obj: 0.002446, Avg Recall: 0.880000, count: 75 -Region Avg IOU: 0.650133, Class: 1.000000, Obj: 0.368679, No Obj: 0.002577, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.657011, Class: 1.000000, Obj: 0.346272, No Obj: 0.002854, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.656337, Class: 1.000000, Obj: 0.409383, No Obj: 0.002825, Avg Recall: 0.823529, count: 119 -Region Avg IOU: 0.652054, Class: 1.000000, Obj: 0.372063, No Obj: 0.002989, Avg Recall: 0.816176, count: 136 -4745: 0.440809, 0.440895 avg, 0.001000 rate, 27.541330 seconds, 1214720 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.661654, Class: 1.000000, Obj: 0.397218, No Obj: 0.002823, Avg Recall: 0.841584, count: 101 -Region Avg IOU: 0.628281, Class: 1.000000, Obj: 0.360018, No Obj: 0.002829, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.683061, Class: 1.000000, Obj: 0.417552, No Obj: 0.002636, Avg Recall: 0.892157, count: 102 -Region Avg IOU: 0.655433, Class: 1.000000, Obj: 0.392819, No Obj: 0.003016, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.662070, Class: 1.000000, Obj: 0.374236, No Obj: 0.002824, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.639795, Class: 1.000000, Obj: 0.454077, No Obj: 0.002594, Avg Recall: 0.760563, count: 71 -Region Avg IOU: 0.657296, Class: 1.000000, Obj: 0.395673, No Obj: 0.003035, Avg Recall: 0.808000, count: 125 -Region Avg IOU: 0.677804, Class: 1.000000, Obj: 0.418670, No Obj: 0.002702, Avg Recall: 0.853211, count: 109 -4746: 0.441846, 0.440991 avg, 0.001000 rate, 28.417242 seconds, 1214976 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.662699, Class: 1.000000, Obj: 0.426702, No Obj: 0.003030, Avg Recall: 0.826446, count: 121 -Region Avg IOU: 0.651918, Class: 1.000000, Obj: 0.420760, No Obj: 0.002603, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.603310, Class: 1.000000, Obj: 0.358501, No Obj: 0.002910, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.656512, Class: 1.000000, Obj: 0.409159, No Obj: 0.002667, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.637902, Class: 1.000000, Obj: 0.387296, No Obj: 0.002477, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.632376, Class: 1.000000, Obj: 0.391338, No Obj: 0.003598, Avg Recall: 0.798658, count: 149 -Region Avg IOU: 0.621574, Class: 1.000000, Obj: 0.406550, No Obj: 0.003099, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.630936, Class: 1.000000, Obj: 0.392873, No Obj: 0.002659, Avg Recall: 0.761062, count: 113 -4747: 0.469627, 0.443854 avg, 0.001000 rate, 27.261919 seconds, 1215232 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.647475, Class: 1.000000, Obj: 0.394667, No Obj: 0.002545, Avg Recall: 0.775000, count: 120 -Region Avg IOU: 0.614830, Class: 1.000000, Obj: 0.378192, No Obj: 0.002922, Avg Recall: 0.775362, count: 138 -Region Avg IOU: 0.649302, Class: 1.000000, Obj: 0.367880, No Obj: 0.002654, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.638830, Class: 1.000000, Obj: 0.353433, No Obj: 0.002522, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.672258, Class: 1.000000, Obj: 0.368368, No Obj: 0.003290, Avg Recall: 0.837838, count: 148 -Region Avg IOU: 0.621586, Class: 1.000000, Obj: 0.342008, No Obj: 0.002967, Avg Recall: 0.770270, count: 148 -Region Avg IOU: 0.623635, Class: 1.000000, Obj: 0.421843, No Obj: 0.002457, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.602861, Class: 1.000000, Obj: 0.365710, No Obj: 0.002563, Avg Recall: 0.712121, count: 132 -4748: 0.500165, 0.449485 avg, 0.001000 rate, 26.816441 seconds, 1215488 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.679457, Class: 1.000000, Obj: 0.459016, No Obj: 0.002421, Avg Recall: 0.814815, count: 81 -Region Avg IOU: 0.654788, Class: 1.000000, Obj: 0.365020, No Obj: 0.002814, Avg Recall: 0.796460, count: 113 -Region Avg IOU: 0.660190, Class: 1.000000, Obj: 0.420616, No Obj: 0.002890, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.647765, Class: 1.000000, Obj: 0.415124, No Obj: 0.002747, Avg Recall: 0.798319, count: 119 -Region Avg IOU: 0.640673, Class: 1.000000, Obj: 0.388448, No Obj: 0.002975, Avg Recall: 0.790698, count: 129 -Region Avg IOU: 0.648944, Class: 1.000000, Obj: 0.407178, No Obj: 0.002708, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.665675, Class: 1.000000, Obj: 0.443832, No Obj: 0.002799, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.639546, Class: 1.000000, Obj: 0.369384, No Obj: 0.002995, Avg Recall: 0.783333, count: 120 -4749: 0.438430, 0.448380 avg, 0.001000 rate, 28.545380 seconds, 1215744 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.645171, Class: 1.000000, Obj: 0.411714, No Obj: 0.002619, Avg Recall: 0.796460, count: 113 -Region Avg IOU: 0.666165, Class: 1.000000, Obj: 0.415803, No Obj: 0.002702, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.679174, Class: 1.000000, Obj: 0.433580, No Obj: 0.002642, Avg Recall: 0.891566, count: 83 -Region Avg IOU: 0.640539, Class: 1.000000, Obj: 0.409346, No Obj: 0.002961, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.702851, Class: 1.000000, Obj: 0.448435, No Obj: 0.003201, Avg Recall: 0.842520, count: 127 -Region Avg IOU: 0.625817, Class: 1.000000, Obj: 0.442868, No Obj: 0.002693, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.583078, Class: 1.000000, Obj: 0.421405, No Obj: 0.002503, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.628266, Class: 1.000000, Obj: 0.384042, No Obj: 0.002888, Avg Recall: 0.767857, count: 112 -4750: 0.434451, 0.446987 avg, 0.001000 rate, 28.157404 seconds, 1216000 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 13.324516 seconds -Region Avg IOU: 0.504665, Class: 1.000000, Obj: 0.300327, No Obj: 0.003948, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.495930, Class: 1.000000, Obj: 0.297992, No Obj: 0.003339, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.544684, Class: 1.000000, Obj: 0.298684, No Obj: 0.003879, Avg Recall: 0.562500, count: 96 -Region Avg IOU: 0.510786, Class: 1.000000, Obj: 0.295717, No Obj: 0.003502, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.423760, Class: 1.000000, Obj: 0.266690, No Obj: 0.003285, Avg Recall: 0.442623, count: 122 -Region Avg IOU: 0.478687, Class: 1.000000, Obj: 0.276214, No Obj: 0.003638, Avg Recall: 0.454545, count: 110 -Region Avg IOU: 0.516810, Class: 1.000000, Obj: 0.318664, No Obj: 0.004518, Avg Recall: 0.582734, count: 139 -Region Avg IOU: 0.518682, Class: 1.000000, Obj: 0.277386, No Obj: 0.003906, Avg Recall: 0.545455, count: 121 -4751: 0.477292, 0.450017 avg, 0.001000 rate, 16.233706 seconds, 1216256 images -Loaded: 4.160557 seconds -Region Avg IOU: 0.518059, Class: 1.000000, Obj: 0.325039, No Obj: 0.003342, Avg Recall: 0.576471, count: 85 -Region Avg IOU: 0.516243, Class: 1.000000, Obj: 0.307155, No Obj: 0.003030, Avg Recall: 0.558442, count: 77 -Region Avg IOU: 0.507586, Class: 1.000000, Obj: 0.279360, No Obj: 0.003976, Avg Recall: 0.578231, count: 147 -Region Avg IOU: 0.491180, Class: 1.000000, Obj: 0.286389, No Obj: 0.003600, Avg Recall: 0.532787, count: 122 -Region Avg IOU: 0.541103, Class: 1.000000, Obj: 0.316352, No Obj: 0.003703, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.507991, Class: 1.000000, Obj: 0.308581, No Obj: 0.003767, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.477436, Class: 1.000000, Obj: 0.282562, No Obj: 0.003727, Avg Recall: 0.542373, count: 118 -Region Avg IOU: 0.439643, Class: 1.000000, Obj: 0.258714, No Obj: 0.003291, Avg Recall: 0.406780, count: 118 -4752: 0.444820, 0.449498 avg, 0.001000 rate, 16.632677 seconds, 1216512 images -Loaded: 6.328490 seconds -Region Avg IOU: 0.501071, Class: 1.000000, Obj: 0.302397, No Obj: 0.004205, Avg Recall: 0.552941, count: 85 -Region Avg IOU: 0.478069, Class: 1.000000, Obj: 0.303647, No Obj: 0.004231, Avg Recall: 0.560811, count: 148 -Region Avg IOU: 0.458806, Class: 1.000000, Obj: 0.290054, No Obj: 0.003525, Avg Recall: 0.504202, count: 119 -Region Avg IOU: 0.504375, Class: 1.000000, Obj: 0.332065, No Obj: 0.003912, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.543063, Class: 1.000000, Obj: 0.345366, No Obj: 0.004208, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.514412, Class: 1.000000, Obj: 0.300812, No Obj: 0.003767, Avg Recall: 0.556818, count: 88 -Region Avg IOU: 0.533541, Class: 1.000000, Obj: 0.307373, No Obj: 0.004056, Avg Recall: 0.584071, count: 113 -Region Avg IOU: 0.530517, Class: 1.000000, Obj: 0.311961, No Obj: 0.004416, Avg Recall: 0.607692, count: 130 -4753: 0.499591, 0.454507 avg, 0.001000 rate, 18.175892 seconds, 1216768 images -Loaded: 3.635945 seconds -Region Avg IOU: 0.507734, Class: 1.000000, Obj: 0.340654, No Obj: 0.003752, Avg Recall: 0.575758, count: 99 -Region Avg IOU: 0.481621, Class: 1.000000, Obj: 0.333340, No Obj: 0.004194, Avg Recall: 0.508475, count: 118 -Region Avg IOU: 0.483137, Class: 1.000000, Obj: 0.314052, No Obj: 0.004417, Avg Recall: 0.519084, count: 131 -Region Avg IOU: 0.533778, Class: 1.000000, Obj: 0.361484, No Obj: 0.004472, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.507703, Class: 1.000000, Obj: 0.353809, No Obj: 0.003889, Avg Recall: 0.540816, count: 98 -Region Avg IOU: 0.523800, Class: 1.000000, Obj: 0.291511, No Obj: 0.004013, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.467431, Class: 1.000000, Obj: 0.288451, No Obj: 0.004773, Avg Recall: 0.496689, count: 151 -Region Avg IOU: 0.532677, Class: 1.000000, Obj: 0.348406, No Obj: 0.004770, Avg Recall: 0.626016, count: 123 -4754: 0.440197, 0.453076 avg, 0.001000 rate, 20.056208 seconds, 1217024 images -Loaded: 1.492481 seconds -Region Avg IOU: 0.505637, Class: 1.000000, Obj: 0.345851, No Obj: 0.004551, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.476729, Class: 1.000000, Obj: 0.340125, No Obj: 0.004240, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.487363, Class: 1.000000, Obj: 0.342007, No Obj: 0.003764, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.478029, Class: 1.000000, Obj: 0.360790, No Obj: 0.003896, Avg Recall: 0.562500, count: 96 -Region Avg IOU: 0.515032, Class: 1.000000, Obj: 0.350593, No Obj: 0.004265, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.520516, Class: 1.000000, Obj: 0.328827, No Obj: 0.004401, Avg Recall: 0.524590, count: 122 -Region Avg IOU: 0.462129, Class: 1.000000, Obj: 0.302134, No Obj: 0.004242, Avg Recall: 0.515152, count: 132 -Region Avg IOU: 0.495016, Class: 1.000000, Obj: 0.355447, No Obj: 0.004291, Avg Recall: 0.571429, count: 112 -4755: 0.440207, 0.451789 avg, 0.001000 rate, 18.239883 seconds, 1217280 images -Loaded: 1.919786 seconds -Region Avg IOU: 0.445176, Class: 1.000000, Obj: 0.342442, No Obj: 0.003451, Avg Recall: 0.455446, count: 101 -Region Avg IOU: 0.459963, Class: 1.000000, Obj: 0.366565, No Obj: 0.004116, Avg Recall: 0.514019, count: 107 -Region Avg IOU: 0.492073, Class: 1.000000, Obj: 0.376390, No Obj: 0.003986, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.460185, Class: 1.000000, Obj: 0.363457, No Obj: 0.004006, Avg Recall: 0.525424, count: 118 -Region Avg IOU: 0.464976, Class: 1.000000, Obj: 0.369343, No Obj: 0.003876, Avg Recall: 0.495050, count: 101 -Region Avg IOU: 0.481349, Class: 1.000000, Obj: 0.345076, No Obj: 0.004616, Avg Recall: 0.534483, count: 116 -Region Avg IOU: 0.534288, Class: 1.000000, Obj: 0.389695, No Obj: 0.004630, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.498775, Class: 1.000000, Obj: 0.394927, No Obj: 0.005093, Avg Recall: 0.515625, count: 128 -4756: 0.454761, 0.452086 avg, 0.001000 rate, 18.399488 seconds, 1217536 images -Loaded: 2.704430 seconds -Region Avg IOU: 0.466001, Class: 1.000000, Obj: 0.406962, No Obj: 0.004771, Avg Recall: 0.507812, count: 128 -Region Avg IOU: 0.523012, Class: 1.000000, Obj: 0.364454, No Obj: 0.004926, Avg Recall: 0.564103, count: 117 -Region Avg IOU: 0.516048, Class: 1.000000, Obj: 0.332338, No Obj: 0.004821, Avg Recall: 0.625000, count: 120 -Region Avg IOU: 0.476846, Class: 1.000000, Obj: 0.346647, No Obj: 0.003897, Avg Recall: 0.533333, count: 75 -Region Avg IOU: 0.448024, Class: 1.000000, Obj: 0.375945, No Obj: 0.004521, Avg Recall: 0.451613, count: 124 -Region Avg IOU: 0.527241, Class: 1.000000, Obj: 0.334223, No Obj: 0.005556, Avg Recall: 0.586957, count: 138 -Region Avg IOU: 0.492375, Class: 1.000000, Obj: 0.366702, No Obj: 0.004815, Avg Recall: 0.567568, count: 111 -Region Avg IOU: 0.568523, Class: 1.000000, Obj: 0.371974, No Obj: 0.005117, Avg Recall: 0.660377, count: 106 -4757: 0.551076, 0.461985 avg, 0.001000 rate, 16.310959 seconds, 1217792 images -Loaded: 3.173081 seconds -Region Avg IOU: 0.503699, Class: 1.000000, Obj: 0.367813, No Obj: 0.006148, Avg Recall: 0.559441, count: 143 -Region Avg IOU: 0.506557, Class: 1.000000, Obj: 0.382466, No Obj: 0.004831, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.481145, Class: 1.000000, Obj: 0.378429, No Obj: 0.004427, Avg Recall: 0.510870, count: 92 -Region Avg IOU: 0.475565, Class: 1.000000, Obj: 0.361930, No Obj: 0.004752, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.477164, Class: 1.000000, Obj: 0.347953, No Obj: 0.005023, Avg Recall: 0.533333, count: 120 -Region Avg IOU: 0.495721, Class: 1.000000, Obj: 0.395136, No Obj: 0.004578, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.454545, Class: 1.000000, Obj: 0.375396, No Obj: 0.005174, Avg Recall: 0.479675, count: 123 -Region Avg IOU: 0.544946, Class: 1.000000, Obj: 0.442508, No Obj: 0.004950, Avg Recall: 0.595506, count: 89 -4758: 0.464846, 0.462271 avg, 0.001000 rate, 18.040983 seconds, 1218048 images -Loaded: 3.025000 seconds -Region Avg IOU: 0.538430, Class: 1.000000, Obj: 0.416675, No Obj: 0.004846, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.494678, Class: 1.000000, Obj: 0.343093, No Obj: 0.005097, Avg Recall: 0.558333, count: 120 -Region Avg IOU: 0.462355, Class: 1.000000, Obj: 0.360615, No Obj: 0.004702, Avg Recall: 0.465517, count: 116 -Region Avg IOU: 0.502447, Class: 1.000000, Obj: 0.393166, No Obj: 0.005052, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.486839, Class: 1.000000, Obj: 0.374885, No Obj: 0.004602, Avg Recall: 0.553571, count: 112 -Region Avg IOU: 0.513451, Class: 1.000000, Obj: 0.381171, No Obj: 0.004917, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.458164, Class: 1.000000, Obj: 0.368021, No Obj: 0.005264, Avg Recall: 0.496350, count: 137 -Region Avg IOU: 0.517260, Class: 1.000000, Obj: 0.353959, No Obj: 0.005564, Avg Recall: 0.594828, count: 116 -4759: 0.486005, 0.464645 avg, 0.001000 rate, 17.789820 seconds, 1218304 images -Loaded: 3.872019 seconds -Region Avg IOU: 0.516115, Class: 1.000000, Obj: 0.376538, No Obj: 0.005093, Avg Recall: 0.571429, count: 112 -Region Avg IOU: 0.523838, Class: 1.000000, Obj: 0.373647, No Obj: 0.004875, Avg Recall: 0.581197, count: 117 -Region Avg IOU: 0.510165, Class: 1.000000, Obj: 0.373465, No Obj: 0.004346, Avg Recall: 0.542857, count: 105 -Region Avg IOU: 0.459453, Class: 1.000000, Obj: 0.364451, No Obj: 0.004516, Avg Recall: 0.439252, count: 107 -Region Avg IOU: 0.532422, Class: 1.000000, Obj: 0.363560, No Obj: 0.004994, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.489570, Class: 1.000000, Obj: 0.375665, No Obj: 0.004682, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.433181, Class: 1.000000, Obj: 0.331357, No Obj: 0.004493, Avg Recall: 0.450820, count: 122 -Region Avg IOU: 0.512694, Class: 1.000000, Obj: 0.363528, No Obj: 0.004530, Avg Recall: 0.568807, count: 109 -4760: 0.460564, 0.464237 avg, 0.001000 rate, 16.457726 seconds, 1218560 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000067 seconds -Region Avg IOU: 0.591027, Class: 1.000000, Obj: 0.439449, No Obj: 0.004991, Avg Recall: 0.717391, count: 138 -Region Avg IOU: 0.607828, Class: 1.000000, Obj: 0.453150, No Obj: 0.004119, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.531425, Class: 1.000000, Obj: 0.408947, No Obj: 0.003845, Avg Recall: 0.586777, count: 121 -Region Avg IOU: 0.577464, Class: 1.000000, Obj: 0.443987, No Obj: 0.003801, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.664687, Class: 1.000000, Obj: 0.507512, No Obj: 0.004579, Avg Recall: 0.808696, count: 115 -Region Avg IOU: 0.628295, Class: 1.000000, Obj: 0.442963, No Obj: 0.004011, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.638139, Class: 1.000000, Obj: 0.488859, No Obj: 0.004066, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.619281, Class: 1.000000, Obj: 0.510245, No Obj: 0.004330, Avg Recall: 0.771930, count: 114 -4761: 0.447802, 0.462593 avg, 0.001000 rate, 23.349480 seconds, 1218816 images -Loaded: 0.000138 seconds -Region Avg IOU: 0.580414, Class: 1.000000, Obj: 0.449192, No Obj: 0.003471, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.581679, Class: 1.000000, Obj: 0.404827, No Obj: 0.003359, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.627536, Class: 1.000000, Obj: 0.411903, No Obj: 0.004096, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.597933, Class: 1.000000, Obj: 0.437223, No Obj: 0.003719, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.553741, Class: 1.000000, Obj: 0.413250, No Obj: 0.003406, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.592312, Class: 1.000000, Obj: 0.435197, No Obj: 0.003686, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.588069, Class: 1.000000, Obj: 0.375090, No Obj: 0.003437, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.566057, Class: 1.000000, Obj: 0.385041, No Obj: 0.003629, Avg Recall: 0.611765, count: 85 -4762: 0.401756, 0.456509 avg, 0.001000 rate, 25.234232 seconds, 1219072 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.610607, Class: 1.000000, Obj: 0.435572, No Obj: 0.003373, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.567749, Class: 1.000000, Obj: 0.409747, No Obj: 0.003850, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.539921, Class: 1.000000, Obj: 0.442452, No Obj: 0.004082, Avg Recall: 0.608696, count: 138 -Region Avg IOU: 0.628481, Class: 1.000000, Obj: 0.411375, No Obj: 0.003925, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.616857, Class: 1.000000, Obj: 0.427327, No Obj: 0.004165, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.617762, Class: 1.000000, Obj: 0.447510, No Obj: 0.003261, Avg Recall: 0.712500, count: 80 -Region Avg IOU: 0.626906, Class: 1.000000, Obj: 0.462067, No Obj: 0.003817, Avg Recall: 0.782609, count: 115 -Region Avg IOU: 0.570802, Class: 1.000000, Obj: 0.363353, No Obj: 0.003728, Avg Recall: 0.696000, count: 125 -4763: 0.459401, 0.456799 avg, 0.001000 rate, 24.271688 seconds, 1219328 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.589776, Class: 1.000000, Obj: 0.397015, No Obj: 0.004123, Avg Recall: 0.675325, count: 154 -Region Avg IOU: 0.645819, Class: 1.000000, Obj: 0.483999, No Obj: 0.002785, Avg Recall: 0.769231, count: 65 -Region Avg IOU: 0.564323, Class: 1.000000, Obj: 0.353248, No Obj: 0.004113, Avg Recall: 0.679245, count: 159 -Region Avg IOU: 0.606365, Class: 1.000000, Obj: 0.395511, No Obj: 0.003076, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.591698, Class: 1.000000, Obj: 0.415468, No Obj: 0.003546, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.559787, Class: 1.000000, Obj: 0.373174, No Obj: 0.003572, Avg Recall: 0.655738, count: 122 -Region Avg IOU: 0.648308, Class: 1.000000, Obj: 0.386919, No Obj: 0.003678, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.609216, Class: 1.000000, Obj: 0.413107, No Obj: 0.003458, Avg Recall: 0.769231, count: 117 -4764: 0.461750, 0.457294 avg, 0.001000 rate, 25.716108 seconds, 1219584 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.645949, Class: 1.000000, Obj: 0.395497, No Obj: 0.002773, Avg Recall: 0.761905, count: 84 -Region Avg IOU: 0.556427, Class: 1.000000, Obj: 0.374808, No Obj: 0.002706, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.593095, Class: 1.000000, Obj: 0.387877, No Obj: 0.002897, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.626972, Class: 1.000000, Obj: 0.328526, No Obj: 0.003339, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.622591, Class: 1.000000, Obj: 0.364374, No Obj: 0.003489, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.584017, Class: 1.000000, Obj: 0.395760, No Obj: 0.003363, Avg Recall: 0.682540, count: 126 -Region Avg IOU: 0.597797, Class: 1.000000, Obj: 0.377819, No Obj: 0.003023, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.616386, Class: 1.000000, Obj: 0.383195, No Obj: 0.003553, Avg Recall: 0.752066, count: 121 -4765: 0.402429, 0.451807 avg, 0.001000 rate, 24.789980 seconds, 1219840 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.572207, Class: 1.000000, Obj: 0.342684, No Obj: 0.002565, Avg Recall: 0.650602, count: 83 -Region Avg IOU: 0.582761, Class: 1.000000, Obj: 0.350462, No Obj: 0.003071, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.646388, Class: 1.000000, Obj: 0.413612, No Obj: 0.002704, Avg Recall: 0.800000, count: 85 -Region Avg IOU: 0.615752, Class: 1.000000, Obj: 0.385726, No Obj: 0.002609, Avg Recall: 0.746988, count: 83 -Region Avg IOU: 0.559531, Class: 1.000000, Obj: 0.332223, No Obj: 0.003499, Avg Recall: 0.655738, count: 122 -Region Avg IOU: 0.586015, Class: 1.000000, Obj: 0.293978, No Obj: 0.002619, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.573546, Class: 1.000000, Obj: 0.345589, No Obj: 0.003326, Avg Recall: 0.644737, count: 152 -Region Avg IOU: 0.584946, Class: 1.000000, Obj: 0.347415, No Obj: 0.003008, Avg Recall: 0.666667, count: 105 -4766: 0.434019, 0.450028 avg, 0.001000 rate, 25.386375 seconds, 1220096 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.604977, Class: 1.000000, Obj: 0.368479, No Obj: 0.002908, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.623099, Class: 1.000000, Obj: 0.365001, No Obj: 0.003039, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.602802, Class: 1.000000, Obj: 0.345436, No Obj: 0.002854, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.579733, Class: 1.000000, Obj: 0.341698, No Obj: 0.002555, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.644303, Class: 1.000000, Obj: 0.376255, No Obj: 0.003004, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.630091, Class: 1.000000, Obj: 0.353138, No Obj: 0.003496, Avg Recall: 0.757576, count: 132 -Region Avg IOU: 0.617548, Class: 1.000000, Obj: 0.384353, No Obj: 0.002593, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.606400, Class: 1.000000, Obj: 0.359377, No Obj: 0.002513, Avg Recall: 0.715909, count: 88 -4767: 0.436716, 0.448697 avg, 0.001000 rate, 25.774508 seconds, 1220352 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.587915, Class: 1.000000, Obj: 0.327628, No Obj: 0.002570, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.661638, Class: 1.000000, Obj: 0.381337, No Obj: 0.002906, Avg Recall: 0.831579, count: 95 -Region Avg IOU: 0.624168, Class: 1.000000, Obj: 0.389210, No Obj: 0.003197, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.613383, Class: 1.000000, Obj: 0.326409, No Obj: 0.002882, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.595664, Class: 1.000000, Obj: 0.356914, No Obj: 0.002929, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.644718, Class: 1.000000, Obj: 0.416405, No Obj: 0.002579, Avg Recall: 0.783132, count: 83 -Region Avg IOU: 0.624609, Class: 1.000000, Obj: 0.391195, No Obj: 0.003557, Avg Recall: 0.735043, count: 117 -Region Avg IOU: 0.581533, Class: 1.000000, Obj: 0.338343, No Obj: 0.003586, Avg Recall: 0.678571, count: 140 -4768: 0.454668, 0.449294 avg, 0.001000 rate, 24.213079 seconds, 1220608 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.590526, Class: 1.000000, Obj: 0.346815, No Obj: 0.002700, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.612624, Class: 1.000000, Obj: 0.384039, No Obj: 0.003369, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.574881, Class: 1.000000, Obj: 0.365960, No Obj: 0.002721, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.616182, Class: 1.000000, Obj: 0.359822, No Obj: 0.003142, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.610749, Class: 1.000000, Obj: 0.368463, No Obj: 0.002735, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.590000, Class: 1.000000, Obj: 0.406500, No Obj: 0.002897, Avg Recall: 0.701149, count: 87 -Region Avg IOU: 0.586388, Class: 1.000000, Obj: 0.363150, No Obj: 0.002709, Avg Recall: 0.675000, count: 80 -Region Avg IOU: 0.602828, Class: 1.000000, Obj: 0.364619, No Obj: 0.002407, Avg Recall: 0.718750, count: 96 -4769: 0.422359, 0.446601 avg, 0.001000 rate, 25.671616 seconds, 1220864 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.620414, Class: 1.000000, Obj: 0.357861, No Obj: 0.003224, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.639630, Class: 1.000000, Obj: 0.378056, No Obj: 0.003398, Avg Recall: 0.779528, count: 127 -Region Avg IOU: 0.593237, Class: 1.000000, Obj: 0.347249, No Obj: 0.002871, Avg Recall: 0.686747, count: 83 -Region Avg IOU: 0.610092, Class: 1.000000, Obj: 0.320122, No Obj: 0.002951, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.641916, Class: 1.000000, Obj: 0.373426, No Obj: 0.003334, Avg Recall: 0.803279, count: 122 -Region Avg IOU: 0.613628, Class: 1.000000, Obj: 0.340096, No Obj: 0.003091, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.599303, Class: 1.000000, Obj: 0.386718, No Obj: 0.003006, Avg Recall: 0.727273, count: 88 -Region Avg IOU: 0.606857, Class: 1.000000, Obj: 0.365088, No Obj: 0.003472, Avg Recall: 0.734375, count: 128 -4770: 0.437362, 0.445677 avg, 0.001000 rate, 25.703688 seconds, 1221120 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.769274 seconds -Region Avg IOU: 0.503606, Class: 1.000000, Obj: 0.302626, No Obj: 0.004128, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.489332, Class: 1.000000, Obj: 0.294910, No Obj: 0.003953, Avg Recall: 0.522059, count: 136 -Region Avg IOU: 0.529881, Class: 1.000000, Obj: 0.294324, No Obj: 0.003936, Avg Recall: 0.587719, count: 114 -Region Avg IOU: 0.499024, Class: 1.000000, Obj: 0.309840, No Obj: 0.004006, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.496231, Class: 1.000000, Obj: 0.282602, No Obj: 0.003949, Avg Recall: 0.588710, count: 124 -Region Avg IOU: 0.514879, Class: 1.000000, Obj: 0.306031, No Obj: 0.003716, Avg Recall: 0.549451, count: 91 -Region Avg IOU: 0.524098, Class: 1.000000, Obj: 0.290705, No Obj: 0.003647, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.564116, Class: 1.000000, Obj: 0.342480, No Obj: 0.003303, Avg Recall: 0.670732, count: 82 -4771: 0.471764, 0.448286 avg, 0.001000 rate, 20.421633 seconds, 1221376 images -Loaded: 1.415278 seconds -Region Avg IOU: 0.528295, Class: 1.000000, Obj: 0.320906, No Obj: 0.004378, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.503786, Class: 1.000000, Obj: 0.297301, No Obj: 0.003679, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.502913, Class: 1.000000, Obj: 0.328086, No Obj: 0.004027, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.470873, Class: 1.000000, Obj: 0.283572, No Obj: 0.004668, Avg Recall: 0.496063, count: 127 -Region Avg IOU: 0.513959, Class: 1.000000, Obj: 0.308872, No Obj: 0.004758, Avg Recall: 0.600000, count: 125 -Region Avg IOU: 0.495107, Class: 1.000000, Obj: 0.307286, No Obj: 0.003261, Avg Recall: 0.500000, count: 84 -Region Avg IOU: 0.446296, Class: 1.000000, Obj: 0.267921, No Obj: 0.004012, Avg Recall: 0.471014, count: 138 -Region Avg IOU: 0.447304, Class: 1.000000, Obj: 0.248621, No Obj: 0.003193, Avg Recall: 0.528090, count: 89 -4772: 0.444064, 0.447864 avg, 0.001000 rate, 15.976212 seconds, 1221632 images -Loaded: 3.146679 seconds -Region Avg IOU: 0.499273, Class: 1.000000, Obj: 0.318613, No Obj: 0.004878, Avg Recall: 0.551020, count: 147 -Region Avg IOU: 0.549527, Class: 1.000000, Obj: 0.347656, No Obj: 0.004213, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.463569, Class: 1.000000, Obj: 0.300737, No Obj: 0.003879, Avg Recall: 0.547009, count: 117 -Region Avg IOU: 0.442156, Class: 1.000000, Obj: 0.295622, No Obj: 0.003923, Avg Recall: 0.505376, count: 93 -Region Avg IOU: 0.500837, Class: 1.000000, Obj: 0.280022, No Obj: 0.004030, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.482400, Class: 1.000000, Obj: 0.285811, No Obj: 0.003965, Avg Recall: 0.463158, count: 95 -Region Avg IOU: 0.472756, Class: 1.000000, Obj: 0.299201, No Obj: 0.004330, Avg Recall: 0.452381, count: 126 -Region Avg IOU: 0.476242, Class: 1.000000, Obj: 0.323609, No Obj: 0.003883, Avg Recall: 0.516949, count: 118 -4773: 0.454439, 0.448521 avg, 0.001000 rate, 21.088715 seconds, 1221888 images -Loaded: 1.378796 seconds -Region Avg IOU: 0.473995, Class: 1.000000, Obj: 0.352154, No Obj: 0.004442, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.502544, Class: 1.000000, Obj: 0.364083, No Obj: 0.004216, Avg Recall: 0.560440, count: 91 -Region Avg IOU: 0.521303, Class: 1.000000, Obj: 0.359896, No Obj: 0.004393, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.531093, Class: 1.000000, Obj: 0.390977, No Obj: 0.004558, Avg Recall: 0.602410, count: 83 -Region Avg IOU: 0.535510, Class: 1.000000, Obj: 0.424658, No Obj: 0.004785, Avg Recall: 0.592593, count: 108 -Region Avg IOU: 0.489806, Class: 1.000000, Obj: 0.350490, No Obj: 0.004817, Avg Recall: 0.545455, count: 121 -Region Avg IOU: 0.514094, Class: 1.000000, Obj: 0.341705, No Obj: 0.004755, Avg Recall: 0.583333, count: 120 -Region Avg IOU: 0.552775, Class: 1.000000, Obj: 0.334291, No Obj: 0.005097, Avg Recall: 0.626016, count: 123 -4774: 0.437463, 0.447415 avg, 0.001000 rate, 17.785233 seconds, 1222144 images -Loaded: 2.733622 seconds -Region Avg IOU: 0.498490, Class: 1.000000, Obj: 0.374035, No Obj: 0.004855, Avg Recall: 0.537190, count: 121 -Region Avg IOU: 0.506946, Class: 1.000000, Obj: 0.410535, No Obj: 0.004607, Avg Recall: 0.554348, count: 92 -Region Avg IOU: 0.492783, Class: 1.000000, Obj: 0.342245, No Obj: 0.004960, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.524136, Class: 1.000000, Obj: 0.385346, No Obj: 0.005278, Avg Recall: 0.584746, count: 118 -Region Avg IOU: 0.496730, Class: 1.000000, Obj: 0.398351, No Obj: 0.004801, Avg Recall: 0.495327, count: 107 -Region Avg IOU: 0.504880, Class: 1.000000, Obj: 0.385644, No Obj: 0.004774, Avg Recall: 0.524272, count: 103 -Region Avg IOU: 0.484836, Class: 1.000000, Obj: 0.352250, No Obj: 0.004614, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.449374, Class: 1.000000, Obj: 0.331179, No Obj: 0.004813, Avg Recall: 0.460784, count: 102 -4775: 0.474135, 0.450087 avg, 0.001000 rate, 17.149599 seconds, 1222400 images -Loaded: 2.837192 seconds -Region Avg IOU: 0.503796, Class: 1.000000, Obj: 0.374397, No Obj: 0.005098, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.483384, Class: 1.000000, Obj: 0.369848, No Obj: 0.005057, Avg Recall: 0.507576, count: 132 -Region Avg IOU: 0.489351, Class: 1.000000, Obj: 0.334403, No Obj: 0.005605, Avg Recall: 0.523809, count: 147 -Region Avg IOU: 0.522550, Class: 1.000000, Obj: 0.376174, No Obj: 0.004869, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.480572, Class: 1.000000, Obj: 0.351329, No Obj: 0.004786, Avg Recall: 0.504065, count: 123 -Region Avg IOU: 0.487246, Class: 1.000000, Obj: 0.385607, No Obj: 0.004603, Avg Recall: 0.514851, count: 101 -Region Avg IOU: 0.470230, Class: 1.000000, Obj: 0.370390, No Obj: 0.005007, Avg Recall: 0.491379, count: 116 -Region Avg IOU: 0.512698, Class: 1.000000, Obj: 0.379639, No Obj: 0.005549, Avg Recall: 0.549180, count: 122 -4776: 0.470293, 0.452108 avg, 0.001000 rate, 20.784794 seconds, 1222656 images -Loaded: 1.369559 seconds -Region Avg IOU: 0.410767, Class: 1.000000, Obj: 0.352714, No Obj: 0.004764, Avg Recall: 0.390909, count: 110 -Region Avg IOU: 0.507762, Class: 1.000000, Obj: 0.377623, No Obj: 0.004849, Avg Recall: 0.575000, count: 120 -Region Avg IOU: 0.576554, Class: 1.000000, Obj: 0.389093, No Obj: 0.005826, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.435268, Class: 1.000000, Obj: 0.359879, No Obj: 0.004893, Avg Recall: 0.462963, count: 108 -Region Avg IOU: 0.532121, Class: 1.000000, Obj: 0.392612, No Obj: 0.005441, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.579069, Class: 1.000000, Obj: 0.464679, No Obj: 0.005202, Avg Recall: 0.681818, count: 88 -Region Avg IOU: 0.532998, Class: 1.000000, Obj: 0.415110, No Obj: 0.006090, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.467045, Class: 1.000000, Obj: 0.402094, No Obj: 0.004433, Avg Recall: 0.494118, count: 85 -4777: 0.443073, 0.451204 avg, 0.001000 rate, 21.127157 seconds, 1222912 images -Loaded: 0.517783 seconds -Region Avg IOU: 0.505901, Class: 1.000000, Obj: 0.387937, No Obj: 0.005247, Avg Recall: 0.589744, count: 117 -Region Avg IOU: 0.484899, Class: 1.000000, Obj: 0.367346, No Obj: 0.005183, Avg Recall: 0.593750, count: 96 -Region Avg IOU: 0.527516, Class: 1.000000, Obj: 0.403580, No Obj: 0.005645, Avg Recall: 0.596774, count: 124 -Region Avg IOU: 0.495076, Class: 1.000000, Obj: 0.402514, No Obj: 0.005223, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.537878, Class: 1.000000, Obj: 0.415259, No Obj: 0.005376, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.474363, Class: 1.000000, Obj: 0.386444, No Obj: 0.005208, Avg Recall: 0.511111, count: 90 -Region Avg IOU: 0.468823, Class: 1.000000, Obj: 0.358278, No Obj: 0.005380, Avg Recall: 0.472000, count: 125 -Region Avg IOU: 0.511040, Class: 1.000000, Obj: 0.382694, No Obj: 0.005366, Avg Recall: 0.564516, count: 124 -4778: 0.449148, 0.450999 avg, 0.001000 rate, 20.029305 seconds, 1223168 images -Loaded: 1.980681 seconds -Region Avg IOU: 0.470137, Class: 1.000000, Obj: 0.323423, No Obj: 0.005776, Avg Recall: 0.540541, count: 148 -Region Avg IOU: 0.498054, Class: 1.000000, Obj: 0.356286, No Obj: 0.005744, Avg Recall: 0.535088, count: 114 -Region Avg IOU: 0.445607, Class: 1.000000, Obj: 0.321794, No Obj: 0.005291, Avg Recall: 0.451613, count: 124 -Region Avg IOU: 0.554809, Class: 1.000000, Obj: 0.340287, No Obj: 0.004252, Avg Recall: 0.666667, count: 69 -Region Avg IOU: 0.439632, Class: 1.000000, Obj: 0.357218, No Obj: 0.004678, Avg Recall: 0.491525, count: 118 -Region Avg IOU: 0.492455, Class: 1.000000, Obj: 0.379377, No Obj: 0.004728, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.494154, Class: 1.000000, Obj: 0.337259, No Obj: 0.005757, Avg Recall: 0.568965, count: 116 -Region Avg IOU: 0.483735, Class: 1.000000, Obj: 0.388242, No Obj: 0.003865, Avg Recall: 0.547619, count: 84 -4779: 0.456357, 0.451535 avg, 0.001000 rate, 16.924622 seconds, 1223424 images -Loaded: 4.107688 seconds -Region Avg IOU: 0.479304, Class: 1.000000, Obj: 0.363443, No Obj: 0.005841, Avg Recall: 0.548387, count: 124 -Region Avg IOU: 0.506612, Class: 1.000000, Obj: 0.380042, No Obj: 0.004246, Avg Recall: 0.533333, count: 90 -Region Avg IOU: 0.519927, Class: 1.000000, Obj: 0.353289, No Obj: 0.004736, Avg Recall: 0.585106, count: 94 -Region Avg IOU: 0.459357, Class: 1.000000, Obj: 0.344194, No Obj: 0.004541, Avg Recall: 0.510417, count: 96 -Region Avg IOU: 0.475431, Class: 1.000000, Obj: 0.312295, No Obj: 0.004872, Avg Recall: 0.557377, count: 122 -Region Avg IOU: 0.493052, Class: 1.000000, Obj: 0.376420, No Obj: 0.003939, Avg Recall: 0.560000, count: 75 -Region Avg IOU: 0.505234, Class: 1.000000, Obj: 0.390066, No Obj: 0.004594, Avg Recall: 0.576087, count: 92 -Region Avg IOU: 0.461126, Class: 1.000000, Obj: 0.382197, No Obj: 0.004625, Avg Recall: 0.476635, count: 107 -4780: 0.412621, 0.447643 avg, 0.001000 rate, 17.218555 seconds, 1223680 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000065 seconds -Region Avg IOU: 0.585451, Class: 1.000000, Obj: 0.429246, No Obj: 0.004422, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.585156, Class: 1.000000, Obj: 0.400989, No Obj: 0.004241, Avg Recall: 0.710938, count: 128 -Region Avg IOU: 0.603150, Class: 1.000000, Obj: 0.435089, No Obj: 0.004257, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.567602, Class: 1.000000, Obj: 0.452885, No Obj: 0.004179, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.538306, Class: 1.000000, Obj: 0.371983, No Obj: 0.003622, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.586552, Class: 1.000000, Obj: 0.402341, No Obj: 0.004409, Avg Recall: 0.706767, count: 133 -Region Avg IOU: 0.598170, Class: 1.000000, Obj: 0.466950, No Obj: 0.004048, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.604786, Class: 1.000000, Obj: 0.444112, No Obj: 0.004575, Avg Recall: 0.699187, count: 123 -4781: 0.464001, 0.449279 avg, 0.001000 rate, 24.904768 seconds, 1223936 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.583667, Class: 1.000000, Obj: 0.460370, No Obj: 0.003323, Avg Recall: 0.691358, count: 81 -Region Avg IOU: 0.581401, Class: 1.000000, Obj: 0.380096, No Obj: 0.003819, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.599369, Class: 1.000000, Obj: 0.379982, No Obj: 0.003581, Avg Recall: 0.717742, count: 124 -Region Avg IOU: 0.600853, Class: 1.000000, Obj: 0.439331, No Obj: 0.004004, Avg Recall: 0.768595, count: 121 -Region Avg IOU: 0.586362, Class: 1.000000, Obj: 0.385930, No Obj: 0.003536, Avg Recall: 0.659341, count: 91 -Region Avg IOU: 0.521827, Class: 1.000000, Obj: 0.362231, No Obj: 0.003510, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.577843, Class: 1.000000, Obj: 0.405129, No Obj: 0.003568, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.588358, Class: 1.000000, Obj: 0.403015, No Obj: 0.003886, Avg Recall: 0.689922, count: 129 -4782: 0.446902, 0.449041 avg, 0.001000 rate, 24.332800 seconds, 1224192 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.629876, Class: 1.000000, Obj: 0.416122, No Obj: 0.003612, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.626544, Class: 1.000000, Obj: 0.428892, No Obj: 0.003775, Avg Recall: 0.752941, count: 85 -Region Avg IOU: 0.522669, Class: 1.000000, Obj: 0.357519, No Obj: 0.003074, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.574863, Class: 1.000000, Obj: 0.357879, No Obj: 0.003201, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.546130, Class: 1.000000, Obj: 0.347423, No Obj: 0.004441, Avg Recall: 0.649007, count: 151 -Region Avg IOU: 0.586538, Class: 1.000000, Obj: 0.399212, No Obj: 0.003663, Avg Recall: 0.666667, count: 117 -Region Avg IOU: 0.564977, Class: 1.000000, Obj: 0.367205, No Obj: 0.003851, Avg Recall: 0.629630, count: 135 -Region Avg IOU: 0.556956, Class: 1.000000, Obj: 0.417963, No Obj: 0.003049, Avg Recall: 0.622222, count: 90 -4783: 0.415722, 0.445709 avg, 0.001000 rate, 24.748549 seconds, 1224448 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.616962, Class: 1.000000, Obj: 0.382896, No Obj: 0.003063, Avg Recall: 0.736264, count: 91 -Region Avg IOU: 0.582136, Class: 1.000000, Obj: 0.390946, No Obj: 0.003416, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.546208, Class: 1.000000, Obj: 0.355017, No Obj: 0.002756, Avg Recall: 0.617978, count: 89 -Region Avg IOU: 0.611853, Class: 1.000000, Obj: 0.358939, No Obj: 0.003878, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.603354, Class: 1.000000, Obj: 0.396964, No Obj: 0.003725, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.593795, Class: 1.000000, Obj: 0.400193, No Obj: 0.002896, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.543780, Class: 1.000000, Obj: 0.346672, No Obj: 0.003348, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.573430, Class: 1.000000, Obj: 0.365580, No Obj: 0.003827, Avg Recall: 0.687500, count: 112 -4784: 0.421965, 0.443335 avg, 0.001000 rate, 22.262457 seconds, 1224704 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.553801, Class: 1.000000, Obj: 0.354479, No Obj: 0.003577, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.563140, Class: 1.000000, Obj: 0.342179, No Obj: 0.003203, Avg Recall: 0.641667, count: 120 -Region Avg IOU: 0.604506, Class: 1.000000, Obj: 0.355304, No Obj: 0.003052, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.642579, Class: 1.000000, Obj: 0.356048, No Obj: 0.003252, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.606252, Class: 1.000000, Obj: 0.400203, No Obj: 0.003102, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.600525, Class: 1.000000, Obj: 0.394334, No Obj: 0.003358, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.593943, Class: 1.000000, Obj: 0.356310, No Obj: 0.003325, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.626414, Class: 1.000000, Obj: 0.386955, No Obj: 0.004412, Avg Recall: 0.743802, count: 121 -4785: 0.424738, 0.441475 avg, 0.001000 rate, 26.030647 seconds, 1224960 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.612901, Class: 1.000000, Obj: 0.360370, No Obj: 0.003407, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.586002, Class: 1.000000, Obj: 0.377660, No Obj: 0.003313, Avg Recall: 0.679688, count: 128 -Region Avg IOU: 0.592813, Class: 1.000000, Obj: 0.368922, No Obj: 0.003128, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.574898, Class: 1.000000, Obj: 0.350111, No Obj: 0.003609, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.657982, Class: 1.000000, Obj: 0.393600, No Obj: 0.003085, Avg Recall: 0.802632, count: 76 -Region Avg IOU: 0.581873, Class: 1.000000, Obj: 0.334764, No Obj: 0.003901, Avg Recall: 0.702479, count: 121 -Region Avg IOU: 0.524880, Class: 1.000000, Obj: 0.297867, No Obj: 0.002998, Avg Recall: 0.556452, count: 124 -Region Avg IOU: 0.560361, Class: 1.000000, Obj: 0.338899, No Obj: 0.003239, Avg Recall: 0.623932, count: 117 -4786: 0.421176, 0.439445 avg, 0.001000 rate, 24.073383 seconds, 1225216 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.619667, Class: 1.000000, Obj: 0.396249, No Obj: 0.003307, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.598473, Class: 1.000000, Obj: 0.412057, No Obj: 0.002749, Avg Recall: 0.741176, count: 85 -Region Avg IOU: 0.619267, Class: 1.000000, Obj: 0.347539, No Obj: 0.003242, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.596947, Class: 1.000000, Obj: 0.348789, No Obj: 0.002944, Avg Recall: 0.716049, count: 81 -Region Avg IOU: 0.625799, Class: 1.000000, Obj: 0.374181, No Obj: 0.002814, Avg Recall: 0.779070, count: 86 -Region Avg IOU: 0.579927, Class: 1.000000, Obj: 0.333593, No Obj: 0.002948, Avg Recall: 0.711111, count: 90 -Region Avg IOU: 0.548897, Class: 1.000000, Obj: 0.312442, No Obj: 0.003069, Avg Recall: 0.630435, count: 92 -Region Avg IOU: 0.623379, Class: 1.000000, Obj: 0.367533, No Obj: 0.003481, Avg Recall: 0.722222, count: 126 -4787: 0.387236, 0.434224 avg, 0.001000 rate, 24.466255 seconds, 1225472 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.592053, Class: 1.000000, Obj: 0.332072, No Obj: 0.003320, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.606868, Class: 1.000000, Obj: 0.374703, No Obj: 0.002887, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.653234, Class: 1.000000, Obj: 0.381459, No Obj: 0.003114, Avg Recall: 0.771084, count: 83 -Region Avg IOU: 0.597220, Class: 1.000000, Obj: 0.370644, No Obj: 0.003043, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.613881, Class: 1.000000, Obj: 0.348818, No Obj: 0.003144, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.594473, Class: 1.000000, Obj: 0.321511, No Obj: 0.003267, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.575952, Class: 1.000000, Obj: 0.330165, No Obj: 0.003831, Avg Recall: 0.664557, count: 158 -Region Avg IOU: 0.552419, Class: 1.000000, Obj: 0.373713, No Obj: 0.003346, Avg Recall: 0.628099, count: 121 -4788: 0.484390, 0.439241 avg, 0.001000 rate, 24.627556 seconds, 1225728 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.561299, Class: 1.000000, Obj: 0.287333, No Obj: 0.003202, Avg Recall: 0.619048, count: 126 -Region Avg IOU: 0.594573, Class: 1.000000, Obj: 0.387704, No Obj: 0.002785, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.601534, Class: 1.000000, Obj: 0.353815, No Obj: 0.003165, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.623495, Class: 1.000000, Obj: 0.330601, No Obj: 0.002969, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.602178, Class: 1.000000, Obj: 0.338584, No Obj: 0.003928, Avg Recall: 0.748387, count: 155 -Region Avg IOU: 0.599121, Class: 1.000000, Obj: 0.331673, No Obj: 0.003339, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.598494, Class: 1.000000, Obj: 0.320665, No Obj: 0.003651, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.584792, Class: 1.000000, Obj: 0.310438, No Obj: 0.003283, Avg Recall: 0.680672, count: 119 -4789: 0.519521, 0.447269 avg, 0.001000 rate, 23.833773 seconds, 1225984 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.574651, Class: 1.000000, Obj: 0.356199, No Obj: 0.003634, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.597080, Class: 1.000000, Obj: 0.389038, No Obj: 0.003408, Avg Recall: 0.702479, count: 121 -Region Avg IOU: 0.626450, Class: 1.000000, Obj: 0.376208, No Obj: 0.003340, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.542963, Class: 1.000000, Obj: 0.348316, No Obj: 0.002974, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.612836, Class: 1.000000, Obj: 0.372531, No Obj: 0.003521, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.590416, Class: 1.000000, Obj: 0.366339, No Obj: 0.003421, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.599235, Class: 1.000000, Obj: 0.373359, No Obj: 0.004172, Avg Recall: 0.653846, count: 130 -Region Avg IOU: 0.572094, Class: 1.000000, Obj: 0.346465, No Obj: 0.003033, Avg Recall: 0.677778, count: 90 -4790: 0.468725, 0.449415 avg, 0.001000 rate, 24.945448 seconds, 1226240 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 17.694176 seconds -Region Avg IOU: 0.534972, Class: 1.000000, Obj: 0.331715, No Obj: 0.004521, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.481137, Class: 1.000000, Obj: 0.253380, No Obj: 0.003588, Avg Recall: 0.510204, count: 98 -Region Avg IOU: 0.447705, Class: 1.000000, Obj: 0.286516, No Obj: 0.003638, Avg Recall: 0.468085, count: 94 -Region Avg IOU: 0.419060, Class: 1.000000, Obj: 0.246577, No Obj: 0.004271, Avg Recall: 0.416667, count: 132 -Region Avg IOU: 0.462436, Class: 1.000000, Obj: 0.262726, No Obj: 0.004458, Avg Recall: 0.511628, count: 129 -Region Avg IOU: 0.529858, Class: 1.000000, Obj: 0.269559, No Obj: 0.004077, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.470573, Class: 1.000000, Obj: 0.266671, No Obj: 0.004303, Avg Recall: 0.500000, count: 124 -Region Avg IOU: 0.452625, Class: 1.000000, Obj: 0.286329, No Obj: 0.003223, Avg Recall: 0.477273, count: 88 -4791: 0.522330, 0.456706 avg, 0.001000 rate, 17.791971 seconds, 1226496 images -Loaded: 3.078855 seconds -Region Avg IOU: 0.457449, Class: 1.000000, Obj: 0.281048, No Obj: 0.004628, Avg Recall: 0.514019, count: 107 -Region Avg IOU: 0.459942, Class: 1.000000, Obj: 0.273111, No Obj: 0.003556, Avg Recall: 0.487179, count: 78 -Region Avg IOU: 0.501275, Class: 1.000000, Obj: 0.330753, No Obj: 0.004278, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.511913, Class: 1.000000, Obj: 0.326756, No Obj: 0.004719, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.456950, Class: 1.000000, Obj: 0.287264, No Obj: 0.004269, Avg Recall: 0.532110, count: 109 -Region Avg IOU: 0.509831, Class: 1.000000, Obj: 0.310078, No Obj: 0.004341, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.527202, Class: 1.000000, Obj: 0.295713, No Obj: 0.004757, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.453790, Class: 1.000000, Obj: 0.269193, No Obj: 0.004956, Avg Recall: 0.463576, count: 151 -4792: 0.514598, 0.462495 avg, 0.001000 rate, 15.231082 seconds, 1226752 images -Loaded: 6.421505 seconds -Region Avg IOU: 0.534267, Class: 1.000000, Obj: 0.369030, No Obj: 0.004947, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.505786, Class: 1.000000, Obj: 0.311728, No Obj: 0.005270, Avg Recall: 0.516393, count: 122 -Region Avg IOU: 0.452558, Class: 1.000000, Obj: 0.307411, No Obj: 0.004364, Avg Recall: 0.466667, count: 105 -Region Avg IOU: 0.483225, Class: 1.000000, Obj: 0.348564, No Obj: 0.005427, Avg Recall: 0.518519, count: 135 -Region Avg IOU: 0.516970, Class: 1.000000, Obj: 0.336314, No Obj: 0.005290, Avg Recall: 0.556452, count: 124 -Region Avg IOU: 0.457010, Class: 1.000000, Obj: 0.306741, No Obj: 0.003827, Avg Recall: 0.453488, count: 86 -Region Avg IOU: 0.443637, Class: 1.000000, Obj: 0.292939, No Obj: 0.004109, Avg Recall: 0.452830, count: 106 -Region Avg IOU: 0.461671, Class: 1.000000, Obj: 0.272394, No Obj: 0.005216, Avg Recall: 0.535433, count: 127 -4793: 0.597889, 0.476035 avg, 0.001000 rate, 18.724506 seconds, 1227008 images -Loaded: 2.266771 seconds -Region Avg IOU: 0.418584, Class: 1.000000, Obj: 0.351843, No Obj: 0.004781, Avg Recall: 0.453488, count: 86 -Region Avg IOU: 0.506020, Class: 1.000000, Obj: 0.364988, No Obj: 0.005138, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.487051, Class: 1.000000, Obj: 0.355915, No Obj: 0.005158, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.442841, Class: 1.000000, Obj: 0.342059, No Obj: 0.004740, Avg Recall: 0.450450, count: 111 -Region Avg IOU: 0.429049, Class: 1.000000, Obj: 0.325686, No Obj: 0.005347, Avg Recall: 0.438017, count: 121 -Region Avg IOU: 0.501726, Class: 1.000000, Obj: 0.341392, No Obj: 0.005005, Avg Recall: 0.516129, count: 124 -Region Avg IOU: 0.439203, Class: 1.000000, Obj: 0.356368, No Obj: 0.004901, Avg Recall: 0.460870, count: 115 -Region Avg IOU: 0.488192, Class: 1.000000, Obj: 0.318334, No Obj: 0.005683, Avg Recall: 0.480000, count: 100 -4794: 0.479506, 0.476382 avg, 0.001000 rate, 18.506100 seconds, 1227264 images -Loaded: 2.027629 seconds -Region Avg IOU: 0.513923, Class: 1.000000, Obj: 0.328728, No Obj: 0.005258, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.453328, Class: 1.000000, Obj: 0.351737, No Obj: 0.005071, Avg Recall: 0.491379, count: 116 -Region Avg IOU: 0.460996, Class: 1.000000, Obj: 0.344998, No Obj: 0.005831, Avg Recall: 0.480000, count: 125 -Region Avg IOU: 0.507664, Class: 1.000000, Obj: 0.354854, No Obj: 0.005372, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.479649, Class: 1.000000, Obj: 0.365004, No Obj: 0.005255, Avg Recall: 0.532710, count: 107 -Region Avg IOU: 0.514213, Class: 1.000000, Obj: 0.374102, No Obj: 0.006116, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.482274, Class: 1.000000, Obj: 0.375790, No Obj: 0.005658, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.446684, Class: 1.000000, Obj: 0.353435, No Obj: 0.005350, Avg Recall: 0.481481, count: 135 -4795: 0.468782, 0.475622 avg, 0.001000 rate, 16.464787 seconds, 1227520 images -Loaded: 3.530661 seconds -Region Avg IOU: 0.482794, Class: 1.000000, Obj: 0.394637, No Obj: 0.004998, Avg Recall: 0.545455, count: 88 -Region Avg IOU: 0.437720, Class: 1.000000, Obj: 0.368474, No Obj: 0.006114, Avg Recall: 0.490566, count: 106 -Region Avg IOU: 0.504396, Class: 1.000000, Obj: 0.353201, No Obj: 0.004862, Avg Recall: 0.537500, count: 80 -Region Avg IOU: 0.422972, Class: 1.000000, Obj: 0.332383, No Obj: 0.005683, Avg Recall: 0.451327, count: 113 -Region Avg IOU: 0.433428, Class: 1.000000, Obj: 0.304554, No Obj: 0.005285, Avg Recall: 0.485437, count: 103 -Region Avg IOU: 0.487974, Class: 1.000000, Obj: 0.409515, No Obj: 0.005209, Avg Recall: 0.500000, count: 76 -Region Avg IOU: 0.444632, Class: 1.000000, Obj: 0.396893, No Obj: 0.005221, Avg Recall: 0.505155, count: 97 -Region Avg IOU: 0.497868, Class: 1.000000, Obj: 0.371364, No Obj: 0.005789, Avg Recall: 0.557522, count: 113 -4796: 0.506259, 0.478686 avg, 0.001000 rate, 16.175261 seconds, 1227776 images -Loaded: 5.281531 seconds -Region Avg IOU: 0.438167, Class: 1.000000, Obj: 0.363682, No Obj: 0.005487, Avg Recall: 0.455285, count: 123 -Region Avg IOU: 0.423860, Class: 1.000000, Obj: 0.347831, No Obj: 0.005903, Avg Recall: 0.474576, count: 118 -Region Avg IOU: 0.481651, Class: 1.000000, Obj: 0.359366, No Obj: 0.005959, Avg Recall: 0.520000, count: 100 -Region Avg IOU: 0.476864, Class: 1.000000, Obj: 0.357959, No Obj: 0.006509, Avg Recall: 0.519685, count: 127 -Region Avg IOU: 0.441176, Class: 1.000000, Obj: 0.385090, No Obj: 0.005146, Avg Recall: 0.467890, count: 109 -Region Avg IOU: 0.517246, Class: 1.000000, Obj: 0.432825, No Obj: 0.005661, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.443767, Class: 1.000000, Obj: 0.380498, No Obj: 0.005048, Avg Recall: 0.470000, count: 100 -Region Avg IOU: 0.486697, Class: 1.000000, Obj: 0.348623, No Obj: 0.006008, Avg Recall: 0.555556, count: 117 -4797: 0.534482, 0.484265 avg, 0.001000 rate, 18.636557 seconds, 1228032 images -Loaded: 2.285295 seconds -Region Avg IOU: 0.460424, Class: 1.000000, Obj: 0.379896, No Obj: 0.005854, Avg Recall: 0.475728, count: 103 -Region Avg IOU: 0.427848, Class: 1.000000, Obj: 0.342475, No Obj: 0.005993, Avg Recall: 0.453704, count: 108 -Region Avg IOU: 0.387051, Class: 1.000000, Obj: 0.307908, No Obj: 0.005975, Avg Recall: 0.365672, count: 134 -Region Avg IOU: 0.433588, Class: 1.000000, Obj: 0.357097, No Obj: 0.006128, Avg Recall: 0.482269, count: 141 -Region Avg IOU: 0.473961, Class: 1.000000, Obj: 0.400043, No Obj: 0.006042, Avg Recall: 0.522936, count: 109 -Region Avg IOU: 0.492016, Class: 1.000000, Obj: 0.407848, No Obj: 0.005210, Avg Recall: 0.544444, count: 90 -Region Avg IOU: 0.495164, Class: 1.000000, Obj: 0.333975, No Obj: 0.006086, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.464906, Class: 1.000000, Obj: 0.355689, No Obj: 0.005841, Avg Recall: 0.470588, count: 119 -4798: 0.541136, 0.489952 avg, 0.001000 rate, 17.523596 seconds, 1228288 images -Loaded: 4.017493 seconds -Region Avg IOU: 0.469125, Class: 1.000000, Obj: 0.340166, No Obj: 0.005011, Avg Recall: 0.485437, count: 103 -Region Avg IOU: 0.464570, Class: 1.000000, Obj: 0.386231, No Obj: 0.006106, Avg Recall: 0.508197, count: 122 -Region Avg IOU: 0.449167, Class: 1.000000, Obj: 0.328401, No Obj: 0.004755, Avg Recall: 0.463636, count: 110 -Region Avg IOU: 0.451418, Class: 1.000000, Obj: 0.312100, No Obj: 0.005695, Avg Recall: 0.532110, count: 109 -Region Avg IOU: 0.465231, Class: 1.000000, Obj: 0.397937, No Obj: 0.005271, Avg Recall: 0.482759, count: 87 -Region Avg IOU: 0.404439, Class: 1.000000, Obj: 0.339974, No Obj: 0.005364, Avg Recall: 0.428571, count: 126 -Region Avg IOU: 0.475168, Class: 1.000000, Obj: 0.403620, No Obj: 0.006228, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.456348, Class: 1.000000, Obj: 0.318938, No Obj: 0.005880, Avg Recall: 0.476562, count: 128 -4799: 0.526845, 0.493641 avg, 0.001000 rate, 18.085560 seconds, 1228544 images -Loaded: 2.792969 seconds -Region Avg IOU: 0.474072, Class: 1.000000, Obj: 0.370512, No Obj: 0.005254, Avg Recall: 0.494505, count: 91 -Region Avg IOU: 0.503611, Class: 1.000000, Obj: 0.363126, No Obj: 0.004721, Avg Recall: 0.613636, count: 88 -Region Avg IOU: 0.456762, Class: 1.000000, Obj: 0.343389, No Obj: 0.004880, Avg Recall: 0.486957, count: 115 -Region Avg IOU: 0.412051, Class: 1.000000, Obj: 0.320690, No Obj: 0.005358, Avg Recall: 0.410596, count: 151 -Region Avg IOU: 0.464251, Class: 1.000000, Obj: 0.351622, No Obj: 0.005504, Avg Recall: 0.512605, count: 119 -Region Avg IOU: 0.434077, Class: 1.000000, Obj: 0.322005, No Obj: 0.004967, Avg Recall: Saving weights to ./person9300/backup/tyvp_93_9c512_4800.weights -0.464286, count: 112 -Region Avg IOU: 0.457040, Class: 1.000000, Obj: 0.356750, No Obj: 0.005168, Avg Recall: 0.491071, count: 112 -Region Avg IOU: 0.472634, Class: 1.000000, Obj: 0.341196, No Obj: 0.005622, Avg Recall: 0.518797, count: 133 -4800: 0.520542, 0.496331 avg, 0.001000 rate, 18.871601 seconds, 1228800 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000069 seconds -Region Avg IOU: 0.633958, Class: 1.000000, Obj: 0.498017, No Obj: 0.003775, Avg Recall: 0.766129, count: 124 -Region Avg IOU: 0.670341, Class: 1.000000, Obj: 0.496488, No Obj: 0.004127, Avg Recall: 0.862069, count: 116 -Region Avg IOU: 0.576307, Class: 1.000000, Obj: 0.476144, No Obj: 0.003792, Avg Recall: 0.709677, count: 124 -Region Avg IOU: 0.618215, Class: 1.000000, Obj: 0.429686, No Obj: 0.003319, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.611761, Class: 1.000000, Obj: 0.475938, No Obj: 0.003498, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.597889, Class: 1.000000, Obj: 0.517673, No Obj: 0.003662, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.635271, Class: 1.000000, Obj: 0.472998, No Obj: 0.003593, Avg Recall: 0.807018, count: 114 -Region Avg IOU: 0.654872, Class: 1.000000, Obj: 0.501300, No Obj: 0.003822, Avg Recall: 0.809091, count: 110 -4801: 0.451275, 0.491826 avg, 0.001000 rate, 26.332878 seconds, 1229056 images -Loaded: 0.000119 seconds -Region Avg IOU: 0.648260, Class: 1.000000, Obj: 0.477596, No Obj: 0.003414, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.639043, Class: 1.000000, Obj: 0.448461, No Obj: 0.003291, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.596104, Class: 1.000000, Obj: 0.440005, No Obj: 0.003446, Avg Recall: 0.705426, count: 129 -Region Avg IOU: 0.644607, Class: 1.000000, Obj: 0.471790, No Obj: 0.004382, Avg Recall: 0.792308, count: 130 -Region Avg IOU: 0.643208, Class: 1.000000, Obj: 0.440597, No Obj: 0.003478, Avg Recall: 0.791304, count: 115 -Region Avg IOU: 0.622502, Class: 1.000000, Obj: 0.391137, No Obj: 0.003226, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.627371, Class: 1.000000, Obj: 0.427433, No Obj: 0.003002, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.588457, Class: 1.000000, Obj: 0.417861, No Obj: 0.003774, Avg Recall: 0.750000, count: 120 -4802: 0.455361, 0.488179 avg, 0.001000 rate, 26.153223 seconds, 1229312 images -Loaded: 0.000119 seconds -Region Avg IOU: 0.668275, Class: 1.000000, Obj: 0.485431, No Obj: 0.002534, Avg Recall: 0.845070, count: 71 -Region Avg IOU: 0.592805, Class: 1.000000, Obj: 0.406365, No Obj: 0.003260, Avg Recall: 0.680328, count: 122 -Region Avg IOU: 0.612909, Class: 1.000000, Obj: 0.397949, No Obj: 0.002685, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.614700, Class: 1.000000, Obj: 0.455987, No Obj: 0.002748, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.645981, Class: 1.000000, Obj: 0.430591, No Obj: 0.003037, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.675221, Class: 1.000000, Obj: 0.393719, No Obj: 0.003156, Avg Recall: 0.845455, count: 110 -Region Avg IOU: 0.637298, Class: 1.000000, Obj: 0.420506, No Obj: 0.002673, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.627718, Class: 1.000000, Obj: 0.376805, No Obj: 0.002850, Avg Recall: 0.790476, count: 105 -4803: 0.375530, 0.476914 avg, 0.001000 rate, 27.084305 seconds, 1229568 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.599889, Class: 1.000000, Obj: 0.346597, No Obj: 0.002735, Avg Recall: 0.712000, count: 125 -Region Avg IOU: 0.635321, Class: 1.000000, Obj: 0.323809, No Obj: 0.002476, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.671423, Class: 1.000000, Obj: 0.354012, No Obj: 0.002636, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.630052, Class: 1.000000, Obj: 0.361989, No Obj: 0.002540, Avg Recall: 0.802083, count: 96 -Region Avg IOU: 0.596281, Class: 1.000000, Obj: 0.379065, No Obj: 0.002687, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.621957, Class: 1.000000, Obj: 0.335393, No Obj: 0.002770, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.621934, Class: 1.000000, Obj: 0.357225, No Obj: 0.002513, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.632825, Class: 1.000000, Obj: 0.374218, No Obj: 0.002602, Avg Recall: 0.776786, count: 112 -4804: 0.460641, 0.475287 avg, 0.001000 rate, 26.445433 seconds, 1229824 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.641508, Class: 1.000000, Obj: 0.366688, No Obj: 0.002626, Avg Recall: 0.804878, count: 123 -Region Avg IOU: 0.615323, Class: 1.000000, Obj: 0.338587, No Obj: 0.002376, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.587545, Class: 1.000000, Obj: 0.367006, No Obj: 0.002182, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.627540, Class: 1.000000, Obj: 0.323475, No Obj: 0.002238, Avg Recall: 0.741176, count: 85 -Region Avg IOU: 0.647267, Class: 1.000000, Obj: 0.391833, No Obj: 0.002354, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.614869, Class: 1.000000, Obj: 0.342794, No Obj: 0.002680, Avg Recall: 0.735294, count: 136 -Region Avg IOU: 0.672313, Class: 1.000000, Obj: 0.314017, No Obj: 0.002333, Avg Recall: 0.859813, count: 107 -Region Avg IOU: 0.655658, Class: 1.000000, Obj: 0.374277, No Obj: 0.002484, Avg Recall: 0.791209, count: 91 -4805: 0.422756, 0.470034 avg, 0.001000 rate, 28.518766 seconds, 1230080 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.666310, Class: 1.000000, Obj: 0.313647, No Obj: 0.002172, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.649522, Class: 1.000000, Obj: 0.307774, No Obj: 0.002083, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.645256, Class: 1.000000, Obj: 0.349119, No Obj: 0.002683, Avg Recall: 0.798319, count: 119 -Region Avg IOU: 0.619157, Class: 1.000000, Obj: 0.318529, No Obj: 0.002516, Avg Recall: 0.764228, count: 123 -Region Avg IOU: 0.666007, Class: 1.000000, Obj: 0.371144, No Obj: 0.002061, Avg Recall: 0.846154, count: 91 -Region Avg IOU: 0.651705, Class: 1.000000, Obj: 0.345881, No Obj: 0.002460, Avg Recall: 0.775862, count: 116 -Region Avg IOU: 0.647493, Class: 1.000000, Obj: 0.304627, No Obj: 0.002278, Avg Recall: 0.787500, count: 80 -Region Avg IOU: 0.636647, Class: 1.000000, Obj: 0.341671, No Obj: 0.001998, Avg Recall: 0.780000, count: 100 -4806: 0.451161, 0.468147 avg, 0.001000 rate, 27.136009 seconds, 1230336 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.685396, Class: 1.000000, Obj: 0.340723, No Obj: 0.002282, Avg Recall: 0.861702, count: 94 -Region Avg IOU: 0.649376, Class: 1.000000, Obj: 0.336963, No Obj: 0.002358, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.670464, Class: 1.000000, Obj: 0.317255, No Obj: 0.002242, Avg Recall: 0.833333, count: 90 -Region Avg IOU: 0.663116, Class: 1.000000, Obj: 0.347635, No Obj: 0.002186, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.623532, Class: 1.000000, Obj: 0.285309, No Obj: 0.002545, Avg Recall: 0.744000, count: 125 -Region Avg IOU: 0.611571, Class: 1.000000, Obj: 0.296228, No Obj: 0.001914, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.648442, Class: 1.000000, Obj: 0.287963, No Obj: 0.002303, Avg Recall: 0.849558, count: 113 -Region Avg IOU: 0.664270, Class: 1.000000, Obj: 0.324431, No Obj: 0.002417, Avg Recall: 0.834862, count: 109 -4807: 0.490049, 0.470337 avg, 0.001000 rate, 26.744274 seconds, 1230592 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.621227, Class: 1.000000, Obj: 0.308922, No Obj: 0.002156, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.640220, Class: 1.000000, Obj: 0.315600, No Obj: 0.002172, Avg Recall: 0.801587, count: 126 -Region Avg IOU: 0.679406, Class: 1.000000, Obj: 0.345361, No Obj: 0.002349, Avg Recall: 0.873786, count: 103 -Region Avg IOU: 0.608418, Class: 1.000000, Obj: 0.303075, No Obj: 0.001927, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.646728, Class: 1.000000, Obj: 0.293248, No Obj: 0.002271, Avg Recall: 0.800000, count: 125 -Region Avg IOU: 0.639070, Class: 1.000000, Obj: 0.341954, No Obj: 0.002089, Avg Recall: 0.844444, count: 90 -Region Avg IOU: 0.630436, Class: 1.000000, Obj: 0.337993, No Obj: 0.002350, Avg Recall: 0.800000, count: 135 -Region Avg IOU: 0.661491, Class: 1.000000, Obj: 0.313350, No Obj: 0.002232, Avg Recall: 0.828571, count: 105 -4808: 0.496165, 0.472920 avg, 0.001000 rate, 28.725376 seconds, 1230848 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.589621, Class: 1.000000, Obj: 0.335628, No Obj: 0.002475, Avg Recall: 0.716216, count: 148 -Region Avg IOU: 0.605842, Class: 1.000000, Obj: 0.292848, No Obj: 0.002573, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.635145, Class: 1.000000, Obj: 0.365782, No Obj: 0.002411, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.622774, Class: 1.000000, Obj: 0.329950, No Obj: 0.002019, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.653402, Class: 1.000000, Obj: 0.358590, No Obj: 0.002840, Avg Recall: 0.827586, count: 116 -Region Avg IOU: 0.677704, Class: 1.000000, Obj: 0.404652, No Obj: 0.002631, Avg Recall: 0.834951, count: 103 -Region Avg IOU: 0.624854, Class: 1.000000, Obj: 0.388428, No Obj: 0.002383, Avg Recall: 0.808219, count: 73 -Region Avg IOU: 0.615646, Class: 1.000000, Obj: 0.282944, No Obj: 0.002627, Avg Recall: 0.743056, count: 144 -4809: 0.466689, 0.472297 avg, 0.001000 rate, 28.068333 seconds, 1231104 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.692271, Class: 1.000000, Obj: 0.420446, No Obj: 0.002522, Avg Recall: 0.860465, count: 86 -Region Avg IOU: 0.649075, Class: 1.000000, Obj: 0.391880, No Obj: 0.002736, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.628401, Class: 1.000000, Obj: 0.365973, No Obj: 0.002706, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.605625, Class: 1.000000, Obj: 0.368478, No Obj: 0.002548, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.575553, Class: 1.000000, Obj: 0.328770, No Obj: 0.002922, Avg Recall: 0.640845, count: 142 -Region Avg IOU: 0.617183, Class: 1.000000, Obj: 0.348094, No Obj: 0.002992, Avg Recall: 0.811475, count: 122 -Region Avg IOU: 0.653865, Class: 1.000000, Obj: 0.356403, No Obj: 0.002697, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.620114, Class: 1.000000, Obj: 0.356194, No Obj: 0.002752, Avg Recall: 0.747967, count: 123 -4810: 0.479496, 0.473017 avg, 0.001000 rate, 26.811115 seconds, 1231360 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000073 seconds -Region Avg IOU: 0.646937, Class: 1.000000, Obj: 0.378415, No Obj: 0.003026, Avg Recall: 0.806897, count: 145 -Region Avg IOU: 0.675181, Class: 1.000000, Obj: 0.387587, No Obj: 0.003173, Avg Recall: 0.840708, count: 113 -Region Avg IOU: 0.684819, Class: 1.000000, Obj: 0.401740, No Obj: 0.002694, Avg Recall: 0.829545, count: 88 -Region Avg IOU: 0.617971, Class: 1.000000, Obj: 0.344268, No Obj: 0.002321, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.635112, Class: 1.000000, Obj: 0.394962, No Obj: 0.003022, Avg Recall: 0.766917, count: 133 -Region Avg IOU: 0.678305, Class: 1.000000, Obj: 0.381170, No Obj: 0.002836, Avg Recall: 0.862069, count: 116 -Region Avg IOU: 0.650417, Class: 1.000000, Obj: 0.417673, No Obj: 0.002532, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.648255, Class: 1.000000, Obj: 0.428660, No Obj: 0.002694, Avg Recall: 0.766355, count: 107 -4811: 0.497457, 0.475461 avg, 0.001000 rate, 28.375996 seconds, 1231616 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.675849, Class: 1.000000, Obj: 0.416248, No Obj: 0.003001, Avg Recall: 0.854701, count: 117 -Region Avg IOU: 0.613117, Class: 1.000000, Obj: 0.393055, No Obj: 0.002952, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.656328, Class: 1.000000, Obj: 0.419491, No Obj: 0.002648, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.653467, Class: 1.000000, Obj: 0.447947, No Obj: 0.002863, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.657741, Class: 1.000000, Obj: 0.412486, No Obj: 0.002848, Avg Recall: 0.840000, count: 100 -Region Avg IOU: 0.629380, Class: 1.000000, Obj: 0.395678, No Obj: 0.003035, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.663521, Class: 1.000000, Obj: 0.472300, No Obj: 0.003142, Avg Recall: 0.824074, count: 108 -Region Avg IOU: 0.641690, Class: 1.000000, Obj: 0.420986, No Obj: 0.003086, Avg Recall: 0.800000, count: 105 -4812: 0.443464, 0.472261 avg, 0.001000 rate, 27.297476 seconds, 1231872 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.630558, Class: 1.000000, Obj: 0.394842, No Obj: 0.003149, Avg Recall: 0.782609, count: 138 -Region Avg IOU: 0.613238, Class: 1.000000, Obj: 0.418359, No Obj: 0.002694, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.598365, Class: 1.000000, Obj: 0.362776, No Obj: 0.002697, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.635458, Class: 1.000000, Obj: 0.420080, No Obj: 0.003139, Avg Recall: 0.805085, count: 118 -Region Avg IOU: 0.625695, Class: 1.000000, Obj: 0.393246, No Obj: 0.003502, Avg Recall: 0.750000, count: 124 -Region Avg IOU: 0.653726, Class: 1.000000, Obj: 0.446927, No Obj: 0.003117, Avg Recall: 0.836364, count: 110 -Region Avg IOU: 0.694208, Class: 1.000000, Obj: 0.446571, No Obj: 0.002828, Avg Recall: 0.858586, count: 99 -Region Avg IOU: 0.659589, Class: 1.000000, Obj: 0.378289, No Obj: 0.002779, Avg Recall: 0.811321, count: 106 -4813: 0.462754, 0.471310 avg, 0.001000 rate, 28.481432 seconds, 1232128 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.649160, Class: 1.000000, Obj: 0.415429, No Obj: 0.002790, Avg Recall: 0.838384, count: 99 -Region Avg IOU: 0.631169, Class: 1.000000, Obj: 0.425801, No Obj: 0.002850, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.628050, Class: 1.000000, Obj: 0.459274, No Obj: 0.003037, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.629251, Class: 1.000000, Obj: 0.376552, No Obj: 0.003028, Avg Recall: 0.785124, count: 121 -Region Avg IOU: 0.723165, Class: 1.000000, Obj: 0.457770, No Obj: 0.002915, Avg Recall: 0.905882, count: 85 -Region Avg IOU: 0.664057, Class: 1.000000, Obj: 0.464939, No Obj: 0.002793, Avg Recall: 0.841584, count: 101 -Region Avg IOU: 0.659557, Class: 1.000000, Obj: 0.407035, No Obj: 0.003486, Avg Recall: 0.852713, count: 129 -Region Avg IOU: 0.684109, Class: 1.000000, Obj: 0.414670, No Obj: 0.003714, Avg Recall: 0.854305, count: 151 -4814: 0.498457, 0.474025 avg, 0.001000 rate, 27.346739 seconds, 1232384 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.608321, Class: 1.000000, Obj: 0.428811, No Obj: 0.002683, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.651667, Class: 1.000000, Obj: 0.428725, No Obj: 0.002972, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.585989, Class: 1.000000, Obj: 0.403549, No Obj: 0.002792, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.658245, Class: 1.000000, Obj: 0.423004, No Obj: 0.002740, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.653434, Class: 1.000000, Obj: 0.467879, No Obj: 0.002738, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.639010, Class: 1.000000, Obj: 0.433266, No Obj: 0.002868, Avg Recall: 0.802198, count: 91 -Region Avg IOU: 0.622340, Class: 1.000000, Obj: 0.416883, No Obj: 0.003087, Avg Recall: 0.713115, count: 122 -Region Avg IOU: 0.613768, Class: 1.000000, Obj: 0.412913, No Obj: 0.002734, Avg Recall: 0.766667, count: 90 -4815: 0.404869, 0.467109 avg, 0.001000 rate, 28.247028 seconds, 1232640 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.653292, Class: 1.000000, Obj: 0.471640, No Obj: 0.002865, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.618027, Class: 1.000000, Obj: 0.421028, No Obj: 0.002707, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.654952, Class: 1.000000, Obj: 0.377791, No Obj: 0.002718, Avg Recall: 0.820000, count: 100 -Region Avg IOU: 0.637197, Class: 1.000000, Obj: 0.380218, No Obj: 0.002903, Avg Recall: 0.796460, count: 113 -Region Avg IOU: 0.624142, Class: 1.000000, Obj: 0.361758, No Obj: 0.002939, Avg Recall: 0.767442, count: 129 -Region Avg IOU: 0.608857, Class: 1.000000, Obj: 0.413893, No Obj: 0.002864, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.602421, Class: 1.000000, Obj: 0.378340, No Obj: 0.003173, Avg Recall: 0.741722, count: 151 -Region Avg IOU: 0.605456, Class: 1.000000, Obj: 0.341672, No Obj: 0.003133, Avg Recall: 0.722222, count: 126 -4816: 0.484202, 0.468819 avg, 0.001000 rate, 28.694569 seconds, 1232896 images -Loaded: 0.000059 seconds -Region Avg IOU: 0.676903, Class: 1.000000, Obj: 0.360148, No Obj: 0.002925, Avg Recall: 0.825688, count: 109 -Region Avg IOU: 0.630613, Class: 1.000000, Obj: 0.369288, No Obj: 0.002787, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.643133, Class: 1.000000, Obj: 0.436930, No Obj: 0.002939, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.615614, Class: 1.000000, Obj: 0.374411, No Obj: 0.002990, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.618717, Class: 1.000000, Obj: 0.425400, No Obj: 0.002834, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.633839, Class: 1.000000, Obj: 0.415606, No Obj: 0.002840, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.642026, Class: 1.000000, Obj: 0.423305, No Obj: 0.002695, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.666972, Class: 1.000000, Obj: 0.356812, No Obj: 0.002576, Avg Recall: 0.847826, count: 92 -4817: 0.455932, 0.467530 avg, 0.001000 rate, 28.288591 seconds, 1233152 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.646867, Class: 1.000000, Obj: 0.351400, No Obj: 0.002695, Avg Recall: 0.795455, count: 132 -Region Avg IOU: 0.634709, Class: 1.000000, Obj: 0.387818, No Obj: 0.002817, Avg Recall: 0.775862, count: 116 -Region Avg IOU: 0.632290, Class: 1.000000, Obj: 0.406125, No Obj: 0.002515, Avg Recall: 0.829545, count: 88 -Region Avg IOU: 0.633625, Class: 1.000000, Obj: 0.389463, No Obj: 0.002880, Avg Recall: 0.771186, count: 118 -Region Avg IOU: 0.648272, Class: 1.000000, Obj: 0.360301, No Obj: 0.002926, Avg Recall: 0.779528, count: 127 -Region Avg IOU: 0.653583, Class: 1.000000, Obj: 0.395434, No Obj: 0.002898, Avg Recall: 0.803150, count: 127 -Region Avg IOU: 0.644107, Class: 1.000000, Obj: 0.397777, No Obj: 0.002655, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.662343, Class: 1.000000, Obj: 0.396358, No Obj: 0.002583, Avg Recall: 0.825243, count: 103 -4818: 0.477107, 0.468488 avg, 0.001000 rate, 28.259407 seconds, 1233408 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.607044, Class: 1.000000, Obj: 0.394607, No Obj: 0.002373, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.660391, Class: 1.000000, Obj: 0.391641, No Obj: 0.002915, Avg Recall: 0.842105, count: 133 -Region Avg IOU: 0.620624, Class: 1.000000, Obj: 0.386982, No Obj: 0.002499, Avg Recall: 0.752000, count: 125 -Region Avg IOU: 0.635268, Class: 1.000000, Obj: 0.412860, No Obj: 0.002528, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.645314, Class: 1.000000, Obj: 0.334801, No Obj: 0.002725, Avg Recall: 0.789916, count: 119 -Region Avg IOU: 0.639238, Class: 1.000000, Obj: 0.373727, No Obj: 0.002389, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.630976, Class: 1.000000, Obj: 0.361253, No Obj: 0.002544, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.650955, Class: 1.000000, Obj: 0.416827, No Obj: 0.002793, Avg Recall: 0.833333, count: 114 -4819: 0.480559, 0.469695 avg, 0.001000 rate, 27.959057 seconds, 1233664 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.652480, Class: 1.000000, Obj: 0.437452, No Obj: 0.002177, Avg Recall: 0.767123, count: 73 -Region Avg IOU: 0.672311, Class: 1.000000, Obj: 0.399080, No Obj: 0.002720, Avg Recall: 0.815126, count: 119 -Region Avg IOU: 0.632078, Class: 1.000000, Obj: 0.377536, No Obj: 0.002603, Avg Recall: 0.818182, count: 110 -Region Avg IOU: 0.674407, Class: 1.000000, Obj: 0.369774, No Obj: 0.002730, Avg Recall: 0.823529, count: 119 -Region Avg IOU: 0.673110, Class: 1.000000, Obj: 0.395934, No Obj: 0.002729, Avg Recall: 0.833333, count: 114 -Region Avg IOU: 0.665041, Class: 1.000000, Obj: 0.382431, No Obj: 0.002483, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.687097, Class: 1.000000, Obj: 0.376291, No Obj: 0.002746, Avg Recall: 0.880000, count: 100 -Region Avg IOU: 0.615610, Class: 1.000000, Obj: 0.378707, No Obj: 0.002193, Avg Recall: 0.740000, count: 100 -4820: 0.457462, 0.468471 avg, 0.001000 rate, 27.699667 seconds, 1233920 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 15.585286 seconds -Region Avg IOU: 0.605151, Class: 1.000000, Obj: 0.340887, No Obj: 0.003232, Avg Recall: 0.744186, count: 129 -Region Avg IOU: 0.588060, Class: 1.000000, Obj: 0.372211, No Obj: 0.002961, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.595107, Class: 1.000000, Obj: 0.383214, No Obj: 0.002934, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.577459, Class: 1.000000, Obj: 0.329763, No Obj: 0.002857, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.559439, Class: 1.000000, Obj: 0.365389, No Obj: 0.002995, Avg Recall: 0.641379, count: 145 -Region Avg IOU: 0.603150, Class: 1.000000, Obj: 0.353776, No Obj: 0.003117, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.551261, Class: 1.000000, Obj: 0.285236, No Obj: 0.003302, Avg Recall: 0.598485, count: 132 -Region Avg IOU: 0.609357, Class: 1.000000, Obj: 0.371420, No Obj: 0.002615, Avg Recall: 0.758242, count: 91 -4821: 0.443555, 0.465980 avg, 0.001000 rate, 26.089476 seconds, 1234176 images -Loaded: 0.000114 seconds -Region Avg IOU: 0.599830, Class: 1.000000, Obj: 0.375867, No Obj: 0.003358, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.577593, Class: 1.000000, Obj: 0.373081, No Obj: 0.002685, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.556961, Class: 1.000000, Obj: 0.304362, No Obj: 0.002662, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.614750, Class: 1.000000, Obj: 0.347555, No Obj: 0.003177, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.581779, Class: 1.000000, Obj: 0.390765, No Obj: 0.003052, Avg Recall: 0.697479, count: 119 -Region Avg IOU: 0.617355, Class: 1.000000, Obj: 0.358701, No Obj: 0.002840, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.624012, Class: 1.000000, Obj: 0.409612, No Obj: 0.003071, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.557506, Class: 1.000000, Obj: 0.336470, No Obj: 0.003377, Avg Recall: 0.678322, count: 143 -4822: 0.458721, 0.465254 avg, 0.001000 rate, 26.514860 seconds, 1234432 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.614421, Class: 1.000000, Obj: 0.379663, No Obj: 0.004130, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.608862, Class: 1.000000, Obj: 0.371016, No Obj: 0.003206, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.577345, Class: 1.000000, Obj: 0.368455, No Obj: 0.003073, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.605679, Class: 1.000000, Obj: 0.390013, No Obj: 0.003507, Avg Recall: 0.732283, count: 127 -Region Avg IOU: 0.551868, Class: 1.000000, Obj: 0.369870, No Obj: 0.002881, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.658791, Class: 1.000000, Obj: 0.398739, No Obj: 0.003760, Avg Recall: 0.862745, count: 102 -Region Avg IOU: 0.552515, Class: 1.000000, Obj: 0.332936, No Obj: 0.003343, Avg Recall: 0.648000, count: 125 -Region Avg IOU: 0.630074, Class: 1.000000, Obj: 0.376677, No Obj: 0.003528, Avg Recall: 0.800000, count: 110 -4823: 0.472580, 0.465986 avg, 0.001000 rate, 23.855921 seconds, 1234688 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.629803, Class: 1.000000, Obj: 0.412551, No Obj: 0.003600, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.592317, Class: 1.000000, Obj: 0.403139, No Obj: 0.002985, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.579803, Class: 1.000000, Obj: 0.376636, No Obj: 0.003068, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.632264, Class: 1.000000, Obj: 0.375100, No Obj: 0.003553, Avg Recall: 0.762712, count: 118 -Region Avg IOU: 0.618019, Class: 1.000000, Obj: 0.421591, No Obj: 0.003315, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.629224, Class: 1.000000, Obj: 0.421857, No Obj: 0.002583, Avg Recall: 0.760000, count: 75 -Region Avg IOU: 0.614680, Class: 1.000000, Obj: 0.358312, No Obj: 0.003436, Avg Recall: 0.767442, count: 129 -Region Avg IOU: 0.619829, Class: 1.000000, Obj: 0.398772, No Obj: 0.003358, Avg Recall: 0.735537, count: 121 -4824: 0.421079, 0.461496 avg, 0.001000 rate, 23.806736 seconds, 1234944 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.570361, Class: 1.000000, Obj: 0.377732, No Obj: 0.003599, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.631990, Class: 1.000000, Obj: 0.448288, No Obj: 0.002918, Avg Recall: 0.788235, count: 85 -Region Avg IOU: 0.598864, Class: 1.000000, Obj: 0.410977, No Obj: 0.003160, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.604637, Class: 1.000000, Obj: 0.395636, No Obj: 0.003531, Avg Recall: 0.757576, count: 132 -Region Avg IOU: 0.578102, Class: 1.000000, Obj: 0.407195, No Obj: 0.003549, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.593192, Class: 1.000000, Obj: 0.357609, No Obj: 0.002959, Avg Recall: 0.723684, count: 76 -Region Avg IOU: 0.612779, Class: 1.000000, Obj: 0.406772, No Obj: 0.003335, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.564976, Class: 1.000000, Obj: 0.365111, No Obj: 0.002903, Avg Recall: 0.647619, count: 105 -4825: 0.436664, 0.459013 avg, 0.001000 rate, 24.325224 seconds, 1235200 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.623846, Class: 1.000000, Obj: 0.414788, No Obj: 0.003611, Avg Recall: 0.726562, count: 128 -Region Avg IOU: 0.616790, Class: 1.000000, Obj: 0.428698, No Obj: 0.003923, Avg Recall: 0.725191, count: 131 -Region Avg IOU: 0.627490, Class: 1.000000, Obj: 0.403418, No Obj: 0.003784, Avg Recall: 0.773438, count: 128 -Region Avg IOU: 0.622524, Class: 1.000000, Obj: 0.460618, No Obj: 0.003422, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.637753, Class: 1.000000, Obj: 0.379010, No Obj: 0.003413, Avg Recall: 0.821429, count: 84 -Region Avg IOU: 0.573042, Class: 1.000000, Obj: 0.395177, No Obj: 0.003588, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.597543, Class: 1.000000, Obj: 0.419151, No Obj: 0.003526, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.585180, Class: 1.000000, Obj: 0.404849, No Obj: 0.003841, Avg Recall: 0.681416, count: 113 -4826: 0.465883, 0.459700 avg, 0.001000 rate, 25.095852 seconds, 1235456 images -Loaded: 0.000121 seconds -Region Avg IOU: 0.615316, Class: 1.000000, Obj: 0.415192, No Obj: 0.003936, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.590486, Class: 1.000000, Obj: 0.411461, No Obj: 0.003024, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.592217, Class: 1.000000, Obj: 0.408090, No Obj: 0.004327, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.631315, Class: 1.000000, Obj: 0.445647, No Obj: 0.003636, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.649872, Class: 1.000000, Obj: 0.469138, No Obj: 0.003525, Avg Recall: 0.820000, count: 100 -Region Avg IOU: 0.620960, Class: 1.000000, Obj: 0.416641, No Obj: 0.004548, Avg Recall: 0.748299, count: 147 -Region Avg IOU: 0.546706, Class: 1.000000, Obj: 0.423622, No Obj: 0.003870, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.577106, Class: 1.000000, Obj: 0.408185, No Obj: 0.003552, Avg Recall: 0.663866, count: 119 -4827: 0.421621, 0.455892 avg, 0.001000 rate, 23.725151 seconds, 1235712 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.567082, Class: 1.000000, Obj: 0.441910, No Obj: 0.003599, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.626778, Class: 1.000000, Obj: 0.478378, No Obj: 0.003594, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.591745, Class: 1.000000, Obj: 0.432326, No Obj: 0.003640, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.562516, Class: 1.000000, Obj: 0.419293, No Obj: 0.003441, Avg Recall: 0.644068, count: 118 -Region Avg IOU: 0.641154, Class: 1.000000, Obj: 0.450861, No Obj: 0.003672, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.634603, Class: 1.000000, Obj: 0.426807, No Obj: 0.004054, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.598591, Class: 1.000000, Obj: 0.415330, No Obj: 0.003792, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.611342, Class: 1.000000, Obj: 0.442656, No Obj: 0.003705, Avg Recall: 0.718447, count: 103 -4828: 0.371151, 0.447418 avg, 0.001000 rate, 25.493876 seconds, 1235968 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.611426, Class: 1.000000, Obj: 0.438847, No Obj: 0.003315, Avg Recall: 0.712329, count: 73 -Region Avg IOU: 0.620416, Class: 1.000000, Obj: 0.442013, No Obj: 0.003651, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.604245, Class: 1.000000, Obj: 0.474303, No Obj: 0.003710, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.576263, Class: 1.000000, Obj: 0.399223, No Obj: 0.003632, Avg Recall: 0.701613, count: 124 -Region Avg IOU: 0.571330, Class: 1.000000, Obj: 0.425327, No Obj: 0.003328, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.572930, Class: 1.000000, Obj: 0.387328, No Obj: 0.003549, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.640961, Class: 1.000000, Obj: 0.473140, No Obj: 0.003866, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.569255, Class: 1.000000, Obj: 0.432194, No Obj: 0.003461, Avg Recall: 0.700000, count: 100 -4829: 0.411025, 0.443778 avg, 0.001000 rate, 27.708956 seconds, 1236224 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.616480, Class: 1.000000, Obj: 0.408711, No Obj: 0.004005, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.604785, Class: 1.000000, Obj: 0.398214, No Obj: 0.003431, Avg Recall: 0.764228, count: 123 -Region Avg IOU: 0.612414, Class: 1.000000, Obj: 0.374061, No Obj: 0.003830, Avg Recall: 0.734848, count: 132 -Region Avg IOU: 0.583951, Class: 1.000000, Obj: 0.452633, No Obj: 0.003518, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.616645, Class: 1.000000, Obj: 0.439605, No Obj: 0.003996, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.573546, Class: 1.000000, Obj: 0.443244, No Obj: 0.004555, Avg Recall: 0.686131, count: 137 -Region Avg IOU: 0.592652, Class: 1.000000, Obj: 0.364978, No Obj: 0.003453, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.623961, Class: 1.000000, Obj: 0.448420, No Obj: 0.003775, Avg Recall: 0.735294, count: 102 -4830: 0.480532, 0.447454 avg, 0.001000 rate, 24.662760 seconds, 1236480 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.532385 seconds -Region Avg IOU: 0.552087, Class: 1.000000, Obj: 0.387526, No Obj: 0.003097, Avg Recall: 0.608108, count: 74 -Region Avg IOU: 0.514614, Class: 1.000000, Obj: 0.328346, No Obj: 0.003931, Avg Recall: 0.566929, count: 127 -Region Avg IOU: 0.605185, Class: 1.000000, Obj: 0.406731, No Obj: 0.004103, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.533940, Class: 1.000000, Obj: 0.332013, No Obj: 0.003618, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.505264, Class: 1.000000, Obj: 0.320312, No Obj: 0.003192, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.522733, Class: 1.000000, Obj: 0.320750, No Obj: 0.003071, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.601968, Class: 1.000000, Obj: 0.438893, No Obj: 0.003398, Avg Recall: 0.726027, count: 73 -Region Avg IOU: 0.552476, Class: 1.000000, Obj: 0.372601, No Obj: 0.003560, Avg Recall: 0.628866, count: 97 -4831: 0.389029, 0.441611 avg, 0.001000 rate, 21.074207 seconds, 1236736 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.577264, Class: 1.000000, Obj: 0.367140, No Obj: 0.004270, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.549402, Class: 1.000000, Obj: 0.297213, No Obj: 0.003072, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.590875, Class: 1.000000, Obj: 0.382728, No Obj: 0.004252, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.555465, Class: 1.000000, Obj: 0.340405, No Obj: 0.003573, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.614529, Class: 1.000000, Obj: 0.426712, No Obj: 0.004075, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.546631, Class: 1.000000, Obj: 0.338937, No Obj: 0.003349, Avg Recall: 0.603306, count: 121 -Region Avg IOU: 0.581514, Class: 1.000000, Obj: 0.346280, No Obj: 0.003634, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.537363, Class: 1.000000, Obj: 0.355668, No Obj: 0.003199, Avg Recall: 0.614583, count: 96 -4832: 0.429700, 0.440420 avg, 0.001000 rate, 22.453312 seconds, 1236992 images -Loaded: 0.650106 seconds -Region Avg IOU: 0.575057, Class: 1.000000, Obj: 0.365004, No Obj: 0.004350, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.513837, Class: 1.000000, Obj: 0.321432, No Obj: 0.003482, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.564339, Class: 1.000000, Obj: 0.355924, No Obj: 0.003322, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.580837, Class: 1.000000, Obj: 0.413216, No Obj: 0.003646, Avg Recall: 0.675325, count: 77 -Region Avg IOU: 0.518643, Class: 1.000000, Obj: 0.336373, No Obj: 0.003590, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.489921, Class: 1.000000, Obj: 0.328158, No Obj: 0.003793, Avg Recall: 0.544000, count: 125 -Region Avg IOU: 0.585066, Class: 1.000000, Obj: 0.351772, No Obj: 0.003512, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.543148, Class: 1.000000, Obj: 0.311357, No Obj: 0.003629, Avg Recall: 0.649485, count: 97 -4833: 0.445139, 0.440892 avg, 0.001000 rate, 22.988083 seconds, 1237248 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.545057, Class: 1.000000, Obj: 0.371441, No Obj: 0.003387, Avg Recall: 0.630137, count: 73 -Region Avg IOU: 0.566709, Class: 1.000000, Obj: 0.352903, No Obj: 0.004110, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.516918, Class: 1.000000, Obj: 0.323012, No Obj: 0.003589, Avg Recall: 0.578125, count: 128 -Region Avg IOU: 0.568716, Class: 1.000000, Obj: 0.367975, No Obj: 0.003545, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.523540, Class: 1.000000, Obj: 0.353964, No Obj: 0.003381, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.607355, Class: 1.000000, Obj: 0.396141, No Obj: 0.003695, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.564089, Class: 1.000000, Obj: 0.336319, No Obj: 0.004034, Avg Recall: 0.680000, count: 125 -Region Avg IOU: 0.552237, Class: 1.000000, Obj: 0.366557, No Obj: 0.004362, Avg Recall: 0.690265, count: 113 -4834: 0.452349, 0.442038 avg, 0.001000 rate, 22.604536 seconds, 1237504 images -Loaded: 0.241367 seconds -Region Avg IOU: 0.550590, Class: 1.000000, Obj: 0.322727, No Obj: 0.003547, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.565425, Class: 1.000000, Obj: 0.371654, No Obj: 0.003512, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.607058, Class: 1.000000, Obj: 0.420734, No Obj: 0.003636, Avg Recall: 0.755556, count: 90 -Region Avg IOU: 0.517675, Class: 1.000000, Obj: 0.330014, No Obj: 0.003267, Avg Recall: 0.574713, count: 87 -Region Avg IOU: 0.462122, Class: 1.000000, Obj: 0.323257, No Obj: 0.003016, Avg Recall: 0.484848, count: 99 -Region Avg IOU: 0.539634, Class: 1.000000, Obj: 0.359646, No Obj: 0.003860, Avg Recall: 0.598485, count: 132 -Region Avg IOU: 0.557495, Class: 1.000000, Obj: 0.395128, No Obj: 0.003622, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.537156, Class: 1.000000, Obj: 0.368954, No Obj: 0.004123, Avg Recall: 0.647541, count: 122 -4835: 0.404768, 0.438311 avg, 0.001000 rate, 21.650324 seconds, 1237760 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.594922, Class: 1.000000, Obj: 0.394756, No Obj: 0.003985, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.584672, Class: 1.000000, Obj: 0.407705, No Obj: 0.004069, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.572052, Class: 1.000000, Obj: 0.405494, No Obj: 0.003846, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.589446, Class: 1.000000, Obj: 0.346794, No Obj: 0.003741, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.549775, Class: 1.000000, Obj: 0.393015, No Obj: 0.003952, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.603761, Class: 1.000000, Obj: 0.396388, No Obj: 0.003707, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.565531, Class: 1.000000, Obj: 0.363455, No Obj: 0.003810, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.593374, Class: 1.000000, Obj: 0.387096, No Obj: 0.003566, Avg Recall: 0.662791, count: 86 -4836: 0.424142, 0.436894 avg, 0.001000 rate, 22.251553 seconds, 1238016 images -Loaded: 0.975685 seconds -Region Avg IOU: 0.480728, Class: 1.000000, Obj: 0.340023, No Obj: 0.003306, Avg Recall: 0.500000, count: 80 -Region Avg IOU: 0.542323, Class: 1.000000, Obj: 0.318825, No Obj: 0.004279, Avg Recall: 0.612403, count: 129 -Region Avg IOU: 0.535711, Class: 1.000000, Obj: 0.353458, No Obj: 0.004138, Avg Recall: 0.615385, count: 130 -Region Avg IOU: 0.527406, Class: 1.000000, Obj: 0.364676, No Obj: 0.004273, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.550964, Class: 1.000000, Obj: 0.339740, No Obj: 0.003343, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.513533, Class: 1.000000, Obj: 0.377765, No Obj: 0.003901, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.541205, Class: 1.000000, Obj: 0.346701, No Obj: 0.004160, Avg Recall: 0.619718, count: 142 -Region Avg IOU: 0.583800, Class: 1.000000, Obj: 0.413252, No Obj: 0.003159, Avg Recall: 0.739726, count: 73 -4837: 0.453204, 0.438525 avg, 0.001000 rate, 21.757324 seconds, 1238272 images -Loaded: 0.663228 seconds -Region Avg IOU: 0.541184, Class: 1.000000, Obj: 0.395598, No Obj: 0.004277, Avg Recall: 0.629870, count: 154 -Region Avg IOU: 0.528163, Class: 1.000000, Obj: 0.366407, No Obj: 0.003893, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.550905, Class: 1.000000, Obj: 0.392677, No Obj: 0.004319, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.553593, Class: 1.000000, Obj: 0.353715, No Obj: 0.003974, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.555246, Class: 1.000000, Obj: 0.383943, No Obj: 0.003927, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.564384, Class: 1.000000, Obj: 0.400251, No Obj: 0.004218, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.549058, Class: 1.000000, Obj: 0.357722, No Obj: 0.003981, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.593009, Class: 1.000000, Obj: 0.415291, No Obj: 0.004004, Avg Recall: 0.701923, count: 104 -4838: 0.427685, 0.437441 avg, 0.001000 rate, 22.143574 seconds, 1238528 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.567994, Class: 1.000000, Obj: 0.396009, No Obj: 0.004556, Avg Recall: 0.640288, count: 139 -Region Avg IOU: 0.526965, Class: 1.000000, Obj: 0.395569, No Obj: 0.003437, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.578824, Class: 1.000000, Obj: 0.416201, No Obj: 0.003946, Avg Recall: 0.636364, count: 88 -Region Avg IOU: 0.534662, Class: 1.000000, Obj: 0.389616, No Obj: 0.004104, Avg Recall: 0.606299, count: 127 -Region Avg IOU: 0.538690, Class: 1.000000, Obj: 0.337154, No Obj: 0.004661, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.573245, Class: 1.000000, Obj: 0.364754, No Obj: 0.003394, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.553998, Class: 1.000000, Obj: 0.395391, No Obj: 0.004142, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.583299, Class: 1.000000, Obj: 0.419923, No Obj: 0.003322, Avg Recall: 0.696203, count: 79 -4839: 0.408608, 0.434558 avg, 0.001000 rate, 23.663565 seconds, 1238784 images -Loaded: 0.437424 seconds -Region Avg IOU: 0.541020, Class: 1.000000, Obj: 0.439254, No Obj: 0.004548, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.555952, Class: 1.000000, Obj: 0.396244, No Obj: 0.003918, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.565066, Class: 1.000000, Obj: 0.385537, No Obj: 0.004038, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.626949, Class: 1.000000, Obj: 0.416401, No Obj: 0.004829, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.565573, Class: 1.000000, Obj: 0.381327, No Obj: 0.003647, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.537365, Class: 1.000000, Obj: 0.369876, No Obj: 0.004367, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.552135, Class: 1.000000, Obj: 0.387429, No Obj: 0.003681, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.580878, Class: 1.000000, Obj: 0.396147, No Obj: 0.004475, Avg Recall: 0.684211, count: 114 -4840: 0.439360, 0.435038 avg, 0.001000 rate, 20.653948 seconds, 1239040 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000083 seconds -Region Avg IOU: 0.676398, Class: 1.000000, Obj: 0.541575, No Obj: 0.003337, Avg Recall: 0.835165, count: 91 -Region Avg IOU: 0.659635, Class: 1.000000, Obj: 0.478968, No Obj: 0.004024, Avg Recall: 0.832168, count: 143 -Region Avg IOU: 0.651635, Class: 1.000000, Obj: 0.451155, No Obj: 0.003127, Avg Recall: 0.804124, count: 97 -Region Avg IOU: 0.624554, Class: 1.000000, Obj: 0.447945, No Obj: 0.003027, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.686323, Class: 1.000000, Obj: 0.455095, No Obj: 0.003678, Avg Recall: 0.849558, count: 113 -Region Avg IOU: 0.630502, Class: 1.000000, Obj: 0.392088, No Obj: 0.003079, Avg Recall: 0.808696, count: 115 -Region Avg IOU: 0.674581, Class: 1.000000, Obj: 0.498185, No Obj: 0.003222, Avg Recall: 0.866667, count: 105 -Region Avg IOU: 0.639626, Class: 1.000000, Obj: 0.406498, No Obj: 0.003402, Avg Recall: 0.788618, count: 123 -4841: 0.436829, 0.435217 avg, 0.001000 rate, 27.394531 seconds, 1239296 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.665831, Class: 1.000000, Obj: 0.481833, No Obj: 0.003136, Avg Recall: 0.814815, count: 108 -Region Avg IOU: 0.655957, Class: 1.000000, Obj: 0.473477, No Obj: 0.003023, Avg Recall: 0.817391, count: 115 -Region Avg IOU: 0.654346, Class: 1.000000, Obj: 0.462885, No Obj: 0.003053, Avg Recall: 0.813559, count: 118 -Region Avg IOU: 0.649966, Class: 1.000000, Obj: 0.475878, No Obj: 0.003185, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.630618, Class: 1.000000, Obj: 0.459910, No Obj: 0.002790, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.625418, Class: 1.000000, Obj: 0.478892, No Obj: 0.003374, Avg Recall: 0.726562, count: 128 -Region Avg IOU: 0.633544, Class: 1.000000, Obj: 0.396019, No Obj: 0.003036, Avg Recall: 0.814159, count: 113 -Region Avg IOU: 0.614782, Class: 1.000000, Obj: 0.493027, No Obj: 0.002961, Avg Recall: 0.767677, count: 99 -4842: 0.424290, 0.434124 avg, 0.001000 rate, 28.521208 seconds, 1239552 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.620752, Class: 1.000000, Obj: 0.470073, No Obj: 0.002778, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.662563, Class: 1.000000, Obj: 0.417289, No Obj: 0.003358, Avg Recall: 0.877049, count: 122 -Region Avg IOU: 0.626871, Class: 1.000000, Obj: 0.450909, No Obj: 0.002849, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.619527, Class: 1.000000, Obj: 0.420022, No Obj: 0.002896, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.656645, Class: 1.000000, Obj: 0.421282, No Obj: 0.003360, Avg Recall: 0.814815, count: 135 -Region Avg IOU: 0.644478, Class: 1.000000, Obj: 0.416728, No Obj: 0.003023, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.609054, Class: 1.000000, Obj: 0.432840, No Obj: 0.002660, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.608028, Class: 1.000000, Obj: 0.418918, No Obj: 0.002972, Avg Recall: 0.752212, count: 113 -4843: 0.440266, 0.434738 avg, 0.001000 rate, 27.831903 seconds, 1239808 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.673110, Class: 1.000000, Obj: 0.406141, No Obj: 0.003184, Avg Recall: 0.827338, count: 139 -Region Avg IOU: 0.605837, Class: 1.000000, Obj: 0.400001, No Obj: 0.003000, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.660399, Class: 1.000000, Obj: 0.450665, No Obj: 0.002631, Avg Recall: 0.824561, count: 114 -Region Avg IOU: 0.695762, Class: 1.000000, Obj: 0.473866, No Obj: 0.002204, Avg Recall: 0.884058, count: 69 -Region Avg IOU: 0.571220, Class: 1.000000, Obj: 0.333149, No Obj: 0.002741, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.621269, Class: 1.000000, Obj: 0.421645, No Obj: 0.002735, Avg Recall: 0.775000, count: 120 -Region Avg IOU: 0.637958, Class: 1.000000, Obj: 0.411600, No Obj: 0.002725, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.663683, Class: 1.000000, Obj: 0.426198, No Obj: 0.002816, Avg Recall: 0.836538, count: 104 -4844: 0.438350, 0.435100 avg, 0.001000 rate, 29.247295 seconds, 1240064 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.633458, Class: 1.000000, Obj: 0.442096, No Obj: 0.002778, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.651108, Class: 1.000000, Obj: 0.420718, No Obj: 0.002785, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.607578, Class: 1.000000, Obj: 0.364085, No Obj: 0.002812, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.652305, Class: 1.000000, Obj: 0.370911, No Obj: 0.002456, Avg Recall: 0.858586, count: 99 -Region Avg IOU: 0.609536, Class: 1.000000, Obj: 0.353957, No Obj: 0.002629, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.619918, Class: 1.000000, Obj: 0.373568, No Obj: 0.003048, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.631836, Class: 1.000000, Obj: 0.374948, No Obj: 0.002779, Avg Recall: 0.790323, count: 124 -Region Avg IOU: 0.627913, Class: 1.000000, Obj: 0.416408, No Obj: 0.002869, Avg Recall: 0.747748, count: 111 -4845: 0.464196, 0.438009 avg, 0.001000 rate, 28.074905 seconds, 1240320 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.584319, Class: 1.000000, Obj: 0.341060, No Obj: 0.002458, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.632586, Class: 1.000000, Obj: 0.362702, No Obj: 0.002876, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.631442, Class: 1.000000, Obj: 0.339223, No Obj: 0.002489, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.687945, Class: 1.000000, Obj: 0.452593, No Obj: 0.002215, Avg Recall: 0.857143, count: 77 -Region Avg IOU: 0.654883, Class: 1.000000, Obj: 0.367942, No Obj: 0.003357, Avg Recall: 0.798507, count: 134 -Region Avg IOU: 0.649256, Class: 1.000000, Obj: 0.359910, No Obj: 0.002440, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.650995, Class: 1.000000, Obj: 0.371756, No Obj: 0.002603, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.628663, Class: 1.000000, Obj: 0.368619, No Obj: 0.002432, Avg Recall: 0.801887, count: 106 -4846: 0.457250, 0.439933 avg, 0.001000 rate, 30.564363 seconds, 1240576 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.568134, Class: 1.000000, Obj: 0.308329, No Obj: 0.002353, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.655576, Class: 1.000000, Obj: 0.376072, No Obj: 0.002706, Avg Recall: 0.825000, count: 120 -Region Avg IOU: 0.631709, Class: 1.000000, Obj: 0.345393, No Obj: 0.002449, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.640877, Class: 1.000000, Obj: 0.375307, No Obj: 0.002354, Avg Recall: 0.794643, count: 112 -Region Avg IOU: 0.680642, Class: 1.000000, Obj: 0.334524, No Obj: 0.002663, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.644305, Class: 1.000000, Obj: 0.358581, No Obj: 0.002848, Avg Recall: 0.748252, count: 143 -Region Avg IOU: 0.642847, Class: 1.000000, Obj: 0.337904, No Obj: 0.002617, Avg Recall: 0.809160, count: 131 -Region Avg IOU: 0.665356, Class: 1.000000, Obj: 0.349212, No Obj: 0.002439, Avg Recall: 0.804878, count: 123 -4847: 0.506640, 0.446604 avg, 0.001000 rate, 28.959007 seconds, 1240832 images -Loaded: 0.000124 seconds -Region Avg IOU: 0.628633, Class: 1.000000, Obj: 0.350177, No Obj: 0.002319, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.647268, Class: 1.000000, Obj: 0.378780, No Obj: 0.002101, Avg Recall: 0.823529, count: 85 -Region Avg IOU: 0.639745, Class: 1.000000, Obj: 0.401698, No Obj: 0.002287, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.605926, Class: 1.000000, Obj: 0.357908, No Obj: 0.002249, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.691159, Class: 1.000000, Obj: 0.356740, No Obj: 0.002526, Avg Recall: 0.913793, count: 116 -Region Avg IOU: 0.645871, Class: 1.000000, Obj: 0.339755, No Obj: 0.002040, Avg Recall: 0.797468, count: 79 -Region Avg IOU: 0.652242, Class: 1.000000, Obj: 0.341287, No Obj: 0.002527, Avg Recall: 0.800000, count: 135 -Region Avg IOU: 0.658832, Class: 1.000000, Obj: 0.354702, No Obj: 0.002506, Avg Recall: 0.815789, count: 114 -4848: 0.446989, 0.446642 avg, 0.001000 rate, 28.009624 seconds, 1241088 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.647944, Class: 1.000000, Obj: 0.381673, No Obj: 0.002458, Avg Recall: 0.803419, count: 117 -Region Avg IOU: 0.686072, Class: 1.000000, Obj: 0.345715, No Obj: 0.002195, Avg Recall: 0.899083, count: 109 -Region Avg IOU: 0.639165, Class: 1.000000, Obj: 0.350465, No Obj: 0.002812, Avg Recall: 0.795918, count: 147 -Region Avg IOU: 0.648396, Class: 1.000000, Obj: 0.393988, No Obj: 0.001803, Avg Recall: 0.815789, count: 76 -Region Avg IOU: 0.627545, Class: 1.000000, Obj: 0.344879, No Obj: 0.002047, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.631916, Class: 1.000000, Obj: 0.351813, No Obj: 0.002348, Avg Recall: 0.712000, count: 125 -Region Avg IOU: 0.655964, Class: 1.000000, Obj: 0.345341, No Obj: 0.002312, Avg Recall: 0.807018, count: 114 -Region Avg IOU: 0.633456, Class: 1.000000, Obj: 0.319687, No Obj: 0.002442, Avg Recall: 0.744186, count: 129 -4849: 0.471802, 0.449158 avg, 0.001000 rate, 29.165903 seconds, 1241344 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.664076, Class: 1.000000, Obj: 0.398687, No Obj: 0.002343, Avg Recall: 0.817391, count: 115 -Region Avg IOU: 0.628696, Class: 1.000000, Obj: 0.369591, No Obj: 0.002403, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.643640, Class: 1.000000, Obj: 0.369852, No Obj: 0.002142, Avg Recall: 0.797297, count: 74 -Region Avg IOU: 0.646473, Class: 1.000000, Obj: 0.349429, No Obj: 0.002532, Avg Recall: 0.762712, count: 118 -Region Avg IOU: 0.644660, Class: 1.000000, Obj: 0.316332, No Obj: 0.002462, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.650090, Class: 1.000000, Obj: 0.354599, No Obj: 0.002639, Avg Recall: 0.824818, count: 137 -Region Avg IOU: 0.662264, Class: 1.000000, Obj: 0.386187, No Obj: 0.002223, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.650074, Class: 1.000000, Obj: 0.339609, No Obj: 0.002059, Avg Recall: 0.773810, count: 84 -4850: 0.444448, 0.448687 avg, 0.001000 rate, 28.488243 seconds, 1241600 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.683713 seconds -Region Avg IOU: 0.468412, Class: 1.000000, Obj: 0.261359, No Obj: 0.003032, Avg Recall: 0.500000, count: 92 -Region Avg IOU: 0.526019, Class: 1.000000, Obj: 0.302127, No Obj: 0.003670, Avg Recall: 0.593750, count: 128 -Region Avg IOU: 0.551928, Class: 1.000000, Obj: 0.325440, No Obj: 0.003407, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.495972, Class: 1.000000, Obj: 0.266032, No Obj: 0.003245, Avg Recall: 0.570175, count: 114 -Region Avg IOU: 0.511055, Class: 1.000000, Obj: 0.271609, No Obj: 0.003655, Avg Recall: 0.537634, count: 93 -Region Avg IOU: 0.515573, Class: 1.000000, Obj: 0.263525, No Obj: 0.003883, Avg Recall: 0.576000, count: 125 -Region Avg IOU: 0.471036, Class: 1.000000, Obj: 0.229282, No Obj: 0.003294, Avg Recall: 0.504274, count: 117 -Region Avg IOU: 0.492813, Class: 1.000000, Obj: 0.282416, No Obj: 0.003024, Avg Recall: 0.557895, count: 95 -4851: 0.535048, 0.457324 avg, 0.001000 rate, 18.308701 seconds, 1241856 images -Loaded: 2.716406 seconds -Region Avg IOU: 0.515699, Class: 1.000000, Obj: 0.276782, No Obj: 0.004378, Avg Recall: 0.633803, count: 142 -Region Avg IOU: 0.507917, Class: 1.000000, Obj: 0.280409, No Obj: 0.003268, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.514259, Class: 1.000000, Obj: 0.285078, No Obj: 0.004462, Avg Recall: 0.568182, count: 132 -Region Avg IOU: 0.543338, Class: 1.000000, Obj: 0.296367, No Obj: 0.003036, Avg Recall: 0.661538, count: 65 -Region Avg IOU: 0.490620, Class: 1.000000, Obj: 0.289789, No Obj: 0.003439, Avg Recall: 0.514851, count: 101 -Region Avg IOU: 0.490522, Class: 1.000000, Obj: 0.277376, No Obj: 0.003628, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.487337, Class: 1.000000, Obj: 0.270122, No Obj: 0.003384, Avg Recall: 0.519084, count: 131 -Region Avg IOU: 0.467015, Class: 1.000000, Obj: 0.249294, No Obj: 0.003143, Avg Recall: 0.491228, count: 114 -4852: 0.522116, 0.463803 avg, 0.001000 rate, 16.289473 seconds, 1242112 images -Loaded: 3.074777 seconds -Region Avg IOU: 0.488739, Class: 1.000000, Obj: 0.311730, No Obj: 0.003601, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.529565, Class: 1.000000, Obj: 0.298400, No Obj: 0.004061, Avg Recall: 0.523810, count: 126 -Region Avg IOU: 0.559555, Class: 1.000000, Obj: 0.295422, No Obj: 0.004030, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.518239, Class: 1.000000, Obj: 0.326908, No Obj: 0.003807, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.469606, Class: 1.000000, Obj: 0.302717, No Obj: 0.003350, Avg Recall: 0.511111, count: 90 -Region Avg IOU: 0.524323, Class: 1.000000, Obj: 0.306742, No Obj: 0.003331, Avg Recall: 0.538462, count: 78 -Region Avg IOU: 0.514906, Class: 1.000000, Obj: 0.305981, No Obj: 0.003548, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.511681, Class: 1.000000, Obj: 0.289974, No Obj: 0.003622, Avg Recall: 0.586538, count: 104 -4853: 0.463230, 0.463746 avg, 0.001000 rate, 19.654135 seconds, 1242368 images -Loaded: 0.652721 seconds -Region Avg IOU: 0.491675, Class: 1.000000, Obj: 0.309169, No Obj: 0.004729, Avg Recall: 0.524823, count: 141 -Region Avg IOU: 0.488313, Class: 1.000000, Obj: 0.351590, No Obj: 0.004130, Avg Recall: 0.534483, count: 116 -Region Avg IOU: 0.497606, Class: 1.000000, Obj: 0.307078, No Obj: 0.003953, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.564817, Class: 1.000000, Obj: 0.342246, No Obj: 0.004616, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.529557, Class: 1.000000, Obj: 0.333588, No Obj: 0.004094, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.490168, Class: 1.000000, Obj: 0.287010, No Obj: 0.004388, Avg Recall: 0.541667, count: 96 -Region Avg IOU: 0.482022, Class: 1.000000, Obj: 0.301741, No Obj: 0.003864, Avg Recall: 0.528926, count: 121 -Region Avg IOU: 0.467017, Class: 1.000000, Obj: 0.306913, No Obj: 0.003350, Avg Recall: 0.528846, count: 104 -4854: 0.449870, 0.462358 avg, 0.001000 rate, 19.886553 seconds, 1242624 images -Loaded: 0.490048 seconds -Region Avg IOU: 0.502876, Class: 1.000000, Obj: 0.338193, No Obj: 0.005350, Avg Recall: 0.577778, count: 135 -Region Avg IOU: 0.515576, Class: 1.000000, Obj: 0.356233, No Obj: 0.005593, Avg Recall: 0.580882, count: 136 -Region Avg IOU: 0.485910, Class: 1.000000, Obj: 0.331156, No Obj: 0.004646, Avg Recall: 0.538462, count: 117 -Region Avg IOU: 0.474216, Class: 1.000000, Obj: 0.307938, No Obj: 0.004034, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.535555, Class: 1.000000, Obj: 0.356103, No Obj: 0.004911, Avg Recall: 0.632479, count: 117 -Region Avg IOU: 0.488002, Class: 1.000000, Obj: 0.329747, No Obj: 0.004835, Avg Recall: 0.547009, count: 117 -Region Avg IOU: 0.509983, Class: 1.000000, Obj: 0.382831, No Obj: 0.005172, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.494055, Class: 1.000000, Obj: 0.341220, No Obj: 0.005373, Avg Recall: 0.510949, count: 137 -4855: 0.544016, 0.470524 avg, 0.001000 rate, 19.106934 seconds, 1242880 images -Loaded: 2.170794 seconds -Region Avg IOU: 0.475328, Class: 1.000000, Obj: 0.357385, No Obj: 0.005462, Avg Recall: 0.484375, count: 128 -Region Avg IOU: 0.484835, Class: 1.000000, Obj: 0.337785, No Obj: 0.004223, Avg Recall: 0.552941, count: 85 -Region Avg IOU: 0.501318, Class: 1.000000, Obj: 0.382701, No Obj: 0.005455, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.515913, Class: 1.000000, Obj: 0.370447, No Obj: 0.005302, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.545695, Class: 1.000000, Obj: 0.411626, No Obj: 0.005684, Avg Recall: 0.606299, count: 127 -Region Avg IOU: 0.466486, Class: 1.000000, Obj: 0.353301, No Obj: 0.005223, Avg Recall: 0.485714, count: 105 -Region Avg IOU: 0.487213, Class: 1.000000, Obj: 0.379392, No Obj: 0.005226, Avg Recall: 0.548387, count: 124 -Region Avg IOU: 0.539264, Class: 1.000000, Obj: 0.440692, No Obj: 0.005241, Avg Recall: 0.576087, count: 92 -4856: 0.457062, 0.469178 avg, 0.001000 rate, 20.089962 seconds, 1243136 images -Loaded: 2.501550 seconds -Region Avg IOU: 0.515014, Class: 1.000000, Obj: 0.400657, No Obj: 0.005358, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.527222, Class: 1.000000, Obj: 0.401598, No Obj: 0.005340, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.528733, Class: 1.000000, Obj: 0.420791, No Obj: 0.005334, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.492744, Class: 1.000000, Obj: 0.398159, No Obj: 0.005137, Avg Recall: 0.544643, count: 112 -Region Avg IOU: 0.488346, Class: 1.000000, Obj: 0.406306, No Obj: 0.004901, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.435056, Class: 1.000000, Obj: 0.349180, No Obj: 0.005119, Avg Recall: 0.460938, count: 128 -Region Avg IOU: 0.504926, Class: 1.000000, Obj: 0.431393, No Obj: 0.005512, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.502907, Class: 1.000000, Obj: 0.389105, No Obj: 0.006003, Avg Recall: 0.576271, count: 118 -4857: 0.551682, 0.477428 avg, 0.001000 rate, 18.089125 seconds, 1243392 images -Loaded: 1.267719 seconds -Region Avg IOU: 0.508510, Class: 1.000000, Obj: 0.430998, No Obj: 0.005904, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.512686, Class: 1.000000, Obj: 0.415480, No Obj: 0.005737, Avg Recall: 0.576577, count: 111 -Region Avg IOU: 0.513648, Class: 1.000000, Obj: 0.376656, No Obj: 0.005492, Avg Recall: 0.584071, count: 113 -Region Avg IOU: 0.504209, Class: 1.000000, Obj: 0.401706, No Obj: 0.005530, Avg Recall: 0.500000, count: 106 -Region Avg IOU: 0.512916, Class: 1.000000, Obj: 0.438338, No Obj: 0.005447, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.472861, Class: 1.000000, Obj: 0.398359, No Obj: 0.005132, Avg Recall: 0.520833, count: 96 -Region Avg IOU: 0.470119, Class: 1.000000, Obj: 0.397281, No Obj: 0.005164, Avg Recall: 0.480392, count: 102 -Region Avg IOU: 0.536550, Class: 1.000000, Obj: 0.398882, No Obj: 0.005651, Avg Recall: 0.620000, count: 100 -4858: 0.458104, 0.475496 avg, 0.001000 rate, 19.886688 seconds, 1243648 images -Loaded: 1.371767 seconds -Region Avg IOU: 0.493407, Class: 1.000000, Obj: 0.379538, No Obj: 0.005731, Avg Recall: 0.518519, count: 135 -Region Avg IOU: 0.425226, Class: 1.000000, Obj: 0.394323, No Obj: 0.005109, Avg Recall: 0.481132, count: 106 -Region Avg IOU: 0.511959, Class: 1.000000, Obj: 0.458355, No Obj: 0.005858, Avg Recall: 0.541667, count: 96 -Region Avg IOU: 0.499580, Class: 1.000000, Obj: 0.421687, No Obj: 0.006058, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.521229, Class: 1.000000, Obj: 0.448594, No Obj: 0.005648, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.468775, Class: 1.000000, Obj: 0.400083, No Obj: 0.005777, Avg Recall: 0.478571, count: 140 -Region Avg IOU: 0.512065, Class: 1.000000, Obj: 0.406942, No Obj: 0.005518, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.540451, Class: 1.000000, Obj: 0.430564, No Obj: 0.005126, Avg Recall: 0.595960, count: 99 -4859: 0.459559, 0.473902 avg, 0.001000 rate, 20.281755 seconds, 1243904 images -Loaded: 1.029610 seconds -Region Avg IOU: 0.457467, Class: 1.000000, Obj: 0.360455, No Obj: 0.005985, Avg Recall: 0.487603, count: 121 -Region Avg IOU: 0.474166, Class: 1.000000, Obj: 0.381827, No Obj: 0.005753, Avg Recall: 0.507937, count: 126 -Region Avg IOU: 0.475676, Class: 1.000000, Obj: 0.409423, No Obj: 0.004475, Avg Recall: 0.475610, count: 82 -Region Avg IOU: 0.513967, Class: 1.000000, Obj: 0.413410, No Obj: 0.005537, Avg Recall: 0.571429, count: 119 -Region Avg IOU: 0.472626, Class: 1.000000, Obj: 0.401865, No Obj: 0.005620, Avg Recall: 0.533333, count: 135 -Region Avg IOU: 0.495172, Class: 1.000000, Obj: 0.392794, No Obj: 0.005605, Avg Recall: 0.532258, count: 124 -Region Avg IOU: 0.534041, Class: 1.000000, Obj: 0.443925, No Obj: 0.006122, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.501363, Class: 1.000000, Obj: 0.406039, No Obj: 0.004441, Avg Recall: 0.560000, count: 75 -4860: 0.491126, 0.475624 avg, 0.001000 rate, 19.207815 seconds, 1244160 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 0.286223 seconds -Region Avg IOU: 0.459373, Class: 1.000000, Obj: 0.400095, No Obj: 0.004456, Avg Recall: 0.500000, count: 96 -Region Avg IOU: 0.554087, Class: 1.000000, Obj: 0.466251, No Obj: 0.004705, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.535742, Class: 1.000000, Obj: 0.430871, No Obj: 0.004552, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.496943, Class: 1.000000, Obj: 0.420377, No Obj: 0.004786, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.568648, Class: 1.000000, Obj: 0.459179, No Obj: 0.005347, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.517754, Class: 1.000000, Obj: 0.412983, No Obj: 0.005738, Avg Recall: 0.574627, count: 134 -Region Avg IOU: 0.522630, Class: 1.000000, Obj: 0.449674, No Obj: 0.004460, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.540181, Class: 1.000000, Obj: 0.461556, No Obj: 0.004647, Avg Recall: 0.605505, count: 109 -4861: 0.421627, 0.470225 avg, 0.001000 rate, 20.634752 seconds, 1244416 images -Loaded: 1.457571 seconds -Region Avg IOU: 0.540144, Class: 1.000000, Obj: 0.407225, No Obj: 0.004696, Avg Recall: 0.622807, count: 114 -Region Avg IOU: 0.569032, Class: 1.000000, Obj: 0.411299, No Obj: 0.004815, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.550604, Class: 1.000000, Obj: 0.383924, No Obj: 0.004400, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.564781, Class: 1.000000, Obj: 0.409493, No Obj: 0.005667, Avg Recall: 0.659722, count: 144 -Region Avg IOU: 0.533904, Class: 1.000000, Obj: 0.385054, No Obj: 0.004930, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.541498, Class: 1.000000, Obj: 0.453081, No Obj: 0.004596, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.561300, Class: 1.000000, Obj: 0.431983, No Obj: 0.004604, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.576254, Class: 1.000000, Obj: 0.405405, No Obj: 0.004444, Avg Recall: 0.673469, count: 98 -4862: 0.427658, 0.465968 avg, 0.001000 rate, 22.354786 seconds, 1244672 images -Loaded: 0.002406 seconds -Region Avg IOU: 0.527146, Class: 1.000000, Obj: 0.405180, No Obj: 0.003805, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.541056, Class: 1.000000, Obj: 0.393490, No Obj: 0.003779, Avg Recall: 0.635294, count: 85 -Region Avg IOU: 0.552514, Class: 1.000000, Obj: 0.373385, No Obj: 0.004351, Avg Recall: 0.643411, count: 129 -Region Avg IOU: 0.540014, Class: 1.000000, Obj: 0.375060, No Obj: 0.003960, Avg Recall: 0.620690, count: 116 -Region Avg IOU: 0.560400, Class: 1.000000, Obj: 0.381803, No Obj: 0.003826, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.545847, Class: 1.000000, Obj: 0.360628, No Obj: 0.003250, Avg Recall: 0.653333, count: 75 -Region Avg IOU: 0.542817, Class: 1.000000, Obj: 0.358480, No Obj: 0.004466, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.548148, Class: 1.000000, Obj: 0.436399, No Obj: 0.003919, Avg Recall: 0.648936, count: 94 -4863: 0.459014, 0.465273 avg, 0.001000 rate, 23.226820 seconds, 1244928 images -Loaded: 0.649431 seconds -Region Avg IOU: 0.565989, Class: 1.000000, Obj: 0.394391, No Obj: 0.003543, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.550684, Class: 1.000000, Obj: 0.379056, No Obj: 0.003800, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.543887, Class: 1.000000, Obj: 0.331421, No Obj: 0.003717, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.575145, Class: 1.000000, Obj: 0.328504, No Obj: 0.003820, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.569170, Class: 1.000000, Obj: 0.344183, No Obj: 0.004097, Avg Recall: 0.669565, count: 115 -Region Avg IOU: 0.564763, Class: 1.000000, Obj: 0.327050, No Obj: 0.003429, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.479379, Class: 1.000000, Obj: 0.308750, No Obj: 0.003278, Avg Recall: 0.553719, count: 121 -Region Avg IOU: 0.574687, Class: 1.000000, Obj: 0.368873, No Obj: 0.003907, Avg Recall: 0.673469, count: 98 -4864: 0.456481, 0.464393 avg, 0.001000 rate, 19.378633 seconds, 1245184 images -Loaded: 1.863890 seconds -Region Avg IOU: 0.570684, Class: 1.000000, Obj: 0.343049, No Obj: 0.003605, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.522080, Class: 1.000000, Obj: 0.305605, No Obj: 0.002613, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.501036, Class: 1.000000, Obj: 0.281101, No Obj: 0.003027, Avg Recall: 0.541667, count: 96 -Region Avg IOU: 0.554685, Class: 1.000000, Obj: 0.336286, No Obj: 0.003456, Avg Recall: 0.648000, count: 125 -Region Avg IOU: 0.509801, Class: 1.000000, Obj: 0.285719, No Obj: 0.003140, Avg Recall: 0.595745, count: 94 -Region Avg IOU: 0.503145, Class: 1.000000, Obj: 0.268837, No Obj: 0.003458, Avg Recall: 0.573643, count: 129 -Region Avg IOU: 0.554289, Class: 1.000000, Obj: 0.319079, No Obj: 0.002947, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.563749, Class: 1.000000, Obj: 0.348262, No Obj: 0.003051, Avg Recall: 0.641304, count: 92 -4865: 0.406840, 0.458638 avg, 0.001000 rate, 22.256903 seconds, 1245440 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.580393, Class: 1.000000, Obj: 0.323768, No Obj: 0.003174, Avg Recall: 0.711111, count: 90 -Region Avg IOU: 0.559290, Class: 1.000000, Obj: 0.352925, No Obj: 0.004133, Avg Recall: 0.658333, count: 120 -Region Avg IOU: 0.533414, Class: 1.000000, Obj: 0.260086, No Obj: 0.003272, Avg Recall: 0.556452, count: 124 -Region Avg IOU: 0.557335, Class: 1.000000, Obj: 0.285320, No Obj: 0.003438, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.508894, Class: 1.000000, Obj: 0.294815, No Obj: 0.003397, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.548691, Class: 1.000000, Obj: 0.295905, No Obj: 0.003366, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.549362, Class: 1.000000, Obj: 0.304268, No Obj: 0.003336, Avg Recall: 0.634146, count: 123 -Region Avg IOU: 0.549578, Class: 1.000000, Obj: 0.324747, No Obj: 0.003020, Avg Recall: 0.647727, count: 88 -4866: 0.462603, 0.459035 avg, 0.001000 rate, 23.494291 seconds, 1245696 images -Loaded: 1.094109 seconds -Region Avg IOU: 0.622681, Class: 1.000000, Obj: 0.346876, No Obj: 0.003355, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.584107, Class: 1.000000, Obj: 0.282564, No Obj: 0.003721, Avg Recall: 0.666667, count: 129 -Region Avg IOU: 0.497090, Class: 1.000000, Obj: 0.308005, No Obj: 0.003304, Avg Recall: 0.549618, count: 131 -Region Avg IOU: 0.531481, Class: 1.000000, Obj: 0.274927, No Obj: 0.003196, Avg Recall: 0.603053, count: 131 -Region Avg IOU: 0.543600, Class: 1.000000, Obj: 0.281183, No Obj: 0.003260, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.508739, Class: 1.000000, Obj: 0.298811, No Obj: 0.003090, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.557681, Class: 1.000000, Obj: 0.284904, No Obj: 0.003358, Avg Recall: 0.684615, count: 130 -Region Avg IOU: 0.553704, Class: 1.000000, Obj: 0.317790, No Obj: 0.003378, Avg Recall: 0.623932, count: 117 -4867: 0.494040, 0.462535 avg, 0.001000 rate, 22.963026 seconds, 1245952 images -Loaded: 0.099727 seconds -Region Avg IOU: 0.528881, Class: 1.000000, Obj: 0.294528, No Obj: 0.003560, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.552050, Class: 1.000000, Obj: 0.340991, No Obj: 0.003027, Avg Recall: 0.616279, count: 86 -Region Avg IOU: 0.563243, Class: 1.000000, Obj: 0.332949, No Obj: 0.003003, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.599173, Class: 1.000000, Obj: 0.353187, No Obj: 0.003495, Avg Recall: 0.756303, count: 119 -Region Avg IOU: 0.505549, Class: 1.000000, Obj: 0.317747, No Obj: 0.002690, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.526388, Class: 1.000000, Obj: 0.255853, No Obj: 0.002813, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.567815, Class: 1.000000, Obj: 0.316241, No Obj: 0.003796, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.548736, Class: 1.000000, Obj: 0.308809, No Obj: 0.003358, Avg Recall: 0.606838, count: 117 -4868: 0.409506, 0.457232 avg, 0.001000 rate, 23.102308 seconds, 1246208 images -Loaded: 0.662726 seconds -Region Avg IOU: 0.623837, Class: 1.000000, Obj: 0.387175, No Obj: 0.003580, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.531548, Class: 1.000000, Obj: 0.376588, No Obj: 0.003588, Avg Recall: 0.587719, count: 114 -Region Avg IOU: 0.576198, Class: 1.000000, Obj: 0.334154, No Obj: 0.003946, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.588463, Class: 1.000000, Obj: 0.383480, No Obj: 0.003780, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.561536, Class: 1.000000, Obj: 0.374644, No Obj: 0.003830, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.560355, Class: 1.000000, Obj: 0.337813, No Obj: 0.003491, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.549811, Class: 1.000000, Obj: 0.331029, No Obj: 0.002978, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.587974, Class: 1.000000, Obj: 0.342333, No Obj: 0.003737, Avg Recall: 0.740157, count: 127 -4869: 0.427680, 0.454277 avg, 0.001000 rate, 21.127356 seconds, 1246464 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.529354, Class: 1.000000, Obj: 0.312139, No Obj: 0.004041, Avg Recall: 0.581967, count: 122 -Region Avg IOU: 0.558176, Class: 1.000000, Obj: 0.326872, No Obj: 0.003952, Avg Recall: 0.633588, count: 131 -Region Avg IOU: 0.524495, Class: 1.000000, Obj: 0.358250, No Obj: 0.003490, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.533356, Class: 1.000000, Obj: 0.317767, No Obj: 0.003620, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.508041, Class: 1.000000, Obj: 0.324364, No Obj: 0.003313, Avg Recall: 0.580000, count: 100 -Region Avg IOU: 0.525000, Class: 1.000000, Obj: 0.368412, No Obj: 0.002846, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.442755, Class: 1.000000, Obj: 0.287891, No Obj: 0.003295, Avg Recall: 0.466667, count: 135 -Region Avg IOU: 0.590643, Class: 1.000000, Obj: 0.420527, No Obj: 0.003387, Avg Recall: 0.702381, count: 84 -4870: 0.452817, 0.454131 avg, 0.001000 rate, 21.144772 seconds, 1246720 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000073 seconds -Region Avg IOU: 0.638550, Class: 1.000000, Obj: 0.444907, No Obj: 0.004323, Avg Recall: 0.758865, count: 141 -Region Avg IOU: 0.567020, Class: 1.000000, Obj: 0.408824, No Obj: 0.003348, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.586628, Class: 1.000000, Obj: 0.414679, No Obj: 0.003598, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.608249, Class: 1.000000, Obj: 0.447332, No Obj: 0.003779, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.610922, Class: 1.000000, Obj: 0.415084, No Obj: 0.003182, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.589426, Class: 1.000000, Obj: 0.395803, No Obj: 0.003299, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.595252, Class: 1.000000, Obj: 0.396859, No Obj: 0.003527, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.635717, Class: 1.000000, Obj: 0.403349, No Obj: 0.003446, Avg Recall: 0.762376, count: 101 -4871: 0.403012, 0.449019 avg, 0.001000 rate, 26.602224 seconds, 1246976 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.615886, Class: 1.000000, Obj: 0.448294, No Obj: 0.004111, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.617397, Class: 1.000000, Obj: 0.435350, No Obj: 0.004227, Avg Recall: 0.769841, count: 126 -Region Avg IOU: 0.640153, Class: 1.000000, Obj: 0.455292, No Obj: 0.003823, Avg Recall: 0.813084, count: 107 -Region Avg IOU: 0.601826, Class: 1.000000, Obj: 0.426430, No Obj: 0.003978, Avg Recall: 0.726562, count: 128 -Region Avg IOU: 0.581166, Class: 1.000000, Obj: 0.452967, No Obj: 0.003332, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.566404, Class: 1.000000, Obj: 0.421168, No Obj: 0.003858, Avg Recall: 0.620968, count: 124 -Region Avg IOU: 0.622357, Class: 1.000000, Obj: 0.435615, No Obj: 0.004081, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.537080, Class: 1.000000, Obj: 0.409794, No Obj: 0.003656, Avg Recall: 0.614173, count: 127 -4872: 0.445627, 0.448680 avg, 0.001000 rate, 26.884407 seconds, 1247232 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.555593, Class: 1.000000, Obj: 0.421343, No Obj: 0.004194, Avg Recall: 0.620155, count: 129 -Region Avg IOU: 0.616073, Class: 1.000000, Obj: 0.427564, No Obj: 0.004151, Avg Recall: 0.742188, count: 128 -Region Avg IOU: 0.560832, Class: 1.000000, Obj: 0.432264, No Obj: 0.003730, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.634869, Class: 1.000000, Obj: 0.447905, No Obj: 0.004173, Avg Recall: 0.752066, count: 121 -Region Avg IOU: 0.615331, Class: 1.000000, Obj: 0.440996, No Obj: 0.003909, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.608082, Class: 1.000000, Obj: 0.464484, No Obj: 0.003536, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.589701, Class: 1.000000, Obj: 0.423143, No Obj: 0.004295, Avg Recall: 0.700000, count: 130 -Region Avg IOU: 0.594745, Class: 1.000000, Obj: 0.416432, No Obj: 0.003793, Avg Recall: 0.678899, count: 109 -4873: 0.407276, 0.444539 avg, 0.001000 rate, 24.456018 seconds, 1247488 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.580127, Class: 1.000000, Obj: 0.442303, No Obj: 0.003717, Avg Recall: 0.680000, count: 125 -Region Avg IOU: 0.575031, Class: 1.000000, Obj: 0.416168, No Obj: 0.003114, Avg Recall: 0.696203, count: 79 -Region Avg IOU: 0.625204, Class: 1.000000, Obj: 0.420279, No Obj: 0.004417, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.591898, Class: 1.000000, Obj: 0.444869, No Obj: 0.003175, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.596697, Class: 1.000000, Obj: 0.423434, No Obj: 0.003878, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.627198, Class: 1.000000, Obj: 0.415606, No Obj: 0.003827, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.580865, Class: 1.000000, Obj: 0.399716, No Obj: 0.003469, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.585741, Class: 1.000000, Obj: 0.443361, No Obj: 0.003864, Avg Recall: 0.679245, count: 106 -4874: 0.429106, 0.442996 avg, 0.001000 rate, 25.050749 seconds, 1247744 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.559996, Class: 1.000000, Obj: 0.416701, No Obj: 0.003156, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.578347, Class: 1.000000, Obj: 0.436362, No Obj: 0.004303, Avg Recall: 0.684615, count: 130 -Region Avg IOU: 0.652517, Class: 1.000000, Obj: 0.480658, No Obj: 0.003842, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.608746, Class: 1.000000, Obj: 0.395458, No Obj: 0.003796, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.553881, Class: 1.000000, Obj: 0.399303, No Obj: 0.003615, Avg Recall: 0.632000, count: 125 -Region Avg IOU: 0.645783, Class: 1.000000, Obj: 0.445117, No Obj: 0.004194, Avg Recall: 0.770492, count: 122 -Region Avg IOU: 0.652634, Class: 1.000000, Obj: 0.425121, No Obj: 0.003385, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.588089, Class: 1.000000, Obj: 0.417363, No Obj: 0.003830, Avg Recall: 0.694215, count: 121 -4875: 0.417427, 0.440439 avg, 0.001000 rate, 23.874298 seconds, 1248000 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.591762, Class: 1.000000, Obj: 0.410447, No Obj: 0.004031, Avg Recall: 0.687023, count: 131 -Region Avg IOU: 0.597091, Class: 1.000000, Obj: 0.411500, No Obj: 0.003260, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.610009, Class: 1.000000, Obj: 0.430135, No Obj: 0.003587, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.596491, Class: 1.000000, Obj: 0.405118, No Obj: 0.003668, Avg Recall: 0.704918, count: 122 -Region Avg IOU: 0.555166, Class: 1.000000, Obj: 0.428618, No Obj: 0.003784, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.617154, Class: 1.000000, Obj: 0.443131, No Obj: 0.003564, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.588544, Class: 1.000000, Obj: 0.397506, No Obj: 0.003738, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.614999, Class: 1.000000, Obj: 0.456131, No Obj: 0.003963, Avg Recall: 0.720721, count: 111 -4876: 0.442937, 0.440689 avg, 0.001000 rate, 24.515123 seconds, 1248256 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.574669, Class: 1.000000, Obj: 0.357843, No Obj: 0.003740, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.612645, Class: 1.000000, Obj: 0.434010, No Obj: 0.003262, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.567433, Class: 1.000000, Obj: 0.386471, No Obj: 0.002839, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.585405, Class: 1.000000, Obj: 0.407095, No Obj: 0.002992, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.567192, Class: 1.000000, Obj: 0.413214, No Obj: 0.003514, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.567139, Class: 1.000000, Obj: 0.386400, No Obj: 0.003549, Avg Recall: 0.706349, count: 126 -Region Avg IOU: 0.593165, Class: 1.000000, Obj: 0.408146, No Obj: 0.003549, Avg Recall: 0.688406, count: 138 -Region Avg IOU: 0.638079, Class: 1.000000, Obj: 0.430178, No Obj: 0.003444, Avg Recall: 0.768519, count: 108 -4877: 0.471874, 0.443807 avg, 0.001000 rate, 25.180124 seconds, 1248512 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.596279, Class: 1.000000, Obj: 0.355092, No Obj: 0.003219, Avg Recall: 0.707692, count: 130 -Region Avg IOU: 0.598348, Class: 1.000000, Obj: 0.447272, No Obj: 0.002931, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.640653, Class: 1.000000, Obj: 0.397133, No Obj: 0.003233, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.578671, Class: 1.000000, Obj: 0.392307, No Obj: 0.003306, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.576174, Class: 1.000000, Obj: 0.391608, No Obj: 0.003144, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.578893, Class: 1.000000, Obj: 0.401078, No Obj: 0.003031, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.637324, Class: 1.000000, Obj: 0.380233, No Obj: 0.003090, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.590962, Class: 1.000000, Obj: 0.399651, No Obj: 0.002752, Avg Recall: 0.704545, count: 88 -4878: 0.412720, 0.440699 avg, 0.001000 rate, 24.930359 seconds, 1248768 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.564919, Class: 1.000000, Obj: 0.311393, No Obj: 0.003098, Avg Recall: 0.682171, count: 129 -Region Avg IOU: 0.573339, Class: 1.000000, Obj: 0.343023, No Obj: 0.003105, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.631079, Class: 1.000000, Obj: 0.408325, No Obj: 0.003260, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.589811, Class: 1.000000, Obj: 0.372009, No Obj: 0.002821, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.544250, Class: 1.000000, Obj: 0.369793, No Obj: 0.002754, Avg Recall: 0.627907, count: 86 -Region Avg IOU: 0.606162, Class: 1.000000, Obj: 0.406790, No Obj: 0.002714, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.602320, Class: 1.000000, Obj: 0.376696, No Obj: 0.003445, Avg Recall: 0.740741, count: 135 -Region Avg IOU: 0.597374, Class: 1.000000, Obj: 0.374916, No Obj: 0.003205, Avg Recall: 0.728070, count: 114 -4879: 0.434427, 0.440072 avg, 0.001000 rate, 25.114183 seconds, 1249024 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.593305, Class: 1.000000, Obj: 0.354808, No Obj: 0.003201, Avg Recall: 0.693548, count: 124 -Region Avg IOU: 0.588314, Class: 1.000000, Obj: 0.382456, No Obj: 0.002951, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.590816, Class: 1.000000, Obj: 0.339744, No Obj: 0.002972, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.563208, Class: 1.000000, Obj: 0.344176, No Obj: 0.003187, Avg Recall: 0.648000, count: 125 -Region Avg IOU: 0.592784, Class: 1.000000, Obj: 0.376132, No Obj: 0.003028, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.650749, Class: 1.000000, Obj: 0.416711, No Obj: 0.002838, Avg Recall: 0.802198, count: 91 -Region Avg IOU: 0.624481, Class: 1.000000, Obj: 0.394964, No Obj: 0.003473, Avg Recall: 0.761905, count: 126 -Region Avg IOU: 0.649411, Class: 1.000000, Obj: 0.394905, No Obj: 0.003025, Avg Recall: 0.782609, count: 92 -4880: 0.418182, 0.437883 avg, 0.001000 rate, 26.577806 seconds, 1249280 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000066 seconds -Region Avg IOU: 0.670930, Class: 1.000000, Obj: 0.395535, No Obj: 0.002568, Avg Recall: 0.831461, count: 89 -Region Avg IOU: 0.614515, Class: 1.000000, Obj: 0.404701, No Obj: 0.002841, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.624397, Class: 1.000000, Obj: 0.372018, No Obj: 0.002718, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.624983, Class: 1.000000, Obj: 0.383919, No Obj: 0.002760, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.625089, Class: 1.000000, Obj: 0.331631, No Obj: 0.002985, Avg Recall: 0.789916, count: 119 -Region Avg IOU: 0.633735, Class: 1.000000, Obj: 0.327551, No Obj: 0.002977, Avg Recall: 0.783333, count: 120 -Region Avg IOU: 0.622406, Class: 1.000000, Obj: 0.382572, No Obj: 0.002770, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.610448, Class: 1.000000, Obj: 0.342651, No Obj: 0.002768, Avg Recall: 0.730496, count: 141 -4881: 0.460409, 0.440135 avg, 0.001000 rate, 27.023977 seconds, 1249536 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.612127, Class: 1.000000, Obj: 0.386734, No Obj: 0.002854, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.593592, Class: 1.000000, Obj: 0.369089, No Obj: 0.003068, Avg Recall: 0.705426, count: 129 -Region Avg IOU: 0.652021, Class: 1.000000, Obj: 0.394421, No Obj: 0.003043, Avg Recall: 0.804511, count: 133 -Region Avg IOU: 0.628046, Class: 1.000000, Obj: 0.385239, No Obj: 0.002843, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.617350, Class: 1.000000, Obj: 0.355013, No Obj: 0.003059, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.630548, Class: 1.000000, Obj: 0.436941, No Obj: 0.003051, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.642345, Class: 1.000000, Obj: 0.403426, No Obj: 0.003016, Avg Recall: 0.764228, count: 123 -Region Avg IOU: 0.599510, Class: 1.000000, Obj: 0.323735, No Obj: 0.002757, Avg Recall: 0.746032, count: 126 -4882: 0.467413, 0.442863 avg, 0.001000 rate, 27.146969 seconds, 1249792 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.608877, Class: 1.000000, Obj: 0.413131, No Obj: 0.002691, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.659140, Class: 1.000000, Obj: 0.373529, No Obj: 0.003250, Avg Recall: 0.801653, count: 121 -Region Avg IOU: 0.614605, Class: 1.000000, Obj: 0.338978, No Obj: 0.002475, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.633733, Class: 1.000000, Obj: 0.383835, No Obj: 0.003218, Avg Recall: 0.776859, count: 121 -Region Avg IOU: 0.657743, Class: 1.000000, Obj: 0.398389, No Obj: 0.003172, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.630502, Class: 1.000000, Obj: 0.345608, No Obj: 0.002924, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.615969, Class: 1.000000, Obj: 0.390446, No Obj: 0.003219, Avg Recall: 0.781250, count: 128 -Region Avg IOU: 0.650219, Class: 1.000000, Obj: 0.409735, No Obj: 0.002401, Avg Recall: 0.804598, count: 87 -4883: 0.449737, 0.443550 avg, 0.001000 rate, 26.764347 seconds, 1250048 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.572253, Class: 1.000000, Obj: 0.364472, No Obj: 0.002269, Avg Recall: 0.705882, count: 85 -Region Avg IOU: 0.638484, Class: 1.000000, Obj: 0.421628, No Obj: 0.003051, Avg Recall: 0.808081, count: 99 -Region Avg IOU: 0.616188, Class: 1.000000, Obj: 0.412845, No Obj: 0.002718, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.599727, Class: 1.000000, Obj: 0.379977, No Obj: 0.002946, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.667513, Class: 1.000000, Obj: 0.444628, No Obj: 0.003554, Avg Recall: 0.840336, count: 119 -Region Avg IOU: 0.576079, Class: 1.000000, Obj: 0.328380, No Obj: 0.002712, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.627526, Class: 1.000000, Obj: 0.387474, No Obj: 0.003639, Avg Recall: 0.776119, count: 134 -Region Avg IOU: 0.635638, Class: 1.000000, Obj: 0.415733, No Obj: 0.003467, Avg Recall: 0.764706, count: 136 -4884: 0.441365, 0.443332 avg, 0.001000 rate, 25.373524 seconds, 1250304 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.623579, Class: 1.000000, Obj: 0.415236, No Obj: 0.003016, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.639400, Class: 1.000000, Obj: 0.413431, No Obj: 0.002783, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.636517, Class: 1.000000, Obj: 0.395840, No Obj: 0.002721, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.588075, Class: 1.000000, Obj: 0.395904, No Obj: 0.002689, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.637767, Class: 1.000000, Obj: 0.345678, No Obj: 0.003474, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.605262, Class: 1.000000, Obj: 0.426966, No Obj: 0.003176, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.627791, Class: 1.000000, Obj: 0.412346, No Obj: 0.003664, Avg Recall: 0.775862, count: 116 -Region Avg IOU: 0.624388, Class: 1.000000, Obj: 0.411927, No Obj: 0.003493, Avg Recall: 0.773109, count: 119 -4885: 0.426125, 0.441611 avg, 0.001000 rate, 28.773335 seconds, 1250560 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.628851, Class: 1.000000, Obj: 0.445695, No Obj: 0.002900, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.598860, Class: 1.000000, Obj: 0.397855, No Obj: 0.002653, Avg Recall: 0.702381, count: 84 -Region Avg IOU: 0.610541, Class: 1.000000, Obj: 0.427062, No Obj: 0.002714, Avg Recall: 0.761905, count: 84 -Region Avg IOU: 0.620989, Class: 1.000000, Obj: 0.412257, No Obj: 0.003150, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.622758, Class: 1.000000, Obj: 0.400639, No Obj: 0.003006, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.614040, Class: 1.000000, Obj: 0.400494, No Obj: 0.003351, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.618564, Class: 1.000000, Obj: 0.391402, No Obj: 0.002423, Avg Recall: 0.716216, count: 74 -Region Avg IOU: 0.624343, Class: 1.000000, Obj: 0.384914, No Obj: 0.002832, Avg Recall: 0.723214, count: 112 -4886: 0.374480, 0.434898 avg, 0.001000 rate, 28.036890 seconds, 1250816 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.628394, Class: 1.000000, Obj: 0.369281, No Obj: 0.003396, Avg Recall: 0.723077, count: 130 -Region Avg IOU: 0.633551, Class: 1.000000, Obj: 0.393672, No Obj: 0.003349, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.584630, Class: 1.000000, Obj: 0.407736, No Obj: 0.002761, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.589372, Class: 1.000000, Obj: 0.411738, No Obj: 0.002757, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.647464, Class: 1.000000, Obj: 0.411451, No Obj: 0.003124, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.626227, Class: 1.000000, Obj: 0.385247, No Obj: 0.003551, Avg Recall: 0.744526, count: 137 -Region Avg IOU: 0.623867, Class: 1.000000, Obj: 0.458063, No Obj: 0.002832, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.592725, Class: 1.000000, Obj: 0.372838, No Obj: 0.003121, Avg Recall: 0.691667, count: 120 -4887: 0.487927, 0.440201 avg, 0.001000 rate, 27.395334 seconds, 1251072 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.626089, Class: 1.000000, Obj: 0.372658, No Obj: 0.003244, Avg Recall: 0.756098, count: 123 -Region Avg IOU: 0.609629, Class: 1.000000, Obj: 0.433315, No Obj: 0.002952, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.625653, Class: 1.000000, Obj: 0.402648, No Obj: 0.003258, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.658665, Class: 1.000000, Obj: 0.459917, No Obj: 0.003325, Avg Recall: 0.800000, count: 115 -Region Avg IOU: 0.593066, Class: 1.000000, Obj: 0.413610, No Obj: 0.003209, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.643288, Class: 1.000000, Obj: 0.402077, No Obj: 0.003049, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.669608, Class: 1.000000, Obj: 0.475981, No Obj: 0.003038, Avg Recall: 0.852273, count: 88 -Region Avg IOU: 0.591501, Class: 1.000000, Obj: 0.408879, No Obj: 0.003063, Avg Recall: 0.724138, count: 116 -4888: 0.426519, 0.438833 avg, 0.001000 rate, 26.410547 seconds, 1251328 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.627527, Class: 1.000000, Obj: 0.379819, No Obj: 0.003031, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.630259, Class: 1.000000, Obj: 0.418192, No Obj: 0.003404, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.642384, Class: 1.000000, Obj: 0.436130, No Obj: 0.003091, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.639780, Class: 1.000000, Obj: 0.443499, No Obj: 0.003147, Avg Recall: 0.768595, count: 121 -Region Avg IOU: 0.628726, Class: 1.000000, Obj: 0.408217, No Obj: 0.003174, Avg Recall: 0.733871, count: 124 -Region Avg IOU: 0.611899, Class: 1.000000, Obj: 0.408612, No Obj: 0.002705, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.604702, Class: 1.000000, Obj: 0.382696, No Obj: 0.002947, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.648362, Class: 1.000000, Obj: 0.453374, No Obj: 0.002924, Avg Recall: 0.825243, count: 103 -4889: 0.449485, 0.439898 avg, 0.001000 rate, 25.950151 seconds, 1251584 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.655885, Class: 1.000000, Obj: 0.421944, No Obj: 0.003321, Avg Recall: 0.850394, count: 127 -Region Avg IOU: 0.626891, Class: 1.000000, Obj: 0.441347, No Obj: 0.002591, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.592795, Class: 1.000000, Obj: 0.420527, No Obj: 0.002786, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.670379, Class: 1.000000, Obj: 0.407126, No Obj: 0.003182, Avg Recall: 0.882883, count: 111 -Region Avg IOU: 0.549711, Class: 1.000000, Obj: 0.360652, No Obj: 0.002922, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.630592, Class: 1.000000, Obj: 0.379773, No Obj: 0.003375, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.622385, Class: 1.000000, Obj: 0.368795, No Obj: 0.002711, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.630780, Class: 1.000000, Obj: 0.393720, No Obj: 0.003466, Avg Recall: 0.793651, count: 126 -4890: 0.431418, 0.439050 avg, 0.001000 rate, 26.572495 seconds, 1251840 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.594000 seconds -Region Avg IOU: 0.580180, Class: 1.000000, Obj: 0.340636, No Obj: 0.004150, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.553955, Class: 1.000000, Obj: 0.307195, No Obj: 0.003931, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.504319, Class: 1.000000, Obj: 0.298302, No Obj: 0.003318, Avg Recall: 0.537037, count: 108 -Region Avg IOU: 0.522847, Class: 1.000000, Obj: 0.306482, No Obj: 0.003503, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.556901, Class: 1.000000, Obj: 0.332709, No Obj: 0.002933, Avg Recall: 0.633333, count: 90 -Region Avg IOU: 0.514216, Class: 1.000000, Obj: 0.278514, No Obj: 0.003451, Avg Recall: 0.566372, count: 113 -Region Avg IOU: 0.471932, Class: 1.000000, Obj: 0.298134, No Obj: 0.002904, Avg Recall: 0.509434, count: 106 -Region Avg IOU: 0.510444, Class: 1.000000, Obj: 0.335699, No Obj: 0.003197, Avg Recall: 0.612500, count: 80 -4891: 0.436770, 0.438822 avg, 0.001000 rate, 20.635853 seconds, 1252096 images -Loaded: 0.667959 seconds -Region Avg IOU: 0.523165, Class: 1.000000, Obj: 0.335888, No Obj: 0.003434, Avg Recall: 0.558140, count: 86 -Region Avg IOU: 0.445551, Class: 1.000000, Obj: 0.295721, No Obj: 0.002853, Avg Recall: 0.474227, count: 97 -Region Avg IOU: 0.511745, Class: 1.000000, Obj: 0.303582, No Obj: 0.003425, Avg Recall: 0.560748, count: 107 -Region Avg IOU: 0.500032, Class: 1.000000, Obj: 0.319869, No Obj: 0.003529, Avg Recall: 0.600000, count: 95 -Region Avg IOU: 0.564233, Class: 1.000000, Obj: 0.374863, No Obj: 0.003421, Avg Recall: 0.658537, count: 82 -Region Avg IOU: 0.515859, Class: 1.000000, Obj: 0.331344, No Obj: 0.003378, Avg Recall: 0.597938, count: 97 -Region Avg IOU: 0.575762, Class: 1.000000, Obj: 0.330564, No Obj: 0.003633, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.534386, Class: 1.000000, Obj: 0.330073, No Obj: 0.003494, Avg Recall: 0.637363, count: 91 -4892: 0.393466, 0.434286 avg, 0.001000 rate, 18.794123 seconds, 1252352 images -Loaded: 2.779515 seconds -Region Avg IOU: 0.481760, Class: 1.000000, Obj: 0.349678, No Obj: 0.003056, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.570092, Class: 1.000000, Obj: 0.366918, No Obj: 0.003745, Avg Recall: 0.651163, count: 86 -Region Avg IOU: 0.515351, Class: 1.000000, Obj: 0.318303, No Obj: 0.003232, Avg Recall: 0.558140, count: 86 -Region Avg IOU: 0.536570, Class: 1.000000, Obj: 0.343843, No Obj: 0.003231, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.551726, Class: 1.000000, Obj: 0.345047, No Obj: 0.003938, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.448490, Class: 1.000000, Obj: 0.279245, No Obj: 0.003234, Avg Recall: 0.509259, count: 108 -Region Avg IOU: 0.540404, Class: 1.000000, Obj: 0.334496, No Obj: 0.004058, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.551494, Class: 1.000000, Obj: 0.341004, No Obj: 0.003501, Avg Recall: 0.641304, count: 92 -4893: 0.421428, 0.433001 avg, 0.001000 rate, 20.382862 seconds, 1252608 images -Loaded: 1.814152 seconds -Region Avg IOU: 0.521333, Class: 1.000000, Obj: 0.364268, No Obj: 0.003374, Avg Recall: 0.589474, count: 95 -Region Avg IOU: 0.535380, Class: 1.000000, Obj: 0.320296, No Obj: 0.003854, Avg Recall: 0.595745, count: 94 -Region Avg IOU: 0.485690, Class: 1.000000, Obj: 0.301679, No Obj: 0.003835, Avg Recall: 0.512397, count: 121 -Region Avg IOU: 0.544933, Class: 1.000000, Obj: 0.322852, No Obj: 0.004270, Avg Recall: 0.629630, count: 135 -Region Avg IOU: 0.561159, Class: 1.000000, Obj: 0.338340, No Obj: 0.004063, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.559033, Class: 1.000000, Obj: 0.353852, No Obj: 0.003838, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.551270, Class: 1.000000, Obj: 0.357402, No Obj: 0.003848, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.584095, Class: 1.000000, Obj: 0.334996, No Obj: 0.003737, Avg Recall: 0.670455, count: 88 -4894: 0.473618, 0.437062 avg, 0.001000 rate, 19.007511 seconds, 1252864 images -Loaded: 2.399141 seconds -Region Avg IOU: 0.502557, Class: 1.000000, Obj: 0.400424, No Obj: 0.003729, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.519441, Class: 1.000000, Obj: 0.341848, No Obj: 0.003702, Avg Recall: 0.575758, count: 99 -Region Avg IOU: 0.561037, Class: 1.000000, Obj: 0.396596, No Obj: 0.003806, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.480562, Class: 1.000000, Obj: 0.342546, No Obj: 0.003785, Avg Recall: 0.520000, count: 100 -Region Avg IOU: 0.535352, Class: 1.000000, Obj: 0.391483, No Obj: 0.004132, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.554179, Class: 1.000000, Obj: 0.334872, No Obj: 0.004078, Avg Recall: 0.663866, count: 119 -Region Avg IOU: 0.524040, Class: 1.000000, Obj: 0.337749, No Obj: 0.003945, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.530892, Class: 1.000000, Obj: 0.358495, No Obj: 0.004164, Avg Recall: 0.616822, count: 107 -4895: 0.418798, 0.435236 avg, 0.001000 rate, 21.089611 seconds, 1253120 images -Loaded: 0.734105 seconds -Region Avg IOU: 0.563007, Class: 1.000000, Obj: 0.388027, No Obj: 0.004545, Avg Recall: 0.669355, count: 124 -Region Avg IOU: 0.542874, Class: 1.000000, Obj: 0.374065, No Obj: 0.004839, Avg Recall: 0.661417, count: 127 -Region Avg IOU: 0.508134, Class: 1.000000, Obj: 0.330429, No Obj: 0.004518, Avg Recall: 0.612403, count: 129 -Region Avg IOU: 0.490018, Class: 1.000000, Obj: 0.361439, No Obj: 0.003718, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.576239, Class: 1.000000, Obj: 0.399809, No Obj: 0.004706, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.555613, Class: 1.000000, Obj: 0.395035, No Obj: 0.004074, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.539303, Class: 1.000000, Obj: 0.399363, No Obj: 0.004378, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.472516, Class: 1.000000, Obj: 0.350525, No Obj: 0.003819, Avg Recall: 0.508621, count: 116 -4896: 0.458127, 0.437525 avg, 0.001000 rate, 23.289568 seconds, 1253376 images -Loaded: 0.035149 seconds -Region Avg IOU: 0.551359, Class: 1.000000, Obj: 0.390432, No Obj: 0.004833, Avg Recall: 0.596639, count: 119 -Region Avg IOU: 0.496136, Class: 1.000000, Obj: 0.360520, No Obj: 0.004050, Avg Recall: 0.570248, count: 121 -Region Avg IOU: 0.510196, Class: 1.000000, Obj: 0.404563, No Obj: 0.004334, Avg Recall: 0.594828, count: 116 -Region Avg IOU: 0.539827, Class: 1.000000, Obj: 0.369426, No Obj: 0.004090, Avg Recall: 0.582418, count: 91 -Region Avg IOU: 0.501897, Class: 1.000000, Obj: 0.416601, No Obj: 0.003740, Avg Recall: 0.555556, count: 81 -Region Avg IOU: 0.539171, Class: 1.000000, Obj: 0.348186, No Obj: 0.004341, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.567825, Class: 1.000000, Obj: 0.376534, No Obj: 0.004942, Avg Recall: 0.688073, count: 109 -Region Avg IOU: 0.550430, Class: 1.000000, Obj: 0.409090, No Obj: 0.004752, Avg Recall: 0.679245, count: 106 -4897: 0.437914, 0.437564 avg, 0.001000 rate, 20.670984 seconds, 1253632 images -Loaded: 1.376827 seconds -Region Avg IOU: 0.589876, Class: 1.000000, Obj: 0.391395, No Obj: 0.004740, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.485423, Class: 1.000000, Obj: 0.348161, No Obj: 0.003746, Avg Recall: 0.510870, count: 92 -Region Avg IOU: 0.548033, Class: 1.000000, Obj: 0.449111, No Obj: 0.004844, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.558453, Class: 1.000000, Obj: 0.353282, No Obj: 0.005025, Avg Recall: 0.653543, count: 127 -Region Avg IOU: 0.551190, Class: 1.000000, Obj: 0.393859, No Obj: 0.004720, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.567691, Class: 1.000000, Obj: 0.414587, No Obj: 0.004508, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.509488, Class: 1.000000, Obj: 0.393984, No Obj: 0.004392, Avg Recall: 0.553571, count: 112 -Region Avg IOU: 0.567495, Class: 1.000000, Obj: 0.380919, No Obj: 0.005760, Avg Recall: 0.658537, count: 123 -4898: 0.433647, 0.437172 avg, 0.001000 rate, 18.766508 seconds, 1253888 images -Loaded: 2.929195 seconds -Region Avg IOU: 0.478380, Class: 1.000000, Obj: 0.362089, No Obj: 0.004191, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.516051, Class: 1.000000, Obj: 0.378436, No Obj: 0.003948, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.551103, Class: 1.000000, Obj: 0.443894, No Obj: 0.004931, Avg Recall: 0.614458, count: 83 -Region Avg IOU: 0.544774, Class: 1.000000, Obj: 0.381262, No Obj: 0.005175, Avg Recall: 0.624000, count: 125 -Region Avg IOU: 0.560269, Class: 1.000000, Obj: 0.410875, No Obj: 0.004706, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.526904, Class: 1.000000, Obj: 0.354898, No Obj: 0.004979, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.483406, Class: 1.000000, Obj: 0.424746, No Obj: 0.004812, Avg Recall: 0.549550, count: 111 -Region Avg IOU: 0.531701, Class: 1.000000, Obj: 0.434406, No Obj: 0.004655, Avg Recall: 0.609524, count: 105 -4899: 0.406588, 0.434114 avg, 0.001000 rate, 22.373724 seconds, 1254144 images -Loaded: 0.648450 seconds -Region Avg IOU: 0.525312, Class: 1.000000, Obj: 0.364410, No Obj: 0.005456, Avg Recall: 0.595588, count: 136 -Region Avg IOU: 0.454678, Class: 1.000000, Obj: 0.359492, No Obj: 0.004173, Avg Recall: 0.464286, count: 112 -Region Avg IOU: 0.523929, Class: 1.000000, Obj: 0.382227, No Obj: 0.004671, Avg Recall: 0.570175, count: 114 -Region Avg IOU: 0.549062, Class: 1.000000, Obj: 0.387998, No Obj: 0.004512, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.565055, Class: 1.000000, Obj: 0.371091, No Obj: 0.005014, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.508990, Class: 1.000000, Obj: 0.359318, No Obj: 0.004604, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.525750, Class: 1.000000, Obj: 0.396863, No Obj: 0.004670, Avg Recall: 0.592593, count: 108 -Region Avg IOU: 0.542060, Class: 1.000000, Obj: 0.356451, No Obj: 0.005118, Avg Recall: 0.629630, count: 135 -4900: 0.527488, 0.443451 avg, 0.001000 rate, 21.240297 seconds, 1254400 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 0.000057 seconds -Region Avg IOU: 0.543866, Class: 1.000000, Obj: 0.403332, No Obj: 0.004290, Avg Recall: 0.613333, count: 75 -Region Avg IOU: 0.538987, Class: 1.000000, Obj: 0.375171, No Obj: 0.004705, Avg Recall: 0.579365, count: 126 -Region Avg IOU: 0.589674, Class: 1.000000, Obj: 0.464747, No Obj: 0.004890, Avg Recall: 0.662791, count: 86 -Region Avg IOU: 0.553362, Class: 1.000000, Obj: 0.388176, No Obj: 0.004256, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.526003, Class: 1.000000, Obj: 0.364348, No Obj: 0.004106, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.586317, Class: 1.000000, Obj: 0.453028, No Obj: 0.004213, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.546966, Class: 1.000000, Obj: 0.430447, No Obj: 0.004464, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.592408, Class: 1.000000, Obj: 0.450088, No Obj: 0.004372, Avg Recall: 0.722222, count: 90 -4901: 0.363452, 0.435451 avg, 0.001000 rate, 21.441315 seconds, 1254656 images -Loaded: 1.040815 seconds -Region Avg IOU: 0.573059, Class: 1.000000, Obj: 0.374660, No Obj: 0.004589, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.546225, Class: 1.000000, Obj: 0.405896, No Obj: 0.004305, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.512232, Class: 1.000000, Obj: 0.398682, No Obj: 0.004345, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.523452, Class: 1.000000, Obj: 0.384415, No Obj: 0.004945, Avg Recall: 0.556452, count: 124 -Region Avg IOU: 0.583604, Class: 1.000000, Obj: 0.447343, No Obj: 0.004715, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.541441, Class: 1.000000, Obj: 0.406534, No Obj: 0.004752, Avg Recall: 0.626984, count: 126 -Region Avg IOU: 0.536922, Class: 1.000000, Obj: 0.400751, No Obj: 0.003792, Avg Recall: 0.540816, count: 98 -Region Avg IOU: 0.563364, Class: 1.000000, Obj: 0.394363, No Obj: 0.003391, Avg Recall: 0.645161, count: 93 -4902: 0.445875, 0.436494 avg, 0.001000 rate, 22.523172 seconds, 1254912 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.520565, Class: 1.000000, Obj: 0.393717, No Obj: 0.004116, Avg Recall: 0.575758, count: 99 -Region Avg IOU: 0.559505, Class: 1.000000, Obj: 0.391844, No Obj: 0.004345, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.565354, Class: 1.000000, Obj: 0.420754, No Obj: 0.004151, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.534884, Class: 1.000000, Obj: 0.453795, No Obj: 0.003360, Avg Recall: 0.608696, count: 69 -Region Avg IOU: 0.539612, Class: 1.000000, Obj: 0.389391, No Obj: 0.003925, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.534230, Class: 1.000000, Obj: 0.369999, No Obj: 0.003935, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.568145, Class: 1.000000, Obj: 0.368960, No Obj: 0.004583, Avg Recall: 0.680000, count: 125 -Region Avg IOU: 0.595736, Class: 1.000000, Obj: 0.412695, No Obj: 0.004550, Avg Recall: 0.715596, count: 109 -4903: 0.417397, 0.434584 avg, 0.001000 rate, 20.408869 seconds, 1255168 images -Loaded: 0.482137 seconds -Region Avg IOU: 0.539703, Class: 1.000000, Obj: 0.401642, No Obj: 0.003999, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.555995, Class: 1.000000, Obj: 0.411048, No Obj: 0.004130, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.583606, Class: 1.000000, Obj: 0.419289, No Obj: 0.004270, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.587750, Class: 1.000000, Obj: 0.380539, No Obj: 0.004856, Avg Recall: 0.681818, count: 132 -Region Avg IOU: 0.542997, Class: 1.000000, Obj: 0.420730, No Obj: 0.004475, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.524347, Class: 1.000000, Obj: 0.391089, No Obj: 0.003655, Avg Recall: 0.586207, count: 87 -Region Avg IOU: 0.575056, Class: 1.000000, Obj: 0.374957, No Obj: 0.003671, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.517922, Class: 1.000000, Obj: 0.356099, No Obj: 0.004539, Avg Recall: 0.594828, count: 116 -4904: 0.418552, 0.432981 avg, 0.001000 rate, 22.454971 seconds, 1255424 images -Loaded: 0.947403 seconds -Region Avg IOU: 0.566949, Class: 1.000000, Obj: 0.345314, No Obj: 0.005055, Avg Recall: 0.629371, count: 143 -Region Avg IOU: 0.614984, Class: 1.000000, Obj: 0.417359, No Obj: 0.004058, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.533266, Class: 1.000000, Obj: 0.416547, No Obj: 0.003557, Avg Recall: 0.617978, count: 89 -Region Avg IOU: 0.551123, Class: 1.000000, Obj: 0.346887, No Obj: 0.003667, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.576382, Class: 1.000000, Obj: 0.403970, No Obj: 0.004229, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.553914, Class: 1.000000, Obj: 0.413542, No Obj: 0.004393, Avg Recall: 0.643478, count: 115 -Region Avg IOU: 0.537826, Class: 1.000000, Obj: 0.372514, No Obj: 0.003828, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.509053, Class: 1.000000, Obj: 0.350264, No Obj: 0.004005, Avg Recall: 0.580357, count: 112 -4905: 0.443500, 0.434033 avg, 0.001000 rate, 21.095732 seconds, 1255680 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.565863, Class: 1.000000, Obj: 0.389035, No Obj: 0.003551, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.521911, Class: 1.000000, Obj: 0.367672, No Obj: 0.003074, Avg Recall: 0.609756, count: 82 -Region Avg IOU: 0.573305, Class: 1.000000, Obj: 0.367331, No Obj: 0.003504, Avg Recall: 0.647059, count: 85 -Region Avg IOU: 0.521161, Class: 1.000000, Obj: 0.327632, No Obj: 0.004186, Avg Recall: 0.585185, count: 135 -Region Avg IOU: 0.559903, Class: 1.000000, Obj: 0.389798, No Obj: 0.003331, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.536609, Class: 1.000000, Obj: 0.340579, No Obj: 0.003816, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.577603, Class: 1.000000, Obj: 0.407912, No Obj: 0.003462, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.518033, Class: 1.000000, Obj: 0.329142, No Obj: 0.004137, Avg Recall: 0.604317, count: 139 -4906: 0.408805, 0.431510 avg, 0.001000 rate, 22.461657 seconds, 1255936 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.579595, Class: 1.000000, Obj: 0.328276, No Obj: 0.004119, Avg Recall: 0.647541, count: 122 -Region Avg IOU: 0.524713, Class: 1.000000, Obj: 0.356407, No Obj: 0.003646, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.575146, Class: 1.000000, Obj: 0.300286, No Obj: 0.003565, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.552244, Class: 1.000000, Obj: 0.353878, No Obj: 0.003466, Avg Recall: 0.652174, count: 92 -Region Avg IOU: 0.516283, Class: 1.000000, Obj: 0.330901, No Obj: 0.003274, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.584015, Class: 1.000000, Obj: 0.377204, No Obj: 0.003718, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.513985, Class: 1.000000, Obj: 0.358764, No Obj: 0.003852, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.512137, Class: 1.000000, Obj: 0.311570, No Obj: 0.003553, Avg Recall: 0.553719, count: 121 -4907: 0.410542, 0.429413 avg, 0.001000 rate, 21.811836 seconds, 1256192 images -Loaded: 1.446770 seconds -Region Avg IOU: 0.529974, Class: 1.000000, Obj: 0.320278, No Obj: 0.003454, Avg Recall: 0.636364, count: 88 -Region Avg IOU: 0.553949, Class: 1.000000, Obj: 0.353350, No Obj: 0.003544, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.490770, Class: 1.000000, Obj: 0.319655, No Obj: 0.003416, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.584115, Class: 1.000000, Obj: 0.392016, No Obj: 0.004174, Avg Recall: 0.697479, count: 119 -Region Avg IOU: 0.544200, Class: 1.000000, Obj: 0.375736, No Obj: 0.004066, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.535774, Class: 1.000000, Obj: 0.321851, No Obj: 0.003466, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.575359, Class: 1.000000, Obj: 0.363418, No Obj: 0.003322, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.522811, Class: 1.000000, Obj: 0.336844, No Obj: 0.003424, Avg Recall: 0.561905, count: 105 -4908: 0.425564, 0.429028 avg, 0.001000 rate, 22.131929 seconds, 1256448 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.586624, Class: 1.000000, Obj: 0.388863, No Obj: 0.004151, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.533733, Class: 1.000000, Obj: 0.331196, No Obj: 0.003854, Avg Recall: 0.635714, count: 140 -Region Avg IOU: 0.539517, Class: 1.000000, Obj: 0.326826, No Obj: 0.004146, Avg Recall: 0.635762, count: 151 -Region Avg IOU: 0.567545, Class: 1.000000, Obj: 0.355143, No Obj: 0.003772, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.581111, Class: 1.000000, Obj: 0.398015, No Obj: 0.003478, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.555366, Class: 1.000000, Obj: 0.367160, No Obj: 0.003795, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.542488, Class: 1.000000, Obj: 0.352397, No Obj: 0.004033, Avg Recall: 0.649007, count: 151 -Region Avg IOU: 0.518070, Class: 1.000000, Obj: 0.345389, No Obj: 0.003341, Avg Recall: 0.592233, count: 103 -4909: 0.473322, 0.433458 avg, 0.001000 rate, 21.451342 seconds, 1256704 images -Loaded: 0.403210 seconds -Region Avg IOU: 0.515867, Class: 1.000000, Obj: 0.366120, No Obj: 0.003610, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.614050, Class: 1.000000, Obj: 0.381070, No Obj: 0.003626, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.525039, Class: 1.000000, Obj: 0.318684, No Obj: 0.003973, Avg Recall: 0.563380, count: 142 -Region Avg IOU: 0.569148, Class: 1.000000, Obj: 0.392298, No Obj: 0.003267, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.580636, Class: 1.000000, Obj: 0.371735, No Obj: 0.004181, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.539203, Class: 1.000000, Obj: 0.394124, No Obj: 0.003313, Avg Recall: 0.583333, count: 96 -Region Avg IOU: 0.520922, Class: 1.000000, Obj: 0.325343, No Obj: 0.003994, Avg Recall: 0.563492, count: 126 -Region Avg IOU: 0.563604, Class: 1.000000, Obj: 0.405884, No Obj: 0.003826, Avg Recall: 0.616071, count: 112 -4910: 0.471414, 0.437253 avg, 0.001000 rate, 23.098444 seconds, 1256960 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.022270 seconds -Region Avg IOU: 0.513621, Class: 1.000000, Obj: 0.391663, No Obj: 0.003144, Avg Recall: 0.602740, count: 146 -Region Avg IOU: 0.554846, Class: 1.000000, Obj: 0.390277, No Obj: 0.003361, Avg Recall: 0.638298, count: 141 -Region Avg IOU: 0.613358, Class: 1.000000, Obj: 0.365983, No Obj: 0.003187, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.619897, Class: 1.000000, Obj: 0.370707, No Obj: 0.003805, Avg Recall: 0.776859, count: 121 -Region Avg IOU: 0.545425, Class: 1.000000, Obj: 0.360991, No Obj: 0.003486, Avg Recall: 0.627907, count: 129 -Region Avg IOU: 0.611957, Class: 1.000000, Obj: 0.439496, No Obj: 0.003467, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.595552, Class: 1.000000, Obj: 0.426989, No Obj: 0.003612, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.601767, Class: 1.000000, Obj: 0.379076, No Obj: 0.003390, Avg Recall: 0.693694, count: 111 -4911: 0.464113, 0.439939 avg, 0.001000 rate, 25.115284 seconds, 1257216 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.589271, Class: 1.000000, Obj: 0.398452, No Obj: 0.003695, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.622708, Class: 1.000000, Obj: 0.408824, No Obj: 0.003619, Avg Recall: 0.775000, count: 120 -Region Avg IOU: 0.567824, Class: 1.000000, Obj: 0.415744, No Obj: 0.003392, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.599047, Class: 1.000000, Obj: 0.437317, No Obj: 0.003426, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.618886, Class: 1.000000, Obj: 0.384951, No Obj: 0.003686, Avg Recall: 0.732283, count: 127 -Region Avg IOU: 0.622291, Class: 1.000000, Obj: 0.411338, No Obj: 0.003353, Avg Recall: 0.744681, count: 94 -Region Avg IOU: 0.591157, Class: 1.000000, Obj: 0.400236, No Obj: 0.003668, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.592218, Class: 1.000000, Obj: 0.409332, No Obj: 0.003148, Avg Recall: 0.659574, count: 94 -4912: 0.465624, 0.442508 avg, 0.001000 rate, 25.862743 seconds, 1257472 images -Loaded: 0.000133 seconds -Region Avg IOU: 0.585229, Class: 1.000000, Obj: 0.413377, No Obj: 0.003337, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.644997, Class: 1.000000, Obj: 0.433144, No Obj: 0.003494, Avg Recall: 0.827586, count: 87 -Region Avg IOU: 0.622207, Class: 1.000000, Obj: 0.401982, No Obj: 0.003490, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.558312, Class: 1.000000, Obj: 0.459679, No Obj: 0.003303, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.556676, Class: 1.000000, Obj: 0.373783, No Obj: 0.003751, Avg Recall: 0.585714, count: 140 -Region Avg IOU: 0.593276, Class: 1.000000, Obj: 0.420890, No Obj: 0.003698, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.592837, Class: 1.000000, Obj: 0.421015, No Obj: 0.003518, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.594151, Class: 1.000000, Obj: 0.421554, No Obj: 0.003608, Avg Recall: 0.679245, count: 106 -4913: 0.387667, 0.437024 avg, 0.001000 rate, 24.794804 seconds, 1257728 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.541578, Class: 1.000000, Obj: 0.421575, No Obj: 0.003186, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.633537, Class: 1.000000, Obj: 0.468661, No Obj: 0.003083, Avg Recall: 0.818182, count: 77 -Region Avg IOU: 0.601027, Class: 1.000000, Obj: 0.421603, No Obj: 0.003905, Avg Recall: 0.717557, count: 131 -Region Avg IOU: 0.629902, Class: 1.000000, Obj: 0.375190, No Obj: 0.003411, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.608132, Class: 1.000000, Obj: 0.435484, No Obj: 0.003784, Avg Recall: 0.732824, count: 131 -Region Avg IOU: 0.579448, Class: 1.000000, Obj: 0.384635, No Obj: 0.003630, Avg Recall: 0.693548, count: 124 -Region Avg IOU: 0.654415, Class: 1.000000, Obj: 0.478462, No Obj: 0.003396, Avg Recall: 0.809524, count: 84 -Region Avg IOU: 0.529569, Class: 1.000000, Obj: 0.362421, No Obj: 0.002736, Avg Recall: 0.595960, count: 99 -4914: 0.387292, 0.432050 avg, 0.001000 rate, 25.865957 seconds, 1257984 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.613682, Class: 1.000000, Obj: 0.409611, No Obj: 0.003594, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.664041, Class: 1.000000, Obj: 0.462722, No Obj: 0.003855, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.619020, Class: 1.000000, Obj: 0.384932, No Obj: 0.003285, Avg Recall: 0.747126, count: 87 -Region Avg IOU: 0.617110, Class: 1.000000, Obj: 0.422943, No Obj: 0.003743, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.592245, Class: 1.000000, Obj: 0.425167, No Obj: 0.003687, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.608315, Class: 1.000000, Obj: 0.440839, No Obj: 0.003233, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.595018, Class: 1.000000, Obj: 0.389992, No Obj: 0.003274, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.617369, Class: 1.000000, Obj: 0.396203, No Obj: 0.003764, Avg Recall: 0.727273, count: 110 -4915: 0.400558, 0.428901 avg, 0.001000 rate, 25.066933 seconds, 1258240 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.609184, Class: 1.000000, Obj: 0.398201, No Obj: 0.003643, Avg Recall: 0.709677, count: 124 -Region Avg IOU: 0.609645, Class: 1.000000, Obj: 0.387229, No Obj: 0.003086, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.581441, Class: 1.000000, Obj: 0.420906, No Obj: 0.003168, Avg Recall: 0.674419, count: 86 -Region Avg IOU: 0.625926, Class: 1.000000, Obj: 0.418053, No Obj: 0.002750, Avg Recall: 0.744186, count: 86 -Region Avg IOU: 0.625306, Class: 1.000000, Obj: 0.375599, No Obj: 0.003298, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.610246, Class: 1.000000, Obj: 0.424765, No Obj: 0.003707, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.581619, Class: 1.000000, Obj: 0.409780, No Obj: 0.003245, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.612426, Class: 1.000000, Obj: 0.400424, No Obj: 0.003214, Avg Recall: 0.729412, count: 85 -4916: 0.363906, 0.422402 avg, 0.001000 rate, 25.461750 seconds, 1258496 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.544692, Class: 1.000000, Obj: 0.370723, No Obj: 0.003566, Avg Recall: 0.608696, count: 138 -Region Avg IOU: 0.616537, Class: 1.000000, Obj: 0.400951, No Obj: 0.003138, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.584729, Class: 1.000000, Obj: 0.360789, No Obj: 0.003318, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.538190, Class: 1.000000, Obj: 0.372382, No Obj: 0.003313, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.577080, Class: 1.000000, Obj: 0.391594, No Obj: 0.002833, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.620192, Class: 1.000000, Obj: 0.464287, No Obj: 0.003258, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.632339, Class: 1.000000, Obj: 0.399162, No Obj: 0.003938, Avg Recall: 0.770370, count: 135 -Region Avg IOU: 0.617035, Class: 1.000000, Obj: 0.422182, No Obj: 0.003393, Avg Recall: 0.743119, count: 109 -4917: 0.409392, 0.421101 avg, 0.001000 rate, 25.195692 seconds, 1258752 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.582629, Class: 1.000000, Obj: 0.381415, No Obj: 0.003265, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.616738, Class: 1.000000, Obj: 0.395646, No Obj: 0.003213, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.617992, Class: 1.000000, Obj: 0.390566, No Obj: 0.003264, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.614263, Class: 1.000000, Obj: 0.383034, No Obj: 0.003252, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.598459, Class: 1.000000, Obj: 0.441453, No Obj: 0.002998, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.594402, Class: 1.000000, Obj: 0.399200, No Obj: 0.003524, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.590421, Class: 1.000000, Obj: 0.392412, No Obj: 0.003213, Avg Recall: 0.688073, count: 109 -Region Avg IOU: 0.609314, Class: 1.000000, Obj: 0.384862, No Obj: 0.003809, Avg Recall: 0.732824, count: 131 -4918: 0.425768, 0.421567 avg, 0.001000 rate, 25.844309 seconds, 1259008 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.605363, Class: 1.000000, Obj: 0.416034, No Obj: 0.003146, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.614145, Class: 1.000000, Obj: 0.366962, No Obj: 0.003365, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.608456, Class: 1.000000, Obj: 0.358871, No Obj: 0.003051, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.553045, Class: 1.000000, Obj: 0.348284, No Obj: 0.003370, Avg Recall: 0.682171, count: 129 -Region Avg IOU: 0.615297, Class: 1.000000, Obj: 0.386411, No Obj: 0.002866, Avg Recall: 0.755814, count: 86 -Region Avg IOU: 0.620809, Class: 1.000000, Obj: 0.340848, No Obj: 0.003311, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.591657, Class: 1.000000, Obj: 0.407808, No Obj: 0.003623, Avg Recall: 0.684615, count: 130 -Region Avg IOU: 0.595010, Class: 1.000000, Obj: 0.325754, No Obj: 0.003964, Avg Recall: 0.706349, count: 126 -4919: 0.432924, 0.422703 avg, 0.001000 rate, 25.573124 seconds, 1259264 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.602723, Class: 1.000000, Obj: 0.418401, No Obj: 0.002934, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.612758, Class: 1.000000, Obj: 0.398732, No Obj: 0.003514, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.594725, Class: 1.000000, Obj: 0.436933, No Obj: 0.002847, Avg Recall: 0.724138, count: 87 -Region Avg IOU: 0.591548, Class: 1.000000, Obj: 0.406939, No Obj: 0.003042, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.581733, Class: 1.000000, Obj: 0.389958, No Obj: 0.002878, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.636281, Class: 1.000000, Obj: 0.425701, No Obj: 0.002766, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.637247, Class: 1.000000, Obj: 0.395748, No Obj: 0.003947, Avg Recall: 0.771186, count: 118 -Region Avg IOU: 0.595109, Class: 1.000000, Obj: 0.383117, No Obj: 0.003480, Avg Recall: 0.691729, count: 133 -4920: 0.406622, 0.421095 avg, 0.001000 rate, 24.836620 seconds, 1259520 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000073 seconds -Region Avg IOU: 0.642590, Class: 1.000000, Obj: 0.426615, No Obj: 0.002786, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.628150, Class: 1.000000, Obj: 0.453991, No Obj: 0.003248, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.671896, Class: 1.000000, Obj: 0.451042, No Obj: 0.003007, Avg Recall: 0.797619, count: 84 -Region Avg IOU: 0.638172, Class: 1.000000, Obj: 0.373292, No Obj: 0.003238, Avg Recall: 0.775194, count: 129 -Region Avg IOU: 0.614077, Class: 1.000000, Obj: 0.422991, No Obj: 0.003054, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.612766, Class: 1.000000, Obj: 0.410370, No Obj: 0.002372, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.569200, Class: 1.000000, Obj: 0.364245, No Obj: 0.002510, Avg Recall: 0.666667, count: 87 -Region Avg IOU: 0.617286, Class: 1.000000, Obj: 0.400311, No Obj: 0.002694, Avg Recall: 0.728395, count: 81 -4921: 0.404552, 0.419441 avg, 0.001000 rate, 25.827539 seconds, 1259776 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.584591, Class: 1.000000, Obj: 0.407678, No Obj: 0.002877, Avg Recall: 0.714286, count: 119 -Region Avg IOU: 0.647172, Class: 1.000000, Obj: 0.407243, No Obj: 0.002966, Avg Recall: 0.762712, count: 118 -Region Avg IOU: 0.609199, Class: 1.000000, Obj: 0.397068, No Obj: 0.002982, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.605411, Class: 1.000000, Obj: 0.386748, No Obj: 0.002622, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.638129, Class: 1.000000, Obj: 0.448351, No Obj: 0.002878, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.642739, Class: 1.000000, Obj: 0.372497, No Obj: 0.002872, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.672836, Class: 1.000000, Obj: 0.461491, No Obj: 0.003242, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.591848, Class: 1.000000, Obj: 0.373229, No Obj: 0.002483, Avg Recall: 0.742268, count: 97 -4922: 0.415825, 0.419079 avg, 0.001000 rate, 25.607693 seconds, 1260032 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.586294, Class: 1.000000, Obj: 0.397829, No Obj: 0.003184, Avg Recall: 0.731707, count: 123 -Region Avg IOU: 0.588891, Class: 1.000000, Obj: 0.342764, No Obj: 0.003141, Avg Recall: 0.716418, count: 134 -Region Avg IOU: 0.600841, Class: 1.000000, Obj: 0.423509, No Obj: 0.002725, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.632766, Class: 1.000000, Obj: 0.404665, No Obj: 0.003133, Avg Recall: 0.811475, count: 122 -Region Avg IOU: 0.629018, Class: 1.000000, Obj: 0.436189, No Obj: 0.002564, Avg Recall: 0.750000, count: 80 -Region Avg IOU: 0.645872, Class: 1.000000, Obj: 0.421683, No Obj: 0.002989, Avg Recall: 0.788235, count: 85 -Region Avg IOU: 0.630379, Class: 1.000000, Obj: 0.388331, No Obj: 0.003139, Avg Recall: 0.785047, count: 107 -Region Avg IOU: 0.594722, Class: 1.000000, Obj: 0.376485, No Obj: 0.002865, Avg Recall: 0.728972, count: 107 -4923: 0.419817, 0.419153 avg, 0.001000 rate, 26.483751 seconds, 1260288 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.616602, Class: 1.000000, Obj: 0.343406, No Obj: 0.003133, Avg Recall: 0.776978, count: 139 -Region Avg IOU: 0.635053, Class: 1.000000, Obj: 0.405305, No Obj: 0.002986, Avg Recall: 0.731707, count: 123 -Region Avg IOU: 0.590543, Class: 1.000000, Obj: 0.360837, No Obj: 0.002739, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.605251, Class: 1.000000, Obj: 0.423225, No Obj: 0.002696, Avg Recall: 0.752941, count: 85 -Region Avg IOU: 0.620851, Class: 1.000000, Obj: 0.363156, No Obj: 0.002803, Avg Recall: 0.738462, count: 130 -Region Avg IOU: 0.645262, Class: 1.000000, Obj: 0.376632, No Obj: 0.003360, Avg Recall: 0.835821, count: 134 -Region Avg IOU: 0.631908, Class: 1.000000, Obj: 0.416324, No Obj: 0.002899, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.623677, Class: 1.000000, Obj: 0.367482, No Obj: 0.002749, Avg Recall: 0.776471, count: 85 -4924: 0.462742, 0.423512 avg, 0.001000 rate, 26.730345 seconds, 1260544 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.628316, Class: 1.000000, Obj: 0.426753, No Obj: 0.002913, Avg Recall: 0.739583, count: 96 -Region Avg IOU: 0.640562, Class: 1.000000, Obj: 0.378386, No Obj: 0.002807, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.641772, Class: 1.000000, Obj: 0.435959, No Obj: 0.002892, Avg Recall: 0.808696, count: 115 -Region Avg IOU: 0.621797, Class: 1.000000, Obj: 0.395431, No Obj: 0.003472, Avg Recall: 0.774648, count: 142 -Region Avg IOU: 0.620737, Class: 1.000000, Obj: 0.393926, No Obj: 0.002920, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.610918, Class: 1.000000, Obj: 0.395747, No Obj: 0.002096, Avg Recall: 0.728571, count: 70 -Region Avg IOU: 0.659609, Class: 1.000000, Obj: 0.424645, No Obj: 0.002774, Avg Recall: 0.831683, count: 101 -Region Avg IOU: 0.636027, Class: 1.000000, Obj: 0.392555, No Obj: 0.002740, Avg Recall: 0.767677, count: 99 -4925: 0.389928, 0.420153 avg, 0.001000 rate, 26.406141 seconds, 1260800 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.639507, Class: 1.000000, Obj: 0.363927, No Obj: 0.002974, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.559757, Class: 1.000000, Obj: 0.375417, No Obj: 0.002739, Avg Recall: 0.653226, count: 124 -Region Avg IOU: 0.629254, Class: 1.000000, Obj: 0.361402, No Obj: 0.002814, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.594111, Class: 1.000000, Obj: 0.390781, No Obj: 0.002870, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.637960, Class: 1.000000, Obj: 0.399514, No Obj: 0.002946, Avg Recall: 0.784000, count: 125 -Region Avg IOU: 0.633171, Class: 1.000000, Obj: 0.345510, No Obj: 0.002445, Avg Recall: 0.806818, count: 88 -Region Avg IOU: 0.626968, Class: 1.000000, Obj: 0.392521, No Obj: 0.002646, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.654030, Class: 1.000000, Obj: 0.366166, No Obj: 0.003604, Avg Recall: 0.805970, count: 134 -4926: 0.424958, 0.420634 avg, 0.001000 rate, 27.321840 seconds, 1261056 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.608129, Class: 1.000000, Obj: 0.384040, No Obj: 0.003236, Avg Recall: 0.728682, count: 129 -Region Avg IOU: 0.607349, Class: 1.000000, Obj: 0.382934, No Obj: 0.002816, Avg Recall: 0.733871, count: 124 -Region Avg IOU: 0.601015, Class: 1.000000, Obj: 0.412226, No Obj: 0.002305, Avg Recall: 0.715909, count: 88 -Region Avg IOU: 0.609350, Class: 1.000000, Obj: 0.379199, No Obj: 0.003106, Avg Recall: 0.673759, count: 141 -Region Avg IOU: 0.571059, Class: 1.000000, Obj: 0.370487, No Obj: 0.002929, Avg Recall: 0.639344, count: 122 -Region Avg IOU: 0.623673, Class: 1.000000, Obj: 0.365397, No Obj: 0.003248, Avg Recall: 0.746269, count: 134 -Region Avg IOU: 0.611813, Class: 1.000000, Obj: 0.341499, No Obj: 0.002869, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.653417, Class: 1.000000, Obj: 0.359431, No Obj: 0.003279, Avg Recall: 0.810606, count: 132 -4927: 0.543770, 0.432948 avg, 0.001000 rate, 27.261896 seconds, 1261312 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.597710, Class: 1.000000, Obj: 0.364177, No Obj: 0.002893, Avg Recall: 0.700787, count: 127 -Region Avg IOU: 0.645918, Class: 1.000000, Obj: 0.426162, No Obj: 0.003154, Avg Recall: 0.775000, count: 120 -Region Avg IOU: 0.601003, Class: 1.000000, Obj: 0.378016, No Obj: 0.003104, Avg Recall: 0.726562, count: 128 -Region Avg IOU: 0.606823, Class: 1.000000, Obj: 0.384789, No Obj: 0.002291, Avg Recall: 0.719512, count: 82 -Region Avg IOU: 0.627242, Class: 1.000000, Obj: 0.399574, No Obj: 0.002523, Avg Recall: 0.790123, count: 81 -Region Avg IOU: 0.645347, Class: 1.000000, Obj: 0.404125, No Obj: 0.003077, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.649004, Class: 1.000000, Obj: 0.376592, No Obj: 0.002982, Avg Recall: 0.834951, count: 103 -Region Avg IOU: 0.622327, Class: 1.000000, Obj: 0.369587, No Obj: 0.002921, Avg Recall: 0.775000, count: 120 -4928: 0.429321, 0.432585 avg, 0.001000 rate, 27.178156 seconds, 1261568 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.629407, Class: 1.000000, Obj: 0.367033, No Obj: 0.003273, Avg Recall: 0.772059, count: 136 -Region Avg IOU: 0.647908, Class: 1.000000, Obj: 0.456489, No Obj: 0.002522, Avg Recall: 0.804598, count: 87 -Region Avg IOU: 0.668487, Class: 1.000000, Obj: 0.435154, No Obj: 0.003006, Avg Recall: 0.847826, count: 92 -Region Avg IOU: 0.599524, Class: 1.000000, Obj: 0.387567, No Obj: 0.002732, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.647530, Class: 1.000000, Obj: 0.425789, No Obj: 0.002842, Avg Recall: 0.813953, count: 86 -Region Avg IOU: 0.594414, Class: 1.000000, Obj: 0.352377, No Obj: 0.002530, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.701201, Class: 1.000000, Obj: 0.457449, No Obj: 0.002541, Avg Recall: 0.864865, count: 74 -Region Avg IOU: 0.591835, Class: 1.000000, Obj: 0.373125, No Obj: 0.002962, Avg Recall: 0.709677, count: 124 -4929: 0.444321, 0.433759 avg, 0.001000 rate, 25.806988 seconds, 1261824 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.642127, Class: 1.000000, Obj: 0.422373, No Obj: 0.003283, Avg Recall: 0.775000, count: 120 -Region Avg IOU: 0.621235, Class: 1.000000, Obj: 0.376228, No Obj: 0.002337, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.672711, Class: 1.000000, Obj: 0.400002, No Obj: 0.002796, Avg Recall: 0.851852, count: 108 -Region Avg IOU: 0.645612, Class: 1.000000, Obj: 0.393806, No Obj: 0.002684, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.629520, Class: 1.000000, Obj: 0.406665, No Obj: 0.003064, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.636257, Class: 1.000000, Obj: 0.444653, No Obj: 0.002791, Avg Recall: 0.823529, count: 85 -Region Avg IOU: 0.588567, Class: 1.000000, Obj: 0.334689, No Obj: 0.002857, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.625518, Class: 1.000000, Obj: 0.420943, No Obj: 0.002862, Avg Recall: 0.782178, count: 101 -4930: 0.406379, 0.431021 avg, 0.001000 rate, 27.826868 seconds, 1262080 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.469852 seconds -Region Avg IOU: 0.559217, Class: 1.000000, Obj: 0.343242, No Obj: 0.004130, Avg Recall: 0.652542, count: 118 -Region Avg IOU: 0.529981, Class: 1.000000, Obj: 0.346265, No Obj: 0.003571, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.573103, Class: 1.000000, Obj: 0.309516, No Obj: 0.004490, Avg Recall: 0.671642, count: 134 -Region Avg IOU: 0.526208, Class: 1.000000, Obj: 0.345473, No Obj: 0.003452, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.570590, Class: 1.000000, Obj: 0.341387, No Obj: 0.003928, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.528242, Class: 1.000000, Obj: 0.318239, No Obj: 0.003614, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.529835, Class: 1.000000, Obj: 0.325280, No Obj: 0.004062, Avg Recall: 0.556452, count: 124 -Region Avg IOU: 0.474962, Class: 1.000000, Obj: 0.285339, No Obj: 0.003795, Avg Recall: 0.479675, count: 123 -4931: 0.435987, 0.431517 avg, 0.001000 rate, 20.270632 seconds, 1262336 images -Loaded: 0.886941 seconds -Region Avg IOU: 0.569780, Class: 1.000000, Obj: 0.345068, No Obj: 0.003896, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.531175, Class: 1.000000, Obj: 0.381078, No Obj: 0.003754, Avg Recall: 0.578313, count: 83 -Region Avg IOU: 0.506510, Class: 1.000000, Obj: 0.345689, No Obj: 0.003764, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.514807, Class: 1.000000, Obj: 0.296582, No Obj: 0.003936, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.517320, Class: 1.000000, Obj: 0.325142, No Obj: 0.003891, Avg Recall: 0.592920, count: 113 -Region Avg IOU: 0.581779, Class: 1.000000, Obj: 0.388378, No Obj: 0.003465, Avg Recall: 0.652174, count: 92 -Region Avg IOU: 0.530165, Class: 1.000000, Obj: 0.364990, No Obj: 0.003793, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.488204, Class: 1.000000, Obj: 0.303874, No Obj: 0.003672, Avg Recall: 0.537815, count: 119 -4932: 0.393952, 0.427761 avg, 0.001000 rate, 20.542685 seconds, 1262592 images -Loaded: 2.266690 seconds -Region Avg IOU: 0.553747, Class: 1.000000, Obj: 0.364469, No Obj: 0.004521, Avg Recall: 0.624000, count: 125 -Region Avg IOU: 0.517751, Class: 1.000000, Obj: 0.321885, No Obj: 0.004388, Avg Recall: 0.525926, count: 135 -Region Avg IOU: 0.515959, Class: 1.000000, Obj: 0.311962, No Obj: 0.003821, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.553405, Class: 1.000000, Obj: 0.322218, No Obj: 0.003635, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.491861, Class: 1.000000, Obj: 0.300121, No Obj: 0.003634, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.506493, Class: 1.000000, Obj: 0.342113, No Obj: 0.004072, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.522869, Class: 1.000000, Obj: 0.303624, No Obj: 0.004162, Avg Recall: 0.639098, count: 133 -Region Avg IOU: 0.512650, Class: 1.000000, Obj: 0.339253, No Obj: 0.004465, Avg Recall: 0.540323, count: 124 -4933: 0.504541, 0.435439 avg, 0.001000 rate, 20.760971 seconds, 1262848 images -Loaded: 2.294126 seconds -Region Avg IOU: 0.539761, Class: 1.000000, Obj: 0.351351, No Obj: 0.004296, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.562091, Class: 1.000000, Obj: 0.389523, No Obj: 0.004408, Avg Recall: 0.655462, count: 119 -Region Avg IOU: 0.527666, Class: 1.000000, Obj: 0.378808, No Obj: 0.003792, Avg Recall: 0.605769, count: 104 -Region Avg IOU: 0.494207, Class: 1.000000, Obj: 0.340027, No Obj: 0.004598, Avg Recall: 0.535433, count: 127 -Region Avg IOU: 0.570827, Class: 1.000000, Obj: 0.371320, No Obj: 0.004562, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.536086, Class: 1.000000, Obj: 0.370068, No Obj: 0.004826, Avg Recall: 0.615385, count: 143 -Region Avg IOU: 0.480085, Class: 1.000000, Obj: 0.316674, No Obj: 0.004019, Avg Recall: 0.500000, count: 132 -Region Avg IOU: 0.525041, Class: 1.000000, Obj: 0.342146, No Obj: 0.005238, Avg Recall: 0.595420, count: 131 -4934: 0.472422, 0.439137 avg, 0.001000 rate, 21.805788 seconds, 1263104 images -Loaded: 0.652920 seconds -Region Avg IOU: 0.548057, Class: 1.000000, Obj: 0.361873, No Obj: 0.004668, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.542448, Class: 1.000000, Obj: 0.437288, No Obj: 0.004435, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.531301, Class: 1.000000, Obj: 0.429165, No Obj: 0.004164, Avg Recall: 0.654762, count: 84 -Region Avg IOU: 0.523573, Class: 1.000000, Obj: 0.357343, No Obj: 0.004103, Avg Recall: 0.586207, count: 116 -Region Avg IOU: 0.557181, Class: 1.000000, Obj: 0.376190, No Obj: 0.005226, Avg Recall: 0.644628, count: 121 -Region Avg IOU: 0.498536, Class: 1.000000, Obj: 0.346831, No Obj: 0.004337, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.454792, Class: 1.000000, Obj: 0.310621, No Obj: 0.003481, Avg Recall: 0.549451, count: 91 -Region Avg IOU: 0.563590, Class: 1.000000, Obj: 0.390943, No Obj: 0.004232, Avg Recall: 0.669903, count: 103 -4935: 0.397606, 0.434984 avg, 0.001000 rate, 20.908987 seconds, 1263360 images -Loaded: 0.166023 seconds -Region Avg IOU: 0.516385, Class: 1.000000, Obj: 0.406806, No Obj: 0.004170, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.549777, Class: 1.000000, Obj: 0.438208, No Obj: 0.004767, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.510920, Class: 1.000000, Obj: 0.362627, No Obj: 0.004784, Avg Recall: 0.581395, count: 129 -Region Avg IOU: 0.535274, Class: 1.000000, Obj: 0.386215, No Obj: 0.004770, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.550812, Class: 1.000000, Obj: 0.413534, No Obj: 0.004384, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.556535, Class: 1.000000, Obj: 0.393489, No Obj: 0.004610, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.554193, Class: 1.000000, Obj: 0.408631, No Obj: 0.004123, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.489419, Class: 1.000000, Obj: 0.333756, No Obj: 0.004504, Avg Recall: 0.547826, count: 115 -4936: 0.412827, 0.432768 avg, 0.001000 rate, 21.295460 seconds, 1263616 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.490098, Class: 1.000000, Obj: 0.352648, No Obj: 0.004053, Avg Recall: 0.504762, count: 105 -Region Avg IOU: 0.478204, Class: 1.000000, Obj: 0.356961, No Obj: 0.004422, Avg Recall: 0.496063, count: 127 -Region Avg IOU: 0.576116, Class: 1.000000, Obj: 0.445944, No Obj: 0.004383, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.498218, Class: 1.000000, Obj: 0.357567, No Obj: 0.005114, Avg Recall: 0.578512, count: 121 -Region Avg IOU: 0.564344, Class: 1.000000, Obj: 0.445692, No Obj: 0.004578, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.565344, Class: 1.000000, Obj: 0.426438, No Obj: 0.005081, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.503622, Class: 1.000000, Obj: 0.432658, No Obj: 0.004734, Avg Recall: 0.533898, count: 118 -Region Avg IOU: 0.554751, Class: 1.000000, Obj: 0.439896, No Obj: 0.004803, Avg Recall: 0.637255, count: 102 -4937: 0.447421, 0.434234 avg, 0.001000 rate, 21.968102 seconds, 1263872 images -Loaded: 0.317200 seconds -Region Avg IOU: 0.520764, Class: 1.000000, Obj: 0.390298, No Obj: 0.004834, Avg Recall: 0.580645, count: 93 -Region Avg IOU: 0.513540, Class: 1.000000, Obj: 0.373975, No Obj: 0.004277, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.457227, Class: 1.000000, Obj: 0.376145, No Obj: 0.004277, Avg Recall: 0.495050, count: 101 -Region Avg IOU: 0.510577, Class: 1.000000, Obj: 0.410670, No Obj: 0.005067, Avg Recall: 0.611570, count: 121 -Region Avg IOU: 0.515067, Class: 1.000000, Obj: 0.401162, No Obj: 0.004510, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.527440, Class: 1.000000, Obj: 0.372392, No Obj: 0.004534, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.522846, Class: 1.000000, Obj: 0.361020, No Obj: 0.005011, Avg Recall: 0.615385, count: 130 -Region Avg IOU: 0.616236, Class: 1.000000, Obj: 0.433125, No Obj: 0.004608, Avg Recall: 0.736842, count: 76 -4938: 0.477167, 0.438527 avg, 0.001000 rate, 22.404984 seconds, 1264128 images -Loaded: 0.111663 seconds -Region Avg IOU: 0.528244, Class: 1.000000, Obj: 0.415638, No Obj: 0.004930, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.552783, Class: 1.000000, Obj: 0.418012, No Obj: 0.005091, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.521422, Class: 1.000000, Obj: 0.378569, No Obj: 0.004726, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.533695, Class: 1.000000, Obj: 0.360013, No Obj: 0.004413, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.463608, Class: 1.000000, Obj: 0.353836, No Obj: 0.004726, Avg Recall: 0.492754, count: 138 -Region Avg IOU: 0.534812, Class: 1.000000, Obj: 0.365124, No Obj: 0.004520, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.484670, Class: 1.000000, Obj: 0.375933, No Obj: 0.004484, Avg Recall: 0.557522, count: 113 -Region Avg IOU: 0.493936, Class: 1.000000, Obj: 0.326139, No Obj: 0.005079, Avg Recall: 0.539062, count: 128 -4939: 0.497360, 0.444410 avg, 0.001000 rate, 18.728605 seconds, 1264384 images -Loaded: 2.945197 seconds -Region Avg IOU: 0.566297, Class: 1.000000, Obj: 0.413970, No Obj: 0.004119, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.484982, Class: 1.000000, Obj: 0.324260, No Obj: 0.004774, Avg Recall: 0.496454, count: 141 -Region Avg IOU: 0.595553, Class: 1.000000, Obj: 0.414702, No Obj: 0.005309, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.588058, Class: 1.000000, Obj: 0.394015, No Obj: 0.005004, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.486072, Class: 1.000000, Obj: 0.392018, No Obj: 0.004658, Avg Recall: 0.520833, count: 96 -Region Avg IOU: 0.531850, Class: 1.000000, Obj: 0.367148, No Obj: 0.004875, Avg Recall: 0.629921, count: 127 -Region Avg IOU: 0.514535, Class: 1.000000, Obj: 0.345001, No Obj: 0.004631, Avg Recall: 0.566176, count: 136 -Region Avg IOU: 0.556978, Class: 1.000000, Obj: 0.404790, No Obj: 0.005613, Avg Recall: 0.643411, count: 129 -4940: 0.536231, 0.453592 avg, 0.001000 rate, 21.651697 seconds, 1264640 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000078 seconds -Region Avg IOU: 0.622186, Class: 1.000000, Obj: 0.467927, No Obj: 0.003396, Avg Recall: 0.752066, count: 121 -Region Avg IOU: 0.601741, Class: 1.000000, Obj: 0.427213, No Obj: 0.003301, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.670400, Class: 1.000000, Obj: 0.470214, No Obj: 0.003223, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.628938, Class: 1.000000, Obj: 0.416872, No Obj: 0.003357, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.576575, Class: 1.000000, Obj: 0.428296, No Obj: 0.002926, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.657609, Class: 1.000000, Obj: 0.467395, No Obj: 0.002886, Avg Recall: 0.876712, count: 73 -Region Avg IOU: 0.635220, Class: 1.000000, Obj: 0.455620, No Obj: 0.003162, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.686139, Class: 1.000000, Obj: 0.500990, No Obj: 0.003412, Avg Recall: 0.844660, count: 103 -4941: 0.437392, 0.451972 avg, 0.001000 rate, 29.932137 seconds, 1264896 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.682683, Class: 1.000000, Obj: 0.425464, No Obj: 0.003012, Avg Recall: 0.877551, count: 98 -Region Avg IOU: 0.641873, Class: 1.000000, Obj: 0.401506, No Obj: 0.003551, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.653143, Class: 1.000000, Obj: 0.441759, No Obj: 0.003319, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.607550, Class: 1.000000, Obj: 0.449154, No Obj: 0.003122, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.632146, Class: 1.000000, Obj: 0.458410, No Obj: 0.003198, Avg Recall: 0.826923, count: 104 -Region Avg IOU: 0.685494, Class: 1.000000, Obj: 0.460310, No Obj: 0.003460, Avg Recall: 0.836735, count: 98 -Region Avg IOU: 0.643131, Class: 1.000000, Obj: 0.512541, No Obj: 0.003119, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.617422, Class: 1.000000, Obj: 0.395593, No Obj: 0.002897, Avg Recall: 0.723214, count: 112 -4942: 0.462654, 0.453040 avg, 0.001000 rate, 29.407366 seconds, 1265152 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.663490, Class: 1.000000, Obj: 0.418856, No Obj: 0.002958, Avg Recall: 0.838384, count: 99 -Region Avg IOU: 0.664091, Class: 1.000000, Obj: 0.412193, No Obj: 0.003118, Avg Recall: 0.860294, count: 136 -Region Avg IOU: 0.660460, Class: 1.000000, Obj: 0.449094, No Obj: 0.002720, Avg Recall: 0.821429, count: 112 -Region Avg IOU: 0.635771, Class: 1.000000, Obj: 0.408172, No Obj: 0.003016, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.670278, Class: 1.000000, Obj: 0.404072, No Obj: 0.002974, Avg Recall: 0.823529, count: 136 -Region Avg IOU: 0.657141, Class: 1.000000, Obj: 0.436240, No Obj: 0.003123, Avg Recall: 0.825000, count: 120 -Region Avg IOU: 0.616595, Class: 1.000000, Obj: 0.413240, No Obj: 0.002767, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.651001, Class: 1.000000, Obj: 0.416335, No Obj: 0.003011, Avg Recall: 0.778846, count: 104 -4943: 0.484534, 0.456190 avg, 0.001000 rate, 27.715828 seconds, 1265408 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.612774, Class: 1.000000, Obj: 0.304539, No Obj: 0.002568, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.623134, Class: 1.000000, Obj: 0.347041, No Obj: 0.002680, Avg Recall: 0.723077, count: 130 -Region Avg IOU: 0.691257, Class: 1.000000, Obj: 0.400942, No Obj: 0.002215, Avg Recall: 0.948052, count: 77 -Region Avg IOU: 0.647611, Class: 1.000000, Obj: 0.360987, No Obj: 0.002239, Avg Recall: 0.838095, count: 105 -Region Avg IOU: 0.667278, Class: 1.000000, Obj: 0.384310, No Obj: 0.002979, Avg Recall: 0.792593, count: 135 -Region Avg IOU: 0.627215, Class: 1.000000, Obj: 0.402068, No Obj: 0.002263, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.660470, Class: 1.000000, Obj: 0.321658, No Obj: 0.002142, Avg Recall: 0.807229, count: 83 -Region Avg IOU: 0.674093, Class: 1.000000, Obj: 0.373516, No Obj: 0.002506, Avg Recall: 0.816514, count: 109 -4944: 0.421385, 0.452709 avg, 0.001000 rate, 29.420080 seconds, 1265664 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.624977, Class: 1.000000, Obj: 0.371008, No Obj: 0.002065, Avg Recall: 0.821429, count: 84 -Region Avg IOU: 0.661778, Class: 1.000000, Obj: 0.346587, No Obj: 0.002316, Avg Recall: 0.841121, count: 107 -Region Avg IOU: 0.678003, Class: 1.000000, Obj: 0.320513, No Obj: 0.002422, Avg Recall: 0.820000, count: 100 -Region Avg IOU: 0.667556, Class: 1.000000, Obj: 0.358884, No Obj: 0.002170, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.650588, Class: 1.000000, Obj: 0.355268, No Obj: 0.002160, Avg Recall: 0.818182, count: 121 -Region Avg IOU: 0.612681, Class: 1.000000, Obj: 0.310501, No Obj: 0.002336, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.633323, Class: 1.000000, Obj: 0.310258, No Obj: 0.002016, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.660683, Class: 1.000000, Obj: 0.324216, No Obj: 0.002416, Avg Recall: 0.826087, count: 115 -4945: 0.481324, 0.455571 avg, 0.001000 rate, 29.037790 seconds, 1265920 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.692455, Class: 1.000000, Obj: 0.363696, No Obj: 0.002103, Avg Recall: 0.895833, count: 96 -Region Avg IOU: 0.648213, Class: 1.000000, Obj: 0.338042, No Obj: 0.002273, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.654027, Class: 1.000000, Obj: 0.289830, No Obj: 0.002475, Avg Recall: 0.831081, count: 148 -Region Avg IOU: 0.691113, Class: 1.000000, Obj: 0.348905, No Obj: 0.002317, Avg Recall: 0.875000, count: 96 -Region Avg IOU: 0.664617, Class: 1.000000, Obj: 0.371363, No Obj: 0.002393, Avg Recall: 0.834862, count: 109 -Region Avg IOU: 0.671568, Class: 1.000000, Obj: 0.374919, No Obj: 0.001915, Avg Recall: 0.853333, count: 75 -Region Avg IOU: 0.655193, Class: 1.000000, Obj: 0.390088, No Obj: 0.002034, Avg Recall: 0.836364, count: 110 -Region Avg IOU: 0.646611, Class: 1.000000, Obj: 0.366964, No Obj: 0.001970, Avg Recall: 0.752809, count: 89 -4946: 0.415510, 0.451565 avg, 0.001000 rate, 29.892502 seconds, 1266176 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.659454, Class: 1.000000, Obj: 0.310518, No Obj: 0.002359, Avg Recall: 0.827869, count: 122 -Region Avg IOU: 0.656056, Class: 1.000000, Obj: 0.320331, No Obj: 0.002030, Avg Recall: 0.829060, count: 117 -Region Avg IOU: 0.662477, Class: 1.000000, Obj: 0.354259, No Obj: 0.001971, Avg Recall: 0.835443, count: 79 -Region Avg IOU: 0.661708, Class: 1.000000, Obj: 0.304288, No Obj: 0.002518, Avg Recall: 0.854015, count: 137 -Region Avg IOU: 0.607071, Class: 1.000000, Obj: 0.302090, No Obj: 0.002196, Avg Recall: 0.757812, count: 128 -Region Avg IOU: 0.680389, Class: 1.000000, Obj: 0.383697, No Obj: 0.002567, Avg Recall: 0.847619, count: 105 -Region Avg IOU: 0.607701, Class: 1.000000, Obj: 0.324084, No Obj: 0.002024, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.635182, Class: 1.000000, Obj: 0.336258, No Obj: 0.002712, Avg Recall: 0.785714, count: 154 -4947: 0.500877, 0.456496 avg, 0.001000 rate, 30.233725 seconds, 1266432 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.617789, Class: 1.000000, Obj: 0.319141, No Obj: 0.002196, Avg Recall: 0.756098, count: 123 -Region Avg IOU: 0.673046, Class: 1.000000, Obj: 0.341854, No Obj: 0.002241, Avg Recall: 0.876289, count: 97 -Region Avg IOU: 0.638668, Class: 1.000000, Obj: 0.317850, No Obj: 0.002204, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.624352, Class: 1.000000, Obj: 0.363667, No Obj: 0.001998, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.637821, Class: 1.000000, Obj: 0.311684, No Obj: 0.002361, Avg Recall: 0.785714, count: 126 -Region Avg IOU: 0.681341, Class: 1.000000, Obj: 0.356767, No Obj: 0.002384, Avg Recall: 0.831933, count: 119 -Region Avg IOU: 0.641459, Class: 1.000000, Obj: 0.313697, No Obj: 0.002175, Avg Recall: 0.782609, count: 115 -Region Avg IOU: 0.638524, Class: 1.000000, Obj: 0.356878, No Obj: 0.001908, Avg Recall: 0.784314, count: 102 -4948: 0.475882, 0.458435 avg, 0.001000 rate, 30.477125 seconds, 1266688 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.669191, Class: 1.000000, Obj: 0.289761, No Obj: 0.002468, Avg Recall: 0.817391, count: 115 -Region Avg IOU: 0.625740, Class: 1.000000, Obj: 0.377126, No Obj: 0.002433, Avg Recall: 0.738095, count: 126 -Region Avg IOU: 0.640799, Class: 1.000000, Obj: 0.324945, No Obj: 0.002387, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.637371, Class: 1.000000, Obj: 0.353014, No Obj: 0.002561, Avg Recall: 0.784000, count: 125 -Region Avg IOU: 0.666576, Class: 1.000000, Obj: 0.295405, No Obj: 0.002185, Avg Recall: 0.813084, count: 107 -Region Avg IOU: 0.657205, Class: 1.000000, Obj: 0.367627, No Obj: 0.002627, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.674746, Class: 1.000000, Obj: 0.385201, No Obj: 0.002486, Avg Recall: 0.841584, count: 101 -Region Avg IOU: 0.652950, Class: 1.000000, Obj: 0.389565, No Obj: 0.002673, Avg Recall: 0.784314, count: 102 -4949: 0.488315, 0.461423 avg, 0.001000 rate, 30.471220 seconds, 1266944 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.641853, Class: 1.000000, Obj: 0.373637, No Obj: 0.002911, Avg Recall: 0.824427, count: 131 -Region Avg IOU: 0.640946, Class: 1.000000, Obj: 0.410329, No Obj: 0.002343, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.685658, Class: 1.000000, Obj: 0.428742, No Obj: 0.002861, Avg Recall: 0.819672, count: 122 -Region Avg IOU: 0.670168, Class: 1.000000, Obj: 0.392254, No Obj: 0.002503, Avg Recall: 0.887755, count: 98 -Region Avg IOU: 0.658383, Class: 1.000000, Obj: 0.335909, No Obj: 0.002298, Avg Recall: 0.826531, count: 98 -Region Avg IOU: 0.661209, Class: 1.000000, Obj: 0.349412, No Obj: 0.002685, Avg Recall: 0.829060, count: 117 -Region Avg IOU: 0.636340, Class: 1.000000, Obj: 0.360623, No Obj: 0.002235, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.653252, Class: 1.000000, Obj: 0.325651, No Obj: 0.002641, Avg Recall: 0.807018, count: 114 -4950: 0.477048, 0.462985 avg, 0.001000 rate, 31.588388 seconds, 1267200 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 9.690888 seconds -Region Avg IOU: 0.575139, Class: 1.000000, Obj: 0.354617, No Obj: 0.003199, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.542063, Class: 1.000000, Obj: 0.326791, No Obj: 0.003202, Avg Recall: 0.591241, count: 137 -Region Avg IOU: 0.571075, Class: 1.000000, Obj: 0.326465, No Obj: 0.003621, Avg Recall: 0.688000, count: 125 -Region Avg IOU: 0.604021, Class: 1.000000, Obj: 0.349348, No Obj: 0.003920, Avg Recall: 0.740458, count: 131 -Region Avg IOU: 0.594043, Class: 1.000000, Obj: 0.348722, No Obj: 0.002628, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.601898, Class: 1.000000, Obj: 0.385676, No Obj: 0.003441, Avg Recall: 0.710938, count: 128 -Region Avg IOU: 0.564029, Class: 1.000000, Obj: 0.312430, No Obj: 0.002761, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.574880, Class: 1.000000, Obj: 0.361996, No Obj: 0.003058, Avg Recall: 0.686869, count: 99 -4951: 0.471418, 0.463828 avg, 0.001000 rate, 24.774204 seconds, 1267456 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.543540, Class: 1.000000, Obj: 0.368689, No Obj: 0.003493, Avg Recall: 0.658333, count: 120 -Region Avg IOU: 0.569468, Class: 1.000000, Obj: 0.376127, No Obj: 0.003334, Avg Recall: 0.650794, count: 126 -Region Avg IOU: 0.549357, Class: 1.000000, Obj: 0.347762, No Obj: 0.002828, Avg Recall: 0.600000, count: 85 -Region Avg IOU: 0.600438, Class: 1.000000, Obj: 0.402947, No Obj: 0.003340, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.632332, Class: 1.000000, Obj: 0.374717, No Obj: 0.003065, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.624061, Class: 1.000000, Obj: 0.396084, No Obj: 0.002978, Avg Recall: 0.797619, count: 84 -Region Avg IOU: 0.548007, Class: 1.000000, Obj: 0.361862, No Obj: 0.003396, Avg Recall: 0.645161, count: 124 -Region Avg IOU: 0.626981, Class: 1.000000, Obj: 0.397328, No Obj: 0.003671, Avg Recall: 0.785124, count: 121 -4952: 0.442476, 0.461693 avg, 0.001000 rate, 24.616364 seconds, 1267712 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.575618, Class: 1.000000, Obj: 0.361336, No Obj: 0.004421, Avg Recall: 0.671642, count: 134 -Region Avg IOU: 0.629669, Class: 1.000000, Obj: 0.418650, No Obj: 0.003742, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.566486, Class: 1.000000, Obj: 0.418586, No Obj: 0.003468, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.616144, Class: 1.000000, Obj: 0.409631, No Obj: 0.003839, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.594718, Class: 1.000000, Obj: 0.388970, No Obj: 0.003689, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.585661, Class: 1.000000, Obj: 0.392837, No Obj: 0.003079, Avg Recall: 0.701299, count: 77 -Region Avg IOU: 0.587922, Class: 1.000000, Obj: 0.409183, No Obj: 0.004581, Avg Recall: 0.719697, count: 132 -Region Avg IOU: 0.546989, Class: 1.000000, Obj: 0.360720, No Obj: 0.003178, Avg Recall: 0.612245, count: 98 -4953: 0.457294, 0.461253 avg, 0.001000 rate, 23.303640 seconds, 1267968 images -Loaded: 0.000116 seconds -Region Avg IOU: 0.540311, Class: 1.000000, Obj: 0.365239, No Obj: 0.002652, Avg Recall: 0.633803, count: 71 -Region Avg IOU: 0.631402, Class: 1.000000, Obj: 0.460515, No Obj: 0.003930, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.597420, Class: 1.000000, Obj: 0.416634, No Obj: 0.003924, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.615233, Class: 1.000000, Obj: 0.385066, No Obj: 0.003252, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.614507, Class: 1.000000, Obj: 0.433504, No Obj: 0.003063, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.606783, Class: 1.000000, Obj: 0.379347, No Obj: 0.004082, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.605628, Class: 1.000000, Obj: 0.408342, No Obj: 0.004028, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.549473, Class: 1.000000, Obj: 0.387888, No Obj: 0.003558, Avg Recall: 0.623762, count: 101 -4954: 0.402757, 0.455404 avg, 0.001000 rate, 22.279739 seconds, 1268224 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.594965, Class: 1.000000, Obj: 0.436694, No Obj: 0.003721, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.588903, Class: 1.000000, Obj: 0.413571, No Obj: 0.004049, Avg Recall: 0.689655, count: 87 -Region Avg IOU: 0.583024, Class: 1.000000, Obj: 0.396889, No Obj: 0.004720, Avg Recall: 0.658537, count: 123 -Region Avg IOU: 0.604572, Class: 1.000000, Obj: 0.446955, No Obj: 0.004531, Avg Recall: 0.690476, count: 126 -Region Avg IOU: 0.553290, Class: 1.000000, Obj: 0.419848, No Obj: 0.004124, Avg Recall: 0.627119, count: 118 -Region Avg IOU: 0.562884, Class: 1.000000, Obj: 0.397394, No Obj: 0.004414, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.571206, Class: 1.000000, Obj: 0.422611, No Obj: 0.004427, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.641478, Class: 1.000000, Obj: 0.464276, No Obj: 0.003784, Avg Recall: 0.763441, count: 93 -4955: 0.439434, 0.453807 avg, 0.001000 rate, 24.020967 seconds, 1268480 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.565897, Class: 1.000000, Obj: 0.442914, No Obj: 0.003755, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.588805, Class: 1.000000, Obj: 0.440127, No Obj: 0.004748, Avg Recall: 0.664000, count: 125 -Region Avg IOU: 0.571278, Class: 1.000000, Obj: 0.445575, No Obj: 0.003278, Avg Recall: 0.670732, count: 82 -Region Avg IOU: 0.627499, Class: 1.000000, Obj: 0.458032, No Obj: 0.003655, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.598933, Class: 1.000000, Obj: 0.467369, No Obj: 0.004443, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.597763, Class: 1.000000, Obj: 0.483557, No Obj: 0.004081, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.580610, Class: 1.000000, Obj: 0.394317, No Obj: 0.004205, Avg Recall: 0.675214, count: 117 -Region Avg IOU: 0.555589, Class: 1.000000, Obj: 0.381705, No Obj: 0.004402, Avg Recall: 0.661157, count: 121 -4956: 0.358878, 0.444314 avg, 0.001000 rate, 23.085293 seconds, 1268736 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.601606, Class: 1.000000, Obj: 0.439750, No Obj: 0.003487, Avg Recall: 0.734940, count: 83 -Region Avg IOU: 0.592290, Class: 1.000000, Obj: 0.453253, No Obj: 0.003517, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.604892, Class: 1.000000, Obj: 0.452182, No Obj: 0.003540, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.596089, Class: 1.000000, Obj: 0.430093, No Obj: 0.004130, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.566526, Class: 1.000000, Obj: 0.442191, No Obj: 0.003629, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.583578, Class: 1.000000, Obj: 0.462066, No Obj: 0.004309, Avg Recall: 0.693548, count: 124 -Region Avg IOU: 0.548661, Class: 1.000000, Obj: 0.427817, No Obj: 0.003282, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.552920, Class: 1.000000, Obj: 0.421604, No Obj: 0.003881, Avg Recall: 0.616071, count: 112 -4957: 0.377746, 0.437657 avg, 0.001000 rate, 23.234066 seconds, 1268992 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.557795, Class: 1.000000, Obj: 0.444969, No Obj: 0.003464, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.592600, Class: 1.000000, Obj: 0.429570, No Obj: 0.003617, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.576828, Class: 1.000000, Obj: 0.390895, No Obj: 0.003847, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.618645, Class: 1.000000, Obj: 0.423499, No Obj: 0.004098, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.562108, Class: 1.000000, Obj: 0.418148, No Obj: 0.003019, Avg Recall: 0.682353, count: 85 -Region Avg IOU: 0.524626, Class: 1.000000, Obj: 0.404771, No Obj: 0.003941, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.656836, Class: 1.000000, Obj: 0.466448, No Obj: 0.003450, Avg Recall: 0.820513, count: 78 -Region Avg IOU: 0.546834, Class: 1.000000, Obj: 0.414757, No Obj: 0.003598, Avg Recall: 0.640000, count: 100 -4958: 0.382040, 0.432095 avg, 0.001000 rate, 23.833069 seconds, 1269248 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.595154, Class: 1.000000, Obj: 0.426977, No Obj: 0.003638, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.627003, Class: 1.000000, Obj: 0.462330, No Obj: 0.003723, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.551604, Class: 1.000000, Obj: 0.421003, No Obj: 0.003931, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.561133, Class: 1.000000, Obj: 0.349926, No Obj: 0.003719, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.617847, Class: 1.000000, Obj: 0.467530, No Obj: 0.003498, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.617865, Class: 1.000000, Obj: 0.455281, No Obj: 0.003706, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.614687, Class: 1.000000, Obj: 0.409509, No Obj: 0.003844, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.599714, Class: 1.000000, Obj: 0.407486, No Obj: 0.004366, Avg Recall: 0.706422, count: 109 -4959: 0.415842, 0.430470 avg, 0.001000 rate, 24.363537 seconds, 1269504 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.570852, Class: 1.000000, Obj: 0.414488, No Obj: 0.002989, Avg Recall: 0.675000, count: 80 -Region Avg IOU: 0.556170, Class: 1.000000, Obj: 0.398378, No Obj: 0.003081, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.598223, Class: 1.000000, Obj: 0.385048, No Obj: 0.003834, Avg Recall: 0.719697, count: 132 -Region Avg IOU: 0.578659, Class: 1.000000, Obj: 0.334958, No Obj: 0.003952, Avg Recall: 0.710938, count: 128 -Region Avg IOU: 0.581862, Class: 1.000000, Obj: 0.387824, No Obj: 0.003874, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.595359, Class: 1.000000, Obj: 0.399175, No Obj: 0.003643, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.597284, Class: 1.000000, Obj: 0.425555, No Obj: 0.003355, Avg Recall: 0.740260, count: 77 -Region Avg IOU: 0.581554, Class: 1.000000, Obj: 0.416326, No Obj: 0.003385, Avg Recall: 0.684685, count: 111 -4960: 0.478899, 0.435313 avg, 0.001000 rate, 24.837738 seconds, 1269760 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 20.014053 seconds -Region Avg IOU: 0.588674, Class: 1.000000, Obj: 0.380842, No Obj: 0.003103, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.560142, Class: 1.000000, Obj: 0.358978, No Obj: 0.002813, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.575009, Class: 1.000000, Obj: 0.366943, No Obj: 0.003177, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.604425, Class: 1.000000, Obj: 0.413807, No Obj: 0.003170, Avg Recall: 0.759494, count: 79 -Region Avg IOU: 0.548501, Class: 1.000000, Obj: 0.339260, No Obj: 0.003974, Avg Recall: 0.631250, count: 160 -Region Avg IOU: 0.556453, Class: 1.000000, Obj: 0.355541, No Obj: 0.003131, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.559646, Class: 1.000000, Obj: 0.342597, No Obj: 0.003438, Avg Recall: 0.641791, count: 134 -Region Avg IOU: 0.585036, Class: 1.000000, Obj: 0.386915, No Obj: 0.003444, Avg Recall: 0.666667, count: 117 -4961: 0.448131, 0.436595 avg, 0.001000 rate, 22.572596 seconds, 1270016 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.554307, Class: 1.000000, Obj: 0.319576, No Obj: 0.003140, Avg Recall: 0.658915, count: 129 -Region Avg IOU: 0.593086, Class: 1.000000, Obj: 0.423880, No Obj: 0.003066, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.610686, Class: 1.000000, Obj: 0.381130, No Obj: 0.003497, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.591782, Class: 1.000000, Obj: 0.370450, No Obj: 0.003054, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.577231, Class: 1.000000, Obj: 0.359838, No Obj: 0.003337, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.559010, Class: 1.000000, Obj: 0.364679, No Obj: 0.003235, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.596703, Class: 1.000000, Obj: 0.414466, No Obj: 0.003153, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.604677, Class: 1.000000, Obj: 0.353337, No Obj: 0.003036, Avg Recall: 0.744186, count: 86 -4962: 0.436322, 0.436567 avg, 0.001000 rate, 24.239851 seconds, 1270272 images -Loaded: 0.000052 seconds -Region Avg IOU: 0.563200, Class: 1.000000, Obj: 0.353157, No Obj: 0.002975, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.577446, Class: 1.000000, Obj: 0.376373, No Obj: 0.003070, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.628532, Class: 1.000000, Obj: 0.344048, No Obj: 0.003260, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.552564, Class: 1.000000, Obj: 0.387149, No Obj: 0.002848, Avg Recall: 0.662791, count: 86 -Region Avg IOU: 0.591783, Class: 1.000000, Obj: 0.420569, No Obj: 0.002721, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.540484, Class: 1.000000, Obj: 0.348005, No Obj: 0.003601, Avg Recall: 0.601307, count: 153 -Region Avg IOU: 0.583179, Class: 1.000000, Obj: 0.383632, No Obj: 0.002860, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.534849, Class: 1.000000, Obj: 0.359675, No Obj: 0.003538, Avg Recall: 0.584000, count: 125 -4963: 0.430090, 0.435920 avg, 0.001000 rate, 24.478706 seconds, 1270528 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.549933, Class: 1.000000, Obj: 0.374057, No Obj: 0.002723, Avg Recall: 0.626506, count: 83 -Region Avg IOU: 0.578246, Class: 1.000000, Obj: 0.353691, No Obj: 0.003711, Avg Recall: 0.702128, count: 141 -Region Avg IOU: 0.621410, Class: 1.000000, Obj: 0.354110, No Obj: 0.003000, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.598288, Class: 1.000000, Obj: 0.369836, No Obj: 0.003401, Avg Recall: 0.677686, count: 121 -Region Avg IOU: 0.531448, Class: 1.000000, Obj: 0.306765, No Obj: 0.003030, Avg Recall: 0.588235, count: 119 -Region Avg IOU: 0.623043, Class: 1.000000, Obj: 0.363189, No Obj: 0.003061, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.553594, Class: 1.000000, Obj: 0.344857, No Obj: 0.002806, Avg Recall: 0.574468, count: 94 -Region Avg IOU: 0.605921, Class: 1.000000, Obj: 0.381354, No Obj: 0.003490, Avg Recall: 0.743590, count: 117 -4964: 0.435452, 0.435873 avg, 0.001000 rate, 22.510544 seconds, 1270784 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.536068, Class: 1.000000, Obj: 0.316010, No Obj: 0.003472, Avg Recall: 0.561983, count: 121 -Region Avg IOU: 0.598167, Class: 1.000000, Obj: 0.379929, No Obj: 0.002908, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.584901, Class: 1.000000, Obj: 0.318371, No Obj: 0.002815, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.582151, Class: 1.000000, Obj: 0.348647, No Obj: 0.003204, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.609836, Class: 1.000000, Obj: 0.394377, No Obj: 0.003755, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.585874, Class: 1.000000, Obj: 0.367210, No Obj: 0.002944, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.649147, Class: 1.000000, Obj: 0.360480, No Obj: 0.003040, Avg Recall: 0.762500, count: 80 -Region Avg IOU: 0.587473, Class: 1.000000, Obj: 0.343964, No Obj: 0.003077, Avg Recall: 0.732673, count: 101 -4965: 0.395496, 0.431835 avg, 0.001000 rate, 23.484024 seconds, 1271040 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.562722, Class: 1.000000, Obj: 0.350325, No Obj: 0.003067, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.592575, Class: 1.000000, Obj: 0.344430, No Obj: 0.003171, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.568147, Class: 1.000000, Obj: 0.364577, No Obj: 0.002816, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.588219, Class: 1.000000, Obj: 0.370113, No Obj: 0.003963, Avg Recall: 0.704545, count: 132 -Region Avg IOU: 0.603168, Class: 1.000000, Obj: 0.366088, No Obj: 0.003140, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.572830, Class: 1.000000, Obj: 0.355661, No Obj: 0.003061, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.605614, Class: 1.000000, Obj: 0.392778, No Obj: 0.003064, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.536476, Class: 1.000000, Obj: 0.320236, No Obj: 0.003302, Avg Recall: 0.634146, count: 123 -4966: 0.448894, 0.433541 avg, 0.001000 rate, 23.990896 seconds, 1271296 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.629032, Class: 1.000000, Obj: 0.439722, No Obj: 0.003766, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.625026, Class: 1.000000, Obj: 0.336812, No Obj: 0.002940, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.601424, Class: 1.000000, Obj: 0.387272, No Obj: 0.003129, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.605014, Class: 1.000000, Obj: 0.379539, No Obj: 0.003269, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.616213, Class: 1.000000, Obj: 0.368940, No Obj: 0.003819, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.593699, Class: 1.000000, Obj: 0.347472, No Obj: 0.003178, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.571287, Class: 1.000000, Obj: 0.327304, No Obj: 0.002892, Avg Recall: 0.652174, count: 92 -Region Avg IOU: 0.609017, Class: 1.000000, Obj: 0.362664, No Obj: 0.003451, Avg Recall: 0.765217, count: 115 -4967: 0.413132, 0.431500 avg, 0.001000 rate, 24.371469 seconds, 1271552 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.618484, Class: 1.000000, Obj: 0.400646, No Obj: 0.003256, Avg Recall: 0.724138, count: 87 -Region Avg IOU: 0.534889, Class: 1.000000, Obj: 0.352226, No Obj: 0.003283, Avg Recall: 0.592000, count: 125 -Region Avg IOU: 0.624411, Class: 1.000000, Obj: 0.443969, No Obj: 0.003998, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.603946, Class: 1.000000, Obj: 0.418748, No Obj: 0.003764, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.556592, Class: 1.000000, Obj: 0.376109, No Obj: 0.003259, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.581468, Class: 1.000000, Obj: 0.329602, No Obj: 0.003317, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.584932, Class: 1.000000, Obj: 0.390703, No Obj: 0.003612, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.590374, Class: 1.000000, Obj: 0.398161, No Obj: 0.003832, Avg Recall: 0.736434, count: 129 -4968: 0.528854, 0.441236 avg, 0.001000 rate, 24.483463 seconds, 1271808 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.605367, Class: 1.000000, Obj: 0.409335, No Obj: 0.003936, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.566426, Class: 1.000000, Obj: 0.370309, No Obj: 0.003599, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.575574, Class: 1.000000, Obj: 0.409642, No Obj: 0.003390, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.570551, Class: 1.000000, Obj: 0.406232, No Obj: 0.003497, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.565852, Class: 1.000000, Obj: 0.399957, No Obj: 0.003553, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.640920, Class: 1.000000, Obj: 0.410604, No Obj: 0.003790, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.639269, Class: 1.000000, Obj: 0.460228, No Obj: 0.003789, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.590263, Class: 1.000000, Obj: 0.358291, No Obj: 0.003780, Avg Recall: 0.741667, count: 120 -4969: 0.419873, 0.439099 avg, 0.001000 rate, 22.855047 seconds, 1272064 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.608544, Class: 1.000000, Obj: 0.420562, No Obj: 0.002965, Avg Recall: 0.698795, count: 83 -Region Avg IOU: 0.662485, Class: 1.000000, Obj: 0.418257, No Obj: 0.003771, Avg Recall: 0.870130, count: 77 -Region Avg IOU: 0.622465, Class: 1.000000, Obj: 0.420492, No Obj: 0.003436, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.573868, Class: 1.000000, Obj: 0.411185, No Obj: 0.003560, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.551506, Class: 1.000000, Obj: 0.375885, No Obj: 0.003455, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.555743, Class: 1.000000, Obj: 0.393524, No Obj: 0.003535, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.611539, Class: 1.000000, Obj: 0.408427, No Obj: 0.003723, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.655795, Class: 1.000000, Obj: 0.418296, No Obj: 0.004255, Avg Recall: 0.815789, count: 114 -4970: 0.385591, 0.433749 avg, 0.001000 rate, 23.554035 seconds, 1272320 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000067 seconds -Region Avg IOU: 0.655853, Class: 1.000000, Obj: 0.447624, No Obj: 0.003199, Avg Recall: 0.813953, count: 86 -Region Avg IOU: 0.659547, Class: 1.000000, Obj: 0.515243, No Obj: 0.003367, Avg Recall: 0.827957, count: 93 -Region Avg IOU: 0.646495, Class: 1.000000, Obj: 0.460107, No Obj: 0.002900, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.620916, Class: 1.000000, Obj: 0.461211, No Obj: 0.003196, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.640750, Class: 1.000000, Obj: 0.449083, No Obj: 0.003562, Avg Recall: 0.771654, count: 127 -Region Avg IOU: 0.651813, Class: 1.000000, Obj: 0.442021, No Obj: 0.003309, Avg Recall: 0.833333, count: 108 -Region Avg IOU: 0.651596, Class: 1.000000, Obj: 0.437227, No Obj: 0.003128, Avg Recall: 0.818182, count: 110 -Region Avg IOU: 0.666398, Class: 1.000000, Obj: 0.416249, No Obj: 0.003155, Avg Recall: 0.854545, count: 110 -4971: 0.435388, 0.433912 avg, 0.001000 rate, 27.519604 seconds, 1272576 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.632241, Class: 1.000000, Obj: 0.485233, No Obj: 0.002978, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.629839, Class: 1.000000, Obj: 0.440137, No Obj: 0.003023, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.619185, Class: 1.000000, Obj: 0.514974, No Obj: 0.002902, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.657206, Class: 1.000000, Obj: 0.375332, No Obj: 0.003241, Avg Recall: 0.830189, count: 106 -Region Avg IOU: 0.613864, Class: 1.000000, Obj: 0.421630, No Obj: 0.003491, Avg Recall: 0.763514, count: 148 -Region Avg IOU: 0.626987, Class: 1.000000, Obj: 0.460584, No Obj: 0.002645, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.619647, Class: 1.000000, Obj: 0.466685, No Obj: 0.002933, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.685146, Class: 1.000000, Obj: 0.430484, No Obj: 0.003974, Avg Recall: 0.865248, count: 141 -4972: 0.439242, 0.434445 avg, 0.001000 rate, 28.417864 seconds, 1272832 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.618725, Class: 1.000000, Obj: 0.400969, No Obj: 0.002545, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.650603, Class: 1.000000, Obj: 0.423854, No Obj: 0.002869, Avg Recall: 0.831776, count: 107 -Region Avg IOU: 0.667647, Class: 1.000000, Obj: 0.445201, No Obj: 0.003265, Avg Recall: 0.851562, count: 128 -Region Avg IOU: 0.636321, Class: 1.000000, Obj: 0.473591, No Obj: 0.003152, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.658951, Class: 1.000000, Obj: 0.506630, No Obj: 0.003156, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.676298, Class: 1.000000, Obj: 0.420574, No Obj: 0.003076, Avg Recall: 0.817204, count: 93 -Region Avg IOU: 0.664859, Class: 1.000000, Obj: 0.440832, No Obj: 0.002589, Avg Recall: 0.815217, count: 92 -Region Avg IOU: 0.623526, Class: 1.000000, Obj: 0.440352, No Obj: 0.002974, Avg Recall: 0.750000, count: 108 -4973: 0.384091, 0.429410 avg, 0.001000 rate, 26.943151 seconds, 1273088 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.629099, Class: 1.000000, Obj: 0.395482, No Obj: 0.003085, Avg Recall: 0.774194, count: 124 -Region Avg IOU: 0.623260, Class: 1.000000, Obj: 0.379022, No Obj: 0.003122, Avg Recall: 0.750000, count: 124 -Region Avg IOU: 0.657731, Class: 1.000000, Obj: 0.412420, No Obj: 0.002724, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.632983, Class: 1.000000, Obj: 0.364362, No Obj: 0.002796, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.631533, Class: 1.000000, Obj: 0.433689, No Obj: 0.002583, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.669498, Class: 1.000000, Obj: 0.471332, No Obj: 0.002321, Avg Recall: 0.836957, count: 92 -Region Avg IOU: 0.666903, Class: 1.000000, Obj: 0.379267, No Obj: 0.002846, Avg Recall: 0.825243, count: 103 -Region Avg IOU: 0.647837, Class: 1.000000, Obj: 0.407492, No Obj: 0.002866, Avg Recall: 0.801653, count: 121 -4974: 0.429993, 0.429468 avg, 0.001000 rate, 29.098373 seconds, 1273344 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.578618, Class: 1.000000, Obj: 0.305494, No Obj: 0.002212, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.656310, Class: 1.000000, Obj: 0.439234, No Obj: 0.002471, Avg Recall: 0.834862, count: 109 -Region Avg IOU: 0.638217, Class: 1.000000, Obj: 0.384016, No Obj: 0.002477, Avg Recall: 0.819048, count: 105 -Region Avg IOU: 0.650592, Class: 1.000000, Obj: 0.408864, No Obj: 0.002375, Avg Recall: 0.811111, count: 90 -Region Avg IOU: 0.631972, Class: 1.000000, Obj: 0.370110, No Obj: 0.002834, Avg Recall: 0.789474, count: 133 -Region Avg IOU: 0.614308, Class: 1.000000, Obj: 0.367277, No Obj: 0.002592, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.690409, Class: 1.000000, Obj: 0.407237, No Obj: 0.002483, Avg Recall: 0.885417, count: 96 -Region Avg IOU: 0.639488, Class: 1.000000, Obj: 0.348466, No Obj: 0.002606, Avg Recall: 0.787402, count: 127 -4975: 0.437139, 0.430235 avg, 0.001000 rate, 27.601986 seconds, 1273600 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.621043, Class: 1.000000, Obj: 0.347166, No Obj: 0.002301, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.635520, Class: 1.000000, Obj: 0.344466, No Obj: 0.002269, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.656962, Class: 1.000000, Obj: 0.375012, No Obj: 0.002301, Avg Recall: 0.833333, count: 108 -Region Avg IOU: 0.619023, Class: 1.000000, Obj: 0.330122, No Obj: 0.002409, Avg Recall: 0.800000, count: 130 -Region Avg IOU: 0.641450, Class: 1.000000, Obj: 0.370688, No Obj: 0.002570, Avg Recall: 0.815789, count: 114 -Region Avg IOU: 0.679957, Class: 1.000000, Obj: 0.359893, No Obj: 0.002602, Avg Recall: 0.826087, count: 115 -Region Avg IOU: 0.673791, Class: 1.000000, Obj: 0.388694, No Obj: 0.002360, Avg Recall: 0.864198, count: 81 -Region Avg IOU: 0.651349, Class: 1.000000, Obj: 0.392072, No Obj: 0.002455, Avg Recall: 0.810000, count: 100 -4976: 0.454776, 0.432689 avg, 0.001000 rate, 25.881783 seconds, 1273856 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.624201, Class: 1.000000, Obj: 0.352125, No Obj: 0.002298, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.652553, Class: 1.000000, Obj: 0.384848, No Obj: 0.002296, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.680107, Class: 1.000000, Obj: 0.394294, No Obj: 0.002397, Avg Recall: 0.858696, count: 92 -Region Avg IOU: 0.634696, Class: 1.000000, Obj: 0.375641, No Obj: 0.002290, Avg Recall: 0.796117, count: 103 -Region Avg IOU: 0.661612, Class: 1.000000, Obj: 0.315074, No Obj: 0.002512, Avg Recall: 0.800000, count: 120 -Region Avg IOU: 0.630307, Class: 1.000000, Obj: 0.368805, No Obj: 0.002033, Avg Recall: 0.764706, count: 85 -Region Avg IOU: 0.635711, Class: 1.000000, Obj: 0.363663, No Obj: 0.002029, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.649403, Class: 1.000000, Obj: 0.395179, No Obj: 0.002282, Avg Recall: 0.826531, count: 98 -4977: 0.419530, 0.431373 avg, 0.001000 rate, 28.748438 seconds, 1274112 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.681809, Class: 1.000000, Obj: 0.374420, No Obj: 0.002729, Avg Recall: 0.848214, count: 112 -Region Avg IOU: 0.608769, Class: 1.000000, Obj: 0.340769, No Obj: 0.002446, Avg Recall: 0.730769, count: 130 -Region Avg IOU: 0.596229, Class: 1.000000, Obj: 0.330592, No Obj: 0.002487, Avg Recall: 0.762590, count: 139 -Region Avg IOU: 0.626012, Class: 1.000000, Obj: 0.387179, No Obj: 0.002665, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.649628, Class: 1.000000, Obj: 0.380716, No Obj: 0.002350, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.640592, Class: 1.000000, Obj: 0.365049, No Obj: 0.002356, Avg Recall: 0.796610, count: 118 -Region Avg IOU: 0.667571, Class: 1.000000, Obj: 0.395244, No Obj: 0.002344, Avg Recall: 0.855556, count: 90 -Region Avg IOU: 0.625544, Class: 1.000000, Obj: 0.358336, No Obj: 0.002489, Avg Recall: 0.737864, count: 103 -4978: 0.474735, 0.435710 avg, 0.001000 rate, 29.293039 seconds, 1274368 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.643765, Class: 1.000000, Obj: 0.413704, No Obj: 0.002325, Avg Recall: 0.818182, count: 88 -Region Avg IOU: 0.643409, Class: 1.000000, Obj: 0.338624, No Obj: 0.002795, Avg Recall: 0.774436, count: 133 -Region Avg IOU: 0.623999, Class: 1.000000, Obj: 0.295124, No Obj: 0.002694, Avg Recall: 0.789474, count: 152 -Region Avg IOU: 0.618560, Class: 1.000000, Obj: 0.377250, No Obj: 0.002680, Avg Recall: 0.740506, count: 158 -Region Avg IOU: 0.659305, Class: 1.000000, Obj: 0.385067, No Obj: 0.002928, Avg Recall: 0.790323, count: 124 -Region Avg IOU: 0.666165, Class: 1.000000, Obj: 0.451549, No Obj: 0.002228, Avg Recall: 0.857143, count: 91 -Region Avg IOU: 0.683712, Class: 1.000000, Obj: 0.415996, No Obj: 0.002610, Avg Recall: 0.871287, count: 101 -Region Avg IOU: 0.630363, Class: 1.000000, Obj: 0.370073, No Obj: 0.002644, Avg Recall: 0.768000, count: 125 -4979: 0.518859, 0.444025 avg, 0.001000 rate, 27.972763 seconds, 1274624 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.649687, Class: 1.000000, Obj: 0.396626, No Obj: 0.002433, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.626145, Class: 1.000000, Obj: 0.364183, No Obj: 0.002616, Avg Recall: 0.770370, count: 135 -Region Avg IOU: 0.658071, Class: 1.000000, Obj: 0.333889, No Obj: 0.002571, Avg Recall: 0.834711, count: 121 -Region Avg IOU: 0.652606, Class: 1.000000, Obj: 0.352506, No Obj: 0.002441, Avg Recall: 0.840336, count: 119 -Region Avg IOU: 0.648266, Class: 1.000000, Obj: 0.323988, No Obj: 0.003098, Avg Recall: 0.811024, count: 127 -Region Avg IOU: 0.602375, Class: 1.000000, Obj: 0.342259, No Obj: 0.002253, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.637723, Class: 1.000000, Obj: 0.385084, No Obj: 0.002490, Avg Recall: 0.825243, count: 103 -Region Avg IOU: 0.629045, Class: 1.000000, Obj: 0.337958, No Obj: 0.002574, Avg Recall: 0.780702, count: 114 -4980: 0.457354, 0.445358 avg, 0.001000 rate, 27.924374 seconds, 1274880 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 18.641851 seconds -Region Avg IOU: 0.628165, Class: 1.000000, Obj: 0.353191, No Obj: 0.002840, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.548773, Class: 1.000000, Obj: 0.307361, No Obj: 0.003220, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.579136, Class: 1.000000, Obj: 0.310860, No Obj: 0.002786, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.562783, Class: 1.000000, Obj: 0.342011, No Obj: 0.002729, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.586798, Class: 1.000000, Obj: 0.322508, No Obj: 0.003614, Avg Recall: 0.674603, count: 126 -Region Avg IOU: 0.559123, Class: 1.000000, Obj: 0.335854, No Obj: 0.003188, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.574452, Class: 1.000000, Obj: 0.354403, No Obj: 0.003057, Avg Recall: 0.681818, count: 88 -Region Avg IOU: 0.601084, Class: 1.000000, Obj: 0.394017, No Obj: 0.002945, Avg Recall: 0.768293, count: 82 -4981: 0.451278, 0.445950 avg, 0.001000 rate, 22.900423 seconds, 1275136 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.549584, Class: 1.000000, Obj: 0.358699, No Obj: 0.003255, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.528872, Class: 1.000000, Obj: 0.317642, No Obj: 0.003246, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.571548, Class: 1.000000, Obj: 0.324196, No Obj: 0.003315, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.592509, Class: 1.000000, Obj: 0.324976, No Obj: 0.004097, Avg Recall: 0.708333, count: 144 -Region Avg IOU: 0.574790, Class: 1.000000, Obj: 0.350397, No Obj: 0.003486, Avg Recall: 0.651852, count: 135 -Region Avg IOU: 0.602287, Class: 1.000000, Obj: 0.395240, No Obj: 0.003326, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.580505, Class: 1.000000, Obj: 0.323047, No Obj: 0.003681, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.587281, Class: 1.000000, Obj: 0.390660, No Obj: 0.003224, Avg Recall: 0.706522, count: 92 -4982: 0.493699, 0.450724 avg, 0.001000 rate, 23.577276 seconds, 1275392 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.560182, Class: 1.000000, Obj: 0.382407, No Obj: 0.003437, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.559663, Class: 1.000000, Obj: 0.378651, No Obj: 0.003238, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.571379, Class: 1.000000, Obj: 0.358362, No Obj: 0.003776, Avg Recall: 0.661290, count: 124 -Region Avg IOU: 0.577514, Class: 1.000000, Obj: 0.393525, No Obj: 0.003607, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.625358, Class: 1.000000, Obj: 0.427517, No Obj: 0.003952, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.576065, Class: 1.000000, Obj: 0.344655, No Obj: 0.003592, Avg Recall: 0.658537, count: 123 -Region Avg IOU: 0.548138, Class: 1.000000, Obj: 0.367821, No Obj: 0.003439, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.623487, Class: 1.000000, Obj: 0.412802, No Obj: 0.003930, Avg Recall: 0.703390, count: 118 -4983: 0.430760, 0.448728 avg, 0.001000 rate, 24.170242 seconds, 1275648 images -Loaded: 0.060434 seconds -Region Avg IOU: 0.590835, Class: 1.000000, Obj: 0.406088, No Obj: 0.003962, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.571243, Class: 1.000000, Obj: 0.391458, No Obj: 0.004138, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.552072, Class: 1.000000, Obj: 0.361681, No Obj: 0.004610, Avg Recall: 0.647059, count: 136 -Region Avg IOU: 0.528280, Class: 1.000000, Obj: 0.375736, No Obj: 0.003511, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.558982, Class: 1.000000, Obj: 0.378941, No Obj: 0.003204, Avg Recall: 0.640449, count: 89 -Region Avg IOU: 0.568893, Class: 1.000000, Obj: 0.384582, No Obj: 0.003987, Avg Recall: 0.650794, count: 126 -Region Avg IOU: 0.553996, Class: 1.000000, Obj: 0.385948, No Obj: 0.003980, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.557486, Class: 1.000000, Obj: 0.383281, No Obj: 0.003583, Avg Recall: 0.635417, count: 96 -4984: 0.396971, 0.443552 avg, 0.001000 rate, 22.748650 seconds, 1275904 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.553189, Class: 1.000000, Obj: 0.404864, No Obj: 0.004220, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.559103, Class: 1.000000, Obj: 0.387681, No Obj: 0.003864, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.576788, Class: 1.000000, Obj: 0.429039, No Obj: 0.004631, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.584985, Class: 1.000000, Obj: 0.402681, No Obj: 0.004081, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.574988, Class: 1.000000, Obj: 0.377748, No Obj: 0.004337, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.570845, Class: 1.000000, Obj: 0.430811, No Obj: 0.004654, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.577654, Class: 1.000000, Obj: 0.410591, No Obj: 0.004073, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.547365, Class: 1.000000, Obj: 0.374226, No Obj: 0.004119, Avg Recall: 0.623853, count: 109 -4985: 0.451313, 0.444328 avg, 0.001000 rate, 22.434507 seconds, 1276160 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.510459, Class: 1.000000, Obj: 0.405646, No Obj: 0.003928, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.596428, Class: 1.000000, Obj: 0.461558, No Obj: 0.003841, Avg Recall: 0.712500, count: 80 -Region Avg IOU: 0.546424, Class: 1.000000, Obj: 0.430137, No Obj: 0.004309, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.581151, Class: 1.000000, Obj: 0.431281, No Obj: 0.004130, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.528535, Class: 1.000000, Obj: 0.368940, No Obj: 0.003828, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.581092, Class: 1.000000, Obj: 0.445520, No Obj: 0.003700, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.597425, Class: 1.000000, Obj: 0.432389, No Obj: 0.004079, Avg Recall: 0.712500, count: 80 -Region Avg IOU: 0.589533, Class: 1.000000, Obj: 0.430635, No Obj: 0.004074, Avg Recall: 0.709677, count: 93 -4986: 0.401316, 0.440027 avg, 0.001000 rate, 22.689501 seconds, 1276416 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.594395, Class: 1.000000, Obj: 0.469771, No Obj: 0.004728, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.583355, Class: 1.000000, Obj: 0.410111, No Obj: 0.004738, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.564742, Class: 1.000000, Obj: 0.445019, No Obj: 0.004573, Avg Recall: 0.687023, count: 131 -Region Avg IOU: 0.548400, Class: 1.000000, Obj: 0.382317, No Obj: 0.004210, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.553777, Class: 1.000000, Obj: 0.412931, No Obj: 0.004316, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.595653, Class: 1.000000, Obj: 0.460185, No Obj: 0.004739, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.588340, Class: 1.000000, Obj: 0.427296, No Obj: 0.005499, Avg Recall: 0.704000, count: 125 -Region Avg IOU: 0.590339, Class: 1.000000, Obj: 0.462030, No Obj: 0.004301, Avg Recall: 0.722222, count: 108 -4987: 0.448185, 0.440843 avg, 0.001000 rate, 23.341436 seconds, 1276672 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.589621, Class: 1.000000, Obj: 0.451413, No Obj: 0.004408, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.572079, Class: 1.000000, Obj: 0.449980, No Obj: 0.003901, Avg Recall: 0.688889, count: 90 -Region Avg IOU: 0.529110, Class: 1.000000, Obj: 0.417667, No Obj: 0.003934, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.556682, Class: 1.000000, Obj: 0.425263, No Obj: 0.004605, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.529422, Class: 1.000000, Obj: 0.417692, No Obj: 0.004328, Avg Recall: 0.601695, count: 118 -Region Avg IOU: 0.583940, Class: 1.000000, Obj: 0.420590, No Obj: 0.004289, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.586390, Class: 1.000000, Obj: 0.425714, No Obj: 0.004300, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.552462, Class: 1.000000, Obj: 0.399142, No Obj: 0.004550, Avg Recall: 0.643939, count: 132 -4988: 0.420155, 0.438774 avg, 0.001000 rate, 23.010368 seconds, 1276928 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.563098, Class: 1.000000, Obj: 0.417521, No Obj: 0.003786, Avg Recall: 0.630952, count: 84 -Region Avg IOU: 0.554184, Class: 1.000000, Obj: 0.426409, No Obj: 0.003704, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.555670, Class: 1.000000, Obj: 0.428574, No Obj: 0.003848, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.605439, Class: 1.000000, Obj: 0.465106, No Obj: 0.004616, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.591989, Class: 1.000000, Obj: 0.424541, No Obj: 0.003888, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.575939, Class: 1.000000, Obj: 0.404145, No Obj: 0.004156, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.528411, Class: 1.000000, Obj: 0.417079, No Obj: 0.003589, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.558813, Class: 1.000000, Obj: 0.438782, No Obj: 0.004278, Avg Recall: 0.643478, count: 115 -4989: 0.407485, 0.435645 avg, 0.001000 rate, 23.590157 seconds, 1277184 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.534302, Class: 1.000000, Obj: 0.418213, No Obj: 0.003525, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.556263, Class: 1.000000, Obj: 0.402897, No Obj: 0.004281, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.553066, Class: 1.000000, Obj: 0.370940, No Obj: 0.003821, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.575641, Class: 1.000000, Obj: 0.412090, No Obj: 0.004221, Avg Recall: 0.633333, count: 120 -Region Avg IOU: 0.572837, Class: 1.000000, Obj: 0.427787, No Obj: 0.004164, Avg Recall: 0.669492, count: 118 -Region Avg IOU: 0.599727, Class: 1.000000, Obj: 0.454613, No Obj: 0.003665, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.487639, Class: 1.000000, Obj: 0.396673, No Obj: 0.003265, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.542559, Class: 1.000000, Obj: 0.389630, No Obj: 0.003381, Avg Recall: 0.656250, count: 96 -4990: 0.395215, 0.431602 avg, 0.001000 rate, 22.654633 seconds, 1277440 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.377260 seconds -Region Avg IOU: 0.532065, Class: 1.000000, Obj: 0.386804, No Obj: 0.004003, Avg Recall: 0.592105, count: 76 -Region Avg IOU: 0.516468, Class: 1.000000, Obj: 0.373931, No Obj: 0.003783, Avg Recall: 0.595238, count: 84 -Region Avg IOU: 0.516733, Class: 1.000000, Obj: 0.388150, No Obj: 0.003780, Avg Recall: 0.537037, count: 108 -Region Avg IOU: 0.513398, Class: 1.000000, Obj: 0.320900, No Obj: 0.003851, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.502203, Class: 1.000000, Obj: 0.315105, No Obj: 0.004493, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.566964, Class: 1.000000, Obj: 0.379516, No Obj: 0.004030, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.521402, Class: 1.000000, Obj: 0.351689, No Obj: 0.004366, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.541934, Class: 1.000000, Obj: 0.363594, No Obj: 0.004802, Avg Recall: 0.633027, count: 109 -4991: 0.413978, 0.429840 avg, 0.001000 rate, 19.686668 seconds, 1277696 images -Loaded: 2.509839 seconds -Region Avg IOU: 0.533568, Class: 1.000000, Obj: 0.346896, No Obj: 0.004147, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.572624, Class: 1.000000, Obj: 0.364734, No Obj: 0.004286, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.558890, Class: 1.000000, Obj: 0.355639, No Obj: 0.004507, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.510545, Class: 1.000000, Obj: 0.374853, No Obj: 0.004032, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.499164, Class: 1.000000, Obj: 0.326862, No Obj: 0.003589, Avg Recall: 0.534653, count: 101 -Region Avg IOU: 0.500730, Class: 1.000000, Obj: 0.313764, No Obj: 0.004122, Avg Recall: 0.522388, count: 134 -Region Avg IOU: 0.567067, Class: 1.000000, Obj: 0.364989, No Obj: 0.004766, Avg Recall: 0.696970, count: 132 -Region Avg IOU: 0.539781, Class: 1.000000, Obj: 0.379816, No Obj: 0.003405, Avg Recall: 0.604938, count: 81 -4992: 0.444209, 0.431277 avg, 0.001000 rate, 18.475874 seconds, 1277952 images -Loaded: 2.163200 seconds -Region Avg IOU: 0.536892, Class: 1.000000, Obj: 0.332861, No Obj: 0.003759, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.547718, Class: 1.000000, Obj: 0.360239, No Obj: 0.003325, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.550488, Class: 1.000000, Obj: 0.393263, No Obj: 0.003973, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.507003, Class: 1.000000, Obj: 0.345540, No Obj: 0.003374, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.539020, Class: 1.000000, Obj: 0.330478, No Obj: 0.004139, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.508139, Class: 1.000000, Obj: 0.340647, No Obj: 0.003399, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.517790, Class: 1.000000, Obj: 0.348124, No Obj: 0.003686, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.531955, Class: 1.000000, Obj: 0.344514, No Obj: 0.003525, Avg Recall: 0.568182, count: 88 -4993: 0.411422, 0.429291 avg, 0.001000 rate, 19.175997 seconds, 1278208 images -Loaded: 1.969457 seconds -Region Avg IOU: 0.541909, Class: 1.000000, Obj: 0.353782, No Obj: 0.003628, Avg Recall: 0.626374, count: 91 -Region Avg IOU: 0.482746, Class: 1.000000, Obj: 0.286649, No Obj: 0.003363, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.531845, Class: 1.000000, Obj: 0.316899, No Obj: 0.003775, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.524219, Class: 1.000000, Obj: 0.322583, No Obj: 0.004191, Avg Recall: 0.585366, count: 123 -Region Avg IOU: 0.533179, Class: 1.000000, Obj: 0.351602, No Obj: 0.003393, Avg Recall: 0.633333, count: 90 -Region Avg IOU: 0.536179, Class: 1.000000, Obj: 0.342796, No Obj: 0.004234, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.511785, Class: 1.000000, Obj: 0.338568, No Obj: 0.003413, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.497644, Class: 1.000000, Obj: 0.355114, No Obj: 0.003540, Avg Recall: 0.569106, count: 123 -4994: 0.478104, 0.434172 avg, 0.001000 rate, 19.187899 seconds, 1278464 images -Loaded: 2.966446 seconds -Region Avg IOU: 0.536404, Class: 1.000000, Obj: 0.326515, No Obj: 0.003829, Avg Recall: 0.641026, count: 117 -Region Avg IOU: 0.538052, Class: 1.000000, Obj: 0.307188, No Obj: 0.003830, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.543279, Class: 1.000000, Obj: 0.357975, No Obj: 0.003767, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.533610, Class: 1.000000, Obj: 0.366755, No Obj: 0.003528, Avg Recall: 0.647727, count: 88 -Region Avg IOU: 0.525663, Class: 1.000000, Obj: 0.342885, No Obj: 0.004104, Avg Recall: 0.611111, count: 126 -Region Avg IOU: 0.517944, Class: 1.000000, Obj: 0.329877, No Obj: 0.003838, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.492647, Class: 1.000000, Obj: 0.283785, No Obj: 0.003459, Avg Recall: 0.571429, count: 105 -Region Avg IOU: 0.514449, Class: 1.000000, Obj: 0.319132, No Obj: 0.003608, Avg Recall: 0.571429, count: 126 -4995: 0.486406, 0.439396 avg, 0.001000 rate, 20.614521 seconds, 1278720 images -Loaded: 1.283513 seconds -Region Avg IOU: 0.583203, Class: 1.000000, Obj: 0.340725, No Obj: 0.004121, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.459551, Class: 1.000000, Obj: 0.291461, No Obj: 0.003986, Avg Recall: 0.532847, count: 137 -Region Avg IOU: 0.559495, Class: 1.000000, Obj: 0.320002, No Obj: 0.003554, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.555254, Class: 1.000000, Obj: 0.310192, No Obj: 0.004007, Avg Recall: 0.643411, count: 129 -Region Avg IOU: 0.541469, Class: 1.000000, Obj: 0.317076, No Obj: 0.004014, Avg Recall: 0.623077, count: 130 -Region Avg IOU: 0.527942, Class: 1.000000, Obj: 0.315921, No Obj: 0.004370, Avg Recall: 0.613793, count: 145 -Region Avg IOU: 0.458981, Class: 1.000000, Obj: 0.296885, No Obj: 0.003188, Avg Recall: 0.482759, count: 116 -Region Avg IOU: 0.485337, Class: 1.000000, Obj: 0.309422, No Obj: 0.003632, Avg Recall: 0.552381, count: 105 -4996: 0.504070, 0.445863 avg, 0.001000 rate, 22.137695 seconds, 1278976 images -Loaded: 0.000056 seconds -Region Avg IOU: 0.534175, Class: 1.000000, Obj: 0.361222, No Obj: 0.003514, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.547264, Class: 1.000000, Obj: 0.343063, No Obj: 0.003769, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.513078, Class: 1.000000, Obj: 0.320385, No Obj: 0.004186, Avg Recall: 0.569106, count: 123 -Region Avg IOU: 0.533023, Class: 1.000000, Obj: 0.291639, No Obj: 0.003570, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.536503, Class: 1.000000, Obj: 0.356667, No Obj: 0.004171, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.556795, Class: 1.000000, Obj: 0.330543, No Obj: 0.003466, Avg Recall: 0.600000, count: 85 -Region Avg IOU: 0.511161, Class: 1.000000, Obj: 0.312142, No Obj: 0.004199, Avg Recall: 0.539683, count: 126 -Region Avg IOU: 0.559153, Class: 1.000000, Obj: 0.362606, No Obj: 0.003386, Avg Recall: 0.641975, count: 81 -4997: 0.438156, 0.445092 avg, 0.001000 rate, 21.243019 seconds, 1279232 images -Loaded: 0.028018 seconds -Region Avg IOU: 0.552946, Class: 1.000000, Obj: 0.364243, No Obj: 0.004072, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.559273, Class: 1.000000, Obj: 0.366023, No Obj: 0.003868, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.542414, Class: 1.000000, Obj: 0.339511, No Obj: 0.003951, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.570142, Class: 1.000000, Obj: 0.400420, No Obj: 0.004512, Avg Recall: 0.652542, count: 118 -Region Avg IOU: 0.586097, Class: 1.000000, Obj: 0.408611, No Obj: 0.003736, Avg Recall: 0.693182, count: 88 -Region Avg IOU: 0.509401, Class: 1.000000, Obj: 0.314563, No Obj: 0.004167, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.540616, Class: 1.000000, Obj: 0.360291, No Obj: 0.004324, Avg Recall: 0.617886, count: 123 -Region Avg IOU: 0.537927, Class: 1.000000, Obj: 0.333330, No Obj: 0.003607, Avg Recall: 0.648936, count: 94 -4998: 0.466475, 0.447231 avg, 0.001000 rate, 20.960489 seconds, 1279488 images -Loaded: 0.826223 seconds -Region Avg IOU: 0.5194Saving weights to ./person9300/backup/tyvp_93_9c512_5000.weights -40, Class: 1.000000, Obj: 0.323033, No Obj: 0.003355, Avg Recall: 0.554217, count: 83 -Region Avg IOU: 0.452386, Class: 1.000000, Obj: 0.332443, No Obj: 0.003826, Avg Recall: 0.487180, count: 117 -Region Avg IOU: 0.549785, Class: 1.000000, Obj: 0.332833, No Obj: 0.004765, Avg Recall: 0.632000, count: 125 -Region Avg IOU: 0.536746, Class: 1.000000, Obj: 0.324980, No Obj: 0.004544, Avg Recall: 0.622047, count: 127 -Region Avg IOU: 0.595112, Class: 1.000000, Obj: 0.404355, No Obj: 0.003949, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.597768, Class: 1.000000, Obj: 0.367763, No Obj: 0.004759, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.533852, Class: 1.000000, Obj: 0.336932, No Obj: 0.004473, Avg Recall: 0.612903, count: 124 -Region Avg IOU: 0.562538, Class: 1.000000, Obj: 0.410454, No Obj: 0.003767, Avg Recall: 0.634409, count: 93 -4999: 0.463484, 0.448856 avg, 0.001000 rate, 22.059963 seconds, 1279744 images -Loaded: 0.381405 seconds -Region Avg IOU: 0.476493, Class: 1.000000, Obj: 0.323509, No Obj: 0.003765, Avg Recall: 0.517241, count: 116 -Region Avg IOU: 0.560997, Class: 1.000000, Obj: 0.359219, No Obj: 0.004758, Avg Recall: 0.700000, count: 120 -Region Avg IOU: 0.556011, Class: 1.000000, Obj: 0.359604, No Obj: 0.005176, Avg Recall: 0.619718, count: 142 -Region Avg IOU: 0.516019, Class: 1.000000, Obj: 0.368170, No Obj: 0.004177, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.538452, Class: 1.000000, Obj: 0.351315, No Obj: 0.004400, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.518332, Class: 1.000000, Obj: 0.358906, No Obj: 0.003754, Avg Recall: 0.565217, count: 92 -Region Avg IOU: 0.574516, Class: 1.000000, Obj: 0.406368, No Obj: 0.004197, Avg Recall: 0.684783, count: 92 -Region Avg IOU: 0.547114, Class: 1.000000, Obj: 0.374232, No Obj: 0.004421, Avg Recall: 0.637255, count: 102 -5000: 0.498448, 0.453815 avg, 0.001000 rate, 20.339624 seconds, 1280000 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000068 seconds -Region Avg IOU: 0.665073, Class: 1.000000, Obj: 0.473268, No Obj: 0.003487, Avg Recall: 0.827586, count: 87 -Region Avg IOU: 0.599294, Class: 1.000000, Obj: 0.428891, No Obj: 0.004112, Avg Recall: 0.723881, count: 134 -Region Avg IOU: 0.599109, Class: 1.000000, Obj: 0.453782, No Obj: 0.003735, Avg Recall: 0.742188, count: 128 -Region Avg IOU: 0.620335, Class: 1.000000, Obj: 0.457160, No Obj: 0.003185, Avg Recall: 0.802326, count: 86 -Region Avg IOU: 0.628647, Class: 1.000000, Obj: 0.451748, No Obj: 0.004350, Avg Recall: 0.768000, count: 125 -Region Avg IOU: 0.642198, Class: 1.000000, Obj: 0.427147, No Obj: 0.003042, Avg Recall: 0.746988, count: 83 -Region Avg IOU: 0.604034, Class: 1.000000, Obj: 0.415400, No Obj: 0.003133, Avg Recall: 0.762500, count: 80 -Region Avg IOU: 0.658703, Class: 1.000000, Obj: 0.496336, No Obj: 0.004215, Avg Recall: 0.818182, count: 110 -5001: 0.415066, 0.449940 avg, 0.001000 rate, 26.141701 seconds, 1280256 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.595995, Class: 1.000000, Obj: 0.427523, No Obj: 0.003379, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.642288, Class: 1.000000, Obj: 0.490776, No Obj: 0.004278, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.635068, Class: 1.000000, Obj: 0.461383, No Obj: 0.003560, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.651463, Class: 1.000000, Obj: 0.446765, No Obj: 0.003844, Avg Recall: 0.819048, count: 105 -Region Avg IOU: 0.675643, Class: 1.000000, Obj: 0.482293, No Obj: 0.004183, Avg Recall: 0.808081, count: 99 -Region Avg IOU: 0.605474, Class: 1.000000, Obj: 0.415475, No Obj: 0.002931, Avg Recall: 0.734177, count: 79 -Region Avg IOU: 0.635360, Class: 1.000000, Obj: 0.491351, No Obj: 0.003591, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.625803, Class: 1.000000, Obj: 0.437427, No Obj: 0.003735, Avg Recall: 0.765217, count: 115 -5002: 0.396033, 0.444550 avg, 0.001000 rate, 28.410572 seconds, 1280512 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.635513, Class: 1.000000, Obj: 0.442641, No Obj: 0.003372, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.633047, Class: 1.000000, Obj: 0.440355, No Obj: 0.003501, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.629617, Class: 1.000000, Obj: 0.447809, No Obj: 0.003890, Avg Recall: 0.785047, count: 107 -Region Avg IOU: 0.657867, Class: 1.000000, Obj: 0.463628, No Obj: 0.004150, Avg Recall: 0.854839, count: 124 -Region Avg IOU: 0.610438, Class: 1.000000, Obj: 0.449632, No Obj: 0.003262, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.594331, Class: 1.000000, Obj: 0.416885, No Obj: 0.004059, Avg Recall: 0.716129, count: 155 -Region Avg IOU: 0.623767, Class: 1.000000, Obj: 0.477136, No Obj: 0.003497, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.588611, Class: 1.000000, Obj: 0.471366, No Obj: 0.003194, Avg Recall: 0.678899, count: 109 -5003: 0.473337, 0.447428 avg, 0.001000 rate, 26.949341 seconds, 1280768 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.622461, Class: 1.000000, Obj: 0.416277, No Obj: 0.003598, Avg Recall: 0.744186, count: 129 -Region Avg IOU: 0.654770, Class: 1.000000, Obj: 0.452978, No Obj: 0.002941, Avg Recall: 0.843137, count: 102 -Region Avg IOU: 0.655170, Class: 1.000000, Obj: 0.460057, No Obj: 0.003108, Avg Recall: 0.833333, count: 96 -Region Avg IOU: 0.634068, Class: 1.000000, Obj: 0.406531, No Obj: 0.003241, Avg Recall: 0.771654, count: 127 -Region Avg IOU: 0.605536, Class: 1.000000, Obj: 0.413749, No Obj: 0.003183, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.632782, Class: 1.000000, Obj: 0.413092, No Obj: 0.003826, Avg Recall: 0.808696, count: 115 -Region Avg IOU: 0.634677, Class: 1.000000, Obj: 0.431881, No Obj: 0.003001, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.636398, Class: 1.000000, Obj: 0.401296, No Obj: 0.003806, Avg Recall: 0.781955, count: 133 -5004: 0.480428, 0.450728 avg, 0.001000 rate, 27.284435 seconds, 1281024 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.584274, Class: 1.000000, Obj: 0.422967, No Obj: 0.003214, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.619795, Class: 1.000000, Obj: 0.401516, No Obj: 0.002792, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.586050, Class: 1.000000, Obj: 0.387833, No Obj: 0.003213, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.606792, Class: 1.000000, Obj: 0.367753, No Obj: 0.003284, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.636245, Class: 1.000000, Obj: 0.446249, No Obj: 0.002929, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.639120, Class: 1.000000, Obj: 0.412506, No Obj: 0.002867, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.647688, Class: 1.000000, Obj: 0.462740, No Obj: 0.003411, Avg Recall: 0.769231, count: 130 -Region Avg IOU: 0.678663, Class: 1.000000, Obj: 0.443998, No Obj: 0.002972, Avg Recall: 0.831325, count: 83 -5005: 0.404551, 0.446111 avg, 0.001000 rate, 26.380924 seconds, 1281280 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.656356, Class: 1.000000, Obj: 0.404974, No Obj: 0.002786, Avg Recall: 0.833333, count: 96 -Region Avg IOU: 0.572473, Class: 1.000000, Obj: 0.363685, No Obj: 0.002512, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.619105, Class: 1.000000, Obj: 0.410395, No Obj: 0.002837, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.606230, Class: 1.000000, Obj: 0.376639, No Obj: 0.002453, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.635916, Class: 1.000000, Obj: 0.442209, No Obj: 0.002832, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.630032, Class: 1.000000, Obj: 0.356617, No Obj: 0.003093, Avg Recall: 0.805085, count: 118 -Region Avg IOU: 0.637232, Class: 1.000000, Obj: 0.337318, No Obj: 0.002522, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.638316, Class: 1.000000, Obj: 0.447520, No Obj: 0.002287, Avg Recall: 0.795181, count: 83 -5006: 0.382757, 0.439775 avg, 0.001000 rate, 27.784098 seconds, 1281536 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.587693, Class: 1.000000, Obj: 0.303144, No Obj: 0.002469, Avg Recall: 0.741176, count: 85 -Region Avg IOU: 0.601435, Class: 1.000000, Obj: 0.379268, No Obj: 0.002898, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.582365, Class: 1.000000, Obj: 0.336750, No Obj: 0.002675, Avg Recall: 0.688406, count: 138 -Region Avg IOU: 0.657232, Class: 1.000000, Obj: 0.332417, No Obj: 0.002397, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.646780, Class: 1.000000, Obj: 0.358889, No Obj: 0.002828, Avg Recall: 0.803279, count: 122 -Region Avg IOU: 0.622890, Class: 1.000000, Obj: 0.366516, No Obj: 0.002769, Avg Recall: 0.758065, count: 124 -Region Avg IOU: 0.631332, Class: 1.000000, Obj: 0.356181, No Obj: 0.002801, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.646738, Class: 1.000000, Obj: 0.381655, No Obj: 0.003085, Avg Recall: 0.780488, count: 123 -5007: 0.513565, 0.447154 avg, 0.001000 rate, 27.294809 seconds, 1281792 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.630522, Class: 1.000000, Obj: 0.380544, No Obj: 0.002536, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.607611, Class: 1.000000, Obj: 0.351052, No Obj: 0.002803, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.565820, Class: 1.000000, Obj: 0.327005, No Obj: 0.002378, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.599734, Class: 1.000000, Obj: 0.318891, No Obj: 0.002421, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.630261, Class: 1.000000, Obj: 0.351914, No Obj: 0.002925, Avg Recall: 0.769231, count: 117 -Region Avg IOU: 0.611272, Class: 1.000000, Obj: 0.326011, No Obj: 0.002780, Avg Recall: 0.748201, count: 139 -Region Avg IOU: 0.613340, Class: 1.000000, Obj: 0.378662, No Obj: 0.002736, Avg Recall: 0.734375, count: 128 -Region Avg IOU: 0.666662, Class: 1.000000, Obj: 0.385535, No Obj: 0.002994, Avg Recall: 0.801724, count: 116 -5008: 0.489687, 0.451408 avg, 0.001000 rate, 25.665215 seconds, 1282048 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.601067, Class: 1.000000, Obj: 0.385785, No Obj: 0.002227, Avg Recall: 0.736264, count: 91 -Region Avg IOU: 0.628789, Class: 1.000000, Obj: 0.348714, No Obj: 0.002634, Avg Recall: 0.756303, count: 119 -Region Avg IOU: 0.610540, Class: 1.000000, Obj: 0.316043, No Obj: 0.002581, Avg Recall: 0.772358, count: 123 -Region Avg IOU: 0.598576, Class: 1.000000, Obj: 0.332490, No Obj: 0.002168, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.600976, Class: 1.000000, Obj: 0.322327, No Obj: 0.002304, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.592838, Class: 1.000000, Obj: 0.347778, No Obj: 0.002188, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.609601, Class: 1.000000, Obj: 0.354473, No Obj: 0.002629, Avg Recall: 0.714286, count: 126 -Region Avg IOU: 0.634225, Class: 1.000000, Obj: 0.366560, No Obj: 0.002507, Avg Recall: 0.735294, count: 102 -5009: 0.435679, 0.449835 avg, 0.001000 rate, 26.817778 seconds, 1282304 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.634386, Class: 1.000000, Obj: 0.323628, No Obj: 0.002580, Avg Recall: 0.762712, count: 118 -Region Avg IOU: 0.649138, Class: 1.000000, Obj: 0.326979, No Obj: 0.002577, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.567380, Class: 1.000000, Obj: 0.297465, No Obj: 0.002615, Avg Recall: 0.685039, count: 127 -Region Avg IOU: 0.634775, Class: 1.000000, Obj: 0.373137, No Obj: 0.002800, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.664194, Class: 1.000000, Obj: 0.355409, No Obj: 0.002362, Avg Recall: 0.797753, count: 89 -Region Avg IOU: 0.652579, Class: 1.000000, Obj: 0.366656, No Obj: 0.002845, Avg Recall: 0.767742, count: 155 -Region Avg IOU: 0.619437, Class: 1.000000, Obj: 0.367375, No Obj: 0.002387, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.644603, Class: 1.000000, Obj: 0.396332, No Obj: 0.002695, Avg Recall: 0.801980, count: 101 -5010: 0.496434, 0.454495 avg, 0.001000 rate, 25.945829 seconds, 1282560 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.516030 seconds -Region Avg IOU: 0.550065, Class: 1.000000, Obj: 0.310961, No Obj: 0.003456, Avg Recall: 0.652542, count: 118 -Region Avg IOU: 0.539141, Class: 1.000000, Obj: 0.336846, No Obj: 0.003091, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.593071, Class: 1.000000, Obj: 0.368252, No Obj: 0.003194, Avg Recall: 0.678161, count: 87 -Region Avg IOU: 0.553528, Class: 1.000000, Obj: 0.302856, No Obj: 0.003510, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.553213, Class: 1.000000, Obj: 0.349020, No Obj: 0.002963, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.579568, Class: 1.000000, Obj: 0.309464, No Obj: 0.002882, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.538103, Class: 1.000000, Obj: 0.331495, No Obj: 0.002948, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.507918, Class: 1.000000, Obj: 0.297966, No Obj: 0.002947, Avg Recall: 0.596330, count: 109 -5011: 0.453463, 0.454391 avg, 0.001000 rate, 22.434376 seconds, 1282816 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.544894, Class: 1.000000, Obj: 0.357638, No Obj: 0.002992, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.546933, Class: 1.000000, Obj: 0.345431, No Obj: 0.003339, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.586973, Class: 1.000000, Obj: 0.373889, No Obj: 0.003435, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.537597, Class: 1.000000, Obj: 0.334304, No Obj: 0.003165, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.553940, Class: 1.000000, Obj: 0.363355, No Obj: 0.003145, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.557052, Class: 1.000000, Obj: 0.350065, No Obj: 0.003509, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.525727, Class: 1.000000, Obj: 0.336466, No Obj: 0.003702, Avg Recall: 0.588235, count: 102 -Region Avg IOU: 0.577749, Class: 1.000000, Obj: 0.388428, No Obj: 0.003464, Avg Recall: 0.657143, count: 105 -5012: 0.411378, 0.450090 avg, 0.001000 rate, 22.514847 seconds, 1283072 images -Loaded: 1.221239 seconds -Region Avg IOU: 0.511889, Class: 1.000000, Obj: 0.345476, No Obj: 0.003397, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.586728, Class: 1.000000, Obj: 0.404947, No Obj: 0.004354, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.555744, Class: 1.000000, Obj: 0.350896, No Obj: 0.003272, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.560576, Class: 1.000000, Obj: 0.372174, No Obj: 0.003944, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.561621, Class: 1.000000, Obj: 0.352126, No Obj: 0.004251, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.559388, Class: 1.000000, Obj: 0.394634, No Obj: 0.004227, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.551464, Class: 1.000000, Obj: 0.363355, No Obj: 0.003011, Avg Recall: 0.626506, count: 83 -Region Avg IOU: 0.543666, Class: 1.000000, Obj: 0.363451, No Obj: 0.003385, Avg Recall: 0.660377, count: 106 -5013: 0.446023, 0.449683 avg, 0.001000 rate, 20.871204 seconds, 1283328 images -Loaded: 0.063827 seconds -Region Avg IOU: 0.545007, Class: 1.000000, Obj: 0.400660, No Obj: 0.003519, Avg Recall: 0.562500, count: 96 -Region Avg IOU: 0.537215, Class: 1.000000, Obj: 0.368716, No Obj: 0.003544, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.558480, Class: 1.000000, Obj: 0.374995, No Obj: 0.004131, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.584834, Class: 1.000000, Obj: 0.365531, No Obj: 0.004315, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.598862, Class: 1.000000, Obj: 0.364985, No Obj: 0.003597, Avg Recall: 0.707865, count: 89 -Region Avg IOU: 0.565742, Class: 1.000000, Obj: 0.391927, No Obj: 0.003636, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.573819, Class: 1.000000, Obj: 0.326778, No Obj: 0.003831, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.522750, Class: 1.000000, Obj: 0.364955, No Obj: 0.003880, Avg Recall: 0.578125, count: 128 -5014: 0.453157, 0.450031 avg, 0.001000 rate, 24.272264 seconds, 1283584 images -Loaded: 0.000057 seconds -Region Avg IOU: 0.568993, Class: 1.000000, Obj: 0.394512, No Obj: 0.004131, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.540368, Class: 1.000000, Obj: 0.408954, No Obj: 0.004038, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.532059, Class: 1.000000, Obj: 0.397665, No Obj: 0.004181, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.541324, Class: 1.000000, Obj: 0.397364, No Obj: 0.004116, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.558736, Class: 1.000000, Obj: 0.392145, No Obj: 0.003654, Avg Recall: 0.670588, count: 85 -Region Avg IOU: 0.540919, Class: 1.000000, Obj: 0.383435, No Obj: 0.003773, Avg Recall: 0.614286, count: 70 -Region Avg IOU: 0.589266, Class: 1.000000, Obj: 0.446568, No Obj: 0.004945, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.546925, Class: 1.000000, Obj: 0.388623, No Obj: 0.004469, Avg Recall: 0.632479, count: 117 -5015: 0.409483, 0.445976 avg, 0.001000 rate, 20.834461 seconds, 1283840 images -Loaded: 0.663069 seconds -Region Avg IOU: 0.584849, Class: 1.000000, Obj: 0.415496, No Obj: 0.004521, Avg Recall: 0.711111, count: 90 -Region Avg IOU: 0.522613, Class: 1.000000, Obj: 0.413562, No Obj: 0.004486, Avg Recall: 0.583333, count: 132 -Region Avg IOU: 0.590943, Class: 1.000000, Obj: 0.415783, No Obj: 0.004699, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.531702, Class: 1.000000, Obj: 0.401337, No Obj: 0.004024, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.538689, Class: 1.000000, Obj: 0.388307, No Obj: 0.004651, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.559357, Class: 1.000000, Obj: 0.429507, No Obj: 0.003566, Avg Recall: 0.625000, count: 80 -Region Avg IOU: 0.542203, Class: 1.000000, Obj: 0.384820, No Obj: 0.004738, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.516482, Class: 1.000000, Obj: 0.400667, No Obj: 0.004194, Avg Recall: 0.610687, count: 131 -5016: 0.405661, 0.441945 avg, 0.001000 rate, 20.566973 seconds, 1284096 images -Loaded: 2.380302 seconds -Region Avg IOU: 0.532233, Class: 1.000000, Obj: 0.450018, No Obj: 0.004742, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.580123, Class: 1.000000, Obj: 0.383181, No Obj: 0.004019, Avg Recall: 0.659341, count: 91 -Region Avg IOU: 0.572383, Class: 1.000000, Obj: 0.414229, No Obj: 0.004793, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.549058, Class: 1.000000, Obj: 0.444658, No Obj: 0.005293, Avg Recall: 0.591241, count: 137 -Region Avg IOU: 0.566293, Class: 1.000000, Obj: 0.410055, No Obj: 0.004224, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.619856, Class: 1.000000, Obj: 0.458293, No Obj: 0.005026, Avg Recall: 0.779070, count: 86 -Region Avg IOU: 0.513914, Class: 1.000000, Obj: 0.355673, No Obj: 0.004197, Avg Recall: 0.607595, count: 79 -Region Avg IOU: 0.546966, Class: 1.000000, Obj: 0.433388, No Obj: 0.004126, Avg Recall: 0.629214, count: 89 -5017: 0.384308, 0.436181 avg, 0.001000 rate, 23.457954 seconds, 1284352 images -Loaded: 0.884804 seconds -Region Avg IOU: 0.546334, Class: 1.000000, Obj: 0.449271, No Obj: 0.004979, Avg Recall: 0.645161, count: 124 -Region Avg IOU: 0.531120, Class: 1.000000, Obj: 0.404627, No Obj: 0.004185, Avg Recall: 0.576471, count: 85 -Region Avg IOU: 0.521252, Class: 1.000000, Obj: 0.423604, No Obj: 0.005033, Avg Recall: 0.590164, count: 122 -Region Avg IOU: 0.567038, Class: 1.000000, Obj: 0.430556, No Obj: 0.004380, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.581693, Class: 1.000000, Obj: 0.452705, No Obj: 0.005093, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.503030, Class: 1.000000, Obj: 0.403569, No Obj: 0.004053, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.501312, Class: 1.000000, Obj: 0.434710, No Obj: 0.004691, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.517732, Class: 1.000000, Obj: 0.411423, No Obj: 0.004630, Avg Recall: 0.568965, count: 116 -5018: 0.427070, 0.435270 avg, 0.001000 rate, 21.351696 seconds, 1284608 images -Loaded: 0.672556 seconds -Region Avg IOU: 0.529225, Class: 1.000000, Obj: 0.368702, No Obj: 0.004571, Avg Recall: 0.591667, count: 120 -Region Avg IOU: 0.569945, Class: 1.000000, Obj: 0.399390, No Obj: 0.004700, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.522197, Class: 1.000000, Obj: 0.393718, No Obj: 0.004918, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.521841, Class: 1.000000, Obj: 0.378637, No Obj: 0.004819, Avg Recall: 0.595420, count: 131 -Region Avg IOU: 0.515344, Class: 1.000000, Obj: 0.372816, No Obj: 0.004588, Avg Recall: 0.554688, count: 128 -Region Avg IOU: 0.523809, Class: 1.000000, Obj: 0.393729, No Obj: 0.004340, Avg Recall: 0.577236, count: 123 -Region Avg IOU: 0.596710, Class: 1.000000, Obj: 0.490984, No Obj: 0.004141, Avg Recall: 0.717647, count: 85 -Region Avg IOU: 0.516134, Class: 1.000000, Obj: 0.380302, No Obj: 0.003894, Avg Recall: 0.577778, count: 90 -5019: 0.459317, 0.437675 avg, 0.001000 rate, 22.635912 seconds, 1284864 images -Loaded: 0.027221 seconds -Region Avg IOU: 0.583523, Class: 1.000000, Obj: 0.432104, No Obj: 0.004739, Avg Recall: 0.669421, count: 121 -Region Avg IOU: 0.535199, Class: 1.000000, Obj: 0.360795, No Obj: 0.004408, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.555661, Class: 1.000000, Obj: 0.390786, No Obj: 0.003957, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.543129, Class: 1.000000, Obj: 0.363593, No Obj: 0.003809, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.567791, Class: 1.000000, Obj: 0.444638, No Obj: 0.004674, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.594819, Class: 1.000000, Obj: 0.442137, No Obj: 0.004494, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.565464, Class: 1.000000, Obj: 0.420815, No Obj: 0.004227, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.560505, Class: 1.000000, Obj: 0.361920, No Obj: 0.004437, Avg Recall: 0.654545, count: 110 -5020: 0.420906, 0.435998 avg, 0.001000 rate, 22.298887 seconds, 1285120 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000080 seconds -Region Avg IOU: 0.586747, Class: 1.000000, Obj: 0.423324, No Obj: 0.004042, Avg Recall: 0.685039, count: 127 -Region Avg IOU: 0.624614, Class: 1.000000, Obj: 0.447120, No Obj: 0.004136, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.585578, Class: 1.000000, Obj: 0.443646, No Obj: 0.003913, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.540547, Class: 1.000000, Obj: 0.410551, No Obj: 0.003590, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.552777, Class: 1.000000, Obj: 0.441181, No Obj: 0.003594, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.569596, Class: 1.000000, Obj: 0.362784, No Obj: 0.003610, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.632497, Class: 1.000000, Obj: 0.453150, No Obj: 0.004156, Avg Recall: 0.788136, count: 118 -Region Avg IOU: 0.580356, Class: 1.000000, Obj: 0.363598, No Obj: 0.003759, Avg Recall: 0.689320, count: 103 -5021: 0.471570, 0.439555 avg, 0.001000 rate, 24.162073 seconds, 1285376 images -Loaded: 0.000115 seconds -Region Avg IOU: 0.600668, Class: 1.000000, Obj: 0.384479, No Obj: 0.004847, Avg Recall: 0.741722, count: 151 -Region Avg IOU: 0.500104, Class: 1.000000, Obj: 0.428062, No Obj: 0.003581, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.530539, Class: 1.000000, Obj: 0.413642, No Obj: 0.003204, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.598665, Class: 1.000000, Obj: 0.434459, No Obj: 0.004250, Avg Recall: 0.746154, count: 130 -Region Avg IOU: 0.586677, Class: 1.000000, Obj: 0.430592, No Obj: 0.003350, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.613172, Class: 1.000000, Obj: 0.403590, No Obj: 0.003319, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.546690, Class: 1.000000, Obj: 0.388336, No Obj: 0.004215, Avg Recall: 0.649635, count: 137 -Region Avg IOU: 0.608322, Class: 1.000000, Obj: 0.436825, No Obj: 0.004042, Avg Recall: 0.686869, count: 99 -5022: 0.436555, 0.439255 avg, 0.001000 rate, 24.298569 seconds, 1285632 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.592233, Class: 1.000000, Obj: 0.389142, No Obj: 0.003639, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.547961, Class: 1.000000, Obj: 0.342661, No Obj: 0.003875, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.602884, Class: 1.000000, Obj: 0.450582, No Obj: 0.003368, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.599495, Class: 1.000000, Obj: 0.381790, No Obj: 0.003926, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.543746, Class: 1.000000, Obj: 0.401461, No Obj: 0.003311, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.608433, Class: 1.000000, Obj: 0.392797, No Obj: 0.003840, Avg Recall: 0.735043, count: 117 -Region Avg IOU: 0.595522, Class: 1.000000, Obj: 0.413450, No Obj: 0.002983, Avg Recall: 0.720930, count: 86 -Region Avg IOU: 0.605436, Class: 1.000000, Obj: 0.384680, No Obj: 0.003530, Avg Recall: 0.716814, count: 113 -5023: 0.422566, 0.437586 avg, 0.001000 rate, 23.574076 seconds, 1285888 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.649500, Class: 1.000000, Obj: 0.395817, No Obj: 0.003554, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.615779, Class: 1.000000, Obj: 0.392838, No Obj: 0.003071, Avg Recall: 0.731707, count: 82 -Region Avg IOU: 0.566089, Class: 1.000000, Obj: 0.403806, No Obj: 0.003102, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.514454, Class: 1.000000, Obj: 0.332598, No Obj: 0.002917, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.568700, Class: 1.000000, Obj: 0.370024, No Obj: 0.003264, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.554839, Class: 1.000000, Obj: 0.324659, No Obj: 0.002833, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.544613, Class: 1.000000, Obj: 0.364460, No Obj: 0.003516, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.554596, Class: 1.000000, Obj: 0.385691, No Obj: 0.003261, Avg Recall: 0.635514, count: 107 -5024: 0.405838, 0.434411 avg, 0.001000 rate, 24.454792 seconds, 1286144 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.596059, Class: 1.000000, Obj: 0.318916, No Obj: 0.002490, Avg Recall: 0.686046, count: 86 -Region Avg IOU: 0.631924, Class: 1.000000, Obj: 0.414800, No Obj: 0.003443, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.615496, Class: 1.000000, Obj: 0.347028, No Obj: 0.003031, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.601550, Class: 1.000000, Obj: 0.366169, No Obj: 0.003369, Avg Recall: 0.735537, count: 121 -Region Avg IOU: 0.603242, Class: 1.000000, Obj: 0.382195, No Obj: 0.003031, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.527922, Class: 1.000000, Obj: 0.321663, No Obj: 0.002941, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.592226, Class: 1.000000, Obj: 0.397310, No Obj: 0.004019, Avg Recall: 0.661290, count: 124 -Region Avg IOU: 0.601830, Class: 1.000000, Obj: 0.343403, No Obj: 0.002794, Avg Recall: 0.742268, count: 97 -5025: 0.407026, 0.431673 avg, 0.001000 rate, 22.285904 seconds, 1286400 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.637942, Class: 1.000000, Obj: 0.373851, No Obj: 0.003017, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.594515, Class: 1.000000, Obj: 0.386550, No Obj: 0.003220, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.643447, Class: 1.000000, Obj: 0.382195, No Obj: 0.003407, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.603434, Class: 1.000000, Obj: 0.379957, No Obj: 0.003374, Avg Recall: 0.746032, count: 126 -Region Avg IOU: 0.619453, Class: 1.000000, Obj: 0.354845, No Obj: 0.003484, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.560355, Class: 1.000000, Obj: 0.350006, No Obj: 0.003177, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.614623, Class: 1.000000, Obj: 0.372319, No Obj: 0.003224, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.601159, Class: 1.000000, Obj: 0.388319, No Obj: 0.002986, Avg Recall: 0.730337, count: 89 -5026: 0.405044, 0.429010 avg, 0.001000 rate, 24.278236 seconds, 1286656 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.597965, Class: 1.000000, Obj: 0.339270, No Obj: 0.003047, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.633046, Class: 1.000000, Obj: 0.372363, No Obj: 0.003228, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.546297, Class: 1.000000, Obj: 0.319795, No Obj: 0.002476, Avg Recall: 0.651163, count: 86 -Region Avg IOU: 0.565024, Class: 1.000000, Obj: 0.364981, No Obj: 0.002980, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.602257, Class: 1.000000, Obj: 0.358804, No Obj: 0.003109, Avg Recall: 0.711111, count: 90 -Region Avg IOU: 0.599472, Class: 1.000000, Obj: 0.357065, No Obj: 0.002970, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.603424, Class: 1.000000, Obj: 0.356165, No Obj: 0.003215, Avg Recall: 0.720930, count: 129 -Region Avg IOU: 0.597076, Class: 1.000000, Obj: 0.367276, No Obj: 0.003465, Avg Recall: 0.691589, count: 107 -5027: 0.458941, 0.432003 avg, 0.001000 rate, 25.077480 seconds, 1286912 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.598310, Class: 1.000000, Obj: 0.312287, No Obj: 0.003501, Avg Recall: 0.727273, count: 132 -Region Avg IOU: 0.629878, Class: 1.000000, Obj: 0.365545, No Obj: 0.002913, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.623713, Class: 1.000000, Obj: 0.378575, No Obj: 0.003242, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.590653, Class: 1.000000, Obj: 0.337774, No Obj: 0.003388, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.591082, Class: 1.000000, Obj: 0.357484, No Obj: 0.003075, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.577499, Class: 1.000000, Obj: 0.291551, No Obj: 0.003471, Avg Recall: 0.669065, count: 139 -Region Avg IOU: 0.620995, Class: 1.000000, Obj: 0.370944, No Obj: 0.003471, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.589451, Class: 1.000000, Obj: 0.347110, No Obj: 0.003155, Avg Recall: 0.656250, count: 128 -5028: 0.494840, 0.438287 avg, 0.001000 rate, 22.866940 seconds, 1287168 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.565005, Class: 1.000000, Obj: 0.332851, No Obj: 0.003759, Avg Recall: 0.668675, count: 166 -Region Avg IOU: 0.551614, Class: 1.000000, Obj: 0.378493, No Obj: 0.002903, Avg Recall: 0.632479, count: 117 -Region Avg IOU: 0.586246, Class: 1.000000, Obj: 0.408183, No Obj: 0.003112, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.589098, Class: 1.000000, Obj: 0.381415, No Obj: 0.002962, Avg Recall: 0.708333, count: 120 -Region Avg IOU: 0.613775, Class: 1.000000, Obj: 0.366939, No Obj: 0.003372, Avg Recall: 0.719298, count: 114 -Region Avg IOU: 0.582186, Class: 1.000000, Obj: 0.399735, No Obj: 0.002519, Avg Recall: 0.666667, count: 78 -Region Avg IOU: 0.619172, Class: 1.000000, Obj: 0.397538, No Obj: 0.003374, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.566067, Class: 1.000000, Obj: 0.347210, No Obj: 0.003372, Avg Recall: 0.650794, count: 126 -5029: 0.458512, 0.440309 avg, 0.001000 rate, 24.629877 seconds, 1287424 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.566588, Class: 1.000000, Obj: 0.381086, No Obj: 0.003214, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.601167, Class: 1.000000, Obj: 0.418026, No Obj: 0.003151, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.577373, Class: 1.000000, Obj: 0.364289, No Obj: 0.004004, Avg Recall: 0.681818, count: 132 -Region Avg IOU: 0.572415, Class: 1.000000, Obj: 0.423257, No Obj: 0.003024, Avg Recall: 0.616279, count: 86 -Region Avg IOU: 0.596334, Class: 1.000000, Obj: 0.421212, No Obj: 0.003539, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.585236, Class: 1.000000, Obj: 0.379514, No Obj: 0.004136, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.593095, Class: 1.000000, Obj: 0.417742, No Obj: 0.003651, Avg Recall: 0.719697, count: 132 -Region Avg IOU: 0.589458, Class: 1.000000, Obj: 0.405729, No Obj: 0.003301, Avg Recall: 0.681035, count: 116 -5030: 0.427943, 0.439073 avg, 0.001000 rate, 23.216185 seconds, 1287680 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 19.781214 seconds -Region Avg IOU: 0.592683, Class: 1.000000, Obj: 0.415508, No Obj: 0.003394, Avg Recall: 0.694118, count: 85 -Region Avg IOU: 0.552105, Class: 1.000000, Obj: 0.408779, No Obj: 0.003596, Avg Recall: 0.615385, count: 91 -Region Avg IOU: 0.611681, Class: 1.000000, Obj: 0.354674, No Obj: 0.004197, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.607804, Class: 1.000000, Obj: 0.429357, No Obj: 0.004044, Avg Recall: 0.748148, count: 135 -Region Avg IOU: 0.554945, Class: 1.000000, Obj: 0.365558, No Obj: 0.003778, Avg Recall: 0.603306, count: 121 -Region Avg IOU: 0.568065, Class: 1.000000, Obj: 0.406635, No Obj: 0.003511, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.574473, Class: 1.000000, Obj: 0.415036, No Obj: 0.003435, Avg Recall: 0.666667, count: 87 -Region Avg IOU: 0.618923, Class: 1.000000, Obj: 0.400904, No Obj: 0.003554, Avg Recall: 0.741935, count: 93 -5031: 0.440871, 0.439252 avg, 0.001000 rate, 23.256990 seconds, 1287936 images -Loaded: 0.012879 seconds -Region Avg IOU: 0.533557, Class: 1.000000, Obj: 0.369951, No Obj: 0.004007, Avg Recall: 0.641221, count: 131 -Region Avg IOU: 0.587651, Class: 1.000000, Obj: 0.427899, No Obj: 0.003914, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.542389, Class: 1.000000, Obj: 0.367152, No Obj: 0.004274, Avg Recall: 0.575540, count: 139 -Region Avg IOU: 0.552341, Class: 1.000000, Obj: 0.445311, No Obj: 0.003361, Avg Recall: 0.635294, count: 85 -Region Avg IOU: 0.590424, Class: 1.000000, Obj: 0.438624, No Obj: 0.003950, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.549499, Class: 1.000000, Obj: 0.374594, No Obj: 0.004038, Avg Recall: 0.595745, count: 94 -Region Avg IOU: 0.612802, Class: 1.000000, Obj: 0.467163, No Obj: 0.003850, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.593874, Class: 1.000000, Obj: 0.389113, No Obj: 0.004279, Avg Recall: 0.697674, count: 129 -5032: 0.478559, 0.443183 avg, 0.001000 rate, 23.133844 seconds, 1288192 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.556817, Class: 1.000000, Obj: 0.376058, No Obj: 0.003955, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.514337, Class: 1.000000, Obj: 0.391404, No Obj: 0.003743, Avg Recall: 0.562500, count: 96 -Region Avg IOU: 0.579427, Class: 1.000000, Obj: 0.410059, No Obj: 0.004158, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.562782, Class: 1.000000, Obj: 0.415895, No Obj: 0.004944, Avg Recall: 0.669065, count: 139 -Region Avg IOU: 0.578129, Class: 1.000000, Obj: 0.422796, No Obj: 0.003659, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.603614, Class: 1.000000, Obj: 0.415282, No Obj: 0.004346, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.562006, Class: 1.000000, Obj: 0.397414, No Obj: 0.004385, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.546416, Class: 1.000000, Obj: 0.411915, No Obj: 0.003672, Avg Recall: 0.647059, count: 102 -5033: 0.439318, 0.442797 avg, 0.001000 rate, 24.644997 seconds, 1288448 images -Loaded: 0.113679 seconds -Region Avg IOU: 0.606705, Class: 1.000000, Obj: 0.456440, No Obj: 0.003845, Avg Recall: 0.681818, count: 88 -Region Avg IOU: 0.562787, Class: 1.000000, Obj: 0.403825, No Obj: 0.004824, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.568111, Class: 1.000000, Obj: 0.402124, No Obj: 0.004859, Avg Recall: 0.633588, count: 131 -Region Avg IOU: 0.596009, Class: 1.000000, Obj: 0.434108, No Obj: 0.004460, Avg Recall: 0.720930, count: 129 -Region Avg IOU: 0.589816, Class: 1.000000, Obj: 0.410932, No Obj: 0.004599, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.518749, Class: 1.000000, Obj: 0.372564, No Obj: 0.003771, Avg Recall: 0.601695, count: 118 -Region Avg IOU: 0.526062, Class: 1.000000, Obj: 0.413424, No Obj: 0.003216, Avg Recall: 0.622222, count: 90 -Region Avg IOU: 0.580548, Class: 1.000000, Obj: 0.437332, No Obj: 0.004648, Avg Recall: 0.625000, count: 120 -5034: 0.455478, 0.444065 avg, 0.001000 rate, 23.112476 seconds, 1288704 images -Loaded: 0.018637 seconds -Region Avg IOU: 0.620050, Class: 1.000000, Obj: 0.492138, No Obj: 0.004177, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.615387, Class: 1.000000, Obj: 0.435876, No Obj: 0.004294, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.587093, Class: 1.000000, Obj: 0.437534, No Obj: 0.003384, Avg Recall: 0.670588, count: 85 -Region Avg IOU: 0.604791, Class: 1.000000, Obj: 0.415221, No Obj: 0.004259, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.566610, Class: 1.000000, Obj: 0.429883, No Obj: 0.004437, Avg Recall: 0.709677, count: 124 -Region Avg IOU: 0.499412, Class: 1.000000, Obj: 0.348719, No Obj: 0.003689, Avg Recall: 0.539683, count: 126 -Region Avg IOU: 0.602601, Class: 1.000000, Obj: 0.429913, No Obj: 0.003781, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.595488, Class: 1.000000, Obj: 0.404796, No Obj: 0.005057, Avg Recall: 0.710345, count: 145 -5035: 0.407212, 0.440380 avg, 0.001000 rate, 23.003508 seconds, 1288960 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.560330, Class: 1.000000, Obj: 0.384715, No Obj: 0.003864, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.544917, Class: 1.000000, Obj: 0.408314, No Obj: 0.003905, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.600334, Class: 1.000000, Obj: 0.452968, No Obj: 0.004638, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.566481, Class: 1.000000, Obj: 0.413204, No Obj: 0.004277, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.541597, Class: 1.000000, Obj: 0.385557, No Obj: 0.003742, Avg Recall: 0.633333, count: 90 -Region Avg IOU: 0.537598, Class: 1.000000, Obj: 0.404012, No Obj: 0.004441, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.571049, Class: 1.000000, Obj: 0.425086, No Obj: 0.004287, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.578564, Class: 1.000000, Obj: 0.374077, No Obj: 0.003725, Avg Recall: 0.680000, count: 100 -5036: 0.453191, 0.441661 avg, 0.001000 rate, 21.861324 seconds, 1289216 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.536640, Class: 1.000000, Obj: 0.367535, No Obj: 0.003394, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.587525, Class: 1.000000, Obj: 0.449092, No Obj: 0.004083, Avg Recall: 0.686956, count: 115 -Region Avg IOU: 0.541334, Class: 1.000000, Obj: 0.390950, No Obj: 0.004283, Avg Recall: 0.619048, count: 126 -Region Avg IOU: 0.567628, Class: 1.000000, Obj: 0.363786, No Obj: 0.004001, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.597989, Class: 1.000000, Obj: 0.388528, No Obj: 0.003967, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.562435, Class: 1.000000, Obj: 0.398773, No Obj: 0.003853, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.584058, Class: 1.000000, Obj: 0.421289, No Obj: 0.004073, Avg Recall: 0.702479, count: 121 -Region Avg IOU: 0.541974, Class: 1.000000, Obj: 0.376821, No Obj: 0.003817, Avg Recall: 0.629032, count: 124 -5037: 0.473663, 0.444861 avg, 0.001000 rate, 22.564589 seconds, 1289472 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.542307, Class: 1.000000, Obj: 0.389685, No Obj: 0.003789, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.543862, Class: 1.000000, Obj: 0.393995, No Obj: 0.003851, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.530246, Class: 1.000000, Obj: 0.380775, No Obj: 0.003633, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.570839, Class: 1.000000, Obj: 0.394219, No Obj: 0.003658, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.603373, Class: 1.000000, Obj: 0.411363, No Obj: 0.004360, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.556346, Class: 1.000000, Obj: 0.400487, No Obj: 0.003781, Avg Recall: 0.650000, count: 120 -Region Avg IOU: 0.643308, Class: 1.000000, Obj: 0.418159, No Obj: 0.004115, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.655420, Class: 1.000000, Obj: 0.447239, No Obj: 0.003962, Avg Recall: 0.806452, count: 93 -5038: 0.433253, 0.443700 avg, 0.001000 rate, 22.469790 seconds, 1289728 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.581262, Class: 1.000000, Obj: 0.381280, No Obj: 0.003769, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.556815, Class: 1.000000, Obj: 0.355658, No Obj: 0.004124, Avg Recall: 0.638462, count: 130 -Region Avg IOU: 0.576945, Class: 1.000000, Obj: 0.385244, No Obj: 0.003455, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.553397, Class: 1.000000, Obj: 0.383526, No Obj: 0.003486, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.569884, Class: 1.000000, Obj: 0.394856, No Obj: 0.003779, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.574059, Class: 1.000000, Obj: 0.401515, No Obj: 0.003496, Avg Recall: 0.681818, count: 88 -Region Avg IOU: 0.549709, Class: 1.000000, Obj: 0.374360, No Obj: 0.003588, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.559705, Class: 1.000000, Obj: 0.351110, No Obj: 0.003691, Avg Recall: 0.690909, count: 110 -5039: 0.468580, 0.446188 avg, 0.001000 rate, 22.290112 seconds, 1289984 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.558179, Class: 1.000000, Obj: 0.387664, No Obj: 0.003095, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.573463, Class: 1.000000, Obj: 0.410715, No Obj: 0.004058, Avg Recall: 0.666667, count: 123 -Region Avg IOU: 0.512579, Class: 1.000000, Obj: 0.358605, No Obj: 0.003812, Avg Recall: 0.593985, count: 133 -Region Avg IOU: 0.601472, Class: 1.000000, Obj: 0.399625, No Obj: 0.004258, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.523243, Class: 1.000000, Obj: 0.320689, No Obj: 0.004078, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.587370, Class: 1.000000, Obj: 0.368588, No Obj: 0.003513, Avg Recall: 0.709302, count: 86 -Region Avg IOU: 0.555325, Class: 1.000000, Obj: 0.375195, No Obj: 0.003564, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.544316, Class: 1.000000, Obj: 0.358582, No Obj: 0.004234, Avg Recall: 0.626016, count: 123 -5040: 0.446686, 0.446238 avg, 0.001000 rate, 24.161516 seconds, 1290240 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000063 seconds -Region Avg IOU: 0.622501, Class: 1.000000, Obj: 0.427123, No Obj: 0.003183, Avg Recall: 0.792857, count: 140 -Region Avg IOU: 0.663123, Class: 1.000000, Obj: 0.393880, No Obj: 0.003161, Avg Recall: 0.796992, count: 133 -Region Avg IOU: 0.649829, Class: 1.000000, Obj: 0.418897, No Obj: 0.002650, Avg Recall: 0.824324, count: 74 -Region Avg IOU: 0.610512, Class: 1.000000, Obj: 0.362125, No Obj: 0.002614, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.667904, Class: 1.000000, Obj: 0.474648, No Obj: 0.003307, Avg Recall: 0.808989, count: 89 -Region Avg IOU: 0.603823, Class: 1.000000, Obj: 0.442071, No Obj: 0.002694, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.654343, Class: 1.000000, Obj: 0.419596, No Obj: 0.003135, Avg Recall: 0.829268, count: 123 -Region Avg IOU: 0.666532, Class: 1.000000, Obj: 0.430314, No Obj: 0.003091, Avg Recall: 0.853211, count: 109 -5041: 0.450748, 0.446689 avg, 0.001000 rate, 30.280216 seconds, 1290496 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.642654, Class: 1.000000, Obj: 0.379794, No Obj: 0.003196, Avg Recall: 0.788618, count: 123 -Region Avg IOU: 0.674963, Class: 1.000000, Obj: 0.437410, No Obj: 0.003026, Avg Recall: 0.836735, count: 98 -Region Avg IOU: 0.639965, Class: 1.000000, Obj: 0.359876, No Obj: 0.003099, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.672813, Class: 1.000000, Obj: 0.443543, No Obj: 0.002570, Avg Recall: 0.857143, count: 112 -Region Avg IOU: 0.626773, Class: 1.000000, Obj: 0.383798, No Obj: 0.002472, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.668589, Class: 1.000000, Obj: 0.398118, No Obj: 0.003040, Avg Recall: 0.826087, count: 115 -Region Avg IOU: 0.676262, Class: 1.000000, Obj: 0.381692, No Obj: 0.002947, Avg Recall: 0.849206, count: 126 -Region Avg IOU: 0.662450, Class: 1.000000, Obj: 0.378031, No Obj: 0.003001, Avg Recall: 0.821429, count: 112 -5042: 0.486730, 0.450693 avg, 0.001000 rate, 29.473732 seconds, 1290752 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.682777, Class: 1.000000, Obj: 0.470398, No Obj: 0.002718, Avg Recall: 0.888889, count: 108 -Region Avg IOU: 0.643018, Class: 1.000000, Obj: 0.386264, No Obj: 0.002657, Avg Recall: 0.795455, count: 132 -Region Avg IOU: 0.616815, Class: 1.000000, Obj: 0.369196, No Obj: 0.002838, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.635379, Class: 1.000000, Obj: 0.431913, No Obj: 0.002646, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.679667, Class: 1.000000, Obj: 0.425484, No Obj: 0.002618, Avg Recall: 0.839623, count: 106 -Region Avg IOU: 0.648307, Class: 1.000000, Obj: 0.431679, No Obj: 0.002514, Avg Recall: 0.851485, count: 101 -Region Avg IOU: 0.649758, Class: 1.000000, Obj: 0.432507, No Obj: 0.002916, Avg Recall: 0.819444, count: 144 -Region Avg IOU: 0.695183, Class: 1.000000, Obj: 0.447594, No Obj: 0.002775, Avg Recall: 0.873786, count: 103 -5043: 0.428384, 0.448462 avg, 0.001000 rate, 29.129892 seconds, 1291008 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.652438, Class: 1.000000, Obj: 0.392847, No Obj: 0.002360, Avg Recall: 0.802326, count: 86 -Region Avg IOU: 0.654339, Class: 1.000000, Obj: 0.363225, No Obj: 0.002657, Avg Recall: 0.834646, count: 127 -Region Avg IOU: 0.680834, Class: 1.000000, Obj: 0.447160, No Obj: 0.002520, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.648095, Class: 1.000000, Obj: 0.361889, No Obj: 0.002229, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.633988, Class: 1.000000, Obj: 0.406189, No Obj: 0.002469, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.635951, Class: 1.000000, Obj: 0.365434, No Obj: 0.002444, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.632324, Class: 1.000000, Obj: 0.376803, No Obj: 0.002933, Avg Recall: 0.788136, count: 118 -Region Avg IOU: 0.680743, Class: 1.000000, Obj: 0.390674, No Obj: 0.002715, Avg Recall: 0.864000, count: 125 -5044: 0.474740, 0.451090 avg, 0.001000 rate, 29.820101 seconds, 1291264 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.642391, Class: 1.000000, Obj: 0.365812, No Obj: 0.002268, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.679922, Class: 1.000000, Obj: 0.370898, No Obj: 0.002406, Avg Recall: 0.826446, count: 121 -Region Avg IOU: 0.595239, Class: 1.000000, Obj: 0.337765, No Obj: 0.002329, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.618071, Class: 1.000000, Obj: 0.307269, No Obj: 0.002321, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.684029, Class: 1.000000, Obj: 0.380953, No Obj: 0.002369, Avg Recall: 0.855769, count: 104 -Region Avg IOU: 0.694813, Class: 1.000000, Obj: 0.411099, No Obj: 0.002303, Avg Recall: 0.882353, count: 85 -Region Avg IOU: 0.622416, Class: 1.000000, Obj: 0.393007, No Obj: 0.002210, Avg Recall: 0.732558, count: 86 -Region Avg IOU: 0.660443, Class: 1.000000, Obj: 0.348592, No Obj: 0.002835, Avg Recall: 0.826772, count: 127 -5045: 0.440202, 0.450001 avg, 0.001000 rate, 28.286524 seconds, 1291520 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.686966, Class: 1.000000, Obj: 0.413674, No Obj: 0.002291, Avg Recall: 0.854167, count: 96 -Region Avg IOU: 0.654597, Class: 1.000000, Obj: 0.300022, No Obj: 0.002293, Avg Recall: 0.808000, count: 125 -Region Avg IOU: 0.643729, Class: 1.000000, Obj: 0.323264, No Obj: 0.002604, Avg Recall: 0.778571, count: 140 -Region Avg IOU: 0.681786, Class: 1.000000, Obj: 0.371276, No Obj: 0.002401, Avg Recall: 0.831933, count: 119 -Region Avg IOU: 0.654124, Class: 1.000000, Obj: 0.349348, No Obj: 0.002252, Avg Recall: 0.843137, count: 102 -Region Avg IOU: 0.661261, Class: 1.000000, Obj: 0.383561, No Obj: 0.002338, Avg Recall: 0.827957, count: 93 -Region Avg IOU: 0.639608, Class: 1.000000, Obj: 0.349152, No Obj: 0.002549, Avg Recall: 0.800000, count: 115 -Region Avg IOU: 0.676238, Class: 1.000000, Obj: 0.343430, No Obj: 0.002430, Avg Recall: 0.842105, count: 95 -5046: 0.479773, 0.452978 avg, 0.001000 rate, 28.488529 seconds, 1291776 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.648207, Class: 1.000000, Obj: 0.364449, No Obj: 0.002548, Avg Recall: 0.807407, count: 135 -Region Avg IOU: 0.631887, Class: 1.000000, Obj: 0.355336, No Obj: 0.002034, Avg Recall: 0.755814, count: 86 -Region Avg IOU: 0.652813, Class: 1.000000, Obj: 0.390434, No Obj: 0.002142, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.662792, Class: 1.000000, Obj: 0.392027, No Obj: 0.001957, Avg Recall: 0.837209, count: 86 -Region Avg IOU: 0.686976, Class: 1.000000, Obj: 0.370987, No Obj: 0.002735, Avg Recall: 0.874074, count: 135 -Region Avg IOU: 0.674960, Class: 1.000000, Obj: 0.393400, No Obj: 0.002320, Avg Recall: 0.870000, count: 100 -Region Avg IOU: 0.624366, Class: 1.000000, Obj: 0.345011, No Obj: 0.002197, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.668485, Class: 1.000000, Obj: 0.329677, No Obj: 0.002441, Avg Recall: 0.858333, count: 120 -5047: 0.470053, 0.454686 avg, 0.001000 rate, 30.050974 seconds, 1292032 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.649164, Class: 1.000000, Obj: 0.353740, No Obj: 0.002388, Avg Recall: 0.789474, count: 114 -Region Avg IOU: 0.655674, Class: 1.000000, Obj: 0.347288, No Obj: 0.002033, Avg Recall: 0.813953, count: 86 -Region Avg IOU: 0.629667, Class: 1.000000, Obj: 0.389338, No Obj: 0.002644, Avg Recall: 0.790698, count: 129 -Region Avg IOU: 0.616281, Class: 1.000000, Obj: 0.343996, No Obj: 0.002519, Avg Recall: 0.759399, count: 133 -Region Avg IOU: 0.634149, Class: 1.000000, Obj: 0.349656, No Obj: 0.002308, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.649928, Class: 1.000000, Obj: 0.371622, No Obj: 0.002485, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.645495, Class: 1.000000, Obj: 0.390036, No Obj: 0.002357, Avg Recall: 0.827586, count: 116 -Region Avg IOU: 0.668743, Class: 1.000000, Obj: 0.297829, No Obj: 0.002359, Avg Recall: 0.880435, count: 92 -5048: 0.473558, 0.456573 avg, 0.001000 rate, 28.624031 seconds, 1292288 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.644399, Class: 1.000000, Obj: 0.379289, No Obj: 0.002275, Avg Recall: 0.817204, count: 93 -Region Avg IOU: 0.671221, Class: 1.000000, Obj: 0.400032, No Obj: 0.002030, Avg Recall: 0.858696, count: 92 -Region Avg IOU: 0.661129, Class: 1.000000, Obj: 0.395325, No Obj: 0.002411, Avg Recall: 0.823009, count: 113 -Region Avg IOU: 0.639867, Class: 1.000000, Obj: 0.403735, No Obj: 0.002659, Avg Recall: 0.783582, count: 134 -Region Avg IOU: 0.648859, Class: 1.000000, Obj: 0.346811, No Obj: 0.002712, Avg Recall: 0.792593, count: 135 -Region Avg IOU: 0.599072, Class: 1.000000, Obj: 0.308146, No Obj: 0.002487, Avg Recall: 0.705426, count: 129 -Region Avg IOU: 0.664899, Class: 1.000000, Obj: 0.351715, No Obj: 0.002518, Avg Recall: 0.820000, count: 100 -Region Avg IOU: 0.675013, Class: 1.000000, Obj: 0.419112, No Obj: 0.002168, Avg Recall: 0.810526, count: 95 -5049: 0.484310, 0.459347 avg, 0.001000 rate, 29.857592 seconds, 1292544 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.643892, Class: 1.000000, Obj: 0.397613, No Obj: 0.002253, Avg Recall: 0.823529, count: 85 -Region Avg IOU: 0.662819, Class: 1.000000, Obj: 0.398155, No Obj: 0.002894, Avg Recall: 0.798319, count: 119 -Region Avg IOU: 0.665954, Class: 1.000000, Obj: 0.418568, No Obj: 0.002699, Avg Recall: 0.820513, count: 117 -Region Avg IOU: 0.696214, Class: 1.000000, Obj: 0.398269, No Obj: 0.002670, Avg Recall: 0.854369, count: 103 -Region Avg IOU: 0.649381, Class: 1.000000, Obj: 0.348492, No Obj: 0.002777, Avg Recall: 0.793651, count: 126 -Region Avg IOU: 0.638084, Class: 1.000000, Obj: 0.332304, No Obj: 0.002615, Avg Recall: 0.837209, count: 129 -Region Avg IOU: 0.677279, Class: 1.000000, Obj: 0.391716, No Obj: 0.002217, Avg Recall: 0.855422, count: 83 -Region Avg IOU: 0.627105, Class: 1.000000, Obj: 0.315268, No Obj: 0.002554, Avg Recall: 0.778626, count: 131 -5050: 0.482781, 0.461690 avg, 0.001000 rate, 28.429085 seconds, 1292800 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 13.705438 seconds -Region Avg IOU: 0.505855, Class: 1.000000, Obj: 0.314520, No Obj: 0.003367, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.514345, Class: 1.000000, Obj: 0.305424, No Obj: 0.003424, Avg Recall: 0.570175, count: 114 -Region Avg IOU: 0.562294, Class: 1.000000, Obj: 0.292824, No Obj: 0.003608, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.553598, Class: 1.000000, Obj: 0.295029, No Obj: 0.003243, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.553560, Class: 1.000000, Obj: 0.346406, No Obj: 0.004079, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.541127, Class: 1.000000, Obj: 0.347773, No Obj: 0.003939, Avg Recall: 0.634146, count: 82 -Region Avg IOU: 0.517943, Class: 1.000000, Obj: 0.322489, No Obj: 0.003395, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.549647, Class: 1.000000, Obj: 0.340526, No Obj: 0.003111, Avg Recall: 0.648936, count: 94 -5051: 0.491567, 0.464678 avg, 0.001000 rate, 21.213764 seconds, 1293056 images -Loaded: 1.200950 seconds -Region Avg IOU: 0.500623, Class: 1.000000, Obj: 0.324824, No Obj: 0.003342, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.513087, Class: 1.000000, Obj: 0.299935, No Obj: 0.003673, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.554816, Class: 1.000000, Obj: 0.374689, No Obj: 0.003646, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.547030, Class: 1.000000, Obj: 0.373806, No Obj: 0.003923, Avg Recall: 0.657895, count: 76 -Region Avg IOU: 0.476630, Class: 1.000000, Obj: 0.278576, No Obj: 0.004313, Avg Recall: 0.524194, count: 124 -Region Avg IOU: 0.541681, Class: 1.000000, Obj: 0.338255, No Obj: 0.003145, Avg Recall: 0.666667, count: 81 -Region Avg IOU: 0.528229, Class: 1.000000, Obj: 0.320380, No Obj: 0.003832, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.522218, Class: 1.000000, Obj: 0.321134, No Obj: 0.003812, Avg Recall: 0.596774, count: 124 -5052: 0.428467, 0.461057 avg, 0.001000 rate, 21.916945 seconds, 1293312 images -Loaded: 0.240666 seconds -Region Avg IOU: 0.523426, Class: 1.000000, Obj: 0.345922, No Obj: 0.004067, Avg Recall: 0.618321, count: 131 -Region Avg IOU: 0.469807, Class: 1.000000, Obj: 0.335060, No Obj: 0.003934, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.542949, Class: 1.000000, Obj: 0.344430, No Obj: 0.003962, Avg Recall: 0.607843, count: 102 -Region Avg IOU: 0.488749, Class: 1.000000, Obj: 0.293616, No Obj: 0.003489, Avg Recall: 0.549180, count: 122 -Region Avg IOU: 0.544849, Class: 1.000000, Obj: 0.375263, No Obj: 0.003705, Avg Recall: 0.615385, count: 91 -Region Avg IOU: 0.544156, Class: 1.000000, Obj: 0.348597, No Obj: 0.003978, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.535245, Class: 1.000000, Obj: 0.361488, No Obj: 0.005275, Avg Recall: 0.593750, count: 160 -Region Avg IOU: 0.519158, Class: 1.000000, Obj: 0.356615, No Obj: 0.003320, Avg Recall: 0.594059, count: 101 -5053: 0.501563, 0.465107 avg, 0.001000 rate, 22.622860 seconds, 1293568 images -Loaded: 0.063038 seconds -Region Avg IOU: 0.556063, Class: 1.000000, Obj: 0.329792, No Obj: 0.005080, Avg Recall: 0.659259, count: 135 -Region Avg IOU: 0.532176, Class: 1.000000, Obj: 0.369409, No Obj: 0.003992, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.519895, Class: 1.000000, Obj: 0.325803, No Obj: 0.004026, Avg Recall: 0.636364, count: 88 -Region Avg IOU: 0.494052, Class: 1.000000, Obj: 0.353913, No Obj: 0.004170, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.531782, Class: 1.000000, Obj: 0.383017, No Obj: 0.004072, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.509377, Class: 1.000000, Obj: 0.334808, No Obj: 0.004996, Avg Recall: 0.563910, count: 133 -Region Avg IOU: 0.552413, Class: 1.000000, Obj: 0.384441, No Obj: 0.003794, Avg Recall: 0.644444, count: 90 -Region Avg IOU: 0.541028, Class: 1.000000, Obj: 0.357809, No Obj: 0.003746, Avg Recall: 0.588235, count: 85 -5054: 0.435005, 0.462097 avg, 0.001000 rate, 21.450668 seconds, 1293824 images -Loaded: 0.569360 seconds -Region Avg IOU: 0.558017, Class: 1.000000, Obj: 0.406556, No Obj: 0.004128, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.509299, Class: 1.000000, Obj: 0.349051, No Obj: 0.004379, Avg Recall: 0.572581, count: 124 -Region Avg IOU: 0.561625, Class: 1.000000, Obj: 0.386766, No Obj: 0.004114, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.518357, Class: 1.000000, Obj: 0.352448, No Obj: 0.004097, Avg Recall: 0.570175, count: 114 -Region Avg IOU: 0.525731, Class: 1.000000, Obj: 0.378568, No Obj: 0.004198, Avg Recall: 0.566372, count: 113 -Region Avg IOU: 0.489846, Class: 1.000000, Obj: 0.334497, No Obj: 0.005359, Avg Recall: 0.521429, count: 140 -Region Avg IOU: 0.558871, Class: 1.000000, Obj: 0.383692, No Obj: 0.004894, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.519476, Class: 1.000000, Obj: 0.353726, No Obj: 0.004146, Avg Recall: 0.584158, count: 101 -5055: 0.447687, 0.460656 avg, 0.001000 rate, 20.299492 seconds, 1294080 images -Loaded: 1.865664 seconds -Region Avg IOU: 0.490783, Class: 1.000000, Obj: 0.388720, No Obj: 0.004377, Avg Recall: 0.561404, count: 114 -Region Avg IOU: 0.590638, Class: 1.000000, Obj: 0.463695, No Obj: 0.004229, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.505457, Class: 1.000000, Obj: 0.382226, No Obj: 0.004562, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.569725, Class: 1.000000, Obj: 0.471250, No Obj: 0.004451, Avg Recall: 0.630952, count: 84 -Region Avg IOU: 0.552634, Class: 1.000000, Obj: 0.367197, No Obj: 0.004630, Avg Recall: 0.602273, count: 88 -Region Avg IOU: 0.594492, Class: 1.000000, Obj: 0.454950, No Obj: 0.005271, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.521355, Class: 1.000000, Obj: 0.358344, No Obj: 0.005082, Avg Recall: 0.609929, count: 141 -Region Avg IOU: 0.554959, Class: 1.000000, Obj: 0.378371, No Obj: 0.004682, Avg Recall: 0.670455, count: 88 -5056: 0.409300, 0.455521 avg, 0.001000 rate, 21.247036 seconds, 1294336 images -Loaded: 0.143888 seconds -Region Avg IOU: 0.503748, Class: 1.000000, Obj: 0.360238, No Obj: 0.004408, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.496073, Class: 1.000000, Obj: 0.391692, No Obj: 0.004538, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.536270, Class: 1.000000, Obj: 0.458769, No Obj: 0.004207, Avg Recall: 0.586207, count: 87 -Region Avg IOU: 0.471755, Class: 1.000000, Obj: 0.368241, No Obj: 0.004757, Avg Recall: 0.500000, count: 108 -Region Avg IOU: 0.532692, Class: 1.000000, Obj: 0.446363, No Obj: 0.005339, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.507054, Class: 1.000000, Obj: 0.423986, No Obj: 0.003939, Avg Recall: 0.544118, count: 68 -Region Avg IOU: 0.554954, Class: 1.000000, Obj: 0.370671, No Obj: 0.004689, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.566464, Class: 1.000000, Obj: 0.413503, No Obj: 0.004106, Avg Recall: 0.682927, count: 82 -5057: 0.392714, 0.449240 avg, 0.001000 rate, 19.175692 seconds, 1294592 images -Loaded: 2.521043 seconds -Region Avg IOU: 0.503427, Class: 1.000000, Obj: 0.417758, No Obj: 0.005073, Avg Recall: 0.570370, count: 135 -Region Avg IOU: 0.520460, Class: 1.000000, Obj: 0.450364, No Obj: 0.003671, Avg Recall: 0.600000, count: 80 -Region Avg IOU: 0.515378, Class: 1.000000, Obj: 0.380730, No Obj: 0.004664, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.598036, Class: 1.000000, Obj: 0.440554, No Obj: 0.004701, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.509643, Class: 1.000000, Obj: 0.404977, No Obj: 0.004008, Avg Recall: 0.594595, count: 74 -Region Avg IOU: 0.495653, Class: 1.000000, Obj: 0.401485, No Obj: 0.004883, Avg Recall: 0.552845, count: 123 -Region Avg IOU: 0.521903, Class: 1.000000, Obj: 0.367904, No Obj: 0.004949, Avg Recall: 0.632812, count: 128 -Region Avg IOU: 0.487229, Class: 1.000000, Obj: 0.347432, No Obj: 0.004881, Avg Recall: 0.535433, count: 127 -5058: 0.477071, 0.452023 avg, 0.001000 rate, 21.160547 seconds, 1294848 images -Loaded: 0.322776 seconds -Region Avg IOU: 0.540526, Class: 1.000000, Obj: 0.400887, No Obj: 0.004683, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.539847, Class: 1.000000, Obj: 0.478009, No Obj: 0.004352, Avg Recall: 0.622222, count: 90 -Region Avg IOU: 0.508217, Class: 1.000000, Obj: 0.374963, No Obj: 0.004506, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.473476, Class: 1.000000, Obj: 0.365773, No Obj: 0.003944, Avg Recall: 0.515464, count: 97 -Region Avg IOU: 0.494352, Class: 1.000000, Obj: 0.388565, No Obj: 0.004690, Avg Recall: 0.560345, count: 116 -Region Avg IOU: 0.505023, Class: 1.000000, Obj: 0.407393, No Obj: 0.004540, Avg Recall: 0.542553, count: 94 -Region Avg IOU: 0.523336, Class: 1.000000, Obj: 0.418745, No Obj: 0.004174, Avg Recall: 0.583333, count: 84 -Region Avg IOU: 0.552835, Class: 1.000000, Obj: 0.401565, No Obj: 0.004353, Avg Recall: 0.676190, count: 105 -5059: 0.454242, 0.452245 avg, 0.001000 rate, 22.436060 seconds, 1295104 images -Loaded: 0.046208 seconds -Region Avg IOU: 0.525776, Class: 1.000000, Obj: 0.378692, No Obj: 0.005580, Avg Recall: 0.596899, count: 129 -Region Avg IOU: 0.561853, Class: 1.000000, Obj: 0.348001, No Obj: 0.004601, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.533260, Class: 1.000000, Obj: 0.419851, No Obj: 0.004651, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.535630, Class: 1.000000, Obj: 0.400140, No Obj: 0.004784, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.532787, Class: 1.000000, Obj: 0.373091, No Obj: 0.005130, Avg Recall: 0.578947, count: 133 -Region Avg IOU: 0.545165, Class: 1.000000, Obj: 0.402657, No Obj: 0.005134, Avg Recall: 0.652174, count: 138 -Region Avg IOU: 0.540447, Class: 1.000000, Obj: 0.374038, No Obj: 0.004543, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.508730, Class: 1.000000, Obj: 0.339144, No Obj: 0.004194, Avg Recall: 0.591837, count: 98 -5060: 0.441094, 0.451130 avg, 0.001000 rate, 22.209370 seconds, 1295360 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000063 seconds -Region Avg IOU: 0.595333, Class: 1.000000, Obj: 0.492982, No Obj: 0.003894, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.595723, Class: 1.000000, Obj: 0.457912, No Obj: 0.003980, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.624615, Class: 1.000000, Obj: 0.420967, No Obj: 0.003537, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.589233, Class: 1.000000, Obj: 0.464373, No Obj: 0.003690, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.650773, Class: 1.000000, Obj: 0.523814, No Obj: 0.003482, Avg Recall: 0.744444, count: 90 -Region Avg IOU: 0.608367, Class: 1.000000, Obj: 0.443340, No Obj: 0.004182, Avg Recall: 0.704918, count: 122 -Region Avg IOU: 0.622602, Class: 1.000000, Obj: 0.446862, No Obj: 0.003666, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.603886, Class: 1.000000, Obj: 0.487465, No Obj: 0.003580, Avg Recall: 0.720430, count: 93 -5061: 0.428996, 0.448916 avg, 0.001000 rate, 24.200571 seconds, 1295616 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.568266, Class: 1.000000, Obj: 0.424058, No Obj: 0.003186, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.624755, Class: 1.000000, Obj: 0.447702, No Obj: 0.003792, Avg Recall: 0.782609, count: 115 -Region Avg IOU: 0.598207, Class: 1.000000, Obj: 0.410141, No Obj: 0.003680, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.568652, Class: 1.000000, Obj: 0.433518, No Obj: 0.003432, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.588638, Class: 1.000000, Obj: 0.446910, No Obj: 0.003482, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.602962, Class: 1.000000, Obj: 0.460190, No Obj: 0.003588, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.628375, Class: 1.000000, Obj: 0.433002, No Obj: 0.003212, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.606089, Class: 1.000000, Obj: 0.489380, No Obj: 0.003730, Avg Recall: 0.710000, count: 100 -5062: 0.407333, 0.444758 avg, 0.001000 rate, 26.536589 seconds, 1295872 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.652280, Class: 1.000000, Obj: 0.467822, No Obj: 0.002903, Avg Recall: 0.797753, count: 89 -Region Avg IOU: 0.628545, Class: 1.000000, Obj: 0.396819, No Obj: 0.003571, Avg Recall: 0.756098, count: 123 -Region Avg IOU: 0.631062, Class: 1.000000, Obj: 0.480237, No Obj: 0.003374, Avg Recall: 0.806818, count: 88 -Region Avg IOU: 0.592780, Class: 1.000000, Obj: 0.347902, No Obj: 0.003269, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.679214, Class: 1.000000, Obj: 0.450307, No Obj: 0.003762, Avg Recall: 0.839080, count: 87 -Region Avg IOU: 0.648782, Class: 1.000000, Obj: 0.414305, No Obj: 0.003423, Avg Recall: 0.854167, count: 96 -Region Avg IOU: 0.669959, Class: 1.000000, Obj: 0.430939, No Obj: 0.003294, Avg Recall: 0.862069, count: 87 -Region Avg IOU: 0.579901, Class: 1.000000, Obj: 0.407692, No Obj: 0.003449, Avg Recall: 0.680328, count: 122 -5063: 0.428023, 0.443085 avg, 0.001000 rate, 24.665392 seconds, 1296128 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.624508, Class: 1.000000, Obj: 0.410195, No Obj: 0.002636, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.612602, Class: 1.000000, Obj: 0.423690, No Obj: 0.002867, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.647398, Class: 1.000000, Obj: 0.405161, No Obj: 0.003024, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.627798, Class: 1.000000, Obj: 0.378996, No Obj: 0.003367, Avg Recall: 0.748148, count: 135 -Region Avg IOU: 0.643747, Class: 1.000000, Obj: 0.381309, No Obj: 0.002687, Avg Recall: 0.759036, count: 83 -Region Avg IOU: 0.588438, Class: 1.000000, Obj: 0.406998, No Obj: 0.002862, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.598594, Class: 1.000000, Obj: 0.378050, No Obj: 0.003124, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.611973, Class: 1.000000, Obj: 0.353506, No Obj: 0.002988, Avg Recall: 0.741379, count: 116 -5064: 0.395055, 0.438282 avg, 0.001000 rate, 25.832191 seconds, 1296384 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.652306, Class: 1.000000, Obj: 0.386050, No Obj: 0.002729, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.640486, Class: 1.000000, Obj: 0.395162, No Obj: 0.003075, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.590922, Class: 1.000000, Obj: 0.357518, No Obj: 0.002335, Avg Recall: 0.685393, count: 89 -Region Avg IOU: 0.608633, Class: 1.000000, Obj: 0.348873, No Obj: 0.002765, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.651778, Class: 1.000000, Obj: 0.371858, No Obj: 0.002403, Avg Recall: 0.790123, count: 81 -Region Avg IOU: 0.601962, Class: 1.000000, Obj: 0.348974, No Obj: 0.002779, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.607708, Class: 1.000000, Obj: 0.351298, No Obj: 0.002628, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.592363, Class: 1.000000, Obj: 0.308496, No Obj: 0.003019, Avg Recall: 0.714286, count: 140 -5065: 0.415993, 0.436053 avg, 0.001000 rate, 24.437153 seconds, 1296640 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.631855, Class: 1.000000, Obj: 0.373665, No Obj: 0.002602, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.603410, Class: 1.000000, Obj: 0.346708, No Obj: 0.002433, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.619805, Class: 1.000000, Obj: 0.371504, No Obj: 0.002652, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.595734, Class: 1.000000, Obj: 0.319465, No Obj: 0.002530, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.587029, Class: 1.000000, Obj: 0.326531, No Obj: 0.002328, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.632618, Class: 1.000000, Obj: 0.309101, No Obj: 0.002719, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.610132, Class: 1.000000, Obj: 0.374472, No Obj: 0.002461, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.659805, Class: 1.000000, Obj: 0.331081, No Obj: 0.002962, Avg Recall: 0.830357, count: 112 -5066: 0.417718, 0.434219 avg, 0.001000 rate, 25.063320 seconds, 1296896 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.636667, Class: 1.000000, Obj: 0.388846, No Obj: 0.002582, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.604989, Class: 1.000000, Obj: 0.357680, No Obj: 0.002678, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.640366, Class: 1.000000, Obj: 0.357479, No Obj: 0.002582, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.568292, Class: 1.000000, Obj: 0.307902, No Obj: 0.002483, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.606779, Class: 1.000000, Obj: 0.313149, No Obj: 0.002275, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.604260, Class: 1.000000, Obj: 0.330477, No Obj: 0.002834, Avg Recall: 0.718121, count: 149 -Region Avg IOU: 0.609722, Class: 1.000000, Obj: 0.363078, No Obj: 0.002565, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.616392, Class: 1.000000, Obj: 0.367605, No Obj: 0.002934, Avg Recall: 0.765217, count: 115 -5067: 0.477494, 0.438547 avg, 0.001000 rate, 26.709089 seconds, 1297152 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.581364, Class: 1.000000, Obj: 0.318048, No Obj: 0.002565, Avg Recall: 0.669421, count: 121 -Region Avg IOU: 0.627644, Class: 1.000000, Obj: 0.346410, No Obj: 0.003055, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.624634, Class: 1.000000, Obj: 0.326716, No Obj: 0.002637, Avg Recall: 0.771186, count: 118 -Region Avg IOU: 0.635125, Class: 1.000000, Obj: 0.438356, No Obj: 0.002679, Avg Recall: 0.804598, count: 87 -Region Avg IOU: 0.618183, Class: 1.000000, Obj: 0.371833, No Obj: 0.002574, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.623953, Class: 1.000000, Obj: 0.371507, No Obj: 0.003119, Avg Recall: 0.768595, count: 121 -Region Avg IOU: 0.646670, Class: 1.000000, Obj: 0.396826, No Obj: 0.002729, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.609255, Class: 1.000000, Obj: 0.400126, No Obj: 0.002952, Avg Recall: 0.752137, count: 117 -5068: 0.457608, 0.440453 avg, 0.001000 rate, 26.255934 seconds, 1297408 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.591191, Class: 1.000000, Obj: 0.357956, No Obj: 0.002537, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.640455, Class: 1.000000, Obj: 0.395117, No Obj: 0.002753, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.630805, Class: 1.000000, Obj: 0.331769, No Obj: 0.002795, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.628308, Class: 1.000000, Obj: 0.372768, No Obj: 0.002893, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.634600, Class: 1.000000, Obj: 0.357358, No Obj: 0.002762, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.691686, Class: 1.000000, Obj: 0.403725, No Obj: 0.002315, Avg Recall: 0.871429, count: 70 -Region Avg IOU: 0.614281, Class: 1.000000, Obj: 0.329289, No Obj: 0.003152, Avg Recall: 0.710938, count: 128 -Region Avg IOU: 0.591707, Class: 1.000000, Obj: 0.309878, No Obj: 0.002954, Avg Recall: 0.714286, count: 119 -5069: 0.436213, 0.440029 avg, 0.001000 rate, 25.689093 seconds, 1297664 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.650973, Class: 1.000000, Obj: 0.396128, No Obj: 0.002544, Avg Recall: 0.849462, count: 93 -Region Avg IOU: 0.639506, Class: 1.000000, Obj: 0.409262, No Obj: 0.002876, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.614570, Class: 1.000000, Obj: 0.450720, No Obj: 0.003218, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.623747, Class: 1.000000, Obj: 0.369712, No Obj: 0.003541, Avg Recall: 0.731707, count: 123 -Region Avg IOU: 0.637066, Class: 1.000000, Obj: 0.355662, No Obj: 0.003052, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.647759, Class: 1.000000, Obj: 0.451703, No Obj: 0.002792, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.617426, Class: 1.000000, Obj: 0.358990, No Obj: 0.003012, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.636032, Class: 1.000000, Obj: 0.388448, No Obj: 0.002874, Avg Recall: 0.812500, count: 96 -5070: 0.393415, 0.435367 avg, 0.001000 rate, 26.014872 seconds, 1297920 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 20.308371 seconds -Region Avg IOU: 0.619378, Class: 1.000000, Obj: 0.383110, No Obj: 0.003467, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.597721, Class: 1.000000, Obj: 0.380953, No Obj: 0.002656, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.581739, Class: 1.000000, Obj: 0.387904, No Obj: 0.002864, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.578141, Class: 1.000000, Obj: 0.354749, No Obj: 0.002563, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.632936, Class: 1.000000, Obj: 0.381735, No Obj: 0.002818, Avg Recall: 0.775281, count: 89 -Region Avg IOU: 0.591537, Class: 1.000000, Obj: 0.413841, No Obj: 0.002799, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.639041, Class: 1.000000, Obj: 0.413453, No Obj: 0.003370, Avg Recall: 0.775000, count: 120 -Region Avg IOU: 0.673424, Class: 1.000000, Obj: 0.413047, No Obj: 0.003124, Avg Recall: 0.805825, count: 103 -5071: 0.418867, 0.433717 avg, 0.001000 rate, 25.677984 seconds, 1298176 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.634511, Class: 1.000000, Obj: 0.402770, No Obj: 0.003282, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.653705, Class: 1.000000, Obj: 0.438846, No Obj: 0.003637, Avg Recall: 0.779528, count: 127 -Region Avg IOU: 0.630591, Class: 1.000000, Obj: 0.389467, No Obj: 0.003144, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.659417, Class: 1.000000, Obj: 0.445971, No Obj: 0.003634, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.582529, Class: 1.000000, Obj: 0.369410, No Obj: 0.003092, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.578041, Class: 1.000000, Obj: 0.420873, No Obj: 0.002809, Avg Recall: 0.658824, count: 85 -Region Avg IOU: 0.617979, Class: 1.000000, Obj: 0.374637, No Obj: 0.003572, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.602195, Class: 1.000000, Obj: 0.427238, No Obj: 0.003556, Avg Recall: 0.688525, count: 122 -5072: 0.442998, 0.434645 avg, 0.001000 rate, 25.527868 seconds, 1298432 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.597674, Class: 1.000000, Obj: 0.423860, No Obj: 0.003443, Avg Recall: 0.717742, count: 124 -Region Avg IOU: 0.639885, Class: 1.000000, Obj: 0.473061, No Obj: 0.003538, Avg Recall: 0.774194, count: 124 -Region Avg IOU: 0.625627, Class: 1.000000, Obj: 0.410352, No Obj: 0.003149, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.629883, Class: 1.000000, Obj: 0.389302, No Obj: 0.003541, Avg Recall: 0.805085, count: 118 -Region Avg IOU: 0.651944, Class: 1.000000, Obj: 0.381110, No Obj: 0.003495, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.587158, Class: 1.000000, Obj: 0.380853, No Obj: 0.002977, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.578026, Class: 1.000000, Obj: 0.393038, No Obj: 0.003002, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.625185, Class: 1.000000, Obj: 0.458642, No Obj: 0.002951, Avg Recall: 0.777778, count: 90 -5073: 0.443000, 0.435481 avg, 0.001000 rate, 25.359264 seconds, 1298688 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.642684, Class: 1.000000, Obj: 0.458541, No Obj: 0.003358, Avg Recall: 0.814159, count: 113 -Region Avg IOU: 0.606131, Class: 1.000000, Obj: 0.438820, No Obj: 0.003160, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.641112, Class: 1.000000, Obj: 0.474414, No Obj: 0.003217, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.659514, Class: 1.000000, Obj: 0.437296, No Obj: 0.003578, Avg Recall: 0.824561, count: 114 -Region Avg IOU: 0.650625, Class: 1.000000, Obj: 0.451009, No Obj: 0.003038, Avg Recall: 0.813187, count: 91 -Region Avg IOU: 0.610316, Class: 1.000000, Obj: 0.410546, No Obj: 0.003184, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.588623, Class: 1.000000, Obj: 0.373247, No Obj: 0.004076, Avg Recall: 0.704142, count: 169 -Region Avg IOU: 0.635919, Class: 1.000000, Obj: 0.463383, No Obj: 0.002935, Avg Recall: 0.789474, count: 95 -5074: 0.397482, 0.431681 avg, 0.001000 rate, 25.516115 seconds, 1298944 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.598907, Class: 1.000000, Obj: 0.424882, No Obj: 0.002889, Avg Recall: 0.723077, count: 65 -Region Avg IOU: 0.605308, Class: 1.000000, Obj: 0.452712, No Obj: 0.003716, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.607954, Class: 1.000000, Obj: 0.417187, No Obj: 0.003874, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.606941, Class: 1.000000, Obj: 0.410271, No Obj: 0.002884, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.616076, Class: 1.000000, Obj: 0.406926, No Obj: 0.002970, Avg Recall: 0.772152, count: 79 -Region Avg IOU: 0.555280, Class: 1.000000, Obj: 0.384160, No Obj: 0.003005, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.597820, Class: 1.000000, Obj: 0.443075, No Obj: 0.003297, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.597813, Class: 1.000000, Obj: 0.469177, No Obj: 0.003251, Avg Recall: 0.743363, count: 113 -5075: 0.388410, 0.427354 avg, 0.001000 rate, 25.258995 seconds, 1299200 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.594561, Class: 1.000000, Obj: 0.429346, No Obj: 0.003251, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.574752, Class: 1.000000, Obj: 0.418164, No Obj: 0.004011, Avg Recall: 0.666667, count: 138 -Region Avg IOU: 0.597578, Class: 1.000000, Obj: 0.432415, No Obj: 0.003235, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.581187, Class: 1.000000, Obj: 0.431132, No Obj: 0.003239, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.614712, Class: 1.000000, Obj: 0.407938, No Obj: 0.003565, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.659974, Class: 1.000000, Obj: 0.433452, No Obj: 0.003928, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.645990, Class: 1.000000, Obj: 0.475704, No Obj: 0.003661, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.631212, Class: 1.000000, Obj: 0.429163, No Obj: 0.003099, Avg Recall: 0.766990, count: 103 -5076: 0.422098, 0.426828 avg, 0.001000 rate, 25.608047 seconds, 1299456 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.607704, Class: 1.000000, Obj: 0.427733, No Obj: 0.002995, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.609866, Class: 1.000000, Obj: 0.403084, No Obj: 0.003845, Avg Recall: 0.722628, count: 137 -Region Avg IOU: 0.633052, Class: 1.000000, Obj: 0.423669, No Obj: 0.003354, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.618912, Class: 1.000000, Obj: 0.401074, No Obj: 0.003514, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.573689, Class: 1.000000, Obj: 0.384029, No Obj: 0.003367, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.647201, Class: 1.000000, Obj: 0.450041, No Obj: 0.003509, Avg Recall: 0.801980, count: 101 -Region Avg IOU: 0.618627, Class: 1.000000, Obj: 0.418282, No Obj: 0.002765, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.592307, Class: 1.000000, Obj: 0.395808, No Obj: 0.003436, Avg Recall: 0.707965, count: 113 -5077: 0.475403, 0.431686 avg, 0.001000 rate, 24.629562 seconds, 1299712 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.630040, Class: 1.000000, Obj: 0.449290, No Obj: 0.002992, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.551760, Class: 1.000000, Obj: 0.442962, No Obj: 0.002698, Avg Recall: 0.616279, count: 86 -Region Avg IOU: 0.592063, Class: 1.000000, Obj: 0.415714, No Obj: 0.002959, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.619941, Class: 1.000000, Obj: 0.413944, No Obj: 0.003281, Avg Recall: 0.808081, count: 99 -Region Avg IOU: 0.611734, Class: 1.000000, Obj: 0.379836, No Obj: 0.003536, Avg Recall: 0.769231, count: 143 -Region Avg IOU: 0.637859, Class: 1.000000, Obj: 0.387263, No Obj: 0.003294, Avg Recall: 0.801980, count: 101 -Region Avg IOU: 0.618362, Class: 1.000000, Obj: 0.451344, No Obj: 0.002980, Avg Recall: 0.715909, count: 88 -Region Avg IOU: 0.630131, Class: 1.000000, Obj: 0.463682, No Obj: 0.002925, Avg Recall: 0.765306, count: 98 -5078: 0.432972, 0.431814 avg, 0.001000 rate, 25.276407 seconds, 1299968 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.645799, Class: 1.000000, Obj: 0.394466, No Obj: 0.002927, Avg Recall: 0.813187, count: 91 -Region Avg IOU: 0.633377, Class: 1.000000, Obj: 0.429797, No Obj: 0.003258, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.596657, Class: 1.000000, Obj: 0.381700, No Obj: 0.002674, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.627242, Class: 1.000000, Obj: 0.376763, No Obj: 0.003160, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.643249, Class: 1.000000, Obj: 0.389592, No Obj: 0.003780, Avg Recall: 0.765152, count: 132 -Region Avg IOU: 0.604619, Class: 1.000000, Obj: 0.423753, No Obj: 0.002946, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.614842, Class: 1.000000, Obj: 0.379687, No Obj: 0.003124, Avg Recall: 0.753968, count: 126 -Region Avg IOU: 0.590024, Class: 1.000000, Obj: 0.392478, No Obj: 0.003525, Avg Recall: 0.676056, count: 142 -5079: 0.418488, 0.430482 avg, 0.001000 rate, 25.523796 seconds, 1300224 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.617160, Class: 1.000000, Obj: 0.366784, No Obj: 0.003079, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.573371, Class: 1.000000, Obj: 0.390088, No Obj: 0.002518, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.613589, Class: 1.000000, Obj: 0.412826, No Obj: 0.002827, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.627479, Class: 1.000000, Obj: 0.366253, No Obj: 0.002811, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.635109, Class: 1.000000, Obj: 0.406585, No Obj: 0.002760, Avg Recall: 0.732558, count: 86 -Region Avg IOU: 0.626285, Class: 1.000000, Obj: 0.398702, No Obj: 0.003038, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.645935, Class: 1.000000, Obj: 0.431591, No Obj: 0.003039, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.609225, Class: 1.000000, Obj: 0.367363, No Obj: 0.002771, Avg Recall: 0.742857, count: 105 -5080: 0.381328, 0.425566 avg, 0.001000 rate, 24.095917 seconds, 1300480 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000066 seconds -Region Avg IOU: 0.621375, Class: 1.000000, Obj: 0.375398, No Obj: 0.002698, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.573073, Class: 1.000000, Obj: 0.344685, No Obj: 0.002338, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.642484, Class: 1.000000, Obj: 0.401039, No Obj: 0.003002, Avg Recall: 0.776000, count: 125 -Region Avg IOU: 0.572694, Class: 1.000000, Obj: 0.355021, No Obj: 0.002397, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.605822, Class: 1.000000, Obj: 0.373737, No Obj: 0.002751, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.613207, Class: 1.000000, Obj: 0.426307, No Obj: 0.002758, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.613002, Class: 1.000000, Obj: 0.383369, No Obj: 0.002757, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.662750, Class: 1.000000, Obj: 0.394292, No Obj: 0.002980, Avg Recall: 0.804348, count: 92 -5081: 0.398395, 0.422849 avg, 0.001000 rate, 27.095032 seconds, 1300736 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.613166, Class: 1.000000, Obj: 0.321852, No Obj: 0.002762, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.637728, Class: 1.000000, Obj: 0.357719, No Obj: 0.002947, Avg Recall: 0.810345, count: 116 -Region Avg IOU: 0.626604, Class: 1.000000, Obj: 0.392215, No Obj: 0.003101, Avg Recall: 0.734375, count: 128 -Region Avg IOU: 0.624057, Class: 1.000000, Obj: 0.410470, No Obj: 0.002328, Avg Recall: 0.768116, count: 69 -Region Avg IOU: 0.634388, Class: 1.000000, Obj: 0.361987, No Obj: 0.002948, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.620037, Class: 1.000000, Obj: 0.328099, No Obj: 0.003347, Avg Recall: 0.751724, count: 145 -Region Avg IOU: 0.594422, Class: 1.000000, Obj: 0.358398, No Obj: 0.002548, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.606314, Class: 1.000000, Obj: 0.366498, No Obj: 0.002269, Avg Recall: 0.694118, count: 85 -5082: 0.445386, 0.425103 avg, 0.001000 rate, 26.900167 seconds, 1300992 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.600806, Class: 1.000000, Obj: 0.377329, No Obj: 0.002908, Avg Recall: 0.715447, count: 123 -Region Avg IOU: 0.653387, Class: 1.000000, Obj: 0.344285, No Obj: 0.002703, Avg Recall: 0.814815, count: 108 -Region Avg IOU: 0.636449, Class: 1.000000, Obj: 0.416804, No Obj: 0.002893, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.612188, Class: 1.000000, Obj: 0.358223, No Obj: 0.002787, Avg Recall: 0.720000, count: 125 -Region Avg IOU: 0.620262, Class: 1.000000, Obj: 0.387876, No Obj: 0.002911, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.616351, Class: 1.000000, Obj: 0.385673, No Obj: 0.003012, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.635603, Class: 1.000000, Obj: 0.397461, No Obj: 0.002529, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.666341, Class: 1.000000, Obj: 0.375528, No Obj: 0.002879, Avg Recall: 0.800000, count: 100 -5083: 0.480731, 0.430666 avg, 0.001000 rate, 26.443846 seconds, 1301248 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.642234, Class: 1.000000, Obj: 0.415292, No Obj: 0.002667, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.595106, Class: 1.000000, Obj: 0.319640, No Obj: 0.002126, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.588262, Class: 1.000000, Obj: 0.337600, No Obj: 0.003169, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.591460, Class: 1.000000, Obj: 0.298575, No Obj: 0.002965, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.653652, Class: 1.000000, Obj: 0.403601, No Obj: 0.002867, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.635654, Class: 1.000000, Obj: 0.386671, No Obj: 0.002886, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.630108, Class: 1.000000, Obj: 0.445600, No Obj: 0.002953, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.660809, Class: 1.000000, Obj: 0.444481, No Obj: 0.003094, Avg Recall: 0.824074, count: 108 -5084: 0.419794, 0.429579 avg, 0.001000 rate, 26.534121 seconds, 1301504 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.643584, Class: 1.000000, Obj: 0.366977, No Obj: 0.002820, Avg Recall: 0.802469, count: 81 -Region Avg IOU: 0.622699, Class: 1.000000, Obj: 0.397232, No Obj: 0.002772, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.616985, Class: 1.000000, Obj: 0.333002, No Obj: 0.002439, Avg Recall: 0.670588, count: 85 -Region Avg IOU: 0.634933, Class: 1.000000, Obj: 0.431387, No Obj: 0.002957, Avg Recall: 0.805310, count: 113 -Region Avg IOU: 0.616104, Class: 1.000000, Obj: 0.358678, No Obj: 0.003374, Avg Recall: 0.750000, count: 148 -Region Avg IOU: 0.682194, Class: 1.000000, Obj: 0.408692, No Obj: 0.002682, Avg Recall: 0.857143, count: 91 -Region Avg IOU: 0.630383, Class: 1.000000, Obj: 0.374990, No Obj: 0.003636, Avg Recall: 0.779412, count: 136 -Region Avg IOU: 0.609336, Class: 1.000000, Obj: 0.366230, No Obj: 0.003102, Avg Recall: 0.758929, count: 112 -5085: 0.459009, 0.432522 avg, 0.001000 rate, 27.185612 seconds, 1301760 images -Loaded: 0.000059 seconds -Region Avg IOU: 0.607419, Class: 1.000000, Obj: 0.415867, No Obj: 0.002539, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.705096, Class: 1.000000, Obj: 0.483355, No Obj: 0.003416, Avg Recall: 0.853211, count: 109 -Region Avg IOU: 0.589819, Class: 1.000000, Obj: 0.330171, No Obj: 0.002542, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.602660, Class: 1.000000, Obj: 0.387905, No Obj: 0.002716, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.641317, Class: 1.000000, Obj: 0.401013, No Obj: 0.002823, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.631903, Class: 1.000000, Obj: 0.418033, No Obj: 0.002908, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.644169, Class: 1.000000, Obj: 0.446699, No Obj: 0.002737, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.637314, Class: 1.000000, Obj: 0.417480, No Obj: 0.002782, Avg Recall: 0.831579, count: 95 -5086: 0.385768, 0.427846 avg, 0.001000 rate, 26.380758 seconds, 1302016 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.664944, Class: 1.000000, Obj: 0.462460, No Obj: 0.002775, Avg Recall: 0.849462, count: 93 -Region Avg IOU: 0.611298, Class: 1.000000, Obj: 0.410744, No Obj: 0.003057, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.651076, Class: 1.000000, Obj: 0.401279, No Obj: 0.003372, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.633172, Class: 1.000000, Obj: 0.461549, No Obj: 0.003200, Avg Recall: 0.824561, count: 114 -Region Avg IOU: 0.605610, Class: 1.000000, Obj: 0.376853, No Obj: 0.002886, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.659884, Class: 1.000000, Obj: 0.424999, No Obj: 0.003074, Avg Recall: 0.836364, count: 110 -Region Avg IOU: 0.649158, Class: 1.000000, Obj: 0.387479, No Obj: 0.003641, Avg Recall: 0.839416, count: 137 -Region Avg IOU: 0.631166, Class: 1.000000, Obj: 0.392464, No Obj: 0.003307, Avg Recall: 0.785714, count: 112 -5087: 0.429332, 0.427995 avg, 0.001000 rate, 25.677248 seconds, 1302272 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.645180, Class: 1.000000, Obj: 0.404530, No Obj: 0.003431, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.642506, Class: 1.000000, Obj: 0.419864, No Obj: 0.002918, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.628598, Class: 1.000000, Obj: 0.387961, No Obj: 0.003114, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.629850, Class: 1.000000, Obj: 0.427924, No Obj: 0.003103, Avg Recall: 0.741176, count: 85 -Region Avg IOU: 0.667279, Class: 1.000000, Obj: 0.491602, No Obj: 0.002544, Avg Recall: 0.800000, count: 75 -Region Avg IOU: 0.642007, Class: 1.000000, Obj: 0.429383, No Obj: 0.002943, Avg Recall: 0.747126, count: 87 -Region Avg IOU: 0.643173, Class: 1.000000, Obj: 0.419266, No Obj: 0.003175, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.610271, Class: 1.000000, Obj: 0.407717, No Obj: 0.003619, Avg Recall: 0.712230, count: 139 -5088: 0.418926, 0.427088 avg, 0.001000 rate, 26.925997 seconds, 1302528 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.613786, Class: 1.000000, Obj: 0.412425, No Obj: 0.003265, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.643117, Class: 1.000000, Obj: 0.430427, No Obj: 0.003283, Avg Recall: 0.807692, count: 130 -Region Avg IOU: 0.641456, Class: 1.000000, Obj: 0.441023, No Obj: 0.003084, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.656505, Class: 1.000000, Obj: 0.443709, No Obj: 0.003577, Avg Recall: 0.793651, count: 126 -Region Avg IOU: 0.670472, Class: 1.000000, Obj: 0.490712, No Obj: 0.003466, Avg Recall: 0.880734, count: 109 -Region Avg IOU: 0.623273, Class: 1.000000, Obj: 0.471364, No Obj: 0.003253, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.636330, Class: 1.000000, Obj: 0.438950, No Obj: 0.002963, Avg Recall: 0.793478, count: 92 -Region Avg IOU: 0.621803, Class: 1.000000, Obj: 0.438299, No Obj: 0.004017, Avg Recall: 0.755396, count: 139 -5089: 0.428967, 0.427276 avg, 0.001000 rate, 25.262804 seconds, 1302784 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.640553, Class: 1.000000, Obj: 0.422521, No Obj: 0.002700, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.660423, Class: 1.000000, Obj: 0.458521, No Obj: 0.003433, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.630178, Class: 1.000000, Obj: 0.419037, No Obj: 0.002881, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.616908, Class: 1.000000, Obj: 0.421342, No Obj: 0.002478, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.614733, Class: 1.000000, Obj: 0.374346, No Obj: 0.002781, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.640342, Class: 1.000000, Obj: 0.413789, No Obj: 0.003335, Avg Recall: 0.782946, count: 129 -Region Avg IOU: 0.618394, Class: 1.000000, Obj: 0.397718, No Obj: 0.003000, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.615032, Class: 1.000000, Obj: 0.391936, No Obj: 0.002748, Avg Recall: 0.736264, count: 91 -5090: 0.413886, 0.425937 avg, 0.001000 rate, 27.946392 seconds, 1303040 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000069 seconds -Region Avg IOU: 0.682922, Class: 1.000000, Obj: 0.430583, No Obj: 0.002926, Avg Recall: 0.838095, count: 105 -Region Avg IOU: 0.656104, Class: 1.000000, Obj: 0.443844, No Obj: 0.002817, Avg Recall: 0.836957, count: 92 -Region Avg IOU: 0.666009, Class: 1.000000, Obj: 0.438597, No Obj: 0.002815, Avg Recall: 0.821429, count: 112 -Region Avg IOU: 0.680740, Class: 1.000000, Obj: 0.423512, No Obj: 0.002935, Avg Recall: 0.827586, count: 87 -Region Avg IOU: 0.665531, Class: 1.000000, Obj: 0.457715, No Obj: 0.003081, Avg Recall: 0.836207, count: 116 -Region Avg IOU: 0.662728, Class: 1.000000, Obj: 0.375549, No Obj: 0.002858, Avg Recall: 0.836364, count: 110 -Region Avg IOU: 0.662580, Class: 1.000000, Obj: 0.391396, No Obj: 0.003428, Avg Recall: 0.837209, count: 129 -Region Avg IOU: 0.637047, Class: 1.000000, Obj: 0.408332, No Obj: 0.002667, Avg Recall: 0.765766, count: 111 -5091: 0.434004, 0.426744 avg, 0.001000 rate, 27.608339 seconds, 1303296 images -Loaded: 0.000122 seconds -Region Avg IOU: 0.679144, Class: 1.000000, Obj: 0.397434, No Obj: 0.002709, Avg Recall: 0.848000, count: 125 -Region Avg IOU: 0.637073, Class: 1.000000, Obj: 0.413123, No Obj: 0.002754, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.661285, Class: 1.000000, Obj: 0.456823, No Obj: 0.002614, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.631706, Class: 1.000000, Obj: 0.404528, No Obj: 0.002777, Avg Recall: 0.764228, count: 123 -Region Avg IOU: 0.688818, Class: 1.000000, Obj: 0.447414, No Obj: 0.003104, Avg Recall: 0.844828, count: 116 -Region Avg IOU: 0.692685, Class: 1.000000, Obj: 0.471332, No Obj: 0.002358, Avg Recall: 0.888889, count: 81 -Region Avg IOU: 0.669452, Class: 1.000000, Obj: 0.420004, No Obj: 0.002590, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.606488, Class: 1.000000, Obj: 0.376729, No Obj: 0.002692, Avg Recall: 0.724138, count: 116 -5092: 0.433273, 0.427396 avg, 0.001000 rate, 29.695206 seconds, 1303552 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.639486, Class: 1.000000, Obj: 0.442413, No Obj: 0.002731, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.619246, Class: 1.000000, Obj: 0.373267, No Obj: 0.002815, Avg Recall: 0.771654, count: 127 -Region Avg IOU: 0.669919, Class: 1.000000, Obj: 0.438190, No Obj: 0.002892, Avg Recall: 0.820000, count: 100 -Region Avg IOU: 0.637048, Class: 1.000000, Obj: 0.364070, No Obj: 0.002406, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.621906, Class: 1.000000, Obj: 0.385332, No Obj: 0.002582, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.635179, Class: 1.000000, Obj: 0.361013, No Obj: 0.002833, Avg Recall: 0.784000, count: 125 -Region Avg IOU: 0.662061, Class: 1.000000, Obj: 0.398053, No Obj: 0.002936, Avg Recall: 0.858491, count: 106 -Region Avg IOU: 0.664636, Class: 1.000000, Obj: 0.432482, No Obj: 0.002416, Avg Recall: 0.825581, count: 86 -5093: 0.428207, 0.427478 avg, 0.001000 rate, 29.088352 seconds, 1303808 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.653357, Class: 1.000000, Obj: 0.367110, No Obj: 0.002423, Avg Recall: 0.824561, count: 114 -Region Avg IOU: 0.643415, Class: 1.000000, Obj: 0.385837, No Obj: 0.003038, Avg Recall: 0.801527, count: 131 -Region Avg IOU: 0.669267, Class: 1.000000, Obj: 0.433777, No Obj: 0.002418, Avg Recall: 0.838710, count: 93 -Region Avg IOU: 0.658653, Class: 1.000000, Obj: 0.437058, No Obj: 0.002491, Avg Recall: 0.837209, count: 86 -Region Avg IOU: 0.737371, Class: 1.000000, Obj: 0.454822, No Obj: 0.002748, Avg Recall: 0.955556, count: 90 -Region Avg IOU: 0.643640, Class: 1.000000, Obj: 0.356723, No Obj: 0.002572, Avg Recall: 0.793103, count: 87 -Region Avg IOU: 0.627309, Class: 1.000000, Obj: 0.375536, No Obj: 0.002222, Avg Recall: 0.804124, count: 97 -Region Avg IOU: 0.657642, Class: 1.000000, Obj: 0.418680, No Obj: 0.002447, Avg Recall: 0.811111, count: 90 -5094: 0.422154, 0.426945 avg, 0.001000 rate, 29.082928 seconds, 1304064 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.632080, Class: 1.000000, Obj: 0.384618, No Obj: 0.002356, Avg Recall: 0.798387, count: 124 -Region Avg IOU: 0.643748, Class: 1.000000, Obj: 0.350319, No Obj: 0.002571, Avg Recall: 0.801527, count: 131 -Region Avg IOU: 0.655554, Class: 1.000000, Obj: 0.381727, No Obj: 0.002159, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.702513, Class: 1.000000, Obj: 0.431405, No Obj: 0.002314, Avg Recall: 0.857143, count: 84 -Region Avg IOU: 0.639625, Class: 1.000000, Obj: 0.365450, No Obj: 0.002141, Avg Recall: 0.796117, count: 103 -Region Avg IOU: 0.667885, Class: 1.000000, Obj: 0.361599, No Obj: 0.002403, Avg Recall: 0.836364, count: 110 -Region Avg IOU: 0.669541, Class: 1.000000, Obj: 0.392397, No Obj: 0.002818, Avg Recall: 0.860294, count: 136 -Region Avg IOU: 0.681664, Class: 1.000000, Obj: 0.416493, No Obj: 0.002478, Avg Recall: 0.848214, count: 112 -5095: 0.465480, 0.430799 avg, 0.001000 rate, 29.429897 seconds, 1304320 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.638929, Class: 1.000000, Obj: 0.310994, No Obj: 0.002581, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.630610, Class: 1.000000, Obj: 0.364137, No Obj: 0.002228, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.648107, Class: 1.000000, Obj: 0.386151, No Obj: 0.002374, Avg Recall: 0.762712, count: 118 -Region Avg IOU: 0.648953, Class: 1.000000, Obj: 0.353803, No Obj: 0.002377, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.662449, Class: 1.000000, Obj: 0.379544, No Obj: 0.002413, Avg Recall: 0.822034, count: 118 -Region Avg IOU: 0.650033, Class: 1.000000, Obj: 0.367323, No Obj: 0.002274, Avg Recall: 0.800000, count: 115 -Region Avg IOU: 0.670024, Class: 1.000000, Obj: 0.347368, No Obj: 0.002112, Avg Recall: 0.872340, count: 94 -Region Avg IOU: 0.677103, Class: 1.000000, Obj: 0.399710, No Obj: 0.002771, Avg Recall: 0.843750, count: 128 -5096: 0.456171, 0.433336 avg, 0.001000 rate, 28.243889 seconds, 1304576 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.625484, Class: 1.000000, Obj: 0.382138, No Obj: 0.002439, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.685147, Class: 1.000000, Obj: 0.397499, No Obj: 0.002254, Avg Recall: 0.883721, count: 86 -Region Avg IOU: 0.657932, Class: 1.000000, Obj: 0.403813, No Obj: 0.002258, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.645176, Class: 1.000000, Obj: 0.406982, No Obj: 0.002327, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.657804, Class: 1.000000, Obj: 0.340417, No Obj: 0.002333, Avg Recall: 0.834711, count: 121 -Region Avg IOU: 0.680207, Class: 1.000000, Obj: 0.380705, No Obj: 0.002302, Avg Recall: 0.880342, count: 117 -Region Avg IOU: 0.665877, Class: 1.000000, Obj: 0.378732, No Obj: 0.002311, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.647433, Class: 1.000000, Obj: 0.352860, No Obj: 0.002190, Avg Recall: 0.781250, count: 96 -5097: 0.438282, 0.433831 avg, 0.001000 rate, 30.651381 seconds, 1304832 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.616164, Class: 1.000000, Obj: 0.328418, No Obj: 0.002136, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.642776, Class: 1.000000, Obj: 0.313392, No Obj: 0.002323, Avg Recall: 0.805310, count: 113 -Region Avg IOU: 0.648450, Class: 1.000000, Obj: 0.426849, No Obj: 0.002079, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.618491, Class: 1.000000, Obj: 0.299385, No Obj: 0.002052, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.634289, Class: 1.000000, Obj: 0.340160, No Obj: 0.002365, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.678257, Class: 1.000000, Obj: 0.415474, No Obj: 0.002298, Avg Recall: 0.857143, count: 98 -Region Avg IOU: 0.678614, Class: 1.000000, Obj: 0.390607, No Obj: 0.002477, Avg Recall: 0.809091, count: 110 -Region Avg IOU: 0.676764, Class: 1.000000, Obj: 0.386420, No Obj: 0.002181, Avg Recall: 0.841584, count: 101 -5098: 0.442413, 0.434689 avg, 0.001000 rate, 29.090445 seconds, 1305088 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.645480, Class: 1.000000, Obj: 0.319766, No Obj: 0.002310, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.668691, Class: 1.000000, Obj: 0.342604, No Obj: 0.002778, Avg Recall: 0.839695, count: 131 -Region Avg IOU: 0.682553, Class: 1.000000, Obj: 0.375923, No Obj: 0.002829, Avg Recall: 0.848921, count: 139 -Region Avg IOU: 0.652517, Class: 1.000000, Obj: 0.348772, No Obj: 0.002546, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.662347, Class: 1.000000, Obj: 0.375010, No Obj: 0.002398, Avg Recall: 0.825688, count: 109 -Region Avg IOU: 0.683248, Class: 1.000000, Obj: 0.406416, No Obj: 0.002127, Avg Recall: 0.807229, count: 83 -Region Avg IOU: 0.610174, Class: 1.000000, Obj: 0.322719, No Obj: 0.002413, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.638239, Class: 1.000000, Obj: 0.418207, No Obj: 0.002083, Avg Recall: 0.726316, count: 95 -5099: 0.476198, 0.438840 avg, 0.001000 rate, 29.969725 seconds, 1305344 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.674037, Class: 1.000000, Obj: 0.400117, No Obj: 0.003037, Avg Recall: 0.857143, count: 119 -Region Avg IOU: 0.627835, Class: 1.000000, Obj: 0.336116, No Obj: 0.002670, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.638151, Class: 1.000000, Obj: 0.311158, No Obj: 0.002476, Avg Recall: 0.800000, count: 135 -Region Avg IOU: 0.631722, Class: 1.000000, Obj: 0.327424, No Obj: 0.002427, Avg Recall: 0.788889, count: 90 -Region Avg IOU: 0.590416, Class: 1.000000, Obj: 0.336320, No Obj: 0.002354, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.656392, Class: 1.000000, Obj: 0.373876, No Obj: 0.002974, Avg Recall: 0.806202, count: 129 -Region Avg IOU: 0.663148, Class: 1.000000, Obj: 0.382460, No Obj: 0.002382, Avg Recall: 0.827957, count: 93 -Region Avg IOU: 0.620859, Class: 1.000000, Obj: 0.367747, No Obj: 0.002267, Avg Recall: 0.772727, count: 110 -5100: 0.499105, 0.444866 avg, 0.001000 rate, 29.166887 seconds, 1305600 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 11.748127 seconds -Region Avg IOU: 0.546428, Class: 1.000000, Obj: 0.314545, No Obj: 0.003202, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.551243, Class: 1.000000, Obj: 0.335742, No Obj: 0.003112, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.561837, Class: 1.000000, Obj: 0.331536, No Obj: 0.003958, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.474505, Class: 1.000000, Obj: 0.273800, No Obj: 0.002944, Avg Recall: 0.504587, count: 109 -Region Avg IOU: 0.492207, Class: 1.000000, Obj: 0.306944, No Obj: 0.003144, Avg Recall: 0.538462, count: 117 -Region Avg IOU: 0.565251, Class: 1.000000, Obj: 0.297316, No Obj: 0.003314, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.564950, Class: 1.000000, Obj: 0.310340, No Obj: 0.003436, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.504601, Class: 1.000000, Obj: 0.289096, No Obj: 0.003915, Avg Recall: 0.597403, count: 154 -5101: 0.513358, 0.451715 avg, 0.001000 rate, 22.066904 seconds, 1305856 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.531910, Class: 1.000000, Obj: 0.288585, No Obj: 0.003195, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.561613, Class: 1.000000, Obj: 0.294112, No Obj: 0.003097, Avg Recall: 0.689189, count: 74 -Region Avg IOU: 0.501359, Class: 1.000000, Obj: 0.288325, No Obj: 0.003336, Avg Recall: 0.574468, count: 94 -Region Avg IOU: 0.550693, Class: 1.000000, Obj: 0.321010, No Obj: 0.003836, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.543486, Class: 1.000000, Obj: 0.329857, No Obj: 0.003740, Avg Recall: 0.606838, count: 117 -Region Avg IOU: 0.558034, Class: 1.000000, Obj: 0.302826, No Obj: 0.003897, Avg Recall: 0.661417, count: 127 -Region Avg IOU: 0.525641, Class: 1.000000, Obj: 0.316796, No Obj: 0.004146, Avg Recall: 0.579310, count: 145 -Region Avg IOU: 0.504389, Class: 1.000000, Obj: 0.340048, No Obj: 0.002820, Avg Recall: 0.534247, count: 73 -5102: 0.420888, 0.448633 avg, 0.001000 rate, 20.221369 seconds, 1306112 images -Loaded: 1.645258 seconds -Region Avg IOU: 0.499459, Class: 1.000000, Obj: 0.347419, No Obj: 0.003890, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.504746, Class: 1.000000, Obj: 0.321899, No Obj: 0.003940, Avg Recall: 0.569106, count: 123 -Region Avg IOU: 0.525285, Class: 1.000000, Obj: 0.318924, No Obj: 0.003673, Avg Recall: 0.578512, count: 121 -Region Avg IOU: 0.535128, Class: 1.000000, Obj: 0.307426, No Obj: 0.003895, Avg Recall: 0.577586, count: 116 -Region Avg IOU: 0.497602, Class: 1.000000, Obj: 0.333632, No Obj: 0.004155, Avg Recall: 0.571429, count: 91 -Region Avg IOU: 0.552839, Class: 1.000000, Obj: 0.312345, No Obj: 0.003618, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.560882, Class: 1.000000, Obj: 0.340380, No Obj: 0.004399, Avg Recall: 0.652542, count: 118 -Region Avg IOU: 0.583359, Class: 1.000000, Obj: 0.365193, No Obj: 0.004147, Avg Recall: 0.677966, count: 118 -5103: 0.487287, 0.452498 avg, 0.001000 rate, 20.637880 seconds, 1306368 images -Loaded: 0.571673 seconds -Region Avg IOU: 0.487765, Class: 1.000000, Obj: 0.342309, No Obj: 0.003830, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.493380, Class: 1.000000, Obj: 0.339142, No Obj: 0.003948, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.573368, Class: 1.000000, Obj: 0.372739, No Obj: 0.003546, Avg Recall: 0.694118, count: 85 -Region Avg IOU: 0.554847, Class: 1.000000, Obj: 0.297343, No Obj: 0.005296, Avg Recall: 0.648438, count: 128 -Region Avg IOU: 0.550052, Class: 1.000000, Obj: 0.384976, No Obj: 0.005371, Avg Recall: 0.629371, count: 143 -Region Avg IOU: 0.539676, Class: 1.000000, Obj: 0.368637, No Obj: 0.004113, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.506843, Class: 1.000000, Obj: 0.351850, No Obj: 0.004467, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.554334, Class: 1.000000, Obj: 0.339389, No Obj: 0.004068, Avg Recall: 0.658333, count: 120 -5104: 0.486468, 0.455895 avg, 0.001000 rate, 20.728491 seconds, 1306624 images -Loaded: 0.927517 seconds -Region Avg IOU: 0.554596, Class: 1.000000, Obj: 0.355492, No Obj: 0.004950, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.570152, Class: 1.000000, Obj: 0.437455, No Obj: 0.004731, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.504961, Class: 1.000000, Obj: 0.353421, No Obj: 0.004194, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.478288, Class: 1.000000, Obj: 0.358617, No Obj: 0.003910, Avg Recall: 0.561798, count: 89 -Region Avg IOU: 0.515094, Class: 1.000000, Obj: 0.367209, No Obj: 0.003676, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.535139, Class: 1.000000, Obj: 0.336290, No Obj: 0.003950, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.554420, Class: 1.000000, Obj: 0.377525, No Obj: 0.004306, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.520882, Class: 1.000000, Obj: 0.357787, No Obj: 0.004669, Avg Recall: 0.591304, count: 115 -5105: 0.445316, 0.454837 avg, 0.001000 rate, 21.231421 seconds, 1306880 images -Loaded: 0.523900 seconds -Region Avg IOU: 0.504159, Class: 1.000000, Obj: 0.371043, No Obj: 0.004198, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.520267, Class: 1.000000, Obj: 0.432565, No Obj: 0.004519, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.555380, Class: 1.000000, Obj: 0.447802, No Obj: 0.005173, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.525244, Class: 1.000000, Obj: 0.385838, No Obj: 0.004627, Avg Recall: 0.613636, count: 88 -Region Avg IOU: 0.513240, Class: 1.000000, Obj: 0.410415, No Obj: 0.003739, Avg Recall: 0.588235, count: 102 -Region Avg IOU: 0.514656, Class: 1.000000, Obj: 0.364574, No Obj: 0.004828, Avg Recall: 0.553719, count: 121 -Region Avg IOU: 0.517879, Class: 1.000000, Obj: 0.411055, No Obj: 0.004698, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.505830, Class: 1.000000, Obj: 0.353904, No Obj: 0.004914, Avg Recall: 0.549180, count: 122 -5106: 0.407482, 0.450102 avg, 0.001000 rate, 21.978796 seconds, 1307136 images -Loaded: 0.082005 seconds -Region Avg IOU: 0.513105, Class: 1.000000, Obj: 0.392779, No Obj: 0.004982, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.556014, Class: 1.000000, Obj: 0.414339, No Obj: 0.005069, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.517549, Class: 1.000000, Obj: 0.394609, No Obj: 0.004456, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.537646, Class: 1.000000, Obj: 0.415254, No Obj: 0.004406, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.523678, Class: 1.000000, Obj: 0.453119, No Obj: 0.004817, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.446785, Class: 1.000000, Obj: 0.333195, No Obj: 0.003863, Avg Recall: 0.483051, count: 118 -Region Avg IOU: 0.535295, Class: 1.000000, Obj: 0.427948, No Obj: 0.004755, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.501123, Class: 1.000000, Obj: 0.399666, No Obj: 0.005180, Avg Recall: 0.547619, count: 126 -5107: 0.446956, 0.449787 avg, 0.001000 rate, 20.929016 seconds, 1307392 images -Loaded: 0.554196 seconds -Region Avg IOU: 0.518064, Class: 1.000000, Obj: 0.417617, No Obj: 0.004033, Avg Recall: 0.565217, count: 92 -Region Avg IOU: 0.501337, Class: 1.000000, Obj: 0.401530, No Obj: 0.005362, Avg Recall: 0.570423, count: 142 -Region Avg IOU: 0.519377, Class: 1.000000, Obj: 0.392333, No Obj: 0.004555, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.488571, Class: 1.000000, Obj: 0.356763, No Obj: 0.004800, Avg Recall: 0.495327, count: 107 -Region Avg IOU: 0.547819, Class: 1.000000, Obj: 0.429536, No Obj: 0.005198, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.571583, Class: 1.000000, Obj: 0.450844, No Obj: 0.004156, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.547061, Class: 1.000000, Obj: 0.420457, No Obj: 0.004860, Avg Recall: 0.608333, count: 120 -Region Avg IOU: 0.477973, Class: 1.000000, Obj: 0.403197, No Obj: 0.004992, Avg Recall: 0.491379, count: 116 -5108: 0.419886, 0.446797 avg, 0.001000 rate, 19.904980 seconds, 1307648 images -Loaded: 1.729302 seconds -Region Avg IOU: 0.528984, Class: 1.000000, Obj: 0.414128, No Obj: 0.005424, Avg Recall: 0.586207, count: 116 -Region Avg IOU: 0.466407, Class: 1.000000, Obj: 0.378969, No Obj: 0.004477, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.552891, Class: 1.000000, Obj: 0.402186, No Obj: 0.005200, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.557714, Class: 1.000000, Obj: 0.432802, No Obj: 0.004540, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.508109, Class: 1.000000, Obj: 0.413727, No Obj: 0.004825, Avg Recall: 0.503876, count: 129 -Region Avg IOU: 0.491067, Class: 1.000000, Obj: 0.373930, No Obj: 0.004565, Avg Recall: 0.531746, count: 126 -Region Avg IOU: 0.532318, Class: 1.000000, Obj: 0.388012, No Obj: 0.005081, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.542043, Class: 1.000000, Obj: 0.416479, No Obj: 0.005828, Avg Recall: 0.609375, count: 128 -5109: 0.522111, 0.454328 avg, 0.001000 rate, 21.746471 seconds, 1307904 images -Loaded: 0.163685 seconds -Region Avg IOU: 0.546234, Class: 1.000000, Obj: 0.402397, No Obj: 0.004381, Avg Recall: 0.647727, count: 88 -Region Avg IOU: 0.524759, Class: 1.000000, Obj: 0.398284, No Obj: 0.005333, Avg Recall: 0.570312, count: 128 -Region Avg IOU: 0.484055, Class: 1.000000, Obj: 0.389952, No Obj: 0.004215, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.560756, Class: 1.000000, Obj: 0.438642, No Obj: 0.004888, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.473182, Class: 1.000000, Obj: 0.358626, No Obj: 0.004851, Avg Recall: 0.520661, count: 121 -Region Avg IOU: 0.502260, Class: 1.000000, Obj: 0.416291, No Obj: 0.004427, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.501900, Class: 1.000000, Obj: 0.383454, No Obj: 0.004736, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.532898, Class: 1.000000, Obj: 0.429997, No Obj: 0.005082, Avg Recall: 0.614035, count: 114 -5110: 0.449539, 0.453849 avg, 0.001000 rate, 22.696903 seconds, 1308160 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000079 seconds -Region Avg IOU: 0.591444, Class: 1.000000, Obj: 0.478061, No Obj: 0.004437, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.620713, Class: 1.000000, Obj: 0.506203, No Obj: 0.003736, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.610971, Class: 1.000000, Obj: 0.472687, No Obj: 0.004675, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.543560, Class: 1.000000, Obj: 0.441663, No Obj: 0.003652, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.633249, Class: 1.000000, Obj: 0.447108, No Obj: 0.003936, Avg Recall: 0.785714, count: 70 -Region Avg IOU: 0.580509, Class: 1.000000, Obj: 0.423666, No Obj: 0.004053, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.595929, Class: 1.000000, Obj: 0.510570, No Obj: 0.003811, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.580140, Class: 1.000000, Obj: 0.491495, No Obj: 0.004851, Avg Recall: 0.640288, count: 139 -5111: 0.415014, 0.449966 avg, 0.001000 rate, 25.216892 seconds, 1308416 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.590540, Class: 1.000000, Obj: 0.443939, No Obj: 0.003976, Avg Recall: 0.715328, count: 137 -Region Avg IOU: 0.614595, Class: 1.000000, Obj: 0.453487, No Obj: 0.004014, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.555509, Class: 1.000000, Obj: 0.440970, No Obj: 0.003625, Avg Recall: 0.650794, count: 126 -Region Avg IOU: 0.538458, Class: 1.000000, Obj: 0.378575, No Obj: 0.003376, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.584299, Class: 1.000000, Obj: 0.408575, No Obj: 0.003473, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.605294, Class: 1.000000, Obj: 0.457963, No Obj: 0.003915, Avg Recall: 0.730159, count: 126 -Region Avg IOU: 0.575411, Class: 1.000000, Obj: 0.438363, No Obj: 0.003713, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.632499, Class: 1.000000, Obj: 0.458337, No Obj: 0.004232, Avg Recall: 0.766667, count: 120 -5112: 0.467275, 0.451697 avg, 0.001000 rate, 25.085072 seconds, 1308672 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.594083, Class: 1.000000, Obj: 0.401132, No Obj: 0.003280, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.596404, Class: 1.000000, Obj: 0.378913, No Obj: 0.003380, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.588884, Class: 1.000000, Obj: 0.444491, No Obj: 0.003364, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.612718, Class: 1.000000, Obj: 0.386405, No Obj: 0.003031, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.615377, Class: 1.000000, Obj: 0.448352, No Obj: 0.003425, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.589276, Class: 1.000000, Obj: 0.434884, No Obj: 0.003196, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.559356, Class: 1.000000, Obj: 0.375044, No Obj: 0.002772, Avg Recall: 0.666667, count: 87 -Region Avg IOU: 0.557912, Class: 1.000000, Obj: 0.407925, No Obj: 0.002892, Avg Recall: 0.666667, count: 87 -5113: 0.378101, 0.444337 avg, 0.001000 rate, 26.109491 seconds, 1308928 images -Loaded: 0.000119 seconds -Region Avg IOU: 0.537515, Class: 1.000000, Obj: 0.424166, No Obj: 0.002404, Avg Recall: 0.647727, count: 88 -Region Avg IOU: 0.632086, Class: 1.000000, Obj: 0.397375, No Obj: 0.003263, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.604523, Class: 1.000000, Obj: 0.376671, No Obj: 0.003384, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.629612, Class: 1.000000, Obj: 0.403425, No Obj: 0.002961, Avg Recall: 0.814433, count: 97 -Region Avg IOU: 0.614176, Class: 1.000000, Obj: 0.406496, No Obj: 0.003090, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.626569, Class: 1.000000, Obj: 0.402867, No Obj: 0.003352, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.636714, Class: 1.000000, Obj: 0.371377, No Obj: 0.002908, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.595049, Class: 1.000000, Obj: 0.384308, No Obj: 0.003213, Avg Recall: 0.719298, count: 114 -5114: 0.458221, 0.445726 avg, 0.001000 rate, 25.296732 seconds, 1309184 images -Loaded: 0.000050 seconds -Region Avg IOU: 0.592618, Class: 1.000000, Obj: 0.339714, No Obj: 0.003117, Avg Recall: 0.677686, count: 121 -Region Avg IOU: 0.651601, Class: 1.000000, Obj: 0.430764, No Obj: 0.002910, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.604643, Class: 1.000000, Obj: 0.355665, No Obj: 0.002978, Avg Recall: 0.685484, count: 124 -Region Avg IOU: 0.610830, Class: 1.000000, Obj: 0.349294, No Obj: 0.003085, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.657515, Class: 1.000000, Obj: 0.370868, No Obj: 0.003229, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.608096, Class: 1.000000, Obj: 0.357232, No Obj: 0.002501, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.555410, Class: 1.000000, Obj: 0.353221, No Obj: 0.003458, Avg Recall: 0.651316, count: 152 -Region Avg IOU: 0.578247, Class: 1.000000, Obj: 0.350316, No Obj: 0.002833, Avg Recall: 0.710000, count: 100 -5115: 0.444780, 0.445631 avg, 0.001000 rate, 24.258389 seconds, 1309440 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.587525, Class: 1.000000, Obj: 0.361900, No Obj: 0.002332, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.584049, Class: 1.000000, Obj: 0.348863, No Obj: 0.002965, Avg Recall: 0.671756, count: 131 -Region Avg IOU: 0.589658, Class: 1.000000, Obj: 0.339828, No Obj: 0.002441, Avg Recall: 0.681818, count: 88 -Region Avg IOU: 0.591801, Class: 1.000000, Obj: 0.361595, No Obj: 0.002447, Avg Recall: 0.728395, count: 81 -Region Avg IOU: 0.643807, Class: 1.000000, Obj: 0.356127, No Obj: 0.002873, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.602467, Class: 1.000000, Obj: 0.335754, No Obj: 0.002779, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.632143, Class: 1.000000, Obj: 0.375080, No Obj: 0.003008, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.589480, Class: 1.000000, Obj: 0.336496, No Obj: 0.002313, Avg Recall: 0.698113, count: 106 -5116: 0.399879, 0.441056 avg, 0.001000 rate, 26.317116 seconds, 1309696 images -Loaded: 0.000038 seconds -Region Avg IOU: 0.628096, Class: 1.000000, Obj: 0.333420, No Obj: 0.002609, Avg Recall: 0.738462, count: 130 -Region Avg IOU: 0.589639, Class: 1.000000, Obj: 0.298353, No Obj: 0.003159, Avg Recall: 0.692308, count: 143 -Region Avg IOU: 0.607785, Class: 1.000000, Obj: 0.333813, No Obj: 0.002545, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.643791, Class: 1.000000, Obj: 0.359626, No Obj: 0.002970, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.623652, Class: 1.000000, Obj: 0.349781, No Obj: 0.002626, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.594097, Class: 1.000000, Obj: 0.368991, No Obj: 0.002516, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.586836, Class: 1.000000, Obj: 0.325801, No Obj: 0.002725, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.615242, Class: 1.000000, Obj: 0.335326, No Obj: 0.002642, Avg Recall: 0.695238, count: 105 -5117: 0.469461, 0.443896 avg, 0.001000 rate, 25.740467 seconds, 1309952 images -Loaded: 0.000049 seconds -Region Avg IOU: 0.625089, Class: 1.000000, Obj: 0.351627, No Obj: 0.002761, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.615630, Class: 1.000000, Obj: 0.305006, No Obj: 0.002289, Avg Recall: 0.741176, count: 85 -Region Avg IOU: 0.603484, Class: 1.000000, Obj: 0.367471, No Obj: 0.002683, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.655426, Class: 1.000000, Obj: 0.423953, No Obj: 0.002680, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.621019, Class: 1.000000, Obj: 0.353482, No Obj: 0.003808, Avg Recall: 0.744828, count: 145 -Region Avg IOU: 0.594888, Class: 1.000000, Obj: 0.361485, No Obj: 0.002874, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.601206, Class: 1.000000, Obj: 0.399877, No Obj: 0.003065, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.664246, Class: 1.000000, Obj: 0.367062, No Obj: 0.002883, Avg Recall: 0.792683, count: 82 -5118: 0.449161, 0.444423 avg, 0.001000 rate, 25.157431 seconds, 1310208 images -Loaded: 0.000053 seconds -Region Avg IOU: 0.606769, Class: 1.000000, Obj: 0.413677, No Obj: 0.002656, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.637638, Class: 1.000000, Obj: 0.443179, No Obj: 0.002812, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.598759, Class: 1.000000, Obj: 0.376663, No Obj: 0.002326, Avg Recall: 0.700000, count: 80 -Region Avg IOU: 0.615072, Class: 1.000000, Obj: 0.364594, No Obj: 0.002684, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.636336, Class: 1.000000, Obj: 0.402035, No Obj: 0.002694, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.604737, Class: 1.000000, Obj: 0.387998, No Obj: 0.002874, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.604831, Class: 1.000000, Obj: 0.348191, No Obj: 0.003094, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.610857, Class: 1.000000, Obj: 0.382411, No Obj: 0.003299, Avg Recall: 0.767241, count: 116 -5119: 0.413967, 0.441377 avg, 0.001000 rate, 25.636040 seconds, 1310464 images -Loaded: 0.000054 seconds -Region Avg IOU: 0.603226, Class: 1.000000, Obj: 0.361397, No Obj: 0.003173, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.628515, Class: 1.000000, Obj: 0.401968, No Obj: 0.003286, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.641996, Class: 1.000000, Obj: 0.399967, No Obj: 0.003195, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.570282, Class: 1.000000, Obj: 0.338926, No Obj: 0.002532, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.597048, Class: 1.000000, Obj: 0.368191, No Obj: 0.003919, Avg Recall: 0.710145, count: 138 -Region Avg IOU: 0.643360, Class: 1.000000, Obj: 0.391472, No Obj: 0.003446, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.597292, Class: 1.000000, Obj: 0.362314, No Obj: 0.003111, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.623427, Class: 1.000000, Obj: 0.415006, No Obj: 0.003661, Avg Recall: 0.780488, count: 123 -5120: 0.455177, 0.442757 avg, 0.001000 rate, 25.591648 seconds, 1310720 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000070 seconds -Region Avg IOU: 0.695150, Class: 1.000000, Obj: 0.427200, No Obj: 0.002730, Avg Recall: 0.905263, count: 95 -Region Avg IOU: 0.668021, Class: 1.000000, Obj: 0.439798, No Obj: 0.002819, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.669804, Class: 1.000000, Obj: 0.465371, No Obj: 0.002993, Avg Recall: 0.828829, count: 111 -Region Avg IOU: 0.658499, Class: 1.000000, Obj: 0.443558, No Obj: 0.002778, Avg Recall: 0.845361, count: 97 -Region Avg IOU: 0.622985, Class: 1.000000, Obj: 0.412928, No Obj: 0.002968, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.645984, Class: 1.000000, Obj: 0.473251, No Obj: 0.002831, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.692711, Class: 1.000000, Obj: 0.455753, No Obj: 0.003120, Avg Recall: 0.845455, count: 110 -Region Avg IOU: 0.653945, Class: 1.000000, Obj: 0.450497, No Obj: 0.002791, Avg Recall: 0.796117, count: 103 -5121: 0.400334, 0.438515 avg, 0.001000 rate, 29.998602 seconds, 1310976 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.638249, Class: 1.000000, Obj: 0.426255, No Obj: 0.003095, Avg Recall: 0.798387, count: 124 -Region Avg IOU: 0.666470, Class: 1.000000, Obj: 0.460810, No Obj: 0.003056, Avg Recall: 0.830508, count: 118 -Region Avg IOU: 0.640165, Class: 1.000000, Obj: 0.395457, No Obj: 0.003040, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.660835, Class: 1.000000, Obj: 0.424723, No Obj: 0.003255, Avg Recall: 0.846774, count: 124 -Region Avg IOU: 0.666737, Class: 1.000000, Obj: 0.460887, No Obj: 0.002866, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.641589, Class: 1.000000, Obj: 0.458970, No Obj: 0.003004, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.670967, Class: 1.000000, Obj: 0.488675, No Obj: 0.003260, Avg Recall: 0.806452, count: 124 -Region Avg IOU: 0.595915, Class: 1.000000, Obj: 0.387076, No Obj: 0.002660, Avg Recall: 0.740000, count: 100 -5122: 0.467888, 0.441452 avg, 0.001000 rate, 29.998440 seconds, 1311232 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.643045, Class: 1.000000, Obj: 0.390960, No Obj: 0.002976, Avg Recall: 0.806452, count: 124 -Region Avg IOU: 0.676398, Class: 1.000000, Obj: 0.416776, No Obj: 0.002975, Avg Recall: 0.835052, count: 97 -Region Avg IOU: 0.671349, Class: 1.000000, Obj: 0.374928, No Obj: 0.003097, Avg Recall: 0.863636, count: 110 -Region Avg IOU: 0.658718, Class: 1.000000, Obj: 0.455566, No Obj: 0.002925, Avg Recall: 0.778761, count: 113 -Region Avg IOU: 0.651591, Class: 1.000000, Obj: 0.419363, No Obj: 0.002646, Avg Recall: 0.835443, count: 79 -Region Avg IOU: 0.666124, Class: 1.000000, Obj: 0.448816, No Obj: 0.002417, Avg Recall: 0.813953, count: 86 -Region Avg IOU: 0.651003, Class: 1.000000, Obj: 0.420588, No Obj: 0.002995, Avg Recall: 0.813008, count: 123 -Region Avg IOU: 0.704359, Class: 1.000000, Obj: 0.432650, No Obj: 0.002897, Avg Recall: 0.915663, count: 83 -5123: 0.424940, 0.439801 avg, 0.001000 rate, 29.302303 seconds, 1311488 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.670322, Class: 1.000000, Obj: 0.383968, No Obj: 0.002956, Avg Recall: 0.832000, count: 125 -Region Avg IOU: 0.665574, Class: 1.000000, Obj: 0.458453, No Obj: 0.002554, Avg Recall: 0.827957, count: 93 -Region Avg IOU: 0.684402, Class: 1.000000, Obj: 0.405508, No Obj: 0.002649, Avg Recall: 0.880000, count: 100 -Region Avg IOU: 0.644080, Class: 1.000000, Obj: 0.425478, No Obj: 0.002470, Avg Recall: 0.820225, count: 89 -Region Avg IOU: 0.643401, Class: 1.000000, Obj: 0.416236, No Obj: 0.002255, Avg Recall: 0.790698, count: 86 -Region Avg IOU: 0.683711, Class: 1.000000, Obj: 0.382020, No Obj: 0.003083, Avg Recall: 0.870968, count: 93 -Region Avg IOU: 0.628863, Class: 1.000000, Obj: 0.364862, No Obj: 0.002613, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.657576, Class: 1.000000, Obj: 0.468082, No Obj: 0.003016, Avg Recall: 0.794393, count: 107 -5124: 0.432987, 0.439120 avg, 0.001000 rate, 29.433664 seconds, 1311744 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.614667, Class: 1.000000, Obj: 0.388994, No Obj: 0.002372, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.699853, Class: 1.000000, Obj: 0.429149, No Obj: 0.002566, Avg Recall: 0.922222, count: 90 -Region Avg IOU: 0.655650, Class: 1.000000, Obj: 0.400972, No Obj: 0.002828, Avg Recall: 0.819048, count: 105 -Region Avg IOU: 0.670387, Class: 1.000000, Obj: 0.420493, No Obj: 0.002392, Avg Recall: 0.877778, count: 90 -Region Avg IOU: 0.652766, Class: 1.000000, Obj: 0.388954, No Obj: 0.002835, Avg Recall: 0.788136, count: 118 -Region Avg IOU: 0.634971, Class: 1.000000, Obj: 0.389139, No Obj: 0.002593, Avg Recall: 0.777778, count: 90 -Region Avg IOU: 0.681937, Class: 1.000000, Obj: 0.453484, No Obj: 0.002728, Avg Recall: 0.813187, count: 91 -Region Avg IOU: 0.630484, Class: 1.000000, Obj: 0.372706, No Obj: 0.002746, Avg Recall: 0.752137, count: 117 -5125: 0.410338, 0.436241 avg, 0.001000 rate, 30.012768 seconds, 1312000 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.647889, Class: 1.000000, Obj: 0.382802, No Obj: 0.002637, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.663291, Class: 1.000000, Obj: 0.383682, No Obj: 0.002886, Avg Recall: 0.848921, count: 139 -Region Avg IOU: 0.616604, Class: 1.000000, Obj: 0.401889, No Obj: 0.002777, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.632237, Class: 1.000000, Obj: 0.409107, No Obj: 0.002046, Avg Recall: 0.747126, count: 87 -Region Avg IOU: 0.646553, Class: 1.000000, Obj: 0.395219, No Obj: 0.002490, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.664447, Class: 1.000000, Obj: 0.428151, No Obj: 0.002152, Avg Recall: 0.804124, count: 97 -Region Avg IOU: 0.680474, Class: 1.000000, Obj: 0.418266, No Obj: 0.002191, Avg Recall: 0.872340, count: 94 -Region Avg IOU: 0.621623, Class: 1.000000, Obj: 0.475301, No Obj: 0.002111, Avg Recall: 0.805556, count: 72 -5126: 0.408764, 0.433494 avg, 0.001000 rate, 28.609110 seconds, 1312256 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.656913, Class: 1.000000, Obj: 0.361283, No Obj: 0.002150, Avg Recall: 0.842697, count: 89 -Region Avg IOU: 0.659713, Class: 1.000000, Obj: 0.396869, No Obj: 0.002106, Avg Recall: 0.843137, count: 102 -Region Avg IOU: 0.640422, Class: 1.000000, Obj: 0.326207, No Obj: 0.002588, Avg Recall: 0.806202, count: 129 -Region Avg IOU: 0.677427, Class: 1.000000, Obj: 0.379395, No Obj: 0.002495, Avg Recall: 0.849558, count: 113 -Region Avg IOU: 0.681489, Class: 1.000000, Obj: 0.358763, No Obj: 0.002196, Avg Recall: 0.869159, count: 107 -Region Avg IOU: 0.645309, Class: 1.000000, Obj: 0.340131, No Obj: 0.002383, Avg Recall: 0.791667, count: 120 -Region Avg IOU: 0.677249, Class: 1.000000, Obj: 0.422618, No Obj: 0.002433, Avg Recall: 0.875000, count: 104 -Region Avg IOU: 0.663367, Class: 1.000000, Obj: 0.390383, No Obj: 0.002455, Avg Recall: 0.834783, count: 115 -5127: 0.479947, 0.438139 avg, 0.001000 rate, 30.164846 seconds, 1312512 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.654869, Class: 1.000000, Obj: 0.356126, No Obj: 0.002211, Avg Recall: 0.833333, count: 114 -Region Avg IOU: 0.657612, Class: 1.000000, Obj: 0.315198, No Obj: 0.002276, Avg Recall: 0.830000, count: 100 -Region Avg IOU: 0.649421, Class: 1.000000, Obj: 0.354729, No Obj: 0.002087, Avg Recall: 0.793478, count: 92 -Region Avg IOU: 0.663785, Class: 1.000000, Obj: 0.376406, No Obj: 0.002557, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.637731, Class: 1.000000, Obj: 0.323202, No Obj: 0.002414, Avg Recall: 0.794521, count: 146 -Region Avg IOU: 0.667588, Class: 1.000000, Obj: 0.364389, No Obj: 0.002254, Avg Recall: 0.843478, count: 115 -Region Avg IOU: 0.663968, Class: 1.000000, Obj: 0.376524, No Obj: 0.002087, Avg Recall: 0.843750, count: 96 -Region Avg IOU: 0.649730, Class: 1.000000, Obj: 0.365957, No Obj: 0.002136, Avg Recall: 0.848485, count: 99 -5128: 0.465982, 0.440923 avg, 0.001000 rate, 28.532972 seconds, 1312768 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.644277, Class: 1.000000, Obj: 0.362411, No Obj: 0.002375, Avg Recall: 0.785714, count: 126 -Region Avg IOU: 0.630356, Class: 1.000000, Obj: 0.418117, No Obj: 0.002039, Avg Recall: 0.762500, count: 80 -Region Avg IOU: 0.630095, Class: 1.000000, Obj: 0.326825, No Obj: 0.002239, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.630484, Class: 1.000000, Obj: 0.380881, No Obj: 0.002344, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.678005, Class: 1.000000, Obj: 0.377191, No Obj: 0.002312, Avg Recall: 0.815217, count: 92 -Region Avg IOU: 0.653817, Class: 1.000000, Obj: 0.388911, No Obj: 0.002228, Avg Recall: 0.781250, count: 96 -Region Avg IOU: 0.642737, Class: 1.000000, Obj: 0.378365, No Obj: 0.002428, Avg Recall: 0.850394, count: 127 -Region Avg IOU: 0.619071, Class: 1.000000, Obj: 0.324020, No Obj: 0.002037, Avg Recall: 0.750000, count: 104 -5129: 0.426373, 0.439468 avg, 0.001000 rate, 30.046667 seconds, 1313024 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.650822, Class: 1.000000, Obj: 0.375250, No Obj: 0.002436, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.644643, Class: 1.000000, Obj: 0.399520, No Obj: 0.002231, Avg Recall: 0.804124, count: 97 -Region Avg IOU: 0.661815, Class: 1.000000, Obj: 0.346799, No Obj: 0.002244, Avg Recall: 0.843750, count: 96 -Region Avg IOU: 0.683309, Class: 1.000000, Obj: 0.325783, No Obj: 0.002369, Avg Recall: 0.831776, count: 107 -Region Avg IOU: 0.604480, Class: 1.000000, Obj: 0.369869, No Obj: 0.002191, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.630958, Class: 1.000000, Obj: 0.402214, No Obj: 0.002481, Avg Recall: 0.782609, count: 115 -Region Avg IOU: 0.674671, Class: 1.000000, Obj: 0.352463, No Obj: 0.002649, Avg Recall: 0.873874, count: 111 -Region Avg IOU: 0.655804, Class: 1.000000, Obj: 0.417010, No Obj: 0.002257, Avg Recall: 0.802326, count: 86 -5130: 0.427052, 0.438227 avg, 0.001000 rate, 30.282103 seconds, 1313280 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 15.458218 seconds -Region Avg IOU: 0.590531, Class: 1.000000, Obj: 0.349786, No Obj: 0.003504, Avg Recall: 0.681481, count: 135 -Region Avg IOU: 0.569160, Class: 1.000000, Obj: 0.379325, No Obj: 0.002748, Avg Recall: 0.602273, count: 88 -Region Avg IOU: 0.558197, Class: 1.000000, Obj: 0.331045, No Obj: 0.002729, Avg Recall: 0.653226, count: 124 -Region Avg IOU: 0.570441, Class: 1.000000, Obj: 0.329164, No Obj: 0.003017, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.582159, Class: 1.000000, Obj: 0.334911, No Obj: 0.002838, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.589556, Class: 1.000000, Obj: 0.317433, No Obj: 0.003032, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.572014, Class: 1.000000, Obj: 0.333657, No Obj: 0.002536, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.519664, Class: 1.000000, Obj: 0.294969, No Obj: 0.002593, Avg Recall: 0.553571, count: 112 -5131: 0.434802, 0.437884 avg, 0.001000 rate, 25.110111 seconds, 1313536 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.633677, Class: 1.000000, Obj: 0.350526, No Obj: 0.003467, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.577452, Class: 1.000000, Obj: 0.373978, No Obj: 0.003022, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.603266, Class: 1.000000, Obj: 0.375659, No Obj: 0.003315, Avg Recall: 0.674419, count: 129 -Region Avg IOU: 0.550653, Class: 1.000000, Obj: 0.341756, No Obj: 0.002818, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.545971, Class: 1.000000, Obj: 0.331798, No Obj: 0.003236, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.500847, Class: 1.000000, Obj: 0.299031, No Obj: 0.002918, Avg Recall: 0.546875, count: 128 -Region Avg IOU: 0.606832, Class: 1.000000, Obj: 0.379309, No Obj: 0.003015, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.586591, Class: 1.000000, Obj: 0.322300, No Obj: 0.002934, Avg Recall: 0.686275, count: 102 -5132: 0.441375, 0.438233 avg, 0.001000 rate, 23.720291 seconds, 1313792 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.564163, Class: 1.000000, Obj: 0.343330, No Obj: 0.003355, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.531813, Class: 1.000000, Obj: 0.343231, No Obj: 0.003106, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.594127, Class: 1.000000, Obj: 0.366026, No Obj: 0.003458, Avg Recall: 0.683761, count: 117 -Region Avg IOU: 0.592531, Class: 1.000000, Obj: 0.390757, No Obj: 0.003165, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.593961, Class: 1.000000, Obj: 0.363822, No Obj: 0.003688, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.564413, Class: 1.000000, Obj: 0.359639, No Obj: 0.003017, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.555835, Class: 1.000000, Obj: 0.336004, No Obj: 0.003110, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.604397, Class: 1.000000, Obj: 0.362571, No Obj: 0.002905, Avg Recall: 0.693069, count: 101 -5133: 0.427914, 0.437201 avg, 0.001000 rate, 24.616154 seconds, 1314048 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.651984, Class: 1.000000, Obj: 0.432644, No Obj: 0.003555, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.594121, Class: 1.000000, Obj: 0.388435, No Obj: 0.002979, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.565022, Class: 1.000000, Obj: 0.369634, No Obj: 0.003278, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.620262, Class: 1.000000, Obj: 0.403549, No Obj: 0.003644, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.549558, Class: 1.000000, Obj: 0.382819, No Obj: 0.002631, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.639362, Class: 1.000000, Obj: 0.396288, No Obj: 0.003972, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.594730, Class: 1.000000, Obj: 0.352751, No Obj: 0.003369, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.561130, Class: 1.000000, Obj: 0.404926, No Obj: 0.003137, Avg Recall: 0.692308, count: 91 -5134: 0.431517, 0.436633 avg, 0.001000 rate, 23.139736 seconds, 1314304 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.613096, Class: 1.000000, Obj: 0.436250, No Obj: 0.003744, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.564957, Class: 1.000000, Obj: 0.395819, No Obj: 0.003525, Avg Recall: 0.652542, count: 118 -Region Avg IOU: 0.588075, Class: 1.000000, Obj: 0.421274, No Obj: 0.003157, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.633922, Class: 1.000000, Obj: 0.395020, No Obj: 0.004220, Avg Recall: 0.781250, count: 128 -Region Avg IOU: 0.612160, Class: 1.000000, Obj: 0.439708, No Obj: 0.004174, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.570317, Class: 1.000000, Obj: 0.378782, No Obj: 0.002811, Avg Recall: 0.658824, count: 85 -Region Avg IOU: 0.587100, Class: 1.000000, Obj: 0.384354, No Obj: 0.003398, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.561696, Class: 1.000000, Obj: 0.408265, No Obj: 0.002942, Avg Recall: 0.621053, count: 95 -5135: 0.411130, 0.434083 avg, 0.001000 rate, 24.615627 seconds, 1314560 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.568594, Class: 1.000000, Obj: 0.354225, No Obj: 0.003798, Avg Recall: 0.698276, count: 116 -Region Avg IOU: 0.583802, Class: 1.000000, Obj: 0.422064, No Obj: 0.003767, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.628752, Class: 1.000000, Obj: 0.420920, No Obj: 0.004273, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.622985, Class: 1.000000, Obj: 0.430771, No Obj: 0.003517, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.561084, Class: 1.000000, Obj: 0.376401, No Obj: 0.003799, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.602368, Class: 1.000000, Obj: 0.421651, No Obj: 0.004249, Avg Recall: 0.703704, count: 135 -Region Avg IOU: 0.640340, Class: 1.000000, Obj: 0.430746, No Obj: 0.003792, Avg Recall: 0.858696, count: 92 -Region Avg IOU: 0.558811, Class: 1.000000, Obj: 0.380589, No Obj: 0.004112, Avg Recall: 0.636364, count: 132 -5136: 0.484720, 0.439146 avg, 0.001000 rate, 24.421152 seconds, 1314816 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.587267, Class: 1.000000, Obj: 0.442258, No Obj: 0.004575, Avg Recall: 0.683761, count: 117 -Region Avg IOU: 0.590408, Class: 1.000000, Obj: 0.418739, No Obj: 0.003808, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.537840, Class: 1.000000, Obj: 0.417506, No Obj: 0.003801, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.611003, Class: 1.000000, Obj: 0.492584, No Obj: 0.003936, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.546002, Class: 1.000000, Obj: 0.380224, No Obj: 0.003499, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.611477, Class: 1.000000, Obj: 0.472745, No Obj: 0.003270, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.642174, Class: 1.000000, Obj: 0.497907, No Obj: 0.004484, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.604001, Class: 1.000000, Obj: 0.452579, No Obj: 0.003876, Avg Recall: 0.734694, count: 98 -5137: 0.425794, 0.437811 avg, 0.001000 rate, 23.262629 seconds, 1315072 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.578159, Class: 1.000000, Obj: 0.457049, No Obj: 0.004047, Avg Recall: 0.680672, count: 119 -Region Avg IOU: 0.635609, Class: 1.000000, Obj: 0.494472, No Obj: 0.003718, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.602724, Class: 1.000000, Obj: 0.485581, No Obj: 0.003171, Avg Recall: 0.698795, count: 83 -Region Avg IOU: 0.603783, Class: 1.000000, Obj: 0.420561, No Obj: 0.003645, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.594672, Class: 1.000000, Obj: 0.442499, No Obj: 0.003882, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.572068, Class: 1.000000, Obj: 0.414703, No Obj: 0.003997, Avg Recall: 0.653543, count: 127 -Region Avg IOU: 0.571423, Class: 1.000000, Obj: 0.416470, No Obj: 0.003980, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.575841, Class: 1.000000, Obj: 0.425297, No Obj: 0.003590, Avg Recall: 0.636364, count: 99 -5138: 0.397896, 0.433820 avg, 0.001000 rate, 24.174250 seconds, 1315328 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.579216, Class: 1.000000, Obj: 0.389353, No Obj: 0.004325, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.571512, Class: 1.000000, Obj: 0.425792, No Obj: 0.003636, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.613632, Class: 1.000000, Obj: 0.442776, No Obj: 0.004267, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.616865, Class: 1.000000, Obj: 0.406960, No Obj: 0.004415, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.555089, Class: 1.000000, Obj: 0.398593, No Obj: 0.003845, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.583787, Class: 1.000000, Obj: 0.423878, No Obj: 0.003826, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.604119, Class: 1.000000, Obj: 0.420964, No Obj: 0.003625, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.605546, Class: 1.000000, Obj: 0.405167, No Obj: 0.003850, Avg Recall: 0.725490, count: 102 -5139: 0.490107, 0.439448 avg, 0.001000 rate, 23.716396 seconds, 1315584 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.629187, Class: 1.000000, Obj: 0.451358, No Obj: 0.004278, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.607637, Class: 1.000000, Obj: 0.493245, No Obj: 0.004087, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.561767, Class: 1.000000, Obj: 0.431821, No Obj: 0.003540, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.588866, Class: 1.000000, Obj: 0.466854, No Obj: 0.003876, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.587857, Class: 1.000000, Obj: 0.387394, No Obj: 0.003579, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.600224, Class: 1.000000, Obj: 0.403608, No Obj: 0.004755, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.669982, Class: 1.000000, Obj: 0.525670, No Obj: 0.003426, Avg Recall: 0.821429, count: 84 -Region Avg IOU: 0.575777, Class: 1.000000, Obj: 0.456288, No Obj: 0.004373, Avg Recall: 0.656250, count: 128 -5140: 0.391503, 0.434654 avg, 0.001000 rate, 24.203472 seconds, 1315840 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 21.539654 seconds -Region Avg IOU: 0.530474, Class: 1.000000, Obj: 0.409349, No Obj: 0.003429, Avg Recall: 0.592593, count: 108 -Region Avg IOU: 0.580842, Class: 1.000000, Obj: 0.399591, No Obj: 0.003872, Avg Recall: 0.717557, count: 131 -Region Avg IOU: 0.591760, Class: 1.000000, Obj: 0.396133, No Obj: 0.003523, Avg Recall: 0.680000, count: 125 -Region Avg IOU: 0.586902, Class: 1.000000, Obj: 0.398684, No Obj: 0.004115, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.593192, Class: 1.000000, Obj: 0.448687, No Obj: 0.004378, Avg Recall: 0.704000, count: 125 -Region Avg IOU: 0.595429, Class: 1.000000, Obj: 0.425807, No Obj: 0.003557, Avg Recall: 0.717647, count: 85 -Region Avg IOU: 0.549466, Class: 1.000000, Obj: 0.424151, No Obj: 0.003404, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.545818, Class: 1.000000, Obj: 0.359359, No Obj: 0.003531, Avg Recall: 0.613445, count: 119 -5141: 0.447448, 0.435933 avg, 0.001000 rate, 25.593935 seconds, 1316096 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.607308, Class: 1.000000, Obj: 0.412867, No Obj: 0.003802, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.616830, Class: 1.000000, Obj: 0.416960, No Obj: 0.004207, Avg Recall: 0.750000, count: 124 -Region Avg IOU: 0.633817, Class: 1.000000, Obj: 0.438197, No Obj: 0.003645, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.636879, Class: 1.000000, Obj: 0.463642, No Obj: 0.003359, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.603260, Class: 1.000000, Obj: 0.414653, No Obj: 0.003837, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.584909, Class: 1.000000, Obj: 0.427757, No Obj: 0.003898, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.599529, Class: 1.000000, Obj: 0.410705, No Obj: 0.003368, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.616577, Class: 1.000000, Obj: 0.402253, No Obj: 0.003330, Avg Recall: 0.745283, count: 106 -5142: 0.443656, 0.436706 avg, 0.001000 rate, 24.719416 seconds, 1316352 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.576388, Class: 1.000000, Obj: 0.333038, No Obj: 0.003823, Avg Recall: 0.659722, count: 144 -Region Avg IOU: 0.564527, Class: 1.000000, Obj: 0.365697, No Obj: 0.003233, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.564421, Class: 1.000000, Obj: 0.369030, No Obj: 0.003512, Avg Recall: 0.644068, count: 118 -Region Avg IOU: 0.584069, Class: 1.000000, Obj: 0.362789, No Obj: 0.004123, Avg Recall: 0.695035, count: 141 -Region Avg IOU: 0.601576, Class: 1.000000, Obj: 0.446899, No Obj: 0.003186, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.603691, Class: 1.000000, Obj: 0.419436, No Obj: 0.003603, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.580602, Class: 1.000000, Obj: 0.396818, No Obj: 0.003085, Avg Recall: 0.707865, count: 89 -Region Avg IOU: 0.550428, Class: 1.000000, Obj: 0.386459, No Obj: 0.002927, Avg Recall: 0.663043, count: 92 -5143: 0.441816, 0.437217 avg, 0.001000 rate, 24.346745 seconds, 1316608 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.602808, Class: 1.000000, Obj: 0.385479, No Obj: 0.003570, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.576610, Class: 1.000000, Obj: 0.363020, No Obj: 0.003375, Avg Recall: 0.666667, count: 120 -Region Avg IOU: 0.614038, Class: 1.000000, Obj: 0.356829, No Obj: 0.003097, Avg Recall: 0.756098, count: 82 -Region Avg IOU: 0.565944, Class: 1.000000, Obj: 0.345450, No Obj: 0.003640, Avg Recall: 0.654412, count: 136 -Region Avg IOU: 0.587282, Class: 1.000000, Obj: 0.435374, No Obj: 0.002958, Avg Recall: 0.702381, count: 84 -Region Avg IOU: 0.601431, Class: 1.000000, Obj: 0.361461, No Obj: 0.003565, Avg Recall: 0.756098, count: 123 -Region Avg IOU: 0.627775, Class: 1.000000, Obj: 0.403934, No Obj: 0.003381, Avg Recall: 0.766667, count: 120 -Region Avg IOU: 0.561436, Class: 1.000000, Obj: 0.372270, No Obj: 0.003128, Avg Recall: 0.653226, count: 124 -5144: 0.451106, 0.438606 avg, 0.001000 rate, 22.572832 seconds, 1316864 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.600348, Class: 1.000000, Obj: 0.365124, No Obj: 0.003372, Avg Recall: 0.694656, count: 131 -Region Avg IOU: 0.531645, Class: 1.000000, Obj: 0.374720, No Obj: 0.003013, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.526654, Class: 1.000000, Obj: 0.347142, No Obj: 0.002818, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.595328, Class: 1.000000, Obj: 0.369860, No Obj: 0.003585, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.616032, Class: 1.000000, Obj: 0.411883, No Obj: 0.003431, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.590096, Class: 1.000000, Obj: 0.391365, No Obj: 0.003159, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.567513, Class: 1.000000, Obj: 0.366193, No Obj: 0.002894, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.636918, Class: 1.000000, Obj: 0.426697, No Obj: 0.003359, Avg Recall: 0.762712, count: 118 -5145: 0.430476, 0.437793 avg, 0.001000 rate, 25.200752 seconds, 1317120 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.616290, Class: 1.000000, Obj: 0.403860, No Obj: 0.002968, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.606914, Class: 1.000000, Obj: 0.383841, No Obj: 0.003644, Avg Recall: 0.712000, count: 125 -Region Avg IOU: 0.531993, Class: 1.000000, Obj: 0.375869, No Obj: 0.003723, Avg Recall: 0.596899, count: 129 -Region Avg IOU: 0.598915, Class: 1.000000, Obj: 0.366401, No Obj: 0.003457, Avg Recall: 0.697674, count: 129 -Region Avg IOU: 0.594438, Class: 1.000000, Obj: 0.358509, No Obj: 0.003427, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.604120, Class: 1.000000, Obj: 0.381190, No Obj: 0.003210, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.616852, Class: 1.000000, Obj: 0.374140, No Obj: 0.003189, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.589391, Class: 1.000000, Obj: 0.392803, No Obj: 0.003469, Avg Recall: 0.683333, count: 120 -5146: 0.478123, 0.441826 avg, 0.001000 rate, 23.996698 seconds, 1317376 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.584522, Class: 1.000000, Obj: 0.381196, No Obj: 0.003205, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.603740, Class: 1.000000, Obj: 0.408918, No Obj: 0.003210, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.625157, Class: 1.000000, Obj: 0.405250, No Obj: 0.003494, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.560906, Class: 1.000000, Obj: 0.350479, No Obj: 0.002966, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.619549, Class: 1.000000, Obj: 0.392821, No Obj: 0.003066, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.610947, Class: 1.000000, Obj: 0.374899, No Obj: 0.003292, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.607990, Class: 1.000000, Obj: 0.327012, No Obj: 0.002856, Avg Recall: 0.722222, count: 90 -Region Avg IOU: 0.584499, Class: 1.000000, Obj: 0.380655, No Obj: 0.003045, Avg Recall: 0.703390, count: 118 -5147: 0.434208, 0.441064 avg, 0.001000 rate, 24.235891 seconds, 1317632 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.596622, Class: 1.000000, Obj: 0.394778, No Obj: 0.003027, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.613362, Class: 1.000000, Obj: 0.409686, No Obj: 0.003083, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.611706, Class: 1.000000, Obj: 0.403525, No Obj: 0.003862, Avg Recall: 0.707692, count: 130 -Region Avg IOU: 0.575269, Class: 1.000000, Obj: 0.343991, No Obj: 0.003445, Avg Recall: 0.679688, count: 128 -Region Avg IOU: 0.593364, Class: 1.000000, Obj: 0.376973, No Obj: 0.003093, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.631324, Class: 1.000000, Obj: 0.384215, No Obj: 0.003665, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.577908, Class: 1.000000, Obj: 0.347192, No Obj: 0.003175, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.650119, Class: 1.000000, Obj: 0.387541, No Obj: 0.002969, Avg Recall: 0.769231, count: 78 -5148: 0.394010, 0.436359 avg, 0.001000 rate, 25.084696 seconds, 1317888 images -Loaded: 0.000059 seconds -Region Avg IOU: 0.626883, Class: 1.000000, Obj: 0.398430, No Obj: 0.003996, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.597785, Class: 1.000000, Obj: 0.382787, No Obj: 0.003263, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.548896, Class: 1.000000, Obj: 0.390775, No Obj: 0.003364, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.577597, Class: 1.000000, Obj: 0.412081, No Obj: 0.003505, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.555094, Class: 1.000000, Obj: 0.363381, No Obj: 0.003028, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.585818, Class: 1.000000, Obj: 0.383982, No Obj: 0.003508, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.619483, Class: 1.000000, Obj: 0.418397, No Obj: 0.003827, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.595744, Class: 1.000000, Obj: 0.382462, No Obj: 0.004167, Avg Recall: 0.728571, count: 140 -5149: 0.469994, 0.439722 avg, 0.001000 rate, 24.549023 seconds, 1318144 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.568051, Class: 1.000000, Obj: 0.419869, No Obj: 0.003424, Avg Recall: 0.658537, count: 123 -Region Avg IOU: 0.529481, Class: 1.000000, Obj: 0.340939, No Obj: 0.003667, Avg Recall: 0.650794, count: 126 -Region Avg IOU: 0.532694, Class: 1.000000, Obj: 0.329176, No Obj: 0.003884, Avg Recall: 0.609023, count: 133 -Region Avg IOU: 0.554650, Class: 1.000000, Obj: 0.335250, No Obj: 0.003713, Avg Recall: 0.625899, count: 139 -Region Avg IOU: 0.612919, Class: 1.000000, Obj: 0.366427, No Obj: 0.003217, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.579132, Class: 1.000000, Obj: 0.390123, No Obj: 0.003738, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.620163, Class: 1.000000, Obj: 0.442961, No Obj: 0.003943, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.604309, Class: 1.000000, Obj: 0.431672, No Obj: 0.003606, Avg Recall: 0.683673, count: 98 -5150: 0.477728, 0.443523 avg, 0.001000 rate, 24.234533 seconds, 1318400 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000064 seconds -Region Avg IOU: 0.647180, Class: 1.000000, Obj: 0.449754, No Obj: 0.003141, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.624500, Class: 1.000000, Obj: 0.436866, No Obj: 0.003515, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.634127, Class: 1.000000, Obj: 0.437561, No Obj: 0.003947, Avg Recall: 0.774436, count: 133 -Region Avg IOU: 0.621568, Class: 1.000000, Obj: 0.455680, No Obj: 0.002716, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.656514, Class: 1.000000, Obj: 0.477549, No Obj: 0.003325, Avg Recall: 0.777778, count: 90 -Region Avg IOU: 0.662062, Class: 1.000000, Obj: 0.478424, No Obj: 0.003365, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.661078, Class: 1.000000, Obj: 0.438770, No Obj: 0.003714, Avg Recall: 0.808696, count: 115 -Region Avg IOU: 0.642535, Class: 1.000000, Obj: 0.496475, No Obj: 0.003285, Avg Recall: 0.815534, count: 103 -5151: 0.457066, 0.444877 avg, 0.001000 rate, 27.225271 seconds, 1318656 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.622385, Class: 1.000000, Obj: 0.401315, No Obj: 0.003565, Avg Recall: 0.755725, count: 131 -Region Avg IOU: 0.616170, Class: 1.000000, Obj: 0.422577, No Obj: 0.003537, Avg Recall: 0.751825, count: 137 -Region Avg IOU: 0.673275, Class: 1.000000, Obj: 0.453830, No Obj: 0.003273, Avg Recall: 0.843137, count: 102 -Region Avg IOU: 0.607397, Class: 1.000000, Obj: 0.448533, No Obj: 0.003024, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.622346, Class: 1.000000, Obj: 0.449631, No Obj: 0.003540, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.606641, Class: 1.000000, Obj: 0.467084, No Obj: 0.003018, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.651328, Class: 1.000000, Obj: 0.433449, No Obj: 0.003752, Avg Recall: 0.786260, count: 131 -Region Avg IOU: 0.649888, Class: 1.000000, Obj: 0.429252, No Obj: 0.003244, Avg Recall: 0.811966, count: 117 -5152: 0.456106, 0.446000 avg, 0.001000 rate, 28.387695 seconds, 1318912 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.654992, Class: 1.000000, Obj: 0.445092, No Obj: 0.003318, Avg Recall: 0.834711, count: 121 -Region Avg IOU: 0.632462, Class: 1.000000, Obj: 0.374894, No Obj: 0.003313, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.651147, Class: 1.000000, Obj: 0.450256, No Obj: 0.003231, Avg Recall: 0.803150, count: 127 -Region Avg IOU: 0.664729, Class: 1.000000, Obj: 0.435530, No Obj: 0.002689, Avg Recall: 0.788235, count: 85 -Region Avg IOU: 0.626795, Class: 1.000000, Obj: 0.418109, No Obj: 0.002794, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.654814, Class: 1.000000, Obj: 0.389623, No Obj: 0.003035, Avg Recall: 0.800000, count: 95 -Region Avg IOU: 0.651596, Class: 1.000000, Obj: 0.456222, No Obj: 0.003656, Avg Recall: 0.798450, count: 129 -Region Avg IOU: 0.646619, Class: 1.000000, Obj: 0.418866, No Obj: 0.002813, Avg Recall: 0.802083, count: 96 -5153: 0.452153, 0.446615 avg, 0.001000 rate, 27.732325 seconds, 1319168 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.625444, Class: 1.000000, Obj: 0.400117, No Obj: 0.002695, Avg Recall: 0.744186, count: 86 -Region Avg IOU: 0.644923, Class: 1.000000, Obj: 0.439698, No Obj: 0.003094, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.658234, Class: 1.000000, Obj: 0.391067, No Obj: 0.002729, Avg Recall: 0.819149, count: 94 -Region Avg IOU: 0.635034, Class: 1.000000, Obj: 0.451168, No Obj: 0.002875, Avg Recall: 0.738095, count: 84 -Region Avg IOU: 0.662911, Class: 1.000000, Obj: 0.418803, No Obj: 0.002803, Avg Recall: 0.846154, count: 104 -Region Avg IOU: 0.611236, Class: 1.000000, Obj: 0.408369, No Obj: 0.002934, Avg Recall: 0.770115, count: 87 -Region Avg IOU: 0.654205, Class: 1.000000, Obj: 0.460060, No Obj: 0.003032, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.668977, Class: 1.000000, Obj: 0.424942, No Obj: 0.002780, Avg Recall: 0.804348, count: 92 -5154: 0.370827, 0.439036 avg, 0.001000 rate, 27.643171 seconds, 1319424 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.694907, Class: 1.000000, Obj: 0.408010, No Obj: 0.003173, Avg Recall: 0.838983, count: 118 -Region Avg IOU: 0.626034, Class: 1.000000, Obj: 0.414009, No Obj: 0.002601, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.626007, Class: 1.000000, Obj: 0.407719, No Obj: 0.002847, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.628437, Class: 1.000000, Obj: 0.412695, No Obj: 0.002956, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.645550, Class: 1.000000, Obj: 0.425968, No Obj: 0.002897, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.653185, Class: 1.000000, Obj: 0.408781, No Obj: 0.002719, Avg Recall: 0.815217, count: 92 -Region Avg IOU: 0.690187, Class: 1.000000, Obj: 0.423823, No Obj: 0.002991, Avg Recall: 0.836957, count: 92 -Region Avg IOU: 0.656402, Class: 1.000000, Obj: 0.402465, No Obj: 0.003179, Avg Recall: 0.800000, count: 130 -5155: 0.445785, 0.439711 avg, 0.001000 rate, 27.320526 seconds, 1319680 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.614731, Class: 1.000000, Obj: 0.411321, No Obj: 0.002584, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.685675, Class: 1.000000, Obj: 0.424853, No Obj: 0.002764, Avg Recall: 0.867925, count: 106 -Region Avg IOU: 0.677756, Class: 1.000000, Obj: 0.398524, No Obj: 0.002746, Avg Recall: 0.852174, count: 115 -Region Avg IOU: 0.608829, Class: 1.000000, Obj: 0.405801, No Obj: 0.002316, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.654854, Class: 1.000000, Obj: 0.418844, No Obj: 0.002347, Avg Recall: 0.783784, count: 74 -Region Avg IOU: 0.606939, Class: 1.000000, Obj: 0.371863, No Obj: 0.002151, Avg Recall: 0.761905, count: 84 -Region Avg IOU: 0.631190, Class: 1.000000, Obj: 0.352660, No Obj: 0.002262, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.683164, Class: 1.000000, Obj: 0.400848, No Obj: 0.002455, Avg Recall: 0.847059, count: 85 -5156: 0.415914, 0.437332 avg, 0.001000 rate, 26.434416 seconds, 1319936 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.655376, Class: 1.000000, Obj: 0.342473, No Obj: 0.002262, Avg Recall: 0.778761, count: 113 -Region Avg IOU: 0.666201, Class: 1.000000, Obj: 0.392507, No Obj: 0.002590, Avg Recall: 0.855856, count: 111 -Region Avg IOU: 0.635555, Class: 1.000000, Obj: 0.382308, No Obj: 0.002451, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.630524, Class: 1.000000, Obj: 0.382838, No Obj: 0.002648, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.640582, Class: 1.000000, Obj: 0.391765, No Obj: 0.002669, Avg Recall: 0.825397, count: 126 -Region Avg IOU: 0.630188, Class: 1.000000, Obj: 0.392175, No Obj: 0.002332, Avg Recall: 0.791209, count: 91 -Region Avg IOU: 0.665766, Class: 1.000000, Obj: 0.432624, No Obj: 0.002991, Avg Recall: 0.848214, count: 112 -Region Avg IOU: 0.673880, Class: 1.000000, Obj: 0.404694, No Obj: 0.002836, Avg Recall: 0.814815, count: 108 -5157: 0.436926, 0.437291 avg, 0.001000 rate, 26.489668 seconds, 1320192 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.662345, Class: 1.000000, Obj: 0.357497, No Obj: 0.002671, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.629322, Class: 1.000000, Obj: 0.361595, No Obj: 0.002594, Avg Recall: 0.766917, count: 133 -Region Avg IOU: 0.619500, Class: 1.000000, Obj: 0.376516, No Obj: 0.002256, Avg Recall: 0.694118, count: 85 -Region Avg IOU: 0.591235, Class: 1.000000, Obj: 0.374688, No Obj: 0.002321, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.635126, Class: 1.000000, Obj: 0.425989, No Obj: 0.002309, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.634069, Class: 1.000000, Obj: 0.389746, No Obj: 0.002511, Avg Recall: 0.753086, count: 81 -Region Avg IOU: 0.619672, Class: 1.000000, Obj: 0.345248, No Obj: 0.002498, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.653987, Class: 1.000000, Obj: 0.384763, No Obj: 0.002483, Avg Recall: 0.798077, count: 104 -5158: 0.422940, 0.435856 avg, 0.001000 rate, 26.619932 seconds, 1320448 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.615228, Class: 1.000000, Obj: 0.379610, No Obj: 0.002770, Avg Recall: 0.737705, count: 122 -Region Avg IOU: 0.627610, Class: 1.000000, Obj: 0.351661, No Obj: 0.002707, Avg Recall: 0.786885, count: 122 -Region Avg IOU: 0.600021, Class: 1.000000, Obj: 0.374827, No Obj: 0.002839, Avg Recall: 0.688000, count: 125 -Region Avg IOU: 0.634607, Class: 1.000000, Obj: 0.355270, No Obj: 0.002703, Avg Recall: 0.756098, count: 123 -Region Avg IOU: 0.627763, Class: 1.000000, Obj: 0.370599, No Obj: 0.002601, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.642476, Class: 1.000000, Obj: 0.417527, No Obj: 0.002355, Avg Recall: 0.826087, count: 92 -Region Avg IOU: 0.640362, Class: 1.000000, Obj: 0.393173, No Obj: 0.002436, Avg Recall: 0.835165, count: 91 -Region Avg IOU: 0.640564, Class: 1.000000, Obj: 0.364309, No Obj: 0.002534, Avg Recall: 0.805970, count: 134 -5159: 0.479298, 0.440200 avg, 0.001000 rate, 26.299885 seconds, 1320704 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.630428, Class: 1.000000, Obj: 0.440490, No Obj: 0.002253, Avg Recall: 0.804124, count: 97 -Region Avg IOU: 0.620765, Class: 1.000000, Obj: 0.372647, No Obj: 0.002888, Avg Recall: 0.733333, count: 120 -Region Avg IOU: 0.628996, Class: 1.000000, Obj: 0.366273, No Obj: 0.002460, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.607116, Class: 1.000000, Obj: 0.407972, No Obj: 0.002914, Avg Recall: 0.727941, count: 136 -Region Avg IOU: 0.624231, Class: 1.000000, Obj: 0.341691, No Obj: 0.002937, Avg Recall: 0.760870, count: 138 -Region Avg IOU: 0.631758, Class: 1.000000, Obj: 0.386468, No Obj: 0.002368, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.643641, Class: 1.000000, Obj: 0.371374, No Obj: 0.002730, Avg Recall: 0.804124, count: 97 -Region Avg IOU: 0.599621, Class: 1.000000, Obj: 0.368958, No Obj: 0.002691, Avg Recall: 0.724138, count: 116 -5160: 0.441267, 0.440307 avg, 0.001000 rate, 27.830608 seconds, 1320960 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 18.059692 seconds -Region Avg IOU: 0.607969, Class: 1.000000, Obj: 0.341105, No Obj: 0.002645, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.626492, Class: 1.000000, Obj: 0.432300, No Obj: 0.002829, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.617300, Class: 1.000000, Obj: 0.397268, No Obj: 0.003189, Avg Recall: 0.761194, count: 134 -Region Avg IOU: 0.607616, Class: 1.000000, Obj: 0.365289, No Obj: 0.003078, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.598207, Class: 1.000000, Obj: 0.368942, No Obj: 0.002856, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.601290, Class: 1.000000, Obj: 0.383717, No Obj: 0.003021, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.609958, Class: 1.000000, Obj: 0.388281, No Obj: 0.002762, Avg Recall: 0.714286, count: 84 -Region Avg IOU: 0.655405, Class: 1.000000, Obj: 0.398164, No Obj: 0.002877, Avg Recall: 0.787234, count: 94 -5161: 0.456880, 0.441964 avg, 0.001000 rate, 25.356424 seconds, 1321216 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.588611, Class: 1.000000, Obj: 0.386617, No Obj: 0.003393, Avg Recall: 0.664234, count: 137 -Region Avg IOU: 0.621564, Class: 1.000000, Obj: 0.404548, No Obj: 0.003002, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.651309, Class: 1.000000, Obj: 0.373122, No Obj: 0.003400, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.630515, Class: 1.000000, Obj: 0.406776, No Obj: 0.002878, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.539127, Class: 1.000000, Obj: 0.334026, No Obj: 0.003250, Avg Recall: 0.624000, count: 125 -Region Avg IOU: 0.599463, Class: 1.000000, Obj: 0.370352, No Obj: 0.003724, Avg Recall: 0.705882, count: 153 -Region Avg IOU: 0.629046, Class: 1.000000, Obj: 0.414724, No Obj: 0.002816, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.629040, Class: 1.000000, Obj: 0.399590, No Obj: 0.003042, Avg Recall: 0.777778, count: 81 -5162: 0.427189, 0.440487 avg, 0.001000 rate, 25.016537 seconds, 1321472 images -Loaded: 0.000148 seconds -Region Avg IOU: 0.613253, Class: 1.000000, Obj: 0.413461, No Obj: 0.003395, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.593939, Class: 1.000000, Obj: 0.383791, No Obj: 0.003180, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.633016, Class: 1.000000, Obj: 0.399745, No Obj: 0.003364, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.606866, Class: 1.000000, Obj: 0.382244, No Obj: 0.003380, Avg Recall: 0.696629, count: 89 -Region Avg IOU: 0.610906, Class: 1.000000, Obj: 0.383930, No Obj: 0.003615, Avg Recall: 0.731343, count: 134 -Region Avg IOU: 0.647308, Class: 1.000000, Obj: 0.431468, No Obj: 0.003173, Avg Recall: 0.829787, count: 94 -Region Avg IOU: 0.621812, Class: 1.000000, Obj: 0.408972, No Obj: 0.003259, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.607638, Class: 1.000000, Obj: 0.377206, No Obj: 0.004048, Avg Recall: 0.715328, count: 137 -5163: 0.448567, 0.441295 avg, 0.001000 rate, 25.374094 seconds, 1321728 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.620737, Class: 1.000000, Obj: 0.430112, No Obj: 0.003662, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.606178, Class: 1.000000, Obj: 0.485411, No Obj: 0.003063, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.612055, Class: 1.000000, Obj: 0.385865, No Obj: 0.003697, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.600257, Class: 1.000000, Obj: 0.414336, No Obj: 0.003691, Avg Recall: 0.671756, count: 131 -Region Avg IOU: 0.600237, Class: 1.000000, Obj: 0.430751, No Obj: 0.003328, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.609734, Class: 1.000000, Obj: 0.398482, No Obj: 0.003386, Avg Recall: 0.712000, count: 125 -Region Avg IOU: 0.609743, Class: 1.000000, Obj: 0.458359, No Obj: 0.003756, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.616589, Class: 1.000000, Obj: 0.405262, No Obj: 0.003160, Avg Recall: 0.709677, count: 93 -5164: 0.399000, 0.437065 avg, 0.001000 rate, 25.063782 seconds, 1321984 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.604375, Class: 1.000000, Obj: 0.421613, No Obj: 0.003882, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.583039, Class: 1.000000, Obj: 0.422060, No Obj: 0.003555, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.550281, Class: 1.000000, Obj: 0.412470, No Obj: 0.003656, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.619873, Class: 1.000000, Obj: 0.407286, No Obj: 0.003641, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.559213, Class: 1.000000, Obj: 0.414259, No Obj: 0.003582, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.559852, Class: 1.000000, Obj: 0.426023, No Obj: 0.003039, Avg Recall: 0.662651, count: 83 -Region Avg IOU: 0.613945, Class: 1.000000, Obj: 0.455294, No Obj: 0.003828, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.617507, Class: 1.000000, Obj: 0.467398, No Obj: 0.003067, Avg Recall: 0.766234, count: 77 -5165: 0.371722, 0.430531 avg, 0.001000 rate, 24.558357 seconds, 1322240 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.579258, Class: 1.000000, Obj: 0.467380, No Obj: 0.003600, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.596590, Class: 1.000000, Obj: 0.450046, No Obj: 0.003735, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.656323, Class: 1.000000, Obj: 0.433203, No Obj: 0.003575, Avg Recall: 0.816667, count: 120 -Region Avg IOU: 0.588385, Class: 1.000000, Obj: 0.391790, No Obj: 0.004284, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.670691, Class: 1.000000, Obj: 0.492573, No Obj: 0.003246, Avg Recall: 0.836957, count: 92 -Region Avg IOU: 0.576915, Class: 1.000000, Obj: 0.392639, No Obj: 0.003565, Avg Recall: 0.685315, count: 143 -Region Avg IOU: 0.586020, Class: 1.000000, Obj: 0.432574, No Obj: 0.004039, Avg Recall: 0.681481, count: 135 -Region Avg IOU: 0.592125, Class: 1.000000, Obj: 0.448445, No Obj: 0.003374, Avg Recall: 0.747253, count: 91 -5166: 0.443190, 0.431797 avg, 0.001000 rate, 25.094645 seconds, 1322496 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.589374, Class: 1.000000, Obj: 0.435497, No Obj: 0.003143, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.613548, Class: 1.000000, Obj: 0.451629, No Obj: 0.003484, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.599705, Class: 1.000000, Obj: 0.412702, No Obj: 0.003490, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.595214, Class: 1.000000, Obj: 0.424476, No Obj: 0.003862, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.605258, Class: 1.000000, Obj: 0.432464, No Obj: 0.003549, Avg Recall: 0.725275, count: 91 -Region Avg IOU: 0.620576, Class: 1.000000, Obj: 0.392657, No Obj: 0.003624, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.611058, Class: 1.000000, Obj: 0.445706, No Obj: 0.003475, Avg Recall: 0.742188, count: 128 -Region Avg IOU: 0.548844, Class: 1.000000, Obj: 0.397912, No Obj: 0.003209, Avg Recall: 0.640351, count: 114 -5167: 0.422310, 0.430848 avg, 0.001000 rate, 25.529491 seconds, 1322752 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.605074, Class: 1.000000, Obj: 0.457027, No Obj: 0.003927, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.634627, Class: 1.000000, Obj: 0.450440, No Obj: 0.003358, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.563627, Class: 1.000000, Obj: 0.416923, No Obj: 0.003485, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.685900, Class: 1.000000, Obj: 0.495130, No Obj: 0.003879, Avg Recall: 0.865385, count: 104 -Region Avg IOU: 0.623222, Class: 1.000000, Obj: 0.450935, No Obj: 0.003713, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.586031, Class: 1.000000, Obj: 0.420057, No Obj: 0.003609, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.623135, Class: 1.000000, Obj: 0.454951, No Obj: 0.003125, Avg Recall: 0.785714, count: 84 -Region Avg IOU: 0.551488, Class: 1.000000, Obj: 0.376355, No Obj: 0.003682, Avg Recall: 0.583333, count: 120 -5168: 0.457521, 0.433515 avg, 0.001000 rate, 25.441532 seconds, 1323008 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.623126, Class: 1.000000, Obj: 0.438988, No Obj: 0.003032, Avg Recall: 0.767442, count: 86 -Region Avg IOU: 0.572778, Class: 1.000000, Obj: 0.397631, No Obj: 0.003371, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.592334, Class: 1.000000, Obj: 0.425507, No Obj: 0.003561, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.617827, Class: 1.000000, Obj: 0.441376, No Obj: 0.003478, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.580015, Class: 1.000000, Obj: 0.423560, No Obj: 0.003361, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.633713, Class: 1.000000, Obj: 0.478282, No Obj: 0.003433, Avg Recall: 0.758621, count: 87 -Region Avg IOU: 0.642830, Class: 1.000000, Obj: 0.480237, No Obj: 0.003502, Avg Recall: 0.814433, count: 97 -Region Avg IOU: 0.637311, Class: 1.000000, Obj: 0.452955, No Obj: 0.004023, Avg Recall: 0.818182, count: 88 -5169: 0.364370, 0.426601 avg, 0.001000 rate, 24.056887 seconds, 1323264 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.618869, Class: 1.000000, Obj: 0.441516, No Obj: 0.003356, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.623852, Class: 1.000000, Obj: 0.399448, No Obj: 0.003485, Avg Recall: 0.752137, count: 117 -Region Avg IOU: 0.622738, Class: 1.000000, Obj: 0.455745, No Obj: 0.003703, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.591765, Class: 1.000000, Obj: 0.390022, No Obj: 0.003543, Avg Recall: 0.698413, count: 126 -Region Avg IOU: 0.671541, Class: 1.000000, Obj: 0.437458, No Obj: 0.002978, Avg Recall: 0.864865, count: 74 -Region Avg IOU: 0.563781, Class: 1.000000, Obj: 0.423113, No Obj: 0.002830, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.568174, Class: 1.000000, Obj: 0.379139, No Obj: 0.003362, Avg Recall: 0.658915, count: 129 -Region Avg IOU: 0.594385, Class: 1.000000, Obj: 0.412881, No Obj: 0.003380, Avg Recall: 0.722689, count: 119 -5170: 0.414189, 0.425360 avg, 0.001000 rate, 25.869436 seconds, 1323520 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000066 seconds -Region Avg IOU: 0.633138, Class: 1.000000, Obj: 0.392307, No Obj: 0.003131, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.583486, Class: 1.000000, Obj: 0.417111, No Obj: 0.002900, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.602413, Class: 1.000000, Obj: 0.391012, No Obj: 0.003165, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.606490, Class: 1.000000, Obj: 0.394260, No Obj: 0.003428, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.568700, Class: 1.000000, Obj: 0.379898, No Obj: 0.003035, Avg Recall: 0.688525, count: 122 -Region Avg IOU: 0.604226, Class: 1.000000, Obj: 0.414576, No Obj: 0.003001, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.618105, Class: 1.000000, Obj: 0.413118, No Obj: 0.003300, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.628602, Class: 1.000000, Obj: 0.450289, No Obj: 0.002877, Avg Recall: 0.770833, count: 96 -5171: 0.396730, 0.422497 avg, 0.001000 rate, 25.997318 seconds, 1323776 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.640898, Class: 1.000000, Obj: 0.418725, No Obj: 0.002896, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.577700, Class: 1.000000, Obj: 0.333351, No Obj: 0.002501, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.624470, Class: 1.000000, Obj: 0.426938, No Obj: 0.003054, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.611403, Class: 1.000000, Obj: 0.418583, No Obj: 0.002994, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.565345, Class: 1.000000, Obj: 0.357441, No Obj: 0.002740, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.612221, Class: 1.000000, Obj: 0.406261, No Obj: 0.003653, Avg Recall: 0.740741, count: 135 -Region Avg IOU: 0.622714, Class: 1.000000, Obj: 0.401869, No Obj: 0.003070, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.567064, Class: 1.000000, Obj: 0.381442, No Obj: 0.003209, Avg Recall: 0.669565, count: 115 -5172: 0.417371, 0.421984 avg, 0.001000 rate, 25.004734 seconds, 1324032 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.580464, Class: 1.000000, Obj: 0.353949, No Obj: 0.002621, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.577390, Class: 1.000000, Obj: 0.394494, No Obj: 0.002639, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.616597, Class: 1.000000, Obj: 0.354033, No Obj: 0.002874, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.582230, Class: 1.000000, Obj: 0.378806, No Obj: 0.003253, Avg Recall: 0.647059, count: 136 -Region Avg IOU: 0.554347, Class: 1.000000, Obj: 0.407438, No Obj: 0.002849, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.605478, Class: 1.000000, Obj: 0.399872, No Obj: 0.003297, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.641572, Class: 1.000000, Obj: 0.403931, No Obj: 0.002998, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.625436, Class: 1.000000, Obj: 0.400423, No Obj: 0.003216, Avg Recall: 0.768519, count: 108 -5173: 0.442355, 0.424021 avg, 0.001000 rate, 26.043934 seconds, 1324288 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.612630, Class: 1.000000, Obj: 0.419868, No Obj: 0.002398, Avg Recall: 0.782051, count: 78 -Region Avg IOU: 0.629222, Class: 1.000000, Obj: 0.386087, No Obj: 0.002986, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.628982, Class: 1.000000, Obj: 0.391914, No Obj: 0.002915, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.614501, Class: 1.000000, Obj: 0.411919, No Obj: 0.002821, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.601642, Class: 1.000000, Obj: 0.344511, No Obj: 0.003172, Avg Recall: 0.714286, count: 140 -Region Avg IOU: 0.600673, Class: 1.000000, Obj: 0.351535, No Obj: 0.002632, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.624360, Class: 1.000000, Obj: 0.374743, No Obj: 0.002762, Avg Recall: 0.736842, count: 133 -Region Avg IOU: 0.564837, Class: 1.000000, Obj: 0.343862, No Obj: 0.002448, Avg Recall: 0.693069, count: 101 -5174: 0.438618, 0.425481 avg, 0.001000 rate, 25.963966 seconds, 1324544 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.621565, Class: 1.000000, Obj: 0.362413, No Obj: 0.002908, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.596622, Class: 1.000000, Obj: 0.345189, No Obj: 0.002599, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.628552, Class: 1.000000, Obj: 0.417327, No Obj: 0.003299, Avg Recall: 0.765152, count: 132 -Region Avg IOU: 0.596396, Class: 1.000000, Obj: 0.376013, No Obj: 0.002868, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.606507, Class: 1.000000, Obj: 0.373820, No Obj: 0.002604, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.637532, Class: 1.000000, Obj: 0.373995, No Obj: 0.003023, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.606556, Class: 1.000000, Obj: 0.386499, No Obj: 0.002747, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.595072, Class: 1.000000, Obj: 0.386863, No Obj: 0.002745, Avg Recall: 0.697248, count: 109 -5175: 0.411924, 0.424125 avg, 0.001000 rate, 26.050472 seconds, 1324800 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.592357, Class: 1.000000, Obj: 0.323480, No Obj: 0.002893, Avg Recall: 0.719298, count: 114 -Region Avg IOU: 0.669764, Class: 1.000000, Obj: 0.452781, No Obj: 0.002851, Avg Recall: 0.816092, count: 87 -Region Avg IOU: 0.603023, Class: 1.000000, Obj: 0.364479, No Obj: 0.002899, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.625559, Class: 1.000000, Obj: 0.394904, No Obj: 0.003385, Avg Recall: 0.740260, count: 154 -Region Avg IOU: 0.582959, Class: 1.000000, Obj: 0.402171, No Obj: 0.002464, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.581729, Class: 1.000000, Obj: 0.389930, No Obj: 0.002850, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.610383, Class: 1.000000, Obj: 0.369367, No Obj: 0.003036, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.611533, Class: 1.000000, Obj: 0.396694, No Obj: 0.002971, Avg Recall: 0.741071, count: 112 -5176: 0.466268, 0.428339 avg, 0.001000 rate, 24.994736 seconds, 1325056 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.630561, Class: 1.000000, Obj: 0.435633, No Obj: 0.003530, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.614378, Class: 1.000000, Obj: 0.409974, No Obj: 0.003112, Avg Recall: 0.720930, count: 129 -Region Avg IOU: 0.627361, Class: 1.000000, Obj: 0.423344, No Obj: 0.003032, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.624858, Class: 1.000000, Obj: 0.447925, No Obj: 0.002462, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.601593, Class: 1.000000, Obj: 0.350599, No Obj: 0.002700, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.632964, Class: 1.000000, Obj: 0.343366, No Obj: 0.003024, Avg Recall: 0.759124, count: 137 -Region Avg IOU: 0.570435, Class: 1.000000, Obj: 0.355409, No Obj: 0.002527, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.591874, Class: 1.000000, Obj: 0.368878, No Obj: 0.002499, Avg Recall: 0.726191, count: 84 -5177: 0.399190, 0.425425 avg, 0.001000 rate, 24.898026 seconds, 1325312 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.646586, Class: 1.000000, Obj: 0.411890, No Obj: 0.003503, Avg Recall: 0.811024, count: 127 -Region Avg IOU: 0.625235, Class: 1.000000, Obj: 0.399883, No Obj: 0.003125, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.647513, Class: 1.000000, Obj: 0.388645, No Obj: 0.003596, Avg Recall: 0.787671, count: 146 -Region Avg IOU: 0.633689, Class: 1.000000, Obj: 0.392492, No Obj: 0.003556, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.609982, Class: 1.000000, Obj: 0.377900, No Obj: 0.002931, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.621774, Class: 1.000000, Obj: 0.411110, No Obj: 0.002987, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.623927, Class: 1.000000, Obj: 0.429239, No Obj: 0.002778, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.582654, Class: 1.000000, Obj: 0.365053, No Obj: 0.003120, Avg Recall: 0.682171, count: 129 -5178: 0.450170, 0.427899 avg, 0.001000 rate, 26.168341 seconds, 1325568 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.640836, Class: 1.000000, Obj: 0.445120, No Obj: 0.003229, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.607620, Class: 1.000000, Obj: 0.395770, No Obj: 0.003708, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.594429, Class: 1.000000, Obj: 0.409886, No Obj: 0.003058, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.629589, Class: 1.000000, Obj: 0.351190, No Obj: 0.003038, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.627646, Class: 1.000000, Obj: 0.365631, No Obj: 0.003389, Avg Recall: 0.766129, count: 124 -Region Avg IOU: 0.574574, Class: 1.000000, Obj: 0.388645, No Obj: 0.003445, Avg Recall: 0.664122, count: 131 -Region Avg IOU: 0.611805, Class: 1.000000, Obj: 0.414648, No Obj: 0.003642, Avg Recall: 0.751825, count: 137 -Region Avg IOU: 0.619424, Class: 1.000000, Obj: 0.391593, No Obj: 0.002768, Avg Recall: 0.782178, count: 101 -5179: 0.462572, 0.431366 avg, 0.001000 rate, 26.050045 seconds, 1325824 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.647255, Class: 1.000000, Obj: 0.405618, No Obj: 0.004010, Avg Recall: 0.809859, count: 142 -Region Avg IOU: 0.668252, Class: 1.000000, Obj: 0.402521, No Obj: 0.004265, Avg Recall: 0.818898, count: 127 -Region Avg IOU: 0.566727, Class: 1.000000, Obj: 0.406158, No Obj: 0.003083, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.619875, Class: 1.000000, Obj: 0.427555, No Obj: 0.003497, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.571336, Class: 1.000000, Obj: 0.386081, No Obj: 0.003091, Avg Recall: 0.648000, count: 125 -Region Avg IOU: 0.626476, Class: 1.000000, Obj: 0.445466, No Obj: 0.003059, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.602625, Class: 1.000000, Obj: 0.381398, No Obj: 0.002590, Avg Recall: 0.750000, count: 80 -Region Avg IOU: 0.656870, Class: 1.000000, Obj: 0.461882, No Obj: 0.002977, Avg Recall: 0.764706, count: 68 -5180: 0.436917, 0.431921 avg, 0.001000 rate, 24.966532 seconds, 1326080 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 16.145485 seconds -Region Avg IOU: 0.614060, Class: 1.000000, Obj: 0.374765, No Obj: 0.004513, Avg Recall: 0.769841, count: 126 -Region Avg IOU: 0.586575, Class: 1.000000, Obj: 0.409681, No Obj: 0.003615, Avg Recall: 0.688525, count: 122 -Region Avg IOU: 0.607395, Class: 1.000000, Obj: 0.455094, No Obj: 0.003000, Avg Recall: 0.738095, count: 84 -Region Avg IOU: 0.622958, Class: 1.000000, Obj: 0.440201, No Obj: 0.004134, Avg Recall: 0.723881, count: 134 -Region Avg IOU: 0.565579, Class: 1.000000, Obj: 0.374510, No Obj: 0.003149, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.636710, Class: 1.000000, Obj: 0.417993, No Obj: 0.004524, Avg Recall: 0.769231, count: 130 -Region Avg IOU: 0.569438, Class: 1.000000, Obj: 0.410248, No Obj: 0.003422, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.573592, Class: 1.000000, Obj: 0.415319, No Obj: 0.002931, Avg Recall: 0.647727, count: 88 -5181: 0.390153, 0.427745 avg, 0.001000 rate, 24.044664 seconds, 1326336 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.556250, Class: 1.000000, Obj: 0.388072, No Obj: 0.003110, Avg Recall: 0.632184, count: 87 -Region Avg IOU: 0.624874, Class: 1.000000, Obj: 0.444982, No Obj: 0.003542, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.566417, Class: 1.000000, Obj: 0.369619, No Obj: 0.003735, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.568228, Class: 1.000000, Obj: 0.391144, No Obj: 0.003920, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.595965, Class: 1.000000, Obj: 0.402591, No Obj: 0.004022, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.588897, Class: 1.000000, Obj: 0.405538, No Obj: 0.004007, Avg Recall: 0.688000, count: 125 -Region Avg IOU: 0.610184, Class: 1.000000, Obj: 0.382726, No Obj: 0.003598, Avg Recall: 0.716418, count: 134 -Region Avg IOU: 0.582437, Class: 1.000000, Obj: 0.398100, No Obj: 0.003158, Avg Recall: 0.686869, count: 99 -5182: 0.408242, 0.425794 avg, 0.001000 rate, 24.738449 seconds, 1326592 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.562986, Class: 1.000000, Obj: 0.366173, No Obj: 0.003385, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.583870, Class: 1.000000, Obj: 0.386145, No Obj: 0.003667, Avg Recall: 0.708333, count: 120 -Region Avg IOU: 0.548442, Class: 1.000000, Obj: 0.389187, No Obj: 0.003826, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.546008, Class: 1.000000, Obj: 0.376339, No Obj: 0.003775, Avg Recall: 0.631579, count: 133 -Region Avg IOU: 0.580379, Class: 1.000000, Obj: 0.391402, No Obj: 0.004056, Avg Recall: 0.712000, count: 125 -Region Avg IOU: 0.560412, Class: 1.000000, Obj: 0.342501, No Obj: 0.003864, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.633374, Class: 1.000000, Obj: 0.465142, No Obj: 0.003344, Avg Recall: 0.797619, count: 84 -Region Avg IOU: 0.554251, Class: 1.000000, Obj: 0.355029, No Obj: 0.003609, Avg Recall: 0.637168, count: 113 -5183: 0.450685, 0.428283 avg, 0.001000 rate, 24.226210 seconds, 1326848 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.555941, Class: 1.000000, Obj: 0.409202, No Obj: 0.003127, Avg Recall: 0.589744, count: 78 -Region Avg IOU: 0.555987, Class: 1.000000, Obj: 0.439505, No Obj: 0.003377, Avg Recall: 0.617978, count: 89 -Region Avg IOU: 0.581350, Class: 1.000000, Obj: 0.429414, No Obj: 0.003596, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.572781, Class: 1.000000, Obj: 0.425713, No Obj: 0.003363, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.597176, Class: 1.000000, Obj: 0.431283, No Obj: 0.003673, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.595281, Class: 1.000000, Obj: 0.413556, No Obj: 0.004479, Avg Recall: 0.703448, count: 145 -Region Avg IOU: 0.591270, Class: 1.000000, Obj: 0.384136, No Obj: 0.003348, Avg Recall: 0.733333, count: 75 -Region Avg IOU: 0.575713, Class: 1.000000, Obj: 0.376294, No Obj: 0.003422, Avg Recall: 0.686869, count: 99 -5184: 0.400946, 0.425550 avg, 0.001000 rate, 24.492420 seconds, 1327104 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.559262, Class: 1.000000, Obj: 0.419980, No Obj: 0.003017, Avg Recall: 0.674419, count: 86 -Region Avg IOU: 0.586324, Class: 1.000000, Obj: 0.428315, No Obj: 0.003823, Avg Recall: 0.680672, count: 119 -Region Avg IOU: 0.619773, Class: 1.000000, Obj: 0.420406, No Obj: 0.004000, Avg Recall: 0.753731, count: 134 -Region Avg IOU: 0.573888, Class: 1.000000, Obj: 0.424033, No Obj: 0.003727, Avg Recall: 0.676471, count: 136 -Region Avg IOU: 0.586879, Class: 1.000000, Obj: 0.419585, No Obj: 0.004304, Avg Recall: 0.696000, count: 125 -Region Avg IOU: 0.562377, Class: 1.000000, Obj: 0.412367, No Obj: 0.002801, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.580655, Class: 1.000000, Obj: 0.386914, No Obj: 0.004163, Avg Recall: 0.682432, count: 148 -Region Avg IOU: 0.586414, Class: 1.000000, Obj: 0.425742, No Obj: 0.003450, Avg Recall: 0.714286, count: 105 -5185: 0.443014, 0.427296 avg, 0.001000 rate, 23.613634 seconds, 1327360 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.561449, Class: 1.000000, Obj: 0.393381, No Obj: 0.003495, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.572000, Class: 1.000000, Obj: 0.413641, No Obj: 0.003382, Avg Recall: 0.674419, count: 86 -Region Avg IOU: 0.572832, Class: 1.000000, Obj: 0.434020, No Obj: 0.003999, Avg Recall: 0.663866, count: 119 -Region Avg IOU: 0.600880, Class: 1.000000, Obj: 0.402242, No Obj: 0.003389, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.589922, Class: 1.000000, Obj: 0.373572, No Obj: 0.003567, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.557971, Class: 1.000000, Obj: 0.395447, No Obj: 0.003285, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.567435, Class: 1.000000, Obj: 0.352838, No Obj: 0.003710, Avg Recall: 0.682927, count: 123 -Region Avg IOU: 0.564272, Class: 1.000000, Obj: 0.383876, No Obj: 0.003345, Avg Recall: 0.636364, count: 110 -5186: 0.419124, 0.426479 avg, 0.001000 rate, 23.594732 seconds, 1327616 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.587694, Class: 1.000000, Obj: 0.334862, No Obj: 0.003758, Avg Recall: 0.676056, count: 142 -Region Avg IOU: 0.592468, Class: 1.000000, Obj: 0.417106, No Obj: 0.002954, Avg Recall: 0.776471, count: 85 -Region Avg IOU: 0.610228, Class: 1.000000, Obj: 0.426910, No Obj: 0.003960, Avg Recall: 0.731707, count: 123 -Region Avg IOU: 0.594884, Class: 1.000000, Obj: 0.415198, No Obj: 0.003145, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.586424, Class: 1.000000, Obj: 0.391636, No Obj: 0.002914, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.562680, Class: 1.000000, Obj: 0.355947, No Obj: 0.003387, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.605982, Class: 1.000000, Obj: 0.399426, No Obj: 0.003299, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.587439, Class: 1.000000, Obj: 0.387170, No Obj: 0.003808, Avg Recall: 0.717557, count: 131 -5187: 0.415550, 0.425386 avg, 0.001000 rate, 24.291397 seconds, 1327872 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.576998, Class: 1.000000, Obj: 0.390494, No Obj: 0.003064, Avg Recall: 0.714286, count: 77 -Region Avg IOU: 0.580305, Class: 1.000000, Obj: 0.419752, No Obj: 0.003111, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.547552, Class: 1.000000, Obj: 0.367264, No Obj: 0.003128, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.568054, Class: 1.000000, Obj: 0.382832, No Obj: 0.003236, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.578331, Class: 1.000000, Obj: 0.365372, No Obj: 0.003662, Avg Recall: 0.728000, count: 125 -Region Avg IOU: 0.628686, Class: 1.000000, Obj: 0.423577, No Obj: 0.003380, Avg Recall: 0.784483, count: 116 -Region Avg IOU: 0.594815, Class: 1.000000, Obj: 0.421921, No Obj: 0.004119, Avg Recall: 0.700787, count: 127 -Region Avg IOU: 0.596748, Class: 1.000000, Obj: 0.367577, No Obj: 0.003622, Avg Recall: 0.686956, count: 115 -5188: 0.425915, 0.425439 avg, 0.001000 rate, 24.657379 seconds, 1328128 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.558896, Class: 1.000000, Obj: 0.343158, No Obj: 0.003376, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.604512, Class: 1.000000, Obj: 0.437111, No Obj: 0.002836, Avg Recall: 0.760563, count: 71 -Region Avg IOU: 0.607380, Class: 1.000000, Obj: 0.373100, No Obj: 0.003257, Avg Recall: 0.736264, count: 91 -Region Avg IOU: 0.618439, Class: 1.000000, Obj: 0.429914, No Obj: 0.003497, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.582512, Class: 1.000000, Obj: 0.371893, No Obj: 0.003179, Avg Recall: 0.701149, count: 87 -Region Avg IOU: 0.611844, Class: 1.000000, Obj: 0.420573, No Obj: 0.003639, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.582880, Class: 1.000000, Obj: 0.433732, No Obj: 0.003466, Avg Recall: 0.676923, count: 130 -Region Avg IOU: 0.579488, Class: 1.000000, Obj: 0.385961, No Obj: 0.003767, Avg Recall: 0.680000, count: 125 -5189: 0.405051, 0.423400 avg, 0.001000 rate, 23.203705 seconds, 1328384 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.565800, Class: 1.000000, Obj: 0.364402, No Obj: 0.003237, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.636555, Class: 1.000000, Obj: 0.431769, No Obj: 0.004164, Avg Recall: 0.796875, count: 128 -Region Avg IOU: 0.529209, Class: 1.000000, Obj: 0.376099, No Obj: 0.003261, Avg Recall: 0.591667, count: 120 -Region Avg IOU: 0.612318, Class: 1.000000, Obj: 0.382639, No Obj: 0.003145, Avg Recall: 0.780488, count: 82 -Region Avg IOU: 0.577832, Class: 1.000000, Obj: 0.419375, No Obj: 0.002914, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.560903, Class: 1.000000, Obj: 0.365915, No Obj: 0.003692, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.598462, Class: 1.000000, Obj: 0.393714, No Obj: 0.003496, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.615408, Class: 1.000000, Obj: 0.390471, No Obj: 0.003541, Avg Recall: 0.769231, count: 104 -5190: 0.428076, 0.423868 avg, 0.001000 rate, 24.254900 seconds, 1328640 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.012060 seconds -Region Avg IOU: 0.472035, Class: 1.000000, Obj: 0.290802, No Obj: 0.004565, Avg Recall: 0.512000, count: 125 -Region Avg IOU: 0.491076, Class: 1.000000, Obj: 0.253723, No Obj: 0.004247, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.487676, Class: 1.000000, Obj: 0.299682, No Obj: 0.003878, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.502062, Class: 1.000000, Obj: 0.269871, No Obj: 0.004493, Avg Recall: 0.545455, count: 121 -Region Avg IOU: 0.422372, Class: 1.000000, Obj: 0.255621, No Obj: 0.003743, Avg Recall: 0.455882, count: 136 -Region Avg IOU: 0.472365, Class: 1.000000, Obj: 0.274657, No Obj: 0.003261, Avg Recall: 0.551282, count: 78 -Region Avg IOU: 0.501999, Class: 1.000000, Obj: 0.296441, No Obj: 0.003808, Avg Recall: 0.547619, count: 84 -Region Avg IOU: 0.486231, Class: 1.000000, Obj: 0.286250, No Obj: 0.004148, Avg Recall: 0.516484, count: 91 -5191: 0.502553, 0.431736 avg, 0.001000 rate, 18.513996 seconds, 1328896 images -Loaded: 2.470273 seconds -Region Avg IOU: 0.471407, Class: 1.000000, Obj: 0.260104, No Obj: 0.003500, Avg Recall: 0.467290, count: 107 -Region Avg IOU: 0.523978, Class: 1.000000, Obj: 0.329586, No Obj: 0.003709, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.410674, Class: 1.000000, Obj: 0.272471, No Obj: 0.003638, Avg Recall: 0.409524, count: 105 -Region Avg IOU: 0.511241, Class: 1.000000, Obj: 0.315126, No Obj: 0.003238, Avg Recall: 0.658537, count: 82 -Region Avg IOU: 0.422929, Class: 1.000000, Obj: 0.237607, No Obj: 0.003481, Avg Recall: 0.450000, count: 100 -Region Avg IOU: 0.505374, Class: 1.000000, Obj: 0.284561, No Obj: 0.004068, Avg Recall: 0.568182, count: 88 -Region Avg IOU: 0.462865, Class: 1.000000, Obj: 0.251994, No Obj: 0.004191, Avg Recall: 0.468468, count: 111 -Region Avg IOU: 0.460857, Class: 1.000000, Obj: 0.293886, No Obj: 0.003822, Avg Recall: 0.510870, count: 92 -5192: 0.466909, 0.435253 avg, 0.001000 rate, 13.003477 seconds, 1329152 images -Loaded: 6.817092 seconds -Region Avg IOU: 0.493087, Class: 1.000000, Obj: 0.288689, No Obj: 0.004175, Avg Recall: 0.539326, count: 89 -Region Avg IOU: 0.463231, Class: 1.000000, Obj: 0.269118, No Obj: 0.004294, Avg Recall: 0.523438, count: 128 -Region Avg IOU: 0.492373, Class: 1.000000, Obj: 0.310880, No Obj: 0.004479, Avg Recall: 0.533333, count: 120 -Region Avg IOU: 0.448808, Class: 1.000000, Obj: 0.274727, No Obj: 0.004043, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.528671, Class: 1.000000, Obj: 0.289144, No Obj: 0.004414, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.504209, Class: 1.000000, Obj: 0.317610, No Obj: 0.004165, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.540854, Class: 1.000000, Obj: 0.302704, No Obj: 0.004692, Avg Recall: 0.563492, count: 126 -Region Avg IOU: 0.451520, Class: 1.000000, Obj: 0.284609, No Obj: 0.003286, Avg Recall: 0.481481, count: 81 -5193: 0.465990, 0.438327 avg, 0.001000 rate, 16.066633 seconds, 1329408 images -Loaded: 5.412905 seconds -Region Avg IOU: 0.490710, Class: 1.000000, Obj: 0.317043, No Obj: 0.003963, Avg Recall: 0.533333, count: 105 -Region Avg IOU: 0.493899, Class: 1.000000, Obj: 0.342072, No Obj: 0.004360, Avg Recall: 0.540000, count: 100 -Region Avg IOU: 0.422403, Class: 1.000000, Obj: 0.286965, No Obj: 0.004063, Avg Recall: 0.406780, count: 118 -Region Avg IOU: 0.465121, Class: 1.000000, Obj: 0.320686, No Obj: 0.003788, Avg Recall: 0.481818, count: 110 -Region Avg IOU: 0.473514, Class: 1.000000, Obj: 0.316073, No Obj: 0.003955, Avg Recall: 0.516129, count: 93 -Region Avg IOU: 0.444077, Class: 1.000000, Obj: 0.276666, No Obj: 0.004506, Avg Recall: 0.427419, count: 124 -Region Avg IOU: 0.527077, Class: 1.000000, Obj: 0.325114, No Obj: 0.004633, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.506717, Class: 1.000000, Obj: 0.313280, No Obj: 0.004374, Avg Recall: 0.583333, count: 96 -5194: 0.489831, 0.443477 avg, 0.001000 rate, 18.103563 seconds, 1329664 images -Loaded: 1.646831 seconds -Region Avg IOU: 0.510731, Class: 1.000000, Obj: 0.370604, No Obj: 0.005099, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.425769, Class: 1.000000, Obj: 0.304419, No Obj: 0.003925, Avg Recall: 0.415094, count: 106 -Region Avg IOU: 0.466450, Class: 1.000000, Obj: 0.345502, No Obj: 0.004219, Avg Recall: 0.505495, count: 91 -Region Avg IOU: 0.515557, Class: 1.000000, Obj: 0.326692, No Obj: 0.004414, Avg Recall: 0.588235, count: 102 -Region Avg IOU: 0.518622, Class: 1.000000, Obj: 0.343161, No Obj: 0.004192, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.505949, Class: 1.000000, Obj: 0.365189, No Obj: 0.004632, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.592661, Class: 1.000000, Obj: 0.354914, No Obj: 0.005228, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.508813, Class: 1.000000, Obj: 0.365412, No Obj: 0.004729, Avg Recall: 0.576577, count: 111 -5195: 0.437105, 0.442840 avg, 0.001000 rate, 16.948912 seconds, 1329920 images -Loaded: 4.386093 seconds -Region Avg IOU: 0.461795, Class: 1.000000, Obj: 0.355885, No Obj: 0.004880, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.504798, Class: 1.000000, Obj: 0.281208, No Obj: 0.004790, Avg Recall: 0.564103, count: 117 -Region Avg IOU: 0.454839, Class: 1.000000, Obj: 0.344508, No Obj: 0.004263, Avg Recall: 0.500000, count: 90 -Region Avg IOU: 0.464353, Class: 1.000000, Obj: 0.361755, No Obj: 0.004772, Avg Recall: 0.491071, count: 112 -Region Avg IOU: 0.459705, Class: 1.000000, Obj: 0.323499, No Obj: 0.004503, Avg Recall: 0.495495, count: 111 -Region Avg IOU: 0.481010, Class: 1.000000, Obj: 0.323764, No Obj: 0.004413, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.496697, Class: 1.000000, Obj: 0.315761, No Obj: 0.004797, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.515219, Class: 1.000000, Obj: 0.409870, No Obj: 0.004893, Avg Recall: 0.593750, count: 96 -5196: 0.456662, 0.444222 avg, 0.001000 rate, 14.981932 seconds, 1330176 images -Loaded: 7.412989 seconds -Region Avg IOU: 0.514920, Class: 1.000000, Obj: 0.366557, No Obj: 0.005826, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.539205, Class: 1.000000, Obj: 0.378469, No Obj: 0.005854, Avg Recall: 0.613445, count: 119 -Region Avg IOU: 0.449779, Class: 1.000000, Obj: 0.367402, No Obj: 0.004607, Avg Recall: 0.489362, count: 94 -Region Avg IOU: Saving weights to ./person9300/backup/tyvp_93_9c512_5200.weights -0.488269, Class: 1.000000, Obj: 0.345744, No Obj: 0.005228, Avg Recall: 0.523364, count: 107 -Region Avg IOU: 0.455175, Class: 1.000000, Obj: 0.309471, No Obj: 0.006020, Avg Recall: 0.470588, count: 153 -Region Avg IOU: 0.518095, Class: 1.000000, Obj: 0.385830, No Obj: 0.004980, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.494251, Class: 1.000000, Obj: 0.338644, No Obj: 0.005477, Avg Recall: 0.545455, count: 132 -Region Avg IOU: 0.488833, Class: 1.000000, Obj: 0.332107, No Obj: 0.005711, Avg Recall: 0.552000, count: 125 -5197: 0.555858, 0.455386 avg, 0.001000 rate, 17.843716 seconds, 1330432 images -Loaded: 1.827242 seconds -Region Avg IOU: 0.419093, Class: 1.000000, Obj: 0.297015, No Obj: 0.004724, Avg Recall: 0.495050, count: 101 -Region Avg IOU: 0.434926, Class: 1.000000, Obj: 0.376289, No Obj: 0.005422, Avg Recall: 0.472727, count: 110 -Region Avg IOU: 0.489331, Class: 1.000000, Obj: 0.363742, No Obj: 0.005724, Avg Recall: 0.523438, count: 128 -Region Avg IOU: 0.468179, Class: 1.000000, Obj: 0.371896, No Obj: 0.005378, Avg Recall: 0.486239, count: 109 -Region Avg IOU: 0.452896, Class: 1.000000, Obj: 0.320283, No Obj: 0.005315, Avg Recall: 0.474138, count: 116 -Region Avg IOU: 0.456275, Class: 1.000000, Obj: 0.366140, No Obj: 0.005657, Avg Recall: 0.508197, count: 122 -Region Avg IOU: 0.419538, Class: 1.000000, Obj: 0.377389, No Obj: 0.005112, Avg Recall: 0.457944, count: 107 -Region Avg IOU: 0.477174, Class: 1.000000, Obj: 0.360617, No Obj: 0.005377, Avg Recall: 0.550000, count: 120 -5198: 0.492001, 0.459047 avg, 0.001000 rate, 18.291008 seconds, 1330688 images -Loaded: 3.222617 seconds -Region Avg IOU: 0.410246, Class: 1.000000, Obj: 0.372137, No Obj: 0.004206, Avg Recall: 0.414894, count: 94 -Region Avg IOU: 0.431698, Class: 1.000000, Obj: 0.312960, No Obj: 0.004894, Avg Recall: 0.454545, count: 99 -Region Avg IOU: 0.462472, Class: 1.000000, Obj: 0.337300, No Obj: 0.006093, Avg Recall: 0.421488, count: 121 -Region Avg IOU: 0.415849, Class: 1.000000, Obj: 0.347773, No Obj: 0.004574, Avg Recall: 0.394231, count: 104 -Region Avg IOU: 0.500431, Class: 1.000000, Obj: 0.370277, No Obj: 0.005545, Avg Recall: 0.615385, count: 130 -Region Avg IOU: 0.441201, Class: 1.000000, Obj: 0.348732, No Obj: 0.006561, Avg Recall: 0.451613, count: 155 -Region Avg IOU: 0.446815, Class: 1.000000, Obj: 0.364809, No Obj: 0.005669, Avg Recall: 0.457944, count: 107 -Region Avg IOU: 0.488408, Class: 1.000000, Obj: 0.393113, No Obj: 0.006412, Avg Recall: 0.535433, count: 127 -5199: 0.500721, 0.463215 avg, 0.001000 rate, 13.849977 seconds, 1330944 images -Loaded: 6.837013 seconds -Region Avg IOU: 0.426393, Class: 1.000000, Obj: 0.307618, No Obj: 0.005569, Avg Recall: 0.434783, count: 115 -Region Avg IOU: 0.492839, Class: 1.000000, Obj: 0.431248, No Obj: 0.004592, Avg Recall: 0.556701, count: 97 -Region Avg IOU: 0.490999, Class: 1.000000, Obj: 0.375235, No Obj: 0.005538, Avg Recall: 0.541667, count: 120 -Region Avg IOU: 0.455422, Class: 1.000000, Obj: 0.340667, No Obj: 0.005473, Avg Recall: 0.518248, count: 137 -Region Avg IOU: 0.455785, Class: 1.000000, Obj: 0.354170, No Obj: 0.004844, Avg Recall: 0.495238, count: 105 -Region Avg IOU: 0.468083, Class: 1.000000, Obj: 0.350773, No Obj: 0.005369, Avg Recall: 0.491803, count: 122 -Region Avg IOU: 0.462520, Class: 1.000000, Obj: 0.382693, No Obj: 0.005696, Avg Recall: 0.495575, count: 113 -Region Avg IOU: 0.499204, Class: 1.000000, Obj: 0.443921, No Obj: 0.005144, Avg Recall: 0.567308, count: 104 -5200: 0.511720, 0.468065 avg, 0.001000 rate, 17.537363 seconds, 1331200 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000067 seconds -Region Avg IOU: 0.594685, Class: 1.000000, Obj: 0.450278, No Obj: 0.004145, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.646206, Class: 1.000000, Obj: 0.481726, No Obj: 0.004885, Avg Recall: 0.800000, count: 95 -Region Avg IOU: 0.622254, Class: 1.000000, Obj: 0.484392, No Obj: 0.004472, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.599984, Class: 1.000000, Obj: 0.445764, No Obj: 0.003768, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.590333, Class: 1.000000, Obj: 0.460942, No Obj: 0.004990, Avg Recall: 0.699248, count: 133 -Region Avg IOU: 0.634564, Class: 1.000000, Obj: 0.546500, No Obj: 0.004156, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.603056, Class: 1.000000, Obj: 0.529409, No Obj: 0.004064, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.620301, Class: 1.000000, Obj: 0.567048, No Obj: 0.004865, Avg Recall: 0.748031, count: 127 -5201: 0.408981, 0.462157 avg, 0.001000 rate, 23.554028 seconds, 1331456 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.623084, Class: 1.000000, Obj: 0.477989, No Obj: 0.004286, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.627732, Class: 1.000000, Obj: 0.470138, No Obj: 0.004166, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.560862, Class: 1.000000, Obj: 0.496564, No Obj: 0.003973, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.642960, Class: 1.000000, Obj: 0.460069, No Obj: 0.003687, Avg Recall: 0.784810, count: 79 -Region Avg IOU: 0.602473, Class: 1.000000, Obj: 0.509774, No Obj: 0.003976, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.521370, Class: 1.000000, Obj: 0.473843, No Obj: 0.002896, Avg Recall: 0.595745, count: 94 -Region Avg IOU: 0.639999, Class: 1.000000, Obj: 0.472672, No Obj: 0.003650, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.601633, Class: 1.000000, Obj: 0.456133, No Obj: 0.004043, Avg Recall: 0.752381, count: 105 -5202: 0.404005, 0.456342 avg, 0.001000 rate, 26.966452 seconds, 1331712 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.600593, Class: 1.000000, Obj: 0.437205, No Obj: 0.003525, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.597825, Class: 1.000000, Obj: 0.400495, No Obj: 0.004057, Avg Recall: 0.761194, count: 134 -Region Avg IOU: 0.630055, Class: 1.000000, Obj: 0.472214, No Obj: 0.003662, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.593693, Class: 1.000000, Obj: 0.425218, No Obj: 0.003410, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.611666, Class: 1.000000, Obj: 0.446168, No Obj: 0.003830, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.666036, Class: 1.000000, Obj: 0.443602, No Obj: 0.004161, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.644706, Class: 1.000000, Obj: 0.478506, No Obj: 0.003662, Avg Recall: 0.785047, count: 107 -Region Avg IOU: 0.603472, Class: 1.000000, Obj: 0.457524, No Obj: 0.004596, Avg Recall: 0.684211, count: 133 -5203: 0.434552, 0.454163 avg, 0.001000 rate, 25.105068 seconds, 1331968 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.610034, Class: 1.000000, Obj: 0.432999, No Obj: 0.003174, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.570025, Class: 1.000000, Obj: 0.409482, No Obj: 0.003350, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.613915, Class: 1.000000, Obj: 0.384980, No Obj: 0.003709, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.582866, Class: 1.000000, Obj: 0.438161, No Obj: 0.003399, Avg Recall: 0.696970, count: 132 -Region Avg IOU: 0.545817, Class: 1.000000, Obj: 0.335580, No Obj: 0.003159, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.623466, Class: 1.000000, Obj: 0.409788, No Obj: 0.003702, Avg Recall: 0.783333, count: 120 -Region Avg IOU: 0.597637, Class: 1.000000, Obj: 0.393412, No Obj: 0.003342, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.640684, Class: 1.000000, Obj: 0.430772, No Obj: 0.003098, Avg Recall: 0.765766, count: 111 -5204: 0.445840, 0.453331 avg, 0.001000 rate, 26.725639 seconds, 1332224 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.599979, Class: 1.000000, Obj: 0.368528, No Obj: 0.002829, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.572579, Class: 1.000000, Obj: 0.347828, No Obj: 0.002884, Avg Recall: 0.680672, count: 119 -Region Avg IOU: 0.557210, Class: 1.000000, Obj: 0.371003, No Obj: 0.002720, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.665741, Class: 1.000000, Obj: 0.392436, No Obj: 0.003377, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.594857, Class: 1.000000, Obj: 0.329756, No Obj: 0.003025, Avg Recall: 0.710938, count: 128 -Region Avg IOU: 0.600192, Class: 1.000000, Obj: 0.384855, No Obj: 0.002716, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.604094, Class: 1.000000, Obj: 0.374851, No Obj: 0.003451, Avg Recall: 0.710938, count: 128 -Region Avg IOU: 0.634690, Class: 1.000000, Obj: 0.358133, No Obj: 0.003072, Avg Recall: 0.785124, count: 121 -5205: 0.474154, 0.455413 avg, 0.001000 rate, 26.735308 seconds, 1332480 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.652964, Class: 1.000000, Obj: 0.371930, No Obj: 0.002964, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.642278, Class: 1.000000, Obj: 0.331417, No Obj: 0.002440, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.608860, Class: 1.000000, Obj: 0.331797, No Obj: 0.002826, Avg Recall: 0.720588, count: 136 -Region Avg IOU: 0.622681, Class: 1.000000, Obj: 0.380014, No Obj: 0.002387, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.619920, Class: 1.000000, Obj: 0.325229, No Obj: 0.002593, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.619510, Class: 1.000000, Obj: 0.367824, No Obj: 0.002900, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.655059, Class: 1.000000, Obj: 0.383555, No Obj: 0.002836, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.630350, Class: 1.000000, Obj: 0.354808, No Obj: 0.002517, Avg Recall: 0.726316, count: 95 -5206: 0.458993, 0.455771 avg, 0.001000 rate, 26.237848 seconds, 1332736 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.645939, Class: 1.000000, Obj: 0.352551, No Obj: 0.002492, Avg Recall: 0.807018, count: 114 -Region Avg IOU: 0.586473, Class: 1.000000, Obj: 0.361770, No Obj: 0.002100, Avg Recall: 0.685393, count: 89 -Region Avg IOU: 0.598363, Class: 1.000000, Obj: 0.381091, No Obj: 0.002614, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.665360, Class: 1.000000, Obj: 0.360080, No Obj: 0.002301, Avg Recall: 0.872340, count: 94 -Region Avg IOU: 0.615090, Class: 1.000000, Obj: 0.338361, No Obj: 0.001980, Avg Recall: 0.772152, count: 79 -Region Avg IOU: 0.680110, Class: 1.000000, Obj: 0.388812, No Obj: 0.002527, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.623426, Class: 1.000000, Obj: 0.321084, No Obj: 0.002245, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.604837, Class: 1.000000, Obj: 0.305307, No Obj: 0.002473, Avg Recall: 0.700855, count: 117 -5207: 0.393273, 0.449521 avg, 0.001000 rate, 25.857191 seconds, 1332992 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.634694, Class: 1.000000, Obj: 0.320307, No Obj: 0.001877, Avg Recall: 0.747126, count: 87 -Region Avg IOU: 0.638897, Class: 1.000000, Obj: 0.309651, No Obj: 0.002509, Avg Recall: 0.775862, count: 116 -Region Avg IOU: 0.618152, Class: 1.000000, Obj: 0.348979, No Obj: 0.002377, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.619658, Class: 1.000000, Obj: 0.358899, No Obj: 0.002025, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.619178, Class: 1.000000, Obj: 0.305843, No Obj: 0.002188, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.621551, Class: 1.000000, Obj: 0.354573, No Obj: 0.002358, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.598308, Class: 1.000000, Obj: 0.338706, No Obj: 0.002368, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.633228, Class: 1.000000, Obj: 0.297446, No Obj: 0.002100, Avg Recall: 0.809524, count: 84 -5208: 0.457895, 0.450358 avg, 0.001000 rate, 25.022017 seconds, 1333248 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.596094, Class: 1.000000, Obj: 0.348538, No Obj: 0.002335, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.628818, Class: 1.000000, Obj: 0.362685, No Obj: 0.002473, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.629563, Class: 1.000000, Obj: 0.343297, No Obj: 0.002052, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.617787, Class: 1.000000, Obj: 0.328340, No Obj: 0.002238, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.620875, Class: 1.000000, Obj: 0.362856, No Obj: 0.002044, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.630692, Class: 1.000000, Obj: 0.321283, No Obj: 0.002054, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.587667, Class: 1.000000, Obj: 0.283130, No Obj: 0.002186, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.615929, Class: 1.000000, Obj: 0.342203, No Obj: 0.002403, Avg Recall: 0.731092, count: 119 -5209: 0.405815, 0.445904 avg, 0.001000 rate, 26.765863 seconds, 1333504 images -Loaded: 0.000123 seconds -Region Avg IOU: 0.626486, Class: 1.000000, Obj: 0.387592, No Obj: 0.002444, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.641413, Class: 1.000000, Obj: 0.338698, No Obj: 0.002769, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.532044, Class: 1.000000, Obj: 0.340230, No Obj: 0.002327, Avg Recall: 0.586207, count: 87 -Region Avg IOU: 0.575174, Class: 1.000000, Obj: 0.312844, No Obj: 0.002581, Avg Recall: 0.626866, count: 134 -Region Avg IOU: 0.579804, Class: 1.000000, Obj: 0.315571, No Obj: 0.002859, Avg Recall: 0.690476, count: 126 -Region Avg IOU: 0.624557, Class: 1.000000, Obj: 0.298595, No Obj: 0.002458, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.635275, Class: 1.000000, Obj: 0.347754, No Obj: 0.002454, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.679232, Class: 1.000000, Obj: 0.376596, No Obj: 0.003214, Avg Recall: 0.878788, count: 132 -5210: 0.464853, 0.447799 avg, 0.001000 rate, 26.851774 seconds, 1333760 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 14.805728 seconds -Region Avg IOU: 0.634576, Class: 1.000000, Obj: 0.423338, No Obj: 0.002622, Avg Recall: 0.750000, count: 88 -Region Avg IOU: 0.612291, Class: 1.000000, Obj: 0.346414, No Obj: 0.002710, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.584146, Class: 1.000000, Obj: 0.307707, No Obj: 0.002579, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.646199, Class: 1.000000, Obj: 0.368776, No Obj: 0.002942, Avg Recall: 0.804348, count: 92 -Region Avg IOU: 0.601941, Class: 1.000000, Obj: 0.352893, No Obj: 0.002628, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.579555, Class: 1.000000, Obj: 0.368939, No Obj: 0.002465, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.633463, Class: 1.000000, Obj: 0.378817, No Obj: 0.002894, Avg Recall: 0.804348, count: 92 -Region Avg IOU: 0.604226, Class: 1.000000, Obj: 0.346205, No Obj: 0.002979, Avg Recall: 0.723214, count: 112 -5211: 0.389035, 0.441923 avg, 0.001000 rate, 25.841080 seconds, 1334016 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.645089, Class: 1.000000, Obj: 0.376106, No Obj: 0.003640, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.637678, Class: 1.000000, Obj: 0.420332, No Obj: 0.003411, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.619585, Class: 1.000000, Obj: 0.401348, No Obj: 0.003384, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.581384, Class: 1.000000, Obj: 0.344817, No Obj: 0.002983, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.667104, Class: 1.000000, Obj: 0.449245, No Obj: 0.003180, Avg Recall: 0.824074, count: 108 -Region Avg IOU: 0.607673, Class: 1.000000, Obj: 0.394325, No Obj: 0.003225, Avg Recall: 0.692913, count: 127 -Region Avg IOU: 0.588067, Class: 1.000000, Obj: 0.373664, No Obj: 0.003309, Avg Recall: 0.709677, count: 124 -Region Avg IOU: 0.556604, Class: 1.000000, Obj: 0.397131, No Obj: 0.003071, Avg Recall: 0.622642, count: 106 -5212: 0.444769, 0.442207 avg, 0.001000 rate, 24.046076 seconds, 1334272 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.625007, Class: 1.000000, Obj: 0.415103, No Obj: 0.002804, Avg Recall: 0.758621, count: 87 -Region Avg IOU: 0.651806, Class: 1.000000, Obj: 0.469237, No Obj: 0.003157, Avg Recall: 0.802083, count: 96 -Region Avg IOU: 0.607596, Class: 1.000000, Obj: 0.423991, No Obj: 0.003822, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.602428, Class: 1.000000, Obj: 0.413872, No Obj: 0.003651, Avg Recall: 0.713178, count: 129 -Region Avg IOU: 0.561377, Class: 1.000000, Obj: 0.384430, No Obj: 0.003734, Avg Recall: 0.658915, count: 129 -Region Avg IOU: 0.619744, Class: 1.000000, Obj: 0.417762, No Obj: 0.003224, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.661309, Class: 1.000000, Obj: 0.456873, No Obj: 0.003595, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.610320, Class: 1.000000, Obj: 0.424688, No Obj: 0.002767, Avg Recall: 0.717949, count: 78 -5213: 0.407865, 0.438773 avg, 0.001000 rate, 25.914360 seconds, 1334528 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.589650, Class: 1.000000, Obj: 0.408106, No Obj: 0.003251, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.565364, Class: 1.000000, Obj: 0.438732, No Obj: 0.003172, Avg Recall: 0.652174, count: 92 -Region Avg IOU: 0.623991, Class: 1.000000, Obj: 0.457208, No Obj: 0.003122, Avg Recall: 0.788889, count: 90 -Region Avg IOU: 0.620363, Class: 1.000000, Obj: 0.437808, No Obj: 0.003835, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.617952, Class: 1.000000, Obj: 0.405115, No Obj: 0.004303, Avg Recall: 0.738095, count: 126 -Region Avg IOU: 0.585582, Class: 1.000000, Obj: 0.369434, No Obj: 0.004253, Avg Recall: 0.669014, count: 142 -Region Avg IOU: 0.615585, Class: 1.000000, Obj: 0.502447, No Obj: 0.003883, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.654409, Class: 1.000000, Obj: 0.462079, No Obj: 0.003161, Avg Recall: 0.750000, count: 84 -5214: 0.392937, 0.434189 avg, 0.001000 rate, 25.255203 seconds, 1334784 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.578352, Class: 1.000000, Obj: 0.439778, No Obj: 0.003718, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.567714, Class: 1.000000, Obj: 0.387087, No Obj: 0.004298, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.566133, Class: 1.000000, Obj: 0.402947, No Obj: 0.003359, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.537319, Class: 1.000000, Obj: 0.429569, No Obj: 0.003798, Avg Recall: 0.611940, count: 134 -Region Avg IOU: 0.588909, Class: 1.000000, Obj: 0.410965, No Obj: 0.004142, Avg Recall: 0.688525, count: 122 -Region Avg IOU: 0.620891, Class: 1.000000, Obj: 0.416325, No Obj: 0.004415, Avg Recall: 0.729323, count: 133 -Region Avg IOU: 0.605090, Class: 1.000000, Obj: 0.446002, No Obj: 0.004123, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.577791, Class: 1.000000, Obj: 0.406760, No Obj: 0.004101, Avg Recall: 0.647619, count: 105 -5215: 0.474272, 0.438198 avg, 0.001000 rate, 24.633722 seconds, 1335040 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.585190, Class: 1.000000, Obj: 0.459755, No Obj: 0.004056, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.633806, Class: 1.000000, Obj: 0.519864, No Obj: 0.003621, Avg Recall: 0.804598, count: 87 -Region Avg IOU: 0.643347, Class: 1.000000, Obj: 0.500442, No Obj: 0.003860, Avg Recall: 0.703297, count: 91 -Region Avg IOU: 0.591523, Class: 1.000000, Obj: 0.528160, No Obj: 0.003634, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.610246, Class: 1.000000, Obj: 0.462709, No Obj: 0.004070, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.623621, Class: 1.000000, Obj: 0.438965, No Obj: 0.004237, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.618691, Class: 1.000000, Obj: 0.512103, No Obj: 0.003922, Avg Recall: 0.763158, count: 76 -Region Avg IOU: 0.546745, Class: 1.000000, Obj: 0.421781, No Obj: 0.003684, Avg Recall: 0.579439, count: 107 -5216: 0.368456, 0.431223 avg, 0.001000 rate, 25.464735 seconds, 1335296 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.587974, Class: 1.000000, Obj: 0.496793, No Obj: 0.003678, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.623404, Class: 1.000000, Obj: 0.399519, No Obj: 0.003489, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.574261, Class: 1.000000, Obj: 0.450010, No Obj: 0.004054, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.674473, Class: 1.000000, Obj: 0.533218, No Obj: 0.003761, Avg Recall: 0.842697, count: 89 -Region Avg IOU: 0.578711, Class: 1.000000, Obj: 0.449823, No Obj: 0.003206, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.619235, Class: 1.000000, Obj: 0.483860, No Obj: 0.003825, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.645914, Class: 1.000000, Obj: 0.506373, No Obj: 0.003904, Avg Recall: 0.837500, count: 80 -Region Avg IOU: 0.611381, Class: 1.000000, Obj: 0.445772, No Obj: 0.003561, Avg Recall: 0.773810, count: 84 -5217: 0.367884, 0.424889 avg, 0.001000 rate, 26.237165 seconds, 1335552 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.587665, Class: 1.000000, Obj: 0.413835, No Obj: 0.003417, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.629052, Class: 1.000000, Obj: 0.489464, No Obj: 0.004098, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.659797, Class: 1.000000, Obj: 0.443215, No Obj: 0.003999, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.620402, Class: 1.000000, Obj: 0.416022, No Obj: 0.003750, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.552873, Class: 1.000000, Obj: 0.451243, No Obj: 0.003724, Avg Recall: 0.614754, count: 122 -Region Avg IOU: 0.569325, Class: 1.000000, Obj: 0.433842, No Obj: 0.003300, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.565425, Class: 1.000000, Obj: 0.363903, No Obj: 0.003368, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.596853, Class: 1.000000, Obj: 0.443593, No Obj: 0.003849, Avg Recall: 0.691667, count: 120 -5218: 0.431960, 0.425596 avg, 0.001000 rate, 27.079725 seconds, 1335808 images -Loaded: 0.000154 seconds -Region Avg IOU: 0.622604, Class: 1.000000, Obj: 0.413792, No Obj: 0.003995, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.574631, Class: 1.000000, Obj: 0.395874, No Obj: 0.003422, Avg Recall: 0.693182, count: 88 -Region Avg IOU: 0.559106, Class: 1.000000, Obj: 0.398520, No Obj: 0.003348, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.648817, Class: 1.000000, Obj: 0.471486, No Obj: 0.003544, Avg Recall: 0.840909, count: 88 -Region Avg IOU: 0.599788, Class: 1.000000, Obj: 0.401311, No Obj: 0.003687, Avg Recall: 0.714286, count: 126 -Region Avg IOU: 0.609522, Class: 1.000000, Obj: 0.405008, No Obj: 0.003781, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.531526, Class: 1.000000, Obj: 0.437904, No Obj: 0.003060, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.598009, Class: 1.000000, Obj: 0.401437, No Obj: 0.003110, Avg Recall: 0.700000, count: 100 -5219: 0.408352, 0.423872 avg, 0.001000 rate, 24.661203 seconds, 1336064 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.603176, Class: 1.000000, Obj: 0.356371, No Obj: 0.003485, Avg Recall: 0.709677, count: 124 -Region Avg IOU: 0.607139, Class: 1.000000, Obj: 0.443452, No Obj: 0.003112, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.570566, Class: 1.000000, Obj: 0.395270, No Obj: 0.003337, Avg Recall: 0.685039, count: 127 -Region Avg IOU: 0.599499, Class: 1.000000, Obj: 0.412982, No Obj: 0.002946, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.600258, Class: 1.000000, Obj: 0.447784, No Obj: 0.003410, Avg Recall: 0.722222, count: 90 -Region Avg IOU: 0.598328, Class: 1.000000, Obj: 0.436722, No Obj: 0.002844, Avg Recall: 0.686046, count: 86 -Region Avg IOU: 0.551967, Class: 1.000000, Obj: 0.407288, No Obj: 0.003033, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.595890, Class: 1.000000, Obj: 0.391359, No Obj: 0.003178, Avg Recall: 0.705357, count: 112 -5220: 0.397090, 0.421194 avg, 0.001000 rate, 24.455902 seconds, 1336320 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000083 seconds -Region Avg IOU: 0.641395, Class: 1.000000, Obj: 0.406293, No Obj: 0.002645, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.680676, Class: 1.000000, Obj: 0.412151, No Obj: 0.002894, Avg Recall: 0.865079, count: 126 -Region Avg IOU: 0.694215, Class: 1.000000, Obj: 0.354094, No Obj: 0.002571, Avg Recall: 0.872093, count: 86 -Region Avg IOU: 0.619561, Class: 1.000000, Obj: 0.393151, No Obj: 0.002394, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.675501, Class: 1.000000, Obj: 0.427825, No Obj: 0.002218, Avg Recall: 0.827586, count: 87 -Region Avg IOU: 0.674290, Class: 1.000000, Obj: 0.441143, No Obj: 0.002179, Avg Recall: 0.822785, count: 79 -Region Avg IOU: 0.656998, Class: 1.000000, Obj: 0.408520, No Obj: 0.002725, Avg Recall: 0.848921, count: 139 -Region Avg IOU: 0.618023, Class: 1.000000, Obj: 0.387778, No Obj: 0.002444, Avg Recall: 0.774510, count: 102 -5221: 0.431413, 0.422216 avg, 0.001000 rate, 28.846523 seconds, 1336576 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.635692, Class: 1.000000, Obj: 0.361604, No Obj: 0.002237, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.645807, Class: 1.000000, Obj: 0.453304, No Obj: 0.002314, Avg Recall: 0.815217, count: 92 -Region Avg IOU: 0.640924, Class: 1.000000, Obj: 0.362735, No Obj: 0.002529, Avg Recall: 0.819549, count: 133 -Region Avg IOU: 0.604220, Class: 1.000000, Obj: 0.341600, No Obj: 0.002564, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.627682, Class: 1.000000, Obj: 0.410318, No Obj: 0.002496, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.630446, Class: 1.000000, Obj: 0.341105, No Obj: 0.002630, Avg Recall: 0.770492, count: 122 -Region Avg IOU: 0.630796, Class: 1.000000, Obj: 0.399214, No Obj: 0.002424, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.639265, Class: 1.000000, Obj: 0.384675, No Obj: 0.002235, Avg Recall: 0.798077, count: 104 -5222: 0.461994, 0.426194 avg, 0.001000 rate, 29.445929 seconds, 1336832 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.639153, Class: 1.000000, Obj: 0.334367, No Obj: 0.002067, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.704184, Class: 1.000000, Obj: 0.381225, No Obj: 0.002305, Avg Recall: 0.939394, count: 99 -Region Avg IOU: 0.638704, Class: 1.000000, Obj: 0.348686, No Obj: 0.002632, Avg Recall: 0.770992, count: 131 -Region Avg IOU: 0.646740, Class: 1.000000, Obj: 0.333759, No Obj: 0.002251, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.623718, Class: 1.000000, Obj: 0.319691, No Obj: 0.002275, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.673739, Class: 1.000000, Obj: 0.373313, No Obj: 0.002355, Avg Recall: 0.884615, count: 104 -Region Avg IOU: 0.671033, Class: 1.000000, Obj: 0.386672, No Obj: 0.002454, Avg Recall: 0.824074, count: 108 -Region Avg IOU: 0.602308, Class: 1.000000, Obj: 0.322214, No Obj: 0.002150, Avg Recall: 0.757576, count: 99 -5223: 0.446674, 0.428242 avg, 0.001000 rate, 28.614191 seconds, 1337088 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.646802, Class: 1.000000, Obj: 0.408512, No Obj: 0.002187, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.655617, Class: 1.000000, Obj: 0.380479, No Obj: 0.002304, Avg Recall: 0.793388, count: 121 -Region Avg IOU: 0.646818, Class: 1.000000, Obj: 0.329111, No Obj: 0.002584, Avg Recall: 0.815385, count: 130 -Region Avg IOU: 0.645064, Class: 1.000000, Obj: 0.378718, No Obj: 0.002280, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.658615, Class: 1.000000, Obj: 0.399400, No Obj: 0.002301, Avg Recall: 0.808081, count: 99 -Region Avg IOU: 0.688507, Class: 1.000000, Obj: 0.353567, No Obj: 0.002399, Avg Recall: 0.897959, count: 98 -Region Avg IOU: 0.673880, Class: 1.000000, Obj: 0.348353, No Obj: 0.002102, Avg Recall: 0.868132, count: 91 -Region Avg IOU: 0.618001, Class: 1.000000, Obj: 0.340407, No Obj: 0.002374, Avg Recall: 0.775510, count: 98 -5224: 0.412833, 0.426701 avg, 0.001000 rate, 30.933178 seconds, 1337344 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.648177, Class: 1.000000, Obj: 0.395546, No Obj: 0.002310, Avg Recall: 0.837838, count: 111 -Region Avg IOU: 0.629032, Class: 1.000000, Obj: 0.350242, No Obj: 0.002202, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.567813, Class: 1.000000, Obj: 0.322491, No Obj: 0.001909, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.630555, Class: 1.000000, Obj: 0.381774, No Obj: 0.002160, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.632084, Class: 1.000000, Obj: 0.368922, No Obj: 0.001958, Avg Recall: 0.777778, count: 90 -Region Avg IOU: 0.631578, Class: 1.000000, Obj: 0.386688, No Obj: 0.002238, Avg Recall: 0.779528, count: 127 -Region Avg IOU: 0.613451, Class: 1.000000, Obj: 0.348783, No Obj: 0.002149, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.651784, Class: 1.000000, Obj: 0.361330, No Obj: 0.002439, Avg Recall: 0.790909, count: 110 -5225: 0.432642, 0.427295 avg, 0.001000 rate, 27.722130 seconds, 1337600 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.592434, Class: 1.000000, Obj: 0.294704, No Obj: 0.002259, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.633377, Class: 1.000000, Obj: 0.343970, No Obj: 0.002144, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.702951, Class: 1.000000, Obj: 0.399918, No Obj: 0.001992, Avg Recall: 0.890625, count: 64 -Region Avg IOU: 0.679844, Class: 1.000000, Obj: 0.389618, No Obj: 0.002458, Avg Recall: 0.878261, count: 115 -Region Avg IOU: 0.642585, Class: 1.000000, Obj: 0.349358, No Obj: 0.002090, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.629444, Class: 1.000000, Obj: 0.388442, No Obj: 0.002643, Avg Recall: 0.803279, count: 122 -Region Avg IOU: 0.697804, Class: 1.000000, Obj: 0.393294, No Obj: 0.002296, Avg Recall: 0.913462, count: 104 -Region Avg IOU: 0.654355, Class: 1.000000, Obj: 0.391349, No Obj: 0.002202, Avg Recall: 0.808989, count: 89 -5226: 0.430795, 0.427645 avg, 0.001000 rate, 30.386620 seconds, 1337856 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.679445, Class: 1.000000, Obj: 0.389800, No Obj: 0.002253, Avg Recall: 0.864865, count: 111 -Region Avg IOU: 0.630123, Class: 1.000000, Obj: 0.325700, No Obj: 0.002136, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.669292, Class: 1.000000, Obj: 0.340088, No Obj: 0.002728, Avg Recall: 0.800000, count: 115 -Region Avg IOU: 0.652424, Class: 1.000000, Obj: 0.367024, No Obj: 0.002481, Avg Recall: 0.854839, count: 124 -Region Avg IOU: 0.622912, Class: 1.000000, Obj: 0.367770, No Obj: 0.002603, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.683623, Class: 1.000000, Obj: 0.382654, No Obj: 0.002549, Avg Recall: 0.834711, count: 121 -Region Avg IOU: 0.670541, Class: 1.000000, Obj: 0.422118, No Obj: 0.002404, Avg Recall: 0.828571, count: 105 -Region Avg IOU: 0.661051, Class: 1.000000, Obj: 0.385413, No Obj: 0.002271, Avg Recall: 0.797753, count: 89 -5227: 0.480812, 0.432962 avg, 0.001000 rate, 29.324680 seconds, 1338112 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.684595, Class: 1.000000, Obj: 0.394243, No Obj: 0.002299, Avg Recall: 0.867347, count: 98 -Region Avg IOU: 0.635491, Class: 1.000000, Obj: 0.362854, No Obj: 0.002415, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.641560, Class: 1.000000, Obj: 0.380125, No Obj: 0.002332, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.685637, Class: 1.000000, Obj: 0.358105, No Obj: 0.002323, Avg Recall: 0.873418, count: 79 -Region Avg IOU: 0.663528, Class: 1.000000, Obj: 0.342273, No Obj: 0.002695, Avg Recall: 0.834646, count: 127 -Region Avg IOU: 0.698874, Class: 1.000000, Obj: 0.362433, No Obj: 0.002435, Avg Recall: 0.890110, count: 91 -Region Avg IOU: 0.676521, Class: 1.000000, Obj: 0.383215, No Obj: 0.002340, Avg Recall: 0.810345, count: 116 -Region Avg IOU: 0.696453, Class: 1.000000, Obj: 0.466665, No Obj: 0.002242, Avg Recall: 0.886598, count: 97 -5228: 0.451372, 0.434803 avg, 0.001000 rate, 28.824080 seconds, 1338368 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.656021, Class: 1.000000, Obj: 0.352896, No Obj: 0.002718, Avg Recall: 0.806452, count: 124 -Region Avg IOU: 0.644179, Class: 1.000000, Obj: 0.389522, No Obj: 0.002625, Avg Recall: 0.770492, count: 122 -Region Avg IOU: 0.616823, Class: 1.000000, Obj: 0.385203, No Obj: 0.002102, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.637075, Class: 1.000000, Obj: 0.384526, No Obj: 0.002514, Avg Recall: 0.801587, count: 126 -Region Avg IOU: 0.640830, Class: 1.000000, Obj: 0.428875, No Obj: 0.002288, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.612956, Class: 1.000000, Obj: 0.328124, No Obj: 0.002272, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.671634, Class: 1.000000, Obj: 0.427179, No Obj: 0.002494, Avg Recall: 0.835165, count: 91 -Region Avg IOU: 0.647270, Class: 1.000000, Obj: 0.378966, No Obj: 0.002894, Avg Recall: 0.789809, count: 157 -5229: 0.464577, 0.437780 avg, 0.001000 rate, 28.972012 seconds, 1338624 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.666574, Class: 1.000000, Obj: 0.433457, No Obj: 0.002406, Avg Recall: 0.851064, count: 94 -Region Avg IOU: 0.642203, Class: 1.000000, Obj: 0.460819, No Obj: 0.002303, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.692292, Class: 1.000000, Obj: 0.411504, No Obj: 0.002745, Avg Recall: 0.885714, count: 105 -Region Avg IOU: 0.694401, Class: 1.000000, Obj: 0.399392, No Obj: 0.002793, Avg Recall: 0.900000, count: 100 -Region Avg IOU: 0.630095, Class: 1.000000, Obj: 0.397635, No Obj: 0.002906, Avg Recall: 0.806452, count: 124 -Region Avg IOU: 0.639376, Class: 1.000000, Obj: 0.391870, No Obj: 0.002835, Avg Recall: 0.756098, count: 123 -Region Avg IOU: 0.663577, Class: 1.000000, Obj: 0.422651, No Obj: 0.002687, Avg Recall: 0.848485, count: 99 -Region Avg IOU: 0.654024, Class: 1.000000, Obj: 0.368121, No Obj: 0.002697, Avg Recall: 0.828125, count: 128 -5230: 0.464248, 0.440427 avg, 0.001000 rate, 29.643772 seconds, 1338880 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 13.043757 seconds -Region Avg IOU: 0.641952, Class: 1.000000, Obj: 0.421270, No Obj: 0.002691, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.616807, Class: 1.000000, Obj: 0.391503, No Obj: 0.002652, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.608435, Class: 1.000000, Obj: 0.378798, No Obj: 0.002918, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.608373, Class: 1.000000, Obj: 0.357783, No Obj: 0.003336, Avg Recall: 0.708661, count: 127 -Region Avg IOU: 0.634922, Class: 1.000000, Obj: 0.413312, No Obj: 0.002577, Avg Recall: 0.783132, count: 83 -Region Avg IOU: 0.645825, Class: 1.000000, Obj: 0.372521, No Obj: 0.003282, Avg Recall: 0.809091, count: 110 -Region Avg IOU: 0.554960, Class: 1.000000, Obj: 0.324836, No Obj: 0.003097, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.638005, Class: 1.000000, Obj: 0.350939, No Obj: 0.003346, Avg Recall: 0.770000, count: 100 -5231: 0.410764, 0.437461 avg, 0.001000 rate, 25.459036 seconds, 1339136 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.570417, Class: 1.000000, Obj: 0.374065, No Obj: 0.003241, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.641904, Class: 1.000000, Obj: 0.432338, No Obj: 0.003061, Avg Recall: 0.809524, count: 84 -Region Avg IOU: 0.637200, Class: 1.000000, Obj: 0.411914, No Obj: 0.003855, Avg Recall: 0.742857, count: 140 -Region Avg IOU: 0.639489, Class: 1.000000, Obj: 0.389534, No Obj: 0.003413, Avg Recall: 0.781250, count: 96 -Region Avg IOU: 0.576779, Class: 1.000000, Obj: 0.386487, No Obj: 0.003070, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.644367, Class: 1.000000, Obj: 0.430037, No Obj: 0.002789, Avg Recall: 0.756757, count: 74 -Region Avg IOU: 0.620645, Class: 1.000000, Obj: 0.362915, No Obj: 0.003537, Avg Recall: 0.795082, count: 122 -Region Avg IOU: 0.584160, Class: 1.000000, Obj: 0.362553, No Obj: 0.003458, Avg Recall: 0.648438, count: 128 -5232: 0.461610, 0.439876 avg, 0.001000 rate, 25.700272 seconds, 1339392 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.517796, Class: 1.000000, Obj: 0.383619, No Obj: 0.003114, Avg Recall: 0.557047, count: 149 -Region Avg IOU: 0.638231, Class: 1.000000, Obj: 0.373940, No Obj: 0.003888, Avg Recall: 0.791667, count: 120 -Region Avg IOU: 0.625209, Class: 1.000000, Obj: 0.430543, No Obj: 0.003409, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.610325, Class: 1.000000, Obj: 0.371470, No Obj: 0.003441, Avg Recall: 0.735043, count: 117 -Region Avg IOU: 0.666999, Class: 1.000000, Obj: 0.509983, No Obj: 0.003021, Avg Recall: 0.797101, count: 69 -Region Avg IOU: 0.585211, Class: 1.000000, Obj: 0.433977, No Obj: 0.003350, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.608873, Class: 1.000000, Obj: 0.375806, No Obj: 0.003234, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.668579, Class: 1.000000, Obj: 0.406551, No Obj: 0.003339, Avg Recall: 0.820000, count: 100 -5233: 0.457205, 0.441608 avg, 0.001000 rate, 24.256409 seconds, 1339648 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.592056, Class: 1.000000, Obj: 0.434632, No Obj: 0.003332, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.605199, Class: 1.000000, Obj: 0.408104, No Obj: 0.003417, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.588471, Class: 1.000000, Obj: 0.392750, No Obj: 0.003709, Avg Recall: 0.679389, count: 131 -Region Avg IOU: 0.572582, Class: 1.000000, Obj: 0.418394, No Obj: 0.002830, Avg Recall: 0.673684, count: 95 -Region Avg IOU: 0.575707, Class: 1.000000, Obj: 0.433174, No Obj: 0.002943, Avg Recall: 0.714286, count: 77 -Region Avg IOU: 0.595056, Class: 1.000000, Obj: 0.412660, No Obj: 0.003054, Avg Recall: 0.707865, count: 89 -Region Avg IOU: 0.667003, Class: 1.000000, Obj: 0.459844, No Obj: 0.003380, Avg Recall: 0.819277, count: 83 -Region Avg IOU: 0.624951, Class: 1.000000, Obj: 0.499300, No Obj: 0.002936, Avg Recall: 0.772727, count: 88 -5234: 0.398102, 0.437258 avg, 0.001000 rate, 25.658640 seconds, 1339904 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.575731, Class: 1.000000, Obj: 0.373510, No Obj: 0.002598, Avg Recall: 0.689655, count: 87 -Region Avg IOU: 0.637892, Class: 1.000000, Obj: 0.466449, No Obj: 0.003960, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.588263, Class: 1.000000, Obj: 0.385131, No Obj: 0.003490, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.648065, Class: 1.000000, Obj: 0.411369, No Obj: 0.003696, Avg Recall: 0.801980, count: 101 -Region Avg IOU: 0.600038, Class: 1.000000, Obj: 0.430903, No Obj: 0.003118, Avg Recall: 0.758621, count: 87 -Region Avg IOU: 0.578481, Class: 1.000000, Obj: 0.388136, No Obj: 0.003577, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.597345, Class: 1.000000, Obj: 0.424280, No Obj: 0.003607, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.628292, Class: 1.000000, Obj: 0.417762, No Obj: 0.003646, Avg Recall: 0.732143, count: 112 -5235: 0.392035, 0.432736 avg, 0.001000 rate, 25.702841 seconds, 1340160 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.591662, Class: 1.000000, Obj: 0.383767, No Obj: 0.003969, Avg Recall: 0.711268, count: 142 -Region Avg IOU: 0.593448, Class: 1.000000, Obj: 0.479606, No Obj: 0.003774, Avg Recall: 0.658333, count: 120 -Region Avg IOU: 0.597327, Class: 1.000000, Obj: 0.414890, No Obj: 0.003549, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.586437, Class: 1.000000, Obj: 0.390011, No Obj: 0.003316, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.573982, Class: 1.000000, Obj: 0.350258, No Obj: 0.003269, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.555002, Class: 1.000000, Obj: 0.401409, No Obj: 0.003332, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.649279, Class: 1.000000, Obj: 0.449481, No Obj: 0.004039, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.621015, Class: 1.000000, Obj: 0.444040, No Obj: 0.003499, Avg Recall: 0.747368, count: 95 -5236: 0.420823, 0.431544 avg, 0.001000 rate, 25.315304 seconds, 1340416 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.620865, Class: 1.000000, Obj: 0.417077, No Obj: 0.004149, Avg Recall: 0.748387, count: 155 -Region Avg IOU: 0.617327, Class: 1.000000, Obj: 0.470378, No Obj: 0.003565, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.605083, Class: 1.000000, Obj: 0.403508, No Obj: 0.003842, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.614828, Class: 1.000000, Obj: 0.394056, No Obj: 0.003351, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.597588, Class: 1.000000, Obj: 0.393851, No Obj: 0.003994, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.580330, Class: 1.000000, Obj: 0.415279, No Obj: 0.003351, Avg Recall: 0.684783, count: 92 -Region Avg IOU: 0.600801, Class: 1.000000, Obj: 0.418860, No Obj: 0.003633, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.581780, Class: 1.000000, Obj: 0.385857, No Obj: 0.003423, Avg Recall: 0.672414, count: 116 -5237: 0.462120, 0.434602 avg, 0.001000 rate, 24.354496 seconds, 1340672 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.643878, Class: 1.000000, Obj: 0.401106, No Obj: 0.003269, Avg Recall: 0.800000, count: 95 -Region Avg IOU: 0.600594, Class: 1.000000, Obj: 0.423593, No Obj: 0.003343, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.594554, Class: 1.000000, Obj: 0.406954, No Obj: 0.003670, Avg Recall: 0.714286, count: 119 -Region Avg IOU: 0.591921, Class: 1.000000, Obj: 0.381297, No Obj: 0.004005, Avg Recall: 0.733333, count: 150 -Region Avg IOU: 0.639360, Class: 1.000000, Obj: 0.451592, No Obj: 0.003589, Avg Recall: 0.786667, count: 75 -Region Avg IOU: 0.612431, Class: 1.000000, Obj: 0.415845, No Obj: 0.003709, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.609102, Class: 1.000000, Obj: 0.429157, No Obj: 0.003491, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.588738, Class: 1.000000, Obj: 0.365868, No Obj: 0.003477, Avg Recall: 0.688073, count: 109 -5238: 0.422532, 0.433395 avg, 0.001000 rate, 25.179722 seconds, 1340928 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.641119, Class: 1.000000, Obj: 0.423408, No Obj: 0.003587, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.606307, Class: 1.000000, Obj: 0.398109, No Obj: 0.003352, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.589437, Class: 1.000000, Obj: 0.399145, No Obj: 0.003238, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.596145, Class: 1.000000, Obj: 0.373801, No Obj: 0.003578, Avg Recall: 0.691057, count: 123 -Region Avg IOU: 0.612239, Class: 1.000000, Obj: 0.428802, No Obj: 0.003906, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.598799, Class: 1.000000, Obj: 0.439174, No Obj: 0.003357, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.612137, Class: 1.000000, Obj: 0.419404, No Obj: 0.003107, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.548117, Class: 1.000000, Obj: 0.389922, No Obj: 0.003366, Avg Recall: 0.645669, count: 127 -5239: 0.463851, 0.436440 avg, 0.001000 rate, 25.771206 seconds, 1341184 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.566223, Class: 1.000000, Obj: 0.376854, No Obj: 0.003040, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.584968, Class: 1.000000, Obj: 0.357172, No Obj: 0.003004, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.578228, Class: 1.000000, Obj: 0.365557, No Obj: 0.003726, Avg Recall: 0.689394, count: 132 -Region Avg IOU: 0.582151, Class: 1.000000, Obj: 0.412314, No Obj: 0.003838, Avg Recall: 0.697674, count: 129 -Region Avg IOU: 0.597327, Class: 1.000000, Obj: 0.376517, No Obj: 0.003280, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.600737, Class: 1.000000, Obj: 0.400184, No Obj: 0.003706, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.623245, Class: 1.000000, Obj: 0.437624, No Obj: 0.002748, Avg Recall: 0.814286, count: 70 -Region Avg IOU: 0.606750, Class: 1.000000, Obj: 0.355476, No Obj: 0.003853, Avg Recall: 0.737589, count: 141 -5240: 0.497968, 0.442593 avg, 0.001000 rate, 25.973577 seconds, 1341440 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.488832 seconds -Region Avg IOU: 0.518441, Class: 1.000000, Obj: 0.269698, No Obj: 0.003344, Avg Recall: 0.590909, count: 88 -Region Avg IOU: 0.485713, Class: 1.000000, Obj: 0.249867, No Obj: 0.003588, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.510966, Class: 1.000000, Obj: 0.295627, No Obj: 0.003694, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.490715, Class: 1.000000, Obj: 0.269501, No Obj: 0.003557, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.499446, Class: 1.000000, Obj: 0.272632, No Obj: 0.004150, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.492634, Class: 1.000000, Obj: 0.250042, No Obj: 0.003779, Avg Recall: 0.541667, count: 96 -Region Avg IOU: 0.517688, Class: 1.000000, Obj: 0.285428, No Obj: 0.003762, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.443812, Class: 1.000000, Obj: 0.272371, No Obj: 0.003782, Avg Recall: 0.533333, count: 105 -5241: 0.470719, 0.445406 avg, 0.001000 rate, 15.269114 seconds, 1341696 images -Loaded: 5.402509 seconds -Region Avg IOU: 0.471851, Class: 1.000000, Obj: 0.264364, No Obj: 0.004269, Avg Recall: 0.514085, count: 142 -Region Avg IOU: 0.439708, Class: 1.000000, Obj: 0.264983, No Obj: 0.003082, Avg Recall: 0.460674, count: 89 -Region Avg IOU: 0.459710, Class: 1.000000, Obj: 0.276103, No Obj: 0.003470, Avg Recall: 0.504505, count: 111 -Region Avg IOU: 0.528833, Class: 1.000000, Obj: 0.265497, No Obj: 0.004318, Avg Recall: 0.593496, count: 123 -Region Avg IOU: 0.451354, Class: 1.000000, Obj: 0.260937, No Obj: 0.003472, Avg Recall: 0.494737, count: 95 -Region Avg IOU: 0.483398, Class: 1.000000, Obj: 0.268502, No Obj: 0.003935, Avg Recall: 0.566667, count: 120 -Region Avg IOU: 0.426669, Class: 1.000000, Obj: 0.239663, No Obj: 0.003378, Avg Recall: 0.439024, count: 123 -Region Avg IOU: 0.508192, Class: 1.000000, Obj: 0.275128, No Obj: 0.004416, Avg Recall: 0.608333, count: 120 -5242: 0.543098, 0.455175 avg, 0.001000 rate, 16.434975 seconds, 1341952 images -Loaded: 3.830672 seconds -Region Avg IOU: 0.532428, Class: 1.000000, Obj: 0.316177, No Obj: 0.004348, Avg Recall: 0.619565, count: 92 -Region Avg IOU: 0.512169, Class: 1.000000, Obj: 0.302832, No Obj: 0.004639, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.532022, Class: 1.000000, Obj: 0.283083, No Obj: 0.004181, Avg Recall: 0.630952, count: 84 -Region Avg IOU: 0.453432, Class: 1.000000, Obj: 0.255458, No Obj: 0.003907, Avg Recall: 0.475410, count: 122 -Region Avg IOU: 0.475683, Class: 1.000000, Obj: 0.297380, No Obj: 0.003153, Avg Recall: 0.516484, count: 91 -Region Avg IOU: 0.513785, Class: 1.000000, Obj: 0.310685, No Obj: 0.004050, Avg Recall: 0.572650, count: 117 -Region Avg IOU: 0.484326, Class: 1.000000, Obj: 0.293884, No Obj: 0.004171, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.483817, Class: 1.000000, Obj: 0.307483, No Obj: 0.003641, Avg Recall: 0.495413, count: 109 -5243: 0.478669, 0.457524 avg, 0.001000 rate, 18.791874 seconds, 1342208 images -Loaded: 2.820781 seconds -Region Avg IOU: 0.518865, Class: 1.000000, Obj: 0.310937, No Obj: 0.004685, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.470412, Class: 1.000000, Obj: 0.272299, No Obj: 0.003784, Avg Recall: 0.547368, count: 95 -Region Avg IOU: 0.495730, Class: 1.000000, Obj: 0.317150, No Obj: 0.004064, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.438489, Class: 1.000000, Obj: 0.293435, No Obj: 0.003823, Avg Recall: 0.442478, count: 113 -Region Avg IOU: 0.479584, Class: 1.000000, Obj: 0.298091, No Obj: 0.004105, Avg Recall: 0.477778, count: 90 -Region Avg IOU: 0.520273, Class: 1.000000, Obj: 0.328325, No Obj: 0.004012, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.411575, Class: 1.000000, Obj: 0.245021, No Obj: 0.004287, Avg Recall: 0.426667, count: 150 -Region Avg IOU: 0.477143, Class: 1.000000, Obj: 0.284397, No Obj: 0.004249, Avg Recall: 0.508772, count: 114 -5244: 0.467261, 0.458498 avg, 0.001000 rate, 17.428219 seconds, 1342464 images -Loaded: 3.889881 seconds -Region Avg IOU: 0.507794, Class: 1.000000, Obj: 0.326524, No Obj: 0.004132, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.427961, Class: 1.000000, Obj: 0.279210, No Obj: 0.004663, Avg Recall: 0.459854, count: 137 -Region Avg IOU: 0.475640, Class: 1.000000, Obj: 0.299914, No Obj: 0.004741, Avg Recall: 0.528846, count: 104 -Region Avg IOU: 0.471450, Class: 1.000000, Obj: 0.259939, No Obj: 0.004023, Avg Recall: 0.500000, count: 116 -Region Avg IOU: 0.468676, Class: 1.000000, Obj: 0.290935, No Obj: 0.004561, Avg Recall: 0.491379, count: 116 -Region Avg IOU: 0.477747, Class: 1.000000, Obj: 0.293197, No Obj: 0.005217, Avg Recall: 0.521429, count: 140 -Region Avg IOU: 0.460883, Class: 1.000000, Obj: 0.282230, No Obj: 0.004293, Avg Recall: 0.504132, count: 121 -Region Avg IOU: 0.487673, Class: 1.000000, Obj: 0.343508, No Obj: 0.004384, Avg Recall: 0.536842, count: 95 -5245: 0.506111, 0.463259 avg, 0.001000 rate, 17.014893 seconds, 1342720 images -Loaded: 3.364840 seconds -Region Avg IOU: 0.494397, Class: 1.000000, Obj: 0.328226, No Obj: 0.004820, Avg Recall: 0.512821, count: 117 -Region Avg IOU: 0.469946, Class: 1.000000, Obj: 0.337788, No Obj: 0.004137, Avg Recall: 0.483146, count: 89 -Region Avg IOU: 0.498387, Class: 1.000000, Obj: 0.329103, No Obj: 0.005105, Avg Recall: 0.515873, count: 126 -Region Avg IOU: 0.504026, Class: 1.000000, Obj: 0.318774, No Obj: 0.004779, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.466767, Class: 1.000000, Obj: 0.335123, No Obj: 0.003876, Avg Recall: 0.516484, count: 91 -Region Avg IOU: 0.494945, Class: 1.000000, Obj: 0.353696, No Obj: 0.004577, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.442496, Class: 1.000000, Obj: 0.342776, No Obj: 0.004444, Avg Recall: 0.448980, count: 98 -Region Avg IOU: 0.489022, Class: 1.000000, Obj: 0.322055, No Obj: 0.004667, Avg Recall: 0.555556, count: 90 -5246: 0.505751, 0.467509 avg, 0.001000 rate, 14.926719 seconds, 1342976 images -Loaded: 4.136238 seconds -Region Avg IOU: 0.499912, Class: 1.000000, Obj: 0.333501, No Obj: 0.004980, Avg Recall: 0.560748, count: 107 -Region Avg IOU: 0.523434, Class: 1.000000, Obj: 0.340033, No Obj: 0.005357, Avg Recall: 0.592593, count: 108 -Region Avg IOU: 0.473215, Class: 1.000000, Obj: 0.322620, No Obj: 0.004724, Avg Recall: 0.450450, count: 111 -Region Avg IOU: 0.433118, Class: 1.000000, Obj: 0.321375, No Obj: 0.004988, Avg Recall: 0.409524, count: 105 -Region Avg IOU: 0.465118, Class: 1.000000, Obj: 0.352243, No Obj: 0.004984, Avg Recall: 0.526316, count: 95 -Region Avg IOU: 0.431091, Class: 1.000000, Obj: 0.339646, No Obj: 0.004472, Avg Recall: 0.505747, count: 87 -Region Avg IOU: 0.509049, Class: 1.000000, Obj: 0.397072, No Obj: 0.004930, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.491896, Class: 1.000000, Obj: 0.383559, No Obj: 0.005030, Avg Recall: 0.571429, count: 98 -5247: 0.448262, 0.465584 avg, 0.001000 rate, 15.888141 seconds, 1343232 images -Loaded: 2.713299 seconds -Region Avg IOU: 0.441561, Class: 1.000000, Obj: 0.365435, No Obj: 0.004527, Avg Recall: 0.471910, count: 89 -Region Avg IOU: 0.451992, Class: 1.000000, Obj: 0.373801, No Obj: 0.005951, Avg Recall: 0.488189, count: 127 -Region Avg IOU: 0.463659, Class: 1.000000, Obj: 0.319336, No Obj: 0.005848, Avg Recall: 0.514925, count: 134 -Region Avg IOU: 0.446280, Class: 1.000000, Obj: 0.316785, No Obj: 0.005413, Avg Recall: 0.473684, count: 114 -Region Avg IOU: 0.465685, Class: 1.000000, Obj: 0.344534, No Obj: 0.005702, Avg Recall: 0.513514, count: 111 -Region Avg IOU: 0.491068, Class: 1.000000, Obj: 0.375459, No Obj: 0.004907, Avg Recall: 0.600000, count: 85 -Region Avg IOU: 0.498571, Class: 1.000000, Obj: 0.380848, No Obj: 0.005389, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.484864, Class: 1.000000, Obj: 0.367814, No Obj: 0.004988, Avg Recall: 0.478992, count: 119 -5248: 0.484247, 0.467450 avg, 0.001000 rate, 17.816872 seconds, 1343488 images -Loaded: 3.977283 seconds -Region Avg IOU: 0.520887, Class: 1.000000, Obj: 0.426396, No Obj: 0.004525, Avg Recall: 0.627119, count: 59 -Region Avg IOU: 0.506333, Class: 1.000000, Obj: 0.399604, No Obj: 0.005988, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.446758, Class: 1.000000, Obj: 0.373322, No Obj: 0.004959, Avg Recall: 0.473214, count: 112 -Region Avg IOU: 0.487514, Class: 1.000000, Obj: 0.371136, No Obj: 0.005075, Avg Recall: 0.565217, count: 92 -Region Avg IOU: 0.497604, Class: 1.000000, Obj: 0.389394, No Obj: 0.005365, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.449631, Class: 1.000000, Obj: 0.369099, No Obj: 0.005084, Avg Recall: 0.489796, count: 98 -Region Avg IOU: 0.480172, Class: 1.000000, Obj: 0.421303, No Obj: 0.005978, Avg Recall: 0.527473, count: 91 -Region Avg IOU: 0.490084, Class: 1.000000, Obj: 0.386359, No Obj: 0.006320, Avg Recall: 0.557522, count: 113 -5249: 0.395757, 0.460281 avg, 0.001000 rate, 16.595879 seconds, 1343744 images -Loaded: 2.697839 seconds -Region Avg IOU: 0.461192, Class: 1.000000, Obj: 0.352111, No Obj: 0.005893, Avg Recall: 0.515464, count: 97 -Region Avg IOU: 0.519533, Class: 1.000000, Obj: 0.379028, No Obj: 0.006022, Avg Recall: 0.593750, count: 96 -Region Avg IOU: 0.556516, Class: 1.000000, Obj: 0.425545, No Obj: 0.005390, Avg Recall: 0.708861, count: 79 -Region Avg IOU: 0.474099, Class: 1.000000, Obj: 0.376505, No Obj: 0.005537, Avg Recall: 0.517241, count: 116 -Region Avg IOU: 0.427768, Class: 1.000000, Obj: 0.376201, No Obj: 0.005399, Avg Recall: 0.406250, count: 128 -Region Avg IOU: 0.446581, Class: 1.000000, Obj: 0.370790, No Obj: 0.005704, Avg Recall: 0.477477, count: 111 -Region Avg IOU: 0.495460, Class: 1.000000, Obj: 0.386589, No Obj: 0.005738, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.416301, Class: 1.000000, Obj: 0.343550, No Obj: 0.005309, Avg Recall: 0.429688, count: 128 -5250: 0.463484, 0.460601 avg, 0.001000 rate, 16.442095 seconds, 1344000 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 0.000110 seconds -Region Avg IOU: 0.588195, Class: 1.000000, Obj: 0.443760, No Obj: 0.005137, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.583473, Class: 1.000000, Obj: 0.464826, No Obj: 0.004821, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.580877, Class: 1.000000, Obj: 0.469874, No Obj: 0.004878, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.603088, Class: 1.000000, Obj: 0.458480, No Obj: 0.004506, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.566272, Class: 1.000000, Obj: 0.452088, No Obj: 0.005444, Avg Recall: 0.657143, count: 140 -Region Avg IOU: 0.559966, Class: 1.000000, Obj: 0.432002, No Obj: 0.004307, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.575147, Class: 1.000000, Obj: 0.540654, No Obj: 0.004817, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.587965, Class: 1.000000, Obj: 0.534375, No Obj: 0.004683, Avg Recall: 0.681818, count: 88 -5251: 0.459990, 0.460540 avg, 0.001000 rate, 22.980600 seconds, 1344256 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.602486, Class: 1.000000, Obj: 0.471782, No Obj: 0.004956, Avg Recall: 0.752809, count: 89 -Region Avg IOU: 0.586663, Class: 1.000000, Obj: 0.506726, No Obj: 0.003863, Avg Recall: 0.696203, count: 79 -Region Avg IOU: 0.589990, Class: 1.000000, Obj: 0.471532, No Obj: 0.004587, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.566529, Class: 1.000000, Obj: 0.448469, No Obj: 0.004762, Avg Recall: 0.658333, count: 120 -Region Avg IOU: 0.583797, Class: 1.000000, Obj: 0.473858, No Obj: 0.005363, Avg Recall: 0.696296, count: 135 -Region Avg IOU: 0.609061, Class: 1.000000, Obj: 0.497252, No Obj: 0.004404, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.572514, Class: 1.000000, Obj: 0.438979, No Obj: 0.004653, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.659235, Class: 1.000000, Obj: 0.495281, No Obj: 0.004106, Avg Recall: 0.823529, count: 85 -5252: 0.382727, 0.452759 avg, 0.001000 rate, 22.456429 seconds, 1344512 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.492172, Class: 1.000000, Obj: 0.439103, No Obj: 0.003520, Avg Recall: 0.494737, count: 95 -Region Avg IOU: 0.543466, Class: 1.000000, Obj: 0.416616, No Obj: 0.005062, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.585715, Class: 1.000000, Obj: 0.431379, No Obj: 0.004828, Avg Recall: 0.709220, count: 141 -Region Avg IOU: 0.615650, Class: 1.000000, Obj: 0.486493, No Obj: 0.004915, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.593012, Class: 1.000000, Obj: 0.446480, No Obj: 0.004566, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.586602, Class: 1.000000, Obj: 0.444952, No Obj: 0.003977, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.549770, Class: 1.000000, Obj: 0.431488, No Obj: 0.004825, Avg Recall: 0.646154, count: 130 -Region Avg IOU: 0.557040, Class: 1.000000, Obj: 0.407642, No Obj: 0.004113, Avg Recall: 0.638655, count: 119 -5253: 0.433570, 0.450840 avg, 0.001000 rate, 23.181608 seconds, 1344768 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.583155, Class: 1.000000, Obj: 0.392306, No Obj: 0.003849, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.605542, Class: 1.000000, Obj: 0.417611, No Obj: 0.004900, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.559040, Class: 1.000000, Obj: 0.440507, No Obj: 0.004156, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.582196, Class: 1.000000, Obj: 0.428074, No Obj: 0.004011, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.494288, Class: 1.000000, Obj: 0.379998, No Obj: 0.003724, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.544065, Class: 1.000000, Obj: 0.381576, No Obj: 0.003826, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.547519, Class: 1.000000, Obj: 0.417822, No Obj: 0.004068, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.577199, Class: 1.000000, Obj: 0.466266, No Obj: 0.004220, Avg Recall: 0.655738, count: 122 -5254: 0.404921, 0.446248 avg, 0.001000 rate, 22.316441 seconds, 1345024 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.501942, Class: 1.000000, Obj: 0.390865, No Obj: 0.003593, Avg Recall: 0.533333, count: 120 -Region Avg IOU: 0.564183, Class: 1.000000, Obj: 0.403639, No Obj: 0.004118, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.573187, Class: 1.000000, Obj: 0.372089, No Obj: 0.003912, Avg Recall: 0.642276, count: 123 -Region Avg IOU: 0.559618, Class: 1.000000, Obj: 0.445009, No Obj: 0.003303, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.537093, Class: 1.000000, Obj: 0.350682, No Obj: 0.004151, Avg Recall: 0.617647, count: 136 -Region Avg IOU: 0.539348, Class: 1.000000, Obj: 0.395562, No Obj: 0.003593, Avg Recall: 0.645669, count: 127 -Region Avg IOU: 0.575305, Class: 1.000000, Obj: 0.409305, No Obj: 0.003537, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.571452, Class: 1.000000, Obj: 0.403522, No Obj: 0.004448, Avg Recall: 0.676056, count: 142 -5255: 0.427026, 0.444326 avg, 0.001000 rate, 23.209892 seconds, 1345280 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.590593, Class: 1.000000, Obj: 0.378032, No Obj: 0.003685, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.568321, Class: 1.000000, Obj: 0.375755, No Obj: 0.004138, Avg Recall: 0.671533, count: 137 -Region Avg IOU: 0.548041, Class: 1.000000, Obj: 0.345809, No Obj: 0.002997, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.529392, Class: 1.000000, Obj: 0.321596, No Obj: 0.003547, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.515980, Class: 1.000000, Obj: 0.348285, No Obj: 0.003161, Avg Recall: 0.601695, count: 118 -Region Avg IOU: 0.531780, Class: 1.000000, Obj: 0.358328, No Obj: 0.003038, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.605481, Class: 1.000000, Obj: 0.380143, No Obj: 0.003589, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.615682, Class: 1.000000, Obj: 0.405129, No Obj: 0.003817, Avg Recall: 0.771930, count: 114 -5256: 0.433232, 0.443216 avg, 0.001000 rate, 23.308559 seconds, 1345536 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.579080, Class: 1.000000, Obj: 0.331336, No Obj: 0.003024, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.608725, Class: 1.000000, Obj: 0.357162, No Obj: 0.003624, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.586115, Class: 1.000000, Obj: 0.353790, No Obj: 0.003101, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.560663, Class: 1.000000, Obj: 0.322003, No Obj: 0.002909, Avg Recall: 0.671053, count: 76 -Region Avg IOU: 0.579408, Class: 1.000000, Obj: 0.340410, No Obj: 0.003708, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.593222, Class: 1.000000, Obj: 0.336123, No Obj: 0.003290, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.530948, Class: 1.000000, Obj: 0.310055, No Obj: 0.002968, Avg Recall: 0.593220, count: 118 -Region Avg IOU: 0.607427, Class: 1.000000, Obj: 0.304937, No Obj: 0.003278, Avg Recall: 0.712871, count: 101 -5257: 0.406092, 0.439504 avg, 0.001000 rate, 24.590504 seconds, 1345792 images -Loaded: 0.061332 seconds -Region Avg IOU: 0.591096, Class: 1.000000, Obj: 0.311293, No Obj: 0.003616, Avg Recall: 0.682171, count: 129 -Region Avg IOU: 0.556145, Class: 1.000000, Obj: 0.292602, No Obj: 0.002851, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.639902, Class: 1.000000, Obj: 0.334793, No Obj: 0.003281, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.585470, Class: 1.000000, Obj: 0.361089, No Obj: 0.002915, Avg Recall: 0.686046, count: 86 -Region Avg IOU: 0.565632, Class: 1.000000, Obj: 0.319823, No Obj: 0.003441, Avg Recall: 0.657143, count: 140 -Region Avg IOU: 0.571636, Class: 1.000000, Obj: 0.365815, No Obj: 0.003074, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.526823, Class: 1.000000, Obj: 0.293920, No Obj: 0.003599, Avg Recall: 0.560811, count: 148 -Region Avg IOU: 0.579049, Class: 1.000000, Obj: 0.335334, No Obj: 0.003511, Avg Recall: 0.690265, count: 113 -5258: 0.458860, 0.441439 avg, 0.001000 rate, 23.098982 seconds, 1346048 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.631197, Class: 1.000000, Obj: 0.408551, No Obj: 0.003124, Avg Recall: 0.802083, count: 96 -Region Avg IOU: 0.555783, Class: 1.000000, Obj: 0.311862, No Obj: 0.002694, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.575691, Class: 1.000000, Obj: 0.346458, No Obj: 0.002874, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.591140, Class: 1.000000, Obj: 0.336731, No Obj: 0.002865, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.566264, Class: 1.000000, Obj: 0.354398, No Obj: 0.002695, Avg Recall: 0.662338, count: 77 -Region Avg IOU: 0.532578, Class: 1.000000, Obj: 0.286832, No Obj: 0.003297, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.602700, Class: 1.000000, Obj: 0.355734, No Obj: 0.003227, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.570185, Class: 1.000000, Obj: 0.331559, No Obj: 0.003049, Avg Recall: 0.644231, count: 104 -5259: 0.447075, 0.442003 avg, 0.001000 rate, 23.690912 seconds, 1346304 images -Loaded: 0.000054 seconds -Region Avg IOU: 0.582789, Class: 1.000000, Obj: 0.345521, No Obj: 0.003100, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.557042, Class: 1.000000, Obj: 0.329037, No Obj: 0.002628, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.570709, Class: 1.000000, Obj: 0.275440, No Obj: 0.003454, Avg Recall: 0.671875, count: 128 -Region Avg IOU: 0.602825, Class: 1.000000, Obj: 0.371737, No Obj: 0.002965, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.608352, Class: 1.000000, Obj: 0.377189, No Obj: 0.003319, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.572276, Class: 1.000000, Obj: 0.394289, No Obj: 0.002982, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.576517, Class: 1.000000, Obj: 0.345390, No Obj: 0.003066, Avg Recall: 0.690476, count: 84 -Region Avg IOU: 0.588330, Class: 1.000000, Obj: 0.337940, No Obj: 0.003461, Avg Recall: 0.671429, count: 140 -5260: 0.454146, 0.443217 avg, 0.001000 rate, 23.709452 seconds, 1346560 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000058 seconds -Region Avg IOU: 0.583066, Class: 1.000000, Obj: 0.359592, No Obj: 0.003316, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.606702, Class: 1.000000, Obj: 0.352877, No Obj: 0.003326, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.579356, Class: 1.000000, Obj: 0.348370, No Obj: 0.002824, Avg Recall: 0.705128, count: 78 -Region Avg IOU: 0.577645, Class: 1.000000, Obj: 0.337490, No Obj: 0.002661, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.589596, Class: 1.000000, Obj: 0.350948, No Obj: 0.003487, Avg Recall: 0.703704, count: 135 -Region Avg IOU: 0.624919, Class: 1.000000, Obj: 0.381898, No Obj: 0.003787, Avg Recall: 0.742424, count: 132 -Region Avg IOU: 0.631976, Class: 1.000000, Obj: 0.374411, No Obj: 0.003058, Avg Recall: 0.784091, count: 88 -Region Avg IOU: 0.554977, Class: 1.000000, Obj: 0.365599, No Obj: 0.003121, Avg Recall: 0.630252, count: 119 -5261: 0.463009, 0.445197 avg, 0.001000 rate, 23.098742 seconds, 1346816 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.622361, Class: 1.000000, Obj: 0.406100, No Obj: 0.003468, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.574827, Class: 1.000000, Obj: 0.392660, No Obj: 0.002931, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.571700, Class: 1.000000, Obj: 0.368416, No Obj: 0.003164, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.598985, Class: 1.000000, Obj: 0.407804, No Obj: 0.003182, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.604125, Class: 1.000000, Obj: 0.371068, No Obj: 0.003461, Avg Recall: 0.733333, count: 120 -Region Avg IOU: 0.628221, Class: 1.000000, Obj: 0.377653, No Obj: 0.003221, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.593749, Class: 1.000000, Obj: 0.413653, No Obj: 0.003183, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.612793, Class: 1.000000, Obj: 0.394744, No Obj: 0.003443, Avg Recall: 0.717742, count: 124 -5262: 0.418211, 0.442498 avg, 0.001000 rate, 23.971754 seconds, 1347072 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.565083, Class: 1.000000, Obj: 0.377046, No Obj: 0.003092, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.531597, Class: 1.000000, Obj: 0.320835, No Obj: 0.003457, Avg Recall: 0.609023, count: 133 -Region Avg IOU: 0.640249, Class: 1.000000, Obj: 0.451056, No Obj: 0.003415, Avg Recall: 0.800000, count: 95 -Region Avg IOU: 0.546001, Class: 1.000000, Obj: 0.378903, No Obj: 0.003511, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.618666, Class: 1.000000, Obj: 0.414070, No Obj: 0.003883, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.566208, Class: 1.000000, Obj: 0.359474, No Obj: 0.003457, Avg Recall: 0.669231, count: 130 -Region Avg IOU: 0.588166, Class: 1.000000, Obj: 0.379726, No Obj: 0.003606, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.541270, Class: 1.000000, Obj: 0.375197, No Obj: 0.002835, Avg Recall: 0.610526, count: 95 -5263: 0.445614, 0.442810 avg, 0.001000 rate, 22.697512 seconds, 1347328 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.603581, Class: 1.000000, Obj: 0.427774, No Obj: 0.003602, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.578200, Class: 1.000000, Obj: 0.385376, No Obj: 0.003676, Avg Recall: 0.696000, count: 125 -Region Avg IOU: 0.600413, Class: 1.000000, Obj: 0.434088, No Obj: 0.003419, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.574915, Class: 1.000000, Obj: 0.415596, No Obj: 0.003546, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.555913, Class: 1.000000, Obj: 0.354958, No Obj: 0.003338, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.596496, Class: 1.000000, Obj: 0.414497, No Obj: 0.003394, Avg Recall: 0.744186, count: 86 -Region Avg IOU: 0.620560, Class: 1.000000, Obj: 0.449774, No Obj: 0.003610, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.614331, Class: 1.000000, Obj: 0.460022, No Obj: 0.003548, Avg Recall: 0.723404, count: 94 -5264: 0.393484, 0.437877 avg, 0.001000 rate, 24.900908 seconds, 1347584 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.568983, Class: 1.000000, Obj: 0.432468, No Obj: 0.003931, Avg Recall: 0.658537, count: 123 -Region Avg IOU: 0.631094, Class: 1.000000, Obj: 0.443280, No Obj: 0.003738, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.606887, Class: 1.000000, Obj: 0.471484, No Obj: 0.003883, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.603439, Class: 1.000000, Obj: 0.441636, No Obj: 0.003844, Avg Recall: 0.701149, count: 87 -Region Avg IOU: 0.581345, Class: 1.000000, Obj: 0.445542, No Obj: 0.003822, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.608375, Class: 1.000000, Obj: 0.467156, No Obj: 0.004141, Avg Recall: 0.712000, count: 125 -Region Avg IOU: 0.579466, Class: 1.000000, Obj: 0.437088, No Obj: 0.003683, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.590903, Class: 1.000000, Obj: 0.456490, No Obj: 0.003441, Avg Recall: 0.700000, count: 80 -5265: 0.408757, 0.434965 avg, 0.001000 rate, 22.744816 seconds, 1347840 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.617505, Class: 1.000000, Obj: 0.454125, No Obj: 0.003476, Avg Recall: 0.710145, count: 69 -Region Avg IOU: 0.558042, Class: 1.000000, Obj: 0.395325, No Obj: 0.003531, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.580463, Class: 1.000000, Obj: 0.411626, No Obj: 0.004114, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.534071, Class: 1.000000, Obj: 0.459756, No Obj: 0.003987, Avg Recall: 0.601626, count: 123 -Region Avg IOU: 0.581303, Class: 1.000000, Obj: 0.421350, No Obj: 0.004328, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.622646, Class: 1.000000, Obj: 0.495489, No Obj: 0.003912, Avg Recall: 0.712644, count: 87 -Region Avg IOU: 0.546007, Class: 1.000000, Obj: 0.429011, No Obj: 0.004072, Avg Recall: 0.624060, count: 133 -Region Avg IOU: 0.582598, Class: 1.000000, Obj: 0.455218, No Obj: 0.004110, Avg Recall: 0.692308, count: 104 -5266: 0.433556, 0.434824 avg, 0.001000 rate, 25.509481 seconds, 1348096 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.577743, Class: 1.000000, Obj: 0.413346, No Obj: 0.004121, Avg Recall: 0.680000, count: 125 -Region Avg IOU: 0.583969, Class: 1.000000, Obj: 0.411668, No Obj: 0.003840, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.577362, Class: 1.000000, Obj: 0.419156, No Obj: 0.004624, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.611096, Class: 1.000000, Obj: 0.463383, No Obj: 0.004349, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.538099, Class: 1.000000, Obj: 0.389224, No Obj: 0.004289, Avg Recall: 0.577778, count: 135 -Region Avg IOU: 0.564132, Class: 1.000000, Obj: 0.449380, No Obj: 0.004438, Avg Recall: 0.651515, count: 132 -Region Avg IOU: 0.571300, Class: 1.000000, Obj: 0.434791, No Obj: 0.003813, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.548668, Class: 1.000000, Obj: 0.407655, No Obj: 0.003901, Avg Recall: 0.650000, count: 120 -5267: 0.490898, 0.440432 avg, 0.001000 rate, 23.529411 seconds, 1348352 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.626611, Class: 1.000000, Obj: 0.417317, No Obj: 0.004506, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.538828, Class: 1.000000, Obj: 0.400797, No Obj: 0.003441, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.599624, Class: 1.000000, Obj: 0.438151, No Obj: 0.002977, Avg Recall: 0.705882, count: 68 -Region Avg IOU: 0.587820, Class: 1.000000, Obj: 0.407359, No Obj: 0.004184, Avg Recall: 0.695312, count: 128 -Region Avg IOU: 0.579955, Class: 1.000000, Obj: 0.453686, No Obj: 0.003880, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.594434, Class: 1.000000, Obj: 0.455123, No Obj: 0.003602, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.536779, Class: 1.000000, Obj: 0.393606, No Obj: 0.003339, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.572109, Class: 1.000000, Obj: 0.441798, No Obj: 0.003579, Avg Recall: 0.680000, count: 100 -5268: 0.394559, 0.435844 avg, 0.001000 rate, 23.242432 seconds, 1348608 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.578987, Class: 1.000000, Obj: 0.421822, No Obj: 0.003496, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.586718, Class: 1.000000, Obj: 0.407065, No Obj: 0.003797, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.565793, Class: 1.000000, Obj: 0.366808, No Obj: 0.003920, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.631898, Class: 1.000000, Obj: 0.417373, No Obj: 0.004515, Avg Recall: 0.820000, count: 100 -Region Avg IOU: 0.614916, Class: 1.000000, Obj: 0.454840, No Obj: 0.003708, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.590023, Class: 1.000000, Obj: 0.471499, No Obj: 0.003059, Avg Recall: 0.679012, count: 81 -Region Avg IOU: 0.594125, Class: 1.000000, Obj: 0.429053, No Obj: 0.003658, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.576235, Class: 1.000000, Obj: 0.417688, No Obj: 0.003807, Avg Recall: 0.680000, count: 100 -5269: 0.444989, 0.436759 avg, 0.001000 rate, 23.697655 seconds, 1348864 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.589540, Class: 1.000000, Obj: 0.387206, No Obj: 0.003422, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.554340, Class: 1.000000, Obj: 0.442195, No Obj: 0.003980, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.567081, Class: 1.000000, Obj: 0.422504, No Obj: 0.003050, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.579932, Class: 1.000000, Obj: 0.414517, No Obj: 0.003699, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.588228, Class: 1.000000, Obj: 0.402874, No Obj: 0.004010, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.564059, Class: 1.000000, Obj: 0.387466, No Obj: 0.003408, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.571505, Class: 1.000000, Obj: 0.395726, No Obj: 0.003294, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.603054, Class: 1.000000, Obj: 0.439737, No Obj: 0.003723, Avg Recall: 0.724771, count: 109 -5270: 0.428658, 0.435949 avg, 0.001000 rate, 23.387409 seconds, 1349120 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 20.976465 seconds -Region Avg IOU: 0.554747, Class: 1.000000, Obj: 0.376139, No Obj: 0.003399, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.571778, Class: 1.000000, Obj: 0.365759, No Obj: 0.004292, Avg Recall: 0.657534, count: 146 -Region Avg IOU: 0.606382, Class: 1.000000, Obj: 0.368246, No Obj: 0.003926, Avg Recall: 0.742188, count: 128 -Region Avg IOU: 0.581017, Class: 1.000000, Obj: 0.339566, No Obj: 0.003897, Avg Recall: 0.676471, count: 136 -Region Avg IOU: 0.546641, Class: 1.000000, Obj: 0.349413, No Obj: 0.003814, Avg Recall: 0.593750, count: 128 -Region Avg IOU: 0.518903, Class: 1.000000, Obj: 0.361034, No Obj: 0.003118, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.589954, Class: 1.000000, Obj: 0.379343, No Obj: 0.003494, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.598842, Class: 1.000000, Obj: 0.329457, No Obj: 0.003473, Avg Recall: 0.723214, count: 112 -5271: 0.484537, 0.440808 avg, 0.001000 rate, 25.145041 seconds, 1349376 images -Loaded: 0.000059 seconds -Region Avg IOU: 0.544196, Class: 1.000000, Obj: 0.370408, No Obj: 0.003376, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.623748, Class: 1.000000, Obj: 0.418791, No Obj: 0.003724, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.604951, Class: 1.000000, Obj: 0.414215, No Obj: 0.003935, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.581197, Class: 1.000000, Obj: 0.394399, No Obj: 0.003039, Avg Recall: 0.670455, count: 88 -Region Avg IOU: 0.595866, Class: 1.000000, Obj: 0.357239, No Obj: 0.003664, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.565244, Class: 1.000000, Obj: 0.350913, No Obj: 0.003196, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.493144, Class: 1.000000, Obj: 0.332702, No Obj: 0.003509, Avg Recall: 0.546875, count: 128 -Region Avg IOU: 0.599076, Class: 1.000000, Obj: 0.386304, No Obj: 0.003704, Avg Recall: 0.694915, count: 118 -5272: 0.406532, 0.437380 avg, 0.001000 rate, 23.328205 seconds, 1349632 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.530099, Class: 1.000000, Obj: 0.341866, No Obj: 0.003136, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.537587, Class: 1.000000, Obj: 0.350168, No Obj: 0.003037, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.566200, Class: 1.000000, Obj: 0.353065, No Obj: 0.003089, Avg Recall: 0.619048, count: 84 -Region Avg IOU: 0.609677, Class: 1.000000, Obj: 0.437398, No Obj: 0.003810, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.532086, Class: 1.000000, Obj: 0.359281, No Obj: 0.003601, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.590996, Class: 1.000000, Obj: 0.350858, No Obj: 0.004153, Avg Recall: 0.702899, count: 138 -Region Avg IOU: 0.575240, Class: 1.000000, Obj: 0.364157, No Obj: 0.003435, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.555442, Class: 1.000000, Obj: 0.365820, No Obj: 0.003264, Avg Recall: 0.613445, count: 119 -5273: 0.437288, 0.437371 avg, 0.001000 rate, 23.461824 seconds, 1349888 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.632977, Class: 1.000000, Obj: 0.432301, No Obj: 0.003311, Avg Recall: 0.743590, count: 78 -Region Avg IOU: 0.572656, Class: 1.000000, Obj: 0.357411, No Obj: 0.003902, Avg Recall: 0.671429, count: 140 -Region Avg IOU: 0.507626, Class: 1.000000, Obj: 0.323037, No Obj: 0.003222, Avg Recall: 0.543307, count: 127 -Region Avg IOU: 0.510072, Class: 1.000000, Obj: 0.368958, No Obj: 0.003040, Avg Recall: 0.500000, count: 110 -Region Avg IOU: 0.566571, Class: 1.000000, Obj: 0.352616, No Obj: 0.003521, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.557067, Class: 1.000000, Obj: 0.336025, No Obj: 0.004248, Avg Recall: 0.629870, count: 154 -Region Avg IOU: 0.606382, Class: 1.000000, Obj: 0.364084, No Obj: 0.004032, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.557080, Class: 1.000000, Obj: 0.378186, No Obj: 0.003025, Avg Recall: 0.615385, count: 78 -5274: 0.449282, 0.438562 avg, 0.001000 rate, 23.255636 seconds, 1350144 images -Loaded: 0.022173 seconds -Region Avg IOU: 0.591781, Class: 1.000000, Obj: 0.369501, No Obj: 0.003393, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.589023, Class: 1.000000, Obj: 0.395701, No Obj: 0.003594, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.571123, Class: 1.000000, Obj: 0.357626, No Obj: 0.003611, Avg Recall: 0.683761, count: 117 -Region Avg IOU: 0.578949, Class: 1.000000, Obj: 0.403281, No Obj: 0.003410, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.540325, Class: 1.000000, Obj: 0.368410, No Obj: 0.003755, Avg Recall: 0.631148, count: 122 -Region Avg IOU: 0.589280, Class: 1.000000, Obj: 0.360459, No Obj: 0.003685, Avg Recall: 0.710526, count: 114 -Region Avg IOU: 0.610752, Class: 1.000000, Obj: 0.385837, No Obj: 0.003853, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.611183, Class: 1.000000, Obj: 0.341331, No Obj: 0.003486, Avg Recall: 0.759259, count: 108 -5275: 0.435399, 0.438246 avg, 0.001000 rate, 23.950056 seconds, 1350400 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.576674, Class: 1.000000, Obj: 0.345613, No Obj: 0.003799, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.564962, Class: 1.000000, Obj: 0.417837, No Obj: 0.003655, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.597124, Class: 1.000000, Obj: 0.375994, No Obj: 0.003917, Avg Recall: 0.685039, count: 127 -Region Avg IOU: 0.544327, Class: 1.000000, Obj: 0.338268, No Obj: 0.003116, Avg Recall: 0.636364, count: 88 -Region Avg IOU: 0.604273, Class: 1.000000, Obj: 0.403122, No Obj: 0.004026, Avg Recall: 0.726562, count: 128 -Region Avg IOU: 0.587018, Class: 1.000000, Obj: 0.374635, No Obj: 0.003893, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.518944, Class: 1.000000, Obj: 0.350798, No Obj: 0.003697, Avg Recall: 0.605042, count: 119 -Region Avg IOU: 0.568211, Class: 1.000000, Obj: 0.342618, No Obj: 0.003199, Avg Recall: 0.686275, count: 102 -5276: 0.447864, 0.439208 avg, 0.001000 rate, 22.271240 seconds, 1350656 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.594090, Class: 1.000000, Obj: 0.382161, No Obj: 0.003014, Avg Recall: 0.739726, count: 73 -Region Avg IOU: 0.547370, Class: 1.000000, Obj: 0.393447, No Obj: 0.003399, Avg Recall: 0.620690, count: 116 -Region Avg IOU: 0.563358, Class: 1.000000, Obj: 0.382687, No Obj: 0.002979, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.605026, Class: 1.000000, Obj: 0.389713, No Obj: 0.003831, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.601227, Class: 1.000000, Obj: 0.384204, No Obj: 0.003580, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.568521, Class: 1.000000, Obj: 0.395400, No Obj: 0.003206, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.538991, Class: 1.000000, Obj: 0.395855, No Obj: 0.003036, Avg Recall: 0.647727, count: 88 -Region Avg IOU: 0.600562, Class: 1.000000, Obj: 0.354022, No Obj: 0.003925, Avg Recall: 0.722222, count: 126 -5277: 0.370545, 0.432341 avg, 0.001000 rate, 23.624176 seconds, 1350912 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.558292, Class: 1.000000, Obj: 0.372375, No Obj: 0.003458, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.529290, Class: 1.000000, Obj: 0.335357, No Obj: 0.003719, Avg Recall: 0.601504, count: 133 -Region Avg IOU: 0.540139, Class: 1.000000, Obj: 0.358559, No Obj: 0.003586, Avg Recall: 0.611111, count: 126 -Region Avg IOU: 0.568098, Class: 1.000000, Obj: 0.407536, No Obj: 0.003328, Avg Recall: 0.648352, count: 91 -Region Avg IOU: 0.573837, Class: 1.000000, Obj: 0.394373, No Obj: 0.003260, Avg Recall: 0.682353, count: 85 -Region Avg IOU: 0.603183, Class: 1.000000, Obj: 0.431204, No Obj: 0.003499, Avg Recall: 0.707865, count: 89 -Region Avg IOU: 0.584653, Class: 1.000000, Obj: 0.421674, No Obj: 0.003690, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.580723, Class: 1.000000, Obj: 0.397024, No Obj: 0.003791, Avg Recall: 0.663265, count: 98 -5278: 0.457107, 0.434818 avg, 0.001000 rate, 22.608913 seconds, 1351168 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.540867, Class: 1.000000, Obj: 0.400478, No Obj: 0.003285, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.594032, Class: 1.000000, Obj: 0.443842, No Obj: 0.003530, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.620288, Class: 1.000000, Obj: 0.437794, No Obj: 0.003552, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.625732, Class: 1.000000, Obj: 0.450983, No Obj: 0.003503, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.568662, Class: 1.000000, Obj: 0.410118, No Obj: 0.004162, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.554671, Class: 1.000000, Obj: 0.407505, No Obj: 0.003917, Avg Recall: 0.658333, count: 120 -Region Avg IOU: 0.594075, Class: 1.000000, Obj: 0.396163, No Obj: 0.003082, Avg Recall: 0.662651, count: 83 -Region Avg IOU: 0.560871, Class: 1.000000, Obj: 0.381780, No Obj: 0.003509, Avg Recall: 0.659341, count: 91 -5279: 0.381203, 0.429456 avg, 0.001000 rate, 23.873894 seconds, 1351424 images -Loaded: 0.301857 seconds -Region Avg IOU: 0.617693, Class: 1.000000, Obj: 0.361292, No Obj: 0.004117, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.627540, Class: 1.000000, Obj: 0.433827, No Obj: 0.003750, Avg Recall: 0.759036, count: 83 -Region Avg IOU: 0.578339, Class: 1.000000, Obj: 0.431233, No Obj: 0.004107, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.561470, Class: 1.000000, Obj: 0.420525, No Obj: 0.004365, Avg Recall: 0.688889, count: 135 -Region Avg IOU: 0.637544, Class: 1.000000, Obj: 0.446212, No Obj: 0.004105, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.580670, Class: 1.000000, Obj: 0.388723, No Obj: 0.003838, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.599194, Class: 1.000000, Obj: 0.380600, No Obj: 0.003503, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.595098, Class: 1.000000, Obj: 0.403802, No Obj: 0.003714, Avg Recall: 0.676471, count: 102 -5280: 0.412638, 0.427775 avg, 0.001000 rate, 22.718409 seconds, 1351680 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000076 seconds -Region Avg IOU: 0.601937, Class: 1.000000, Obj: 0.421049, No Obj: 0.003163, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.538247, Class: 1.000000, Obj: 0.368612, No Obj: 0.003068, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.581659, Class: 1.000000, Obj: 0.385991, No Obj: 0.003180, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.593546, Class: 1.000000, Obj: 0.435808, No Obj: 0.003449, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.580739, Class: 1.000000, Obj: 0.432358, No Obj: 0.003570, Avg Recall: 0.691667, count: 120 -Region Avg IOU: 0.595908, Class: 1.000000, Obj: 0.452183, No Obj: 0.004253, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.600867, Class: 1.000000, Obj: 0.457371, No Obj: 0.003714, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.691372, Class: 1.000000, Obj: 0.500946, No Obj: 0.003606, Avg Recall: 0.876543, count: 81 -5281: 0.376426, 0.422640 avg, 0.001000 rate, 24.354752 seconds, 1351936 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.613532, Class: 1.000000, Obj: 0.438714, No Obj: 0.003909, Avg Recall: 0.726562, count: 128 -Region Avg IOU: 0.659200, Class: 1.000000, Obj: 0.420973, No Obj: 0.004514, Avg Recall: 0.813953, count: 129 -Region Avg IOU: 0.579892, Class: 1.000000, Obj: 0.428047, No Obj: 0.004188, Avg Recall: 0.698529, count: 136 -Region Avg IOU: 0.606169, Class: 1.000000, Obj: 0.445496, No Obj: 0.002815, Avg Recall: 0.717647, count: 85 -Region Avg IOU: 0.562120, Class: 1.000000, Obj: 0.408000, No Obj: 0.004121, Avg Recall: 0.637681, count: 138 -Region Avg IOU: 0.591921, Class: 1.000000, Obj: 0.458962, No Obj: 0.003286, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.626659, Class: 1.000000, Obj: 0.437818, No Obj: 0.004010, Avg Recall: 0.763780, count: 127 -Region Avg IOU: 0.609305, Class: 1.000000, Obj: 0.417966, No Obj: 0.003550, Avg Recall: 0.699029, count: 103 -5282: 0.446595, 0.425035 avg, 0.001000 rate, 24.118284 seconds, 1352192 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.615557, Class: 1.000000, Obj: 0.481028, No Obj: 0.003572, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.568985, Class: 1.000000, Obj: 0.401805, No Obj: 0.003543, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.600098, Class: 1.000000, Obj: 0.387215, No Obj: 0.003071, Avg Recall: 0.740260, count: 77 -Region Avg IOU: 0.565002, Class: 1.000000, Obj: 0.373802, No Obj: 0.003410, Avg Recall: 0.669565, count: 115 -Region Avg IOU: 0.560223, Class: 1.000000, Obj: 0.464701, No Obj: 0.003461, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.564714, Class: 1.000000, Obj: 0.410651, No Obj: 0.003614, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.634796, Class: 1.000000, Obj: 0.483530, No Obj: 0.003705, Avg Recall: 0.814433, count: 97 -Region Avg IOU: 0.620023, Class: 1.000000, Obj: 0.409324, No Obj: 0.004170, Avg Recall: 0.789855, count: 138 -5283: 0.395316, 0.422063 avg, 0.001000 rate, 25.028025 seconds, 1352448 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.583910, Class: 1.000000, Obj: 0.404280, No Obj: 0.003565, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.665177, Class: 1.000000, Obj: 0.469576, No Obj: 0.003116, Avg Recall: 0.841270, count: 63 -Region Avg IOU: 0.605685, Class: 1.000000, Obj: 0.454545, No Obj: 0.003379, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.601191, Class: 1.000000, Obj: 0.397401, No Obj: 0.003374, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.585631, Class: 1.000000, Obj: 0.380432, No Obj: 0.002985, Avg Recall: 0.714286, count: 84 -Region Avg IOU: 0.605980, Class: 1.000000, Obj: 0.413284, No Obj: 0.003446, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.595963, Class: 1.000000, Obj: 0.402701, No Obj: 0.003798, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.602577, Class: 1.000000, Obj: 0.456764, No Obj: 0.003765, Avg Recall: 0.729730, count: 111 -5284: 0.403589, 0.420216 avg, 0.001000 rate, 25.745899 seconds, 1352704 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.589097, Class: 1.000000, Obj: 0.409795, No Obj: 0.003156, Avg Recall: 0.709302, count: 86 -Region Avg IOU: 0.603709, Class: 1.000000, Obj: 0.405892, No Obj: 0.003852, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.599908, Class: 1.000000, Obj: 0.432852, No Obj: 0.003457, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.625043, Class: 1.000000, Obj: 0.408086, No Obj: 0.003890, Avg Recall: 0.767442, count: 129 -Region Avg IOU: 0.655252, Class: 1.000000, Obj: 0.438356, No Obj: 0.003544, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.579491, Class: 1.000000, Obj: 0.376472, No Obj: 0.003383, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.570882, Class: 1.000000, Obj: 0.398916, No Obj: 0.002782, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.537192, Class: 1.000000, Obj: 0.361432, No Obj: 0.003253, Avg Recall: 0.655738, count: 122 -5285: 0.466064, 0.424801 avg, 0.001000 rate, 24.403296 seconds, 1352960 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.597176, Class: 1.000000, Obj: 0.410665, No Obj: 0.002818, Avg Recall: 0.720930, count: 86 -Region Avg IOU: 0.583149, Class: 1.000000, Obj: 0.395999, No Obj: 0.003080, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.578719, Class: 1.000000, Obj: 0.383349, No Obj: 0.003227, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.584681, Class: 1.000000, Obj: 0.411036, No Obj: 0.003512, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.594230, Class: 1.000000, Obj: 0.363300, No Obj: 0.003681, Avg Recall: 0.731343, count: 134 -Region Avg IOU: 0.599580, Class: 1.000000, Obj: 0.445787, No Obj: 0.003667, Avg Recall: 0.683761, count: 117 -Region Avg IOU: 0.575796, Class: 1.000000, Obj: 0.421315, No Obj: 0.002552, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.572816, Class: 1.000000, Obj: 0.377288, No Obj: 0.003013, Avg Recall: 0.698113, count: 106 -5286: 0.408341, 0.423155 avg, 0.001000 rate, 25.533176 seconds, 1353216 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.674616, Class: 1.000000, Obj: 0.405085, No Obj: 0.003063, Avg Recall: 0.877778, count: 90 -Region Avg IOU: 0.633128, Class: 1.000000, Obj: 0.396682, No Obj: 0.003242, Avg Recall: 0.833333, count: 114 -Region Avg IOU: 0.613231, Class: 1.000000, Obj: 0.421917, No Obj: 0.002574, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.597519, Class: 1.000000, Obj: 0.397531, No Obj: 0.003153, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.637191, Class: 1.000000, Obj: 0.403719, No Obj: 0.003496, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.637036, Class: 1.000000, Obj: 0.396204, No Obj: 0.003287, Avg Recall: 0.766667, count: 120 -Region Avg IOU: 0.624177, Class: 1.000000, Obj: 0.425932, No Obj: 0.003345, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.610834, Class: 1.000000, Obj: 0.395000, No Obj: 0.002966, Avg Recall: 0.734940, count: 83 -5287: 0.418776, 0.422717 avg, 0.001000 rate, 25.667631 seconds, 1353472 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.596062, Class: 1.000000, Obj: 0.346134, No Obj: 0.003097, Avg Recall: 0.740458, count: 131 -Region Avg IOU: 0.594147, Class: 1.000000, Obj: 0.376843, No Obj: 0.002666, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.640615, Class: 1.000000, Obj: 0.386005, No Obj: 0.003564, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.595373, Class: 1.000000, Obj: 0.380627, No Obj: 0.003057, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.619641, Class: 1.000000, Obj: 0.407041, No Obj: 0.003021, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.595333, Class: 1.000000, Obj: 0.421729, No Obj: 0.003150, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.576402, Class: 1.000000, Obj: 0.345211, No Obj: 0.003595, Avg Recall: 0.635135, count: 148 -Region Avg IOU: 0.577767, Class: 1.000000, Obj: 0.398463, No Obj: 0.002831, Avg Recall: 0.705263, count: 95 -5288: 0.450784, 0.425524 avg, 0.001000 rate, 26.188364 seconds, 1353728 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.569222, Class: 1.000000, Obj: 0.371316, No Obj: 0.002175, Avg Recall: 0.666667, count: 75 -Region Avg IOU: 0.564162, Class: 1.000000, Obj: 0.359862, No Obj: 0.003135, Avg Recall: 0.632000, count: 125 -Region Avg IOU: 0.607698, Class: 1.000000, Obj: 0.364989, No Obj: 0.003478, Avg Recall: 0.748092, count: 131 -Region Avg IOU: 0.636221, Class: 1.000000, Obj: 0.431105, No Obj: 0.003210, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.628853, Class: 1.000000, Obj: 0.386562, No Obj: 0.002391, Avg Recall: 0.767442, count: 86 -Region Avg IOU: 0.591696, Class: 1.000000, Obj: 0.350072, No Obj: 0.003549, Avg Recall: 0.690476, count: 126 -Region Avg IOU: 0.611841, Class: 1.000000, Obj: 0.373622, No Obj: 0.003094, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.579997, Class: 1.000000, Obj: 0.360914, No Obj: 0.003292, Avg Recall: 0.704348, count: 115 -5289: 0.430316, 0.426003 avg, 0.001000 rate, 23.592985 seconds, 1353984 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.587849, Class: 1.000000, Obj: 0.354138, No Obj: 0.003003, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.586221, Class: 1.000000, Obj: 0.425398, No Obj: 0.002959, Avg Recall: 0.675214, count: 117 -Region Avg IOU: 0.630947, Class: 1.000000, Obj: 0.383895, No Obj: 0.003114, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.613372, Class: 1.000000, Obj: 0.428897, No Obj: 0.002610, Avg Recall: 0.702381, count: 84 -Region Avg IOU: 0.659558, Class: 1.000000, Obj: 0.392117, No Obj: 0.003006, Avg Recall: 0.825581, count: 86 -Region Avg IOU: 0.538563, Class: 1.000000, Obj: 0.379546, No Obj: 0.003127, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.627001, Class: 1.000000, Obj: 0.369796, No Obj: 0.002868, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.622704, Class: 1.000000, Obj: 0.374750, No Obj: 0.003077, Avg Recall: 0.721649, count: 97 -5290: 0.381619, 0.421564 avg, 0.001000 rate, 25.268602 seconds, 1354240 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.148159 seconds -Region Avg IOU: 0.546189, Class: 1.000000, Obj: 0.330958, No Obj: 0.003163, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.542650, Class: 1.000000, Obj: 0.290435, No Obj: 0.003516, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.508407, Class: 1.000000, Obj: 0.293403, No Obj: 0.003550, Avg Recall: 0.560000, count: 125 -Region Avg IOU: 0.519628, Class: 1.000000, Obj: 0.293344, No Obj: 0.003148, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.591675, Class: 1.000000, Obj: 0.360789, No Obj: 0.003458, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.540060, Class: 1.000000, Obj: 0.355219, No Obj: 0.003018, Avg Recall: 0.574468, count: 94 -Region Avg IOU: 0.567203, Class: 1.000000, Obj: 0.346175, No Obj: 0.003366, Avg Recall: 0.654762, count: 84 -Region Avg IOU: 0.572541, Class: 1.000000, Obj: 0.390699, No Obj: 0.003681, Avg Recall: 0.659091, count: 88 -5291: 0.422437, 0.421652 avg, 0.001000 rate, 20.779976 seconds, 1354496 images -Loaded: 1.501027 seconds -Region Avg IOU: 0.562169, Class: 1.000000, Obj: 0.365970, No Obj: 0.003917, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.543540, Class: 1.000000, Obj: 0.374102, No Obj: 0.003261, Avg Recall: 0.652174, count: 92 -Region Avg IOU: 0.538506, Class: 1.000000, Obj: 0.337489, No Obj: 0.003209, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.509414, Class: 1.000000, Obj: 0.307091, No Obj: 0.003760, Avg Recall: 0.544715, count: 123 -Region Avg IOU: 0.544147, Class: 1.000000, Obj: 0.345012, No Obj: 0.003113, Avg Recall: 0.622222, count: 90 -Region Avg IOU: 0.536418, Class: 1.000000, Obj: 0.308481, No Obj: 0.003758, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.568116, Class: 1.000000, Obj: 0.372495, No Obj: 0.003714, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.525839, Class: 1.000000, Obj: 0.322813, No Obj: 0.004379, Avg Recall: 0.574074, count: 162 -5292: 0.481904, 0.427677 avg, 0.001000 rate, 22.216818 seconds, 1354752 images -Loaded: 0.050543 seconds -Region Avg IOU: 0.529034, Class: 1.000000, Obj: 0.339469, No Obj: 0.004314, Avg Recall: 0.592000, count: 125 -Region Avg IOU: 0.566794, Class: 1.000000, Obj: 0.385751, No Obj: 0.004035, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.498118, Class: 1.000000, Obj: 0.362453, No Obj: 0.003502, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.525084, Class: 1.000000, Obj: 0.354247, No Obj: 0.004146, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.551822, Class: 1.000000, Obj: 0.388416, No Obj: 0.004324, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.587248, Class: 1.000000, Obj: 0.354649, No Obj: 0.004192, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.514229, Class: 1.000000, Obj: 0.330970, No Obj: 0.003638, Avg Recall: 0.518072, count: 83 -Region Avg IOU: 0.504996, Class: 1.000000, Obj: 0.307802, No Obj: 0.003568, Avg Recall: 0.556701, count: 97 -5293: 0.437872, 0.428696 avg, 0.001000 rate, 19.405865 seconds, 1355008 images -Loaded: 1.134059 seconds -Region Avg IOU: 0.565389, Class: 1.000000, Obj: 0.414938, No Obj: 0.003978, Avg Recall: 0.659091, count: 88 -Region Avg IOU: 0.570376, Class: 1.000000, Obj: 0.377885, No Obj: 0.004669, Avg Recall: 0.633588, count: 131 -Region Avg IOU: 0.577738, Class: 1.000000, Obj: 0.389773, No Obj: 0.003999, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.495792, Class: 1.000000, Obj: 0.310751, No Obj: 0.004090, Avg Recall: 0.561538, count: 130 -Region Avg IOU: 0.538376, Class: 1.000000, Obj: 0.415428, No Obj: 0.003480, Avg Recall: 0.662791, count: 86 -Region Avg IOU: 0.488153, Class: 1.000000, Obj: 0.346942, No Obj: 0.003533, Avg Recall: 0.554455, count: 101 -Region Avg IOU: 0.513627, Class: 1.000000, Obj: 0.361986, No Obj: 0.004412, Avg Recall: 0.555556, count: 135 -Region Avg IOU: 0.546813, Class: 1.000000, Obj: 0.363072, No Obj: 0.004040, Avg Recall: 0.618182, count: 110 -5294: 0.484667, 0.434294 avg, 0.001000 rate, 22.134796 seconds, 1355264 images -Loaded: 0.523899 seconds -Region Avg IOU: 0.583451, Class: 1.000000, Obj: 0.412722, No Obj: 0.004351, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.541162, Class: 1.000000, Obj: 0.389769, No Obj: 0.004400, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.542199, Class: 1.000000, Obj: 0.429291, No Obj: 0.003589, Avg Recall: 0.628571, count: 70 -Region Avg IOU: 0.562697, Class: 1.000000, Obj: 0.425633, No Obj: 0.004791, Avg Recall: 0.677686, count: 121 -Region Avg IOU: 0.572256, Class: 1.000000, Obj: 0.391099, No Obj: 0.004971, Avg Recall: 0.691729, count: 133 -Region Avg IOU: 0.506749, Class: 1.000000, Obj: 0.377960, No Obj: 0.004066, Avg Recall: 0.578947, count: 114 -Region Avg IOU: 0.566525, Class: 1.000000, Obj: 0.365082, No Obj: 0.005306, Avg Recall: 0.656934, count: 137 -Region Avg IOU: 0.589117, Class: 1.000000, Obj: 0.395729, No Obj: 0.004813, Avg Recall: 0.720721, count: 111 -5295: 0.444308, 0.435295 avg, 0.001000 rate, 21.019327 seconds, 1355520 images -Loaded: 0.728320 seconds -Region Avg IOU: 0.548925, Class: 1.000000, Obj: 0.408946, No Obj: 0.004159, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.507915, Class: 1.000000, Obj: 0.403467, No Obj: 0.004632, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.497156, Class: 1.000000, Obj: 0.383721, No Obj: 0.004069, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.523963, Class: 1.000000, Obj: 0.371236, No Obj: 0.004479, Avg Recall: 0.593220, count: 118 -Region Avg IOU: 0.528927, Class: 1.000000, Obj: 0.377024, No Obj: 0.003914, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.507497, Class: 1.000000, Obj: 0.385037, No Obj: 0.004451, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.517702, Class: 1.000000, Obj: 0.375842, No Obj: 0.003948, Avg Recall: 0.576471, count: 85 -Region Avg IOU: 0.495299, Class: 1.000000, Obj: 0.398358, No Obj: 0.004507, Avg Recall: 0.558824, count: 102 -5296: 0.422414, 0.434007 avg, 0.001000 rate, 18.507792 seconds, 1355776 images -Loaded: 2.648419 seconds -Region Avg IOU: 0.526368, Class: 1.000000, Obj: 0.435946, No Obj: 0.005100, Avg Recall: 0.595588, count: 136 -Region Avg IOU: 0.510861, Class: 1.000000, Obj: 0.378580, No Obj: 0.004935, Avg Recall: 0.569106, count: 123 -Region Avg IOU: 0.535321, Class: 1.000000, Obj: 0.414392, No Obj: 0.004422, Avg Recall: 0.570312, count: 128 -Region Avg IOU: 0.560286, Class: 1.000000, Obj: 0.412864, No Obj: 0.004879, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.529544, Class: 1.000000, Obj: 0.418527, No Obj: 0.004754, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.518833, Class: 1.000000, Obj: 0.367557, No Obj: 0.004139, Avg Recall: 0.549550, count: 111 -Region Avg IOU: 0.548953, Class: 1.000000, Obj: 0.426900, No Obj: 0.004472, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.461788, Class: 1.000000, Obj: 0.377942, No Obj: 0.004529, Avg Recall: 0.500000, count: 126 -5297: 0.439793, 0.434586 avg, 0.001000 rate, 21.491861 seconds, 1356032 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.508753, Class: 1.000000, Obj: 0.415272, No Obj: 0.005056, Avg Recall: 0.581197, count: 117 -Region Avg IOU: 0.543253, Class: 1.000000, Obj: 0.397662, No Obj: 0.005490, Avg Recall: 0.601504, count: 133 -Region Avg IOU: 0.510771, Class: 1.000000, Obj: 0.406883, No Obj: 0.005246, Avg Recall: 0.582090, count: 134 -Region Avg IOU: 0.527103, Class: 1.000000, Obj: 0.400243, No Obj: 0.004784, Avg Recall: 0.609756, count: 123 -Region Avg IOU: 0.505355, Class: 1.000000, Obj: 0.417004, No Obj: 0.004339, Avg Recall: 0.542857, count: 105 -Region Avg IOU: 0.532083, Class: 1.000000, Obj: 0.371810, No Obj: 0.004869, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.552872, Class: 1.000000, Obj: 0.450492, No Obj: 0.004704, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.543399, Class: 1.000000, Obj: 0.432784, No Obj: 0.004211, Avg Recall: 0.617021, count: 94 -5298: 0.445946, 0.435722 avg, 0.001000 rate, 21.436840 seconds, 1356288 images -Loaded: 0.697871 seconds -Region Avg IOU: 0.533358, Class: 1.000000, Obj: 0.439468, No Obj: 0.005276, Avg Recall: 0.592000, count: 125 -Region Avg IOU: 0.476606, Class: 1.000000, Obj: 0.364786, No Obj: 0.005338, Avg Recall: 0.531469, count: 143 -Region Avg IOU: 0.518224, Class: 1.000000, Obj: 0.396008, No Obj: 0.004054, Avg Recall: 0.559140, count: 93 -Region Avg IOU: 0.528437, Class: 1.000000, Obj: 0.408176, No Obj: 0.004692, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.509350, Class: 1.000000, Obj: 0.350787, No Obj: 0.004005, Avg Recall: 0.523364, count: 107 -Region Avg IOU: 0.528475, Class: 1.000000, Obj: 0.404635, No Obj: 0.004430, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.555794, Class: 1.000000, Obj: 0.446712, No Obj: 0.004520, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.592247, Class: 1.000000, Obj: 0.417629, No Obj: 0.004834, Avg Recall: 0.692308, count: 91 -5299: 0.448848, 0.437034 avg, 0.001000 rate, 20.933163 seconds, 1356544 images -Loaded: 1.634219 seconds -Region Avg IOU: 0.579223, Class: 1.000000, Obj: 0.403059, No Obj: 0.004510, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.554924, Class: 1.000000, Obj: 0.420281, No Obj: 0.004938, Avg Recall: 0.608000, count: 125 -Region Avg IOU: 0.530719, Class: 1.000000, Obj: 0.423541, No Obj: 0.004597, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.547471, Class: 1.000000, Obj: 0.409074, No Obj: 0.004443, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.542296, Class: 1.000000, Obj: 0.416064, No Obj: 0.005213, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.496561, Class: 1.000000, Obj: 0.385484, No Obj: 0.004626, Avg Recall: 0.475000, count: 120 -Region Avg IOU: 0.507556, Class: 1.000000, Obj: 0.392058, No Obj: 0.004675, Avg Recall: 0.611570, count: 121 -Region Avg IOU: 0.513316, Class: 1.000000, Obj: 0.376814, No Obj: 0.004359, Avg Recall: 0.613861, count: 101 -5300: 0.467548, 0.440086 avg, 0.001000 rate, 20.623140 seconds, 1356800 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 0.046120 seconds -Region Avg IOU: 0.557956, Class: 1.000000, Obj: 0.353869, No Obj: 0.005288, Avg Recall: 0.622222, count: 135 -Region Avg IOU: 0.562489, Class: 1.000000, Obj: 0.418650, No Obj: 0.004528, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.560142, Class: 1.000000, Obj: 0.374421, No Obj: 0.004055, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.562299, Class: 1.000000, Obj: 0.413602, No Obj: 0.005106, Avg Recall: 0.664122, count: 131 -Region Avg IOU: 0.556199, Class: 1.000000, Obj: 0.448866, No Obj: 0.004002, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.567563, Class: 1.000000, Obj: 0.405134, No Obj: 0.004596, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.500465, Class: 1.000000, Obj: 0.387869, No Obj: 0.004536, Avg Recall: 0.571429, count: 112 -Region Avg IOU: 0.507077, Class: 1.000000, Obj: 0.411373, No Obj: 0.004734, Avg Recall: 0.528000, count: 125 -5301: 0.470933, 0.443170 avg, 0.001000 rate, 21.122732 seconds, 1357056 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.513929, Class: 1.000000, Obj: 0.414927, No Obj: 0.004133, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.615764, Class: 1.000000, Obj: 0.454472, No Obj: 0.003802, Avg Recall: 0.771084, count: 83 -Region Avg IOU: 0.597401, Class: 1.000000, Obj: 0.451242, No Obj: 0.004405, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.554985, Class: 1.000000, Obj: 0.408054, No Obj: 0.004861, Avg Recall: 0.600000, count: 120 -Region Avg IOU: 0.541199, Class: 1.000000, Obj: 0.374554, No Obj: 0.003952, Avg Recall: 0.571429, count: 119 -Region Avg IOU: 0.558831, Class: 1.000000, Obj: 0.400920, No Obj: 0.004593, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.572742, Class: 1.000000, Obj: 0.432065, No Obj: 0.004328, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.534297, Class: 1.000000, Obj: 0.358930, No Obj: 0.004427, Avg Recall: 0.601504, count: 133 -5302: 0.402065, 0.439060 avg, 0.001000 rate, 21.382240 seconds, 1357312 images -Loaded: 0.658976 seconds -Region Avg IOU: 0.517758, Class: 1.000000, Obj: 0.364161, No Obj: 0.004136, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.491717, Class: 1.000000, Obj: 0.336300, No Obj: 0.003593, Avg Recall: 0.563636, count: 110 -Region Avg IOU: 0.540243, Class: 1.000000, Obj: 0.368511, No Obj: 0.003836, Avg Recall: 0.588235, count: 102 -Region Avg IOU: 0.504081, Class: 1.000000, Obj: 0.372299, No Obj: 0.003468, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.538348, Class: 1.000000, Obj: 0.403381, No Obj: 0.003380, Avg Recall: 0.647727, count: 88 -Region Avg IOU: 0.593448, Class: 1.000000, Obj: 0.419273, No Obj: 0.003728, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.545171, Class: 1.000000, Obj: 0.367627, No Obj: 0.003389, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.585619, Class: 1.000000, Obj: 0.385526, No Obj: 0.003917, Avg Recall: 0.688172, count: 93 -5303: 0.370108, 0.432165 avg, 0.001000 rate, 21.278505 seconds, 1357568 images -Loaded: 1.218456 seconds -Region Avg IOU: 0.580552, Class: 1.000000, Obj: 0.376676, No Obj: 0.003882, Avg Recall: 0.666667, count: 126 -Region Avg IOU: 0.536886, Class: 1.000000, Obj: 0.392065, No Obj: 0.003699, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.568569, Class: 1.000000, Obj: 0.418158, No Obj: 0.004422, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.548535, Class: 1.000000, Obj: 0.328517, No Obj: 0.003770, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.559686, Class: 1.000000, Obj: 0.385863, No Obj: 0.004827, Avg Recall: 0.633588, count: 131 -Region Avg IOU: 0.558056, Class: 1.000000, Obj: 0.360006, No Obj: 0.003631, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.513384, Class: 1.000000, Obj: 0.381771, No Obj: 0.003966, Avg Recall: 0.569231, count: 130 -Region Avg IOU: 0.551590, Class: 1.000000, Obj: 0.380775, No Obj: 0.004196, Avg Recall: 0.636364, count: 121 -5304: 0.446087, 0.433557 avg, 0.001000 rate, 22.937170 seconds, 1357824 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.576962, Class: 1.000000, Obj: 0.396319, No Obj: 0.003594, Avg Recall: 0.674419, count: 86 -Region Avg IOU: 0.547195, Class: 1.000000, Obj: 0.399781, No Obj: 0.004190, Avg Recall: 0.577586, count: 116 -Region Avg IOU: 0.605656, Class: 1.000000, Obj: 0.367175, No Obj: 0.003420, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.526388, Class: 1.000000, Obj: 0.353795, No Obj: 0.003504, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.477181, Class: 1.000000, Obj: 0.327091, No Obj: 0.003907, Avg Recall: 0.485294, count: 136 -Region Avg IOU: 0.541523, Class: 1.000000, Obj: 0.357385, No Obj: 0.004298, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.581094, Class: 1.000000, Obj: 0.393898, No Obj: 0.003808, Avg Recall: 0.698795, count: 83 -Region Avg IOU: 0.534749, Class: 1.000000, Obj: 0.353239, No Obj: 0.003471, Avg Recall: 0.646465, count: 99 -5305: 0.404573, 0.430658 avg, 0.001000 rate, 22.078264 seconds, 1358080 images -Loaded: 1.025572 seconds -Region Avg IOU: 0.587028, Class: 1.000000, Obj: 0.370939, No Obj: 0.004155, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.570420, Class: 1.000000, Obj: 0.342223, No Obj: 0.003861, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.522998, Class: 1.000000, Obj: 0.335523, No Obj: 0.003690, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.571044, Class: 1.000000, Obj: 0.349698, No Obj: 0.004038, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.567358, Class: 1.000000, Obj: 0.317118, No Obj: 0.003588, Avg Recall: 0.627119, count: 118 -Region Avg IOU: 0.564216, Class: 1.000000, Obj: 0.342973, No Obj: 0.003642, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.561804, Class: 1.000000, Obj: 0.365223, No Obj: 0.003834, Avg Recall: 0.622047, count: 127 -Region Avg IOU: 0.532303, Class: 1.000000, Obj: 0.348283, No Obj: 0.003990, Avg Recall: 0.597015, count: 134 -5306: 0.444285, 0.432021 avg, 0.001000 rate, 21.353384 seconds, 1358336 images -Loaded: 0.999452 seconds -Region Avg IOU: 0.600368, Class: 1.000000, Obj: 0.421014, No Obj: 0.002953, Avg Recall: 0.704225, count: 71 -Region Avg IOU: 0.631923, Class: 1.000000, Obj: 0.403327, No Obj: 0.004583, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.531718, Class: 1.000000, Obj: 0.309139, No Obj: 0.003406, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.545225, Class: 1.000000, Obj: 0.367948, No Obj: 0.003955, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.557245, Class: 1.000000, Obj: 0.327845, No Obj: 0.003838, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.591950, Class: 1.000000, Obj: 0.352070, No Obj: 0.003891, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.580488, Class: 1.000000, Obj: 0.396632, No Obj: 0.003526, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.573577, Class: 1.000000, Obj: 0.394582, No Obj: 0.003091, Avg Recall: 0.688525, count: 61 -5307: 0.400544, 0.428873 avg, 0.001000 rate, 19.918600 seconds, 1358592 images -Loaded: 1.725541 seconds -Region Avg IOU: 0.547771, Class: 1.000000, Obj: 0.360209, No Obj: 0.003934, Avg Recall: 0.645161, count: 124 -Region Avg IOU: 0.558888, Class: 1.000000, Obj: 0.367580, No Obj: 0.003905, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.543382, Class: 1.000000, Obj: 0.399683, No Obj: 0.003780, Avg Recall: 0.601770, count: 113 -Region Avg IOU: 0.587553, Class: 1.000000, Obj: 0.332775, No Obj: 0.003870, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.537544, Class: 1.000000, Obj: 0.336521, No Obj: 0.003864, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.584279, Class: 1.000000, Obj: 0.357224, No Obj: 0.003357, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.592740, Class: 1.000000, Obj: 0.403532, No Obj: 0.004032, Avg Recall: 0.734848, count: 132 -Region Avg IOU: 0.592617, Class: 1.000000, Obj: 0.373509, No Obj: 0.003213, Avg Recall: 0.740000, count: 100 -5308: 0.437854, 0.429771 avg, 0.001000 rate, 20.583391 seconds, 1358848 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.550948, Class: 1.000000, Obj: 0.365327, No Obj: 0.003404, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.611592, Class: 1.000000, Obj: 0.407566, No Obj: 0.003549, Avg Recall: 0.714286, count: 77 -Region Avg IOU: 0.569816, Class: 1.000000, Obj: 0.351064, No Obj: 0.003637, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.592097, Class: 1.000000, Obj: 0.423977, No Obj: 0.003919, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.507099, Class: 1.000000, Obj: 0.371571, No Obj: 0.003532, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.587951, Class: 1.000000, Obj: 0.395329, No Obj: 0.003262, Avg Recall: 0.684783, count: 92 -Region Avg IOU: 0.564313, Class: 1.000000, Obj: 0.344615, No Obj: 0.004542, Avg Recall: 0.632353, count: 136 -Region Avg IOU: 0.561228, Class: 1.000000, Obj: 0.368946, No Obj: 0.003671, Avg Recall: 0.652632, count: 95 -5309: 0.413906, 0.428185 avg, 0.001000 rate, 21.037418 seconds, 1359104 images -Loaded: 1.785480 seconds -Region Avg IOU: 0.536453, Class: 1.000000, Obj: 0.303961, No Obj: 0.003776, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.540907, Class: 1.000000, Obj: 0.345839, No Obj: 0.004043, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.596644, Class: 1.000000, Obj: 0.382121, No Obj: 0.003901, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.491078, Class: 1.000000, Obj: 0.355973, No Obj: 0.003752, Avg Recall: 0.576577, count: 111 -Region Avg IOU: 0.511229, Class: 1.000000, Obj: 0.359332, No Obj: 0.003645, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.546289, Class: 1.000000, Obj: 0.379371, No Obj: 0.003522, Avg Recall: 0.627119, count: 118 -Region Avg IOU: 0.524222, Class: 1.000000, Obj: 0.361313, No Obj: 0.003625, Avg Recall: 0.597938, count: 97 -Region Avg IOU: 0.543272, Class: 1.000000, Obj: 0.368480, No Obj: 0.003851, Avg Recall: 0.632000, count: 125 -5310: 0.426153, 0.427982 avg, 0.001000 rate, 22.606558 seconds, 1359360 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000070 seconds -Region Avg IOU: 0.607475, Class: 1.000000, Obj: 0.479691, No Obj: 0.002826, Avg Recall: 0.709302, count: 86 -Region Avg IOU: 0.629191, Class: 1.000000, Obj: 0.389640, No Obj: 0.002784, Avg Recall: 0.736842, count: 76 -Region Avg IOU: 0.623075, Class: 1.000000, Obj: 0.449244, No Obj: 0.002809, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.622101, Class: 1.000000, Obj: 0.455006, No Obj: 0.002776, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.653821, Class: 1.000000, Obj: 0.439398, No Obj: 0.003114, Avg Recall: 0.840909, count: 88 -Region Avg IOU: 0.628767, Class: 1.000000, Obj: 0.421950, No Obj: 0.003162, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.652674, Class: 1.000000, Obj: 0.422028, No Obj: 0.003833, Avg Recall: 0.785124, count: 121 -Region Avg IOU: 0.646742, Class: 1.000000, Obj: 0.442526, No Obj: 0.003222, Avg Recall: 0.800000, count: 100 -5311: 0.400877, 0.425271 avg, 0.001000 rate, 26.655508 seconds, 1359616 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.660599, Class: 1.000000, Obj: 0.450613, No Obj: 0.003792, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.641256, Class: 1.000000, Obj: 0.462211, No Obj: 0.003756, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.651648, Class: 1.000000, Obj: 0.449954, No Obj: 0.003314, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.673073, Class: 1.000000, Obj: 0.517647, No Obj: 0.003218, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.673361, Class: 1.000000, Obj: 0.426668, No Obj: 0.002872, Avg Recall: 0.844444, count: 90 -Region Avg IOU: 0.664478, Class: 1.000000, Obj: 0.430907, No Obj: 0.003273, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.637947, Class: 1.000000, Obj: 0.464265, No Obj: 0.003278, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.715318, Class: 1.000000, Obj: 0.471027, No Obj: 0.003662, Avg Recall: 0.950495, count: 101 -5312: 0.430559, 0.425800 avg, 0.001000 rate, 28.004992 seconds, 1359872 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.649009, Class: 1.000000, Obj: 0.420709, No Obj: 0.002829, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.697823, Class: 1.000000, Obj: 0.476813, No Obj: 0.003253, Avg Recall: 0.872549, count: 102 -Region Avg IOU: 0.587469, Class: 1.000000, Obj: 0.429081, No Obj: 0.002752, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.666784, Class: 1.000000, Obj: 0.397672, No Obj: 0.003298, Avg Recall: 0.823529, count: 119 -Region Avg IOU: 0.627965, Class: 1.000000, Obj: 0.465990, No Obj: 0.003334, Avg Recall: 0.780488, count: 123 -Region Avg IOU: 0.627136, Class: 1.000000, Obj: 0.476321, No Obj: 0.003005, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.621624, Class: 1.000000, Obj: 0.429933, No Obj: 0.003001, Avg Recall: 0.755725, count: 131 -Region Avg IOU: 0.645609, Class: 1.000000, Obj: 0.475693, No Obj: 0.003241, Avg Recall: 0.801980, count: 101 -5313: 0.410941, 0.424314 avg, 0.001000 rate, 27.261379 seconds, 1360128 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.642786, Class: 1.000000, Obj: 0.403674, No Obj: 0.002550, Avg Recall: 0.811111, count: 90 -Region Avg IOU: 0.620524, Class: 1.000000, Obj: 0.416727, No Obj: 0.003227, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.612419, Class: 1.000000, Obj: 0.439797, No Obj: 0.003369, Avg Recall: 0.709677, count: 124 -Region Avg IOU: 0.623688, Class: 1.000000, Obj: 0.458689, No Obj: 0.002981, Avg Recall: 0.757812, count: 128 -Region Avg IOU: 0.662769, Class: 1.000000, Obj: 0.446974, No Obj: 0.002986, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.637023, Class: 1.000000, Obj: 0.424402, No Obj: 0.003082, Avg Recall: 0.772358, count: 123 -Region Avg IOU: 0.669244, Class: 1.000000, Obj: 0.452966, No Obj: 0.002631, Avg Recall: 0.817204, count: 93 -Region Avg IOU: 0.638997, Class: 1.000000, Obj: 0.420315, No Obj: 0.002866, Avg Recall: 0.793651, count: 126 -5314: 0.444406, 0.426323 avg, 0.001000 rate, 26.039316 seconds, 1360384 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.647376, Class: 1.000000, Obj: 0.385551, No Obj: 0.003434, Avg Recall: 0.809160, count: 131 -Region Avg IOU: 0.659434, Class: 1.000000, Obj: 0.385031, No Obj: 0.002665, Avg Recall: 0.830357, count: 112 -Region Avg IOU: 0.632845, Class: 1.000000, Obj: 0.385357, No Obj: 0.002771, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.585101, Class: 1.000000, Obj: 0.416799, No Obj: 0.002659, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.665498, Class: 1.000000, Obj: 0.386640, No Obj: 0.003274, Avg Recall: 0.827068, count: 133 -Region Avg IOU: 0.643166, Class: 1.000000, Obj: 0.413513, No Obj: 0.002439, Avg Recall: 0.746988, count: 83 -Region Avg IOU: 0.644857, Class: 1.000000, Obj: 0.421047, No Obj: 0.002691, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.636471, Class: 1.000000, Obj: 0.323282, No Obj: 0.003212, Avg Recall: 0.783582, count: 134 -5315: 0.456999, 0.429391 avg, 0.001000 rate, 28.088505 seconds, 1360640 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.625416, Class: 1.000000, Obj: 0.381326, No Obj: 0.002963, Avg Recall: 0.759399, count: 133 -Region Avg IOU: 0.623317, Class: 1.000000, Obj: 0.385598, No Obj: 0.002811, Avg Recall: 0.806452, count: 124 -Region Avg IOU: 0.640669, Class: 1.000000, Obj: 0.352667, No Obj: 0.002958, Avg Recall: 0.798319, count: 119 -Region Avg IOU: 0.585076, Class: 1.000000, Obj: 0.363102, No Obj: 0.002940, Avg Recall: 0.685039, count: 127 -Region Avg IOU: 0.639240, Class: 1.000000, Obj: 0.380496, No Obj: 0.003327, Avg Recall: 0.776119, count: 134 -Region Avg IOU: 0.580890, Class: 1.000000, Obj: 0.381709, No Obj: 0.002380, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.667807, Class: 1.000000, Obj: 0.440482, No Obj: 0.002506, Avg Recall: 0.867470, count: 83 -Region Avg IOU: 0.649764, Class: 1.000000, Obj: 0.390507, No Obj: 0.003068, Avg Recall: 0.811475, count: 122 -5316: 0.503902, 0.436842 avg, 0.001000 rate, 27.381395 seconds, 1360896 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.614369, Class: 1.000000, Obj: 0.390468, No Obj: 0.002791, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.668656, Class: 1.000000, Obj: 0.423717, No Obj: 0.002167, Avg Recall: 0.812500, count: 80 -Region Avg IOU: 0.610921, Class: 1.000000, Obj: 0.370611, No Obj: 0.002917, Avg Recall: 0.715385, count: 130 -Region Avg IOU: 0.622312, Class: 1.000000, Obj: 0.385578, No Obj: 0.002697, Avg Recall: 0.763780, count: 127 -Region Avg IOU: 0.615073, Class: 1.000000, Obj: 0.352325, No Obj: 0.002964, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.651699, Class: 1.000000, Obj: 0.363827, No Obj: 0.003036, Avg Recall: 0.812500, count: 128 -Region Avg IOU: 0.645662, Class: 1.000000, Obj: 0.408213, No Obj: 0.002222, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.598534, Class: 1.000000, Obj: 0.357582, No Obj: 0.002369, Avg Recall: 0.726415, count: 106 -5317: 0.445408, 0.437699 avg, 0.001000 rate, 27.734659 seconds, 1361152 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.628371, Class: 1.000000, Obj: 0.371677, No Obj: 0.002695, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.612501, Class: 1.000000, Obj: 0.363530, No Obj: 0.002125, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.667217, Class: 1.000000, Obj: 0.441857, No Obj: 0.002365, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.653427, Class: 1.000000, Obj: 0.370051, No Obj: 0.002313, Avg Recall: 0.829787, count: 94 -Region Avg IOU: 0.599867, Class: 1.000000, Obj: 0.386508, No Obj: 0.002590, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.594956, Class: 1.000000, Obj: 0.334371, No Obj: 0.002054, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.619071, Class: 1.000000, Obj: 0.366133, No Obj: 0.002453, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.580835, Class: 1.000000, Obj: 0.371040, No Obj: 0.002518, Avg Recall: 0.733945, count: 109 -5318: 0.402069, 0.434136 avg, 0.001000 rate, 28.693905 seconds, 1361408 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.640882, Class: 1.000000, Obj: 0.391403, No Obj: 0.002932, Avg Recall: 0.781955, count: 133 -Region Avg IOU: 0.617898, Class: 1.000000, Obj: 0.359073, No Obj: 0.002770, Avg Recall: 0.700787, count: 127 -Region Avg IOU: 0.645736, Class: 1.000000, Obj: 0.377125, No Obj: 0.002973, Avg Recall: 0.808696, count: 115 -Region Avg IOU: 0.625852, Class: 1.000000, Obj: 0.341312, No Obj: 0.003118, Avg Recall: 0.779661, count: 118 -Region Avg IOU: 0.617190, Class: 1.000000, Obj: 0.380357, No Obj: 0.002486, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.597396, Class: 1.000000, Obj: 0.363831, No Obj: 0.002362, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.652545, Class: 1.000000, Obj: 0.376718, No Obj: 0.002543, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.675763, Class: 1.000000, Obj: 0.419899, No Obj: 0.002532, Avg Recall: 0.827586, count: 87 -5319: 0.465822, 0.437304 avg, 0.001000 rate, 27.485775 seconds, 1361664 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.648007, Class: 1.000000, Obj: 0.420664, No Obj: 0.002692, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.602471, Class: 1.000000, Obj: 0.404730, No Obj: 0.002442, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.654357, Class: 1.000000, Obj: 0.379715, No Obj: 0.002119, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.677119, Class: 1.000000, Obj: 0.405239, No Obj: 0.002376, Avg Recall: 0.847059, count: 85 -Region Avg IOU: 0.649536, Class: 1.000000, Obj: 0.402297, No Obj: 0.002873, Avg Recall: 0.819672, count: 122 -Region Avg IOU: 0.621562, Class: 1.000000, Obj: 0.332482, No Obj: 0.003076, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.615027, Class: 1.000000, Obj: 0.375369, No Obj: 0.002411, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.590540, Class: 1.000000, Obj: 0.357347, No Obj: 0.003028, Avg Recall: 0.705036, count: 139 -5320: 0.446408, 0.438215 avg, 0.001000 rate, 27.171623 seconds, 1361920 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.333483 seconds -Region Avg IOU: 0.504328, Class: 1.000000, Obj: 0.247832, No Obj: 0.003457, Avg Recall: 0.529412, count: 119 -Region Avg IOU: 0.427620, Class: 1.000000, Obj: 0.249886, No Obj: 0.003049, Avg Recall: 0.438776, count: 98 -Region Avg IOU: 0.442911, Class: 1.000000, Obj: 0.254582, No Obj: 0.003069, Avg Recall: 0.447368, count: 114 -Region Avg IOU: 0.479729, Class: 1.000000, Obj: 0.250726, No Obj: 0.004062, Avg Recall: 0.557252, count: 131 -Region Avg IOU: 0.551644, Class: 1.000000, Obj: 0.302071, No Obj: 0.004115, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.486772, Class: 1.000000, Obj: 0.276351, No Obj: 0.003814, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.463891, Class: 1.000000, Obj: 0.273761, No Obj: 0.003876, Avg Recall: 0.514019, count: 107 -Region Avg IOU: 0.461601, Class: 1.000000, Obj: 0.267997, No Obj: 0.003298, Avg Recall: 0.512500, count: 80 -5321: 0.545268, 0.448920 avg, 0.001000 rate, 15.451274 seconds, 1362176 images -Loaded: 6.146673 seconds -Region Avg IOU: 0.492896, Class: 1.000000, Obj: 0.270707, No Obj: 0.004128, Avg Recall: 0.551181, count: 127 -Region Avg IOU: 0.437708, Class: 1.000000, Obj: 0.259462, No Obj: 0.003606, Avg Recall: 0.467890, count: 109 -Region Avg IOU: 0.504066, Class: 1.000000, Obj: 0.298355, No Obj: 0.003682, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.432083, Class: 1.000000, Obj: 0.266563, No Obj: 0.003551, Avg Recall: 0.378378, count: 111 -Region Avg IOU: 0.489190, Class: 1.000000, Obj: 0.298447, No Obj: 0.003741, Avg Recall: 0.490196, count: 102 -Region Avg IOU: 0.435432, Class: 1.000000, Obj: 0.251606, No Obj: 0.003636, Avg Recall: 0.424528, count: 106 -Region Avg IOU: 0.497070, Class: 1.000000, Obj: 0.262177, No Obj: 0.003805, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.428840, Class: 1.000000, Obj: 0.264095, No Obj: 0.003491, Avg Recall: 0.452991, count: 117 -5322: 0.482658, 0.452294 avg, 0.001000 rate, 13.748892 seconds, 1362432 images -Loaded: 6.956572 seconds -Region Avg IOU: 0.506431, Class: 1.000000, Obj: 0.306751, No Obj: 0.004216, Avg Recall: 0.540741, count: 135 -Region Avg IOU: 0.509165, Class: 1.000000, Obj: 0.311808, No Obj: 0.004298, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.479584, Class: 1.000000, Obj: 0.280173, No Obj: 0.003976, Avg Recall: 0.504762, count: 105 -Region Avg IOU: 0.466750, Class: 1.000000, Obj: 0.283797, No Obj: 0.004604, Avg Recall: 0.507812, count: 128 -Region Avg IOU: 0.474476, Class: 1.000000, Obj: 0.283750, No Obj: 0.004003, Avg Recall: 0.524272, count: 103 -Region Avg IOU: 0.490468, Class: 1.000000, Obj: 0.299216, No Obj: 0.004644, Avg Recall: 0.525862, count: 116 -Region Avg IOU: 0.446802, Class: 1.000000, Obj: 0.320993, No Obj: 0.004417, Avg Recall: 0.462810, count: 121 -Region Avg IOU: 0.504908, Class: 1.000000, Obj: 0.311230, No Obj: 0.003615, Avg Recall: 0.518987, count: 79 -5323: 0.508983, 0.457963 avg, 0.001000 rate, 15.878707 seconds, 1362688 images -Loaded: 5.038089 seconds -Region Avg IOU: 0.524638, Class: 1.000000, Obj: 0.350605, No Obj: 0.003804, Avg Recall: 0.615385, count: 91 -Region Avg IOU: 0.494172, Class: 1.000000, Obj: 0.332750, No Obj: 0.004322, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.492834, Class: 1.000000, Obj: 0.325040, No Obj: 0.004875, Avg Recall: 0.504348, count: 115 -Region Avg IOU: 0.453414, Class: 1.000000, Obj: 0.319387, No Obj: 0.004064, Avg Recall: 0.500000, count: 112 -Region Avg IOU: 0.444857, Class: 1.000000, Obj: 0.326826, No Obj: 0.004378, Avg Recall: 0.477876, count: 113 -Region Avg IOU: 0.473382, Class: 1.000000, Obj: 0.318163, No Obj: 0.004308, Avg Recall: 0.544444, count: 90 -Region Avg IOU: 0.444534, Class: 1.000000, Obj: 0.268704, No Obj: 0.004483, Avg Recall: 0.478992, count: 119 -Region Avg IOU: 0.490198, Class: 1.000000, Obj: 0.324297, No Obj: 0.004140, Avg Recall: 0.544554, count: 101 -5324: 0.431484, 0.455315 avg, 0.001000 rate, 15.216555 seconds, 1362944 images -Loaded: 5.498125 seconds -Region Avg IOU: 0.454963, Class: 1.000000, Obj: 0.281577, No Obj: 0.004930, Avg Recall: 0.482456, count: 114 -Region Avg IOU: 0.484137, Class: 1.000000, Obj: 0.322106, No Obj: 0.004169, Avg Recall: 0.533333, count: 90 -Region Avg IOU: 0.479167, Class: 1.000000, Obj: 0.339695, No Obj: 0.004983, Avg Recall: 0.532847, count: 137 -Region Avg IOU: 0.459229, Class: 1.000000, Obj: 0.330097, No Obj: 0.004739, Avg Recall: 0.528455, count: 123 -Region Avg IOU: 0.541669, Class: 1.000000, Obj: 0.343462, No Obj: 0.004338, Avg Recall: 0.613636, count: 88 -Region Avg IOU: 0.391978, Class: 1.000000, Obj: 0.278954, No Obj: 0.003759, Avg Recall: 0.341463, count: 82 -Region Avg IOU: 0.467965, Class: 1.000000, Obj: 0.321974, No Obj: 0.005284, Avg Recall: 0.534653, count: 101 -Region Avg IOU: 0.502650, Class: 1.000000, Obj: 0.308599, No Obj: 0.005204, Avg Recall: 0.548148, count: 135 -5325: 0.446792, 0.454462 avg, 0.001000 rate, 13.317694 seconds, 1363200 images -Loaded: 7.994029 seconds -Region Avg IOU: 0.502140, Class: 1.000000, Obj: 0.412431, No Obj: 0.004827, Avg Recall: 0.520833, count: 96 -Region Avg IOU: 0.478516, Class: 1.000000, Obj: 0.438232, No Obj: 0.005209, Avg Recall: 0.512605, count: 119 -Region Avg IOU: 0.443917, Class: 1.000000, Obj: 0.306403, No Obj: 0.004827, Avg Recall: 0.503759, count: 133 -Region Avg IOU: 0.448679, Class: 1.000000, Obj: 0.378202, No Obj: 0.005983, Avg Recall: 0.486111, count: 144 -Region Avg IOU: 0.456068, Class: 1.000000, Obj: 0.329178, No Obj: 0.004778, Avg Recall: 0.446429, count: 112 -Region Avg IOU: 0.477645, Class: 1.000000, Obj: 0.365866, No Obj: 0.004763, Avg Recall: 0.547826, count: 115 -Region Avg IOU: 0.490798, Class: 1.000000, Obj: 0.356080, No Obj: 0.005081, Avg Recall: 0.504854, count: 103 -Region Avg IOU: 0.475512, Class: 1.000000, Obj: 0.338466, No Obj: 0.005431, Avg Recall: 0.521739, count: 115 -5326: 0.508482, 0.459864 avg, 0.001000 rate, 15.208475 seconds, 1363456 images -Loaded: 4.687456 seconds -Region Avg IOU: 0.520763, Class: 1.000000, Obj: 0.408765, No Obj: 0.005245, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.474671, Class: 1.000000, Obj: 0.338533, No Obj: 0.004248, Avg Recall: 0.515790, count: 95 -Region Avg IOU: 0.447624, Class: 1.000000, Obj: 0.325368, No Obj: 0.005344, Avg Recall: 0.474074, count: 135 -Region Avg IOU: 0.463057, Class: 1.000000, Obj: 0.347598, No Obj: 0.004613, Avg Recall: 0.508197, count: 122 -Region Avg IOU: 0.482268, Class: 1.000000, Obj: 0.316613, No Obj: 0.005883, Avg Recall: 0.511278, count: 133 -Region Avg IOU: 0.464935, Class: 1.000000, Obj: 0.359797, No Obj: 0.005781, Avg Recall: 0.500000, count: 110 -Region Avg IOU: 0.484241, Class: 1.000000, Obj: 0.383713, No Obj: 0.004126, Avg Recall: 0.554054, count: 74 -Region Avg IOU: 0.459290, Class: 1.000000, Obj: 0.375471, No Obj: 0.004503, Avg Recall: 0.520548, count: 73 -5327: 0.448126, 0.458690 avg, 0.001000 rate, 15.255744 seconds, 1363712 images -Loaded: 6.316391 seconds -Region Avg IOU: 0.489474, Class: 1.000000, Obj: 0.359842, No Obj: 0.005778, Avg Recall: 0.549020, count: 102 -Region Avg IOU: 0.452565, Class: 1.000000, Obj: 0.372920, No Obj: 0.006102, Avg Recall: 0.495868, count: 121 -Region Avg IOU: 0.504309, Class: 1.000000, Obj: 0.409185, No Obj: 0.005461, Avg Recall: 0.517544, count: 114 -Region Avg IOU: 0.484228, Class: 1.000000, Obj: 0.387050, No Obj: 0.004656, Avg Recall: 0.516484, count: 91 -Region Avg IOU: 0.475076, Class: 1.000000, Obj: 0.384881, No Obj: 0.006738, Avg Recall: 0.532787, count: 122 -Region Avg IOU: 0.402324, Class: 1.000000, Obj: 0.362141, No Obj: 0.004453, Avg Recall: 0.404040, count: 99 -Region Avg IOU: 0.416646, Class: 1.000000, Obj: 0.382916, No Obj: 0.005070, Avg Recall: 0.421569, count: 102 -Region Avg IOU: 0.478898, Class: 1.000000, Obj: 0.392913, No Obj: 0.005575, Avg Recall: 0.508333, count: 120 -5328: 0.450739, 0.457895 avg, 0.001000 rate, 14.090380 seconds, 1363968 images -Loaded: 6.741362 seconds -Region Avg IOU: 0.504682, Class: 1.000000, Obj: 0.375160, No Obj: 0.005387, Avg Recall: 0.575758, count: 99 -Region Avg IOU: 0.452673, Class: 1.000000, Obj: 0.345845, No Obj: 0.004911, Avg Recall: 0.461538, count: 104 -Region Avg IOU: 0.482189, Class: 1.000000, Obj: 0.405600, No Obj: 0.005138, Avg Recall: 0.562500, count: 96 -Region Avg IOU: 0.421476, Class: 1.000000, Obj: 0.323512, No Obj: 0.005085, Avg Recall: 0.446281, count: 121 -Region Avg IOU: 0.559818, Class: 1.000000, Obj: 0.443188, No Obj: 0.005438, Avg Recall: 0.632184, count: 87 -Region Avg IOU: 0.482754, Class: 1.000000, Obj: 0.309718, No Obj: 0.004270, Avg Recall: 0.510204, count: 98 -Region Avg IOU: 0.485415, Class: 1.000000, Obj: 0.389624, No Obj: 0.005665, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.409345, Class: 1.000000, Obj: 0.362413, No Obj: 0.004721, Avg Recall: 0.427184, count: 103 -5329: 0.458950, 0.458001 avg, 0.001000 rate, 17.390924 seconds, 1364224 images -Loaded: 3.048991 seconds -Region Avg IOU: 0.447135, Class: 1.000000, Obj: 0.341144, No Obj: 0.005657, Avg Recall: 0.480315, count: 127 -Region Avg IOU: 0.504980, Class: 1.000000, Obj: 0.362464, No Obj: 0.005483, Avg Recall: 0.592920, count: 113 -Region Avg IOU: 0.530986, Class: 1.000000, Obj: 0.385296, No Obj: 0.005779, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.472729, Class: 1.000000, Obj: 0.360681, No Obj: 0.004541, Avg Recall: 0.440000, count: 75 -Region Avg IOU: 0.514849, Class: 1.000000, Obj: 0.388559, No Obj: 0.005862, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.488106, Class: 1.000000, Obj: 0.337829, No Obj: 0.005139, Avg Recall: 0.508772, count: 114 -Region Avg IOU: 0.448376, Class: 1.000000, Obj: 0.378847, No Obj: 0.006208, Avg Recall: 0.512821, count: 117 -Region Avg IOU: 0.439949, Class: 1.000000, Obj: 0.341917, No Obj: 0.005333, Avg Recall: 0.473282, count: 131 -5330: 0.449116, 0.457112 avg, 0.001000 rate, 14.199385 seconds, 1364480 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000107 seconds -Region Avg IOU: 0.657168, Class: 1.000000, Obj: 0.482513, No Obj: 0.003164, Avg Recall: 0.804124, count: 97 -Region Avg IOU: 0.671884, Class: 1.000000, Obj: 0.492528, No Obj: 0.003262, Avg Recall: 0.846154, count: 104 -Region Avg IOU: 0.661856, Class: 1.000000, Obj: 0.534123, No Obj: 0.003791, Avg Recall: 0.870968, count: 124 -Region Avg IOU: 0.634802, Class: 1.000000, Obj: 0.455525, No Obj: 0.003319, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.622550, Class: 1.000000, Obj: 0.380710, No Obj: 0.003602, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.613866, Class: 1.000000, Obj: 0.486592, No Obj: 0.003805, Avg Recall: 0.765625, count: 128 -Region Avg IOU: 0.653162, Class: 1.000000, Obj: 0.388109, No Obj: 0.003458, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.667124, Class: 1.000000, Obj: 0.531031, No Obj: 0.003530, Avg Recall: 0.810526, count: 95 -5331: 0.446849, 0.456086 avg, 0.001000 rate, 30.244556 seconds, 1364736 images -Loaded: 0.000158 seconds -Region Avg IOU: 0.665585, Class: 1.000000, Obj: 0.515877, No Obj: 0.003139, Avg Recall: 0.824742, count: 97 -Region Avg IOU: 0.682029, Class: 1.000000, Obj: 0.490789, No Obj: 0.003696, Avg Recall: 0.882353, count: 119 -Region Avg IOU: 0.643002, Class: 1.000000, Obj: 0.398530, No Obj: 0.003620, Avg Recall: 0.771429, count: 140 -Region Avg IOU: 0.609862, Class: 1.000000, Obj: 0.371255, No Obj: 0.003147, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.658427, Class: 1.000000, Obj: 0.439395, No Obj: 0.003454, Avg Recall: 0.854545, count: 110 -Region Avg IOU: 0.661969, Class: 1.000000, Obj: 0.486914, No Obj: 0.003156, Avg Recall: 0.838983, count: 118 -Region Avg IOU: 0.664825, Class: 1.000000, Obj: 0.466829, No Obj: 0.003661, Avg Recall: 0.827586, count: 116 -Region Avg IOU: 0.663813, Class: 1.000000, Obj: 0.477424, No Obj: 0.003732, Avg Recall: 0.816667, count: 120 -5332: 0.488752, 0.459353 avg, 0.001000 rate, 30.155008 seconds, 1364992 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.668820, Class: 1.000000, Obj: 0.426254, No Obj: 0.002926, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.662103, Class: 1.000000, Obj: 0.447320, No Obj: 0.003130, Avg Recall: 0.855932, count: 118 -Region Avg IOU: 0.661957, Class: 1.000000, Obj: 0.473691, No Obj: 0.002689, Avg Recall: 0.840000, count: 100 -Region Avg IOU: 0.663195, Class: 1.000000, Obj: 0.434305, No Obj: 0.002808, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.667685, Class: 1.000000, Obj: 0.435253, No Obj: 0.003038, Avg Recall: 0.844262, count: 122 -Region Avg IOU: 0.604346, Class: 1.000000, Obj: 0.413737, No Obj: 0.002838, Avg Recall: 0.730769, count: 130 -Region Avg IOU: 0.647319, Class: 1.000000, Obj: 0.386324, No Obj: 0.002686, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.669875, Class: 1.000000, Obj: 0.462548, No Obj: 0.002706, Avg Recall: 0.864583, count: 96 -5333: 0.442419, 0.457659 avg, 0.001000 rate, 29.200624 seconds, 1365248 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.649679, Class: 1.000000, Obj: 0.377341, No Obj: 0.002714, Avg Recall: 0.833333, count: 126 -Region Avg IOU: 0.650862, Class: 1.000000, Obj: 0.391421, No Obj: 0.002558, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.658880, Class: 1.000000, Obj: 0.327462, No Obj: 0.002599, Avg Recall: 0.823529, count: 119 -Region Avg IOU: 0.632779, Class: 1.000000, Obj: 0.372609, No Obj: 0.002876, Avg Recall: 0.739726, count: 146 -Region Avg IOU: 0.634350, Class: 1.000000, Obj: 0.403912, No Obj: 0.002192, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.649372, Class: 1.000000, Obj: 0.386021, No Obj: 0.002803, Avg Recall: 0.793893, count: 131 -Region Avg IOU: 0.620955, Class: 1.000000, Obj: 0.335273, No Obj: 0.002195, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.694910, Class: 1.000000, Obj: 0.433784, No Obj: 0.002717, Avg Recall: 0.862385, count: 109 -5334: 0.498564, 0.461750 avg, 0.001000 rate, 30.514944 seconds, 1365504 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.653897, Class: 1.000000, Obj: 0.370308, No Obj: 0.002074, Avg Recall: 0.830357, count: 112 -Region Avg IOU: 0.663100, Class: 1.000000, Obj: 0.354377, No Obj: 0.003039, Avg Recall: 0.813953, count: 172 -Region Avg IOU: 0.661660, Class: 1.000000, Obj: 0.355137, No Obj: 0.002220, Avg Recall: 0.827586, count: 116 -Region Avg IOU: 0.682412, Class: 1.000000, Obj: 0.359273, No Obj: 0.002555, Avg Recall: 0.844828, count: 116 -Region Avg IOU: 0.660396, Class: 1.000000, Obj: 0.350161, No Obj: 0.002595, Avg Recall: 0.821138, count: 123 -Region Avg IOU: 0.624123, Class: 1.000000, Obj: 0.355582, No Obj: 0.002322, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.657860, Class: 1.000000, Obj: 0.353036, No Obj: 0.002499, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.636412, Class: 1.000000, Obj: 0.395917, No Obj: 0.002199, Avg Recall: 0.758242, count: 91 -5335: 0.487468, 0.464322 avg, 0.001000 rate, 28.123243 seconds, 1365760 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.658461, Class: 1.000000, Obj: 0.344304, No Obj: 0.002172, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.674761, Class: 1.000000, Obj: 0.350660, No Obj: 0.002027, Avg Recall: 0.847619, count: 105 -Region Avg IOU: 0.638084, Class: 1.000000, Obj: 0.339485, No Obj: 0.002218, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.611834, Class: 1.000000, Obj: 0.346160, No Obj: 0.001988, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.637499, Class: 1.000000, Obj: 0.328364, No Obj: 0.002738, Avg Recall: 0.783784, count: 148 -Region Avg IOU: 0.638734, Class: 1.000000, Obj: 0.289564, No Obj: 0.002164, Avg Recall: 0.789474, count: 114 -Region Avg IOU: 0.640888, Class: 1.000000, Obj: 0.315990, No Obj: 0.002274, Avg Recall: 0.798450, count: 129 -Region Avg IOU: 0.667109, Class: 1.000000, Obj: 0.347111, No Obj: 0.002173, Avg Recall: 0.845455, count: 110 -5336: 0.499900, 0.467879 avg, 0.001000 rate, 30.498804 seconds, 1366016 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.628220, Class: 1.000000, Obj: 0.317635, No Obj: 0.001819, Avg Recall: 0.761364, count: 88 -Region Avg IOU: 0.631966, Class: 1.000000, Obj: 0.291392, No Obj: 0.001887, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.634123, Class: 1.000000, Obj: 0.348460, No Obj: 0.002268, Avg Recall: 0.755725, count: 131 -Region Avg IOU: 0.672639, Class: 1.000000, Obj: 0.330687, No Obj: 0.002258, Avg Recall: 0.819549, count: 133 -Region Avg IOU: 0.632224, Class: 1.000000, Obj: 0.285098, No Obj: 0.001803, Avg Recall: 0.788235, count: 85 -Region Avg IOU: 0.659585, Class: 1.000000, Obj: 0.310344, No Obj: 0.002049, Avg Recall: 0.817204, count: 93 -Region Avg IOU: 0.635127, Class: 1.000000, Obj: 0.327729, No Obj: 0.002087, Avg Recall: 0.818182, count: 121 -Region Avg IOU: 0.694577, Class: 1.000000, Obj: 0.375858, No Obj: 0.002476, Avg Recall: 0.887850, count: 107 -5337: 0.472821, 0.468374 avg, 0.001000 rate, 28.670816 seconds, 1366272 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.654769, Class: 1.000000, Obj: 0.334429, No Obj: 0.002066, Avg Recall: 0.820000, count: 100 -Region Avg IOU: 0.642052, Class: 1.000000, Obj: 0.354027, No Obj: 0.002102, Avg Recall: 0.815789, count: 114 -Region Avg IOU: 0.674275, Class: 1.000000, Obj: 0.326244, No Obj: 0.002477, Avg Recall: 0.857143, count: 133 -Region Avg IOU: 0.635661, Class: 1.000000, Obj: 0.288941, No Obj: 0.001850, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.622905, Class: 1.000000, Obj: 0.333911, No Obj: 0.001905, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.653905, Class: 1.000000, Obj: 0.298029, No Obj: 0.002163, Avg Recall: 0.792308, count: 130 -Region Avg IOU: 0.668322, Class: 1.000000, Obj: 0.359897, No Obj: 0.001862, Avg Recall: 0.873239, count: 71 -Region Avg IOU: 0.645301, Class: 1.000000, Obj: 0.291230, No Obj: 0.002051, Avg Recall: 0.787037, count: 108 -5338: 0.492744, 0.470811 avg, 0.001000 rate, 28.283728 seconds, 1366528 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.668448, Class: 1.000000, Obj: 0.359681, No Obj: 0.002465, Avg Recall: 0.830000, count: 100 -Region Avg IOU: 0.671520, Class: 1.000000, Obj: 0.371137, No Obj: 0.002543, Avg Recall: 0.832061, count: 131 -Region Avg IOU: 0.584215, Class: 1.000000, Obj: 0.291116, No Obj: 0.002158, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.639723, Class: 1.000000, Obj: 0.355521, No Obj: 0.001986, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.671005, Class: 1.000000, Obj: 0.411764, No Obj: 0.001936, Avg Recall: 0.836735, count: 98 -Region Avg IOU: 0.619658, Class: 1.000000, Obj: 0.325294, No Obj: 0.002061, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.654163, Class: 1.000000, Obj: 0.323963, No Obj: 0.002386, Avg Recall: 0.789855, count: 138 -Region Avg IOU: 0.625855, Class: 1.000000, Obj: 0.347264, No Obj: 0.002057, Avg Recall: 0.709677, count: 93 -5339: 0.471569, 0.470886 avg, 0.001000 rate, 30.697235 seconds, 1366784 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.641704, Class: 1.000000, Obj: 0.341736, No Obj: 0.002301, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.668589, Class: 1.000000, Obj: 0.332660, No Obj: 0.002078, Avg Recall: 0.846939, count: 98 -Region Avg IOU: 0.604820, Class: 1.000000, Obj: 0.340963, No Obj: 0.002249, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.597915, Class: 1.000000, Obj: 0.347630, No Obj: 0.002367, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.640575, Class: 1.000000, Obj: 0.316287, No Obj: 0.002638, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.658587, Class: 1.000000, Obj: 0.389365, No Obj: 0.002277, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.645479, Class: 1.000000, Obj: 0.361775, No Obj: 0.002429, Avg Recall: 0.780488, count: 123 -Region Avg IOU: 0.629495, Class: 1.000000, Obj: 0.369594, No Obj: 0.002271, Avg Recall: 0.747967, count: 123 -5340: 0.480362, 0.471834 avg, 0.001000 rate, 29.196297 seconds, 1367040 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 14.409909 seconds -Region Avg IOU: 0.594708, Class: 1.000000, Obj: 0.341991, No Obj: 0.003153, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.595408, Class: 1.000000, Obj: 0.343538, No Obj: 0.003076, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.568374, Class: 1.000000, Obj: 0.363856, No Obj: 0.003356, Avg Recall: 0.640625, count: 128 -Region Avg IOU: 0.593538, Class: 1.000000, Obj: 0.345148, No Obj: 0.002917, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.553693, Class: 1.000000, Obj: 0.298750, No Obj: 0.002915, Avg Recall: 0.645669, count: 127 -Region Avg IOU: 0.567875, Class: 1.000000, Obj: 0.354298, No Obj: 0.003354, Avg Recall: 0.627119, count: 118 -Region Avg IOU: 0.622193, Class: 1.000000, Obj: 0.351351, No Obj: 0.003465, Avg Recall: 0.746269, count: 134 -Region Avg IOU: 0.594928, Class: 1.000000, Obj: 0.322908, No Obj: 0.003096, Avg Recall: 0.700000, count: 110 -5341: 0.494391, 0.474090 avg, 0.001000 rate, 23.945332 seconds, 1367296 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.627694, Class: 1.000000, Obj: 0.373203, No Obj: 0.003558, Avg Recall: 0.762712, count: 118 -Region Avg IOU: 0.580436, Class: 1.000000, Obj: 0.350690, No Obj: 0.003358, Avg Recall: 0.666667, count: 117 -Region Avg IOU: 0.592970, Class: 1.000000, Obj: 0.391951, No Obj: 0.003219, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.611373, Class: 1.000000, Obj: 0.368598, No Obj: 0.003942, Avg Recall: 0.717742, count: 124 -Region Avg IOU: 0.608150, Class: 1.000000, Obj: 0.330767, No Obj: 0.003312, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.629072, Class: 1.000000, Obj: 0.359241, No Obj: 0.003306, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.577791, Class: 1.000000, Obj: 0.381601, No Obj: 0.003450, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.617896, Class: 1.000000, Obj: 0.415838, No Obj: 0.003275, Avg Recall: 0.736842, count: 114 -5342: 0.445204, 0.471201 avg, 0.001000 rate, 23.899502 seconds, 1367552 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.620437, Class: 1.000000, Obj: 0.376397, No Obj: 0.003180, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.595586, Class: 1.000000, Obj: 0.357481, No Obj: 0.003461, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.535241, Class: 1.000000, Obj: 0.363484, No Obj: 0.003671, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.659050, Class: 1.000000, Obj: 0.451138, No Obj: 0.003424, Avg Recall: 0.800000, count: 95 -Region Avg IOU: 0.571228, Class: 1.000000, Obj: 0.346202, No Obj: 0.003548, Avg Recall: 0.664062, count: 128 -Region Avg IOU: 0.531779, Class: 1.000000, Obj: 0.351377, No Obj: 0.003392, Avg Recall: 0.596774, count: 124 -Region Avg IOU: 0.593835, Class: 1.000000, Obj: 0.369081, No Obj: 0.003884, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.600894, Class: 1.000000, Obj: 0.380233, No Obj: 0.003894, Avg Recall: 0.696000, count: 125 -5343: 0.463356, 0.470417 avg, 0.001000 rate, 25.981863 seconds, 1367808 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.567478, Class: 1.000000, Obj: 0.428378, No Obj: 0.003903, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.598538, Class: 1.000000, Obj: 0.418033, No Obj: 0.004663, Avg Recall: 0.689922, count: 129 -Region Avg IOU: 0.622825, Class: 1.000000, Obj: 0.444847, No Obj: 0.004189, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.618037, Class: 1.000000, Obj: 0.443401, No Obj: 0.003481, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.565868, Class: 1.000000, Obj: 0.404704, No Obj: 0.003971, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.619968, Class: 1.000000, Obj: 0.492788, No Obj: 0.004296, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.650571, Class: 1.000000, Obj: 0.490337, No Obj: 0.004564, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.603066, Class: 1.000000, Obj: 0.399526, No Obj: 0.004522, Avg Recall: 0.700855, count: 117 -5344: 0.423818, 0.465757 avg, 0.001000 rate, 24.328934 seconds, 1368064 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.570677, Class: 1.000000, Obj: 0.411669, No Obj: 0.003723, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.566922, Class: 1.000000, Obj: 0.423408, No Obj: 0.003973, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.591230, Class: 1.000000, Obj: 0.409548, No Obj: 0.004214, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.610581, Class: 1.000000, Obj: 0.484680, No Obj: 0.003792, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.609781, Class: 1.000000, Obj: 0.435238, No Obj: 0.003994, Avg Recall: 0.720930, count: 86 -Region Avg IOU: 0.622130, Class: 1.000000, Obj: 0.452193, No Obj: 0.004429, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.561965, Class: 1.000000, Obj: 0.435023, No Obj: 0.003650, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.570949, Class: 1.000000, Obj: 0.408674, No Obj: 0.004147, Avg Recall: 0.653846, count: 104 -5345: 0.455703, 0.464751 avg, 0.001000 rate, 25.578020 seconds, 1368320 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.632453, Class: 1.000000, Obj: 0.480029, No Obj: 0.004735, Avg Recall: 0.755725, count: 131 -Region Avg IOU: 0.575343, Class: 1.000000, Obj: 0.413920, No Obj: 0.003981, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.588779, Class: 1.000000, Obj: 0.473043, No Obj: 0.003671, Avg Recall: 0.681818, count: 88 -Region Avg IOU: 0.558070, Class: 1.000000, Obj: 0.485941, No Obj: 0.004059, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.614103, Class: 1.000000, Obj: 0.484356, No Obj: 0.004506, Avg Recall: 0.734375, count: 128 -Region Avg IOU: 0.568972, Class: 1.000000, Obj: 0.372943, No Obj: 0.003724, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.594222, Class: 1.000000, Obj: 0.447301, No Obj: 0.004622, Avg Recall: 0.698276, count: 116 -Region Avg IOU: 0.629681, Class: 1.000000, Obj: 0.490212, No Obj: 0.003695, Avg Recall: 0.729412, count: 85 -5346: 0.398977, 0.458174 avg, 0.001000 rate, 23.701124 seconds, 1368576 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.604244, Class: 1.000000, Obj: 0.469113, No Obj: 0.004229, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.581289, Class: 1.000000, Obj: 0.471038, No Obj: 0.003976, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.593637, Class: 1.000000, Obj: 0.437617, No Obj: 0.004215, Avg Recall: 0.677419, count: 124 -Region Avg IOU: 0.589525, Class: 1.000000, Obj: 0.488380, No Obj: 0.003727, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.562386, Class: 1.000000, Obj: 0.361900, No Obj: 0.005020, Avg Recall: 0.685315, count: 143 -Region Avg IOU: 0.571096, Class: 1.000000, Obj: 0.458537, No Obj: 0.003873, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.601524, Class: 1.000000, Obj: 0.462305, No Obj: 0.004198, Avg Recall: 0.691057, count: 123 -Region Avg IOU: 0.579956, Class: 1.000000, Obj: 0.405679, No Obj: 0.004071, Avg Recall: 0.672566, count: 113 -5347: 0.395197, 0.451876 avg, 0.001000 rate, 23.199463 seconds, 1368832 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.561750, Class: 1.000000, Obj: 0.415353, No Obj: 0.004684, Avg Recall: 0.671533, count: 137 -Region Avg IOU: 0.565968, Class: 1.000000, Obj: 0.449453, No Obj: 0.003855, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.600665, Class: 1.000000, Obj: 0.476868, No Obj: 0.004109, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.595891, Class: 1.000000, Obj: 0.451715, No Obj: 0.004426, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.597777, Class: 1.000000, Obj: 0.470458, No Obj: 0.003680, Avg Recall: 0.744444, count: 90 -Region Avg IOU: 0.600154, Class: 1.000000, Obj: 0.417263, No Obj: 0.004395, Avg Recall: 0.700787, count: 127 -Region Avg IOU: 0.577524, Class: 1.000000, Obj: 0.489003, No Obj: 0.003835, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.556280, Class: 1.000000, Obj: 0.402250, No Obj: 0.004127, Avg Recall: 0.640000, count: 125 -5348: 0.462450, 0.452934 avg, 0.001000 rate, 24.175308 seconds, 1369088 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.614907, Class: 1.000000, Obj: 0.434681, No Obj: 0.003925, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.591341, Class: 1.000000, Obj: 0.400236, No Obj: 0.003948, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.608891, Class: 1.000000, Obj: 0.400976, No Obj: 0.004081, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.546418, Class: 1.000000, Obj: 0.468491, No Obj: 0.003142, Avg Recall: 0.616279, count: 86 -Region Avg IOU: 0.566677, Class: 1.000000, Obj: 0.437481, No Obj: 0.003853, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.558231, Class: 1.000000, Obj: 0.448734, No Obj: 0.003616, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.623956, Class: 1.000000, Obj: 0.469412, No Obj: 0.004514, Avg Recall: 0.737705, count: 122 -Region Avg IOU: 0.553875, Class: 1.000000, Obj: 0.405075, No Obj: 0.003988, Avg Recall: 0.681481, count: 135 -5349: 0.460966, 0.453737 avg, 0.001000 rate, 24.639784 seconds, 1369344 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.588744, Class: 1.000000, Obj: 0.419054, No Obj: 0.004193, Avg Recall: 0.718310, count: 142 -Region Avg IOU: 0.608324, Class: 1.000000, Obj: 0.424127, No Obj: 0.004052, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.577387, Class: 1.000000, Obj: 0.391142, No Obj: 0.003860, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.574183, Class: 1.000000, Obj: 0.420061, No Obj: 0.003967, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.576089, Class: 1.000000, Obj: 0.456350, No Obj: 0.003443, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.565498, Class: 1.000000, Obj: 0.428268, No Obj: 0.003654, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.592967, Class: 1.000000, Obj: 0.447555, No Obj: 0.003753, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.574283, Class: 1.000000, Obj: 0.419886, No Obj: 0.003993, Avg Recall: 0.707071, count: 99 -5350: 0.449022, 0.453265 avg, 0.001000 rate, 25.934525 seconds, 1369600 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000087 seconds -Region Avg IOU: 0.565537, Class: 1.000000, Obj: 0.407452, No Obj: 0.002657, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.553485, Class: 1.000000, Obj: 0.399152, No Obj: 0.003022, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.619078, Class: 1.000000, Obj: 0.407155, No Obj: 0.003091, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.616925, Class: 1.000000, Obj: 0.392066, No Obj: 0.003360, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.632112, Class: 1.000000, Obj: 0.426588, No Obj: 0.002979, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.649887, Class: 1.000000, Obj: 0.424112, No Obj: 0.003631, Avg Recall: 0.792000, count: 125 -Region Avg IOU: 0.612884, Class: 1.000000, Obj: 0.419468, No Obj: 0.003582, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.603311, Class: 1.000000, Obj: 0.390767, No Obj: 0.003595, Avg Recall: 0.753968, count: 126 -5351: 0.466466, 0.454585 avg, 0.001000 rate, 24.895714 seconds, 1369856 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.638271, Class: 1.000000, Obj: 0.459326, No Obj: 0.002735, Avg Recall: 0.765432, count: 81 -Region Avg IOU: 0.611745, Class: 1.000000, Obj: 0.421504, No Obj: 0.002837, Avg Recall: 0.728395, count: 81 -Region Avg IOU: 0.616835, Class: 1.000000, Obj: 0.408120, No Obj: 0.003306, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.603114, Class: 1.000000, Obj: 0.383020, No Obj: 0.002994, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.574813, Class: 1.000000, Obj: 0.408411, No Obj: 0.003259, Avg Recall: 0.671329, count: 143 -Region Avg IOU: 0.618181, Class: 1.000000, Obj: 0.386548, No Obj: 0.003353, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.598118, Class: 1.000000, Obj: 0.360165, No Obj: 0.003163, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.584193, Class: 1.000000, Obj: 0.397466, No Obj: 0.003150, Avg Recall: 0.640625, count: 128 -5352: 0.437907, 0.452918 avg, 0.001000 rate, 25.035236 seconds, 1370112 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.630906, Class: 1.000000, Obj: 0.447915, No Obj: 0.002351, Avg Recall: 0.743902, count: 82 -Region Avg IOU: 0.627345, Class: 1.000000, Obj: 0.394757, No Obj: 0.003327, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.617021, Class: 1.000000, Obj: 0.369191, No Obj: 0.003208, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.633788, Class: 1.000000, Obj: 0.402013, No Obj: 0.003196, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.610331, Class: 1.000000, Obj: 0.393421, No Obj: 0.002775, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.634817, Class: 1.000000, Obj: 0.380307, No Obj: 0.002909, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.648369, Class: 1.000000, Obj: 0.387519, No Obj: 0.003515, Avg Recall: 0.844828, count: 116 -Region Avg IOU: 0.633994, Class: 1.000000, Obj: 0.423573, No Obj: 0.003144, Avg Recall: 0.770000, count: 100 -5353: 0.427236, 0.450349 avg, 0.001000 rate, 25.936911 seconds, 1370368 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.604579, Class: 1.000000, Obj: 0.364615, No Obj: 0.003145, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.620815, Class: 1.000000, Obj: 0.368132, No Obj: 0.003083, Avg Recall: 0.763780, count: 127 -Region Avg IOU: 0.643007, Class: 1.000000, Obj: 0.422001, No Obj: 0.003495, Avg Recall: 0.779661, count: 118 -Region Avg IOU: 0.611068, Class: 1.000000, Obj: 0.336835, No Obj: 0.002980, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.633845, Class: 1.000000, Obj: 0.373480, No Obj: 0.002939, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.592084, Class: 1.000000, Obj: 0.347556, No Obj: 0.002850, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.597643, Class: 1.000000, Obj: 0.335773, No Obj: 0.003141, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.600648, Class: 1.000000, Obj: 0.339997, No Obj: 0.002706, Avg Recall: 0.735043, count: 117 -5354: 0.510626, 0.456377 avg, 0.001000 rate, 25.430672 seconds, 1370624 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.634959, Class: 1.000000, Obj: 0.379566, No Obj: 0.002907, Avg Recall: 0.821429, count: 112 -Region Avg IOU: 0.674537, Class: 1.000000, Obj: 0.425130, No Obj: 0.003001, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.571742, Class: 1.000000, Obj: 0.336011, No Obj: 0.002592, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.590009, Class: 1.000000, Obj: 0.355287, No Obj: 0.003072, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.605950, Class: 1.000000, Obj: 0.334647, No Obj: 0.003003, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.606139, Class: 1.000000, Obj: 0.385576, No Obj: 0.002972, Avg Recall: 0.729508, count: 122 -Region Avg IOU: 0.633438, Class: 1.000000, Obj: 0.377951, No Obj: 0.002918, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.628429, Class: 1.000000, Obj: 0.358426, No Obj: 0.002948, Avg Recall: 0.781818, count: 110 -5355: 0.453074, 0.456047 avg, 0.001000 rate, 25.374319 seconds, 1370880 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.666284, Class: 1.000000, Obj: 0.401658, No Obj: 0.003395, Avg Recall: 0.826087, count: 115 -Region Avg IOU: 0.622496, Class: 1.000000, Obj: 0.426030, No Obj: 0.002798, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.640922, Class: 1.000000, Obj: 0.370662, No Obj: 0.003579, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.595122, Class: 1.000000, Obj: 0.375716, No Obj: 0.002870, Avg Recall: 0.710526, count: 114 -Region Avg IOU: 0.579188, Class: 1.000000, Obj: 0.352703, No Obj: 0.002944, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.644459, Class: 1.000000, Obj: 0.420198, No Obj: 0.003143, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.620270, Class: 1.000000, Obj: 0.371933, No Obj: 0.002662, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.627813, Class: 1.000000, Obj: 0.388023, No Obj: 0.003286, Avg Recall: 0.789916, count: 119 -5356: 0.414235, 0.451866 avg, 0.001000 rate, 24.517889 seconds, 1371136 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.632772, Class: 1.000000, Obj: 0.361313, No Obj: 0.003307, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.695436, Class: 1.000000, Obj: 0.453099, No Obj: 0.002891, Avg Recall: 0.855422, count: 83 -Region Avg IOU: 0.583586, Class: 1.000000, Obj: 0.352347, No Obj: 0.002896, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.658146, Class: 1.000000, Obj: 0.425458, No Obj: 0.002968, Avg Recall: 0.838384, count: 99 -Region Avg IOU: 0.605322, Class: 1.000000, Obj: 0.462727, No Obj: 0.002324, Avg Recall: 0.701299, count: 77 -Region Avg IOU: 0.645107, Class: 1.000000, Obj: 0.359896, No Obj: 0.003040, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.668610, Class: 1.000000, Obj: 0.421810, No Obj: 0.003180, Avg Recall: 0.839623, count: 106 -Region Avg IOU: 0.628938, Class: 1.000000, Obj: 0.389470, No Obj: 0.002993, Avg Recall: 0.791667, count: 96 -5357: 0.402595, 0.446939 avg, 0.001000 rate, 25.127045 seconds, 1371392 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.622145, Class: 1.000000, Obj: 0.389905, No Obj: 0.003667, Avg Recall: 0.779412, count: 136 -Region Avg IOU: 0.612519, Class: 1.000000, Obj: 0.394066, No Obj: 0.002861, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.609376, Class: 1.000000, Obj: 0.381115, No Obj: 0.002686, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.584488, Class: 1.000000, Obj: 0.332138, No Obj: 0.002908, Avg Recall: 0.674074, count: 135 -Region Avg IOU: 0.554152, Class: 1.000000, Obj: 0.326383, No Obj: 0.003140, Avg Recall: 0.666667, count: 120 -Region Avg IOU: 0.649602, Class: 1.000000, Obj: 0.396370, No Obj: 0.003238, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.661021, Class: 1.000000, Obj: 0.425130, No Obj: 0.003014, Avg Recall: 0.808989, count: 89 -Region Avg IOU: 0.578796, Class: 1.000000, Obj: 0.374871, No Obj: 0.003347, Avg Recall: 0.678571, count: 140 -5358: 0.461849, 0.448430 avg, 0.001000 rate, 26.055412 seconds, 1371648 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.670093, Class: 1.000000, Obj: 0.503942, No Obj: 0.002943, Avg Recall: 0.840909, count: 88 -Region Avg IOU: 0.661284, Class: 1.000000, Obj: 0.386390, No Obj: 0.003374, Avg Recall: 0.831776, count: 107 -Region Avg IOU: 0.599892, Class: 1.000000, Obj: 0.415055, No Obj: 0.002963, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.653987, Class: 1.000000, Obj: 0.427706, No Obj: 0.003458, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.561510, Class: 1.000000, Obj: 0.365461, No Obj: 0.002876, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.601328, Class: 1.000000, Obj: 0.413408, No Obj: 0.003218, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.632699, Class: 1.000000, Obj: 0.402793, No Obj: 0.003613, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.575174, Class: 1.000000, Obj: 0.345552, No Obj: 0.002680, Avg Recall: 0.680412, count: 97 -5359: 0.419123, 0.445499 avg, 0.001000 rate, 26.134472 seconds, 1371904 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.635927, Class: 1.000000, Obj: 0.425708, No Obj: 0.003766, Avg Recall: 0.800000, count: 130 -Region Avg IOU: 0.648894, Class: 1.000000, Obj: 0.447379, No Obj: 0.002996, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.633461, Class: 1.000000, Obj: 0.443039, No Obj: 0.003428, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.665333, Class: 1.000000, Obj: 0.429256, No Obj: 0.002606, Avg Recall: 0.831461, count: 89 -Region Avg IOU: 0.594775, Class: 1.000000, Obj: 0.397476, No Obj: 0.003732, Avg Recall: 0.705036, count: 139 -Region Avg IOU: 0.615774, Class: 1.000000, Obj: 0.367953, No Obj: 0.002886, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.614619, Class: 1.000000, Obj: 0.404067, No Obj: 0.003230, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.657290, Class: 1.000000, Obj: 0.385190, No Obj: 0.003866, Avg Recall: 0.813953, count: 129 -5360: 0.444995, 0.445449 avg, 0.001000 rate, 26.225727 seconds, 1372160 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000069 seconds -Region Avg IOU: 0.681756, Class: 1.000000, Obj: 0.444562, No Obj: 0.002905, Avg Recall: 0.828829, count: 111 -Region Avg IOU: 0.673445, Class: 1.000000, Obj: 0.473838, No Obj: 0.003051, Avg Recall: 0.818182, count: 110 -Region Avg IOU: 0.667558, Class: 1.000000, Obj: 0.493660, No Obj: 0.002882, Avg Recall: 0.815217, count: 92 -Region Avg IOU: 0.649236, Class: 1.000000, Obj: 0.431721, No Obj: 0.003116, Avg Recall: 0.806723, count: 119 -Region Avg IOU: 0.650992, Class: 1.000000, Obj: 0.437390, No Obj: 0.002708, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.660690, Class: 1.000000, Obj: 0.479222, No Obj: 0.003024, Avg Recall: 0.844660, count: 103 -Region Avg IOU: 0.630038, Class: 1.000000, Obj: 0.409145, No Obj: 0.002628, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.654806, Class: 1.000000, Obj: 0.428139, No Obj: 0.002874, Avg Recall: 0.826531, count: 98 -5361: 0.399730, 0.440877 avg, 0.001000 rate, 26.776711 seconds, 1372416 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.638308, Class: 1.000000, Obj: 0.461964, No Obj: 0.003066, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.658179, Class: 1.000000, Obj: 0.445650, No Obj: 0.003041, Avg Recall: 0.766129, count: 124 -Region Avg IOU: 0.645455, Class: 1.000000, Obj: 0.475609, No Obj: 0.002640, Avg Recall: 0.785714, count: 84 -Region Avg IOU: 0.642853, Class: 1.000000, Obj: 0.453198, No Obj: 0.002999, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.617908, Class: 1.000000, Obj: 0.434526, No Obj: 0.003227, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.625517, Class: 1.000000, Obj: 0.425900, No Obj: 0.002740, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.631030, Class: 1.000000, Obj: 0.452584, No Obj: 0.002727, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.666682, Class: 1.000000, Obj: 0.423344, No Obj: 0.003010, Avg Recall: 0.795455, count: 88 -5362: 0.426593, 0.439448 avg, 0.001000 rate, 28.017241 seconds, 1372672 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.637586, Class: 1.000000, Obj: 0.419584, No Obj: 0.002756, Avg Recall: 0.811475, count: 122 -Region Avg IOU: 0.542498, Class: 1.000000, Obj: 0.404465, No Obj: 0.002477, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.594710, Class: 1.000000, Obj: 0.426609, No Obj: 0.002323, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.660680, Class: 1.000000, Obj: 0.463545, No Obj: 0.002706, Avg Recall: 0.831325, count: 83 -Region Avg IOU: 0.607605, Class: 1.000000, Obj: 0.436925, No Obj: 0.003277, Avg Recall: 0.751825, count: 137 -Region Avg IOU: 0.636163, Class: 1.000000, Obj: 0.441628, No Obj: 0.002779, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.624565, Class: 1.000000, Obj: 0.384626, No Obj: 0.002992, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.646091, Class: 1.000000, Obj: 0.441787, No Obj: 0.002710, Avg Recall: 0.768519, count: 108 -5363: 0.415516, 0.437055 avg, 0.001000 rate, 27.725416 seconds, 1372928 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.625385, Class: 1.000000, Obj: 0.363314, No Obj: 0.002620, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.642224, Class: 1.000000, Obj: 0.423094, No Obj: 0.003015, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.631470, Class: 1.000000, Obj: 0.396287, No Obj: 0.002605, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.639365, Class: 1.000000, Obj: 0.412654, No Obj: 0.002569, Avg Recall: 0.815217, count: 92 -Region Avg IOU: 0.672983, Class: 1.000000, Obj: 0.422500, No Obj: 0.002958, Avg Recall: 0.866142, count: 127 -Region Avg IOU: 0.652507, Class: 1.000000, Obj: 0.452668, No Obj: 0.002654, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.709774, Class: 1.000000, Obj: 0.472834, No Obj: 0.002510, Avg Recall: 0.891892, count: 74 -Region Avg IOU: 0.683877, Class: 1.000000, Obj: 0.438579, No Obj: 0.003072, Avg Recall: 0.853448, count: 116 -5364: 0.417680, 0.435118 avg, 0.001000 rate, 27.508450 seconds, 1373184 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.608095, Class: 1.000000, Obj: 0.381277, No Obj: 0.002275, Avg Recall: 0.740260, count: 77 -Region Avg IOU: 0.622090, Class: 1.000000, Obj: 0.363959, No Obj: 0.002156, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.621536, Class: 1.000000, Obj: 0.391239, No Obj: 0.002384, Avg Recall: 0.762500, count: 80 -Region Avg IOU: 0.668399, Class: 1.000000, Obj: 0.419777, No Obj: 0.002255, Avg Recall: 0.813333, count: 75 -Region Avg IOU: 0.648343, Class: 1.000000, Obj: 0.415565, No Obj: 0.002337, Avg Recall: 0.755556, count: 90 -Region Avg IOU: 0.644455, Class: 1.000000, Obj: 0.374969, No Obj: 0.002962, Avg Recall: 0.808696, count: 115 -Region Avg IOU: 0.631085, Class: 1.000000, Obj: 0.424640, No Obj: 0.002505, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.666254, Class: 1.000000, Obj: 0.398806, No Obj: 0.002652, Avg Recall: 0.848485, count: 99 -5365: 0.379299, 0.429536 avg, 0.001000 rate, 29.090281 seconds, 1373440 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.628065, Class: 1.000000, Obj: 0.341336, No Obj: 0.002754, Avg Recall: 0.746269, count: 134 -Region Avg IOU: 0.651554, Class: 1.000000, Obj: 0.364052, No Obj: 0.002722, Avg Recall: 0.830357, count: 112 -Region Avg IOU: 0.644585, Class: 1.000000, Obj: 0.372340, No Obj: 0.002652, Avg Recall: 0.805310, count: 113 -Region Avg IOU: 0.638239, Class: 1.000000, Obj: 0.389604, No Obj: 0.002559, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.617020, Class: 1.000000, Obj: 0.340279, No Obj: 0.002653, Avg Recall: 0.729508, count: 122 -Region Avg IOU: 0.639762, Class: 1.000000, Obj: 0.364342, No Obj: 0.002726, Avg Recall: 0.786325, count: 117 -Region Avg IOU: 0.622249, Class: 1.000000, Obj: 0.390742, No Obj: 0.002399, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.672661, Class: 1.000000, Obj: 0.401990, No Obj: 0.002203, Avg Recall: 0.860215, count: 93 -5366: 0.452900, 0.431872 avg, 0.001000 rate, 26.883512 seconds, 1373696 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.669664, Class: 1.000000, Obj: 0.433609, No Obj: 0.002285, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.671737, Class: 1.000000, Obj: 0.392921, No Obj: 0.002645, Avg Recall: 0.817391, count: 115 -Region Avg IOU: 0.662416, Class: 1.000000, Obj: 0.398165, No Obj: 0.002065, Avg Recall: 0.857143, count: 84 -Region Avg IOU: 0.652873, Class: 1.000000, Obj: 0.412833, No Obj: 0.002446, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.640404, Class: 1.000000, Obj: 0.364354, No Obj: 0.002612, Avg Recall: 0.785185, count: 135 -Region Avg IOU: 0.688608, Class: 1.000000, Obj: 0.383709, No Obj: 0.002624, Avg Recall: 0.885965, count: 114 -Region Avg IOU: 0.643150, Class: 1.000000, Obj: 0.329001, No Obj: 0.002399, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.653561, Class: 1.000000, Obj: 0.379715, No Obj: 0.002268, Avg Recall: 0.839286, count: 112 -5367: 0.450585, 0.433743 avg, 0.001000 rate, 27.519148 seconds, 1373952 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.649008, Class: 1.000000, Obj: 0.328769, No Obj: 0.002626, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.611815, Class: 1.000000, Obj: 0.377750, No Obj: 0.002307, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.635173, Class: 1.000000, Obj: 0.297898, No Obj: 0.002732, Avg Recall: 0.818182, count: 143 -Region Avg IOU: 0.666194, Class: 1.000000, Obj: 0.385498, No Obj: 0.002663, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.628869, Class: 1.000000, Obj: 0.356613, No Obj: 0.002131, Avg Recall: 0.744681, count: 94 -Region Avg IOU: 0.657954, Class: 1.000000, Obj: 0.398059, No Obj: 0.002558, Avg Recall: 0.827586, count: 116 -Region Avg IOU: 0.626655, Class: 1.000000, Obj: 0.364827, No Obj: 0.002785, Avg Recall: 0.762590, count: 139 -Region Avg IOU: 0.696613, Class: 1.000000, Obj: 0.405476, No Obj: 0.002340, Avg Recall: 0.864865, count: 111 -5368: 0.503403, 0.440709 avg, 0.001000 rate, 29.460600 seconds, 1374208 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.668502, Class: 1.000000, Obj: 0.410755, No Obj: 0.002474, Avg Recall: 0.843750, count: 96 -Region Avg IOU: 0.657080, Class: 1.000000, Obj: 0.381334, No Obj: 0.002991, Avg Recall: 0.814286, count: 140 -Region Avg IOU: 0.630121, Class: 1.000000, Obj: 0.386566, No Obj: 0.002880, Avg Recall: 0.772414, count: 145 -Region Avg IOU: 0.666679, Class: 1.000000, Obj: 0.397296, No Obj: 0.002325, Avg Recall: 0.825581, count: 86 -Region Avg IOU: 0.647431, Class: 1.000000, Obj: 0.380300, No Obj: 0.002601, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.621335, Class: 1.000000, Obj: 0.377314, No Obj: 0.002529, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.654398, Class: 1.000000, Obj: 0.419790, No Obj: 0.002319, Avg Recall: 0.831461, count: 89 -Region Avg IOU: 0.656223, Class: 1.000000, Obj: 0.345951, No Obj: 0.002561, Avg Recall: 0.800000, count: 115 -5369: 0.459001, 0.442539 avg, 0.001000 rate, 27.562708 seconds, 1374464 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.620000, Class: 1.000000, Obj: 0.348768, No Obj: 0.002528, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.654702, Class: 1.000000, Obj: 0.379237, No Obj: 0.002608, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.634975, Class: 1.000000, Obj: 0.390853, No Obj: 0.002279, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.636428, Class: 1.000000, Obj: 0.373636, No Obj: 0.002764, Avg Recall: 0.786885, count: 122 -Region Avg IOU: 0.600802, Class: 1.000000, Obj: 0.371741, No Obj: 0.002781, Avg Recall: 0.688000, count: 125 -Region Avg IOU: 0.655179, Class: 1.000000, Obj: 0.419201, No Obj: 0.003048, Avg Recall: 0.822222, count: 135 -Region Avg IOU: 0.658600, Class: 1.000000, Obj: 0.376087, No Obj: 0.002814, Avg Recall: 0.854839, count: 124 -Region Avg IOU: 0.661632, Class: 1.000000, Obj: 0.363695, No Obj: 0.002189, Avg Recall: 0.827160, count: 81 -5370: 0.455635, 0.443848 avg, 0.001000 rate, 29.351864 seconds, 1374720 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 13.991922 seconds -Region Avg IOU: 0.488430, Class: 1.000000, Obj: 0.300594, No Obj: 0.003897, Avg Recall: 0.516949, count: 118 -Region Avg IOU: 0.456461, Class: 1.000000, Obj: 0.274118, No Obj: 0.003061, Avg Recall: 0.523438, count: 128 -Region Avg IOU: 0.466205, Class: 1.000000, Obj: 0.264759, No Obj: 0.003602, Avg Recall: 0.518182, count: 110 -Region Avg IOU: 0.532467, Class: 1.000000, Obj: 0.313041, No Obj: 0.003790, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.525251, Class: 1.000000, Obj: 0.278635, No Obj: 0.004756, Avg Recall: 0.598592, count: 142 -Region Avg IOU: 0.538195, Class: 1.000000, Obj: 0.297876, No Obj: 0.003676, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.548183, Class: 1.000000, Obj: 0.379198, No Obj: 0.003395, Avg Recall: 0.628205, count: 78 -Region Avg IOU: 0.482907, Class: 1.000000, Obj: 0.277054, No Obj: 0.003635, Avg Recall: 0.484375, count: 128 -5371: 0.486061, 0.448070 avg, 0.001000 rate, 17.983397 seconds, 1374976 images -Loaded: 3.440950 seconds -Region Avg IOU: 0.451646, Class: 1.000000, Obj: 0.240660, No Obj: 0.003149, Avg Recall: 0.486239, count: 109 -Region Avg IOU: 0.514467, Class: 1.000000, Obj: 0.297225, No Obj: 0.003384, Avg Recall: 0.589474, count: 95 -Region Avg IOU: 0.453813, Class: 1.000000, Obj: 0.256656, No Obj: 0.003451, Avg Recall: 0.448276, count: 116 -Region Avg IOU: 0.482225, Class: 1.000000, Obj: 0.312667, No Obj: 0.003399, Avg Recall: 0.582418, count: 91 -Region Avg IOU: 0.481748, Class: 1.000000, Obj: 0.300108, No Obj: 0.003529, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.530522, Class: 1.000000, Obj: 0.315005, No Obj: 0.004068, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.520398, Class: 1.000000, Obj: 0.315423, No Obj: 0.004191, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.479069, Class: 1.000000, Obj: 0.274993, No Obj: 0.003593, Avg Recall: 0.546154, count: 130 -5372: 0.532251, 0.456488 avg, 0.001000 rate, 20.734703 seconds, 1375232 images -Loaded: 1.052849 seconds -Region Avg IOU: 0.498726, Class: 1.000000, Obj: 0.308523, No Obj: 0.003857, Avg Recall: 0.570175, count: 114 -Region Avg IOU: 0.552871, Class: 1.000000, Obj: 0.325541, No Obj: 0.004381, Avg Recall: 0.658333, count: 120 -Region Avg IOU: 0.500441, Class: 1.000000, Obj: 0.309325, No Obj: 0.004743, Avg Recall: 0.569106, count: 123 -Region Avg IOU: 0.541385, Class: 1.000000, Obj: 0.359005, No Obj: 0.004472, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.484689, Class: 1.000000, Obj: 0.308384, No Obj: 0.003160, Avg Recall: 0.553191, count: 94 -Region Avg IOU: 0.497008, Class: 1.000000, Obj: 0.312091, No Obj: 0.004080, Avg Recall: 0.513274, count: 113 -Region Avg IOU: 0.486493, Class: 1.000000, Obj: 0.320981, No Obj: 0.003686, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.474224, Class: 1.000000, Obj: 0.299916, No Obj: 0.003814, Avg Recall: 0.524752, count: 101 -5373: 0.430347, 0.453874 avg, 0.001000 rate, 19.590153 seconds, 1375488 images -Loaded: 1.561663 seconds -Region Avg IOU: 0.585587, Class: 1.000000, Obj: 0.357856, No Obj: 0.004118, Avg Recall: 0.679487, count: 78 -Region Avg IOU: 0.475110, Class: 1.000000, Obj: 0.337431, No Obj: 0.004653, Avg Recall: 0.508621, count: 116 -Region Avg IOU: 0.535394, Class: 1.000000, Obj: 0.370892, No Obj: 0.004014, Avg Recall: 0.602273, count: 88 -Region Avg IOU: 0.565466, Class: 1.000000, Obj: 0.324552, No Obj: 0.004646, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.448109, Class: 1.000000, Obj: 0.288845, No Obj: 0.003928, Avg Recall: 0.473684, count: 114 -Region Avg IOU: 0.503395, Class: 1.000000, Obj: 0.363747, No Obj: 0.004617, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.484639, Class: 1.000000, Obj: 0.329100, No Obj: 0.004057, Avg Recall: 0.511278, count: 133 -Region Avg IOU: 0.472603, Class: 1.000000, Obj: 0.336850, No Obj: 0.003836, Avg Recall: 0.532609, count: 92 -5374: 0.471701, 0.455656 avg, 0.001000 rate, 16.430126 seconds, 1375744 images -Loaded: 4.321481 seconds -Region Avg IOU: 0.518224, Class: 1.000000, Obj: 0.398138, No Obj: 0.004611, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.527864, Class: 1.000000, Obj: 0.349993, No Obj: 0.004251, Avg Recall: 0.610169, count: 118 -Region Avg IOU: 0.492052, Class: 1.000000, Obj: 0.319540, No Obj: 0.004579, Avg Recall: 0.525253, count: 99 -Region Avg IOU: 0.533170, Class: 1.000000, Obj: 0.372510, No Obj: 0.004460, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.546367, Class: 1.000000, Obj: 0.346436, No Obj: 0.004750, Avg Recall: 0.575342, count: 73 -Region Avg IOU: 0.496646, Class: 1.000000, Obj: 0.347765, No Obj: 0.004704, Avg Recall: 0.543103, count: 116 -Region Avg IOU: 0.501894, Class: 1.000000, Obj: 0.358970, No Obj: 0.004147, Avg Recall: 0.548387, count: 93 -Region Avg IOU: 0.514859, Class: 1.000000, Obj: 0.349084, No Obj: 0.004758, Avg Recall: 0.628571, count: 105 -5375: 0.448693, 0.454960 avg, 0.001000 rate, 18.433825 seconds, 1376000 images -Loaded: 2.619847 seconds -Region Avg IOU: 0.554951, Class: 1.000000, Obj: 0.420330, No Obj: 0.005002, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.466455, Class: 1.000000, Obj: 0.355193, No Obj: 0.003968, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.551011, Class: 1.000000, Obj: 0.367003, No Obj: 0.005035, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.518963, Class: 1.000000, Obj: 0.360153, No Obj: 0.005191, Avg Recall: 0.573643, count: 129 -Region Avg IOU: 0.520523, Class: 1.000000, Obj: 0.393349, No Obj: 0.004586, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.503051, Class: 1.000000, Obj: 0.348171, No Obj: 0.004691, Avg Recall: 0.552083, count: 96 -Region Avg IOU: 0.495522, Class: 1.000000, Obj: 0.369472, No Obj: 0.004249, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.505227, Class: 1.000000, Obj: 0.358813, No Obj: 0.005068, Avg Recall: 0.565574, count: 122 -5376: 0.460733, 0.455537 avg, 0.001000 rate, 18.563034 seconds, 1376256 images -Loaded: 1.714497 seconds -Region Avg IOU: 0.517841, Class: 1.000000, Obj: 0.415565, No Obj: 0.005085, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.496424, Class: 1.000000, Obj: 0.417693, No Obj: 0.004697, Avg Recall: 0.528846, count: 104 -Region Avg IOU: 0.489859, Class: 1.000000, Obj: 0.351818, No Obj: 0.004623, Avg Recall: 0.495727, count: 117 -Region Avg IOU: 0.458727, Class: 1.000000, Obj: 0.338800, No Obj: 0.005030, Avg Recall: 0.507692, count: 130 -Region Avg IOU: 0.496341, Class: 1.000000, Obj: 0.352116, No Obj: 0.004686, Avg Recall: 0.559140, count: 93 -Region Avg IOU: 0.508308, Class: 1.000000, Obj: 0.403126, No Obj: 0.005075, Avg Recall: 0.576577, count: 111 -Region Avg IOU: 0.475881, Class: 1.000000, Obj: 0.354015, No Obj: 0.005650, Avg Recall: 0.517986, count: 139 -Region Avg IOU: 0.515886, Class: 1.000000, Obj: 0.368611, No Obj: 0.004776, Avg Recall: 0.523810, count: 84 -5377: 0.452104, 0.455194 avg, 0.001000 rate, 19.576223 seconds, 1376512 images -Loaded: 1.232198 seconds -Region Avg IOU: 0.573914, Class: 1.000000, Obj: 0.421503, No Obj: 0.006297, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.457041, Class: 1.000000, Obj: 0.379759, No Obj: 0.004844, Avg Recall: 0.522523, count: 111 -Region Avg IOU: 0.503959, Class: 1.000000, Obj: 0.393154, No Obj: 0.006295, Avg Recall: 0.528000, count: 125 -Region Avg IOU: 0.534643, Class: 1.000000, Obj: 0.375054, No Obj: 0.005286, Avg Recall: 0.601695, count: 118 -Region Avg IOU: 0.540138, Class: 1.000000, Obj: 0.434378, No Obj: 0.004346, Avg Recall: 0.571429, count: 77 -Region Avg IOU: 0.491152, Class: 1.000000, Obj: 0.347749, No Obj: 0.004569, Avg Recall: 0.495575, count: 113 -Region Avg IOU: 0.582797, Class: 1.000000, Obj: 0.468118, No Obj: 0.005822, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.465112, Class: 1.000000, Obj: 0.380935, No Obj: 0.005148, Avg Recall: 0.452055, count: 146 -5378: 0.461840, 0.455859 avg, 0.001000 rate, 17.674227 seconds, 1376768 images -Loaded: 3.903403 seconds -Region Avg IOU: 0.528928, Class: 1.000000, Obj: 0.452185, No Obj: 0.004679, Avg Recall: 0.604651, count: 86 -Region Avg IOU: 0.467245, Class: 1.000000, Obj: 0.387313, No Obj: 0.005621, Avg Recall: 0.500000, count: 124 -Region Avg IOU: 0.518273, Class: 1.000000, Obj: 0.382427, No Obj: 0.005429, Avg Recall: 0.587719, count: 114 -Region Avg IOU: 0.485360, Class: 1.000000, Obj: 0.392110, No Obj: 0.005495, Avg Recall: 0.508621, count: 116 -Region Avg IOU: 0.456035, Class: 1.000000, Obj: 0.340782, No Obj: 0.005036, Avg Recall: 0.513043, count: 115 -Region Avg IOU: 0.488499, Class: 1.000000, Obj: 0.385037, No Obj: 0.004948, Avg Recall: 0.529412, count: 102 -Region Avg IOU: 0.549396, Class: 1.000000, Obj: 0.443963, No Obj: 0.004696, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.468234, Class: 1.000000, Obj: 0.353257, No Obj: 0.005554, Avg Recall: 0.487603, count: 121 -5379: 0.450699, 0.455343 avg, 0.001000 rate, 19.130886 seconds, 1377024 images -Loaded: 2.572048 seconds -Region Avg IOU: 0.513505, Class: 1.000000, Obj: 0.386802, No Obj: 0.004950, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.505162, Class: 1.000000, Obj: 0.410913, No Obj: 0.005390, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.487865, Class: 1.000000, Obj: 0.346703, No Obj: 0.004332, Avg Recall: 0.543478, count: 92 -Region Avg IOU: 0.511989, Class: 1.000000, Obj: 0.379871, No Obj: 0.005658, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.496022, Class: 1.000000, Obj: 0.358395, No Obj: 0.004915, Avg Recall: 0.560748, count: 107 -Region Avg IOU: 0.495532, Class: 1.000000, Obj: 0.432689, No Obj: 0.005273, Avg Recall: 0.500000, count: 110 -Region Avg IOU: 0.524308, Class: 1.000000, Obj: 0.367294, No Obj: 0.005207, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.523278, Class: 1.000000, Obj: 0.384340, No Obj: 0.005888, Avg Recall: 0.601770, count: 113 -5380: 0.459121, 0.455720 avg, 0.001000 rate, 18.702108 seconds, 1377280 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000080 seconds -Region Avg IOU: 0.558849, Class: 1.000000, Obj: 0.488826, No Obj: 0.004414, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.583607, Class: 1.000000, Obj: 0.455928, No Obj: 0.004182, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.591556, Class: 1.000000, Obj: 0.490565, No Obj: 0.003673, Avg Recall: 0.681818, count: 88 -Region Avg IOU: 0.631182, Class: 1.000000, Obj: 0.516080, No Obj: 0.005421, Avg Recall: 0.770992, count: 131 -Region Avg IOU: 0.581158, Class: 1.000000, Obj: 0.506330, No Obj: 0.004797, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.607743, Class: 1.000000, Obj: 0.503962, No Obj: 0.005204, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.610352, Class: 1.000000, Obj: 0.451549, No Obj: 0.004835, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.552537, Class: 1.000000, Obj: 0.512296, No Obj: 0.003985, Avg Recall: 0.613208, count: 106 -5381: 0.405112, 0.450660 avg, 0.001000 rate, 23.019737 seconds, 1377536 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.622928, Class: 1.000000, Obj: 0.453720, No Obj: 0.004646, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.585776, Class: 1.000000, Obj: 0.479850, No Obj: 0.003903, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.533221, Class: 1.000000, Obj: 0.440839, No Obj: 0.004691, Avg Recall: 0.595745, count: 141 -Region Avg IOU: 0.579563, Class: 1.000000, Obj: 0.482137, No Obj: 0.004488, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.606845, Class: 1.000000, Obj: 0.495993, No Obj: 0.003876, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.590884, Class: 1.000000, Obj: 0.465414, No Obj: 0.003446, Avg Recall: 0.715909, count: 88 -Region Avg IOU: 0.601308, Class: 1.000000, Obj: 0.451274, No Obj: 0.003748, Avg Recall: 0.737500, count: 80 -Region Avg IOU: 0.641381, Class: 1.000000, Obj: 0.423062, No Obj: 0.004058, Avg Recall: 0.831461, count: 89 -5382: 0.401319, 0.445726 avg, 0.001000 rate, 23.959856 seconds, 1377792 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.635327, Class: 1.000000, Obj: 0.448237, No Obj: 0.003805, Avg Recall: 0.777778, count: 90 -Region Avg IOU: 0.579730, Class: 1.000000, Obj: 0.470853, No Obj: 0.003979, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.558517, Class: 1.000000, Obj: 0.453110, No Obj: 0.004300, Avg Recall: 0.625954, count: 131 -Region Avg IOU: 0.610251, Class: 1.000000, Obj: 0.486468, No Obj: 0.003837, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.536583, Class: 1.000000, Obj: 0.369511, No Obj: 0.003931, Avg Recall: 0.616541, count: 133 -Region Avg IOU: 0.616412, Class: 1.000000, Obj: 0.424360, No Obj: 0.004338, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.590992, Class: 1.000000, Obj: 0.471467, No Obj: 0.004348, Avg Recall: 0.708333, count: 120 -Region Avg IOU: 0.579315, Class: 1.000000, Obj: 0.387824, No Obj: 0.004102, Avg Recall: 0.640000, count: 100 -5383: 0.410214, 0.442174 avg, 0.001000 rate, 23.806402 seconds, 1378048 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.576954, Class: 1.000000, Obj: 0.391148, No Obj: 0.003112, Avg Recall: 0.702381, count: 84 -Region Avg IOU: 0.604477, Class: 1.000000, Obj: 0.433864, No Obj: 0.003996, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.578921, Class: 1.000000, Obj: 0.442783, No Obj: 0.003874, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.640933, Class: 1.000000, Obj: 0.469964, No Obj: 0.003822, Avg Recall: 0.804124, count: 97 -Region Avg IOU: 0.546405, Class: 1.000000, Obj: 0.413942, No Obj: 0.003086, Avg Recall: 0.644444, count: 90 -Region Avg IOU: 0.589339, Class: 1.000000, Obj: 0.433655, No Obj: 0.003897, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.559478, Class: 1.000000, Obj: 0.412534, No Obj: 0.003283, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.538224, Class: 1.000000, Obj: 0.411771, No Obj: 0.003132, Avg Recall: 0.610619, count: 113 -5384: 0.372468, 0.435204 avg, 0.001000 rate, 25.011314 seconds, 1378304 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.549336, Class: 1.000000, Obj: 0.344784, No Obj: 0.003736, Avg Recall: 0.616667, count: 120 -Region Avg IOU: 0.574689, Class: 1.000000, Obj: 0.395635, No Obj: 0.003196, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.586048, Class: 1.000000, Obj: 0.387593, No Obj: 0.003379, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.615363, Class: 1.000000, Obj: 0.371748, No Obj: 0.003088, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.576539, Class: 1.000000, Obj: 0.389050, No Obj: 0.003578, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.634478, Class: 1.000000, Obj: 0.373156, No Obj: 0.003464, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.555397, Class: 1.000000, Obj: 0.378374, No Obj: 0.003220, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.517121, Class: 1.000000, Obj: 0.344347, No Obj: 0.002714, Avg Recall: 0.611111, count: 108 -5385: 0.445813, 0.436265 avg, 0.001000 rate, 23.862083 seconds, 1378560 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.609850, Class: 1.000000, Obj: 0.398089, No Obj: 0.002956, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.643631, Class: 1.000000, Obj: 0.385310, No Obj: 0.003060, Avg Recall: 0.764706, count: 85 -Region Avg IOU: 0.518789, Class: 1.000000, Obj: 0.316795, No Obj: 0.003094, Avg Recall: 0.575758, count: 132 -Region Avg IOU: 0.571650, Class: 1.000000, Obj: 0.387871, No Obj: 0.002744, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.595685, Class: 1.000000, Obj: 0.354100, No Obj: 0.003315, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.528063, Class: 1.000000, Obj: 0.335443, No Obj: 0.002700, Avg Recall: 0.593750, count: 96 -Region Avg IOU: 0.580311, Class: 1.000000, Obj: 0.329600, No Obj: 0.003025, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.599363, Class: 1.000000, Obj: 0.382695, No Obj: 0.002914, Avg Recall: 0.690909, count: 110 -5386: 0.418338, 0.434472 avg, 0.001000 rate, 24.098572 seconds, 1378816 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.552567, Class: 1.000000, Obj: 0.304034, No Obj: 0.002908, Avg Recall: 0.666667, count: 126 -Region Avg IOU: 0.616865, Class: 1.000000, Obj: 0.384753, No Obj: 0.003081, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.608370, Class: 1.000000, Obj: 0.328976, No Obj: 0.002973, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.591055, Class: 1.000000, Obj: 0.357491, No Obj: 0.002838, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.621086, Class: 1.000000, Obj: 0.364803, No Obj: 0.003247, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.593842, Class: 1.000000, Obj: 0.321184, No Obj: 0.002768, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.530780, Class: 1.000000, Obj: 0.302938, No Obj: 0.002402, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.581447, Class: 1.000000, Obj: 0.343441, No Obj: 0.002934, Avg Recall: 0.672727, count: 110 -5387: 0.469021, 0.437927 avg, 0.001000 rate, 24.984400 seconds, 1379072 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.548532, Class: 1.000000, Obj: 0.318800, No Obj: 0.003032, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.606004, Class: 1.000000, Obj: 0.342395, No Obj: 0.002885, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.586673, Class: 1.000000, Obj: 0.284627, No Obj: 0.003188, Avg Recall: 0.691176, count: 136 -Region Avg IOU: 0.580758, Class: 1.000000, Obj: 0.353590, No Obj: 0.002602, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.595828, Class: 1.000000, Obj: 0.352147, No Obj: 0.003014, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.596715, Class: 1.000000, Obj: 0.304254, No Obj: 0.002524, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.606690, Class: 1.000000, Obj: 0.309330, No Obj: 0.003374, Avg Recall: 0.715278, count: 144 -Region Avg IOU: 0.589991, Class: 1.000000, Obj: 0.331889, No Obj: 0.003194, Avg Recall: 0.676692, count: 133 -5388: 0.468768, 0.441011 avg, 0.001000 rate, 23.534563 seconds, 1379328 images -Loaded: 0.000115 seconds -Region Avg IOU: 0.608409, Class: 1.000000, Obj: 0.365665, No Obj: 0.002189, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.580481, Class: 1.000000, Obj: 0.307842, No Obj: 0.003323, Avg Recall: 0.705882, count: 136 -Region Avg IOU: 0.571660, Class: 1.000000, Obj: 0.315453, No Obj: 0.002908, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.603685, Class: 1.000000, Obj: 0.351601, No Obj: 0.003249, Avg Recall: 0.700730, count: 137 -Region Avg IOU: 0.586195, Class: 1.000000, Obj: 0.326653, No Obj: 0.002834, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.570047, Class: 1.000000, Obj: 0.346087, No Obj: 0.002666, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.612726, Class: 1.000000, Obj: 0.347422, No Obj: 0.003200, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.633250, Class: 1.000000, Obj: 0.376238, No Obj: 0.002629, Avg Recall: 0.760000, count: 100 -5389: 0.422828, 0.439193 avg, 0.001000 rate, 24.686289 seconds, 1379584 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.615348, Class: 1.000000, Obj: 0.347871, No Obj: 0.003022, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.627383, Class: 1.000000, Obj: 0.409706, No Obj: 0.002945, Avg Recall: 0.740741, count: 81 -Region Avg IOU: 0.589306, Class: 1.000000, Obj: 0.365373, No Obj: 0.003016, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.597348, Class: 1.000000, Obj: 0.378092, No Obj: 0.003026, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.582898, Class: 1.000000, Obj: 0.328880, No Obj: 0.003000, Avg Recall: 0.656250, count: 128 -Region Avg IOU: 0.605206, Class: 1.000000, Obj: 0.330707, No Obj: 0.002837, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.557843, Class: 1.000000, Obj: 0.340473, No Obj: 0.002648, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.573835, Class: 1.000000, Obj: 0.392915, No Obj: 0.003002, Avg Recall: 0.675926, count: 108 -5390: 0.440108, 0.439284 avg, 0.001000 rate, 23.768982 seconds, 1379840 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000083 seconds -Region Avg IOU: 0.636635, Class: 1.000000, Obj: 0.425230, No Obj: 0.003227, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.614406, Class: 1.000000, Obj: 0.380308, No Obj: 0.002731, Avg Recall: 0.730769, count: 78 -Region Avg IOU: 0.600272, Class: 1.000000, Obj: 0.347241, No Obj: 0.002846, Avg Recall: 0.682927, count: 123 -Region Avg IOU: 0.632680, Class: 1.000000, Obj: 0.401237, No Obj: 0.003616, Avg Recall: 0.787879, count: 132 -Region Avg IOU: 0.625878, Class: 1.000000, Obj: 0.441024, No Obj: 0.002795, Avg Recall: 0.769231, count: 78 -Region Avg IOU: 0.593582, Class: 1.000000, Obj: 0.355462, No Obj: 0.002850, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.612042, Class: 1.000000, Obj: 0.386449, No Obj: 0.002972, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.595337, Class: 1.000000, Obj: 0.348341, No Obj: 0.003165, Avg Recall: 0.747368, count: 95 -5391: 0.410078, 0.436364 avg, 0.001000 rate, 25.167091 seconds, 1380096 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.582570, Class: 1.000000, Obj: 0.365326, No Obj: 0.003391, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.578172, Class: 1.000000, Obj: 0.416681, No Obj: 0.003044, Avg Recall: 0.662162, count: 148 -Region Avg IOU: 0.649246, Class: 1.000000, Obj: 0.424752, No Obj: 0.003002, Avg Recall: 0.806818, count: 88 -Region Avg IOU: 0.573877, Class: 1.000000, Obj: 0.381402, No Obj: 0.003513, Avg Recall: 0.656000, count: 125 -Region Avg IOU: 0.656021, Class: 1.000000, Obj: 0.454702, No Obj: 0.003001, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.634994, Class: 1.000000, Obj: 0.403702, No Obj: 0.003206, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.606855, Class: 1.000000, Obj: 0.397099, No Obj: 0.003272, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.591128, Class: 1.000000, Obj: 0.447945, No Obj: 0.002865, Avg Recall: 0.641304, count: 92 -5392: 0.426988, 0.435426 avg, 0.001000 rate, 26.150082 seconds, 1380352 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.621031, Class: 1.000000, Obj: 0.439675, No Obj: 0.003659, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.594591, Class: 1.000000, Obj: 0.381716, No Obj: 0.003296, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.605497, Class: 1.000000, Obj: 0.459209, No Obj: 0.003486, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.607841, Class: 1.000000, Obj: 0.450865, No Obj: 0.003368, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.620453, Class: 1.000000, Obj: 0.369472, No Obj: 0.003339, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.630894, Class: 1.000000, Obj: 0.374533, No Obj: 0.003765, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.621972, Class: 1.000000, Obj: 0.426066, No Obj: 0.003136, Avg Recall: 0.705882, count: 85 -Region Avg IOU: 0.658443, Class: 1.000000, Obj: 0.466177, No Obj: 0.003667, Avg Recall: 0.819820, count: 111 -5393: 0.410246, 0.432908 avg, 0.001000 rate, 24.143009 seconds, 1380608 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.599494, Class: 1.000000, Obj: 0.421987, No Obj: 0.004260, Avg Recall: 0.683824, count: 136 -Region Avg IOU: 0.598935, Class: 1.000000, Obj: 0.455857, No Obj: 0.003474, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.604289, Class: 1.000000, Obj: 0.419982, No Obj: 0.003824, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.562677, Class: 1.000000, Obj: 0.403346, No Obj: 0.003471, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.593767, Class: 1.000000, Obj: 0.404384, No Obj: 0.003559, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.620584, Class: 1.000000, Obj: 0.428404, No Obj: 0.003106, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.583353, Class: 1.000000, Obj: 0.370943, No Obj: 0.003480, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.630255, Class: 1.000000, Obj: 0.438771, No Obj: 0.003742, Avg Recall: 0.760000, count: 100 -5394: 0.447010, 0.434318 avg, 0.001000 rate, 24.636671 seconds, 1380864 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.663071, Class: 1.000000, Obj: 0.461603, No Obj: 0.003851, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.598329, Class: 1.000000, Obj: 0.445426, No Obj: 0.003884, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.537813, Class: 1.000000, Obj: 0.406455, No Obj: 0.003837, Avg Recall: 0.642857, count: 126 -Region Avg IOU: 0.562335, Class: 1.000000, Obj: 0.435157, No Obj: 0.003281, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.616418, Class: 1.000000, Obj: 0.471419, No Obj: 0.004049, Avg Recall: 0.719697, count: 132 -Region Avg IOU: 0.625101, Class: 1.000000, Obj: 0.423312, No Obj: 0.003734, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.622973, Class: 1.000000, Obj: 0.477089, No Obj: 0.003618, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.623467, Class: 1.000000, Obj: 0.444948, No Obj: 0.004235, Avg Recall: 0.720000, count: 125 -5395: 0.437513, 0.434638 avg, 0.001000 rate, 25.164967 seconds, 1381120 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.588406, Class: 1.000000, Obj: 0.405648, No Obj: 0.003399, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.619904, Class: 1.000000, Obj: 0.433454, No Obj: 0.003638, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.626948, Class: 1.000000, Obj: 0.455280, No Obj: 0.003642, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.597867, Class: 1.000000, Obj: 0.512373, No Obj: 0.003655, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.593278, Class: 1.000000, Obj: 0.411184, No Obj: 0.003828, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.610942, Class: 1.000000, Obj: 0.468958, No Obj: 0.003914, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.583151, Class: 1.000000, Obj: 0.471187, No Obj: 0.003830, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.633594, Class: 1.000000, Obj: 0.467832, No Obj: 0.004073, Avg Recall: 0.733333, count: 105 -5396: 0.376455, 0.428820 avg, 0.001000 rate, 25.351404 seconds, 1381376 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.641972, Class: 1.000000, Obj: 0.435757, No Obj: 0.003550, Avg Recall: 0.797753, count: 89 -Region Avg IOU: 0.632306, Class: 1.000000, Obj: 0.435003, No Obj: 0.003752, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.610997, Class: 1.000000, Obj: 0.461581, No Obj: 0.003720, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.589156, Class: 1.000000, Obj: 0.430433, No Obj: 0.002991, Avg Recall: 0.628205, count: 78 -Region Avg IOU: 0.656326, Class: 1.000000, Obj: 0.496313, No Obj: 0.003638, Avg Recall: 0.813187, count: 91 -Region Avg IOU: 0.629565, Class: 1.000000, Obj: 0.472728, No Obj: 0.004009, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.642390, Class: 1.000000, Obj: 0.471268, No Obj: 0.004106, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.608704, Class: 1.000000, Obj: 0.400144, No Obj: 0.004591, Avg Recall: 0.738562, count: 153 -5397: 0.436516, 0.429589 avg, 0.001000 rate, 24.977589 seconds, 1381632 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.598163, Class: 1.000000, Obj: 0.403886, No Obj: 0.003350, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.584401, Class: 1.000000, Obj: 0.453441, No Obj: 0.003979, Avg Recall: 0.682927, count: 123 -Region Avg IOU: 0.576381, Class: 1.000000, Obj: 0.440470, No Obj: 0.003860, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.680663, Class: 1.000000, Obj: 0.495038, No Obj: 0.004021, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.575277, Class: 1.000000, Obj: 0.396343, No Obj: 0.003624, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.640900, Class: 1.000000, Obj: 0.433274, No Obj: 0.004003, Avg Recall: 0.796460, count: 113 -Region Avg IOU: 0.635550, Class: 1.000000, Obj: 0.495766, No Obj: 0.003036, Avg Recall: 0.746835, count: 79 -Region Avg IOU: 0.572359, Class: 1.000000, Obj: 0.405817, No Obj: 0.003488, Avg Recall: 0.666667, count: 99 -5398: 0.413630, 0.427993 avg, 0.001000 rate, 25.747372 seconds, 1381888 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.583640, Class: 1.000000, Obj: 0.412716, No Obj: 0.003468, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.588156, Class: 1.000000, Obj: 0.395670, No Obj: 0.003494, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.592560, Class: 1.000000, Obj: 0.441862, No Obj: 0.003390, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.559250, Class: 1.000000, Obj: 0.374510, No Obj: 0.003515, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.634589, Class: 1.000000, Obj: 0.424713, No Obj: 0.003482, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.621144, Class: 1.000000, Obj: 0.400578, No Obj: 0.003299, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.582390, Class: 1.000000, Obj: 0.429636, No Obj: 0.003653, Avg Recall: 0.661417, count: 127 -Region Avg IOU: 0.593555, Class: 1.000000, Obj: 0.419357, No Obj: 0.003529, Avg ReSaving weights to ./person9300/backup/tyvp_93_9c512_5400.weights -call: 0.723577, count: 123 -5399: 0.450231, 0.430217 avg, 0.001000 rate, 26.750765 seconds, 1382144 images -Loaded: 0.000118 seconds -Region Avg IOU: 0.605206, Class: 1.000000, Obj: 0.400149, No Obj: 0.003770, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.569867, Class: 1.000000, Obj: 0.383112, No Obj: 0.003475, Avg Recall: 0.680328, count: 122 -Region Avg IOU: 0.679997, Class: 1.000000, Obj: 0.429647, No Obj: 0.003029, Avg Recall: 0.845238, count: 84 -Region Avg IOU: 0.562200, Class: 1.000000, Obj: 0.371733, No Obj: 0.003311, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.617969, Class: 1.000000, Obj: 0.435774, No Obj: 0.003700, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.635025, Class: 1.000000, Obj: 0.469341, No Obj: 0.003326, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.535174, Class: 1.000000, Obj: 0.378129, No Obj: 0.003417, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.612516, Class: 1.000000, Obj: 0.387475, No Obj: 0.003375, Avg Recall: 0.743363, count: 113 -5400: 0.435785, 0.430774 avg, 0.001000 rate, 26.022301 seconds, 1382400 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.209911 seconds -Region Avg IOU: 0.490268, Class: 1.000000, Obj: 0.292512, No Obj: 0.004037, Avg Recall: 0.522059, count: 136 -Region Avg IOU: 0.517420, Class: 1.000000, Obj: 0.353961, No Obj: 0.004026, Avg Recall: 0.559322, count: 118 -Region Avg IOU: 0.518487, Class: 1.000000, Obj: 0.335728, No Obj: 0.004191, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.490071, Class: 1.000000, Obj: 0.323818, No Obj: 0.003257, Avg Recall: 0.578947, count: 114 -Region Avg IOU: 0.504027, Class: 1.000000, Obj: 0.294129, No Obj: 0.003555, Avg Recall: 0.530973, count: 113 -Region Avg IOU: 0.563521, Class: 1.000000, Obj: 0.332522, No Obj: 0.003618, Avg Recall: 0.673684, count: 95 -Region Avg IOU: 0.527826, Class: 1.000000, Obj: 0.323784, No Obj: 0.003599, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.442818, Class: 1.000000, Obj: 0.303951, No Obj: 0.003355, Avg Recall: 0.481818, count: 110 -5401: 0.448255, 0.432522 avg, 0.001000 rate, 19.375462 seconds, 1382656 images -Loaded: 2.153328 seconds -Region Avg IOU: 0.490770, Class: 1.000000, Obj: 0.325348, No Obj: 0.003520, Avg Recall: 0.528846, count: 104 -Region Avg IOU: 0.532255, Class: 1.000000, Obj: 0.293396, No Obj: 0.003870, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.497637, Class: 1.000000, Obj: 0.307222, No Obj: 0.003320, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.499888, Class: 1.000000, Obj: 0.333380, No Obj: 0.003436, Avg Recall: 0.515464, count: 97 -Region Avg IOU: 0.482414, Class: 1.000000, Obj: 0.303651, No Obj: 0.003217, Avg Recall: 0.515873, count: 126 -Region Avg IOU: 0.443906, Class: 1.000000, Obj: 0.244412, No Obj: 0.003536, Avg Recall: 0.538462, count: 117 -Region Avg IOU: 0.535746, Class: 1.000000, Obj: 0.293332, No Obj: 0.003998, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.541981, Class: 1.000000, Obj: 0.318598, No Obj: 0.004435, Avg Recall: 0.628788, count: 132 -5402: 0.458314, 0.435101 avg, 0.001000 rate, 18.460554 seconds, 1382912 images -Loaded: 3.863027 seconds -Region Avg IOU: 0.531277, Class: 1.000000, Obj: 0.305797, No Obj: 0.003528, Avg Recall: 0.558442, count: 77 -Region Avg IOU: 0.498582, Class: 1.000000, Obj: 0.292745, No Obj: 0.003510, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.501561, Class: 1.000000, Obj: 0.305175, No Obj: 0.003568, Avg Recall: 0.523364, count: 107 -Region Avg IOU: 0.505484, Class: 1.000000, Obj: 0.301457, No Obj: 0.004419, Avg Recall: 0.587838, count: 148 -Region Avg IOU: 0.491127, Class: 1.000000, Obj: 0.304397, No Obj: 0.003504, Avg Recall: 0.593750, count: 96 -Region Avg IOU: 0.446197, Class: 1.000000, Obj: 0.271130, No Obj: 0.003073, Avg Recall: 0.464912, count: 114 -Region Avg IOU: 0.513365, Class: 1.000000, Obj: 0.364864, No Obj: 0.003815, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.570576, Class: 1.000000, Obj: 0.324228, No Obj: 0.004136, Avg Recall: 0.663366, count: 101 -5403: 0.477277, 0.439319 avg, 0.001000 rate, 19.831528 seconds, 1383168 images -Loaded: 1.191243 seconds -Region Avg IOU: 0.508039, Class: 1.000000, Obj: 0.343572, No Obj: 0.003868, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.538472, Class: 1.000000, Obj: 0.316515, No Obj: 0.004249, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.516824, Class: 1.000000, Obj: 0.302611, No Obj: 0.003883, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.506444, Class: 1.000000, Obj: 0.326574, No Obj: 0.003785, Avg Recall: 0.547368, count: 95 -Region Avg IOU: 0.508609, Class: 1.000000, Obj: 0.275312, No Obj: 0.002965, Avg Recall: 0.617284, count: 81 -Region Avg IOU: 0.502425, Class: 1.000000, Obj: 0.325977, No Obj: 0.003468, Avg Recall: 0.567568, count: 111 -Region Avg IOU: 0.508974, Class: 1.000000, Obj: 0.306736, No Obj: 0.003544, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.509898, Class: 1.000000, Obj: 0.264292, No Obj: 0.003845, Avg Recall: 0.608696, count: 115 -5404: 0.433944, 0.438781 avg, 0.001000 rate, 18.743973 seconds, 1383424 images -Loaded: 3.212593 seconds -Region Avg IOU: 0.516255, Class: 1.000000, Obj: 0.338968, No Obj: 0.004355, Avg Recall: 0.604839, count: 124 -Region Avg IOU: 0.515524, Class: 1.000000, Obj: 0.371924, No Obj: 0.004439, Avg Recall: 0.593220, count: 118 -Region Avg IOU: 0.510837, Class: 1.000000, Obj: 0.299279, No Obj: 0.004647, Avg Recall: 0.551471, count: 136 -Region Avg IOU: 0.522235, Class: 1.000000, Obj: 0.302678, No Obj: 0.004211, Avg Recall: 0.587786, count: 131 -Region Avg IOU: 0.494868, Class: 1.000000, Obj: 0.332277, No Obj: 0.003480, Avg Recall: 0.494737, count: 95 -Region Avg IOU: 0.444665, Class: 1.000000, Obj: 0.298123, No Obj: 0.003320, Avg Recall: 0.469388, count: 98 -Region Avg IOU: 0.499270, Class: 1.000000, Obj: 0.303434, No Obj: 0.004728, Avg Recall: 0.512605, count: 119 -Region Avg IOU: 0.534072, Class: 1.000000, Obj: 0.330115, No Obj: 0.004247, Avg Recall: 0.616667, count: 120 -5405: 0.453443, 0.440247 avg, 0.001000 rate, 16.577101 seconds, 1383680 images -Loaded: 4.219351 seconds -Region Avg IOU: 0.613075, Class: 1.000000, Obj: 0.389690, No Obj: 0.004135, Avg Recall: 0.725000, count: 80 -Region Avg IOU: 0.534301, Class: 1.000000, Obj: 0.337064, No Obj: 0.004461, Avg Recall: 0.624000, count: 125 -Region Avg IOU: 0.526461, Class: 1.000000, Obj: 0.361874, No Obj: 0.004716, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.564170, Class: 1.000000, Obj: 0.364367, No Obj: 0.003812, Avg Recall: 0.701299, count: 77 -Region Avg IOU: 0.552965, Class: 1.000000, Obj: 0.357156, No Obj: 0.004915, Avg Recall: 0.664000, count: 125 -Region Avg IOU: 0.483484, Class: 1.000000, Obj: 0.320491, No Obj: 0.003370, Avg Recall: 0.543478, count: 92 -Region Avg IOU: 0.505508, Class: 1.000000, Obj: 0.331482, No Obj: 0.004312, Avg Recall: 0.569106, count: 123 -Region Avg IOU: 0.510800, Class: 1.000000, Obj: 0.288280, No Obj: 0.003329, Avg Recall: 0.520000, count: 75 -5406: 0.410153, 0.437238 avg, 0.001000 rate, 18.623425 seconds, 1383936 images -Loaded: 2.139953 seconds -Region Avg IOU: 0.537761, Class: 1.000000, Obj: 0.389788, No Obj: 0.004892, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.492105, Class: 1.000000, Obj: 0.334949, No Obj: 0.003866, Avg Recall: 0.554348, count: 92 -Region Avg IOU: 0.545856, Class: 1.000000, Obj: 0.328580, No Obj: 0.003919, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.534856, Class: 1.000000, Obj: 0.373347, No Obj: 0.004019, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.541803, Class: 1.000000, Obj: 0.360190, No Obj: 0.004885, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.494692, Class: 1.000000, Obj: 0.320908, No Obj: 0.004074, Avg Recall: 0.565217, count: 92 -Region Avg IOU: 0.461501, Class: 1.000000, Obj: 0.348721, No Obj: 0.004174, Avg Recall: 0.525253, count: 99 -Region Avg IOU: 0.516102, Class: 1.000000, Obj: 0.325916, No Obj: 0.004909, Avg Recall: 0.569106, count: 123 -5407: 0.413906, 0.434905 avg, 0.001000 rate, 18.230556 seconds, 1384192 images -Loaded: 0.945630 seconds -Region Avg IOU: 0.501429, Class: 1.000000, Obj: 0.363627, No Obj: 0.005186, Avg Recall: 0.550725, count: 138 -Region Avg IOU: 0.545570, Class: 1.000000, Obj: 0.370122, No Obj: 0.005090, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.463467, Class: 1.000000, Obj: 0.347244, No Obj: 0.004662, Avg Recall: 0.471698, count: 106 -Region Avg IOU: 0.558181, Class: 1.000000, Obj: 0.413835, No Obj: 0.004348, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.504355, Class: 1.000000, Obj: 0.355559, No Obj: 0.004342, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.524788, Class: 1.000000, Obj: 0.394221, No Obj: 0.004275, Avg Recall: 0.606742, count: 89 -Region Avg IOU: 0.493591, Class: 1.000000, Obj: 0.362281, No Obj: 0.004276, Avg Recall: 0.559140, count: 93 -Region Avg IOU: 0.540892, Class: 1.000000, Obj: 0.343840, No Obj: 0.004197, Avg Recall: 0.640449, count: 89 -5408: 0.430444, 0.434459 avg, 0.001000 rate, 17.512884 seconds, 1384448 images -Loaded: 3.571247 seconds -Region Avg IOU: 0.486274, Class: 1.000000, Obj: 0.411171, No Obj: 0.005056, Avg Recall: 0.508772, count: 114 -Region Avg IOU: 0.522356, Class: 1.000000, Obj: 0.375581, No Obj: 0.005184, Avg Recall: 0.598485, count: 132 -Region Avg IOU: 0.591321, Class: 1.000000, Obj: 0.396572, No Obj: 0.004947, Avg Recall: 0.616438, count: 73 -Region Avg IOU: 0.514482, Class: 1.000000, Obj: 0.417314, No Obj: 0.005715, Avg Recall: 0.587786, count: 131 -Region Avg IOU: 0.528073, Class: 1.000000, Obj: 0.363180, No Obj: 0.004959, Avg Recall: 0.578947, count: 114 -Region Avg IOU: 0.471927, Class: 1.000000, Obj: 0.378773, No Obj: 0.004881, Avg Recall: 0.496241, count: 133 -Region Avg IOU: 0.478646, Class: 1.000000, Obj: 0.386175, No Obj: 0.004585, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.543184, Class: 1.000000, Obj: 0.421942, No Obj: 0.005256, Avg Recall: 0.598214, count: 112 -5409: 0.443803, 0.435393 avg, 0.001000 rate, 20.110647 seconds, 1384704 images -Loaded: 1.750637 seconds -Region Avg IOU: 0.548540, Class: 1.000000, Obj: 0.409234, No Obj: 0.005149, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.537216, Class: 1.000000, Obj: 0.404187, No Obj: 0.005510, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.508140, Class: 1.000000, Obj: 0.361529, No Obj: 0.005262, Avg Recall: 0.563025, count: 119 -Region Avg IOU: 0.523521, Class: 1.000000, Obj: 0.375332, No Obj: 0.004786, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.507997, Class: 1.000000, Obj: 0.389125, No Obj: 0.004664, Avg Recall: 0.579545, count: 88 -Region Avg IOU: 0.544629, Class: 1.000000, Obj: 0.417699, No Obj: 0.004481, Avg Recall: 0.609195, count: 87 -Region Avg IOU: 0.475977, Class: 1.000000, Obj: 0.407292, No Obj: 0.005469, Avg Recall: 0.511278, count: 133 -Region Avg IOU: 0.491662, Class: 1.000000, Obj: 0.364659, No Obj: 0.005128, Avg Recall: 0.533333, count: 120 -5410: 0.462417, 0.438096 avg, 0.001000 rate, 20.680859 seconds, 1384960 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000066 seconds -Region Avg IOU: 0.663029, Class: 1.000000, Obj: 0.511212, No Obj: 0.003824, Avg Recall: 0.813008, count: 123 -Region Avg IOU: 0.665246, Class: 1.000000, Obj: 0.541172, No Obj: 0.003692, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.650719, Class: 1.000000, Obj: 0.543547, No Obj: 0.003306, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.647061, Class: 1.000000, Obj: 0.480469, No Obj: 0.003422, Avg Recall: 0.835052, count: 97 -Region Avg IOU: 0.648914, Class: 1.000000, Obj: 0.492567, No Obj: 0.003692, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.643385, Class: 1.000000, Obj: 0.507807, No Obj: 0.002977, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.628931, Class: 1.000000, Obj: 0.515471, No Obj: 0.003209, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.659136, Class: 1.000000, Obj: 0.478770, No Obj: 0.003427, Avg Recall: 0.806122, count: 98 -5411: 0.402598, 0.434546 avg, 0.001000 rate, 29.496368 seconds, 1385216 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.673184, Class: 1.000000, Obj: 0.440705, No Obj: 0.002933, Avg Recall: 0.809524, count: 84 -Region Avg IOU: 0.632827, Class: 1.000000, Obj: 0.409993, No Obj: 0.003911, Avg Recall: 0.771242, count: 153 -Region Avg IOU: 0.674555, Class: 1.000000, Obj: 0.469723, No Obj: 0.003689, Avg Recall: 0.833333, count: 108 -Region Avg IOU: 0.665998, Class: 1.000000, Obj: 0.451781, No Obj: 0.003455, Avg Recall: 0.858586, count: 99 -Region Avg IOU: 0.633123, Class: 1.000000, Obj: 0.525836, No Obj: 0.003076, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.660647, Class: 1.000000, Obj: 0.499366, No Obj: 0.003341, Avg Recall: 0.851852, count: 108 -Region Avg IOU: 0.681855, Class: 1.000000, Obj: 0.455844, No Obj: 0.004153, Avg Recall: 0.817460, count: 126 -Region Avg IOU: 0.653136, Class: 1.000000, Obj: 0.470616, No Obj: 0.003461, Avg Recall: 0.804348, count: 92 -5412: 0.466281, 0.437719 avg, 0.001000 rate, 29.962364 seconds, 1385472 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.614009, Class: 1.000000, Obj: 0.438392, No Obj: 0.002972, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.639864, Class: 1.000000, Obj: 0.458290, No Obj: 0.002770, Avg Recall: 0.817308, count: 104 -Region Avg IOU: 0.679728, Class: 1.000000, Obj: 0.423086, No Obj: 0.003193, Avg Recall: 0.864407, count: 118 -Region Avg IOU: 0.636562, Class: 1.000000, Obj: 0.427659, No Obj: 0.002390, Avg Recall: 0.811765, count: 85 -Region Avg IOU: 0.682409, Class: 1.000000, Obj: 0.489131, No Obj: 0.002838, Avg Recall: 0.839080, count: 87 -Region Avg IOU: 0.631586, Class: 1.000000, Obj: 0.381411, No Obj: 0.002631, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.671005, Class: 1.000000, Obj: 0.488198, No Obj: 0.003237, Avg Recall: 0.813008, count: 123 -Region Avg IOU: 0.634230, Class: 1.000000, Obj: 0.453973, No Obj: 0.003092, Avg Recall: 0.750000, count: 104 -5413: 0.436801, 0.437627 avg, 0.001000 rate, 29.273100 seconds, 1385728 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.613729, Class: 1.000000, Obj: 0.398040, No Obj: 0.002215, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.647238, Class: 1.000000, Obj: 0.366519, No Obj: 0.002833, Avg Recall: 0.791667, count: 120 -Region Avg IOU: 0.624680, Class: 1.000000, Obj: 0.457921, No Obj: 0.002381, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.676376, Class: 1.000000, Obj: 0.363272, No Obj: 0.002611, Avg Recall: 0.843137, count: 102 -Region Avg IOU: 0.665416, Class: 1.000000, Obj: 0.500453, No Obj: 0.002249, Avg Recall: 0.827586, count: 87 -Region Avg IOU: 0.629520, Class: 1.000000, Obj: 0.440700, No Obj: 0.002496, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.657512, Class: 1.000000, Obj: 0.374530, No Obj: 0.002738, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.675020, Class: 1.000000, Obj: 0.406942, No Obj: 0.002545, Avg Recall: 0.818182, count: 110 -5414: 0.455977, 0.439462 avg, 0.001000 rate, 29.476412 seconds, 1385984 images -Loaded: 0.000120 seconds -Region Avg IOU: 0.640788, Class: 1.000000, Obj: 0.433153, No Obj: 0.002227, Avg Recall: 0.805970, count: 134 -Region Avg IOU: 0.649866, Class: 1.000000, Obj: 0.407996, No Obj: 0.002368, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.647280, Class: 1.000000, Obj: 0.343878, No Obj: 0.002430, Avg Recall: 0.793651, count: 126 -Region Avg IOU: 0.653979, Class: 1.000000, Obj: 0.381604, No Obj: 0.002151, Avg Recall: 0.825000, count: 80 -Region Avg IOU: 0.690546, Class: 1.000000, Obj: 0.441837, No Obj: 0.002138, Avg Recall: 0.871287, count: 101 -Region Avg IOU: 0.655773, Class: 1.000000, Obj: 0.377948, No Obj: 0.002661, Avg Recall: 0.791667, count: 120 -Region Avg IOU: 0.673736, Class: 1.000000, Obj: 0.431391, No Obj: 0.002061, Avg Recall: 0.851064, count: 94 -Region Avg IOU: 0.675529, Class: 1.000000, Obj: 0.371142, No Obj: 0.002411, Avg Recall: 0.850877, count: 114 -5415: 0.447167, 0.440233 avg, 0.001000 rate, 28.353592 seconds, 1386240 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.658559, Class: 1.000000, Obj: 0.314122, No Obj: 0.002138, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.672301, Class: 1.000000, Obj: 0.409645, No Obj: 0.001958, Avg Recall: 0.855670, count: 97 -Region Avg IOU: 0.611260, Class: 1.000000, Obj: 0.347322, No Obj: 0.001697, Avg Recall: 0.740260, count: 77 -Region Avg IOU: 0.651055, Class: 1.000000, Obj: 0.353496, No Obj: 0.002125, Avg Recall: 0.826923, count: 104 -Region Avg IOU: 0.630618, Class: 1.000000, Obj: 0.348037, No Obj: 0.002321, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.683104, Class: 1.000000, Obj: 0.392645, No Obj: 0.002363, Avg Recall: 0.869565, count: 115 -Region Avg IOU: 0.651160, Class: 1.000000, Obj: 0.321100, No Obj: 0.002257, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.636427, Class: 1.000000, Obj: 0.343782, No Obj: 0.002210, Avg Recall: 0.773913, count: 115 -5416: 0.449733, 0.441183 avg, 0.001000 rate, 30.316759 seconds, 1386496 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.667909, Class: 1.000000, Obj: 0.332046, No Obj: 0.001980, Avg Recall: 0.803571, count: 112 -Region Avg IOU: 0.601134, Class: 1.000000, Obj: 0.310131, No Obj: 0.001894, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.624916, Class: 1.000000, Obj: 0.325287, No Obj: 0.002136, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.652989, Class: 1.000000, Obj: 0.301192, No Obj: 0.002271, Avg Recall: 0.826087, count: 138 -Region Avg IOU: 0.613481, Class: 1.000000, Obj: 0.350112, No Obj: 0.001549, Avg Recall: 0.703704, count: 81 -Region Avg IOU: 0.695037, Class: 1.000000, Obj: 0.377547, No Obj: 0.001956, Avg Recall: 0.872340, count: 94 -Region Avg IOU: 0.656556, Class: 1.000000, Obj: 0.322216, No Obj: 0.001890, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.674316, Class: 1.000000, Obj: 0.336937, No Obj: 0.002085, Avg Recall: 0.858586, count: 99 -5417: 0.510856, 0.448150 avg, 0.001000 rate, 30.043444 seconds, 1386752 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.646416, Class: 1.000000, Obj: 0.359397, No Obj: 0.002078, Avg Recall: 0.846154, count: 91 -Region Avg IOU: 0.686794, Class: 1.000000, Obj: 0.332231, No Obj: 0.001869, Avg Recall: 0.876190, count: 105 -Region Avg IOU: 0.656872, Class: 1.000000, Obj: 0.397865, No Obj: 0.001897, Avg Recall: 0.819277, count: 83 -Region Avg IOU: 0.655857, Class: 1.000000, Obj: 0.296961, No Obj: 0.002254, Avg Recall: 0.823529, count: 119 -Region Avg IOU: 0.680338, Class: 1.000000, Obj: 0.340665, No Obj: 0.002339, Avg Recall: 0.854962, count: 131 -Region Avg IOU: 0.709084, Class: 1.000000, Obj: 0.379373, No Obj: 0.002164, Avg Recall: 0.891304, count: 92 -Region Avg IOU: 0.635453, Class: 1.000000, Obj: 0.334892, No Obj: 0.002032, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.640466, Class: 1.000000, Obj: 0.285705, No Obj: 0.001920, Avg Recall: 0.816326, count: 98 -5418: 0.437015, 0.447037 avg, 0.001000 rate, 29.168438 seconds, 1387008 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.650218, Class: 1.000000, Obj: 0.373017, No Obj: 0.002131, Avg Recall: 0.818182, count: 88 -Region Avg IOU: 0.670121, Class: 1.000000, Obj: 0.392274, No Obj: 0.001999, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.654865, Class: 1.000000, Obj: 0.274568, No Obj: 0.002069, Avg Recall: 0.842520, count: 127 -Region Avg IOU: 0.708834, Class: 1.000000, Obj: 0.385904, No Obj: 0.002339, Avg Recall: 0.899083, count: 109 -Region Avg IOU: 0.617640, Class: 1.000000, Obj: 0.312578, No Obj: 0.001776, Avg Recall: 0.790698, count: 86 -Region Avg IOU: 0.650316, Class: 1.000000, Obj: 0.406822, No Obj: 0.001833, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.659126, Class: 1.000000, Obj: 0.323853, No Obj: 0.002330, Avg Recall: 0.857143, count: 112 -Region Avg IOU: 0.673067, Class: 1.000000, Obj: 0.386519, No Obj: 0.002408, Avg Recall: 0.822917, count: 96 -5419: 0.434536, 0.445787 avg, 0.001000 rate, 28.376801 seconds, 1387264 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.618988, Class: 1.000000, Obj: 0.373487, No Obj: 0.001949, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.657256, Class: 1.000000, Obj: 0.408180, No Obj: 0.001703, Avg Recall: 0.837500, count: 80 -Region Avg IOU: 0.671198, Class: 1.000000, Obj: 0.394527, No Obj: 0.002186, Avg Recall: 0.841584, count: 101 -Region Avg IOU: 0.662814, Class: 1.000000, Obj: 0.329478, No Obj: 0.002210, Avg Recall: 0.845455, count: 110 -Region Avg IOU: 0.657208, Class: 1.000000, Obj: 0.352337, No Obj: 0.002192, Avg Recall: 0.848740, count: 119 -Region Avg IOU: 0.640528, Class: 1.000000, Obj: 0.318027, No Obj: 0.002115, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.640640, Class: 1.000000, Obj: 0.302901, No Obj: 0.001921, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.619339, Class: 1.000000, Obj: 0.337992, No Obj: 0.001994, Avg Recall: 0.730435, count: 115 -5420: 0.397430, 0.440951 avg, 0.001000 rate, 30.853992 seconds, 1387520 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 11.538367 seconds -Region Avg IOU: 0.640866, Class: 1.000000, Obj: 0.373703, No Obj: 0.002189, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.621124, Class: 1.000000, Obj: 0.343616, No Obj: 0.002540, Avg Recall: 0.766917, count: 133 -Region Avg IOU: 0.671945, Class: 1.000000, Obj: 0.364602, No Obj: 0.002432, Avg Recall: 0.848740, count: 119 -Region Avg IOU: 0.659839, Class: 1.000000, Obj: 0.385845, No Obj: 0.002764, Avg Recall: 0.775000, count: 120 -Region Avg IOU: 0.631305, Class: 1.000000, Obj: 0.361047, No Obj: 0.002299, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.701150, Class: 1.000000, Obj: 0.394094, No Obj: 0.001901, Avg Recall: 0.903614, count: 83 -Region Avg IOU: 0.644464, Class: 1.000000, Obj: 0.330047, No Obj: 0.002264, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.645517, Class: 1.000000, Obj: 0.352634, No Obj: 0.002702, Avg Recall: 0.789916, count: 119 -5421: 0.430162, 0.439872 avg, 0.001000 rate, 28.590599 seconds, 1387776 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.654780, Class: 1.000000, Obj: 0.381785, No Obj: 0.003180, Avg Recall: 0.795918, count: 147 -Region Avg IOU: 0.694908, Class: 1.000000, Obj: 0.431399, No Obj: 0.002487, Avg Recall: 0.880435, count: 92 -Region Avg IOU: 0.651737, Class: 1.000000, Obj: 0.405205, No Obj: 0.002429, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.664518, Class: 1.000000, Obj: 0.366710, No Obj: 0.002874, Avg Recall: 0.820895, count: 134 -Region Avg IOU: 0.621065, Class: 1.000000, Obj: 0.373392, No Obj: 0.002572, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.637679, Class: 1.000000, Obj: 0.395348, No Obj: 0.002636, Avg Recall: 0.814815, count: 108 -Region Avg IOU: 0.698766, Class: 1.000000, Obj: 0.436112, No Obj: 0.002342, Avg Recall: 0.895349, count: 86 -Region Avg IOU: 0.666821, Class: 1.000000, Obj: 0.431202, No Obj: 0.002571, Avg Recall: 0.821782, count: 101 -5422: 0.438056, 0.439691 avg, 0.001000 rate, 29.940849 seconds, 1388032 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.664635, Class: 1.000000, Obj: 0.404144, No Obj: 0.003051, Avg Recall: 0.833333, count: 120 -Region Avg IOU: 0.643725, Class: 1.000000, Obj: 0.418350, No Obj: 0.002381, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.675111, Class: 1.000000, Obj: 0.425894, No Obj: 0.002448, Avg Recall: 0.850000, count: 80 -Region Avg IOU: 0.677794, Class: 1.000000, Obj: 0.468835, No Obj: 0.002206, Avg Recall: 0.837838, count: 74 -Region Avg IOU: 0.667476, Class: 1.000000, Obj: 0.405640, No Obj: 0.002850, Avg Recall: 0.851240, count: 121 -Region Avg IOU: 0.660186, Class: 1.000000, Obj: 0.373299, No Obj: 0.003024, Avg Recall: 0.798319, count: 119 -Region Avg IOU: 0.658243, Class: 1.000000, Obj: 0.442340, No Obj: 0.002720, Avg Recall: 0.843137, count: 102 -Region Avg IOU: 0.636746, Class: 1.000000, Obj: 0.400506, No Obj: 0.002943, Avg Recall: 0.789062, count: 128 -5423: 0.438043, 0.439526 avg, 0.001000 rate, 29.376377 seconds, 1388288 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.624669, Class: 1.000000, Obj: 0.397091, No Obj: 0.002794, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.705434, Class: 1.000000, Obj: 0.425395, No Obj: 0.002567, Avg Recall: 0.917526, count: 97 -Region Avg IOU: 0.638182, Class: 1.000000, Obj: 0.443630, No Obj: 0.002660, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.659256, Class: 1.000000, Obj: 0.413093, No Obj: 0.002792, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.662245, Class: 1.000000, Obj: 0.405620, No Obj: 0.003372, Avg Recall: 0.816794, count: 131 -Region Avg IOU: 0.674048, Class: 1.000000, Obj: 0.421935, No Obj: 0.002394, Avg Recall: 0.835443, count: 79 -Region Avg IOU: 0.636356, Class: 1.000000, Obj: 0.434235, No Obj: 0.002764, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.610759, Class: 1.000000, Obj: 0.371585, No Obj: 0.002453, Avg Recall: 0.737864, count: 103 -5424: 0.411264, 0.436700 avg, 0.001000 rate, 29.946083 seconds, 1388544 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.663376, Class: 1.000000, Obj: 0.404015, No Obj: 0.003106, Avg Recall: 0.822034, count: 118 -Region Avg IOU: 0.676160, Class: 1.000000, Obj: 0.425768, No Obj: 0.002659, Avg Recall: 0.831776, count: 107 -Region Avg IOU: 0.638650, Class: 1.000000, Obj: 0.385427, No Obj: 0.002995, Avg Recall: 0.793388, count: 121 -Region Avg IOU: 0.625432, Class: 1.000000, Obj: 0.390971, No Obj: 0.002516, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.681920, Class: 1.000000, Obj: 0.446368, No Obj: 0.002907, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.648250, Class: 1.000000, Obj: 0.373695, No Obj: 0.002613, Avg Recall: 0.809524, count: 84 -Region Avg IOU: 0.690605, Class: 1.000000, Obj: 0.412149, No Obj: 0.002591, Avg Recall: 0.887097, count: 124 -Region Avg IOU: 0.626970, Class: 1.000000, Obj: 0.400145, No Obj: 0.002962, Avg Recall: 0.771654, count: 127 -5425: 0.453090, 0.438339 avg, 0.001000 rate, 29.598856 seconds, 1388800 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.697603, Class: 1.000000, Obj: 0.420631, No Obj: 0.002833, Avg Recall: 0.878788, count: 99 -Region Avg IOU: 0.640422, Class: 1.000000, Obj: 0.427168, No Obj: 0.003046, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.686232, Class: 1.000000, Obj: 0.476273, No Obj: 0.002754, Avg Recall: 0.848485, count: 99 -Region Avg IOU: 0.664125, Class: 1.000000, Obj: 0.405605, No Obj: 0.003098, Avg Recall: 0.808696, count: 115 -Region Avg IOU: 0.606945, Class: 1.000000, Obj: 0.383213, No Obj: 0.002783, Avg Recall: 0.746032, count: 126 -Region Avg IOU: 0.657267, Class: 1.000000, Obj: 0.415656, No Obj: 0.002692, Avg Recall: 0.819048, count: 105 -Region Avg IOU: 0.661803, Class: 1.000000, Obj: 0.443334, No Obj: 0.002511, Avg Recall: 0.857143, count: 84 -Region Avg IOU: 0.701808, Class: 1.000000, Obj: 0.460216, No Obj: 0.002427, Avg Recall: 0.916667, count: 84 -5426: 0.427298, 0.437235 avg, 0.001000 rate, 30.151960 seconds, 1389056 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.670781, Class: 1.000000, Obj: 0.407107, No Obj: 0.002746, Avg Recall: 0.880734, count: 109 -Region Avg IOU: 0.634088, Class: 1.000000, Obj: 0.415187, No Obj: 0.002530, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.619636, Class: 1.000000, Obj: 0.369454, No Obj: 0.002778, Avg Recall: 0.762712, count: 118 -Region Avg IOU: 0.614586, Class: 1.000000, Obj: 0.393499, No Obj: 0.002500, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.652161, Class: 1.000000, Obj: 0.365230, No Obj: 0.002519, Avg Recall: 0.844037, count: 109 -Region Avg IOU: 0.642359, Class: 1.000000, Obj: 0.398000, No Obj: 0.003067, Avg Recall: 0.806897, count: 145 -Region Avg IOU: 0.660376, Class: 1.000000, Obj: 0.407575, No Obj: 0.002581, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.668822, Class: 1.000000, Obj: 0.437370, No Obj: 0.002721, Avg Recall: 0.836364, count: 110 -5427: 0.472592, 0.440770 avg, 0.001000 rate, 29.687897 seconds, 1389312 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.638646, Class: 1.000000, Obj: 0.395294, No Obj: 0.002537, Avg Recall: 0.786260, count: 131 -Region Avg IOU: 0.630155, Class: 1.000000, Obj: 0.344381, No Obj: 0.002495, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.644637, Class: 1.000000, Obj: 0.342279, No Obj: 0.002468, Avg Recall: 0.793893, count: 131 -Region Avg IOU: 0.680160, Class: 1.000000, Obj: 0.367865, No Obj: 0.002321, Avg Recall: 0.813953, count: 86 -Region Avg IOU: 0.636528, Class: 1.000000, Obj: 0.404060, No Obj: 0.002632, Avg Recall: 0.820513, count: 117 -Region Avg IOU: 0.664164, Class: 1.000000, Obj: 0.375924, No Obj: 0.002195, Avg Recall: 0.822222, count: 90 -Region Avg IOU: 0.661825, Class: 1.000000, Obj: 0.422236, No Obj: 0.002594, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.629964, Class: 1.000000, Obj: 0.357114, No Obj: 0.002623, Avg Recall: 0.784483, count: 116 -5428: 0.455754, 0.442269 avg, 0.001000 rate, 30.636362 seconds, 1389568 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.582087, Class: 1.000000, Obj: 0.352662, No Obj: 0.002168, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.675544, Class: 1.000000, Obj: 0.458594, No Obj: 0.002318, Avg Recall: 0.823529, count: 85 -Region Avg IOU: 0.643170, Class: 1.000000, Obj: 0.399768, No Obj: 0.002580, Avg Recall: 0.800000, count: 120 -Region Avg IOU: 0.661321, Class: 1.000000, Obj: 0.385067, No Obj: 0.002279, Avg Recall: 0.826531, count: 98 -Region Avg IOU: 0.644654, Class: 1.000000, Obj: 0.425066, No Obj: 0.002479, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.696689, Class: 1.000000, Obj: 0.454651, No Obj: 0.002665, Avg Recall: 0.847619, count: 105 -Region Avg IOU: 0.652330, Class: 1.000000, Obj: 0.372123, No Obj: 0.002658, Avg Recall: 0.827586, count: 116 -Region Avg IOU: 0.616118, Class: 1.000000, Obj: 0.396107, No Obj: 0.002389, Avg Recall: 0.735043, count: 117 -5429: 0.424056, 0.440447 avg, 0.001000 rate, 28.895845 seconds, 1389824 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.617736, Class: 1.000000, Obj: 0.325470, No Obj: 0.002540, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.636100, Class: 1.000000, Obj: 0.331383, No Obj: 0.002067, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.652852, Class: 1.000000, Obj: 0.400884, No Obj: 0.002413, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.687080, Class: 1.000000, Obj: 0.465721, No Obj: 0.002424, Avg Recall: 0.863158, count: 95 -Region Avg IOU: 0.618632, Class: 1.000000, Obj: 0.318775, No Obj: 0.002616, Avg Recall: 0.810811, count: 148 -Region Avg IOU: 0.696254, Class: 1.000000, Obj: 0.435511, No Obj: 0.002311, Avg Recall: 0.906977, count: 86 -Region Avg IOU: 0.650245, Class: 1.000000, Obj: 0.339601, No Obj: 0.002432, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.639611, Class: 1.000000, Obj: 0.338935, No Obj: 0.002462, Avg Recall: 0.785185, count: 135 -5430: 0.466369, 0.443040 avg, 0.001000 rate, 28.805414 seconds, 1390080 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 13.298043 seconds -Region Avg IOU: 0.597909, Class: 1.000000, Obj: 0.366568, No Obj: 0.002936, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.602506, Class: 1.000000, Obj: 0.373437, No Obj: 0.002703, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.580367, Class: 1.000000, Obj: 0.366100, No Obj: 0.002804, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.613163, Class: 1.000000, Obj: 0.373830, No Obj: 0.002515, Avg Recall: 0.710526, count: 76 -Region Avg IOU: 0.640109, Class: 1.000000, Obj: 0.443191, No Obj: 0.002958, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.593056, Class: 1.000000, Obj: 0.400358, No Obj: 0.002468, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.605403, Class: 1.000000, Obj: 0.415020, No Obj: 0.003031, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.616247, Class: 1.000000, Obj: 0.372063, No Obj: 0.002798, Avg Recall: 0.756098, count: 123 -5431: 0.437440, 0.442480 avg, 0.001000 rate, 25.182436 seconds, 1390336 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.597349, Class: 1.000000, Obj: 0.375794, No Obj: 0.002892, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.646754, Class: 1.000000, Obj: 0.412982, No Obj: 0.002864, Avg Recall: 0.803279, count: 122 -Region Avg IOU: 0.617640, Class: 1.000000, Obj: 0.392623, No Obj: 0.002747, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.609807, Class: 1.000000, Obj: 0.342907, No Obj: 0.003007, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.635751, Class: 1.000000, Obj: 0.353846, No Obj: 0.002949, Avg Recall: 0.782609, count: 138 -Region Avg IOU: 0.636245, Class: 1.000000, Obj: 0.366563, No Obj: 0.002720, Avg Recall: 0.809917, count: 121 -Region Avg IOU: 0.656368, Class: 1.000000, Obj: 0.367830, No Obj: 0.003002, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.605685, Class: 1.000000, Obj: 0.403411, No Obj: 0.003081, Avg Recall: 0.722772, count: 101 -5432: 0.452016, 0.443433 avg, 0.001000 rate, 26.858162 seconds, 1390592 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.633476, Class: 1.000000, Obj: 0.421592, No Obj: 0.002926, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.612984, Class: 1.000000, Obj: 0.349280, No Obj: 0.003056, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.601529, Class: 1.000000, Obj: 0.380330, No Obj: 0.002709, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.588131, Class: 1.000000, Obj: 0.376706, No Obj: 0.003210, Avg Recall: 0.718518, count: 135 -Region Avg IOU: 0.655623, Class: 1.000000, Obj: 0.373533, No Obj: 0.003356, Avg Recall: 0.813008, count: 123 -Region Avg IOU: 0.596653, Class: 1.000000, Obj: 0.335736, No Obj: 0.003267, Avg Recall: 0.724359, count: 156 -Region Avg IOU: 0.642497, Class: 1.000000, Obj: 0.414903, No Obj: 0.002981, Avg Recall: 0.791667, count: 120 -Region Avg IOU: 0.554831, Class: 1.000000, Obj: 0.363349, No Obj: 0.002676, Avg Recall: 0.626168, count: 107 -5433: 0.502619, 0.449352 avg, 0.001000 rate, 25.706226 seconds, 1390848 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.618818, Class: 1.000000, Obj: 0.364720, No Obj: 0.002981, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.616541, Class: 1.000000, Obj: 0.403021, No Obj: 0.002816, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.619733, Class: 1.000000, Obj: 0.405361, No Obj: 0.002995, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.634207, Class: 1.000000, Obj: 0.383377, No Obj: 0.002837, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.610198, Class: 1.000000, Obj: 0.376528, No Obj: 0.002878, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.654039, Class: 1.000000, Obj: 0.423382, No Obj: 0.002800, Avg Recall: 0.752809, count: 89 -Region Avg IOU: 0.583403, Class: 1.000000, Obj: 0.390710, No Obj: 0.002818, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.649632, Class: 1.000000, Obj: 0.414570, No Obj: 0.003081, Avg Recall: 0.787037, count: 108 -5434: 0.417131, 0.446130 avg, 0.001000 rate, 25.202135 seconds, 1391104 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.633630, Class: 1.000000, Obj: 0.395611, No Obj: 0.003220, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.607597, Class: 1.000000, Obj: 0.330837, No Obj: 0.003456, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.605666, Class: 1.000000, Obj: 0.413395, No Obj: 0.003050, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.632123, Class: 1.000000, Obj: 0.360108, No Obj: 0.003217, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.626765, Class: 1.000000, Obj: 0.400055, No Obj: 0.003393, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.606848, Class: 1.000000, Obj: 0.381505, No Obj: 0.002792, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.653684, Class: 1.000000, Obj: 0.449454, No Obj: 0.003024, Avg Recall: 0.788889, count: 90 -Region Avg IOU: 0.661040, Class: 1.000000, Obj: 0.395335, No Obj: 0.002726, Avg Recall: 0.847059, count: 85 -5435: 0.408634, 0.442380 avg, 0.001000 rate, 25.363020 seconds, 1391360 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.648643, Class: 1.000000, Obj: 0.420839, No Obj: 0.003374, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.620821, Class: 1.000000, Obj: 0.419697, No Obj: 0.003478, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.654066, Class: 1.000000, Obj: 0.413973, No Obj: 0.003352, Avg Recall: 0.824742, count: 97 -Region Avg IOU: 0.640544, Class: 1.000000, Obj: 0.451602, No Obj: 0.003609, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.552688, Class: 1.000000, Obj: 0.376759, No Obj: 0.002714, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.634858, Class: 1.000000, Obj: 0.409690, No Obj: 0.003064, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.578654, Class: 1.000000, Obj: 0.369418, No Obj: 0.003315, Avg Recall: 0.682171, count: 129 -Region Avg IOU: 0.584898, Class: 1.000000, Obj: 0.414876, No Obj: 0.002757, Avg Recall: 0.663366, count: 101 -5436: 0.408436, 0.438986 avg, 0.001000 rate, 26.180391 seconds, 1391616 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.602626, Class: 1.000000, Obj: 0.434317, No Obj: 0.003121, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.632186, Class: 1.000000, Obj: 0.452262, No Obj: 0.003423, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.648025, Class: 1.000000, Obj: 0.441780, No Obj: 0.003761, Avg Recall: 0.791667, count: 120 -Region Avg IOU: 0.595607, Class: 1.000000, Obj: 0.380586, No Obj: 0.004217, Avg Recall: 0.713450, count: 171 -Region Avg IOU: 0.605027, Class: 1.000000, Obj: 0.413309, No Obj: 0.003230, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.614296, Class: 1.000000, Obj: 0.412107, No Obj: 0.003529, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.644216, Class: 1.000000, Obj: 0.479564, No Obj: 0.003476, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.611558, Class: 1.000000, Obj: 0.420082, No Obj: 0.003533, Avg Recall: 0.711864, count: 118 -5437: 0.477561, 0.442843 avg, 0.001000 rate, 25.359730 seconds, 1391872 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.643172, Class: 1.000000, Obj: 0.442252, No Obj: 0.003422, Avg Recall: 0.804348, count: 92 -Region Avg IOU: 0.625747, Class: 1.000000, Obj: 0.404223, No Obj: 0.003517, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.592066, Class: 1.000000, Obj: 0.409574, No Obj: 0.003200, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.573105, Class: 1.000000, Obj: 0.373579, No Obj: 0.003214, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.608669, Class: 1.000000, Obj: 0.447440, No Obj: 0.003362, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.648223, Class: 1.000000, Obj: 0.474023, No Obj: 0.003234, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.602437, Class: 1.000000, Obj: 0.401369, No Obj: 0.002994, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.626989, Class: 1.000000, Obj: 0.436095, No Obj: 0.003442, Avg Recall: 0.756303, count: 119 -5438: 0.420048, 0.440564 avg, 0.001000 rate, 24.600897 seconds, 1392128 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.620620, Class: 1.000000, Obj: 0.433550, No Obj: 0.003184, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.682083, Class: 1.000000, Obj: 0.454370, No Obj: 0.003817, Avg Recall: 0.864000, count: 125 -Region Avg IOU: 0.617826, Class: 1.000000, Obj: 0.421719, No Obj: 0.003244, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.607856, Class: 1.000000, Obj: 0.403787, No Obj: 0.003196, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.608934, Class: 1.000000, Obj: 0.446424, No Obj: 0.003405, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.588046, Class: 1.000000, Obj: 0.430855, No Obj: 0.003161, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.525680, Class: 1.000000, Obj: 0.400235, No Obj: 0.003266, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.598050, Class: 1.000000, Obj: 0.408367, No Obj: 0.003702, Avg Recall: 0.707965, count: 113 -5439: 0.396378, 0.436145 avg, 0.001000 rate, 25.154196 seconds, 1392384 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.640629, Class: 1.000000, Obj: 0.395075, No Obj: 0.003246, Avg Recall: 0.802198, count: 91 -Region Avg IOU: 0.601964, Class: 1.000000, Obj: 0.401781, No Obj: 0.003310, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.647672, Class: 1.000000, Obj: 0.418541, No Obj: 0.003654, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.635448, Class: 1.000000, Obj: 0.406861, No Obj: 0.003229, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.589674, Class: 1.000000, Obj: 0.417737, No Obj: 0.003097, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.662739, Class: 1.000000, Obj: 0.457377, No Obj: 0.003474, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.644301, Class: 1.000000, Obj: 0.427401, No Obj: 0.003675, Avg Recall: 0.763359, count: 131 -Region Avg IOU: 0.630341, Class: 1.000000, Obj: 0.436902, No Obj: 0.003488, Avg Recall: 0.747663, count: 107 -5440: 0.388081, 0.431339 avg, 0.001000 rate, 25.410645 seconds, 1392640 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000084 seconds -Region Avg IOU: 0.639946, Class: 1.000000, Obj: 0.428713, No Obj: 0.003250, Avg Recall: 0.783333, count: 120 -Region Avg IOU: 0.679851, Class: 1.000000, Obj: 0.438399, No Obj: 0.003444, Avg Recall: 0.860000, count: 100 -Region Avg IOU: 0.632160, Class: 1.000000, Obj: 0.491561, No Obj: 0.002738, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.646176, Class: 1.000000, Obj: 0.406122, No Obj: 0.003120, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.647767, Class: 1.000000, Obj: 0.419496, No Obj: 0.002744, Avg Recall: 0.811966, count: 117 -Region Avg IOU: 0.644673, Class: 1.000000, Obj: 0.482215, No Obj: 0.002753, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.661163, Class: 1.000000, Obj: 0.472425, No Obj: 0.003696, Avg Recall: 0.819444, count: 144 -Region Avg IOU: 0.647623, Class: 1.000000, Obj: 0.465762, No Obj: 0.002941, Avg Recall: 0.757282, count: 103 -5441: 0.442166, 0.432422 avg, 0.001000 rate, 29.486340 seconds, 1392896 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.606239, Class: 1.000000, Obj: 0.388454, No Obj: 0.003519, Avg Recall: 0.702532, count: 158 -Region Avg IOU: 0.661925, Class: 1.000000, Obj: 0.453930, No Obj: 0.003289, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.684657, Class: 1.000000, Obj: 0.391577, No Obj: 0.002765, Avg Recall: 0.870968, count: 93 -Region Avg IOU: 0.653359, Class: 1.000000, Obj: 0.417959, No Obj: 0.002924, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.662625, Class: 1.000000, Obj: 0.442311, No Obj: 0.002541, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.634120, Class: 1.000000, Obj: 0.384833, No Obj: 0.003113, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.646909, Class: 1.000000, Obj: 0.418717, No Obj: 0.002981, Avg Recall: 0.804124, count: 97 -Region Avg IOU: 0.688249, Class: 1.000000, Obj: 0.380730, No Obj: 0.002957, Avg Recall: 0.875000, count: 120 -5442: 0.451447, 0.434324 avg, 0.001000 rate, 28.708580 seconds, 1393152 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.672310, Class: 1.000000, Obj: 0.369467, No Obj: 0.002757, Avg Recall: 0.839623, count: 106 -Region Avg IOU: 0.649595, Class: 1.000000, Obj: 0.416101, No Obj: 0.002753, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.654702, Class: 1.000000, Obj: 0.364387, No Obj: 0.002835, Avg Recall: 0.820513, count: 117 -Region Avg IOU: 0.685907, Class: 1.000000, Obj: 0.410049, No Obj: 0.002952, Avg Recall: 0.880342, count: 117 -Region Avg IOU: 0.649809, Class: 1.000000, Obj: 0.431809, No Obj: 0.002641, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.672022, Class: 1.000000, Obj: 0.432089, No Obj: 0.002695, Avg Recall: 0.838710, count: 93 -Region Avg IOU: 0.659828, Class: 1.000000, Obj: 0.415517, No Obj: 0.002675, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.655475, Class: 1.000000, Obj: 0.381978, No Obj: 0.002581, Avg Recall: 0.790000, count: 100 -5443: 0.443280, 0.435220 avg, 0.001000 rate, 28.038099 seconds, 1393408 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.631317, Class: 1.000000, Obj: 0.412904, No Obj: 0.002869, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.639640, Class: 1.000000, Obj: 0.327442, No Obj: 0.002948, Avg Recall: 0.796992, count: 133 -Region Avg IOU: 0.651018, Class: 1.000000, Obj: 0.369722, No Obj: 0.002554, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.674551, Class: 1.000000, Obj: 0.436462, No Obj: 0.002835, Avg Recall: 0.834783, count: 115 -Region Avg IOU: 0.645218, Class: 1.000000, Obj: 0.386631, No Obj: 0.002753, Avg Recall: 0.815126, count: 119 -Region Avg IOU: 0.642609, Class: 1.000000, Obj: 0.388773, No Obj: 0.002506, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.705543, Class: 1.000000, Obj: 0.437875, No Obj: 0.002486, Avg Recall: 0.882979, count: 94 -Region Avg IOU: 0.618664, Class: 1.000000, Obj: 0.383706, No Obj: 0.002336, Avg Recall: 0.739130, count: 92 -5444: 0.433598, 0.435057 avg, 0.001000 rate, 30.728064 seconds, 1393664 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.673049, Class: 1.000000, Obj: 0.378042, No Obj: 0.002604, Avg Recall: 0.865546, count: 119 -Region Avg IOU: 0.670282, Class: 1.000000, Obj: 0.362628, No Obj: 0.002876, Avg Recall: 0.837607, count: 117 -Region Avg IOU: 0.632157, Class: 1.000000, Obj: 0.395954, No Obj: 0.002567, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.635833, Class: 1.000000, Obj: 0.348001, No Obj: 0.003152, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.616855, Class: 1.000000, Obj: 0.348556, No Obj: 0.002808, Avg Recall: 0.733813, count: 139 -Region Avg IOU: 0.646862, Class: 1.000000, Obj: 0.351874, No Obj: 0.002905, Avg Recall: 0.794118, count: 136 -Region Avg IOU: 0.621917, Class: 1.000000, Obj: 0.377253, No Obj: 0.002721, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.648953, Class: 1.000000, Obj: 0.365406, No Obj: 0.002858, Avg Recall: 0.800000, count: 120 -5445: 0.505317, 0.442083 avg, 0.001000 rate, 28.637735 seconds, 1393920 images -Loaded: 0.000119 seconds -Region Avg IOU: 0.654132, Class: 1.000000, Obj: 0.339051, No Obj: 0.002544, Avg Recall: 0.845528, count: 123 -Region Avg IOU: 0.663969, Class: 1.000000, Obj: 0.386228, No Obj: 0.002412, Avg Recall: 0.813187, count: 91 -Region Avg IOU: 0.671205, Class: 1.000000, Obj: 0.485776, No Obj: 0.002367, Avg Recall: 0.878049, count: 82 -Region Avg IOU: 0.663896, Class: 1.000000, Obj: 0.383857, No Obj: 0.002591, Avg Recall: 0.845361, count: 97 -Region Avg IOU: 0.634756, Class: 1.000000, Obj: 0.380821, No Obj: 0.003096, Avg Recall: 0.735714, count: 140 -Region Avg IOU: 0.682437, Class: 1.000000, Obj: 0.347658, No Obj: 0.002374, Avg Recall: 0.903226, count: 93 -Region Avg IOU: 0.667018, Class: 1.000000, Obj: 0.382433, No Obj: 0.002571, Avg Recall: 0.839623, count: 106 -Region Avg IOU: 0.626450, Class: 1.000000, Obj: 0.394719, No Obj: 0.002865, Avg Recall: 0.763158, count: 114 -5446: 0.418749, 0.439750 avg, 0.001000 rate, 27.126482 seconds, 1394176 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.665439, Class: 1.000000, Obj: 0.398999, No Obj: 0.002421, Avg Recall: 0.805085, count: 118 -Region Avg IOU: 0.681585, Class: 1.000000, Obj: 0.425643, No Obj: 0.002681, Avg Recall: 0.836538, count: 104 -Region Avg IOU: 0.606779, Class: 1.000000, Obj: 0.364585, No Obj: 0.002210, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.667663, Class: 1.000000, Obj: 0.432184, No Obj: 0.002352, Avg Recall: 0.858491, count: 106 -Region Avg IOU: 0.642690, Class: 1.000000, Obj: 0.358060, No Obj: 0.002499, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.659397, Class: 1.000000, Obj: 0.366073, No Obj: 0.002475, Avg Recall: 0.833333, count: 114 -Region Avg IOU: 0.630373, Class: 1.000000, Obj: 0.364964, No Obj: 0.002645, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.642932, Class: 1.000000, Obj: 0.329369, No Obj: 0.002728, Avg Recall: 0.787402, count: 127 -5447: 0.451316, 0.440907 avg, 0.001000 rate, 27.626673 seconds, 1394432 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.673998, Class: 1.000000, Obj: 0.409295, No Obj: 0.002433, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.649435, Class: 1.000000, Obj: 0.394488, No Obj: 0.002725, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.668294, Class: 1.000000, Obj: 0.381659, No Obj: 0.002376, Avg Recall: 0.811765, count: 85 -Region Avg IOU: 0.647769, Class: 1.000000, Obj: 0.310122, No Obj: 0.002669, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.685165, Class: 1.000000, Obj: 0.398274, No Obj: 0.002697, Avg Recall: 0.867347, count: 98 -Region Avg IOU: 0.637779, Class: 1.000000, Obj: 0.395070, No Obj: 0.002536, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.645696, Class: 1.000000, Obj: 0.353389, No Obj: 0.002573, Avg Recall: 0.829457, count: 129 -Region Avg IOU: 0.672942, Class: 1.000000, Obj: 0.403419, No Obj: 0.002760, Avg Recall: 0.837607, count: 117 -5448: 0.432592, 0.440075 avg, 0.001000 rate, 28.130884 seconds, 1394688 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.630184, Class: 1.000000, Obj: 0.347331, No Obj: 0.002823, Avg Recall: 0.728682, count: 129 -Region Avg IOU: 0.638170, Class: 1.000000, Obj: 0.366828, No Obj: 0.002906, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.653308, Class: 1.000000, Obj: 0.431135, No Obj: 0.002025, Avg Recall: 0.805556, count: 72 -Region Avg IOU: 0.688262, Class: 1.000000, Obj: 0.376136, No Obj: 0.003068, Avg Recall: 0.864000, count: 125 -Region Avg IOU: 0.616343, Class: 1.000000, Obj: 0.312826, No Obj: 0.002545, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.657369, Class: 1.000000, Obj: 0.440978, No Obj: 0.002676, Avg Recall: 0.822581, count: 124 -Region Avg IOU: 0.666363, Class: 1.000000, Obj: 0.439319, No Obj: 0.002417, Avg Recall: 0.820000, count: 100 -Region Avg IOU: 0.659018, Class: 1.000000, Obj: 0.383486, No Obj: 0.002636, Avg Recall: 0.805825, count: 103 -5449: 0.481327, 0.444200 avg, 0.001000 rate, 29.023321 seconds, 1394944 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.606660, Class: 1.000000, Obj: 0.381842, No Obj: 0.002508, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.653922, Class: 1.000000, Obj: 0.357662, No Obj: 0.002904, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.659974, Class: 1.000000, Obj: 0.388563, No Obj: 0.002707, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.659234, Class: 1.000000, Obj: 0.374320, No Obj: 0.002860, Avg Recall: 0.785714, count: 126 -Region Avg IOU: 0.666015, Class: 1.000000, Obj: 0.424278, No Obj: 0.002297, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.663924, Class: 1.000000, Obj: 0.417157, No Obj: 0.002952, Avg Recall: 0.844262, count: 122 -Region Avg IOU: 0.654583, Class: 1.000000, Obj: 0.382249, No Obj: 0.002704, Avg Recall: 0.830645, count: 124 -Region Avg IOU: 0.638801, Class: 1.000000, Obj: 0.358164, No Obj: 0.002718, Avg Recall: 0.752137, count: 117 -5450: 0.429068, 0.442687 avg, 0.001000 rate, 28.831451 seconds, 1395200 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 17.306425 seconds -Region Avg IOU: 0.626866, Class: 1.000000, Obj: 0.450965, No Obj: 0.002800, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.649318, Class: 1.000000, Obj: 0.414927, No Obj: 0.002738, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.660121, Class: 1.000000, Obj: 0.388924, No Obj: 0.002588, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.635323, Class: 1.000000, Obj: 0.343108, No Obj: 0.002971, Avg Recall: 0.798450, count: 129 -Region Avg IOU: 0.584154, Class: 1.000000, Obj: 0.352485, No Obj: 0.002581, Avg Recall: 0.669421, count: 121 -Region Avg IOU: 0.679297, Class: 1.000000, Obj: 0.477105, No Obj: 0.002978, Avg Recall: 0.868132, count: 91 -Region Avg IOU: 0.623946, Class: 1.000000, Obj: 0.455359, No Obj: 0.002245, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.655306, Class: 1.000000, Obj: 0.432992, No Obj: 0.002663, Avg Recall: 0.808081, count: 99 -5451: 0.423934, 0.440812 avg, 0.001000 rate, 27.427889 seconds, 1395456 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.684551, Class: 1.000000, Obj: 0.393966, No Obj: 0.002712, Avg Recall: 0.865854, count: 82 -Region Avg IOU: 0.658784, Class: 1.000000, Obj: 0.412707, No Obj: 0.003206, Avg Recall: 0.801653, count: 121 -Region Avg IOU: 0.632409, Class: 1.000000, Obj: 0.438286, No Obj: 0.002917, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.625047, Class: 1.000000, Obj: 0.392904, No Obj: 0.002507, Avg Recall: 0.741176, count: 85 -Region Avg IOU: 0.644366, Class: 1.000000, Obj: 0.458755, No Obj: 0.002823, Avg Recall: 0.788235, count: 85 -Region Avg IOU: 0.648727, Class: 1.000000, Obj: 0.380117, No Obj: 0.002950, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.633258, Class: 1.000000, Obj: 0.406159, No Obj: 0.002847, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.671796, Class: 1.000000, Obj: 0.430219, No Obj: 0.003347, Avg Recall: 0.852459, count: 122 -5452: 0.437632, 0.440494 avg, 0.001000 rate, 27.842045 seconds, 1395712 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.624817, Class: 1.000000, Obj: 0.415797, No Obj: 0.002680, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.645828, Class: 1.000000, Obj: 0.366557, No Obj: 0.002562, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.659350, Class: 1.000000, Obj: 0.458461, No Obj: 0.002895, Avg Recall: 0.797872, count: 94 -Region Avg IOU: 0.633939, Class: 1.000000, Obj: 0.403551, No Obj: 0.003296, Avg Recall: 0.729323, count: 133 -Region Avg IOU: 0.676198, Class: 1.000000, Obj: 0.405188, No Obj: 0.002471, Avg Recall: 0.855422, count: 83 -Region Avg IOU: 0.646614, Class: 1.000000, Obj: 0.381060, No Obj: 0.003329, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.632619, Class: 1.000000, Obj: 0.399137, No Obj: 0.003055, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.607639, Class: 1.000000, Obj: 0.369468, No Obj: 0.002995, Avg Recall: 0.690476, count: 126 -5453: 0.426419, 0.439086 avg, 0.001000 rate, 28.406237 seconds, 1395968 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.664513, Class: 1.000000, Obj: 0.423680, No Obj: 0.002992, Avg Recall: 0.800000, count: 95 -Region Avg IOU: 0.650640, Class: 1.000000, Obj: 0.395734, No Obj: 0.002994, Avg Recall: 0.798450, count: 129 -Region Avg IOU: 0.674288, Class: 1.000000, Obj: 0.464100, No Obj: 0.003006, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.660467, Class: 1.000000, Obj: 0.419837, No Obj: 0.002968, Avg Recall: 0.803419, count: 117 -Region Avg IOU: 0.618832, Class: 1.000000, Obj: 0.440483, No Obj: 0.002525, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.645963, Class: 1.000000, Obj: 0.430351, No Obj: 0.003044, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.668074, Class: 1.000000, Obj: 0.434628, No Obj: 0.002912, Avg Recall: 0.793103, count: 87 -Region Avg IOU: 0.634498, Class: 1.000000, Obj: 0.421165, No Obj: 0.002945, Avg Recall: 0.801980, count: 101 -5454: 0.410839, 0.436261 avg, 0.001000 rate, 26.616062 seconds, 1396224 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.655441, Class: 1.000000, Obj: 0.449232, No Obj: 0.002626, Avg Recall: 0.768293, count: 82 -Region Avg IOU: 0.636018, Class: 1.000000, Obj: 0.366619, No Obj: 0.002895, Avg Recall: 0.815789, count: 114 -Region Avg IOU: 0.626293, Class: 1.000000, Obj: 0.381739, No Obj: 0.003046, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.670614, Class: 1.000000, Obj: 0.458256, No Obj: 0.003137, Avg Recall: 0.814815, count: 108 -Region Avg IOU: 0.638276, Class: 1.000000, Obj: 0.410365, No Obj: 0.003023, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.690911, Class: 1.000000, Obj: 0.450475, No Obj: 0.003286, Avg Recall: 0.865079, count: 126 -Region Avg IOU: 0.644205, Class: 1.000000, Obj: 0.427381, No Obj: 0.002947, Avg Recall: 0.775862, count: 116 -Region Avg IOU: 0.665355, Class: 1.000000, Obj: 0.421972, No Obj: 0.002630, Avg Recall: 0.833333, count: 84 -5455: 0.399117, 0.432547 avg, 0.001000 rate, 27.530964 seconds, 1396480 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.634062, Class: 1.000000, Obj: 0.405518, No Obj: 0.002683, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.628305, Class: 1.000000, Obj: 0.435965, No Obj: 0.002628, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.650204, Class: 1.000000, Obj: 0.369532, No Obj: 0.003476, Avg Recall: 0.809160, count: 131 -Region Avg IOU: 0.635799, Class: 1.000000, Obj: 0.437906, No Obj: 0.003231, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.605822, Class: 1.000000, Obj: 0.401959, No Obj: 0.003058, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.618834, Class: 1.000000, Obj: 0.422839, No Obj: 0.002698, Avg Recall: 0.769231, count: 78 -Region Avg IOU: 0.677418, Class: 1.000000, Obj: 0.456785, No Obj: 0.002487, Avg Recall: 0.831579, count: 95 -Region Avg IOU: 0.615276, Class: 1.000000, Obj: 0.357090, No Obj: 0.002681, Avg Recall: 0.677778, count: 90 -5456: 0.388768, 0.428169 avg, 0.001000 rate, 27.464617 seconds, 1396736 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.624891, Class: 1.000000, Obj: 0.369407, No Obj: 0.002976, Avg Recall: 0.776000, count: 125 -Region Avg IOU: 0.606431, Class: 1.000000, Obj: 0.407537, No Obj: 0.003030, Avg Recall: 0.683761, count: 117 -Region Avg IOU: 0.643677, Class: 1.000000, Obj: 0.418707, No Obj: 0.002488, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.662061, Class: 1.000000, Obj: 0.455833, No Obj: 0.002748, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.677723, Class: 1.000000, Obj: 0.415192, No Obj: 0.003177, Avg Recall: 0.803571, count: 112 -Region Avg IOU: 0.607078, Class: 1.000000, Obj: 0.422313, No Obj: 0.002841, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.626229, Class: 1.000000, Obj: 0.368371, No Obj: 0.002623, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.676652, Class: 1.000000, Obj: 0.486516, No Obj: 0.003362, Avg Recall: 0.797101, count: 138 -5457: 0.411097, 0.426462 avg, 0.001000 rate, 26.370419 seconds, 1396992 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.644227, Class: 1.000000, Obj: 0.451939, No Obj: 0.003076, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.593899, Class: 1.000000, Obj: 0.406094, No Obj: 0.002542, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.623201, Class: 1.000000, Obj: 0.383184, No Obj: 0.002820, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.618712, Class: 1.000000, Obj: 0.424790, No Obj: 0.003222, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.641181, Class: 1.000000, Obj: 0.404941, No Obj: 0.003554, Avg Recall: 0.789474, count: 152 -Region Avg IOU: 0.679307, Class: 1.000000, Obj: 0.399450, No Obj: 0.002785, Avg Recall: 0.826087, count: 115 -Region Avg IOU: 0.671833, Class: 1.000000, Obj: 0.436626, No Obj: 0.002874, Avg Recall: 0.817204, count: 93 -Region Avg IOU: 0.651648, Class: 1.000000, Obj: 0.461093, No Obj: 0.002593, Avg Recall: 0.802326, count: 86 -5458: 0.451038, 0.428920 avg, 0.001000 rate, 26.015263 seconds, 1397248 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.617564, Class: 1.000000, Obj: 0.408996, No Obj: 0.002142, Avg Recall: 0.802817, count: 71 -Region Avg IOU: 0.605748, Class: 1.000000, Obj: 0.358961, No Obj: 0.002169, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.628521, Class: 1.000000, Obj: 0.425954, No Obj: 0.002413, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.624890, Class: 1.000000, Obj: 0.425206, No Obj: 0.002691, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.638004, Class: 1.000000, Obj: 0.368803, No Obj: 0.002616, Avg Recall: 0.827273, count: 110 -Region Avg IOU: 0.630025, Class: 1.000000, Obj: 0.401888, No Obj: 0.003210, Avg Recall: 0.763780, count: 127 -Region Avg IOU: 0.586903, Class: 1.000000, Obj: 0.365393, No Obj: 0.002568, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.658159, Class: 1.000000, Obj: 0.470260, No Obj: 0.002769, Avg Recall: 0.796296, count: 108 -5459: 0.386040, 0.424632 avg, 0.001000 rate, 28.481739 seconds, 1397504 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.641092, Class: 1.000000, Obj: 0.424130, No Obj: 0.003011, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.615040, Class: 1.000000, Obj: 0.391508, No Obj: 0.002714, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.639076, Class: 1.000000, Obj: 0.439754, No Obj: 0.003136, Avg Recall: 0.729508, count: 122 -Region Avg IOU: 0.660534, Class: 1.000000, Obj: 0.425382, No Obj: 0.002641, Avg Recall: 0.753086, count: 81 -Region Avg IOU: 0.642915, Class: 1.000000, Obj: 0.401843, No Obj: 0.002849, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.607999, Class: 1.000000, Obj: 0.407178, No Obj: 0.002763, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.657838, Class: 1.000000, Obj: 0.376869, No Obj: 0.003014, Avg Recall: 0.813559, count: 118 -Region Avg IOU: 0.666177, Class: 1.000000, Obj: 0.410503, No Obj: 0.002854, Avg Recall: 0.786408, count: 103 -5460: 0.410381, 0.423207 avg, 0.001000 rate, 27.343628 seconds, 1397760 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.291485 seconds -Region Avg IOU: 0.512046, Class: 1.000000, Obj: 0.302430, No Obj: 0.004084, Avg Recall: 0.546296, count: 108 -Region Avg IOU: 0.480921, Class: 1.000000, Obj: 0.289227, No Obj: 0.003311, Avg Recall: 0.505050, count: 99 -Region Avg IOU: 0.479342, Class: 1.000000, Obj: 0.279031, No Obj: 0.003667, Avg Recall: 0.525253, count: 99 -Region Avg IOU: 0.498038, Class: 1.000000, Obj: 0.293580, No Obj: 0.004233, Avg Recall: 0.523364, count: 107 -Region Avg IOU: 0.469043, Class: 1.000000, Obj: 0.272043, No Obj: 0.003662, Avg Recall: 0.554348, count: 92 -Region Avg IOU: 0.513333, Class: 1.000000, Obj: 0.274602, No Obj: 0.004098, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.479644, Class: 1.000000, Obj: 0.256326, No Obj: 0.003346, Avg Recall: 0.525641, count: 78 -Region Avg IOU: 0.512646, Class: 1.000000, Obj: 0.304667, No Obj: 0.004365, Avg Recall: 0.527559, count: 127 -5461: 0.487262, 0.429612 avg, 0.001000 rate, 15.523271 seconds, 1398016 images -Loaded: 6.036011 seconds -Region Avg IOU: 0.461446, Class: 1.000000, Obj: 0.257959, No Obj: 0.003628, Avg Recall: 0.553719, count: 121 -Region Avg IOU: 0.497401, Class: 1.000000, Obj: 0.325208, No Obj: 0.003818, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.533348, Class: 1.000000, Obj: 0.290278, No Obj: 0.004486, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.484921, Class: 1.000000, Obj: 0.302428, No Obj: 0.004309, Avg Recall: 0.520325, count: 123 -Region Avg IOU: 0.488569, Class: 1.000000, Obj: 0.273841, No Obj: 0.004442, Avg Recall: 0.512195, count: 123 -Region Avg IOU: 0.511071, Class: 1.000000, Obj: 0.301705, No Obj: 0.003976, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.444553, Class: 1.000000, Obj: 0.258014, No Obj: 0.003216, Avg Recall: 0.488636, count: 88 -Region Avg IOU: 0.483789, Class: 1.000000, Obj: 0.288702, No Obj: 0.004415, Avg Recall: 0.525180, count: 139 -5462: 0.495709, 0.436222 avg, 0.001000 rate, 16.366369 seconds, 1398272 images -Loaded: 3.784401 seconds -Region Avg IOU: 0.467946, Class: 1.000000, Obj: 0.317335, No Obj: 0.003983, Avg Recall: 0.537037, count: 108 -Region Avg IOU: 0.475213, Class: 1.000000, Obj: 0.276556, No Obj: 0.004479, Avg Recall: 0.526316, count: 133 -Region Avg IOU: 0.489919, Class: 1.000000, Obj: 0.315168, No Obj: 0.003866, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.522449, Class: 1.000000, Obj: 0.297012, No Obj: 0.003693, Avg Recall: 0.586207, count: 87 -Region Avg IOU: 0.441020, Class: 1.000000, Obj: 0.291674, No Obj: 0.003992, Avg Recall: 0.475000, count: 120 -Region Avg IOU: 0.486476, Class: 1.000000, Obj: 0.286978, No Obj: 0.004187, Avg Recall: 0.546154, count: 130 -Region Avg IOU: 0.508082, Class: 1.000000, Obj: 0.331795, No Obj: 0.004155, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.468730, Class: 1.000000, Obj: 0.286050, No Obj: 0.004425, Avg Recall: 0.451327, count: 113 -5463: 0.471394, 0.439739 avg, 0.001000 rate, 16.047760 seconds, 1398528 images -Loaded: 5.173339 seconds -Region Avg IOU: 0.499424, Class: 1.000000, Obj: 0.300896, No Obj: 0.004266, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.495090, Class: 1.000000, Obj: 0.307909, No Obj: 0.004223, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.509060, Class: 1.000000, Obj: 0.320348, No Obj: 0.004507, Avg Recall: 0.543103, count: 116 -Region Avg IOU: 0.446690, Class: 1.000000, Obj: 0.264028, No Obj: 0.004021, Avg Recall: 0.476191, count: 105 -Region Avg IOU: 0.524330, Class: 1.000000, Obj: 0.341569, No Obj: 0.004943, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.502385, Class: 1.000000, Obj: 0.319651, No Obj: 0.004886, Avg Recall: 0.518797, count: 133 -Region Avg IOU: 0.520471, Class: 1.000000, Obj: 0.334979, No Obj: 0.004546, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.493649, Class: 1.000000, Obj: 0.330130, No Obj: 0.004048, Avg Recall: 0.516129, count: 93 -5464: 0.559398, 0.451705 avg, 0.001000 rate, 15.619735 seconds, 1398784 images -Loaded: 6.604105 seconds -Region Avg IOU: 0.484777, Class: 1.000000, Obj: 0.335694, No Obj: 0.005520, Avg Recall: 0.534351, count: 131 -Region Avg IOU: 0.463770, Class: 1.000000, Obj: 0.338884, No Obj: 0.005049, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.497641, Class: 1.000000, Obj: 0.334876, No Obj: 0.004251, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.563644, Class: 1.000000, Obj: 0.376572, No Obj: 0.005028, Avg Recall: 0.673684, count: 95 -Region Avg IOU: 0.455184, Class: 1.000000, Obj: 0.333784, No Obj: 0.004606, Avg Recall: 0.500000, count: 116 -Region Avg IOU: 0.450777, Class: 1.000000, Obj: 0.336038, No Obj: 0.004397, Avg Recall: 0.494845, count: 97 -Region Avg IOU: 0.447595, Class: 1.000000, Obj: 0.349179, No Obj: 0.003886, Avg Recall: 0.518182, count: 110 -Region Avg IOU: 0.501511, Class: 1.000000, Obj: 0.353426, No Obj: 0.005066, Avg Recall: 0.545455, count: 121 -5465: 0.458284, 0.452363 avg, 0.001000 rate, 17.150972 seconds, 1399040 images -Loaded: 3.842193 seconds -Region Avg IOU: 0.492419, Class: 1.000000, Obj: 0.353939, No Obj: 0.004819, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.496012, Class: 1.000000, Obj: 0.355786, No Obj: 0.004625, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.520659, Class: 1.000000, Obj: 0.399676, No Obj: 0.004299, Avg Recall: 0.581081, count: 74 -Region Avg IOU: 0.409557, Class: 1.000000, Obj: 0.328304, No Obj: 0.004584, Avg Recall: 0.412698, count: 126 -Region Avg IOU: 0.479285, Class: 1.000000, Obj: 0.378834, No Obj: 0.004579, Avg Recall: 0.534884, count: 86 -Region Avg IOU: 0.451731, Class: 1.000000, Obj: 0.366221, No Obj: 0.005006, Avg Recall: 0.464912, count: 114 -Region Avg IOU: 0.485541, Class: 1.000000, Obj: 0.331700, No Obj: 0.004826, Avg Recall: 0.508621, count: 116 -Region Avg IOU: 0.450695, Class: 1.000000, Obj: 0.348273, No Obj: 0.005319, Avg Recall: 0.443396, count: 106 -5466: 0.413695, 0.448496 avg, 0.001000 rate, 17.468460 seconds, 1399296 images -Loaded: 4.041119 seconds -Region Avg IOU: 0.462831, Class: 1.000000, Obj: 0.375494, No Obj: 0.005373, Avg Recall: 0.500000, count: 114 -Region Avg IOU: 0.465502, Class: 1.000000, Obj: 0.313042, No Obj: 0.004962, Avg Recall: 0.537037, count: 108 -Region Avg IOU: 0.436521, Class: 1.000000, Obj: 0.349678, No Obj: 0.005652, Avg Recall: 0.451327, count: 113 -Region Avg IOU: 0.527791, Class: 1.000000, Obj: 0.362328, No Obj: 0.005511, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.505821, Class: 1.000000, Obj: 0.402858, No Obj: 0.004882, Avg Recall: 0.566667, count: 90 -Region Avg IOU: 0.465038, Class: 1.000000, Obj: 0.366796, No Obj: 0.005377, Avg Recall: 0.478633, count: 117 -Region Avg IOU: 0.487355, Class: 1.000000, Obj: 0.346713, No Obj: 0.004742, Avg Recall: 0.552941, count: 85 -Region Avg IOU: 0.439293, Class: 1.000000, Obj: 0.369955, No Obj: 0.005322, Avg Recall: 0.465649, count: 131 -5467: 0.478252, 0.451472 avg, 0.001000 rate, 17.412308 seconds, 1399552 images -Loaded: 3.968520 seconds -Region Avg IOU: 0.488305, Class: 1.000000, Obj: 0.362690, No Obj: 0.006119, Avg Recall: 0.520548, count: 146 -Region Avg IOU: 0.505599, Class: 1.000000, Obj: 0.396860, No Obj: 0.005715, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.409593, Class: 1.000000, Obj: 0.327158, No Obj: 0.003956, Avg Recall: 0.376471, count: 85 -Region Avg IOU: 0.479417, Class: 1.000000, Obj: 0.335918, No Obj: 0.005765, Avg Recall: 0.518797, count: 133 -Region Avg IOU: 0.419788, Class: 1.000000, Obj: 0.357951, No Obj: 0.005140, Avg Recall: 0.466667, count: 105 -Region Avg IOU: 0.459535, Class: 1.000000, Obj: 0.366923, No Obj: 0.005221, Avg Recall: 0.512397, count: 121 -Region Avg IOU: 0.495353, Class: 1.000000, Obj: 0.355284, No Obj: 0.004739, Avg Recall: 0.532110, count: 109 -Region Avg IOU: 0.439413, Class: 1.000000, Obj: 0.334345, No Obj: 0.005172, Avg Recall: 0.475410, count: 122 -5468: 0.481403, 0.454465 avg, 0.001000 rate, 17.247314 seconds, 1399808 images -Loaded: 4.472770 seconds -Region Avg IOU: 0.450464, Class: 1.000000, Obj: 0.387452, No Obj: 0.005144, Avg Recall: 0.465347, count: 101 -Region Avg IOU: 0.442923, Class: 1.000000, Obj: 0.371427, No Obj: 0.005135, Avg Recall: 0.495050, count: 101 -Region Avg IOU: 0.467176, Class: 1.000000, Obj: 0.393006, No Obj: 0.006051, Avg Recall: 0.488889, count: 135 -Region Avg IOU: 0.471042, Class: 1.000000, Obj: 0.370628, No Obj: 0.006661, Avg Recall: 0.512195, count: 164 -Region Avg IOU: 0.442016, Class: 1.000000, Obj: 0.348307, No Obj: 0.004807, Avg Recall: 0.455446, count: 101 -Region Avg IOU: 0.510096, Class: 1.000000, Obj: 0.389727, No Obj: 0.005165, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.471427, Class: 1.000000, Obj: 0.354607, No Obj: 0.005029, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.470071, Class: 1.000000, Obj: 0.336437, No Obj: 0.005352, Avg Recall: 0.478992, count: 119 -5469: 0.472002, 0.456219 avg, 0.001000 rate, 17.475172 seconds, 1400064 images -Loaded: 2.627622 seconds -Region Avg IOU: 0.462856, Class: 1.000000, Obj: 0.364469, No Obj: 0.005686, Avg Recall: 0.461538, count: 117 -Region Avg IOU: 0.509136, Class: 1.000000, Obj: 0.383154, No Obj: 0.006026, Avg Recall: 0.584071, count: 113 -Region Avg IOU: 0.476833, Class: 1.000000, Obj: 0.399323, No Obj: 0.004698, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.527567, Class: 1.000000, Obj: 0.413947, No Obj: 0.005416, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.564679, Class: 1.000000, Obj: 0.438488, No Obj: 0.004632, Avg Recall: 0.676056, count: 71 -Region Avg IOU: 0.393891, Class: 1.000000, Obj: 0.315294, No Obj: 0.005066, Avg Recall: 0.370370, count: 108 -Region Avg IOU: 0.522571, Class: 1.000000, Obj: 0.401191, No Obj: 0.005511, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.464624, Class: 1.000000, Obj: 0.376556, No Obj: 0.004788, Avg Recall: 0.548387, count: 93 -5470: 0.447498, 0.455346 avg, 0.001000 rate, 17.205273 seconds, 1400320 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000074 seconds -Region Avg IOU: 0.606443, Class: 1.000000, Obj: 0.493086, No Obj: 0.004046, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.630327, Class: 1.000000, Obj: 0.495124, No Obj: 0.003994, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.593598, Class: 1.000000, Obj: 0.396012, No Obj: 0.003959, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.651787, Class: 1.000000, Obj: 0.478532, No Obj: 0.004318, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.585036, Class: 1.000000, Obj: 0.500174, No Obj: 0.003515, Avg Recall: 0.654321, count: 81 -Region Avg IOU: 0.569799, Class: 1.000000, Obj: 0.507663, No Obj: 0.003310, Avg Recall: 0.643678, count: 87 -Region Avg IOU: 0.617781, Class: 1.000000, Obj: 0.510159, No Obj: 0.004170, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.628094, Class: 1.000000, Obj: 0.403831, No Obj: 0.003616, Avg Recall: 0.747826, count: 115 -5471: 0.378163, 0.447628 avg, 0.001000 rate, 26.122730 seconds, 1400576 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.631240, Class: 1.000000, Obj: 0.488079, No Obj: 0.003557, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.642241, Class: 1.000000, Obj: 0.463041, No Obj: 0.003483, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.637783, Class: 1.000000, Obj: 0.483016, No Obj: 0.003643, Avg Recall: 0.755556, count: 90 -Region Avg IOU: 0.613853, Class: 1.000000, Obj: 0.484360, No Obj: 0.003626, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.608648, Class: 1.000000, Obj: 0.481994, No Obj: 0.004237, Avg Recall: 0.688000, count: 125 -Region Avg IOU: 0.658102, Class: 1.000000, Obj: 0.499102, No Obj: 0.003726, Avg Recall: 0.845361, count: 97 -Region Avg IOU: 0.560505, Class: 1.000000, Obj: 0.427151, No Obj: 0.003424, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.630786, Class: 1.000000, Obj: 0.415832, No Obj: 0.004231, Avg Recall: 0.770642, count: 109 -5472: 0.436641, 0.446529 avg, 0.001000 rate, 27.311203 seconds, 1400832 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.578826, Class: 1.000000, Obj: 0.392309, No Obj: 0.003486, Avg Recall: 0.664000, count: 125 -Region Avg IOU: 0.614124, Class: 1.000000, Obj: 0.492472, No Obj: 0.002902, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.625966, Class: 1.000000, Obj: 0.416470, No Obj: 0.003579, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.681073, Class: 1.000000, Obj: 0.461496, No Obj: 0.003409, Avg Recall: 0.848837, count: 86 -Region Avg IOU: 0.630098, Class: 1.000000, Obj: 0.433991, No Obj: 0.004331, Avg Recall: 0.755556, count: 135 -Region Avg IOU: 0.630857, Class: 1.000000, Obj: 0.443223, No Obj: 0.003371, Avg Recall: 0.790698, count: 86 -Region Avg IOU: 0.625406, Class: 1.000000, Obj: 0.409641, No Obj: 0.003673, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.571474, Class: 1.000000, Obj: 0.439404, No Obj: 0.003048, Avg Recall: 0.712871, count: 101 -5473: 0.389230, 0.440799 avg, 0.001000 rate, 25.563091 seconds, 1401088 images -Loaded: 0.000131 seconds -Region Avg IOU: 0.562405, Class: 1.000000, Obj: 0.409926, No Obj: 0.003387, Avg Recall: 0.659259, count: 135 -Region Avg IOU: 0.655483, Class: 1.000000, Obj: 0.446738, No Obj: 0.003092, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.644305, Class: 1.000000, Obj: 0.455432, No Obj: 0.003241, Avg Recall: 0.778761, count: 113 -Region Avg IOU: 0.611451, Class: 1.000000, Obj: 0.370610, No Obj: 0.002916, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.590093, Class: 1.000000, Obj: 0.396655, No Obj: 0.003115, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.639782, Class: 1.000000, Obj: 0.427142, No Obj: 0.003549, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.632451, Class: 1.000000, Obj: 0.463248, No Obj: 0.003521, Avg Recall: 0.735537, count: 121 -Region Avg IOU: 0.607565, Class: 1.000000, Obj: 0.369634, No Obj: 0.003193, Avg Recall: 0.755906, count: 127 -5474: 0.415474, 0.438267 avg, 0.001000 rate, 25.819334 seconds, 1401344 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.643792, Class: 1.000000, Obj: 0.378521, No Obj: 0.002805, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.604390, Class: 1.000000, Obj: 0.395159, No Obj: 0.003164, Avg Recall: 0.715385, count: 130 -Region Avg IOU: 0.597149, Class: 1.000000, Obj: 0.411145, No Obj: 0.002829, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.609946, Class: 1.000000, Obj: 0.405935, No Obj: 0.002757, Avg Recall: 0.752809, count: 89 -Region Avg IOU: 0.587552, Class: 1.000000, Obj: 0.383197, No Obj: 0.002788, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.566882, Class: 1.000000, Obj: 0.402659, No Obj: 0.002856, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.623724, Class: 1.000000, Obj: 0.389511, No Obj: 0.003129, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.632822, Class: 1.000000, Obj: 0.387649, No Obj: 0.002678, Avg Recall: 0.800000, count: 75 -5475: 0.376234, 0.432064 avg, 0.001000 rate, 24.921356 seconds, 1401600 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.597970, Class: 1.000000, Obj: 0.353336, No Obj: 0.002661, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.610221, Class: 1.000000, Obj: 0.360286, No Obj: 0.002768, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.618423, Class: 1.000000, Obj: 0.366000, No Obj: 0.003092, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.612573, Class: 1.000000, Obj: 0.401148, No Obj: 0.002631, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.620403, Class: 1.000000, Obj: 0.385272, No Obj: 0.002921, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.634332, Class: 1.000000, Obj: 0.386789, No Obj: 0.002787, Avg Recall: 0.795181, count: 83 -Region Avg IOU: 0.595809, Class: 1.000000, Obj: 0.375619, No Obj: 0.002632, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.635868, Class: 1.000000, Obj: 0.391519, No Obj: 0.003060, Avg Recall: 0.761905, count: 105 -5476: 0.423154, 0.431173 avg, 0.001000 rate, 24.538023 seconds, 1401856 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.648956, Class: 1.000000, Obj: 0.391252, No Obj: 0.002399, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.609149, Class: 1.000000, Obj: 0.342833, No Obj: 0.003031, Avg Recall: 0.736000, count: 125 -Region Avg IOU: 0.607462, Class: 1.000000, Obj: 0.347916, No Obj: 0.002815, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.664275, Class: 1.000000, Obj: 0.405474, No Obj: 0.002919, Avg Recall: 0.770115, count: 87 -Region Avg IOU: 0.663635, Class: 1.000000, Obj: 0.397800, No Obj: 0.002452, Avg Recall: 0.843137, count: 102 -Region Avg IOU: 0.638569, Class: 1.000000, Obj: 0.329814, No Obj: 0.003030, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.646377, Class: 1.000000, Obj: 0.361953, No Obj: 0.002578, Avg Recall: 0.809524, count: 84 -Region Avg IOU: 0.609222, Class: 1.000000, Obj: 0.345901, No Obj: 0.002791, Avg Recall: 0.752000, count: 125 -5477: 0.443256, 0.432381 avg, 0.001000 rate, 24.993896 seconds, 1402112 images -Loaded: 0.000126 seconds -Region Avg IOU: 0.666705, Class: 1.000000, Obj: 0.393489, No Obj: 0.002550, Avg Recall: 0.853933, count: 89 -Region Avg IOU: 0.591569, Class: 1.000000, Obj: 0.303078, No Obj: 0.002697, Avg Recall: 0.686747, count: 83 -Region Avg IOU: 0.619703, Class: 1.000000, Obj: 0.355999, No Obj: 0.002514, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.602119, Class: 1.000000, Obj: 0.389724, No Obj: 0.002169, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.625139, Class: 1.000000, Obj: 0.365543, No Obj: 0.002333, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.643396, Class: 1.000000, Obj: 0.409760, No Obj: 0.002691, Avg Recall: 0.797619, count: 84 -Region Avg IOU: 0.627432, Class: 1.000000, Obj: 0.348685, No Obj: 0.002379, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.621088, Class: 1.000000, Obj: 0.362156, No Obj: 0.002612, Avg Recall: 0.756757, count: 111 -5478: 0.365311, 0.425674 avg, 0.001000 rate, 26.155712 seconds, 1402368 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.614694, Class: 1.000000, Obj: 0.393186, No Obj: 0.002886, Avg Recall: 0.674419, count: 129 -Region Avg IOU: 0.558289, Class: 1.000000, Obj: 0.304943, No Obj: 0.003182, Avg Recall: 0.657718, count: 149 -Region Avg IOU: 0.607145, Class: 1.000000, Obj: 0.351974, No Obj: 0.002929, Avg Recall: 0.715385, count: 130 -Region Avg IOU: 0.617413, Class: 1.000000, Obj: 0.325255, No Obj: 0.002497, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.604421, Class: 1.000000, Obj: 0.320730, No Obj: 0.002830, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.593074, Class: 1.000000, Obj: 0.350157, No Obj: 0.002861, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.637590, Class: 1.000000, Obj: 0.386871, No Obj: 0.003092, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.645973, Class: 1.000000, Obj: 0.393502, No Obj: 0.002670, Avg Recall: 0.744444, count: 90 -5479: 0.455617, 0.428668 avg, 0.001000 rate, 25.991571 seconds, 1402624 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.573919, Class: 1.000000, Obj: 0.341066, No Obj: 0.002113, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.652532, Class: 1.000000, Obj: 0.411417, No Obj: 0.002725, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.587670, Class: 1.000000, Obj: 0.364386, No Obj: 0.002761, Avg Recall: 0.680328, count: 122 -Region Avg IOU: 0.609563, Class: 1.000000, Obj: 0.345232, No Obj: 0.002937, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.666607, Class: 1.000000, Obj: 0.359497, No Obj: 0.002863, Avg Recall: 0.857143, count: 105 -Region Avg IOU: 0.635129, Class: 1.000000, Obj: 0.340797, No Obj: 0.002410, Avg Recall: 0.777778, count: 81 -Region Avg IOU: 0.604892, Class: 1.000000, Obj: 0.351013, No Obj: 0.002864, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.644248, Class: 1.000000, Obj: 0.351830, No Obj: 0.002988, Avg Recall: 0.782258, count: 124 -5480: 0.429332, 0.428735 avg, 0.001000 rate, 26.172832 seconds, 1402880 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000086 seconds -Region Avg IOU: 0.669946, Class: 1.000000, Obj: 0.404799, No Obj: 0.002330, Avg Recall: 0.807018, count: 114 -Region Avg IOU: 0.654935, Class: 1.000000, Obj: 0.339899, No Obj: 0.002586, Avg Recall: 0.815789, count: 114 -Region Avg IOU: 0.632324, Class: 1.000000, Obj: 0.364717, No Obj: 0.002560, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.699501, Class: 1.000000, Obj: 0.416020, No Obj: 0.002495, Avg Recall: 0.866667, count: 90 -Region Avg IOU: 0.699418, Class: 1.000000, Obj: 0.428862, No Obj: 0.002591, Avg Recall: 0.892308, count: 130 -Region Avg IOU: 0.687916, Class: 1.000000, Obj: 0.404479, No Obj: 0.002174, Avg Recall: 0.866667, count: 90 -Region Avg IOU: 0.649287, Class: 1.000000, Obj: 0.345801, No Obj: 0.002536, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.650214, Class: 1.000000, Obj: 0.404846, No Obj: 0.002543, Avg Recall: 0.784615, count: 130 -5481: 0.433970, 0.429258 avg, 0.001000 rate, 27.663036 seconds, 1403136 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.673502, Class: 1.000000, Obj: 0.383602, No Obj: 0.002600, Avg Recall: 0.850877, count: 114 -Region Avg IOU: 0.679727, Class: 1.000000, Obj: 0.438944, No Obj: 0.002340, Avg Recall: 0.872549, count: 102 -Region Avg IOU: 0.674396, Class: 1.000000, Obj: 0.406622, No Obj: 0.002423, Avg Recall: 0.896226, count: 106 -Region Avg IOU: 0.679930, Class: 1.000000, Obj: 0.431900, No Obj: 0.002334, Avg Recall: 0.833333, count: 90 -Region Avg IOU: 0.653119, Class: 1.000000, Obj: 0.366398, No Obj: 0.002914, Avg Recall: 0.810458, count: 153 -Region Avg IOU: 0.641343, Class: 1.000000, Obj: 0.398108, No Obj: 0.002642, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.668892, Class: 1.000000, Obj: 0.432146, No Obj: 0.002435, Avg Recall: 0.824742, count: 97 -Region Avg IOU: 0.649975, Class: 1.000000, Obj: 0.375420, No Obj: 0.002734, Avg Recall: 0.823077, count: 130 -5482: 0.460100, 0.432342 avg, 0.001000 rate, 30.327806 seconds, 1403392 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.656640, Class: 1.000000, Obj: 0.390218, No Obj: 0.002620, Avg Recall: 0.833333, count: 108 -Region Avg IOU: 0.644994, Class: 1.000000, Obj: 0.443113, No Obj: 0.002353, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.721875, Class: 1.000000, Obj: 0.442800, No Obj: 0.002585, Avg Recall: 0.941176, count: 85 -Region Avg IOU: 0.618264, Class: 1.000000, Obj: 0.379542, No Obj: 0.002383, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.652618, Class: 1.000000, Obj: 0.384328, No Obj: 0.002650, Avg Recall: 0.854545, count: 110 -Region Avg IOU: 0.624120, Class: 1.000000, Obj: 0.390888, No Obj: 0.002912, Avg Recall: 0.779528, count: 127 -Region Avg IOU: 0.684343, Class: 1.000000, Obj: 0.375684, No Obj: 0.002511, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.641438, Class: 1.000000, Obj: 0.442325, No Obj: 0.002431, Avg Recall: 0.819149, count: 94 -5483: 0.455322, 0.434640 avg, 0.001000 rate, 29.375916 seconds, 1403648 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.652777, Class: 1.000000, Obj: 0.423069, No Obj: 0.002510, Avg Recall: 0.836364, count: 110 -Region Avg IOU: 0.652045, Class: 1.000000, Obj: 0.442732, No Obj: 0.002563, Avg Recall: 0.838095, count: 105 -Region Avg IOU: 0.673110, Class: 1.000000, Obj: 0.351874, No Obj: 0.003375, Avg Recall: 0.840000, count: 150 -Region Avg IOU: 0.668825, Class: 1.000000, Obj: 0.428040, No Obj: 0.002687, Avg Recall: 0.838095, count: 105 -Region Avg IOU: 0.651840, Class: 1.000000, Obj: 0.407935, No Obj: 0.002354, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.662287, Class: 1.000000, Obj: 0.386835, No Obj: 0.002421, Avg Recall: 0.814815, count: 81 -Region Avg IOU: 0.683265, Class: 1.000000, Obj: 0.446645, No Obj: 0.002301, Avg Recall: 0.842105, count: 76 -Region Avg IOU: 0.677170, Class: 1.000000, Obj: 0.363339, No Obj: 0.003115, Avg Recall: 0.853846, count: 130 -5484: 0.426801, 0.433856 avg, 0.001000 rate, 30.054728 seconds, 1403904 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.669458, Class: 1.000000, Obj: 0.454614, No Obj: 0.002810, Avg Recall: 0.844828, count: 116 -Region Avg IOU: 0.708085, Class: 1.000000, Obj: 0.436242, No Obj: 0.002416, Avg Recall: 0.863636, count: 66 -Region Avg IOU: 0.632992, Class: 1.000000, Obj: 0.321251, No Obj: 0.003013, Avg Recall: 0.814815, count: 108 -Region Avg IOU: 0.645437, Class: 1.000000, Obj: 0.418141, No Obj: 0.002682, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.631772, Class: 1.000000, Obj: 0.391391, No Obj: 0.002715, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.678360, Class: 1.000000, Obj: 0.422504, No Obj: 0.002956, Avg Recall: 0.840336, count: 119 -Region Avg IOU: 0.665383, Class: 1.000000, Obj: 0.427160, No Obj: 0.002667, Avg Recall: 0.804348, count: 92 -Region Avg IOU: 0.665709, Class: 1.000000, Obj: 0.386877, No Obj: 0.002845, Avg Recall: 0.830508, count: 118 -5485: 0.433493, 0.433820 avg, 0.001000 rate, 30.137487 seconds, 1404160 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.620226, Class: 1.000000, Obj: 0.390577, No Obj: 0.002995, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.646939, Class: 1.000000, Obj: 0.415301, No Obj: 0.002809, Avg Recall: 0.825000, count: 80 -Region Avg IOU: 0.659571, Class: 1.000000, Obj: 0.432150, No Obj: 0.002699, Avg Recall: 0.820225, count: 89 -Region Avg IOU: 0.652896, Class: 1.000000, Obj: 0.413778, No Obj: 0.003059, Avg Recall: 0.804878, count: 123 -Region Avg IOU: 0.682361, Class: 1.000000, Obj: 0.436453, No Obj: 0.002757, Avg Recall: 0.854545, count: 110 -Region Avg IOU: 0.628749, Class: 1.000000, Obj: 0.400581, No Obj: 0.002701, Avg Recall: 0.727273, count: 143 -Region Avg IOU: 0.685250, Class: 1.000000, Obj: 0.370306, No Obj: 0.002829, Avg Recall: 0.834862, count: 109 -Region Avg IOU: 0.645329, Class: 1.000000, Obj: 0.428418, No Obj: 0.003045, Avg Recall: 0.796992, count: 133 -5486: 0.491469, 0.439585 avg, 0.001000 rate, 31.249975 seconds, 1404416 images -Loaded: 0.000148 seconds -Region Avg IOU: 0.617459, Class: 1.000000, Obj: 0.422126, No Obj: 0.002912, Avg Recall: 0.781955, count: 133 -Region Avg IOU: 0.656719, Class: 1.000000, Obj: 0.416121, No Obj: 0.002953, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.673573, Class: 1.000000, Obj: 0.430254, No Obj: 0.002812, Avg Recall: 0.829787, count: 94 -Region Avg IOU: 0.628419, Class: 1.000000, Obj: 0.454958, No Obj: 0.002449, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.611648, Class: 1.000000, Obj: 0.382945, No Obj: 0.002767, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.642431, Class: 1.000000, Obj: 0.421675, No Obj: 0.003156, Avg Recall: 0.773438, count: 128 -Region Avg IOU: 0.654714, Class: 1.000000, Obj: 0.422829, No Obj: 0.003387, Avg Recall: 0.797297, count: 148 -Region Avg IOU: 0.588094, Class: 1.000000, Obj: 0.325216, No Obj: 0.002586, Avg Recall: 0.693694, count: 111 -5487: 0.503689, 0.445995 avg, 0.001000 rate, 29.337051 seconds, 1404672 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.636127, Class: 1.000000, Obj: 0.379470, No Obj: 0.002442, Avg Recall: 0.831776, count: 107 -Region Avg IOU: 0.686695, Class: 1.000000, Obj: 0.383835, No Obj: 0.002584, Avg Recall: 0.935897, count: 78 -Region Avg IOU: 0.666613, Class: 1.000000, Obj: 0.430579, No Obj: 0.002502, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.670821, Class: 1.000000, Obj: 0.453756, No Obj: 0.002955, Avg Recall: 0.825688, count: 109 -Region Avg IOU: 0.652520, Class: 1.000000, Obj: 0.406527, No Obj: 0.002459, Avg Recall: 0.772152, count: 79 -Region Avg IOU: 0.641605, Class: 1.000000, Obj: 0.407504, No Obj: 0.002608, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.686026, Class: 1.000000, Obj: 0.426745, No Obj: 0.002789, Avg Recall: 0.877551, count: 98 -Region Avg IOU: 0.641999, Class: 1.000000, Obj: 0.446449, No Obj: 0.002294, Avg Recall: 0.804348, count: 92 -5488: 0.412536, 0.442649 avg, 0.001000 rate, 29.159447 seconds, 1404928 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.651936, Class: 1.000000, Obj: 0.438183, No Obj: 0.002663, Avg Recall: 0.821429, count: 112 -Region Avg IOU: 0.606230, Class: 1.000000, Obj: 0.391703, No Obj: 0.002762, Avg Recall: 0.740741, count: 135 -Region Avg IOU: 0.679430, Class: 1.000000, Obj: 0.425997, No Obj: 0.002944, Avg Recall: 0.876190, count: 105 -Region Avg IOU: 0.671544, Class: 1.000000, Obj: 0.380941, No Obj: 0.002516, Avg Recall: 0.854369, count: 103 -Region Avg IOU: 0.687953, Class: 1.000000, Obj: 0.408404, No Obj: 0.002994, Avg Recall: 0.878505, count: 107 -Region Avg IOU: 0.684280, Class: 1.000000, Obj: 0.413362, No Obj: 0.002609, Avg Recall: 0.831776, count: 107 -Region Avg IOU: 0.636208, Class: 1.000000, Obj: 0.392251, No Obj: 0.002647, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.671821, Class: 1.000000, Obj: 0.453992, No Obj: 0.002504, Avg Recall: 0.797872, count: 94 -5489: 0.434618, 0.441846 avg, 0.001000 rate, 29.668045 seconds, 1405184 images -Loaded: 0.000118 seconds -Region Avg IOU: 0.641085, Class: 1.000000, Obj: 0.402099, No Obj: 0.002336, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.670689, Class: 1.000000, Obj: 0.380434, No Obj: 0.002932, Avg Recall: 0.870504, count: 139 -Region Avg IOU: 0.654354, Class: 1.000000, Obj: 0.389383, No Obj: 0.002397, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.650200, Class: 1.000000, Obj: 0.369405, No Obj: 0.002745, Avg Recall: 0.792593, count: 135 -Region Avg IOU: 0.656047, Class: 1.000000, Obj: 0.397332, No Obj: 0.002861, Avg Recall: 0.809859, count: 142 -Region Avg IOU: 0.642081, Class: 1.000000, Obj: 0.381605, No Obj: 0.002691, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.677730, Class: 1.000000, Obj: 0.358507, No Obj: 0.002776, Avg Recall: 0.847458, count: 118 -Region Avg IOU: 0.669336, Class: 1.000000, Obj: 0.420109, No Obj: 0.002428, Avg Recall: 0.830000, count: 100 -5490: 0.468468, 0.444508 avg, 0.001000 rate, 28.326590 seconds, 1405440 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 13.288012 seconds -Region Avg IOU: 0.598797, Class: 1.000000, Obj: 0.345206, No Obj: 0.002636, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.670170, Class: 1.000000, Obj: 0.421147, No Obj: 0.002163, Avg Recall: 0.835443, count: 79 -Region Avg IOU: 0.671798, Class: 1.000000, Obj: 0.378579, No Obj: 0.002727, Avg Recall: 0.845455, count: 110 -Region Avg IOU: 0.638236, Class: 1.000000, Obj: 0.379094, No Obj: 0.002665, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.656277, Class: 1.000000, Obj: 0.373845, No Obj: 0.002802, Avg Recall: 0.813793, count: 145 -Region Avg IOU: 0.662848, Class: 1.000000, Obj: 0.403361, No Obj: 0.002655, Avg Recall: 0.830189, count: 106 -Region Avg IOU: 0.632710, Class: 1.000000, Obj: 0.388382, No Obj: 0.002762, Avg Recall: 0.735294, count: 136 -Region Avg IOU: 0.616914, Class: 1.000000, Obj: 0.354948, No Obj: 0.002958, Avg Recall: 0.723404, count: 141 -5491: 0.481009, 0.448158 avg, 0.001000 rate, 29.654440 seconds, 1405696 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.652516, Class: 1.000000, Obj: 0.422197, No Obj: 0.002300, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.640768, Class: 1.000000, Obj: 0.432343, No Obj: 0.002575, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.641587, Class: 1.000000, Obj: 0.402221, No Obj: 0.002690, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.659162, Class: 1.000000, Obj: 0.365436, No Obj: 0.002645, Avg Recall: 0.806818, count: 88 -Region Avg IOU: 0.684340, Class: 1.000000, Obj: 0.375725, No Obj: 0.002539, Avg Recall: 0.840000, count: 100 -Region Avg IOU: 0.631704, Class: 1.000000, Obj: 0.354345, No Obj: 0.002357, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.659309, Class: 1.000000, Obj: 0.420100, No Obj: 0.003087, Avg Recall: 0.816667, count: 120 -Region Avg IOU: 0.651874, Class: 1.000000, Obj: 0.394983, No Obj: 0.002379, Avg Recall: 0.812500, count: 112 -5492: 0.411601, 0.444503 avg, 0.001000 rate, 29.495592 seconds, 1405952 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.648251, Class: 1.000000, Obj: 0.404117, No Obj: 0.002514, Avg Recall: 0.785124, count: 121 -Region Avg IOU: 0.660274, Class: 1.000000, Obj: 0.425464, No Obj: 0.002754, Avg Recall: 0.828829, count: 111 -Region Avg IOU: 0.665630, Class: 1.000000, Obj: 0.394802, No Obj: 0.002395, Avg Recall: 0.847826, count: 92 -Region Avg IOU: 0.651296, Class: 1.000000, Obj: 0.422259, No Obj: 0.002965, Avg Recall: 0.806202, count: 129 -Region Avg IOU: 0.638848, Class: 1.000000, Obj: 0.418553, No Obj: 0.002331, Avg Recall: 0.772152, count: 79 -Region Avg IOU: 0.693552, Class: 1.000000, Obj: 0.415679, No Obj: 0.002367, Avg Recall: 0.839623, count: 106 -Region Avg IOU: 0.600161, Class: 1.000000, Obj: 0.401145, No Obj: 0.002245, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.653643, Class: 1.000000, Obj: 0.384531, No Obj: 0.002095, Avg Recall: 0.810000, count: 100 -5493: 0.395875, 0.439640 avg, 0.001000 rate, 28.621576 seconds, 1406208 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.647718, Class: 1.000000, Obj: 0.411409, No Obj: 0.002624, Avg Recall: 0.800000, count: 115 -Region Avg IOU: 0.661890, Class: 1.000000, Obj: 0.385696, No Obj: 0.002527, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.691280, Class: 1.000000, Obj: 0.375839, No Obj: 0.002460, Avg Recall: 0.865385, count: 104 -Region Avg IOU: 0.666934, Class: 1.000000, Obj: 0.406307, No Obj: 0.002581, Avg Recall: 0.864865, count: 111 -Region Avg IOU: 0.624286, Class: 1.000000, Obj: 0.378709, No Obj: 0.002479, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.627386, Class: 1.000000, Obj: 0.359910, No Obj: 0.002314, Avg Recall: 0.771084, count: 83 -Region Avg IOU: 0.635698, Class: 1.000000, Obj: 0.386677, No Obj: 0.002540, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.619791, Class: 1.000000, Obj: 0.368030, No Obj: 0.002793, Avg Recall: 0.730435, count: 115 -5494: 0.443823, 0.440058 avg, 0.001000 rate, 29.233231 seconds, 1406464 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.645230, Class: 1.000000, Obj: 0.365821, No Obj: 0.002851, Avg Recall: 0.801471, count: 136 -Region Avg IOU: 0.673480, Class: 1.000000, Obj: 0.365386, No Obj: 0.003325, Avg Recall: 0.836735, count: 147 -Region Avg IOU: 0.615840, Class: 1.000000, Obj: 0.360915, No Obj: 0.002814, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.676868, Class: 1.000000, Obj: 0.424335, No Obj: 0.002252, Avg Recall: 0.858824, count: 85 -Region Avg IOU: 0.677777, Class: 1.000000, Obj: 0.410915, No Obj: 0.002245, Avg Recall: 0.875000, count: 88 -Region Avg IOU: 0.686353, Class: 1.000000, Obj: 0.385965, No Obj: 0.002486, Avg Recall: 0.835165, count: 91 -Region Avg IOU: 0.626381, Class: 1.000000, Obj: 0.398722, No Obj: 0.002360, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.664263, Class: 1.000000, Obj: 0.396334, No Obj: 0.002228, Avg Recall: 0.802198, count: 91 -5495: 0.457312, 0.441784 avg, 0.001000 rate, 28.963600 seconds, 1406720 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.683396, Class: 1.000000, Obj: 0.391914, No Obj: 0.002390, Avg Recall: 0.921053, count: 76 -Region Avg IOU: 0.605353, Class: 1.000000, Obj: 0.410321, No Obj: 0.002161, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.640398, Class: 1.000000, Obj: 0.386947, No Obj: 0.002607, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.658562, Class: 1.000000, Obj: 0.360851, No Obj: 0.003024, Avg Recall: 0.830000, count: 100 -Region Avg IOU: 0.636162, Class: 1.000000, Obj: 0.375491, No Obj: 0.003372, Avg Recall: 0.797101, count: 138 -Region Avg IOU: 0.657104, Class: 1.000000, Obj: 0.385883, No Obj: 0.002184, Avg Recall: 0.816901, count: 71 -Region Avg IOU: 0.652833, Class: 1.000000, Obj: 0.393892, No Obj: 0.002922, Avg Recall: 0.836066, count: 122 -Region Avg IOU: 0.652164, Class: 1.000000, Obj: 0.428042, No Obj: 0.002490, Avg Recall: 0.802198, count: 91 -5496: 0.421814, 0.439787 avg, 0.001000 rate, 28.507723 seconds, 1406976 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.626457, Class: 1.000000, Obj: 0.443817, No Obj: 0.002358, Avg Recall: 0.703297, count: 91 -Region Avg IOU: 0.646323, Class: 1.000000, Obj: 0.455185, No Obj: 0.002494, Avg Recall: 0.808081, count: 99 -Region Avg IOU: 0.605221, Class: 1.000000, Obj: 0.336734, No Obj: 0.003014, Avg Recall: 0.716312, count: 141 -Region Avg IOU: 0.637145, Class: 1.000000, Obj: 0.391353, No Obj: 0.002771, Avg Recall: 0.798319, count: 119 -Region Avg IOU: 0.673967, Class: 1.000000, Obj: 0.411308, No Obj: 0.003276, Avg Recall: 0.851613, count: 155 -Region Avg IOU: 0.676179, Class: 1.000000, Obj: 0.369743, No Obj: 0.002567, Avg Recall: 0.829787, count: 94 -Region Avg IOU: 0.698658, Class: 1.000000, Obj: 0.402019, No Obj: 0.003099, Avg Recall: 0.901786, count: 112 -Region Avg IOU: 0.661114, Class: 1.000000, Obj: 0.425886, No Obj: 0.002933, Avg Recall: 0.819149, count: 94 -5497: 0.447171, 0.440525 avg, 0.001000 rate, 26.781397 seconds, 1407232 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.676312, Class: 1.000000, Obj: 0.471187, No Obj: 0.003007, Avg Recall: 0.880734, count: 109 -Region Avg IOU: 0.664393, Class: 1.000000, Obj: 0.406859, No Obj: 0.002653, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.637231, Class: 1.000000, Obj: 0.372795, No Obj: 0.002566, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.684270, Class: 1.000000, Obj: 0.433736, No Obj: 0.002618, Avg Recall: 0.879518, count: 83 -Region Avg IOU: 0.698461, Class: 1.000000, Obj: 0.432342, No Obj: 0.002763, Avg Recall: 0.892857, count: 84 -Region Avg IOU: 0.636222, Class: 1.000000, Obj: 0.436393, No Obj: 0.002487, Avg Recall: 0.797872, count: 94 -Region Avg IOU: 0.666343, Class: 1.000000, Obj: 0.430066, No Obj: 0.002666, Avg Recall: 0.831579, count: 95 -Region Avg IOU: 0.680451, Class: 1.000000, Obj: 0.411189, No Obj: 0.002464, Avg Recall: 0.844660, count: 103 -5498: 0.387711, 0.435244 avg, 0.001000 rate, 28.843048 seconds, 1407488 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.638660, Class: 1.000000, Obj: 0.459903, No Obj: 0.002237, Avg Recall: 0.772727, count: 88 -Region Avg IOU: 0.647825, Class: 1.000000, Obj: 0.431927, No Obj: 0.002579, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.640318, Class: 1.000000, Obj: 0.387067, No Obj: 0.002688, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.629312, Class: 1.000000, Obj: 0.428577, No Obj: 0.002595, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.620716, Class: 1.000000, Obj: 0.390309, No Obj: 0.002456, Avg Recall: 0.781609, count: 87 -Region Avg IOU: 0.663988, Class: 1.000000, Obj: 0.373417, No Obj: 0.002689, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.651836, Class: 1.000000, Obj: 0.422630, No Obj: 0.002881, Avg Recall: 0.800000, count: 125 -Region Avg IOU: 0.676665, Class: 1.000000, Obj: 0.372008, No Obj: 0.002688, Avg Recall: 0.852941, count: 102 -5499: 0.391164, 0.430836 avg, 0.001000 rate, 28.606356 seconds, 1407744 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.598495, Class: 1.000000, Obj: 0.415380, No Obj: 0.003166, Avg Recall: 0.701613, count: 124 -Region Avg IOU: 0.659930, Class: 1.000000, Obj: 0.428502, No Obj: 0.002965, Avg Recall: 0.837838, count: 111 -Region Avg IOU: 0.640164, Class: 1.000000, Obj: 0.344198, No Obj: 0.002418, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.672581, Class: 1.000000, Obj: 0.370869, No Obj: 0.003074, Avg Recall: 0.858407, count: 113 -Region Avg IOU: 0.622463, Class: 1.000000, Obj: 0.379152, No Obj: 0.002458, Avg Recall: 0.788889, count: 90 -Region Avg IOU: 0.689263, Class: 1.000000, Obj: 0.492555, No Obj: 0.002705, Avg Recall: 0.848101, count: 79 -Region Avg IOU: 0.672790, Class: 1.000000, Obj: 0.434356, No Obj: 0.002546, Avg Recall: 0.860215, count: 93 -Region Avg IOU: 0.651765, Class: 1.000000, Obj: 0.409046, No Obj: 0.002537, Avg Recall: 0.778846, count: 104 -5500: 0.421121, 0.429864 avg, 0.001000 rate, 26.567345 seconds, 1408000 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 13.909837 seconds -Region Avg IOU: 0.641212, Class: 1.000000, Obj: 0.359714, No Obj: 0.002641, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.648521, Class: 1.000000, Obj: 0.433717, No Obj: 0.002821, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.625482, Class: 1.000000, Obj: 0.415708, No Obj: 0.002669, Avg Recall: 0.762712, count: 118 -Region Avg IOU: 0.628316, Class: 1.000000, Obj: 0.376032, No Obj: 0.003049, Avg Recall: 0.760870, count: 138 -Region Avg IOU: 0.666467, Class: 1.000000, Obj: 0.463819, No Obj: 0.002247, Avg Recall: 0.782609, count: 69 -Region Avg IOU: 0.617299, Class: 1.000000, Obj: 0.437141, No Obj: 0.002981, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.629668, Class: 1.000000, Obj: 0.440863, No Obj: 0.002541, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.680617, Class: 1.000000, Obj: 0.417086, No Obj: 0.002996, Avg Recall: 0.819820, count: 111 -5501: 0.420432, 0.428921 avg, 0.001000 rate, 26.937300 seconds, 1408256 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.626618, Class: 1.000000, Obj: 0.379255, No Obj: 0.002566, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.635839, Class: 1.000000, Obj: 0.371309, No Obj: 0.002614, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.654475, Class: 1.000000, Obj: 0.438134, No Obj: 0.002473, Avg Recall: 0.797619, count: 84 -Region Avg IOU: 0.635413, Class: 1.000000, Obj: 0.442708, No Obj: 0.002433, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.632478, Class: 1.000000, Obj: 0.427032, No Obj: 0.002808, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.613228, Class: 1.000000, Obj: 0.371628, No Obj: 0.002438, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.602570, Class: 1.000000, Obj: 0.338753, No Obj: 0.002589, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.644827, Class: 1.000000, Obj: 0.421725, No Obj: 0.003325, Avg Recall: 0.779661, count: 118 -5502: 0.409014, 0.426930 avg, 0.001000 rate, 28.712120 seconds, 1408512 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.675115, Class: 1.000000, Obj: 0.443248, No Obj: 0.002534, Avg Recall: 0.843750, count: 96 -Region Avg IOU: 0.656313, Class: 1.000000, Obj: 0.449654, No Obj: 0.002376, Avg Recall: 0.800000, count: 80 -Region Avg IOU: 0.630908, Class: 1.000000, Obj: 0.428707, No Obj: 0.002627, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.596342, Class: 1.000000, Obj: 0.402119, No Obj: 0.002799, Avg Recall: 0.706349, count: 126 -Region Avg IOU: 0.649092, Class: 1.000000, Obj: 0.370128, No Obj: 0.002547, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.651385, Class: 1.000000, Obj: 0.382637, No Obj: 0.002704, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.647884, Class: 1.000000, Obj: 0.403560, No Obj: 0.002717, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.645112, Class: 1.000000, Obj: 0.412186, No Obj: 0.002711, Avg Recall: 0.781818, count: 110 -5503: 0.385078, 0.422745 avg, 0.001000 rate, 27.478403 seconds, 1408768 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.647582, Class: 1.000000, Obj: 0.446896, No Obj: 0.002536, Avg Recall: 0.797872, count: 94 -Region Avg IOU: 0.658078, Class: 1.000000, Obj: 0.433518, No Obj: 0.002357, Avg Recall: 0.831325, count: 83 -Region Avg IOU: 0.643881, Class: 1.000000, Obj: 0.442865, No Obj: 0.002659, Avg Recall: 0.815789, count: 114 -Region Avg IOU: 0.655335, Class: 1.000000, Obj: 0.397500, No Obj: 0.002519, Avg Recall: 0.771084, count: 83 -Region Avg IOU: 0.625249, Class: 1.000000, Obj: 0.396576, No Obj: 0.002384, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.674617, Class: 1.000000, Obj: 0.404006, No Obj: 0.002900, Avg Recall: 0.843284, count: 134 -Region Avg IOU: 0.656630, Class: 1.000000, Obj: 0.389242, No Obj: 0.002799, Avg Recall: 0.825243, count: 103 -Region Avg IOU: 0.665648, Class: 1.000000, Obj: 0.382525, No Obj: 0.002823, Avg Recall: 0.811966, count: 117 -5504: 0.408903, 0.421361 avg, 0.001000 rate, 27.438639 seconds, 1409024 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.625019, Class: 1.000000, Obj: 0.384980, No Obj: 0.002807, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.660033, Class: 1.000000, Obj: 0.416718, No Obj: 0.002601, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.661490, Class: 1.000000, Obj: 0.376816, No Obj: 0.002416, Avg Recall: 0.824742, count: 97 -Region Avg IOU: 0.657295, Class: 1.000000, Obj: 0.441713, No Obj: 0.003071, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.653470, Class: 1.000000, Obj: 0.387097, No Obj: 0.003212, Avg Recall: 0.807692, count: 130 -Region Avg IOU: 0.670622, Class: 1.000000, Obj: 0.412346, No Obj: 0.002990, Avg Recall: 0.839286, count: 112 -Region Avg IOU: 0.598636, Class: 1.000000, Obj: 0.332318, No Obj: 0.002394, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.592179, Class: 1.000000, Obj: 0.370600, No Obj: 0.002357, Avg Recall: 0.737374, count: 99 -5505: 0.470890, 0.426314 avg, 0.001000 rate, 27.633690 seconds, 1409280 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.647076, Class: 1.000000, Obj: 0.368919, No Obj: 0.003123, Avg Recall: 0.785124, count: 121 -Region Avg IOU: 0.683161, Class: 1.000000, Obj: 0.414812, No Obj: 0.002513, Avg Recall: 0.823529, count: 85 -Region Avg IOU: 0.602925, Class: 1.000000, Obj: 0.376149, No Obj: 0.002598, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.653371, Class: 1.000000, Obj: 0.465239, No Obj: 0.002972, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.670670, Class: 1.000000, Obj: 0.451420, No Obj: 0.002155, Avg Recall: 0.831461, count: 89 -Region Avg IOU: 0.648541, Class: 1.000000, Obj: 0.401448, No Obj: 0.002618, Avg Recall: 0.788889, count: 90 -Region Avg IOU: 0.655146, Class: 1.000000, Obj: 0.385057, No Obj: 0.003150, Avg Recall: 0.808333, count: 120 -Region Avg IOU: 0.654771, Class: 1.000000, Obj: 0.408100, No Obj: 0.002871, Avg Recall: 0.836364, count: 110 -5506: 0.428662, 0.426549 avg, 0.001000 rate, 28.066244 seconds, 1409536 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.662112, Class: 1.000000, Obj: 0.435581, No Obj: 0.003161, Avg Recall: 0.859504, count: 121 -Region Avg IOU: 0.657920, Class: 1.000000, Obj: 0.424354, No Obj: 0.002491, Avg Recall: 0.818182, count: 88 -Region Avg IOU: 0.663139, Class: 1.000000, Obj: 0.447167, No Obj: 0.002749, Avg Recall: 0.828829, count: 111 -Region Avg IOU: 0.638449, Class: 1.000000, Obj: 0.390053, No Obj: 0.003204, Avg Recall: 0.801653, count: 121 -Region Avg IOU: 0.645459, Class: 1.000000, Obj: 0.413401, No Obj: 0.003217, Avg Recall: 0.782258, count: 124 -Region Avg IOU: 0.673832, Class: 1.000000, Obj: 0.431916, No Obj: 0.002594, Avg Recall: 0.819048, count: 105 -Region Avg IOU: 0.630867, Class: 1.000000, Obj: 0.406250, No Obj: 0.002907, Avg Recall: 0.762295, count: 122 -Region Avg IOU: 0.600401, Class: 1.000000, Obj: 0.331349, No Obj: 0.002664, Avg Recall: 0.752381, count: 105 -5507: 0.421570, 0.426051 avg, 0.001000 rate, 26.819923 seconds, 1409792 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.632126, Class: 1.000000, Obj: 0.437090, No Obj: 0.002780, Avg Recall: 0.794643, count: 112 -Region Avg IOU: 0.646747, Class: 1.000000, Obj: 0.421735, No Obj: 0.003026, Avg Recall: 0.783333, count: 120 -Region Avg IOU: 0.687153, Class: 1.000000, Obj: 0.425737, No Obj: 0.003244, Avg Recall: 0.877193, count: 114 -Region Avg IOU: 0.638374, Class: 1.000000, Obj: 0.418338, No Obj: 0.003033, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.670386, Class: 1.000000, Obj: 0.435044, No Obj: 0.002635, Avg Recall: 0.826087, count: 92 -Region Avg IOU: 0.682857, Class: 1.000000, Obj: 0.465402, No Obj: 0.002866, Avg Recall: 0.845455, count: 110 -Region Avg IOU: 0.638030, Class: 1.000000, Obj: 0.386168, No Obj: 0.002980, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.658933, Class: 1.000000, Obj: 0.430559, No Obj: 0.002959, Avg Recall: 0.783505, count: 97 -5508: 0.454603, 0.428906 avg, 0.001000 rate, 25.969494 seconds, 1410048 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.618917, Class: 1.000000, Obj: 0.418044, No Obj: 0.002832, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.637686, Class: 1.000000, Obj: 0.464691, No Obj: 0.002831, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.675002, Class: 1.000000, Obj: 0.443828, No Obj: 0.002958, Avg Recall: 0.852273, count: 88 -Region Avg IOU: 0.614925, Class: 1.000000, Obj: 0.428269, No Obj: 0.003013, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.654739, Class: 1.000000, Obj: 0.418771, No Obj: 0.003400, Avg Recall: 0.766917, count: 133 -Region Avg IOU: 0.680054, Class: 1.000000, Obj: 0.433857, No Obj: 0.003465, Avg Recall: 0.830645, count: 124 -Region Avg IOU: 0.638743, Class: 1.000000, Obj: 0.418339, No Obj: 0.003130, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.657565, Class: 1.000000, Obj: 0.436820, No Obj: 0.002886, Avg Recall: 0.830189, count: 106 -5509: 0.416352, 0.427651 avg, 0.001000 rate, 27.919212 seconds, 1410304 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.596533, Class: 1.000000, Obj: 0.415393, No Obj: 0.003079, Avg Recall: 0.696296, count: 135 -Region Avg IOU: 0.678485, Class: 1.000000, Obj: 0.407071, No Obj: 0.002870, Avg Recall: 0.868132, count: 91 -Region Avg IOU: 0.638541, Class: 1.000000, Obj: 0.501289, No Obj: 0.002932, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.628100, Class: 1.000000, Obj: 0.420024, No Obj: 0.003540, Avg Recall: 0.778571, count: 140 -Region Avg IOU: 0.649070, Class: 1.000000, Obj: 0.440801, No Obj: 0.003058, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.657373, Class: 1.000000, Obj: 0.442252, No Obj: 0.003604, Avg Recall: 0.826923, count: 156 -Region Avg IOU: 0.658362, Class: 1.000000, Obj: 0.470751, No Obj: 0.003031, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.644820, Class: 1.000000, Obj: 0.438568, No Obj: 0.002971, Avg Recall: 0.747748, count: 111 -5510: 0.436971, 0.428583 avg, 0.001000 rate, 26.517511 seconds, 1410560 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 12.693379 seconds -Region Avg IOU: 0.609262, Class: 1.000000, Obj: 0.425839, No Obj: 0.003326, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.580935, Class: 1.000000, Obj: 0.385671, No Obj: 0.002811, Avg Recall: 0.657895, count: 76 -Region Avg IOU: 0.627967, Class: 1.000000, Obj: 0.434414, No Obj: 0.002955, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.615046, Class: 1.000000, Obj: 0.403366, No Obj: 0.003532, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.648030, Class: 1.000000, Obj: 0.444451, No Obj: 0.003866, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.631791, Class: 1.000000, Obj: 0.411737, No Obj: 0.003594, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.602243, Class: 1.000000, Obj: 0.413493, No Obj: 0.003117, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.642863, Class: 1.000000, Obj: 0.447543, No Obj: 0.003047, Avg Recall: 0.795699, count: 93 -5511: 0.348532, 0.420578 avg, 0.001000 rate, 25.671230 seconds, 1410816 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.631994, Class: 1.000000, Obj: 0.427276, No Obj: 0.003694, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.622269, Class: 1.000000, Obj: 0.444148, No Obj: 0.003060, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.568935, Class: 1.000000, Obj: 0.395222, No Obj: 0.003086, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.611946, Class: 1.000000, Obj: 0.411300, No Obj: 0.003182, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.589585, Class: 1.000000, Obj: 0.428198, No Obj: 0.003508, Avg Recall: 0.715447, count: 123 -Region Avg IOU: 0.613721, Class: 1.000000, Obj: 0.427165, No Obj: 0.003319, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.618580, Class: 1.000000, Obj: 0.414402, No Obj: 0.003559, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.635518, Class: 1.000000, Obj: 0.459279, No Obj: 0.003671, Avg Recall: 0.766667, count: 120 -5512: 0.395470, 0.418067 avg, 0.001000 rate, 25.048237 seconds, 1411072 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.547453, Class: 1.000000, Obj: 0.359886, No Obj: 0.003740, Avg Recall: 0.632653, count: 147 -Region Avg IOU: 0.599670, Class: 1.000000, Obj: 0.398177, No Obj: 0.003391, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.627883, Class: 1.000000, Obj: 0.414066, No Obj: 0.003311, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.627107, Class: 1.000000, Obj: 0.463980, No Obj: 0.003457, Avg Recall: 0.759036, count: 83 -Region Avg IOU: 0.625369, Class: 1.000000, Obj: 0.445040, No Obj: 0.003611, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.647649, Class: 1.000000, Obj: 0.403127, No Obj: 0.003682, Avg Recall: 0.785714, count: 126 -Region Avg IOU: 0.581735, Class: 1.000000, Obj: 0.360085, No Obj: 0.003074, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.646712, Class: 1.000000, Obj: 0.450845, No Obj: 0.002958, Avg Recall: 0.780220, count: 91 -5513: 0.416933, 0.417953 avg, 0.001000 rate, 25.118153 seconds, 1411328 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.603448, Class: 1.000000, Obj: 0.389529, No Obj: 0.003144, Avg Recall: 0.750000, count: 88 -Region Avg IOU: 0.561534, Class: 1.000000, Obj: 0.381976, No Obj: 0.003624, Avg Recall: 0.643939, count: 132 -Region Avg IOU: 0.578354, Class: 1.000000, Obj: 0.376903, No Obj: 0.002943, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.600491, Class: 1.000000, Obj: 0.383414, No Obj: 0.003582, Avg Recall: 0.713178, count: 129 -Region Avg IOU: 0.653514, Class: 1.000000, Obj: 0.425529, No Obj: 0.003161, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.586668, Class: 1.000000, Obj: 0.372338, No Obj: 0.002997, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.596231, Class: 1.000000, Obj: 0.392876, No Obj: 0.003332, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.604724, Class: 1.000000, Obj: 0.379964, No Obj: 0.003660, Avg Recall: 0.723810, count: 105 -5514: 0.431907, 0.419349 avg, 0.001000 rate, 25.349812 seconds, 1411584 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.624531, Class: 1.000000, Obj: 0.415520, No Obj: 0.003008, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.601502, Class: 1.000000, Obj: 0.382275, No Obj: 0.003437, Avg Recall: 0.744000, count: 125 -Region Avg IOU: 0.587815, Class: 1.000000, Obj: 0.370154, No Obj: 0.003761, Avg Recall: 0.687943, count: 141 -Region Avg IOU: 0.623741, Class: 1.000000, Obj: 0.402069, No Obj: 0.003520, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.583255, Class: 1.000000, Obj: 0.415095, No Obj: 0.003215, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.618611, Class: 1.000000, Obj: 0.405836, No Obj: 0.003324, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.649312, Class: 1.000000, Obj: 0.390643, No Obj: 0.003600, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.604587, Class: 1.000000, Obj: 0.443564, No Obj: 0.003070, Avg Recall: 0.693069, count: 101 -5515: 0.421133, 0.419527 avg, 0.001000 rate, 24.826687 seconds, 1411840 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.628992, Class: 1.000000, Obj: 0.400187, No Obj: 0.002988, Avg Recall: 0.773810, count: 84 -Region Avg IOU: 0.539506, Class: 1.000000, Obj: 0.353765, No Obj: 0.003251, Avg Recall: 0.597015, count: 134 -Region Avg IOU: 0.619241, Class: 1.000000, Obj: 0.407872, No Obj: 0.002846, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.606718, Class: 1.000000, Obj: 0.460761, No Obj: 0.002945, Avg Recall: 0.741176, count: 85 -Region Avg IOU: 0.620972, Class: 1.000000, Obj: 0.402417, No Obj: 0.003414, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.590803, Class: 1.000000, Obj: 0.384354, No Obj: 0.002947, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.580138, Class: 1.000000, Obj: 0.407054, No Obj: 0.003275, Avg Recall: 0.712000, count: 125 -Region Avg IOU: 0.576846, Class: 1.000000, Obj: 0.329218, No Obj: 0.002619, Avg Recall: 0.682353, count: 85 -5516: 0.387273, 0.416302 avg, 0.001000 rate, 25.206469 seconds, 1412096 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.558332, Class: 1.000000, Obj: 0.366800, No Obj: 0.003554, Avg Recall: 0.668874, count: 151 -Region Avg IOU: 0.558614, Class: 1.000000, Obj: 0.362259, No Obj: 0.002765, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.585613, Class: 1.000000, Obj: 0.385358, No Obj: 0.003026, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.612670, Class: 1.000000, Obj: 0.403251, No Obj: 0.003381, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.615008, Class: 1.000000, Obj: 0.404469, No Obj: 0.003315, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.635801, Class: 1.000000, Obj: 0.456423, No Obj: 0.003336, Avg Recall: 0.797872, count: 94 -Region Avg IOU: 0.631716, Class: 1.000000, Obj: 0.423313, No Obj: 0.003147, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.600753, Class: 1.000000, Obj: 0.396125, No Obj: 0.003043, Avg Recall: 0.733945, count: 109 -5517: 0.430672, 0.417739 avg, 0.001000 rate, 24.345482 seconds, 1412352 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.564229, Class: 1.000000, Obj: 0.387166, No Obj: 0.002716, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.600597, Class: 1.000000, Obj: 0.422169, No Obj: 0.002923, Avg Recall: 0.712644, count: 87 -Region Avg IOU: 0.604894, Class: 1.000000, Obj: 0.406729, No Obj: 0.002973, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.626736, Class: 1.000000, Obj: 0.416700, No Obj: 0.003472, Avg Recall: 0.753968, count: 126 -Region Avg IOU: 0.615263, Class: 1.000000, Obj: 0.397733, No Obj: 0.003220, Avg Recall: 0.744186, count: 86 -Region Avg IOU: 0.622294, Class: 1.000000, Obj: 0.411893, No Obj: 0.003503, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.594588, Class: 1.000000, Obj: 0.393095, No Obj: 0.003063, Avg Recall: 0.688073, count: 109 -Region Avg IOU: 0.618137, Class: 1.000000, Obj: 0.404376, No Obj: 0.003046, Avg Recall: 0.765306, count: 98 -5518: 0.374416, 0.413407 avg, 0.001000 rate, 24.417166 seconds, 1412608 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.617518, Class: 1.000000, Obj: 0.437989, No Obj: 0.003099, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.561993, Class: 1.000000, Obj: 0.365765, No Obj: 0.003035, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.650557, Class: 1.000000, Obj: 0.381073, No Obj: 0.003707, Avg Recall: 0.787879, count: 132 -Region Avg IOU: 0.556187, Class: 1.000000, Obj: 0.390590, No Obj: 0.003891, Avg Recall: 0.659722, count: 144 -Region Avg IOU: 0.626612, Class: 1.000000, Obj: 0.390038, No Obj: 0.003505, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.596876, Class: 1.000000, Obj: 0.337521, No Obj: 0.003413, Avg Recall: 0.725926, count: 135 -Region Avg IOU: 0.558462, Class: 1.000000, Obj: 0.406074, No Obj: 0.003109, Avg Recall: 0.651852, count: 135 -Region Avg IOU: 0.626074, Class: 1.000000, Obj: 0.420456, No Obj: 0.003209, Avg Recall: 0.736842, count: 95 -5519: 0.515458, 0.423612 avg, 0.001000 rate, 25.251030 seconds, 1412864 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.608134, Class: 1.000000, Obj: 0.379314, No Obj: 0.003004, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.607310, Class: 1.000000, Obj: 0.375592, No Obj: 0.003521, Avg Recall: 0.675214, count: 117 -Region Avg IOU: 0.547348, Class: 1.000000, Obj: 0.327565, No Obj: 0.003323, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.673399, Class: 1.000000, Obj: 0.432293, No Obj: 0.003288, Avg Recall: 0.848485, count: 99 -Region Avg IOU: 0.624634, Class: 1.000000, Obj: 0.404545, No Obj: 0.003180, Avg Recall: 0.722222, count: 90 -Region Avg IOU: 0.603679, Class: 1.000000, Obj: 0.424028, No Obj: 0.002729, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.528049, Class: 1.000000, Obj: 0.363000, No Obj: 0.003378, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.653142, Class: 1.000000, Obj: 0.420754, No Obj: 0.003070, Avg Recall: 0.761905, count: 84 -5520: 0.402418, 0.421492 avg, 0.001000 rate, 22.893660 seconds, 1413120 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000073 seconds -Region Avg IOU: 0.611577, Class: 1.000000, Obj: 0.419662, No Obj: 0.003377, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.619828, Class: 1.000000, Obj: 0.396418, No Obj: 0.002921, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.594298, Class: 1.000000, Obj: 0.397364, No Obj: 0.002708, Avg Recall: 0.725275, count: 91 -Region Avg IOU: 0.601015, Class: 1.000000, Obj: 0.389880, No Obj: 0.002566, Avg Recall: 0.759494, count: 79 -Region Avg IOU: 0.613594, Class: 1.000000, Obj: 0.394378, No Obj: 0.003177, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.601316, Class: 1.000000, Obj: 0.389808, No Obj: 0.003105, Avg Recall: 0.705426, count: 129 -Region Avg IOU: 0.623820, Class: 1.000000, Obj: 0.428210, No Obj: 0.003123, Avg Recall: 0.755814, count: 86 -Region Avg IOU: 0.546352, Class: 1.000000, Obj: 0.382982, No Obj: 0.002840, Avg Recall: 0.626168, count: 107 -5521: 0.413492, 0.420692 avg, 0.001000 rate, 24.404345 seconds, 1413376 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.610494, Class: 1.000000, Obj: 0.384088, No Obj: 0.002759, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.608110, Class: 1.000000, Obj: 0.400610, No Obj: 0.003200, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.635351, Class: 1.000000, Obj: 0.391805, No Obj: 0.003259, Avg Recall: 0.813084, count: 107 -Region Avg IOU: 0.639013, Class: 1.000000, Obj: 0.404257, No Obj: 0.003200, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.602338, Class: 1.000000, Obj: 0.339147, No Obj: 0.003449, Avg Recall: 0.721429, count: 140 -Region Avg IOU: 0.630436, Class: 1.000000, Obj: 0.451580, No Obj: 0.002790, Avg Recall: 0.765432, count: 81 -Region Avg IOU: 0.613965, Class: 1.000000, Obj: 0.433021, No Obj: 0.002675, Avg Recall: 0.783132, count: 83 -Region Avg IOU: 0.646270, Class: 1.000000, Obj: 0.437104, No Obj: 0.002990, Avg Recall: 0.803922, count: 102 -5522: 0.375760, 0.416199 avg, 0.001000 rate, 26.313919 seconds, 1413632 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.600832, Class: 1.000000, Obj: 0.431704, No Obj: 0.003028, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.584207, Class: 1.000000, Obj: 0.420653, No Obj: 0.003161, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.586115, Class: 1.000000, Obj: 0.417702, No Obj: 0.003257, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.664840, Class: 1.000000, Obj: 0.462137, No Obj: 0.003164, Avg Recall: 0.819149, count: 94 -Region Avg IOU: 0.673266, Class: 1.000000, Obj: 0.454903, No Obj: 0.003637, Avg Recall: 0.849057, count: 106 -Region Avg IOU: 0.659886, Class: 1.000000, Obj: 0.458031, No Obj: 0.003131, Avg Recall: 0.812500, count: 80 -Region Avg IOU: 0.666816, Class: 1.000000, Obj: 0.400373, No Obj: 0.002611, Avg Recall: 0.787500, count: 80 -Region Avg IOU: 0.619275, Class: 1.000000, Obj: 0.423987, No Obj: 0.003042, Avg Recall: 0.691589, count: 107 -5523: 0.426767, 0.417256 avg, 0.001000 rate, 26.826500 seconds, 1413888 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.620764, Class: 1.000000, Obj: 0.381162, No Obj: 0.003105, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.653095, Class: 1.000000, Obj: 0.439342, No Obj: 0.003476, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.628762, Class: 1.000000, Obj: 0.402146, No Obj: 0.003561, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.666001, Class: 1.000000, Obj: 0.443663, No Obj: 0.003775, Avg Recall: 0.787402, count: 127 -Region Avg IOU: 0.688739, Class: 1.000000, Obj: 0.471062, No Obj: 0.003385, Avg Recall: 0.851485, count: 101 -Region Avg IOU: 0.632276, Class: 1.000000, Obj: 0.429714, No Obj: 0.003068, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.590470, Class: 1.000000, Obj: 0.374089, No Obj: 0.003742, Avg Recall: 0.703125, count: 128 -Region Avg IOU: 0.601269, Class: 1.000000, Obj: 0.429582, No Obj: 0.003019, Avg Recall: 0.685185, count: 108 -5524: 0.399789, 0.415509 avg, 0.001000 rate, 26.082026 seconds, 1414144 images -Loaded: 0.000148 seconds -Region Avg IOU: 0.584132, Class: 1.000000, Obj: 0.364616, No Obj: 0.002726, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.643606, Class: 1.000000, Obj: 0.443080, No Obj: 0.003465, Avg Recall: 0.791304, count: 115 -Region Avg IOU: 0.640954, Class: 1.000000, Obj: 0.426360, No Obj: 0.003488, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.558224, Class: 1.000000, Obj: 0.386501, No Obj: 0.003203, Avg Recall: 0.676692, count: 133 -Region Avg IOU: 0.644587, Class: 1.000000, Obj: 0.468131, No Obj: 0.003285, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.594412, Class: 1.000000, Obj: 0.417628, No Obj: 0.002696, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.684486, Class: 1.000000, Obj: 0.451529, No Obj: 0.003925, Avg Recall: 0.830000, count: 100 -Region Avg IOU: 0.639586, Class: 1.000000, Obj: 0.433937, No Obj: 0.002940, Avg Recall: 0.780000, count: 100 -5525: 0.436757, 0.417634 avg, 0.001000 rate, 25.168003 seconds, 1414400 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.600433, Class: 1.000000, Obj: 0.391587, No Obj: 0.003114, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.608501, Class: 1.000000, Obj: 0.396033, No Obj: 0.003273, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.619174, Class: 1.000000, Obj: 0.426075, No Obj: 0.002432, Avg Recall: 0.710526, count: 76 -Region Avg IOU: 0.584233, Class: 1.000000, Obj: 0.377905, No Obj: 0.002781, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.663408, Class: 1.000000, Obj: 0.422475, No Obj: 0.003101, Avg Recall: 0.821429, count: 112 -Region Avg IOU: 0.609108, Class: 1.000000, Obj: 0.400639, No Obj: 0.002787, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.648249, Class: 1.000000, Obj: 0.412759, No Obj: 0.003112, Avg Recall: 0.793478, count: 92 -Region Avg IOU: 0.586858, Class: 1.000000, Obj: 0.389784, No Obj: 0.002625, Avg Recall: 0.750000, count: 92 -5526: 0.415012, 0.417372 avg, 0.001000 rate, 25.772697 seconds, 1414656 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.583097, Class: 1.000000, Obj: 0.384039, No Obj: 0.003009, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.572016, Class: 1.000000, Obj: 0.442000, No Obj: 0.002861, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.624428, Class: 1.000000, Obj: 0.418900, No Obj: 0.003794, Avg Recall: 0.751773, count: 141 -Region Avg IOU: 0.674153, Class: 1.000000, Obj: 0.437975, No Obj: 0.003150, Avg Recall: 0.844037, count: 109 -Region Avg IOU: 0.617657, Class: 1.000000, Obj: 0.366574, No Obj: 0.002876, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.584612, Class: 1.000000, Obj: 0.407036, No Obj: 0.002827, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.598731, Class: 1.000000, Obj: 0.414707, No Obj: 0.003137, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.611775, Class: 1.000000, Obj: 0.383096, No Obj: 0.003377, Avg Recall: 0.739837, count: 123 -5527: 0.443616, 0.419996 avg, 0.001000 rate, 26.508932 seconds, 1414912 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.580700, Class: 1.000000, Obj: 0.396354, No Obj: 0.003170, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.628291, Class: 1.000000, Obj: 0.395838, No Obj: 0.002688, Avg Recall: 0.804878, count: 82 -Region Avg IOU: 0.612480, Class: 1.000000, Obj: 0.402945, No Obj: 0.003119, Avg Recall: 0.754098, count: 122 -Region Avg IOU: 0.608463, Class: 1.000000, Obj: 0.379038, No Obj: 0.003405, Avg Recall: 0.715385, count: 130 -Region Avg IOU: 0.616362, Class: 1.000000, Obj: 0.441926, No Obj: 0.003435, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.628601, Class: 1.000000, Obj: 0.423569, No Obj: 0.003700, Avg Recall: 0.748201, count: 139 -Region Avg IOU: 0.624020, Class: 1.000000, Obj: 0.423036, No Obj: 0.003634, Avg Recall: 0.766129, count: 124 -Region Avg IOU: 0.614805, Class: 1.000000, Obj: 0.461440, No Obj: 0.003299, Avg Recall: 0.721739, count: 115 -5528: 0.434864, 0.421483 avg, 0.001000 rate, 24.916861 seconds, 1415168 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.639725, Class: 1.000000, Obj: 0.423609, No Obj: 0.002844, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.647116, Class: 1.000000, Obj: 0.484773, No Obj: 0.003141, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.620096, Class: 1.000000, Obj: 0.416465, No Obj: 0.002845, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.642211, Class: 1.000000, Obj: 0.402207, No Obj: 0.003188, Avg Recall: 0.776859, count: 121 -Region Avg IOU: 0.606983, Class: 1.000000, Obj: 0.400090, No Obj: 0.002883, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.586139, Class: 1.000000, Obj: 0.396051, No Obj: 0.003241, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.659127, Class: 1.000000, Obj: 0.396183, No Obj: 0.002827, Avg Recall: 0.815217, count: 92 -Region Avg IOU: 0.614990, Class: 1.000000, Obj: 0.440909, No Obj: 0.003216, Avg Recall: 0.722772, count: 101 -5529: 0.396599, 0.418994 avg, 0.001000 rate, 24.952663 seconds, 1415424 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.638373, Class: 1.000000, Obj: 0.435464, No Obj: 0.003196, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.660006, Class: 1.000000, Obj: 0.390675, No Obj: 0.002965, Avg Recall: 0.825243, count: 103 -Region Avg IOU: 0.660484, Class: 1.000000, Obj: 0.412611, No Obj: 0.002773, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.645187, Class: 1.000000, Obj: 0.407133, No Obj: 0.002856, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.581218, Class: 1.000000, Obj: 0.413525, No Obj: 0.002777, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.618039, Class: 1.000000, Obj: 0.402309, No Obj: 0.003337, Avg Recall: 0.744186, count: 129 -Region Avg IOU: 0.613988, Class: 1.000000, Obj: 0.371537, No Obj: 0.003226, Avg Recall: 0.733333, count: 120 -Region Avg IOU: 0.585926, Class: 1.000000, Obj: 0.393435, No Obj: 0.003296, Avg Recall: 0.669492, count: 118 -5530: 0.400026, 0.417098 avg, 0.001000 rate, 25.675806 seconds, 1415680 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 20.736431 seconds -Region Avg IOU: 0.610935, Class: 1.000000, Obj: 0.407726, No Obj: 0.004712, Avg Recall: 0.714286, count: 154 -Region Avg IOU: 0.526649, Class: 1.000000, Obj: 0.367511, No Obj: 0.003291, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.572700, Class: 1.000000, Obj: 0.367808, No Obj: 0.003930, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.578625, Class: 1.000000, Obj: 0.342890, No Obj: 0.003331, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.569234, Class: 1.000000, Obj: 0.427323, No Obj: 0.003355, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.591190, Class: 1.000000, Obj: 0.365418, No Obj: 0.003511, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.535151, Class: 1.000000, Obj: 0.345394, No Obj: 0.003996, Avg Recall: 0.611511, count: 139 -Region Avg IOU: 0.587375, Class: 1.000000, Obj: 0.348322, No Obj: 0.003270, Avg Recall: 0.693182, count: 88 -5531: 0.440007, 0.419389 avg, 0.001000 rate, 22.886953 seconds, 1415936 images -Loaded: 0.106156 seconds -Region Avg IOU: 0.543952, Class: 1.000000, Obj: 0.318599, No Obj: 0.004082, Avg Recall: 0.633333, count: 150 -Region Avg IOU: 0.503024, Class: 1.000000, Obj: 0.338059, No Obj: 0.003409, Avg Recall: 0.578947, count: 114 -Region Avg IOU: 0.569477, Class: 1.000000, Obj: 0.378565, No Obj: 0.003305, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.597974, Class: 1.000000, Obj: 0.400609, No Obj: 0.003330, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.557563, Class: 1.000000, Obj: 0.377932, No Obj: 0.003189, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.598944, Class: 1.000000, Obj: 0.421293, No Obj: 0.003144, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.595380, Class: 1.000000, Obj: 0.431509, No Obj: 0.003789, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.610620, Class: 1.000000, Obj: 0.392796, No Obj: 0.003392, Avg Recall: 0.777778, count: 81 -5532: 0.397605, 0.417210 avg, 0.001000 rate, 24.277191 seconds, 1416192 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.588679, Class: 1.000000, Obj: 0.368967, No Obj: 0.003852, Avg Recall: 0.740157, count: 127 -Region Avg IOU: 0.631240, Class: 1.000000, Obj: 0.420544, No Obj: 0.003632, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.546021, Class: 1.000000, Obj: 0.333909, No Obj: 0.003768, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.575005, Class: 1.000000, Obj: 0.401883, No Obj: 0.003863, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.546871, Class: 1.000000, Obj: 0.398366, No Obj: 0.003565, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.593150, Class: 1.000000, Obj: 0.378864, No Obj: 0.003539, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.583522, Class: 1.000000, Obj: 0.397932, No Obj: 0.003343, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.542615, Class: 1.000000, Obj: 0.350367, No Obj: 0.003861, Avg Recall: 0.629921, count: 127 -5533: 0.489237, 0.424413 avg, 0.001000 rate, 24.201630 seconds, 1416448 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.530227, Class: 1.000000, Obj: 0.363150, No Obj: 0.004332, Avg Recall: 0.641026, count: 117 -Region Avg IOU: 0.600342, Class: 1.000000, Obj: 0.376019, No Obj: 0.003624, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.501467, Class: 1.000000, Obj: 0.389049, No Obj: 0.003243, Avg Recall: 0.540816, count: 98 -Region Avg IOU: 0.577042, Class: 1.000000, Obj: 0.399399, No Obj: 0.003398, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.594096, Class: 1.000000, Obj: 0.412710, No Obj: 0.004084, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.593818, Class: 1.000000, Obj: 0.422322, No Obj: 0.003938, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.564375, Class: 1.000000, Obj: 0.413438, No Obj: 0.003556, Avg Recall: 0.619565, count: 92 -Region Avg IOU: 0.585789, Class: 1.000000, Obj: 0.418630, No Obj: 0.003533, Avg Recall: 0.696429, count: 112 -5534: 0.412633, 0.423235 avg, 0.001000 rate, 22.443075 seconds, 1416704 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.604238, Class: 1.000000, Obj: 0.434258, No Obj: 0.004194, Avg Recall: 0.688000, count: 125 -Region Avg IOU: 0.572173, Class: 1.000000, Obj: 0.374424, No Obj: 0.003482, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.535130, Class: 1.000000, Obj: 0.409676, No Obj: 0.003999, Avg Recall: 0.616667, count: 120 -Region Avg IOU: 0.617883, Class: 1.000000, Obj: 0.407185, No Obj: 0.002972, Avg Recall: 0.750000, count: 80 -Region Avg IOU: 0.558352, Class: 1.000000, Obj: 0.404898, No Obj: 0.003374, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.583595, Class: 1.000000, Obj: 0.451013, No Obj: 0.003645, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.600982, Class: 1.000000, Obj: 0.412237, No Obj: 0.004078, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.576644, Class: 1.000000, Obj: 0.379900, No Obj: 0.004219, Avg Recall: 0.675439, count: 114 -5535: 0.412487, 0.422160 avg, 0.001000 rate, 23.904472 seconds, 1416960 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.591750, Class: 1.000000, Obj: 0.409724, No Obj: 0.004115, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.515445, Class: 1.000000, Obj: 0.355483, No Obj: 0.003809, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.566889, Class: 1.000000, Obj: 0.417719, No Obj: 0.004146, Avg Recall: 0.652893, count: 121 -Region Avg IOU: 0.542114, Class: 1.000000, Obj: 0.373457, No Obj: 0.004442, Avg Recall: 0.645833, count: 144 -Region Avg IOU: 0.579854, Class: 1.000000, Obj: 0.461200, No Obj: 0.003527, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.614398, Class: 1.000000, Obj: 0.430654, No Obj: 0.004708, Avg Recall: 0.761905, count: 126 -Region Avg IOU: 0.627559, Class: 1.000000, Obj: 0.481876, No Obj: 0.004281, Avg Recall: 0.717647, count: 85 -Region Avg IOU: 0.572200, Class: 1.000000, Obj: 0.410622, No Obj: 0.004024, Avg Recall: 0.637931, count: 116 -5536: 0.459703, 0.425914 avg, 0.001000 rate, 24.113111 seconds, 1417216 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.531014, Class: 1.000000, Obj: 0.370464, No Obj: 0.003159, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.587808, Class: 1.000000, Obj: 0.417536, No Obj: 0.003611, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.611060, Class: 1.000000, Obj: 0.445134, No Obj: 0.004256, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.598421, Class: 1.000000, Obj: 0.429789, No Obj: 0.003878, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.592475, Class: 1.000000, Obj: 0.403733, No Obj: 0.004185, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.524325, Class: 1.000000, Obj: 0.380977, No Obj: 0.004194, Avg Recall: 0.641026, count: 117 -Region Avg IOU: 0.531041, Class: 1.000000, Obj: 0.351733, No Obj: 0.004770, Avg Recall: 0.610294, count: 136 -Region Avg IOU: 0.613536, Class: 1.000000, Obj: 0.437113, No Obj: 0.004453, Avg Recall: 0.737864, count: 103 -5537: 0.464443, 0.429767 avg, 0.001000 rate, 23.920624 seconds, 1417472 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.604623, Class: 1.000000, Obj: 0.395952, No Obj: 0.004516, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.575326, Class: 1.000000, Obj: 0.419253, No Obj: 0.003923, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.614949, Class: 1.000000, Obj: 0.445597, No Obj: 0.003803, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.572796, Class: 1.000000, Obj: 0.397647, No Obj: 0.003992, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.601473, Class: 1.000000, Obj: 0.466687, No Obj: 0.003889, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.627046, Class: 1.000000, Obj: 0.420273, No Obj: 0.004423, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.558313, Class: 1.000000, Obj: 0.417267, No Obj: 0.004265, Avg Recall: 0.680000, count: 125 -Region Avg IOU: 0.582997, Class: 1.000000, Obj: 0.411215, No Obj: 0.004020, Avg Recall: 0.669643, count: 112 -5538: 0.455380, 0.432328 avg, 0.001000 rate, 22.084221 seconds, 1417728 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.569320, Class: 1.000000, Obj: 0.357141, No Obj: 0.004374, Avg Recall: 0.676471, count: 136 -Region Avg IOU: 0.575066, Class: 1.000000, Obj: 0.395284, No Obj: 0.004091, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.565267, Class: 1.000000, Obj: 0.424388, No Obj: 0.004514, Avg Recall: 0.669231, count: 130 -Region Avg IOU: 0.581715, Class: 1.000000, Obj: 0.445151, No Obj: 0.004220, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.538231, Class: 1.000000, Obj: 0.410318, No Obj: 0.003805, Avg Recall: 0.617886, count: 123 -Region Avg IOU: 0.598112, Class: 1.000000, Obj: 0.445749, No Obj: 0.004365, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.579935, Class: 1.000000, Obj: 0.417718, No Obj: 0.004240, Avg Recall: 0.696000, count: 125 -Region Avg IOU: 0.571877, Class: 1.000000, Obj: 0.413097, No Obj: 0.004458, Avg Recall: 0.622378, count: 143 -5539: 0.486366, 0.437732 avg, 0.001000 rate, 22.860949 seconds, 1417984 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.550160, Class: 1.000000, Obj: 0.393677, No Obj: 0.004010, Avg Recall: 0.601626, count: 123 -Region Avg IOU: 0.544650, Class: 1.000000, Obj: 0.398602, No Obj: 0.004089, Avg Recall: 0.624000, count: 125 -Region Avg IOU: 0.562626, Class: 1.000000, Obj: 0.412296, No Obj: 0.003523, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.560482, Class: 1.000000, Obj: 0.423832, No Obj: 0.004106, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.630093, Class: 1.000000, Obj: 0.418225, No Obj: 0.003346, Avg Recall: 0.761364, count: 88 -Region Avg IOU: 0.559802, Class: 1.000000, Obj: 0.388780, No Obj: 0.004528, Avg Recall: 0.654930, count: 142 -Region Avg IOU: 0.550273, Class: 1.000000, Obj: 0.400486, No Obj: 0.003550, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.627164, Class: 1.000000, Obj: 0.460102, No Obj: 0.003538, Avg Recall: 0.790000, count: 100 -5540: 0.431094, 0.437068 avg, 0.001000 rate, 23.070702 seconds, 1418240 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000079 seconds -Region Avg IOU: 0.580753, Class: 1.000000, Obj: 0.451979, No Obj: 0.003305, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.590715, Class: 1.000000, Obj: 0.420922, No Obj: 0.003792, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.598345, Class: 1.000000, Obj: 0.434435, No Obj: 0.003925, Avg Recall: 0.701613, count: 124 -Region Avg IOU: 0.596629, Class: 1.000000, Obj: 0.431057, No Obj: 0.004216, Avg Recall: 0.679688, count: 128 -Region Avg IOU: 0.614607, Class: 1.000000, Obj: 0.469771, No Obj: 0.003537, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.602081, Class: 1.000000, Obj: 0.423738, No Obj: 0.003727, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.596225, Class: 1.000000, Obj: 0.431941, No Obj: 0.003351, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.630394, Class: 1.000000, Obj: 0.423410, No Obj: 0.003639, Avg Recall: 0.776596, count: 94 -5541: 0.395178, 0.432879 avg, 0.001000 rate, 24.645687 seconds, 1418496 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.625702, Class: 1.000000, Obj: 0.472274, No Obj: 0.003259, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.593473, Class: 1.000000, Obj: 0.416079, No Obj: 0.003437, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.589429, Class: 1.000000, Obj: 0.399427, No Obj: 0.003562, Avg Recall: 0.718750, count: 128 -Region Avg IOU: 0.557674, Class: 1.000000, Obj: 0.393922, No Obj: 0.003800, Avg Recall: 0.636364, count: 132 -Region Avg IOU: 0.571696, Class: 1.000000, Obj: 0.404797, No Obj: 0.003573, Avg Recall: 0.666667, count: 120 -Region Avg IOU: 0.601809, Class: 1.000000, Obj: 0.404852, No Obj: 0.003631, Avg Recall: 0.698276, count: 116 -Region Avg IOU: 0.644444, Class: 1.000000, Obj: 0.464318, No Obj: 0.003503, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.615872, Class: 1.000000, Obj: 0.460410, No Obj: 0.003192, Avg Recall: 0.777778, count: 99 -5542: 0.436712, 0.433263 avg, 0.001000 rate, 25.803705 seconds, 1418752 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.604966, Class: 1.000000, Obj: 0.400035, No Obj: 0.003706, Avg Recall: 0.705036, count: 139 -Region Avg IOU: 0.620345, Class: 1.000000, Obj: 0.398688, No Obj: 0.003727, Avg Recall: 0.722222, count: 126 -Region Avg IOU: 0.624942, Class: 1.000000, Obj: 0.398623, No Obj: 0.003987, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.587538, Class: 1.000000, Obj: 0.431561, No Obj: 0.003089, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.595576, Class: 1.000000, Obj: 0.450946, No Obj: 0.003508, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.619290, Class: 1.000000, Obj: 0.398752, No Obj: 0.003539, Avg Recall: 0.718518, count: 135 -Region Avg IOU: 0.563034, Class: 1.000000, Obj: 0.441793, No Obj: 0.002802, Avg Recall: 0.636364, count: 88 -Region Avg IOU: 0.603205, Class: 1.000000, Obj: 0.382270, No Obj: 0.003421, Avg Recall: 0.769912, count: 113 -5543: 0.463080, 0.436244 avg, 0.001000 rate, 23.764999 seconds, 1419008 images -Loaded: 0.000150 seconds -Region Avg IOU: 0.609998, Class: 1.000000, Obj: 0.415239, No Obj: 0.003055, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.648763, Class: 1.000000, Obj: 0.425030, No Obj: 0.002911, Avg Recall: 0.793478, count: 92 -Region Avg IOU: 0.565767, Class: 1.000000, Obj: 0.396434, No Obj: 0.003385, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.620817, Class: 1.000000, Obj: 0.453954, No Obj: 0.002853, Avg Recall: 0.756098, count: 82 -Region Avg IOU: 0.607597, Class: 1.000000, Obj: 0.436531, No Obj: 0.002959, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.603491, Class: 1.000000, Obj: 0.350858, No Obj: 0.003324, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.583541, Class: 1.000000, Obj: 0.373996, No Obj: 0.003212, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.582706, Class: 1.000000, Obj: 0.405526, No Obj: 0.003270, Avg Recall: 0.709091, count: 110 -5544: 0.384436, 0.431063 avg, 0.001000 rate, 25.466908 seconds, 1419264 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.638805, Class: 1.000000, Obj: 0.470225, No Obj: 0.003190, Avg Recall: 0.727273, count: 77 -Region Avg IOU: 0.630291, Class: 1.000000, Obj: 0.415815, No Obj: 0.002993, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.618421, Class: 1.000000, Obj: 0.412827, No Obj: 0.003295, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.587538, Class: 1.000000, Obj: 0.370117, No Obj: 0.003231, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.598441, Class: 1.000000, Obj: 0.366645, No Obj: 0.002959, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.623735, Class: 1.000000, Obj: 0.404909, No Obj: 0.003268, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.534595, Class: 1.000000, Obj: 0.343432, No Obj: 0.003201, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.663265, Class: 1.000000, Obj: 0.386540, No Obj: 0.003396, Avg Recall: 0.820755, count: 106 -5545: 0.418992, 0.429856 avg, 0.001000 rate, 24.407145 seconds, 1419520 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.623214, Class: 1.000000, Obj: 0.385166, No Obj: 0.003163, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.582546, Class: 1.000000, Obj: 0.389578, No Obj: 0.003063, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.610808, Class: 1.000000, Obj: 0.430877, No Obj: 0.002941, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.585373, Class: 1.000000, Obj: 0.385768, No Obj: 0.002776, Avg Recall: 0.750000, count: 76 -Region Avg IOU: 0.582006, Class: 1.000000, Obj: 0.380535, No Obj: 0.003291, Avg Recall: 0.705036, count: 139 -Region Avg IOU: 0.592472, Class: 1.000000, Obj: 0.384729, No Obj: 0.003011, Avg Recall: 0.669421, count: 121 -Region Avg IOU: 0.623503, Class: 1.000000, Obj: 0.407011, No Obj: 0.002847, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.643514, Class: 1.000000, Obj: 0.440036, No Obj: 0.003015, Avg Recall: 0.795455, count: 88 -5546: 0.422985, 0.429169 avg, 0.001000 rate, 24.903650 seconds, 1419776 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.587283, Class: 1.000000, Obj: 0.428749, No Obj: 0.003031, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.630881, Class: 1.000000, Obj: 0.346880, No Obj: 0.003033, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.587967, Class: 1.000000, Obj: 0.362411, No Obj: 0.003557, Avg Recall: 0.687075, count: 147 -Region Avg IOU: 0.633586, Class: 1.000000, Obj: 0.429431, No Obj: 0.003647, Avg Recall: 0.784483, count: 116 -Region Avg IOU: 0.586159, Class: 1.000000, Obj: 0.406653, No Obj: 0.003424, Avg Recall: 0.678832, count: 137 -Region Avg IOU: 0.609995, Class: 1.000000, Obj: 0.392529, No Obj: 0.003113, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.592102, Class: 1.000000, Obj: 0.386802, No Obj: 0.002822, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.642447, Class: 1.000000, Obj: 0.434968, No Obj: 0.003005, Avg Recall: 0.752577, count: 97 -5547: 0.433415, 0.429594 avg, 0.001000 rate, 25.970709 seconds, 1420032 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.614401, Class: 1.000000, Obj: 0.380228, No Obj: 0.003307, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.602704, Class: 1.000000, Obj: 0.384583, No Obj: 0.003040, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.634911, Class: 1.000000, Obj: 0.398602, No Obj: 0.003264, Avg Recall: 0.786260, count: 131 -Region Avg IOU: 0.615080, Class: 1.000000, Obj: 0.386221, No Obj: 0.003304, Avg Recall: 0.715385, count: 130 -Region Avg IOU: 0.602733, Class: 1.000000, Obj: 0.408430, No Obj: 0.002933, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.624352, Class: 1.000000, Obj: 0.414559, No Obj: 0.002957, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.658349, Class: 1.000000, Obj: 0.412494, No Obj: 0.003442, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.582488, Class: 1.000000, Obj: 0.355537, No Obj: 0.003227, Avg Recall: 0.692913, count: 127 -5548: 0.443408, 0.430975 avg, 0.001000 rate, 24.481735 seconds, 1420288 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.606444, Class: 1.000000, Obj: 0.375252, No Obj: 0.003170, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.623592, Class: 1.000000, Obj: 0.384315, No Obj: 0.003033, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.586237, Class: 1.000000, Obj: 0.414360, No Obj: 0.003610, Avg Recall: 0.691176, count: 136 -Region Avg IOU: 0.587933, Class: 1.000000, Obj: 0.401348, No Obj: 0.003038, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.583312, Class: 1.000000, Obj: 0.357641, No Obj: 0.003050, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.617137, Class: 1.000000, Obj: 0.438945, No Obj: 0.003297, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.566233, Class: 1.000000, Obj: 0.415218, No Obj: 0.002940, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.608429, Class: 1.000000, Obj: 0.403197, No Obj: 0.002848, Avg Recall: 0.765306, count: 98 -5549: 0.417528, 0.429630 avg, 0.001000 rate, 25.004356 seconds, 1420544 images -Loaded: 0.000114 seconds -Region Avg IOU: 0.610991, Class: 1.000000, Obj: 0.351230, No Obj: 0.003457, Avg Recall: 0.744186, count: 129 -Region Avg IOU: 0.622447, Class: 1.000000, Obj: 0.404361, No Obj: 0.003309, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.592348, Class: 1.000000, Obj: 0.420426, No Obj: 0.003385, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.618479, Class: 1.000000, Obj: 0.397774, No Obj: 0.003131, Avg Recall: 0.750000, count: 84 -Region Avg IOU: 0.603536, Class: 1.000000, Obj: 0.397263, No Obj: 0.002969, Avg Recall: 0.713115, count: 122 -Region Avg IOU: 0.627324, Class: 1.000000, Obj: 0.429883, No Obj: 0.003350, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.581735, Class: 1.000000, Obj: 0.391002, No Obj: 0.003277, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.628129, Class: 1.000000, Obj: 0.414095, No Obj: 0.003327, Avg Recall: 0.745098, count: 102 -5550: 0.469116, 0.433579 avg, 0.001000 rate, 24.516457 seconds, 1420800 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.550341 seconds -Region Avg IOU: 0.499628, Class: 1.000000, Obj: 0.330243, No Obj: 0.004166, Avg Recall: 0.541667, count: 120 -Region Avg IOU: 0.487683, Class: 1.000000, Obj: 0.311586, No Obj: 0.004051, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.565349, Class: 1.000000, Obj: 0.332999, No Obj: 0.003676, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.531337, Class: 1.000000, Obj: 0.375281, No Obj: 0.003908, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.492067, Class: 1.000000, Obj: 0.324029, No Obj: 0.003721, Avg Recall: 0.495146, count: 103 -Region Avg IOU: 0.481632, Class: 1.000000, Obj: 0.303360, No Obj: 0.004312, Avg Recall: 0.581081, count: 148 -Region Avg IOU: 0.485653, Class: 1.000000, Obj: 0.287932, No Obj: 0.003836, Avg Recall: 0.559633, count: 109 -Region Avg IOU: 0.520467, Class: 1.000000, Obj: 0.340281, No Obj: 0.004660, Avg Recall: 0.621622, count: 111 -5551: 0.490800, 0.439301 avg, 0.001000 rate, 20.985640 seconds, 1421056 images -Loaded: 0.821326 seconds -Region Avg IOU: 0.545598, Class: 1.000000, Obj: 0.368621, No Obj: 0.004420, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.554878, Class: 1.000000, Obj: 0.353573, No Obj: 0.004212, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.491707, Class: 1.000000, Obj: 0.292984, No Obj: 0.004478, Avg Recall: 0.539683, count: 126 -Region Avg IOU: 0.514559, Class: 1.000000, Obj: 0.334409, No Obj: 0.003295, Avg Recall: 0.568182, count: 88 -Region Avg IOU: 0.565784, Class: 1.000000, Obj: 0.363464, No Obj: 0.003134, Avg Recall: 0.657895, count: 76 -Region Avg IOU: 0.488643, Class: 1.000000, Obj: 0.323089, No Obj: 0.004690, Avg Recall: 0.536765, count: 136 -Region Avg IOU: 0.434529, Class: 1.000000, Obj: 0.316706, No Obj: 0.003970, Avg Recall: 0.436508, count: 126 -Region Avg IOU: 0.517547, Class: 1.000000, Obj: 0.348264, No Obj: 0.003955, Avg Recall: 0.590476, count: 105 -5552: 0.439526, 0.439324 avg, 0.001000 rate, 19.234589 seconds, 1421312 images -Loaded: 0.614490 seconds -Region Avg IOU: 0.553995, Class: 1.000000, Obj: 0.342171, No Obj: 0.004283, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.520790, Class: 1.000000, Obj: 0.369639, No Obj: 0.004230, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.472783, Class: 1.000000, Obj: 0.325600, No Obj: 0.004468, Avg Recall: 0.519685, count: 127 -Region Avg IOU: 0.476535, Class: 1.000000, Obj: 0.307977, No Obj: 0.004260, Avg Recall: 0.518868, count: 106 -Region Avg IOU: 0.484179, Class: 1.000000, Obj: 0.321347, No Obj: 0.004380, Avg Recall: 0.507812, count: 128 -Region Avg IOU: 0.490250, Class: 1.000000, Obj: 0.312650, No Obj: 0.003669, Avg Recall: 0.590909, count: 88 -Region Avg IOU: 0.538599, Class: 1.000000, Obj: 0.336317, No Obj: 0.004018, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.512551, Class: 1.000000, Obj: 0.345835, No Obj: 0.003984, Avg Recall: 0.568627, count: 102 -5553: 0.430825, 0.438474 avg, 0.001000 rate, 16.971943 seconds, 1421568 images -Loaded: 4.403888 seconds -Region Avg IOU: 0.511334, Class: 1.000000, Obj: 0.315983, No Obj: 0.003975, Avg Recall: 0.546667, count: 75 -Region Avg IOU: 0.513481, Class: 1.000000, Obj: 0.315150, No Obj: 0.005138, Avg Recall: 0.572414, count: 145 -Region Avg IOU: 0.503371, Class: 1.000000, Obj: 0.345309, No Obj: 0.004969, Avg Recall: 0.533835, count: 133 -Region Avg IOU: 0.522824, Class: 1.000000, Obj: 0.317273, No Obj: 0.004634, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.514679, Class: 1.000000, Obj: 0.362615, No Obj: 0.004597, Avg Recall: 0.572650, count: 117 -Region Avg IOU: 0.521094, Class: 1.000000, Obj: 0.342353, No Obj: 0.004449, Avg Recall: 0.570175, count: 114 -Region Avg IOU: 0.528307, Class: 1.000000, Obj: 0.409282, No Obj: 0.004740, Avg Recall: 0.570175, count: 114 -Region Avg IOU: 0.523058, Class: 1.000000, Obj: 0.347545, No Obj: 0.004609, Avg Recall: 0.598039, count: 102 -5554: 0.443454, 0.438972 avg, 0.001000 rate, 19.972456 seconds, 1421824 images -Loaded: 2.343163 seconds -Region Avg IOU: 0.516259, Class: 1.000000, Obj: 0.377831, No Obj: 0.004772, Avg Recall: 0.576087, count: 92 -Region Avg IOU: 0.536774, Class: 1.000000, Obj: 0.377725, No Obj: 0.004736, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.564248, Class: 1.000000, Obj: 0.339787, No Obj: 0.005370, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.493453, Class: 1.000000, Obj: 0.361566, No Obj: 0.005239, Avg Recall: 0.555556, count: 135 -Region Avg IOU: 0.484199, Class: 1.000000, Obj: 0.368008, No Obj: 0.005292, Avg Recall: 0.514493, count: 138 -Region Avg IOU: 0.504725, Class: 1.000000, Obj: 0.366762, No Obj: 0.004778, Avg Recall: 0.554622, count: 119 -Region Avg IOU: 0.485885, Class: 1.000000, Obj: 0.362191, No Obj: 0.004332, Avg Recall: 0.513514, count: 111 -Region Avg IOU: 0.510113, Class: 1.000000, Obj: 0.360747, No Obj: 0.005127, Avg Recall: 0.563025, count: 119 -5555: 0.473815, 0.442456 avg, 0.001000 rate, 17.401228 seconds, 1422080 images -Loaded: 5.496586 seconds -Region Avg IOU: 0.490606, Class: 1.000000, Obj: 0.364100, No Obj: 0.005266, Avg Recall: 0.519084, count: 131 -Region Avg IOU: 0.537712, Class: 1.000000, Obj: 0.372571, No Obj: 0.004711, Avg Recall: 0.573171, count: 82 -Region Avg IOU: 0.468484, Class: 1.000000, Obj: 0.377526, No Obj: 0.004389, Avg Recall: 0.521739, count: 115 -Region Avg IOU: 0.494472, Class: 1.000000, Obj: 0.351130, No Obj: 0.005109, Avg Recall: 0.534351, count: 131 -Region Avg IOU: 0.490145, Class: 1.000000, Obj: 0.321923, No Obj: 0.004371, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.509449, Class: 1.000000, Obj: 0.360170, No Obj: 0.005170, Avg Recall: 0.575000, count: 120 -Region Avg IOU: 0.531804, Class: 1.000000, Obj: 0.368192, No Obj: 0.004823, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.506765, Class: 1.000000, Obj: 0.345018, No Obj: 0.005307, Avg Recall: 0.568000, count: 125 -5556: 0.416149, 0.439825 avg, 0.001000 rate, 20.231581 seconds, 1422336 images -Loaded: 0.149129 seconds -Region Avg IOU: 0.510120, Class: 1.000000, Obj: 0.366802, No Obj: 0.004521, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.489072, Class: 1.000000, Obj: 0.381644, No Obj: 0.004926, Avg Recall: 0.544643, count: 112 -Region Avg IOU: 0.485910, Class: 1.000000, Obj: 0.315199, No Obj: 0.004857, Avg Recall: 0.500000, count: 138 -Region Avg IOU: 0.501946, Class: 1.000000, Obj: 0.362478, No Obj: 0.005418, Avg Recall: 0.553719, count: 121 -Region Avg IOU: 0.463154, Class: 1.000000, Obj: 0.368136, No Obj: 0.004562, Avg Recall: 0.511111, count: 90 -Region Avg IOU: 0.517763, Class: 1.000000, Obj: 0.361664, No Obj: 0.004594, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.504906, Class: 1.000000, Obj: 0.370803, No Obj: 0.004490, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.540371, Class: 1.000000, Obj: 0.400854, No Obj: 0.005551, Avg Recall: 0.600000, count: 135 -5557: 0.453691, 0.441212 avg, 0.001000 rate, 19.571440 seconds, 1422592 images -Loaded: 0.189123 seconds -Region Avg IOU: 0.538764, Class: 1.000000, Obj: 0.448346, No Obj: 0.004929, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.490816, Class: 1.000000, Obj: 0.351121, No Obj: 0.005392, Avg Recall: 0.556452, count: 124 -Region Avg IOU: 0.522903, Class: 1.000000, Obj: 0.387738, No Obj: 0.005064, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.545276, Class: 1.000000, Obj: 0.349274, No Obj: 0.004541, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.514522, Class: 1.000000, Obj: 0.378948, No Obj: 0.005483, Avg Recall: 0.588235, count: 119 -Region Avg IOU: 0.434918, Class: 1.000000, Obj: 0.329618, No Obj: 0.004017, Avg Recall: 0.449153, count: 118 -Region Avg IOU: 0.535532, Class: 1.000000, Obj: 0.389043, No Obj: 0.004689, Avg Recall: 0.607595, count: 79 -Region Avg IOU: 0.481885, Class: 1.000000, Obj: 0.393402, No Obj: 0.003949, Avg Recall: 0.536082, count: 97 -5558: 0.427122, 0.439803 avg, 0.001000 rate, 20.534264 seconds, 1422848 images -Loaded: 0.386239 seconds -Region Avg IOU: 0.530233, Class: 1.000000, Obj: 0.375912, No Obj: 0.004403, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.500195, Class: 1.000000, Obj: 0.372618, No Obj: 0.005134, Avg Recall: 0.580420, count: 143 -Region Avg IOU: 0.503420, Class: 1.000000, Obj: 0.371136, No Obj: 0.004436, Avg Recall: 0.566372, count: 113 -Region Avg IOU: 0.524814, Class: 1.000000, Obj: 0.368436, No Obj: 0.004598, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.500472, Class: 1.000000, Obj: 0.377096, No Obj: 0.004383, Avg Recall: 0.510000, count: 100 -Region Avg IOU: 0.475710, Class: 1.000000, Obj: 0.368244, No Obj: 0.004416, Avg Recall: 0.513514, count: 111 -Region Avg IOU: 0.550327, Class: 1.000000, Obj: 0.407554, No Obj: 0.005187, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.494701, Class: 1.000000, Obj: 0.374420, No Obj: 0.004347, Avg Recall: 0.546296, count: 108 -5559: 0.414176, 0.437240 avg, 0.001000 rate, 17.287563 seconds, 1423104 images -Loaded: 3.762232 seconds -Region Avg IOU: 0.521437, Class: 1.000000, Obj: 0.373139, No Obj: 0.004609, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.520385, Class: 1.000000, Obj: 0.403738, No Obj: 0.004776, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.502178, Class: 1.000000, Obj: 0.338723, No Obj: 0.004367, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.526814, Class: 1.000000, Obj: 0.393529, No Obj: 0.004734, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.464482, Class: 1.000000, Obj: 0.374896, No Obj: 0.005454, Avg Recall: 0.550388, count: 129 -Region Avg IOU: 0.522386, Class: 1.000000, Obj: 0.361841, No Obj: 0.004398, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.500909, Class: 1.000000, Obj: 0.359798, No Obj: 0.003633, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.528217, Class: 1.000000, Obj: 0.413235, No Obj: 0.005100, Avg Recall: 0.632184, count: 87 -5560: 0.457579, 0.439274 avg, 0.001000 rate, 17.868608 seconds, 1423360 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 0.100388 seconds -Region Avg IOU: 0.538808, Class: 1.000000, Obj: 0.392149, No Obj: 0.004730, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.529954, Class: 1.000000, Obj: 0.377996, No Obj: 0.004997, Avg Recall: 0.630769, count: 130 -Region Avg IOU: 0.489447, Class: 1.000000, Obj: 0.325766, No Obj: 0.004853, Avg Recall: 0.539007, count: 141 -Region Avg IOU: 0.490426, Class: 1.000000, Obj: 0.358358, No Obj: 0.004251, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.510980, Class: 1.000000, Obj: 0.386576, No Obj: 0.004703, Avg Recall: 0.594828, count: 116 -Region Avg IOU: 0.485242, Class: 1.000000, Obj: 0.383646, No Obj: 0.004195, Avg Recall: 0.491228, count: 114 -Region Avg IOU: 0.512051, Class: 1.000000, Obj: 0.407741, No Obj: 0.004438, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.479844, Class: 1.000000, Obj: 0.369126, No Obj: 0.004529, Avg Recall: 0.530769, count: 130 -5561: 0.457077, 0.441054 avg, 0.001000 rate, 22.475519 seconds, 1423616 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.529547, Class: 1.000000, Obj: 0.397360, No Obj: 0.004602, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.511315, Class: 1.000000, Obj: 0.365289, No Obj: 0.004828, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.557345, Class: 1.000000, Obj: 0.419372, No Obj: 0.005542, Avg Recall: 0.587719, count: 114 -Region Avg IOU: 0.494494, Class: 1.000000, Obj: 0.389259, No Obj: 0.004130, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.514694, Class: 1.000000, Obj: 0.382478, No Obj: 0.004512, Avg Recall: 0.573770, count: 122 -Region Avg IOU: 0.516147, Class: 1.000000, Obj: 0.378824, No Obj: 0.004661, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.527435, Class: 1.000000, Obj: 0.389345, No Obj: 0.003937, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.542021, Class: 1.000000, Obj: 0.420124, No Obj: 0.004679, Avg Recall: 0.639175, count: 97 -5562: 0.421382, 0.439087 avg, 0.001000 rate, 19.796196 seconds, 1423872 images -Loaded: 0.773961 seconds -Region Avg IOU: 0.503930, Class: 1.000000, Obj: 0.347750, No Obj: 0.004537, Avg Recall: 0.566667, count: 120 -Region Avg IOU: 0.566197, Class: 1.000000, Obj: 0.415007, No Obj: 0.003976, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.500218, Class: 1.000000, Obj: 0.368285, No Obj: 0.003457, Avg Recall: 0.616279, count: 86 -Region Avg IOU: 0.511260, Class: 1.000000, Obj: 0.378331, No Obj: 0.004454, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.467693, Class: 1.000000, Obj: 0.327130, No Obj: 0.004260, Avg Recall: 0.544715, count: 123 -Region Avg IOU: 0.520273, Class: 1.000000, Obj: 0.346265, No Obj: 0.004722, Avg Recall: 0.568345, count: 139 -Region Avg IOU: 0.514416, Class: 1.000000, Obj: 0.394445, No Obj: 0.004922, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.457715, Class: 1.000000, Obj: 0.352310, No Obj: 0.003569, Avg Recall: 0.509434, count: 106 -5563: 0.477638, 0.442942 avg, 0.001000 rate, 20.027077 seconds, 1424128 images -Loaded: 1.506113 seconds -Region Avg IOU: 0.511495, Class: 1.000000, Obj: 0.341317, No Obj: 0.004047, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.554779, Class: 1.000000, Obj: 0.379958, No Obj: 0.004369, Avg Recall: 0.636364, count: 88 -Region Avg IOU: 0.564303, Class: 1.000000, Obj: 0.376820, No Obj: 0.005130, Avg Recall: 0.666667, count: 135 -Region Avg IOU: 0.518995, Class: 1.000000, Obj: 0.376692, No Obj: 0.003840, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.502920, Class: 1.000000, Obj: 0.348081, No Obj: 0.004652, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.544413, Class: 1.000000, Obj: 0.375446, No Obj: 0.004085, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.503097, Class: 1.000000, Obj: 0.359928, No Obj: 0.003786, Avg Recall: 0.557522, count: 113 -Region Avg IOU: 0.536874, Class: 1.000000, Obj: 0.388661, No Obj: 0.004380, Avg Recall: 0.601695, count: 118 -5564: 0.461877, 0.444836 avg, 0.001000 rate, 21.101194 seconds, 1424384 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.494679, Class: 1.000000, Obj: 0.355659, No Obj: 0.003326, Avg Recall: 0.566667, count: 90 -Region Avg IOU: 0.536964, Class: 1.000000, Obj: 0.331276, No Obj: 0.004552, Avg Recall: 0.650000, count: 140 -Region Avg IOU: 0.531315, Class: 1.000000, Obj: 0.428972, No Obj: 0.003503, Avg Recall: 0.547619, count: 84 -Region Avg IOU: 0.506365, Class: 1.000000, Obj: 0.333333, No Obj: 0.004034, Avg Recall: 0.578125, count: 128 -Region Avg IOU: 0.535500, Class: 1.000000, Obj: 0.308485, No Obj: 0.004363, Avg Recall: 0.588235, count: 136 -Region Avg IOU: 0.553101, Class: 1.000000, Obj: 0.396997, No Obj: 0.003574, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.484875, Class: 1.000000, Obj: 0.310652, No Obj: 0.003250, Avg Recall: 0.517241, count: 87 -Region Avg IOU: 0.562664, Class: 1.000000, Obj: 0.347184, No Obj: 0.004549, Avg Recall: 0.615385, count: 117 -5565: 0.448379, 0.445190 avg, 0.001000 rate, 20.176208 seconds, 1424640 images -Loaded: 2.164170 seconds -Region Avg IOU: 0.566058, Class: 1.000000, Obj: 0.368506, No Obj: 0.003437, Avg Recall: 0.659091, count: 88 -Region Avg IOU: 0.535901, Class: 1.000000, Obj: 0.386745, No Obj: 0.004130, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.490505, Class: 1.000000, Obj: 0.301228, No Obj: 0.003504, Avg Recall: 0.554348, count: 92 -Region Avg IOU: 0.499097, Class: 1.000000, Obj: 0.339751, No Obj: 0.004117, Avg Recall: 0.552000, count: 125 -Region Avg IOU: 0.558828, Class: 1.000000, Obj: 0.355847, No Obj: 0.005199, Avg Recall: 0.664234, count: 137 -Region Avg IOU: 0.627042, Class: 1.000000, Obj: 0.413873, No Obj: 0.004359, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.558810, Class: 1.000000, Obj: 0.384692, No Obj: 0.004107, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.525714, Class: 1.000000, Obj: 0.352188, No Obj: 0.003246, Avg Recall: 0.602564, count: 78 -5566: 0.423330, 0.443004 avg, 0.001000 rate, 20.361380 seconds, 1424896 images -Loaded: 2.235053 seconds -Region Avg IOU: 0.518147, Class: 1.000000, Obj: 0.335027, No Obj: 0.003896, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.504730, Class: 1.000000, Obj: 0.351737, No Obj: 0.004032, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.496076, Class: 1.000000, Obj: 0.314926, No Obj: 0.004248, Avg Recall: 0.563910, count: 133 -Region Avg IOU: 0.518055, Class: 1.000000, Obj: 0.332540, No Obj: 0.003459, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.535392, Class: 1.000000, Obj: 0.348697, No Obj: 0.004065, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.562710, Class: 1.000000, Obj: 0.363326, No Obj: 0.003757, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.487446, Class: 1.000000, Obj: 0.325174, No Obj: 0.004261, Avg Recall: 0.514085, count: 142 -Region Avg IOU: 0.574305, Class: 1.000000, Obj: 0.368834, No Obj: 0.003937, Avg Recall: 0.659091, count: 88 -5567: 0.453151, 0.444019 avg, 0.001000 rate, 21.667534 seconds, 1425152 images -Loaded: 0.734450 seconds -Region Avg IOU: 0.497961, Class: 1.000000, Obj: 0.321442, No Obj: 0.003393, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.540925, Class: 1.000000, Obj: 0.292493, No Obj: 0.003779, Avg Recall: 0.612903, count: 124 -Region Avg IOU: 0.533329, Class: 1.000000, Obj: 0.326178, No Obj: 0.003557, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.526816, Class: 1.000000, Obj: 0.399199, No Obj: 0.003854, Avg Recall: 0.622222, count: 90 -Region Avg IOU: 0.537472, Class: 1.000000, Obj: 0.359443, No Obj: 0.004360, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.443995, Class: 1.000000, Obj: 0.296836, No Obj: 0.003951, Avg Recall: 0.461538, count: 117 -Region Avg IOU: 0.547582, Class: 1.000000, Obj: 0.351081, No Obj: 0.004530, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.477718, Class: 1.000000, Obj: 0.334035, No Obj: 0.003452, Avg Recall: 0.518519, count: 108 -5568: 0.463645, 0.445981 avg, 0.001000 rate, 19.336740 seconds, 1425408 images -Loaded: 1.713514 seconds -Region Avg IOU: 0.574184, Class: 1.000000, Obj: 0.390592, No Obj: 0.005133, Avg Recall: 0.661538, count: 130 -Region Avg IOU: 0.524978, Class: 1.000000, Obj: 0.361694, No Obj: 0.003898, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.554059, Class: 1.000000, Obj: 0.333015, No Obj: 0.004508, Avg Recall: 0.671875, count: 128 -Region Avg IOU: 0.559698, Class: 1.000000, Obj: 0.387905, No Obj: 0.004182, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.535557, Class: 1.000000, Obj: 0.397197, No Obj: 0.004043, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.554031, Class: 1.000000, Obj: 0.353119, No Obj: 0.004124, Avg Recall: 0.631579, count: 133 -Region Avg IOU: 0.499878, Class: 1.000000, Obj: 0.362803, No Obj: 0.004129, Avg Recall: 0.553846, count: 130 -Region Avg IOU: 0.550661, Class: 1.000000, Obj: 0.352694, No Obj: 0.004372, Avg Recall: 0.606838, count: 117 -5569: 0.417633, 0.443147 avg, 0.001000 rate, 19.553680 seconds, 1425664 images -Loaded: 3.549883 seconds -Region Avg IOU: 0.500692, Class: 1.000000, Obj: 0.370619, No Obj: 0.003274, Avg Recall: 0.571429, count: 77 -Region Avg IOU: 0.489394, Class: 1.000000, Obj: 0.333642, No Obj: 0.003904, Avg Recall: 0.495868, count: 121 -Region Avg IOU: 0.556125, Class: 1.000000, Obj: 0.419695, No Obj: 0.003431, Avg Recall: 0.628571, count: 70 -Region Avg IOU: 0.544400, Class: 1.000000, Obj: 0.345017, No Obj: 0.004380, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.544647, Class: 1.000000, Obj: 0.426882, No Obj: 0.004093, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.521209, Class: 1.000000, Obj: 0.351596, No Obj: 0.004421, Avg Recall: 0.581560, count: 141 -Region Avg IOU: 0.560117, Class: 1.000000, Obj: 0.415227, No Obj: 0.004173, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.547962, Class: 1.000000, Obj: 0.380727, No Obj: 0.003627, Avg Recall: 0.647059, count: 85 -5570: 0.424155, 0.441247 avg, 0.001000 rate, 22.453979 seconds, 1425920 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 0.190387 seconds -Region Avg IOU: 0.596627, Class: 1.000000, Obj: 0.447571, No Obj: 0.004354, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.609867, Class: 1.000000, Obj: 0.378192, No Obj: 0.003879, Avg Recall: 0.720930, count: 86 -Region Avg IOU: 0.552236, Class: 1.000000, Obj: 0.414659, No Obj: 0.004073, Avg Recall: 0.602941, count: 136 -Region Avg IOU: 0.553445, Class: 1.000000, Obj: 0.372165, No Obj: 0.003299, Avg Recall: 0.659091, count: 88 -Region Avg IOU: 0.566310, Class: 1.000000, Obj: 0.403149, No Obj: 0.003762, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.587870, Class: 1.000000, Obj: 0.424587, No Obj: 0.003521, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.594041, Class: 1.000000, Obj: 0.428903, No Obj: 0.003668, Avg Recall: 0.715909, count: 88 -Region Avg IOU: 0.601807, Class: 1.000000, Obj: 0.445772, No Obj: 0.003539, Avg Recall: 0.678161, count: 87 -5571: 0.403064, 0.437429 avg, 0.001000 rate, 23.865431 seconds, 1426176 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.582560, Class: 1.000000, Obj: 0.422820, No Obj: 0.004246, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.588039, Class: 1.000000, Obj: 0.430679, No Obj: 0.003869, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.627921, Class: 1.000000, Obj: 0.428367, No Obj: 0.003972, Avg Recall: 0.755556, count: 90 -Region Avg IOU: 0.567800, Class: 1.000000, Obj: 0.405722, No Obj: 0.003476, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.585334, Class: 1.000000, Obj: 0.388739, No Obj: 0.004095, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.569194, Class: 1.000000, Obj: 0.362836, No Obj: 0.003754, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.551439, Class: 1.000000, Obj: 0.410565, No Obj: 0.003937, Avg Recall: 0.644068, count: 118 -Region Avg IOU: 0.570085, Class: 1.000000, Obj: 0.431893, No Obj: 0.003611, Avg Recall: 0.660377, count: 106 -5572: 0.393439, 0.433030 avg, 0.001000 rate, 23.045912 seconds, 1426432 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.568648, Class: 1.000000, Obj: 0.392047, No Obj: 0.003761, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.605128, Class: 1.000000, Obj: 0.455097, No Obj: 0.004425, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.588131, Class: 1.000000, Obj: 0.399151, No Obj: 0.003590, Avg Recall: 0.678161, count: 87 -Region Avg IOU: 0.547153, Class: 1.000000, Obj: 0.403400, No Obj: 0.004029, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.557185, Class: 1.000000, Obj: 0.417528, No Obj: 0.003874, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.575132, Class: 1.000000, Obj: 0.373176, No Obj: 0.004071, Avg Recall: 0.692913, count: 127 -Region Avg IOU: 0.540077, Class: 1.000000, Obj: 0.398297, No Obj: 0.003494, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.587731, Class: 1.000000, Obj: 0.393864, No Obj: 0.003992, Avg Recall: 0.720000, count: 100 -5573: 0.385772, 0.428304 avg, 0.001000 rate, 23.036154 seconds, 1426688 images -Loaded: 0.000116 seconds -Region Avg IOU: 0.590693, Class: 1.000000, Obj: 0.383730, No Obj: 0.004174, Avg Recall: 0.715447, count: 123 -Region Avg IOU: 0.557904, Class: 1.000000, Obj: 0.383551, No Obj: 0.004116, Avg Recall: 0.651163, count: 129 -Region Avg IOU: 0.604749, Class: 1.000000, Obj: 0.407877, No Obj: 0.004199, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.519638, Class: 1.000000, Obj: 0.370642, No Obj: 0.003279, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.583708, Class: 1.000000, Obj: 0.407084, No Obj: 0.003637, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.586668, Class: 1.000000, Obj: 0.418859, No Obj: 0.004056, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.586734, Class: 1.000000, Obj: 0.433827, No Obj: 0.004602, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.584261, Class: 1.000000, Obj: 0.412813, No Obj: 0.003647, Avg Recall: 0.659341, count: 91 -5574: 0.428791, 0.428353 avg, 0.001000 rate, 23.199749 seconds, 1426944 images -Loaded: 0.001513 seconds -Region Avg IOU: 0.598754, Class: 1.000000, Obj: 0.421171, No Obj: 0.004013, Avg Recall: 0.717742, count: 124 -Region Avg IOU: 0.591197, Class: 1.000000, Obj: 0.403095, No Obj: 0.003700, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.584418, Class: 1.000000, Obj: 0.400337, No Obj: 0.003882, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.608747, Class: 1.000000, Obj: 0.456236, No Obj: 0.003436, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.610722, Class: 1.000000, Obj: 0.391969, No Obj: 0.004109, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.606647, Class: 1.000000, Obj: 0.398905, No Obj: 0.003788, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.568534, Class: 1.000000, Obj: 0.415675, No Obj: 0.004054, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.587935, Class: 1.000000, Obj: 0.378806, No Obj: 0.004143, Avg Recall: 0.709402, count: 117 -5575: 0.415451, 0.427063 avg, 0.001000 rate, 21.890999 seconds, 1427200 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.575999, Class: 1.000000, Obj: 0.417529, No Obj: 0.003592, Avg Recall: 0.659341, count: 91 -Region Avg IOU: 0.563327, Class: 1.000000, Obj: 0.427958, No Obj: 0.003648, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.623430, Class: 1.000000, Obj: 0.444165, No Obj: 0.003807, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.605185, Class: 1.000000, Obj: 0.399788, No Obj: 0.003834, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.567359, Class: 1.000000, Obj: 0.392026, No Obj: 0.003380, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.568019, Class: 1.000000, Obj: 0.397818, No Obj: 0.003900, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.571770, Class: 1.000000, Obj: 0.389460, No Obj: 0.003702, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.550590, Class: 1.000000, Obj: 0.417254, No Obj: 0.003628, Avg Recall: 0.628571, count: 105 -5576: 0.393095, 0.423666 avg, 0.001000 rate, 24.369467 seconds, 1427456 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.561327, Class: 1.000000, Obj: 0.350262, No Obj: 0.004309, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.616978, Class: 1.000000, Obj: 0.414415, No Obj: 0.003739, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.541602, Class: 1.000000, Obj: 0.376294, No Obj: 0.004328, Avg Recall: 0.614754, count: 122 -Region Avg IOU: 0.608916, Class: 1.000000, Obj: 0.449561, No Obj: 0.003938, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.586525, Class: 1.000000, Obj: 0.395489, No Obj: 0.004077, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.537242, Class: 1.000000, Obj: 0.364821, No Obj: 0.003580, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.568978, Class: 1.000000, Obj: 0.354192, No Obj: 0.004334, Avg Recall: 0.708955, count: 134 -Region Avg IOU: 0.647530, Class: 1.000000, Obj: 0.459429, No Obj: 0.004336, Avg Recall: 0.785714, count: 98 -5577: 0.419537, 0.423253 avg, 0.001000 rate, 23.506721 seconds, 1427712 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.564148, Class: 1.000000, Obj: 0.379632, No Obj: 0.003828, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.646162, Class: 1.000000, Obj: 0.470146, No Obj: 0.004125, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.560176, Class: 1.000000, Obj: 0.338402, No Obj: 0.003804, Avg Recall: 0.669355, count: 124 -Region Avg IOU: 0.585071, Class: 1.000000, Obj: 0.380973, No Obj: 0.003724, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.541011, Class: 1.000000, Obj: 0.378562, No Obj: 0.003570, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.557604, Class: 1.000000, Obj: 0.386199, No Obj: 0.003932, Avg Recall: 0.636364, count: 132 -Region Avg IOU: 0.532248, Class: 1.000000, Obj: 0.409870, No Obj: 0.003895, Avg Recall: 0.610169, count: 118 -Region Avg IOU: 0.556732, Class: 1.000000, Obj: 0.423048, No Obj: 0.003410, Avg Recall: 0.620000, count: 100 -5578: 0.417557, 0.422683 avg, 0.001000 rate, 22.806295 seconds, 1427968 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.615708, Class: 1.000000, Obj: 0.415883, No Obj: 0.003302, Avg Recall: 0.727273, count: 88 -Region Avg IOU: 0.594928, Class: 1.000000, Obj: 0.435537, No Obj: 0.003324, Avg Recall: 0.711111, count: 90 -Region Avg IOU: 0.580372, Class: 1.000000, Obj: 0.364595, No Obj: 0.003571, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.577690, Class: 1.000000, Obj: 0.367215, No Obj: 0.003655, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.570778, Class: 1.000000, Obj: 0.439071, No Obj: 0.003309, Avg Recall: 0.678161, count: 87 -Region Avg IOU: 0.584817, Class: 1.000000, Obj: 0.392236, No Obj: 0.003246, Avg Recall: 0.686046, count: 86 -Region Avg IOU: 0.566840, Class: 1.000000, Obj: 0.384444, No Obj: 0.004350, Avg Recall: 0.611111, count: 144 -Region Avg IOU: 0.560993, Class: 1.000000, Obj: 0.383591, No Obj: 0.003313, Avg Recall: 0.662921, count: 89 -5579: 0.347709, 0.415186 avg, 0.001000 rate, 22.733721 seconds, 1428224 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.606035, Class: 1.000000, Obj: 0.441477, No Obj: 0.003805, Avg Recall: 0.675676, count: 74 -Region Avg IOU: 0.594345, Class: 1.000000, Obj: 0.406235, No Obj: 0.003735, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.543539, Class: 1.000000, Obj: 0.387983, No Obj: 0.003642, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.574382, Class: 1.000000, Obj: 0.355056, No Obj: 0.003337, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.553340, Class: 1.000000, Obj: 0.413830, No Obj: 0.004244, Avg Recall: 0.629032, count: 124 -Region Avg IOU: 0.624357, Class: 1.000000, Obj: 0.427275, No Obj: 0.004011, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.612700, Class: 1.000000, Obj: 0.430130, No Obj: 0.004205, Avg Recall: 0.700787, count: 127 -Region Avg IOU: 0.601514, Class: 1.000000, Obj: 0.382710, No Obj: 0.003567, Avg Recall: 0.685393, count: 89 -5580: 0.411030, 0.414770 avg, 0.001000 rate, 21.954887 seconds, 1428480 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.765013 seconds -Region Avg IOU: 0.472330, Class: 1.000000, Obj: 0.333154, No Obj: 0.004160, Avg Recall: 0.465347, count: 101 -Region Avg IOU: 0.504077, Class: 1.000000, Obj: 0.315840, No Obj: 0.003907, Avg Recall: 0.544444, count: 90 -Region Avg IOU: 0.484173, Class: 1.000000, Obj: 0.276359, No Obj: 0.004113, Avg Recall: 0.516393, count: 122 -Region Avg IOU: 0.459123, Class: 1.000000, Obj: 0.334908, No Obj: 0.005045, Avg Recall: 0.517544, count: 114 -Region Avg IOU: 0.488730, Class: 1.000000, Obj: 0.288954, No Obj: 0.004080, Avg Recall: 0.541667, count: 96 -Region Avg IOU: 0.411110, Class: 1.000000, Obj: 0.317494, No Obj: 0.004043, Avg Recall: 0.419643, count: 112 -Region Avg IOU: 0.481401, Class: 1.000000, Obj: 0.299978, No Obj: 0.004489, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.452316, Class: 1.000000, Obj: 0.223116, No Obj: 0.004032, Avg Recall: 0.464567, count: 127 -5581: 0.494262, 0.422720 avg, 0.001000 rate, 14.763154 seconds, 1428736 images -Loaded: 6.663709 seconds -Region Avg IOU: 0.528890, Class: 1.000000, Obj: 0.346205, No Obj: 0.004099, Avg Recall: 0.604651, count: 86 -Region Avg IOU: 0.547572, Class: 1.000000, Obj: 0.378824, No Obj: 0.004158, Avg Recall: 0.586957, count: 92 -Region Avg IOU: 0.556431, Class: 1.000000, Obj: 0.405795, No Obj: 0.004374, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.482726, Class: 1.000000, Obj: 0.310031, No Obj: 0.004547, Avg Recall: 0.563025, count: 119 -Region Avg IOU: 0.456424, Class: 1.000000, Obj: 0.299179, No Obj: 0.003480, Avg Recall: 0.510417, count: 96 -Region Avg IOU: 0.517545, Class: 1.000000, Obj: 0.290434, No Obj: 0.004051, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.481183, Class: 1.000000, Obj: 0.324657, No Obj: 0.004730, Avg Recall: 0.541985, count: 131 -Region Avg IOU: 0.486556, Class: 1.000000, Obj: 0.309727, No Obj: 0.003442, Avg Recall: 0.535714, count: 84 -5582: 0.428508, 0.423298 avg, 0.001000 rate, 14.108577 seconds, 1428992 images -Loaded: 7.113772 seconds -Region Avg IOU: 0.456646, Class: 1.000000, Obj: 0.325083, No Obj: 0.003968, Avg Recall: 0.470588, count: 102 -Region Avg IOU: 0.527632, Class: 1.000000, Obj: 0.356242, No Obj: 0.004089, Avg Recall: 0.588889, count: 90 -Region Avg IOU: 0.524145, Class: 1.000000, Obj: 0.354698, No Obj: 0.004362, Avg Recall: 0.588235, count: 102 -Region Avg IOU: 0.489742, Class: 1.000000, Obj: 0.301571, No Obj: 0.004358, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.466550, Class: 1.000000, Obj: 0.288636, No Obj: 0.004259, Avg Recall: 0.508475, count: 118 -Region Avg IOU: 0.425118, Class: 1.000000, Obj: 0.294275, No Obj: 0.004384, Avg Recall: 0.442308, count: 104 -Region Avg IOU: 0.545544, Class: 1.000000, Obj: 0.337043, No Obj: 0.004839, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.464218, Class: 1.000000, Obj: 0.338325, No Obj: 0.004011, Avg Recall: 0.500000, count: 102 -5583: 0.391584, 0.420127 avg, 0.001000 rate, 17.706993 seconds, 1429248 images -Loaded: 4.120654 seconds -Region Avg IOU: 0.513637, Class: 1.000000, Obj: 0.350592, No Obj: 0.004294, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.477662, Class: 1.000000, Obj: 0.298012, No Obj: 0.004492, Avg Recall: 0.491525, count: 118 -Region Avg IOU: 0.491083, Class: 1.000000, Obj: 0.316041, No Obj: 0.004193, Avg Recall: 0.548673, count: 113 -Region Avg IOU: 0.484349, Class: 1.000000, Obj: 0.330054, No Obj: 0.004517, Avg Recall: 0.509804, count: 102 -Region Avg IOU: 0.488982, Class: 1.000000, Obj: 0.300372, No Obj: 0.004133, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.464050, Class: 1.000000, Obj: 0.287960, No Obj: 0.003748, Avg Recall: 0.458333, count: 96 -Region Avg IOU: 0.481234, Class: 1.000000, Obj: 0.318992, No Obj: 0.004615, Avg Recall: 0.570248, count: 121 -Region Avg IOU: 0.442716, Class: 1.000000, Obj: 0.289987, No Obj: 0.004585, Avg Recall: 0.456693, count: 127 -5584: 0.469483, 0.425063 avg, 0.001000 rate, 18.473829 seconds, 1429504 images -Loaded: 1.207910 seconds -Region Avg IOU: 0.450824, Class: 1.000000, Obj: 0.313572, No Obj: 0.004514, Avg Recall: 0.471698, count: 159 -Region Avg IOU: 0.459089, Class: 1.000000, Obj: 0.323828, No Obj: 0.004037, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.459225, Class: 1.000000, Obj: 0.309049, No Obj: 0.004264, Avg Recall: 0.500000, count: 106 -Region Avg IOU: 0.444188, Class: 1.000000, Obj: 0.288813, No Obj: 0.003913, Avg Recall: 0.491071, count: 112 -Region Avg IOU: 0.478024, Class: 1.000000, Obj: 0.332115, No Obj: 0.004064, Avg Recall: 0.494505, count: 91 -Region Avg IOU: 0.529205, Class: 1.000000, Obj: 0.359909, No Obj: 0.005007, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.496877, Class: 1.000000, Obj: 0.355944, No Obj: 0.004442, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.468598, Class: 1.000000, Obj: 0.302975, No Obj: 0.004629, Avg Recall: 0.439024, count: 123 -5585: 0.448760, 0.427432 avg, 0.001000 rate, 14.123128 seconds, 1429760 images -Loaded: 5.327227 seconds -Region Avg IOU: 0.490311, Class: 1.000000, Obj: 0.354971, No Obj: 0.004434, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.533529, Class: 1.000000, Obj: 0.370435, No Obj: 0.004662, Avg Recall: 0.626374, count: 91 -Region Avg IOU: 0.513341, Class: 1.000000, Obj: 0.337912, No Obj: 0.004879, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.447859, Class: 1.000000, Obj: 0.330729, No Obj: 0.004445, Avg Recall: 0.484536, count: 97 -Region Avg IOU: 0.487912, Class: 1.000000, Obj: 0.365817, No Obj: 0.005195, Avg Recall: 0.525000, count: 120 -Region Avg IOU: 0.480908, Class: 1.000000, Obj: 0.365088, No Obj: 0.004459, Avg Recall: 0.534884, count: 86 -Region Avg IOU: 0.466581, Class: 1.000000, Obj: 0.344137, No Obj: 0.004387, Avg Recall: 0.456790, count: 81 -Region Avg IOU: 0.485077, Class: 1.000000, Obj: 0.383920, No Obj: 0.004380, Avg Recall: 0.514286, count: 105 -5586: 0.397210, 0.424410 avg, 0.001000 rate, 15.485629 seconds, 1430016 images -Loaded: 4.536408 seconds -Region Avg IOU: 0.462040, Class: 1.000000, Obj: 0.348620, No Obj: 0.004827, Avg Recall: 0.476191, count: 105 -Region Avg IOU: 0.470540, Class: 1.000000, Obj: 0.382517, No Obj: 0.004966, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.449963, Class: 1.000000, Obj: 0.325670, No Obj: 0.004708, Avg Recall: 0.447619, count: 105 -Region Avg IOU: 0.474127, Class: 1.000000, Obj: 0.321967, No Obj: 0.004412, Avg Recall: 0.494845, count: 97 -Region Avg IOU: 0.507616, Class: 1.000000, Obj: 0.402002, No Obj: 0.004809, Avg Recall: 0.558140, count: 86 -Region Avg IOU: 0.513657, Class: 1.000000, Obj: 0.363598, No Obj: 0.005778, Avg Recall: 0.566372, count: 113 -Region Avg IOU: 0.472171, Class: 1.000000, Obj: 0.358707, No Obj: 0.005295, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.432685, Class: 1.000000, Obj: 0.340903, No Obj: 0.004678, Avg Recall: 0.439655, count: 116 -5587: 0.446726, 0.426642 avg, 0.001000 rate, 14.858909 seconds, 1430272 images -Loaded: 7.229565 seconds -Region Avg IOU: 0.489060, Class: 1.000000, Obj: 0.389610, No Obj: 0.005421, Avg Recall: 0.516667, count: 120 -Region Avg IOU: 0.517177, Class: 1.000000, Obj: 0.415381, No Obj: 0.005283, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.468879, Class: 1.000000, Obj: 0.315953, No Obj: 0.004829, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.466495, Class: 1.000000, Obj: 0.369768, No Obj: 0.005094, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.487958, Class: 1.000000, Obj: 0.360938, No Obj: 0.004628, Avg Recall: 0.518868, count: 106 -Region Avg IOU: 0.490022, Class: 1.000000, Obj: 0.377019, No Obj: 0.005125, Avg Recall: 0.519380, count: 129 -Region Avg IOU: 0.461788, Class: 1.000000, Obj: 0.336245, No Obj: 0.004956, Avg Recall: 0.509434, count: 106 -Region Avg IOU: 0.484304, Class: 1.000000, Obj: 0.384906, No Obj: 0.004635, Avg Recall: 0.544444, count: 90 -5588: 0.448413, 0.428819 avg, 0.001000 rate, 19.423332 seconds, 1430528 images -Loaded: 2.407272 seconds -Region Avg IOU: 0.425127, Class: 1.000000, Obj: 0.307033, No Obj: 0.004693, Avg Recall: 0.485714, count: 105 -Region Avg IOU: 0.523079, Class: 1.000000, Obj: 0.324739, No Obj: 0.005431, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.447372, Class: 1.000000, Obj: 0.379478, No Obj: 0.005190, Avg Recall: 0.475248, count: 101 -Region Avg IOU: 0.493555, Class: 1.000000, Obj: 0.354556, No Obj: 0.006041, Avg Recall: 0.576000, count: 125 -Region Avg IOU: 0.475169, Class: 1.000000, Obj: 0.355569, No Obj: 0.005331, Avg Recall: 0.526786, count: 112 -Region Avg IOU: 0.504880, Class: 1.000000, Obj: 0.413610, No Obj: 0.004912, Avg Recall: 0.583333, count: 84 -Region Avg IOU: 0.500553, Class: 1.000000, Obj: 0.401900, No Obj: 0.005223, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.501262, Class: 1.000000, Obj: 0.383178, No Obj: 0.004873, Avg Recall: 0.571429, count: 98 -5589: 0.471865, 0.433123 avg, 0.001000 rate, 16.131924 seconds, 1430784 images -Loaded: 4.389304 seconds -Region Avg IOU: 0.464840, Class: 1.000000, Obj: 0.382520, No Obj: 0.004264, Avg Recall: 0.523256, count: 86 -Region Avg IOU: 0.473566, Class: 1.000000, Obj: 0.343926, No Obj: 0.005813, Avg Recall: 0.481481, count: 135 -Region Avg IOU: 0.492869, Class: 1.000000, Obj: 0.384865, No Obj: 0.005002, Avg Recall: 0.588889, count: 90 -Region Avg IOU: 0.483610, Class: 1.000000, Obj: 0.382621, No Obj: 0.004653, Avg Recall: 0.523364, count: 107 -Region Avg IOU: 0.481376, Class: 1.000000, Obj: 0.357031, No Obj: 0.004982, Avg Recall: 0.494845, count: 97 -Region Avg IOU: 0.492023, Class: 1.000000, Obj: 0.375927, No Obj: 0.005493, Avg Recall: 0.542636, count: 129 -Region Avg IOU: 0.404846, Class: 1.000000, Obj: 0.326664, No Obj: 0.004565, Avg Recall: 0.378151, count: 119 -Region Avg IOU: 0.522076, Class: 1.000000, Obj: 0.402617, No Obj: 0.005391, Avg Recall: 0.568627, count: 102 -5590: 0.472442, 0.437055 avg, 0.001000 rate, 20.162319 seconds, 1431040 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 0.008408 seconds -Region Avg IOU: 0.589841, Class: 1.000000, Obj: 0.491395, No Obj: 0.003945, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.634052, Class: 1.000000, Obj: 0.433100, No Obj: 0.004417, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.543381, Class: 1.000000, Obj: 0.389052, No Obj: 0.004445, Avg Recall: 0.609375, count: 128 -Region Avg IOU: 0.570142, Class: 1.000000, Obj: 0.459151, No Obj: 0.003925, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.552951, Class: 1.000000, Obj: 0.407137, No Obj: 0.004249, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.589982, Class: 1.000000, Obj: 0.435729, No Obj: 0.003652, Avg Recall: 0.707317, count: 82 -Region Avg IOU: 0.527747, Class: 1.000000, Obj: 0.419916, No Obj: 0.003452, Avg Recall: 0.616279, count: 86 -Region Avg IOU: 0.597148, Class: 1.000000, Obj: 0.447081, No Obj: 0.004389, Avg Recall: 0.701923, count: 104 -5591: 0.387502, 0.432100 avg, 0.001000 rate, 23.478279 seconds, 1431296 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.608843, Class: 1.000000, Obj: 0.471248, No Obj: 0.004218, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.539555, Class: 1.000000, Obj: 0.387676, No Obj: 0.003895, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.548734, Class: 1.000000, Obj: 0.414697, No Obj: 0.004070, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.575083, Class: 1.000000, Obj: 0.438493, No Obj: 0.003496, Avg Recall: 0.714286, count: 84 -Region Avg IOU: 0.573277, Class: 1.000000, Obj: 0.381336, No Obj: 0.004495, Avg Recall: 0.674603, count: 126 -Region Avg IOU: 0.576844, Class: 1.000000, Obj: 0.424069, No Obj: 0.004768, Avg Recall: 0.653543, count: 127 -Region Avg IOU: 0.564314, Class: 1.000000, Obj: 0.405079, No Obj: 0.004123, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.582492, Class: 1.000000, Obj: 0.460127, No Obj: 0.003613, Avg Recall: 0.654762, count: 84 -5592: 0.409228, 0.429813 avg, 0.001000 rate, 24.265013 seconds, 1431552 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.509952, Class: 1.000000, Obj: 0.374692, No Obj: 0.003447, Avg Recall: 0.573770, count: 122 -Region Avg IOU: 0.594156, Class: 1.000000, Obj: 0.427909, No Obj: 0.004513, Avg Recall: 0.698413, count: 126 -Region Avg IOU: 0.559653, Class: 1.000000, Obj: 0.400442, No Obj: 0.003736, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.602810, Class: 1.000000, Obj: 0.445166, No Obj: 0.003794, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.587887, Class: 1.000000, Obj: 0.421171, No Obj: 0.004430, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.594414, Class: 1.000000, Obj: 0.389125, No Obj: 0.003949, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.555402, Class: 1.000000, Obj: 0.404457, No Obj: 0.003246, Avg Recall: 0.639535, count: 86 -Region Avg IOU: 0.499337, Class: 1.000000, Obj: 0.429738, No Obj: 0.003218, Avg Recall: 0.554348, count: 92 -5593: 0.398865, 0.426718 avg, 0.001000 rate, 24.264832 seconds, 1431808 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.551816, Class: 1.000000, Obj: 0.401321, No Obj: 0.003332, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.574672, Class: 1.000000, Obj: 0.428794, No Obj: 0.003707, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.599706, Class: 1.000000, Obj: 0.422960, No Obj: 0.003899, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.631264, Class: 1.000000, Obj: 0.437573, No Obj: 0.003916, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.583684, Class: 1.000000, Obj: 0.449748, No Obj: 0.003565, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.591125, Class: 1.000000, Obj: 0.414817, No Obj: 0.003754, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.558254, Class: 1.000000, Obj: 0.367854, No Obj: 0.003724, Avg Recall: 0.656250, count: 128 -Region Avg IOU: 0.665295, Class: 1.000000, Obj: 0.501331, No Obj: 0.003464, Avg Recall: 0.833333, count: 78 -5594: 0.386402, 0.422686 avg, 0.001000 rate, 24.012480 seconds, 1432064 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.566637, Class: 1.000000, Obj: 0.399001, No Obj: 0.003559, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.596349, Class: 1.000000, Obj: 0.366656, No Obj: 0.002920, Avg Recall: 0.658228, count: 79 -Region Avg IOU: 0.552639, Class: 1.000000, Obj: 0.393925, No Obj: 0.003080, Avg Recall: 0.629214, count: 89 -Region Avg IOU: 0.598544, Class: 1.000000, Obj: 0.366575, No Obj: 0.003916, Avg Recall: 0.750000, count: 132 -Region Avg IOU: 0.611255, Class: 1.000000, Obj: 0.410775, No Obj: 0.003998, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.595308, Class: 1.000000, Obj: 0.401617, No Obj: 0.004005, Avg Recall: 0.686567, count: 134 -Region Avg IOU: 0.626475, Class: 1.000000, Obj: 0.481034, No Obj: 0.003320, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.573040, Class: 1.000000, Obj: 0.383289, No Obj: 0.003931, Avg Recall: 0.645390, count: 141 -5595: 0.468949, 0.427313 avg, 0.001000 rate, 23.016983 seconds, 1432320 images -Loaded: 0.000127 seconds -Region Avg IOU: 0.550722, Class: 1.000000, Obj: 0.415209, No Obj: 0.003594, Avg Recall: 0.631148, count: 122 -Region Avg IOU: 0.591310, Class: 1.000000, Obj: 0.369809, No Obj: 0.003728, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.586073, Class: 1.000000, Obj: 0.393906, No Obj: 0.003402, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.609240, Class: 1.000000, Obj: 0.367134, No Obj: 0.003644, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.584312, Class: 1.000000, Obj: 0.440000, No Obj: 0.003958, Avg Recall: 0.658333, count: 120 -Region Avg IOU: 0.604487, Class: 1.000000, Obj: 0.386461, No Obj: 0.003623, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.588645, Class: 1.000000, Obj: 0.408503, No Obj: 0.003583, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.663019, Class: 1.000000, Obj: 0.438725, No Obj: 0.003379, Avg Recall: 0.822222, count: 90 -5596: 0.405975, 0.425179 avg, 0.001000 rate, 24.027988 seconds, 1432576 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.584274, Class: 1.000000, Obj: 0.414705, No Obj: 0.003808, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.552243, Class: 1.000000, Obj: 0.350353, No Obj: 0.003767, Avg Recall: 0.664062, count: 128 -Region Avg IOU: 0.547168, Class: 1.000000, Obj: 0.353471, No Obj: 0.003396, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.559717, Class: 1.000000, Obj: 0.376411, No Obj: 0.002966, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.563806, Class: 1.000000, Obj: 0.395602, No Obj: 0.003368, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.547569, Class: 1.000000, Obj: 0.365607, No Obj: 0.003134, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.570425, Class: 1.000000, Obj: 0.379179, No Obj: 0.003348, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.556073, Class: 1.000000, Obj: 0.362472, No Obj: 0.003958, Avg Recall: 0.630769, count: 130 -5597: 0.423866, 0.425048 avg, 0.001000 rate, 23.020891 seconds, 1432832 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.551904, Class: 1.000000, Obj: 0.346663, No Obj: 0.003145, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.596818, Class: 1.000000, Obj: 0.413001, No Obj: 0.003504Saving weights to ./person9300/backup/tyvp_93_9c512_5600.weights -, Avg Recall: 0.674419, count: 86 -Region Avg IOU: 0.599843, Class: 1.000000, Obj: 0.342004, No Obj: 0.004244, Avg Recall: 0.748148, count: 135 -Region Avg IOU: 0.603068, Class: 1.000000, Obj: 0.400958, No Obj: 0.003721, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.537081, Class: 1.000000, Obj: 0.342975, No Obj: 0.002634, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.546350, Class: 1.000000, Obj: 0.369333, No Obj: 0.003636, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.583234, Class: 1.000000, Obj: 0.369776, No Obj: 0.003756, Avg Recall: 0.701613, count: 124 -Region Avg IOU: 0.529131, Class: 1.000000, Obj: 0.347515, No Obj: 0.003058, Avg Recall: 0.590476, count: 105 -5598: 0.415442, 0.424087 avg, 0.001000 rate, 23.888620 seconds, 1433088 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.577693, Class: 1.000000, Obj: 0.373821, No Obj: 0.003813, Avg Recall: 0.666667, count: 120 -Region Avg IOU: 0.586136, Class: 1.000000, Obj: 0.381786, No Obj: 0.003767, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.557481, Class: 1.000000, Obj: 0.383347, No Obj: 0.003554, Avg Recall: 0.650000, count: 120 -Region Avg IOU: 0.575801, Class: 1.000000, Obj: 0.322307, No Obj: 0.003411, Avg Recall: 0.632000, count: 125 -Region Avg IOU: 0.557854, Class: 1.000000, Obj: 0.377077, No Obj: 0.003195, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.591675, Class: 1.000000, Obj: 0.390737, No Obj: 0.003624, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.584085, Class: 1.000000, Obj: 0.379177, No Obj: 0.003244, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.600411, Class: 1.000000, Obj: 0.379061, No Obj: 0.004218, Avg Recall: 0.730769, count: 130 -5599: 0.467692, 0.428447 avg, 0.001000 rate, 23.588783 seconds, 1433344 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.619261, Class: 1.000000, Obj: 0.436151, No Obj: 0.003190, Avg Recall: 0.752941, count: 85 -Region Avg IOU: 0.592819, Class: 1.000000, Obj: 0.375501, No Obj: 0.003846, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.643928, Class: 1.000000, Obj: 0.395402, No Obj: 0.003907, Avg Recall: 0.812500, count: 80 -Region Avg IOU: 0.532276, Class: 1.000000, Obj: 0.354058, No Obj: 0.003150, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.560835, Class: 1.000000, Obj: 0.379102, No Obj: 0.003789, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.613134, Class: 1.000000, Obj: 0.431471, No Obj: 0.003239, Avg Recall: 0.746988, count: 83 -Region Avg IOU: 0.579326, Class: 1.000000, Obj: 0.366502, No Obj: 0.003649, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.562101, Class: 1.000000, Obj: 0.375597, No Obj: 0.003307, Avg Recall: 0.583333, count: 84 -5600: 0.386561, 0.424259 avg, 0.001000 rate, 23.712324 seconds, 1433600 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 17.179956 seconds -Region Avg IOU: 0.490078, Class: 1.000000, Obj: 0.357832, No Obj: 0.004562, Avg Recall: 0.500000, count: 120 -Region Avg IOU: 0.446606, Class: 1.000000, Obj: 0.297942, No Obj: 0.004421, Avg Recall: 0.476191, count: 105 -Region Avg IOU: 0.458938, Class: 1.000000, Obj: 0.277598, No Obj: 0.004539, Avg Recall: 0.495413, count: 109 -Region Avg IOU: 0.516431, Class: 1.000000, Obj: 0.323843, No Obj: 0.005109, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.480274, Class: 1.000000, Obj: 0.346928, No Obj: 0.004691, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.506001, Class: 1.000000, Obj: 0.322442, No Obj: 0.004211, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.508205, Class: 1.000000, Obj: 0.295352, No Obj: 0.003877, Avg Recall: 0.565217, count: 92 -Region Avg IOU: 0.472810, Class: 1.000000, Obj: 0.311111, No Obj: 0.003808, Avg Recall: 0.548077, count: 104 -5601: 0.483881, 0.430221 avg, 0.001000 rate, 14.539778 seconds, 1433856 images -Loaded: 5.883157 seconds -Region Avg IOU: 0.468715, Class: 1.000000, Obj: 0.311459, No Obj: 0.003722, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.468594, Class: 1.000000, Obj: 0.315587, No Obj: 0.004509, Avg Recall: 0.491667, count: 120 -Region Avg IOU: 0.489733, Class: 1.000000, Obj: 0.327931, No Obj: 0.004280, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.507610, Class: 1.000000, Obj: 0.341536, No Obj: 0.004535, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.494695, Class: 1.000000, Obj: 0.328617, No Obj: 0.004692, Avg Recall: 0.528455, count: 123 -Region Avg IOU: 0.506723, Class: 1.000000, Obj: 0.324716, No Obj: 0.003848, Avg Recall: 0.537634, count: 93 -Region Avg IOU: 0.479131, Class: 1.000000, Obj: 0.349661, No Obj: 0.004133, Avg Recall: 0.513761, count: 109 -Region Avg IOU: 0.447250, Class: 1.000000, Obj: 0.288118, No Obj: 0.004991, Avg Recall: 0.459259, count: 135 -5602: 0.523270, 0.439526 avg, 0.001000 rate, 14.259752 seconds, 1434112 images -Loaded: 6.724106 seconds -Region Avg IOU: 0.467066, Class: 1.000000, Obj: 0.308898, No Obj: 0.004623, Avg Recall: 0.444444, count: 99 -Region Avg IOU: 0.521875, Class: 1.000000, Obj: 0.329577, No Obj: 0.004234, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.477233, Class: 1.000000, Obj: 0.315026, No Obj: 0.004311, Avg Recall: 0.549020, count: 102 -Region Avg IOU: 0.549014, Class: 1.000000, Obj: 0.361841, No Obj: 0.004875, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.450278, Class: 1.000000, Obj: 0.336064, No Obj: 0.004742, Avg Recall: 0.477612, count: 134 -Region Avg IOU: 0.489382, Class: 1.000000, Obj: 0.359108, No Obj: 0.004148, Avg Recall: 0.568182, count: 88 -Region Avg IOU: 0.452185, Class: 1.000000, Obj: 0.282921, No Obj: 0.003848, Avg Recall: 0.468468, count: 111 -Region Avg IOU: 0.485737, Class: 1.000000, Obj: 0.335694, No Obj: 0.005483, Avg Recall: 0.524476, count: 143 -5603: 0.452231, 0.440797 avg, 0.001000 rate, 15.222910 seconds, 1434368 images -Loaded: 6.223928 seconds -Region Avg IOU: 0.476479, Class: 1.000000, Obj: 0.374815, No Obj: 0.004320, Avg Recall: 0.460000, count: 100 -Region Avg IOU: 0.477702, Class: 1.000000, Obj: 0.356386, No Obj: 0.004479, Avg Recall: 0.553191, count: 94 -Region Avg IOU: 0.488156, Class: 1.000000, Obj: 0.380199, No Obj: 0.004990, Avg Recall: 0.530435, count: 115 -Region Avg IOU: 0.499439, Class: 1.000000, Obj: 0.363174, No Obj: 0.004806, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.533373, Class: 1.000000, Obj: 0.357676, No Obj: 0.004744, Avg Recall: 0.626374, count: 91 -Region Avg IOU: 0.527884, Class: 1.000000, Obj: 0.356442, No Obj: 0.005447, Avg Recall: 0.580645, count: 93 -Region Avg IOU: 0.507361, Class: 1.000000, Obj: 0.377705, No Obj: 0.004396, Avg Recall: 0.552083, count: 96 -Region Avg IOU: 0.485226, Class: 1.000000, Obj: 0.333702, No Obj: 0.005202, Avg Recall: 0.543307, count: 127 -5604: 0.459611, 0.442678 avg, 0.001000 rate, 14.914086 seconds, 1434624 images -Loaded: 6.374573 seconds -Region Avg IOU: 0.479380, Class: 1.000000, Obj: 0.349439, No Obj: 0.005672, Avg Recall: 0.510638, count: 141 -Region Avg IOU: 0.455918, Class: 1.000000, Obj: 0.347666, No Obj: 0.005527, Avg Recall: 0.495050, count: 101 -Region Avg IOU: 0.418286, Class: 1.000000, Obj: 0.350287, No Obj: 0.004524, Avg Recall: 0.394495, count: 109 -Region Avg IOU: 0.516004, Class: 1.000000, Obj: 0.334476, No Obj: 0.005465, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.494657, Class: 1.000000, Obj: 0.347802, No Obj: 0.004684, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.439640, Class: 1.000000, Obj: 0.339883, No Obj: 0.005189, Avg Recall: 0.428571, count: 126 -Region Avg IOU: 0.502262, Class: 1.000000, Obj: 0.338289, No Obj: 0.004806, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.461497, Class: 1.000000, Obj: 0.345708, No Obj: 0.005322, Avg Recall: 0.473684, count: 133 -5605: 0.593786, 0.457789 avg, 0.001000 rate, 20.163914 seconds, 1434880 images -Loaded: 0.943319 seconds -Region Avg IOU: 0.505018, Class: 1.000000, Obj: 0.413931, No Obj: 0.004910, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.479624, Class: 1.000000, Obj: 0.358228, No Obj: 0.005602, Avg Recall: 0.531746, count: 126 -Region Avg IOU: 0.458755, Class: 1.000000, Obj: 0.355498, No Obj: 0.004603, Avg Recall: 0.467290, count: 107 -Region Avg IOU: 0.431171, Class: 1.000000, Obj: 0.389671, No Obj: 0.004574, Avg Recall: 0.440367, count: 109 -Region Avg IOU: 0.440123, Class: 1.000000, Obj: 0.338882, No Obj: 0.004845, Avg Recall: 0.481481, count: 108 -Region Avg IOU: 0.515907, Class: 1.000000, Obj: 0.391249, No Obj: 0.005555, Avg Recall: 0.566667, count: 120 -Region Avg IOU: 0.455556, Class: 1.000000, Obj: 0.341608, No Obj: 0.005320, Avg Recall: 0.489051, count: 137 -Region Avg IOU: 0.483295, Class: 1.000000, Obj: 0.355800, No Obj: 0.005203, Avg Recall: 0.598291, count: 117 -5606: 0.546188, 0.466629 avg, 0.001000 rate, 14.547285 seconds, 1435136 images -Loaded: 5.390161 seconds -Region Avg IOU: 0.494697, Class: 1.000000, Obj: 0.405405, No Obj: 0.005423, Avg Recall: 0.519231, count: 104 -Region Avg IOU: 0.473274, Class: 1.000000, Obj: 0.323098, No Obj: 0.005170, Avg Recall: 0.544715, count: 123 -Region Avg IOU: 0.457499, Class: 1.000000, Obj: 0.368375, No Obj: 0.005163, Avg Recall: 0.449153, count: 118 -Region Avg IOU: 0.434945, Class: 1.000000, Obj: 0.297634, No Obj: 0.005189, Avg Recall: 0.471545, count: 123 -Region Avg IOU: 0.506185, Class: 1.000000, Obj: 0.385260, No Obj: 0.005313, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.511581, Class: 1.000000, Obj: 0.396473, No Obj: 0.005431, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.455098, Class: 1.000000, Obj: 0.330283, No Obj: 0.006059, Avg Recall: 0.534884, count: 129 -Region Avg IOU: 0.471250, Class: 1.000000, Obj: 0.368343, No Obj: 0.004707, Avg Recall: 0.528090, count: 89 -5607: 0.501685, 0.470134 avg, 0.001000 rate, 18.522676 seconds, 1435392 images -Loaded: 3.494194 seconds -Region Avg IOU: 0.461973, Class: 1.000000, Obj: 0.349101, No Obj: 0.005636, Avg Recall: 0.526786, count: 112 -Region Avg IOU: 0.468468, Class: 1.000000, Obj: 0.343404, No Obj: 0.005445, Avg Recall: 0.553846, count: 130 -Region Avg IOU: 0.498659, Class: 1.000000, Obj: 0.353133, No Obj: 0.006296, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.496515, Class: 1.000000, Obj: 0.351876, No Obj: 0.005197, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.444433, Class: 1.000000, Obj: 0.323911, No Obj: 0.005404, Avg Recall: 0.465116, count: 129 -Region Avg IOU: 0.440759, Class: 1.000000, Obj: 0.346541, No Obj: 0.005004, Avg Recall: 0.441667, count: 120 -Region Avg IOU: 0.464979, Class: 1.000000, Obj: 0.323134, No Obj: 0.005182, Avg Recall: 0.489796, count: 98 -Region Avg IOU: 0.498696, Class: 1.000000, Obj: 0.379755, No Obj: 0.004646, Avg Recall: 0.578313, count: 83 -5608: 0.506599, 0.473781 avg, 0.001000 rate, 16.476646 seconds, 1435648 images -Loaded: 5.086153 seconds -Region Avg IOU: 0.483558, Class: 1.000000, Obj: 0.355438, No Obj: 0.006062, Avg Recall: 0.529412, count: 136 -Region Avg IOU: 0.439512, Class: 1.000000, Obj: 0.344535, No Obj: 0.004955, Avg Recall: 0.472727, count: 110 -Region Avg IOU: 0.483827, Class: 1.000000, Obj: 0.349522, No Obj: 0.004634, Avg Recall: 0.530435, count: 115 -Region Avg IOU: 0.483369, Class: 1.000000, Obj: 0.364491, No Obj: 0.004920, Avg Recall: 0.514851, count: 101 -Region Avg IOU: 0.445398, Class: 1.000000, Obj: 0.306836, No Obj: 0.004207, Avg Recall: 0.500000, count: 86 -Region Avg IOU: 0.504369, Class: 1.000000, Obj: 0.386571, No Obj: 0.005392, Avg Recall: 0.535088, count: 114 -Region Avg IOU: 0.446906, Class: 1.000000, Obj: 0.312713, No Obj: 0.005137, Avg Recall: 0.481132, count: 106 -Region Avg IOU: 0.425160, Class: 1.000000, Obj: 0.369239, No Obj: 0.004762, Avg Recall: 0.443396, count: 106 -5609: 0.478607, 0.474263 avg, 0.001000 rate, 15.086780 seconds, 1435904 images -Loaded: 6.086576 seconds -Region Avg IOU: 0.447551, Class: 1.000000, Obj: 0.283684, No Obj: 0.004535, Avg Recall: 0.475000, count: 120 -Region Avg IOU: 0.499177, Class: 1.000000, Obj: 0.385068, No Obj: 0.004841, Avg Recall: 0.600000, count: 70 -Region Avg IOU: 0.443757, Class: 1.000000, Obj: 0.350816, No Obj: 0.004378, Avg Recall: 0.458333, count: 96 -Region Avg IOU: 0.472966, Class: 1.000000, Obj: 0.328855, No Obj: 0.005597, Avg Recall: 0.519685, count: 127 -Region Avg IOU: 0.453887, Class: 1.000000, Obj: 0.313717, No Obj: 0.004598, Avg Recall: 0.456522, count: 92 -Region Avg IOU: 0.542334, Class: 1.000000, Obj: 0.383189, No Obj: 0.005914, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.470273, Class: 1.000000, Obj: 0.358996, No Obj: 0.005019, Avg Recall: 0.508772, count: 114 -Region Avg IOU: 0.484443, Class: 1.000000, Obj: 0.355005, No Obj: 0.005620, Avg Recall: 0.587786, count: 131 -5610: 0.457269, 0.472564 avg, 0.001000 rate, 15.882956 seconds, 1436160 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.015979 seconds -Region Avg IOU: 0.582654, Class: 1.000000, Obj: 0.479705, No Obj: 0.003905, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.565626, Class: 1.000000, Obj: 0.418567, No Obj: 0.003974, Avg Recall: 0.691667, count: 120 -Region Avg IOU: 0.595070, Class: 1.000000, Obj: 0.458622, No Obj: 0.004539, Avg Recall: 0.697479, count: 119 -Region Avg IOU: 0.632122, Class: 1.000000, Obj: 0.439204, No Obj: 0.003750, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.626676, Class: 1.000000, Obj: 0.459763, No Obj: 0.004020, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.623811, Class: 1.000000, Obj: 0.434761, No Obj: 0.003654, Avg Recall: 0.729412, count: 85 -Region Avg IOU: 0.580578, Class: 1.000000, Obj: 0.362083, No Obj: 0.004305, Avg Recall: 0.678832, count: 137 -Region Avg IOU: 0.554730, Class: 1.000000, Obj: 0.445211, No Obj: 0.003626, Avg Recall: 0.595745, count: 94 -5611: 0.415544, 0.466862 avg, 0.001000 rate, 24.568787 seconds, 1436416 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.569354, Class: 1.000000, Obj: 0.401548, No Obj: 0.004148, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.566975, Class: 1.000000, Obj: 0.422413, No Obj: 0.004187, Avg Recall: 0.639344, count: 122 -Region Avg IOU: 0.634168, Class: 1.000000, Obj: 0.457615, No Obj: 0.004101, Avg Recall: 0.805310, count: 113 -Region Avg IOU: 0.569786, Class: 1.000000, Obj: 0.420030, No Obj: 0.003882, Avg Recall: 0.664122, count: 131 -Region Avg IOU: 0.571312, Class: 1.000000, Obj: 0.431825, No Obj: 0.004159, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.644443, Class: 1.000000, Obj: 0.438288, No Obj: 0.003399, Avg Recall: 0.785714, count: 84 -Region Avg IOU: 0.586766, Class: 1.000000, Obj: 0.454711, No Obj: 0.004071, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.633597, Class: 1.000000, Obj: 0.459390, No Obj: 0.004452, Avg Recall: 0.781250, count: 128 -5612: 0.426739, 0.462850 avg, 0.001000 rate, 24.578253 seconds, 1436672 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.582703, Class: 1.000000, Obj: 0.428482, No Obj: 0.003492, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.623252, Class: 1.000000, Obj: 0.465421, No Obj: 0.003565, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.535960, Class: 1.000000, Obj: 0.402736, No Obj: 0.003405, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.623960, Class: 1.000000, Obj: 0.431645, No Obj: 0.004033, Avg Recall: 0.764228, count: 123 -Region Avg IOU: 0.604256, Class: 1.000000, Obj: 0.383245, No Obj: 0.003920, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.564517, Class: 1.000000, Obj: 0.382015, No Obj: 0.003947, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.582113, Class: 1.000000, Obj: 0.433523, No Obj: 0.003256, Avg Recall: 0.717949, count: 78 -Region Avg IOU: 0.609690, Class: 1.000000, Obj: 0.404179, No Obj: 0.004005, Avg Recall: 0.731481, count: 108 -5613: 0.375482, 0.454113 avg, 0.001000 rate, 25.640045 seconds, 1436928 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.622871, Class: 1.000000, Obj: 0.365704, No Obj: 0.003733, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.613837, Class: 1.000000, Obj: 0.447720, No Obj: 0.003324, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.581569, Class: 1.000000, Obj: 0.345229, No Obj: 0.003843, Avg Recall: 0.669421, count: 121 -Region Avg IOU: 0.599916, Class: 1.000000, Obj: 0.356381, No Obj: 0.003326, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.510420, Class: 1.000000, Obj: 0.348649, No Obj: 0.002900, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.589867, Class: 1.000000, Obj: 0.411934, No Obj: 0.002931, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.581222, Class: 1.000000, Obj: 0.420490, No Obj: 0.003784, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.583309, Class: 1.000000, Obj: 0.372645, No Obj: 0.003180, Avg Recall: 0.617978, count: 89 -5614: 0.430811, 0.451783 avg, 0.001000 rate, 24.077141 seconds, 1437184 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.638291, Class: 1.000000, Obj: 0.405767, No Obj: 0.003650, Avg Recall: 0.795455, count: 88 -Region Avg IOU: 0.611205, Class: 1.000000, Obj: 0.398111, No Obj: 0.004121, Avg Recall: 0.764286, count: 140 -Region Avg IOU: 0.586543, Class: 1.000000, Obj: 0.400398, No Obj: 0.003359, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.569245, Class: 1.000000, Obj: 0.414706, No Obj: 0.003121, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.577051, Class: 1.000000, Obj: 0.371267, No Obj: 0.003528, Avg Recall: 0.680672, count: 119 -Region Avg IOU: 0.640210, Class: 1.000000, Obj: 0.350099, No Obj: 0.003813, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.608043, Class: 1.000000, Obj: 0.397911, No Obj: 0.002978, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.607360, Class: 1.000000, Obj: 0.380027, No Obj: 0.002744, Avg Recall: 0.716216, count: 74 -5615: 0.395188, 0.446123 avg, 0.001000 rate, 23.102421 seconds, 1437440 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.586704, Class: 1.000000, Obj: 0.374708, No Obj: 0.003061, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.574633, Class: 1.000000, Obj: 0.353827, No Obj: 0.002641, Avg Recall: 0.654321, count: 81 -Region Avg IOU: 0.596936, Class: 1.000000, Obj: 0.387394, No Obj: 0.003062, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.604637, Class: 1.000000, Obj: 0.398173, No Obj: 0.003160, Avg Recall: 0.694118, count: 85 -Region Avg IOU: 0.566509, Class: 1.000000, Obj: 0.321644, No Obj: 0.002881, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.567961, Class: 1.000000, Obj: 0.314726, No Obj: 0.003410, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.578204, Class: 1.000000, Obj: 0.334005, No Obj: 0.003113, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.575880, Class: 1.000000, Obj: 0.355369, No Obj: 0.003159, Avg Recall: 0.646552, count: 116 -5616: 0.423563, 0.443867 avg, 0.001000 rate, 24.367815 seconds, 1437696 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.576490, Class: 1.000000, Obj: 0.392471, No Obj: 0.003385, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.577754, Class: 1.000000, Obj: 0.354583, No Obj: 0.003603, Avg Recall: 0.671642, count: 134 -Region Avg IOU: 0.570199, Class: 1.000000, Obj: 0.340505, No Obj: 0.002958, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.566787, Class: 1.000000, Obj: 0.381723, No Obj: 0.003207, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.573040, Class: 1.000000, Obj: 0.386947, No Obj: 0.003020, Avg Recall: 0.663866, count: 119 -Region Avg IOU: 0.616850, Class: 1.000000, Obj: 0.397382, No Obj: 0.003443, Avg Recall: 0.750000, count: 124 -Region Avg IOU: 0.648867, Class: 1.000000, Obj: 0.395920, No Obj: 0.002981, Avg Recall: 0.827586, count: 87 -Region Avg IOU: 0.588990, Class: 1.000000, Obj: 0.408269, No Obj: 0.002893, Avg Recall: 0.693878, count: 98 -5617: 0.453828, 0.444863 avg, 0.001000 rate, 23.376661 seconds, 1437952 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.622949, Class: 1.000000, Obj: 0.364408, No Obj: 0.003353, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.615987, Class: 1.000000, Obj: 0.395162, No Obj: 0.003281, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.606319, Class: 1.000000, Obj: 0.427971, No Obj: 0.003058, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.560527, Class: 1.000000, Obj: 0.351331, No Obj: 0.002922, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.583575, Class: 1.000000, Obj: 0.373213, No Obj: 0.003107, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.595734, Class: 1.000000, Obj: 0.385789, No Obj: 0.003380, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.628355, Class: 1.000000, Obj: 0.342743, No Obj: 0.003312, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.592398, Class: 1.000000, Obj: 0.350547, No Obj: 0.003247, Avg Recall: 0.697674, count: 129 -5618: 0.442870, 0.444664 avg, 0.001000 rate, 23.823460 seconds, 1438208 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.611095, Class: 1.000000, Obj: 0.397870, No Obj: 0.002934, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.574859, Class: 1.000000, Obj: 0.386267, No Obj: 0.003035, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.573311, Class: 1.000000, Obj: 0.346071, No Obj: 0.002836, Avg Recall: 0.680672, count: 119 -Region Avg IOU: 0.607276, Class: 1.000000, Obj: 0.391959, No Obj: 0.003579, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.604654, Class: 1.000000, Obj: 0.318629, No Obj: 0.003796, Avg Recall: 0.731707, count: 123 -Region Avg IOU: 0.617832, Class: 1.000000, Obj: 0.365852, No Obj: 0.003206, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.635718, Class: 1.000000, Obj: 0.415639, No Obj: 0.003041, Avg Recall: 0.752809, count: 89 -Region Avg IOU: 0.632388, Class: 1.000000, Obj: 0.403506, No Obj: 0.003020, Avg Recall: 0.786408, count: 103 -5619: 0.458224, 0.446020 avg, 0.001000 rate, 23.769974 seconds, 1438464 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.628304, Class: 1.000000, Obj: 0.351144, No Obj: 0.003878, Avg Recall: 0.767442, count: 129 -Region Avg IOU: 0.584956, Class: 1.000000, Obj: 0.397997, No Obj: 0.003151, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.590114, Class: 1.000000, Obj: 0.385579, No Obj: 0.003430, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.592196, Class: 1.000000, Obj: 0.433057, No Obj: 0.002980, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.637607, Class: 1.000000, Obj: 0.445328, No Obj: 0.003207, Avg Recall: 0.759494, count: 79 -Region Avg IOU: 0.591270, Class: 1.000000, Obj: 0.400698, No Obj: 0.003684, Avg Recall: 0.706349, count: 126 -Region Avg IOU: 0.607594, Class: 1.000000, Obj: 0.389366, No Obj: 0.003225, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.579422, Class: 1.000000, Obj: 0.387854, No Obj: 0.003108, Avg Recall: 0.672897, count: 107 -5620: 0.449397, 0.446358 avg, 0.001000 rate, 23.901024 seconds, 1438720 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000069 seconds -Region Avg IOU: 0.649055, Class: 1.000000, Obj: 0.362881, No Obj: 0.002677, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.634423, Class: 1.000000, Obj: 0.453376, No Obj: 0.002804, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.604820, Class: 1.000000, Obj: 0.389368, No Obj: 0.003134, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.624386, Class: 1.000000, Obj: 0.426028, No Obj: 0.002708, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.672752, Class: 1.000000, Obj: 0.461945, No Obj: 0.003103, Avg Recall: 0.882883, count: 111 -Region Avg IOU: 0.605758, Class: 1.000000, Obj: 0.431565, No Obj: 0.003484, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.637069, Class: 1.000000, Obj: 0.446467, No Obj: 0.003068, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.648489, Class: 1.000000, Obj: 0.467379, No Obj: 0.003421, Avg Recall: 0.788462, count: 104 -5621: 0.406910, 0.442413 avg, 0.001000 rate, 25.262640 seconds, 1438976 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.612241, Class: 1.000000, Obj: 0.422496, No Obj: 0.003343, Avg Recall: 0.752137, count: 117 -Region Avg IOU: 0.640515, Class: 1.000000, Obj: 0.442242, No Obj: 0.003269, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.599190, Class: 1.000000, Obj: 0.425868, No Obj: 0.003147, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.617733, Class: 1.000000, Obj: 0.417309, No Obj: 0.003406, Avg Recall: 0.791667, count: 120 -Region Avg IOU: 0.604010, Class: 1.000000, Obj: 0.441747, No Obj: 0.002576, Avg Recall: 0.711111, count: 90 -Region Avg IOU: 0.673415, Class: 1.000000, Obj: 0.446226, No Obj: 0.003203, Avg Recall: 0.825243, count: 103 -Region Avg IOU: 0.643073, Class: 1.000000, Obj: 0.403427, No Obj: 0.003593, Avg Recall: 0.773438, count: 128 -Region Avg IOU: 0.567977, Class: 1.000000, Obj: 0.407583, No Obj: 0.002764, Avg Recall: 0.626374, count: 91 -5622: 0.387032, 0.436875 avg, 0.001000 rate, 25.783764 seconds, 1439232 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.642823, Class: 1.000000, Obj: 0.418392, No Obj: 0.003460, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.613653, Class: 1.000000, Obj: 0.410519, No Obj: 0.003177, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.611262, Class: 1.000000, Obj: 0.421335, No Obj: 0.003798, Avg Recall: 0.721804, count: 133 -Region Avg IOU: 0.674654, Class: 1.000000, Obj: 0.436049, No Obj: 0.003710, Avg Recall: 0.862745, count: 102 -Region Avg IOU: 0.659075, Class: 1.000000, Obj: 0.409546, No Obj: 0.003548, Avg Recall: 0.846847, count: 111 -Region Avg IOU: 0.576419, Class: 1.000000, Obj: 0.377993, No Obj: 0.002895, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.676537, Class: 1.000000, Obj: 0.495533, No Obj: 0.003438, Avg Recall: 0.864198, count: 81 -Region Avg IOU: 0.606830, Class: 1.000000, Obj: 0.397266, No Obj: 0.003183, Avg Recall: 0.697248, count: 109 -5623: 0.428152, 0.436003 avg, 0.001000 rate, 23.949104 seconds, 1439488 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.625909, Class: 1.000000, Obj: 0.413048, No Obj: 0.003002, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.570409, Class: 1.000000, Obj: 0.416834, No Obj: 0.003709, Avg Recall: 0.682540, count: 126 -Region Avg IOU: 0.671102, Class: 1.000000, Obj: 0.540742, No Obj: 0.003148, Avg Recall: 0.859155, count: 71 -Region Avg IOU: 0.621193, Class: 1.000000, Obj: 0.478509, No Obj: 0.003113, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.630073, Class: 1.000000, Obj: 0.389654, No Obj: 0.003322, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.636233, Class: 1.000000, Obj: 0.411373, No Obj: 0.003597, Avg Recall: 0.784000, count: 125 -Region Avg IOU: 0.630152, Class: 1.000000, Obj: 0.478578, No Obj: 0.003290, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.613873, Class: 1.000000, Obj: 0.446737, No Obj: 0.003488, Avg Recall: 0.758333, count: 120 -5624: 0.396315, 0.432034 avg, 0.001000 rate, 25.661425 seconds, 1439744 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.594294, Class: 1.000000, Obj: 0.412076, No Obj: 0.003316, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.649000, Class: 1.000000, Obj: 0.428022, No Obj: 0.003264, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.610371, Class: 1.000000, Obj: 0.408607, No Obj: 0.003508, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.620900, Class: 1.000000, Obj: 0.449838, No Obj: 0.003308, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.570143, Class: 1.000000, Obj: 0.408185, No Obj: 0.003093, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.625243, Class: 1.000000, Obj: 0.444838, No Obj: 0.003147, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.616401, Class: 1.000000, Obj: 0.463189, No Obj: 0.002723, Avg Recall: 0.717647, count: 85 -Region Avg IOU: 0.630486, Class: 1.000000, Obj: 0.463075, No Obj: 0.002863, Avg Recall: 0.707865, count: 89 -5625: 0.403951, 0.429226 avg, 0.001000 rate, 25.791885 seconds, 1440000 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.640194, Class: 1.000000, Obj: 0.460828, No Obj: 0.003481, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.620601, Class: 1.000000, Obj: 0.426911, No Obj: 0.002738, Avg Recall: 0.766234, count: 77 -Region Avg IOU: 0.611062, Class: 1.000000, Obj: 0.407361, No Obj: 0.003433, Avg Recall: 0.726619, count: 139 -Region Avg IOU: 0.676419, Class: 1.000000, Obj: 0.464574, No Obj: 0.003078, Avg Recall: 0.826923, count: 104 -Region Avg IOU: 0.607942, Class: 1.000000, Obj: 0.426207, No Obj: 0.003099, Avg Recall: 0.762712, count: 118 -Region Avg IOU: 0.525411, Class: 1.000000, Obj: 0.330726, No Obj: 0.003499, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.591999, Class: 1.000000, Obj: 0.414847, No Obj: 0.003407, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.602902, Class: 1.000000, Obj: 0.408628, No Obj: 0.003118, Avg Recall: 0.696970, count: 99 -5626: 0.420278, 0.428331 avg, 0.001000 rate, 26.389664 seconds, 1440256 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.637738, Class: 1.000000, Obj: 0.426081, No Obj: 0.003776, Avg Recall: 0.770492, count: 122 -Region Avg IOU: 0.596376, Class: 1.000000, Obj: 0.382548, No Obj: 0.003282, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.578136, Class: 1.000000, Obj: 0.384554, No Obj: 0.003217, Avg Recall: 0.626984, count: 126 -Region Avg IOU: 0.667825, Class: 1.000000, Obj: 0.445456, No Obj: 0.003772, Avg Recall: 0.834783, count: 115 -Region Avg IOU: 0.647338, Class: 1.000000, Obj: 0.455716, No Obj: 0.002975, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.602458, Class: 1.000000, Obj: 0.468511, No Obj: 0.003047, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.645171, Class: 1.000000, Obj: 0.468954, No Obj: 0.003254, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.584731, Class: 1.000000, Obj: 0.389063, No Obj: 0.003603, Avg Recall: 0.708029, count: 137 -5627: 0.441346, 0.429632 avg, 0.001000 rate, 25.520803 seconds, 1440512 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.618673, Class: 1.000000, Obj: 0.432245, No Obj: 0.002886, Avg Recall: 0.775000, count: 80 -Region Avg IOU: 0.600355, Class: 1.000000, Obj: 0.396022, No Obj: 0.002842, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.634302, Class: 1.000000, Obj: 0.413431, No Obj: 0.002982, Avg Recall: 0.729412, count: 85 -Region Avg IOU: 0.631903, Class: 1.000000, Obj: 0.435616, No Obj: 0.002923, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.571440, Class: 1.000000, Obj: 0.382383, No Obj: 0.003092, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.599066, Class: 1.000000, Obj: 0.413944, No Obj: 0.003117, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.615649, Class: 1.000000, Obj: 0.439892, No Obj: 0.002851, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.604336, Class: 1.000000, Obj: 0.388247, No Obj: 0.003550, Avg Recall: 0.730435, count: 115 -5628: 0.399409, 0.426610 avg, 0.001000 rate, 25.968861 seconds, 1440768 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.594017, Class: 1.000000, Obj: 0.395924, No Obj: 0.002408, Avg Recall: 0.693182, count: 88 -Region Avg IOU: 0.621831, Class: 1.000000, Obj: 0.440302, No Obj: 0.002865, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.620098, Class: 1.000000, Obj: 0.367196, No Obj: 0.003428, Avg Recall: 0.746154, count: 130 -Region Avg IOU: 0.623522, Class: 1.000000, Obj: 0.393925, No Obj: 0.002705, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.625740, Class: 1.000000, Obj: 0.360092, No Obj: 0.003041, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.597073, Class: 1.000000, Obj: 0.383038, No Obj: 0.003058, Avg Recall: 0.676923, count: 130 -Region Avg IOU: 0.604301, Class: 1.000000, Obj: 0.400554, No Obj: 0.002978, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.632864, Class: 1.000000, Obj: 0.399751, No Obj: 0.003121, Avg Recall: 0.713115, count: 122 -5629: 0.429393, 0.426888 avg, 0.001000 rate, 25.677153 seconds, 1441024 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.594667, Class: 1.000000, Obj: 0.390252, No Obj: 0.002712, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.615145, Class: 1.000000, Obj: 0.416967, No Obj: 0.002823, Avg Recall: 0.735537, count: 121 -Region Avg IOU: 0.626053, Class: 1.000000, Obj: 0.398878, No Obj: 0.003158, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.611038, Class: 1.000000, Obj: 0.427825, No Obj: 0.002570, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.589041, Class: 1.000000, Obj: 0.389080, No Obj: 0.002765, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.613776, Class: 1.000000, Obj: 0.363705, No Obj: 0.002629, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.597898, Class: 1.000000, Obj: 0.368592, No Obj: 0.002308, Avg Recall: 0.717647, count: 85 -Region Avg IOU: 0.602814, Class: 1.000000, Obj: 0.404106, No Obj: 0.003006, Avg Recall: 0.710145, count: 138 -5630: 0.368604, 0.421060 avg, 0.001000 rate, 26.036264 seconds, 1441280 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.610504 seconds -Region Avg IOU: 0.531221, Class: 1.000000, Obj: 0.356934, No Obj: 0.003744, Avg Recall: 0.576923, count: 130 -Region Avg IOU: 0.524267, Class: 1.000000, Obj: 0.341709, No Obj: 0.003204, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.527448, Class: 1.000000, Obj: 0.359635, No Obj: 0.002801, Avg Recall: 0.584416, count: 77 -Region Avg IOU: 0.581095, Class: 1.000000, Obj: 0.372470, No Obj: 0.002905, Avg Recall: 0.705882, count: 85 -Region Avg IOU: 0.518317, Class: 1.000000, Obj: 0.315381, No Obj: 0.003251, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.582841, Class: 1.000000, Obj: 0.347169, No Obj: 0.003801, Avg Recall: 0.680000, count: 125 -Region Avg IOU: 0.607866, Class: 1.000000, Obj: 0.358322, No Obj: 0.003661, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.588582, Class: 1.000000, Obj: 0.400903, No Obj: 0.002928, Avg Recall: 0.741573, count: 89 -5631: 0.424161, 0.421370 avg, 0.001000 rate, 23.693689 seconds, 1441536 images -Loaded: 0.073595 seconds -Region Avg IOU: 0.568513, Class: 1.000000, Obj: 0.320317, No Obj: 0.003414, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.567895, Class: 1.000000, Obj: 0.319345, No Obj: 0.002967, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.601788, Class: 1.000000, Obj: 0.383690, No Obj: 0.002788, Avg Recall: 0.675000, count: 80 -Region Avg IOU: 0.571560, Class: 1.000000, Obj: 0.346425, No Obj: 0.004014, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.553084, Class: 1.000000, Obj: 0.334307, No Obj: 0.003538, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.617459, Class: 1.000000, Obj: 0.362913, No Obj: 0.003726, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.569914, Class: 1.000000, Obj: 0.357078, No Obj: 0.004046, Avg Recall: 0.681818, count: 132 -Region Avg IOU: 0.558070, Class: 1.000000, Obj: 0.308859, No Obj: 0.004218, Avg Recall: 0.642276, count: 123 -5632: 0.455964, 0.424829 avg, 0.001000 rate, 21.471516 seconds, 1441792 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.597528, Class: 1.000000, Obj: 0.421574, No Obj: 0.003403, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.630286, Class: 1.000000, Obj: 0.424029, No Obj: 0.003476, Avg Recall: 0.752809, count: 89 -Region Avg IOU: 0.553450, Class: 1.000000, Obj: 0.359663, No Obj: 0.003419, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.499730, Class: 1.000000, Obj: 0.283774, No Obj: 0.003551, Avg Recall: 0.523438, count: 128 -Region Avg IOU: 0.556480, Class: 1.000000, Obj: 0.363151, No Obj: 0.003966, Avg Recall: 0.632812, count: 128 -Region Avg IOU: 0.559484, Class: 1.000000, Obj: 0.371269, No Obj: 0.003418, Avg Recall: 0.630435, count: 92 -Region Avg IOU: 0.579362, Class: 1.000000, Obj: 0.408846, No Obj: 0.003609, Avg Recall: 0.702381, count: 84 -Region Avg IOU: 0.540290, Class: 1.000000, Obj: 0.352549, No Obj: 0.003607, Avg Recall: 0.633027, count: 109 -5633: 0.406264, 0.422973 avg, 0.001000 rate, 21.418396 seconds, 1442048 images -Loaded: 0.088794 seconds -Region Avg IOU: 0.553843, Class: 1.000000, Obj: 0.409228, No Obj: 0.003274, Avg Recall: 0.617284, count: 81 -Region Avg IOU: 0.512452, Class: 1.000000, Obj: 0.341804, No Obj: 0.003130, Avg Recall: 0.589474, count: 95 -Region Avg IOU: 0.588617, Class: 1.000000, Obj: 0.401235, No Obj: 0.003201, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.551141, Class: 1.000000, Obj: 0.357687, No Obj: 0.004070, Avg Recall: 0.644068, count: 118 -Region Avg IOU: 0.568727, Class: 1.000000, Obj: 0.371765, No Obj: 0.003051, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.584207, Class: 1.000000, Obj: 0.405153, No Obj: 0.003431, Avg Recall: 0.679487, count: 78 -Region Avg IOU: 0.588672, Class: 1.000000, Obj: 0.389295, No Obj: 0.004458, Avg Recall: 0.703125, count: 128 -Region Avg IOU: 0.554701, Class: 1.000000, Obj: 0.388918, No Obj: 0.003618, Avg Recall: 0.623853, count: 109 -5634: 0.376520, 0.418328 avg, 0.001000 rate, 22.401457 seconds, 1442304 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.573239, Class: 1.000000, Obj: 0.404489, No Obj: 0.003560, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.567866, Class: 1.000000, Obj: 0.357888, No Obj: 0.004311, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.544272, Class: 1.000000, Obj: 0.392778, No Obj: 0.003651, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.573269, Class: 1.000000, Obj: 0.352913, No Obj: 0.004083, Avg Recall: 0.669291, count: 127 -Region Avg IOU: 0.558647, Class: 1.000000, Obj: 0.375086, No Obj: 0.003394, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.614335, Class: 1.000000, Obj: 0.361390, No Obj: 0.003952, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.562092, Class: 1.000000, Obj: 0.392812, No Obj: 0.003553, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.565136, Class: 1.000000, Obj: 0.399583, No Obj: 0.004742, Avg Recall: 0.647059, count: 153 -5635: 0.424137, 0.418909 avg, 0.001000 rate, 22.791586 seconds, 1442560 images -Loaded: 0.104876 seconds -Region Avg IOU: 0.542781, Class: 1.000000, Obj: 0.383232, No Obj: 0.003677, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.602232, Class: 1.000000, Obj: 0.425145, No Obj: 0.004255, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.593066, Class: 1.000000, Obj: 0.364801, No Obj: 0.004025, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.536909, Class: 1.000000, Obj: 0.357557, No Obj: 0.004146, Avg Recall: 0.584615, count: 130 -Region Avg IOU: 0.584711, Class: 1.000000, Obj: 0.362289, No Obj: 0.003813, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.515396, Class: 1.000000, Obj: 0.417255, No Obj: 0.003350, Avg Recall: 0.583333, count: 96 -Region Avg IOU: 0.587289, Class: 1.000000, Obj: 0.428382, No Obj: 0.003849, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.520146, Class: 1.000000, Obj: 0.326805, No Obj: 0.003479, Avg Recall: 0.555556, count: 108 -5636: 0.386422, 0.415660 avg, 0.001000 rate, 21.962908 seconds, 1442816 images -Loaded: 0.529392 seconds -Region Avg IOU: 0.589850, Class: 1.000000, Obj: 0.463642, No Obj: 0.004184, Avg Recall: 0.684783, count: 92 -Region Avg IOU: 0.574317, Class: 1.000000, Obj: 0.443138, No Obj: 0.004283, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.555713, Class: 1.000000, Obj: 0.422673, No Obj: 0.003941, Avg Recall: 0.655462, count: 119 -Region Avg IOU: 0.583879, Class: 1.000000, Obj: 0.418694, No Obj: 0.004112, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.548182, Class: 1.000000, Obj: 0.412067, No Obj: 0.003542, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.520970, Class: 1.000000, Obj: 0.376081, No Obj: 0.004224, Avg Recall: 0.573770, count: 122 -Region Avg IOU: 0.540494, Class: 1.000000, Obj: 0.365254, No Obj: 0.003826, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.581447, Class: 1.000000, Obj: 0.382744, No Obj: 0.004821, Avg Recall: 0.664062, count: 128 -5637: 0.412568, 0.415351 avg, 0.001000 rate, 21.414492 seconds, 1443072 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.539335, Class: 1.000000, Obj: 0.434191, No Obj: 0.004796, Avg Recall: 0.634146, count: 123 -Region Avg IOU: 0.537184, Class: 1.000000, Obj: 0.437975, No Obj: 0.004852, Avg Recall: 0.639344, count: 122 -Region Avg IOU: 0.560184, Class: 1.000000, Obj: 0.383254, No Obj: 0.004423, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.540112, Class: 1.000000, Obj: 0.437438, No Obj: 0.003455, Avg Recall: 0.619565, count: 92 -Region Avg IOU: 0.522111, Class: 1.000000, Obj: 0.378045, No Obj: 0.004058, Avg Recall: 0.584000, count: 125 -Region Avg IOU: 0.563697, Class: 1.000000, Obj: 0.392076, No Obj: 0.004655, Avg Recall: 0.658537, count: 123 -Region Avg IOU: 0.554278, Class: 1.000000, Obj: 0.418400, No Obj: 0.004498, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.568389, Class: 1.000000, Obj: 0.403126, No Obj: 0.004666, Avg Recall: 0.660870, count: 115 -5638: 0.468256, 0.420641 avg, 0.001000 rate, 21.456472 seconds, 1443328 images -Loaded: 0.340371 seconds -Region Avg IOU: 0.565404, Class: 1.000000, Obj: 0.454440, No Obj: 0.003864, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.568394, Class: 1.000000, Obj: 0.450855, No Obj: 0.004710, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.580456, Class: 1.000000, Obj: 0.395918, No Obj: 0.004087, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.586145, Class: 1.000000, Obj: 0.362208, No Obj: 0.004688, Avg Recall: 0.679688, count: 128 -Region Avg IOU: 0.573283, Class: 1.000000, Obj: 0.445497, No Obj: 0.004718, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.590046, Class: 1.000000, Obj: 0.445769, No Obj: 0.005392, Avg Recall: 0.684932, count: 146 -Region Avg IOU: 0.593944, Class: 1.000000, Obj: 0.477435, No Obj: 0.004430, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.552963, Class: 1.000000, Obj: 0.448444, No Obj: 0.004085, Avg Recall: 0.646018, count: 113 -5639: 0.417987, 0.420376 avg, 0.001000 rate, 23.142363 seconds, 1443584 images -Loaded: 0.382704 seconds -Region Avg IOU: 0.574137, Class: 1.000000, Obj: 0.414848, No Obj: 0.005076, Avg Recall: 0.722222, count: 144 -Region Avg IOU: 0.572838, Class: 1.000000, Obj: 0.397687, No Obj: 0.004138, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.554382, Class: 1.000000, Obj: 0.369784, No Obj: 0.005051, Avg Recall: 0.643312, count: 157 -Region Avg IOU: 0.582363, Class: 1.000000, Obj: 0.502017, No Obj: 0.004207, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.604141, Class: 1.000000, Obj: 0.432349, No Obj: 0.004720, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.532580, Class: 1.000000, Obj: 0.376319, No Obj: 0.003810, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.527962, Class: 1.000000, Obj: 0.411864, No Obj: 0.003646, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.594835, Class: 1.000000, Obj: 0.396452, No Obj: 0.004934, Avg Recall: 0.725191, count: 131 -5640: 0.439730, 0.422311 avg, 0.001000 rate, 21.227156 seconds, 1443840 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.412212 seconds -Region Avg IOU: 0.583987, Class: 1.000000, Obj: 0.377899, No Obj: 0.004915, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.535726, Class: 1.000000, Obj: 0.403155, No Obj: 0.006575, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.434487, Class: 1.000000, Obj: 0.301454, No Obj: 0.005029, Avg Recall: 0.447761, count: 134 -Region Avg IOU: 0.492396, Class: 1.000000, Obj: 0.346690, No Obj: 0.004824, Avg Recall: 0.511111, count: 90 -Region Avg IOU: 0.496088, Class: 1.000000, Obj: 0.353795, No Obj: 0.005304, Avg Recall: 0.580153, count: 131 -Region Avg IOU: 0.517509, Class: 1.000000, Obj: 0.354153, No Obj: 0.004955, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.480520, Class: 1.000000, Obj: 0.315017, No Obj: 0.005044, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.479355, Class: 1.000000, Obj: 0.385027, No Obj: 0.005359, Avg Recall: 0.515152, count: 99 -5641: 0.491361, 0.429216 avg, 0.001000 rate, 16.419909 seconds, 1444096 images -Loaded: 4.571830 seconds -Region Avg IOU: 0.432902, Class: 1.000000, Obj: 0.297144, No Obj: 0.004452, Avg Recall: 0.446154, count: 130 -Region Avg IOU: 0.553513, Class: 1.000000, Obj: 0.415903, No Obj: 0.005055, Avg Recall: 0.623529, count: 85 -Region Avg IOU: 0.488283, Class: 1.000000, Obj: 0.392923, No Obj: 0.004666, Avg Recall: 0.469388, count: 98 -Region Avg IOU: 0.440421, Class: 1.000000, Obj: 0.341706, No Obj: 0.004192, Avg Recall: 0.431034, count: 116 -Region Avg IOU: 0.484908, Class: 1.000000, Obj: 0.334969, No Obj: 0.005113, Avg Recall: 0.495652, count: 115 -Region Avg IOU: 0.481657, Class: 1.000000, Obj: 0.340759, No Obj: 0.004435, Avg Recall: 0.543478, count: 92 -Region Avg IOU: 0.474821, Class: 1.000000, Obj: 0.349977, No Obj: 0.004595, Avg Recall: 0.553398, count: 103 -Region Avg IOU: 0.491331, Class: 1.000000, Obj: 0.364255, No Obj: 0.004807, Avg Recall: 0.550000, count: 100 -5642: 0.420804, 0.428375 avg, 0.001000 rate, 18.014215 seconds, 1444352 images -Loaded: 3.069551 seconds -Region Avg IOU: 0.532975, Class: 1.000000, Obj: 0.373899, No Obj: 0.005406, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.553319, Class: 1.000000, Obj: 0.395229, No Obj: 0.004723, Avg Recall: 0.639535, count: 86 -Region Avg IOU: 0.549676, Class: 1.000000, Obj: 0.355909, No Obj: 0.004813, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.456816, Class: 1.000000, Obj: 0.322498, No Obj: 0.004731, Avg Recall: 0.472727, count: 110 -Region Avg IOU: 0.524197, Class: 1.000000, Obj: 0.356955, No Obj: 0.005551, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.451132, Class: 1.000000, Obj: 0.336492, No Obj: 0.004099, Avg Recall: 0.505263, count: 95 -Region Avg IOU: 0.500632, Class: 1.000000, Obj: 0.351175, No Obj: 0.004718, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.522042, Class: 1.000000, Obj: 0.370485, No Obj: 0.005670, Avg Recall: 0.557377, count: 122 -5643: 0.437381, 0.429276 avg, 0.001000 rate, 16.288897 seconds, 1444608 images -Loaded: 4.419482 seconds -Region Avg IOU: 0.436325, Class: 1.000000, Obj: 0.339060, No Obj: 0.004868, Avg Recall: 0.491935, count: 124 -Region Avg IOU: 0.496997, Class: 1.000000, Obj: 0.340470, No Obj: 0.004758, Avg Recall: 0.585106, count: 94 -Region Avg IOU: 0.491948, Class: 1.000000, Obj: 0.349232, No Obj: 0.004472, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.445448, Class: 1.000000, Obj: 0.307955, No Obj: 0.004697, Avg Recall: 0.454545, count: 110 -Region Avg IOU: 0.512365, Class: 1.000000, Obj: 0.373419, No Obj: 0.005470, Avg Recall: 0.567568, count: 111 -Region Avg IOU: 0.455977, Class: 1.000000, Obj: 0.337956, No Obj: 0.005103, Avg Recall: 0.523810, count: 126 -Region Avg IOU: 0.468684, Class: 1.000000, Obj: 0.327844, No Obj: 0.005078, Avg Recall: 0.477273, count: 132 -Region Avg IOU: 0.468317, Class: 1.000000, Obj: 0.347898, No Obj: 0.005199, Avg Recall: 0.469565, count: 115 -5644: 0.448593, 0.431207 avg, 0.001000 rate, 16.152506 seconds, 1444864 images -Loaded: 3.963897 seconds -Region Avg IOU: 0.423533, Class: 1.000000, Obj: 0.319465, No Obj: 0.003602, Avg Recall: 0.443182, count: 88 -Region Avg IOU: 0.478470, Class: 1.000000, Obj: 0.305539, No Obj: 0.004954, Avg Recall: 0.535088, count: 114 -Region Avg IOU: 0.436487, Class: 1.000000, Obj: 0.298164, No Obj: 0.004014, Avg Recall: 0.380000, count: 100 -Region Avg IOU: 0.437185, Class: 1.000000, Obj: 0.340118, No Obj: 0.004489, Avg Recall: 0.490741, count: 108 -Region Avg IOU: 0.432542, Class: 1.000000, Obj: 0.320646, No Obj: 0.005112, Avg Recall: 0.425532, count: 141 -Region Avg IOU: 0.464773, Class: 1.000000, Obj: 0.348326, No Obj: 0.004784, Avg Recall: 0.491803, count: 122 -Region Avg IOU: 0.454133, Class: 1.000000, Obj: 0.295072, No Obj: 0.005056, Avg Recall: 0.466019, count: 103 -Region Avg IOU: 0.501569, Class: 1.000000, Obj: 0.354174, No Obj: 0.005568, Avg Recall: 0.547619, count: 126 -5645: 0.445973, 0.432684 avg, 0.001000 rate, 16.816635 seconds, 1445120 images -Loaded: 3.575257 seconds -Region Avg IOU: 0.382887, Class: 1.000000, Obj: 0.295130, No Obj: 0.003795, Avg Recall: 0.380531, count: 113 -Region Avg IOU: 0.518207, Class: 1.000000, Obj: 0.306591, No Obj: 0.005224, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.461968, Class: 1.000000, Obj: 0.373711, No Obj: 0.005058, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.442732, Class: 1.000000, Obj: 0.333908, No Obj: 0.004032, Avg Recall: 0.482759, count: 87 -Region Avg IOU: 0.492156, Class: 1.000000, Obj: 0.356234, No Obj: 0.004743, Avg Recall: 0.565657, count: 99 -Region Avg IOU: 0.451166, Class: 1.000000, Obj: 0.330852, No Obj: 0.005363, Avg Recall: 0.477941, count: 136 -Region Avg IOU: 0.462875, Class: 1.000000, Obj: 0.340306, No Obj: 0.004209, Avg Recall: 0.451613, count: 93 -Region Avg IOU: 0.450613, Class: 1.000000, Obj: 0.291177, No Obj: 0.004450, Avg Recall: 0.471545, count: 123 -5646: 0.495361, 0.438951 avg, 0.001000 rate, 16.296003 seconds, 1445376 images -Loaded: 4.874965 seconds -Region Avg IOU: 0.482782, Class: 1.000000, Obj: 0.344942, No Obj: 0.005331, Avg Recall: 0.544643, count: 112 -Region Avg IOU: 0.508856, Class: 1.000000, Obj: 0.360454, No Obj: 0.005274, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.486151, Class: 1.000000, Obj: 0.316533, No Obj: 0.005009, Avg Recall: 0.550000, count: 120 -Region Avg IOU: 0.414786, Class: 1.000000, Obj: 0.289096, No Obj: 0.004024, Avg Recall: 0.395349, count: 86 -Region Avg IOU: 0.504071, Class: 1.000000, Obj: 0.360444, No Obj: 0.004123, Avg Recall: 0.554217, count: 83 -Region Avg IOU: 0.458642, Class: 1.000000, Obj: 0.312795, No Obj: 0.005072, Avg Recall: 0.496124, count: 129 -Region Avg IOU: 0.487075, Class: 1.000000, Obj: 0.396315, No Obj: 0.005229, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.456551, Class: 1.000000, Obj: 0.367606, No Obj: 0.004555, Avg Recall: 0.500000, count: 94 -5647: 0.470192, 0.442075 avg, 0.001000 rate, 14.183914 seconds, 1445632 images -Loaded: 6.660885 seconds -Region Avg IOU: 0.488257, Class: 1.000000, Obj: 0.339849, No Obj: 0.004291, Avg Recall: 0.580645, count: 93 -Region Avg IOU: 0.463285, Class: 1.000000, Obj: 0.349963, No Obj: 0.004677, Avg Recall: 0.516393, count: 122 -Region Avg IOU: 0.443738, Class: 1.000000, Obj: 0.322756, No Obj: 0.004152, Avg Recall: 0.474227, count: 97 -Region Avg IOU: 0.471773, Class: 1.000000, Obj: 0.334366, No Obj: 0.004503, Avg Recall: 0.514851, count: 101 -Region Avg IOU: 0.500391, Class: 1.000000, Obj: 0.350417, No Obj: 0.004693, Avg Recall: 0.540000, count: 100 -Region Avg IOU: 0.485780, Class: 1.000000, Obj: 0.329774, No Obj: 0.004923, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.446290, Class: 1.000000, Obj: 0.304366, No Obj: 0.004468, Avg Recall: 0.453846, count: 130 -Region Avg IOU: 0.437901, Class: 1.000000, Obj: 0.339198, No Obj: 0.003938, Avg Recall: 0.523364, count: 107 -5648: 0.472893, 0.445157 avg, 0.001000 rate, 14.980639 seconds, 1445888 images -Loaded: 4.035751 seconds -Region Avg IOU: 0.482566, Class: 1.000000, Obj: 0.347625, No Obj: 0.003753, Avg Recall: 0.566265, count: 83 -Region Avg IOU: 0.487223, Class: 1.000000, Obj: 0.310025, No Obj: 0.005055, Avg Recall: 0.495727, count: 117 -Region Avg IOU: 0.444491, Class: 1.000000, Obj: 0.303745, No Obj: 0.004818, Avg Recall: 0.477941, count: 136 -Region Avg IOU: 0.486480, Class: 1.000000, Obj: 0.332219, No Obj: 0.004940, Avg Recall: 0.508929, count: 112 -Region Avg IOU: 0.502441, Class: 1.000000, Obj: 0.325237, No Obj: 0.004057, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.490541, Class: 1.000000, Obj: 0.355669, No Obj: 0.004583, Avg Recall: 0.535714, count: 84 -Region Avg IOU: 0.482352, Class: 1.000000, Obj: 0.336006, No Obj: 0.005376, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.525615, Class: 1.000000, Obj: 0.304989, No Obj: 0.004753, Avg Recall: 0.537736, count: 106 -5649: 0.436672, 0.444309 avg, 0.001000 rate, 15.961507 seconds, 1446144 images -Loaded: 5.667979 seconds -Region Avg IOU: 0.441389, Class: 1.000000, Obj: 0.349967, No Obj: 0.005361, Avg Recall: 0.435644, count: 101 -Region Avg IOU: 0.434317, Class: 1.000000, Obj: 0.303098, No Obj: 0.004239, Avg Recall: 0.495495, count: 111 -Region Avg IOU: 0.531256, Class: 1.000000, Obj: 0.413117, No Obj: 0.004896, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.483819, Class: 1.000000, Obj: 0.344844, No Obj: 0.005328, Avg Recall: 0.534483, count: 116 -Region Avg IOU: 0.430283, Class: 1.000000, Obj: 0.280626, No Obj: 0.004604, Avg Recall: 0.458716, count: 109 -Region Avg IOU: 0.545167, Class: 1.000000, Obj: 0.335441, No Obj: 0.004826, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.502468, Class: 1.000000, Obj: 0.385851, No Obj: 0.004757, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.471032, Class: 1.000000, Obj: 0.315771, No Obj: 0.004880, Avg Recall: 0.572581, count: 124 -5650: 0.463415, 0.446219 avg, 0.001000 rate, 17.619215 seconds, 1446400 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000062 seconds -Region Avg IOU: 0.648980, Class: 1.000000, Obj: 0.506399, No Obj: 0.003714, Avg Recall: 0.785047, count: 107 -Region Avg IOU: 0.588497, Class: 1.000000, Obj: 0.450922, No Obj: 0.002983, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.610523, Class: 1.000000, Obj: 0.462504, No Obj: 0.003242, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.641462, Class: 1.000000, Obj: 0.470704, No Obj: 0.003826, Avg Recall: 0.779310, count: 145 -Region Avg IOU: 0.626877, Class: 1.000000, Obj: 0.459930, No Obj: 0.003353, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.630230, Class: 1.000000, Obj: 0.440424, No Obj: 0.004417, Avg Recall: 0.779221, count: 154 -Region Avg IOU: 0.656608, Class: 1.000000, Obj: 0.414301, No Obj: 0.002993, Avg Recall: 0.831579, count: 95 -Region Avg IOU: 0.655460, Class: 1.000000, Obj: 0.507119, No Obj: 0.003340, Avg Recall: 0.812500, count: 96 -5651: 0.406783, 0.442276 avg, 0.001000 rate, 26.420853 seconds, 1446656 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.657968, Class: 1.000000, Obj: 0.496663, No Obj: 0.003843, Avg Recall: 0.833333, count: 114 -Region Avg IOU: 0.605671, Class: 1.000000, Obj: 0.450410, No Obj: 0.003483, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.630855, Class: 1.000000, Obj: 0.432554, No Obj: 0.003873, Avg Recall: 0.767606, count: 142 -Region Avg IOU: 0.639343, Class: 1.000000, Obj: 0.495949, No Obj: 0.003316, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.601505, Class: 1.000000, Obj: 0.485733, No Obj: 0.003740, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.670021, Class: 1.000000, Obj: 0.498353, No Obj: 0.003694, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.627907, Class: 1.000000, Obj: 0.476245, No Obj: 0.003149, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.610659, Class: 1.000000, Obj: 0.420500, No Obj: 0.003586, Avg Recall: 0.766355, count: 107 -5652: 0.419541, 0.440002 avg, 0.001000 rate, 26.279568 seconds, 1446912 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.596936, Class: 1.000000, Obj: 0.434841, No Obj: 0.002985, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.609430, Class: 1.000000, Obj: 0.417366, No Obj: 0.003436, Avg Recall: 0.733871, count: 124 -Region Avg IOU: 0.625658, Class: 1.000000, Obj: 0.469482, No Obj: 0.003080, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.669015, Class: 1.000000, Obj: 0.502402, No Obj: 0.003343, Avg Recall: 0.864583, count: 96 -Region Avg IOU: 0.605858, Class: 1.000000, Obj: 0.392682, No Obj: 0.002965, Avg Recall: 0.736264, count: 91 -Region Avg IOU: 0.600176, Class: 1.000000, Obj: 0.447295, No Obj: 0.002785, Avg Recall: 0.703297, count: 91 -Region Avg IOU: 0.658217, Class: 1.000000, Obj: 0.494704, No Obj: 0.002979, Avg Recall: 0.800000, count: 80 -Region Avg IOU: 0.638651, Class: 1.000000, Obj: 0.498660, No Obj: 0.003690, Avg Recall: 0.752137, count: 117 -5653: 0.394411, 0.435443 avg, 0.001000 rate, 27.533039 seconds, 1447168 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.578738, Class: 1.000000, Obj: 0.424929, No Obj: 0.003179, Avg Recall: 0.664000, count: 125 -Region Avg IOU: 0.624833, Class: 1.000000, Obj: 0.406282, No Obj: 0.002514, Avg Recall: 0.744681, count: 94 -Region Avg IOU: 0.631302, Class: 1.000000, Obj: 0.404596, No Obj: 0.002923, Avg Recall: 0.796117, count: 103 -Region Avg IOU: 0.595596, Class: 1.000000, Obj: 0.389151, No Obj: 0.002965, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.656260, Class: 1.000000, Obj: 0.456427, No Obj: 0.002740, Avg Recall: 0.806818, count: 88 -Region Avg IOU: 0.571970, Class: 1.000000, Obj: 0.391498, No Obj: 0.003608, Avg Recall: 0.666667, count: 120 -Region Avg IOU: 0.659474, Class: 1.000000, Obj: 0.407568, No Obj: 0.003646, Avg Recall: 0.788618, count: 123 -Region Avg IOU: 0.594645, Class: 1.000000, Obj: 0.405547, No Obj: 0.003553, Avg Recall: 0.722581, count: 155 -5654: 0.428648, 0.434764 avg, 0.001000 rate, 27.251709 seconds, 1447424 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.638161, Class: 1.000000, Obj: 0.391859, No Obj: 0.003332, Avg Recall: 0.801587, count: 126 -Region Avg IOU: 0.580335, Class: 1.000000, Obj: 0.361180, No Obj: 0.002582, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.652342, Class: 1.000000, Obj: 0.462821, No Obj: 0.002832, Avg Recall: 0.834862, count: 109 -Region Avg IOU: 0.639813, Class: 1.000000, Obj: 0.415952, No Obj: 0.002667, Avg Recall: 0.756098, count: 82 -Region Avg IOU: 0.621395, Class: 1.000000, Obj: 0.380720, No Obj: 0.002955, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.654500, Class: 1.000000, Obj: 0.402114, No Obj: 0.002746, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.584706, Class: 1.000000, Obj: 0.418343, No Obj: 0.002534, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.598427, Class: 1.000000, Obj: 0.389707, No Obj: 0.002567, Avg Recall: 0.670455, count: 88 -5655: 0.422730, 0.433560 avg, 0.001000 rate, 26.901749 seconds, 1447680 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.632387, Class: 1.000000, Obj: 0.407852, No Obj: 0.002501, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.665916, Class: 1.000000, Obj: 0.415594, No Obj: 0.002653, Avg Recall: 0.890110, count: 91 -Region Avg IOU: 0.584575, Class: 1.000000, Obj: 0.351215, No Obj: 0.002567, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.653045, Class: 1.000000, Obj: 0.361862, No Obj: 0.002544, Avg Recall: 0.829060, count: 117 -Region Avg IOU: 0.658598, Class: 1.000000, Obj: 0.399704, No Obj: 0.002439, Avg Recall: 0.850575, count: 87 -Region Avg IOU: 0.642889, Class: 1.000000, Obj: 0.416531, No Obj: 0.002738, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.587419, Class: 1.000000, Obj: 0.391288, No Obj: 0.002465, Avg Recall: 0.720930, count: 129 -Region Avg IOU: 0.641583, Class: 1.000000, Obj: 0.393204, No Obj: 0.002339, Avg Recall: 0.793478, count: 92 -5656: 0.417053, 0.431910 avg, 0.001000 rate, 27.758907 seconds, 1447936 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.635933, Class: 1.000000, Obj: 0.433279, No Obj: 0.002689, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.593314, Class: 1.000000, Obj: 0.353793, No Obj: 0.001998, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.652244, Class: 1.000000, Obj: 0.405020, No Obj: 0.002566, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.642982, Class: 1.000000, Obj: 0.349957, No Obj: 0.002637, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.601812, Class: 1.000000, Obj: 0.344882, No Obj: 0.002509, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.666731, Class: 1.000000, Obj: 0.420582, No Obj: 0.002258, Avg Recall: 0.893333, count: 75 -Region Avg IOU: 0.630971, Class: 1.000000, Obj: 0.336166, No Obj: 0.003084, Avg Recall: 0.801282, count: 156 -Region Avg IOU: 0.622664, Class: 1.000000, Obj: 0.330465, No Obj: 0.002282, Avg Recall: 0.760870, count: 92 -5657: 0.430814, 0.431800 avg, 0.001000 rate, 26.825888 seconds, 1448192 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.629337, Class: 1.000000, Obj: 0.417463, No Obj: 0.002432, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.614716, Class: 1.000000, Obj: 0.323192, No Obj: 0.002562, Avg Recall: 0.731343, count: 134 -Region Avg IOU: 0.622650, Class: 1.000000, Obj: 0.390970, No Obj: 0.002296, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.619972, Class: 1.000000, Obj: 0.388800, No Obj: 0.002524, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.653543, Class: 1.000000, Obj: 0.365826, No Obj: 0.002990, Avg Recall: 0.776119, count: 134 -Region Avg IOU: 0.658531, Class: 1.000000, Obj: 0.338552, No Obj: 0.002475, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.634022, Class: 1.000000, Obj: 0.386123, No Obj: 0.002241, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.597779, Class: 1.000000, Obj: 0.372221, No Obj: 0.002989, Avg Recall: 0.738806, count: 134 -5658: 0.474422, 0.436062 avg, 0.001000 rate, 27.586872 seconds, 1448448 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.600347, Class: 1.000000, Obj: 0.335084, No Obj: 0.002148, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.614788, Class: 1.000000, Obj: 0.349694, No Obj: 0.002820, Avg Recall: 0.722222, count: 126 -Region Avg IOU: 0.620220, Class: 1.000000, Obj: 0.356731, No Obj: 0.002468, Avg Recall: 0.748031, count: 127 -Region Avg IOU: 0.614056, Class: 1.000000, Obj: 0.365597, No Obj: 0.002534, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.628915, Class: 1.000000, Obj: 0.333102, No Obj: 0.002591, Avg Recall: 0.778626, count: 131 -Region Avg IOU: 0.664206, Class: 1.000000, Obj: 0.381476, No Obj: 0.002404, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.592703, Class: 1.000000, Obj: 0.373568, No Obj: 0.002053, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.612770, Class: 1.000000, Obj: 0.375472, No Obj: 0.002358, Avg Recall: 0.678899, count: 109 -5659: 0.509720, 0.443428 avg, 0.001000 rate, 27.311779 seconds, 1448704 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.632453, Class: 1.000000, Obj: 0.373099, No Obj: 0.002485, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.676222, Class: 1.000000, Obj: 0.329433, No Obj: 0.002727, Avg Recall: 0.886598, count: 97 -Region Avg IOU: 0.657292, Class: 1.000000, Obj: 0.375436, No Obj: 0.002868, Avg Recall: 0.842105, count: 133 -Region Avg IOU: 0.628976, Class: 1.000000, Obj: 0.363504, No Obj: 0.003077, Avg Recall: 0.769231, count: 130 -Region Avg IOU: 0.657769, Class: 1.000000, Obj: 0.415857, No Obj: 0.002927, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.601245, Class: 1.000000, Obj: 0.398820, No Obj: 0.002649, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.609871, Class: 1.000000, Obj: 0.386510, No Obj: 0.002147, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.649560, Class: 1.000000, Obj: 0.383868, No Obj: 0.002769, Avg Recall: 0.811024, count: 127 -5660: 0.453716, 0.444457 avg, 0.001000 rate, 27.145498 seconds, 1448960 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.300764 seconds -Region Avg IOU: 0.534561, Class: 1.000000, Obj: 0.347721, No Obj: 0.004371, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.509761, Class: 1.000000, Obj: 0.306335, No Obj: 0.003495, Avg Recall: 0.579545, count: 88 -Region Avg IOU: 0.495521, Class: 1.000000, Obj: 0.321313, No Obj: 0.002942, Avg Recall: 0.533333, count: 90 -Region Avg IOU: 0.541392, Class: 1.000000, Obj: 0.333337, No Obj: 0.003577, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.516865, Class: 1.000000, Obj: 0.346757, No Obj: 0.003361, Avg Recall: 0.576471, count: 85 -Region Avg IOU: 0.498871, Class: 1.000000, Obj: 0.283464, No Obj: 0.003878, Avg Recall: 0.526718, count: 131 -Region Avg IOU: 0.526080, Class: 1.000000, Obj: 0.317002, No Obj: 0.004392, Avg Recall: 0.579365, count: 126 -Region Avg IOU: 0.495900, Class: 1.000000, Obj: 0.334529, No Obj: 0.004379, Avg Recall: 0.519380, count: 129 -5661: 0.466041, 0.446615 avg, 0.001000 rate, 19.679998 seconds, 1449216 images -Loaded: 1.380643 seconds -Region Avg IOU: 0.514865, Class: 1.000000, Obj: 0.327377, No Obj: 0.003634, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.448387, Class: 1.000000, Obj: 0.283351, No Obj: 0.003893, Avg Recall: 0.492647, count: 136 -Region Avg IOU: 0.559820, Class: 1.000000, Obj: 0.338408, No Obj: 0.004226, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.505436, Class: 1.000000, Obj: 0.314853, No Obj: 0.003795, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.571286, Class: 1.000000, Obj: 0.365007, No Obj: 0.004073, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.497003, Class: 1.000000, Obj: 0.336842, No Obj: 0.003893, Avg Recall: 0.526316, count: 114 -Region Avg IOU: 0.488235, Class: 1.000000, Obj: 0.329246, No Obj: 0.003943, Avg Recall: 0.556522, count: 115 -Region Avg IOU: 0.512239, Class: 1.000000, Obj: 0.342161, No Obj: 0.004164, Avg Recall: 0.547619, count: 126 -5662: 0.453128, 0.447266 avg, 0.001000 rate, 21.266788 seconds, 1449472 images -Loaded: 0.605783 seconds -Region Avg IOU: 0.500167, Class: 1.000000, Obj: 0.310114, No Obj: 0.004549, Avg Recall: 0.554688, count: 128 -Region Avg IOU: 0.489320, Class: 1.000000, Obj: 0.333440, No Obj: 0.005133, Avg Recall: 0.565891, count: 129 -Region Avg IOU: 0.494955, Class: 1.000000, Obj: 0.364461, No Obj: 0.004691, Avg Recall: 0.536000, count: 125 -Region Avg IOU: 0.486695, Class: 1.000000, Obj: 0.356873, No Obj: 0.003829, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.504720, Class: 1.000000, Obj: 0.330784, No Obj: 0.004551, Avg Recall: 0.580645, count: 124 -Region Avg IOU: 0.495030, Class: 1.000000, Obj: 0.339394, No Obj: 0.004312, Avg Recall: 0.567797, count: 118 -Region Avg IOU: 0.531370, Class: 1.000000, Obj: 0.349287, No Obj: 0.004964, Avg Recall: 0.597015, count: 134 -Region Avg IOU: 0.501698, Class: 1.000000, Obj: 0.331593, No Obj: 0.004218, Avg Recall: 0.552845, count: 123 -5663: 0.546083, 0.457148 avg, 0.001000 rate, 20.240379 seconds, 1449728 images -Loaded: 0.809243 seconds -Region Avg IOU: 0.556542, Class: 1.000000, Obj: 0.402084, No Obj: 0.004157, Avg Recall: 0.658537, count: 82 -Region Avg IOU: 0.536161, Class: 1.000000, Obj: 0.371413, No Obj: 0.005040, Avg Recall: 0.581197, count: 117 -Region Avg IOU: 0.468726, Class: 1.000000, Obj: 0.347116, No Obj: 0.004219, Avg Recall: 0.509804, count: 102 -Region Avg IOU: 0.463194, Class: 1.000000, Obj: 0.315690, No Obj: 0.004456, Avg Recall: 0.503704, count: 135 -Region Avg IOU: 0.513427, Class: 1.000000, Obj: 0.361104, No Obj: 0.003616, Avg Recall: 0.518072, count: 83 -Region Avg IOU: 0.524732, Class: 1.000000, Obj: 0.322201, No Obj: 0.005531, Avg Recall: 0.640000, count: 125 -Region Avg IOU: 0.524171, Class: 1.000000, Obj: 0.353820, No Obj: 0.004156, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.512521, Class: 1.000000, Obj: 0.378015, No Obj: 0.004680, Avg Recall: 0.589286, count: 112 -5664: 0.449391, 0.456372 avg, 0.001000 rate, 17.492067 seconds, 1449984 images -Loaded: 4.211136 seconds -Region Avg IOU: 0.509446, Class: 1.000000, Obj: 0.360097, No Obj: 0.005572, Avg Recall: 0.583333, count: 132 -Region Avg IOU: 0.487870, Class: 1.000000, Obj: 0.365461, No Obj: 0.005411, Avg Recall: 0.524823, count: 141 -Region Avg IOU: 0.492578, Class: 1.000000, Obj: 0.350735, No Obj: 0.004561, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.526025, Class: 1.000000, Obj: 0.366765, No Obj: 0.005037, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.462800, Class: 1.000000, Obj: 0.378073, No Obj: 0.004009, Avg Recall: 0.478723, count: 94 -Region Avg IOU: 0.533848, Class: 1.000000, Obj: 0.399992, No Obj: 0.004821, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.451136, Class: 1.000000, Obj: 0.346728, No Obj: 0.004688, Avg Recall: 0.530973, count: 113 -Region Avg IOU: 0.471681, Class: 1.000000, Obj: 0.366018, No Obj: 0.004436, Avg Recall: 0.526316, count: 95 -5665: 0.444310, 0.455166 avg, 0.001000 rate, 19.095106 seconds, 1450240 images -Loaded: 2.012098 seconds -Region Avg IOU: 0.531865, Class: 1.000000, Obj: 0.405327, No Obj: 0.005348, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.506002, Class: 1.000000, Obj: 0.372391, No Obj: 0.005853, Avg Recall: 0.571429, count: 133 -Region Avg IOU: 0.511436, Class: 1.000000, Obj: 0.426016, No Obj: 0.005249, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.517533, Class: 1.000000, Obj: 0.458064, No Obj: 0.005321, Avg Recall: 0.567568, count: 111 -Region Avg IOU: 0.543085, Class: 1.000000, Obj: 0.386422, No Obj: 0.005465, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.522194, Class: 1.000000, Obj: 0.454963, No Obj: 0.004809, Avg Recall: 0.586207, count: 87 -Region Avg IOU: 0.541154, Class: 1.000000, Obj: 0.419067, No Obj: 0.004858, Avg Recall: 0.602410, count: 83 -Region Avg IOU: 0.541100, Class: 1.000000, Obj: 0.361453, No Obj: 0.005735, Avg Recall: 0.635659, count: 129 -5666: 0.392858, 0.448935 avg, 0.001000 rate, 17.281115 seconds, 1450496 images -Loaded: 1.010818 seconds -Region Avg IOU: 0.522817, Class: 1.000000, Obj: 0.438083, No Obj: 0.005508, Avg Recall: 0.596639, count: 119 -Region Avg IOU: 0.499604, Class: 1.000000, Obj: 0.417423, No Obj: 0.005135, Avg Recall: 0.525000, count: 80 -Region Avg IOU: 0.465898, Class: 1.000000, Obj: 0.413610, No Obj: 0.005209, Avg Recall: 0.472727, count: 110 -Region Avg IOU: 0.486401, Class: 1.000000, Obj: 0.410656, No Obj: 0.004799, Avg Recall: 0.542553, count: 94 -Region Avg IOU: 0.517606, Class: 1.000000, Obj: 0.425397, No Obj: 0.005352, Avg Recall: 0.596774, count: 124 -Region Avg IOU: 0.493966, Class: 1.000000, Obj: 0.434319, No Obj: 0.005040, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.509683, Class: 1.000000, Obj: 0.396921, No Obj: 0.004810, Avg Recall: 0.576087, count: 92 -Region Avg IOU: 0.518360, Class: 1.000000, Obj: 0.419093, No Obj: 0.004958, Avg Recall: 0.570000, count: 100 -5667: 0.416681, 0.445710 avg, 0.001000 rate, 15.434479 seconds, 1450752 images -Loaded: 4.401090 seconds -Region Avg IOU: 0.521565, Class: 1.000000, Obj: 0.399453, No Obj: 0.005090, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.440650, Class: 1.000000, Obj: 0.380571, No Obj: 0.004830, Avg Recall: 0.426229, count: 122 -Region Avg IOU: 0.525716, Class: 1.000000, Obj: 0.439364, No Obj: 0.005428, Avg Recall: 0.587719, count: 114 -Region Avg IOU: 0.477320, Class: 1.000000, Obj: 0.385961, No Obj: 0.005761, Avg Recall: 0.504274, count: 117 -Region Avg IOU: 0.499499, Class: 1.000000, Obj: 0.397527, No Obj: 0.005304, Avg Recall: 0.511811, count: 127 -Region Avg IOU: 0.450139, Class: 1.000000, Obj: 0.381767, No Obj: 0.005525, Avg Recall: 0.443548, count: 124 -Region Avg IOU: 0.573556, Class: 1.000000, Obj: 0.397696, No Obj: 0.004738, Avg Recall: 0.722222, count: 72 -Region Avg IOU: 0.494943, Class: 1.000000, Obj: 0.422295, No Obj: 0.004516, Avg Recall: 0.550000, count: 80 -5668: 0.476605, 0.448800 avg, 0.001000 rate, 20.094383 seconds, 1451008 images -Loaded: 1.244409 seconds -Region Avg IOU: 0.495118, Class: 1.000000, Obj: 0.372667, No Obj: 0.004464, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.464527, Class: 1.000000, Obj: 0.363762, No Obj: 0.004854, Avg Recall: 0.530435, count: 115 -Region Avg IOU: 0.518404, Class: 1.000000, Obj: 0.405218, No Obj: 0.005729, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.450184, Class: 1.000000, Obj: 0.370057, No Obj: 0.004380, Avg Recall: 0.500000, count: 104 -Region Avg IOU: 0.514862, Class: 1.000000, Obj: 0.447048, No Obj: 0.005634, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.501238, Class: 1.000000, Obj: 0.393445, No Obj: 0.004913, Avg Recall: 0.515152, count: 99 -Region Avg IOU: 0.496617, Class: 1.000000, Obj: 0.428457, No Obj: 0.004479, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.543598, Class: 1.000000, Obj: 0.371718, No Obj: 0.006085, Avg Recall: 0.607843, count: 102 -5669: 0.441857, 0.448105 avg, 0.001000 rate, 19.723381 seconds, 1451264 images -Loaded: 0.701146 seconds -Region Avg IOU: 0.460724, Class: 1.000000, Obj: 0.382975, No Obj: 0.004487, Avg Recall: 0.527273, count: 110 -Region Avg IOU: 0.457110, Class: 1.000000, Obj: 0.353858, No Obj: 0.005091, Avg Recall: 0.450820, count: 122 -Region Avg IOU: 0.519300, Class: 1.000000, Obj: 0.407089, No Obj: 0.004879, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.511920, Class: 1.000000, Obj: 0.393121, No Obj: 0.005235, Avg Recall: 0.530435, count: 115 -Region Avg IOU: 0.472159, Class: 1.000000, Obj: 0.370039, No Obj: 0.004687, Avg Recall: 0.547009, count: 117 -Region Avg IOU: 0.481469, Class: 1.000000, Obj: 0.414465, No Obj: 0.004663, Avg Recall: 0.500000, count: 122 -Region Avg IOU: 0.586053, Class: 1.000000, Obj: 0.452073, No Obj: 0.005429, Avg Recall: 0.687500, count: 80 -Region Avg IOU: 0.467082, Class: 1.000000, Obj: 0.396273, No Obj: 0.004393, Avg Recall: 0.491071, count: 112 -5670: 0.483367, 0.451631 avg, 0.001000 rate, 17.634989 seconds, 1451520 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000087 seconds -Region Avg IOU: 0.549962, Class: 1.000000, Obj: 0.478090, No Obj: 0.002888, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.635472, Class: 1.000000, Obj: 0.417130, No Obj: 0.003752, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.618871, Class: 1.000000, Obj: 0.499624, No Obj: 0.003315, Avg Recall: 0.761905, count: 84 -Region Avg IOU: 0.614238, Class: 1.000000, Obj: 0.486142, No Obj: 0.003588, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.631994, Class: 1.000000, Obj: 0.506996, No Obj: 0.003701, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.651864, Class: 1.000000, Obj: 0.498923, No Obj: 0.003606, Avg Recall: 0.788235, count: 85 -Region Avg IOU: 0.598380, Class: 1.000000, Obj: 0.466473, No Obj: 0.003632, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.614008, Class: 1.000000, Obj: 0.436971, No Obj: 0.004439, Avg Recall: 0.750000, count: 136 -5671: 0.387371, 0.445205 avg, 0.001000 rate, 26.659719 seconds, 1451776 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.602695, Class: 1.000000, Obj: 0.403324, No Obj: 0.003814, Avg Recall: 0.752137, count: 117 -Region Avg IOU: 0.654598, Class: 1.000000, Obj: 0.448720, No Obj: 0.003651, Avg Recall: 0.801980, count: 101 -Region Avg IOU: 0.601733, Class: 1.000000, Obj: 0.435434, No Obj: 0.003328, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.619065, Class: 1.000000, Obj: 0.434245, No Obj: 0.003568, Avg Recall: 0.744000, count: 125 -Region Avg IOU: 0.620285, Class: 1.000000, Obj: 0.421474, No Obj: 0.003974, Avg Recall: 0.704918, count: 122 -Region Avg IOU: 0.596224, Class: 1.000000, Obj: 0.419358, No Obj: 0.003866, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.579588, Class: 1.000000, Obj: 0.387952, No Obj: 0.002899, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.626047, Class: 1.000000, Obj: 0.401548, No Obj: 0.003843, Avg Recall: 0.798165, count: 109 -5672: 0.456918, 0.446377 avg, 0.001000 rate, 28.028795 seconds, 1452032 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.644335, Class: 1.000000, Obj: 0.426223, No Obj: 0.003017, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.585482, Class: 1.000000, Obj: 0.404824, No Obj: 0.003116, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.629490, Class: 1.000000, Obj: 0.398803, No Obj: 0.003591, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.684172, Class: 1.000000, Obj: 0.407853, No Obj: 0.003852, Avg Recall: 0.871795, count: 117 -Region Avg IOU: 0.615794, Class: 1.000000, Obj: 0.376556, No Obj: 0.003052, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.607358, Class: 1.000000, Obj: 0.404183, No Obj: 0.003495, Avg Recall: 0.752066, count: 121 -Region Avg IOU: 0.581205, Class: 1.000000, Obj: 0.375920, No Obj: 0.003234, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.619444, Class: 1.000000, Obj: 0.424393, No Obj: 0.002724, Avg Recall: 0.773333, count: 75 -5673: 0.427320, 0.444471 avg, 0.001000 rate, 27.066824 seconds, 1452288 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.615186, Class: 1.000000, Obj: 0.399830, No Obj: 0.003274, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.612606, Class: 1.000000, Obj: 0.353800, No Obj: 0.003471, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.648410, Class: 1.000000, Obj: 0.409380, No Obj: 0.003543, Avg Recall: 0.823009, count: 113 -Region Avg IOU: 0.629756, Class: 1.000000, Obj: 0.424543, No Obj: 0.003093, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.623992, Class: 1.000000, Obj: 0.368171, No Obj: 0.003020, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.595216, Class: 1.000000, Obj: 0.372811, No Obj: 0.003390, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.579546, Class: 1.000000, Obj: 0.358578, No Obj: 0.002982, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.656425, Class: 1.000000, Obj: 0.402851, No Obj: 0.003374, Avg Recall: 0.791304, count: 115 -5674: 0.484623, 0.448486 avg, 0.001000 rate, 25.655052 seconds, 1452544 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.586347, Class: 1.000000, Obj: 0.373625, No Obj: 0.003358, Avg Recall: 0.697183, count: 142 -Region Avg IOU: 0.580501, Class: 1.000000, Obj: 0.315338, No Obj: 0.002412, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.606694, Class: 1.000000, Obj: 0.342507, No Obj: 0.003466, Avg Recall: 0.691275, count: 149 -Region Avg IOU: 0.660711, Class: 1.000000, Obj: 0.388859, No Obj: 0.002939, Avg Recall: 0.855422, count: 83 -Region Avg IOU: 0.656604, Class: 1.000000, Obj: 0.406798, No Obj: 0.002760, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.629371, Class: 1.000000, Obj: 0.354441, No Obj: 0.002369, Avg Recall: 0.775000, count: 80 -Region Avg IOU: 0.581412, Class: 1.000000, Obj: 0.357126, No Obj: 0.002755, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.640725, Class: 1.000000, Obj: 0.395182, No Obj: 0.002961, Avg Recall: 0.790698, count: 129 -5675: 0.445875, 0.448225 avg, 0.001000 rate, 26.152664 seconds, 1452800 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.645489, Class: 1.000000, Obj: 0.399018, No Obj: 0.002239, Avg Recall: 0.813187, count: 91 -Region Avg IOU: 0.623472, Class: 1.000000, Obj: 0.362157, No Obj: 0.002624, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.626485, Class: 1.000000, Obj: 0.382673, No Obj: 0.002519, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.616151, Class: 1.000000, Obj: 0.369955, No Obj: 0.002341, Avg Recall: 0.752809, count: 89 -Region Avg IOU: 0.654177, Class: 1.000000, Obj: 0.340012, No Obj: 0.002586, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.603910, Class: 1.000000, Obj: 0.343253, No Obj: 0.002888, Avg Recall: 0.732824, count: 131 -Region Avg IOU: 0.650082, Class: 1.000000, Obj: 0.380348, No Obj: 0.002858, Avg Recall: 0.803419, count: 117 -Region Avg IOU: 0.589225, Class: 1.000000, Obj: 0.343626, No Obj: 0.002603, Avg Recall: 0.706422, count: 109 -5676: 0.429206, 0.446323 avg, 0.001000 rate, 24.549004 seconds, 1453056 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.610468, Class: 1.000000, Obj: 0.323964, No Obj: 0.002653, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.644178, Class: 1.000000, Obj: 0.398169, No Obj: 0.002539, Avg Recall: 0.855556, count: 90 -Region Avg IOU: 0.659585, Class: 1.000000, Obj: 0.405133, No Obj: 0.002280, Avg Recall: 0.806818, count: 88 -Region Avg IOU: 0.609324, Class: 1.000000, Obj: 0.350523, No Obj: 0.002790, Avg Recall: 0.742647, count: 136 -Region Avg IOU: 0.668003, Class: 1.000000, Obj: 0.394839, No Obj: 0.002637, Avg Recall: 0.822222, count: 90 -Region Avg IOU: 0.661229, Class: 1.000000, Obj: 0.403602, No Obj: 0.002806, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.609047, Class: 1.000000, Obj: 0.308287, No Obj: 0.002300, Avg Recall: 0.728395, count: 81 -Region Avg IOU: 0.657908, Class: 1.000000, Obj: 0.366209, No Obj: 0.002842, Avg Recall: 0.847619, count: 105 -5677: 0.432219, 0.444913 avg, 0.001000 rate, 26.574619 seconds, 1453312 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.620572, Class: 1.000000, Obj: 0.362436, No Obj: 0.002719, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.608162, Class: 1.000000, Obj: 0.345408, No Obj: 0.002426, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.625295, Class: 1.000000, Obj: 0.360483, No Obj: 0.002686, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.638647, Class: 1.000000, Obj: 0.358227, No Obj: 0.002806, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.615782, Class: 1.000000, Obj: 0.437645, No Obj: 0.002846, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.629637, Class: 1.000000, Obj: 0.359859, No Obj: 0.002530, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.610534, Class: 1.000000, Obj: 0.349888, No Obj: 0.002656, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.641603, Class: 1.000000, Obj: 0.373542, No Obj: 0.002621, Avg Recall: 0.744444, count: 90 -5678: 0.447411, 0.445163 avg, 0.001000 rate, 25.851944 seconds, 1453568 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.628478, Class: 1.000000, Obj: 0.411642, No Obj: 0.002004, Avg Recall: 0.757143, count: 70 -Region Avg IOU: 0.613564, Class: 1.000000, Obj: 0.406481, No Obj: 0.002556, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.611027, Class: 1.000000, Obj: 0.375160, No Obj: 0.003163, Avg Recall: 0.756303, count: 119 -Region Avg IOU: 0.635602, Class: 1.000000, Obj: 0.354646, No Obj: 0.002537, Avg Recall: 0.752809, count: 89 -Region Avg IOU: 0.644074, Class: 1.000000, Obj: 0.378957, No Obj: 0.002891, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.624477, Class: 1.000000, Obj: 0.404250, No Obj: 0.002678, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.621402, Class: 1.000000, Obj: 0.370890, No Obj: 0.002643, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.611664, Class: 1.000000, Obj: 0.366882, No Obj: 0.003017, Avg Recall: 0.757576, count: 132 -5679: 0.431216, 0.443768 avg, 0.001000 rate, 25.491314 seconds, 1453824 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.612158, Class: 1.000000, Obj: 0.377007, No Obj: 0.002781, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.618176, Class: 1.000000, Obj: 0.469329, No Obj: 0.002577, Avg Recall: 0.714286, count: 84 -Region Avg IOU: 0.614616, Class: 1.000000, Obj: 0.379569, No Obj: 0.002694, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.669036, Class: 1.000000, Obj: 0.436446, No Obj: 0.002701, Avg Recall: 0.844660, count: 103 -Region Avg IOU: 0.628218, Class: 1.000000, Obj: 0.383611, No Obj: 0.003451, Avg Recall: 0.736434, count: 129 -Region Avg IOU: 0.575527, Class: 1.000000, Obj: 0.361850, No Obj: 0.002579, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.587427, Class: 1.000000, Obj: 0.438021, No Obj: 0.002644, Avg Recall: 0.640449, count: 89 -Region Avg IOU: 0.611182, Class: 1.000000, Obj: 0.374301, No Obj: 0.002827, Avg Recall: 0.743802, count: 121 -5680: 0.395344, 0.438926 avg, 0.001000 rate, 26.352156 seconds, 1454080 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 17.269964 seconds -Region Avg IOU: 0.595513, Class: 1.000000, Obj: 0.360261, No Obj: 0.004186, Avg Recall: 0.714286, count: 119 -Region Avg IOU: 0.555667, Class: 1.000000, Obj: 0.316879, No Obj: 0.003704, Avg Recall: 0.658537, count: 123 -Region Avg IOU: 0.510962, Class: 1.000000, Obj: 0.336311, No Obj: 0.003183, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.492985, Class: 1.000000, Obj: 0.280768, No Obj: 0.003358, Avg Recall: 0.500000, count: 126 -Region Avg IOU: 0.593898, Class: 1.000000, Obj: 0.410849, No Obj: 0.003487, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.519017, Class: 1.000000, Obj: 0.357105, No Obj: 0.003295, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.575301, Class: 1.000000, Obj: 0.353387, No Obj: 0.003929, Avg Recall: 0.681818, count: 132 -Region Avg IOU: 0.612654, Class: 1.000000, Obj: 0.342491, No Obj: 0.003476, Avg Recall: 0.699115, count: 113 -5681: 0.509351, 0.445968 avg, 0.001000 rate, 23.009296 seconds, 1454336 images -Loaded: 0.259975 seconds -Region Avg IOU: 0.597467, Class: 1.000000, Obj: 0.413710, No Obj: 0.003218, Avg Recall: 0.729412, count: 85 -Region Avg IOU: 0.554235, Class: 1.000000, Obj: 0.383142, No Obj: 0.003834, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.526545, Class: 1.000000, Obj: 0.351954, No Obj: 0.003458, Avg Recall: 0.565574, count: 122 -Region Avg IOU: 0.578673, Class: 1.000000, Obj: 0.438917, No Obj: 0.003912, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.528710, Class: 1.000000, Obj: 0.363730, No Obj: 0.003083, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.581008, Class: 1.000000, Obj: 0.400020, No Obj: 0.003732, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.597965, Class: 1.000000, Obj: 0.414427, No Obj: 0.003291, Avg Recall: 0.726191, count: 84 -Region Avg IOU: 0.525776, Class: 1.000000, Obj: 0.360193, No Obj: 0.004072, Avg Recall: 0.600000, count: 130 -5682: 0.440197, 0.445391 avg, 0.001000 rate, 22.490820 seconds, 1454592 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.558684, Class: 1.000000, Obj: 0.382545, No Obj: 0.003916, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.583645, Class: 1.000000, Obj: 0.421996, No Obj: 0.003365, Avg Recall: 0.666667, count: 78 -Region Avg IOU: 0.568226, Class: 1.000000, Obj: 0.432485, No Obj: 0.003848, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.585006, Class: 1.000000, Obj: 0.427790, No Obj: 0.004270, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.526440, Class: 1.000000, Obj: 0.383608, No Obj: 0.004509, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.537222, Class: 1.000000, Obj: 0.324303, No Obj: 0.003688, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.573549, Class: 1.000000, Obj: 0.413573, No Obj: 0.004130, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.544581, Class: 1.000000, Obj: 0.386488, No Obj: 0.003824, Avg Recall: 0.604167, count: 96 -5683: 0.406153, 0.441467 avg, 0.001000 rate, 21.644564 seconds, 1454848 images -Loaded: 0.027405 seconds -Region Avg IOU: 0.515886, Class: 1.000000, Obj: 0.403922, No Obj: 0.004093, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.556144, Class: 1.000000, Obj: 0.390520, No Obj: 0.003799, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.538614, Class: 1.000000, Obj: 0.327425, No Obj: 0.003586, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.605524, Class: 1.000000, Obj: 0.487144, No Obj: 0.004249, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.592504, Class: 1.000000, Obj: 0.445151, No Obj: 0.004034, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.544484, Class: 1.000000, Obj: 0.410343, No Obj: 0.004361, Avg Recall: 0.587719, count: 114 -Region Avg IOU: 0.567684, Class: 1.000000, Obj: 0.404921, No Obj: 0.003725, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.513556, Class: 1.000000, Obj: 0.410823, No Obj: 0.003597, Avg Recall: 0.588889, count: 90 -5684: 0.437866, 0.441107 avg, 0.001000 rate, 22.138666 seconds, 1455104 images -Loaded: 0.000053 seconds -Region Avg IOU: 0.523466, Class: 1.000000, Obj: 0.390498, No Obj: 0.004490, Avg Recall: 0.588710, count: 124 -Region Avg IOU: 0.612275, Class: 1.000000, Obj: 0.434373, No Obj: 0.004401, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.545204, Class: 1.000000, Obj: 0.427804, No Obj: 0.003611, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.535410, Class: 1.000000, Obj: 0.448784, No Obj: 0.004108, Avg Recall: 0.578947, count: 114 -Region Avg IOU: 0.560270, Class: 1.000000, Obj: 0.424338, No Obj: 0.004347, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.623579, Class: 1.000000, Obj: 0.447750, No Obj: 0.004064, Avg Recall: 0.775281, count: 89 -Region Avg IOU: 0.507015, Class: 1.000000, Obj: 0.410868, No Obj: 0.003748, Avg Recall: 0.549550, count: 111 -Region Avg IOU: 0.578981, Class: 1.000000, Obj: 0.448521, No Obj: 0.004303, Avg Recall: 0.713043, count: 115 -5685: 0.370926, 0.434089 avg, 0.001000 rate, 22.341928 seconds, 1455360 images -Loaded: 0.630793 seconds -Region Avg IOU: 0.533264, Class: 1.000000, Obj: 0.372372, No Obj: 0.004266, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.599335, Class: 1.000000, Obj: 0.437255, No Obj: 0.004160, Avg Recall: 0.719512, count: 82 -Region Avg IOU: 0.559992, Class: 1.000000, Obj: 0.377187, No Obj: 0.004290, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.557267, Class: 1.000000, Obj: 0.390662, No Obj: 0.004754, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.544196, Class: 1.000000, Obj: 0.396623, No Obj: 0.005564, Avg Recall: 0.626866, count: 134 -Region Avg IOU: 0.583915, Class: 1.000000, Obj: 0.413168, No Obj: 0.004487, Avg Recall: 0.661654, count: 133 -Region Avg IOU: 0.596680, Class: 1.000000, Obj: 0.472399, No Obj: 0.004576, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.572294, Class: 1.000000, Obj: 0.403940, No Obj: 0.004852, Avg Recall: 0.689922, count: 129 -5686: 0.434101, 0.434090 avg, 0.001000 rate, 23.748009 seconds, 1455616 images -Loaded: 0.136149 seconds -Region Avg IOU: 0.518343, Class: 1.000000, Obj: 0.413608, No Obj: 0.003983, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.597614, Class: 1.000000, Obj: 0.451788, No Obj: 0.005110, Avg Recall: 0.680328, count: 122 -Region Avg IOU: 0.616621, Class: 1.000000, Obj: 0.446698, No Obj: 0.004791, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.624813, Class: 1.000000, Obj: 0.461467, No Obj: 0.004674, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.522402, Class: 1.000000, Obj: 0.394847, No Obj: 0.004095, Avg Recall: 0.576577, count: 111 -Region Avg IOU: 0.568419, Class: 1.000000, Obj: 0.374723, No Obj: 0.004694, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.584765, Class: 1.000000, Obj: 0.470082, No Obj: 0.004455, Avg Recall: 0.735043, count: 117 -Region Avg IOU: 0.555140, Class: 1.000000, Obj: 0.438061, No Obj: 0.003893, Avg Recall: 0.658824, count: 85 -5687: 0.437042, 0.434385 avg, 0.001000 rate, 23.790468 seconds, 1455872 images -Loaded: 0.000114 seconds -Region Avg IOU: 0.575309, Class: 1.000000, Obj: 0.447117, No Obj: 0.004205, Avg Recall: 0.685393, count: 89 -Region Avg IOU: 0.532812, Class: 1.000000, Obj: 0.404422, No Obj: 0.004779, Avg Recall: 0.596639, count: 119 -Region Avg IOU: 0.524538, Class: 1.000000, Obj: 0.384894, No Obj: 0.004064, Avg Recall: 0.542857, count: 105 -Region Avg IOU: 0.536918, Class: 1.000000, Obj: 0.401489, No Obj: 0.005457, Avg Recall: 0.600000, count: 140 -Region Avg IOU: 0.608602, Class: 1.000000, Obj: 0.469325, No Obj: 0.005598, Avg Recall: 0.735714, count: 140 -Region Avg IOU: 0.590158, Class: 1.000000, Obj: 0.403185, No Obj: 0.004627, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.575881, Class: 1.000000, Obj: 0.368839, No Obj: 0.005751, Avg Recall: 0.678832, count: 137 -Region Avg IOU: 0.512419, Class: 1.000000, Obj: 0.386851, No Obj: 0.004876, Avg Recall: 0.538462, count: 130 -5688: 0.465812, 0.437528 avg, 0.001000 rate, 22.003176 seconds, 1456128 images -Loaded: 0.552289 seconds -Region Avg IOU: 0.554332, Class: 1.000000, Obj: 0.416690, No Obj: 0.004838, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.559535, Class: 1.000000, Obj: 0.410895, No Obj: 0.004041, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.575013, Class: 1.000000, Obj: 0.418472, No Obj: 0.004484, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.625963, Class: 1.000000, Obj: 0.484882, No Obj: 0.004483, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.522225, Class: 1.000000, Obj: 0.406885, No Obj: 0.004876, Avg Recall: 0.566667, count: 120 -Region Avg IOU: 0.576230, Class: 1.000000, Obj: 0.397684, No Obj: 0.004840, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.545472, Class: 1.000000, Obj: 0.419091, No Obj: 0.004249, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.555034, Class: 1.000000, Obj: 0.450385, No Obj: 0.004735, Avg Recall: 0.638554, count: 83 -5689: 0.438057, 0.437581 avg, 0.001000 rate, 22.305397 seconds, 1456384 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.515491, Class: 1.000000, Obj: 0.385399, No Obj: 0.004427, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.567553, Class: 1.000000, Obj: 0.383445, No Obj: 0.004524, Avg Recall: 0.666667, count: 132 -Region Avg IOU: 0.549139, Class: 1.000000, Obj: 0.432656, No Obj: 0.003967, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.528400, Class: 1.000000, Obj: 0.422790, No Obj: 0.004054, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.559692, Class: 1.000000, Obj: 0.390039, No Obj: 0.003642, Avg Recall: 0.666667, count: 87 -Region Avg IOU: 0.574508, Class: 1.000000, Obj: 0.409510, No Obj: 0.004766, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.503369, Class: 1.000000, Obj: 0.359737, No Obj: 0.004922, Avg Recall: 0.575540, count: 139 -Region Avg IOU: 0.528871, Class: 1.000000, Obj: 0.367552, No Obj: 0.003972, Avg Recall: 0.674157, count: 89 -5690: 0.444350, 0.438258 avg, 0.001000 rate, 21.161049 seconds, 1456640 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000086 seconds -Region Avg IOU: 0.639172, Class: 1.000000, Obj: 0.481031, No Obj: 0.003006, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.638582, Class: 1.000000, Obj: 0.486059, No Obj: 0.003033, Avg Recall: 0.826087, count: 92 -Region Avg IOU: 0.646563, Class: 1.000000, Obj: 0.510544, No Obj: 0.003584, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.630115, Class: 1.000000, Obj: 0.443955, No Obj: 0.003271, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.633741, Class: 1.000000, Obj: 0.464394, No Obj: 0.003219, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.638588, Class: 1.000000, Obj: 0.512856, No Obj: 0.003149, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.666337, Class: 1.000000, Obj: 0.524855, No Obj: 0.003550, Avg Recall: 0.823529, count: 102 -Region Avg IOU: 0.665739, Class: 1.000000, Obj: 0.468754, No Obj: 0.003363, Avg Recall: 0.831858, count: 113 -5691: 0.415004, 0.435933 avg, 0.001000 rate, 27.934467 seconds, 1456896 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.690325, Class: 1.000000, Obj: 0.446755, No Obj: 0.003032, Avg Recall: 0.870968, count: 93 -Region Avg IOU: 0.664571, Class: 1.000000, Obj: 0.433050, No Obj: 0.003063, Avg Recall: 0.838384, count: 99 -Region Avg IOU: 0.652590, Class: 1.000000, Obj: 0.443085, No Obj: 0.003642, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.635754, Class: 1.000000, Obj: 0.456492, No Obj: 0.002975, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.646928, Class: 1.000000, Obj: 0.421653, No Obj: 0.002732, Avg Recall: 0.855263, count: 76 -Region Avg IOU: 0.638803, Class: 1.000000, Obj: 0.426068, No Obj: 0.003022, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.690997, Class: 1.000000, Obj: 0.474498, No Obj: 0.003129, Avg Recall: 0.864583, count: 96 -Region Avg IOU: 0.656091, Class: 1.000000, Obj: 0.412775, No Obj: 0.003418, Avg Recall: 0.833333, count: 132 -5692: 0.400842, 0.432423 avg, 0.001000 rate, 27.968088 seconds, 1457152 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.658729, Class: 1.000000, Obj: 0.389811, No Obj: 0.003263, Avg Recall: 0.832061, count: 131 -Region Avg IOU: 0.649624, Class: 1.000000, Obj: 0.470467, No Obj: 0.002658, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.681059, Class: 1.000000, Obj: 0.451122, No Obj: 0.003104, Avg Recall: 0.823009, count: 113 -Region Avg IOU: 0.682467, Class: 1.000000, Obj: 0.443813, No Obj: 0.002449, Avg Recall: 0.868687, count: 99 -Region Avg IOU: 0.672931, Class: 1.000000, Obj: 0.405169, No Obj: 0.003129, Avg Recall: 0.842697, count: 89 -Region Avg IOU: 0.626279, Class: 1.000000, Obj: 0.377976, No Obj: 0.003112, Avg Recall: 0.747967, count: 123 -Region Avg IOU: 0.634362, Class: 1.000000, Obj: 0.392478, No Obj: 0.002817, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.656172, Class: 1.000000, Obj: 0.388101, No Obj: 0.002503, Avg Recall: 0.804124, count: 97 -5693: 0.440733, 0.433254 avg, 0.001000 rate, 27.897196 seconds, 1457408 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.662957, Class: 1.000000, Obj: 0.426410, No Obj: 0.002493, Avg Recall: 0.831776, count: 107 -Region Avg IOU: 0.614299, Class: 1.000000, Obj: 0.429290, No Obj: 0.002537, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.705411, Class: 1.000000, Obj: 0.397687, No Obj: 0.002992, Avg Recall: 0.887755, count: 98 -Region Avg IOU: 0.689052, Class: 1.000000, Obj: 0.412695, No Obj: 0.002643, Avg Recall: 0.842105, count: 95 -Region Avg IOU: 0.676137, Class: 1.000000, Obj: 0.398123, No Obj: 0.002430, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.657731, Class: 1.000000, Obj: 0.354691, No Obj: 0.002642, Avg Recall: 0.789062, count: 128 -Region Avg IOU: 0.631799, Class: 1.000000, Obj: 0.408998, No Obj: 0.002624, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.647291, Class: 1.000000, Obj: 0.448187, No Obj: 0.002420, Avg Recall: 0.781818, count: 110 -5694: 0.440840, 0.434013 avg, 0.001000 rate, 26.332533 seconds, 1457664 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.658346, Class: 1.000000, Obj: 0.416154, No Obj: 0.002112, Avg Recall: 0.820755, count: 106 -Region Avg IOU: 0.679540, Class: 1.000000, Obj: 0.430902, No Obj: 0.002370, Avg Recall: 0.851485, count: 101 -Region Avg IOU: 0.674014, Class: 1.000000, Obj: 0.386597, No Obj: 0.002243, Avg Recall: 0.870000, count: 100 -Region Avg IOU: 0.647988, Class: 1.000000, Obj: 0.362090, No Obj: 0.002499, Avg Recall: 0.801587, count: 126 -Region Avg IOU: 0.650229, Class: 1.000000, Obj: 0.384096, No Obj: 0.002325, Avg Recall: 0.811111, count: 90 -Region Avg IOU: 0.684024, Class: 1.000000, Obj: 0.388222, No Obj: 0.002264, Avg Recall: 0.853933, count: 89 -Region Avg IOU: 0.654144, Class: 1.000000, Obj: 0.355623, No Obj: 0.002261, Avg Recall: 0.847826, count: 92 -Region Avg IOU: 0.616564, Class: 1.000000, Obj: 0.367556, No Obj: 0.002054, Avg Recall: 0.750000, count: 100 -5695: 0.427813, 0.433393 avg, 0.001000 rate, 28.772013 seconds, 1457920 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.649991, Class: 1.000000, Obj: 0.303196, No Obj: 0.002284, Avg Recall: 0.788136, count: 118 -Region Avg IOU: 0.670146, Class: 1.000000, Obj: 0.337625, No Obj: 0.002303, Avg Recall: 0.833333, count: 108 -Region Avg IOU: 0.672018, Class: 1.000000, Obj: 0.363403, No Obj: 0.002139, Avg Recall: 0.836538, count: 104 -Region Avg IOU: 0.643066, Class: 1.000000, Obj: 0.375364, No Obj: 0.002119, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.679109, Class: 1.000000, Obj: 0.398885, No Obj: 0.002249, Avg Recall: 0.857143, count: 105 -Region Avg IOU: 0.661584, Class: 1.000000, Obj: 0.347763, No Obj: 0.002382, Avg Recall: 0.831776, count: 107 -Region Avg IOU: 0.646862, Class: 1.000000, Obj: 0.344126, No Obj: 0.002128, Avg Recall: 0.847619, count: 105 -Region Avg IOU: 0.677226, Class: 1.000000, Obj: 0.366696, No Obj: 0.002178, Avg Recall: 0.815534, count: 103 -5696: 0.437273, 0.433781 avg, 0.001000 rate, 28.804548 seconds, 1458176 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.635166, Class: 1.000000, Obj: 0.349451, No Obj: 0.002044, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.663058, Class: 1.000000, Obj: 0.387509, No Obj: 0.002488, Avg Recall: 0.825688, count: 109 -Region Avg IOU: 0.654686, Class: 1.000000, Obj: 0.326719, No Obj: 0.002166, Avg Recall: 0.824074, count: 108 -Region Avg IOU: 0.659389, Class: 1.000000, Obj: 0.377475, No Obj: 0.002341, Avg Recall: 0.851852, count: 108 -Region Avg IOU: 0.675781, Class: 1.000000, Obj: 0.375487, No Obj: 0.002386, Avg Recall: 0.836207, count: 116 -Region Avg IOU: 0.691403, Class: 1.000000, Obj: 0.410120, No Obj: 0.002108, Avg Recall: 0.898876, count: 89 -Region Avg IOU: 0.630823, Class: 1.000000, Obj: 0.409545, No Obj: 0.002161, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.658633, Class: 1.000000, Obj: 0.331609, No Obj: 0.002101, Avg Recall: 0.846154, count: 104 -5697: 0.420568, 0.432460 avg, 0.001000 rate, 27.966656 seconds, 1458432 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.635000, Class: 1.000000, Obj: 0.366842, No Obj: 0.001919, Avg Recall: 0.775000, count: 80 -Region Avg IOU: 0.670055, Class: 1.000000, Obj: 0.345869, No Obj: 0.002126, Avg Recall: 0.860215, count: 93 -Region Avg IOU: 0.655240, Class: 1.000000, Obj: 0.360847, No Obj: 0.002245, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.649361, Class: 1.000000, Obj: 0.416458, No Obj: 0.002256, Avg Recall: 0.824742, count: 97 -Region Avg IOU: 0.624939, Class: 1.000000, Obj: 0.305018, No Obj: 0.002231, Avg Recall: 0.761905, count: 126 -Region Avg IOU: 0.660453, Class: 1.000000, Obj: 0.395958, No Obj: 0.002338, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.690356, Class: 1.000000, Obj: 0.409106, No Obj: 0.002229, Avg Recall: 0.850000, count: 100 -Region Avg IOU: 0.634255, Class: 1.000000, Obj: 0.330865, No Obj: 0.002208, Avg Recall: 0.750000, count: 120 -5698: 0.431014, 0.432315 avg, 0.001000 rate, 28.131205 seconds, 1458688 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.686176, Class: 1.000000, Obj: 0.380564, No Obj: 0.002549, Avg Recall: 0.875912, count: 137 -Region Avg IOU: 0.647353, Class: 1.000000, Obj: 0.398350, No Obj: 0.002274, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.665108, Class: 1.000000, Obj: 0.406828, No Obj: 0.002295, Avg Recall: 0.794643, count: 112 -Region Avg IOU: 0.639985, Class: 1.000000, Obj: 0.321175, No Obj: 0.002544, Avg Recall: 0.831579, count: 95 -Region Avg IOU: 0.648171, Class: 1.000000, Obj: 0.340932, No Obj: 0.002120, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.681717, Class: 1.000000, Obj: 0.423779, No Obj: 0.002122, Avg Recall: 0.835294, count: 85 -Region Avg IOU: 0.645000, Class: 1.000000, Obj: 0.389231, No Obj: 0.002519, Avg Recall: 0.801587, count: 126 -Region Avg IOU: 0.640406, Class: 1.000000, Obj: 0.403870, No Obj: 0.002315, Avg Recall: 0.818182, count: 110 -5699: 0.415032, 0.430587 avg, 0.001000 rate, 28.577160 seconds, 1458944 images -Loaded: 0.000118 seconds -Region Avg IOU: 0.704983, Class: 1.000000, Obj: 0.458432, No Obj: 0.002657, Avg Recall: 0.912621, count: 103 -Region Avg IOU: 0.647702, Class: 1.000000, Obj: 0.386647, No Obj: 0.002412, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.669242, Class: 1.000000, Obj: 0.384029, No Obj: 0.002403, Avg Recall: 0.831461, count: 89 -Region Avg IOU: 0.666848, Class: 1.000000, Obj: 0.439152, No Obj: 0.002535, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.633012, Class: 1.000000, Obj: 0.402304, No Obj: 0.002427, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.639523, Class: 1.000000, Obj: 0.357119, No Obj: 0.002778, Avg Recall: 0.800000, count: 130 -Region Avg IOU: 0.657952, Class: 1.000000, Obj: 0.417162, No Obj: 0.002156, Avg Recall: 0.785714, count: 70 -Region Avg IOU: 0.693978, Class: 1.000000, Obj: 0.442701, No Obj: 0.002597, Avg Recall: 0.838384, count: 99 -5700: 0.381320, 0.425660 avg, 0.001000 rate, 27.757910 seconds, 1459200 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 14.920722 seconds -Region Avg IOU: 0.595112, Class: 1.000000, Obj: 0.414867, No Obj: 0.003202, Avg Recall: 0.705036, count: 139 -Region Avg IOU: 0.644951, Class: 1.000000, Obj: 0.488251, No Obj: 0.002826, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.602172, Class: 1.000000, Obj: 0.383373, No Obj: 0.002840, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.617334, Class: 1.000000, Obj: 0.404837, No Obj: 0.002821, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.624411, Class: 1.000000, Obj: 0.394049, No Obj: 0.002709, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.631253, Class: 1.000000, Obj: 0.392140, No Obj: 0.002692, Avg Recall: 0.785714, count: 84 -Region Avg IOU: 0.642347, Class: 1.000000, Obj: 0.348999, No Obj: 0.002603, Avg Recall: 0.797753, count: 89 -Region Avg IOU: 0.567641, Class: 1.000000, Obj: 0.439146, No Obj: 0.002530, Avg Recall: 0.643678, count: 87 -5701: 0.402828, 0.423377 avg, 0.001000 rate, 27.255947 seconds, 1459456 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.579720, Class: 1.000000, Obj: 0.405578, No Obj: 0.003235, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.597901, Class: 1.000000, Obj: 0.385023, No Obj: 0.003352, Avg Recall: 0.687943, count: 141 -Region Avg IOU: 0.642093, Class: 1.000000, Obj: 0.415015, No Obj: 0.002909, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.660720, Class: 1.000000, Obj: 0.445353, No Obj: 0.002620, Avg Recall: 0.819277, count: 83 -Region Avg IOU: 0.597135, Class: 1.000000, Obj: 0.414376, No Obj: 0.002731, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.604919, Class: 1.000000, Obj: 0.414430, No Obj: 0.002670, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.653842, Class: 1.000000, Obj: 0.453913, No Obj: 0.002551, Avg Recall: 0.808989, count: 89 -Region Avg IOU: 0.638152, Class: 1.000000, Obj: 0.373020, No Obj: 0.002805, Avg Recall: 0.816092, count: 87 -5702: 0.393429, 0.420382 avg, 0.001000 rate, 26.423391 seconds, 1459712 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.658861, Class: 1.000000, Obj: 0.441430, No Obj: 0.003128, Avg Recall: 0.829268, count: 82 -Region Avg IOU: 0.594072, Class: 1.000000, Obj: 0.404198, No Obj: 0.002743, Avg Recall: 0.642857, count: 84 -Region Avg IOU: 0.622255, Class: 1.000000, Obj: 0.420536, No Obj: 0.003407, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.612098, Class: 1.000000, Obj: 0.402412, No Obj: 0.003318, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.630979, Class: 1.000000, Obj: 0.415506, No Obj: 0.002739, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.625560, Class: 1.000000, Obj: 0.452399, No Obj: 0.003470, Avg Recall: 0.811024, count: 127 -Region Avg IOU: 0.668218, Class: 1.000000, Obj: 0.454286, No Obj: 0.002944, Avg Recall: 0.813333, count: 75 -Region Avg IOU: 0.661631, Class: 1.000000, Obj: 0.457096, No Obj: 0.003416, Avg Recall: 0.804878, count: 82 -5703: 0.392611, 0.417605 avg, 0.001000 rate, 25.317158 seconds, 1459968 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.611097, Class: 1.000000, Obj: 0.402358, No Obj: 0.002805, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.636134, Class: 1.000000, Obj: 0.412184, No Obj: 0.003229, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.631369, Class: 1.000000, Obj: 0.388978, No Obj: 0.002795, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.614948, Class: 1.000000, Obj: 0.445587, No Obj: 0.003217, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.623026, Class: 1.000000, Obj: 0.432400, No Obj: 0.002957, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.617714, Class: 1.000000, Obj: 0.405233, No Obj: 0.002665, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.613321, Class: 1.000000, Obj: 0.414302, No Obj: 0.002594, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.676847, Class: 1.000000, Obj: 0.410133, No Obj: 0.003203, Avg Recall: 0.831683, count: 101 -5704: 0.408968, 0.416741 avg, 0.001000 rate, 27.295914 seconds, 1460224 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.651371, Class: 1.000000, Obj: 0.428318, No Obj: 0.002998, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.695496, Class: 1.000000, Obj: 0.493488, No Obj: 0.002460, Avg Recall: 0.896104, count: 77 -Region Avg IOU: 0.643929, Class: 1.000000, Obj: 0.392905, No Obj: 0.002802, Avg Recall: 0.784810, count: 79 -Region Avg IOU: 0.638204, Class: 1.000000, Obj: 0.456197, No Obj: 0.002988, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.658289, Class: 1.000000, Obj: 0.449400, No Obj: 0.002768, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.653632, Class: 1.000000, Obj: 0.485584, No Obj: 0.002812, Avg Recall: 0.784091, count: 88 -Region Avg IOU: 0.627098, Class: 1.000000, Obj: 0.413045, No Obj: 0.003409, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.612354, Class: 1.000000, Obj: 0.402868, No Obj: 0.002560, Avg Recall: 0.761905, count: 105 -5705: 0.398732, 0.414940 avg, 0.001000 rate, 27.113195 seconds, 1460480 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.632246, Class: 1.000000, Obj: 0.431039, No Obj: 0.003388, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.626369, Class: 1.000000, Obj: 0.409726, No Obj: 0.003180, Avg Recall: 0.756303, count: 119 -Region Avg IOU: 0.609836, Class: 1.000000, Obj: 0.401728, No Obj: 0.002929, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.632098, Class: 1.000000, Obj: 0.400750, No Obj: 0.003345, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.695595, Class: 1.000000, Obj: 0.493740, No Obj: 0.003443, Avg Recall: 0.843750, count: 96 -Region Avg IOU: 0.624001, Class: 1.000000, Obj: 0.433008, No Obj: 0.003194, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.612819, Class: 1.000000, Obj: 0.370430, No Obj: 0.003418, Avg Recall: 0.761905, count: 126 -Region Avg IOU: 0.649666, Class: 1.000000, Obj: 0.426928, No Obj: 0.002536, Avg Recall: 0.792208, count: 77 -5706: 0.427213, 0.416167 avg, 0.001000 rate, 25.503056 seconds, 1460736 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.603849, Class: 1.000000, Obj: 0.401921, No Obj: 0.003261, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.626668, Class: 1.000000, Obj: 0.494789, No Obj: 0.002717, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.625843, Class: 1.000000, Obj: 0.414955, No Obj: 0.002650, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.637630, Class: 1.000000, Obj: 0.447591, No Obj: 0.003110, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.660450, Class: 1.000000, Obj: 0.443680, No Obj: 0.003077, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.660048, Class: 1.000000, Obj: 0.454707, No Obj: 0.002876, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.635867, Class: 1.000000, Obj: 0.434279, No Obj: 0.002487, Avg Recall: 0.760000, count: 75 -Region Avg IOU: 0.656856, Class: 1.000000, Obj: 0.425112, No Obj: 0.003303, Avg Recall: 0.778688, count: 122 -5707: 0.387261, 0.413277 avg, 0.001000 rate, 26.354048 seconds, 1460992 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.626081, Class: 1.000000, Obj: 0.389029, No Obj: 0.002962, Avg Recall: 0.766129, count: 124 -Region Avg IOU: 0.568011, Class: 1.000000, Obj: 0.413768, No Obj: 0.002791, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.639920, Class: 1.000000, Obj: 0.393754, No Obj: 0.002908, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.654245, Class: 1.000000, Obj: 0.464862, No Obj: 0.002883, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.617745, Class: 1.000000, Obj: 0.390071, No Obj: 0.002971, Avg Recall: 0.764228, count: 123 -Region Avg IOU: 0.646164, Class: 1.000000, Obj: 0.457617, No Obj: 0.002698, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.646437, Class: 1.000000, Obj: 0.417996, No Obj: 0.002732, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.601578, Class: 1.000000, Obj: 0.368601, No Obj: 0.002719, Avg Recall: 0.714286, count: 98 -5708: 0.392039, 0.411153 avg, 0.001000 rate, 27.046303 seconds, 1461248 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.650657, Class: 1.000000, Obj: 0.407542, No Obj: 0.002496, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.598902, Class: 1.000000, Obj: 0.429064, No Obj: 0.002622, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.594622, Class: 1.000000, Obj: 0.384767, No Obj: 0.002768, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.627986, Class: 1.000000, Obj: 0.460071, No Obj: 0.002756, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.589664, Class: 1.000000, Obj: 0.386768, No Obj: 0.002468, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.659943, Class: 1.000000, Obj: 0.397321, No Obj: 0.002331, Avg Recall: 0.809524, count: 84 -Region Avg IOU: 0.621671, Class: 1.000000, Obj: 0.407156, No Obj: 0.002685, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.673012, Class: 1.000000, Obj: 0.417417, No Obj: 0.002993, Avg Recall: 0.857143, count: 105 -5709: 0.370824, 0.407120 avg, 0.001000 rate, 25.549112 seconds, 1461504 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.631780, Class: 1.000000, Obj: 0.429231, No Obj: 0.002712, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.600366, Class: 1.000000, Obj: 0.418744, No Obj: 0.002553, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.614124, Class: 1.000000, Obj: 0.410157, No Obj: 0.002500, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.667939, Class: 1.000000, Obj: 0.417487, No Obj: 0.002795, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.615381, Class: 1.000000, Obj: 0.349070, No Obj: 0.002928, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.638632, Class: 1.000000, Obj: 0.386367, No Obj: 0.002778, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.651943, Class: 1.000000, Obj: 0.406608, No Obj: 0.002879, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.636237, Class: 1.000000, Obj: 0.413547, No Obj: 0.002661, Avg Recall: 0.788991, count: 109 -5710: 0.396795, 0.406088 avg, 0.001000 rate, 25.828094 seconds, 1461760 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 19.852600 seconds -Region Avg IOU: 0.575116, Class: 1.000000, Obj: 0.363068, No Obj: 0.002611, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.596476, Class: 1.000000, Obj: 0.357097, No Obj: 0.003477, Avg Recall: 0.685484, count: 124 -Region Avg IOU: 0.532495, Class: 1.000000, Obj: 0.344094, No Obj: 0.002612, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.578607, Class: 1.000000, Obj: 0.341042, No Obj: 0.003160, Avg Recall: 0.674603, count: 126 -Region Avg IOU: 0.565080, Class: 1.000000, Obj: 0.402757, No Obj: 0.002888, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.554217, Class: 1.000000, Obj: 0.348134, No Obj: 0.003732, Avg Recall: 0.642857, count: 126 -Region Avg IOU: 0.546831, Class: 1.000000, Obj: 0.360255, No Obj: 0.002748, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.591778, Class: 1.000000, Obj: 0.348507, No Obj: 0.003335, Avg Recall: 0.716814, count: 113 -5711: 0.425404, 0.408019 avg, 0.001000 rate, 23.790298 seconds, 1462016 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.574155, Class: 1.000000, Obj: 0.353771, No Obj: 0.003017, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.581992, Class: 1.000000, Obj: 0.363417, No Obj: 0.003829, Avg Recall: 0.661871, count: 139 -Region Avg IOU: 0.606612, Class: 1.000000, Obj: 0.364979, No Obj: 0.003264, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.612941, Class: 1.000000, Obj: 0.376126, No Obj: 0.003106, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.577605, Class: 1.000000, Obj: 0.378919, No Obj: 0.003097, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.562289, Class: 1.000000, Obj: 0.374134, No Obj: 0.003617, Avg Recall: 0.669421, count: 121 -Region Avg IOU: 0.486955, Class: 1.000000, Obj: 0.342115, No Obj: 0.002580, Avg Recall: 0.516484, count: 91 -Region Avg IOU: 0.577834, Class: 1.000000, Obj: 0.356319, No Obj: 0.003620, Avg Recall: 0.697842, count: 139 -5712: 0.444378, 0.411655 avg, 0.001000 rate, 23.054955 seconds, 1462272 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.585500, Class: 1.000000, Obj: 0.440952, No Obj: 0.002949, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.609730, Class: 1.000000, Obj: 0.347185, No Obj: 0.003695, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.533002, Class: 1.000000, Obj: 0.337784, No Obj: 0.003268, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.590136, Class: 1.000000, Obj: 0.398271, No Obj: 0.003112, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.609043, Class: 1.000000, Obj: 0.333939, No Obj: 0.003567, Avg Recall: 0.719298, count: 114 -Region Avg IOU: 0.608898, Class: 1.000000, Obj: 0.402232, No Obj: 0.003127, Avg Recall: 0.720930, count: 86 -Region Avg IOU: 0.583675, Class: 1.000000, Obj: 0.329233, No Obj: 0.003617, Avg Recall: 0.700730, count: 137 -Region Avg IOU: 0.576900, Class: 1.000000, Obj: 0.378513, No Obj: 0.003663, Avg Recall: 0.683333, count: 120 -5713: 0.412604, 0.411750 avg, 0.001000 rate, 22.945908 seconds, 1462528 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.618679, Class: 1.000000, Obj: 0.397947, No Obj: 0.003415, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.527394, Class: 1.000000, Obj: 0.345772, No Obj: 0.003742, Avg Recall: 0.636364, count: 154 -Region Avg IOU: 0.536205, Class: 1.000000, Obj: 0.390901, No Obj: 0.002825, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.553890, Class: 1.000000, Obj: 0.393487, No Obj: 0.003492, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.605457, Class: 1.000000, Obj: 0.435844, No Obj: 0.002911, Avg Recall: 0.714286, count: 77 -Region Avg IOU: 0.537637, Class: 1.000000, Obj: 0.347360, No Obj: 0.003366, Avg Recall: 0.586207, count: 116 -Region Avg IOU: 0.581860, Class: 1.000000, Obj: 0.422682, No Obj: 0.002852, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.656446, Class: 1.000000, Obj: 0.430798, No Obj: 0.003277, Avg Recall: 0.808081, count: 99 -5714: 0.405248, 0.411100 avg, 0.001000 rate, 22.788767 seconds, 1462784 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.551224, Class: 1.000000, Obj: 0.390362, No Obj: 0.003308, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.566391, Class: 1.000000, Obj: 0.404963, No Obj: 0.003480, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.561427, Class: 1.000000, Obj: 0.412830, No Obj: 0.003840, Avg Recall: 0.643411, count: 129 -Region Avg IOU: 0.570132, Class: 1.000000, Obj: 0.385704, No Obj: 0.003334, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.628412, Class: 1.000000, Obj: 0.399869, No Obj: 0.003796, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.588677, Class: 1.000000, Obj: 0.372257, No Obj: 0.003888, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.577355, Class: 1.000000, Obj: 0.407653, No Obj: 0.003295, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.588405, Class: 1.000000, Obj: 0.382438, No Obj: 0.003534, Avg Recall: 0.685393, count: 89 -5715: 0.451089, 0.415099 avg, 0.001000 rate, 23.766504 seconds, 1463040 images -Loaded: 0.304988 seconds -Region Avg IOU: 0.559228, Class: 1.000000, Obj: 0.448739, No Obj: 0.003303, Avg Recall: 0.629214, count: 89 -Region Avg IOU: 0.608548, Class: 1.000000, Obj: 0.410833, No Obj: 0.004352, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.611708, Class: 1.000000, Obj: 0.461579, No Obj: 0.003799, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.574308, Class: 1.000000, Obj: 0.402320, No Obj: 0.003770, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.538078, Class: 1.000000, Obj: 0.436023, No Obj: 0.003142, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.600293, Class: 1.000000, Obj: 0.450602, No Obj: 0.003450, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.576311, Class: 1.000000, Obj: 0.452381, No Obj: 0.003863, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.529792, Class: 1.000000, Obj: 0.418480, No Obj: 0.003119, Avg Recall: 0.597701, count: 87 -5716: 0.421488, 0.415738 avg, 0.001000 rate, 22.285030 seconds, 1463296 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.528180, Class: 1.000000, Obj: 0.372446, No Obj: 0.003571, Avg Recall: 0.581967, count: 122 -Region Avg IOU: 0.536178, Class: 1.000000, Obj: 0.389295, No Obj: 0.003985, Avg Recall: 0.612903, count: 124 -Region Avg IOU: 0.539635, Class: 1.000000, Obj: 0.411216, No Obj: 0.003299, Avg Recall: 0.629214, count: 89 -Region Avg IOU: 0.582696, Class: 1.000000, Obj: 0.480724, No Obj: 0.003945, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.523171, Class: 1.000000, Obj: 0.379823, No Obj: 0.003620, Avg Recall: 0.593750, count: 96 -Region Avg IOU: 0.589792, Class: 1.000000, Obj: 0.437535, No Obj: 0.004006, Avg Recall: 0.687500, count: 128 -Region Avg IOU: 0.569291, Class: 1.000000, Obj: 0.452135, No Obj: 0.004106, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.598681, Class: 1.000000, Obj: 0.424090, No Obj: 0.003594, Avg Recall: 0.726316, count: 95 -5717: 0.433517, 0.417515 avg, 0.001000 rate, 23.129999 seconds, 1463552 images -Loaded: 0.000051 seconds -Region Avg IOU: 0.540899, Class: 1.000000, Obj: 0.419686, No Obj: 0.004666, Avg Recall: 0.593333, count: 150 -Region Avg IOU: 0.586353, Class: 1.000000, Obj: 0.413619, No Obj: 0.003613, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.555793, Class: 1.000000, Obj: 0.422897, No Obj: 0.004499, Avg Recall: 0.619835, count: 121 -Region Avg IOU: 0.584023, Class: 1.000000, Obj: 0.414940, No Obj: 0.004268, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.541369, Class: 1.000000, Obj: 0.406859, No Obj: 0.004142, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.618089, Class: 1.000000, Obj: 0.422463, No Obj: 0.004023, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.567049, Class: 1.000000, Obj: 0.444389, No Obj: 0.004392, Avg Recall: 0.658333, count: 120 -Region Avg IOU: 0.499712, Class: 1.000000, Obj: 0.354801, No Obj: 0.003902, Avg Recall: 0.576577, count: 111 -5718: 0.428731, 0.418637 avg, 0.001000 rate, 23.579723 seconds, 1463808 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.543099, Class: 1.000000, Obj: 0.404897, No Obj: 0.003412, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.585310, Class: 1.000000, Obj: 0.433508, No Obj: 0.003873, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.615285, Class: 1.000000, Obj: 0.433628, No Obj: 0.003588, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.557611, Class: 1.000000, Obj: 0.442665, No Obj: 0.004350, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.547731, Class: 1.000000, Obj: 0.428394, No Obj: 0.003968, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.585701, Class: 1.000000, Obj: 0.484380, No Obj: 0.004588, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.566837, Class: 1.000000, Obj: 0.403940, No Obj: 0.004221, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.600917, Class: 1.000000, Obj: 0.436798, No Obj: 0.004077, Avg Recall: 0.659794, count: 97 -5719: 0.421959, 0.418969 avg, 0.001000 rate, 23.557024 seconds, 1464064 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.565067, Class: 1.000000, Obj: 0.424885, No Obj: 0.004554, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.579896, Class: 1.000000, Obj: 0.425041, No Obj: 0.003170, Avg Recall: 0.705882, count: 85 -Region Avg IOU: 0.575536, Class: 1.000000, Obj: 0.376000, No Obj: 0.004478, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.572695, Class: 1.000000, Obj: 0.478032, No Obj: 0.003629, Avg Recall: 0.686747, count: 83 -Region Avg IOU: 0.603835, Class: 1.000000, Obj: 0.476501, No Obj: 0.003865, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.488284, Class: 1.000000, Obj: 0.374689, No Obj: 0.003506, Avg Recall: 0.522936, count: 109 -Region Avg IOU: 0.611575, Class: 1.000000, Obj: 0.453242, No Obj: 0.004497, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.534098, Class: 1.000000, Obj: 0.433713, No Obj: 0.003692, Avg Recall: 0.590164, count: 122 -5720: 0.411391, 0.418211 avg, 0.001000 rate, 22.169359 seconds, 1464320 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000068 seconds -Region Avg IOU: 0.609770, Class: 1.000000, Obj: 0.488425, No Obj: 0.003625, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.569327, Class: 1.000000, Obj: 0.445319, No Obj: 0.003794, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.647238, Class: 1.000000, Obj: 0.474156, No Obj: 0.003428, Avg Recall: 0.823529, count: 85 -Region Avg IOU: 0.614340, Class: 1.000000, Obj: 0.427018, No Obj: 0.004102, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.611851, Class: 1.000000, Obj: 0.453207, No Obj: 0.003465, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.579259, Class: 1.000000, Obj: 0.429311, No Obj: 0.003318, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.642446, Class: 1.000000, Obj: 0.483999, No Obj: 0.003845, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.603986, Class: 1.000000, Obj: 0.474068, No Obj: 0.003349, Avg Recall: 0.773913, count: 115 -5721: 0.387266, 0.415117 avg, 0.001000 rate, 24.278740 seconds, 1464576 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.661435, Class: 1.000000, Obj: 0.454207, No Obj: 0.003422, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.618189, Class: 1.000000, Obj: 0.476948, No Obj: 0.003605, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.644292, Class: 1.000000, Obj: 0.483576, No Obj: 0.003602, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.639034, Class: 1.000000, Obj: 0.500295, No Obj: 0.003324, Avg Recall: 0.831683, count: 101 -Region Avg IOU: 0.643407, Class: 1.000000, Obj: 0.439242, No Obj: 0.003467, Avg Recall: 0.794872, count: 78 -Region Avg IOU: 0.643039, Class: 1.000000, Obj: 0.440219, No Obj: 0.003625, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.570628, Class: 1.000000, Obj: 0.407462, No Obj: 0.003093, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.625361, Class: 1.000000, Obj: 0.470401, No Obj: 0.003024, Avg Recall: 0.709302, count: 86 -5722: 0.359548, 0.409560 avg, 0.001000 rate, 24.573673 seconds, 1464832 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.596377, Class: 1.000000, Obj: 0.398548, No Obj: 0.003529, Avg Recall: 0.671533, count: 137 -Region Avg IOU: 0.662555, Class: 1.000000, Obj: 0.408876, No Obj: 0.003565, Avg Recall: 0.837838, count: 111 -Region Avg IOU: 0.624049, Class: 1.000000, Obj: 0.407638, No Obj: 0.003482, Avg Recall: 0.733333, count: 120 -Region Avg IOU: 0.595489, Class: 1.000000, Obj: 0.407807, No Obj: 0.003217, Avg Recall: 0.733333, count: 120 -Region Avg IOU: 0.664571, Class: 1.000000, Obj: 0.460526, No Obj: 0.003120, Avg Recall: 0.820225, count: 89 -Region Avg IOU: 0.607035, Class: 1.000000, Obj: 0.379400, No Obj: 0.003580, Avg Recall: 0.705426, count: 129 -Region Avg IOU: 0.636797, Class: 1.000000, Obj: 0.418279, No Obj: 0.003611, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.649486, Class: 1.000000, Obj: 0.447254, No Obj: 0.003572, Avg Recall: 0.796610, count: 118 -5723: 0.450632, 0.413667 avg, 0.001000 rate, 25.825617 seconds, 1465088 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.600124, Class: 1.000000, Obj: 0.411620, No Obj: 0.002957, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.598312, Class: 1.000000, Obj: 0.427241, No Obj: 0.002687, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.576195, Class: 1.000000, Obj: 0.396167, No Obj: 0.002915, Avg Recall: 0.688525, count: 122 -Region Avg IOU: 0.666644, Class: 1.000000, Obj: 0.448640, No Obj: 0.003366, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.588751, Class: 1.000000, Obj: 0.401679, No Obj: 0.003294, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.657366, Class: 1.000000, Obj: 0.457530, No Obj: 0.002873, Avg Recall: 0.789474, count: 76 -Region Avg IOU: 0.627187, Class: 1.000000, Obj: 0.386870, No Obj: 0.003133, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.623286, Class: 1.000000, Obj: 0.377401, No Obj: 0.002894, Avg Recall: 0.752381, count: 105 -5724: 0.414680, 0.413768 avg, 0.001000 rate, 25.278021 seconds, 1465344 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.616885, Class: 1.000000, Obj: 0.388580, No Obj: 0.003285, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.610409, Class: 1.000000, Obj: 0.373000, No Obj: 0.003078, Avg Recall: 0.735537, count: 121 -Region Avg IOU: 0.641813, Class: 1.000000, Obj: 0.363576, No Obj: 0.003459, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.645015, Class: 1.000000, Obj: 0.389585, No Obj: 0.003019, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.619716, Class: 1.000000, Obj: 0.368888, No Obj: 0.003135, Avg Recall: 0.740458, count: 131 -Region Avg IOU: 0.620780, Class: 1.000000, Obj: 0.433465, No Obj: 0.002927, Avg Recall: 0.734940, count: 83 -Region Avg IOU: 0.648887, Class: 1.000000, Obj: 0.438096, No Obj: 0.002802, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.675563, Class: 1.000000, Obj: 0.408483, No Obj: 0.003188, Avg Recall: 0.834783, count: 115 -5725: 0.449559, 0.417348 avg, 0.001000 rate, 24.893745 seconds, 1465600 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.665032, Class: 1.000000, Obj: 0.385454, No Obj: 0.003118, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.661718, Class: 1.000000, Obj: 0.415064, No Obj: 0.003457, Avg Recall: 0.785124, count: 121 -Region Avg IOU: 0.595451, Class: 1.000000, Obj: 0.352001, No Obj: 0.002882, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.655522, Class: 1.000000, Obj: 0.430422, No Obj: 0.002313, Avg Recall: 0.744681, count: 94 -Region Avg IOU: 0.609606, Class: 1.000000, Obj: 0.379690, No Obj: 0.002840, Avg Recall: 0.704545, count: 132 -Region Avg IOU: 0.640091, Class: 1.000000, Obj: 0.427487, No Obj: 0.003523, Avg Recall: 0.748148, count: 135 -Region Avg IOU: 0.594584, Class: 1.000000, Obj: 0.352492, No Obj: 0.002732, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.617126, Class: 1.000000, Obj: 0.405304, No Obj: 0.003223, Avg Recall: 0.740458, count: 131 -5726: 0.437842, 0.419397 avg, 0.001000 rate, 25.672148 seconds, 1465856 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.581841, Class: 1.000000, Obj: 0.410080, No Obj: 0.002490, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.630064, Class: 1.000000, Obj: 0.358476, No Obj: 0.002984, Avg Recall: 0.787402, count: 127 -Region Avg IOU: 0.561012, Class: 1.000000, Obj: 0.344430, No Obj: 0.002195, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.610286, Class: 1.000000, Obj: 0.356794, No Obj: 0.002500, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.571693, Class: 1.000000, Obj: 0.328713, No Obj: 0.002647, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.628398, Class: 1.000000, Obj: 0.365906, No Obj: 0.002427, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.627548, Class: 1.000000, Obj: 0.398116, No Obj: 0.002764, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.661704, Class: 1.000000, Obj: 0.478038, No Obj: 0.002192, Avg Recall: 0.812500, count: 64 -5727: 0.366062, 0.414063 avg, 0.001000 rate, 25.675945 seconds, 1466112 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.604740, Class: 1.000000, Obj: 0.382146, No Obj: 0.002746, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.650597, Class: 1.000000, Obj: 0.431334, No Obj: 0.002935, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.644056, Class: 1.000000, Obj: 0.329650, No Obj: 0.003093, Avg Recall: 0.784483, count: 116 -Region Avg IOU: 0.625525, Class: 1.000000, Obj: 0.356736, No Obj: 0.002770, Avg Recall: 0.779661, count: 118 -Region Avg IOU: 0.610268, Class: 1.000000, Obj: 0.359269, No Obj: 0.003122, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.597582, Class: 1.000000, Obj: 0.401068, No Obj: 0.002516, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.634235, Class: 1.000000, Obj: 0.383020, No Obj: 0.002802, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.608568, Class: 1.000000, Obj: 0.402430, No Obj: 0.002645, Avg Recall: 0.686275, count: 102 -5728: 0.443119, 0.416969 avg, 0.001000 rate, 25.749023 seconds, 1466368 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.586780, Class: 1.000000, Obj: 0.386850, No Obj: 0.002449, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.580484, Class: 1.000000, Obj: 0.367647, No Obj: 0.002919, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.615671, Class: 1.000000, Obj: 0.362166, No Obj: 0.002978, Avg Recall: 0.738462, count: 130 -Region Avg IOU: 0.632231, Class: 1.000000, Obj: 0.400154, No Obj: 0.002558, Avg Recall: 0.750000, count: 84 -Region Avg IOU: 0.628595, Class: 1.000000, Obj: 0.399067, No Obj: 0.002469, Avg Recall: 0.772727, count: 88 -Region Avg IOU: 0.623645, Class: 1.000000, Obj: 0.397453, No Obj: 0.002773, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.556521, Class: 1.000000, Obj: 0.311477, No Obj: 0.002528, Avg Recall: 0.631148, count: 122 -Region Avg IOU: 0.637079, Class: 1.000000, Obj: 0.368491, No Obj: 0.002748, Avg Recall: 0.814815, count: 108 -5729: 0.437583, 0.419030 avg, 0.001000 rate, 24.024492 seconds, 1466624 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.584233, Class: 1.000000, Obj: 0.360565, No Obj: 0.003071, Avg Recall: 0.677686, count: 121 -Region Avg IOU: 0.645418, Class: 1.000000, Obj: 0.413794, No Obj: 0.003292, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.633316, Class: 1.000000, Obj: 0.405587, No Obj: 0.003043, Avg Recall: 0.766917, count: 133 -Region Avg IOU: 0.659357, Class: 1.000000, Obj: 0.427262, No Obj: 0.002765, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.607990, Class: 1.000000, Obj: 0.359901, No Obj: 0.002723, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.626924, Class: 1.000000, Obj: 0.452604, No Obj: 0.002574, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.651786, Class: 1.000000, Obj: 0.420915, No Obj: 0.002721, Avg Recall: 0.802083, count: 96 -Region Avg IOU: 0.621321, Class: 1.000000, Obj: 0.401056, No Obj: 0.002667, Avg Recall: 0.769231, count: 104 -5730: 0.439074, 0.421035 avg, 0.001000 rate, 26.221380 seconds, 1466880 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 17.349604 seconds -Region Avg IOU: 0.453428, Class: 1.000000, Obj: 0.270110, No Obj: 0.003714, Avg Recall: 0.455285, count: 123 -Region Avg IOU: 0.481953, Class: 1.000000, Obj: 0.273291, No Obj: 0.004115, Avg Recall: 0.522936, count: 109 -Region Avg IOU: 0.506239, Class: 1.000000, Obj: 0.305380, No Obj: 0.003759, Avg Recall: 0.550000, count: 100 -Region Avg IOU: 0.523289, Class: 1.000000, Obj: 0.278148, No Obj: 0.003714, Avg Recall: 0.579545, count: 88 -Region Avg IOU: 0.505931, Class: 1.000000, Obj: 0.303219, No Obj: 0.003615, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.533894, Class: 1.000000, Obj: 0.282939, No Obj: 0.003483, Avg Recall: 0.611765, count: 85 -Region Avg IOU: 0.476829, Class: 1.000000, Obj: 0.246987, No Obj: 0.003462, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.479991, Class: 1.000000, Obj: 0.249764, No Obj: 0.003981, Avg Recall: 0.500000, count: 120 -5731: 0.473447, 0.426276 avg, 0.001000 rate, 16.713511 seconds, 1467136 images -Loaded: 4.749236 seconds -Region Avg IOU: 0.517999, Class: 1.000000, Obj: 0.307631, No Obj: 0.004769, Avg Recall: 0.562500, count: 128 -Region Avg IOU: 0.498538, Class: 1.000000, Obj: 0.305238, No Obj: 0.004133, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.460440, Class: 1.000000, Obj: 0.286021, No Obj: 0.003446, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.436520, Class: 1.000000, Obj: 0.249332, No Obj: 0.004018, Avg Recall: 0.430769, count: 130 -Region Avg IOU: 0.484819, Class: 1.000000, Obj: 0.293348, No Obj: 0.004034, Avg Recall: 0.529412, count: 102 -Region Avg IOU: 0.444449, Class: 1.000000, Obj: 0.278171, No Obj: 0.003974, Avg Recall: 0.488000, count: 125 -Region Avg IOU: 0.367925, Class: 1.000000, Obj: 0.235424, No Obj: 0.003827, Avg Recall: 0.390476, count: 105 -Region Avg IOU: 0.478127, Class: 1.000000, Obj: 0.271844, No Obj: 0.004035, Avg Recall: 0.517544, count: 114 -5732: 0.513838, 0.435032 avg, 0.001000 rate, 16.837437 seconds, 1467392 images -Loaded: 3.875507 seconds -Region Avg IOU: 0.535797, Class: 1.000000, Obj: 0.329053, No Obj: 0.004179, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.458750, Class: 1.000000, Obj: 0.254639, No Obj: 0.004655, Avg Recall: 0.475000, count: 160 -Region Avg IOU: 0.470268, Class: 1.000000, Obj: 0.294889, No Obj: 0.003763, Avg Recall: 0.469027, count: 113 -Region Avg IOU: 0.437931, Class: 1.000000, Obj: 0.300518, No Obj: 0.003631, Avg Recall: 0.440860, count: 93 -Region Avg IOU: 0.452905, Class: 1.000000, Obj: 0.300557, No Obj: 0.004039, Avg Recall: 0.477064, count: 109 -Region Avg IOU: 0.497355, Class: 1.000000, Obj: 0.323611, No Obj: 0.005093, Avg Recall: 0.573770, count: 122 -Region Avg IOU: 0.493338, Class: 1.000000, Obj: 0.297461, No Obj: 0.004048, Avg Recall: 0.512397, count: 121 -Region Avg IOU: 0.474521, Class: 1.000000, Obj: 0.300694, No Obj: 0.003735, Avg Recall: 0.495327, count: 107 -5733: 0.552942, 0.446823 avg, 0.001000 rate, 19.111019 seconds, 1467648 images -Loaded: 2.184125 seconds -Region Avg IOU: 0.506764, Class: 1.000000, Obj: 0.377937, No Obj: 0.004943, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.471357, Class: 1.000000, Obj: 0.330736, No Obj: 0.004040, Avg Recall: 0.500000, count: 92 -Region Avg IOU: 0.510135, Class: 1.000000, Obj: 0.355451, No Obj: 0.004567, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.507958, Class: 1.000000, Obj: 0.379101, No Obj: 0.004191, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.487890, Class: 1.000000, Obj: 0.368923, No Obj: 0.004663, Avg Recall: 0.537879, count: 132 -Region Avg IOU: 0.502848, Class: 1.000000, Obj: 0.368245, No Obj: 0.005227, Avg Recall: 0.564516, count: 124 -Region Avg IOU: 0.499039, Class: 1.000000, Obj: 0.330343, No Obj: 0.005067, Avg Recall: 0.577778, count: 135 -Region Avg IOU: 0.450872, Class: 1.000000, Obj: 0.353794, No Obj: 0.004349, Avg Recall: 0.475410, count: 122 -5734: 0.441720, 0.446313 avg, 0.001000 rate, 18.454365 seconds, 1467904 images -Loaded: 1.764331 seconds -Region Avg IOU: 0.470969, Class: 1.000000, Obj: 0.359509, No Obj: 0.005028, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.528348, Class: 1.000000, Obj: 0.394221, No Obj: 0.004442, Avg Recall: 0.579545, count: 88 -Region Avg IOU: 0.460051, Class: 1.000000, Obj: 0.371873, No Obj: 0.005313, Avg Recall: 0.512397, count: 121 -Region Avg IOU: 0.479685, Class: 1.000000, Obj: 0.361962, No Obj: 0.005375, Avg Recall: 0.561151, count: 139 -Region Avg IOU: 0.447439, Class: 1.000000, Obj: 0.342232, No Obj: 0.005013, Avg Recall: 0.466667, count: 135 -Region Avg IOU: 0.509854, Class: 1.000000, Obj: 0.403309, No Obj: 0.004388, Avg Recall: 0.523810, count: 84 -Region Avg IOU: 0.565273, Class: 1.000000, Obj: 0.412670, No Obj: 0.004234, Avg Recall: 0.653846, count: 78 -Region Avg IOU: 0.517475, Class: 1.000000, Obj: 0.402493, No Obj: 0.004891, Avg Recall: 0.584158, count: 101 -5735: 0.396782, 0.441360 avg, 0.001000 rate, 15.636737 seconds, 1468160 images -Loaded: 4.576559 seconds -Region Avg IOU: 0.475612, Class: 1.000000, Obj: 0.382451, No Obj: 0.005327, Avg Recall: 0.515873, count: 126 -Region Avg IOU: 0.477135, Class: 1.000000, Obj: 0.366605, No Obj: 0.006083, Avg Recall: 0.533333, count: 120 -Region Avg IOU: 0.474677, Class: 1.000000, Obj: 0.358354, No Obj: 0.005043, Avg Recall: 0.485714, count: 105 -Region Avg IOU: 0.508809, Class: 1.000000, Obj: 0.368262, No Obj: 0.006023, Avg Recall: 0.543307, count: 127 -Region Avg IOU: 0.454000, Class: 1.000000, Obj: 0.354062, No Obj: 0.005249, Avg Recall: 0.500000, count: 116 -Region Avg IOU: 0.446663, Class: 1.000000, Obj: 0.361711, No Obj: 0.004564, Avg Recall: 0.468750, count: 96 -Region Avg IOU: 0.421068, Class: 1.000000, Obj: 0.365394, No Obj: 0.004161, Avg Recall: 0.423529, count: 85 -Region Avg IOU: 0.479852, Class: 1.000000, Obj: 0.344832, No Obj: 0.005076, Avg Recall: 0.517241, count: 116 -5736: 0.521663, 0.449390 avg, 0.001000 rate, 16.569517 seconds, 1468416 images -Loaded: 5.143495 seconds -Region Avg IOU: 0.421061, Class: 1.000000, Obj: 0.406436, No Obj: 0.003809, Avg Recall: 0.433735, count: 83 -Region Avg IOU: 0.504904, Class: 1.000000, Obj: 0.383480, No Obj: 0.005920, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.456889, Class: 1.000000, Obj: 0.377834, No Obj: 0.005527, Avg Recall: 0.450450, count: 111 -Region Avg IOU: 0.442402, Class: 1.000000, Obj: 0.392664, No Obj: 0.004100, Avg Recall: 0.494118, count: 85 -Region Avg IOU: 0.478948, Class: 1.000000, Obj: 0.371336, No Obj: 0.005519, Avg Recall: 0.495495, count: 111 -Region Avg IOU: 0.457516, Class: 1.000000, Obj: 0.383509, No Obj: 0.005726, Avg Recall: 0.520000, count: 100 -Region Avg IOU: 0.458844, Class: 1.000000, Obj: 0.376129, No Obj: 0.005200, Avg Recall: 0.504587, count: 109 -Region Avg IOU: 0.465301, Class: 1.000000, Obj: 0.390816, No Obj: 0.005371, Avg Recall: 0.491379, count: 116 -5737: 0.406707, 0.445122 avg, 0.001000 rate, 15.268337 seconds, 1468672 images -Loaded: 4.958365 seconds -Region Avg IOU: 0.427613, Class: 1.000000, Obj: 0.359093, No Obj: 0.004901, Avg Recall: 0.447154, count: 123 -Region Avg IOU: 0.451834, Class: 1.000000, Obj: 0.376362, No Obj: 0.005946, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.438707, Class: 1.000000, Obj: 0.377283, No Obj: 0.004666, Avg Recall: 0.511628, count: 86 -Region Avg IOU: 0.484411, Class: 1.000000, Obj: 0.356452, No Obj: 0.005812, Avg Recall: 0.546219, count: 119 -Region Avg IOU: 0.456850, Class: 1.000000, Obj: 0.381190, No Obj: 0.004523, Avg Recall: 0.494118, count: 85 -Region Avg IOU: 0.478845, Class: 1.000000, Obj: 0.406160, No Obj: 0.005535, Avg Recall: 0.500000, count: 114 -Region Avg IOU: 0.477564, Class: 1.000000, Obj: 0.350862, No Obj: 0.004923, Avg Recall: 0.550000, count: 100 -Region Avg IOU: 0.487331, Class: 1.000000, Obj: 0.397058, No Obj: 0.005851, Avg Recall: 0.512000, count: 125 -5738: 0.534261, 0.454036 avg, 0.001000 rate, 19.512190 seconds, 1468928 images -Loaded: 2.391886 seconds -Region Avg IOU: 0.482866, Class: 1.000000, Obj: 0.395155, No Obj: 0.005823, Avg Recall: 0.505050, count: 99 -Region Avg IOU: 0.478715, Class: 1.000000, Obj: 0.383801, No Obj: 0.005039, Avg Recall: 0.476191, count: 105 -Region Avg IOU: 0.465514, Class: 1.000000, Obj: 0.380536, No Obj: 0.005132, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.447821, Class: 1.000000, Obj: 0.367235, No Obj: 0.005651, Avg Recall: 0.484848, count: 132 -Region Avg IOU: 0.532871, Class: 1.000000, Obj: 0.412070, No Obj: 0.005365, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.500274, Class: 1.000000, Obj: 0.355692, No Obj: 0.006761, Avg Recall: 0.556452, count: 124 -Region Avg IOU: 0.515561, Class: 1.000000, Obj: 0.436449, No Obj: 0.005691, Avg Recall: 0.588235, count: 85 -Region Avg IOU: 0.420942, Class: 1.000000, Obj: 0.365266, No Obj: 0.005324, Avg Recall: 0.481481, count: 108 -5739: 0.450903, 0.453722 avg, 0.001000 rate, 14.386973 seconds, 1469184 images -Loaded: 6.685293 seconds -Region Avg IOU: 0.461105, Class: 1.000000, Obj: 0.329931, No Obj: 0.006108, Avg Recall: 0.479339, count: 121 -Region Avg IOU: 0.471704, Class: 1.000000, Obj: 0.375767, No Obj: 0.005606, Avg Recall: 0.492064, count: 126 -Region Avg IOU: 0.503356, Class: 1.000000, Obj: 0.384738, No Obj: 0.005064, Avg Recall: 0.488889, count: 90 -Region Avg IOU: 0.490486, Class: 1.000000, Obj: 0.364403, No Obj: 0.005676, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.498161, Class: 1.000000, Obj: 0.349955, No Obj: 0.005869, Avg Recall: 0.551020, count: 98 -Region Avg IOU: 0.510803, Class: 1.000000, Obj: 0.380449, No Obj: 0.004730, Avg Recall: 0.573034, count: 89 -Region Avg IOU: 0.460960, Class: 1.000000, Obj: 0.381721, No Obj: 0.005365, Avg Recall: 0.471154, count: 104 -Region Avg IOU: 0.440086, Class: 1.000000, Obj: 0.361519, No Obj: 0.005177, Avg Recall: 0.421053, count: 95 -5740: 0.433520, 0.451702 avg, 0.001000 rate, 17.190784 seconds, 1469440 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000083 seconds -Region Avg IOU: 0.602263, Class: 1.000000, Obj: 0.485968, No Obj: 0.003808, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.625171, Class: 1.000000, Obj: 0.482868, No Obj: 0.003751, Avg Recall: 0.816000, count: 125 -Region Avg IOU: 0.605714, Class: 1.000000, Obj: 0.437467, No Obj: 0.004169, Avg Recall: 0.741379, count: 116 -Region Avg IOU: 0.641797, Class: 1.000000, Obj: 0.480266, No Obj: 0.003992, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.632918, Class: 1.000000, Obj: 0.530322, No Obj: 0.003839, Avg Recall: 0.739583, count: 96 -Region Avg IOU: 0.648243, Class: 1.000000, Obj: 0.481278, No Obj: 0.004208, Avg Recall: 0.803279, count: 122 -Region Avg IOU: 0.604020, Class: 1.000000, Obj: 0.492292, No Obj: 0.004535, Avg Recall: 0.696970, count: 132 -Region Avg IOU: 0.659785, Class: 1.000000, Obj: 0.538437, No Obj: 0.003667, Avg Recall: 0.825243, count: 103 -5741: 0.453866, 0.451919 avg, 0.001000 rate, 27.487375 seconds, 1469696 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.636928, Class: 1.000000, Obj: 0.447331, No Obj: 0.003144, Avg Recall: 0.788235, count: 85 -Region Avg IOU: 0.672080, Class: 1.000000, Obj: 0.493794, No Obj: 0.003969, Avg Recall: 0.852174, count: 115 -Region Avg IOU: 0.644412, Class: 1.000000, Obj: 0.517733, No Obj: 0.003163, Avg Recall: 0.802469, count: 81 -Region Avg IOU: 0.614286, Class: 1.000000, Obj: 0.451163, No Obj: 0.002985, Avg Recall: 0.753086, count: 81 -Region Avg IOU: 0.658051, Class: 1.000000, Obj: 0.452013, No Obj: 0.003525, Avg Recall: 0.833333, count: 114 -Region Avg IOU: 0.632641, Class: 1.000000, Obj: 0.520105, No Obj: 0.003219, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.663104, Class: 1.000000, Obj: 0.456215, No Obj: 0.003062, Avg Recall: 0.836735, count: 98 -Region Avg IOU: 0.633232, Class: 1.000000, Obj: 0.370569, No Obj: 0.004314, Avg Recall: 0.798611, count: 144 -5742: 0.409865, 0.447713 avg, 0.001000 rate, 27.529852 seconds, 1469952 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.557881, Class: 1.000000, Obj: 0.338373, No Obj: 0.003164, Avg Recall: 0.659091, count: 132 -Region Avg IOU: 0.681339, Class: 1.000000, Obj: 0.475400, No Obj: 0.002902, Avg Recall: 0.898876, count: 89 -Region Avg IOU: 0.668109, Class: 1.000000, Obj: 0.480222, No Obj: 0.002943, Avg Recall: 0.838710, count: 93 -Region Avg IOU: 0.646394, Class: 1.000000, Obj: 0.466235, No Obj: 0.003115, Avg Recall: 0.833333, count: 114 -Region Avg IOU: 0.646225, Class: 1.000000, Obj: 0.450337, No Obj: 0.003203, Avg Recall: 0.798387, count: 124 -Region Avg IOU: 0.640784, Class: 1.000000, Obj: 0.461870, No Obj: 0.003422, Avg Recall: 0.782609, count: 115 -Region Avg IOU: 0.644876, Class: 1.000000, Obj: 0.441091, No Obj: 0.003308, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.620537, Class: 1.000000, Obj: 0.495850, No Obj: 0.002966, Avg Recall: 0.719626, count: 107 -5743: 0.414017, 0.444344 avg, 0.001000 rate, 29.385164 seconds, 1470208 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.643606, Class: 1.000000, Obj: 0.405516, No Obj: 0.003377, Avg Recall: 0.804054, count: 148 -Region Avg IOU: 0.659992, Class: 1.000000, Obj: 0.458715, No Obj: 0.002495, Avg Recall: 0.817073, count: 82 -Region Avg IOU: 0.672306, Class: 1.000000, Obj: 0.414828, No Obj: 0.003001, Avg Recall: 0.814516, count: 124 -Region Avg IOU: 0.626082, Class: 1.000000, Obj: 0.386321, No Obj: 0.002772, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.680724, Class: 1.000000, Obj: 0.398512, No Obj: 0.002574, Avg Recall: 0.849462, count: 93 -Region Avg IOU: 0.704835, Class: 1.000000, Obj: 0.429533, No Obj: 0.002950, Avg Recall: 0.881818, count: 110 -Region Avg IOU: 0.604502, Class: 1.000000, Obj: 0.345556, No Obj: 0.003028, Avg Recall: 0.722222, count: 144 -Region Avg IOU: 0.661730, Class: 1.000000, Obj: 0.450111, No Obj: 0.002342, Avg Recall: 0.805970, count: 67 -5744: 0.471541, 0.447063 avg, 0.001000 rate, 27.568989 seconds, 1470464 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.609464, Class: 1.000000, Obj: 0.347806, No Obj: 0.002851, Avg Recall: 0.741007, count: 139 -Region Avg IOU: 0.694278, Class: 1.000000, Obj: 0.422662, No Obj: 0.002148, Avg Recall: 0.879518, count: 83 -Region Avg IOU: 0.640094, Class: 1.000000, Obj: 0.348767, No Obj: 0.002543, Avg Recall: 0.798387, count: 124 -Region Avg IOU: 0.636239, Class: 1.000000, Obj: 0.380779, No Obj: 0.002619, Avg Recall: 0.736000, count: 125 -Region Avg IOU: 0.653954, Class: 1.000000, Obj: 0.385099, No Obj: 0.002421, Avg Recall: 0.806723, count: 119 -Region Avg IOU: 0.594282, Class: 1.000000, Obj: 0.348635, No Obj: 0.002292, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.653796, Class: 1.000000, Obj: 0.358566, No Obj: 0.002361, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.650488, Class: 1.000000, Obj: 0.365203, No Obj: 0.002994, Avg Recall: 0.829268, count: 123 -5745: 0.461957, 0.448553 avg, 0.001000 rate, 27.776058 seconds, 1470720 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.630582, Class: 1.000000, Obj: 0.349181, No Obj: 0.002152, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.671057, Class: 1.000000, Obj: 0.389620, No Obj: 0.002352, Avg Recall: 0.795455, count: 88 -Region Avg IOU: 0.667204, Class: 1.000000, Obj: 0.376200, No Obj: 0.002545, Avg Recall: 0.825000, count: 120 -Region Avg IOU: 0.627512, Class: 1.000000, Obj: 0.352067, No Obj: 0.002741, Avg Recall: 0.773973, count: 146 -Region Avg IOU: 0.648405, Class: 1.000000, Obj: 0.364818, No Obj: 0.002309, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.602880, Class: 1.000000, Obj: 0.344405, No Obj: 0.002080, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.646107, Class: 1.000000, Obj: 0.365636, No Obj: 0.002118, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.635265, Class: 1.000000, Obj: 0.387159, No Obj: 0.002163, Avg Recall: 0.781609, count: 87 -5746: 0.437956, 0.447493 avg, 0.001000 rate, 28.271200 seconds, 1470976 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.598505, Class: 1.000000, Obj: 0.329556, No Obj: 0.002512, Avg Recall: 0.693878, count: 147 -Region Avg IOU: 0.600312, Class: 1.000000, Obj: 0.289804, No Obj: 0.002447, Avg Recall: 0.712230, count: 139 -Region Avg IOU: 0.635048, Class: 1.000000, Obj: 0.332269, No Obj: 0.002103, Avg Recall: 0.780488, count: 123 -Region Avg IOU: 0.655036, Class: 1.000000, Obj: 0.356115, No Obj: 0.002616, Avg Recall: 0.782946, count: 129 -Region Avg IOU: 0.609990, Class: 1.000000, Obj: 0.346357, No Obj: 0.001982, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.642040, Class: 1.000000, Obj: 0.342518, No Obj: 0.002024, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.609829, Class: 1.000000, Obj: 0.323714, No Obj: 0.002310, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.645964, Class: 1.000000, Obj: 0.312627, No Obj: 0.002135, Avg Recall: 0.766355, count: 107 -5747: 0.496547, 0.452398 avg, 0.001000 rate, 27.494596 seconds, 1471232 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.615935, Class: 1.000000, Obj: 0.366878, No Obj: 0.002358, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.673703, Class: 1.000000, Obj: 0.324603, No Obj: 0.002489, Avg Recall: 0.848000, count: 125 -Region Avg IOU: 0.644350, Class: 1.000000, Obj: 0.295858, No Obj: 0.002253, Avg Recall: 0.825688, count: 109 -Region Avg IOU: 0.636792, Class: 1.000000, Obj: 0.373570, No Obj: 0.002354, Avg Recall: 0.736000, count: 125 -Region Avg IOU: 0.632076, Class: 1.000000, Obj: 0.307911, No Obj: 0.002314, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.657324, Class: 1.000000, Obj: 0.359845, No Obj: 0.002367, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.641134, Class: 1.000000, Obj: 0.338417, No Obj: 0.002184, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.639132, Class: 1.000000, Obj: 0.376457, No Obj: 0.002121, Avg Recall: 0.794118, count: 102 -5748: 0.478688, 0.455027 avg, 0.001000 rate, 27.458006 seconds, 1471488 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.652222, Class: 1.000000, Obj: 0.324618, No Obj: 0.002402, Avg Recall: 0.804124, count: 97 -Region Avg IOU: 0.624390, Class: 1.000000, Obj: 0.346782, No Obj: 0.001964, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.602607, Class: 1.000000, Obj: 0.335117, No Obj: 0.002232, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.643319, Class: 1.000000, Obj: 0.358160, No Obj: 0.002527, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.644367, Class: 1.000000, Obj: 0.350994, No Obj: 0.002092, Avg Recall: 0.814159, count: 113 -Region Avg IOU: 0.625441, Class: 1.000000, Obj: 0.333220, No Obj: 0.002434, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.664228, Class: 1.000000, Obj: 0.369078, No Obj: 0.002319, Avg Recall: 0.836538, count: 104 -Region Avg IOU: 0.631845, Class: 1.000000, Obj: 0.347755, No Obj: 0.002380, Avg Recall: 0.753731, count: 134 -5749: 0.436965, 0.453221 avg, 0.001000 rate, 26.134216 seconds, 1471744 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.676828, Class: 1.000000, Obj: 0.363171, No Obj: 0.002410, Avg Recall: 0.863158, count: 95 -Region Avg IOU: 0.601233, Class: 1.000000, Obj: 0.366056, No Obj: 0.002451, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.657946, Class: 1.000000, Obj: 0.391392, No Obj: 0.002585, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.654563, Class: 1.000000, Obj: 0.371287, No Obj: 0.002536, Avg Recall: 0.759494, count: 79 -Region Avg IOU: 0.695038, Class: 1.000000, Obj: 0.433512, No Obj: 0.002445, Avg Recall: 0.876405, count: 89 -Region Avg IOU: 0.649803, Class: 1.000000, Obj: 0.371251, No Obj: 0.002763, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.663927, Class: 1.000000, Obj: 0.365513, No Obj: 0.002350, Avg Recall: 0.830000, count: 100 -Region Avg IOU: 0.632776, Class: 1.000000, Obj: 0.362802, No Obj: 0.002177, Avg Recall: 0.752941, count: 85 -5750: 0.390897, 0.446989 avg, 0.001000 rate, 28.414116 seconds, 1472000 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000137 seconds -Region Avg IOU: 0.693459, Class: 1.000000, Obj: 0.422287, No Obj: 0.002324, Avg Recall: 0.839506, count: 81 -Region Avg IOU: 0.633424, Class: 1.000000, Obj: 0.369939, No Obj: 0.002492, Avg Recall: 0.793651, count: 126 -Region Avg IOU: 0.668204, Class: 1.000000, Obj: 0.376669, No Obj: 0.002518, Avg Recall: 0.852941, count: 102 -Region Avg IOU: 0.656681, Class: 1.000000, Obj: 0.372292, No Obj: 0.002763, Avg Recall: 0.840708, count: 113 -Region Avg IOU: 0.696032, Class: 1.000000, Obj: 0.472371, No Obj: 0.002871, Avg Recall: 0.866071, count: 112 -Region Avg IOU: 0.668569, Class: 1.000000, Obj: 0.447174, No Obj: 0.002365, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.674135, Class: 1.000000, Obj: 0.360250, No Obj: 0.002764, Avg Recall: 0.830508, count: 118 -Region Avg IOU: 0.675036, Class: 1.000000, Obj: 0.412077, No Obj: 0.002053, Avg Recall: 0.848837, count: 86 -5751: 0.403593, 0.442649 avg, 0.001000 rate, 28.688805 seconds, 1472256 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.639885, Class: 1.000000, Obj: 0.403328, No Obj: 0.003097, Avg Recall: 0.744828, count: 145 -Region Avg IOU: 0.660659, Class: 1.000000, Obj: 0.403050, No Obj: 0.002848, Avg Recall: 0.838462, count: 130 -Region Avg IOU: 0.686401, Class: 1.000000, Obj: 0.467777, No Obj: 0.002815, Avg Recall: 0.838384, count: 99 -Region Avg IOU: 0.648515, Class: 1.000000, Obj: 0.397992, No Obj: 0.002773, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.652538, Class: 1.000000, Obj: 0.393373, No Obj: 0.002489, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.642856, Class: 1.000000, Obj: 0.396015, No Obj: 0.002990, Avg Recall: 0.809524, count: 147 -Region Avg IOU: 0.693045, Class: 1.000000, Obj: 0.497332, No Obj: 0.002635, Avg Recall: 0.851064, count: 94 -Region Avg IOU: 0.643932, Class: 1.000000, Obj: 0.388128, No Obj: 0.002608, Avg Recall: 0.784314, count: 102 -5752: 0.493766, 0.447761 avg, 0.001000 rate, 30.902256 seconds, 1472512 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.650706, Class: 1.000000, Obj: 0.436904, No Obj: 0.002956, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.650207, Class: 1.000000, Obj: 0.434610, No Obj: 0.002923, Avg Recall: 0.837838, count: 111 -Region Avg IOU: 0.631777, Class: 1.000000, Obj: 0.433411, No Obj: 0.003028, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.649546, Class: 1.000000, Obj: 0.370529, No Obj: 0.002891, Avg Recall: 0.823009, count: 113 -Region Avg IOU: 0.676615, Class: 1.000000, Obj: 0.397864, No Obj: 0.003261, Avg Recall: 0.889831, count: 118 -Region Avg IOU: 0.657655, Class: 1.000000, Obj: 0.429872, No Obj: 0.002655, Avg Recall: 0.779070, count: 86 -Region Avg IOU: 0.654808, Class: 1.000000, Obj: 0.404797, No Obj: 0.002935, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.647463, Class: 1.000000, Obj: 0.443641, No Obj: 0.002977, Avg Recall: 0.808000, count: 125 -5753: 0.447507, 0.447736 avg, 0.001000 rate, 30.679352 seconds, 1472768 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.675134, Class: 1.000000, Obj: 0.465143, No Obj: 0.003133, Avg Recall: 0.814516, count: 124 -Region Avg IOU: 0.639904, Class: 1.000000, Obj: 0.447476, No Obj: 0.002810, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.646400, Class: 1.000000, Obj: 0.414211, No Obj: 0.003042, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.650666, Class: 1.000000, Obj: 0.423240, No Obj: 0.002650, Avg Recall: 0.851064, count: 94 -Region Avg IOU: 0.671883, Class: 1.000000, Obj: 0.380095, No Obj: 0.002905, Avg Recall: 0.855932, count: 118 -Region Avg IOU: 0.660344, Class: 1.000000, Obj: 0.459289, No Obj: 0.002710, Avg Recall: 0.831461, count: 89 -Region Avg IOU: 0.628512, Class: 1.000000, Obj: 0.418189, No Obj: 0.002675, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.638180, Class: 1.000000, Obj: 0.402227, No Obj: 0.003453, Avg Recall: 0.797297, count: 148 -5754: 0.487603, 0.451722 avg, 0.001000 rate, 30.454824 seconds, 1473024 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.630340, Class: 1.000000, Obj: 0.423411, No Obj: 0.002920, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.668789, Class: 1.000000, Obj: 0.474395, No Obj: 0.002551, Avg Recall: 0.831325, count: 83 -Region Avg IOU: 0.683537, Class: 1.000000, Obj: 0.421188, No Obj: 0.003224, Avg Recall: 0.864407, count: 118 -Region Avg IOU: 0.654018, Class: 1.000000, Obj: 0.453693, No Obj: 0.002743, Avg Recall: 0.835443, count: 79 -Region Avg IOU: 0.646150, Class: 1.000000, Obj: 0.430438, No Obj: 0.003079, Avg Recall: 0.786765, count: 136 -Region Avg IOU: 0.654634, Class: 1.000000, Obj: 0.482052, No Obj: 0.002915, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.610750, Class: 1.000000, Obj: 0.452242, No Obj: 0.003269, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.663921, Class: 1.000000, Obj: 0.438948, No Obj: 0.003365, Avg Recall: 0.829787, count: 141 -5755: 0.425800, 0.449130 avg, 0.001000 rate, 29.844908 seconds, 1473280 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.678915, Class: 1.000000, Obj: 0.441560, No Obj: 0.003209, Avg Recall: 0.840580, count: 138 -Region Avg IOU: 0.688152, Class: 1.000000, Obj: 0.416969, No Obj: 0.002664, Avg Recall: 0.828829, count: 111 -Region Avg IOU: 0.646740, Class: 1.000000, Obj: 0.370988, No Obj: 0.002406, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.692398, Class: 1.000000, Obj: 0.469630, No Obj: 0.002558, Avg Recall: 0.828829, count: 111 -Region Avg IOU: 0.666227, Class: 1.000000, Obj: 0.378333, No Obj: 0.003099, Avg Recall: 0.848000, count: 125 -Region Avg IOU: 0.643330, Class: 1.000000, Obj: 0.423334, No Obj: 0.002573, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.640616, Class: 1.000000, Obj: 0.405421, No Obj: 0.002601, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.625039, Class: 1.000000, Obj: 0.474206, No Obj: 0.002653, Avg Recall: 0.773585, count: 106 -5756: 0.443139, 0.448531 avg, 0.001000 rate, 28.255852 seconds, 1473536 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.672357, Class: 1.000000, Obj: 0.450769, No Obj: 0.002391, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.629738, Class: 1.000000, Obj: 0.404740, No Obj: 0.002720, Avg Recall: 0.794643, count: 112 -Region Avg IOU: 0.647144, Class: 1.000000, Obj: 0.405556, No Obj: 0.002723, Avg Recall: 0.796992, count: 133 -Region Avg IOU: 0.675471, Class: 1.000000, Obj: 0.452697, No Obj: 0.002606, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.653036, Class: 1.000000, Obj: 0.422317, No Obj: 0.002728, Avg Recall: 0.836207, count: 116 -Region Avg IOU: 0.662930, Class: 1.000000, Obj: 0.448144, No Obj: 0.002279, Avg Recall: 0.851064, count: 94 -Region Avg IOU: 0.637756, Class: 1.000000, Obj: 0.404335, No Obj: 0.002271, Avg Recall: 0.775281, count: 89 -Region Avg IOU: 0.650367, Class: 1.000000, Obj: 0.382792, No Obj: 0.002246, Avg Recall: 0.867470, count: 83 -5757: 0.389661, 0.442644 avg, 0.001000 rate, 29.178782 seconds, 1473792 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.651335, Class: 1.000000, Obj: 0.344932, No Obj: 0.002399, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.626647, Class: 1.000000, Obj: 0.341516, No Obj: 0.002524, Avg Recall: 0.756303, count: 119 -Region Avg IOU: 0.668913, Class: 1.000000, Obj: 0.392199, No Obj: 0.002242, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.653633, Class: 1.000000, Obj: 0.376942, No Obj: 0.002117, Avg Recall: 0.797753, count: 89 -Region Avg IOU: 0.654471, Class: 1.000000, Obj: 0.420160, No Obj: 0.002187, Avg Recall: 0.824176, count: 91 -Region Avg IOU: 0.641152, Class: 1.000000, Obj: 0.359216, No Obj: 0.002766, Avg Recall: 0.783582, count: 134 -Region Avg IOU: 0.664360, Class: 1.000000, Obj: 0.402646, No Obj: 0.002575, Avg Recall: 0.846154, count: 117 -Region Avg IOU: 0.650958, Class: 1.000000, Obj: 0.368370, No Obj: 0.002769, Avg Recall: 0.800000, count: 135 -5758: 0.492922, 0.447672 avg, 0.001000 rate, 28.928543 seconds, 1474048 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.689635, Class: 1.000000, Obj: 0.376334, No Obj: 0.002233, Avg Recall: 0.857143, count: 105 -Region Avg IOU: 0.635172, Class: 1.000000, Obj: 0.359739, No Obj: 0.002308, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.655319, Class: 1.000000, Obj: 0.411935, No Obj: 0.002525, Avg Recall: 0.805085, count: 118 -Region Avg IOU: 0.640306, Class: 1.000000, Obj: 0.383136, No Obj: 0.001911, Avg Recall: 0.785714, count: 70 -Region Avg IOU: 0.669245, Class: 1.000000, Obj: 0.427592, No Obj: 0.002479, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.626421, Class: 1.000000, Obj: 0.436724, No Obj: 0.002084, Avg Recall: 0.788235, count: 85 -Region Avg IOU: 0.687309, Class: 1.000000, Obj: 0.345910, No Obj: 0.002735, Avg Recall: 0.864000, count: 125 -Region Avg IOU: 0.665177, Class: 1.000000, Obj: 0.397880, No Obj: 0.002341, Avg Recall: 0.833333, count: 102 -5759: 0.426851, 0.445590 avg, 0.001000 rate, 29.971239 seconds, 1474304 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.695859, Class: 1.000000, Obj: 0.409185, No Obj: 0.002102, Avg Recall: 0.862069, count: 87 -Region Avg IOU: 0.621625, Class: 1.000000, Obj: 0.384294, No Obj: 0.002309, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.644316, Class: 1.000000, Obj: 0.385438, No Obj: 0.002664, Avg Recall: 0.797101, count: 138 -Region Avg IOU: 0.711977, Class: 1.000000, Obj: 0.414426, No Obj: 0.002422, Avg Recall: 0.905660, count: 106 -Region Avg IOU: 0.668358, Class: 1.000000, Obj: 0.381693, No Obj: 0.002416, Avg Recall: 0.827273, count: 110 -Region Avg IOU: 0.618345, Class: 1.000000, Obj: 0.395517, No Obj: 0.002256, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.656182, Class: 1.000000, Obj: 0.392790, No Obj: 0.002366, Avg Recall: 0.830189, count: 106 -Region Avg IOU: 0.632723, Class: 1.000000, Obj: 0.427462, No Obj: 0.002248, Avg Recall: 0.778947, count: 95 -5760: 0.444265, 0.445457 avg, 0.001000 rate, 30.063372 seconds, 1474560 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 14.808187 seconds -Region Avg IOU: 0.616254, Class: 1.000000, Obj: 0.374163, No Obj: 0.002361, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.591025, Class: 1.000000, Obj: 0.300084, No Obj: 0.002913, Avg Recall: 0.691057, count: 123 -Region Avg IOU: 0.613673, Class: 1.000000, Obj: 0.339978, No Obj: 0.002763, Avg Recall: 0.709677, count: 124 -Region Avg IOU: 0.645972, Class: 1.000000, Obj: 0.383462, No Obj: 0.002692, Avg Recall: 0.819048, count: 105 -Region Avg IOU: 0.545032, Class: 1.000000, Obj: 0.317257, No Obj: 0.002143, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.591230, Class: 1.000000, Obj: 0.389514, No Obj: 0.002110, Avg Recall: 0.702381, count: 84 -Region Avg IOU: 0.621318, Class: 1.000000, Obj: 0.359599, No Obj: 0.002608, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.636424, Class: 1.000000, Obj: 0.392583, No Obj: 0.002981, Avg Recall: 0.760684, count: 117 -5761: 0.439033, 0.444815 avg, 0.001000 rate, 26.626072 seconds, 1474816 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.595172, Class: 1.000000, Obj: 0.377620, No Obj: 0.002628, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.590505, Class: 1.000000, Obj: 0.394682, No Obj: 0.002676, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.602803, Class: 1.000000, Obj: 0.340255, No Obj: 0.002782, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.629349, Class: 1.000000, Obj: 0.359993, No Obj: 0.002788, Avg Recall: 0.744444, count: 90 -Region Avg IOU: 0.638158, Class: 1.000000, Obj: 0.435842, No Obj: 0.002506, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.674667, Class: 1.000000, Obj: 0.406407, No Obj: 0.002831, Avg Recall: 0.827273, count: 110 -Region Avg IOU: 0.619546, Class: 1.000000, Obj: 0.404265, No Obj: 0.002940, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.640943, Class: 1.000000, Obj: 0.380085, No Obj: 0.002535, Avg Recall: 0.790000, count: 100 -5762: 0.422378, 0.442571 avg, 0.001000 rate, 25.809649 seconds, 1475072 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.598832, Class: 1.000000, Obj: 0.344433, No Obj: 0.002413, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.595414, Class: 1.000000, Obj: 0.318919, No Obj: 0.002441, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.621561, Class: 1.000000, Obj: 0.399909, No Obj: 0.002639, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.646577, Class: 1.000000, Obj: 0.360177, No Obj: 0.002673, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.653308, Class: 1.000000, Obj: 0.382471, No Obj: 0.002889, Avg Recall: 0.818182, count: 121 -Region Avg IOU: 0.646674, Class: 1.000000, Obj: 0.378489, No Obj: 0.002762, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.604122, Class: 1.000000, Obj: 0.351135, No Obj: 0.002803, Avg Recall: 0.720930, count: 129 -Region Avg IOU: 0.621878, Class: 1.000000, Obj: 0.375896, No Obj: 0.002803, Avg Recall: 0.717949, count: 117 -5763: 0.479342, 0.446248 avg, 0.001000 rate, 24.752357 seconds, 1475328 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.632919, Class: 1.000000, Obj: 0.411696, No Obj: 0.003149, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.577545, Class: 1.000000, Obj: 0.370671, No Obj: 0.002703, Avg Recall: 0.669492, count: 118 -Region Avg IOU: 0.623362, Class: 1.000000, Obj: 0.383818, No Obj: 0.002470, Avg Recall: 0.752941, count: 85 -Region Avg IOU: 0.642812, Class: 1.000000, Obj: 0.460111, No Obj: 0.002877, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.660974, Class: 1.000000, Obj: 0.421922, No Obj: 0.002686, Avg Recall: 0.815217, count: 92 -Region Avg IOU: 0.607136, Class: 1.000000, Obj: 0.406136, No Obj: 0.002642, Avg Recall: 0.772152, count: 79 -Region Avg IOU: 0.609598, Class: 1.000000, Obj: 0.407481, No Obj: 0.002761, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.594686, Class: 1.000000, Obj: 0.396768, No Obj: 0.002553, Avg Recall: 0.731183, count: 93 -5764: 0.367944, 0.438418 avg, 0.001000 rate, 25.190765 seconds, 1475584 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.604795, Class: 1.000000, Obj: 0.369731, No Obj: 0.002963, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.632623, Class: 1.000000, Obj: 0.388055, No Obj: 0.003143, Avg Recall: 0.801587, count: 126 -Region Avg IOU: 0.646830, Class: 1.000000, Obj: 0.414580, No Obj: 0.003373, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.636424, Class: 1.000000, Obj: 0.410273, No Obj: 0.002893, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.609040, Class: 1.000000, Obj: 0.407487, No Obj: 0.002542, Avg Recall: 0.682353, count: 85 -Region Avg IOU: 0.633096, Class: 1.000000, Obj: 0.395895, No Obj: 0.003502, Avg Recall: 0.811024, count: 127 -Region Avg IOU: 0.655849, Class: 1.000000, Obj: 0.410743, No Obj: 0.002755, Avg Recall: 0.807018, count: 114 -Region Avg IOU: 0.644283, Class: 1.000000, Obj: 0.410819, No Obj: 0.002464, Avg Recall: 0.756098, count: 82 -5765: 0.456044, 0.440180 avg, 0.001000 rate, 25.728226 seconds, 1475840 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.640097, Class: 1.000000, Obj: 0.410477, No Obj: 0.003235, Avg Recall: 0.775000, count: 120 -Region Avg IOU: 0.639169, Class: 1.000000, Obj: 0.452295, No Obj: 0.003210, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.647692, Class: 1.000000, Obj: 0.403900, No Obj: 0.003451, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.581202, Class: 1.000000, Obj: 0.379673, No Obj: 0.002370, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.626730, Class: 1.000000, Obj: 0.445967, No Obj: 0.003279, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.657900, Class: 1.000000, Obj: 0.430985, No Obj: 0.003089, Avg Recall: 0.834862, count: 109 -Region Avg IOU: 0.601495, Class: 1.000000, Obj: 0.384949, No Obj: 0.003198, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.641084, Class: 1.000000, Obj: 0.419707, No Obj: 0.002745, Avg Recall: 0.808081, count: 99 -5766: 0.409184, 0.437081 avg, 0.001000 rate, 25.112461 seconds, 1476096 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.640996, Class: 1.000000, Obj: 0.450005, No Obj: 0.003305, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.617298, Class: 1.000000, Obj: 0.441223, No Obj: 0.002840, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.615264, Class: 1.000000, Obj: 0.391198, No Obj: 0.003375, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.646374, Class: 1.000000, Obj: 0.472203, No Obj: 0.003321, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.601222, Class: 1.000000, Obj: 0.413639, No Obj: 0.003847, Avg Recall: 0.723926, count: 163 -Region Avg IOU: 0.600729, Class: 1.000000, Obj: 0.429231, No Obj: 0.002989, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.658749, Class: 1.000000, Obj: 0.440606, No Obj: 0.003057, Avg Recall: 0.825000, count: 80 -Region Avg IOU: 0.639002, Class: 1.000000, Obj: 0.462558, No Obj: 0.003056, Avg Recall: 0.741935, count: 93 -5767: 0.407215, 0.434094 avg, 0.001000 rate, 26.413799 seconds, 1476352 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.633585, Class: 1.000000, Obj: 0.462279, No Obj: 0.002826, Avg Recall: 0.795455, count: 88 -Region Avg IOU: 0.655433, Class: 1.000000, Obj: 0.416419, No Obj: 0.003654, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.641133, Class: 1.000000, Obj: 0.427417, No Obj: 0.003775, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.619376, Class: 1.000000, Obj: 0.445803, No Obj: 0.003003, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.611482, Class: 1.000000, Obj: 0.429750, No Obj: 0.003651, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.600827, Class: 1.000000, Obj: 0.471586, No Obj: 0.003329, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.609764, Class: 1.000000, Obj: 0.451620, No Obj: 0.003572, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.680834, Class: 1.000000, Obj: 0.530554, No Obj: 0.003264, Avg Recall: 0.835165, count: 91 -5768: 0.389925, 0.429677 avg, 0.001000 rate, 24.006563 seconds, 1476608 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.611201, Class: 1.000000, Obj: 0.470901, No Obj: 0.003347, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.647067, Class: 1.000000, Obj: 0.449929, No Obj: 0.003830, Avg Recall: 0.836538, count: 104 -Region Avg IOU: 0.623257, Class: 1.000000, Obj: 0.486576, No Obj: 0.003150, Avg Recall: 0.712644, count: 87 -Region Avg IOU: 0.638729, Class: 1.000000, Obj: 0.447213, No Obj: 0.003677, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.671119, Class: 1.000000, Obj: 0.438954, No Obj: 0.003480, Avg Recall: 0.855670, count: 97 -Region Avg IOU: 0.626715, Class: 1.000000, Obj: 0.409399, No Obj: 0.003872, Avg Recall: 0.760000, count: 125 -Region Avg IOU: 0.639942, Class: 1.000000, Obj: 0.488156, No Obj: 0.003435, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.628890, Class: 1.000000, Obj: 0.458300, No Obj: 0.003252, Avg Recall: 0.766990, count: 103 -5769: 0.392571, 0.425967 avg, 0.001000 rate, 26.272230 seconds, 1476864 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.626178, Class: 1.000000, Obj: 0.433565, No Obj: 0.003505, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.642540, Class: 1.000000, Obj: 0.474700, No Obj: 0.003599, Avg Recall: 0.783333, count: 120 -Region Avg IOU: 0.634588, Class: 1.000000, Obj: 0.421936, No Obj: 0.003810, Avg Recall: 0.758065, count: 124 -Region Avg IOU: 0.600586, Class: 1.000000, Obj: 0.411054, No Obj: 0.003496, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.630780, Class: 1.000000, Obj: 0.454445, No Obj: 0.003361, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.573503, Class: 1.000000, Obj: 0.373440, No Obj: 0.003875, Avg Recall: 0.637795, count: 127 -Region Avg IOU: 0.641630, Class: 1.000000, Obj: 0.478018, No Obj: 0.004064, Avg Recall: 0.776859, count: 121 -Region Avg IOU: 0.589645, Class: 1.000000, Obj: 0.462972, No Obj: 0.003397, Avg Recall: 0.712963, count: 108 -5770: 0.397626, 0.423133 avg, 0.001000 rate, 25.286924 seconds, 1477120 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 20.823786 seconds -Region Avg IOU: 0.591063, Class: 1.000000, Obj: 0.461679, No Obj: 0.002763, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.563865, Class: 1.000000, Obj: 0.377061, No Obj: 0.003208, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.598766, Class: 1.000000, Obj: 0.394405, No Obj: 0.002960, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.626086, Class: 1.000000, Obj: 0.412319, No Obj: 0.003634, Avg Recall: 0.777778, count: 126 -Region Avg IOU: 0.574419, Class: 1.000000, Obj: 0.414395, No Obj: 0.003016, Avg Recall: 0.652542, count: 118 -Region Avg IOU: 0.604101, Class: 1.000000, Obj: 0.401167, No Obj: 0.003410, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.679683, Class: 1.000000, Obj: 0.489402, No Obj: 0.003251, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.591431, Class: 1.000000, Obj: 0.383346, No Obj: 0.003812, Avg Recall: 0.674699, count: 166 -5771: 0.400140, 0.420833 avg, 0.001000 rate, 26.836412 seconds, 1477376 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.594012, Class: 1.000000, Obj: 0.378039, No Obj: 0.003037, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.602022, Class: 1.000000, Obj: 0.434451, No Obj: 0.002618, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.604905, Class: 1.000000, Obj: 0.412298, No Obj: 0.003369, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.634606, Class: 1.000000, Obj: 0.438667, No Obj: 0.003541, Avg Recall: 0.804688, count: 128 -Region Avg IOU: 0.663796, Class: 1.000000, Obj: 0.462982, No Obj: 0.002675, Avg Recall: 0.810127, count: 79 -Region Avg IOU: 0.641221, Class: 1.000000, Obj: 0.424561, No Obj: 0.003783, Avg Recall: 0.753968, count: 126 -Region Avg IOU: 0.522656, Class: 1.000000, Obj: 0.355103, No Obj: 0.003015, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.607135, Class: 1.000000, Obj: 0.444288, No Obj: 0.003252, Avg Recall: 0.700000, count: 110 -5772: 0.398796, 0.418630 avg, 0.001000 rate, 26.731380 seconds, 1477632 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.639334, Class: 1.000000, Obj: 0.411791, No Obj: 0.003341, Avg Recall: 0.774194, count: 124 -Region Avg IOU: 0.592976, Class: 1.000000, Obj: 0.404869, No Obj: 0.003647, Avg Recall: 0.700730, count: 137 -Region Avg IOU: 0.636040, Class: 1.000000, Obj: 0.412198, No Obj: 0.003228, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.656979, Class: 1.000000, Obj: 0.462832, No Obj: 0.003172, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.617771, Class: 1.000000, Obj: 0.369785, No Obj: 0.003212, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.615869, Class: 1.000000, Obj: 0.393714, No Obj: 0.003617, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.628965, Class: 1.000000, Obj: 0.435602, No Obj: 0.003266, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.613344, Class: 1.000000, Obj: 0.400978, No Obj: 0.003203, Avg Recall: 0.687500, count: 112 -5773: 0.441050, 0.420872 avg, 0.001000 rate, 27.034027 seconds, 1477888 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.592956, Class: 1.000000, Obj: 0.437162, No Obj: 0.002591, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.602001, Class: 1.000000, Obj: 0.416970, No Obj: 0.003473, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.662865, Class: 1.000000, Obj: 0.429178, No Obj: 0.003102, Avg Recall: 0.834951, count: 103 -Region Avg IOU: 0.648348, Class: 1.000000, Obj: 0.421454, No Obj: 0.003256, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.598149, Class: 1.000000, Obj: 0.393757, No Obj: 0.003530, Avg Recall: 0.723077, count: 130 -Region Avg IOU: 0.577267, Class: 1.000000, Obj: 0.372191, No Obj: 0.003133, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.610978, Class: 1.000000, Obj: 0.417862, No Obj: 0.003223, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.643131, Class: 1.000000, Obj: 0.368200, No Obj: 0.003691, Avg Recall: 0.768657, count: 134 -5774: 0.446781, 0.423463 avg, 0.001000 rate, 24.633589 seconds, 1478144 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.576476, Class: 1.000000, Obj: 0.338486, No Obj: 0.002649, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.649064, Class: 1.000000, Obj: 0.445503, No Obj: 0.002819, Avg Recall: 0.804878, count: 82 -Region Avg IOU: 0.644690, Class: 1.000000, Obj: 0.385769, No Obj: 0.003572, Avg Recall: 0.804878, count: 123 -Region Avg IOU: 0.611530, Class: 1.000000, Obj: 0.449746, No Obj: 0.003270, Avg Recall: 0.658537, count: 123 -Region Avg IOU: 0.628382, Class: 1.000000, Obj: 0.438653, No Obj: 0.003066, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.635631, Class: 1.000000, Obj: 0.394048, No Obj: 0.003469, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.601469, Class: 1.000000, Obj: 0.377041, No Obj: 0.003056, Avg Recall: 0.723077, count: 130 -Region Avg IOU: 0.638903, Class: 1.000000, Obj: 0.429028, No Obj: 0.002828, Avg Recall: 0.793478, count: 92 -5775: 0.435384, 0.424655 avg, 0.001000 rate, 24.951296 seconds, 1478400 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.602208, Class: 1.000000, Obj: 0.364114, No Obj: 0.003180, Avg Recall: 0.692913, count: 127 -Region Avg IOU: 0.652950, Class: 1.000000, Obj: 0.415549, No Obj: 0.003124, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.596038, Class: 1.000000, Obj: 0.386790, No Obj: 0.003233, Avg Recall: 0.689076, count: 119 -Region Avg IOU: 0.603847, Class: 1.000000, Obj: 0.380244, No Obj: 0.002866, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.654563, Class: 1.000000, Obj: 0.423767, No Obj: 0.003319, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.644247, Class: 1.000000, Obj: 0.434956, No Obj: 0.003056, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.614602, Class: 1.000000, Obj: 0.402979, No Obj: 0.003583, Avg Recall: 0.758621, count: 145 -Region Avg IOU: 0.592917, Class: 1.000000, Obj: 0.365654, No Obj: 0.002524, Avg Recall: 0.752941, count: 85 -5776: 0.415438, 0.423733 avg, 0.001000 rate, 25.505571 seconds, 1478656 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.585278, Class: 1.000000, Obj: 0.409044, No Obj: 0.002562, Avg Recall: 0.717647, count: 85 -Region Avg IOU: 0.643915, Class: 1.000000, Obj: 0.405254, No Obj: 0.003324, Avg Recall: 0.762295, count: 122 -Region Avg IOU: 0.615155, Class: 1.000000, Obj: 0.386856, No Obj: 0.003149, Avg Recall: 0.764286, count: 140 -Region Avg IOU: 0.591077, Class: 1.000000, Obj: 0.308532, No Obj: 0.002828, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.618896, Class: 1.000000, Obj: 0.418008, No Obj: 0.002481, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.662234, Class: 1.000000, Obj: 0.460915, No Obj: 0.002926, Avg Recall: 0.829268, count: 82 -Region Avg IOU: 0.618564, Class: 1.000000, Obj: 0.368988, No Obj: 0.003234, Avg Recall: 0.774436, count: 133 -Region Avg IOU: 0.614909, Class: 1.000000, Obj: 0.422797, No Obj: 0.003061, Avg Recall: 0.692982, count: 114 -5777: 0.410397, 0.422400 avg, 0.001000 rate, 27.317829 seconds, 1478912 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.597785, Class: 1.000000, Obj: 0.393387, No Obj: 0.002549, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.655882, Class: 1.000000, Obj: 0.429322, No Obj: 0.002613, Avg Recall: 0.804598, count: 87 -Region Avg IOU: 0.648084, Class: 1.000000, Obj: 0.390095, No Obj: 0.002888, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.633121, Class: 1.000000, Obj: 0.401943, No Obj: 0.002808, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.622280, Class: 1.000000, Obj: 0.446042, No Obj: 0.002660, Avg Recall: 0.760000, count: 75 -Region Avg IOU: 0.619991, Class: 1.000000, Obj: 0.358451, No Obj: 0.002446, Avg Recall: 0.750000, count: 88 -Region Avg IOU: 0.641658, Class: 1.000000, Obj: 0.416085, No Obj: 0.002848, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.663165, Class: 1.000000, Obj: 0.380854, No Obj: 0.003647, Avg Recall: 0.795775, count: 142 -5778: 0.405996, 0.420759 avg, 0.001000 rate, 24.762213 seconds, 1479168 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.632762, Class: 1.000000, Obj: 0.404412, No Obj: 0.002942, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.619750, Class: 1.000000, Obj: 0.352492, No Obj: 0.003075, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.637999, Class: 1.000000, Obj: 0.397781, No Obj: 0.002796, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.614726, Class: 1.000000, Obj: 0.397537, No Obj: 0.002756, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.658918, Class: 1.000000, Obj: 0.389473, No Obj: 0.003324, Avg Recall: 0.792593, count: 135 -Region Avg IOU: 0.596242, Class: 1.000000, Obj: 0.361355, No Obj: 0.002949, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.622322, Class: 1.000000, Obj: 0.372824, No Obj: 0.002786, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.632713, Class: 1.000000, Obj: 0.352071, No Obj: 0.002872, Avg Recall: 0.770642, count: 109 -5779: 0.416230, 0.420306 avg, 0.001000 rate, 25.662338 seconds, 1479424 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.591795, Class: 1.000000, Obj: 0.372019, No Obj: 0.003371, Avg Recall: 0.721804, count: 133 -Region Avg IOU: 0.638836, Class: 1.000000, Obj: 0.403412, No Obj: 0.003423, Avg Recall: 0.752000, count: 125 -Region Avg IOU: 0.682765, Class: 1.000000, Obj: 0.437657, No Obj: 0.002836, Avg Recall: 0.852632, count: 95 -Region Avg IOU: 0.642682, Class: 1.000000, Obj: 0.431427, No Obj: 0.003010, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.629206, Class: 1.000000, Obj: 0.381418, No Obj: 0.002891, Avg Recall: 0.804124, count: 97 -Region Avg IOU: 0.622249, Class: 1.000000, Obj: 0.437149, No Obj: 0.002861, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.667609, Class: 1.000000, Obj: 0.420855, No Obj: 0.002932, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.615918, Class: 1.000000, Obj: 0.389325, No Obj: 0.002894, Avg Recall: 0.741379, count: 116 -5780: 0.418764, 0.420152 avg, 0.001000 rate, 27.339781 seconds, 1479680 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000085 seconds -Region Avg IOU: 0.664100, Class: 1.000000, Obj: 0.473927, No Obj: 0.002684, Avg Recall: 0.819277, count: 83 -Region Avg IOU: 0.615870, Class: 1.000000, Obj: 0.409515, No Obj: 0.003013, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.701751, Class: 1.000000, Obj: 0.473880, No Obj: 0.002487, Avg Recall: 0.884615, count: 78 -Region Avg IOU: 0.687678, Class: 1.000000, Obj: 0.438648, No Obj: 0.002594, Avg Recall: 0.816092, count: 87 -Region Avg IOU: 0.637957, Class: 1.000000, Obj: 0.362436, No Obj: 0.002598, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.621161, Class: 1.000000, Obj: 0.437358, No Obj: 0.002772, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.643802, Class: 1.000000, Obj: 0.422545, No Obj: 0.003329, Avg Recall: 0.777778, count: 126 -Region Avg IOU: 0.640165, Class: 1.000000, Obj: 0.442631, No Obj: 0.003099, Avg Recall: 0.803571, count: 112 -5781: 0.369205, 0.415057 avg, 0.001000 rate, 25.996077 seconds, 1479936 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.632484, Class: 1.000000, Obj: 0.422458, No Obj: 0.003065, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.563718, Class: 1.000000, Obj: 0.376780, No Obj: 0.003118, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.620641, Class: 1.000000, Obj: 0.422088, No Obj: 0.003013, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.656024, Class: 1.000000, Obj: 0.389715, No Obj: 0.003024, Avg Recall: 0.819048, count: 105 -Region Avg IOU: 0.633314, Class: 1.000000, Obj: 0.450460, No Obj: 0.003171, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.628091, Class: 1.000000, Obj: 0.419806, No Obj: 0.003115, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.646174, Class: 1.000000, Obj: 0.420400, No Obj: 0.003270, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.659717, Class: 1.000000, Obj: 0.399777, No Obj: 0.003488, Avg Recall: 0.803279, count: 122 -5782: 0.450378, 0.418589 avg, 0.001000 rate, 26.681005 seconds, 1480192 images -Loaded: 0.000134 seconds -Region Avg IOU: 0.634905, Class: 1.000000, Obj: 0.398346, No Obj: 0.002914, Avg Recall: 0.802083, count: 96 -Region Avg IOU: 0.614607, Class: 1.000000, Obj: 0.443448, No Obj: 0.002949, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.641512, Class: 1.000000, Obj: 0.453380, No Obj: 0.002804, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.586660, Class: 1.000000, Obj: 0.422664, No Obj: 0.003380, Avg Recall: 0.700787, count: 127 -Region Avg IOU: 0.623295, Class: 1.000000, Obj: 0.454104, No Obj: 0.003467, Avg Recall: 0.735043, count: 117 -Region Avg IOU: 0.699399, Class: 1.000000, Obj: 0.463810, No Obj: 0.002741, Avg Recall: 0.857143, count: 84 -Region Avg IOU: 0.616853, Class: 1.000000, Obj: 0.405556, No Obj: 0.003351, Avg Recall: 0.696970, count: 132 -Region Avg IOU: 0.572637, Class: 1.000000, Obj: 0.395554, No Obj: 0.002525, Avg Recall: 0.628205, count: 78 -5783: 0.396561, 0.416387 avg, 0.001000 rate, 24.881207 seconds, 1480448 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.656704, Class: 1.000000, Obj: 0.434912, No Obj: 0.003179, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.623079, Class: 1.000000, Obj: 0.406492, No Obj: 0.003296, Avg Recall: 0.785185, count: 135 -Region Avg IOU: 0.633478, Class: 1.000000, Obj: 0.388098, No Obj: 0.003132, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.662663, Class: 1.000000, Obj: 0.459022, No Obj: 0.003377, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.622018, Class: 1.000000, Obj: 0.410291, No Obj: 0.002549, Avg Recall: 0.773810, count: 84 -Region Avg IOU: 0.631205, Class: 1.000000, Obj: 0.419414, No Obj: 0.002947, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.614902, Class: 1.000000, Obj: 0.396686, No Obj: 0.003208, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.669466, Class: 1.000000, Obj: 0.512894, No Obj: 0.003106, Avg Recall: 0.818182, count: 88 -5784: 0.404833, 0.415231 avg, 0.001000 rate, 25.447098 seconds, 1480704 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.635675, Class: 1.000000, Obj: 0.383693, No Obj: 0.003815, Avg Recall: 0.798387, count: 124 -Region Avg IOU: 0.661671, Class: 1.000000, Obj: 0.453020, No Obj: 0.002948, Avg Recall: 0.843750, count: 96 -Region Avg IOU: 0.708314, Class: 1.000000, Obj: 0.506931, No Obj: 0.002659, Avg Recall: 0.866667, count: 60 -Region Avg IOU: 0.640410, Class: 1.000000, Obj: 0.454531, No Obj: 0.003083, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.676270, Class: 1.000000, Obj: 0.416916, No Obj: 0.002870, Avg Recall: 0.818182, count: 77 -Region Avg IOU: 0.642808, Class: 1.000000, Obj: 0.468531, No Obj: 0.003091, Avg Recall: 0.804348, count: 92 -Region Avg IOU: 0.631349, Class: 1.000000, Obj: 0.444139, No Obj: 0.003184, Avg Recall: 0.789916, count: 119 -Region Avg IOU: 0.639753, Class: 1.000000, Obj: 0.494292, No Obj: 0.003042, Avg Recall: 0.776699, count: 103 -5785: 0.347296, 0.408438 avg, 0.001000 rate, 26.873531 seconds, 1480960 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.661135, Class: 1.000000, Obj: 0.452349, No Obj: 0.003604, Avg Recall: 0.814286, count: 140 -Region Avg IOU: 0.634971, Class: 1.000000, Obj: 0.412562, No Obj: 0.002658, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.632612, Class: 1.000000, Obj: 0.435706, No Obj: 0.002932, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.562345, Class: 1.000000, Obj: 0.392193, No Obj: 0.002722, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.662992, Class: 1.000000, Obj: 0.423363, No Obj: 0.003178, Avg Recall: 0.838384, count: 99 -Region Avg IOU: 0.643157, Class: 1.000000, Obj: 0.466048, No Obj: 0.003369, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.645286, Class: 1.000000, Obj: 0.441950, No Obj: 0.003432, Avg Recall: 0.807018, count: 114 -Region Avg IOU: 0.649066, Class: 1.000000, Obj: 0.434119, No Obj: 0.002901, Avg Recall: 0.796460, count: 113 -5786: 0.412360, 0.408830 avg, 0.001000 rate, 26.248108 seconds, 1481216 images -Loaded: 0.000120 seconds -Region Avg IOU: 0.649039, Class: 1.000000, Obj: 0.477329, No Obj: 0.002832, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.619946, Class: 1.000000, Obj: 0.434252, No Obj: 0.002694, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.673681, Class: 1.000000, Obj: 0.477056, No Obj: 0.003009, Avg Recall: 0.876190, count: 105 -Region Avg IOU: 0.615134, Class: 1.000000, Obj: 0.363187, No Obj: 0.003009, Avg Recall: 0.714286, count: 126 -Region Avg IOU: 0.645788, Class: 1.000000, Obj: 0.423272, No Obj: 0.002984, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.608216, Class: 1.000000, Obj: 0.409755, No Obj: 0.003314, Avg Recall: 0.703125, count: 128 -Region Avg IOU: 0.656281, Class: 1.000000, Obj: 0.473832, No Obj: 0.002829, Avg Recall: 0.814815, count: 81 -Region Avg IOU: 0.651094, Class: 1.000000, Obj: 0.402533, No Obj: 0.002802, Avg Recall: 0.815534, count: 103 -5787: 0.385489, 0.406496 avg, 0.001000 rate, 28.180449 seconds, 1481472 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.581993, Class: 1.000000, Obj: 0.388153, No Obj: 0.003266, Avg Recall: 0.629032, count: 124 -Region Avg IOU: 0.643029, Class: 1.000000, Obj: 0.420990, No Obj: 0.002950, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.656018, Class: 1.000000, Obj: 0.401832, No Obj: 0.002848, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.591111, Class: 1.000000, Obj: 0.421932, No Obj: 0.002995, Avg Recall: 0.678832, count: 137 -Region Avg IOU: 0.607597, Class: 1.000000, Obj: 0.385726, No Obj: 0.002516, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.625060, Class: 1.000000, Obj: 0.421601, No Obj: 0.002651, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.645321, Class: 1.000000, Obj: 0.405725, No Obj: 0.003077, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.641968, Class: 1.000000, Obj: 0.437917, No Obj: 0.002492, Avg Recall: 0.810811, count: 74 -5788: 0.418710, 0.407717 avg, 0.001000 rate, 25.850348 seconds, 1481728 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.649938, Class: 1.000000, Obj: 0.404549, No Obj: 0.002419, Avg Recall: 0.787500, count: 80 -Region Avg IOU: 0.640876, Class: 1.000000, Obj: 0.418498, No Obj: 0.002944, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.659395, Class: 1.000000, Obj: 0.396416, No Obj: 0.003071, Avg Recall: 0.808696, count: 115 -Region Avg IOU: 0.635291, Class: 1.000000, Obj: 0.392622, No Obj: 0.002949, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.595056, Class: 1.000000, Obj: 0.373194, No Obj: 0.002715, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.639745, Class: 1.000000, Obj: 0.421042, No Obj: 0.002537, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.640040, Class: 1.000000, Obj: 0.416802, No Obj: 0.003214, Avg Recall: 0.751938, count: 129 -Region Avg IOU: 0.611262, Class: 1.000000, Obj: 0.359076, No Obj: 0.002824, Avg Recall: 0.730435, count: 115 -5789: 0.425524, 0.409498 avg, 0.001000 rate, 26.176758 seconds, 1481984 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.623445, Class: 1.000000, Obj: 0.405046, No Obj: 0.002742, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.656503, Class: 1.000000, Obj: 0.412479, No Obj: 0.002526, Avg Recall: 0.821429, count: 84 -Region Avg IOU: 0.615091, Class: 1.000000, Obj: 0.374677, No Obj: 0.002883, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.659474, Class: 1.000000, Obj: 0.410839, No Obj: 0.002964, Avg Recall: 0.782609, count: 115 -Region Avg IOU: 0.691631, Class: 1.000000, Obj: 0.449556, No Obj: 0.003010, Avg Recall: 0.870370, count: 108 -Region Avg IOU: 0.593098, Class: 1.000000, Obj: 0.389327, No Obj: 0.002845, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.673430, Class: 1.000000, Obj: 0.479479, No Obj: 0.002648, Avg Recall: 0.827957, count: 93 -Region Avg IOU: 0.656777, Class: 1.000000, Obj: 0.456183, No Obj: 0.002721, Avg Recall: 0.774194, count: 93 -5790: 0.437802, 0.412328 avg, 0.001000 rate, 25.335009 seconds, 1482240 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.033480 seconds -Region Avg IOU: 0.557165, Class: 1.000000, Obj: 0.328714, No Obj: 0.003017, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.516412, Class: 1.000000, Obj: 0.329252, No Obj: 0.002876, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.488183, Class: 1.000000, Obj: 0.325327, No Obj: 0.002749, Avg Recall: 0.580000, count: 100 -Region Avg IOU: 0.515582, Class: 1.000000, Obj: 0.302034, No Obj: 0.003175, Avg Recall: 0.542857, count: 105 -Region Avg IOU: 0.468335, Class: 1.000000, Obj: 0.295098, No Obj: 0.002522, Avg Recall: 0.521277, count: 94 -Region Avg IOU: 0.540168, Class: 1.000000, Obj: 0.298172, No Obj: 0.003142, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.567291, Class: 1.000000, Obj: 0.312405, No Obj: 0.003270, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.531165, Class: 1.000000, Obj: 0.298004, No Obj: 0.003512, Avg Recall: 0.606061, count: 132 -5791: 0.463421, 0.417438 avg, 0.001000 rate, 22.592012 seconds, 1482496 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.554401, Class: 1.000000, Obj: 0.342793, No Obj: 0.003163, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.547189, Class: 1.000000, Obj: 0.323250, No Obj: 0.004136, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.563756, Class: 1.000000, Obj: 0.363987, No Obj: 0.002730, Avg Recall: 0.627907, count: 86 -Region Avg IOU: 0.551505, Class: 1.000000, Obj: 0.334472, No Obj: 0.003285, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.491136, Class: 1.000000, Obj: 0.321419, No Obj: 0.002940, Avg Recall: 0.546392, count: 97 -Region Avg IOU: 0.550498, Class: 1.000000, Obj: 0.348752, No Obj: 0.003342, Avg Recall: 0.646154, count: 130 -Region Avg IOU: 0.525764, Class: 1.000000, Obj: 0.332877, No Obj: 0.002848, Avg Recall: 0.571429, count: 91 -Region Avg IOU: 0.599324, Class: 1.000000, Obj: 0.351911, No Obj: 0.003481, Avg Recall: 0.691589, count: 107 -5792: 0.444944, 0.420188 avg, 0.001000 rate, 23.241840 seconds, 1482752 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.568989, Class: 1.000000, Obj: 0.359287, No Obj: 0.003595, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.616025, Class: 1.000000, Obj: 0.415678, No Obj: 0.003375, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.541120, Class: 1.000000, Obj: 0.333872, No Obj: 0.003394, Avg Recall: 0.620690, count: 116 -Region Avg IOU: 0.568415, Class: 1.000000, Obj: 0.308943, No Obj: 0.003648, Avg Recall: 0.658333, count: 120 -Region Avg IOU: 0.521376, Class: 1.000000, Obj: 0.345964, No Obj: 0.003385, Avg Recall: 0.592593, count: 81 -Region Avg IOU: 0.577317, Class: 1.000000, Obj: 0.358795, No Obj: 0.004493, Avg Recall: 0.673913, count: 138 -Region Avg IOU: 0.567985, Class: 1.000000, Obj: 0.348798, No Obj: 0.003811, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.553573, Class: 1.000000, Obj: 0.297748, No Obj: 0.003235, Avg Recall: 0.648936, count: 94 -5793: 0.483702, 0.426540 avg, 0.001000 rate, 21.367781 seconds, 1483008 images -Loaded: 0.361522 seconds -Region Avg IOU: 0.551599, Class: 1.000000, Obj: 0.327220, No Obj: 0.003504, Avg Recall: 0.608247, count: 97 -Region Avg IOU: 0.547175, Class: 1.000000, Obj: 0.375406, No Obj: 0.003487, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.536859, Class: 1.000000, Obj: 0.332943, No Obj: 0.004100, Avg Recall: 0.615385, count: 130 -Region Avg IOU: 0.583310, Class: 1.000000, Obj: 0.404558, No Obj: 0.003951, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.577033, Class: 1.000000, Obj: 0.403421, No Obj: 0.003346, Avg Recall: 0.688889, count: 90 -Region Avg IOU: 0.624457, Class: 1.000000, Obj: 0.374697, No Obj: 0.003722, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.557099, Class: 1.000000, Obj: 0.382379, No Obj: 0.004163, Avg Recall: 0.666667, count: 123 -Region Avg IOU: 0.556939, Class: 1.000000, Obj: 0.356052, No Obj: 0.003055, Avg Recall: 0.631068, count: 103 -5794: 0.439435, 0.427829 avg, 0.001000 rate, 22.627199 seconds, 1483264 images -Loaded: 0.007506 seconds -Region Avg IOU: 0.552756, Class: 1.000000, Obj: 0.406896, No Obj: 0.003718, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.539466, Class: 1.000000, Obj: 0.353009, No Obj: 0.004054, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.540330, Class: 1.000000, Obj: 0.375109, No Obj: 0.003879, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.536665, Class: 1.000000, Obj: 0.380829, No Obj: 0.003591, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.586577, Class: 1.000000, Obj: 0.418573, No Obj: 0.003539, Avg Recall: 0.696629, count: 89 -Region Avg IOU: 0.539620, Class: 1.000000, Obj: 0.389598, No Obj: 0.003485, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.529198, Class: 1.000000, Obj: 0.379148, No Obj: 0.003703, Avg Recall: 0.610169, count: 118 -Region Avg IOU: 0.591181, Class: 1.000000, Obj: 0.398423, No Obj: 0.003748, Avg Recall: 0.681818, count: 88 -5795: 0.396651, 0.424711 avg, 0.001000 rate, 22.223757 seconds, 1483520 images -Loaded: 0.133837 seconds -Region Avg IOU: 0.574549, Class: 1.000000, Obj: 0.406927, No Obj: 0.004633, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.538575, Class: 1.000000, Obj: 0.384838, No Obj: 0.004618, Avg Recall: 0.620438, count: 137 -Region Avg IOU: 0.562496, Class: 1.000000, Obj: 0.404359, No Obj: 0.003959, Avg Recall: 0.689076, count: 119 -Region Avg IOU: 0.575021, Class: 1.000000, Obj: 0.428572, No Obj: 0.003704, Avg Recall: 0.640449, count: 89 -Region Avg IOU: 0.513348, Class: 1.000000, Obj: 0.387122, No Obj: 0.003802, Avg Recall: 0.565574, count: 122 -Region Avg IOU: 0.578551, Class: 1.000000, Obj: 0.392253, No Obj: 0.004555, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.589626, Class: 1.000000, Obj: 0.475687, No Obj: 0.003593, Avg Recall: 0.710526, count: 76 -Region Avg IOU: 0.534031, Class: 1.000000, Obj: 0.407264, No Obj: 0.004083, Avg Recall: 0.593220, count: 118 -5796: 0.445058, 0.426746 avg, 0.001000 rate, 22.767988 seconds, 1483776 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.531257, Class: 1.000000, Obj: 0.435895, No Obj: 0.004679, Avg Recall: 0.610169, count: 118 -Region Avg IOU: 0.578508, Class: 1.000000, Obj: 0.414740, No Obj: 0.004938, Avg Recall: 0.684615, count: 130 -Region Avg IOU: 0.585585, Class: 1.000000, Obj: 0.431960, No Obj: 0.004687, Avg Recall: 0.697479, count: 119 -Region Avg IOU: 0.569334, Class: 1.000000, Obj: 0.390439, No Obj: 0.004240, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.593110, Class: 1.000000, Obj: 0.433246, No Obj: 0.004955, Avg Recall: 0.714286, count: 133 -Region Avg IOU: 0.563651, Class: 1.000000, Obj: 0.421390, No Obj: 0.004915, Avg Recall: 0.616000, count: 125 -Region Avg IOU: 0.566870, Class: 1.000000, Obj: 0.423302, No Obj: 0.003929, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.544453, Class: 1.000000, Obj: 0.396714, No Obj: 0.003833, Avg Recall: 0.640449, count: 89 -5797: 0.432468, 0.427318 avg, 0.001000 rate, 23.246475 seconds, 1484032 images -Loaded: 0.390600 seconds -Region Avg IOU: 0.586888, Class: 1.000000, Obj: 0.420956, No Obj: 0.004848, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.600576, Class: 1.000000, Obj: 0.459200, No Obj: 0.004058, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.569845, Class: 1.000000, Obj: 0.407699, No Obj: 0.004454, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.547176, Class: 1.000000, Obj: 0.377979, No Obj: 0.004663, Avg Recall: 0.647059, count: 136 -Region Avg IOU: 0.549721, Class: 1.000000, Obj: 0.431295, No Obj: 0.004615, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.564203, Class: 1.000000, Obj: 0.393311, No Obj: 0.004821, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.599776, Class: 1.000000, Obj: 0.426497, No Obj: 0.003752, Avg Recall: 0.727273, count: 88 -Region Avg IOU: 0.552825, Class: 1.000000, Obj: 0.462061, No Obj: 0.003153, Avg Recall: 0.639535, count: 86 -5798: 0.382245, 0.422811 avg, 0.001000 rate, 24.012835 seconds, 1484288 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.542728, Class: 1.000000, Obj: 0.392069, No Obj: 0.004636, Avg Recall: 0.645161, count: 124 -Region Avg IOU: 0.545180, Class: 1.000000, Obj: 0.450454, No Obj: 0.004053, Avg Recall: 0.597938, count: 97 -Region Avg IOU: 0.538622, Class: 1.000000, Obj: 0.430363, No Obj: 0.004314, Avg Recall: 0.634146, count: 123 -Region Avg IOU: 0.579375, Class: 1.000000, Obj: 0.383786, No Obj: 0.005508, Avg Recall: 0.704698, count: 149 -Region Avg IOU: 0.555171, Class: 1.000000, Obj: 0.417839, No Obj: 0.004993, Avg Recall: 0.651515, count: 132 -Region Avg IOU: 0.550943, Class: 1.000000, Obj: 0.422549, No Obj: 0.003677, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.545199, Class: 1.000000, Obj: 0.422815, No Obj: 0.005379, Avg Recall: 0.659864, count: 147 -Region Avg IOU: 0.544891, Class: 1.000000, Obj: 0.440765, No Obj: 0.004440, Avg Recall: 0.625000, count: 112 -5799: 0.481113, 0.428641 avg, 0.001000 rate, 21.513823 seconds, 1484544 images -Loaded: 0.000058 seconds -Region Avg IOU: 0.566557, Class: 1.000000, Obj: 0.434321, No Obj: 0.003517, Avg Recall: 0.706667, count: 75 -Region Avg IOU: 0.554676, Class: 1.000000, Obj: 0.431964, No Obj: 0.005102, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.536240, Class: 1.000000, Obj: 0.434488, No Obj: 0.003986, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.580852, Class: 1.000000, Obj: 0.435200, No Obj: 0.004336, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.562105, Class: 1.000000, Obj: 0.435973, No Obj: 0.004234, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.565108, Class: 1.000000, Obj: 0.396249, No Obj: 0.004120, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.627173, Class: 1.000000, Obj: 0.445568, No Obj: 0.004515, Avg Recall: 0.757895, Saving weights to ./person9300/backup/tyvp_93_9c512_5800.weights -count: 95 -Region Avg IOU: 0.541888, Class: 1.000000, Obj: 0.426625, No Obj: 0.004356, Avg Recall: 0.582524, count: 103 -5800: 0.413686, 0.427146 avg, 0.001000 rate, 21.481676 seconds, 1484800 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.051847 seconds -Region Avg IOU: 0.422390, Class: 1.000000, Obj: 0.314704, No Obj: 0.003822, Avg Recall: 0.470588, count: 102 -Region Avg IOU: 0.519748, Class: 1.000000, Obj: 0.392483, No Obj: 0.004855, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.437998, Class: 1.000000, Obj: 0.328495, No Obj: 0.005003, Avg Recall: 0.471014, count: 138 -Region Avg IOU: 0.476670, Class: 1.000000, Obj: 0.337027, No Obj: 0.004602, Avg Recall: 0.460870, count: 115 -Region Avg IOU: 0.529085, Class: 1.000000, Obj: 0.352990, No Obj: 0.004884, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.462336, Class: 1.000000, Obj: 0.363579, No Obj: 0.004458, Avg Recall: 0.460784, count: 102 -Region Avg IOU: 0.468610, Class: 1.000000, Obj: 0.309812, No Obj: 0.005328, Avg Recall: 0.484615, count: 130 -Region Avg IOU: 0.472843, Class: 1.000000, Obj: 0.293700, No Obj: 0.004538, Avg Recall: 0.482143, count: 112 -5801: 0.531509, 0.437582 avg, 0.001000 rate, 18.399160 seconds, 1485056 images -Loaded: 2.650706 seconds -Region Avg IOU: 0.466562, Class: 1.000000, Obj: 0.306977, No Obj: 0.004398, Avg Recall: 0.470588, count: 102 -Region Avg IOU: 0.478956, Class: 1.000000, Obj: 0.301988, No Obj: 0.004713, Avg Recall: 0.508065, count: 124 -Region Avg IOU: 0.447647, Class: 1.000000, Obj: 0.327769, No Obj: 0.003782, Avg Recall: 0.437500, count: 96 -Region Avg IOU: 0.486228, Class: 1.000000, Obj: 0.324521, No Obj: 0.005471, Avg Recall: 0.561151, count: 139 -Region Avg IOU: 0.439900, Class: 1.000000, Obj: 0.346824, No Obj: 0.004306, Avg Recall: 0.436170, count: 94 -Region Avg IOU: 0.427330, Class: 1.000000, Obj: 0.290658, No Obj: 0.003500, Avg Recall: 0.434343, count: 99 -Region Avg IOU: 0.490206, Class: 1.000000, Obj: 0.365258, No Obj: 0.004583, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.498005, Class: 1.000000, Obj: 0.343324, No Obj: 0.004886, Avg Recall: 0.561905, count: 105 -5802: 0.433435, 0.437167 avg, 0.001000 rate, 15.017496 seconds, 1485312 images -Loaded: 5.983694 seconds -Region Avg IOU: 0.470590, Class: 1.000000, Obj: 0.332661, No Obj: 0.004438, Avg Recall: 0.572727, count: 110 -Region Avg IOU: 0.489096, Class: 1.000000, Obj: 0.355582, No Obj: 0.004581, Avg Recall: 0.536082, count: 97 -Region Avg IOU: 0.508921, Class: 1.000000, Obj: 0.335761, No Obj: 0.005136, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.484883, Class: 1.000000, Obj: 0.354624, No Obj: 0.004871, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.445649, Class: 1.000000, Obj: 0.316367, No Obj: 0.003678, Avg Recall: 0.490196, count: 102 -Region Avg IOU: 0.475592, Class: 1.000000, Obj: 0.344297, No Obj: 0.004998, Avg Recall: 0.500000, count: 106 -Region Avg IOU: 0.469246, Class: 1.000000, Obj: 0.333087, No Obj: 0.004600, Avg Recall: 0.470588, count: 102 -Region Avg IOU: 0.518338, Class: 1.000000, Obj: 0.352438, No Obj: 0.004985, Avg Recall: 0.633929, count: 112 -5803: 0.480769, 0.441527 avg, 0.001000 rate, 14.259905 seconds, 1485568 images -Loaded: 6.565226 seconds -Region Avg IOU: 0.541979, Class: 1.000000, Obj: 0.387497, No Obj: 0.004908, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.483651, Class: 1.000000, Obj: 0.342633, No Obj: 0.005034, Avg Recall: 0.526316, count: 133 -Region Avg IOU: 0.492858, Class: 1.000000, Obj: 0.320827, No Obj: 0.004089, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.538040, Class: 1.000000, Obj: 0.359999, No Obj: 0.004412, Avg Recall: 0.589744, count: 78 -Region Avg IOU: 0.472530, Class: 1.000000, Obj: 0.299042, No Obj: 0.004007, Avg Recall: 0.517241, count: 87 -Region Avg IOU: 0.444238, Class: 1.000000, Obj: 0.296274, No Obj: 0.004256, Avg Recall: 0.528736, count: 87 -Region Avg IOU: 0.468005, Class: 1.000000, Obj: 0.342130, No Obj: 0.004779, Avg Recall: 0.495727, count: 117 -Region Avg IOU: 0.472586, Class: 1.000000, Obj: 0.333755, No Obj: 0.004060, Avg Recall: 0.546392, count: 97 -5804: 0.407093, 0.438084 avg, 0.001000 rate, 15.379337 seconds, 1485824 images -Loaded: 5.115094 seconds -Region Avg IOU: 0.484292, Class: 1.000000, Obj: 0.333292, No Obj: 0.004086, Avg Recall: 0.526882, count: 93 -Region Avg IOU: 0.456697, Class: 1.000000, Obj: 0.296653, No Obj: 0.005077, Avg Recall: 0.514286, count: 140 -Region Avg IOU: 0.457996, Class: 1.000000, Obj: 0.314514, No Obj: 0.004916, Avg Recall: 0.472222, count: 144 -Region Avg IOU: 0.454654, Class: 1.000000, Obj: 0.312740, No Obj: 0.004478, Avg Recall: 0.512605, count: 119 -Region Avg IOU: 0.460362, Class: 1.000000, Obj: 0.294172, No Obj: 0.004938, Avg Recall: 0.476923, count: 130 -Region Avg IOU: 0.496175, Class: 1.000000, Obj: 0.331307, No Obj: 0.005090, Avg Recall: 0.532258, count: 124 -Region Avg IOU: 0.494621, Class: 1.000000, Obj: 0.321834, No Obj: 0.003894, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.539549, Class: 1.000000, Obj: 0.368554, No Obj: 0.004472, Avg Recall: 0.615385, count: 91 -5805: 0.463423, 0.440618 avg, 0.001000 rate, 19.092548 seconds, 1486080 images -Loaded: 3.158954 seconds -Region Avg IOU: 0.535550, Class: 1.000000, Obj: 0.356354, No Obj: 0.005309, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.473541, Class: 1.000000, Obj: 0.341206, No Obj: 0.004245, Avg Recall: 0.515152, count: 99 -Region Avg IOU: 0.438307, Class: 1.000000, Obj: 0.325342, No Obj: 0.004663, Avg Recall: 0.500000, count: 124 -Region Avg IOU: 0.433196, Class: 1.000000, Obj: 0.277456, No Obj: 0.004929, Avg Recall: 0.500000, count: 146 -Region Avg IOU: 0.493480, Class: 1.000000, Obj: 0.355243, No Obj: 0.003973, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.530787, Class: 1.000000, Obj: 0.367154, No Obj: 0.004823, Avg Recall: 0.573171, count: 82 -Region Avg IOU: 0.488323, Class: 1.000000, Obj: 0.318306, No Obj: 0.004475, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.480038, Class: 1.000000, Obj: 0.313439, No Obj: 0.004762, Avg Recall: 0.528302, count: 106 -5806: 0.476457, 0.444202 avg, 0.001000 rate, 17.665939 seconds, 1486336 images -Loaded: 1.494064 seconds -Region Avg IOU: 0.589914, Class: 1.000000, Obj: 0.410939, No Obj: 0.004369, Avg Recall: 0.685714, count: 70 -Region Avg IOU: 0.460353, Class: 1.000000, Obj: 0.330276, No Obj: 0.004082, Avg Recall: 0.571429, count: 84 -Region Avg IOU: 0.508487, Class: 1.000000, Obj: 0.299445, No Obj: 0.004766, Avg Recall: 0.581197, count: 117 -Region Avg IOU: 0.446184, Class: 1.000000, Obj: 0.301963, No Obj: 0.004871, Avg Recall: 0.472222, count: 108 -Region Avg IOU: 0.473992, Class: 1.000000, Obj: 0.319482, No Obj: 0.004571, Avg Recall: 0.532710, count: 107 -Region Avg IOU: 0.461363, Class: 1.000000, Obj: 0.387207, No Obj: 0.004371, Avg Recall: 0.470000, count: 100 -Region Avg IOU: 0.507500, Class: 1.000000, Obj: 0.364793, No Obj: 0.004547, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.459877, Class: 1.000000, Obj: 0.324973, No Obj: 0.004653, Avg Recall: 0.474227, count: 97 -5807: 0.459552, 0.445737 avg, 0.001000 rate, 19.826231 seconds, 1486592 images -Loaded: 1.430779 seconds -Region Avg IOU: 0.463296, Class: 1.000000, Obj: 0.339402, No Obj: 0.005409, Avg Recall: 0.495575, count: 113 -Region Avg IOU: 0.479169, Class: 1.000000, Obj: 0.359573, No Obj: 0.004549, Avg Recall: 0.500000, count: 114 -Region Avg IOU: 0.442971, Class: 1.000000, Obj: 0.336515, No Obj: 0.004659, Avg Recall: 0.440367, count: 109 -Region Avg IOU: 0.408688, Class: 1.000000, Obj: 0.298607, No Obj: 0.004508, Avg Recall: 0.405405, count: 111 -Region Avg IOU: 0.485240, Class: 1.000000, Obj: 0.330663, No Obj: 0.004949, Avg Recall: 0.514286, count: 105 -Region Avg IOU: 0.501201, Class: 1.000000, Obj: 0.390798, No Obj: 0.003995, Avg Recall: 0.551020, count: 98 -Region Avg IOU: 0.570437, Class: 1.000000, Obj: 0.388534, No Obj: 0.004427, Avg Recall: 0.714286, count: 84 -Region Avg IOU: 0.503385, Class: 1.000000, Obj: 0.338699, No Obj: 0.005480, Avg Recall: 0.540816, count: 98 -5808: 0.421752, 0.443338 avg, 0.001000 rate, 14.619614 seconds, 1486848 images -Loaded: 5.706889 seconds -Region Avg IOU: 0.505127, Class: 1.000000, Obj: 0.320088, No Obj: 0.004902, Avg Recall: 0.580000, count: 100 -Region Avg IOU: 0.445458, Class: 1.000000, Obj: 0.307171, No Obj: 0.004425, Avg Recall: 0.438095, count: 105 -Region Avg IOU: 0.442842, Class: 1.000000, Obj: 0.354915, No Obj: 0.004342, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.452076, Class: 1.000000, Obj: 0.341632, No Obj: 0.004323, Avg Recall: 0.483871, count: 93 -Region Avg IOU: 0.479243, Class: 1.000000, Obj: 0.304480, No Obj: 0.005051, Avg Recall: 0.516667, count: 120 -Region Avg IOU: 0.497049, Class: 1.000000, Obj: 0.349988, No Obj: 0.004814, Avg Recall: 0.549550, count: 111 -Region Avg IOU: 0.469599, Class: 1.000000, Obj: 0.328499, No Obj: 0.005701, Avg Recall: 0.503448, count: 145 -Region Avg IOU: 0.494265, Class: 1.000000, Obj: 0.319513, No Obj: 0.004908, Avg Recall: 0.550459, count: 109 -5809: 0.484989, 0.447503 avg, 0.001000 rate, 14.198135 seconds, 1487104 images -Loaded: 6.790435 seconds -Region Avg IOU: 0.519184, Class: 1.000000, Obj: 0.378563, No Obj: 0.004728, Avg Recall: 0.605769, count: 104 -Region Avg IOU: 0.520714, Class: 1.000000, Obj: 0.367970, No Obj: 0.004549, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.449476, Class: 1.000000, Obj: 0.346282, No Obj: 0.004640, Avg Recall: 0.500000, count: 116 -Region Avg IOU: 0.467244, Class: 1.000000, Obj: 0.321484, No Obj: 0.004657, Avg Recall: 0.513043, count: 115 -Region Avg IOU: 0.466911, Class: 1.000000, Obj: 0.318970, No Obj: 0.004685, Avg Recall: 0.478261, count: 115 -Region Avg IOU: 0.430611, Class: 1.000000, Obj: 0.338765, No Obj: 0.004188, Avg Recall: 0.442308, count: 104 -Region Avg IOU: 0.482540, Class: 1.000000, Obj: 0.350817, No Obj: 0.005096, Avg Recall: 0.537037, count: 108 -Region Avg IOU: 0.476315, Class: 1.000000, Obj: 0.334231, No Obj: 0.005334, Avg Recall: 0.519380, count: 129 -5810: 0.556974, 0.458450 avg, 0.001000 rate, 16.840487 seconds, 1487360 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000079 seconds -Region Avg IOU: 0.603282, Class: 1.000000, Obj: 0.499901, No Obj: 0.003740, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.584358, Class: 1.000000, Obj: 0.435310, No Obj: 0.004408, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.582329, Class: 1.000000, Obj: 0.459891, No Obj: 0.003688, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.604500, Class: 1.000000, Obj: 0.495730, No Obj: 0.004021, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.533821, Class: 1.000000, Obj: 0.428729, No Obj: 0.003982, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.618445, Class: 1.000000, Obj: 0.436169, No Obj: 0.004103, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.600390, Class: 1.000000, Obj: 0.437115, No Obj: 0.005100, Avg Recall: 0.702899, count: 138 -Region Avg IOU: 0.605670, Class: 1.000000, Obj: 0.440656, No Obj: 0.003896, Avg Recall: 0.697917, count: 96 -5811: 0.434681, 0.456073 avg, 0.001000 rate, 24.608124 seconds, 1487616 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.638732, Class: 1.000000, Obj: 0.536018, No Obj: 0.003550, Avg Recall: 0.788732, count: 71 -Region Avg IOU: 0.660583, Class: 1.000000, Obj: 0.445729, No Obj: 0.004274, Avg Recall: 0.818965, count: 116 -Region Avg IOU: 0.609668, Class: 1.000000, Obj: 0.450415, No Obj: 0.003959, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.652956, Class: 1.000000, Obj: 0.451781, No Obj: 0.003526, Avg Recall: 0.802469, count: 81 -Region Avg IOU: 0.524274, Class: 1.000000, Obj: 0.404008, No Obj: 0.003647, Avg Recall: 0.584071, count: 113 -Region Avg IOU: 0.635376, Class: 1.000000, Obj: 0.480636, No Obj: 0.004351, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.598194, Class: 1.000000, Obj: 0.440541, No Obj: 0.003658, Avg Recall: 0.724138, count: 58 -Region Avg IOU: 0.656856, Class: 1.000000, Obj: 0.426545, No Obj: 0.003588, Avg Recall: 0.783132, count: 83 -5812: 0.362063, 0.446672 avg, 0.001000 rate, 25.245180 seconds, 1487872 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.569964, Class: 1.000000, Obj: 0.406602, No Obj: 0.003812, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.638374, Class: 1.000000, Obj: 0.401061, No Obj: 0.004158, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.651669, Class: 1.000000, Obj: 0.488482, No Obj: 0.004012, Avg Recall: 0.823529, count: 102 -Region Avg IOU: 0.626314, Class: 1.000000, Obj: 0.492529, No Obj: 0.003900, Avg Recall: 0.784091, count: 88 -Region Avg IOU: 0.559975, Class: 1.000000, Obj: 0.396828, No Obj: 0.004691, Avg Recall: 0.682119, count: 151 -Region Avg IOU: 0.544522, Class: 1.000000, Obj: 0.397576, No Obj: 0.003668, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.584378, Class: 1.000000, Obj: 0.387494, No Obj: 0.003603, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.575632, Class: 1.000000, Obj: 0.395967, No Obj: 0.003944, Avg Recall: 0.666667, count: 132 -5813: 0.419972, 0.444002 avg, 0.001000 rate, 24.711256 seconds, 1488128 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.569038, Class: 1.000000, Obj: 0.407797, No Obj: 0.003730, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.605662, Class: 1.000000, Obj: 0.460162, No Obj: 0.003659, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.593750, Class: 1.000000, Obj: 0.390871, No Obj: 0.004464, Avg Recall: 0.703125, count: 128 -Region Avg IOU: 0.625638, Class: 1.000000, Obj: 0.441553, No Obj: 0.004609, Avg Recall: 0.736434, count: 129 -Region Avg IOU: 0.594937, Class: 1.000000, Obj: 0.437161, No Obj: 0.003673, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.567637, Class: 1.000000, Obj: 0.400013, No Obj: 0.003624, Avg Recall: 0.647541, count: 122 -Region Avg IOU: 0.622115, Class: 1.000000, Obj: 0.444168, No Obj: 0.003476, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.562806, Class: 1.000000, Obj: 0.382370, No Obj: 0.003463, Avg Recall: 0.616162, count: 99 -5814: 0.473047, 0.446907 avg, 0.001000 rate, 25.635763 seconds, 1488384 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.596235, Class: 1.000000, Obj: 0.393189, No Obj: 0.003905, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.649983, Class: 1.000000, Obj: 0.446356, No Obj: 0.003801, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.627421, Class: 1.000000, Obj: 0.427856, No Obj: 0.004126, Avg Recall: 0.780000, count: 150 -Region Avg IOU: 0.625636, Class: 1.000000, Obj: 0.418349, No Obj: 0.004328, Avg Recall: 0.728682, count: 129 -Region Avg IOU: 0.581330, Class: 1.000000, Obj: 0.402928, No Obj: 0.003612, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.649658, Class: 1.000000, Obj: 0.435829, No Obj: 0.003514, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.575900, Class: 1.000000, Obj: 0.382315, No Obj: 0.003462, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.598208, Class: 1.000000, Obj: 0.417454, No Obj: 0.004061, Avg Recall: 0.732824, count: 131 -5815: 0.479752, 0.450191 avg, 0.001000 rate, 24.478855 seconds, 1488640 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.585637, Class: 1.000000, Obj: 0.379649, No Obj: 0.003441, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.582744, Class: 1.000000, Obj: 0.370584, No Obj: 0.003472, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.593157, Class: 1.000000, Obj: 0.393616, No Obj: 0.003593, Avg Recall: 0.676259, count: 139 -Region Avg IOU: 0.607462, Class: 1.000000, Obj: 0.408337, No Obj: 0.002690, Avg Recall: 0.794872, count: 78 -Region Avg IOU: 0.590690, Class: 1.000000, Obj: 0.388698, No Obj: 0.003438, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.584864, Class: 1.000000, Obj: 0.383060, No Obj: 0.003232, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.608926, Class: 1.000000, Obj: 0.398784, No Obj: 0.003550, Avg Recall: 0.710526, count: 114 -Region Avg IOU: 0.584902, Class: 1.000000, Obj: 0.368361, No Obj: 0.002971, Avg Recall: 0.742857, count: 105 -5816: 0.467749, 0.451947 avg, 0.001000 rate, 25.425255 seconds, 1488896 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.582872, Class: 1.000000, Obj: 0.390168, No Obj: 0.002973, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.603777, Class: 1.000000, Obj: 0.407492, No Obj: 0.003194, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.606483, Class: 1.000000, Obj: 0.397507, No Obj: 0.003214, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.600767, Class: 1.000000, Obj: 0.334480, No Obj: 0.003312, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.533789, Class: 1.000000, Obj: 0.324994, No Obj: 0.003456, Avg Recall: 0.609375, count: 128 -Region Avg IOU: 0.622526, Class: 1.000000, Obj: 0.389043, No Obj: 0.003417, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.611430, Class: 1.000000, Obj: 0.377121, No Obj: 0.003512, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.584495, Class: 1.000000, Obj: 0.372640, No Obj: 0.003960, Avg Recall: 0.687075, count: 147 -5817: 0.434469, 0.450199 avg, 0.001000 rate, 24.660912 seconds, 1489152 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.592115, Class: 1.000000, Obj: 0.392449, No Obj: 0.002702, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.603929, Class: 1.000000, Obj: 0.355493, No Obj: 0.003651, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.582577, Class: 1.000000, Obj: 0.409017, No Obj: 0.002884, Avg Recall: 0.643678, count: 87 -Region Avg IOU: 0.618953, Class: 1.000000, Obj: 0.349789, No Obj: 0.003043, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.585279, Class: 1.000000, Obj: 0.415268, No Obj: 0.002991, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.599525, Class: 1.000000, Obj: 0.399491, No Obj: 0.003493, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.599801, Class: 1.000000, Obj: 0.325633, No Obj: 0.003958, Avg Recall: 0.707143, count: 140 -Region Avg IOU: 0.582846, Class: 1.000000, Obj: 0.357299, No Obj: 0.002806, Avg Recall: 0.725490, count: 102 -5818: 0.423827, 0.447562 avg, 0.001000 rate, 24.897305 seconds, 1489408 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.595332, Class: 1.000000, Obj: 0.402897, No Obj: 0.003472, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.601680, Class: 1.000000, Obj: 0.369240, No Obj: 0.003435, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.556590, Class: 1.000000, Obj: 0.351787, No Obj: 0.003170, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.626031, Class: 1.000000, Obj: 0.410752, No Obj: 0.003539, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.626696, Class: 1.000000, Obj: 0.395605, No Obj: 0.003221, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.577445, Class: 1.000000, Obj: 0.357724, No Obj: 0.003570, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.543901, Class: 1.000000, Obj: 0.349485, No Obj: 0.003005, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.556370, Class: 1.000000, Obj: 0.366837, No Obj: 0.003323, Avg Recall: 0.637168, count: 113 -5819: 0.425718, 0.445378 avg, 0.001000 rate, 25.452881 seconds, 1489664 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.541407, Class: 1.000000, Obj: 0.337082, No Obj: 0.003299, Avg Recall: 0.600000, count: 130 -Region Avg IOU: 0.628198, Class: 1.000000, Obj: 0.389191, No Obj: 0.002978, Avg Recall: 0.773810, count: 84 -Region Avg IOU: 0.627381, Class: 1.000000, Obj: 0.402811, No Obj: 0.003233, Avg Recall: 0.793103, count: 87 -Region Avg IOU: 0.633167, Class: 1.000000, Obj: 0.380158, No Obj: 0.003420, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.618829, Class: 1.000000, Obj: 0.369685, No Obj: 0.003146, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.619490, Class: 1.000000, Obj: 0.380533, No Obj: 0.003173, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.595372, Class: 1.000000, Obj: 0.373802, No Obj: 0.003369, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.625811, Class: 1.000000, Obj: 0.423318, No Obj: 0.003580, Avg Recall: 0.766355, count: 107 -5820: 0.422862, 0.443126 avg, 0.001000 rate, 23.375885 seconds, 1489920 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000074 seconds -Region Avg IOU: 0.638595, Class: 1.000000, Obj: 0.394506, No Obj: 0.003043, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.614408, Class: 1.000000, Obj: 0.410875, No Obj: 0.002903, Avg Recall: 0.773438, count: 128 -Region Avg IOU: 0.637960, Class: 1.000000, Obj: 0.369988, No Obj: 0.003317, Avg Recall: 0.788618, count: 123 -Region Avg IOU: 0.659288, Class: 1.000000, Obj: 0.423455, No Obj: 0.003227, Avg Recall: 0.813559, count: 118 -Region Avg IOU: 0.662205, Class: 1.000000, Obj: 0.394694, No Obj: 0.003298, Avg Recall: 0.828571, count: 105 -Region Avg IOU: 0.622633, Class: 1.000000, Obj: 0.387508, No Obj: 0.002772, Avg Recall: 0.766667, count: 120 -Region Avg IOU: 0.622365, Class: 1.000000, Obj: 0.371302, No Obj: 0.002939, Avg Recall: 0.754098, count: 122 -Region Avg IOU: 0.616360, Class: 1.000000, Obj: 0.374901, No Obj: 0.003100, Avg Recall: 0.757353, count: 136 -5821: 0.471465, 0.445960 avg, 0.001000 rate, 26.358984 seconds, 1490176 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.630412, Class: 1.000000, Obj: 0.403682, No Obj: 0.002739, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.658552, Class: 1.000000, Obj: 0.431772, No Obj: 0.002784, Avg Recall: 0.769231, count: 117 -Region Avg IOU: 0.648885, Class: 1.000000, Obj: 0.470290, No Obj: 0.003016, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.667038, Class: 1.000000, Obj: 0.413837, No Obj: 0.002485, Avg Recall: 0.883721, count: 86 -Region Avg IOU: 0.621095, Class: 1.000000, Obj: 0.349226, No Obj: 0.002990, Avg Recall: 0.789474, count: 114 -Region Avg IOU: 0.667411, Class: 1.000000, Obj: 0.404408, No Obj: 0.003018, Avg Recall: 0.833333, count: 126 -Region Avg IOU: 0.583436, Class: 1.000000, Obj: 0.374672, No Obj: 0.002648, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.602158, Class: 1.000000, Obj: 0.410427, No Obj: 0.002652, Avg Recall: 0.672897, count: 107 -5822: 0.398200, 0.441184 avg, 0.001000 rate, 27.311842 seconds, 1490432 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.629167, Class: 1.000000, Obj: 0.422353, No Obj: 0.003173, Avg Recall: 0.783582, count: 134 -Region Avg IOU: 0.642938, Class: 1.000000, Obj: 0.413018, No Obj: 0.002630, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.610988, Class: 1.000000, Obj: 0.362767, No Obj: 0.002941, Avg Recall: 0.751880, count: 133 -Region Avg IOU: 0.671380, Class: 1.000000, Obj: 0.451351, No Obj: 0.002924, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.645433, Class: 1.000000, Obj: 0.444689, No Obj: 0.002492, Avg Recall: 0.772727, count: 88 -Region Avg IOU: 0.665021, Class: 1.000000, Obj: 0.417148, No Obj: 0.002742, Avg Recall: 0.827586, count: 87 -Region Avg IOU: 0.643550, Class: 1.000000, Obj: 0.360088, No Obj: 0.003419, Avg Recall: 0.762712, count: 118 -Region Avg IOU: 0.624123, Class: 1.000000, Obj: 0.414870, No Obj: 0.002611, Avg Recall: 0.735294, count: 102 -5823: 0.412715, 0.438337 avg, 0.001000 rate, 27.958212 seconds, 1490688 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.636546, Class: 1.000000, Obj: 0.451004, No Obj: 0.002486, Avg Recall: 0.784810, count: 79 -Region Avg IOU: 0.629529, Class: 1.000000, Obj: 0.392459, No Obj: 0.002760, Avg Recall: 0.779661, count: 118 -Region Avg IOU: 0.643435, Class: 1.000000, Obj: 0.396031, No Obj: 0.003073, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.677323, Class: 1.000000, Obj: 0.444415, No Obj: 0.003201, Avg Recall: 0.828125, count: 128 -Region Avg IOU: 0.589035, Class: 1.000000, Obj: 0.371209, No Obj: 0.002676, Avg Recall: 0.709677, count: 124 -Region Avg IOU: 0.595290, Class: 1.000000, Obj: 0.411436, No Obj: 0.002924, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.698075, Class: 1.000000, Obj: 0.475570, No Obj: 0.002513, Avg Recall: 0.857143, count: 77 -Region Avg IOU: 0.622087, Class: 1.000000, Obj: 0.434432, No Obj: 0.002829, Avg Recall: 0.800000, count: 135 -5824: 0.432989, 0.437802 avg, 0.001000 rate, 27.593513 seconds, 1490944 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.647177, Class: 1.000000, Obj: 0.442527, No Obj: 0.003383, Avg Recall: 0.809859, count: 142 -Region Avg IOU: 0.595646, Class: 1.000000, Obj: 0.379244, No Obj: 0.003142, Avg Recall: 0.681481, count: 135 -Region Avg IOU: 0.678147, Class: 1.000000, Obj: 0.440224, No Obj: 0.002865, Avg Recall: 0.873786, count: 103 -Region Avg IOU: 0.645436, Class: 1.000000, Obj: 0.462396, No Obj: 0.002606, Avg Recall: 0.851852, count: 81 -Region Avg IOU: 0.664493, Class: 1.000000, Obj: 0.465024, No Obj: 0.003084, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.673212, Class: 1.000000, Obj: 0.412850, No Obj: 0.003431, Avg Recall: 0.803571, count: 112 -Region Avg IOU: 0.639831, Class: 1.000000, Obj: 0.410889, No Obj: 0.003097, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.650455, Class: 1.000000, Obj: 0.467795, No Obj: 0.002936, Avg Recall: 0.785714, count: 98 -5825: 0.458942, 0.439916 avg, 0.001000 rate, 27.011587 seconds, 1491200 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.649261, Class: 1.000000, Obj: 0.440985, No Obj: 0.002917, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.632504, Class: 1.000000, Obj: 0.394620, No Obj: 0.003027, Avg Recall: 0.808696, count: 115 -Region Avg IOU: 0.648685, Class: 1.000000, Obj: 0.474067, No Obj: 0.002982, Avg Recall: 0.775862, count: 116 -Region Avg IOU: 0.666369, Class: 1.000000, Obj: 0.445483, No Obj: 0.002860, Avg Recall: 0.836957, count: 92 -Region Avg IOU: 0.637982, Class: 1.000000, Obj: 0.445830, No Obj: 0.002780, Avg Recall: 0.796117, count: 103 -Region Avg IOU: 0.606722, Class: 1.000000, Obj: 0.385374, No Obj: 0.003054, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.693245, Class: 1.000000, Obj: 0.412095, No Obj: 0.003076, Avg Recall: 0.879630, count: 108 -Region Avg IOU: 0.656263, Class: 1.000000, Obj: 0.428096, No Obj: 0.002754, Avg Recall: 0.852632, count: 95 -5826: 0.435267, 0.439451 avg, 0.001000 rate, 26.193205 seconds, 1491456 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.652512, Class: 1.000000, Obj: 0.427964, No Obj: 0.002963, Avg Recall: 0.803571, count: 112 -Region Avg IOU: 0.687577, Class: 1.000000, Obj: 0.475027, No Obj: 0.002814, Avg Recall: 0.879121, count: 91 -Region Avg IOU: 0.632777, Class: 1.000000, Obj: 0.421025, No Obj: 0.002924, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.651514, Class: 1.000000, Obj: 0.401906, No Obj: 0.002707, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.588659, Class: 1.000000, Obj: 0.436036, No Obj: 0.002914, Avg Recall: 0.715385, count: 130 -Region Avg IOU: 0.642617, Class: 1.000000, Obj: 0.374779, No Obj: 0.003098, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.646741, Class: 1.000000, Obj: 0.436805, No Obj: 0.003211, Avg Recall: 0.796748, count: 123 -Region Avg IOU: 0.627864, Class: 1.000000, Obj: 0.416003, No Obj: 0.002627, Avg Recall: 0.771429, count: 105 -5827: 0.441776, 0.439684 avg, 0.001000 rate, 26.745632 seconds, 1491712 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.659440, Class: 1.000000, Obj: 0.454346, No Obj: 0.002459, Avg Recall: 0.850575, count: 87 -Region Avg IOU: 0.669157, Class: 1.000000, Obj: 0.439128, No Obj: 0.002813, Avg Recall: 0.834951, count: 103 -Region Avg IOU: 0.631015, Class: 1.000000, Obj: 0.415837, No Obj: 0.002700, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.608898, Class: 1.000000, Obj: 0.432556, No Obj: 0.002791, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.685911, Class: 1.000000, Obj: 0.464168, No Obj: 0.003026, Avg Recall: 0.872340, count: 94 -Region Avg IOU: 0.634950, Class: 1.000000, Obj: 0.421520, No Obj: 0.002874, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.634714, Class: 1.000000, Obj: 0.395638, No Obj: 0.003179, Avg Recall: 0.750000, count: 132 -Region Avg IOU: 0.657803, Class: 1.000000, Obj: 0.450824, No Obj: 0.002551, Avg Recall: 0.790698, count: 86 -5828: 0.379855, 0.433701 avg, 0.001000 rate, 27.675594 seconds, 1491968 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.636696, Class: 1.000000, Obj: 0.425597, No Obj: 0.002307, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.690552, Class: 1.000000, Obj: 0.428407, No Obj: 0.002753, Avg Recall: 0.863636, count: 88 -Region Avg IOU: 0.587417, Class: 1.000000, Obj: 0.416386, No Obj: 0.002742, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.673733, Class: 1.000000, Obj: 0.461216, No Obj: 0.003362, Avg Recall: 0.803571, count: 112 -Region Avg IOU: 0.643645, Class: 1.000000, Obj: 0.360436, No Obj: 0.002560, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.612182, Class: 1.000000, Obj: 0.461991, No Obj: 0.002914, Avg Recall: 0.718750, count: 128 -Region Avg IOU: 0.670240, Class: 1.000000, Obj: 0.413666, No Obj: 0.003398, Avg Recall: 0.819672, count: 122 -Region Avg IOU: 0.662172, Class: 1.000000, Obj: 0.413115, No Obj: 0.002901, Avg Recall: 0.830357, count: 112 -5829: 0.435403, 0.433871 avg, 0.001000 rate, 27.744013 seconds, 1492224 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.620861, Class: 1.000000, Obj: 0.420023, No Obj: 0.003079, Avg Recall: 0.787402, count: 127 -Region Avg IOU: 0.670815, Class: 1.000000, Obj: 0.396122, No Obj: 0.002534, Avg Recall: 0.854545, count: 110 -Region Avg IOU: 0.655820, Class: 1.000000, Obj: 0.409020, No Obj: 0.002645, Avg Recall: 0.813187, count: 91 -Region Avg IOU: 0.641138, Class: 1.000000, Obj: 0.377908, No Obj: 0.003201, Avg Recall: 0.797468, count: 158 -Region Avg IOU: 0.631635, Class: 1.000000, Obj: 0.417121, No Obj: 0.003073, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.669424, Class: 1.000000, Obj: 0.440519, No Obj: 0.002600, Avg Recall: 0.830000, count: 100 -Region Avg IOU: 0.644331, Class: 1.000000, Obj: 0.386961, No Obj: 0.003058, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.646711, Class: 1.000000, Obj: 0.393209, No Obj: 0.002854, Avg Recall: 0.794393, count: 107 -5830: 0.418227, 0.432307 avg, 0.001000 rate, 28.098196 seconds, 1492480 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 13.828841 seconds -Region Avg IOU: 0.556788, Class: 1.000000, Obj: 0.351996, No Obj: 0.003510, Avg Recall: 0.630952, count: 84 -Region Avg IOU: 0.507627, Class: 1.000000, Obj: 0.315255, No Obj: 0.003190, Avg Recall: 0.564706, count: 85 -Region Avg IOU: 0.427389, Class: 1.000000, Obj: 0.284385, No Obj: 0.003259, Avg Recall: 0.452174, count: 115 -Region Avg IOU: 0.524156, Class: 1.000000, Obj: 0.297656, No Obj: 0.003164, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.536466, Class: 1.000000, Obj: 0.371449, No Obj: 0.003481, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.497694, Class: 1.000000, Obj: 0.290859, No Obj: 0.004148, Avg Recall: 0.520270, count: 148 -Region Avg IOU: 0.437475, Class: 1.000000, Obj: 0.258942, No Obj: 0.003403, Avg Recall: 0.448598, count: 107 -Region Avg IOU: 0.515247, Class: 1.000000, Obj: 0.292051, No Obj: 0.003892, Avg Recall: 0.560000, count: 125 -5831: 0.466675, 0.435744 avg, 0.001000 rate, 16.034422 seconds, 1492736 images -Loaded: 4.425326 seconds -Region Avg IOU: 0.509209, Class: 1.000000, Obj: 0.317204, No Obj: 0.004012, Avg Recall: 0.571429, count: 105 -Region Avg IOU: 0.518448, Class: 1.000000, Obj: 0.312140, No Obj: 0.003398, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.498786, Class: 1.000000, Obj: 0.315376, No Obj: 0.003638, Avg Recall: 0.536364, count: 110 -Region Avg IOU: 0.472812, Class: 1.000000, Obj: 0.282993, No Obj: 0.002966, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.522097, Class: 1.000000, Obj: 0.336615, No Obj: 0.003783, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.589349, Class: 1.000000, Obj: 0.349264, No Obj: 0.004014, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.517714, Class: 1.000000, Obj: 0.282178, No Obj: 0.003397, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.557983, Class: 1.000000, Obj: 0.329235, No Obj: 0.004076, Avg Recall: 0.657895, count: 114 -5832: 0.462185, 0.438388 avg, 0.001000 rate, 19.794115 seconds, 1492992 images -Loaded: 0.706901 seconds -Region Avg IOU: 0.540249, Class: 1.000000, Obj: 0.319092, No Obj: 0.004389, Avg Recall: 0.606299, count: 127 -Region Avg IOU: 0.485118, Class: 1.000000, Obj: 0.324912, No Obj: 0.003371, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.468512, Class: 1.000000, Obj: 0.302797, No Obj: 0.002730, Avg Recall: 0.505747, count: 87 -Region Avg IOU: 0.479186, Class: 1.000000, Obj: 0.314367, No Obj: 0.003190, Avg Recall: 0.490196, count: 102 -Region Avg IOU: 0.583081, Class: 1.000000, Obj: 0.320693, No Obj: 0.003902, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.534085, Class: 1.000000, Obj: 0.306257, No Obj: 0.004405, Avg Recall: 0.586777, count: 121 -Region Avg IOU: 0.457077, Class: 1.000000, Obj: 0.277737, No Obj: 0.003372, Avg Recall: 0.485437, count: 103 -Region Avg IOU: 0.528874, Class: 1.000000, Obj: 0.316706, No Obj: 0.003272, Avg Recall: 0.640000, count: 75 -5833: 0.386500, 0.433199 avg, 0.001000 rate, 19.141695 seconds, 1493248 images -Loaded: 1.302091 seconds -Region Avg IOU: 0.511101, Class: 1.000000, Obj: 0.321557, No Obj: 0.003760, Avg Recall: 0.548673, count: 113 -Region Avg IOU: 0.528482, Class: 1.000000, Obj: 0.385302, No Obj: 0.003408, Avg Recall: 0.563218, count: 87 -Region Avg IOU: 0.509969, Class: 1.000000, Obj: 0.327787, No Obj: 0.003984, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.522192, Class: 1.000000, Obj: 0.317285, No Obj: 0.003590, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.512940, Class: 1.000000, Obj: 0.335663, No Obj: 0.004689, Avg Recall: 0.559441, count: 143 -Region Avg IOU: 0.459911, Class: 1.000000, Obj: 0.306024, No Obj: 0.004165, Avg Recall: 0.467290, count: 107 -Region Avg IOU: 0.558109, Class: 1.000000, Obj: 0.332743, No Obj: 0.004033, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.518072, Class: 1.000000, Obj: 0.319131, No Obj: 0.003971, Avg Recall: 0.585859, count: 99 -5834: 0.411754, 0.431055 avg, 0.001000 rate, 20.723330 seconds, 1493504 images -Loaded: 0.842798 seconds -Region Avg IOU: 0.524016, Class: 1.000000, Obj: 0.352146, No Obj: 0.004049, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.459744, Class: 1.000000, Obj: 0.317908, No Obj: 0.004857, Avg Recall: 0.527397, count: 146 -Region Avg IOU: 0.523239, Class: 1.000000, Obj: 0.365860, No Obj: 0.003837, Avg Recall: 0.586207, count: 87 -Region Avg IOU: 0.548250, Class: 1.000000, Obj: 0.389856, No Obj: 0.004382, Avg Recall: 0.636364, count: 88 -Region Avg IOU: 0.550166, Class: 1.000000, Obj: 0.329748, No Obj: 0.004505, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.535024, Class: 1.000000, Obj: 0.363504, No Obj: 0.003758, Avg Recall: 0.604651, count: 86 -Region Avg IOU: 0.548102, Class: 1.000000, Obj: 0.379925, No Obj: 0.004634, Avg Recall: 0.612403, count: 129 -Region Avg IOU: 0.473348, Class: 1.000000, Obj: 0.304715, No Obj: 0.004151, Avg Recall: 0.537815, count: 119 -5835: 0.515364, 0.439485 avg, 0.001000 rate, 17.178469 seconds, 1493760 images -Loaded: 3.285934 seconds -Region Avg IOU: 0.512391, Class: 1.000000, Obj: 0.358008, No Obj: 0.005415, Avg Recall: 0.551402, count: 107 -Region Avg IOU: 0.431007, Class: 1.000000, Obj: 0.328493, No Obj: 0.004076, Avg Recall: 0.495413, count: 109 -Region Avg IOU: 0.491279, Class: 1.000000, Obj: 0.333405, No Obj: 0.004004, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.448191, Class: 1.000000, Obj: 0.323863, No Obj: 0.004408, Avg Recall: 0.489796, count: 147 -Region Avg IOU: 0.570710, Class: 1.000000, Obj: 0.375731, No Obj: 0.004746, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.544350, Class: 1.000000, Obj: 0.400801, No Obj: 0.004595, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.557130, Class: 1.000000, Obj: 0.379861, No Obj: 0.004323, Avg Recall: 0.643678, count: 87 -Region Avg IOU: 0.538191, Class: 1.000000, Obj: 0.370021, No Obj: 0.004730, Avg Recall: 0.613861, count: 101 -5836: 0.509981, 0.446535 avg, 0.001000 rate, 19.686522 seconds, 1494016 images -Loaded: 0.887087 seconds -Region Avg IOU: 0.521961, Class: 1.000000, Obj: 0.391538, No Obj: 0.004182, Avg Recall: 0.641975, count: 81 -Region Avg IOU: 0.530044, Class: 1.000000, Obj: 0.421187, No Obj: 0.004847, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.511496, Class: 1.000000, Obj: 0.416976, No Obj: 0.004287, Avg Recall: 0.586957, count: 92 -Region Avg IOU: 0.468598, Class: 1.000000, Obj: 0.336377, No Obj: 0.004557, Avg Recall: 0.524194, count: 124 -Region Avg IOU: 0.447831, Class: 1.000000, Obj: 0.332135, No Obj: 0.004029, Avg Recall: 0.530973, count: 113 -Region Avg IOU: 0.579665, Class: 1.000000, Obj: 0.436650, No Obj: 0.004919, Avg Recall: 0.737500, count: 80 -Region Avg IOU: 0.473101, Class: 1.000000, Obj: 0.356259, No Obj: 0.004624, Avg Recall: 0.480769, count: 104 -Region Avg IOU: 0.489433, Class: 1.000000, Obj: 0.317466, No Obj: 0.004379, Avg Recall: 0.581633, count: 98 -5837: 0.392744, 0.441156 avg, 0.001000 rate, 15.944781 seconds, 1494272 images -Loaded: 3.303356 seconds -Region Avg IOU: 0.480966, Class: 1.000000, Obj: 0.371756, No Obj: 0.004496, Avg Recall: 0.538462, count: 117 -Region Avg IOU: 0.577158, Class: 1.000000, Obj: 0.407196, No Obj: 0.005126, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.489040, Class: 1.000000, Obj: 0.366777, No Obj: 0.004652, Avg Recall: 0.537736, count: 106 -Region Avg IOU: 0.482953, Class: 1.000000, Obj: 0.383246, No Obj: 0.004620, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.503318, Class: 1.000000, Obj: 0.412317, No Obj: 0.004854, Avg Recall: 0.557522, count: 113 -Region Avg IOU: 0.454027, Class: 1.000000, Obj: 0.376809, No Obj: 0.005265, Avg Recall: 0.466667, count: 120 -Region Avg IOU: 0.468556, Class: 1.000000, Obj: 0.390056, No Obj: 0.004885, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.500422, Class: 1.000000, Obj: 0.398775, No Obj: 0.004439, Avg Recall: 0.587156, count: 109 -5838: 0.437568, 0.440797 avg, 0.001000 rate, 17.986198 seconds, 1494528 images -Loaded: 2.992942 seconds -Region Avg IOU: 0.484840, Class: 1.000000, Obj: 0.380367, No Obj: 0.005588, Avg Recall: 0.532258, count: 124 -Region Avg IOU: 0.551123, Class: 1.000000, Obj: 0.436409, No Obj: 0.005125, Avg Recall: 0.632184, count: 87 -Region Avg IOU: 0.522366, Class: 1.000000, Obj: 0.390198, No Obj: 0.005157, Avg Recall: 0.577778, count: 90 -Region Avg IOU: 0.487610, Class: 1.000000, Obj: 0.393522, No Obj: 0.004428, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.595590, Class: 1.000000, Obj: 0.468938, No Obj: 0.004689, Avg Recall: 0.734940, count: 83 -Region Avg IOU: 0.487795, Class: 1.000000, Obj: 0.376990, No Obj: 0.004465, Avg Recall: 0.567568, count: 111 -Region Avg IOU: 0.562245, Class: 1.000000, Obj: 0.397673, No Obj: 0.005403, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.491960, Class: 1.000000, Obj: 0.371196, No Obj: 0.005526, Avg Recall: 0.534351, count: 131 -5839: 0.475275, 0.444245 avg, 0.001000 rate, 17.449602 seconds, 1494784 images -Loaded: 1.865580 seconds -Region Avg IOU: 0.532247, Class: 1.000000, Obj: 0.409509, No Obj: 0.004906, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.470623, Class: 1.000000, Obj: 0.375495, No Obj: 0.004929, Avg Recall: 0.500000, count: 106 -Region Avg IOU: 0.515649, Class: 1.000000, Obj: 0.405724, No Obj: 0.005152, Avg Recall: 0.572650, count: 117 -Region Avg IOU: 0.479672, Class: 1.000000, Obj: 0.386510, No Obj: 0.004162, Avg Recall: 0.509434, count: 106 -Region Avg IOU: 0.533536, Class: 1.000000, Obj: 0.391734, No Obj: 0.004008, Avg Recall: 0.600000, count: 65 -Region Avg IOU: 0.504650, Class: 1.000000, Obj: 0.382624, No Obj: 0.005064, Avg Recall: 0.570370, count: 135 -Region Avg IOU: 0.528062, Class: 1.000000, Obj: 0.400353, No Obj: 0.004852, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.443171, Class: 1.000000, Obj: 0.352420, No Obj: 0.005150, Avg Recall: 0.462121, count: 132 -5840: 0.481549, 0.447975 avg, 0.001000 rate, 20.128893 seconds, 1495040 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000071 seconds -Region Avg IOU: 0.677496, Class: 1.000000, Obj: 0.468185, No Obj: 0.003688, Avg Recall: 0.800000, count: 130 -Region Avg IOU: 0.639517, Class: 1.000000, Obj: 0.532832, No Obj: 0.003059, Avg Recall: 0.730159, count: 126 -Region Avg IOU: 0.658204, Class: 1.000000, Obj: 0.490032, No Obj: 0.002916, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.640629, Class: 1.000000, Obj: 0.429698, No Obj: 0.003312, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.657849, Class: 1.000000, Obj: 0.535249, No Obj: 0.003128, Avg Recall: 0.831325, count: 83 -Region Avg IOU: 0.689829, Class: 1.000000, Obj: 0.500657, No Obj: 0.003860, Avg Recall: 0.861789, count: 123 -Region Avg IOU: 0.682147, Class: 1.000000, Obj: 0.543512, No Obj: 0.003289, Avg Recall: 0.861386, count: 101 -Region Avg IOU: 0.674532, Class: 1.000000, Obj: 0.477904, No Obj: 0.003195, Avg Recall: 0.838095, count: 105 -5841: 0.409701, 0.444148 avg, 0.001000 rate, 28.615896 seconds, 1495296 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.666110, Class: 1.000000, Obj: 0.538130, No Obj: 0.002995, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.657135, Class: 1.000000, Obj: 0.555235, No Obj: 0.002765, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.657477, Class: 1.000000, Obj: 0.472966, No Obj: 0.003680, Avg Recall: 0.827068, count: 133 -Region Avg IOU: 0.641141, Class: 1.000000, Obj: 0.412981, No Obj: 0.003150, Avg Recall: 0.789916, count: 119 -Region Avg IOU: 0.650177, Class: 1.000000, Obj: 0.458742, No Obj: 0.003350, Avg Recall: 0.798387, count: 124 -Region Avg IOU: 0.652117, Class: 1.000000, Obj: 0.406384, No Obj: 0.003815, Avg Recall: 0.778571, count: 140 -Region Avg IOU: 0.629314, Class: 1.000000, Obj: 0.425059, No Obj: 0.003406, Avg Recall: 0.746154, count: 130 -Region Avg IOU: 0.654291, Class: 1.000000, Obj: 0.468197, No Obj: 0.002973, Avg Recall: 0.808989, count: 89 -5842: 0.453373, 0.445070 avg, 0.001000 rate, 30.342171 seconds, 1495552 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.636456, Class: 1.000000, Obj: 0.403369, No Obj: 0.002843, Avg Recall: 0.795082, count: 122 -Region Avg IOU: 0.674810, Class: 1.000000, Obj: 0.420633, No Obj: 0.002714, Avg Recall: 0.882979, count: 94 -Region Avg IOU: 0.662020, Class: 1.000000, Obj: 0.467509, No Obj: 0.002866, Avg Recall: 0.861789, count: 123 -Region Avg IOU: 0.644153, Class: 1.000000, Obj: 0.425767, No Obj: 0.002821, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.683651, Class: 1.000000, Obj: 0.431522, No Obj: 0.002757, Avg Recall: 0.864078, count: 103 -Region Avg IOU: 0.662020, Class: 1.000000, Obj: 0.365711, No Obj: 0.002851, Avg Recall: 0.842975, count: 121 -Region Avg IOU: 0.637050, Class: 1.000000, Obj: 0.434190, No Obj: 0.002478, Avg Recall: 0.836957, count: 92 -Region Avg IOU: 0.698373, Class: 1.000000, Obj: 0.412214, No Obj: 0.002751, Avg Recall: 0.914894, count: 94 -5843: 0.451462, 0.445710 avg, 0.001000 rate, 28.334429 seconds, 1495808 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.675837, Class: 1.000000, Obj: 0.452916, No Obj: 0.002666, Avg Recall: 0.816000, count: 125 -Region Avg IOU: 0.650178, Class: 1.000000, Obj: 0.383497, No Obj: 0.002709, Avg Recall: 0.826087, count: 138 -Region Avg IOU: 0.619061, Class: 1.000000, Obj: 0.412529, No Obj: 0.002122, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.648406, Class: 1.000000, Obj: 0.425130, No Obj: 0.002390, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.642321, Class: 1.000000, Obj: 0.385300, No Obj: 0.002573, Avg Recall: 0.790323, count: 124 -Region Avg IOU: 0.654055, Class: 1.000000, Obj: 0.423665, No Obj: 0.002161, Avg Recall: 0.777778, count: 90 -Region Avg IOU: 0.652413, Class: 1.000000, Obj: 0.406768, No Obj: 0.002322, Avg Recall: 0.785047, count: 107 -Region Avg IOU: 0.606675, Class: 1.000000, Obj: 0.389878, No Obj: 0.002506, Avg Recall: 0.709924, count: 131 -5844: 0.430045, 0.444143 avg, 0.001000 rate, 29.447924 seconds, 1496064 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.572352, Class: 1.000000, Obj: 0.323009, No Obj: 0.002081, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.652773, Class: 1.000000, Obj: 0.353251, No Obj: 0.002212, Avg Recall: 0.807018, count: 114 -Region Avg IOU: 0.653112, Class: 1.000000, Obj: 0.421810, No Obj: 0.002207, Avg Recall: 0.829268, count: 82 -Region Avg IOU: 0.674877, Class: 1.000000, Obj: 0.402594, No Obj: 0.002324, Avg Recall: 0.829268, count: 82 -Region Avg IOU: 0.682951, Class: 1.000000, Obj: 0.420364, No Obj: 0.002041, Avg Recall: 0.885057, count: 87 -Region Avg IOU: 0.688256, Class: 1.000000, Obj: 0.362002, No Obj: 0.002010, Avg Recall: 0.895833, count: 96 -Region Avg IOU: 0.647135, Class: 1.000000, Obj: 0.319279, No Obj: 0.002439, Avg Recall: 0.795620, count: 137 -Region Avg IOU: 0.577955, Class: 1.000000, Obj: 0.331155, No Obj: 0.002576, Avg Recall: 0.733766, count: 154 -5845: 0.480160, 0.447745 avg, 0.001000 rate, 30.760910 seconds, 1496320 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.677374, Class: 1.000000, Obj: 0.417498, No Obj: 0.002101, Avg Recall: 0.813187, count: 91 -Region Avg IOU: 0.654308, Class: 1.000000, Obj: 0.343611, No Obj: 0.001982, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.655618, Class: 1.000000, Obj: 0.330108, No Obj: 0.002129, Avg Recall: 0.795082, count: 122 -Region Avg IOU: 0.645279, Class: 1.000000, Obj: 0.390021, No Obj: 0.002182, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.672141, Class: 1.000000, Obj: 0.347176, No Obj: 0.002159, Avg Recall: 0.851852, count: 108 -Region Avg IOU: 0.676998, Class: 1.000000, Obj: 0.395310, No Obj: 0.002408, Avg Recall: 0.834783, count: 115 -Region Avg IOU: 0.672915, Class: 1.000000, Obj: 0.354350, No Obj: 0.002014, Avg Recall: 0.875000, count: 80 -Region Avg IOU: 0.653795, Class: 1.000000, Obj: 0.382155, No Obj: 0.002329, Avg Recall: 0.818182, count: 121 -5846: 0.456241, 0.448594 avg, 0.001000 rate, 27.815739 seconds, 1496576 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.668189, Class: 1.000000, Obj: 0.342802, No Obj: 0.002175, Avg Recall: 0.823009, count: 113 -Region Avg IOU: 0.697335, Class: 1.000000, Obj: 0.341515, No Obj: 0.002488, Avg Recall: 0.855932, count: 118 -Region Avg IOU: 0.681085, Class: 1.000000, Obj: 0.366995, No Obj: 0.001889, Avg Recall: 0.868687, count: 99 -Region Avg IOU: 0.651010, Class: 1.000000, Obj: 0.335419, No Obj: 0.001979, Avg Recall: 0.822222, count: 90 -Region Avg IOU: 0.667353, Class: 1.000000, Obj: 0.380020, No Obj: 0.001989, Avg Recall: 0.823529, count: 119 -Region Avg IOU: 0.583553, Class: 1.000000, Obj: 0.355370, No Obj: 0.002135, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.653044, Class: 1.000000, Obj: 0.342746, No Obj: 0.002051, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.623585, Class: 1.000000, Obj: 0.340153, No Obj: 0.001659, Avg Recall: 0.732558, count: 86 -5847: 0.444164, 0.448151 avg, 0.001000 rate, 29.276264 seconds, 1496832 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.668759, Class: 1.000000, Obj: 0.361315, No Obj: 0.002271, Avg Recall: 0.861386, count: 101 -Region Avg IOU: 0.655918, Class: 1.000000, Obj: 0.357633, No Obj: 0.002185, Avg Recall: 0.831683, count: 101 -Region Avg IOU: 0.629867, Class: 1.000000, Obj: 0.275669, No Obj: 0.002075, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.649995, Class: 1.000000, Obj: 0.348482, No Obj: 0.002048, Avg Recall: 0.827586, count: 116 -Region Avg IOU: 0.620806, Class: 1.000000, Obj: 0.338324, No Obj: 0.001963, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.676967, Class: 1.000000, Obj: 0.368622, No Obj: 0.002618, Avg Recall: 0.841667, count: 120 -Region Avg IOU: 0.641957, Class: 1.000000, Obj: 0.352274, No Obj: 0.002299, Avg Recall: 0.766667, count: 120 -Region Avg IOU: 0.658345, Class: 1.000000, Obj: 0.334880, No Obj: 0.002530, Avg Recall: 0.823529, count: 136 -5848: 0.485445, 0.451881 avg, 0.001000 rate, 30.815351 seconds, 1497088 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.684953, Class: 1.000000, Obj: 0.387777, No Obj: 0.002175, Avg Recall: 0.860870, count: 115 -Region Avg IOU: 0.645192, Class: 1.000000, Obj: 0.357192, No Obj: 0.002098, Avg Recall: 0.804348, count: 92 -Region Avg IOU: 0.680551, Class: 1.000000, Obj: 0.370000, No Obj: 0.002134, Avg Recall: 0.842105, count: 95 -Region Avg IOU: 0.629306, Class: 1.000000, Obj: 0.340538, No Obj: 0.002003, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.657453, Class: 1.000000, Obj: 0.336473, No Obj: 0.002307, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.681450, Class: 1.000000, Obj: 0.377581, No Obj: 0.002529, Avg Recall: 0.849057, count: 106 -Region Avg IOU: 0.665186, Class: 1.000000, Obj: 0.331324, No Obj: 0.002388, Avg Recall: 0.840000, count: 125 -Region Avg IOU: 0.657577, Class: 1.000000, Obj: 0.350853, No Obj: 0.002602, Avg Recall: 0.801370, count: 146 -5849: 0.469987, 0.453691 avg, 0.001000 rate, 29.050827 seconds, 1497344 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.690555, Class: 1.000000, Obj: 0.450999, No Obj: 0.002202, Avg Recall: 0.869565, count: 92 -Region Avg IOU: 0.664841, Class: 1.000000, Obj: 0.398460, No Obj: 0.002292, Avg Recall: 0.836066, count: 122 -Region Avg IOU: 0.639866, Class: 1.000000, Obj: 0.366890, No Obj: 0.002264, Avg Recall: 0.819149, count: 94 -Region Avg IOU: 0.653850, Class: 1.000000, Obj: 0.413431, No Obj: 0.002468, Avg Recall: 0.861386, count: 101 -Region Avg IOU: 0.699370, Class: 1.000000, Obj: 0.413257, No Obj: 0.002331, Avg Recall: 0.885057, count: 87 -Region Avg IOU: 0.649676, Class: 1.000000, Obj: 0.347965, No Obj: 0.002639, Avg Recall: 0.817460, count: 126 -Region Avg IOU: 0.671238, Class: 1.000000, Obj: 0.330960, No Obj: 0.002170, Avg Recall: 0.827957, count: 93 -Region Avg IOU: 0.725842, Class: 1.000000, Obj: 0.393004, No Obj: 0.002172, Avg Recall: 0.920455, count: 88 -5850: 0.418533, 0.450175 avg, 0.001000 rate, 30.662638 seconds, 1497600 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 13.596758 seconds -Region Avg IOU: 0.586189, Class: 1.000000, Obj: 0.326383, No Obj: 0.003552, Avg Recall: 0.702899, count: 138 -Region Avg IOU: 0.647126, Class: 1.000000, Obj: 0.373096, No Obj: 0.003805, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.558586, Class: 1.000000, Obj: 0.286731, No Obj: 0.002757, Avg Recall: 0.629032, count: 124 -Region Avg IOU: 0.592067, Class: 1.000000, Obj: 0.388359, No Obj: 0.003702, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.597429, Class: 1.000000, Obj: 0.379806, No Obj: 0.003742, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.626357, Class: 1.000000, Obj: 0.394481, No Obj: 0.003698, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.584853, Class: 1.000000, Obj: 0.351565, No Obj: 0.002901, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.568962, Class: 1.000000, Obj: 0.393342, No Obj: 0.003499, Avg Recall: 0.654206, count: 107 -5851: 0.443863, 0.449544 avg, 0.001000 rate, 23.290985 seconds, 1497856 images -Loaded: 0.571003 seconds -Region Avg IOU: 0.572448, Class: 1.000000, Obj: 0.340326, No Obj: 0.003819, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.544850, Class: 1.000000, Obj: 0.354340, No Obj: 0.003421, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.595951, Class: 1.000000, Obj: 0.416053, No Obj: 0.003733, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.580815, Class: 1.000000, Obj: 0.374055, No Obj: 0.003809, Avg Recall: 0.683824, count: 136 -Region Avg IOU: 0.583854, Class: 1.000000, Obj: 0.329371, No Obj: 0.003856, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.573178, Class: 1.000000, Obj: 0.386899, No Obj: 0.003573, Avg Recall: 0.669421, count: 121 -Region Avg IOU: 0.608250, Class: 1.000000, Obj: 0.397538, No Obj: 0.003547, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.612580, Class: 1.000000, Obj: 0.365833, No Obj: 0.004010, Avg Recall: 0.709677, count: 93 -5852: 0.424352, 0.447025 avg, 0.001000 rate, 23.801464 seconds, 1498112 images -Loaded: 0.681845 seconds -Region Avg IOU: 0.588665, Class: 1.000000, Obj: 0.363172, No Obj: 0.003703, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.559265, Class: 1.000000, Obj: 0.361722, No Obj: 0.003476, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.555635, Class: 1.000000, Obj: 0.393673, No Obj: 0.003598, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.578226, Class: 1.000000, Obj: 0.380536, No Obj: 0.004365, Avg Recall: 0.666667, count: 123 -Region Avg IOU: 0.564891, Class: 1.000000, Obj: 0.386223, No Obj: 0.003484, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.598658, Class: 1.000000, Obj: 0.402514, No Obj: 0.004087, Avg Recall: 0.728571, count: 140 -Region Avg IOU: 0.601361, Class: 1.000000, Obj: 0.407138, No Obj: 0.004012, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.652882, Class: 1.000000, Obj: 0.421818, No Obj: 0.003800, Avg Recall: 0.750000, count: 76 -5853: 0.416134, 0.443936 avg, 0.001000 rate, 24.023075 seconds, 1498368 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.564160, Class: 1.000000, Obj: 0.390467, No Obj: 0.003492, Avg Recall: 0.634146, count: 82 -Region Avg IOU: 0.581032, Class: 1.000000, Obj: 0.419521, No Obj: 0.004710, Avg Recall: 0.678832, count: 137 -Region Avg IOU: 0.630087, Class: 1.000000, Obj: 0.422505, No Obj: 0.003251, Avg Recall: 0.743590, count: 78 -Region Avg IOU: 0.575882, Class: 1.000000, Obj: 0.395577, No Obj: 0.004155, Avg Recall: 0.687023, count: 131 -Region Avg IOU: 0.617951, Class: 1.000000, Obj: 0.420559, No Obj: 0.003867, Avg Recall: 0.685393, count: 89 -Region Avg IOU: 0.552485, Class: 1.000000, Obj: 0.389177, No Obj: 0.003785, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.562160, Class: 1.000000, Obj: 0.441769, No Obj: 0.004145, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.570551, Class: 1.000000, Obj: 0.409683, No Obj: 0.003667, Avg Recall: 0.631579, count: 95 -5854: 0.399976, 0.439540 avg, 0.001000 rate, 23.239580 seconds, 1498624 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.573355, Class: 1.000000, Obj: 0.422811, No Obj: 0.004298, Avg Recall: 0.658537, count: 123 -Region Avg IOU: 0.635879, Class: 1.000000, Obj: 0.510225, No Obj: 0.003740, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.613813, Class: 1.000000, Obj: 0.411474, No Obj: 0.005230, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.533398, Class: 1.000000, Obj: 0.383789, No Obj: 0.003761, Avg Recall: 0.572650, count: 117 -Region Avg IOU: 0.538173, Class: 1.000000, Obj: 0.387120, No Obj: 0.004414, Avg Recall: 0.586957, count: 138 -Region Avg IOU: 0.643115, Class: 1.000000, Obj: 0.475595, No Obj: 0.004373, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.605601, Class: 1.000000, Obj: 0.444226, No Obj: 0.004372, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.588461, Class: 1.000000, Obj: 0.485600, No Obj: 0.003690, Avg Recall: 0.615385, count: 91 -5855: 0.400353, 0.435621 avg, 0.001000 rate, 22.677912 seconds, 1498880 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.555970, Class: 1.000000, Obj: 0.401429, No Obj: 0.003839, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.606536, Class: 1.000000, Obj: 0.495577, No Obj: 0.003898, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.576921, Class: 1.000000, Obj: 0.407145, No Obj: 0.004900, Avg Recall: 0.694444, count: 144 -Region Avg IOU: 0.559811, Class: 1.000000, Obj: 0.435176, No Obj: 0.004277, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.564264, Class: 1.000000, Obj: 0.457978, No Obj: 0.003606, Avg Recall: 0.642857, count: 84 -Region Avg IOU: 0.508140, Class: 1.000000, Obj: 0.366287, No Obj: 0.004096, Avg Recall: 0.575758, count: 132 -Region Avg IOU: 0.577688, Class: 1.000000, Obj: 0.444595, No Obj: 0.004251, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.580075, Class: 1.000000, Obj: 0.433912, No Obj: 0.004194, Avg Recall: 0.675926, count: 108 -5856: 0.410147, 0.433074 avg, 0.001000 rate, 24.163336 seconds, 1499136 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.558790, Class: 1.000000, Obj: 0.462642, No Obj: 0.003973, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.568072, Class: 1.000000, Obj: 0.443800, No Obj: 0.004697, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.590746, Class: 1.000000, Obj: 0.433688, No Obj: 0.005051, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.560878, Class: 1.000000, Obj: 0.405304, No Obj: 0.004524, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.571086, Class: 1.000000, Obj: 0.500119, No Obj: 0.003609, Avg Recall: 0.686747, count: 83 -Region Avg IOU: 0.535914, Class: 1.000000, Obj: 0.463401, No Obj: 0.003819, Avg Recall: 0.600000, count: 120 -Region Avg IOU: 0.512637, Class: 1.000000, Obj: 0.413543, No Obj: 0.004136, Avg Recall: 0.534884, count: 86 -Region Avg IOU: 0.575468, Class: 1.000000, Obj: 0.440522, No Obj: 0.004329, Avg Recall: 0.697479, count: 119 -5857: 0.394322, 0.429199 avg, 0.001000 rate, 24.251305 seconds, 1499392 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.584990, Class: 1.000000, Obj: 0.437914, No Obj: 0.004564, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.549779, Class: 1.000000, Obj: 0.396967, No Obj: 0.004050, Avg Recall: 0.651163, count: 129 -Region Avg IOU: 0.619200, Class: 1.000000, Obj: 0.428496, No Obj: 0.003829, Avg Recall: 0.784810, count: 79 -Region Avg IOU: 0.541604, Class: 1.000000, Obj: 0.392178, No Obj: 0.003641, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.573997, Class: 1.000000, Obj: 0.435784, No Obj: 0.003975, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.609669, Class: 1.000000, Obj: 0.497291, No Obj: 0.004117, Avg Recall: 0.688889, count: 90 -Region Avg IOU: 0.592131, Class: 1.000000, Obj: 0.414110, No Obj: 0.004808, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.574676, Class: 1.000000, Obj: 0.458580, No Obj: 0.004022, Avg Recall: 0.705882, count: 85 -5858: 0.411881, 0.427467 avg, 0.001000 rate, 23.439837 seconds, 1499648 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.582810, Class: 1.000000, Obj: 0.397332, No Obj: 0.004829, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.579779, Class: 1.000000, Obj: 0.460732, No Obj: 0.004485, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.593515, Class: 1.000000, Obj: 0.413567, No Obj: 0.004683, Avg Recall: 0.702899, count: 138 -Region Avg IOU: 0.513009, Class: 1.000000, Obj: 0.366056, No Obj: 0.004070, Avg Recall: 0.603306, count: 121 -Region Avg IOU: 0.566985, Class: 1.000000, Obj: 0.423171, No Obj: 0.004140, Avg Recall: 0.679012, count: 81 -Region Avg IOU: 0.583070, Class: 1.000000, Obj: 0.469736, No Obj: 0.004384, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.619035, Class: 1.000000, Obj: 0.500917, No Obj: 0.003651, Avg Recall: 0.718310, count: 71 -Region Avg IOU: 0.596448, Class: 1.000000, Obj: 0.429379, No Obj: 0.004143, Avg Recall: 0.728155, count: 103 -5859: 0.429543, 0.427674 avg, 0.001000 rate, 23.253246 seconds, 1499904 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.609186, Class: 1.000000, Obj: 0.494984, No Obj: 0.003457, Avg Recall: 0.726191, count: 84 -Region Avg IOU: 0.572627, Class: 1.000000, Obj: 0.405654, No Obj: 0.003891, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.522699, Class: 1.000000, Obj: 0.414716, No Obj: 0.003529, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.560570, Class: 1.000000, Obj: 0.381001, No Obj: 0.004251, Avg Recall: 0.632479, count: 117 -Region Avg IOU: 0.554713, Class: 1.000000, Obj: 0.424021, No Obj: 0.004088, Avg Recall: 0.659259, count: 135 -Region Avg IOU: 0.600216, Class: 1.000000, Obj: 0.407705, No Obj: 0.004288, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.569156, Class: 1.000000, Obj: 0.444425, No Obj: 0.003225, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.598039, Class: 1.000000, Obj: 0.476162, No Obj: 0.004092, Avg Recall: 0.731481, count: 108 -5860: 0.491254, 0.434032 avg, 0.001000 rate, 24.320906 seconds, 1500160 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.932583 seconds -Region Avg IOU: 0.505852, Class: 1.000000, Obj: 0.336005, No Obj: 0.004319, Avg Recall: 0.565657, count: 99 -Region Avg IOU: 0.483298, Class: 1.000000, Obj: 0.365747, No Obj: 0.004259, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.470979, Class: 1.000000, Obj: 0.310210, No Obj: 0.003902, Avg Recall: 0.517544, count: 114 -Region Avg IOU: 0.475961, Class: 1.000000, Obj: 0.288596, No Obj: 0.004667, Avg Recall: 0.515385, count: 130 -Region Avg IOU: 0.469646, Class: 1.000000, Obj: 0.311675, No Obj: 0.004007, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.458921, Class: 1.000000, Obj: 0.290820, No Obj: 0.004918, Avg Recall: 0.503817, count: 131 -Region Avg IOU: 0.438353, Class: 1.000000, Obj: 0.295901, No Obj: 0.004926, Avg Recall: 0.470588, count: 136 -Region Avg IOU: 0.524945, Class: 1.000000, Obj: 0.338378, No Obj: 0.005050, Avg Recall: 0.610619, count: 113 -5861: 0.503797, 0.441009 avg, 0.001000 rate, 18.952463 seconds, 1500416 images -Loaded: 1.533961 seconds -Region Avg IOU: 0.461839, Class: 1.000000, Obj: 0.285209, No Obj: 0.004126, Avg Recall: 0.471014, count: 138 -Region Avg IOU: 0.461758, Class: 1.000000, Obj: 0.312348, No Obj: 0.003782, Avg Recall: 0.522222, count: 90 -Region Avg IOU: 0.447082, Class: 1.000000, Obj: 0.289159, No Obj: 0.004107, Avg Recall: 0.473214, count: 112 -Region Avg IOU: 0.485834, Class: 1.000000, Obj: 0.292723, No Obj: 0.003911, Avg Recall: 0.532468, count: 77 -Region Avg IOU: 0.518757, Class: 1.000000, Obj: 0.321863, No Obj: 0.003713, Avg Recall: 0.581395, count: 86 -Region Avg IOU: 0.482892, Class: 1.000000, Obj: 0.316880, No Obj: 0.004371, Avg Recall: 0.547826, count: 115 -Region Avg IOU: 0.519363, Class: 1.000000, Obj: 0.335060, No Obj: 0.003747, Avg Recall: 0.612500, count: 80 -Region Avg IOU: 0.490458, Class: 1.000000, Obj: 0.288375, No Obj: 0.004315, Avg Recall: 0.544643, count: 112 -5862: 0.466217, 0.443530 avg, 0.001000 rate, 17.080008 seconds, 1500672 images -Loaded: 4.175265 seconds -Region Avg IOU: 0.513475, Class: 1.000000, Obj: 0.326402, No Obj: 0.004100, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.549618, Class: 1.000000, Obj: 0.341921, No Obj: 0.004675, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.512813, Class: 1.000000, Obj: 0.319360, No Obj: 0.004731, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.432356, Class: 1.000000, Obj: 0.267920, No Obj: 0.004187, Avg Recall: 0.455285, count: 123 -Region Avg IOU: 0.451855, Class: 1.000000, Obj: 0.285826, No Obj: 0.004844, Avg Recall: 0.486842, count: 152 -Region Avg IOU: 0.477252, Class: 1.000000, Obj: 0.296886, No Obj: 0.004247, Avg Recall: 0.503876, count: 129 -Region Avg IOU: 0.511163, Class: 1.000000, Obj: 0.307303, No Obj: 0.004352, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.437434, Class: 1.000000, Obj: 0.273872, No Obj: 0.004388, Avg Recall: 0.453125, count: 128 -5863: 0.503300, 0.449507 avg, 0.001000 rate, 16.244310 seconds, 1500928 images -Loaded: 2.779591 seconds -Region Avg IOU: 0.428799, Class: 1.000000, Obj: 0.270331, No Obj: 0.003915, Avg Recall: 0.481132, count: 106 -Region Avg IOU: 0.481019, Class: 1.000000, Obj: 0.321240, No Obj: 0.004193, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.461720, Class: 1.000000, Obj: 0.308517, No Obj: 0.003887, Avg Recall: 0.509259, count: 108 -Region Avg IOU: 0.485710, Class: 1.000000, Obj: 0.306313, No Obj: 0.003933, Avg Recall: 0.490741, count: 108 -Region Avg IOU: 0.521459, Class: 1.000000, Obj: 0.306881, No Obj: 0.004142, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.459827, Class: 1.000000, Obj: 0.285712, No Obj: 0.004379, Avg Recall: 0.477941, count: 136 -Region Avg IOU: 0.507372, Class: 1.000000, Obj: 0.327388, No Obj: 0.004298, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.544599, Class: 1.000000, Obj: 0.287644, No Obj: 0.004297, Avg Recall: 0.600000, count: 110 -5864: 0.500936, 0.454650 avg, 0.001000 rate, 17.594557 seconds, 1501184 images -Loaded: 3.499990 seconds -Region Avg IOU: 0.497735, Class: 1.000000, Obj: 0.271808, No Obj: 0.004561, Avg Recall: 0.559702, count: 134 -Region Avg IOU: 0.491519, Class: 1.000000, Obj: 0.350818, No Obj: 0.003175, Avg Recall: 0.596774, count: 62 -Region Avg IOU: 0.505652, Class: 1.000000, Obj: 0.307050, No Obj: 0.004883, Avg Recall: 0.546875, count: 128 -Region Avg IOU: 0.456739, Class: 1.000000, Obj: 0.290801, No Obj: 0.004691, Avg Recall: 0.473684, count: 133 -Region Avg IOU: 0.482524, Class: 1.000000, Obj: 0.338237, No Obj: 0.003892, Avg Recall: 0.521277, count: 94 -Region Avg IOU: 0.467492, Class: 1.000000, Obj: 0.289376, No Obj: 0.004173, Avg Recall: 0.504000, count: 125 -Region Avg IOU: 0.495373, Class: 1.000000, Obj: 0.334824, No Obj: 0.004355, Avg Recall: 0.515790, count: 95 -Region Avg IOU: 0.529511, Class: 1.000000, Obj: 0.338731, No Obj: 0.003758, Avg Recall: 0.607143, count: 84 -5865: 0.457655, 0.454950 avg, 0.001000 rate, 14.689160 seconds, 1501440 images -Loaded: 3.652316 seconds -Region Avg IOU: 0.534328, Class: 1.000000, Obj: 0.396549, No Obj: 0.004403, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.469141, Class: 1.000000, Obj: 0.326373, No Obj: 0.003436, Avg Recall: 0.463158, count: 95 -Region Avg IOU: 0.477711, Class: 1.000000, Obj: 0.287415, No Obj: 0.004512, Avg Recall: 0.535088, count: 114 -Region Avg IOU: 0.497661, Class: 1.000000, Obj: 0.315851, No Obj: 0.004357, Avg Recall: 0.568182, count: 88 -Region Avg IOU: 0.468000, Class: 1.000000, Obj: 0.326271, No Obj: 0.003988, Avg Recall: 0.500000, count: 104 -Region Avg IOU: 0.448396, Class: 1.000000, Obj: 0.305882, No Obj: 0.004114, Avg Recall: 0.470086, count: 117 -Region Avg IOU: 0.483795, Class: 1.000000, Obj: 0.352056, No Obj: 0.003992, Avg Recall: 0.527273, count: 110 -Region Avg IOU: 0.445948, Class: 1.000000, Obj: 0.306412, No Obj: 0.004459, Avg Recall: 0.442623, count: 122 -5866: 0.404692, 0.449924 avg, 0.001000 rate, 14.985418 seconds, 1501696 images -Loaded: 5.771258 seconds -Region Avg IOU: 0.506913, Class: 1.000000, Obj: 0.338340, No Obj: 0.004361, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.453446, Class: 1.000000, Obj: 0.319107, No Obj: 0.004607, Avg Recall: 0.518868, count: 106 -Region Avg IOU: 0.472673, Class: 1.000000, Obj: 0.349465, No Obj: 0.004464, Avg Recall: 0.500000, count: 106 -Region Avg IOU: 0.410296, Class: 1.000000, Obj: 0.286993, No Obj: 0.004661, Avg Recall: 0.437500, count: 112 -Region Avg IOU: 0.520575, Class: 1.000000, Obj: 0.331527, No Obj: 0.005258, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.462416, Class: 1.000000, Obj: 0.305844, No Obj: 0.004573, Avg Recall: 0.483607, count: 122 -Region Avg IOU: 0.546194, Class: 1.000000, Obj: 0.361642, No Obj: 0.004614, Avg Recall: 0.620690, count: 87 -Region Avg IOU: 0.525254, Class: 1.000000, Obj: 0.338651, No Obj: 0.005028, Avg Recall: 0.598485, count: 132 -5867: 0.452128, 0.450145 avg, 0.001000 rate, 14.111140 seconds, 1501952 images -Loaded: 7.702187 seconds -Region Avg IOU: 0.551031, Class: 1.000000, Obj: 0.363436, No Obj: 0.005251, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.407174, Class: 1.000000, Obj: 0.320191, No Obj: 0.004686, Avg Recall: 0.420168, count: 119 -Region Avg IOU: 0.474086, Class: 1.000000, Obj: 0.319365, No Obj: 0.004593, Avg Recall: 0.514019, count: 107 -Region Avg IOU: 0.481824, Class: 1.000000, Obj: 0.341776, No Obj: 0.004682, Avg Recall: 0.528926, count: 121 -Region Avg IOU: 0.493371, Class: 1.000000, Obj: 0.326168, No Obj: 0.004916, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.505399, Class: 1.000000, Obj: 0.338219, No Obj: 0.004262, Avg Recall: 0.551724, count: 87 -Region Avg IOU: 0.479192, Class: 1.000000, Obj: 0.300839, No Obj: 0.004895, Avg Recall: 0.550847, count: 118 -Region Avg IOU: 0.504465, Class: 1.000000, Obj: 0.332293, No Obj: 0.004317, Avg Recall: 0.543478, count: 92 -5868: 0.421062, 0.447236 avg, 0.001000 rate, 15.380892 seconds, 1502208 images -Loaded: 5.647449 seconds -Region Avg IOU: 0.516635, Class: 1.000000, Obj: 0.373940, No Obj: 0.004714, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.525242, Class: 1.000000, Obj: 0.371900, No Obj: 0.004698, Avg Recall: 0.575758, count: 99 -Region Avg IOU: 0.481666, Class: 1.000000, Obj: 0.358183, No Obj: 0.005380, Avg Recall: 0.511628, count: 129 -Region Avg IOU: 0.482357, Class: 1.000000, Obj: 0.323864, No Obj: 0.005008, Avg Recall: 0.580952, count: 105 -Region Avg IOU: 0.589264, Class: 1.000000, Obj: 0.460092, No Obj: 0.004938, Avg Recall: 0.690476, count: 84 -Region Avg IOU: 0.474514, Class: 1.000000, Obj: 0.326432, No Obj: 0.004436, Avg Recall: 0.544444, count: 90 -Region Avg IOU: 0.455029, Class: 1.000000, Obj: 0.310435, No Obj: 0.004398, Avg Recall: 0.489362, count: 94 -Region Avg IOU: 0.470133, Class: 1.000000, Obj: 0.336050, No Obj: 0.003457, Avg Recall: 0.505495, count: 91 -5869: 0.411808, 0.443694 avg, 0.001000 rate, 18.040337 seconds, 1502464 images -Loaded: 2.381712 seconds -Region Avg IOU: 0.462379, Class: 1.000000, Obj: 0.377864, No Obj: 0.005492, Avg Recall: 0.491379, count: 116 -Region Avg IOU: 0.566400, Class: 1.000000, Obj: 0.410047, No Obj: 0.005780, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.438244, Class: 1.000000, Obj: 0.314017, No Obj: 0.004473, Avg Recall: 0.406250, count: 96 -Region Avg IOU: 0.478479, Class: 1.000000, Obj: 0.343168, No Obj: 0.004864, Avg Recall: 0.523364, count: 107 -Region Avg IOU: 0.422646, Class: 1.000000, Obj: 0.308586, No Obj: 0.004632, Avg Recall: 0.433628, count: 113 -Region Avg IOU: 0.511310, Class: 1.000000, Obj: 0.397418, No Obj: 0.004521, Avg Recall: 0.547619, count: 84 -Region Avg IOU: 0.486403, Class: 1.000000, Obj: 0.371918, No Obj: 0.004763, Avg Recall: 0.517241, count: 87 -Region Avg IOU: 0.490862, Class: 1.000000, Obj: 0.350719, No Obj: 0.005854, Avg Recall: 0.552845, count: 123 -5870: 0.468770, 0.446201 avg, 0.001000 rate, 16.430992 seconds, 1502720 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000058 seconds -Region Avg IOU: 0.630507, Class: 1.000000, Obj: 0.468282, No Obj: 0.005207, Avg Recall: 0.750000, count: 136 -Region Avg IOU: 0.616193, Class: 1.000000, Obj: 0.483378, No Obj: 0.004199, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.576899, Class: 1.000000, Obj: 0.446591, No Obj: 0.003922, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.597896, Class: 1.000000, Obj: 0.498924, No Obj: 0.004790, Avg Recall: 0.710938, count: 128 -Region Avg IOU: 0.592073, Class: 1.000000, Obj: 0.521135, No Obj: 0.003766, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.569529, Class: 1.000000, Obj: 0.474693, No Obj: 0.004259, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.604635, Class: 1.000000, Obj: 0.469380, No Obj: 0.004360, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.583391, Class: 1.000000, Obj: 0.427757, No Obj: 0.004226, Avg Recall: 0.743363, count: 113 -5871: 0.438281, 0.445409 avg, 0.001000 rate, 24.398272 seconds, 1502976 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.602971, Class: 1.000000, Obj: 0.486054, No Obj: 0.003792, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.588913, Class: 1.000000, Obj: 0.453347, No Obj: 0.003885, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.612072, Class: 1.000000, Obj: 0.491993, No Obj: 0.004104, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.592080, Class: 1.000000, Obj: 0.460239, No Obj: 0.003789, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.595653, Class: 1.000000, Obj: 0.438974, No Obj: 0.005069, Avg Recall: 0.671429, count: 140 -Region Avg IOU: 0.611366, Class: 1.000000, Obj: 0.454395, No Obj: 0.004314, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.563653, Class: 1.000000, Obj: 0.404992, No Obj: 0.003616, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.572930, Class: 1.000000, Obj: 0.455765, No Obj: 0.003592, Avg Recall: 0.663043, count: 92 -5872: 0.415178, 0.442386 avg, 0.001000 rate, 24.173719 seconds, 1503232 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.577898, Class: 1.000000, Obj: 0.465189, No Obj: 0.003470, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.580392, Class: 1.000000, Obj: 0.458078, No Obj: 0.004437, Avg Recall: 0.674603, count: 126 -Region Avg IOU: 0.593341, Class: 1.000000, Obj: 0.471760, No Obj: 0.004536, Avg Recall: 0.691057, count: 123 -Region Avg IOU: 0.641428, Class: 1.000000, Obj: 0.531328, No Obj: 0.003638, Avg Recall: 0.789474, count: 76 -Region Avg IOU: 0.591493, Class: 1.000000, Obj: 0.459573, No Obj: 0.004418, Avg Recall: 0.688073, count: 109 -Region Avg IOU: 0.623399, Class: 1.000000, Obj: 0.522502, No Obj: 0.004096, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.597848, Class: 1.000000, Obj: 0.438499, No Obj: 0.004155, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.618749, Class: 1.000000, Obj: 0.460237, No Obj: 0.004338, Avg Recall: 0.744681, count: 94 -5873: 0.386165, 0.436764 avg, 0.001000 rate, 23.936466 seconds, 1503488 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.551393, Class: 1.000000, Obj: 0.410443, No Obj: 0.003702, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.562170, Class: 1.000000, Obj: 0.454732, No Obj: 0.002770, Avg Recall: 0.662338, count: 77 -Region Avg IOU: 0.583014, Class: 1.000000, Obj: 0.463261, No Obj: 0.003946, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.573808, Class: 1.000000, Obj: 0.424680, No Obj: 0.004343, Avg Recall: 0.656716, count: 134 -Region Avg IOU: 0.536376, Class: 1.000000, Obj: 0.441290, No Obj: 0.003829, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.574395, Class: 1.000000, Obj: 0.434394, No Obj: 0.003411, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.614418, Class: 1.000000, Obj: 0.427422, No Obj: 0.004160, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.578283, Class: 1.000000, Obj: 0.407744, No Obj: 0.003941, Avg Recall: 0.682243, count: 107 -5874: 0.468368, 0.439924 avg, 0.001000 rate, 24.814783 seconds, 1503744 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.539640, Class: 1.000000, Obj: 0.399147, No Obj: 0.003755, Avg Recall: 0.606557, count: 122 -Region Avg IOU: 0.595988, Class: 1.000000, Obj: 0.443763, No Obj: 0.003801, Avg Recall: 0.691057, count: 123 -Region Avg IOU: 0.633467, Class: 1.000000, Obj: 0.461436, No Obj: 0.003843, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.613120, Class: 1.000000, Obj: 0.455312, No Obj: 0.003423, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.591632, Class: 1.000000, Obj: 0.384831, No Obj: 0.003393, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.587149, Class: 1.000000, Obj: 0.463963, No Obj: 0.003479, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.551618, Class: 1.000000, Obj: 0.441437, No Obj: 0.003217, Avg Recall: 0.647727, count: 88 -Region Avg IOU: 0.605553, Class: 1.000000, Obj: 0.415166, No Obj: 0.004459, Avg Recall: 0.723404, count: 141 -5875: 0.404621, 0.436394 avg, 0.001000 rate, 23.908503 seconds, 1504000 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.641796, Class: 1.000000, Obj: 0.462183, No Obj: 0.003446, Avg Recall: 0.837209, count: 86 -Region Avg IOU: 0.611025, Class: 1.000000, Obj: 0.456648, No Obj: 0.003980, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.581343, Class: 1.000000, Obj: 0.423513, No Obj: 0.004086, Avg Recall: 0.642336, count: 137 -Region Avg IOU: 0.540204, Class: 1.000000, Obj: 0.339624, No Obj: 0.003156, Avg Recall: 0.572650, count: 117 -Region Avg IOU: 0.570833, Class: 1.000000, Obj: 0.399881, No Obj: 0.003702, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.610744, Class: 1.000000, Obj: 0.431008, No Obj: 0.003817, Avg Recall: 0.746269, count: 134 -Region Avg IOU: 0.563116, Class: 1.000000, Obj: 0.401404, No Obj: 0.003565, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.595113, Class: 1.000000, Obj: 0.435213, No Obj: 0.003168, Avg Recall: 0.699029, count: 103 -5876: 0.408813, 0.433636 avg, 0.001000 rate, 23.147360 seconds, 1504256 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.577021, Class: 1.000000, Obj: 0.385477, No Obj: 0.003074, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.609280, Class: 1.000000, Obj: 0.396968, No Obj: 0.003437, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.599903, Class: 1.000000, Obj: 0.378167, No Obj: 0.002996, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.578135, Class: 1.000000, Obj: 0.344846, No Obj: 0.002926, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.616673, Class: 1.000000, Obj: 0.377907, No Obj: 0.002679, Avg Recall: 0.716049, count: 81 -Region Avg IOU: 0.606940, Class: 1.000000, Obj: 0.389354, No Obj: 0.003264, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.653180, Class: 1.000000, Obj: 0.412389, No Obj: 0.003086, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.592687, Class: 1.000000, Obj: 0.382803, No Obj: 0.003440, Avg Recall: 0.747748, count: 111 -5877: 0.385489, 0.428821 avg, 0.001000 rate, 24.429102 seconds, 1504512 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.626638, Class: 1.000000, Obj: 0.411248, No Obj: 0.003023, Avg Recall: 0.755556, count: 90 -Region Avg IOU: 0.552698, Class: 1.000000, Obj: 0.355771, No Obj: 0.003018, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.625167, Class: 1.000000, Obj: 0.421682, No Obj: 0.003497, Avg Recall: 0.768000, count: 125 -Region Avg IOU: 0.612422, Class: 1.000000, Obj: 0.382996, No Obj: 0.003389, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.563492, Class: 1.000000, Obj: 0.340090, No Obj: 0.003163, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.616393, Class: 1.000000, Obj: 0.408065, No Obj: 0.003343, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.601862, Class: 1.000000, Obj: 0.365139, No Obj: 0.002729, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.614478, Class: 1.000000, Obj: 0.385236, No Obj: 0.003112, Avg Recall: 0.742574, count: 101 -5878: 0.411620, 0.427101 avg, 0.001000 rate, 24.514078 seconds, 1504768 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.609805, Class: 1.000000, Obj: 0.395293, No Obj: 0.003062, Avg Recall: 0.739583, count: 96 -Region Avg IOU: 0.552859, Class: 1.000000, Obj: 0.357991, No Obj: 0.003301, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.627335, Class: 1.000000, Obj: 0.391163, No Obj: 0.003515, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.574154, Class: 1.000000, Obj: 0.308454, No Obj: 0.002817, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.618061, Class: 1.000000, Obj: 0.380451, No Obj: 0.003437, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.580542, Class: 1.000000, Obj: 0.376268, No Obj: 0.003266, Avg Recall: 0.648438, count: 128 -Region Avg IOU: 0.622152, Class: 1.000000, Obj: 0.395486, No Obj: 0.003159, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.575404, Class: 1.000000, Obj: 0.355678, No Obj: 0.002847, Avg Recall: 0.682243, count: 107 -5879: 0.421413, 0.426532 avg, 0.001000 rate, 23.705915 seconds, 1505024 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.599045, Class: 1.000000, Obj: 0.385107, No Obj: 0.003026, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.579435, Class: 1.000000, Obj: 0.361199, No Obj: 0.002590, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.582053, Class: 1.000000, Obj: 0.349483, No Obj: 0.002560, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.515691, Class: 1.000000, Obj: 0.315869, No Obj: 0.002907, Avg Recall: 0.565574, count: 122 -Region Avg IOU: 0.571941, Class: 1.000000, Obj: 0.331890, No Obj: 0.002585, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.621920, Class: 1.000000, Obj: 0.368426, No Obj: 0.003809, Avg Recall: 0.761538, count: 130 -Region Avg IOU: 0.592349, Class: 1.000000, Obj: 0.321760, No Obj: 0.003072, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.609054, Class: 1.000000, Obj: 0.356586, No Obj: 0.003078, Avg Recall: 0.714286, count: 119 -5880: 0.432279, 0.427107 avg, 0.001000 rate, 23.899591 seconds, 1505280 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 20.413307 seconds -Region Avg IOU: 0.547674, Class: 1.000000, Obj: 0.365520, No Obj: 0.003461, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.539520, Class: 1.000000, Obj: 0.337481, No Obj: 0.003776, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.517293, Class: 1.000000, Obj: 0.308162, No Obj: 0.003967, Avg Recall: 0.601399, count: 143 -Region Avg IOU: 0.535155, Class: 1.000000, Obj: 0.317573, No Obj: 0.003868, Avg Recall: 0.593750, count: 96 -Region Avg IOU: 0.520108, Class: 1.000000, Obj: 0.338077, No Obj: 0.003492, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.560205, Class: 1.000000, Obj: 0.360986, No Obj: 0.003694, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.494075, Class: 1.000000, Obj: 0.277647, No Obj: 0.003081, Avg Recall: 0.566929, count: 127 -Region Avg IOU: 0.539533, Class: 1.000000, Obj: 0.336529, No Obj: 0.003541, Avg Recall: 0.627273, count: 110 -5881: 0.407646, 0.425161 avg, 0.001000 rate, 22.029875 seconds, 1505536 images -Loaded: 0.020173 seconds -Region Avg IOU: 0.532174, Class: 1.000000, Obj: 0.324518, No Obj: 0.003915, Avg Recall: 0.603175, count: 126 -Region Avg IOU: 0.638977, Class: 1.000000, Obj: 0.392718, No Obj: 0.003761, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.532334, Class: 1.000000, Obj: 0.341304, No Obj: 0.003814, Avg Recall: 0.596899, count: 129 -Region Avg IOU: 0.516219, Class: 1.000000, Obj: 0.350765, No Obj: 0.003160, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.541313, Class: 1.000000, Obj: 0.305736, No Obj: 0.003760, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.516243, Class: 1.000000, Obj: 0.331313, No Obj: 0.003559, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.478678, Class: 1.000000, Obj: 0.293358, No Obj: 0.003035, Avg Recall: 0.505495, count: 91 -Region Avg IOU: 0.525124, Class: 1.000000, Obj: 0.302166, No Obj: 0.003891, Avg Recall: 0.557143, count: 140 -5882: 0.425955, 0.425240 avg, 0.001000 rate, 20.358412 seconds, 1505792 images -Loaded: 2.090607 seconds -Region Avg IOU: 0.520194, Class: 1.000000, Obj: 0.329330, No Obj: 0.003859, Avg Recall: 0.593220, count: 118 -Region Avg IOU: 0.571891, Class: 1.000000, Obj: 0.357511, No Obj: 0.003347, Avg Recall: 0.717949, count: 78 -Region Avg IOU: 0.546538, Class: 1.000000, Obj: 0.379093, No Obj: 0.003794, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.581511, Class: 1.000000, Obj: 0.348521, No Obj: 0.003509, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.503979, Class: 1.000000, Obj: 0.336199, No Obj: 0.004323, Avg Recall: 0.580153, count: 131 -Region Avg IOU: 0.551830, Class: 1.000000, Obj: 0.329370, No Obj: 0.003969, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.624638, Class: 1.000000, Obj: 0.401168, No Obj: 0.004224, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.490006, Class: 1.000000, Obj: 0.324014, No Obj: 0.003259, Avg Recall: 0.556522, count: 115 -5883: 0.419976, 0.424714 avg, 0.001000 rate, 21.972944 seconds, 1506048 images -Loaded: 0.017071 seconds -Region Avg IOU: 0.540885, Class: 1.000000, Obj: 0.387119, No Obj: 0.004566, Avg Recall: 0.656000, count: 125 -Region Avg IOU: 0.550586, Class: 1.000000, Obj: 0.363558, No Obj: 0.004183, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.563395, Class: 1.000000, Obj: 0.399294, No Obj: 0.004291, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.583638, Class: 1.000000, Obj: 0.398532, No Obj: 0.004220, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.521246, Class: 1.000000, Obj: 0.393749, No Obj: 0.004418, Avg Recall: 0.560606, count: 132 -Region Avg IOU: 0.581687, Class: 1.000000, Obj: 0.405880, No Obj: 0.003767, Avg Recall: 0.698795, count: 83 -Region Avg IOU: 0.554259, Class: 1.000000, Obj: 0.368799, No Obj: 0.003386, Avg Recall: 0.658824, count: 85 -Region Avg IOU: 0.480628, Class: 1.000000, Obj: 0.366474, No Obj: 0.003090, Avg Recall: 0.516854, count: 89 -5884: 0.399804, 0.422223 avg, 0.001000 rate, 20.562338 seconds, 1506304 images -Loaded: 1.503333 seconds -Region Avg IOU: 0.565541, Class: 1.000000, Obj: 0.385100, No Obj: 0.004194, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.560398, Class: 1.000000, Obj: 0.424129, No Obj: 0.003753, Avg Recall: 0.629214, count: 89 -Region Avg IOU: 0.531915, Class: 1.000000, Obj: 0.402467, No Obj: 0.003596, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.578426, Class: 1.000000, Obj: 0.433939, No Obj: 0.004796, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.552907, Class: 1.000000, Obj: 0.404494, No Obj: 0.003721, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.563301, Class: 1.000000, Obj: 0.405243, No Obj: 0.004825, Avg Recall: 0.675214, count: 117 -Region Avg IOU: 0.510163, Class: 1.000000, Obj: 0.371170, No Obj: 0.003771, Avg Recall: 0.595745, count: 94 -Region Avg IOU: 0.557061, Class: 1.000000, Obj: 0.399228, No Obj: 0.004043, Avg Recall: 0.646465, count: 99 -5885: 0.401782, 0.420179 avg, 0.001000 rate, 19.023380 seconds, 1506560 images -Loaded: 2.354472 seconds -Region Avg IOU: 0.597918, Class: 1.000000, Obj: 0.412861, No Obj: 0.004835, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.558893, Class: 1.000000, Obj: 0.458045, No Obj: 0.004211, Avg Recall: 0.648352, count: 91 -Region Avg IOU: 0.501720, Class: 1.000000, Obj: 0.393618, No Obj: 0.004300, Avg Recall: 0.531915, count: 94 -Region Avg IOU: 0.579169, Class: 1.000000, Obj: 0.431335, No Obj: 0.005239, Avg Recall: 0.661290, count: 124 -Region Avg IOU: 0.534011, Class: 1.000000, Obj: 0.424296, No Obj: 0.004356, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.589328, Class: 1.000000, Obj: 0.455671, No Obj: 0.004218, Avg Recall: 0.670455, count: 88 -Region Avg IOU: 0.533869, Class: 1.000000, Obj: 0.437668, No Obj: 0.004806, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.559366, Class: 1.000000, Obj: 0.417424, No Obj: 0.004579, Avg Recall: 0.610526, count: 95 -5886: 0.359713, 0.414132 avg, 0.001000 rate, 20.162504 seconds, 1506816 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.546690, Class: 1.000000, Obj: 0.388375, No Obj: 0.004681, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.513620, Class: 1.000000, Obj: 0.404024, No Obj: 0.005258, Avg Recall: 0.542253, count: 142 -Region Avg IOU: 0.569694, Class: 1.000000, Obj: 0.429406, No Obj: 0.005055, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.552556, Class: 1.000000, Obj: 0.411856, No Obj: 0.005489, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.491274, Class: 1.000000, Obj: 0.399572, No Obj: 0.003972, Avg Recall: 0.540000, count: 100 -Region Avg IOU: 0.531417, Class: 1.000000, Obj: 0.402390, No Obj: 0.004239, Avg Recall: 0.605769, count: 104 -Region Avg IOU: 0.507512, Class: 1.000000, Obj: 0.408910, No Obj: 0.005206, Avg Recall: 0.535211, count: 142 -Region Avg IOU: 0.531570, Class: 1.000000, Obj: 0.418068, No Obj: 0.004168, Avg Recall: 0.569892, count: 93 -5887: 0.456066, 0.418326 avg, 0.001000 rate, 20.984785 seconds, 1507072 images -Loaded: 1.910573 seconds -Region Avg IOU: 0.518298, Class: 1.000000, Obj: 0.407392, No Obj: 0.004589, Avg Recall: 0.595745, count: 94 -Region Avg IOU: 0.534377, Class: 1.000000, Obj: 0.420386, No Obj: 0.004337, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.546984, Class: 1.000000, Obj: 0.446092, No Obj: 0.005336, Avg Recall: 0.650406, count: 123 -Region Avg IOU: 0.598876, Class: 1.000000, Obj: 0.459058, No Obj: 0.005855, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.551436, Class: 1.000000, Obj: 0.373767, No Obj: 0.004979, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.547656, Class: 1.000000, Obj: 0.447062, No Obj: 0.004398, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.487273, Class: 1.000000, Obj: 0.378368, No Obj: 0.004862, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.494859, Class: 1.000000, Obj: 0.393678, No Obj: 0.004710, Avg Recall: 0.518182, count: 110 -5888: 0.447954, 0.421288 avg, 0.001000 rate, 18.605928 seconds, 1507328 images -Loaded: 3.600263 seconds -Region Avg IOU: 0.538636, Class: 1.000000, Obj: 0.440041, No Obj: 0.005765, Avg Recall: 0.617886, count: 123 -Region Avg IOU: 0.580090, Class: 1.000000, Obj: 0.466844, No Obj: 0.004580, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.555262, Class: 1.000000, Obj: 0.440738, No Obj: 0.004989, Avg Recall: 0.688000, count: 125 -Region Avg IOU: 0.474541, Class: 1.000000, Obj: 0.391995, No Obj: 0.004297, Avg Recall: 0.504065, count: 123 -Region Avg IOU: 0.563205, Class: 1.000000, Obj: 0.420258, No Obj: 0.005251, Avg Recall: 0.651515, count: 132 -Region Avg IOU: 0.517247, Class: 1.000000, Obj: 0.391612, No Obj: 0.004764, Avg Recall: 0.577586, count: 116 -Region Avg IOU: 0.486009, Class: 1.000000, Obj: 0.377305, No Obj: 0.005142, Avg Recall: 0.482456, count: 114 -Region Avg IOU: 0.521103, Class: 1.000000, Obj: 0.396090, No Obj: 0.005603, Avg Recall: 0.611511, count: 139 -5889: 0.488489, 0.428008 avg, 0.001000 rate, 20.625813 seconds, 1507584 images -Loaded: 0.986114 seconds -Region Avg IOU: 0.519654, Class: 1.000000, Obj: 0.419067, No Obj: 0.004785, Avg Recall: 0.589147, count: 129 -Region Avg IOU: 0.572252, Class: 1.000000, Obj: 0.445281, No Obj: 0.005032, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.558024, Class: 1.000000, Obj: 0.419308, No Obj: 0.004432, Avg Recall: 0.678571, count: 84 -Region Avg IOU: 0.563855, Class: 1.000000, Obj: 0.447390, No Obj: 0.004093, Avg Recall: 0.630952, count: 84 -Region Avg IOU: 0.572645, Class: 1.000000, Obj: 0.415496, No Obj: 0.004964, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.518334, Class: 1.000000, Obj: 0.404946, No Obj: 0.004871, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.508072, Class: 1.000000, Obj: 0.429665, No Obj: 0.004673, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.563884, Class: 1.000000, Obj: 0.435824, No Obj: 0.004896, Avg Recall: 0.603960, count: 101 -5890: 0.382150, 0.423423 avg, 0.001000 rate, 19.460892 seconds, 1507840 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000073 seconds -Region Avg IOU: 0.634494, Class: 1.000000, Obj: 0.452069, No Obj: 0.003536, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.669276, Class: 1.000000, Obj: 0.525792, No Obj: 0.003452, Avg Recall: 0.843137, count: 102 -Region Avg IOU: 0.641130, Class: 1.000000, Obj: 0.495639, No Obj: 0.002933, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.661606, Class: 1.000000, Obj: 0.486215, No Obj: 0.003038, Avg Recall: 0.825397, count: 63 -Region Avg IOU: 0.632423, Class: 1.000000, Obj: 0.511249, No Obj: 0.003351, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.672813, Class: 1.000000, Obj: 0.446177, No Obj: 0.003955, Avg Recall: 0.809524, count: 126 -Region Avg IOU: 0.617047, Class: 1.000000, Obj: 0.406130, No Obj: 0.003730, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.678583, Class: 1.000000, Obj: 0.497104, No Obj: 0.003408, Avg Recall: 0.840000, count: 100 -5891: 0.381058, 0.419186 avg, 0.001000 rate, 26.637997 seconds, 1508096 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.631840, Class: 1.000000, Obj: 0.460489, No Obj: 0.002958, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.620257, Class: 1.000000, Obj: 0.435537, No Obj: 0.003330, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.604418, Class: 1.000000, Obj: 0.483521, No Obj: 0.002510, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.632649, Class: 1.000000, Obj: 0.428423, No Obj: 0.003730, Avg Recall: 0.801370, count: 146 -Region Avg IOU: 0.679629, Class: 1.000000, Obj: 0.480791, No Obj: 0.003513, Avg Recall: 0.864865, count: 111 -Region Avg IOU: 0.661927, Class: 1.000000, Obj: 0.476802, No Obj: 0.003313, Avg Recall: 0.866667, count: 105 -Region Avg IOU: 0.654523, Class: 1.000000, Obj: 0.446027, No Obj: 0.003488, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.626181, Class: 1.000000, Obj: 0.422758, No Obj: 0.003005, Avg Recall: 0.766355, count: 107 -5892: 0.420610, 0.419329 avg, 0.001000 rate, 27.217442 seconds, 1508352 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.651256, Class: 1.000000, Obj: 0.427288, No Obj: 0.003105, Avg Recall: 0.791667, count: 120 -Region Avg IOU: 0.638186, Class: 1.000000, Obj: 0.441520, No Obj: 0.002568, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.647634, Class: 1.000000, Obj: 0.368067, No Obj: 0.003027, Avg Recall: 0.830357, count: 112 -Region Avg IOU: 0.654276, Class: 1.000000, Obj: 0.394790, No Obj: 0.003432, Avg Recall: 0.814159, count: 113 -Region Avg IOU: 0.633258, Class: 1.000000, Obj: 0.413879, No Obj: 0.002209, Avg Recall: 0.744186, count: 86 -Region Avg IOU: 0.608618, Class: 1.000000, Obj: 0.429530, No Obj: 0.002892, Avg Recall: 0.769841, count: 126 -Region Avg IOU: 0.638157, Class: 1.000000, Obj: 0.444775, No Obj: 0.003317, Avg Recall: 0.753968, count: 126 -Region Avg IOU: 0.671631, Class: 1.000000, Obj: 0.424525, No Obj: 0.002780, Avg Recall: 0.813187, count: 91 -5893: 0.450360, 0.422432 avg, 0.001000 rate, 28.765812 seconds, 1508608 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.608112, Class: 1.000000, Obj: 0.354213, No Obj: 0.002461, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.664217, Class: 1.000000, Obj: 0.408128, No Obj: 0.002917, Avg Recall: 0.831776, count: 107 -Region Avg IOU: 0.577446, Class: 1.000000, Obj: 0.368958, No Obj: 0.002724, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.639085, Class: 1.000000, Obj: 0.424673, No Obj: 0.002686, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.610116, Class: 1.000000, Obj: 0.349595, No Obj: 0.002809, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.619997, Class: 1.000000, Obj: 0.442450, No Obj: 0.002326, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.687700, Class: 1.000000, Obj: 0.436843, No Obj: 0.002796, Avg Recall: 0.855856, count: 111 -Region Avg IOU: 0.659116, Class: 1.000000, Obj: 0.465388, No Obj: 0.002702, Avg Recall: 0.825243, count: 103 -5894: 0.436049, 0.423793 avg, 0.001000 rate, 28.553692 seconds, 1508864 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.651057, Class: 1.000000, Obj: 0.415805, No Obj: 0.002458, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.588872, Class: 1.000000, Obj: 0.353805, No Obj: 0.002340, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.616511, Class: 1.000000, Obj: 0.397514, No Obj: 0.002547, Avg Recall: 0.755556, count: 135 -Region Avg IOU: 0.609956, Class: 1.000000, Obj: 0.383801, No Obj: 0.002753, Avg Recall: 0.772727, count: 132 -Region Avg IOU: 0.610310, Class: 1.000000, Obj: 0.368907, No Obj: 0.002597, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.670720, Class: 1.000000, Obj: 0.397625, No Obj: 0.002751, Avg Recall: 0.805085, count: 118 -Region Avg IOU: 0.674143, Class: 1.000000, Obj: 0.456657, No Obj: 0.002643, Avg Recall: 0.827273, count: 110 -Region Avg IOU: 0.641276, Class: 1.000000, Obj: 0.380413, No Obj: 0.002674, Avg Recall: 0.750000, count: 120 -5895: 0.451941, 0.426608 avg, 0.001000 rate, 27.584112 seconds, 1509120 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.624165, Class: 1.000000, Obj: 0.411828, No Obj: 0.002363, Avg Recall: 0.735537, count: 121 -Region Avg IOU: 0.684146, Class: 1.000000, Obj: 0.427850, No Obj: 0.002518, Avg Recall: 0.882979, count: 94 -Region Avg IOU: 0.673254, Class: 1.000000, Obj: 0.382029, No Obj: 0.002429, Avg Recall: 0.840708, count: 113 -Region Avg IOU: 0.639237, Class: 1.000000, Obj: 0.351940, No Obj: 0.002475, Avg Recall: 0.811024, count: 127 -Region Avg IOU: 0.620590, Class: 1.000000, Obj: 0.345377, No Obj: 0.002219, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.656179, Class: 1.000000, Obj: 0.379956, No Obj: 0.002234, Avg Recall: 0.827586, count: 87 -Region Avg IOU: 0.670889, Class: 1.000000, Obj: 0.418856, No Obj: 0.002365, Avg Recall: 0.800000, count: 85 -Region Avg IOU: 0.617860, Class: 1.000000, Obj: 0.294664, No Obj: 0.002199, Avg Recall: 0.718750, count: 96 -5896: 0.392373, 0.423185 avg, 0.001000 rate, 26.885904 seconds, 1509376 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.665873, Class: 1.000000, Obj: 0.409569, No Obj: 0.002256, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.610873, Class: 1.000000, Obj: 0.299383, No Obj: 0.002220, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.599811, Class: 1.000000, Obj: 0.354171, No Obj: 0.002395, Avg Recall: 0.702479, count: 121 -Region Avg IOU: 0.684000, Class: 1.000000, Obj: 0.344254, No Obj: 0.002287, Avg Recall: 0.894231, count: 104 -Region Avg IOU: 0.600998, Class: 1.000000, Obj: 0.314142, No Obj: 0.002407, Avg Recall: 0.737705, count: 122 -Region Avg IOU: 0.644462, Class: 1.000000, Obj: 0.342228, No Obj: 0.002019, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.667267, Class: 1.000000, Obj: 0.413944, No Obj: 0.002190, Avg Recall: 0.831683, count: 101 -Region Avg IOU: 0.671382, Class: 1.000000, Obj: 0.383087, No Obj: 0.002035, Avg Recall: 0.835165, count: 91 -5897: 0.442271, 0.425093 avg, 0.001000 rate, 27.517763 seconds, 1509632 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.702882, Class: 1.000000, Obj: 0.341447, No Obj: 0.002739, Avg Recall: 0.900826, count: 121 -Region Avg IOU: 0.632257, Class: 1.000000, Obj: 0.396564, No Obj: 0.002227, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.637486, Class: 1.000000, Obj: 0.389291, No Obj: 0.002357, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.640231, Class: 1.000000, Obj: 0.392933, No Obj: 0.002371, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.665418, Class: 1.000000, Obj: 0.357009, No Obj: 0.002519, Avg Recall: 0.821138, count: 123 -Region Avg IOU: 0.659415, Class: 1.000000, Obj: 0.393132, No Obj: 0.002212, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.636381, Class: 1.000000, Obj: 0.339363, No Obj: 0.002233, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.664397, Class: 1.000000, Obj: 0.365964, No Obj: 0.002453, Avg Recall: 0.800000, count: 90 -5898: 0.411963, 0.423780 avg, 0.001000 rate, 26.775782 seconds, 1509888 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.657795, Class: 1.000000, Obj: 0.371590, No Obj: 0.002838, Avg Recall: 0.821138, count: 123 -Region Avg IOU: 0.654057, Class: 1.000000, Obj: 0.400878, No Obj: 0.002583, Avg Recall: 0.785047, count: 107 -Region Avg IOU: 0.624507, Class: 1.000000, Obj: 0.381463, No Obj: 0.002696, Avg Recall: 0.750000, count: 136 -Region Avg IOU: 0.687209, Class: 1.000000, Obj: 0.356393, No Obj: 0.002400, Avg Recall: 0.861386, count: 101 -Region Avg IOU: 0.638263, Class: 1.000000, Obj: 0.356477, No Obj: 0.002546, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.661221, Class: 1.000000, Obj: 0.373147, No Obj: 0.002390, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.635243, Class: 1.000000, Obj: 0.407372, No Obj: 0.002284, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.631862, Class: 1.000000, Obj: 0.340098, No Obj: 0.002102, Avg Recall: 0.797872, count: 94 -5899: 0.459731, 0.427375 avg, 0.001000 rate, 26.578281 seconds, 1510144 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.604641, Class: 1.000000, Obj: 0.381581, No Obj: 0.002220, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.671394, Class: 1.000000, Obj: 0.410873, No Obj: 0.002532, Avg Recall: 0.849462, count: 93 -Region Avg IOU: 0.698514, Class: 1.000000, Obj: 0.459745, No Obj: 0.002172, Avg Recall: 0.864198, count: 81 -Region Avg IOU: 0.662419, Class: 1.000000, Obj: 0.373083, No Obj: 0.002669, Avg Recall: 0.827586, count: 116 -Region Avg IOU: 0.629069, Class: 1.000000, Obj: 0.440688, No Obj: 0.002426, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.671429, Class: 1.000000, Obj: 0.361509, No Obj: 0.002659, Avg Recall: 0.840000, count: 100 -Region Avg IOU: 0.644171, Class: 1.000000, Obj: 0.419975, No Obj: 0.002224, Avg Recall: 0.810127, count: 79 -Region Avg IOU: 0.610237, Class: 1.000000, Obj: 0.383890, No Obj: 0.002094, Avg Recall: 0.771739, count: 92 -5900: 0.403172, 0.424955 avg, 0.001000 rate, 28.677029 seconds, 1510400 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 18.994028 seconds -Region Avg IOU: 0.645652, Class: 1.000000, Obj: 0.442705, No Obj: 0.002480, Avg Recall: 0.795181, count: 83 -Region Avg IOU: 0.619068, Class: 1.000000, Obj: 0.437851, No Obj: 0.002504, Avg Recall: 0.727273, count: 88 -Region Avg IOU: 0.597560, Class: 1.000000, Obj: 0.384235, No Obj: 0.003148, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.649085, Class: 1.000000, Obj: 0.433274, No Obj: 0.003002, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.646398, Class: 1.000000, Obj: 0.426993, No Obj: 0.003066, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.656370, Class: 1.000000, Obj: 0.408079, No Obj: 0.002964, Avg Recall: 0.778761, count: 113 -Region Avg IOU: 0.633364, Class: 1.000000, Obj: 0.415237, No Obj: 0.003091, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.629416, Class: 1.000000, Obj: 0.366985, No Obj: 0.003056, Avg Recall: 0.765766, count: 111 -5901: 0.368067, 0.419266 avg, 0.001000 rate, 25.842134 seconds, 1510656 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.627405, Class: 1.000000, Obj: 0.448927, No Obj: 0.002889, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.604377, Class: 1.000000, Obj: 0.368658, No Obj: 0.002723, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.633969, Class: 1.000000, Obj: 0.416694, No Obj: 0.003076, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.616262, Class: 1.000000, Obj: 0.410879, No Obj: 0.003302, Avg Recall: 0.782946, count: 129 -Region Avg IOU: 0.604221, Class: 1.000000, Obj: 0.401393, No Obj: 0.002713, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.627212, Class: 1.000000, Obj: 0.455402, No Obj: 0.002827, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.652664, Class: 1.000000, Obj: 0.422206, No Obj: 0.003156, Avg Recall: 0.800000, count: 95 -Region Avg IOU: 0.596802, Class: 1.000000, Obj: 0.398720, No Obj: 0.002990, Avg Recall: 0.718447, count: 103 -5902: 0.377150, 0.415055 avg, 0.001000 rate, 24.748552 seconds, 1510912 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.629987, Class: 1.000000, Obj: 0.446140, No Obj: 0.002980, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.640134, Class: 1.000000, Obj: 0.439872, No Obj: 0.002955, Avg Recall: 0.762500, count: 80 -Region Avg IOU: 0.619376, Class: 1.000000, Obj: 0.425676, No Obj: 0.002926, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.634602, Class: 1.000000, Obj: 0.415714, No Obj: 0.003468, Avg Recall: 0.768000, count: 125 -Region Avg IOU: 0.660006, Class: 1.000000, Obj: 0.466438, No Obj: 0.002845, Avg Recall: 0.806818, count: 88 -Region Avg IOU: 0.627657, Class: 1.000000, Obj: 0.397532, No Obj: 0.003428, Avg Recall: 0.798450, count: 129 -Region Avg IOU: 0.628318, Class: 1.000000, Obj: 0.426588, No Obj: 0.003125, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.645262, Class: 1.000000, Obj: 0.423091, No Obj: 0.003133, Avg Recall: 0.774194, count: 93 -5903: 0.403476, 0.413897 avg, 0.001000 rate, 25.707598 seconds, 1511168 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.608372, Class: 1.000000, Obj: 0.438110, No Obj: 0.003443, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.631297, Class: 1.000000, Obj: 0.416613, No Obj: 0.003737, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.646714, Class: 1.000000, Obj: 0.433946, No Obj: 0.003502, Avg Recall: 0.791667, count: 120 -Region Avg IOU: 0.597873, Class: 1.000000, Obj: 0.441988, No Obj: 0.003466, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.625695, Class: 1.000000, Obj: 0.436477, No Obj: 0.003390, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.632970, Class: 1.000000, Obj: 0.466153, No Obj: 0.003498, Avg Recall: 0.761538, count: 130 -Region Avg IOU: 0.610063, Class: 1.000000, Obj: 0.406527, No Obj: 0.004483, Avg Recall: 0.739437, count: 142 -Region Avg IOU: 0.615640, Class: 1.000000, Obj: 0.405299, No Obj: 0.003188, Avg Recall: 0.745098, count: 102 -5904: 0.455027, 0.418010 avg, 0.001000 rate, 26.595230 seconds, 1511424 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.639206, Class: 1.000000, Obj: 0.429484, No Obj: 0.003571, Avg Recall: 0.766129, count: 124 -Region Avg IOU: 0.644151, Class: 1.000000, Obj: 0.435086, No Obj: 0.003420, Avg Recall: 0.825688, count: 109 -Region Avg IOU: 0.621134, Class: 1.000000, Obj: 0.412332, No Obj: 0.003278, Avg Recall: 0.756303, count: 119 -Region Avg IOU: 0.658062, Class: 1.000000, Obj: 0.450057, No Obj: 0.004181, Avg Recall: 0.821138, count: 123 -Region Avg IOU: 0.649227, Class: 1.000000, Obj: 0.448603, No Obj: 0.003377, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.592482, Class: 1.000000, Obj: 0.394501, No Obj: 0.003676, Avg Recall: 0.720280, count: 143 -Region Avg IOU: 0.649584, Class: 1.000000, Obj: 0.504951, No Obj: 0.002762, Avg Recall: 0.782051, count: 78 -Region Avg IOU: 0.661204, Class: 1.000000, Obj: 0.484887, No Obj: 0.003445, Avg Recall: 0.825688, count: 109 -5905: 0.455028, 0.421712 avg, 0.001000 rate, 25.688663 seconds, 1511680 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.603484, Class: 1.000000, Obj: 0.430063, No Obj: 0.003382, Avg Recall: 0.720000, count: 125 -Region Avg IOU: 0.636422, Class: 1.000000, Obj: 0.452729, No Obj: 0.003117, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.610553, Class: 1.000000, Obj: 0.438931, No Obj: 0.004252, Avg Recall: 0.746377, count: 138 -Region Avg IOU: 0.595122, Class: 1.000000, Obj: 0.437972, No Obj: 0.003305, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.607348, Class: 1.000000, Obj: 0.430095, No Obj: 0.003059, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.647433, Class: 1.000000, Obj: 0.544001, No Obj: 0.002758, Avg Recall: 0.765432, count: 81 -Region Avg IOU: 0.629179, Class: 1.000000, Obj: 0.474980, No Obj: 0.003103, Avg Recall: 0.790698, count: 86 -Region Avg IOU: 0.637471, Class: 1.000000, Obj: 0.478370, No Obj: 0.002918, Avg Recall: 0.770833, count: 96 -5906: 0.377035, 0.417244 avg, 0.001000 rate, 24.885780 seconds, 1511936 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.635815, Class: 1.000000, Obj: 0.449847, No Obj: 0.003022, Avg Recall: 0.804348, count: 92 -Region Avg IOU: 0.619315, Class: 1.000000, Obj: 0.467755, No Obj: 0.003603, Avg Recall: 0.712000, count: 125 -Region Avg IOU: 0.630315, Class: 1.000000, Obj: 0.436971, No Obj: 0.003701, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.612376, Class: 1.000000, Obj: 0.480085, No Obj: 0.003397, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.629532, Class: 1.000000, Obj: 0.424459, No Obj: 0.003221, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.626369, Class: 1.000000, Obj: 0.470225, No Obj: 0.003282, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.674029, Class: 1.000000, Obj: 0.479150, No Obj: 0.003716, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.605074, Class: 1.000000, Obj: 0.419393, No Obj: 0.002428, Avg Recall: 0.737500, count: 80 -5907: 0.363333, 0.411853 avg, 0.001000 rate, 26.934828 seconds, 1512192 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.605733, Class: 1.000000, Obj: 0.442877, No Obj: 0.003352, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.612268, Class: 1.000000, Obj: 0.415348, No Obj: 0.003412, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.621011, Class: 1.000000, Obj: 0.453466, No Obj: 0.003059, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.624812, Class: 1.000000, Obj: 0.452923, No Obj: 0.003636, Avg Recall: 0.722222, count: 126 -Region Avg IOU: 0.629761, Class: 1.000000, Obj: 0.392542, No Obj: 0.004176, Avg Recall: 0.784722, count: 144 -Region Avg IOU: 0.563815, Class: 1.000000, Obj: 0.402780, No Obj: 0.003032, Avg Recall: 0.666667, count: 123 -Region Avg IOU: 0.634247, Class: 1.000000, Obj: 0.461260, No Obj: 0.002937, Avg Recall: 0.743590, count: 78 -Region Avg IOU: 0.624328, Class: 1.000000, Obj: 0.401658, No Obj: 0.003694, Avg Recall: 0.771186, count: 118 -5908: 0.442383, 0.414906 avg, 0.001000 rate, 26.407585 seconds, 1512448 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.588407, Class: 1.000000, Obj: 0.419763, No Obj: 0.003386, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.669657, Class: 1.000000, Obj: 0.461929, No Obj: 0.003351, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.601814, Class: 1.000000, Obj: 0.419420, No Obj: 0.002967, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.568343, Class: 1.000000, Obj: 0.432851, No Obj: 0.003254, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.671157, Class: 1.000000, Obj: 0.480104, No Obj: 0.003861, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.664311, Class: 1.000000, Obj: 0.488429, No Obj: 0.003153, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.619760, Class: 1.000000, Obj: 0.417397, No Obj: 0.002902, Avg Recall: 0.781250, count: 96 -Region Avg IOU: 0.586680, Class: 1.000000, Obj: 0.437698, No Obj: 0.003473, Avg Recall: 0.683099, count: 142 -5909: 0.399171, 0.413332 avg, 0.001000 rate, 25.969063 seconds, 1512704 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.587268, Class: 1.000000, Obj: 0.389907, No Obj: 0.002885, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.589579, Class: 1.000000, Obj: 0.396905, No Obj: 0.002764, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.659875, Class: 1.000000, Obj: 0.478880, No Obj: 0.002969, Avg Recall: 0.772152, count: 79 -Region Avg IOU: 0.637285, Class: 1.000000, Obj: 0.447577, No Obj: 0.003101, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.543887, Class: 1.000000, Obj: 0.456825, No Obj: 0.003145, Avg Recall: 0.573034, count: 89 -Region Avg IOU: 0.649722, Class: 1.000000, Obj: 0.374486, No Obj: 0.003040, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.634417, Class: 1.000000, Obj: 0.443294, No Obj: 0.003731, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.614039, Class: 1.000000, Obj: 0.396267, No Obj: 0.003239, Avg Recall: 0.694737, count: 95 -5910: 0.440203, 0.416019 avg, 0.001000 rate, 25.683836 seconds, 1512960 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000089 seconds -Region Avg IOU: 0.634123, Class: 1.000000, Obj: 0.399651, No Obj: 0.002599, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.645972, Class: 1.000000, Obj: 0.405159, No Obj: 0.003324, Avg Recall: 0.809524, count: 126 -Region Avg IOU: 0.673542, Class: 1.000000, Obj: 0.434161, No Obj: 0.002889, Avg Recall: 0.861386, count: 101 -Region Avg IOU: 0.645303, Class: 1.000000, Obj: 0.444981, No Obj: 0.002762, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.643465, Class: 1.000000, Obj: 0.423478, No Obj: 0.002334, Avg Recall: 0.780488, count: 82 -Region Avg IOU: 0.631058, Class: 1.000000, Obj: 0.411352, No Obj: 0.002747, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.661596, Class: 1.000000, Obj: 0.484297, No Obj: 0.002585, Avg Recall: 0.817308, count: 104 -Region Avg IOU: 0.607429, Class: 1.000000, Obj: 0.398647, No Obj: 0.002792, Avg Recall: 0.736842, count: 95 -5911: 0.420969, 0.416514 avg, 0.001000 rate, 26.656418 seconds, 1513216 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.639322, Class: 1.000000, Obj: 0.433478, No Obj: 0.002419, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.650416, Class: 1.000000, Obj: 0.411029, No Obj: 0.002231, Avg Recall: 0.797619, count: 84 -Region Avg IOU: 0.655502, Class: 1.000000, Obj: 0.397736, No Obj: 0.002463, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.615315, Class: 1.000000, Obj: 0.399506, No Obj: 0.003504, Avg Recall: 0.750000, count: 144 -Region Avg IOU: 0.674929, Class: 1.000000, Obj: 0.419721, No Obj: 0.002619, Avg Recall: 0.858824, count: 85 -Region Avg IOU: 0.636378, Class: 1.000000, Obj: 0.405149, No Obj: 0.002914, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.602336, Class: 1.000000, Obj: 0.378434, No Obj: 0.002693, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.664121, Class: 1.000000, Obj: 0.401084, No Obj: 0.002972, Avg Recall: 0.817460, count: 126 -5912: 0.423413, 0.417204 avg, 0.001000 rate, 26.826769 seconds, 1513472 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.661597, Class: 1.000000, Obj: 0.399924, No Obj: 0.002523, Avg Recall: 0.804348, count: 92 -Region Avg IOU: 0.604706, Class: 1.000000, Obj: 0.363694, No Obj: 0.002602, Avg Recall: 0.711111, count: 90 -Region Avg IOU: 0.662707, Class: 1.000000, Obj: 0.427534, No Obj: 0.002841, Avg Recall: 0.864078, count: 103 -Region Avg IOU: 0.634959, Class: 1.000000, Obj: 0.402437, No Obj: 0.003152, Avg Recall: 0.757353, count: 136 -Region Avg IOU: 0.655605, Class: 1.000000, Obj: 0.460467, No Obj: 0.002148, Avg Recall: 0.826667, count: 75 -Region Avg IOU: 0.652367, Class: 1.000000, Obj: 0.353239, No Obj: 0.002740, Avg Recall: 0.844262, count: 122 -Region Avg IOU: 0.617113, Class: 1.000000, Obj: 0.341496, No Obj: 0.002802, Avg Recall: 0.754098, count: 122 -Region Avg IOU: 0.650632, Class: 1.000000, Obj: 0.438468, No Obj: 0.002941, Avg Recall: 0.763636, count: 110 -5913: 0.398115, 0.415295 avg, 0.001000 rate, 27.349953 seconds, 1513728 images -Loaded: 0.000122 seconds -Region Avg IOU: 0.642242, Class: 1.000000, Obj: 0.396080, No Obj: 0.002314, Avg Recall: 0.823529, count: 85 -Region Avg IOU: 0.652782, Class: 1.000000, Obj: 0.356623, No Obj: 0.002621, Avg Recall: 0.801653, count: 121 -Region Avg IOU: 0.620726, Class: 1.000000, Obj: 0.387765, No Obj: 0.003088, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.666889, Class: 1.000000, Obj: 0.426440, No Obj: 0.002509, Avg Recall: 0.819048, count: 105 -Region Avg IOU: 0.663025, Class: 1.000000, Obj: 0.398871, No Obj: 0.002588, Avg Recall: 0.823529, count: 102 -Region Avg IOU: 0.659020, Class: 1.000000, Obj: 0.415359, No Obj: 0.002807, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.630321, Class: 1.000000, Obj: 0.386230, No Obj: 0.002525, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.669882, Class: 1.000000, Obj: 0.398988, No Obj: 0.002837, Avg Recall: 0.837607, count: 117 -5914: 0.447764, 0.418542 avg, 0.001000 rate, 28.758711 seconds, 1513984 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.635828, Class: 1.000000, Obj: 0.420734, No Obj: 0.002754, Avg Recall: 0.780303, count: 132 -Region Avg IOU: 0.629266, Class: 1.000000, Obj: 0.390850, No Obj: 0.002195, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.659865, Class: 1.000000, Obj: 0.433845, No Obj: 0.002669, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.594663, Class: 1.000000, Obj: 0.350460, No Obj: 0.002821, Avg Recall: 0.714286, count: 133 -Region Avg IOU: 0.630064, Class: 1.000000, Obj: 0.381159, No Obj: 0.003029, Avg Recall: 0.801418, count: 141 -Region Avg IOU: 0.684717, Class: 1.000000, Obj: 0.434643, No Obj: 0.002771, Avg Recall: 0.854167, count: 96 -Region Avg IOU: 0.668212, Class: 1.000000, Obj: 0.393338, No Obj: 0.002385, Avg Recall: 0.815217, count: 92 -Region Avg IOU: 0.578535, Class: 1.000000, Obj: 0.401791, No Obj: 0.002387, Avg Recall: 0.722772, count: 101 -5915: 0.481414, 0.424829 avg, 0.001000 rate, 26.490499 seconds, 1514240 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.663313, Class: 1.000000, Obj: 0.387168, No Obj: 0.002608, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.609177, Class: 1.000000, Obj: 0.344678, No Obj: 0.002290, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.627299, Class: 1.000000, Obj: 0.444620, No Obj: 0.002274, Avg Recall: 0.764706, count: 68 -Region Avg IOU: 0.678191, Class: 1.000000, Obj: 0.465912, No Obj: 0.002780, Avg Recall: 0.817204, count: 93 -Region Avg IOU: 0.613163, Class: 1.000000, Obj: 0.388589, No Obj: 0.002682, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.576497, Class: 1.000000, Obj: 0.360594, No Obj: 0.002553, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.677059, Class: 1.000000, Obj: 0.365832, No Obj: 0.002352, Avg Recall: 0.876712, count: 73 -Region Avg IOU: 0.650969, Class: 1.000000, Obj: 0.376620, No Obj: 0.001966, Avg Recall: 0.809524, count: 63 -5916: 0.378150, 0.420161 avg, 0.001000 rate, 29.195532 seconds, 1514496 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.648602, Class: 1.000000, Obj: 0.419838, No Obj: 0.003058, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.642325, Class: 1.000000, Obj: 0.459174, No Obj: 0.002454, Avg Recall: 0.767442, count: 86 -Region Avg IOU: 0.666331, Class: 1.000000, Obj: 0.424506, No Obj: 0.002729, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.625686, Class: 1.000000, Obj: 0.383861, No Obj: 0.003059, Avg Recall: 0.770833, count: 144 -Region Avg IOU: 0.632261, Class: 1.000000, Obj: 0.370816, No Obj: 0.003198, Avg Recall: 0.748201, count: 139 -Region Avg IOU: 0.636048, Class: 1.000000, Obj: 0.385795, No Obj: 0.002954, Avg Recall: 0.784173, count: 139 -Region Avg IOU: 0.651227, Class: 1.000000, Obj: 0.392622, No Obj: 0.002618, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.625876, Class: 1.000000, Obj: 0.378025, No Obj: 0.003299, Avg Recall: 0.741722, count: 151 -5917: 0.452645, 0.423410 avg, 0.001000 rate, 26.791021 seconds, 1514752 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.699041, Class: 1.000000, Obj: 0.413281, No Obj: 0.003246, Avg Recall: 0.854701, count: 117 -Region Avg IOU: 0.664054, Class: 1.000000, Obj: 0.304718, No Obj: 0.002683, Avg Recall: 0.845528, count: 123 -Region Avg IOU: 0.620334, Class: 1.000000, Obj: 0.380641, No Obj: 0.002716, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.669199, Class: 1.000000, Obj: 0.389725, No Obj: 0.002968, Avg Recall: 0.814815, count: 108 -Region Avg IOU: 0.666826, Class: 1.000000, Obj: 0.418176, No Obj: 0.002853, Avg Recall: 0.825243, count: 103 -Region Avg IOU: 0.670992, Class: 1.000000, Obj: 0.392950, No Obj: 0.002843, Avg Recall: 0.833333, count: 96 -Region Avg IOU: 0.669677, Class: 1.000000, Obj: 0.377178, No Obj: 0.002692, Avg Recall: 0.792683, count: 82 -Region Avg IOU: 0.648949, Class: 1.000000, Obj: 0.389694, No Obj: 0.002682, Avg Recall: 0.781513, count: 119 -5918: 0.479168, 0.428986 avg, 0.001000 rate, 28.487164 seconds, 1515008 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.615557, Class: 1.000000, Obj: 0.370046, No Obj: 0.002969, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.668807, Class: 1.000000, Obj: 0.409466, No Obj: 0.003148, Avg Recall: 0.826772, count: 127 -Region Avg IOU: 0.681358, Class: 1.000000, Obj: 0.441342, No Obj: 0.002838, Avg Recall: 0.859813, count: 107 -Region Avg IOU: 0.636167, Class: 1.000000, Obj: 0.396685, No Obj: 0.002963, Avg Recall: 0.742188, count: 128 -Region Avg IOU: 0.635219, Class: 1.000000, Obj: 0.406368, No Obj: 0.003355, Avg Recall: 0.776119, count: 134 -Region Avg IOU: 0.682758, Class: 1.000000, Obj: 0.478784, No Obj: 0.003031, Avg Recall: 0.846154, count: 104 -Region Avg IOU: 0.650691, Class: 1.000000, Obj: 0.429717, No Obj: 0.003467, Avg Recall: 0.765152, count: 132 -Region Avg IOU: 0.631566, Class: 1.000000, Obj: 0.420296, No Obj: 0.002781, Avg Recall: 0.764706, count: 102 -5919: 0.465181, 0.432605 avg, 0.001000 rate, 26.020405 seconds, 1515264 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.672853, Class: 1.000000, Obj: 0.438444, No Obj: 0.002976, Avg Recall: 0.900000, count: 110 -Region Avg IOU: 0.650848, Class: 1.000000, Obj: 0.460366, No Obj: 0.002857, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.671292, Class: 1.000000, Obj: 0.434714, No Obj: 0.002864, Avg Recall: 0.835052, count: 97 -Region Avg IOU: 0.644188, Class: 1.000000, Obj: 0.475648, No Obj: 0.002714, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.657459, Class: 1.000000, Obj: 0.450532, No Obj: 0.002399, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.647353, Class: 1.000000, Obj: 0.435690, No Obj: 0.002869, Avg Recall: 0.814815, count: 108 -Region Avg IOU: 0.653302, Class: 1.000000, Obj: 0.469998, No Obj: 0.002805, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.614875, Class: 1.000000, Obj: 0.380426, No Obj: 0.002968, Avg Recall: 0.744681, count: 141 -5920: 0.421531, 0.431498 avg, 0.001000 rate, 28.291620 seconds, 1515520 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000072 seconds -Region Avg IOU: 0.655469, Class: 1.000000, Obj: 0.467321, No Obj: 0.003147, Avg Recall: 0.829060, count: 117 -Region Avg IOU: 0.671429, Class: 1.000000, Obj: 0.421628, No Obj: 0.002721, Avg Recall: 0.836735, count: 98 -Region Avg IOU: 0.698479, Class: 1.000000, Obj: 0.435688, No Obj: 0.002533, Avg Recall: 0.871287, count: 101 -Region Avg IOU: 0.678354, Class: 1.000000, Obj: 0.411748, No Obj: 0.002998, Avg Recall: 0.824742, count: 97 -Region Avg IOU: 0.687874, Class: 1.000000, Obj: 0.459564, No Obj: 0.003202, Avg Recall: 0.840336, count: 119 -Region Avg IOU: 0.650448, Class: 1.000000, Obj: 0.405989, No Obj: 0.002617, Avg Recall: 0.808081, count: 99 -Region Avg IOU: 0.644427, Class: 1.000000, Obj: 0.475634, No Obj: 0.002990, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.694587, Class: 1.000000, Obj: 0.472194, No Obj: 0.002360, Avg Recall: 0.926829, count: 82 -5921: 0.419553, 0.430303 avg, 0.001000 rate, 28.725985 seconds, 1515776 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.646244, Class: 1.000000, Obj: 0.428421, No Obj: 0.002855, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.705018, Class: 1.000000, Obj: 0.521769, No Obj: 0.002582, Avg Recall: 0.866667, count: 90 -Region Avg IOU: 0.622761, Class: 1.000000, Obj: 0.434165, No Obj: 0.002727, Avg Recall: 0.743802, count: 121 -Region Avg IOU: 0.691923, Class: 1.000000, Obj: 0.470752, No Obj: 0.002939, Avg Recall: 0.864407, count: 118 -Region Avg IOU: 0.649438, Class: 1.000000, Obj: 0.418590, No Obj: 0.002977, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.634074, Class: 1.000000, Obj: 0.459604, No Obj: 0.002951, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.663203, Class: 1.000000, Obj: 0.469790, No Obj: 0.002612, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.669065, Class: 1.000000, Obj: 0.467478, No Obj: 0.003172, Avg Recall: 0.860656, count: 122 -5922: 0.414141, 0.428687 avg, 0.001000 rate, 30.368755 seconds, 1516032 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.635481, Class: 1.000000, Obj: 0.419763, No Obj: 0.002670, Avg Recall: 0.731707, count: 123 -Region Avg IOU: 0.678231, Class: 1.000000, Obj: 0.374427, No Obj: 0.002926, Avg Recall: 0.840708, count: 113 -Region Avg IOU: 0.682048, Class: 1.000000, Obj: 0.439566, No Obj: 0.003340, Avg Recall: 0.827869, count: 122 -Region Avg IOU: 0.666873, Class: 1.000000, Obj: 0.447983, No Obj: 0.002800, Avg Recall: 0.831579, count: 95 -Region Avg IOU: 0.682664, Class: 1.000000, Obj: 0.447735, No Obj: 0.002669, Avg Recall: 0.882353, count: 119 -Region Avg IOU: 0.667137, Class: 1.000000, Obj: 0.433177, No Obj: 0.002971, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.703389, Class: 1.000000, Obj: 0.445968, No Obj: 0.002727, Avg Recall: 0.894737, count: 95 -Region Avg IOU: 0.639247, Class: 1.000000, Obj: 0.416971, No Obj: 0.002521, Avg Recall: 0.765766, count: 111 -5923: 0.432745, 0.429093 avg, 0.001000 rate, 30.047514 seconds, 1516288 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.680174, Class: 1.000000, Obj: 0.472587, No Obj: 0.002739, Avg Recall: 0.848485, count: 99 -Region Avg IOU: 0.652919, Class: 1.000000, Obj: 0.418318, No Obj: 0.002698, Avg Recall: 0.803030, count: 132 -Region Avg IOU: 0.617292, Class: 1.000000, Obj: 0.426100, No Obj: 0.002223, Avg Recall: 0.780488, count: 82 -Region Avg IOU: 0.623149, Class: 1.000000, Obj: 0.386913, No Obj: 0.002610, Avg Recall: 0.819149, count: 94 -Region Avg IOU: 0.642873, Class: 1.000000, Obj: 0.388115, No Obj: 0.002573, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.677634, Class: 1.000000, Obj: 0.369556, No Obj: 0.002711, Avg Recall: 0.855670, count: 97 -Region Avg IOU: 0.652540, Class: 1.000000, Obj: 0.358374, No Obj: 0.002537, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.706700, Class: 1.000000, Obj: 0.462976, No Obj: 0.002454, Avg Recall: 0.876543, count: 81 -5924: 0.415981, 0.427782 avg, 0.001000 rate, 33.490700 seconds, 1516544 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.647715, Class: 1.000000, Obj: 0.463467, No Obj: 0.002186, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.633315, Class: 1.000000, Obj: 0.373796, No Obj: 0.002729, Avg Recall: 0.781690, count: 142 -Region Avg IOU: 0.658020, Class: 1.000000, Obj: 0.388245, No Obj: 0.002699, Avg Recall: 0.822581, count: 124 -Region Avg IOU: 0.628032, Class: 1.000000, Obj: 0.426817, No Obj: 0.002462, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.626258, Class: 1.000000, Obj: 0.409086, No Obj: 0.002841, Avg Recall: 0.758065, count: 124 -Region Avg IOU: 0.625287, Class: 1.000000, Obj: 0.346287, No Obj: 0.002580, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.627417, Class: 1.000000, Obj: 0.390308, No Obj: 0.003109, Avg Recall: 0.762238, count: 143 -Region Avg IOU: 0.651025, Class: 1.000000, Obj: 0.441039, No Obj: 0.002568, Avg Recall: 0.849558, count: 113 -5925: 0.471855, 0.432189 avg, 0.001000 rate, 32.777309 seconds, 1516800 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.719536, Class: 1.000000, Obj: 0.454696, No Obj: 0.002423, Avg Recall: 0.925532, count: 94 -Region Avg IOU: 0.615517, Class: 1.000000, Obj: 0.376540, No Obj: 0.002420, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.627811, Class: 1.000000, Obj: 0.367577, No Obj: 0.002802, Avg Recall: 0.727891, count: 147 -Region Avg IOU: 0.652207, Class: 1.000000, Obj: 0.423057, No Obj: 0.002594, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.648291, Class: 1.000000, Obj: 0.404581, No Obj: 0.002323, Avg Recall: 0.820755, count: 106 -Region Avg IOU: 0.653806, Class: 1.000000, Obj: 0.422627, No Obj: 0.002726, Avg Recall: 0.805085, count: 118 -Region Avg IOU: 0.608116, Class: 1.000000, Obj: 0.347318, No Obj: 0.002595, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.670231, Class: 1.000000, Obj: 0.428121, No Obj: 0.002449, Avg Recall: 0.845361, count: 97 -5926: 0.462762, 0.435246 avg, 0.001000 rate, 31.083712 seconds, 1517056 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.689316, Class: 1.000000, Obj: 0.410759, No Obj: 0.002425, Avg Recall: 0.852941, count: 102 -Region Avg IOU: 0.650774, Class: 1.000000, Obj: 0.373914, No Obj: 0.002294, Avg Recall: 0.796117, count: 103 -Region Avg IOU: 0.634256, Class: 1.000000, Obj: 0.397473, No Obj: 0.001913, Avg Recall: 0.807692, count: 78 -Region Avg IOU: 0.701702, Class: 1.000000, Obj: 0.394696, No Obj: 0.002101, Avg Recall: 0.892857, count: 84 -Region Avg IOU: 0.592119, Class: 1.000000, Obj: 0.320423, No Obj: 0.002255, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.650583, Class: 1.000000, Obj: 0.385440, No Obj: 0.002462, Avg Recall: 0.822034, count: 118 -Region Avg IOU: 0.625072, Class: 1.000000, Obj: 0.431435, No Obj: 0.002511, Avg Recall: 0.752000, count: 125 -Region Avg IOU: 0.640508, Class: 1.000000, Obj: 0.392176, No Obj: 0.002412, Avg Recall: 0.765306, count: 98 -5927: 0.416126, 0.433334 avg, 0.001000 rate, 28.650618 seconds, 1517312 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.664813, Class: 1.000000, Obj: 0.347085, No Obj: 0.002610, Avg Recall: 0.839416, count: 137 -Region Avg IOU: 0.655294, Class: 1.000000, Obj: 0.380906, No Obj: 0.002310, Avg Recall: 0.826446, count: 121 -Region Avg IOU: 0.618158, Class: 1.000000, Obj: 0.356466, No Obj: 0.002189, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.654329, Class: 1.000000, Obj: 0.406901, No Obj: 0.001993, Avg Recall: 0.835616, count: 73 -Region Avg IOU: 0.645949, Class: 1.000000, Obj: 0.394752, No Obj: 0.002204, Avg Recall: 0.827273, count: 110 -Region Avg IOU: 0.678622, Class: 1.000000, Obj: 0.401485, No Obj: 0.002182, Avg Recall: 0.883495, count: 103 -Region Avg IOU: 0.681212, Class: 1.000000, Obj: 0.416873, No Obj: 0.002542, Avg Recall: 0.844037, count: 109 -Region Avg IOU: 0.652023, Class: 1.000000, Obj: 0.361419, No Obj: 0.002282, Avg Recall: 0.823009, count: 113 -5928: 0.484117, 0.438413 avg, 0.001000 rate, 30.193308 seconds, 1517568 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.677566, Class: 1.000000, Obj: 0.404861, No Obj: 0.002487, Avg Recall: 0.805085, count: 118 -Region Avg IOU: 0.688711, Class: 1.000000, Obj: 0.406363, No Obj: 0.002224, Avg Recall: 0.837838, count: 74 -Region Avg IOU: 0.684234, Class: 1.000000, Obj: 0.393646, No Obj: 0.002612, Avg Recall: 0.867769, count: 121 -Region Avg IOU: 0.665022, Class: 1.000000, Obj: 0.401900, No Obj: 0.002189, Avg Recall: 0.852632, count: 95 -Region Avg IOU: 0.666500, Class: 1.000000, Obj: 0.337842, No Obj: 0.002746, Avg Recall: 0.854015, count: 137 -Region Avg IOU: 0.629197, Class: 1.000000, Obj: 0.358225, No Obj: 0.002224, Avg Recall: 0.775000, count: 120 -Region Avg IOU: 0.639143, Class: 1.000000, Obj: 0.431197, No Obj: 0.002395, Avg Recall: 0.829268, count: 123 -Region Avg IOU: 0.626047, Class: 1.000000, Obj: 0.365715, No Obj: 0.002034, Avg Recall: 0.782178, count: 101 -5929: 0.461931, 0.440764 avg, 0.001000 rate, 29.113873 seconds, 1517824 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.684116, Class: 1.000000, Obj: 0.434293, No Obj: 0.002047, Avg Recall: 0.823529, count: 85 -Region Avg IOU: 0.639826, Class: 1.000000, Obj: 0.375403, No Obj: 0.002005, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.687967, Class: 1.000000, Obj: 0.383293, No Obj: 0.002317, Avg Recall: 0.855556, count: 90 -Region Avg IOU: 0.662764, Class: 1.000000, Obj: 0.458675, No Obj: 0.002299, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.679780, Class: 1.000000, Obj: 0.363722, No Obj: 0.002320, Avg Recall: 0.850877, count: 114 -Region Avg IOU: 0.607402, Class: 1.000000, Obj: 0.391022, No Obj: 0.002484, Avg Recall: 0.788136, count: 118 -Region Avg IOU: 0.615482, Class: 1.000000, Obj: 0.343845, No Obj: 0.002613, Avg Recall: 0.748031, count: 127 -Region Avg IOU: 0.651467, Class: 1.000000, Obj: 0.383864, No Obj: 0.002112, Avg Recall: 0.782609, count: 92 -5930: 0.455099, 0.442198 avg, 0.001000 rate, 30.672709 seconds, 1518080 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 11.051988 seconds -Region Avg IOU: 0.657319, Class: 1.000000, Obj: 0.425754, No Obj: 0.002147, Avg Recall: 0.849462, count: 93 -Region Avg IOU: 0.634664, Class: 1.000000, Obj: 0.379097, No Obj: 0.002656, Avg Recall: 0.786885, count: 122 -Region Avg IOU: 0.709433, Class: 1.000000, Obj: 0.512344, No Obj: 0.002799, Avg Recall: 0.906250, count: 96 -Region Avg IOU: 0.628180, Class: 1.000000, Obj: 0.383973, No Obj: 0.002460, Avg Recall: 0.726562, count: 128 -Region Avg IOU: 0.676463, Class: 1.000000, Obj: 0.374457, No Obj: 0.002773, Avg Recall: 0.848000, count: 125 -Region Avg IOU: 0.674383, Class: 1.000000, Obj: 0.340507, No Obj: 0.002795, Avg Recall: 0.858268, count: 127 -Region Avg IOU: 0.623668, Class: 1.000000, Obj: 0.415777, No Obj: 0.002529, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.643960, Class: 1.000000, Obj: 0.364187, No Obj: 0.002428, Avg Recall: 0.794118, count: 102 -5931: 0.483970, 0.446375 avg, 0.001000 rate, 28.156677 seconds, 1518336 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.657273, Class: 1.000000, Obj: 0.384250, No Obj: 0.003013, Avg Recall: 0.790698, count: 129 -Region Avg IOU: 0.645647, Class: 1.000000, Obj: 0.427504, No Obj: 0.002585, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.666899, Class: 1.000000, Obj: 0.419938, No Obj: 0.002533, Avg Recall: 0.824074, count: 108 -Region Avg IOU: 0.673590, Class: 1.000000, Obj: 0.364932, No Obj: 0.002669, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.619624, Class: 1.000000, Obj: 0.377800, No Obj: 0.002928, Avg Recall: 0.803419, count: 117 -Region Avg IOU: 0.660566, Class: 1.000000, Obj: 0.396307, No Obj: 0.002514, Avg Recall: 0.808696, count: 115 -Region Avg IOU: 0.669523, Class: 1.000000, Obj: 0.438036, No Obj: 0.002645, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.679464, Class: 1.000000, Obj: 0.449722, No Obj: 0.002245, Avg Recall: 0.795455, count: 88 -5932: 0.446495, 0.446387 avg, 0.001000 rate, 32.296982 seconds, 1518592 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.614910, Class: 1.000000, Obj: 0.433145, No Obj: 0.002695, Avg Recall: 0.782609, count: 115 -Region Avg IOU: 0.684708, Class: 1.000000, Obj: 0.429319, No Obj: 0.002965, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.668201, Class: 1.000000, Obj: 0.405887, No Obj: 0.002750, Avg Recall: 0.824074, count: 108 -Region Avg IOU: 0.642229, Class: 1.000000, Obj: 0.395974, No Obj: 0.002843, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.660018, Class: 1.000000, Obj: 0.390715, No Obj: 0.002515, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.655271, Class: 1.000000, Obj: 0.435437, No Obj: 0.002709, Avg Recall: 0.782609, count: 115 -Region Avg IOU: 0.649482, Class: 1.000000, Obj: 0.404501, No Obj: 0.002943, Avg Recall: 0.805970, count: 134 -Region Avg IOU: 0.655385, Class: 1.000000, Obj: 0.432572, No Obj: 0.002710, Avg Recall: 0.798319, count: 119 -5933: 0.448622, 0.446611 avg, 0.001000 rate, 27.267733 seconds, 1518848 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.663298, Class: 1.000000, Obj: 0.413161, No Obj: 0.002758, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.659159, Class: 1.000000, Obj: 0.429307, No Obj: 0.002547, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.662569, Class: 1.000000, Obj: 0.349921, No Obj: 0.002285, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.616706, Class: 1.000000, Obj: 0.408377, No Obj: 0.002694, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.635748, Class: 1.000000, Obj: 0.374296, No Obj: 0.002628, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.644851, Class: 1.000000, Obj: 0.411233, No Obj: 0.002104, Avg Recall: 0.750000, count: 88 -Region Avg IOU: 0.654665, Class: 1.000000, Obj: 0.471855, No Obj: 0.002473, Avg Recall: 0.787500, count: 80 -Region Avg IOU: 0.621492, Class: 1.000000, Obj: 0.378180, No Obj: 0.003031, Avg Recall: 0.779528, count: 127 -5934: 0.424691, 0.444419 avg, 0.001000 rate, 28.286936 seconds, 1519104 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.665241, Class: 1.000000, Obj: 0.407401, No Obj: 0.002901, Avg Recall: 0.777778, count: 90 -Region Avg IOU: 0.617426, Class: 1.000000, Obj: 0.391636, No Obj: 0.002993, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.645597, Class: 1.000000, Obj: 0.442031, No Obj: 0.002953, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.679692, Class: 1.000000, Obj: 0.432158, No Obj: 0.002702, Avg Recall: 0.852941, count: 102 -Region Avg IOU: 0.662345, Class: 1.000000, Obj: 0.371678, No Obj: 0.002915, Avg Recall: 0.823009, count: 113 -Region Avg IOU: 0.665783, Class: 1.000000, Obj: 0.450215, No Obj: 0.002505, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.641300, Class: 1.000000, Obj: 0.409621, No Obj: 0.002765, Avg Recall: 0.790323, count: 124 -Region Avg IOU: 0.647244, Class: 1.000000, Obj: 0.411944, No Obj: 0.002748, Avg Recall: 0.822430, count: 107 -5935: 0.431412, 0.443118 avg, 0.001000 rate, 27.939224 seconds, 1519360 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.649031, Class: 1.000000, Obj: 0.360522, No Obj: 0.002468, Avg Recall: 0.788235, count: 85 -Region Avg IOU: 0.643310, Class: 1.000000, Obj: 0.460615, No Obj: 0.002598, Avg Recall: 0.767442, count: 86 -Region Avg IOU: 0.679669, Class: 1.000000, Obj: 0.475278, No Obj: 0.002416, Avg Recall: 0.857143, count: 77 -Region Avg IOU: 0.656018, Class: 1.000000, Obj: 0.458379, No Obj: 0.002472, Avg Recall: 0.777778, count: 81 -Region Avg IOU: 0.653940, Class: 1.000000, Obj: 0.367448, No Obj: 0.002679, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.662822, Class: 1.000000, Obj: 0.399903, No Obj: 0.002482, Avg Recall: 0.841584, count: 101 -Region Avg IOU: 0.629182, Class: 1.000000, Obj: 0.434478, No Obj: 0.003217, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.681819, Class: 1.000000, Obj: 0.453423, No Obj: 0.003040, Avg Recall: 0.867188, count: 128 -5936: 0.381257, 0.436932 avg, 0.001000 rate, 27.760925 seconds, 1519616 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.666282, Class: 1.000000, Obj: 0.447642, No Obj: 0.003258, Avg Recall: 0.863636, count: 132 -Region Avg IOU: 0.650801, Class: 1.000000, Obj: 0.419877, No Obj: 0.002781, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.630800, Class: 1.000000, Obj: 0.430899, No Obj: 0.002879, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.624220, Class: 1.000000, Obj: 0.443299, No Obj: 0.002632, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.645153, Class: 1.000000, Obj: 0.430481, No Obj: 0.002431, Avg Recall: 0.802083, count: 96 -Region Avg IOU: 0.617218, Class: 1.000000, Obj: 0.401525, No Obj: 0.002670, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.642400, Class: 1.000000, Obj: 0.401661, No Obj: 0.002860, Avg Recall: 0.795082, count: 122 -Region Avg IOU: 0.657532, Class: 1.000000, Obj: 0.417846, No Obj: 0.002972, Avg Recall: 0.805556, count: 108 -5937: 0.460750, 0.439314 avg, 0.001000 rate, 27.288916 seconds, 1519872 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.688882, Class: 1.000000, Obj: 0.479680, No Obj: 0.002786, Avg Recall: 0.864583, count: 96 -Region Avg IOU: 0.649683, Class: 1.000000, Obj: 0.432434, No Obj: 0.002881, Avg Recall: 0.793893, count: 131 -Region Avg IOU: 0.652877, Class: 1.000000, Obj: 0.442913, No Obj: 0.002727, Avg Recall: 0.813559, count: 118 -Region Avg IOU: 0.673723, Class: 1.000000, Obj: 0.410742, No Obj: 0.002720, Avg Recall: 0.855856, count: 111 -Region Avg IOU: 0.679935, Class: 1.000000, Obj: 0.453609, No Obj: 0.002892, Avg Recall: 0.837838, count: 111 -Region Avg IOU: 0.645147, Class: 1.000000, Obj: 0.453538, No Obj: 0.002580, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.636436, Class: 1.000000, Obj: 0.380824, No Obj: 0.002719, Avg Recall: 0.786885, count: 122 -Region Avg IOU: 0.643597, Class: 1.000000, Obj: 0.411206, No Obj: 0.002341, Avg Recall: 0.790000, count: 100 -5938: 0.417275, 0.437110 avg, 0.001000 rate, 28.569111 seconds, 1520128 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.684564, Class: 1.000000, Obj: 0.434560, No Obj: 0.002432, Avg Recall: 0.829787, count: 94 -Region Avg IOU: 0.619532, Class: 1.000000, Obj: 0.408121, No Obj: 0.002501, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.675386, Class: 1.000000, Obj: 0.409674, No Obj: 0.002738, Avg Recall: 0.869159, count: 107 -Region Avg IOU: 0.636926, Class: 1.000000, Obj: 0.404094, No Obj: 0.002393, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.661933, Class: 1.000000, Obj: 0.408493, No Obj: 0.002484, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.679535, Class: 1.000000, Obj: 0.464212, No Obj: 0.002552, Avg Recall: 0.815789, count: 114 -Region Avg IOU: 0.674354, Class: 1.000000, Obj: 0.383490, No Obj: 0.002592, Avg Recall: 0.836538, count: 104 -Region Avg IOU: 0.696847, Class: 1.000000, Obj: 0.473144, No Obj: 0.002250, Avg Recall: 0.857143, count: 70 -5939: 0.411342, 0.434533 avg, 0.001000 rate, 30.474060 seconds, 1520384 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.645580, Class: 1.000000, Obj: 0.438768, No Obj: 0.002380, Avg Recall: 0.773333, count: 75 -Region Avg IOU: 0.624795, Class: 1.000000, Obj: 0.444197, No Obj: 0.002481, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.644779, Class: 1.000000, Obj: 0.386233, No Obj: 0.002746, Avg Recall: 0.787402, count: 127 -Region Avg IOU: 0.636033, Class: 1.000000, Obj: 0.390418, No Obj: 0.002809, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.655276, Class: 1.000000, Obj: 0.403058, No Obj: 0.002526, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.652095, Class: 1.000000, Obj: 0.364837, No Obj: 0.002411, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.623165, Class: 1.000000, Obj: 0.384870, No Obj: 0.002524, Avg Recall: 0.793388, count: 121 -Region Avg IOU: 0.629308, Class: 1.000000, Obj: 0.412405, No Obj: 0.002555, Avg Recall: 0.750000, count: 112 -5940: 0.467759, 0.437856 avg, 0.001000 rate, 28.605429 seconds, 1520640 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.962915 seconds -Region Avg IOU: 0.476697, Class: 1.000000, Obj: 0.261045, No Obj: 0.003589, Avg Recall: 0.491228, count: 114 -Region Avg IOU: 0.475051, Class: 1.000000, Obj: 0.252664, No Obj: 0.003350, Avg Recall: 0.544554, count: 101 -Region Avg IOU: 0.499418, Class: 1.000000, Obj: 0.292440, No Obj: 0.003642, Avg Recall: 0.567568, count: 111 -Region Avg IOU: 0.513523, Class: 1.000000, Obj: 0.288177, No Obj: 0.003907, Avg Recall: 0.620690, count: 116 -Region Avg IOU: 0.487864, Class: 1.000000, Obj: 0.293492, No Obj: 0.003615, Avg Recall: 0.518868, count: 106 -Region Avg IOU: 0.483031, Class: 1.000000, Obj: 0.275296, No Obj: 0.003848, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.426184, Class: 1.000000, Obj: 0.278684, No Obj: 0.002969, Avg Recall: 0.434211, count: 76 -Region Avg IOU: 0.531809, Class: 1.000000, Obj: 0.296808, No Obj: 0.004269, Avg Recall: 0.622951, count: 122 -5941: 0.465146, 0.440585 avg, 0.001000 rate, 16.243208 seconds, 1520896 images -Loaded: 3.266806 seconds -Region Avg IOU: 0.528881, Class: 1.000000, Obj: 0.270191, No Obj: 0.003648, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.469094, Class: 1.000000, Obj: 0.290853, No Obj: 0.003814, Avg Recall: 0.479675, count: 123 -Region Avg IOU: 0.529577, Class: 1.000000, Obj: 0.298939, No Obj: 0.003927, Avg Recall: 0.643478, count: 115 -Region Avg IOU: 0.504867, Class: 1.000000, Obj: 0.280335, No Obj: 0.004045, Avg Recall: 0.533333, count: 135 -Region Avg IOU: 0.513951, Class: 1.000000, Obj: 0.294289, No Obj: 0.004053, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.461225, Class: 1.000000, Obj: 0.311081, No Obj: 0.003148, Avg Recall: 0.473118, count: 93 -Region Avg IOU: 0.418145, Class: 1.000000, Obj: 0.251352, No Obj: 0.003043, Avg Recall: 0.389474, count: 95 -Region Avg IOU: 0.511582, Class: 1.000000, Obj: 0.296407, No Obj: 0.004219, Avg Recall: 0.572727, count: 110 -5942: 0.532306, 0.449757 avg, 0.001000 rate, 19.271900 seconds, 1521152 images -Loaded: 0.200533 seconds -Region Avg IOU: 0.465782, Class: 1.000000, Obj: 0.299497, No Obj: 0.003656, Avg Recall: 0.504274, count: 117 -Region Avg IOU: 0.488284, Class: 1.000000, Obj: 0.270887, No Obj: 0.003772, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.510252, Class: 1.000000, Obj: 0.300071, No Obj: 0.003947, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.470969, Class: 1.000000, Obj: 0.271895, No Obj: 0.003647, Avg Recall: 0.491379, count: 116 -Region Avg IOU: 0.479537, Class: 1.000000, Obj: 0.320402, No Obj: 0.004286, Avg Recall: 0.521008, count: 119 -Region Avg IOU: 0.493527, Class: 1.000000, Obj: 0.303641, No Obj: 0.004206, Avg Recall: 0.528455, count: 123 -Region Avg IOU: 0.500014, Class: 1.000000, Obj: 0.359040, No Obj: 0.003514, Avg Recall: 0.552941, count: 85 -Region Avg IOU: 0.467088, Class: 1.000000, Obj: 0.269144, No Obj: 0.003357, Avg Recall: 0.522727, count: 88 -5943: 0.431631, 0.447944 avg, 0.001000 rate, 17.048916 seconds, 1521408 images -Loaded: 4.003143 seconds -Region Avg IOU: 0.526145, Class: 1.000000, Obj: 0.353563, No Obj: 0.003953, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.518835, Class: 1.000000, Obj: 0.308697, No Obj: 0.004209, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.448491, Class: 1.000000, Obj: 0.287489, No Obj: 0.004511, Avg Recall: 0.479675, count: 123 -Region Avg IOU: 0.493308, Class: 1.000000, Obj: 0.327225, No Obj: 0.004399, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.502462, Class: 1.000000, Obj: 0.339198, No Obj: 0.003915, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.525154, Class: 1.000000, Obj: 0.332338, No Obj: 0.004314, Avg Recall: 0.579545, count: 88 -Region Avg IOU: 0.467428, Class: 1.000000, Obj: 0.314879, No Obj: 0.003993, Avg Recall: 0.470588, count: 102 -Region Avg IOU: 0.526613, Class: 1.000000, Obj: 0.345004, No Obj: 0.004538, Avg Recall: 0.571429, count: 119 -5944: 0.452989, 0.448449 avg, 0.001000 rate, 15.913450 seconds, 1521664 images -Loaded: 3.273252 seconds -Region Avg IOU: 0.479198, Class: 1.000000, Obj: 0.320168, No Obj: 0.003984, Avg Recall: 0.527273, count: 110 -Region Avg IOU: 0.498257, Class: 1.000000, Obj: 0.342705, No Obj: 0.003635, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.498125, Class: 1.000000, Obj: 0.330891, No Obj: 0.003393, Avg Recall: 0.546667, count: 75 -Region Avg IOU: 0.518779, Class: 1.000000, Obj: 0.369234, No Obj: 0.004567, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.532653, Class: 1.000000, Obj: 0.358230, No Obj: 0.005672, Avg Recall: 0.615942, count: 138 -Region Avg IOU: 0.490257, Class: 1.000000, Obj: 0.341161, No Obj: 0.004096, Avg Recall: 0.535714, count: 84 -Region Avg IOU: 0.503174, Class: 1.000000, Obj: 0.331382, No Obj: 0.004630, Avg Recall: 0.510000, count: 100 -Region Avg IOU: 0.476181, Class: 1.000000, Obj: 0.329880, No Obj: 0.004053, Avg Recall: 0.551020, count: 98 -5945: 0.419829, 0.445587 avg, 0.001000 rate, 16.587128 seconds, 1521920 images -Loaded: 1.891333 seconds -Region Avg IOU: 0.492603, Class: 1.000000, Obj: 0.367317, No Obj: 0.004552, Avg Recall: 0.559140, count: 93 -Region Avg IOU: 0.506931, Class: 1.000000, Obj: 0.303088, No Obj: 0.004841, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.540052, Class: 1.000000, Obj: 0.361303, No Obj: 0.004919, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.537597, Class: 1.000000, Obj: 0.375713, No Obj: 0.004805, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.470383, Class: 1.000000, Obj: 0.334279, No Obj: 0.004244, Avg Recall: 0.500000, count: 96 -Region Avg IOU: 0.535614, Class: 1.000000, Obj: 0.372430, No Obj: 0.004156, Avg Recall: 0.573171, count: 82 -Region Avg IOU: 0.460683, Class: 1.000000, Obj: 0.332950, No Obj: 0.004607, Avg Recall: 0.490741, count: 108 -Region Avg IOU: 0.458031, Class: 1.000000, Obj: 0.342621, No Obj: 0.004434, Avg Recall: 0.471698, count: 106 -5946: 0.452975, 0.446325 avg, 0.001000 rate, 14.301797 seconds, 1522176 images -Loaded: 7.546534 seconds -Region Avg IOU: 0.450263, Class: 1.000000, Obj: 0.342596, No Obj: 0.005176, Avg Recall: 0.449664, count: 149 -Region Avg IOU: 0.413959, Class: 1.000000, Obj: 0.302931, No Obj: 0.004574, Avg Recall: 0.409091, count: 110 -Region Avg IOU: 0.491980, Class: 1.000000, Obj: 0.382536, No Obj: 0.004781, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.510302, Class: 1.000000, Obj: 0.368247, No Obj: 0.005712, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.439793, Class: 1.000000, Obj: 0.341993, No Obj: 0.004351, Avg Recall: 0.453608, count: 97 -Region Avg IOU: 0.405195, Class: 1.000000, Obj: 0.348347, No Obj: 0.004558, Avg Recall: 0.377049, count: 122 -Region Avg IOU: 0.465793, Class: 1.000000, Obj: 0.341045, No Obj: 0.004914, Avg Recall: 0.518182, count: 110 -Region Avg IOU: 0.466546, Class: 1.000000, Obj: 0.383330, No Obj: 0.004609, Avg Recall: 0.485981, count: 107 -5947: 0.552427, 0.456936 avg, 0.001000 rate, 14.127245 seconds, 1522432 images -Loaded: 7.044176 seconds -Region Avg IOU: 0.531477, Class: 1.000000, Obj: 0.424778, No Obj: 0.005380, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.488464, Class: 1.000000, Obj: 0.353654, No Obj: 0.004980, Avg Recall: 0.495935, count: 123 -Region Avg IOU: 0.473775, Class: 1.000000, Obj: 0.343302, No Obj: 0.004623, Avg Recall: 0.523810, count: 105 -Region Avg IOU: 0.482023, Class: 1.000000, Obj: 0.391490, No Obj: 0.005681, Avg Recall: 0.526316, count: 133 -Region Avg IOU: 0.467391, Class: 1.000000, Obj: 0.384471, No Obj: 0.004579, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.465103, Class: 1.000000, Obj: 0.385609, No Obj: 0.005252, Avg Recall: 0.522059, count: 136 -Region Avg IOU: 0.492189, Class: 1.000000, Obj: 0.340406, No Obj: 0.004946, Avg Recall: 0.607843, count: 102 -Region Avg IOU: 0.500309, Class: 1.000000, Obj: 0.351079, No Obj: 0.005540, Avg Recall: 0.543103, count: 116 -5948: 0.469999, 0.458242 avg, 0.001000 rate, 16.891796 seconds, 1522688 images -Loaded: 3.194220 seconds -Region Avg IOU: 0.420397, Class: 1.000000, Obj: 0.317897, No Obj: 0.005249, Avg Recall: 0.420690, count: 145 -Region Avg IOU: 0.484489, Class: 1.000000, Obj: 0.387452, No Obj: 0.005079, Avg Recall: 0.557522, count: 113 -Region Avg IOU: 0.513017, Class: 1.000000, Obj: 0.413590, No Obj: 0.005165, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.468233, Class: 1.000000, Obj: 0.370617, No Obj: 0.005503, Avg Recall: 0.512000, count: 125 -Region Avg IOU: 0.479515, Class: 1.000000, Obj: 0.377289, No Obj: 0.005675, Avg Recall: 0.509615, count: 104 -Region Avg IOU: 0.459757, Class: 1.000000, Obj: 0.408738, No Obj: 0.005254, Avg Recall: 0.515464, count: 97 -Region Avg IOU: 0.456139, Class: 1.000000, Obj: 0.384689, No Obj: 0.004177, Avg Recall: 0.528736, count: 87 -Region Avg IOU: 0.450751, Class: 1.000000, Obj: 0.378303, No Obj: 0.005004, Avg Recall: 0.471698, count: 106 -5949: 0.511145, 0.463532 avg, 0.001000 rate, 15.064920 seconds, 1522944 images -Loaded: 5.857759 seconds -Region Avg IOU: 0.434861, Class: 1.000000, Obj: 0.350361, No Obj: 0.005685, Avg Recall: 0.437500, count: 144 -Region Avg IOU: 0.434301, Class: 1.000000, Obj: 0.362451, No Obj: 0.005254, Avg Recall: 0.421429, count: 140 -Region Avg IOU: 0.493533, Class: 1.000000, Obj: 0.381609, No Obj: 0.005183, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.445148, Class: 1.000000, Obj: 0.373205, No Obj: 0.005127, Avg Recall: 0.495575, count: 113 -Region Avg IOU: 0.506223, Class: 1.000000, Obj: 0.370321, No Obj: 0.005474, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.446291, Class: 1.000000, Obj: 0.339688, No Obj: 0.006018, Avg Recall: 0.465753, count: 146 -Region Avg IOU: 0.500412, Class: 1.000000, Obj: 0.380670, No Obj: 0.005249, Avg Recall: 0.575758, count: 99 -Region Avg IOU: 0.523230, Class: 1.000000, Obj: 0.425357, No Obj: 0.004727, Avg Recall: 0.627907, count: 86 -5950: 0.513090, 0.468488 avg, 0.001000 rate, 15.158710 seconds, 1523200 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 0.080592 seconds -Region Avg IOU: 0.507601, Class: 1.000000, Obj: 0.402356, No Obj: 0.004855, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.506756, Class: 1.000000, Obj: 0.393053, No Obj: 0.005415, Avg Recall: 0.521368, count: 117 -Region Avg IOU: 0.470654, Class: 1.000000, Obj: 0.418542, No Obj: 0.004578, Avg Recall: 0.537037, count: 108 -Region Avg IOU: 0.474538, Class: 1.000000, Obj: 0.353506, No Obj: 0.005418, Avg Recall: 0.527778, count: 144 -Region Avg IOU: 0.497275, Class: 1.000000, Obj: 0.328432, No Obj: 0.005339, Avg Recall: 0.532847, count: 137 -Region Avg IOU: 0.488918, Class: 1.000000, Obj: 0.367375, No Obj: 0.004659, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.537859, Class: 1.000000, Obj: 0.395435, No Obj: 0.004841, Avg Recall: 0.617978, count: 89 -Region Avg IOU: 0.512830, Class: 1.000000, Obj: 0.388691, No Obj: 0.004205, Avg Recall: 0.544444, count: 90 -5951: 0.459675, 0.467607 avg, 0.001000 rate, 19.639406 seconds, 1523456 images -Loaded: 2.872737 seconds -Region Avg IOU: 0.506346, Class: 1.000000, Obj: 0.362333, No Obj: 0.004744, Avg Recall: 0.544643, count: 112 -Region Avg IOU: 0.508159, Class: 1.000000, Obj: 0.368768, No Obj: 0.004943, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.505904, Class: 1.000000, Obj: 0.393339, No Obj: 0.005042, Avg Recall: 0.557377, count: 122 -Region Avg IOU: 0.494234, Class: 1.000000, Obj: 0.355861, No Obj: 0.004638, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.496038, Class: 1.000000, Obj: 0.365099, No Obj: 0.005528, Avg Recall: 0.592000, count: 125 -Region Avg IOU: 0.474864, Class: 1.000000, Obj: 0.375323, No Obj: 0.005876, Avg Recall: 0.520270, count: 148 -Region Avg IOU: 0.494748, Class: 1.000000, Obj: 0.372787, No Obj: 0.004603, Avg Recall: 0.537736, count: 106 -Region Avg IOU: 0.489801, Class: 1.000000, Obj: 0.343780, No Obj: 0.005636, Avg Recall: 0.511811, count: 127 -5952: 0.476684, 0.468514 avg, 0.001000 rate, 17.688061 seconds, 1523712 images -Loaded: 3.244382 seconds -Region Avg IOU: 0.539070, Class: 1.000000, Obj: 0.359564, No Obj: 0.006189, Avg Recall: 0.622378, count: 143 -Region Avg IOU: 0.561531, Class: 1.000000, Obj: 0.375509, No Obj: 0.005063, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.500127, Class: 1.000000, Obj: 0.378963, No Obj: 0.005144, Avg Recall: 0.534483, count: 116 -Region Avg IOU: 0.521454, Class: 1.000000, Obj: 0.389774, No Obj: 0.004055, Avg Recall: 0.585366, count: 82 -Region Avg IOU: 0.592339, Class: 1.000000, Obj: 0.382331, No Obj: 0.004873, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.519727, Class: 1.000000, Obj: 0.402482, No Obj: 0.005310, Avg Recall: 0.543478, count: 138 -Region Avg IOU: 0.468617, Class: 1.000000, Obj: 0.379242, No Obj: 0.004839, Avg Recall: 0.479339, count: 121 -Region Avg IOU: 0.582426, Class: 1.000000, Obj: 0.416916, No Obj: 0.004905, Avg Recall: 0.658824, count: 85 -5953: 0.462757, 0.467939 avg, 0.001000 rate, 19.986029 seconds, 1523968 images -Loaded: 1.913009 seconds -Region Avg IOU: 0.499036, Class: 1.000000, Obj: 0.358078, No Obj: 0.004901, Avg Recall: 0.531746, count: 126 -Region Avg IOU: 0.510986, Class: 1.000000, Obj: 0.392804, No Obj: 0.005802, Avg Recall: 0.589147, count: 129 -Region Avg IOU: 0.491869, Class: 1.000000, Obj: 0.342564, No Obj: 0.005119, Avg Recall: 0.522124, count: 113 -Region Avg IOU: 0.475173, Class: 1.000000, Obj: 0.320928, No Obj: 0.004681, Avg Recall: 0.520833, count: 144 -Region Avg IOU: 0.525821, Class: 1.000000, Obj: 0.396115, No Obj: 0.004672, Avg Recall: 0.574468, count: 94 -Region Avg IOU: 0.495925, Class: 1.000000, Obj: 0.344328, No Obj: 0.004228, Avg Recall: 0.533333, count: 105 -Region Avg IOU: 0.497490, Class: 1.000000, Obj: 0.345885, No Obj: 0.004649, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.513733, Class: 1.000000, Obj: 0.381106, No Obj: 0.004349, Avg Recall: 0.631579, count: 95 -5954: 0.519021, 0.473047 avg, 0.001000 rate, 17.547804 seconds, 1524224 images -Loaded: 3.523430 seconds -Region Avg IOU: 0.554908, Class: 1.000000, Obj: 0.371810, No Obj: 0.005622, Avg Recall: 0.642857, count: 140 -Region Avg IOU: 0.526957, Class: 1.000000, Obj: 0.356014, No Obj: 0.005043, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.508678, Class: 1.000000, Obj: 0.354417, No Obj: 0.005509, Avg Recall: 0.562500, count: 128 -Region Avg IOU: 0.436596, Class: 1.000000, Obj: 0.314811, No Obj: 0.003793, Avg Recall: 0.450549, count: 91 -Region Avg IOU: 0.530365, Class: 1.000000, Obj: 0.371457, No Obj: 0.005268, Avg Recall: 0.568000, count: 125 -Region Avg IOU: 0.530684, Class: 1.000000, Obj: 0.360115, No Obj: 0.004253, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.499435, Class: 1.000000, Obj: 0.328319, No Obj: 0.005114, Avg Recall: 0.543478, count: 138 -Region Avg IOU: 0.485308, Class: 1.000000, Obj: 0.333755, No Obj: 0.004448, Avg Recall: 0.513043, count: 115 -5955: 0.444112, 0.470153 avg, 0.001000 rate, 19.313574 seconds, 1524480 images -Loaded: 2.931983 seconds -Region Avg IOU: 0.531786, Class: 1.000000, Obj: 0.371306, No Obj: 0.005450, Avg Recall: 0.608000, count: 125 -Region Avg IOU: 0.566261, Class: 1.000000, Obj: 0.438662, No Obj: 0.004354, Avg Recall: 0.643678, count: 87 -Region Avg IOU: 0.543132, Class: 1.000000, Obj: 0.355190, No Obj: 0.004786, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.544303, Class: 1.000000, Obj: 0.357893, No Obj: 0.005119, Avg Recall: 0.636364, count: 132 -Region Avg IOU: 0.461559, Class: 1.000000, Obj: 0.339141, No Obj: 0.004513, Avg Recall: 0.519685, count: 127 -Region Avg IOU: 0.468527, Class: 1.000000, Obj: 0.339605, No Obj: 0.004059, Avg Recall: 0.523364, count: 107 -Region Avg IOU: 0.515727, Class: 1.000000, Obj: 0.370004, No Obj: 0.004212, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.523549, Class: 1.000000, Obj: 0.390429, No Obj: 0.005449, Avg Recall: 0.570248, count: 121 -5956: 0.433107, 0.466449 avg, 0.001000 rate, 19.710110 seconds, 1524736 images -Loaded: 2.462197 seconds -Region Avg IOU: 0.477391, Class: 1.000000, Obj: 0.322566, No Obj: 0.004882, Avg Recall: 0.484848, count: 132 -Region Avg IOU: 0.495482, Class: 1.000000, Obj: 0.329029, No Obj: 0.004960, Avg Recall: 0.577465, count: 142 -Region Avg IOU: 0.525446, Class: 1.000000, Obj: 0.371432, No Obj: 0.003996, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.556875, Class: 1.000000, Obj: 0.374436, No Obj: 0.004752, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.492988, Class: 1.000000, Obj: 0.335255, No Obj: 0.003765, Avg Recall: 0.504854, count: 103 -Region Avg IOU: 0.552661, Class: 1.000000, Obj: 0.366242, No Obj: 0.004660, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.462485, Class: 1.000000, Obj: 0.310800, No Obj: 0.005119, Avg Recall: 0.469231, count: 130 -Region Avg IOU: 0.515061, Class: 1.000000, Obj: 0.339499, No Obj: 0.004507, Avg Recall: 0.567568, count: 111 -5957: 0.462692, 0.466073 avg, 0.001000 rate, 19.205606 seconds, 1524992 images -Loaded: 1.479581 seconds -Region Avg IOU: 0.504413, Class: 1.000000, Obj: 0.367820, No Obj: 0.004827, Avg Recall: 0.553571, count: 112 -Region Avg IOU: 0.512893, Class: 1.000000, Obj: 0.353591, No Obj: 0.004255, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.494158, Class: 1.000000, Obj: 0.355503, No Obj: 0.004043, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.555687, Class: 1.000000, Obj: 0.373017, No Obj: 0.005104, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.488145, Class: 1.000000, Obj: 0.361985, No Obj: 0.004755, Avg Recall: 0.488550, count: 131 -Region Avg IOU: 0.526463, Class: 1.000000, Obj: 0.339369, No Obj: 0.004256, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.509644, Class: 1.000000, Obj: 0.344275, No Obj: 0.004428, Avg Recall: 0.504000, count: 125 -Region Avg IOU: 0.487428, Class: 1.000000, Obj: 0.335732, No Obj: 0.005200, Avg Recall: 0.522388, count: 134 -5958: 0.430312, 0.462497 avg, 0.001000 rate, 18.009380 seconds, 1525248 images -Loaded: 2.841521 seconds -Region Avg IOU: 0.525526, Class: 1.000000, Obj: 0.376614, No Obj: 0.004978, Avg Recall: 0.573643, count: 129 -Region Avg IOU: 0.531550, Class: 1.000000, Obj: 0.347446, No Obj: 0.004426, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.574834, Class: 1.000000, Obj: 0.392300, No Obj: 0.005021, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.495275, Class: 1.000000, Obj: 0.345577, No Obj: 0.004331, Avg Recall: 0.564103, count: 117 -Region Avg IOU: 0.575426, Class: 1.000000, Obj: 0.413710, No Obj: 0.004568, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.572835, Class: 1.000000, Obj: 0.393763, No Obj: 0.004916, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.550493, Class: 1.000000, Obj: 0.361569, No Obj: 0.004459, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.492479, Class: 1.000000, Obj: 0.307255, No Obj: 0.004266, Avg Recall: 0.536364, count: 110 -5959: 0.452354, 0.461483 avg, 0.001000 rate, 19.565496 seconds, 1525504 images -Loaded: 0.422528 seconds -Region Avg IOU: 0.564147, Class: 1.000000, Obj: 0.354269, No Obj: 0.005035, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.498984, Class: 1.000000, Obj: 0.332335, No Obj: 0.004599, Avg Recall: 0.549550, count: 111 -Region Avg IOU: 0.536161, Class: 1.000000, Obj: 0.328613, No Obj: 0.004603, Avg Recall: 0.561404, count: 114 -Region Avg IOU: 0.492612, Class: 1.000000, Obj: 0.335870, No Obj: 0.004980, Avg Recall: 0.528986, count: 138 -Region Avg IOU: 0.499746, Class: 1.000000, Obj: 0.354668, No Obj: 0.004655, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.503885, Class: 1.000000, Obj: 0.377122, No Obj: 0.003675, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.510991, Class: 1.000000, Obj: 0.375177, No Obj: 0.005197, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.499741, Class: 1.000000, Obj: 0.344867, No Obj: 0.004239, Avg Recall: 0.549618, count: 131 -5960: 0.467334, 0.462068 avg, 0.001000 rate, 16.887283 seconds, 1525760 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 0.232108 seconds -Region Avg IOU: 0.560725, Class: 1.000000, Obj: 0.414556, No Obj: 0.004333, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.571323, Class: 1.000000, Obj: 0.402753, No Obj: 0.004377, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.627703, Class: 1.000000, Obj: 0.456974, No Obj: 0.003907, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.542022, Class: 1.000000, Obj: 0.420609, No Obj: 0.003425, Avg Recall: 0.627907, count: 86 -Region Avg IOU: 0.611660, Class: 1.000000, Obj: 0.437903, No Obj: 0.004809, Avg Recall: 0.713115, count: 122 -Region Avg IOU: 0.496026, Class: 1.000000, Obj: 0.403815, No Obj: 0.003606, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.568580, Class: 1.000000, Obj: 0.372256, No Obj: 0.004943, Avg Recall: 0.674074, count: 135 -Region Avg IOU: 0.595523, Class: 1.000000, Obj: 0.408279, No Obj: 0.004750, Avg Recall: 0.718447, count: 103 -5961: 0.385237, 0.454385 avg, 0.001000 rate, 23.930861 seconds, 1526016 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.503932, Class: 1.000000, Obj: 0.338025, No Obj: 0.004155, Avg Recall: 0.568000, count: 125 -Region Avg IOU: 0.590137, Class: 1.000000, Obj: 0.422497, No Obj: 0.004496, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.565952, Class: 1.000000, Obj: 0.409723, No Obj: 0.004408, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.571458, Class: 1.000000, Obj: 0.399658, No Obj: 0.004671, Avg Recall: 0.654135, count: 133 -Region Avg IOU: 0.548864, Class: 1.000000, Obj: 0.389737, No Obj: 0.004119, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.543688, Class: 1.000000, Obj: 0.374626, No Obj: 0.004005, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.548298, Class: 1.000000, Obj: 0.385056, No Obj: 0.003802, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.556931, Class: 1.000000, Obj: 0.355775, No Obj: 0.004137, Avg Recall: 0.650000, count: 100 -5962: 0.436287, 0.452575 avg, 0.001000 rate, 20.195507 seconds, 1526272 images -Loaded: 2.413522 seconds -Region Avg IOU: 0.567307, Class: 1.000000, Obj: 0.421146, No Obj: 0.003681, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.600703, Class: 1.000000, Obj: 0.448455, No Obj: 0.003864, Avg Recall: 0.720930, count: 86 -Region Avg IOU: 0.532379, Class: 1.000000, Obj: 0.397587, No Obj: 0.004078, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.598755, Class: 1.000000, Obj: 0.494095, No Obj: 0.003964, Avg Recall: 0.711111, count: 90 -Region Avg IOU: 0.571334, Class: 1.000000, Obj: 0.370379, No Obj: 0.003718, Avg Recall: 0.679012, count: 81 -Region Avg IOU: 0.590377, Class: 1.000000, Obj: 0.427318, No Obj: 0.004476, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.514632, Class: 1.000000, Obj: 0.346531, No Obj: 0.004381, Avg Recall: 0.611111, count: 126 -Region Avg IOU: 0.496592, Class: 1.000000, Obj: 0.388704, No Obj: 0.004087, Avg Recall: 0.558824, count: 102 -5963: 0.398703, 0.447188 avg, 0.001000 rate, 21.861101 seconds, 1526528 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.529032, Class: 1.000000, Obj: 0.390288, No Obj: 0.004254, Avg Recall: 0.563380, count: 142 -Region Avg IOU: 0.545137, Class: 1.000000, Obj: 0.422280, No Obj: 0.003967, Avg Recall: 0.577778, count: 90 -Region Avg IOU: 0.569748, Class: 1.000000, Obj: 0.428287, No Obj: 0.004139, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.576512, Class: 1.000000, Obj: 0.396060, No Obj: 0.003928, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.522424, Class: 1.000000, Obj: 0.389169, No Obj: 0.004472, Avg Recall: 0.583333, count: 120 -Region Avg IOU: 0.586425, Class: 1.000000, Obj: 0.424158, No Obj: 0.004722, Avg Recall: 0.666667, count: 135 -Region Avg IOU: 0.545456, Class: 1.000000, Obj: 0.391733, No Obj: 0.005059, Avg Recall: 0.581081, count: 148 -Region Avg IOU: 0.597648, Class: 1.000000, Obj: 0.428051, No Obj: 0.004419, Avg Recall: 0.700000, count: 110 -5964: 0.426690, 0.445138 avg, 0.001000 rate, 20.850349 seconds, 1526784 images -Loaded: 2.346582 seconds -Region Avg IOU: 0.556738, Class: 1.000000, Obj: 0.389762, No Obj: 0.004970, Avg Recall: 0.674419, count: 129 -Region Avg IOU: 0.583856, Class: 1.000000, Obj: 0.448485, No Obj: 0.003660, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.557996, Class: 1.000000, Obj: 0.424034, No Obj: 0.004276, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.510843, Class: 1.000000, Obj: 0.355621, No Obj: 0.003969, Avg Recall: 0.608000, count: 125 -Region Avg IOU: 0.586191, Class: 1.000000, Obj: 0.365907, No Obj: 0.004322, Avg Recall: 0.704918, count: 122 -Region Avg IOU: 0.581339, Class: 1.000000, Obj: 0.409445, No Obj: 0.003354, Avg Recall: 0.686747, count: 83 -Region Avg IOU: 0.581238, Class: 1.000000, Obj: 0.443091, No Obj: 0.004274, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.557914, Class: 1.000000, Obj: 0.386256, No Obj: 0.003467, Avg Recall: 0.670000, count: 100 -5965: 0.450403, 0.445664 avg, 0.001000 rate, 23.101254 seconds, 1527040 images -Loaded: 0.904729 seconds -Region Avg IOU: 0.548590, Class: 1.000000, Obj: 0.422676, No Obj: 0.003503, Avg Recall: 0.619048, count: 84 -Region Avg IOU: 0.497412, Class: 1.000000, Obj: 0.363825, No Obj: 0.003705, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.532943, Class: 1.000000, Obj: 0.375642, No Obj: 0.003545, Avg Recall: 0.650602, count: 83 -Region Avg IOU: 0.567220, Class: 1.000000, Obj: 0.453592, No Obj: 0.004838, Avg Recall: 0.632812, count: 128 -Region Avg IOU: 0.601783, Class: 1.000000, Obj: 0.437268, No Obj: 0.004329, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.610268, Class: 1.000000, Obj: 0.442748, No Obj: 0.003421, Avg Recall: 0.743590, count: 78 -Region Avg IOU: 0.589330, Class: 1.000000, Obj: 0.402313, No Obj: 0.004457, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.547431, Class: 1.000000, Obj: 0.382182, No Obj: 0.004650, Avg Recall: 0.617886, count: 123 -5966: 0.390335, 0.440131 avg, 0.001000 rate, 19.270044 seconds, 1527296 images -Loaded: 2.598853 seconds -Region Avg IOU: 0.576480, Class: 1.000000, Obj: 0.428147, No Obj: 0.004065, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.554514, Class: 1.000000, Obj: 0.386262, No Obj: 0.003733, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.551742, Class: 1.000000, Obj: 0.392070, No Obj: 0.004423, Avg Recall: 0.634783, count: 115 -Region Avg IOU: 0.558654, Class: 1.000000, Obj: 0.432692, No Obj: 0.003559, Avg Recall: 0.673684, count: 95 -Region Avg IOU: 0.570340, Class: 1.000000, Obj: 0.436429, No Obj: 0.004575, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.539740, Class: 1.000000, Obj: 0.375273, No Obj: 0.003916, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.568472, Class: 1.000000, Obj: 0.362290, No Obj: 0.004217, Avg Recall: 0.698276, count: 116 -Region Avg IOU: 0.585737, Class: 1.000000, Obj: 0.421295, No Obj: 0.003935, Avg Recall: 0.679612, count: 103 -5967: 0.413914, 0.437510 avg, 0.001000 rate, 22.126400 seconds, 1527552 images -Loaded: 1.723326 seconds -Region Avg IOU: 0.516799, Class: 1.000000, Obj: 0.372910, No Obj: 0.004033, Avg Recall: 0.580952, count: 105 -Region Avg IOU: 0.535770, Class: 1.000000, Obj: 0.384865, No Obj: 0.004552, Avg Recall: 0.591549, count: 142 -Region Avg IOU: 0.556468, Class: 1.000000, Obj: 0.419242, No Obj: 0.004096, Avg Recall: 0.606061, count: 99 -Region Avg IOU: 0.564587, Class: 1.000000, Obj: 0.427021, No Obj: 0.003800, Avg Recall: 0.633333, count: 90 -Region Avg IOU: 0.541468, Class: 1.000000, Obj: 0.407027, No Obj: 0.003730, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.603122, Class: 1.000000, Obj: 0.419523, No Obj: 0.004037, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.526791, Class: 1.000000, Obj: 0.402237, No Obj: 0.004147, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.594244, Class: 1.000000, Obj: 0.388068, No Obj: 0.003743, Avg Recall: 0.741573, count: 89 -5968: 0.432525, 0.437011 avg, 0.001000 rate, 20.555805 seconds, 1527808 images -Loaded: 1.519994 seconds -Region Avg IOU: 0.579718, Class: 1.000000, Obj: 0.414490, No Obj: 0.004153, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.563658, Class: 1.000000, Obj: 0.399869, No Obj: 0.004231, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.564483, Class: 1.000000, Obj: 0.380036, No Obj: 0.004501, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.546658, Class: 1.000000, Obj: 0.396954, No Obj: 0.003994, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.592529, Class: 1.000000, Obj: 0.454293, No Obj: 0.004275, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.511574, Class: 1.000000, Obj: 0.352160, No Obj: 0.004004, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.555191, Class: 1.000000, Obj: 0.366151, No Obj: 0.004778, Avg Recall: 0.642276, count: 123 -Region Avg IOU: 0.557423, Class: 1.000000, Obj: 0.343544, No Obj: 0.003975, Avg Recall: 0.633027, count: 109 -5969: 0.407856, 0.434096 avg, 0.001000 rate, 22.491003 seconds, 1528064 images -Loaded: 1.863220 seconds -Region Avg IOU: 0.572514, Class: 1.000000, Obj: 0.418160, No Obj: 0.004207, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.558307, Class: 1.000000, Obj: 0.388637, No Obj: 0.004208, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.543327, Class: 1.000000, Obj: 0.325067, No Obj: 0.003710, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.578440, Class: 1.000000, Obj: 0.397573, No Obj: 0.004627, Avg Recall: 0.684211, count: 133 -Region Avg IOU: 0.577955, Class: 1.000000, Obj: 0.398023, No Obj: 0.003826, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.563516, Class: 1.000000, Obj: 0.388790, No Obj: 0.004728, Avg Recall: 0.664000, count: 125 -Region Avg IOU: 0.571395, Class: 1.000000, Obj: 0.376062, No Obj: 0.004607, Avg Recall: 0.664179, count: 134 -Region Avg IOU: 0.559229, Class: 1.000000, Obj: 0.386483, No Obj: 0.004321, Avg Recall: 0.657895, count: 114 -5970: 0.430671, 0.433753 avg, 0.001000 rate, 22.275780 seconds, 1528320 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 20.906933 seconds -Region Avg IOU: 0.456845, Class: 1.000000, Obj: 0.261167, No Obj: 0.004179, Avg Recall: 0.457627, count: 118 -Region Avg IOU: 0.483284, Class: 1.000000, Obj: 0.308190, No Obj: 0.004156, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.493072, Class: 1.000000, Obj: 0.299559, No Obj: 0.004903, Avg Recall: 0.542373, count: 118 -Region Avg IOU: 0.494182, Class: 1.000000, Obj: 0.309315, No Obj: 0.004570, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.491881, Class: 1.000000, Obj: 0.335393, No Obj: 0.004601, Avg Recall: 0.557522, count: 113 -Region Avg IOU: 0.481810, Class: 1.000000, Obj: 0.276536, No Obj: 0.004412, Avg Recall: 0.508621, count: 116 -Region Avg IOU: 0.462523, Class: 1.000000, Obj: 0.302782, No Obj: 0.004757, Avg Recall: 0.500000, count: 138 -Region Avg IOU: 0.455165, Class: 1.000000, Obj: 0.272834, No Obj: 0.004741, Avg Recall: 0.463415, count: 123 -5971: 0.496492, 0.440027 avg, 0.001000 rate, 18.533810 seconds, 1528576 images -Loaded: 2.667650 seconds -Region Avg IOU: 0.503637, Class: 1.000000, Obj: 0.345414, No Obj: 0.004473, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.494215, Class: 1.000000, Obj: 0.306688, No Obj: 0.005178, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.544925, Class: 1.000000, Obj: 0.353391, No Obj: 0.004233, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.501196, Class: 1.000000, Obj: 0.322603, No Obj: 0.004794, Avg Recall: 0.580645, count: 124 -Region Avg IOU: 0.475395, Class: 1.000000, Obj: 0.294881, No Obj: 0.004492, Avg Recall: 0.484375, count: 128 -Region Avg IOU: 0.460725, Class: 1.000000, Obj: 0.306666, No Obj: 0.004485, Avg Recall: 0.503937, count: 127 -Region Avg IOU: 0.467160, Class: 1.000000, Obj: 0.316969, No Obj: 0.004461, Avg Recall: 0.550000, count: 120 -Region Avg IOU: 0.475857, Class: 1.000000, Obj: 0.331843, No Obj: 0.003793, Avg Recall: 0.488095, count: 84 -5972: 0.446206, 0.440645 avg, 0.001000 rate, 15.645198 seconds, 1528832 images -Loaded: 3.886335 seconds -Region Avg IOU: 0.542455, Class: 1.000000, Obj: 0.350771, No Obj: 0.004732, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.472355, Class: 1.000000, Obj: 0.316619, No Obj: 0.004622, Avg Recall: 0.484848, count: 99 -Region Avg IOU: 0.511780, Class: 1.000000, Obj: 0.354784, No Obj: 0.005316, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.471437, Class: 1.000000, Obj: 0.333592, No Obj: 0.005007, Avg Recall: 0.525000, count: 120 -Region Avg IOU: 0.510537, Class: 1.000000, Obj: 0.345066, No Obj: 0.003909, Avg Recall: 0.597222, count: 72 -Region Avg IOU: 0.476537, Class: 1.000000, Obj: 0.315303, No Obj: 0.004272, Avg Recall: 0.487180, count: 117 -Region Avg IOU: 0.495475, Class: 1.000000, Obj: 0.328725, No Obj: 0.004494, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.484046, Class: 1.000000, Obj: 0.301104, No Obj: 0.003403, Avg Recall: 0.573171, count: 82 -5973: 0.465822, 0.443163 avg, 0.001000 rate, 14.300518 seconds, 1529088 images -Loaded: 5.781761 seconds -Region Avg IOU: 0.546658, Class: 1.000000, Obj: 0.324606, No Obj: 0.005458, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.550897, Class: 1.000000, Obj: 0.351959, No Obj: 0.005735, Avg Recall: 0.631148, count: 122 -Region Avg IOU: 0.463231, Class: 1.000000, Obj: 0.335794, No Obj: 0.004216, Avg Recall: 0.464646, count: 99 -Region Avg IOU: 0.516711, Class: 1.000000, Obj: 0.335137, No Obj: 0.003340, Avg Recall: 0.545455, count: 66 -Region Avg IOU: 0.462109, Class: 1.000000, Obj: 0.274562, No Obj: 0.004513, Avg Recall: 0.508772, count: 114 -Region Avg IOU: 0.487293, Class: 1.000000, Obj: 0.375015, No Obj: 0.004753, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.498699, Class: 1.000000, Obj: 0.355477, No Obj: 0.004060, Avg Recall: 0.552083, count: 96 -Region Avg IOU: 0.484440, Class: 1.000000, Obj: 0.350976, No Obj: 0.005371, Avg Recall: 0.554794, count: 146 -5974: 0.430210, 0.441867 avg, 0.001000 rate, 14.316794 seconds, 1529344 images -Loaded: 2.756479 seconds -Region Avg IOU: 0.470956, Class: 1.000000, Obj: 0.324789, No Obj: 0.004189, Avg Recall: 0.542857, count: 105 -Region Avg IOU: 0.502759, Class: 1.000000, Obj: 0.396151, No Obj: 0.005336, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.540812, Class: 1.000000, Obj: 0.387608, No Obj: 0.004599, Avg Recall: 0.607143, count: 84 -Region Avg IOU: 0.487930, Class: 1.000000, Obj: 0.353076, No Obj: 0.004690, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.478816, Class: 1.000000, Obj: 0.356767, No Obj: 0.005300, Avg Recall: 0.486957, count: 115 -Region Avg IOU: 0.465828, Class: 1.000000, Obj: 0.319485, No Obj: 0.004394, Avg Recall: 0.459459, count: 111 -Region Avg IOU: 0.457085, Class: 1.000000, Obj: 0.329471, No Obj: 0.005127, Avg Recall: 0.496296, count: 135 -Region Avg IOU: 0.496889, Class: 1.000000, Obj: 0.312299, No Obj: 0.004103, Avg Recall: 0.553191, count: 94 -5975: 0.476963, 0.445377 avg, 0.001000 rate, 17.924480 seconds, 1529600 images -Loaded: 3.186224 seconds -Region Avg IOU: 0.490459, Class: 1.000000, Obj: 0.353641, No Obj: 0.004454, Avg Recall: 0.535714, count: 84 -Region Avg IOU: 0.509221, Class: 1.000000, Obj: 0.356678, No Obj: 0.005131, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.484679, Class: 1.000000, Obj: 0.337958, No Obj: 0.004449, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.487755, Class: 1.000000, Obj: 0.378625, No Obj: 0.004431, Avg Recall: 0.500000, count: 84 -Region Avg IOU: 0.438261, Class: 1.000000, Obj: 0.328709, No Obj: 0.004581, Avg Recall: 0.441441, count: 111 -Region Avg IOU: 0.479199, Class: 1.000000, Obj: 0.348003, No Obj: 0.005074, Avg Recall: 0.570175, count: 114 -Region Avg IOU: 0.473763, Class: 1.000000, Obj: 0.363199, No Obj: 0.005241, Avg Recall: 0.537037, count: 108 -Region Avg IOU: 0.559169, Class: 1.000000, Obj: 0.387523, No Obj: 0.005174, Avg Recall: 0.651786, count: 112 -5976: 0.469947, 0.447834 avg, 0.001000 rate, 16.290051 seconds, 1529856 images -Loaded: 5.829054 seconds -Region Avg IOU: 0.503769, Class: 1.000000, Obj: 0.340564, No Obj: 0.004741, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.489096, Class: 1.000000, Obj: 0.392155, No Obj: 0.005453, Avg Recall: 0.528000, count: 125 -Region Avg IOU: 0.476448, Class: 1.000000, Obj: 0.347868, No Obj: 0.005240, Avg Recall: 0.488000, count: 125 -Region Avg IOU: 0.498633, Class: 1.000000, Obj: 0.362216, No Obj: 0.005528, Avg Recall: 0.534483, count: 116 -Region Avg IOU: 0.462586, Class: 1.000000, Obj: 0.331760, No Obj: 0.005173, Avg Recall: 0.533333, count: 105 -Region Avg IOU: 0.522202, Class: 1.000000, Obj: 0.322066, No Obj: 0.005172, Avg Recall: 0.555556, count: 99 -Region Avg IOU: 0.494370, Class: 1.000000, Obj: 0.363026, No Obj: 0.004925, Avg Recall: 0.514286, count: 105 -Region Avg IOU: 0.449741, Class: 1.000000, Obj: 0.382822, No Obj: 0.004621, Avg Recall: 0.488889, count: 90 -5977: 0.489271, 0.451978 avg, 0.001000 rate, 17.610600 seconds, 1530112 images -Loaded: 1.946743 seconds -Region Avg IOU: 0.500357, Class: 1.000000, Obj: 0.379045, No Obj: 0.005616, Avg Recall: 0.532110, count: 109 -Region Avg IOU: 0.472584, Class: 1.000000, Obj: 0.396412, No Obj: 0.004517, Avg Recall: 0.555556, count: 90 -Region Avg IOU: 0.398310, Class: 1.000000, Obj: 0.301769, No Obj: 0.004350, Avg Recall: 0.413793, count: 116 -Region Avg IOU: 0.482740, Class: 1.000000, Obj: 0.322300, No Obj: 0.005389, Avg Recall: 0.517241, count: 116 -Region Avg IOU: 0.512259, Class: 1.000000, Obj: 0.388585, No Obj: 0.005508, Avg Recall: 0.567568, count: 111 -Region Avg IOU: 0.415687, Class: 1.000000, Obj: 0.302522, No Obj: 0.005198, Avg Recall: 0.435714, count: 140 -Region Avg IOU: 0.499866, Class: 1.000000, Obj: 0.370583, No Obj: 0.005337, Avg Recall: 0.530435, count: 115 -Region Avg IOU: 0.468664, Class: 1.000000, Obj: 0.367170, No Obj: 0.004642, Avg Recall: 0.527778, count: 108 -5978: 0.464466, 0.453227 avg, 0.001000 rate, 18.768780 seconds, 1530368 images -Loaded: 1.969967 seconds -Region Avg IOU: 0.489311, Class: 1.000000, Obj: 0.374738, No Obj: 0.004780, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.519728, Class: 1.000000, Obj: 0.393630, No Obj: 0.005042, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.467202, Class: 1.000000, Obj: 0.344371, No Obj: 0.004333, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.464822, Class: 1.000000, Obj: 0.350875, No Obj: 0.005039, Avg Recall: 0.508333, count: 120 -Region Avg IOU: 0.436981, Class: 1.000000, Obj: 0.316343, No Obj: 0.004846, Avg Recall: 0.466102, count: 118 -Region Avg IOU: 0.489694, Class: 1.000000, Obj: 0.345506, No Obj: 0.005209, Avg Recall: 0.553571, count: 112 -Region Avg IOU: 0.452087, Class: 1.000000, Obj: 0.390169, No Obj: 0.004756, Avg Recall: 0.495327, count: 107 -Region Avg IOU: 0.487027, Class: 1.000000, Obj: 0.422642, No Obj: 0.004717, Avg Recall: 0.521739, count: 92 -5979: 0.467130, 0.454617 avg, 0.001000 rate, 17.484024 seconds, 1530624 images -Loaded: 4.687512 seconds -Region Avg IOU: 0.459081, Class: 1.000000, Obj: 0.360388, No Obj: 0.005462, Avg Recall: 0.523810, count: 126 -Region Avg IOU: 0.441420, Class: 1.000000, Obj: 0.395339, No Obj: 0.004563, Avg Recall: 0.486239, count: 109 -Region Avg IOU: 0.462978, Class: 1.000000, Obj: 0.337098, No Obj: 0.004876, Avg Recall: 0.473118, count: 93 -Region Avg IOU: 0.484138, Class: 1.000000, Obj: 0.358321, No Obj: 0.005769, Avg Recall: 0.517241, count: 116 -Region Avg IOU: 0.480662, Class: 1.000000, Obj: 0.372122, No Obj: 0.004587, Avg Recall: 0.539326, count: 89 -Region Avg IOU: 0.477922, Class: 1.000000, Obj: 0.349537, No Obj: 0.005173, Avg Recall: 0.537815, count: 119 -Region Avg IOU: 0.488697, Class: 1.000000, Obj: 0.343366, No Obj: 0.005612, Avg Recall: 0.559055, count: 127 -Region Avg IOU: 0.483126, Class: 1.000000, Obj: 0.395839, No Obj: 0.004207, Avg Recall: 0.510204, count: 98 -5980: 0.530397, 0.462195 avg, 0.001000 rate, 15.383102 seconds, 1530880 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000065 seconds -Region Avg IOU: 0.642325, Class: 1.000000, Obj: 0.465475, No Obj: 0.003458, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.638528, Class: 1.000000, Obj: 0.501074, No Obj: 0.003244, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.636637, Class: 1.000000, Obj: 0.475190, No Obj: 0.003400, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.642400, Class: 1.000000, Obj: 0.470390, No Obj: 0.004302, Avg Recall: 0.768657, count: 134 -Region Avg IOU: 0.614750, Class: 1.000000, Obj: 0.492500, No Obj: 0.003019, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.627240, Class: 1.000000, Obj: 0.483440, No Obj: 0.003253, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.620055, Class: 1.000000, Obj: 0.493449, No Obj: 0.003108, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.662653, Class: 1.000000, Obj: 0.459371, No Obj: 0.003722, Avg Recall: 0.811881, count: 101 -5981: 0.430849, 0.459060 avg, 0.001000 rate, 28.375198 seconds, 1531136 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.622792, Class: 1.000000, Obj: 0.461608, No Obj: 0.003634, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.648381, Class: 1.000000, Obj: 0.461415, No Obj: 0.003253, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.648554, Class: 1.000000, Obj: 0.429185, No Obj: 0.004186, Avg Recall: 0.821705, count: 129 -Region Avg IOU: 0.609935, Class: 1.000000, Obj: 0.420536, No Obj: 0.004302, Avg Recall: 0.689189, count: 148 -Region Avg IOU: 0.611264, Class: 1.000000, Obj: 0.483337, No Obj: 0.003349, Avg Recall: 0.756303, count: 119 -Region Avg IOU: 0.606358, Class: 1.000000, Obj: 0.483122, No Obj: 0.003256, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.620524, Class: 1.000000, Obj: 0.413067, No Obj: 0.003617, Avg Recall: 0.754098, count: 122 -Region Avg IOU: 0.632885, Class: 1.000000, Obj: 0.498217, No Obj: 0.002843, Avg Recall: 0.746988, count: 83 -5982: 0.444383, 0.457593 avg, 0.001000 rate, 27.638346 seconds, 1531392 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.644810, Class: 1.000000, Obj: 0.427115, No Obj: 0.003350, Avg Recall: 0.815126, count: 119 -Region Avg IOU: 0.682244, Class: 1.000000, Obj: 0.456746, No Obj: 0.003500, Avg Recall: 0.844037, count: 109 -Region Avg IOU: 0.640936, Class: 1.000000, Obj: 0.467349, No Obj: 0.003254, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.624979, Class: 1.000000, Obj: 0.433583, No Obj: 0.003023, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.626454, Class: 1.000000, Obj: 0.465272, No Obj: 0.003065, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.662727, Class: 1.000000, Obj: 0.452857, No Obj: 0.003277, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.654525, Class: 1.000000, Obj: 0.469080, No Obj: 0.002956, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.634482, Class: 1.000000, Obj: 0.406810, No Obj: 0.003805, Avg Recall: 0.783217, count: 143 -5983: 0.414789, 0.453312 avg, 0.001000 rate, 29.134798 seconds, 1531648 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.663937, Class: 1.000000, Obj: 0.380358, No Obj: 0.003094, Avg Recall: 0.806723, count: 119 -Region Avg IOU: 0.624753, Class: 1.000000, Obj: 0.445396, No Obj: 0.002980, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.650926, Class: 1.000000, Obj: 0.401487, No Obj: 0.003599, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.646072, Class: 1.000000, Obj: 0.429208, No Obj: 0.003336, Avg Recall: 0.833333, count: 132 -Region Avg IOU: 0.644088, Class: 1.000000, Obj: 0.467870, No Obj: 0.002780, Avg Recall: 0.744186, count: 86 -Region Avg IOU: 0.618012, Class: 1.000000, Obj: 0.445711, No Obj: 0.003470, Avg Recall: 0.767442, count: 129 -Region Avg IOU: 0.627821, Class: 1.000000, Obj: 0.389819, No Obj: 0.002758, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.609124, Class: 1.000000, Obj: 0.374325, No Obj: 0.003085, Avg Recall: 0.766129, count: 124 -5984: 0.469163, 0.454897 avg, 0.001000 rate, 29.111179 seconds, 1531904 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.627439, Class: 1.000000, Obj: 0.398500, No Obj: 0.002294, Avg Recall: 0.755556, count: 90 -Region Avg IOU: 0.629294, Class: 1.000000, Obj: 0.419156, No Obj: 0.002503, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.676366, Class: 1.000000, Obj: 0.437633, No Obj: 0.002712, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.603552, Class: 1.000000, Obj: 0.386607, No Obj: 0.002699, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.617096, Class: 1.000000, Obj: 0.376033, No Obj: 0.002396, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.617278, Class: 1.000000, Obj: 0.344663, No Obj: 0.003177, Avg Recall: 0.760563, count: 142 -Region Avg IOU: 0.653844, Class: 1.000000, Obj: 0.403801, No Obj: 0.003093, Avg Recall: 0.796992, count: 133 -Region Avg IOU: 0.626890, Class: 1.000000, Obj: 0.380253, No Obj: 0.003134, Avg Recall: 0.757576, count: 132 -5985: 0.448672, 0.454275 avg, 0.001000 rate, 29.426949 seconds, 1532160 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.669769, Class: 1.000000, Obj: 0.426429, No Obj: 0.002715, Avg Recall: 0.858586, count: 99 -Region Avg IOU: 0.631233, Class: 1.000000, Obj: 0.396483, No Obj: 0.002654, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.607622, Class: 1.000000, Obj: 0.344419, No Obj: 0.002673, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.608499, Class: 1.000000, Obj: 0.397670, No Obj: 0.002597, Avg Recall: 0.731707, count: 82 -Region Avg IOU: 0.627900, Class: 1.000000, Obj: 0.378616, No Obj: 0.003196, Avg Recall: 0.753521, count: 142 -Region Avg IOU: 0.634051, Class: 1.000000, Obj: 0.434635, No Obj: 0.002309, Avg Recall: 0.771084, count: 83 -Region Avg IOU: 0.650235, Class: 1.000000, Obj: 0.421381, No Obj: 0.002805, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.636825, Class: 1.000000, Obj: 0.419118, No Obj: 0.002777, Avg Recall: 0.783019, count: 106 -5986: 0.404983, 0.449346 avg, 0.001000 rate, 27.662394 seconds, 1532416 images -Loaded: 0.806028 seconds -Region Avg IOU: 0.610435, Class: 1.000000, Obj: 0.358475, No Obj: 0.002449, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.567520, Class: 1.000000, Obj: 0.305482, No Obj: 0.002206, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.644287, Class: 1.000000, Obj: 0.423267, No Obj: 0.002794, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.649012, Class: 1.000000, Obj: 0.382568, No Obj: 0.002483, Avg Recall: 0.823529, count: 85 -Region Avg IOU: 0.638079, Class: 1.000000, Obj: 0.396591, No Obj: 0.002976, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.682422, Class: 1.000000, Obj: 0.389280, No Obj: 0.002844, Avg Recall: 0.851852, count: 108 -Region Avg IOU: 0.628881, Class: 1.000000, Obj: 0.392699, No Obj: 0.003014, Avg Recall: 0.761194, count: 134 -Region Avg IOU: 0.648281, Class: 1.000000, Obj: 0.394955, No Obj: 0.002335, Avg Recall: 0.846154, count: 91 -5987: 0.412759, 0.445687 avg, 0.001000 rate, 29.181391 seconds, 1532672 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.629650, Class: 1.000000, Obj: 0.421245, No Obj: 0.002615, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.670078, Class: 1.000000, Obj: 0.449183, No Obj: 0.002266, Avg Recall: 0.822785, count: 79 -Region Avg IOU: 0.625914, Class: 1.000000, Obj: 0.388733, No Obj: 0.002510, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.644207, Class: 1.000000, Obj: 0.389164, No Obj: 0.002412, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.602387, Class: 1.000000, Obj: 0.354261, No Obj: 0.003003, Avg Recall: 0.706349, count: 126 -Region Avg IOU: 0.642113, Class: 1.000000, Obj: 0.396095, No Obj: 0.002694, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.640064, Class: 1.000000, Obj: 0.386134, No Obj: 0.002464, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.655420, Class: 1.000000, Obj: 0.423508, No Obj: 0.002513, Avg Recall: 0.760870, count: 92 -5988: 0.426843, 0.443803 avg, 0.001000 rate, 28.234936 seconds, 1532928 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.661643, Class: 1.000000, Obj: 0.387335, No Obj: 0.002690, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.591746, Class: 1.000000, Obj: 0.347702, No Obj: 0.002692, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.601139, Class: 1.000000, Obj: 0.373141, No Obj: 0.002814, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.665972, Class: 1.000000, Obj: 0.426260, No Obj: 0.002498, Avg Recall: 0.813187, count: 91 -Region Avg IOU: 0.647257, Class: 1.000000, Obj: 0.394714, No Obj: 0.002619, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.620291, Class: 1.000000, Obj: 0.378576, No Obj: 0.002586, Avg Recall: 0.784000, count: 125 -Region Avg IOU: 0.628588, Class: 1.000000, Obj: 0.352693, No Obj: 0.002458, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.632443, Class: 1.000000, Obj: 0.361553, No Obj: 0.002877, Avg Recall: 0.747899, count: 119 -5989: 0.409545, 0.440377 avg, 0.001000 rate, 30.239513 seconds, 1533184 images -Loaded: 0.019402 seconds -Region Avg IOU: 0.641670, Class: 1.000000, Obj: 0.399447, No Obj: 0.002774, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.582037, Class: 1.000000, Obj: 0.391090, No Obj: 0.002691, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.667072, Class: 1.000000, Obj: 0.416125, No Obj: 0.002645, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.616498, Class: 1.000000, Obj: 0.343179, No Obj: 0.002821, Avg Recall: 0.775862, count: 116 -Region Avg IOU: 0.604605, Class: 1.000000, Obj: 0.371371, No Obj: 0.002428, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.627561, Class: 1.000000, Obj: 0.396196, No Obj: 0.003075, Avg Recall: 0.782609, count: 138 -Region Avg IOU: 0.646301, Class: 1.000000, Obj: 0.377134, No Obj: 0.002460, Avg Recall: 0.769231, count: 78 -Region Avg IOU: 0.676848, Class: 1.000000, Obj: 0.470077, No Obj: 0.002671, Avg Recall: 0.812500, count: 96 -5990: 0.392228, 0.435562 avg, 0.001000 rate, 28.829693 seconds, 1533440 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 13.387053 seconds -Region Avg IOU: 0.640542, Class: 1.000000, Obj: 0.393553, No Obj: 0.003211, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.660583, Class: 1.000000, Obj: 0.356538, No Obj: 0.003148, Avg Recall: 0.825243, count: 103 -Region Avg IOU: 0.618984, Class: 1.000000, Obj: 0.418937, No Obj: 0.002895, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.564059, Class: 1.000000, Obj: 0.368958, No Obj: 0.002653, Avg Recall: 0.669492, count: 118 -Region Avg IOU: 0.636118, Class: 1.000000, Obj: 0.396867, No Obj: 0.002983, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.644953, Class: 1.000000, Obj: 0.411377, No Obj: 0.002790, Avg Recall: 0.822430, count: 107 -Region Avg IOU: 0.665335, Class: 1.000000, Obj: 0.445300, No Obj: 0.002563, Avg Recall: 0.825688, count: 109 -Region Avg IOU: 0.624750, Class: 1.000000, Obj: 0.373474, No Obj: 0.002525, Avg Recall: 0.754545, count: 110 -5991: 0.440208, 0.436027 avg, 0.001000 rate, 27.586815 seconds, 1533696 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.610044, Class: 1.000000, Obj: 0.386589, No Obj: 0.002936, Avg Recall: 0.756303, count: 119 -Region Avg IOU: 0.646222, Class: 1.000000, Obj: 0.434553, No Obj: 0.002904, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.656277, Class: 1.000000, Obj: 0.426819, No Obj: 0.002840, Avg Recall: 0.800000, count: 95 -Region Avg IOU: 0.672677, Class: 1.000000, Obj: 0.418715, No Obj: 0.002557, Avg Recall: 0.817204, count: 93 -Region Avg IOU: 0.677974, Class: 1.000000, Obj: 0.410319, No Obj: 0.002728, Avg Recall: 0.826923, count: 104 -Region Avg IOU: 0.627146, Class: 1.000000, Obj: 0.411543, No Obj: 0.002747, Avg Recall: 0.744681, count: 94 -Region Avg IOU: 0.632908, Class: 1.000000, Obj: 0.379375, No Obj: 0.003324, Avg Recall: 0.764706, count: 136 -Region Avg IOU: 0.596794, Class: 1.000000, Obj: 0.358471, No Obj: 0.002581, Avg Recall: 0.694737, count: 95 -5992: 0.457515, 0.438175 avg, 0.001000 rate, 28.534960 seconds, 1533952 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.645020, Class: 1.000000, Obj: 0.478886, No Obj: 0.002764, Avg Recall: 0.775281, count: 89 -Region Avg IOU: 0.641474, Class: 1.000000, Obj: 0.391399, No Obj: 0.002510, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.638324, Class: 1.000000, Obj: 0.400860, No Obj: 0.003771, Avg Recall: 0.783582, count: 134 -Region Avg IOU: 0.606953, Class: 1.000000, Obj: 0.423323, No Obj: 0.002765, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.594191, Class: 1.000000, Obj: 0.404058, No Obj: 0.002572, Avg Recall: 0.722222, count: 90 -Region Avg IOU: 0.618602, Class: 1.000000, Obj: 0.424900, No Obj: 0.002631, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.625985, Class: 1.000000, Obj: 0.459218, No Obj: 0.002873, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.660885, Class: 1.000000, Obj: 0.473631, No Obj: 0.002751, Avg Recall: 0.825000, count: 80 -5993: 0.376390, 0.431997 avg, 0.001000 rate, 28.212336 seconds, 1534208 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.646391, Class: 1.000000, Obj: 0.469981, No Obj: 0.002731, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.619451, Class: 1.000000, Obj: 0.393346, No Obj: 0.002828, Avg Recall: 0.725275, count: 91 -Region Avg IOU: 0.661034, Class: 1.000000, Obj: 0.419783, No Obj: 0.002916, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.644039, Class: 1.000000, Obj: 0.416189, No Obj: 0.002771, Avg Recall: 0.755556, count: 90 -Region Avg IOU: 0.616664, Class: 1.000000, Obj: 0.370058, No Obj: 0.003927, Avg Recall: 0.723684, count: 152 -Region Avg IOU: 0.639049, Class: 1.000000, Obj: 0.370779, No Obj: 0.003064, Avg Recall: 0.793388, count: 121 -Region Avg IOU: 0.656413, Class: 1.000000, Obj: 0.395378, No Obj: 0.003599, Avg Recall: 0.827068, count: 133 -Region Avg IOU: 0.657549, Class: 1.000000, Obj: 0.446178, No Obj: 0.003210, Avg Recall: 0.832061, count: 131 -5994: 0.481554, 0.436953 avg, 0.001000 rate, 28.708893 seconds, 1534464 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.640245, Class: 1.000000, Obj: 0.432000, No Obj: 0.003211, Avg Recall: 0.785124, count: 121 -Region Avg IOU: 0.636694, Class: 1.000000, Obj: 0.427973, No Obj: 0.003244, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.659637, Class: 1.000000, Obj: 0.408066, No Obj: 0.002957, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.688457, Class: 1.000000, Obj: 0.436266, No Obj: 0.002941, Avg Recall: 0.870968, count: 93 -Region Avg IOU: 0.626299, Class: 1.000000, Obj: 0.411848, No Obj: 0.003030, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.610322, Class: 1.000000, Obj: 0.375557, No Obj: 0.003358, Avg Recall: 0.707692, count: 130 -Region Avg IOU: 0.594667, Class: 1.000000, Obj: 0.401442, No Obj: 0.002690, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.590428, Class: 1.000000, Obj: 0.392449, No Obj: 0.002694, Avg Recall: 0.747253, count: 91 -5995: 0.421433, 0.435401 avg, 0.001000 rate, 28.239336 seconds, 1534720 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.657970, Class: 1.000000, Obj: 0.454405, No Obj: 0.002922, Avg Recall: 0.804598, count: 87 -Region Avg IOU: 0.671111, Class: 1.000000, Obj: 0.468382, No Obj: 0.002832, Avg Recall: 0.797619, count: 84 -Region Avg IOU: 0.625693, Class: 1.000000, Obj: 0.410364, No Obj: 0.004008, Avg Recall: 0.758242, count: 182 -Region Avg IOU: 0.617590, Class: 1.000000, Obj: 0.453186, No Obj: 0.002914, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.595737, Class: 1.000000, Obj: 0.418808, No Obj: 0.003038, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.645019, Class: 1.000000, Obj: 0.405576, No Obj: 0.003162, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.653010, Class: 1.000000, Obj: 0.457155, No Obj: 0.003438, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.619443, Class: 1.000000, Obj: 0.413686, No Obj: 0.002792, Avg Recall: 0.795699, count: 93 -5996: 0.420854, 0.433946 avg, 0.001000 rate, 28.486071 seconds, 1534976 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.667868, Class: 1.000000, Obj: 0.477094, No Obj: 0.002883, Avg Recall: 0.814433, count: 97 -Region Avg IOU: 0.658955, Class: 1.000000, Obj: 0.487862, No Obj: 0.003135, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.656698, Class: 1.000000, Obj: 0.447687, No Obj: 0.003762, Avg Recall: 0.804688, count: 128 -Region Avg IOU: 0.618479, Class: 1.000000, Obj: 0.383773, No Obj: 0.003058, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.660607, Class: 1.000000, Obj: 0.453310, No Obj: 0.002939, Avg Recall: 0.777778, count: 90 -Region Avg IOU: 0.627685, Class: 1.000000, Obj: 0.400256, No Obj: 0.003100, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.643858, Class: 1.000000, Obj: 0.405056, No Obj: 0.003085, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.642932, Class: 1.000000, Obj: 0.417567, No Obj: 0.003433, Avg Recall: 0.780303, count: 132 -5997: 0.431135, 0.433665 avg, 0.001000 rate, 28.604664 seconds, 1535232 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.662833, Class: 1.000000, Obj: 0.437826, No Obj: 0.002981, Avg Recall: 0.853211, count: 109 -Region Avg IOU: 0.631735, Class: 1.000000, Obj: 0.489473, No Obj: 0.002910, Avg Recall: 0.779070, count: 86 -Region Avg IOU: 0.661822, Class: 1.000000, Obj: 0.490555, No Obj: 0.003298, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.631869, Class: 1.000000, Obj: 0.419270, No Obj: 0.003018, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.607356, Class: 1.000000, Obj: 0.385978, No Obj: 0.003107, Avg Recall: 0.715447, count: 123 -Region Avg IOU: 0.632991, Class: 1.000000, Obj: 0.421109, No Obj: 0.003468, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.652239, Class: 1.000000, Obj: 0.450695, No Obj: 0.003290, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.649868, Class: 1.000000, Obj: 0.431866, No Obj: 0.003106, Avg Recall: 0.784483, count: 116 -5998: 0.400883, 0.430387 avg, 0.001000 rate, 28.582291 seconds, 1535488 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.583923, Class: 1.000000, Obj: 0.375519, No Obj: 0.002986, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.5Saving weights to ./person9300/backup/tyvp_93_9c512_6000.weights -98883, Class: 1.000000, Obj: 0.407871, No Obj: 0.002525, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.623694, Class: 1.000000, Obj: 0.439544, No Obj: 0.003399, Avg Recall: 0.755556, count: 135 -Region Avg IOU: 0.611146, Class: 1.000000, Obj: 0.430186, No Obj: 0.002686, Avg Recall: 0.759036, count: 83 -Region Avg IOU: 0.599953, Class: 1.000000, Obj: 0.406794, No Obj: 0.003017, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.624304, Class: 1.000000, Obj: 0.405773, No Obj: 0.003105, Avg Recall: 0.775000, count: 120 -Region Avg IOU: 0.645861, Class: 1.000000, Obj: 0.416763, No Obj: 0.003072, Avg Recall: 0.744681, count: 94 -Region Avg IOU: 0.631603, Class: 1.000000, Obj: 0.411756, No Obj: 0.002672, Avg Recall: 0.794118, count: 102 -5999: 0.427203, 0.430068 avg, 0.001000 rate, 28.325069 seconds, 1535744 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.630475, Class: 1.000000, Obj: 0.403669, No Obj: 0.003135, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.609143, Class: 1.000000, Obj: 0.382426, No Obj: 0.002849, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.684677, Class: 1.000000, Obj: 0.468220, No Obj: 0.003196, Avg Recall: 0.862745, count: 102 -Region Avg IOU: 0.636586, Class: 1.000000, Obj: 0.485733, No Obj: 0.002802, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.624756, Class: 1.000000, Obj: 0.450366, No Obj: 0.002896, Avg Recall: 0.750000, count: 88 -Region Avg IOU: 0.634825, Class: 1.000000, Obj: 0.439251, No Obj: 0.003342, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.632511, Class: 1.000000, Obj: 0.428288, No Obj: 0.002981, Avg Recall: 0.826923, count: 104 -Region Avg IOU: 0.565654, Class: 1.000000, Obj: 0.375127, No Obj: 0.002530, Avg Recall: 0.605263, count: 76 -6000: 0.423568, 0.429418 avg, 0.001000 rate, 28.731483 seconds, 1536000 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 15.710239 seconds -Region Avg IOU: 0.611201, Class: 1.000000, Obj: 0.430122, No Obj: 0.003183, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.661901, Class: 1.000000, Obj: 0.442240, No Obj: 0.003346, Avg Recall: 0.852459, count: 122 -Region Avg IOU: 0.651415, Class: 1.000000, Obj: 0.439831, No Obj: 0.002630, Avg Recall: 0.813953, count: 86 -Region Avg IOU: 0.615744, Class: 1.000000, Obj: 0.391559, No Obj: 0.002167, Avg Recall: 0.769231, count: 65 -Region Avg IOU: 0.613115, Class: 1.000000, Obj: 0.413402, No Obj: 0.003015, Avg Recall: 0.719697, count: 132 -Region Avg IOU: 0.615218, Class: 1.000000, Obj: 0.406504, No Obj: 0.003031, Avg Recall: 0.728000, count: 125 -Region Avg IOU: 0.651190, Class: 1.000000, Obj: 0.407027, No Obj: 0.002798, Avg Recall: 0.808696, count: 115 -Region Avg IOU: 0.584502, Class: 1.000000, Obj: 0.365738, No Obj: 0.002813, Avg Recall: 0.711712, count: 111 -6001: 0.437001, 0.430177 avg, 0.001000 rate, 28.584372 seconds, 1536256 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.647418, Class: 1.000000, Obj: 0.451274, No Obj: 0.003142, Avg Recall: 0.771654, count: 127 -Region Avg IOU: 0.620647, Class: 1.000000, Obj: 0.417259, No Obj: 0.002444, Avg Recall: 0.753086, count: 81 -Region Avg IOU: 0.670855, Class: 1.000000, Obj: 0.467125, No Obj: 0.002818, Avg Recall: 0.830000, count: 100 -Region Avg IOU: 0.572546, Class: 1.000000, Obj: 0.384307, No Obj: 0.002965, Avg Recall: 0.658333, count: 120 -Region Avg IOU: 0.629790, Class: 1.000000, Obj: 0.411476, No Obj: 0.002977, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.574069, Class: 1.000000, Obj: 0.398169, No Obj: 0.002702, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.666132, Class: 1.000000, Obj: 0.450955, No Obj: 0.003028, Avg Recall: 0.827273, count: 110 -Region Avg IOU: 0.645674, Class: 1.000000, Obj: 0.463022, No Obj: 0.002664, Avg Recall: 0.807229, count: 83 -6002: 0.393769, 0.426536 avg, 0.001000 rate, 29.746872 seconds, 1536512 images -Loaded: 0.028835 seconds -Region Avg IOU: 0.647588, Class: 1.000000, Obj: 0.406754, No Obj: 0.003378, Avg Recall: 0.791304, count: 115 -Region Avg IOU: 0.653337, Class: 1.000000, Obj: 0.390211, No Obj: 0.003394, Avg Recall: 0.796460, count: 113 -Region Avg IOU: 0.647422, Class: 1.000000, Obj: 0.432188, No Obj: 0.002724, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.623360, Class: 1.000000, Obj: 0.387385, No Obj: 0.003347, Avg Recall: 0.731884, count: 138 -Region Avg IOU: 0.619585, Class: 1.000000, Obj: 0.428567, No Obj: 0.002310, Avg Recall: 0.730769, count: 78 -Region Avg IOU: 0.628197, Class: 1.000000, Obj: 0.377389, No Obj: 0.002889, Avg Recall: 0.758621, count: 87 -Region Avg IOU: 0.659343, Class: 1.000000, Obj: 0.434775, No Obj: 0.002869, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.658485, Class: 1.000000, Obj: 0.395084, No Obj: 0.003004, Avg Recall: 0.805085, count: 118 -6003: 0.410482, 0.424930 avg, 0.001000 rate, 28.547447 seconds, 1536768 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.639122, Class: 1.000000, Obj: 0.418891, No Obj: 0.002805, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.645871, Class: 1.000000, Obj: 0.378799, No Obj: 0.002829, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.648875, Class: 1.000000, Obj: 0.393488, No Obj: 0.003065, Avg Recall: 0.796610, count: 118 -Region Avg IOU: 0.655050, Class: 1.000000, Obj: 0.417576, No Obj: 0.002931, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.630807, Class: 1.000000, Obj: 0.392665, No Obj: 0.002326, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.633354, Class: 1.000000, Obj: 0.410015, No Obj: 0.002960, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.636447, Class: 1.000000, Obj: 0.409017, No Obj: 0.002523, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.648069, Class: 1.000000, Obj: 0.374092, No Obj: 0.002677, Avg Recall: 0.803738, count: 107 -6004: 0.400959, 0.422533 avg, 0.001000 rate, 28.741596 seconds, 1537024 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.609776, Class: 1.000000, Obj: 0.378248, No Obj: 0.002940, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.577343, Class: 1.000000, Obj: 0.365988, No Obj: 0.002221, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.646328, Class: 1.000000, Obj: 0.373148, No Obj: 0.002740, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.659786, Class: 1.000000, Obj: 0.385420, No Obj: 0.002796, Avg Recall: 0.791304, count: 115 -Region Avg IOU: 0.660705, Class: 1.000000, Obj: 0.388390, No Obj: 0.003154, Avg Recall: 0.815126, count: 119 -Region Avg IOU: 0.592277, Class: 1.000000, Obj: 0.366477, No Obj: 0.002911, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.645949, Class: 1.000000, Obj: 0.422835, No Obj: 0.002531, Avg Recall: 0.788889, count: 90 -Region Avg IOU: 0.649162, Class: 1.000000, Obj: 0.397212, No Obj: 0.002684, Avg Recall: 0.812500, count: 96 -6005: 0.460284, 0.426308 avg, 0.001000 rate, 30.019009 seconds, 1537280 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.657201, Class: 1.000000, Obj: 0.369308, No Obj: 0.002882, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.682122, Class: 1.000000, Obj: 0.440840, No Obj: 0.002479, Avg Recall: 0.825000, count: 80 -Region Avg IOU: 0.631474, Class: 1.000000, Obj: 0.352739, No Obj: 0.002676, Avg Recall: 0.750000, count: 88 -Region Avg IOU: 0.647269, Class: 1.000000, Obj: 0.390721, No Obj: 0.002768, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.615264, Class: 1.000000, Obj: 0.368155, No Obj: 0.002820, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.620785, Class: 1.000000, Obj: 0.393394, No Obj: 0.002803, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.655352, Class: 1.000000, Obj: 0.409271, No Obj: 0.003061, Avg Recall: 0.842105, count: 114 -Region Avg IOU: 0.658518, Class: 1.000000, Obj: 0.408131, No Obj: 0.002664, Avg Recall: 0.813953, count: 86 -6006: 0.393552, 0.423033 avg, 0.001000 rate, 28.250160 seconds, 1537536 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.621389, Class: 1.000000, Obj: 0.380888, No Obj: 0.002514, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.677057, Class: 1.000000, Obj: 0.409804, No Obj: 0.003066, Avg Recall: 0.864865, count: 111 -Region Avg IOU: 0.629589, Class: 1.000000, Obj: 0.352676, No Obj: 0.002326, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.608663, Class: 1.000000, Obj: 0.376655, No Obj: 0.002892, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.601858, Class: 1.000000, Obj: 0.355136, No Obj: 0.003278, Avg Recall: 0.710526, count: 152 -Region Avg IOU: 0.626720, Class: 1.000000, Obj: 0.454758, No Obj: 0.002474, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.631264, Class: 1.000000, Obj: 0.468838, No Obj: 0.002803, Avg Recall: 0.739583, count: 96 -Region Avg IOU: 0.651366, Class: 1.000000, Obj: 0.404518, No Obj: 0.002225, Avg Recall: 0.804598, count: 87 -6007: 0.407620, 0.421491 avg, 0.001000 rate, 29.032553 seconds, 1537792 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.657926, Class: 1.000000, Obj: 0.395967, No Obj: 0.002998, Avg Recall: 0.794643, count: 112 -Region Avg IOU: 0.631637, Class: 1.000000, Obj: 0.418835, No Obj: 0.003027, Avg Recall: 0.780488, count: 123 -Region Avg IOU: 0.640174, Class: 1.000000, Obj: 0.353278, No Obj: 0.002859, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.634201, Class: 1.000000, Obj: 0.421419, No Obj: 0.002446, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.597825, Class: 1.000000, Obj: 0.403230, No Obj: 0.002643, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.653386, Class: 1.000000, Obj: 0.389909, No Obj: 0.002951, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.609948, Class: 1.000000, Obj: 0.432976, No Obj: 0.002782, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.622287, Class: 1.000000, Obj: 0.439418, No Obj: 0.002762, Avg Recall: 0.757576, count: 99 -6008: 0.416538, 0.420996 avg, 0.001000 rate, 28.052605 seconds, 1538048 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.616921, Class: 1.000000, Obj: 0.376554, No Obj: 0.002628, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.624686, Class: 1.000000, Obj: 0.398624, No Obj: 0.002688, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.628899, Class: 1.000000, Obj: 0.419461, No Obj: 0.003032, Avg Recall: 0.756098, count: 123 -Region Avg IOU: 0.609625, Class: 1.000000, Obj: 0.395884, No Obj: 0.002814, Avg Recall: 0.744681, count: 94 -Region Avg IOU: 0.590169, Class: 1.000000, Obj: 0.377969, No Obj: 0.002777, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.604063, Class: 1.000000, Obj: 0.410050, No Obj: 0.003181, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.612731, Class: 1.000000, Obj: 0.418722, No Obj: 0.002692, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.609220, Class: 1.000000, Obj: 0.406843, No Obj: 0.002849, Avg Recall: 0.755102, count: 98 -6009: 0.413762, 0.420273 avg, 0.001000 rate, 27.597876 seconds, 1538304 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.641843, Class: 1.000000, Obj: 0.424983, No Obj: 0.003446, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.665537, Class: 1.000000, Obj: 0.453062, No Obj: 0.002813, Avg Recall: 0.826087, count: 92 -Region Avg IOU: 0.604746, Class: 1.000000, Obj: 0.387363, No Obj: 0.003116, Avg Recall: 0.746032, count: 126 -Region Avg IOU: 0.660939, Class: 1.000000, Obj: 0.439316, No Obj: 0.003255, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.627138, Class: 1.000000, Obj: 0.405468, No Obj: 0.003091, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.654478, Class: 1.000000, Obj: 0.468785, No Obj: 0.003161, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.654420, Class: 1.000000, Obj: 0.453327, No Obj: 0.002885, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.611364, Class: 1.000000, Obj: 0.461293, No Obj: 0.002346, Avg Recall: 0.717647, count: 85 -6010: 0.440022, 0.422248 avg, 0.001000 rate, 28.864967 seconds, 1538560 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 14.945664 seconds -Region Avg IOU: 0.641714, Class: 1.000000, Obj: 0.457247, No Obj: 0.003596, Avg Recall: 0.805085, count: 118 -Region Avg IOU: 0.604324, Class: 1.000000, Obj: 0.412276, No Obj: 0.003267, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.627256, Class: 1.000000, Obj: 0.441721, No Obj: 0.003049, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.591789, Class: 1.000000, Obj: 0.421025, No Obj: 0.003036, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.617996, Class: 1.000000, Obj: 0.382814, No Obj: 0.003914, Avg Recall: 0.773973, count: 146 -Region Avg IOU: 0.612435, Class: 1.000000, Obj: 0.396035, No Obj: 0.003398, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.608539, Class: 1.000000, Obj: 0.436546, No Obj: 0.003012, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.653094, Class: 1.000000, Obj: 0.403393, No Obj: 0.003483, Avg Recall: 0.776923, count: 130 -6011: 0.443968, 0.424420 avg, 0.001000 rate, 29.306385 seconds, 1538816 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.614774, Class: 1.000000, Obj: 0.430181, No Obj: 0.003409, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.637021, Class: 1.000000, Obj: 0.424672, No Obj: 0.003481, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.627132, Class: 1.000000, Obj: 0.457933, No Obj: 0.003339, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.650163, Class: 1.000000, Obj: 0.456068, No Obj: 0.003348, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.625402, Class: 1.000000, Obj: 0.396816, No Obj: 0.003198, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.656709, Class: 1.000000, Obj: 0.463368, No Obj: 0.003153, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.613878, Class: 1.000000, Obj: 0.459692, No Obj: 0.003027, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.646707, Class: 1.000000, Obj: 0.445372, No Obj: 0.002856, Avg Recall: 0.781609, count: 87 -6012: 0.406563, 0.422634 avg, 0.001000 rate, 25.137957 seconds, 1539072 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.653547, Class: 1.000000, Obj: 0.464101, No Obj: 0.003253, Avg Recall: 0.825243, count: 103 -Region Avg IOU: 0.614876, Class: 1.000000, Obj: 0.423555, No Obj: 0.003345, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.638224, Class: 1.000000, Obj: 0.420537, No Obj: 0.003416, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.618059, Class: 1.000000, Obj: 0.437734, No Obj: 0.002815, Avg Recall: 0.800000, count: 65 -Region Avg IOU: 0.629925, Class: 1.000000, Obj: 0.449668, No Obj: 0.003074, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.614452, Class: 1.000000, Obj: 0.448377, No Obj: 0.003972, Avg Recall: 0.762590, count: 139 -Region Avg IOU: 0.588272, Class: 1.000000, Obj: 0.484772, No Obj: 0.003245, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.613665, Class: 1.000000, Obj: 0.404507, No Obj: 0.003067, Avg Recall: 0.765766, count: 111 -6013: 0.372377, 0.417608 avg, 0.001000 rate, 30.192076 seconds, 1539328 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.637802, Class: 1.000000, Obj: 0.419315, No Obj: 0.003088, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.600626, Class: 1.000000, Obj: 0.438011, No Obj: 0.003144, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.644314, Class: 1.000000, Obj: 0.429228, No Obj: 0.003314, Avg Recall: 0.777778, count: 81 -Region Avg IOU: 0.642273, Class: 1.000000, Obj: 0.429293, No Obj: 0.003094, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.629033, Class: 1.000000, Obj: 0.462556, No Obj: 0.003259, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.615465, Class: 1.000000, Obj: 0.409933, No Obj: 0.003330, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.640018, Class: 1.000000, Obj: 0.402282, No Obj: 0.002720, Avg Recall: 0.753623, count: 69 -Region Avg IOU: 0.621276, Class: 1.000000, Obj: 0.436484, No Obj: 0.003353, Avg Recall: 0.761905, count: 126 -6014: 0.377099, 0.413557 avg, 0.001000 rate, 27.839247 seconds, 1539584 images -Loaded: 0.072783 seconds -Region Avg IOU: 0.614286, Class: 1.000000, Obj: 0.405871, No Obj: 0.002585, Avg Recall: 0.732558, count: 86 -Region Avg IOU: 0.595611, Class: 1.000000, Obj: 0.423249, No Obj: 0.003334, Avg Recall: 0.704545, count: 132 -Region Avg IOU: 0.682319, Class: 1.000000, Obj: 0.448022, No Obj: 0.003317, Avg Recall: 0.855769, count: 104 -Region Avg IOU: 0.602876, Class: 1.000000, Obj: 0.423474, No Obj: 0.003048, Avg Recall: 0.708661, count: 127 -Region Avg IOU: 0.610377, Class: 1.000000, Obj: 0.442398, No Obj: 0.003158, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.636885, Class: 1.000000, Obj: 0.403285, No Obj: 0.002915, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.675984, Class: 1.000000, Obj: 0.445801, No Obj: 0.003228, Avg Recall: 0.848485, count: 99 -Region Avg IOU: 0.639149, Class: 1.000000, Obj: 0.476586, No Obj: 0.002897, Avg Recall: 0.753247, count: 77 -6015: 0.408609, 0.413062 avg, 0.001000 rate, 27.839323 seconds, 1539840 images -Loaded: 0.604677 seconds -Region Avg IOU: 0.575471, Class: 1.000000, Obj: 0.368587, No Obj: 0.003075, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.665630, Class: 1.000000, Obj: 0.476550, No Obj: 0.002606, Avg Recall: 0.792208, count: 77 -Region Avg IOU: 0.634499, Class: 1.000000, Obj: 0.417086, No Obj: 0.002760, Avg Recall: 0.793103, count: 87 -Region Avg IOU: 0.653233, Class: 1.000000, Obj: 0.434847, No Obj: 0.003705, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.566142, Class: 1.000000, Obj: 0.380681, No Obj: 0.003560, Avg Recall: 0.666667, count: 117 -Region Avg IOU: 0.615919, Class: 1.000000, Obj: 0.409142, No Obj: 0.003286, Avg Recall: 0.762590, count: 139 -Region Avg IOU: 0.623322, Class: 1.000000, Obj: 0.418112, No Obj: 0.003040, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.614593, Class: 1.000000, Obj: 0.403867, No Obj: 0.003472, Avg Recall: 0.768212, count: 151 -6016: 0.414522, 0.413208 avg, 0.001000 rate, 26.521505 seconds, 1540096 images -Loaded: 1.812662 seconds -Region Avg IOU: 0.612263, Class: 1.000000, Obj: 0.399627, No Obj: 0.002507, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.624141, Class: 1.000000, Obj: 0.439943, No Obj: 0.003637, Avg Recall: 0.764706, count: 136 -Region Avg IOU: 0.605585, Class: 1.000000, Obj: 0.356245, No Obj: 0.002812, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.603415, Class: 1.000000, Obj: 0.388388, No Obj: 0.003231, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.634420, Class: 1.000000, Obj: 0.429132, No Obj: 0.003139, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.653688, Class: 1.000000, Obj: 0.447164, No Obj: 0.003819, Avg Recall: 0.801324, count: 151 -Region Avg IOU: 0.573205, Class: 1.000000, Obj: 0.397873, No Obj: 0.003439, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.590309, Class: 1.000000, Obj: 0.405909, No Obj: 0.003418, Avg Recall: 0.708661, count: 127 -6017: 0.455488, 0.417436 avg, 0.001000 rate, 28.036160 seconds, 1540352 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.624665, Class: 1.000000, Obj: 0.369446, No Obj: 0.003223, Avg Recall: 0.769231, count: 130 -Region Avg IOU: 0.596435, Class: 1.000000, Obj: 0.387454, No Obj: 0.003183, Avg Recall: 0.677686, count: 121 -Region Avg IOU: 0.642923, Class: 1.000000, Obj: 0.409667, No Obj: 0.002824, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.660406, Class: 1.000000, Obj: 0.495680, No Obj: 0.002475, Avg Recall: 0.802198, count: 91 -Region Avg IOU: 0.652607, Class: 1.000000, Obj: 0.444278, No Obj: 0.002931, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.612594, Class: 1.000000, Obj: 0.399326, No Obj: 0.002824, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.663533, Class: 1.000000, Obj: 0.408114, No Obj: 0.003165, Avg Recall: 0.828571, count: 105 -Region Avg IOU: 0.639469, Class: 1.000000, Obj: 0.397031, No Obj: 0.003193, Avg Recall: 0.833333, count: 102 -6018: 0.426344, 0.418327 avg, 0.001000 rate, 27.014584 seconds, 1540608 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.628435, Class: 1.000000, Obj: 0.414231, No Obj: 0.002666, Avg Recall: 0.746988, count: 83 -Region Avg IOU: 0.579113, Class: 1.000000, Obj: 0.375920, No Obj: 0.002848, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.593020, Class: 1.000000, Obj: 0.399118, No Obj: 0.003127, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.617118, Class: 1.000000, Obj: 0.415801, No Obj: 0.002478, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.622086, Class: 1.000000, Obj: 0.383210, No Obj: 0.003524, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.594805, Class: 1.000000, Obj: 0.373746, No Obj: 0.002658, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.597698, Class: 1.000000, Obj: 0.394633, No Obj: 0.003240, Avg Recall: 0.687943, count: 141 -Region Avg IOU: 0.625867, Class: 1.000000, Obj: 0.388760, No Obj: 0.002791, Avg Recall: 0.737864, count: 103 -6019: 0.406981, 0.417193 avg, 0.001000 rate, 27.669151 seconds, 1540864 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.674143, Class: 1.000000, Obj: 0.406468, No Obj: 0.003095, Avg Recall: 0.845361, count: 97 -Region Avg IOU: 0.639267, Class: 1.000000, Obj: 0.390703, No Obj: 0.003035, Avg Recall: 0.801653, count: 121 -Region Avg IOU: 0.659160, Class: 1.000000, Obj: 0.425773, No Obj: 0.003504, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.567167, Class: 1.000000, Obj: 0.343661, No Obj: 0.002315, Avg Recall: 0.654321, count: 81 -Region Avg IOU: 0.606690, Class: 1.000000, Obj: 0.364299, No Obj: 0.002769, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.573817, Class: 1.000000, Obj: 0.381448, No Obj: 0.003275, Avg Recall: 0.666667, count: 129 -Region Avg IOU: 0.641856, Class: 1.000000, Obj: 0.443038, No Obj: 0.003345, Avg Recall: 0.789474, count: 133 -Region Avg IOU: 0.669829, Class: 1.000000, Obj: 0.417314, No Obj: 0.002333, Avg Recall: 0.826087, count: 69 -6020: 0.418721, 0.417345 avg, 0.001000 rate, 27.323410 seconds, 1541120 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000072 seconds -Region Avg IOU: 0.637667, Class: 1.000000, Obj: 0.411304, No Obj: 0.002510, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.666223, Class: 1.000000, Obj: 0.423366, No Obj: 0.002701, Avg Recall: 0.849558, count: 113 -Region Avg IOU: 0.684091, Class: 1.000000, Obj: 0.417867, No Obj: 0.002725, Avg Recall: 0.893204, count: 103 -Region Avg IOU: 0.645534, Class: 1.000000, Obj: 0.400269, No Obj: 0.002756, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.648710, Class: 1.000000, Obj: 0.388347, No Obj: 0.002639, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.660563, Class: 1.000000, Obj: 0.449255, No Obj: 0.002393, Avg Recall: 0.775000, count: 80 -Region Avg IOU: 0.669220, Class: 1.000000, Obj: 0.391560, No Obj: 0.002484, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.666087, Class: 1.000000, Obj: 0.404088, No Obj: 0.002464, Avg Recall: 0.845455, count: 110 -6021: 0.434688, 0.419080 avg, 0.001000 rate, 32.613407 seconds, 1541376 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.673477, Class: 1.000000, Obj: 0.469015, No Obj: 0.002380, Avg Recall: 0.830508, count: 118 -Region Avg IOU: 0.670064, Class: 1.000000, Obj: 0.432725, No Obj: 0.002781, Avg Recall: 0.831579, count: 95 -Region Avg IOU: 0.642386, Class: 1.000000, Obj: 0.400739, No Obj: 0.002833, Avg Recall: 0.773438, count: 128 -Region Avg IOU: 0.664464, Class: 1.000000, Obj: 0.384028, No Obj: 0.002776, Avg Recall: 0.832117, count: 137 -Region Avg IOU: 0.647556, Class: 1.000000, Obj: 0.390054, No Obj: 0.003059, Avg Recall: 0.798507, count: 134 -Region Avg IOU: 0.644515, Class: 1.000000, Obj: 0.381476, No Obj: 0.002686, Avg Recall: 0.778626, count: 131 -Region Avg IOU: 0.637869, Class: 1.000000, Obj: 0.422099, No Obj: 0.002418, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.646480, Class: 1.000000, Obj: 0.436308, No Obj: 0.002587, Avg Recall: 0.828571, count: 105 -6022: 0.464995, 0.423671 avg, 0.001000 rate, 32.425751 seconds, 1541632 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.673726, Class: 1.000000, Obj: 0.432064, No Obj: 0.002681, Avg Recall: 0.859813, count: 107 -Region Avg IOU: 0.667379, Class: 1.000000, Obj: 0.455263, No Obj: 0.002475, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.635950, Class: 1.000000, Obj: 0.393687, No Obj: 0.002803, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.634299, Class: 1.000000, Obj: 0.397446, No Obj: 0.002712, Avg Recall: 0.752137, count: 117 -Region Avg IOU: 0.689896, Class: 1.000000, Obj: 0.411874, No Obj: 0.002736, Avg Recall: 0.854167, count: 96 -Region Avg IOU: 0.662611, Class: 1.000000, Obj: 0.379622, No Obj: 0.003102, Avg Recall: 0.812950, count: 139 -Region Avg IOU: 0.615137, Class: 1.000000, Obj: 0.386011, No Obj: 0.002498, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.649661, Class: 1.000000, Obj: 0.378693, No Obj: 0.002432, Avg Recall: 0.838710, count: 93 -6023: 0.435322, 0.424836 avg, 0.001000 rate, 32.392998 seconds, 1541888 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.647511, Class: 1.000000, Obj: 0.381305, No Obj: 0.002550, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.662327, Class: 1.000000, Obj: 0.406348, No Obj: 0.002596, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.641283, Class: 1.000000, Obj: 0.368449, No Obj: 0.002833, Avg Recall: 0.798387, count: 124 -Region Avg IOU: 0.697578, Class: 1.000000, Obj: 0.414957, No Obj: 0.002249, Avg Recall: 0.909091, count: 66 -Region Avg IOU: 0.665147, Class: 1.000000, Obj: 0.360753, No Obj: 0.002963, Avg Recall: 0.823529, count: 136 -Region Avg IOU: 0.681603, Class: 1.000000, Obj: 0.429319, No Obj: 0.002805, Avg Recall: 0.860465, count: 129 -Region Avg IOU: 0.717717, Class: 1.000000, Obj: 0.437213, No Obj: 0.002289, Avg Recall: 0.897436, count: 117 -Region Avg IOU: 0.637586, Class: 1.000000, Obj: 0.386134, No Obj: 0.002055, Avg Recall: 0.800000, count: 70 -6024: 0.448160, 0.427169 avg, 0.001000 rate, 33.026466 seconds, 1542144 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.693157, Class: 1.000000, Obj: 0.369752, No Obj: 0.002499, Avg Recall: 0.837838, count: 111 -Region Avg IOU: 0.691663, Class: 1.000000, Obj: 0.452200, No Obj: 0.002430, Avg Recall: 0.827957, count: 93 -Region Avg IOU: 0.670377, Class: 1.000000, Obj: 0.419409, No Obj: 0.002658, Avg Recall: 0.842975, count: 121 -Region Avg IOU: 0.641095, Class: 1.000000, Obj: 0.389728, No Obj: 0.002243, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.662619, Class: 1.000000, Obj: 0.353857, No Obj: 0.002655, Avg Recall: 0.858268, count: 127 -Region Avg IOU: 0.680901, Class: 1.000000, Obj: 0.411415, No Obj: 0.002798, Avg Recall: 0.833333, count: 120 -Region Avg IOU: 0.693577, Class: 1.000000, Obj: 0.449644, No Obj: 0.002492, Avg Recall: 0.850000, count: 100 -Region Avg IOU: 0.661961, Class: 1.000000, Obj: 0.386951, No Obj: 0.002830, Avg Recall: 0.810811, count: 111 -6025: 0.473611, 0.431813 avg, 0.001000 rate, 32.479290 seconds, 1542400 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.624535, Class: 1.000000, Obj: 0.370620, No Obj: 0.002625, Avg Recall: 0.814159, count: 113 -Region Avg IOU: 0.654179, Class: 1.000000, Obj: 0.401657, No Obj: 0.002770, Avg Recall: 0.828571, count: 105 -Region Avg IOU: 0.646155, Class: 1.000000, Obj: 0.398982, No Obj: 0.002574, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.693762, Class: 1.000000, Obj: 0.416063, No Obj: 0.002274, Avg Recall: 0.860759, count: 79 -Region Avg IOU: 0.695413, Class: 1.000000, Obj: 0.366048, No Obj: 0.002453, Avg Recall: 0.895833, count: 96 -Region Avg IOU: 0.679645, Class: 1.000000, Obj: 0.443170, No Obj: 0.002487, Avg Recall: 0.836957, count: 92 -Region Avg IOU: 0.652027, Class: 1.000000, Obj: 0.402541, No Obj: 0.002437, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.663922, Class: 1.000000, Obj: 0.389555, No Obj: 0.002607, Avg Recall: 0.822581, count: 124 -6026: 0.433919, 0.432023 avg, 0.001000 rate, 32.417583 seconds, 1542656 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.662665, Class: 1.000000, Obj: 0.386894, No Obj: 0.002741, Avg Recall: 0.858696, count: 92 -Region Avg IOU: 0.640867, Class: 1.000000, Obj: 0.402193, No Obj: 0.002380, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.672799, Class: 1.000000, Obj: 0.378740, No Obj: 0.002557, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.677736, Class: 1.000000, Obj: 0.423456, No Obj: 0.002224, Avg Recall: 0.854167, count: 96 -Region Avg IOU: 0.645209, Class: 1.000000, Obj: 0.399927, No Obj: 0.002665, Avg Recall: 0.786260, count: 131 -Region Avg IOU: 0.649098, Class: 1.000000, Obj: 0.418266, No Obj: 0.002478, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.665837, Class: 1.000000, Obj: 0.432530, No Obj: 0.002351, Avg Recall: 0.830189, count: 106 -Region Avg IOU: 0.676099, Class: 1.000000, Obj: 0.406699, No Obj: 0.002399, Avg Recall: 0.821782, count: 101 -6027: 0.425106, 0.431332 avg, 0.001000 rate, 33.448032 seconds, 1542912 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.660616, Class: 1.000000, Obj: 0.458026, No Obj: 0.002645, Avg Recall: 0.811966, count: 117 -Region Avg IOU: 0.665055, Class: 1.000000, Obj: 0.400801, No Obj: 0.002813, Avg Recall: 0.865546, count: 119 -Region Avg IOU: 0.622908, Class: 1.000000, Obj: 0.362246, No Obj: 0.002948, Avg Recall: 0.782258, count: 124 -Region Avg IOU: 0.659069, Class: 1.000000, Obj: 0.426319, No Obj: 0.002605, Avg Recall: 0.839286, count: 112 -Region Avg IOU: 0.626795, Class: 1.000000, Obj: 0.375280, No Obj: 0.002449, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.644752, Class: 1.000000, Obj: 0.402008, No Obj: 0.002667, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.645989, Class: 1.000000, Obj: 0.384676, No Obj: 0.002686, Avg Recall: 0.815789, count: 114 -Region Avg IOU: 0.701925, Class: 1.000000, Obj: 0.464880, No Obj: 0.003031, Avg Recall: 0.885965, count: 114 -6028: 0.461934, 0.434392 avg, 0.001000 rate, 32.589825 seconds, 1543168 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.666428, Class: 1.000000, Obj: 0.395987, No Obj: 0.002366, Avg Recall: 0.814815, count: 108 -Region Avg IOU: 0.645970, Class: 1.000000, Obj: 0.339955, No Obj: 0.002471, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.658561, Class: 1.000000, Obj: 0.404138, No Obj: 0.003101, Avg Recall: 0.806452, count: 124 -Region Avg IOU: 0.691164, Class: 1.000000, Obj: 0.391731, No Obj: 0.002387, Avg Recall: 0.893617, count: 94 -Region Avg IOU: 0.689368, Class: 1.000000, Obj: 0.415867, No Obj: 0.002341, Avg Recall: 0.827586, count: 87 -Region Avg IOU: 0.691621, Class: 1.000000, Obj: 0.395633, No Obj: 0.002472, Avg Recall: 0.866667, count: 105 -Region Avg IOU: 0.681513, Class: 1.000000, Obj: 0.445802, No Obj: 0.002711, Avg Recall: 0.871795, count: 117 -Region Avg IOU: 0.629625, Class: 1.000000, Obj: 0.418963, No Obj: 0.002660, Avg Recall: 0.776000, count: 125 -6029: 0.455075, 0.436460 avg, 0.001000 rate, 32.152401 seconds, 1543424 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.664271, Class: 1.000000, Obj: 0.431149, No Obj: 0.002846, Avg Recall: 0.830189, count: 106 -Region Avg IOU: 0.685112, Class: 1.000000, Obj: 0.428754, No Obj: 0.002406, Avg Recall: 0.817073, count: 82 -Region Avg IOU: 0.662262, Class: 1.000000, Obj: 0.455307, No Obj: 0.002288, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.693430, Class: 1.000000, Obj: 0.445553, No Obj: 0.002367, Avg Recall: 0.880435, count: 92 -Region Avg IOU: 0.694977, Class: 1.000000, Obj: 0.412634, No Obj: 0.002535, Avg Recall: 0.840000, count: 100 -Region Avg IOU: 0.648279, Class: 1.000000, Obj: 0.460527, No Obj: 0.002262, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.626079, Class: 1.000000, Obj: 0.410788, No Obj: 0.002773, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.682912, Class: 1.000000, Obj: 0.460900, No Obj: 0.002353, Avg Recall: 0.865979, count: 97 -6030: 0.408211, 0.433635 avg, 0.001000 rate, 31.236376 seconds, 1543680 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.396083 seconds -Region Avg IOU: 0.503184, Class: 1.000000, Obj: 0.299582, No Obj: 0.004006, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.493106, Class: 1.000000, Obj: 0.277361, No Obj: 0.003372, Avg Recall: 0.542169, count: 83 -Region Avg IOU: 0.429760, Class: 1.000000, Obj: 0.257096, No Obj: 0.003743, Avg Recall: 0.478633, count: 117 -Region Avg IOU: 0.462831, Class: 1.000000, Obj: 0.281441, No Obj: 0.003840, Avg Recall: 0.495495, count: 111 -Region Avg IOU: 0.512726, Class: 1.000000, Obj: 0.310104, No Obj: 0.003714, Avg Recall: 0.586207, count: 116 -Region Avg IOU: 0.496049, Class: 1.000000, Obj: 0.287781, No Obj: 0.003939, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.520809, Class: 1.000000, Obj: 0.318353, No Obj: 0.003942, Avg Recall: 0.606742, count: 89 -Region Avg IOU: 0.517135, Class: 1.000000, Obj: 0.329089, No Obj: 0.003467, Avg Recall: 0.604938, count: 81 -6031: 0.454257, 0.435698 avg, 0.001000 rate, 16.917189 seconds, 1543936 images -Loaded: 3.986729 seconds -Region Avg IOU: 0.553170, Class: 1.000000, Obj: 0.349325, No Obj: 0.004404, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.515722, Class: 1.000000, Obj: 0.306920, No Obj: 0.004172, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.441511, Class: 1.000000, Obj: 0.271825, No Obj: 0.003726, Avg Recall: 0.454545, count: 99 -Region Avg IOU: 0.515795, Class: 1.000000, Obj: 0.298297, No Obj: 0.004299, Avg Recall: 0.567568, count: 111 -Region Avg IOU: 0.537064, Class: 1.000000, Obj: 0.323793, No Obj: 0.004183, Avg Recall: 0.590361, count: 83 -Region Avg IOU: 0.483222, Class: 1.000000, Obj: 0.309843, No Obj: 0.003770, Avg Recall: 0.521277, count: 94 -Region Avg IOU: 0.520763, Class: 1.000000, Obj: 0.307818, No Obj: 0.004134, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.496193, Class: 1.000000, Obj: 0.311376, No Obj: 0.004300, Avg Recall: 0.593750, count: 96 -6032: 0.491022, 0.441230 avg, 0.001000 rate, 18.324368 seconds, 1544192 images -Loaded: 2.610723 seconds -Region Avg IOU: 0.509970, Class: 1.000000, Obj: 0.344685, No Obj: 0.004657, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.431325, Class: 1.000000, Obj: 0.274439, No Obj: 0.003279, Avg Recall: 0.401961, count: 102 -Region Avg IOU: 0.459011, Class: 1.000000, Obj: 0.290574, No Obj: 0.004565, Avg Recall: 0.462687, count: 134 -Region Avg IOU: 0.506059, Class: 1.000000, Obj: 0.369999, No Obj: 0.004556, Avg Recall: 0.566372, count: 113 -Region Avg IOU: 0.561836, Class: 1.000000, Obj: 0.324692, No Obj: 0.004063, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.450082, Class: 1.000000, Obj: 0.305255, No Obj: 0.003878, Avg Recall: 0.435484, count: 124 -Region Avg IOU: 0.461346, Class: 1.000000, Obj: 0.299534, No Obj: 0.004566, Avg Recall: 0.492188, count: 128 -Region Avg IOU: 0.496735, Class: 1.000000, Obj: 0.309909, No Obj: 0.004701, Avg Recall: 0.573770, count: 122 -6033: 0.470843, 0.444191 avg, 0.001000 rate, 13.972623 seconds, 1544448 images -Loaded: 5.689004 seconds -Region Avg IOU: 0.460239, Class: 1.000000, Obj: 0.304419, No Obj: 0.004015, Avg Recall: 0.483051, count: 118 -Region Avg IOU: 0.472690, Class: 1.000000, Obj: 0.280823, No Obj: 0.003933, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.467542, Class: 1.000000, Obj: 0.358137, No Obj: 0.004180, Avg Recall: 0.509615, count: 104 -Region Avg IOU: 0.467548, Class: 1.000000, Obj: 0.306809, No Obj: 0.004545, Avg Recall: 0.522936, count: 109 -Region Avg IOU: 0.487407, Class: 1.000000, Obj: 0.352469, No Obj: 0.004966, Avg Recall: 0.571429, count: 133 -Region Avg IOU: 0.470349, Class: 1.000000, Obj: 0.304078, No Obj: 0.004199, Avg Recall: 0.500000, count: 94 -Region Avg IOU: 0.502491, Class: 1.000000, Obj: 0.357220, No Obj: 0.004173, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.461713, Class: 1.000000, Obj: 0.294372, No Obj: 0.004187, Avg Recall: 0.489583, count: 96 -6034: 0.437888, 0.443561 avg, 0.001000 rate, 19.132776 seconds, 1544704 images -Loaded: 1.856243 seconds -Region Avg IOU: 0.446856, Class: 1.000000, Obj: 0.330946, No Obj: 0.004238, Avg Recall: 0.516129, count: 93 -Region Avg IOU: 0.432747, Class: 1.000000, Obj: 0.296641, No Obj: 0.004707, Avg Recall: 0.454545, count: 110 -Region Avg IOU: 0.537046, Class: 1.000000, Obj: 0.371256, No Obj: 0.003980, Avg Recall: 0.641975, count: 81 -Region Avg IOU: 0.516665, Class: 1.000000, Obj: 0.358502, No Obj: 0.004989, Avg Recall: 0.576000, count: 125 -Region Avg IOU: 0.511100, Class: 1.000000, Obj: 0.372647, No Obj: 0.004908, Avg Recall: 0.566667, count: 120 -Region Avg IOU: 0.499058, Class: 1.000000, Obj: 0.331817, No Obj: 0.004223, Avg Recall: 0.542553, count: 94 -Region Avg IOU: 0.496686, Class: 1.000000, Obj: 0.371682, No Obj: 0.004388, Avg Recall: 0.509434, count: 106 -Region Avg IOU: 0.463467, Class: 1.000000, Obj: 0.334630, No Obj: 0.004504, Avg Recall: 0.480392, count: 102 -6035: 0.462386, 0.445443 avg, 0.001000 rate, 18.970137 seconds, 1544960 images -Loaded: 2.117532 seconds -Region Avg IOU: 0.448256, Class: 1.000000, Obj: 0.365106, No Obj: 0.004457, Avg Recall: 0.485149, count: 101 -Region Avg IOU: 0.491963, Class: 1.000000, Obj: 0.353301, No Obj: 0.005174, Avg Recall: 0.536364, count: 110 -Region Avg IOU: 0.489782, Class: 1.000000, Obj: 0.358027, No Obj: 0.005110, Avg Recall: 0.500000, count: 108 -Region Avg IOU: 0.426206, Class: 1.000000, Obj: 0.350222, No Obj: 0.004432, Avg Recall: 0.455357, count: 112 -Region Avg IOU: 0.462822, Class: 1.000000, Obj: 0.351931, No Obj: 0.005281, Avg Recall: 0.481818, count: 110 -Region Avg IOU: 0.520918, Class: 1.000000, Obj: 0.381662, No Obj: 0.005493, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.470347, Class: 1.000000, Obj: 0.350777, No Obj: 0.004825, Avg Recall: 0.489796, count: 98 -Region Avg IOU: 0.535943, Class: 1.000000, Obj: 0.387604, No Obj: 0.005281, Avg Recall: 0.636364, count: 99 -6036: 0.476816, 0.448581 avg, 0.001000 rate, 15.381436 seconds, 1545216 images -Loaded: 6.662822 seconds -Region Avg IOU: 0.502354, Class: 1.000000, Obj: 0.365735, No Obj: 0.005406, Avg Recall: 0.566667, count: 120 -Region Avg IOU: 0.537790, Class: 1.000000, Obj: 0.410015, No Obj: 0.005054, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.483361, Class: 1.000000, Obj: 0.344195, No Obj: 0.004996, Avg Recall: 0.522936, count: 109 -Region Avg IOU: 0.435380, Class: 1.000000, Obj: 0.365838, No Obj: 0.005003, Avg Recall: 0.486957, count: 115 -Region Avg IOU: 0.479842, Class: 1.000000, Obj: 0.330145, No Obj: 0.004924, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.483930, Class: 1.000000, Obj: 0.374773, No Obj: 0.004511, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.456444, Class: 1.000000, Obj: 0.345751, No Obj: 0.005634, Avg Recall: 0.510791, count: 139 -Region Avg IOU: 0.428533, Class: 1.000000, Obj: 0.341674, No Obj: 0.004751, Avg Recall: 0.419355, count: 124 -6037: 0.470166, 0.450739 avg, 0.001000 rate, 13.427458 seconds, 1545472 images -Loaded: 6.160231 seconds -Region Avg IOU: 0.474110, Class: 1.000000, Obj: 0.402895, No Obj: 0.005827, Avg Recall: 0.488722, count: 133 -Region Avg IOU: 0.439442, Class: 1.000000, Obj: 0.390067, No Obj: 0.005039, Avg Recall: 0.474138, count: 116 -Region Avg IOU: 0.485696, Class: 1.000000, Obj: 0.376403, No Obj: 0.005171, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.490319, Class: 1.000000, Obj: 0.320571, No Obj: 0.004941, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.456870, Class: 1.000000, Obj: 0.365036, No Obj: 0.005679, Avg Recall: 0.539683, count: 126 -Region Avg IOU: 0.481725, Class: 1.000000, Obj: 0.333649, No Obj: 0.004683, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.451052, Class: 1.000000, Obj: 0.370147, No Obj: 0.004694, Avg Recall: 0.462500, count: 80 -Region Avg IOU: 0.428528, Class: 1.000000, Obj: 0.354005, No Obj: 0.004660, Avg Recall: 0.444444, count: 108 -6038: 0.478243, 0.453490 avg, 0.001000 rate, 16.453079 seconds, 1545728 images -Loaded: 4.168779 seconds -Region Avg IOU: 0.484536, Class: 1.000000, Obj: 0.381190, No Obj: 0.004769, Avg Recall: 0.554217, count: 83 -Region Avg IOU: 0.465025, Class: 1.000000, Obj: 0.386236, No Obj: 0.005775, Avg Recall: 0.513274, count: 113 -Region Avg IOU: 0.500793, Class: 1.000000, Obj: 0.384657, No Obj: 0.004745, Avg Recall: 0.541667, count: 96 -Region Avg IOU: 0.470168, Class: 1.000000, Obj: 0.359260, No Obj: 0.005802, Avg Recall: 0.500000, count: 138 -Region Avg IOU: 0.470596, Class: 1.000000, Obj: 0.397002, No Obj: 0.004473, Avg Recall: 0.473118, count: 93 -Region Avg IOU: 0.511630, Class: 1.000000, Obj: 0.413752, No Obj: 0.004759, Avg Recall: 0.600000, count: 80 -Region Avg IOU: 0.496203, Class: 1.000000, Obj: 0.359689, No Obj: 0.005113, Avg Recall: 0.552381, count: 105 -Region Avg IOU: 0.466051, Class: 1.000000, Obj: 0.376805, No Obj: 0.004714, Avg Recall: 0.500000, count: 94 -6039: 0.403689, 0.448509 avg, 0.001000 rate, 13.616888 seconds, 1545984 images -Loaded: 7.406606 seconds -Region Avg IOU: 0.471248, Class: 1.000000, Obj: 0.394766, No Obj: 0.005238, Avg Recall: 0.521277, count: 94 -Region Avg IOU: 0.495428, Class: 1.000000, Obj: 0.356026, No Obj: 0.004875, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.454041, Class: 1.000000, Obj: 0.325801, No Obj: 0.005472, Avg Recall: 0.511450, count: 131 -Region Avg IOU: 0.490220, Class: 1.000000, Obj: 0.400863, No Obj: 0.005705, Avg Recall: 0.552000, count: 125 -Region Avg IOU: 0.513689, Class: 1.000000, Obj: 0.400500, No Obj: 0.005351, Avg Recall: 0.535354, count: 99 -Region Avg IOU: 0.502963, Class: 1.000000, Obj: 0.344007, No Obj: 0.005625, Avg Recall: 0.586777, count: 121 -Region Avg IOU: 0.503622, Class: 1.000000, Obj: 0.383354, No Obj: 0.005106, Avg Recall: 0.543103, count: 116 -Region Avg IOU: 0.484823, Class: 1.000000, Obj: 0.383379, No Obj: 0.004708, Avg Recall: 0.533333, count: 90 -6040: 0.481461, 0.451805 avg, 0.001000 rate, 15.519119 seconds, 1546240 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000061 seconds -Region Avg IOU: 0.616945, Class: 1.000000, Obj: 0.441382, No Obj: 0.004284, Avg Recall: 0.734375, count: 128 -Region Avg IOU: 0.634636, Class: 1.000000, Obj: 0.458321, No Obj: 0.003818, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.640822, Class: 1.000000, Obj: 0.509700, No Obj: 0.003527, Avg Recall: 0.772727, count: 88 -Region Avg IOU: 0.578088, Class: 1.000000, Obj: 0.478734, No Obj: 0.003357, Avg Recall: 0.682353, count: 85 -Region Avg IOU: 0.585054, Class: 1.000000, Obj: 0.459304, No Obj: 0.003529, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.580231, Class: 1.000000, Obj: 0.467802, No Obj: 0.003923, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.595375, Class: 1.000000, Obj: 0.418881, No Obj: 0.004424, Avg Recall: 0.704000, count: 125 -Region Avg IOU: 0.557568, Class: 1.000000, Obj: 0.467934, No Obj: 0.004228, Avg Recall: 0.609524, count: 105 -6041: 0.405173, 0.447141 avg, 0.001000 rate, 23.433331 seconds, 1546496 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.568818, Class: 1.000000, Obj: 0.410688, No Obj: 0.004244, Avg Recall: 0.661765, count: 136 -Region Avg IOU: 0.602423, Class: 1.000000, Obj: 0.461023, No Obj: 0.003689, Avg Recall: 0.719512, count: 82 -Region Avg IOU: 0.582851, Class: 1.000000, Obj: 0.422193, No Obj: 0.004074, Avg Recall: 0.658730, count: 126 -Region Avg IOU: 0.591692, Class: 1.000000, Obj: 0.460860, No Obj: 0.003750, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.580346, Class: 1.000000, Obj: 0.469304, No Obj: 0.003991, Avg Recall: 0.641791, count: 134 -Region Avg IOU: 0.597524, Class: 1.000000, Obj: 0.483810, No Obj: 0.004002, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.585898, Class: 1.000000, Obj: 0.468240, No Obj: 0.004075, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.545591, Class: 1.000000, Obj: 0.414167, No Obj: 0.003603, Avg Recall: 0.623077, count: 130 -6042: 0.429079, 0.445335 avg, 0.001000 rate, 25.365866 seconds, 1546752 images -Loaded: 0.016729 seconds -Region Avg IOU: 0.578067, Class: 1.000000, Obj: 0.424476, No Obj: 0.003756, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.600463, Class: 1.000000, Obj: 0.405496, No Obj: 0.003897, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.605306, Class: 1.000000, Obj: 0.453776, No Obj: 0.003780, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.652607, Class: 1.000000, Obj: 0.487888, No Obj: 0.003513, Avg Recall: 0.767442, count: 86 -Region Avg IOU: 0.595850, Class: 1.000000, Obj: 0.464523, No Obj: 0.003857, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.624961, Class: 1.000000, Obj: 0.467049, No Obj: 0.003474, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.657850, Class: 1.000000, Obj: 0.471854, No Obj: 0.003747, Avg Recall: 0.809091, count: 110 -Region Avg IOU: 0.624659, Class: 1.000000, Obj: 0.466117, No Obj: 0.003631, Avg Recall: 0.743363, count: 113 -6043: 0.393827, 0.440184 avg, 0.001000 rate, 24.030697 seconds, 1547008 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.546457, Class: 1.000000, Obj: 0.363624, No Obj: 0.003409, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.563289, Class: 1.000000, Obj: 0.406271, No Obj: 0.002825, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.578255, Class: 1.000000, Obj: 0.413416, No Obj: 0.003400, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.572010, Class: 1.000000, Obj: 0.377165, No Obj: 0.003531, Avg Recall: 0.619835, count: 121 -Region Avg IOU: 0.585230, Class: 1.000000, Obj: 0.458334, No Obj: 0.003513, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.557195, Class: 1.000000, Obj: 0.369553, No Obj: 0.003384, Avg Recall: 0.666667, count: 132 -Region Avg IOU: 0.588847, Class: 1.000000, Obj: 0.423628, No Obj: 0.003474, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.587346, Class: 1.000000, Obj: 0.445587, No Obj: 0.003590, Avg Recall: 0.731183, count: 93 -6044: 0.433825, 0.439548 avg, 0.001000 rate, 22.936428 seconds, 1547264 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.516646, Class: 1.000000, Obj: 0.347325, No Obj: 0.002707, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.609123, Class: 1.000000, Obj: 0.392129, No Obj: 0.004248, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.552426, Class: 1.000000, Obj: 0.351220, No Obj: 0.002632, Avg Recall: 0.625000, count: 88 -Region Avg IOU: 0.570957, Class: 1.000000, Obj: 0.348503, No Obj: 0.003288, Avg Recall: 0.666667, count: 126 -Region Avg IOU: 0.599206, Class: 1.000000, Obj: 0.377296, No Obj: 0.002659, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.589747, Class: 1.000000, Obj: 0.398842, No Obj: 0.003063, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.610149, Class: 1.000000, Obj: 0.407650, No Obj: 0.003146, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.568450, Class: 1.000000, Obj: 0.402450, No Obj: 0.003584, Avg Recall: 0.640777, count: 103 -6045: 0.409168, 0.436510 avg, 0.001000 rate, 23.376680 seconds, 1547520 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.561417, Class: 1.000000, Obj: 0.370249, No Obj: 0.003047, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.601742, Class: 1.000000, Obj: 0.379651, No Obj: 0.002385, Avg Recall: 0.714286, count: 84 -Region Avg IOU: 0.591974, Class: 1.000000, Obj: 0.335809, No Obj: 0.003227, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.601094, Class: 1.000000, Obj: 0.394014, No Obj: 0.002903, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.602200, Class: 1.000000, Obj: 0.370135, No Obj: 0.003136, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.598827, Class: 1.000000, Obj: 0.437754, No Obj: 0.002852, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.570087, Class: 1.000000, Obj: 0.362561, No Obj: 0.003241, Avg Recall: 0.666667, count: 132 -Region Avg IOU: 0.615319, Class: 1.000000, Obj: 0.360607, No Obj: 0.003523, Avg Recall: 0.706349, count: 126 -6046: 0.419001, 0.434759 avg, 0.001000 rate, 25.404221 seconds, 1547776 images -Loaded: 0.041985 seconds -Region Avg IOU: 0.628440, Class: 1.000000, Obj: 0.404763, No Obj: 0.003512, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.607077, Class: 1.000000, Obj: 0.374738, No Obj: 0.003099, Avg Recall: 0.705882, count: 85 -Region Avg IOU: 0.610096, Class: 1.000000, Obj: 0.361176, No Obj: 0.003446, Avg Recall: 0.738806, count: 134 -Region Avg IOU: 0.569870, Class: 1.000000, Obj: 0.322763, No Obj: 0.002825, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.586950, Class: 1.000000, Obj: 0.342889, No Obj: 0.002926, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.592430, Class: 1.000000, Obj: 0.381215, No Obj: 0.002907, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.605294, Class: 1.000000, Obj: 0.412090, No Obj: 0.002690, Avg Recall: 0.702703, count: 74 -Region Avg IOU: 0.620322, Class: 1.000000, Obj: 0.393591, No Obj: 0.003418, Avg Recall: 0.771186, count: 118 -6047: 0.410437, 0.432327 avg, 0.001000 rate, 25.021809 seconds, 1548032 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.626795, Class: 1.000000, Obj: 0.390854, No Obj: 0.002883, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.606270, Class: 1.000000, Obj: 0.385347, No Obj: 0.003110, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.551989, Class: 1.000000, Obj: 0.309129, No Obj: 0.003152, Avg Recall: 0.664430, count: 149 -Region Avg IOU: 0.579823, Class: 1.000000, Obj: 0.343796, No Obj: 0.002983, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.576153, Class: 1.000000, Obj: 0.379418, No Obj: 0.003452, Avg Recall: 0.682171, count: 129 -Region Avg IOU: 0.565955, Class: 1.000000, Obj: 0.391768, No Obj: 0.002319, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.609112, Class: 1.000000, Obj: 0.376647, No Obj: 0.003389, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.551795, Class: 1.000000, Obj: 0.332289, No Obj: 0.003180, Avg Recall: 0.648438, count: 128 -6048: 0.443428, 0.433437 avg, 0.001000 rate, 24.660999 seconds, 1548288 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.611606, Class: 1.000000, Obj: 0.392921, No Obj: 0.003054, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.627900, Class: 1.000000, Obj: 0.329978, No Obj: 0.002640, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.606661, Class: 1.000000, Obj: 0.394794, No Obj: 0.002874, Avg Recall: 0.679487, count: 78 -Region Avg IOU: 0.602762, Class: 1.000000, Obj: 0.351472, No Obj: 0.003466, Avg Recall: 0.697987, count: 149 -Region Avg IOU: 0.603734, Class: 1.000000, Obj: 0.340861, No Obj: 0.003471, Avg Recall: 0.729927, count: 137 -Region Avg IOU: 0.606570, Class: 1.000000, Obj: 0.410739, No Obj: 0.002923, Avg Recall: 0.750000, count: 88 -Region Avg IOU: 0.652158, Class: 1.000000, Obj: 0.414662, No Obj: 0.003295, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.595196, Class: 1.000000, Obj: 0.356384, No Obj: 0.003130, Avg Recall: 0.683333, count: 120 -6049: 0.405531, 0.430647 avg, 0.001000 rate, 24.309452 seconds, 1548544 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.629571, Class: 1.000000, Obj: 0.405546, No Obj: 0.003415, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.612411, Class: 1.000000, Obj: 0.393661, No Obj: 0.003017, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.598737, Class: 1.000000, Obj: 0.345085, No Obj: 0.003262, Avg Recall: 0.718750, count: 128 -Region Avg IOU: 0.626950, Class: 1.000000, Obj: 0.393800, No Obj: 0.002760, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.569098, Class: 1.000000, Obj: 0.341503, No Obj: 0.002855, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.614546, Class: 1.000000, Obj: 0.391860, No Obj: 0.003142, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.619405, Class: 1.000000, Obj: 0.380862, No Obj: 0.003035, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.597280, Class: 1.000000, Obj: 0.398042, No Obj: 0.003045, Avg Recall: 0.663551, count: 107 -6050: 0.384919, 0.426074 avg, 0.001000 rate, 24.405142 seconds, 1548800 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000070 seconds -Region Avg IOU: 0.652186, Class: 1.000000, Obj: 0.430307, No Obj: 0.002385, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.639391, Class: 1.000000, Obj: 0.362963, No Obj: 0.002589, Avg Recall: 0.764228, count: 123 -Region Avg IOU: 0.650086, Class: 1.000000, Obj: 0.440240, No Obj: 0.002944, Avg Recall: 0.798387, count: 124 -Region Avg IOU: 0.636471, Class: 1.000000, Obj: 0.398715, No Obj: 0.002644, Avg Recall: 0.807018, count: 114 -Region Avg IOU: 0.634626, Class: 1.000000, Obj: 0.442295, No Obj: 0.002851, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.697483, Class: 1.000000, Obj: 0.456287, No Obj: 0.002559, Avg Recall: 0.853658, count: 82 -Region Avg IOU: 0.627432, Class: 1.000000, Obj: 0.386266, No Obj: 0.002733, Avg Recall: 0.752000, count: 125 -Region Avg IOU: 0.677029, Class: 1.000000, Obj: 0.419208, No Obj: 0.003030, Avg Recall: 0.863248, count: 117 -6051: 0.439709, 0.427437 avg, 0.001000 rate, 28.748545 seconds, 1549056 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.632265, Class: 1.000000, Obj: 0.432237, No Obj: 0.002441, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.646458, Class: 1.000000, Obj: 0.408851, No Obj: 0.003289, Avg Recall: 0.840000, count: 125 -Region Avg IOU: 0.712198, Class: 1.000000, Obj: 0.454793, No Obj: 0.002613, Avg Recall: 0.909091, count: 88 -Region Avg IOU: 0.652602, Class: 1.000000, Obj: 0.463358, No Obj: 0.002972, Avg Recall: 0.788136, count: 118 -Region Avg IOU: 0.697231, Class: 1.000000, Obj: 0.488851, No Obj: 0.002643, Avg Recall: 0.852273, count: 88 -Region Avg IOU: 0.696159, Class: 1.000000, Obj: 0.425942, No Obj: 0.002881, Avg Recall: 0.882353, count: 102 -Region Avg IOU: 0.656415, Class: 1.000000, Obj: 0.407092, No Obj: 0.002697, Avg Recall: 0.817073, count: 82 -Region Avg IOU: 0.656300, Class: 1.000000, Obj: 0.400546, No Obj: 0.003188, Avg Recall: 0.795276, count: 127 -6052: 0.450336, 0.429727 avg, 0.001000 rate, 27.922928 seconds, 1549312 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.651234, Class: 1.000000, Obj: 0.419368, No Obj: 0.002964, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.643023, Class: 1.000000, Obj: 0.406643, No Obj: 0.002975, Avg Recall: 0.789474, count: 114 -Region Avg IOU: 0.612664, Class: 1.000000, Obj: 0.439846, No Obj: 0.002419, Avg Recall: 0.734940, count: 83 -Region Avg IOU: 0.594282, Class: 1.000000, Obj: 0.357605, No Obj: 0.002537, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.683607, Class: 1.000000, Obj: 0.404639, No Obj: 0.002971, Avg Recall: 0.847619, count: 105 -Region Avg IOU: 0.697418, Class: 1.000000, Obj: 0.484485, No Obj: 0.002839, Avg Recall: 0.865385, count: 104 -Region Avg IOU: 0.649620, Class: 1.000000, Obj: 0.490372, No Obj: 0.002725, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.674560, Class: 1.000000, Obj: 0.473600, No Obj: 0.002826, Avg Recall: 0.830000, count: 100 -6053: 0.416167, 0.428371 avg, 0.001000 rate, 28.946159 seconds, 1549568 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.653975, Class: 1.000000, Obj: 0.415927, No Obj: 0.002963, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.639450, Class: 1.000000, Obj: 0.463943, No Obj: 0.002476, Avg Recall: 0.802632, count: 76 -Region Avg IOU: 0.646391, Class: 1.000000, Obj: 0.479069, No Obj: 0.002571, Avg Recall: 0.781609, count: 87 -Region Avg IOU: 0.659014, Class: 1.000000, Obj: 0.462239, No Obj: 0.002783, Avg Recall: 0.838384, count: 99 -Region Avg IOU: 0.648150, Class: 1.000000, Obj: 0.430041, No Obj: 0.003317, Avg Recall: 0.827586, count: 116 -Region Avg IOU: 0.649314, Class: 1.000000, Obj: 0.412993, No Obj: 0.002610, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.689727, Class: 1.000000, Obj: 0.415645, No Obj: 0.003360, Avg Recall: 0.879032, count: 124 -Region Avg IOU: 0.637120, Class: 1.000000, Obj: 0.415496, No Obj: 0.003313, Avg Recall: 0.794326, count: 141 -6054: 0.433258, 0.428860 avg, 0.001000 rate, 27.884392 seconds, 1549824 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.648603, Class: 1.000000, Obj: 0.461059, No Obj: 0.002716, Avg Recall: 0.820755, count: 106 -Region Avg IOU: 0.689003, Class: 1.000000, Obj: 0.492266, No Obj: 0.003052, Avg Recall: 0.884615, count: 104 -Region Avg IOU: 0.661595, Class: 1.000000, Obj: 0.431711, No Obj: 0.003053, Avg Recall: 0.830769, count: 130 -Region Avg IOU: 0.686208, Class: 1.000000, Obj: 0.468796, No Obj: 0.003035, Avg Recall: 0.870968, count: 93 -Region Avg IOU: 0.687654, Class: 1.000000, Obj: 0.414795, No Obj: 0.003534, Avg Recall: 0.805085, count: 118 -Region Avg IOU: 0.583932, Class: 1.000000, Obj: 0.414388, No Obj: 0.002834, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.668831, Class: 1.000000, Obj: 0.471906, No Obj: 0.002784, Avg Recall: 0.833333, count: 96 -Region Avg IOU: 0.648989, Class: 1.000000, Obj: 0.438054, No Obj: 0.002423, Avg Recall: 0.802198, count: 91 -6055: 0.405755, 0.426549 avg, 0.001000 rate, 28.308590 seconds, 1550080 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.659222, Class: 1.000000, Obj: 0.386022, No Obj: 0.003009, Avg Recall: 0.846774, count: 124 -Region Avg IOU: 0.648296, Class: 1.000000, Obj: 0.440251, No Obj: 0.002594, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.674035, Class: 1.000000, Obj: 0.467549, No Obj: 0.002719, Avg Recall: 0.833333, count: 108 -Region Avg IOU: 0.665147, Class: 1.000000, Obj: 0.441666, No Obj: 0.002798, Avg Recall: 0.831776, count: 107 -Region Avg IOU: 0.683623, Class: 1.000000, Obj: 0.487027, No Obj: 0.003012, Avg Recall: 0.790698, count: 86 -Region Avg IOU: 0.687464, Class: 1.000000, Obj: 0.429007, No Obj: 0.003160, Avg Recall: 0.849057, count: 106 -Region Avg IOU: 0.598570, Class: 1.000000, Obj: 0.424966, No Obj: 0.003059, Avg Recall: 0.750000, count: 124 -Region Avg IOU: 0.657903, Class: 1.000000, Obj: 0.359638, No Obj: 0.003066, Avg Recall: 0.810811, count: 111 -6056: 0.425681, 0.426463 avg, 0.001000 rate, 28.556377 seconds, 1550336 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.628752, Class: 1.000000, Obj: 0.378119, No Obj: 0.002750, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.665535, Class: 1.000000, Obj: 0.458126, No Obj: 0.002845, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.626680, Class: 1.000000, Obj: 0.454914, No Obj: 0.002696, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.663299, Class: 1.000000, Obj: 0.473307, No Obj: 0.002228, Avg Recall: 0.840425, count: 94 -Region Avg IOU: 0.610333, Class: 1.000000, Obj: 0.377936, No Obj: 0.003104, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.655065, Class: 1.000000, Obj: 0.383765, No Obj: 0.002566, Avg Recall: 0.802198, count: 91 -Region Avg IOU: 0.689043, Class: 1.000000, Obj: 0.412317, No Obj: 0.003164, Avg Recall: 0.875000, count: 128 -Region Avg IOU: 0.625652, Class: 1.000000, Obj: 0.373003, No Obj: 0.002670, Avg Recall: 0.752294, count: 109 -6057: 0.440948, 0.427911 avg, 0.001000 rate, 29.557999 seconds, 1550592 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.691256, Class: 1.000000, Obj: 0.432916, No Obj: 0.002378, Avg Recall: 0.879518, count: 83 -Region Avg IOU: 0.662991, Class: 1.000000, Obj: 0.402278, No Obj: 0.002978, Avg Recall: 0.816000, count: 125 -Region Avg IOU: 0.669123, Class: 1.000000, Obj: 0.401059, No Obj: 0.003093, Avg Recall: 0.873950, count: 119 -Region Avg IOU: 0.657144, Class: 1.000000, Obj: 0.384477, No Obj: 0.002891, Avg Recall: 0.804688, count: 128 -Region Avg IOU: 0.658174, Class: 1.000000, Obj: 0.434883, No Obj: 0.002676, Avg Recall: 0.776471, count: 85 -Region Avg IOU: 0.599158, Class: 1.000000, Obj: 0.399294, No Obj: 0.001970, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.634880, Class: 1.000000, Obj: 0.408626, No Obj: 0.002308, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.694927, Class: 1.000000, Obj: 0.444373, No Obj: 0.002391, Avg Recall: 0.875000, count: 88 -6058: 0.406185, 0.425739 avg, 0.001000 rate, 29.407354 seconds, 1550848 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.688953, Class: 1.000000, Obj: 0.427601, No Obj: 0.002174, Avg Recall: 0.870588, count: 85 -Region Avg IOU: 0.623995, Class: 1.000000, Obj: 0.420991, No Obj: 0.002574, Avg Recall: 0.738462, count: 130 -Region Avg IOU: 0.677375, Class: 1.000000, Obj: 0.411351, No Obj: 0.002671, Avg Recall: 0.816667, count: 120 -Region Avg IOU: 0.610533, Class: 1.000000, Obj: 0.392341, No Obj: 0.002897, Avg Recall: 0.720280, count: 143 -Region Avg IOU: 0.646801, Class: 1.000000, Obj: 0.412570, No Obj: 0.002864, Avg Recall: 0.804511, count: 133 -Region Avg IOU: 0.655294, Class: 1.000000, Obj: 0.370818, No Obj: 0.002516, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.642290, Class: 1.000000, Obj: 0.331502, No Obj: 0.003088, Avg Recall: 0.763158, count: 152 -Region Avg IOU: 0.657288, Class: 1.000000, Obj: 0.461343, No Obj: 0.001932, Avg Recall: 0.765625, count: 64 -6059: 0.450781, 0.428243 avg, 0.001000 rate, 29.723961 seconds, 1551104 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.652131, Class: 1.000000, Obj: 0.324605, No Obj: 0.002760, Avg Recall: 0.803571, count: 112 -Region Avg IOU: 0.623203, Class: 1.000000, Obj: 0.341123, No Obj: 0.002506, Avg Recall: 0.769231, count: 117 -Region Avg IOU: 0.652196, Class: 1.000000, Obj: 0.390933, No Obj: 0.002841, Avg Recall: 0.785047, count: 107 -Region Avg IOU: 0.664761, Class: 1.000000, Obj: 0.303998, No Obj: 0.002678, Avg Recall: 0.838384, count: 99 -Region Avg IOU: 0.638204, Class: 1.000000, Obj: 0.382723, No Obj: 0.002696, Avg Recall: 0.806452, count: 124 -Region Avg IOU: 0.617899, Class: 1.000000, Obj: 0.335590, No Obj: 0.002386, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.642245, Class: 1.000000, Obj: 0.351415, No Obj: 0.002690, Avg Recall: 0.782946, count: 129 -Region Avg IOU: 0.675521, Class: 1.000000, Obj: 0.414756, No Obj: 0.002654, Avg Recall: 0.834646, count: 127 -6060: 0.487898, 0.434208 avg, 0.001000 rate, 28.942137 seconds, 1551360 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 14.199357 seconds -Region Avg IOU: 0.622331, Class: 1.000000, Obj: 0.372362, No Obj: 0.002661, Avg Recall: 0.743802, count: 121 -Region Avg IOU: 0.630366, Class: 1.000000, Obj: 0.403683, No Obj: 0.002495, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.624838, Class: 1.000000, Obj: 0.412493, No Obj: 0.002282, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.665812, Class: 1.000000, Obj: 0.352140, No Obj: 0.002731, Avg Recall: 0.828829, count: 111 -Region Avg IOU: 0.641074, Class: 1.000000, Obj: 0.390512, No Obj: 0.002664, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.660268, Class: 1.000000, Obj: 0.397029, No Obj: 0.002522, Avg Recall: 0.833333, count: 120 -Region Avg IOU: 0.615503, Class: 1.000000, Obj: 0.354743, No Obj: 0.002567, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.639395, Class: 1.000000, Obj: 0.355203, No Obj: 0.002017, Avg Recall: 0.788235, count: 85 -6061: 0.418475, 0.432635 avg, 0.001000 rate, 28.225121 seconds, 1551616 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.670275, Class: 1.000000, Obj: 0.440086, No Obj: 0.002980, Avg Recall: 0.814516, count: 124 -Region Avg IOU: 0.658077, Class: 1.000000, Obj: 0.399448, No Obj: 0.002723, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.647162, Class: 1.000000, Obj: 0.407116, No Obj: 0.002393, Avg Recall: 0.793478, count: 92 -Region Avg IOU: 0.624274, Class: 1.000000, Obj: 0.390267, No Obj: 0.002555, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.661894, Class: 1.000000, Obj: 0.397278, No Obj: 0.002942, Avg Recall: 0.795276, count: 127 -Region Avg IOU: 0.648321, Class: 1.000000, Obj: 0.384129, No Obj: 0.002364, Avg Recall: 0.764706, count: 85 -Region Avg IOU: 0.650207, Class: 1.000000, Obj: 0.401273, No Obj: 0.002692, Avg Recall: 0.802083, count: 96 -Region Avg IOU: 0.684564, Class: 1.000000, Obj: 0.414841, No Obj: 0.002193, Avg Recall: 0.893939, count: 66 -6062: 0.417940, 0.431165 avg, 0.001000 rate, 27.505920 seconds, 1551872 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.640992, Class: 1.000000, Obj: 0.370390, No Obj: 0.002738, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.691185, Class: 1.000000, Obj: 0.358812, No Obj: 0.002466, Avg Recall: 0.888889, count: 108 -Region Avg IOU: 0.673635, Class: 1.000000, Obj: 0.435785, No Obj: 0.002720, Avg Recall: 0.846154, count: 104 -Region Avg IOU: 0.686564, Class: 1.000000, Obj: 0.373807, No Obj: 0.002918, Avg Recall: 0.847458, count: 118 -Region Avg IOU: 0.660824, Class: 1.000000, Obj: 0.445015, No Obj: 0.002302, Avg Recall: 0.784091, count: 88 -Region Avg IOU: 0.618849, Class: 1.000000, Obj: 0.382861, No Obj: 0.003099, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.653904, Class: 1.000000, Obj: 0.356850, No Obj: 0.002528, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.624412, Class: 1.000000, Obj: 0.405744, No Obj: 0.002667, Avg Recall: 0.808696, count: 115 -6063: 0.419197, 0.429969 avg, 0.001000 rate, 27.911638 seconds, 1552128 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.626027, Class: 1.000000, Obj: 0.365247, No Obj: 0.002756, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.680562, Class: 1.000000, Obj: 0.493173, No Obj: 0.002885, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.628616, Class: 1.000000, Obj: 0.372746, No Obj: 0.002657, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.651531, Class: 1.000000, Obj: 0.467136, No Obj: 0.002589, Avg Recall: 0.779070, count: 86 -Region Avg IOU: 0.686910, Class: 1.000000, Obj: 0.402432, No Obj: 0.003198, Avg Recall: 0.840708, count: 113 -Region Avg IOU: 0.670121, Class: 1.000000, Obj: 0.372232, No Obj: 0.002860, Avg Recall: 0.880734, count: 109 -Region Avg IOU: 0.664219, Class: 1.000000, Obj: 0.383368, No Obj: 0.003682, Avg Recall: 0.833333, count: 144 -Region Avg IOU: 0.627362, Class: 1.000000, Obj: 0.428102, No Obj: 0.003020, Avg Recall: 0.739837, count: 123 -6064: 0.439056, 0.430877 avg, 0.001000 rate, 27.929216 seconds, 1552384 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.640673, Class: 1.000000, Obj: 0.420191, No Obj: 0.002700, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.648358, Class: 1.000000, Obj: 0.387530, No Obj: 0.002946, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.660132, Class: 1.000000, Obj: 0.390804, No Obj: 0.002927, Avg Recall: 0.807018, count: 114 -Region Avg IOU: 0.698410, Class: 1.000000, Obj: 0.422112, No Obj: 0.002963, Avg Recall: 0.879121, count: 91 -Region Avg IOU: 0.622138, Class: 1.000000, Obj: 0.403722, No Obj: 0.003006, Avg Recall: 0.735537, count: 121 -Region Avg IOU: 0.643707, Class: 1.000000, Obj: 0.413688, No Obj: 0.002999, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.661356, Class: 1.000000, Obj: 0.432189, No Obj: 0.002997, Avg Recall: 0.817391, count: 115 -Region Avg IOU: 0.629584, Class: 1.000000, Obj: 0.387890, No Obj: 0.002812, Avg Recall: 0.762376, count: 101 -6065: 0.428289, 0.430618 avg, 0.001000 rate, 26.721905 seconds, 1552640 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.672996, Class: 1.000000, Obj: 0.444469, No Obj: 0.002841, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.640695, Class: 1.000000, Obj: 0.417269, No Obj: 0.002769, Avg Recall: 0.819048, count: 105 -Region Avg IOU: 0.618129, Class: 1.000000, Obj: 0.375065, No Obj: 0.002996, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.663841, Class: 1.000000, Obj: 0.458532, No Obj: 0.002972, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.647940, Class: 1.000000, Obj: 0.475985, No Obj: 0.002889, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.646190, Class: 1.000000, Obj: 0.397349, No Obj: 0.003504, Avg Recall: 0.857143, count: 140 -Region Avg IOU: 0.684994, Class: 1.000000, Obj: 0.410350, No Obj: 0.003469, Avg Recall: 0.858268, count: 127 -Region Avg IOU: 0.648324, Class: 1.000000, Obj: 0.466460, No Obj: 0.002925, Avg Recall: 0.780952, count: 105 -6066: 0.441393, 0.431696 avg, 0.001000 rate, 27.601818 seconds, 1552896 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.625023, Class: 1.000000, Obj: 0.459654, No Obj: 0.003430, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.647349, Class: 1.000000, Obj: 0.411780, No Obj: 0.003058, Avg Recall: 0.819672, count: 122 -Region Avg IOU: 0.646952, Class: 1.000000, Obj: 0.415035, No Obj: 0.003825, Avg Recall: 0.796748, count: 123 -Region Avg IOU: 0.588020, Class: 1.000000, Obj: 0.414764, No Obj: 0.003175, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.647094, Class: 1.000000, Obj: 0.443404, No Obj: 0.003438, Avg Recall: 0.785124, count: 121 -Region Avg IOU: 0.640357, Class: 1.000000, Obj: 0.417330, No Obj: 0.003195, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.674324, Class: 1.000000, Obj: 0.438545, No Obj: 0.003128, Avg Recall: 0.842593, count: 108 -Region Avg IOU: 0.642957, Class: 1.000000, Obj: 0.449430, No Obj: 0.003511, Avg Recall: 0.770270, count: 148 -6067: 0.469502, 0.435476 avg, 0.001000 rate, 29.109800 seconds, 1553152 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.650746, Class: 1.000000, Obj: 0.458320, No Obj: 0.003216, Avg Recall: 0.818182, count: 121 -Region Avg IOU: 0.678925, Class: 1.000000, Obj: 0.425852, No Obj: 0.003417, Avg Recall: 0.869565, count: 115 -Region Avg IOU: 0.629431, Class: 1.000000, Obj: 0.417925, No Obj: 0.002941, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.569687, Class: 1.000000, Obj: 0.404753, No Obj: 0.002835, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.653421, Class: 1.000000, Obj: 0.446307, No Obj: 0.003586, Avg Recall: 0.836066, count: 122 -Region Avg IOU: 0.648792, Class: 1.000000, Obj: 0.444034, No Obj: 0.002948, Avg Recall: 0.794643, count: 112 -Region Avg IOU: 0.633991, Class: 1.000000, Obj: 0.447253, No Obj: 0.003298, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.661363, Class: 1.000000, Obj: 0.455029, No Obj: 0.002816, Avg Recall: 0.780000, count: 100 -6068: 0.423820, 0.434311 avg, 0.001000 rate, 27.448051 seconds, 1553408 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.636054, Class: 1.000000, Obj: 0.462601, No Obj: 0.002555, Avg Recall: 0.793478, count: 92 -Region Avg IOU: 0.633293, Class: 1.000000, Obj: 0.421817, No Obj: 0.002782, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.636576, Class: 1.000000, Obj: 0.416076, No Obj: 0.002762, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.600339, Class: 1.000000, Obj: 0.401246, No Obj: 0.002955, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.674998, Class: 1.000000, Obj: 0.455898, No Obj: 0.002390, Avg Recall: 0.805195, count: 77 -Region Avg IOU: 0.635589, Class: 1.000000, Obj: 0.420505, No Obj: 0.002821, Avg Recall: 0.841584, count: 101 -Region Avg IOU: 0.670758, Class: 1.000000, Obj: 0.442084, No Obj: 0.003481, Avg Recall: 0.837607, count: 117 -Region Avg IOU: 0.649096, Class: 1.000000, Obj: 0.415298, No Obj: 0.003137, Avg Recall: 0.793103, count: 116 -6069: 0.450965, 0.435976 avg, 0.001000 rate, 26.113457 seconds, 1553664 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.624837, Class: 1.000000, Obj: 0.374384, No Obj: 0.002947, Avg Recall: 0.776859, count: 121 -Region Avg IOU: 0.653703, Class: 1.000000, Obj: 0.486054, No Obj: 0.002968, Avg Recall: 0.765625, count: 128 -Region Avg IOU: 0.688105, Class: 1.000000, Obj: 0.424502, No Obj: 0.003019, Avg Recall: 0.901961, count: 102 -Region Avg IOU: 0.642727, Class: 1.000000, Obj: 0.388291, No Obj: 0.002663, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.638491, Class: 1.000000, Obj: 0.412236, No Obj: 0.002478, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.678838, Class: 1.000000, Obj: 0.415884, No Obj: 0.002936, Avg Recall: 0.844660, count: 103 -Region Avg IOU: 0.660736, Class: 1.000000, Obj: 0.468399, No Obj: 0.002873, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.617713, Class: 1.000000, Obj: 0.420528, No Obj: 0.002882, Avg Recall: 0.743119, count: 109 -6070: 0.416612, 0.434040 avg, 0.001000 rate, 27.633238 seconds, 1553920 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 15.600631 seconds -Region Avg IOU: 0.603072, Class: 1.000000, Obj: 0.366127, No Obj: 0.002301, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.660301, Class: 1.000000, Obj: 0.420248, No Obj: 0.002870, Avg Recall: 0.800000, count: 120 -Region Avg IOU: 0.608128, Class: 1.000000, Obj: 0.407168, No Obj: 0.002567, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.615029, Class: 1.000000, Obj: 0.426887, No Obj: 0.003279, Avg Recall: 0.750000, count: 116 -Region Avg IOU: 0.625309, Class: 1.000000, Obj: 0.411442, No Obj: 0.002802, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.658826, Class: 1.000000, Obj: 0.381319, No Obj: 0.002678, Avg Recall: 0.793103, count: 87 -Region Avg IOU: 0.613385, Class: 1.000000, Obj: 0.415392, No Obj: 0.002388, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.639055, Class: 1.000000, Obj: 0.433612, No Obj: 0.002945, Avg Recall: 0.816514, count: 109 -6071: 0.436710, 0.434307 avg, 0.001000 rate, 26.913984 seconds, 1554176 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.638985, Class: 1.000000, Obj: 0.412198, No Obj: 0.002960, Avg Recall: 0.771429, count: 140 -Region Avg IOU: 0.656672, Class: 1.000000, Obj: 0.408963, No Obj: 0.002779, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.646180, Class: 1.000000, Obj: 0.406526, No Obj: 0.002835, Avg Recall: 0.805310, count: 113 -Region Avg IOU: 0.671875, Class: 1.000000, Obj: 0.375804, No Obj: 0.002264, Avg Recall: 0.852273, count: 88 -Region Avg IOU: 0.663090, Class: 1.000000, Obj: 0.390256, No Obj: 0.002684, Avg Recall: 0.822430, count: 107 -Region Avg IOU: 0.624703, Class: 1.000000, Obj: 0.388114, No Obj: 0.002733, Avg Recall: 0.748031, count: 127 -Region Avg IOU: 0.663084, Class: 1.000000, Obj: 0.408342, No Obj: 0.002551, Avg Recall: 0.833333, count: 96 -Region Avg IOU: 0.635023, Class: 1.000000, Obj: 0.434384, No Obj: 0.002734, Avg Recall: 0.780000, count: 100 -6072: 0.464855, 0.437362 avg, 0.001000 rate, 27.057346 seconds, 1554432 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.624425, Class: 1.000000, Obj: 0.427489, No Obj: 0.003174, Avg Recall: 0.736434, count: 129 -Region Avg IOU: 0.656550, Class: 1.000000, Obj: 0.351894, No Obj: 0.003160, Avg Recall: 0.829630, count: 135 -Region Avg IOU: 0.656940, Class: 1.000000, Obj: 0.412610, No Obj: 0.002509, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.646469, Class: 1.000000, Obj: 0.416072, No Obj: 0.002716, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.637251, Class: 1.000000, Obj: 0.394262, No Obj: 0.003014, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.612748, Class: 1.000000, Obj: 0.411512, No Obj: 0.002424, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.664251, Class: 1.000000, Obj: 0.456332, No Obj: 0.002504, Avg Recall: 0.835165, count: 91 -Region Avg IOU: 0.647158, Class: 1.000000, Obj: 0.375461, No Obj: 0.003182, Avg Recall: 0.765517, count: 145 -6073: 0.446385, 0.438264 avg, 0.001000 rate, 26.755943 seconds, 1554688 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.642326, Class: 1.000000, Obj: 0.390744, No Obj: 0.002586, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.658231, Class: 1.000000, Obj: 0.459534, No Obj: 0.002610, Avg Recall: 0.813084, count: 107 -Region Avg IOU: 0.679228, Class: 1.000000, Obj: 0.340196, No Obj: 0.002529, Avg Recall: 0.879630, count: 108 -Region Avg IOU: 0.630679, Class: 1.000000, Obj: 0.375218, No Obj: 0.002822, Avg Recall: 0.776000, count: 125 -Region Avg IOU: 0.642883, Class: 1.000000, Obj: 0.389415, No Obj: 0.002869, Avg Recall: 0.748031, count: 127 -Region Avg IOU: 0.659036, Class: 1.000000, Obj: 0.430454, No Obj: 0.002611, Avg Recall: 0.843137, count: 102 -Region Avg IOU: 0.626442, Class: 1.000000, Obj: 0.353232, No Obj: 0.002361, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.656170, Class: 1.000000, Obj: 0.370957, No Obj: 0.002704, Avg Recall: 0.844037, count: 109 -6074: 0.458246, 0.440262 avg, 0.001000 rate, 27.909739 seconds, 1554944 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.621931, Class: 1.000000, Obj: 0.376874, No Obj: 0.002624, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.629166, Class: 1.000000, Obj: 0.425794, No Obj: 0.002836, Avg Recall: 0.754098, count: 122 -Region Avg IOU: 0.643116, Class: 1.000000, Obj: 0.427957, No Obj: 0.002530, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.616757, Class: 1.000000, Obj: 0.404327, No Obj: 0.002553, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.685469, Class: 1.000000, Obj: 0.406487, No Obj: 0.002343, Avg Recall: 0.783132, count: 83 -Region Avg IOU: 0.674781, Class: 1.000000, Obj: 0.444772, No Obj: 0.002604, Avg Recall: 0.853658, count: 82 -Region Avg IOU: 0.646051, Class: 1.000000, Obj: 0.413752, No Obj: 0.002675, Avg Recall: 0.789474, count: 114 -Region Avg IOU: 0.626204, Class: 1.000000, Obj: 0.406540, No Obj: 0.002699, Avg Recall: 0.734694, count: 98 -6075: 0.441180, 0.440354 avg, 0.001000 rate, 28.187153 seconds, 1555200 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.632503, Class: 1.000000, Obj: 0.387260, No Obj: 0.003103, Avg Recall: 0.760274, count: 146 -Region Avg IOU: 0.653419, Class: 1.000000, Obj: 0.462474, No Obj: 0.003087, Avg Recall: 0.752000, count: 125 -Region Avg IOU: 0.662760, Class: 1.000000, Obj: 0.405095, No Obj: 0.002729, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.634637, Class: 1.000000, Obj: 0.403898, No Obj: 0.003215, Avg Recall: 0.800000, count: 120 -Region Avg IOU: 0.655794, Class: 1.000000, Obj: 0.409196, No Obj: 0.003023, Avg Recall: 0.806452, count: 124 -Region Avg IOU: 0.635643, Class: 1.000000, Obj: 0.474732, No Obj: 0.002898, Avg Recall: 0.778761, count: 113 -Region Avg IOU: 0.639682, Class: 1.000000, Obj: 0.427538, No Obj: 0.002645, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.638428, Class: 1.000000, Obj: 0.399424, No Obj: 0.002757, Avg Recall: 0.820000, count: 100 -6076: 0.514464, 0.447765 avg, 0.001000 rate, 27.793888 seconds, 1555456 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.641572, Class: 1.000000, Obj: 0.375512, No Obj: 0.002547, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.655167, Class: 1.000000, Obj: 0.443616, No Obj: 0.002756, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.673447, Class: 1.000000, Obj: 0.434627, No Obj: 0.002743, Avg Recall: 0.854167, count: 96 -Region Avg IOU: 0.684562, Class: 1.000000, Obj: 0.440216, No Obj: 0.003050, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.655443, Class: 1.000000, Obj: 0.420590, No Obj: 0.002877, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.636125, Class: 1.000000, Obj: 0.436331, No Obj: 0.002534, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.624486, Class: 1.000000, Obj: 0.381922, No Obj: 0.003144, Avg Recall: 0.769231, count: 130 -Region Avg IOU: 0.597917, Class: 1.000000, Obj: 0.354857, No Obj: 0.002832, Avg Recall: 0.715517, count: 116 -6077: 0.431672, 0.446156 avg, 0.001000 rate, 28.999657 seconds, 1555712 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.646755, Class: 1.000000, Obj: 0.436829, No Obj: 0.002710, Avg Recall: 0.776316, count: 76 -Region Avg IOU: 0.646380, Class: 1.000000, Obj: 0.457905, No Obj: 0.002324, Avg Recall: 0.800000, count: 70 -Region Avg IOU: 0.587401, Class: 1.000000, Obj: 0.414909, No Obj: 0.002677, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.650326, Class: 1.000000, Obj: 0.470665, No Obj: 0.002952, Avg Recall: 0.827586, count: 116 -Region Avg IOU: 0.686857, Class: 1.000000, Obj: 0.468472, No Obj: 0.003031, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.630390, Class: 1.000000, Obj: 0.425559, No Obj: 0.003153, Avg Recall: 0.793388, count: 121 -Region Avg IOU: 0.627604, Class: 1.000000, Obj: 0.396978, No Obj: 0.002981, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.628888, Class: 1.000000, Obj: 0.430855, No Obj: 0.002810, Avg Recall: 0.757282, count: 103 -6078: 0.365856, 0.438126 avg, 0.001000 rate, 28.496582 seconds, 1555968 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.661806, Class: 1.000000, Obj: 0.426689, No Obj: 0.003081, Avg Recall: 0.826772, count: 127 -Region Avg IOU: 0.631052, Class: 1.000000, Obj: 0.455401, No Obj: 0.003019, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.656913, Class: 1.000000, Obj: 0.461805, No Obj: 0.002849, Avg Recall: 0.800000, count: 75 -Region Avg IOU: 0.659869, Class: 1.000000, Obj: 0.405362, No Obj: 0.002990, Avg Recall: 0.840000, count: 100 -Region Avg IOU: 0.661101, Class: 1.000000, Obj: 0.427750, No Obj: 0.002926, Avg Recall: 0.820000, count: 100 -Region Avg IOU: 0.630250, Class: 1.000000, Obj: 0.422444, No Obj: 0.003142, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.668761, Class: 1.000000, Obj: 0.452962, No Obj: 0.002974, Avg Recall: 0.817308, count: 104 -Region Avg IOU: 0.662432, Class: 1.000000, Obj: 0.420632, No Obj: 0.003231, Avg Recall: 0.848214, count: 112 -6079: 0.411237, 0.435437 avg, 0.001000 rate, 27.452965 seconds, 1556224 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.631966, Class: 1.000000, Obj: 0.430539, No Obj: 0.002649, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.632770, Class: 1.000000, Obj: 0.467060, No Obj: 0.003082, Avg Recall: 0.761364, count: 88 -Region Avg IOU: 0.641816, Class: 1.000000, Obj: 0.409321, No Obj: 0.002909, Avg Recall: 0.766129, count: 124 -Region Avg IOU: 0.658061, Class: 1.000000, Obj: 0.487627, No Obj: 0.002963, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.638451, Class: 1.000000, Obj: 0.420927, No Obj: 0.002825, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.606852, Class: 1.000000, Obj: 0.405758, No Obj: 0.002810, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.645637, Class: 1.000000, Obj: 0.458733, No Obj: 0.002392, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.692079, Class: 1.000000, Obj: 0.456978, No Obj: 0.003600, Avg Recall: 0.896825, count: 126 -6080: 0.396982, 0.431591 avg, 0.001000 rate, 28.202917 seconds, 1556480 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 13.324203 seconds -Region Avg IOU: 0.585268, Class: 1.000000, Obj: 0.372997, No Obj: 0.004822, Avg Recall: 0.686956, count: 115 -Region Avg IOU: 0.520992, Class: 1.000000, Obj: 0.346316, No Obj: 0.003382, Avg Recall: 0.566265, count: 83 -Region Avg IOU: 0.526175, Class: 1.000000, Obj: 0.340316, No Obj: 0.003794, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.480098, Class: 1.000000, Obj: 0.300348, No Obj: 0.004774, Avg Recall: 0.496689, count: 151 -Region Avg IOU: 0.537794, Class: 1.000000, Obj: 0.355044, No Obj: 0.004624, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.593456, Class: 1.000000, Obj: 0.381815, No Obj: 0.003609, Avg Recall: 0.688312, count: 77 -Region Avg IOU: 0.521221, Class: 1.000000, Obj: 0.311200, No Obj: 0.003933, Avg Recall: 0.605042, count: 119 -Region Avg IOU: 0.493393, Class: 1.000000, Obj: 0.321972, No Obj: 0.004127, Avg Recall: 0.596639, count: 119 -6081: 0.464003, 0.434833 avg, 0.001000 rate, 16.897591 seconds, 1556736 images -Loaded: 5.381448 seconds -Region Avg IOU: 0.510960, Class: 1.000000, Obj: 0.362808, No Obj: 0.004883, Avg Recall: 0.586466, count: 133 -Region Avg IOU: 0.461448, Class: 1.000000, Obj: 0.271186, No Obj: 0.004301, Avg Recall: 0.456522, count: 138 -Region Avg IOU: 0.458548, Class: 1.000000, Obj: 0.334248, No Obj: 0.004641, Avg Recall: 0.513043, count: 115 -Region Avg IOU: 0.521508, Class: 1.000000, Obj: 0.344818, No Obj: 0.004188, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.562534, Class: 1.000000, Obj: 0.336291, No Obj: 0.004996, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.515403, Class: 1.000000, Obj: 0.308242, No Obj: 0.003872, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.536956, Class: 1.000000, Obj: 0.320961, No Obj: 0.004539, Avg Recall: 0.590164, count: 122 -Region Avg IOU: 0.511797, Class: 1.000000, Obj: 0.345312, No Obj: 0.003921, Avg Recall: 0.536842, count: 95 -6082: 0.486637, 0.440013 avg, 0.001000 rate, 17.974945 seconds, 1556992 images -Loaded: 2.820086 seconds -Region Avg IOU: 0.497677, Class: 1.000000, Obj: 0.299552, No Obj: 0.004081, Avg Recall: 0.559633, count: 109 -Region Avg IOU: 0.478318, Class: 1.000000, Obj: 0.311028, No Obj: 0.004704, Avg Recall: 0.500000, count: 124 -Region Avg IOU: 0.533792, Class: 1.000000, Obj: 0.346983, No Obj: 0.004745, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.503229, Class: 1.000000, Obj: 0.360222, No Obj: 0.003757, Avg Recall: 0.538462, count: 104 -Region Avg IOU: 0.530573, Class: 1.000000, Obj: 0.353171, No Obj: 0.004854, Avg Recall: 0.578947, count: 114 -Region Avg IOU: 0.489306, Class: 1.000000, Obj: 0.320488, No Obj: 0.004149, Avg Recall: 0.500000, count: 118 -Region Avg IOU: 0.512848, Class: 1.000000, Obj: 0.325905, No Obj: 0.004260, Avg Recall: 0.557522, count: 113 -Region Avg IOU: 0.513562, Class: 1.000000, Obj: 0.323358, No Obj: 0.004046, Avg Recall: 0.622807, count: 114 -6083: 0.445019, 0.440514 avg, 0.001000 rate, 16.503420 seconds, 1557248 images -Loaded: 6.061129 seconds -Region Avg IOU: 0.493626, Class: 1.000000, Obj: 0.387573, No Obj: 0.004157, Avg Recall: 0.526786, count: 112 -Region Avg IOU: 0.505558, Class: 1.000000, Obj: 0.344337, No Obj: 0.004786, Avg Recall: 0.533898, count: 118 -Region Avg IOU: 0.532733, Class: 1.000000, Obj: 0.367095, No Obj: 0.004609, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.520498, Class: 1.000000, Obj: 0.332709, No Obj: 0.004665, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.474917, Class: 1.000000, Obj: 0.336611, No Obj: 0.004688, Avg Recall: 0.511278, count: 133 -Region Avg IOU: 0.504353, Class: 1.000000, Obj: 0.329598, No Obj: 0.004447, Avg Recall: 0.529851, count: 134 -Region Avg IOU: 0.470468, Class: 1.000000, Obj: 0.346856, No Obj: 0.004219, Avg Recall: 0.535354, count: 99 -Region Avg IOU: 0.500461, Class: 1.000000, Obj: 0.342563, No Obj: 0.004277, Avg Recall: 0.524752, count: 101 -6084: 0.465701, 0.443032 avg, 0.001000 rate, 17.163845 seconds, 1557504 images -Loaded: 2.833403 seconds -Region Avg IOU: 0.501708, Class: 1.000000, Obj: 0.359945, No Obj: 0.005003, Avg Recall: 0.531746, count: 126 -Region Avg IOU: 0.483976, Class: 1.000000, Obj: 0.341586, No Obj: 0.004404, Avg Recall: 0.504348, count: 115 -Region Avg IOU: 0.537640, Class: 1.000000, Obj: 0.379103, No Obj: 0.004670, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.563830, Class: 1.000000, Obj: 0.401912, No Obj: 0.003689, Avg Recall: 0.704225, count: 71 -Region Avg IOU: 0.498309, Class: 1.000000, Obj: 0.372784, No Obj: 0.004514, Avg Recall: 0.601695, count: 118 -Region Avg IOU: 0.539289, Class: 1.000000, Obj: 0.411001, No Obj: 0.004427, Avg Recall: 0.611940, count: 67 -Region Avg IOU: 0.529731, Class: 1.000000, Obj: 0.412233, No Obj: 0.003999, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.512118, Class: 1.000000, Obj: 0.354336, No Obj: 0.004996, Avg Recall: 0.534351, count: 131 -6085: 0.396508, 0.438380 avg, 0.001000 rate, 16.566120 seconds, 1557760 images -Loaded: 3.635303 seconds -Region Avg IOU: 0.571065, Class: 1.000000, Obj: 0.403702, No Obj: 0.004336, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.509965, Class: 1.000000, Obj: 0.344146, No Obj: 0.004212, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.489689, Class: 1.000000, Obj: 0.347977, No Obj: 0.004227, Avg Recall: 0.523810, count: 105 -Region Avg IOU: 0.549897, Class: 1.000000, Obj: 0.376320, No Obj: 0.005241, Avg Recall: 0.579710, count: 138 -Region Avg IOU: 0.509645, Class: 1.000000, Obj: 0.390027, No Obj: 0.004670, Avg Recall: 0.578947, count: 114 -Region Avg IOU: 0.542478, Class: 1.000000, Obj: 0.377703, No Obj: 0.004683, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.527059, Class: 1.000000, Obj: 0.373420, No Obj: 0.004142, Avg Recall: 0.582278, count: 79 -Region Avg IOU: 0.502030, Class: 1.000000, Obj: 0.357901, No Obj: 0.004226, Avg Recall: 0.573913, count: 115 -6086: 0.438741, 0.438416 avg, 0.001000 rate, 18.224804 seconds, 1558016 images -Loaded: 2.250273 seconds -Region Avg IOU: 0.465425, Class: 1.000000, Obj: 0.344213, No Obj: 0.004877, Avg Recall: 0.530769, count: 130 -Region Avg IOU: 0.533494, Class: 1.000000, Obj: 0.376471, No Obj: 0.005130, Avg Recall: 0.608000, count: 125 -Region Avg IOU: 0.520028, Class: 1.000000, Obj: 0.374888, No Obj: 0.004769, Avg Recall: 0.575758, count: 99 -Region Avg IOU: 0.540730, Class: 1.000000, Obj: 0.374337, No Obj: 0.004819, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.507168, Class: 1.000000, Obj: 0.364150, No Obj: 0.003974, Avg Recall: 0.560440, count: 91 -Region Avg IOU: 0.539179, Class: 1.000000, Obj: 0.391173, No Obj: 0.004553, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.535348, Class: 1.000000, Obj: 0.380495, No Obj: 0.004462, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.540129, Class: 1.000000, Obj: 0.397555, No Obj: 0.004546, Avg Recall: 0.592593, count: 81 -6087: 0.449232, 0.439498 avg, 0.001000 rate, 17.797064 seconds, 1558272 images -Loaded: 5.591257 seconds -Region Avg IOU: 0.542333, Class: 1.000000, Obj: 0.423225, No Obj: 0.004747, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.560045, Class: 1.000000, Obj: 0.388324, No Obj: 0.005153, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.475141, Class: 1.000000, Obj: 0.383017, No Obj: 0.004909, Avg Recall: 0.500000, count: 118 -Region Avg IOU: 0.518069, Class: 1.000000, Obj: 0.405786, No Obj: 0.004840, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.474874, Class: 1.000000, Obj: 0.345315, No Obj: 0.003947, Avg Recall: 0.522727, count: 88 -Region Avg IOU: 0.560936, Class: 1.000000, Obj: 0.410658, No Obj: 0.005446, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.528363, Class: 1.000000, Obj: 0.401485, No Obj: 0.004280, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.485997, Class: 1.000000, Obj: 0.381983, No Obj: 0.005084, Avg Recall: 0.523364, count: 107 -6088: 0.429987, 0.438546 avg, 0.001000 rate, 20.885599 seconds, 1558528 images -Loaded: 0.984493 seconds -Region Avg IOU: 0.546241, Class: 1.000000, Obj: 0.405380, No Obj: 0.005577, Avg Recall: 0.603053, count: 131 -Region Avg IOU: 0.530602, Class: 1.000000, Obj: 0.418097, No Obj: 0.004823, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.532300, Class: 1.000000, Obj: 0.404993, No Obj: 0.005012, Avg Recall: 0.572727, count: 110 -Region Avg IOU: 0.504183, Class: 1.000000, Obj: 0.383737, No Obj: 0.004448, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.542591, Class: 1.000000, Obj: 0.409349, No Obj: 0.004298, Avg Recall: 0.625000, count: 72 -Region Avg IOU: 0.460427, Class: 1.000000, Obj: 0.339526, No Obj: 0.004461, Avg Recall: 0.486486, count: 111 -Region Avg IOU: 0.516644, Class: 1.000000, Obj: 0.404438, No Obj: 0.004490, Avg Recall: 0.633333, count: 90 -Region Avg IOU: 0.525984, Class: 1.000000, Obj: 0.404650, No Obj: 0.004599, Avg Recall: 0.605769, count: 104 -6089: 0.419351, 0.436627 avg, 0.001000 rate, 17.797115 seconds, 1558784 images -Loaded: 3.101241 seconds -Region Avg IOU: 0.538010, Class: 1.000000, Obj: 0.392320, No Obj: 0.005668, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.516698, Class: 1.000000, Obj: 0.346641, No Obj: 0.005517, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.479421, Class: 1.000000, Obj: 0.366591, No Obj: 0.004401, Avg Recall: 0.532110, count: 109 -Region Avg IOU: 0.515587, Class: 1.000000, Obj: 0.376120, No Obj: 0.004695, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.513462, Class: 1.000000, Obj: 0.370632, No Obj: 0.005068, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.489758, Class: 1.000000, Obj: 0.367308, No Obj: 0.005346, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.483013, Class: 1.000000, Obj: 0.375315, No Obj: 0.004562, Avg Recall: 0.536585, count: 123 -Region Avg IOU: 0.506089, Class: 1.000000, Obj: 0.369534, No Obj: 0.003951, Avg Recall: 0.584270, count: 89 -6090: 0.448617, 0.437826 avg, 0.001000 rate, 16.661463 seconds, 1559040 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 0.180892 seconds -Region Avg IOU: 0.555927, Class: 1.000000, Obj: 0.396857, No Obj: 0.004478, Avg Recall: 0.630252, count: 119 -Region Avg IOU: 0.572919, Class: 1.000000, Obj: 0.422496, No Obj: 0.003770, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.627270, Class: 1.000000, Obj: 0.459863, No Obj: 0.003360, Avg Recall: 0.744444, count: 90 -Region Avg IOU: 0.621403, Class: 1.000000, Obj: 0.457485, No Obj: 0.004004, Avg Recall: 0.765432, count: 81 -Region Avg IOU: 0.556550, Class: 1.000000, Obj: 0.427286, No Obj: 0.004934, Avg Recall: 0.642857, count: 126 -Region Avg IOU: 0.569448, Class: 1.000000, Obj: 0.436540, No Obj: 0.003671, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.598514, Class: 1.000000, Obj: 0.499378, No Obj: 0.004070, Avg Recall: 0.705882, count: 85 -Region Avg IOU: 0.532285, Class: 1.000000, Obj: 0.464410, No Obj: 0.003490, Avg Recall: 0.592593, count: 81 -6091: 0.429533, 0.436997 avg, 0.001000 rate, 22.028845 seconds, 1559296 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.574680, Class: 1.000000, Obj: 0.439096, No Obj: 0.004014, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.607253, Class: 1.000000, Obj: 0.411298, No Obj: 0.004317, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.522733, Class: 1.000000, Obj: 0.393175, No Obj: 0.004176, Avg Recall: 0.557377, count: 122 -Region Avg IOU: 0.621741, Class: 1.000000, Obj: 0.476904, No Obj: 0.003539, Avg Recall: 0.756410, count: 78 -Region Avg IOU: 0.614284, Class: 1.000000, Obj: 0.455059, No Obj: 0.003489, Avg Recall: 0.728395, count: 81 -Region Avg IOU: 0.525125, Class: 1.000000, Obj: 0.414231, No Obj: 0.003646, Avg Recall: 0.579365, count: 126 -Region Avg IOU: 0.568841, Class: 1.000000, Obj: 0.414983, No Obj: 0.004342, Avg Recall: 0.691729, count: 133 -Region Avg IOU: 0.560416, Class: 1.000000, Obj: 0.408445, No Obj: 0.003883, Avg Recall: 0.644860, count: 107 -6092: 0.400782, 0.433375 avg, 0.001000 rate, 23.339939 seconds, 1559552 images -Loaded: 0.108355 seconds -Region Avg IOU: 0.618647, Class: 1.000000, Obj: 0.391037, No Obj: 0.003402, Avg Recall: 0.775281, count: 89 -Region Avg IOU: 0.568303, Class: 1.000000, Obj: 0.420610, No Obj: 0.003308, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.572631, Class: 1.000000, Obj: 0.445060, No Obj: 0.003575, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.607543, Class: 1.000000, Obj: 0.432128, No Obj: 0.003969, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.563042, Class: 1.000000, Obj: 0.394717, No Obj: 0.003535, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.620556, Class: 1.000000, Obj: 0.404754, No Obj: 0.004365, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.618196, Class: 1.000000, Obj: 0.433796, No Obj: 0.004110, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.586348, Class: 1.000000, Obj: 0.443274, No Obj: 0.004065, Avg Recall: 0.655172, count: 116 -6093: 0.398466, 0.429884 avg, 0.001000 rate, 24.372309 seconds, 1559808 images -Loaded: 0.063304 seconds -Region Avg IOU: 0.601820, Class: 1.000000, Obj: 0.415694, No Obj: 0.003626, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.560783, Class: 1.000000, Obj: 0.371110, No Obj: 0.003814, Avg Recall: 0.663866, count: 119 -Region Avg IOU: 0.600331, Class: 1.000000, Obj: 0.416334, No Obj: 0.003423, Avg Recall: 0.685393, count: 89 -Region Avg IOU: 0.616225, Class: 1.000000, Obj: 0.459000, No Obj: 0.004252, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.624327, Class: 1.000000, Obj: 0.412195, No Obj: 0.004162, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.537779, Class: 1.000000, Obj: 0.366216, No Obj: 0.003661, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.562590, Class: 1.000000, Obj: 0.384619, No Obj: 0.004069, Avg Recall: 0.658730, count: 126 -Region Avg IOU: 0.584649, Class: 1.000000, Obj: 0.448994, No Obj: 0.004166, Avg Recall: 0.697674, count: 86 -6094: 0.438692, 0.430765 avg, 0.001000 rate, 24.702587 seconds, 1560064 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.595502, Class: 1.000000, Obj: 0.378539, No Obj: 0.003873, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.550798, Class: 1.000000, Obj: 0.375578, No Obj: 0.003714, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.615144, Class: 1.000000, Obj: 0.377058, No Obj: 0.003005, Avg Recall: 0.706667, count: 75 -Region Avg IOU: 0.549137, Class: 1.000000, Obj: 0.390953, No Obj: 0.003257, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.567799, Class: 1.000000, Obj: 0.383013, No Obj: 0.003840, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.598235, Class: 1.000000, Obj: 0.376019, No Obj: 0.002956, Avg Recall: 0.750000, count: 84 -Region Avg IOU: 0.627311, Class: 1.000000, Obj: 0.400238, No Obj: 0.003602, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.602653, Class: 1.000000, Obj: 0.444389, No Obj: 0.003376, Avg Recall: 0.736364, count: 110 -6095: 0.400449, 0.427733 avg, 0.001000 rate, 22.696873 seconds, 1560320 images -Loaded: 3.139259 seconds -Region Avg IOU: 0.545823, Class: 1.000000, Obj: 0.375839, No Obj: 0.003366, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.647162, Class: 1.000000, Obj: 0.454726, No Obj: 0.003945, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.601742, Class: 1.000000, Obj: 0.365131, No Obj: 0.003799, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.579615, Class: 1.000000, Obj: 0.387101, No Obj: 0.004301, Avg Recall: 0.687023, count: 131 -Region Avg IOU: 0.560553, Class: 1.000000, Obj: 0.408408, No Obj: 0.003601, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.593288, Class: 1.000000, Obj: 0.378374, No Obj: 0.003379, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.543639, Class: 1.000000, Obj: 0.383023, No Obj: 0.003395, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.567674, Class: 1.000000, Obj: 0.367592, No Obj: 0.003275, Avg Recall: 0.627273, count: 110 -6096: 0.395311, 0.424491 avg, 0.001000 rate, 24.012396 seconds, 1560576 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.588813, Class: 1.000000, Obj: 0.380754, No Obj: 0.002946, Avg Recall: 0.682353, count: 85 -Region Avg IOU: 0.537001, Class: 1.000000, Obj: 0.330972, No Obj: 0.003653, Avg Recall: 0.630435, count: 138 -Region Avg IOU: 0.560638, Class: 1.000000, Obj: 0.358428, No Obj: 0.003263, Avg Recall: 0.650000, count: 120 -Region Avg IOU: 0.619645, Class: 1.000000, Obj: 0.383833, No Obj: 0.004603, Avg Recall: 0.744526, count: 137 -Region Avg IOU: 0.584044, Class: 1.000000, Obj: 0.357620, No Obj: 0.003776, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.563260, Class: 1.000000, Obj: 0.355771, No Obj: 0.003219, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.607567, Class: 1.000000, Obj: 0.445657, No Obj: 0.002975, Avg Recall: 0.680556, count: 72 -Region Avg IOU: 0.604688, Class: 1.000000, Obj: 0.374705, No Obj: 0.004065, Avg Recall: 0.741935, count: 124 -6097: 0.452053, 0.427247 avg, 0.001000 rate, 24.085993 seconds, 1560832 images -Loaded: 0.234140 seconds -Region Avg IOU: 0.600085, Class: 1.000000, Obj: 0.408175, No Obj: 0.003519, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.572424, Class: 1.000000, Obj: 0.356808, No Obj: 0.003166, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.570841, Class: 1.000000, Obj: 0.383482, No Obj: 0.004194, Avg Recall: 0.639098, count: 133 -Region Avg IOU: 0.580300, Class: 1.000000, Obj: 0.362160, No Obj: 0.003668, Avg Recall: 0.684615, count: 130 -Region Avg IOU: 0.546985, Class: 1.000000, Obj: 0.362809, No Obj: 0.003212, Avg Recall: 0.598361, count: 122 -Region Avg IOU: 0.594789, Class: 1.000000, Obj: 0.374645, No Obj: 0.003310, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.547843, Class: 1.000000, Obj: 0.364610, No Obj: 0.003422, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.557328, Class: 1.000000, Obj: 0.357499, No Obj: 0.003196, Avg Recall: 0.608247, count: 97 -6098: 0.451760, 0.429699 avg, 0.001000 rate, 24.632294 seconds, 1561088 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.564600, Class: 1.000000, Obj: 0.365565, No Obj: 0.003435, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.577947, Class: 1.000000, Obj: 0.347815, No Obj: 0.003809, Avg Recall: 0.649635, count: 137 -Region Avg IOU: 0.581610, Class: 1.000000, Obj: 0.421395, No Obj: 0.003743, Avg Recall: 0.688073, count: 109 -Region Avg IOU: 0.562766, Class: 1.000000, Obj: 0.363669, No Obj: 0.004108, Avg Recall: 0.646667, count: 150 -Region Avg IOU: 0.627463, Class: 1.000000, Obj: 0.392889, No Obj: 0.003059, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.564949, Class: 1.000000, Obj: 0.382674, No Obj: 0.004218, Avg Recall: 0.639344, count: 122 -Region Avg IOU: 0.574053, Class: 1.000000, Obj: 0.334268, No Obj: 0.003686, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.598314, Class: 1.000000, Obj: 0.406378, No Obj: 0.003548, Avg Recall: 0.708738, count: 103 -6099: 0.417358, 0.428465 avg, 0.001000 rate, 23.520802 seconds, 1561344 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.605692, Class: 1.000000, Obj: 0.445419, No Obj: 0.003535, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.572963, Class: 1.000000, Obj: 0.416489, No Obj: 0.003310, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.613879, Class: 1.000000, Obj: 0.417327, No Obj: 0.003866, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.578031, Class: 1.000000, Obj: 0.377016, No Obj: 0.004198, Avg Recall: 0.650000, count: 140 -Region Avg IOU: 0.626686, Class: 1.000000, Obj: 0.419566, No Obj: 0.003781, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.563567, Class: 1.000000, Obj: 0.424244, No Obj: 0.004030, Avg Recall: 0.669565, count: 115 -Region Avg IOU: 0.594634, Class: 1.000000, Obj: 0.359721, No Obj: 0.003503, Avg Recall: 0.688889, count: 90 -Region Avg IOU: 0.567302, Class: 1.000000, Obj: 0.395850, No Obj: 0.004049, Avg Recall: 0.666667, count: 123 -6100: 0.431042, 0.428722 avg, 0.001000 rate, 23.866539 seconds, 1561600 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.908030 seconds -Region Avg IOU: 0.533564, Class: 1.000000, Obj: 0.372811, No Obj: 0.003804, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.504369, Class: 1.000000, Obj: 0.364868, No Obj: 0.004296, Avg Recall: 0.585938, count: 128 -Region Avg IOU: 0.530223, Class: 1.000000, Obj: 0.384949, No Obj: 0.004223, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.512825, Class: 1.000000, Obj: 0.365731, No Obj: 0.004031, Avg Recall: 0.565574, count: 122 -Region Avg IOU: 0.533378, Class: 1.000000, Obj: 0.365181, No Obj: 0.003793, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.554526, Class: 1.000000, Obj: 0.338649, No Obj: 0.004840, Avg Recall: 0.627907, count: 129 -Region Avg IOU: 0.542034, Class: 1.000000, Obj: 0.380918, No Obj: 0.004272, Avg Recall: 0.625000, count: 120 -Region Avg IOU: 0.559201, Class: 1.000000, Obj: 0.367925, No Obj: 0.004113, Avg Recall: 0.663551, count: 107 -6101: 0.497817, 0.435632 avg, 0.001000 rate, 21.548498 seconds, 1561856 images -Loaded: 0.562277 seconds -Region Avg IOU: 0.546836, Class: 1.000000, Obj: 0.392672, No Obj: 0.004392, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.536640, Class: 1.000000, Obj: 0.394171, No Obj: 0.003804, Avg Recall: 0.594937, count: 79 -Region Avg IOU: 0.496493, Class: 1.000000, Obj: 0.411345, No Obj: 0.003804, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.550423, Class: 1.000000, Obj: 0.424322, No Obj: 0.004257, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.551844, Class: 1.000000, Obj: 0.353456, No Obj: 0.004491, Avg Recall: 0.625000, count: 120 -Region Avg IOU: 0.550758, Class: 1.000000, Obj: 0.378628, No Obj: 0.004267, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.509884, Class: 1.000000, Obj: 0.374127, No Obj: 0.004516, Avg Recall: 0.538462, count: 117 -Region Avg IOU: 0.536071, Class: 1.000000, Obj: 0.391927, No Obj: 0.004890, Avg Recall: 0.621212, count: 132 -6102: 0.433851, 0.435454 avg, 0.001000 rate, 19.389790 seconds, 1562112 images -Loaded: 2.686266 seconds -Region Avg IOU: 0.533331, Class: 1.000000, Obj: 0.388472, No Obj: 0.004693, Avg Recall: 0.563636, count: 110 -Region Avg IOU: 0.497990, Class: 1.000000, Obj: 0.365278, No Obj: 0.004263, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.553751, Class: 1.000000, Obj: 0.402369, No Obj: 0.004530, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.497623, Class: 1.000000, Obj: 0.348297, No Obj: 0.003850, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.567893, Class: 1.000000, Obj: 0.396868, No Obj: 0.004725, Avg Recall: 0.666667, count: 123 -Region Avg IOU: 0.573586, Class: 1.000000, Obj: 0.420260, No Obj: 0.004163, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.518878, Class: 1.000000, Obj: 0.422088, No Obj: 0.004466, Avg Recall: 0.550847, count: 118 -Region Avg IOU: 0.525208, Class: 1.000000, Obj: 0.392539, No Obj: 0.004508, Avg Recall: 0.621622, count: 111 -6103: 0.448745, 0.436783 avg, 0.001000 rate, 18.783813 seconds, 1562368 images -Loaded: 2.258145 seconds -Region Avg IOU: 0.547694, Class: 1.000000, Obj: 0.423997, No Obj: 0.005043, Avg Recall: 0.643411, count: 129 -Region Avg IOU: 0.501029, Class: 1.000000, Obj: 0.363639, No Obj: 0.003943, Avg Recall: 0.515464, count: 97 -Region Avg IOU: 0.545121, Class: 1.000000, Obj: 0.401306, No Obj: 0.004265, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.543548, Class: 1.000000, Obj: 0.400862, No Obj: 0.004346, Avg Recall: 0.632184, count: 87 -Region Avg IOU: 0.556006, Class: 1.000000, Obj: 0.386411, No Obj: 0.005118, Avg Recall: 0.669291, count: 127 -Region Avg IOU: 0.563987, Class: 1.000000, Obj: 0.426527, No Obj: 0.004246, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.539620, Class: 1.000000, Obj: 0.382087, No Obj: 0.004594, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.551175, Class: 1.000000, Obj: 0.454670, No Obj: 0.004904, Avg Recall: 0.628571, count: 105 -6104: 0.454841, 0.438589 avg, 0.001000 rate, 21.811005 seconds, 1562624 images -Loaded: 1.010837 seconds -Region Avg IOU: 0.520331, Class: 1.000000, Obj: 0.415977, No Obj: 0.004212, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.531823, Class: 1.000000, Obj: 0.466989, No Obj: 0.004221, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.487635, Class: 1.000000, Obj: 0.400218, No Obj: 0.004272, Avg Recall: 0.490741, count: 108 -Region Avg IOU: 0.493906, Class: 1.000000, Obj: 0.372114, No Obj: 0.004140, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.526249, Class: 1.000000, Obj: 0.407308, No Obj: 0.004503, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.520231, Class: 1.000000, Obj: 0.397291, No Obj: 0.004544, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.520422, Class: 1.000000, Obj: 0.413622, No Obj: 0.004664, Avg Recall: 0.594828, count: 116 -Region Avg IOU: 0.554343, Class: 1.000000, Obj: 0.427602, No Obj: 0.003964, Avg Recall: 0.636364, count: 77 -6105: 0.420695, 0.436799 avg, 0.001000 rate, 19.108650 seconds, 1562880 images -Loaded: 1.125394 seconds -Region Avg IOU: 0.489705, Class: 1.000000, Obj: 0.385727, No Obj: 0.004237, Avg Recall: 0.532710, count: 107 -Region Avg IOU: 0.563877, Class: 1.000000, Obj: 0.425913, No Obj: 0.003820, Avg Recall: 0.683544, count: 79 -Region Avg IOU: 0.540859, Class: 1.000000, Obj: 0.423540, No Obj: 0.004877, Avg Recall: 0.635659, count: 129 -Region Avg IOU: 0.506948, Class: 1.000000, Obj: 0.391477, No Obj: 0.004193, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.571412, Class: 1.000000, Obj: 0.413486, No Obj: 0.004432, Avg Recall: 0.670455, count: 88 -Region Avg IOU: 0.518456, Class: 1.000000, Obj: 0.435053, No Obj: 0.004117, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.479156, Class: 1.000000, Obj: 0.390950, No Obj: 0.004158, Avg Recall: 0.500000, count: 104 -Region Avg IOU: 0.500513, Class: 1.000000, Obj: 0.380341, No Obj: 0.004161, Avg Recall: 0.559633, count: 109 -6106: 0.434463, 0.436566 avg, 0.001000 rate, 18.962383 seconds, 1563136 images -Loaded: 2.142524 seconds -Region Avg IOU: 0.554675, Class: 1.000000, Obj: 0.458111, No Obj: 0.004423, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.522926, Class: 1.000000, Obj: 0.338830, No Obj: 0.004129, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.451254, Class: 1.000000, Obj: 0.366583, No Obj: 0.003841, Avg Recall: 0.516484, count: 91 -Region Avg IOU: 0.489475, Class: 1.000000, Obj: 0.390125, No Obj: 0.003443, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.478756, Class: 1.000000, Obj: 0.375165, No Obj: 0.003868, Avg Recall: 0.521739, count: 92 -Region Avg IOU: 0.499687, Class: 1.000000, Obj: 0.330483, No Obj: 0.004345, Avg Recall: 0.580645, count: 124 -Region Avg IOU: 0.565406, Class: 1.000000, Obj: 0.438762, No Obj: 0.004281, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.537671, Class: 1.000000, Obj: 0.414853, No Obj: 0.004368, Avg Recall: 0.598214, count: 112 -6107: 0.415968, 0.434506 avg, 0.001000 rate, 18.867874 seconds, 1563392 images -Loaded: 3.529190 seconds -Region Avg IOU: 0.454716, Class: 1.000000, Obj: 0.347398, No Obj: 0.004197, Avg Recall: 0.465517, count: 116 -Region Avg IOU: 0.519646, Class: 1.000000, Obj: 0.373417, No Obj: 0.004211, Avg Recall: 0.538462, count: 117 -Region Avg IOU: 0.463548, Class: 1.000000, Obj: 0.351832, No Obj: 0.003847, Avg Recall: 0.530973, count: 113 -Region Avg IOU: 0.555175, Class: 1.000000, Obj: 0.375454, No Obj: 0.003779, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.516391, Class: 1.000000, Obj: 0.406733, No Obj: 0.004063, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.540683, Class: 1.000000, Obj: 0.400016, No Obj: 0.004365, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.525988, Class: 1.000000, Obj: 0.418776, No Obj: 0.003680, Avg Recall: 0.586957, count: 92 -Region Avg IOU: 0.563179, Class: 1.000000, Obj: 0.389935, No Obj: 0.004901, Avg Recall: 0.671875, count: 128 -6108: 0.427792, 0.433835 avg, 0.001000 rate, 19.864656 seconds, 1563648 images -Loaded: 2.558330 seconds -Region Avg IOU: 0.494144, Class: 1.000000, Obj: 0.373152, No Obj: 0.004098, Avg Recall: 0.481481, count: 108 -Region Avg IOU: 0.507449, Class: 1.000000, Obj: 0.355873, No Obj: 0.003782, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.542990, Class: 1.000000, Obj: 0.384732, No Obj: 0.004762, Avg Recall: 0.652893, count: 121 -Region Avg IOU: 0.500632, Class: 1.000000, Obj: 0.381813, No Obj: 0.003890, Avg Recall: 0.540816, count: 98 -Region Avg IOU: 0.504182, Class: 1.000000, Obj: 0.341378, No Obj: 0.004749, Avg Recall: 0.598361, count: 122 -Region Avg IOU: 0.535780, Class: 1.000000, Obj: 0.396665, No Obj: 0.003786, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.538160, Class: 1.000000, Obj: 0.374431, No Obj: 0.004084, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.522186, Class: 1.000000, Obj: 0.386844, No Obj: 0.003835, Avg Recall: 0.563218, count: 87 -6109: 0.442986, 0.434750 avg, 0.001000 rate, 20.271984 seconds, 1563904 images -Loaded: 2.685225 seconds -Region Avg IOU: 0.535686, Class: 1.000000, Obj: 0.347142, No Obj: 0.003463, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.537291, Class: 1.000000, Obj: 0.376879, No Obj: 0.003925, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.479322, Class: 1.000000, Obj: 0.350148, No Obj: 0.003740, Avg Recall: 0.482143, count: 112 -Region Avg IOU: 0.514019, Class: 1.000000, Obj: 0.358635, No Obj: 0.004360, Avg Recall: 0.586777, count: 121 -Region Avg IOU: 0.522108, Class: 1.000000, Obj: 0.357874, No Obj: 0.004017, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.509255, Class: 1.000000, Obj: 0.367567, No Obj: 0.003592, Avg Recall: 0.544554, count: 101 -Region Avg IOU: 0.542522, Class: 1.000000, Obj: 0.381162, No Obj: 0.004020, Avg Recall: 0.669565, count: 115 -Region Avg IOU: 0.564794, Class: 1.000000, Obj: 0.413789, No Obj: 0.003757, Avg Recall: 0.641304, count: 92 -6110: 0.415943, 0.432869 avg, 0.001000 rate, 20.335491 seconds, 1564160 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000076 seconds -Region Avg IOU: 0.627688, Class: 1.000000, Obj: 0.428926, No Obj: 0.002662, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.612371, Class: 1.000000, Obj: 0.414890, No Obj: 0.003293, Avg Recall: 0.763359, count: 131 -Region Avg IOU: 0.642956, Class: 1.000000, Obj: 0.414918, No Obj: 0.003075, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.645136, Class: 1.000000, Obj: 0.459227, No Obj: 0.002911, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.621284, Class: 1.000000, Obj: 0.414303, No Obj: 0.003196, Avg Recall: 0.760563, count: 142 -Region Avg IOU: 0.644323, Class: 1.000000, Obj: 0.453936, No Obj: 0.003672, Avg Recall: 0.797297, count: 148 -Region Avg IOU: 0.629530, Class: 1.000000, Obj: 0.378416, No Obj: 0.002694, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.652510, Class: 1.000000, Obj: 0.393666, No Obj: 0.002886, Avg Recall: 0.815217, count: 92 -6111: 0.451208, 0.434703 avg, 0.001000 rate, 26.447172 seconds, 1564416 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.644603, Class: 1.000000, Obj: 0.424385, No Obj: 0.003052, Avg Recall: 0.775862, count: 116 -Region Avg IOU: 0.612919, Class: 1.000000, Obj: 0.494844, No Obj: 0.002988, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.664212, Class: 1.000000, Obj: 0.485602, No Obj: 0.003114, Avg Recall: 0.801980, count: 101 -Region Avg IOU: 0.606666, Class: 1.000000, Obj: 0.391432, No Obj: 0.003102, Avg Recall: 0.729927, count: 137 -Region Avg IOU: 0.628104, Class: 1.000000, Obj: 0.444563, No Obj: 0.002937, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.621324, Class: 1.000000, Obj: 0.439842, No Obj: 0.002695, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.644899, Class: 1.000000, Obj: 0.412127, No Obj: 0.002899, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.651394, Class: 1.000000, Obj: 0.430859, No Obj: 0.003134, Avg Recall: 0.777778, count: 99 -6112: 0.411132, 0.432346 avg, 0.001000 rate, 27.071159 seconds, 1564672 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.595091, Class: 1.000000, Obj: 0.379350, No Obj: 0.002503, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.639317, Class: 1.000000, Obj: 0.395524, No Obj: 0.003247, Avg Recall: 0.761194, count: 134 -Region Avg IOU: 0.644026, Class: 1.000000, Obj: 0.383388, No Obj: 0.002713, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.638504, Class: 1.000000, Obj: 0.446172, No Obj: 0.003272, Avg Recall: 0.787402, count: 127 -Region Avg IOU: 0.603446, Class: 1.000000, Obj: 0.360253, No Obj: 0.003283, Avg Recall: 0.686131, count: 137 -Region Avg IOU: 0.598883, Class: 1.000000, Obj: 0.401841, No Obj: 0.002755, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.659675, Class: 1.000000, Obj: 0.427962, No Obj: 0.003087, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.608085, Class: 1.000000, Obj: 0.436152, No Obj: 0.002788, Avg Recall: 0.715686, count: 102 -6113: 0.424256, 0.431537 avg, 0.001000 rate, 27.033899 seconds, 1564928 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.623471, Class: 1.000000, Obj: 0.414011, No Obj: 0.002728, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.629500, Class: 1.000000, Obj: 0.437431, No Obj: 0.002222, Avg Recall: 0.750000, count: 88 -Region Avg IOU: 0.645888, Class: 1.000000, Obj: 0.363640, No Obj: 0.003369, Avg Recall: 0.779528, count: 127 -Region Avg IOU: 0.656453, Class: 1.000000, Obj: 0.455421, No Obj: 0.002775, Avg Recall: 0.813187, count: 91 -Region Avg IOU: 0.668056, Class: 1.000000, Obj: 0.452939, No Obj: 0.002834, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.681086, Class: 1.000000, Obj: 0.482014, No Obj: 0.002586, Avg Recall: 0.810127, count: 79 -Region Avg IOU: 0.639617, Class: 1.000000, Obj: 0.413915, No Obj: 0.003143, Avg Recall: 0.779661, count: 118 -Region Avg IOU: 0.626167, Class: 1.000000, Obj: 0.456254, No Obj: 0.002701, Avg Recall: 0.750000, count: 100 -6114: 0.384038, 0.426787 avg, 0.001000 rate, 29.194569 seconds, 1565184 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.601650, Class: 1.000000, Obj: 0.434077, No Obj: 0.003346, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.654033, Class: 1.000000, Obj: 0.428145, No Obj: 0.002695, Avg Recall: 0.813084, count: 107 -Region Avg IOU: 0.642135, Class: 1.000000, Obj: 0.356664, No Obj: 0.003007, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.650121, Class: 1.000000, Obj: 0.361805, No Obj: 0.002699, Avg Recall: 0.811111, count: 90 -Region Avg IOU: 0.621754, Class: 1.000000, Obj: 0.429786, No Obj: 0.002845, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.642299, Class: 1.000000, Obj: 0.428546, No Obj: 0.002768, Avg Recall: 0.807229, count: 83 -Region Avg IOU: 0.614772, Class: 1.000000, Obj: 0.421225, No Obj: 0.002418, Avg Recall: 0.779070, count: 86 -Region Avg IOU: 0.619426, Class: 1.000000, Obj: 0.426843, No Obj: 0.002945, Avg Recall: 0.764706, count: 119 -6115: 0.401116, 0.424220 avg, 0.001000 rate, 28.157579 seconds, 1565440 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.617730, Class: 1.000000, Obj: 0.365692, No Obj: 0.003264, Avg Recall: 0.775862, count: 116 -Region Avg IOU: 0.669076, Class: 1.000000, Obj: 0.455616, No Obj: 0.002524, Avg Recall: 0.820225, count: 89 -Region Avg IOU: 0.658120, Class: 1.000000, Obj: 0.399021, No Obj: 0.002519, Avg Recall: 0.833333, count: 90 -Region Avg IOU: 0.616107, Class: 1.000000, Obj: 0.405200, No Obj: 0.002864, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.604196, Class: 1.000000, Obj: 0.399798, No Obj: 0.002497, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.645891, Class: 1.000000, Obj: 0.440999, No Obj: 0.003010, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.648627, Class: 1.000000, Obj: 0.359745, No Obj: 0.002488, Avg Recall: 0.822222, count: 90 -Region Avg IOU: 0.641714, Class: 1.000000, Obj: 0.406750, No Obj: 0.002980, Avg Recall: 0.801653, count: 121 -6116: 0.406668, 0.422465 avg, 0.001000 rate, 29.193113 seconds, 1565696 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.666602, Class: 1.000000, Obj: 0.438100, No Obj: 0.002804, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.545175, Class: 1.000000, Obj: 0.360600, No Obj: 0.002673, Avg Recall: 0.632812, count: 128 -Region Avg IOU: 0.640004, Class: 1.000000, Obj: 0.405044, No Obj: 0.002939, Avg Recall: 0.829457, count: 129 -Region Avg IOU: 0.638309, Class: 1.000000, Obj: 0.428242, No Obj: 0.002736, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.658887, Class: 1.000000, Obj: 0.401608, No Obj: 0.003467, Avg Recall: 0.791045, count: 134 -Region Avg IOU: 0.637751, Class: 1.000000, Obj: 0.433766, No Obj: 0.002911, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.667982, Class: 1.000000, Obj: 0.407088, No Obj: 0.002678, Avg Recall: 0.824176, count: 91 -Region Avg IOU: 0.657710, Class: 1.000000, Obj: 0.424958, No Obj: 0.003748, Avg Recall: 0.797297, count: 148 -6117: 0.459775, 0.426196 avg, 0.001000 rate, 28.148163 seconds, 1565952 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.666364, Class: 1.000000, Obj: 0.473280, No Obj: 0.002643, Avg Recall: 0.804878, count: 82 -Region Avg IOU: 0.615952, Class: 1.000000, Obj: 0.409905, No Obj: 0.003185, Avg Recall: 0.726562, count: 128 -Region Avg IOU: 0.675618, Class: 1.000000, Obj: 0.435421, No Obj: 0.003006, Avg Recall: 0.858586, count: 99 -Region Avg IOU: 0.630154, Class: 1.000000, Obj: 0.424477, No Obj: 0.003117, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.634917, Class: 1.000000, Obj: 0.431816, No Obj: 0.003032, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.683215, Class: 1.000000, Obj: 0.448708, No Obj: 0.003189, Avg Recall: 0.836538, count: 104 -Region Avg IOU: 0.638147, Class: 1.000000, Obj: 0.408368, No Obj: 0.003143, Avg Recall: 0.808000, count: 125 -Region Avg IOU: 0.637899, Class: 1.000000, Obj: 0.427152, No Obj: 0.002884, Avg Recall: 0.785714, count: 98 -6118: 0.381675, 0.421744 avg, 0.001000 rate, 27.649475 seconds, 1566208 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.630921, Class: 1.000000, Obj: 0.397863, No Obj: 0.003371, Avg Recall: 0.783217, count: 143 -Region Avg IOU: 0.598876, Class: 1.000000, Obj: 0.352686, No Obj: 0.003031, Avg Recall: 0.710938, count: 128 -Region Avg IOU: 0.663923, Class: 1.000000, Obj: 0.445406, No Obj: 0.002477, Avg Recall: 0.848101, count: 79 -Region Avg IOU: 0.651118, Class: 1.000000, Obj: 0.397665, No Obj: 0.003428, Avg Recall: 0.791667, count: 120 -Region Avg IOU: 0.680405, Class: 1.000000, Obj: 0.471181, No Obj: 0.003221, Avg Recall: 0.846154, count: 117 -Region Avg IOU: 0.589664, Class: 1.000000, Obj: 0.397288, No Obj: 0.002902, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.630697, Class: 1.000000, Obj: 0.453726, No Obj: 0.002797, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.636234, Class: 1.000000, Obj: 0.408793, No Obj: 0.002521, Avg Recall: 0.767442, count: 86 -6119: 0.474640, 0.427033 avg, 0.001000 rate, 27.009552 seconds, 1566464 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.631524, Class: 1.000000, Obj: 0.429376, No Obj: 0.003646, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.655275, Class: 1.000000, Obj: 0.465088, No Obj: 0.003235, Avg Recall: 0.808989, count: 89 -Region Avg IOU: 0.637077, Class: 1.000000, Obj: 0.412559, No Obj: 0.002979, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.639667, Class: 1.000000, Obj: 0.404287, No Obj: 0.003966, Avg Recall: 0.769697, count: 165 -Region Avg IOU: 0.621750, Class: 1.000000, Obj: 0.444615, No Obj: 0.002808, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.652218, Class: 1.000000, Obj: 0.433673, No Obj: 0.003296, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.602716, Class: 1.000000, Obj: 0.381423, No Obj: 0.002992, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.689488, Class: 1.000000, Obj: 0.417819, No Obj: 0.003361, Avg Recall: 0.876190, count: 105 -6120: 0.442651, 0.428595 avg, 0.001000 rate, 26.404356 seconds, 1566720 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 17.198244 seconds -Region Avg IOU: 0.594916, Class: 1.000000, Obj: 0.421043, No Obj: 0.003056, Avg Recall: 0.658537, count: 82 -Region Avg IOU: 0.638094, Class: 1.000000, Obj: 0.461501, No Obj: 0.002956, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.635465, Class: 1.000000, Obj: 0.432620, No Obj: 0.002743, Avg Recall: 0.795455, count: 88 -Region Avg IOU: 0.629229, Class: 1.000000, Obj: 0.453131, No Obj: 0.003838, Avg Recall: 0.754098, count: 122 -Region Avg IOU: 0.552353, Class: 1.000000, Obj: 0.376401, No Obj: 0.003109, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.606939, Class: 1.000000, Obj: 0.410648, No Obj: 0.003185, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.618271, Class: 1.000000, Obj: 0.388430, No Obj: 0.003167, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.581738, Class: 1.000000, Obj: 0.416950, No Obj: 0.003275, Avg Recall: 0.700000, count: 100 -6121: 0.404695, 0.426205 avg, 0.001000 rate, 26.925833 seconds, 1566976 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.583928, Class: 1.000000, Obj: 0.369704, No Obj: 0.003042, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.608486, Class: 1.000000, Obj: 0.417261, No Obj: 0.002706, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.606762, Class: 1.000000, Obj: 0.378037, No Obj: 0.003223, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.686528, Class: 1.000000, Obj: 0.429251, No Obj: 0.003417, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.637359, Class: 1.000000, Obj: 0.445514, No Obj: 0.003367, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.636614, Class: 1.000000, Obj: 0.387625, No Obj: 0.003022, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.579336, Class: 1.000000, Obj: 0.350906, No Obj: 0.003750, Avg Recall: 0.694268, count: 157 -Region Avg IOU: 0.624793, Class: 1.000000, Obj: 0.429403, No Obj: 0.003400, Avg Recall: 0.723810, count: 105 -6122: 0.432559, 0.426840 avg, 0.001000 rate, 27.216576 seconds, 1567232 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.589378, Class: 1.000000, Obj: 0.393510, No Obj: 0.003650, Avg Recall: 0.717241, count: 145 -Region Avg IOU: 0.585853, Class: 1.000000, Obj: 0.447531, No Obj: 0.003254, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.602719, Class: 1.000000, Obj: 0.387713, No Obj: 0.003448, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.557155, Class: 1.000000, Obj: 0.370176, No Obj: 0.002999, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.623776, Class: 1.000000, Obj: 0.371905, No Obj: 0.003536, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.642719, Class: 1.000000, Obj: 0.436708, No Obj: 0.003910, Avg Recall: 0.759690, count: 129 -Region Avg IOU: 0.620499, Class: 1.000000, Obj: 0.394196, No Obj: 0.003103, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.649382, Class: 1.000000, Obj: 0.418012, No Obj: 0.003136, Avg Recall: 0.776596, count: 94 -6123: 0.446165, 0.428773 avg, 0.001000 rate, 24.119797 seconds, 1567488 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.671509, Class: 1.000000, Obj: 0.458580, No Obj: 0.003367, Avg Recall: 0.802632, count: 76 -Region Avg IOU: 0.641240, Class: 1.000000, Obj: 0.392247, No Obj: 0.003007, Avg Recall: 0.797753, count: 89 -Region Avg IOU: 0.686577, Class: 1.000000, Obj: 0.446299, No Obj: 0.003676, Avg Recall: 0.849558, count: 113 -Region Avg IOU: 0.629039, Class: 1.000000, Obj: 0.390341, No Obj: 0.003053, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.600810, Class: 1.000000, Obj: 0.404370, No Obj: 0.003736, Avg Recall: 0.735537, count: 121 -Region Avg IOU: 0.647238, Class: 1.000000, Obj: 0.450797, No Obj: 0.003377, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.629553, Class: 1.000000, Obj: 0.429580, No Obj: 0.003779, Avg Recall: 0.744681, count: 141 -Region Avg IOU: 0.639370, Class: 1.000000, Obj: 0.444460, No Obj: 0.003165, Avg Recall: 0.775281, count: 89 -6124: 0.377032, 0.423599 avg, 0.001000 rate, 26.497875 seconds, 1567744 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.675717, Class: 1.000000, Obj: 0.410584, No Obj: 0.003204, Avg Recall: 0.849462, count: 93 -Region Avg IOU: 0.584586, Class: 1.000000, Obj: 0.411840, No Obj: 0.003531, Avg Recall: 0.692857, count: 140 -Region Avg IOU: 0.630356, Class: 1.000000, Obj: 0.446356, No Obj: 0.003462, Avg Recall: 0.752137, count: 117 -Region Avg IOU: 0.609983, Class: 1.000000, Obj: 0.434637, No Obj: 0.003307, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.595944, Class: 1.000000, Obj: 0.398236, No Obj: 0.004279, Avg Recall: 0.697987, count: 149 -Region Avg IOU: 0.567764, Class: 1.000000, Obj: 0.370676, No Obj: 0.002834, Avg Recall: 0.625000, count: 120 -Region Avg IOU: 0.600261, Class: 1.000000, Obj: 0.410397, No Obj: 0.003529, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.629101, Class: 1.000000, Obj: 0.417733, No Obj: 0.003622, Avg Recall: 0.750000, count: 112 -6125: 0.444480, 0.425687 avg, 0.001000 rate, 25.628502 seconds, 1568000 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.601596, Class: 1.000000, Obj: 0.418429, No Obj: 0.002768, Avg Recall: 0.704545, count: 88 -Region Avg IOU: 0.574641, Class: 1.000000, Obj: 0.398324, No Obj: 0.003441, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.657558, Class: 1.000000, Obj: 0.439003, No Obj: 0.003017, Avg Recall: 0.813953, count: 86 -Region Avg IOU: 0.608656, Class: 1.000000, Obj: 0.422760, No Obj: 0.003445, Avg Recall: 0.752066, count: 121 -Region Avg IOU: 0.629517, Class: 1.000000, Obj: 0.427616, No Obj: 0.003322, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.588064, Class: 1.000000, Obj: 0.410785, No Obj: 0.003478, Avg Recall: 0.694656, count: 131 -Region Avg IOU: 0.627189, Class: 1.000000, Obj: 0.470052, No Obj: 0.003357, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.594755, Class: 1.000000, Obj: 0.432785, No Obj: 0.003295, Avg Recall: 0.688073, count: 109 -6126: 0.390977, 0.422216 avg, 0.001000 rate, 25.521343 seconds, 1568256 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.598596, Class: 1.000000, Obj: 0.470354, No Obj: 0.002936, Avg Recall: 0.666667, count: 81 -Region Avg IOU: 0.617245, Class: 1.000000, Obj: 0.428838, No Obj: 0.002722, Avg Recall: 0.734940, count: 83 -Region Avg IOU: 0.633377, Class: 1.000000, Obj: 0.452482, No Obj: 0.003420, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.598919, Class: 1.000000, Obj: 0.458173, No Obj: 0.003397, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.574210, Class: 1.000000, Obj: 0.456750, No Obj: 0.003323, Avg Recall: 0.655738, count: 122 -Region Avg IOU: 0.634261, Class: 1.000000, Obj: 0.440803, No Obj: 0.003305, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.605832, Class: 1.000000, Obj: 0.444864, No Obj: 0.003331, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.628840, Class: 1.000000, Obj: 0.478887, No Obj: 0.003434, Avg Recall: 0.707071, count: 99 -6127: 0.366335, 0.416628 avg, 0.001000 rate, 26.048861 seconds, 1568512 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.592544, Class: 1.000000, Obj: 0.427423, No Obj: 0.003005, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.593127, Class: 1.000000, Obj: 0.416341, No Obj: 0.004083, Avg Recall: 0.722222, count: 144 -Region Avg IOU: 0.633210, Class: 1.000000, Obj: 0.482444, No Obj: 0.003926, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.599112, Class: 1.000000, Obj: 0.424128, No Obj: 0.003256, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.620958, Class: 1.000000, Obj: 0.391765, No Obj: 0.004057, Avg Recall: 0.753968, count: 126 -Region Avg IOU: 0.619152, Class: 1.000000, Obj: 0.442743, No Obj: 0.003630, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.647417, Class: 1.000000, Obj: 0.440739, No Obj: 0.003376, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.636587, Class: 1.000000, Obj: 0.468224, No Obj: 0.003440, Avg Recall: 0.807018, count: 114 -6128: 0.451372, 0.420102 avg, 0.001000 rate, 26.783804 seconds, 1568768 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.626833, Class: 1.000000, Obj: 0.432969, No Obj: 0.004049, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.603400, Class: 1.000000, Obj: 0.459288, No Obj: 0.003730, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.585959, Class: 1.000000, Obj: 0.432493, No Obj: 0.003422, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.611355, Class: 1.000000, Obj: 0.403583, No Obj: 0.003715, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.613671, Class: 1.000000, Obj: 0.405385, No Obj: 0.003804, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.563340, Class: 1.000000, Obj: 0.462465, No Obj: 0.002843, Avg Recall: 0.697368, count: 76 -Region Avg IOU: 0.617426, Class: 1.000000, Obj: 0.386188, No Obj: 0.004624, Avg Recall: 0.723270, count: 159 -Region Avg IOU: 0.620348, Class: 1.000000, Obj: 0.436968, No Obj: 0.003416, Avg Recall: 0.757282, count: 103 -6129: 0.433181, 0.421410 avg, 0.001000 rate, 27.493809 seconds, 1569024 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.613629, Class: 1.000000, Obj: 0.476278, No Obj: 0.003282, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.624081, Class: 1.000000, Obj: 0.434219, No Obj: 0.003274, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.594004, Class: 1.000000, Obj: 0.417341, No Obj: 0.003635, Avg Recall: 0.743802, count: 121 -Region Avg IOU: 0.622926, Class: 1.000000, Obj: 0.463842, No Obj: 0.003733, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.650556, Class: 1.000000, Obj: 0.448345, No Obj: 0.003762, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.596208, Class: 1.000000, Obj: 0.452296, No Obj: 0.004017, Avg Recall: 0.724638, count: 138 -Region Avg IOU: 0.600457, Class: 1.000000, Obj: 0.437825, No Obj: 0.003648, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.603746, Class: 1.000000, Obj: 0.424524, No Obj: 0.004086, Avg Recall: 0.732759, count: 116 -6130: 0.424055, 0.421675 avg, 0.001000 rate, 27.364834 seconds, 1569280 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000067 seconds -Region Avg IOU: 0.646745, Class: 1.000000, Obj: 0.495442, No Obj: 0.003087, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.649284, Class: 1.000000, Obj: 0.478003, No Obj: 0.002444, Avg Recall: 0.773333, count: 75 -Region Avg IOU: 0.610596, Class: 1.000000, Obj: 0.431169, No Obj: 0.003328, Avg Recall: 0.712000, count: 125 -Region Avg IOU: 0.657447, Class: 1.000000, Obj: 0.465904, No Obj: 0.003764, Avg Recall: 0.813793, count: 145 -Region Avg IOU: 0.675364, Class: 1.000000, Obj: 0.450296, No Obj: 0.002625, Avg Recall: 0.840000, count: 75 -Region Avg IOU: 0.632423, Class: 1.000000, Obj: 0.397187, No Obj: 0.003127, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.646624, Class: 1.000000, Obj: 0.467071, No Obj: 0.002955, Avg Recall: 0.802083, count: 96 -Region Avg IOU: 0.693180, Class: 1.000000, Obj: 0.467741, No Obj: 0.002963, Avg Recall: 0.865169, count: 89 -6131: 0.407402, 0.420247 avg, 0.001000 rate, 29.332876 seconds, 1569536 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.659748, Class: 1.000000, Obj: 0.418364, No Obj: 0.003054, Avg Recall: 0.804878, count: 123 -Region Avg IOU: 0.652361, Class: 1.000000, Obj: 0.482332, No Obj: 0.003002, Avg Recall: 0.814815, count: 108 -Region Avg IOU: 0.657750, Class: 1.000000, Obj: 0.504232, No Obj: 0.002986, Avg Recall: 0.826087, count: 92 -Region Avg IOU: 0.689825, Class: 1.000000, Obj: 0.444881, No Obj: 0.003100, Avg Recall: 0.866667, count: 105 -Region Avg IOU: 0.666010, Class: 1.000000, Obj: 0.467320, No Obj: 0.003230, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.623606, Class: 1.000000, Obj: 0.417013, No Obj: 0.003177, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.639537, Class: 1.000000, Obj: 0.438566, No Obj: 0.003071, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.643963, Class: 1.000000, Obj: 0.426998, No Obj: 0.003444, Avg Recall: 0.776923, count: 130 -6132: 0.407045, 0.418927 avg, 0.001000 rate, 29.085340 seconds, 1569792 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.643146, Class: 1.000000, Obj: 0.387949, No Obj: 0.003055, Avg Recall: 0.818182, count: 121 -Region Avg IOU: 0.623465, Class: 1.000000, Obj: 0.408399, No Obj: 0.002682, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.651310, Class: 1.000000, Obj: 0.427832, No Obj: 0.003260, Avg Recall: 0.804511, count: 133 -Region Avg IOU: 0.666212, Class: 1.000000, Obj: 0.401327, No Obj: 0.002631, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.650250, Class: 1.000000, Obj: 0.425240, No Obj: 0.002851, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.647101, Class: 1.000000, Obj: 0.437160, No Obj: 0.002747, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.661539, Class: 1.000000, Obj: 0.423441, No Obj: 0.002723, Avg Recall: 0.797872, count: 94 -Region Avg IOU: 0.675206, Class: 1.000000, Obj: 0.455013, No Obj: 0.002581, Avg Recall: 0.851485, count: 101 -6133: 0.428743, 0.419909 avg, 0.001000 rate, 27.615025 seconds, 1570048 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.665600, Class: 1.000000, Obj: 0.394597, No Obj: 0.002777, Avg Recall: 0.839623, count: 106 -Region Avg IOU: 0.671113, Class: 1.000000, Obj: 0.412236, No Obj: 0.002700, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.649472, Class: 1.000000, Obj: 0.413739, No Obj: 0.002596, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.647256, Class: 1.000000, Obj: 0.440864, No Obj: 0.002977, Avg Recall: 0.823529, count: 102 -Region Avg IOU: 0.601313, Class: 1.000000, Obj: 0.377680, No Obj: 0.002682, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.645541, Class: 1.000000, Obj: 0.382471, No Obj: 0.002702, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.644275, Class: 1.000000, Obj: 0.427562, No Obj: 0.002890, Avg Recall: 0.810345, count: 116 -Region Avg IOU: 0.662680, Class: 1.000000, Obj: 0.463203, No Obj: 0.002825, Avg Recall: 0.810526, count: 95 -6134: 0.406973, 0.418615 avg, 0.001000 rate, 28.932156 seconds, 1570304 images -Loaded: 0.000116 seconds -Region Avg IOU: 0.661506, Class: 1.000000, Obj: 0.452986, No Obj: 0.002816, Avg Recall: 0.820144, count: 139 -Region Avg IOU: 0.603323, Class: 1.000000, Obj: 0.374804, No Obj: 0.002544, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.659980, Class: 1.000000, Obj: 0.409568, No Obj: 0.002945, Avg Recall: 0.806723, count: 119 -Region Avg IOU: 0.605781, Class: 1.000000, Obj: 0.349914, No Obj: 0.002701, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.653559, Class: 1.000000, Obj: 0.459912, No Obj: 0.002941, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.657252, Class: 1.000000, Obj: 0.355606, No Obj: 0.003277, Avg Recall: 0.797101, count: 138 -Region Avg IOU: 0.647229, Class: 1.000000, Obj: 0.397054, No Obj: 0.002705, Avg Recall: 0.796460, count: 113 -Region Avg IOU: 0.614909, Class: 1.000000, Obj: 0.391725, No Obj: 0.002925, Avg Recall: 0.734694, count: 147 -6135: 0.446841, 0.421438 avg, 0.001000 rate, 28.991703 seconds, 1570560 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.681190, Class: 1.000000, Obj: 0.423001, No Obj: 0.002628, Avg Recall: 0.863636, count: 88 -Region Avg IOU: 0.654463, Class: 1.000000, Obj: 0.390684, No Obj: 0.002684, Avg Recall: 0.824742, count: 97 -Region Avg IOU: 0.658194, Class: 1.000000, Obj: 0.407799, No Obj: 0.002237, Avg Recall: 0.821429, count: 84 -Region Avg IOU: 0.622065, Class: 1.000000, Obj: 0.369720, No Obj: 0.002289, Avg Recall: 0.761905, count: 84 -Region Avg IOU: 0.585582, Class: 1.000000, Obj: 0.321064, No Obj: 0.002895, Avg Recall: 0.708861, count: 158 -Region Avg IOU: 0.670507, Class: 1.000000, Obj: 0.451134, No Obj: 0.002405, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.643031, Class: 1.000000, Obj: 0.377966, No Obj: 0.002633, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.649145, Class: 1.000000, Obj: 0.404203, No Obj: 0.002258, Avg Recall: 0.818182, count: 110 -6136: 0.430092, 0.422303 avg, 0.001000 rate, 27.572405 seconds, 1570816 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.627728, Class: 1.000000, Obj: 0.419157, No Obj: 0.002867, Avg Recall: 0.719298, count: 114 -Region Avg IOU: 0.601948, Class: 1.000000, Obj: 0.432817, No Obj: 0.002483, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.683455, Class: 1.000000, Obj: 0.440198, No Obj: 0.002033, Avg Recall: 0.867470, count: 83 -Region Avg IOU: 0.621125, Class: 1.000000, Obj: 0.382319, No Obj: 0.002549, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.643939, Class: 1.000000, Obj: 0.379611, No Obj: 0.002648, Avg Recall: 0.803279, count: 122 -Region Avg IOU: 0.620873, Class: 1.000000, Obj: 0.385149, No Obj: 0.002420, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.635931, Class: 1.000000, Obj: 0.385306, No Obj: 0.002457, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.631148, Class: 1.000000, Obj: 0.385450, No Obj: 0.002652, Avg Recall: 0.750000, count: 108 -6137: 0.398342, 0.419907 avg, 0.001000 rate, 31.045277 seconds, 1571072 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.627481, Class: 1.000000, Obj: 0.410825, No Obj: 0.002378, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.631754, Class: 1.000000, Obj: 0.404270, No Obj: 0.002600, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.667337, Class: 1.000000, Obj: 0.424516, No Obj: 0.002603, Avg Recall: 0.827160, count: 81 -Region Avg IOU: 0.644286, Class: 1.000000, Obj: 0.402550, No Obj: 0.003198, Avg Recall: 0.769841, count: 126 -Region Avg IOU: 0.670553, Class: 1.000000, Obj: 0.452309, No Obj: 0.002854, Avg Recall: 0.786885, count: 122 -Region Avg IOU: 0.613567, Class: 1.000000, Obj: 0.388044, No Obj: 0.002863, Avg Recall: 0.719298, count: 114 -Region Avg IOU: 0.605066, Class: 1.000000, Obj: 0.392682, No Obj: 0.002608, Avg Recall: 0.720280, count: 143 -Region Avg IOU: 0.635664, Class: 1.000000, Obj: 0.403092, No Obj: 0.002866, Avg Recall: 0.745763, count: 118 -6138: 0.472219, 0.425138 avg, 0.001000 rate, 29.444578 seconds, 1571328 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.659186, Class: 1.000000, Obj: 0.394844, No Obj: 0.003517, Avg Recall: 0.800000, count: 140 -Region Avg IOU: 0.687116, Class: 1.000000, Obj: 0.457934, No Obj: 0.002890, Avg Recall: 0.850467, count: 107 -Region Avg IOU: 0.655527, Class: 1.000000, Obj: 0.359688, No Obj: 0.002314, Avg Recall: 0.801980, count: 101 -Region Avg IOU: 0.682495, Class: 1.000000, Obj: 0.380505, No Obj: 0.002925, Avg Recall: 0.849462, count: 93 -Region Avg IOU: 0.643394, Class: 1.000000, Obj: 0.390714, No Obj: 0.002705, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.654558, Class: 1.000000, Obj: 0.407988, No Obj: 0.002872, Avg Recall: 0.842975, count: 121 -Region Avg IOU: 0.659601, Class: 1.000000, Obj: 0.406422, No Obj: 0.003210, Avg Recall: 0.770270, count: 148 -Region Avg IOU: 0.622756, Class: 1.000000, Obj: 0.364444, No Obj: 0.002762, Avg Recall: 0.804878, count: 123 -6139: 0.482435, 0.430868 avg, 0.001000 rate, 32.025265 seconds, 1571584 images -Loaded: 0.000057 seconds -Region Avg IOU: 0.672551, Class: 1.000000, Obj: 0.439343, No Obj: 0.002142, Avg Recall: 0.833333, count: 78 -Region Avg IOU: 0.677611, Class: 1.000000, Obj: 0.415826, No Obj: 0.002603, Avg Recall: 0.837838, count: 111 -Region Avg IOU: 0.667317, Class: 1.000000, Obj: 0.456878, No Obj: 0.002793, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.657754, Class: 1.000000, Obj: 0.376467, No Obj: 0.002949, Avg Recall: 0.813953, count: 129 -Region Avg IOU: 0.637111, Class: 1.000000, Obj: 0.320911, No Obj: 0.002653, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.638493, Class: 1.000000, Obj: 0.479102, No Obj: 0.002797, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.664277, Class: 1.000000, Obj: 0.429048, No Obj: 0.002879, Avg Recall: 0.807692, count: 130 -Region Avg IOU: 0.656073, Class: 1.000000, Obj: 0.450127, No Obj: 0.002941, Avg Recall: 0.844660, count: 103 -6140: 0.398597, 0.427641 avg, 0.001000 rate, 29.021114 seconds, 1571840 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000063 seconds -Region Avg IOU: 0.700138, Class: 1.000000, Obj: 0.503706, No Obj: 0.002655, Avg Recall: 0.840909, count: 88 -Region Avg IOU: 0.633755, Class: 1.000000, Obj: 0.365697, No Obj: 0.002674, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.662137, Class: 1.000000, Obj: 0.472289, No Obj: 0.002497, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.659502, Class: 1.000000, Obj: 0.445235, No Obj: 0.002459, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.678658, Class: 1.000000, Obj: 0.421312, No Obj: 0.003135, Avg Recall: 0.856000, count: 125 -Region Avg IOU: 0.664862, Class: 1.000000, Obj: 0.411391, No Obj: 0.003021, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.637888, Class: 1.000000, Obj: 0.385294, No Obj: 0.002844, Avg Recall: 0.782258, count: 124 -Region Avg IOU: 0.644931, Class: 1.000000, Obj: 0.418783, No Obj: 0.003051, Avg Recall: 0.804511, count: 133 -6141: 0.482556, 0.433132 avg, 0.001000 rate, 29.602407 seconds, 1572096 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.677612, Class: 1.000000, Obj: 0.450553, No Obj: 0.002820, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.656117, Class: 1.000000, Obj: 0.450914, No Obj: 0.003092, Avg Recall: 0.812500, count: 128 -Region Avg IOU: 0.672175, Class: 1.000000, Obj: 0.451278, No Obj: 0.002529, Avg Recall: 0.825243, count: 103 -Region Avg IOU: 0.688907, Class: 1.000000, Obj: 0.474102, No Obj: 0.002681, Avg Recall: 0.888889, count: 90 -Region Avg IOU: 0.671658, Class: 1.000000, Obj: 0.447369, No Obj: 0.002876, Avg Recall: 0.814433, count: 97 -Region Avg IOU: 0.624785, Class: 1.000000, Obj: 0.440416, No Obj: 0.002748, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.676777, Class: 1.000000, Obj: 0.434043, No Obj: 0.002958, Avg Recall: 0.821429, count: 112 -Region Avg IOU: 0.677498, Class: 1.000000, Obj: 0.477915, No Obj: 0.002547, Avg Recall: 0.818182, count: 88 -6142: 0.388521, 0.428671 avg, 0.001000 rate, 31.202677 seconds, 1572352 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.664820, Class: 1.000000, Obj: 0.405088, No Obj: 0.002098, Avg Recall: 0.810811, count: 74 -Region Avg IOU: 0.656174, Class: 1.000000, Obj: 0.447395, No Obj: 0.003116, Avg Recall: 0.834783, count: 115 -Region Avg IOU: 0.663732, Class: 1.000000, Obj: 0.413803, No Obj: 0.003510, Avg Recall: 0.845528, count: 123 -Region Avg IOU: 0.615287, Class: 1.000000, Obj: 0.384241, No Obj: 0.002727, Avg Recall: 0.730769, count: 130 -Region Avg IOU: 0.634198, Class: 1.000000, Obj: 0.422924, No Obj: 0.002694, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.668503, Class: 1.000000, Obj: 0.454248, No Obj: 0.002824, Avg Recall: 0.835938, count: 128 -Region Avg IOU: 0.635829, Class: 1.000000, Obj: 0.438680, No Obj: 0.002819, Avg Recall: 0.786325, count: 117 -Region Avg IOU: 0.625631, Class: 1.000000, Obj: 0.357605, No Obj: 0.002793, Avg Recall: 0.768595, count: 121 -6143: 0.426044, 0.428408 avg, 0.001000 rate, 31.286585 seconds, 1572608 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.664195, Class: 1.000000, Obj: 0.442897, No Obj: 0.002475, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.677696, Class: 1.000000, Obj: 0.478423, No Obj: 0.002625, Avg Recall: 0.845238, count: 84 -Region Avg IOU: 0.705703, Class: 1.000000, Obj: 0.473717, No Obj: 0.003314, Avg Recall: 0.872549, count: 102 -Region Avg IOU: 0.660456, Class: 1.000000, Obj: 0.464502, No Obj: 0.002529, Avg Recall: 0.826923, count: 104 -Region Avg IOU: 0.636436, Class: 1.000000, Obj: 0.421995, No Obj: 0.002618, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.660933, Class: 1.000000, Obj: 0.449502, No Obj: 0.003033, Avg Recall: 0.818182, count: 121 -Region Avg IOU: 0.663834, Class: 1.000000, Obj: 0.434922, No Obj: 0.002846, Avg Recall: 0.836538, count: 104 -Region Avg IOU: 0.561366, Class: 1.000000, Obj: 0.331455, No Obj: 0.002222, Avg Recall: 0.637500, count: 80 -6144: 0.374300, 0.422998 avg, 0.001000 rate, 31.019783 seconds, 1572864 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.649182, Class: 1.000000, Obj: 0.386771, No Obj: 0.002992, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.673280, Class: 1.000000, Obj: 0.410366, No Obj: 0.002829, Avg Recall: 0.830000, count: 100 -Region Avg IOU: 0.692013, Class: 1.000000, Obj: 0.422797, No Obj: 0.003459, Avg Recall: 0.896825, count: 126 -Region Avg IOU: 0.654273, Class: 1.000000, Obj: 0.442311, No Obj: 0.002547, Avg Recall: 0.840000, count: 100 -Region Avg IOU: 0.680749, Class: 1.000000, Obj: 0.387508, No Obj: 0.002734, Avg Recall: 0.851852, count: 108 -Region Avg IOU: 0.663658, Class: 1.000000, Obj: 0.421739, No Obj: 0.002889, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.631310, Class: 1.000000, Obj: 0.432837, No Obj: 0.003082, Avg Recall: 0.735043, count: 117 -Region Avg IOU: 0.666949, Class: 1.000000, Obj: 0.382456, No Obj: 0.003076, Avg Recall: 0.795276, count: 127 -6145: 0.448618, 0.425560 avg, 0.001000 rate, 30.292295 seconds, 1573120 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.627258, Class: 1.000000, Obj: 0.409055, No Obj: 0.002315, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.701485, Class: 1.000000, Obj: 0.474299, No Obj: 0.002787, Avg Recall: 0.887640, count: 89 -Region Avg IOU: 0.631477, Class: 1.000000, Obj: 0.397858, No Obj: 0.002383, Avg Recall: 0.703704, count: 81 -Region Avg IOU: 0.630227, Class: 1.000000, Obj: 0.397453, No Obj: 0.002738, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.684223, Class: 1.000000, Obj: 0.436130, No Obj: 0.002589, Avg Recall: 0.875000, count: 104 -Region Avg IOU: 0.654194, Class: 1.000000, Obj: 0.426950, No Obj: 0.002487, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.635877, Class: 1.000000, Obj: 0.449402, No Obj: 0.002634, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.674796, Class: 1.000000, Obj: 0.406976, No Obj: 0.002870, Avg Recall: 0.849206, count: 126 -6146: 0.421736, 0.425177 avg, 0.001000 rate, 29.636940 seconds, 1573376 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.660145, Class: 1.000000, Obj: 0.419176, No Obj: 0.002537, Avg Recall: 0.839623, count: 106 -Region Avg IOU: 0.658023, Class: 1.000000, Obj: 0.427224, No Obj: 0.002528, Avg Recall: 0.797753, count: 89 -Region Avg IOU: 0.620680, Class: 1.000000, Obj: 0.402651, No Obj: 0.002753, Avg Recall: 0.731707, count: 123 -Region Avg IOU: 0.623128, Class: 1.000000, Obj: 0.420557, No Obj: 0.002571, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.690616, Class: 1.000000, Obj: 0.389281, No Obj: 0.002654, Avg Recall: 0.857143, count: 91 -Region Avg IOU: 0.636697, Class: 1.000000, Obj: 0.412939, No Obj: 0.002448, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.661691, Class: 1.000000, Obj: 0.391037, No Obj: 0.002472, Avg Recall: 0.828829, count: 111 -Region Avg IOU: 0.627611, Class: 1.000000, Obj: 0.367140, No Obj: 0.003310, Avg Recall: 0.768750, count: 160 -6147: 0.459239, 0.428583 avg, 0.001000 rate, 28.638456 seconds, 1573632 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.661208, Class: 1.000000, Obj: 0.396192, No Obj: 0.002524, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.632341, Class: 1.000000, Obj: 0.444029, No Obj: 0.002334, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.644569, Class: 1.000000, Obj: 0.389273, No Obj: 0.002544, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.702881, Class: 1.000000, Obj: 0.403983, No Obj: 0.002534, Avg Recall: 0.883495, count: 103 -Region Avg IOU: 0.670227, Class: 1.000000, Obj: 0.412666, No Obj: 0.002359, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.687520, Class: 1.000000, Obj: 0.381903, No Obj: 0.002781, Avg Recall: 0.866667, count: 105 -Region Avg IOU: 0.648555, Class: 1.000000, Obj: 0.422354, No Obj: 0.002478, Avg Recall: 0.826087, count: 115 -Region Avg IOU: 0.620662, Class: 1.000000, Obj: 0.394384, No Obj: 0.002191, Avg Recall: 0.766667, count: 90 -6148: 0.416917, 0.427417 avg, 0.001000 rate, 29.987389 seconds, 1573888 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.641096, Class: 1.000000, Obj: 0.376456, No Obj: 0.002076, Avg Recall: 0.800000, count: 85 -Region Avg IOU: 0.614404, Class: 1.000000, Obj: 0.310143, No Obj: 0.002215, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.637775, Class: 1.000000, Obj: 0.434932, No Obj: 0.002306, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.667330, Class: 1.000000, Obj: 0.420146, No Obj: 0.002470, Avg Recall: 0.834711, count: 121 -Region Avg IOU: 0.626067, Class: 1.000000, Obj: 0.390856, No Obj: 0.002365, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.672349, Class: 1.000000, Obj: 0.377836, No Obj: 0.002644, Avg Recall: 0.808696, count: 115 -Region Avg IOU: 0.675825, Class: 1.000000, Obj: 0.441914, No Obj: 0.002522, Avg Recall: 0.860870, count: 115 -Region Avg IOU: 0.648549, Class: 1.000000, Obj: 0.355199, No Obj: 0.002595, Avg Recall: 0.804688, count: 128 -6149: 0.429672, 0.427642 avg, 0.001000 rate, 29.924080 seconds, 1574144 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.676028, Class: 1.000000, Obj: 0.411470, No Obj: 0.002332, Avg Recall: 0.820755, count: 106 -Region Avg IOU: 0.664973, Class: 1.000000, Obj: 0.358033, No Obj: 0.002471, Avg Recall: 0.836735, count: 98 -Region Avg IOU: 0.676119, Class: 1.000000, Obj: 0.423130, No Obj: 0.002226, Avg Recall: 0.837209, count: 86 -Region Avg IOU: 0.669000, Class: 1.000000, Obj: 0.437995, No Obj: 0.002942, Avg Recall: 0.818182, count: 132 -Region Avg IOU: 0.633904, Class: 1.000000, Obj: 0.341168, No Obj: 0.002499, Avg Recall: 0.798507, count: 134 -Region Avg IOU: 0.704414, Class: 1.000000, Obj: 0.451568, No Obj: 0.002398, Avg Recall: 0.847826, count: 92 -Region Avg IOU: 0.668523, Class: 1.000000, Obj: 0.462013, No Obj: 0.002298, Avg Recall: 0.829787, count: 94 -Region Avg IOU: 0.691813, Class: 1.000000, Obj: 0.432682, No Obj: 0.002404, Avg Recall: 0.864865, count: 111 -6150: 0.441520, 0.429030 avg, 0.001000 rate, 30.785849 seconds, 1574400 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 17.371931 seconds -Region Avg IOU: 0.559721, Class: 1.000000, Obj: 0.342742, No Obj: 0.003373, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.503542, Class: 1.000000, Obj: 0.308643, No Obj: 0.003836, Avg Recall: 0.546875, count: 128 -Region Avg IOU: 0.542516, Class: 1.000000, Obj: 0.345293, No Obj: 0.003809, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.533382, Class: 1.000000, Obj: 0.289971, No Obj: 0.003797, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.554122, Class: 1.000000, Obj: 0.325654, No Obj: 0.003951, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.512461, Class: 1.000000, Obj: 0.321716, No Obj: 0.003447, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.519747, Class: 1.000000, Obj: 0.330930, No Obj: 0.004272, Avg Recall: 0.578125, count: 128 -Region Avg IOU: 0.512385, Class: 1.000000, Obj: 0.292747, No Obj: 0.004288, Avg Recall: 0.572650, count: 117 -6151: 0.440138, 0.430141 avg, 0.001000 rate, 19.835222 seconds, 1574656 images -Loaded: 2.249176 seconds -Region Avg IOU: 0.530280, Class: 1.000000, Obj: 0.311556, No Obj: 0.003938, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.485032, Class: 1.000000, Obj: 0.308230, No Obj: 0.004466, Avg Recall: 0.531034, count: 145 -Region Avg IOU: 0.549039, Class: 1.000000, Obj: 0.360062, No Obj: 0.003677, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.538781, Class: 1.000000, Obj: 0.362708, No Obj: 0.003575, Avg Recall: 0.590909, count: 88 -Region Avg IOU: 0.482909, Class: 1.000000, Obj: 0.304667, No Obj: 0.003705, Avg Recall: 0.516949, count: 118 -Region Avg IOU: 0.488426, Class: 1.000000, Obj: 0.334408, No Obj: 0.004249, Avg Recall: 0.532787, count: 122 -Region Avg IOU: 0.490876, Class: 1.000000, Obj: 0.316759, No Obj: 0.003818, Avg Recall: 0.495495, count: 111 -Region Avg IOU: 0.535637, Class: 1.000000, Obj: 0.345258, No Obj: 0.004204, Avg Recall: 0.568627, count: 102 -6152: 0.422336, 0.429360 avg, 0.001000 rate, 16.176558 seconds, 1574912 images -Loaded: 3.478227 seconds -Region Avg IOU: 0.502407, Class: 1.000000, Obj: 0.294229, No Obj: 0.003469, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.526536, Class: 1.000000, Obj: 0.333782, No Obj: 0.003878, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.509712, Class: 1.000000, Obj: 0.350060, No Obj: 0.003617, Avg Recall: 0.559524, count: 84 -Region Avg IOU: 0.512999, Class: 1.000000, Obj: 0.357288, No Obj: 0.003697, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.501702, Class: 1.000000, Obj: 0.342181, No Obj: 0.003540, Avg Recall: 0.571429, count: 91 -Region Avg IOU: 0.517325, Class: 1.000000, Obj: 0.375351, No Obj: 0.004347, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.509686, Class: 1.000000, Obj: 0.323984, No Obj: 0.003729, Avg Recall: 0.553719, count: 121 -Region Avg IOU: 0.568021, Class: 1.000000, Obj: 0.361969, No Obj: 0.003979, Avg Recall: 0.704082, count: 98 -6153: 0.454408, 0.431865 avg, 0.001000 rate, 14.273221 seconds, 1575168 images -Loaded: 2.712084 seconds -Region Avg IOU: 0.494949, Class: 1.000000, Obj: 0.338875, No Obj: 0.004218, Avg Recall: 0.522059, count: 136 -Region Avg IOU: 0.520649, Class: 1.000000, Obj: 0.399185, No Obj: 0.004467, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.503633, Class: 1.000000, Obj: 0.378516, No Obj: 0.004142, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.472596, Class: 1.000000, Obj: 0.337871, No Obj: 0.004219, Avg Recall: 0.508333, count: 120 -Region Avg IOU: 0.571173, Class: 1.000000, Obj: 0.372911, No Obj: 0.005278, Avg Recall: 0.669492, count: 118 -Region Avg IOU: 0.495361, Class: 1.000000, Obj: 0.353581, No Obj: 0.004329, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.466939, Class: 1.000000, Obj: 0.325823, No Obj: 0.003870, Avg Recall: 0.468468, count: 111 -Region Avg IOU: 0.503372, Class: 1.000000, Obj: 0.318187, No Obj: 0.003661, Avg Recall: 0.577778, count: 90 -6154: 0.437323, 0.432411 avg, 0.001000 rate, 18.040209 seconds, 1575424 images -Loaded: 4.142775 seconds -Region Avg IOU: 0.522883, Class: 1.000000, Obj: 0.361265, No Obj: 0.004278, Avg Recall: 0.658228, count: 79 -Region Avg IOU: 0.500133, Class: 1.000000, Obj: 0.380412, No Obj: 0.004591, Avg Recall: 0.549550, count: 111 -Region Avg IOU: 0.518574, Class: 1.000000, Obj: 0.352729, No Obj: 0.004660, Avg Recall: 0.611570, count: 121 -Region Avg IOU: 0.507368, Class: 1.000000, Obj: 0.369069, No Obj: 0.003920, Avg Recall: 0.569620, count: 79 -Region Avg IOU: 0.500059, Class: 1.000000, Obj: 0.381056, No Obj: 0.004177, Avg Recall: 0.551020, count: 98 -Region Avg IOU: 0.483158, Class: 1.000000, Obj: 0.383779, No Obj: 0.004647, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.523140, Class: 1.000000, Obj: 0.372350, No Obj: 0.003973, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.514004, Class: 1.000000, Obj: 0.352673, No Obj: 0.004591, Avg Recall: 0.582609, count: 115 -6155: 0.380748, 0.427245 avg, 0.001000 rate, 19.485422 seconds, 1575680 images -Loaded: 1.304188 seconds -Region Avg IOU: 0.535764, Class: 1.000000, Obj: 0.435934, No Obj: 0.005209, Avg Recall: 0.637795, count: 127 -Region Avg IOU: 0.459252, Class: 1.000000, Obj: 0.345181, No Obj: 0.004048, Avg Recall: 0.500000, count: 80 -Region Avg IOU: 0.565636, Class: 1.000000, Obj: 0.409283, No Obj: 0.003957, Avg Recall: 0.645570, count: 79 -Region Avg IOU: 0.471657, Class: 1.000000, Obj: 0.343838, No Obj: 0.004761, Avg Recall: 0.526786, count: 112 -Region Avg IOU: 0.509927, Class: 1.000000, Obj: 0.391256, No Obj: 0.004309, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.511437, Class: 1.000000, Obj: 0.345615, No Obj: 0.003663, Avg Recall: 0.538462, count: 78 -Region Avg IOU: 0.478314, Class: 1.000000, Obj: 0.330693, No Obj: 0.005067, Avg Recall: 0.519380, count: 129 -Region Avg IOU: 0.502253, Class: 1.000000, Obj: 0.371042, No Obj: 0.004257, Avg Recall: 0.572917, count: 96 -6156: 0.440653, 0.428585 avg, 0.001000 rate, 19.882416 seconds, 1575936 images -Loaded: 2.406392 seconds -Region Avg IOU: 0.542906, Class: 1.000000, Obj: 0.411473, No Obj: 0.004969, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.551492, Class: 1.000000, Obj: 0.388951, No Obj: 0.005725, Avg Recall: 0.626866, count: 134 -Region Avg IOU: 0.520009, Class: 1.000000, Obj: 0.385523, No Obj: 0.005860, Avg Recall: 0.637037, count: 135 -Region Avg IOU: 0.469280, Class: 1.000000, Obj: 0.376135, No Obj: 0.005214, Avg Recall: 0.551471, count: 136 -Region Avg IOU: 0.491089, Class: 1.000000, Obj: 0.371215, No Obj: 0.004878, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.574232, Class: 1.000000, Obj: 0.443590, No Obj: 0.005283, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.520865, Class: 1.000000, Obj: 0.369831, No Obj: 0.005573, Avg Recall: 0.600000, count: 135 -Region Avg IOU: 0.521401, Class: 1.000000, Obj: 0.388520, No Obj: 0.005096, Avg Recall: 0.611570, count: 121 -6157: 0.514579, 0.437185 avg, 0.001000 rate, 18.736582 seconds, 1576192 images -Loaded: 1.836712 seconds -Region Avg IOU: 0.507209, Class: 1.000000, Obj: 0.360143, No Obj: 0.004436, Avg Recall: 0.605042, count: 119 -Region Avg IOU: 0.523945, Class: 1.000000, Obj: 0.445178, No Obj: 0.005196, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.470131, Class: 1.000000, Obj: 0.388393, No Obj: 0.004477, Avg Recall: 0.500000, count: 114 -Region Avg IOU: 0.470358, Class: 1.000000, Obj: 0.378351, No Obj: 0.004461, Avg Recall: 0.509434, count: 106 -Region Avg IOU: 0.534764, Class: 1.000000, Obj: 0.391357, No Obj: 0.004880, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.482499, Class: 1.000000, Obj: 0.327658, No Obj: 0.004800, Avg Recall: 0.491935, count: 124 -Region Avg IOU: 0.543693, Class: 1.000000, Obj: 0.365057, No Obj: 0.005849, Avg Recall: 0.625899, count: 139 -Region Avg IOU: 0.554172, Class: 1.000000, Obj: 0.374078, No Obj: 0.004970, Avg Recall: 0.653846, count: 104 -6158: 0.473930, 0.440859 avg, 0.001000 rate, 18.582975 seconds, 1576448 images -Loaded: 1.820278 seconds -Region Avg IOU: 0.524668, Class: 1.000000, Obj: 0.456056, No Obj: 0.005044, Avg Recall: 0.518519, count: 108 -Region Avg IOU: 0.534444, Class: 1.000000, Obj: 0.440925, No Obj: 0.004502, Avg Recall: 0.526882, count: 93 -Region Avg IOU: 0.537999, Class: 1.000000, Obj: 0.381591, No Obj: 0.004537, Avg Recall: 0.600000, count: 95 -Region Avg IOU: 0.480537, Class: 1.000000, Obj: 0.423174, No Obj: 0.005088, Avg Recall: 0.500000, count: 114 -Region Avg IOU: 0.524212, Class: 1.000000, Obj: 0.384381, No Obj: 0.006010, Avg Recall: 0.608392, count: 143 -Region Avg IOU: 0.501112, Class: 1.000000, Obj: 0.367451, No Obj: 0.005470, Avg Recall: 0.486726, count: 113 -Region Avg IOU: 0.458260, Class: 1.000000, Obj: 0.410104, No Obj: 0.004438, Avg Recall: 0.471264, count: 87 -Region Avg IOU: 0.528235, Class: 1.000000, Obj: 0.422169, No Obj: 0.004697, Avg Recall: 0.550459, count: 109 -6159: 0.404713, 0.437245 avg, 0.001000 rate, 15.847234 seconds, 1576704 images -Loaded: 5.641761 seconds -Region Avg IOU: 0.496989, Class: 1.000000, Obj: 0.390176, No Obj: 0.004270, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.518895, Class: 1.000000, Obj: 0.395252, No Obj: 0.005588, Avg Recall: 0.610619, count: 113 -Region Avg IOU: 0.572263, Class: 1.000000, Obj: 0.442245, No Obj: 0.004898, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.471512, Class: 1.000000, Obj: 0.399117, No Obj: 0.004430, Avg Recall: 0.485149, count: 101 -Region Avg IOU: 0.531071, Class: 1.000000, Obj: 0.384299, No Obj: 0.005369, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.521556, Class: 1.000000, Obj: 0.405745, No Obj: 0.004777, Avg Recall: 0.630435, count: 92 -Region Avg IOU: 0.537170, Class: 1.000000, Obj: 0.411414, No Obj: 0.005152, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.531703, Class: 1.000000, Obj: 0.349804, No Obj: 0.005225, Avg Recall: 0.627451, count: 102 -6160: 0.410192, 0.434539 avg, 0.001000 rate, 19.325462 seconds, 1576960 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 17.681120 seconds -Region Avg IOU: 0.471255, Class: 1.000000, Obj: 0.353221, No Obj: 0.003791, Avg Recall: 0.479592, count: 98 -Region Avg IOU: 0.542629, Class: 1.000000, Obj: 0.414191, No Obj: 0.004909, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.498079, Class: 1.000000, Obj: 0.372908, No Obj: 0.005870, Avg Recall: 0.534722, count: 144 -Region Avg IOU: 0.473819, Class: 1.000000, Obj: 0.343582, No Obj: 0.004586, Avg Recall: 0.504348, count: 115 -Region Avg IOU: 0.542492, Class: 1.000000, Obj: 0.401166, No Obj: 0.004730, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.530031, Class: 1.000000, Obj: 0.401643, No Obj: 0.005342, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.509961, Class: 1.000000, Obj: 0.371831, No Obj: 0.005056, Avg Recall: 0.548673, count: 113 -Region Avg IOU: 0.507858, Class: 1.000000, Obj: 0.371537, No Obj: 0.004839, Avg Recall: 0.564103, count: 117 -6161: 0.462050, 0.437290 avg, 0.001000 rate, 17.904852 seconds, 1577216 images -Loaded: 1.811821 seconds -Region Avg IOU: 0.481023, Class: 1.000000, Obj: 0.380107, No Obj: 0.004878, Avg Recall: 0.525000, count: 120 -Region Avg IOU: 0.494296, Class: 1.000000, Obj: 0.350414, No Obj: 0.005319, Avg Recall: 0.569106, count: 123 -Region Avg IOU: 0.504119, Class: 1.000000, Obj: 0.369716, No Obj: 0.004538, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.604512, Class: 1.000000, Obj: 0.446752, No Obj: 0.005152, Avg Recall: 0.715909, count: 88 -Region Avg IOU: 0.558163, Class: 1.000000, Obj: 0.398527, No Obj: 0.005302, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.519346, Class: 1.000000, Obj: 0.399043, No Obj: 0.003936, Avg Recall: 0.584270, count: 89 -Region Avg IOU: 0.509561, Class: 1.000000, Obj: 0.374731, No Obj: 0.004661, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.516162, Class: 1.000000, Obj: 0.358048, No Obj: 0.004930, Avg Recall: 0.604651, count: 129 -6162: 0.487825, 0.442344 avg, 0.001000 rate, 20.060076 seconds, 1577472 images -Loaded: 2.411035 seconds -Region Avg IOU: 0.490273, Class: 1.000000, Obj: 0.388422, No Obj: 0.004843, Avg Recall: 0.528926, count: 121 -Region Avg IOU: 0.510777, Class: 1.000000, Obj: 0.376126, No Obj: 0.003976, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.532843, Class: 1.000000, Obj: 0.400206, No Obj: 0.005547, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.485476, Class: 1.000000, Obj: 0.342949, No Obj: 0.004902, Avg Recall: 0.521368, count: 117 -Region Avg IOU: 0.538405, Class: 1.000000, Obj: 0.381867, No Obj: 0.005002, Avg Recall: 0.606557, count: 122 -Region Avg IOU: 0.505433, Class: 1.000000, Obj: 0.332225, No Obj: 0.003892, Avg Recall: 0.521277, count: 94 -Region Avg IOU: 0.535913, Class: 1.000000, Obj: 0.371521, No Obj: 0.004943, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.548191, Class: 1.000000, Obj: 0.366297, No Obj: 0.004875, Avg Recall: 0.678571, count: 112 -6163: 0.426440, 0.440754 avg, 0.001000 rate, 19.590719 seconds, 1577728 images -Loaded: 2.690702 seconds -Region Avg IOU: 0.525093, Class: 1.000000, Obj: 0.367848, No Obj: 0.003269, Avg Recall: 0.637500, count: 80 -Region Avg IOU: 0.484294, Class: 1.000000, Obj: 0.364362, No Obj: 0.004194, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.517805, Class: 1.000000, Obj: 0.353378, No Obj: 0.005057, Avg Recall: 0.584071, count: 113 -Region Avg IOU: 0.489538, Class: 1.000000, Obj: 0.346635, No Obj: 0.004155, Avg Recall: 0.508065, count: 124 -Region Avg IOU: 0.490685, Class: 1.000000, Obj: 0.365213, No Obj: 0.004876, Avg Recall: 0.536000, count: 125 -Region Avg IOU: 0.521144, Class: 1.000000, Obj: 0.341280, No Obj: 0.004760, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.482188, Class: 1.000000, Obj: 0.344383, No Obj: 0.004833, Avg Recall: 0.496000, count: 125 -Region Avg IOU: 0.517574, Class: 1.000000, Obj: 0.358252, No Obj: 0.004661, Avg Recall: 0.585586, count: 111 -6164: 0.482171, 0.444895 avg, 0.001000 rate, 18.042961 seconds, 1577984 images -Loaded: 3.527282 seconds -Region Avg IOU: 0.510630, Class: 1.000000, Obj: 0.341775, No Obj: 0.004712, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.535302, Class: 1.000000, Obj: 0.405844, No Obj: 0.004627, Avg Recall: 0.611570, count: 121 -Region Avg IOU: 0.537986, Class: 1.000000, Obj: 0.362577, No Obj: 0.003827, Avg Recall: 0.606383, count: 94 -Region Avg IOU: 0.519310, Class: 1.000000, Obj: 0.401891, No Obj: 0.004193, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.485926, Class: 1.000000, Obj: 0.359058, No Obj: 0.004295, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.535810, Class: 1.000000, Obj: 0.412990, No Obj: 0.004264, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.504172, Class: 1.000000, Obj: 0.380581, No Obj: 0.004344, Avg Recall: 0.541667, count: 120 -Region Avg IOU: 0.519830, Class: 1.000000, Obj: 0.335631, No Obj: 0.004760, Avg Recall: 0.598361, count: 122 -6165: 0.465437, 0.446949 avg, 0.001000 rate, 18.107592 seconds, 1578240 images -Loaded: 3.109391 seconds -Region Avg IOU: 0.484851, Class: 1.000000, Obj: 0.339420, No Obj: 0.004133, Avg Recall: 0.512000, count: 125 -Region Avg IOU: 0.467409, Class: 1.000000, Obj: 0.336177, No Obj: 0.004567, Avg Recall: 0.496000, count: 125 -Region Avg IOU: 0.511378, Class: 1.000000, Obj: 0.338738, No Obj: 0.004464, Avg Recall: 0.584746, count: 118 -Region Avg IOU: 0.508882, Class: 1.000000, Obj: 0.329266, No Obj: 0.004397, Avg Recall: 0.581967, count: 122 -Region Avg IOU: 0.480739, Class: 1.000000, Obj: 0.297174, No Obj: 0.004586, Avg Recall: 0.555556, count: 99 -Region Avg IOU: 0.550058, Class: 1.000000, Obj: 0.362840, No Obj: 0.004466, Avg Recall: 0.630252, count: 119 -Region Avg IOU: 0.481071, Class: 1.000000, Obj: 0.302329, No Obj: 0.003766, Avg Recall: 0.517241, count: 87 -Region Avg IOU: 0.547623, Class: 1.000000, Obj: 0.336779, No Obj: 0.004543, Avg Recall: 0.598291, count: 117 -6166: 0.485057, 0.450760 avg, 0.001000 rate, 19.491220 seconds, 1578496 images -Loaded: 1.643957 seconds -Region Avg IOU: 0.481540, Class: 1.000000, Obj: 0.315263, No Obj: 0.004286, Avg Recall: 0.527778, count: 144 -Region Avg IOU: 0.508737, Class: 1.000000, Obj: 0.345107, No Obj: 0.004286, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.511930, Class: 1.000000, Obj: 0.335859, No Obj: 0.003971, Avg Recall: 0.556701, count: 97 -Region Avg IOU: 0.513726, Class: 1.000000, Obj: 0.333147, No Obj: 0.004084, Avg Recall: 0.568421, count: 95 -Region Avg IOU: 0.520814, Class: 1.000000, Obj: 0.305822, No Obj: 0.003983, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.528606, Class: 1.000000, Obj: 0.375584, No Obj: 0.004425, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.475567, Class: 1.000000, Obj: 0.347780, No Obj: 0.004053, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.490431, Class: 1.000000, Obj: 0.341187, No Obj: 0.004266, Avg Recall: 0.558559, count: 111 -6167: 0.457146, 0.451399 avg, 0.001000 rate, 16.614815 seconds, 1578752 images -Loaded: 4.413354 seconds -Region Avg IOU: 0.504735, Class: 1.000000, Obj: 0.304807, No Obj: 0.004348, Avg Recall: 0.553846, count: 130 -Region Avg IOU: 0.552047, Class: 1.000000, Obj: 0.355610, No Obj: 0.003877, Avg Recall: 0.588235, count: 85 -Region Avg IOU: 0.498061, Class: 1.000000, Obj: 0.322936, No Obj: 0.004801, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.483659, Class: 1.000000, Obj: 0.338596, No Obj: 0.004359, Avg Recall: 0.492064, count: 126 -Region Avg IOU: 0.580667, Class: 1.000000, Obj: 0.364431, No Obj: 0.004657, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.537101, Class: 1.000000, Obj: 0.346295, No Obj: 0.004729, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.538175, Class: 1.000000, Obj: 0.344605, No Obj: 0.003946, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.573542, Class: 1.000000, Obj: 0.358488, No Obj: 0.004669, Avg Recall: 0.685393, count: 89 -6168: 0.445540, 0.450813 avg, 0.001000 rate, 19.909300 seconds, 1579008 images -Loaded: 2.191001 seconds -Region Avg IOU: 0.509606, Class: 1.000000, Obj: 0.317598, No Obj: 0.004544, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.512195, Class: 1.000000, Obj: 0.340888, No Obj: 0.003747, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.484029, Class: 1.000000, Obj: 0.325011, No Obj: 0.004252, Avg Recall: 0.516129, count: 124 -Region Avg IOU: 0.591581, Class: 1.000000, Obj: 0.350863, No Obj: 0.004223, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.481282, Class: 1.000000, Obj: 0.315950, No Obj: 0.003704, Avg Recall: 0.517544, count: 114 -Region Avg IOU: 0.499046, Class: 1.000000, Obj: 0.366170, No Obj: 0.003765, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.528754, Class: 1.000000, Obj: 0.340183, No Obj: 0.004262, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.529940, Class: 1.000000, Obj: 0.361482, No Obj: 0.004333, Avg Recall: 0.592233, count: 103 -6169: 0.440332, 0.449765 avg, 0.001000 rate, 19.574291 seconds, 1579264 images -Loaded: 2.044403 seconds -Region Avg IOU: 0.508044, Class: 1.000000, Obj: 0.368243, No Obj: 0.003892, Avg Recall: 0.521277, count: 94 -Region Avg IOU: 0.516274, Class: 1.000000, Obj: 0.355670, No Obj: 0.004884, Avg Recall: 0.592000, count: 125 -Region Avg IOU: 0.535129, Class: 1.000000, Obj: 0.351830, No Obj: 0.004483, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.496961, Class: 1.000000, Obj: 0.312727, No Obj: 0.004328, Avg Recall: 0.539062, count: 128 -Region Avg IOU: 0.540495, Class: 1.000000, Obj: 0.368810, No Obj: 0.004868, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.506277, Class: 1.000000, Obj: 0.379459, No Obj: 0.003827, Avg Recall: 0.560976, count: 82 -Region Avg IOU: 0.504876, Class: 1.000000, Obj: 0.357246, No Obj: 0.004419, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.523216, Class: 1.000000, Obj: 0.355107, No Obj: 0.004047, Avg Recall: 0.598039, count: 102 -6170: 0.412045, 0.445993 avg, 0.001000 rate, 19.369448 seconds, 1579520 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000071 seconds -Region Avg IOU: 0.661904, Class: 1.000000, Obj: 0.497273, No Obj: 0.004008, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.652703, Class: 1.000000, Obj: 0.497780, No Obj: 0.003268, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.639113, Class: 1.000000, Obj: 0.464854, No Obj: 0.003087, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.610381, Class: 1.000000, Obj: 0.416089, No Obj: 0.003368, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.592699, Class: 1.000000, Obj: 0.429972, No Obj: 0.003336, Avg Recall: 0.726191, count: 84 -Region Avg IOU: 0.676150, Class: 1.000000, Obj: 0.470491, No Obj: 0.003301, Avg Recall: 0.858586, count: 99 -Region Avg IOU: 0.644501, Class: 1.000000, Obj: 0.449834, No Obj: 0.003029, Avg Recall: 0.816092, count: 87 -Region Avg IOU: 0.614764, Class: 1.000000, Obj: 0.400084, No Obj: 0.004237, Avg Recall: 0.780303, count: 132 -6171: 0.408121, 0.442206 avg, 0.001000 rate, 27.234749 seconds, 1579776 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.589210, Class: 1.000000, Obj: 0.490256, No Obj: 0.003469, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.608697, Class: 1.000000, Obj: 0.462494, No Obj: 0.003223, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.637714, Class: 1.000000, Obj: 0.410770, No Obj: 0.003789, Avg Recall: 0.779661, count: 118 -Region Avg IOU: 0.625733, Class: 1.000000, Obj: 0.458316, No Obj: 0.003242, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.682411, Class: 1.000000, Obj: 0.551902, No Obj: 0.003745, Avg Recall: 0.846154, count: 78 -Region Avg IOU: 0.558692, Class: 1.000000, Obj: 0.419412, No Obj: 0.003235, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.646334, Class: 1.000000, Obj: 0.531620, No Obj: 0.003288, Avg Recall: 0.807229, count: 83 -Region Avg IOU: 0.649553, Class: 1.000000, Obj: 0.466356, No Obj: 0.003784, Avg Recall: 0.770492, count: 122 -6172: 0.384916, 0.436477 avg, 0.001000 rate, 27.172544 seconds, 1580032 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.660983, Class: 1.000000, Obj: 0.458028, No Obj: 0.003346, Avg Recall: 0.820225, count: 89 -Region Avg IOU: 0.656586, Class: 1.000000, Obj: 0.436186, No Obj: 0.003791, Avg Recall: 0.770992, count: 131 -Region Avg IOU: 0.585865, Class: 1.000000, Obj: 0.399288, No Obj: 0.003260, Avg Recall: 0.669291, count: 127 -Region Avg IOU: 0.594475, Class: 1.000000, Obj: 0.436712, No Obj: 0.003169, Avg Recall: 0.704918, count: 122 -Region Avg IOU: 0.607234, Class: 1.000000, Obj: 0.449094, No Obj: 0.003831, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.592184, Class: 1.000000, Obj: 0.459525, No Obj: 0.003240, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.640054, Class: 1.000000, Obj: 0.453432, No Obj: 0.003377, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.650073, Class: 1.000000, Obj: 0.485995, No Obj: 0.003659, Avg Recall: 0.796610, count: 118 -6173: 0.432165, 0.436045 avg, 0.001000 rate, 27.314249 seconds, 1580288 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.656549, Class: 1.000000, Obj: 0.420532, No Obj: 0.003198, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.593214, Class: 1.000000, Obj: 0.410704, No Obj: 0.003268, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.590354, Class: 1.000000, Obj: 0.388581, No Obj: 0.003156, Avg Recall: 0.723077, count: 130 -Region Avg IOU: 0.629228, Class: 1.000000, Obj: 0.463554, No Obj: 0.002751, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.636982, Class: 1.000000, Obj: 0.459911, No Obj: 0.003399, Avg Recall: 0.769231, count: 117 -Region Avg IOU: 0.604824, Class: 1.000000, Obj: 0.408186, No Obj: 0.003167, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.645710, Class: 1.000000, Obj: 0.455549, No Obj: 0.003098, Avg Recall: 0.801980, count: 101 -Region Avg IOU: 0.584224, Class: 1.000000, Obj: 0.431706, No Obj: 0.003332, Avg Recall: 0.701923, count: 104 -6174: 0.385482, 0.430989 avg, 0.001000 rate, 28.492868 seconds, 1580544 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.603594, Class: 1.000000, Obj: 0.390755, No Obj: 0.003433, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.610870, Class: 1.000000, Obj: 0.438891, No Obj: 0.002558, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.630305, Class: 1.000000, Obj: 0.434296, No Obj: 0.003306, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.634890, Class: 1.000000, Obj: 0.387917, No Obj: 0.003049, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.637455, Class: 1.000000, Obj: 0.453151, No Obj: 0.002861, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.599300, Class: 1.000000, Obj: 0.426346, No Obj: 0.003060, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.593707, Class: 1.000000, Obj: 0.327340, No Obj: 0.003088, Avg Recall: 0.700000, count: 130 -Region Avg IOU: 0.604007, Class: 1.000000, Obj: 0.382221, No Obj: 0.003158, Avg Recall: 0.752294, count: 109 -6175: 0.401719, 0.428062 avg, 0.001000 rate, 27.867418 seconds, 1580800 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.645344, Class: 1.000000, Obj: 0.380243, No Obj: 0.003029, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.582608, Class: 1.000000, Obj: 0.356529, No Obj: 0.003438, Avg Recall: 0.703947, count: 152 -Region Avg IOU: 0.641065, Class: 1.000000, Obj: 0.428183, No Obj: 0.002804, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.641993, Class: 1.000000, Obj: 0.357936, No Obj: 0.003146, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.657534, Class: 1.000000, Obj: 0.432916, No Obj: 0.002939, Avg Recall: 0.842105, count: 95 -Region Avg IOU: 0.586500, Class: 1.000000, Obj: 0.391723, No Obj: 0.003230, Avg Recall: 0.664234, count: 137 -Region Avg IOU: 0.602330, Class: 1.000000, Obj: 0.429356, No Obj: 0.002834, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.589649, Class: 1.000000, Obj: 0.398513, No Obj: 0.003124, Avg Recall: 0.682540, count: 126 -6176: 0.472159, 0.432472 avg, 0.001000 rate, 27.226643 seconds, 1581056 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.637771, Class: 1.000000, Obj: 0.461978, No Obj: 0.002677, Avg Recall: 0.797872, count: 94 -Region Avg IOU: 0.640483, Class: 1.000000, Obj: 0.356232, No Obj: 0.003082, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.619220, Class: 1.000000, Obj: 0.396809, No Obj: 0.003263, Avg Recall: 0.750000, count: 124 -Region Avg IOU: 0.649234, Class: 1.000000, Obj: 0.375847, No Obj: 0.003044, Avg Recall: 0.781955, count: 133 -Region Avg IOU: 0.617066, Class: 1.000000, Obj: 0.404640, No Obj: 0.002755, Avg Recall: 0.762712, count: 118 -Region Avg IOU: 0.662615, Class: 1.000000, Obj: 0.424840, No Obj: 0.002693, Avg Recall: 0.770115, count: 87 -Region Avg IOU: 0.664090, Class: 1.000000, Obj: 0.380292, No Obj: 0.003358, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.655579, Class: 1.000000, Obj: 0.438375, No Obj: 0.003336, Avg Recall: 0.790476, count: 105 -6177: 0.503678, 0.439592 avg, 0.001000 rate, 27.486128 seconds, 1581312 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.640613, Class: 1.000000, Obj: 0.440857, No Obj: 0.002858, Avg Recall: 0.788889, count: 90 -Region Avg IOU: 0.631189, Class: 1.000000, Obj: 0.406570, No Obj: 0.003018, Avg Recall: 0.762712, count: 118 -Region Avg IOU: 0.629678, Class: 1.000000, Obj: 0.422410, No Obj: 0.002797, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.636556, Class: 1.000000, Obj: 0.414345, No Obj: 0.003021, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.614381, Class: 1.000000, Obj: 0.418005, No Obj: 0.003014, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.637672, Class: 1.000000, Obj: 0.385462, No Obj: 0.002972, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.605142, Class: 1.000000, Obj: 0.385956, No Obj: 0.002967, Avg Recall: 0.686956, count: 115 -Region Avg IOU: 0.603048, Class: 1.000000, Obj: 0.384165, No Obj: 0.003481, Avg Recall: 0.718121, count: 149 -6178: 0.465773, 0.442211 avg, 0.001000 rate, 27.752554 seconds, 1581568 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.628043, Class: 1.000000, Obj: 0.455707, No Obj: 0.003148, Avg Recall: 0.790698, count: 86 -Region Avg IOU: 0.649543, Class: 1.000000, Obj: 0.483115, No Obj: 0.002884, Avg Recall: 0.777778, count: 90 -Region Avg IOU: 0.612222, Class: 1.000000, Obj: 0.404194, No Obj: 0.003219, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.588483, Class: 1.000000, Obj: 0.367481, No Obj: 0.002932, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.617760, Class: 1.000000, Obj: 0.412470, No Obj: 0.003209, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.658791, Class: 1.000000, Obj: 0.463932, No Obj: 0.003544, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.562596, Class: 1.000000, Obj: 0.361465, No Obj: 0.003432, Avg Recall: 0.639344, count: 122 -Region Avg IOU: 0.627937, Class: 1.000000, Obj: 0.443592, No Obj: 0.003063, Avg Recall: 0.756757, count: 111 -6179: 0.394166, 0.437406 avg, 0.001000 rate, 27.546244 seconds, 1581824 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.641925, Class: 1.000000, Obj: 0.470800, No Obj: 0.003222, Avg Recall: 0.786325, count: 117 -Region Avg IOU: 0.614385, Class: 1.000000, Obj: 0.407525, No Obj: 0.003089, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.590546, Class: 1.000000, Obj: 0.362733, No Obj: 0.003124, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.635774, Class: 1.000000, Obj: 0.399188, No Obj: 0.002884, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.584262, Class: 1.000000, Obj: 0.374736, No Obj: 0.003096, Avg Recall: 0.639344, count: 122 -Region Avg IOU: 0.610514, Class: 1.000000, Obj: 0.406338, No Obj: 0.003429, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.655413, Class: 1.000000, Obj: 0.414127, No Obj: 0.003390, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.678235, Class: 1.000000, Obj: 0.463078, No Obj: 0.003015, Avg Recall: 0.830000, count: 100 -6180: 0.401372, 0.433803 avg, 0.001000 rate, 27.661381 seconds, 1582080 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 16.831333 seconds -Region Avg IOU: 0.537019, Class: 1.000000, Obj: 0.358022, No Obj: 0.003845, Avg Recall: 0.603175, count: 126 -Region Avg IOU: 0.552939, Class: 1.000000, Obj: 0.366798, No Obj: 0.003363, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.590858, Class: 1.000000, Obj: 0.410609, No Obj: 0.004050, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.535213, Class: 1.000000, Obj: 0.346142, No Obj: 0.003495, Avg Recall: 0.607692, count: 130 -Region Avg IOU: 0.541274, Class: 1.000000, Obj: 0.345528, No Obj: 0.003772, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.589320, Class: 1.000000, Obj: 0.423410, No Obj: 0.003635, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.615472, Class: 1.000000, Obj: 0.430640, No Obj: 0.003541, Avg Recall: 0.697368, count: 76 -Region Avg IOU: 0.596074, Class: 1.000000, Obj: 0.356229, No Obj: 0.003977, Avg Recall: 0.720000, count: 125 -6181: 0.427244, 0.433147 avg, 0.001000 rate, 23.344343 seconds, 1582336 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.608112, Class: 1.000000, Obj: 0.424261, No Obj: 0.003503, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.613583, Class: 1.000000, Obj: 0.396435, No Obj: 0.003831, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.532358, Class: 1.000000, Obj: 0.362321, No Obj: 0.003492, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.583879, Class: 1.000000, Obj: 0.401912, No Obj: 0.004234, Avg Recall: 0.708955, count: 134 -Region Avg IOU: 0.629305, Class: 1.000000, Obj: 0.366486, No Obj: 0.003790, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.554232, Class: 1.000000, Obj: 0.357013, No Obj: 0.003819, Avg Recall: 0.641791, count: 134 -Region Avg IOU: 0.582797, Class: 1.000000, Obj: 0.387639, No Obj: 0.003291, Avg Recall: 0.719512, count: 82 -Region Avg IOU: 0.572924, Class: 1.000000, Obj: 0.380351, No Obj: 0.003144, Avg Recall: 0.666667, count: 87 -6182: 0.436158, 0.433448 avg, 0.001000 rate, 24.054737 seconds, 1582592 images -Loaded: 0.916446 seconds -Region Avg IOU: 0.594919, Class: 1.000000, Obj: 0.374712, No Obj: 0.003064, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.559274, Class: 1.000000, Obj: 0.419595, No Obj: 0.003434, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.614735, Class: 1.000000, Obj: 0.405139, No Obj: 0.003445, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.571344, Class: 1.000000, Obj: 0.379807, No Obj: 0.003613, Avg Recall: 0.714286, count: 126 -Region Avg IOU: 0.557821, Class: 1.000000, Obj: 0.372886, No Obj: 0.003277, Avg Recall: 0.633333, count: 120 -Region Avg IOU: 0.629877, Class: 1.000000, Obj: 0.439858, No Obj: 0.003561, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.575217, Class: 1.000000, Obj: 0.417005, No Obj: 0.003242, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.607215, Class: 1.000000, Obj: 0.453253, No Obj: 0.003856, Avg Recall: 0.717391, count: 92 -6183: 0.394252, 0.429528 avg, 0.001000 rate, 23.611324 seconds, 1582848 images -Loaded: 0.976100 seconds -Region Avg IOU: 0.537971, Class: 1.000000, Obj: 0.423530, No Obj: 0.003731, Avg Recall: 0.584746, count: 118 -Region Avg IOU: 0.588894, Class: 1.000000, Obj: 0.374794, No Obj: 0.004077, Avg Recall: 0.671533, count: 137 -Region Avg IOU: 0.602159, Class: 1.000000, Obj: 0.366105, No Obj: 0.003615, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.566284, Class: 1.000000, Obj: 0.399113, No Obj: 0.004081, Avg Recall: 0.617188, count: 128 -Region Avg IOU: 0.623977, Class: 1.000000, Obj: 0.410608, No Obj: 0.003815, Avg Recall: 0.784483, count: 116 -Region Avg IOU: 0.567172, Class: 1.000000, Obj: 0.377588, No Obj: 0.003854, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.557633, Class: 1.000000, Obj: 0.385753, No Obj: 0.003716, Avg Recall: 0.661765, count: 136 -Region Avg IOU: 0.579085, Class: 1.000000, Obj: 0.424073, No Obj: 0.003888, Avg Recall: 0.654545, count: 110 -6184: 0.477658, 0.434341 avg, 0.001000 rate, 23.308287 seconds, 1583104 images -Loaded: 0.228036 seconds -Region Avg IOU: 0.556730, Class: 1.000000, Obj: 0.429503, No Obj: 0.003760, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.551534, Class: 1.000000, Obj: 0.373257, No Obj: 0.004681, Avg Recall: 0.635036, count: 137 -Region Avg IOU: 0.550451, Class: 1.000000, Obj: 0.362938, No Obj: 0.003896, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.576887, Class: 1.000000, Obj: 0.398828, No Obj: 0.003645, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.590245, Class: 1.000000, Obj: 0.429890, No Obj: 0.003944, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.585923, Class: 1.000000, Obj: 0.429628, No Obj: 0.003459, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.622488, Class: 1.000000, Obj: 0.451131, No Obj: 0.003296, Avg Recall: 0.792683, count: 82 -Region Avg IOU: 0.556658, Class: 1.000000, Obj: 0.379426, No Obj: 0.003927, Avg Recall: 0.664000, count: 125 -6185: 0.454630, 0.436370 avg, 0.001000 rate, 23.168663 seconds, 1583360 images -Loaded: 0.124597 seconds -Region Avg IOU: 0.599845, Class: 1.000000, Obj: 0.438782, No Obj: 0.004375, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.592518, Class: 1.000000, Obj: 0.407987, No Obj: 0.003902, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.612883, Class: 1.000000, Obj: 0.467145, No Obj: 0.003524, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.577679, Class: 1.000000, Obj: 0.415105, No Obj: 0.003549, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.612767, Class: 1.000000, Obj: 0.447950, No Obj: 0.003751, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.591287, Class: 1.000000, Obj: 0.454861, No Obj: 0.004230, Avg Recall: 0.709924, count: 131 -Region Avg IOU: 0.588476, Class: 1.000000, Obj: 0.393495, No Obj: 0.004498, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.612947, Class: 1.000000, Obj: 0.460262, No Obj: 0.004176, Avg Recall: 0.775701, count: 107 -6186: 0.399879, 0.432721 avg, 0.001000 rate, 24.585323 seconds, 1583616 images -Loaded: 0.347633 seconds -Region Avg IOU: 0.613168, Class: 1.000000, Obj: 0.420484, No Obj: 0.004489, Avg Recall: 0.742188, count: 128 -Region Avg IOU: 0.628308, Class: 1.000000, Obj: 0.467348, No Obj: 0.004754, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.607950, Class: 1.000000, Obj: 0.393183, No Obj: 0.003989, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.545726, Class: 1.000000, Obj: 0.432785, No Obj: 0.003594, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.620855, Class: 1.000000, Obj: 0.465402, No Obj: 0.003908, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.597652, Class: 1.000000, Obj: 0.455010, No Obj: 0.004289, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.539892, Class: 1.000000, Obj: 0.365308, No Obj: 0.003770, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.579070, Class: 1.000000, Obj: 0.414208, No Obj: 0.003174, Avg Recall: 0.703297, count: 91 -6187: 0.402171, 0.429666 avg, 0.001000 rate, 23.086704 seconds, 1583872 images -Loaded: 0.994760 seconds -Region Avg IOU: 0.571389, Class: 1.000000, Obj: 0.421968, No Obj: 0.003921, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.520978, Class: 1.000000, Obj: 0.393189, No Obj: 0.004165, Avg Recall: 0.606061, count: 132 -Region Avg IOU: 0.550257, Class: 1.000000, Obj: 0.448874, No Obj: 0.004574, Avg Recall: 0.611465, count: 157 -Region Avg IOU: 0.585395, Class: 1.000000, Obj: 0.468046, No Obj: 0.003772, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.602239, Class: 1.000000, Obj: 0.449732, No Obj: 0.003771, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.578552, Class: 1.000000, Obj: 0.499212, No Obj: 0.004183, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.588100, Class: 1.000000, Obj: 0.468306, No Obj: 0.003650, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.606878, Class: 1.000000, Obj: 0.426972, No Obj: 0.004025, Avg Recall: 0.745098, count: 102 -6188: 0.373751, 0.424075 avg, 0.001000 rate, 23.424330 seconds, 1584128 images -Loaded: 0.353370 seconds -Region Avg IOU: 0.544055, Class: 1.000000, Obj: 0.422552, No Obj: 0.004203, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.563500, Class: 1.000000, Obj: 0.415845, No Obj: 0.003934, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.593085, Class: 1.000000, Obj: 0.436529, No Obj: 0.003876, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.588328, Class: 1.000000, Obj: 0.450092, No Obj: 0.004584, Avg Recall: 0.724409, count: 127 -Region Avg IOU: 0.589395, Class: 1.000000, Obj: 0.393369, No Obj: 0.004343, Avg Recall: 0.718750, count: 128 -Region Avg IOU: 0.613299, Class: 1.000000, Obj: 0.431643, No Obj: 0.003946, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.526845, Class: 1.000000, Obj: 0.375202, No Obj: 0.003800, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.566843, Class: 1.000000, Obj: 0.420562, No Obj: 0.003553, Avg Recall: 0.673469, count: 98 -6189: 0.417413, 0.423408 avg, 0.001000 rate, 24.302092 seconds, 1584384 images -Loaded: 1.110842 seconds -Region Avg IOU: 0.590785, Class: 1.000000, Obj: 0.427369, No Obj: 0.003819, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.556926, Class: 1.000000, Obj: 0.414142, No Obj: 0.004210, Avg Recall: 0.655462, count: 119 -Region Avg IOU: 0.583800, Class: 1.000000, Obj: 0.466508, No Obj: 0.003415, Avg Recall: 0.670732, count: 82 -Region Avg IOU: 0.593736, Class: 1.000000, Obj: 0.417813, No Obj: 0.004204, Avg Recall: 0.634146, count: 123 -Region Avg IOU: 0.557897, Class: 1.000000, Obj: 0.396704, No Obj: 0.003794, Avg Recall: 0.639344, count: 122 -Region Avg IOU: 0.551433, Class: 1.000000, Obj: 0.414123, No Obj: 0.003806, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.559898, Class: 1.000000, Obj: 0.442708, No Obj: 0.003771, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.551179, Class: 1.000000, Obj: 0.358779, No Obj: 0.003097, Avg Recall: 0.631579, count: 95 -6190: 0.390772, 0.420145 avg, 0.001000 rate, 24.082691 seconds, 1584640 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 16.390285 seconds -Region Avg IOU: 0.562062, Class: 1.000000, Obj: 0.423476, No Obj: 0.003039, Avg Recall: 0.708861, count: 79 -Region Avg IOU: 0.600501, Class: 1.000000, Obj: 0.395394, No Obj: 0.003817, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.576740, Class: 1.000000, Obj: 0.388764, No Obj: 0.003991, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.563416, Class: 1.000000, Obj: 0.403837, No Obj: 0.004599, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.562658, Class: 1.000000, Obj: 0.410609, No Obj: 0.003603, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.576439, Class: 1.000000, Obj: 0.404725, No Obj: 0.003480, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.581691, Class: 1.000000, Obj: 0.426081, No Obj: 0.004089, Avg Recall: 0.715447, count: 123 -Region Avg IOU: 0.569195, Class: 1.000000, Obj: 0.419012, No Obj: 0.003499, Avg Recall: 0.629630, count: 108 -6191: 0.401229, 0.418253 avg, 0.001000 rate, 22.650028 seconds, 1584896 images -Loaded: 2.730097 seconds -Region Avg IOU: 0.538458, Class: 1.000000, Obj: 0.394864, No Obj: 0.003347, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.624946, Class: 1.000000, Obj: 0.402712, No Obj: 0.003858, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.527774, Class: 1.000000, Obj: 0.365931, No Obj: 0.003094, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.588494, Class: 1.000000, Obj: 0.426112, No Obj: 0.003124, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.506422, Class: 1.000000, Obj: 0.433323, No Obj: 0.003229, Avg Recall: 0.534483, count: 116 -Region Avg IOU: 0.580615, Class: 1.000000, Obj: 0.401146, No Obj: 0.003065, Avg Recall: 0.719512, count: 82 -Region Avg IOU: 0.547402, Class: 1.000000, Obj: 0.377438, No Obj: 0.004479, Avg Recall: 0.674419, count: 129 -Region Avg IOU: 0.495571, Class: 1.000000, Obj: 0.384958, No Obj: 0.003252, Avg Recall: 0.556522, count: 115 -6192: 0.385708, 0.414999 avg, 0.001000 rate, 23.670671 seconds, 1585152 images -Loaded: 1.146148 seconds -Region Avg IOU: 0.586323, Class: 1.000000, Obj: 0.431969, No Obj: 0.003901, Avg Recall: 0.666667, count: 123 -Region Avg IOU: 0.571375, Class: 1.000000, Obj: 0.364779, No Obj: 0.003098, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.644888, Class: 1.000000, Obj: 0.450364, No Obj: 0.003575, Avg Recall: 0.802083, count: 96 -Region Avg IOU: 0.594282, Class: 1.000000, Obj: 0.424636, No Obj: 0.003717, Avg Recall: 0.715328, count: 137 -Region Avg IOU: 0.603711, Class: 1.000000, Obj: 0.393598, No Obj: 0.003601, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.577669, Class: 1.000000, Obj: 0.375224, No Obj: 0.003796, Avg Recall: 0.664122, count: 131 -Region Avg IOU: 0.586330, Class: 1.000000, Obj: 0.376236, No Obj: 0.004113, Avg Recall: 0.658537, count: 123 -Region Avg IOU: 0.544739, Class: 1.000000, Obj: 0.387257, No Obj: 0.003724, Avg Recall: 0.636364, count: 110 -6193: 0.453252, 0.418824 avg, 0.001000 rate, 23.830456 seconds, 1585408 images -Loaded: 1.377884 seconds -Region Avg IOU: 0.576546, Class: 1.000000, Obj: 0.378130, No Obj: 0.003469, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.458488, Class: 1.000000, Obj: 0.340346, No Obj: 0.002788, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.595344, Class: 1.000000, Obj: 0.408415, No Obj: 0.003660, Avg Recall: 0.650794, count: 126 -Region Avg IOU: 0.526782, Class: 1.000000, Obj: 0.364919, No Obj: 0.003802, Avg Recall: 0.621795, count: 156 -Region Avg IOU: 0.568254, Class: 1.000000, Obj: 0.406684, No Obj: 0.003404, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.583922, Class: 1.000000, Obj: 0.431568, No Obj: 0.003029, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.576119, Class: 1.000000, Obj: 0.389636, No Obj: 0.004295, Avg Recall: 0.655172, count: 145 -Region Avg IOU: 0.598042, Class: 1.000000, Obj: 0.417805, No Obj: 0.003455, Avg Recall: 0.685185, count: 108 -6194: 0.527154, 0.429657 avg, 0.001000 rate, 25.343502 seconds, 1585664 images -Loaded: 0.709274 seconds -Region Avg IOU: 0.580872, Class: 1.000000, Obj: 0.374691, No Obj: 0.003156, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.591222, Class: 1.000000, Obj: 0.389846, No Obj: 0.003479, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.578659, Class: 1.000000, Obj: 0.421066, No Obj: 0.003193, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.559293, Class: 1.000000, Obj: 0.377561, No Obj: 0.003517, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.543689, Class: 1.000000, Obj: 0.406793, No Obj: 0.003047, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.592237, Class: 1.000000, Obj: 0.416109, No Obj: 0.003971, Avg Recall: 0.710145, count: 138 -Region Avg IOU: 0.620407, Class: 1.000000, Obj: 0.447645, No Obj: 0.003565, Avg Recall: 0.797872, count: 94 -Region Avg IOU: 0.562733, Class: 1.000000, Obj: 0.383309, No Obj: 0.002795, Avg Recall: 0.692308, count: 78 -6195: 0.425920, 0.429283 avg, 0.001000 rate, 25.335491 seconds, 1585920 images -Loaded: 0.100433 seconds -Region Avg IOU: 0.562357, Class: 1.000000, Obj: 0.382450, No Obj: 0.003226, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.583337, Class: 1.000000, Obj: 0.356274, No Obj: 0.003933, Avg Recall: 0.697674, count: 129 -Region Avg IOU: 0.601887, Class: 1.000000, Obj: 0.403477, No Obj: 0.003773, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.551434, Class: 1.000000, Obj: 0.398913, No Obj: 0.003575, Avg Recall: 0.620968, count: 124 -Region Avg IOU: 0.608771, Class: 1.000000, Obj: 0.426834, No Obj: 0.003428, Avg Recall: 0.727273, count: 88 -Region Avg IOU: 0.563998, Class: 1.000000, Obj: 0.321439, No Obj: 0.003319, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.628521, Class: 1.000000, Obj: 0.441047, No Obj: 0.003912, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.593405, Class: 1.000000, Obj: 0.402265, No Obj: 0.003771, Avg Recall: 0.712963, count: 108 -6196: 0.418944, 0.428249 avg, 0.001000 rate, 23.251310 seconds, 1586176 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.568244, Class: 1.000000, Obj: 0.416145, No Obj: 0.003168, Avg Recall: 0.710843, count: 83 -Region Avg IOU: 0.586916, Class: 1.000000, Obj: 0.386481, No Obj: 0.003911, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.572753, Class: 1.000000, Obj: 0.405635, No Obj: 0.003183, Avg Recall: 0.662791, count: 86 -Region Avg IOU: 0.563609, Class: 1.000000, Obj: 0.415156, No Obj: 0.003206, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.597350, Class: 1.000000, Obj: 0.399559, No Obj: 0.003713,Saving weights to ./person9300/backup/tyvp_93_9c512_6200.weights - Avg Recall: 0.682927, count: 123 -Region Avg IOU: 0.660536, Class: 1.000000, Obj: 0.464814, No Obj: 0.003224, Avg Recall: 0.835294, count: 85 -Region Avg IOU: 0.604888, Class: 1.000000, Obj: 0.402802, No Obj: 0.003999, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.583848, Class: 1.000000, Obj: 0.397744, No Obj: 0.003525, Avg Recall: 0.700855, count: 117 -6197: 0.381457, 0.423570 avg, 0.001000 rate, 24.870071 seconds, 1586432 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.573465, Class: 1.000000, Obj: 0.398435, No Obj: 0.004006, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.555532, Class: 1.000000, Obj: 0.395253, No Obj: 0.003244, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.548371, Class: 1.000000, Obj: 0.374745, No Obj: 0.003360, Avg Recall: 0.669492, count: 118 -Region Avg IOU: 0.541474, Class: 1.000000, Obj: 0.374663, No Obj: 0.003659, Avg Recall: 0.630252, count: 119 -Region Avg IOU: 0.619495, Class: 1.000000, Obj: 0.386819, No Obj: 0.004105, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.566663, Class: 1.000000, Obj: 0.444394, No Obj: 0.003314, Avg Recall: 0.696629, count: 89 -Region Avg IOU: 0.598355, Class: 1.000000, Obj: 0.432862, No Obj: 0.003956, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.602821, Class: 1.000000, Obj: 0.395625, No Obj: 0.003180, Avg Recall: 0.750000, count: 88 -6198: 0.414314, 0.422645 avg, 0.001000 rate, 24.610855 seconds, 1586688 images -Loaded: 0.477635 seconds -Region Avg IOU: 0.535514, Class: 1.000000, Obj: 0.394263, No Obj: 0.003734, Avg Recall: 0.606299, count: 127 -Region Avg IOU: 0.577172, Class: 1.000000, Obj: 0.433901, No Obj: 0.003190, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.599084, Class: 1.000000, Obj: 0.364942, No Obj: 0.003771, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.547308, Class: 1.000000, Obj: 0.374675, No Obj: 0.003681, Avg Recall: 0.645161, count: 124 -Region Avg IOU: 0.579601, Class: 1.000000, Obj: 0.395703, No Obj: 0.003293, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.582074, Class: 1.000000, Obj: 0.431069, No Obj: 0.003730, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.616862, Class: 1.000000, Obj: 0.431928, No Obj: 0.003943, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.599654, Class: 1.000000, Obj: 0.391022, No Obj: 0.003884, Avg Recall: 0.736364, count: 110 -6199: 0.441826, 0.424563 avg, 0.001000 rate, 23.001755 seconds, 1586944 images -Loaded: 2.229632 seconds -Region Avg IOU: 0.552639, Class: 1.000000, Obj: 0.414899, No Obj: 0.004018, Avg Recall: 0.633588, count: 131 -Region Avg IOU: 0.528122, Class: 1.000000, Obj: 0.369828, No Obj: 0.003231, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.595306, Class: 1.000000, Obj: 0.488269, No Obj: 0.003792, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.592812, Class: 1.000000, Obj: 0.466008, No Obj: 0.003124, Avg Recall: 0.716049, count: 81 -Region Avg IOU: 0.606360, Class: 1.000000, Obj: 0.455567, No Obj: 0.003738, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.573067, Class: 1.000000, Obj: 0.427121, No Obj: 0.003201, Avg Recall: 0.642857, count: 84 -Region Avg IOU: 0.586709, Class: 1.000000, Obj: 0.418423, No Obj: 0.003558, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.569014, Class: 1.000000, Obj: 0.437860, No Obj: 0.003890, Avg Recall: 0.695652, count: 115 -6200: 0.393283, 0.421435 avg, 0.001000 rate, 22.963408 seconds, 1587200 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 17.823448 seconds -Region Avg IOU: 0.577867, Class: 1.000000, Obj: 0.377796, No Obj: 0.003946, Avg Recall: 0.714286, count: 119 -Region Avg IOU: 0.545948, Class: 1.000000, Obj: 0.412061, No Obj: 0.003768, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.579754, Class: 1.000000, Obj: 0.425873, No Obj: 0.004263, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.553438, Class: 1.000000, Obj: 0.410641, No Obj: 0.004199, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.490406, Class: 1.000000, Obj: 0.382268, No Obj: 0.003672, Avg Recall: 0.554622, count: 119 -Region Avg IOU: 0.538800, Class: 1.000000, Obj: 0.418828, No Obj: 0.003483, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.586845, Class: 1.000000, Obj: 0.426040, No Obj: 0.003978, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.527927, Class: 1.000000, Obj: 0.385636, No Obj: 0.004365, Avg Recall: 0.596491, count: 114 -6201: 0.435527, 0.422844 avg, 0.001000 rate, 23.081821 seconds, 1587456 images -Loaded: 1.526611 seconds -Region Avg IOU: 0.577693, Class: 1.000000, Obj: 0.394918, No Obj: 0.003865, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.565734, Class: 1.000000, Obj: 0.413252, No Obj: 0.004225, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.568743, Class: 1.000000, Obj: 0.388830, No Obj: 0.003428, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.508381, Class: 1.000000, Obj: 0.346452, No Obj: 0.004191, Avg Recall: 0.561644, count: 146 -Region Avg IOU: 0.554235, Class: 1.000000, Obj: 0.390738, No Obj: 0.004606, Avg Recall: 0.633333, count: 120 -Region Avg IOU: 0.570589, Class: 1.000000, Obj: 0.432599, No Obj: 0.004338, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.552583, Class: 1.000000, Obj: 0.436120, No Obj: 0.003702, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.561056, Class: 1.000000, Obj: 0.439006, No Obj: 0.003528, Avg Recall: 0.656566, count: 99 -6202: 0.422365, 0.422796 avg, 0.001000 rate, 22.005962 seconds, 1587712 images -Loaded: 1.475201 seconds -Region Avg IOU: 0.570956, Class: 1.000000, Obj: 0.381997, No Obj: 0.003228, Avg Recall: 0.643678, count: 87 -Region Avg IOU: 0.562485, Class: 1.000000, Obj: 0.434842, No Obj: 0.003578, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.575876, Class: 1.000000, Obj: 0.426215, No Obj: 0.004921, Avg Recall: 0.655462, count: 119 -Region Avg IOU: 0.580438, Class: 1.000000, Obj: 0.396106, No Obj: 0.003811, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.549788, Class: 1.000000, Obj: 0.379663, No Obj: 0.004791, Avg Recall: 0.658915, count: 129 -Region Avg IOU: 0.532352, Class: 1.000000, Obj: 0.381487, No Obj: 0.003897, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.565683, Class: 1.000000, Obj: 0.425507, No Obj: 0.004293, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.498470, Class: 1.000000, Obj: 0.358751, No Obj: 0.003925, Avg Recall: 0.588710, count: 124 -6203: 0.439297, 0.424446 avg, 0.001000 rate, 22.282164 seconds, 1587968 images -Loaded: 0.926435 seconds -Region Avg IOU: 0.545976, Class: 1.000000, Obj: 0.356697, No Obj: 0.003520, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.583378, Class: 1.000000, Obj: 0.413447, No Obj: 0.004104, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.575967, Class: 1.000000, Obj: 0.404545, No Obj: 0.003660, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.529963, Class: 1.000000, Obj: 0.420485, No Obj: 0.003531, Avg Recall: 0.601770, count: 113 -Region Avg IOU: 0.561031, Class: 1.000000, Obj: 0.382568, No Obj: 0.003716, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.572552, Class: 1.000000, Obj: 0.459744, No Obj: 0.004074, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.533511, Class: 1.000000, Obj: 0.395588, No Obj: 0.004463, Avg Recall: 0.643939, count: 132 -Region Avg IOU: 0.517022, Class: 1.000000, Obj: 0.400317, No Obj: 0.004294, Avg Recall: 0.573913, count: 115 -6204: 0.398257, 0.421827 avg, 0.001000 rate, 21.705980 seconds, 1588224 images -Loaded: 0.882604 seconds -Region Avg IOU: 0.530781, Class: 1.000000, Obj: 0.368122, No Obj: 0.003442, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.519488, Class: 1.000000, Obj: 0.385184, No Obj: 0.004891, Avg Recall: 0.576642, count: 137 -Region Avg IOU: 0.565534, Class: 1.000000, Obj: 0.432750, No Obj: 0.004276, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.557194, Class: 1.000000, Obj: 0.397828, No Obj: 0.003816, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.545878, Class: 1.000000, Obj: 0.359847, No Obj: 0.004148, Avg Recall: 0.585366, count: 123 -Region Avg IOU: 0.545129, Class: 1.000000, Obj: 0.402835, No Obj: 0.003304, Avg Recall: 0.641026, count: 78 -Region Avg IOU: 0.603407, Class: 1.000000, Obj: 0.422971, No Obj: 0.004196, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.614135, Class: 1.000000, Obj: 0.457842, No Obj: 0.003440, Avg Recall: 0.744186, count: 86 -6205: 0.393885, 0.419033 avg, 0.001000 rate, 22.259996 seconds, 1588480 images -Loaded: 1.761292 seconds -Region Avg IOU: 0.556531, Class: 1.000000, Obj: 0.389764, No Obj: 0.004240, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.579534, Class: 1.000000, Obj: 0.402544, No Obj: 0.003746, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.540661, Class: 1.000000, Obj: 0.368722, No Obj: 0.004419, Avg Recall: 0.632653, count: 147 -Region Avg IOU: 0.553888, Class: 1.000000, Obj: 0.367102, No Obj: 0.004467, Avg Recall: 0.606838, count: 117 -Region Avg IOU: 0.570711, Class: 1.000000, Obj: 0.384022, No Obj: 0.003811, Avg Recall: 0.666667, count: 123 -Region Avg IOU: 0.518132, Class: 1.000000, Obj: 0.377049, No Obj: 0.004847, Avg Recall: 0.561404, count: 171 -Region Avg IOU: 0.548616, Class: 1.000000, Obj: 0.406420, No Obj: 0.003593, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.506831, Class: 1.000000, Obj: 0.343474, No Obj: 0.004437, Avg Recall: 0.575163, count: 153 -6206: 0.436027, 0.420732 avg, 0.001000 rate, 19.718908 seconds, 1588736 images -Loaded: 2.781241 seconds -Region Avg IOU: 0.475866, Class: 1.000000, Obj: 0.372463, No Obj: 0.004073, Avg Recall: 0.488889, count: 135 -Region Avg IOU: 0.560642, Class: 1.000000, Obj: 0.410177, No Obj: 0.004056, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.646841, Class: 1.000000, Obj: 0.463180, No Obj: 0.003922, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.564575, Class: 1.000000, Obj: 0.377590, No Obj: 0.003671, Avg Recall: 0.643478, count: 115 -Region Avg IOU: 0.532050, Class: 1.000000, Obj: 0.422517, No Obj: 0.004000, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.547130, Class: 1.000000, Obj: 0.404314, No Obj: 0.003757, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.563847, Class: 1.000000, Obj: 0.414301, No Obj: 0.003433, Avg Recall: 0.659341, count: 91 -Region Avg IOU: 0.557414, Class: 1.000000, Obj: 0.397960, No Obj: 0.003512, Avg Recall: 0.620000, count: 100 -6207: 0.384006, 0.417060 avg, 0.001000 rate, 20.745636 seconds, 1588992 images -Loaded: 2.256077 seconds -Region Avg IOU: 0.534154, Class: 1.000000, Obj: 0.400242, No Obj: 0.003998, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.561054, Class: 1.000000, Obj: 0.406421, No Obj: 0.003246, Avg Recall: 0.626374, count: 91 -Region Avg IOU: 0.544890, Class: 1.000000, Obj: 0.371896, No Obj: 0.004265, Avg Recall: 0.617450, count: 149 -Region Avg IOU: 0.586897, Class: 1.000000, Obj: 0.447184, No Obj: 0.004244, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.602424, Class: 1.000000, Obj: 0.377156, No Obj: 0.003666, Avg Recall: 0.752809, count: 89 -Region Avg IOU: 0.555771, Class: 1.000000, Obj: 0.377043, No Obj: 0.004569, Avg Recall: 0.646154, count: 130 -Region Avg IOU: 0.605255, Class: 1.000000, Obj: 0.437385, No Obj: 0.004061, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.543280, Class: 1.000000, Obj: 0.376272, No Obj: 0.004604, Avg Recall: 0.620000, count: 150 -6208: 0.444157, 0.419770 avg, 0.001000 rate, 22.894783 seconds, 1589248 images -Loaded: 0.633456 seconds -Region Avg IOU: 0.562284, Class: 1.000000, Obj: 0.357448, No Obj: 0.004500, Avg Recall: 0.626866, count: 134 -Region Avg IOU: 0.571806, Class: 1.000000, Obj: 0.380906, No Obj: 0.004157, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.556776, Class: 1.000000, Obj: 0.372580, No Obj: 0.003707, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.553895, Class: 1.000000, Obj: 0.416481, No Obj: 0.003747, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.520447, Class: 1.000000, Obj: 0.331940, No Obj: 0.003403, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.536540, Class: 1.000000, Obj: 0.363591, No Obj: 0.003963, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.583650, Class: 1.000000, Obj: 0.436691, No Obj: 0.003793, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.556617, Class: 1.000000, Obj: 0.396089, No Obj: 0.003533, Avg Recall: 0.647619, count: 105 -6209: 0.425481, 0.420341 avg, 0.001000 rate, 22.814316 seconds, 1589504 images -Loaded: 0.486102 seconds -Region Avg IOU: 0.557376, Class: 1.000000, Obj: 0.404781, No Obj: 0.003819, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.585776, Class: 1.000000, Obj: 0.403670, No Obj: 0.003483, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.552453, Class: 1.000000, Obj: 0.378548, No Obj: 0.003424, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.577208, Class: 1.000000, Obj: 0.391527, No Obj: 0.004194, Avg Recall: 0.652542, count: 118 -Region Avg IOU: 0.579934, Class: 1.000000, Obj: 0.388055, No Obj: 0.003518, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.537933, Class: 1.000000, Obj: 0.408162, No Obj: 0.003769, Avg Recall: 0.606557, count: 122 -Region Avg IOU: 0.501828, Class: 1.000000, Obj: 0.366374, No Obj: 0.003611, Avg Recall: 0.525862, count: 116 -Region Avg IOU: 0.601228, Class: 1.000000, Obj: 0.406782, No Obj: 0.004220, Avg Recall: 0.722222, count: 126 -6210: 0.391151, 0.417422 avg, 0.001000 rate, 22.236830 seconds, 1589760 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000076 seconds -Region Avg IOU: 0.622530, Class: 1.000000, Obj: 0.462021, No Obj: 0.003634, Avg Recall: 0.776471, count: 85 -Region Avg IOU: 0.553314, Class: 1.000000, Obj: 0.400543, No Obj: 0.003568, Avg Recall: 0.669355, count: 124 -Region Avg IOU: 0.606454, Class: 1.000000, Obj: 0.401388, No Obj: 0.003347, Avg Recall: 0.666667, count: 117 -Region Avg IOU: 0.637357, Class: 1.000000, Obj: 0.490306, No Obj: 0.003497, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.608394, Class: 1.000000, Obj: 0.416462, No Obj: 0.003913, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.627553, Class: 1.000000, Obj: 0.438993, No Obj: 0.003128, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.565199, Class: 1.000000, Obj: 0.414036, No Obj: 0.003621, Avg Recall: 0.687500, count: 128 -Region Avg IOU: 0.593590, Class: 1.000000, Obj: 0.422216, No Obj: 0.003354, Avg Recall: 0.712963, count: 108 -6211: 0.402432, 0.415923 avg, 0.001000 rate, 24.946768 seconds, 1590016 images -Loaded: 0.000059 seconds -Region Avg IOU: 0.596826, Class: 1.000000, Obj: 0.366476, No Obj: 0.003234, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.590871, Class: 1.000000, Obj: 0.404817, No Obj: 0.004102, Avg Recall: 0.713115, count: 122 -Region Avg IOU: 0.573220, Class: 1.000000, Obj: 0.387830, No Obj: 0.004031, Avg Recall: 0.666667, count: 138 -Region Avg IOU: 0.619317, Class: 1.000000, Obj: 0.411380, No Obj: 0.004300, Avg Recall: 0.709677, count: 155 -Region Avg IOU: 0.600682, Class: 1.000000, Obj: 0.403765, No Obj: 0.003448, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.569050, Class: 1.000000, Obj: 0.426500, No Obj: 0.003043, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.629213, Class: 1.000000, Obj: 0.424187, No Obj: 0.003414, Avg Recall: 0.744444, count: 90 -Region Avg IOU: 0.587545, Class: 1.000000, Obj: 0.417552, No Obj: 0.003807, Avg Recall: 0.682927, count: 123 -6212: 0.425220, 0.416852 avg, 0.001000 rate, 25.563274 seconds, 1590272 images -Loaded: 0.000059 seconds -Region Avg IOU: 0.613645, Class: 1.000000, Obj: 0.404913, No Obj: 0.002936, Avg Recall: 0.750000, count: 76 -Region Avg IOU: 0.598026, Class: 1.000000, Obj: 0.406157, No Obj: 0.003374, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.559933, Class: 1.000000, Obj: 0.369996, No Obj: 0.004031, Avg Recall: 0.672956, count: 159 -Region Avg IOU: 0.577514, Class: 1.000000, Obj: 0.396416, No Obj: 0.003552, Avg Recall: 0.698276, count: 116 -Region Avg IOU: 0.614021, Class: 1.000000, Obj: 0.411572, No Obj: 0.003179, Avg Recall: 0.734940, count: 83 -Region Avg IOU: 0.592613, Class: 1.000000, Obj: 0.443877, No Obj: 0.003154, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.547072, Class: 1.000000, Obj: 0.414352, No Obj: 0.002956, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.582923, Class: 1.000000, Obj: 0.433098, No Obj: 0.003707, Avg Recall: 0.663793, count: 116 -6213: 0.385550, 0.413722 avg, 0.001000 rate, 26.181303 seconds, 1590528 images -Loaded: 0.053907 seconds -Region Avg IOU: 0.612259, Class: 1.000000, Obj: 0.482783, No Obj: 0.003296, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.593138, Class: 1.000000, Obj: 0.404052, No Obj: 0.003352, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.590180, Class: 1.000000, Obj: 0.392583, No Obj: 0.003221, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.622723, Class: 1.000000, Obj: 0.381802, No Obj: 0.004115, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.577601, Class: 1.000000, Obj: 0.405928, No Obj: 0.003814, Avg Recall: 0.671329, count: 143 -Region Avg IOU: 0.575975, Class: 1.000000, Obj: 0.406863, No Obj: 0.003424, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.625794, Class: 1.000000, Obj: 0.503500, No Obj: 0.003131, Avg Recall: 0.720930, count: 86 -Region Avg IOU: 0.594496, Class: 1.000000, Obj: 0.443743, No Obj: 0.003245, Avg Recall: 0.685185, count: 108 -6214: 0.392535, 0.411604 avg, 0.001000 rate, 24.728071 seconds, 1590784 images -Loaded: 1.641566 seconds -Region Avg IOU: 0.607467, Class: 1.000000, Obj: 0.428034, No Obj: 0.003453, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.625158, Class: 1.000000, Obj: 0.392392, No Obj: 0.003434, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.623984, Class: 1.000000, Obj: 0.465257, No Obj: 0.003638, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.578249, Class: 1.000000, Obj: 0.421287, No Obj: 0.003497, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.653799, Class: 1.000000, Obj: 0.427132, No Obj: 0.003349, Avg Recall: 0.808081, count: 99 -Region Avg IOU: 0.613904, Class: 1.000000, Obj: 0.428138, No Obj: 0.003837, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.551104, Class: 1.000000, Obj: 0.393288, No Obj: 0.003423, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.576027, Class: 1.000000, Obj: 0.376201, No Obj: 0.003790, Avg Recall: 0.666667, count: 102 -6215: 0.437174, 0.414161 avg, 0.001000 rate, 25.303757 seconds, 1591040 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.627714, Class: 1.000000, Obj: 0.439813, No Obj: 0.002990, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.539040, Class: 1.000000, Obj: 0.349110, No Obj: 0.003161, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.615576, Class: 1.000000, Obj: 0.410074, No Obj: 0.003806, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.524663, Class: 1.000000, Obj: 0.389018, No Obj: 0.003486, Avg Recall: 0.598361, count: 122 -Region Avg IOU: 0.618866, Class: 1.000000, Obj: 0.363296, No Obj: 0.003453, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.562095, Class: 1.000000, Obj: 0.366616, No Obj: 0.002824, Avg Recall: 0.615385, count: 91 -Region Avg IOU: 0.598425, Class: 1.000000, Obj: 0.397327, No Obj: 0.003631, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.573244, Class: 1.000000, Obj: 0.376205, No Obj: 0.003464, Avg Recall: 0.649573, count: 117 -6216: 0.412821, 0.414027 avg, 0.001000 rate, 24.948225 seconds, 1591296 images -Loaded: 0.805143 seconds -Region Avg IOU: 0.609013, Class: 1.000000, Obj: 0.447727, No Obj: 0.003454, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.658153, Class: 1.000000, Obj: 0.431013, No Obj: 0.003859, Avg Recall: 0.803571, count: 112 -Region Avg IOU: 0.597148, Class: 1.000000, Obj: 0.430729, No Obj: 0.003676, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.641251, Class: 1.000000, Obj: 0.409911, No Obj: 0.003266, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.595519, Class: 1.000000, Obj: 0.410698, No Obj: 0.003270, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.632197, Class: 1.000000, Obj: 0.442259, No Obj: 0.003821, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.599385, Class: 1.000000, Obj: 0.404310, No Obj: 0.004139, Avg Recall: 0.712121, count: 132 -Region Avg IOU: 0.610148, Class: 1.000000, Obj: 0.395216, No Obj: 0.003090, Avg Recall: 0.742574, count: 101 -6217: 0.412482, 0.413872 avg, 0.001000 rate, 25.586132 seconds, 1591552 images -Loaded: 1.087842 seconds -Region Avg IOU: 0.613092, Class: 1.000000, Obj: 0.401541, No Obj: 0.003695, Avg Recall: 0.715385, count: 130 -Region Avg IOU: 0.577669, Class: 1.000000, Obj: 0.398880, No Obj: 0.003348, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.639504, Class: 1.000000, Obj: 0.416604, No Obj: 0.003680, Avg Recall: 0.735043, count: 117 -Region Avg IOU: 0.599778, Class: 1.000000, Obj: 0.466014, No Obj: 0.003206, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.570079, Class: 1.000000, Obj: 0.408002, No Obj: 0.003490, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.575014, Class: 1.000000, Obj: 0.417654, No Obj: 0.003730, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.645199, Class: 1.000000, Obj: 0.471657, No Obj: 0.003595, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.589750, Class: 1.000000, Obj: 0.402796, No Obj: 0.003493, Avg Recall: 0.689076, count: 119 -6218: 0.430918, 0.415577 avg, 0.001000 rate, 24.870455 seconds, 1591808 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.587706, Class: 1.000000, Obj: 0.408891, No Obj: 0.003256, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.607049, Class: 1.000000, Obj: 0.400436, No Obj: 0.003538, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.631709, Class: 1.000000, Obj: 0.428648, No Obj: 0.003589, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.593038, Class: 1.000000, Obj: 0.415689, No Obj: 0.003616, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.580417, Class: 1.000000, Obj: 0.402275, No Obj: 0.002991, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.572142, Class: 1.000000, Obj: 0.390904, No Obj: 0.002695, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.593707, Class: 1.000000, Obj: 0.387918, No Obj: 0.003330, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.557412, Class: 1.000000, Obj: 0.362216, No Obj: 0.003227, Avg Recall: 0.666667, count: 93 -6219: 0.430728, 0.417092 avg, 0.001000 rate, 25.177336 seconds, 1592064 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.635176, Class: 1.000000, Obj: 0.414836, No Obj: 0.003049, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.571282, Class: 1.000000, Obj: 0.424764, No Obj: 0.003149, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.565856, Class: 1.000000, Obj: 0.372509, No Obj: 0.003233, Avg Recall: 0.652542, count: 118 -Region Avg IOU: 0.617162, Class: 1.000000, Obj: 0.373323, No Obj: 0.003454, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.590850, Class: 1.000000, Obj: 0.389311, No Obj: 0.003190, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.581444, Class: 1.000000, Obj: 0.384094, No Obj: 0.004235, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.613145, Class: 1.000000, Obj: 0.413250, No Obj: 0.003855, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.601607, Class: 1.000000, Obj: 0.406830, No Obj: 0.003570, Avg Recall: 0.731481, count: 108 -6220: 0.422552, 0.417638 avg, 0.001000 rate, 25.660202 seconds, 1592320 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.156631 seconds -Region Avg IOU: 0.526601, Class: 1.000000, Obj: 0.327563, No Obj: 0.004028, Avg Recall: 0.576271, count: 118 -Region Avg IOU: 0.509833, Class: 1.000000, Obj: 0.315653, No Obj: 0.004505, Avg Recall: 0.558140, count: 129 -Region Avg IOU: 0.533613, Class: 1.000000, Obj: 0.407735, No Obj: 0.003704, Avg Recall: 0.560440, count: 91 -Region Avg IOU: 0.517767, Class: 1.000000, Obj: 0.369842, No Obj: 0.003787, Avg Recall: 0.576577, count: 111 -Region Avg IOU: 0.562156, Class: 1.000000, Obj: 0.352201, No Obj: 0.004021, Avg Recall: 0.615385, count: 78 -Region Avg IOU: 0.467693, Class: 1.000000, Obj: 0.293948, No Obj: 0.003633, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.518233, Class: 1.000000, Obj: 0.328832, No Obj: 0.004519, Avg Recall: 0.596639, count: 119 -Region Avg IOU: 0.545943, Class: 1.000000, Obj: 0.372252, No Obj: 0.004160, Avg Recall: 0.632184, count: 87 -6221: 0.457340, 0.421608 avg, 0.001000 rate, 17.168823 seconds, 1592576 images -Loaded: 3.864754 seconds -Region Avg IOU: 0.500574, Class: 1.000000, Obj: 0.348914, No Obj: 0.004535, Avg Recall: 0.580153, count: 131 -Region Avg IOU: 0.538839, Class: 1.000000, Obj: 0.338160, No Obj: 0.003947, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.525893, Class: 1.000000, Obj: 0.348363, No Obj: 0.003731, Avg Recall: 0.609195, count: 87 -Region Avg IOU: 0.438978, Class: 1.000000, Obj: 0.290866, No Obj: 0.003317, Avg Recall: 0.474747, count: 99 -Region Avg IOU: 0.476798, Class: 1.000000, Obj: 0.335131, No Obj: 0.003765, Avg Recall: 0.485981, count: 107 -Region Avg IOU: 0.533117, Class: 1.000000, Obj: 0.350004, No Obj: 0.005140, Avg Recall: 0.582734, count: 139 -Region Avg IOU: 0.555788, Class: 1.000000, Obj: 0.377809, No Obj: 0.003642, Avg Recall: 0.640449, count: 89 -Region Avg IOU: 0.504552, Class: 1.000000, Obj: 0.347418, No Obj: 0.004268, Avg Recall: 0.543478, count: 138 -6222: 0.455289, 0.424976 avg, 0.001000 rate, 18.472704 seconds, 1592832 images -Loaded: 3.043760 seconds -Region Avg IOU: 0.471498, Class: 1.000000, Obj: 0.366723, No Obj: 0.003900, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.500430, Class: 1.000000, Obj: 0.299882, No Obj: 0.003949, Avg Recall: 0.551020, count: 98 -Region Avg IOU: 0.542446, Class: 1.000000, Obj: 0.375685, No Obj: 0.004083, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.488630, Class: 1.000000, Obj: 0.317611, No Obj: 0.004523, Avg Recall: 0.545455, count: 132 -Region Avg IOU: 0.539489, Class: 1.000000, Obj: 0.357867, No Obj: 0.004413, Avg Recall: 0.584071, count: 113 -Region Avg IOU: 0.525076, Class: 1.000000, Obj: 0.339267, No Obj: 0.004595, Avg Recall: 0.558140, count: 129 -Region Avg IOU: 0.463976, Class: 1.000000, Obj: 0.344735, No Obj: 0.004359, Avg Recall: 0.518868, count: 106 -Region Avg IOU: 0.480099, Class: 1.000000, Obj: 0.344791, No Obj: 0.004036, Avg Recall: 0.519231, count: 104 -6223: 0.484106, 0.430889 avg, 0.001000 rate, 18.578312 seconds, 1593088 images -Loaded: 3.756892 seconds -Region Avg IOU: 0.509898, Class: 1.000000, Obj: 0.381287, No Obj: 0.004583, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.508059, Class: 1.000000, Obj: 0.357715, No Obj: 0.004664, Avg Recall: 0.555556, count: 135 -Region Avg IOU: 0.557876, Class: 1.000000, Obj: 0.390379, No Obj: 0.004231, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.497023, Class: 1.000000, Obj: 0.355775, No Obj: 0.004538, Avg Recall: 0.526718, count: 131 -Region Avg IOU: 0.515377, Class: 1.000000, Obj: 0.344047, No Obj: 0.004500, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.494716, Class: 1.000000, Obj: 0.289767, No Obj: 0.004391, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.496108, Class: 1.000000, Obj: 0.350607, No Obj: 0.004604, Avg Recall: 0.540984, count: 122 -Region Avg IOU: 0.487356, Class: 1.000000, Obj: 0.334757, No Obj: 0.005383, Avg Recall: 0.532374, count: 139 -6224: 0.504210, 0.438221 avg, 0.001000 rate, 18.559267 seconds, 1593344 images -Loaded: 1.722989 seconds -Region Avg IOU: 0.559834, Class: 1.000000, Obj: 0.409246, No Obj: 0.004365, Avg Recall: 0.651163, count: 86 -Region Avg IOU: 0.493749, Class: 1.000000, Obj: 0.364497, No Obj: 0.004770, Avg Recall: 0.540323, count: 124 -Region Avg IOU: 0.535373, Class: 1.000000, Obj: 0.369806, No Obj: 0.004550, Avg Recall: 0.627907, count: 86 -Region Avg IOU: 0.495020, Class: 1.000000, Obj: 0.369820, No Obj: 0.004593, Avg Recall: 0.525424, count: 118 -Region Avg IOU: 0.527191, Class: 1.000000, Obj: 0.355212, No Obj: 0.004306, Avg Recall: 0.547368, count: 95 -Region Avg IOU: 0.501280, Class: 1.000000, Obj: 0.383968, No Obj: 0.004739, Avg Recall: 0.586207, count: 116 -Region Avg IOU: 0.507267, Class: 1.000000, Obj: 0.399143, No Obj: 0.004166, Avg Recall: 0.525773, count: 97 -Region Avg IOU: 0.568266, Class: 1.000000, Obj: 0.429001, No Obj: 0.004083, Avg Recall: 0.617284, count: 81 -6225: 0.397950, 0.434194 avg, 0.001000 rate, 18.035568 seconds, 1593600 images -Loaded: 2.929117 seconds -Region Avg IOU: 0.475638, Class: 1.000000, Obj: 0.391545, No Obj: 0.004144, Avg Recall: 0.511364, count: 88 -Region Avg IOU: 0.529289, Class: 1.000000, Obj: 0.341661, No Obj: 0.004265, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.565252, Class: 1.000000, Obj: 0.409303, No Obj: 0.004938, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.524855, Class: 1.000000, Obj: 0.402076, No Obj: 0.004314, Avg Recall: 0.570175, count: 114 -Region Avg IOU: 0.485780, Class: 1.000000, Obj: 0.416116, No Obj: 0.003860, Avg Recall: 0.510417, count: 96 -Region Avg IOU: 0.524359, Class: 1.000000, Obj: 0.365968, No Obj: 0.005064, Avg Recall: 0.598361, count: 122 -Region Avg IOU: 0.548923, Class: 1.000000, Obj: 0.444368, No Obj: 0.004280, Avg Recall: 0.607143, count: 84 -Region Avg IOU: 0.587502, Class: 1.000000, Obj: 0.437752, No Obj: 0.005220, Avg Recall: 0.713115, count: 122 -6226: 0.375685, 0.428343 avg, 0.001000 rate, 17.578983 seconds, 1593856 images -Loaded: 4.146193 seconds -Region Avg IOU: 0.533035, Class: 1.000000, Obj: 0.380850, No Obj: 0.004623, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.477024, Class: 1.000000, Obj: 0.368272, No Obj: 0.004252, Avg Recall: 0.490566, count: 106 -Region Avg IOU: 0.524469, Class: 1.000000, Obj: 0.373954, No Obj: 0.005047, Avg Recall: 0.590164, count: 122 -Region Avg IOU: 0.551574, Class: 1.000000, Obj: 0.396261, No Obj: 0.004078, Avg Recall: 0.595745, count: 94 -Region Avg IOU: 0.475488, Class: 1.000000, Obj: 0.368247, No Obj: 0.004967, Avg Recall: 0.495652, count: 115 -Region Avg IOU: 0.568101, Class: 1.000000, Obj: 0.392834, No Obj: 0.005606, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.511970, Class: 1.000000, Obj: 0.396001, No Obj: 0.004584, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.512701, Class: 1.000000, Obj: 0.437545, No Obj: 0.004960, Avg Recall: 0.596491, count: 114 -6227: 0.449550, 0.430464 avg, 0.001000 rate, 16.277243 seconds, 1594112 images -Loaded: 3.375195 seconds -Region Avg IOU: 0.504225, Class: 1.000000, Obj: 0.409889, No Obj: 0.004288, Avg Recall: 0.579545, count: 88 -Region Avg IOU: 0.540336, Class: 1.000000, Obj: 0.371885, No Obj: 0.005618, Avg Recall: 0.623077, count: 130 -Region Avg IOU: 0.468699, Class: 1.000000, Obj: 0.399507, No Obj: 0.004073, Avg Recall: 0.509804, count: 102 -Region Avg IOU: 0.560704, Class: 1.000000, Obj: 0.420785, No Obj: 0.004763, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.550081, Class: 1.000000, Obj: 0.435429, No Obj: 0.005129, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.464620, Class: 1.000000, Obj: 0.348624, No Obj: 0.005434, Avg Recall: 0.479339, count: 121 -Region Avg IOU: 0.494166, Class: 1.000000, Obj: 0.363566, No Obj: 0.004348, Avg Recall: 0.540816, count: 98 -Region Avg IOU: 0.487665, Class: 1.000000, Obj: 0.367658, No Obj: 0.004885, Avg Recall: 0.533333, count: 120 -6228: 0.391614, 0.426579 avg, 0.001000 rate, 18.992193 seconds, 1594368 images -Loaded: 2.319751 seconds -Region Avg IOU: 0.468143, Class: 1.000000, Obj: 0.367305, No Obj: 0.004195, Avg Recall: 0.495050, count: 101 -Region Avg IOU: 0.493199, Class: 1.000000, Obj: 0.388958, No Obj: 0.004556, Avg Recall: 0.544554, count: 101 -Region Avg IOU: 0.546278, Class: 1.000000, Obj: 0.423819, No Obj: 0.005334, Avg Recall: 0.627119, count: 118 -Region Avg IOU: 0.507470, Class: 1.000000, Obj: 0.382509, No Obj: 0.004867, Avg Recall: 0.542373, count: 118 -Region Avg IOU: 0.527266, Class: 1.000000, Obj: 0.397250, No Obj: 0.005516, Avg Recall: 0.571429, count: 133 -Region Avg IOU: 0.548394, Class: 1.000000, Obj: 0.388993, No Obj: 0.004842, Avg Recall: 0.629214, count: 89 -Region Avg IOU: 0.538800, Class: 1.000000, Obj: 0.420861, No Obj: 0.004388, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.550934, Class: 1.000000, Obj: 0.435759, No Obj: 0.005063, Avg Recall: 0.616822, count: 107 -6229: 0.396358, 0.423557 avg, 0.001000 rate, 18.050545 seconds, 1594624 images -Loaded: 3.558177 seconds -Region Avg IOU: 0.499392, Class: 1.000000, Obj: 0.389052, No Obj: 0.004582, Avg Recall: 0.551724, count: 116 -Region Avg IOU: 0.444719, Class: 1.000000, Obj: 0.388385, No Obj: 0.004006, Avg Recall: 0.446602, count: 103 -Region Avg IOU: 0.517338, Class: 1.000000, Obj: 0.381326, No Obj: 0.005037, Avg Recall: 0.597826, count: 92 -Region Avg IOU: 0.543605, Class: 1.000000, Obj: 0.373575, No Obj: 0.004338, Avg Recall: 0.611765, count: 85 -Region Avg IOU: 0.502371, Class: 1.000000, Obj: 0.347359, No Obj: 0.004478, Avg Recall: 0.537736, count: 106 -Region Avg IOU: 0.435126, Class: 1.000000, Obj: 0.338686, No Obj: 0.004629, Avg Recall: 0.391304, count: 115 -Region Avg IOU: 0.499905, Class: 1.000000, Obj: 0.363883, No Obj: 0.004407, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.483034, Class: 1.000000, Obj: 0.392639, No Obj: 0.004584, Avg Recall: 0.480000, count: 100 -6230: 0.371251, 0.418326 avg, 0.001000 rate, 16.473541 seconds, 1594880 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000089 seconds -Region Avg IOU: 0.600994, Class: 1.000000, Obj: 0.371447, No Obj: 0.002907, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.670389, Class: 1.000000, Obj: 0.424836, No Obj: 0.003368, Avg Recall: 0.846154, count: 117 -Region Avg IOU: 0.654545, Class: 1.000000, Obj: 0.432430, No Obj: 0.002857, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.658398, Class: 1.000000, Obj: 0.428275, No Obj: 0.002920, Avg Recall: 0.806818, count: 88 -Region Avg IOU: 0.630715, Class: 1.000000, Obj: 0.448977, No Obj: 0.002622, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.611219, Class: 1.000000, Obj: 0.469787, No Obj: 0.002854, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.619783, Class: 1.000000, Obj: 0.426483, No Obj: 0.002732, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.647926, Class: 1.000000, Obj: 0.426912, No Obj: 0.002788, Avg Recall: 0.800000, count: 95 -6231: 0.412494, 0.417743 avg, 0.001000 rate, 28.929796 seconds, 1595136 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.635444, Class: 1.000000, Obj: 0.413227, No Obj: 0.002578, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.649790, Class: 1.000000, Obj: 0.453489, No Obj: 0.003324, Avg Recall: 0.811024, count: 127 -Region Avg IOU: 0.635100, Class: 1.000000, Obj: 0.469631, No Obj: 0.002209, Avg Recall: 0.780822, count: 73 -Region Avg IOU: 0.680994, Class: 1.000000, Obj: 0.555539, No Obj: 0.002996, Avg Recall: 0.834862, count: 109 -Region Avg IOU: 0.636675, Class: 1.000000, Obj: 0.460432, No Obj: 0.002669, Avg Recall: 0.798319, count: 119 -Region Avg IOU: 0.647592, Class: 1.000000, Obj: 0.417251, No Obj: 0.003141, Avg Recall: 0.754098, count: 122 -Region Avg IOU: 0.657951, Class: 1.000000, Obj: 0.417406, No Obj: 0.002860, Avg Recall: 0.820000, count: 100 -Region Avg IOU: 0.645750, Class: 1.000000, Obj: 0.416381, No Obj: 0.002999, Avg Recall: 0.790909, count: 110 -6232: 0.421989, 0.418168 avg, 0.001000 rate, 30.167358 seconds, 1595392 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.667123, Class: 1.000000, Obj: 0.433650, No Obj: 0.002583, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.689923, Class: 1.000000, Obj: 0.382763, No Obj: 0.003156, Avg Recall: 0.869919, count: 123 -Region Avg IOU: 0.659303, Class: 1.000000, Obj: 0.426613, No Obj: 0.002782, Avg Recall: 0.829787, count: 94 -Region Avg IOU: 0.658089, Class: 1.000000, Obj: 0.439306, No Obj: 0.002292, Avg Recall: 0.813953, count: 86 -Region Avg IOU: 0.677992, Class: 1.000000, Obj: 0.466537, No Obj: 0.002555, Avg Recall: 0.831461, count: 89 -Region Avg IOU: 0.659031, Class: 1.000000, Obj: 0.435452, No Obj: 0.002780, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.659645, Class: 1.000000, Obj: 0.452542, No Obj: 0.002660, Avg Recall: 0.838095, count: 105 -Region Avg IOU: 0.678232, Class: 1.000000, Obj: 0.390424, No Obj: 0.003042, Avg Recall: 0.781818, count: 110 -6233: 0.396576, 0.416009 avg, 0.001000 rate, 32.292759 seconds, 1595648 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.644792, Class: 1.000000, Obj: 0.348441, No Obj: 0.002826, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.645747, Class: 1.000000, Obj: 0.392492, No Obj: 0.002677, Avg Recall: 0.805310, count: 113 -Region Avg IOU: 0.658925, Class: 1.000000, Obj: 0.441526, No Obj: 0.002700, Avg Recall: 0.830189, count: 106 -Region Avg IOU: 0.657061, Class: 1.000000, Obj: 0.462092, No Obj: 0.002443, Avg Recall: 0.817308, count: 104 -Region Avg IOU: 0.674399, Class: 1.000000, Obj: 0.402385, No Obj: 0.002513, Avg Recall: 0.881720, count: 93 -Region Avg IOU: 0.691929, Class: 1.000000, Obj: 0.441804, No Obj: 0.002566, Avg Recall: 0.872549, count: 102 -Region Avg IOU: 0.629207, Class: 1.000000, Obj: 0.432170, No Obj: 0.002865, Avg Recall: 0.746154, count: 130 -Region Avg IOU: 0.675811, Class: 1.000000, Obj: 0.394924, No Obj: 0.002768, Avg Recall: 0.838462, count: 130 -6234: 0.434667, 0.417874 avg, 0.001000 rate, 30.249247 seconds, 1595904 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.665459, Class: 1.000000, Obj: 0.403101, No Obj: 0.002456, Avg Recall: 0.831776, count: 107 -Region Avg IOU: 0.592865, Class: 1.000000, Obj: 0.384048, No Obj: 0.002230, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.672860, Class: 1.000000, Obj: 0.358743, No Obj: 0.002434, Avg Recall: 0.857143, count: 105 -Region Avg IOU: 0.669875, Class: 1.000000, Obj: 0.362279, No Obj: 0.002465, Avg Recall: 0.815126, count: 119 -Region Avg IOU: 0.661976, Class: 1.000000, Obj: 0.389927, No Obj: 0.002238, Avg Recall: 0.834951, count: 103 -Region Avg IOU: 0.577298, Class: 1.000000, Obj: 0.328732, No Obj: 0.002498, Avg Recall: 0.669421, count: 121 -Region Avg IOU: 0.663767, Class: 1.000000, Obj: 0.427289, No Obj: 0.002220, Avg Recall: 0.833333, count: 84 -Region Avg IOU: 0.681266, Class: 1.000000, Obj: 0.457031, No Obj: 0.002235, Avg Recall: 0.814433, count: 97 -6235: 0.399019, 0.415989 avg, 0.001000 rate, 29.411390 seconds, 1596160 images -Loaded: 3.887948 seconds -Region Avg IOU: 0.673359, Class: 1.000000, Obj: 0.361988, No Obj: 0.002457, Avg Recall: 0.828829, count: 111 -Region Avg IOU: 0.664422, Class: 1.000000, Obj: 0.370619, No Obj: 0.002472, Avg Recall: 0.805085, count: 118 -Region Avg IOU: 0.646742, Class: 1.000000, Obj: 0.364336, No Obj: 0.002513, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.656707, Class: 1.000000, Obj: 0.371409, No Obj: 0.002248, Avg Recall: 0.806723, count: 119 -Region Avg IOU: 0.667123, Class: 1.000000, Obj: 0.400048, No Obj: 0.002434, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.619326, Class: 1.000000, Obj: 0.364770, No Obj: 0.002050, Avg Recall: 0.710526, count: 114 -Region Avg IOU: 0.672486, Class: 1.000000, Obj: 0.425357, No Obj: 0.002587, Avg Recall: 0.830189, count: 106 -Region Avg IOU: 0.663558, Class: 1.000000, Obj: 0.332597, No Obj: 0.002469, Avg Recall: 0.828571, count: 105 -6236: 0.443870, 0.418777 avg, 0.001000 rate, 30.986967 seconds, 1596416 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.602926, Class: 1.000000, Obj: 0.357251, No Obj: 0.002125, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.722044, Class: 1.000000, Obj: 0.405798, No Obj: 0.002429, Avg Recall: 0.931373, count: 102 -Region Avg IOU: 0.600714, Class: 1.000000, Obj: 0.356127, No Obj: 0.002650, Avg Recall: 0.720588, count: 136 -Region Avg IOU: 0.616319, Class: 1.000000, Obj: 0.346283, No Obj: 0.002754, Avg Recall: 0.761538, count: 130 -Region Avg IOU: 0.657979, Class: 1.000000, Obj: 0.391016, No Obj: 0.002551, Avg Recall: 0.826772, count: 127 -Region Avg IOU: 0.635953, Class: 1.000000, Obj: 0.363547, No Obj: 0.002037, Avg Recall: 0.788235, count: 85 -Region Avg IOU: 0.658669, Class: 1.000000, Obj: 0.343069, No Obj: 0.002528, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.679442, Class: 1.000000, Obj: 0.422975, No Obj: 0.002261, Avg Recall: 0.872093, count: 86 -6237: 0.493393, 0.426239 avg, 0.001000 rate, 31.687395 seconds, 1596672 images -Loaded: 0.804935 seconds -Region Avg IOU: 0.645044, Class: 1.000000, Obj: 0.394354, No Obj: 0.002536, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.654236, Class: 1.000000, Obj: 0.355162, No Obj: 0.002672, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.637727, Class: 1.000000, Obj: 0.352109, No Obj: 0.002464, Avg Recall: 0.753968, count: 126 -Region Avg IOU: 0.632648, Class: 1.000000, Obj: 0.370443, No Obj: 0.002316, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.608493, Class: 1.000000, Obj: 0.354753, No Obj: 0.002514, Avg Recall: 0.731707, count: 123 -Region Avg IOU: 0.676061, Class: 1.000000, Obj: 0.384380, No Obj: 0.002961, Avg Recall: 0.865672, count: 134 -Region Avg IOU: 0.655536, Class: 1.000000, Obj: 0.367739, No Obj: 0.002463, Avg Recall: 0.809091, count: 110 -Region Avg IOU: 0.654405, Class: 1.000000, Obj: 0.373319, No Obj: 0.002363, Avg Recall: 0.837838, count: 111 -6238: 0.440210, 0.427636 avg, 0.001000 rate, 30.368647 seconds, 1596928 images -Loaded: 0.748738 seconds -Region Avg IOU: 0.683627, Class: 1.000000, Obj: 0.351679, No Obj: 0.002628, Avg Recall: 0.848000, count: 125 -Region Avg IOU: 0.654986, Class: 1.000000, Obj: 0.410406, No Obj: 0.002315, Avg Recall: 0.814815, count: 108 -Region Avg IOU: 0.672373, Class: 1.000000, Obj: 0.407463, No Obj: 0.002544, Avg Recall: 0.776859, count: 121 -Region Avg IOU: 0.655981, Class: 1.000000, Obj: 0.369881, No Obj: 0.002858, Avg Recall: 0.797101, count: 138 -Region Avg IOU: 0.608252, Class: 1.000000, Obj: 0.402049, No Obj: 0.002239, Avg Recall: 0.690476, count: 84 -Region Avg IOU: 0.672819, Class: 1.000000, Obj: 0.378598, No Obj: 0.002668, Avg Recall: 0.834862, count: 109 -Region Avg IOU: 0.672140, Class: 1.000000, Obj: 0.435926, No Obj: 0.002627, Avg Recall: 0.844037, count: 109 -Region Avg IOU: 0.664503, Class: 1.000000, Obj: 0.389865, No Obj: 0.002726, Avg Recall: 0.845455, count: 110 -6239: 0.457392, 0.430611 avg, 0.001000 rate, 31.998739 seconds, 1597184 images -Loaded: 0.768246 seconds -Region Avg IOU: 0.602226, Class: 1.000000, Obj: 0.419805, No Obj: 0.002481, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.639169, Class: 1.000000, Obj: 0.396111, No Obj: 0.002710, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.640871, Class: 1.000000, Obj: 0.419882, No Obj: 0.002333, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.658220, Class: 1.000000, Obj: 0.400199, No Obj: 0.002555, Avg Recall: 0.842697, count: 89 -Region Avg IOU: 0.645965, Class: 1.000000, Obj: 0.397682, No Obj: 0.003115, Avg Recall: 0.784000, count: 125 -Region Avg IOU: 0.672509, Class: 1.000000, Obj: 0.353461, No Obj: 0.002373, Avg Recall: 0.860000, count: 100 -Region Avg IOU: 0.683132, Class: 1.000000, Obj: 0.451719, No Obj: 0.002808, Avg Recall: 0.864078, count: 103 -Region Avg IOU: 0.604451, Class: 1.000000, Obj: 0.397112, No Obj: 0.002810, Avg Recall: 0.684615, count: 130 -6240: 0.412636, 0.428814 avg, 0.001000 rate, 32.132038 seconds, 1597440 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 14.902228 seconds -Region Avg IOU: 0.592790, Class: 1.000000, Obj: 0.371639, No Obj: 0.002551, Avg Recall: 0.748031, count: 127 -Region Avg IOU: 0.632310, Class: 1.000000, Obj: 0.390138, No Obj: 0.002752, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.639643, Class: 1.000000, Obj: 0.407473, No Obj: 0.002232, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.626383, Class: 1.000000, Obj: 0.402479, No Obj: 0.002806, Avg Recall: 0.790323, count: 124 -Region Avg IOU: 0.668624, Class: 1.000000, Obj: 0.414964, No Obj: 0.002439, Avg Recall: 0.846154, count: 104 -Region Avg IOU: 0.680377, Class: 1.000000, Obj: 0.441251, No Obj: 0.002744, Avg Recall: 0.879121, count: 91 -Region Avg IOU: 0.657562, Class: 1.000000, Obj: 0.472557, No Obj: 0.002806, Avg Recall: 0.831776, count: 107 -Region Avg IOU: 0.643137, Class: 1.000000, Obj: 0.382186, No Obj: 0.002908, Avg Recall: 0.781955, count: 133 -6241: 0.434234, 0.429356 avg, 0.001000 rate, 28.219757 seconds, 1597696 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.690888, Class: 1.000000, Obj: 0.468112, No Obj: 0.003017, Avg Recall: 0.888889, count: 117 -Region Avg IOU: 0.622770, Class: 1.000000, Obj: 0.428419, No Obj: 0.002590, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.681035, Class: 1.000000, Obj: 0.427067, No Obj: 0.002542, Avg Recall: 0.861702, count: 94 -Region Avg IOU: 0.647514, Class: 1.000000, Obj: 0.394449, No Obj: 0.002853, Avg Recall: 0.773438, count: 128 -Region Avg IOU: 0.647292, Class: 1.000000, Obj: 0.418485, No Obj: 0.002914, Avg Recall: 0.787879, count: 132 -Region Avg IOU: 0.658192, Class: 1.000000, Obj: 0.392391, No Obj: 0.003140, Avg Recall: 0.823077, count: 130 -Region Avg IOU: 0.634428, Class: 1.000000, Obj: 0.426342, No Obj: 0.002568, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.684242, Class: 1.000000, Obj: 0.444864, No Obj: 0.002610, Avg Recall: 0.860215, count: 93 -6242: 0.428618, 0.429282 avg, 0.001000 rate, 30.851852 seconds, 1597952 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.665278, Class: 1.000000, Obj: 0.483101, No Obj: 0.002469, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.711152, Class: 1.000000, Obj: 0.497190, No Obj: 0.003079, Avg Recall: 0.855856, count: 111 -Region Avg IOU: 0.644777, Class: 1.000000, Obj: 0.438789, No Obj: 0.002938, Avg Recall: 0.755725, count: 131 -Region Avg IOU: 0.639023, Class: 1.000000, Obj: 0.409380, No Obj: 0.002329, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.646000, Class: 1.000000, Obj: 0.492340, No Obj: 0.003116, Avg Recall: 0.821138, count: 123 -Region Avg IOU: 0.617583, Class: 1.000000, Obj: 0.421787, No Obj: 0.002699, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.625003, Class: 1.000000, Obj: 0.397941, No Obj: 0.003061, Avg Recall: 0.734848, count: 132 -Region Avg IOU: 0.648493, Class: 1.000000, Obj: 0.449071, No Obj: 0.002758, Avg Recall: 0.808081, count: 99 -6243: 0.420513, 0.428405 avg, 0.001000 rate, 28.566477 seconds, 1598208 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.628025, Class: 1.000000, Obj: 0.417058, No Obj: 0.002672, Avg Recall: 0.791045, count: 134 -Region Avg IOU: 0.623652, Class: 1.000000, Obj: 0.441255, No Obj: 0.002752, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.679815, Class: 1.000000, Obj: 0.438548, No Obj: 0.003215, Avg Recall: 0.890625, count: 128 -Region Avg IOU: 0.641148, Class: 1.000000, Obj: 0.464490, No Obj: 0.002662, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.646768, Class: 1.000000, Obj: 0.482607, No Obj: 0.002645, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.678366, Class: 1.000000, Obj: 0.432550, No Obj: 0.002844, Avg Recall: 0.855932, count: 118 -Region Avg IOU: 0.657802, Class: 1.000000, Obj: 0.455520, No Obj: 0.002836, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.672615, Class: 1.000000, Obj: 0.423849, No Obj: 0.002990, Avg Recall: 0.848000, count: 125 -6244: 0.436617, 0.429226 avg, 0.001000 rate, 29.717184 seconds, 1598464 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.679139, Class: 1.000000, Obj: 0.449011, No Obj: 0.002855, Avg Recall: 0.818182, count: 110 -Region Avg IOU: 0.620625, Class: 1.000000, Obj: 0.437847, No Obj: 0.002561, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.684043, Class: 1.000000, Obj: 0.466933, No Obj: 0.002549, Avg Recall: 0.900000, count: 90 -Region Avg IOU: 0.631680, Class: 1.000000, Obj: 0.420232, No Obj: 0.002983, Avg Recall: 0.776000, count: 125 -Region Avg IOU: 0.690367, Class: 1.000000, Obj: 0.433320, No Obj: 0.002390, Avg Recall: 0.855263, count: 76 -Region Avg IOU: 0.652050, Class: 1.000000, Obj: 0.431602, No Obj: 0.002858, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.644709, Class: 1.000000, Obj: 0.465283, No Obj: 0.002479, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.642395, Class: 1.000000, Obj: 0.353385, No Obj: 0.002912, Avg Recall: 0.747899, count: 119 -6245: 0.400396, 0.426343 avg, 0.001000 rate, 27.858540 seconds, 1598720 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.669178, Class: 1.000000, Obj: 0.431023, No Obj: 0.002221, Avg Recall: 0.794521, count: 73 -Region Avg IOU: 0.646382, Class: 1.000000, Obj: 0.387628, No Obj: 0.002526, Avg Recall: 0.809091, count: 110 -Region Avg IOU: 0.656435, Class: 1.000000, Obj: 0.438826, No Obj: 0.002881, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.622122, Class: 1.000000, Obj: 0.405684, No Obj: 0.002740, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.669976, Class: 1.000000, Obj: 0.414720, No Obj: 0.002267, Avg Recall: 0.838384, count: 99 -Region Avg IOU: 0.639222, Class: 1.000000, Obj: 0.442637, No Obj: 0.002580, Avg Recall: 0.801980, count: 101 -Region Avg IOU: 0.691572, Class: 1.000000, Obj: 0.461484, No Obj: 0.002911, Avg Recall: 0.871795, count: 117 -Region Avg IOU: 0.614941, Class: 1.000000, Obj: 0.421771, No Obj: 0.002462, Avg Recall: 0.774194, count: 93 -6246: 0.394052, 0.423114 avg, 0.001000 rate, 28.223179 seconds, 1598976 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.663762, Class: 1.000000, Obj: 0.412561, No Obj: 0.002839, Avg Recall: 0.858333, count: 120 -Region Avg IOU: 0.679611, Class: 1.000000, Obj: 0.371618, No Obj: 0.002115, Avg Recall: 0.933333, count: 75 -Region Avg IOU: 0.650296, Class: 1.000000, Obj: 0.392068, No Obj: 0.003278, Avg Recall: 0.798561, count: 139 -Region Avg IOU: 0.644522, Class: 1.000000, Obj: 0.402884, No Obj: 0.002607, Avg Recall: 0.842593, count: 108 -Region Avg IOU: 0.665246, Class: 1.000000, Obj: 0.394265, No Obj: 0.002600, Avg Recall: 0.815126, count: 119 -Region Avg IOU: 0.634983, Class: 1.000000, Obj: 0.439199, No Obj: 0.002601, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.647121, Class: 1.000000, Obj: 0.405980, No Obj: 0.002305, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.662001, Class: 1.000000, Obj: 0.380679, No Obj: 0.002779, Avg Recall: 0.830508, count: 118 -6247: 0.411067, 0.421909 avg, 0.001000 rate, 28.647591 seconds, 1599232 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.657678, Class: 1.000000, Obj: 0.387765, No Obj: 0.002942, Avg Recall: 0.824561, count: 114 -Region Avg IOU: 0.661385, Class: 1.000000, Obj: 0.382702, No Obj: 0.002593, Avg Recall: 0.808081, count: 99 -Region Avg IOU: 0.684899, Class: 1.000000, Obj: 0.425794, No Obj: 0.002781, Avg Recall: 0.870370, count: 108 -Region Avg IOU: 0.614165, Class: 1.000000, Obj: 0.421984, No Obj: 0.002444, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.672658, Class: 1.000000, Obj: 0.404456, No Obj: 0.002763, Avg Recall: 0.867257, count: 113 -Region Avg IOU: 0.632616, Class: 1.000000, Obj: 0.423235, No Obj: 0.002583, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.618350, Class: 1.000000, Obj: 0.363427, No Obj: 0.002553, Avg Recall: 0.744000, count: 125 -Region Avg IOU: 0.679429, Class: 1.000000, Obj: 0.392687, No Obj: 0.003066, Avg Recall: 0.850394, count: 127 -6248: 0.482719, 0.427990 avg, 0.001000 rate, 29.800320 seconds, 1599488 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.650508, Class: 1.000000, Obj: 0.441785, No Obj: 0.002280, Avg Recall: 0.788889, count: 90 -Region Avg IOU: 0.660188, Class: 1.000000, Obj: 0.411575, No Obj: 0.002491, Avg Recall: 0.826923, count: 104 -Region Avg IOU: 0.618944, Class: 1.000000, Obj: 0.387576, No Obj: 0.002325, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.604027, Class: 1.000000, Obj: 0.381460, No Obj: 0.002055, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.612390, Class: 1.000000, Obj: 0.344809, No Obj: 0.002412, Avg Recall: 0.747967, count: 123 -Region Avg IOU: 0.696878, Class: 1.000000, Obj: 0.485143, No Obj: 0.002324, Avg Recall: 0.862069, count: 87 -Region Avg IOU: 0.654841, Class: 1.000000, Obj: 0.395936, No Obj: 0.002734, Avg Recall: 0.842520, count: 127 -Region Avg IOU: 0.640117, Class: 1.000000, Obj: 0.384581, No Obj: 0.002530, Avg Recall: 0.800000, count: 100 -6249: 0.427025, 0.427894 avg, 0.001000 rate, 27.313433 seconds, 1599744 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.640641, Class: 1.000000, Obj: 0.374701, No Obj: 0.002649, Avg Recall: 0.788321, count: 137 -Region Avg IOU: 0.661484, Class: 1.000000, Obj: 0.376634, No Obj: 0.002544, Avg Recall: 0.847826, count: 92 -Region Avg IOU: 0.643226, Class: 1.000000, Obj: 0.426361, No Obj: 0.002545, Avg Recall: 0.776471, count: 85 -Region Avg IOU: 0.643456, Class: 1.000000, Obj: 0.393649, No Obj: 0.002454, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.642077, Class: 1.000000, Obj: 0.409608, No Obj: 0.002233, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.621856, Class: 1.000000, Obj: 0.376541, No Obj: 0.002916, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.614445, Class: 1.000000, Obj: 0.393944, No Obj: 0.002583, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.670712, Class: 1.000000, Obj: 0.445431, No Obj: 0.002412, Avg Recall: 0.839506, count: 81 -6250: 0.428371, 0.427941 avg, 0.001000 rate, 27.414202 seconds, 1600000 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 15.577932 seconds -Region Avg IOU: 0.626213, Class: 1.000000, Obj: 0.423206, No Obj: 0.003380, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.608598, Class: 1.000000, Obj: 0.390678, No Obj: 0.002921, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.562990, Class: 1.000000, Obj: 0.370231, No Obj: 0.003005, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.602241, Class: 1.000000, Obj: 0.374966, No Obj: 0.003319, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.556091, Class: 1.000000, Obj: 0.372241, No Obj: 0.002929, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.587941, Class: 1.000000, Obj: 0.370579, No Obj: 0.003413, Avg Recall: 0.689394, count: 132 -Region Avg IOU: 0.593803, Class: 1.000000, Obj: 0.406625, No Obj: 0.003438, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.601467, Class: 1.000000, Obj: 0.386092, No Obj: 0.003203, Avg Recall: 0.707317, count: 123 -6251: 0.418819, 0.427029 avg, 0.001000 rate, 22.211809 seconds, 1600256 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.630638, Class: 1.000000, Obj: 0.428735, No Obj: 0.002892, Avg Recall: 0.720930, count: 86 -Region Avg IOU: 0.542538, Class: 1.000000, Obj: 0.363296, No Obj: 0.003338, Avg Recall: 0.603053, count: 131 -Region Avg IOU: 0.539122, Class: 1.000000, Obj: 0.363535, No Obj: 0.003022, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.608677, Class: 1.000000, Obj: 0.388289, No Obj: 0.003511, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.600236, Class: 1.000000, Obj: 0.413389, No Obj: 0.002815, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.640591, Class: 1.000000, Obj: 0.398725, No Obj: 0.003259, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.569415, Class: 1.000000, Obj: 0.378747, No Obj: 0.003415, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.591636, Class: 1.000000, Obj: 0.345055, No Obj: 0.003646, Avg Recall: 0.691589, count: 107 -6252: 0.414242, 0.425751 avg, 0.001000 rate, 24.547491 seconds, 1600512 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.606382, Class: 1.000000, Obj: 0.388435, No Obj: 0.003794, Avg Recall: 0.702290, count: 131 -Region Avg IOU: 0.569189, Class: 1.000000, Obj: 0.354921, No Obj: 0.003385, Avg Recall: 0.642857, count: 126 -Region Avg IOU: 0.546172, Class: 1.000000, Obj: 0.369693, No Obj: 0.002999, Avg Recall: 0.607843, count: 102 -Region Avg IOU: 0.701896, Class: 1.000000, Obj: 0.457375, No Obj: 0.003573, Avg Recall: 0.891304, count: 92 -Region Avg IOU: 0.630703, Class: 1.000000, Obj: 0.392865, No Obj: 0.003858, Avg Recall: 0.735294, count: 136 -Region Avg IOU: 0.652293, Class: 1.000000, Obj: 0.411716, No Obj: 0.003450, Avg Recall: 0.835165, count: 91 -Region Avg IOU: 0.564748, Class: 1.000000, Obj: 0.360426, No Obj: 0.003261, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.576428, Class: 1.000000, Obj: 0.344174, No Obj: 0.004095, Avg Recall: 0.666667, count: 135 -6253: 0.461204, 0.429296 avg, 0.001000 rate, 24.604454 seconds, 1600768 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.645104, Class: 1.000000, Obj: 0.417355, No Obj: 0.003964, Avg Recall: 0.833333, count: 90 -Region Avg IOU: 0.590711, Class: 1.000000, Obj: 0.388061, No Obj: 0.002899, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.556347, Class: 1.000000, Obj: 0.395059, No Obj: 0.003641, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.642123, Class: 1.000000, Obj: 0.415205, No Obj: 0.003570, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.600384, Class: 1.000000, Obj: 0.384336, No Obj: 0.003312, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.555218, Class: 1.000000, Obj: 0.368756, No Obj: 0.003499, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.562421, Class: 1.000000, Obj: 0.353292, No Obj: 0.003556, Avg Recall: 0.646154, count: 130 -Region Avg IOU: 0.573834, Class: 1.000000, Obj: 0.395706, No Obj: 0.003271, Avg Recall: 0.675214, count: 117 -6254: 0.448158, 0.431182 avg, 0.001000 rate, 24.851072 seconds, 1601024 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.579325, Class: 1.000000, Obj: 0.434187, No Obj: 0.003392, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.580007, Class: 1.000000, Obj: 0.397895, No Obj: 0.003474, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.602235, Class: 1.000000, Obj: 0.400895, No Obj: 0.003632, Avg Recall: 0.721804, count: 133 -Region Avg IOU: 0.648774, Class: 1.000000, Obj: 0.458229, No Obj: 0.003593, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.614782, Class: 1.000000, Obj: 0.387572, No Obj: 0.003858, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.613593, Class: 1.000000, Obj: 0.402766, No Obj: 0.003619, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.611171, Class: 1.000000, Obj: 0.352304, No Obj: 0.003299, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.587379, Class: 1.000000, Obj: 0.385115, No Obj: 0.003170, Avg Recall: 0.700000, count: 90 -6255: 0.414987, 0.429563 avg, 0.001000 rate, 24.428659 seconds, 1601280 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.618973, Class: 1.000000, Obj: 0.451024, No Obj: 0.003532, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.569114, Class: 1.000000, Obj: 0.406984, No Obj: 0.004024, Avg Recall: 0.713235, count: 136 -Region Avg IOU: 0.609360, Class: 1.000000, Obj: 0.459976, No Obj: 0.003957, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.571348, Class: 1.000000, Obj: 0.382821, No Obj: 0.003466, Avg Recall: 0.650794, count: 126 -Region Avg IOU: 0.561924, Class: 1.000000, Obj: 0.412820, No Obj: 0.004156, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.632275, Class: 1.000000, Obj: 0.445049, No Obj: 0.004071, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.595862, Class: 1.000000, Obj: 0.405987, No Obj: 0.004396, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.578384, Class: 1.000000, Obj: 0.399690, No Obj: 0.003192, Avg Recall: 0.697917, count: 96 -6256: 0.400582, 0.426665 avg, 0.001000 rate, 24.610373 seconds, 1601536 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.626657, Class: 1.000000, Obj: 0.431097, No Obj: 0.003481, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.566028, Class: 1.000000, Obj: 0.387673, No Obj: 0.003193, Avg Recall: 0.652893, count: 121 -Region Avg IOU: 0.592929, Class: 1.000000, Obj: 0.429388, No Obj: 0.003410, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.573332, Class: 1.000000, Obj: 0.404013, No Obj: 0.003951, Avg Recall: 0.674074, count: 135 -Region Avg IOU: 0.554484, Class: 1.000000, Obj: 0.429244, No Obj: 0.003268, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.591615, Class: 1.000000, Obj: 0.398500, No Obj: 0.003492, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.600264, Class: 1.000000, Obj: 0.460059, No Obj: 0.003033, Avg Recall: 0.707317, count: 82 -Region Avg IOU: 0.604102, Class: 1.000000, Obj: 0.470144, No Obj: 0.003747, Avg Recall: 0.686275, count: 102 -6257: 0.386225, 0.422621 avg, 0.001000 rate, 24.995989 seconds, 1601792 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.560728, Class: 1.000000, Obj: 0.408849, No Obj: 0.003268, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.599607, Class: 1.000000, Obj: 0.423583, No Obj: 0.003924, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.585542, Class: 1.000000, Obj: 0.424374, No Obj: 0.003559, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.596649, Class: 1.000000, Obj: 0.419244, No Obj: 0.003589, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.638186, Class: 1.000000, Obj: 0.451027, No Obj: 0.003657, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.602352, Class: 1.000000, Obj: 0.426392, No Obj: 0.003124, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.592690, Class: 1.000000, Obj: 0.422156, No Obj: 0.004464, Avg Recall: 0.687500, count: 144 -Region Avg IOU: 0.562619, Class: 1.000000, Obj: 0.402974, No Obj: 0.003595, Avg Recall: 0.640777, count: 103 -6258: 0.439271, 0.424286 avg, 0.001000 rate, 24.244272 seconds, 1602048 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.611216, Class: 1.000000, Obj: 0.432135, No Obj: 0.003364, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.651951, Class: 1.000000, Obj: 0.405905, No Obj: 0.003445, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.555336, Class: 1.000000, Obj: 0.414485, No Obj: 0.003626, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.577154, Class: 1.000000, Obj: 0.446695, No Obj: 0.003536, Avg Recall: 0.655462, count: 119 -Region Avg IOU: 0.628833, Class: 1.000000, Obj: 0.460147, No Obj: 0.003590, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.640191, Class: 1.000000, Obj: 0.429951, No Obj: 0.003842, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.576899, Class: 1.000000, Obj: 0.426244, No Obj: 0.003702, Avg Recall: 0.675214, count: 117 -Region Avg IOU: 0.528552, Class: 1.000000, Obj: 0.389865, No Obj: 0.003863, Avg Recall: 0.570423, count: 142 -6259: 0.399605, 0.421818 avg, 0.001000 rate, 26.139715 seconds, 1602304 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.591572, Class: 1.000000, Obj: 0.399914, No Obj: 0.004532, Avg Recall: 0.740157, count: 127 -Region Avg IOU: 0.570059, Class: 1.000000, Obj: 0.386466, No Obj: 0.003887, Avg Recall: 0.666667, count: 141 -Region Avg IOU: 0.564651, Class: 1.000000, Obj: 0.416272, No Obj: 0.003677, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.590586, Class: 1.000000, Obj: 0.437983, No Obj: 0.003579, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.607722, Class: 1.000000, Obj: 0.391208, No Obj: 0.004015, Avg Recall: 0.742424, count: 132 -Region Avg IOU: 0.591823, Class: 1.000000, Obj: 0.401895, No Obj: 0.003740, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.602259, Class: 1.000000, Obj: 0.404977, No Obj: 0.003550, Avg Recall: 0.690476, count: 126 -Region Avg IOU: 0.615325, Class: 1.000000, Obj: 0.432258, No Obj: 0.003706, Avg Recall: 0.691667, count: 120 -6260: 0.449695, 0.424605 avg, 0.001000 rate, 24.099457 seconds, 1602560 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.474283 seconds -Region Avg IOU: 0.461599, Class: 1.000000, Obj: 0.324320, No Obj: 0.004314, Avg Recall: 0.476923, count: 130 -Region Avg IOU: 0.519973, Class: 1.000000, Obj: 0.315485, No Obj: 0.003858, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.553094, Class: 1.000000, Obj: 0.363549, No Obj: 0.004594, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.435866, Class: 1.000000, Obj: 0.299370, No Obj: 0.004023, Avg Recall: 0.434783, count: 115 -Region Avg IOU: 0.468566, Class: 1.000000, Obj: 0.343362, No Obj: 0.004469, Avg Recall: 0.486239, count: 109 -Region Avg IOU: 0.491952, Class: 1.000000, Obj: 0.304342, No Obj: 0.004642, Avg Recall: 0.528455, count: 123 -Region Avg IOU: 0.482941, Class: 1.000000, Obj: 0.344841, No Obj: 0.004175, Avg Recall: 0.535354, count: 99 -Region Avg IOU: 0.507178, Class: 1.000000, Obj: 0.307802, No Obj: 0.004692, Avg Recall: 0.537815, count: 119 -6261: 0.450754, 0.427220 avg, 0.001000 rate, 17.110495 seconds, 1602816 images -Loaded: 3.084955 seconds -Region Avg IOU: 0.495708, Class: 1.000000, Obj: 0.315400, No Obj: 0.004735, Avg Recall: 0.560345, count: 116 -Region Avg IOU: 0.424685, Class: 1.000000, Obj: 0.276212, No Obj: 0.004130, Avg Recall: 0.413223, count: 121 -Region Avg IOU: 0.484413, Class: 1.000000, Obj: 0.326562, No Obj: 0.004595, Avg Recall: 0.521739, count: 92 -Region Avg IOU: 0.465667, Class: 1.000000, Obj: 0.305904, No Obj: 0.004013, Avg Recall: 0.527273, count: 110 -Region Avg IOU: 0.523379, Class: 1.000000, Obj: 0.337444, No Obj: 0.004840, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.470508, Class: 1.000000, Obj: 0.272479, No Obj: 0.004247, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.502103, Class: 1.000000, Obj: 0.321645, No Obj: 0.004646, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.495858, Class: 1.000000, Obj: 0.303125, No Obj: 0.004419, Avg Recall: 0.553571, count: 112 -6262: 0.485082, 0.433006 avg, 0.001000 rate, 15.647389 seconds, 1603072 images -Loaded: 4.715352 seconds -Region Avg IOU: 0.409878, Class: 1.000000, Obj: 0.283316, No Obj: 0.004076, Avg Recall: 0.457627, count: 118 -Region Avg IOU: 0.504336, Class: 1.000000, Obj: 0.335542, No Obj: 0.004770, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.532541, Class: 1.000000, Obj: 0.364742, No Obj: 0.004672, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.513718, Class: 1.000000, Obj: 0.348170, No Obj: 0.004575, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.513418, Class: 1.000000, Obj: 0.334756, No Obj: 0.004716, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.480040, Class: 1.000000, Obj: 0.355570, No Obj: 0.004624, Avg Recall: 0.521739, count: 115 -Region Avg IOU: 0.456551, Class: 1.000000, Obj: 0.314154, No Obj: 0.004550, Avg Recall: 0.464567, count: 127 -Region Avg IOU: 0.467711, Class: 1.000000, Obj: 0.314437, No Obj: 0.004421, Avg Recall: 0.480000, count: 100 -6263: 0.454738, 0.435180 avg, 0.001000 rate, 17.040890 seconds, 1603328 images -Loaded: 4.661126 seconds -Region Avg IOU: 0.458182, Class: 1.000000, Obj: 0.305376, No Obj: 0.004210, Avg Recall: 0.505050, count: 99 -Region Avg IOU: 0.465391, Class: 1.000000, Obj: 0.324894, No Obj: 0.004151, Avg Recall: 0.516667, count: 120 -Region Avg IOU: 0.464476, Class: 1.000000, Obj: 0.334885, No Obj: 0.005007, Avg Recall: 0.480315, count: 127 -Region Avg IOU: 0.486583, Class: 1.000000, Obj: 0.320691, No Obj: 0.005198, Avg Recall: 0.531469, count: 143 -Region Avg IOU: 0.475049, Class: 1.000000, Obj: 0.338078, No Obj: 0.005012, Avg Recall: 0.504132, count: 121 -Region Avg IOU: 0.450717, Class: 1.000000, Obj: 0.317747, No Obj: 0.004640, Avg Recall: 0.488550, count: 131 -Region Avg IOU: 0.487023, Class: 1.000000, Obj: 0.297812, No Obj: 0.004665, Avg Recall: 0.523810, count: 126 -Region Avg IOU: 0.514880, Class: 1.000000, Obj: 0.374901, No Obj: 0.004528, Avg Recall: 0.586957, count: 92 -6264: 0.517016, 0.443363 avg, 0.001000 rate, 17.310513 seconds, 1603584 images -Loaded: 2.780668 seconds -Region Avg IOU: 0.471390, Class: 1.000000, Obj: 0.341691, No Obj: 0.004809, Avg Recall: 0.521429, count: 140 -Region Avg IOU: 0.498311, Class: 1.000000, Obj: 0.382355, No Obj: 0.004384, Avg Recall: 0.556701, count: 97 -Region Avg IOU: 0.511905, Class: 1.000000, Obj: 0.318388, No Obj: 0.004228, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.474906, Class: 1.000000, Obj: 0.320028, No Obj: 0.004891, Avg Recall: 0.521368, count: 117 -Region Avg IOU: 0.521343, Class: 1.000000, Obj: 0.373514, No Obj: 0.005164, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.441827, Class: 1.000000, Obj: 0.298351, No Obj: 0.004333, Avg Recall: 0.454545, count: 132 -Region Avg IOU: 0.456029, Class: 1.000000, Obj: 0.361210, No Obj: 0.004299, Avg Recall: 0.500000, count: 106 -Region Avg IOU: 0.523717, Class: 1.000000, Obj: 0.372458, No Obj: 0.005212, Avg Recall: 0.582609, count: 115 -6265: 0.440590, 0.443086 avg, 0.001000 rate, 17.660664 seconds, 1603840 images -Loaded: 3.657284 seconds -Region Avg IOU: 0.494782, Class: 1.000000, Obj: 0.353437, No Obj: 0.004961, Avg Recall: 0.553571, count: 112 -Region Avg IOU: 0.484330, Class: 1.000000, Obj: 0.344067, No Obj: 0.004940, Avg Recall: 0.511628, count: 129 -Region Avg IOU: 0.496429, Class: 1.000000, Obj: 0.371696, No Obj: 0.004751, Avg Recall: 0.547009, count: 117 -Region Avg IOU: 0.481228, Class: 1.000000, Obj: 0.341851, No Obj: 0.004521, Avg Recall: 0.545455, count: 121 -Region Avg IOU: 0.483378, Class: 1.000000, Obj: 0.304889, No Obj: 0.005719, Avg Recall: 0.542253, count: 142 -Region Avg IOU: 0.461075, Class: 1.000000, Obj: 0.366513, No Obj: 0.004587, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.437232, Class: 1.000000, Obj: 0.318088, No Obj: 0.003608, Avg Recall: 0.477273, count: 88 -Region Avg IOU: 0.418358, Class: 1.000000, Obj: 0.322905, No Obj: 0.004775, Avg Recall: 0.465517, count: 116 -6266: 0.477329, 0.446510 avg, 0.001000 rate, 15.236067 seconds, 1604096 images -Loaded: 7.007699 seconds -Region Avg IOU: 0.484349, Class: 1.000000, Obj: 0.379074, No Obj: 0.005431, Avg Recall: 0.573770, count: 122 -Region Avg IOU: 0.465445, Class: 1.000000, Obj: 0.345094, No Obj: 0.005121, Avg Recall: 0.528986, count: 138 -Region Avg IOU: 0.443808, Class: 1.000000, Obj: 0.317143, No Obj: 0.004654, Avg Recall: 0.511111, count: 90 -Region Avg IOU: 0.496560, Class: 1.000000, Obj: 0.360892, No Obj: 0.005664, Avg Recall: 0.547009, count: 117 -Region Avg IOU: 0.467818, Class: 1.000000, Obj: 0.351848, No Obj: 0.005987, Avg Recall: 0.489362, count: 141 -Region Avg IOU: 0.484169, Class: 1.000000, Obj: 0.373671, No Obj: 0.004832, Avg Recall: 0.480000, count: 100 -Region Avg IOU: 0.447937, Class: 1.000000, Obj: 0.335359, No Obj: 0.004355, Avg Recall: 0.443299, count: 97 -Region Avg IOU: 0.464286, Class: 1.000000, Obj: 0.305856, No Obj: 0.006096, Avg Recall: 0.493976, count: 166 -6267: 0.544044, 0.456263 avg, 0.001000 rate, 15.162215 seconds, 1604352 images -Loaded: 3.976034 seconds -Region Avg IOU: 0.529248, Class: 1.000000, Obj: 0.396992, No Obj: 0.005611, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.481868, Class: 1.000000, Obj: 0.297809, No Obj: 0.004565, Avg Recall: 0.542056, count: 107 -Region Avg IOU: 0.503449, Class: 1.000000, Obj: 0.365412, No Obj: 0.005677, Avg Recall: 0.564516, count: 124 -Region Avg IOU: 0.450548, Class: 1.000000, Obj: 0.363349, No Obj: 0.004486, Avg Recall: 0.473684, count: 95 -Region Avg IOU: 0.522518, Class: 1.000000, Obj: 0.370897, No Obj: 0.005205, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.472285, Class: 1.000000, Obj: 0.363999, No Obj: 0.005089, Avg Recall: 0.504854, count: 103 -Region Avg IOU: 0.468576, Class: 1.000000, Obj: 0.354257, No Obj: 0.004666, Avg Recall: 0.505263, count: 95 -Region Avg IOU: 0.443064, Class: 1.000000, Obj: 0.354840, No Obj: 0.005075, Avg Recall: 0.469231, count: 130 -6268: 0.440259, 0.454663 avg, 0.001000 rate, 14.935458 seconds, 1604608 images -Loaded: 5.479809 seconds -Region Avg IOU: 0.503283, Class: 1.000000, Obj: 0.334842, No Obj: 0.005518, Avg Recall: 0.550000, count: 100 -Region Avg IOU: 0.475158, Class: 1.000000, Obj: 0.373435, No Obj: 0.004445, Avg Recall: 0.485149, count: 101 -Region Avg IOU: 0.507153, Class: 1.000000, Obj: 0.325383, No Obj: 0.004825, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.486525, Class: 1.000000, Obj: 0.351505, No Obj: 0.005581, Avg Recall: 0.590909, count: 132 -Region Avg IOU: 0.463885, Class: 1.000000, Obj: 0.351798, No Obj: 0.004872, Avg Recall: 0.500000, count: 118 -Region Avg IOU: 0.500508, Class: 1.000000, Obj: 0.386467, No Obj: 0.006231, Avg Recall: 0.519380, count: 129 -Region Avg IOU: 0.502694, Class: 1.000000, Obj: 0.370773, No Obj: 0.005103, Avg Recall: 0.557522, count: 113 -Region Avg IOU: 0.407184, Class: 1.000000, Obj: 0.339013, No Obj: 0.005070, Avg Recall: 0.429688, count: 128 -6269: 0.550966, 0.464293 avg, 0.001000 rate, 18.397367 seconds, 1604864 images -Loaded: 3.545559 seconds -Region Avg IOU: 0.444451, Class: 1.000000, Obj: 0.335705, No Obj: 0.005520, Avg Recall: 0.489051, count: 137 -Region Avg IOU: 0.453835, Class: 1.000000, Obj: 0.317044, No Obj: 0.005598, Avg Recall: 0.484127, count: 126 -Region Avg IOU: 0.504743, Class: 1.000000, Obj: 0.380515, No Obj: 0.005523, Avg Recall: 0.576087, count: 92 -Region Avg IOU: 0.470923, Class: 1.000000, Obj: 0.350857, No Obj: 0.005736, Avg Recall: 0.535714, count: 140 -Region Avg IOU: 0.507138, Class: 1.000000, Obj: 0.403327, No Obj: 0.005117, Avg Recall: 0.636364, count: 88 -Region Avg IOU: 0.423525, Class: 1.000000, Obj: 0.361075, No Obj: 0.004218, Avg Recall: 0.410526, count: 95 -Region Avg IOU: 0.472281, Class: 1.000000, Obj: 0.350549, No Obj: 0.005220, Avg Recall: 0.535088, count: 114 -Region Avg IOU: 0.514903, Class: 1.000000, Obj: 0.364303, No Obj: 0.004266, Avg Recall: 0.614458, count: 83 -6270: 0.452076, 0.463072 avg, 0.001000 rate, 14.537384 seconds, 1605120 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.002435 seconds -Region Avg IOU: 0.601131, Class: 1.000000, Obj: 0.449070, No Obj: 0.004241, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.556580, Class: 1.000000, Obj: 0.441697, No Obj: 0.003462, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.611013, Class: 1.000000, Obj: 0.449674, No Obj: 0.004259, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.575478, Class: 1.000000, Obj: 0.454411, No Obj: 0.004127, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.615712, Class: 1.000000, Obj: 0.429549, No Obj: 0.004664, Avg Recall: 0.760000, count: 125 -Region Avg IOU: 0.569301, Class: 1.000000, Obj: 0.410110, No Obj: 0.004474, Avg Recall: 0.640625, count: 128 -Region Avg IOU: 0.597814, Class: 1.000000, Obj: 0.502619, No Obj: 0.003787, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.567250, Class: 1.000000, Obj: 0.446776, No Obj: 0.004284, Avg Recall: 0.686441, count: 118 -6271: 0.406400, 0.457404 avg, 0.001000 rate, 23.167988 seconds, 1605376 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.615693, Class: 1.000000, Obj: 0.495882, No Obj: 0.003593, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.590259, Class: 1.000000, Obj: 0.444408, No Obj: 0.003972, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.533922, Class: 1.000000, Obj: 0.406545, No Obj: 0.003537, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.580416, Class: 1.000000, Obj: 0.420951, No Obj: 0.002981, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.583348, Class: 1.000000, Obj: 0.478047, No Obj: 0.003996, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.626573, Class: 1.000000, Obj: 0.495617, No Obj: 0.003642, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.577984, Class: 1.000000, Obj: 0.398242, No Obj: 0.004347, Avg Recall: 0.715278, count: 144 -Region Avg IOU: 0.582529, Class: 1.000000, Obj: 0.465375, No Obj: 0.003555, Avg Recall: 0.670732, count: 82 -6272: 0.369109, 0.448575 avg, 0.001000 rate, 23.816420 seconds, 1605632 images -Loaded: 0.000114 seconds -Region Avg IOU: 0.576598, Class: 1.000000, Obj: 0.406815, No Obj: 0.003537, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.644360, Class: 1.000000, Obj: 0.458890, No Obj: 0.003332, Avg Recall: 0.795181, count: 83 -Region Avg IOU: 0.527579, Class: 1.000000, Obj: 0.438647, No Obj: 0.003571, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.588682, Class: 1.000000, Obj: 0.421189, No Obj: 0.003429, Avg Recall: 0.703297, count: 91 -Region Avg IOU: 0.598769, Class: 1.000000, Obj: 0.439312, No Obj: 0.003620, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.581301, Class: 1.000000, Obj: 0.397560, No Obj: 0.003647, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.585529, Class: 1.000000, Obj: 0.383655, No Obj: 0.003995, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.579766, Class: 1.000000, Obj: 0.448713, No Obj: 0.004072, Avg Recall: 0.669565, count: 115 -6273: 0.433566, 0.447074 avg, 0.001000 rate, 23.541332 seconds, 1605888 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.573541, Class: 1.000000, Obj: 0.422437, No Obj: 0.003854, Avg Recall: 0.616000, count: 125 -Region Avg IOU: 0.600448, Class: 1.000000, Obj: 0.417675, No Obj: 0.003113, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.581498, Class: 1.000000, Obj: 0.435763, No Obj: 0.003558, Avg Recall: 0.669492, count: 118 -Region Avg IOU: 0.641640, Class: 1.000000, Obj: 0.458972, No Obj: 0.004191, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.621573, Class: 1.000000, Obj: 0.434381, No Obj: 0.003640, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.571565, Class: 1.000000, Obj: 0.381654, No Obj: 0.002933, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.590217, Class: 1.000000, Obj: 0.441148, No Obj: 0.003351, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.602280, Class: 1.000000, Obj: 0.430464, No Obj: 0.003218, Avg Recall: 0.696970, count: 99 -6274: 0.374841, 0.439851 avg, 0.001000 rate, 23.595224 seconds, 1606144 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.552992, Class: 1.000000, Obj: 0.391338, No Obj: 0.003688, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.550786, Class: 1.000000, Obj: 0.378661, No Obj: 0.003213, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.602912, Class: 1.000000, Obj: 0.448473, No Obj: 0.003205, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.622987, Class: 1.000000, Obj: 0.425896, No Obj: 0.003479, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.583742, Class: 1.000000, Obj: 0.389277, No Obj: 0.003292, Avg Recall: 0.689076, count: 119 -Region Avg IOU: 0.567118, Class: 1.000000, Obj: 0.314126, No Obj: 0.002900, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.610853, Class: 1.000000, Obj: 0.422407, No Obj: 0.004020, Avg Recall: 0.686131, count: 137 -Region Avg IOU: 0.631108, Class: 1.000000, Obj: 0.399110, No Obj: 0.003720, Avg Recall: 0.773913, count: 115 -6275: 0.410285, 0.436894 avg, 0.001000 rate, 24.017715 seconds, 1606400 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.614050, Class: 1.000000, Obj: 0.373879, No Obj: 0.003316, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.612298, Class: 1.000000, Obj: 0.404557, No Obj: 0.003140, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.602123, Class: 1.000000, Obj: 0.433900, No Obj: 0.003120, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.568411, Class: 1.000000, Obj: 0.324641, No Obj: 0.003245, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.594441, Class: 1.000000, Obj: 0.397891, No Obj: 0.003476, Avg Recall: 0.675214, count: 117 -Region Avg IOU: 0.553419, Class: 1.000000, Obj: 0.365622, No Obj: 0.003099, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.593702, Class: 1.000000, Obj: 0.376362, No Obj: 0.003494, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.642346, Class: 1.000000, Obj: 0.399054, No Obj: 0.003931, Avg Recall: 0.788618, count: 123 -6276: 0.417187, 0.434923 avg, 0.001000 rate, 23.468206 seconds, 1606656 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.619776, Class: 1.000000, Obj: 0.353671, No Obj: 0.002922, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.606444, Class: 1.000000, Obj: 0.389898, No Obj: 0.003155, Avg Recall: 0.693548, count: 124 -Region Avg IOU: 0.657964, Class: 1.000000, Obj: 0.413765, No Obj: 0.003377, Avg Recall: 0.783132, count: 83 -Region Avg IOU: 0.621491, Class: 1.000000, Obj: 0.400342, No Obj: 0.003621, Avg Recall: 0.770992, count: 131 -Region Avg IOU: 0.604444, Class: 1.000000, Obj: 0.422066, No Obj: 0.003299, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.575860, Class: 1.000000, Obj: 0.367283, No Obj: 0.003159, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.616512, Class: 1.000000, Obj: 0.413116, No Obj: 0.003395, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.648609, Class: 1.000000, Obj: 0.397946, No Obj: 0.003607, Avg Recall: 0.761062, count: 113 -6277: 0.449885, 0.436419 avg, 0.001000 rate, 22.870447 seconds, 1606912 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.614505, Class: 1.000000, Obj: 0.398737, No Obj: 0.002946, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.641383, Class: 1.000000, Obj: 0.402676, No Obj: 0.002928, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.561330, Class: 1.000000, Obj: 0.327725, No Obj: 0.002722, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.601412, Class: 1.000000, Obj: 0.360821, No Obj: 0.002873, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.612664, Class: 1.000000, Obj: 0.403663, No Obj: 0.003004, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.628565, Class: 1.000000, Obj: 0.437718, No Obj: 0.003015, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.631059, Class: 1.000000, Obj: 0.378960, No Obj: 0.003559, Avg Recall: 0.764228, count: 123 -Region Avg IOU: 0.595187, Class: 1.000000, Obj: 0.363839, No Obj: 0.004024, Avg Recall: 0.736842, count: 152 -6278: 0.428530, 0.435631 avg, 0.001000 rate, 22.970184 seconds, 1607168 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.586265, Class: 1.000000, Obj: 0.350211, No Obj: 0.003721, Avg Recall: 0.685315, count: 143 -Region Avg IOU: 0.639196, Class: 1.000000, Obj: 0.410287, No Obj: 0.003105, Avg Recall: 0.802198, count: 91 -Region Avg IOU: 0.597160, Class: 1.000000, Obj: 0.366005, No Obj: 0.003305, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.597535, Class: 1.000000, Obj: 0.433801, No Obj: 0.003008, Avg Recall: 0.752941, count: 85 -Region Avg IOU: 0.650704, Class: 1.000000, Obj: 0.434969, No Obj: 0.002808, Avg Recall: 0.780488, count: 82 -Region Avg IOU: 0.624140, Class: 1.000000, Obj: 0.384552, No Obj: 0.003184, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.580583, Class: 1.000000, Obj: 0.372938, No Obj: 0.003226, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.580253, Class: 1.000000, Obj: 0.355442, No Obj: 0.002974, Avg Recall: 0.656863, count: 102 -6279: 0.403076, 0.432375 avg, 0.001000 rate, 23.046598 seconds, 1607424 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.564990, Class: 1.000000, Obj: 0.363974, No Obj: 0.003392, Avg Recall: 0.664062, count: 128 -Region Avg IOU: 0.578383, Class: 1.000000, Obj: 0.410963, No Obj: 0.003515, Avg Recall: 0.650794, count: 126 -Region Avg IOU: 0.619939, Class: 1.000000, Obj: 0.362022, No Obj: 0.003642, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.558128, Class: 1.000000, Obj: 0.337668, No Obj: 0.003106, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.645141, Class: 1.000000, Obj: 0.421219, No Obj: 0.003908, Avg Recall: 0.783333, count: 120 -Region Avg IOU: 0.593091, Class: 1.000000, Obj: 0.399448, No Obj: 0.003407, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.655648, Class: 1.000000, Obj: 0.479624, No Obj: 0.003049, Avg Recall: 0.791667, count: 72 -Region Avg IOU: 0.586308, Class: 1.000000, Obj: 0.371340, No Obj: 0.002961, Avg Recall: 0.747573, count: 103 -6280: 0.426553, 0.431793 avg, 0.001000 rate, 24.098677 seconds, 1607680 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000060 seconds -Region Avg IOU: 0.623491, Class: 1.000000, Obj: 0.393467, No Obj: 0.003290, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.659611, Class: 1.000000, Obj: 0.412667, No Obj: 0.003573, Avg Recall: 0.848214, count: 112 -Region Avg IOU: 0.653362, Class: 1.000000, Obj: 0.429128, No Obj: 0.003098, Avg Recall: 0.817308, count: 104 -Region Avg IOU: 0.620382, Class: 1.000000, Obj: 0.411107, No Obj: 0.002866, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.635624, Class: 1.000000, Obj: 0.421105, No Obj: 0.002848, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.627774, Class: 1.000000, Obj: 0.458269, No Obj: 0.003094, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.600434, Class: 1.000000, Obj: 0.461119, No Obj: 0.003205, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.644863, Class: 1.000000, Obj: 0.466324, No Obj: 0.003361, Avg Recall: 0.732283, count: 127 -6281: 0.387639, 0.427378 avg, 0.001000 rate, 25.597496 seconds, 1607936 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.640314, Class: 1.000000, Obj: 0.363628, No Obj: 0.003630, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.606726, Class: 1.000000, Obj: 0.403996, No Obj: 0.003354, Avg Recall: 0.696970, count: 132 -Region Avg IOU: 0.631916, Class: 1.000000, Obj: 0.431819, No Obj: 0.003921, Avg Recall: 0.757812, count: 128 -Region Avg IOU: 0.581326, Class: 1.000000, Obj: 0.403356, No Obj: 0.003299, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.624507, Class: 1.000000, Obj: 0.427784, No Obj: 0.002937, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.608023, Class: 1.000000, Obj: 0.471976, No Obj: 0.002907, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.578434, Class: 1.000000, Obj: 0.397422, No Obj: 0.003597, Avg Recall: 0.680000, count: 150 -Region Avg IOU: 0.583915, Class: 1.000000, Obj: 0.385011, No Obj: 0.003316, Avg Recall: 0.699248, count: 133 -6282: 0.431804, 0.427820 avg, 0.001000 rate, 26.529119 seconds, 1608192 images -Loaded: 0.000118 seconds -Region Avg IOU: 0.643378, Class: 1.000000, Obj: 0.469510, No Obj: 0.003248, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.612244, Class: 1.000000, Obj: 0.433904, No Obj: 0.002993, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.592290, Class: 1.000000, Obj: 0.416360, No Obj: 0.002983, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.618958, Class: 1.000000, Obj: 0.413190, No Obj: 0.003370, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.643778, Class: 1.000000, Obj: 0.465899, No Obj: 0.003255, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.644809, Class: 1.000000, Obj: 0.422218, No Obj: 0.004003, Avg Recall: 0.817518, count: 137 -Region Avg IOU: 0.580936, Class: 1.000000, Obj: 0.406567, No Obj: 0.003140, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.577673, Class: 1.000000, Obj: 0.365649, No Obj: 0.002867, Avg Recall: 0.663462, count: 104 -6283: 0.482856, 0.433324 avg, 0.001000 rate, 26.295979 seconds, 1608448 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.631257, Class: 1.000000, Obj: 0.419383, No Obj: 0.003519, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.612733, Class: 1.000000, Obj: 0.430087, No Obj: 0.003059, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.627289, Class: 1.000000, Obj: 0.414660, No Obj: 0.003416, Avg Recall: 0.739583, count: 96 -Region Avg IOU: 0.640589, Class: 1.000000, Obj: 0.476800, No Obj: 0.002818, Avg Recall: 0.809524, count: 84 -Region Avg IOU: 0.615465, Class: 1.000000, Obj: 0.425683, No Obj: 0.003294, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.626730, Class: 1.000000, Obj: 0.477000, No Obj: 0.003725, Avg Recall: 0.742424, count: 132 -Region Avg IOU: 0.648166, Class: 1.000000, Obj: 0.458953, No Obj: 0.003402, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.691811, Class: 1.000000, Obj: 0.496038, No Obj: 0.003421, Avg Recall: 0.863636, count: 88 -6284: 0.411531, 0.431145 avg, 0.001000 rate, 26.986132 seconds, 1608704 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.674087, Class: 1.000000, Obj: 0.454245, No Obj: 0.003250, Avg Recall: 0.817204, count: 93 -Region Avg IOU: 0.600732, Class: 1.000000, Obj: 0.394561, No Obj: 0.003001, Avg Recall: 0.710526, count: 114 -Region Avg IOU: 0.638451, Class: 1.000000, Obj: 0.434479, No Obj: 0.003088, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.619232, Class: 1.000000, Obj: 0.479417, No Obj: 0.003255, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.644653, Class: 1.000000, Obj: 0.405118, No Obj: 0.003493, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.593859, Class: 1.000000, Obj: 0.471610, No Obj: 0.003257, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.657620, Class: 1.000000, Obj: 0.441529, No Obj: 0.003245, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.598054, Class: 1.000000, Obj: 0.375505, No Obj: 0.003241, Avg Recall: 0.700000, count: 120 -6285: 0.420762, 0.430106 avg, 0.001000 rate, 25.605808 seconds, 1608960 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.683070, Class: 1.000000, Obj: 0.442161, No Obj: 0.002897, Avg Recall: 0.860000, count: 100 -Region Avg IOU: 0.652348, Class: 1.000000, Obj: 0.468663, No Obj: 0.003413, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.642134, Class: 1.000000, Obj: 0.411827, No Obj: 0.003286, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.598308, Class: 1.000000, Obj: 0.453600, No Obj: 0.003094, Avg Recall: 0.692913, count: 127 -Region Avg IOU: 0.582319, Class: 1.000000, Obj: 0.437481, No Obj: 0.003451, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.634376, Class: 1.000000, Obj: 0.455282, No Obj: 0.003461, Avg Recall: 0.761538, count: 130 -Region Avg IOU: 0.623663, Class: 1.000000, Obj: 0.450443, No Obj: 0.003090, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.611213, Class: 1.000000, Obj: 0.413288, No Obj: 0.002948, Avg Recall: 0.741573, count: 89 -6286: 0.408590, 0.427955 avg, 0.001000 rate, 25.502895 seconds, 1609216 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.626274, Class: 1.000000, Obj: 0.453923, No Obj: 0.002855, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.585311, Class: 1.000000, Obj: 0.391190, No Obj: 0.002619, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.594426, Class: 1.000000, Obj: 0.400338, No Obj: 0.003462, Avg Recall: 0.695035, count: 141 -Region Avg IOU: 0.604756, Class: 1.000000, Obj: 0.405566, No Obj: 0.002904, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.633918, Class: 1.000000, Obj: 0.441236, No Obj: 0.002853, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.623501, Class: 1.000000, Obj: 0.381924, No Obj: 0.003262, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.637777, Class: 1.000000, Obj: 0.410860, No Obj: 0.003483, Avg Recall: 0.768657, count: 134 -Region Avg IOU: 0.660325, Class: 1.000000, Obj: 0.417017, No Obj: 0.003076, Avg Recall: 0.806818, count: 88 -6287: 0.425639, 0.427723 avg, 0.001000 rate, 26.048058 seconds, 1609472 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.583433, Class: 1.000000, Obj: 0.410405, No Obj: 0.002919, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.622289, Class: 1.000000, Obj: 0.395184, No Obj: 0.003364, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.629349, Class: 1.000000, Obj: 0.404178, No Obj: 0.003315, Avg Recall: 0.750000, count: 124 -Region Avg IOU: 0.621543, Class: 1.000000, Obj: 0.435974, No Obj: 0.003157, Avg Recall: 0.725191, count: 131 -Region Avg IOU: 0.647310, Class: 1.000000, Obj: 0.374051, No Obj: 0.003445, Avg Recall: 0.780488, count: 123 -Region Avg IOU: 0.611007, Class: 1.000000, Obj: 0.392392, No Obj: 0.002988, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.607048, Class: 1.000000, Obj: 0.421460, No Obj: 0.003592, Avg Recall: 0.730769, count: 130 -Region Avg IOU: 0.561629, Class: 1.000000, Obj: 0.403275, No Obj: 0.003140, Avg Recall: 0.650794, count: 126 -6288: 0.489749, 0.433926 avg, 0.001000 rate, 26.175333 seconds, 1609728 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.585857, Class: 1.000000, Obj: 0.402664, No Obj: 0.002631, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.658696, Class: 1.000000, Obj: 0.435017, No Obj: 0.003182, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.586721, Class: 1.000000, Obj: 0.406953, No Obj: 0.003371, Avg Recall: 0.699301, count: 143 -Region Avg IOU: 0.669668, Class: 1.000000, Obj: 0.471578, No Obj: 0.003046, Avg Recall: 0.830000, count: 100 -Region Avg IOU: 0.648115, Class: 1.000000, Obj: 0.472413, No Obj: 0.003049, Avg Recall: 0.791045, count: 67 -Region Avg IOU: 0.611814, Class: 1.000000, Obj: 0.410034, No Obj: 0.003375, Avg Recall: 0.753731, count: 134 -Region Avg IOU: 0.580839, Class: 1.000000, Obj: 0.387018, No Obj: 0.002879, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.583996, Class: 1.000000, Obj: 0.446563, No Obj: 0.002792, Avg Recall: 0.688525, count: 122 -6289: 0.416459, 0.432179 avg, 0.001000 rate, 25.048231 seconds, 1609984 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.610715, Class: 1.000000, Obj: 0.413357, No Obj: 0.003049, Avg Recall: 0.744000, count: 125 -Region Avg IOU: 0.624528, Class: 1.000000, Obj: 0.415126, No Obj: 0.002899, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.617234, Class: 1.000000, Obj: 0.441406, No Obj: 0.002965, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.628473, Class: 1.000000, Obj: 0.401901, No Obj: 0.002660, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.631106, Class: 1.000000, Obj: 0.410493, No Obj: 0.003104, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.593762, Class: 1.000000, Obj: 0.399050, No Obj: 0.003197, Avg Recall: 0.678832, count: 137 -Region Avg IOU: 0.655590, Class: 1.000000, Obj: 0.401540, No Obj: 0.003340, Avg Recall: 0.801527, count: 131 -Region Avg IOU: 0.572931, Class: 1.000000, Obj: 0.401739, No Obj: 0.002877, Avg Recall: 0.652174, count: 115 -6290: 0.446478, 0.433609 avg, 0.001000 rate, 25.445496 seconds, 1610240 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 20.918140 seconds -Region Avg IOU: 0.636120, Class: 1.000000, Obj: 0.422026, No Obj: 0.003574, Avg Recall: 0.776923, count: 130 -Region Avg IOU: 0.607558, Class: 1.000000, Obj: 0.401159, No Obj: 0.003219, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.590072, Class: 1.000000, Obj: 0.384771, No Obj: 0.003015, Avg Recall: 0.688525, count: 122 -Region Avg IOU: 0.615998, Class: 1.000000, Obj: 0.380085, No Obj: 0.002914, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.599123, Class: 1.000000, Obj: 0.388125, No Obj: 0.002675, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.571854, Class: 1.000000, Obj: 0.393246, No Obj: 0.002980, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.635214, Class: 1.000000, Obj: 0.400775, No Obj: 0.002996, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.606144, Class: 1.000000, Obj: 0.412811, No Obj: 0.003421, Avg Recall: 0.715328, count: 137 -6291: 0.457914, 0.436039 avg, 0.001000 rate, 25.706560 seconds, 1610496 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.582204, Class: 1.000000, Obj: 0.417877, No Obj: 0.002824, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.625448, Class: 1.000000, Obj: 0.414815, No Obj: 0.003250, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.656035, Class: 1.000000, Obj: 0.415851, No Obj: 0.002895, Avg Recall: 0.840425, count: 94 -Region Avg IOU: 0.633110, Class: 1.000000, Obj: 0.402436, No Obj: 0.003485, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.603694, Class: 1.000000, Obj: 0.398302, No Obj: 0.003069, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.619983, Class: 1.000000, Obj: 0.417086, No Obj: 0.002684, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.591016, Class: 1.000000, Obj: 0.393624, No Obj: 0.003325, Avg Recall: 0.697479, count: 119 -Region Avg IOU: 0.632569, Class: 1.000000, Obj: 0.475135, No Obj: 0.002768, Avg Recall: 0.756098, count: 82 -6292: 0.411428, 0.433578 avg, 0.001000 rate, 25.514635 seconds, 1610752 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.627339, Class: 1.000000, Obj: 0.416983, No Obj: 0.002820, Avg Recall: 0.804598, count: 87 -Region Avg IOU: 0.593356, Class: 1.000000, Obj: 0.407414, No Obj: 0.003311, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.609509, Class: 1.000000, Obj: 0.426993, No Obj: 0.002790, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.615958, Class: 1.000000, Obj: 0.384027, No Obj: 0.003079, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.675125, Class: 1.000000, Obj: 0.469982, No Obj: 0.002993, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.588536, Class: 1.000000, Obj: 0.386904, No Obj: 0.003041, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.618692, Class: 1.000000, Obj: 0.392975, No Obj: 0.003342, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.610261, Class: 1.000000, Obj: 0.401776, No Obj: 0.002975, Avg Recall: 0.751938, count: 129 -6293: 0.402635, 0.430484 avg, 0.001000 rate, 25.098156 seconds, 1611008 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.597961, Class: 1.000000, Obj: 0.408231, No Obj: 0.003079, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.633754, Class: 1.000000, Obj: 0.422229, No Obj: 0.003434, Avg Recall: 0.744000, count: 125 -Region Avg IOU: 0.613654, Class: 1.000000, Obj: 0.419669, No Obj: 0.002820, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.596317, Class: 1.000000, Obj: 0.381951, No Obj: 0.003234, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.599357, Class: 1.000000, Obj: 0.433822, No Obj: 0.002706, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.589184, Class: 1.000000, Obj: 0.408007, No Obj: 0.003154, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.626311, Class: 1.000000, Obj: 0.445658, No Obj: 0.003036, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.639665, Class: 1.000000, Obj: 0.412883, No Obj: 0.003257, Avg Recall: 0.779817, count: 109 -6294: 0.381289, 0.425564 avg, 0.001000 rate, 25.327108 seconds, 1611264 images -Loaded: 0.000127 seconds -Region Avg IOU: 0.612517, Class: 1.000000, Obj: 0.445798, No Obj: 0.002357, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.617212, Class: 1.000000, Obj: 0.378041, No Obj: 0.002903, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.635587, Class: 1.000000, Obj: 0.434063, No Obj: 0.003004, Avg Recall: 0.744444, count: 90 -Region Avg IOU: 0.621550, Class: 1.000000, Obj: 0.433571, No Obj: 0.002603, Avg Recall: 0.711111, count: 90 -Region Avg IOU: 0.644350, Class: 1.000000, Obj: 0.419169, No Obj: 0.003021, Avg Recall: 0.785047, count: 107 -Region Avg IOU: 0.674533, Class: 1.000000, Obj: 0.480492, No Obj: 0.002972, Avg Recall: 0.829545, count: 88 -Region Avg IOU: 0.676552, Class: 1.000000, Obj: 0.474206, No Obj: 0.003416, Avg Recall: 0.822430, count: 107 -Region Avg IOU: 0.649534, Class: 1.000000, Obj: 0.414785, No Obj: 0.002922, Avg Recall: 0.760000, count: 100 -6295: 0.390326, 0.422041 avg, 0.001000 rate, 25.122662 seconds, 1611520 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.640137, Class: 1.000000, Obj: 0.424009, No Obj: 0.002712, Avg Recall: 0.793478, count: 92 -Region Avg IOU: 0.649027, Class: 1.000000, Obj: 0.404094, No Obj: 0.003227, Avg Recall: 0.821429, count: 112 -Region Avg IOU: 0.626069, Class: 1.000000, Obj: 0.438858, No Obj: 0.003038, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.651130, Class: 1.000000, Obj: 0.401245, No Obj: 0.002890, Avg Recall: 0.835165, count: 91 -Region Avg IOU: 0.635582, Class: 1.000000, Obj: 0.433907, No Obj: 0.002884, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.611656, Class: 1.000000, Obj: 0.410413, No Obj: 0.002806, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.662128, Class: 1.000000, Obj: 0.420771, No Obj: 0.002669, Avg Recall: 0.822785, count: 79 -Region Avg IOU: 0.692517, Class: 1.000000, Obj: 0.441045, No Obj: 0.003448, Avg Recall: 0.876405, count: 89 -6296: 0.372267, 0.417063 avg, 0.001000 rate, 25.437328 seconds, 1611776 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.603155, Class: 1.000000, Obj: 0.397488, No Obj: 0.003225, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.661404, Class: 1.000000, Obj: 0.395951, No Obj: 0.003700, Avg Recall: 0.829787, count: 141 -Region Avg IOU: 0.598980, Class: 1.000000, Obj: 0.420578, No Obj: 0.002423, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.601136, Class: 1.000000, Obj: 0.419165, No Obj: 0.003175, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.595241, Class: 1.000000, Obj: 0.375321, No Obj: 0.003230, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.627276, Class: 1.000000, Obj: 0.404316, No Obj: 0.003202, Avg Recall: 0.752137, count: 117 -Region Avg IOU: 0.646806, Class: 1.000000, Obj: 0.461417, No Obj: 0.003620, Avg Recall: 0.798387, count: 124 -Region Avg IOU: 0.568908, Class: 1.000000, Obj: 0.341450, No Obj: 0.002624, Avg Recall: 0.697248, count: 109 -6297: 0.465704, 0.421927 avg, 0.001000 rate, 25.719000 seconds, 1612032 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.656107, Class: 1.000000, Obj: 0.492407, No Obj: 0.003107, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.615738, Class: 1.000000, Obj: 0.403972, No Obj: 0.003419, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.600472, Class: 1.000000, Obj: 0.385253, No Obj: 0.002732, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.634586, Class: 1.000000, Obj: 0.410848, No Obj: 0.002772, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.664845, Class: 1.000000, Obj: 0.393591, No Obj: 0.003243, Avg Recall: 0.857143, count: 84 -Region Avg IOU: 0.642390, Class: 1.000000, Obj: 0.425068, No Obj: 0.002971, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.643878, Class: 1.000000, Obj: 0.430650, No Obj: 0.003365, Avg Recall: 0.809524, count: 147 -Region Avg IOU: 0.601322, Class: 1.000000, Obj: 0.398606, No Obj: 0.003498, Avg Recall: 0.757282, count: 103 -6298: 0.404042, 0.420139 avg, 0.001000 rate, 26.176985 seconds, 1612288 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.609831, Class: 1.000000, Obj: 0.428796, No Obj: 0.002970, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.612796, Class: 1.000000, Obj: 0.440967, No Obj: 0.003217, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.636306, Class: 1.000000, Obj: 0.400256, No Obj: 0.003361, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.625296, Class: 1.000000, Obj: 0.408832, No Obj: 0.003399, Avg Recall: 0.812950, count: 139 -Region Avg IOU: 0.643444, Class: 1.000000, Obj: 0.392412, No Obj: 0.002988, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.628709, Class: 1.000000, Obj: 0.446083, No Obj: 0.002577, Avg Recall: 0.795181, count: 83 -Region Avg IOU: 0.685826, Class: 1.000000, Obj: 0.478506, No Obj: 0.003115, Avg Recall: 0.827957, count: 93 -Region Avg IOU: 0.604856, Class: 1.000000, Obj: 0.437866, No Obj: 0.003229, Avg Recall: 0.708661, count: 127 -6299: 0.425152, 0.420640 avg, 0.001000 rate, 25.598026 seconds, 1612544 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.658826, Class: 1.000000, Obj: 0.437187, No Obj: 0.003004, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.662329, Class: 1.000000, Obj: 0.481582, No Obj: 0.003004, Avg Recall: 0.820225, count: 89 -Region Avg IOU: 0.618882, Class: 1.000000, Obj: 0.417359, No Obj: 0.003522, Avg Recall: 0.731343, count: 134 -Region Avg IOU: 0.636568, Class: 1.000000, Obj: 0.429063, No Obj: 0.002941, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.631459, Class: 1.000000, Obj: 0.438301, No Obj: 0.002632, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.614685, Class: 1.000000, Obj: 0.439885, No Obj: 0.003318, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.671322, Class: 1.000000, Obj: 0.464782, No Obj: 0.003326, Avg Recall: 0.870690, count: 116 -Region Avg IOU: 0.638318, Class: 1.000000, Obj: 0.425139, No Obj: 0.002579, Avg Recall: 0.825581, count: 86 -6300: 0.402611, 0.418837 avg, 0.001000 rate, 25.349764 seconds, 1612800 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 17.234215 seconds -Region Avg IOU: 0.502638, Class: 1.000000, Obj: 0.336376, No Obj: 0.004258, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.472050, Class: 1.000000, Obj: 0.309079, No Obj: 0.004042, Avg Recall: 0.504854, count: 103 -Region Avg IOU: 0.481974, Class: 1.000000, Obj: 0.303567, No Obj: 0.003714, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.454231, Class: 1.000000, Obj: 0.277594, No Obj: 0.004076, Avg Recall: 0.468750, count: 128 -Region Avg IOU: 0.441059, Class: 1.000000, Obj: 0.268780, No Obj: 0.003807, Avg Recall: 0.468750, count: 128 -Region Avg IOU: 0.496888, Class: 1.000000, Obj: 0.279422, No Obj: 0.004591, Avg Recall: 0.566667, count: 120 -Region Avg IOU: 0.555155, Class: 1.000000, Obj: 0.327046, No Obj: 0.004272, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.460278, Class: 1.000000, Obj: 0.305837, No Obj: 0.003303, Avg Recall: 0.456522, count: 92 -6301: 0.515646, 0.428518 avg, 0.001000 rate, 15.077691 seconds, 1613056 images -Loaded: 6.208566 seconds -Region Avg IOU: 0.494665, Class: 1.000000, Obj: 0.302014, No Obj: 0.003524, Avg Recall: 0.481481, count: 81 -Region Avg IOU: 0.460047, Class: 1.000000, Obj: 0.284716, No Obj: 0.003399, Avg Recall: 0.474227, count: 97 -Region Avg IOU: 0.476594, Class: 1.000000, Obj: 0.283696, No Obj: 0.003672, Avg Recall: 0.456311, count: 103 -Region Avg IOU: 0.445527, Class: 1.000000, Obj: 0.288002, No Obj: 0.004500, Avg Recall: 0.447154, count: 123 -Region Avg IOU: 0.467620, Class: 1.000000, Obj: 0.277298, No Obj: 0.003929, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.482535, Class: 1.000000, Obj: 0.333358, No Obj: 0.004737, Avg Recall: 0.544776, count: 134 -Region Avg IOU: 0.543408, Class: 1.000000, Obj: 0.281837, No Obj: 0.004765, Avg Recall: 0.596639, count: 119 -Region Avg IOU: 0.442185, Class: 1.000000, Obj: 0.274645, No Obj: 0.004436, Avg Recall: 0.496124, count: 129 -6302: 0.535447, 0.439211 avg, 0.001000 rate, 17.794403 seconds, 1613312 images -Loaded: 4.150734 seconds -Region Avg IOU: 0.461099, Class: 1.000000, Obj: 0.289436, No Obj: 0.003795, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.512298, Class: 1.000000, Obj: 0.394826, No Obj: 0.005304, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.458489, Class: 1.000000, Obj: 0.351351, No Obj: 0.004846, Avg Recall: 0.479675, count: 123 -Region Avg IOU: 0.501851, Class: 1.000000, Obj: 0.344298, No Obj: 0.003962, Avg Recall: 0.564706, count: 85 -Region Avg IOU: 0.558316, Class: 1.000000, Obj: 0.406217, No Obj: 0.004942, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.494943, Class: 1.000000, Obj: 0.305707, No Obj: 0.004457, Avg Recall: 0.523810, count: 105 -Region Avg IOU: 0.510773, Class: 1.000000, Obj: 0.329917, No Obj: 0.004921, Avg Recall: 0.573770, count: 122 -Region Avg IOU: 0.448543, Class: 1.000000, Obj: 0.313812, No Obj: 0.004475, Avg Recall: 0.500000, count: 126 -6303: 0.551593, 0.450449 avg, 0.001000 rate, 16.227755 seconds, 1613568 images -Loaded: 4.690854 seconds -Region Avg IOU: 0.515266, Class: 1.000000, Obj: 0.345664, No Obj: 0.004811, Avg Recall: 0.537037, count: 108 -Region Avg IOU: 0.454806, Class: 1.000000, Obj: 0.353134, No Obj: 0.005280, Avg Recall: 0.492857, count: 140 -Region Avg IOU: 0.522543, Class: 1.000000, Obj: 0.354807, No Obj: 0.005276, Avg Recall: 0.593220, count: 118 -Region Avg IOU: 0.502205, Class: 1.000000, Obj: 0.330749, No Obj: 0.004874, Avg Recall: 0.579365, count: 126 -Region Avg IOU: 0.501415, Class: 1.000000, Obj: 0.321168, No Obj: 0.004464, Avg Recall: 0.509615, count: 104 -Region Avg IOU: 0.493190, Class: 1.000000, Obj: 0.341137, No Obj: 0.005065, Avg Recall: 0.553571, count: 112 -Region Avg IOU: 0.544977, Class: 1.000000, Obj: 0.364006, No Obj: 0.004946, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.458409, Class: 1.000000, Obj: 0.323699, No Obj: 0.005253, Avg Recall: 0.479675, count: 123 -6304: 0.508872, 0.456291 avg, 0.001000 rate, 18.300396 seconds, 1613824 images -Loaded: 1.964888 seconds -Region Avg IOU: 0.470317, Class: 1.000000, Obj: 0.367765, No Obj: 0.005434, Avg Recall: 0.520325, count: 123 -Region Avg IOU: 0.507703, Class: 1.000000, Obj: 0.394443, No Obj: 0.004898, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.477615, Class: 1.000000, Obj: 0.344659, No Obj: 0.004629, Avg Recall: 0.495050, count: 101 -Region Avg IOU: 0.452666, Class: 1.000000, Obj: 0.305261, No Obj: 0.004549, Avg Recall: 0.485714, count: 105 -Region Avg IOU: 0.489461, Class: 1.000000, Obj: 0.338882, No Obj: 0.005475, Avg Recall: 0.503597, count: 139 -Region Avg IOU: 0.535710, Class: 1.000000, Obj: 0.403085, No Obj: 0.005440, Avg Recall: 0.601695, count: 118 -Region Avg IOU: 0.500010, Class: 1.000000, Obj: 0.382815, No Obj: 0.004698, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.453613, Class: 1.000000, Obj: 0.376495, No Obj: 0.005181, Avg Recall: 0.462121, count: 132 -6305: 0.534074, 0.464070 avg, 0.001000 rate, 15.156637 seconds, 1614080 images -Loaded: 5.994876 seconds -Region Avg IOU: 0.496952, Class: 1.000000, Obj: 0.386054, No Obj: 0.004889, Avg Recall: 0.548387, count: 93 -Region Avg IOU: 0.518996, Class: 1.000000, Obj: 0.400689, No Obj: 0.005324, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.458460, Class: 1.000000, Obj: 0.364410, No Obj: 0.004789, Avg Recall: 0.500000, count: 90 -Region Avg IOU: 0.476233, Class: 1.000000, Obj: 0.364608, No Obj: 0.004937, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.482434, Class: 1.000000, Obj: 0.367631, No Obj: 0.005141, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.445056, Class: 1.000000, Obj: 0.341399, No Obj: 0.004846, Avg Recall: 0.504132, count: 121 -Region Avg IOU: 0.501058, Class: 1.000000, Obj: 0.346856, No Obj: 0.003594, Avg Recall: 0.569620, count: 79 -Region Avg IOU: 0.458352, Class: 1.000000, Obj: 0.388835, No Obj: 0.004361, Avg Recall: 0.476190, count: 84 -6306: 0.428352, 0.460498 avg, 0.001000 rate, 18.615200 seconds, 1614336 images -Loaded: 0.823755 seconds -Region Avg IOU: 0.454280, Class: 1.000000, Obj: 0.411933, No Obj: 0.005382, Avg Recall: 0.462963, count: 108 -Region Avg IOU: 0.427043, Class: 1.000000, Obj: 0.365730, No Obj: 0.004220, Avg Recall: 0.457143, count: 105 -Region Avg IOU: 0.506907, Class: 1.000000, Obj: 0.354923, No Obj: 0.005321, Avg Recall: 0.595238, count: 126 -Region Avg IOU: 0.489200, Class: 1.000000, Obj: 0.363601, No Obj: 0.005274, Avg Recall: 0.522936, count: 109 -Region Avg IOU: 0.472657, Class: 1.000000, Obj: 0.355919, No Obj: 0.005107, Avg Recall: 0.521739, count: 92 -Region Avg IOU: 0.407166, Class: 1.000000, Obj: 0.375858, No Obj: 0.004781, Avg Recall: 0.405660, count: 106 -Region Avg IOU: 0.511847, Class: 1.000000, Obj: 0.427643, No Obj: 0.005756, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.468494, Class: 1.000000, Obj: 0.380987, No Obj: 0.004974, Avg Recall: 0.475000, count: 80 -6307: 0.433429, 0.457791 avg, 0.001000 rate, 16.897907 seconds, 1614592 images -Loaded: 1.444464 seconds -Region Avg IOU: 0.439359, Class: 1.000000, Obj: 0.410906, No Obj: 0.005335, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.461214, Class: 1.000000, Obj: 0.414625, No Obj: 0.005637, Avg Recall: 0.491525, count: 118 -Region Avg IOU: 0.463758, Class: 1.000000, Obj: 0.392844, No Obj: 0.004951, Avg Recall: 0.460784, count: 102 -Region Avg IOU: 0.539903, Class: 1.000000, Obj: 0.416887, No Obj: 0.005845, Avg Recall: 0.594828, count: 116 -Region Avg IOU: 0.426027, Class: 1.000000, Obj: 0.365751, No Obj: 0.004931, Avg Recall: 0.445455, count: 110 -Region Avg IOU: 0.496254, Class: 1.000000, Obj: 0.420984, No Obj: 0.004514, Avg Recall: 0.530120, count: 83 -Region Avg IOU: 0.423570, Class: 1.000000, Obj: 0.351603, No Obj: 0.005570, Avg Recall: 0.395833, count: 96 -Region Avg IOU: 0.502099, Class: 1.000000, Obj: 0.390783, No Obj: 0.005441, Avg Recall: 0.600000, count: 95 -6308: 0.458646, 0.457876 avg, 0.001000 rate, 18.844208 seconds, 1614848 images -Loaded: 2.213730 seconds -Region Avg IOU: 0.450813, Class: 1.000000, Obj: 0.383863, No Obj: 0.006169, Avg Recall: 0.446429, count: 112 -Region Avg IOU: 0.428358, Class: 1.000000, Obj: 0.361258, No Obj: 0.004202, Avg Recall: 0.488095, count: 84 -Region Avg IOU: 0.494041, Class: 1.000000, Obj: 0.400627, No Obj: 0.005737, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.516305, Class: 1.000000, Obj: 0.400556, No Obj: 0.006008, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.455945, Class: 1.000000, Obj: 0.369223, No Obj: 0.006174, Avg Recall: 0.514493, count: 138 -Region Avg IOU: 0.530241, Class: 1.000000, Obj: 0.409550, No Obj: 0.006188, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.499159, Class: 1.000000, Obj: 0.374106, No Obj: 0.004201, Avg Recall: 0.554217, count: 83 -Region Avg IOU: 0.519017, Class: 1.000000, Obj: 0.375654, No Obj: 0.005520, Avg Recall: 0.577982, count: 109 -6309: 0.442510, 0.456340 avg, 0.001000 rate, 15.125199 seconds, 1615104 images -Loaded: 5.145801 seconds -Region Avg IOU: 0.488826, Class: 1.000000, Obj: 0.378261, No Obj: 0.005410, Avg Recall: 0.532110, count: 109 -Region Avg IOU: 0.451412, Class: 1.000000, Obj: 0.356236, No Obj: 0.005255, Avg Recall: 0.440678, count: 118 -Region Avg IOU: 0.480829, Class: 1.000000, Obj: 0.363025, No Obj: 0.005719, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.497639, Class: 1.000000, Obj: 0.390764, No Obj: 0.004568, Avg Recall: 0.566265, count: 83 -Region Avg IOU: 0.449188, Class: 1.000000, Obj: 0.329869, No Obj: 0.004764, Avg Recall: 0.487180, count: 117 -Region Avg IOU: 0.436314, Class: 1.000000, Obj: 0.294065, No Obj: 0.004761, Avg Recall: 0.456897, count: 116 -Region Avg IOU: 0.453394, Class: 1.000000, Obj: 0.342354, No Obj: 0.005104, Avg Recall: 0.475410, count: 122 -Region Avg IOU: 0.518549, Class: 1.000000, Obj: 0.431280, No Obj: 0.005298, Avg Recall: 0.617647, count: 102 -6310: 0.477496, 0.458455 avg, 0.001000 rate, 18.486084 seconds, 1615360 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000067 seconds -Region Avg IOU: 0.678210, Class: 1.000000, Obj: 0.495911, No Obj: 0.003952, Avg Recall: 0.843750, count: 128 -Region Avg IOU: 0.625082, Class: 1.000000, Obj: 0.475746, No Obj: 0.003372, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.618433, Class: 1.000000, Obj: 0.471330, No Obj: 0.003421, Avg Recall: 0.768000, count: 125 -Region Avg IOU: 0.657306, Class: 1.000000, Obj: 0.471944, No Obj: 0.003491, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.623784, Class: 1.000000, Obj: 0.474327, No Obj: 0.002799, Avg Recall: 0.791667, count: 72 -Region Avg IOU: 0.631521, Class: 1.000000, Obj: 0.490652, No Obj: 0.003256, Avg Recall: 0.752809, count: 89 -Region Avg IOU: 0.664957, Class: 1.000000, Obj: 0.480915, No Obj: 0.004120, Avg Recall: 0.823077, count: 130 -Region Avg IOU: 0.652416, Class: 1.000000, Obj: 0.482811, No Obj: 0.003814, Avg Recall: 0.853448, count: 116 -6311: 0.401597, 0.452770 avg, 0.001000 rate, 26.939877 seconds, 1615616 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.615713, Class: 1.000000, Obj: 0.458958, No Obj: 0.003648, Avg Recall: 0.773438, count: 128 -Region Avg IOU: 0.670379, Class: 1.000000, Obj: 0.490545, No Obj: 0.003844, Avg Recall: 0.818841, count: 138 -Region Avg IOU: 0.582471, Class: 1.000000, Obj: 0.418194, No Obj: 0.003365, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.651611, Class: 1.000000, Obj: 0.461769, No Obj: 0.003676, Avg Recall: 0.811966, count: 117 -Region Avg IOU: 0.647817, Class: 1.000000, Obj: 0.489936, No Obj: 0.003560, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.660834, Class: 1.000000, Obj: 0.494279, No Obj: 0.003395, Avg Recall: 0.814159, count: 113 -Region Avg IOU: 0.621796, Class: 1.000000, Obj: 0.411126, No Obj: 0.003294, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.604977, Class: 1.000000, Obj: 0.481643, No Obj: 0.003819, Avg Recall: 0.740741, count: 135 -6312: 0.452589, 0.452752 avg, 0.001000 rate, 27.214363 seconds, 1615872 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.624675, Class: 1.000000, Obj: 0.466663, No Obj: 0.003085, Avg Recall: 0.744000, count: 125 -Region Avg IOU: 0.656960, Class: 1.000000, Obj: 0.436026, No Obj: 0.003722, Avg Recall: 0.840336, count: 119 -Region Avg IOU: 0.622927, Class: 1.000000, Obj: 0.437513, No Obj: 0.003586, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.617492, Class: 1.000000, Obj: 0.423984, No Obj: 0.003475, Avg Recall: 0.734266, count: 143 -Region Avg IOU: 0.626114, Class: 1.000000, Obj: 0.468370, No Obj: 0.003321, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.657236, Class: 1.000000, Obj: 0.420671, No Obj: 0.003163, Avg Recall: 0.791209, count: 91 -Region Avg IOU: 0.631928, Class: 1.000000, Obj: 0.418268, No Obj: 0.003314, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.601532, Class: 1.000000, Obj: 0.413431, No Obj: 0.003221, Avg Recall: 0.732283, count: 127 -6313: 0.454984, 0.452975 avg, 0.001000 rate, 26.880953 seconds, 1616128 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.620057, Class: 1.000000, Obj: 0.436398, No Obj: 0.003301, Avg Recall: 0.759690, count: 129 -Region Avg IOU: 0.631345, Class: 1.000000, Obj: 0.446449, No Obj: 0.003355, Avg Recall: 0.709924, count: 131 -Region Avg IOU: 0.648211, Class: 1.000000, Obj: 0.467516, No Obj: 0.003029, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.672774, Class: 1.000000, Obj: 0.437572, No Obj: 0.003156, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.626007, Class: 1.000000, Obj: 0.408697, No Obj: 0.002929, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.667277, Class: 1.000000, Obj: 0.428660, No Obj: 0.002570, Avg Recall: 0.831461, count: 89 -Region Avg IOU: 0.614673, Class: 1.000000, Obj: 0.413810, No Obj: 0.002667, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.587136, Class: 1.000000, Obj: 0.429622, No Obj: 0.002679, Avg Recall: 0.693694, count: 111 -6314: 0.431448, 0.450822 avg, 0.001000 rate, 26.620056 seconds, 1616384 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.613830, Class: 1.000000, Obj: 0.410468, No Obj: 0.002589, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.673344, Class: 1.000000, Obj: 0.430912, No Obj: 0.002744, Avg Recall: 0.814815, count: 108 -Region Avg IOU: 0.607777, Class: 1.000000, Obj: 0.401205, No Obj: 0.002944, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.638325, Class: 1.000000, Obj: 0.350827, No Obj: 0.002792, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.619458, Class: 1.000000, Obj: 0.379767, No Obj: 0.002675, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.654094, Class: 1.000000, Obj: 0.402169, No Obj: 0.003091, Avg Recall: 0.822034, count: 118 -Region Avg IOU: 0.612289, Class: 1.000000, Obj: 0.398058, No Obj: 0.002915, Avg Recall: 0.729167, count: 144 -Region Avg IOU: 0.626823, Class: 1.000000, Obj: 0.366962, No Obj: 0.002598, Avg Recall: 0.769231, count: 91 -6315: 0.424574, 0.448197 avg, 0.001000 rate, 27.153080 seconds, 1616640 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.644430, Class: 1.000000, Obj: 0.416767, No Obj: 0.002883, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.644242, Class: 1.000000, Obj: 0.360123, No Obj: 0.002720, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.618458, Class: 1.000000, Obj: 0.366991, No Obj: 0.002864, Avg Recall: 0.776923, count: 130 -Region Avg IOU: 0.616042, Class: 1.000000, Obj: 0.379515, No Obj: 0.002649, Avg Recall: 0.770492, count: 122 -Region Avg IOU: 0.635193, Class: 1.000000, Obj: 0.426774, No Obj: 0.002755, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.636454, Class: 1.000000, Obj: 0.402272, No Obj: 0.002694, Avg Recall: 0.789474, count: 114 -Region Avg IOU: 0.614126, Class: 1.000000, Obj: 0.332209, No Obj: 0.003090, Avg Recall: 0.740000, count: 150 -Region Avg IOU: 0.635421, Class: 1.000000, Obj: 0.376326, No Obj: 0.002507, Avg Recall: 0.776000, count: 125 -6316: 0.486240, 0.452002 avg, 0.001000 rate, 26.751480 seconds, 1616896 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.681333, Class: 1.000000, Obj: 0.393562, No Obj: 0.002677, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.689129, Class: 1.000000, Obj: 0.378353, No Obj: 0.002300, Avg Recall: 0.890110, count: 91 -Region Avg IOU: 0.635496, Class: 1.000000, Obj: 0.392881, No Obj: 0.002487, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.583534, Class: 1.000000, Obj: 0.313527, No Obj: 0.002215, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.590124, Class: 1.000000, Obj: 0.346262, No Obj: 0.002596, Avg Recall: 0.738806, count: 134 -Region Avg IOU: 0.598333, Class: 1.000000, Obj: 0.314820, No Obj: 0.002494, Avg Recall: 0.743802, count: 121 -Region Avg IOU: 0.651684, Class: 1.000000, Obj: 0.383661, No Obj: 0.002975, Avg Recall: 0.805310, count: 113 -Region Avg IOU: 0.682482, Class: 1.000000, Obj: 0.379058, No Obj: 0.003002, Avg Recall: 0.830000, count: 100 -6317: 0.451743, 0.451976 avg, 0.001000 rate, 27.129145 seconds, 1617152 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.665393, Class: 1.000000, Obj: 0.397395, No Obj: 0.003041, Avg Recall: 0.850000, count: 120 -Region Avg IOU: 0.623734, Class: 1.000000, Obj: 0.351361, No Obj: 0.002943, Avg Recall: 0.738806, count: 134 -Region Avg IOU: 0.654097, Class: 1.000000, Obj: 0.388431, No Obj: 0.002718, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.661079, Class: 1.000000, Obj: 0.460361, No Obj: 0.002787, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.625097, Class: 1.000000, Obj: 0.368708, No Obj: 0.002588, Avg Recall: 0.734848, count: 132 -Region Avg IOU: 0.663098, Class: 1.000000, Obj: 0.346956, No Obj: 0.002989, Avg Recall: 0.809160, count: 131 -Region Avg IOU: 0.552723, Class: 1.000000, Obj: 0.371674, No Obj: 0.002514, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.623450, Class: 1.000000, Obj: 0.365655, No Obj: 0.002859, Avg Recall: 0.793103, count: 116 -6318: 0.477850, 0.454563 avg, 0.001000 rate, 26.455355 seconds, 1617408 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.673988, Class: 1.000000, Obj: 0.411939, No Obj: 0.002141, Avg Recall: 0.881579, count: 76 -Region Avg IOU: 0.626303, Class: 1.000000, Obj: 0.364924, No Obj: 0.003484, Avg Recall: 0.777027, count: 148 -Region Avg IOU: 0.658277, Class: 1.000000, Obj: 0.437579, No Obj: 0.002891, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.647369, Class: 1.000000, Obj: 0.398982, No Obj: 0.002210, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.662652, Class: 1.000000, Obj: 0.380442, No Obj: 0.003080, Avg Recall: 0.814815, count: 108 -Region Avg IOU: 0.628438, Class: 1.000000, Obj: 0.413303, No Obj: 0.002482, Avg Recall: 0.828947, count: 76 -Region Avg IOU: 0.651951, Class: 1.000000, Obj: 0.420340, No Obj: 0.002958, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.579197, Class: 1.000000, Obj: 0.373644, No Obj: 0.002790, Avg Recall: 0.663934, count: 122 -6319: 0.388573, 0.447964 avg, 0.001000 rate, 26.159136 seconds, 1617664 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.659445, Class: 1.000000, Obj: 0.425063, No Obj: 0.003138, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.616604, Class: 1.000000, Obj: 0.403786, No Obj: 0.002649, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.640112, Class: 1.000000, Obj: 0.396340, No Obj: 0.003073, Avg Recall: 0.752066, count: 121 -Region Avg IOU: 0.662302, Class: 1.000000, Obj: 0.438935, No Obj: 0.002697, Avg Recall: 0.827957, count: 93 -Region Avg IOU: 0.627684, Class: 1.000000, Obj: 0.437390, No Obj: 0.002801, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.608446, Class: 1.000000, Obj: 0.379504, No Obj: 0.002348, Avg Recall: 0.734940, count: 83 -Region Avg IOU: 0.633307, Class: 1.000000, Obj: 0.380188, No Obj: 0.003176, Avg Recall: 0.751825, count: 137 -Region Avg IOU: 0.635160, Class: 1.000000, Obj: 0.363903, No Obj: 0.002566, Avg Recall: 0.766355, count: 107 -6320: 0.406142, 0.443782 avg, 0.001000 rate, 27.956305 seconds, 1617920 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.078467 seconds -Region Avg IOU: 0.608508, Class: 1.000000, Obj: 0.352338, No Obj: 0.002974, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.551849, Class: 1.000000, Obj: 0.335255, No Obj: 0.003380, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.549712, Class: 1.000000, Obj: 0.354349, No Obj: 0.003626, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.583503, Class: 1.000000, Obj: 0.380493, No Obj: 0.003721, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.589707, Class: 1.000000, Obj: 0.389730, No Obj: 0.003415, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.559132, Class: 1.000000, Obj: 0.347151, No Obj: 0.003529, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.560988, Class: 1.000000, Obj: 0.338573, No Obj: 0.003516, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.638797, Class: 1.000000, Obj: 0.447201, No Obj: 0.003428, Avg Recall: 0.731959, count: 97 -6321: 0.441037, 0.443507 avg, 0.001000 rate, 22.842407 seconds, 1618176 images -Loaded: 0.026353 seconds -Region Avg IOU: 0.567090, Class: 1.000000, Obj: 0.376605, No Obj: 0.002957, Avg Recall: 0.653333, count: 75 -Region Avg IOU: 0.546967, Class: 1.000000, Obj: 0.355604, No Obj: 0.003475, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.520393, Class: 1.000000, Obj: 0.325366, No Obj: 0.004067, Avg Recall: 0.607407, count: 135 -Region Avg IOU: 0.584736, Class: 1.000000, Obj: 0.385091, No Obj: 0.003538, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.538083, Class: 1.000000, Obj: 0.339930, No Obj: 0.004270, Avg Recall: 0.617450, count: 149 -Region Avg IOU: 0.568623, Class: 1.000000, Obj: 0.447778, No Obj: 0.003837, Avg Recall: 0.674699, count: 83 -Region Avg IOU: 0.524018, Class: 1.000000, Obj: 0.357609, No Obj: 0.004153, Avg Recall: 0.577465, count: 142 -Region Avg IOU: 0.611317, Class: 1.000000, Obj: 0.391956, No Obj: 0.003345, Avg Recall: 0.682927, count: 82 -6322: 0.490843, 0.448241 avg, 0.001000 rate, 22.747343 seconds, 1618432 images -Loaded: 1.428917 seconds -Region Avg IOU: 0.564763, Class: 1.000000, Obj: 0.408585, No Obj: 0.003741, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.588471, Class: 1.000000, Obj: 0.364365, No Obj: 0.004798, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.562641, Class: 1.000000, Obj: 0.404455, No Obj: 0.003929, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.575317, Class: 1.000000, Obj: 0.414750, No Obj: 0.004084, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.529911, Class: 1.000000, Obj: 0.351689, No Obj: 0.003847, Avg Recall: 0.577236, count: 123 -Region Avg IOU: 0.605796, Class: 1.000000, Obj: 0.417551, No Obj: 0.003976, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.561095, Class: 1.000000, Obj: 0.367119, No Obj: 0.004326, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.559496, Class: 1.000000, Obj: 0.407300, No Obj: 0.003945, Avg Recall: 0.653465, count: 101 -6323: 0.437409, 0.447158 avg, 0.001000 rate, 20.612993 seconds, 1618688 images -Loaded: 1.714413 seconds -Region Avg IOU: 0.575451, Class: 1.000000, Obj: 0.425387, No Obj: 0.004429, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.623107, Class: 1.000000, Obj: 0.429885, No Obj: 0.005018, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.513806, Class: 1.000000, Obj: 0.343708, No Obj: 0.003380, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.529234, Class: 1.000000, Obj: 0.422209, No Obj: 0.003930, Avg Recall: 0.554348, count: 92 -Region Avg IOU: 0.574927, Class: 1.000000, Obj: 0.421626, No Obj: 0.004442, Avg Recall: 0.610687, count: 131 -Region Avg IOU: 0.565667, Class: 1.000000, Obj: 0.405240, No Obj: 0.004368, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.556827, Class: 1.000000, Obj: 0.401989, No Obj: 0.004443, Avg Recall: 0.647541, count: 122 -Region Avg IOU: 0.598481, Class: 1.000000, Obj: 0.448913, No Obj: 0.004140, Avg Recall: 0.725490, count: 102 -6324: 0.444223, 0.446864 avg, 0.001000 rate, 19.975296 seconds, 1618944 images -Loaded: 1.669792 seconds -Region Avg IOU: 0.542803, Class: 1.000000, Obj: 0.413522, No Obj: 0.005217, Avg Recall: 0.648000, count: 125 -Region Avg IOU: 0.564251, Class: 1.000000, Obj: 0.392168, No Obj: 0.004661, Avg Recall: 0.650685, count: 146 -Region Avg IOU: 0.569000, Class: 1.000000, Obj: 0.456795, No Obj: 0.003880, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.566043, Class: 1.000000, Obj: 0.406684, No Obj: 0.004398, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.610665, Class: 1.000000, Obj: 0.440374, No Obj: 0.004257, Avg Recall: 0.725275, count: 91 -Region Avg IOU: 0.521352, Class: 1.000000, Obj: 0.416095, No Obj: 0.003377, Avg Recall: 0.563218, count: 87 -Region Avg IOU: 0.549187, Class: 1.000000, Obj: 0.373503, No Obj: 0.004561, Avg Recall: 0.651515, count: 132 -Region Avg IOU: 0.611475, Class: 1.000000, Obj: 0.484391, No Obj: 0.004265, Avg Recall: 0.704545, count: 88 -6325: 0.441620, 0.446340 avg, 0.001000 rate, 23.077774 seconds, 1619200 images -Loaded: 0.001708 seconds -Region Avg IOU: 0.544416, Class: 1.000000, Obj: 0.449588, No Obj: 0.003807, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.557920, Class: 1.000000, Obj: 0.424956, No Obj: 0.004486, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.569993, Class: 1.000000, Obj: 0.413034, No Obj: 0.004056, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.562311, Class: 1.000000, Obj: 0.430641, No Obj: 0.003544, Avg Recall: 0.671233, count: 73 -Region Avg IOU: 0.532532, Class: 1.000000, Obj: 0.422649, No Obj: 0.004969, Avg Recall: 0.637795, count: 127 -Region Avg IOU: 0.524859, Class: 1.000000, Obj: 0.400706, No Obj: 0.004573, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.535769, Class: 1.000000, Obj: 0.431750, No Obj: 0.004539, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.563667, Class: 1.000000, Obj: 0.414937, No Obj: 0.004969, Avg Recall: 0.717949, count: 117 -6326: 0.371680, 0.438874 avg, 0.001000 rate, 22.192184 seconds, 1619456 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.565700, Class: 1.000000, Obj: 0.427860, No Obj: 0.004139, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.537179, Class: 1.000000, Obj: 0.400152, No Obj: 0.005008, Avg Recall: 0.652542, count: 118 -Region Avg IOU: 0.573871, Class: 1.000000, Obj: 0.489499, No Obj: 0.004886, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.508440, Class: 1.000000, Obj: 0.412649, No Obj: 0.004870, Avg Recall: 0.571429, count: 126 -Region Avg IOU: 0.551363, Class: 1.000000, Obj: 0.488459, No Obj: 0.003829, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.572456, Class: 1.000000, Obj: 0.461231, No Obj: 0.004581, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.577416, Class: 1.000000, Obj: 0.503294, No Obj: 0.004348, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.565748, Class: 1.000000, Obj: 0.454318, No Obj: 0.004937, Avg Recall: 0.645161, count: 93 -6327: 0.393834, 0.434370 avg, 0.001000 rate, 23.104614 seconds, 1619712 images -Loaded: 0.300224 seconds -Region Avg IOU: 0.550703, Class: 1.000000, Obj: 0.410080, No Obj: 0.004685, Avg Recall: 0.631579, count: 133 -Region Avg IOU: 0.599479, Class: 1.000000, Obj: 0.480836, No Obj: 0.004456, Avg Recall: 0.720930, count: 86 -Region Avg IOU: 0.558180, Class: 1.000000, Obj: 0.407457, No Obj: 0.003885, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.536511, Class: 1.000000, Obj: 0.415616, No Obj: 0.003913, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.530716, Class: 1.000000, Obj: 0.422528, No Obj: 0.004573, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.533936, Class: 1.000000, Obj: 0.407157, No Obj: 0.005571, Avg Recall: 0.606897, count: 145 -Region Avg IOU: 0.603831, Class: 1.000000, Obj: 0.407502, No Obj: 0.004042, Avg Recall: 0.752809, count: 89 -Region Avg IOU: 0.537351, Class: 1.000000, Obj: 0.412905, No Obj: 0.004642, Avg Recall: 0.628866, count: 97 -6328: 0.447020, 0.435635 avg, 0.001000 rate, 22.863811 seconds, 1619968 images -Loaded: 0.522716 seconds -Region Avg IOU: 0.597408, Class: 1.000000, Obj: 0.423725, No Obj: 0.004012, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.536182, Class: 1.000000, Obj: 0.448588, No Obj: 0.004343, Avg Recall: 0.604651, count: 129 -Region Avg IOU: 0.492276, Class: 1.000000, Obj: 0.367139, No Obj: 0.004709, Avg Recall: 0.554688, count: 128 -Region Avg IOU: 0.530146, Class: 1.000000, Obj: 0.389980, No Obj: 0.003835, Avg Recall: 0.629214, count: 89 -Region Avg IOU: 0.500710, Class: 1.000000, Obj: 0.406883, No Obj: 0.004152, Avg Recall: 0.550847, count: 118 -Region Avg IOU: 0.590260, Class: 1.000000, Obj: 0.406813, No Obj: 0.004624, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.515198, Class: 1.000000, Obj: 0.411298, No Obj: 0.003536, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.519631, Class: 1.000000, Obj: 0.417868, No Obj: 0.003236, Avg Recall: 0.500000, count: 78 -6329: 0.454600, 0.437531 avg, 0.001000 rate, 20.601900 seconds, 1620224 images -Loaded: 2.173980 seconds -Region Avg IOU: 0.597035, Class: 1.000000, Obj: 0.432703, No Obj: 0.004215, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.551277, Class: 1.000000, Obj: 0.420352, No Obj: 0.003483, Avg Recall: 0.635135, count: 74 -Region Avg IOU: 0.548627, Class: 1.000000, Obj: 0.378409, No Obj: 0.004437, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.561537, Class: 1.000000, Obj: 0.375222, No Obj: 0.003805, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.583719, Class: 1.000000, Obj: 0.395490, No Obj: 0.003809, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.566910, Class: 1.000000, Obj: 0.398239, No Obj: 0.004103, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.544804, Class: 1.000000, Obj: 0.421415, No Obj: 0.003782, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.536340, Class: 1.000000, Obj: 0.364981, No Obj: 0.004219, Avg Recall: 0.611940, count: 134 -6330: 0.390858, 0.432864 avg, 0.001000 rate, 21.783209 seconds, 1620480 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000062 seconds -Region Avg IOU: 0.580335, Class: 1.000000, Obj: 0.361048, No Obj: 0.003475, Avg Recall: 0.702479, count: 121 -Region Avg IOU: 0.595699, Class: 1.000000, Obj: 0.396949, No Obj: 0.003562, Avg Recall: 0.692308, count: 130 -Region Avg IOU: 0.606677, Class: 1.000000, Obj: 0.437741, No Obj: 0.002995, Avg Recall: 0.673684, count: 95 -Region Avg IOU: 0.614070, Class: 1.000000, Obj: 0.407110, No Obj: 0.003521, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.573226, Class: 1.000000, Obj: 0.408904, No Obj: 0.003033, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.586131, Class: 1.000000, Obj: 0.436336, No Obj: 0.003216, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.631165, Class: 1.000000, Obj: 0.471672, No Obj: 0.003340, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.579599, Class: 1.000000, Obj: 0.422747, No Obj: 0.003220, Avg Recall: 0.631068, count: 103 -6331: 0.441532, 0.433731 avg, 0.001000 rate, 23.835617 seconds, 1620736 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.595248, Class: 1.000000, Obj: 0.401866, No Obj: 0.003071, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.609738, Class: 1.000000, Obj: 0.384553, No Obj: 0.003140, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.606813, Class: 1.000000, Obj: 0.440630, No Obj: 0.003213, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.621550, Class: 1.000000, Obj: 0.384584, No Obj: 0.003580, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.611381, Class: 1.000000, Obj: 0.439118, No Obj: 0.003481, Avg Recall: 0.740458, count: 131 -Region Avg IOU: 0.629812, Class: 1.000000, Obj: 0.404143, No Obj: 0.003377, Avg Recall: 0.796875, count: 128 -Region Avg IOU: 0.584141, Class: 1.000000, Obj: 0.411064, No Obj: 0.003216, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.624810, Class: 1.000000, Obj: 0.451971, No Obj: 0.003125, Avg Recall: 0.734694, count: 98 -6332: 0.420345, 0.432392 avg, 0.001000 rate, 24.463728 seconds, 1620992 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.572938, Class: 1.000000, Obj: 0.404768, No Obj: 0.003149, Avg Recall: 0.632479, count: 117 -Region Avg IOU: 0.600457, Class: 1.000000, Obj: 0.374062, No Obj: 0.003544, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.578194, Class: 1.000000, Obj: 0.377523, No Obj: 0.003089, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.615839, Class: 1.000000, Obj: 0.458566, No Obj: 0.003079, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.650495, Class: 1.000000, Obj: 0.426904, No Obj: 0.002827, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.552170, Class: 1.000000, Obj: 0.363346, No Obj: 0.003634, Avg Recall: 0.597122, count: 139 -Region Avg IOU: 0.602161, Class: 1.000000, Obj: 0.387077, No Obj: 0.002857, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.577431, Class: 1.000000, Obj: 0.371208, No Obj: 0.002964, Avg Recall: 0.666667, count: 111 -6333: 0.413725, 0.430526 avg, 0.001000 rate, 25.655432 seconds, 1621248 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.611145, Class: 1.000000, Obj: 0.400663, No Obj: 0.003259, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.639365, Class: 1.000000, Obj: 0.421912, No Obj: 0.003397, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.634649, Class: 1.000000, Obj: 0.464058, No Obj: 0.002708, Avg Recall: 0.779070, count: 86 -Region Avg IOU: 0.558410, Class: 1.000000, Obj: 0.382859, No Obj: 0.002951, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.621759, Class: 1.000000, Obj: 0.373190, No Obj: 0.003334, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.566149, Class: 1.000000, Obj: 0.385477, No Obj: 0.003445, Avg Recall: 0.656489, count: 131 -Region Avg IOU: 0.641448, Class: 1.000000, Obj: 0.399074, No Obj: 0.003239, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.630733, Class: 1.000000, Obj: 0.417224, No Obj: 0.003058, Avg Recall: 0.795699, count: 93 -6334: 0.407408, 0.428214 avg, 0.001000 rate, 24.577751 seconds, 1621504 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.576506, Class: 1.000000, Obj: 0.397281, No Obj: 0.003875, Avg Recall: 0.658064, count: 155 -Region Avg IOU: 0.627570, Class: 1.000000, Obj: 0.414360, No Obj: 0.002751, Avg Recall: 0.818182, count: 88 -Region Avg IOU: 0.625082, Class: 1.000000, Obj: 0.397749, No Obj: 0.003143, Avg Recall: 0.735537, count: 121 -Region Avg IOU: 0.658927, Class: 1.000000, Obj: 0.426257, No Obj: 0.003285, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.634307, Class: 1.000000, Obj: 0.375614, No Obj: 0.003185, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.574224, Class: 1.000000, Obj: 0.379236, No Obj: 0.003223, Avg Recall: 0.700000, count: 130 -Region Avg IOU: 0.580161, Class: 1.000000, Obj: 0.361938, No Obj: 0.003084, Avg Recall: 0.673469, count: 147 -Region Avg IOU: 0.590440, Class: 1.000000, Obj: 0.358716, No Obj: 0.002723, Avg Recall: 0.711340, count: 97 -6335: 0.509471, 0.436340 avg, 0.001000 rate, 26.810406 seconds, 1621760 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.599654, Class: 1.000000, Obj: 0.341605, No Obj: 0.002731, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.590299, Class: 1.000000, Obj: 0.398371, No Obj: 0.003355, Avg Recall: 0.693548, count: 124 -Region Avg IOU: 0.596952, Class: 1.000000, Obj: 0.379254, No Obj: 0.003496, Avg Recall: 0.689922, count: 129 -Region Avg IOU: 0.608495, Class: 1.000000, Obj: 0.390135, No Obj: 0.003503, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.631357, Class: 1.000000, Obj: 0.384052, No Obj: 0.003842, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.585869, Class: 1.000000, Obj: 0.398107, No Obj: 0.002981, Avg Recall: 0.687500, count: 128 -Region Avg IOU: 0.618096, Class: 1.000000, Obj: 0.424421, No Obj: 0.002856, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.669740, Class: 1.000000, Obj: 0.447940, No Obj: 0.003529, Avg Recall: 0.846774, count: 124 -6336: 0.450499, 0.437756 avg, 0.001000 rate, 24.123837 seconds, 1622016 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.589601, Class: 1.000000, Obj: 0.402917, No Obj: 0.002551, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.610771, Class: 1.000000, Obj: 0.427362, No Obj: 0.003360, Avg Recall: 0.723077, count: 130 -Region Avg IOU: 0.575125, Class: 1.000000, Obj: 0.373601, No Obj: 0.002688, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.580444, Class: 1.000000, Obj: 0.379378, No Obj: 0.002874, Avg Recall: 0.661654, count: 133 -Region Avg IOU: 0.600730, Class: 1.000000, Obj: 0.403339, No Obj: 0.002276, Avg Recall: 0.712500, count: 80 -Region Avg IOU: 0.625557, Class: 1.000000, Obj: 0.388659, No Obj: 0.002975, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.574914, Class: 1.000000, Obj: 0.388905, No Obj: 0.002724, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.612207, Class: 1.000000, Obj: 0.420642, No Obj: 0.002878, Avg Recall: 0.717172, count: 99 -6337: 0.425595, 0.436540 avg, 0.001000 rate, 25.169907 seconds, 1622272 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.587765, Class: 1.000000, Obj: 0.369414, No Obj: 0.003203, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.629753, Class: 1.000000, Obj: 0.429272, No Obj: 0.003263, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.661447, Class: 1.000000, Obj: 0.439495, No Obj: 0.003574, Avg Recall: 0.840000, count: 100 -Region Avg IOU: 0.614330, Class: 1.000000, Obj: 0.422347, No Obj: 0.003149, Avg Recall: 0.755814, count: 86 -Region Avg IOU: 0.638261, Class: 1.000000, Obj: 0.403160, No Obj: 0.003223, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.609365, Class: 1.000000, Obj: 0.396096, No Obj: 0.003056, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.585923, Class: 1.000000, Obj: 0.392540, No Obj: 0.003473, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.608477, Class: 1.000000, Obj: 0.405664, No Obj: 0.002885, Avg Recall: 0.777778, count: 90 -6338: 0.370518, 0.429937 avg, 0.001000 rate, 25.233189 seconds, 1622528 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.651651, Class: 1.000000, Obj: 0.435732, No Obj: 0.003602, Avg Recall: 0.788618, count: 123 -Region Avg IOU: 0.645329, Class: 1.000000, Obj: 0.469814, No Obj: 0.003573, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.653343, Class: 1.000000, Obj: 0.402621, No Obj: 0.003227, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.597648, Class: 1.000000, Obj: 0.375190, No Obj: 0.003682, Avg Recall: 0.701389, count: 144 -Region Avg IOU: 0.645162, Class: 1.000000, Obj: 0.450166, No Obj: 0.003026, Avg Recall: 0.764706, count: 85 -Region Avg IOU: 0.624231, Class: 1.000000, Obj: 0.382124, No Obj: 0.003863, Avg Recall: 0.760563, count: 142 -Region Avg IOU: 0.610702, Class: 1.000000, Obj: 0.378385, No Obj: 0.003091, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.604270, Class: 1.000000, Obj: 0.403206, No Obj: 0.003280, Avg Recall: 0.722689, count: 119 -6339: 0.453133, 0.432257 avg, 0.001000 rate, 26.339602 seconds, 1622784 images -Loaded: 0.000119 seconds -Region Avg IOU: 0.609491, Class: 1.000000, Obj: 0.408004, No Obj: 0.003815, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.601673, Class: 1.000000, Obj: 0.399760, No Obj: 0.003456, Avg Recall: 0.755556, count: 90 -Region Avg IOU: 0.673488, Class: 1.000000, Obj: 0.477372, No Obj: 0.003338, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.634347, Class: 1.000000, Obj: 0.449530, No Obj: 0.003163, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.612759, Class: 1.000000, Obj: 0.371587, No Obj: 0.003193, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.619352, Class: 1.000000, Obj: 0.430814, No Obj: 0.003997, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.632303, Class: 1.000000, Obj: 0.419912, No Obj: 0.002794, Avg Recall: 0.759036, count: 83 -Region Avg IOU: 0.622959, Class: 1.000000, Obj: 0.431642, No Obj: 0.003826, Avg Recall: 0.729508, count: 122 -6340: 0.391011, 0.428132 avg, 0.001000 rate, 24.329931 seconds, 1623040 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.881737 seconds -Region Avg IOU: 0.536723, Class: 1.000000, Obj: 0.382762, No Obj: 0.004181, Avg Recall: 0.633333, count: 120 -Region Avg IOU: 0.594225, Class: 1.000000, Obj: 0.429638, No Obj: 0.003846, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.596533, Class: 1.000000, Obj: 0.401490, No Obj: 0.004202, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.560494, Class: 1.000000, Obj: 0.384359, No Obj: 0.004028, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.593946, Class: 1.000000, Obj: 0.362432, No Obj: 0.004242, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.579602, Class: 1.000000, Obj: 0.380124, No Obj: 0.004263, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.580590, Class: 1.000000, Obj: 0.439331, No Obj: 0.003880, Avg Recall: 0.648352, count: 91 -Region Avg IOU: 0.604432, Class: 1.000000, Obj: 0.390768, No Obj: 0.003541, Avg Recall: 0.738095, count: 84 -6341: 0.422094, 0.427528 avg, 0.001000 rate, 21.446611 seconds, 1623296 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.534057, Class: 1.000000, Obj: 0.381763, No Obj: 0.003377, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.601993, Class: 1.000000, Obj: 0.390149, No Obj: 0.004091, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.558986, Class: 1.000000, Obj: 0.395060, No Obj: 0.003664, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.549969, Class: 1.000000, Obj: 0.425169, No Obj: 0.003506, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.608236, Class: 1.000000, Obj: 0.449366, No Obj: 0.004102, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.610095, Class: 1.000000, Obj: 0.454030, No Obj: 0.003851, Avg Recall: 0.746988, count: 83 -Region Avg IOU: 0.612361, Class: 1.000000, Obj: 0.424898, No Obj: 0.004071, Avg Recall: 0.750000, count: 84 -Region Avg IOU: 0.549781, Class: 1.000000, Obj: 0.390090, No Obj: 0.004329, Avg Recall: 0.604478, count: 134 -6342: 0.376119, 0.422387 avg, 0.001000 rate, 21.967943 seconds, 1623552 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.549448, Class: 1.000000, Obj: 0.392204, No Obj: 0.004103, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.558261, Class: 1.000000, Obj: 0.438197, No Obj: 0.003756, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.559545, Class: 1.000000, Obj: 0.395568, No Obj: 0.003492, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.589271, Class: 1.000000, Obj: 0.472866, No Obj: 0.003219, Avg Recall: 0.725000, count: 80 -Region Avg IOU: 0.526134, Class: 1.000000, Obj: 0.366186, No Obj: 0.003700, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.521778, Class: 1.000000, Obj: 0.373630, No Obj: 0.004658, Avg Recall: 0.600000, count: 125 -Region Avg IOU: 0.563893, Class: 1.000000, Obj: 0.422874, No Obj: 0.003517, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.564391, Class: 1.000000, Obj: 0.386944, No Obj: 0.004438, Avg Recall: 0.669565, count: 115 -6343: 0.364625, 0.416611 avg, 0.001000 rate, 22.532648 seconds, 1623808 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.556764, Class: 1.000000, Obj: 0.374554, No Obj: 0.004015, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.542682, Class: 1.000000, Obj: 0.378309, No Obj: 0.003768, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.542565, Class: 1.000000, Obj: 0.401897, No Obj: 0.003594, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.542461, Class: 1.000000, Obj: 0.390733, No Obj: 0.004067, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.614654, Class: 1.000000, Obj: 0.450510, No Obj: 0.003780, Avg Recall: 0.753086, count: 81 -Region Avg IOU: 0.581248, Class: 1.000000, Obj: 0.433111, No Obj: 0.004050, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.520752, Class: 1.000000, Obj: 0.413933, No Obj: 0.003280, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.576926, Class: 1.000000, Obj: 0.391258, No Obj: 0.004004, Avg Recall: 0.708333, count: 120 -6344: 0.356165, 0.410567 avg, 0.001000 rate, 22.854782 seconds, 1624064 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.573515, Class: 1.000000, Obj: 0.396961, No Obj: 0.004326, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.576751, Class: 1.000000, Obj: 0.438792, No Obj: 0.003662, Avg Recall: 0.659091, count: 88 -Region Avg IOU: 0.543414, Class: 1.000000, Obj: 0.397429, No Obj: 0.003905, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.566440, Class: 1.000000, Obj: 0.415047, No Obj: 0.004458, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.519612, Class: 1.000000, Obj: 0.425823, No Obj: 0.003565, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.526197, Class: 1.000000, Obj: 0.385941, No Obj: 0.004123, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.556313, Class: 1.000000, Obj: 0.395826, No Obj: 0.004231, Avg Recall: 0.679688, count: 128 -Region Avg IOU: 0.562394, Class: 1.000000, Obj: 0.435533, No Obj: 0.003928, Avg Recall: 0.638554, count: 83 -6345: 0.431958, 0.412706 avg, 0.001000 rate, 21.768320 seconds, 1624320 images -Loaded: 0.000056 seconds -Region Avg IOU: 0.570459, Class: 1.000000, Obj: 0.388554, No Obj: 0.003955, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.544663, Class: 1.000000, Obj: 0.373408, No Obj: 0.004534, Avg Recall: 0.648855, count: 131 -Region Avg IOU: 0.589431, Class: 1.000000, Obj: 0.418178, No Obj: 0.003530, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.583224, Class: 1.000000, Obj: 0.390997, No Obj: 0.003763, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.549825, Class: 1.000000, Obj: 0.426125, No Obj: 0.003173, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.595068, Class: 1.000000, Obj: 0.391641, No Obj: 0.004186, Avg Recall: 0.717742, count: 124 -Region Avg IOU: 0.544283, Class: 1.000000, Obj: 0.355889, No Obj: 0.004335, Avg Recall: 0.676471, count: 136 -Region Avg IOU: 0.575407, Class: 1.000000, Obj: 0.434778, No Obj: 0.004034, Avg Recall: 0.673913, count: 92 -6346: 0.433871, 0.414822 avg, 0.001000 rate, 22.145483 seconds, 1624576 images -Loaded: 0.703763 seconds -Region Avg IOU: 0.530817, Class: 1.000000, Obj: 0.417306, No Obj: 0.003310, Avg Recall: 0.612500, count: 80 -Region Avg IOU: 0.530202, Class: 1.000000, Obj: 0.439492, No Obj: 0.003743, Avg Recall: 0.605042, count: 119 -Region Avg IOU: 0.575847, Class: 1.000000, Obj: 0.396325, No Obj: 0.003905, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.560905, Class: 1.000000, Obj: 0.384347, No Obj: 0.004058, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.552793, Class: 1.000000, Obj: 0.394345, No Obj: 0.003776, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.509378, Class: 1.000000, Obj: 0.342725, No Obj: 0.003953, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.585966, Class: 1.000000, Obj: 0.430573, No Obj: 0.003502, Avg Recall: 0.662651, count: 83 -Region Avg IOU: 0.556075, Class: 1.000000, Obj: 0.414776, No Obj: 0.003240, Avg Recall: 0.590909, count: 88 -6347: 0.386566, 0.411997 avg, 0.001000 rate, 21.117201 seconds, 1624832 images -Loaded: 1.309252 seconds -Region Avg IOU: 0.605130, Class: 1.000000, Obj: 0.409513, No Obj: 0.003969, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.542698, Class: 1.000000, Obj: 0.380873, No Obj: 0.003495, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.510767, Class: 1.000000, Obj: 0.387412, No Obj: 0.003228, Avg Recall: 0.542553, count: 94 -Region Avg IOU: 0.504568, Class: 1.000000, Obj: 0.380592, No Obj: 0.003338, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.537772, Class: 1.000000, Obj: 0.337661, No Obj: 0.004172, Avg Recall: 0.616438, count: 146 -Region Avg IOU: 0.551260, Class: 1.000000, Obj: 0.400138, No Obj: 0.004269, Avg Recall: 0.659420, count: 138 -Region Avg IOU: 0.555441, Class: 1.000000, Obj: 0.425423, No Obj: 0.004073, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.538293, Class: 1.000000, Obj: 0.372228, No Obj: 0.003581, Avg Recall: 0.638095, count: 105 -6348: 0.498690, 0.420666 avg, 0.001000 rate, 21.282663 seconds, 1625088 images -Loaded: 2.098232 seconds -Region Avg IOU: 0.526458, Class: 1.000000, Obj: 0.397771, No Obj: 0.004021, Avg Recall: 0.600000, count: 120 -Region Avg IOU: 0.557602, Class: 1.000000, Obj: 0.373683, No Obj: 0.004259, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.570717, Class: 1.000000, Obj: 0.409913, No Obj: 0.004045, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.539172, Class: 1.000000, Obj: 0.380111, No Obj: 0.004470, Avg Recall: 0.587413, count: 143 -Region Avg IOU: 0.532703, Class: 1.000000, Obj: 0.368044, No Obj: 0.004124, Avg Recall: 0.631579, count: 133 -Region Avg IOU: 0.530611, Class: 1.000000, Obj: 0.412735, No Obj: 0.003944, Avg Recall: 0.611111, count: 126 -Region Avg IOU: 0.595760, Class: 1.000000, Obj: 0.402278, No Obj: 0.003769, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.481347, Class: 1.000000, Obj: 0.332447, No Obj: 0.003818, Avg Recall: 0.515385, count: 130 -6349: 0.478810, 0.426480 avg, 0.001000 rate, 21.631266 seconds, 1625344 images -Loaded: 2.232458 seconds -Region Avg IOU: 0.538937, Class: 1.000000, Obj: 0.348761, No Obj: 0.003909, Avg Recall: 0.532847, count: 137 -Region Avg IOU: 0.523173, Class: 1.000000, Obj: 0.327760, No Obj: 0.003714, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.592768, Class: 1.000000, Obj: 0.423976, No Obj: 0.003926, Avg Recall: 0.704545, count: 88 -Region Avg IOU: 0.523308, Class: 1.000000, Obj: 0.404987, No Obj: 0.002916, Avg Recall: 0.571429, count: 77 -Region Avg IOU: 0.557041, Class: 1.000000, Obj: 0.371265, No Obj: 0.003803, Avg Recall: 0.597938, count: 97 -Region Avg IOU: 0.550143, Class: 1.000000, Obj: 0.338515, No Obj: 0.004240, Avg Recall: 0.656250, count: 128 -Region Avg IOU: 0.526648, Class: 1.000000, Obj: 0.368036, No Obj: 0.003504, Avg Recall: 0.612903, count: 124 -Region Avg IOU: 0.616676, Class: 1.000000, Obj: 0.392240, No Obj: 0.003897, Avg Recall: 0.789474, count: 76 -6350: 0.412358, 0.425068 avg, 0.001000 rate, 21.406015 seconds, 1625600 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000072 seconds -Region Avg IOU: 0.596152, Class: 1.000000, Obj: 0.390487, No Obj: 0.003938, Avg Recall: 0.715278, count: 144 -Region Avg IOU: 0.636666, Class: 1.000000, Obj: 0.464245, No Obj: 0.003097, Avg Recall: 0.788889, count: 90 -Region Avg IOU: 0.649474, Class: 1.000000, Obj: 0.419305, No Obj: 0.003824, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.591277, Class: 1.000000, Obj: 0.460014, No Obj: 0.002946, Avg Recall: 0.775281, count: 89 -Region Avg IOU: 0.596941, Class: 1.000000, Obj: 0.435718, No Obj: 0.003342, Avg Recall: 0.700787, count: 127 -Region Avg IOU: 0.570128, Class: 1.000000, Obj: 0.443104, No Obj: 0.003342, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.576869, Class: 1.000000, Obj: 0.440641, No Obj: 0.003450, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.605709, Class: 1.000000, Obj: 0.414342, No Obj: 0.003249, Avg Recall: 0.691589, count: 107 -6351: 0.389580, 0.421519 avg, 0.001000 rate, 25.209225 seconds, 1625856 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.634091, Class: 1.000000, Obj: 0.470039, No Obj: 0.003206, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.539204, Class: 1.000000, Obj: 0.418922, No Obj: 0.003091, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.657033, Class: 1.000000, Obj: 0.472957, No Obj: 0.003432, Avg Recall: 0.802469, count: 81 -Region Avg IOU: 0.608641, Class: 1.000000, Obj: 0.399701, No Obj: 0.003298, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.624521, Class: 1.000000, Obj: 0.409559, No Obj: 0.003939, Avg Recall: 0.748092, count: 131 -Region Avg IOU: 0.642898, Class: 1.000000, Obj: 0.460769, No Obj: 0.003157, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.615536, Class: 1.000000, Obj: 0.451927, No Obj: 0.003077, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.627885, Class: 1.000000, Obj: 0.456485, No Obj: 0.003637, Avg Recall: 0.767241, count: 116 -6352: 0.359572, 0.415325 avg, 0.001000 rate, 24.996227 seconds, 1626112 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.602255, Class: 1.000000, Obj: 0.418412, No Obj: 0.003385, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.637279, Class: 1.000000, Obj: 0.408102, No Obj: 0.003569, Avg Recall: 0.766667, count: 120 -Region Avg IOU: 0.591788, Class: 1.000000, Obj: 0.407322, No Obj: 0.003821, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.573137, Class: 1.000000, Obj: 0.397359, No Obj: 0.003454, Avg Recall: 0.666667, count: 123 -Region Avg IOU: 0.608054, Class: 1.000000, Obj: 0.510065, No Obj: 0.003229, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.597771, Class: 1.000000, Obj: 0.372595, No Obj: 0.003320, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.593430, Class: 1.000000, Obj: 0.425519, No Obj: 0.003280, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.537966, Class: 1.000000, Obj: 0.337277, No Obj: 0.003788, Avg Recall: 0.650000, count: 140 -6353: 0.477260, 0.421518 avg, 0.001000 rate, 23.947001 seconds, 1626368 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.635693, Class: 1.000000, Obj: 0.446678, No Obj: 0.003153, Avg Recall: 0.775281, count: 89 -Region Avg IOU: 0.648172, Class: 1.000000, Obj: 0.432604, No Obj: 0.004201, Avg Recall: 0.797203, count: 143 -Region Avg IOU: 0.619806, Class: 1.000000, Obj: 0.424862, No Obj: 0.003757, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.628887, Class: 1.000000, Obj: 0.425822, No Obj: 0.003507, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.636709, Class: 1.000000, Obj: 0.449767, No Obj: 0.003438, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.628293, Class: 1.000000, Obj: 0.443242, No Obj: 0.003875, Avg Recall: 0.719424, count: 139 -Region Avg IOU: 0.637235, Class: 1.000000, Obj: 0.442058, No Obj: 0.003148, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.650635, Class: 1.000000, Obj: 0.433899, No Obj: 0.003224, Avg Recall: 0.829545, count: 88 -6354: 0.418246, 0.421191 avg, 0.001000 rate, 24.678965 seconds, 1626624 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.616494, Class: 1.000000, Obj: 0.421802, No Obj: 0.003429, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.595033, Class: 1.000000, Obj: 0.381080, No Obj: 0.003121, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.544499, Class: 1.000000, Obj: 0.394225, No Obj: 0.003018, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.586847, Class: 1.000000, Obj: 0.426338, No Obj: 0.003430, Avg Recall: 0.697479, count: 119 -Region Avg IOU: 0.603722, Class: 1.000000, Obj: 0.422218, No Obj: 0.003126, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.644295, Class: 1.000000, Obj: 0.443017, No Obj: 0.003023, Avg Recall: 0.793103, count: 87 -Region Avg IOU: 0.638430, Class: 1.000000, Obj: 0.430512, No Obj: 0.003921, Avg Recall: 0.775194, count: 129 -Region Avg IOU: 0.601096, Class: 1.000000, Obj: 0.412351, No Obj: 0.003000, Avg Recall: 0.724138, count: 116 -6355: 0.395212, 0.418593 avg, 0.001000 rate, 25.445696 seconds, 1626880 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.591927, Class: 1.000000, Obj: 0.394224, No Obj: 0.003126, Avg Recall: 0.736434, count: 129 -Region Avg IOU: 0.594913, Class: 1.000000, Obj: 0.397416, No Obj: 0.003295, Avg Recall: 0.719697, count: 132 -Region Avg IOU: 0.633949, Class: 1.000000, Obj: 0.368384, No Obj: 0.003401, Avg Recall: 0.800000, count: 115 -Region Avg IOU: 0.584053, Class: 1.000000, Obj: 0.353148, No Obj: 0.003356, Avg Recall: 0.688889, count: 135 -Region Avg IOU: 0.695201, Class: 1.000000, Obj: 0.419915, No Obj: 0.003225, Avg Recall: 0.850000, count: 80 -Region Avg IOU: 0.623045, Class: 1.000000, Obj: 0.421802, No Obj: 0.003700, Avg Recall: 0.732394, count: 142 -Region Avg IOU: 0.552400, Class: 1.000000, Obj: 0.352659, No Obj: 0.003935, Avg Recall: 0.611940, count: 134 -Region Avg IOU: 0.650611, Class: 1.000000, Obj: 0.446141, No Obj: 0.003543, Avg Recall: 0.773109, count: 119 -6356: 0.475933, 0.424327 avg, 0.001000 rate, 24.045044 seconds, 1627136 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.598448, Class: 1.000000, Obj: 0.358773, No Obj: 0.003373, Avg Recall: 0.701613, count: 124 -Region Avg IOU: 0.632373, Class: 1.000000, Obj: 0.415150, No Obj: 0.003221, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.602835, Class: 1.000000, Obj: 0.396908, No Obj: 0.003207, Avg Recall: 0.715447, count: 123 -Region Avg IOU: 0.637163, Class: 1.000000, Obj: 0.424179, No Obj: 0.003126, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.570023, Class: 1.000000, Obj: 0.394444, No Obj: 0.002854, Avg Recall: 0.644444, count: 90 -Region Avg IOU: 0.632233, Class: 1.000000, Obj: 0.420072, No Obj: 0.003636, Avg Recall: 0.770992, count: 131 -Region Avg IOU: 0.611069, Class: 1.000000, Obj: 0.458342, No Obj: 0.003550, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.625144, Class: 1.000000, Obj: 0.410420, No Obj: 0.002854, Avg Recall: 0.724138, count: 87 -6357: 0.463066, 0.428201 avg, 0.001000 rate, 25.817816 seconds, 1627392 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.607458, Class: 1.000000, Obj: 0.396831, No Obj: 0.003318, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.628531, Class: 1.000000, Obj: 0.429784, No Obj: 0.003394, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.599717, Class: 1.000000, Obj: 0.401223, No Obj: 0.002984, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.599934, Class: 1.000000, Obj: 0.391976, No Obj: 0.003037, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.622915, Class: 1.000000, Obj: 0.390096, No Obj: 0.003735, Avg Recall: 0.757576, count: 132 -Region Avg IOU: 0.664860, Class: 1.000000, Obj: 0.431012, No Obj: 0.003426, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.644177, Class: 1.000000, Obj: 0.430070, No Obj: 0.003391, Avg Recall: 0.793893, count: 131 -Region Avg IOU: 0.641247, Class: 1.000000, Obj: 0.435528, No Obj: 0.002652, Avg Recall: 0.810127, count: 79 -6358: 0.436637, 0.429045 avg, 0.001000 rate, 26.393976 seconds, 1627648 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.660715, Class: 1.000000, Obj: 0.400788, No Obj: 0.003140, Avg Recall: 0.840708, count: 113 -Region Avg IOU: 0.586448, Class: 1.000000, Obj: 0.389047, No Obj: 0.003814, Avg Recall: 0.707792, count: 154 -Region Avg IOU: 0.618338, Class: 1.000000, Obj: 0.383200, No Obj: 0.003603, Avg Recall: 0.725191, count: 131 -Region Avg IOU: 0.552940, Class: 1.000000, Obj: 0.359823, No Obj: 0.003204, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.646047, Class: 1.000000, Obj: 0.437940, No Obj: 0.003428, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.616721, Class: 1.000000, Obj: 0.414795, No Obj: 0.003459, Avg Recall: 0.779661, count: 118 -Region Avg IOU: 0.621417, Class: 1.000000, Obj: 0.449250, No Obj: 0.002923, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.573314, Class: 1.000000, Obj: 0.410889, No Obj: 0.003120, Avg Recall: 0.633929, count: 112 -6359: 0.476412, 0.433781 avg, 0.001000 rate, 25.857197 seconds, 1627904 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.651753, Class: 1.000000, Obj: 0.451626, No Obj: 0.003092, Avg Recall: 0.819277, count: 83 -Region Avg IOU: 0.628693, Class: 1.000000, Obj: 0.410826, No Obj: 0.003234, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.676335, Class: 1.000000, Obj: 0.445890, No Obj: 0.003112, Avg Recall: 0.839080, count: 87 -Region Avg IOU: 0.545151, Class: 1.000000, Obj: 0.345854, No Obj: 0.002932, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.595175, Class: 1.000000, Obj: 0.392732, No Obj: 0.003407, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.660842, Class: 1.000000, Obj: 0.468640, No Obj: 0.003574, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.608962, Class: 1.000000, Obj: 0.400988, No Obj: 0.003772, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.607772, Class: 1.000000, Obj: 0.408115, No Obj: 0.003504, Avg Recall: 0.730435, count: 115 -6360: 0.435322, 0.433935 avg, 0.001000 rate, 25.379343 seconds, 1628160 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 17.279718 seconds -Region Avg IOU: 0.618390, Class: 1.000000, Obj: 0.417649, No Obj: 0.003560, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.584827, Class: 1.000000, Obj: 0.396368, No Obj: 0.002993, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.583869, Class: 1.000000, Obj: 0.388666, No Obj: 0.003944, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.617327, Class: 1.000000, Obj: 0.409337, No Obj: 0.003159, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.605572, Class: 1.000000, Obj: 0.387502, No Obj: 0.003512, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.621589, Class: 1.000000, Obj: 0.431211, No Obj: 0.003598, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.586403, Class: 1.000000, Obj: 0.364433, No Obj: 0.003409, Avg Recall: 0.729323, count: 133 -Region Avg IOU: 0.587262, Class: 1.000000, Obj: 0.359040, No Obj: 0.003192, Avg Recall: 0.690000, count: 100 -6361: 0.461074, 0.436649 avg, 0.001000 rate, 23.858727 seconds, 1628416 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.602317, Class: 1.000000, Obj: 0.393361, No Obj: 0.003807, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.560479, Class: 1.000000, Obj: 0.415716, No Obj: 0.003036, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.632487, Class: 1.000000, Obj: 0.453074, No Obj: 0.003471, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.619378, Class: 1.000000, Obj: 0.392366, No Obj: 0.003217, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.637736, Class: 1.000000, Obj: 0.424084, No Obj: 0.003254, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.603898, Class: 1.000000, Obj: 0.399301, No Obj: 0.003932, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.589598, Class: 1.000000, Obj: 0.405615, No Obj: 0.003243, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.617467, Class: 1.000000, Obj: 0.411179, No Obj: 0.003927, Avg Recall: 0.711864, count: 118 -6362: 0.448753, 0.437860 avg, 0.001000 rate, 24.477224 seconds, 1628672 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.540915, Class: 1.000000, Obj: 0.376910, No Obj: 0.003476, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.633487, Class: 1.000000, Obj: 0.384152, No Obj: 0.003615, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.574856, Class: 1.000000, Obj: 0.393268, No Obj: 0.004426, Avg Recall: 0.650000, count: 160 -Region Avg IOU: 0.646509, Class: 1.000000, Obj: 0.421489, No Obj: 0.003690, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.611907, Class: 1.000000, Obj: 0.424243, No Obj: 0.003609, Avg Recall: 0.715447, count: 123 -Region Avg IOU: 0.579418, Class: 1.000000, Obj: 0.418281, No Obj: 0.003391, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.601101, Class: 1.000000, Obj: 0.412974, No Obj: 0.003790, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.559408, Class: 1.000000, Obj: 0.357732, No Obj: 0.003194, Avg Recall: 0.687500, count: 128 -6363: 0.455548, 0.439629 avg, 0.001000 rate, 24.386251 seconds, 1628928 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.589042, Class: 1.000000, Obj: 0.454857, No Obj: 0.003501, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.591411, Class: 1.000000, Obj: 0.404613, No Obj: 0.003257, Avg Recall: 0.702381, count: 84 -Region Avg IOU: 0.575927, Class: 1.000000, Obj: 0.403714, No Obj: 0.004102, Avg Recall: 0.661417, count: 127 -Region Avg IOU: 0.619497, Class: 1.000000, Obj: 0.438836, No Obj: 0.003863, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.569483, Class: 1.000000, Obj: 0.391038, No Obj: 0.003059, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.599361, Class: 1.000000, Obj: 0.427897, No Obj: 0.004136, Avg Recall: 0.674603, count: 126 -Region Avg IOU: 0.596171, Class: 1.000000, Obj: 0.411643, No Obj: 0.003799, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.613005, Class: 1.000000, Obj: 0.454500, No Obj: 0.003504, Avg Recall: 0.722222, count: 108 -6364: 0.395415, 0.435207 avg, 0.001000 rate, 23.389780 seconds, 1629184 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.596219, Class: 1.000000, Obj: 0.418680, No Obj: 0.003429, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.556128, Class: 1.000000, Obj: 0.444345, No Obj: 0.003369, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.587574, Class: 1.000000, Obj: 0.416802, No Obj: 0.003232, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.606848, Class: 1.000000, Obj: 0.421058, No Obj: 0.004017, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.615273, Class: 1.000000, Obj: 0.411789, No Obj: 0.003848, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.670258, Class: 1.000000, Obj: 0.506840, No Obj: 0.003727, Avg Recall: 0.817204, count: 93 -Region Avg IOU: 0.584498, Class: 1.000000, Obj: 0.435617, No Obj: 0.002713, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.598173, Class: 1.000000, Obj: 0.380911, No Obj: 0.003407, Avg Recall: 0.709091, count: 110 -6365: 0.397065, 0.431393 avg, 0.001000 rate, 23.923231 seconds, 1629440 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.548761, Class: 1.000000, Obj: 0.424316, No Obj: 0.003769, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.608153, Class: 1.000000, Obj: 0.392488, No Obj: 0.003467, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.587560, Class: 1.000000, Obj: 0.453143, No Obj: 0.003535, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.542227, Class: 1.000000, Obj: 0.402599, No Obj: 0.003019, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.586499, Class: 1.000000, Obj: 0.402131, No Obj: 0.003628, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.599471, Class: 1.000000, Obj: 0.450632, No Obj: 0.003976, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.590637, Class: 1.000000, Obj: 0.413908, No Obj: 0.003751, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.551916, Class: 1.000000, Obj: 0.381289, No Obj: 0.003931, Avg Recall: 0.617886, count: 123 -6366: 0.436873, 0.431941 avg, 0.001000 rate, 24.546038 seconds, 1629696 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.605180, Class: 1.000000, Obj: 0.411971, No Obj: 0.003608, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.632686, Class: 1.000000, Obj: 0.446097, No Obj: 0.004018, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.626698, Class: 1.000000, Obj: 0.483766, No Obj: 0.003329, Avg Recall: 0.744186, count: 86 -Region Avg IOU: 0.561637, Class: 1.000000, Obj: 0.351805, No Obj: 0.004298, Avg Recall: 0.619048, count: 147 -Region Avg IOU: 0.614458, Class: 1.000000, Obj: 0.457844, No Obj: 0.004330, Avg Recall: 0.744966, count: 149 -Region Avg IOU: 0.559675, Class: 1.000000, Obj: 0.419678, No Obj: 0.003357, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.602632, Class: 1.000000, Obj: 0.449391, No Obj: 0.003644, Avg Recall: 0.700787, count: 127 -Region Avg IOU: 0.657445, Class: 1.000000, Obj: 0.433629, No Obj: 0.003883, Avg Recall: 0.809524, count: 105 -6367: 0.458031, 0.434550 avg, 0.001000 rate, 23.086386 seconds, 1629952 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.603628, Class: 1.000000, Obj: 0.410023, No Obj: 0.004199, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.603935, Class: 1.000000, Obj: 0.405232, No Obj: 0.003184, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.605056, Class: 1.000000, Obj: 0.413735, No Obj: 0.003983, Avg Recall: 0.708661, count: 127 -Region Avg IOU: 0.559331, Class: 1.000000, Obj: 0.446153, No Obj: 0.003569, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.632201, Class: 1.000000, Obj: 0.409012, No Obj: 0.003850, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.606543, Class: 1.000000, Obj: 0.492367, No Obj: 0.003750, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.601640, Class: 1.000000, Obj: 0.462449, No Obj: 0.004057, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.622393, Class: 1.000000, Obj: 0.398073, No Obj: 0.003347, Avg Recall: 0.730000, count: 100 -6368: 0.389845, 0.430080 avg, 0.001000 rate, 24.601185 seconds, 1630208 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.562173, Class: 1.000000, Obj: 0.466907, No Obj: 0.003009, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.619632, Class: 1.000000, Obj: 0.460173, No Obj: 0.004087, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.612412, Class: 1.000000, Obj: 0.424907, No Obj: 0.003269, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.587638, Class: 1.000000, Obj: 0.453781, No Obj: 0.003948, Avg Recall: 0.658333, count: 120 -Region Avg IOU: 0.607263, Class: 1.000000, Obj: 0.444674, No Obj: 0.004242, Avg Recall: 0.731343, count: 134 -Region Avg IOU: 0.606779, Class: 1.000000, Obj: 0.475757, No Obj: 0.003704, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.600168, Class: 1.000000, Obj: 0.474512, No Obj: 0.003497, Avg Recall: 0.681818, count: 88 -Region Avg IOU: 0.578676, Class: 1.000000, Obj: 0.374890, No Obj: 0.003156, Avg Recall: 0.656863, count: 102 -6369: 0.413468, 0.428418 avg, 0.001000 rate, 24.352327 seconds, 1630464 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.572824, Class: 1.000000, Obj: 0.397147, No Obj: 0.003737, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.601506, Class: 1.000000, Obj: 0.462326, No Obj: 0.003581, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.568019, Class: 1.000000, Obj: 0.391166, No Obj: 0.003403, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.613909, Class: 1.000000, Obj: 0.453444, No Obj: 0.003723, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.658721, Class: 1.000000, Obj: 0.493827, No Obj: 0.003505, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.635530, Class: 1.000000, Obj: 0.484652, No Obj: 0.003720, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.627456, Class: 1.000000, Obj: 0.473930, No Obj: 0.003193, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.646195, Class: 1.000000, Obj: 0.459456, No Obj: 0.003532, Avg Recall: 0.836538, count: 104 -6370: 0.381419, 0.423718 avg, 0.001000 rate, 24.817760 seconds, 1630720 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000060 seconds -Region Avg IOU: 0.664046, Class: 1.000000, Obj: 0.460816, No Obj: 0.003602, Avg Recall: 0.823529, count: 136 -Region Avg IOU: 0.655899, Class: 1.000000, Obj: 0.453938, No Obj: 0.003308, Avg Recall: 0.817460, count: 126 -Region Avg IOU: 0.638445, Class: 1.000000, Obj: 0.431169, No Obj: 0.003558, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.632148, Class: 1.000000, Obj: 0.427277, No Obj: 0.003184, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.663369, Class: 1.000000, Obj: 0.414012, No Obj: 0.003369, Avg Recall: 0.835938, count: 128 -Region Avg IOU: 0.622492, Class: 1.000000, Obj: 0.481724, No Obj: 0.002700, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.643110, Class: 1.000000, Obj: 0.410588, No Obj: 0.003286, Avg Recall: 0.831858, count: 113 -Region Avg IOU: 0.637007, Class: 1.000000, Obj: 0.432640, No Obj: 0.002860, Avg Recall: 0.793103, count: 116 -6371: 0.458086, 0.427155 avg, 0.001000 rate, 28.154055 seconds, 1630976 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.629919, Class: 1.000000, Obj: 0.431357, No Obj: 0.003143, Avg Recall: 0.757812, count: 128 -Region Avg IOU: 0.659703, Class: 1.000000, Obj: 0.470905, No Obj: 0.002966, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.655647, Class: 1.000000, Obj: 0.434477, No Obj: 0.003454, Avg Recall: 0.829457, count: 129 -Region Avg IOU: 0.592880, Class: 1.000000, Obj: 0.417941, No Obj: 0.002687, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.656850, Class: 1.000000, Obj: 0.423057, No Obj: 0.003027, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.627822, Class: 1.000000, Obj: 0.396122, No Obj: 0.002697, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.664375, Class: 1.000000, Obj: 0.457057, No Obj: 0.002906, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.644501, Class: 1.000000, Obj: 0.449742, No Obj: 0.003152, Avg Recall: 0.793103, count: 116 -6372: 0.418542, 0.426294 avg, 0.001000 rate, 26.960884 seconds, 1631232 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.648217, Class: 1.000000, Obj: 0.423183, No Obj: 0.002843, Avg Recall: 0.822034, count: 118 -Region Avg IOU: 0.663614, Class: 1.000000, Obj: 0.445080, No Obj: 0.002862, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.655401, Class: 1.000000, Obj: 0.440257, No Obj: 0.002464, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.653847, Class: 1.000000, Obj: 0.420249, No Obj: 0.003317, Avg Recall: 0.832061, count: 131 -Region Avg IOU: 0.651639, Class: 1.000000, Obj: 0.469551, No Obj: 0.002605, Avg Recall: 0.752809, count: 89 -Region Avg IOU: 0.699279, Class: 1.000000, Obj: 0.442879, No Obj: 0.002765, Avg Recall: 0.880435, count: 92 -Region Avg IOU: 0.661724, Class: 1.000000, Obj: 0.501687, No Obj: 0.002915, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.675297, Class: 1.000000, Obj: 0.492692, No Obj: 0.002693, Avg Recall: 0.854369, count: 103 -6373: 0.382469, 0.421911 avg, 0.001000 rate, 26.969095 seconds, 1631488 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.647602, Class: 1.000000, Obj: 0.391098, No Obj: 0.002973, Avg Recall: 0.797101, count: 138 -Region Avg IOU: 0.655998, Class: 1.000000, Obj: 0.431951, No Obj: 0.002698, Avg Recall: 0.806723, count: 119 -Region Avg IOU: 0.638664, Class: 1.000000, Obj: 0.429393, No Obj: 0.003036, Avg Recall: 0.732824, count: 131 -Region Avg IOU: 0.653401, Class: 1.000000, Obj: 0.466843, No Obj: 0.002555, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.645922, Class: 1.000000, Obj: 0.388555, No Obj: 0.002651, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.616072, Class: 1.000000, Obj: 0.407929, No Obj: 0.002379, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.654771, Class: 1.000000, Obj: 0.431975, No Obj: 0.002604, Avg Recall: 0.831683, count: 101 -Region Avg IOU: 0.657471, Class: 1.000000, Obj: 0.480290, No Obj: 0.002631, Avg Recall: 0.786517, count: 89 -6374: 0.401211, 0.419841 avg, 0.001000 rate, 27.571564 seconds, 1631744 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.679168, Class: 1.000000, Obj: 0.453123, No Obj: 0.002413, Avg Recall: 0.804598, count: 87 -Region Avg IOU: 0.662851, Class: 1.000000, Obj: 0.438808, No Obj: 0.003148, Avg Recall: 0.816901, count: 142 -Region Avg IOU: 0.637665, Class: 1.000000, Obj: 0.386721, No Obj: 0.002673, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.655131, Class: 1.000000, Obj: 0.454335, No Obj: 0.002539, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.615400, Class: 1.000000, Obj: 0.409441, No Obj: 0.002513, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.707740, Class: 1.000000, Obj: 0.448187, No Obj: 0.002626, Avg Recall: 0.952941, count: 85 -Region Avg IOU: 0.581108, Class: 1.000000, Obj: 0.335474, No Obj: 0.002302, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.614748, Class: 1.000000, Obj: 0.425443, No Obj: 0.002507, Avg Recall: 0.728000, count: 125 -6375: 0.389311, 0.416788 avg, 0.001000 rate, 27.737122 seconds, 1632000 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.657916, Class: 1.000000, Obj: 0.436688, No Obj: 0.002526, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.659203, Class: 1.000000, Obj: 0.475447, No Obj: 0.002335, Avg Recall: 0.842697, count: 89 -Region Avg IOU: 0.601239, Class: 1.000000, Obj: 0.353106, No Obj: 0.002543, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.590266, Class: 1.000000, Obj: 0.393358, No Obj: 0.002848, Avg Recall: 0.705882, count: 119 -Region Avg IOU: 0.632069, Class: 1.000000, Obj: 0.416789, No Obj: 0.002210, Avg Recall: 0.772727, count: 88 -Region Avg IOU: 0.630892, Class: 1.000000, Obj: 0.396307, No Obj: 0.003207, Avg Recall: 0.789474, count: 133 -Region Avg IOU: 0.631530, Class: 1.000000, Obj: 0.348137, No Obj: 0.002653, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.692875, Class: 1.000000, Obj: 0.446032, No Obj: 0.002687, Avg Recall: 0.846154, count: 91 -6376: 0.435088, 0.418618 avg, 0.001000 rate, 27.502407 seconds, 1632256 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.682895, Class: 1.000000, Obj: 0.432787, No Obj: 0.002533, Avg Recall: 0.882979, count: 94 -Region Avg IOU: 0.580172, Class: 1.000000, Obj: 0.384248, No Obj: 0.002327, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.668977, Class: 1.000000, Obj: 0.454530, No Obj: 0.002545, Avg Recall: 0.794643, count: 112 -Region Avg IOU: 0.669795, Class: 1.000000, Obj: 0.388892, No Obj: 0.002726, Avg Recall: 0.882353, count: 102 -Region Avg IOU: 0.647761, Class: 1.000000, Obj: 0.421340, No Obj: 0.002770, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.613206, Class: 1.000000, Obj: 0.406965, No Obj: 0.002486, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.644591, Class: 1.000000, Obj: 0.367104, No Obj: 0.002101, Avg Recall: 0.822222, count: 90 -Region Avg IOU: 0.668299, Class: 1.000000, Obj: 0.434362, No Obj: 0.002658, Avg Recall: 0.828571, count: 105 -6377: 0.425441, 0.419300 avg, 0.001000 rate, 27.274212 seconds, 1632512 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.618254, Class: 1.000000, Obj: 0.319702, No Obj: 0.002703, Avg Recall: 0.769231, count: 117 -Region Avg IOU: 0.633928, Class: 1.000000, Obj: 0.381984, No Obj: 0.002735, Avg Recall: 0.754237, count: 118 -Region Avg IOU: 0.651474, Class: 1.000000, Obj: 0.395252, No Obj: 0.002519, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.668062, Class: 1.000000, Obj: 0.412849, No Obj: 0.002570, Avg Recall: 0.814433, count: 97 -Region Avg IOU: 0.631383, Class: 1.000000, Obj: 0.352696, No Obj: 0.002682, Avg Recall: 0.783582, count: 134 -Region Avg IOU: 0.609210, Class: 1.000000, Obj: 0.376100, No Obj: 0.002331, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.658451, Class: 1.000000, Obj: 0.433020, No Obj: 0.002716, Avg Recall: 0.841121, count: 107 -Region Avg IOU: 0.662609, Class: 1.000000, Obj: 0.452085, No Obj: 0.002277, Avg Recall: 0.838384, count: 99 -6378: 0.411872, 0.418558 avg, 0.001000 rate, 27.522034 seconds, 1632768 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.642339, Class: 1.000000, Obj: 0.437145, No Obj: 0.002501, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.618648, Class: 1.000000, Obj: 0.402158, No Obj: 0.003141, Avg Recall: 0.757576, count: 132 -Region Avg IOU: 0.665951, Class: 1.000000, Obj: 0.407472, No Obj: 0.003417, Avg Recall: 0.828125, count: 128 -Region Avg IOU: 0.645345, Class: 1.000000, Obj: 0.407371, No Obj: 0.002583, Avg Recall: 0.809091, count: 110 -Region Avg IOU: 0.674177, Class: 1.000000, Obj: 0.409372, No Obj: 0.002906, Avg Recall: 0.796610, count: 118 -Region Avg IOU: 0.625686, Class: 1.000000, Obj: 0.403199, No Obj: 0.002530, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.639046, Class: 1.000000, Obj: 0.365716, No Obj: 0.002633, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.696343, Class: 1.000000, Obj: 0.470433, No Obj: 0.002954, Avg Recall: 0.895833, count: 96 -6379: 0.442514, 0.420953 avg, 0.001000 rate, 28.309340 seconds, 1633024 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.644056, Class: 1.000000, Obj: 0.424386, No Obj: 0.002815, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.684042, Class: 1.000000, Obj: 0.486115, No Obj: 0.003033, Avg Recall: 0.837398, count: 123 -Region Avg IOU: 0.625605, Class: 1.000000, Obj: 0.453651, No Obj: 0.002515, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.633282, Class: 1.000000, Obj: 0.384772, No Obj: 0.003227, Avg Recall: 0.774194, count: 155 -Region Avg IOU: 0.627170, Class: 1.000000, Obj: 0.369619, No Obj: 0.002493, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.647467, Class: 1.000000, Obj: 0.405843, No Obj: 0.002774, Avg Recall: 0.842105, count: 114 -Region Avg IOU: 0.646750, Class: 1.000000, Obj: 0.370914, No Obj: 0.002479, Avg Recall: 0.817073, count: 82 -Region Avg IOU: 0.651758, Class: 1.000000, Obj: 0.388634, No Obj: 0.002432, Avg Recall: 0.767677, count: 99 -6380: 0.426799, 0.421538 avg, 0.001000 rate, 26.496466 seconds, 1633280 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 15.033947 seconds -Region Avg IOU: 0.637262, Class: 1.000000, Obj: 0.358153, No Obj: 0.003270, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.575963, Class: 1.000000, Obj: 0.353358, No Obj: 0.002570, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.641200, Class: 1.000000, Obj: 0.360173, No Obj: 0.003241, Avg Recall: 0.828829, count: 111 -Region Avg IOU: 0.620782, Class: 1.000000, Obj: 0.385881, No Obj: 0.003803, Avg Recall: 0.762295, count: 122 -Region Avg IOU: 0.603063, Class: 1.000000, Obj: 0.415388, No Obj: 0.003036, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.658482, Class: 1.000000, Obj: 0.440752, No Obj: 0.002861, Avg Recall: 0.855670, count: 97 -Region Avg IOU: 0.637138, Class: 1.000000, Obj: 0.379212, No Obj: 0.003674, Avg Recall: 0.785185, count: 135 -Region Avg IOU: 0.584186, Class: 1.000000, Obj: 0.391718, No Obj: 0.003567, Avg Recall: 0.718310, count: 142 -6381: 0.460220, 0.425406 avg, 0.001000 rate, 23.388367 seconds, 1633536 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.578181, Class: 1.000000, Obj: 0.364585, No Obj: 0.003178, Avg Recall: 0.682171, count: 129 -Region Avg IOU: 0.651963, Class: 1.000000, Obj: 0.429042, No Obj: 0.003318, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.612847, Class: 1.000000, Obj: 0.442555, No Obj: 0.003067, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.628412, Class: 1.000000, Obj: 0.389833, No Obj: 0.003890, Avg Recall: 0.776119, count: 134 -Region Avg IOU: 0.616466, Class: 1.000000, Obj: 0.408886, No Obj: 0.004067, Avg Recall: 0.753623, count: 138 -Region Avg IOU: 0.623310, Class: 1.000000, Obj: 0.405293, No Obj: 0.003677, Avg Recall: 0.744526, count: 137 -Region Avg IOU: 0.619148, Class: 1.000000, Obj: 0.418610, No Obj: 0.003495, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.602261, Class: 1.000000, Obj: 0.381286, No Obj: 0.003497, Avg Recall: 0.718750, count: 128 -6382: 0.449502, 0.427816 avg, 0.001000 rate, 26.091610 seconds, 1633792 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.584579, Class: 1.000000, Obj: 0.406728, No Obj: 0.004009, Avg Recall: 0.693750, count: 160 -Region Avg IOU: 0.594067, Class: 1.000000, Obj: 0.432546, No Obj: 0.003850, Avg Recall: 0.708333, count: 120 -Region Avg IOU: 0.565485, Class: 1.000000, Obj: 0.385567, No Obj: 0.003611, Avg Recall: 0.653543, count: 127 -Region Avg IOU: 0.548262, Class: 1.000000, Obj: 0.427780, No Obj: 0.003730, Avg Recall: 0.626984, count: 126 -Region Avg IOU: 0.625595, Class: 1.000000, Obj: 0.416482, No Obj: 0.003863, Avg Recall: 0.752000, count: 125 -Region Avg IOU: 0.633389, Class: 1.000000, Obj: 0.428828, No Obj: 0.003576, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.629838, Class: 1.000000, Obj: 0.416993, No Obj: 0.003351, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.537894, Class: 1.000000, Obj: 0.342005, No Obj: 0.003158, Avg Recall: 0.639344, count: 122 -6383: 0.475502, 0.432584 avg, 0.001000 rate, 24.823816 seconds, 1634048 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.594047, Class: 1.000000, Obj: 0.425732, No Obj: 0.003956, Avg Recall: 0.657895, count: 114 -Region Avg IOU: 0.630017, Class: 1.000000, Obj: 0.454406, No Obj: 0.003814, Avg Recall: 0.758065, count: 124 -Region Avg IOU: 0.632490, Class: 1.000000, Obj: 0.485471, No Obj: 0.003581, Avg Recall: 0.769231, count: 78 -Region Avg IOU: 0.637822, Class: 1.000000, Obj: 0.460091, No Obj: 0.003971, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.592973, Class: 1.000000, Obj: 0.421155, No Obj: 0.003971, Avg Recall: 0.740458, count: 131 -Region Avg IOU: 0.575179, Class: 1.000000, Obj: 0.395717, No Obj: 0.003524, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.566245, Class: 1.000000, Obj: 0.418799, No Obj: 0.003392, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.599326, Class: 1.000000, Obj: 0.430412, No Obj: 0.004004, Avg Recall: 0.717949, count: 117 -6384: 0.416479, 0.430974 avg, 0.001000 rate, 23.854567 seconds, 1634304 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.609725, Class: 1.000000, Obj: 0.491419, No Obj: 0.003823, Avg Recall: 0.691667, count: 120 -Region Avg IOU: 0.595003, Class: 1.000000, Obj: 0.467848, No Obj: 0.003496, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.582275, Class: 1.000000, Obj: 0.395389, No Obj: 0.004180, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.633324, Class: 1.000000, Obj: 0.466582, No Obj: 0.003187, Avg Recall: 0.787500, count: 80 -Region Avg IOU: 0.587400, Class: 1.000000, Obj: 0.407908, No Obj: 0.003986, Avg Recall: 0.659091, count: 132 -Region Avg IOU: 0.605472, Class: 1.000000, Obj: 0.405379, No Obj: 0.003829, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.586183, Class: 1.000000, Obj: 0.471695, No Obj: 0.003742, Avg Recall: 0.669565, count: 115 -Region Avg IOU: 0.605104, Class: 1.000000, Obj: 0.455774, No Obj: 0.004100, Avg Recall: 0.704762, count: 105 -6385: 0.436412, 0.431518 avg, 0.001000 rate, 23.335173 seconds, 1634560 images -Loaded: 0.000153 seconds -Region Avg IOU: 0.591971, Class: 1.000000, Obj: 0.458925, No Obj: 0.003899, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.623011, Class: 1.000000, Obj: 0.399456, No Obj: 0.003949, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.567816, Class: 1.000000, Obj: 0.443988, No Obj: 0.003667, Avg Recall: 0.690476, count: 126 -Region Avg IOU: 0.587124, Class: 1.000000, Obj: 0.444424, No Obj: 0.003776, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.615924, Class: 1.000000, Obj: 0.483573, No Obj: 0.003694, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.593412, Class: 1.000000, Obj: 0.440445, No Obj: 0.004066, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.604344, Class: 1.000000, Obj: 0.437869, No Obj: 0.003633, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.613942, Class: 1.000000, Obj: 0.458428, No Obj: 0.003768, Avg Recall: 0.734694, count: 98 -6386: 0.392856, 0.427651 avg, 0.001000 rate, 24.645191 seconds, 1634816 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.609423, Class: 1.000000, Obj: 0.424093, No Obj: 0.003753, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.635868, Class: 1.000000, Obj: 0.506795, No Obj: 0.003908, Avg Recall: 0.752941, count: 85 -Region Avg IOU: 0.571408, Class: 1.000000, Obj: 0.403160, No Obj: 0.003955, Avg Recall: 0.669421, count: 121 -Region Avg IOU: 0.567492, Class: 1.000000, Obj: 0.425248, No Obj: 0.003724, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.608561, Class: 1.000000, Obj: 0.414994, No Obj: 0.004111, Avg Recall: 0.748092, count: 131 -Region Avg IOU: 0.615588, Class: 1.000000, Obj: 0.445748, No Obj: 0.004022, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.543937, Class: 1.000000, Obj: 0.442512, No Obj: 0.003561, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.545417, Class: 1.000000, Obj: 0.434938, No Obj: 0.003800, Avg Recall: 0.622807, count: 114 -6387: 0.435477, 0.428434 avg, 0.001000 rate, 24.076775 seconds, 1635072 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.610655, Class: 1.000000, Obj: 0.430880, No Obj: 0.004409, Avg Recall: 0.663866, count: 119 -Region Avg IOU: 0.585148, Class: 1.000000, Obj: 0.428548, No Obj: 0.004423, Avg Recall: 0.666667, count: 144 -Region Avg IOU: 0.631665, Class: 1.000000, Obj: 0.510056, No Obj: 0.004177, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.615802, Class: 1.000000, Obj: 0.449134, No Obj: 0.004034, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.623235, Class: 1.000000, Obj: 0.442395, No Obj: 0.003945, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.561879, Class: 1.000000, Obj: 0.417975, No Obj: 0.003743, Avg Recall: 0.661290, count: 124 -Region Avg IOU: 0.620874, Class: 1.000000, Obj: 0.441812, No Obj: 0.003029, Avg Recall: 0.727273, count: 77 -Region Avg IOU: 0.623981, Class: 1.000000, Obj: 0.469328, No Obj: 0.003205, Avg Recall: 0.750000, count: 88 -6388: 0.425089, 0.428099 avg, 0.001000 rate, 22.606421 seconds, 1635328 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.626932, Class: 1.000000, Obj: 0.417924, No Obj: 0.004000, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.619367, Class: 1.000000, Obj: 0.527042, No Obj: 0.003119, Avg Recall: 0.743590, count: 78 -Region Avg IOU: 0.601988, Class: 1.000000, Obj: 0.434044, No Obj: 0.003963, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.600889, Class: 1.000000, Obj: 0.446712, No Obj: 0.003589, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.596488, Class: 1.000000, Obj: 0.455924, No Obj: 0.003082, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.576531, Class: 1.000000, Obj: 0.401603, No Obj: 0.003964, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.592938, Class: 1.000000, Obj: 0.387175, No Obj: 0.003965, Avg Recall: 0.717647, count: 85 -Region Avg IOU: 0.669925, Class: 1.000000, Obj: 0.462331, No Obj: 0.004316, Avg Recall: 0.801887, count: 106 -6389: 0.390351, 0.424325 avg, 0.001000 rate, 23.805527 seconds, 1635584 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.633717, Class: 1.000000, Obj: 0.465735, No Obj: 0.003391, Avg Recall: 0.811765, count: 85 -Region Avg IOU: 0.578489, Class: 1.000000, Obj: 0.398697, No Obj: 0.003723, Avg Recall: 0.673913, count: 138 -Region Avg IOU: 0.611490, Class: 1.000000, Obj: 0.407023, No Obj: 0.004168, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.549371, Class: 1.000000, Obj: 0.443492, No Obj: 0.003469, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.594736, Class: 1.000000, Obj: 0.400329, No Obj: 0.003326, Avg Recall: 0.778761, count: 113 -Region Avg IOU: 0.590070, Class: 1.000000, Obj: 0.377968, No Obj: 0.004206, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.597422, Class: 1.000000, Obj: 0.443760, No Obj: 0.003347, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.623989, Class: 1.000000, Obj: 0.405584, No Obj: 0.004358, Avg Recall: 0.745902, count: 122 -6390: 0.427854, 0.424678 avg, 0.001000 rate, 23.084364 seconds, 1635840 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.066830 seconds -Region Avg IOU: 0.526482, Class: 1.000000, Obj: 0.343990, No Obj: 0.004408, Avg Recall: 0.598485, count: 132 -Region Avg IOU: 0.576831, Class: 1.000000, Obj: 0.360554, No Obj: 0.003686, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.536007, Class: 1.000000, Obj: 0.358395, No Obj: 0.003594, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.513943, Class: 1.000000, Obj: 0.328081, No Obj: 0.003689, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.541510, Class: 1.000000, Obj: 0.354909, No Obj: 0.004140, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.521695, Class: 1.000000, Obj: 0.344381, No Obj: 0.003571, Avg Recall: 0.553191, count: 94 -Region Avg IOU: 0.558124, Class: 1.000000, Obj: 0.359804, No Obj: 0.003894, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.538760, Class: 1.000000, Obj: 0.370345, No Obj: 0.004014, Avg Recall: 0.611111, count: 108 -6391: 0.453886, 0.427598 avg, 0.001000 rate, 19.481913 seconds, 1636096 images -Loaded: 1.978253 seconds -Region Avg IOU: 0.534703, Class: 1.000000, Obj: 0.364607, No Obj: 0.003219, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.497172, Class: 1.000000, Obj: 0.334903, No Obj: 0.003324, Avg Recall: 0.540230, count: 87 -Region Avg IOU: 0.563705, Class: 1.000000, Obj: 0.381595, No Obj: 0.004010, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.517867, Class: 1.000000, Obj: 0.316070, No Obj: 0.003254, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.535483, Class: 1.000000, Obj: 0.369808, No Obj: 0.004117, Avg Recall: 0.554622, count: 119 -Region Avg IOU: 0.479908, Class: 1.000000, Obj: 0.333696, No Obj: 0.004166, Avg Recall: 0.510949, count: 137 -Region Avg IOU: 0.561553, Class: 1.000000, Obj: 0.332020, No Obj: 0.004392, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.541650, Class: 1.000000, Obj: 0.379579, No Obj: 0.002965, Avg Recall: 0.636364, count: 77 -6392: 0.440470, 0.428886 avg, 0.001000 rate, 19.660341 seconds, 1636352 images -Loaded: 2.496279 seconds -Region Avg IOU: 0.520567, Class: 1.000000, Obj: 0.327974, No Obj: 0.003647, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.535186, Class: 1.000000, Obj: 0.351165, No Obj: 0.003832, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.563055, Class: 1.000000, Obj: 0.351623, No Obj: 0.003901, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.531563, Class: 1.000000, Obj: 0.349262, No Obj: 0.003302, Avg Recall: 0.571429, count: 84 -Region Avg IOU: 0.571405, Class: 1.000000, Obj: 0.373802, No Obj: 0.004269, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.532130, Class: 1.000000, Obj: 0.323009, No Obj: 0.004144, Avg Recall: 0.578512, count: 121 -Region Avg IOU: 0.524733, Class: 1.000000, Obj: 0.339359, No Obj: 0.003575, Avg Recall: 0.622807, count: 114 -Region Avg IOU: 0.520660, Class: 1.000000, Obj: 0.378176, No Obj: 0.003324, Avg Recall: 0.555556, count: 72 -6393: 0.408694, 0.426866 avg, 0.001000 rate, 21.261312 seconds, 1636608 images -Loaded: 1.101577 seconds -Region Avg IOU: 0.519553, Class: 1.000000, Obj: 0.337126, No Obj: 0.003909, Avg Recall: 0.611570, count: 121 -Region Avg IOU: 0.490518, Class: 1.000000, Obj: 0.391067, No Obj: 0.003366, Avg Recall: 0.489362, count: 94 -Region Avg IOU: 0.551192, Class: 1.000000, Obj: 0.367014, No Obj: 0.004405, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.573421, Class: 1.000000, Obj: 0.372778, No Obj: 0.003686, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.571032, Class: 1.000000, Obj: 0.365642, No Obj: 0.003722, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.532209, Class: 1.000000, Obj: 0.367069, No Obj: 0.004153, Avg Recall: 0.606557, count: 122 -Region Avg IOU: 0.576009, Class: 1.000000, Obj: 0.379468, No Obj: 0.003725, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.527251, Class: 1.000000, Obj: 0.361130, No Obj: 0.004465, Avg Recall: 0.577778, count: 135 -6394: 0.429992, 0.427179 avg, 0.001000 rate, 21.183292 seconds, 1636864 images -Loaded: 0.239324 seconds -Region Avg IOU: 0.581753, Class: 1.000000, Obj: 0.373243, No Obj: 0.003777, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.552522, Class: 1.000000, Obj: 0.378238, No Obj: 0.003971, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.580906, Class: 1.000000, Obj: 0.363614, No Obj: 0.004379, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.520257, Class: 1.000000, Obj: 0.336223, No Obj: 0.003542, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.510457, Class: 1.000000, Obj: 0.340967, No Obj: 0.003309, Avg Recall: 0.580357, count: 112 -Region Avg IOU: 0.569433, Class: 1.000000, Obj: 0.327792, No Obj: 0.004098, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.545567, Class: 1.000000, Obj: 0.347980, No Obj: 0.004019, Avg Recall: 0.671756, count: 131 -Region Avg IOU: 0.495051, Class: 1.000000, Obj: 0.292827, No Obj: 0.003693, Avg Recall: 0.562500, count: 112 -6395: 0.441693, 0.428630 avg, 0.001000 rate, 20.967438 seconds, 1637120 images -Loaded: 0.627080 seconds -Region Avg IOU: 0.553511, Class: 1.000000, Obj: 0.375858, No Obj: 0.003471, Avg Recall: 0.621622, count: 74 -Region Avg IOU: 0.522718, Class: 1.000000, Obj: 0.346841, No Obj: 0.003422, Avg Recall: 0.553571, count: 112 -Region Avg IOU: 0.522643, Class: 1.000000, Obj: 0.356909, No Obj: 0.003355, Avg Recall: 0.580645, count: 93 -Region Avg IOU: 0.555206, Class: 1.000000, Obj: 0.389136, No Obj: 0.003738, Avg Recall: 0.666667, count: 75 -Region Avg IOU: 0.552089, Class: 1.000000, Obj: 0.369602, No Obj: 0.004053, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.578276, Class: 1.000000, Obj: 0.414842, No Obj: 0.003436, Avg Recall: 0.666667, count: 87 -Region Avg IOU: 0.545272, Class: 1.000000, Obj: 0.372240, No Obj: 0.004377, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.503774, Class: 1.000000, Obj: 0.334588, No Obj: 0.004103, Avg Recall: 0.603604, count: 111 -6396: 0.402059, 0.425973 avg, 0.001000 rate, 19.674566 seconds, 1637376 images -Loaded: 1.743970 seconds -Region Avg IOU: 0.591904, Class: 1.000000, Obj: 0.469542, No Obj: 0.004086, Avg Recall: 0.700000, count: 80 -Region Avg IOU: 0.501418, Class: 1.000000, Obj: 0.337423, No Obj: 0.003719, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.566843, Class: 1.000000, Obj: 0.409269, No Obj: 0.003498, Avg Recall: 0.612500, count: 80 -Region Avg IOU: 0.567661, Class: 1.000000, Obj: 0.387504, No Obj: 0.004146, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.509643, Class: 1.000000, Obj: 0.365128, No Obj: 0.003496, Avg Recall: 0.600000, count: 85 -Region Avg IOU: 0.522926, Class: 1.000000, Obj: 0.377491, No Obj: 0.004468, Avg Recall: 0.581395, count: 129 -Region Avg IOU: 0.571079, Class: 1.000000, Obj: 0.425896, No Obj: 0.003673, Avg Recall: 0.670732, count: 82 -Region Avg IOU: 0.543218, Class: 1.000000, Obj: 0.386198, No Obj: 0.003658, Avg Recall: 0.595745, count: 94 -6397: 0.403005, 0.423676 avg, 0.001000 rate, 19.833340 seconds, 1637632 images -Loaded: 2.267643 seconds -Region Avg IOU: 0.533834, Class: 1.000000, Obj: 0.360538, No Obj: 0.003947, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.594107, Class: 1.000000, Obj: 0.426161, No Obj: 0.003796, Avg Recall: 0.701754, count: 57 -Region Avg IOU: 0.568097, Class: 1.000000, Obj: 0.433471, No Obj: 0.004123, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.579425, Class: 1.000000, Obj: 0.380697, No Obj: 0.004075, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.513945, Class: 1.000000, Obj: 0.413528, No Obj: 0.004326, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.561700, Class: 1.000000, Obj: 0.393818, No Obj: 0.004184, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.514573, Class: 1.000000, Obj: 0.329086, No Obj: 0.003703, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.500724, Class: 1.000000, Obj: 0.352501, No Obj: 0.004107, Avg Recall: 0.545455, count: 121 -6398: 0.410440, 0.422353 avg, 0.001000 rate, 20.241228 seconds, 1637888 images -Loaded: 0.950095 seconds -Region Avg IOU: 0.566324, Class: 1.000000, Obj: 0.437377, No Obj: 0.004222, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.547570, Class: 1.000000, Obj: 0.396577, No Obj: 0.004730, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.493523, Class: 1.000000, Obj: 0.380566, No Obj: 0.004253, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.570721, Class: 1.000000, Obj: 0.403617, No Obj: 0.004311, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.562540, Class: 1.000000, Obj: 0.391978, No Obj: 0.005050, Avg Recall: 0.629921, count: 127 -Region Avg IOU: 0.582295, Class: 1.000000, Obj: 0.407543, No Obj: 0.004765, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.529715, Class: 1.000000, Obj: 0.412690, No Obj: 0.004850, Avg Recall: 0.595745, count: 141 -Region Avg IOU: 0.537651, Class: 1.000000, Obj: 0.441510, No Obj: 0.003883, Avg Recall: 0.613861, count: 101 -6399: 0.429784, 0.423096 avg, 0.001000 rate, 19.459641 seconds, 1638144 images -Loaded: 1.578928 seconds -Region Avg IOU: 0.534843, Class: 1.000000, Obj: 0.430125, No Obj: 0.004396, Avg Recall: 0.602041, countSaving weights to ./person9300/backup/tyvp_93_9c512_6400.weights -: 98 -Region Avg IOU: 0.472366, Class: 1.000000, Obj: 0.383574, No Obj: 0.004489, Avg Recall: 0.535088, count: 114 -Region Avg IOU: 0.521215, Class: 1.000000, Obj: 0.416587, No Obj: 0.003684, Avg Recall: 0.589744, count: 78 -Region Avg IOU: 0.488713, Class: 1.000000, Obj: 0.394651, No Obj: 0.004371, Avg Recall: 0.500000, count: 120 -Region Avg IOU: 0.546797, Class: 1.000000, Obj: 0.414604, No Obj: 0.004017, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.584557, Class: 1.000000, Obj: 0.450203, No Obj: 0.004664, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.513423, Class: 1.000000, Obj: 0.414678, No Obj: 0.004449, Avg Recall: 0.578125, count: 128 -Region Avg IOU: 0.492654, Class: 1.000000, Obj: 0.358717, No Obj: 0.003771, Avg Recall: 0.566667, count: 90 -6400: 0.359042, 0.416690 avg, 0.001000 rate, 17.269552 seconds, 1638400 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000077 seconds -Region Avg IOU: 0.631734, Class: 1.000000, Obj: 0.482337, No Obj: 0.003549, Avg Recall: 0.755906, count: 127 -Region Avg IOU: 0.690352, Class: 1.000000, Obj: 0.506028, No Obj: 0.003556, Avg Recall: 0.887755, count: 98 -Region Avg IOU: 0.591742, Class: 1.000000, Obj: 0.410258, No Obj: 0.003023, Avg Recall: 0.684685, count: 111 -Region Avg IOU: 0.672629, Class: 1.000000, Obj: 0.418965, No Obj: 0.003743, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.642748, Class: 1.000000, Obj: 0.477691, No Obj: 0.003008, Avg Recall: 0.777778, count: 90 -Region Avg IOU: 0.665519, Class: 1.000000, Obj: 0.507756, No Obj: 0.003814, Avg Recall: 0.825758, count: 132 -Region Avg IOU: 0.644626, Class: 1.000000, Obj: 0.531861, No Obj: 0.003116, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.659122, Class: 1.000000, Obj: 0.480093, No Obj: 0.003197, Avg Recall: 0.842593, count: 108 -6401: 0.428097, 0.417831 avg, 0.001000 rate, 27.579641 seconds, 1638656 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.634121, Class: 1.000000, Obj: 0.446643, No Obj: 0.003491, Avg Recall: 0.786765, count: 136 -Region Avg IOU: 0.627872, Class: 1.000000, Obj: 0.427429, No Obj: 0.002966, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.680993, Class: 1.000000, Obj: 0.506345, No Obj: 0.003159, Avg Recall: 0.854369, count: 103 -Region Avg IOU: 0.589844, Class: 1.000000, Obj: 0.402566, No Obj: 0.002877, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.650292, Class: 1.000000, Obj: 0.480047, No Obj: 0.003833, Avg Recall: 0.762590, count: 139 -Region Avg IOU: 0.660125, Class: 1.000000, Obj: 0.465185, No Obj: 0.003404, Avg Recall: 0.788136, count: 118 -Region Avg IOU: 0.654961, Class: 1.000000, Obj: 0.501928, No Obj: 0.002592, Avg Recall: 0.804878, count: 82 -Region Avg IOU: 0.653642, Class: 1.000000, Obj: 0.505797, No Obj: 0.002978, Avg Recall: 0.805310, count: 113 -6402: 0.466539, 0.422702 avg, 0.001000 rate, 27.433224 seconds, 1638912 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.601201, Class: 1.000000, Obj: 0.429360, No Obj: 0.002845, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.628578, Class: 1.000000, Obj: 0.476920, No Obj: 0.002993, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.668498, Class: 1.000000, Obj: 0.490128, No Obj: 0.003242, Avg Recall: 0.822430, count: 107 -Region Avg IOU: 0.678185, Class: 1.000000, Obj: 0.453886, No Obj: 0.002866, Avg Recall: 0.847826, count: 92 -Region Avg IOU: 0.636443, Class: 1.000000, Obj: 0.463503, No Obj: 0.003431, Avg Recall: 0.776119, count: 134 -Region Avg IOU: 0.633941, Class: 1.000000, Obj: 0.466021, No Obj: 0.003075, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.659300, Class: 1.000000, Obj: 0.465039, No Obj: 0.003094, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.645522, Class: 1.000000, Obj: 0.451588, No Obj: 0.003644, Avg Recall: 0.801527, count: 131 -6403: 0.428470, 0.423279 avg, 0.001000 rate, 27.522787 seconds, 1639168 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.659141, Class: 1.000000, Obj: 0.418018, No Obj: 0.003601, Avg Recall: 0.824561, count: 114 -Region Avg IOU: 0.670753, Class: 1.000000, Obj: 0.398116, No Obj: 0.002946, Avg Recall: 0.862069, count: 87 -Region Avg IOU: 0.580448, Class: 1.000000, Obj: 0.430482, No Obj: 0.002642, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.584169, Class: 1.000000, Obj: 0.408784, No Obj: 0.002313, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.651953, Class: 1.000000, Obj: 0.438895, No Obj: 0.002922, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.642186, Class: 1.000000, Obj: 0.425534, No Obj: 0.003033, Avg Recall: 0.806202, count: 129 -Region Avg IOU: 0.600598, Class: 1.000000, Obj: 0.381308, No Obj: 0.002795, Avg Recall: 0.766667, count: 120 -Region Avg IOU: 0.620480, Class: 1.000000, Obj: 0.396636, No Obj: 0.002803, Avg Recall: 0.744681, count: 94 -6404: 0.428422, 0.423793 avg, 0.001000 rate, 27.533636 seconds, 1639424 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.664010, Class: 1.000000, Obj: 0.405716, No Obj: 0.002785, Avg Recall: 0.800000, count: 95 -Region Avg IOU: 0.678446, Class: 1.000000, Obj: 0.454834, No Obj: 0.002424, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.642492, Class: 1.000000, Obj: 0.383320, No Obj: 0.002772, Avg Recall: 0.827273, count: 110 -Region Avg IOU: 0.677819, Class: 1.000000, Obj: 0.461230, No Obj: 0.002657, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.627848, Class: 1.000000, Obj: 0.388606, No Obj: 0.002665, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.662993, Class: 1.000000, Obj: 0.421054, No Obj: 0.002621, Avg Recall: 0.839506, count: 81 -Region Avg IOU: 0.667889, Class: 1.000000, Obj: 0.395153, No Obj: 0.002890, Avg Recall: 0.825758, count: 132 -Region Avg IOU: 0.626528, Class: 1.000000, Obj: 0.399663, No Obj: 0.002505, Avg Recall: 0.733945, count: 109 -6405: 0.430690, 0.424483 avg, 0.001000 rate, 27.251499 seconds, 1639680 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.609868, Class: 1.000000, Obj: 0.399752, No Obj: 0.002445, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.574405, Class: 1.000000, Obj: 0.371549, No Obj: 0.001861, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.657069, Class: 1.000000, Obj: 0.398575, No Obj: 0.002215, Avg Recall: 0.808081, count: 99 -Region Avg IOU: 0.625561, Class: 1.000000, Obj: 0.391830, No Obj: 0.002652, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.643106, Class: 1.000000, Obj: 0.347650, No Obj: 0.003004, Avg Recall: 0.762295, count: 122 -Region Avg IOU: 0.626546, Class: 1.000000, Obj: 0.353186, No Obj: 0.002493, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.643929, Class: 1.000000, Obj: 0.410188, No Obj: 0.002361, Avg Recall: 0.813084, count: 107 -Region Avg IOU: 0.686769, Class: 1.000000, Obj: 0.375333, No Obj: 0.002993, Avg Recall: 0.844444, count: 135 -6406: 0.465953, 0.428630 avg, 0.001000 rate, 28.118635 seconds, 1639936 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.617296, Class: 1.000000, Obj: 0.384327, No Obj: 0.002843, Avg Recall: 0.725191, count: 131 -Region Avg IOU: 0.664342, Class: 1.000000, Obj: 0.372372, No Obj: 0.002777, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.617108, Class: 1.000000, Obj: 0.387441, No Obj: 0.002509, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.639373, Class: 1.000000, Obj: 0.389900, No Obj: 0.002673, Avg Recall: 0.788618, count: 123 -Region Avg IOU: 0.604002, Class: 1.000000, Obj: 0.380095, No Obj: 0.002388, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.665239, Class: 1.000000, Obj: 0.400262, No Obj: 0.002580, Avg Recall: 0.821429, count: 112 -Region Avg IOU: 0.637576, Class: 1.000000, Obj: 0.368004, No Obj: 0.002584, Avg Recall: 0.785124, count: 121 -Region Avg IOU: 0.652313, Class: 1.000000, Obj: 0.412516, No Obj: 0.002396, Avg Recall: 0.758621, count: 116 -6407: 0.465749, 0.432342 avg, 0.001000 rate, 27.484035 seconds, 1640192 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.673321, Class: 1.000000, Obj: 0.427678, No Obj: 0.002594, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.667697, Class: 1.000000, Obj: 0.367912, No Obj: 0.002593, Avg Recall: 0.826446, count: 121 -Region Avg IOU: 0.694820, Class: 1.000000, Obj: 0.437944, No Obj: 0.003227, Avg Recall: 0.850394, count: 127 -Region Avg IOU: 0.650181, Class: 1.000000, Obj: 0.387406, No Obj: 0.002529, Avg Recall: 0.821138, count: 123 -Region Avg IOU: 0.670981, Class: 1.000000, Obj: 0.390108, No Obj: 0.002482, Avg Recall: 0.848101, count: 79 -Region Avg IOU: 0.651604, Class: 1.000000, Obj: 0.377953, No Obj: 0.002685, Avg Recall: 0.818965, count: 116 -Region Avg IOU: 0.628940, Class: 1.000000, Obj: 0.325602, No Obj: 0.002559, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.627739, Class: 1.000000, Obj: 0.378739, No Obj: 0.002895, Avg Recall: 0.780000, count: 150 -6408: 0.463577, 0.435465 avg, 0.001000 rate, 28.398388 seconds, 1640448 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.685643, Class: 1.000000, Obj: 0.458728, No Obj: 0.002993, Avg Recall: 0.849057, count: 106 -Region Avg IOU: 0.664764, Class: 1.000000, Obj: 0.388576, No Obj: 0.002335, Avg Recall: 0.755814, count: 86 -Region Avg IOU: 0.650984, Class: 1.000000, Obj: 0.398603, No Obj: 0.002594, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.678684, Class: 1.000000, Obj: 0.382164, No Obj: 0.002712, Avg Recall: 0.894309, count: 123 -Region Avg IOU: 0.597457, Class: 1.000000, Obj: 0.362491, No Obj: 0.002217, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.646816, Class: 1.000000, Obj: 0.380426, No Obj: 0.002385, Avg Recall: 0.797753, count: 89 -Region Avg IOU: 0.612651, Class: 1.000000, Obj: 0.379843, No Obj: 0.002118, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.612603, Class: 1.000000, Obj: 0.389219, No Obj: 0.002606, Avg Recall: 0.715328, count: 137 -6409: 0.391061, 0.431025 avg, 0.001000 rate, 25.963367 seconds, 1640704 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.660948, Class: 1.000000, Obj: 0.416576, No Obj: 0.002626, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.675727, Class: 1.000000, Obj: 0.425095, No Obj: 0.002350, Avg Recall: 0.867470, count: 83 -Region Avg IOU: 0.651282, Class: 1.000000, Obj: 0.428339, No Obj: 0.002146, Avg Recall: 0.810127, count: 79 -Region Avg IOU: 0.676393, Class: 1.000000, Obj: 0.404965, No Obj: 0.002556, Avg Recall: 0.796460, count: 113 -Region Avg IOU: 0.650547, Class: 1.000000, Obj: 0.413669, No Obj: 0.002223, Avg Recall: 0.776471, count: 85 -Region Avg IOU: 0.654769, Class: 1.000000, Obj: 0.412455, No Obj: 0.002524, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.624175, Class: 1.000000, Obj: 0.363292, No Obj: 0.002158, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.678333, Class: 1.000000, Obj: 0.424717, No Obj: 0.002638, Avg Recall: 0.861386, count: 101 -6410: 0.343204, 0.422243 avg, 0.001000 rate, 27.224421 seconds, 1640960 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000073 seconds -Region Avg IOU: 0.645080, Class: 1.000000, Obj: 0.353522, No Obj: 0.002652, Avg Recall: 0.808333, count: 120 -Region Avg IOU: 0.645098, Class: 1.000000, Obj: 0.400401, No Obj: 0.002158, Avg Recall: 0.835165, count: 91 -Region Avg IOU: 0.651196, Class: 1.000000, Obj: 0.412932, No Obj: 0.002571, Avg Recall: 0.806723, count: 119 -Region Avg IOU: 0.687031, Class: 1.000000, Obj: 0.398660, No Obj: 0.002630, Avg Recall: 0.851852, count: 108 -Region Avg IOU: 0.690012, Class: 1.000000, Obj: 0.444498, No Obj: 0.002469, Avg Recall: 0.865385, count: 104 -Region Avg IOU: 0.689571, Class: 1.000000, Obj: 0.377035, No Obj: 0.002540, Avg Recall: 0.884615, count: 104 -Region Avg IOU: 0.652804, Class: 1.000000, Obj: 0.387336, No Obj: 0.001989, Avg Recall: 0.831461, count: 89 -Region Avg IOU: 0.665722, Class: 1.000000, Obj: 0.446632, No Obj: 0.002695, Avg Recall: 0.813433, count: 134 -6411: 0.432753, 0.423294 avg, 0.001000 rate, 29.102793 seconds, 1641216 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.735736, Class: 1.000000, Obj: 0.407247, No Obj: 0.002497, Avg Recall: 0.943182, count: 88 -Region Avg IOU: 0.615851, Class: 1.000000, Obj: 0.390057, No Obj: 0.002994, Avg Recall: 0.741007, count: 139 -Region Avg IOU: 0.688028, Class: 1.000000, Obj: 0.493183, No Obj: 0.002418, Avg Recall: 0.870588, count: 85 -Region Avg IOU: 0.718627, Class: 1.000000, Obj: 0.460178, No Obj: 0.002774, Avg Recall: 0.886956, count: 115 -Region Avg IOU: 0.694503, Class: 1.000000, Obj: 0.434460, No Obj: 0.002809, Avg Recall: 0.883495, count: 103 -Region Avg IOU: 0.654994, Class: 1.000000, Obj: 0.401254, No Obj: 0.002675, Avg Recall: 0.813084, count: 107 -Region Avg IOU: 0.618353, Class: 1.000000, Obj: 0.378391, No Obj: 0.002574, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.678841, Class: 1.000000, Obj: 0.457700, No Obj: 0.002553, Avg Recall: 0.838095, count: 105 -6412: 0.445465, 0.425511 avg, 0.001000 rate, 28.052607 seconds, 1641472 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.657634, Class: 1.000000, Obj: 0.399827, No Obj: 0.002525, Avg Recall: 0.824176, count: 91 -Region Avg IOU: 0.677483, Class: 1.000000, Obj: 0.397106, No Obj: 0.002603, Avg Recall: 0.844037, count: 109 -Region Avg IOU: 0.707548, Class: 1.000000, Obj: 0.470509, No Obj: 0.002953, Avg Recall: 0.876289, count: 97 -Region Avg IOU: 0.651160, Class: 1.000000, Obj: 0.448262, No Obj: 0.002873, Avg Recall: 0.801587, count: 126 -Region Avg IOU: 0.680113, Class: 1.000000, Obj: 0.430621, No Obj: 0.002932, Avg Recall: 0.847619, count: 105 -Region Avg IOU: 0.684447, Class: 1.000000, Obj: 0.432121, No Obj: 0.002890, Avg Recall: 0.861702, count: 94 -Region Avg IOU: 0.699092, Class: 1.000000, Obj: 0.497168, No Obj: 0.002688, Avg Recall: 0.894737, count: 95 -Region Avg IOU: 0.687675, Class: 1.000000, Obj: 0.468554, No Obj: 0.002706, Avg Recall: 0.877358, count: 106 -6413: 0.424667, 0.425426 avg, 0.001000 rate, 29.505159 seconds, 1641728 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.660741, Class: 1.000000, Obj: 0.407890, No Obj: 0.002830, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.662032, Class: 1.000000, Obj: 0.402528, No Obj: 0.002469, Avg Recall: 0.831858, count: 113 -Region Avg IOU: 0.640070, Class: 1.000000, Obj: 0.419553, No Obj: 0.002657, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.631504, Class: 1.000000, Obj: 0.406794, No Obj: 0.002485, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.661031, Class: 1.000000, Obj: 0.487257, No Obj: 0.002586, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.652546, Class: 1.000000, Obj: 0.476479, No Obj: 0.002432, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.637160, Class: 1.000000, Obj: 0.383524, No Obj: 0.002546, Avg Recall: 0.791667, count: 120 -Region Avg IOU: 0.712062, Class: 1.000000, Obj: 0.478233, No Obj: 0.002827, Avg Recall: 0.902655, count: 113 -6414: 0.436393, 0.426523 avg, 0.001000 rate, 29.546616 seconds, 1641984 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.667633, Class: 1.000000, Obj: 0.432610, No Obj: 0.002972, Avg Recall: 0.836364, count: 110 -Region Avg IOU: 0.683743, Class: 1.000000, Obj: 0.440685, No Obj: 0.002910, Avg Recall: 0.863636, count: 132 -Region Avg IOU: 0.626925, Class: 1.000000, Obj: 0.412373, No Obj: 0.002828, Avg Recall: 0.728000, count: 125 -Region Avg IOU: 0.680517, Class: 1.000000, Obj: 0.426637, No Obj: 0.002423, Avg Recall: 0.875000, count: 104 -Region Avg IOU: 0.670371, Class: 1.000000, Obj: 0.416778, No Obj: 0.002649, Avg Recall: 0.858407, count: 113 -Region Avg IOU: 0.673668, Class: 1.000000, Obj: 0.437809, No Obj: 0.002276, Avg Recall: 0.835165, count: 91 -Region Avg IOU: 0.582195, Class: 1.000000, Obj: 0.430705, No Obj: 0.002580, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.660114, Class: 1.000000, Obj: 0.422645, No Obj: 0.002450, Avg Recall: 0.782178, count: 101 -6415: 0.420877, 0.425959 avg, 0.001000 rate, 29.325329 seconds, 1642240 images -Loaded: 0.000124 seconds -Region Avg IOU: 0.656828, Class: 1.000000, Obj: 0.374189, No Obj: 0.002773, Avg Recall: 0.820690, count: 145 -Region Avg IOU: 0.617956, Class: 1.000000, Obj: 0.456832, No Obj: 0.002264, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.675494, Class: 1.000000, Obj: 0.474533, No Obj: 0.002742, Avg Recall: 0.821429, count: 112 -Region Avg IOU: 0.663366, Class: 1.000000, Obj: 0.391861, No Obj: 0.002753, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.627814, Class: 1.000000, Obj: 0.394777, No Obj: 0.002647, Avg Recall: 0.729927, count: 137 -Region Avg IOU: 0.687278, Class: 1.000000, Obj: 0.470138, No Obj: 0.002332, Avg Recall: 0.857143, count: 77 -Region Avg IOU: 0.724466, Class: 1.000000, Obj: 0.506340, No Obj: 0.002443, Avg Recall: 0.887500, count: 80 -Region Avg IOU: 0.681498, Class: 1.000000, Obj: 0.452621, No Obj: 0.002675, Avg Recall: 0.866667, count: 120 -6416: 0.435743, 0.426937 avg, 0.001000 rate, 30.568480 seconds, 1642496 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.668414, Class: 1.000000, Obj: 0.407876, No Obj: 0.003333, Avg Recall: 0.823899, count: 159 -Region Avg IOU: 0.632222, Class: 1.000000, Obj: 0.432268, No Obj: 0.002265, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.691887, Class: 1.000000, Obj: 0.454108, No Obj: 0.002309, Avg Recall: 0.920792, count: 101 -Region Avg IOU: 0.666849, Class: 1.000000, Obj: 0.436330, No Obj: 0.002254, Avg Recall: 0.825000, count: 80 -Region Avg IOU: 0.663380, Class: 1.000000, Obj: 0.398310, No Obj: 0.002698, Avg Recall: 0.830189, count: 106 -Region Avg IOU: 0.600038, Class: 1.000000, Obj: 0.392955, No Obj: 0.002445, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.673851, Class: 1.000000, Obj: 0.425202, No Obj: 0.002740, Avg Recall: 0.850877, count: 114 -Region Avg IOU: 0.677613, Class: 1.000000, Obj: 0.414941, No Obj: 0.002965, Avg Recall: 0.838028, count: 142 -6417: 0.462310, 0.430474 avg, 0.001000 rate, 29.957428 seconds, 1642752 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.665082, Class: 1.000000, Obj: 0.375043, No Obj: 0.002909, Avg Recall: 0.829630, count: 135 -Region Avg IOU: 0.670212, Class: 1.000000, Obj: 0.466202, No Obj: 0.002665, Avg Recall: 0.830882, count: 136 -Region Avg IOU: 0.705640, Class: 1.000000, Obj: 0.439397, No Obj: 0.002317, Avg Recall: 0.892473, count: 93 -Region Avg IOU: 0.676652, Class: 1.000000, Obj: 0.418497, No Obj: 0.002431, Avg Recall: 0.811475, count: 122 -Region Avg IOU: 0.666856, Class: 1.000000, Obj: 0.446215, No Obj: 0.002770, Avg Recall: 0.836207, count: 116 -Region Avg IOU: 0.700179, Class: 1.000000, Obj: 0.416456, No Obj: 0.002709, Avg Recall: 0.869565, count: 92 -Region Avg IOU: 0.663712, Class: 1.000000, Obj: 0.412326, No Obj: 0.002454, Avg Recall: 0.844444, count: 90 -Region Avg IOU: 0.664927, Class: 1.000000, Obj: 0.383549, No Obj: 0.002520, Avg Recall: 0.816514, count: 109 -6418: 0.446717, 0.432098 avg, 0.001000 rate, 28.871365 seconds, 1643008 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.647318, Class: 1.000000, Obj: 0.378328, No Obj: 0.002428, Avg Recall: 0.807407, count: 135 -Region Avg IOU: 0.639358, Class: 1.000000, Obj: 0.427003, No Obj: 0.002434, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.675103, Class: 1.000000, Obj: 0.433336, No Obj: 0.002649, Avg Recall: 0.817391, count: 115 -Region Avg IOU: 0.644311, Class: 1.000000, Obj: 0.367478, No Obj: 0.002710, Avg Recall: 0.795082, count: 122 -Region Avg IOU: 0.672323, Class: 1.000000, Obj: 0.372983, No Obj: 0.002697, Avg Recall: 0.841270, count: 126 -Region Avg IOU: 0.677022, Class: 1.000000, Obj: 0.399392, No Obj: 0.002593, Avg Recall: 0.826087, count: 115 -Region Avg IOU: 0.657925, Class: 1.000000, Obj: 0.417113, No Obj: 0.002482, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.674913, Class: 1.000000, Obj: 0.429626, No Obj: 0.002551, Avg Recall: 0.844037, count: 109 -6419: 0.506166, 0.439505 avg, 0.001000 rate, 29.157656 seconds, 1643264 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.636694, Class: 1.000000, Obj: 0.449633, No Obj: 0.002229, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.683945, Class: 1.000000, Obj: 0.409548, No Obj: 0.002555, Avg Recall: 0.864865, count: 111 -Region Avg IOU: 0.660606, Class: 1.000000, Obj: 0.417678, No Obj: 0.002548, Avg Recall: 0.825000, count: 120 -Region Avg IOU: 0.648705, Class: 1.000000, Obj: 0.359235, No Obj: 0.002759, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.667965, Class: 1.000000, Obj: 0.427501, No Obj: 0.002824, Avg Recall: 0.861111, count: 108 -Region Avg IOU: 0.670163, Class: 1.000000, Obj: 0.420580, No Obj: 0.002442, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.653788, Class: 1.000000, Obj: 0.473583, No Obj: 0.002457, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.676907, Class: 1.000000, Obj: 0.414320, No Obj: 0.002645, Avg Recall: 0.860000, count: 100 -6420: 0.441120, 0.439667 avg, 0.001000 rate, 28.531712 seconds, 1643520 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 9.694144 seconds -Region Avg IOU: 0.652858, Class: 1.000000, Obj: 0.413499, No Obj: 0.002671, Avg Recall: 0.815126, count: 119 -Region Avg IOU: 0.653043, Class: 1.000000, Obj: 0.373150, No Obj: 0.002734, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.678749, Class: 1.000000, Obj: 0.433036, No Obj: 0.002587, Avg Recall: 0.845455, count: 110 -Region Avg IOU: 0.581304, Class: 1.000000, Obj: 0.368705, No Obj: 0.002275, Avg Recall: 0.663866, count: 119 -Region Avg IOU: 0.621381, Class: 1.000000, Obj: 0.368343, No Obj: 0.002567, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.677150, Class: 1.000000, Obj: 0.416823, No Obj: 0.002696, Avg Recall: 0.836538, count: 104 -Region Avg IOU: 0.689871, Class: 1.000000, Obj: 0.436632, No Obj: 0.002694, Avg Recall: 0.852941, count: 102 -Region Avg IOU: 0.623850, Class: 1.000000, Obj: 0.423049, No Obj: 0.002543, Avg Recall: 0.791667, count: 96 -6421: 0.416016, 0.437302 avg, 0.001000 rate, 28.504591 seconds, 1643776 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.672566, Class: 1.000000, Obj: 0.434648, No Obj: 0.002613, Avg Recall: 0.815126, count: 119 -Region Avg IOU: 0.637051, Class: 1.000000, Obj: 0.403688, No Obj: 0.002640, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.622095, Class: 1.000000, Obj: 0.370209, No Obj: 0.002869, Avg Recall: 0.735537, count: 121 -Region Avg IOU: 0.619258, Class: 1.000000, Obj: 0.417181, No Obj: 0.002549, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.644307, Class: 1.000000, Obj: 0.421088, No Obj: 0.002415, Avg Recall: 0.814159, count: 113 -Region Avg IOU: 0.683189, Class: 1.000000, Obj: 0.414239, No Obj: 0.003034, Avg Recall: 0.887097, count: 124 -Region Avg IOU: 0.670832, Class: 1.000000, Obj: 0.461676, No Obj: 0.002519, Avg Recall: 0.836735, count: 98 -Region Avg IOU: 0.680127, Class: 1.000000, Obj: 0.453397, No Obj: 0.002516, Avg Recall: 0.844037, count: 109 -6422: 0.425723, 0.436144 avg, 0.001000 rate, 28.436005 seconds, 1644032 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.602299, Class: 1.000000, Obj: 0.371975, No Obj: 0.002835, Avg Recall: 0.701493, count: 134 -Region Avg IOU: 0.681546, Class: 1.000000, Obj: 0.367928, No Obj: 0.002245, Avg Recall: 0.904762, count: 84 -Region Avg IOU: 0.651485, Class: 1.000000, Obj: 0.392048, No Obj: 0.002443, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.648719, Class: 1.000000, Obj: 0.402555, No Obj: 0.002664, Avg Recall: 0.820312, count: 128 -Region Avg IOU: 0.708897, Class: 1.000000, Obj: 0.414002, No Obj: 0.002551, Avg Recall: 0.880734, count: 109 -Region Avg IOU: 0.651289, Class: 1.000000, Obj: 0.381752, No Obj: 0.002674, Avg Recall: 0.831858, count: 113 -Region Avg IOU: 0.610232, Class: 1.000000, Obj: 0.405701, No Obj: 0.002704, Avg Recall: 0.713115, count: 122 -Region Avg IOU: 0.653308, Class: 1.000000, Obj: 0.387609, No Obj: 0.002801, Avg Recall: 0.822581, count: 124 -6423: 0.460081, 0.438538 avg, 0.001000 rate, 29.242085 seconds, 1644288 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.637778, Class: 1.000000, Obj: 0.413771, No Obj: 0.002907, Avg Recall: 0.780303, count: 132 -Region Avg IOU: 0.643376, Class: 1.000000, Obj: 0.389502, No Obj: 0.003099, Avg Recall: 0.784000, count: 125 -Region Avg IOU: 0.622461, Class: 1.000000, Obj: 0.376552, No Obj: 0.002513, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.646832, Class: 1.000000, Obj: 0.410879, No Obj: 0.002909, Avg Recall: 0.771654, count: 127 -Region Avg IOU: 0.645181, Class: 1.000000, Obj: 0.426952, No Obj: 0.002612, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.659590, Class: 1.000000, Obj: 0.447850, No Obj: 0.002500, Avg Recall: 0.842105, count: 95 -Region Avg IOU: 0.652162, Class: 1.000000, Obj: 0.432892, No Obj: 0.003049, Avg Recall: 0.771654, count: 127 -Region Avg IOU: 0.627222, Class: 1.000000, Obj: 0.388858, No Obj: 0.002255, Avg Recall: 0.804348, count: 92 -6424: 0.467060, 0.441390 avg, 0.001000 rate, 28.429876 seconds, 1644544 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.667026, Class: 1.000000, Obj: 0.432808, No Obj: 0.002659, Avg Recall: 0.842105, count: 114 -Region Avg IOU: 0.685163, Class: 1.000000, Obj: 0.423477, No Obj: 0.002960, Avg Recall: 0.873016, count: 126 -Region Avg IOU: 0.638759, Class: 1.000000, Obj: 0.375137, No Obj: 0.002478, Avg Recall: 0.786885, count: 122 -Region Avg IOU: 0.654208, Class: 1.000000, Obj: 0.407959, No Obj: 0.002605, Avg Recall: 0.829545, count: 88 -Region Avg IOU: 0.664270, Class: 1.000000, Obj: 0.409324, No Obj: 0.002854, Avg Recall: 0.838235, count: 136 -Region Avg IOU: 0.666305, Class: 1.000000, Obj: 0.403447, No Obj: 0.003033, Avg Recall: 0.801471, count: 136 -Region Avg IOU: 0.652126, Class: 1.000000, Obj: 0.416181, No Obj: 0.002561, Avg Recall: 0.817391, count: 115 -Region Avg IOU: 0.691682, Class: 1.000000, Obj: 0.426914, No Obj: 0.002404, Avg Recall: 0.875000, count: 88 -6425: 0.446118, 0.441863 avg, 0.001000 rate, 27.834141 seconds, 1644800 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.712190, Class: 1.000000, Obj: 0.459376, No Obj: 0.002433, Avg Recall: 0.892473, count: 93 -Region Avg IOU: 0.652087, Class: 1.000000, Obj: 0.440037, No Obj: 0.002357, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.682090, Class: 1.000000, Obj: 0.436477, No Obj: 0.002521, Avg Recall: 0.851485, count: 101 -Region Avg IOU: 0.665502, Class: 1.000000, Obj: 0.418897, No Obj: 0.002760, Avg Recall: 0.831858, count: 113 -Region Avg IOU: 0.666834, Class: 1.000000, Obj: 0.413591, No Obj: 0.002330, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.652184, Class: 1.000000, Obj: 0.420459, No Obj: 0.002511, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.667786, Class: 1.000000, Obj: 0.413837, No Obj: 0.002637, Avg Recall: 0.854369, count: 103 -Region Avg IOU: 0.667239, Class: 1.000000, Obj: 0.411396, No Obj: 0.002338, Avg Recall: 0.824176, count: 91 -6426: 0.394693, 0.437146 avg, 0.001000 rate, 27.678137 seconds, 1645056 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.628419, Class: 1.000000, Obj: 0.441368, No Obj: 0.002733, Avg Recall: 0.761538, count: 130 -Region Avg IOU: 0.685167, Class: 1.000000, Obj: 0.457465, No Obj: 0.002814, Avg Recall: 0.854369, count: 103 -Region Avg IOU: 0.654787, Class: 1.000000, Obj: 0.419382, No Obj: 0.002171, Avg Recall: 0.876543, count: 81 -Region Avg IOU: 0.689220, Class: 1.000000, Obj: 0.454803, No Obj: 0.002739, Avg Recall: 0.858407, count: 113 -Region Avg IOU: 0.630003, Class: 1.000000, Obj: 0.379614, No Obj: 0.002458, Avg Recall: 0.732673, count: 101 -Region Avg IOU: 0.643905, Class: 1.000000, Obj: 0.365308, No Obj: 0.002625, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.667913, Class: 1.000000, Obj: 0.414657, No Obj: 0.002576, Avg Recall: 0.842593, count: 108 -Region Avg IOU: 0.674240, Class: 1.000000, Obj: 0.408997, No Obj: 0.002732, Avg Recall: 0.826923, count: 104 -6427: 0.436623, 0.437093 avg, 0.001000 rate, 26.617264 seconds, 1645312 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.662846, Class: 1.000000, Obj: 0.434466, No Obj: 0.002353, Avg Recall: 0.835616, count: 73 -Region Avg IOU: 0.670744, Class: 1.000000, Obj: 0.371056, No Obj: 0.002856, Avg Recall: 0.815126, count: 119 -Region Avg IOU: 0.661205, Class: 1.000000, Obj: 0.395823, No Obj: 0.002555, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.672441, Class: 1.000000, Obj: 0.456003, No Obj: 0.002925, Avg Recall: 0.821138, count: 123 -Region Avg IOU: 0.667640, Class: 1.000000, Obj: 0.412003, No Obj: 0.002488, Avg Recall: 0.834783, count: 115 -Region Avg IOU: 0.654047, Class: 1.000000, Obj: 0.391917, No Obj: 0.002818, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.689467, Class: 1.000000, Obj: 0.435495, No Obj: 0.002220, Avg Recall: 0.864198, count: 81 -Region Avg IOU: 0.692674, Class: 1.000000, Obj: 0.400272, No Obj: 0.002463, Avg Recall: 0.866667, count: 90 -6428: 0.400390, 0.433423 avg, 0.001000 rate, 27.288620 seconds, 1645568 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.687424, Class: 1.000000, Obj: 0.463482, No Obj: 0.002599, Avg Recall: 0.872340, count: 94 -Region Avg IOU: 0.690312, Class: 1.000000, Obj: 0.423445, No Obj: 0.002831, Avg Recall: 0.870690, count: 116 -Region Avg IOU: 0.672992, Class: 1.000000, Obj: 0.436929, No Obj: 0.002782, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.613547, Class: 1.000000, Obj: 0.342353, No Obj: 0.002690, Avg Recall: 0.724138, count: 145 -Region Avg IOU: 0.681131, Class: 1.000000, Obj: 0.393485, No Obj: 0.002565, Avg Recall: 0.838710, count: 124 -Region Avg IOU: 0.641045, Class: 1.000000, Obj: 0.354236, No Obj: 0.002643, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.627700, Class: 1.000000, Obj: 0.402310, No Obj: 0.002448, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.646154, Class: 1.000000, Obj: 0.410940, No Obj: 0.002659, Avg Recall: 0.780702, count: 114 -6429: 0.467979, 0.436879 avg, 0.001000 rate, 27.519901 seconds, 1645824 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.645595, Class: 1.000000, Obj: 0.412664, No Obj: 0.002588, Avg Recall: 0.801980, count: 101 -Region Avg IOU: 0.666768, Class: 1.000000, Obj: 0.443435, No Obj: 0.002986, Avg Recall: 0.845588, count: 136 -Region Avg IOU: 0.676442, Class: 1.000000, Obj: 0.459866, No Obj: 0.002820, Avg Recall: 0.827273, count: 110 -Region Avg IOU: 0.655742, Class: 1.000000, Obj: 0.382849, No Obj: 0.002198, Avg Recall: 0.819277, count: 83 -Region Avg IOU: 0.658515, Class: 1.000000, Obj: 0.403031, No Obj: 0.002800, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.703705, Class: 1.000000, Obj: 0.434469, No Obj: 0.002815, Avg Recall: 0.888889, count: 117 -Region Avg IOU: 0.685053, Class: 1.000000, Obj: 0.382069, No Obj: 0.002809, Avg Recall: 0.837838, count: 111 -Region Avg IOU: 0.631830, Class: 1.000000, Obj: 0.430489, No Obj: 0.002759, Avg Recall: 0.736364, count: 110 -6430: 0.435006, 0.436691 avg, 0.001000 rate, 28.672527 seconds, 1646080 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 14.013126 seconds -Region Avg IOU: 0.676700, Class: 1.000000, Obj: 0.427605, No Obj: 0.002562, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.649004, Class: 1.000000, Obj: 0.409159, No Obj: 0.003065, Avg Recall: 0.795276, count: 127 -Region Avg IOU: 0.646866, Class: 1.000000, Obj: 0.445550, No Obj: 0.002533, Avg Recall: 0.824742, count: 97 -Region Avg IOU: 0.649945, Class: 1.000000, Obj: 0.415501, No Obj: 0.003114, Avg Recall: 0.778626, count: 131 -Region Avg IOU: 0.662529, Class: 1.000000, Obj: 0.471849, No Obj: 0.002404, Avg Recall: 0.806818, count: 88 -Region Avg IOU: 0.652894, Class: 1.000000, Obj: 0.396910, No Obj: 0.002507, Avg Recall: 0.851485, count: 101 -Region Avg IOU: 0.648448, Class: 1.000000, Obj: 0.390693, No Obj: 0.002864, Avg Recall: 0.786885, count: 122 -Region Avg IOU: 0.658116, Class: 1.000000, Obj: 0.411638, No Obj: 0.002848, Avg Recall: 0.815126, count: 119 -6431: 0.413906, 0.434413 avg, 0.001000 rate, 26.616697 seconds, 1646336 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.632666, Class: 1.000000, Obj: 0.404262, No Obj: 0.002880, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.670435, Class: 1.000000, Obj: 0.411650, No Obj: 0.002905, Avg Recall: 0.823529, count: 102 -Region Avg IOU: 0.640524, Class: 1.000000, Obj: 0.419290, No Obj: 0.002878, Avg Recall: 0.776859, count: 121 -Region Avg IOU: 0.681280, Class: 1.000000, Obj: 0.447641, No Obj: 0.003069, Avg Recall: 0.863248, count: 117 -Region Avg IOU: 0.676051, Class: 1.000000, Obj: 0.418385, No Obj: 0.003031, Avg Recall: 0.842975, count: 121 -Region Avg IOU: 0.670480, Class: 1.000000, Obj: 0.437743, No Obj: 0.002711, Avg Recall: 0.865979, count: 97 -Region Avg IOU: 0.674305, Class: 1.000000, Obj: 0.493681, No Obj: 0.002266, Avg Recall: 0.857143, count: 84 -Region Avg IOU: 0.619503, Class: 1.000000, Obj: 0.430117, No Obj: 0.002781, Avg Recall: 0.726496, count: 117 -6432: 0.424323, 0.433404 avg, 0.001000 rate, 27.850027 seconds, 1646592 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.635283, Class: 1.000000, Obj: 0.390490, No Obj: 0.002924, Avg Recall: 0.757353, count: 136 -Region Avg IOU: 0.683133, Class: 1.000000, Obj: 0.459847, No Obj: 0.002794, Avg Recall: 0.834783, count: 115 -Region Avg IOU: 0.694049, Class: 1.000000, Obj: 0.452781, No Obj: 0.002858, Avg Recall: 0.890909, count: 110 -Region Avg IOU: 0.637075, Class: 1.000000, Obj: 0.387114, No Obj: 0.003088, Avg Recall: 0.781690, count: 142 -Region Avg IOU: 0.662594, Class: 1.000000, Obj: 0.465963, No Obj: 0.002632, Avg Recall: 0.830357, count: 112 -Region Avg IOU: 0.679564, Class: 1.000000, Obj: 0.409077, No Obj: 0.002468, Avg Recall: 0.885417, count: 96 -Region Avg IOU: 0.654326, Class: 1.000000, Obj: 0.391066, No Obj: 0.002453, Avg Recall: 0.829545, count: 88 -Region Avg IOU: 0.687319, Class: 1.000000, Obj: 0.461702, No Obj: 0.003243, Avg Recall: 0.827869, count: 122 -6433: 0.459352, 0.435999 avg, 0.001000 rate, 28.197891 seconds, 1646848 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.646883, Class: 1.000000, Obj: 0.453014, No Obj: 0.002758, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.646906, Class: 1.000000, Obj: 0.443256, No Obj: 0.002662, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.601436, Class: 1.000000, Obj: 0.434825, No Obj: 0.002710, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.662464, Class: 1.000000, Obj: 0.414470, No Obj: 0.002927, Avg Recall: 0.847328, count: 131 -Region Avg IOU: 0.617435, Class: 1.000000, Obj: 0.373433, No Obj: 0.002843, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.645912, Class: 1.000000, Obj: 0.467949, No Obj: 0.003204, Avg Recall: 0.765625, count: 128 -Region Avg IOU: 0.680116, Class: 1.000000, Obj: 0.417132, No Obj: 0.003174, Avg Recall: 0.840580, count: 138 -Region Avg IOU: 0.650235, Class: 1.000000, Obj: 0.431366, No Obj: 0.002935, Avg Recall: 0.801653, count: 121 -6434: 0.471317, 0.439531 avg, 0.001000 rate, 27.260067 seconds, 1647104 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.671613, Class: 1.000000, Obj: 0.528886, No Obj: 0.002484, Avg Recall: 0.816092, count: 87 -Region Avg IOU: 0.667624, Class: 1.000000, Obj: 0.499818, No Obj: 0.002449, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.678264, Class: 1.000000, Obj: 0.442241, No Obj: 0.002787, Avg Recall: 0.839286, count: 112 -Region Avg IOU: 0.670857, Class: 1.000000, Obj: 0.397490, No Obj: 0.002378, Avg Recall: 0.829545, count: 88 -Region Avg IOU: 0.657812, Class: 1.000000, Obj: 0.427155, No Obj: 0.002689, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.637355, Class: 1.000000, Obj: 0.354862, No Obj: 0.002454, Avg Recall: 0.781609, count: 87 -Region Avg IOU: 0.688680, Class: 1.000000, Obj: 0.446419, No Obj: 0.003095, Avg Recall: 0.839695, count: 131 -Region Avg IOU: 0.637118, Class: 1.000000, Obj: 0.448763, No Obj: 0.003022, Avg Recall: 0.766990, count: 103 -6435: 0.397217, 0.435299 avg, 0.001000 rate, 28.439455 seconds, 1647360 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.647698, Class: 1.000000, Obj: 0.445662, No Obj: 0.002466, Avg Recall: 0.819149, count: 94 -Region Avg IOU: 0.641513, Class: 1.000000, Obj: 0.412111, No Obj: 0.003104, Avg Recall: 0.823529, count: 119 -Region Avg IOU: 0.708793, Class: 1.000000, Obj: 0.473612, No Obj: 0.003372, Avg Recall: 0.893443, count: 122 -Region Avg IOU: 0.673116, Class: 1.000000, Obj: 0.410524, No Obj: 0.002930, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.629819, Class: 1.000000, Obj: 0.376363, No Obj: 0.002616, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.661899, Class: 1.000000, Obj: 0.412312, No Obj: 0.003776, Avg Recall: 0.814815, count: 162 -Region Avg IOU: 0.670741, Class: 1.000000, Obj: 0.436777, No Obj: 0.002418, Avg Recall: 0.808989, count: 89 -Region Avg IOU: 0.592794, Class: 1.000000, Obj: 0.416581, No Obj: 0.002312, Avg Recall: 0.703704, count: 108 -6436: 0.447340, 0.436503 avg, 0.001000 rate, 29.388462 seconds, 1647616 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.646175, Class: 1.000000, Obj: 0.430958, No Obj: 0.002287, Avg Recall: 0.753623, count: 69 -Region Avg IOU: 0.684355, Class: 1.000000, Obj: 0.485641, No Obj: 0.002900, Avg Recall: 0.791667, count: 120 -Region Avg IOU: 0.654941, Class: 1.000000, Obj: 0.415460, No Obj: 0.002579, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.672461, Class: 1.000000, Obj: 0.403435, No Obj: 0.002897, Avg Recall: 0.817308, count: 104 -Region Avg IOU: 0.651933, Class: 1.000000, Obj: 0.387218, No Obj: 0.002647, Avg Recall: 0.817308, count: 104 -Region Avg IOU: 0.706929, Class: 1.000000, Obj: 0.457110, No Obj: 0.002841, Avg Recall: 0.889908, count: 109 -Region Avg IOU: 0.674316, Class: 1.000000, Obj: 0.437461, No Obj: 0.002553, Avg Recall: 0.847619, count: 105 -Region Avg IOU: 0.711213, Class: 1.000000, Obj: 0.453518, No Obj: 0.002948, Avg Recall: 0.897196, count: 107 -6437: 0.409326, 0.433786 avg, 0.001000 rate, 27.954592 seconds, 1647872 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.681745, Class: 1.000000, Obj: 0.409053, No Obj: 0.002427, Avg Recall: 0.820225, count: 89 -Region Avg IOU: 0.732166, Class: 1.000000, Obj: 0.419652, No Obj: 0.002399, Avg Recall: 0.939759, count: 83 -Region Avg IOU: 0.641250, Class: 1.000000, Obj: 0.355748, No Obj: 0.002950, Avg Recall: 0.785124, count: 121 -Region Avg IOU: 0.650388, Class: 1.000000, Obj: 0.392222, No Obj: 0.002648, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.682313, Class: 1.000000, Obj: 0.406749, No Obj: 0.002586, Avg Recall: 0.833333, count: 96 -Region Avg IOU: 0.659404, Class: 1.000000, Obj: 0.461983, No Obj: 0.002753, Avg Recall: 0.804124, count: 97 -Region Avg IOU: 0.687918, Class: 1.000000, Obj: 0.419581, No Obj: 0.002670, Avg Recall: 0.871560, count: 109 -Region Avg IOU: 0.675731, Class: 1.000000, Obj: 0.378759, No Obj: 0.002719, Avg Recall: 0.820755, count: 106 -6438: 0.426973, 0.433104 avg, 0.001000 rate, 26.518766 seconds, 1648128 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.655753, Class: 1.000000, Obj: 0.388107, No Obj: 0.002664, Avg Recall: 0.806723, count: 119 -Region Avg IOU: 0.685754, Class: 1.000000, Obj: 0.423158, No Obj: 0.002540, Avg Recall: 0.865979, count: 97 -Region Avg IOU: 0.617546, Class: 1.000000, Obj: 0.378932, No Obj: 0.002573, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.601879, Class: 1.000000, Obj: 0.408574, No Obj: 0.002031, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.670676, Class: 1.000000, Obj: 0.455677, No Obj: 0.002237, Avg Recall: 0.807692, count: 78 -Region Avg IOU: 0.668272, Class: 1.000000, Obj: 0.426056, No Obj: 0.002728, Avg Recall: 0.839623, count: 106 -Region Avg IOU: 0.672293, Class: 1.000000, Obj: 0.418008, No Obj: 0.002520, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.595364, Class: 1.000000, Obj: 0.356442, No Obj: 0.002213, Avg Recall: 0.709091, count: 110 -6439: 0.419329, 0.431727 avg, 0.001000 rate, 27.758200 seconds, 1648384 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.687507, Class: 1.000000, Obj: 0.425726, No Obj: 0.002536, Avg Recall: 0.851485, count: 101 -Region Avg IOU: 0.644415, Class: 1.000000, Obj: 0.425155, No Obj: 0.002093, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.688632, Class: 1.000000, Obj: 0.492867, No Obj: 0.002423, Avg Recall: 0.875000, count: 88 -Region Avg IOU: 0.652355, Class: 1.000000, Obj: 0.428939, No Obj: 0.002435, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.646784, Class: 1.000000, Obj: 0.358870, No Obj: 0.002580, Avg Recall: 0.795276, count: 127 -Region Avg IOU: 0.630666, Class: 1.000000, Obj: 0.380223, No Obj: 0.002869, Avg Recall: 0.781690, count: 142 -Region Avg IOU: 0.669272, Class: 1.000000, Obj: 0.397179, No Obj: 0.002556, Avg Recall: 0.813084, count: 107 -Region Avg IOU: 0.630221, Class: 1.000000, Obj: 0.423803, No Obj: 0.002145, Avg Recall: 0.744681, count: 94 -6440: 0.412745, 0.429829 avg, 0.001000 rate, 28.763945 seconds, 1648640 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 15.854778 seconds -Region Avg IOU: 0.587481, Class: 1.000000, Obj: 0.372732, No Obj: 0.002780, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.588165, Class: 1.000000, Obj: 0.346617, No Obj: 0.002739, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.606319, Class: 1.000000, Obj: 0.390234, No Obj: 0.003035, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.625490, Class: 1.000000, Obj: 0.378825, No Obj: 0.002937, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.590596, Class: 1.000000, Obj: 0.367929, No Obj: 0.003384, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.669650, Class: 1.000000, Obj: 0.395732, No Obj: 0.003062, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.641086, Class: 1.000000, Obj: 0.431919, No Obj: 0.003253, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.603785, Class: 1.000000, Obj: 0.389830, No Obj: 0.002961, Avg Recall: 0.708333, count: 120 -6441: 0.444779, 0.431324 avg, 0.001000 rate, 24.997952 seconds, 1648896 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.614144, Class: 1.000000, Obj: 0.368439, No Obj: 0.004052, Avg Recall: 0.715232, count: 151 -Region Avg IOU: 0.654207, Class: 1.000000, Obj: 0.447748, No Obj: 0.002641, Avg Recall: 0.807229, count: 83 -Region Avg IOU: 0.567819, Class: 1.000000, Obj: 0.380006, No Obj: 0.002467, Avg Recall: 0.647059, count: 85 -Region Avg IOU: 0.634943, Class: 1.000000, Obj: 0.408357, No Obj: 0.003062, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.604030, Class: 1.000000, Obj: 0.390591, No Obj: 0.003083, Avg Recall: 0.746032, count: 126 -Region Avg IOU: 0.591884, Class: 1.000000, Obj: 0.373490, No Obj: 0.002775, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.633775, Class: 1.000000, Obj: 0.417028, No Obj: 0.002826, Avg Recall: 0.795455, count: 88 -Region Avg IOU: 0.568630, Class: 1.000000, Obj: 0.386393, No Obj: 0.002792, Avg Recall: 0.666667, count: 108 -6442: 0.424848, 0.430676 avg, 0.001000 rate, 24.767931 seconds, 1649152 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.641080, Class: 1.000000, Obj: 0.415602, No Obj: 0.003153, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.666633, Class: 1.000000, Obj: 0.399108, No Obj: 0.002996, Avg Recall: 0.845361, count: 97 -Region Avg IOU: 0.620353, Class: 1.000000, Obj: 0.426976, No Obj: 0.003618, Avg Recall: 0.752066, count: 121 -Region Avg IOU: 0.625236, Class: 1.000000, Obj: 0.425839, No Obj: 0.002830, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.634992, Class: 1.000000, Obj: 0.445337, No Obj: 0.003481, Avg Recall: 0.777778, count: 126 -Region Avg IOU: 0.601659, Class: 1.000000, Obj: 0.441778, No Obj: 0.002659, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.612966, Class: 1.000000, Obj: 0.440024, No Obj: 0.002823, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.609157, Class: 1.000000, Obj: 0.387979, No Obj: 0.003143, Avg Recall: 0.723577, count: 123 -6443: 0.424978, 0.430106 avg, 0.001000 rate, 25.521894 seconds, 1649408 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.584982, Class: 1.000000, Obj: 0.396483, No Obj: 0.003348, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.579300, Class: 1.000000, Obj: 0.396306, No Obj: 0.002981, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.645349, Class: 1.000000, Obj: 0.431747, No Obj: 0.003234, Avg Recall: 0.781250, count: 96 -Region Avg IOU: 0.586263, Class: 1.000000, Obj: 0.376539, No Obj: 0.003603, Avg Recall: 0.704545, count: 132 -Region Avg IOU: 0.621657, Class: 1.000000, Obj: 0.426385, No Obj: 0.002917, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.644062, Class: 1.000000, Obj: 0.425505, No Obj: 0.003578, Avg Recall: 0.800000, count: 120 -Region Avg IOU: 0.565814, Class: 1.000000, Obj: 0.340074, No Obj: 0.003056, Avg Recall: 0.688000, count: 125 -Region Avg IOU: 0.625775, Class: 1.000000, Obj: 0.369119, No Obj: 0.002987, Avg Recall: 0.755319, count: 94 -6444: 0.415081, 0.428604 avg, 0.001000 rate, 24.354237 seconds, 1649664 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.656423, Class: 1.000000, Obj: 0.425813, No Obj: 0.003032, Avg Recall: 0.793478, count: 92 -Region Avg IOU: 0.575472, Class: 1.000000, Obj: 0.428249, No Obj: 0.002807, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.600857, Class: 1.000000, Obj: 0.406837, No Obj: 0.003090, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.623483, Class: 1.000000, Obj: 0.469613, No Obj: 0.003330, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.612765, Class: 1.000000, Obj: 0.413365, No Obj: 0.002903, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.597037, Class: 1.000000, Obj: 0.419683, No Obj: 0.003560, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.634184, Class: 1.000000, Obj: 0.453819, No Obj: 0.002892, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.634284, Class: 1.000000, Obj: 0.454077, No Obj: 0.003552, Avg Recall: 0.772727, count: 132 -6445: 0.377118, 0.423455 avg, 0.001000 rate, 22.793943 seconds, 1649920 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.647521, Class: 1.000000, Obj: 0.447035, No Obj: 0.002971, Avg Recall: 0.750000, count: 84 -Region Avg IOU: 0.609708, Class: 1.000000, Obj: 0.443956, No Obj: 0.003420, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.652459, Class: 1.000000, Obj: 0.466751, No Obj: 0.002962, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.606951, Class: 1.000000, Obj: 0.421327, No Obj: 0.003105, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.636564, Class: 1.000000, Obj: 0.465371, No Obj: 0.003241, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.620825, Class: 1.000000, Obj: 0.434736, No Obj: 0.003610, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.545850, Class: 1.000000, Obj: 0.377255, No Obj: 0.003014, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.608405, Class: 1.000000, Obj: 0.470126, No Obj: 0.002998, Avg Recall: 0.719298, count: 114 -6446: 0.402837, 0.421393 avg, 0.001000 rate, 24.570032 seconds, 1650176 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.609767, Class: 1.000000, Obj: 0.406135, No Obj: 0.003413, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.616346, Class: 1.000000, Obj: 0.413485, No Obj: 0.003699, Avg Recall: 0.711864, count: 118 -Region Avg IOU: 0.589916, Class: 1.000000, Obj: 0.376894, No Obj: 0.003658, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.662797, Class: 1.000000, Obj: 0.487192, No Obj: 0.003549, Avg Recall: 0.822222, count: 90 -Region Avg IOU: 0.598385, Class: 1.000000, Obj: 0.464937, No Obj: 0.003867, Avg Recall: 0.680000, count: 125 -Region Avg IOU: 0.539425, Class: 1.000000, Obj: 0.431468, No Obj: 0.002930, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.599011, Class: 1.000000, Obj: 0.470365, No Obj: 0.003060, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.635464, Class: 1.000000, Obj: 0.470413, No Obj: 0.003325, Avg Recall: 0.766667, count: 90 -6447: 0.376034, 0.416857 avg, 0.001000 rate, 25.918385 seconds, 1650432 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.622903, Class: 1.000000, Obj: 0.469348, No Obj: 0.002742, Avg Recall: 0.759036, count: 83 -Region Avg IOU: 0.607680, Class: 1.000000, Obj: 0.452360, No Obj: 0.003832, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.608888, Class: 1.000000, Obj: 0.436444, No Obj: 0.003402, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.583458, Class: 1.000000, Obj: 0.406081, No Obj: 0.003752, Avg Recall: 0.671141, count: 149 -Region Avg IOU: 0.572863, Class: 1.000000, Obj: 0.407833, No Obj: 0.003322, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.661054, Class: 1.000000, Obj: 0.419741, No Obj: 0.003592, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.560047, Class: 1.000000, Obj: 0.374218, No Obj: 0.003276, Avg Recall: 0.653543, count: 127 -Region Avg IOU: 0.656922, Class: 1.000000, Obj: 0.461895, No Obj: 0.004064, Avg Recall: 0.825758, count: 132 -6448: 0.445101, 0.419682 avg, 0.001000 rate, 26.254858 seconds, 1650688 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.662039, Class: 1.000000, Obj: 0.454046, No Obj: 0.003258, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.612274, Class: 1.000000, Obj: 0.437703, No Obj: 0.003433, Avg Recall: 0.743802, count: 121 -Region Avg IOU: 0.586374, Class: 1.000000, Obj: 0.417636, No Obj: 0.003615, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.600656, Class: 1.000000, Obj: 0.407845, No Obj: 0.003483, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.637163, Class: 1.000000, Obj: 0.452815, No Obj: 0.003668, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.643066, Class: 1.000000, Obj: 0.500075, No Obj: 0.003573, Avg Recall: 0.796117, count: 103 -Region Avg IOU: 0.588413, Class: 1.000000, Obj: 0.426631, No Obj: 0.003546, Avg Recall: 0.669421, count: 121 -Region Avg IOU: 0.618676, Class: 1.000000, Obj: 0.446152, No Obj: 0.003891, Avg Recall: 0.779528, count: 127 -6449: 0.411343, 0.418848 avg, 0.001000 rate, 26.950857 seconds, 1650944 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.579497, Class: 1.000000, Obj: 0.428769, No Obj: 0.003559, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.581921, Class: 1.000000, Obj: 0.428127, No Obj: 0.003010, Avg Recall: 0.711111, count: 90 -Region Avg IOU: 0.609459, Class: 1.000000, Obj: 0.454691, No Obj: 0.003637, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.598666, Class: 1.000000, Obj: 0.423187, No Obj: 0.003605, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.591432, Class: 1.000000, Obj: 0.431877, No Obj: 0.003995, Avg Recall: 0.683099, count: 142 -Region Avg IOU: 0.603469, Class: 1.000000, Obj: 0.420918, No Obj: 0.004100, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.574916, Class: 1.000000, Obj: 0.411376, No Obj: 0.003809, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.587813, Class: 1.000000, Obj: 0.445473, No Obj: 0.003219, Avg Recall: 0.718750, count: 96 -6450: 0.472403, 0.424203 avg, 0.001000 rate, 24.378044 seconds, 1651200 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000088 seconds -Region Avg IOU: 0.658524, Class: 1.000000, Obj: 0.415032, No Obj: 0.003600, Avg Recall: 0.843972, count: 141 -Region Avg IOU: 0.657633, Class: 1.000000, Obj: 0.420795, No Obj: 0.002901, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.653736, Class: 1.000000, Obj: 0.464685, No Obj: 0.003079, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.668400, Class: 1.000000, Obj: 0.428068, No Obj: 0.003034, Avg Recall: 0.854701, count: 117 -Region Avg IOU: 0.685092, Class: 1.000000, Obj: 0.445250, No Obj: 0.002976, Avg Recall: 0.854545, count: 110 -Region Avg IOU: 0.666558, Class: 1.000000, Obj: 0.447830, No Obj: 0.002894, Avg Recall: 0.846774, count: 124 -Region Avg IOU: 0.658701, Class: 1.000000, Obj: 0.411807, No Obj: 0.002934, Avg Recall: 0.825397, count: 126 -Region Avg IOU: 0.632142, Class: 1.000000, Obj: 0.419646, No Obj: 0.003261, Avg Recall: 0.776000, count: 125 -6451: 0.482866, 0.430070 avg, 0.001000 rate, 28.681639 seconds, 1651456 images -Loaded: 0.000138 seconds -Region Avg IOU: 0.641613, Class: 1.000000, Obj: 0.393368, No Obj: 0.002968, Avg Recall: 0.796992, count: 133 -Region Avg IOU: 0.621488, Class: 1.000000, Obj: 0.437843, No Obj: 0.002394, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.651075, Class: 1.000000, Obj: 0.424452, No Obj: 0.002686, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.649233, Class: 1.000000, Obj: 0.470127, No Obj: 0.002730, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.621077, Class: 1.000000, Obj: 0.426614, No Obj: 0.002658, Avg Recall: 0.755556, count: 90 -Region Avg IOU: 0.619128, Class: 1.000000, Obj: 0.418176, No Obj: 0.002516, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.671664, Class: 1.000000, Obj: 0.456399, No Obj: 0.003003, Avg Recall: 0.857143, count: 119 -Region Avg IOU: 0.636707, Class: 1.000000, Obj: 0.411582, No Obj: 0.003221, Avg Recall: 0.770833, count: 144 -6452: 0.511018, 0.438164 avg, 0.001000 rate, 28.802902 seconds, 1651712 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.662788, Class: 1.000000, Obj: 0.495908, No Obj: 0.002933, Avg Recall: 0.821429, count: 112 -Region Avg IOU: 0.654488, Class: 1.000000, Obj: 0.448052, No Obj: 0.003017, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.651845, Class: 1.000000, Obj: 0.393633, No Obj: 0.002398, Avg Recall: 0.840000, count: 100 -Region Avg IOU: 0.638902, Class: 1.000000, Obj: 0.367808, No Obj: 0.002911, Avg Recall: 0.758621, count: 145 -Region Avg IOU: 0.683774, Class: 1.000000, Obj: 0.428563, No Obj: 0.002901, Avg Recall: 0.848214, count: 112 -Region Avg IOU: 0.634553, Class: 1.000000, Obj: 0.362672, No Obj: 0.002398, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.683674, Class: 1.000000, Obj: 0.432997, No Obj: 0.002743, Avg Recall: 0.871795, count: 117 -Region Avg IOU: 0.673398, Class: 1.000000, Obj: 0.415940, No Obj: 0.002841, Avg Recall: 0.812500, count: 112 -6453: 0.460627, 0.440411 avg, 0.001000 rate, 28.102495 seconds, 1651968 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.675147, Class: 1.000000, Obj: 0.407103, No Obj: 0.002490, Avg Recall: 0.844037, count: 109 -Region Avg IOU: 0.679959, Class: 1.000000, Obj: 0.462980, No Obj: 0.002668, Avg Recall: 0.870000, count: 100 -Region Avg IOU: 0.560210, Class: 1.000000, Obj: 0.371080, No Obj: 0.002301, Avg Recall: 0.651163, count: 86 -Region Avg IOU: 0.649416, Class: 1.000000, Obj: 0.412908, No Obj: 0.002457, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.667556, Class: 1.000000, Obj: 0.395393, No Obj: 0.002381, Avg Recall: 0.801980, count: 101 -Region Avg IOU: 0.694028, Class: 1.000000, Obj: 0.422394, No Obj: 0.002272, Avg Recall: 0.833333, count: 84 -Region Avg IOU: 0.642741, Class: 1.000000, Obj: 0.408841, No Obj: 0.002174, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.681302, Class: 1.000000, Obj: 0.421950, No Obj: 0.002429, Avg Recall: 0.851064, count: 94 -6454: 0.401412, 0.436511 avg, 0.001000 rate, 28.624001 seconds, 1652224 images -Loaded: 0.000131 seconds -Region Avg IOU: 0.624606, Class: 1.000000, Obj: 0.374837, No Obj: 0.002361, Avg Recall: 0.756303, count: 119 -Region Avg IOU: 0.625855, Class: 1.000000, Obj: 0.380396, No Obj: 0.002443, Avg Recall: 0.766667, count: 120 -Region Avg IOU: 0.670736, Class: 1.000000, Obj: 0.395972, No Obj: 0.002538, Avg Recall: 0.860870, count: 115 -Region Avg IOU: 0.698056, Class: 1.000000, Obj: 0.409590, No Obj: 0.002567, Avg Recall: 0.877551, count: 98 -Region Avg IOU: 0.634193, Class: 1.000000, Obj: 0.396309, No Obj: 0.002277, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.644829, Class: 1.000000, Obj: 0.373669, No Obj: 0.002405, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.677951, Class: 1.000000, Obj: 0.461417, No Obj: 0.002384, Avg Recall: 0.846847, count: 111 -Region Avg IOU: 0.659749, Class: 1.000000, Obj: 0.436423, No Obj: 0.002458, Avg Recall: 0.803419, count: 117 -6455: 0.454141, 0.438274 avg, 0.001000 rate, 26.980366 seconds, 1652480 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.615066, Class: 1.000000, Obj: 0.369723, No Obj: 0.002580, Avg Recall: 0.788732, count: 142 -Region Avg IOU: 0.650088, Class: 1.000000, Obj: 0.415803, No Obj: 0.001967, Avg Recall: 0.795455, count: 88 -Region Avg IOU: 0.714462, Class: 1.000000, Obj: 0.384435, No Obj: 0.002291, Avg Recall: 0.893617, count: 94 -Region Avg IOU: 0.654040, Class: 1.000000, Obj: 0.408303, No Obj: 0.002085, Avg Recall: 0.813953, count: 86 -Region Avg IOU: 0.701549, Class: 1.000000, Obj: 0.447886, No Obj: 0.002102, Avg Recall: 0.896552, count: 87 -Region Avg IOU: 0.673146, Class: 1.000000, Obj: 0.412594, No Obj: 0.002738, Avg Recall: 0.849624, count: 133 -Region Avg IOU: 0.657932, Class: 1.000000, Obj: 0.393548, No Obj: 0.002448, Avg Recall: 0.814159, count: 113 -Region Avg IOU: 0.686641, Class: 1.000000, Obj: 0.427024, No Obj: 0.002627, Avg Recall: 0.831858, count: 113 -6456: 0.445794, 0.439026 avg, 0.001000 rate, 27.944775 seconds, 1652736 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.658424, Class: 1.000000, Obj: 0.421147, No Obj: 0.002220, Avg Recall: 0.820225, count: 89 -Region Avg IOU: 0.646718, Class: 1.000000, Obj: 0.399735, No Obj: 0.002729, Avg Recall: 0.788321, count: 137 -Region Avg IOU: 0.638363, Class: 1.000000, Obj: 0.421931, No Obj: 0.002349, Avg Recall: 0.789474, count: 114 -Region Avg IOU: 0.652043, Class: 1.000000, Obj: 0.414210, No Obj: 0.002133, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.632949, Class: 1.000000, Obj: 0.403003, No Obj: 0.002259, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.643909, Class: 1.000000, Obj: 0.393771, No Obj: 0.002569, Avg Recall: 0.787402, count: 127 -Region Avg IOU: 0.690876, Class: 1.000000, Obj: 0.435861, No Obj: 0.002658, Avg Recall: 0.849558, count: 113 -Region Avg IOU: 0.693156, Class: 1.000000, Obj: 0.370425, No Obj: 0.002281, Avg Recall: 0.892473, count: 93 -6457: 0.434986, 0.438622 avg, 0.001000 rate, 27.195335 seconds, 1652992 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.694333, Class: 1.000000, Obj: 0.432242, No Obj: 0.002193, Avg Recall: 0.861702, count: 94 -Region Avg IOU: 0.682042, Class: 1.000000, Obj: 0.400708, No Obj: 0.002256, Avg Recall: 0.896552, count: 87 -Region Avg IOU: 0.655311, Class: 1.000000, Obj: 0.373776, No Obj: 0.002575, Avg Recall: 0.796748, count: 123 -Region Avg IOU: 0.628635, Class: 1.000000, Obj: 0.316719, No Obj: 0.002477, Avg Recall: 0.731707, count: 123 -Region Avg IOU: 0.689232, Class: 1.000000, Obj: 0.456932, No Obj: 0.002778, Avg Recall: 0.890756, count: 119 -Region Avg IOU: 0.634867, Class: 1.000000, Obj: 0.372336, No Obj: 0.002765, Avg Recall: 0.796992, count: 133 -Region Avg IOU: 0.675535, Class: 1.000000, Obj: 0.403624, No Obj: 0.002621, Avg Recall: 0.842593, count: 108 -Region Avg IOU: 0.640004, Class: 1.000000, Obj: 0.405026, No Obj: 0.002609, Avg Recall: 0.779817, count: 109 -6458: 0.438912, 0.438651 avg, 0.001000 rate, 27.927994 seconds, 1653248 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.662878, Class: 1.000000, Obj: 0.424510, No Obj: 0.002273, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.615383, Class: 1.000000, Obj: 0.353974, No Obj: 0.002332, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.664985, Class: 1.000000, Obj: 0.467834, No Obj: 0.002449, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.678005, Class: 1.000000, Obj: 0.424202, No Obj: 0.002606, Avg Recall: 0.860656, count: 122 -Region Avg IOU: 0.675094, Class: 1.000000, Obj: 0.488881, No Obj: 0.002718, Avg Recall: 0.831858, count: 113 -Region Avg IOU: 0.643743, Class: 1.000000, Obj: 0.445232, No Obj: 0.002155, Avg Recall: 0.833333, count: 78 -Region Avg IOU: 0.668134, Class: 1.000000, Obj: 0.444832, No Obj: 0.002719, Avg Recall: 0.834951, count: 103 -Region Avg IOU: 0.698180, Class: 1.000000, Obj: 0.464841, No Obj: 0.002807, Avg Recall: 0.892157, count: 102 -6459: 0.386900, 0.433476 avg, 0.001000 rate, 29.363121 seconds, 1653504 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.674844, Class: 1.000000, Obj: 0.439499, No Obj: 0.002756, Avg Recall: 0.868687, count: 99 -Region Avg IOU: 0.652652, Class: 1.000000, Obj: 0.434140, No Obj: 0.002937, Avg Recall: 0.766129, count: 124 -Region Avg IOU: 0.626184, Class: 1.000000, Obj: 0.398882, No Obj: 0.002497, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.670375, Class: 1.000000, Obj: 0.470736, No Obj: 0.002522, Avg Recall: 0.784091, count: 88 -Region Avg IOU: 0.621360, Class: 1.000000, Obj: 0.398515, No Obj: 0.003004, Avg Recall: 0.746377, count: 138 -Region Avg IOU: 0.621086, Class: 1.000000, Obj: 0.395984, No Obj: 0.002737, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.633045, Class: 1.000000, Obj: 0.428265, No Obj: 0.003210, Avg Recall: 0.777778, count: 135 -Region Avg IOU: 0.668644, Class: 1.000000, Obj: 0.413993, No Obj: 0.002912, Avg Recall: 0.821138, count: 123 -6460: 0.457870, 0.435915 avg, 0.001000 rate, 28.701481 seconds, 1653760 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.021523 seconds -Region Avg IOU: 0.541760, Class: 1.000000, Obj: 0.355362, No Obj: 0.004093, Avg Recall: 0.611940, count: 134 -Region Avg IOU: 0.534743, Class: 1.000000, Obj: 0.309411, No Obj: 0.003514, Avg Recall: 0.607843, count: 102 -Region Avg IOU: 0.490674, Class: 1.000000, Obj: 0.353399, No Obj: 0.003291, Avg Recall: 0.518182, count: 110 -Region Avg IOU: 0.543428, Class: 1.000000, Obj: 0.336082, No Obj: 0.003737, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.559987, Class: 1.000000, Obj: 0.373729, No Obj: 0.003929, Avg Recall: 0.658730, count: 126 -Region Avg IOU: 0.602134, Class: 1.000000, Obj: 0.390226, No Obj: 0.003076, Avg Recall: 0.719512, count: 82 -Region Avg IOU: 0.538220, Class: 1.000000, Obj: 0.333800, No Obj: 0.003025, Avg Recall: 0.615385, count: 91 -Region Avg IOU: 0.551873, Class: 1.000000, Obj: 0.406467, No Obj: 0.003700, Avg Recall: 0.619048, count: 105 -6461: 0.415182, 0.433842 avg, 0.001000 rate, 21.060196 seconds, 1654016 images -Loaded: 1.706646 seconds -Region Avg IOU: 0.525292, Class: 1.000000, Obj: 0.313379, No Obj: 0.003903, Avg Recall: 0.613636, count: 132 -Region Avg IOU: 0.554363, Class: 1.000000, Obj: 0.348804, No Obj: 0.003347, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.562454, Class: 1.000000, Obj: 0.366332, No Obj: 0.003841, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.537869, Class: 1.000000, Obj: 0.373554, No Obj: 0.003919, Avg Recall: 0.625000, count: 120 -Region Avg IOU: 0.501718, Class: 1.000000, Obj: 0.307586, No Obj: 0.003637, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.567460, Class: 1.000000, Obj: 0.383799, No Obj: 0.004013, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.543880, Class: 1.000000, Obj: 0.345543, No Obj: 0.004077, Avg Recall: 0.597122, count: 139 -Region Avg IOU: 0.494266, Class: 1.000000, Obj: 0.340047, No Obj: 0.003854, Avg Recall: 0.563636, count: 110 -6462: 0.551897, 0.445647 avg, 0.001000 rate, 22.628296 seconds, 1654272 images -Loaded: 0.002679 seconds -Region Avg IOU: 0.556607, Class: 1.000000, Obj: 0.377242, No Obj: 0.004521, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.534820, Class: 1.000000, Obj: 0.388085, No Obj: 0.003879, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.543699, Class: 1.000000, Obj: 0.409663, No Obj: 0.003990, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.530471, Class: 1.000000, Obj: 0.350018, No Obj: 0.004382, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.545037, Class: 1.000000, Obj: 0.352632, No Obj: 0.004353, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.520944, Class: 1.000000, Obj: 0.354736, No Obj: 0.004375, Avg Recall: 0.564885, count: 131 -Region Avg IOU: 0.524335, Class: 1.000000, Obj: 0.317251, No Obj: 0.003945, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.533336, Class: 1.000000, Obj: 0.358970, No Obj: 0.004105, Avg Recall: 0.581197, count: 117 -6463: 0.476556, 0.448738 avg, 0.001000 rate, 21.238365 seconds, 1654528 images -Loaded: 0.104923 seconds -Region Avg IOU: 0.534408, Class: 1.000000, Obj: 0.374073, No Obj: 0.004476, Avg Recall: 0.600000, count: 110 -Region Avg IOU: 0.503643, Class: 1.000000, Obj: 0.344326, No Obj: 0.003969, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.560891, Class: 1.000000, Obj: 0.416610, No Obj: 0.004517, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.558773, Class: 1.000000, Obj: 0.396019, No Obj: 0.003998, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.543037, Class: 1.000000, Obj: 0.394199, No Obj: 0.004408, Avg Recall: 0.580645, count: 124 -Region Avg IOU: 0.546678, Class: 1.000000, Obj: 0.376445, No Obj: 0.003942, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.564922, Class: 1.000000, Obj: 0.415189, No Obj: 0.003958, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.526961, Class: 1.000000, Obj: 0.358270, No Obj: 0.003662, Avg Recall: 0.607843, count: 102 -6464: 0.430227, 0.446887 avg, 0.001000 rate, 20.523527 seconds, 1654784 images -Loaded: 0.741160 seconds -Region Avg IOU: 0.528111, Class: 1.000000, Obj: 0.385976, No Obj: 0.005693, Avg Recall: 0.574324, count: 148 -Region Avg IOU: 0.523766, Class: 1.000000, Obj: 0.390939, No Obj: 0.004430, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.523471, Class: 1.000000, Obj: 0.380523, No Obj: 0.003853, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.636268, Class: 1.000000, Obj: 0.487124, No Obj: 0.004157, Avg Recall: 0.758621, count: 87 -Region Avg IOU: 0.533686, Class: 1.000000, Obj: 0.409983, No Obj: 0.005473, Avg Recall: 0.583942, count: 137 -Region Avg IOU: 0.507485, Class: 1.000000, Obj: 0.402894, No Obj: 0.004084, Avg Recall: 0.586207, count: 87 -Region Avg IOU: 0.613059, Class: 1.000000, Obj: 0.409690, No Obj: 0.004649, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.567703, Class: 1.000000, Obj: 0.396037, No Obj: 0.004626, Avg Recall: 0.684685, count: 111 -6465: 0.430981, 0.445297 avg, 0.001000 rate, 19.176905 seconds, 1655040 images -Loaded: 2.322104 seconds -Region Avg IOU: 0.586412, Class: 1.000000, Obj: 0.405068, No Obj: 0.004956, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.598694, Class: 1.000000, Obj: 0.468833, No Obj: 0.004738, Avg Recall: 0.759036, count: 83 -Region Avg IOU: 0.531823, Class: 1.000000, Obj: 0.416956, No Obj: 0.004269, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.538285, Class: 1.000000, Obj: 0.391130, No Obj: 0.003941, Avg Recall: 0.597561, count: 82 -Region Avg IOU: 0.598516, Class: 1.000000, Obj: 0.449955, No Obj: 0.004866, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.479697, Class: 1.000000, Obj: 0.402013, No Obj: 0.004337, Avg Recall: 0.471154, count: 104 -Region Avg IOU: 0.423286, Class: 1.000000, Obj: 0.343542, No Obj: 0.004387, Avg Recall: 0.464567, count: 127 -Region Avg IOU: 0.541533, Class: 1.000000, Obj: 0.393504, No Obj: 0.004805, Avg Recall: 0.600000, count: 110 -6466: 0.393845, 0.440151 avg, 0.001000 rate, 20.930079 seconds, 1655296 images -Loaded: 0.618043 seconds -Region Avg IOU: 0.562621, Class: 1.000000, Obj: 0.408423, No Obj: 0.004884, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.466037, Class: 1.000000, Obj: 0.365517, No Obj: 0.003907, Avg Recall: 0.489362, count: 94 -Region Avg IOU: 0.486319, Class: 1.000000, Obj: 0.395730, No Obj: 0.003862, Avg Recall: 0.536842, count: 95 -Region Avg IOU: 0.539128, Class: 1.000000, Obj: 0.428104, No Obj: 0.004472, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.493936, Class: 1.000000, Obj: 0.416840, No Obj: 0.003638, Avg Recall: 0.547368, count: 95 -Region Avg IOU: 0.512193, Class: 1.000000, Obj: 0.437840, No Obj: 0.003940, Avg Recall: 0.579545, count: 88 -Region Avg IOU: 0.534726, Class: 1.000000, Obj: 0.415015, No Obj: 0.004625, Avg Recall: 0.622222, count: 90 -Region Avg IOU: 0.541478, Class: 1.000000, Obj: 0.418429, No Obj: 0.004787, Avg Recall: 0.604839, count: 124 -6467: 0.437485, 0.439885 avg, 0.001000 rate, 20.215994 seconds, 1655552 images -Loaded: 1.464882 seconds -Region Avg IOU: 0.512702, Class: 1.000000, Obj: 0.436399, No Obj: 0.004341, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.543484, Class: 1.000000, Obj: 0.460325, No Obj: 0.003925, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.515181, Class: 1.000000, Obj: 0.376916, No Obj: 0.005513, Avg Recall: 0.600000, count: 140 -Region Avg IOU: 0.496733, Class: 1.000000, Obj: 0.430094, No Obj: 0.004474, Avg Recall: 0.539062, count: 128 -Region Avg IOU: 0.545538, Class: 1.000000, Obj: 0.432834, No Obj: 0.004552, Avg Recall: 0.575000, count: 120 -Region Avg IOU: 0.526999, Class: 1.000000, Obj: 0.391139, No Obj: 0.004534, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.528078, Class: 1.000000, Obj: 0.396716, No Obj: 0.005083, Avg Recall: 0.650794, count: 126 -Region Avg IOU: 0.555703, Class: 1.000000, Obj: 0.426154, No Obj: 0.004551, Avg Recall: 0.594340, count: 106 -6468: 0.449597, 0.440856 avg, 0.001000 rate, 19.602406 seconds, 1655808 images -Loaded: 2.836568 seconds -Region Avg IOU: 0.536724, Class: 1.000000, Obj: 0.418634, No Obj: 0.004224, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.583496, Class: 1.000000, Obj: 0.430429, No Obj: 0.004613, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.511932, Class: 1.000000, Obj: 0.371481, No Obj: 0.004078, Avg Recall: 0.537736, count: 106 -Region Avg IOU: 0.518234, Class: 1.000000, Obj: 0.394778, No Obj: 0.003705, Avg Recall: 0.542553, count: 94 -Region Avg IOU: 0.545059, Class: 1.000000, Obj: 0.377905, No Obj: 0.005024, Avg Recall: 0.623077, count: 130 -Region Avg IOU: 0.549223, Class: 1.000000, Obj: 0.434641, No Obj: 0.004268, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.598938, Class: 1.000000, Obj: 0.420587, No Obj: 0.005642, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.539642, Class: 1.000000, Obj: 0.431132, No Obj: 0.003387, Avg Recall: 0.628205, count: 78 -6469: 0.450629, 0.441833 avg, 0.001000 rate, 22.336697 seconds, 1656064 images -Loaded: 0.745475 seconds -Region Avg IOU: 0.542211, Class: 1.000000, Obj: 0.385115, No Obj: 0.004192, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.569990, Class: 1.000000, Obj: 0.402544, No Obj: 0.004517, Avg Recall: 0.653846, count: 130 -Region Avg IOU: 0.505398, Class: 1.000000, Obj: 0.419782, No Obj: 0.004155, Avg Recall: 0.544304, count: 79 -Region Avg IOU: 0.566341, Class: 1.000000, Obj: 0.429824, No Obj: 0.004920, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.537861, Class: 1.000000, Obj: 0.388015, No Obj: 0.004818, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.571691, Class: 1.000000, Obj: 0.424151, No Obj: 0.004795, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.551034, Class: 1.000000, Obj: 0.401689, No Obj: 0.004431, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.528873, Class: 1.000000, Obj: 0.394071, No Obj: 0.004051, Avg Recall: 0.613861, count: 101 -6470: 0.469594, 0.444609 avg, 0.001000 rate, 21.662266 seconds, 1656320 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000059 seconds -Region Avg IOU: 0.596544, Class: 1.000000, Obj: 0.413645, No Obj: 0.003549, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.567088, Class: 1.000000, Obj: 0.413452, No Obj: 0.003712, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.621956, Class: 1.000000, Obj: 0.471496, No Obj: 0.003716, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.589372, Class: 1.000000, Obj: 0.459015, No Obj: 0.003681, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.646364, Class: 1.000000, Obj: 0.461520, No Obj: 0.003914, Avg Recall: 0.802083, count: 96 -Region Avg IOU: 0.554692, Class: 1.000000, Obj: 0.409837, No Obj: 0.004153, Avg Recall: 0.646154, count: 130 -Region Avg IOU: 0.533927, Class: 1.000000, Obj: 0.437994, No Obj: 0.003524, Avg Recall: 0.613445, count: 119 -Region Avg IOU: 0.591971, Class: 1.000000, Obj: 0.458497, No Obj: 0.004040, Avg Recall: 0.730435, count: 115 -6471: 0.452270, 0.445375 avg, 0.001000 rate, 25.923223 seconds, 1656576 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.526988, Class: 1.000000, Obj: 0.398269, No Obj: 0.003965, Avg Recall: 0.615942, count: 138 -Region Avg IOU: 0.571219, Class: 1.000000, Obj: 0.460256, No Obj: 0.003823, Avg Recall: 0.688073, count: 109 -Region Avg IOU: 0.590635, Class: 1.000000, Obj: 0.417472, No Obj: 0.003514, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.623184, Class: 1.000000, Obj: 0.433767, No Obj: 0.003424, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.573445, Class: 1.000000, Obj: 0.431314, No Obj: 0.003568, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.635323, Class: 1.000000, Obj: 0.434206, No Obj: 0.003920, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.615030, Class: 1.000000, Obj: 0.437496, No Obj: 0.004002, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.618431, Class: 1.000000, Obj: 0.404815, No Obj: 0.004603, Avg Recall: 0.701389, count: 144 -6472: 0.441658, 0.445004 avg, 0.001000 rate, 23.782784 seconds, 1656832 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.642008, Class: 1.000000, Obj: 0.410362, No Obj: 0.003821, Avg Recall: 0.769231, count: 117 -Region Avg IOU: 0.630729, Class: 1.000000, Obj: 0.495811, No Obj: 0.003488, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.567594, Class: 1.000000, Obj: 0.376549, No Obj: 0.003394, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.627005, Class: 1.000000, Obj: 0.440386, No Obj: 0.003122, Avg Recall: 0.722222, count: 90 -Region Avg IOU: 0.602533, Class: 1.000000, Obj: 0.394911, No Obj: 0.003818, Avg Recall: 0.720000, count: 125 -Region Avg IOU: 0.608065, Class: 1.000000, Obj: 0.388685, No Obj: 0.003126, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.607989, Class: 1.000000, Obj: 0.435435, No Obj: 0.004220, Avg Recall: 0.703125, count: 128 -Region Avg IOU: 0.635393, Class: 1.000000, Obj: 0.414848, No Obj: 0.003300, Avg Recall: 0.776699, count: 103 -6473: 0.382959, 0.438799 avg, 0.001000 rate, 24.097776 seconds, 1657088 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.585087, Class: 1.000000, Obj: 0.379512, No Obj: 0.002780, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.600113, Class: 1.000000, Obj: 0.439933, No Obj: 0.003362, Avg Recall: 0.703297, count: 91 -Region Avg IOU: 0.595345, Class: 1.000000, Obj: 0.418449, No Obj: 0.003136, Avg Recall: 0.689655, count: 87 -Region Avg IOU: 0.587602, Class: 1.000000, Obj: 0.389495, No Obj: 0.003619, Avg Recall: 0.680000, count: 125 -Region Avg IOU: 0.621106, Class: 1.000000, Obj: 0.392962, No Obj: 0.003528, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.615146, Class: 1.000000, Obj: 0.434227, No Obj: 0.002811, Avg Recall: 0.770270, count: 74 -Region Avg IOU: 0.592965, Class: 1.000000, Obj: 0.472372, No Obj: 0.003368, Avg Recall: 0.680328, count: 122 -Region Avg IOU: 0.646798, Class: 1.000000, Obj: 0.404595, No Obj: 0.003248, Avg Recall: 0.763441, count: 93 -6474: 0.373229, 0.432242 avg, 0.001000 rate, 23.813805 seconds, 1657344 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.594851, Class: 1.000000, Obj: 0.377943, No Obj: 0.003026, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.600195, Class: 1.000000, Obj: 0.405052, No Obj: 0.003256, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.572279, Class: 1.000000, Obj: 0.395673, No Obj: 0.002891, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.550508, Class: 1.000000, Obj: 0.360305, No Obj: 0.003278, Avg Recall: 0.630769, count: 130 -Region Avg IOU: 0.643690, Class: 1.000000, Obj: 0.428260, No Obj: 0.003578, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.578934, Class: 1.000000, Obj: 0.391894, No Obj: 0.003622, Avg Recall: 0.681159, count: 138 -Region Avg IOU: 0.572114, Class: 1.000000, Obj: 0.385374, No Obj: 0.003508, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.607471, Class: 1.000000, Obj: 0.393985, No Obj: 0.003504, Avg Recall: 0.728972, count: 107 -6475: 0.442676, 0.433286 avg, 0.001000 rate, 23.921757 seconds, 1657600 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.585756, Class: 1.000000, Obj: 0.399682, No Obj: 0.003022, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.605609, Class: 1.000000, Obj: 0.411325, No Obj: 0.002922, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.579422, Class: 1.000000, Obj: 0.394384, No Obj: 0.002758, Avg Recall: 0.670588, count: 85 -Region Avg IOU: 0.622796, Class: 1.000000, Obj: 0.390719, No Obj: 0.003246, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.561584, Class: 1.000000, Obj: 0.356380, No Obj: 0.002767, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.610597, Class: 1.000000, Obj: 0.426578, No Obj: 0.003318, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.567041, Class: 1.000000, Obj: 0.398062, No Obj: 0.003638, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.675093, Class: 1.000000, Obj: 0.418296, No Obj: 0.002753, Avg Recall: 0.867647, count: 68 -6476: 0.388705, 0.428827 avg, 0.001000 rate, 24.807274 seconds, 1657856 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.613769, Class: 1.000000, Obj: 0.391110, No Obj: 0.003630, Avg Recall: 0.720930, count: 129 -Region Avg IOU: 0.585143, Class: 1.000000, Obj: 0.357212, No Obj: 0.003074, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.658172, Class: 1.000000, Obj: 0.453884, No Obj: 0.003674, Avg Recall: 0.855769, count: 104 -Region Avg IOU: 0.625704, Class: 1.000000, Obj: 0.391321, No Obj: 0.003152, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.590248, Class: 1.000000, Obj: 0.367843, No Obj: 0.003185, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.569334, Class: 1.000000, Obj: 0.355931, No Obj: 0.003698, Avg Recall: 0.644444, count: 135 -Region Avg IOU: 0.599940, Class: 1.000000, Obj: 0.406022, No Obj: 0.002810, Avg Recall: 0.709302, count: 86 -Region Avg IOU: 0.602581, Class: 1.000000, Obj: 0.423233, No Obj: 0.003713, Avg Recall: 0.697479, count: 119 -6477: 0.422968, 0.428242 avg, 0.001000 rate, 23.970375 seconds, 1658112 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.601527, Class: 1.000000, Obj: 0.360032, No Obj: 0.002991, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.653542, Class: 1.000000, Obj: 0.421134, No Obj: 0.003344, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.596517, Class: 1.000000, Obj: 0.381205, No Obj: 0.003403, Avg Recall: 0.704918, count: 122 -Region Avg IOU: 0.624959, Class: 1.000000, Obj: 0.409350, No Obj: 0.003242, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.608400, Class: 1.000000, Obj: 0.401509, No Obj: 0.003603, Avg Recall: 0.741007, count: 139 -Region Avg IOU: 0.607830, Class: 1.000000, Obj: 0.427831, No Obj: 0.002724, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.563550, Class: 1.000000, Obj: 0.331084, No Obj: 0.003518, Avg Recall: 0.647541, count: 122 -Region Avg IOU: 0.607032, Class: 1.000000, Obj: 0.398159, No Obj: 0.003033, Avg Recall: 0.738636, count: 88 -6478: 0.446941, 0.430111 avg, 0.001000 rate, 24.162771 seconds, 1658368 images -Loaded: 0.000120 seconds -Region Avg IOU: 0.631418, Class: 1.000000, Obj: 0.439435, No Obj: 0.003732, Avg Recall: 0.779661, count: 118 -Region Avg IOU: 0.516028, Class: 1.000000, Obj: 0.368241, No Obj: 0.002986, Avg Recall: 0.572727, count: 110 -Region Avg IOU: 0.603055, Class: 1.000000, Obj: 0.418160, No Obj: 0.003975, Avg Recall: 0.702479, count: 121 -Region Avg IOU: 0.603687, Class: 1.000000, Obj: 0.471214, No Obj: 0.003493, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.613421, Class: 1.000000, Obj: 0.420109, No Obj: 0.003640, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.577699, Class: 1.000000, Obj: 0.376419, No Obj: 0.003370, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.572298, Class: 1.000000, Obj: 0.376260, No Obj: 0.002860, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.627750, Class: 1.000000, Obj: 0.434647, No Obj: 0.003216, Avg Recall: 0.705882, count: 102 -6479: 0.428293, 0.429930 avg, 0.001000 rate, 22.393965 seconds, 1658624 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.593380, Class: 1.000000, Obj: 0.442443, No Obj: 0.002839, Avg Recall: 0.741573, count: 89 -Region Avg IOU: 0.626947, Class: 1.000000, Obj: 0.426754, No Obj: 0.003662, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.570661, Class: 1.000000, Obj: 0.422338, No Obj: 0.002854, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.622581, Class: 1.000000, Obj: 0.461283, No Obj: 0.003599, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.604807, Class: 1.000000, Obj: 0.415803, No Obj: 0.003612, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.600455, Class: 1.000000, Obj: 0.446624, No Obj: 0.003870, Avg Recall: 0.676923, count: 130 -Region Avg IOU: 0.584948, Class: 1.000000, Obj: 0.444554, No Obj: 0.003396, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.543016, Class: 1.000000, Obj: 0.380352, No Obj: 0.002633, Avg Recall: 0.567568, count: 74 -6480: 0.368407, 0.423777 avg, 0.001000 rate, 23.532917 seconds, 1658880 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 19.272854 seconds -Region Avg IOU: 0.581690, Class: 1.000000, Obj: 0.371077, No Obj: 0.003665, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.592924, Class: 1.000000, Obj: 0.414780, No Obj: 0.004063, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.575591, Class: 1.000000, Obj: 0.403583, No Obj: 0.002948, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.602075, Class: 1.000000, Obj: 0.432257, No Obj: 0.003884, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.556995, Class: 1.000000, Obj: 0.404304, No Obj: 0.003933, Avg Recall: 0.653226, count: 124 -Region Avg IOU: 0.605620, Class: 1.000000, Obj: 0.439180, No Obj: 0.003705, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.668157, Class: 1.000000, Obj: 0.405198, No Obj: 0.003608, Avg Recall: 0.822430, count: 107 -Region Avg IOU: 0.631253, Class: 1.000000, Obj: 0.428228, No Obj: 0.003664, Avg Recall: 0.752212, count: 113 -6481: 0.423576, 0.423757 avg, 0.001000 rate, 23.180941 seconds, 1659136 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.611342, Class: 1.000000, Obj: 0.434778, No Obj: 0.003883, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.561579, Class: 1.000000, Obj: 0.395914, No Obj: 0.003499, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.573927, Class: 1.000000, Obj: 0.409883, No Obj: 0.003752, Avg Recall: 0.656000, count: 125 -Region Avg IOU: 0.621132, Class: 1.000000, Obj: 0.482306, No Obj: 0.003059, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.610922, Class: 1.000000, Obj: 0.434865, No Obj: 0.003581, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.679377, Class: 1.000000, Obj: 0.496909, No Obj: 0.003336, Avg Recall: 0.850575, count: 87 -Region Avg IOU: 0.563676, Class: 1.000000, Obj: 0.391698, No Obj: 0.003771, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.554770, Class: 1.000000, Obj: 0.423925, No Obj: 0.003367, Avg Recall: 0.652174, count: 115 -6482: 0.396571, 0.421039 avg, 0.001000 rate, 23.073889 seconds, 1659392 images -Loaded: 0.000154 seconds -Region Avg IOU: 0.558598, Class: 1.000000, Obj: 0.434203, No Obj: 0.003557, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.577041, Class: 1.000000, Obj: 0.439711, No Obj: 0.003167, Avg Recall: 0.698276, count: 116 -Region Avg IOU: 0.602996, Class: 1.000000, Obj: 0.405083, No Obj: 0.003633, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.591324, Class: 1.000000, Obj: 0.412662, No Obj: 0.003068, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.549726, Class: 1.000000, Obj: 0.414236, No Obj: 0.003929, Avg Recall: 0.628571, count: 140 -Region Avg IOU: 0.648377, Class: 1.000000, Obj: 0.443823, No Obj: 0.003254, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.614062, Class: 1.000000, Obj: 0.453311, No Obj: 0.003602, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.602583, Class: 1.000000, Obj: 0.408678, No Obj: 0.004113, Avg Recall: 0.700855, count: 117 -6483: 0.396046, 0.418539 avg, 0.001000 rate, 23.944244 seconds, 1659648 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.580637, Class: 1.000000, Obj: 0.428630, No Obj: 0.004180, Avg Recall: 0.648438, count: 128 -Region Avg IOU: 0.618842, Class: 1.000000, Obj: 0.421588, No Obj: 0.004230, Avg Recall: 0.730159, count: 126 -Region Avg IOU: 0.598651, Class: 1.000000, Obj: 0.428660, No Obj: 0.003878, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.579995, Class: 1.000000, Obj: 0.397646, No Obj: 0.004154, Avg Recall: 0.682927, count: 123 -Region Avg IOU: 0.558225, Class: 1.000000, Obj: 0.401978, No Obj: 0.003315, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.585713, Class: 1.000000, Obj: 0.455069, No Obj: 0.003483, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.532092, Class: 1.000000, Obj: 0.389457, No Obj: 0.003463, Avg Recall: 0.596491, count: 114 -Region Avg IOU: 0.569448, Class: 1.000000, Obj: 0.400699, No Obj: 0.003425, Avg Recall: 0.693878, count: 98 -6484: 0.465356, 0.423221 avg, 0.001000 rate, 24.374027 seconds, 1659904 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.596522, Class: 1.000000, Obj: 0.425471, No Obj: 0.003887, Avg Recall: 0.680328, count: 122 -Region Avg IOU: 0.634717, Class: 1.000000, Obj: 0.458001, No Obj: 0.003458, Avg Recall: 0.752941, count: 85 -Region Avg IOU: 0.582902, Class: 1.000000, Obj: 0.443202, No Obj: 0.003510, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.576098, Class: 1.000000, Obj: 0.429851, No Obj: 0.003843, Avg Recall: 0.649635, count: 137 -Region Avg IOU: 0.585703, Class: 1.000000, Obj: 0.475666, No Obj: 0.003535, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.613140, Class: 1.000000, Obj: 0.463283, No Obj: 0.003262, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.627366, Class: 1.000000, Obj: 0.407552, No Obj: 0.004034, Avg Recall: 0.750000, count: 124 -Region Avg IOU: 0.585855, Class: 1.000000, Obj: 0.430098, No Obj: 0.003138, Avg Recall: 0.686046, count: 86 -6485: 0.400968, 0.420996 avg, 0.001000 rate, 22.883320 seconds, 1660160 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.566951, Class: 1.000000, Obj: 0.405641, No Obj: 0.003113, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.581590, Class: 1.000000, Obj: 0.442322, No Obj: 0.002798, Avg Recall: 0.705128, count: 78 -Region Avg IOU: 0.623150, Class: 1.000000, Obj: 0.432274, No Obj: 0.003116, Avg Recall: 0.744681, count: 94 -Region Avg IOU: 0.596207, Class: 1.000000, Obj: 0.458090, No Obj: 0.003198, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.543662, Class: 1.000000, Obj: 0.413779, No Obj: 0.002939, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.572622, Class: 1.000000, Obj: 0.390070, No Obj: 0.003378, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.613469, Class: 1.000000, Obj: 0.418199, No Obj: 0.003232, Avg Recall: 0.762500, count: 80 -Region Avg IOU: 0.603446, Class: 1.000000, Obj: 0.453081, No Obj: 0.003188, Avg Recall: 0.776596, count: 94 -6486: 0.381056, 0.417002 avg, 0.001000 rate, 24.597780 seconds, 1660416 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.641696, Class: 1.000000, Obj: 0.415125, No Obj: 0.003566, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.613321, Class: 1.000000, Obj: 0.421099, No Obj: 0.004097, Avg Recall: 0.720930, count: 129 -Region Avg IOU: 0.592737, Class: 1.000000, Obj: 0.395784, No Obj: 0.003134, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.597855, Class: 1.000000, Obj: 0.374028, No Obj: 0.003401, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.560216, Class: 1.000000, Obj: 0.385254, No Obj: 0.003421, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.625194, Class: 1.000000, Obj: 0.392054, No Obj: 0.003791, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.574514, Class: 1.000000, Obj: 0.382910, No Obj: 0.003330, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.592046, Class: 1.000000, Obj: 0.414116, No Obj: 0.004349, Avg Recall: 0.669291, count: 127 -6487: 0.447096, 0.420011 avg, 0.001000 rate, 22.305243 seconds, 1660672 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.581055, Class: 1.000000, Obj: 0.431686, No Obj: 0.003465, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.588117, Class: 1.000000, Obj: 0.414511, No Obj: 0.003529, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.588231, Class: 1.000000, Obj: 0.361762, No Obj: 0.003444, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.628657, Class: 1.000000, Obj: 0.418780, No Obj: 0.003342, Avg Recall: 0.802083, count: 96 -Region Avg IOU: 0.604823, Class: 1.000000, Obj: 0.416388, No Obj: 0.003389, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.533935, Class: 1.000000, Obj: 0.434864, No Obj: 0.002665, Avg Recall: 0.629630, count: 81 -Region Avg IOU: 0.583515, Class: 1.000000, Obj: 0.411642, No Obj: 0.003159, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.582306, Class: 1.000000, Obj: 0.430846, No Obj: 0.003456, Avg Recall: 0.702970, count: 101 -6488: 0.377927, 0.415803 avg, 0.001000 rate, 23.073269 seconds, 1660928 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.575089, Class: 1.000000, Obj: 0.356203, No Obj: 0.003050, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.623861, Class: 1.000000, Obj: 0.434833, No Obj: 0.004226, Avg Recall: 0.761589, count: 151 -Region Avg IOU: 0.606389, Class: 1.000000, Obj: 0.429117, No Obj: 0.003358, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.597497, Class: 1.000000, Obj: 0.419546, No Obj: 0.003550, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.629919, Class: 1.000000, Obj: 0.447507, No Obj: 0.003300, Avg Recall: 0.719512, count: 82 -Region Avg IOU: 0.621548, Class: 1.000000, Obj: 0.460055, No Obj: 0.003323, Avg Recall: 0.761364, count: 88 -Region Avg IOU: 0.678164, Class: 1.000000, Obj: 0.513555, No Obj: 0.003589, Avg Recall: 0.795455, count: 88 -Region Avg IOU: 0.616980, Class: 1.000000, Obj: 0.431993, No Obj: 0.003100, Avg Recall: 0.750000, count: 100 -6489: 0.398357, 0.414058 avg, 0.001000 rate, 25.687792 seconds, 1661184 images -Loaded: 0.000141 seconds -Region Avg IOU: 0.642269, Class: 1.000000, Obj: 0.397132, No Obj: 0.003472, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.602732, Class: 1.000000, Obj: 0.378551, No Obj: 0.004414, Avg Recall: 0.708609, count: 151 -Region Avg IOU: 0.635816, Class: 1.000000, Obj: 0.355836, No Obj: 0.003397, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.606682, Class: 1.000000, Obj: 0.418134, No Obj: 0.003346, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.652225, Class: 1.000000, Obj: 0.392768, No Obj: 0.003983, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.579750, Class: 1.000000, Obj: 0.371557, No Obj: 0.003167, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.631611, Class: 1.000000, Obj: 0.420336, No Obj: 0.004033, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.593918, Class: 1.000000, Obj: 0.412529, No Obj: 0.004130, Avg Recall: 0.743056, count: 144 -6490: 0.458630, 0.418515 avg, 0.001000 rate, 24.216774 seconds, 1661440 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000059 seconds -Region Avg IOU: 0.673007, Class: 1.000000, Obj: 0.429542, No Obj: 0.002632, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.705486, Class: 1.000000, Obj: 0.413095, No Obj: 0.002978, Avg Recall: 0.871287, count: 101 -Region Avg IOU: 0.655608, Class: 1.000000, Obj: 0.476319, No Obj: 0.003126, Avg Recall: 0.822034, count: 118 -Region Avg IOU: 0.653940, Class: 1.000000, Obj: 0.411879, No Obj: 0.002922, Avg Recall: 0.800000, count: 120 -Region Avg IOU: 0.697848, Class: 1.000000, Obj: 0.478555, No Obj: 0.002984, Avg Recall: 0.870370, count: 108 -Region Avg IOU: 0.692369, Class: 1.000000, Obj: 0.471213, No Obj: 0.002554, Avg Recall: 0.795455, count: 88 -Region Avg IOU: 0.651825, Class: 1.000000, Obj: 0.443173, No Obj: 0.002606, Avg Recall: 0.818182, count: 88 -Region Avg IOU: 0.639123, Class: 1.000000, Obj: 0.455283, No Obj: 0.002360, Avg Recall: 0.789474, count: 95 -6491: 0.391245, 0.415788 avg, 0.001000 rate, 28.828783 seconds, 1661696 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.667064, Class: 1.000000, Obj: 0.410950, No Obj: 0.002483, Avg Recall: 0.808989, count: 89 -Region Avg IOU: 0.672260, Class: 1.000000, Obj: 0.450631, No Obj: 0.002717, Avg Recall: 0.855856, count: 111 -Region Avg IOU: 0.660049, Class: 1.000000, Obj: 0.463855, No Obj: 0.002509, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.687898, Class: 1.000000, Obj: 0.503125, No Obj: 0.002835, Avg Recall: 0.844660, count: 103 -Region Avg IOU: 0.622432, Class: 1.000000, Obj: 0.442261, No Obj: 0.002501, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.663668, Class: 1.000000, Obj: 0.398625, No Obj: 0.002642, Avg Recall: 0.809091, count: 110 -Region Avg IOU: 0.640159, Class: 1.000000, Obj: 0.430235, No Obj: 0.002400, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.655613, Class: 1.000000, Obj: 0.450882, No Obj: 0.002770, Avg Recall: 0.852632, count: 95 -6492: 0.388667, 0.413076 avg, 0.001000 rate, 27.701040 seconds, 1661952 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.661610, Class: 1.000000, Obj: 0.403533, No Obj: 0.002702, Avg Recall: 0.831776, count: 107 -Region Avg IOU: 0.677606, Class: 1.000000, Obj: 0.438118, No Obj: 0.002508, Avg Recall: 0.824176, count: 91 -Region Avg IOU: 0.681190, Class: 1.000000, Obj: 0.437712, No Obj: 0.002940, Avg Recall: 0.843137, count: 102 -Region Avg IOU: 0.686247, Class: 1.000000, Obj: 0.416194, No Obj: 0.002623, Avg Recall: 0.861702, count: 94 -Region Avg IOU: 0.658088, Class: 1.000000, Obj: 0.385957, No Obj: 0.002673, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.702609, Class: 1.000000, Obj: 0.459346, No Obj: 0.002592, Avg Recall: 0.910714, count: 112 -Region Avg IOU: 0.675240, Class: 1.000000, Obj: 0.384074, No Obj: 0.002660, Avg Recall: 0.846847, count: 111 -Region Avg IOU: 0.662447, Class: 1.000000, Obj: 0.432579, No Obj: 0.002548, Avg Recall: 0.840000, count: 100 -6493: 0.419466, 0.413715 avg, 0.001000 rate, 29.077637 seconds, 1662208 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.675639, Class: 1.000000, Obj: 0.421063, No Obj: 0.002715, Avg Recall: 0.848740, count: 119 -Region Avg IOU: 0.651981, Class: 1.000000, Obj: 0.425019, No Obj: 0.002498, Avg Recall: 0.836364, count: 110 -Region Avg IOU: 0.682323, Class: 1.000000, Obj: 0.414978, No Obj: 0.002333, Avg Recall: 0.836735, count: 98 -Region Avg IOU: 0.672361, Class: 1.000000, Obj: 0.388586, No Obj: 0.002507, Avg Recall: 0.858491, count: 106 -Region Avg IOU: 0.694427, Class: 1.000000, Obj: 0.434533, No Obj: 0.002443, Avg Recall: 0.884211, count: 95 -Region Avg IOU: 0.654945, Class: 1.000000, Obj: 0.392588, No Obj: 0.002038, Avg Recall: 0.826531, count: 98 -Region Avg IOU: 0.695907, Class: 1.000000, Obj: 0.423333, No Obj: 0.002899, Avg Recall: 0.896000, count: 125 -Region Avg IOU: 0.606406, Class: 1.000000, Obj: 0.371171, No Obj: 0.002453, Avg Recall: 0.718447, count: 103 -6494: 0.441490, 0.416493 avg, 0.001000 rate, 28.905821 seconds, 1662464 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.637005, Class: 1.000000, Obj: 0.411833, No Obj: 0.002459, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.650977, Class: 1.000000, Obj: 0.411289, No Obj: 0.002837, Avg Recall: 0.802632, count: 152 -Region Avg IOU: 0.672896, Class: 1.000000, Obj: 0.400755, No Obj: 0.002372, Avg Recall: 0.850575, count: 87 -Region Avg IOU: 0.684047, Class: 1.000000, Obj: 0.396412, No Obj: 0.002675, Avg Recall: 0.866071, count: 112 -Region Avg IOU: 0.676497, Class: 1.000000, Obj: 0.392998, No Obj: 0.002352, Avg Recall: 0.870968, count: 93 -Region Avg IOU: 0.683321, Class: 1.000000, Obj: 0.452228, No Obj: 0.002450, Avg Recall: 0.877358, count: 106 -Region Avg IOU: 0.674470, Class: 1.000000, Obj: 0.355564, No Obj: 0.002456, Avg Recall: 0.879121, count: 91 -Region Avg IOU: 0.646336, Class: 1.000000, Obj: 0.342965, No Obj: 0.002346, Avg Recall: 0.756522, count: 115 -6495: 0.463516, 0.421195 avg, 0.001000 rate, 30.424179 seconds, 1662720 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.698093, Class: 1.000000, Obj: 0.444288, No Obj: 0.002699, Avg Recall: 0.854701, count: 117 -Region Avg IOU: 0.670645, Class: 1.000000, Obj: 0.394340, No Obj: 0.002307, Avg Recall: 0.843750, count: 96 -Region Avg IOU: 0.682116, Class: 1.000000, Obj: 0.413125, No Obj: 0.002640, Avg Recall: 0.859813, count: 107 -Region Avg IOU: 0.681994, Class: 1.000000, Obj: 0.415960, No Obj: 0.002057, Avg Recall: 0.836957, count: 92 -Region Avg IOU: 0.632507, Class: 1.000000, Obj: 0.385978, No Obj: 0.002278, Avg Recall: 0.829787, count: 94 -Region Avg IOU: 0.706459, Class: 1.000000, Obj: 0.520811, No Obj: 0.001916, Avg Recall: 0.879518, count: 83 -Region Avg IOU: 0.684458, Class: 1.000000, Obj: 0.412509, No Obj: 0.002344, Avg Recall: 0.848214, count: 112 -Region Avg IOU: 0.654488, Class: 1.000000, Obj: 0.409055, No Obj: 0.002333, Avg Recall: 0.806122, count: 98 -6496: 0.410878, 0.420163 avg, 0.001000 rate, 27.024967 seconds, 1662976 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.672740, Class: 1.000000, Obj: 0.415665, No Obj: 0.002364, Avg Recall: 0.845588, count: 136 -Region Avg IOU: 0.687702, Class: 1.000000, Obj: 0.408020, No Obj: 0.002264, Avg Recall: 0.857143, count: 91 -Region Avg IOU: 0.678023, Class: 1.000000, Obj: 0.416035, No Obj: 0.002144, Avg Recall: 0.840425, count: 94 -Region Avg IOU: 0.655563, Class: 1.000000, Obj: 0.431061, No Obj: 0.002644, Avg Recall: 0.811966, count: 117 -Region Avg IOU: 0.629466, Class: 1.000000, Obj: 0.434702, No Obj: 0.001947, Avg Recall: 0.744186, count: 86 -Region Avg IOU: 0.653089, Class: 1.000000, Obj: 0.391119, No Obj: 0.002426, Avg Recall: 0.786885, count: 122 -Region Avg IOU: 0.677858, Class: 1.000000, Obj: 0.395447, No Obj: 0.002685, Avg Recall: 0.875000, count: 120 -Region Avg IOU: 0.642816, Class: 1.000000, Obj: 0.387960, No Obj: 0.002288, Avg Recall: 0.785714, count: 112 -6497: 0.443263, 0.422473 avg, 0.001000 rate, 30.062244 seconds, 1663232 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.675017, Class: 1.000000, Obj: 0.465966, No Obj: 0.002481, Avg Recall: 0.858491, count: 106 -Region Avg IOU: 0.639790, Class: 1.000000, Obj: 0.427164, No Obj: 0.002184, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.643471, Class: 1.000000, Obj: 0.414488, No Obj: 0.002576, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.699935, Class: 1.000000, Obj: 0.405873, No Obj: 0.002529, Avg Recall: 0.870370, count: 108 -Region Avg IOU: 0.663381, Class: 1.000000, Obj: 0.359908, No Obj: 0.002567, Avg Recall: 0.838462, count: 130 -Region Avg IOU: 0.693683, Class: 1.000000, Obj: 0.434192, No Obj: 0.002365, Avg Recall: 0.907216, count: 97 -Region Avg IOU: 0.625329, Class: 1.000000, Obj: 0.403796, No Obj: 0.002241, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.661093, Class: 1.000000, Obj: 0.367714, No Obj: 0.002570, Avg Recall: 0.809091, count: 110 -6498: 0.406336, 0.420860 avg, 0.001000 rate, 29.528439 seconds, 1663488 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.685041, Class: 1.000000, Obj: 0.440516, No Obj: 0.001993, Avg Recall: 0.865854, count: 82 -Region Avg IOU: 0.669102, Class: 1.000000, Obj: 0.396763, No Obj: 0.002417, Avg Recall: 0.850394, count: 127 -Region Avg IOU: 0.663913, Class: 1.000000, Obj: 0.350123, No Obj: 0.002542, Avg Recall: 0.843478, count: 115 -Region Avg IOU: 0.657237, Class: 1.000000, Obj: 0.352525, No Obj: 0.002255, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.618654, Class: 1.000000, Obj: 0.391872, No Obj: 0.002256, Avg Recall: 0.758621, count: 87 -Region Avg IOU: 0.650539, Class: 1.000000, Obj: 0.463468, No Obj: 0.002330, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.707311, Class: 1.000000, Obj: 0.449863, No Obj: 0.002306, Avg Recall: 0.895522, count: 67 -Region Avg IOU: 0.606779, Class: 1.000000, Obj: 0.373763, No Obj: 0.002321, Avg Recall: 0.737374, count: 99 -6499: 0.419763, 0.420750 avg, 0.001000 rate, 29.191837 seconds, 1663744 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.650745, Class: 1.000000, Obj: 0.421361, No Obj: 0.002538, Avg Recall: 0.837209, count: 129 -Region Avg IOU: 0.633850, Class: 1.000000, Obj: 0.403041, No Obj: 0.002593, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.669286, Class: 1.000000, Obj: 0.433741, No Obj: 0.002502, Avg Recall: 0.839623, count: 106 -Region Avg IOU: 0.669497, Class: 1.000000, Obj: 0.409544, No Obj: 0.002255, Avg Recall: 0.844660, count: 103 -Region Avg IOU: 0.661364, Class: 1.000000, Obj: 0.379342, No Obj: 0.002694, Avg Recall: 0.843284, count: 134 -Region Avg IOU: 0.658734, Class: 1.000000, Obj: 0.418076, No Obj: 0.002419, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.645166, Class: 1.000000, Obj: 0.404521, No Obj: 0.002714, Avg Recall: 0.803419, count: 117 -Region Avg IOU: 0.634928, Class: 1.000000, Obj: 0.354982, No Obj: 0.002252, Avg Recall: 0.757576, count: 99 -6500: 0.432036, 0.421879 avg, 0.001000 rate, 29.086332 seconds, 1664000 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 12.459014 seconds -Region Avg IOU: 0.577298, Class: 1.000000, Obj: 0.364626, No Obj: 0.003281, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.614651, Class: 1.000000, Obj: 0.409122, No Obj: 0.003252, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.563369, Class: 1.000000, Obj: 0.342367, No Obj: 0.003172, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.589989, Class: 1.000000, Obj: 0.412171, No Obj: 0.003022, Avg Recall: 0.689655, count: 87 -Region Avg IOU: 0.544523, Class: 1.000000, Obj: 0.349101, No Obj: 0.003098, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.587994, Class: 1.000000, Obj: 0.363031, No Obj: 0.003741, Avg Recall: 0.664062, count: 128 -Region Avg IOU: 0.554110, Class: 1.000000, Obj: 0.338273, No Obj: 0.003225, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.610244, Class: 1.000000, Obj: 0.382431, No Obj: 0.003613, Avg Recall: 0.728814, count: 118 -6501: 0.427617, 0.422452 avg, 0.001000 rate, 22.994984 seconds, 1664256 images -Loaded: 0.000059 seconds -Region Avg IOU: 0.623038, Class: 1.000000, Obj: 0.395018, No Obj: 0.003547, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.564142, Class: 1.000000, Obj: 0.345537, No Obj: 0.003401, Avg Recall: 0.644068, count: 118 -Region Avg IOU: 0.551870, Class: 1.000000, Obj: 0.364215, No Obj: 0.003051, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.573570, Class: 1.000000, Obj: 0.376240, No Obj: 0.003870, Avg Recall: 0.620155, count: 129 -Region Avg IOU: 0.608627, Class: 1.000000, Obj: 0.379494, No Obj: 0.003462, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.556132, Class: 1.000000, Obj: 0.346534, No Obj: 0.003716, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.604159, Class: 1.000000, Obj: 0.357498, No Obj: 0.003642, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.588432, Class: 1.000000, Obj: 0.388824, No Obj: 0.003384, Avg Recall: 0.683673, count: 98 -6502: 0.459275, 0.426135 avg, 0.001000 rate, 22.463648 seconds, 1664512 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.604090, Class: 1.000000, Obj: 0.364634, No Obj: 0.003557, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.588135, Class: 1.000000, Obj: 0.397594, No Obj: 0.003885, Avg Recall: 0.679389, count: 131 -Region Avg IOU: 0.569240, Class: 1.000000, Obj: 0.345113, No Obj: 0.003973, Avg Recall: 0.609375, count: 128 -Region Avg IOU: 0.567101, Class: 1.000000, Obj: 0.384558, No Obj: 0.003434, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.589285, Class: 1.000000, Obj: 0.424150, No Obj: 0.004089, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.554150, Class: 1.000000, Obj: 0.364546, No Obj: 0.003059, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.613513, Class: 1.000000, Obj: 0.478516, No Obj: 0.003085, Avg Recall: 0.736111, count: 72 -Region Avg IOU: 0.550409, Class: 1.000000, Obj: 0.330539, No Obj: 0.003226, Avg Recall: 0.621849, count: 119 -6503: 0.430250, 0.426546 avg, 0.001000 rate, 24.262512 seconds, 1664768 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.569944, Class: 1.000000, Obj: 0.412449, No Obj: 0.004050, Avg Recall: 0.658915, count: 129 -Region Avg IOU: 0.570733, Class: 1.000000, Obj: 0.398103, No Obj: 0.003516, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.623629, Class: 1.000000, Obj: 0.398378, No Obj: 0.003511, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.557667, Class: 1.000000, Obj: 0.426011, No Obj: 0.003211, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.566325, Class: 1.000000, Obj: 0.413887, No Obj: 0.003923, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.593936, Class: 1.000000, Obj: 0.423098, No Obj: 0.003510, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.605144, Class: 1.000000, Obj: 0.415622, No Obj: 0.003736, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.553154, Class: 1.000000, Obj: 0.355251, No Obj: 0.004214, Avg Recall: 0.627907, count: 129 -6504: 0.423258, 0.426217 avg, 0.001000 rate, 23.749220 seconds, 1665024 images -Loaded: 0.000058 seconds -Region Avg IOU: 0.600463, Class: 1.000000, Obj: 0.436327, No Obj: 0.003457, Avg Recall: 0.709302, count: 86 -Region Avg IOU: 0.587369, Class: 1.000000, Obj: 0.429000, No Obj: 0.004047, Avg Recall: 0.655462, count: 119 -Region Avg IOU: 0.642647, Class: 1.000000, Obj: 0.431854, No Obj: 0.003664, Avg Recall: 0.823529, count: 102 -Region Avg IOU: 0.579692, Class: 1.000000, Obj: 0.367938, No Obj: 0.003347, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.573622, Class: 1.000000, Obj: 0.389988, No Obj: 0.003955, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.574386, Class: 1.000000, Obj: 0.377007, No Obj: 0.004507, Avg Recall: 0.652482, count: 141 -Region Avg IOU: 0.567610, Class: 1.000000, Obj: 0.370865, No Obj: 0.003597, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.593059, Class: 1.000000, Obj: 0.414474, No Obj: 0.003910, Avg Recall: 0.683761, count: 117 -6505: 0.391643, 0.422760 avg, 0.001000 rate, 23.141586 seconds, 1665280 images -Loaded: 0.025331 seconds -Region Avg IOU: 0.588047, Class: 1.000000, Obj: 0.434450, No Obj: 0.003741, Avg Recall: 0.688525, count: 122 -Region Avg IOU: 0.578465, Class: 1.000000, Obj: 0.411322, No Obj: 0.004063, Avg Recall: 0.655738, count: 122 -Region Avg IOU: 0.570328, Class: 1.000000, Obj: 0.406823, No Obj: 0.003536, Avg Recall: 0.696000, count: 125 -Region Avg IOU: 0.575760, Class: 1.000000, Obj: 0.434984, No Obj: 0.003835, Avg Recall: 0.680328, count: 122 -Region Avg IOU: 0.553460, Class: 1.000000, Obj: 0.408425, No Obj: 0.004039, Avg Recall: 0.656000, count: 125 -Region Avg IOU: 0.587428, Class: 1.000000, Obj: 0.425382, No Obj: 0.002991, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.603262, Class: 1.000000, Obj: 0.405377, No Obj: 0.003487, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.582567, Class: 1.000000, Obj: 0.396482, No Obj: 0.004083, Avg Recall: 0.698529, count: 136 -6506: 0.424315, 0.422915 avg, 0.001000 rate, 22.643126 seconds, 1665536 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.591402, Class: 1.000000, Obj: 0.419975, No Obj: 0.004289, Avg Recall: 0.705426, count: 129 -Region Avg IOU: 0.630009, Class: 1.000000, Obj: 0.452401, No Obj: 0.004163, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.548450, Class: 1.000000, Obj: 0.397636, No Obj: 0.003217, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.571807, Class: 1.000000, Obj: 0.432352, No Obj: 0.003401, Avg Recall: 0.659091, count: 88 -Region Avg IOU: 0.618125, Class: 1.000000, Obj: 0.458421, No Obj: 0.003125, Avg Recall: 0.746835, count: 79 -Region Avg IOU: 0.590320, Class: 1.000000, Obj: 0.431056, No Obj: 0.004189, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.603272, Class: 1.000000, Obj: 0.393851, No Obj: 0.003420, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.589617, Class: 1.000000, Obj: 0.439493, No Obj: 0.003976, Avg Recall: 0.710000, count: 100 -6507: 0.387570, 0.419381 avg, 0.001000 rate, 22.686890 seconds, 1665792 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.621101, Class: 1.000000, Obj: 0.460293, No Obj: 0.004007, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.557866, Class: 1.000000, Obj: 0.456097, No Obj: 0.004148, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.577412, Class: 1.000000, Obj: 0.415753, No Obj: 0.003524, Avg Recall: 0.660714, count: 112 -Region Avg IOU: 0.566110, Class: 1.000000, Obj: 0.441791, No Obj: 0.003158, Avg Recall: 0.666667, count: 87 -Region Avg IOU: 0.601091, Class: 1.000000, Obj: 0.451717, No Obj: 0.003316, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.560038, Class: 1.000000, Obj: 0.442155, No Obj: 0.004051, Avg Recall: 0.671756, count: 131 -Region Avg IOU: 0.619999, Class: 1.000000, Obj: 0.491276, No Obj: 0.003606, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.513882, Class: 1.000000, Obj: 0.404586, No Obj: 0.003773, Avg Recall: 0.598425, count: 127 -6508: 0.432170, 0.420660 avg, 0.001000 rate, 22.972946 seconds, 1666048 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.568401, Class: 1.000000, Obj: 0.445890, No Obj: 0.003616, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.617684, Class: 1.000000, Obj: 0.462236, No Obj: 0.003703, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.493685, Class: 1.000000, Obj: 0.397146, No Obj: 0.003347, Avg Recall: 0.530973, count: 113 -Region Avg IOU: 0.549457, Class: 1.000000, Obj: 0.417711, No Obj: 0.003666, Avg Recall: 0.670588, count: 85 -Region Avg IOU: 0.542138, Class: 1.000000, Obj: 0.432476, No Obj: 0.003677, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.609820, Class: 1.000000, Obj: 0.450581, No Obj: 0.003968, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.586265, Class: 1.000000, Obj: 0.405570, No Obj: 0.003792, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.537131, Class: 1.000000, Obj: 0.422984, No Obj: 0.004087, Avg Recall: 0.579710, count: 138 -6509: 0.457737, 0.424368 avg, 0.001000 rate, 23.976408 seconds, 1666304 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.576798, Class: 1.000000, Obj: 0.391044, No Obj: 0.004029, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.651794, Class: 1.000000, Obj: 0.530028, No Obj: 0.003551, Avg Recall: 0.759494, count: 79 -Region Avg IOU: 0.592784, Class: 1.000000, Obj: 0.409302, No Obj: 0.003893, Avg Recall: 0.694915, count: 118 -Region Avg IOU: 0.562017, Class: 1.000000, Obj: 0.430248, No Obj: 0.003742, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.592260, Class: 1.000000, Obj: 0.393530, No Obj: 0.004180, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.568302, Class: 1.000000, Obj: 0.391937, No Obj: 0.003238, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.568055, Class: 1.000000, Obj: 0.381213, No Obj: 0.004068, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.588733, Class: 1.000000, Obj: 0.436372, No Obj: 0.003839, Avg Recall: 0.718447, count: 103 -6510: 0.409418, 0.422873 avg, 0.001000 rate, 22.955441 seconds, 1666560 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 20.283876 seconds -Region Avg IOU: 0.585404, Class: 1.000000, Obj: 0.383514, No Obj: 0.003834, Avg Recall: 0.692982, count: 114 -Region Avg IOU: 0.548828, Class: 1.000000, Obj: 0.394267, No Obj: 0.003956, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.614117, Class: 1.000000, Obj: 0.406919, No Obj: 0.004138, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.626154, Class: 1.000000, Obj: 0.429860, No Obj: 0.003735, Avg Recall: 0.782051, count: 78 -Region Avg IOU: 0.571510, Class: 1.000000, Obj: 0.374148, No Obj: 0.003728, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.619922, Class: 1.000000, Obj: 0.391964, No Obj: 0.003793, Avg Recall: 0.758621, count: 87 -Region Avg IOU: 0.601865, Class: 1.000000, Obj: 0.384685, No Obj: 0.003584, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.559670, Class: 1.000000, Obj: 0.388797, No Obj: 0.003901, Avg Recall: 0.656566, count: 99 -6511: 0.445332, 0.425118 avg, 0.001000 rate, 23.139351 seconds, 1666816 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.603751, Class: 1.000000, Obj: 0.418789, No Obj: 0.003628, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.627569, Class: 1.000000, Obj: 0.420415, No Obj: 0.003617, Avg Recall: 0.737374, count: 99 -Region Avg IOU: 0.586329, Class: 1.000000, Obj: 0.406315, No Obj: 0.003534, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.564374, Class: 1.000000, Obj: 0.398656, No Obj: 0.003877, Avg Recall: 0.630252, count: 119 -Region Avg IOU: 0.608791, Class: 1.000000, Obj: 0.437906, No Obj: 0.003399, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.646182, Class: 1.000000, Obj: 0.464237, No Obj: 0.004313, Avg Recall: 0.807018, count: 114 -Region Avg IOU: 0.583321, Class: 1.000000, Obj: 0.388502, No Obj: 0.003875, Avg Recall: 0.708333, count: 120 -Region Avg IOU: 0.595835, Class: 1.000000, Obj: 0.420712, No Obj: 0.003706, Avg Recall: 0.718447, count: 103 -6512: 0.386153, 0.421222 avg, 0.001000 rate, 22.242432 seconds, 1667072 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.638196, Class: 1.000000, Obj: 0.413817, No Obj: 0.003785, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.568315, Class: 1.000000, Obj: 0.436390, No Obj: 0.003091, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.613983, Class: 1.000000, Obj: 0.392377, No Obj: 0.004435, Avg Recall: 0.744681, count: 141 -Region Avg IOU: 0.566672, Class: 1.000000, Obj: 0.418090, No Obj: 0.003957, Avg Recall: 0.661538, count: 130 -Region Avg IOU: 0.595552, Class: 1.000000, Obj: 0.415691, No Obj: 0.003561, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.576700, Class: 1.000000, Obj: 0.442681, No Obj: 0.003278, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.559190, Class: 1.000000, Obj: 0.388142, No Obj: 0.003213, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.627344, Class: 1.000000, Obj: 0.394835, No Obj: 0.004489, Avg Recall: 0.738095, count: 126 -6513: 0.414561, 0.420556 avg, 0.001000 rate, 21.943636 seconds, 1667328 images -Loaded: 0.000131 seconds -Region Avg IOU: 0.537125, Class: 1.000000, Obj: 0.414437, No Obj: 0.003000, Avg Recall: 0.643678, count: 87 -Region Avg IOU: 0.542595, Class: 1.000000, Obj: 0.390452, No Obj: 0.003443, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.549120, Class: 1.000000, Obj: 0.408058, No Obj: 0.003874, Avg Recall: 0.661538, count: 130 -Region Avg IOU: 0.584440, Class: 1.000000, Obj: 0.352542, No Obj: 0.003605, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.533278, Class: 1.000000, Obj: 0.379363, No Obj: 0.003399, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.590409, Class: 1.000000, Obj: 0.428140, No Obj: 0.003171, Avg Recall: 0.679012, count: 81 -Region Avg IOU: 0.591015, Class: 1.000000, Obj: 0.387024, No Obj: 0.003209, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.560684, Class: 1.000000, Obj: 0.378897, No Obj: 0.003871, Avg Recall: 0.671329, count: 143 -6514: 0.419604, 0.420461 avg, 0.001000 rate, 23.426867 seconds, 1667584 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.556826, Class: 1.000000, Obj: 0.396592, No Obj: 0.003887, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.663339, Class: 1.000000, Obj: 0.417568, No Obj: 0.003801, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.578484, Class: 1.000000, Obj: 0.409098, No Obj: 0.004141, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.594305, Class: 1.000000, Obj: 0.397045, No Obj: 0.003561, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.595747, Class: 1.000000, Obj: 0.421715, No Obj: 0.003480, Avg Recall: 0.708333, count: 72 -Region Avg IOU: 0.557171, Class: 1.000000, Obj: 0.355055, No Obj: 0.003534, Avg Recall: 0.624000, count: 125 -Region Avg IOU: 0.604088, Class: 1.000000, Obj: 0.432086, No Obj: 0.003552, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.659823, Class: 1.000000, Obj: 0.463190, No Obj: 0.003317, Avg Recall: 0.825000, count: 80 -6515: 0.416691, 0.420084 avg, 0.001000 rate, 24.342728 seconds, 1667840 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.566857, Class: 1.000000, Obj: 0.367942, No Obj: 0.003895, Avg Recall: 0.694656, count: 131 -Region Avg IOU: 0.662741, Class: 1.000000, Obj: 0.432454, No Obj: 0.003565, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.580814, Class: 1.000000, Obj: 0.392427, No Obj: 0.003248, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.619173, Class: 1.000000, Obj: 0.408749, No Obj: 0.003855, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.563842, Class: 1.000000, Obj: 0.381815, No Obj: 0.003281, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.582151, Class: 1.000000, Obj: 0.416098, No Obj: 0.003519, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.549446, Class: 1.000000, Obj: 0.378114, No Obj: 0.002976, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.627357, Class: 1.000000, Obj: 0.406224, No Obj: 0.003963, Avg Recall: 0.771429, count: 105 -6516: 0.421391, 0.420214 avg, 0.001000 rate, 22.539141 seconds, 1668096 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.575977, Class: 1.000000, Obj: 0.411724, No Obj: 0.003281, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.583645, Class: 1.000000, Obj: 0.373945, No Obj: 0.003386, Avg Recall: 0.645570, count: 79 -Region Avg IOU: 0.542565, Class: 1.000000, Obj: 0.419305, No Obj: 0.003116, Avg Recall: 0.609756, count: 82 -Region Avg IOU: 0.504641, Class: 1.000000, Obj: 0.350116, No Obj: 0.003523, Avg Recall: 0.571429, count: 126 -Region Avg IOU: 0.582586, Class: 1.000000, Obj: 0.421001, No Obj: 0.003591, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.572762, Class: 1.000000, Obj: 0.411298, No Obj: 0.003138, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.566613, Class: 1.000000, Obj: 0.361383, No Obj: 0.003607, Avg Recall: 0.664062, count: 128 -Region Avg IOU: 0.588495, Class: 1.000000, Obj: 0.378202, No Obj: 0.003212, Avg Recall: 0.692308, count: 104 -6517: 0.405802, 0.418773 avg, 0.001000 rate, 23.704676 seconds, 1668352 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.549142, Class: 1.000000, Obj: 0.417222, No Obj: 0.003806, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.605825, Class: 1.000000, Obj: 0.413554, No Obj: 0.003947, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.559062, Class: 1.000000, Obj: 0.386739, No Obj: 0.003314, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.533350, Class: 1.000000, Obj: 0.362285, No Obj: 0.003403, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.583923, Class: 1.000000, Obj: 0.374816, No Obj: 0.003800, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.633024, Class: 1.000000, Obj: 0.459537, No Obj: 0.003640, Avg Recall: 0.761364, count: 88 -Region Avg IOU: 0.604190, Class: 1.000000, Obj: 0.412078, No Obj: 0.003965, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.507418, Class: 1.000000, Obj: 0.347368, No Obj: 0.003340, Avg Recall: 0.564516, count: 124 -6518: 0.442978, 0.421194 avg, 0.001000 rate, 23.309799 seconds, 1668608 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.606043, Class: 1.000000, Obj: 0.398056, No Obj: 0.004002, Avg Recall: 0.736842, count: 133 -Region Avg IOU: 0.571948, Class: 1.000000, Obj: 0.371589, No Obj: 0.004040, Avg Recall: 0.666667, count: 120 -Region Avg IOU: 0.541912, Class: 1.000000, Obj: 0.419564, No Obj: 0.003741, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.570183, Class: 1.000000, Obj: 0.432119, No Obj: 0.003692, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.572195, Class: 1.000000, Obj: 0.408315, No Obj: 0.003881, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.585047, Class: 1.000000, Obj: 0.404821, No Obj: 0.004232, Avg Recall: 0.664122, count: 131 -Region Avg IOU: 0.583483, Class: 1.000000, Obj: 0.394146, No Obj: 0.003619, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.582702, Class: 1.000000, Obj: 0.383469, No Obj: 0.004145, Avg Recall: 0.706897, count: 116 -6519: 0.465520, 0.425626 avg, 0.001000 rate, 23.582796 seconds, 1668864 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.615005, Class: 1.000000, Obj: 0.428020, No Obj: 0.003593, Avg Recall: 0.755556, count: 90 -Region Avg IOU: 0.632365, Class: 1.000000, Obj: 0.469784, No Obj: 0.003361, Avg Recall: 0.775000, count: 80 -Region Avg IOU: 0.560158, Class: 1.000000, Obj: 0.367503, No Obj: 0.003686, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.614044, Class: 1.000000, Obj: 0.433338, No Obj: 0.003665, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.634340, Class: 1.000000, Obj: 0.432052, No Obj: 0.004187, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.579570, Class: 1.000000, Obj: 0.455448, No Obj: 0.003320, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.622148, Class: 1.000000, Obj: 0.449770, No Obj: 0.003747, Avg Recall: 0.744444, count: 90 -Region Avg IOU: 0.572965, Class: 1.000000, Obj: 0.429185, No Obj: 0.003472, Avg Recall: 0.653465, count: 101 -6520: 0.360160, 0.419080 avg, 0.001000 rate, 23.010008 seconds, 1669120 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000063 seconds -Region Avg IOU: 0.575124, Class: 1.000000, Obj: 0.445789, No Obj: 0.002928, Avg Recall: 0.682927, count: 82 -Region Avg IOU: 0.634044, Class: 1.000000, Obj: 0.475897, No Obj: 0.003267, Avg Recall: 0.761364, count: 88 -Region Avg IOU: 0.608434, Class: 1.000000, Obj: 0.437732, No Obj: 0.003637, Avg Recall: 0.726562, count: 128 -Region Avg IOU: 0.637501, Class: 1.000000, Obj: 0.509177, No Obj: 0.003797, Avg Recall: 0.764228, count: 123 -Region Avg IOU: 0.645831, Class: 1.000000, Obj: 0.492563, No Obj: 0.003178, Avg Recall: 0.768293, count: 82 -Region Avg IOU: 0.622686, Class: 1.000000, Obj: 0.438975, No Obj: 0.003579, Avg Recall: 0.737226, count: 137 -Region Avg IOU: 0.627851, Class: 1.000000, Obj: 0.466847, No Obj: 0.003679, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.622211, Class: 1.000000, Obj: 0.440111, No Obj: 0.003087, Avg Recall: 0.759259, count: 108 -6521: 0.374240, 0.414596 avg, 0.001000 rate, 25.033279 seconds, 1669376 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.630168, Class: 1.000000, Obj: 0.420027, No Obj: 0.003602, Avg Recall: 0.818182, count: 132 -Region Avg IOU: 0.599346, Class: 1.000000, Obj: 0.459905, No Obj: 0.003381, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.631303, Class: 1.000000, Obj: 0.480959, No Obj: 0.003037, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.593997, Class: 1.000000, Obj: 0.465814, No Obj: 0.003409, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.654375, Class: 1.000000, Obj: 0.460582, No Obj: 0.003491, Avg Recall: 0.808696, count: 115 -Region Avg IOU: 0.612497, Class: 1.000000, Obj: 0.411334, No Obj: 0.003839, Avg Recall: 0.727273, count: 143 -Region Avg IOU: 0.624946, Class: 1.000000, Obj: 0.390665, No Obj: 0.003708, Avg Recall: 0.771186, count: 118 -Region Avg IOU: 0.626795, Class: 1.000000, Obj: 0.478522, No Obj: 0.003413, Avg Recall: 0.744898, count: 98 -6522: 0.434109, 0.416547 avg, 0.001000 rate, 25.802423 seconds, 1669632 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.648681, Class: 1.000000, Obj: 0.452587, No Obj: 0.003200, Avg Recall: 0.802198, count: 91 -Region Avg IOU: 0.593671, Class: 1.000000, Obj: 0.408045, No Obj: 0.003279, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.595066, Class: 1.000000, Obj: 0.380203, No Obj: 0.002970, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.593615, Class: 1.000000, Obj: 0.405818, No Obj: 0.002993, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.583217, Class: 1.000000, Obj: 0.400644, No Obj: 0.003117, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.628301, Class: 1.000000, Obj: 0.450515, No Obj: 0.003248, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.654569, Class: 1.000000, Obj: 0.425977, No Obj: 0.003619, Avg Recall: 0.829268, count: 123 -Region Avg IOU: 0.644564, Class: 1.000000, Obj: 0.432855, No Obj: 0.004241, Avg Recall: 0.765957, count: 141 -6523: 0.448392, 0.419731 avg, 0.001000 rate, 26.522083 seconds, 1669888 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.613241, Class: 1.000000, Obj: 0.424935, No Obj: 0.003289, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.667941, Class: 1.000000, Obj: 0.440024, No Obj: 0.003051, Avg Recall: 0.794643, count: 112 -Region Avg IOU: 0.586007, Class: 1.000000, Obj: 0.360265, No Obj: 0.003161, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.664954, Class: 1.000000, Obj: 0.480612, No Obj: 0.003303, Avg Recall: 0.841584, count: 101 -Region Avg IOU: 0.619579, Class: 1.000000, Obj: 0.454553, No Obj: 0.003275, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.652745, Class: 1.000000, Obj: 0.458524, No Obj: 0.002828, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.618254, Class: 1.000000, Obj: 0.413546, No Obj: 0.002789, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.582100, Class: 1.000000, Obj: 0.414021, No Obj: 0.003157, Avg Recall: 0.650943, count: 106 -6524: 0.397514, 0.417510 avg, 0.001000 rate, 25.492064 seconds, 1670144 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.604867, Class: 1.000000, Obj: 0.428757, No Obj: 0.002809, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.655037, Class: 1.000000, Obj: 0.400453, No Obj: 0.002655, Avg Recall: 0.839080, count: 87 -Region Avg IOU: 0.614454, Class: 1.000000, Obj: 0.459571, No Obj: 0.003107, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.665709, Class: 1.000000, Obj: 0.448797, No Obj: 0.002636, Avg Recall: 0.815789, count: 76 -Region Avg IOU: 0.637585, Class: 1.000000, Obj: 0.449780, No Obj: 0.002855, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.643521, Class: 1.000000, Obj: 0.495744, No Obj: 0.002933, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.570921, Class: 1.000000, Obj: 0.405575, No Obj: 0.003001, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.634684, Class: 1.000000, Obj: 0.409021, No Obj: 0.003029, Avg Recall: 0.759615, count: 104 -6525: 0.369342, 0.412693 avg, 0.001000 rate, 24.700348 seconds, 1670400 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.569779, Class: 1.000000, Obj: 0.397724, No Obj: 0.002617, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.653428, Class: 1.000000, Obj: 0.357789, No Obj: 0.002716, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.653820, Class: 1.000000, Obj: 0.439277, No Obj: 0.003056, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.644423, Class: 1.000000, Obj: 0.387494, No Obj: 0.003253, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.570804, Class: 1.000000, Obj: 0.426659, No Obj: 0.002628, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.634364, Class: 1.000000, Obj: 0.395990, No Obj: 0.002947, Avg Recall: 0.808824, count: 136 -Region Avg IOU: 0.634757, Class: 1.000000, Obj: 0.432350, No Obj: 0.002615, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.645640, Class: 1.000000, Obj: 0.433037, No Obj: 0.002860, Avg Recall: 0.782178, count: 101 -6526: 0.405428, 0.411967 avg, 0.001000 rate, 26.727669 seconds, 1670656 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.621045, Class: 1.000000, Obj: 0.358840, No Obj: 0.003139, Avg Recall: 0.744361, count: 133 -Region Avg IOU: 0.632188, Class: 1.000000, Obj: 0.377946, No Obj: 0.002984, Avg Recall: 0.739130, count: 138 -Region Avg IOU: 0.597037, Class: 1.000000, Obj: 0.394479, No Obj: 0.002690, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.571943, Class: 1.000000, Obj: 0.347947, No Obj: 0.002773, Avg Recall: 0.637795, count: 127 -Region Avg IOU: 0.593466, Class: 1.000000, Obj: 0.386677, No Obj: 0.002570, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.616357, Class: 1.000000, Obj: 0.389954, No Obj: 0.003189, Avg Recall: 0.786325, count: 117 -Region Avg IOU: 0.586464, Class: 1.000000, Obj: 0.370422, No Obj: 0.002176, Avg Recall: 0.675325, count: 77 -Region Avg IOU: 0.656932, Class: 1.000000, Obj: 0.399731, No Obj: 0.002630, Avg Recall: 0.795699, count: 93 -6527: 0.423898, 0.413160 avg, 0.001000 rate, 24.786230 seconds, 1670912 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.622524, Class: 1.000000, Obj: 0.403648, No Obj: 0.002136, Avg Recall: 0.743902, count: 82 -Region Avg IOU: 0.636901, Class: 1.000000, Obj: 0.442702, No Obj: 0.003295, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.599359, Class: 1.000000, Obj: 0.418894, No Obj: 0.002850, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.606725, Class: 1.000000, Obj: 0.385858, No Obj: 0.002909, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.633252, Class: 1.000000, Obj: 0.436305, No Obj: 0.003006, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.651211, Class: 1.000000, Obj: 0.403725, No Obj: 0.002553, Avg Recall: 0.804348, count: 92 -Region Avg IOU: 0.617379, Class: 1.000000, Obj: 0.386724, No Obj: 0.002933, Avg Recall: 0.706349, count: 126 -Region Avg IOU: 0.652039, Class: 1.000000, Obj: 0.375229, No Obj: 0.002912, Avg Recall: 0.824074, count: 108 -6528: 0.420756, 0.413919 avg, 0.001000 rate, 24.721409 seconds, 1671168 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.610988, Class: 1.000000, Obj: 0.376547, No Obj: 0.003180, Avg Recall: 0.742188, count: 128 -Region Avg IOU: 0.640370, Class: 1.000000, Obj: 0.430272, No Obj: 0.003118, Avg Recall: 0.808000, count: 125 -Region Avg IOU: 0.631059, Class: 1.000000, Obj: 0.404181, No Obj: 0.003274, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.657939, Class: 1.000000, Obj: 0.360901, No Obj: 0.002428, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.640558, Class: 1.000000, Obj: 0.476099, No Obj: 0.002953, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.619533, Class: 1.000000, Obj: 0.442282, No Obj: 0.003023, Avg Recall: 0.766129, count: 124 -Region Avg IOU: 0.606676, Class: 1.000000, Obj: 0.397864, No Obj: 0.002909, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.636083, Class: 1.000000, Obj: 0.421022, No Obj: 0.002506, Avg Recall: 0.744186, count: 86 -6529: 0.406570, 0.413184 avg, 0.001000 rate, 26.489504 seconds, 1671424 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.659704, Class: 1.000000, Obj: 0.413781, No Obj: 0.002644, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.636700, Class: 1.000000, Obj: 0.385977, No Obj: 0.003385, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.689456, Class: 1.000000, Obj: 0.507956, No Obj: 0.002110, Avg Recall: 0.865672, count: 67 -Region Avg IOU: 0.646844, Class: 1.000000, Obj: 0.433020, No Obj: 0.003043, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.630176, Class: 1.000000, Obj: 0.438075, No Obj: 0.003076, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.593373, Class: 1.000000, Obj: 0.403599, No Obj: 0.002668, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.643048, Class: 1.000000, Obj: 0.434831, No Obj: 0.002344, Avg Recall: 0.773810, count: 84 -Region Avg IOU: 0.625339, Class: 1.000000, Obj: 0.362424, No Obj: 0.002833, Avg Recall: 0.770492, count: 122 -6530: 0.387458, 0.410612 avg, 0.001000 rate, 26.077547 seconds, 1671680 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 16.040789 seconds -Region Avg IOU: 0.625187, Class: 1.000000, Obj: 0.420918, No Obj: 0.003200, Avg Recall: 0.727273, count: 132 -Region Avg IOU: 0.609423, Class: 1.000000, Obj: 0.404058, No Obj: 0.002845, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.662256, Class: 1.000000, Obj: 0.382233, No Obj: 0.003204, Avg Recall: 0.791304, count: 115 -Region Avg IOU: 0.609992, Class: 1.000000, Obj: 0.452099, No Obj: 0.002542, Avg Recall: 0.755814, count: 86 -Region Avg IOU: 0.620867, Class: 1.000000, Obj: 0.472082, No Obj: 0.002742, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.680299, Class: 1.000000, Obj: 0.458270, No Obj: 0.003446, Avg Recall: 0.861111, count: 108 -Region Avg IOU: 0.580195, Class: 1.000000, Obj: 0.405683, No Obj: 0.002584, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.636362, Class: 1.000000, Obj: 0.422809, No Obj: 0.002916, Avg Recall: 0.762295, count: 122 -6531: 0.395767, 0.409127 avg, 0.001000 rate, 25.858240 seconds, 1671936 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.583112, Class: 1.000000, Obj: 0.381199, No Obj: 0.003426, Avg Recall: 0.691275, count: 149 -Region Avg IOU: 0.635147, Class: 1.000000, Obj: 0.432985, No Obj: 0.003291, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.660832, Class: 1.000000, Obj: 0.467460, No Obj: 0.003653, Avg Recall: 0.863248, count: 117 -Region Avg IOU: 0.624259, Class: 1.000000, Obj: 0.459335, No Obj: 0.003405, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.595146, Class: 1.000000, Obj: 0.379792, No Obj: 0.002600, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.648519, Class: 1.000000, Obj: 0.440094, No Obj: 0.003678, Avg Recall: 0.798319, count: 119 -Region Avg IOU: 0.643911, Class: 1.000000, Obj: 0.428706, No Obj: 0.003077, Avg Recall: 0.822034, count: 118 -Region Avg IOU: 0.646802, Class: 1.000000, Obj: 0.443549, No Obj: 0.003128, Avg Recall: 0.794118, count: 102 -6532: 0.452044, 0.413419 avg, 0.001000 rate, 25.834328 seconds, 1672192 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.593001, Class: 1.000000, Obj: 0.408245, No Obj: 0.002847, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.642824, Class: 1.000000, Obj: 0.465951, No Obj: 0.003048, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.622510, Class: 1.000000, Obj: 0.401491, No Obj: 0.002992, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.644062, Class: 1.000000, Obj: 0.454194, No Obj: 0.002748, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.577903, Class: 1.000000, Obj: 0.473174, No Obj: 0.003171, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.597349, Class: 1.000000, Obj: 0.445530, No Obj: 0.002903, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.614672, Class: 1.000000, Obj: 0.401493, No Obj: 0.002978, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.626594, Class: 1.000000, Obj: 0.418539, No Obj: 0.003293, Avg Recall: 0.766990, count: 103 -6533: 0.449068, 0.416984 avg, 0.001000 rate, 26.463545 seconds, 1672448 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.613612, Class: 1.000000, Obj: 0.434036, No Obj: 0.003697, Avg Recall: 0.720000, count: 125 -Region Avg IOU: 0.604134, Class: 1.000000, Obj: 0.389532, No Obj: 0.003088, Avg Recall: 0.717742, count: 124 -Region Avg IOU: 0.655545, Class: 1.000000, Obj: 0.445565, No Obj: 0.003203, Avg Recall: 0.826531, count: 98 -Region Avg IOU: 0.596232, Class: 1.000000, Obj: 0.394164, No Obj: 0.002964, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.630592, Class: 1.000000, Obj: 0.427780, No Obj: 0.003045, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.644700, Class: 1.000000, Obj: 0.460735, No Obj: 0.003504, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.618231, Class: 1.000000, Obj: 0.425582, No Obj: 0.002565, Avg Recall: 0.720930, count: 86 -Region Avg IOU: 0.562751, Class: 1.000000, Obj: 0.454429, No Obj: 0.003220, Avg Recall: 0.651515, count: 132 -6534: 0.431132, 0.418399 avg, 0.001000 rate, 25.842844 seconds, 1672704 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.622003, Class: 1.000000, Obj: 0.454273, No Obj: 0.002849, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.642426, Class: 1.000000, Obj: 0.362767, No Obj: 0.002716, Avg Recall: 0.808989, count: 89 -Region Avg IOU: 0.663510, Class: 1.000000, Obj: 0.438699, No Obj: 0.003420, Avg Recall: 0.824561, count: 114 -Region Avg IOU: 0.627727, Class: 1.000000, Obj: 0.443736, No Obj: 0.002929, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.649570, Class: 1.000000, Obj: 0.484317, No Obj: 0.002949, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.651497, Class: 1.000000, Obj: 0.396727, No Obj: 0.003005, Avg Recall: 0.809917, count: 121 -Region Avg IOU: 0.641659, Class: 1.000000, Obj: 0.462197, No Obj: 0.003279, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.670989, Class: 1.000000, Obj: 0.445996, No Obj: 0.003186, Avg Recall: 0.822430, count: 107 -6535: 0.386087, 0.415167 avg, 0.001000 rate, 24.667936 seconds, 1672960 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.636958, Class: 1.000000, Obj: 0.456015, No Obj: 0.003008, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.657870, Class: 1.000000, Obj: 0.464711, No Obj: 0.003062, Avg Recall: 0.808081, count: 99 -Region Avg IOU: 0.611501, Class: 1.000000, Obj: 0.379654, No Obj: 0.003630, Avg Recall: 0.733813, count: 139 -Region Avg IOU: 0.625819, Class: 1.000000, Obj: 0.493166, No Obj: 0.002839, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.661260, Class: 1.000000, Obj: 0.403406, No Obj: 0.003085, Avg Recall: 0.854167, count: 96 -Region Avg IOU: 0.645930, Class: 1.000000, Obj: 0.413334, No Obj: 0.002596, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.618628, Class: 1.000000, Obj: 0.410713, No Obj: 0.003416, Avg Recall: 0.750000, count: 132 -Region Avg IOU: 0.636460, Class: 1.000000, Obj: 0.416800, No Obj: 0.003138, Avg Recall: 0.727273, count: 110 -6536: 0.402266, 0.413877 avg, 0.001000 rate, 25.925406 seconds, 1673216 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.567525, Class: 1.000000, Obj: 0.418955, No Obj: 0.002901, Avg Recall: 0.635593, count: 118 -Region Avg IOU: 0.632938, Class: 1.000000, Obj: 0.462144, No Obj: 0.003138, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.688860, Class: 1.000000, Obj: 0.447772, No Obj: 0.003294, Avg Recall: 0.878505, count: 107 -Region Avg IOU: 0.628785, Class: 1.000000, Obj: 0.403626, No Obj: 0.002768, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.566090, Class: 1.000000, Obj: 0.422349, No Obj: 0.002534, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.620211, Class: 1.000000, Obj: 0.451289, No Obj: 0.003348, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.639674, Class: 1.000000, Obj: 0.456505, No Obj: 0.003195, Avg Recall: 0.817308, count: 104 -Region Avg IOU: 0.617873, Class: 1.000000, Obj: 0.389286, No Obj: 0.002955, Avg Recall: 0.738318, count: 107 -6537: 0.422577, 0.414747 avg, 0.001000 rate, 25.018675 seconds, 1673472 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.618624, Class: 1.000000, Obj: 0.371244, No Obj: 0.003200, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.597260, Class: 1.000000, Obj: 0.439347, No Obj: 0.002785, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.608033, Class: 1.000000, Obj: 0.362711, No Obj: 0.002462, Avg Recall: 0.743590, count: 78 -Region Avg IOU: 0.625018, Class: 1.000000, Obj: 0.449650, No Obj: 0.003117, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.554115, Class: 1.000000, Obj: 0.446042, No Obj: 0.002868, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.604130, Class: 1.000000, Obj: 0.442296, No Obj: 0.003121, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.598484, Class: 1.000000, Obj: 0.420065, No Obj: 0.002886, Avg Recall: 0.644444, count: 90 -Region Avg IOU: 0.648299, Class: 1.000000, Obj: 0.476279, No Obj: 0.003041, Avg Recall: 0.810811, count: 111 -6538: 0.402165, 0.413489 avg, 0.001000 rate, 25.533567 seconds, 1673728 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.607848, Class: 1.000000, Obj: 0.436289, No Obj: 0.002554, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.613557, Class: 1.000000, Obj: 0.435624, No Obj: 0.002844, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.619771, Class: 1.000000, Obj: 0.429668, No Obj: 0.003013, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.638248, Class: 1.000000, Obj: 0.428574, No Obj: 0.003920, Avg Recall: 0.755245, count: 143 -Region Avg IOU: 0.647693, Class: 1.000000, Obj: 0.411664, No Obj: 0.003279, Avg Recall: 0.775862, count: 116 -Region Avg IOU: 0.618176, Class: 1.000000, Obj: 0.420200, No Obj: 0.002652, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.638537, Class: 1.000000, Obj: 0.471445, No Obj: 0.002572, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.624455, Class: 1.000000, Obj: 0.482281, No Obj: 0.002943, Avg Recall: 0.736842, count: 114 -6539: 0.414716, 0.413612 avg, 0.001000 rate, 27.640684 seconds, 1673984 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.658454, Class: 1.000000, Obj: 0.422635, No Obj: 0.003277, Avg Recall: 0.836957, count: 92 -Region Avg IOU: 0.641418, Class: 1.000000, Obj: 0.421881, No Obj: 0.003296, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.634366, Class: 1.000000, Obj: 0.430806, No Obj: 0.003279, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.663761, Class: 1.000000, Obj: 0.396450, No Obj: 0.002834, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.648378, Class: 1.000000, Obj: 0.475522, No Obj: 0.003211, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.629320, Class: 1.000000, Obj: 0.430665, No Obj: 0.003004, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.634616, Class: 1.000000, Obj: 0.400760, No Obj: 0.003092, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.587732, Class: 1.000000, Obj: 0.435119, No Obj: 0.003003, Avg Recall: 0.677419, count: 93 -6540: 0.404624, 0.412713 avg, 0.001000 rate, 26.076511 seconds, 1674240 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.647086 seconds -Region Avg IOU: 0.531789, Class: 1.000000, Obj: 0.332855, No Obj: 0.004015, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.547791, Class: 1.000000, Obj: 0.354532, No Obj: 0.004690, Avg Recall: 0.639344, count: 122 -Region Avg IOU: 0.543290, Class: 1.000000, Obj: 0.310153, No Obj: 0.003883, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.472114, Class: 1.000000, Obj: 0.330530, No Obj: 0.002899, Avg Recall: 0.565217, count: 69 -Region Avg IOU: 0.490028, Class: 1.000000, Obj: 0.320428, No Obj: 0.003606, Avg Recall: 0.537634, count: 93 -Region Avg IOU: 0.495033, Class: 1.000000, Obj: 0.311952, No Obj: 0.003531, Avg Recall: 0.526786, count: 112 -Region Avg IOU: 0.508696, Class: 1.000000, Obj: 0.295881, No Obj: 0.003143, Avg Recall: 0.547368, count: 95 -Region Avg IOU: 0.476294, Class: 1.000000, Obj: 0.301538, No Obj: 0.003228, Avg Recall: 0.517857, count: 112 -6541: 0.428957, 0.414337 avg, 0.001000 rate, 19.176441 seconds, 1674496 images -Loaded: 0.887128 seconds -Region Avg IOU: 0.513855, Class: 1.000000, Obj: 0.343941, No Obj: 0.003851, Avg Recall: 0.553398, count: 103 -Region Avg IOU: 0.592963, Class: 1.000000, Obj: 0.336556, No Obj: 0.003746, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.517179, Class: 1.000000, Obj: 0.358212, No Obj: 0.003757, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.522403, Class: 1.000000, Obj: 0.330296, No Obj: 0.003554, Avg Recall: 0.574713, count: 87 -Region Avg IOU: 0.547237, Class: 1.000000, Obj: 0.332495, No Obj: 0.004076, Avg Recall: 0.632479, count: 117 -Region Avg IOU: 0.537382, Class: 1.000000, Obj: 0.325549, No Obj: 0.003754, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.504643, Class: 1.000000, Obj: 0.314309, No Obj: 0.004097, Avg Recall: 0.565217, count: 115 -Region Avg IOU: 0.539945, Class: 1.000000, Obj: 0.376556, No Obj: 0.003747, Avg Recall: 0.588889, count: 90 -6542: 0.386428, 0.411546 avg, 0.001000 rate, 20.447683 seconds, 1674752 images -Loaded: 1.097184 seconds -Region Avg IOU: 0.565620, Class: 1.000000, Obj: 0.335398, No Obj: 0.004639, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.485120, Class: 1.000000, Obj: 0.294523, No Obj: 0.003967, Avg Recall: 0.503597, count: 139 -Region Avg IOU: 0.583456, Class: 1.000000, Obj: 0.362828, No Obj: 0.004303, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.485374, Class: 1.000000, Obj: 0.317261, No Obj: 0.004085, Avg Recall: 0.511811, count: 127 -Region Avg IOU: 0.518693, Class: 1.000000, Obj: 0.354952, No Obj: 0.003973, Avg Recall: 0.593750, count: 96 -Region Avg IOU: 0.545896, Class: 1.000000, Obj: 0.399000, No Obj: 0.004506, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.528703, Class: 1.000000, Obj: 0.325737, No Obj: 0.003872, Avg Recall: 0.600000, count: 95 -Region Avg IOU: 0.532037, Class: 1.000000, Obj: 0.336190, No Obj: 0.004114, Avg Recall: 0.600000, count: 115 -6543: 0.481417, 0.418534 avg, 0.001000 rate, 19.881536 seconds, 1675008 images -Loaded: 3.471585 seconds -Region Avg IOU: 0.535058, Class: 1.000000, Obj: 0.312153, No Obj: 0.004194, Avg Recall: 0.601770, count: 113 -Region Avg IOU: 0.530430, Class: 1.000000, Obj: 0.367241, No Obj: 0.004085, Avg Recall: 0.593023, count: 86 -Region Avg IOU: 0.546916, Class: 1.000000, Obj: 0.372860, No Obj: 0.004004, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.511570, Class: 1.000000, Obj: 0.345929, No Obj: 0.003965, Avg Recall: 0.527273, count: 110 -Region Avg IOU: 0.511366, Class: 1.000000, Obj: 0.383836, No Obj: 0.004474, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.537457, Class: 1.000000, Obj: 0.385915, No Obj: 0.004121, Avg Recall: 0.629214, count: 89 -Region Avg IOU: 0.490093, Class: 1.000000, Obj: 0.323386, No Obj: 0.004672, Avg Recall: 0.561538, count: 130 -Region Avg IOU: 0.546190, Class: 1.000000, Obj: 0.345395, No Obj: 0.005147, Avg Recall: 0.649635, count: 137 -6544: 0.423774, 0.419058 avg, 0.001000 rate, 18.032385 seconds, 1675264 images -Loaded: 1.653385 seconds -Region Avg IOU: 0.532545, Class: 1.000000, Obj: 0.327937, No Obj: 0.005260, Avg Recall: 0.596899, count: 129 -Region Avg IOU: 0.552073, Class: 1.000000, Obj: 0.394441, No Obj: 0.004615, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.527635, Class: 1.000000, Obj: 0.358726, No Obj: 0.004685, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.539846, Class: 1.000000, Obj: 0.366730, No Obj: 0.004009, Avg Recall: 0.630952, count: 84 -Region Avg IOU: 0.629753, Class: 1.000000, Obj: 0.438598, No Obj: 0.004615, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.535391, Class: 1.000000, Obj: 0.388034, No Obj: 0.004513, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.474340, Class: 1.000000, Obj: 0.334546, No Obj: 0.003764, Avg Recall: 0.451327, count: 113 -Region Avg IOU: 0.552349, Class: 1.000000, Obj: 0.394095, No Obj: 0.004655, Avg Recall: 0.631148, count: 122 -6545: 0.404932, 0.417645 avg, 0.001000 rate, 19.452713 seconds, 1675520 images -Loaded: 1.076263 seconds -Region Avg IOU: 0.492075, Class: 1.000000, Obj: 0.347098, No Obj: 0.005132, Avg Recall: 0.553846, count: 130 -Region Avg IOU: 0.510809, Class: 1.000000, Obj: 0.407659, No Obj: 0.004822, Avg Recall: 0.556452, count: 124 -Region Avg IOU: 0.497581, Class: 1.000000, Obj: 0.425535, No Obj: 0.004739, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.569898, Class: 1.000000, Obj: 0.403179, No Obj: 0.004395, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.556031, Class: 1.000000, Obj: 0.419803, No Obj: 0.003729, Avg Recall: 0.619048, count: 84 -Region Avg IOU: 0.499959, Class: 1.000000, Obj: 0.357104, No Obj: 0.005114, Avg Recall: 0.568627, count: 153 -Region Avg IOU: 0.461499, Class: 1.000000, Obj: 0.357731, No Obj: 0.003226, Avg Recall: 0.441176, count: 68 -Region Avg IOU: 0.549648, Class: 1.000000, Obj: 0.428438, No Obj: 0.004336, Avg Recall: 0.597826, count: 92 -6546: 0.462076, 0.422088 avg, 0.001000 rate, 17.689732 seconds, 1675776 images -Loaded: 2.174151 seconds -Region Avg IOU: 0.490902, Class: 1.000000, Obj: 0.399176, No Obj: 0.004516, Avg Recall: 0.562500, count: 128 -Region Avg IOU: 0.519310, Class: 1.000000, Obj: 0.427509, No Obj: 0.004641, Avg Recall: 0.579545, count: 88 -Region Avg IOU: 0.573463, Class: 1.000000, Obj: 0.392755, No Obj: 0.004266, Avg Recall: 0.650602, count: 83 -Region Avg IOU: 0.491064, Class: 1.000000, Obj: 0.381136, No Obj: 0.004898, Avg Recall: 0.544118, count: 136 -Region Avg IOU: 0.546820, Class: 1.000000, Obj: 0.410895, No Obj: 0.005498, Avg Recall: 0.616000, count: 125 -Region Avg IOU: 0.493018, Class: 1.000000, Obj: 0.403565, No Obj: 0.004354, Avg Recall: 0.541176, count: 85 -Region Avg IOU: 0.588502, Class: 1.000000, Obj: 0.446786, No Obj: 0.005114, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.543217, Class: 1.000000, Obj: 0.439284, No Obj: 0.004856, Avg Recall: 0.649485, count: 97 -6547: 0.402296, 0.420109 avg, 0.001000 rate, 20.547728 seconds, 1676032 images -Loaded: 0.864089 seconds -Region Avg IOU: 0.494467, Class: 1.000000, Obj: 0.417803, No Obj: 0.004492, Avg Recall: 0.495238, count: 105 -Region Avg IOU: 0.524193, Class: 1.000000, Obj: 0.397090, No Obj: 0.004919, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.515205, Class: 1.000000, Obj: 0.378560, No Obj: 0.005636, Avg Recall: 0.581395, count: 129 -Region Avg IOU: 0.511067, Class: 1.000000, Obj: 0.406686, No Obj: 0.004771, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.509559, Class: 1.000000, Obj: 0.432947, No Obj: 0.004243, Avg Recall: 0.555556, count: 90 -Region Avg IOU: 0.492433, Class: 1.000000, Obj: 0.400288, No Obj: 0.004510, Avg Recall: 0.562500, count: 96 -Region Avg IOU: 0.501125, Class: 1.000000, Obj: 0.385737, No Obj: 0.004977, Avg Recall: 0.550847, count: 118 -Region Avg IOU: 0.541365, Class: 1.000000, Obj: 0.386733, No Obj: 0.004435, Avg Recall: 0.637363, count: 91 -6548: 0.387153, 0.416813 avg, 0.001000 rate, 18.255287 seconds, 1676288 images -Loaded: 3.851356 seconds -Region Avg IOU: 0.513917, Class: 1.000000, Obj: 0.420272, No Obj: 0.005677, Avg Recall: 0.617978, count: 89 -Region Avg IOU: 0.572805, Class: 1.000000, Obj: 0.458927, No Obj: 0.004679, Avg Recall: 0.662338, count: 77 -Region Avg IOU: 0.533187, Class: 1.000000, Obj: 0.413677, No Obj: 0.005776, Avg Recall: 0.620155, count: 129 -Region Avg IOU: 0.526873, Class: 1.000000, Obj: 0.403916, No Obj: 0.005802, Avg Recall: 0.603306, count: 121 -Region Avg IOU: 0.439119, Class: 1.000000, Obj: 0.397242, No Obj: 0.004647, Avg Recall: 0.450000, count: 100 -Region Avg IOU: 0.511068, Class: 1.000000, Obj: 0.397601, No Obj: 0.005076, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.535193, Class: 1.000000, Obj: 0.408558, No Obj: 0.006189, Avg Recall: 0.618705, count: 139 -Region Avg IOU: 0.529444, Class: 1.000000, Obj: 0.412599, No Obj: 0.004329, Avg Recall: 0.620253, count: 79 -6549: 0.454074, 0.420539 avg, 0.001000 rate, 18.061396 seconds, 1676544 images -Loaded: 3.300879 seconds -Region Avg IOU: 0.478202, Class: 1.000000, Obj: 0.403586, No Obj: 0.004539, Avg Recall: 0.542857, count: 105 -Region Avg IOU: 0.513610, Class: 1.000000, Obj: 0.390119, No Obj: 0.005168, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.543717, Class: 1.000000, Obj: 0.420580, No Obj: 0.004827, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.548219, Class: 1.000000, Obj: 0.427280, No Obj: 0.005006, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.513491, Class: 1.000000, Obj: 0.403442, No Obj: 0.004495, Avg Recall: 0.556818, count: 88 -Region Avg IOU: 0.512540, Class: 1.000000, Obj: 0.378763, No Obj: 0.005941, Avg Recall: 0.551471, count: 136 -Region Avg IOU: 0.485618, Class: 1.000000, Obj: 0.391698, No Obj: 0.004357, Avg Recall: 0.485149, count: 101 -Region Avg IOU: 0.485637, Class: 1.000000, Obj: 0.424272, No Obj: 0.004674, Avg Recall: 0.520000, count: 100 -6550: 0.465053, 0.424991 avg, 0.001000 rate, 20.017029 seconds, 1676800 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000070 seconds -Region Avg IOU: 0.668136, Class: 1.000000, Obj: 0.450385, No Obj: 0.004192, Avg Recall: 0.838095, count: 105 -Region Avg IOU: 0.591109, Class: 1.000000, Obj: 0.488457, No Obj: 0.003552, Avg Recall: 0.697479, count: 119 -Region Avg IOU: 0.654188, Class: 1.000000, Obj: 0.508995, No Obj: 0.003914, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.627196, Class: 1.000000, Obj: 0.485339, No Obj: 0.003307, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.602953, Class: 1.000000, Obj: 0.468072, No Obj: 0.003456, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.669487, Class: 1.000000, Obj: 0.458933, No Obj: 0.003557, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.662437, Class: 1.000000, Obj: 0.566250, No Obj: 0.003674, Avg Recall: 0.827586, count: 87 -Region Avg IOU: 0.641911, Class: 1.000000, Obj: 0.459047, No Obj: 0.004228, Avg Recall: 0.759690, count: 129 -6551: 0.381107, 0.420602 avg, 0.001000 rate, 24.110254 seconds, 1677056 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.616266, Class: 1.000000, Obj: 0.495859, No Obj: 0.003086, Avg Recall: 0.730769, count: 78 -Region Avg IOU: 0.619757, Class: 1.000000, Obj: 0.447885, No Obj: 0.002999, Avg Recall: 0.730000, count: 100 -Region Avg IOU: 0.590512, Class: 1.000000, Obj: 0.473060, No Obj: 0.003391, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.658107, Class: 1.000000, Obj: 0.494224, No Obj: 0.003299, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.580427, Class: 1.000000, Obj: 0.451521, No Obj: 0.003888, Avg Recall: 0.674242, count: 132 -Region Avg IOU: 0.635287, Class: 1.000000, Obj: 0.509863, No Obj: 0.003620, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.609515, Class: 1.000000, Obj: 0.418803, No Obj: 0.003350, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.628339, Class: 1.000000, Obj: 0.490744, No Obj: 0.003639, Avg Recall: 0.740385, count: 104 -6552: 0.407330, 0.419275 avg, 0.001000 rate, 25.941263 seconds, 1677312 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.611140, Class: 1.000000, Obj: 0.415842, No Obj: 0.003070, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.610751, Class: 1.000000, Obj: 0.387808, No Obj: 0.003470, Avg Recall: 0.728682, count: 129 -Region Avg IOU: 0.590575, Class: 1.000000, Obj: 0.488252, No Obj: 0.002900, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.612833, Class: 1.000000, Obj: 0.462625, No Obj: 0.003185, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.637311, Class: 1.000000, Obj: 0.441812, No Obj: 0.003325, Avg Recall: 0.764706, count: 136 -Region Avg IOU: 0.641363, Class: 1.000000, Obj: 0.453955, No Obj: 0.003414, Avg Recall: 0.754098, count: 122 -Region Avg IOU: 0.635307, Class: 1.000000, Obj: 0.416929, No Obj: 0.003860, Avg Recall: 0.750000, count: 140 -Region Avg IOU: 0.639880, Class: 1.000000, Obj: 0.451691, No Obj: 0.003152, Avg Recall: 0.741935, count: 93 -6553: 0.431343, 0.420482 avg, 0.001000 rate, 25.858149 seconds, 1677568 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.661410, Class: 1.000000, Obj: 0.461934, No Obj: 0.002996, Avg Recall: 0.842697, count: 89 -Region Avg IOU: 0.637408, Class: 1.000000, Obj: 0.413662, No Obj: 0.002991, Avg Recall: 0.802326, count: 86 -Region Avg IOU: 0.662936, Class: 1.000000, Obj: 0.416115, No Obj: 0.003290, Avg Recall: 0.796748, count: 123 -Region Avg IOU: 0.622671, Class: 1.000000, Obj: 0.436191, No Obj: 0.002816, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.613704, Class: 1.000000, Obj: 0.410588, No Obj: 0.003400, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.629938, Class: 1.000000, Obj: 0.457196, No Obj: 0.003199, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.634991, Class: 1.000000, Obj: 0.373801, No Obj: 0.003308, Avg Recall: 0.768000, count: 125 -Region Avg IOU: 0.644985, Class: 1.000000, Obj: 0.403967, No Obj: 0.003484, Avg Recall: 0.813433, count: 134 -6554: 0.401199, 0.418554 avg, 0.001000 rate, 26.617380 seconds, 1677824 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.599886, Class: 1.000000, Obj: 0.378368, No Obj: 0.003172, Avg Recall: 0.700000, count: 130 -Region Avg IOU: 0.567330, Class: 1.000000, Obj: 0.342565, No Obj: 0.002882, Avg Recall: 0.640000, count: 125 -Region Avg IOU: 0.590306, Class: 1.000000, Obj: 0.363690, No Obj: 0.003077, Avg Recall: 0.737705, count: 122 -Region Avg IOU: 0.633364, Class: 1.000000, Obj: 0.379024, No Obj: 0.003257, Avg Recall: 0.773438, count: 128 -Region Avg IOU: 0.615191, Class: 1.000000, Obj: 0.350894, No Obj: 0.002825, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.630430, Class: 1.000000, Obj: 0.387632, No Obj: 0.002682, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.636411, Class: 1.000000, Obj: 0.450180, No Obj: 0.002759, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.649931, Class: 1.000000, Obj: 0.411523, No Obj: 0.002682, Avg Recall: 0.802198, count: 91 -6555: 0.463178, 0.423016 avg, 0.001000 rate, 26.629105 seconds, 1678080 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.614788, Class: 1.000000, Obj: 0.412458, No Obj: 0.002413, Avg Recall: 0.734940, count: 83 -Region Avg IOU: 0.629597, Class: 1.000000, Obj: 0.351997, No Obj: 0.002417, Avg Recall: 0.777778, count: 81 -Region Avg IOU: 0.650004, Class: 1.000000, Obj: 0.392509, No Obj: 0.003289, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.622628, Class: 1.000000, Obj: 0.407096, No Obj: 0.002469, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.639657, Class: 1.000000, Obj: 0.402230, No Obj: 0.003062, Avg Recall: 0.795082, count: 122 -Region Avg IOU: 0.666084, Class: 1.000000, Obj: 0.420028, No Obj: 0.002433, Avg Recall: 0.759494, count: 79 -Region Avg IOU: 0.647694, Class: 1.000000, Obj: 0.395174, No Obj: 0.002720, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.636718, Class: 1.000000, Obj: 0.398803, No Obj: 0.002651, Avg Recall: 0.742268, count: 97 -6556: 0.369363, 0.417651 avg, 0.001000 rate, 25.601404 seconds, 1678336 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.646402, Class: 1.000000, Obj: 0.338450, No Obj: 0.003125, Avg Recall: 0.777778, count: 144 -Region Avg IOU: 0.616222, Class: 1.000000, Obj: 0.360860, No Obj: 0.002489, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.656989, Class: 1.000000, Obj: 0.431221, No Obj: 0.003274, Avg Recall: 0.776859, count: 121 -Region Avg IOU: 0.637284, Class: 1.000000, Obj: 0.344396, No Obj: 0.002556, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.642198, Class: 1.000000, Obj: 0.394038, No Obj: 0.003270, Avg Recall: 0.783333, count: 120 -Region Avg IOU: 0.604295, Class: 1.000000, Obj: 0.390132, No Obj: 0.002337, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.638064, Class: 1.000000, Obj: 0.413350, No Obj: 0.002752, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.600707, Class: 1.000000, Obj: 0.383737, No Obj: 0.002077, Avg Recall: 0.687500, count: 96 -6557: 0.430049, 0.418891 avg, 0.001000 rate, 24.459997 seconds, 1678592 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.651140, Class: 1.000000, Obj: 0.413821, No Obj: 0.002640, Avg Recall: 0.802083, count: 96 -Region Avg IOU: 0.633260, Class: 1.000000, Obj: 0.376755, No Obj: 0.002990, Avg Recall: 0.785714, count: 126 -Region Avg IOU: 0.657465, Class: 1.000000, Obj: 0.401117, No Obj: 0.002902, Avg Recall: 0.822034, count: 118 -Region Avg IOU: 0.698994, Class: 1.000000, Obj: 0.393544, No Obj: 0.002415, Avg Recall: 0.873418, count: 79 -Region Avg IOU: 0.643001, Class: 1.000000, Obj: 0.420407, No Obj: 0.002556, Avg Recall: 0.811111, count: 90 -Region Avg IOU: 0.678277, Class: 1.000000, Obj: 0.457659, No Obj: 0.002634, Avg Recall: 0.852273, count: 88 -Region Avg IOU: 0.584111, Class: 1.000000, Obj: 0.391798, No Obj: 0.002526, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.666955, Class: 1.000000, Obj: 0.403246, No Obj: 0.002614, Avg Recall: 0.772727, count: 88 -6558: 0.388144, 0.415816 avg, 0.001000 rate, 25.369688 seconds, 1678848 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.638136, Class: 1.000000, Obj: 0.393155, No Obj: 0.002512, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.630045, Class: 1.000000, Obj: 0.426498, No Obj: 0.002736, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.637208, Class: 1.000000, Obj: 0.399597, No Obj: 0.002682, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.608661, Class: 1.000000, Obj: 0.321569, No Obj: 0.002862, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.638862, Class: 1.000000, Obj: 0.428197, No Obj: 0.002944, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.644899, Class: 1.000000, Obj: 0.410565, No Obj: 0.003405, Avg Recall: 0.798450, count: 129 -Region Avg IOU: 0.637354, Class: 1.000000, Obj: 0.385223, No Obj: 0.002882, Avg Recall: 0.782258, count: 124 -Region Avg IOU: 0.630764, Class: 1.000000, Obj: 0.408516, No Obj: 0.003545, Avg Recall: 0.753333, count: 150 -6559: 0.431069, 0.417341 avg, 0.001000 rate, 26.709312 seconds, 1679104 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.645026, Class: 1.000000, Obj: 0.381835, No Obj: 0.003197, Avg Recall: 0.776923, count: 130 -Region Avg IOU: 0.653727, Class: 1.000000, Obj: 0.394603, No Obj: 0.002830, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.629434, Class: 1.000000, Obj: 0.399392, No Obj: 0.003259, Avg Recall: 0.761905, count: 126 -Region Avg IOU: 0.634028, Class: 1.000000, Obj: 0.379124, No Obj: 0.002940, Avg Recall: 0.748092, count: 131 -Region Avg IOU: 0.648752, Class: 1.000000, Obj: 0.413427, No Obj: 0.003286, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.623372, Class: 1.000000, Obj: 0.386658, No Obj: 0.002781, Avg Recall: 0.705426, count: 129 -Region Avg IOU: 0.639625, Class: 1.000000, Obj: 0.425452, No Obj: 0.003370, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.644927, Class: 1.000000, Obj: 0.422812, No Obj: 0.002882, Avg Recall: 0.739583, count: 96 -6560: 0.460195, 0.421627 avg, 0.001000 rate, 24.970232 seconds, 1679360 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000092 seconds -Region Avg IOU: 0.621216, Class: 1.000000, Obj: 0.424569, No Obj: 0.002981, Avg Recall: 0.782258, count: 124 -Region Avg IOU: 0.666179, Class: 1.000000, Obj: 0.440584, No Obj: 0.003037, Avg Recall: 0.808696, count: 115 -Region Avg IOU: 0.619844, Class: 1.000000, Obj: 0.369991, No Obj: 0.003073, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.581128, Class: 1.000000, Obj: 0.405684, No Obj: 0.002371, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.646786, Class: 1.000000, Obj: 0.484717, No Obj: 0.003317, Avg Recall: 0.782258, count: 124 -Region Avg IOU: 0.630277, Class: 1.000000, Obj: 0.447665, No Obj: 0.002606, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.652240, Class: 1.000000, Obj: 0.425596, No Obj: 0.003072, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.608207, Class: 1.000000, Obj: 0.436582, No Obj: 0.002398, Avg Recall: 0.715447, count: 123 -6561: 0.413676, 0.420832 avg, 0.001000 rate, 25.042196 seconds, 1679616 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.627149, Class: 1.000000, Obj: 0.477676, No Obj: 0.002333, Avg Recall: 0.710526, count: 76 -Region Avg IOU: 0.631157, Class: 1.000000, Obj: 0.479312, No Obj: 0.003096, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.621543, Class: 1.000000, Obj: 0.429709, No Obj: 0.003009, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.620848, Class: 1.000000, Obj: 0.419726, No Obj: 0.002883, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.676269, Class: 1.000000, Obj: 0.510698, No Obj: 0.002824, Avg Recall: 0.814433, count: 97 -Region Avg IOU: 0.665153, Class: 1.000000, Obj: 0.448927, No Obj: 0.002806, Avg Recall: 0.831776, count: 107 -Region Avg IOU: 0.665425, Class: 1.000000, Obj: 0.477376, No Obj: 0.003656, Avg Recall: 0.833333, count: 114 -Region Avg IOU: 0.654014, Class: 1.000000, Obj: 0.500771, No Obj: 0.002795, Avg Recall: 0.764706, count: 85 -6562: 0.384217, 0.417170 avg, 0.001000 rate, 26.691109 seconds, 1679872 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.623559, Class: 1.000000, Obj: 0.458926, No Obj: 0.003398, Avg Recall: 0.771186, count: 118 -Region Avg IOU: 0.610376, Class: 1.000000, Obj: 0.398212, No Obj: 0.003800, Avg Recall: 0.685315, count: 143 -Region Avg IOU: 0.650042, Class: 1.000000, Obj: 0.482555, No Obj: 0.003326, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.636411, Class: 1.000000, Obj: 0.449128, No Obj: 0.003018, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.622441, Class: 1.000000, Obj: 0.472666, No Obj: 0.003767, Avg Recall: 0.731544, count: 149 -Region Avg IOU: 0.670855, Class: 1.000000, Obj: 0.416769, No Obj: 0.003428, Avg Recall: 0.858407, count: 113 -Region Avg IOU: 0.633365, Class: 1.000000, Obj: 0.446554, No Obj: 0.003278, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.609743, Class: 1.000000, Obj: 0.467469, No Obj: 0.002987, Avg Recall: 0.717647, count: 85 -6563: 0.423095, 0.417763 avg, 0.001000 rate, 28.127363 seconds, 1680128 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.639466, Class: 1.000000, Obj: 0.494801, No Obj: 0.003216, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.654880, Class: 1.000000, Obj: 0.490367, No Obj: 0.003478, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.638645, Class: 1.000000, Obj: 0.486933, No Obj: 0.003077, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.651540, Class: 1.000000, Obj: 0.482531, No Obj: 0.003490, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.673371, Class: 1.000000, Obj: 0.523743, No Obj: 0.003125, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.632136, Class: 1.000000, Obj: 0.466020, No Obj: 0.003490, Avg Recall: 0.764228, count: 123 -Region Avg IOU: 0.656961, Class: 1.000000, Obj: 0.495624, No Obj: 0.003683, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.653725, Class: 1.000000, Obj: 0.475754, No Obj: 0.003121, Avg Recall: 0.784946, count: 93 -6564: 0.369282, 0.412915 avg, 0.001000 rate, 26.577620 seconds, 1680384 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.656640, Class: 1.000000, Obj: 0.468985, No Obj: 0.003184, Avg Recall: 0.817308, count: 104 -Region Avg IOU: 0.605842, Class: 1.000000, Obj: 0.454846, No Obj: 0.003051, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.589243, Class: 1.000000, Obj: 0.408058, No Obj: 0.003182, Avg Recall: 0.693548, count: 124 -Region Avg IOU: 0.612163, Class: 1.000000, Obj: 0.444994, No Obj: 0.003179, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.641312, Class: 1.000000, Obj: 0.492158, No Obj: 0.003500, Avg Recall: 0.776859, count: 121 -Region Avg IOU: 0.658939, Class: 1.000000, Obj: 0.464742, No Obj: 0.003575, Avg Recall: 0.797101, count: 138 -Region Avg IOU: 0.632140, Class: 1.000000, Obj: 0.423024, No Obj: 0.002704, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.613175, Class: 1.000000, Obj: 0.431180, No Obj: 0.003402, Avg Recall: 0.698113, count: 106 -6565: 0.486760, 0.420299 avg, 0.001000 rate, 27.257959 seconds, 1680640 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.609529, Class: 1.000000, Obj: 0.431050, No Obj: 0.003102, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.661900, Class: 1.000000, Obj: 0.440964, No Obj: 0.003046, Avg Recall: 0.784483, count: 116 -Region Avg IOU: 0.582578, Class: 1.000000, Obj: 0.413026, No Obj: 0.003307, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.664350, Class: 1.000000, Obj: 0.389458, No Obj: 0.002999, Avg Recall: 0.852941, count: 102 -Region Avg IOU: 0.635904, Class: 1.000000, Obj: 0.454716, No Obj: 0.002676, Avg Recall: 0.797753, count: 89 -Region Avg IOU: 0.634418, Class: 1.000000, Obj: 0.452444, No Obj: 0.003073, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.628267, Class: 1.000000, Obj: 0.447151, No Obj: 0.002934, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.642512, Class: 1.000000, Obj: 0.480697, No Obj: 0.002949, Avg Recall: 0.788462, count: 104 -6566: 0.428870, 0.421156 avg, 0.001000 rate, 26.528080 seconds, 1680896 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.626288, Class: 1.000000, Obj: 0.434904, No Obj: 0.003138, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.637200, Class: 1.000000, Obj: 0.414666, No Obj: 0.003379, Avg Recall: 0.806723, count: 119 -Region Avg IOU: 0.660545, Class: 1.000000, Obj: 0.396734, No Obj: 0.002960, Avg Recall: 0.825688, count: 109 -Region Avg IOU: 0.630507, Class: 1.000000, Obj: 0.434019, No Obj: 0.003025, Avg Recall: 0.766667, count: 120 -Region Avg IOU: 0.654470, Class: 1.000000, Obj: 0.486947, No Obj: 0.002876, Avg Recall: 0.813187, count: 91 -Region Avg IOU: 0.586386, Class: 1.000000, Obj: 0.425346, No Obj: 0.002889, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.657077, Class: 1.000000, Obj: 0.436135, No Obj: 0.002686, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.627006, Class: 1.000000, Obj: 0.424750, No Obj: 0.003044, Avg Recall: 0.783784, count: 111 -6567: 0.407856, 0.419826 avg, 0.001000 rate, 27.818415 seconds, 1681152 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.646763, Class: 1.000000, Obj: 0.400503, No Obj: 0.002683, Avg Recall: 0.815217, count: 92 -Region Avg IOU: 0.623298, Class: 1.000000, Obj: 0.411683, No Obj: 0.002986, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.666040, Class: 1.000000, Obj: 0.436461, No Obj: 0.002979, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.700337, Class: 1.000000, Obj: 0.440581, No Obj: 0.003039, Avg Recall: 0.871560, count: 109 -Region Avg IOU: 0.614113, Class: 1.000000, Obj: 0.395887, No Obj: 0.002687, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.648645, Class: 1.000000, Obj: 0.397437, No Obj: 0.003099, Avg Recall: 0.775281, count: 89 -Region Avg IOU: 0.618429, Class: 1.000000, Obj: 0.399968, No Obj: 0.003191, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.663661, Class: 1.000000, Obj: 0.441589, No Obj: 0.002743, Avg Recall: 0.813953, count: 86 -6568: 0.422129, 0.420056 avg, 0.001000 rate, 25.790836 seconds, 1681408 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.555341, Class: 1.000000, Obj: 0.380155, No Obj: 0.002567, Avg Recall: 0.651163, count: 129 -Region Avg IOU: 0.659082, Class: 1.000000, Obj: 0.408419, No Obj: 0.003134, Avg Recall: 0.792000, count: 125 -Region Avg IOU: 0.605651, Class: 1.000000, Obj: 0.412821, No Obj: 0.002412, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.600978, Class: 1.000000, Obj: 0.381701, No Obj: 0.002384, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.648060, Class: 1.000000, Obj: 0.416441, No Obj: 0.002415, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.616845, Class: 1.000000, Obj: 0.421782, No Obj: 0.002493, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.617272, Class: 1.000000, Obj: 0.404742, No Obj: 0.002185, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.615372, Class: 1.000000, Obj: 0.396546, No Obj: 0.002844, Avg Recall: 0.721739, count: 115 -6569: 0.416932, 0.419744 avg, 0.001000 rate, 24.776060 seconds, 1681664 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.637646, Class: 1.000000, Obj: 0.424984, No Obj: 0.003002, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.633916, Class: 1.000000, Obj: 0.345482, No Obj: 0.002797, Avg Recall: 0.732283, count: 127 -Region Avg IOU: 0.651609, Class: 1.000000, Obj: 0.420748, No Obj: 0.002491, Avg Recall: 0.813187, count: 91 -Region Avg IOU: 0.650139, Class: 1.000000, Obj: 0.410834, No Obj: 0.002497, Avg Recall: 0.827957, count: 93 -Region Avg IOU: 0.644101, Class: 1.000000, Obj: 0.433922, No Obj: 0.003065, Avg Recall: 0.791304, count: 115 -Region Avg IOU: 0.638356, Class: 1.000000, Obj: 0.435791, No Obj: 0.002466, Avg Recall: 0.710843, count: 83 -Region Avg IOU: 0.675568, Class: 1.000000, Obj: 0.408694, No Obj: 0.002526, Avg Recall: 0.807229, count: 83 -Region Avg IOU: 0.651503, Class: 1.000000, Obj: 0.397548, No Obj: 0.002699, Avg Recall: 0.778947, count: 95 -6570: 0.387437, 0.416513 avg, 0.001000 rate, 26.474222 seconds, 1681920 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000083 seconds -Region Avg IOU: 0.636003, Class: 1.000000, Obj: 0.408317, No Obj: 0.002592, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.677197, Class: 1.000000, Obj: 0.409809, No Obj: 0.002770, Avg Recall: 0.834783, count: 115 -Region Avg IOU: 0.658362, Class: 1.000000, Obj: 0.366289, No Obj: 0.002747, Avg Recall: 0.809160, count: 131 -Region Avg IOU: 0.630872, Class: 1.000000, Obj: 0.385055, No Obj: 0.002220, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.695134, Class: 1.000000, Obj: 0.438212, No Obj: 0.002548, Avg Recall: 0.860465, count: 86 -Region Avg IOU: 0.609450, Class: 1.000000, Obj: 0.396957, No Obj: 0.002503, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.673207, Class: 1.000000, Obj: 0.431832, No Obj: 0.002805, Avg Recall: 0.861111, count: 108 -Region Avg IOU: 0.672136, Class: 1.000000, Obj: 0.458505, No Obj: 0.002244, Avg Recall: 0.825581, count: 86 -6571: 0.426831, 0.417545 avg, 0.001000 rate, 26.874763 seconds, 1682176 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.641208, Class: 1.000000, Obj: 0.394781, No Obj: 0.002410, Avg Recall: 0.766667, count: 120 -Region Avg IOU: 0.641811, Class: 1.000000, Obj: 0.401000, No Obj: 0.002747, Avg Recall: 0.807018, count: 114 -Region Avg IOU: 0.610179, Class: 1.000000, Obj: 0.407819, No Obj: 0.002663, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.676737, Class: 1.000000, Obj: 0.402794, No Obj: 0.002328, Avg Recall: 0.844660, count: 103 -Region Avg IOU: 0.609410, Class: 1.000000, Obj: 0.385186, No Obj: 0.002340, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.613365, Class: 1.000000, Obj: 0.375792, No Obj: 0.002565, Avg Recall: 0.706349, count: 126 -Region Avg IOU: 0.642253, Class: 1.000000, Obj: 0.398772, No Obj: 0.002688, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.627648, Class: 1.000000, Obj: 0.452323, No Obj: 0.002154, Avg Recall: 0.775000, count: 80 -6572: 0.383791, 0.414170 avg, 0.001000 rate, 26.734552 seconds, 1682432 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.671582, Class: 1.000000, Obj: 0.432606, No Obj: 0.003021, Avg Recall: 0.850000, count: 120 -Region Avg IOU: 0.672384, Class: 1.000000, Obj: 0.393847, No Obj: 0.002901, Avg Recall: 0.854839, count: 124 -Region Avg IOU: 0.638327, Class: 1.000000, Obj: 0.409472, No Obj: 0.002425, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.614036, Class: 1.000000, Obj: 0.379767, No Obj: 0.002438, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.599890, Class: 1.000000, Obj: 0.402285, No Obj: 0.002440, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.602555, Class: 1.000000, Obj: 0.346465, No Obj: 0.002576, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.654077, Class: 1.000000, Obj: 0.420940, No Obj: 0.002757, Avg Recall: 0.823009, count: 113 -Region Avg IOU: 0.687263, Class: 1.000000, Obj: 0.429578, No Obj: 0.002941, Avg Recall: 0.836538, count: 104 -6573: 0.407189, 0.413472 avg, 0.001000 rate, 28.477535 seconds, 1682688 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.617392, Class: 1.000000, Obj: 0.457355, No Obj: 0.002780, Avg Recall: 0.696629, count: 89 -Region Avg IOU: 0.668858, Class: 1.000000, Obj: 0.442176, No Obj: 0.003008, Avg Recall: 0.793388, count: 121 -Region Avg IOU: 0.607283, Class: 1.000000, Obj: 0.430617, No Obj: 0.002342, Avg Recall: 0.712500, count: 80 -Region Avg IOU: 0.683926, Class: 1.000000, Obj: 0.398018, No Obj: 0.002879, Avg Recall: 0.805085, count: 118 -Region Avg IOU: 0.659041, Class: 1.000000, Obj: 0.415785, No Obj: 0.002742, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.698181, Class: 1.000000, Obj: 0.520871, No Obj: 0.002904, Avg Recall: 0.897959, count: 98 -Region Avg IOU: 0.678882, Class: 1.000000, Obj: 0.452972, No Obj: 0.002452, Avg Recall: 0.852632, count: 95 -Region Avg IOU: 0.636485, Class: 1.000000, Obj: 0.417286, No Obj: 0.002451, Avg Recall: 0.750000, count: 100 -6574: 0.380251, 0.410150 avg, 0.001000 rate, 27.092545 seconds, 1682944 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.653210, Class: 1.000000, Obj: 0.464389, No Obj: 0.002546, Avg Recall: 0.747126, count: 87 -Region Avg IOU: 0.643285, Class: 1.000000, Obj: 0.422029, No Obj: 0.002240, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.636506, Class: 1.000000, Obj: 0.439358, No Obj: 0.002984, Avg Recall: 0.737705, count: 122 -Region Avg IOU: 0.620455, Class: 1.000000, Obj: 0.341949, No Obj: 0.003108, Avg Recall: 0.746269, count: 134 -Region Avg IOU: 0.663565, Class: 1.000000, Obj: 0.434969, No Obj: 0.002936, Avg Recall: 0.824074, count: 108 -Region Avg IOU: 0.656029, Class: 1.000000, Obj: 0.435060, No Obj: 0.002723, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.652241, Class: 1.000000, Obj: 0.413340, No Obj: 0.002785, Avg Recall: 0.766667, count: 120 -Region Avg IOU: 0.691180, Class: 1.000000, Obj: 0.507933, No Obj: 0.002725, Avg Recall: 0.875000, count: 96 -6575: 0.407167, 0.409851 avg, 0.001000 rate, 28.286119 seconds, 1683200 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.654641, Class: 1.000000, Obj: 0.452853, No Obj: 0.002482, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.641455, Class: 1.000000, Obj: 0.456023, No Obj: 0.002839, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.643590, Class: 1.000000, Obj: 0.408972, No Obj: 0.002550, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.657379, Class: 1.000000, Obj: 0.409533, No Obj: 0.002736, Avg Recall: 0.814433, count: 97 -Region Avg IOU: 0.669513, Class: 1.000000, Obj: 0.426444, No Obj: 0.002410, Avg Recall: 0.863636, count: 88 -Region Avg IOU: 0.666103, Class: 1.000000, Obj: 0.454251, No Obj: 0.003049, Avg Recall: 0.813953, count: 129 -Region Avg IOU: 0.650535, Class: 1.000000, Obj: 0.415302, No Obj: 0.002786, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.629901, Class: 1.000000, Obj: 0.415270, No Obj: 0.002998, Avg Recall: 0.789062, count: 128 -6576: 0.418287, 0.410695 avg, 0.001000 rate, 27.341536 seconds, 1683456 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.657757, Class: 1.000000, Obj: 0.483420, No Obj: 0.002921, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.690281, Class: 1.000000, Obj: 0.397842, No Obj: 0.002581, Avg Recall: 0.846154, count: 91 -Region Avg IOU: 0.613770, Class: 1.000000, Obj: 0.431699, No Obj: 0.002319, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.640028, Class: 1.000000, Obj: 0.448069, No Obj: 0.002717, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.650201, Class: 1.000000, Obj: 0.417952, No Obj: 0.002898, Avg Recall: 0.805085, count: 118 -Region Avg IOU: 0.645045, Class: 1.000000, Obj: 0.453042, No Obj: 0.002503, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.635380, Class: 1.000000, Obj: 0.472958, No Obj: 0.002660, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.650948, Class: 1.000000, Obj: 0.456696, No Obj: 0.002644, Avg Recall: 0.800000, count: 85 -6577: 0.396057, 0.409231 avg, 0.001000 rate, 27.374613 seconds, 1683712 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.662458, Class: 1.000000, Obj: 0.412445, No Obj: 0.002590, Avg Recall: 0.833333, count: 90 -Region Avg IOU: 0.668877, Class: 1.000000, Obj: 0.459697, No Obj: 0.002990, Avg Recall: 0.851485, count: 101 -Region Avg IOU: 0.678161, Class: 1.000000, Obj: 0.476994, No Obj: 0.003168, Avg Recall: 0.829268, count: 123 -Region Avg IOU: 0.655248, Class: 1.000000, Obj: 0.484342, No Obj: 0.003145, Avg Recall: 0.801587, count: 126 -Region Avg IOU: 0.606535, Class: 1.000000, Obj: 0.428000, No Obj: 0.003169, Avg Recall: 0.751825, count: 137 -Region Avg IOU: 0.590868, Class: 1.000000, Obj: 0.404302, No Obj: 0.002513, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.645833, Class: 1.000000, Obj: 0.429105, No Obj: 0.002861, Avg Recall: 0.823529, count: 119 -Region Avg IOU: 0.569512, Class: 1.000000, Obj: 0.386408, No Obj: 0.002659, Avg Recall: 0.658120, count: 117 -6578: 0.435514, 0.411859 avg, 0.001000 rate, 26.297531 seconds, 1683968 images -Loaded: 0.000130 seconds -Region Avg IOU: 0.647226, Class: 1.000000, Obj: 0.460081, No Obj: 0.002645, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.621041, Class: 1.000000, Obj: 0.420394, No Obj: 0.002277, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.677782, Class: 1.000000, Obj: 0.448495, No Obj: 0.002565, Avg Recall: 0.876405, count: 89 -Region Avg IOU: 0.681002, Class: 1.000000, Obj: 0.477113, No Obj: 0.002277, Avg Recall: 0.855263, count: 76 -Region Avg IOU: 0.676255, Class: 1.000000, Obj: 0.441104, No Obj: 0.003202, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.629202, Class: 1.000000, Obj: 0.437087, No Obj: 0.003021, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.650860, Class: 1.000000, Obj: 0.499347, No Obj: 0.002447, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.701663, Class: 1.000000, Obj: 0.461652, No Obj: 0.003024, Avg Recall: 0.903226, count: 93 -6579: 0.362737, 0.406947 avg, 0.001000 rate, 28.458143 seconds, 1684224 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.691488, Class: 1.000000, Obj: 0.463416, No Obj: 0.003117, Avg Recall: 0.866071, count: 112 -Region Avg IOU: 0.632046, Class: 1.000000, Obj: 0.393831, No Obj: 0.002919, Avg Recall: 0.734848, count: 132 -Region Avg IOU: 0.658134, Class: 1.000000, Obj: 0.463050, No Obj: 0.003134, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.615227, Class: 1.000000, Obj: 0.441651, No Obj: 0.003285, Avg Recall: 0.764706, count: 136 -Region Avg IOU: 0.654939, Class: 1.000000, Obj: 0.385432, No Obj: 0.003869, Avg Recall: 0.796053, count: 152 -Region Avg IOU: 0.652193, Class: 1.000000, Obj: 0.462480, No Obj: 0.002850, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.646221, Class: 1.000000, Obj: 0.420641, No Obj: 0.002991, Avg Recall: 0.788618, count: 123 -Region Avg IOU: 0.650106, Class: 1.000000, Obj: 0.412369, No Obj: 0.003050, Avg Recall: 0.793103, count: 116 -6580: 0.452771, 0.411529 avg, 0.001000 rate, 27.892639 seconds, 1684480 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.094744 seconds -Region Avg IOU: 0.526244, Class: 1.000000, Obj: 0.300628, No Obj: 0.004301, Avg Recall: 0.587786, count: 131 -Region Avg IOU: 0.491870, Class: 1.000000, Obj: 0.305395, No Obj: 0.003915, Avg Recall: 0.537313, count: 134 -Region Avg IOU: 0.508908, Class: 1.000000, Obj: 0.283331, No Obj: 0.004121, Avg Recall: 0.556338, count: 142 -Region Avg IOU: 0.533981, Class: 1.000000, Obj: 0.337716, No Obj: 0.003306, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.502401, Class: 1.000000, Obj: 0.351200, No Obj: 0.004592, Avg Recall: 0.558140, count: 129 -Region Avg IOU: 0.522787, Class: 1.000000, Obj: 0.308542, No Obj: 0.003491, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.418750, Class: 1.000000, Obj: 0.270572, No Obj: 0.003226, Avg Recall: 0.420635, count: 126 -Region Avg IOU: 0.524220, Class: 1.000000, Obj: 0.343400, No Obj: 0.003843, Avg Recall: 0.564815, count: 108 -6581: 0.472469, 0.417623 avg, 0.001000 rate, 17.141548 seconds, 1684736 images -Loaded: 3.385082 seconds -Region Avg IOU: 0.463669, Class: 1.000000, Obj: 0.303353, No Obj: 0.003336, Avg Recall: 0.500000, count: 96 -Region Avg IOU: 0.543782, Class: 1.000000, Obj: 0.326644, No Obj: 0.003427, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.525167, Class: 1.000000, Obj: 0.307562, No Obj: 0.003703, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.508856, Class: 1.000000, Obj: 0.325139, No Obj: 0.003377, Avg Recall: 0.553191, count: 94 -Region Avg IOU: 0.510372, Class: 1.000000, Obj: 0.305117, No Obj: 0.003899, Avg Recall: 0.603175, count: 126 -Region Avg IOU: 0.537053, Class: 1.000000, Obj: 0.321948, No Obj: 0.004682, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.530344, Class: 1.000000, Obj: 0.344177, No Obj: 0.004161, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.476695, Class: 1.000000, Obj: 0.296905, No Obj: 0.004023, Avg Recall: 0.525424, count: 118 -6582: 0.482155, 0.424076 avg, 0.001000 rate, 18.008017 seconds, 1684992 images -Loaded: 2.011932 seconds -Region Avg IOU: 0.495446, Class: 1.000000, Obj: 0.320432, No Obj: 0.003755, Avg Recall: 0.537037, count: 108 -Region Avg IOU: 0.501022, Class: 1.000000, Obj: 0.297112, No Obj: 0.003531, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.545502, Class: 1.000000, Obj: 0.354164, No Obj: 0.004004, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.499439, Class: 1.000000, Obj: 0.316968, No Obj: 0.004032, Avg Recall: 0.508621, count: 116 -Region Avg IOU: 0.481397, Class: 1.000000, Obj: 0.314535, No Obj: 0.003354, Avg Recall: 0.575000, count: 80 -Region Avg IOU: 0.523725, Class: 1.000000, Obj: 0.300801, No Obj: 0.004708, Avg Recall: 0.621429, count: 140 -Region Avg IOU: 0.561327, Class: 1.000000, Obj: 0.374366, No Obj: 0.003645, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.496598, Class: 1.000000, Obj: 0.310923, No Obj: 0.004037, Avg Recall: 0.551402, count: 107 -6583: 0.437704, 0.425439 avg, 0.001000 rate, 17.883692 seconds, 1685248 images -Loaded: 2.496586 seconds -Region Avg IOU: 0.486078, Class: 1.000000, Obj: 0.336748, No Obj: 0.003827, Avg Recall: 0.559322, count: 118 -Region Avg IOU: 0.486506, Class: 1.000000, Obj: 0.344880, No Obj: 0.003978, Avg Recall: 0.516129, count: 124 -Region Avg IOU: 0.502554, Class: 1.000000, Obj: 0.327517, No Obj: 0.004752, Avg Recall: 0.585714, count: 140 -Region Avg IOU: 0.543138, Class: 1.000000, Obj: 0.357463, No Obj: 0.004133, Avg Recall: 0.627907, count: 129 -Region Avg IOU: 0.517410, Class: 1.000000, Obj: 0.311576, No Obj: 0.003893, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.513892, Class: 1.000000, Obj: 0.366017, No Obj: 0.004375, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.599508, Class: 1.000000, Obj: 0.380628, No Obj: 0.003852, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.536208, Class: 1.000000, Obj: 0.388397, No Obj: 0.003767, Avg Recall: 0.577778, count: 90 -6584: 0.448162, 0.427712 avg, 0.001000 rate, 20.790640 seconds, 1685504 images -Loaded: 1.466726 seconds -Region Avg IOU: 0.487432, Class: 1.000000, Obj: 0.314768, No Obj: 0.003637, Avg Recall: 0.521739, count: 92 -Region Avg IOU: 0.498958, Class: 1.000000, Obj: 0.406944, No Obj: 0.003943, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.493266, Class: 1.000000, Obj: 0.363850, No Obj: 0.003984, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.501750, Class: 1.000000, Obj: 0.399031, No Obj: 0.004077, Avg Recall: 0.549550, count: 111 -Region Avg IOU: 0.544173, Class: 1.000000, Obj: 0.388609, No Obj: 0.004553, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.480691, Class: 1.000000, Obj: 0.328691, No Obj: 0.004433, Avg Recall: 0.504000, count: 125 -Region Avg IOU: 0.526928, Class: 1.000000, Obj: 0.345397, No Obj: 0.004222, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.504549, Class: 1.000000, Obj: 0.361354, No Obj: 0.003691, Avg Recall: 0.552632, count: 76 -6585: 0.472814, 0.432222 avg, 0.001000 rate, 18.995556 seconds, 1685760 images -Loaded: 1.109265 seconds -Region Avg IOU: 0.497587, Class: 1.000000, Obj: 0.391973, No Obj: 0.003997, Avg Recall: 0.579545, count: 88 -Region Avg IOU: 0.459607, Class: 1.000000, Obj: 0.325322, No Obj: 0.004149, Avg Recall: 0.491935, count: 124 -Region Avg IOU: 0.525847, Class: 1.000000, Obj: 0.366374, No Obj: 0.004824, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.573964, Class: 1.000000, Obj: 0.406410, No Obj: 0.004273, Avg Recall: 0.726191, count: 84 -Region Avg IOU: 0.530434, Class: 1.000000, Obj: 0.397412, No Obj: 0.004563, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.512753, Class: 1.000000, Obj: 0.421480, No Obj: 0.004028, Avg Recall: 0.550000, count: 80 -Region Avg IOU: 0.524828, Class: 1.000000, Obj: 0.395123, No Obj: 0.003780, Avg Recall: 0.571429, count: 84 -Region Avg IOU: 0.492563, Class: 1.000000, Obj: 0.375341, No Obj: 0.004363, Avg Recall: 0.581818, count: 110 -6586: 0.367829, 0.425782 avg, 0.001000 rate, 18.485584 seconds, 1686016 images -Loaded: 1.708012 seconds -Region Avg IOU: 0.526374, Class: 1.000000, Obj: 0.368697, No Obj: 0.004344, Avg Recall: 0.521739, count: 92 -Region Avg IOU: 0.491654, Class: 1.000000, Obj: 0.356288, No Obj: 0.003760, Avg Recall: 0.505747, count: 87 -Region Avg IOU: 0.503973, Class: 1.000000, Obj: 0.395767, No Obj: 0.004061, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.503699, Class: 1.000000, Obj: 0.389956, No Obj: 0.004126, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.515615, Class: 1.000000, Obj: 0.379270, No Obj: 0.005281, Avg Recall: 0.594828, count: 116 -Region Avg IOU: 0.457294, Class: 1.000000, Obj: 0.351949, No Obj: 0.004208, Avg Recall: 0.518182, count: 110 -Region Avg IOU: 0.486768, Class: 1.000000, Obj: 0.394547, No Obj: 0.003780, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.463992, Class: 1.000000, Obj: 0.327167, No Obj: 0.004508, Avg Recall: 0.532710, count: 107 -6587: 0.437160, 0.426920 avg, 0.001000 rate, 17.849257 seconds, 1686272 images -Loaded: 4.441421 seconds -Region Avg IOU: 0.508366, Class: 1.000000, Obj: 0.343735, No Obj: 0.004800, Avg Recall: 0.568000, count: 125 -Region Avg IOU: 0.556488, Class: 1.000000, Obj: 0.410086, No Obj: 0.005148, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.527632, Class: 1.000000, Obj: 0.365526, No Obj: 0.004684, Avg Recall: 0.610169, count: 118 -Region Avg IOU: 0.490956, Class: 1.000000, Obj: 0.373399, No Obj: 0.004137, Avg Recall: 0.526316, count: 95 -Region Avg IOU: 0.510891, Class: 1.000000, Obj: 0.415040, No Obj: 0.005122, Avg Recall: 0.566667, count: 120 -Region Avg IOU: 0.465608, Class: 1.000000, Obj: 0.415683, No Obj: 0.004380, Avg Recall: 0.522523, count: 111 -Region Avg IOU: 0.511615, Class: 1.000000, Obj: 0.381544, No Obj: 0.004215, Avg Recall: 0.577586, count: 116 -Region Avg IOU: 0.542453, Class: 1.000000, Obj: 0.412071, No Obj: 0.004051, Avg Recall: 0.640000, count: 75 -6588: 0.470673, 0.431296 avg, 0.001000 rate, 19.414846 seconds, 1686528 images -Loaded: 1.380028 seconds -Region Avg IOU: 0.470814, Class: 1.000000, Obj: 0.389319, No Obj: 0.004033, Avg Recall: 0.521277, count: 94 -Region Avg IOU: 0.545465, Class: 1.000000, Obj: 0.414158, No Obj: 0.004600, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.542312, Class: 1.000000, Obj: 0.405248, No Obj: 0.004770, Avg Recall: 0.600000, count: 85 -Region Avg IOU: 0.537660, Class: 1.000000, Obj: 0.399697, No Obj: 0.004307, Avg Recall: 0.616279, count: 86 -Region Avg IOU: 0.536067, Class: 1.000000, Obj: 0.371961, No Obj: 0.004254, Avg Recall: 0.678571, count: 84 -Region Avg IOU: 0.542137, Class: 1.000000, Obj: 0.395666, No Obj: 0.004453, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.498152, Class: 1.000000, Obj: 0.370961, No Obj: 0.005083, Avg Recall: 0.550847, count: 118 -Region Avg IOU: 0.542974, Class: 1.000000, Obj: 0.394973, No Obj: 0.004714, Avg Recall: 0.679612, count: 103 -6589: 0.372772, 0.425443 avg, 0.001000 rate, 15.896443 seconds, 1686784 images -Loaded: 6.215761 seconds -Region Avg IOU: 0.512597, Class: 1.000000, Obj: 0.370709, No Obj: 0.004860, Avg Recall: 0.537500, count: 80 -Region Avg IOU: 0.559330, Class: 1.000000, Obj: 0.416531, No Obj: 0.005338, Avg Recall: 0.646154, count: 130 -Region Avg IOU: 0.531827, Class: 1.000000, Obj: 0.368899, No Obj: 0.004087, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.528961, Class: 1.000000, Obj: 0.400287, No Obj: 0.004951, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.576768, Class: 1.000000, Obj: 0.440169, No Obj: 0.004680, Avg Recall: 0.685393, count: 89 -Region Avg IOU: 0.518891, Class: 1.000000, Obj: 0.409777, No Obj: 0.004392, Avg Recall: 0.568182, count: 88 -Region Avg IOU: 0.525123, Class: 1.000000, Obj: 0.404981, No Obj: 0.004142, Avg Recall: 0.585106, count: 94 -Region Avg IOU: 0.533143, Class: 1.000000, Obj: 0.416720, No Obj: 0.005024, Avg Recall: 0.553191, count: 94 -6590: 0.367696, 0.419668 avg, 0.001000 rate, 17.098148 seconds, 1687040 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000063 seconds -Region Avg IOU: 0.652590, Class: 1.000000, Obj: 0.495643, No Obj: 0.003513, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.625078, Class: 1.000000, Obj: 0.507955, No Obj: 0.003635, Avg Recall: 0.772727, count: 132 -Region Avg IOU: 0.667849, Class: 1.000000, Obj: 0.470250, No Obj: 0.003530, Avg Recall: 0.846154, count: 117 -Region Avg IOU: 0.617741, Class: 1.000000, Obj: 0.479634, No Obj: 0.002864, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.638042, Class: 1.000000, Obj: 0.478880, No Obj: 0.003476, Avg Recall: 0.768595, count: 121 -Region Avg IOU: 0.635286, Class: 1.000000, Obj: 0.468555, No Obj: 0.003535, Avg Recall: 0.823009, count: 113 -Region Avg IOU: 0.637261, Class: 1.000000, Obj: 0.482157, No Obj: 0.003666, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.655936, Class: 1.000000, Obj: 0.530077, No Obj: 0.003432, Avg Recall: 0.807692, count: 104 -6591: 0.400969, 0.417798 avg, 0.001000 rate, 26.343664 seconds, 1687296 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.633832, Class: 1.000000, Obj: 0.459374, No Obj: 0.002845, Avg Recall: 0.752809, count: 89 -Region Avg IOU: 0.636918, Class: 1.000000, Obj: 0.529101, No Obj: 0.003387, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.624708, Class: 1.000000, Obj: 0.452361, No Obj: 0.003143, Avg Recall: 0.750000, count: 88 -Region Avg IOU: 0.627356, Class: 1.000000, Obj: 0.431632, No Obj: 0.003416, Avg Recall: 0.766129, count: 124 -Region Avg IOU: 0.651928, Class: 1.000000, Obj: 0.501441, No Obj: 0.003119, Avg Recall: 0.797872, count: 94 -Region Avg IOU: 0.633355, Class: 1.000000, Obj: 0.476050, No Obj: 0.003826, Avg Recall: 0.768657, count: 134 -Region Avg IOU: 0.576481, Class: 1.000000, Obj: 0.416540, No Obj: 0.003571, Avg Recall: 0.658915, count: 129 -Region Avg IOU: 0.628010, Class: 1.000000, Obj: 0.453978, No Obj: 0.003240, Avg Recall: 0.757895, count: 95 -6592: 0.400052, 0.416024 avg, 0.001000 rate, 26.599531 seconds, 1687552 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.618238, Class: 1.000000, Obj: 0.423230, No Obj: 0.002818, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.636181, Class: 1.000000, Obj: 0.421182, No Obj: 0.003153, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.581235, Class: 1.000000, Obj: 0.421900, No Obj: 0.002959, Avg Recall: 0.672000, count: 125 -Region Avg IOU: 0.641151, Class: 1.000000, Obj: 0.448140, No Obj: 0.003524, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.669055, Class: 1.000000, Obj: 0.474106, No Obj: 0.003081, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.678886, Class: 1.000000, Obj: 0.429441, No Obj: 0.003056, Avg Recall: 0.831683, count: 101 -Region Avg IOU: 0.666688, Class: 1.000000, Obj: 0.466747, No Obj: 0.003081, Avg Recall: 0.833333, count: 96 -Region Avg IOU: 0.628347, Class: 1.000000, Obj: 0.467255, No Obj: 0.002992, Avg Recall: 0.760417, count: 96 -6593: 0.386250, 0.413046 avg, 0.001000 rate, 26.829880 seconds, 1687808 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.641461, Class: 1.000000, Obj: 0.411962, No Obj: 0.002731, Avg Recall: 0.796117, count: 103 -Region Avg IOU: 0.659491, Class: 1.000000, Obj: 0.437042, No Obj: 0.002679, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.651658, Class: 1.000000, Obj: 0.450515, No Obj: 0.003161, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.647041, Class: 1.000000, Obj: 0.451358, No Obj: 0.002609, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.663393, Class: 1.000000, Obj: 0.470834, No Obj: 0.002685, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.619079, Class: 1.000000, Obj: 0.404902, No Obj: 0.003019, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.665733, Class: 1.000000, Obj: 0.403741, No Obj: 0.002782, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.599639, Class: 1.000000, Obj: 0.370172, No Obj: 0.002504, Avg Recall: 0.739130, count: 115 -6594: 0.462105, 0.417952 avg, 0.001000 rate, 26.995808 seconds, 1688064 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.608897, Class: 1.000000, Obj: 0.432912, No Obj: 0.002365, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.648429, Class: 1.000000, Obj: 0.433326, No Obj: 0.002636, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.639005, Class: 1.000000, Obj: 0.392936, No Obj: 0.002666, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.693839, Class: 1.000000, Obj: 0.475356, No Obj: 0.002789, Avg Recall: 0.846154, count: 78 -Region Avg IOU: 0.608030, Class: 1.000000, Obj: 0.374892, No Obj: 0.002929, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.619925, Class: 1.000000, Obj: 0.400323, No Obj: 0.002550, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.625840, Class: 1.000000, Obj: 0.413194, No Obj: 0.002961, Avg Recall: 0.768116, count: 138 -Region Avg IOU: 0.603907, Class: 1.000000, Obj: 0.374950, No Obj: 0.002663, Avg Recall: 0.688525, count: 122 -6595: 0.410094, 0.417167 avg, 0.001000 rate, 27.273598 seconds, 1688320 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.669322, Class: 1.000000, Obj: 0.402664, No Obj: 0.002839, Avg Recall: 0.847458, count: 118 -Region Avg IOU: 0.652836, Class: 1.000000, Obj: 0.402215, No Obj: 0.002416, Avg Recall: 0.835052, count: 97 -Region Avg IOU: 0.658713, Class: 1.000000, Obj: 0.413150, No Obj: 0.002631, Avg Recall: 0.800000, count: 95 -Region Avg IOU: 0.634987, Class: 1.000000, Obj: 0.374873, No Obj: 0.002161, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.654346, Class: 1.000000, Obj: 0.388869, No Obj: 0.002717, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.680762, Class: 1.000000, Obj: 0.382690, No Obj: 0.002927, Avg Recall: 0.858156, count: 141 -Region Avg IOU: 0.655566, Class: 1.000000, Obj: 0.430641, No Obj: 0.002301, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.628664, Class: 1.000000, Obj: 0.410130, No Obj: 0.002711, Avg Recall: 0.756303, count: 119 -6596: 0.408555, 0.416305 avg, 0.001000 rate, 26.989807 seconds, 1688576 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.606567, Class: 1.000000, Obj: 0.373056, No Obj: 0.002283, Avg Recall: 0.739583, count: 96 -Region Avg IOU: 0.623842, Class: 1.000000, Obj: 0.376677, No Obj: 0.002621, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.621121, Class: 1.000000, Obj: 0.381457, No Obj: 0.002548, Avg Recall: 0.721804, count: 133 -Region Avg IOU: 0.654314, Class: 1.000000, Obj: 0.396450, No Obj: 0.002251, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.594000, Class: 1.000000, Obj: 0.348618, No Obj: 0.002071, Avg Recall: 0.765432, count: 81 -Region Avg IOU: 0.629394, Class: 1.000000, Obj: 0.370559, No Obj: 0.002865, Avg Recall: 0.747967, count: 123 -Region Avg IOU: 0.621506, Class: 1.000000, Obj: 0.402782, No Obj: 0.002716, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.649358, Class: 1.000000, Obj: 0.383893, No Obj: 0.003064, Avg Recall: 0.813433, count: 134 -6597: 0.448014, 0.419476 avg, 0.001000 rate, 27.041145 seconds, 1688832 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.607477, Class: 1.000000, Obj: 0.319711, No Obj: 0.002862, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.634703, Class: 1.000000, Obj: 0.378508, No Obj: 0.002380, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.646259, Class: 1.000000, Obj: 0.353580, No Obj: 0.002379, Avg Recall: 0.833333, count: 96 -Region Avg IOU: 0.709475, Class: 1.000000, Obj: 0.412805, No Obj: 0.002354, Avg Recall: 0.891892, count: 74 -Region Avg IOU: 0.646917,Saving weights to ./person9300/backup/tyvp_93_9c512_6600.weights - Class: 1.000000, Obj: 0.355739, No Obj: 0.002811, Avg Recall: 0.778626, count: 131 -Region Avg IOU: 0.604422, Class: 1.000000, Obj: 0.333577, No Obj: 0.002509, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.644721, Class: 1.000000, Obj: 0.436453, No Obj: 0.002727, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.631019, Class: 1.000000, Obj: 0.403370, No Obj: 0.002672, Avg Recall: 0.722689, count: 119 -6598: 0.447622, 0.422291 avg, 0.001000 rate, 26.326355 seconds, 1689088 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.639124, Class: 1.000000, Obj: 0.414311, No Obj: 0.002645, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.668446, Class: 1.000000, Obj: 0.403238, No Obj: 0.002711, Avg Recall: 0.817308, count: 104 -Region Avg IOU: 0.625687, Class: 1.000000, Obj: 0.365134, No Obj: 0.002486, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.637061, Class: 1.000000, Obj: 0.372574, No Obj: 0.002847, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.701008, Class: 1.000000, Obj: 0.409043, No Obj: 0.002673, Avg Recall: 0.905263, count: 95 -Region Avg IOU: 0.641372, Class: 1.000000, Obj: 0.409806, No Obj: 0.002465, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.598390, Class: 1.000000, Obj: 0.439773, No Obj: 0.002259, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.638177, Class: 1.000000, Obj: 0.374512, No Obj: 0.002645, Avg Recall: 0.722222, count: 108 -6599: 0.411925, 0.421254 avg, 0.001000 rate, 28.438007 seconds, 1689344 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.664307, Class: 1.000000, Obj: 0.409804, No Obj: 0.003652, Avg Recall: 0.796875, count: 128 -Region Avg IOU: 0.647445, Class: 1.000000, Obj: 0.386537, No Obj: 0.003333, Avg Recall: 0.803030, count: 132 -Region Avg IOU: 0.621673, Class: 1.000000, Obj: 0.429577, No Obj: 0.002887, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.640268, Class: 1.000000, Obj: 0.411807, No Obj: 0.002884, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.634696, Class: 1.000000, Obj: 0.375670, No Obj: 0.002804, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.644754, Class: 1.000000, Obj: 0.403620, No Obj: 0.002391, Avg Recall: 0.797872, count: 94 -Region Avg IOU: 0.662637, Class: 1.000000, Obj: 0.363603, No Obj: 0.002797, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.613557, Class: 1.000000, Obj: 0.358278, No Obj: 0.002893, Avg Recall: 0.754386, count: 114 -6600: 0.445238, 0.423653 avg, 0.001000 rate, 26.648701 seconds, 1689600 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.525608 seconds -Region Avg IOU: 0.552137, Class: 1.000000, Obj: 0.361225, No Obj: 0.003159, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.568181, Class: 1.000000, Obj: 0.344076, No Obj: 0.003611, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.578735, Class: 1.000000, Obj: 0.338604, No Obj: 0.004045, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.565808, Class: 1.000000, Obj: 0.396452, No Obj: 0.004044, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.521644, Class: 1.000000, Obj: 0.376277, No Obj: 0.004175, Avg Recall: 0.596774, count: 124 -Region Avg IOU: 0.527772, Class: 1.000000, Obj: 0.325700, No Obj: 0.003428, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.551526, Class: 1.000000, Obj: 0.359945, No Obj: 0.004385, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.611607, Class: 1.000000, Obj: 0.350700, No Obj: 0.004498, Avg Recall: 0.716667, count: 120 -6601: 0.421203, 0.423408 avg, 0.001000 rate, 20.949320 seconds, 1689856 images -Loaded: 2.638970 seconds -Region Avg IOU: 0.541190, Class: 1.000000, Obj: 0.355156, No Obj: 0.003787, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.522075, Class: 1.000000, Obj: 0.350873, No Obj: 0.004374, Avg Recall: 0.608000, count: 125 -Region Avg IOU: 0.522429, Class: 1.000000, Obj: 0.353112, No Obj: 0.004210, Avg Recall: 0.547826, count: 115 -Region Avg IOU: 0.573584, Class: 1.000000, Obj: 0.397389, No Obj: 0.004191, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.611380, Class: 1.000000, Obj: 0.397404, No Obj: 0.004278, Avg Recall: 0.728395, count: 81 -Region Avg IOU: 0.511121, Class: 1.000000, Obj: 0.318442, No Obj: 0.004062, Avg Recall: 0.564103, count: 117 -Region Avg IOU: 0.545824, Class: 1.000000, Obj: 0.357163, No Obj: 0.004585, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.568801, Class: 1.000000, Obj: 0.371841, No Obj: 0.003698, Avg Recall: 0.650000, count: 80 -6602: 0.463077, 0.427374 avg, 0.001000 rate, 19.284704 seconds, 1690112 images -Loaded: 1.611872 seconds -Region Avg IOU: 0.548859, Class: 1.000000, Obj: 0.383328, No Obj: 0.003592, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.502036, Class: 1.000000, Obj: 0.342657, No Obj: 0.004376, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.527095, Class: 1.000000, Obj: 0.348458, No Obj: 0.003662, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.534605, Class: 1.000000, Obj: 0.345912, No Obj: 0.004174, Avg Recall: 0.605042, count: 119 -Region Avg IOU: 0.517038, Class: 1.000000, Obj: 0.381479, No Obj: 0.003419, Avg Recall: 0.580645, count: 93 -Region Avg IOU: 0.606300, Class: 1.000000, Obj: 0.408805, No Obj: 0.004314, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.515575, Class: 1.000000, Obj: 0.347408, No Obj: 0.003821, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.511250, Class: 1.000000, Obj: 0.352068, No Obj: 0.003904, Avg Recall: 0.568807, count: 109 -6603: 0.390997, 0.423737 avg, 0.001000 rate, 18.331003 seconds, 1690368 images -Loaded: 4.461676 seconds -Region Avg IOU: 0.539661, Class: 1.000000, Obj: 0.366145, No Obj: 0.005023, Avg Recall: 0.640000, count: 125 -Region Avg IOU: 0.541324, Class: 1.000000, Obj: 0.372685, No Obj: 0.004685, Avg Recall: 0.609524, count: 105 -Region Avg IOU: 0.555918, Class: 1.000000, Obj: 0.387081, No Obj: 0.004300, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.517694, Class: 1.000000, Obj: 0.383442, No Obj: 0.004140, Avg Recall: 0.605769, count: 104 -Region Avg IOU: 0.545210, Class: 1.000000, Obj: 0.449266, No Obj: 0.004725, Avg Recall: 0.605769, count: 104 -Region Avg IOU: 0.532233, Class: 1.000000, Obj: 0.423241, No Obj: 0.004012, Avg Recall: 0.582418, count: 91 -Region Avg IOU: 0.514778, Class: 1.000000, Obj: 0.394884, No Obj: 0.004485, Avg Recall: 0.560345, count: 116 -Region Avg IOU: 0.519947, Class: 1.000000, Obj: 0.352564, No Obj: 0.004236, Avg Recall: 0.611570, count: 121 -6604: 0.401119, 0.421475 avg, 0.001000 rate, 19.504845 seconds, 1690624 images -Loaded: 0.929814 seconds -Region Avg IOU: 0.555677, Class: 1.000000, Obj: 0.384300, No Obj: 0.005623, Avg Recall: 0.639098, count: 133 -Region Avg IOU: 0.564185, Class: 1.000000, Obj: 0.421384, No Obj: 0.004934, Avg Recall: 0.666667, count: 120 -Region Avg IOU: 0.551198, Class: 1.000000, Obj: 0.379420, No Obj: 0.005229, Avg Recall: 0.628571, count: 140 -Region Avg IOU: 0.588273, Class: 1.000000, Obj: 0.426389, No Obj: 0.004091, Avg Recall: 0.724138, count: 87 -Region Avg IOU: 0.560606, Class: 1.000000, Obj: 0.404342, No Obj: 0.004986, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.554436, Class: 1.000000, Obj: 0.419010, No Obj: 0.005012, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.543665, Class: 1.000000, Obj: 0.413426, No Obj: 0.003954, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.533590, Class: 1.000000, Obj: 0.433023, No Obj: 0.004649, Avg Recall: 0.600000, count: 110 -6605: 0.411116, 0.420439 avg, 0.001000 rate, 20.105963 seconds, 1690880 images -Loaded: 1.976726 seconds -Region Avg IOU: 0.553590, Class: 1.000000, Obj: 0.440354, No Obj: 0.005562, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.529848, Class: 1.000000, Obj: 0.398673, No Obj: 0.005018, Avg Recall: 0.628788, count: 132 -Region Avg IOU: 0.523273, Class: 1.000000, Obj: 0.399151, No Obj: 0.005290, Avg Recall: 0.583333, count: 132 -Region Avg IOU: 0.484135, Class: 1.000000, Obj: 0.410297, No Obj: 0.004512, Avg Recall: 0.544643, count: 112 -Region Avg IOU: 0.579293, Class: 1.000000, Obj: 0.442602, No Obj: 0.004810, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.519922, Class: 1.000000, Obj: 0.412119, No Obj: 0.004862, Avg Recall: 0.597015, count: 134 -Region Avg IOU: 0.514111, Class: 1.000000, Obj: 0.452208, No Obj: 0.003959, Avg Recall: 0.569767, count: 86 -Region Avg IOU: 0.562149, Class: 1.000000, Obj: 0.444744, No Obj: 0.004736, Avg Recall: 0.642276, count: 123 -6606: 0.495461, 0.427941 avg, 0.001000 rate, 21.027285 seconds, 1691136 images -Loaded: 0.027965 seconds -Region Avg IOU: 0.569960, Class: 1.000000, Obj: 0.368612, No Obj: 0.004381, Avg Recall: 0.674419, count: 86 -Region Avg IOU: 0.548610, Class: 1.000000, Obj: 0.474675, No Obj: 0.003774, Avg Recall: 0.675000, count: 80 -Region Avg IOU: 0.540049, Class: 1.000000, Obj: 0.457692, No Obj: 0.004610, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.513688, Class: 1.000000, Obj: 0.424004, No Obj: 0.005086, Avg Recall: 0.569106, count: 123 -Region Avg IOU: 0.586473, Class: 1.000000, Obj: 0.436626, No Obj: 0.005049, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.485261, Class: 1.000000, Obj: 0.395058, No Obj: 0.004923, Avg Recall: 0.571429, count: 119 -Region Avg IOU: 0.508908, Class: 1.000000, Obj: 0.410117, No Obj: 0.004783, Avg Recall: 0.572727, count: 110 -Region Avg IOU: 0.543653, Class: 1.000000, Obj: 0.425904, No Obj: 0.005053, Avg Recall: 0.649573, count: 117 -6607: 0.405700, 0.425717 avg, 0.001000 rate, 18.952665 seconds, 1691392 images -Loaded: 3.265181 seconds -Region Avg IOU: 0.529944, Class: 1.000000, Obj: 0.434449, No Obj: 0.004291, Avg Recall: 0.597222, count: 72 -Region Avg IOU: 0.550128, Class: 1.000000, Obj: 0.416702, No Obj: 0.005342, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.543506, Class: 1.000000, Obj: 0.375165, No Obj: 0.005429, Avg Recall: 0.601449, count: 138 -Region Avg IOU: 0.606895, Class: 1.000000, Obj: 0.390621, No Obj: 0.004321, Avg Recall: 0.752941, count: 85 -Region Avg IOU: 0.498398, Class: 1.000000, Obj: 0.402311, No Obj: 0.004753, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.533218, Class: 1.000000, Obj: 0.433990, No Obj: 0.004522, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.518359, Class: 1.000000, Obj: 0.346453, No Obj: 0.004128, Avg Recall: 0.586207, count: 116 -Region Avg IOU: 0.533976, Class: 1.000000, Obj: 0.447179, No Obj: 0.004428, Avg Recall: 0.578431, count: 102 -6608: 0.447162, 0.427862 avg, 0.001000 rate, 21.778440 seconds, 1691648 images -Loaded: 1.170274 seconds -Region Avg IOU: 0.556307, Class: 1.000000, Obj: 0.417889, No Obj: 0.004422, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.562066, Class: 1.000000, Obj: 0.393321, No Obj: 0.006239, Avg Recall: 0.641026, count: 156 -Region Avg IOU: 0.557766, Class: 1.000000, Obj: 0.465679, No Obj: 0.004617, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.523982, Class: 1.000000, Obj: 0.373963, No Obj: 0.004335, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.503601, Class: 1.000000, Obj: 0.363725, No Obj: 0.005166, Avg Recall: 0.558140, count: 129 -Region Avg IOU: 0.563914, Class: 1.000000, Obj: 0.406021, No Obj: 0.004536, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.527575, Class: 1.000000, Obj: 0.444393, No Obj: 0.004801, Avg Recall: 0.608333, count: 120 -Region Avg IOU: 0.555440, Class: 1.000000, Obj: 0.470043, No Obj: 0.004129, Avg Recall: 0.591398, count: 93 -6609: 0.458547, 0.430930 avg, 0.001000 rate, 17.445536 seconds, 1691904 images -Loaded: 3.487023 seconds -Region Avg IOU: 0.561418, Class: 1.000000, Obj: 0.431065, No Obj: 0.004695, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.507391, Class: 1.000000, Obj: 0.434256, No Obj: 0.004567, Avg Recall: 0.525253, count: 99 -Region Avg IOU: 0.504285, Class: 1.000000, Obj: 0.396146, No Obj: 0.004878, Avg Recall: 0.556522, count: 115 -Region Avg IOU: 0.536968, Class: 1.000000, Obj: 0.428535, No Obj: 0.004310, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.619244, Class: 1.000000, Obj: 0.422990, No Obj: 0.003879, Avg Recall: 0.714286, count: 63 -Region Avg IOU: 0.492077, Class: 1.000000, Obj: 0.381845, No Obj: 0.004338, Avg Recall: 0.504202, count: 119 -Region Avg IOU: 0.521515, Class: 1.000000, Obj: 0.414700, No Obj: 0.003650, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.559471, Class: 1.000000, Obj: 0.409359, No Obj: 0.004223, Avg Recall: 0.686869, count: 99 -6610: 0.367070, 0.424544 avg, 0.001000 rate, 19.851908 seconds, 1692160 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000064 seconds -Region Avg IOU: 0.662831, Class: 1.000000, Obj: 0.477115, No Obj: 0.002924, Avg Recall: 0.800000, count: 80 -Region Avg IOU: 0.613424, Class: 1.000000, Obj: 0.452194, No Obj: 0.002767, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.681794, Class: 1.000000, Obj: 0.442642, No Obj: 0.003205, Avg Recall: 0.854545, count: 110 -Region Avg IOU: 0.654799, Class: 1.000000, Obj: 0.444099, No Obj: 0.003039, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.647388, Class: 1.000000, Obj: 0.466376, No Obj: 0.002935, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.616558, Class: 1.000000, Obj: 0.476220, No Obj: 0.002881, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.610577, Class: 1.000000, Obj: 0.483037, No Obj: 0.003153, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.621255, Class: 1.000000, Obj: 0.467394, No Obj: 0.003180, Avg Recall: 0.757895, count: 95 -6611: 0.401372, 0.422227 avg, 0.001000 rate, 28.551476 seconds, 1692416 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.627130, Class: 1.000000, Obj: 0.437956, No Obj: 0.003036, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.663571, Class: 1.000000, Obj: 0.448256, No Obj: 0.003714, Avg Recall: 0.818898, count: 127 -Region Avg IOU: 0.645123, Class: 1.000000, Obj: 0.472225, No Obj: 0.003071, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.614960, Class: 1.000000, Obj: 0.408764, No Obj: 0.002773, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.670779, Class: 1.000000, Obj: 0.446417, No Obj: 0.003014, Avg Recall: 0.813187, count: 91 -Region Avg IOU: 0.632209, Class: 1.000000, Obj: 0.451247, No Obj: 0.003225, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.617883, Class: 1.000000, Obj: 0.364029, No Obj: 0.002922, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.653460, Class: 1.000000, Obj: 0.396790, No Obj: 0.003236, Avg Recall: 0.793651, count: 126 -6612: 0.434656, 0.423470 avg, 0.001000 rate, 27.139977 seconds, 1692672 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.630019, Class: 1.000000, Obj: 0.428654, No Obj: 0.002805, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.661861, Class: 1.000000, Obj: 0.445875, No Obj: 0.003138, Avg Recall: 0.822034, count: 118 -Region Avg IOU: 0.664327, Class: 1.000000, Obj: 0.454692, No Obj: 0.003216, Avg Recall: 0.816000, count: 125 -Region Avg IOU: 0.645973, Class: 1.000000, Obj: 0.417648, No Obj: 0.003223, Avg Recall: 0.776119, count: 134 -Region Avg IOU: 0.570816, Class: 1.000000, Obj: 0.419986, No Obj: 0.002326, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.677645, Class: 1.000000, Obj: 0.429409, No Obj: 0.002984, Avg Recall: 0.867925, count: 106 -Region Avg IOU: 0.674224, Class: 1.000000, Obj: 0.421268, No Obj: 0.002544, Avg Recall: 0.829268, count: 82 -Region Avg IOU: 0.650878, Class: 1.000000, Obj: 0.432173, No Obj: 0.003071, Avg Recall: 0.795699, count: 93 -6613: 0.419805, 0.423103 avg, 0.001000 rate, 26.426355 seconds, 1692928 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.632587, Class: 1.000000, Obj: 0.459502, No Obj: 0.002261, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.645644, Class: 1.000000, Obj: 0.411305, No Obj: 0.002886, Avg Recall: 0.815126, count: 119 -Region Avg IOU: 0.609662, Class: 1.000000, Obj: 0.391958, No Obj: 0.002419, Avg Recall: 0.750000, count: 88 -Region Avg IOU: 0.663363, Class: 1.000000, Obj: 0.447025, No Obj: 0.002319, Avg Recall: 0.855556, count: 90 -Region Avg IOU: 0.648024, Class: 1.000000, Obj: 0.438417, No Obj: 0.002785, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.648189, Class: 1.000000, Obj: 0.390239, No Obj: 0.002512, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.655430, Class: 1.000000, Obj: 0.422057, No Obj: 0.003092, Avg Recall: 0.798507, count: 134 -Region Avg IOU: 0.682149, Class: 1.000000, Obj: 0.471469, No Obj: 0.002257, Avg Recall: 0.831169, count: 77 -6614: 0.400697, 0.420863 avg, 0.001000 rate, 27.805229 seconds, 1693184 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.605251, Class: 1.000000, Obj: 0.345700, No Obj: 0.002318, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.645641, Class: 1.000000, Obj: 0.389958, No Obj: 0.002498, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.613784, Class: 1.000000, Obj: 0.352796, No Obj: 0.002471, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.609662, Class: 1.000000, Obj: 0.405959, No Obj: 0.002123, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.662778, Class: 1.000000, Obj: 0.397472, No Obj: 0.002662, Avg Recall: 0.831858, count: 113 -Region Avg IOU: 0.643918, Class: 1.000000, Obj: 0.422569, No Obj: 0.002385, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.629521, Class: 1.000000, Obj: 0.404872, No Obj: 0.002658, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.648008, Class: 1.000000, Obj: 0.429580, No Obj: 0.002416, Avg Recall: 0.789474, count: 114 -6615: 0.438325, 0.422609 avg, 0.001000 rate, 27.986612 seconds, 1693440 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.659961, Class: 1.000000, Obj: 0.419280, No Obj: 0.002305, Avg Recall: 0.804878, count: 82 -Region Avg IOU: 0.659319, Class: 1.000000, Obj: 0.403468, No Obj: 0.002694, Avg Recall: 0.800000, count: 125 -Region Avg IOU: 0.647644, Class: 1.000000, Obj: 0.421975, No Obj: 0.002261, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.651776, Class: 1.000000, Obj: 0.423735, No Obj: 0.002619, Avg Recall: 0.801980, count: 101 -Region Avg IOU: 0.635748, Class: 1.000000, Obj: 0.382677, No Obj: 0.002630, Avg Recall: 0.762295, count: 122 -Region Avg IOU: 0.627506, Class: 1.000000, Obj: 0.418030, No Obj: 0.002639, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.647674, Class: 1.000000, Obj: 0.361039, No Obj: 0.003073, Avg Recall: 0.815789, count: 114 -Region Avg IOU: 0.632454, Class: 1.000000, Obj: 0.409601, No Obj: 0.002087, Avg Recall: 0.752941, count: 85 -6616: 0.414448, 0.421793 avg, 0.001000 rate, 29.101656 seconds, 1693696 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.632697, Class: 1.000000, Obj: 0.362303, No Obj: 0.002696, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.652770, Class: 1.000000, Obj: 0.359222, No Obj: 0.002319, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.625326, Class: 1.000000, Obj: 0.411712, No Obj: 0.002477, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.640037, Class: 1.000000, Obj: 0.409542, No Obj: 0.002457, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.625189, Class: 1.000000, Obj: 0.365313, No Obj: 0.002613, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.589393, Class: 1.000000, Obj: 0.361899, No Obj: 0.002564, Avg Recall: 0.700680, count: 147 -Region Avg IOU: 0.691902, Class: 1.000000, Obj: 0.374342, No Obj: 0.002490, Avg Recall: 0.836538, count: 104 -Region Avg IOU: 0.652036, Class: 1.000000, Obj: 0.380887, No Obj: 0.002643, Avg Recall: 0.800000, count: 120 -6617: 0.463170, 0.425931 avg, 0.001000 rate, 26.894041 seconds, 1693952 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.690991, Class: 1.000000, Obj: 0.426570, No Obj: 0.002775, Avg Recall: 0.866071, count: 112 -Region Avg IOU: 0.681757, Class: 1.000000, Obj: 0.397780, No Obj: 0.002304, Avg Recall: 0.857143, count: 98 -Region Avg IOU: 0.639986, Class: 1.000000, Obj: 0.390164, No Obj: 0.002807, Avg Recall: 0.813953, count: 129 -Region Avg IOU: 0.568650, Class: 1.000000, Obj: 0.318044, No Obj: 0.002533, Avg Recall: 0.690265, count: 113 -Region Avg IOU: 0.636210, Class: 1.000000, Obj: 0.334444, No Obj: 0.002222, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.663949, Class: 1.000000, Obj: 0.470637, No Obj: 0.002290, Avg Recall: 0.823529, count: 102 -Region Avg IOU: 0.633465, Class: 1.000000, Obj: 0.342360, No Obj: 0.002854, Avg Recall: 0.779412, count: 136 -Region Avg IOU: 0.659643, Class: 1.000000, Obj: 0.355374, No Obj: 0.002773, Avg Recall: 0.829060, count: 117 -6618: 0.471308, 0.430468 avg, 0.001000 rate, 27.636330 seconds, 1694208 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.680222, Class: 1.000000, Obj: 0.474982, No Obj: 0.002243, Avg Recall: 0.858696, count: 92 -Region Avg IOU: 0.641777, Class: 1.000000, Obj: 0.401501, No Obj: 0.002700, Avg Recall: 0.816000, count: 125 -Region Avg IOU: 0.615249, Class: 1.000000, Obj: 0.329525, No Obj: 0.002721, Avg Recall: 0.746377, count: 138 -Region Avg IOU: 0.690986, Class: 1.000000, Obj: 0.404104, No Obj: 0.002277, Avg Recall: 0.869048, count: 84 -Region Avg IOU: 0.654963, Class: 1.000000, Obj: 0.424843, No Obj: 0.002557, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.644334, Class: 1.000000, Obj: 0.385225, No Obj: 0.002760, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.629284, Class: 1.000000, Obj: 0.328729, No Obj: 0.002160, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.611779, Class: 1.000000, Obj: 0.401497, No Obj: 0.002646, Avg Recall: 0.695238, count: 105 -6619: 0.429424, 0.430364 avg, 0.001000 rate, 27.729277 seconds, 1694464 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.656503, Class: 1.000000, Obj: 0.407043, No Obj: 0.003045, Avg Recall: 0.790698, count: 129 -Region Avg IOU: 0.673199, Class: 1.000000, Obj: 0.431649, No Obj: 0.002617, Avg Recall: 0.838095, count: 105 -Region Avg IOU: 0.640905, Class: 1.000000, Obj: 0.393429, No Obj: 0.002424, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.667546, Class: 1.000000, Obj: 0.422153, No Obj: 0.002902, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.650228, Class: 1.000000, Obj: 0.444724, No Obj: 0.002599, Avg Recall: 0.814815, count: 108 -Region Avg IOU: 0.651035, Class: 1.000000, Obj: 0.401057, No Obj: 0.002317, Avg Recall: 0.788235, count: 85 -Region Avg IOU: 0.652884, Class: 1.000000, Obj: 0.404282, No Obj: 0.002564, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.679242, Class: 1.000000, Obj: 0.423673, No Obj: 0.002604, Avg Recall: 0.816092, count: 87 -6620: 0.405117, 0.427839 avg, 0.001000 rate, 27.024145 seconds, 1694720 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.347639 seconds -Region Avg IOU: 0.494053, Class: 1.000000, Obj: 0.273538, No Obj: 0.004217, Avg Recall: 0.552000, count: 125 -Region Avg IOU: 0.489117, Class: 1.000000, Obj: 0.252276, No Obj: 0.002771, Avg Recall: 0.549451, count: 91 -Region Avg IOU: 0.518323, Class: 1.000000, Obj: 0.316423, No Obj: 0.003613, Avg Recall: 0.594828, count: 116 -Region Avg IOU: 0.556538, Class: 1.000000, Obj: 0.307569, No Obj: 0.003457, Avg Recall: 0.612500, count: 80 -Region Avg IOU: 0.530812, Class: 1.000000, Obj: 0.306833, No Obj: 0.003448, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.528205, Class: 1.000000, Obj: 0.304184, No Obj: 0.004613, Avg Recall: 0.601351, count: 148 -Region Avg IOU: 0.545081, Class: 1.000000, Obj: 0.317004, No Obj: 0.004631, Avg Recall: 0.631579, count: 133 -Region Avg IOU: 0.501317, Class: 1.000000, Obj: 0.331868, No Obj: 0.003962, Avg Recall: 0.567308, count: 104 -6621: 0.504980, 0.435553 avg, 0.001000 rate, 16.881161 seconds, 1694976 images -Loaded: 2.991534 seconds -Region Avg IOU: 0.560767, Class: 1.000000, Obj: 0.345877, No Obj: 0.003668, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.502663, Class: 1.000000, Obj: 0.338263, No Obj: 0.003043, Avg Recall: 0.566667, count: 90 -Region Avg IOU: 0.540562, Class: 1.000000, Obj: 0.345666, No Obj: 0.004770, Avg Recall: 0.628788, count: 132 -Region Avg IOU: 0.570533, Class: 1.000000, Obj: 0.365982, No Obj: 0.004294, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.505411, Class: 1.000000, Obj: 0.324685, No Obj: 0.003329, Avg Recall: 0.540000, count: 100 -Region Avg IOU: 0.513095, Class: 1.000000, Obj: 0.362325, No Obj: 0.004081, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.527829, Class: 1.000000, Obj: 0.344129, No Obj: 0.004481, Avg Recall: 0.601695, count: 118 -Region Avg IOU: 0.617835, Class: 1.000000, Obj: 0.366919, No Obj: 0.004433, Avg Recall: 0.790000, count: 100 -6622: 0.414081, 0.433406 avg, 0.001000 rate, 18.495043 seconds, 1695232 images -Loaded: 3.171973 seconds -Region Avg IOU: 0.559390, Class: 1.000000, Obj: 0.345697, No Obj: 0.003796, Avg Recall: 0.597561, count: 82 -Region Avg IOU: 0.503797, Class: 1.000000, Obj: 0.333287, No Obj: 0.003704, Avg Recall: 0.582418, count: 91 -Region Avg IOU: 0.509592, Class: 1.000000, Obj: 0.359202, No Obj: 0.003926, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.588643, Class: 1.000000, Obj: 0.376239, No Obj: 0.004710, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.518065, Class: 1.000000, Obj: 0.354449, No Obj: 0.005553, Avg Recall: 0.586667, count: 150 -Region Avg IOU: 0.600635, Class: 1.000000, Obj: 0.362624, No Obj: 0.005065, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.454743, Class: 1.000000, Obj: 0.270603, No Obj: 0.003864, Avg Recall: 0.516949, count: 118 -Region Avg IOU: 0.505567, Class: 1.000000, Obj: 0.331032, No Obj: 0.004407, Avg Recall: 0.549180, count: 122 -6623: 0.425669, 0.432632 avg, 0.001000 rate, 20.456007 seconds, 1695488 images -Loaded: 0.966177 seconds -Region Avg IOU: 0.477289, Class: 1.000000, Obj: 0.300116, No Obj: 0.004121, Avg Recall: 0.523810, count: 126 -Region Avg IOU: 0.501822, Class: 1.000000, Obj: 0.411008, No Obj: 0.004318, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.600583, Class: 1.000000, Obj: 0.410747, No Obj: 0.004852, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.516741, Class: 1.000000, Obj: 0.413064, No Obj: 0.005025, Avg Recall: 0.588235, count: 102 -Region Avg IOU: 0.487161, Class: 1.000000, Obj: 0.362134, No Obj: 0.004669, Avg Recall: 0.500000, count: 126 -Region Avg IOU: 0.493661, Class: 1.000000, Obj: 0.314521, No Obj: 0.004320, Avg Recall: 0.528926, count: 121 -Region Avg IOU: 0.497603, Class: 1.000000, Obj: 0.374203, No Obj: 0.004352, Avg Recall: 0.509804, count: 102 -Region Avg IOU: 0.443243, Class: 1.000000, Obj: 0.346554, No Obj: 0.004139, Avg Recall: 0.457143, count: 105 -6624: 0.450878, 0.434457 avg, 0.001000 rate, 18.175278 seconds, 1695744 images -Loaded: 2.143900 seconds -Region Avg IOU: 0.518332, Class: 1.000000, Obj: 0.420801, No Obj: 0.005097, Avg Recall: 0.577586, count: 116 -Region Avg IOU: 0.518583, Class: 1.000000, Obj: 0.412476, No Obj: 0.004574, Avg Recall: 0.586207, count: 87 -Region Avg IOU: 0.516329, Class: 1.000000, Obj: 0.395813, No Obj: 0.004367, Avg Recall: 0.565657, count: 99 -Region Avg IOU: 0.551544, Class: 1.000000, Obj: 0.405635, No Obj: 0.004832, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.472343, Class: 1.000000, Obj: 0.392558, No Obj: 0.004910, Avg Recall: 0.500000, count: 112 -Region Avg IOU: 0.530064, Class: 1.000000, Obj: 0.388716, No Obj: 0.005767, Avg Recall: 0.589744, count: 117 -Region Avg IOU: 0.511762, Class: 1.000000, Obj: 0.344729, No Obj: 0.004298, Avg Recall: 0.616279, count: 86 -Region Avg IOU: 0.577132, Class: 1.000000, Obj: 0.419093, No Obj: 0.005154, Avg Recall: 0.708333, count: 96 -6625: 0.443236, 0.435335 avg, 0.001000 rate, 17.136860 seconds, 1696000 images -Loaded: 4.313270 seconds -Region Avg IOU: 0.457199, Class: 1.000000, Obj: 0.398201, No Obj: 0.004465, Avg Recall: 0.516854, count: 89 -Region Avg IOU: 0.489974, Class: 1.000000, Obj: 0.409788, No Obj: 0.005014, Avg Recall: 0.530612, count: 98 -Region Avg IOU: 0.523768, Class: 1.000000, Obj: 0.479322, No Obj: 0.005173, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.532217, Class: 1.000000, Obj: 0.424352, No Obj: 0.005202, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.539621, Class: 1.000000, Obj: 0.433168, No Obj: 0.005702, Avg Recall: 0.595238, count: 126 -Region Avg IOU: 0.504307, Class: 1.000000, Obj: 0.398236, No Obj: 0.004734, Avg Recall: 0.520408, count: 98 -Region Avg IOU: 0.522937, Class: 1.000000, Obj: 0.394057, No Obj: 0.005286, Avg Recall: 0.596774, count: 124 -Region Avg IOU: 0.465109, Class: 1.000000, Obj: 0.397260, No Obj: 0.004780, Avg Recall: 0.504762, count: 105 -6626: 0.468594, 0.438661 avg, 0.001000 rate, 17.797840 seconds, 1696256 images -Loaded: 4.167741 seconds -Region Avg IOU: 0.526991, Class: 1.000000, Obj: 0.467010, No Obj: 0.005689, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.534273, Class: 1.000000, Obj: 0.382999, No Obj: 0.005079, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.528734, Class: 1.000000, Obj: 0.434309, No Obj: 0.005990, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.538872, Class: 1.000000, Obj: 0.406670, No Obj: 0.005758, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.496527, Class: 1.000000, Obj: 0.453783, No Obj: 0.005116, Avg Recall: 0.560748, count: 107 -Region Avg IOU: 0.486688, Class: 1.000000, Obj: 0.359256, No Obj: 0.005400, Avg Recall: 0.516393, count: 122 -Region Avg IOU: 0.562329, Class: 1.000000, Obj: 0.436937, No Obj: 0.004835, Avg Recall: 0.621951, count: 82 -Region Avg IOU: 0.474116, Class: 1.000000, Obj: 0.382489, No Obj: 0.005153, Avg Recall: 0.507812, count: 128 -6627: 0.412896, 0.436084 avg, 0.001000 rate, 20.051434 seconds, 1696512 images -Loaded: 1.196945 seconds -Region Avg IOU: 0.532784, Class: 1.000000, Obj: 0.405121, No Obj: 0.005779, Avg Recall: 0.595745, count: 94 -Region Avg IOU: 0.470715, Class: 1.000000, Obj: 0.440671, No Obj: 0.004922, Avg Recall: 0.442105, count: 95 -Region Avg IOU: 0.516101, Class: 1.000000, Obj: 0.420726, No Obj: 0.005368, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.532100, Class: 1.000000, Obj: 0.417059, No Obj: 0.004830, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.498955, Class: 1.000000, Obj: 0.388595, No Obj: 0.006093, Avg Recall: 0.595238, count: 126 -Region Avg IOU: 0.447587, Class: 1.000000, Obj: 0.390293, No Obj: 0.004755, Avg Recall: 0.455357, count: 112 -Region Avg IOU: 0.479462, Class: 1.000000, Obj: 0.423947, No Obj: 0.004947, Avg Recall: 0.512000, count: 125 -Region Avg IOU: 0.512412, Class: 1.000000, Obj: 0.429258, No Obj: 0.005341, Avg Recall: 0.568000, count: 125 -6628: 0.468710, 0.439347 avg, 0.001000 rate, 20.822184 seconds, 1696768 images -Loaded: 0.945539 seconds -Region Avg IOU: 0.482875, Class: 1.000000, Obj: 0.353626, No Obj: 0.005305, Avg Recall: 0.550388, count: 129 -Region Avg IOU: 0.507082, Class: 1.000000, Obj: 0.388125, No Obj: 0.004800, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.547765, Class: 1.000000, Obj: 0.468495, No Obj: 0.005268, Avg Recall: 0.563218, count: 87 -Region Avg IOU: 0.532981, Class: 1.000000, Obj: 0.463284, No Obj: 0.005419, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.501792, Class: 1.000000, Obj: 0.425127, No Obj: 0.004130, Avg Recall: 0.579545, count: 88 -Region Avg IOU: 0.466277, Class: 1.000000, Obj: 0.350469, No Obj: 0.004169, Avg Recall: 0.505376, count: 93 -Region Avg IOU: 0.510546, Class: 1.000000, Obj: 0.391544, No Obj: 0.005128, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.527992, Class: 1.000000, Obj: 0.419546, No Obj: 0.004757, Avg Recall: 0.641304, count: 92 -6629: 0.429834, 0.438396 avg, 0.001000 rate, 20.298052 seconds, 1697024 images -Loaded: 1.058103 seconds -Region Avg IOU: 0.464430, Class: 1.000000, Obj: 0.352723, No Obj: 0.004699, Avg Recall: 0.539823, count: 113 -Region Avg IOU: 0.500587, Class: 1.000000, Obj: 0.386182, No Obj: 0.005094, Avg Recall: 0.558333, count: 120 -Region Avg IOU: 0.515140, Class: 1.000000, Obj: 0.391978, No Obj: 0.005141, Avg Recall: 0.587719, count: 114 -Region Avg IOU: 0.468498, Class: 1.000000, Obj: 0.350386, No Obj: 0.004527, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.488550, Class: 1.000000, Obj: 0.383809, No Obj: 0.004957, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.488769, Class: 1.000000, Obj: 0.387905, No Obj: 0.004941, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.467435, Class: 1.000000, Obj: 0.374784, No Obj: 0.004135, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.524584, Class: 1.000000, Obj: 0.411707, No Obj: 0.004534, Avg Recall: 0.601942, count: 103 -6630: 0.463434, 0.440899 avg, 0.001000 rate, 20.484217 seconds, 1697280 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 17.318205 seconds -Region Avg IOU: 0.407773, Class: 1.000000, Obj: 0.276077, No Obj: 0.004472, Avg Recall: 0.420561, count: 107 -Region Avg IOU: 0.464809, Class: 1.000000, Obj: 0.334816, No Obj: 0.004381, Avg Recall: 0.527473, count: 91 -Region Avg IOU: 0.428651, Class: 1.000000, Obj: 0.331478, No Obj: 0.004869, Avg Recall: 0.430894, count: 123 -Region Avg IOU: 0.458948, Class: 1.000000, Obj: 0.344654, No Obj: 0.004905, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.430181, Class: 1.000000, Obj: 0.323081, No Obj: 0.004762, Avg Recall: 0.440678, count: 118 -Region Avg IOU: 0.490515, Class: 1.000000, Obj: 0.396949, No Obj: 0.004942, Avg Recall: 0.530612, count: 98 -Region Avg IOU: 0.454987, Class: 1.000000, Obj: 0.293200, No Obj: 0.004921, Avg Recall: 0.442623, count: 122 -Region Avg IOU: 0.480643, Class: 1.000000, Obj: 0.377785, No Obj: 0.004152, Avg Recall: 0.582278, count: 79 -6631: 0.495988, 0.446408 avg, 0.001000 rate, 15.936536 seconds, 1697536 images -Loaded: 2.983899 seconds -Region Avg IOU: 0.462062, Class: 1.000000, Obj: 0.330844, No Obj: 0.004036, Avg Recall: 0.528736, count: 87 -Region Avg IOU: 0.458494, Class: 1.000000, Obj: 0.301459, No Obj: 0.003779, Avg Recall: 0.488095, count: 84 -Region Avg IOU: 0.488245, Class: 1.000000, Obj: 0.326021, No Obj: 0.004328, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.503456, Class: 1.000000, Obj: 0.336246, No Obj: 0.004727, Avg Recall: 0.553719, count: 121 -Region Avg IOU: 0.545131, Class: 1.000000, Obj: 0.369280, No Obj: 0.004141, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.456535, Class: 1.000000, Obj: 0.279528, No Obj: 0.003705, Avg Recall: 0.480769, count: 104 -Region Avg IOU: 0.402799, Class: 1.000000, Obj: 0.290755, No Obj: 0.004212, Avg Recall: 0.452174, count: 115 -Region Avg IOU: 0.446226, Class: 1.000000, Obj: 0.286960, No Obj: 0.004612, Avg Recall: 0.472000, count: 125 -6632: 0.419653, 0.443733 avg, 0.001000 rate, 17.800608 seconds, 1697792 images -Loaded: 2.744150 seconds -Region Avg IOU: 0.484917, Class: 1.000000, Obj: 0.326082, No Obj: 0.004342, Avg Recall: 0.484615, count: 130 -Region Avg IOU: 0.513470, Class: 1.000000, Obj: 0.359940, No Obj: 0.004786, Avg Recall: 0.605769, count: 104 -Region Avg IOU: 0.519011, Class: 1.000000, Obj: 0.337558, No Obj: 0.004946, Avg Recall: 0.617647, count: 102 -Region Avg IOU: 0.430117, Class: 1.000000, Obj: 0.320232, No Obj: 0.003580, Avg Recall: 0.485981, count: 107 -Region Avg IOU: 0.468133, Class: 1.000000, Obj: 0.342490, No Obj: 0.004233, Avg Recall: 0.505050, count: 99 -Region Avg IOU: 0.507926, Class: 1.000000, Obj: 0.307497, No Obj: 0.004273, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.502443, Class: 1.000000, Obj: 0.332356, No Obj: 0.004073, Avg Recall: 0.595238, count: 84 -Region Avg IOU: 0.473620, Class: 1.000000, Obj: 0.324290, No Obj: 0.004503, Avg Recall: 0.495935, count: 123 -6633: 0.441924, 0.443552 avg, 0.001000 rate, 17.948055 seconds, 1698048 images -Loaded: 2.501758 seconds -Region Avg IOU: 0.521163, Class: 1.000000, Obj: 0.340968, No Obj: 0.004152, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.520645, Class: 1.000000, Obj: 0.327608, No Obj: 0.004600, Avg Recall: 0.607692, count: 130 -Region Avg IOU: 0.525206, Class: 1.000000, Obj: 0.322232, No Obj: 0.004163, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.527025, Class: 1.000000, Obj: 0.310248, No Obj: 0.004420, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.505331, Class: 1.000000, Obj: 0.341523, No Obj: 0.004270, Avg Recall: 0.541667, count: 96 -Region Avg IOU: 0.493373, Class: 1.000000, Obj: 0.311001, No Obj: 0.003788, Avg Recall: 0.489130, count: 92 -Region Avg IOU: 0.514342, Class: 1.000000, Obj: 0.329360, No Obj: 0.004239, Avg Recall: 0.570000, count: 100 -Region Avg IOU: 0.430507, Class: 1.000000, Obj: 0.268727, No Obj: 0.003172, Avg Recall: 0.450549, count: 91 -6634: 0.410638, 0.440260 avg, 0.001000 rate, 17.302868 seconds, 1698304 images -Loaded: 4.523550 seconds -Region Avg IOU: 0.469856, Class: 1.000000, Obj: 0.279427, No Obj: 0.003766, Avg Recall: 0.500000, count: 114 -Region Avg IOU: 0.431386, Class: 1.000000, Obj: 0.264288, No Obj: 0.003804, Avg Recall: 0.431193, count: 109 -Region Avg IOU: 0.495010, Class: 1.000000, Obj: 0.316108, No Obj: 0.003562, Avg Recall: 0.513889, count: 72 -Region Avg IOU: 0.464316, Class: 1.000000, Obj: 0.273481, No Obj: 0.004186, Avg Recall: 0.524590, count: 122 -Region Avg IOU: 0.459109, Class: 1.000000, Obj: 0.285237, No Obj: 0.003967, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.503776, Class: 1.000000, Obj: 0.325701, No Obj: 0.004277, Avg Recall: 0.577236, count: 123 -Region Avg IOU: 0.500188, Class: 1.000000, Obj: 0.326571, No Obj: 0.004284, Avg Recall: 0.566372, count: 113 -Region Avg IOU: 0.527052, Class: 1.000000, Obj: 0.308744, No Obj: 0.004103, Avg Recall: 0.653333, count: 75 -6635: 0.450539, 0.441288 avg, 0.001000 rate, 16.040834 seconds, 1698560 images -Loaded: 3.890413 seconds -Region Avg IOU: 0.506250, Class: 1.000000, Obj: 0.323754, No Obj: 0.004086, Avg Recall: 0.584270, count: 89 -Region Avg IOU: 0.538477, Class: 1.000000, Obj: 0.326594, No Obj: 0.004344, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.507445, Class: 1.000000, Obj: 0.270372, No Obj: 0.004526, Avg Recall: 0.561538, count: 130 -Region Avg IOU: 0.491966, Class: 1.000000, Obj: 0.327433, No Obj: 0.004596, Avg Recall: 0.557377, count: 122 -Region Avg IOU: 0.470774, Class: 1.000000, Obj: 0.291340, No Obj: 0.003785, Avg Recall: 0.521368, count: 117 -Region Avg IOU: 0.392477, Class: 1.000000, Obj: 0.230221, No Obj: 0.003259, Avg Recall: 0.438095, count: 105 -Region Avg IOU: 0.524830, Class: 1.000000, Obj: 0.336600, No Obj: 0.004643, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.510270, Class: 1.000000, Obj: 0.301763, No Obj: 0.004168, Avg Recall: 0.551020, count: 98 -6636: 0.463225, 0.443482 avg, 0.001000 rate, 18.869215 seconds, 1698816 images -Loaded: 2.374101 seconds -Region Avg IOU: 0.465689, Class: 1.000000, Obj: 0.290598, No Obj: 0.003918, Avg Recall: 0.451923, count: 104 -Region Avg IOU: 0.470722, Class: 1.000000, Obj: 0.323120, No Obj: 0.004295, Avg Recall: 0.508621, count: 116 -Region Avg IOU: 0.495329, Class: 1.000000, Obj: 0.318060, No Obj: 0.004590, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.474515, Class: 1.000000, Obj: 0.279341, No Obj: 0.004126, Avg Recall: 0.532609, count: 92 -Region Avg IOU: 0.529621, Class: 1.000000, Obj: 0.345059, No Obj: 0.004872, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.485903, Class: 1.000000, Obj: 0.322252, No Obj: 0.003893, Avg Recall: 0.545455, count: 121 -Region Avg IOU: 0.480558, Class: 1.000000, Obj: 0.300604, No Obj: 0.005435, Avg Recall: 0.542373, count: 118 -Region Avg IOU: 0.516237, Class: 1.000000, Obj: 0.312679, No Obj: 0.005157, Avg Recall: 0.609375, count: 128 -6637: 0.579600, 0.457094 avg, 0.001000 rate, 14.904973 seconds, 1699072 images -Loaded: 5.346251 seconds -Region Avg IOU: 0.518217, Class: 1.000000, Obj: 0.358050, No Obj: 0.004920, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.468903, Class: 1.000000, Obj: 0.340311, No Obj: 0.003824, Avg Recall: 0.450000, count: 100 -Region Avg IOU: 0.470728, Class: 1.000000, Obj: 0.319493, No Obj: 0.004647, Avg Recall: 0.523810, count: 105 -Region Avg IOU: 0.509827, Class: 1.000000, Obj: 0.317956, No Obj: 0.004786, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.471830, Class: 1.000000, Obj: 0.324233, No Obj: 0.004333, Avg Recall: 0.521739, count: 92 -Region Avg IOU: 0.526886, Class: 1.000000, Obj: 0.356914, No Obj: 0.004271, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.506200, Class: 1.000000, Obj: 0.346104, No Obj: 0.004463, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.477944, Class: 1.000000, Obj: 0.327188, No Obj: 0.004570, Avg Recall: 0.562500, count: 112 -6638: 0.460292, 0.457414 avg, 0.001000 rate, 16.481798 seconds, 1699328 images -Loaded: 4.343279 seconds -Region Avg IOU: 0.468164, Class: 1.000000, Obj: 0.323795, No Obj: 0.004456, Avg Recall: 0.515464, count: 97 -Region Avg IOU: 0.500286, Class: 1.000000, Obj: 0.367446, No Obj: 0.005441, Avg Recall: 0.531746, count: 126 -Region Avg IOU: 0.492622, Class: 1.000000, Obj: 0.362068, No Obj: 0.004151, Avg Recall: 0.517647, count: 85 -Region Avg IOU: 0.507373, Class: 1.000000, Obj: 0.370718, No Obj: 0.005016, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.523308, Class: 1.000000, Obj: 0.363259, No Obj: 0.004104, Avg Recall: 0.580247, count: 81 -Region Avg IOU: 0.533822, Class: 1.000000, Obj: 0.349677, No Obj: 0.003966, Avg Recall: 0.650602, count: 83 -Region Avg IOU: 0.525989, Class: 1.000000, Obj: 0.371526, No Obj: 0.005073, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.521902, Class: 1.000000, Obj: 0.378261, No Obj: 0.004192, Avg Recall: 0.550562, count: 89 -6639: 0.404346, 0.452107 avg, 0.001000 rate, 15.699210 seconds, 1699584 images -Loaded: 2.328144 seconds -Region Avg IOU: 0.497955, Class: 1.000000, Obj: 0.370786, No Obj: 0.004679, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.502805, Class: 1.000000, Obj: 0.376216, No Obj: 0.004818, Avg Recall: 0.561404, count: 114 -Region Avg IOU: 0.451416, Class: 1.000000, Obj: 0.369160, No Obj: 0.004828, Avg Recall: 0.445255, count: 137 -Region Avg IOU: 0.443003, Class: 1.000000, Obj: 0.339142, No Obj: 0.005379, Avg Recall: 0.469697, count: 132 -Region Avg IOU: 0.473400, Class: 1.000000, Obj: 0.357994, No Obj: 0.004816, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.515713, Class: 1.000000, Obj: 0.379920, No Obj: 0.005493, Avg Recall: 0.552000, count: 125 -Region Avg IOU: 0.529320, Class: 1.000000, Obj: 0.374400, No Obj: 0.004348, Avg Recall: 0.568421, count: 95 -Region Avg IOU: 0.468340, Class: 1.000000, Obj: 0.397497, No Obj: 0.004590, Avg Recall: 0.474747, count: 99 -6640: 0.455203, 0.452416 avg, 0.001000 rate, 18.543571 seconds, 1699840 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 0.100068 seconds -Region Avg IOU: 0.571833, Class: 1.000000, Obj: 0.454103, No Obj: 0.004910, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.530394, Class: 1.000000, Obj: 0.384952, No Obj: 0.004993, Avg Recall: 0.626866, count: 134 -Region Avg IOU: 0.590300, Class: 1.000000, Obj: 0.434974, No Obj: 0.004810, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.524320, Class: 1.000000, Obj: 0.442075, No Obj: 0.005087, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.571751, Class: 1.000000, Obj: 0.460719, No Obj: 0.004767, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.588073, Class: 1.000000, Obj: 0.477361, No Obj: 0.004854, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.581806, Class: 1.000000, Obj: 0.482144, No Obj: 0.004554, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.521707, Class: 1.000000, Obj: 0.421989, No Obj: 0.004717, Avg Recall: 0.588235, count: 119 -6641: 0.426284, 0.449803 avg, 0.001000 rate, 22.050808 seconds, 1700096 images -Loaded: 0.390192 seconds -Region Avg IOU: 0.547927, Class: 1.000000, Obj: 0.448848, No Obj: 0.005072, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.551003, Class: 1.000000, Obj: 0.427274, No Obj: 0.004614, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.578010, Class: 1.000000, Obj: 0.452935, No Obj: 0.004326, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.569296, Class: 1.000000, Obj: 0.447217, No Obj: 0.004904, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.485843, Class: 1.000000, Obj: 0.433979, No Obj: 0.003801, Avg Recall: 0.500000, count: 100 -Region Avg IOU: 0.538176, Class: 1.000000, Obj: 0.422708, No Obj: 0.004012, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.602365, Class: 1.000000, Obj: 0.448407, No Obj: 0.005203, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.499749, Class: 1.000000, Obj: 0.423525, No Obj: 0.004871, Avg Recall: 0.552448, count: 143 -6642: 0.471506, 0.451973 avg, 0.001000 rate, 21.907818 seconds, 1700352 images -Loaded: 1.124899 seconds -Region Avg IOU: 0.550380, Class: 1.000000, Obj: 0.463582, No Obj: 0.004706, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.595227, Class: 1.000000, Obj: 0.453826, No Obj: 0.004528, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.526679, Class: 1.000000, Obj: 0.458755, No Obj: 0.004033, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.595386, Class: 1.000000, Obj: 0.465501, No Obj: 0.004224, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.579544, Class: 1.000000, Obj: 0.484496, No Obj: 0.004127, Avg Recall: 0.702381, count: 84 -Region Avg IOU: 0.560880, Class: 1.000000, Obj: 0.443915, No Obj: 0.004785, Avg Recall: 0.652542, count: 118 -Region Avg IOU: 0.553360, Class: 1.000000, Obj: 0.433838, No Obj: 0.004760, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.509301, Class: 1.000000, Obj: 0.418858, No Obj: 0.005046, Avg Recall: 0.514925, count: 134 -6643: 0.433122, 0.450088 avg, 0.001000 rate, 23.076488 seconds, 1700608 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.546772, Class: 1.000000, Obj: 0.451693, No Obj: 0.003823, Avg Recall: 0.644860, count: 107 -Region Avg IOU: 0.547850, Class: 1.000000, Obj: 0.410426, No Obj: 0.004839, Avg Recall: 0.592308, count: 130 -Region Avg IOU: 0.553283, Class: 1.000000, Obj: 0.434932, No Obj: 0.004639, Avg Recall: 0.653846, count: 130 -Region Avg IOU: 0.577761, Class: 1.000000, Obj: 0.442367, No Obj: 0.004320, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.563228, Class: 1.000000, Obj: 0.436667, No Obj: 0.004655, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.524284, Class: 1.000000, Obj: 0.401378, No Obj: 0.004562, Avg Recall: 0.601562, count: 128 -Region Avg IOU: 0.567568, Class: 1.000000, Obj: 0.425796, No Obj: 0.004505, Avg Recall: 0.705882, count: 119 -Region Avg IOU: 0.544573, Class: 1.000000, Obj: 0.455230, No Obj: 0.004251, Avg Recall: 0.634615, count: 104 -6644: 0.487447, 0.453824 avg, 0.001000 rate, 22.573467 seconds, 1700864 images -Loaded: 0.541065 seconds -Region Avg IOU: 0.623352, Class: 1.000000, Obj: 0.449241, No Obj: 0.003970, Avg Recall: 0.707317, count: 82 -Region Avg IOU: 0.533788, Class: 1.000000, Obj: 0.374101, No Obj: 0.003699, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.513903, Class: 1.000000, Obj: 0.423661, No Obj: 0.004440, Avg Recall: 0.573770, count: 122 -Region Avg IOU: 0.585107, Class: 1.000000, Obj: 0.459680, No Obj: 0.004644, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.547558, Class: 1.000000, Obj: 0.398840, No Obj: 0.004246, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.563303, Class: 1.000000, Obj: 0.433245, No Obj: 0.003833, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.578772, Class: 1.000000, Obj: 0.374459, No Obj: 0.004843, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.556603, Class: 1.000000, Obj: 0.434717, No Obj: 0.004573, Avg Recall: 0.639098, count: 133 -6645: 0.393403, 0.447782 avg, 0.001000 rate, 21.570513 seconds, 1701120 images -Loaded: 0.983769 seconds -Region Avg IOU: 0.514853, Class: 1.000000, Obj: 0.368092, No Obj: 0.003402, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.518129, Class: 1.000000, Obj: 0.378442, No Obj: 0.004177, Avg Recall: 0.580952, count: 105 -Region Avg IOU: 0.551168, Class: 1.000000, Obj: 0.338412, No Obj: 0.004227, Avg Recall: 0.629310, count: 116 -Region Avg IOU: 0.521140, Class: 1.000000, Obj: 0.410278, No Obj: 0.003490, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.535666, Class: 1.000000, Obj: 0.378818, No Obj: 0.004331, Avg Recall: 0.619403, count: 134 -Region Avg IOU: 0.598641, Class: 1.000000, Obj: 0.454558, No Obj: 0.004383, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.525172, Class: 1.000000, Obj: 0.370305, No Obj: 0.004443, Avg Recall: 0.609375, count: 128 -Region Avg IOU: 0.568898, Class: 1.000000, Obj: 0.371759, No Obj: 0.003718, Avg Recall: 0.695122, count: 82 -6646: 0.407907, 0.443794 avg, 0.001000 rate, 23.747892 seconds, 1701376 images -Loaded: 0.000053 seconds -Region Avg IOU: 0.517542, Class: 1.000000, Obj: 0.367741, No Obj: 0.003050, Avg Recall: 0.572727, count: 110 -Region Avg IOU: 0.551117, Class: 1.000000, Obj: 0.360751, No Obj: 0.003618, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.584202, Class: 1.000000, Obj: 0.354880, No Obj: 0.003544, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.558853, Class: 1.000000, Obj: 0.355000, No Obj: 0.003748, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.551186, Class: 1.000000, Obj: 0.378611, No Obj: 0.003095, Avg Recall: 0.615385, count: 78 -Region Avg IOU: 0.565773, Class: 1.000000, Obj: 0.343146, No Obj: 0.003248, Avg Recall: 0.638889, count: 108 -Region Avg IOU: 0.561064, Class: 1.000000, Obj: 0.421706, No Obj: 0.003930, Avg Recall: 0.697248, count: 109 -Region Avg IOU: 0.546943, Class: 1.000000, Obj: 0.327929, No Obj: 0.003538, Avg Recall: 0.633929, count: 112 -6647: 0.423759, 0.441791 avg, 0.001000 rate, 21.731712 seconds, 1701632 images -Loaded: 1.971771 seconds -Region Avg IOU: 0.585406, Class: 1.000000, Obj: 0.362655, No Obj: 0.003858, Avg Recall: 0.658730, count: 126 -Region Avg IOU: 0.519608, Class: 1.000000, Obj: 0.337189, No Obj: 0.003617, Avg Recall: 0.535088, count: 114 -Region Avg IOU: 0.590831, Class: 1.000000, Obj: 0.352479, No Obj: 0.003579, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.504300, Class: 1.000000, Obj: 0.344042, No Obj: 0.003536, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.547884, Class: 1.000000, Obj: 0.361371, No Obj: 0.004003, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.542528, Class: 1.000000, Obj: 0.352297, No Obj: 0.003760, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.551637, Class: 1.000000, Obj: 0.370990, No Obj: 0.003573, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.607918, Class: 1.000000, Obj: 0.364379, No Obj: 0.004407, Avg Recall: 0.695312, count: 128 -6648: 0.465965, 0.444208 avg, 0.001000 rate, 22.133560 seconds, 1701888 images -Loaded: 1.021234 seconds -Region Avg IOU: 0.626157, Class: 1.000000, Obj: 0.404820, No Obj: 0.004257, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.566801, Class: 1.000000, Obj: 0.392924, No Obj: 0.003591, Avg Recall: 0.641667, count: 120 -Region Avg IOU: 0.563118, Class: 1.000000, Obj: 0.414593, No Obj: 0.003764, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.588666, Class: 1.000000, Obj: 0.368595, No Obj: 0.003285, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.562098, Class: 1.000000, Obj: 0.333849, No Obj: 0.003351, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.578241, Class: 1.000000, Obj: 0.345540, No Obj: 0.003822, Avg Recall: 0.671875, count: 128 -Region Avg IOU: 0.568073, Class: 1.000000, Obj: 0.355599, No Obj: 0.004029, Avg Recall: 0.650000, count: 120 -Region Avg IOU: 0.589661, Class: 1.000000, Obj: 0.376225, No Obj: 0.003225, Avg Recall: 0.692308, count: 91 -6649: 0.463734, 0.446161 avg, 0.001000 rate, 19.485184 seconds, 1702144 images -Loaded: 1.557580 seconds -Region Avg IOU: 0.591300, Class: 1.000000, Obj: 0.383162, No Obj: 0.003384, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.578276, Class: 1.000000, Obj: 0.385769, No Obj: 0.003525, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.562395, Class: 1.000000, Obj: 0.390481, No Obj: 0.003812, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.558513, Class: 1.000000, Obj: 0.354414, No Obj: 0.003871, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.557072, Class: 1.000000, Obj: 0.351600, No Obj: 0.003470, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.560052, Class: 1.000000, Obj: 0.363408, No Obj: 0.003217, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.591494, Class: 1.000000, Obj: 0.376491, No Obj: 0.003338, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.616285, Class: 1.000000, Obj: 0.382580, No Obj: 0.003292, Avg Recall: 0.760417, count: 96 -6650: 0.410957, 0.442641 avg, 0.001000 rate, 20.359076 seconds, 1702400 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.480812 seconds -Region Avg IOU: 0.512075, Class: 1.000000, Obj: 0.357890, No Obj: 0.003201, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.595704, Class: 1.000000, Obj: 0.348819, No Obj: 0.003855, Avg Recall: 0.714286, count: 77 -Region Avg IOU: 0.550557, Class: 1.000000, Obj: 0.383674, No Obj: 0.004787, Avg Recall: 0.609023, count: 133 -Region Avg IOU: 0.574812, Class: 1.000000, Obj: 0.383283, No Obj: 0.004086, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.604891, Class: 1.000000, Obj: 0.407339, No Obj: 0.004433, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.515616, Class: 1.000000, Obj: 0.327943, No Obj: 0.003342, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.556796, Class: 1.000000, Obj: 0.378604, No Obj: 0.003516, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.534980, Class: 1.000000, Obj: 0.386862, No Obj: 0.003424, Avg Recall: 0.606742, count: 89 -6651: 0.433726, 0.441749 avg, 0.001000 rate, 20.675728 seconds, 1702656 images -Loaded: 1.773385 seconds -Region Avg IOU: 0.555457, Class: 1.000000, Obj: 0.346938, No Obj: 0.003472, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.533615, Class: 1.000000, Obj: 0.355453, No Obj: 0.004363, Avg Recall: 0.608000, count: 125 -Region Avg IOU: 0.515952, Class: 1.000000, Obj: 0.364197, No Obj: 0.003631, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.528079, Class: 1.000000, Obj: 0.350552, No Obj: 0.003579, Avg Recall: 0.580000, count: 100 -Region Avg IOU: 0.573137, Class: 1.000000, Obj: 0.383706, No Obj: 0.003622, Avg Recall: 0.619565, count: 92 -Region Avg IOU: 0.557970, Class: 1.000000, Obj: 0.378999, No Obj: 0.004201, Avg Recall: 0.661765, count: 136 -Region Avg IOU: 0.515259, Class: 1.000000, Obj: 0.356609, No Obj: 0.003682, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.544327, Class: 1.000000, Obj: 0.368611, No Obj: 0.004183, Avg Recall: 0.600000, count: 130 -6652: 0.404521, 0.438026 avg, 0.001000 rate, 21.992510 seconds, 1702912 images -Loaded: 1.350640 seconds -Region Avg IOU: 0.536550, Class: 1.000000, Obj: 0.362151, No Obj: 0.005642, Avg Recall: 0.625000, count: 160 -Region Avg IOU: 0.561837, Class: 1.000000, Obj: 0.342944, No Obj: 0.004482, Avg Recall: 0.688000, count: 125 -Region Avg IOU: 0.555096, Class: 1.000000, Obj: 0.354955, No Obj: 0.003850, Avg Recall: 0.619565, count: 92 -Region Avg IOU: 0.534478, Class: 1.000000, Obj: 0.381101, No Obj: 0.003760, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.529538, Class: 1.000000, Obj: 0.399358, No Obj: 0.003988, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.591203, Class: 1.000000, Obj: 0.435140, No Obj: 0.004164, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.545627, Class: 1.000000, Obj: 0.405440, No Obj: 0.004094, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.576365, Class: 1.000000, Obj: 0.376542, No Obj: 0.004838, Avg Recall: 0.656489, count: 131 -6653: 0.495889, 0.443813 avg, 0.001000 rate, 22.448179 seconds, 1703168 images -Loaded: 0.126512 seconds -Region Avg IOU: 0.581575, Class: 1.000000, Obj: 0.420413, No Obj: 0.003661, Avg Recall: 0.700000, count: 80 -Region Avg IOU: 0.562245, Class: 1.000000, Obj: 0.408221, No Obj: 0.003993, Avg Recall: 0.620690, count: 87 -Region Avg IOU: 0.508189, Class: 1.000000, Obj: 0.396561, No Obj: 0.004734, Avg Recall: 0.548872, count: 133 -Region Avg IOU: 0.558745, Class: 1.000000, Obj: 0.422278, No Obj: 0.003997, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.555098, Class: 1.000000, Obj: 0.379028, No Obj: 0.004644, Avg Recall: 0.627119, count: 118 -Region Avg IOU: 0.539079, Class: 1.000000, Obj: 0.371441, No Obj: 0.004718, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.541899, Class: 1.000000, Obj: 0.439326, No Obj: 0.003875, Avg Recall: 0.644444, count: 90 -Region Avg IOU: 0.540877, Class: 1.000000, Obj: 0.394198, No Obj: 0.004693, Avg Recall: 0.609756, count: 123 -6654: 0.391237, 0.438555 avg, 0.001000 rate, 20.889986 seconds, 1703424 images -Loaded: 0.010398 seconds -Region Avg IOU: 0.569820, Class: 1.000000, Obj: 0.392828, No Obj: 0.004490, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.528788, Class: 1.000000, Obj: 0.384524, No Obj: 0.004827, Avg Recall: 0.592308, count: 130 -Region Avg IOU: 0.498179, Class: 1.000000, Obj: 0.365500, No Obj: 0.003935, Avg Recall: 0.563025, count: 119 -Region Avg IOU: 0.517761, Class: 1.000000, Obj: 0.406627, No Obj: 0.004064, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.493378, Class: 1.000000, Obj: 0.394995, No Obj: 0.004702, Avg Recall: 0.466165, count: 133 -Region Avg IOU: 0.501906, Class: 1.000000, Obj: 0.380868, No Obj: 0.003986, Avg Recall: 0.551020, count: 98 -Region Avg IOU: 0.522970, Class: 1.000000, Obj: 0.428801, No Obj: 0.004162, Avg Recall: 0.601770, count: 113 -Region Avg IOU: 0.543087, Class: 1.000000, Obj: 0.419741, No Obj: 0.004453, Avg Recall: 0.563025, count: 119 -6655: 0.417674, 0.436467 avg, 0.001000 rate, 20.231125 seconds, 1703680 images -Loaded: 1.692068 seconds -Region Avg IOU: 0.611743, Class: 1.000000, Obj: 0.439665, No Obj: 0.004866, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.474870, Class: 1.000000, Obj: 0.400905, No Obj: 0.004227, Avg Recall: 0.496124, count: 129 -Region Avg IOU: 0.571125, Class: 1.000000, Obj: 0.479185, No Obj: 0.004802, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.594575, Class: 1.000000, Obj: 0.451601, No Obj: 0.004103, Avg Recall: 0.704545, count: 88 -Region Avg IOU: 0.474196, Class: 1.000000, Obj: 0.342675, No Obj: 0.004820, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.528795, Class: 1.000000, Obj: 0.379407, No Obj: 0.004568, Avg Recall: 0.608000, count: 125 -Region Avg IOU: 0.578941, Class: 1.000000, Obj: 0.434985, No Obj: 0.004335, Avg Recall: 0.660550, count: 109 -Region Avg IOU: 0.598926, Class: 1.000000, Obj: 0.436093, No Obj: 0.004090, Avg Recall: 0.696629, count: 89 -6656: 0.426049, 0.435425 avg, 0.001000 rate, 21.771952 seconds, 1703936 images -Loaded: 0.035409 seconds -Region Avg IOU: 0.550098, Class: 1.000000, Obj: 0.405170, No Obj: 0.005445, Avg Recall: 0.656250, count: 128 -Region Avg IOU: 0.550840, Class: 1.000000, Obj: 0.408085, No Obj: 0.004645, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.543085, Class: 1.000000, Obj: 0.438034, No Obj: 0.004284, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.544680, Class: 1.000000, Obj: 0.403497, No Obj: 0.004157, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.526591, Class: 1.000000, Obj: 0.419588, No Obj: 0.004794, Avg Recall: 0.554622, count: 119 -Region Avg IOU: 0.598678, Class: 1.000000, Obj: 0.411537, No Obj: 0.004204, Avg Recall: 0.724138, count: 87 -Region Avg IOU: 0.514227, Class: 1.000000, Obj: 0.400203, No Obj: 0.004130, Avg Recall: 0.592593, count: 108 -Region Avg IOU: 0.542540, Class: 1.000000, Obj: 0.456674, No Obj: 0.004032, Avg Recall: 0.628205, count: 78 -6657: 0.405278, 0.432410 avg, 0.001000 rate, 19.125561 seconds, 1704192 images -Loaded: 2.871110 seconds -Region Avg IOU: 0.530686, Class: 1.000000, Obj: 0.392845, No Obj: 0.004090, Avg Recall: 0.509804, count: 102 -Region Avg IOU: 0.574807, Class: 1.000000, Obj: 0.416760, No Obj: 0.005564, Avg Recall: 0.681481, count: 135 -Region Avg IOU: 0.635702, Class: 1.000000, Obj: 0.450447, No Obj: 0.004774, Avg Recall: 0.797619, count: 84 -Region Avg IOU: 0.544691, Class: 1.000000, Obj: 0.375245, No Obj: 0.004070, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.510435, Class: 1.000000, Obj: 0.400920, No Obj: 0.003594, Avg Recall: 0.556818, count: 88 -Region Avg IOU: 0.523814, Class: 1.000000, Obj: 0.372258, No Obj: 0.004496, Avg Recall: 0.569106, count: 123 -Region Avg IOU: 0.552648, Class: 1.000000, Obj: 0.410630, No Obj: 0.005014, Avg Recall: 0.623077, count: 130 -Region Avg IOU: 0.539606, Class: 1.000000, Obj: 0.377363, No Obj: 0.004660, Avg Recall: 0.625000, count: 96 -6658: 0.441327, 0.433302 avg, 0.001000 rate, 22.403189 seconds, 1704448 images -Loaded: 0.313050 seconds -Region Avg IOU: 0.529489, Class: 1.000000, Obj: 0.378108, No Obj: 0.004423, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.510526, Class: 1.000000, Obj: 0.399549, No Obj: 0.004131, Avg Recall: 0.547009, count: 117 -Region Avg IOU: 0.514620, Class: 1.000000, Obj: 0.374611, No Obj: 0.004906, Avg Recall: 0.587786, count: 131 -Region Avg IOU: 0.584305, Class: 1.000000, Obj: 0.434335, No Obj: 0.004090, Avg Recall: 0.673684, count: 95 -Region Avg IOU: 0.524157, Class: 1.000000, Obj: 0.362077, No Obj: 0.004336, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.517933, Class: 1.000000, Obj: 0.388264, No Obj: 0.005189, Avg Recall: 0.629630, count: 135 -Region Avg IOU: 0.564966, Class: 1.000000, Obj: 0.400715, No Obj: 0.005045, Avg Recall: 0.669421, count: 121 -Region Avg IOU: 0.522215, Class: 1.000000, Obj: 0.391084, No Obj: 0.003888, Avg Recall: 0.593023, count: 86 -6659: 0.492333, 0.439205 avg, 0.001000 rate, 19.504190 seconds, 1704704 images -Loaded: 2.632004 seconds -Region Avg IOU: 0.527404, Class: 1.000000, Obj: 0.336353, No Obj: 0.004236, Avg Recall: 0.639344, count: 122 -Region Avg IOU: 0.528030, Class: 1.000000, Obj: 0.383698, No Obj: 0.004417, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.511444, Class: 1.000000, Obj: 0.378997, No Obj: 0.004104, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.623681, Class: 1.000000, Obj: 0.419538, No Obj: 0.004115, Avg Recall: 0.746988, count: 83 -Region Avg IOU: 0.556807, Class: 1.000000, Obj: 0.339700, No Obj: 0.005369, Avg Recall: 0.618056, count: 144 -Region Avg IOU: 0.579350, Class: 1.000000, Obj: 0.399654, No Obj: 0.004725, Avg Recall: 0.666667, count: 120 -Region Avg IOU: 0.535611, Class: 1.000000, Obj: 0.384243, No Obj: 0.004011, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.543537, Class: 1.000000, Obj: 0.343421, No Obj: 0.004142, Avg Recall: 0.629630, count: 108 -6660: 0.462802, 0.441565 avg, 0.001000 rate, 19.339657 seconds, 1704960 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000065 seconds -Region Avg IOU: 0.655332, Class: 1.000000, Obj: 0.411386, No Obj: 0.002953, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.672204, Class: 1.000000, Obj: 0.453923, No Obj: 0.002575, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.649444, Class: 1.000000, Obj: 0.457271, No Obj: 0.003254, Avg Recall: 0.787879, count: 132 -Region Avg IOU: 0.645225, Class: 1.000000, Obj: 0.524510, No Obj: 0.002561, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.655856, Class: 1.000000, Obj: 0.462081, No Obj: 0.003279, Avg Recall: 0.813559, count: 118 -Region Avg IOU: 0.612927, Class: 1.000000, Obj: 0.482395, No Obj: 0.002809, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.647763, Class: 1.000000, Obj: 0.460196, No Obj: 0.002783, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.646931, Class: 1.000000, Obj: 0.465910, No Obj: 0.002735, Avg Recall: 0.785714, count: 98 -6661: 0.420569, 0.439465 avg, 0.001000 rate, 30.643061 seconds, 1705216 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.684340, Class: 1.000000, Obj: 0.474225, No Obj: 0.003298, Avg Recall: 0.882353, count: 119 -Region Avg IOU: 0.592761, Class: 1.000000, Obj: 0.415047, No Obj: 0.002771, Avg Recall: 0.762963, count: 135 -Region Avg IOU: 0.653428, Class: 1.000000, Obj: 0.476175, No Obj: 0.002944, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.658563, Class: 1.000000, Obj: 0.495792, No Obj: 0.002763, Avg Recall: 0.819149, count: 94 -Region Avg IOU: 0.672538, Class: 1.000000, Obj: 0.481800, No Obj: 0.003104, Avg Recall: 0.872340, count: 94 -Region Avg IOU: 0.665739, Class: 1.000000, Obj: 0.456034, No Obj: 0.003032, Avg Recall: 0.853448, count: 116 -Region Avg IOU: 0.681465, Class: 1.000000, Obj: 0.506887, No Obj: 0.002677, Avg Recall: 0.865385, count: 104 -Region Avg IOU: 0.629017, Class: 1.000000, Obj: 0.436185, No Obj: 0.003239, Avg Recall: 0.800000, count: 130 -6662: 0.432109, 0.438730 avg, 0.001000 rate, 29.955296 seconds, 1705472 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.646233, Class: 1.000000, Obj: 0.473036, No Obj: 0.002513, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.673510, Class: 1.000000, Obj: 0.474052, No Obj: 0.002616, Avg Recall: 0.861111, count: 72 -Region Avg IOU: 0.672349, Class: 1.000000, Obj: 0.450533, No Obj: 0.002857, Avg Recall: 0.849057, count: 106 -Region Avg IOU: 0.667395, Class: 1.000000, Obj: 0.420594, No Obj: 0.002973, Avg Recall: 0.846154, count: 117 -Region Avg IOU: 0.666151, Class: 1.000000, Obj: 0.411459, No Obj: 0.003092, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.641262, Class: 1.000000, Obj: 0.440407, No Obj: 0.003053, Avg Recall: 0.767442, count: 129 -Region Avg IOU: 0.713662, Class: 1.000000, Obj: 0.429243, No Obj: 0.002580, Avg Recall: 0.914894, count: 94 -Region Avg IOU: 0.668199, Class: 1.000000, Obj: 0.403442, No Obj: 0.003258, Avg Recall: 0.812950, count: 139 -6663: 0.441688, 0.439025 avg, 0.001000 rate, 29.305668 seconds, 1705728 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.574297, Class: 1.000000, Obj: 0.353869, No Obj: 0.002168, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.656212, Class: 1.000000, Obj: 0.383532, No Obj: 0.002884, Avg Recall: 0.803279, count: 122 -Region Avg IOU: 0.695567, Class: 1.000000, Obj: 0.415971, No Obj: 0.002699, Avg Recall: 0.861111, count: 108 -Region Avg IOU: 0.690000, Class: 1.000000, Obj: 0.412918, No Obj: 0.002470, Avg Recall: 0.882979, count: 94 -Region Avg IOU: 0.626983, Class: 1.000000, Obj: 0.407706, No Obj: 0.002915, Avg Recall: 0.792000, count: 125 -Region Avg IOU: 0.672231, Class: 1.000000, Obj: 0.421625, No Obj: 0.002378, Avg Recall: 0.878788, count: 99 -Region Avg IOU: 0.647719, Class: 1.000000, Obj: 0.428669, No Obj: 0.002893, Avg Recall: 0.795620, count: 137 -Region Avg IOU: 0.640806, Class: 1.000000, Obj: 0.438646, No Obj: 0.002169, Avg Recall: 0.793814, count: 97 -6664: 0.448994, 0.440022 avg, 0.001000 rate, 29.036869 seconds, 1705984 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.646269, Class: 1.000000, Obj: 0.430572, No Obj: 0.002391, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.654230, Class: 1.000000, Obj: 0.416464, No Obj: 0.002554, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.659963, Class: 1.000000, Obj: 0.417547, No Obj: 0.002488, Avg Recall: 0.834783, count: 115 -Region Avg IOU: 0.603970, Class: 1.000000, Obj: 0.344890, No Obj: 0.002210, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.598717, Class: 1.000000, Obj: 0.376310, No Obj: 0.002242, Avg Recall: 0.690722, count: 97 -Region Avg IOU: 0.671853, Class: 1.000000, Obj: 0.414654, No Obj: 0.002080, Avg Recall: 0.870968, count: 93 -Region Avg IOU: 0.672137, Class: 1.000000, Obj: 0.433690, No Obj: 0.002459, Avg Recall: 0.830508, count: 118 -Region Avg IOU: 0.667227, Class: 1.000000, Obj: 0.414271, No Obj: 0.002300, Avg Recall: 0.807018, count: 114 -6665: 0.411314, 0.437151 avg, 0.001000 rate, 28.106152 seconds, 1706240 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.671639, Class: 1.000000, Obj: 0.327208, No Obj: 0.002365, Avg Recall: 0.811966, count: 117 -Region Avg IOU: 0.679205, Class: 1.000000, Obj: 0.384379, No Obj: 0.002397, Avg Recall: 0.880952, count: 126 -Region Avg IOU: 0.650791, Class: 1.000000, Obj: 0.385289, No Obj: 0.002071, Avg Recall: 0.858586, count: 99 -Region Avg IOU: 0.661266, Class: 1.000000, Obj: 0.391190, No Obj: 0.001934, Avg Recall: 0.860465, count: 86 -Region Avg IOU: 0.648877, Class: 1.000000, Obj: 0.357290, No Obj: 0.002508, Avg Recall: 0.808696, count: 115 -Region Avg IOU: 0.672615, Class: 1.000000, Obj: 0.413605, No Obj: 0.002368, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.665503, Class: 1.000000, Obj: 0.373298, No Obj: 0.002208, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.642671, Class: 1.000000, Obj: 0.396895, No Obj: 0.002162, Avg Recall: 0.809091, count: 110 -6666: 0.437232, 0.437160 avg, 0.001000 rate, 27.778906 seconds, 1706496 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.691049, Class: 1.000000, Obj: 0.329048, No Obj: 0.002054, Avg Recall: 0.873684, count: 95 -Region Avg IOU: 0.664687, Class: 1.000000, Obj: 0.367509, No Obj: 0.002373, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.689535, Class: 1.000000, Obj: 0.393872, No Obj: 0.002186, Avg Recall: 0.865385, count: 104 -Region Avg IOU: 0.653635, Class: 1.000000, Obj: 0.413332, No Obj: 0.002074, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.645883, Class: 1.000000, Obj: 0.376580, No Obj: 0.002049, Avg Recall: 0.796460, count: 113 -Region Avg IOU: 0.675589, Class: 1.000000, Obj: 0.320495, No Obj: 0.002030, Avg Recall: 0.849057, count: 106 -Region Avg IOU: 0.646325, Class: 1.000000, Obj: 0.328810, No Obj: 0.001996, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.658193, Class: 1.000000, Obj: 0.372911, No Obj: 0.001950, Avg Recall: 0.836957, count: 92 -6667: 0.444441, 0.437888 avg, 0.001000 rate, 31.602083 seconds, 1706752 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.669620, Class: 1.000000, Obj: 0.360375, No Obj: 0.002159, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.649535, Class: 1.000000, Obj: 0.373962, No Obj: 0.002205, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.631137, Class: 1.000000, Obj: 0.359038, No Obj: 0.002165, Avg Recall: 0.786885, count: 122 -Region Avg IOU: 0.642068, Class: 1.000000, Obj: 0.344933, No Obj: 0.001760, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.693630, Class: 1.000000, Obj: 0.345004, No Obj: 0.002495, Avg Recall: 0.855932, count: 118 -Region Avg IOU: 0.638711, Class: 1.000000, Obj: 0.340302, No Obj: 0.002238, Avg Recall: 0.766129, count: 124 -Region Avg IOU: 0.692082, Class: 1.000000, Obj: 0.404690, No Obj: 0.001905, Avg Recall: 0.850000, count: 80 -Region Avg IOU: 0.632445, Class: 1.000000, Obj: 0.384155, No Obj: 0.002377, Avg Recall: 0.761538, count: 130 -6668: 0.452553, 0.439354 avg, 0.001000 rate, 30.347843 seconds, 1707008 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.667513, Class: 1.000000, Obj: 0.345929, No Obj: 0.002327, Avg Recall: 0.855856, count: 111 -Region Avg IOU: 0.665344, Class: 1.000000, Obj: 0.407278, No Obj: 0.002421, Avg Recall: 0.849558, count: 113 -Region Avg IOU: 0.679531, Class: 1.000000, Obj: 0.399640, No Obj: 0.002438, Avg Recall: 0.840708, count: 113 -Region Avg IOU: 0.688769, Class: 1.000000, Obj: 0.426718, No Obj: 0.001979, Avg Recall: 0.835165, count: 91 -Region Avg IOU: 0.679751, Class: 1.000000, Obj: 0.356957, No Obj: 0.002319, Avg Recall: 0.867257, count: 113 -Region Avg IOU: 0.644311, Class: 1.000000, Obj: 0.359438, No Obj: 0.002476, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.685508, Class: 1.000000, Obj: 0.389789, No Obj: 0.002226, Avg Recall: 0.827273, count: 110 -Region Avg IOU: 0.653899, Class: 1.000000, Obj: 0.364385, No Obj: 0.002359, Avg Recall: 0.851240, count: 121 -6669: 0.435914, 0.439010 avg, 0.001000 rate, 29.105604 seconds, 1707264 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.672054, Class: 1.000000, Obj: 0.392951, No Obj: 0.002312, Avg Recall: 0.869565, count: 92 -Region Avg IOU: 0.650163, Class: 1.000000, Obj: 0.409207, No Obj: 0.002594, Avg Recall: 0.809917, count: 121 -Region Avg IOU: 0.652143, Class: 1.000000, Obj: 0.371709, No Obj: 0.002373, Avg Recall: 0.788136, count: 118 -Region Avg IOU: 0.674810, Class: 1.000000, Obj: 0.432423, No Obj: 0.002740, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.654261, Class: 1.000000, Obj: 0.405897, No Obj: 0.002419, Avg Recall: 0.801653, count: 121 -Region Avg IOU: 0.675447, Class: 1.000000, Obj: 0.432272, No Obj: 0.002338, Avg Recall: 0.858586, count: 99 -Region Avg IOU: 0.651944, Class: 1.000000, Obj: 0.356261, No Obj: 0.002170, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.629811, Class: 1.000000, Obj: 0.413257, No Obj: 0.002116, Avg Recall: 0.733333, count: 90 -6670: 0.416580, 0.436767 avg, 0.001000 rate, 29.116520 seconds, 1707520 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.342604 seconds -Region Avg IOU: 0.523790, Class: 1.000000, Obj: 0.344331, No Obj: 0.002884, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.574057, Class: 1.000000, Obj: 0.335805, No Obj: 0.003353, Avg Recall: 0.684211, count: 133 -Region Avg IOU: 0.599915, Class: 1.000000, Obj: 0.348871, No Obj: 0.003914, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.592046, Class: 1.000000, Obj: 0.366871, No Obj: 0.003210, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.569193, Class: 1.000000, Obj: 0.361031, No Obj: 0.003225, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.592432, Class: 1.000000, Obj: 0.395492, No Obj: 0.002946, Avg Recall: 0.710843, count: 83 -Region Avg IOU: 0.578197, Class: 1.000000, Obj: 0.364149, No Obj: 0.003824, Avg Recall: 0.648855, count: 131 -Region Avg IOU: 0.560725, Class: 1.000000, Obj: 0.332966, No Obj: 0.003406, Avg Recall: 0.632000, count: 125 -6671: 0.457937, 0.438884 avg, 0.001000 rate, 22.182255 seconds, 1707776 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.601941, Class: 1.000000, Obj: 0.380323, No Obj: 0.002791, Avg Recall: 0.743902, count: 82 -Region Avg IOU: 0.577344, Class: 1.000000, Obj: 0.364252, No Obj: 0.003464, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.610631, Class: 1.000000, Obj: 0.388004, No Obj: 0.004009, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.608204, Class: 1.000000, Obj: 0.389648, No Obj: 0.003531, Avg Recall: 0.719512, count: 82 -Region Avg IOU: 0.558553, Class: 1.000000, Obj: 0.336653, No Obj: 0.004107, Avg Recall: 0.630435, count: 138 -Region Avg IOU: 0.580694, Class: 1.000000, Obj: 0.362190, No Obj: 0.003491, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.572572, Class: 1.000000, Obj: 0.352943, No Obj: 0.003141, Avg Recall: 0.666667, count: 87 -Region Avg IOU: 0.612962, Class: 1.000000, Obj: 0.357815, No Obj: 0.003760, Avg Recall: 0.752294, count: 109 -6672: 0.485569, 0.443553 avg, 0.001000 rate, 23.346544 seconds, 1708032 images -Loaded: 0.575166 seconds -Region Avg IOU: 0.555663, Class: 1.000000, Obj: 0.358619, No Obj: 0.004062, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.577639, Class: 1.000000, Obj: 0.473722, No Obj: 0.003409, Avg Recall: 0.690476, count: 84 -Region Avg IOU: 0.531746, Class: 1.000000, Obj: 0.339591, No Obj: 0.003572, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.574384, Class: 1.000000, Obj: 0.371950, No Obj: 0.003395, Avg Recall: 0.682353, count: 85 -Region Avg IOU: 0.540566, Class: 1.000000, Obj: 0.389784, No Obj: 0.003405, Avg Recall: 0.610526, count: 95 -Region Avg IOU: 0.556024, Class: 1.000000, Obj: 0.384024, No Obj: 0.003628, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.585084, Class: 1.000000, Obj: 0.393924, No Obj: 0.003511, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.545101, Class: 1.000000, Obj: 0.341681, No Obj: 0.003772, Avg Recall: 0.577778, count: 135 -6673: 0.417071, 0.440905 avg, 0.001000 rate, 23.078012 seconds, 1708288 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.560725, Class: 1.000000, Obj: 0.397337, No Obj: 0.004082, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.595219, Class: 1.000000, Obj: 0.394736, No Obj: 0.004093, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.603992, Class: 1.000000, Obj: 0.454475, No Obj: 0.004107, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.539036, Class: 1.000000, Obj: 0.391905, No Obj: 0.004367, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.564300, Class: 1.000000, Obj: 0.367393, No Obj: 0.003087, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.608017, Class: 1.000000, Obj: 0.422000, No Obj: 0.003704, Avg Recall: 0.717391, count: 92 -Region Avg IOU: 0.547094, Class: 1.000000, Obj: 0.388175, No Obj: 0.004547, Avg Recall: 0.593496, count: 123 -Region Avg IOU: 0.581206, Class: 1.000000, Obj: 0.426753, No Obj: 0.004463, Avg Recall: 0.658730, count: 126 -6674: 0.396960, 0.436510 avg, 0.001000 rate, 23.319704 seconds, 1708544 images -Loaded: 0.515157 seconds -Region Avg IOU: 0.585682, Class: 1.000000, Obj: 0.432895, No Obj: 0.004021, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.562432, Class: 1.000000, Obj: 0.438841, No Obj: 0.004517, Avg Recall: 0.672000, count: 125 -Region Avg IOU: 0.570434, Class: 1.000000, Obj: 0.401197, No Obj: 0.004437, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.555709, Class: 1.000000, Obj: 0.412602, No Obj: 0.004410, Avg Recall: 0.625000, count: 104 -Region Avg IOU: 0.578991, Class: 1.000000, Obj: 0.418242, No Obj: 0.003331, Avg Recall: 0.686747, count: 83 -Region Avg IOU: 0.529075, Class: 1.000000, Obj: 0.388340, No Obj: 0.003975, Avg Recall: 0.580952, count: 105 -Region Avg IOU: 0.590201, Class: 1.000000, Obj: 0.450681, No Obj: 0.004486, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.578677, Class: 1.000000, Obj: 0.453149, No Obj: 0.004656, Avg Recall: 0.663462, count: 104 -6675: 0.381145, 0.430974 avg, 0.001000 rate, 21.327391 seconds, 1708800 images -Loaded: 0.307080 seconds -Region Avg IOU: 0.533296, Class: 1.000000, Obj: 0.407393, No Obj: 0.004476, Avg Recall: 0.625000, count: 136 -Region Avg IOU: 0.560124, Class: 1.000000, Obj: 0.419719, No Obj: 0.004397, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.538518, Class: 1.000000, Obj: 0.429867, No Obj: 0.004359, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.576518, Class: 1.000000, Obj: 0.462040, No Obj: 0.004698, Avg Recall: 0.674797, count: 123 -Region Avg IOU: 0.539891, Class: 1.000000, Obj: 0.402540, No Obj: 0.004174, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.499439, Class: 1.000000, Obj: 0.428117, No Obj: 0.004318, Avg Recall: 0.522523, count: 111 -Region Avg IOU: 0.555219, Class: 1.000000, Obj: 0.460421, No Obj: 0.004485, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.561230, Class: 1.000000, Obj: 0.417040, No Obj: 0.004857, Avg Recall: 0.660000, count: 100 -6676: 0.439520, 0.431828 avg, 0.001000 rate, 20.277948 seconds, 1709056 images -Loaded: 2.018824 seconds -Region Avg IOU: 0.542713, Class: 1.000000, Obj: 0.449102, No Obj: 0.004119, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.527139, Class: 1.000000, Obj: 0.414007, No Obj: 0.004409, Avg Recall: 0.559055, count: 127 -Region Avg IOU: 0.579707, Class: 1.000000, Obj: 0.452872, No Obj: 0.003885, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.571218, Class: 1.000000, Obj: 0.498645, No Obj: 0.004975, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.560513, Class: 1.000000, Obj: 0.451806, No Obj: 0.004320, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.623568, Class: 1.000000, Obj: 0.426998, No Obj: 0.005458, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.546663, Class: 1.000000, Obj: 0.491038, No Obj: 0.004929, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.582831, Class: 1.000000, Obj: 0.482705, No Obj: 0.004638, Avg Recall: 0.675676, count: 111 -6677: 0.397851, 0.428430 avg, 0.001000 rate, 21.848927 seconds, 1709312 images -Loaded: 0.467343 seconds -Region Avg IOU: 0.579858, Class: 1.000000, Obj: 0.448435, No Obj: 0.004581, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.579888, Class: 1.000000, Obj: 0.468404, No Obj: 0.004723, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.516948, Class: 1.000000, Obj: 0.446181, No Obj: 0.004588, Avg Recall: 0.586957, count: 138 -Region Avg IOU: 0.573206, Class: 1.000000, Obj: 0.463116, No Obj: 0.005017, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.543833, Class: 1.000000, Obj: 0.463425, No Obj: 0.004502, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.579762, Class: 1.000000, Obj: 0.494897, No Obj: 0.004280, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.584631, Class: 1.000000, Obj: 0.487933, No Obj: 0.004575, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.565162, Class: 1.000000, Obj: 0.460959, No Obj: 0.003591, Avg Recall: 0.697368, count: 76 -6678: 0.405458, 0.426133 avg, 0.001000 rate, 21.896442 seconds, 1709568 images -Loaded: 0.371358 seconds -Region Avg IOU: 0.530213, Class: 1.000000, Obj: 0.401284, No Obj: 0.004320, Avg Recall: 0.634146, count: 123 -Region Avg IOU: 0.513631, Class: 1.000000, Obj: 0.380177, No Obj: 0.004445, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.634642, Class: 1.000000, Obj: 0.524698, No Obj: 0.003721, Avg Recall: 0.789474, count: 76 -Region Avg IOU: 0.559392, Class: 1.000000, Obj: 0.416239, No Obj: 0.004701, Avg Recall: 0.650000, count: 120 -Region Avg IOU: 0.582486, Class: 1.000000, Obj: 0.405083, No Obj: 0.005326, Avg Recall: 0.681481, count: 135 -Region Avg IOU: 0.593180, Class: 1.000000, Obj: 0.433911, No Obj: 0.004254, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.606033, Class: 1.000000, Obj: 0.434520, No Obj: 0.004128, Avg Recall: 0.734043, count: 94 -Region Avg IOU: 0.524204, Class: 1.000000, Obj: 0.395960, No Obj: 0.003926, Avg Recall: 0.589286, count: 112 -6679: 0.459396, 0.429460 avg, 0.001000 rate, 22.865189 seconds, 1709824 images -Loaded: 0.581876 seconds -Region Avg IOU: 0.560257, Class: 1.000000, Obj: 0.373632, No Obj: 0.004640, Avg Recall: 0.702479, count: 121 -Region Avg IOU: 0.556078, Class: 1.000000, Obj: 0.392353, No Obj: 0.004122, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.589765, Class: 1.000000, Obj: 0.433030, No Obj: 0.004273, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.532016, Class: 1.000000, Obj: 0.412985, No Obj: 0.003768, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.510814, Class: 1.000000, Obj: 0.410426, No Obj: 0.003814, Avg Recall: 0.590164, count: 122 -Region Avg IOU: 0.544167, Class: 1.000000, Obj: 0.445282, No Obj: 0.003625, Avg Recall: 0.607143, count: 84 -Region Avg IOU: 0.502252, Class: 1.000000, Obj: 0.391875, No Obj: 0.003879, Avg Recall: 0.583333, count: 120 -Region Avg IOU: 0.547248, Class: 1.000000, Obj: 0.395139, No Obj: 0.004393, Avg Recall: 0.645161, count: 124 -6680: 0.493346, 0.435848 avg, 0.001000 rate, 21.824677 seconds, 1710080 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.767523 seconds -Region Avg IOU: 0.597785, Class: 1.000000, Obj: 0.407001, No Obj: 0.004143, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.542915, Class: 1.000000, Obj: 0.352834, No Obj: 0.003823, Avg Recall: 0.600000, count: 140 -Region Avg IOU: 0.550420, Class: 1.000000, Obj: 0.387304, No Obj: 0.003710, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.555172, Class: 1.000000, Obj: 0.417702, No Obj: 0.003745, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.535185, Class: 1.000000, Obj: 0.394672, No Obj: 0.004146, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.545706, Class: 1.000000, Obj: 0.400805, No Obj: 0.004217, Avg Recall: 0.570312, count: 128 -Region Avg IOU: 0.579634, Class: 1.000000, Obj: 0.437313, No Obj: 0.003846, Avg Recall: 0.663366, count: 101 -Region Avg IOU: 0.560306, Class: 1.000000, Obj: 0.375674, No Obj: 0.004559, Avg Recall: 0.674603, count: 126 -6681: 0.465326, 0.438796 avg, 0.001000 rate, 20.282812 seconds, 1710336 images -Loaded: 1.402192 seconds -Region Avg IOU: 0.589170, Class: 1.000000, Obj: 0.416221, No Obj: 0.004129, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.589551, Class: 1.000000, Obj: 0.394975, No Obj: 0.004288, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.554755, Class: 1.000000, Obj: 0.403406, No Obj: 0.003254, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.561428, Class: 1.000000, Obj: 0.414535, No Obj: 0.003557, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.550343, Class: 1.000000, Obj: 0.364260, No Obj: 0.003739, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.538850, Class: 1.000000, Obj: 0.369672, No Obj: 0.003506, Avg Recall: 0.630252, count: 119 -Region Avg IOU: 0.581979, Class: 1.000000, Obj: 0.400776, No Obj: 0.004027, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.540725, Class: 1.000000, Obj: 0.400032, No Obj: 0.003476, Avg Recall: 0.586207, count: 87 -6682: 0.394973, 0.434414 avg, 0.001000 rate, 23.180351 seconds, 1710592 images -Loaded: 0.153248 seconds -Region Avg IOU: 0.576389, Class: 1.000000, Obj: 0.346146, No Obj: 0.003831, Avg Recall: 0.627119, count: 118 -Region Avg IOU: 0.613926, Class: 1.000000, Obj: 0.417229, No Obj: 0.003793, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.544458, Class: 1.000000, Obj: 0.358732, No Obj: 0.003686, Avg Recall: 0.570175, count: 114 -Region Avg IOU: 0.563667, Class: 1.000000, Obj: 0.373438, No Obj: 0.003679, Avg Recall: 0.634921, count: 126 -Region Avg IOU: 0.495180, Class: 1.000000, Obj: 0.323843, No Obj: 0.003457, Avg Recall: 0.571429, count: 126 -Region Avg IOU: 0.598939, Class: 1.000000, Obj: 0.432681, No Obj: 0.003589, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.585137, Class: 1.000000, Obj: 0.383986, No Obj: 0.004607, Avg Recall: 0.649635, count: 137 -Region Avg IOU: 0.553126, Class: 1.000000, Obj: 0.378630, No Obj: 0.003460, Avg Recall: 0.612245, count: 98 -6683: 0.460087, 0.436981 avg, 0.001000 rate, 22.609167 seconds, 1710848 images -Loaded: 0.000053 seconds -Region Avg IOU: 0.557610, Class: 1.000000, Obj: 0.327638, No Obj: 0.004033, Avg Recall: 0.661417, count: 127 -Region Avg IOU: 0.574227, Class: 1.000000, Obj: 0.381430, No Obj: 0.003086, Avg Recall: 0.728571, count: 70 -Region Avg IOU: 0.558943, Class: 1.000000, Obj: 0.360434, No Obj: 0.003647, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.574259, Class: 1.000000, Obj: 0.374675, No Obj: 0.003519, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.572062, Class: 1.000000, Obj: 0.383147, No Obj: 0.003794, Avg Recall: 0.669355, count: 124 -Region Avg IOU: 0.611350, Class: 1.000000, Obj: 0.394192, No Obj: 0.003333, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.564662, Class: 1.000000, Obj: 0.390598, No Obj: 0.003415, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.548783, Class: 1.000000, Obj: 0.370038, No Obj: 0.003467, Avg Recall: 0.626168, count: 107 -6684: 0.430581, 0.436341 avg, 0.001000 rate, 22.693253 seconds, 1711104 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.600229, Class: 1.000000, Obj: 0.382779, No Obj: 0.004104, Avg Recall: 0.682540, count: 126 -Region Avg IOU: 0.579004, Class: 1.000000, Obj: 0.354739, No Obj: 0.003849, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.580147, Class: 1.000000, Obj: 0.382353, No Obj: 0.004186, Avg Recall: 0.669355, count: 124 -Region Avg IOU: 0.532006, Class: 1.000000, Obj: 0.367558, No Obj: 0.003321, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.584735, Class: 1.000000, Obj: 0.420094, No Obj: 0.003616, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.574981, Class: 1.000000, Obj: 0.407153, No Obj: 0.003446, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.559693, Class: 1.000000, Obj: 0.371936, No Obj: 0.004192, Avg Recall: 0.626866, count: 134 -Region Avg IOU: 0.547611, Class: 1.000000, Obj: 0.341769, No Obj: 0.003554, Avg Recall: 0.622807, count: 114 -6685: 0.433315, 0.436038 avg, 0.001000 rate, 21.545259 seconds, 1711360 images -Loaded: 1.238009 seconds -Region Avg IOU: 0.593472, Class: 1.000000, Obj: 0.407645, No Obj: 0.003356, Avg Recall: 0.732558, count: 86 -Region Avg IOU: 0.573441, Class: 1.000000, Obj: 0.398742, No Obj: 0.004168, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.586302, Class: 1.000000, Obj: 0.389547, No Obj: 0.003917, Avg Recall: 0.666667, count: 117 -Region Avg IOU: 0.553416, Class: 1.000000, Obj: 0.384808, No Obj: 0.004097, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.481386, Class: 1.000000, Obj: 0.361974, No Obj: 0.002971, Avg Recall: 0.533333, count: 90 -Region Avg IOU: 0.586667, Class: 1.000000, Obj: 0.410630, No Obj: 0.004043, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.554981, Class: 1.000000, Obj: 0.385517, No Obj: 0.004221, Avg Recall: 0.661290, count: 124 -Region Avg IOU: 0.568106, Class: 1.000000, Obj: 0.408775, No Obj: 0.003703, Avg Recall: 0.640000, count: 100 -6686: 0.399097, 0.432344 avg, 0.001000 rate, 22.137873 seconds, 1711616 images -Loaded: 0.056123 seconds -Region Avg IOU: 0.545734, Class: 1.000000, Obj: 0.383890, No Obj: 0.003318, Avg Recall: 0.650000, count: 80 -Region Avg IOU: 0.580053, Class: 1.000000, Obj: 0.386571, No Obj: 0.003655, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.545570, Class: 1.000000, Obj: 0.371363, No Obj: 0.003663, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.559167, Class: 1.000000, Obj: 0.390008, No Obj: 0.003673, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.598561, Class: 1.000000, Obj: 0.398853, No Obj: 0.004056, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.565117, Class: 1.000000, Obj: 0.399473, No Obj: 0.003661, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.584866, Class: 1.000000, Obj: 0.388998, No Obj: 0.003494, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.583479, Class: 1.000000, Obj: 0.399102, No Obj: 0.003792, Avg Recall: 0.679612, count: 103 -6687: 0.400118, 0.429122 avg, 0.001000 rate, 22.993477 seconds, 1711872 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.534583, Class: 1.000000, Obj: 0.383374, No Obj: 0.004246, Avg Recall: 0.602740, count: 146 -Region Avg IOU: 0.550747, Class: 1.000000, Obj: 0.376043, No Obj: 0.004004, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.618526, Class: 1.000000, Obj: 0.411334, No Obj: 0.004325, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.559175, Class: 1.000000, Obj: 0.382278, No Obj: 0.003775, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.566185, Class: 1.000000, Obj: 0.404743, No Obj: 0.003405, Avg Recall: 0.659341, count: 91 -Region Avg IOU: 0.534335, Class: 1.000000, Obj: 0.400546, No Obj: 0.003139, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.569946, Class: 1.000000, Obj: 0.415763, No Obj: 0.003811, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.571137, Class: 1.000000, Obj: 0.410356, No Obj: 0.003703, Avg Recall: 0.666667, count: 105 -6688: 0.442225, 0.430432 avg, 0.001000 rate, 23.044861 seconds, 1712128 images -Loaded: 0.108995 seconds -Region Avg IOU: 0.574549, Class: 1.000000, Obj: 0.405220, No Obj: 0.004042, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.578013, Class: 1.000000, Obj: 0.404354, No Obj: 0.004238, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.567444, Class: 1.000000, Obj: 0.412117, No Obj: 0.004207, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.587164, Class: 1.000000, Obj: 0.408779, No Obj: 0.003932, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.552936, Class: 1.000000, Obj: 0.390037, No Obj: 0.004414, Avg Recall: 0.676923, count: 130 -Region Avg IOU: 0.578412, Class: 1.000000, Obj: 0.402230, No Obj: 0.003668, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.612246, Class: 1.000000, Obj: 0.400637, No Obj: 0.004195, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.589747, Class: 1.000000, Obj: 0.403713, No Obj: 0.004289, Avg Recall: 0.692857, count: 140 -6689: 0.391904, 0.426579 avg, 0.001000 rate, 23.464891 seconds, 1712384 images -Loaded: 0.049068 seconds -Region Avg IOU: 0.577850, Class: 1.000000, Obj: 0.420208, No Obj: 0.004406, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.581893, Class: 1.000000, Obj: 0.453771, No Obj: 0.004332, Avg Recall: 0.696000, count: 125 -Region Avg IOU: 0.553841, Class: 1.000000, Obj: 0.398550, No Obj: 0.004101, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.599719, Class: 1.000000, Obj: 0.452764, No Obj: 0.004267, Avg Recall: 0.724409, count: 127 -Region Avg IOU: 0.551761, Class: 1.000000, Obj: 0.425592, No Obj: 0.003914, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.534341, Class: 1.000000, Obj: 0.396703, No Obj: 0.003756, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.558555, Class: 1.000000, Obj: 0.433007, No Obj: 0.004012, Avg Recall: 0.673267, count: 101 -Region Avg IOU: 0.592127, Class: 1.000000, Obj: 0.379216, No Obj: 0.003277, Avg Recall: 0.704225, count: 71 -6690: 0.388937, 0.422815 avg, 0.001000 rate, 21.374109 seconds, 1712640 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.097252 seconds -Region Avg IOU: 0.480313, Class: 1.000000, Obj: 0.315671, No Obj: 0.004812, Avg Recall: 0.537815, count: 119 -Region Avg IOU: 0.543007, Class: 1.000000, Obj: 0.366711, No Obj: 0.004811, Avg Recall: 0.585106, count: 94 -Region Avg IOU: 0.495529, Class: 1.000000, Obj: 0.330358, No Obj: 0.005190, Avg Recall: 0.584507, count: 142 -Region Avg IOU: 0.525439, Class: 1.000000, Obj: 0.328065, No Obj: 0.004573, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.485788, Class: 1.000000, Obj: 0.342031, No Obj: 0.004628, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.493683, Class: 1.000000, Obj: 0.350159, No Obj: 0.005012, Avg Recall: 0.541985, count: 131 -Region Avg IOU: 0.486115, Class: 1.000000, Obj: 0.368042, No Obj: 0.004237, Avg Recall: 0.550562, count: 89 -Region Avg IOU: 0.522893, Class: 1.000000, Obj: 0.362244, No Obj: 0.004559, Avg Recall: 0.553398, count: 103 -6691: 0.527411, 0.433275 avg, 0.001000 rate, 17.650509 seconds, 1712896 images -Loaded: 3.093418 seconds -Region Avg IOU: 0.468469, Class: 1.000000, Obj: 0.326470, No Obj: 0.005162, Avg Recall: 0.500000, count: 134 -Region Avg IOU: 0.497002, Class: 1.000000, Obj: 0.337240, No Obj: 0.004769, Avg Recall: 0.508333, count: 120 -Region Avg IOU: 0.479355, Class: 1.000000, Obj: 0.318642, No Obj: 0.004449, Avg Recall: 0.553398, count: 103 -Region Avg IOU: 0.475134, Class: 1.000000, Obj: 0.347812, No Obj: 0.004161, Avg Recall: 0.550000, count: 100 -Region Avg IOU: 0.499022, Class: 1.000000, Obj: 0.339579, No Obj: 0.004236, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.535858, Class: 1.000000, Obj: 0.373011, No Obj: 0.004263, Avg Recall: 0.612903, count: 93 -Region Avg IOU: 0.480342, Class: 1.000000, Obj: 0.356693, No Obj: 0.005667, Avg Recall: 0.470588, count: 136 -Region Avg IOU: 0.505978, Class: 1.000000, Obj: 0.339171, No Obj: 0.005336, Avg Recall: 0.600000, count: 120 -6692: 0.453496, 0.435297 avg, 0.001000 rate, 14.880584 seconds, 1713152 images -Loaded: 6.198901 seconds -Region Avg IOU: 0.485723, Class: 1.000000, Obj: 0.353578, No Obj: 0.004936, Avg Recall: 0.512605, count: 119 -Region Avg IOU: 0.506565, Class: 1.000000, Obj: 0.364472, No Obj: 0.005067, Avg Recall: 0.572581, count: 124 -Region Avg IOU: 0.503213, Class: 1.000000, Obj: 0.368938, No Obj: 0.004581, Avg Recall: 0.563218, count: 87 -Region Avg IOU: 0.485085, Class: 1.000000, Obj: 0.361469, No Obj: 0.005397, Avg Recall: 0.520325, count: 123 -Region Avg IOU: 0.474755, Class: 1.000000, Obj: 0.347106, No Obj: 0.004795, Avg Recall: 0.483051, count: 118 -Region Avg IOU: 0.534684, Class: 1.000000, Obj: 0.341913, No Obj: 0.004690, Avg Recall: 0.612500, count: 80 -Region Avg IOU: 0.476251, Class: 1.000000, Obj: 0.365193, No Obj: 0.004495, Avg Recall: 0.523364, count: 107 -Region Avg IOU: 0.494087, Class: 1.000000, Obj: 0.353277, No Obj: 0.004956, Avg Recall: 0.571429, count: 105 -6693: 0.454179, 0.437185 avg, 0.001000 rate, 16.748260 seconds, 1713408 images -Loaded: 3.788539 seconds -Region Avg IOU: 0.488320, Class: 1.000000, Obj: 0.340945, No Obj: 0.004808, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.540175, Class: 1.000000, Obj: 0.421832, No Obj: 0.004345, Avg Recall: 0.693548, count: 62 -Region Avg IOU: 0.500142, Class: 1.000000, Obj: 0.369174, No Obj: 0.005125, Avg Recall: 0.572650, count: 117 -Region Avg IOU: 0.449189, Class: 1.000000, Obj: 0.313188, No Obj: 0.004918, Avg Recall: 0.469565, count: 115 -Region Avg IOU: 0.532650, Class: 1.000000, Obj: 0.372729, No Obj: 0.004258, Avg Recall: 0.636364, count: 88 -Region Avg IOU: 0.516606, Class: 1.000000, Obj: 0.385180, No Obj: 0.005269, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.508241, Class: 1.000000, Obj: 0.393686, No Obj: 0.005751, Avg Recall: 0.537736, count: 106 -Region Avg IOU: 0.540904, Class: 1.000000, Obj: 0.341203, No Obj: 0.005289, Avg Recall: 0.606557, count: 122 -6694: 0.456348, 0.439101 avg, 0.001000 rate, 16.322090 seconds, 1713664 images -Loaded: 3.479411 seconds -Region Avg IOU: 0.478140, Class: 1.000000, Obj: 0.337667, No Obj: 0.004460, Avg Recall: 0.568421, count: 95 -Region Avg IOU: 0.516058, Class: 1.000000, Obj: 0.349909, No Obj: 0.004458, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.532473, Class: 1.000000, Obj: 0.379849, No Obj: 0.005023, Avg Recall: 0.607143, count: 84 -Region Avg IOU: 0.480847, Class: 1.000000, Obj: 0.330817, No Obj: 0.005050, Avg Recall: 0.466019, count: 103 -Region Avg IOU: 0.508742, Class: 1.000000, Obj: 0.380790, No Obj: 0.004902, Avg Recall: 0.515464, count: 97 -Region Avg IOU: 0.474009, Class: 1.000000, Obj: 0.378931, No Obj: 0.004209, Avg Recall: 0.530864, count: 81 -Region Avg IOU: 0.518511, Class: 1.000000, Obj: 0.422263, No Obj: 0.004762, Avg Recall: 0.544444, count: 90 -Region Avg IOU: 0.491759, Class: 1.000000, Obj: 0.415436, No Obj: 0.005017, Avg Recall: 0.522222, count: 90 -6695: 0.424819, 0.437673 avg, 0.001000 rate, 14.890378 seconds, 1713920 images -Loaded: 5.743104 seconds -Region Avg IOU: 0.526639, Class: 1.000000, Obj: 0.409363, No Obj: 0.004135, Avg Recall: 0.600000, count: 80 -Region Avg IOU: 0.500169, Class: 1.000000, Obj: 0.383864, No Obj: 0.005158, Avg Recall: 0.537037, count: 108 -Region Avg IOU: 0.463103, Class: 1.000000, Obj: 0.372381, No Obj: 0.004117, Avg Recall: 0.467391, count: 92 -Region Avg IOU: 0.436524, Class: 1.000000, Obj: 0.302762, No Obj: 0.004873, Avg Recall: 0.480000, count: 125 -Region Avg IOU: 0.514412, Class: 1.000000, Obj: 0.372153, No Obj: 0.004454, Avg Recall: 0.574468, count: 94 -Region Avg IOU: 0.483359, Class: 1.000000, Obj: 0.384075, No Obj: 0.004794, Avg Recall: 0.476190, count: 84 -Region Avg IOU: 0.505344, Class: 1.000000, Obj: 0.368314, No Obj: 0.005268, Avg Recall: 0.542373, count: 118 -Region Avg IOU: 0.440646, Class: 1.000000, Obj: 0.359811, No Obj: 0.004671, Avg Recall: 0.453704, count: 108 -6696: 0.429220, 0.436828 avg, 0.001000 rate, 14.839039 seconds, 1714176 images -Loaded: 5.768163 seconds -Region Avg IOU: 0.478011, Class: 1.000000, Obj: 0.300893, No Obj: 0.005124, Avg Recall: 0.510638, count: 141 -Region Avg IOU: 0.506400, Class: 1.000000, Obj: 0.378849, No Obj: 0.005220, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.453787, Class: 1.000000, Obj: 0.345043, No Obj: 0.005266, Avg Recall: 0.480315, count: 127 -Region Avg IOU: 0.498033, Class: 1.000000, Obj: 0.402376, No Obj: 0.004115, Avg Recall: 0.561798, count: 89 -Region Avg IOU: 0.465078, Class: 1.000000, Obj: 0.340990, No Obj: 0.004892, Avg Recall: 0.481818, count: 110 -Region Avg IOU: 0.501129, Class: 1.000000, Obj: 0.391079, No Obj: 0.005228, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.453900, Class: 1.000000, Obj: 0.363359, No Obj: 0.004138, Avg Recall: 0.481013, count: 79 -Region Avg IOU: 0.488946, Class: 1.000000, Obj: 0.334511, No Obj: 0.004723, Avg Recall: 0.583333, count: 108 -6697: 0.530509, 0.446196 avg, 0.001000 rate, 17.673145 seconds, 1714432 images -Loaded: 2.479579 seconds -Region Avg IOU: 0.421591, Class: 1.000000, Obj: 0.306870, No Obj: 0.004046, Avg Recall: 0.433628, count: 113 -Region Avg IOU: 0.538885, Class: 1.000000, Obj: 0.420088, No Obj: 0.004190, Avg Recall: 0.621622, count: 74 -Region Avg IOU: 0.538995, Class: 1.000000, Obj: 0.372197, No Obj: 0.005059, Avg Recall: 0.610000, count: 100 -Region Avg IOU: 0.505932, Class: 1.000000, Obj: 0.399173, No Obj: 0.004910, Avg Recall: 0.562500, count: 96 -Region Avg IOU: 0.468860, Class: 1.000000, Obj: 0.353297, No Obj: 0.004740, Avg Recall: 0.466019, count: 103 -Region Avg IOU: 0.475567, Class: 1.000000, Obj: 0.385353, No Obj: 0.004836, Avg Recall: 0.521739, count: 115 -Region Avg IOU: 0.471178, Class: 1.000000, Obj: 0.348512, No Obj: 0.005669, Avg Recall: 0.517483, count: 143 -Region Avg IOU: 0.521234, Class: 1.000000, Obj: 0.414533, No Obj: 0.005235, Avg Recall: 0.592233, count: 103 -6698: 0.505042, 0.452080 avg, 0.001000 rate, 15.037764 seconds, 1714688 images -Loaded: 5.137913 seconds -Region Avg IOU: 0.471576, Class: 1.000000, Obj: 0.345887, No Obj: 0.004688, Avg Recall: 0.523810, count: 105 -Region Avg IOU: 0.506980, Class: 1.000000, Obj: 0.356867, No Obj: 0.005937, Avg Recall: 0.562963, count: 135 -Region Avg IOU: 0.488373, Class: 1.000000, Obj: 0.375718, No Obj: 0.004506, Avg Recall: 0.525773, count: 97 -Region Avg IOU: 0.448218, Class: 1.000000, Obj: 0.304823, No Obj: 0.004347, Avg Recall: 0.477064, count: 109 -Region Avg IOU: 0.446842, Class: 1.000000, Obj: 0.375130, No Obj: 0.004300, Avg Recall: 0.482456, count: 114 -Region Avg IOU: 0.467903, Class: 1.000000, Obj: 0.324810, No Obj: 0.005186, Avg Recall: 0.532258, count: 124 -Region Avg IOU: 0.525996, Class: 1.000000, Obj: 0.384035, No Obj: 0.004107, Avg Recall: 0.666667, count: 75 -Region Avg IOU: 0.430127, Class: 1.000000, Obj: 0.358192, No Obj: 0.004581, Avg Recall: 0.410000, count: 100 -6699: 0.528974, 0.459770 avg, 0.001000 rate, 15.144861 seconds, 1714944 images -Loaded: 5.897153 seconds -Region Avg IOU: 0.519413, Class: 1.000000, Obj: 0.372739, No Obj: 0.004172, Avg Recall: 0.582090, count: 67 -Region Avg IOU: 0.520070, Class: 1.000000, Obj: 0.365294, No Obj: 0.004445, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.481308, Class: 1.000000, Obj: 0.355940, No Obj: 0.003908, Avg Recall: 0.562500, count: 80 -Region Avg IOU: 0.479753, Class: 1.000000, Obj: 0.333035, No Obj: 0.005091, Avg Recall: 0.515152, count: 132 -Region Avg IOU: 0.465507, Class: 1.000000, Obj: 0.370422, No Obj: 0.004190, Avg Recall: 0.513158, count: 76 -Region Avg IOU: 0.473814, Class: 1.000000, Obj: 0.339069, No Obj: 0.004897, Avg Recall: 0.483607, count: 122 -Region Avg IOU: 0.517681, Class: 1.000000, Obj: 0.360058, No Obj: 0.005541, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.537810, Class: 1.000000, Obj: 0.355447, No Obj: 0.005165, Avg Recall: 0.594340, count: 106 -6700: 0.424482, 0.456241 avg, 0.001000 rate, 15.294640 seconds, 1715200 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000067 seconds -Region Avg IOU: 0.666852, Class: 1.000000, Obj: 0.543543, No Obj: 0.004483, Avg Recall: 0.849558, count: 113 -Region Avg IOU: 0.634068, Class: 1.000000, Obj: 0.465601, No Obj: 0.003689, Avg Recall: 0.730337, count: 89 -Region Avg IOU: 0.624301, Class: 1.000000, Obj: 0.448459, No Obj: 0.004396, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.606178, Class: 1.000000, Obj: 0.456421, No Obj: 0.002850, Avg Recall: 0.729412, count: 85 -Region Avg IOU: 0.598138, Class: 1.000000, Obj: 0.464703, No Obj: 0.003737, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.634090, Class: 1.000000, Obj: 0.501798, No Obj: 0.004122, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.635796, Class: 1.000000, Obj: 0.479706, No Obj: 0.003353, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.673729, Class: 1.000000, Obj: 0.444931, No Obj: 0.004136, Avg Recall: 0.833333, count: 120 -6701: 0.391492, 0.449766 avg, 0.001000 rate, 24.825727 seconds, 1715456 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.631550, Class: 1.000000, Obj: 0.465831, No Obj: 0.004131, Avg Recall: 0.798611, count: 144 -Region Avg IOU: 0.657690, Class: 1.000000, Obj: 0.505557, No Obj: 0.004144, Avg Recall: 0.819149, count: 94 -Region Avg IOU: 0.647505, Class: 1.000000, Obj: 0.543554, No Obj: 0.003857, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.600397, Class: 1.000000, Obj: 0.432880, No Obj: 0.003489, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.657584, Class: 1.000000, Obj: 0.500845, No Obj: 0.003536, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.640175, Class: 1.000000, Obj: 0.463916, No Obj: 0.004060, Avg Recall: 0.759690, count: 129 -Region Avg IOU: 0.625874, Class: 1.000000, Obj: 0.439572, No Obj: 0.003451, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.578382, Class: 1.000000, Obj: 0.434017, No Obj: 0.003847, Avg Recall: 0.698529, count: 136 -6702: 0.438313, 0.448621 avg, 0.001000 rate, 26.796480 seconds, 1715712 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.672377, Class: 1.000000, Obj: 0.467297, No Obj: 0.004133, Avg Recall: 0.825000, count: 120 -Region Avg IOU: 0.597010, Class: 1.000000, Obj: 0.454142, No Obj: 0.003680, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.595333, Class: 1.000000, Obj: 0.427950, No Obj: 0.002792, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.639552, Class: 1.000000, Obj: 0.460669, No Obj: 0.003980, Avg Recall: 0.786260, count: 131 -Region Avg IOU: 0.611597, Class: 1.000000, Obj: 0.410712, No Obj: 0.003720, Avg Recall: 0.740458, count: 131 -Region Avg IOU: 0.604719, Class: 1.000000, Obj: 0.445262, No Obj: 0.003261, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.639887, Class: 1.000000, Obj: 0.494287, No Obj: 0.003491, Avg Recall: 0.791209, count: 91 -Region Avg IOU: 0.612002, Class: 1.000000, Obj: 0.464539, No Obj: 0.004095, Avg Recall: 0.717949, count: 117 -6703: 0.432370, 0.446996 avg, 0.001000 rate, 26.438112 seconds, 1715968 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.638889, Class: 1.000000, Obj: 0.447841, No Obj: 0.003136, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.666762, Class: 1.000000, Obj: 0.461538, No Obj: 0.003427, Avg Recall: 0.836735, count: 98 -Region Avg IOU: 0.675492, Class: 1.000000, Obj: 0.464011, No Obj: 0.002906, Avg Recall: 0.802469, count: 81 -Region Avg IOU: 0.653455, Class: 1.000000, Obj: 0.468540, No Obj: 0.003080, Avg Recall: 0.831461, count: 89 -Region Avg IOU: 0.680472, Class: 1.000000, Obj: 0.499544, No Obj: 0.004003, Avg Recall: 0.834646, count: 127 -Region Avg IOU: 0.677024, Class: 1.000000, Obj: 0.463268, No Obj: 0.003390, Avg Recall: 0.843750, count: 96 -Region Avg IOU: 0.668803, Class: 1.000000, Obj: 0.446919, No Obj: 0.003380, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.644086, Class: 1.000000, Obj: 0.433210, No Obj: 0.002884, Avg Recall: 0.777778, count: 90 -6704: 0.381588, 0.440455 avg, 0.001000 rate, 24.194258 seconds, 1716224 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.651142, Class: 1.000000, Obj: 0.395431, No Obj: 0.002688, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.622495, Class: 1.000000, Obj: 0.411207, No Obj: 0.003068, Avg Recall: 0.701613, count: 124 -Region Avg IOU: 0.608434, Class: 1.000000, Obj: 0.420949, No Obj: 0.002841, Avg Recall: 0.744444, count: 90 -Region Avg IOU: 0.650629, Class: 1.000000, Obj: 0.404492, No Obj: 0.002743, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.674361, Class: 1.000000, Obj: 0.425538, No Obj: 0.003182, Avg Recall: 0.833333, count: 96 -Region Avg IOU: 0.644066, Class: 1.000000, Obj: 0.407513, No Obj: 0.003055, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.617510, Class: 1.000000, Obj: 0.411638, No Obj: 0.002581, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.623401, Class: 1.000000, Obj: 0.448171, No Obj: 0.002686, Avg Recall: 0.750000, count: 104 -6705: 0.393630, 0.435772 avg, 0.001000 rate, 25.566893 seconds, 1716480 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.593110, Class: 1.000000, Obj: 0.401169, No Obj: 0.002660, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.639188, Class: 1.000000, Obj: 0.420137, No Obj: 0.002667, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.618846, Class: 1.000000, Obj: 0.360816, No Obj: 0.002585, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.630526, Class: 1.000000, Obj: 0.386427, No Obj: 0.002806, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.650461, Class: 1.000000, Obj: 0.427484, No Obj: 0.003294, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.595285, Class: 1.000000, Obj: 0.369099, No Obj: 0.002923, Avg Recall: 0.688000, count: 125 -Region Avg IOU: 0.636172, Class: 1.000000, Obj: 0.382673, No Obj: 0.003055, Avg Recall: 0.769231, count: 117 -Region Avg IOU: 0.651943, Class: 1.000000, Obj: 0.419023, No Obj: 0.003141, Avg Recall: 0.800000, count: 110 -6706: 0.425213, 0.434717 avg, 0.001000 rate, 25.404997 seconds, 1716736 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.663981, Class: 1.000000, Obj: 0.354632, No Obj: 0.002903, Avg Recall: 0.837209, count: 86 -Region Avg IOU: 0.650180, Class: 1.000000, Obj: 0.411752, No Obj: 0.002833, Avg Recall: 0.796117, count: 103 -Region Avg IOU: 0.623632, Class: 1.000000, Obj: 0.370433, No Obj: 0.002901, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.595754, Class: 1.000000, Obj: 0.375650, No Obj: 0.002445, Avg Recall: 0.706767, count: 133 -Region Avg IOU: 0.636848, Class: 1.000000, Obj: 0.430277, No Obj: 0.002452, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.623276, Class: 1.000000, Obj: 0.344688, No Obj: 0.002774, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.656100, Class: 1.000000, Obj: 0.359995, No Obj: 0.002568, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.622529, Class: 1.000000, Obj: 0.400148, No Obj: 0.002911, Avg Recall: 0.711538, count: 104 -6707: 0.428203, 0.434065 avg, 0.001000 rate, 25.452772 seconds, 1716992 images -Loaded: 0.000130 seconds -Region Avg IOU: 0.624180, Class: 1.000000, Obj: 0.405316, No Obj: 0.003116, Avg Recall: 0.793651, count: 126 -Region Avg IOU: 0.665499, Class: 1.000000, Obj: 0.448787, No Obj: 0.002618, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.543056, Class: 1.000000, Obj: 0.347573, No Obj: 0.002753, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.639725, Class: 1.000000, Obj: 0.400694, No Obj: 0.003029, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.613469, Class: 1.000000, Obj: 0.399985, No Obj: 0.002402, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.624728, Class: 1.000000, Obj: 0.391060, No Obj: 0.002885, Avg Recall: 0.771186, count: 118 -Region Avg IOU: 0.652681, Class: 1.000000, Obj: 0.422581, No Obj: 0.002622, Avg Recall: 0.822222, count: 90 -Region Avg IOU: 0.613102, Class: 1.000000, Obj: 0.397241, No Obj: 0.002403, Avg Recall: 0.741379, count: 116 -6708: 0.415824, 0.432241 avg, 0.001000 rate, 24.980976 seconds, 1717248 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.633425, Class: 1.000000, Obj: 0.372897, No Obj: 0.002818, Avg Recall: 0.795082, count: 122 -Region Avg IOU: 0.618063, Class: 1.000000, Obj: 0.393451, No Obj: 0.002615, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.651638, Class: 1.000000, Obj: 0.354468, No Obj: 0.002578, Avg Recall: 0.827586, count: 87 -Region Avg IOU: 0.631288, Class: 1.000000, Obj: 0.368971, No Obj: 0.002997, Avg Recall: 0.771186, count: 118 -Region Avg IOU: 0.598898, Class: 1.000000, Obj: 0.349210, No Obj: 0.002919, Avg Recall: 0.688525, count: 122 -Region Avg IOU: 0.595088, Class: 1.000000, Obj: 0.405817, No Obj: 0.002633, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.607400, Class: 1.000000, Obj: 0.361167, No Obj: 0.002334, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.629784, Class: 1.000000, Obj: 0.382859, No Obj: 0.002617, Avg Recall: 0.752475, count: 101 -6709: 0.431589, 0.432176 avg, 0.001000 rate, 25.562904 seconds, 1717504 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.671810, Class: 1.000000, Obj: 0.392189, No Obj: 0.002745, Avg Recall: 0.824074, count: 108 -Region Avg IOU: 0.638882, Class: 1.000000, Obj: 0.413646, No Obj: 0.003392, Avg Recall: 0.740741, count: 135 -Region Avg IOU: 0.632097, Class: 1.000000, Obj: 0.376268, No Obj: 0.002795, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.627639, Class: 1.000000, Obj: 0.398160, No Obj: 0.002900, Avg Recall: 0.759690, count: 129 -Region Avg IOU: 0.656631, Class: 1.000000, Obj: 0.442420, No Obj: 0.002564, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.638386, Class: 1.000000, Obj: 0.406983, No Obj: 0.003237, Avg Recall: 0.746032, count: 126 -Region Avg IOU: 0.650530, Class: 1.000000, Obj: 0.427593, No Obj: 0.002454, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.627223, Class: 1.000000, Obj: 0.435931, No Obj: 0.002224, Avg Recall: 0.755319, count: 94 -6710: 0.407700, 0.429728 avg, 0.001000 rate, 26.182278 seconds, 1717760 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 20.274464 seconds -Region Avg IOU: 0.614192, Class: 1.000000, Obj: 0.421195, No Obj: 0.002791, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.665765, Class: 1.000000, Obj: 0.407029, No Obj: 0.003050, Avg Recall: 0.818182, count: 132 -Region Avg IOU: 0.651046, Class: 1.000000, Obj: 0.471811, No Obj: 0.002970, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.631829, Class: 1.000000, Obj: 0.444360, No Obj: 0.002802, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.585110, Class: 1.000000, Obj: 0.394007, No Obj: 0.002402, Avg Recall: 0.666667, count: 117 -Region Avg IOU: 0.638921, Class: 1.000000, Obj: 0.435336, No Obj: 0.003330, Avg Recall: 0.790323, count: 124 -Region Avg IOU: 0.604285, Class: 1.000000, Obj: 0.381205, No Obj: 0.002860, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.604843, Class: 1.000000, Obj: 0.363131, No Obj: 0.002401, Avg Recall: 0.673267, count: 101 -6711: 0.409434, 0.427699 avg, 0.001000 rate, 26.083384 seconds, 1718016 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.634854, Class: 1.000000, Obj: 0.458068, No Obj: 0.003125, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.611624, Class: 1.000000, Obj: 0.458474, No Obj: 0.002525, Avg Recall: 0.723684, count: 76 -Region Avg IOU: 0.651093, Class: 1.000000, Obj: 0.473783, No Obj: 0.002926, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.575617, Class: 1.000000, Obj: 0.409807, No Obj: 0.003414, Avg Recall: 0.669065, count: 139 -Region Avg IOU: 0.608879, Class: 1.000000, Obj: 0.407898, No Obj: 0.003178, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.607976, Class: 1.000000, Obj: 0.374293, No Obj: 0.003125, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.637455, Class: 1.000000, Obj: 0.442439, No Obj: 0.003059, Avg Recall: 0.788136, count: 118 -Region Avg IOU: 0.628303, Class: 1.000000, Obj: 0.448021, No Obj: 0.002689, Avg Recall: 0.747748, count: 111 -6712: 0.407405, 0.425669 avg, 0.001000 rate, 25.146202 seconds, 1718272 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.595640, Class: 1.000000, Obj: 0.451373, No Obj: 0.002746, Avg Recall: 0.670732, count: 82 -Region Avg IOU: 0.629737, Class: 1.000000, Obj: 0.466627, No Obj: 0.003165, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.647402, Class: 1.000000, Obj: 0.516288, No Obj: 0.003033, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.554432, Class: 1.000000, Obj: 0.409176, No Obj: 0.002456, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.606799, Class: 1.000000, Obj: 0.463372, No Obj: 0.002673, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.632098, Class: 1.000000, Obj: 0.450971, No Obj: 0.003072, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.659750, Class: 1.000000, Obj: 0.433591, No Obj: 0.003221, Avg Recall: 0.796875, count: 128 -Region Avg IOU: 0.622861, Class: 1.000000, Obj: 0.353218, No Obj: 0.003159, Avg Recall: 0.733333, count: 135 -6713: 0.401238, 0.423226 avg, 0.001000 rate, 25.195333 seconds, 1718528 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.649630, Class: 1.000000, Obj: 0.523478, No Obj: 0.002909, Avg Recall: 0.785714, count: 84 -Region Avg IOU: 0.623564, Class: 1.000000, Obj: 0.433377, No Obj: 0.003009, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.653377, Class: 1.000000, Obj: 0.434206, No Obj: 0.003636, Avg Recall: 0.822034, count: 118 -Region Avg IOU: 0.620840, Class: 1.000000, Obj: 0.440304, No Obj: 0.003755, Avg Recall: 0.774194, count: 124 -Region Avg IOU: 0.624404, Class: 1.000000, Obj: 0.438402, No Obj: 0.003160, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.648210, Class: 1.000000, Obj: 0.477156, No Obj: 0.003320, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.671297, Class: 1.000000, Obj: 0.488421, No Obj: 0.002735, Avg Recall: 0.841463, count: 82 -Region Avg IOU: 0.591477, Class: 1.000000, Obj: 0.384540, No Obj: 0.003129, Avg Recall: 0.730769, count: 104 -6714: 0.394811, 0.420385 avg, 0.001000 rate, 26.812544 seconds, 1718784 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.619361, Class: 1.000000, Obj: 0.455306, No Obj: 0.003354, Avg Recall: 0.744000, count: 125 -Region Avg IOU: 0.592435, Class: 1.000000, Obj: 0.450156, No Obj: 0.003736, Avg Recall: 0.697183, count: 142 -Region Avg IOU: 0.630429, Class: 1.000000, Obj: 0.476458, No Obj: 0.002658, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.568235, Class: 1.000000, Obj: 0.441548, No Obj: 0.003317, Avg Recall: 0.671756, count: 131 -Region Avg IOU: 0.674309, Class: 1.000000, Obj: 0.460967, No Obj: 0.003290, Avg Recall: 0.814159, count: 113 -Region Avg IOU: 0.588239, Class: 1.000000, Obj: 0.429822, No Obj: 0.002775, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.575263, Class: 1.000000, Obj: 0.422377, No Obj: 0.003289, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.653154, Class: 1.000000, Obj: 0.478559, No Obj: 0.002988, Avg Recall: 0.804878, count: 82 -6715: 0.409824, 0.419329 avg, 0.001000 rate, 26.187063 seconds, 1719040 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.622604, Class: 1.000000, Obj: 0.431408, No Obj: 0.003163, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.630463, Class: 1.000000, Obj: 0.480371, No Obj: 0.003428, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.661888, Class: 1.000000, Obj: 0.467947, No Obj: 0.003285, Avg Recall: 0.843137, count: 102 -Region Avg IOU: 0.625825, Class: 1.000000, Obj: 0.373982, No Obj: 0.003932, Avg Recall: 0.729730, count: 148 -Region Avg IOU: 0.634970, Class: 1.000000, Obj: 0.446627, No Obj: 0.003303, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.591422, Class: 1.000000, Obj: 0.436569, No Obj: 0.003537, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.649294, Class: 1.000000, Obj: 0.427307, No Obj: 0.003337, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.611797, Class: 1.000000, Obj: 0.431132, No Obj: 0.003371, Avg Recall: 0.725191, count: 131 -6716: 0.452289, 0.422625 avg, 0.001000 rate, 27.420668 seconds, 1719296 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.643806, Class: 1.000000, Obj: 0.469663, No Obj: 0.003212, Avg Recall: 0.814433, count: 97 -Region Avg IOU: 0.652675, Class: 1.000000, Obj: 0.482918, No Obj: 0.003823, Avg Recall: 0.800000, count: 125 -Region Avg IOU: 0.625705, Class: 1.000000, Obj: 0.440330, No Obj: 0.003412, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.579626, Class: 1.000000, Obj: 0.438924, No Obj: 0.003515, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.625788, Class: 1.000000, Obj: 0.443837, No Obj: 0.003175, Avg Recall: 0.772727, count: 88 -Region Avg IOU: 0.635833, Class: 1.000000, Obj: 0.433206, No Obj: 0.003891, Avg Recall: 0.777778, count: 135 -Region Avg IOU: 0.638977, Class: 1.000000, Obj: 0.477563, No Obj: 0.003527, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.613650, Class: 1.000000, Obj: 0.433469, No Obj: 0.003396, Avg Recall: 0.770492, count: 122 -6717: 0.412836, 0.421646 avg, 0.001000 rate, 25.700272 seconds, 1719552 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.595704, Class: 1.000000, Obj: 0.399631, No Obj: 0.002990, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.619395, Class: 1.000000, Obj: 0.419119, No Obj: 0.003176, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.590337, Class: 1.000000, Obj: 0.487548, No Obj: 0.002485, Avg Recall: 0.666667, count: 81 -Region Avg IOU: 0.680658, Class: 1.000000, Obj: 0.475247, No Obj: 0.003018, Avg Recall: 0.870968, count: 93 -Region Avg IOU: 0.606685, Class: 1.000000, Obj: 0.452789, No Obj: 0.003357, Avg Recall: 0.735537, count: 121 -Region Avg IOU: 0.632453, Class: 1.000000, Obj: 0.400089, No Obj: 0.003595, Avg Recall: 0.744526, count: 137 -Region Avg IOU: 0.636952, Class: 1.000000, Obj: 0.434579, No Obj: 0.003748, Avg Recall: 0.762712, count: 118 -Region Avg IOU: 0.564094, Class: 1.000000, Obj: 0.405743, No Obj: 0.002892, Avg Recall: 0.666667, count: 123 -6718: 0.467810, 0.426262 avg, 0.001000 rate, 25.581804 seconds, 1719808 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.556505, Class: 1.000000, Obj: 0.413899, No Obj: 0.002793, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.671827, Class: 1.000000, Obj: 0.450471, No Obj: 0.003376, Avg Recall: 0.822034, count: 118 -Region Avg IOU: 0.599810, Class: 1.000000, Obj: 0.397920, No Obj: 0.002894, Avg Recall: 0.721154, count: 104 -Region Avg IOU: 0.611500, Class: 1.000000, Obj: 0.423756, No Obj: 0.002948, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.587778, Class: 1.000000, Obj: 0.387921, No Obj: 0.003211, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.646940, Class: 1.000000, Obj: 0.457302, No Obj: 0.003004, Avg Recall: 0.800000, count: 130 -Region Avg IOU: 0.653089, Class: 1.000000, Obj: 0.434500, No Obj: 0.002781, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.609138, Class: 1.000000, Obj: 0.396507, No Obj: 0.003003, Avg Recall: 0.704762, count: 105 -6719: 0.420191, 0.425655 avg, 0.001000 rate, 27.888512 seconds, 1720064 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.633270, Class: 1.000000, Obj: 0.413068, No Obj: 0.003696, Avg Recall: 0.756757, count: 148 -Region Avg IOU: 0.602398, Class: 1.000000, Obj: 0.387584, No Obj: 0.003133, Avg Recall: 0.720000, count: 125 -Region Avg IOU: 0.614416, Class: 1.000000, Obj: 0.423244, No Obj: 0.002883, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.606508, Class: 1.000000, Obj: 0.434776, No Obj: 0.003048, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.608203, Class: 1.000000, Obj: 0.392996, No Obj: 0.003317, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.627820, Class: 1.000000, Obj: 0.424254, No Obj: 0.003007, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.629470, Class: 1.000000, Obj: 0.410649, No Obj: 0.003085, Avg Recall: 0.756303, count: 119 -Region Avg IOU: 0.614416, Class: 1.000000, Obj: 0.419973, No Obj: 0.003121, Avg Recall: 0.707547, count: 106 -6720: 0.473894, 0.430479 avg, 0.001000 rate, 25.707836 seconds, 1720320 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000068 seconds -Region Avg IOU: 0.658373, Class: 1.000000, Obj: 0.390354, No Obj: 0.002750, Avg Recall: 0.824561, count: 114 -Region Avg IOU: 0.627362, Class: 1.000000, Obj: 0.431558, No Obj: 0.002388, Avg Recall: 0.705882, count: 85 -Region Avg IOU: 0.629166, Class: 1.000000, Obj: 0.411582, No Obj: 0.002589, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.654494, Class: 1.000000, Obj: 0.438204, No Obj: 0.002872, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.628919, Class: 1.000000, Obj: 0.431629, No Obj: 0.002969, Avg Recall: 0.788321, count: 137 -Region Avg IOU: 0.637602, Class: 1.000000, Obj: 0.430978, No Obj: 0.002990, Avg Recall: 0.766917, count: 133 -Region Avg IOU: 0.639369, Class: 1.000000, Obj: 0.380425, No Obj: 0.002188, Avg Recall: 0.792683, count: 82 -Region Avg IOU: 0.638335, Class: 1.000000, Obj: 0.418896, No Obj: 0.002682, Avg Recall: 0.784000, count: 125 -6721: 0.447060, 0.432137 avg, 0.001000 rate, 28.429274 seconds, 1720576 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.636113, Class: 1.000000, Obj: 0.391773, No Obj: 0.002685, Avg Recall: 0.792000, count: 125 -Region Avg IOU: 0.661462, Class: 1.000000, Obj: 0.400892, No Obj: 0.002696, Avg Recall: 0.837607, count: 117 -Region Avg IOU: 0.644546, Class: 1.000000, Obj: 0.431817, No Obj: 0.002405, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.672561, Class: 1.000000, Obj: 0.413544, No Obj: 0.002681, Avg Recall: 0.814433, count: 97 -Region Avg IOU: 0.651068, Class: 1.000000, Obj: 0.398288, No Obj: 0.002746, Avg Recall: 0.788618, count: 123 -Region Avg IOU: 0.629241, Class: 1.000000, Obj: 0.425151, No Obj: 0.002831, Avg Recall: 0.777778, count: 126 -Region Avg IOU: 0.662182, Class: 1.000000, Obj: 0.454406, No Obj: 0.002587, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.660534, Class: 1.000000, Obj: 0.431648, No Obj: 0.003111, Avg Recall: 0.851562, count: 128 -6722: 0.440502, 0.432974 avg, 0.001000 rate, 28.916704 seconds, 1720832 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.690545, Class: 1.000000, Obj: 0.402964, No Obj: 0.002785, Avg Recall: 0.837838, count: 111 -Region Avg IOU: 0.658726, Class: 1.000000, Obj: 0.410219, No Obj: 0.002584, Avg Recall: 0.787402, count: 127 -Region Avg IOU: 0.673128, Class: 1.000000, Obj: 0.414944, No Obj: 0.002207, Avg Recall: 0.819277, count: 83 -Region Avg IOU: 0.658467, Class: 1.000000, Obj: 0.460030, No Obj: 0.002914, Avg Recall: 0.806723, count: 119 -Region Avg IOU: 0.668030, Class: 1.000000, Obj: 0.392097, No Obj: 0.002620, Avg Recall: 0.835165, count: 91 -Region Avg IOU: 0.664638, Class: 1.000000, Obj: 0.506421, No Obj: 0.002673, Avg Recall: 0.829787, count: 94 -Region Avg IOU: 0.634756, Class: 1.000000, Obj: 0.350592, No Obj: 0.002506, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.654060, Class: 1.000000, Obj: 0.404471, No Obj: 0.002611, Avg Recall: 0.810811, count: 111 -6723: 0.422437, 0.431920 avg, 0.001000 rate, 26.629559 seconds, 1721088 images -Loaded: 0.000115 seconds -Region Avg IOU: 0.686478, Class: 1.000000, Obj: 0.403416, No Obj: 0.002751, Avg Recall: 0.865385, count: 104 -Region Avg IOU: 0.720053, Class: 1.000000, Obj: 0.484504, No Obj: 0.002228, Avg Recall: 0.944444, count: 90 -Region Avg IOU: 0.659116, Class: 1.000000, Obj: 0.468154, No Obj: 0.002904, Avg Recall: 0.825397, count: 126 -Region Avg IOU: 0.565636, Class: 1.000000, Obj: 0.370986, No Obj: 0.002214, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.585413, Class: 1.000000, Obj: 0.342889, No Obj: 0.002684, Avg Recall: 0.709677, count: 124 -Region Avg IOU: 0.654919, Class: 1.000000, Obj: 0.468476, No Obj: 0.002228, Avg Recall: 0.827160, count: 81 -Region Avg IOU: 0.639522, Class: 1.000000, Obj: 0.432074, No Obj: 0.002636, Avg Recall: 0.756098, count: 123 -Region Avg IOU: 0.671388, Class: 1.000000, Obj: 0.460846, No Obj: 0.002863, Avg Recall: 0.845528, count: 123 -6724: 0.419265, 0.430654 avg, 0.001000 rate, 27.364704 seconds, 1721344 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.688767, Class: 1.000000, Obj: 0.448544, No Obj: 0.002643, Avg Recall: 0.838384, count: 99 -Region Avg IOU: 0.638054, Class: 1.000000, Obj: 0.463501, No Obj: 0.002719, Avg Recall: 0.777778, count: 126 -Region Avg IOU: 0.633532, Class: 1.000000, Obj: 0.436143, No Obj: 0.002363, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.685129, Class: 1.000000, Obj: 0.416430, No Obj: 0.002669, Avg Recall: 0.881818, count: 110 -Region Avg IOU: 0.631553, Class: 1.000000, Obj: 0.427780, No Obj: 0.002343, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.660789, Class: 1.000000, Obj: 0.364613, No Obj: 0.002952, Avg Recall: 0.820690, count: 145 -Region Avg IOU: 0.642358, Class: 1.000000, Obj: 0.356566, No Obj: 0.002845, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.650225, Class: 1.000000, Obj: 0.467972, No Obj: 0.002287, Avg Recall: 0.813953, count: 86 -6725: 0.439259, 0.431515 avg, 0.001000 rate, 28.749207 seconds, 1721600 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.677453, Class: 1.000000, Obj: 0.443218, No Obj: 0.002486, Avg Recall: 0.881818, count: 110 -Region Avg IOU: 0.674035, Class: 1.000000, Obj: 0.431221, No Obj: 0.002669, Avg Recall: 0.831461, count: 89 -Region Avg IOU: 0.663640, Class: 1.000000, Obj: 0.375279, No Obj: 0.003133, Avg Recall: 0.834483, count: 145 -Region Avg IOU: 0.653731, Class: 1.000000, Obj: 0.409348, No Obj: 0.002490, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.661207, Class: 1.000000, Obj: 0.356753, No Obj: 0.002880, Avg Recall: 0.771654, count: 127 -Region Avg IOU: 0.663714, Class: 1.000000, Obj: 0.415808, No Obj: 0.002569, Avg Recall: 0.834711, count: 121 -Region Avg IOU: 0.690687, Class: 1.000000, Obj: 0.418816, No Obj: 0.002703, Avg Recall: 0.892157, count: 102 -Region Avg IOU: 0.618201, Class: 1.000000, Obj: 0.405176, No Obj: 0.002784, Avg Recall: 0.750000, count: 116 -6726: 0.435993, 0.431963 avg, 0.001000 rate, 27.214199 seconds, 1721856 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.668367, Class: 1.000000, Obj: 0.394443, No Obj: 0.002653, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.647004, Class: 1.000000, Obj: 0.363311, No Obj: 0.002483, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.672811, Class: 1.000000, Obj: 0.395197, No Obj: 0.002267, Avg Recall: 0.851852, count: 81 -Region Avg IOU: 0.669288, Class: 1.000000, Obj: 0.397921, No Obj: 0.002536, Avg Recall: 0.813953, count: 86 -Region Avg IOU: 0.644547, Class: 1.000000, Obj: 0.437629, No Obj: 0.002570, Avg Recall: 0.793388, count: 121 -Region Avg IOU: 0.654355, Class: 1.000000, Obj: 0.442512, No Obj: 0.002491, Avg Recall: 0.813953, count: 86 -Region Avg IOU: 0.677461, Class: 1.000000, Obj: 0.415018, No Obj: 0.002133, Avg Recall: 0.878049, count: 82 -Region Avg IOU: 0.642791, Class: 1.000000, Obj: 0.444224, No Obj: 0.002862, Avg Recall: 0.758929, count: 112 -6727: 0.419872, 0.430754 avg, 0.001000 rate, 29.056084 seconds, 1722112 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.682433, Class: 1.000000, Obj: 0.443616, No Obj: 0.002486, Avg Recall: 0.865546, count: 119 -Region Avg IOU: 0.645664, Class: 1.000000, Obj: 0.396919, No Obj: 0.002569, Avg Recall: 0.800000, count: 120 -Region Avg IOU: 0.693373, Class: 1.000000, Obj: 0.396389, No Obj: 0.002358, Avg Recall: 0.826923, count: 104 -Region Avg IOU: 0.691050, Class: 1.000000, Obj: 0.448424, No Obj: 0.002370, Avg Recall: 0.858696, count: 92 -Region Avg IOU: 0.648349, Class: 1.000000, Obj: 0.445041, No Obj: 0.002066, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.618784, Class: 1.000000, Obj: 0.425456, No Obj: 0.002346, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.636260, Class: 1.000000, Obj: 0.420530, No Obj: 0.002282, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.671763, Class: 1.000000, Obj: 0.444436, No Obj: 0.002754, Avg Recall: 0.789916, count: 119 -6728: 0.393623, 0.427040 avg, 0.001000 rate, 28.447884 seconds, 1722368 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.649998, Class: 1.000000, Obj: 0.443593, No Obj: 0.002434, Avg Recall: 0.800000, count: 115 -Region Avg IOU: 0.659425, Class: 1.000000, Obj: 0.384302, No Obj: 0.002659, Avg Recall: 0.808824, count: 136 -Region Avg IOU: 0.696822, Class: 1.000000, Obj: 0.426785, No Obj: 0.002778, Avg Recall: 0.855556, count: 90 -Region Avg IOU: 0.670923, Class: 1.000000, Obj: 0.409335, No Obj: 0.002944, Avg Recall: 0.790698, count: 129 -Region Avg IOU: 0.690693, Class: 1.000000, Obj: 0.445274, No Obj: 0.002473, Avg Recall: 0.854545, count: 110 -Region Avg IOU: 0.648593, Class: 1.000000, Obj: 0.381726, No Obj: 0.002714, Avg Recall: 0.830357, count: 112 -Region Avg IOU: 0.684615, Class: 1.000000, Obj: 0.416889, No Obj: 0.002539, Avg Recall: 0.826531, count: 98 -Region Avg IOU: 0.608500, Class: 1.000000, Obj: 0.373015, No Obj: 0.002509, Avg Recall: 0.727273, count: 99 -6729: 0.460761, 0.430413 avg, 0.001000 rate, 29.015512 seconds, 1722624 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.646372, Class: 1.000000, Obj: 0.405927, No Obj: 0.002541, Avg Recall: 0.808696, count: 115 -Region Avg IOU: 0.650167, Class: 1.000000, Obj: 0.410508, No Obj: 0.002465, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.640499, Class: 1.000000, Obj: 0.426805, No Obj: 0.002364, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.663342, Class: 1.000000, Obj: 0.417051, No Obj: 0.002740, Avg Recall: 0.844037, count: 109 -Region Avg IOU: 0.642322, Class: 1.000000, Obj: 0.362024, No Obj: 0.002943, Avg Recall: 0.789062, count: 128 -Region Avg IOU: 0.630876, Class: 1.000000, Obj: 0.412639, No Obj: 0.002655, Avg Recall: 0.755725, count: 131 -Region Avg IOU: 0.651641, Class: 1.000000, Obj: 0.416766, No Obj: 0.002436, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.670580, Class: 1.000000, Obj: 0.413430, No Obj: 0.003265, Avg Recall: 0.843537, count: 147 -6730: 0.446827, 0.432054 avg, 0.001000 rate, 28.742895 seconds, 1722880 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000073 seconds -Region Avg IOU: 0.667556, Class: 1.000000, Obj: 0.408685, No Obj: 0.002559, Avg Recall: 0.809091, count: 110 -Region Avg IOU: 0.724068, Class: 1.000000, Obj: 0.510733, No Obj: 0.002516, Avg Recall: 0.933333, count: 90 -Region Avg IOU: 0.649446, Class: 1.000000, Obj: 0.414319, No Obj: 0.002536, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.666260, Class: 1.000000, Obj: 0.420939, No Obj: 0.002581, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.658633, Class: 1.000000, Obj: 0.423292, No Obj: 0.002979, Avg Recall: 0.812950, count: 139 -Region Avg IOU: 0.708808, Class: 1.000000, Obj: 0.468593, No Obj: 0.002545, Avg Recall: 0.917526, count: 97 -Region Avg IOU: 0.701569, Class: 1.000000, Obj: 0.438070, No Obj: 0.002371, Avg Recall: 0.872727, count: 110 -Region Avg IOU: 0.674595, Class: 1.000000, Obj: 0.431088, No Obj: 0.002379, Avg Recall: 0.810811, count: 74 -6731: 0.400269, 0.428876 avg, 0.001000 rate, 27.866184 seconds, 1723136 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.631189, Class: 1.000000, Obj: 0.372638, No Obj: 0.002970, Avg Recall: 0.771429, count: 140 -Region Avg IOU: 0.671296, Class: 1.000000, Obj: 0.420239, No Obj: 0.002141, Avg Recall: 0.817204, count: 93 -Region Avg IOU: 0.669168, Class: 1.000000, Obj: 0.494078, No Obj: 0.002315, Avg Recall: 0.851852, count: 81 -Region Avg IOU: 0.644549, Class: 1.000000, Obj: 0.387197, No Obj: 0.002757, Avg Recall: 0.806202, count: 129 -Region Avg IOU: 0.645471, Class: 1.000000, Obj: 0.381344, No Obj: 0.003137, Avg Recall: 0.832168, count: 143 -Region Avg IOU: 0.655629, Class: 1.000000, Obj: 0.419830, No Obj: 0.002619, Avg Recall: 0.834862, count: 109 -Region Avg IOU: 0.676205, Class: 1.000000, Obj: 0.340142, No Obj: 0.003010, Avg Recall: 0.844262, count: 122 -Region Avg IOU: 0.644429, Class: 1.000000, Obj: 0.461113, No Obj: 0.002522, Avg Recall: 0.761062, count: 113 -6732: 0.465333, 0.432521 avg, 0.001000 rate, 29.566408 seconds, 1723392 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.639733, Class: 1.000000, Obj: 0.391266, No Obj: 0.002659, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.636441, Class: 1.000000, Obj: 0.418144, No Obj: 0.002250, Avg Recall: 0.772727, count: 88 -Region Avg IOU: 0.712030, Class: 1.000000, Obj: 0.465009, No Obj: 0.002526, Avg Recall: 0.886364, count: 88 -Region Avg IOU: 0.694429, Class: 1.000000, Obj: 0.463320, No Obj: 0.002552, Avg Recall: 0.867470, count: 83 -Region Avg IOU: 0.673184, Class: 1.000000, Obj: 0.404457, No Obj: 0.002248, Avg Recall: 0.860465, count: 86 -Region Avg IOU: 0.682349, Class: 1.000000, Obj: 0.439833, No Obj: 0.002579, Avg Recall: 0.847619, count: 105 -Region Avg IOU: 0.654857, Class: 1.000000, Obj: 0.416030, No Obj: 0.002617, Avg Recall: 0.831858, count: 113 -Region Avg IOU: 0.685506, Class: 1.000000, Obj: 0.467159, No Obj: 0.002709, Avg Recall: 0.833333, count: 96 -6733: 0.370376, 0.426307 avg, 0.001000 rate, 27.421175 seconds, 1723648 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.657245, Class: 1.000000, Obj: 0.434055, No Obj: 0.002624, Avg Recall: 0.798319, count: 119 -Region Avg IOU: 0.624208, Class: 1.000000, Obj: 0.456411, No Obj: 0.002540, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.615042, Class: 1.000000, Obj: 0.417995, No Obj: 0.002351, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.690978, Class: 1.000000, Obj: 0.502557, No Obj: 0.002437, Avg Recall: 0.898990, count: 99 -Region Avg IOU: 0.691090, Class: 1.000000, Obj: 0.479324, No Obj: 0.002585, Avg Recall: 0.873684, count: 95 -Region Avg IOU: 0.688501, Class: 1.000000, Obj: 0.467577, No Obj: 0.002249, Avg Recall: 0.892857, count: 84 -Region Avg IOU: 0.675513, Class: 1.000000, Obj: 0.432653, No Obj: 0.002439, Avg Recall: 0.844037, count: 109 -Region Avg IOU: 0.634295, Class: 1.000000, Obj: 0.350949, No Obj: 0.002606, Avg Recall: 0.764706, count: 136 -6734: 0.411885, 0.424865 avg, 0.001000 rate, 28.852312 seconds, 1723904 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.646831, Class: 1.000000, Obj: 0.456122, No Obj: 0.002255, Avg Recall: 0.773810, count: 84 -Region Avg IOU: 0.651252, Class: 1.000000, Obj: 0.358626, No Obj: 0.002417, Avg Recall: 0.817204, count: 93 -Region Avg IOU: 0.697128, Class: 1.000000, Obj: 0.399997, No Obj: 0.002712, Avg Recall: 0.938053, count: 113 -Region Avg IOU: 0.645569, Class: 1.000000, Obj: 0.409200, No Obj: 0.002525, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.684788, Class: 1.000000, Obj: 0.441058, No Obj: 0.002866, Avg Recall: 0.820312, count: 128 -Region Avg IOU: 0.667617, Class: 1.000000, Obj: 0.462155, No Obj: 0.002524, Avg Recall: 0.827957, count: 93 -Region Avg IOU: 0.646819, Class: 1.000000, Obj: 0.406050, No Obj: 0.002428, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.694986, Class: 1.000000, Obj: 0.443428, No Obj: 0.002741, Avg Recall: 0.864407, count: 118 -6735: 0.415516, 0.423930 avg, 0.001000 rate, 31.379328 seconds, 1724160 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.655789, Class: 1.000000, Obj: 0.432352, No Obj: 0.002321, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.642514, Class: 1.000000, Obj: 0.383769, No Obj: 0.002508, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.673488, Class: 1.000000, Obj: 0.447012, No Obj: 0.002350, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.676243, Class: 1.000000, Obj: 0.409421, No Obj: 0.002344, Avg Recall: 0.850000, count: 100 -Region Avg IOU: 0.608098, Class: 1.000000, Obj: 0.365004, No Obj: 0.001970, Avg Recall: 0.708861, count: 79 -Region Avg IOU: 0.676473, Class: 1.000000, Obj: 0.400693, No Obj: 0.002763, Avg Recall: 0.819549, count: 133 -Region Avg IOU: 0.607734, Class: 1.000000, Obj: 0.372448, No Obj: 0.002257, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.627211, Class: 1.000000, Obj: 0.377774, No Obj: 0.002853, Avg Recall: 0.739726, count: 146 -6736: 0.437306, 0.425267 avg, 0.001000 rate, 30.227150 seconds, 1724416 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.672050, Class: 1.000000, Obj: 0.402463, No Obj: 0.002830, Avg Recall: 0.861538, count: 130 -Region Avg IOU: 0.668478, Class: 1.000000, Obj: 0.426141, No Obj: 0.002129, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.686086, Class: 1.000000, Obj: 0.482149, No Obj: 0.002514, Avg Recall: 0.865979, count: 97 -Region Avg IOU: 0.650946, Class: 1.000000, Obj: 0.413593, No Obj: 0.002733, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.657734, Class: 1.000000, Obj: 0.414465, No Obj: 0.002319, Avg Recall: 0.845361, count: 97 -Region Avg IOU: 0.634538, Class: 1.000000, Obj: 0.380214, No Obj: 0.002447, Avg Recall: 0.760000, count: 125 -Region Avg IOU: 0.659743, Class: 1.000000, Obj: 0.394477, No Obj: 0.002434, Avg Recall: 0.821429, count: 112 -Region Avg IOU: 0.645604, Class: 1.000000, Obj: 0.405685, No Obj: 0.002731, Avg Recall: 0.792308, count: 130 -6737: 0.439914, 0.426732 avg, 0.001000 rate, 29.709911 seconds, 1724672 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.697755, Class: 1.000000, Obj: 0.422940, No Obj: 0.002583, Avg Recall: 0.871287, count: 101 -Region Avg IOU: 0.633000, Class: 1.000000, Obj: 0.414799, No Obj: 0.002115, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.686408, Class: 1.000000, Obj: 0.446435, No Obj: 0.002628, Avg Recall: 0.860656, count: 122 -Region Avg IOU: 0.663373, Class: 1.000000, Obj: 0.448934, No Obj: 0.002290, Avg Recall: 0.814433, count: 97 -Region Avg IOU: 0.665683, Class: 1.000000, Obj: 0.387508, No Obj: 0.002458, Avg Recall: 0.864000, count: 125 -Region Avg IOU: 0.672235, Class: 1.000000, Obj: 0.431626, No Obj: 0.002524, Avg Recall: 0.834711, count: 121 -Region Avg IOU: 0.645860, Class: 1.000000, Obj: 0.381775, No Obj: 0.002298, Avg Recall: 0.782609, count: 115 -Region Avg IOU: 0.662496, Class: 1.000000, Obj: 0.366679, No Obj: 0.002437, Avg Recall: 0.783019, count: 106 -6738: 0.437884, 0.427847 avg, 0.001000 rate, 29.095324 seconds, 1724928 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.649759, Class: 1.000000, Obj: 0.476113, No Obj: 0.002648, Avg Recall: 0.795455, count: 132 -Region Avg IOU: 0.699730, Class: 1.000000, Obj: 0.478472, No Obj: 0.002371, Avg Recall: 0.882353, count: 85 -Region Avg IOU: 0.666135, Class: 1.000000, Obj: 0.425937, No Obj: 0.002485, Avg Recall: 0.827273, count: 110 -Region Avg IOU: 0.645436, Class: 1.000000, Obj: 0.367393, No Obj: 0.002507, Avg Recall: 0.789062, count: 128 -Region Avg IOU: 0.638583, Class: 1.000000, Obj: 0.375371, No Obj: 0.002167, Avg Recall: 0.795181, count: 83 -Region Avg IOU: 0.685606, Class: 1.000000, Obj: 0.435964, No Obj: 0.002218, Avg Recall: 0.846154, count: 91 -Region Avg IOU: 0.665298, Class: 1.000000, Obj: 0.453768, No Obj: 0.002322, Avg Recall: 0.834951, count: 103 -Region Avg IOU: 0.683865, Class: 1.000000, Obj: 0.410568, No Obj: 0.002480, Avg Recall: 0.831683, count: 101 -6739: 0.453719, 0.430434 avg, 0.001000 rate, 30.947296 seconds, 1725184 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.685187, Class: 1.000000, Obj: 0.400937, No Obj: 0.002327, Avg Recall: 0.844660, count: 103 -Region Avg IOU: 0.668971, Class: 1.000000, Obj: 0.371064, No Obj: 0.002384, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.673540, Class: 1.000000, Obj: 0.411452, No Obj: 0.002669, Avg Recall: 0.845455, count: 110 -Region Avg IOU: 0.706141, Class: 1.000000, Obj: 0.411957, No Obj: 0.002743, Avg Recall: 0.878505, count: 107 -Region Avg IOU: 0.682349, Class: 1.000000, Obj: 0.427569, No Obj: 0.002704, Avg Recall: 0.869565, count: 115 -Region Avg IOU: 0.703688, Class: 1.000000, Obj: 0.422776, No Obj: 0.002663, Avg Recall: 0.860000, count: 100 -Region Avg IOU: 0.648178, Class: 1.000000, Obj: 0.421671, No Obj: 0.002109, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.658667, Class: 1.000000, Obj: 0.391000, No Obj: 0.002506, Avg Recall: 0.840708, count: 113 -6740: 0.445772, 0.431968 avg, 0.001000 rate, 29.388914 seconds, 1725440 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.343076 seconds -Region Avg IOU: 0.529265, Class: 1.000000, Obj: 0.295503, No Obj: 0.003356, Avg Recall: 0.630252, count: 119 -Region Avg IOU: 0.538302, Class: 1.000000, Obj: 0.327370, No Obj: 0.003145, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.524911, Class: 1.000000, Obj: 0.318040, No Obj: 0.002956, Avg Recall: 0.595238, count: 84 -Region Avg IOU: 0.575959, Class: 1.000000, Obj: 0.320099, No Obj: 0.003284, Avg Recall: 0.663265, count: 98 -Region Avg IOU: 0.542591, Class: 1.000000, Obj: 0.341878, No Obj: 0.003796, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.582908, Class: 1.000000, Obj: 0.316001, No Obj: 0.003254, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.536232, Class: 1.000000, Obj: 0.338507, No Obj: 0.003341, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.560294, Class: 1.000000, Obj: 0.339348, No Obj: 0.003755, Avg Recall: 0.627273, count: 110 -6741: 0.428067, 0.431578 avg, 0.001000 rate, 19.221231 seconds, 1725696 images -Loaded: 2.348450 seconds -Region Avg IOU: 0.541896, Class: 1.000000, Obj: 0.292550, No Obj: 0.003655, Avg Recall: 0.648438, count: 128 -Region Avg IOU: 0.536537, Class: 1.000000, Obj: 0.331619, No Obj: 0.002991, Avg Recall: 0.566372, count: 113 -Region Avg IOU: 0.591001, Class: 1.000000, Obj: 0.364962, No Obj: 0.004176, Avg Recall: 0.666667, count: 111 -Region Avg IOU: 0.592127, Class: 1.000000, Obj: 0.384508, No Obj: 0.004101, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.510405, Class: 1.000000, Obj: 0.305037, No Obj: 0.003434, Avg Recall: 0.564885, count: 131 -Region Avg IOU: 0.576856, Class: 1.000000, Obj: 0.322840, No Obj: 0.003017, Avg Recall: 0.666667, count: 84 -Region Avg IOU: 0.490712, Class: 1.000000, Obj: 0.261767, No Obj: 0.003296, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.543385, Class: 1.000000, Obj: 0.337675, No Obj: 0.002771, Avg Recall: 0.616279, count: 86 -6742: 0.490472, 0.437467 avg, 0.001000 rate, 17.528603 seconds, 1725952 images -Loaded: 2.284223 seconds -Region Avg IOU: 0.539189, Class: 1.000000, Obj: 0.386197, No Obj: 0.003152, Avg Recall: 0.657895, count: 76 -Region Avg IOU: 0.556740, Class: 1.000000, Obj: 0.331408, No Obj: 0.003083, Avg Recall: 0.654321, count: 81 -Region Avg IOU: 0.551640, Class: 1.000000, Obj: 0.342672, No Obj: 0.003275, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.474055, Class: 1.000000, Obj: 0.307135, No Obj: 0.003055, Avg Recall: 0.549020, count: 102 -Region Avg IOU: 0.508563, Class: 1.000000, Obj: 0.329284, No Obj: 0.003477, Avg Recall: 0.572650, count: 117 -Region Avg IOU: 0.517297, Class: 1.000000, Obj: 0.325745, No Obj: 0.003338, Avg Recall: 0.588889, count: 90 -Region Avg IOU: 0.554400, Class: 1.000000, Obj: 0.386478, No Obj: 0.003207, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.539716, Class: 1.000000, Obj: 0.326788, No Obj: 0.003789, Avg Recall: 0.625000, count: 120 -6743: 0.398099, 0.433531 avg, 0.001000 rate, 21.751930 seconds, 1726208 images -Loaded: 0.125108 seconds -Region Avg IOU: 0.560123, Class: 1.000000, Obj: 0.370009, No Obj: 0.003628, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.569457, Class: 1.000000, Obj: 0.363678, No Obj: 0.003429, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.600082, Class: 1.000000, Obj: 0.448133, No Obj: 0.004080, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.566825, Class: 1.000000, Obj: 0.384377, No Obj: 0.003394, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.500585, Class: 1.000000, Obj: 0.320701, No Obj: 0.003353, Avg Recall: 0.571429, count: 119 -Region Avg IOU: 0.536603, Class: 1.000000, Obj: 0.342345, No Obj: 0.003841, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.541649, Class: 1.000000, Obj: 0.408547, No Obj: 0.003783, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.532370, Class: 1.000000, Obj: 0.336792, No Obj: 0.004765, Avg Recall: 0.601307, count: 153 -6744: 0.403123, 0.430490 avg, 0.001000 rate, 21.515787 seconds, 1726464 images -Loaded: 0.012958 seconds -Region Avg IOU: 0.539153, Class: 1.000000, Obj: 0.375608, No Obj: 0.004058, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.503763, Class: 1.000000, Obj: 0.347966, No Obj: 0.003587, Avg Recall: 0.590476, count: 105 -Region Avg IOU: 0.576758, Class: 1.000000, Obj: 0.393321, No Obj: 0.003865, Avg Recall: 0.674419, count: 86 -Region Avg IOU: 0.491588, Class: 1.000000, Obj: 0.335413, No Obj: 0.003730, Avg Recall: 0.543103, count: 116 -Region Avg IOU: 0.510446, Class: 1.000000, Obj: 0.383098, No Obj: 0.004428, Avg Recall: 0.531469, count: 143 -Region Avg IOU: 0.510055, Class: 1.000000, Obj: 0.371870, No Obj: 0.003636, Avg Recall: 0.556522, count: 115 -Region Avg IOU: 0.558478, Class: 1.000000, Obj: 0.417691, No Obj: 0.004277, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.553128, Class: 1.000000, Obj: 0.400338, No Obj: 0.003863, Avg Recall: 0.642857, count: 98 -6745: 0.434353, 0.430876 avg, 0.001000 rate, 20.250532 seconds, 1726720 images -Loaded: 1.555831 seconds -Region Avg IOU: 0.573451, Class: 1.000000, Obj: 0.485158, No Obj: 0.004575, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.583305, Class: 1.000000, Obj: 0.414010, No Obj: 0.004220, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.559334, Class: 1.000000, Obj: 0.421992, No Obj: 0.004287, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.503781, Class: 1.000000, Obj: 0.345746, No Obj: 0.003845, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.579085, Class: 1.000000, Obj: 0.431979, No Obj: 0.004238, Avg Recall: 0.666667, count: 81 -Region Avg IOU: 0.577390, Class: 1.000000, Obj: 0.432278, No Obj: 0.004565, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.520197, Class: 1.000000, Obj: 0.364086, No Obj: 0.004367, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.528175, Class: 1.000000, Obj: 0.407874, No Obj: 0.003304, Avg Recall: 0.612500, count: 80 -6746: 0.350631, 0.422852 avg, 0.001000 rate, 20.958487 seconds, 1726976 images -Loaded: 0.574867 seconds -Region Avg IOU: 0.537126, Class: 1.000000, Obj: 0.381625, No Obj: 0.004242, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.554310, Class: 1.000000, Obj: 0.411254, No Obj: 0.004470, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.561959, Class: 1.000000, Obj: 0.397684, No Obj: 0.004453, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.482148, Class: 1.000000, Obj: 0.414604, No Obj: 0.004979, Avg Recall: 0.540541, count: 111 -Region Avg IOU: 0.533614, Class: 1.000000, Obj: 0.417659, No Obj: 0.005184, Avg Recall: 0.609375, count: 128 -Region Avg IOU: 0.529881, Class: 1.000000, Obj: 0.402473, No Obj: 0.003979, Avg Recall: 0.604651, count: 86 -Region Avg IOU: 0.637916, Class: 1.000000, Obj: 0.494199, No Obj: 0.004135, Avg Recall: 0.740260, count: 77 -Region Avg IOU: 0.521754, Class: 1.000000, Obj: 0.414022, No Obj: 0.004489, Avg Recall: 0.570093, count: 107 -6747: 0.417319, 0.422298 avg, 0.001000 rate, 20.456291 seconds, 1727232 images -Loaded: 1.529760 seconds -Region Avg IOU: 0.545867, Class: 1.000000, Obj: 0.415267, No Obj: 0.004605, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.562367, Class: 1.000000, Obj: 0.426230, No Obj: 0.005542, Avg Recall: 0.637795, count: 127 -Region Avg IOU: 0.544408, Class: 1.000000, Obj: 0.460754, No Obj: 0.004366, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.554892, Class: 1.000000, Obj: 0.414211, No Obj: 0.004623, Avg Recall: 0.645161, count: 124 -Region Avg IOU: 0.513457, Class: 1.000000, Obj: 0.407904, No Obj: 0.004600, Avg Recall: 0.549180, count: 122 -Region Avg IOU: 0.532483, Class: 1.000000, Obj: 0.438134, No Obj: 0.005319, Avg Recall: 0.578512, count: 121 -Region Avg IOU: 0.600667, Class: 1.000000, Obj: 0.514045, No Obj: 0.004319, Avg Recall: 0.700000, count: 70 -Region Avg IOU: 0.568726, Class: 1.000000, Obj: 0.458362, No Obj: 0.004477, Avg Recall: 0.686046, count: 86 -6748: 0.434394, 0.423508 avg, 0.001000 rate, 20.836300 seconds, 1727488 images -Loaded: 0.869862 seconds -Region Avg IOU: 0.539150, Class: 1.000000, Obj: 0.483782, No Obj: 0.004683, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.525424, Class: 1.000000, Obj: 0.456533, No Obj: 0.004230, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.567893, Class: 1.000000, Obj: 0.422231, No Obj: 0.005028, Avg Recall: 0.666667, count: 126 -Region Avg IOU: 0.580138, Class: 1.000000, Obj: 0.468206, No Obj: 0.005829, Avg Recall: 0.651163, count: 129 -Region Avg IOU: 0.570807, Class: 1.000000, Obj: 0.457918, No Obj: 0.004800, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.547446, Class: 1.000000, Obj: 0.399917, No Obj: 0.005239, Avg Recall: 0.593750, count: 128 -Region Avg IOU: 0.546156, Class: 1.000000, Obj: 0.425831, No Obj: 0.004699, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.550243, Class: 1.000000, Obj: 0.434427, No Obj: 0.005555, Avg Recall: 0.653226, count: 124 -6749: 0.439071, 0.425064 avg, 0.001000 rate, 19.318716 seconds, 1727744 images -Loaded: 2.331957 seconds -Region Avg IOU: 0.529133, Class: 1.000000, Obj: 0.414320, No Obj: 0.004420, Avg Recall: 0.619469, count: 113 -Region Avg IOU: 0.531055, Class: 1.000000, Obj: 0.408561, No Obj: 0.004462, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.548309, Class: 1.000000, Obj: 0.446810, No Obj: 0.004639, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.570920, Class: 1.000000, Obj: 0.486837, No Obj: 0.004668, Avg Recall: 0.629214, count: 89 -Region Avg IOU: 0.540623, Class: 1.000000, Obj: 0.457029, No Obj: 0.004819, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.471848, Class: 1.000000, Obj: 0.455960, No Obj: 0.004398, Avg Recall: 0.469388, count: 98 -Region Avg IOU: 0.556010, Class: 1.000000, Obj: 0.479872, No Obj: 0.003679, Avg Recall: 0.659091, count: 88 -Region Avg IOU: 0.530512, Class: 1.000000, Obj: 0.408858, No Obj: 0.005644, Avg Recall: 0.581818, count: 165 -6750: 0.383934, 0.420951 avg, 0.001000 rate, 20.536608 seconds, 1728000 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.841433 seconds -Region Avg IOU: 0.450212, Class: 1.000000, Obj: 0.353479, No Obj: 0.004489, Avg Recall: 0.505263, count: 95 -Region Avg IOU: 0.504203, Class: 1.000000, Obj: 0.350856, No Obj: 0.005344, Avg Recall: 0.588710, count: 124 -Region Avg IOU: 0.514780, Class: 1.000000, Obj: 0.351875, No Obj: 0.004933, Avg Recall: 0.589744, count: 117 -Region Avg IOU: 0.465651, Class: 1.000000, Obj: 0.341998, No Obj: 0.004292, Avg Recall: 0.524752, count: 101 -Region Avg IOU: 0.534027, Class: 1.000000, Obj: 0.388304, No Obj: 0.005498, Avg Recall: 0.594059, count: 101 -Region Avg IOU: 0.474717, Class: 1.000000, Obj: 0.350032, No Obj: 0.004963, Avg Recall: 0.511628, count: 129 -Region Avg IOU: 0.457706, Class: 1.000000, Obj: 0.333655, No Obj: 0.004538, Avg Recall: 0.509091, count: 110 -Region Avg IOU: 0.513752, Class: 1.000000, Obj: 0.373456, No Obj: 0.004956, Avg Recall: 0.622807, count: 114 -6751: 0.529257, 0.431782 avg, 0.001000 rate, 15.411515 seconds, 1728256 images -Loaded: 5.984179 seconds -Region Avg IOU: 0.501811, Class: 1.000000, Obj: 0.328931, No Obj: 0.005676, Avg Recall: 0.568000, count: 125 -Region Avg IOU: 0.523770, Class: 1.000000, Obj: 0.371916, No Obj: 0.004798, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.510925, Class: 1.000000, Obj: 0.353343, No Obj: 0.004584, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.464116, Class: 1.000000, Obj: 0.323001, No Obj: 0.005593, Avg Recall: 0.484375, count: 128 -Region Avg IOU: 0.501048, Class: 1.000000, Obj: 0.363696, No Obj: 0.005234, Avg Recall: 0.566667, count: 120 -Region Avg IOU: 0.462348, Class: 1.000000, Obj: 0.319455, No Obj: 0.005410, Avg Recall: 0.484127, count: 126 -Region Avg IOU: 0.488439, Class: 1.000000, Obj: 0.326957, No Obj: 0.004443, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.427850, Class: 1.000000, Obj: 0.314808, No Obj: 0.003683, Avg Recall: 0.422222, count: 90 -6752: 0.497821, 0.438386 avg, 0.001000 rate, 16.865297 seconds, 1728512 images -Loaded: 1.834496 seconds -Region Avg IOU: 0.506337, Class: 1.000000, Obj: 0.341711, No Obj: 0.004700, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.451548, Class: 1.000000, Obj: 0.339290, No Obj: 0.004361, Avg Recall: 0.466667, count: 105 -Region Avg IOU: 0.513641, Class: 1.000000, Obj: 0.373453, No Obj: 0.004471, Avg Recall: 0.597826, count: 92 -Region Avg IOU: 0.504773, Class: 1.000000, Obj: 0.371727, No Obj: 0.004872, Avg Recall: 0.532110, count: 109 -Region Avg IOU: 0.518911, Class: 1.000000, Obj: 0.333491, No Obj: 0.004265, Avg Recall: 0.607143, count: 112 -Region Avg IOU: 0.498317, Class: 1.000000, Obj: 0.369470, No Obj: 0.004094, Avg Recall: 0.528736, count: 87 -Region Avg IOU: 0.472098, Class: 1.000000, Obj: 0.287135, No Obj: 0.005394, Avg Recall: 0.518293, count: 164 -Region Avg IOU: 0.479906, Class: 1.000000, Obj: 0.303328, No Obj: 0.004328, Avg Recall: 0.519231, count: 104 -6753: 0.464213, 0.440968 avg, 0.001000 rate, 19.444427 seconds, 1728768 images -Loaded: 2.265780 seconds -Region Avg IOU: 0.436339, Class: 1.000000, Obj: 0.295003, No Obj: 0.003272, Avg Recall: 0.450980, count: 102 -Region Avg IOU: 0.504756, Class: 1.000000, Obj: 0.337272, No Obj: 0.004695, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.497173, Class: 1.000000, Obj: 0.302962, No Obj: 0.004180, Avg Recall: 0.569892, count: 93 -Region Avg IOU: 0.461345, Class: 1.000000, Obj: 0.301161, No Obj: 0.003829, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.521101, Class: 1.000000, Obj: 0.334614, No Obj: 0.003934, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.508748, Class: 1.000000, Obj: 0.348270, No Obj: 0.004599, Avg Recall: 0.567797, count: 118 -Region Avg IOU: 0.554710, Class: 1.000000, Obj: 0.327149, No Obj: 0.004361, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.453745, Class: 1.000000, Obj: 0.316117, No Obj: 0.003574, Avg Recall: 0.505495, count: 91 -6754: 0.457930, 0.442665 avg, 0.001000 rate, 18.071133 seconds, 1729024 images -Loaded: 3.068644 seconds -Region Avg IOU: 0.528847, Class: 1.000000, Obj: 0.322104, No Obj: 0.004252, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.523703, Class: 1.000000, Obj: 0.343876, No Obj: 0.004688, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.441373, Class: 1.000000, Obj: 0.292614, No Obj: 0.003946, Avg Recall: 0.468085, count: 94 -Region Avg IOU: 0.471610, Class: 1.000000, Obj: 0.299212, No Obj: 0.003794, Avg Recall: 0.526316, count: 95 -Region Avg IOU: 0.490362, Class: 1.000000, Obj: 0.319395, No Obj: 0.004274, Avg Recall: 0.521008, count: 119 -Region Avg IOU: 0.542582, Class: 1.000000, Obj: 0.332262, No Obj: 0.004157, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.420499, Class: 1.000000, Obj: 0.279220, No Obj: 0.003745, Avg Recall: 0.473684, count: 95 -Region Avg IOU: 0.482178, Class: 1.000000, Obj: 0.324229, No Obj: 0.004447, Avg Recall: 0.536000, count: 125 -6755: 0.442228, 0.442621 avg, 0.001000 rate, 15.404107 seconds, 1729280 images -Loaded: 5.752059 seconds -Region Avg IOU: 0.549067, Class: 1.000000, Obj: 0.357966, No Obj: 0.004921, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.505945, Class: 1.000000, Obj: 0.349741, No Obj: 0.005139, Avg Recall: 0.495146, count: 103 -Region Avg IOU: 0.499018, Class: 1.000000, Obj: 0.357552, No Obj: 0.003917, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.495386, Class: 1.000000, Obj: 0.333429, No Obj: 0.003879, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.499448, Class: 1.000000, Obj: 0.354220, No Obj: 0.003929, Avg Recall: 0.556701, count: 97 -Region Avg IOU: 0.475198, Class: 1.000000, Obj: 0.306927, No Obj: 0.004370, Avg Recall: 0.464286, count: 112 -Region Avg IOU: 0.478136, Class: 1.000000, Obj: 0.329849, No Obj: 0.004476, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.489003, Class: 1.000000, Obj: 0.291069, No Obj: 0.003898, Avg Recall: 0.564815, count: 108 -6756: 0.407837, 0.439143 avg, 0.001000 rate, 16.630762 seconds, 1729536 images -Loaded: 2.189820 seconds -Region Avg IOU: 0.501943, Class: 1.000000, Obj: 0.368609, No Obj: 0.004330, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.480908, Class: 1.000000, Obj: 0.328596, No Obj: 0.004618, Avg Recall: 0.495050, count: 101 -Region Avg IOU: 0.487669, Class: 1.000000, Obj: 0.326431, No Obj: 0.004970, Avg Recall: 0.543478, count: 138 -Region Avg IOU: 0.482687, Class: 1.000000, Obj: 0.285478, No Obj: 0.005100, Avg Recall: 0.520548, count: 146 -Region Avg IOU: 0.465003, Class: 1.000000, Obj: 0.289343, No Obj: 0.003562, Avg Recall: 0.537500, count: 80 -Region Avg IOU: 0.524733, Class: 1.000000, Obj: 0.340539, No Obj: 0.003581, Avg Recall: 0.613636, count: 88 -Region Avg IOU: 0.487842, Class: 1.000000, Obj: 0.299119, No Obj: 0.004780, Avg Recall: 0.544118, count: 136 -Region Avg IOU: 0.518688, Class: 1.000000, Obj: 0.337901, No Obj: 0.004253, Avg Recall: 0.601942, count: 103 -6757: 0.450282, 0.440257 avg, 0.001000 rate, 17.067827 seconds, 1729792 images -Loaded: 2.190194 seconds -Region Avg IOU: 0.524188, Class: 1.000000, Obj: 0.361094, No Obj: 0.004302, Avg Recall: 0.604651, count: 86 -Region Avg IOU: 0.549366, Class: 1.000000, Obj: 0.326719, No Obj: 0.003594, Avg Recall: 0.633333, count: 90 -Region Avg IOU: 0.491742, Class: 1.000000, Obj: 0.282350, No Obj: 0.004295, Avg Recall: 0.517857, count: 112 -Region Avg IOU: 0.500709, Class: 1.000000, Obj: 0.364150, No Obj: 0.004152, Avg Recall: 0.567901, count: 81 -Region Avg IOU: 0.479268, Class: 1.000000, Obj: 0.291018, No Obj: 0.004200, Avg Recall: 0.527559, count: 127 -Region Avg IOU: 0.500208, Class: 1.000000, Obj: 0.338760, No Obj: 0.004718, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.511277, Class: 1.000000, Obj: 0.347137, No Obj: 0.004896, Avg Recall: 0.546392, count: 97 -Region Avg IOU: 0.499084, Class: 1.000000, Obj: 0.356832, No Obj: 0.004301, Avg Recall: 0.571429, count: 91 -6758: 0.424789, 0.438710 avg, 0.001000 rate, 14.374007 seconds, 1730048 images -Loaded: 5.050803 seconds -Region Avg IOU: 0.478038, Class: 1.000000, Obj: 0.313500, No Obj: 0.004843, Avg Recall: 0.504673, count: 107 -Region Avg IOU: 0.442096, Class: 1.000000, Obj: 0.352452, No Obj: 0.005075, Avg Recall: 0.513043, count: 115 -Region Avg IOU: 0.548947, Class: 1.000000, Obj: 0.377514, No Obj: 0.005087, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.499001, Class: 1.000000, Obj: 0.370251, No Obj: 0.004079, Avg Recall: 0.560000, count: 75 -Region Avg IOU: 0.485661, Class: 1.000000, Obj: 0.332466, No Obj: 0.004163, Avg Recall: 0.525862, count: 116 -Region Avg IOU: 0.510580, Class: 1.000000, Obj: 0.334918, No Obj: 0.004812, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.516663, Class: 1.000000, Obj: 0.332915, No Obj: 0.004711, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.576021, Class: 1.000000, Obj: 0.445533, No Obj: 0.005005, Avg Recall: 0.678161, count: 87 -6759: 0.453203, 0.440159 avg, 0.001000 rate, 17.411705 seconds, 1730304 images -Loaded: 4.090405 seconds -Region Avg IOU: 0.520974, Class: 1.000000, Obj: 0.377968, No Obj: 0.005372, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.451077, Class: 1.000000, Obj: 0.342353, No Obj: 0.004778, Avg Recall: 0.494624, count: 93 -Region Avg IOU: 0.466196, Class: 1.000000, Obj: 0.348886, No Obj: 0.004560, Avg Recall: 0.484375, count: 128 -Region Avg IOU: 0.505378, Class: 1.000000, Obj: 0.333173, No Obj: 0.005194, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.524231, Class: 1.000000, Obj: 0.405126, No Obj: 0.004479, Avg Recall: 0.619048, count: 84 -Region Avg IOU: 0.470240, Class: 1.000000, Obj: 0.374905, No Obj: 0.004898, Avg Recall: 0.494949, count: 99 -Region Avg IOU: 0.488610, Class: 1.000000, Obj: 0.335565, No Obj: 0.005496, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.479932, Class: 1.000000, Obj: 0.364941, No Obj: 0.004028, Avg Recall: 0.560440, count: 91 -6760: 0.479571, 0.444100 avg, 0.001000 rate, 14.611453 seconds, 1730560 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000080 seconds -Region Avg IOU: 0.571628, Class: 1.000000, Obj: 0.494545, No Obj: 0.003562, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.613534, Class: 1.000000, Obj: 0.464191, No Obj: 0.004441, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.578870, Class: 1.000000, Obj: 0.505118, No Obj: 0.004360, Avg Recall: 0.669355, count: 124 -Region Avg IOU: 0.604252, Class: 1.000000, Obj: 0.455440, No Obj: 0.004666, Avg Recall: 0.698413, count: 126 -Region Avg IOU: 0.630218, Class: 1.000000, Obj: 0.476551, No Obj: 0.004059, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.570517, Class: 1.000000, Obj: 0.421540, No Obj: 0.004574, Avg Recall: 0.664384, count: 146 -Region Avg IOU: 0.562730, Class: 1.000000, Obj: 0.481534, No Obj: 0.004176, Avg Recall: 0.666667, count: 117 -Region Avg IOU: 0.511778, Class: 1.000000, Obj: 0.459877, No Obj: 0.003611, Avg Recall: 0.558140, count: 86 -6761: 0.436455, 0.443336 avg, 0.001000 rate, 22.830416 seconds, 1730816 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.591814, Class: 1.000000, Obj: 0.517223, No Obj: 0.004086, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.572993, Class: 1.000000, Obj: 0.492403, No Obj: 0.003353, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.639496, Class: 1.000000, Obj: 0.523302, No Obj: 0.003837, Avg Recall: 0.790123, count: 81 -Region Avg IOU: 0.565412, Class: 1.000000, Obj: 0.443455, No Obj: 0.003915, Avg Recall: 0.643478, count: 115 -Region Avg IOU: 0.581944, Class: 1.000000, Obj: 0.482410, No Obj: 0.003742, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.579008, Class: 1.000000, Obj: 0.506388, No Obj: 0.003673, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.583399, Class: 1.000000, Obj: 0.441692, No Obj: 0.003989, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.558707, Class: 1.000000, Obj: 0.439043, No Obj: 0.004252, Avg Recall: 0.645390, count: 141 -6762: 0.357715, 0.434774 avg, 0.001000 rate, 24.959694 seconds, 1731072 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.607515, Class: 1.000000, Obj: 0.466843, No Obj: 0.004252, Avg Recall: 0.691729, count: 133 -Region Avg IOU: 0.608842, Class: 1.000000, Obj: 0.464709, No Obj: 0.004401, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.617669, Class: 1.000000, Obj: 0.449588, No Obj: 0.003990, Avg Recall: 0.734940, count: 83 -Region Avg IOU: 0.566023, Class: 1.000000, Obj: 0.446829, No Obj: 0.004599, Avg Recall: 0.653846, count: 130 -Region Avg IOU: 0.639089, Class: 1.000000, Obj: 0.470499, No Obj: 0.004311, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.645329, Class: 1.000000, Obj: 0.517721, No Obj: 0.003864, Avg Recall: 0.800000, count: 85 -Region Avg IOU: 0.671561, Class: 1.000000, Obj: 0.489569, No Obj: 0.004195, Avg Recall: 0.838710, count: 93 -Region Avg IOU: 0.626070, Class: 1.000000, Obj: 0.472612, No Obj: 0.004227, Avg Recall: 0.734513, count: 113 -6763: 0.382143, 0.429511 avg, 0.001000 rate, 24.849096 seconds, 1731328 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.625599, Class: 1.000000, Obj: 0.437384, No Obj: 0.004462, Avg Recall: 0.739437, count: 142 -Region Avg IOU: 0.642709, Class: 1.000000, Obj: 0.467858, No Obj: 0.004211, Avg Recall: 0.801653, count: 121 -Region Avg IOU: 0.628476, Class: 1.000000, Obj: 0.412860, No Obj: 0.003781, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.586679, Class: 1.000000, Obj: 0.413656, No Obj: 0.003264, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.567246, Class: 1.000000, Obj: 0.408172, No Obj: 0.003688, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.589255, Class: 1.000000, Obj: 0.431972, No Obj: 0.003682, Avg Recall: 0.689655, count: 87 -Region Avg IOU: 0.542622, Class: 1.000000, Obj: 0.428789, No Obj: 0.003294, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.684030, Class: 1.000000, Obj: 0.456751, No Obj: 0.004251, Avg Recall: 0.833333, count: 102 -6764: 0.387161, 0.425276 avg, 0.001000 rate, 24.687756 seconds, 1731584 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.575569, Class: 1.000000, Obj: 0.450952, No Obj: 0.003499, Avg Recall: 0.670588, count: 85 -Region Avg IOU: 0.631934, Class: 1.000000, Obj: 0.436064, No Obj: 0.004030, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.629292, Class: 1.000000, Obj: 0.396153, No Obj: 0.004199, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.624876, Class: 1.000000, Obj: 0.415804, No Obj: 0.003239, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.613464, Class: 1.000000, Obj: 0.446455, No Obj: 0.003542, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.639313, Class: 1.000000, Obj: 0.414590, No Obj: 0.003298, Avg Recall: 0.767442, count: 86 -Region Avg IOU: 0.639903, Class: 1.000000, Obj: 0.403802, No Obj: 0.003537, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.606300, Class: 1.000000, Obj: 0.384116, No Obj: 0.003407, Avg Recall: 0.718447, count: 103 -6765: 0.439397, 0.426688 avg, 0.001000 rate, 23.547621 seconds, 1731840 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.550777, Class: 1.000000, Obj: 0.358561, No Obj: 0.003532, Avg Recall: 0.620155, count: 129 -Region Avg IOU: 0.635113, Class: 1.000000, Obj: 0.466423, No Obj: 0.003966, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.629027, Class: 1.000000, Obj: 0.390327, No Obj: 0.003307, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.622806, Class: 1.000000, Obj: 0.376198, No Obj: 0.003292, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.626304, Class: 1.000000, Obj: 0.438308, No Obj: 0.002870, Avg Recall: 0.779070, count: 86 -Region Avg IOU: 0.577097, Class: 1.000000, Obj: 0.390443, No Obj: 0.003448, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.558632, Class: 1.000000, Obj: 0.362662, No Obj: 0.003181, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.628100, Class: 1.000000, Obj: 0.411078, No Obj: 0.003266, Avg Recall: 0.744898, count: 98 -6766: 0.409419, 0.424961 avg, 0.001000 rate, 24.061169 seconds, 1732096 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.625181, Class: 1.000000, Obj: 0.400787, No Obj: 0.003717, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.589634, Class: 1.000000, Obj: 0.390348, No Obj: 0.002780, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.593826, Class: 1.000000, Obj: 0.405628, No Obj: 0.003062, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.605434, Class: 1.000000, Obj: 0.402542, No Obj: 0.003154, Avg Recall: 0.706349, count: 126 -Region Avg IOU: 0.605340, Class: 1.000000, Obj: 0.400501, No Obj: 0.003836, Avg Recall: 0.688000, count: 125 -Region Avg IOU: 0.651944, Class: 1.000000, Obj: 0.411446, No Obj: 0.003059, Avg Recall: 0.833333, count: 84 -Region Avg IOU: 0.599441, Class: 1.000000, Obj: 0.362555, No Obj: 0.003209, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.620246, Class: 1.000000, Obj: 0.400943, No Obj: 0.003298, Avg Recall: 0.716814, count: 113 -6767: 0.412285, 0.423693 avg, 0.001000 rate, 24.188276 seconds, 1732352 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.622883, Class: 1.000000, Obj: 0.406165, No Obj: 0.003551, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.613274, Class: 1.000000, Obj: 0.369430, No Obj: 0.003083, Avg Recall: 0.702381, count: 84 -Region Avg IOU: 0.597203, Class: 1.000000, Obj: 0.380280, No Obj: 0.003322, Avg Recall: 0.710938, count: 128 -Region Avg IOU: 0.588093, Class: 1.000000, Obj: 0.333013, No Obj: 0.003000, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.582602, Class: 1.000000, Obj: 0.406284, No Obj: 0.002787, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.616206, Class: 1.000000, Obj: 0.334237, No Obj: 0.003425, Avg Recall: 0.770492, count: 122 -Region Avg IOU: 0.592320, Class: 1.000000, Obj: 0.386281, No Obj: 0.002819, Avg Recall: 0.683761, count: 117 -Region Avg IOU: 0.627772, Class: 1.000000, Obj: 0.381971, No Obj: 0.003141, Avg Recall: 0.745098, count: 102 -6768: 0.428117, 0.424136 avg, 0.001000 rate, 24.529787 seconds, 1732608 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.600523, Class: 1.000000, Obj: 0.357326, No Obj: 0.003455, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.598198, Class: 1.000000, Obj: 0.381473, No Obj: 0.002910, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.629099, Class: 1.000000, Obj: 0.385948, No Obj: 0.003334, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.577821, Class: 1.000000, Obj: 0.403640, No Obj: 0.002674, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.668368, Class: 1.000000, Obj: 0.442784, No Obj: 0.002851, Avg Recall: 0.822222, count: 90 -Region Avg IOU: 0.602762, Class: 1.000000, Obj: 0.397614, No Obj: 0.002410, Avg Recall: 0.703704, count: 81 -Region Avg IOU: 0.576157, Class: 1.000000, Obj: 0.380778, No Obj: 0.002880, Avg Recall: 0.686046, count: 86 -Region Avg IOU: 0.625156, Class: 1.000000, Obj: 0.385296, No Obj: 0.003168, Avg Recall: 0.729167, count: 96 -6769: 0.394480, 0.421170 avg, 0.001000 rate, 23.262005 seconds, 1732864 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.667645, Class: 1.000000, Obj: 0.428867, No Obj: 0.003639, Avg Recall: 0.795082, count: 122 -Region Avg IOU: 0.509468, Class: 1.000000, Obj: 0.330621, No Obj: 0.002944, Avg Recall: 0.608000, count: 125 -Region Avg IOU: 0.545373, Class: 1.000000, Obj: 0.315215, No Obj: 0.002776, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.590560, Class: 1.000000, Obj: 0.386466, No Obj: 0.002719, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.637468, Class: 1.000000, Obj: 0.390545, No Obj: 0.003794, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.592404, Class: 1.000000, Obj: 0.363590, No Obj: 0.003406, Avg Recall: 0.715447, count: 123 -Region Avg IOU: 0.628551, Class: 1.000000, Obj: 0.379512, No Obj: 0.002876, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.560968, Class: 1.000000, Obj: 0.399815, No Obj: 0.003067, Avg Recall: 0.663717, count: 113 -6770: 0.441465, 0.423200 avg, 0.001000 rate, 24.738813 seconds, 1733120 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000074 seconds -Region Avg IOU: 0.628399, Class: 1.000000, Obj: 0.425024, No Obj: 0.002694, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.622877, Class: 1.000000, Obj: 0.412916, No Obj: 0.002710, Avg Recall: 0.733871, count: 124 -Region Avg IOU: 0.635129, Class: 1.000000, Obj: 0.421301, No Obj: 0.002988, Avg Recall: 0.769231, count: 143 -Region Avg IOU: 0.689148, Class: 1.000000, Obj: 0.436023, No Obj: 0.002571, Avg Recall: 0.883721, count: 86 -Region Avg IOU: 0.659525, Class: 1.000000, Obj: 0.384376, No Obj: 0.002398, Avg Recall: 0.812500, count: 80 -Region Avg IOU: 0.666948, Class: 1.000000, Obj: 0.446051, No Obj: 0.002649, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.647297, Class: 1.000000, Obj: 0.417963, No Obj: 0.002733, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.668800, Class: 1.000000, Obj: 0.376156, No Obj: 0.002705, Avg Recall: 0.800000, count: 110 -6771: 0.401651, 0.421045 avg, 0.001000 rate, 28.076571 seconds, 1733376 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.668425, Class: 1.000000, Obj: 0.437663, No Obj: 0.002516, Avg Recall: 0.864583, count: 96 -Region Avg IOU: 0.685905, Class: 1.000000, Obj: 0.455986, No Obj: 0.002469, Avg Recall: 0.893333, count: 75 -Region Avg IOU: 0.675649, Class: 1.000000, Obj: 0.421190, No Obj: 0.003147, Avg Recall: 0.888000, count: 125 -Region Avg IOU: 0.611501, Class: 1.000000, Obj: 0.386676, No Obj: 0.002931, Avg Recall: 0.762295, count: 122 -Region Avg IOU: 0.672319, Class: 1.000000, Obj: 0.464189, No Obj: 0.002767, Avg Recall: 0.851485, count: 101 -Region Avg IOU: 0.684646, Class: 1.000000, Obj: 0.490963, No Obj: 0.002874, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.653070, Class: 1.000000, Obj: 0.398688, No Obj: 0.002933, Avg Recall: 0.822034, count: 118 -Region Avg IOU: 0.685237, Class: 1.000000, Obj: 0.442384, No Obj: 0.002645, Avg Recall: 0.865979, count: 97 -6772: 0.430588, 0.421999 avg, 0.001000 rate, 28.756458 seconds, 1733632 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.636995, Class: 1.000000, Obj: 0.415909, No Obj: 0.002825, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.593204, Class: 1.000000, Obj: 0.425405, No Obj: 0.002571, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.645441, Class: 1.000000, Obj: 0.412353, No Obj: 0.003008, Avg Recall: 0.737589, count: 141 -Region Avg IOU: 0.642041, Class: 1.000000, Obj: 0.443783, No Obj: 0.002981, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.668751, Class: 1.000000, Obj: 0.454814, No Obj: 0.002684, Avg Recall: 0.804348, count: 92 -Region Avg IOU: 0.652215, Class: 1.000000, Obj: 0.421678, No Obj: 0.002578, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.641622, Class: 1.000000, Obj: 0.398539, No Obj: 0.002998, Avg Recall: 0.769841, count: 126 -Region Avg IOU: 0.657231, Class: 1.000000, Obj: 0.455073, No Obj: 0.002667, Avg Recall: 0.829268, count: 82 -6773: 0.450111, 0.424810 avg, 0.001000 rate, 27.202017 seconds, 1733888 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.649919, Class: 1.000000, Obj: 0.461791, No Obj: 0.002369, Avg Recall: 0.797619, count: 84 -Region Avg IOU: 0.649970, Class: 1.000000, Obj: 0.437388, No Obj: 0.002933, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.671856, Class: 1.000000, Obj: 0.448343, No Obj: 0.002624, Avg Recall: 0.846939, count: 98 -Region Avg IOU: 0.640996, Class: 1.000000, Obj: 0.473677, No Obj: 0.002956, Avg Recall: 0.777778, count: 90 -Region Avg IOU: 0.645269, Class: 1.000000, Obj: 0.417279, No Obj: 0.003073, Avg Recall: 0.784483, count: 116 -Region Avg IOU: 0.639140, Class: 1.000000, Obj: 0.492617, No Obj: 0.002863, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.640210, Class: 1.000000, Obj: 0.435863, No Obj: 0.002980, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.673957, Class: 1.000000, Obj: 0.442665, No Obj: 0.002414, Avg Recall: 0.847222, count: 72 -6774: 0.424971, 0.424826 avg, 0.001000 rate, 28.040792 seconds, 1734144 images -Loaded: 0.000116 seconds -Region Avg IOU: 0.614079, Class: 1.000000, Obj: 0.489266, No Obj: 0.002429, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.683070, Class: 1.000000, Obj: 0.480940, No Obj: 0.002829, Avg Recall: 0.868687, count: 99 -Region Avg IOU: 0.670555, Class: 1.000000, Obj: 0.446402, No Obj: 0.002777, Avg Recall: 0.834862, count: 109 -Region Avg IOU: 0.662974, Class: 1.000000, Obj: 0.471088, No Obj: 0.002783, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.675837, Class: 1.000000, Obj: 0.406536, No Obj: 0.002568, Avg Recall: 0.870968, count: 93 -Region Avg IOU: 0.650482, Class: 1.000000, Obj: 0.446058, No Obj: 0.002022, Avg Recall: 0.859375, count: 64 -Region Avg IOU: 0.649133, Class: 1.000000, Obj: 0.419296, No Obj: 0.003089, Avg Recall: 0.804878, count: 123 -Region Avg IOU: 0.638421, Class: 1.000000, Obj: 0.413701, No Obj: 0.002869, Avg Recall: 0.779661, count: 118 -6775: 0.388646, 0.421208 avg, 0.001000 rate, 28.931456 seconds, 1734400 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.655896, Class: 1.000000, Obj: 0.414675, No Obj: 0.002692, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.680327, Class: 1.000000, Obj: 0.490846, No Obj: 0.002588, Avg Recall: 0.866667, count: 90 -Region Avg IOU: 0.674262, Class: 1.000000, Obj: 0.419324, No Obj: 0.002789, Avg Recall: 0.833333, count: 120 -Region Avg IOU: 0.638873, Class: 1.000000, Obj: 0.449052, No Obj: 0.002184, Avg Recall: 0.747126, count: 87 -Region Avg IOU: 0.682199, Class: 1.000000, Obj: 0.439669, No Obj: 0.002930, Avg Recall: 0.841121, count: 107 -Region Avg IOU: 0.650200, Class: 1.000000, Obj: 0.422363, No Obj: 0.003185, Avg Recall: 0.797101, count: 138 -Region Avg IOU: 0.668460, Class: 1.000000, Obj: 0.419298, No Obj: 0.003004, Avg Recall: 0.823529, count: 119 -Region Avg IOU: 0.708102, Class: 1.000000, Obj: 0.445515, No Obj: 0.002907, Avg Recall: 0.869565, count: 115 -6776: 0.434976, 0.422585 avg, 0.001000 rate, 28.149429 seconds, 1734656 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.632445, Class: 1.000000, Obj: 0.403092, No Obj: 0.002777, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.668185, Class: 1.000000, Obj: 0.445911, No Obj: 0.002875, Avg Recall: 0.865546, count: 119 -Region Avg IOU: 0.635426, Class: 1.000000, Obj: 0.379323, No Obj: 0.002646, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.660808, Class: 1.000000, Obj: 0.419272, No Obj: 0.003222, Avg Recall: 0.765152, count: 132 -Region Avg IOU: 0.653784, Class: 1.000000, Obj: 0.424798, No Obj: 0.002767, Avg Recall: 0.796610, count: 118 -Region Avg IOU: 0.638316, Class: 1.000000, Obj: 0.420643, No Obj: 0.002150, Avg Recall: 0.846154, count: 91 -Region Avg IOU: 0.681108, Class: 1.000000, Obj: 0.417460, No Obj: 0.002668, Avg Recall: 0.833333, count: 120 -Region Avg IOU: 0.663592, Class: 1.000000, Obj: 0.459733, No Obj: 0.002876, Avg Recall: 0.855670, count: 97 -6777: 0.429058, 0.423232 avg, 0.001000 rate, 28.910992 seconds, 1734912 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.680743, Class: 1.000000, Obj: 0.446234, No Obj: 0.002389, Avg Recall: 0.836957, count: 92 -Region Avg IOU: 0.636810, Class: 1.000000, Obj: 0.422979, No Obj: 0.002108, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.657219, Class: 1.000000, Obj: 0.387724, No Obj: 0.002432, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.649187, Class: 1.000000, Obj: 0.384770, No Obj: 0.002602, Avg Recall: 0.787879, count: 132 -Region Avg IOU: 0.680140, Class: 1.000000, Obj: 0.444593, No Obj: 0.002677, Avg Recall: 0.848214, count: 112 -Region Avg IOU: 0.614657, Class: 1.000000, Obj: 0.388343, No Obj: 0.002638, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.664192, Class: 1.000000, Obj: 0.449396, No Obj: 0.002316, Avg Recall: 0.805195, count: 77 -Region Avg IOU: 0.658243, Class: 1.000000, Obj: 0.428747, No Obj: 0.002476, Avg Recall: 0.797753, count: 89 -6778: 0.395819, 0.420491 avg, 0.001000 rate, 29.545612 seconds, 1735168 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.629632, Class: 1.000000, Obj: 0.402667, No Obj: 0.002269, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.653908, Class: 1.000000, Obj: 0.438150, No Obj: 0.002728, Avg Recall: 0.834711, count: 121 -Region Avg IOU: 0.682411, Class: 1.000000, Obj: 0.410443, No Obj: 0.002845, Avg Recall: 0.821705, count: 129 -Region Avg IOU: 0.622636, Class: 1.000000, Obj: 0.389861, No Obj: 0.002550, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.625457, Class: 1.000000, Obj: 0.385553, No Obj: 0.002653, Avg Recall: 0.750000, count: 132 -Region Avg IOU: 0.690306, Class: 1.000000, Obj: 0.455420, No Obj: 0.002224, Avg Recall: 0.869565, count: 92 -Region Avg IOU: 0.655741, Class: 1.000000, Obj: 0.401137, No Obj: 0.002261, Avg Recall: 0.805310, count: 113 -Region Avg IOU: 0.650987, Class: 1.000000, Obj: 0.418793, No Obj: 0.002328, Avg Recall: 0.785714, count: 98 -6779: 0.444252, 0.422867 avg, 0.001000 rate, 29.376932 seconds, 1735424 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.685065, Class: 1.000000, Obj: 0.429961, No Obj: 0.002780, Avg Recall: 0.850467, count: 107 -Region Avg IOU: 0.679764, Class: 1.000000, Obj: 0.373452, No Obj: 0.002591, Avg Recall: 0.883929, count: 112 -Region Avg IOU: 0.664525, Class: 1.000000, Obj: 0.415770, No Obj: 0.002712, Avg Recall: 0.848740, count: 119 -Region Avg IOU: 0.681908, Class: 1.000000, Obj: 0.411786, No Obj: 0.003049, Avg Recall: 0.857143, count: 119 -Region Avg IOU: 0.596169, Class: 1.000000, Obj: 0.407680, No Obj: 0.002184, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.665417, Class: 1.000000, Obj: 0.413841, No Obj: 0.002924, Avg Recall: 0.854545, count: 110 -Region Avg IOU: 0.676029, Class: 1.000000, Obj: 0.393919, No Obj: 0.002214, Avg Recall: 0.860215, count: 93 -Region Avg IOU: 0.600449, Class: 1.000000, Obj: 0.398952, No Obj: 0.002312, Avg Recall: 0.726415, count: 106 -6780: 0.429423, 0.423523 avg, 0.001000 rate, 29.245176 seconds, 1735680 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.986043 seconds -Region Avg IOU: 0.549612, Class: 1.000000, Obj: 0.346153, No Obj: 0.004275, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.510520, Class: 1.000000, Obj: 0.352228, No Obj: 0.003592, Avg Recall: 0.573034, count: 89 -Region Avg IOU: 0.598133, Class: 1.000000, Obj: 0.374091, No Obj: 0.004199, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.515563, Class: 1.000000, Obj: 0.300268, No Obj: 0.004452, Avg Recall: 0.548148, count: 135 -Region Avg IOU: 0.492799, Class: 1.000000, Obj: 0.300741, No Obj: 0.003582, Avg Recall: 0.527559, count: 127 -Region Avg IOU: 0.543002, Class: 1.000000, Obj: 0.326236, No Obj: 0.004086, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.506012, Class: 1.000000, Obj: 0.282410, No Obj: 0.003922, Avg Recall: 0.564626, count: 147 -Region Avg IOU: 0.574133, Class: 1.000000, Obj: 0.319263, No Obj: 0.004436, Avg Recall: 0.696552, count: 145 -6781: 0.540491, 0.435220 avg, 0.001000 rate, 18.308041 seconds, 1735936 images -Loaded: 4.727621 seconds -Region Avg IOU: 0.483704, Class: 1.000000, Obj: 0.303118, No Obj: 0.004005, Avg Recall: 0.540741, count: 135 -Region Avg IOU: 0.523620, Class: 1.000000, Obj: 0.340657, No Obj: 0.003266, Avg Recall: 0.574468, count: 94 -Region Avg IOU: 0.510927, Class: 1.000000, Obj: 0.348668, No Obj: 0.003205, Avg Recall: 0.494118, count: 85 -Region Avg IOU: 0.512693, Class: 1.000000, Obj: 0.291046, No Obj: 0.003113, Avg Recall: 0.574468, count: 94 -Region Avg IOU: 0.541725, Class: 1.000000, Obj: 0.361860, No Obj: 0.003693, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.532404, Class: 1.000000, Obj: 0.333857, No Obj: 0.003922, Avg Recall: 0.602941, count: 136 -Region Avg IOU: 0.522287, Class: 1.000000, Obj: 0.316049, No Obj: 0.004114, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.514371, Class: 1.000000, Obj: 0.326204, No Obj: 0.004257, Avg Recall: 0.563025, count: 119 -6782: 0.419559, 0.433654 avg, 0.001000 rate, 19.441124 seconds, 1736192 images -Loaded: 2.985567 seconds -Region Avg IOU: 0.506835, Class: 1.000000, Obj: 0.324436, No Obj: 0.003995, Avg Recall: 0.573770, count: 122 -Region Avg IOU: 0.475632, Class: 1.000000, Obj: 0.282994, No Obj: 0.004173, Avg Recall: 0.493151, count: 146 -Region Avg IOU: 0.562413, Class: 1.000000, Obj: 0.364933, No Obj: 0.003609, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.510816, Class: 1.000000, Obj: 0.309410, No Obj: 0.003739, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.519946, Class: 1.000000, Obj: 0.357502, No Obj: 0.003190, Avg Recall: 0.571429, count: 70 -Region Avg IOU: 0.527327, Class: 1.000000, Obj: 0.327741, No Obj: 0.003867, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.484239, Class: 1.000000, Obj: 0.302431, No Obj: 0.003608, Avg Recall: 0.480392, count: 102 -Region Avg IOU: 0.532030, Class: 1.000000, Obj: 0.373030, No Obj: 0.003694, Avg Recall: 0.571429, count: 98 -6783: 0.424030, 0.432691 avg, 0.001000 rate, 20.382010 seconds, 1736448 images -Loaded: 0.413118 seconds -Region Avg IOU: 0.477563, Class: 1.000000, Obj: 0.322705, No Obj: 0.004049, Avg Recall: 0.548387, count: 124 -Region Avg IOU: 0.518126, Class: 1.000000, Obj: 0.389115, No Obj: 0.004685, Avg Recall: 0.598425, count: 127 -Region Avg IOU: 0.509069, Class: 1.000000, Obj: 0.405584, No Obj: 0.004658, Avg Recall: 0.529915, count: 117 -Region Avg IOU: 0.515919, Class: 1.000000, Obj: 0.321429, No Obj: 0.003759, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.540515, Class: 1.000000, Obj: 0.355939, No Obj: 0.004325, Avg Recall: 0.642857, count: 126 -Region Avg IOU: 0.550974, Class: 1.000000, Obj: 0.374683, No Obj: 0.004029, Avg Recall: 0.633333, count: 90 -Region Avg IOU: 0.494821, Class: 1.000000, Obj: 0.360874, No Obj: 0.004734, Avg Recall: 0.544715, count: 123 -Region Avg IOU: 0.528844, Class: 1.000000, Obj: 0.340771, No Obj: 0.004801, Avg Recall: 0.629371, count: 143 -6784: 0.509566, 0.440379 avg, 0.001000 rate, 16.287737 seconds, 1736704 images -Loaded: 4.213664 seconds -Region Avg IOU: 0.541314, Class: 1.000000, Obj: 0.384527, No Obj: 0.005384, Avg Recall: 0.619403, count: 134 -Region Avg IOU: 0.544240, Class: 1.000000, Obj: 0.346615, No Obj: 0.004714, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.612853, Class: 1.000000, Obj: 0.449625, No Obj: 0.004549, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.516774, Class: 1.000000, Obj: 0.379136, No Obj: 0.004743, Avg Recall: 0.560345, count: 116 -Region Avg IOU: 0.582324, Class: 1.000000, Obj: 0.428567, No Obj: 0.004665, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.517831, Class: 1.000000, Obj: 0.365241, No Obj: 0.004811, Avg Recall: 0.608000, count: 125 -Region Avg IOU: 0.500955, Class: 1.000000, Obj: 0.367806, No Obj: 0.004303, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.541230, Class: 1.000000, Obj: 0.394077, No Obj: 0.004545, Avg Recall: 0.606742, count: 89 -6785: 0.395928, 0.435934 avg, 0.001000 rate, 17.149361 seconds, 1736960 images -Loaded: 3.367885 seconds -Region Avg IOU: 0.463319, Class: 1.000000, Obj: 0.361065, No Obj: 0.004279, Avg Recall: 0.456311, count: 103 -Region Avg IOU: 0.524183, Class: 1.000000, Obj: 0.403609, No Obj: 0.005156, Avg Recall: 0.534483, count: 116 -Region Avg IOU: 0.537196, Class: 1.000000, Obj: 0.400855, No Obj: 0.004898, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.536621, Class: 1.000000, Obj: 0.417993, No Obj: 0.005138, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.470986, Class: 1.000000, Obj: 0.379863, No Obj: 0.004513, Avg Recall: 0.511628, count: 129 -Region Avg IOU: 0.519027, Class: 1.000000, Obj: 0.427464, No Obj: 0.004666, Avg Recall: 0.575758, count: 99 -Region Avg IOU: 0.505424, Class: 1.000000, Obj: 0.372540, No Obj: 0.005322, Avg Recall: 0.560345, count: 116 -Region Avg IOU: 0.575168, Class: 1.000000, Obj: 0.419355, No Obj: 0.005047, Avg Recall: 0.684783, count: 92 -6786: 0.399623, 0.432303 avg, 0.001000 rate, 20.084818 seconds, 1737216 images -Loaded: 0.585922 seconds -Region Avg IOU: 0.531766, Class: 1.000000, Obj: 0.470460, No Obj: 0.005113, Avg Recall: 0.584270, count: 89 -Region Avg IOU: 0.572692, Class: 1.000000, Obj: 0.457129, No Obj: 0.004047, Avg Recall: 0.662338, count: 77 -Region Avg IOU: 0.534996, Class: 1.000000, Obj: 0.389495, No Obj: 0.004620, Avg Recall: 0.560748, count: 107 -Region Avg IOU: 0.564739, Class: 1.000000, Obj: 0.456528, No Obj: 0.004913, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.499655, Class: 1.000000, Obj: 0.417362, No Obj: 0.006235, Avg Recall: 0.506494, count: 154 -Region Avg IOU: 0.580612, Class: 1.000000, Obj: 0.443394, No Obj: 0.004602, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.518270, Class: 1.000000, Obj: 0.428743, No Obj: 0.004265, Avg Recall: 0.617978, count: 89 -Region Avg IOU: 0.531868, Class: 1.000000, Obj: 0.442442, No Obj: 0.004196, Avg Recall: 0.647059, count: 85 -6787: 0.349410, 0.424013 avg, 0.001000 rate, 18.323252 seconds, 1737472 images -Loaded: 2.676951 seconds -Region Avg IOU: 0.564579, Class: 1.000000, Obj: 0.457938, No Obj: 0.005347, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.488341, Class: 1.000000, Obj: 0.390366, No Obj: 0.004839, Avg Recall: 0.553719, count: 121 -Region Avg IOU: 0.555739, Class: 1.000000, Obj: 0.410261, No Obj: 0.004626, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.517569, Class: 1.000000, Obj: 0.397396, No Obj: 0.004658, Avg Recall: 0.626168, count: 107 -Region Avg IOU: 0.569545, Class: 1.000000, Obj: 0.473273, No Obj: 0.004565, Avg Recall: 0.657534, count: 73 -Region Avg IOU: 0.487205, Class: 1.000000, Obj: 0.418595, No Obj: 0.004429, Avg Recall: 0.520833, count: 96 -Region Avg IOU: 0.562287, Class: 1.000000, Obj: 0.443529, No Obj: 0.005681, Avg Recall: 0.616000, count: 125 -Region Avg IOU: 0.527663, Class: 1.000000, Obj: 0.456710, No Obj: 0.004968, Avg Recall: 0.625000, count: 104 -6788: 0.409560, 0.422568 avg, 0.001000 rate, 17.392916 seconds, 1737728 images -Loaded: 3.640417 seconds -Region Avg IOU: 0.556949, Class: 1.000000, Obj: 0.436630, No Obj: 0.005000, Avg Recall: 0.628319, count: 113 -Region Avg IOU: 0.532243, Class: 1.000000, Obj: 0.422345, No Obj: 0.004728, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.535390, Class: 1.000000, Obj: 0.443577, No Obj: 0.005208, Avg Recall: 0.607843, count: 102 -Region Avg IOU: 0.491145, Class: 1.000000, Obj: 0.343632, No Obj: 0.005324, Avg Recall: 0.536765, count: 136 -Region Avg IOU: 0.519928, Class: 1.000000, Obj: 0.436311, No Obj: 0.004275, Avg Recall: 0.581081, count: 74 -Region Avg IOU: 0.562837, Class: 1.000000, Obj: 0.443006, No Obj: 0.005127, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.509428, Class: 1.000000, Obj: 0.412630, No Obj: 0.004803, Avg Recall: 0.586207, count: 87 -Region Avg IOU: 0.554337, Class: 1.000000, Obj: 0.427189, No Obj: 0.004896, Avg Recall: 0.651685, count: 89 -6789: 0.463887, 0.426700 avg, 0.001000 rate, 18.010931 seconds, 1737984 images -Loaded: 1.847679 seconds -Region Avg IOU: 0.523007, Class: 1.000000, Obj: 0.429084, No Obj: 0.005247, Avg Recall: 0.564815, count: 108 -Region Avg IOU: 0.497429, Class: 1.000000, Obj: 0.437723, No Obj: 0.004968, Avg Recall: 0.517544, count: 114 -Region Avg IOU: 0.550250, Class: 1.000000, Obj: 0.465458, No Obj: 0.004477, Avg Recall: 0.647727, count: 88 -Region Avg IOU: 0.506969, Class: 1.000000, Obj: 0.381119, No Obj: 0.004816, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.531320, Class: 1.000000, Obj: 0.415875, No Obj: 0.004443, Avg Recall: 0.602410, count: 83 -Region Avg IOU: 0.521508, Class: 1.000000, Obj: 0.428080, No Obj: 0.005082, Avg Recall: 0.605263, count: 114 -Region Avg IOU: 0.533011, Class: 1.000000, Obj: 0.429348, No Obj: 0.004416, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.497321, Class: 1.000000, Obj: 0.437352, No Obj: 0.004066, Avg Recall: 0.531250, count: 96 -6790: 0.377355, 0.421765 avg, 0.001000 rate, 17.854206 seconds, 1738240 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 19.210211 seconds -Region Avg IOU: 0.469395, Class: 1.000000, Obj: 0.385150, No Obj: 0.005332, Avg Recall: 0.504950, count: 101 -Region Avg IOU: 0.512705, Class: 1.000000, Obj: 0.390349, No Obj: 0.005203, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.489229, Class: 1.000000, Obj: 0.363327, No Obj: 0.004744, Avg Recall: 0.528846, count: 104 -Region Avg IOU: 0.476950, Class: 1.000000, Obj: 0.381797, No Obj: 0.004633, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.469405, Class: 1.000000, Obj: 0.348238, No Obj: 0.005346, Avg Recall: 0.500000, count: 138 -Region Avg IOU: 0.472317, Class: 1.000000, Obj: 0.355303, No Obj: 0.004223, Avg Recall: 0.523256, count: 86 -Region Avg IOU: 0.478927, Class: 1.000000, Obj: 0.360032, No Obj: 0.004315, Avg Recall: 0.573171, count: 82 -Region Avg IOU: 0.492286, Class: 1.000000, Obj: 0.392883, No Obj: 0.005316, Avg Recall: 0.572727, count: 110 -6791: 0.433921, 0.422981 avg, 0.001000 rate, 13.793791 seconds, 1738496 images -Loaded: 7.261442 seconds -Region Avg IOU: 0.480911, Class: 1.000000, Obj: 0.337674, No Obj: 0.005584, Avg Recall: 0.548148, count: 135 -Region Avg IOU: 0.535799, Class: 1.000000, Obj: 0.387009, No Obj: 0.004553, Avg Recall: 0.595745, count: 94 -Region Avg IOU: 0.531733, Class: 1.000000, Obj: 0.402555, No Obj: 0.004559, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.493909, Class: 1.000000, Obj: 0.335533, No Obj: 0.003582, Avg Recall: 0.566265, count: 83 -Region Avg IOU: 0.440292, Class: 1.000000, Obj: 0.323039, No Obj: 0.004232, Avg Recall: 0.456311, count: 103 -Region Avg IOU: 0.512554, Class: 1.000000, Obj: 0.381785, No Obj: 0.004990, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.516976, Class: 1.000000, Obj: 0.396711, No Obj: 0.005366, Avg Recall: 0.552845, count: 123 -Region Avg IOU: 0.520916, Class: 1.000000, Obj: 0.353764, No Obj: 0.005115, Avg Recall: 0.600000, count: 115 -6792: 0.419246, 0.422607 avg, 0.001000 rate, 18.397766 seconds, 1738752 images -Loaded: 2.538639 seconds -Region Avg IOU: 0.476851, Class: 1.000000, Obj: 0.386754, No Obj: 0.004383, Avg Recall: 0.510417, count: 96 -Region Avg IOU: 0.509596, Class: 1.000000, Obj: 0.343107, No Obj: 0.004335, Avg Recall: 0.555556, count: 99 -Region Avg IOU: 0.492352, Class: 1.000000, Obj: 0.351935, No Obj: 0.004968, Avg Recall: 0.500000, count: 118 -Region Avg IOU: 0.512818, Class: 1.000000, Obj: 0.371389, No Obj: 0.004587, Avg Recall: 0.553191, count: 94 -Region Avg IOU: 0.434557, Class: 1.000000, Obj: 0.283789, No Obj: 0.003668, Avg Recall: 0.444444, count: 99 -Region Avg IOU: 0.450172, Class: 1.000000, Obj: 0.330029, No Obj: 0.005142, Avg Recall: 0.467213, count: 122 -Region Avg IOU: 0.472539, Class: 1.000000, Obj: 0.341036, No Obj: 0.003965, Avg Recall: 0.516484, count: 91 -Region Avg IOU: 0.503731, Class: 1.000000, Obj: 0.364563, No Obj: 0.005019, Avg Recall: 0.548077, count: 104 -6793: 0.443739, 0.424720 avg, 0.001000 rate, 15.265843 seconds, 1739008 images -Loaded: 5.371013 seconds -Region Avg IOU: 0.477892, Class: 1.000000, Obj: 0.347667, No Obj: 0.004605, Avg Recall: 0.515625, count: 128 -Region Avg IOU: 0.493441, Class: 1.000000, Obj: 0.338178, No Obj: 0.004096, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.520443, Class: 1.000000, Obj: 0.365569, No Obj: 0.004806, Avg Recall: 0.559633, count: 109 -Region Avg IOU: 0.435014, Class: 1.000000, Obj: 0.297806, No Obj: 0.004648, Avg Recall: 0.444444, count: 108 -Region Avg IOU: 0.517474, Class: 1.000000, Obj: 0.352806, No Obj: 0.004498, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.492155, Class: 1.000000, Obj: 0.316024, No Obj: 0.004725, Avg Recall: 0.558333, count: 120 -Region Avg IOU: 0.436155, Class: 1.000000, Obj: 0.309404, No Obj: 0.004043, Avg Recall: 0.482456, count: 114 -Region Avg IOU: 0.480404, Class: 1.000000, Obj: 0.334976, No Obj: 0.004680, Avg Recall: 0.532110, count: 109 -6794: 0.473362, 0.429585 avg, 0.001000 rate, 14.322608 seconds, 1739264 images -Loaded: 5.676873 seconds -Region Avg IOU: 0.491487, Class: 1.000000, Obj: 0.359831, No Obj: 0.004677, Avg Recall: 0.532710, count: 107 -Region Avg IOU: 0.479145, Class: 1.000000, Obj: 0.326871, No Obj: 0.004658, Avg Recall: 0.505155, count: 97 -Region Avg IOU: 0.483558, Class: 1.000000, Obj: 0.291158, No Obj: 0.004090, Avg Recall: 0.541176, count: 85 -Region Avg IOU: 0.531063, Class: 1.000000, Obj: 0.334431, No Obj: 0.004618, Avg Recall: 0.605042, count: 119 -Region Avg IOU: 0.520118, Class: 1.000000, Obj: 0.323002, No Obj: 0.004111, Avg Recall: 0.595238, count: 84 -Region Avg IOU: 0.559715, Class: 1.000000, Obj: 0.341355, No Obj: 0.004421, Avg Recall: 0.670732, count: 82 -Region Avg IOU: 0.528033, Class: 1.000000, Obj: 0.347044, No Obj: 0.004493, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.518066, Class: 1.000000, Obj: 0.380620, No Obj: 0.004359, Avg Recall: 0.604167, count: 96 -6795: 0.393501, 0.425976 avg, 0.001000 rate, 14.376008 seconds, 1739520 images -Loaded: 6.516865 seconds -Region Avg IOU: 0.489896, Class: 1.000000, Obj: 0.317513, No Obj: 0.005144, Avg Recall: 0.540323, count: 124 -Region Avg IOU: 0.497944, Class: 1.000000, Obj: 0.350641, No Obj: 0.004855, Avg Recall: 0.536000, count: 125 -Region Avg IOU: 0.456420, Class: 1.000000, Obj: 0.321266, No Obj: 0.005089, Avg Recall: 0.496124, count: 129 -Region Avg IOU: 0.517181, Class: 1.000000, Obj: 0.370920, No Obj: 0.004057, Avg Recall: 0.590361, count: 83 -Region Avg IOU: 0.458000, Class: 1.000000, Obj: 0.341026, No Obj: 0.004149, Avg Recall: 0.445378, count: 119 -Region Avg IOU: 0.505510, Class: 1.000000, Obj: 0.334282, No Obj: 0.004705, Avg Recall: 0.570248, count: 121 -Region Avg IOU: 0.498495, Class: 1.000000, Obj: 0.305831, No Obj: 0.004898, Avg Recall: 0.521739, count: 115 -Region Avg IOU: 0.520156, Class: 1.000000, Obj: 0.380249, No Obj: 0.00Saving weights to ./person9300/backup/tyvp_93_9c512_6800.weights -4685, Avg Recall: 0.579545, count: 88 -6796: 0.442897, 0.427668 avg, 0.001000 rate, 16.328623 seconds, 1739776 images -Loaded: 4.379666 seconds -Region Avg IOU: 0.511087, Class: 1.000000, Obj: 0.348595, No Obj: 0.004466, Avg Recall: 0.520833, count: 96 -Region Avg IOU: 0.486101, Class: 1.000000, Obj: 0.322040, No Obj: 0.004617, Avg Recall: 0.532847, count: 137 -Region Avg IOU: 0.531559, Class: 1.000000, Obj: 0.426282, No Obj: 0.004930, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.479620, Class: 1.000000, Obj: 0.323010, No Obj: 0.004936, Avg Recall: 0.500000, count: 112 -Region Avg IOU: 0.504932, Class: 1.000000, Obj: 0.354660, No Obj: 0.004125, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.495398, Class: 1.000000, Obj: 0.375187, No Obj: 0.004337, Avg Recall: 0.527473, count: 91 -Region Avg IOU: 0.494487, Class: 1.000000, Obj: 0.306689, No Obj: 0.004522, Avg Recall: 0.549180, count: 122 -Region Avg IOU: 0.443642, Class: 1.000000, Obj: 0.296873, No Obj: 0.004383, Avg Recall: 0.491803, count: 122 -6797: 0.479695, 0.432871 avg, 0.001000 rate, 13.555478 seconds, 1740032 images -Loaded: 7.589231 seconds -Region Avg IOU: 0.540041, Class: 1.000000, Obj: 0.343468, No Obj: 0.004547, Avg Recall: 0.629214, count: 89 -Region Avg IOU: 0.470721, Class: 1.000000, Obj: 0.317253, No Obj: 0.004539, Avg Recall: 0.474576, count: 118 -Region Avg IOU: 0.501451, Class: 1.000000, Obj: 0.332808, No Obj: 0.005360, Avg Recall: 0.555556, count: 135 -Region Avg IOU: 0.497498, Class: 1.000000, Obj: 0.334136, No Obj: 0.004760, Avg Recall: 0.539130, count: 115 -Region Avg IOU: 0.482523, Class: 1.000000, Obj: 0.330085, No Obj: 0.004786, Avg Recall: 0.504505, count: 111 -Region Avg IOU: 0.459272, Class: 1.000000, Obj: 0.315415, No Obj: 0.004901, Avg Recall: 0.500000, count: 112 -Region Avg IOU: 0.492762, Class: 1.000000, Obj: 0.346322, No Obj: 0.003818, Avg Recall: 0.519481, count: 77 -Region Avg IOU: 0.526697, Class: 1.000000, Obj: 0.366172, No Obj: 0.004620, Avg Recall: 0.602273, count: 88 -6798: 0.444621, 0.434046 avg, 0.001000 rate, 15.022115 seconds, 1740288 images -Loaded: 4.574495 seconds -Region Avg IOU: 0.470848, Class: 1.000000, Obj: 0.362061, No Obj: 0.004308, Avg Recall: 0.552845, count: 123 -Region Avg IOU: 0.433478, Class: 1.000000, Obj: 0.337620, No Obj: 0.003746, Avg Recall: 0.461538, count: 91 -Region Avg IOU: 0.505331, Class: 1.000000, Obj: 0.339209, No Obj: 0.004304, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.521263, Class: 1.000000, Obj: 0.386711, No Obj: 0.004056, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.537700, Class: 1.000000, Obj: 0.360711, No Obj: 0.005286, Avg Recall: 0.625000, count: 120 -Region Avg IOU: 0.550559, Class: 1.000000, Obj: 0.372675, No Obj: 0.005013, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.537397, Class: 1.000000, Obj: 0.349097, No Obj: 0.004272, Avg Recall: 0.619565, count: 92 -Region Avg IOU: 0.525767, Class: 1.000000, Obj: 0.334405, No Obj: 0.004882, Avg Recall: 0.566038, count: 106 -6799: 0.434376, 0.434079 avg, 0.001000 rate, 16.715712 seconds, 1740544 images -Loaded: 3.944322 seconds -Region Avg IOU: 0.519975, Class: 1.000000, Obj: 0.353618, No Obj: 0.005923, Avg Recall: 0.587786, count: 131 -Region Avg IOU: 0.563257, Class: 1.000000, Obj: 0.372701, No Obj: 0.005157, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.508286, Class: 1.000000, Obj: 0.352166, No Obj: 0.005070, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.510231, Class: 1.000000, Obj: 0.352485, No Obj: 0.004990, Avg Recall: 0.567797, count: 118 -Region Avg IOU: 0.448269, Class: 1.000000, Obj: 0.358706, No Obj: 0.004009, Avg Recall: 0.515464, count: 97 -Region Avg IOU: 0.470856, Class: 1.000000, Obj: 0.404839, No Obj: 0.004778, Avg Recall: 0.495327, count: 107 -Region Avg IOU: 0.499204, Class: 1.000000, Obj: 0.346636, No Obj: 0.005082, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.440056, Class: 1.000000, Obj: 0.338830, No Obj: 0.005007, Avg Recall: 0.478633, count: 117 -6800: 0.452145, 0.435886 avg, 0.001000 rate, 18.063934 seconds, 1740800 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 2.375332 seconds -Region Avg IOU: 0.577308, Class: 1.000000, Obj: 0.412909, No Obj: 0.005597, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.510116, Class: 1.000000, Obj: 0.405963, No Obj: 0.005444, Avg Recall: 0.544118, count: 136 -Region Avg IOU: 0.501455, Class: 1.000000, Obj: 0.405226, No Obj: 0.005326, Avg Recall: 0.525000, count: 120 -Region Avg IOU: 0.512632, Class: 1.000000, Obj: 0.371312, No Obj: 0.005252, Avg Recall: 0.588710, count: 124 -Region Avg IOU: 0.478097, Class: 1.000000, Obj: 0.355005, No Obj: 0.005062, Avg Recall: 0.496350, count: 137 -Region Avg IOU: 0.508120, Class: 1.000000, Obj: 0.383328, No Obj: 0.004775, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.536288, Class: 1.000000, Obj: 0.412233, No Obj: 0.005358, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.544324, Class: 1.000000, Obj: 0.416028, No Obj: 0.004806, Avg Recall: 0.626087, count: 115 -6801: 0.489547, 0.441252 avg, 0.001000 rate, 21.218861 seconds, 1741056 images -Loaded: 0.562745 seconds -Region Avg IOU: 0.461340, Class: 1.000000, Obj: 0.364049, No Obj: 0.004741, Avg Recall: 0.480769, count: 104 -Region Avg IOU: 0.490232, Class: 1.000000, Obj: 0.388068, No Obj: 0.005537, Avg Recall: 0.542636, count: 129 -Region Avg IOU: 0.566038, Class: 1.000000, Obj: 0.422650, No Obj: 0.004787, Avg Recall: 0.680851, count: 94 -Region Avg IOU: 0.546693, Class: 1.000000, Obj: 0.424589, No Obj: 0.004895, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.506830, Class: 1.000000, Obj: 0.350692, No Obj: 0.004410, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.511443, Class: 1.000000, Obj: 0.417943, No Obj: 0.003846, Avg Recall: 0.554217, count: 83 -Region Avg IOU: 0.559658, Class: 1.000000, Obj: 0.403457, No Obj: 0.006169, Avg Recall: 0.642857, count: 140 -Region Avg IOU: 0.527904, Class: 1.000000, Obj: 0.385406, No Obj: 0.005014, Avg Recall: 0.614035, count: 114 -6802: 0.437596, 0.440886 avg, 0.001000 rate, 18.517927 seconds, 1741312 images -Loaded: 1.366868 seconds -Region Avg IOU: 0.542677, Class: 1.000000, Obj: 0.451941, No Obj: 0.004683, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.508424, Class: 1.000000, Obj: 0.362547, No Obj: 0.005465, Avg Recall: 0.563025, count: 119 -Region Avg IOU: 0.546403, Class: 1.000000, Obj: 0.411319, No Obj: 0.004323, Avg Recall: 0.629214, count: 89 -Region Avg IOU: 0.554444, Class: 1.000000, Obj: 0.421569, No Obj: 0.005039, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.569429, Class: 1.000000, Obj: 0.430435, No Obj: 0.004368, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.507757, Class: 1.000000, Obj: 0.374490, No Obj: 0.005996, Avg Recall: 0.573333, count: 150 -Region Avg IOU: 0.558551, Class: 1.000000, Obj: 0.406282, No Obj: 0.004313, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.514530, Class: 1.000000, Obj: 0.391256, No Obj: 0.005185, Avg Recall: 0.555556, count: 108 -6803: 0.434688, 0.440266 avg, 0.001000 rate, 17.065800 seconds, 1741568 images -Loaded: 5.732672 seconds -Region Avg IOU: 0.562723, Class: 1.000000, Obj: 0.450467, No Obj: 0.005126, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.530283, Class: 1.000000, Obj: 0.432689, No Obj: 0.005067, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.593789, Class: 1.000000, Obj: 0.446645, No Obj: 0.005662, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.488836, Class: 1.000000, Obj: 0.369736, No Obj: 0.004487, Avg Recall: 0.515464, count: 97 -Region Avg IOU: 0.456232, Class: 1.000000, Obj: 0.352926, No Obj: 0.004276, Avg Recall: 0.491525, count: 118 -Region Avg IOU: 0.535998, Class: 1.000000, Obj: 0.360881, No Obj: 0.005773, Avg Recall: 0.616541, count: 133 -Region Avg IOU: 0.557737, Class: 1.000000, Obj: 0.414735, No Obj: 0.004889, Avg Recall: 0.620690, count: 87 -Region Avg IOU: 0.491226, Class: 1.000000, Obj: 0.364740, No Obj: 0.004831, Avg Recall: 0.566176, count: 136 -6804: 0.468310, 0.443071 avg, 0.001000 rate, 20.922102 seconds, 1741824 images -Loaded: 0.966099 seconds -Region Avg IOU: 0.495633, Class: 1.000000, Obj: 0.411250, No Obj: 0.004394, Avg Recall: 0.515464, count: 97 -Region Avg IOU: 0.476530, Class: 1.000000, Obj: 0.368451, No Obj: 0.004549, Avg Recall: 0.495146, count: 103 -Region Avg IOU: 0.523364, Class: 1.000000, Obj: 0.390643, No Obj: 0.004932, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.506627, Class: 1.000000, Obj: 0.397057, No Obj: 0.004854, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.522418, Class: 1.000000, Obj: 0.419581, No Obj: 0.005293, Avg Recall: 0.605839, count: 137 -Region Avg IOU: 0.447560, Class: 1.000000, Obj: 0.377788, No Obj: 0.004070, Avg Recall: 0.451613, count: 93 -Region Avg IOU: 0.501790, Class: 1.000000, Obj: 0.368201, No Obj: 0.006017, Avg Recall: 0.588710, count: 124 -Region Avg IOU: 0.537373, Class: 1.000000, Obj: 0.419248, No Obj: 0.004783, Avg Recall: 0.602151, count: 93 -6805: 0.413367, 0.440100 avg, 0.001000 rate, 20.170645 seconds, 1742080 images -Loaded: 2.647778 seconds -Region Avg IOU: 0.538254, Class: 1.000000, Obj: 0.394965, No Obj: 0.005297, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.510925, Class: 1.000000, Obj: 0.396303, No Obj: 0.004389, Avg Recall: 0.564706, count: 85 -Region Avg IOU: 0.575304, Class: 1.000000, Obj: 0.418706, No Obj: 0.004818, Avg Recall: 0.650485, count: 103 -Region Avg IOU: 0.521064, Class: 1.000000, Obj: 0.362921, No Obj: 0.004500, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.514127, Class: 1.000000, Obj: 0.400621, No Obj: 0.005482, Avg Recall: 0.581197, count: 117 -Region Avg IOU: 0.532448, Class: 1.000000, Obj: 0.382720, No Obj: 0.004890, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.562839, Class: 1.000000, Obj: 0.392850, No Obj: 0.005426, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.485617, Class: 1.000000, Obj: 0.397201, No Obj: 0.004934, Avg Recall: 0.513274, count: 113 -6806: 0.402824, 0.436373 avg, 0.001000 rate, 19.682177 seconds, 1742336 images -Loaded: 0.345075 seconds -Region Avg IOU: 0.472821, Class: 1.000000, Obj: 0.355356, No Obj: 0.004699, Avg Recall: 0.532258, count: 124 -Region Avg IOU: 0.496531, Class: 1.000000, Obj: 0.386094, No Obj: 0.003655, Avg Recall: 0.459459, count: 74 -Region Avg IOU: 0.529442, Class: 1.000000, Obj: 0.342668, No Obj: 0.004095, Avg Recall: 0.589474, count: 95 -Region Avg IOU: 0.535960, Class: 1.000000, Obj: 0.399437, No Obj: 0.004662, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.491480, Class: 1.000000, Obj: 0.394071, No Obj: 0.004431, Avg Recall: 0.513514, count: 111 -Region Avg IOU: 0.498035, Class: 1.000000, Obj: 0.361476, No Obj: 0.004313, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.473388, Class: 1.000000, Obj: 0.362978, No Obj: 0.004238, Avg Recall: 0.521368, count: 117 -Region Avg IOU: 0.512193, Class: 1.000000, Obj: 0.360423, No Obj: 0.004386, Avg Recall: 0.601770, count: 113 -6807: 0.371566, 0.429892 avg, 0.001000 rate, 17.366814 seconds, 1742592 images -Loaded: 3.991196 seconds -Region Avg IOU: 0.469284, Class: 1.000000, Obj: 0.340178, No Obj: 0.003745, Avg Recall: 0.529412, count: 102 -Region Avg IOU: 0.569421, Class: 1.000000, Obj: 0.405404, No Obj: 0.004233, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.484582, Class: 1.000000, Obj: 0.340658, No Obj: 0.004753, Avg Recall: 0.527273, count: 110 -Region Avg IOU: 0.546077, Class: 1.000000, Obj: 0.411711, No Obj: 0.005261, Avg Recall: 0.630252, count: 119 -Region Avg IOU: 0.516229, Class: 1.000000, Obj: 0.381853, No Obj: 0.004084, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.437367, Class: 1.000000, Obj: 0.328986, No Obj: 0.004292, Avg Recall: 0.471545, count: 123 -Region Avg IOU: 0.480415, Class: 1.000000, Obj: 0.338217, No Obj: 0.003923, Avg Recall: 0.524752, count: 101 -Region Avg IOU: 0.515681, Class: 1.000000, Obj: 0.353739, No Obj: 0.004808, Avg Recall: 0.560976, count: 123 -6808: 0.439511, 0.430854 avg, 0.001000 rate, 17.958750 seconds, 1742848 images -Loaded: 3.177189 seconds -Region Avg IOU: 0.453838, Class: 1.000000, Obj: 0.340477, No Obj: 0.003881, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.482199, Class: 1.000000, Obj: 0.327497, No Obj: 0.004347, Avg Recall: 0.518868, count: 106 -Region Avg IOU: 0.515768, Class: 1.000000, Obj: 0.400760, No Obj: 0.004309, Avg Recall: 0.576577, count: 111 -Region Avg IOU: 0.474779, Class: 1.000000, Obj: 0.370737, No Obj: 0.003974, Avg Recall: 0.483051, count: 118 -Region Avg IOU: 0.563945, Class: 1.000000, Obj: 0.401247, No Obj: 0.004837, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.529772, Class: 1.000000, Obj: 0.364586, No Obj: 0.004319, Avg Recall: 0.627907, count: 86 -Region Avg IOU: 0.559937, Class: 1.000000, Obj: 0.405137, No Obj: 0.004224, Avg Recall: 0.632911, count: 79 -Region Avg IOU: 0.486423, Class: 1.000000, Obj: 0.317599, No Obj: 0.004447, Avg Recall: 0.570248, count: 121 -6809: 0.406978, 0.428466 avg, 0.001000 rate, 17.352240 seconds, 1743104 images -Loaded: 1.683421 seconds -Region Avg IOU: 0.509440, Class: 1.000000, Obj: 0.346882, No Obj: 0.003988, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.471119, Class: 1.000000, Obj: 0.385935, No Obj: 0.003467, Avg Recall: 0.564706, count: 85 -Region Avg IOU: 0.535128, Class: 1.000000, Obj: 0.353981, No Obj: 0.004114, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.518236, Class: 1.000000, Obj: 0.384257, No Obj: 0.004633, Avg Recall: 0.557252, count: 131 -Region Avg IOU: 0.508981, Class: 1.000000, Obj: 0.320024, No Obj: 0.004284, Avg Recall: 0.584071, count: 113 -Region Avg IOU: 0.465027, Class: 1.000000, Obj: 0.355013, No Obj: 0.004429, Avg Recall: 0.491935, count: 124 -Region Avg IOU: 0.484705, Class: 1.000000, Obj: 0.385040, No Obj: 0.003902, Avg Recall: 0.554348, count: 92 -Region Avg IOU: 0.509284, Class: 1.000000, Obj: 0.334206, No Obj: 0.004322, Avg Recall: 0.571429, count: 105 -6810: 0.405775, 0.426197 avg, 0.001000 rate, 19.214523 seconds, 1743360 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000062 seconds -Region Avg IOU: 0.626805, Class: 1.000000, Obj: 0.471481, No Obj: 0.003627, Avg Recall: 0.776978, count: 139 -Region Avg IOU: 0.641810, Class: 1.000000, Obj: 0.498320, No Obj: 0.003159, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.650686, Class: 1.000000, Obj: 0.452158, No Obj: 0.002841, Avg Recall: 0.829787, count: 94 -Region Avg IOU: 0.668224, Class: 1.000000, Obj: 0.472990, No Obj: 0.003396, Avg Recall: 0.818182, count: 121 -Region Avg IOU: 0.629583, Class: 1.000000, Obj: 0.440287, No Obj: 0.002803, Avg Recall: 0.750000, count: 88 -Region Avg IOU: 0.579584, Class: 1.000000, Obj: 0.382552, No Obj: 0.002679, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.663140, Class: 1.000000, Obj: 0.502654, No Obj: 0.002539, Avg Recall: 0.802817, count: 71 -Region Avg IOU: 0.606975, Class: 1.000000, Obj: 0.459849, No Obj: 0.002492, Avg Recall: 0.684211, count: 76 -6811: 0.358207, 0.419398 avg, 0.001000 rate, 26.906351 seconds, 1743616 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.658667, Class: 1.000000, Obj: 0.427881, No Obj: 0.003251, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.636103, Class: 1.000000, Obj: 0.439387, No Obj: 0.003444, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.652700, Class: 1.000000, Obj: 0.447917, No Obj: 0.003254, Avg Recall: 0.818965, count: 116 -Region Avg IOU: 0.622069, Class: 1.000000, Obj: 0.356968, No Obj: 0.003558, Avg Recall: 0.721429, count: 140 -Region Avg IOU: 0.628350, Class: 1.000000, Obj: 0.432867, No Obj: 0.003415, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.675082, Class: 1.000000, Obj: 0.456419, No Obj: 0.002524, Avg Recall: 0.844156, count: 77 -Region Avg IOU: 0.611087, Class: 1.000000, Obj: 0.437368, No Obj: 0.002930, Avg Recall: 0.673684, count: 95 -Region Avg IOU: 0.622304, Class: 1.000000, Obj: 0.448340, No Obj: 0.002840, Avg Recall: 0.759259, count: 108 -6812: 0.433860, 0.420844 avg, 0.001000 rate, 26.599707 seconds, 1743872 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.616538, Class: 1.000000, Obj: 0.410943, No Obj: 0.003011, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.627774, Class: 1.000000, Obj: 0.431676, No Obj: 0.003202, Avg Recall: 0.744186, count: 129 -Region Avg IOU: 0.640618, Class: 1.000000, Obj: 0.445027, No Obj: 0.002824, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.654792, Class: 1.000000, Obj: 0.472989, No Obj: 0.002646, Avg Recall: 0.777778, count: 90 -Region Avg IOU: 0.603753, Class: 1.000000, Obj: 0.399338, No Obj: 0.002547, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.654839, Class: 1.000000, Obj: 0.409209, No Obj: 0.002597, Avg Recall: 0.810127, count: 79 -Region Avg IOU: 0.649519, Class: 1.000000, Obj: 0.418074, No Obj: 0.003099, Avg Recall: 0.806202, count: 129 -Region Avg IOU: 0.634736, Class: 1.000000, Obj: 0.397711, No Obj: 0.003244, Avg Recall: 0.760563, count: 142 -6813: 0.455923, 0.424352 avg, 0.001000 rate, 25.950636 seconds, 1744128 images -Loaded: 0.000065 seconds -Region Avg IOU: 0.614879, Class: 1.000000, Obj: 0.432627, No Obj: 0.002825, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.641127, Class: 1.000000, Obj: 0.432081, No Obj: 0.002432, Avg Recall: 0.759494, count: 79 -Region Avg IOU: 0.596067, Class: 1.000000, Obj: 0.418136, No Obj: 0.002436, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.610026, Class: 1.000000, Obj: 0.379758, No Obj: 0.002741, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.635412, Class: 1.000000, Obj: 0.407537, No Obj: 0.002671, Avg Recall: 0.758621, count: 87 -Region Avg IOU: 0.668760, Class: 1.000000, Obj: 0.428875, No Obj: 0.002671, Avg Recall: 0.830189, count: 106 -Region Avg IOU: 0.656528, Class: 1.000000, Obj: 0.459307, No Obj: 0.003148, Avg Recall: 0.817308, count: 104 -Region Avg IOU: 0.660899, Class: 1.000000, Obj: 0.411017, No Obj: 0.002181, Avg Recall: 0.833333, count: 90 -6814: 0.355806, 0.417498 avg, 0.001000 rate, 26.280651 seconds, 1744384 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.642781, Class: 1.000000, Obj: 0.370498, No Obj: 0.002442, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.628911, Class: 1.000000, Obj: 0.399051, No Obj: 0.002812, Avg Recall: 0.756303, count: 119 -Region Avg IOU: 0.629093, Class: 1.000000, Obj: 0.408297, No Obj: 0.002905, Avg Recall: 0.776859, count: 121 -Region Avg IOU: 0.659506, Class: 1.000000, Obj: 0.433301, No Obj: 0.002381, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.620402, Class: 1.000000, Obj: 0.396971, No Obj: 0.002672, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.642313, Class: 1.000000, Obj: 0.372647, No Obj: 0.003169, Avg Recall: 0.763514, count: 148 -Region Avg IOU: 0.670840, Class: 1.000000, Obj: 0.425454, No Obj: 0.002644, Avg Recall: 0.831325, count: 83 -Region Avg IOU: 0.639029, Class: 1.000000, Obj: 0.404877, No Obj: 0.002680, Avg Recall: 0.831579, count: 95 -6815: 0.452189, 0.420967 avg, 0.001000 rate, 26.193602 seconds, 1744640 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.620489, Class: 1.000000, Obj: 0.341239, No Obj: 0.002303, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.608175, Class: 1.000000, Obj: 0.355717, No Obj: 0.002782, Avg Recall: 0.718750, count: 128 -Region Avg IOU: 0.627834, Class: 1.000000, Obj: 0.408917, No Obj: 0.002806, Avg Recall: 0.750000, count: 124 -Region Avg IOU: 0.664485, Class: 1.000000, Obj: 0.417630, No Obj: 0.002735, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.635874, Class: 1.000000, Obj: 0.412344, No Obj: 0.002404, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.617291, Class: 1.000000, Obj: 0.399903, No Obj: 0.002540, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.641768, Class: 1.000000, Obj: 0.405748, No Obj: 0.003067, Avg Recall: 0.741007, count: 139 -Region Avg IOU: 0.625927, Class: 1.000000, Obj: 0.378800, No Obj: 0.002944, Avg Recall: 0.745455, count: 110 -6816: 0.475432, 0.426413 avg, 0.001000 rate, 26.987860 seconds, 1744896 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.654923, Class: 1.000000, Obj: 0.365114, No Obj: 0.002784, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.644905, Class: 1.000000, Obj: 0.445667, No Obj: 0.002587, Avg Recall: 0.810127, count: 79 -Region Avg IOU: 0.634782, Class: 1.000000, Obj: 0.377692, No Obj: 0.002715, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.640335, Class: 1.000000, Obj: 0.399426, No Obj: 0.002515, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.669642, Class: 1.000000, Obj: 0.368345, No Obj: 0.002671, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.665456, Class: 1.000000, Obj: 0.416191, No Obj: 0.002749, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.653216, Class: 1.000000, Obj: 0.423635, No Obj: 0.003098, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.644483, Class: 1.000000, Obj: 0.431772, No Obj: 0.002802, Avg Recall: 0.800000, count: 105 -6817: 0.446679, 0.428440 avg, 0.001000 rate, 26.938431 seconds, 1745152 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.633245, Class: 1.000000, Obj: 0.414914, No Obj: 0.003211, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.641320, Class: 1.000000, Obj: 0.382937, No Obj: 0.002752, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.628464, Class: 1.000000, Obj: 0.436202, No Obj: 0.002573, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.624239, Class: 1.000000, Obj: 0.422033, No Obj: 0.002622, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.647615, Class: 1.000000, Obj: 0.437696, No Obj: 0.002893, Avg Recall: 0.786885, count: 122 -Region Avg IOU: 0.658048, Class: 1.000000, Obj: 0.444404, No Obj: 0.002999, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.612992, Class: 1.000000, Obj: 0.369286, No Obj: 0.002887, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.653469, Class: 1.000000, Obj: 0.441523, No Obj: 0.003276, Avg Recall: 0.770992, count: 131 -6818: 0.442803, 0.429876 avg, 0.001000 rate, 25.777948 seconds, 1745408 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.642086, Class: 1.000000, Obj: 0.386813, No Obj: 0.002991, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.648241, Class: 1.000000, Obj: 0.438534, No Obj: 0.003354, Avg Recall: 0.791304, count: 115 -Region Avg IOU: 0.644338, Class: 1.000000, Obj: 0.435210, No Obj: 0.002934, Avg Recall: 0.826446, count: 121 -Region Avg IOU: 0.683976, Class: 1.000000, Obj: 0.462394, No Obj: 0.003189, Avg Recall: 0.838384, count: 99 -Region Avg IOU: 0.640278, Class: 1.000000, Obj: 0.431830, No Obj: 0.002616, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.655511, Class: 1.000000, Obj: 0.437347, No Obj: 0.002734, Avg Recall: 0.802198, count: 91 -Region Avg IOU: 0.643284, Class: 1.000000, Obj: 0.421383, No Obj: 0.002679, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.630914, Class: 1.000000, Obj: 0.386066, No Obj: 0.002773, Avg Recall: 0.729167, count: 96 -6819: 0.386831, 0.425572 avg, 0.001000 rate, 25.307995 seconds, 1745664 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.638872, Class: 1.000000, Obj: 0.439898, No Obj: 0.003282, Avg Recall: 0.757143, count: 140 -Region Avg IOU: 0.631557, Class: 1.000000, Obj: 0.414047, No Obj: 0.002947, Avg Recall: 0.752137, count: 117 -Region Avg IOU: 0.661258, Class: 1.000000, Obj: 0.427446, No Obj: 0.002880, Avg Recall: 0.819048, count: 105 -Region Avg IOU: 0.651735, Class: 1.000000, Obj: 0.402943, No Obj: 0.003055, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.635764, Class: 1.000000, Obj: 0.449784, No Obj: 0.002806, Avg Recall: 0.801980, count: 101 -Region Avg IOU: 0.663835, Class: 1.000000, Obj: 0.386992, No Obj: 0.003517, Avg Recall: 0.792308, count: 130 -Region Avg IOU: 0.634558, Class: 1.000000, Obj: 0.426085, No Obj: 0.002732, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.606206, Class: 1.000000, Obj: 0.432025, No Obj: 0.002805, Avg Recall: 0.716981, count: 106 -6820: 0.419227, 0.424937 avg, 0.001000 rate, 26.707157 seconds, 1745920 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.186017 seconds -Region Avg IOU: 0.553540, Class: 1.000000, Obj: 0.320949, No Obj: 0.003976, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.518733, Class: 1.000000, Obj: 0.325120, No Obj: 0.004235, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.490681, Class: 1.000000, Obj: 0.309449, No Obj: 0.004450, Avg Recall: 0.609375, count: 128 -Region Avg IOU: 0.580691, Class: 1.000000, Obj: 0.338902, No Obj: 0.004798, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.498502, Class: 1.000000, Obj: 0.293960, No Obj: 0.003286, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.549105, Class: 1.000000, Obj: 0.393194, No Obj: 0.004475, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.509884, Class: 1.000000, Obj: 0.335624, No Obj: 0.004242, Avg Recall: 0.570248, count: 121 -Region Avg IOU: 0.596742, Class: 1.000000, Obj: 0.369186, No Obj: 0.003679, Avg Recall: 0.700000, count: 80 -6821: 0.458572, 0.428301 avg, 0.001000 rate, 18.907152 seconds, 1746176 images -Loaded: 1.187659 seconds -Region Avg IOU: 0.570952, Class: 1.000000, Obj: 0.358929, No Obj: 0.004240, Avg Recall: 0.703297, count: 91 -Region Avg IOU: 0.493982, Class: 1.000000, Obj: 0.323610, No Obj: 0.003886, Avg Recall: 0.522936, count: 109 -Region Avg IOU: 0.517192, Class: 1.000000, Obj: 0.323979, No Obj: 0.004009, Avg Recall: 0.557692, count: 104 -Region Avg IOU: 0.567300, Class: 1.000000, Obj: 0.381041, No Obj: 0.004459, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.604298, Class: 1.000000, Obj: 0.396595, No Obj: 0.004479, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.535418, Class: 1.000000, Obj: 0.365405, No Obj: 0.004471, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.528973, Class: 1.000000, Obj: 0.353474, No Obj: 0.003828, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.535425, Class: 1.000000, Obj: 0.344404, No Obj: 0.004324, Avg Recall: 0.606061, count: 99 -6822: 0.391128, 0.424583 avg, 0.001000 rate, 20.412952 seconds, 1746432 images -Loaded: 1.127188 seconds -Region Avg IOU: 0.480938, Class: 1.000000, Obj: 0.351526, No Obj: 0.003793, Avg Recall: 0.500000, count: 116 -Region Avg IOU: 0.495710, Class: 1.000000, Obj: 0.326046, No Obj: 0.004438, Avg Recall: 0.548872, count: 133 -Region Avg IOU: 0.467758, Class: 1.000000, Obj: 0.345741, No Obj: 0.004142, Avg Recall: 0.533333, count: 105 -Region Avg IOU: 0.540270, Class: 1.000000, Obj: 0.386916, No Obj: 0.004798, Avg Recall: 0.620155, count: 129 -Region Avg IOU: 0.490861, Class: 1.000000, Obj: 0.348209, No Obj: 0.004602, Avg Recall: 0.546154, count: 130 -Region Avg IOU: 0.534606, Class: 1.000000, Obj: 0.416642, No Obj: 0.004641, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.541503, Class: 1.000000, Obj: 0.346671, No Obj: 0.004401, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.512461, Class: 1.000000, Obj: 0.356408, No Obj: 0.004224, Avg Recall: 0.576923, count: 104 -6823: 0.506491, 0.432774 avg, 0.001000 rate, 19.041464 seconds, 1746688 images -Loaded: 0.685866 seconds -Region Avg IOU: 0.501414, Class: 1.000000, Obj: 0.360675, No Obj: 0.003918, Avg Recall: 0.550000, count: 100 -Region Avg IOU: 0.524274, Class: 1.000000, Obj: 0.410590, No Obj: 0.004582, Avg Recall: 0.625000, count: 120 -Region Avg IOU: 0.526728, Class: 1.000000, Obj: 0.390031, No Obj: 0.004137, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.545507, Class: 1.000000, Obj: 0.407656, No Obj: 0.004710, Avg Recall: 0.636364, count: 121 -Region Avg IOU: 0.502469, Class: 1.000000, Obj: 0.363836, No Obj: 0.005109, Avg Recall: 0.524823, count: 141 -Region Avg IOU: 0.486680, Class: 1.000000, Obj: 0.342605, No Obj: 0.004124, Avg Recall: 0.510000, count: 100 -Region Avg IOU: 0.558742, Class: 1.000000, Obj: 0.398783, No Obj: 0.003987, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.478453, Class: 1.000000, Obj: 0.309434, No Obj: 0.004117, Avg Recall: 0.459459, count: 111 -6824: 0.427325, 0.432229 avg, 0.001000 rate, 14.946384 seconds, 1746944 images -Loaded: 3.560914 seconds -Region Avg IOU: 0.558209, Class: 1.000000, Obj: 0.434147, No Obj: 0.005805, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.469685, Class: 1.000000, Obj: 0.363816, No Obj: 0.004405, Avg Recall: 0.480000, count: 125 -Region Avg IOU: 0.535599, Class: 1.000000, Obj: 0.391344, No Obj: 0.003879, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.477134, Class: 1.000000, Obj: 0.354407, No Obj: 0.003752, Avg Recall: 0.505618, count: 89 -Region Avg IOU: 0.543483, Class: 1.000000, Obj: 0.389365, No Obj: 0.004789, Avg Recall: 0.577586, count: 116 -Region Avg IOU: 0.483483, Class: 1.000000, Obj: 0.349628, No Obj: 0.005260, Avg Recall: 0.544715, count: 123 -Region Avg IOU: 0.502088, Class: 1.000000, Obj: 0.387033, No Obj: 0.004897, Avg Recall: 0.560748, count: 107 -Region Avg IOU: 0.536730, Class: 1.000000, Obj: 0.435334, No Obj: 0.004858, Avg Recall: 0.626263, count: 99 -6825: 0.427524, 0.431759 avg, 0.001000 rate, 19.064724 seconds, 1747200 images -Loaded: 0.833522 seconds -Region Avg IOU: 0.495726, Class: 1.000000, Obj: 0.384262, No Obj: 0.005514, Avg Recall: 0.556391, count: 133 -Region Avg IOU: 0.577766, Class: 1.000000, Obj: 0.412263, No Obj: 0.004967, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.499509, Class: 1.000000, Obj: 0.368270, No Obj: 0.005149, Avg Recall: 0.527778, count: 108 -Region Avg IOU: 0.455617, Class: 1.000000, Obj: 0.350107, No Obj: 0.005515, Avg Recall: 0.507463, count: 134 -Region Avg IOU: 0.558231, Class: 1.000000, Obj: 0.458238, No Obj: 0.005336, Avg Recall: 0.601770, count: 113 -Region Avg IOU: 0.536924, Class: 1.000000, Obj: 0.412981, No Obj: 0.004758, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.466228, Class: 1.000000, Obj: 0.378754, No Obj: 0.004925, Avg Recall: 0.525926, count: 135 -Region Avg IOU: 0.492759, Class: 1.000000, Obj: 0.365575, No Obj: 0.005836, Avg Recall: 0.532544, count: 169 -6826: 0.482952, 0.436878 avg, 0.001000 rate, 18.524595 seconds, 1747456 images -Loaded: 2.790318 seconds -Region Avg IOU: 0.488315, Class: 1.000000, Obj: 0.415782, No Obj: 0.004451, Avg Recall: 0.560976, count: 82 -Region Avg IOU: 0.492782, Class: 1.000000, Obj: 0.386875, No Obj: 0.004233, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.558700, Class: 1.000000, Obj: 0.478425, No Obj: 0.005334, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.481013, Class: 1.000000, Obj: 0.425572, No Obj: 0.004892, Avg Recall: 0.520000, count: 100 -Region Avg IOU: 0.549053, Class: 1.000000, Obj: 0.422726, No Obj: 0.004447, Avg Recall: 0.641975, count: 81 -Region Avg IOU: 0.540277, Class: 1.000000, Obj: 0.390751, No Obj: 0.005576, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.538156, Class: 1.000000, Obj: 0.406001, No Obj: 0.004117, Avg Recall: 0.608247, count: 97 -Region Avg IOU: 0.535102, Class: 1.000000, Obj: 0.424033, No Obj: 0.005525, Avg Recall: 0.603774, count: 106 -6827: 0.377898, 0.430980 avg, 0.001000 rate, 14.810527 seconds, 1747712 images -Loaded: 5.629221 seconds -Region Avg IOU: 0.483687, Class: 1.000000, Obj: 0.397588, No Obj: 0.004663, Avg Recall: 0.546392, count: 97 -Region Avg IOU: 0.587693, Class: 1.000000, Obj: 0.415344, No Obj: 0.005015, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.550266, Class: 1.000000, Obj: 0.435737, No Obj: 0.004554, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.431891, Class: 1.000000, Obj: 0.353658, No Obj: 0.003793, Avg Recall: 0.434783, count: 92 -Region Avg IOU: 0.504462, Class: 1.000000, Obj: 0.403025, No Obj: 0.004163, Avg Recall: 0.602410, count: 83 -Region Avg IOU: 0.477890, Class: 1.000000, Obj: 0.322276, No Obj: 0.004880, Avg Recall: 0.525253, count: 99 -Region Avg IOU: 0.508227, Class: 1.000000, Obj: 0.376374, No Obj: 0.005047, Avg Recall: 0.577982, count: 109 -Region Avg IOU: 0.511110, Class: 1.000000, Obj: 0.392086, No Obj: 0.004936, Avg Recall: 0.573913, count: 115 -6828: 0.385163, 0.426398 avg, 0.001000 rate, 16.950422 seconds, 1747968 images -Loaded: 4.773819 seconds -Region Avg IOU: 0.541956, Class: 1.000000, Obj: 0.420492, No Obj: 0.005716, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.578116, Class: 1.000000, Obj: 0.431583, No Obj: 0.005239, Avg Recall: 0.696629, count: 89 -Region Avg IOU: 0.489147, Class: 1.000000, Obj: 0.390555, No Obj: 0.005934, Avg Recall: 0.557252, count: 131 -Region Avg IOU: 0.490917, Class: 1.000000, Obj: 0.381974, No Obj: 0.004724, Avg Recall: 0.528846, count: 104 -Region Avg IOU: 0.523370, Class: 1.000000, Obj: 0.422605, No Obj: 0.005053, Avg Recall: 0.616822, count: 107 -Region Avg IOU: 0.520076, Class: 1.000000, Obj: 0.390386, No Obj: 0.005085, Avg Recall: 0.603306, count: 121 -Region Avg IOU: 0.484675, Class: 1.000000, Obj: 0.381385, No Obj: 0.005046, Avg Recall: 0.507812, count: 128 -Region Avg IOU: 0.494560, Class: 1.000000, Obj: 0.319549, No Obj: 0.004176, Avg Recall: 0.540000, count: 100 -6829: 0.487277, 0.432486 avg, 0.001000 rate, 17.462337 seconds, 1748224 images -Loaded: 4.571661 seconds -Region Avg IOU: 0.518192, Class: 1.000000, Obj: 0.382014, No Obj: 0.004697, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.494883, Class: 1.000000, Obj: 0.380148, No Obj: 0.005105, Avg Recall: 0.542857, count: 105 -Region Avg IOU: 0.541615, Class: 1.000000, Obj: 0.394209, No Obj: 0.004943, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.498308, Class: 1.000000, Obj: 0.380696, No Obj: 0.005113, Avg Recall: 0.609375, count: 128 -Region Avg IOU: 0.515212, Class: 1.000000, Obj: 0.396414, No Obj: 0.004966, Avg Recall: 0.559322, count: 118 -Region Avg IOU: 0.514748, Class: 1.000000, Obj: 0.361346, No Obj: 0.004699, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.484044, Class: 1.000000, Obj: 0.360760, No Obj: 0.004674, Avg Recall: 0.513043, count: 115 -Region Avg IOU: 0.479815, Class: 1.000000, Obj: 0.367649, No Obj: 0.004582, Avg Recall: 0.526786, count: 112 -6830: 0.490377, 0.438275 avg, 0.001000 rate, 18.801445 seconds, 1748480 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000056 seconds -Region Avg IOU: 0.644966, Class: 1.000000, Obj: 0.519004, No Obj: 0.003807, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.616938, Class: 1.000000, Obj: 0.478302, No Obj: 0.003473, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.579753, Class: 1.000000, Obj: 0.504204, No Obj: 0.003605, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.622206, Class: 1.000000, Obj: 0.477369, No Obj: 0.004293, Avg Recall: 0.783333, count: 120 -Region Avg IOU: 0.599003, Class: 1.000000, Obj: 0.445047, No Obj: 0.003733, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.602733, Class: 1.000000, Obj: 0.471470, No Obj: 0.003481, Avg Recall: 0.711340, count: 97 -Region Avg IOU: 0.600526, Class: 1.000000, Obj: 0.442511, No Obj: 0.003718, Avg Recall: 0.742188, count: 128 -Region Avg IOU: 0.595146, Class: 1.000000, Obj: 0.427473, No Obj: 0.004377, Avg Recall: 0.721854, count: 151 -6831: 0.450426, 0.439490 avg, 0.001000 rate, 24.329002 seconds, 1748736 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.535900, Class: 1.000000, Obj: 0.419292, No Obj: 0.003152, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.543460, Class: 1.000000, Obj: 0.409768, No Obj: 0.003045, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.618789, Class: 1.000000, Obj: 0.467739, No Obj: 0.003063, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.619934, Class: 1.000000, Obj: 0.446759, No Obj: 0.003584, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.672937, Class: 1.000000, Obj: 0.484813, No Obj: 0.003732, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.604360, Class: 1.000000, Obj: 0.421602, No Obj: 0.003842, Avg Recall: 0.730769, count: 130 -Region Avg IOU: 0.592368, Class: 1.000000, Obj: 0.426955, No Obj: 0.003911, Avg Recall: 0.682171, count: 129 -Region Avg IOU: 0.620118, Class: 1.000000, Obj: 0.448643, No Obj: 0.003888, Avg Recall: 0.736364, count: 110 -6832: 0.465546, 0.442096 avg, 0.001000 rate, 24.756927 seconds, 1748992 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.607879, Class: 1.000000, Obj: 0.446962, No Obj: 0.003527, Avg Recall: 0.728000, count: 125 -Region Avg IOU: 0.596779, Class: 1.000000, Obj: 0.408857, No Obj: 0.003511, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.580570, Class: 1.000000, Obj: 0.411282, No Obj: 0.003142, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.649253, Class: 1.000000, Obj: 0.404367, No Obj: 0.003570, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.589375, Class: 1.000000, Obj: 0.413854, No Obj: 0.003649, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.624438, Class: 1.000000, Obj: 0.452271, No Obj: 0.003970, Avg Recall: 0.717742, count: 124 -Region Avg IOU: 0.636500, Class: 1.000000, Obj: 0.442043, No Obj: 0.003598, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.560563, Class: 1.000000, Obj: 0.421322, No Obj: 0.002982, Avg Recall: 0.626263, count: 99 -6833: 0.408193, 0.438706 avg, 0.001000 rate, 24.156412 seconds, 1749248 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.590160, Class: 1.000000, Obj: 0.366827, No Obj: 0.003417, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.604703, Class: 1.000000, Obj: 0.426577, No Obj: 0.003002, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.610876, Class: 1.000000, Obj: 0.423142, No Obj: 0.002886, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.656172, Class: 1.000000, Obj: 0.391885, No Obj: 0.002738, Avg Recall: 0.805195, count: 77 -Region Avg IOU: 0.629595, Class: 1.000000, Obj: 0.453094, No Obj: 0.002785, Avg Recall: 0.703297, count: 91 -Region Avg IOU: 0.645095, Class: 1.000000, Obj: 0.450379, No Obj: 0.003417, Avg Recall: 0.762712, count: 118 -Region Avg IOU: 0.548448, Class: 1.000000, Obj: 0.370092, No Obj: 0.003332, Avg Recall: 0.633588, count: 131 -Region Avg IOU: 0.600468, Class: 1.000000, Obj: 0.399448, No Obj: 0.003095, Avg Recall: 0.706897, count: 116 -6834: 0.383810, 0.433216 avg, 0.001000 rate, 25.571411 seconds, 1749504 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.614547, Class: 1.000000, Obj: 0.413326, No Obj: 0.003180, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.587544, Class: 1.000000, Obj: 0.416055, No Obj: 0.003176, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.652002, Class: 1.000000, Obj: 0.400711, No Obj: 0.003533, Avg Recall: 0.833333, count: 108 -Region Avg IOU: 0.644267, Class: 1.000000, Obj: 0.405080, No Obj: 0.002791, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.603354, Class: 1.000000, Obj: 0.366567, No Obj: 0.003150, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.642829, Class: 1.000000, Obj: 0.396627, No Obj: 0.003408, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.572604, Class: 1.000000, Obj: 0.370383, No Obj: 0.003170, Avg Recall: 0.661290, count: 124 -Region Avg IOU: 0.651969, Class: 1.000000, Obj: 0.461069, No Obj: 0.003120, Avg Recall: 0.795699, count: 93 -6835: 0.439036, 0.433798 avg, 0.001000 rate, 25.497042 seconds, 1749760 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.621017, Class: 1.000000, Obj: 0.402693, No Obj: 0.002799, Avg Recall: 0.818182, count: 77 -Region Avg IOU: 0.613795, Class: 1.000000, Obj: 0.401194, No Obj: 0.003180, Avg Recall: 0.746154, count: 130 -Region Avg IOU: 0.645849, Class: 1.000000, Obj: 0.435357, No Obj: 0.002887, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.589038, Class: 1.000000, Obj: 0.377507, No Obj: 0.003038, Avg Recall: 0.651852, count: 135 -Region Avg IOU: 0.665797, Class: 1.000000, Obj: 0.426541, No Obj: 0.002871, Avg Recall: 0.808989, count: 89 -Region Avg IOU: 0.587401, Class: 1.000000, Obj: 0.372769, No Obj: 0.002923, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.611355, Class: 1.000000, Obj: 0.369232, No Obj: 0.002872, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.617569, Class: 1.000000, Obj: 0.408327, No Obj: 0.002894, Avg Recall: 0.731959, count: 97 -6836: 0.376610, 0.428079 avg, 0.001000 rate, 23.845768 seconds, 1750016 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.633171, Class: 1.000000, Obj: 0.399378, No Obj: 0.003093, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.622118, Class: 1.000000, Obj: 0.448184, No Obj: 0.002743, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.623498, Class: 1.000000, Obj: 0.384051, No Obj: 0.003197, Avg Recall: 0.744681, count: 94 -Region Avg IOU: 0.658310, Class: 1.000000, Obj: 0.410032, No Obj: 0.002734, Avg Recall: 0.800000, count: 85 -Region Avg IOU: 0.637753, Class: 1.000000, Obj: 0.383828, No Obj: 0.002612, Avg Recall: 0.750000, count: 88 -Region Avg IOU: 0.631956, Class: 1.000000, Obj: 0.388381, No Obj: 0.002657, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.582863, Class: 1.000000, Obj: 0.370435, No Obj: 0.002901, Avg Recall: 0.707965, count: 113 -Region Avg IOU: 0.580625, Class: 1.000000, Obj: 0.362785, No Obj: 0.003409, Avg Recall: 0.664336, count: 143 -6837: 0.408069, 0.426078 avg, 0.001000 rate, 24.071104 seconds, 1750272 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.611315, Class: 1.000000, Obj: 0.373100, No Obj: 0.003025, Avg Recall: 0.766917, count: 133 -Region Avg IOU: 0.606928, Class: 1.000000, Obj: 0.389260, No Obj: 0.003066, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.639305, Class: 1.000000, Obj: 0.429810, No Obj: 0.002608, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.585597, Class: 1.000000, Obj: 0.338027, No Obj: 0.002982, Avg Recall: 0.643478, count: 115 -Region Avg IOU: 0.631022, Class: 1.000000, Obj: 0.362831, No Obj: 0.003233, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.660737, Class: 1.000000, Obj: 0.466391, No Obj: 0.003966, Avg Recall: 0.818182, count: 121 -Region Avg IOU: 0.668500, Class: 1.000000, Obj: 0.430685, No Obj: 0.002797, Avg Recall: 0.820225, count: 89 -Region Avg IOU: 0.637929, Class: 1.000000, Obj: 0.403212, No Obj: 0.003044, Avg Recall: 0.767123, count: 73 -6838: 0.413838, 0.424854 avg, 0.001000 rate, 25.480154 seconds, 1750528 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.604765, Class: 1.000000, Obj: 0.396476, No Obj: 0.002737, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.643854, Class: 1.000000, Obj: 0.424570, No Obj: 0.002649, Avg Recall: 0.795181, count: 83 -Region Avg IOU: 0.582640, Class: 1.000000, Obj: 0.315690, No Obj: 0.003386, Avg Recall: 0.685039, count: 127 -Region Avg IOU: 0.595660, Class: 1.000000, Obj: 0.398522, No Obj: 0.002860, Avg Recall: 0.689655, count: 87 -Region Avg IOU: 0.643758, Class: 1.000000, Obj: 0.408058, No Obj: 0.003398, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.641526, Class: 1.000000, Obj: 0.397658, No Obj: 0.002875, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.631553, Class: 1.000000, Obj: 0.366692, No Obj: 0.002882, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.634591, Class: 1.000000, Obj: 0.390110, No Obj: 0.003318, Avg Recall: 0.781250, count: 128 -6839: 0.407002, 0.423069 avg, 0.001000 rate, 24.913359 seconds, 1750784 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.617891, Class: 1.000000, Obj: 0.377317, No Obj: 0.003574, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.611936, Class: 1.000000, Obj: 0.375850, No Obj: 0.002599, Avg Recall: 0.707317, count: 82 -Region Avg IOU: 0.655792, Class: 1.000000, Obj: 0.414796, No Obj: 0.002837, Avg Recall: 0.801980, count: 101 -Region Avg IOU: 0.671352, Class: 1.000000, Obj: 0.468549, No Obj: 0.003532, Avg Recall: 0.837398, count: 123 -Region Avg IOU: 0.607719, Class: 1.000000, Obj: 0.428214, No Obj: 0.002879, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.618964, Class: 1.000000, Obj: 0.398883, No Obj: 0.002938, Avg Recall: 0.688073, count: 109 -Region Avg IOU: 0.614182, Class: 1.000000, Obj: 0.404988, No Obj: 0.002989, Avg Recall: 0.735849, count: 106 -Region Avg IOU: 0.629245, Class: 1.000000, Obj: 0.402881, No Obj: 0.002841, Avg Recall: 0.768421, count: 95 -6840: 0.384768, 0.419239 avg, 0.001000 rate, 25.679527 seconds, 1751040 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000066 seconds -Region Avg IOU: 0.659061, Class: 1.000000, Obj: 0.381253, No Obj: 0.002988, Avg Recall: 0.831776, count: 107 -Region Avg IOU: 0.658435, Class: 1.000000, Obj: 0.404986, No Obj: 0.002692, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.689830, Class: 1.000000, Obj: 0.456236, No Obj: 0.002529, Avg Recall: 0.875000, count: 88 -Region Avg IOU: 0.676881, Class: 1.000000, Obj: 0.443645, No Obj: 0.002502, Avg Recall: 0.842593, count: 108 -Region Avg IOU: 0.650727, Class: 1.000000, Obj: 0.394974, No Obj: 0.002487, Avg Recall: 0.817308, count: 104 -Region Avg IOU: 0.642958, Class: 1.000000, Obj: 0.413029, No Obj: 0.002634, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.661625, Class: 1.000000, Obj: 0.480569, No Obj: 0.002423, Avg Recall: 0.808081, count: 99 -Region Avg IOU: 0.657375, Class: 1.000000, Obj: 0.407965, No Obj: 0.002671, Avg Recall: 0.795276, count: 127 -6841: 0.434026, 0.420718 avg, 0.001000 rate, 29.090321 seconds, 1751296 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.639382, Class: 1.000000, Obj: 0.351556, No Obj: 0.002614, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.667359, Class: 1.000000, Obj: 0.437021, No Obj: 0.003176, Avg Recall: 0.806452, count: 124 -Region Avg IOU: 0.641006, Class: 1.000000, Obj: 0.377118, No Obj: 0.002522, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.640654, Class: 1.000000, Obj: 0.359611, No Obj: 0.002613, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.666362, Class: 1.000000, Obj: 0.432596, No Obj: 0.002202, Avg Recall: 0.845238, count: 84 -Region Avg IOU: 0.698127, Class: 1.000000, Obj: 0.450959, No Obj: 0.002659, Avg Recall: 0.843478, count: 115 -Region Avg IOU: 0.603733, Class: 1.000000, Obj: 0.385984, No Obj: 0.002734, Avg Recall: 0.743802, count: 121 -Region Avg IOU: 0.720351, Class: 1.000000, Obj: 0.500682, No Obj: 0.002977, Avg Recall: 0.929293, count: 99 -6842: 0.449015, 0.423547 avg, 0.001000 rate, 28.585272 seconds, 1751552 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.656640, Class: 1.000000, Obj: 0.448924, No Obj: 0.002797, Avg Recall: 0.827586, count: 116 -Region Avg IOU: 0.688412, Class: 1.000000, Obj: 0.481318, No Obj: 0.002477, Avg Recall: 0.868132, count: 91 -Region Avg IOU: 0.667563, Class: 1.000000, Obj: 0.453946, No Obj: 0.002701, Avg Recall: 0.846154, count: 117 -Region Avg IOU: 0.669604, Class: 1.000000, Obj: 0.450997, No Obj: 0.002798, Avg Recall: 0.861314, count: 137 -Region Avg IOU: 0.644851, Class: 1.000000, Obj: 0.415051, No Obj: 0.002418, Avg Recall: 0.810811, count: 74 -Region Avg IOU: 0.679180, Class: 1.000000, Obj: 0.440642, No Obj: 0.002644, Avg Recall: 0.867347, count: 98 -Region Avg IOU: 0.675979, Class: 1.000000, Obj: 0.433758, No Obj: 0.002773, Avg Recall: 0.859813, count: 107 -Region Avg IOU: 0.638486, Class: 1.000000, Obj: 0.453141, No Obj: 0.002162, Avg Recall: 0.762887, count: 97 -6843: 0.448210, 0.426014 avg, 0.001000 rate, 29.001221 seconds, 1751808 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.646212, Class: 1.000000, Obj: 0.489513, No Obj: 0.002830, Avg Recall: 0.821138, count: 123 -Region Avg IOU: 0.645923, Class: 1.000000, Obj: 0.404565, No Obj: 0.002607, Avg Recall: 0.746988, count: 83 -Region Avg IOU: 0.657288, Class: 1.000000, Obj: 0.410164, No Obj: 0.002859, Avg Recall: 0.812500, count: 128 -Region Avg IOU: 0.622914, Class: 1.000000, Obj: 0.375046, No Obj: 0.002460, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.704700, Class: 1.000000, Obj: 0.445688, No Obj: 0.002675, Avg Recall: 0.884956, count: 113 -Region Avg IOU: 0.693736, Class: 1.000000, Obj: 0.503476, No Obj: 0.002500, Avg Recall: 0.851485, count: 101 -Region Avg IOU: 0.659738, Class: 1.000000, Obj: 0.429675, No Obj: 0.002837, Avg Recall: 0.818182, count: 121 -Region Avg IOU: 0.668795, Class: 1.000000, Obj: 0.523465, No Obj: 0.002736, Avg Recall: 0.817073, count: 82 -6844: 0.426667, 0.426079 avg, 0.001000 rate, 29.795704 seconds, 1752064 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.631132, Class: 1.000000, Obj: 0.417860, No Obj: 0.002210, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.651049, Class: 1.000000, Obj: 0.439676, No Obj: 0.002196, Avg Recall: 0.835165, count: 91 -Region Avg IOU: 0.653858, Class: 1.000000, Obj: 0.422930, No Obj: 0.002360, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.618863, Class: 1.000000, Obj: 0.397162, No Obj: 0.002682, Avg Recall: 0.714286, count: 133 -Region Avg IOU: 0.657077, Class: 1.000000, Obj: 0.424292, No Obj: 0.002741, Avg Recall: 0.803279, count: 122 -Region Avg IOU: 0.671467, Class: 1.000000, Obj: 0.357511, No Obj: 0.003121, Avg Recall: 0.845161, count: 155 -Region Avg IOU: 0.641086, Class: 1.000000, Obj: 0.457058, No Obj: 0.002344, Avg Recall: 0.795455, count: 88 -Region Avg IOU: 0.662107, Class: 1.000000, Obj: 0.444161, No Obj: 0.002553, Avg Recall: 0.795918, count: 98 -6845: 0.449911, 0.428462 avg, 0.001000 rate, 28.691748 seconds, 1752320 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.641260, Class: 1.000000, Obj: 0.397116, No Obj: 0.002740, Avg Recall: 0.752066, count: 121 -Region Avg IOU: 0.638692, Class: 1.000000, Obj: 0.411903, No Obj: 0.002863, Avg Recall: 0.811475, count: 122 -Region Avg IOU: 0.664725, Class: 1.000000, Obj: 0.404588, No Obj: 0.002509, Avg Recall: 0.828829, count: 111 -Region Avg IOU: 0.667319, Class: 1.000000, Obj: 0.389751, No Obj: 0.002500, Avg Recall: 0.853211, count: 109 -Region Avg IOU: 0.652203, Class: 1.000000, Obj: 0.427380, No Obj: 0.002667, Avg Recall: 0.828829, count: 111 -Region Avg IOU: 0.709511, Class: 1.000000, Obj: 0.495271, No Obj: 0.002201, Avg Recall: 0.913043, count: 69 -Region Avg IOU: 0.672380, Class: 1.000000, Obj: 0.377217, No Obj: 0.003154, Avg Recall: 0.820513, count: 156 -Region Avg IOU: 0.615896, Class: 1.000000, Obj: 0.408276, No Obj: 0.002475, Avg Recall: 0.725664, count: 113 -6846: 0.431374, 0.428753 avg, 0.001000 rate, 29.071945 seconds, 1752576 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.675499, Class: 1.000000, Obj: 0.417237, No Obj: 0.002278, Avg Recall: 0.849462, count: 93 -Region Avg IOU: 0.687186, Class: 1.000000, Obj: 0.382926, No Obj: 0.002722, Avg Recall: 0.861789, count: 123 -Region Avg IOU: 0.657694, Class: 1.000000, Obj: 0.398686, No Obj: 0.002544, Avg Recall: 0.833333, count: 114 -Region Avg IOU: 0.680098, Class: 1.000000, Obj: 0.423510, No Obj: 0.002255, Avg Recall: 0.852632, count: 95 -Region Avg IOU: 0.659575, Class: 1.000000, Obj: 0.412086, No Obj: 0.002395, Avg Recall: 0.846847, count: 111 -Region Avg IOU: 0.620970, Class: 1.000000, Obj: 0.351124, No Obj: 0.001971, Avg Recall: 0.831169, count: 77 -Region Avg IOU: 0.659877, Class: 1.000000, Obj: 0.411214, No Obj: 0.002348, Avg Recall: 0.795455, count: 132 -Region Avg IOU: 0.675574, Class: 1.000000, Obj: 0.437418, No Obj: 0.002275, Avg Recall: 0.850000, count: 100 -6847: 0.429805, 0.428859 avg, 0.001000 rate, 30.277136 seconds, 1752832 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.681472, Class: 1.000000, Obj: 0.455050, No Obj: 0.002220, Avg Recall: 0.841121, count: 107 -Region Avg IOU: 0.650850, Class: 1.000000, Obj: 0.401387, No Obj: 0.002164, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.700447, Class: 1.000000, Obj: 0.412508, No Obj: 0.002639, Avg Recall: 0.889908, count: 109 -Region Avg IOU: 0.700974, Class: 1.000000, Obj: 0.372387, No Obj: 0.002283, Avg Recall: 0.888889, count: 90 -Region Avg IOU: 0.682409, Class: 1.000000, Obj: 0.427548, No Obj: 0.002413, Avg Recall: 0.826923, count: 104 -Region Avg IOU: 0.683599, Class: 1.000000, Obj: 0.446899, No Obj: 0.002304, Avg Recall: 0.830189, count: 106 -Region Avg IOU: 0.657408, Class: 1.000000, Obj: 0.378237, No Obj: 0.002337, Avg Recall: 0.873786, count: 103 -Region Avg IOU: 0.667864, Class: 1.000000, Obj: 0.455911, No Obj: 0.002085, Avg Recall: 0.829787, count: 94 -6848: 0.399749, 0.425948 avg, 0.001000 rate, 29.999489 seconds, 1753088 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.645095, Class: 1.000000, Obj: 0.420144, No Obj: 0.002409, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.633479, Class: 1.000000, Obj: 0.345084, No Obj: 0.002660, Avg Recall: 0.780488, count: 123 -Region Avg IOU: 0.693692, Class: 1.000000, Obj: 0.420634, No Obj: 0.002337, Avg Recall: 0.895833, count: 96 -Region Avg IOU: 0.657088, Class: 1.000000, Obj: 0.424717, No Obj: 0.002545, Avg Recall: 0.829787, count: 94 -Region Avg IOU: 0.700227, Class: 1.000000, Obj: 0.365147, No Obj: 0.002795, Avg Recall: 0.854701, count: 117 -Region Avg IOU: 0.666540, Class: 1.000000, Obj: 0.419562, No Obj: 0.002293, Avg Recall: 0.865385, count: 104 -Region Avg IOU: 0.658401, Class: 1.000000, Obj: 0.407351, No Obj: 0.002430, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.705762, Class: 1.000000, Obj: 0.393578, No Obj: 0.002686, Avg Recall: 0.890625, count: 128 -6849: 0.459043, 0.429257 avg, 0.001000 rate, 30.424919 seconds, 1753344 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.678937, Class: 1.000000, Obj: 0.401453, No Obj: 0.002597, Avg Recall: 0.853448, count: 116 -Region Avg IOU: 0.628254, Class: 1.000000, Obj: 0.366080, No Obj: 0.002613, Avg Recall: 0.795082, count: 122 -Region Avg IOU: 0.696809, Class: 1.000000, Obj: 0.443856, No Obj: 0.002444, Avg Recall: 0.918605, count: 86 -Region Avg IOU: 0.644362, Class: 1.000000, Obj: 0.372902, No Obj: 0.002972, Avg Recall: 0.781250, count: 128 -Region Avg IOU: 0.661806, Class: 1.000000, Obj: 0.416464, No Obj: 0.002683, Avg Recall: 0.823529, count: 119 -Region Avg IOU: 0.665112, Class: 1.000000, Obj: 0.403758, No Obj: 0.003012, Avg Recall: 0.798507, count: 134 -Region Avg IOU: 0.644433, Class: 1.000000, Obj: 0.397144, No Obj: 0.001944, Avg Recall: 0.805556, count: 72 -Region Avg IOU: 0.668986, Class: 1.000000, Obj: 0.409359, No Obj: 0.002199, Avg Recall: 0.843750, count: 96 -6850: 0.450462, 0.431378 avg, 0.001000 rate, 30.935787 seconds, 1753600 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 12.163320 seconds -Region Avg IOU: 0.561667, Class: 1.000000, Obj: 0.355378, No Obj: 0.003239, Avg Recall: 0.646707, count: 167 -Region Avg IOU: 0.655523, Class: 1.000000, Obj: 0.393196, No Obj: 0.003033, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.643887, Class: 1.000000, Obj: 0.390645, No Obj: 0.003619, Avg Recall: 0.761905, count: 126 -Region Avg IOU: 0.624719, Class: 1.000000, Obj: 0.380785, No Obj: 0.002803, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.544569, Class: 1.000000, Obj: 0.364690, No Obj: 0.003090, Avg Recall: 0.637931, count: 116 -Region Avg IOU: 0.632781, Class: 1.000000, Obj: 0.391707, No Obj: 0.003301, Avg Recall: 0.798561, count: 139 -Region Avg IOU: 0.574498, Class: 1.000000, Obj: 0.327442, No Obj: 0.002667, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.579771, Class: 1.000000, Obj: 0.398543, No Obj: 0.002790, Avg Recall: 0.673267, count: 101 -6851: 0.466503, 0.434890 avg, 0.001000 rate, 24.931517 seconds, 1753856 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.591013, Class: 1.000000, Obj: 0.403891, No Obj: 0.003217, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.603576, Class: 1.000000, Obj: 0.392396, No Obj: 0.002751, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.573325, Class: 1.000000, Obj: 0.374760, No Obj: 0.002755, Avg Recall: 0.669565, count: 115 -Region Avg IOU: 0.653777, Class: 1.000000, Obj: 0.428334, No Obj: 0.003269, Avg Recall: 0.834951, count: 103 -Region Avg IOU: 0.631265, Class: 1.000000, Obj: 0.414164, No Obj: 0.002813, Avg Recall: 0.724138, count: 87 -Region Avg IOU: 0.600067, Class: 1.000000, Obj: 0.362308, No Obj: 0.002792, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.584525, Class: 1.000000, Obj: 0.399789, No Obj: 0.003050, Avg Recall: 0.679389, count: 131 -Region Avg IOU: 0.565634, Class: 1.000000, Obj: 0.388181, No Obj: 0.002996, Avg Recall: 0.669811, count: 106 -6852: 0.415124, 0.432914 avg, 0.001000 rate, 24.881752 seconds, 1754112 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.599753, Class: 1.000000, Obj: 0.391272, No Obj: 0.003368, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.571440, Class: 1.000000, Obj: 0.420054, No Obj: 0.003018, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.596373, Class: 1.000000, Obj: 0.340877, No Obj: 0.002700, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.646798, Class: 1.000000, Obj: 0.446158, No Obj: 0.003150, Avg Recall: 0.793478, count: 92 -Region Avg IOU: 0.661321, Class: 1.000000, Obj: 0.470584, No Obj: 0.003346, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.613778, Class: 1.000000, Obj: 0.449868, No Obj: 0.003210, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.637973, Class: 1.000000, Obj: 0.464461, No Obj: 0.003491, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.602841, Class: 1.000000, Obj: 0.437194, No Obj: 0.003217, Avg Recall: 0.707317, count: 123 -6853: 0.372482, 0.426870 avg, 0.001000 rate, 25.947111 seconds, 1754368 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.623541, Class: 1.000000, Obj: 0.465846, No Obj: 0.003553, Avg Recall: 0.758333, count: 120 -Region Avg IOU: 0.648559, Class: 1.000000, Obj: 0.408738, No Obj: 0.003584, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.615440, Class: 1.000000, Obj: 0.386190, No Obj: 0.003250, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.634863, Class: 1.000000, Obj: 0.451655, No Obj: 0.003471, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.622920, Class: 1.000000, Obj: 0.447232, No Obj: 0.003392, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.610732, Class: 1.000000, Obj: 0.457362, No Obj: 0.003413, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.653934, Class: 1.000000, Obj: 0.438494, No Obj: 0.003186, Avg Recall: 0.802083, count: 96 -Region Avg IOU: 0.566720, Class: 1.000000, Obj: 0.410467, No Obj: 0.003302, Avg Recall: 0.677165, count: 127 -6854: 0.392280, 0.423411 avg, 0.001000 rate, 24.601715 seconds, 1754624 images -Loaded: 0.000120 seconds -Region Avg IOU: 0.613601, Class: 1.000000, Obj: 0.417615, No Obj: 0.003120, Avg Recall: 0.696629, count: 89 -Region Avg IOU: 0.653073, Class: 1.000000, Obj: 0.411579, No Obj: 0.003832, Avg Recall: 0.817460, count: 126 -Region Avg IOU: 0.596730, Class: 1.000000, Obj: 0.429158, No Obj: 0.003012, Avg Recall: 0.716981, count: 106 -Region Avg IOU: 0.600190, Class: 1.000000, Obj: 0.430227, No Obj: 0.003490, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.614459, Class: 1.000000, Obj: 0.416573, No Obj: 0.003568, Avg Recall: 0.744000, count: 125 -Region Avg IOU: 0.634289, Class: 1.000000, Obj: 0.457700, No Obj: 0.003556, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.570053, Class: 1.000000, Obj: 0.414758, No Obj: 0.003077, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.599636, Class: 1.000000, Obj: 0.457586, No Obj: 0.003152, Avg Recall: 0.695238, count: 105 -6855: 0.423101, 0.423380 avg, 0.001000 rate, 24.998266 seconds, 1754880 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.636971, Class: 1.000000, Obj: 0.428793, No Obj: 0.003123, Avg Recall: 0.770115, count: 87 -Region Avg IOU: 0.659622, Class: 1.000000, Obj: 0.426508, No Obj: 0.003987, Avg Recall: 0.795181, count: 83 -Region Avg IOU: 0.662701, Class: 1.000000, Obj: 0.430686, No Obj: 0.003384, Avg Recall: 0.794643, count: 112 -Region Avg IOU: 0.616361, Class: 1.000000, Obj: 0.433775, No Obj: 0.003568, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.633907, Class: 1.000000, Obj: 0.477358, No Obj: 0.003490, Avg Recall: 0.796460, count: 113 -Region Avg IOU: 0.665767, Class: 1.000000, Obj: 0.478734, No Obj: 0.003117, Avg Recall: 0.836957, count: 92 -Region Avg IOU: 0.596661, Class: 1.000000, Obj: 0.447172, No Obj: 0.004196, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.594588, Class: 1.000000, Obj: 0.423878, No Obj: 0.003508, Avg Recall: 0.726415, count: 106 -6856: 0.383995, 0.419442 avg, 0.001000 rate, 25.645901 seconds, 1755136 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.652792, Class: 1.000000, Obj: 0.470235, No Obj: 0.003168, Avg Recall: 0.783132, count: 83 -Region Avg IOU: 0.606542, Class: 1.000000, Obj: 0.457975, No Obj: 0.003421, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.607342, Class: 1.000000, Obj: 0.417731, No Obj: 0.003609, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.591951, Class: 1.000000, Obj: 0.454758, No Obj: 0.003530, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.652409, Class: 1.000000, Obj: 0.484904, No Obj: 0.002835, Avg Recall: 0.790698, count: 86 -Region Avg IOU: 0.641529, Class: 1.000000, Obj: 0.452452, No Obj: 0.003825, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.653059, Class: 1.000000, Obj: 0.450843, No Obj: 0.003407, Avg Recall: 0.825243, count: 103 -Region Avg IOU: 0.634893, Class: 1.000000, Obj: 0.493120, No Obj: 0.003181, Avg Recall: 0.743363, count: 113 -6857: 0.394094, 0.416907 avg, 0.001000 rate, 26.566116 seconds, 1755392 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.613857, Class: 1.000000, Obj: 0.400014, No Obj: 0.003594, Avg Recall: 0.775000, count: 120 -Region Avg IOU: 0.617289, Class: 1.000000, Obj: 0.462775, No Obj: 0.003006, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.610992, Class: 1.000000, Obj: 0.409460, No Obj: 0.003684, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.568144, Class: 1.000000, Obj: 0.404397, No Obj: 0.003044, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.601120, Class: 1.000000, Obj: 0.447004, No Obj: 0.003343, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.660440, Class: 1.000000, Obj: 0.425447, No Obj: 0.003576, Avg Recall: 0.800000, count: 115 -Region Avg IOU: 0.596141, Class: 1.000000, Obj: 0.433289, No Obj: 0.003359, Avg Recall: 0.706422, count: 109 -Region Avg IOU: 0.648805, Class: 1.000000, Obj: 0.491940, No Obj: 0.003043, Avg Recall: 0.757143, count: 70 -6858: 0.432405, 0.418457 avg, 0.001000 rate, 26.527451 seconds, 1755648 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.628707, Class: 1.000000, Obj: 0.435763, No Obj: 0.004017, Avg Recall: 0.785185, count: 135 -Region Avg IOU: 0.655177, Class: 1.000000, Obj: 0.456433, No Obj: 0.003340, Avg Recall: 0.781609, count: 87 -Region Avg IOU: 0.627373, Class: 1.000000, Obj: 0.434593, No Obj: 0.003224, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.601641, Class: 1.000000, Obj: 0.432193, No Obj: 0.003252, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.643936, Class: 1.000000, Obj: 0.449092, No Obj: 0.003665, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.613770, Class: 1.000000, Obj: 0.436506, No Obj: 0.002800, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.610968, Class: 1.000000, Obj: 0.465130, No Obj: 0.003532, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.624125, Class: 1.000000, Obj: 0.498940, No Obj: 0.003341, Avg Recall: 0.750000, count: 112 -6859: 0.429886, 0.419600 avg, 0.001000 rate, 23.800035 seconds, 1755904 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.632440, Class: 1.000000, Obj: 0.404567, No Obj: 0.003350, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.585743, Class: 1.000000, Obj: 0.427486, No Obj: 0.003054, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.618486, Class: 1.000000, Obj: 0.459338, No Obj: 0.003749, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.612626, Class: 1.000000, Obj: 0.441344, No Obj: 0.002940, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.640642, Class: 1.000000, Obj: 0.429535, No Obj: 0.003528, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.594308, Class: 1.000000, Obj: 0.416152, No Obj: 0.003400, Avg Recall: 0.716535, count: 127 -Region Avg IOU: 0.581888, Class: 1.000000, Obj: 0.437491, No Obj: 0.003164, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.590674, Class: 1.000000, Obj: 0.402950, No Obj: 0.002789, Avg Recall: 0.712644, count: 87 -6860: 0.431528, 0.420793 avg, 0.001000 rate, 24.509617 seconds, 1756160 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.691814 seconds -Region Avg IOU: 0.554524, Class: 1.000000, Obj: 0.383705, No Obj: 0.003832, Avg Recall: 0.630952, count: 84 -Region Avg IOU: 0.567424, Class: 1.000000, Obj: 0.348151, No Obj: 0.004755, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.518346, Class: 1.000000, Obj: 0.345881, No Obj: 0.003939, Avg Recall: 0.586466, count: 133 -Region Avg IOU: 0.502917, Class: 1.000000, Obj: 0.340753, No Obj: 0.003639, Avg Recall: 0.515464, count: 97 -Region Avg IOU: 0.519893, Class: 1.000000, Obj: 0.330415, No Obj: 0.003867, Avg Recall: 0.560748, count: 107 -Region Avg IOU: 0.538907, Class: 1.000000, Obj: 0.346680, No Obj: 0.004223, Avg Recall: 0.581967, count: 122 -Region Avg IOU: 0.522463, Class: 1.000000, Obj: 0.329377, No Obj: 0.004104, Avg Recall: 0.583333, count: 120 -Region Avg IOU: 0.517380, Class: 1.000000, Obj: 0.350971, No Obj: 0.003763, Avg Recall: 0.568807, count: 109 -6861: 0.430697, 0.421783 avg, 0.001000 rate, 17.990936 seconds, 1756416 images -Loaded: 3.323425 seconds -Region Avg IOU: 0.507394, Class: 1.000000, Obj: 0.350262, No Obj: 0.003514, Avg Recall: 0.543210, count: 81 -Region Avg IOU: 0.533212, Class: 1.000000, Obj: 0.362719, No Obj: 0.004659, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.534773, Class: 1.000000, Obj: 0.341853, No Obj: 0.003774, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.544405, Class: 1.000000, Obj: 0.366200, No Obj: 0.003840, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.510122, Class: 1.000000, Obj: 0.318083, No Obj: 0.004043, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.532169, Class: 1.000000, Obj: 0.320783, No Obj: 0.004000, Avg Recall: 0.556701, count: 97 -Region Avg IOU: 0.544556, Class: 1.000000, Obj: 0.342337, No Obj: 0.004684, Avg Recall: 0.609756, count: 123 -Region Avg IOU: 0.542326, Class: 1.000000, Obj: 0.347224, No Obj: 0.004590, Avg Recall: 0.622951, count: 122 -6862: 0.420365, 0.421641 avg, 0.001000 rate, 18.212400 seconds, 1756672 images -Loaded: 2.196310 seconds -Region Avg IOU: 0.543676, Class: 1.000000, Obj: 0.350092, No Obj: 0.004273, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.451769, Class: 1.000000, Obj: 0.353902, No Obj: 0.003917, Avg Recall: 0.481818, count: 110 -Region Avg IOU: 0.508593, Class: 1.000000, Obj: 0.333839, No Obj: 0.003562, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.496211, Class: 1.000000, Obj: 0.320072, No Obj: 0.003959, Avg Recall: 0.546219, count: 119 -Region Avg IOU: 0.529567, Class: 1.000000, Obj: 0.369958, No Obj: 0.004283, Avg Recall: 0.573643, count: 129 -Region Avg IOU: 0.517188, Class: 1.000000, Obj: 0.313445, No Obj: 0.004333, Avg Recall: 0.590164, count: 122 -Region Avg IOU: 0.549312, Class: 1.000000, Obj: 0.363674, No Obj: 0.003799, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.467658, Class: 1.000000, Obj: 0.332711, No Obj: 0.003643, Avg Recall: 0.491525, count: 118 -6863: 0.498420, 0.429319 avg, 0.001000 rate, 20.080828 seconds, 1756928 images -Loaded: 0.957371 seconds -Region Avg IOU: 0.467752, Class: 1.000000, Obj: 0.341049, No Obj: 0.003920, Avg Recall: 0.487395, count: 119 -Region Avg IOU: 0.544427, Class: 1.000000, Obj: 0.366084, No Obj: 0.003385, Avg Recall: 0.629214, count: 89 -Region Avg IOU: 0.540837, Class: 1.000000, Obj: 0.330716, No Obj: 0.004403, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.541709, Class: 1.000000, Obj: 0.401287, No Obj: 0.003216, Avg Recall: 0.629630, count: 81 -Region Avg IOU: 0.460271, Class: 1.000000, Obj: 0.326698, No Obj: 0.003997, Avg Recall: 0.519084, count: 131 -Region Avg IOU: 0.504292, Class: 1.000000, Obj: 0.297950, No Obj: 0.004462, Avg Recall: 0.569106, count: 123 -Region Avg IOU: 0.500721, Class: 1.000000, Obj: 0.323247, No Obj: 0.004204, Avg Recall: 0.588235, count: 119 -Region Avg IOU: 0.480447, Class: 1.000000, Obj: 0.344432, No Obj: 0.004337, Avg Recall: 0.480000, count: 125 -6864: 0.483580, 0.434745 avg, 0.001000 rate, 17.735567 seconds, 1757184 images -Loaded: 3.268522 seconds -Region Avg IOU: 0.536178, Class: 1.000000, Obj: 0.412073, No Obj: 0.004283, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.572083, Class: 1.000000, Obj: 0.400651, No Obj: 0.004958, Avg Recall: 0.680328, count: 122 -Region Avg IOU: 0.508137, Class: 1.000000, Obj: 0.324491, No Obj: 0.004733, Avg Recall: 0.578571, count: 140 -Region Avg IOU: 0.505001, Class: 1.000000, Obj: 0.362859, No Obj: 0.004367, Avg Recall: 0.570248, count: 121 -Region Avg IOU: 0.578503, Class: 1.000000, Obj: 0.418220, No Obj: 0.004778, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.536306, Class: 1.000000, Obj: 0.390856, No Obj: 0.004958, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.595556, Class: 1.000000, Obj: 0.385799, No Obj: 0.004726, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.534823, Class: 1.000000, Obj: 0.373291, No Obj: 0.004961, Avg Recall: 0.566372, count: 113 -6865: 0.458556, 0.437126 avg, 0.001000 rate, 18.425812 seconds, 1757440 images -Loaded: 2.458245 seconds -Region Avg IOU: 0.529244, Class: 1.000000, Obj: 0.340302, No Obj: 0.005704, Avg Recall: 0.582090, count: 134 -Region Avg IOU: 0.551616, Class: 1.000000, Obj: 0.369986, No Obj: 0.003543, Avg Recall: 0.670886, count: 79 -Region Avg IOU: 0.525667, Class: 1.000000, Obj: 0.364596, No Obj: 0.005414, Avg Recall: 0.578125, count: 128 -Region Avg IOU: 0.515612, Class: 1.000000, Obj: 0.388367, No Obj: 0.004237, Avg Recall: 0.547170, count: 106 -Region Avg IOU: 0.517026, Class: 1.000000, Obj: 0.344028, No Obj: 0.004579, Avg Recall: 0.544643, count: 112 -Region Avg IOU: 0.464198, Class: 1.000000, Obj: 0.354793, No Obj: 0.004028, Avg Recall: 0.500000, count: 100 -Region Avg IOU: 0.544892, Class: 1.000000, Obj: 0.417737, No Obj: 0.004050, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.483866, Class: 1.000000, Obj: 0.389839, No Obj: 0.003894, Avg Recall: 0.522727, count: 88 -6866: 0.405242, 0.433938 avg, 0.001000 rate, 20.220768 seconds, 1757696 images -Loaded: 1.959053 seconds -Region Avg IOU: 0.542746, Class: 1.000000, Obj: 0.353129, No Obj: 0.005344, Avg Recall: 0.607692, count: 130 -Region Avg IOU: 0.493408, Class: 1.000000, Obj: 0.366394, No Obj: 0.004331, Avg Recall: 0.509434, count: 106 -Region Avg IOU: 0.566878, Class: 1.000000, Obj: 0.430356, No Obj: 0.004852, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.491869, Class: 1.000000, Obj: 0.394167, No Obj: 0.004745, Avg Recall: 0.573529, count: 136 -Region Avg IOU: 0.462265, Class: 1.000000, Obj: 0.320843, No Obj: 0.004429, Avg Recall: 0.529412, count: 119 -Region Avg IOU: 0.564863, Class: 1.000000, Obj: 0.383814, No Obj: 0.004684, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.563665, Class: 1.000000, Obj: 0.411800, No Obj: 0.004224, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.523372, Class: 1.000000, Obj: 0.380309, No Obj: 0.004624, Avg Recall: 0.619048, count: 105 -6867: 0.496967, 0.440241 avg, 0.001000 rate, 16.796453 seconds, 1757952 images -Loaded: 3.705868 seconds -Region Avg IOU: 0.499954, Class: 1.000000, Obj: 0.382414, No Obj: 0.004165, Avg Recall: 0.543103, count: 116 -Region Avg IOU: 0.537460, Class: 1.000000, Obj: 0.394347, No Obj: 0.005042, Avg Recall: 0.565217, count: 92 -Region Avg IOU: 0.532850, Class: 1.000000, Obj: 0.401372, No Obj: 0.004321, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.568390, Class: 1.000000, Obj: 0.417661, No Obj: 0.004958, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.560067, Class: 1.000000, Obj: 0.376900, No Obj: 0.005267, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.546860, Class: 1.000000, Obj: 0.411219, No Obj: 0.004501, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.495154, Class: 1.000000, Obj: 0.398811, No Obj: 0.004061, Avg Recall: 0.518072, count: 83 -Region Avg IOU: 0.488160, Class: 1.000000, Obj: 0.387969, No Obj: 0.004664, Avg Recall: 0.504505, count: 111 -6868: 0.393783, 0.435595 avg, 0.001000 rate, 17.181900 seconds, 1758208 images -Loaded: 1.381745 seconds -Region Avg IOU: 0.564254, Class: 1.000000, Obj: 0.452837, No Obj: 0.004902, Avg Recall: 0.666667, count: 96 -Region Avg IOU: 0.531679, Class: 1.000000, Obj: 0.413889, No Obj: 0.005198, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.538862, Class: 1.000000, Obj: 0.413515, No Obj: 0.004890, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.509188, Class: 1.000000, Obj: 0.394461, No Obj: 0.004198, Avg Recall: 0.600000, count: 95 -Region Avg IOU: 0.536483, Class: 1.000000, Obj: 0.462959, No Obj: 0.004985, Avg Recall: 0.608247, count: 97 -Region Avg IOU: 0.544011, Class: 1.000000, Obj: 0.405158, No Obj: 0.005550, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.569019, Class: 1.000000, Obj: 0.429575, No Obj: 0.004803, Avg Recall: 0.633333, count: 90 -Region Avg IOU: 0.508317, Class: 1.000000, Obj: 0.366340, No Obj: 0.004336, Avg Recall: 0.564356, count: 101 -6869: 0.391975, 0.431233 avg, 0.001000 rate, 17.787556 seconds, 1758464 images -Loaded: 2.037648 seconds -Region Avg IOU: 0.487578, Class: 1.000000, Obj: 0.411642, No Obj: 0.004360, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.492341, Class: 1.000000, Obj: 0.386961, No Obj: 0.004731, Avg Recall: 0.581197, count: 117 -Region Avg IOU: 0.535006, Class: 1.000000, Obj: 0.384070, No Obj: 0.004353, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.543381, Class: 1.000000, Obj: 0.399288, No Obj: 0.004203, Avg Recall: 0.638298, count: 94 -Region Avg IOU: 0.456531, Class: 1.000000, Obj: 0.338129, No Obj: 0.003755, Avg Recall: 0.506329, count: 79 -Region Avg IOU: 0.533925, Class: 1.000000, Obj: 0.401990, No Obj: 0.004686, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.531755, Class: 1.000000, Obj: 0.403134, No Obj: 0.004580, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.485242, Class: 1.000000, Obj: 0.391309, No Obj: 0.003810, Avg Recall: 0.529412, count: 102 -6870: 0.411636, 0.429273 avg, 0.001000 rate, 18.234257 seconds, 1758720 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000071 seconds -Region Avg IOU: 0.597019, Class: 1.000000, Obj: 0.453928, No Obj: 0.004387, Avg Recall: 0.700730, count: 137 -Region Avg IOU: 0.598033, Class: 1.000000, Obj: 0.469596, No Obj: 0.004554, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.568169, Class: 1.000000, Obj: 0.506911, No Obj: 0.003873, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.606058, Class: 1.000000, Obj: 0.506451, No Obj: 0.003580, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.608290, Class: 1.000000, Obj: 0.482338, No Obj: 0.003504, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.607714, Class: 1.000000, Obj: 0.449397, No Obj: 0.004018, Avg Recall: 0.736000, count: 125 -Region Avg IOU: 0.590411, Class: 1.000000, Obj: 0.465352, No Obj: 0.003738, Avg Recall: 0.715789, count: 95 -Region Avg IOU: 0.588391, Class: 1.000000, Obj: 0.504669, No Obj: 0.003678, Avg Recall: 0.710000, count: 100 -6871: 0.456778, 0.432024 avg, 0.001000 rate, 23.262384 seconds, 1758976 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.579940, Class: 1.000000, Obj: 0.412195, No Obj: 0.004084, Avg Recall: 0.703704, count: 135 -Region Avg IOU: 0.574759, Class: 1.000000, Obj: 0.441197, No Obj: 0.003951, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.615019, Class: 1.000000, Obj: 0.492533, No Obj: 0.004327, Avg Recall: 0.746032, count: 126 -Region Avg IOU: 0.604663, Class: 1.000000, Obj: 0.474099, No Obj: 0.003086, Avg Recall: 0.708861, count: 79 -Region Avg IOU: 0.549271, Class: 1.000000, Obj: 0.414847, No Obj: 0.003391, Avg Recall: 0.623529, count: 85 -Region Avg IOU: 0.557853, Class: 1.000000, Obj: 0.471417, No Obj: 0.003816, Avg Recall: 0.626866, count: 134 -Region Avg IOU: 0.646986, Class: 1.000000, Obj: 0.535172, No Obj: 0.003736, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.587613, Class: 1.000000, Obj: 0.472921, No Obj: 0.003951, Avg Recall: 0.713043, count: 115 -6872: 0.411644, 0.429986 avg, 0.001000 rate, 22.269691 seconds, 1759232 images -Loaded: 0.000124 seconds -Region Avg IOU: 0.576676, Class: 1.000000, Obj: 0.439433, No Obj: 0.003554, Avg Recall: 0.700935, count: 107 -Region Avg IOU: 0.641362, Class: 1.000000, Obj: 0.454428, No Obj: 0.003846, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.580678, Class: 1.000000, Obj: 0.438716, No Obj: 0.003536, Avg Recall: 0.707071, count: 99 -Region Avg IOU: 0.645132, Class: 1.000000, Obj: 0.499508, No Obj: 0.003821, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.635941, Class: 1.000000, Obj: 0.434179, No Obj: 0.004516, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.659668, Class: 1.000000, Obj: 0.485655, No Obj: 0.003982, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.623665, Class: 1.000000, Obj: 0.474900, No Obj: 0.003386, Avg Recall: 0.755556, count: 90 -Region Avg IOU: 0.593623, Class: 1.000000, Obj: 0.432403, No Obj: 0.003882, Avg Recall: 0.733333, count: 90 -6873: 0.400223, 0.427010 avg, 0.001000 rate, 24.496788 seconds, 1759488 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.575586, Class: 1.000000, Obj: 0.445041, No Obj: 0.003357, Avg Recall: 0.657143, count: 105 -Region Avg IOU: 0.634038, Class: 1.000000, Obj: 0.490296, No Obj: 0.004012, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.624444, Class: 1.000000, Obj: 0.454012, No Obj: 0.004185, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.606705, Class: 1.000000, Obj: 0.426389, No Obj: 0.003650, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.580071, Class: 1.000000, Obj: 0.437722, No Obj: 0.003836, Avg Recall: 0.651163, count: 129 -Region Avg IOU: 0.560933, Class: 1.000000, Obj: 0.398306, No Obj: 0.003743, Avg Recall: 0.664179, count: 134 -Region Avg IOU: 0.602586, Class: 1.000000, Obj: 0.438931, No Obj: 0.003753, Avg Recall: 0.719424, count: 139 -Region Avg IOU: 0.636530, Class: 1.000000, Obj: 0.441439, No Obj: 0.003783, Avg Recall: 0.783019, count: 106 -6874: 0.403495, 0.424658 avg, 0.001000 rate, 24.864977 seconds, 1759744 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.604811, Class: 1.000000, Obj: 0.411707, No Obj: 0.003583, Avg Recall: 0.675676, count: 111 -Region Avg IOU: 0.566589, Class: 1.000000, Obj: 0.370327, No Obj: 0.003064, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.609599, Class: 1.000000, Obj: 0.420688, No Obj: 0.003855, Avg Recall: 0.744361, count: 133 -Region Avg IOU: 0.635380, Class: 1.000000, Obj: 0.418576, No Obj: 0.003807, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.567351, Class: 1.000000, Obj: 0.402714, No Obj: 0.002700, Avg Recall: 0.700000, count: 90 -Region Avg IOU: 0.593061, Class: 1.000000, Obj: 0.402324, No Obj: 0.003154, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.580873, Class: 1.000000, Obj: 0.401069, No Obj: 0.003080, Avg Recall: 0.696429, count: 112 -Region Avg IOU: 0.556013, Class: 1.000000, Obj: 0.374738, No Obj: 0.003043, Avg Recall: 0.611650, count: 103 -6875: 0.406609, 0.422853 avg, 0.001000 rate, 23.580137 seconds, 1760000 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.635353, Class: 1.000000, Obj: 0.416431, No Obj: 0.003618, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.540537, Class: 1.000000, Obj: 0.352713, No Obj: 0.003401, Avg Recall: 0.650000, count: 140 -Region Avg IOU: 0.621711, Class: 1.000000, Obj: 0.444939, No Obj: 0.003512, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.521884, Class: 1.000000, Obj: 0.377139, No Obj: 0.003000, Avg Recall: 0.605042, count: 119 -Region Avg IOU: 0.573011, Class: 1.000000, Obj: 0.375519, No Obj: 0.003132, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.568728, Class: 1.000000, Obj: 0.402964, No Obj: 0.003786, Avg Recall: 0.687500, count: 128 -Region Avg IOU: 0.592161, Class: 1.000000, Obj: 0.417792, No Obj: 0.003354, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.609289, Class: 1.000000, Obj: 0.390025, No Obj: 0.003143, Avg Recall: 0.750000, count: 108 -6876: 0.488209, 0.429389 avg, 0.001000 rate, 24.674679 seconds, 1760256 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.572878, Class: 1.000000, Obj: 0.389013, No Obj: 0.003238, Avg Recall: 0.712963, count: 108 -Region Avg IOU: 0.607673, Class: 1.000000, Obj: 0.447551, No Obj: 0.003157, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.587025, Class: 1.000000, Obj: 0.383574, No Obj: 0.002794, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.635542, Class: 1.000000, Obj: 0.395941, No Obj: 0.003389, Avg Recall: 0.826087, count: 92 -Region Avg IOU: 0.582001, Class: 1.000000, Obj: 0.419683, No Obj: 0.002879, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.568362, Class: 1.000000, Obj: 0.405882, No Obj: 0.003264, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.530989, Class: 1.000000, Obj: 0.346950, No Obj: 0.002920, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.604166, Class: 1.000000, Obj: 0.416145, No Obj: 0.003462, Avg Recall: 0.688073, count: 109 -6877: 0.408559, 0.427306 avg, 0.001000 rate, 24.440674 seconds, 1760512 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.619056, Class: 1.000000, Obj: 0.397251, No Obj: 0.003072, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.666014, Class: 1.000000, Obj: 0.438601, No Obj: 0.003194, Avg Recall: 0.860215, count: 93 -Region Avg IOU: 0.599139, Class: 1.000000, Obj: 0.383203, No Obj: 0.003266, Avg Recall: 0.709091, count: 110 -Region Avg IOU: 0.585553, Class: 1.000000, Obj: 0.365590, No Obj: 0.003444, Avg Recall: 0.682540, count: 126 -Region Avg IOU: 0.591195, Class: 1.000000, Obj: 0.355633, No Obj: 0.003387, Avg Recall: 0.700000, count: 120 -Region Avg IOU: 0.625288, Class: 1.000000, Obj: 0.392192, No Obj: 0.003593, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.628654, Class: 1.000000, Obj: 0.383155, No Obj: 0.003796, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.594044, Class: 1.000000, Obj: 0.382147, No Obj: 0.003432, Avg Recall: 0.704000, count: 125 -6878: 0.445915, 0.429167 avg, 0.001000 rate, 24.236572 seconds, 1760768 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.625433, Class: 1.000000, Obj: 0.350580, No Obj: 0.003527, Avg Recall: 0.769231, count: 130 -Region Avg IOU: 0.624582, Class: 1.000000, Obj: 0.387802, No Obj: 0.003411, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.618319, Class: 1.000000, Obj: 0.358482, No Obj: 0.003552, Avg Recall: 0.752066, count: 121 -Region Avg IOU: 0.581211, Class: 1.000000, Obj: 0.371581, No Obj: 0.003474, Avg Recall: 0.677686, count: 121 -Region Avg IOU: 0.634737, Class: 1.000000, Obj: 0.447847, No Obj: 0.003686, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.627362, Class: 1.000000, Obj: 0.396882, No Obj: 0.002765, Avg Recall: 0.720930, count: 86 -Region Avg IOU: 0.598168, Class: 1.000000, Obj: 0.392670, No Obj: 0.003804, Avg Recall: 0.697674, count: 129 -Region Avg IOU: 0.600486, Class: 1.000000, Obj: 0.373042, No Obj: 0.003462, Avg Recall: 0.691489, count: 94 -6879: 0.482081, 0.434458 avg, 0.001000 rate, 24.258635 seconds, 1761024 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.614356, Class: 1.000000, Obj: 0.411287, No Obj: 0.002979, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.586278, Class: 1.000000, Obj: 0.358481, No Obj: 0.003247, Avg Recall: 0.705263, count: 95 -Region Avg IOU: 0.585428, Class: 1.000000, Obj: 0.340014, No Obj: 0.003520, Avg Recall: 0.704545, count: 132 -Region Avg IOU: 0.650453, Class: 1.000000, Obj: 0.458596, No Obj: 0.003078, Avg Recall: 0.737500, count: 80 -Region Avg IOU: 0.641052, Class: 1.000000, Obj: 0.401128, No Obj: 0.003650, Avg Recall: 0.755725, count: 131 -Region Avg IOU: 0.615761, Class: 1.000000, Obj: 0.411469, No Obj: 0.003500, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.611099, Class: 1.000000, Obj: 0.397822, No Obj: 0.003347, Avg Recall: 0.739583, count: 96 -Region Avg IOU: 0.651587, Class: 1.000000, Obj: 0.439948, No Obj: 0.003562, Avg Recall: 0.798077, count: 104 -6880: 0.406169, 0.431629 avg, 0.001000 rate, 23.141136 seconds, 1761280 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.892857 seconds -Region Avg IOU: 0.533051, Class: 1.000000, Obj: 0.337528, No Obj: 0.004498, Avg Recall: 0.583333, count: 132 -Region Avg IOU: 0.506805, Class: 1.000000, Obj: 0.325627, No Obj: 0.003759, Avg Recall: 0.563107, count: 103 -Region Avg IOU: 0.439293, Class: 1.000000, Obj: 0.296914, No Obj: 0.004174, Avg Recall: 0.478261, count: 138 -Region Avg IOU: 0.496189, Class: 1.000000, Obj: 0.339749, No Obj: 0.003808, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.594909, Class: 1.000000, Obj: 0.367994, No Obj: 0.003930, Avg Recall: 0.666667, count: 69 -Region Avg IOU: 0.526362, Class: 1.000000, Obj: 0.324982, No Obj: 0.003505, Avg Recall: 0.588235, count: 102 -Region Avg IOU: 0.532823, Class: 1.000000, Obj: 0.321616, No Obj: 0.004267, Avg Recall: 0.593220, count: 118 -Region Avg IOU: 0.540218, Class: 1.000000, Obj: 0.367382, No Obj: 0.004030, Avg Recall: 0.597938, count: 97 -6881: 0.486148, 0.437081 avg, 0.001000 rate, 18.182098 seconds, 1761536 images -Loaded: 5.023074 seconds -Region Avg IOU: 0.510571, Class: 1.000000, Obj: 0.355928, No Obj: 0.004283, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.485774, Class: 1.000000, Obj: 0.300768, No Obj: 0.004143, Avg Recall: 0.590164, count: 122 -Region Avg IOU: 0.501273, Class: 1.000000, Obj: 0.354002, No Obj: 0.004473, Avg Recall: 0.554622, count: 119 -Region Avg IOU: 0.519652, Class: 1.000000, Obj: 0.347429, No Obj: 0.004652, Avg Recall: 0.568807, count: 109 -Region Avg IOU: 0.533139, Class: 1.000000, Obj: 0.307098, No Obj: 0.004676, Avg Recall: 0.604167, count: 144 -Region Avg IOU: 0.526581, Class: 1.000000, Obj: 0.337890, No Obj: 0.004492, Avg Recall: 0.600000, count: 135 -Region Avg IOU: 0.520266, Class: 1.000000, Obj: 0.327894, No Obj: 0.003794, Avg Recall: 0.607143, count: 84 -Region Avg IOU: 0.524678, Class: 1.000000, Obj: 0.357445, No Obj: 0.004842, Avg Recall: 0.587786, count: 131 -6882: 0.554715, 0.448844 avg, 0.001000 rate, 20.336843 seconds, 1761792 images -Loaded: 1.288802 seconds -Region Avg IOU: 0.543970, Class: 1.000000, Obj: 0.385566, No Obj: 0.003972, Avg Recall: 0.597938, count: 97 -Region Avg IOU: 0.524646, Class: 1.000000, Obj: 0.384040, No Obj: 0.004079, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.519140, Class: 1.000000, Obj: 0.398779, No Obj: 0.004902, Avg Recall: 0.633588, count: 131 -Region Avg IOU: 0.555442, Class: 1.000000, Obj: 0.352195, No Obj: 0.004872, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.516574, Class: 1.000000, Obj: 0.365367, No Obj: 0.004119, Avg Recall: 0.615385, count: 78 -Region Avg IOU: 0.556148, Class: 1.000000, Obj: 0.426313, No Obj: 0.004463, Avg Recall: 0.682243, count: 107 -Region Avg IOU: 0.519489, Class: 1.000000, Obj: 0.331756, No Obj: 0.004637, Avg Recall: 0.593985, count: 133 -Region Avg IOU: 0.589501, Class: 1.000000, Obj: 0.412565, No Obj: 0.004790, Avg Recall: 0.725490, count: 102 -6883: 0.402177, 0.444178 avg, 0.001000 rate, 15.913597 seconds, 1762048 images -Loaded: 5.377436 seconds -Region Avg IOU: 0.486819, Class: 1.000000, Obj: 0.390105, No Obj: 0.004467, Avg Recall: 0.535088, count: 114 -Region Avg IOU: 0.502841, Class: 1.000000, Obj: 0.366992, No Obj: 0.004852, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.541980, Class: 1.000000, Obj: 0.386959, No Obj: 0.004729, Avg Recall: 0.568627, count: 102 -Region Avg IOU: 0.538933, Class: 1.000000, Obj: 0.368092, No Obj: 0.004575, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.533010, Class: 1.000000, Obj: 0.365349, No Obj: 0.004087, Avg Recall: 0.594937, count: 79 -Region Avg IOU: 0.534703, Class: 1.000000, Obj: 0.394988, No Obj: 0.005430, Avg Recall: 0.586466, count: 133 -Region Avg IOU: 0.486828, Class: 1.000000, Obj: 0.357614, No Obj: 0.004613, Avg Recall: 0.580645, count: 124 -Region Avg IOU: 0.491691, Class: 1.000000, Obj: 0.369805, No Obj: 0.004942, Avg Recall: 0.539823, count: 113 -6884: 0.454674, 0.445227 avg, 0.001000 rate, 22.026472 seconds, 1762304 images -Loaded: 0.508841 seconds -Region Avg IOU: 0.462423, Class: 1.000000, Obj: 0.352514, No Obj: 0.004562, Avg Recall: 0.466019, count: 103 -Region Avg IOU: 0.523725, Class: 1.000000, Obj: 0.383770, No Obj: 0.004704, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.526079, Class: 1.000000, Obj: 0.368253, No Obj: 0.004176, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.500827, Class: 1.000000, Obj: 0.348258, No Obj: 0.005122, Avg Recall: 0.519685, count: 127 -Region Avg IOU: 0.499733, Class: 1.000000, Obj: 0.355512, No Obj: 0.004728, Avg Recall: 0.570175, count: 114 -Region Avg IOU: 0.581617, Class: 1.000000, Obj: 0.415447, No Obj: 0.005084, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.506544, Class: 1.000000, Obj: 0.395343, No Obj: 0.005209, Avg Recall: 0.577236, count: 123 -Region Avg IOU: 0.545838, Class: 1.000000, Obj: 0.391740, No Obj: 0.005736, Avg Recall: 0.610526, count: 95 -6885: 0.492918, 0.449996 avg, 0.001000 rate, 17.252058 seconds, 1762560 images -Loaded: 4.247364 seconds -Region Avg IOU: 0.507072, Class: 1.000000, Obj: 0.383289, No Obj: 0.005936, Avg Recall: 0.567164, count: 134 -Region Avg IOU: 0.472727, Class: 1.000000, Obj: 0.361748, No Obj: 0.004517, Avg Recall: 0.545455, count: 99 -Region Avg IOU: 0.579791, Class: 1.000000, Obj: 0.409886, No Obj: 0.004301, Avg Recall: 0.719512, count: 82 -Region Avg IOU: 0.527678, Class: 1.000000, Obj: 0.354317, No Obj: 0.004249, Avg Recall: 0.578313, count: 83 -Region Avg IOU: 0.538017, Class: 1.000000, Obj: 0.406754, No Obj: 0.005339, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.549032, Class: 1.000000, Obj: 0.438404, No Obj: 0.005239, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.522400, Class: 1.000000, Obj: 0.405943, No Obj: 0.005181, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.505710, Class: 1.000000, Obj: 0.404283, No Obj: 0.004835, Avg Recall: 0.586957, count: 92 -6886: 0.387135, 0.443710 avg, 0.001000 rate, 19.481613 seconds, 1762816 images -Loaded: 2.268352 seconds -Region Avg IOU: 0.488689, Class: 1.000000, Obj: 0.421899, No Obj: 0.005034, Avg Recall: 0.538462, count: 117 -Region Avg IOU: 0.539620, Class: 1.000000, Obj: 0.446601, No Obj: 0.004561, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.561011, Class: 1.000000, Obj: 0.393640, No Obj: 0.005581, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.456726, Class: 1.000000, Obj: 0.361247, No Obj: 0.004735, Avg Recall: 0.483607, count: 122 -Region Avg IOU: 0.481923, Class: 1.000000, Obj: 0.391698, No Obj: 0.005755, Avg Recall: 0.513043, count: 115 -Region Avg IOU: 0.502114, Class: 1.000000, Obj: 0.406494, No Obj: 0.004489, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.537326, Class: 1.000000, Obj: 0.428029, No Obj: 0.004953, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.556639, Class: 1.000000, Obj: 0.433967, No Obj: 0.004767, Avg Recall: 0.662500, count: 80 -6887: 0.502120, 0.449551 avg, 0.001000 rate, 21.556768 seconds, 1763072 images -Loaded: 0.774063 seconds -Region Avg IOU: 0.517193, Class: 1.000000, Obj: 0.419289, No Obj: 0.004725, Avg Recall: 0.538462, count: 91 -Region Avg IOU: 0.474134, Class: 1.000000, Obj: 0.397529, No Obj: 0.004614, Avg Recall: 0.513274, count: 113 -Region Avg IOU: 0.542256, Class: 1.000000, Obj: 0.431466, No Obj: 0.005308, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.476534, Class: 1.000000, Obj: 0.395149, No Obj: 0.005123, Avg Recall: 0.500000, count: 130 -Region Avg IOU: 0.519733, Class: 1.000000, Obj: 0.406784, No Obj: 0.005172, Avg Recall: 0.565891, count: 129 -Region Avg IOU: 0.543136, Class: 1.000000, Obj: 0.417056, No Obj: 0.005911, Avg Recall: 0.595238, count: 126 -Region Avg IOU: 0.495010, Class: 1.000000, Obj: 0.392754, No Obj: 0.005503, Avg Recall: 0.558824, count: 136 -Region Avg IOU: 0.528196, Class: 1.000000, Obj: 0.438871, No Obj: 0.004423, Avg Recall: 0.616279, count: 86 -6888: 0.474375, 0.452034 avg, 0.001000 rate, 20.657778 seconds, 1763328 images -Loaded: 0.573961 seconds -Region Avg IOU: 0.517987, Class: 1.000000, Obj: 0.394497, No Obj: 0.005014, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.494896, Class: 1.000000, Obj: 0.400796, No Obj: 0.005233, Avg Recall: 0.542373, count: 118 -Region Avg IOU: 0.479466, Class: 1.000000, Obj: 0.403784, No Obj: 0.004987, Avg Recall: 0.512397, count: 121 -Region Avg IOU: 0.503095, Class: 1.000000, Obj: 0.368183, No Obj: 0.005014, Avg Recall: 0.551020, count: 98 -Region Avg IOU: 0.532123, Class: 1.000000, Obj: 0.380945, No Obj: 0.004429, Avg Recall: 0.556818, count: 88 -Region Avg IOU: 0.477557, Class: 1.000000, Obj: 0.374437, No Obj: 0.004629, Avg Recall: 0.504425, count: 113 -Region Avg IOU: 0.499256, Class: 1.000000, Obj: 0.361096, No Obj: 0.005073, Avg Recall: 0.546875, count: 128 -Region Avg IOU: 0.541628, Class: 1.000000, Obj: 0.424756, No Obj: 0.005095, Avg Recall: 0.632075, count: 106 -6889: 0.541365, 0.460967 avg, 0.001000 rate, 18.156839 seconds, 1763584 images -Loaded: 2.744341 seconds -Region Avg IOU: 0.524392, Class: 1.000000, Obj: 0.403466, No Obj: 0.005188, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.465654, Class: 1.000000, Obj: 0.347731, No Obj: 0.004772, Avg Recall: 0.521739, count: 115 -Region Avg IOU: 0.535905, Class: 1.000000, Obj: 0.417461, No Obj: 0.004752, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.529075, Class: 1.000000, Obj: 0.374009, No Obj: 0.004818, Avg Recall: 0.637681, count: 138 -Region Avg IOU: 0.510284, Class: 1.000000, Obj: 0.409380, No Obj: 0.004549, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.553588, Class: 1.000000, Obj: 0.359315, No Obj: 0.005267, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.476373, Class: 1.000000, Obj: 0.366755, No Obj: 0.004936, Avg Recall: 0.521008, count: 119 -Region Avg IOU: 0.495356, Class: 1.000000, Obj: 0.395370, No Obj: 0.004763, Avg Recall: 0.537736, count: 106 -6890: 0.422896, 0.457160 avg, 0.001000 rate, 20.387260 seconds, 1763840 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 2.994235 seconds -Region Avg IOU: 0.570612, Class: 1.000000, Obj: 0.434538, No Obj: 0.004361, Avg Recall: 0.664000, count: 125 -Region Avg IOU: 0.590389, Class: 1.000000, Obj: 0.418073, No Obj: 0.005222, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.572430, Class: 1.000000, Obj: 0.404893, No Obj: 0.003930, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.529684, Class: 1.000000, Obj: 0.396257, No Obj: 0.004203, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.565046, Class: 1.000000, Obj: 0.433614, No Obj: 0.004345, Avg Recall: 0.709677, count: 124 -Region Avg IOU: 0.534844, Class: 1.000000, Obj: 0.427713, No Obj: 0.004064, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.535101, Class: 1.000000, Obj: 0.399358, No Obj: 0.003249, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.571966, Class: 1.000000, Obj: 0.430414, No Obj: 0.004740, Avg Recall: 0.682692, count: 104 -6891: 0.448159, 0.456260 avg, 0.001000 rate, 20.993315 seconds, 1764096 images -Loaded: 0.696207 seconds -Region Avg IOU: 0.577889, Class: 1.000000, Obj: 0.418217, No Obj: 0.004309, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.562025, Class: 1.000000, Obj: 0.397932, No Obj: 0.004839, Avg Recall: 0.692913, count: 127 -Region Avg IOU: 0.549959, Class: 1.000000, Obj: 0.393307, No Obj: 0.003963, Avg Recall: 0.630952, count: 84 -Region Avg IOU: 0.584576, Class: 1.000000, Obj: 0.472206, No Obj: 0.003236, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.519207, Class: 1.000000, Obj: 0.379029, No Obj: 0.003045, Avg Recall: 0.566667, count: 90 -Region Avg IOU: 0.559648, Class: 1.000000, Obj: 0.394202, No Obj: 0.004718, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.538173, Class: 1.000000, Obj: 0.412292, No Obj: 0.003981, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.552906, Class: 1.000000, Obj: 0.409090, No Obj: 0.004421, Avg Recall: 0.615385, count: 117 -6892: 0.404855, 0.451119 avg, 0.001000 rate, 23.210930 seconds, 1764352 images -Loaded: 0.662389 seconds -Region Avg IOU: 0.575572, Class: 1.000000, Obj: 0.394344, No Obj: 0.003939, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.541061, Class: 1.000000, Obj: 0.386861, No Obj: 0.003264, Avg Recall: 0.620253, count: 79 -Region Avg IOU: 0.582412, Class: 1.000000, Obj: 0.398987, No Obj: 0.004530, Avg Recall: 0.661654, count: 133 -Region Avg IOU: 0.585300, Class: 1.000000, Obj: 0.417389, No Obj: 0.004257, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.566765, Class: 1.000000, Obj: 0.356061, No Obj: 0.004700, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.588881, Class: 1.000000, Obj: 0.392078, No Obj: 0.004025, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.550234, Class: 1.000000, Obj: 0.349132, No Obj: 0.003218, Avg Recall: 0.702381, count: 84 -Region Avg IOU: 0.556797, Class: 1.000000, Obj: 0.346127, No Obj: 0.003422, Avg Recall: 0.688889, count: 90 -6893: 0.388720, 0.444879 avg, 0.001000 rate, 21.823530 seconds, 1764608 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.626104, Class: 1.000000, Obj: 0.443755, No Obj: 0.004064, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.548230, Class: 1.000000, Obj: 0.363480, No Obj: 0.003175, Avg Recall: 0.618557, count: 97 -Region Avg IOU: 0.591569, Class: 1.000000, Obj: 0.419699, No Obj: 0.004400, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.539236, Class: 1.000000, Obj: 0.373268, No Obj: 0.004045, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.531007, Class: 1.000000, Obj: 0.374557, No Obj: 0.004155, Avg Recall: 0.592593, count: 108 -Region Avg IOU: 0.555957, Class: 1.000000, Obj: 0.375273, No Obj: 0.004302, Avg Recall: 0.640288, count: 139 -Region Avg IOU: 0.562559, Class: 1.000000, Obj: 0.390015, No Obj: 0.003907, Avg Recall: 0.674419, count: 86 -Region Avg IOU: 0.584721, Class: 1.000000, Obj: 0.374607, No Obj: 0.004101, Avg Recall: 0.651376, count: 109 -6894: 0.430043, 0.443396 avg, 0.001000 rate, 21.597685 seconds, 1764864 images -Loaded: 0.049072 seconds -Region Avg IOU: 0.558175, Class: 1.000000, Obj: 0.389837, No Obj: 0.003849, Avg Recall: 0.628099, count: 121 -Region Avg IOU: 0.576409, Class: 1.000000, Obj: 0.369746, No Obj: 0.004218, Avg Recall: 0.684615, count: 130 -Region Avg IOU: 0.569549, Class: 1.000000, Obj: 0.415025, No Obj: 0.003115, Avg Recall: 0.696203, count: 79 -Region Avg IOU: 0.585803, Class: 1.000000, Obj: 0.389271, No Obj: 0.003822, Avg Recall: 0.677419, count: 124 -Region Avg IOU: 0.548928, Class: 1.000000, Obj: 0.357300, No Obj: 0.003752, Avg Recall: 0.617188, count: 128 -Region Avg IOU: 0.590623, Class: 1.000000, Obj: 0.436267, No Obj: 0.003605, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.524109, Class: 1.000000, Obj: 0.360228, No Obj: 0.003767, Avg Recall: 0.553719, count: 121 -Region Avg IOU: 0.504579, Class: 1.000000, Obj: 0.328170, No Obj: 0.003540, Avg Recall: 0.578947, count: 114 -6895: 0.429374, 0.441993 avg, 0.001000 rate, 22.475664 seconds, 1765120 images -Loaded: 0.325220 seconds -Region Avg IOU: 0.571033, Class: 1.000000, Obj: 0.383391, No Obj: 0.003106, Avg Recall: 0.670455, count: 88 -Region Avg IOU: 0.645026, Class: 1.000000, Obj: 0.389393, No Obj: 0.003678, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.559436, Class: 1.000000, Obj: 0.368659, No Obj: 0.003690, Avg Recall: 0.643411, count: 129 -Region Avg IOU: 0.579415, Class: 1.000000, Obj: 0.344809, No Obj: 0.003399, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.586501, Class: 1.000000, Obj: 0.354883, No Obj: 0.003107, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.597623, Class: 1.000000, Obj: 0.391013, No Obj: 0.003555, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.615765, Class: 1.000000, Obj: 0.380396, No Obj: 0.004492, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.542514, Class: 1.000000, Obj: 0.336889, No Obj: 0.003606, Avg Recall: 0.583333, count: 108 -6896: 0.433113, 0.441105 avg, 0.001000 rate, 20.574984 seconds, 1765376 images -Loaded: 1.533651 seconds -Region Avg IOU: 0.564742, Class: 1.000000, Obj: 0.395223, No Obj: 0.003699, Avg Recall: 0.644068, count: 118 -Region Avg IOU: 0.590307, Class: 1.000000, Obj: 0.357985, No Obj: 0.003675, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.574429, Class: 1.000000, Obj: 0.373654, No Obj: 0.003677, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.601295, Class: 1.000000, Obj: 0.389465, No Obj: 0.003328, Avg Recall: 0.730769, count: 78 -Region Avg IOU: 0.576176, Class: 1.000000, Obj: 0.416911, No Obj: 0.003877, Avg Recall: 0.675214, count: 117 -Region Avg IOU: 0.605340, Class: 1.000000, Obj: 0.383123, No Obj: 0.005244, Avg Recall: 0.704698, count: 149 -Region Avg IOU: 0.636800, Class: 1.000000, Obj: 0.472282, No Obj: 0.003545, Avg Recall: 0.737500, count: 80 -Region Avg IOU: 0.558558, Class: 1.000000, Obj: 0.369264, No Obj: 0.003991, Avg Recall: 0.688525, count: 122 -6897: 0.419738, 0.438969 avg, 0.001000 rate, 23.252640 seconds, 1765632 images -Loaded: 0.077834 seconds -Region Avg IOU: 0.523196, Class: 1.000000, Obj: 0.361347, No Obj: 0.003210, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.538105, Class: 1.000000, Obj: 0.330293, No Obj: 0.003566, Avg Recall: 0.607407, count: 135 -Region Avg IOU: 0.578811, Class: 1.000000, Obj: 0.372170, No Obj: 0.004465, Avg Recall: 0.674603, count: 126 -Region Avg IOU: 0.560793, Class: 1.000000, Obj: 0.378200, No Obj: 0.003964, Avg Recall: 0.669811, count: 106 -Region Avg IOU: 0.572087, Class: 1.000000, Obj: 0.397830, No Obj: 0.003164, Avg Recall: 0.653846, count: 78 -Region Avg IOU: 0.534042, Class: 1.000000, Obj: 0.369658, No Obj: 0.003960, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.536527, Class: 1.000000, Obj: 0.395431, No Obj: 0.003201, Avg Recall: 0.593023, count: 86 -Region Avg IOU: 0.589393, Class: 1.000000, Obj: 0.361718, No Obj: 0.003360, Avg Recall: 0.711340, count: 97 -6898: 0.436494, 0.438721 avg, 0.001000 rate, 24.032270 seconds, 1765888 images -Loaded: 0.226268 seconds -Region Avg IOU: 0.578132, Class: 1.000000, Obj: 0.416929, No Obj: 0.004092, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.608961, Class: 1.000000, Obj: 0.430156, No Obj: 0.004415, Avg Recall: 0.709677, count: 124 -Region Avg IOU: 0.579573, Class: 1.000000, Obj: 0.384636, No Obj: 0.004452, Avg Recall: 0.688889, count: 135 -Region Avg IOU: 0.579632, Class: 1.000000, Obj: 0.428311, No Obj: 0.003690, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.529864, Class: 1.000000, Obj: 0.353912, No Obj: 0.004349, Avg Recall: 0.635714, count: 140 -Region Avg IOU: 0.593394, Class: 1.000000, Obj: 0.422726, No Obj: 0.004646, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.524758, Class: 1.000000, Obj: 0.382972, No Obj: 0.004265, Avg Recall: 0.578947, count: 133 -Region Avg IOU: 0.614759, Class: 1.000000, Obj: 0.428301, No Obj: 0.004468, Avg Recall: 0.750000, count: 132 -6899: 0.513028, 0.446152 avg, 0.001000 rate, 22.493711 seconds, 1766144 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.586862, Class: 1.000000, Obj: 0.410528, No Obj: 0.004245, Avg Recall: 0.671756, count: 131 -Region Avg IOU: 0.561574, Class: 1.000000, Obj: 0.421746, No Obj: 0.003434, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.576035, Class: 1.000000, Obj: 0.420664, No Obj: 0.003993, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.562570, Class: 1.000000, Obj: 0.374733, No Obj: 0.004359, Avg Recall: 0.659091, count: 132 -Region Avg IOU: 0.554763, Class: 1.000000, Obj: 0.394851, No Obj: 0.003363, Avg Recall: 0.650602, count: 83 -Region Avg IOU: 0.556459, Class: 1.000000, Obj: 0.420529, No Obj: 0.004018, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.585041, Class: 1.000000, Obj: 0.428970, No Obj: 0.004142, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.557085, Class: 1.000000, Obj: 0.358629, No Obj: 0.004395, Avg Recall: 0.630769, count: 130 -6900: 0.449954, 0.446532 avg, 0.001000 rate, 23.246037 seconds, 1766400 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000066 seconds -Region Avg IOU: 0.619886, Class: 1.000000, Obj: 0.478984, No Obj: 0.002809, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.658563, Class: 1.000000, Obj: 0.456049, No Obj: 0.003551, Avg Recall: 0.809917, count: 121 -Region Avg IOU: 0.639416, Class: 1.000000, Obj: 0.475288, No Obj: 0.003124, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.612882, Class: 1.000000, Obj: 0.456976, No Obj: 0.003216, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.645412, Class: 1.000000, Obj: 0.439230, No Obj: 0.003287, Avg Recall: 0.796610, count: 118 -Region Avg IOU: 0.627124, Class: 1.000000, Obj: 0.407653, No Obj: 0.003350, Avg Recall: 0.732143, count: 112 -Region Avg IOU: 0.668507, Class: 1.000000, Obj: 0.500743, No Obj: 0.003652, Avg Recall: 0.837838, count: 111 -Region Avg IOU: 0.655557, Class: 1.000000, Obj: 0.470436, No Obj: 0.002831, Avg Recall: 0.816092, count: 87 -6901: 0.384801, 0.440359 avg, 0.001000 rate, 27.264742 seconds, 1766656 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.622077, Class: 1.000000, Obj: 0.437363, No Obj: 0.003441, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.671665, Class: 1.000000, Obj: 0.486083, No Obj: 0.003309, Avg Recall: 0.823529, count: 102 -Region Avg IOU: 0.668514, Class: 1.000000, Obj: 0.521083, No Obj: 0.003557, Avg Recall: 0.830000, count: 100 -Region Avg IOU: 0.635890, Class: 1.000000, Obj: 0.458678, No Obj: 0.003266, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.583998, Class: 1.000000, Obj: 0.458119, No Obj: 0.002936, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.622731, Class: 1.000000, Obj: 0.480672, No Obj: 0.002874, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.644174, Class: 1.000000, Obj: 0.477189, No Obj: 0.003903, Avg Recall: 0.801418, count: 141 -Region Avg IOU: 0.663074, Class: 1.000000, Obj: 0.480286, No Obj: 0.003542, Avg Recall: 0.855769, count: 104 -6902: 0.399932, 0.436316 avg, 0.001000 rate, 27.216436 seconds, 1766912 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.632746, Class: 1.000000, Obj: 0.478671, No Obj: 0.003364, Avg Recall: 0.795082, count: 122 -Region Avg IOU: 0.691739, Class: 1.000000, Obj: 0.486941, No Obj: 0.003439, Avg Recall: 0.847826, count: 92 -Region Avg IOU: 0.664904, Class: 1.000000, Obj: 0.472558, No Obj: 0.003666, Avg Recall: 0.858491, count: 106 -Region Avg IOU: 0.686573, Class: 1.000000, Obj: 0.448683, No Obj: 0.003339, Avg Recall: 0.849057, count: 106 -Region Avg IOU: 0.625967, Class: 1.000000, Obj: 0.457627, No Obj: 0.003163, Avg Recall: 0.753846, count: 130 -Region Avg IOU: 0.590952, Class: 1.000000, Obj: 0.478549, No Obj: 0.002895, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.674302, Class: 1.000000, Obj: 0.471392, No Obj: 0.003715, Avg Recall: 0.840000, count: 125 -Region Avg IOU: 0.621367, Class: 1.000000, Obj: 0.447663, No Obj: 0.002777, Avg Recall: 0.804348, count: 92 -6903: 0.391921, 0.431877 avg, 0.001000 rate, 26.210880 seconds, 1767168 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.619957, Class: 1.000000, Obj: 0.453849, No Obj: 0.003093, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.642710, Class: 1.000000, Obj: 0.424308, No Obj: 0.002955, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.629682, Class: 1.000000, Obj: 0.525551, No Obj: 0.002443, Avg Recall: 0.746988, count: 83 -Region Avg IOU: 0.585759, Class: 1.000000, Obj: 0.418087, No Obj: 0.002992, Avg Recall: 0.707692, count: 130 -Region Avg IOU: 0.619651, Class: 1.000000, Obj: 0.450619, No Obj: 0.003026, Avg Recall: 0.733333, count: 120 -Region Avg IOU: 0.642065, Class: 1.000000, Obj: 0.451569, No Obj: 0.003328, Avg Recall: 0.777778, count: 126 -Region Avg IOU: 0.638303, Class: 1.000000, Obj: 0.487526, No Obj: 0.002822, Avg Recall: 0.813187, count: 91 -Region Avg IOU: 0.644175, Class: 1.000000, Obj: 0.468550, No Obj: 0.003126, Avg Recall: 0.754545, count: 110 -6904: 0.396550, 0.428344 avg, 0.001000 rate, 26.213367 seconds, 1767424 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.630682, Class: 1.000000, Obj: 0.441282, No Obj: 0.002697, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.607292, Class: 1.000000, Obj: 0.433564, No Obj: 0.002784, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.605456, Class: 1.000000, Obj: 0.425536, No Obj: 0.002688, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.677783, Class: 1.000000, Obj: 0.453984, No Obj: 0.002722, Avg Recall: 0.882353, count: 85 -Region Avg IOU: 0.659871, Class: 1.000000, Obj: 0.452398, No Obj: 0.003167, Avg Recall: 0.809524, count: 126 -Region Avg IOU: 0.617429, Class: 1.000000, Obj: 0.429988, No Obj: 0.002941, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.683901, Class: 1.000000, Obj: 0.504806, No Obj: 0.003209, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.642708, Class: 1.000000, Obj: 0.446237, No Obj: 0.002741, Avg Recall: 0.771739, count: 92 -6905: 0.381846, 0.423694 avg, 0.001000 rate, 27.589712 seconds, 1767680 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.650801, Class: 1.000000, Obj: 0.429490, No Obj: 0.002601, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.615329, Class: 1.000000, Obj: 0.402927, No Obj: 0.003028, Avg Recall: 0.747967, count: 123 -Region Avg IOU: 0.646369, Class: 1.000000, Obj: 0.406836, No Obj: 0.002629, Avg Recall: 0.791667, count: 120 -Region Avg IOU: 0.611588, Class: 1.000000, Obj: 0.379302, No Obj: 0.002980, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.601087, Class: 1.000000, Obj: 0.396212, No Obj: 0.002838, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.617840, Class: 1.000000, Obj: 0.403795, No Obj: 0.003220, Avg Recall: 0.742188, count: 128 -Region Avg IOU: 0.640593, Class: 1.000000, Obj: 0.385608, No Obj: 0.002567, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.642080, Class: 1.000000, Obj: 0.442670, No Obj: 0.002647, Avg Recall: 0.784091, count: 88 -6906: 0.430351, 0.424360 avg, 0.001000 rate, 27.326185 seconds, 1767936 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.597037, Class: 1.000000, Obj: 0.391501, No Obj: 0.002567, Avg Recall: 0.713178, count: 129 -Region Avg IOU: 0.625951, Class: 1.000000, Obj: 0.468456, No Obj: 0.002415, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.599269, Class: 1.000000, Obj: 0.401039, No Obj: 0.002724, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.615700, Class: 1.000000, Obj: 0.421400, No Obj: 0.002390, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.635269, Class: 1.000000, Obj: 0.424304, No Obj: 0.002661, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.634091, Class: 1.000000, Obj: 0.425741, No Obj: 0.003105, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.604686, Class: 1.000000, Obj: 0.353501, No Obj: 0.002949, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.629127, Class: 1.000000, Obj: 0.418334, No Obj: 0.002873, Avg Recall: 0.741071, count: 112 -6907: 0.414255, 0.423349 avg, 0.001000 rate, 27.886251 seconds, 1768192 images -Loaded: 0.000118 seconds -Region Avg IOU: 0.625017, Class: 1.000000, Obj: 0.364575, No Obj: 0.002933, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.663279, Class: 1.000000, Obj: 0.388225, No Obj: 0.003033, Avg Recall: 0.803150, count: 127 -Region Avg IOU: 0.627562, Class: 1.000000, Obj: 0.400647, No Obj: 0.002947, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.593774, Class: 1.000000, Obj: 0.445236, No Obj: 0.002381, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.579997, Class: 1.000000, Obj: 0.375441, No Obj: 0.002681, Avg Recall: 0.669492, count: 118 -Region Avg IOU: 0.637528, Class: 1.000000, Obj: 0.385670, No Obj: 0.002955, Avg Recall: 0.780488, count: 123 -Region Avg IOU: 0.653264, Class: 1.000000, Obj: 0.375143, No Obj: 0.002840, Avg Recall: 0.780952, count: 105 -Region Avg IOU: 0.624598, Class: 1.000000, Obj: 0.368743, No Obj: 0.002878, Avg Recall: 0.791367, count: 139 -6908: 0.469083, 0.427923 avg, 0.001000 rate, 26.336611 seconds, 1768448 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.648552, Class: 1.000000, Obj: 0.373183, No Obj: 0.002683, Avg Recall: 0.809524, count: 126 -Region Avg IOU: 0.646276, Class: 1.000000, Obj: 0.444584, No Obj: 0.002572, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.649895, Class: 1.000000, Obj: 0.404289, No Obj: 0.002105, Avg Recall: 0.831461, count: 89 -Region Avg IOU: 0.640642, Class: 1.000000, Obj: 0.396924, No Obj: 0.002471, Avg Recall: 0.781609, count: 87 -Region Avg IOU: 0.654505, Class: 1.000000, Obj: 0.367346, No Obj: 0.002654, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.602166, Class: 1.000000, Obj: 0.407698, No Obj: 0.002563, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.642675, Class: 1.000000, Obj: 0.365479, No Obj: 0.002607, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.655949, Class: 1.000000, Obj: 0.446991, No Obj: 0.002954, Avg Recall: 0.807339, count: 109 -6909: 0.388519, 0.423982 avg, 0.001000 rate, 27.300957 seconds, 1768704 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.662067, Class: 1.000000, Obj: 0.434895, No Obj: 0.002764, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.614841, Class: 1.000000, Obj: 0.395771, No Obj: 0.002559, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.626530, Class: 1.000000, Obj: 0.378838, No Obj: 0.002625, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.627380, Class: 1.000000, Obj: 0.410412, No Obj: 0.002941, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.637131, Class: 1.000000, Obj: 0.435997, No Obj: 0.002795, Avg Recall: 0.750000, count: 124 -Region Avg IOU: 0.642433, Class: 1.000000, Obj: 0.405070, No Obj: 0.002862, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.655446, Class: 1.000000, Obj: 0.448924, No Obj: 0.002856, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.644569, Class: 1.000000, Obj: 0.407946, No Obj: 0.002807, Avg Recall: 0.764228, count: 123 -6910: 0.431674, 0.424752 avg, 0.001000 rate, 26.337240 seconds, 1768960 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.111762 seconds -Region Avg IOU: 0.593335, Class: 1.000000, Obj: 0.379161, No Obj: 0.003508, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.550147, Class: 1.000000, Obj: 0.331350, No Obj: 0.003308, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.526742, Class: 1.000000, Obj: 0.396071, No Obj: 0.003569, Avg Recall: 0.584071, count: 113 -Region Avg IOU: 0.576119, Class: 1.000000, Obj: 0.339668, No Obj: 0.003220, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.525062, Class: 1.000000, Obj: 0.326660, No Obj: 0.002570, Avg Recall: 0.591398, count: 93 -Region Avg IOU: 0.566876, Class: 1.000000, Obj: 0.347338, No Obj: 0.003669, Avg Recall: 0.661157, count: 121 -Region Avg IOU: 0.568920, Class: 1.000000, Obj: 0.383583, No Obj: 0.002601, Avg Recall: 0.617284, count: 81 -Region Avg IOU: 0.614821, Class: 1.000000, Obj: 0.329376, No Obj: 0.003707, Avg Recall: 0.752941, count: 85 -6911: 0.403726, 0.422649 avg, 0.001000 rate, 21.846224 seconds, 1769216 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.553102, Class: 1.000000, Obj: 0.336148, No Obj: 0.004102, Avg Recall: 0.647482, count: 139 -Region Avg IOU: 0.614050, Class: 1.000000, Obj: 0.432412, No Obj: 0.002805, Avg Recall: 0.753425, count: 73 -Region Avg IOU: 0.552957, Class: 1.000000, Obj: 0.377148, No Obj: 0.003762, Avg Recall: 0.641791, count: 134 -Region Avg IOU: 0.561718, Class: 1.000000, Obj: 0.380944, No Obj: 0.003616, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.553717, Class: 1.000000, Obj: 0.375131, No Obj: 0.003578, Avg Recall: 0.611111, count: 126 -Region Avg IOU: 0.585014, Class: 1.000000, Obj: 0.403489, No Obj: 0.003193, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.580653, Class: 1.000000, Obj: 0.382280, No Obj: 0.002904, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.591972, Class: 1.000000, Obj: 0.398953, No Obj: 0.003101, Avg Recall: 0.700000, count: 90 -6912: 0.419482, 0.422332 avg, 0.001000 rate, 22.662107 seconds, 1769472 images -Loaded: 0.203062 seconds -Region Avg IOU: 0.580393, Class: 1.000000, Obj: 0.395420, No Obj: 0.004145, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.482775, Class: 1.000000, Obj: 0.358835, No Obj: 0.003100, Avg Recall: 0.518519, count: 108 -Region Avg IOU: 0.590404, Class: 1.000000, Obj: 0.439205, No Obj: 0.003241, Avg Recall: 0.715909, count: 88 -Region Avg IOU: 0.540379, Class: 1.000000, Obj: 0.394980, No Obj: 0.003768, Avg Recall: 0.606838, count: 117 -Region Avg IOU: 0.555695, Class: 1.000000, Obj: 0.384301, No Obj: 0.004043, Avg Recall: 0.642857, count: 126 -Region Avg IOU: 0.568815, Class: 1.000000, Obj: 0.416666, No Obj: 0.003087, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.615915, Class: 1.000000, Obj: 0.440212, No Obj: 0.002944, Avg Recall: 0.711111, count: 90 -Region Avg IOU: 0.519384, Class: 1.000000, Obj: 0.349302, No Obj: 0.003456, Avg Recall: 0.553719, count: 121 -6913: 0.397607, 0.419860 avg, 0.001000 rate, 22.114376 seconds, 1769728 images -Loaded: 0.152420 seconds -Region Avg IOU: 0.566531, Class: 1.000000, Obj: 0.373727, No Obj: 0.004402, Avg Recall: 0.651163, count: 129 -Region Avg IOU: 0.604725, Class: 1.000000, Obj: 0.439380, No Obj: 0.003951, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.567275, Class: 1.000000, Obj: 0.399033, No Obj: 0.003950, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.542541, Class: 1.000000, Obj: 0.396789, No Obj: 0.003295, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.543604, Class: 1.000000, Obj: 0.400877, No Obj: 0.004070, Avg Recall: 0.616667, count: 120 -Region Avg IOU: 0.549218, Class: 1.000000, Obj: 0.406043, No Obj: 0.004085, Avg Recall: 0.613445, count: 119 -Region Avg IOU: 0.527344, Class: 1.000000, Obj: 0.329583, No Obj: 0.003462, Avg Recall: 0.576577, count: 111 -Region Avg IOU: 0.583736, Class: 1.000000, Obj: 0.395375, No Obj: 0.004008, Avg Recall: 0.688679, count: 106 -6914: 0.454076, 0.423281 avg, 0.001000 rate, 21.670332 seconds, 1769984 images -Loaded: 1.022462 seconds -Region Avg IOU: 0.550584, Class: 1.000000, Obj: 0.407944, No Obj: 0.004532, Avg Recall: 0.624060, count: 133 -Region Avg IOU: 0.551079, Class: 1.000000, Obj: 0.413451, No Obj: 0.004061, Avg Recall: 0.670000, count: 100 -Region Avg IOU: 0.581546, Class: 1.000000, Obj: 0.422585, No Obj: 0.004100, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.615896, Class: 1.000000, Obj: 0.371257, No Obj: 0.004717, Avg Recall: 0.750000, count: 128 -Region Avg IOU: 0.554364, Class: 1.000000, Obj: 0.392440, No Obj: 0.003709, Avg Recall: 0.650000, count: 120 -Region Avg IOU: 0.542208, Class: 1.000000, Obj: 0.382193, No Obj: 0.003964, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.569561, Class: 1.000000, Obj: 0.419661, No Obj: 0.003256, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.552169, Class: 1.000000, Obj: 0.420130, No Obj: 0.004447, Avg Recall: 0.592920, count: 113 -6915: 0.424966, 0.423450 avg, 0.001000 rate, 22.430370 seconds, 1770240 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.598210, Class: 1.000000, Obj: 0.439007, No Obj: 0.004167, Avg Recall: 0.653846, count: 104 -Region Avg IOU: 0.572504, Class: 1.000000, Obj: 0.450328, No Obj: 0.004258, Avg Recall: 0.637500, count: 80 -Region Avg IOU: 0.615220, Class: 1.000000, Obj: 0.432684, No Obj: 0.003880, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.548324, Class: 1.000000, Obj: 0.392814, No Obj: 0.003838, Avg Recall: 0.641667, count: 120 -Region Avg IOU: 0.568533, Class: 1.000000, Obj: 0.384121, No Obj: 0.003363, Avg Recall: 0.670732, count: 82 -Region Avg IOU: 0.575393, Class: 1.000000, Obj: 0.390596, No Obj: 0.003817, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.558952, Class: 1.000000, Obj: 0.443988, No Obj: 0.004277, Avg Recall: 0.613445, count: 119 -Region Avg IOU: 0.594469, Class: 1.000000, Obj: 0.431847, No Obj: 0.003984, Avg Recall: 0.663366, count: 101 -6916: 0.385495, 0.419654 avg, 0.001000 rate, 22.766754 seconds, 1770496 images -Loaded: 0.003847 seconds -Region Avg IOU: 0.619387, Class: 1.000000, Obj: 0.408348, No Obj: 0.004853, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.522250, Class: 1.000000, Obj: 0.377767, No Obj: 0.004606, Avg Recall: 0.584270, count: 89 -Region Avg IOU: 0.574497, Class: 1.000000, Obj: 0.416748, No Obj: 0.005130, Avg Recall: 0.666667, count: 120 -Region Avg IOU: 0.569867, Class: 1.000000, Obj: 0.417757, No Obj: 0.005105, Avg Recall: 0.688406, count: 138 -Region Avg IOU: 0.561630, Class: 1.000000, Obj: 0.476707, No Obj: 0.004091, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.547807, Class: 1.000000, Obj: 0.426961, No Obj: 0.003922, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.571328, Class: 1.000000, Obj: 0.437030, No Obj: 0.004787, Avg Recall: 0.675214, count: 117 -Region Avg IOU: 0.502134, Class: 1.000000, Obj: 0.341632, No Obj: 0.004079, Avg Recall: 0.601626, count: 123 -6917: 0.473044, 0.424993 avg, 0.001000 rate, 22.200712 seconds, 1770752 images -Loaded: 0.018921 seconds -Region Avg IOU: 0.549057, Class: 1.000000, Obj: 0.404536, No Obj: 0.004746, Avg Recall: 0.641791, count: 134 -Region Avg IOU: 0.586772, Class: 1.000000, Obj: 0.432297, No Obj: 0.003816, Avg Recall: 0.685714, count: 70 -Region Avg IOU: 0.545719, Class: 1.000000, Obj: 0.416265, No Obj: 0.004466, Avg Recall: 0.621212, count: 132 -Region Avg IOU: 0.544276, Class: 1.000000, Obj: 0.372189, No Obj: 0.004587, Avg Recall: 0.598361, count: 122 -Region Avg IOU: 0.590203, Class: 1.000000, Obj: 0.430581, No Obj: 0.004590, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.577417, Class: 1.000000, Obj: 0.427172, No Obj: 0.004965, Avg Recall: 0.672414, count: 116 -Region Avg IOU: 0.580094, Class: 1.000000, Obj: 0.453039, No Obj: 0.004276, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.572030, Class: 1.000000, Obj: 0.466550, No Obj: 0.004285, Avg Recall: 0.654545, count: 110 -6918: 0.461499, 0.428644 avg, 0.001000 rate, 21.744862 seconds, 1771008 images -Loaded: 1.854443 seconds -Region Avg IOU: 0.598011, Class: 1.000000, Obj: 0.441168, No Obj: 0.003945, Avg Recall: 0.784091, count: 88 -Region Avg IOU: 0.548063, Class: 1.000000, Obj: 0.439069, No Obj: 0.005077, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.545408, Class: 1.000000, Obj: 0.442689, No Obj: 0.004070, Avg Recall: 0.632184, count: 87 -Region Avg IOU: 0.539566, Class: 1.000000, Obj: 0.402132, No Obj: 0.004207, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.582472, Class: 1.000000, Obj: 0.405395, No Obj: 0.005366, Avg Recall: 0.733813, count: 139 -Region Avg IOU: 0.572801, Class: 1.000000, Obj: 0.463789, No Obj: 0.003883, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.535937, Class: 1.000000, Obj: 0.462846, No Obj: 0.003859, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.491373, Class: 1.000000, Obj: 0.380750, No Obj: 0.003105, Avg Recall: 0.578313, count: 83 -6919: 0.400715, 0.425851 avg, 0.001000 rate, 22.698032 seconds, 1771264 images -Loaded: 0.376410 seconds -Region Avg IOU: 0.536706, Class: 1.000000, Obj: 0.420068, No Obj: 0.003796, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.581619, Class: 1.000000, Obj: 0.400609, No Obj: 0.003807, Avg Recall: 0.685393, count: 89 -Region Avg IOU: 0.544799, Class: 1.000000, Obj: 0.400941, No Obj: 0.004015, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.564695, Class: 1.000000, Obj: 0.362482, No Obj: 0.004452, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.585755, Class: 1.000000, Obj: 0.432429, No Obj: 0.004741, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.603152, Class: 1.000000, Obj: 0.453598, No Obj: 0.004393, Avg Recall: 0.707317, count: 123 -Region Avg IOU: 0.578501, Class: 1.000000, Obj: 0.427196, No Obj: 0.004128, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.591798, Class: 1.000000, Obj: 0.459920, No Obj: 0.004423, Avg Recall: 0.691489, count: 94 -6920: 0.416773, 0.424943 avg, 0.001000 rate, 22.591022 seconds, 1771520 images -Resizing -480 - try to allocate workspace = 78675968 * sizeof(float), CUDA allocate done! -Loaded: 0.000063 seconds -Region Avg IOU: 0.553207, Class: 1.000000, Obj: 0.409041, No Obj: 0.003587, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.582485, Class: 1.000000, Obj: 0.411771, No Obj: 0.004808, Avg Recall: 0.659091, count: 132 -Region Avg IOU: 0.576377, Class: 1.000000, Obj: 0.453729, No Obj: 0.003189, Avg Recall: 0.686869, count: 99 -Region Avg IOU: 0.611618, Class: 1.000000, Obj: 0.405653, No Obj: 0.003818, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.594282, Class: 1.000000, Obj: 0.451479, No Obj: 0.003463, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.642273, Class: 1.000000, Obj: 0.481156, No Obj: 0.004012, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.583354, Class: 1.000000, Obj: 0.439950, No Obj: 0.003873, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.595349, Class: 1.000000, Obj: 0.393530, No Obj: 0.003546, Avg Recall: 0.703297, count: 91 -6921: 0.406196, 0.423069 avg, 0.001000 rate, 22.381176 seconds, 1771776 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.619268, Class: 1.000000, Obj: 0.440499, No Obj: 0.004275, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.643795, Class: 1.000000, Obj: 0.459387, No Obj: 0.003656, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.596988, Class: 1.000000, Obj: 0.450338, No Obj: 0.003770, Avg Recall: 0.694118, count: 85 -Region Avg IOU: 0.546894, Class: 1.000000, Obj: 0.362572, No Obj: 0.003293, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.603058, Class: 1.000000, Obj: 0.457688, No Obj: 0.003369, Avg Recall: 0.707317, count: 82 -Region Avg IOU: 0.600509, Class: 1.000000, Obj: 0.433994, No Obj: 0.003523, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.667669, Class: 1.000000, Obj: 0.418737, No Obj: 0.004739, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.590205, Class: 1.000000, Obj: 0.421576, No Obj: 0.004148, Avg Recall: 0.672727, count: 110 -6922: 0.377356, 0.418497 avg, 0.001000 rate, 23.184113 seconds, 1772032 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.586469, Class: 1.000000, Obj: 0.386034, No Obj: 0.003337, Avg Recall: 0.705882, count: 85 -Region Avg IOU: 0.564664, Class: 1.000000, Obj: 0.432840, No Obj: 0.003280, Avg Recall: 0.593750, count: 96 -Region Avg IOU: 0.606999, Class: 1.000000, Obj: 0.404220, No Obj: 0.003960, Avg Recall: 0.746032, count: 126 -Region Avg IOU: 0.566110, Class: 1.000000, Obj: 0.372083, No Obj: 0.003117, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.594490, Class: 1.000000, Obj: 0.416330, No Obj: 0.003244, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.553634, Class: 1.000000, Obj: 0.436585, No Obj: 0.003407, Avg Recall: 0.666667, count: 105 -Region Avg IOU: 0.599021, Class: 1.000000, Obj: 0.381834, No Obj: 0.003527, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.605035, Class: 1.000000, Obj: 0.407698, No Obj: 0.003443, Avg Recall: 0.769231, count: 104 -6923: 0.397366, 0.416384 avg, 0.001000 rate, 23.002075 seconds, 1772288 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.638710, Class: 1.000000, Obj: 0.460019, No Obj: 0.004066, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.539274, Class: 1.000000, Obj: 0.420448, No Obj: 0.003591, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.587682, Class: 1.000000, Obj: 0.453780, No Obj: 0.003801, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.525790, Class: 1.000000, Obj: 0.353567, No Obj: 0.003515, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.606517, Class: 1.000000, Obj: 0.428907, No Obj: 0.003612, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.553914, Class: 1.000000, Obj: 0.386032, No Obj: 0.003053, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.610997, Class: 1.000000, Obj: 0.455092, No Obj: 0.004548, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.569878, Class: 1.000000, Obj: 0.388201, No Obj: 0.004060, Avg Recall: 0.622047, count: 127 -6924: 0.399615, 0.414707 avg, 0.001000 rate, 23.473242 seconds, 1772544 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.617358, Class: 1.000000, Obj: 0.400116, No Obj: 0.003462, Avg Recall: 0.736842, count: 95 -Region Avg IOU: 0.597476, Class: 1.000000, Obj: 0.406474, No Obj: 0.003646, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.621794, Class: 1.000000, Obj: 0.427277, No Obj: 0.003425, Avg Recall: 0.800000, count: 90 -Region Avg IOU: 0.572445, Class: 1.000000, Obj: 0.389985, No Obj: 0.003609, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.579160, Class: 1.000000, Obj: 0.371942, No Obj: 0.003958, Avg Recall: 0.703448, count: 145 -Region Avg IOU: 0.575541, Class: 1.000000, Obj: 0.377579, No Obj: 0.003730, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.607009, Class: 1.000000, Obj: 0.418353, No Obj: 0.003869, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.557142, Class: 1.000000, Obj: 0.391035, No Obj: 0.003473, Avg Recall: 0.653846, count: 104 -6925: 0.417293, 0.414966 avg, 0.001000 rate, 22.749708 seconds, 1772800 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.562316, Class: 1.000000, Obj: 0.388179, No Obj: 0.003644, Avg Recall: 0.655462, count: 119 -Region Avg IOU: 0.582314, Class: 1.000000, Obj: 0.393274, No Obj: 0.004059, Avg Recall: 0.666667, count: 117 -Region Avg IOU: 0.597626, Class: 1.000000, Obj: 0.432584, No Obj: 0.003842, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.561140, Class: 1.000000, Obj: 0.382570, No Obj: 0.003944, Avg Recall: 0.661654, count: 133 -Region Avg IOU: 0.639990, Class: 1.000000, Obj: 0.453381, No Obj: 0.003133, Avg Recall: 0.802632, count: 76 -Region Avg IOU: 0.615174, Class: 1.000000, Obj: 0.370490, No Obj: 0.003803, Avg Recall: 0.737705, count: 122 -Region Avg IOU: 0.569174, Class: 1.000000, Obj: 0.400536, No Obj: 0.003869, Avg Recall: 0.661972, count: 142 -Region Avg IOU: 0.562818, Class: 1.000000, Obj: 0.422761, No Obj: 0.003435, Avg Recall: 0.639535, count: 86 -6926: 0.437360, 0.417205 avg, 0.001000 rate, 22.807804 seconds, 1773056 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.586282, Class: 1.000000, Obj: 0.424622, No Obj: 0.003867, Avg Recall: 0.658915, count: 129 -Region Avg IOU: 0.602080, Class: 1.000000, Obj: 0.386354, No Obj: 0.003905, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.566147, Class: 1.000000, Obj: 0.384660, No Obj: 0.003351, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.619205, Class: 1.000000, Obj: 0.432162, No Obj: 0.003758, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.618151, Class: 1.000000, Obj: 0.390402, No Obj: 0.003508, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.623137, Class: 1.000000, Obj: 0.450151, No Obj: 0.003942, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.593037, Class: 1.000000, Obj: 0.444612, No Obj: 0.003341, Avg Recall: 0.698795, count: 83 -Region Avg IOU: 0.567262, Class: 1.000000, Obj: 0.385692, No Obj: 0.003325, Avg Recall: 0.653846, count: 104 -6927: 0.395902, 0.415075 avg, 0.001000 rate, 23.876984 seconds, 1773312 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.608567, Class: 1.000000, Obj: 0.424329, No Obj: 0.003902, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.609457, Class: 1.000000, Obj: 0.413433, No Obj: 0.003253, Avg Recall: 0.720430, count: 93 -Region Avg IOU: 0.491005, Class: 1.000000, Obj: 0.377286, No Obj: 0.002974, Avg Recall: 0.505050, count: 99 -Region Avg IOU: 0.627912, Class: 1.000000, Obj: 0.432885, No Obj: 0.003705, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.562524, Class: 1.000000, Obj: 0.407384, No Obj: 0.003713, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.564970, Class: 1.000000, Obj: 0.394687, No Obj: 0.003775, Avg Recall: 0.658915, count: 129 -Region Avg IOU: 0.612885, Class: 1.000000, Obj: 0.430852, No Obj: 0.003480, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.628097, Class: 1.000000, Obj: 0.451037, No Obj: 0.005196, Avg Recall: 0.742647, count: 136 -6928: 0.408913, 0.414459 avg, 0.001000 rate, 23.312555 seconds, 1773568 images -Loaded: 0.000110 seconds -Region Avg IOU: 0.562277, Class: 1.000000, Obj: 0.419490, No Obj: 0.004179, Avg Recall: 0.652174, count: 138 -Region Avg IOU: 0.582789, Class: 1.000000, Obj: 0.455356, No Obj: 0.003357, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.565524, Class: 1.000000, Obj: 0.393544, No Obj: 0.004006, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.576940, Class: 1.000000, Obj: 0.388782, No Obj: 0.003299, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.558795, Class: 1.000000, Obj: 0.382164, No Obj: 0.003848, Avg Recall: 0.639098, count: 133 -Region Avg IOU: 0.578491, Class: 1.000000, Obj: 0.439471, No Obj: 0.003420, Avg Recall: 0.674699, count: 83 -Region Avg IOU: 0.611793, Class: 1.000000, Obj: 0.429516, No Obj: 0.003338, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.604064, Class: 1.000000, Obj: 0.406293, No Obj: 0.004010, Avg Recall: 0.731481, count: 108 -6929: 0.383089, 0.411322 avg, 0.001000 rate, 23.632429 seconds, 1773824 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.583628, Class: 1.000000, Obj: 0.419372, No Obj: 0.003459, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.584683, Class: 1.000000, Obj: 0.414077, No Obj: 0.003748, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.552280, Class: 1.000000, Obj: 0.411155, No Obj: 0.003607, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.540300, Class: 1.000000, Obj: 0.398479, No Obj: 0.003368, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.597853, Class: 1.000000, Obj: 0.424018, No Obj: 0.003362, Avg Recall: 0.701031, count: 97 -Region Avg IOU: 0.608103, Class: 1.000000, Obj: 0.412610, No Obj: 0.004139, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.540230, Class: 1.000000, Obj: 0.365309, No Obj: 0.003820, Avg Recall: 0.629921, count: 127 -Region Avg IOU: 0.562398, Class: 1.000000, Obj: 0.407043, No Obj: 0.004014, Avg Recall: 0.656489, count: 131 -6930: 0.404442, 0.410634 avg, 0.001000 rate, 23.062216 seconds, 1774080 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000082 seconds -Region Avg IOU: 0.535336, Class: 1.000000, Obj: 0.377267, No Obj: 0.003889, Avg Recall: 0.608696, count: 138 -Region Avg IOU: 0.531773, Class: 1.000000, Obj: 0.413779, No Obj: 0.003092, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.618691, Class: 1.000000, Obj: 0.470462, No Obj: 0.004314, Avg Recall: 0.714286, count: 140 -Region Avg IOU: 0.608895, Class: 1.000000, Obj: 0.424890, No Obj: 0.004248, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.619828, Class: 1.000000, Obj: 0.415518, No Obj: 0.003060, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.578602, Class: 1.000000, Obj: 0.406028, No Obj: 0.003852, Avg Recall: 0.657658, count: 111 -Region Avg IOU: 0.565603, Class: 1.000000, Obj: 0.455441, No Obj: 0.003786, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.638711, Class: 1.000000, Obj: 0.443366, No Obj: 0.004519, Avg Recall: 0.740506, count: 158 -6931: 0.506109, 0.420181 avg, 0.001000 rate, 23.954430 seconds, 1774336 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.640074, Class: 1.000000, Obj: 0.447106, No Obj: 0.004734, Avg Recall: 0.806897, count: 145 -Region Avg IOU: 0.590615, Class: 1.000000, Obj: 0.444420, No Obj: 0.003946, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.587788, Class: 1.000000, Obj: 0.470966, No Obj: 0.003536, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.617713, Class: 1.000000, Obj: 0.463334, No Obj: 0.003792, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.548586, Class: 1.000000, Obj: 0.400162, No Obj: 0.003764, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.664052, Class: 1.000000, Obj: 0.476310, No Obj: 0.003433, Avg Recall: 0.775000, count: 80 -Region Avg IOU: 0.637107, Class: 1.000000, Obj: 0.433755, No Obj: 0.004030, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.599254, Class: 1.000000, Obj: 0.391542, No Obj: 0.003564, Avg Recall: 0.663551, count: 107 -6932: 0.395760, 0.417739 avg, 0.001000 rate, 23.239256 seconds, 1774592 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.606321, Class: 1.000000, Obj: 0.415448, No Obj: 0.003218, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.579504, Class: 1.000000, Obj: 0.441581, No Obj: 0.003304, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.568686, Class: 1.000000, Obj: 0.415753, No Obj: 0.003130, Avg Recall: 0.613861, count: 101 -Region Avg IOU: 0.635173, Class: 1.000000, Obj: 0.440655, No Obj: 0.003654, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.631093, Class: 1.000000, Obj: 0.478117, No Obj: 0.003538, Avg Recall: 0.781250, count: 96 -Region Avg IOU: 0.587601, Class: 1.000000, Obj: 0.410343, No Obj: 0.003914, Avg Recall: 0.720000, count: 125 -Region Avg IOU: 0.622519, Class: 1.000000, Obj: 0.429043, No Obj: 0.003606, Avg Recall: 0.731707, count: 82 -Region Avg IOU: 0.624553, Class: 1.000000, Obj: 0.433258, No Obj: 0.004051, Avg Recall: 0.728155, count: 103 -6933: 0.382290, 0.414194 avg, 0.001000 rate, 23.391556 seconds, 1774848 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.589701, Class: 1.000000, Obj: 0.416979, No Obj: 0.003397, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.622980, Class: 1.000000, Obj: 0.459644, No Obj: 0.003931, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.622831, Class: 1.000000, Obj: 0.433657, No Obj: 0.003397, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.599110, Class: 1.000000, Obj: 0.379261, No Obj: 0.003883, Avg Recall: 0.726562, count: 128 -Region Avg IOU: 0.595723, Class: 1.000000, Obj: 0.441981, No Obj: 0.003372, Avg Recall: 0.756098, count: 82 -Region Avg IOU: 0.604718, Class: 1.000000, Obj: 0.481531, No Obj: 0.002988, Avg Recall: 0.696629, count: 89 -Region Avg IOU: 0.600551, Class: 1.000000, Obj: 0.418978, No Obj: 0.002981, Avg Recall: 0.707547, count: 106 -Region Avg IOU: 0.626617, Class: 1.000000, Obj: 0.489136, No Obj: 0.003669, Avg Recall: 0.783019, count: 106 -6934: 0.399350, 0.412710 avg, 0.001000 rate, 24.526794 seconds, 1775104 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.604247, Class: 1.000000, Obj: 0.413551, No Obj: 0.003520, Avg Recall: 0.731707, count: 123 -Region Avg IOU: 0.602380, Class: 1.000000, Obj: 0.412498, No Obj: 0.003711, Avg Recall: 0.752000, count: 125 -Region Avg IOU: 0.594698, Class: 1.000000, Obj: 0.416176, No Obj: 0.003481, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.591928, Class: 1.000000, Obj: 0.400582, No Obj: 0.003771, Avg Recall: 0.719298, count: 114 -Region Avg IOU: 0.612665, Class: 1.000000, Obj: 0.467993, No Obj: 0.003351, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.594946, Class: 1.000000, Obj: 0.409617, No Obj: 0.003664, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.625477, Class: 1.000000, Obj: 0.470470, No Obj: 0.003148, Avg Recall: 0.777778, count: 81 -Region Avg IOU: 0.572043, Class: 1.000000, Obj: 0.406009, No Obj: 0.003405, Avg Recall: 0.689655, count: 116 -6935: 0.414104, 0.412849 avg, 0.001000 rate, 23.971527 seconds, 1775360 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.600338, Class: 1.000000, Obj: 0.445863, No Obj: 0.003289, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.615321, Class: 1.000000, Obj: 0.441550, No Obj: 0.003867, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.622390, Class: 1.000000, Obj: 0.443476, No Obj: 0.003330, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.598018, Class: 1.000000, Obj: 0.425894, No Obj: 0.002859, Avg Recall: 0.752809, count: 89 -Region Avg IOU: 0.619629, Class: 1.000000, Obj: 0.417609, No Obj: 0.003252, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.614321, Class: 1.000000, Obj: 0.414609, No Obj: 0.003899, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.616016, Class: 1.000000, Obj: 0.442156, No Obj: 0.003369, Avg Recall: 0.698276, count: 116 -Region Avg IOU: 0.601441, Class: 1.000000, Obj: 0.426341, No Obj: 0.003827, Avg Recall: 0.744526, count: 137 -6936: 0.410969, 0.412661 avg, 0.001000 rate, 23.852997 seconds, 1775616 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.560528, Class: 1.000000, Obj: 0.390610, No Obj: 0.003325, Avg Recall: 0.663934, count: 122 -Region Avg IOU: 0.627263, Class: 1.000000, Obj: 0.435445, No Obj: 0.003408, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.602745, Class: 1.000000, Obj: 0.380360, No Obj: 0.003230, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.565773, Class: 1.000000, Obj: 0.397994, No Obj: 0.003925, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.593147, Class: 1.000000, Obj: 0.376096, No Obj: 0.002986, Avg Recall: 0.745098, count: 102 -Region Avg IOU: 0.598291, Class: 1.000000, Obj: 0.383845, No Obj: 0.003314, Avg Recall: 0.715385, count: 130 -Region Avg IOU: 0.628045, Class: 1.000000, Obj: 0.454473, No Obj: 0.003243, Avg Recall: 0.734940, count: 83 -Region Avg IOU: 0.588112, Class: 1.000000, Obj: 0.397904, No Obj: 0.003226, Avg Recall: 0.728070, count: 114 -6937: 0.427530, 0.414148 avg, 0.001000 rate, 25.469868 seconds, 1775872 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.613747, Class: 1.000000, Obj: 0.421514, No Obj: 0.003002, Avg Recall: 0.729167, count: 96 -Region Avg IOU: 0.605089, Class: 1.000000, Obj: 0.455630, No Obj: 0.003439, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.629838, Class: 1.000000, Obj: 0.419660, No Obj: 0.003508, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.614375, Class: 1.000000, Obj: 0.382148, No Obj: 0.003412, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.602419, Class: 1.000000, Obj: 0.414010, No Obj: 0.003405, Avg Recall: 0.709677, count: 124 -Region Avg IOU: 0.554072, Class: 1.000000, Obj: 0.400210, No Obj: 0.003151, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.579160, Class: 1.000000, Obj: 0.390656, No Obj: 0.003128, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.561657, Class: 1.000000, Obj: 0.374176, No Obj: 0.003117, Avg Recall: 0.638095, count: 105 -6938: 0.414228, 0.414156 avg, 0.001000 rate, 23.406712 seconds, 1776128 images -Loaded: 0.000114 seconds -Region Avg IOU: 0.600943, Class: 1.000000, Obj: 0.426050, No Obj: 0.003005, Avg Recall: 0.725000, count: 120 -Region Avg IOU: 0.579252, Class: 1.000000, Obj: 0.359939, No Obj: 0.003551, Avg Recall: 0.674603, count: 126 -Region Avg IOU: 0.622912, Class: 1.000000, Obj: 0.373539, No Obj: 0.002633, Avg Recall: 0.746667, count: 75 -Region Avg IOU: 0.583727, Class: 1.000000, Obj: 0.432647, No Obj: 0.003465, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.634486, Class: 1.000000, Obj: 0.451670, No Obj: 0.003160, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.580437, Class: 1.000000, Obj: 0.376979, No Obj: 0.003660, Avg Recall: 0.669421, count: 121 -Region Avg IOU: 0.604283, Class: 1.000000, Obj: 0.439443, No Obj: 0.003378, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.590925, Class: 1.000000, Obj: 0.375289, No Obj: 0.003430, Avg Recall: 0.719697, count: 132 -6939: 0.428934, 0.415634 avg, 0.001000 rate, 24.350191 seconds, 1776384 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.593529, Class: 1.000000, Obj: 0.422001, No Obj: 0.002978, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.604082, Class: 1.000000, Obj: 0.415036, No Obj: 0.002849, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.581215, Class: 1.000000, Obj: 0.382524, No Obj: 0.002831, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.568584, Class: 1.000000, Obj: 0.402591, No Obj: 0.003037, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.610765, Class: 1.000000, Obj: 0.385012, No Obj: 0.003237, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.584930, Class: 1.000000, Obj: 0.369202, No Obj: 0.003166, Avg Recall: 0.703297, count: 91 -Region Avg IOU: 0.564011, Class: 1.000000, Obj: 0.390306, No Obj: 0.003492, Avg Recall: 0.682171, count: 129 -Region Avg IOU: 0.613933, Class: 1.000000, Obj: 0.432368, No Obj: 0.003587, Avg Recall: 0.747826, count: 115 -6940: 0.414889, 0.415559 avg, 0.001000 rate, 24.554749 seconds, 1776640 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 17.128740 seconds -Region Avg IOU: 0.584883, Class: 1.000000, Obj: 0.401712, No Obj: 0.003889, Avg Recall: 0.658537, count: 123 -Region Avg IOU: 0.539660, Class: 1.000000, Obj: 0.383615, No Obj: 0.002835, Avg Recall: 0.597826, count: 92 -Region Avg IOU: 0.622560, Class: 1.000000, Obj: 0.419839, No Obj: 0.003653, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.571020, Class: 1.000000, Obj: 0.349726, No Obj: 0.003779, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.606484, Class: 1.000000, Obj: 0.467575, No Obj: 0.003060, Avg Recall: 0.717647, count: 85 -Region Avg IOU: 0.558372, Class: 1.000000, Obj: 0.365300, No Obj: 0.002948, Avg Recall: 0.621359, count: 103 -Region Avg IOU: 0.615413, Class: 1.000000, Obj: 0.427447, No Obj: 0.003432, Avg Recall: 0.705882, count: 102 -Region Avg IOU: 0.619652, Class: 1.000000, Obj: 0.385660, No Obj: 0.003291, Avg Recall: 0.741379, count: 116 -6941: 0.420643, 0.416068 avg, 0.001000 rate, 23.670918 seconds, 1776896 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.649857, Class: 1.000000, Obj: 0.440340, No Obj: 0.003085, Avg Recall: 0.815789, count: 76 -Region Avg IOU: 0.585267, Class: 1.000000, Obj: 0.376004, No Obj: 0.003022, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.611486, Class: 1.000000, Obj: 0.438288, No Obj: 0.003280, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.606097, Class: 1.000000, Obj: 0.468050, No Obj: 0.003201, Avg Recall: 0.717647, count: 85 -Region Avg IOU: 0.598542, Class: 1.000000, Obj: 0.395696, No Obj: 0.003499, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.601648, Class: 1.000000, Obj: 0.460935, No Obj: 0.002990, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.591669, Class: 1.000000, Obj: 0.392497, No Obj: 0.003070, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.582968, Class: 1.000000, Obj: 0.402452, No Obj: 0.003337, Avg Recall: 0.640777, count: 103 -6942: 0.365720, 0.411033 avg, 0.001000 rate, 24.836229 seconds, 1777152 images -Loaded: 0.000102 seconds -Region Avg IOU: 0.632524, Class: 1.000000, Obj: 0.404071, No Obj: 0.003564, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.576902, Class: 1.000000, Obj: 0.448055, No Obj: 0.004200, Avg Recall: 0.680272, count: 147 -Region Avg IOU: 0.577944, Class: 1.000000, Obj: 0.405694, No Obj: 0.002758, Avg Recall: 0.676056, count: 71 -Region Avg IOU: 0.620041, Class: 1.000000, Obj: 0.392454, No Obj: 0.003363, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.607733, Class: 1.000000, Obj: 0.443501, No Obj: 0.002939, Avg Recall: 0.734940, count: 83 -Region Avg IOU: 0.577353, Class: 1.000000, Obj: 0.353937, No Obj: 0.003088, Avg Recall: 0.650000, count: 120 -Region Avg IOU: 0.628462, Class: 1.000000, Obj: 0.432599, No Obj: 0.003498, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.639122, Class: 1.000000, Obj: 0.402463, No Obj: 0.004041, Avg Recall: 0.768657, count: 134 -6943: 0.442141, 0.414144 avg, 0.001000 rate, 24.380384 seconds, 1777408 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.607618, Class: 1.000000, Obj: 0.426914, No Obj: 0.003084, Avg Recall: 0.737500, count: 80 -Region Avg IOU: 0.621976, Class: 1.000000, Obj: 0.458743, No Obj: 0.003141, Avg Recall: 0.709302, count: 86 -Region Avg IOU: 0.630008, Class: 1.000000, Obj: 0.438694, No Obj: 0.003527, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.589974, Class: 1.000000, Obj: 0.415734, No Obj: 0.003204, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.574046, Class: 1.000000, Obj: 0.423634, No Obj: 0.003454, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.600571, Class: 1.000000, Obj: 0.417389, No Obj: 0.003281, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.548825, Class: 1.000000, Obj: 0.422029, No Obj: 0.003134, Avg Recall: 0.666667, count: 120 -Region Avg IOU: 0.601058, Class: 1.000000, Obj: 0.421752, No Obj: 0.003134, Avg Recall: 0.695652, count: 115 -6944: 0.373295, 0.410059 avg, 0.001000 rate, 23.610643 seconds, 1777664 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.588351, Class: 1.000000, Obj: 0.403506, No Obj: 0.003739, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.604120, Class: 1.000000, Obj: 0.433313, No Obj: 0.003714, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.605565, Class: 1.000000, Obj: 0.422879, No Obj: 0.003464, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.637912, Class: 1.000000, Obj: 0.432048, No Obj: 0.003766, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.607701, Class: 1.000000, Obj: 0.458667, No Obj: 0.003607, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.588596, Class: 1.000000, Obj: 0.414920, No Obj: 0.003339, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.649167, Class: 1.000000, Obj: 0.423907, No Obj: 0.002745, Avg Recall: 0.833333, count: 66 -Region Avg IOU: 0.590684, Class: 1.000000, Obj: 0.428539, No Obj: 0.004319, Avg Recall: 0.700787, count: 127 -6945: 0.449551, 0.414008 avg, 0.001000 rate, 23.852345 seconds, 1777920 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.625024, Class: 1.000000, Obj: 0.432353, No Obj: 0.003750, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.650150, Class: 1.000000, Obj: 0.525858, No Obj: 0.003695, Avg Recall: 0.752688, count: 93 -Region Avg IOU: 0.603844, Class: 1.000000, Obj: 0.438997, No Obj: 0.003334, Avg Recall: 0.677083, count: 96 -Region Avg IOU: 0.636358, Class: 1.000000, Obj: 0.441867, No Obj: 0.003646, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.625588, Class: 1.000000, Obj: 0.486915, No Obj: 0.003857, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.622524, Class: 1.000000, Obj: 0.424675, No Obj: 0.004033, Avg Recall: 0.726562, count: 128 -Region Avg IOU: 0.613081, Class: 1.000000, Obj: 0.450790, No Obj: 0.004053, Avg Recall: 0.704545, count: 132 -Region Avg IOU: 0.580756, Class: 1.000000, Obj: 0.441096, No Obj: 0.003359, Avg Recall: 0.672414, count: 116 -6946: 0.381858, 0.410793 avg, 0.001000 rate, 24.184387 seconds, 1778176 images -Loaded: 0.000157 seconds -Region Avg IOU: 0.528585, Class: 1.000000, Obj: 0.417047, No Obj: 0.003448, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.620274, Class: 1.000000, Obj: 0.455576, No Obj: 0.003515, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.621259, Class: 1.000000, Obj: 0.430700, No Obj: 0.003910, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.589850, Class: 1.000000, Obj: 0.397458, No Obj: 0.003458, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.580488, Class: 1.000000, Obj: 0.396177, No Obj: 0.003256, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.573921, Class: 1.000000, Obj: 0.445312, No Obj: 0.003545, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.636345, Class: 1.000000, Obj: 0.453281, No Obj: 0.003532, Avg Recall: 0.755814, count: 86 -Region Avg IOU: 0.616559, Class: 1.000000, Obj: 0.475558, No Obj: 0.003660, Avg Recall: 0.711712, count: 111 -6947: 0.358123, 0.405526 avg, 0.001000 rate, 24.057907 seconds, 1778432 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.592673, Class: 1.000000, Obj: 0.447093, No Obj: 0.003652, Avg Recall: 0.678571, count: 84 -Region Avg IOU: 0.602560, Class: 1.000000, Obj: 0.430092, No Obj: 0.003985, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.604075, Class: 1.000000, Obj: 0.447040, No Obj: 0.003917, Avg Recall: 0.726496, count: 117 -Region Avg IOU: 0.616879, Class: 1.000000, Obj: 0.462906, No Obj: 0.003469, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.608793, Class: 1.000000, Obj: 0.418504, No Obj: 0.004007, Avg Recall: 0.683761, count: 117 -Region Avg IOU: 0.578197, Class: 1.000000, Obj: 0.442438, No Obj: 0.004156, Avg Recall: 0.646617, count: 133 -Region Avg IOU: 0.614215, Class: 1.000000, Obj: 0.462700, No Obj: 0.004156, Avg Recall: 0.735294, count: 136 -Region Avg IOU: 0.579062, Class: 1.000000, Obj: 0.409368, No Obj: 0.003286, Avg Recall: 0.669903, count: 103 -6948: 0.459862, 0.410960 avg, 0.001000 rate, 22.552868 seconds, 1778688 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.588482, Class: 1.000000, Obj: 0.434021, No Obj: 0.003413, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.620984, Class: 1.000000, Obj: 0.437145, No Obj: 0.003142, Avg Recall: 0.772152, count: 79 -Region Avg IOU: 0.568598, Class: 1.000000, Obj: 0.394267, No Obj: 0.003598, Avg Recall: 0.680000, count: 125 -Region Avg IOU: 0.643079, Class: 1.000000, Obj: 0.409669, No Obj: 0.003274, Avg Recall: 0.797101, count: 69 -Region Avg IOU: 0.606389, Class: 1.000000, Obj: 0.446117, No Obj: 0.004677, Avg Recall: 0.731343, count: 134 -Region Avg IOU: 0.628041, Class: 1.000000, Obj: 0.480678, No Obj: 0.003554, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.501699, Class: 1.000000, Obj: 0.384972, No Obj: 0.003474, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.578278, Class: 1.000000, Obj: 0.460773, No Obj: 0.003657, Avg Recall: 0.689655, count: 116 -6949: 0.377776, 0.407641 avg, 0.001000 rate, 24.550104 seconds, 1778944 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.608942, Class: 1.000000, Obj: 0.453649, No Obj: 0.003337, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.615997, Class: 1.000000, Obj: 0.441301, No Obj: 0.003606, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.623313, Class: 1.000000, Obj: 0.431872, No Obj: 0.003885, Avg Recall: 0.733333, count: 135 -Region Avg IOU: 0.626253, Class: 1.000000, Obj: 0.444054, No Obj: 0.004133, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.534564, Class: 1.000000, Obj: 0.384095, No Obj: 0.003049, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.613641, Class: 1.000000, Obj: 0.415378, No Obj: 0.003940, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.665327, Class: 1.000000, Obj: 0.468274, No Obj: 0.002946, Avg Recall: 0.828947, count: 76 -Region Avg IOU: 0.617282, Class: 1.000000, Obj: 0.429551, No Obj: 0.004175, Avg Recall: 0.738462, count: 130 -6950: 0.371839, 0.404061 avg, 0.001000 rate, 25.283005 seconds, 1779200 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000073 seconds -Region Avg IOU: 0.678491, Class: 1.000000, Obj: 0.445837, No Obj: 0.003543, Avg Recall: 0.851240, count: 121 -Region Avg IOU: 0.650383, Class: 1.000000, Obj: 0.481813, No Obj: 0.003270, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.601705, Class: 1.000000, Obj: 0.381939, No Obj: 0.003285, Avg Recall: 0.737226, count: 137 -Region Avg IOU: 0.627210, Class: 1.000000, Obj: 0.499972, No Obj: 0.002889, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.651950, Class: 1.000000, Obj: 0.466232, No Obj: 0.002699, Avg Recall: 0.797872, count: 94 -Region Avg IOU: 0.631414, Class: 1.000000, Obj: 0.429209, No Obj: 0.003028, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.594160, Class: 1.000000, Obj: 0.434486, No Obj: 0.002793, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.656498, Class: 1.000000, Obj: 0.447624, No Obj: 0.002914, Avg Recall: 0.802326, count: 86 -6951: 0.392486, 0.402903 avg, 0.001000 rate, 27.700834 seconds, 1779456 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.688598, Class: 1.000000, Obj: 0.416487, No Obj: 0.003049, Avg Recall: 0.841463, count: 82 -Region Avg IOU: 0.644580, Class: 1.000000, Obj: 0.457479, No Obj: 0.003133, Avg Recall: 0.784314, count: 102 -Region Avg IOU: 0.690459, Class: 1.000000, Obj: 0.456447, No Obj: 0.003428, Avg Recall: 0.884297, count: 121 -Region Avg IOU: 0.610090, Class: 1.000000, Obj: 0.401771, No Obj: 0.003279, Avg Recall: 0.718750, count: 128 -Region Avg IOU: 0.637956, Class: 1.000000, Obj: 0.476841, No Obj: 0.003024, Avg Recall: 0.781250, count: 96 -Region Avg IOU: 0.662936, Class: 1.000000, Obj: 0.464758, No Obj: 0.002896, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.635922, Class: 1.000000, Obj: 0.428161, No Obj: 0.003513, Avg Recall: 0.817460, count: 126 -Region Avg IOU: 0.670418, Class: 1.000000, Obj: 0.462081, No Obj: 0.003157, Avg Recall: 0.818182, count: 110 -6952: 0.437983, 0.406411 avg, 0.001000 rate, 26.516500 seconds, 1779712 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.657826, Class: 1.000000, Obj: 0.448843, No Obj: 0.002664, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.620717, Class: 1.000000, Obj: 0.435479, No Obj: 0.002851, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.640181, Class: 1.000000, Obj: 0.452270, No Obj: 0.003139, Avg Recall: 0.760000, count: 125 -Region Avg IOU: 0.642504, Class: 1.000000, Obj: 0.425488, No Obj: 0.002591, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.619387, Class: 1.000000, Obj: 0.413795, No Obj: 0.002831, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.617665, Class: 1.000000, Obj: 0.382326, No Obj: 0.002632, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.635693, Class: 1.000000, Obj: 0.418854, No Obj: 0.002908, Avg Recall: 0.795276, count: 127 -Region Avg IOU: 0.655015, Class: 1.000000, Obj: 0.463164, No Obj: 0.003137, Avg Recall: 0.788136, count: 118 -6953: 0.404180, 0.406188 avg, 0.001000 rate, 26.315151 seconds, 1779968 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.648993, Class: 1.000000, Obj: 0.454494, No Obj: 0.002906, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.652420, Class: 1.000000, Obj: 0.415595, No Obj: 0.002781, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.655378, Class: 1.000000, Obj: 0.391833, No Obj: 0.003374, Avg Recall: 0.813008, count: 123 -Region Avg IOU: 0.609991, Class: 1.000000, Obj: 0.350216, No Obj: 0.002627, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.637327, Class: 1.000000, Obj: 0.422393, No Obj: 0.002701, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.656477, Class: 1.000000, Obj: 0.450987, No Obj: 0.002578, Avg Recall: 0.831683, count: 101 -Region Avg IOU: 0.645754, Class: 1.000000, Obj: 0.411122, No Obj: 0.003285, Avg Recall: 0.798561, count: 139 -Region Avg IOU: 0.612521, Class: 1.000000, Obj: 0.393196, No Obj: 0.002639, Avg Recall: 0.716981, count: 106 -6954: 0.469557, 0.412525 avg, 0.001000 rate, 25.537323 seconds, 1780224 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.585746, Class: 1.000000, Obj: 0.364638, No Obj: 0.002634, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.606256, Class: 1.000000, Obj: 0.461923, No Obj: 0.002403, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.603640, Class: 1.000000, Obj: 0.378683, No Obj: 0.002909, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.662197, Class: 1.000000, Obj: 0.399647, No Obj: 0.002925, Avg Recall: 0.842105, count: 114 -Region Avg IOU: 0.621266, Class: 1.000000, Obj: 0.426645, No Obj: 0.002528, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.645681, Class: 1.000000, Obj: 0.437373, No Obj: 0.002784, Avg Recall: 0.778947, count: 95 -Region Avg IOU: 0.590399, Class: 1.000000, Obj: 0.415572, No Obj: 0.002390, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.649468, Class: 1.000000, Obj: 0.415808, No Obj: 0.003053, Avg Recall: 0.777778, count: 117 -6955: 0.443084, 0.415581 avg, 0.001000 rate, 25.720909 seconds, 1780480 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.658645, Class: 1.000000, Obj: 0.423018, No Obj: 0.002841, Avg Recall: 0.828571, count: 105 -Region Avg IOU: 0.635357, Class: 1.000000, Obj: 0.385028, No Obj: 0.003233, Avg Recall: 0.806452, count: 124 -Region Avg IOU: 0.585682, Class: 1.000000, Obj: 0.382964, No Obj: 0.002579, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.624596, Class: 1.000000, Obj: 0.420033, No Obj: 0.002703, Avg Recall: 0.702703, count: 111 -Region Avg IOU: 0.661068, Class: 1.000000, Obj: 0.402549, No Obj: 0.002945, Avg Recall: 0.840708, count: 113 -Region Avg IOU: 0.635153, Class: 1.000000, Obj: 0.376579, No Obj: 0.002550, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.633374, Class: 1.000000, Obj: 0.414447, No Obj: 0.002293, Avg Recall: 0.752809, count: 89 -Region Avg IOU: 0.604132, Class: 1.000000, Obj: 0.412458, No Obj: 0.002778, Avg Recall: 0.718750, count: 96 -6956: 0.387935, 0.412816 avg, 0.001000 rate, 26.865427 seconds, 1780736 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.638037, Class: 1.000000, Obj: 0.399037, No Obj: 0.002722, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.600070, Class: 1.000000, Obj: 0.385670, No Obj: 0.002826, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.699730, Class: 1.000000, Obj: 0.462643, No Obj: 0.002393, Avg Recall: 0.897436, count: 78 -Region Avg IOU: 0.630900, Class: 1.000000, Obj: 0.396442, No Obj: 0.003063, Avg Recall: 0.748031, count: 127 -Region Avg IOU: 0.608541, Class: 1.000000, Obj: 0.399606, No Obj: 0.002453, Avg Recall: 0.734694, count: 98 -Region Avg IOU: 0.675416, Class: 1.000000, Obj: 0.457005, No Obj: 0.002500, Avg Recall: 0.781609, count: 87 -Region Avg IOU: 0.627670, Class: 1.000000, Obj: 0.409994, No Obj: 0.003169, Avg Recall: 0.777778, count: 135 -Region Avg IOU: 0.659530, Class: 1.000000, Obj: 0.426759, No Obj: 0.002859, Avg Recall: 0.812500, count: 112 -6957: 0.408223, 0.412357 avg, 0.001000 rate, 27.387106 seconds, 1780992 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.630679, Class: 1.000000, Obj: 0.419420, No Obj: 0.002331, Avg Recall: 0.738095, count: 84 -Region Avg IOU: 0.670894, Class: 1.000000, Obj: 0.443824, No Obj: 0.003060, Avg Recall: 0.818841, count: 138 -Region Avg IOU: 0.641322, Class: 1.000000, Obj: 0.417826, No Obj: 0.003181, Avg Recall: 0.795276, count: 127 -Region Avg IOU: 0.658459, Class: 1.000000, Obj: 0.375926, No Obj: 0.002936, Avg Recall: 0.805085, count: 118 -Region Avg IOU: 0.651859, Class: 1.000000, Obj: 0.430795, No Obj: 0.002886, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.659796, Class: 1.000000, Obj: 0.431585, No Obj: 0.002974, Avg Recall: 0.783333, count: 120 -Region Avg IOU: 0.650597, Class: 1.000000, Obj: 0.449041, No Obj: 0.002970, Avg Recall: 0.827273, count: 110 -Region Avg IOU: 0.634297, Class: 1.000000, Obj: 0.399937, No Obj: 0.002719, Avg Recall: 0.785714, count: 112 -6958: 0.453918, 0.416513 avg, 0.001000 rate, 25.762455 seconds, 1781248 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.661512, Class: 1.000000, Obj: 0.460193, No Obj: 0.002652, Avg Recall: 0.845238, count: 84 -Region Avg IOU: 0.656928, Class: 1.000000, Obj: 0.442265, No Obj: 0.002781, Avg Recall: 0.813084, count: 107 -Region Avg IOU: 0.629918, Class: 1.000000, Obj: 0.420948, No Obj: 0.002231, Avg Recall: 0.759036, count: 83 -Region Avg IOU: 0.619449, Class: 1.000000, Obj: 0.392447, No Obj: 0.002504, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.665512, Class: 1.000000, Obj: 0.440976, No Obj: 0.002566, Avg Recall: 0.816092, count: 87 -Region Avg IOU: 0.607806, Class: 1.000000, Obj: 0.397506, No Obj: 0.002916, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.645988, Class: 1.000000, Obj: 0.406540, No Obj: 0.002659, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.643388, Class: 1.000000, Obj: 0.451334, No Obj: 0.003014, Avg Recall: 0.766129, count: 124 -6959: 0.419081, 0.416770 avg, 0.001000 rate, 26.425411 seconds, 1781504 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.646923, Class: 1.000000, Obj: 0.386813, No Obj: 0.002974, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.657240, Class: 1.000000, Obj: 0.420335, No Obj: 0.003282, Avg Recall: 0.818898, count: 127 -Region Avg IOU: 0.620051, Class: 1.000000, Obj: 0.446292, No Obj: 0.002699, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.663867, Class: 1.000000, Obj: 0.490510, No Obj: 0.003306, Avg Recall: 0.831579, count: 95 -Region Avg IOU: 0.662702, Class: 1.000000, Obj: 0.432900, No Obj: 0.003118, Avg Recall: 0.852459, count: 122 -Region Avg IOU: 0.620098, Class: 1.000000, Obj: 0.451548, No Obj: 0.002877, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.705194, Class: 1.000000, Obj: 0.502302, No Obj: 0.002596, Avg Recall: 0.878378, count: 74 -Region Avg IOU: 0.592197, Class: 1.000000, Obj: 0.400822, No Obj: 0.003063, Avg Recall: 0.719424, count: 139 -6960: 0.416190, 0.416712 avg, 0.001000 rate, 26.904760 seconds, 1781760 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.941412 seconds -Region Avg IOU: 0.491626, Class: 1.000000, Obj: 0.350234, No Obj: 0.004528, Avg Recall: 0.523810, count: 126 -Region Avg IOU: 0.559686, Class: 1.000000, Obj: 0.335831, No Obj: 0.004478, Avg Recall: 0.627907, count: 129 -Region Avg IOU: 0.517751, Class: 1.000000, Obj: 0.361230, No Obj: 0.004450, Avg Recall: 0.567376, count: 141 -Region Avg IOU: 0.493405, Class: 1.000000, Obj: 0.306979, No Obj: 0.003847, Avg Recall: 0.525253, count: 99 -Region Avg IOU: 0.543355, Class: 1.000000, Obj: 0.355091, No Obj: 0.004059, Avg Recall: 0.602151, count: 93 -Region Avg IOU: 0.512782, Class: 1.000000, Obj: 0.350216, No Obj: 0.003721, Avg Recall: 0.588235, count: 102 -Region Avg IOU: 0.481530, Class: 1.000000, Obj: 0.304880, No Obj: 0.004398, Avg Recall: 0.541985, count: 131 -Region Avg IOU: 0.591565, Class: 1.000000, Obj: 0.363962, No Obj: 0.004125, Avg Recall: 0.729167, count: 96 -6961: 0.440313, 0.419072 avg, 0.001000 rate, 16.327074 seconds, 1782016 images -Loaded: 5.606001 seconds -Region Avg IOU: 0.518265, Class: 1.000000, Obj: 0.343421, No Obj: 0.004141, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.540641, Class: 1.000000, Obj: 0.412224, No Obj: 0.004048, Avg Recall: 0.632184, count: 87 -Region Avg IOU: 0.458481, Class: 1.000000, Obj: 0.332391, No Obj: 0.003728, Avg Recall: 0.460674, count: 89 -Region Avg IOU: 0.512911, Class: 1.000000, Obj: 0.392775, No Obj: 0.004494, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.548855, Class: 1.000000, Obj: 0.349968, No Obj: 0.005539, Avg Recall: 0.601351, count: 148 -Region Avg IOU: 0.557759, Class: 1.000000, Obj: 0.383168, No Obj: 0.004389, Avg Recall: 0.675439, count: 114 -Region Avg IOU: 0.561504, Class: 1.000000, Obj: 0.395054, No Obj: 0.004223, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.536216, Class: 1.000000, Obj: 0.349916, No Obj: 0.003663, Avg Recall: 0.625000, count: 80 -6962: 0.380764, 0.415241 avg, 0.001000 rate, 16.728027 seconds, 1782272 images -Loaded: 2.437364 seconds -Region Avg IOU: 0.492017, Class: 1.000000, Obj: 0.377115, No Obj: 0.004382, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.573465, Class: 1.000000, Obj: 0.377774, No Obj: 0.004603, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.550607, Class: 1.000000, Obj: 0.377773, No Obj: 0.005449, Avg Recall: 0.651852, count: 135 -Region Avg IOU: 0.603511, Class: 1.000000, Obj: 0.424036, No Obj: 0.004512, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.489102, Class: 1.000000, Obj: 0.385489, No Obj: 0.004436, Avg Recall: 0.517241, count: 116 -Region Avg IOU: 0.560405, Class: 1.000000, Obj: 0.404072, No Obj: 0.005313, Avg Recall: 0.644628, count: 121 -Region Avg IOU: 0.497937, Class: 1.000000, Obj: 0.364804, No Obj: 0.004146, Avg Recall: 0.531250, count: 96 -Region Avg IOU: 0.543045, Class: 1.000000, Obj: 0.356291, No Obj: 0.004143, Avg Recall: 0.632653, count: 98 -6963: 0.428561, 0.416573 avg, 0.001000 rate, 19.520145 seconds, 1782528 images -Loaded: 1.627043 seconds -Region Avg IOU: 0.489682, Class: 1.000000, Obj: 0.368857, No Obj: 0.004214, Avg Recall: 0.530000, count: 100 -Region Avg IOU: 0.520439, Class: 1.000000, Obj: 0.373943, No Obj: 0.004166, Avg Recall: 0.592233, count: 103 -Region Avg IOU: 0.526030, Class: 1.000000, Obj: 0.361960, No Obj: 0.004176, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.538659, Class: 1.000000, Obj: 0.391685, No Obj: 0.005010, Avg Recall: 0.586538, count: 104 -Region Avg IOU: 0.528196, Class: 1.000000, Obj: 0.391968, No Obj: 0.004228, Avg Recall: 0.607595, count: 79 -Region Avg IOU: 0.540502, Class: 1.000000, Obj: 0.387669, No Obj: 0.004561, Avg Recall: 0.630000, count: 100 -Region Avg IOU: 0.500206, Class: 1.000000, Obj: 0.346227, No Obj: 0.004500, Avg Recall: 0.553191, count: 94 -Region Avg IOU: 0.480073, Class: 1.000000, Obj: 0.378971, No Obj: 0.004110, Avg Recall: 0.568421, count: 95 -6964: 0.369942, 0.411910 avg, 0.001000 rate, 18.543447 seconds, 1782784 images -Loaded: 2.675080 seconds -Region Avg IOU: 0.498839, Class: 1.000000, Obj: 0.366465, No Obj: 0.003775, Avg Recall: 0.580645, count: 93 -Region Avg IOU: 0.536701, Class: 1.000000, Obj: 0.424752, No Obj: 0.005195, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.518480, Class: 1.000000, Obj: 0.361214, No Obj: 0.004582, Avg Recall: 0.631148, count: 122 -Region Avg IOU: 0.454656, Class: 1.000000, Obj: 0.362081, No Obj: 0.004115, Avg Recall: 0.500000, count: 102 -Region Avg IOU: 0.515086, Class: 1.000000, Obj: 0.364942, No Obj: 0.003974, Avg Recall: 0.612500, count: 80 -Region Avg IOU: 0.512536, Class: 1.000000, Obj: 0.392855, No Obj: 0.004914, Avg Recall: 0.544715, count: 123 -Region Avg IOU: 0.474532, Class: 1.000000, Obj: 0.399661, No Obj: 0.003472, Avg Recall: 0.550562, count: 89 -Region Avg IOU: 0.477221, Class: 1.000000, Obj: 0.334816, No Obj: 0.004695, Avg Recall: 0.521008, count: 119 -6965: 0.508387, 0.421558 avg, 0.001000 rate, 20.598423 seconds, 1783040 images -Loaded: 0.966660 seconds -Region Avg IOU: 0.491581, Class: 1.000000, Obj: 0.408861, No Obj: 0.004903, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.508564, Class: 1.000000, Obj: 0.368435, No Obj: 0.004756, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.513974, Class: 1.000000, Obj: 0.420503, No Obj: 0.004813, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.514829, Class: 1.000000, Obj: 0.404284, No Obj: 0.005030, Avg Recall: 0.528302, count: 106 -Region Avg IOU: 0.523489, Class: 1.000000, Obj: 0.365001, No Obj: 0.004030, Avg Recall: 0.576087, count: 92 -Region Avg IOU: 0.561288, Class: 1.000000, Obj: 0.424786, No Obj: 0.004584, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.544969, Class: 1.000000, Obj: 0.410612, No Obj: 0.005368, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.493369, Class: 1.000000, Obj: 0.354653, No Obj: 0.005063, Avg Recall: 0.510490, count: 143 -6966: 0.393737, 0.418776 avg, 0.001000 rate, 19.746901 seconds, 1783296 images -Loaded: 0.187286 seconds -Region Avg IOU: 0.585213, Class: 1.000000, Obj: 0.461847, No Obj: 0.004319, Avg Recall: 0.738095, count: 84 -Region Avg IOU: 0.548715, Class: 1.000000, Obj: 0.416125, No Obj: 0.005376, Avg Recall: 0.630252, count: 119 -Region Avg IOU: 0.578206, Class: 1.000000, Obj: 0.434538, No Obj: 0.004945, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.511817, Class: 1.000000, Obj: 0.398152, No Obj: 0.004362, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.539382, Class: 1.000000, Obj: 0.410349, No Obj: 0.005175, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.533837, Class: 1.000000, Obj: 0.400632, No Obj: 0.004936, Avg Recall: 0.617886, count: 123 -Region Avg IOU: 0.492286, Class: 1.000000, Obj: 0.446461, No Obj: 0.004356, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.523751, Class: 1.000000, Obj: 0.403978, No Obj: 0.004737, Avg Recall: 0.607843, count: 102 -6967: 0.411514, 0.418049 avg, 0.001000 rate, 21.173258 seconds, 1783552 images -Loaded: 1.243659 seconds -Region Avg IOU: 0.490653, Class: 1.000000, Obj: 0.355008, No Obj: 0.004297, Avg Recall: 0.555556, count: 99 -Region Avg IOU: 0.546174, Class: 1.000000, Obj: 0.389708, No Obj: 0.004909, Avg Recall: 0.650794, count: 126 -Region Avg IOU: 0.514787, Class: 1.000000, Obj: 0.337290, No Obj: 0.004145, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.536149, Class: 1.000000, Obj: 0.379165, No Obj: 0.005264, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.514198, Class: 1.000000, Obj: 0.373105, No Obj: 0.005024, Avg Recall: 0.608333, count: 120 -Region Avg IOU: 0.522183, Class: 1.000000, Obj: 0.370957, No Obj: 0.004938, Avg Recall: 0.591304, count: 115 -Region Avg IOU: 0.478046, Class: 1.000000, Obj: 0.349355, No Obj: 0.004290, Avg Recall: 0.584270, count: 89 -Region Avg IOU: 0.504269, Class: 1.000000, Obj: 0.370794, No Obj: 0.004962, Avg Recall: 0.525926, count: 135 -6968: 0.463493, 0.422594 avg, 0.001000 rate, 17.391836 seconds, 1783808 images -Loaded: 4.759031 seconds -Region Avg IOU: 0.517231, Class: 1.000000, Obj: 0.368211, No Obj: 0.005097, Avg Recall: 0.631579, count: 114 -Region Avg IOU: 0.501967, Class: 1.000000, Obj: 0.362180, No Obj: 0.004579, Avg Recall: 0.576577, count: 111 -Region Avg IOU: 0.523981, Class: 1.000000, Obj: 0.412417, No Obj: 0.004745, Avg Recall: 0.582524, count: 103 -Region Avg IOU: 0.539240, Class: 1.000000, Obj: 0.404129, No Obj: 0.005107, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.515416, Class: 1.000000, Obj: 0.400831, No Obj: 0.004400, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.566167, Class: 1.000000, Obj: 0.402472, No Obj: 0.004673, Avg Recall: 0.626374, count: 91 -Region Avg IOU: 0.497716, Class: 1.000000, Obj: 0.322000, No Obj: 0.004451, Avg Recall: 0.596639, count: 119 -Region Avg IOU: 0.561973, Class: 1.000000, Obj: 0.421047, No Obj: 0.004486, Avg Recall: 0.634409, count: 93 -6969: 0.425894, 0.422924 avg, 0.001000 rate, 16.686367 seconds, 1784064 images -Loaded: 6.304351 seconds -Region Avg IOU: 0.541005, Class: 1.000000, Obj: 0.401929, No Obj: 0.004898, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.498357, Class: 1.000000, Obj: 0.394307, No Obj: 0.005357, Avg Recall: 0.531746, count: 126 -Region Avg IOU: 0.500291, Class: 1.000000, Obj: 0.360672, No Obj: 0.004802, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.526556, Class: 1.000000, Obj: 0.397605, No Obj: 0.005661, Avg Recall: 0.593220, count: 118 -Region Avg IOU: 0.530890, Class: 1.000000, Obj: 0.421399, No Obj: 0.004632, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.522138, Class: 1.000000, Obj: 0.389917, No Obj: 0.004142, Avg Recall: 0.617978, count: 89 -Region Avg IOU: 0.519368, Class: 1.000000, Obj: 0.393019, No Obj: 0.004220, Avg Recall: 0.597701, count: 87 -Region Avg IOU: 0.513957, Class: 1.000000, Obj: 0.377587, No Obj: 0.004983, Avg Recall: 0.583333, count: 120 -6970: 0.434967, 0.424128 avg, 0.001000 rate, 21.142551 seconds, 1784320 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.033771 seconds -Region Avg IOU: 0.606559, Class: 1.000000, Obj: 0.443060, No Obj: 0.004847, Avg Recall: 0.743421, count: 152 -Region Avg IOU: 0.590926, Class: 1.000000, Obj: 0.435299, No Obj: 0.003587, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.584411, Class: 1.000000, Obj: 0.452171, No Obj: 0.003948, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.595812, Class: 1.000000, Obj: 0.466825, No Obj: 0.003555, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.578507, Class: 1.000000, Obj: 0.451263, No Obj: 0.003995, Avg Recall: 0.650794, count: 126 -Region Avg IOU: 0.608542, Class: 1.000000, Obj: 0.502508, No Obj: 0.003218, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.612341, Class: 1.000000, Obj: 0.518933, No Obj: 0.003420, Avg Recall: 0.743902, count: 82 -Region Avg IOU: 0.574905, Class: 1.000000, Obj: 0.411233, No Obj: 0.002583, Avg Recall: 0.675325, count: 77 -6971: 0.386892, 0.420404 avg, 0.001000 rate, 23.372280 seconds, 1784576 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.580610, Class: 1.000000, Obj: 0.426511, No Obj: 0.003621, Avg Recall: 0.672000, count: 125 -Region Avg IOU: 0.579415, Class: 1.000000, Obj: 0.412062, No Obj: 0.003291, Avg Recall: 0.663043, count: 92 -Region Avg IOU: 0.633628, Class: 1.000000, Obj: 0.470554, No Obj: 0.003393, Avg Recall: 0.788235, count: 85 -Region Avg IOU: 0.578954, Class: 1.000000, Obj: 0.421078, No Obj: 0.004248, Avg Recall: 0.697842, count: 139 -Region Avg IOU: 0.610345, Class: 1.000000, Obj: 0.451634, No Obj: 0.003225, Avg Recall: 0.772152, count: 79 -Region Avg IOU: 0.598750, Class: 1.000000, Obj: 0.445669, No Obj: 0.003313, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.607351, Class: 1.000000, Obj: 0.465358, No Obj: 0.003736, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.622229, Class: 1.000000, Obj: 0.426791, No Obj: 0.004542, Avg Recall: 0.760331, count: 121 -6972: 0.448764, 0.423240 avg, 0.001000 rate, 24.706114 seconds, 1784832 images -Loaded: 0.055240 seconds -Region Avg IOU: 0.608607, Class: 1.000000, Obj: 0.396215, No Obj: 0.003445, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.575617, Class: 1.000000, Obj: 0.423751, No Obj: 0.003468, Avg Recall: 0.669231, count: 130 -Region Avg IOU: 0.639076, Class: 1.000000, Obj: 0.430187, No Obj: 0.003659, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.598265, Class: 1.000000, Obj: 0.369360, No Obj: 0.004134, Avg Recall: 0.701613, count: 124 -Region Avg IOU: 0.610959, Class: 1.000000, Obj: 0.435260, No Obj: 0.003304, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.611812, Class: 1.000000, Obj: 0.450249, No Obj: 0.003497, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.609430, Class: 1.000000, Obj: 0.486664, No Obj: 0.002997, Avg Recall: 0.701299, count: 77 -Region Avg IOU: 0.557652, Class: 1.000000, Obj: 0.451481, No Obj: 0.003449, Avg Recall: 0.659794, count: 97 -6973: 0.373817, 0.418298 avg, 0.001000 rate, 24.471245 seconds, 1785088 images -Loaded: 0.000127 seconds -Region Avg IOU: 0.615150, Class: 1.000000, Obj: 0.426808, No Obj: 0.003798, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.624148, Class: 1.000000, Obj: 0.413446, No Obj: 0.003428, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.612654, Class: 1.000000, Obj: 0.385472, No Obj: 0.003703, Avg Recall: 0.732394, count: 142 -Region Avg IOU: 0.570934, Class: 1.000000, Obj: 0.367900, No Obj: 0.002860, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.560305, Class: 1.000000, Obj: 0.375634, No Obj: 0.002980, Avg Recall: 0.622807, count: 114 -Region Avg IOU: 0.601084, Class: 1.000000, Obj: 0.429578, No Obj: 0.002974, Avg Recall: 0.686046, count: 86 -Region Avg IOU: 0.550723, Class: 1.000000, Obj: 0.388527, No Obj: 0.002423, Avg Recall: 0.602273, count: 88 -Region Avg IOU: 0.602911, Class: 1.000000, Obj: 0.411836, No Obj: 0.003334, Avg Recall: 0.719008, count: 121 -6974: 0.431948, 0.419663 avg, 0.001000 rate, 23.722092 seconds, 1785344 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.559650, Class: 1.000000, Obj: 0.355912, No Obj: 0.003551, Avg Recall: 0.648438, count: 128 -Region Avg IOU: 0.570685, Class: 1.000000, Obj: 0.387617, No Obj: 0.003411, Avg Recall: 0.669291, count: 127 -Region Avg IOU: 0.680672, Class: 1.000000, Obj: 0.486493, No Obj: 0.003128, Avg Recall: 0.848485, count: 66 -Region Avg IOU: 0.607870, Class: 1.000000, Obj: 0.411753, No Obj: 0.003490, Avg Recall: 0.722222, count: 126 -Region Avg IOU: 0.590573, Class: 1.000000, Obj: 0.375440, No Obj: 0.003122, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.636855, Class: 1.000000, Obj: 0.454020, No Obj: 0.003033, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.602659, Class: 1.000000, Obj: 0.436046, No Obj: 0.003361, Avg Recall: 0.703704, count: 108 -Region Avg IOU: 0.592400, Class: 1.000000, Obj: 0.417845, No Obj: 0.002968, Avg Recall: 0.720430, count: 93 -6975: 0.391970, 0.416894 avg, 0.001000 rate, 23.254192 seconds, 1785600 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.678813, Class: 1.000000, Obj: 0.442981, No Obj: 0.002818, Avg Recall: 0.867647, count: 68 -Region Avg IOU: 0.601799, Class: 1.000000, Obj: 0.428219, No Obj: 0.003138, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.622509, Class: 1.000000, Obj: 0.410721, No Obj: 0.003520, Avg Recall: 0.740458, count: 131 -Region Avg IOU: 0.605461, Class: 1.000000, Obj: 0.365073, No Obj: 0.003125, Avg Recall: 0.691667, count: 120 -Region Avg IOU: 0.594867, Class: 1.000000, Obj: 0.395023, No Obj: 0.002979, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.564632, Class: 1.000000, Obj: 0.350104, No Obj: 0.002340, Avg Recall: 0.632184, count: 87 -Region Avg IOU: 0.627229, Class: 1.000000, Obj: 0.411169, No Obj: 0.002702, Avg Recall: 0.775281, count: 89 -Region Avg IOU: 0.600629, Class: 1.000000, Obj: 0.408058, No Obj: 0.003331, Avg Recall: 0.725490, count: 102 -6976: 0.427488, 0.417953 avg, 0.001000 rate, 23.771130 seconds, 1785856 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.620261, Class: 1.000000, Obj: 0.414957, No Obj: 0.003134, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.616756, Class: 1.000000, Obj: 0.422619, No Obj: 0.003227, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.615652, Class: 1.000000, Obj: 0.407951, No Obj: 0.002456, Avg Recall: 0.666667, count: 72 -Region Avg IOU: 0.577362, Class: 1.000000, Obj: 0.404487, No Obj: 0.003164, Avg Recall: 0.705426, count: 129 -Region Avg IOU: 0.588871, Class: 1.000000, Obj: 0.351539, No Obj: 0.003368, Avg Recall: 0.655462, count: 119 -Region Avg IOU: 0.592925, Class: 1.000000, Obj: 0.375209, No Obj: 0.003293, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.581409, Class: 1.000000, Obj: 0.358967, No Obj: 0.003030, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.599508, Class: 1.000000, Obj: 0.425913, No Obj: 0.002924, Avg Recall: 0.722222, count: 90 -6977: 0.391582, 0.415316 avg, 0.001000 rate, 23.493176 seconds, 1786112 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.536100, Class: 1.000000, Obj: 0.363527, No Obj: 0.002617, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.606198, Class: 1.000000, Obj: 0.386754, No Obj: 0.002862, Avg Recall: 0.731183, count: 93 -Region Avg IOU: 0.558391, Class: 1.000000, Obj: 0.366805, No Obj: 0.002520, Avg Recall: 0.674419, count: 86 -Region Avg IOU: 0.614405, Class: 1.000000, Obj: 0.395249, No Obj: 0.003712, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.585347, Class: 1.000000, Obj: 0.374732, No Obj: 0.003440, Avg Recall: 0.706349, count: 126 -Region Avg IOU: 0.624230, Class: 1.000000, Obj: 0.422992, No Obj: 0.003487, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.642639, Class: 1.000000, Obj: 0.452020, No Obj: 0.003440, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.602247, Class: 1.000000, Obj: 0.410361, No Obj: 0.003529, Avg Recall: 0.736000, count: 125 -6978: 0.422770, 0.416061 avg, 0.001000 rate, 23.455221 seconds, 1786368 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.607775, Class: 1.000000, Obj: 0.400969, No Obj: 0.003702, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.632072, Class: 1.000000, Obj: 0.458513, No Obj: 0.003002, Avg Recall: 0.795181, count: 83 -Region Avg IOU: 0.597771, Class: 1.000000, Obj: 0.391738, No Obj: 0.003634, Avg Recall: 0.706349, count: 126 -Region Avg IOU: 0.593747, Class: 1.000000, Obj: 0.434441, No Obj: 0.003769, Avg Recall: 0.685484, count: 124 -Region Avg IOU: 0.604278, Class: 1.000000, Obj: 0.412477, No Obj: 0.003031, Avg Recall: 0.722689, count: 119 -Region Avg IOU: 0.629258, Class: 1.000000, Obj: 0.434933, No Obj: 0.003743, Avg Recall: 0.772059, count: 136 -Region Avg IOU: 0.619319, Class: 1.000000, Obj: 0.416532, No Obj: 0.003558, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.625526, Class: 1.000000, Obj: 0.448172, No Obj: 0.003329, Avg Recall: 0.719626, count: 107 -6979: 0.425271, 0.416982 avg, 0.001000 rate, 23.271332 seconds, 1786624 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.629115, Class: 1.000000, Obj: 0.407200, No Obj: 0.002828, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.582686, Class: 1.000000, Obj: 0.391632, No Obj: 0.003721, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.576351, Class: 1.000000, Obj: 0.400232, No Obj: 0.003087, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.645603, Class: 1.000000, Obj: 0.445336, No Obj: 0.003254, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.674230, Class: 1.000000, Obj: 0.478744, No Obj: 0.003568, Avg Recall: 0.825581, count: 86 -Region Avg IOU: 0.615330, Class: 1.000000, Obj: 0.449369, No Obj: 0.003129, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.605202, Class: 1.000000, Obj: 0.422918, No Obj: 0.003762, Avg Recall: 0.684615, count: 130 -Region Avg IOU: 0.612433, Class: 1.000000, Obj: 0.416709, No Obj: 0.003598, Avg Recall: 0.752137, count: 117 -6980: 0.386299, 0.413914 avg, 0.001000 rate, 23.680304 seconds, 1786880 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 17.976583 seconds -Region Avg IOU: 0.613925, Class: 1.000000, Obj: 0.413082, No Obj: 0.003860, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.572272, Class: 1.000000, Obj: 0.407944, No Obj: 0.004449, Avg Recall: 0.664062, count: 128 -Region Avg IOU: 0.589254, Class: 1.000000, Obj: 0.420277, No Obj: 0.003664, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.557168, Class: 1.000000, Obj: 0.382520, No Obj: 0.003598, Avg Recall: 0.666667, count: 108 -Region Avg IOU: 0.555538, Class: 1.000000, Obj: 0.412117, No Obj: 0.003714, Avg Recall: 0.591667, count: 120 -Region Avg IOU: 0.586403, Class: 1.000000, Obj: 0.423658, No Obj: 0.003476, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.588144, Class: 1.000000, Obj: 0.378903, No Obj: 0.003782, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.561249, Class: 1.000000, Obj: 0.437046, No Obj: 0.003171, Avg Recall: 0.609756, count: 82 -6981: 0.440777, 0.416600 avg, 0.001000 rate, 23.981344 seconds, 1787136 images -Loaded: 0.078898 seconds -Region Avg IOU: 0.549239, Class: 1.000000, Obj: 0.350318, No Obj: 0.004200, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.589309, Class: 1.000000, Obj: 0.420720, No Obj: 0.004055, Avg Recall: 0.666667, count: 99 -Region Avg IOU: 0.582231, Class: 1.000000, Obj: 0.379194, No Obj: 0.004260, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.609063, Class: 1.000000, Obj: 0.420983, No Obj: 0.004612, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.557296, Class: 1.000000, Obj: 0.390216, No Obj: 0.003782, Avg Recall: 0.649485, count: 97 -Region Avg IOU: 0.626202, Class: 1.000000, Obj: 0.444491, No Obj: 0.004556, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.557124, Class: 1.000000, Obj: 0.363001, No Obj: 0.003978, Avg Recall: 0.650406, count: 123 -Region Avg IOU: 0.582783, Class: 1.000000, Obj: 0.434301, No Obj: 0.004644, Avg Recall: 0.731481, count: 108 -6982: 0.433754, 0.418316 avg, 0.001000 rate, 22.456743 seconds, 1787392 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.609740, Class: 1.000000, Obj: 0.460601, No Obj: 0.003694, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.566369, Class: 1.000000, Obj: 0.411338, No Obj: 0.003859, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.560513, Class: 1.000000, Obj: 0.390887, No Obj: 0.004020, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.555990, Class: 1.000000, Obj: 0.394873, No Obj: 0.004439, Avg Recall: 0.633333, count: 120 -Region Avg IOU: 0.578967, Class: 1.000000, Obj: 0.412753, No Obj: 0.004624, Avg Recall: 0.685039, count: 127 -Region Avg IOU: 0.557056, Class: 1.000000, Obj: 0.384037, No Obj: 0.004192, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.593424, Class: 1.000000, Obj: 0.402348, No Obj: 0.003954, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.532187, Class: 1.000000, Obj: 0.388907, No Obj: 0.004286, Avg Recall: 0.560345, count: 116 -6983: 0.475244, 0.424009 avg, 0.001000 rate, 22.959724 seconds, 1787648 images -Loaded: 1.246964 seconds -Region Avg IOU: 0.586570, Class: 1.000000, Obj: 0.437408, No Obj: 0.003834, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.558119, Class: 1.000000, Obj: 0.411621, No Obj: 0.004130, Avg Recall: 0.663551, count: 107 -Region Avg IOU: 0.541645, Class: 1.000000, Obj: 0.437261, No Obj: 0.003734, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.585404, Class: 1.000000, Obj: 0.412780, No Obj: 0.004262, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.552234, Class: 1.000000, Obj: 0.386344, No Obj: 0.003573, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.652596, Class: 1.000000, Obj: 0.450912, No Obj: 0.004788, Avg Recall: 0.797753, count: 89 -Region Avg IOU: 0.546243, Class: 1.000000, Obj: 0.380980, No Obj: 0.003625, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.563229, Class: 1.000000, Obj: 0.397382, No Obj: 0.004495, Avg Recall: 0.661157, count: 121 -6984: 0.412419, 0.422850 avg, 0.001000 rate, 21.338879 seconds, 1787904 images -Loaded: 1.015624 seconds -Region Avg IOU: 0.537884, Class: 1.000000, Obj: 0.442135, No Obj: 0.004185, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.637757, Class: 1.000000, Obj: 0.477651, No Obj: 0.004906, Avg Recall: 0.790698, count: 86 -Region Avg IOU: 0.542811, Class: 1.000000, Obj: 0.412760, No Obj: 0.004441, Avg Recall: 0.644628, count: 121 -Region Avg IOU: 0.590114, Class: 1.000000, Obj: 0.467282, No Obj: 0.003828, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.605295, Class: 1.000000, Obj: 0.407955, No Obj: 0.004567, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.542608, Class: 1.000000, Obj: 0.437679, No Obj: 0.004484, Avg Recall: 0.598361, count: 122 -Region Avg IOU: 0.543957, Class: 1.000000, Obj: 0.387184, No Obj: 0.004266, Avg Recall: 0.628788, count: 132 -Region Avg IOU: 0.471634, Class: 1.000000, Obj: 0.346085, No Obj: 0.003633, Avg Recall: 0.540323, count: 124 -6985: 0.449315, 0.425496 avg, 0.001000 rate, 22.947006 seconds, 1788160 images -Loaded: 0.000128 seconds -Region Avg IOU: 0.585842, Class: 1.000000, Obj: 0.410942, No Obj: 0.005256, Avg Recall: 0.669355, count: 124 -Region Avg IOU: 0.625059, Class: 1.000000, Obj: 0.446346, No Obj: 0.003767, Avg Recall: 0.755814, count: 86 -Region Avg IOU: 0.626874, Class: 1.000000, Obj: 0.442863, No Obj: 0.004898, Avg Recall: 0.781250, count: 96 -Region Avg IOU: 0.559099, Class: 1.000000, Obj: 0.433694, No Obj: 0.004742, Avg Recall: 0.685950, count: 121 -Region Avg IOU: 0.522831, Class: 1.000000, Obj: 0.383729, No Obj: 0.004720, Avg Recall: 0.606452, count: 155 -Region Avg IOU: 0.595891, Class: 1.000000, Obj: 0.419146, No Obj: 0.003423, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.582694, Class: 1.000000, Obj: 0.442328, No Obj: 0.004207, Avg Recall: 0.694118, count: 85 -Region Avg IOU: 0.620231, Class: 1.000000, Obj: 0.440649, No Obj: 0.004441, Avg Recall: 0.757009, count: 107 -6986: 0.384857, 0.421432 avg, 0.001000 rate, 20.364132 seconds, 1788416 images -Loaded: 1.759422 seconds -Region Avg IOU: 0.546884, Class: 1.000000, Obj: 0.423201, No Obj: 0.003987, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.540394, Class: 1.000000, Obj: 0.419034, No Obj: 0.004111, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.498581, Class: 1.000000, Obj: 0.383692, No Obj: 0.004145, Avg Recall: 0.561404, count: 114 -Region Avg IOU: 0.621798, Class: 1.000000, Obj: 0.449847, No Obj: 0.004462, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.589841, Class: 1.000000, Obj: 0.431714, No Obj: 0.004225, Avg Recall: 0.718750, count: 128 -Region Avg IOU: 0.478947, Class: 1.000000, Obj: 0.359572, No Obj: 0.003791, Avg Recall: 0.548673, count: 113 -Region Avg IOU: 0.531998, Class: 1.000000, Obj: 0.408569, No Obj: 0.004210, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.515716, Class: 1.000000, Obj: 0.439994, No Obj: 0.003962, Avg Recall: 0.551020, count: 98 -6987: 0.403375, 0.419627 avg, 0.001000 rate, 20.630451 seconds, 1788672 images -Loaded: 1.532327 seconds -Region Avg IOU: 0.545521, Class: 1.000000, Obj: 0.406438, No Obj: 0.004340, Avg Recall: 0.608696, count: 92 -Region Avg IOU: 0.540160, Class: 1.000000, Obj: 0.447115, No Obj: 0.003275, Avg Recall: 0.605634, count: 71 -Region Avg IOU: 0.530908, Class: 1.000000, Obj: 0.416775, No Obj: 0.003758, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.551967, Class: 1.000000, Obj: 0.385610, No Obj: 0.004335, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.555402, Class: 1.000000, Obj: 0.402844, No Obj: 0.004764, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.602919, Class: 1.000000, Obj: 0.413281, No Obj: 0.004853, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.558725, Class: 1.000000, Obj: 0.439537, No Obj: 0.004642, Avg Recall: 0.677966, count: 118 -Region Avg IOU: 0.582174, Class: 1.000000, Obj: 0.415776, No Obj: 0.004564, Avg Recall: 0.691589, count: 107 -6988: 0.448007, 0.422465 avg, 0.001000 rate, 23.341209 seconds, 1788928 images -Loaded: 0.578760 seconds -Region Avg IOU: 0.561371, Class: 1.000000, Obj: 0.428378, No Obj: 0.003490, Avg Recall: 0.682353, count: 85 -Region Avg IOU: 0.599468, Class: 1.000000, Obj: 0.385214, No Obj: 0.004949, Avg Recall: 0.702479, count: 121 -Region Avg IOU: 0.577465, Class: 1.000000, Obj: 0.379707, No Obj: 0.005012, Avg Recall: 0.697674, count: 129 -Region Avg IOU: 0.551702, Class: 1.000000, Obj: 0.422386, No Obj: 0.004301, Avg Recall: 0.669355, count: 124 -Region Avg IOU: 0.498419, Class: 1.000000, Obj: 0.365924, No Obj: 0.004000, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.546616, Class: 1.000000, Obj: 0.419741, No Obj: 0.004384, Avg Recall: 0.663793, count: 116 -Region Avg IOU: 0.585532, Class: 1.000000, Obj: 0.428062, No Obj: 0.004182, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.577555, Class: 1.000000, Obj: 0.410243, No Obj: 0.004310, Avg Recall: 0.690722, count: 97 -6989: 0.428252, 0.423043 avg, 0.001000 rate, 21.391859 seconds, 1789184 images -Loaded: 1.052106 seconds -Region Avg IOU: 0.552655, Class: 1.000000, Obj: 0.362046, No Obj: 0.004248, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.564423, Class: 1.000000, Obj: 0.437290, No Obj: 0.003869, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.572128, Class: 1.000000, Obj: 0.376512, No Obj: 0.004160, Avg Recall: 0.672131, count: 122 -Region Avg IOU: 0.540433, Class: 1.000000, Obj: 0.398777, No Obj: 0.004274, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.575291, Class: 1.000000, Obj: 0.436718, No Obj: 0.003367, Avg Recall: 0.674699, count: 83 -Region Avg IOU: 0.509675, Class: 1.000000, Obj: 0.375218, No Obj: 0.003736, Avg Recall: 0.616667, count: 120 -Region Avg IOU: 0.564106, Class: 1.000000, Obj: 0.429879, No Obj: 0.004488, Avg Recall: 0.652542, count: 118 -Region Avg IOU: 0.581552, Class: 1.000000, Obj: 0.432828, No Obj: 0.004507, Avg Recall: 0.656250, count: 96 -6990: 0.397909, 0.420530 avg, 0.001000 rate, 21.999594 seconds, 1789440 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000068 seconds -Region Avg IOU: 0.660117, Class: 1.000000, Obj: 0.492784, No Obj: 0.003333, Avg Recall: 0.817073, count: 82 -Region Avg IOU: 0.593199, Class: 1.000000, Obj: 0.458932, No Obj: 0.003352, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.632443, Class: 1.000000, Obj: 0.511954, No Obj: 0.002688, Avg Recall: 0.780488, count: 82 -Region Avg IOU: 0.615625, Class: 1.000000, Obj: 0.424316, No Obj: 0.003969, Avg Recall: 0.731884, count: 138 -Region Avg IOU: 0.632979, Class: 1.000000, Obj: 0.408603, No Obj: 0.003131, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.553686, Class: 1.000000, Obj: 0.401487, No Obj: 0.002948, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.577424, Class: 1.000000, Obj: 0.391002, No Obj: 0.003885, Avg Recall: 0.690476, count: 126 -Region Avg IOU: 0.606792, Class: 1.000000, Obj: 0.486089, No Obj: 0.003645, Avg Recall: 0.736842, count: 114 -6991: 0.408673, 0.419344 avg, 0.001000 rate, 24.943325 seconds, 1789696 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.670464, Class: 1.000000, Obj: 0.501170, No Obj: 0.003487, Avg Recall: 0.848214, count: 112 -Region Avg IOU: 0.585530, Class: 1.000000, Obj: 0.423750, No Obj: 0.003523, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.596549, Class: 1.000000, Obj: 0.400066, No Obj: 0.003074, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.601741, Class: 1.000000, Obj: 0.433026, No Obj: 0.003130, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.676050, Class: 1.000000, Obj: 0.427894, No Obj: 0.003720, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.611066, Class: 1.000000, Obj: 0.444721, No Obj: 0.003612, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.598544, Class: 1.000000, Obj: 0.449877, No Obj: 0.003699, Avg Recall: 0.712000, count: 125 -Region Avg IOU: 0.671533, Class: 1.000000, Obj: 0.483153, No Obj: 0.003518, Avg Recall: 0.855769, count: 104 -6992: 0.442757, 0.421686 avg, 0.001000 rate, 25.165928 seconds, 1789952 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.606189, Class: 1.000000, Obj: 0.426074, No Obj: 0.002969, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.539061, Class: 1.000000, Obj: 0.389041, No Obj: 0.003053, Avg Recall: 0.619048, count: 126 -Region Avg IOU: 0.608496, Class: 1.000000, Obj: 0.382776, No Obj: 0.003177, Avg Recall: 0.750000, count: 104 -Region Avg IOU: 0.668914, Class: 1.000000, Obj: 0.472493, No Obj: 0.003972, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.618756, Class: 1.000000, Obj: 0.416934, No Obj: 0.003127, Avg Recall: 0.750000, count: 120 -Region Avg IOU: 0.619554, Class: 1.000000, Obj: 0.397735, No Obj: 0.002751, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.614198, Class: 1.000000, Obj: 0.430254, No Obj: 0.003270, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.605190, Class: 1.000000, Obj: 0.434559, No Obj: 0.003428, Avg Recall: 0.629310, count: 116 -6993: 0.408617, 0.420379 avg, 0.001000 rate, 24.325190 seconds, 1790208 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.654225, Class: 1.000000, Obj: 0.443285, No Obj: 0.003392, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.600991, Class: 1.000000, Obj: 0.403765, No Obj: 0.003065, Avg Recall: 0.679389, count: 131 -Region Avg IOU: 0.635709, Class: 1.000000, Obj: 0.442166, No Obj: 0.002905, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.595039, Class: 1.000000, Obj: 0.436033, No Obj: 0.002826, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.634402, Class: 1.000000, Obj: 0.428438, No Obj: 0.003068, Avg Recall: 0.743902, count: 82 -Region Avg IOU: 0.618853, Class: 1.000000, Obj: 0.386074, No Obj: 0.003053, Avg Recall: 0.736842, count: 114 -Region Avg IOU: 0.626300, Class: 1.000000, Obj: 0.418790, No Obj: 0.003500, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.633207, Class: 1.000000, Obj: 0.417967, No Obj: 0.003566, Avg Recall: 0.786765, count: 136 -6994: 0.400766, 0.418417 avg, 0.001000 rate, 25.675272 seconds, 1790464 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.558924, Class: 1.000000, Obj: 0.405670, No Obj: 0.002601, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.594128, Class: 1.000000, Obj: 0.413111, No Obj: 0.002965, Avg Recall: 0.673684, count: 95 -Region Avg IOU: 0.586990, Class: 1.000000, Obj: 0.351486, No Obj: 0.003640, Avg Recall: 0.684615, count: 130 -Region Avg IOU: 0.636997, Class: 1.000000, Obj: 0.405039, No Obj: 0.002817, Avg Recall: 0.806818, count: 88 -Region Avg IOU: 0.582474, Class: 1.000000, Obj: 0.377273, No Obj: 0.003024, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.623938, Class: 1.000000, Obj: 0.396126, No Obj: 0.003094, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.592328, Class: 1.000000, Obj: 0.421098, No Obj: 0.002940, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.652381, Class: 1.000000, Obj: 0.403062, No Obj: 0.003428, Avg Recall: 0.790323, count: 124 -6995: 0.391869, 0.415763 avg, 0.001000 rate, 26.326984 seconds, 1790720 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.620743, Class: 1.000000, Obj: 0.407261, No Obj: 0.002913, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.583345, Class: 1.000000, Obj: 0.389533, No Obj: 0.002837, Avg Recall: 0.712000, count: 125 -Region Avg IOU: 0.597793, Class: 1.000000, Obj: 0.401986, No Obj: 0.003111, Avg Recall: 0.708661, count: 127 -Region Avg IOU: 0.616559, Class: 1.000000, Obj: 0.398409, No Obj: 0.002702, Avg Recall: 0.735632, count: 87 -Region Avg IOU: 0.606541, Class: 1.000000, Obj: 0.400141, No Obj: 0.002809, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.605923, Class: 1.000000, Obj: 0.434360, No Obj: 0.002846, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.638907, Class: 1.000000, Obj: 0.420162, No Obj: 0.003743, Avg Recall: 0.786207, count: 145 -Region Avg IOU: 0.625497, Class: 1.000000, Obj: 0.402453, No Obj: 0.002430, Avg Recall: 0.771084, count: 83 -6996: 0.389403, 0.413127 avg, 0.001000 rate, 24.517992 seconds, 1790976 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.638026, Class: 1.000000, Obj: 0.409717, No Obj: 0.003111, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.663605, Class: 1.000000, Obj: 0.388630, No Obj: 0.002761, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.645603, Class: 1.000000, Obj: 0.443069, No Obj: 0.003651, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.649393, Class: 1.000000, Obj: 0.451211, No Obj: 0.003204, Avg Recall: 0.770000, count: 100 -Region Avg IOU: 0.591461, Class: 1.000000, Obj: 0.400338, No Obj: 0.002927, Avg Recall: 0.683168, count: 101 -Region Avg IOU: 0.604881, Class: 1.000000, Obj: 0.328957, No Obj: 0.003114, Avg Recall: 0.774194, count: 124 -Region Avg IOU: 0.669488, Class: 1.000000, Obj: 0.464373, No Obj: 0.002855, Avg Recall: 0.817073, count: 82 -Region Avg IOU: 0.570032, Class: 1.000000, Obj: 0.390222, No Obj: 0.002654, Avg Recall: 0.679245, count: 106 -6997: 0.406282, 0.412442 avg, 0.001000 rate, 24.426296 seconds, 1791232 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.625964, Class: 1.000000, Obj: 0.431596, No Obj: 0.003431, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.594980, Class: 1.000000, Obj: 0.424037, No Obj: 0.002755, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.615370, Class: 1.000000, Obj: 0.407750, No Obj: 0.002931, Avg Recall: 0.782609, count: 115 -Region Avg IOU: 0.663604, Class: 1.000000, Obj: 0.448109, No Obj: 0.002921, Avg Recall: 0.800000, count: 85 -Region Avg IOU: 0.628786, Class: 1.000000, Obj: 0.394328, No Obj: 0.003125, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.618764, Class: 1.000000, Obj: 0.403017, No Obj: 0.003057, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.544807, Class: 1.000000, Obj: 0.383009, No Obj: 0.002696, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.631757, Class: 1.000000, Obj: 0.433754, No Obj: 0.002865, Avg Recall: 0.781250, count: 96 -6998: 0.410419, 0.412240 avg, 0.001000 rate, 23.748232 seconds, 1791488 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.600524, Class: 1.000000, Obj: 0.442768, No Obj: 0.003397, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.585790, Class: 1.000000, Obj: 0.394389, No Obj: 0.002886, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.608528, Class: 1.000000, Obj: 0.373636, No Obj: 0.003423, Avg Recall: 0.742857, count: 140 -Region Avg IOU: 0.562860, Class: 1.000000, Obj: 0.413851, No Obj: 0.003020, Avg Recall: 0.652174,Saving weights to ./person9300/backup/tyvp_93_9c512_7000.weights - count: 115 -Region Avg IOU: 0.621845, Class: 1.000000, Obj: 0.415258, No Obj: 0.003072, Avg Recall: 0.731707, count: 123 -Region Avg IOU: 0.655830, Class: 1.000000, Obj: 0.438495, No Obj: 0.003334, Avg Recall: 0.798319, count: 119 -Region Avg IOU: 0.611944, Class: 1.000000, Obj: 0.425220, No Obj: 0.002877, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.613928, Class: 1.000000, Obj: 0.418142, No Obj: 0.002840, Avg Recall: 0.730769, count: 104 -6999: 0.443942, 0.415410 avg, 0.001000 rate, 26.165844 seconds, 1791744 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.573228, Class: 1.000000, Obj: 0.437154, No Obj: 0.002967, Avg Recall: 0.659091, count: 88 -Region Avg IOU: 0.660349, Class: 1.000000, Obj: 0.498314, No Obj: 0.003067, Avg Recall: 0.818182, count: 88 -Region Avg IOU: 0.585061, Class: 1.000000, Obj: 0.465383, No Obj: 0.002626, Avg Recall: 0.688172, count: 93 -Region Avg IOU: 0.649827, Class: 1.000000, Obj: 0.416905, No Obj: 0.003909, Avg Recall: 0.729508, count: 122 -Region Avg IOU: 0.628152, Class: 1.000000, Obj: 0.457939, No Obj: 0.002615, Avg Recall: 0.762500, count: 80 -Region Avg IOU: 0.658852, Class: 1.000000, Obj: 0.430457, No Obj: 0.002820, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.633766, Class: 1.000000, Obj: 0.422337, No Obj: 0.003544, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.614331, Class: 1.000000, Obj: 0.470996, No Obj: 0.002982, Avg Recall: 0.731959, count: 97 -7000: 0.347218, 0.408591 avg, 0.001000 rate, 25.493086 seconds, 1792000 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000070 seconds -Region Avg IOU: 0.645918, Class: 1.000000, Obj: 0.455453, No Obj: 0.002781, Avg Recall: 0.800000, count: 115 -Region Avg IOU: 0.630561, Class: 1.000000, Obj: 0.447087, No Obj: 0.002942, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.654024, Class: 1.000000, Obj: 0.456781, No Obj: 0.002887, Avg Recall: 0.811475, count: 122 -Region Avg IOU: 0.694344, Class: 1.000000, Obj: 0.458399, No Obj: 0.002940, Avg Recall: 0.870370, count: 108 -Region Avg IOU: 0.620428, Class: 1.000000, Obj: 0.431789, No Obj: 0.002427, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.708501, Class: 1.000000, Obj: 0.495095, No Obj: 0.002550, Avg Recall: 0.906250, count: 96 -Region Avg IOU: 0.637977, Class: 1.000000, Obj: 0.451725, No Obj: 0.002865, Avg Recall: 0.785047, count: 107 -Region Avg IOU: 0.627487, Class: 1.000000, Obj: 0.456862, No Obj: 0.002270, Avg Recall: 0.741176, count: 85 -7001: 0.418664, 0.409598 avg, 0.001000 rate, 27.509432 seconds, 1792256 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.636821, Class: 1.000000, Obj: 0.432605, No Obj: 0.002808, Avg Recall: 0.798319, count: 119 -Region Avg IOU: 0.703959, Class: 1.000000, Obj: 0.488259, No Obj: 0.002879, Avg Recall: 0.889908, count: 109 -Region Avg IOU: 0.638684, Class: 1.000000, Obj: 0.434058, No Obj: 0.002663, Avg Recall: 0.765766, count: 111 -Region Avg IOU: 0.679128, Class: 1.000000, Obj: 0.468824, No Obj: 0.003165, Avg Recall: 0.865385, count: 104 -Region Avg IOU: 0.641525, Class: 1.000000, Obj: 0.449866, No Obj: 0.002492, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.643445, Class: 1.000000, Obj: 0.505333, No Obj: 0.003064, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.637403, Class: 1.000000, Obj: 0.411755, No Obj: 0.003217, Avg Recall: 0.783333, count: 120 -Region Avg IOU: 0.666756, Class: 1.000000, Obj: 0.476212, No Obj: 0.002956, Avg Recall: 0.839286, count: 112 -7002: 0.382404, 0.406879 avg, 0.001000 rate, 28.619339 seconds, 1792512 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.623125, Class: 1.000000, Obj: 0.481469, No Obj: 0.002562, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.680072, Class: 1.000000, Obj: 0.456708, No Obj: 0.002857, Avg Recall: 0.859649, count: 114 -Region Avg IOU: 0.640570, Class: 1.000000, Obj: 0.488139, No Obj: 0.002440, Avg Recall: 0.779070, count: 86 -Region Avg IOU: 0.635456, Class: 1.000000, Obj: 0.395751, No Obj: 0.003417, Avg Recall: 0.742647, count: 136 -Region Avg IOU: 0.692014, Class: 1.000000, Obj: 0.439196, No Obj: 0.002683, Avg Recall: 0.854369, count: 103 -Region Avg IOU: 0.648931, Class: 1.000000, Obj: 0.396173, No Obj: 0.002597, Avg Recall: 0.820755, count: 106 -Region Avg IOU: 0.643702, Class: 1.000000, Obj: 0.429852, No Obj: 0.002741, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.621679, Class: 1.000000, Obj: 0.424952, No Obj: 0.002794, Avg Recall: 0.745763, count: 118 -7003: 0.403373, 0.406528 avg, 0.001000 rate, 30.425388 seconds, 1792768 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.662570, Class: 1.000000, Obj: 0.468188, No Obj: 0.003268, Avg Recall: 0.837398, count: 123 -Region Avg IOU: 0.638156, Class: 1.000000, Obj: 0.431964, No Obj: 0.002750, Avg Recall: 0.787402, count: 127 -Region Avg IOU: 0.636612, Class: 1.000000, Obj: 0.421062, No Obj: 0.002642, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.641251, Class: 1.000000, Obj: 0.395845, No Obj: 0.003123, Avg Recall: 0.793388, count: 121 -Region Avg IOU: 0.703912, Class: 1.000000, Obj: 0.451919, No Obj: 0.002530, Avg Recall: 0.876289, count: 97 -Region Avg IOU: 0.646215, Class: 1.000000, Obj: 0.374046, No Obj: 0.002710, Avg Recall: 0.804598, count: 87 -Region Avg IOU: 0.686316, Class: 1.000000, Obj: 0.468160, No Obj: 0.003214, Avg Recall: 0.865546, count: 119 -Region Avg IOU: 0.695690, Class: 1.000000, Obj: 0.454542, No Obj: 0.002735, Avg Recall: 0.900000, count: 90 -7004: 0.434695, 0.409345 avg, 0.001000 rate, 29.027971 seconds, 1793024 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.672977, Class: 1.000000, Obj: 0.408006, No Obj: 0.002324, Avg Recall: 0.829268, count: 82 -Region Avg IOU: 0.652642, Class: 1.000000, Obj: 0.442571, No Obj: 0.002671, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.682204, Class: 1.000000, Obj: 0.501082, No Obj: 0.002548, Avg Recall: 0.831461, count: 89 -Region Avg IOU: 0.661259, Class: 1.000000, Obj: 0.458457, No Obj: 0.002760, Avg Recall: 0.828829, count: 111 -Region Avg IOU: 0.682271, Class: 1.000000, Obj: 0.442968, No Obj: 0.002596, Avg Recall: 0.809091, count: 110 -Region Avg IOU: 0.627327, Class: 1.000000, Obj: 0.425835, No Obj: 0.002587, Avg Recall: 0.733333, count: 120 -Region Avg IOU: 0.638177, Class: 1.000000, Obj: 0.413301, No Obj: 0.003053, Avg Recall: 0.765625, count: 128 -Region Avg IOU: 0.666694, Class: 1.000000, Obj: 0.471518, No Obj: 0.002943, Avg Recall: 0.845528, count: 123 -7005: 0.435539, 0.411964 avg, 0.001000 rate, 28.722576 seconds, 1793280 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.662284, Class: 1.000000, Obj: 0.391692, No Obj: 0.002241, Avg Recall: 0.837209, count: 86 -Region Avg IOU: 0.657929, Class: 1.000000, Obj: 0.387676, No Obj: 0.002528, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.650205, Class: 1.000000, Obj: 0.407065, No Obj: 0.002596, Avg Recall: 0.785714, count: 126 -Region Avg IOU: 0.661070, Class: 1.000000, Obj: 0.376222, No Obj: 0.002503, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.692691, Class: 1.000000, Obj: 0.417832, No Obj: 0.002736, Avg Recall: 0.881818, count: 110 -Region Avg IOU: 0.670802, Class: 1.000000, Obj: 0.431999, No Obj: 0.002870, Avg Recall: 0.849558, count: 113 -Region Avg IOU: 0.638396, Class: 1.000000, Obj: 0.352555, No Obj: 0.002839, Avg Recall: 0.760000, count: 125 -Region Avg IOU: 0.664907, Class: 1.000000, Obj: 0.457927, No Obj: 0.002558, Avg Recall: 0.845361, count: 97 -7006: 0.427284, 0.413496 avg, 0.001000 rate, 29.068636 seconds, 1793536 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.668384, Class: 1.000000, Obj: 0.470161, No Obj: 0.002010, Avg Recall: 0.820513, count: 78 -Region Avg IOU: 0.668325, Class: 1.000000, Obj: 0.418170, No Obj: 0.002513, Avg Recall: 0.879121, count: 91 -Region Avg IOU: 0.670202, Class: 1.000000, Obj: 0.384034, No Obj: 0.003182, Avg Recall: 0.842857, count: 140 -Region Avg IOU: 0.708975, Class: 1.000000, Obj: 0.445580, No Obj: 0.002631, Avg Recall: 0.902174, count: 92 -Region Avg IOU: 0.661043, Class: 1.000000, Obj: 0.445621, No Obj: 0.002507, Avg Recall: 0.842105, count: 95 -Region Avg IOU: 0.679869, Class: 1.000000, Obj: 0.452685, No Obj: 0.002095, Avg Recall: 0.848101, count: 79 -Region Avg IOU: 0.663780, Class: 1.000000, Obj: 0.453785, No Obj: 0.002034, Avg Recall: 0.784091, count: 88 -Region Avg IOU: 0.675000, Class: 1.000000, Obj: 0.403683, No Obj: 0.002497, Avg Recall: 0.798077, count: 104 -7007: 0.378072, 0.409954 avg, 0.001000 rate, 27.760111 seconds, 1793792 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.620674, Class: 1.000000, Obj: 0.406026, No Obj: 0.002332, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.668380, Class: 1.000000, Obj: 0.398331, No Obj: 0.002204, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.705952, Class: 1.000000, Obj: 0.451093, No Obj: 0.002688, Avg Recall: 0.875000, count: 96 -Region Avg IOU: 0.670618, Class: 1.000000, Obj: 0.401570, No Obj: 0.002223, Avg Recall: 0.822430, count: 107 -Region Avg IOU: 0.650054, Class: 1.000000, Obj: 0.396826, No Obj: 0.002516, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.698805, Class: 1.000000, Obj: 0.439748, No Obj: 0.002646, Avg Recall: 0.883495, count: 103 -Region Avg IOU: 0.645154, Class: 1.000000, Obj: 0.442852, No Obj: 0.002001, Avg Recall: 0.755556, count: 90 -Region Avg IOU: 0.658640, Class: 1.000000, Obj: 0.422693, No Obj: 0.002047, Avg Recall: 0.790000, count: 100 -7008: 0.402121, 0.409171 avg, 0.001000 rate, 28.684116 seconds, 1794048 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.616557, Class: 1.000000, Obj: 0.387343, No Obj: 0.002354, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.670658, Class: 1.000000, Obj: 0.420957, No Obj: 0.002722, Avg Recall: 0.866667, count: 105 -Region Avg IOU: 0.697250, Class: 1.000000, Obj: 0.411863, No Obj: 0.002193, Avg Recall: 0.892473, count: 93 -Region Avg IOU: 0.636173, Class: 1.000000, Obj: 0.378355, No Obj: 0.002447, Avg Recall: 0.739583, count: 96 -Region Avg IOU: 0.642778, Class: 1.000000, Obj: 0.411495, No Obj: 0.002609, Avg Recall: 0.774194, count: 124 -Region Avg IOU: 0.667297, Class: 1.000000, Obj: 0.378121, No Obj: 0.002946, Avg Recall: 0.791667, count: 144 -Region Avg IOU: 0.652319, Class: 1.000000, Obj: 0.402022, No Obj: 0.002607, Avg Recall: 0.824074, count: 108 -Region Avg IOU: 0.621797, Class: 1.000000, Obj: 0.393657, No Obj: 0.002816, Avg Recall: 0.787402, count: 127 -7009: 0.444915, 0.412745 avg, 0.001000 rate, 27.477068 seconds, 1794304 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.689577, Class: 1.000000, Obj: 0.417122, No Obj: 0.002286, Avg Recall: 0.876405, count: 89 -Region Avg IOU: 0.647790, Class: 1.000000, Obj: 0.388004, No Obj: 0.002336, Avg Recall: 0.788618, count: 123 -Region Avg IOU: 0.692364, Class: 1.000000, Obj: 0.485146, No Obj: 0.002509, Avg Recall: 0.802083, count: 96 -Region Avg IOU: 0.674357, Class: 1.000000, Obj: 0.426520, No Obj: 0.002766, Avg Recall: 0.834532, count: 139 -Region Avg IOU: 0.671142, Class: 1.000000, Obj: 0.461980, No Obj: 0.002710, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.672224, Class: 1.000000, Obj: 0.439680, No Obj: 0.002652, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.681156, Class: 1.000000, Obj: 0.417101, No Obj: 0.002489, Avg Recall: 0.863636, count: 88 -Region Avg IOU: 0.632306, Class: 1.000000, Obj: 0.423927, No Obj: 0.002607, Avg Recall: 0.798246, count: 114 -7010: 0.431741, 0.414645 avg, 0.001000 rate, 28.522533 seconds, 1794560 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000072 seconds -Region Avg IOU: 0.691707, Class: 1.000000, Obj: 0.407219, No Obj: 0.002621, Avg Recall: 0.870370, count: 108 -Region Avg IOU: 0.699750, Class: 1.000000, Obj: 0.430809, No Obj: 0.002535, Avg Recall: 0.918367, count: 98 -Region Avg IOU: 0.668884, Class: 1.000000, Obj: 0.410433, No Obj: 0.002906, Avg Recall: 0.844961, count: 129 -Region Avg IOU: 0.690250, Class: 1.000000, Obj: 0.419375, No Obj: 0.002568, Avg Recall: 0.864865, count: 111 -Region Avg IOU: 0.664944, Class: 1.000000, Obj: 0.443583, No Obj: 0.002434, Avg Recall: 0.880435, count: 92 -Region Avg IOU: 0.643274, Class: 1.000000, Obj: 0.371032, No Obj: 0.002539, Avg Recall: 0.851852, count: 108 -Region Avg IOU: 0.687405, Class: 1.000000, Obj: 0.446573, No Obj: 0.002392, Avg Recall: 0.870968, count: 93 -Region Avg IOU: 0.664768, Class: 1.000000, Obj: 0.410890, No Obj: 0.002589, Avg Recall: 0.815126, count: 119 -7011: 0.429614, 0.416141 avg, 0.001000 rate, 28.293190 seconds, 1794816 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.635409, Class: 1.000000, Obj: 0.380460, No Obj: 0.002651, Avg Recall: 0.769231, count: 130 -Region Avg IOU: 0.693942, Class: 1.000000, Obj: 0.468444, No Obj: 0.002519, Avg Recall: 0.843750, count: 96 -Region Avg IOU: 0.701307, Class: 1.000000, Obj: 0.424289, No Obj: 0.002735, Avg Recall: 0.870968, count: 124 -Region Avg IOU: 0.658624, Class: 1.000000, Obj: 0.403458, No Obj: 0.002783, Avg Recall: 0.773723, count: 137 -Region Avg IOU: 0.685250, Class: 1.000000, Obj: 0.451027, No Obj: 0.002792, Avg Recall: 0.850394, count: 127 -Region Avg IOU: 0.701140, Class: 1.000000, Obj: 0.485738, No Obj: 0.002406, Avg Recall: 0.847059, count: 85 -Region Avg IOU: 0.661236, Class: 1.000000, Obj: 0.464160, No Obj: 0.002546, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.661296, Class: 1.000000, Obj: 0.418002, No Obj: 0.002297, Avg Recall: 0.864078, count: 103 -7012: 0.443107, 0.418838 avg, 0.001000 rate, 30.479273 seconds, 1795072 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.679220, Class: 1.000000, Obj: 0.428336, No Obj: 0.002595, Avg Recall: 0.866667, count: 105 -Region Avg IOU: 0.701265, Class: 1.000000, Obj: 0.399485, No Obj: 0.002663, Avg Recall: 0.903509, count: 114 -Region Avg IOU: 0.704846, Class: 1.000000, Obj: 0.428307, No Obj: 0.002473, Avg Recall: 0.891089, count: 101 -Region Avg IOU: 0.634775, Class: 1.000000, Obj: 0.430629, No Obj: 0.002993, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.660228, Class: 1.000000, Obj: 0.520547, No Obj: 0.002551, Avg Recall: 0.791209, count: 91 -Region Avg IOU: 0.695958, Class: 1.000000, Obj: 0.466067, No Obj: 0.002483, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.680821, Class: 1.000000, Obj: 0.445326, No Obj: 0.003046, Avg Recall: 0.826389, count: 144 -Region Avg IOU: 0.679201, Class: 1.000000, Obj: 0.430630, No Obj: 0.002524, Avg Recall: 0.830508, count: 118 -7013: 0.428085, 0.419763 avg, 0.001000 rate, 29.558125 seconds, 1795328 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.666676, Class: 1.000000, Obj: 0.453435, No Obj: 0.002648, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.665765, Class: 1.000000, Obj: 0.424634, No Obj: 0.002233, Avg Recall: 0.842697, count: 89 -Region Avg IOU: 0.661730, Class: 1.000000, Obj: 0.427596, No Obj: 0.002502, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.648731, Class: 1.000000, Obj: 0.417295, No Obj: 0.002432, Avg Recall: 0.808081, count: 99 -Region Avg IOU: 0.633765, Class: 1.000000, Obj: 0.405408, No Obj: 0.002531, Avg Recall: 0.804124, count: 97 -Region Avg IOU: 0.679955, Class: 1.000000, Obj: 0.442397, No Obj: 0.002354, Avg Recall: 0.873786, count: 103 -Region Avg IOU: 0.630477, Class: 1.000000, Obj: 0.432684, No Obj: 0.002553, Avg Recall: 0.782609, count: 115 -Region Avg IOU: 0.678335, Class: 1.000000, Obj: 0.439145, No Obj: 0.002550, Avg Recall: 0.845361, count: 97 -7014: 0.381368, 0.415923 avg, 0.001000 rate, 29.302456 seconds, 1795584 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.680834, Class: 1.000000, Obj: 0.429357, No Obj: 0.002679, Avg Recall: 0.870690, count: 116 -Region Avg IOU: 0.651071, Class: 1.000000, Obj: 0.362822, No Obj: 0.002366, Avg Recall: 0.823529, count: 119 -Region Avg IOU: 0.635143, Class: 1.000000, Obj: 0.390314, No Obj: 0.002238, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.709508, Class: 1.000000, Obj: 0.418486, No Obj: 0.002606, Avg Recall: 0.915789, count: 95 -Region Avg IOU: 0.651893, Class: 1.000000, Obj: 0.424828, No Obj: 0.002759, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.616357, Class: 1.000000, Obj: 0.436176, No Obj: 0.002755, Avg Recall: 0.733813, count: 139 -Region Avg IOU: 0.683210, Class: 1.000000, Obj: 0.411162, No Obj: 0.002630, Avg Recall: 0.850000, count: 120 -Region Avg IOU: 0.691280, Class: 1.000000, Obj: 0.464129, No Obj: 0.002495, Avg Recall: 0.876289, count: 97 -7015: 0.432480, 0.417579 avg, 0.001000 rate, 28.575253 seconds, 1795840 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.640815, Class: 1.000000, Obj: 0.429164, No Obj: 0.002563, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.677855, Class: 1.000000, Obj: 0.460449, No Obj: 0.002374, Avg Recall: 0.826923, count: 104 -Region Avg IOU: 0.639001, Class: 1.000000, Obj: 0.407846, No Obj: 0.002677, Avg Recall: 0.760000, count: 125 -Region Avg IOU: 0.666378, Class: 1.000000, Obj: 0.385684, No Obj: 0.001985, Avg Recall: 0.813333, count: 75 -Region Avg IOU: 0.680597, Class: 1.000000, Obj: 0.448537, No Obj: 0.002363, Avg Recall: 0.868687, count: 99 -Region Avg IOU: 0.640317, Class: 1.000000, Obj: 0.438359, No Obj: 0.002746, Avg Recall: 0.792593, count: 135 -Region Avg IOU: 0.717640, Class: 1.000000, Obj: 0.489332, No Obj: 0.002315, Avg Recall: 0.913979, count: 93 -Region Avg IOU: 0.668709, Class: 1.000000, Obj: 0.373364, No Obj: 0.002591, Avg Recall: 0.839623, count: 106 -7016: 0.391807, 0.415002 avg, 0.001000 rate, 28.749163 seconds, 1796096 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.630686, Class: 1.000000, Obj: 0.392724, No Obj: 0.002261, Avg Recall: 0.783132, count: 83 -Region Avg IOU: 0.665882, Class: 1.000000, Obj: 0.440026, No Obj: 0.002065, Avg Recall: 0.818182, count: 77 -Region Avg IOU: 0.678358, Class: 1.000000, Obj: 0.480123, No Obj: 0.002552, Avg Recall: 0.849057, count: 106 -Region Avg IOU: 0.706014, Class: 1.000000, Obj: 0.426951, No Obj: 0.002451, Avg Recall: 0.930000, count: 100 -Region Avg IOU: 0.680623, Class: 1.000000, Obj: 0.394726, No Obj: 0.002366, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.654444, Class: 1.000000, Obj: 0.396601, No Obj: 0.002754, Avg Recall: 0.838028, count: 142 -Region Avg IOU: 0.652094, Class: 1.000000, Obj: 0.383147, No Obj: 0.002354, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.663742, Class: 1.000000, Obj: 0.379057, No Obj: 0.002651, Avg Recall: 0.820312, count: 128 -7017: 0.414778, 0.414979 avg, 0.001000 rate, 29.865469 seconds, 1796352 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.656548, Class: 1.000000, Obj: 0.380073, No Obj: 0.002252, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.703981, Class: 1.000000, Obj: 0.463747, No Obj: 0.002385, Avg Recall: 0.865385, count: 104 -Region Avg IOU: 0.641088, Class: 1.000000, Obj: 0.399418, No Obj: 0.002243, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.652520, Class: 1.000000, Obj: 0.429223, No Obj: 0.002261, Avg Recall: 0.815217, count: 92 -Region Avg IOU: 0.689609, Class: 1.000000, Obj: 0.411964, No Obj: 0.002818, Avg Recall: 0.846774, count: 124 -Region Avg IOU: 0.680758, Class: 1.000000, Obj: 0.461705, No Obj: 0.002212, Avg Recall: 0.841463, count: 82 -Region Avg IOU: 0.661527, Class: 1.000000, Obj: 0.407538, No Obj: 0.002404, Avg Recall: 0.798319, count: 119 -Region Avg IOU: 0.688062, Class: 1.000000, Obj: 0.425670, No Obj: 0.002756, Avg Recall: 0.881890, count: 127 -7018: 0.427373, 0.416219 avg, 0.001000 rate, 30.311396 seconds, 1796608 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.670118, Class: 1.000000, Obj: 0.416643, No Obj: 0.002632, Avg Recall: 0.866071, count: 112 -Region Avg IOU: 0.654842, Class: 1.000000, Obj: 0.385841, No Obj: 0.002395, Avg Recall: 0.794643, count: 112 -Region Avg IOU: 0.670752, Class: 1.000000, Obj: 0.391191, No Obj: 0.003060, Avg Recall: 0.867550, count: 151 -Region Avg IOU: 0.645000, Class: 1.000000, Obj: 0.407091, No Obj: 0.002369, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.649159, Class: 1.000000, Obj: 0.394393, No Obj: 0.002852, Avg Recall: 0.775362, count: 138 -Region Avg IOU: 0.669505, Class: 1.000000, Obj: 0.469506, No Obj: 0.002414, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.690246, Class: 1.000000, Obj: 0.398706, No Obj: 0.002367, Avg Recall: 0.884615, count: 104 -Region Avg IOU: 0.631474, Class: 1.000000, Obj: 0.404862, No Obj: 0.002376, Avg Recall: 0.754545, count: 110 -7019: 0.462506, 0.420848 avg, 0.001000 rate, 27.221731 seconds, 1796864 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.672378, Class: 1.000000, Obj: 0.462739, No Obj: 0.002598, Avg Recall: 0.835294, count: 85 -Region Avg IOU: 0.665012, Class: 1.000000, Obj: 0.360311, No Obj: 0.002052, Avg Recall: 0.858824, count: 85 -Region Avg IOU: 0.695416, Class: 1.000000, Obj: 0.457575, No Obj: 0.002736, Avg Recall: 0.866071, count: 112 -Region Avg IOU: 0.659503, Class: 1.000000, Obj: 0.407793, No Obj: 0.002365, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.687408, Class: 1.000000, Obj: 0.479241, No Obj: 0.002012, Avg Recall: 0.830986, count: 71 -Region Avg IOU: 0.655339, Class: 1.000000, Obj: 0.392658, No Obj: 0.002702, Avg Recall: 0.814516, count: 124 -Region Avg IOU: 0.693274, Class: 1.000000, Obj: 0.419960, No Obj: 0.002318, Avg Recall: 0.867257, count: 113 -Region Avg IOU: 0.682857, Class: 1.000000, Obj: 0.395107, No Obj: 0.002127, Avg Recall: 0.870000, count: 100 -7020: 0.411014, 0.419864 avg, 0.001000 rate, 30.122070 seconds, 1797120 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 13.276872 seconds -Region Avg IOU: 0.546519, Class: 1.000000, Obj: 0.326326, No Obj: 0.003530, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.525085, Class: 1.000000, Obj: 0.292272, No Obj: 0.003525, Avg Recall: 0.598485, count: 132 -Region Avg IOU: 0.589561, Class: 1.000000, Obj: 0.375975, No Obj: 0.002995, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.551836, Class: 1.000000, Obj: 0.364073, No Obj: 0.003216, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.514289, Class: 1.000000, Obj: 0.314595, No Obj: 0.003004, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.613019, Class: 1.000000, Obj: 0.388404, No Obj: 0.003917, Avg Recall: 0.696629, count: 89 -Region Avg IOU: 0.505840, Class: 1.000000, Obj: 0.335916, No Obj: 0.003151, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.553775, Class: 1.000000, Obj: 0.357275, No Obj: 0.003644, Avg Recall: 0.619835, count: 121 -7021: 0.414585, 0.419336 avg, 0.001000 rate, 19.079319 seconds, 1797376 images -Loaded: 2.305364 seconds -Region Avg IOU: 0.558018, Class: 1.000000, Obj: 0.316915, No Obj: 0.003533, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.586208, Class: 1.000000, Obj: 0.336961, No Obj: 0.003764, Avg Recall: 0.663866, count: 119 -Region Avg IOU: 0.499885, Class: 1.000000, Obj: 0.324168, No Obj: 0.003199, Avg Recall: 0.523810, count: 105 -Region Avg IOU: 0.551042, Class: 1.000000, Obj: 0.338359, No Obj: 0.003792, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.563433, Class: 1.000000, Obj: 0.328874, No Obj: 0.003352, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.538685, Class: 1.000000, Obj: 0.331832, No Obj: 0.003187, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.546772, Class: 1.000000, Obj: 0.368780, No Obj: 0.003607, Avg Recall: 0.641026, count: 117 -Region Avg IOU: 0.553218, Class: 1.000000, Obj: 0.330064, No Obj: 0.003859, Avg Recall: 0.616822, count: 107 -7022: 0.471154, 0.424518 avg, 0.001000 rate, 19.131849 seconds, 1797632 images -Loaded: 3.060231 seconds -Region Avg IOU: 0.516497, Class: 1.000000, Obj: 0.343725, No Obj: 0.003490, Avg Recall: 0.580000, count: 100 -Region Avg IOU: 0.466385, Class: 1.000000, Obj: 0.314630, No Obj: 0.003258, Avg Recall: 0.512195, count: 123 -Region Avg IOU: 0.510597, Class: 1.000000, Obj: 0.356386, No Obj: 0.003398, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.534680, Class: 1.000000, Obj: 0.336083, No Obj: 0.003655, Avg Recall: 0.601852, count: 108 -Region Avg IOU: 0.500628, Class: 1.000000, Obj: 0.336219, No Obj: 0.003189, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.534294, Class: 1.000000, Obj: 0.337534, No Obj: 0.003738, Avg Recall: 0.623762, count: 101 -Region Avg IOU: 0.516216, Class: 1.000000, Obj: 0.350497, No Obj: 0.003208, Avg Recall: 0.573034, count: 89 -Region Avg IOU: 0.589245, Class: 1.000000, Obj: 0.422702, No Obj: 0.003482, Avg Recall: 0.636364, count: 99 -7023: 0.400426, 0.422109 avg, 0.001000 rate, 18.858608 seconds, 1797888 images -Loaded: 3.189700 seconds -Region Avg IOU: 0.560421, Class: 1.000000, Obj: 0.417772, No Obj: 0.003811, Avg Recall: 0.666667, count: 84 -Region Avg IOU: 0.539698, Class: 1.000000, Obj: 0.370882, No Obj: 0.004124, Avg Recall: 0.635659, count: 129 -Region Avg IOU: 0.519723, Class: 1.000000, Obj: 0.365168, No Obj: 0.003554, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.523368, Class: 1.000000, Obj: 0.355597, No Obj: 0.003798, Avg Recall: 0.586207, count: 116 -Region Avg IOU: 0.518223, Class: 1.000000, Obj: 0.400723, No Obj: 0.004054, Avg Recall: 0.579439, count: 107 -Region Avg IOU: 0.523769, Class: 1.000000, Obj: 0.380957, No Obj: 0.004177, Avg Recall: 0.619048, count: 126 -Region Avg IOU: 0.513904, Class: 1.000000, Obj: 0.369336, No Obj: 0.003758, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.537139, Class: 1.000000, Obj: 0.372043, No Obj: 0.004071, Avg Recall: 0.646552, count: 116 -7024: 0.425767, 0.422475 avg, 0.001000 rate, 19.453323 seconds, 1798144 images -Loaded: 2.187551 seconds -Region Avg IOU: 0.528917, Class: 1.000000, Obj: 0.383881, No Obj: 0.004172, Avg Recall: 0.608247, count: 97 -Region Avg IOU: 0.497283, Class: 1.000000, Obj: 0.397366, No Obj: 0.004335, Avg Recall: 0.552845, count: 123 -Region Avg IOU: 0.538045, Class: 1.000000, Obj: 0.365769, No Obj: 0.004331, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.531846, Class: 1.000000, Obj: 0.418657, No Obj: 0.004693, Avg Recall: 0.596774, count: 124 -Region Avg IOU: 0.578955, Class: 1.000000, Obj: 0.431209, No Obj: 0.003917, Avg Recall: 0.658824, count: 85 -Region Avg IOU: 0.516336, Class: 1.000000, Obj: 0.413542, No Obj: 0.004035, Avg Recall: 0.568421, count: 95 -Region Avg IOU: 0.543732, Class: 1.000000, Obj: 0.415901, No Obj: 0.004737, Avg Recall: 0.595238, count: 126 -Region Avg IOU: 0.597152, Class: 1.000000, Obj: 0.461428, No Obj: 0.004256, Avg Recall: 0.724771, count: 109 -7025: 0.413596, 0.421587 avg, 0.001000 rate, 20.339144 seconds, 1798400 images -Loaded: 0.818235 seconds -Region Avg IOU: 0.554079, Class: 1.000000, Obj: 0.357279, No Obj: 0.004003, Avg Recall: 0.622807, count: 114 -Region Avg IOU: 0.572448, Class: 1.000000, Obj: 0.440216, No Obj: 0.005047, Avg Recall: 0.683761, count: 117 -Region Avg IOU: 0.551492, Class: 1.000000, Obj: 0.384948, No Obj: 0.004199, Avg Recall: 0.627660, count: 94 -Region Avg IOU: 0.579980, Class: 1.000000, Obj: 0.435046, No Obj: 0.004519, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.504128, Class: 1.000000, Obj: 0.401839, No Obj: 0.004106, Avg Recall: 0.532110, count: 109 -Region Avg IOU: 0.473633, Class: 1.000000, Obj: 0.343064, No Obj: 0.003806, Avg Recall: 0.514563, count: 103 -Region Avg IOU: 0.570528, Class: 1.000000, Obj: 0.415799, No Obj: 0.004892, Avg Recall: 0.700000, count: 130 -Region Avg IOU: 0.529027, Class: 1.000000, Obj: 0.436924, No Obj: 0.003764, Avg Recall: 0.593407, count: 91 -7026: 0.417138, 0.421142 avg, 0.001000 rate, 21.774572 seconds, 1798656 images -Loaded: 0.879741 seconds -Region Avg IOU: 0.567525, Class: 1.000000, Obj: 0.404979, No Obj: 0.005665, Avg Recall: 0.685039, count: 127 -Region Avg IOU: 0.566486, Class: 1.000000, Obj: 0.401255, No Obj: 0.004721, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.519888, Class: 1.000000, Obj: 0.392093, No Obj: 0.004619, Avg Recall: 0.594595, count: 111 -Region Avg IOU: 0.482484, Class: 1.000000, Obj: 0.411812, No Obj: 0.004102, Avg Recall: 0.494949, count: 99 -Region Avg IOU: 0.552898, Class: 1.000000, Obj: 0.430261, No Obj: 0.004563, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.547128, Class: 1.000000, Obj: 0.447135, No Obj: 0.004608, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.567555, Class: 1.000000, Obj: 0.451962, No Obj: 0.004783, Avg Recall: 0.649573, count: 117 -Region Avg IOU: 0.602748, Class: 1.000000, Obj: 0.429538, No Obj: 0.005015, Avg Recall: 0.742857, count: 105 -7027: 0.445437, 0.423571 avg, 0.001000 rate, 20.657558 seconds, 1798912 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.581992, Class: 1.000000, Obj: 0.523045, No Obj: 0.004729, Avg Recall: 0.626667, count: 75 -Region Avg IOU: 0.513776, Class: 1.000000, Obj: 0.416288, No Obj: 0.004642, Avg Recall: 0.594828, count: 116 -Region Avg IOU: 0.520870, Class: 1.000000, Obj: 0.455020, No Obj: 0.004433, Avg Recall: 0.553571, count: 112 -Region Avg IOU: 0.494261, Class: 1.000000, Obj: 0.375789, No Obj: 0.004613, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.599068, Class: 1.000000, Obj: 0.459775, No Obj: 0.005069, Avg Recall: 0.685393, count: 89 -Region Avg IOU: 0.531156, Class: 1.000000, Obj: 0.449918, No Obj: 0.003873, Avg Recall: 0.554217, count: 83 -Region Avg IOU: 0.567283, Class: 1.000000, Obj: 0.418668, No Obj: 0.004056, Avg Recall: 0.659341, count: 91 -Region Avg IOU: 0.525402, Class: 1.000000, Obj: 0.449997, No Obj: 0.004643, Avg Recall: 0.600000, count: 110 -7028: 0.366048, 0.417819 avg, 0.001000 rate, 21.299257 seconds, 1799168 images -Loaded: 1.974092 seconds -Region Avg IOU: 0.518568, Class: 1.000000, Obj: 0.445821, No Obj: 0.003916, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.558301, Class: 1.000000, Obj: 0.454660, No Obj: 0.004588, Avg Recall: 0.614583, count: 96 -Region Avg IOU: 0.573295, Class: 1.000000, Obj: 0.469678, No Obj: 0.005311, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.505062, Class: 1.000000, Obj: 0.414434, No Obj: 0.004646, Avg Recall: 0.564103, count: 117 -Region Avg IOU: 0.526408, Class: 1.000000, Obj: 0.368151, No Obj: 0.004407, Avg Recall: 0.576923, count: 104 -Region Avg IOU: 0.524776, Class: 1.000000, Obj: 0.438403, No Obj: 0.004219, Avg Recall: 0.589474, count: 95 -Region Avg IOU: 0.535986, Class: 1.000000, Obj: 0.430916, No Obj: 0.005073, Avg Recall: 0.589147, count: 129 -Region Avg IOU: 0.544976, Class: 1.000000, Obj: 0.404860, No Obj: 0.005124, Avg Recall: 0.632812, count: 128 -7029: 0.429677, 0.419005 avg, 0.001000 rate, 19.743532 seconds, 1799424 images -Loaded: 2.161803 seconds -Region Avg IOU: 0.548153, Class: 1.000000, Obj: 0.416669, No Obj: 0.005032, Avg Recall: 0.669903, count: 103 -Region Avg IOU: 0.581981, Class: 1.000000, Obj: 0.461240, No Obj: 0.004536, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.559634, Class: 1.000000, Obj: 0.413136, No Obj: 0.005057, Avg Recall: 0.603448, count: 116 -Region Avg IOU: 0.552405, Class: 1.000000, Obj: 0.403400, No Obj: 0.004915, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.547055, Class: 1.000000, Obj: 0.431883, No Obj: 0.004940, Avg Recall: 0.605042, count: 119 -Region Avg IOU: 0.568674, Class: 1.000000, Obj: 0.429487, No Obj: 0.004853, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.567578, Class: 1.000000, Obj: 0.469722, No Obj: 0.003883, Avg Recall: 0.655172, count: 87 -Region Avg IOU: 0.535157, Class: 1.000000, Obj: 0.417324, No Obj: 0.004239, Avg Recall: 0.595745, count: 94 -7030: 0.428255, 0.419930 avg, 0.001000 rate, 21.248924 seconds, 1799680 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000063 seconds -Region Avg IOU: 0.622820, Class: 1.000000, Obj: 0.514026, No Obj: 0.002505, Avg Recall: 0.784091, count: 88 -Region Avg IOU: 0.641757, Class: 1.000000, Obj: 0.504820, No Obj: 0.003107, Avg Recall: 0.812500, count: 80 -Region Avg IOU: 0.611572, Class: 1.000000, Obj: 0.465301, No Obj: 0.003094, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.604594, Class: 1.000000, Obj: 0.456167, No Obj: 0.003900, Avg Recall: 0.715447, count: 123 -Region Avg IOU: 0.609876, Class: 1.000000, Obj: 0.455404, No Obj: 0.003861, Avg Recall: 0.742188, count: 128 -Region Avg IOU: 0.609568, Class: 1.000000, Obj: 0.474374, No Obj: 0.003673, Avg Recall: 0.773196, count: 97 -Region Avg IOU: 0.616943, Class: 1.000000, Obj: 0.442460, No Obj: 0.003741, Avg Recall: 0.747899, count: 119 -Region Avg IOU: 0.583477, Class: 1.000000, Obj: 0.442703, No Obj: 0.003600, Avg Recall: 0.683761, count: 117 -7031: 0.427896, 0.420727 avg, 0.001000 rate, 24.197351 seconds, 1799936 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.630141, Class: 1.000000, Obj: 0.462438, No Obj: 0.003238, Avg Recall: 0.733333, count: 90 -Region Avg IOU: 0.678210, Class: 1.000000, Obj: 0.483584, No Obj: 0.003740, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.604991, Class: 1.000000, Obj: 0.475103, No Obj: 0.003200, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.624502, Class: 1.000000, Obj: 0.480452, No Obj: 0.003373, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.639489, Class: 1.000000, Obj: 0.485606, No Obj: 0.003912, Avg Recall: 0.756098, count: 123 -Region Avg IOU: 0.599660, Class: 1.000000, Obj: 0.460906, No Obj: 0.003455, Avg Recall: 0.711111, count: 135 -Region Avg IOU: 0.605248, Class: 1.000000, Obj: 0.462388, No Obj: 0.003265, Avg Recall: 0.727273, count: 121 -Region Avg IOU: 0.616794, Class: 1.000000, Obj: 0.450197, No Obj: 0.002977, Avg Recall: 0.712766, count: 94 -7032: 0.387690, 0.417423 avg, 0.001000 rate, 25.135046 seconds, 1800192 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.576549, Class: 1.000000, Obj: 0.397372, No Obj: 0.003024, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.648278, Class: 1.000000, Obj: 0.409502, No Obj: 0.002799, Avg Recall: 0.804348, count: 92 -Region Avg IOU: 0.640476, Class: 1.000000, Obj: 0.414766, No Obj: 0.002953, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.597091, Class: 1.000000, Obj: 0.400096, No Obj: 0.002818, Avg Recall: 0.670213, count: 94 -Region Avg IOU: 0.604005, Class: 1.000000, Obj: 0.408420, No Obj: 0.003099, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.662275, Class: 1.000000, Obj: 0.441550, No Obj: 0.003236, Avg Recall: 0.803419, count: 117 -Region Avg IOU: 0.608134, Class: 1.000000, Obj: 0.389704, No Obj: 0.003379, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.587507, Class: 1.000000, Obj: 0.412736, No Obj: 0.003524, Avg Recall: 0.688000, count: 125 -7033: 0.455561, 0.421237 avg, 0.001000 rate, 25.636019 seconds, 1800448 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.621737, Class: 1.000000, Obj: 0.407616, No Obj: 0.002424, Avg Recall: 0.719512, count: 82 -Region Avg IOU: 0.605794, Class: 1.000000, Obj: 0.379042, No Obj: 0.003322, Avg Recall: 0.718310, count: 142 -Region Avg IOU: 0.628495, Class: 1.000000, Obj: 0.327546, No Obj: 0.003528, Avg Recall: 0.761905, count: 126 -Region Avg IOU: 0.591111, Class: 1.000000, Obj: 0.389334, No Obj: 0.003450, Avg Recall: 0.700000, count: 140 -Region Avg IOU: 0.646826, Class: 1.000000, Obj: 0.449522, No Obj: 0.002565, Avg Recall: 0.795181, count: 83 -Region Avg IOU: 0.690411, Class: 1.000000, Obj: 0.446448, No Obj: 0.002813, Avg Recall: 0.885057, count: 87 -Region Avg IOU: 0.614252, Class: 1.000000, Obj: 0.409774, No Obj: 0.002761, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.654542, Class: 1.000000, Obj: 0.380831, No Obj: 0.002806, Avg Recall: 0.813187, count: 91 -7034: 0.416415, 0.420755 avg, 0.001000 rate, 26.331230 seconds, 1800704 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.623224, Class: 1.000000, Obj: 0.395451, No Obj: 0.002647, Avg Recall: 0.755556, count: 90 -Region Avg IOU: 0.657471, Class: 1.000000, Obj: 0.428208, No Obj: 0.002608, Avg Recall: 0.813187, count: 91 -Region Avg IOU: 0.645273, Class: 1.000000, Obj: 0.419800, No Obj: 0.002519, Avg Recall: 0.806818, count: 88 -Region Avg IOU: 0.602583, Class: 1.000000, Obj: 0.416980, No Obj: 0.003114, Avg Recall: 0.728070, count: 114 -Region Avg IOU: 0.610520, Class: 1.000000, Obj: 0.410445, No Obj: 0.002821, Avg Recall: 0.720930, count: 86 -Region Avg IOU: 0.627838, Class: 1.000000, Obj: 0.413833, No Obj: 0.002780, Avg Recall: 0.764706, count: 85 -Region Avg IOU: 0.655726, Class: 1.000000, Obj: 0.345514, No Obj: 0.003129, Avg Recall: 0.811475, count: 122 -Region Avg IOU: 0.675963, Class: 1.000000, Obj: 0.461719, No Obj: 0.003393, Avg Recall: 0.833333, count: 102 -7035: 0.375041, 0.416183 avg, 0.001000 rate, 25.924616 seconds, 1800960 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.662374, Class: 1.000000, Obj: 0.403598, No Obj: 0.003016, Avg Recall: 0.820513, count: 117 -Region Avg IOU: 0.606407, Class: 1.000000, Obj: 0.362362, No Obj: 0.003476, Avg Recall: 0.685535, count: 159 -Region Avg IOU: 0.601489, Class: 1.000000, Obj: 0.342958, No Obj: 0.003273, Avg Recall: 0.740157, count: 127 -Region Avg IOU: 0.598074, Class: 1.000000, Obj: 0.322540, No Obj: 0.002774, Avg Recall: 0.736434, count: 129 -Region Avg IOU: 0.645762, Class: 1.000000, Obj: 0.382417, No Obj: 0.003256, Avg Recall: 0.775362, count: 138 -Region Avg IOU: 0.578366, Class: 1.000000, Obj: 0.376135, No Obj: 0.002556, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.593679, Class: 1.000000, Obj: 0.352655, No Obj: 0.002511, Avg Recall: 0.710938, count: 128 -Region Avg IOU: 0.653453, Class: 1.000000, Obj: 0.404218, No Obj: 0.002349, Avg Recall: 0.760870, count: 92 -7036: 0.498789, 0.424444 avg, 0.001000 rate, 26.408487 seconds, 1801216 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.625048, Class: 1.000000, Obj: 0.392358, No Obj: 0.003036, Avg Recall: 0.722628, count: 137 -Region Avg IOU: 0.653752, Class: 1.000000, Obj: 0.457396, No Obj: 0.002752, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.630917, Class: 1.000000, Obj: 0.309143, No Obj: 0.002563, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.688194, Class: 1.000000, Obj: 0.397848, No Obj: 0.002437, Avg Recall: 0.852632, count: 95 -Region Avg IOU: 0.605594, Class: 1.000000, Obj: 0.373620, No Obj: 0.003102, Avg Recall: 0.731707, count: 123 -Region Avg IOU: 0.592095, Class: 1.000000, Obj: 0.338756, No Obj: 0.002764, Avg Recall: 0.742188, count: 128 -Region Avg IOU: 0.608230, Class: 1.000000, Obj: 0.389641, No Obj: 0.003093, Avg Recall: 0.735537, count: 121 -Region Avg IOU: 0.636033, Class: 1.000000, Obj: 0.402057, No Obj: 0.002838, Avg Recall: 0.733333, count: 120 -7037: 0.485795, 0.430579 avg, 0.001000 rate, 25.476360 seconds, 1801472 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.636276, Class: 1.000000, Obj: 0.339676, No Obj: 0.002683, Avg Recall: 0.824074, count: 108 -Region Avg IOU: 0.653008, Class: 1.000000, Obj: 0.430682, No Obj: 0.002680, Avg Recall: 0.742574, count: 101 -Region Avg IOU: 0.675060, Class: 1.000000, Obj: 0.469152, No Obj: 0.003002, Avg Recall: 0.841584, count: 101 -Region Avg IOU: 0.623648, Class: 1.000000, Obj: 0.387330, No Obj: 0.002855, Avg Recall: 0.754098, count: 122 -Region Avg IOU: 0.596811, Class: 1.000000, Obj: 0.346286, No Obj: 0.002519, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.642912, Class: 1.000000, Obj: 0.404527, No Obj: 0.002761, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.622732, Class: 1.000000, Obj: 0.361220, No Obj: 0.003215, Avg Recall: 0.784615, count: 130 -Region Avg IOU: 0.636940, Class: 1.000000, Obj: 0.457918, No Obj: 0.002685, Avg Recall: 0.784946, count: 93 -7038: 0.415414, 0.429062 avg, 0.001000 rate, 25.774963 seconds, 1801728 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.645561, Class: 1.000000, Obj: 0.464639, No Obj: 0.003283, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.630128, Class: 1.000000, Obj: 0.453747, No Obj: 0.002738, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.663003, Class: 1.000000, Obj: 0.451940, No Obj: 0.002777, Avg Recall: 0.811765, count: 85 -Region Avg IOU: 0.673938, Class: 1.000000, Obj: 0.462444, No Obj: 0.002823, Avg Recall: 0.793478, count: 92 -Region Avg IOU: 0.649350, Class: 1.000000, Obj: 0.403377, No Obj: 0.002704, Avg Recall: 0.813187, count: 91 -Region Avg IOU: 0.624938, Class: 1.000000, Obj: 0.411164, No Obj: 0.002385, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.580801, Class: 1.000000, Obj: 0.325695, No Obj: 0.002663, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.668633, Class: 1.000000, Obj: 0.380251, No Obj: 0.003221, Avg Recall: 0.833333, count: 114 -7039: 0.361940, 0.422350 avg, 0.001000 rate, 24.547831 seconds, 1801984 images -Loaded: 0.000111 seconds -Region Avg IOU: 0.643065, Class: 1.000000, Obj: 0.452800, No Obj: 0.003333, Avg Recall: 0.796748, count: 123 -Region Avg IOU: 0.596462, Class: 1.000000, Obj: 0.411858, No Obj: 0.002787, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.635321, Class: 1.000000, Obj: 0.438127, No Obj: 0.002892, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.643240, Class: 1.000000, Obj: 0.426656, No Obj: 0.002777, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.654430, Class: 1.000000, Obj: 0.432064, No Obj: 0.002648, Avg Recall: 0.833333, count: 84 -Region Avg IOU: 0.623427, Class: 1.000000, Obj: 0.432151, No Obj: 0.002873, Avg Recall: 0.754545, count: 110 -Region Avg IOU: 0.656360, Class: 1.000000, Obj: 0.424441, No Obj: 0.003733, Avg Recall: 0.813953, count: 129 -Region Avg IOU: 0.597497, Class: 1.000000, Obj: 0.427247, No Obj: 0.002925, Avg Recall: 0.717172, count: 99 -7040: 0.351344, 0.415250 avg, 0.001000 rate, 25.274752 seconds, 1802240 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000078 seconds -Region Avg IOU: 0.675359, Class: 1.000000, Obj: 0.477768, No Obj: 0.002178, Avg Recall: 0.828571, count: 70 -Region Avg IOU: 0.645531, Class: 1.000000, Obj: 0.415494, No Obj: 0.003501, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.620138, Class: 1.000000, Obj: 0.442009, No Obj: 0.002920, Avg Recall: 0.725275, count: 91 -Region Avg IOU: 0.634363, Class: 1.000000, Obj: 0.445690, No Obj: 0.003291, Avg Recall: 0.763780, count: 127 -Region Avg IOU: 0.655040, Class: 1.000000, Obj: 0.449860, No Obj: 0.003337, Avg Recall: 0.803419, count: 117 -Region Avg IOU: 0.638538, Class: 1.000000, Obj: 0.482831, No Obj: 0.003914, Avg Recall: 0.758621, count: 145 -Region Avg IOU: 0.661758, Class: 1.000000, Obj: 0.445550, No Obj: 0.003350, Avg Recall: 0.796875, count: 128 -Region Avg IOU: 0.631316, Class: 1.000000, Obj: 0.392622, No Obj: 0.003264, Avg Recall: 0.792593, count: 135 -7041: 0.437329, 0.417458 avg, 0.001000 rate, 27.877483 seconds, 1802496 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.643319, Class: 1.000000, Obj: 0.437034, No Obj: 0.003208, Avg Recall: 0.776859, count: 121 -Region Avg IOU: 0.653425, Class: 1.000000, Obj: 0.386337, No Obj: 0.003159, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.666334, Class: 1.000000, Obj: 0.497602, No Obj: 0.003285, Avg Recall: 0.824000, count: 125 -Region Avg IOU: 0.629083, Class: 1.000000, Obj: 0.414716, No Obj: 0.003028, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.655544, Class: 1.000000, Obj: 0.455748, No Obj: 0.003626, Avg Recall: 0.811475, count: 122 -Region Avg IOU: 0.591680, Class: 1.000000, Obj: 0.436356, No Obj: 0.003102, Avg Recall: 0.704000, count: 125 -Region Avg IOU: 0.661584, Class: 1.000000, Obj: 0.493971, No Obj: 0.003402, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.671184, Class: 1.000000, Obj: 0.478966, No Obj: 0.003844, Avg Recall: 0.801724, count: 116 -7042: 0.420515, 0.417763 avg, 0.001000 rate, 26.941959 seconds, 1802752 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.657776, Class: 1.000000, Obj: 0.458477, No Obj: 0.003528, Avg Recall: 0.827586, count: 116 -Region Avg IOU: 0.624733, Class: 1.000000, Obj: 0.488439, No Obj: 0.003165, Avg Recall: 0.773109, count: 119 -Region Avg IOU: 0.658619, Class: 1.000000, Obj: 0.536867, No Obj: 0.003123, Avg Recall: 0.817308, count: 104 -Region Avg IOU: 0.622828, Class: 1.000000, Obj: 0.481477, No Obj: 0.003153, Avg Recall: 0.752294, count: 109 -Region Avg IOU: 0.694968, Class: 1.000000, Obj: 0.556964, No Obj: 0.002652, Avg Recall: 0.827586, count: 87 -Region Avg IOU: 0.663692, Class: 1.000000, Obj: 0.454078, No Obj: 0.002796, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.652113, Class: 1.000000, Obj: 0.432521, No Obj: 0.003270, Avg Recall: 0.785185, count: 135 -Region Avg IOU: 0.668600, Class: 1.000000, Obj: 0.437955, No Obj: 0.003648, Avg Recall: 0.806723, count: 119 -7043: 0.368432, 0.412830 avg, 0.001000 rate, 28.247726 seconds, 1803008 images -Loaded: 0.000104 seconds -Region Avg IOU: 0.645157, Class: 1.000000, Obj: 0.436206, No Obj: 0.002959, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.627748, Class: 1.000000, Obj: 0.472058, No Obj: 0.002987, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.658150, Class: 1.000000, Obj: 0.478165, No Obj: 0.003121, Avg Recall: 0.851485, count: 101 -Region Avg IOU: 0.642914, Class: 1.000000, Obj: 0.431905, No Obj: 0.003414, Avg Recall: 0.801724, count: 116 -Region Avg IOU: 0.659153, Class: 1.000000, Obj: 0.507576, No Obj: 0.003302, Avg Recall: 0.805310, count: 113 -Region Avg IOU: 0.630749, Class: 1.000000, Obj: 0.422419, No Obj: 0.003356, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.627608, Class: 1.000000, Obj: 0.461488, No Obj: 0.003264, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.638626, Class: 1.000000, Obj: 0.453574, No Obj: 0.003579, Avg Recall: 0.795082, count: 122 -7044: 0.447168, 0.416264 avg, 0.001000 rate, 26.565712 seconds, 1803264 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.632467, Class: 1.000000, Obj: 0.402197, No Obj: 0.002903, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.658317, Class: 1.000000, Obj: 0.460431, No Obj: 0.003468, Avg Recall: 0.811475, count: 122 -Region Avg IOU: 0.660884, Class: 1.000000, Obj: 0.471330, No Obj: 0.003225, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.657077, Class: 1.000000, Obj: 0.506260, No Obj: 0.003076, Avg Recall: 0.836735, count: 98 -Region Avg IOU: 0.617036, Class: 1.000000, Obj: 0.458200, No Obj: 0.002964, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.624882, Class: 1.000000, Obj: 0.406025, No Obj: 0.002607, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.611628, Class: 1.000000, Obj: 0.398411, No Obj: 0.003307, Avg Recall: 0.705479, count: 146 -Region Avg IOU: 0.656325, Class: 1.000000, Obj: 0.507983, No Obj: 0.003184, Avg Recall: 0.785714, count: 98 -7045: 0.409009, 0.415538 avg, 0.001000 rate, 26.199848 seconds, 1803520 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.614646, Class: 1.000000, Obj: 0.491365, No Obj: 0.002797, Avg Recall: 0.719626, count: 107 -Region Avg IOU: 0.676622, Class: 1.000000, Obj: 0.470719, No Obj: 0.002575, Avg Recall: 0.863014, count: 73 -Region Avg IOU: 0.656250, Class: 1.000000, Obj: 0.482019, No Obj: 0.003053, Avg Recall: 0.826923, count: 104 -Region Avg IOU: 0.657970, Class: 1.000000, Obj: 0.433330, No Obj: 0.002989, Avg Recall: 0.835165, count: 91 -Region Avg IOU: 0.672026, Class: 1.000000, Obj: 0.437664, No Obj: 0.003229, Avg Recall: 0.851240, count: 121 -Region Avg IOU: 0.589455, Class: 1.000000, Obj: 0.401095, No Obj: 0.002811, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.639381, Class: 1.000000, Obj: 0.517941, No Obj: 0.003131, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.646974, Class: 1.000000, Obj: 0.490726, No Obj: 0.003552, Avg Recall: 0.764706, count: 102 -7046: 0.431176, 0.417102 avg, 0.001000 rate, 27.139584 seconds, 1803776 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.675554, Class: 1.000000, Obj: 0.417098, No Obj: 0.002602, Avg Recall: 0.809524, count: 84 -Region Avg IOU: 0.626007, Class: 1.000000, Obj: 0.425915, No Obj: 0.002512, Avg Recall: 0.738636, count: 88 -Region Avg IOU: 0.697750, Class: 1.000000, Obj: 0.502408, No Obj: 0.002768, Avg Recall: 0.891566, count: 83 -Region Avg IOU: 0.637696, Class: 1.000000, Obj: 0.431193, No Obj: 0.003447, Avg Recall: 0.751724, count: 145 -Region Avg IOU: 0.602553, Class: 1.000000, Obj: 0.441566, No Obj: 0.003211, Avg Recall: 0.700855, count: 117 -Region Avg IOU: 0.646330, Class: 1.000000, Obj: 0.446190, No Obj: 0.003112, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.644726, Class: 1.000000, Obj: 0.425154, No Obj: 0.002707, Avg Recall: 0.822917, count: 96 -Region Avg IOU: 0.649682, Class: 1.000000, Obj: 0.452574, No Obj: 0.003039, Avg Recall: 0.775000, count: 120 -7047: 0.381521, 0.413544 avg, 0.001000 rate, 26.718271 seconds, 1804032 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.562469, Class: 1.000000, Obj: 0.378881, No Obj: 0.002226, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.640327, Class: 1.000000, Obj: 0.449194, No Obj: 0.002641, Avg Recall: 0.793478, count: 92 -Region Avg IOU: 0.655533, Class: 1.000000, Obj: 0.416461, No Obj: 0.003205, Avg Recall: 0.806452, count: 124 -Region Avg IOU: 0.655653, Class: 1.000000, Obj: 0.440334, No Obj: 0.002832, Avg Recall: 0.828571, count: 105 -Region Avg IOU: 0.647595, Class: 1.000000, Obj: 0.445611, No Obj: 0.003018, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.650390, Class: 1.000000, Obj: 0.430975, No Obj: 0.002701, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.650257, Class: 1.000000, Obj: 0.424138, No Obj: 0.002409, Avg Recall: 0.793814, count: 97 -Region Avg IOU: 0.640714, Class: 1.000000, Obj: 0.395431, No Obj: 0.003106, Avg Recall: 0.790323, count: 124 -7048: 0.442206, 0.416410 avg, 0.001000 rate, 27.414610 seconds, 1804288 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.632279, Class: 1.000000, Obj: 0.403599, No Obj: 0.002751, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.662378, Class: 1.000000, Obj: 0.469369, No Obj: 0.002828, Avg Recall: 0.833333, count: 108 -Region Avg IOU: 0.647229, Class: 1.000000, Obj: 0.410653, No Obj: 0.002412, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.666528, Class: 1.000000, Obj: 0.431124, No Obj: 0.002672, Avg Recall: 0.833333, count: 90 -Region Avg IOU: 0.646035, Class: 1.000000, Obj: 0.415903, No Obj: 0.002813, Avg Recall: 0.820755, count: 106 -Region Avg IOU: 0.680697, Class: 1.000000, Obj: 0.431939, No Obj: 0.002778, Avg Recall: 0.872549, count: 102 -Region Avg IOU: 0.600759, Class: 1.000000, Obj: 0.406739, No Obj: 0.002657, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.645484, Class: 1.000000, Obj: 0.380005, No Obj: 0.003232, Avg Recall: 0.790698, count: 129 -7049: 0.467488, 0.421518 avg, 0.001000 rate, 25.461596 seconds, 1804544 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.637000, Class: 1.000000, Obj: 0.379148, No Obj: 0.002323, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.671980, Class: 1.000000, Obj: 0.395044, No Obj: 0.002499, Avg Recall: 0.840000, count: 100 -Region Avg IOU: 0.667911, Class: 1.000000, Obj: 0.420074, No Obj: 0.003060, Avg Recall: 0.809917, count: 121 -Region Avg IOU: 0.631621, Class: 1.000000, Obj: 0.417168, No Obj: 0.002646, Avg Recall: 0.787037, count: 108 -Region Avg IOU: 0.666166, Class: 1.000000, Obj: 0.397318, No Obj: 0.002734, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.676454, Class: 1.000000, Obj: 0.440871, No Obj: 0.002613, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.579887, Class: 1.000000, Obj: 0.391084, No Obj: 0.002812, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.699368, Class: 1.000000, Obj: 0.416326, No Obj: 0.002905, Avg Recall: 0.872549, count: 102 -7050: 0.457502, 0.425117 avg, 0.001000 rate, 27.225756 seconds, 1804800 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000075 seconds -Region Avg IOU: 0.652832, Class: 1.000000, Obj: 0.364474, No Obj: 0.002399, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.666660, Class: 1.000000, Obj: 0.409017, No Obj: 0.002338, Avg Recall: 0.831579, count: 95 -Region Avg IOU: 0.658270, Class: 1.000000, Obj: 0.363920, No Obj: 0.002921, Avg Recall: 0.801527, count: 131 -Region Avg IOU: 0.674377, Class: 1.000000, Obj: 0.426036, No Obj: 0.002862, Avg Recall: 0.848740, count: 119 -Region Avg IOU: 0.649284, Class: 1.000000, Obj: 0.401130, No Obj: 0.002764, Avg Recall: 0.796992, count: 133 -Region Avg IOU: 0.644493, Class: 1.000000, Obj: 0.409703, No Obj: 0.002784, Avg Recall: 0.777778, count: 126 -Region Avg IOU: 0.686754, Class: 1.000000, Obj: 0.387762, No Obj: 0.002644, Avg Recall: 0.872727, count: 110 -Region Avg IOU: 0.638436, Class: 1.000000, Obj: 0.380204, No Obj: 0.002489, Avg Recall: 0.784000, count: 125 -7051: 0.495038, 0.432109 avg, 0.001000 rate, 28.007277 seconds, 1805056 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.683107, Class: 1.000000, Obj: 0.437215, No Obj: 0.002503, Avg Recall: 0.834951, count: 103 -Region Avg IOU: 0.672376, Class: 1.000000, Obj: 0.398373, No Obj: 0.002728, Avg Recall: 0.857143, count: 112 -Region Avg IOU: 0.669319, Class: 1.000000, Obj: 0.423213, No Obj: 0.002744, Avg Recall: 0.868687, count: 99 -Region Avg IOU: 0.646304, Class: 1.000000, Obj: 0.427430, No Obj: 0.002643, Avg Recall: 0.774194, count: 124 -Region Avg IOU: 0.623194, Class: 1.000000, Obj: 0.388819, No Obj: 0.002790, Avg Recall: 0.734375, count: 128 -Region Avg IOU: 0.643747, Class: 1.000000, Obj: 0.445672, No Obj: 0.003188, Avg Recall: 0.780822, count: 146 -Region Avg IOU: 0.658467, Class: 1.000000, Obj: 0.448953, No Obj: 0.002599, Avg Recall: 0.815789, count: 76 -Region Avg IOU: 0.669929, Class: 1.000000, Obj: 0.436672, No Obj: 0.002953, Avg Recall: 0.825688, count: 109 -7052: 0.435925, 0.432490 avg, 0.001000 rate, 28.501516 seconds, 1805312 images -Loaded: 0.000115 seconds -Region Avg IOU: 0.647442, Class: 1.000000, Obj: 0.396396, No Obj: 0.002703, Avg Recall: 0.815126, count: 119 -Region Avg IOU: 0.682108, Class: 1.000000, Obj: 0.461450, No Obj: 0.002828, Avg Recall: 0.810345, count: 116 -Region Avg IOU: 0.661038, Class: 1.000000, Obj: 0.438860, No Obj: 0.002601, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.591567, Class: 1.000000, Obj: 0.401180, No Obj: 0.003039, Avg Recall: 0.681416, count: 113 -Region Avg IOU: 0.619819, Class: 1.000000, Obj: 0.419521, No Obj: 0.002563, Avg Recall: 0.766129, count: 124 -Region Avg IOU: 0.652854, Class: 1.000000, Obj: 0.423607, No Obj: 0.002531, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.645860, Class: 1.000000, Obj: 0.388090, No Obj: 0.003039, Avg Recall: 0.798450, count: 129 -Region Avg IOU: 0.673755, Class: 1.000000, Obj: 0.410159, No Obj: 0.002325, Avg Recall: 0.837838, count: 74 -7053: 0.468466, 0.436088 avg, 0.001000 rate, 29.465324 seconds, 1805568 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.648128, Class: 1.000000, Obj: 0.374712, No Obj: 0.003251, Avg Recall: 0.797101, count: 138 -Region Avg IOU: 0.638950, Class: 1.000000, Obj: 0.419999, No Obj: 0.002605, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.641747, Class: 1.000000, Obj: 0.358885, No Obj: 0.002446, Avg Recall: 0.785047, count: 107 -Region Avg IOU: 0.683474, Class: 1.000000, Obj: 0.418487, No Obj: 0.003012, Avg Recall: 0.868687, count: 99 -Region Avg IOU: 0.644332, Class: 1.000000, Obj: 0.380676, No Obj: 0.002455, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.616588, Class: 1.000000, Obj: 0.406497, No Obj: 0.002522, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.670177, Class: 1.000000, Obj: 0.414423, No Obj: 0.003359, Avg Recall: 0.818182, count: 121 -Region Avg IOU: 0.673036, Class: 1.000000, Obj: 0.481643, No Obj: 0.002510, Avg Recall: 0.802198, count: 91 -7054: 0.435907, 0.436070 avg, 0.001000 rate, 28.940544 seconds, 1805824 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.684036, Class: 1.000000, Obj: 0.474213, No Obj: 0.002390, Avg Recall: 0.853333, count: 75 -Region Avg IOU: 0.679126, Class: 1.000000, Obj: 0.424087, No Obj: 0.002905, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.636056, Class: 1.000000, Obj: 0.382049, No Obj: 0.002194, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.715247, Class: 1.000000, Obj: 0.423967, No Obj: 0.002857, Avg Recall: 0.891892, count: 111 -Region Avg IOU: 0.648081, Class: 1.000000, Obj: 0.387160, No Obj: 0.002829, Avg Recall: 0.798387, count: 124 -Region Avg IOU: 0.660860, Class: 1.000000, Obj: 0.427513, No Obj: 0.002831, Avg Recall: 0.833333, count: 108 -Region Avg IOU: 0.663217, Class: 1.000000, Obj: 0.474220, No Obj: 0.002640, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.677053, Class: 1.000000, Obj: 0.442520, No Obj: 0.002768, Avg Recall: 0.813084, count: 107 -7055: 0.411032, 0.433566 avg, 0.001000 rate, 29.169758 seconds, 1806080 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.638352, Class: 1.000000, Obj: 0.416460, No Obj: 0.002703, Avg Recall: 0.815126, count: 119 -Region Avg IOU: 0.656149, Class: 1.000000, Obj: 0.414002, No Obj: 0.002244, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.646831, Class: 1.000000, Obj: 0.408496, No Obj: 0.002532, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.649968, Class: 1.000000, Obj: 0.447383, No Obj: 0.002816, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.642846, Class: 1.000000, Obj: 0.491113, No Obj: 0.002495, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.641625, Class: 1.000000, Obj: 0.390215, No Obj: 0.002689, Avg Recall: 0.775000, count: 120 -Region Avg IOU: 0.656717, Class: 1.000000, Obj: 0.439134, No Obj: 0.002962, Avg Recall: 0.779528, count: 127 -Region Avg IOU: 0.695757, Class: 1.000000, Obj: 0.444139, No Obj: 0.002747, Avg Recall: 0.850467, count: 107 -7056: 0.433409, 0.433550 avg, 0.001000 rate, 28.805580 seconds, 1806336 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.663307, Class: 1.000000, Obj: 0.482226, No Obj: 0.002845, Avg Recall: 0.807339, count: 109 -Region Avg IOU: 0.681995, Class: 1.000000, Obj: 0.468156, No Obj: 0.002356, Avg Recall: 0.845238, count: 84 -Region Avg IOU: 0.641943, Class: 1.000000, Obj: 0.393035, No Obj: 0.002710, Avg Recall: 0.798246, count: 114 -Region Avg IOU: 0.639233, Class: 1.000000, Obj: 0.433152, No Obj: 0.002556, Avg Recall: 0.785047, count: 107 -Region Avg IOU: 0.612518, Class: 1.000000, Obj: 0.434037, No Obj: 0.002691, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.681535, Class: 1.000000, Obj: 0.424031, No Obj: 0.002657, Avg Recall: 0.837607, count: 117 -Region Avg IOU: 0.690259, Class: 1.000000, Obj: 0.450300, No Obj: 0.002672, Avg Recall: 0.877358, count: 106 -Region Avg IOU: 0.678936, Class: 1.000000, Obj: 0.465315, No Obj: 0.002613, Avg Recall: 0.883929, count: 112 -7057: 0.459955, 0.436191 avg, 0.001000 rate, 28.045652 seconds, 1806592 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.650376, Class: 1.000000, Obj: 0.447161, No Obj: 0.002935, Avg Recall: 0.774194, count: 124 -Region Avg IOU: 0.711203, Class: 1.000000, Obj: 0.429595, No Obj: 0.002643, Avg Recall: 0.877551, count: 98 -Region Avg IOU: 0.688677, Class: 1.000000, Obj: 0.454502, No Obj: 0.002489, Avg Recall: 0.841584, count: 101 -Region Avg IOU: 0.680146, Class: 1.000000, Obj: 0.437761, No Obj: 0.002401, Avg Recall: 0.858586, count: 99 -Region Avg IOU: 0.695169, Class: 1.000000, Obj: 0.423629, No Obj: 0.002883, Avg Recall: 0.895238, count: 105 -Region Avg IOU: 0.645288, Class: 1.000000, Obj: 0.441564, No Obj: 0.002779, Avg Recall: 0.828829, count: 111 -Region Avg IOU: 0.607277, Class: 1.000000, Obj: 0.404990, No Obj: 0.002612, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.674348, Class: 1.000000, Obj: 0.505017, No Obj: 0.002794, Avg Recall: 0.830189, count: 106 -7058: 0.416553, 0.434227 avg, 0.001000 rate, 27.731483 seconds, 1806848 images -Loaded: 0.000059 seconds -Region Avg IOU: 0.678158, Class: 1.000000, Obj: 0.392382, No Obj: 0.002492, Avg Recall: 0.860000, count: 100 -Region Avg IOU: 0.668590, Class: 1.000000, Obj: 0.401498, No Obj: 0.002713, Avg Recall: 0.855769, count: 104 -Region Avg IOU: 0.642768, Class: 1.000000, Obj: 0.391107, No Obj: 0.002726, Avg Recall: 0.765152, count: 132 -Region Avg IOU: 0.626788, Class: 1.000000, Obj: 0.386283, No Obj: 0.002636, Avg Recall: 0.766129, count: 124 -Region Avg IOU: 0.626925, Class: 1.000000, Obj: 0.445819, No Obj: 0.002418, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.644518, Class: 1.000000, Obj: 0.456202, No Obj: 0.002225, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.646465, Class: 1.000000, Obj: 0.414411, No Obj: 0.002813, Avg Recall: 0.780488, count: 123 -Region Avg IOU: 0.652217, Class: 1.000000, Obj: 0.381843, No Obj: 0.002695, Avg Recall: 0.813559, count: 118 -7059: 0.434123, 0.434217 avg, 0.001000 rate, 29.154221 seconds, 1807104 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.643307, Class: 1.000000, Obj: 0.471940, No Obj: 0.002546, Avg Recall: 0.772358, count: 123 -Region Avg IOU: 0.646001, Class: 1.000000, Obj: 0.409499, No Obj: 0.002858, Avg Recall: 0.789474, count: 133 -Region Avg IOU: 0.659511, Class: 1.000000, Obj: 0.434132, No Obj: 0.002702, Avg Recall: 0.785124, count: 121 -Region Avg IOU: 0.602483, Class: 1.000000, Obj: 0.383358, No Obj: 0.002580, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.655310, Class: 1.000000, Obj: 0.423344, No Obj: 0.002693, Avg Recall: 0.815126, count: 119 -Region Avg IOU: 0.681691, Class: 1.000000, Obj: 0.420824, No Obj: 0.002427, Avg Recall: 0.822222, count: 90 -Region Avg IOU: 0.614507, Class: 1.000000, Obj: 0.426506, No Obj: 0.002268, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.685662, Class: 1.000000, Obj: 0.440354, No Obj: 0.002787, Avg Recall: 0.848214, count: 112 -7060: 0.447332, 0.435528 avg, 0.001000 rate, 29.451376 seconds, 1807360 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 13.799337 seconds -Region Avg IOU: 0.570925, Class: 1.000000, Obj: 0.360844, No Obj: 0.003727, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.512926, Class: 1.000000, Obj: 0.299301, No Obj: 0.002878, Avg Recall: 0.597938, count: 97 -Region Avg IOU: 0.591120, Class: 1.000000, Obj: 0.353359, No Obj: 0.003404, Avg Recall: 0.689320, count: 103 -Region Avg IOU: 0.534748, Class: 1.000000, Obj: 0.355789, No Obj: 0.003505, Avg Recall: 0.567308, count: 104 -Region Avg IOU: 0.518396, Class: 1.000000, Obj: 0.317091, No Obj: 0.003333, Avg Recall: 0.575221, count: 113 -Region Avg IOU: 0.573978, Class: 1.000000, Obj: 0.347697, No Obj: 0.003157, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.548655, Class: 1.000000, Obj: 0.341692, No Obj: 0.003649, Avg Recall: 0.623853, count: 109 -Region Avg IOU: 0.537003, Class: 1.000000, Obj: 0.349630, No Obj: 0.003248, Avg Recall: 0.617978, count: 89 -7061: 0.404964, 0.432472 avg, 0.001000 rate, 19.548492 seconds, 1807616 images -Loaded: 2.863048 seconds -Region Avg IOU: 0.570287, Class: 1.000000, Obj: 0.386057, No Obj: 0.003958, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.515067, Class: 1.000000, Obj: 0.309782, No Obj: 0.004200, Avg Recall: 0.602941, count: 136 -Region Avg IOU: 0.585914, Class: 1.000000, Obj: 0.398775, No Obj: 0.003659, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.535109, Class: 1.000000, Obj: 0.344424, No Obj: 0.003345, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.559026, Class: 1.000000, Obj: 0.381751, No Obj: 0.003426, Avg Recall: 0.687500, count: 80 -Region Avg IOU: 0.559350, Class: 1.000000, Obj: 0.315907, No Obj: 0.003000, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.585458, Class: 1.000000, Obj: 0.359508, No Obj: 0.003498, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.554615, Class: 1.000000, Obj: 0.341109, No Obj: 0.003074, Avg Recall: 0.679245, count: 106 -7062: 0.447949, 0.434019 avg, 0.001000 rate, 21.382439 seconds, 1807872 images -Loaded: 0.652013 seconds -Region Avg IOU: 0.519767, Class: 1.000000, Obj: 0.367023, No Obj: 0.003345, Avg Recall: 0.621622, count: 111 -Region Avg IOU: 0.613300, Class: 1.000000, Obj: 0.386698, No Obj: 0.004316, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.562155, Class: 1.000000, Obj: 0.363906, No Obj: 0.002982, Avg Recall: 0.698795, count: 83 -Region Avg IOU: 0.533975, Class: 1.000000, Obj: 0.351808, No Obj: 0.003970, Avg Recall: 0.614754, count: 122 -Region Avg IOU: 0.540529, Class: 1.000000, Obj: 0.348783, No Obj: 0.003612, Avg Recall: 0.596154, count: 104 -Region Avg IOU: 0.554945, Class: 1.000000, Obj: 0.408670, No Obj: 0.003760, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.557639, Class: 1.000000, Obj: 0.351629, No Obj: 0.003454, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.496860, Class: 1.000000, Obj: 0.349043, No Obj: 0.003675, Avg Recall: 0.552381, count: 105 -7063: 0.433296, 0.433947 avg, 0.001000 rate, 21.777006 seconds, 1808128 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.564525, Class: 1.000000, Obj: 0.393153, No Obj: 0.004136, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.511854, Class: 1.000000, Obj: 0.316779, No Obj: 0.003520, Avg Recall: 0.590551, count: 127 -Region Avg IOU: 0.531686, Class: 1.000000, Obj: 0.415960, No Obj: 0.003275, Avg Recall: 0.604651, count: 86 -Region Avg IOU: 0.588903, Class: 1.000000, Obj: 0.412306, No Obj: 0.003860, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.488485, Class: 1.000000, Obj: 0.372404, No Obj: 0.003145, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.536062, Class: 1.000000, Obj: 0.364668, No Obj: 0.004181, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.565659, Class: 1.000000, Obj: 0.429119, No Obj: 0.003185, Avg Recall: 0.666667, count: 84 -Region Avg IOU: 0.577056, Class: 1.000000, Obj: 0.361127, No Obj: 0.003863, Avg Recall: 0.650485, count: 103 -7064: 0.403437, 0.430896 avg, 0.001000 rate, 21.434059 seconds, 1808384 images -Loaded: 1.575771 seconds -Region Avg IOU: 0.512541, Class: 1.000000, Obj: 0.374757, No Obj: 0.003838, Avg Recall: 0.545455, count: 88 -Region Avg IOU: 0.531101, Class: 1.000000, Obj: 0.385488, No Obj: 0.003435, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.544535, Class: 1.000000, Obj: 0.358077, No Obj: 0.004623, Avg Recall: 0.571429, count: 133 -Region Avg IOU: 0.582387, Class: 1.000000, Obj: 0.427216, No Obj: 0.004383, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.542200, Class: 1.000000, Obj: 0.423288, No Obj: 0.003837, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.572695, Class: 1.000000, Obj: 0.383384, No Obj: 0.004080, Avg Recall: 0.655556, count: 90 -Region Avg IOU: 0.523679, Class: 1.000000, Obj: 0.348019, No Obj: 0.003733, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.534000, Class: 1.000000, Obj: 0.383602, No Obj: 0.004200, Avg Recall: 0.591304, count: 115 -7065: 0.453158, 0.433122 avg, 0.001000 rate, 20.433056 seconds, 1808640 images -Loaded: 1.135143 seconds -Region Avg IOU: 0.526385, Class: 1.000000, Obj: 0.378041, No Obj: 0.004995, Avg Recall: 0.638158, count: 152 -Region Avg IOU: 0.579302, Class: 1.000000, Obj: 0.441946, No Obj: 0.004226, Avg Recall: 0.683673, count: 98 -Region Avg IOU: 0.562035, Class: 1.000000, Obj: 0.413143, No Obj: 0.004328, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.561957, Class: 1.000000, Obj: 0.425725, No Obj: 0.004595, Avg Recall: 0.637255, count: 102 -Region Avg IOU: 0.498486, Class: 1.000000, Obj: 0.368188, No Obj: 0.003873, Avg Recall: 0.568182, count: 88 -Region Avg IOU: 0.544238, Class: 1.000000, Obj: 0.392177, No Obj: 0.005692, Avg Recall: 0.635220, count: 159 -Region Avg IOU: 0.563893, Class: 1.000000, Obj: 0.419667, No Obj: 0.004122, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.530456, Class: 1.000000, Obj: 0.447968, No Obj: 0.003609, Avg Recall: 0.595238, count: 84 -7066: 0.429007, 0.432711 avg, 0.001000 rate, 20.661167 seconds, 1808896 images -Loaded: 0.185445 seconds -Region Avg IOU: 0.547277, Class: 1.000000, Obj: 0.389494, No Obj: 0.004440, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.567565, Class: 1.000000, Obj: 0.457697, No Obj: 0.004467, Avg Recall: 0.660194, count: 103 -Region Avg IOU: 0.536112, Class: 1.000000, Obj: 0.399907, No Obj: 0.004698, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.560613, Class: 1.000000, Obj: 0.427123, No Obj: 0.004617, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.563108, Class: 1.000000, Obj: 0.464561, No Obj: 0.004754, Avg Recall: 0.706522, count: 92 -Region Avg IOU: 0.498300, Class: 1.000000, Obj: 0.423524, No Obj: 0.003863, Avg Recall: 0.578431, count: 102 -Region Avg IOU: 0.562685, Class: 1.000000, Obj: 0.426853, No Obj: 0.004638, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.575802, Class: 1.000000, Obj: 0.467424, No Obj: 0.004257, Avg Recall: 0.629214, count: 89 -7067: 0.368564, 0.426296 avg, 0.001000 rate, 21.294468 seconds, 1809152 images -Loaded: 1.520523 seconds -Region Avg IOU: 0.543092, Class: 1.000000, Obj: 0.424442, No Obj: 0.005013, Avg Recall: 0.605769, count: 104 -Region Avg IOU: 0.553520, Class: 1.000000, Obj: 0.427427, No Obj: 0.005133, Avg Recall: 0.584158, count: 101 -Region Avg IOU: 0.557530, Class: 1.000000, Obj: 0.429182, No Obj: 0.005164, Avg Recall: 0.637097, count: 124 -Region Avg IOU: 0.602275, Class: 1.000000, Obj: 0.454913, No Obj: 0.005075, Avg Recall: 0.694118, count: 85 -Region Avg IOU: 0.575031, Class: 1.000000, Obj: 0.456657, No Obj: 0.005040, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.472623, Class: 1.000000, Obj: 0.360735, No Obj: 0.004227, Avg Recall: 0.494845, count: 97 -Region Avg IOU: 0.590538, Class: 1.000000, Obj: 0.451331, No Obj: 0.005096, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.511273, Class: 1.000000, Obj: 0.415873, No Obj: 0.004970, Avg Recall: 0.531746, count: 126 -7068: 0.477543, 0.431421 avg, 0.001000 rate, 21.524599 seconds, 1809408 images -Loaded: 1.043871 seconds -Region Avg IOU: 0.560888, Class: 1.000000, Obj: 0.432033, No Obj: 0.004395, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.538493, Class: 1.000000, Obj: 0.448075, No Obj: 0.005580, Avg Recall: 0.564885, count: 131 -Region Avg IOU: 0.559569, Class: 1.000000, Obj: 0.414470, No Obj: 0.004695, Avg Recall: 0.697674, count: 129 -Region Avg IOU: 0.572076, Class: 1.000000, Obj: 0.417988, No Obj: 0.005218, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.596116, Class: 1.000000, Obj: 0.423441, No Obj: 0.004647, Avg Recall: 0.707865, count: 89 -Region Avg IOU: 0.520532, Class: 1.000000, Obj: 0.443301, No Obj: 0.005025, Avg Recall: 0.581197, count: 117 -Region Avg IOU: 0.544375, Class: 1.000000, Obj: 0.433531, No Obj: 0.004488, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.524632, Class: 1.000000, Obj: 0.431408, No Obj: 0.004558, Avg Recall: 0.550459, count: 109 -7069: 0.425767, 0.430855 avg, 0.001000 rate, 21.663424 seconds, 1809664 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.523134, Class: 1.000000, Obj: 0.424089, No Obj: 0.005147, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.546196, Class: 1.000000, Obj: 0.422482, No Obj: 0.005048, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.586676, Class: 1.000000, Obj: 0.467654, No Obj: 0.005304, Avg Recall: 0.672269, count: 119 -Region Avg IOU: 0.550609, Class: 1.000000, Obj: 0.462863, No Obj: 0.004369, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.522779, Class: 1.000000, Obj: 0.423999, No Obj: 0.004395, Avg Recall: 0.543689, count: 103 -Region Avg IOU: 0.531821, Class: 1.000000, Obj: 0.357619, No Obj: 0.004750, Avg Recall: 0.636364, count: 110 -Region Avg IOU: 0.621939, Class: 1.000000, Obj: 0.512244, No Obj: 0.003752, Avg Recall: 0.739726, count: 73 -Region Avg IOU: 0.523245, Class: 1.000000, Obj: 0.400407, No Obj: 0.004313, Avg Recall: 0.584158, count: 101 -7070: 0.379324, 0.425702 avg, 0.001000 rate, 20.239891 seconds, 1809920 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 20.822788 seconds -Region Avg IOU: 0.534827, Class: 1.000000, Obj: 0.364900, No Obj: 0.003953, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.520420, Class: 1.000000, Obj: 0.398713, No Obj: 0.005073, Avg Recall: 0.600000, count: 135 -Region Avg IOU: 0.479273, Class: 1.000000, Obj: 0.381659, No Obj: 0.004266, Avg Recall: 0.505050, count: 99 -Region Avg IOU: 0.506795, Class: 1.000000, Obj: 0.388528, No Obj: 0.003939, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.572223, Class: 1.000000, Obj: 0.451101, No Obj: 0.004921, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.546614, Class: 1.000000, Obj: 0.418639, No Obj: 0.004069, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.577436, Class: 1.000000, Obj: 0.427511, No Obj: 0.005776, Avg Recall: 0.700000, count: 120 -Region Avg IOU: 0.528997, Class: 1.000000, Obj: 0.377440, No Obj: 0.004267, Avg Recall: 0.632075, count: 106 -7071: 0.423461, 0.425478 avg, 0.001000 rate, 19.506821 seconds, 1810176 images -Loaded: 2.292138 seconds -Region Avg IOU: 0.574364, Class: 1.000000, Obj: 0.405398, No Obj: 0.004054, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.536039, Class: 1.000000, Obj: 0.356798, No Obj: 0.004493, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.554804, Class: 1.000000, Obj: 0.409872, No Obj: 0.004728, Avg Recall: 0.683333, count: 120 -Region Avg IOU: 0.524226, Class: 1.000000, Obj: 0.411726, No Obj: 0.004685, Avg Recall: 0.561983, count: 121 -Region Avg IOU: 0.519304, Class: 1.000000, Obj: 0.390203, No Obj: 0.004566, Avg Recall: 0.581633, count: 98 -Region Avg IOU: 0.453595, Class: 1.000000, Obj: 0.316989, No Obj: 0.003866, Avg Recall: 0.483871, count: 124 -Region Avg IOU: 0.492199, Class: 1.000000, Obj: 0.405707, No Obj: 0.004047, Avg Recall: 0.529412, count: 102 -Region Avg IOU: 0.513218, Class: 1.000000, Obj: 0.416917, No Obj: 0.003846, Avg Recall: 0.580645, count: 93 -7072: 0.462485, 0.429179 avg, 0.001000 rate, 21.468363 seconds, 1810432 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.512314, Class: 1.000000, Obj: 0.354174, No Obj: 0.004114, Avg Recall: 0.555556, count: 117 -Region Avg IOU: 0.570906, Class: 1.000000, Obj: 0.379575, No Obj: 0.004254, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.564064, Class: 1.000000, Obj: 0.391723, No Obj: 0.005562, Avg Recall: 0.632353, count: 136 -Region Avg IOU: 0.560144, Class: 1.000000, Obj: 0.412341, No Obj: 0.004610, Avg Recall: 0.653846, count: 130 -Region Avg IOU: 0.522623, Class: 1.000000, Obj: 0.349474, No Obj: 0.004284, Avg Recall: 0.626087, count: 115 -Region Avg IOU: 0.546762, Class: 1.000000, Obj: 0.406866, No Obj: 0.004041, Avg Recall: 0.589474, count: 95 -Region Avg IOU: 0.561653, Class: 1.000000, Obj: 0.381585, No Obj: 0.003879, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.471628, Class: 1.000000, Obj: 0.317978, No Obj: 0.003542, Avg Recall: 0.516667, count: 120 -7073: 0.510377, 0.437299 avg, 0.001000 rate, 20.764072 seconds, 1810688 images -Loaded: 0.970310 seconds -Region Avg IOU: 0.512772, Class: 1.000000, Obj: 0.385680, No Obj: 0.003865, Avg Recall: 0.509615, count: 104 -Region Avg IOU: 0.548127, Class: 1.000000, Obj: 0.344964, No Obj: 0.004266, Avg Recall: 0.656000, count: 125 -Region Avg IOU: 0.490095, Class: 1.000000, Obj: 0.357657, No Obj: 0.003913, Avg Recall: 0.478633, count: 117 -Region Avg IOU: 0.556671, Class: 1.000000, Obj: 0.389239, No Obj: 0.004140, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.547414, Class: 1.000000, Obj: 0.360360, No Obj: 0.004071, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.564806, Class: 1.000000, Obj: 0.376392, No Obj: 0.003943, Avg Recall: 0.663462, count: 104 -Region Avg IOU: 0.597087, Class: 1.000000, Obj: 0.399863, No Obj: 0.004967, Avg Recall: 0.714286, count: 126 -Region Avg IOU: 0.533410, Class: 1.000000, Obj: 0.369539, No Obj: 0.004630, Avg Recall: 0.637931, count: 116 -7074: 0.427787, 0.436347 avg, 0.001000 rate, 22.841702 seconds, 1810944 images -Loaded: 0.349196 seconds -Region Avg IOU: 0.474921, Class: 1.000000, Obj: 0.301965, No Obj: 0.004539, Avg Recall: 0.522876, count: 153 -Region Avg IOU: 0.572952, Class: 1.000000, Obj: 0.373742, No Obj: 0.003906, Avg Recall: 0.627451, count: 102 -Region Avg IOU: 0.545677, Class: 1.000000, Obj: 0.366495, No Obj: 0.003753, Avg Recall: 0.603604, count: 111 -Region Avg IOU: 0.588804, Class: 1.000000, Obj: 0.358973, No Obj: 0.003923, Avg Recall: 0.681035, count: 116 -Region Avg IOU: 0.569229, Class: 1.000000, Obj: 0.347371, No Obj: 0.003873, Avg Recall: 0.622449, count: 98 -Region Avg IOU: 0.537733, Class: 1.000000, Obj: 0.355494, No Obj: 0.003761, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.569318, Class: 1.000000, Obj: 0.371669, No Obj: 0.003933, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.486629, Class: 1.000000, Obj: 0.353105, No Obj: 0.003625, Avg Recall: 0.495413, count: 109 -7075: 0.435269, 0.436240 avg, 0.001000 rate, 22.354296 seconds, 1811200 images -Loaded: 0.026153 seconds -Region Avg IOU: 0.533077, Class: 1.000000, Obj: 0.353183, No Obj: 0.003158, Avg Recall: 0.619048, count: 84 -Region Avg IOU: 0.578907, Class: 1.000000, Obj: 0.366455, No Obj: 0.004143, Avg Recall: 0.677419, count: 124 -Region Avg IOU: 0.586758, Class: 1.000000, Obj: 0.350384, No Obj: 0.004119, Avg Recall: 0.661290, count: 124 -Region Avg IOU: 0.545942, Class: 1.000000, Obj: 0.389413, No Obj: 0.005038, Avg Recall: 0.647482, count: 139 -Region Avg IOU: 0.534910, Class: 1.000000, Obj: 0.366501, No Obj: 0.003797, Avg Recall: 0.575472, count: 106 -Region Avg IOU: 0.590921, Class: 1.000000, Obj: 0.425381, No Obj: 0.004256, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.517437, Class: 1.000000, Obj: 0.362480, No Obj: 0.004524, Avg Recall: 0.626984, count: 126 -Region Avg IOU: 0.512081, Class: 1.000000, Obj: 0.339440, No Obj: 0.003706, Avg Recall: 0.565217, count: 115 -7076: 0.444500, 0.437066 avg, 0.001000 rate, 19.270004 seconds, 1811456 images -Loaded: 2.584433 seconds -Region Avg IOU: 0.558070, Class: 1.000000, Obj: 0.325446, No Obj: 0.004014, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.565608, Class: 1.000000, Obj: 0.412124, No Obj: 0.003999, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.531027, Class: 1.000000, Obj: 0.351122, No Obj: 0.003490, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.520724, Class: 1.000000, Obj: 0.378953, No Obj: 0.004043, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.555259, Class: 1.000000, Obj: 0.374727, No Obj: 0.004499, Avg Recall: 0.588235, count: 119 -Region Avg IOU: 0.535614, Class: 1.000000, Obj: 0.389338, No Obj: 0.003807, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.534994, Class: 1.000000, Obj: 0.336733, No Obj: 0.004483, Avg Recall: 0.648000, count: 125 -Region Avg IOU: 0.573476, Class: 1.000000, Obj: 0.399009, No Obj: 0.003502, Avg Recall: 0.727273, count: 77 -7077: 0.461691, 0.439528 avg, 0.001000 rate, 22.879213 seconds, 1811712 images -Loaded: 0.566008 seconds -Region Avg IOU: 0.571859, Class: 1.000000, Obj: 0.392332, No Obj: 0.003684, Avg Recall: 0.648649, count: 74 -Region Avg IOU: 0.526829, Class: 1.000000, Obj: 0.334636, No Obj: 0.004108, Avg Recall: 0.603774, count: 106 -Region Avg IOU: 0.586622, Class: 1.000000, Obj: 0.374979, No Obj: 0.004276, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.563992, Class: 1.000000, Obj: 0.367627, No Obj: 0.004378, Avg Recall: 0.639344, count: 122 -Region Avg IOU: 0.531815, Class: 1.000000, Obj: 0.386819, No Obj: 0.003466, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.556910, Class: 1.000000, Obj: 0.375493, No Obj: 0.004475, Avg Recall: 0.653846, count: 130 -Region Avg IOU: 0.511495, Class: 1.000000, Obj: 0.335296, No Obj: 0.003189, Avg Recall: 0.555556, count: 90 -Region Avg IOU: 0.528907, Class: 1.000000, Obj: 0.354452, No Obj: 0.004496, Avg Recall: 0.591667, count: 120 -7078: 0.458426, 0.441418 avg, 0.001000 rate, 22.335836 seconds, 1811968 images -Loaded: 0.113383 seconds -Region Avg IOU: 0.537077, Class: 1.000000, Obj: 0.383590, No Obj: 0.004694, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.541072, Class: 1.000000, Obj: 0.372710, No Obj: 0.003504, Avg Recall: 0.617978, count: 89 -Region Avg IOU: 0.511289, Class: 1.000000, Obj: 0.370434, No Obj: 0.004157, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.587394, Class: 1.000000, Obj: 0.379442, No Obj: 0.004180, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.542712, Class: 1.000000, Obj: 0.396860, No Obj: 0.004086, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.544964, Class: 1.000000, Obj: 0.393913, No Obj: 0.003919, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.514432, Class: 1.000000, Obj: 0.358015, No Obj: 0.004629, Avg Recall: 0.578571, count: 140 -Region Avg IOU: 0.547989, Class: 1.000000, Obj: 0.383162, No Obj: 0.004363, Avg Recall: 0.638298, count: 94 -7079: 0.437035, 0.440980 avg, 0.001000 rate, 21.645382 seconds, 1812224 images -Loaded: 0.064561 seconds -Region Avg IOU: 0.514215, Class: 1.000000, Obj: 0.365732, No Obj: 0.003841, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.566163, Class: 1.000000, Obj: 0.425253, No Obj: 0.004430, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.555951, Class: 1.000000, Obj: 0.377480, No Obj: 0.003919, Avg Recall: 0.618182, count: 110 -Region Avg IOU: 0.580007, Class: 1.000000, Obj: 0.382975, No Obj: 0.004243, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.524268, Class: 1.000000, Obj: 0.394711, No Obj: 0.004754, Avg Recall: 0.583333, count: 132 -Region Avg IOU: 0.564662, Class: 1.000000, Obj: 0.418707, No Obj: 0.003598, Avg Recall: 0.630435, count: 92 -Region Avg IOU: 0.561282, Class: 1.000000, Obj: 0.392870, No Obj: 0.004319, Avg Recall: 0.686747, count: 83 -Region Avg IOU: 0.504782, Class: 1.000000, Obj: 0.382387, No Obj: 0.003440, Avg Recall: 0.526882, count: 93 -7080: 0.402181, 0.437100 avg, 0.001000 rate, 20.551785 seconds, 1812480 images -Resizing -448 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 0.000054 seconds -Region Avg IOU: 0.577403, Class: 1.000000, Obj: 0.389693, No Obj: 0.003904, Avg Recall: 0.652893, count: 121 -Region Avg IOU: 0.602212, Class: 1.000000, Obj: 0.425429, No Obj: 0.004649, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.517312, Class: 1.000000, Obj: 0.377075, No Obj: 0.004018, Avg Recall: 0.605042, count: 119 -Region Avg IOU: 0.603550, Class: 1.000000, Obj: 0.465327, No Obj: 0.003423, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.561303, Class: 1.000000, Obj: 0.396615, No Obj: 0.004232, Avg Recall: 0.654867, count: 113 -Region Avg IOU: 0.538497, Class: 1.000000, Obj: 0.388239, No Obj: 0.003870, Avg Recall: 0.616000, count: 125 -Region Avg IOU: 0.568451, Class: 1.000000, Obj: 0.425171, No Obj: 0.004004, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.564592, Class: 1.000000, Obj: 0.402169, No Obj: 0.004502, Avg Recall: 0.669643, count: 112 -7081: 0.441655, 0.437555 avg, 0.001000 rate, 22.400869 seconds, 1812736 images -Loaded: 0.555454 seconds -Region Avg IOU: 0.577597, Class: 1.000000, Obj: 0.416187, No Obj: 0.004777, Avg Recall: 0.648148, count: 108 -Region Avg IOU: 0.594956, Class: 1.000000, Obj: 0.425812, No Obj: 0.004602, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.528640, Class: 1.000000, Obj: 0.394351, No Obj: 0.005016, Avg Recall: 0.589552, count: 134 -Region Avg IOU: 0.546311, Class: 1.000000, Obj: 0.435865, No Obj: 0.004345, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.545983, Class: 1.000000, Obj: 0.401862, No Obj: 0.004503, Avg Recall: 0.663717, count: 113 -Region Avg IOU: 0.597214, Class: 1.000000, Obj: 0.467676, No Obj: 0.004471, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.566149, Class: 1.000000, Obj: 0.436577, No Obj: 0.004384, Avg Recall: 0.655738, count: 122 -Region Avg IOU: 0.572245, Class: 1.000000, Obj: 0.412658, No Obj: 0.004563, Avg Recall: 0.663717, count: 113 -7082: 0.416219, 0.435422 avg, 0.001000 rate, 22.086998 seconds, 1812992 images -Loaded: 0.327785 seconds -Region Avg IOU: 0.568137, Class: 1.000000, Obj: 0.433516, No Obj: 0.004202, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.524012, Class: 1.000000, Obj: 0.406513, No Obj: 0.004360, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.561385, Class: 1.000000, Obj: 0.438324, No Obj: 0.003856, Avg Recall: 0.614458, count: 83 -Region Avg IOU: 0.509590, Class: 1.000000, Obj: 0.412001, No Obj: 0.004425, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.565065, Class: 1.000000, Obj: 0.440313, No Obj: 0.003567, Avg Recall: 0.648352, count: 91 -Region Avg IOU: 0.550597, Class: 1.000000, Obj: 0.419259, No Obj: 0.004587, Avg Recall: 0.634328, count: 134 -Region Avg IOU: 0.575817, Class: 1.000000, Obj: 0.434189, No Obj: 0.004048, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.577037, Class: 1.000000, Obj: 0.431083, No Obj: 0.004343, Avg Recall: 0.699187, count: 123 -7083: 0.412385, 0.433118 avg, 0.001000 rate, 20.655212 seconds, 1813248 images -Loaded: 1.294386 seconds -Region Avg IOU: 0.544329, Class: 1.000000, Obj: 0.358454, No Obj: 0.003981, Avg Recall: 0.619835, count: 121 -Region Avg IOU: 0.522450, Class: 1.000000, Obj: 0.394507, No Obj: 0.003958, Avg Recall: 0.578125, count: 128 -Region Avg IOU: 0.564941, Class: 1.000000, Obj: 0.415681, No Obj: 0.004566, Avg Recall: 0.656000, count: 125 -Region Avg IOU: 0.590916, Class: 1.000000, Obj: 0.424793, No Obj: 0.004259, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.583256, Class: 1.000000, Obj: 0.427595, No Obj: 0.004282, Avg Recall: 0.672000, count: 125 -Region Avg IOU: 0.595621, Class: 1.000000, Obj: 0.427469, No Obj: 0.004566, Avg Recall: 0.680672, count: 119 -Region Avg IOU: 0.558558, Class: 1.000000, Obj: 0.427121, No Obj: 0.005150, Avg Recall: 0.636364, count: 143 -Region Avg IOU: 0.574611, Class: 1.000000, Obj: 0.444442, No Obj: 0.004018, Avg Recall: 0.709091, count: 110 -7084: 0.442775, 0.434084 avg, 0.001000 rate, 19.520042 seconds, 1813504 images -Loaded: 2.648360 seconds -Region Avg IOU: 0.585123, Class: 1.000000, Obj: 0.410285, No Obj: 0.004490, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.518893, Class: 1.000000, Obj: 0.424237, No Obj: 0.003724, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.512128, Class: 1.000000, Obj: 0.405849, No Obj: 0.003746, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.582069, Class: 1.000000, Obj: 0.440284, No Obj: 0.004211, Avg Recall: 0.651163, count: 86 -Region Avg IOU: 0.556108, Class: 1.000000, Obj: 0.384445, No Obj: 0.003819, Avg Recall: 0.641509, count: 106 -Region Avg IOU: 0.570114, Class: 1.000000, Obj: 0.397406, No Obj: 0.003849, Avg Recall: 0.700730, count: 137 -Region Avg IOU: 0.555452, Class: 1.000000, Obj: 0.390438, No Obj: 0.003637, Avg Recall: 0.615385, count: 91 -Region Avg IOU: 0.549316, Class: 1.000000, Obj: 0.413067, No Obj: 0.003596, Avg Recall: 0.617647, count: 102 -7085: 0.419339, 0.432609 avg, 0.001000 rate, 21.243948 seconds, 1813760 images -Loaded: 0.422945 seconds -Region Avg IOU: 0.536854, Class: 1.000000, Obj: 0.390743, No Obj: 0.003976, Avg Recall: 0.598131, count: 107 -Region Avg IOU: 0.572731, Class: 1.000000, Obj: 0.372949, No Obj: 0.004930, Avg Recall: 0.671141, count: 149 -Region Avg IOU: 0.569553, Class: 1.000000, Obj: 0.428590, No Obj: 0.004116, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.548707, Class: 1.000000, Obj: 0.404748, No Obj: 0.004255, Avg Recall: 0.630435, count: 92 -Region Avg IOU: 0.580759, Class: 1.000000, Obj: 0.436640, No Obj: 0.004350, Avg Recall: 0.650000, count: 120 -Region Avg IOU: 0.519616, Class: 1.000000, Obj: 0.389527, No Obj: 0.003535, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.556847, Class: 1.000000, Obj: 0.399394, No Obj: 0.003739, Avg Recall: 0.640777, count: 103 -Region Avg IOU: 0.528218, Class: 1.000000, Obj: 0.430827, No Obj: 0.004036, Avg Recall: 0.552381, count: 105 -7086: 0.402363, 0.429585 avg, 0.001000 rate, 23.790661 seconds, 1814016 images -Loaded: 0.004269 seconds -Region Avg IOU: 0.598519, Class: 1.000000, Obj: 0.434647, No Obj: 0.004638, Avg Recall: 0.669355, count: 124 -Region Avg IOU: 0.571700, Class: 1.000000, Obj: 0.375518, No Obj: 0.003953, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.557004, Class: 1.000000, Obj: 0.376057, No Obj: 0.003859, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.524747, Class: 1.000000, Obj: 0.355367, No Obj: 0.004064, Avg Recall: 0.584071, count: 113 -Region Avg IOU: 0.561205, Class: 1.000000, Obj: 0.425242, No Obj: 0.003925, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.606192, Class: 1.000000, Obj: 0.430282, No Obj: 0.003712, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.592979, Class: 1.000000, Obj: 0.375989, No Obj: 0.004297, Avg Recall: 0.689076, count: 119 -Region Avg IOU: 0.570636, Class: 1.000000, Obj: 0.412912, No Obj: 0.004131, Avg Recall: 0.649485, count: 97 -7087: 0.448285, 0.431455 avg, 0.001000 rate, 23.101860 seconds, 1814272 images -Loaded: 0.298314 seconds -Region Avg IOU: 0.566926, Class: 1.000000, Obj: 0.399516, No Obj: 0.003521, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.575397, Class: 1.000000, Obj: 0.410386, No Obj: 0.003323, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.570848, Class: 1.000000, Obj: 0.370472, No Obj: 0.003993, Avg Recall: 0.689189, count: 148 -Region Avg IOU: 0.617392, Class: 1.000000, Obj: 0.433939, No Obj: 0.004166, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.525890, Class: 1.000000, Obj: 0.298986, No Obj: 0.004287, Avg Recall: 0.625000, count: 144 -Region Avg IOU: 0.552049, Class: 1.000000, Obj: 0.408838, No Obj: 0.003917, Avg Recall: 0.650794, count: 126 -Region Avg IOU: 0.536240, Class: 1.000000, Obj: 0.358462, No Obj: 0.003828, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.545360, Class: 1.000000, Obj: 0.397473, No Obj: 0.004077, Avg Recall: 0.632479, count: 117 -7088: 0.430302, 0.431339 avg, 0.001000 rate, 22.305367 seconds, 1814528 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.514417, Class: 1.000000, Obj: 0.338148, No Obj: 0.004220, Avg Recall: 0.568345, count: 139 -Region Avg IOU: 0.549267, Class: 1.000000, Obj: 0.418623, No Obj: 0.002998, Avg Recall: 0.600000, count: 85 -Region Avg IOU: 0.592507, Class: 1.000000, Obj: 0.404203, No Obj: 0.004052, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.582050, Class: 1.000000, Obj: 0.388073, No Obj: 0.003872, Avg Recall: 0.700000, count: 100 -Region Avg IOU: 0.534261, Class: 1.000000, Obj: 0.376143, No Obj: 0.003553, Avg Recall: 0.535354, count: 99 -Region Avg IOU: 0.619770, Class: 1.000000, Obj: 0.375253, No Obj: 0.004820, Avg Recall: 0.782609, count: 115 -Region Avg IOU: 0.599115, Class: 1.000000, Obj: 0.437922, No Obj: 0.003724, Avg Recall: 0.670732, count: 82 -Region Avg IOU: 0.593514, Class: 1.000000, Obj: 0.405920, No Obj: 0.004104, Avg Recall: 0.676190, count: 105 -7089: 0.426946, 0.430900 avg, 0.001000 rate, 22.462704 seconds, 1814784 images -Loaded: 0.219329 seconds -Region Avg IOU: 0.593557, Class: 1.000000, Obj: 0.428109, No Obj: 0.003171, Avg Recall: 0.705128, count: 78 -Region Avg IOU: 0.567687, Class: 1.000000, Obj: 0.369714, No Obj: 0.003832, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.581308, Class: 1.000000, Obj: 0.398574, No Obj: 0.003708, Avg Recall: 0.663866, count: 119 -Region Avg IOU: 0.567491, Class: 1.000000, Obj: 0.407027, No Obj: 0.004537, Avg Recall: 0.635714, count: 140 -Region Avg IOU: 0.574355, Class: 1.000000, Obj: 0.384536, No Obj: 0.003480, Avg Recall: 0.647059, count: 85 -Region Avg IOU: 0.618295, Class: 1.000000, Obj: 0.416462, No Obj: 0.003817, Avg Recall: 0.728261, count: 92 -Region Avg IOU: 0.559543, Class: 1.000000, Obj: 0.330178, No Obj: 0.003262, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.567309, Class: 1.000000, Obj: 0.371623, No Obj: 0.003681, Avg Recall: 0.660870, count: 115 -7090: 0.396231, 0.427433 avg, 0.001000 rate, 23.191223 seconds, 1815040 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 14.682602 seconds -Region Avg IOU: 0.478430, Class: 1.000000, Obj: 0.340803, No Obj: 0.004217, Avg Recall: 0.495413, count: 109 -Region Avg IOU: 0.538834, Class: 1.000000, Obj: 0.327924, No Obj: 0.004041, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.515803, Class: 1.000000, Obj: 0.351882, No Obj: 0.004600, Avg Recall: 0.563830, count: 94 -Region Avg IOU: 0.548211, Class: 1.000000, Obj: 0.369188, No Obj: 0.004709, Avg Recall: 0.620370, count: 108 -Region Avg IOU: 0.470859, Class: 1.000000, Obj: 0.343498, No Obj: 0.004792, Avg Recall: 0.474576, count: 118 -Region Avg IOU: 0.470923, Class: 1.000000, Obj: 0.305179, No Obj: 0.004167, Avg Recall: 0.527273, count: 110 -Region Avg IOU: 0.514719, Class: 1.000000, Obj: 0.269028, No Obj: 0.004130, Avg Recall: 0.562500, count: 112 -Region Avg IOU: 0.503777, Class: 1.000000, Obj: 0.320892, No Obj: 0.004263, Avg Recall: 0.574468, count: 94 -7091: 0.399182, 0.424608 avg, 0.001000 rate, 13.469261 seconds, 1815296 images -Loaded: 8.602345 seconds -Region Avg IOU: 0.478190, Class: 1.000000, Obj: 0.307655, No Obj: 0.003840, Avg Recall: 0.536082, count: 97 -Region Avg IOU: 0.503611, Class: 1.000000, Obj: 0.354810, No Obj: 0.003507, Avg Recall: 0.573171, count: 82 -Region Avg IOU: 0.484141, Class: 1.000000, Obj: 0.328937, No Obj: 0.004089, Avg Recall: 0.555556, count: 99 -Region Avg IOU: 0.488998, Class: 1.000000, Obj: 0.312552, No Obj: 0.004260, Avg Recall: 0.504348, count: 115 -Region Avg IOU: 0.504360, Class: 1.000000, Obj: 0.322454, No Obj: 0.004271, Avg Recall: 0.515152, count: 99 -Region Avg IOU: 0.476780, Class: 1.000000, Obj: 0.309032, No Obj: 0.004510, Avg Recall: 0.525424, count: 118 -Region Avg IOU: 0.512740, Class: 1.000000, Obj: 0.367868, No Obj: 0.004577, Avg Recall: 0.580645, count: 93 -Region Avg IOU: 0.533865, Class: 1.000000, Obj: 0.312810, No Obj: 0.004069, Avg Recall: 0.617021, count: 94 -7092: 0.477793, 0.429926 avg, 0.001000 rate, 14.703919 seconds, 1815552 images -Loaded: 6.089716 seconds -Region Avg IOU: 0.502345, Class: 1.000000, Obj: 0.324970, No Obj: 0.004328, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.511012, Class: 1.000000, Obj: 0.321083, No Obj: 0.004224, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.506328, Class: 1.000000, Obj: 0.355016, No Obj: 0.004594, Avg Recall: 0.562500, count: 96 -Region Avg IOU: 0.533221, Class: 1.000000, Obj: 0.334402, No Obj: 0.004998, Avg Recall: 0.619565, count: 92 -Region Avg IOU: 0.493304, Class: 1.000000, Obj: 0.349819, No Obj: 0.005214, Avg Recall: 0.536585, count: 123 -Region Avg IOU: 0.498246, Class: 1.000000, Obj: 0.331363, No Obj: 0.004599, Avg Recall: 0.584071, count: 113 -Region Avg IOU: 0.524399, Class: 1.000000, Obj: 0.372747, No Obj: 0.004193, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.465266, Class: 1.000000, Obj: 0.297339, No Obj: 0.005061, Avg Recall: 0.458904, count: 146 -7093: 0.484673, 0.435401 avg, 0.001000 rate, 14.499437 seconds, 1815808 images -Loaded: 4.775549 seconds -Region Avg IOU: 0.534749, Class: 1.000000, Obj: 0.395718, No Obj: 0.004642, Avg Recall: 0.570093, count: 107 -Region Avg IOU: 0.511243, Class: 1.000000, Obj: 0.329686, No Obj: 0.004202, Avg Recall: 0.524390, count: 82 -Region Avg IOU: 0.485403, Class: 1.000000, Obj: 0.344875, No Obj: 0.004225, Avg Recall: 0.540000, count: 100 -Region Avg IOU: 0.472282, Class: 1.000000, Obj: 0.325498, No Obj: 0.004270, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.529938, Class: 1.000000, Obj: 0.363406, No Obj: 0.005852, Avg Recall: 0.648438, count: 128 -Region Avg IOU: 0.488882, Class: 1.000000, Obj: 0.354694, No Obj: 0.004034, Avg Recall: 0.546392, count: 97 -Region Avg IOU: 0.517024, Class: 1.000000, Obj: 0.382926, No Obj: 0.004765, Avg Recall: 0.565574, count: 122 -Region Avg IOU: 0.473948, Class: 1.000000, Obj: 0.304340, No Obj: 0.003901, Avg Recall: 0.505155, count: 97 -7094: 0.434464, 0.435307 avg, 0.001000 rate, 17.754234 seconds, 1816064 images -Loaded: 3.127263 seconds -Region Avg IOU: 0.490458, Class: 1.000000, Obj: 0.342580, No Obj: 0.004506, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.549958, Class: 1.000000, Obj: 0.391469, No Obj: 0.005956, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.479625, Class: 1.000000, Obj: 0.342880, No Obj: 0.005562, Avg Recall: 0.549618, count: 131 -Region Avg IOU: 0.573260, Class: 1.000000, Obj: 0.398860, No Obj: 0.004583, Avg Recall: 0.670588, count: 85 -Region Avg IOU: 0.546323, Class: 1.000000, Obj: 0.380889, No Obj: 0.004619, Avg Recall: 0.659091, count: 88 -Region Avg IOU: 0.428642, Class: 1.000000, Obj: 0.348614, No Obj: 0.004258, Avg Recall: 0.447059, count: 85 -Region Avg IOU: 0.479493, Class: 1.000000, Obj: 0.358028, No Obj: 0.004687, Avg Recall: 0.516393, count: 122 -Region Avg IOU: 0.493512, Class: 1.000000, Obj: 0.324001, No Obj: 0.004236, Avg Recall: 0.612613, count: 111 -7095: 0.398453, 0.431622 avg, 0.001000 rate, 17.783060 seconds, 1816320 images -Loaded: 1.464918 seconds -Region Avg IOU: 0.509526, Class: 1.000000, Obj: 0.349333, No Obj: 0.004676, Avg Recall: 0.547368, count: 95 -Region Avg IOU: 0.469437, Class: 1.000000, Obj: 0.330942, No Obj: 0.004304, Avg Recall: 0.538462, count: 91 -Region Avg IOU: 0.442527, Class: 1.000000, Obj: 0.353793, No Obj: 0.004628, Avg Recall: 0.436893, count: 103 -Region Avg IOU: 0.483811, Class: 1.000000, Obj: 0.362651, No Obj: 0.005080, Avg Recall: 0.532110, count: 109 -Region Avg IOU: 0.486663, Class: 1.000000, Obj: 0.350883, No Obj: 0.004705, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.493603, Class: 1.000000, Obj: 0.371317, No Obj: 0.004635, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.450186, Class: 1.000000, Obj: 0.282840, No Obj: 0.004618, Avg Recall: 0.500000, count: 112 -Region Avg IOU: 0.510212, Class: 1.000000, Obj: 0.405488, No Obj: 0.005610, Avg Recall: 0.601562, count: 128 -7096: 0.415303, 0.429990 avg, 0.001000 rate, 17.629456 seconds, 1816576 images -Loaded: 2.124153 seconds -Region Avg IOU: 0.484420, Class: 1.000000, Obj: 0.345870, No Obj: 0.005784, Avg Recall: 0.546296, count: 108 -Region Avg IOU: 0.470825, Class: 1.000000, Obj: 0.362728, No Obj: 0.006109, Avg Recall: 0.511278, count: 133 -Region Avg IOU: 0.529275, Class: 1.000000, Obj: 0.473611, No Obj: 0.004529, Avg Recall: 0.605634, count: 71 -Region Avg IOU: 0.442695, Class: 1.000000, Obj: 0.321398, No Obj: 0.005369, Avg Recall: 0.452055, count: 146 -Region Avg IOU: 0.422676, Class: 1.000000, Obj: 0.271990, No Obj: 0.003923, Avg Recall: 0.460784, count: 102 -Region Avg IOU: 0.504322, Class: 1.000000, Obj: 0.397141, No Obj: 0.004521, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.505006, Class: 1.000000, Obj: 0.399406, No Obj: 0.006035, Avg Recall: 0.546154, count: 130 -Region Avg IOU: 0.513874, Class: 1.000000, Obj: 0.383021, No Obj: 0.005256, Avg Recall: 0.556701, count: 97 -7097: 0.458044, 0.432795 avg, 0.001000 rate, 18.670849 seconds, 1816832 images -Loaded: 1.703402 seconds -Region Avg IOU: 0.479184, Class: 1.000000, Obj: 0.353884, No Obj: 0.004644, Avg Recall: 0.511111, count: 90 -Region Avg IOU: 0.438566, Class: 1.000000, Obj: 0.322571, No Obj: 0.005159, Avg Recall: 0.491525, count: 118 -Region Avg IOU: 0.514120, Class: 1.000000, Obj: 0.406524, No Obj: 0.004576, Avg Recall: 0.519481, count: 77 -Region Avg IOU: 0.477412, Class: 1.000000, Obj: 0.392291, No Obj: 0.004742, Avg Recall: 0.445783, count: 83 -Region Avg IOU: 0.473930, Class: 1.000000, Obj: 0.345425, No Obj: 0.004650, Avg Recall: 0.485714, count: 105 -Region Avg IOU: 0.459183, Class: 1.000000, Obj: 0.338479, No Obj: 0.005158, Avg Recall: 0.509259, count: 108 -Region Avg IOU: 0.481233, Class: 1.000000, Obj: 0.385764, No Obj: 0.004664, Avg Recall: 0.505155, count: 97 -Region Avg IOU: 0.519363, Class: 1.000000, Obj: 0.374368, No Obj: 0.004779, Avg Recall: 0.617021, count: 94 -7098: 0.399276, 0.429444 avg, 0.001000 rate, 17.124245 seconds, 1817088 images -Loaded: 4.386157 seconds -Region Avg IOU: 0.517090, Class: 1.000000, Obj: 0.400107, No Obj: 0.005687, Avg Recall: 0.566038, count: 106 -Region Avg IOU: 0.508177, Class: 1.000000, Obj: 0.399699, No Obj: 0.004916, Avg Recall: 0.554217, count: 83 -Region Avg IOU: 0.501393, Class: 1.000000, Obj: 0.420161, No Obj: 0.005501, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.528325, Class: 1.000000, Obj: 0.411538, No Obj: 0.005060, Avg Recall: 0.611650, count: 103 -Region Avg IOU: 0.452067, Class: 1.000000, Obj: 0.365021, No Obj: 0.004173, Avg Recall: 0.476635, count: 107 -Region Avg IOU: 0.512536, Class: 1.000000, Obj: 0.394823, No Obj: 0.004952, Avg Recall: 0.604651, count: 86 -Region Avg IOU: 0.490012, Class: 1.000000, Obj: 0.362034, No Obj: 0.005836, Avg Recall: 0.515873, count: 126 -Region Avg IOU: 0.437632, Class: 1.000000, Obj: 0.324430, No Obj: 0.005343, Avg Recall: 0.443609, count: 133 -7099: 0.417243, 0.428223 avg, 0.001000 rate, 14.686530 seconds, 1817344 images -Loaded: 5.001938 seconds -Region Avg IOU: 0.448979, Class: 1.000000, Obj: 0.368320, No Obj: 0.004881, Avg Recall: 0.477876, count: 113 -Region Avg IOU: 0.494734, Class: 1.000000, Obj: 0.394543, No Obj: 0.005023, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.527183, Class: 1.000000, Obj: 0.394655, No Obj: 0.005284, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.435831, Class: 1.000000, Obj: 0.330788, No Obj: 0.003946, Avg Recall: 0.460784, count: 102 -Region Avg IOU: 0.531915, Class: 1.000000, Obj: 0.403265, No Obj: 0.004102, Avg Recall: 0.591549, count: 71 -Region Avg IOU: 0.492416, Class: 1.000000, Obj: 0.370766, No Obj: 0.005341, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.502911, Class: 1.000000, Obj: 0.343370, No Obj: 0.004693, Avg Recall: 0.543860, count: 114 -Region Avg IOU: 0.461206, Class: 1.000000, Obj: 0.386642, No Obj: 0.005721, Avg Recall: 0.510791, count: 139 -7100: 0.482872, 0.433688 avg, 0.001000 rate, 15.394170 seconds, 1817600 images -Resizing -352 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.262079 seconds -Region Avg IOU: 0.511226, Class: 1.000000, Obj: 0.412376, No Obj: 0.005418, Avg Recall: 0.559140, count: 93 -Region Avg IOU: 0.519533, Class: 1.000000, Obj: 0.394466, No Obj: 0.004655, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.490565, Class: 1.000000, Obj: 0.426071, No Obj: 0.005544, Avg Recall: 0.500000, count: 100 -Region Avg IOU: 0.515001, Class: 1.000000, Obj: 0.389018, No Obj: 0.004712, Avg Recall: 0.573034, count: 89 -Region Avg IOU: 0.500729, Class: 1.000000, Obj: 0.356501, No Obj: 0.004606, Avg Recall: 0.564706, count: 85 -Region Avg IOU: 0.464779, Class: 1.000000, Obj: 0.376653, No Obj: 0.005171, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.524013, Class: 1.000000, Obj: 0.404676, No Obj: 0.005177, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.503859, Class: 1.000000, Obj: 0.416006, No Obj: 0.004247, Avg Recall: 0.516484, count: 91 -7101: 0.394396, 0.429759 avg, 0.001000 rate, 18.253828 seconds, 1817856 images -Loaded: 2.479593 seconds -Region Avg IOU: 0.476158, Class: 1.000000, Obj: 0.372954, No Obj: 0.004663, Avg Recall: 0.549020, count: 102 -Region Avg IOU: 0.499374, Class: 1.000000, Obj: 0.395637, No Obj: 0.004717, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.533973, Class: 1.000000, Obj: 0.376388, No Obj: 0.004965, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.469775, Class: 1.000000, Obj: 0.321012, No Obj: 0.005196, Avg Recall: 0.509259, count: 108 -Region Avg IOU: 0.430108, Class: 1.000000, Obj: 0.328662, No Obj: 0.005234, Avg Recall: 0.429752, count: 121 -Region Avg IOU: 0.486085, Class: 1.000000, Obj: 0.375964, No Obj: 0.004161, Avg Recall: 0.500000, count: 98 -Region Avg IOU: 0.484168, Class: 1.000000, Obj: 0.326047, No Obj: 0.005334, Avg Recall: 0.531532, count: 111 -Region Avg IOU: 0.502802, Class: 1.000000, Obj: 0.326234, No Obj: 0.005886, Avg Recall: 0.514085, count: 142 -7102: 0.483911, 0.435174 avg, 0.001000 rate, 14.893896 seconds, 1818112 images -Loaded: 6.017164 seconds -Region Avg IOU: 0.406228, Class: 1.000000, Obj: 0.314181, No Obj: 0.004217, Avg Recall: 0.420635, count: 126 -Region Avg IOU: 0.472285, Class: 1.000000, Obj: 0.341348, No Obj: 0.004579, Avg Recall: 0.524590, count: 122 -Region Avg IOU: 0.486887, Class: 1.000000, Obj: 0.354088, No Obj: 0.005187, Avg Recall: 0.559322, count: 118 -Region Avg IOU: 0.461598, Class: 1.000000, Obj: 0.317821, No Obj: 0.003842, Avg Recall: 0.486486, count: 111 -Region Avg IOU: 0.495401, Class: 1.000000, Obj: 0.342290, No Obj: 0.005245, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.468659, Class: 1.000000, Obj: 0.358958, No Obj: 0.004184, Avg Recall: 0.480769, count: 104 -Region Avg IOU: 0.507063, Class: 1.000000, Obj: 0.356673, No Obj: 0.005019, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.448162, Class: 1.000000, Obj: 0.323289, No Obj: 0.004196, Avg Recall: 0.485437, count: 103 -7103: 0.440385, 0.435695 avg, 0.001000 rate, 14.199788 seconds, 1818368 images -Loaded: 6.625386 seconds -Region Avg IOU: 0.517465, Class: 1.000000, Obj: 0.405231, No Obj: 0.004947, Avg Recall: 0.565657, count: 99 -Region Avg IOU: 0.502947, Class: 1.000000, Obj: 0.341026, No Obj: 0.004597, Avg Recall: 0.539216, count: 102 -Region Avg IOU: 0.534451, Class: 1.000000, Obj: 0.358984, No Obj: 0.004495, Avg Recall: 0.611111, count: 108 -Region Avg IOU: 0.517113, Class: 1.000000, Obj: 0.362145, No Obj: 0.004764, Avg Recall: 0.583333, count: 108 -Region Avg IOU: 0.518018, Class: 1.000000, Obj: 0.379043, No Obj: 0.004768, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.505416, Class: 1.000000, Obj: 0.334682, No Obj: 0.004227, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.499456, Class: 1.000000, Obj: 0.338456, No Obj: 0.005096, Avg Recall: 0.563492, count: 126 -Region Avg IOU: 0.538485, Class: 1.000000, Obj: 0.376381, No Obj: 0.004454, Avg Recall: 0.584158, count: 101 -7104: 0.438874, 0.436013 avg, 0.001000 rate, 15.596585 seconds, 1818624 images -Loaded: 4.797016 seconds -Region Avg IOU: 0.498654, Class: 1.000000, Obj: 0.368505, No Obj: 0.004250, Avg Recall: 0.557895, count: 95 -Region Avg IOU: 0.478002, Class: 1.000000, Obj: 0.302638, No Obj: 0.005379, Avg Recall: 0.529412, count: 136 -Region Avg IOU: 0.486764, Class: 1.000000, Obj: 0.350352, No Obj: 0.004540, Avg Recall: 0.550459, count: 109 -Region Avg IOU: 0.472290, Class: 1.000000, Obj: 0.354915, No Obj: 0.004349, Avg Recall: 0.518868, count: 106 -Region Avg IOU: 0.491729, Class: 1.000000, Obj: 0.320076, No Obj: 0.003824, Avg Recall: 0.524390, count: 82 -Region Avg IOU: 0.488115, Class: 1.000000, Obj: 0.307495, No Obj: 0.004729, Avg Recall: 0.500000, count: 104 -Region Avg IOU: 0.454609, Class: 1.000000, Obj: 0.350200, No Obj: 0.004450, Avg Recall: 0.466667, count: 90 -Region Avg IOU: 0.477074, Class: 1.000000, Obj: 0.339049, No Obj: 0.004615, Avg Recall: 0.512000, count: 125 -7105: 0.417835, 0.434195 avg, 0.001000 rate, 15.531610 seconds, 1818880 images -Loaded: 5.820908 seconds -Region Avg IOU: 0.463322, Class: 1.000000, Obj: 0.311984, No Obj: 0.003533, Avg Recall: 0.500000, count: 82 -Region Avg IOU: 0.460550, Class: 1.000000, Obj: 0.349323, No Obj: 0.004379, Avg Recall: 0.515152, count: 99 -Region Avg IOU: 0.465140, Class: 1.000000, Obj: 0.347554, No Obj: 0.004945, Avg Recall: 0.451613, count: 124 -Region Avg IOU: 0.462055, Class: 1.000000, Obj: 0.311702, No Obj: 0.004468, Avg Recall: 0.526882, count: 93 -Region Avg IOU: 0.528643, Class: 1.000000, Obj: 0.367903, No Obj: 0.005094, Avg Recall: 0.587629, count: 97 -Region Avg IOU: 0.462597, Class: 1.000000, Obj: 0.335262, No Obj: 0.005181, Avg Recall: 0.495935, count: 123 -Region Avg IOU: 0.459212, Class: 1.000000, Obj: 0.308035, No Obj: 0.004300, Avg Recall: 0.525000, count: 120 -Region Avg IOU: 0.533300, Class: 1.000000, Obj: 0.369003, No Obj: 0.004512, Avg Recall: 0.601942, count: 103 -7106: 0.452883, 0.436064 avg, 0.001000 rate, 17.171015 seconds, 1819136 images -Loaded: 3.031703 seconds -Region Avg IOU: 0.483497, Class: 1.000000, Obj: 0.331747, No Obj: 0.004261, Avg Recall: 0.541284, count: 109 -Region Avg IOU: 0.542443, Class: 1.000000, Obj: 0.356508, No Obj: 0.004884, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.491665, Class: 1.000000, Obj: 0.309704, No Obj: 0.004384, Avg Recall: 0.549020, count: 102 -Region Avg IOU: 0.482367, Class: 1.000000, Obj: 0.347584, No Obj: 0.004681, Avg Recall: 0.544715, count: 123 -Region Avg IOU: 0.471461, Class: 1.000000, Obj: 0.350287, No Obj: 0.003856, Avg Recall: 0.495413, count: 109 -Region Avg IOU: 0.486117, Class: 1.000000, Obj: 0.356447, No Obj: 0.004704, Avg Recall: 0.495575, count: 113 -Region Avg IOU: 0.497817, Class: 1.000000, Obj: 0.336531, No Obj: 0.005445, Avg Recall: 0.543307, count: 127 -Region Avg IOU: 0.497012, Class: 1.000000, Obj: 0.337401, No Obj: 0.004307, Avg Recall: 0.549451, count: 91 -7107: 0.443389, 0.436797 avg, 0.001000 rate, 20.046911 seconds, 1819392 images -Loaded: 0.508128 seconds -Region Avg IOU: 0.516929, Class: 1.000000, Obj: 0.358196, No Obj: 0.005152, Avg Recall: 0.595041, count: 121 -Region Avg IOU: 0.522103, Class: 1.000000, Obj: 0.379932, No Obj: 0.005444, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.543980, Class: 1.000000, Obj: 0.359517, No Obj: 0.004748, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.486412, Class: 1.000000, Obj: 0.352122, No Obj: 0.005172, Avg Recall: 0.550000, count: 120 -Region Avg IOU: 0.436927, Class: 1.000000, Obj: 0.321153, No Obj: 0.003774, Avg Recall: 0.382979, count: 94 -Region Avg IOU: 0.483774, Class: 1.000000, Obj: 0.361512, No Obj: 0.004225, Avg Recall: 0.542857, count: 105 -Region Avg IOU: 0.487562, Class: 1.000000, Obj: 0.338811, No Obj: 0.004394, Avg Recall: 0.536082, count: 97 -Region Avg IOU: 0.575017, Class: 1.000000, Obj: 0.362341, No Obj: 0.004989, Avg Recall: 0.679612, count: 103 -7108: 0.479439, 0.441061 avg, 0.001000 rate, 19.138332 seconds, 1819648 images -Loaded: 1.656100 seconds -Region Avg IOU: 0.525309, Class: 1.000000, Obj: 0.393021, No Obj: 0.004876, Avg Recall: 0.590909, count: 88 -Region Avg IOU: 0.460216, Class: 1.000000, Obj: 0.384283, No Obj: 0.004380, Avg Recall: 0.465116, count: 86 -Region Avg IOU: 0.475712, Class: 1.000000, Obj: 0.366742, No Obj: 0.004774, Avg Recall: 0.526316, count: 114 -Region Avg IOU: 0.527844, Class: 1.000000, Obj: 0.352717, No Obj: 0.003855, Avg Recall: 0.588235, count: 85 -Region Avg IOU: 0.453875, Class: 1.000000, Obj: 0.322215, No Obj: 0.004208, Avg Recall: 0.455446, count: 101 -Region Avg IOU: 0.538764, Class: 1.000000, Obj: 0.415930, No Obj: 0.004434, Avg Recall: 0.564706, count: 85 -Region Avg IOU: 0.490908, Class: 1.000000, Obj: 0.318094, No Obj: 0.003984, Avg Recall: 0.567010, count: 97 -Region Avg IOU: 0.482713, Class: 1.000000, Obj: 0.333195, No Obj: 0.005611, Avg Recall: 0.506944, count: 144 -7109: 0.438413, 0.440796 avg, 0.001000 rate, 18.192305 seconds, 1819904 images -Loaded: 2.718048 seconds -Region Avg IOU: 0.532674, Class: 1.000000, Obj: 0.385839, No Obj: 0.004103, Avg Recall: 0.595506, count: 89 -Region Avg IOU: 0.481454, Class: 1.000000, Obj: 0.386365, No Obj: 0.005351, Avg Recall: 0.508197, count: 122 -Region Avg IOU: 0.460106, Class: 1.000000, Obj: 0.378898, No Obj: 0.004581, Avg Recall: 0.545455, count: 77 -Region Avg IOU: 0.530059, Class: 1.000000, Obj: 0.352853, No Obj: 0.004276, Avg Recall: 0.574468, count: 94 -Region Avg IOU: 0.478224, Class: 1.000000, Obj: 0.354925, No Obj: 0.003861, Avg Recall: 0.540230, count: 87 -Region Avg IOU: 0.532856, Class: 1.000000, Obj: 0.383625, No Obj: 0.004874, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.547651, Class: 1.000000, Obj: 0.416665, No Obj: 0.005670, Avg Recall: 0.658537, count: 123 -Region Avg IOU: 0.519108, Class: 1.000000, Obj: 0.377396, No Obj: 0.004737, Avg Recall: 0.598131, count: 107 -7110: 0.412707, 0.437987 avg, 0.001000 rate, 15.897110 seconds, 1820160 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 2.732449 seconds -Region Avg IOU: 0.551515, Class: 1.000000, Obj: 0.406509, No Obj: 0.005193, Avg Recall: 0.609023, count: 133 -Region Avg IOU: 0.576981, Class: 1.000000, Obj: 0.396711, No Obj: 0.004937, Avg Recall: 0.666667, count: 123 -Region Avg IOU: 0.599056, Class: 1.000000, Obj: 0.453000, No Obj: 0.004426, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.553304, Class: 1.000000, Obj: 0.443979, No Obj: 0.004641, Avg Recall: 0.616667, count: 120 -Region Avg IOU: 0.507583, Class: 1.000000, Obj: 0.370813, No Obj: 0.004128, Avg Recall: 0.526316, count: 114 -Region Avg IOU: 0.481288, Class: 1.000000, Obj: 0.366406, No Obj: 0.004895, Avg Recall: 0.521368, count: 117 -Region Avg IOU: 0.542042, Class: 1.000000, Obj: 0.405134, No Obj: 0.005059, Avg Recall: 0.595238, count: 126 -Region Avg IOU: 0.525251, Class: 1.000000, Obj: 0.400870, No Obj: 0.005329, Avg Recall: 0.562500, count: 144 -7111: 0.462943, 0.440483 avg, 0.001000 rate, 19.606119 seconds, 1820416 images -Loaded: 2.734666 seconds -Region Avg IOU: 0.518338, Class: 1.000000, Obj: 0.367683, No Obj: 0.004022, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.494944, Class: 1.000000, Obj: 0.382216, No Obj: 0.005679, Avg Recall: 0.539062, count: 128 -Region Avg IOU: 0.581434, Class: 1.000000, Obj: 0.400291, No Obj: 0.005407, Avg Recall: 0.638462, count: 130 -Region Avg IOU: 0.558417, Class: 1.000000, Obj: 0.405645, No Obj: 0.004452, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.506484, Class: 1.000000, Obj: 0.409157, No Obj: 0.004790, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.534590, Class: 1.000000, Obj: 0.420324, No Obj: 0.004222, Avg Recall: 0.567568, count: 74 -Region Avg IOU: 0.565820, Class: 1.000000, Obj: 0.453417, No Obj: 0.004733, Avg Recall: 0.640000, count: 100 -Region Avg IOU: 0.545163, Class: 1.000000, Obj: 0.422876, No Obj: 0.005837, Avg Recall: 0.612403, count: 129 -7112: 0.404402, 0.436875 avg, 0.001000 rate, 22.039551 seconds, 1820672 images -Loaded: 0.000060 seconds -Region Avg IOU: 0.563859, Class: 1.000000, Obj: 0.435865, No Obj: 0.004510, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.578685, Class: 1.000000, Obj: 0.436908, No Obj: 0.004604, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.570977, Class: 1.000000, Obj: 0.446600, No Obj: 0.004382, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.564392, Class: 1.000000, Obj: 0.407975, No Obj: 0.004334, Avg Recall: 0.674157, count: 89 -Region Avg IOU: 0.546677, Class: 1.000000, Obj: 0.417250, No Obj: 0.004876, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.489214, Class: 1.000000, Obj: 0.391027, No Obj: 0.004366, Avg Recall: 0.532110, count: 109 -Region Avg IOU: 0.551518, Class: 1.000000, Obj: 0.436574, No Obj: 0.004466, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.536279, Class: 1.000000, Obj: 0.408674, No Obj: 0.004195, Avg Recall: 0.628866, count: 97 -7113: 0.435841, 0.436771 avg, 0.001000 rate, 20.901104 seconds, 1820928 images -Loaded: 1.629147 seconds -Region Avg IOU: 0.513365, Class: 1.000000, Obj: 0.414144, No Obj: 0.004660, Avg Recall: 0.582609, count: 115 -Region Avg IOU: 0.629326, Class: 1.000000, Obj: 0.429372, No Obj: 0.005038, Avg Recall: 0.793103, count: 87 -Region Avg IOU: 0.513030, Class: 1.000000, Obj: 0.408028, No Obj: 0.004194, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.536893, Class: 1.000000, Obj: 0.393076, No Obj: 0.004346, Avg Recall: 0.638655, count: 119 -Region Avg IOU: 0.548926, Class: 1.000000, Obj: 0.423517, No Obj: 0.004201, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.532308, Class: 1.000000, Obj: 0.407393, No Obj: 0.004715, Avg Recall: 0.591667, count: 120 -Region Avg IOU: 0.525451, Class: 1.000000, Obj: 0.387228, No Obj: 0.004427, Avg Recall: 0.554545, count: 110 -Region Avg IOU: 0.568167, Class: 1.000000, Obj: 0.399063, No Obj: 0.004340, Avg Recall: 0.626263, count: 99 -7114: 0.452868, 0.438381 avg, 0.001000 rate, 18.416971 seconds, 1821184 images -Loaded: 5.471227 seconds -Region Avg IOU: 0.505810, Class: 1.000000, Obj: 0.397563, No Obj: 0.004759, Avg Recall: 0.562963, count: 135 -Region Avg IOU: 0.516538, Class: 1.000000, Obj: 0.378104, No Obj: 0.004727, Avg Recall: 0.592308, count: 130 -Region Avg IOU: 0.557764, Class: 1.000000, Obj: 0.432110, No Obj: 0.004630, Avg Recall: 0.681818, count: 110 -Region Avg IOU: 0.527331, Class: 1.000000, Obj: 0.361883, No Obj: 0.003744, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.493598, Class: 1.000000, Obj: 0.342182, No Obj: 0.004136, Avg Recall: 0.550847, count: 118 -Region Avg IOU: 0.554501, Class: 1.000000, Obj: 0.401040, No Obj: 0.004114, Avg Recall: 0.672566, count: 113 -Region Avg IOU: 0.525174, Class: 1.000000, Obj: 0.384591, No Obj: 0.004212, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.495757, Class: 1.000000, Obj: 0.369145, No Obj: 0.004477, Avg Recall: 0.523810, count: 105 -7115: 0.541841, 0.448727 avg, 0.001000 rate, 20.817705 seconds, 1821440 images -Loaded: 0.211617 seconds -Region Avg IOU: 0.488440, Class: 1.000000, Obj: 0.332714, No Obj: 0.003813, Avg Recall: 0.528736, count: 87 -Region Avg IOU: 0.562846, Class: 1.000000, Obj: 0.407249, No Obj: 0.004253, Avg Recall: 0.676768, count: 99 -Region Avg IOU: 0.544223, Class: 1.000000, Obj: 0.397879, No Obj: 0.004699, Avg Recall: 0.626016, count: 123 -Region Avg IOU: 0.504385, Class: 1.000000, Obj: 0.368933, No Obj: 0.004075, Avg Recall: 0.532258, count: 124 -Region Avg IOU: 0.548726, Class: 1.000000, Obj: 0.406254, No Obj: 0.003852, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.474358, Class: 1.000000, Obj: 0.373634, No Obj: 0.003762, Avg Recall: 0.477064, count: 109 -Region Avg IOU: 0.506408, Class: 1.000000, Obj: 0.378469, No Obj: 0.004127, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.541600, Class: 1.000000, Obj: 0.415487, No Obj: 0.003533, Avg Recall: 0.595506, count: 89 -7116: 0.436314, 0.447486 avg, 0.001000 rate, 20.228388 seconds, 1821696 images -Loaded: 1.969115 seconds -Region Avg IOU: 0.559103, Class: 1.000000, Obj: 0.415172, No Obj: 0.004726, Avg Recall: 0.674419, count: 129 -Region Avg IOU: 0.527385, Class: 1.000000, Obj: 0.371908, No Obj: 0.003817, Avg Recall: 0.561224, count: 98 -Region Avg IOU: 0.548558, Class: 1.000000, Obj: 0.411918, No Obj: 0.004224, Avg Recall: 0.610169, count: 118 -Region Avg IOU: 0.477052, Class: 1.000000, Obj: 0.362685, No Obj: 0.003792, Avg Recall: 0.500000, count: 104 -Region Avg IOU: 0.555731, Class: 1.000000, Obj: 0.395087, No Obj: 0.004403, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.543512, Class: 1.000000, Obj: 0.341074, No Obj: 0.004700, Avg Recall: 0.656934, count: 137 -Region Avg IOU: 0.479596, Class: 1.000000, Obj: 0.321014, No Obj: 0.004412, Avg Recall: 0.510067, count: 149 -Region Avg IOU: 0.564379, Class: 1.000000, Obj: 0.355100, No Obj: 0.004475, Avg Recall: 0.650000, count: 120 -7117: 0.505293, 0.453267 avg, 0.001000 rate, 20.896660 seconds, 1821952 images -Loaded: 0.000051 seconds -Region Avg IOU: 0.536036, Class: 1.000000, Obj: 0.382620, No Obj: 0.004024, Avg Recall: 0.604167, count: 96 -Region Avg IOU: 0.581749, Class: 1.000000, Obj: 0.376415, No Obj: 0.003941, Avg Recall: 0.691489, count: 94 -Region Avg IOU: 0.545526, Class: 1.000000, Obj: 0.331349, No Obj: 0.004657, Avg Recall: 0.608333, count: 120 -Region Avg IOU: 0.594113, Class: 1.000000, Obj: 0.392912, No Obj: 0.004382, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.493435, Class: 1.000000, Obj: 0.395533, No Obj: 0.004086, Avg Recall: 0.549020, count: 102 -Region Avg IOU: 0.570301, Class: 1.000000, Obj: 0.410310, No Obj: 0.004789, Avg Recall: 0.666667, count: 117 -Region Avg IOU: 0.524031, Class: 1.000000, Obj: 0.372135, No Obj: 0.004132, Avg Recall: 0.561905, count: 105 -Region Avg IOU: 0.520679, Class: 1.000000, Obj: 0.342117, No Obj: 0.003712, Avg Recall: 0.573643, count: 129 -7118: 0.419316, 0.449872 avg, 0.001000 rate, 19.736217 seconds, 1822208 images -Loaded: 1.246127 seconds -Region Avg IOU: 0.525710, Class: 1.000000, Obj: 0.363804, No Obj: 0.004009, Avg Recall: 0.613208, count: 106 -Region Avg IOU: 0.566354, Class: 1.000000, Obj: 0.391874, No Obj: 0.004237, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.590914, Class: 1.000000, Obj: 0.396826, No Obj: 0.004179, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.502479, Class: 1.000000, Obj: 0.356961, No Obj: 0.003849, Avg Recall: 0.512605, count: 119 -Region Avg IOU: 0.557616, Class: 1.000000, Obj: 0.385101, No Obj: 0.003957, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.554471, Class: 1.000000, Obj: 0.399199, No Obj: 0.004041, Avg Recall: 0.641026, count: 117 -Region Avg IOU: 0.533472, Class: 1.000000, Obj: 0.338296, No Obj: 0.003879, Avg Recall: 0.620968, count: 124 -Region Avg IOU: 0.561253, Class: 1.000000, Obj: 0.383893, No Obj: 0.004644, Avg Recall: 0.661017, count: 118 -7119: 0.427940, 0.447678 avg, 0.001000 rate, 20.606632 seconds, 1822464 images -Loaded: 0.649748 seconds -Region Avg IOU: 0.548299, Class: 1.000000, Obj: 0.404115, No Obj: 0.003653, Avg Recall: 0.617021, count: 94 -Region Avg IOU: 0.504350, Class: 1.000000, Obj: 0.372419, No Obj: 0.004573, Avg Recall: 0.587719, count: 114 -Region Avg IOU: 0.539767, Class: 1.000000, Obj: 0.379923, No Obj: 0.003960, Avg Recall: 0.598291, count: 117 -Region Avg IOU: 0.489343, Class: 1.000000, Obj: 0.339746, No Obj: 0.003648, Avg Recall: 0.548077, count: 104 -Region Avg IOU: 0.542064, Class: 1.000000, Obj: 0.343876, No Obj: 0.003286, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.545252, Class: 1.000000, Obj: 0.378498, No Obj: 0.003909, Avg Recall: 0.656566, count: 99 -Region Avg IOU: 0.550254, Class: 1.000000, Obj: 0.398601, No Obj: 0.004596, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.586766, Class: 1.000000, Obj: 0.385518, No Obj: 0.004048, Avg Recall: 0.700000, count: 90 -7120: 0.355908, 0.438501 avg, 0.001000 rate, 21.728687 seconds, 1822720 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000064 seconds -Region Avg IOU: 0.638010, Class: 1.000000, Obj: 0.422586, No Obj: 0.003440, Avg Recall: 0.789855, count: 138 -Region Avg IOU: 0.652150, Class: 1.000000, Obj: 0.465748, No Obj: 0.003012, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.605207, Class: 1.000000, Obj: 0.413255, No Obj: 0.002608, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.648628, Class: 1.000000, Obj: 0.489487, No Obj: 0.002600, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.652978, Class: 1.000000, Obj: 0.435644, No Obj: 0.002709, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.652843, Class: 1.000000, Obj: 0.459305, No Obj: 0.002782, Avg Recall: 0.820000, count: 100 -Region Avg IOU: 0.633939, Class: 1.000000, Obj: 0.466565, No Obj: 0.002353, Avg Recall: 0.793103, count: 87 -Region Avg IOU: 0.702344, Class: 1.000000, Obj: 0.552881, No Obj: 0.002754, Avg Recall: 0.880952, count: 84 -7121: 0.394924, 0.434144 avg, 0.001000 rate, 27.834528 seconds, 1822976 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.629701, Class: 1.000000, Obj: 0.451758, No Obj: 0.002943, Avg Recall: 0.752577, count: 97 -Region Avg IOU: 0.679824, Class: 1.000000, Obj: 0.451174, No Obj: 0.003007, Avg Recall: 0.844828, count: 116 -Region Avg IOU: 0.692508, Class: 1.000000, Obj: 0.458675, No Obj: 0.002845, Avg Recall: 0.882883, count: 111 -Region Avg IOU: 0.638782, Class: 1.000000, Obj: 0.407320, No Obj: 0.003180, Avg Recall: 0.780000, count: 150 -Region Avg IOU: 0.639280, Class: 1.000000, Obj: 0.437753, No Obj: 0.002315, Avg Recall: 0.793478, count: 92 -Region Avg IOU: 0.679796, Class: 1.000000, Obj: 0.434086, No Obj: 0.002647, Avg Recall: 0.851485, count: 101 -Region Avg IOU: 0.700438, Class: 1.000000, Obj: 0.461635, No Obj: 0.002573, Avg Recall: 0.861702, count: 94 -Region Avg IOU: 0.637333, Class: 1.000000, Obj: 0.461111, No Obj: 0.002801, Avg Recall: 0.809524, count: 105 -7122: 0.409903, 0.431719 avg, 0.001000 rate, 28.555319 seconds, 1823232 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.654837, Class: 1.000000, Obj: 0.434753, No Obj: 0.002902, Avg Recall: 0.808333, count: 120 -Region Avg IOU: 0.641208, Class: 1.000000, Obj: 0.460648, No Obj: 0.002515, Avg Recall: 0.802326, count: 86 -Region Avg IOU: 0.706254, Class: 1.000000, Obj: 0.459478, No Obj: 0.002894, Avg Recall: 0.895652, count: 115 -Region Avg IOU: 0.653082, Class: 1.000000, Obj: 0.435267, No Obj: 0.002488, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.659147, Class: 1.000000, Obj: 0.427759, No Obj: 0.002436, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.665812, Class: 1.000000, Obj: 0.429143, No Obj: 0.003236, Avg Recall: 0.793388, count: 121 -Region Avg IOU: 0.657321, Class: 1.000000, Obj: 0.435306, No Obj: 0.002776, Avg Recall: 0.809091, count: 110 -Region Avg IOU: 0.622141, Class: 1.000000, Obj: 0.375131, No Obj: 0.002935, Avg Recall: 0.773050, count: 141 -7123: 0.445276, 0.433075 avg, 0.001000 rate, 29.852451 seconds, 1823488 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.618298, Class: 1.000000, Obj: 0.388196, No Obj: 0.002463, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.689409, Class: 1.000000, Obj: 0.488067, No Obj: 0.002941, Avg Recall: 0.863636, count: 110 -Region Avg IOU: 0.665903, Class: 1.000000, Obj: 0.470861, No Obj: 0.002729, Avg Recall: 0.819048, count: 105 -Region Avg IOU: 0.663292, Class: 1.000000, Obj: 0.381011, No Obj: 0.002953, Avg Recall: 0.838235, count: 136 -Region Avg IOU: 0.701045, Class: 1.000000, Obj: 0.455734, No Obj: 0.003196, Avg Recall: 0.879032, count: 124 -Region Avg IOU: 0.640451, Class: 1.000000, Obj: 0.405497, No Obj: 0.003060, Avg Recall: 0.793103, count: 145 -Region Avg IOU: 0.670362, Class: 1.000000, Obj: 0.446117, No Obj: 0.002990, Avg Recall: 0.808824, count: 136 -Region Avg IOU: 0.671579, Class: 1.000000, Obj: 0.384549, No Obj: 0.003038, Avg Recall: 0.837209, count: 129 -7124: 0.479978, 0.437765 avg, 0.001000 rate, 27.360094 seconds, 1823744 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.647610, Class: 1.000000, Obj: 0.397219, No Obj: 0.002466, Avg Recall: 0.740385, count: 104 -Region Avg IOU: 0.652966, Class: 1.000000, Obj: 0.356896, No Obj: 0.002465, Avg Recall: 0.817204, count: 93 -Region Avg IOU: 0.624821, Class: 1.000000, Obj: 0.392705, No Obj: 0.002761, Avg Recall: 0.753425, count: 146 -Region Avg IOU: 0.685282, Class: 1.000000, Obj: 0.429418, No Obj: 0.002780, Avg Recall: 0.867347, count: 98 -Region Avg IOU: 0.634902, Class: 1.000000, Obj: 0.417480, No Obj: 0.002388, Avg Recall: 0.811765, count: 85 -Region Avg IOU: 0.641487, Class: 1.000000, Obj: 0.431019, No Obj: 0.002403, Avg Recall: 0.782609, count: 115 -Region Avg IOU: 0.643313, Class: 1.000000, Obj: 0.358314, No Obj: 0.002455, Avg Recall: 0.827273, count: 110 -Region Avg IOU: 0.673801, Class: 1.000000, Obj: 0.455104, No Obj: 0.002854, Avg Recall: 0.836538, count: 104 -7125: 0.436939, 0.437683 avg, 0.001000 rate, 29.682320 seconds, 1824000 images -Loaded: 0.000059 seconds -Region Avg IOU: 0.597001, Class: 1.000000, Obj: 0.427596, No Obj: 0.002035, Avg Recall: 0.722892, count: 83 -Region Avg IOU: 0.682640, Class: 1.000000, Obj: 0.433555, No Obj: 0.002513, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.678052, Class: 1.000000, Obj: 0.459505, No Obj: 0.002875, Avg Recall: 0.796610, count: 118 -Region Avg IOU: 0.666780, Class: 1.000000, Obj: 0.378783, No Obj: 0.002917, Avg Recall: 0.833333, count: 114 -Region Avg IOU: 0.660748, Class: 1.000000, Obj: 0.411951, No Obj: 0.002388, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.653712, Class: 1.000000, Obj: 0.445152, No Obj: 0.002456, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.665947, Class: 1.000000, Obj: 0.426493, No Obj: 0.002662, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.689438, Class: 1.000000, Obj: 0.425815, No Obj: 0.002548, Avg Recall: 0.847619, count: 105 -7126: 0.410590, 0.434973 avg, 0.001000 rate, 27.875692 seconds, 1824256 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.634717, Class: 1.000000, Obj: 0.343671, No Obj: 0.002032, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.665448, Class: 1.000000, Obj: 0.435433, No Obj: 0.002564, Avg Recall: 0.844660, count: 103 -Region Avg IOU: 0.623866, Class: 1.000000, Obj: 0.337985, No Obj: 0.002788, Avg Recall: 0.761538, count: 130 -Region Avg IOU: 0.651526, Class: 1.000000, Obj: 0.434069, No Obj: 0.002325, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.660165, Class: 1.000000, Obj: 0.425593, No Obj: 0.002414, Avg Recall: 0.833333, count: 96 -Region Avg IOU: 0.598193, Class: 1.000000, Obj: 0.381036, No Obj: 0.002591, Avg Recall: 0.737226, count: 137 -Region Avg IOU: 0.703354, Class: 1.000000, Obj: 0.440181, No Obj: 0.002891, Avg Recall: 0.897638, count: 127 -Region Avg IOU: 0.667017, Class: 1.000000, Obj: 0.374756, No Obj: 0.002503, Avg Recall: 0.801653, count: 121 -7127: 0.452515, 0.436728 avg, 0.001000 rate, 27.574434 seconds, 1824512 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.652944, Class: 1.000000, Obj: 0.356360, No Obj: 0.002689, Avg Recall: 0.841121, count: 107 -Region Avg IOU: 0.659665, Class: 1.000000, Obj: 0.445790, No Obj: 0.002738, Avg Recall: 0.818182, count: 110 -Region Avg IOU: 0.643628, Class: 1.000000, Obj: 0.409979, No Obj: 0.002741, Avg Recall: 0.803419, count: 117 -Region Avg IOU: 0.667163, Class: 1.000000, Obj: 0.417179, No Obj: 0.002157, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.701815, Class: 1.000000, Obj: 0.423517, No Obj: 0.002519, Avg Recall: 0.898148, count: 108 -Region Avg IOU: 0.657985, Class: 1.000000, Obj: 0.423317, No Obj: 0.002446, Avg Recall: 0.866667, count: 90 -Region Avg IOU: 0.641407, Class: 1.000000, Obj: 0.456144, No Obj: 0.002206, Avg Recall: 0.800000, count: 85 -Region Avg IOU: 0.648231, Class: 1.000000, Obj: 0.391196, No Obj: 0.002270, Avg Recall: 0.776786, count: 112 -7128: 0.392763, 0.432331 avg, 0.001000 rate, 27.801155 seconds, 1824768 images -Loaded: 0.000113 seconds -Region Avg IOU: 0.607570, Class: 1.000000, Obj: 0.350006, No Obj: 0.002260, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.656027, Class: 1.000000, Obj: 0.419810, No Obj: 0.002170, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.655473, Class: 1.000000, Obj: 0.395226, No Obj: 0.002489, Avg Recall: 0.810345, count: 116 -Region Avg IOU: 0.647196, Class: 1.000000, Obj: 0.389110, No Obj: 0.002327, Avg Recall: 0.767677, count: 99 -Region Avg IOU: 0.648471, Class: 1.000000, Obj: 0.381320, No Obj: 0.002546, Avg Recall: 0.838983, count: 118 -Region Avg IOU: 0.637873, Class: 1.000000, Obj: 0.432264, No Obj: 0.002574, Avg Recall: 0.761905, count: 126 -Region Avg IOU: 0.659905, Class: 1.000000, Obj: 0.407377, No Obj: 0.002573, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.679270, Class: 1.000000, Obj: 0.473130, No Obj: 0.002250, Avg Recall: 0.851852, count: 81 -7129: 0.429375, 0.432036 avg, 0.001000 rate, 27.746420 seconds, 1825024 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.671184, Class: 1.000000, Obj: 0.387146, No Obj: 0.002268, Avg Recall: 0.827586, count: 87 -Region Avg IOU: 0.712965, Class: 1.000000, Obj: 0.440590, No Obj: 0.003053, Avg Recall: 0.910569, count: 123 -Region Avg IOU: 0.723540, Class: 1.000000, Obj: 0.424326, No Obj: 0.002501, Avg Recall: 0.912088, count: 91 -Region Avg IOU: 0.644924, Class: 1.000000, Obj: 0.343185, No Obj: 0.002814, Avg Recall: 0.765101, count: 149 -Region Avg IOU: 0.670422, Class: 1.000000, Obj: 0.350876, No Obj: 0.002735, Avg Recall: 0.817460, count: 126 -Region Avg IOU: 0.642242, Class: 1.000000, Obj: 0.425360, No Obj: 0.002931, Avg Recall: 0.752137, count: 117 -Region Avg IOU: 0.685531, Class: 1.000000, Obj: 0.425998, No Obj: 0.002496, Avg Recall: 0.858696, count: 92 -Region Avg IOU: 0.675200, Class: 1.000000, Obj: 0.410437, No Obj: 0.002266, Avg Recall: 0.876289, count: 97 -7130: 0.450477, 0.433880 avg, 0.001000 rate, 28.910059 seconds, 1825280 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.636681 seconds -Region Avg IOU: 0.564406, Class: 1.000000, Obj: 0.385913, No Obj: 0.003761, Avg Recall: 0.607477, count: 107 -Region Avg IOU: 0.456291, Class: 1.000000, Obj: 0.286814, No Obj: 0.003432, Avg Recall: 0.475410, count: 122 -Region Avg IOU: 0.574791, Class: 1.000000, Obj: 0.335923, No Obj: 0.003860, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.508758, Class: 1.000000, Obj: 0.296574, No Obj: 0.003474, Avg Recall: 0.572727, count: 110 -Region Avg IOU: 0.516285, Class: 1.000000, Obj: 0.319069, No Obj: 0.004263, Avg Recall: 0.570248, count: 121 -Region Avg IOU: 0.507007, Class: 1.000000, Obj: 0.326238, No Obj: 0.004026, Avg Recall: 0.579365, count: 126 -Region Avg IOU: 0.593562, Class: 1.000000, Obj: 0.358535, No Obj: 0.003953, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.495445, Class: 1.000000, Obj: 0.329413, No Obj: 0.003732, Avg Recall: 0.571429, count: 126 -7131: 0.524277, 0.442919 avg, 0.001000 rate, 19.158400 seconds, 1825536 images -Loaded: 1.064912 seconds -Region Avg IOU: 0.556833, Class: 1.000000, Obj: 0.304998, No Obj: 0.003498, Avg Recall: 0.645161, count: 93 -Region Avg IOU: 0.557403, Class: 1.000000, Obj: 0.345755, No Obj: 0.003517, Avg Recall: 0.666667, count: 81 -Region Avg IOU: 0.511703, Class: 1.000000, Obj: 0.297257, No Obj: 0.003592, Avg Recall: 0.534653, count: 101 -Region Avg IOU: 0.547595, Class: 1.000000, Obj: 0.356333, No Obj: 0.003825, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.563681, Class: 1.000000, Obj: 0.327236, No Obj: 0.004073, Avg Recall: 0.678261, count: 115 -Region Avg IOU: 0.510517, Class: 1.000000, Obj: 0.325951, No Obj: 0.004103, Avg Recall: 0.570175, count: 114 -Region Avg IOU: 0.566142, Class: 1.000000, Obj: 0.298288, No Obj: 0.003496, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.500076, Class: 1.000000, Obj: 0.337573, No Obj: 0.004467, Avg Recall: 0.536585, count: 123 -7132: 0.459852, 0.444613 avg, 0.001000 rate, 18.799444 seconds, 1825792 images -Loaded: 2.276473 seconds -Region Avg IOU: 0.482673, Class: 1.000000, Obj: 0.351882, No Obj: 0.003745, Avg Recall: 0.514851, count: 101 -Region Avg IOU: 0.476822, Class: 1.000000, Obj: 0.385719, No Obj: 0.003382, Avg Recall: 0.511628, count: 86 -Region Avg IOU: 0.568560, Class: 1.000000, Obj: 0.355014, No Obj: 0.004314, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.509267, Class: 1.000000, Obj: 0.335362, No Obj: 0.003509, Avg Recall: 0.600000, count: 90 -Region Avg IOU: 0.591275, Class: 1.000000, Obj: 0.411959, No Obj: 0.004399, Avg Recall: 0.694737, count: 95 -Region Avg IOU: 0.540363, Class: 1.000000, Obj: 0.381444, No Obj: 0.004415, Avg Recall: 0.632075, count: 106 -Region Avg IOU: 0.497265, Class: 1.000000, Obj: 0.339810, No Obj: 0.003899, Avg Recall: 0.553571, count: 112 -Region Avg IOU: 0.531506, Class: 1.000000, Obj: 0.370471, No Obj: 0.004024, Avg Recall: 0.604167, count: 96 -7133: 0.404607, 0.440612 avg, 0.001000 rate, 17.235020 seconds, 1826048 images -Loaded: 4.909682 seconds -Region Avg IOU: 0.520029, Class: 1.000000, Obj: 0.382924, No Obj: 0.005014, Avg Recall: 0.589552, count: 134 -Region Avg IOU: 0.509940, Class: 1.000000, Obj: 0.351599, No Obj: 0.004629, Avg Recall: 0.584000, count: 125 -Region Avg IOU: 0.524314, Class: 1.000000, Obj: 0.395898, No Obj: 0.004241, Avg Recall: 0.593220, count: 118 -Region Avg IOU: 0.486923, Class: 1.000000, Obj: 0.329306, No Obj: 0.004260, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.528540, Class: 1.000000, Obj: 0.363145, No Obj: 0.004194, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.560323, Class: 1.000000, Obj: 0.424061, No Obj: 0.003731, Avg Recall: 0.637681, count: 69 -Region Avg IOU: 0.520273, Class: 1.000000, Obj: 0.392223, No Obj: 0.004866, Avg Recall: 0.591667, count: 120 -Region Avg IOU: 0.518851, Class: 1.000000, Obj: 0.353472, No Obj: 0.003789, Avg Recall: 0.553398, count: 103 -7134: 0.416383, 0.438189 avg, 0.001000 rate, 18.583740 seconds, 1826304 images -Loaded: 1.547523 seconds -Region Avg IOU: 0.508261, Class: 1.000000, Obj: 0.410831, No Obj: 0.004794, Avg Recall: 0.557377, count: 122 -Region Avg IOU: 0.486941, Class: 1.000000, Obj: 0.371015, No Obj: 0.005199, Avg Recall: 0.556452, count: 124 -Region Avg IOU: 0.485886, Class: 1.000000, Obj: 0.391107, No Obj: 0.004550, Avg Recall: 0.556604, count: 106 -Region Avg IOU: 0.473327, Class: 1.000000, Obj: 0.394574, No Obj: 0.004914, Avg Recall: 0.535433, count: 127 -Region Avg IOU: 0.579865, Class: 1.000000, Obj: 0.403417, No Obj: 0.004264, Avg Recall: 0.700000, count: 80 -Region Avg IOU: 0.505060, Class: 1.000000, Obj: 0.376771, No Obj: 0.004172, Avg Recall: 0.545455, count: 110 -Region Avg IOU: 0.523227, Class: 1.000000, Obj: 0.394425, No Obj: 0.004403, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.531592, Class: 1.000000, Obj: 0.410264, No Obj: 0.004294, Avg Recall: 0.587156, count: 109 -7135: 0.425012, 0.436871 avg, 0.001000 rate, 17.475410 seconds, 1826560 images -Loaded: 4.203071 seconds -Region Avg IOU: 0.467669, Class: 1.000000, Obj: 0.394428, No Obj: 0.004251, Avg Recall: 0.466019, count: 103 -Region Avg IOU: 0.456792, Class: 1.000000, Obj: 0.359324, No Obj: 0.004276, Avg Recall: 0.494382, count: 89 -Region Avg IOU: 0.511625, Class: 1.000000, Obj: 0.435009, No Obj: 0.004987, Avg Recall: 0.570175, count: 114 -Region Avg IOU: 0.538615, Class: 1.000000, Obj: 0.415837, No Obj: 0.005627, Avg Recall: 0.595238, count: 126 -Region Avg IOU: 0.545193, Class: 1.000000, Obj: 0.432963, No Obj: 0.005060, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.528270, Class: 1.000000, Obj: 0.396915, No Obj: 0.004500, Avg Recall: 0.603960, count: 101 -Region Avg IOU: 0.531000, Class: 1.000000, Obj: 0.405672, No Obj: 0.004681, Avg Recall: 0.561798, count: 89 -Region Avg IOU: 0.496573, Class: 1.000000, Obj: 0.374075, No Obj: 0.004856, Avg Recall: 0.573770, count: 122 -7136: 0.462669, 0.439451 avg, 0.001000 rate, 22.126436 seconds, 1826816 images -Loaded: 0.160927 seconds -Region Avg IOU: 0.544186, Class: 1.000000, Obj: 0.426929, No Obj: 0.004929, Avg Recall: 0.600000, count: 95 -Region Avg IOU: 0.575398, Class: 1.000000, Obj: 0.459050, No Obj: 0.005410, Avg Recall: 0.680412, count: 97 -Region Avg IOU: 0.547902, Class: 1.000000, Obj: 0.444971, No Obj: 0.004980, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.572262, Class: 1.000000, Obj: 0.457594, No Obj: 0.004744, Avg Recall: 0.673469, count: 98 -Region Avg IOU: 0.565457, Class: 1.000000, Obj: 0.421687, No Obj: 0.005581, Avg Recall: 0.688525, count: 122 -Region Avg IOU: 0.500872, Class: 1.000000, Obj: 0.395275, No Obj: 0.005388, Avg Recall: 0.539568, count: 139 -Region Avg IOU: 0.524459, Class: 1.000000, Obj: 0.447822, No Obj: 0.005448, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.518280, Class: 1.000000, Obj: 0.408994, No Obj: 0.004831, Avg Recall: 0.576923, count: 104 -7137: 0.424000, 0.437906 avg, 0.001000 rate, 19.254875 seconds, 1827072 images -Loaded: 1.068525 seconds -Region Avg IOU: 0.461148, Class: 1.000000, Obj: 0.414234, No Obj: 0.003962, Avg Recall: 0.500000, count: 90 -Region Avg IOU: 0.563482, Class: 1.000000, Obj: 0.448808, No Obj: 0.005379, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.443504, Class: 1.000000, Obj: 0.395116, No Obj: 0.005066, Avg Recall: 0.466667, count: 105 -Region Avg IOU: 0.533412, Class: 1.000000, Obj: 0.378801, No Obj: 0.004878, Avg Recall: 0.651376, count: 109 -Region Avg IOU: 0.535354, Class: 1.000000, Obj: 0.379493, No Obj: 0.004993, Avg Recall: 0.598039, count: 102 -Region Avg IOU: 0.489620, Class: 1.000000, Obj: 0.391887, No Obj: 0.005473, Avg Recall: 0.566667, count: 120 -Region Avg IOU: 0.479204, Class: 1.000000, Obj: 0.393227, No Obj: 0.004352, Avg Recall: 0.553398, count: 103 -Region Avg IOU: 0.525743, Class: 1.000000, Obj: 0.424437, No Obj: 0.005349, Avg Recall: 0.598214, count: 112 -7138: 0.401769, 0.434292 avg, 0.001000 rate, 17.284479 seconds, 1827328 images -Loaded: 4.379141 seconds -Region Avg IOU: 0.548932, Class: 1.000000, Obj: 0.437484, No Obj: 0.005678, Avg Recall: 0.622807, count: 114 -Region Avg IOU: 0.514839, Class: 1.000000, Obj: 0.467757, No Obj: 0.005084, Avg Recall: 0.564706, count: 85 -Region Avg IOU: 0.518362, Class: 1.000000, Obj: 0.378290, No Obj: 0.004816, Avg Recall: 0.612245, count: 98 -Region Avg IOU: 0.484584, Class: 1.000000, Obj: 0.365874, No Obj: 0.004818, Avg Recall: 0.481132, count: 106 -Region Avg IOU: 0.547845, Class: 1.000000, Obj: 0.435276, No Obj: 0.005500, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.479626, Class: 1.000000, Obj: 0.379701, No Obj: 0.005799, Avg Recall: 0.517857, count: 168 -Region Avg IOU: 0.548845, Class: 1.000000, Obj: 0.416312, No Obj: 0.004603, Avg Recall: 0.662651, count: 83 -Region Avg IOU: 0.491339, Class: 1.000000, Obj: 0.441735, No Obj: 0.004077, Avg Recall: 0.584416, count: 77 -7139: 0.408325, 0.431696 avg, 0.001000 rate, 18.366140 seconds, 1827584 images -Loaded: 2.722183 seconds -Region Avg IOU: 0.514766, Class: 1.000000, Obj: 0.402018, No Obj: 0.004975, Avg Recall: 0.584746, count: 118 -Region Avg IOU: 0.502016, Class: 1.000000, Obj: 0.402675, No Obj: 0.004761, Avg Recall: 0.569106, count: 123 -Region Avg IOU: 0.548097, Class: 1.000000, Obj: 0.407393, No Obj: 0.004595, Avg Recall: 0.604651, count: 86 -Region Avg IOU: 0.476080, Class: 1.000000, Obj: 0.386527, No Obj: 0.004581, Avg Recall: 0.500000, count: 112 -Region Avg IOU: 0.509755, Class: 1.000000, Obj: 0.416742, No Obj: 0.005482, Avg Recall: 0.581967, count: 122 -Region Avg IOU: 0.541551, Class: 1.000000, Obj: 0.411394, No Obj: 0.005320, Avg Recall: 0.615385, count: 117 -Region Avg IOU: 0.509261, Class: 1.000000, Obj: 0.386904, No Obj: 0.005606, Avg Recall: 0.593750, count: 128 -Region Avg IOU: 0.495777, Class: 1.000000, Obj: 0.388553, No Obj: 0.005650, Avg Recall: 0.560606, count: 132 -7140: 0.457403, 0.434266 avg, 0.001000 rate, 16.287762 seconds, 1827840 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000068 seconds -Region Avg IOU: 0.589752, Class: 1.000000, Obj: 0.452551, No Obj: 0.003090, Avg Recall: 0.661417, count: 127 -Region Avg IOU: 0.629294, Class: 1.000000, Obj: 0.515918, No Obj: 0.003396, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.633433, Class: 1.000000, Obj: 0.507533, No Obj: 0.003122, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.596023, Class: 1.000000, Obj: 0.482604, No Obj: 0.002775, Avg Recall: 0.693182, count: 88 -Region Avg IOU: 0.614823, Class: 1.000000, Obj: 0.456442, No Obj: 0.003746, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.662736, Class: 1.000000, Obj: 0.535523, No Obj: 0.003323, Avg Recall: 0.814815, count: 108 -Region Avg IOU: 0.633964, Class: 1.000000, Obj: 0.509525, No Obj: 0.003358, Avg Recall: 0.816000, count: 125 -Region Avg IOU: 0.659957, Class: 1.000000, Obj: 0.520516, No Obj: 0.003314, Avg Recall: 0.822222, count: 90 -7141: 0.421772, 0.433017 avg, 0.001000 rate, 26.884323 seconds, 1828096 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.623462, Class: 1.000000, Obj: 0.435834, No Obj: 0.003032, Avg Recall: 0.733333, count: 120 -Region Avg IOU: 0.622002, Class: 1.000000, Obj: 0.465125, No Obj: 0.003297, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.639924, Class: 1.000000, Obj: 0.434655, No Obj: 0.003355, Avg Recall: 0.759399, count: 133 -Region Avg IOU: 0.636597, Class: 1.000000, Obj: 0.405183, No Obj: 0.002810, Avg Recall: 0.804598, count: 87 -Region Avg IOU: 0.625255, Class: 1.000000, Obj: 0.394931, No Obj: 0.002889, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.648572, Class: 1.000000, Obj: 0.474747, No Obj: 0.002705, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.576931, Class: 1.000000, Obj: 0.465521, No Obj: 0.002953, Avg Recall: 0.672000, count: 125 -Region Avg IOU: 0.612114, Class: 1.000000, Obj: 0.413824, No Obj: 0.002980, Avg Recall: 0.757009, count: 107 -7142: 0.423250, 0.432040 avg, 0.001000 rate, 27.903540 seconds, 1828352 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.660876, Class: 1.000000, Obj: 0.470428, No Obj: 0.002891, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.614638, Class: 1.000000, Obj: 0.464279, No Obj: 0.002962, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.661977, Class: 1.000000, Obj: 0.360869, No Obj: 0.002773, Avg Recall: 0.851064, count: 94 -Region Avg IOU: 0.622070, Class: 1.000000, Obj: 0.469960, No Obj: 0.002857, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.626972, Class: 1.000000, Obj: 0.403837, No Obj: 0.002969, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.689998, Class: 1.000000, Obj: 0.423097, No Obj: 0.003675, Avg Recall: 0.842105, count: 133 -Region Avg IOU: 0.647752, Class: 1.000000, Obj: 0.453709, No Obj: 0.002760, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.639978, Class: 1.000000, Obj: 0.389911, No Obj: 0.003218, Avg Recall: 0.789916, count: 119 -7143: 0.438029, 0.432639 avg, 0.001000 rate, 26.701910 seconds, 1828608 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.669996, Class: 1.000000, Obj: 0.458937, No Obj: 0.002723, Avg Recall: 0.815126, count: 119 -Region Avg IOU: 0.601154, Class: 1.000000, Obj: 0.403746, No Obj: 0.002497, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.631159, Class: 1.000000, Obj: 0.441891, No Obj: 0.002633, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.656509, Class: 1.000000, Obj: 0.431563, No Obj: 0.002381, Avg Recall: 0.740741, count: 81 -Region Avg IOU: 0.619128, Class: 1.000000, Obj: 0.384062, No Obj: 0.002860, Avg Recall: 0.761905, count: 126 -Region Avg IOU: 0.706026, Class: 1.000000, Obj: 0.468033, No Obj: 0.002417, Avg Recall: 0.872093, count: 86 -Region Avg IOU: 0.620038, Class: 1.000000, Obj: 0.438007, No Obj: 0.002737, Avg Recall: 0.763636, count: 110 -Region Avg IOU: 0.624607, Class: 1.000000, Obj: 0.379537, No Obj: 0.002855, Avg Recall: 0.772727, count: 110 -7144: 0.372374, 0.426613 avg, 0.001000 rate, 27.786016 seconds, 1828864 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.656541, Class: 1.000000, Obj: 0.398592, No Obj: 0.002883, Avg Recall: 0.784722, count: 144 -Region Avg IOU: 0.643903, Class: 1.000000, Obj: 0.411789, No Obj: 0.002546, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.613324, Class: 1.000000, Obj: 0.391508, No Obj: 0.002429, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.623064, Class: 1.000000, Obj: 0.425987, No Obj: 0.002641, Avg Recall: 0.728814, count: 118 -Region Avg IOU: 0.687202, Class: 1.000000, Obj: 0.429496, No Obj: 0.002808, Avg Recall: 0.870690, count: 116 -Region Avg IOU: 0.652756, Class: 1.000000, Obj: 0.411767, No Obj: 0.002728, Avg Recall: 0.823529, count: 136 -Region Avg IOU: 0.630750, Class: 1.000000, Obj: 0.367758, No Obj: 0.002124, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.677477, Class: 1.000000, Obj: 0.412178, No Obj: 0.002986, Avg Recall: 0.836207, count: 116 -7145: 0.476144, 0.431566 avg, 0.001000 rate, 26.473637 seconds, 1829120 images -Loaded: 0.000128 seconds -Region Avg IOU: 0.674936, Class: 1.000000, Obj: 0.351911, No Obj: 0.002267, Avg Recall: 0.836538, count: 104 -Region Avg IOU: 0.621474, Class: 1.000000, Obj: 0.380921, No Obj: 0.002183, Avg Recall: 0.787879, count: 99 -Region Avg IOU: 0.657582, Class: 1.000000, Obj: 0.403775, No Obj: 0.002381, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.613867, Class: 1.000000, Obj: 0.324208, No Obj: 0.002695, Avg Recall: 0.752212, count: 113 -Region Avg IOU: 0.654693, Class: 1.000000, Obj: 0.375315, No Obj: 0.002427, Avg Recall: 0.851485, count: 101 -Region Avg IOU: 0.633302, Class: 1.000000, Obj: 0.353261, No Obj: 0.002364, Avg Recall: 0.747967, count: 123 -Region Avg IOU: 0.664997, Class: 1.000000, Obj: 0.351646, No Obj: 0.002326, Avg Recall: 0.802198, count: 91 -Region Avg IOU: 0.667339, Class: 1.000000, Obj: 0.368882, No Obj: 0.002559, Avg Recall: 0.858491, count: 106 -7146: 0.442948, 0.432704 avg, 0.001000 rate, 28.518040 seconds, 1829376 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.608340, Class: 1.000000, Obj: 0.364890, No Obj: 0.002146, Avg Recall: 0.723810, count: 105 -Region Avg IOU: 0.675771, Class: 1.000000, Obj: 0.381313, No Obj: 0.002340, Avg Recall: 0.857143, count: 98 -Region Avg IOU: 0.658474, Class: 1.000000, Obj: 0.345203, No Obj: 0.002601, Avg Recall: 0.857143, count: 119 -Region Avg IOU: 0.640180, Class: 1.000000, Obj: 0.372220, No Obj: 0.002384, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.643239, Class: 1.000000, Obj: 0.350184, No Obj: 0.002008, Avg Recall: 0.783132, count: 83 -Region Avg IOU: 0.659077, Class: 1.000000, Obj: 0.326131, No Obj: 0.002494, Avg Recall: 0.838983, count: 118 -Region Avg IOU: 0.615551, Class: 1.000000, Obj: 0.325157, No Obj: 0.002392, Avg Recall: 0.735537, count: 121 -Region Avg IOU: 0.688970, Class: 1.000000, Obj: 0.355393, No Obj: 0.002111, Avg Recall: 0.868687, count: 99 -7147: 0.466115, 0.436045 avg, 0.001000 rate, 27.516712 seconds, 1829632 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.687258, Class: 1.000000, Obj: 0.378385, No Obj: 0.002308, Avg Recall: 0.897727, count: 88 -Region Avg IOU: 0.651725, Class: 1.000000, Obj: 0.422139, No Obj: 0.002106, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.672232, Class: 1.000000, Obj: 0.342731, No Obj: 0.002851, Avg Recall: 0.813008, count: 123 -Region Avg IOU: 0.681511, Class: 1.000000, Obj: 0.384280, No Obj: 0.002284, Avg Recall: 0.866667, count: 105 -Region Avg IOU: 0.671320, Class: 1.000000, Obj: 0.407899, No Obj: 0.002491, Avg Recall: 0.813953, count: 86 -Region Avg IOU: 0.653516, Class: 1.000000, Obj: 0.375170, No Obj: 0.002251, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.666440, Class: 1.000000, Obj: 0.388786, No Obj: 0.002232, Avg Recall: 0.814433, count: 97 -Region Avg IOU: 0.644462, Class: 1.000000, Obj: 0.399528, No Obj: 0.002289, Avg Recall: 0.794118, count: 102 -7148: 0.449073, 0.437348 avg, 0.001000 rate, 29.050678 seconds, 1829888 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.677290, Class: 1.000000, Obj: 0.442691, No Obj: 0.002175, Avg Recall: 0.839506, count: 81 -Region Avg IOU: 0.637072, Class: 1.000000, Obj: 0.365564, No Obj: 0.002703, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.651959, Class: 1.000000, Obj: 0.413003, No Obj: 0.002278, Avg Recall: 0.828571, count: 105 -Region Avg IOU: 0.672970, Class: 1.000000, Obj: 0.415355, No Obj: 0.002524, Avg Recall: 0.870968, count: 93 -Region Avg IOU: 0.678387, Class: 1.000000, Obj: 0.418199, No Obj: 0.002429, Avg Recall: 0.830000, count: 100 -Region Avg IOU: 0.619511, Class: 1.000000, Obj: 0.373552, No Obj: 0.002479, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.631311, Class: 1.000000, Obj: 0.414398, No Obj: 0.002364, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.676470, Class: 1.000000, Obj: 0.365819, No Obj: 0.002971, Avg Recall: 0.872000, count: 125 -7149: 0.422680, 0.435881 avg, 0.001000 rate, 27.321222 seconds, 1830144 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.673412, Class: 1.000000, Obj: 0.429643, No Obj: 0.002875, Avg Recall: 0.810811, count: 111 -Region Avg IOU: 0.620431, Class: 1.000000, Obj: 0.372237, No Obj: 0.002983, Avg Recall: 0.752000, count: 125 -Region Avg IOU: 0.656424, Class: 1.000000, Obj: 0.460259, No Obj: 0.002647, Avg Recall: 0.795082, count: 122 -Region Avg IOU: 0.657759, Class: 1.000000, Obj: 0.433383, No Obj: 0.002704, Avg Recall: 0.814516, count: 124 -Region Avg IOU: 0.677429, Class: 1.000000, Obj: 0.414087, No Obj: 0.002790, Avg Recall: 0.830645, count: 124 -Region Avg IOU: 0.669938, Class: 1.000000, Obj: 0.475436, No Obj: 0.002448, Avg Recall: 0.823529, count: 85 -Region Avg IOU: 0.656970, Class: 1.000000, Obj: 0.399134, No Obj: 0.002245, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.608074, Class: 1.000000, Obj: 0.388741, No Obj: 0.002604, Avg Recall: 0.673267, count: 101 -7150: 0.438393, 0.436132 avg, 0.001000 rate, 27.946020 seconds, 1830400 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000070 seconds -Region Avg IOU: 0.718636, Class: 1.000000, Obj: 0.488405, No Obj: 0.002674, Avg Recall: 0.895349, count: 86 -Region Avg IOU: 0.607526, Class: 1.000000, Obj: 0.416373, No Obj: 0.002276, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.699796, Class: 1.000000, Obj: 0.388832, No Obj: 0.002708, Avg Recall: 0.876289, count: 97 -Region Avg IOU: 0.678873, Class: 1.000000, Obj: 0.487452, No Obj: 0.002609, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.629388, Class: 1.000000, Obj: 0.346131, No Obj: 0.002518, Avg Recall: 0.797468, count: 79 -Region Avg IOU: 0.666695, Class: 1.000000, Obj: 0.438519, No Obj: 0.002812, Avg Recall: 0.836364, count: 110 -Region Avg IOU: 0.644566, Class: 1.000000, Obj: 0.430930, No Obj: 0.002829, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.665656, Class: 1.000000, Obj: 0.487248, No Obj: 0.003045, Avg Recall: 0.839695, count: 131 -7151: 0.413405, 0.433859 avg, 0.001000 rate, 27.930796 seconds, 1830656 images -Loaded: 0.000108 seconds -Region Avg IOU: 0.675795, Class: 1.000000, Obj: 0.455279, No Obj: 0.002809, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.692603, Class: 1.000000, Obj: 0.380425, No Obj: 0.003331, Avg Recall: 0.833333, count: 126 -Region Avg IOU: 0.690010, Class: 1.000000, Obj: 0.429493, No Obj: 0.002831, Avg Recall: 0.847826, count: 92 -Region Avg IOU: 0.687499, Class: 1.000000, Obj: 0.463248, No Obj: 0.002924, Avg Recall: 0.843750, count: 96 -Region Avg IOU: 0.677155, Class: 1.000000, Obj: 0.430980, No Obj: 0.003541, Avg Recall: 0.813953, count: 129 -Region Avg IOU: 0.673360, Class: 1.000000, Obj: 0.467730, No Obj: 0.002900, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.701509, Class: 1.000000, Obj: 0.462559, No Obj: 0.002637, Avg Recall: 0.888889, count: 81 -Region Avg IOU: 0.661151, Class: 1.000000, Obj: 0.441886, No Obj: 0.002968, Avg Recall: 0.819048, count: 105 -7152: 0.451093, 0.435583 avg, 0.001000 rate, 28.048672 seconds, 1830912 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.693994, Class: 1.000000, Obj: 0.470244, No Obj: 0.002960, Avg Recall: 0.908257, count: 109 -Region Avg IOU: 0.628932, Class: 1.000000, Obj: 0.404034, No Obj: 0.002742, Avg Recall: 0.698276, count: 116 -Region Avg IOU: 0.624898, Class: 1.000000, Obj: 0.402121, No Obj: 0.002798, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.687765, Class: 1.000000, Obj: 0.447685, No Obj: 0.002317, Avg Recall: 0.869048, count: 84 -Region Avg IOU: 0.650575, Class: 1.000000, Obj: 0.474412, No Obj: 0.002715, Avg Recall: 0.803922, count: 102 -Region Avg IOU: 0.633368, Class: 1.000000, Obj: 0.426460, No Obj: 0.003320, Avg Recall: 0.785714, count: 154 -Region Avg IOU: 0.648512, Class: 1.000000, Obj: 0.455619, No Obj: 0.002749, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.616735, Class: 1.000000, Obj: 0.396003, No Obj: 0.003115, Avg Recall: 0.723404, count: 141 -7153: 0.445842, 0.436609 avg, 0.001000 rate, 27.554092 seconds, 1831168 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.663173, Class: 1.000000, Obj: 0.454450, No Obj: 0.002659, Avg Recall: 0.863636, count: 88 -Region Avg IOU: 0.645942, Class: 1.000000, Obj: 0.454789, No Obj: 0.002622, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.636640, Class: 1.000000, Obj: 0.453497, No Obj: 0.002832, Avg Recall: 0.769231, count: 104 -Region Avg IOU: 0.650766, Class: 1.000000, Obj: 0.440657, No Obj: 0.002981, Avg Recall: 0.806452, count: 124 -Region Avg IOU: 0.702868, Class: 1.000000, Obj: 0.466399, No Obj: 0.002904, Avg Recall: 0.894231, count: 104 -Region Avg IOU: 0.657489, Class: 1.000000, Obj: 0.442229, No Obj: 0.003008, Avg Recall: 0.823529, count: 119 -Region Avg IOU: 0.655842, Class: 1.000000, Obj: 0.441123, No Obj: 0.003120, Avg Recall: 0.842105, count: 95 -Region Avg IOU: 0.669880, Class: 1.000000, Obj: 0.454354, No Obj: 0.002666, Avg Recall: 0.813333, count: 75 -7154: 0.396619, 0.432610 avg, 0.001000 rate, 26.524696 seconds, 1831424 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.658075, Class: 1.000000, Obj: 0.471343, No Obj: 0.002740, Avg Recall: 0.824561, count: 114 -Region Avg IOU: 0.643316, Class: 1.000000, Obj: 0.449382, No Obj: 0.002631, Avg Recall: 0.780488, count: 123 -Region Avg IOU: 0.672618, Class: 1.000000, Obj: 0.413388, No Obj: 0.002752, Avg Recall: 0.830645, count: 124 -Region Avg IOU: 0.695170, Class: 1.000000, Obj: 0.470965, No Obj: 0.002887, Avg Recall: 0.893204, count: 103 -Region Avg IOU: 0.634871, Class: 1.000000, Obj: 0.460954, No Obj: 0.002428, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.674620, Class: 1.000000, Obj: 0.507837, No Obj: 0.002687, Avg Recall: 0.842105, count: 95 -Region Avg IOU: 0.676264, Class: 1.000000, Obj: 0.479230, No Obj: 0.002787, Avg Recall: 0.831683, count: 101 -Region Avg IOU: 0.625213, Class: 1.000000, Obj: 0.460115, No Obj: 0.003018, Avg Recall: 0.760684, count: 117 -7155: 0.442810, 0.433630 avg, 0.001000 rate, 29.523844 seconds, 1831680 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.666406, Class: 1.000000, Obj: 0.384996, No Obj: 0.003088, Avg Recall: 0.807407, count: 135 -Region Avg IOU: 0.692031, Class: 1.000000, Obj: 0.435452, No Obj: 0.002743, Avg Recall: 0.855856, count: 111 -Region Avg IOU: 0.673262, Class: 1.000000, Obj: 0.443999, No Obj: 0.002262, Avg Recall: 0.829268, count: 82 -Region Avg IOU: 0.662382, Class: 1.000000, Obj: 0.443201, No Obj: 0.002506, Avg Recall: 0.823529, count: 119 -Region Avg IOU: 0.665038, Class: 1.000000, Obj: 0.445336, No Obj: 0.003046, Avg Recall: 0.818182, count: 121 -Region Avg IOU: 0.643870, Class: 1.000000, Obj: 0.404351, No Obj: 0.002554, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.650883, Class: 1.000000, Obj: 0.430313, No Obj: 0.002153, Avg Recall: 0.806818, count: 88 -Region Avg IOU: 0.658834, Class: 1.000000, Obj: 0.394678, No Obj: 0.002774, Avg Recall: 0.829457, count: 129 -7156: 0.449913, 0.435258 avg, 0.001000 rate, 28.244465 seconds, 1831936 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.636166, Class: 1.000000, Obj: 0.439427, No Obj: 0.002403, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.697143, Class: 1.000000, Obj: 0.413938, No Obj: 0.002405, Avg Recall: 0.919192, count: 99 -Region Avg IOU: 0.637517, Class: 1.000000, Obj: 0.432840, No Obj: 0.002532, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.687298, Class: 1.000000, Obj: 0.412942, No Obj: 0.003204, Avg Recall: 0.864000, count: 125 -Region Avg IOU: 0.684799, Class: 1.000000, Obj: 0.361089, No Obj: 0.002932, Avg Recall: 0.844037, count: 109 -Region Avg IOU: 0.666814, Class: 1.000000, Obj: 0.459244, No Obj: 0.002543, Avg Recall: 0.831858, count: 113 -Region Avg IOU: 0.675972, Class: 1.000000, Obj: 0.410766, No Obj: 0.002411, Avg Recall: 0.831579, count: 95 -Region Avg IOU: 0.642146, Class: 1.000000, Obj: 0.366681, No Obj: 0.002754, Avg Recall: 0.785185, count: 135 -7157: 0.456077, 0.437340 avg, 0.001000 rate, 28.699810 seconds, 1832192 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.616463, Class: 1.000000, Obj: 0.406572, No Obj: 0.002830, Avg Recall: 0.739837, count: 123 -Region Avg IOU: 0.670844, Class: 1.000000, Obj: 0.331842, No Obj: 0.002665, Avg Recall: 0.834783, count: 115 -Region Avg IOU: 0.650458, Class: 1.000000, Obj: 0.407734, No Obj: 0.002289, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.718622, Class: 1.000000, Obj: 0.494310, No Obj: 0.002189, Avg Recall: 0.892308, count: 65 -Region Avg IOU: 0.668856, Class: 1.000000, Obj: 0.411630, No Obj: 0.002720, Avg Recall: 0.833333, count: 120 -Region Avg IOU: 0.657771, Class: 1.000000, Obj: 0.413357, No Obj: 0.002303, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.642782, Class: 1.000000, Obj: 0.383202, No Obj: 0.002592, Avg Recall: 0.806452, count: 124 -Region Avg IOU: 0.692892, Class: 1.000000, Obj: 0.426955, No Obj: 0.002456, Avg Recall: 0.899083, count: 109 -7158: 0.432652, 0.436871 avg, 0.001000 rate, 29.347015 seconds, 1832448 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.658041, Class: 1.000000, Obj: 0.406935, No Obj: 0.002578, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.648814, Class: 1.000000, Obj: 0.414011, No Obj: 0.002144, Avg Recall: 0.783132, count: 83 -Region Avg IOU: 0.702121, Class: 1.000000, Obj: 0.486317, No Obj: 0.002649, Avg Recall: 0.857143, count: 105 -Region Avg IOU: 0.687042, Class: 1.000000, Obj: 0.379417, No Obj: 0.002709, Avg Recall: 0.861789, count: 123 -Region Avg IOU: 0.661386, Class: 1.000000, Obj: 0.463049, No Obj: 0.002614, Avg Recall: 0.801653, count: 121 -Region Avg IOU: 0.677777, Class: 1.000000, Obj: 0.440558, No Obj: 0.002741, Avg Recall: 0.878049, count: 123 -Region Avg IOU: 0.689034, Class: 1.000000, Obj: 0.498673, No Obj: 0.002427, Avg Recall: 0.847826, count: 92 -Region Avg IOU: 0.627884, Class: 1.000000, Obj: 0.373187, No Obj: 0.002601, Avg Recall: 0.763889, count: 144 -7159: 0.437929, 0.436977 avg, 0.001000 rate, 28.228956 seconds, 1832704 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.700873, Class: 1.000000, Obj: 0.461026, No Obj: 0.002458, Avg Recall: 0.913979, count: 93 -Region Avg IOU: 0.667327, Class: 1.000000, Obj: 0.374405, No Obj: 0.002499, Avg Recall: 0.877358, count: 106 -Region Avg IOU: 0.637064, Class: 1.000000, Obj: 0.464722, No Obj: 0.002367, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.633789, Class: 1.000000, Obj: 0.368406, No Obj: 0.002413, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.652182, Class: 1.000000, Obj: 0.405621, No Obj: 0.002584, Avg Recall: 0.813084, count: 107 -Region Avg IOU: 0.631991, Class: 1.000000, Obj: 0.365564, No Obj: 0.002555, Avg Recall: 0.770492, count: 122 -Region Avg IOU: 0.663579, Class: 1.000000, Obj: 0.423200, No Obj: 0.002741, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.617582, Class: 1.000000, Obj: 0.413855, No Obj: 0.002465, Avg Recall: 0.739130, count: 115 -7160: 0.429689, 0.436248 avg, 0.001000 rate, 27.724771 seconds, 1832960 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 15.358650 seconds -Region Avg IOU: 0.662074, Class: 1.000000, Obj: 0.360784, No Obj: 0.002873, Avg Recall: 0.811966, count: 117 -Region Avg IOU: 0.638476, Class: 1.000000, Obj: 0.407984, No Obj: 0.002236, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.644319, Class: 1.000000, Obj: 0.405338, No Obj: 0.002411, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.655826, Class: 1.000000, Obj: 0.455075, No Obj: 0.002556, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.667990, Class: 1.000000, Obj: 0.443254, No Obj: 0.002473, Avg Recall: 0.805310, count: 113 -Region Avg IOU: 0.660450, Class: 1.000000, Obj: 0.401534, No Obj: 0.002543, Avg Recall: 0.853211, count: 109 -Region Avg IOU: 0.691027, Class: 1.000000, Obj: 0.415673, No Obj: 0.002935, Avg Recall: 0.857143, count: 105 -Region Avg IOU: 0.626249, Class: 1.000000, Obj: 0.380276, No Obj: 0.002780, Avg Recall: 0.739496, count: 119 -7161: 0.433476, 0.435971 avg, 0.001000 rate, 28.731331 seconds, 1833216 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.647604, Class: 1.000000, Obj: 0.462594, No Obj: 0.002822, Avg Recall: 0.790323, count: 124 -Region Avg IOU: 0.646176, Class: 1.000000, Obj: 0.384640, No Obj: 0.002527, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.666963, Class: 1.000000, Obj: 0.434488, No Obj: 0.002458, Avg Recall: 0.793103, count: 87 -Region Avg IOU: 0.634204, Class: 1.000000, Obj: 0.401107, No Obj: 0.002353, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.659197, Class: 1.000000, Obj: 0.391516, No Obj: 0.002625, Avg Recall: 0.824000, count: 125 -Region Avg IOU: 0.591428, Class: 1.000000, Obj: 0.393191, No Obj: 0.002120, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.626513, Class: 1.000000, Obj: 0.406762, No Obj: 0.002507, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.691681, Class: 1.000000, Obj: 0.452710, No Obj: 0.002696, Avg Recall: 0.873874, count: 111 -7162: 0.428844, 0.435258 avg, 0.001000 rate, 27.748312 seconds, 1833472 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.625554, Class: 1.000000, Obj: 0.427763, No Obj: 0.002595, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.640821, Class: 1.000000, Obj: 0.423368, No Obj: 0.002263, Avg Recall: 0.804348, count: 92 -Region Avg IOU: 0.667010, Class: 1.000000, Obj: 0.450055, No Obj: 0.002828, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.672256, Class: 1.000000, Obj: 0.410879, No Obj: 0.003168, Avg Recall: 0.834587, count: 133 -Region Avg IOU: 0.687695, Class: 1.000000, Obj: 0.482386, No Obj: 0.002463, Avg Recall: 0.867347, count: 98 -Region Avg IOU: 0.646611, Class: 1.000000, Obj: 0.418560, No Obj: 0.002553, Avg Recall: 0.834951, count: 103 -Region Avg IOU: 0.644780, Class: 1.000000, Obj: 0.391876, No Obj: 0.002591, Avg Recall: 0.816514, count: 109 -Region Avg IOU: 0.652047, Class: 1.000000, Obj: 0.381747, No Obj: 0.002008, Avg Recall: 0.812500, count: 80 -7163: 0.428531, 0.434586 avg, 0.001000 rate, 28.615513 seconds, 1833728 images -Loaded: 0.000106 seconds -Region Avg IOU: 0.627846, Class: 1.000000, Obj: 0.432357, No Obj: 0.002429, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.593674, Class: 1.000000, Obj: 0.396682, No Obj: 0.002338, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.661456, Class: 1.000000, Obj: 0.464136, No Obj: 0.002467, Avg Recall: 0.820225, count: 89 -Region Avg IOU: 0.642626, Class: 1.000000, Obj: 0.415682, No Obj: 0.003000, Avg Recall: 0.798450, count: 129 -Region Avg IOU: 0.642234, Class: 1.000000, Obj: 0.387475, No Obj: 0.002266, Avg Recall: 0.752941, count: 85 -Region Avg IOU: 0.697343, Class: 1.000000, Obj: 0.493747, No Obj: 0.002748, Avg Recall: 0.882979, count: 94 -Region Avg IOU: 0.645650, Class: 1.000000, Obj: 0.399856, No Obj: 0.002482, Avg Recall: 0.845238, count: 84 -Region Avg IOU: 0.623900, Class: 1.000000, Obj: 0.373764, No Obj: 0.002491, Avg Recall: 0.725000, count: 120 -7164: 0.410058, 0.432133 avg, 0.001000 rate, 27.878000 seconds, 1833984 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.683118, Class: 1.000000, Obj: 0.455758, No Obj: 0.002261, Avg Recall: 0.862069, count: 87 -Region Avg IOU: 0.674967, Class: 1.000000, Obj: 0.473574, No Obj: 0.002756, Avg Recall: 0.825243, count: 103 -Region Avg IOU: 0.639256, Class: 1.000000, Obj: 0.397054, No Obj: 0.002894, Avg Recall: 0.738806, count: 134 -Region Avg IOU: 0.618924, Class: 1.000000, Obj: 0.389192, No Obj: 0.002286, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.651855, Class: 1.000000, Obj: 0.394635, No Obj: 0.002370, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.652512, Class: 1.000000, Obj: 0.388583, No Obj: 0.002988, Avg Recall: 0.793893, count: 131 -Region Avg IOU: 0.658078, Class: 1.000000, Obj: 0.423908, No Obj: 0.002834, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.667494, Class: 1.000000, Obj: 0.438601, No Obj: 0.003137, Avg Recall: 0.837037, count: 135 -7165: 0.439716, 0.432891 avg, 0.001000 rate, 27.523369 seconds, 1834240 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.677227, Class: 1.000000, Obj: 0.492252, No Obj: 0.002256, Avg Recall: 0.837209, count: 86 -Region Avg IOU: 0.649584, Class: 1.000000, Obj: 0.388686, No Obj: 0.002602, Avg Recall: 0.776000, count: 125 -Region Avg IOU: 0.646793, Class: 1.000000, Obj: 0.421767, No Obj: 0.002641, Avg Recall: 0.776859, count: 121 -Region Avg IOU: 0.642866, Class: 1.000000, Obj: 0.419417, No Obj: 0.002378, Avg Recall: 0.772277, count: 101 -Region Avg IOU: 0.643299, Class: 1.000000, Obj: 0.394080, No Obj: 0.002867, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.689183, Class: 1.000000, Obj: 0.478369, No Obj: 0.002193, Avg Recall: 0.863636, count: 88 -Region Avg IOU: 0.636385, Class: 1.000000, Obj: 0.428044, No Obj: 0.002528, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.652111, Class: 1.000000, Obj: 0.416573, No Obj: 0.002479, Avg Recall: 0.795181, count: 83 -7166: 0.389303, 0.428532 avg, 0.001000 rate, 26.901876 seconds, 1834496 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.652853, Class: 1.000000, Obj: 0.475077, No Obj: 0.002356, Avg Recall: 0.758242, count: 91 -Region Avg IOU: 0.684656, Class: 1.000000, Obj: 0.408359, No Obj: 0.002940, Avg Recall: 0.845455, count: 110 -Region Avg IOU: 0.664792, Class: 1.000000, Obj: 0.452854, No Obj: 0.002048, Avg Recall: 0.818182, count: 66 -Region Avg IOU: 0.629842, Class: 1.000000, Obj: 0.396385, No Obj: 0.002876, Avg Recall: 0.759399, count: 133 -Region Avg IOU: 0.626607, Class: 1.000000, Obj: 0.452002, No Obj: 0.002332, Avg Recall: 0.760417, count: 96 -Region Avg IOU: 0.681663, Class: 1.000000, Obj: 0.383184, No Obj: 0.002518, Avg Recall: 0.864583, count: 96 -Region Avg IOU: 0.654234, Class: 1.000000, Obj: 0.404918, No Obj: 0.002067, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.642329, Class: 1.000000, Obj: 0.481401, No Obj: 0.002568, Avg Recall: 0.793478, count: 92 -7167: 0.395524, 0.425232 avg, 0.001000 rate, 28.441795 seconds, 1834752 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.650474, Class: 1.000000, Obj: 0.397252, No Obj: 0.003130, Avg Recall: 0.831933, count: 119 -Region Avg IOU: 0.684880, Class: 1.000000, Obj: 0.479665, No Obj: 0.002514, Avg Recall: 0.842105, count: 95 -Region Avg IOU: 0.639522, Class: 1.000000, Obj: 0.445868, No Obj: 0.002647, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.606091, Class: 1.000000, Obj: 0.374835, No Obj: 0.002358, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.617166, Class: 1.000000, Obj: 0.404082, No Obj: 0.002133, Avg Recall: 0.699029, count: 103 -Region Avg IOU: 0.679706, Class: 1.000000, Obj: 0.419459, No Obj: 0.002439, Avg Recall: 0.845361, count: 97 -Region Avg IOU: 0.676315, Class: 1.000000, Obj: 0.461059, No Obj: 0.002525, Avg Recall: 0.824742, count: 97 -Region Avg IOU: 0.592653, Class: 1.000000, Obj: 0.373516, No Obj: 0.002484, Avg Recall: 0.709402, count: 117 -7168: 0.408820, 0.423590 avg, 0.001000 rate, 28.135080 seconds, 1835008 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.642712, Class: 1.000000, Obj: 0.385420, No Obj: 0.002634, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.661649, Class: 1.000000, Obj: 0.412390, No Obj: 0.002790, Avg Recall: 0.829457, count: 129 -Region Avg IOU: 0.681661, Class: 1.000000, Obj: 0.453030, No Obj: 0.002548, Avg Recall: 0.831683, count: 101 -Region Avg IOU: 0.667370, Class: 1.000000, Obj: 0.387303, No Obj: 0.002461, Avg Recall: 0.833333, count: 96 -Region Avg IOU: 0.698281, Class: 1.000000, Obj: 0.376722, No Obj: 0.002550, Avg Recall: 0.892473, count: 93 -Region Avg IOU: 0.729421, Class: 1.000000, Obj: 0.424519, No Obj: 0.002372, Avg Recall: 0.920455, count: 88 -Region Avg IOU: 0.654750, Class: 1.000000, Obj: 0.412178, No Obj: 0.002202, Avg Recall: 0.809524, count: 84 -Region Avg IOU: 0.649121, Class: 1.000000, Obj: 0.398387, No Obj: 0.002845, Avg Recall: 0.815603, count: 141 -7169: 0.449606, 0.426192 avg, 0.001000 rate, 28.966272 seconds, 1835264 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.691154, Class: 1.000000, Obj: 0.459890, No Obj: 0.002348, Avg Recall: 0.865854, count: 82 -Region Avg IOU: 0.684040, Class: 1.000000, Obj: 0.439862, No Obj: 0.002832, Avg Recall: 0.883929, count: 112 -Region Avg IOU: 0.672983, Class: 1.000000, Obj: 0.392122, No Obj: 0.002725, Avg Recall: 0.859813, count: 107 -Region Avg IOU: 0.685015, Class: 1.000000, Obj: 0.430586, No Obj: 0.002827, Avg Recall: 0.816000, count: 125 -Region Avg IOU: 0.666443, Class: 1.000000, Obj: 0.377932, No Obj: 0.002860, Avg Recall: 0.784483, count: 116 -Region Avg IOU: 0.650741, Class: 1.000000, Obj: 0.410186, No Obj: 0.002396, Avg Recall: 0.819149, count: 94 -Region Avg IOU: 0.638562, Class: 1.000000, Obj: 0.366294, No Obj: 0.002837, Avg Recall: 0.786260, count: 131 -Region Avg IOU: 0.669194, Class: 1.000000, Obj: 0.412531, No Obj: 0.002649, Avg Recall: 0.826446, count: 121 -7170: 0.454419, 0.429015 avg, 0.001000 rate, 29.071350 seconds, 1835520 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 15.048348 seconds -Region Avg IOU: 0.562973, Class: 1.000000, Obj: 0.322706, No Obj: 0.002963, Avg Recall: 0.626263, count: 99 -Region Avg IOU: 0.527841, Class: 1.000000, Obj: 0.331375, No Obj: 0.004255, Avg Recall: 0.616541, count: 133 -Region Avg IOU: 0.552304, Class: 1.000000, Obj: 0.321638, No Obj: 0.003373, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.541720, Class: 1.000000, Obj: 0.327252, No Obj: 0.003277, Avg Recall: 0.585586, count: 111 -Region Avg IOU: 0.539024, Class: 1.000000, Obj: 0.349980, No Obj: 0.003440, Avg Recall: 0.577320, count: 97 -Region Avg IOU: 0.544780, Class: 1.000000, Obj: 0.323237, No Obj: 0.003872, Avg Recall: 0.607407, count: 135 -Region Avg IOU: 0.579684, Class: 1.000000, Obj: 0.315350, No Obj: 0.003753, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.536689, Class: 1.000000, Obj: 0.311878, No Obj: 0.003611, Avg Recall: 0.617886, count: 123 -7171: 0.469232, 0.433036 avg, 0.001000 rate, 20.629160 seconds, 1835776 images -Loaded: 0.205342 seconds -Region Avg IOU: 0.601533, Class: 1.000000, Obj: 0.363596, No Obj: 0.004744, Avg Recall: 0.692913, count: 127 -Region Avg IOU: 0.532012, Class: 1.000000, Obj: 0.330593, No Obj: 0.003196, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.540007, Class: 1.000000, Obj: 0.326398, No Obj: 0.003614, Avg Recall: 0.626866, count: 134 -Region Avg IOU: 0.533698, Class: 1.000000, Obj: 0.336475, No Obj: 0.003371, Avg Recall: 0.606838, count: 117 -Region Avg IOU: 0.529701, Class: 1.000000, Obj: 0.331779, No Obj: 0.003296, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.570336, Class: 1.000000, Obj: 0.326360, No Obj: 0.003330, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.588575, Class: 1.000000, Obj: 0.350682, No Obj: 0.003478, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.586937, Class: 1.000000, Obj: 0.360544, No Obj: 0.003233, Avg Recall: 0.734940, count: 83 -7172: 0.418236, 0.431556 avg, 0.001000 rate, 18.933151 seconds, 1836032 images -Loaded: 1.394472 seconds -Region Avg IOU: 0.527790, Class: 1.000000, Obj: 0.382366, No Obj: 0.004205, Avg Recall: 0.565574, count: 122 -Region Avg IOU: 0.525719, Class: 1.000000, Obj: 0.331615, No Obj: 0.003925, Avg Recall: 0.632479, count: 117 -Region Avg IOU: 0.512276, Class: 1.000000, Obj: 0.343502, No Obj: 0.003567, Avg Recall: 0.576923, count: 130 -Region Avg IOU: 0.535332, Class: 1.000000, Obj: 0.370513, No Obj: 0.004380, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.510834, Class: 1.000000, Obj: 0.346850, No Obj: 0.003500, Avg Recall: 0.530612, count: 98 -Region Avg IOU: 0.510082, Class: 1.000000, Obj: 0.352498, No Obj: 0.003758, Avg Recall: 0.536765, count: 136 -Region Avg IOU: 0.511881, Class: 1.000000, Obj: 0.341192, No Obj: 0.003686, Avg Recall: 0.550000, count: 100 -Region Avg IOU: 0.534362, Class: 1.000000, Obj: 0.363680, No Obj: 0.003923, Avg Recall: 0.608696, count: 115 -7173: 0.483568, 0.436758 avg, 0.001000 rate, 20.807060 seconds, 1836288 images -Loaded: 0.199601 seconds -Region Avg IOU: 0.561845, Class: 1.000000, Obj: 0.413296, No Obj: 0.004215, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.561100, Class: 1.000000, Obj: 0.405917, No Obj: 0.003769, Avg Recall: 0.578947, count: 95 -Region Avg IOU: 0.550840, Class: 1.000000, Obj: 0.388778, No Obj: 0.003780, Avg Recall: 0.616162, count: 99 -Region Avg IOU: 0.614970, Class: 1.000000, Obj: 0.424150, No Obj: 0.003694, Avg Recall: 0.732558, count: 86 -Region Avg IOU: 0.580270, Class: 1.000000, Obj: 0.339676, No Obj: 0.003845, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.541827, Class: 1.000000, Obj: 0.416514, No Obj: 0.003190, Avg Recall: 0.650602, count: 83 -Region Avg IOU: 0.528284, Class: 1.000000, Obj: 0.380651, No Obj: 0.003697, Avg Recall: 0.567568, count: 111 -Region Avg IOU: 0.585302, Class: 1.000000, Obj: 0.396688, No Obj: 0.004088, Avg Recall: 0.678261, count: 115 -7174: 0.438851, 0.436967 avg, 0.001000 rate, 19.700392 seconds, 1836544 images -Loaded: 1.656997 seconds -Region Avg IOU: 0.499976, Class: 1.000000, Obj: 0.365223, No Obj: 0.004226, Avg Recall: 0.581818, count: 110 -Region Avg IOU: 0.518423, Class: 1.000000, Obj: 0.350814, No Obj: 0.004479, Avg Recall: 0.603175, count: 126 -Region Avg IOU: 0.548728, Class: 1.000000, Obj: 0.396119, No Obj: 0.003934, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.558392, Class: 1.000000, Obj: 0.375456, No Obj: 0.004382, Avg Recall: 0.655738, count: 122 -Region Avg IOU: 0.556460, Class: 1.000000, Obj: 0.433504, No Obj: 0.004097, Avg Recall: 0.632653, count: 98 -Region Avg IOU: 0.515870, Class: 1.000000, Obj: 0.384504, No Obj: 0.003730, Avg Recall: 0.601626, count: 123 -Region Avg IOU: 0.520424, Class: 1.000000, Obj: 0.388727, No Obj: 0.004133, Avg Recall: 0.561404, count: 114 -Region Avg IOU: 0.560835, Class: 1.000000, Obj: 0.385393, No Obj: 0.003617, Avg Recall: 0.673469, count: 98 -7175: 0.417630, 0.435033 avg, 0.001000 rate, 20.304527 seconds, 1836800 images -Loaded: 0.785748 seconds -Region Avg IOU: 0.547307, Class: 1.000000, Obj: 0.440243, No Obj: 0.004061, Avg Recall: 0.635294, count: 85 -Region Avg IOU: 0.541357, Class: 1.000000, Obj: 0.377371, No Obj: 0.004874, Avg Recall: 0.619835, count: 121 -Region Avg IOU: 0.546655, Class: 1.000000, Obj: 0.402050, No Obj: 0.004289, Avg Recall: 0.610169, count: 118 -Region Avg IOU: 0.558770, Class: 1.000000, Obj: 0.424429, No Obj: 0.004329, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.540865, Class: 1.000000, Obj: 0.418486, No Obj: 0.004883, Avg Recall: 0.597015, count: 134 -Region Avg IOU: 0.556612, Class: 1.000000, Obj: 0.459533, No Obj: 0.004809, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.564592, Class: 1.000000, Obj: 0.409810, No Obj: 0.005009, Avg Recall: 0.634615, count: 104 -Region Avg IOU: 0.544557, Class: 1.000000, Obj: 0.455953, No Obj: 0.004185, Avg Recall: 0.626667, count: 75 -7176: 0.384134, 0.429943 avg, 0.001000 rate, 21.868467 seconds, 1837056 images -Loaded: 0.922497 seconds -Region Avg IOU: 0.536483, Class: 1.000000, Obj: 0.419041, No Obj: 0.004513, Avg Recall: 0.594340, count: 106 -Region Avg IOU: 0.490828, Class: 1.000000, Obj: 0.383227, No Obj: 0.004759, Avg Recall: 0.532258, count: 124 -Region Avg IOU: 0.585806, Class: 1.000000, Obj: 0.470753, No Obj: 0.004778, Avg Recall: 0.677778, count: 90 -Region Avg IOU: 0.546561, Class: 1.000000, Obj: 0.404817, No Obj: 0.005458, Avg Recall: 0.634328, count: 134 -Region Avg IOU: 0.528042, Class: 1.000000, Obj: 0.413111, No Obj: 0.004846, Avg Recall: 0.642857, count: 98 -Region Avg IOU: 0.502979, Class: 1.000000, Obj: 0.411477, No Obj: 0.003822, Avg Recall: 0.525000, count: 80 -Region Avg IOU: 0.532890, Class: 1.000000, Obj: 0.426725, No Obj: 0.004456, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.530834, Class: 1.000000, Obj: 0.432259, No Obj: 0.004327, Avg Recall: 0.600000, count: 95 -7177: 0.445284, 0.431477 avg, 0.001000 rate, 21.861937 seconds, 1837312 images -Loaded: 0.565465 seconds -Region Avg IOU: 0.530340, Class: 1.000000, Obj: 0.422974, No Obj: 0.004176, Avg Recall: 0.589474, count: 95 -Region Avg IOU: 0.513137, Class: 1.000000, Obj: 0.413281, No Obj: 0.004878, Avg Recall: 0.565891, count: 129 -Region Avg IOU: 0.522985, Class: 1.000000, Obj: 0.381046, No Obj: 0.004966, Avg Recall: 0.605042, count: 119 -Region Avg IOU: 0.538682, Class: 1.000000, Obj: 0.411869, No Obj: 0.005186, Avg Recall: 0.617647, count: 136 -Region Avg IOU: 0.502803, Class: 1.000000, Obj: 0.455772, No Obj: 0.004636, Avg Recall: 0.530973, count: 113 -Region Avg IOU: 0.532173, Class: 1.000000, Obj: 0.420494, No Obj: 0.005400, Avg Recall: 0.580882, count: 136 -Region Avg IOU: 0.580581, Class: 1.000000, Obj: 0.448421, No Obj: 0.005475, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.587280, Class: 1.000000, Obj: 0.435627, No Obj: 0.005416, Avg Recall: 0.675000, count: 120 -7178: 0.482645, 0.436594 avg, 0.001000 rate, 19.728228 seconds, 1837568 images -Loaded: 2.297231 seconds -Region Avg IOU: 0.546197, Class: 1.000000, Obj: 0.458140, No Obj: 0.004016, Avg Recall: 0.602273, count: 88 -Region Avg IOU: 0.508965, Class: 1.000000, Obj: 0.393603, No Obj: 0.004967, Avg Recall: 0.583942, count: 137 -Region Avg IOU: 0.561196, Class: 1.000000, Obj: 0.469215, No Obj: 0.004735, Avg Recall: 0.659341, count: 91 -Region Avg IOU: 0.511435, Class: 1.000000, Obj: 0.431616, No Obj: 0.004830, Avg Recall: 0.592000, count: 125 -Region Avg IOU: 0.563582, Class: 1.000000, Obj: 0.442114, No Obj: 0.004741, Avg Recall: 0.695652, count: 92 -Region Avg IOU: 0.571645, Class: 1.000000, Obj: 0.469297, No Obj: 0.005003, Avg Recall: 0.693069, count: 101 -Region Avg IOU: 0.589234, Class: 1.000000, Obj: 0.484610, No Obj: 0.004746, Avg Recall: 0.679612, count: 103 -Region Avg IOU: 0.544430, Class: 1.000000, Obj: 0.419873, No Obj: 0.005495, Avg Recall: 0.658730, count: 126 -7179: 0.412747, 0.434209 avg, 0.001000 rate, 19.449512 seconds, 1837824 images -Loaded: 3.448843 seconds -Region Avg IOU: 0.574959, Class: 1.000000, Obj: 0.403944, No Obj: 0.005525, Avg Recall: 0.648276, count: 145 -Region Avg IOU: 0.536812, Class: 1.000000, Obj: 0.413433, No Obj: 0.004512, Avg Recall: 0.614754, count: 122 -Region Avg IOU: 0.537393, Class: 1.000000, Obj: 0.366884, No Obj: 0.004992, Avg Recall: 0.635714, count: 140 -Region Avg IOU: 0.508344, Class: 1.000000, Obj: 0.436542, No Obj: 0.004451, Avg Recall: 0.525773, count: 97 -Region Avg IOU: 0.578869, Class: 1.000000, Obj: 0.444638, No Obj: 0.004316, Avg Recall: 0.666667, count: 102 -Region Avg IOU: 0.507705, Class: 1.000000, Obj: 0.447505, No Obj: 0.003657, Avg Recall: 0.544444, count: 90 -Region Avg IOU: 0.543082, Class: 1.000000, Obj: 0.343107, No Obj: 0.005081, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.545412, Class: 1.000000, Obj: 0.457123, No Obj: 0.004543, Avg Recall: 0.618182, count: 110 -7180: 0.435971, 0.434386 avg, 0.001000 rate, 20.231443 seconds, 1838080 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000060 seconds -Region Avg IOU: 0.579492, Class: 1.000000, Obj: 0.445003, No Obj: 0.003397, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.633958, Class: 1.000000, Obj: 0.497999, No Obj: 0.003515, Avg Recall: 0.790698, count: 86 -Region Avg IOU: 0.608152, Class: 1.000000, Obj: 0.454327, No Obj: 0.004531, Avg Recall: 0.738095, count: 126 -Region Avg IOU: 0.603283, Class: 1.000000, Obj: 0.462875, No Obj: 0.003530, Avg Recall: 0.688889, count: 90 -Region Avg IOU: 0.628181, Class: 1.000000, Obj: 0.463571, No Obj: 0.003636, Avg Recall: 0.738095, count: 84 -Region Avg IOU: 0.579010, Class: 1.000000, Obj: 0.526289, No Obj: 0.003701, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.575045, Class: 1.000000, Obj: 0.468288, No Obj: 0.003766, Avg Recall: 0.642105, count: 95 -Region Avg IOU: 0.605983, Class: 1.000000, Obj: 0.467619, No Obj: 0.004670, Avg Recall: 0.739130, count: 138 -7181: 0.370716, 0.428019 avg, 0.001000 rate, 24.331400 seconds, 1838336 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.608587, Class: 1.000000, Obj: 0.413606, No Obj: 0.003912, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.624933, Class: 1.000000, Obj: 0.504025, No Obj: 0.003668, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.607476, Class: 1.000000, Obj: 0.446333, No Obj: 0.003376, Avg Recall: 0.707317, count: 82 -Region Avg IOU: 0.636400, Class: 1.000000, Obj: 0.440752, No Obj: 0.004227, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.631167, Class: 1.000000, Obj: 0.509622, No Obj: 0.003693, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.600627, Class: 1.000000, Obj: 0.444272, No Obj: 0.004441, Avg Recall: 0.746753, count: 154 -Region Avg IOU: 0.631114, Class: 1.000000, Obj: 0.443170, No Obj: 0.003999, Avg Recall: 0.729323, count: 133 -Region Avg IOU: 0.626936, Class: 1.000000, Obj: 0.416062, No Obj: 0.004308, Avg Recall: 0.782258, count: 124 -7182: 0.422879, 0.427505 avg, 0.001000 rate, 24.712393 seconds, 1838592 images -Loaded: 0.000112 seconds -Region Avg IOU: 0.617951, Class: 1.000000, Obj: 0.432317, No Obj: 0.004039, Avg Recall: 0.752000, count: 125 -Region Avg IOU: 0.582106, Class: 1.000000, Obj: 0.434127, No Obj: 0.003411, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.640766, Class: 1.000000, Obj: 0.424540, No Obj: 0.004236, Avg Recall: 0.758065, count: 124 -Region Avg IOU: 0.564629, Class: 1.000000, Obj: 0.402500, No Obj: 0.003967, Avg Recall: 0.656000, count: 125 -Region Avg IOU: 0.596131, Class: 1.000000, Obj: 0.410008, No Obj: 0.003574, Avg Recall: 0.718750, count: 96 -Region Avg IOU: 0.583994, Class: 1.000000, Obj: 0.387761, No Obj: 0.004170, Avg Recall: 0.682171, count: 129 -Region Avg IOU: 0.556764, Class: 1.000000, Obj: 0.377838, No Obj: 0.003266, Avg Recall: 0.654206, count: 107 -Region Avg IOU: 0.617963, Class: 1.000000, Obj: 0.470391, No Obj: 0.004340, Avg Recall: 0.745455, count: 110 -7183: 0.442857, 0.429040 avg, 0.001000 rate, 24.045687 seconds, 1838848 images -Loaded: 0.000091 seconds -Region Avg IOU: 0.627366, Class: 1.000000, Obj: 0.432265, No Obj: 0.003528, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.637249, Class: 1.000000, Obj: 0.377374, No Obj: 0.004021, Avg Recall: 0.796460, count: 113 -Region Avg IOU: 0.602118, Class: 1.000000, Obj: 0.409520, No Obj: 0.004455, Avg Recall: 0.682119, count: 151 -Region Avg IOU: 0.644693, Class: 1.000000, Obj: 0.488746, No Obj: 0.004122, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.596104, Class: 1.000000, Obj: 0.416937, No Obj: 0.003308, Avg Recall: 0.703704, count: 81 -Region Avg IOU: 0.602651, Class: 1.000000, Obj: 0.402626, No Obj: 0.002794, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.590604, Class: 1.000000, Obj: 0.403899, No Obj: 0.003601, Avg Recall: 0.664179, count: 134 -Region Avg IOU: 0.605963, Class: 1.000000, Obj: 0.404013, No Obj: 0.004238, Avg Recall: 0.746377, count: 138 -7184: 0.438454, 0.429981 avg, 0.001000 rate, 23.877880 seconds, 1839104 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.657155, Class: 1.000000, Obj: 0.434796, No Obj: 0.003950, Avg Recall: 0.798319, count: 119 -Region Avg IOU: 0.546307, Class: 1.000000, Obj: 0.369331, No Obj: 0.002882, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.624499, Class: 1.000000, Obj: 0.379051, No Obj: 0.003368, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.561079, Class: 1.000000, Obj: 0.426845, No Obj: 0.003265, Avg Recall: 0.612613, count: 111 -Region Avg IOU: 0.585837, Class: 1.000000, Obj: 0.400492, No Obj: 0.003060, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.573109, Class: 1.000000, Obj: 0.357552, No Obj: 0.003589, Avg Recall: 0.631148, count: 122 -Region Avg IOU: 0.636389, Class: 1.000000, Obj: 0.421042, No Obj: 0.003684, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.592022, Class: 1.000000, Obj: 0.407003, No Obj: 0.003668, Avg Recall: 0.722689, count: 119 -7185: 0.518052, 0.438788 avg, 0.001000 rate, 24.183125 seconds, 1839360 images -Loaded: 0.000056 seconds -Region Avg IOU: 0.614483, Class: 1.000000, Obj: 0.429229, No Obj: 0.003591, Avg Recall: 0.718182, count: 110 -Region Avg IOU: 0.583289, Class: 1.000000, Obj: 0.366576, No Obj: 0.003261, Avg Recall: 0.730159, count: 126 -Region Avg IOU: 0.606796, Class: 1.000000, Obj: 0.377788, No Obj: 0.002616, Avg Recall: 0.697674, count: 86 -Region Avg IOU: 0.585001, Class: 1.000000, Obj: 0.395437, No Obj: 0.004080, Avg Recall: 0.691176, count: 136 -Region Avg IOU: 0.604553, Class: 1.000000, Obj: 0.436406, No Obj: 0.003054, Avg Recall: 0.719512, count: 82 -Region Avg IOU: 0.560263, Class: 1.000000, Obj: 0.354600, No Obj: 0.003613, Avg Recall: 0.651515, count: 132 -Region Avg IOU: 0.618726, Class: 1.000000, Obj: 0.399782, No Obj: 0.002701, Avg Recall: 0.758621, count: 87 -Region Avg IOU: 0.625838, Class: 1.000000, Obj: 0.436587, No Obj: 0.003440, Avg Recall: 0.707965, count: 113 -7186: 0.372975, 0.432207 avg, 0.001000 rate, 24.126036 seconds, 1839616 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.659544, Class: 1.000000, Obj: 0.398498, No Obj: 0.003493, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.604726, Class: 1.000000, Obj: 0.407430, No Obj: 0.003235, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.610628, Class: 1.000000, Obj: 0.441771, No Obj: 0.003242, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.652191, Class: 1.000000, Obj: 0.445222, No Obj: 0.003082, Avg Recall: 0.781250, count: 96 -Region Avg IOU: 0.667962, Class: 1.000000, Obj: 0.387717, No Obj: 0.002979, Avg Recall: 0.835165, count: 91 -Region Avg IOU: 0.602246, Class: 1.000000, Obj: 0.383825, No Obj: 0.003445, Avg Recall: 0.739496, count: 119 -Region Avg IOU: 0.608515, Class: 1.000000, Obj: 0.399681, No Obj: 0.003191, Avg Recall: 0.720339, count: 118 -Region Avg IOU: 0.601979, Class: 1.000000, Obj: 0.341336, No Obj: 0.003531, Avg Recall: 0.727273, count: 121 -7187: 0.439637, 0.432950 avg, 0.001000 rate, 24.384731 seconds, 1839872 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.613638, Class: 1.000000, Obj: 0.408966, No Obj: 0.002721, Avg Recall: 0.761364, count: 88 -Region Avg IOU: 0.598397, Class: 1.000000, Obj: 0.405734, No Obj: 0.003636, Avg Recall: 0.696000, count: 125 -Region Avg IOU: 0.601311, Class: 1.000000, Obj: 0.397799, No Obj: 0.004305, Avg Recall: 0.727848, count: 158 -Region Avg IOU: 0.586486, Class: 1.000000, Obj: 0.390911, No Obj: 0.003682, Avg Recall: 0.695652, count: 138 -Region Avg IOU: 0.650070, Class: 1.000000, Obj: 0.446356, No Obj: 0.003269, Avg Recall: 0.802326, count: 86 -Region Avg IOU: 0.602014, Class: 1.000000, Obj: 0.377918, No Obj: 0.003668, Avg Recall: 0.696970, count: 132 -Region Avg IOU: 0.628452, Class: 1.000000, Obj: 0.396767, No Obj: 0.003184, Avg Recall: 0.785047, count: 107 -Region Avg IOU: 0.599835, Class: 1.000000, Obj: 0.406685, No Obj: 0.003450, Avg Recall: 0.681035, count: 116 -7188: 0.430371, 0.432692 avg, 0.001000 rate, 23.288895 seconds, 1840128 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.584835, Class: 1.000000, Obj: 0.401465, No Obj: 0.003261, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.612084, Class: 1.000000, Obj: 0.425320, No Obj: 0.003488, Avg Recall: 0.731092, count: 119 -Region Avg IOU: 0.626984, Class: 1.000000, Obj: 0.418472, No Obj: 0.002637, Avg Recall: 0.712644, count: 87 -Region Avg IOU: 0.579444, Class: 1.000000, Obj: 0.400681, No Obj: 0.003074, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.604282, Class: 1.000000, Obj: 0.413615, No Obj: 0.003276, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.593459, Class: 1.000000, Obj: 0.360677, No Obj: 0.002758, Avg Recall: 0.717949, count: 78 -Region Avg IOU: 0.604518, Class: 1.000000, Obj: 0.372571, No Obj: 0.003657, Avg Recall: 0.734848, count: 132 -Region Avg IOU: 0.597501, Class: 1.000000, Obj: 0.397229, No Obj: 0.003170, Avg Recall: 0.710526, count: 114 -7189: 0.447914, 0.434214 avg, 0.001000 rate, 23.880316 seconds, 1840384 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.589759, Class: 1.000000, Obj: 0.447750, No Obj: 0.003150, Avg Recall: 0.750000, count: 88 -Region Avg IOU: 0.635061, Class: 1.000000, Obj: 0.430317, No Obj: 0.003073, Avg Recall: 0.747573, count: 103 -Region Avg IOU: 0.559381, Class: 1.000000, Obj: 0.367287, No Obj: 0.003341, Avg Recall: 0.643939, count: 132 -Region Avg IOU: 0.612092, Class: 1.000000, Obj: 0.410801, No Obj: 0.003557, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.622184, Class: 1.000000, Obj: 0.426780, No Obj: 0.003824, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.617106, Class: 1.000000, Obj: 0.391328, No Obj: 0.003231, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.600592, Class: 1.000000, Obj: 0.377531, No Obj: 0.004597, Avg Recall: 0.724409, count: 127 -Region Avg IOU: 0.608100, Class: 1.000000, Obj: 0.447644, No Obj: 0.003222, Avg Recall: 0.720721, count: 111 -7190: 0.439013, 0.434694 avg, 0.001000 rate, 24.132431 seconds, 1840640 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000073 seconds -Region Avg IOU: 0.670974, Class: 1.000000, Obj: 0.532352, No Obj: 0.002700, Avg Recall: 0.821918, count: 73 -Region Avg IOU: 0.665804, Class: 1.000000, Obj: 0.476453, No Obj: 0.003626, Avg Recall: 0.814516, count: 124 -Region Avg IOU: 0.611029, Class: 1.000000, Obj: 0.462334, No Obj: 0.003267, Avg Recall: 0.708661, count: 127 -Region Avg IOU: 0.631775, Class: 1.000000, Obj: 0.436203, No Obj: 0.002881, Avg Recall: 0.797980, count: 99 -Region Avg IOU: 0.633835, Class: 1.000000, Obj: 0.460914, No Obj: 0.002938, Avg Recall: 0.715909, count: 88 -Region Avg IOU: 0.642458, Class: 1.000000, Obj: 0.440362, No Obj: 0.003110, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.588398, Class: 1.000000, Obj: 0.413106, No Obj: 0.003469, Avg Recall: 0.674419, count: 129 -Region Avg IOU: 0.631349, Class: 1.000000, Obj: 0.446498, No Obj: 0.003084, Avg Recall: 0.723810, count: 105 -7191: 0.385359, 0.429761 avg, 0.001000 rate, 26.726011 seconds, 1840896 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.657114, Class: 1.000000, Obj: 0.436639, No Obj: 0.003338, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.610360, Class: 1.000000, Obj: 0.430652, No Obj: 0.003749, Avg Recall: 0.705521, count: 163 -Region Avg IOU: 0.579463, Class: 1.000000, Obj: 0.445164, No Obj: 0.002685, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.654562, Class: 1.000000, Obj: 0.458470, No Obj: 0.003308, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.664554, Class: 1.000000, Obj: 0.493100, No Obj: 0.003914, Avg Recall: 0.785185, count: 135 -Region Avg IOU: 0.588591, Class: 1.000000, Obj: 0.431973, No Obj: 0.003145, Avg Recall: 0.731959, count: 97 -Region Avg IOU: 0.593914, Class: 1.000000, Obj: 0.433048, No Obj: 0.002934, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.671890, Class: 1.000000, Obj: 0.441306, No Obj: 0.003199, Avg Recall: 0.817391, count: 115 -7192: 0.408599, 0.427645 avg, 0.001000 rate, 26.541317 seconds, 1841152 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.659861, Class: 1.000000, Obj: 0.475831, No Obj: 0.003409, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.664372, Class: 1.000000, Obj: 0.448041, No Obj: 0.004006, Avg Recall: 0.835938, count: 128 -Region Avg IOU: 0.629098, Class: 1.000000, Obj: 0.437790, No Obj: 0.003627, Avg Recall: 0.737705, count: 122 -Region Avg IOU: 0.647857, Class: 1.000000, Obj: 0.467026, No Obj: 0.004022, Avg Recall: 0.785185, count: 135 -Region Avg IOU: 0.636869, Class: 1.000000, Obj: 0.438560, No Obj: 0.002615, Avg Recall: 0.740741, count: 81 -Region Avg IOU: 0.618362, Class: 1.000000, Obj: 0.478058, No Obj: 0.003484, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.603455, Class: 1.000000, Obj: 0.447500, No Obj: 0.003293, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.680046, Class: 1.000000, Obj: 0.504901, No Obj: 0.003441, Avg Recall: 0.830000, count: 100 -7193: 0.389194, 0.423799 avg, 0.001000 rate, 26.073074 seconds, 1841408 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.640192, Class: 1.000000, Obj: 0.467211, No Obj: 0.003708, Avg Recall: 0.786260, count: 131 -Region Avg IOU: 0.608195, Class: 1.000000, Obj: 0.470468, No Obj: 0.002868, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.647770, Class: 1.000000, Obj: 0.427492, No Obj: 0.004260, Avg Recall: 0.798507, count: 134 -Region Avg IOU: 0.619020, Class: 1.000000, Obj: 0.421237, No Obj: 0.003262, Avg Recall: 0.769231, count: 117 -Region Avg IOU: 0.636543, Class: 1.000000, Obj: 0.471032, No Obj: 0.003572, Avg Recall: 0.785124, count: 121 -Region Avg IOU: 0.662922, Class: 1.000000, Obj: 0.439092, No Obj: 0.003020, Avg Recall: 0.827957, count: 93 -Region Avg IOU: 0.619941, Class: 1.000000, Obj: 0.448648, No Obj: 0.003764, Avg Recall: 0.755396, count: 139 -Region Avg IOU: 0.608579, Class: 1.000000, Obj: 0.422202, No Obj: 0.002455, Avg Recall: 0.696203, count: 79 -7194: 0.411331, 0.422553 avg, 0.001000 rate, 25.863968 seconds, 1841664 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.615728, Class: 1.000000, Obj: 0.456704, No Obj: 0.003510, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.635244, Class: 1.000000, Obj: 0.438541, No Obj: 0.003434, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.653397, Class: 1.000000, Obj: 0.484965, No Obj: 0.003494, Avg Recall: 0.778626, count: 131 -Region Avg IOU: 0.586993, Class: 1.000000, Obj: 0.437201, No Obj: 0.003076, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.623045, Class: 1.000000, Obj: 0.463518, No Obj: 0.003436, Avg Recall: 0.730769, count: 104 -Region Avg IOU: 0.647060, Class: 1.000000, Obj: 0.488138, No Obj: 0.003432, Avg Recall: 0.800000, count: 95 -Region Avg IOU: 0.650875, Class: 1.000000, Obj: 0.473947, No Obj: 0.003333, Avg Recall: 0.760684, count: 117 -Region Avg IOU: 0.627040, Class: 1.000000, Obj: 0.467390, No Obj: 0.003486, Avg Recall: 0.734848, count: 132 -7195: 0.418037, 0.422101 avg, 0.001000 rate, 25.976006 seconds, 1841920 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.615497, Class: 1.000000, Obj: 0.437640, No Obj: 0.003017, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.652015, Class: 1.000000, Obj: 0.443856, No Obj: 0.002859, Avg Recall: 0.821918, count: 73 -Region Avg IOU: 0.591431, Class: 1.000000, Obj: 0.363286, No Obj: 0.003409, Avg Recall: 0.689394, count: 132 -Region Avg IOU: 0.607074, Class: 1.000000, Obj: 0.402171, No Obj: 0.003088, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.616560, Class: 1.000000, Obj: 0.447162, No Obj: 0.003563, Avg Recall: 0.759399, count: 133 -Region Avg IOU: 0.648252, Class: 1.000000, Obj: 0.463155, No Obj: 0.003849, Avg Recall: 0.791667, count: 120 -Region Avg IOU: 0.595934, Class: 1.000000, Obj: 0.400379, No Obj: 0.003445, Avg Recall: 0.700000, count: 120 -Region Avg IOU: 0.636338, Class: 1.000000, Obj: 0.517098, No Obj: 0.003086, Avg Recall: 0.755814, count: 86 -7196: 0.446251, 0.424516 avg, 0.001000 rate, 25.127188 seconds, 1842176 images -Loaded: 0.000063 seconds -Region Avg IOU: 0.653795, Class: 1.000000, Obj: 0.441778, No Obj: 0.003418, Avg Recall: 0.794643, count: 112 -Region Avg IOU: 0.624397, Class: 1.000000, Obj: 0.445599, No Obj: 0.003271, Avg Recall: 0.729508, count: 122 -Region Avg IOU: 0.593553, Class: 1.000000, Obj: 0.421471, No Obj: 0.003109, Avg Recall: 0.721739, count: 115 -Region Avg IOU: 0.675780, Class: 1.000000, Obj: 0.458174, No Obj: 0.003519, Avg Recall: 0.811966, count: 117 -Region Avg IOU: 0.631741, Class: 1.000000, Obj: 0.429978, No Obj: 0.003670, Avg Recall: 0.757353, count: 136 -Region Avg IOU: 0.636351, Class: 1.000000, Obj: 0.395777, No Obj: 0.003567, Avg Recall: 0.775362, count: 138 -Region Avg IOU: 0.588645, Class: 1.000000, Obj: 0.419942, No Obj: 0.003018, Avg Recall: 0.678571, count: 84 -Region Avg IOU: 0.587020, Class: 1.000000, Obj: 0.417Saving weights to ./person9300/backup/tyvp_93_9c512_7200.weights -434, No Obj: 0.003028, Avg Recall: 0.695652, count: 115 -7197: 0.421110, 0.424175 avg, 0.001000 rate, 26.887432 seconds, 1842432 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.622789, Class: 1.000000, Obj: 0.453159, No Obj: 0.003030, Avg Recall: 0.710526, count: 114 -Region Avg IOU: 0.604006, Class: 1.000000, Obj: 0.449875, No Obj: 0.002917, Avg Recall: 0.747253, count: 91 -Region Avg IOU: 0.633474, Class: 1.000000, Obj: 0.434796, No Obj: 0.003464, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.598973, Class: 1.000000, Obj: 0.445896, No Obj: 0.002834, Avg Recall: 0.725490, count: 102 -Region Avg IOU: 0.630352, Class: 1.000000, Obj: 0.425253, No Obj: 0.003709, Avg Recall: 0.776859, count: 121 -Region Avg IOU: 0.652617, Class: 1.000000, Obj: 0.469757, No Obj: 0.003163, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.647884, Class: 1.000000, Obj: 0.472272, No Obj: 0.003244, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.652924, Class: 1.000000, Obj: 0.483578, No Obj: 0.003528, Avg Recall: 0.787879, count: 99 -7198: 0.380896, 0.419847 avg, 0.001000 rate, 26.603060 seconds, 1842688 images -Loaded: 0.000118 seconds -Region Avg IOU: 0.630777, Class: 1.000000, Obj: 0.445128, No Obj: 0.002760, Avg Recall: 0.784946, count: 93 -Region Avg IOU: 0.624868, Class: 1.000000, Obj: 0.454072, No Obj: 0.003162, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.623740, Class: 1.000000, Obj: 0.433482, No Obj: 0.002949, Avg Recall: 0.752809, count: 89 -Region Avg IOU: 0.654271, Class: 1.000000, Obj: 0.499306, No Obj: 0.003289, Avg Recall: 0.800000, count: 105 -Region Avg IOU: 0.653404, Class: 1.000000, Obj: 0.430250, No Obj: 0.003676, Avg Recall: 0.791045, count: 134 -Region Avg IOU: 0.571500, Class: 1.000000, Obj: 0.372472, No Obj: 0.003127, Avg Recall: 0.680328, count: 122 -Region Avg IOU: 0.625633, Class: 1.000000, Obj: 0.435080, No Obj: 0.003587, Avg Recall: 0.725806, count: 124 -Region Avg IOU: 0.618675, Class: 1.000000, Obj: 0.403853, No Obj: 0.003229, Avg Recall: 0.743590, count: 117 -7199: 0.429238, 0.420786 avg, 0.001000 rate, 26.332571 seconds, 1842944 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.652631, Class: 1.000000, Obj: 0.437465, No Obj: 0.003255, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.680814, Class: 1.000000, Obj: 0.410387, No Obj: 0.002475, Avg Recall: 0.885057, count: 87 -Region Avg IOU: 0.622522, Class: 1.000000, Obj: 0.444154, No Obj: 0.002829, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.679691, Class: 1.000000, Obj: 0.425770, No Obj: 0.002769, Avg Recall: 0.862069, count: 87 -Region Avg IOU: 0.652599, Class: 1.000000, Obj: 0.424428, No Obj: 0.003334, Avg Recall: 0.818182, count: 132 -Region Avg IOU: 0.589877, Class: 1.000000, Obj: 0.379394, No Obj: 0.002717, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.627140, Class: 1.000000, Obj: 0.425285, No Obj: 0.002949, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.619006, Class: 1.000000, Obj: 0.419475, No Obj: 0.002792, Avg Recall: 0.757576, count: 99 -7200: 0.385446, 0.417252 avg, 0.001000 rate, 26.195480 seconds, 1843200 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000062 seconds -Region Avg IOU: 0.706304, Class: 1.000000, Obj: 0.415504, No Obj: 0.002441, Avg Recall: 0.854369, count: 103 -Region Avg IOU: 0.654354, Class: 1.000000, Obj: 0.422927, No Obj: 0.002785, Avg Recall: 0.850000, count: 120 -Region Avg IOU: 0.653956, Class: 1.000000, Obj: 0.389246, No Obj: 0.002610, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.687740, Class: 1.000000, Obj: 0.373144, No Obj: 0.002833, Avg Recall: 0.868217, count: 129 -Region Avg IOU: 0.659045, Class: 1.000000, Obj: 0.444199, No Obj: 0.002964, Avg Recall: 0.777778, count: 135 -Region Avg IOU: 0.674914, Class: 1.000000, Obj: 0.448681, No Obj: 0.002809, Avg Recall: 0.851064, count: 94 -Region Avg IOU: 0.661143, Class: 1.000000, Obj: 0.410570, No Obj: 0.002381, Avg Recall: 0.843137, count: 102 -Region Avg IOU: 0.692229, Class: 1.000000, Obj: 0.505269, No Obj: 0.002366, Avg Recall: 0.831169, count: 77 -7201: 0.405308, 0.416058 avg, 0.001000 rate, 28.411428 seconds, 1843456 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.660116, Class: 1.000000, Obj: 0.386985, No Obj: 0.002296, Avg Recall: 0.881720, count: 93 -Region Avg IOU: 0.624058, Class: 1.000000, Obj: 0.384519, No Obj: 0.002689, Avg Recall: 0.764706, count: 136 -Region Avg IOU: 0.616226, Class: 1.000000, Obj: 0.455251, No Obj: 0.002547, Avg Recall: 0.769231, count: 117 -Region Avg IOU: 0.678105, Class: 1.000000, Obj: 0.428170, No Obj: 0.002607, Avg Recall: 0.857143, count: 98 -Region Avg IOU: 0.652556, Class: 1.000000, Obj: 0.424840, No Obj: 0.002461, Avg Recall: 0.805825, count: 103 -Region Avg IOU: 0.658623, Class: 1.000000, Obj: 0.443753, No Obj: 0.002500, Avg Recall: 0.802083, count: 96 -Region Avg IOU: 0.665988, Class: 1.000000, Obj: 0.405614, No Obj: 0.002523, Avg Recall: 0.833333, count: 96 -Region Avg IOU: 0.606783, Class: 1.000000, Obj: 0.381042, No Obj: 0.002044, Avg Recall: 0.729167, count: 96 -7202: 0.426377, 0.417090 avg, 0.001000 rate, 27.862167 seconds, 1843712 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.649897, Class: 1.000000, Obj: 0.443198, No Obj: 0.002227, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.653568, Class: 1.000000, Obj: 0.399696, No Obj: 0.002703, Avg Recall: 0.846847, count: 111 -Region Avg IOU: 0.672387, Class: 1.000000, Obj: 0.466344, No Obj: 0.002812, Avg Recall: 0.842105, count: 114 -Region Avg IOU: 0.634610, Class: 1.000000, Obj: 0.394670, No Obj: 0.002669, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.673084, Class: 1.000000, Obj: 0.468572, No Obj: 0.002607, Avg Recall: 0.828571, count: 105 -Region Avg IOU: 0.645556, Class: 1.000000, Obj: 0.411870, No Obj: 0.002691, Avg Recall: 0.791304, count: 115 -Region Avg IOU: 0.685873, Class: 1.000000, Obj: 0.375568, No Obj: 0.003204, Avg Recall: 0.851852, count: 135 -Region Avg IOU: 0.698510, Class: 1.000000, Obj: 0.429576, No Obj: 0.002682, Avg Recall: 0.868852, count: 122 -7203: 0.433483, 0.418729 avg, 0.001000 rate, 28.847769 seconds, 1843968 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.681735, Class: 1.000000, Obj: 0.504047, No Obj: 0.002202, Avg Recall: 0.855556, count: 90 -Region Avg IOU: 0.684474, Class: 1.000000, Obj: 0.396964, No Obj: 0.002706, Avg Recall: 0.842105, count: 95 -Region Avg IOU: 0.666699, Class: 1.000000, Obj: 0.354895, No Obj: 0.002767, Avg Recall: 0.826446, count: 121 -Region Avg IOU: 0.685730, Class: 1.000000, Obj: 0.429500, No Obj: 0.002270, Avg Recall: 0.852273, count: 88 -Region Avg IOU: 0.596848, Class: 1.000000, Obj: 0.390595, No Obj: 0.002428, Avg Recall: 0.699187, count: 123 -Region Avg IOU: 0.675891, Class: 1.000000, Obj: 0.458484, No Obj: 0.002273, Avg Recall: 0.819277, count: 83 -Region Avg IOU: 0.638353, Class: 1.000000, Obj: 0.412977, No Obj: 0.002879, Avg Recall: 0.794521, count: 146 -Region Avg IOU: 0.641323, Class: 1.000000, Obj: 0.402879, No Obj: 0.002633, Avg Recall: 0.768000, count: 125 -7204: 0.432276, 0.420084 avg, 0.001000 rate, 27.745155 seconds, 1844224 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.664725, Class: 1.000000, Obj: 0.423738, No Obj: 0.002396, Avg Recall: 0.833333, count: 120 -Region Avg IOU: 0.666967, Class: 1.000000, Obj: 0.392764, No Obj: 0.002495, Avg Recall: 0.843478, count: 115 -Region Avg IOU: 0.625074, Class: 1.000000, Obj: 0.436682, No Obj: 0.002565, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.705952, Class: 1.000000, Obj: 0.442713, No Obj: 0.002564, Avg Recall: 0.894737, count: 114 -Region Avg IOU: 0.664395, Class: 1.000000, Obj: 0.450296, No Obj: 0.002722, Avg Recall: 0.836207, count: 116 -Region Avg IOU: 0.648278, Class: 1.000000, Obj: 0.455521, No Obj: 0.002281, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.671645, Class: 1.000000, Obj: 0.457421, No Obj: 0.002472, Avg Recall: 0.780220, count: 91 -Region Avg IOU: 0.665348, Class: 1.000000, Obj: 0.401169, No Obj: 0.002560, Avg Recall: 0.837607, count: 117 -7205: 0.419661, 0.420042 avg, 0.001000 rate, 29.667179 seconds, 1844480 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.699120, Class: 1.000000, Obj: 0.500205, No Obj: 0.002576, Avg Recall: 0.865979, count: 97 -Region Avg IOU: 0.720206, Class: 1.000000, Obj: 0.442999, No Obj: 0.002855, Avg Recall: 0.900901, count: 111 -Region Avg IOU: 0.654993, Class: 1.000000, Obj: 0.446046, No Obj: 0.002435, Avg Recall: 0.818182, count: 110 -Region Avg IOU: 0.661799, Class: 1.000000, Obj: 0.379478, No Obj: 0.002540, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.685935, Class: 1.000000, Obj: 0.440494, No Obj: 0.002831, Avg Recall: 0.852459, count: 122 -Region Avg IOU: 0.638969, Class: 1.000000, Obj: 0.398552, No Obj: 0.002520, Avg Recall: 0.832000, count: 125 -Region Avg IOU: 0.664255, Class: 1.000000, Obj: 0.400308, No Obj: 0.003115, Avg Recall: 0.842105, count: 133 -Region Avg IOU: 0.648619, Class: 1.000000, Obj: 0.447737, No Obj: 0.002313, Avg Recall: 0.804598, count: 87 -7206: 0.413315, 0.419369 avg, 0.001000 rate, 28.570723 seconds, 1844736 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.672708, Class: 1.000000, Obj: 0.442799, No Obj: 0.002634, Avg Recall: 0.853211, count: 109 -Region Avg IOU: 0.631325, Class: 1.000000, Obj: 0.382663, No Obj: 0.002748, Avg Recall: 0.766423, count: 137 -Region Avg IOU: 0.662067, Class: 1.000000, Obj: 0.427626, No Obj: 0.002814, Avg Recall: 0.814159, count: 113 -Region Avg IOU: 0.688914, Class: 1.000000, Obj: 0.397611, No Obj: 0.002457, Avg Recall: 0.871287, count: 101 -Region Avg IOU: 0.690327, Class: 1.000000, Obj: 0.442362, No Obj: 0.002936, Avg Recall: 0.876190, count: 105 -Region Avg IOU: 0.672997, Class: 1.000000, Obj: 0.405738, No Obj: 0.003108, Avg Recall: 0.858268, count: 127 -Region Avg IOU: 0.658044, Class: 1.000000, Obj: 0.389387, No Obj: 0.002734, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.699802, Class: 1.000000, Obj: 0.470526, No Obj: 0.002613, Avg Recall: 0.876289, count: 97 -7207: 0.468466, 0.424279 avg, 0.001000 rate, 28.730175 seconds, 1844992 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.686163, Class: 1.000000, Obj: 0.423484, No Obj: 0.002722, Avg Recall: 0.849057, count: 106 -Region Avg IOU: 0.702739, Class: 1.000000, Obj: 0.445462, No Obj: 0.002761, Avg Recall: 0.857143, count: 98 -Region Avg IOU: 0.659991, Class: 1.000000, Obj: 0.454274, No Obj: 0.002351, Avg Recall: 0.790698, count: 86 -Region Avg IOU: 0.632628, Class: 1.000000, Obj: 0.435303, No Obj: 0.002827, Avg Recall: 0.758065, count: 124 -Region Avg IOU: 0.678680, Class: 1.000000, Obj: 0.472439, No Obj: 0.002638, Avg Recall: 0.828829, count: 111 -Region Avg IOU: 0.641047, Class: 1.000000, Obj: 0.451085, No Obj: 0.002657, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.653440, Class: 1.000000, Obj: 0.444531, No Obj: 0.002516, Avg Recall: 0.804348, count: 92 -Region Avg IOU: 0.673634, Class: 1.000000, Obj: 0.451472, No Obj: 0.002999, Avg Recall: 0.825397, count: 126 -7208: 0.405065, 0.422357 avg, 0.001000 rate, 28.608498 seconds, 1845248 images -Loaded: 0.000115 seconds -Region Avg IOU: 0.632840, Class: 1.000000, Obj: 0.462138, No Obj: 0.002736, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.681493, Class: 1.000000, Obj: 0.437482, No Obj: 0.003188, Avg Recall: 0.888000, count: 125 -Region Avg IOU: 0.648605, Class: 1.000000, Obj: 0.442481, No Obj: 0.002825, Avg Recall: 0.780702, count: 114 -Region Avg IOU: 0.666425, Class: 1.000000, Obj: 0.469475, No Obj: 0.002769, Avg Recall: 0.818182, count: 121 -Region Avg IOU: 0.638288, Class: 1.000000, Obj: 0.429745, No Obj: 0.002564, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.648001, Class: 1.000000, Obj: 0.405396, No Obj: 0.002695, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.607325, Class: 1.000000, Obj: 0.416485, No Obj: 0.002356, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.636512, Class: 1.000000, Obj: 0.400059, No Obj: 0.002811, Avg Recall: 0.798165, count: 109 -7209: 0.440038, 0.424125 avg, 0.001000 rate, 28.082729 seconds, 1845504 images -Loaded: 0.000114 seconds -Region Avg IOU: 0.686927, Class: 1.000000, Obj: 0.435275, No Obj: 0.002866, Avg Recall: 0.866667, count: 105 -Region Avg IOU: 0.673733, Class: 1.000000, Obj: 0.447766, No Obj: 0.002861, Avg Recall: 0.840000, count: 100 -Region Avg IOU: 0.628122, Class: 1.000000, Obj: 0.417057, No Obj: 0.002859, Avg Recall: 0.774194, count: 124 -Region Avg IOU: 0.729924, Class: 1.000000, Obj: 0.529929, No Obj: 0.002741, Avg Recall: 0.916667, count: 96 -Region Avg IOU: 0.639685, Class: 1.000000, Obj: 0.423803, No Obj: 0.002487, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.661765, Class: 1.000000, Obj: 0.484323, No Obj: 0.002714, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.628614, Class: 1.000000, Obj: 0.449900, No Obj: 0.002683, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.654747, Class: 1.000000, Obj: 0.394704, No Obj: 0.003040, Avg Recall: 0.829268, count: 123 -7210: 0.454888, 0.427202 avg, 0.001000 rate, 28.329809 seconds, 1845760 images -Resizing -576 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 18.183826 seconds -Region Avg IOU: 0.685273, Class: 1.000000, Obj: 0.490399, No Obj: 0.003398, Avg Recall: 0.872549, count: 102 -Region Avg IOU: 0.634786, Class: 1.000000, Obj: 0.457008, No Obj: 0.002655, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.613339, Class: 1.000000, Obj: 0.407977, No Obj: 0.003143, Avg Recall: 0.717742, count: 124 -Region Avg IOU: 0.637104, Class: 1.000000, Obj: 0.447828, No Obj: 0.003035, Avg Recall: 0.789474, count: 114 -Region Avg IOU: 0.639123, Class: 1.000000, Obj: 0.399202, No Obj: 0.003417, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.688112, Class: 1.000000, Obj: 0.470425, No Obj: 0.002782, Avg Recall: 0.776471, count: 85 -Region Avg IOU: 0.614823, Class: 1.000000, Obj: 0.433012, No Obj: 0.003506, Avg Recall: 0.746032, count: 126 -Region Avg IOU: 0.666662, Class: 1.000000, Obj: 0.437197, No Obj: 0.002948, Avg Recall: 0.817204, count: 93 -7211: 0.391887, 0.423670 avg, 0.001000 rate, 26.535236 seconds, 1846016 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.657036, Class: 1.000000, Obj: 0.450258, No Obj: 0.002955, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.615769, Class: 1.000000, Obj: 0.405892, No Obj: 0.003408, Avg Recall: 0.753846, count: 130 -Region Avg IOU: 0.628755, Class: 1.000000, Obj: 0.428744, No Obj: 0.003368, Avg Recall: 0.777778, count: 126 -Region Avg IOU: 0.673378, Class: 1.000000, Obj: 0.445235, No Obj: 0.003109, Avg Recall: 0.828571, count: 105 -Region Avg IOU: 0.579872, Class: 1.000000, Obj: 0.437415, No Obj: 0.003094, Avg Recall: 0.702479, count: 121 -Region Avg IOU: 0.594657, Class: 1.000000, Obj: 0.470713, No Obj: 0.003058, Avg Recall: 0.703297, count: 91 -Region Avg IOU: 0.632909, Class: 1.000000, Obj: 0.473589, No Obj: 0.003643, Avg Recall: 0.741007, count: 139 -Region Avg IOU: 0.652854, Class: 1.000000, Obj: 0.409109, No Obj: 0.003489, Avg Recall: 0.778688, count: 122 -7212: 0.412618, 0.422565 avg, 0.001000 rate, 25.762304 seconds, 1846272 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.610053, Class: 1.000000, Obj: 0.460987, No Obj: 0.002545, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.625889, Class: 1.000000, Obj: 0.484376, No Obj: 0.003697, Avg Recall: 0.786325, count: 117 -Region Avg IOU: 0.651203, Class: 1.000000, Obj: 0.487154, No Obj: 0.002940, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.603870, Class: 1.000000, Obj: 0.410630, No Obj: 0.003090, Avg Recall: 0.715328, count: 137 -Region Avg IOU: 0.596288, Class: 1.000000, Obj: 0.427437, No Obj: 0.002704, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.653385, Class: 1.000000, Obj: 0.423630, No Obj: 0.003231, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.612677, Class: 1.000000, Obj: 0.420466, No Obj: 0.003252, Avg Recall: 0.743802, count: 121 -Region Avg IOU: 0.671708, Class: 1.000000, Obj: 0.437629, No Obj: 0.002847, Avg Recall: 0.831461, count: 89 -7213: 0.401054, 0.420414 avg, 0.001000 rate, 26.835434 seconds, 1846528 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.631384, Class: 1.000000, Obj: 0.456191, No Obj: 0.003318, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.616623, Class: 1.000000, Obj: 0.465909, No Obj: 0.002840, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.626222, Class: 1.000000, Obj: 0.417561, No Obj: 0.003026, Avg Recall: 0.747663, count: 107 -Region Avg IOU: 0.598889, Class: 1.000000, Obj: 0.386902, No Obj: 0.003588, Avg Recall: 0.718750, count: 128 -Region Avg IOU: 0.610702, Class: 1.000000, Obj: 0.413361, No Obj: 0.003573, Avg Recall: 0.701493, count: 134 -Region Avg IOU: 0.651005, Class: 1.000000, Obj: 0.435940, No Obj: 0.003260, Avg Recall: 0.788618, count: 123 -Region Avg IOU: 0.616614, Class: 1.000000, Obj: 0.441556, No Obj: 0.002979, Avg Recall: 0.729730, count: 111 -Region Avg IOU: 0.637518, Class: 1.000000, Obj: 0.432469, No Obj: 0.002712, Avg Recall: 0.777778, count: 90 -7214: 0.445059, 0.422878 avg, 0.001000 rate, 25.753807 seconds, 1846784 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.619061, Class: 1.000000, Obj: 0.392122, No Obj: 0.003085, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.616040, Class: 1.000000, Obj: 0.384971, No Obj: 0.003210, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.626166, Class: 1.000000, Obj: 0.451079, No Obj: 0.002977, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.672002, Class: 1.000000, Obj: 0.457989, No Obj: 0.002720, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.590695, Class: 1.000000, Obj: 0.380594, No Obj: 0.002971, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.617248, Class: 1.000000, Obj: 0.430316, No Obj: 0.002965, Avg Recall: 0.757009, count: 107 -Region Avg IOU: 0.613809, Class: 1.000000, Obj: 0.479457, No Obj: 0.002727, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.680735, Class: 1.000000, Obj: 0.498099, No Obj: 0.002989, Avg Recall: 0.850575, count: 87 -7215: 0.381084, 0.418699 avg, 0.001000 rate, 26.329384 seconds, 1847040 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.618671, Class: 1.000000, Obj: 0.417916, No Obj: 0.002862, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.620710, Class: 1.000000, Obj: 0.488002, No Obj: 0.003170, Avg Recall: 0.747967, count: 123 -Region Avg IOU: 0.617822, Class: 1.000000, Obj: 0.444849, No Obj: 0.003027, Avg Recall: 0.748031, count: 127 -Region Avg IOU: 0.680124, Class: 1.000000, Obj: 0.548179, No Obj: 0.002814, Avg Recall: 0.858696, count: 92 -Region Avg IOU: 0.578618, Class: 1.000000, Obj: 0.382366, No Obj: 0.003243, Avg Recall: 0.694656, count: 131 -Region Avg IOU: 0.655468, Class: 1.000000, Obj: 0.456344, No Obj: 0.002863, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.660683, Class: 1.000000, Obj: 0.528435, No Obj: 0.002680, Avg Recall: 0.838710, count: 62 -Region Avg IOU: 0.601516, Class: 1.000000, Obj: 0.413471, No Obj: 0.003095, Avg Recall: 0.750000, count: 112 -7216: 0.383853, 0.415214 avg, 0.001000 rate, 26.383142 seconds, 1847296 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.611907, Class: 1.000000, Obj: 0.402634, No Obj: 0.003093, Avg Recall: 0.741935, count: 124 -Region Avg IOU: 0.634390, Class: 1.000000, Obj: 0.459029, No Obj: 0.002797, Avg Recall: 0.771084, count: 83 -Region Avg IOU: 0.596693, Class: 1.000000, Obj: 0.438057, No Obj: 0.002591, Avg Recall: 0.726316, count: 95 -Region Avg IOU: 0.665067, Class: 1.000000, Obj: 0.417549, No Obj: 0.003521, Avg Recall: 0.817460, count: 126 -Region Avg IOU: 0.643497, Class: 1.000000, Obj: 0.391340, No Obj: 0.003510, Avg Recall: 0.787879, count: 132 -Region Avg IOU: 0.650731, Class: 1.000000, Obj: 0.407492, No Obj: 0.002879, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.600909, Class: 1.000000, Obj: 0.413905, No Obj: 0.002771, Avg Recall: 0.709677, count: 93 -Region Avg IOU: 0.622221, Class: 1.000000, Obj: 0.473533, No Obj: 0.002930, Avg Recall: 0.750000, count: 104 -7217: 0.435459, 0.417239 avg, 0.001000 rate, 27.079847 seconds, 1847552 images -Loaded: 0.000151 seconds -Region Avg IOU: 0.623581, Class: 1.000000, Obj: 0.404768, No Obj: 0.003304, Avg Recall: 0.738095, count: 126 -Region Avg IOU: 0.609165, Class: 1.000000, Obj: 0.400164, No Obj: 0.002759, Avg Recall: 0.754386, count: 114 -Region Avg IOU: 0.628519, Class: 1.000000, Obj: 0.401034, No Obj: 0.002516, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.641451, Class: 1.000000, Obj: 0.419668, No Obj: 0.003157, Avg Recall: 0.814159, count: 113 -Region Avg IOU: 0.607236, Class: 1.000000, Obj: 0.407170, No Obj: 0.003124, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.633344, Class: 1.000000, Obj: 0.488908, No Obj: 0.002330, Avg Recall: 0.794521, count: 73 -Region Avg IOU: 0.658096, Class: 1.000000, Obj: 0.470319, No Obj: 0.002734, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.650605, Class: 1.000000, Obj: 0.424260, No Obj: 0.003250, Avg Recall: 0.790323, count: 124 -7218: 0.407542, 0.416269 avg, 0.001000 rate, 25.531160 seconds, 1847808 images -Loaded: 0.000098 seconds -Region Avg IOU: 0.641888, Class: 1.000000, Obj: 0.460563, No Obj: 0.003158, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.638118, Class: 1.000000, Obj: 0.418142, No Obj: 0.003193, Avg Recall: 0.758621, count: 116 -Region Avg IOU: 0.640452, Class: 1.000000, Obj: 0.432823, No Obj: 0.003102, Avg Recall: 0.792308, count: 130 -Region Avg IOU: 0.646449, Class: 1.000000, Obj: 0.436787, No Obj: 0.002913, Avg Recall: 0.788462, count: 104 -Region Avg IOU: 0.652509, Class: 1.000000, Obj: 0.432355, No Obj: 0.003173, Avg Recall: 0.798319, count: 119 -Region Avg IOU: 0.633154, Class: 1.000000, Obj: 0.417066, No Obj: 0.003103, Avg Recall: 0.761538, count: 130 -Region Avg IOU: 0.585237, Class: 1.000000, Obj: 0.390351, No Obj: 0.002635, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.630129, Class: 1.000000, Obj: 0.417786, No Obj: 0.002740, Avg Recall: 0.767677, count: 99 -7219: 0.404409, 0.415083 avg, 0.001000 rate, 25.241575 seconds, 1848064 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.639488, Class: 1.000000, Obj: 0.419962, No Obj: 0.002678, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.625159, Class: 1.000000, Obj: 0.403449, No Obj: 0.002850, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.650465, Class: 1.000000, Obj: 0.463069, No Obj: 0.003277, Avg Recall: 0.834783, count: 115 -Region Avg IOU: 0.634234, Class: 1.000000, Obj: 0.409292, No Obj: 0.002922, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.643923, Class: 1.000000, Obj: 0.442983, No Obj: 0.002577, Avg Recall: 0.804348, count: 92 -Region Avg IOU: 0.634782, Class: 1.000000, Obj: 0.410151, No Obj: 0.003335, Avg Recall: 0.776859, count: 121 -Region Avg IOU: 0.644293, Class: 1.000000, Obj: 0.473655, No Obj: 0.003052, Avg Recall: 0.741935, count: 93 -Region Avg IOU: 0.591240, Class: 1.000000, Obj: 0.455137, No Obj: 0.002520, Avg Recall: 0.704762, count: 105 -7220: 0.422305, 0.415805 avg, 0.001000 rate, 26.231134 seconds, 1848320 images -Resizing -704 - used slow CUDNN algo without Workspace! - try to allocate workspace = 1181220864 * sizeof(float), CUDA allocate done! -Loaded: 0.000087 seconds -Region Avg IOU: 0.676871, Class: 1.000000, Obj: 0.465839, No Obj: 0.002468, Avg Recall: 0.840425, count: 94 -Region Avg IOU: 0.661837, Class: 1.000000, Obj: 0.432589, No Obj: 0.003030, Avg Recall: 0.828125, count: 128 -Region Avg IOU: 0.687910, Class: 1.000000, Obj: 0.470891, No Obj: 0.002184, Avg Recall: 0.837500, count: 80 -Region Avg IOU: 0.616574, Class: 1.000000, Obj: 0.416465, No Obj: 0.002949, Avg Recall: 0.760870, count: 138 -Region Avg IOU: 0.622465, Class: 1.000000, Obj: 0.419650, No Obj: 0.002804, Avg Recall: 0.710145, count: 138 -Region Avg IOU: 0.725710, Class: 1.000000, Obj: 0.416625, No Obj: 0.002444, Avg Recall: 0.906667, count: 75 -Region Avg IOU: 0.678151, Class: 1.000000, Obj: 0.447756, No Obj: 0.002683, Avg Recall: 0.882883, count: 111 -Region Avg IOU: 0.647857, Class: 1.000000, Obj: 0.407792, No Obj: 0.002404, Avg Recall: 0.783019, count: 106 -7221: 0.450495, 0.419274 avg, 0.001000 rate, 29.293053 seconds, 1848576 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.669401, Class: 1.000000, Obj: 0.477137, No Obj: 0.002207, Avg Recall: 0.821053, count: 95 -Region Avg IOU: 0.664899, Class: 1.000000, Obj: 0.423503, No Obj: 0.002485, Avg Recall: 0.852273, count: 88 -Region Avg IOU: 0.701039, Class: 1.000000, Obj: 0.451668, No Obj: 0.002339, Avg Recall: 0.914894, count: 94 -Region Avg IOU: 0.682471, Class: 1.000000, Obj: 0.479057, No Obj: 0.002684, Avg Recall: 0.831858, count: 113 -Region Avg IOU: 0.652348, Class: 1.000000, Obj: 0.448815, No Obj: 0.002539, Avg Recall: 0.812500, count: 96 -Region Avg IOU: 0.640970, Class: 1.000000, Obj: 0.386629, No Obj: 0.003000, Avg Recall: 0.771429, count: 140 -Region Avg IOU: 0.650395, Class: 1.000000, Obj: 0.397985, No Obj: 0.002901, Avg Recall: 0.836066, count: 122 -Region Avg IOU: 0.649813, Class: 1.000000, Obj: 0.415955, No Obj: 0.002344, Avg Recall: 0.798165, count: 109 -7222: 0.399363, 0.417283 avg, 0.001000 rate, 29.203899 seconds, 1848832 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.654984, Class: 1.000000, Obj: 0.424529, No Obj: 0.002442, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.685677, Class: 1.000000, Obj: 0.496820, No Obj: 0.002233, Avg Recall: 0.850575, count: 87 -Region Avg IOU: 0.700879, Class: 1.000000, Obj: 0.407971, No Obj: 0.002999, Avg Recall: 0.890625, count: 128 -Region Avg IOU: 0.642965, Class: 1.000000, Obj: 0.427515, No Obj: 0.002646, Avg Recall: 0.792308, count: 130 -Region Avg IOU: 0.675132, Class: 1.000000, Obj: 0.446534, No Obj: 0.002185, Avg Recall: 0.817204, count: 93 -Region Avg IOU: 0.672747, Class: 1.000000, Obj: 0.451283, No Obj: 0.002432, Avg Recall: 0.833333, count: 96 -Region Avg IOU: 0.655808, Class: 1.000000, Obj: 0.420739, No Obj: 0.002865, Avg Recall: 0.828571, count: 140 -Region Avg IOU: 0.673936, Class: 1.000000, Obj: 0.441891, No Obj: 0.002711, Avg Recall: 0.866667, count: 105 -7223: 0.433110, 0.418866 avg, 0.001000 rate, 30.133333 seconds, 1849088 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.670736, Class: 1.000000, Obj: 0.420348, No Obj: 0.002730, Avg Recall: 0.860870, count: 115 -Region Avg IOU: 0.690153, Class: 1.000000, Obj: 0.496659, No Obj: 0.002252, Avg Recall: 0.894118, count: 85 -Region Avg IOU: 0.646969, Class: 1.000000, Obj: 0.402496, No Obj: 0.002549, Avg Recall: 0.770492, count: 122 -Region Avg IOU: 0.668632, Class: 1.000000, Obj: 0.406002, No Obj: 0.002836, Avg Recall: 0.830645, count: 124 -Region Avg IOU: 0.633504, Class: 1.000000, Obj: 0.424761, No Obj: 0.002669, Avg Recall: 0.776923, count: 130 -Region Avg IOU: 0.613310, Class: 1.000000, Obj: 0.402543, No Obj: 0.002757, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.665710, Class: 1.000000, Obj: 0.447949, No Obj: 0.002564, Avg Recall: 0.851240, count: 121 -Region Avg IOU: 0.672703, Class: 1.000000, Obj: 0.476444, No Obj: 0.002505, Avg Recall: 0.850000, count: 100 -7224: 0.433669, 0.420346 avg, 0.001000 rate, 29.582319 seconds, 1849344 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.646948, Class: 1.000000, Obj: 0.399798, No Obj: 0.002716, Avg Recall: 0.808000, count: 125 -Region Avg IOU: 0.670410, Class: 1.000000, Obj: 0.404288, No Obj: 0.002523, Avg Recall: 0.844660, count: 103 -Region Avg IOU: 0.696770, Class: 1.000000, Obj: 0.391712, No Obj: 0.002687, Avg Recall: 0.900901, count: 111 -Region Avg IOU: 0.660144, Class: 1.000000, Obj: 0.423906, No Obj: 0.002675, Avg Recall: 0.785714, count: 112 -Region Avg IOU: 0.645969, Class: 1.000000, Obj: 0.419709, No Obj: 0.001944, Avg Recall: 0.824176, count: 91 -Region Avg IOU: 0.666712, Class: 1.000000, Obj: 0.492211, No Obj: 0.002326, Avg Recall: 0.853658, count: 82 -Region Avg IOU: 0.650414, Class: 1.000000, Obj: 0.366761, No Obj: 0.002916, Avg Recall: 0.834711, count: 121 -Region Avg IOU: 0.690234, Class: 1.000000, Obj: 0.402497, No Obj: 0.002255, Avg Recall: 0.845238, count: 84 -7225: 0.415488, 0.419860 avg, 0.001000 rate, 29.487473 seconds, 1849600 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.690575, Class: 1.000000, Obj: 0.459287, No Obj: 0.002291, Avg Recall: 0.868132, count: 91 -Region Avg IOU: 0.667413, Class: 1.000000, Obj: 0.473836, No Obj: 0.002443, Avg Recall: 0.847826, count: 92 -Region Avg IOU: 0.645729, Class: 1.000000, Obj: 0.459315, No Obj: 0.002485, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.675224, Class: 1.000000, Obj: 0.404208, No Obj: 0.002435, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.670709, Class: 1.000000, Obj: 0.512668, No Obj: 0.002573, Avg Recall: 0.858407, count: 113 -Region Avg IOU: 0.620675, Class: 1.000000, Obj: 0.384684, No Obj: 0.002933, Avg Recall: 0.738806, count: 134 -Region Avg IOU: 0.670307, Class: 1.000000, Obj: 0.438589, No Obj: 0.002534, Avg Recall: 0.831683, count: 101 -Region Avg IOU: 0.612330, Class: 1.000000, Obj: 0.463787, No Obj: 0.002279, Avg Recall: 0.730769, count: 104 -7226: 0.399658, 0.417840 avg, 0.001000 rate, 29.364592 seconds, 1849856 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.642337, Class: 1.000000, Obj: 0.369870, No Obj: 0.002586, Avg Recall: 0.736434, count: 129 -Region Avg IOU: 0.652214, Class: 1.000000, Obj: 0.405735, No Obj: 0.002211, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.694993, Class: 1.000000, Obj: 0.485230, No Obj: 0.002396, Avg Recall: 0.913580, count: 81 -Region Avg IOU: 0.641354, Class: 1.000000, Obj: 0.405751, No Obj: 0.002550, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.688059, Class: 1.000000, Obj: 0.460018, No Obj: 0.002241, Avg Recall: 0.855556, count: 90 -Region Avg IOU: 0.653895, Class: 1.000000, Obj: 0.444419, No Obj: 0.002158, Avg Recall: 0.849315, count: 73 -Region Avg IOU: 0.628874, Class: 1.000000, Obj: 0.406124, No Obj: 0.002416, Avg Recall: 0.769912, count: 113 -Region Avg IOU: 0.681859, Class: 1.000000, Obj: 0.432864, No Obj: 0.002585, Avg Recall: 0.857143, count: 112 -7227: 0.409930, 0.417049 avg, 0.001000 rate, 30.440819 seconds, 1850112 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.697818, Class: 1.000000, Obj: 0.437370, No Obj: 0.002472, Avg Recall: 0.892157, count: 102 -Region Avg IOU: 0.674738, Class: 1.000000, Obj: 0.406709, No Obj: 0.002704, Avg Recall: 0.857143, count: 112 -Region Avg IOU: 0.704615, Class: 1.000000, Obj: 0.482093, No Obj: 0.002308, Avg Recall: 0.917647, count: 85 -Region Avg IOU: 0.643211, Class: 1.000000, Obj: 0.377461, No Obj: 0.002725, Avg Recall: 0.806202, count: 129 -Region Avg IOU: 0.651592, Class: 1.000000, Obj: 0.424128, No Obj: 0.002145, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.652849, Class: 1.000000, Obj: 0.363441, No Obj: 0.002570, Avg Recall: 0.795276, count: 127 -Region Avg IOU: 0.658896, Class: 1.000000, Obj: 0.374688, No Obj: 0.002712, Avg Recall: 0.800000, count: 130 -Region Avg IOU: 0.681831, Class: 1.000000, Obj: 0.346637, No Obj: 0.002342, Avg Recall: 0.862385, count: 109 -7228: 0.459120, 0.421256 avg, 0.001000 rate, 29.069401 seconds, 1850368 images -Loaded: 0.000068 seconds -Region Avg IOU: 0.710278, Class: 1.000000, Obj: 0.428733, No Obj: 0.002421, Avg Recall: 0.893204, count: 103 -Region Avg IOU: 0.645207, Class: 1.000000, Obj: 0.392828, No Obj: 0.002439, Avg Recall: 0.760331, count: 121 -Region Avg IOU: 0.664940, Class: 1.000000, Obj: 0.366132, No Obj: 0.002461, Avg Recall: 0.820513, count: 117 -Region Avg IOU: 0.618633, Class: 1.000000, Obj: 0.319673, No Obj: 0.002634, Avg Recall: 0.761062, count: 113 -Region Avg IOU: 0.668487, Class: 1.000000, Obj: 0.414047, No Obj: 0.002216, Avg Recall: 0.857143, count: 98 -Region Avg IOU: 0.673083, Class: 1.000000, Obj: 0.400975, No Obj: 0.002432, Avg Recall: 0.827957, count: 93 -Region Avg IOU: 0.698916, Class: 1.000000, Obj: 0.489901, No Obj: 0.002015, Avg Recall: 0.876289, count: 97 -Region Avg IOU: 0.681510, Class: 1.000000, Obj: 0.362092, No Obj: 0.002356, Avg Recall: 0.877193, count: 114 -7229: 0.426610, 0.421792 avg, 0.001000 rate, 28.290205 seconds, 1850624 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.656642, Class: 1.000000, Obj: 0.430305, No Obj: 0.002084, Avg Recall: 0.806818, count: 88 -Region Avg IOU: 0.689541, Class: 1.000000, Obj: 0.418254, No Obj: 0.002384, Avg Recall: 0.850000, count: 80 -Region Avg IOU: 0.674706, Class: 1.000000, Obj: 0.394231, No Obj: 0.002592, Avg Recall: 0.846154, count: 130 -Region Avg IOU: 0.695370, Class: 1.000000, Obj: 0.410569, No Obj: 0.002642, Avg Recall: 0.853846, count: 130 -Region Avg IOU: 0.630248, Class: 1.000000, Obj: 0.382942, No Obj: 0.002043, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.653083, Class: 1.000000, Obj: 0.408902, No Obj: 0.002577, Avg Recall: 0.816000, count: 125 -Region Avg IOU: 0.657713, Class: 1.000000, Obj: 0.382470, No Obj: 0.002250, Avg Recall: 0.808081, count: 99 -Region Avg IOU: 0.646774, Class: 1.000000, Obj: 0.365760, No Obj: 0.002700, Avg Recall: 0.812950, count: 139 -7230: 0.445911, 0.424204 avg, 0.001000 rate, 30.530516 seconds, 1850880 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 10.253010 seconds -Region Avg IOU: 0.654163, Class: 1.000000, Obj: 0.428351, No Obj: 0.003360, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.586450, Class: 1.000000, Obj: 0.382299, No Obj: 0.002797, Avg Recall: 0.698276, count: 116 -Region Avg IOU: 0.631094, Class: 1.000000, Obj: 0.433464, No Obj: 0.002719, Avg Recall: 0.771429, count: 70 -Region Avg IOU: 0.615167, Class: 1.000000, Obj: 0.403415, No Obj: 0.003087, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.619897, Class: 1.000000, Obj: 0.377247, No Obj: 0.002991, Avg Recall: 0.745283, count: 106 -Region Avg IOU: 0.573331, Class: 1.000000, Obj: 0.422688, No Obj: 0.002620, Avg Recall: 0.666667, count: 87 -Region Avg IOU: 0.646657, Class: 1.000000, Obj: 0.407058, No Obj: 0.002833, Avg Recall: 0.758621, count: 87 -Region Avg IOU: 0.611507, Class: 1.000000, Obj: 0.396761, No Obj: 0.002874, Avg Recall: 0.711340, count: 97 -7231: 0.372450, 0.419028 avg, 0.001000 rate, 22.792799 seconds, 1851136 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.620319, Class: 1.000000, Obj: 0.367057, No Obj: 0.002987, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.625273, Class: 1.000000, Obj: 0.387476, No Obj: 0.003352, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.630414, Class: 1.000000, Obj: 0.419096, No Obj: 0.003078, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.608928, Class: 1.000000, Obj: 0.361994, No Obj: 0.003189, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.598096, Class: 1.000000, Obj: 0.363382, No Obj: 0.003215, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.564348, Class: 1.000000, Obj: 0.397379, No Obj: 0.003229, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.621314, Class: 1.000000, Obj: 0.417053, No Obj: 0.002886, Avg Recall: 0.771930, count: 114 -Region Avg IOU: 0.636166, Class: 1.000000, Obj: 0.427003, No Obj: 0.003372, Avg Recall: 0.757282, count: 103 -7232: 0.413007, 0.418426 avg, 0.001000 rate, 24.563120 seconds, 1851392 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.576974, Class: 1.000000, Obj: 0.422870, No Obj: 0.003113, Avg Recall: 0.685185, count: 108 -Region Avg IOU: 0.663990, Class: 1.000000, Obj: 0.444002, No Obj: 0.003556, Avg Recall: 0.793478, count: 92 -Region Avg IOU: 0.605268, Class: 1.000000, Obj: 0.464568, No Obj: 0.003368, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.581334, Class: 1.000000, Obj: 0.393867, No Obj: 0.002973, Avg Recall: 0.636364, count: 99 -Region Avg IOU: 0.612173, Class: 1.000000, Obj: 0.427830, No Obj: 0.003941, Avg Recall: 0.722581, count: 155 -Region Avg IOU: 0.556148, Class: 1.000000, Obj: 0.362280, No Obj: 0.003187, Avg Recall: 0.655738, count: 122 -Region Avg IOU: 0.574238, Class: 1.000000, Obj: 0.354577, No Obj: 0.003558, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.617248, Class: 1.000000, Obj: 0.428586, No Obj: 0.003510, Avg Recall: 0.757009, count: 107 -7233: 0.402807, 0.416864 avg, 0.001000 rate, 23.292015 seconds, 1851648 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.576700, Class: 1.000000, Obj: 0.440471, No Obj: 0.003284, Avg Recall: 0.656863, count: 102 -Region Avg IOU: 0.627328, Class: 1.000000, Obj: 0.409732, No Obj: 0.003622, Avg Recall: 0.756303, count: 119 -Region Avg IOU: 0.600790, Class: 1.000000, Obj: 0.454563, No Obj: 0.003557, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.597910, Class: 1.000000, Obj: 0.457121, No Obj: 0.003150, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.602353, Class: 1.000000, Obj: 0.431605, No Obj: 0.003108, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.535998, Class: 1.000000, Obj: 0.372688, No Obj: 0.003135, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.646626, Class: 1.000000, Obj: 0.406196, No Obj: 0.003439, Avg Recall: 0.785714, count: 98 -Region Avg IOU: 0.579147, Class: 1.000000, Obj: 0.413806, No Obj: 0.002797, Avg Recall: 0.739130, count: 92 -7234: 0.349259, 0.410104 avg, 0.001000 rate, 24.024712 seconds, 1851904 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.634066, Class: 1.000000, Obj: 0.481880, No Obj: 0.003775, Avg Recall: 0.777778, count: 108 -Region Avg IOU: 0.665861, Class: 1.000000, Obj: 0.476863, No Obj: 0.003843, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.577954, Class: 1.000000, Obj: 0.430363, No Obj: 0.003745, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.610845, Class: 1.000000, Obj: 0.481453, No Obj: 0.003191, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.580208, Class: 1.000000, Obj: 0.414043, No Obj: 0.003190, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.583985, Class: 1.000000, Obj: 0.398347, No Obj: 0.003944, Avg Recall: 0.720588, count: 136 -Region Avg IOU: 0.612534, Class: 1.000000, Obj: 0.418494, No Obj: 0.003647, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.584894, Class: 1.000000, Obj: 0.398456, No Obj: 0.003365, Avg Recall: 0.690722, count: 97 -7235: 0.429717, 0.412065 avg, 0.001000 rate, 24.807892 seconds, 1852160 images -Loaded: 0.000080 seconds -Region Avg IOU: 0.619291, Class: 1.000000, Obj: 0.421108, No Obj: 0.003860, Avg Recall: 0.750000, count: 92 -Region Avg IOU: 0.614340, Class: 1.000000, Obj: 0.480896, No Obj: 0.004162, Avg Recall: 0.704000, count: 125 -Region Avg IOU: 0.632251, Class: 1.000000, Obj: 0.468502, No Obj: 0.003353, Avg Recall: 0.791209, count: 91 -Region Avg IOU: 0.669062, Class: 1.000000, Obj: 0.494384, No Obj: 0.003745, Avg Recall: 0.847059, count: 85 -Region Avg IOU: 0.622915, Class: 1.000000, Obj: 0.425595, No Obj: 0.003931, Avg Recall: 0.754902, count: 102 -Region Avg IOU: 0.600815, Class: 1.000000, Obj: 0.444106, No Obj: 0.003543, Avg Recall: 0.744898, count: 98 -Region Avg IOU: 0.572097, Class: 1.000000, Obj: 0.432902, No Obj: 0.003841, Avg Recall: 0.687500, count: 112 -Region Avg IOU: 0.609216, Class: 1.000000, Obj: 0.429042, No Obj: 0.004247, Avg Recall: 0.718518, count: 135 -7236: 0.412589, 0.412117 avg, 0.001000 rate, 23.705654 seconds, 1852416 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.601516, Class: 1.000000, Obj: 0.490892, No Obj: 0.003319, Avg Recall: 0.722222, count: 108 -Region Avg IOU: 0.569804, Class: 1.000000, Obj: 0.400750, No Obj: 0.003469, Avg Recall: 0.644628, count: 121 -Region Avg IOU: 0.590234, Class: 1.000000, Obj: 0.439880, No Obj: 0.003295, Avg Recall: 0.722772, count: 101 -Region Avg IOU: 0.643020, Class: 1.000000, Obj: 0.433205, No Obj: 0.004480, Avg Recall: 0.771654, count: 127 -Region Avg IOU: 0.627513, Class: 1.000000, Obj: 0.455760, No Obj: 0.003621, Avg Recall: 0.712644, count: 87 -Region Avg IOU: 0.656232, Class: 1.000000, Obj: 0.466090, No Obj: 0.003318, Avg Recall: 0.824176, count: 91 -Region Avg IOU: 0.601753, Class: 1.000000, Obj: 0.406034, No Obj: 0.003773, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.579986, Class: 1.000000, Obj: 0.434990, No Obj: 0.003598, Avg Recall: 0.693878, count: 98 -7237: 0.381748, 0.409081 avg, 0.001000 rate, 24.612076 seconds, 1852672 images -Loaded: 0.000107 seconds -Region Avg IOU: 0.616954, Class: 1.000000, Obj: 0.467020, No Obj: 0.003848, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.636692, Class: 1.000000, Obj: 0.503033, No Obj: 0.003809, Avg Recall: 0.752475, count: 101 -Region Avg IOU: 0.629682, Class: 1.000000, Obj: 0.486452, No Obj: 0.003959, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.660784, Class: 1.000000, Obj: 0.485933, No Obj: 0.003635, Avg Recall: 0.808511, count: 94 -Region Avg IOU: 0.540900, Class: 1.000000, Obj: 0.402485, No Obj: 0.004073, Avg Recall: 0.630769, count: 130 -Region Avg IOU: 0.563554, Class: 1.000000, Obj: 0.438702, No Obj: 0.003978, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.600630, Class: 1.000000, Obj: 0.500068, No Obj: 0.003065, Avg Recall: 0.702128, count: 94 -Region Avg IOU: 0.596344, Class: 1.000000, Obj: 0.455188, No Obj: 0.003491, Avg Recall: 0.734694, count: 98 -7238: 0.387783, 0.406951 avg, 0.001000 rate, 23.742107 seconds, 1852928 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.607124, Class: 1.000000, Obj: 0.440186, No Obj: 0.003518, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.655870, Class: 1.000000, Obj: 0.457403, No Obj: 0.003366, Avg Recall: 0.797468, count: 79 -Region Avg IOU: 0.590711, Class: 1.000000, Obj: 0.412626, No Obj: 0.003775, Avg Recall: 0.716667, count: 120 -Region Avg IOU: 0.604969, Class: 1.000000, Obj: 0.458898, No Obj: 0.003385, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.612301, Class: 1.000000, Obj: 0.487442, No Obj: 0.003715, Avg Recall: 0.747475, count: 99 -Region Avg IOU: 0.603055, Class: 1.000000, Obj: 0.464735, No Obj: 0.004020, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.570453, Class: 1.000000, Obj: 0.440514, No Obj: 0.003767, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.646628, Class: 1.000000, Obj: 0.476513, No Obj: 0.002938, Avg Recall: 0.805195, count: 77 -7239: 0.381852, 0.404441 avg, 0.001000 rate, 24.402697 seconds, 1853184 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.601265, Class: 1.000000, Obj: 0.454299, No Obj: 0.003826, Avg Recall: 0.699115, count: 113 -Region Avg IOU: 0.587784, Class: 1.000000, Obj: 0.425306, No Obj: 0.003386, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.616520, Class: 1.000000, Obj: 0.421313, No Obj: 0.003404, Avg Recall: 0.717949, count: 117 -Region Avg IOU: 0.595210, Class: 1.000000, Obj: 0.445271, No Obj: 0.003292, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.651770, Class: 1.000000, Obj: 0.498626, No Obj: 0.003656, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.592918, Class: 1.000000, Obj: 0.395578, No Obj: 0.003653, Avg Recall: 0.710744, count: 121 -Region Avg IOU: 0.586698, Class: 1.000000, Obj: 0.387708, No Obj: 0.002919, Avg Recall: 0.692308, count: 91 -Region Avg IOU: 0.573492, Class: 1.000000, Obj: 0.392909, No Obj: 0.003357, Avg Recall: 0.677419, count: 124 -7240: 0.444159, 0.408413 avg, 0.001000 rate, 23.149733 seconds, 1853440 images -Resizing -640 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000063 seconds -Region Avg IOU: 0.639487, Class: 1.000000, Obj: 0.426122, No Obj: 0.003533, Avg Recall: 0.794702, count: 151 -Region Avg IOU: 0.658903, Class: 1.000000, Obj: 0.454108, No Obj: 0.003383, Avg Recall: 0.826087, count: 115 -Region Avg IOU: 0.630071, Class: 1.000000, Obj: 0.395323, No Obj: 0.003059, Avg Recall: 0.768421, count: 95 -Region Avg IOU: 0.599059, Class: 1.000000, Obj: 0.437063, No Obj: 0.002802, Avg Recall: 0.685714, count: 105 -Region Avg IOU: 0.659245, Class: 1.000000, Obj: 0.441072, No Obj: 0.002991, Avg Recall: 0.803419, count: 117 -Region Avg IOU: 0.711436, Class: 1.000000, Obj: 0.543316, No Obj: 0.002699, Avg Recall: 0.890110, count: 91 -Region Avg IOU: 0.636060, Class: 1.000000, Obj: 0.466775, No Obj: 0.003436, Avg Recall: 0.763780, count: 127 -Region Avg IOU: 0.652132, Class: 1.000000, Obj: 0.437238, No Obj: 0.002966, Avg Recall: 0.787402, count: 127 -7241: 0.427869, 0.410358 avg, 0.001000 rate, 25.059916 seconds, 1853696 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.631768, Class: 1.000000, Obj: 0.448841, No Obj: 0.002626, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.611724, Class: 1.000000, Obj: 0.435893, No Obj: 0.002942, Avg Recall: 0.743590, count: 117 -Region Avg IOU: 0.660230, Class: 1.000000, Obj: 0.463278, No Obj: 0.002740, Avg Recall: 0.836735, count: 98 -Region Avg IOU: 0.680149, Class: 1.000000, Obj: 0.473938, No Obj: 0.002905, Avg Recall: 0.838384, count: 99 -Region Avg IOU: 0.631931, Class: 1.000000, Obj: 0.423000, No Obj: 0.002863, Avg Recall: 0.779661, count: 118 -Region Avg IOU: 0.665444, Class: 1.000000, Obj: 0.413878, No Obj: 0.003198, Avg Recall: 0.819820, count: 111 -Region Avg IOU: 0.645310, Class: 1.000000, Obj: 0.418205, No Obj: 0.002612, Avg Recall: 0.827586, count: 87 -Region Avg IOU: 0.623439, Class: 1.000000, Obj: 0.473527, No Obj: 0.002357, Avg Recall: 0.750000, count: 100 -7242: 0.405748, 0.409897 avg, 0.001000 rate, 27.209356 seconds, 1853952 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.649818, Class: 1.000000, Obj: 0.381653, No Obj: 0.002856, Avg Recall: 0.805556, count: 108 -Region Avg IOU: 0.679015, Class: 1.000000, Obj: 0.482965, No Obj: 0.003047, Avg Recall: 0.883495, count: 103 -Region Avg IOU: 0.651930, Class: 1.000000, Obj: 0.429549, No Obj: 0.002692, Avg Recall: 0.783019, count: 106 -Region Avg IOU: 0.630139, Class: 1.000000, Obj: 0.421984, No Obj: 0.002512, Avg Recall: 0.756522, count: 115 -Region Avg IOU: 0.654747, Class: 1.000000, Obj: 0.439557, No Obj: 0.002255, Avg Recall: 0.784091, count: 88 -Region Avg IOU: 0.620338, Class: 1.000000, Obj: 0.378303, No Obj: 0.002753, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.663264, Class: 1.000000, Obj: 0.430313, No Obj: 0.002369, Avg Recall: 0.813953, count: 86 -Region Avg IOU: 0.627299, Class: 1.000000, Obj: 0.424871, No Obj: 0.003163, Avg Recall: 0.726316, count: 95 -7243: 0.415556, 0.410463 avg, 0.001000 rate, 27.609419 seconds, 1854208 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.682748, Class: 1.000000, Obj: 0.443210, No Obj: 0.002486, Avg Recall: 0.802326, count: 86 -Region Avg IOU: 0.690677, Class: 1.000000, Obj: 0.435464, No Obj: 0.002607, Avg Recall: 0.833333, count: 96 -Region Avg IOU: 0.627501, Class: 1.000000, Obj: 0.396454, No Obj: 0.002967, Avg Recall: 0.740458, count: 131 -Region Avg IOU: 0.617730, Class: 1.000000, Obj: 0.374234, No Obj: 0.002847, Avg Recall: 0.750000, count: 140 -Region Avg IOU: 0.627669, Class: 1.000000, Obj: 0.415153, No Obj: 0.002206, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.608713, Class: 1.000000, Obj: 0.376489, No Obj: 0.002889, Avg Recall: 0.753623, count: 138 -Region Avg IOU: 0.664839, Class: 1.000000, Obj: 0.455763, No Obj: 0.002325, Avg Recall: 0.835294, count: 85 -Region Avg IOU: 0.716297, Class: 1.000000, Obj: 0.498946, No Obj: 0.002560, Avg Recall: 0.869048, count: 84 -7244: 0.399737, 0.409390 avg, 0.001000 rate, 26.533060 seconds, 1854464 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.651123, Class: 1.000000, Obj: 0.408195, No Obj: 0.002297, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.637982, Class: 1.000000, Obj: 0.423893, No Obj: 0.002570, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.613966, Class: 1.000000, Obj: 0.416396, No Obj: 0.002655, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.672976, Class: 1.000000, Obj: 0.472960, No Obj: 0.002529, Avg Recall: 0.814433, count: 97 -Region Avg IOU: 0.634104, Class: 1.000000, Obj: 0.368883, No Obj: 0.002522, Avg Recall: 0.741071, count: 112 -Region Avg IOU: 0.617452, Class: 1.000000, Obj: 0.412348, No Obj: 0.002888, Avg Recall: 0.753521, count: 142 -Region Avg IOU: 0.626690, Class: 1.000000, Obj: 0.368265, No Obj: 0.002276, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.608282, Class: 1.000000, Obj: 0.389797, No Obj: 0.002476, Avg Recall: 0.752577, count: 97 -7245: 0.423045, 0.410756 avg, 0.001000 rate, 26.403316 seconds, 1854720 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.613038, Class: 1.000000, Obj: 0.372482, No Obj: 0.002357, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.648249, Class: 1.000000, Obj: 0.392654, No Obj: 0.002202, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.694465, Class: 1.000000, Obj: 0.443829, No Obj: 0.002158, Avg Recall: 0.858974, count: 78 -Region Avg IOU: 0.645297, Class: 1.000000, Obj: 0.428068, No Obj: 0.002586, Avg Recall: 0.759259, count: 108 -Region Avg IOU: 0.670454, Class: 1.000000, Obj: 0.465026, No Obj: 0.002832, Avg Recall: 0.830508, count: 118 -Region Avg IOU: 0.637533, Class: 1.000000, Obj: 0.402650, No Obj: 0.002855, Avg Recall: 0.722222, count: 126 -Region Avg IOU: 0.661048, Class: 1.000000, Obj: 0.430142, No Obj: 0.002578, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.666222, Class: 1.000000, Obj: 0.444067, No Obj: 0.002468, Avg Recall: 0.821053, count: 95 -7246: 0.410837, 0.410764 avg, 0.001000 rate, 26.731956 seconds, 1854976 images -Loaded: 0.000072 seconds -Region Avg IOU: 0.630777, Class: 1.000000, Obj: 0.419532, No Obj: 0.002569, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.600843, Class: 1.000000, Obj: 0.388173, No Obj: 0.002363, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.641335, Class: 1.000000, Obj: 0.399501, No Obj: 0.002296, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.643487, Class: 1.000000, Obj: 0.407492, No Obj: 0.002541, Avg Recall: 0.787234, count: 94 -Region Avg IOU: 0.678615, Class: 1.000000, Obj: 0.393122, No Obj: 0.002875, Avg Recall: 0.823077, count: 130 -Region Avg IOU: 0.693306, Class: 1.000000, Obj: 0.431479, No Obj: 0.002732, Avg Recall: 0.870690, count: 116 -Region Avg IOU: 0.625942, Class: 1.000000, Obj: 0.324647, No Obj: 0.002506, Avg Recall: 0.767241, count: 116 -Region Avg IOU: 0.641551, Class: 1.000000, Obj: 0.403492, No Obj: 0.002297, Avg Recall: 0.757282, count: 103 -7247: 0.452442, 0.414932 avg, 0.001000 rate, 28.100157 seconds, 1855232 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.649157, Class: 1.000000, Obj: 0.431640, No Obj: 0.002432, Avg Recall: 0.791667, count: 96 -Region Avg IOU: 0.608475, Class: 1.000000, Obj: 0.409907, No Obj: 0.003253, Avg Recall: 0.732824, count: 131 -Region Avg IOU: 0.622382, Class: 1.000000, Obj: 0.402791, No Obj: 0.002736, Avg Recall: 0.750000, count: 132 -Region Avg IOU: 0.604734, Class: 1.000000, Obj: 0.455545, No Obj: 0.002774, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.638216, Class: 1.000000, Obj: 0.397034, No Obj: 0.002679, Avg Recall: 0.771186, count: 118 -Region Avg IOU: 0.652839, Class: 1.000000, Obj: 0.414764, No Obj: 0.002269, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.653995, Class: 1.000000, Obj: 0.419318, No Obj: 0.002433, Avg Recall: 0.806818, count: 88 -Region Avg IOU: 0.649503, Class: 1.000000, Obj: 0.461584, No Obj: 0.002629, Avg Recall: 0.772727, count: 110 -7248: 0.417634, 0.415202 avg, 0.001000 rate, 27.425768 seconds, 1855488 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.628619, Class: 1.000000, Obj: 0.411447, No Obj: 0.002752, Avg Recall: 0.764228, count: 123 -Region Avg IOU: 0.643718, Class: 1.000000, Obj: 0.408941, No Obj: 0.002769, Avg Recall: 0.763158, count: 114 -Region Avg IOU: 0.656915, Class: 1.000000, Obj: 0.448079, No Obj: 0.002566, Avg Recall: 0.809091, count: 110 -Region Avg IOU: 0.655784, Class: 1.000000, Obj: 0.380597, No Obj: 0.002773, Avg Recall: 0.794872, count: 117 -Region Avg IOU: 0.633969, Class: 1.000000, Obj: 0.430548, No Obj: 0.002582, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.659871, Class: 1.000000, Obj: 0.409783, No Obj: 0.002316, Avg Recall: 0.849462, count: 93 -Region Avg IOU: 0.665646, Class: 1.000000, Obj: 0.450073, No Obj: 0.002183, Avg Recall: 0.790698, count: 86 -Region Avg IOU: 0.637448, Class: 1.000000, Obj: 0.417630, No Obj: 0.002382, Avg Recall: 0.787037, count: 108 -7249: 0.409425, 0.414624 avg, 0.001000 rate, 28.902628 seconds, 1855744 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.620313, Class: 1.000000, Obj: 0.421547, No Obj: 0.002665, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.662713, Class: 1.000000, Obj: 0.457375, No Obj: 0.002762, Avg Recall: 0.811475, count: 122 -Region Avg IOU: 0.665882, Class: 1.000000, Obj: 0.432638, No Obj: 0.002387, Avg Recall: 0.862745, count: 102 -Region Avg IOU: 0.662447, Class: 1.000000, Obj: 0.386565, No Obj: 0.002335, Avg Recall: 0.820000, count: 100 -Region Avg IOU: 0.680920, Class: 1.000000, Obj: 0.492068, No Obj: 0.003025, Avg Recall: 0.850467, count: 107 -Region Avg IOU: 0.645516, Class: 1.000000, Obj: 0.424494, No Obj: 0.002913, Avg Recall: 0.801527, count: 131 -Region Avg IOU: 0.608028, Class: 1.000000, Obj: 0.390936, No Obj: 0.002791, Avg Recall: 0.733945, count: 109 -Region Avg IOU: 0.628035, Class: 1.000000, Obj: 0.425522, No Obj: 0.002841, Avg Recall: 0.798246, count: 114 -7250: 0.420152, 0.415177 avg, 0.001000 rate, 27.002527 seconds, 1856000 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 18.488264 seconds -Region Avg IOU: 0.588629, Class: 1.000000, Obj: 0.420435, No Obj: 0.003392, Avg Recall: 0.732759, count: 116 -Region Avg IOU: 0.603285, Class: 1.000000, Obj: 0.396541, No Obj: 0.003106, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.606734, Class: 1.000000, Obj: 0.399914, No Obj: 0.003348, Avg Recall: 0.737705, count: 122 -Region Avg IOU: 0.582694, Class: 1.000000, Obj: 0.420171, No Obj: 0.002881, Avg Recall: 0.653226, count: 124 -Region Avg IOU: 0.601111, Class: 1.000000, Obj: 0.416477, No Obj: 0.003027, Avg Recall: 0.737864, count: 103 -Region Avg IOU: 0.582240, Class: 1.000000, Obj: 0.399393, No Obj: 0.003118, Avg Recall: 0.704918, count: 122 -Region Avg IOU: 0.621162, Class: 1.000000, Obj: 0.407073, No Obj: 0.003565, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.629847, Class: 1.000000, Obj: 0.429052, No Obj: 0.004052, Avg Recall: 0.777778, count: 117 -7251: 0.458399, 0.419499 avg, 0.001000 rate, 22.772602 seconds, 1856256 images -Loaded: 0.000071 seconds -Region Avg IOU: 0.603454, Class: 1.000000, Obj: 0.410018, No Obj: 0.003311, Avg Recall: 0.686275, count: 102 -Region Avg IOU: 0.636232, Class: 1.000000, Obj: 0.429572, No Obj: 0.003459, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.558131, Class: 1.000000, Obj: 0.372087, No Obj: 0.003036, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.578820, Class: 1.000000, Obj: 0.388676, No Obj: 0.002873, Avg Recall: 0.673913, count: 92 -Region Avg IOU: 0.555505, Class: 1.000000, Obj: 0.383689, No Obj: 0.003621, Avg Recall: 0.666667, count: 126 -Region Avg IOU: 0.634702, Class: 1.000000, Obj: 0.405756, No Obj: 0.003644, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.593972, Class: 1.000000, Obj: 0.477119, No Obj: 0.003000, Avg Recall: 0.688889, count: 90 -Region Avg IOU: 0.636369, Class: 1.000000, Obj: 0.419821, No Obj: 0.004277, Avg Recall: 0.781250, count: 128 -7252: 0.396392, 0.417189 avg, 0.001000 rate, 23.358751 seconds, 1856512 images -Loaded: 0.000067 seconds -Region Avg IOU: 0.644991, Class: 1.000000, Obj: 0.481625, No Obj: 0.003546, Avg Recall: 0.796117, count: 103 -Region Avg IOU: 0.592430, Class: 1.000000, Obj: 0.427410, No Obj: 0.003035, Avg Recall: 0.738095, count: 84 -Region Avg IOU: 0.647318, Class: 1.000000, Obj: 0.470736, No Obj: 0.003323, Avg Recall: 0.764045, count: 89 -Region Avg IOU: 0.639085, Class: 1.000000, Obj: 0.464376, No Obj: 0.002867, Avg Recall: 0.750000, count: 80 -Region Avg IOU: 0.671714, Class: 1.000000, Obj: 0.448578, No Obj: 0.003871, Avg Recall: 0.806452, count: 93 -Region Avg IOU: 0.624947, Class: 1.000000, Obj: 0.476715, No Obj: 0.003614, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.571818, Class: 1.000000, Obj: 0.410477, No Obj: 0.003654, Avg Recall: 0.684615, count: 130 -Region Avg IOU: 0.617172, Class: 1.000000, Obj: 0.460954, No Obj: 0.003211, Avg Recall: 0.735632, count: 87 -7253: 0.336336, 0.409103 avg, 0.001000 rate, 22.855448 seconds, 1856768 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.575515, Class: 1.000000, Obj: 0.396335, No Obj: 0.003369, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.583104, Class: 1.000000, Obj: 0.451486, No Obj: 0.003291, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.577225, Class: 1.000000, Obj: 0.456547, No Obj: 0.003078, Avg Recall: 0.647059, count: 102 -Region Avg IOU: 0.600706, Class: 1.000000, Obj: 0.410060, No Obj: 0.003853, Avg Recall: 0.686441, count: 118 -Region Avg IOU: 0.615183, Class: 1.000000, Obj: 0.516619, No Obj: 0.003867, Avg Recall: 0.734513, count: 113 -Region Avg IOU: 0.605378, Class: 1.000000, Obj: 0.441903, No Obj: 0.002961, Avg Recall: 0.705128, count: 78 -Region Avg IOU: 0.589575, Class: 1.000000, Obj: 0.411045, No Obj: 0.003194, Avg Recall: 0.708738, count: 103 -Region Avg IOU: 0.687164, Class: 1.000000, Obj: 0.494761, No Obj: 0.003449, Avg Recall: 0.773810, count: 84 -7254: 0.356079, 0.403801 avg, 0.001000 rate, 24.397476 seconds, 1857024 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.572465, Class: 1.000000, Obj: 0.416635, No Obj: 0.003827, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.584412, Class: 1.000000, Obj: 0.398523, No Obj: 0.004431, Avg Recall: 0.690909, count: 110 -Region Avg IOU: 0.622848, Class: 1.000000, Obj: 0.459959, No Obj: 0.004038, Avg Recall: 0.730435, count: 115 -Region Avg IOU: 0.601908, Class: 1.000000, Obj: 0.416214, No Obj: 0.003391, Avg Recall: 0.731481, count: 108 -Region Avg IOU: 0.644709, Class: 1.000000, Obj: 0.477281, No Obj: 0.002802, Avg Recall: 0.800000, count: 80 -Region Avg IOU: 0.547690, Class: 1.000000, Obj: 0.409957, No Obj: 0.003112, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.596885, Class: 1.000000, Obj: 0.418352, No Obj: 0.002888, Avg Recall: 0.712500, count: 80 -Region Avg IOU: 0.589922, Class: 1.000000, Obj: 0.427278, No Obj: 0.003187, Avg Recall: 0.704082, count: 98 -7255: 0.383979, 0.401819 avg, 0.001000 rate, 24.457178 seconds, 1857280 images -Loaded: 0.000073 seconds -Region Avg IOU: 0.655287, Class: 1.000000, Obj: 0.450217, No Obj: 0.003999, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.610095, Class: 1.000000, Obj: 0.435563, No Obj: 0.003919, Avg Recall: 0.724138, count: 116 -Region Avg IOU: 0.616526, Class: 1.000000, Obj: 0.439883, No Obj: 0.004545, Avg Recall: 0.742424, count: 132 -Region Avg IOU: 0.606050, Class: 1.000000, Obj: 0.406793, No Obj: 0.004210, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.643233, Class: 1.000000, Obj: 0.470945, No Obj: 0.003344, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.592521, Class: 1.000000, Obj: 0.462313, No Obj: 0.003788, Avg Recall: 0.651786, count: 112 -Region Avg IOU: 0.572717, Class: 1.000000, Obj: 0.453193, No Obj: 0.003337, Avg Recall: 0.670330, count: 91 -Region Avg IOU: 0.615811, Class: 1.000000, Obj: 0.412541, No Obj: 0.003577, Avg Recall: 0.741379, count: 116 -7256: 0.401756, 0.401812 avg, 0.001000 rate, 23.771444 seconds, 1857536 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.586865, Class: 1.000000, Obj: 0.471656, No Obj: 0.003132, Avg Recall: 0.684211, count: 76 -Region Avg IOU: 0.621771, Class: 1.000000, Obj: 0.446951, No Obj: 0.003455, Avg Recall: 0.747368, count: 95 -Region Avg IOU: 0.576137, Class: 1.000000, Obj: 0.443472, No Obj: 0.003880, Avg Recall: 0.669355, count: 124 -Region Avg IOU: 0.599624, Class: 1.000000, Obj: 0.421477, No Obj: 0.003914, Avg Recall: 0.721311, count: 122 -Region Avg IOU: 0.663775, Class: 1.000000, Obj: 0.514385, No Obj: 0.003883, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.623352, Class: 1.000000, Obj: 0.484184, No Obj: 0.003520, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.616013, Class: 1.000000, Obj: 0.422892, No Obj: 0.003707, Avg Recall: 0.772727, count: 110 -Region Avg IOU: 0.597115, Class: 1.000000, Obj: 0.419725, No Obj: 0.002858, Avg Recall: 0.770833, count: 96 -7257: 0.402111, 0.401842 avg, 0.001000 rate, 23.825775 seconds, 1857792 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.560411, Class: 1.000000, Obj: 0.423330, No Obj: 0.003216, Avg Recall: 0.638095, count: 105 -Region Avg IOU: 0.614840, Class: 1.000000, Obj: 0.471032, No Obj: 0.003454, Avg Recall: 0.724490, count: 98 -Region Avg IOU: 0.593378, Class: 1.000000, Obj: 0.424468, No Obj: 0.003832, Avg Recall: 0.694656, count: 131 -Region Avg IOU: 0.589462, Class: 1.000000, Obj: 0.441954, No Obj: 0.003908, Avg Recall: 0.709402, count: 117 -Region Avg IOU: 0.647603, Class: 1.000000, Obj: 0.410655, No Obj: 0.003389, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.536012, Class: 1.000000, Obj: 0.389220, No Obj: 0.003728, Avg Recall: 0.590164, count: 122 -Region Avg IOU: 0.649510, Class: 1.000000, Obj: 0.483595, No Obj: 0.003474, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.616065, Class: 1.000000, Obj: 0.450761, No Obj: 0.003359, Avg Recall: 0.718750, count: 96 -7258: 0.389829, 0.400641 avg, 0.001000 rate, 23.334724 seconds, 1858048 images -Loaded: 0.000069 seconds -Region Avg IOU: 0.547305, Class: 1.000000, Obj: 0.391039, No Obj: 0.003038, Avg Recall: 0.622642, count: 106 -Region Avg IOU: 0.609348, Class: 1.000000, Obj: 0.449799, No Obj: 0.003742, Avg Recall: 0.714286, count: 112 -Region Avg IOU: 0.569064, Class: 1.000000, Obj: 0.407833, No Obj: 0.003587, Avg Recall: 0.692308, count: 117 -Region Avg IOU: 0.660796, Class: 1.000000, Obj: 0.450359, No Obj: 0.003912, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.649159, Class: 1.000000, Obj: 0.453876, No Obj: 0.003553, Avg Recall: 0.790476, count: 105 -Region Avg IOU: 0.614214, Class: 1.000000, Obj: 0.425353, No Obj: 0.003490, Avg Recall: 0.723577, count: 123 -Region Avg IOU: 0.615343, Class: 1.000000, Obj: 0.434597, No Obj: 0.003359, Avg Recall: 0.758929, count: 112 -Region Avg IOU: 0.617284, Class: 1.000000, Obj: 0.470407, No Obj: 0.003478, Avg Recall: 0.733333, count: 90 -7259: 0.407086, 0.401286 avg, 0.001000 rate, 24.290943 seconds, 1858304 images -Loaded: 0.000121 seconds -Region Avg IOU: 0.580865, Class: 1.000000, Obj: 0.418411, No Obj: 0.003231, Avg Recall: 0.690000, count: 100 -Region Avg IOU: 0.589747, Class: 1.000000, Obj: 0.404822, No Obj: 0.003269, Avg Recall: 0.700000, count: 110 -Region Avg IOU: 0.573726, Class: 1.000000, Obj: 0.405942, No Obj: 0.003037, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.573995, Class: 1.000000, Obj: 0.430561, No Obj: 0.003723, Avg Recall: 0.682540, count: 126 -Region Avg IOU: 0.589188, Class: 1.000000, Obj: 0.416604, No Obj: 0.003852, Avg Recall: 0.700000, count: 120 -Region Avg IOU: 0.588573, Class: 1.000000, Obj: 0.422787, No Obj: 0.003332, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.616315, Class: 1.000000, Obj: 0.473716, No Obj: 0.003109, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.598739, Class: 1.000000, Obj: 0.384394, No Obj: 0.003612, Avg Recall: 0.721154, count: 104 -7260: 0.428250, 0.403982 avg, 0.001000 rate, 22.360308 seconds, 1858560 images -Resizing -512 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 16.623701 seconds -Region Avg IOU: 0.597030, Class: 1.000000, Obj: 0.387162, No Obj: 0.003035, Avg Recall: 0.732558, count: 86 -Region Avg IOU: 0.608644, Class: 1.000000, Obj: 0.414049, No Obj: 0.003453, Avg Recall: 0.744681, count: 94 -Region Avg IOU: 0.610648, Class: 1.000000, Obj: 0.481747, No Obj: 0.003069, Avg Recall: 0.684211, count: 95 -Region Avg IOU: 0.549601, Class: 1.000000, Obj: 0.404169, No Obj: 0.003193, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.590065, Class: 1.000000, Obj: 0.352820, No Obj: 0.003581, Avg Recall: 0.715517, count: 116 -Region Avg IOU: 0.615337, Class: 1.000000, Obj: 0.423014, No Obj: 0.004336, Avg Recall: 0.768657, count: 134 -Region Avg IOU: 0.605854, Class: 1.000000, Obj: 0.424596, No Obj: 0.002850, Avg Recall: 0.682927, count: 82 -Region Avg IOU: 0.579333, Class: 1.000000, Obj: 0.410394, No Obj: 0.003408, Avg Recall: 0.681818, count: 132 -7261: 0.401195, 0.403703 avg, 0.001000 rate, 23.687037 seconds, 1858816 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.597310, Class: 1.000000, Obj: 0.408195, No Obj: 0.003464, Avg Recall: 0.713043, count: 115 -Region Avg IOU: 0.607216, Class: 1.000000, Obj: 0.391817, No Obj: 0.003052, Avg Recall: 0.727273, count: 99 -Region Avg IOU: 0.611984, Class: 1.000000, Obj: 0.367616, No Obj: 0.002929, Avg Recall: 0.724771, count: 109 -Region Avg IOU: 0.581573, Class: 1.000000, Obj: 0.414474, No Obj: 0.003186, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.636062, Class: 1.000000, Obj: 0.410728, No Obj: 0.003504, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.587413, Class: 1.000000, Obj: 0.405496, No Obj: 0.003593, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.602703, Class: 1.000000, Obj: 0.478720, No Obj: 0.002964, Avg Recall: 0.682353, count: 85 -Region Avg IOU: 0.638091, Class: 1.000000, Obj: 0.435578, No Obj: 0.003528, Avg Recall: 0.785714, count: 126 -7262: 0.436230, 0.406956 avg, 0.001000 rate, 24.165445 seconds, 1859072 images -Loaded: 0.000092 seconds -Region Avg IOU: 0.607069, Class: 1.000000, Obj: 0.396354, No Obj: 0.002998, Avg Recall: 0.693694, count: 111 -Region Avg IOU: 0.596835, Class: 1.000000, Obj: 0.444976, No Obj: 0.003394, Avg Recall: 0.717172, count: 99 -Region Avg IOU: 0.595879, Class: 1.000000, Obj: 0.402883, No Obj: 0.003829, Avg Recall: 0.694656, count: 131 -Region Avg IOU: 0.591810, Class: 1.000000, Obj: 0.415043, No Obj: 0.003170, Avg Recall: 0.669643, count: 112 -Region Avg IOU: 0.616891, Class: 1.000000, Obj: 0.386330, No Obj: 0.003549, Avg Recall: 0.723881, count: 134 -Region Avg IOU: 0.572959, Class: 1.000000, Obj: 0.426306, No Obj: 0.003427, Avg Recall: 0.655738, count: 122 -Region Avg IOU: 0.608917, Class: 1.000000, Obj: 0.449267, No Obj: 0.002922, Avg Recall: 0.723404, count: 94 -Region Avg IOU: 0.600393, Class: 1.000000, Obj: 0.440159, No Obj: 0.003210, Avg Recall: 0.680000, count: 100 -7263: 0.478155, 0.414076 avg, 0.001000 rate, 24.201864 seconds, 1859328 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.653117, Class: 1.000000, Obj: 0.409403, No Obj: 0.003585, Avg Recall: 0.839623, count: 106 -Region Avg IOU: 0.564241, Class: 1.000000, Obj: 0.378723, No Obj: 0.003313, Avg Recall: 0.661765, count: 136 -Region Avg IOU: 0.636284, Class: 1.000000, Obj: 0.378898, No Obj: 0.003583, Avg Recall: 0.771739, count: 92 -Region Avg IOU: 0.582214, Class: 1.000000, Obj: 0.374543, No Obj: 0.003560, Avg Recall: 0.666667, count: 123 -Region Avg IOU: 0.594894, Class: 1.000000, Obj: 0.397931, No Obj: 0.003622, Avg Recall: 0.677686, count: 121 -Region Avg IOU: 0.573272, Class: 1.000000, Obj: 0.393772, No Obj: 0.003176, Avg Recall: 0.698113, count: 106 -Region Avg IOU: 0.616810, Class: 1.000000, Obj: 0.426861, No Obj: 0.003136, Avg Recall: 0.728972, count: 107 -Region Avg IOU: 0.661814, Class: 1.000000, Obj: 0.421591, No Obj: 0.003628, Avg Recall: 0.790476, count: 105 -7264: 0.419826, 0.414651 avg, 0.001000 rate, 23.325439 seconds, 1859584 images -Loaded: 0.000088 seconds -Region Avg IOU: 0.593990, Class: 1.000000, Obj: 0.444990, No Obj: 0.003329, Avg Recall: 0.678899, count: 109 -Region Avg IOU: 0.567535, Class: 1.000000, Obj: 0.375986, No Obj: 0.003654, Avg Recall: 0.695652, count: 115 -Region Avg IOU: 0.592657, Class: 1.000000, Obj: 0.422093, No Obj: 0.002888, Avg Recall: 0.693878, count: 98 -Region Avg IOU: 0.621388, Class: 1.000000, Obj: 0.410310, No Obj: 0.002916, Avg Recall: 0.786517, count: 89 -Region Avg IOU: 0.579841, Class: 1.000000, Obj: 0.419091, No Obj: 0.003506, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.589842, Class: 1.000000, Obj: 0.418573, No Obj: 0.003062, Avg Recall: 0.663158, count: 95 -Region Avg IOU: 0.583379, Class: 1.000000, Obj: 0.401710, No Obj: 0.003155, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.629798, Class: 1.000000, Obj: 0.400889, No Obj: 0.003977, Avg Recall: 0.794118, count: 136 -7265: 0.400202, 0.413206 avg, 0.001000 rate, 23.636749 seconds, 1859840 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.601497, Class: 1.000000, Obj: 0.414513, No Obj: 0.003438, Avg Recall: 0.703297, count: 91 -Region Avg IOU: 0.556481, Class: 1.000000, Obj: 0.395816, No Obj: 0.003432, Avg Recall: 0.637681, count: 138 -Region Avg IOU: 0.587006, Class: 1.000000, Obj: 0.386830, No Obj: 0.003549, Avg Recall: 0.703390, count: 118 -Region Avg IOU: 0.593183, Class: 1.000000, Obj: 0.397522, No Obj: 0.003368, Avg Recall: 0.684615, count: 130 -Region Avg IOU: 0.591325, Class: 1.000000, Obj: 0.421422, No Obj: 0.003492, Avg Recall: 0.691057, count: 123 -Region Avg IOU: 0.545113, Class: 1.000000, Obj: 0.395631, No Obj: 0.002610, Avg Recall: 0.572917, count: 96 -Region Avg IOU: 0.596529, Class: 1.000000, Obj: 0.416188, No Obj: 0.003249, Avg Recall: 0.686046, count: 86 -Region Avg IOU: 0.651906, Class: 1.000000, Obj: 0.428153, No Obj: 0.003391, Avg Recall: 0.770833, count: 96 -7266: 0.435139, 0.415399 avg, 0.001000 rate, 22.867285 seconds, 1860096 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.602115, Class: 1.000000, Obj: 0.420282, No Obj: 0.003460, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.614169, Class: 1.000000, Obj: 0.442900, No Obj: 0.003046, Avg Recall: 0.698795, count: 83 -Region Avg IOU: 0.603872, Class: 1.000000, Obj: 0.398011, No Obj: 0.003701, Avg Recall: 0.733333, count: 120 -Region Avg IOU: 0.601158, Class: 1.000000, Obj: 0.448422, No Obj: 0.003409, Avg Recall: 0.694444, count: 108 -Region Avg IOU: 0.627799, Class: 1.000000, Obj: 0.419800, No Obj: 0.003582, Avg Recall: 0.739130, count: 115 -Region Avg IOU: 0.591343, Class: 1.000000, Obj: 0.406196, No Obj: 0.003143, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.610550, Class: 1.000000, Obj: 0.467175, No Obj: 0.003603, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.602423, Class: 1.000000, Obj: 0.429956, No Obj: 0.003568, Avg Recall: 0.731481, count: 108 -7267: 0.451464, 0.419006 avg, 0.001000 rate, 24.802689 seconds, 1860352 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.571874, Class: 1.000000, Obj: 0.402531, No Obj: 0.003474, Avg Recall: 0.684211, count: 114 -Region Avg IOU: 0.582794, Class: 1.000000, Obj: 0.441667, No Obj: 0.003192, Avg Recall: 0.704762, count: 105 -Region Avg IOU: 0.607179, Class: 1.000000, Obj: 0.455725, No Obj: 0.003773, Avg Recall: 0.688073, count: 109 -Region Avg IOU: 0.548200, Class: 1.000000, Obj: 0.386217, No Obj: 0.003208, Avg Recall: 0.612903, count: 124 -Region Avg IOU: 0.558740, Class: 1.000000, Obj: 0.390570, No Obj: 0.003607, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.586695, Class: 1.000000, Obj: 0.441407, No Obj: 0.003520, Avg Recall: 0.678571, count: 112 -Region Avg IOU: 0.580384, Class: 1.000000, Obj: 0.419996, No Obj: 0.003261, Avg Recall: 0.687500, count: 96 -Region Avg IOU: 0.677251, Class: 1.000000, Obj: 0.509008, No Obj: 0.003479, Avg Recall: 0.858824, count: 85 -7268: 0.361801, 0.413285 avg, 0.001000 rate, 22.573154 seconds, 1860608 images -Loaded: 0.000109 seconds -Region Avg IOU: 0.600138, Class: 1.000000, Obj: 0.459847, No Obj: 0.003386, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.591277, Class: 1.000000, Obj: 0.426614, No Obj: 0.003336, Avg Recall: 0.718447, count: 103 -Region Avg IOU: 0.621229, Class: 1.000000, Obj: 0.444529, No Obj: 0.003373, Avg Recall: 0.728155, count: 103 -Region Avg IOU: 0.637120, Class: 1.000000, Obj: 0.443105, No Obj: 0.003369, Avg Recall: 0.788991, count: 109 -Region Avg IOU: 0.535017, Class: 1.000000, Obj: 0.382862, No Obj: 0.003191, Avg Recall: 0.616071, count: 112 -Region Avg IOU: 0.609433, Class: 1.000000, Obj: 0.423707, No Obj: 0.003727, Avg Recall: 0.740000, count: 100 -Region Avg IOU: 0.580477, Class: 1.000000, Obj: 0.448959, No Obj: 0.003452, Avg Recall: 0.694215, count: 121 -Region Avg IOU: 0.593675, Class: 1.000000, Obj: 0.475867, No Obj: 0.003229, Avg Recall: 0.669811, count: 106 -7269: 0.372206, 0.409177 avg, 0.001000 rate, 24.622414 seconds, 1860864 images -Loaded: 0.000103 seconds -Region Avg IOU: 0.644039, Class: 1.000000, Obj: 0.455554, No Obj: 0.003070, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.559576, Class: 1.000000, Obj: 0.385710, No Obj: 0.003478, Avg Recall: 0.680672, count: 119 -Region Avg IOU: 0.568360, Class: 1.000000, Obj: 0.472361, No Obj: 0.002864, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.597823, Class: 1.000000, Obj: 0.404466, No Obj: 0.003860, Avg Recall: 0.715596, count: 109 -Region Avg IOU: 0.596187, Class: 1.000000, Obj: 0.417593, No Obj: 0.002905, Avg Recall: 0.714286, count: 77 -Region Avg IOU: 0.599884, Class: 1.000000, Obj: 0.435796, No Obj: 0.003361, Avg Recall: 0.666667, count: 90 -Region Avg IOU: 0.649459, Class: 1.000000, Obj: 0.448548, No Obj: 0.004066, Avg Recall: 0.779661, count: 118 -Region Avg IOU: 0.624028, Class: 1.000000, Obj: 0.427184, No Obj: 0.003568, Avg Recall: 0.785047, count: 107 -7270: 0.376944, 0.405954 avg, 0.001000 rate, 23.205631 seconds, 1861120 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000065 seconds -Region Avg IOU: 0.642267, Class: 1.000000, Obj: 0.452518, No Obj: 0.003688, Avg Recall: 0.801653, count: 121 -Region Avg IOU: 0.593733, Class: 1.000000, Obj: 0.482909, No Obj: 0.002955, Avg Recall: 0.688679, count: 106 -Region Avg IOU: 0.611241, Class: 1.000000, Obj: 0.372001, No Obj: 0.003265, Avg Recall: 0.738095, count: 126 -Region Avg IOU: 0.658264, Class: 1.000000, Obj: 0.456789, No Obj: 0.003335, Avg Recall: 0.818965, count: 116 -Region Avg IOU: 0.663062, Class: 1.000000, Obj: 0.464051, No Obj: 0.003207, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.640144, Class: 1.000000, Obj: 0.408025, No Obj: 0.003961, Avg Recall: 0.778626, count: 131 -Region Avg IOU: 0.656870, Class: 1.000000, Obj: 0.444811, No Obj: 0.003293, Avg Recall: 0.866667, count: 105 -Region Avg IOU: 0.605682, Class: 1.000000, Obj: 0.470440, No Obj: 0.002870, Avg Recall: 0.727273, count: 99 -7271: 0.433732, 0.408732 avg, 0.001000 rate, 27.179859 seconds, 1861376 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.663226, Class: 1.000000, Obj: 0.417359, No Obj: 0.003702, Avg Recall: 0.808333, count: 120 -Region Avg IOU: 0.645384, Class: 1.000000, Obj: 0.462758, No Obj: 0.003154, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.641054, Class: 1.000000, Obj: 0.402515, No Obj: 0.003402, Avg Recall: 0.801980, count: 101 -Region Avg IOU: 0.637709, Class: 1.000000, Obj: 0.492942, No Obj: 0.002858, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.592974, Class: 1.000000, Obj: 0.448819, No Obj: 0.002760, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.612765, Class: 1.000000, Obj: 0.467729, No Obj: 0.002988, Avg Recall: 0.702970, count: 101 -Region Avg IOU: 0.675986, Class: 1.000000, Obj: 0.571693, No Obj: 0.002817, Avg Recall: 0.855422, count: 83 -Region Avg IOU: 0.616104, Class: 1.000000, Obj: 0.458055, No Obj: 0.002620, Avg Recall: 0.756098, count: 82 -7272: 0.378863, 0.405745 avg, 0.001000 rate, 25.973951 seconds, 1861632 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.670076, Class: 1.000000, Obj: 0.429154, No Obj: 0.003436, Avg Recall: 0.816000, count: 125 -Region Avg IOU: 0.664722, Class: 1.000000, Obj: 0.421634, No Obj: 0.003006, Avg Recall: 0.837607, count: 117 -Region Avg IOU: 0.644724, Class: 1.000000, Obj: 0.453735, No Obj: 0.002854, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.675072, Class: 1.000000, Obj: 0.495487, No Obj: 0.003211, Avg Recall: 0.816326, count: 98 -Region Avg IOU: 0.674088, Class: 1.000000, Obj: 0.527986, No Obj: 0.002976, Avg Recall: 0.813953, count: 86 -Region Avg IOU: 0.621753, Class: 1.000000, Obj: 0.432949, No Obj: 0.002841, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.630299, Class: 1.000000, Obj: 0.418531, No Obj: 0.003149, Avg Recall: 0.762963, count: 135 -Region Avg IOU: 0.621111, Class: 1.000000, Obj: 0.412387, No Obj: 0.002803, Avg Recall: 0.746154, count: 130 -7273: 0.420028, 0.407173 avg, 0.001000 rate, 26.893127 seconds, 1861888 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.649286, Class: 1.000000, Obj: 0.416567, No Obj: 0.003126, Avg Recall: 0.811966, count: 117 -Region Avg IOU: 0.639187, Class: 1.000000, Obj: 0.410579, No Obj: 0.002452, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.660645, Class: 1.000000, Obj: 0.474806, No Obj: 0.002520, Avg Recall: 0.811765, count: 85 -Region Avg IOU: 0.684582, Class: 1.000000, Obj: 0.442133, No Obj: 0.002824, Avg Recall: 0.844444, count: 90 -Region Avg IOU: 0.623760, Class: 1.000000, Obj: 0.363473, No Obj: 0.003489, Avg Recall: 0.772727, count: 154 -Region Avg IOU: 0.668215, Class: 1.000000, Obj: 0.432426, No Obj: 0.002611, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.652358, Class: 1.000000, Obj: 0.410202, No Obj: 0.002810, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.656825, Class: 1.000000, Obj: 0.430058, No Obj: 0.002857, Avg Recall: 0.789474, count: 95 -7274: 0.388085, 0.405265 avg, 0.001000 rate, 26.386875 seconds, 1862144 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.652068, Class: 1.000000, Obj: 0.438068, No Obj: 0.002926, Avg Recall: 0.778688, count: 122 -Region Avg IOU: 0.653749, Class: 1.000000, Obj: 0.445709, No Obj: 0.002432, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.614130, Class: 1.000000, Obj: 0.428603, No Obj: 0.002510, Avg Recall: 0.721649, count: 97 -Region Avg IOU: 0.628624, Class: 1.000000, Obj: 0.387034, No Obj: 0.002732, Avg Recall: 0.766355, count: 107 -Region Avg IOU: 0.616579, Class: 1.000000, Obj: 0.405274, No Obj: 0.002936, Avg Recall: 0.708661, count: 127 -Region Avg IOU: 0.641400, Class: 1.000000, Obj: 0.429462, No Obj: 0.002727, Avg Recall: 0.788136, count: 118 -Region Avg IOU: 0.704808, Class: 1.000000, Obj: 0.472454, No Obj: 0.002659, Avg Recall: 0.901099, count: 91 -Region Avg IOU: 0.628252, Class: 1.000000, Obj: 0.419711, No Obj: 0.002941, Avg Recall: 0.689076, count: 119 -7275: 0.428670, 0.407605 avg, 0.001000 rate, 26.170221 seconds, 1862400 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.643051, Class: 1.000000, Obj: 0.379769, No Obj: 0.002527, Avg Recall: 0.740741, count: 108 -Region Avg IOU: 0.664505, Class: 1.000000, Obj: 0.463280, No Obj: 0.002599, Avg Recall: 0.781609, count: 87 -Region Avg IOU: 0.607464, Class: 1.000000, Obj: 0.392731, No Obj: 0.002865, Avg Recall: 0.711538, count: 104 -Region Avg IOU: 0.639392, Class: 1.000000, Obj: 0.442097, No Obj: 0.002566, Avg Recall: 0.804124, count: 97 -Region Avg IOU: 0.670187, Class: 1.000000, Obj: 0.467374, No Obj: 0.002776, Avg Recall: 0.843750, count: 96 -Region Avg IOU: 0.657759, Class: 1.000000, Obj: 0.464889, No Obj: 0.002730, Avg Recall: 0.792793, count: 111 -Region Avg IOU: 0.655977, Class: 1.000000, Obj: 0.431062, No Obj: 0.002692, Avg Recall: 0.808696, count: 115 -Region Avg IOU: 0.611682, Class: 1.000000, Obj: 0.378569, No Obj: 0.003085, Avg Recall: 0.723404, count: 141 -7276: 0.403229, 0.407167 avg, 0.001000 rate, 26.633812 seconds, 1862656 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.617098, Class: 1.000000, Obj: 0.417361, No Obj: 0.002876, Avg Recall: 0.704348, count: 115 -Region Avg IOU: 0.694851, Class: 1.000000, Obj: 0.451832, No Obj: 0.002878, Avg Recall: 0.890110, count: 91 -Region Avg IOU: 0.675788, Class: 1.000000, Obj: 0.424451, No Obj: 0.003008, Avg Recall: 0.851240, count: 121 -Region Avg IOU: 0.666185, Class: 1.000000, Obj: 0.398517, No Obj: 0.003077, Avg Recall: 0.824000, count: 125 -Region Avg IOU: 0.634633, Class: 1.000000, Obj: 0.435477, No Obj: 0.002645, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.630686, Class: 1.000000, Obj: 0.450720, No Obj: 0.002798, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.669853, Class: 1.000000, Obj: 0.460127, No Obj: 0.002542, Avg Recall: 0.818182, count: 77 -Region Avg IOU: 0.618509, Class: 1.000000, Obj: 0.417769, No Obj: 0.002585, Avg Recall: 0.736842, count: 114 -7277: 0.400998, 0.406551 avg, 0.001000 rate, 27.022556 seconds, 1862912 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.631136, Class: 1.000000, Obj: 0.414501, No Obj: 0.002717, Avg Recall: 0.785047, count: 107 -Region Avg IOU: 0.641252, Class: 1.000000, Obj: 0.479916, No Obj: 0.002446, Avg Recall: 0.759036, count: 83 -Region Avg IOU: 0.610560, Class: 1.000000, Obj: 0.338653, No Obj: 0.002414, Avg Recall: 0.701754, count: 114 -Region Avg IOU: 0.631815, Class: 1.000000, Obj: 0.509127, No Obj: 0.002905, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.625345, Class: 1.000000, Obj: 0.409759, No Obj: 0.002840, Avg Recall: 0.768519, count: 108 -Region Avg IOU: 0.655719, Class: 1.000000, Obj: 0.406337, No Obj: 0.003509, Avg Recall: 0.800000, count: 125 -Region Avg IOU: 0.624921, Class: 1.000000, Obj: 0.397096, No Obj: 0.003008, Avg Recall: 0.764706, count: 119 -Region Avg IOU: 0.645091, Class: 1.000000, Obj: 0.383634, No Obj: 0.003182, Avg Recall: 0.777778, count: 126 -7278: 0.451280, 0.411023 avg, 0.001000 rate, 26.013081 seconds, 1863168 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.654927, Class: 1.000000, Obj: 0.477228, No Obj: 0.002940, Avg Recall: 0.789474, count: 95 -Region Avg IOU: 0.654535, Class: 1.000000, Obj: 0.389440, No Obj: 0.002885, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.668715, Class: 1.000000, Obj: 0.434144, No Obj: 0.002854, Avg Recall: 0.817308, count: 104 -Region Avg IOU: 0.659512, Class: 1.000000, Obj: 0.442487, No Obj: 0.003242, Avg Recall: 0.834951, count: 103 -Region Avg IOU: 0.648052, Class: 1.000000, Obj: 0.433142, No Obj: 0.003395, Avg Recall: 0.785185, count: 135 -Region Avg IOU: 0.699235, Class: 1.000000, Obj: 0.423864, No Obj: 0.002960, Avg Recall: 0.895833, count: 96 -Region Avg IOU: 0.626597, Class: 1.000000, Obj: 0.429109, No Obj: 0.003078, Avg Recall: 0.759124, count: 137 -Region Avg IOU: 0.646785, Class: 1.000000, Obj: 0.475194, No Obj: 0.002866, Avg Recall: 0.800000, count: 105 -7279: 0.424512, 0.412372 avg, 0.001000 rate, 25.263210 seconds, 1863424 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.679128, Class: 1.000000, Obj: 0.477283, No Obj: 0.002893, Avg Recall: 0.830357, count: 112 -Region Avg IOU: 0.587223, Class: 1.000000, Obj: 0.423278, No Obj: 0.002888, Avg Recall: 0.725664, count: 113 -Region Avg IOU: 0.650344, Class: 1.000000, Obj: 0.412069, No Obj: 0.002759, Avg Recall: 0.774775, count: 111 -Region Avg IOU: 0.649016, Class: 1.000000, Obj: 0.475966, No Obj: 0.003332, Avg Recall: 0.774194, count: 124 -Region Avg IOU: 0.666235, Class: 1.000000, Obj: 0.471540, No Obj: 0.002735, Avg Recall: 0.811765, count: 85 -Region Avg IOU: 0.660642, Class: 1.000000, Obj: 0.453173, No Obj: 0.003109, Avg Recall: 0.817518, count: 137 -Region Avg IOU: 0.650013, Class: 1.000000, Obj: 0.438374, No Obj: 0.002505, Avg Recall: 0.806818, count: 88 -Region Avg IOU: 0.621187, Class: 1.000000, Obj: 0.428065, No Obj: 0.003216, Avg Recall: 0.705426, count: 129 -7280: 0.427418, 0.413877 avg, 0.001000 rate, 26.818815 seconds, 1863680 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 21.786840 seconds -Region Avg IOU: 0.643769, Class: 1.000000, Obj: 0.453644, No Obj: 0.002860, Avg Recall: 0.807692, count: 104 -Region Avg IOU: 0.624180, Class: 1.000000, Obj: 0.391442, No Obj: 0.003353, Avg Recall: 0.795082, count: 122 -Region Avg IOU: 0.621710, Class: 1.000000, Obj: 0.427134, No Obj: 0.002892, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.593265, Class: 1.000000, Obj: 0.399849, No Obj: 0.002629, Avg Recall: 0.659574, count: 94 -Region Avg IOU: 0.625895, Class: 1.000000, Obj: 0.466488, No Obj: 0.003113, Avg Recall: 0.741667, count: 120 -Region Avg IOU: 0.650529, Class: 1.000000, Obj: 0.502392, No Obj: 0.002659, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.626847, Class: 1.000000, Obj: 0.430260, No Obj: 0.002705, Avg Recall: 0.752381, count: 105 -Region Avg IOU: 0.632724, Class: 1.000000, Obj: 0.413997, No Obj: 0.003091, Avg Recall: 0.740741, count: 108 -7281: 0.410871, 0.413576 avg, 0.001000 rate, 26.571640 seconds, 1863936 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.602965, Class: 1.000000, Obj: 0.432746, No Obj: 0.002647, Avg Recall: 0.701923, count: 104 -Region Avg IOU: 0.665931, Class: 1.000000, Obj: 0.452921, No Obj: 0.002831, Avg Recall: 0.824176, count: 91 -Region Avg IOU: 0.641974, Class: 1.000000, Obj: 0.511767, No Obj: 0.002739, Avg Recall: 0.782609, count: 92 -Region Avg IOU: 0.672161, Class: 1.000000, Obj: 0.480587, No Obj: 0.002792, Avg Recall: 0.836538, count: 104 -Region Avg IOU: 0.646880, Class: 1.000000, Obj: 0.433596, No Obj: 0.003357, Avg Recall: 0.757812, count: 128 -Region Avg IOU: 0.655701, Class: 1.000000, Obj: 0.519754, No Obj: 0.002994, Avg Recall: 0.794393, count: 107 -Region Avg IOU: 0.612118, Class: 1.000000, Obj: 0.414000, No Obj: 0.003041, Avg Recall: 0.702479, count: 121 -Region Avg IOU: 0.612430, Class: 1.000000, Obj: 0.501870, No Obj: 0.002594, Avg Recall: 0.752809, count: 89 -7282: 0.400687, 0.412287 avg, 0.001000 rate, 26.351219 seconds, 1864192 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.718428, Class: 1.000000, Obj: 0.506541, No Obj: 0.003209, Avg Recall: 0.918367, count: 98 -Region Avg IOU: 0.648834, Class: 1.000000, Obj: 0.523753, No Obj: 0.002744, Avg Recall: 0.787500, count: 80 -Region Avg IOU: 0.611332, Class: 1.000000, Obj: 0.393371, No Obj: 0.003690, Avg Recall: 0.756098, count: 164 -Region Avg IOU: 0.663853, Class: 1.000000, Obj: 0.485495, No Obj: 0.003173, Avg Recall: 0.814815, count: 108 -Region Avg IOU: 0.610118, Class: 1.000000, Obj: 0.431338, No Obj: 0.002572, Avg Recall: 0.704082, count: 98 -Region Avg IOU: 0.646224, Class: 1.000000, Obj: 0.498096, No Obj: 0.002921, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.677582, Class: 1.000000, Obj: 0.467512, No Obj: 0.003211, Avg Recall: 0.834783, count: 115 -Region Avg IOU: 0.675280, Class: 1.000000, Obj: 0.482550, No Obj: 0.003109, Avg Recall: 0.838095, count: 105 -7283: 0.386987, 0.409757 avg, 0.001000 rate, 27.755157 seconds, 1864448 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.645519, Class: 1.000000, Obj: 0.407275, No Obj: 0.002996, Avg Recall: 0.829268, count: 123 -Region Avg IOU: 0.645390, Class: 1.000000, Obj: 0.421209, No Obj: 0.003562, Avg Recall: 0.782609, count: 161 -Region Avg IOU: 0.640238, Class: 1.000000, Obj: 0.457777, No Obj: 0.002864, Avg Recall: 0.791667, count: 120 -Region Avg IOU: 0.592264, Class: 1.000000, Obj: 0.443563, No Obj: 0.002798, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.626350, Class: 1.000000, Obj: 0.445444, No Obj: 0.003283, Avg Recall: 0.708661, count: 127 -Region Avg IOU: 0.628665, Class: 1.000000, Obj: 0.432884, No Obj: 0.003070, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.659486, Class: 1.000000, Obj: 0.413650, No Obj: 0.003499, Avg Recall: 0.803030, count: 132 -Region Avg IOU: 0.693016, Class: 1.000000, Obj: 0.403262, No Obj: 0.002803, Avg Recall: 0.820755, count: 106 -7284: 0.456745, 0.414456 avg, 0.001000 rate, 27.723539 seconds, 1864704 images -Loaded: 0.000077 seconds -Region Avg IOU: 0.612857, Class: 1.000000, Obj: 0.404109, No Obj: 0.002458, Avg Recall: 0.760000, count: 100 -Region Avg IOU: 0.597807, Class: 1.000000, Obj: 0.417774, No Obj: 0.002562, Avg Recall: 0.693333, count: 75 -Region Avg IOU: 0.638483, Class: 1.000000, Obj: 0.434305, No Obj: 0.003303, Avg Recall: 0.798165, count: 109 -Region Avg IOU: 0.619203, Class: 1.000000, Obj: 0.390814, No Obj: 0.003027, Avg Recall: 0.734848, count: 132 -Region Avg IOU: 0.647329, Class: 1.000000, Obj: 0.440263, No Obj: 0.002964, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.593680, Class: 1.000000, Obj: 0.421625, No Obj: 0.002997, Avg Recall: 0.692308, count: 130 -Region Avg IOU: 0.628726, Class: 1.000000, Obj: 0.433209, No Obj: 0.002610, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.648815, Class: 1.000000, Obj: 0.428379, No Obj: 0.002964, Avg Recall: 0.807692, count: 104 -7285: 0.396670, 0.412677 avg, 0.001000 rate, 26.096020 seconds, 1864960 images -Loaded: 0.000061 seconds -Region Avg IOU: 0.654113, Class: 1.000000, Obj: 0.440219, No Obj: 0.003137, Avg Recall: 0.789474, count: 114 -Region Avg IOU: 0.664373, Class: 1.000000, Obj: 0.448696, No Obj: 0.002676, Avg Recall: 0.816092, count: 87 -Region Avg IOU: 0.653893, Class: 1.000000, Obj: 0.431298, No Obj: 0.002691, Avg Recall: 0.781818, count: 110 -Region Avg IOU: 0.656246, Class: 1.000000, Obj: 0.438056, No Obj: 0.003029, Avg Recall: 0.801587, count: 126 -Region Avg IOU: 0.655597, Class: 1.000000, Obj: 0.404598, No Obj: 0.003070, Avg Recall: 0.788321, count: 137 -Region Avg IOU: 0.649927, Class: 1.000000, Obj: 0.398615, No Obj: 0.003503, Avg Recall: 0.830645, count: 124 -Region Avg IOU: 0.634747, Class: 1.000000, Obj: 0.501897, No Obj: 0.002756, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.634870, Class: 1.000000, Obj: 0.440777, No Obj: 0.002784, Avg Recall: 0.790476, count: 105 -7286: 0.422094, 0.413619 avg, 0.001000 rate, 26.240812 seconds, 1865216 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.686866, Class: 1.000000, Obj: 0.444730, No Obj: 0.002737, Avg Recall: 0.840000, count: 100 -Region Avg IOU: 0.645819, Class: 1.000000, Obj: 0.448082, No Obj: 0.003454, Avg Recall: 0.760563, count: 142 -Region Avg IOU: 0.668475, Class: 1.000000, Obj: 0.470804, No Obj: 0.003408, Avg Recall: 0.804348, count: 138 -Region Avg IOU: 0.626871, Class: 1.000000, Obj: 0.429075, No Obj: 0.003075, Avg Recall: 0.765152, count: 132 -Region Avg IOU: 0.662379, Class: 1.000000, Obj: 0.495665, No Obj: 0.002919, Avg Recall: 0.819048, count: 105 -Region Avg IOU: 0.635671, Class: 1.000000, Obj: 0.401126, No Obj: 0.002737, Avg Recall: 0.800000, count: 115 -Region Avg IOU: 0.635270, Class: 1.000000, Obj: 0.452396, No Obj: 0.003177, Avg Recall: 0.777778, count: 126 -Region Avg IOU: 0.636239, Class: 1.000000, Obj: 0.410415, No Obj: 0.002291, Avg Recall: 0.792683, count: 82 -7287: 0.401057, 0.412363 avg, 0.001000 rate, 27.272892 seconds, 1865472 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.656135, Class: 1.000000, Obj: 0.430673, No Obj: 0.003759, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.699999, Class: 1.000000, Obj: 0.449784, No Obj: 0.002919, Avg Recall: 0.842105, count: 95 -Region Avg IOU: 0.675377, Class: 1.000000, Obj: 0.492553, No Obj: 0.003068, Avg Recall: 0.841584, count: 101 -Region Avg IOU: 0.656386, Class: 1.000000, Obj: 0.447197, No Obj: 0.002733, Avg Recall: 0.836364, count: 110 -Region Avg IOU: 0.640908, Class: 1.000000, Obj: 0.423441, No Obj: 0.003175, Avg Recall: 0.757576, count: 132 -Region Avg IOU: 0.641222, Class: 1.000000, Obj: 0.481048, No Obj: 0.002935, Avg Recall: 0.738739, count: 111 -Region Avg IOU: 0.596100, Class: 1.000000, Obj: 0.382756, No Obj: 0.002785, Avg Recall: 0.714286, count: 91 -Region Avg IOU: 0.640437, Class: 1.000000, Obj: 0.437850, No Obj: 0.003032, Avg Recall: 0.793103, count: 116 -7288: 0.419967, 0.413123 avg, 0.001000 rate, 26.812344 seconds, 1865728 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.635699, Class: 1.000000, Obj: 0.477754, No Obj: 0.003000, Avg Recall: 0.811881, count: 101 -Region Avg IOU: 0.577234, Class: 1.000000, Obj: 0.396834, No Obj: 0.002474, Avg Recall: 0.673077, count: 104 -Region Avg IOU: 0.630623, Class: 1.000000, Obj: 0.426988, No Obj: 0.002963, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.655621, Class: 1.000000, Obj: 0.444605, No Obj: 0.002772, Avg Recall: 0.795181, count: 83 -Region Avg IOU: 0.608606, Class: 1.000000, Obj: 0.438006, No Obj: 0.002984, Avg Recall: 0.675000, count: 120 -Region Avg IOU: 0.624746, Class: 1.000000, Obj: 0.397127, No Obj: 0.002764, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.640629, Class: 1.000000, Obj: 0.438707, No Obj: 0.003266, Avg Recall: 0.754717, count: 106 -Region Avg IOU: 0.636522, Class: 1.000000, Obj: 0.428963, No Obj: 0.003167, Avg Recall: 0.732759, count: 116 -7289: 0.430555, 0.414867 avg, 0.001000 rate, 24.698229 seconds, 1865984 images -Loaded: 0.000089 seconds -Region Avg IOU: 0.663021, Class: 1.000000, Obj: 0.504264, No Obj: 0.002569, Avg Recall: 0.763441, count: 93 -Region Avg IOU: 0.622062, Class: 1.000000, Obj: 0.447641, No Obj: 0.002793, Avg Recall: 0.738318, count: 107 -Region Avg IOU: 0.624041, Class: 1.000000, Obj: 0.443032, No Obj: 0.003179, Avg Recall: 0.750000, count: 132 -Region Avg IOU: 0.661488, Class: 1.000000, Obj: 0.463564, No Obj: 0.002814, Avg Recall: 0.791209, count: 91 -Region Avg IOU: 0.700984, Class: 1.000000, Obj: 0.468849, No Obj: 0.002625, Avg Recall: 0.821429, count: 84 -Region Avg IOU: 0.669830, Class: 1.000000, Obj: 0.449765, No Obj: 0.003459, Avg Recall: 0.806452, count: 124 -Region Avg IOU: 0.597939, Class: 1.000000, Obj: 0.403609, No Obj: 0.002530, Avg Recall: 0.712766, count: 94 -Region Avg IOU: 0.676291, Class: 1.000000, Obj: 0.466990, No Obj: 0.002242, Avg Recall: 0.820895, count: 67 -7290: 0.368172, 0.410197 avg, 0.001000 rate, 27.137575 seconds, 1866240 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.129698 seconds -Region Avg IOU: 0.519496, Class: 1.000000, Obj: 0.320643, No Obj: 0.003219, Avg Recall: 0.582418, count: 91 -Region Avg IOU: 0.508709, Class: 1.000000, Obj: 0.321057, No Obj: 0.004557, Avg Recall: 0.588652, count: 141 -Region Avg IOU: 0.526688, Class: 1.000000, Obj: 0.329799, No Obj: 0.003916, Avg Recall: 0.612069, count: 116 -Region Avg IOU: 0.485426, Class: 1.000000, Obj: 0.325960, No Obj: 0.004443, Avg Recall: 0.533333, count: 135 -Region Avg IOU: 0.573429, Class: 1.000000, Obj: 0.349704, No Obj: 0.003979, Avg Recall: 0.641304, count: 92 -Region Avg IOU: 0.513551, Class: 1.000000, Obj: 0.326856, No Obj: 0.004481, Avg Recall: 0.560000, count: 125 -Region Avg IOU: 0.493484, Class: 1.000000, Obj: 0.338338, No Obj: 0.003514, Avg Recall: 0.518519, count: 108 -Region Avg IOU: 0.522596, Class: 1.000000, Obj: 0.334503, No Obj: 0.004049, Avg Recall: 0.613861, count: 101 -7291: 0.470418, 0.416219 avg, 0.001000 rate, 17.592897 seconds, 1866496 images -Loaded: 2.849520 seconds -Region Avg IOU: 0.546412, Class: 1.000000, Obj: 0.337963, No Obj: 0.004213, Avg Recall: 0.646018, count: 113 -Region Avg IOU: 0.538355, Class: 1.000000, Obj: 0.339970, No Obj: 0.003240, Avg Recall: 0.638554, count: 83 -Region Avg IOU: 0.549956, Class: 1.000000, Obj: 0.352122, No Obj: 0.004590, Avg Recall: 0.637168, count: 113 -Region Avg IOU: 0.504786, Class: 1.000000, Obj: 0.345731, No Obj: 0.004228, Avg Recall: 0.530303, count: 132 -Region Avg IOU: 0.529967, Class: 1.000000, Obj: 0.340899, No Obj: 0.004328, Avg Recall: 0.573913, count: 115 -Region Avg IOU: 0.565403, Class: 1.000000, Obj: 0.394785, No Obj: 0.004478, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.522452, Class: 1.000000, Obj: 0.327971, No Obj: 0.003610, Avg Recall: 0.555556, count: 108 -Region Avg IOU: 0.485460, Class: 1.000000, Obj: 0.328924, No Obj: 0.004020, Avg Recall: 0.504065, count: 123 -7292: 0.473030, 0.421900 avg, 0.001000 rate, 17.799911 seconds, 1866752 images -Loaded: 2.575388 seconds -Region Avg IOU: 0.518300, Class: 1.000000, Obj: 0.292966, No Obj: 0.003865, Avg Recall: 0.629630, count: 108 -Region Avg IOU: 0.547413, Class: 1.000000, Obj: 0.374313, No Obj: 0.004307, Avg Recall: 0.652893, count: 121 -Region Avg IOU: 0.558306, Class: 1.000000, Obj: 0.382167, No Obj: 0.004397, Avg Recall: 0.633027, count: 109 -Region Avg IOU: 0.565934, Class: 1.000000, Obj: 0.348634, No Obj: 0.003679, Avg Recall: 0.645833, count: 96 -Region Avg IOU: 0.497636, Class: 1.000000, Obj: 0.338185, No Obj: 0.004636, Avg Recall: 0.556391, count: 133 -Region Avg IOU: 0.465682, Class: 1.000000, Obj: 0.302850, No Obj: 0.004081, Avg Recall: 0.474138, count: 116 -Region Avg IOU: 0.555413, Class: 1.000000, Obj: 0.372416, No Obj: 0.004663, Avg Recall: 0.640000, count: 125 -Region Avg IOU: 0.540529, Class: 1.000000, Obj: 0.332911, No Obj: 0.003270, Avg Recall: 0.644737, count: 76 -7293: 0.470404, 0.426751 avg, 0.001000 rate, 17.263880 seconds, 1867008 images -Loaded: 3.027700 seconds -Region Avg IOU: 0.564600, Class: 1.000000, Obj: 0.401451, No Obj: 0.004072, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.490987, Class: 1.000000, Obj: 0.343273, No Obj: 0.003614, Avg Recall: 0.553398, count: 103 -Region Avg IOU: 0.540568, Class: 1.000000, Obj: 0.348593, No Obj: 0.003701, Avg Recall: 0.602041, count: 98 -Region Avg IOU: 0.518705, Class: 1.000000, Obj: 0.384104, No Obj: 0.003848, Avg Recall: 0.542857, count: 105 -Region Avg IOU: 0.564880, Class: 1.000000, Obj: 0.371831, No Obj: 0.004304, Avg Recall: 0.653061, count: 98 -Region Avg IOU: 0.495791, Class: 1.000000, Obj: 0.382190, No Obj: 0.003631, Avg Recall: 0.547619, count: 84 -Region Avg IOU: 0.491491, Class: 1.000000, Obj: 0.332636, No Obj: 0.004090, Avg Recall: 0.530435, count: 115 -Region Avg IOU: 0.521421, Class: 1.000000, Obj: 0.326465, No Obj: 0.003375, Avg Recall: 0.596154, count: 104 -7294: 0.394864, 0.423562 avg, 0.001000 rate, 17.003101 seconds, 1867264 images -Loaded: 3.878513 seconds -Region Avg IOU: 0.592390, Class: 1.000000, Obj: 0.408701, No Obj: 0.004855, Avg Recall: 0.670103, count: 97 -Region Avg IOU: 0.542934, Class: 1.000000, Obj: 0.370409, No Obj: 0.004485, Avg Recall: 0.634409, count: 93 -Region Avg IOU: 0.501357, Class: 1.000000, Obj: 0.352676, No Obj: 0.004207, Avg Recall: 0.560000, count: 100 -Region Avg IOU: 0.491923, Class: 1.000000, Obj: 0.359361, No Obj: 0.004710, Avg Recall: 0.564885, count: 131 -Region Avg IOU: 0.557893, Class: 1.000000, Obj: 0.402818, No Obj: 0.005122, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.544392, Class: 1.000000, Obj: 0.414740, No Obj: 0.004633, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.531216, Class: 1.000000, Obj: 0.398042, No Obj: 0.005008, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.547590, Class: 1.000000, Obj: 0.375501, No Obj: 0.004437, Avg Recall: 0.632075, count: 106 -7295: 0.432065, 0.424412 avg, 0.001000 rate, 18.922318 seconds, 1867520 images -Loaded: 3.650166 seconds -Region Avg IOU: 0.457767, Class: 1.000000, Obj: 0.372347, No Obj: 0.004831, Avg Recall: 0.515873, count: 126 -Region Avg IOU: 0.531254, Class: 1.000000, Obj: 0.390708, No Obj: 0.004827, Avg Recall: 0.605505, count: 109 -Region Avg IOU: 0.518990, Class: 1.000000, Obj: 0.379753, No Obj: 0.004704, Avg Recall: 0.535714, count: 112 -Region Avg IOU: 0.543014, Class: 1.000000, Obj: 0.378799, No Obj: 0.005862, Avg Recall: 0.630573, count: 157 -Region Avg IOU: 0.484448, Class: 1.000000, Obj: 0.361425, No Obj: 0.003880, Avg Recall: 0.539326, count: 89 -Region Avg IOU: 0.576838, Class: 1.000000, Obj: 0.389518, No Obj: 0.004688, Avg Recall: 0.666667, count: 93 -Region Avg IOU: 0.479869, Class: 1.000000, Obj: 0.397732, No Obj: 0.003989, Avg Recall: 0.494624, count: 93 -Region Avg IOU: 0.471564, Class: 1.000000, Obj: 0.411960, No Obj: 0.004218, Avg Recall: 0.500000, count: 110 -7296: 0.428497, 0.424821 avg, 0.001000 rate, 17.114775 seconds, 1867776 images -Loaded: 3.933389 seconds -Region Avg IOU: 0.506811, Class: 1.000000, Obj: 0.424303, No Obj: 0.004681, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.561509, Class: 1.000000, Obj: 0.451931, No Obj: 0.004657, Avg Recall: 0.682692, count: 104 -Region Avg IOU: 0.550881, Class: 1.000000, Obj: 0.405590, No Obj: 0.005050, Avg Recall: 0.663636, count: 110 -Region Avg IOU: 0.529876, Class: 1.000000, Obj: 0.442065, No Obj: 0.004758, Avg Recall: 0.574074, count: 108 -Region Avg IOU: 0.453578, Class: 1.000000, Obj: 0.371623, No Obj: 0.004668, Avg Recall: 0.478992, count: 119 -Region Avg IOU: 0.474350, Class: 1.000000, Obj: 0.356113, No Obj: 0.005399, Avg Recall: 0.524194, count: 124 -Region Avg IOU: 0.537414, Class: 1.000000, Obj: 0.427975, No Obj: 0.004740, Avg Recall: 0.621053, count: 95 -Region Avg IOU: 0.490886, Class: 1.000000, Obj: 0.351761, No Obj: 0.004755, Avg Recall: 0.566372, count: 113 -7297: 0.419421, 0.424281 avg, 0.001000 rate, 20.242376 seconds, 1868032 images -Loaded: 0.680754 seconds -Region Avg IOU: 0.548570, Class: 1.000000, Obj: 0.446417, No Obj: 0.004768, Avg Recall: 0.672897, count: 107 -Region Avg IOU: 0.522201, Class: 1.000000, Obj: 0.442897, No Obj: 0.004320, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.530772, Class: 1.000000, Obj: 0.412270, No Obj: 0.005214, Avg Recall: 0.616667, count: 120 -Region Avg IOU: 0.512541, Class: 1.000000, Obj: 0.427389, No Obj: 0.004161, Avg Recall: 0.590000, count: 100 -Region Avg IOU: 0.501513, Class: 1.000000, Obj: 0.396296, No Obj: 0.004794, Avg Recall: 0.509259, count: 108 -Region Avg IOU: 0.567518, Class: 1.000000, Obj: 0.398929, No Obj: 0.004845, Avg Recall: 0.676471, count: 102 -Region Avg IOU: 0.545466, Class: 1.000000, Obj: 0.396929, No Obj: 0.004511, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.526089, Class: 1.000000, Obj: 0.361627, No Obj: 0.004887, Avg Recall: 0.592233, count: 103 -7298: 0.384172, 0.420270 avg, 0.001000 rate, 20.396690 seconds, 1868288 images -Loaded: 0.778488 seconds -Region Avg IOU: 0.486461, Class: 1.000000, Obj: 0.371873, No Obj: 0.005412, Avg Recall: 0.578947, count: 114 -Region Avg IOU: 0.518850, Class: 1.000000, Obj: 0.408695, No Obj: 0.005025, Avg Recall: 0.558824, count: 102 -Region Avg IOU: 0.526382, Class: 1.000000, Obj: 0.399728, No Obj: 0.006557, Avg Recall: 0.614865, count: 148 -Region Avg IOU: 0.521989, Class: 1.000000, Obj: 0.412106, No Obj: 0.004775, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.477196, Class: 1.000000, Obj: 0.413666, No Obj: 0.005172, Avg Recall: 0.538462, count: 130 -Region Avg IOU: 0.512832, Class: 1.000000, Obj: 0.435442, No Obj: 0.004559, Avg Recall: 0.572727, count: 110 -Region Avg IOU: 0.549350, Class: 1.000000, Obj: 0.422391, No Obj: 0.005283, Avg Recall: 0.645455, count: 110 -Region Avg IOU: 0.533406, Class: 1.000000, Obj: 0.460341, No Obj: 0.005382, Avg Recall: 0.561905, count: 105 -7299: 0.495029, 0.427746 avg, 0.001000 rate, 20.728655 seconds, 1868544 images -Loaded: 1.165427 seconds -Region Avg IOU: 0.565325, Class: 1.000000, Obj: 0.429004, No Obj: 0.004799, Avg Recall: 0.676190, count: 105 -Region Avg IOU: 0.448362, Class: 1.000000, Obj: 0.366098, No Obj: 0.004874, Avg Recall: 0.456000, count: 125 -Region Avg IOU: 0.453510, Class: 1.000000, Obj: 0.384140, No Obj: 0.004042, Avg Recall: 0.500000, count: 106 -Region Avg IOU: 0.505263, Class: 1.000000, Obj: 0.397800, No Obj: 0.004245, Avg Recall: 0.597938, count: 97 -Region Avg IOU: 0.463706, Class: 1.000000, Obj: 0.417012, No Obj: 0.004877, Avg Recall: 0.504348, count: 115 -Region Avg IOU: 0.583644, Class: 1.000000, Obj: 0.426887, No Obj: 0.005162, Avg Recall: 0.646465, count: 99 -Region Avg IOU: 0.535974, Class: 1.000000, Obj: 0.415555, No Obj: 0.005279, Avg Recall: 0.625000, count: 128 -Region Avg IOU: 0.513141, Class: 1.000000, Obj: 0.389862, No Obj: 0.004280, Avg Recall: 0.588235, count: 85 -7300: 0.420651, 0.427036 avg, 0.001000 rate, 20.234703 seconds, 1868800 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000067 seconds -Region Avg IOU: 0.656624, Class: 1.000000, Obj: 0.447598, No Obj: 0.003485, Avg Recall: 0.818965, count: 116 -Region Avg IOU: 0.670550, Class: 1.000000, Obj: 0.490823, No Obj: 0.003137, Avg Recall: 0.879121, count: 91 -Region Avg IOU: 0.697258, Class: 1.000000, Obj: 0.501255, No Obj: 0.002995, Avg Recall: 0.890110, count: 91 -Region Avg IOU: 0.667303, Class: 1.000000, Obj: 0.480761, No Obj: 0.003180, Avg Recall: 0.821782, count: 101 -Region Avg IOU: 0.621916, Class: 1.000000, Obj: 0.496910, No Obj: 0.002813, Avg Recall: 0.736264, count: 91 -Region Avg IOU: 0.657169, Class: 1.000000, Obj: 0.451756, No Obj: 0.003390, Avg Recall: 0.845455, count: 110 -Region Avg IOU: 0.644182, Class: 1.000000, Obj: 0.532057, No Obj: 0.003007, Avg Recall: 0.779221, count: 77 -Region Avg IOU: 0.687664, Class: 1.000000, Obj: 0.513071, No Obj: 0.003291, Avg Recall: 0.840000, count: 100 -7301: 0.401876, 0.424520 avg, 0.001000 rate, 28.174599 seconds, 1869056 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.649001, Class: 1.000000, Obj: 0.453316, No Obj: 0.003171, Avg Recall: 0.796992, count: 133 -Region Avg IOU: 0.654396, Class: 1.000000, Obj: 0.493015, No Obj: 0.003196, Avg Recall: 0.838710, count: 124 -Region Avg IOU: 0.640812, Class: 1.000000, Obj: 0.468111, No Obj: 0.003079, Avg Recall: 0.777778, count: 117 -Region Avg IOU: 0.646244, Class: 1.000000, Obj: 0.519893, No Obj: 0.003062, Avg Recall: 0.806122, count: 98 -Region Avg IOU: 0.649898, Class: 1.000000, Obj: 0.445642, No Obj: 0.003262, Avg Recall: 0.826087, count: 115 -Region Avg IOU: 0.626925, Class: 1.000000, Obj: 0.490331, No Obj: 0.002752, Avg Recall: 0.783505, count: 97 -Region Avg IOU: 0.625257, Class: 1.000000, Obj: 0.510648, No Obj: 0.003667, Avg Recall: 0.750000, count: 136 -Region Avg IOU: 0.638740, Class: 1.000000, Obj: 0.463180, No Obj: 0.003072, Avg Recall: 0.777778, count: 126 -7302: 0.463886, 0.428457 avg, 0.001000 rate, 26.418844 seconds, 1869312 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.628484, Class: 1.000000, Obj: 0.450587, No Obj: 0.002579, Avg Recall: 0.769231, count: 117 -Region Avg IOU: 0.640549, Class: 1.000000, Obj: 0.411233, No Obj: 0.002522, Avg Recall: 0.840425, count: 94 -Region Avg IOU: 0.635203, Class: 1.000000, Obj: 0.411001, No Obj: 0.002899, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.664992, Class: 1.000000, Obj: 0.464377, No Obj: 0.002712, Avg Recall: 0.851852, count: 108 -Region Avg IOU: 0.667058, Class: 1.000000, Obj: 0.420572, No Obj: 0.002664, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.655996, Class: 1.000000, Obj: 0.438973, No Obj: 0.002932, Avg Recall: 0.841667, count: 120 -Region Avg IOU: 0.617512, Class: 1.000000, Obj: 0.445627, No Obj: 0.002155, Avg Recall: 0.807692, count: 78 -Region Avg IOU: 0.653899, Class: 1.000000, Obj: 0.463014, No Obj: 0.002791, Avg Recall: 0.787611, count: 113 -7303: 0.441561, 0.429767 avg, 0.001000 rate, 28.315989 seconds, 1869568 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.649661, Class: 1.000000, Obj: 0.434947, No Obj: 0.002325, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.673176, Class: 1.000000, Obj: 0.450154, No Obj: 0.002523, Avg Recall: 0.812500, count: 112 -Region Avg IOU: 0.651311, Class: 1.000000, Obj: 0.411694, No Obj: 0.002421, Avg Recall: 0.824561, count: 114 -Region Avg IOU: 0.642643, Class: 1.000000, Obj: 0.394259, No Obj: 0.002058, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.646945, Class: 1.000000, Obj: 0.366699, No Obj: 0.002631, Avg Recall: 0.782258, count: 124 -Region Avg IOU: 0.652965, Class: 1.000000, Obj: 0.411145, No Obj: 0.002501, Avg Recall: 0.828829, count: 111 -Region Avg IOU: 0.638410, Class: 1.000000, Obj: 0.409806, No Obj: 0.002462, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.661919, Class: 1.000000, Obj: 0.450106, No Obj: 0.002102, Avg Recall: 0.822430, count: 107 -7304: 0.448410, 0.431632 avg, 0.001000 rate, 27.996778 seconds, 1869824 images -Loaded: 0.000101 seconds -Region Avg IOU: 0.672745, Class: 1.000000, Obj: 0.322992, No Obj: 0.002196, Avg Recall: 0.836735, count: 98 -Region Avg IOU: 0.614404, Class: 1.000000, Obj: 0.395077, No Obj: 0.002102, Avg Recall: 0.715686, count: 102 -Region Avg IOU: 0.643943, Class: 1.000000, Obj: 0.371762, No Obj: 0.002298, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.642874, Class: 1.000000, Obj: 0.392543, No Obj: 0.002526, Avg Recall: 0.800000, count: 130 -Region Avg IOU: 0.647288, Class: 1.000000, Obj: 0.389008, No Obj: 0.002506, Avg Recall: 0.809524, count: 126 -Region Avg IOU: 0.679287, Class: 1.000000, Obj: 0.399979, No Obj: 0.002215, Avg Recall: 0.861386, count: 101 -Region Avg IOU: 0.675121, Class: 1.000000, Obj: 0.388146, No Obj: 0.002144, Avg Recall: 0.858696, count: 92 -Region Avg IOU: 0.646105, Class: 1.000000, Obj: 0.346253, No Obj: 0.002218, Avg Recall: 0.786408, count: 103 -7305: 0.431281, 0.431596 avg, 0.001000 rate, 28.411856 seconds, 1870080 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.677088, Class: 1.000000, Obj: 0.362210, No Obj: 0.002598, Avg Recall: 0.853846, count: 130 -Region Avg IOU: 0.640691, Class: 1.000000, Obj: 0.385943, No Obj: 0.002022, Avg Recall: 0.790000, count: 100 -Region Avg IOU: 0.676442, Class: 1.000000, Obj: 0.375398, No Obj: 0.002155, Avg Recall: 0.828571, count: 105 -Region Avg IOU: 0.666379, Class: 1.000000, Obj: 0.386200, No Obj: 0.001959, Avg Recall: 0.873563, count: 87 -Region Avg IOU: 0.667394, Class: 1.000000, Obj: 0.352873, No Obj: 0.002465, Avg Recall: 0.832168, count: 143 -Region Avg IOU: 0.633123, Class: 1.000000, Obj: 0.366338, No Obj: 0.002705, Avg Recall: 0.751634, count: 153 -Region Avg IOU: 0.660022, Class: 1.000000, Obj: 0.361688, No Obj: 0.002242, Avg Recall: 0.822430, count: 107 -Region Avg IOU: 0.629529, Class: 1.000000, Obj: 0.366829, No Obj: 0.002487, Avg Recall: 0.766917, count: 133 -7306: 0.497075, 0.438144 avg, 0.001000 rate, 28.849588 seconds, 1870336 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.633299, Class: 1.000000, Obj: 0.368755, No Obj: 0.001961, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.660724, Class: 1.000000, Obj: 0.327092, No Obj: 0.001741, Avg Recall: 0.835443, count: 79 -Region Avg IOU: 0.633601, Class: 1.000000, Obj: 0.391852, No Obj: 0.002069, Avg Recall: 0.762376, count: 101 -Region Avg IOU: 0.665957, Class: 1.000000, Obj: 0.383599, No Obj: 0.002535, Avg Recall: 0.837209, count: 129 -Region Avg IOU: 0.683368, Class: 1.000000, Obj: 0.372548, No Obj: 0.002337, Avg Recall: 0.839286, count: 112 -Region Avg IOU: 0.649161, Class: 1.000000, Obj: 0.377650, No Obj: 0.002069, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.649363, Class: 1.000000, Obj: 0.359522, No Obj: 0.002098, Avg Recall: 0.805310, count: 113 -Region Avg IOU: 0.667611, Class: 1.000000, Obj: 0.362168, No Obj: 0.002360, Avg Recall: 0.841584, count: 101 -7307: 0.417366, 0.436066 avg, 0.001000 rate, 28.902821 seconds, 1870592 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.723569, Class: 1.000000, Obj: 0.411554, No Obj: 0.002515, Avg Recall: 0.882979, count: 94 -Region Avg IOU: 0.654650, Class: 1.000000, Obj: 0.408231, No Obj: 0.002395, Avg Recall: 0.764706, count: 102 -Region Avg IOU: 0.664416, Class: 1.000000, Obj: 0.426773, No Obj: 0.002712, Avg Recall: 0.819549, count: 133 -Region Avg IOU: 0.660325, Class: 1.000000, Obj: 0.425072, No Obj: 0.002166, Avg Recall: 0.795455, count: 88 -Region Avg IOU: 0.632185, Class: 1.000000, Obj: 0.381180, No Obj: 0.002397, Avg Recall: 0.788136, count: 118 -Region Avg IOU: 0.674244, Class: 1.000000, Obj: 0.379523, No Obj: 0.002281, Avg Recall: 0.835052, count: 97 -Region Avg IOU: 0.601721, Class: 1.000000, Obj: 0.391182, No Obj: 0.002125, Avg Recall: 0.742268, count: 97 -Region Avg IOU: 0.652172, Class: 1.000000, Obj: 0.376164, No Obj: 0.001891, Avg Recall: 0.804348, count: 92 -7308: 0.385379, 0.430998 avg, 0.001000 rate, 27.961428 seconds, 1870848 images -Loaded: 0.000075 seconds -Region Avg IOU: 0.644838, Class: 1.000000, Obj: 0.343877, No Obj: 0.002455, Avg Recall: 0.782946, count: 129 -Region Avg IOU: 0.670117, Class: 1.000000, Obj: 0.403856, No Obj: 0.002995, Avg Recall: 0.853846, count: 130 -Region Avg IOU: 0.607645, Class: 1.000000, Obj: 0.311964, No Obj: 0.002132, Avg Recall: 0.720721, count: 111 -Region Avg IOU: 0.640727, Class: 1.000000, Obj: 0.342248, No Obj: 0.002541, Avg Recall: 0.774194, count: 124 -Region Avg IOU: 0.623203, Class: 1.000000, Obj: 0.372434, No Obj: 0.002147, Avg Recall: 0.776699, count: 103 -Region Avg IOU: 0.681609, Class: 1.000000, Obj: 0.380369, No Obj: 0.002222, Avg Recall: 0.868132, count: 91 -Region Avg IOU: 0.649833, Class: 1.000000, Obj: 0.380417, No Obj: 0.002398, Avg Recall: 0.785714, count: 126 -Region Avg IOU: 0.683305, Class: 1.000000, Obj: 0.427216, No Obj: 0.002180, Avg Recall: 0.890244, count: 82 -7309: 0.460594, 0.433957 avg, 0.001000 rate, 28.850119 seconds, 1871104 images -Loaded: 0.000066 seconds -Region Avg IOU: 0.635747, Class: 1.000000, Obj: 0.328640, No Obj: 0.002386, Avg Recall: 0.787611, count: 113 -Region Avg IOU: 0.649068, Class: 1.000000, Obj: 0.422328, No Obj: 0.002607, Avg Recall: 0.769841, count: 126 -Region Avg IOU: 0.655268, Class: 1.000000, Obj: 0.407840, No Obj: 0.002374, Avg Recall: 0.807018, count: 114 -Region Avg IOU: 0.664514, Class: 1.000000, Obj: 0.422766, No Obj: 0.002374, Avg Recall: 0.846154, count: 91 -Region Avg IOU: 0.670409, Class: 1.000000, Obj: 0.492042, No Obj: 0.002513, Avg Recall: 0.835165, count: 91 -Region Avg IOU: 0.642172, Class: 1.000000, Obj: 0.378837, No Obj: 0.002258, Avg Recall: 0.757282, count: 103 -Region Avg IOU: 0.651317, Class: 1.000000, Obj: 0.411148, No Obj: 0.001876, Avg Recall: 0.833333, count: 90 -Region Avg IOU: 0.687253, Class: 1.000000, Obj: 0.427136, No Obj: 0.003156, Avg Recall: 0.852459, count: 122 -7310: 0.468331, 0.437395 avg, 0.001000 rate, 28.507647 seconds, 1871360 images -Resizing -384 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 16.661449 seconds -Region Avg IOU: 0.514085, Class: 1.000000, Obj: 0.297048, No Obj: 0.003377, Avg Recall: 0.600000, count: 115 -Region Avg IOU: 0.574883, Class: 1.000000, Obj: 0.323722, No Obj: 0.003764, Avg Recall: 0.625000, count: 120 -Region Avg IOU: 0.581705, Class: 1.000000, Obj: 0.369505, No Obj: 0.003360, Avg Recall: 0.651685, count: 89 -Region Avg IOU: 0.575375, Class: 1.000000, Obj: 0.383850, No Obj: 0.003857, Avg Recall: 0.659794, count: 97 -Region Avg IOU: 0.566225, Class: 1.000000, Obj: 0.305986, No Obj: 0.003812, Avg Recall: 0.657407, count: 108 -Region Avg IOU: 0.518768, Class: 1.000000, Obj: 0.307815, No Obj: 0.003624, Avg Recall: 0.561404, count: 114 -Region Avg IOU: 0.543193, Class: 1.000000, Obj: 0.287014, No Obj: 0.003826, Avg Recall: 0.622951, count: 122 -Region Avg IOU: 0.497555, Class: 1.000000, Obj: 0.286495, No Obj: 0.004111, Avg Recall: 0.526316, count: 114 -7311: 0.461021, 0.439757 avg, 0.001000 rate, 17.153809 seconds, 1871616 images -Loaded: 3.947655 seconds -Region Avg IOU: 0.507188, Class: 1.000000, Obj: 0.323010, No Obj: 0.003985, Avg Recall: 0.540000, count: 100 -Region Avg IOU: 0.498049, Class: 1.000000, Obj: 0.352975, No Obj: 0.003622, Avg Recall: 0.584270, count: 89 -Region Avg IOU: 0.537876, Class: 1.000000, Obj: 0.307389, No Obj: 0.004411, Avg Recall: 0.573643, count: 129 -Region Avg IOU: 0.466710, Class: 1.000000, Obj: 0.301281, No Obj: 0.004004, Avg Recall: 0.533898, count: 118 -Region Avg IOU: 0.542354, Class: 1.000000, Obj: 0.336127, No Obj: 0.003275, Avg Recall: 0.620690, count: 87 -Region Avg IOU: 0.538231, Class: 1.000000, Obj: 0.318669, No Obj: 0.003928, Avg Recall: 0.598214, count: 112 -Region Avg IOU: 0.519214, Class: 1.000000, Obj: 0.286464, No Obj: 0.003753, Avg Recall: 0.578947, count: 114 -Region Avg IOU: 0.506814, Class: 1.000000, Obj: 0.298351, No Obj: 0.003935, Avg Recall: 0.539216, count: 102 -7312: 0.464711, 0.442253 avg, 0.001000 rate, 19.944748 seconds, 1871872 images -Loaded: 0.562861 seconds -Region Avg IOU: 0.556631, Class: 1.000000, Obj: 0.359697, No Obj: 0.004000, Avg Recall: 0.641975, count: 81 -Region Avg IOU: 0.504064, Class: 1.000000, Obj: 0.339989, No Obj: 0.004651, Avg Recall: 0.543103, count: 116 -Region Avg IOU: 0.515767, Class: 1.000000, Obj: 0.366637, No Obj: 0.004308, Avg Recall: 0.563025, count: 119 -Region Avg IOU: 0.538156, Class: 1.000000, Obj: 0.345070, No Obj: 0.004156, Avg Recall: 0.631579, count: 95 -Region Avg IOU: 0.569323, Class: 1.000000, Obj: 0.366404, No Obj: 0.003672, Avg Recall: 0.697917, count: 96 -Region Avg IOU: 0.554913, Class: 1.000000, Obj: 0.404312, No Obj: 0.004042, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.518638, Class: 1.000000, Obj: 0.352811, No Obj: 0.004234, Avg Recall: 0.578947, count: 114 -Region Avg IOU: 0.534974, Class: 1.000000, Obj: 0.352018, No Obj: 0.003739, Avg Recall: 0.614583, count: 96 -7313: 0.395927, 0.437620 avg, 0.001000 rate, 17.078945 seconds, 1872128 images -Loaded: 4.077651 seconds -Region Avg IOU: 0.537165, Class: 1.000000, Obj: 0.376094, No Obj: 0.005008, Avg Recall: 0.581967, count: 122 -Region Avg IOU: 0.486128, Class: 1.000000, Obj: 0.370034, No Obj: 0.004772, Avg Recall: 0.529851, count: 134 -Region Avg IOU: 0.595919, Class: 1.000000, Obj: 0.472572, No Obj: 0.005273, Avg Recall: 0.714286, count: 105 -Region Avg IOU: 0.548248, Class: 1.000000, Obj: 0.417263, No Obj: 0.005510, Avg Recall: 0.643478, count: 115 -Region Avg IOU: 0.515761, Class: 1.000000, Obj: 0.345785, No Obj: 0.004250, Avg Recall: 0.549020, count: 102 -Region Avg IOU: 0.482634, Class: 1.000000, Obj: 0.326754, No Obj: 0.004317, Avg Recall: 0.532374, count: 139 -Region Avg IOU: 0.515988, Class: 1.000000, Obj: 0.373080, No Obj: 0.003297, Avg Recall: 0.605263, count: 76 -Region Avg IOU: 0.514806, Class: 1.000000, Obj: 0.366477, No Obj: 0.004449, Avg Recall: 0.539823, count: 113 -7314: 0.473108, 0.441169 avg, 0.001000 rate, 18.139725 seconds, 1872384 images -Loaded: 2.739747 seconds -Region Avg IOU: 0.546165, Class: 1.000000, Obj: 0.416984, No Obj: 0.004313, Avg Recall: 0.625000, count: 96 -Region Avg IOU: 0.515535, Class: 1.000000, Obj: 0.397041, No Obj: 0.004741, Avg Recall: 0.574257, count: 101 -Region Avg IOU: 0.494990, Class: 1.000000, Obj: 0.419811, No Obj: 0.005291, Avg Recall: 0.549020, count: 102 -Region Avg IOU: 0.502639, Class: 1.000000, Obj: 0.426769, No Obj: 0.004882, Avg Recall: 0.534483, count: 116 -Region Avg IOU: 0.528893, Class: 1.000000, Obj: 0.372640, No Obj: 0.004889, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.527299, Class: 1.000000, Obj: 0.434412, No Obj: 0.005161, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.515127, Class: 1.000000, Obj: 0.361891, No Obj: 0.005444, Avg Recall: 0.552845, count: 123 -Region Avg IOU: 0.549613, Class: 1.000000, Obj: 0.409167, No Obj: 0.005243, Avg Recall: 0.658333, count: 120 -7315: 0.461809, 0.443233 avg, 0.001000 rate, 19.676374 seconds, 1872640 images -Loaded: 2.304001 seconds -Region Avg IOU: 0.550774, Class: 1.000000, Obj: 0.462371, No Obj: 0.005393, Avg Recall: 0.627273, count: 110 -Region Avg IOU: 0.479308, Class: 1.000000, Obj: 0.389143, No Obj: 0.004780, Avg Recall: 0.495050, count: 101 -Region Avg IOU: 0.489667, Class: 1.000000, Obj: 0.382381, No Obj: 0.005253, Avg Recall: 0.536765, count: 136 -Region Avg IOU: 0.537695, Class: 1.000000, Obj: 0.420492, No Obj: 0.005631, Avg Recall: 0.588235, count: 119 -Region Avg IOU: 0.551580, Class: 1.000000, Obj: 0.442852, No Obj: 0.004995, Avg Recall: 0.637363, count: 91 -Region Avg IOU: 0.542040, Class: 1.000000, Obj: 0.389053, No Obj: 0.005294, Avg Recall: 0.627119, count: 118 -Region Avg IOU: 0.523729, Class: 1.000000, Obj: 0.443118, No Obj: 0.004998, Avg Recall: 0.571429, count: 112 -Region Avg IOU: 0.516184, Class: 1.000000, Obj: 0.409324, No Obj: 0.004978, Avg Recall: 0.631068, count: 103 -7316: 0.457878, 0.444697 avg, 0.001000 rate, 18.290249 seconds, 1872896 images -Loaded: 3.537046 seconds -Region Avg IOU: 0.465823, Class: 1.000000, Obj: 0.403419, No Obj: 0.004404, Avg Recall: 0.493827, count: 81 -Region Avg IOU: 0.549655, Class: 1.000000, Obj: 0.440332, No Obj: 0.006186, Avg Recall: 0.606557, count: 122 -Region Avg IOU: 0.513139, Class: 1.000000, Obj: 0.425736, No Obj: 0.005454, Avg Recall: 0.583333, count: 120 -Region Avg IOU: 0.565939, Class: 1.000000, Obj: 0.450722, No Obj: 0.005646, Avg Recall: 0.669725, count: 109 -Region Avg IOU: 0.520322, Class: 1.000000, Obj: 0.449255, No Obj: 0.005942, Avg Recall: 0.559322, count: 118 -Region Avg IOU: 0.531814, Class: 1.000000, Obj: 0.449989, No Obj: 0.006793, Avg Recall: 0.604317, count: 139 -Region Avg IOU: 0.551846, Class: 1.000000, Obj: 0.412467, No Obj: 0.005110, Avg Recall: 0.640351, count: 114 -Region Avg IOU: 0.539615, Class: 1.000000, Obj: 0.429997, No Obj: 0.004786, Avg Recall: 0.622222, count: 90 -7317: 0.435624, 0.443790 avg, 0.001000 rate, 20.803396 seconds, 1873152 images -Loaded: 2.659178 seconds -Region Avg IOU: 0.551244, Class: 1.000000, Obj: 0.436342, No Obj: 0.004761, Avg Recall: 0.662921, count: 89 -Region Avg IOU: 0.540594, Class: 1.000000, Obj: 0.436051, No Obj: 0.004533, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.495676, Class: 1.000000, Obj: 0.382793, No Obj: 0.004902, Avg Recall: 0.595960, count: 99 -Region Avg IOU: 0.477321, Class: 1.000000, Obj: 0.429443, No Obj: 0.005114, Avg Recall: 0.543478, count: 92 -Region Avg IOU: 0.531136, Class: 1.000000, Obj: 0.402669, No Obj: 0.004593, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.542600, Class: 1.000000, Obj: 0.431232, No Obj: 0.004368, Avg Recall: 0.631579, count: 57 -Region Avg IOU: 0.536817, Class: 1.000000, Obj: 0.444113, No Obj: 0.005347, Avg Recall: 0.633929, count: 112 -Region Avg IOU: 0.513027, Class: 1.000000, Obj: 0.400397, No Obj: 0.005313, Avg Recall: 0.567568, count: 111 -7318: 0.392750, 0.438686 avg, 0.001000 rate, 20.045799 seconds, 1873408 images -Loaded: 1.284478 seconds -Region Avg IOU: 0.482244, Class: 1.000000, Obj: 0.401397, No Obj: 0.004703, Avg Recall: 0.516949, count: 118 -Region Avg IOU: 0.551326, Class: 1.000000, Obj: 0.430165, No Obj: 0.005819, Avg Recall: 0.674242, count: 132 -Region Avg IOU: 0.544645, Class: 1.000000, Obj: 0.447432, No Obj: 0.005731, Avg Recall: 0.617391, count: 115 -Region Avg IOU: 0.500655, Class: 1.000000, Obj: 0.390267, No Obj: 0.005340, Avg Recall: 0.552632, count: 114 -Region Avg IOU: 0.508695, Class: 1.000000, Obj: 0.421821, No Obj: 0.005268, Avg Recall: 0.572650, count: 117 -Region Avg IOU: 0.520044, Class: 1.000000, Obj: 0.401544, No Obj: 0.004379, Avg Recall: 0.623656, count: 93 -Region Avg IOU: 0.527881, Class: 1.000000, Obj: 0.383167, No Obj: 0.005721, Avg Recall: 0.617021, count: 141 -Region Avg IOU: 0.490448, Class: 1.000000, Obj: 0.432017, No Obj: 0.005664, Avg Recall: 0.550459, count: 109 -7319: 0.518439, 0.446661 avg, 0.001000 rate, 19.185900 seconds, 1873664 images -Loaded: 2.087268 seconds -Region Avg IOU: 0.528629, Class: 1.000000, Obj: 0.406828, No Obj: 0.004945, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.544783, Class: 1.000000, Obj: 0.409841, No Obj: 0.004951, Avg Recall: 0.600000, count: 105 -Region Avg IOU: 0.474443, Class: 1.000000, Obj: 0.376886, No Obj: 0.004884, Avg Recall: 0.520661, count: 121 -Region Avg IOU: 0.532824, Class: 1.000000, Obj: 0.373857, No Obj: 0.005818, Avg Recall: 0.642857, count: 112 -Region Avg IOU: 0.469738, Class: 1.000000, Obj: 0.354917, No Obj: 0.004131, Avg Recall: 0.500000, count: 120 -Region Avg IOU: 0.556888, Class: 1.000000, Obj: 0.432401, No Obj: 0.005419, Avg Recall: 0.639640, count: 111 -Region Avg IOU: 0.489987, Class: 1.000000, Obj: 0.382754, No Obj: 0.005436, Avg Recall: 0.532110, count: 109 -Region Avg IOU: 0.490410, Class: 1.000000, Obj: 0.408212, No Obj: 0.005104, Avg Recall: 0.542373, count: 118 -7320: 0.478077, 0.449803 avg, 0.001000 rate, 17.982800 seconds, 1873920 images -Resizing -544 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000063 seconds -Region Avg IOU: 0.606432, Class: 1.000000, Obj: 0.449400, No Obj: 0.004459, Avg Recall: 0.719178, count: 146 -Region Avg IOU: 0.592046, Class: 1.000000, Obj: 0.473186, No Obj: 0.003713, Avg Recall: 0.698413, count: 126 -Region Avg IOU: 0.582836, Class: 1.000000, Obj: 0.432998, No Obj: 0.004011, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.576866, Class: 1.000000, Obj: 0.438004, No Obj: 0.003422, Avg Recall: 0.710000, count: 100 -Region Avg IOU: 0.671231, Class: 1.000000, Obj: 0.539748, No Obj: 0.003900, Avg Recall: 0.818182, count: 110 -Region Avg IOU: 0.582912, Class: 1.000000, Obj: 0.481505, No Obj: 0.003572, Avg Recall: 0.755319, count: 94 -Region Avg IOU: 0.637880, Class: 1.000000, Obj: 0.487925, No Obj: 0.004076, Avg Recall: 0.767857, count: 112 -Region Avg IOU: 0.621308, Class: 1.000000, Obj: 0.491598, No Obj: 0.002833, Avg Recall: 0.712500, count: 80 -7321: 0.424974, 0.447320 avg, 0.001000 rate, 25.626993 seconds, 1874176 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.619036, Class: 1.000000, Obj: 0.457635, No Obj: 0.003857, Avg Recall: 0.783333, count: 120 -Region Avg IOU: 0.617375, Class: 1.000000, Obj: 0.447758, No Obj: 0.003240, Avg Recall: 0.744444, count: 90 -Region Avg IOU: 0.600269, Class: 1.000000, Obj: 0.459837, No Obj: 0.003470, Avg Recall: 0.660000, count: 100 -Region Avg IOU: 0.563003, Class: 1.000000, Obj: 0.397276, No Obj: 0.003506, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.612855, Class: 1.000000, Obj: 0.382524, No Obj: 0.004847, Avg Recall: 0.748344, count: 151 -Region Avg IOU: 0.650398, Class: 1.000000, Obj: 0.490018, No Obj: 0.003680, Avg Recall: 0.801802, count: 111 -Region Avg IOU: 0.619415, Class: 1.000000, Obj: 0.464313, No Obj: 0.003108, Avg Recall: 0.770833, count: 96 -Region Avg IOU: 0.556097, Class: 1.000000, Obj: 0.461955, No Obj: 0.003729, Avg Recall: 0.619565, count: 92 -7322: 0.431843, 0.445772 avg, 0.001000 rate, 25.259771 seconds, 1874432 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.627875, Class: 1.000000, Obj: 0.452766, No Obj: 0.003452, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.621806, Class: 1.000000, Obj: 0.447817, No Obj: 0.003842, Avg Recall: 0.726415, count: 106 -Region Avg IOU: 0.657041, Class: 1.000000, Obj: 0.448675, No Obj: 0.003280, Avg Recall: 0.782051, count: 78 -Region Avg IOU: 0.618939, Class: 1.000000, Obj: 0.458438, No Obj: 0.004176, Avg Recall: 0.696721, count: 122 -Region Avg IOU: 0.614452, Class: 1.000000, Obj: 0.436707, No Obj: 0.003929, Avg Recall: 0.719424, count: 139 -Region Avg IOU: 0.642918, Class: 1.000000, Obj: 0.432160, No Obj: 0.003603, Avg Recall: 0.805085, count: 118 -Region Avg IOU: 0.602680, Class: 1.000000, Obj: 0.379251, No Obj: 0.003078, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.563844, Class: 1.000000, Obj: 0.423747, No Obj: 0.002890, Avg Recall: 0.642105, count: 95 -7323: 0.412954, 0.442491 avg, 0.001000 rate, 25.825443 seconds, 1874688 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.636103, Class: 1.000000, Obj: 0.426053, No Obj: 0.003196, Avg Recall: 0.765306, count: 98 -Region Avg IOU: 0.555140, Class: 1.000000, Obj: 0.430552, No Obj: 0.002689, Avg Recall: 0.666667, count: 81 -Region Avg IOU: 0.605277, Class: 1.000000, Obj: 0.385580, No Obj: 0.003769, Avg Recall: 0.694656, count: 131 -Region Avg IOU: 0.647325, Class: 1.000000, Obj: 0.394040, No Obj: 0.003591, Avg Recall: 0.783784, count: 111 -Region Avg IOU: 0.640768, Class: 1.000000, Obj: 0.377651, No Obj: 0.002778, Avg Recall: 0.750000, count: 72 -Region Avg IOU: 0.615885, Class: 1.000000, Obj: 0.405012, No Obj: 0.003535, Avg Recall: 0.745763, count: 118 -Region Avg IOU: 0.605341, Class: 1.000000, Obj: 0.388346, No Obj: 0.003240, Avg Recall: 0.712000, count: 125 -Region Avg IOU: 0.585456, Class: 1.000000, Obj: 0.381781, No Obj: 0.003923, Avg Recall: 0.690141, count: 142 -7324: 0.430714, 0.441313 avg, 0.001000 rate, 26.136930 seconds, 1874944 images -Loaded: 0.000078 seconds -Region Avg IOU: 0.640702, Class: 1.000000, Obj: 0.388398, No Obj: 0.003379, Avg Recall: 0.792453, count: 106 -Region Avg IOU: 0.608671, Class: 1.000000, Obj: 0.396291, No Obj: 0.003124, Avg Recall: 0.761468, count: 109 -Region Avg IOU: 0.615282, Class: 1.000000, Obj: 0.361781, No Obj: 0.003560, Avg Recall: 0.743802, count: 121 -Region Avg IOU: 0.583427, Class: 1.000000, Obj: 0.339882, No Obj: 0.003443, Avg Recall: 0.731544, count: 149 -Region Avg IOU: 0.626082, Class: 1.000000, Obj: 0.405634, No Obj: 0.002866, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.610016, Class: 1.000000, Obj: 0.350194, No Obj: 0.002881, Avg Recall: 0.711712, count: 111 -Region Avg IOU: 0.634905, Class: 1.000000, Obj: 0.424255, No Obj: 0.003162, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.610383, Class: 1.000000, Obj: 0.413313, No Obj: 0.002784, Avg Recall: 0.718447, count: 103 -7325: 0.436424, 0.440824 avg, 0.001000 rate, 24.531063 seconds, 1875200 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.616046, Class: 1.000000, Obj: 0.377577, No Obj: 0.002617, Avg Recall: 0.768000, count: 125 -Region Avg IOU: 0.675758, Class: 1.000000, Obj: 0.444680, No Obj: 0.003040, Avg Recall: 0.810000, count: 100 -Region Avg IOU: 0.623474, Class: 1.000000, Obj: 0.370432, No Obj: 0.002995, Avg Recall: 0.780000, count: 100 -Region Avg IOU: 0.568226, Class: 1.000000, Obj: 0.314399, No Obj: 0.002992, Avg Recall: 0.656716, count: 134 -Region Avg IOU: 0.611162, Class: 1.000000, Obj: 0.398623, No Obj: 0.002973, Avg Recall: 0.716814, count: 113 -Region Avg IOU: 0.593571, Class: 1.000000, Obj: 0.369274, No Obj: 0.002731, Avg Recall: 0.702479, count: 121 -Region Avg IOU: 0.634639, Class: 1.000000, Obj: 0.399072, No Obj: 0.002961, Avg Recall: 0.743363, count: 113 -Region Avg IOU: 0.661907, Class: 1.000000, Obj: 0.389594, No Obj: 0.002585, Avg Recall: 0.800000, count: 95 -7326: 0.459624, 0.442704 avg, 0.001000 rate, 25.100336 seconds, 1875456 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.651840, Class: 1.000000, Obj: 0.412260, No Obj: 0.003123, Avg Recall: 0.811966, count: 117 -Region Avg IOU: 0.623351, Class: 1.000000, Obj: 0.379243, No Obj: 0.003072, Avg Recall: 0.719008, count: 121 -Region Avg IOU: 0.627710, Class: 1.000000, Obj: 0.378712, No Obj: 0.003079, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.599020, Class: 1.000000, Obj: 0.350593, No Obj: 0.002685, Avg Recall: 0.680000, count: 100 -Region Avg IOU: 0.578257, Class: 1.000000, Obj: 0.368240, No Obj: 0.002762, Avg Recall: 0.658730, count: 126 -Region Avg IOU: 0.641868, Class: 1.000000, Obj: 0.379060, No Obj: 0.002959, Avg Recall: 0.776596, count: 94 -Region Avg IOU: 0.588309, Class: 1.000000, Obj: 0.346208, No Obj: 0.002455, Avg Recall: 0.677419, count: 93 -Region Avg IOU: 0.634802, Class: 1.000000, Obj: 0.408359, No Obj: 0.002839, Avg Recall: 0.775510, count: 98 -7327: 0.434089, 0.441843 avg, 0.001000 rate, 24.891005 seconds, 1875712 images -Loaded: 0.000087 seconds -Region Avg IOU: 0.557227, Class: 1.000000, Obj: 0.364121, No Obj: 0.002923, Avg Recall: 0.620155, count: 129 -Region Avg IOU: 0.638104, Class: 1.000000, Obj: 0.361302, No Obj: 0.002803, Avg Recall: 0.793478, count: 92 -Region Avg IOU: 0.603735, Class: 1.000000, Obj: 0.393394, No Obj: 0.002810, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.646107, Class: 1.000000, Obj: 0.369224, No Obj: 0.002652, Avg Recall: 0.778846, count: 104 -Region Avg IOU: 0.630930, Class: 1.000000, Obj: 0.448602, No Obj: 0.002544, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.607328, Class: 1.000000, Obj: 0.358617, No Obj: 0.002658, Avg Recall: 0.750000, count: 96 -Region Avg IOU: 0.671384, Class: 1.000000, Obj: 0.444199, No Obj: 0.002856, Avg Recall: 0.802632, count: 76 -Region Avg IOU: 0.600355, Class: 1.000000, Obj: 0.369784, No Obj: 0.003535, Avg Recall: 0.669173, count: 133 -7328: 0.393660, 0.437024 avg, 0.001000 rate, 24.561872 seconds, 1875968 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.598966, Class: 1.000000, Obj: 0.404750, No Obj: 0.002931, Avg Recall: 0.719298, count: 114 -Region Avg IOU: 0.655400, Class: 1.000000, Obj: 0.388823, No Obj: 0.002893, Avg Recall: 0.820225, count: 89 -Region Avg IOU: 0.669584, Class: 1.000000, Obj: 0.499106, No Obj: 0.003167, Avg Recall: 0.813084, count: 107 -Region Avg IOU: 0.659464, Class: 1.000000, Obj: 0.460414, No Obj: 0.003354, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.607654, Class: 1.000000, Obj: 0.428067, No Obj: 0.002893, Avg Recall: 0.756757, count: 111 -Region Avg IOU: 0.616813, Class: 1.000000, Obj: 0.432412, No Obj: 0.002500, Avg Recall: 0.740260, count: 77 -Region Avg IOU: 0.638134, Class: 1.000000, Obj: 0.359149, No Obj: 0.002538, Avg Recall: 0.782051, count: 78 -Region Avg IOU: 0.625014, Class: 1.000000, Obj: 0.418100, No Obj: 0.003380, Avg Recall: 0.708333, count: 120 -7329: 0.377990, 0.431121 avg, 0.001000 rate, 24.594124 seconds, 1876224 images -Loaded: 0.000099 seconds -Region Avg IOU: 0.572513, Class: 1.000000, Obj: 0.403543, No Obj: 0.002681, Avg Recall: 0.620690, count: 87 -Region Avg IOU: 0.608547, Class: 1.000000, Obj: 0.402400, No Obj: 0.002937, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.597938, Class: 1.000000, Obj: 0.398994, No Obj: 0.002968, Avg Recall: 0.695238, count: 105 -Region Avg IOU: 0.652122, Class: 1.000000, Obj: 0.443807, No Obj: 0.002496, Avg Recall: 0.808219, count: 73 -Region Avg IOU: 0.653826, Class: 1.000000, Obj: 0.389039, No Obj: 0.002935, Avg Recall: 0.788235, count: 85 -Region Avg IOU: 0.610023, Class: 1.000000, Obj: 0.415315, No Obj: 0.003222, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.588271, Class: 1.000000, Obj: 0.402546, No Obj: 0.002992, Avg Recall: 0.644231, count: 104 -Region Avg IOU: 0.612759, Class: 1.000000, Obj: 0.380704, No Obj: 0.003024, Avg Recall: 0.736842, count: 114 -7330: 0.371421, 0.425151 avg, 0.001000 rate, 24.629396 seconds, 1876480 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 18.141731 seconds -Region Avg IOU: 0.551428, Class: 1.000000, Obj: 0.369258, No Obj: 0.004200, Avg Recall: 0.623932, count: 117 -Region Avg IOU: 0.497444, Class: 1.000000, Obj: 0.341186, No Obj: 0.003636, Avg Recall: 0.533981, count: 103 -Region Avg IOU: 0.558153, Class: 1.000000, Obj: 0.351161, No Obj: 0.004136, Avg Recall: 0.613333, count: 150 -Region Avg IOU: 0.577532, Class: 1.000000, Obj: 0.390790, No Obj: 0.003446, Avg Recall: 0.656250, count: 96 -Region Avg IOU: 0.559102, Class: 1.000000, Obj: 0.359501, No Obj: 0.003218, Avg Recall: 0.678571, count: 84 -Region Avg IOU: 0.580078, Class: 1.000000, Obj: 0.340613, No Obj: 0.003860, Avg Recall: 0.675926, count: 108 -Region Avg IOU: 0.577108, Class: 1.000000, Obj: 0.388644, No Obj: 0.004799, Avg Recall: 0.648855, count: 131 -Region Avg IOU: 0.534308, Class: 1.000000, Obj: 0.327390, No Obj: 0.004406, Avg Recall: 0.639344, count: 122 -7331: 0.445051, 0.427141 avg, 0.001000 rate, 21.528116 seconds, 1876736 images -Loaded: 0.230848 seconds -Region Avg IOU: 0.522458, Class: 1.000000, Obj: 0.330352, No Obj: 0.004205, Avg Recall: 0.604651, count: 129 -Region Avg IOU: 0.540728, Class: 1.000000, Obj: 0.363846, No Obj: 0.003939, Avg Recall: 0.589286, count: 112 -Region Avg IOU: 0.551182, Class: 1.000000, Obj: 0.375872, No Obj: 0.004735, Avg Recall: 0.619403, count: 134 -Region Avg IOU: 0.555083, Class: 1.000000, Obj: 0.357970, No Obj: 0.003603, Avg Recall: 0.648352, count: 91 -Region Avg IOU: 0.495596, Class: 1.000000, Obj: 0.357693, No Obj: 0.003645, Avg Recall: 0.513761, count: 109 -Region Avg IOU: 0.548980, Class: 1.000000, Obj: 0.398080, No Obj: 0.003859, Avg Recall: 0.628571, count: 105 -Region Avg IOU: 0.570221, Class: 1.000000, Obj: 0.375710, No Obj: 0.003853, Avg Recall: 0.692308, count: 104 -Region Avg IOU: 0.586312, Class: 1.000000, Obj: 0.445916, No Obj: 0.003598, Avg Recall: 0.710843, count: 83 -7332: 0.386717, 0.423098 avg, 0.001000 rate, 18.510008 seconds, 1876992 images -Loaded: 3.628838 seconds -Region Avg IOU: 0.568696, Class: 1.000000, Obj: 0.419244, No Obj: 0.004457, Avg Recall: 0.658915, count: 129 -Region Avg IOU: 0.539228, Class: 1.000000, Obj: 0.390894, No Obj: 0.004585, Avg Recall: 0.643478, count: 115 -Region Avg IOU: 0.544220, Class: 1.000000, Obj: 0.413662, No Obj: 0.004849, Avg Recall: 0.601562, count: 128 -Region Avg IOU: 0.553293, Class: 1.000000, Obj: 0.412785, No Obj: 0.004356, Avg Recall: 0.641026, count: 117 -Region Avg IOU: 0.560075, Class: 1.000000, Obj: 0.396030, No Obj: 0.004067, Avg Recall: 0.632184, count: 87 -Region Avg IOU: 0.577909, Class: 1.000000, Obj: 0.436292, No Obj: 0.004696, Avg Recall: 0.650943, count: 106 -Region Avg IOU: 0.571673, Class: 1.000000, Obj: 0.411617, No Obj: 0.004511, Avg Recall: 0.648649, count: 111 -Region Avg IOU: 0.545560, Class: 1.000000, Obj: 0.329741, No Obj: 0.004339, Avg Recall: 0.643939, count: 132 -7333: 0.460990, 0.426888 avg, 0.001000 rate, 19.066854 seconds, 1877248 images -Loaded: 3.774280 seconds -Region Avg IOU: 0.598893, Class: 1.000000, Obj: 0.420251, No Obj: 0.004782, Avg Recall: 0.720000, count: 100 -Region Avg IOU: 0.528922, Class: 1.000000, Obj: 0.382824, No Obj: 0.004923, Avg Recall: 0.592593, count: 135 -Region Avg IOU: 0.530828, Class: 1.000000, Obj: 0.362336, No Obj: 0.005186, Avg Recall: 0.582822, count: 163 -Region Avg IOU: 0.554093, Class: 1.000000, Obj: 0.378813, No Obj: 0.004410, Avg Recall: 0.633663, count: 101 -Region Avg IOU: 0.545962, Class: 1.000000, Obj: 0.398952, No Obj: 0.004399, Avg Recall: 0.623077, count: 130 -Region Avg IOU: 0.544055, Class: 1.000000, Obj: 0.390761, No Obj: 0.004557, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.563816, Class: 1.000000, Obj: 0.427051, No Obj: 0.004118, Avg Recall: 0.650000, count: 100 -Region Avg IOU: 0.543206, Class: 1.000000, Obj: 0.371568, No Obj: 0.003505, Avg Recall: 0.591398, count: 93 -7334: 0.459447, 0.430143 avg, 0.001000 rate, 18.605881 seconds, 1877504 images -Loaded: 3.018460 seconds -Region Avg IOU: 0.531945, Class: 1.000000, Obj: 0.447325, No Obj: 0.005231, Avg Recall: 0.584906, count: 106 -Region Avg IOU: 0.540409, Class: 1.000000, Obj: 0.379812, No Obj: 0.005084, Avg Recall: 0.661765, count: 136 -Region Avg IOU: 0.558429, Class: 1.000000, Obj: 0.453426, No Obj: 0.005035, Avg Recall: 0.666667, count: 114 -Region Avg IOU: 0.559300, Class: 1.000000, Obj: 0.426630, No Obj: 0.004864, Avg Recall: 0.647619, count: 105 -Region Avg IOU: 0.554358, Class: 1.000000, Obj: 0.419194, No Obj: 0.004434, Avg Recall: 0.644444, count: 90 -Region Avg IOU: 0.536651, Class: 1.000000, Obj: 0.395196, No Obj: 0.003815, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.543069, Class: 1.000000, Obj: 0.432263, No Obj: 0.004239, Avg Recall: 0.615385, count: 104 -Region Avg IOU: 0.558250, Class: 1.000000, Obj: 0.432289, No Obj: 0.005069, Avg Recall: 0.681416, count: 113 -7335: 0.467099, 0.433839 avg, 0.001000 rate, 18.519182 seconds, 1877760 images -Loaded: 2.518936 seconds -Region Avg IOU: 0.493112, Class: 1.000000, Obj: 0.399446, No Obj: 0.004598, Avg Recall: 0.537190, count: 121 -Region Avg IOU: 0.505563, Class: 1.000000, Obj: 0.378197, No Obj: 0.004056, Avg Recall: 0.544444, count: 90 -Region Avg IOU: 0.513285, Class: 1.000000, Obj: 0.409776, No Obj: 0.004784, Avg Recall: 0.560000, count: 125 -Region Avg IOU: 0.525779, Class: 1.000000, Obj: 0.469489, No Obj: 0.004830, Avg Recall: 0.604396, count: 91 -Region Avg IOU: 0.525786, Class: 1.000000, Obj: 0.445798, No Obj: 0.005132, Avg Recall: 0.592593, count: 135 -Region Avg IOU: 0.539351, Class: 1.000000, Obj: 0.460007, No Obj: 0.004133, Avg Recall: 0.584416, count: 77 -Region Avg IOU: 0.524312, Class: 1.000000, Obj: 0.385754, No Obj: 0.004736, Avg Recall: 0.590909, count: 110 -Region Avg IOU: 0.589061, Class: 1.000000, Obj: 0.446905, No Obj: 0.004103, Avg Recall: 0.684783, count: 92 -7336: 0.426363, 0.433091 avg, 0.001000 rate, 20.111267 seconds, 1878016 images -Loaded: 1.552781 seconds -Region Avg IOU: 0.513936, Class: 1.000000, Obj: 0.452874, No Obj: 0.004401, Avg Recall: 0.586957, count: 92 -Region Avg IOU: 0.522801, Class: 1.000000, Obj: 0.424477, No Obj: 0.005129, Avg Recall: 0.558559, count: 111 -Region Avg IOU: 0.557259, Class: 1.000000, Obj: 0.443047, No Obj: 0.004771, Avg Recall: 0.622807, count: 114 -Region Avg IOU: 0.550615, Class: 1.000000, Obj: 0.408871, No Obj: 0.004980, Avg Recall: 0.652174, count: 115 -Region Avg IOU: 0.538197, Class: 1.000000, Obj: 0.431717, No Obj: 0.004154, Avg Recall: 0.600000, count: 100 -Region Avg IOU: 0.523240, Class: 1.000000, Obj: 0.411884, No Obj: 0.004660, Avg Recall: 0.579832, count: 119 -Region Avg IOU: 0.533862, Class: 1.000000, Obj: 0.416448, No Obj: 0.005068, Avg Recall: 0.636364, count: 132 -Region Avg IOU: 0.552463, Class: 1.000000, Obj: 0.384369, No Obj: 0.004295, Avg Recall: 0.610526, count: 95 -7337: 0.401953, 0.429978 avg, 0.001000 rate, 21.453260 seconds, 1878272 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.531479, Class: 1.000000, Obj: 0.453585, No Obj: 0.004233, Avg Recall: 0.580000, count: 100 -Region Avg IOU: 0.519457, Class: 1.000000, Obj: 0.427972, No Obj: 0.003987, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.521923, Class: 1.000000, Obj: 0.429604, No Obj: 0.004446, Avg Recall: 0.554217, count: 83 -Region Avg IOU: 0.567740, Class: 1.000000, Obj: 0.433443, No Obj: 0.005241, Avg Recall: 0.608696, count: 115 -Region Avg IOU: 0.585562, Class: 1.000000, Obj: 0.407023, No Obj: 0.005008, Avg Recall: 0.688073, count: 109 -Region Avg IOU: 0.531576, Class: 1.000000, Obj: 0.371835, No Obj: 0.004691, Avg Recall: 0.614679, count: 109 -Region Avg IOU: 0.520600, Class: 1.000000, Obj: 0.440668, No Obj: 0.003941, Avg Recall: 0.571429, count: 98 -Region Avg IOU: 0.545340, Class: 1.000000, Obj: 0.392947, No Obj: 0.003826, Avg Recall: 0.626263, count: 99 -7338: 0.369049, 0.423885 avg, 0.001000 rate, 22.444653 seconds, 1878528 images -Loaded: 0.420138 seconds -Region Avg IOU: 0.539041, Class: 1.000000, Obj: 0.413886, No Obj: 0.005457, Avg Recall: 0.612500, count: 160 -Region Avg IOU: 0.547792, Class: 1.000000, Obj: 0.421146, No Obj: 0.004949, Avg Recall: 0.616667, count: 120 -Region Avg IOU: 0.511407, Class: 1.000000, Obj: 0.400142, No Obj: 0.004786, Avg Recall: 0.563492, count: 126 -Region Avg IOU: 0.539634, Class: 1.000000, Obj: 0.417460, No Obj: 0.004250, Avg Recall: 0.646552, count: 116 -Region Avg IOU: 0.553893, Class: 1.000000, Obj: 0.423874, No Obj: 0.005027, Avg Recall: 0.679245, count: 106 -Region Avg IOU: 0.540606, Class: 1.000000, Obj: 0.401907, No Obj: 0.004251, Avg Recall: 0.628866, count: 97 -Region Avg IOU: 0.550640, Class: 1.000000, Obj: 0.431382, No Obj: 0.004386, Avg Recall: 0.609195, count: 87 -Region Avg IOU: 0.528000, Class: 1.000000, Obj: 0.377543, No Obj: 0.004139, Avg Recall: 0.638889, count: 108 -7339: 0.450807, 0.426577 avg, 0.001000 rate, 22.090620 seconds, 1878784 images -Loaded: 0.275395 seconds -Region Avg IOU: 0.543394, Class: 1.000000, Obj: 0.371122, No Obj: 0.005155, Avg Recall: 0.654135, count: 133 -Region Avg IOU: 0.545129, Class: 1.000000, Obj: 0.391543, No Obj: 0.004490, Avg Recall: 0.660377, count: 106 -Region Avg IOU: 0.540567, Class: 1.000000, Obj: 0.383920, No Obj: 0.004231, Avg Recall: 0.591837, count: 98 -Region Avg IOU: 0.577341, Class: 1.000000, Obj: 0.417202, No Obj: 0.003939, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.581831, Class: 1.000000, Obj: 0.426024, No Obj: 0.003919, Avg Recall: 0.670455, count: 88 -Region Avg IOU: 0.552407, Class: 1.000000, Obj: 0.381731, No Obj: 0.004369, Avg Recall: 0.635417, count: 96 -Region Avg IOU: 0.568262, Class: 1.000000, Obj: 0.402925, No Obj: 0.004954, Avg Recall: 0.664234, count: 137 -Region Avg IOU: 0.520887, Class: 1.000000, Obj: 0.400038, No Obj: 0.003420, Avg Recall: 0.590361, count: 83 -7340: 0.441693, 0.428089 avg, 0.001000 rate, 22.114286 seconds, 1879040 images -Resizing -416 - try to allocate workspace = 632291328 * sizeof(float), CUDA allocate done! -Loaded: 21.838696 seconds -Region Avg IOU: 0.527351, Class: 1.000000, Obj: 0.398710, No Obj: 0.004050, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.496630, Class: 1.000000, Obj: 0.369192, No Obj: 0.004328, Avg Recall: 0.559633, count: 109 -Region Avg IOU: 0.509882, Class: 1.000000, Obj: 0.365573, No Obj: 0.004282, Avg Recall: 0.601695, count: 118 -Region Avg IOU: 0.579553, Class: 1.000000, Obj: 0.403572, No Obj: 0.004608, Avg Recall: 0.706897, count: 116 -Region Avg IOU: 0.558579, Class: 1.000000, Obj: 0.385664, No Obj: 0.004404, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.535913, Class: 1.000000, Obj: 0.410921, No Obj: 0.003932, Avg Recall: 0.596330, count: 109 -Region Avg IOU: 0.585980, Class: 1.000000, Obj: 0.412877, No Obj: 0.003767, Avg Recall: 0.691358, count: 81 -Region Avg IOU: 0.519103, Class: 1.000000, Obj: 0.377730, No Obj: 0.004102, Avg Recall: 0.558333, count: 120 -7341: 0.409957, 0.426275 avg, 0.001000 rate, 19.089054 seconds, 1879296 images -Loaded: 0.374952 seconds -Region Avg IOU: 0.523791, Class: 1.000000, Obj: 0.377010, No Obj: 0.004153, Avg Recall: 0.588785, count: 107 -Region Avg IOU: 0.535263, Class: 1.000000, Obj: 0.371547, No Obj: 0.003793, Avg Recall: 0.619835, count: 121 -Region Avg IOU: 0.535080, Class: 1.000000, Obj: 0.387157, No Obj: 0.003984, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.519946, Class: 1.000000, Obj: 0.364363, No Obj: 0.004448, Avg Recall: 0.621849, count: 119 -Region Avg IOU: 0.512768, Class: 1.000000, Obj: 0.367335, No Obj: 0.003352, Avg Recall: 0.593407, count: 91 -Region Avg IOU: 0.485197, Class: 1.000000, Obj: 0.362215, No Obj: 0.004079, Avg Recall: 0.535714, count: 140 -Region Avg IOU: 0.525558, Class: 1.000000, Obj: 0.373827, No Obj: 0.004181, Avg Recall: 0.564356, count: 101 -Region Avg IOU: 0.561456, Class: 1.000000, Obj: 0.381668, No Obj: 0.004653, Avg Recall: 0.654930, count: 142 -7342: 0.443235, 0.427971 avg, 0.001000 rate, 20.033333 seconds, 1879552 images -Loaded: 2.405285 seconds -Region Avg IOU: 0.550087, Class: 1.000000, Obj: 0.368677, No Obj: 0.003898, Avg Recall: 0.626374, count: 91 -Region Avg IOU: 0.516708, Class: 1.000000, Obj: 0.324133, No Obj: 0.003194, Avg Recall: 0.548387, count: 93 -Region Avg IOU: 0.567110, Class: 1.000000, Obj: 0.398011, No Obj: 0.004556, Avg Recall: 0.653846, count: 130 -Region Avg IOU: 0.565215, Class: 1.000000, Obj: 0.394175, No Obj: 0.004761, Avg Recall: 0.675214, count: 117 -Region Avg IOU: 0.527678, Class: 1.000000, Obj: 0.343223, No Obj: 0.004019, Avg Recall: 0.630631, count: 111 -Region Avg IOU: 0.519431, Class: 1.000000, Obj: 0.349102, No Obj: 0.003307, Avg Recall: 0.585859, count: 99 -Region Avg IOU: 0.556644, Class: 1.000000, Obj: 0.395866, No Obj: 0.004670, Avg Recall: 0.655172, count: 116 -Region Avg IOU: 0.595033, Class: 1.000000, Obj: 0.410433, No Obj: 0.004102, Avg Recall: 0.682243, count: 107 -7343: 0.408269, 0.426001 avg, 0.001000 rate, 20.406343 seconds, 1879808 images -Loaded: 1.031162 seconds -Region Avg IOU: 0.555890, Class: 1.000000, Obj: 0.415195, No Obj: 0.003878, Avg Recall: 0.710280, count: 107 -Region Avg IOU: 0.577336, Class: 1.000000, Obj: 0.354073, No Obj: 0.004399, Avg Recall: 0.689655, count: 116 -Region Avg IOU: 0.499349, Class: 1.000000, Obj: 0.339981, No Obj: 0.004078, Avg Recall: 0.587156, count: 109 -Region Avg IOU: 0.594855, Class: 1.000000, Obj: 0.444180, No Obj: 0.004059, Avg Recall: 0.705357, count: 112 -Region Avg IOU: 0.540724, Class: 1.000000, Obj: 0.396803, No Obj: 0.003469, Avg Recall: 0.578947, count: 76 -Region Avg IOU: 0.560819, Class: 1.000000, Obj: 0.358339, No Obj: 0.004111, Avg Recall: 0.660870, count: 115 -Region Avg IOU: 0.551718, Class: 1.000000, Obj: 0.387330, No Obj: 0.003800, Avg Recall: 0.620000, count: 100 -Region Avg IOU: 0.565165, Class: 1.000000, Obj: 0.356442, No Obj: 0.004272, Avg Recall: 0.669565, count: 115 -7344: 0.397593, 0.423160 avg, 0.001000 rate, 19.040899 seconds, 1880064 images -Loaded: 2.668437 seconds -Region Avg IOU: 0.612954, Class: 1.000000, Obj: 0.444593, No Obj: 0.004306, Avg Recall: 0.691358, count: 81 -Region Avg IOU: 0.556677, Class: 1.000000, Obj: 0.406088, No Obj: 0.003484, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.558730, Class: 1.000000, Obj: 0.407780, No Obj: 0.004258, Avg Recall: 0.648936, count: 94 -Region Avg IOU: 0.560328, Class: 1.000000, Obj: 0.411860, No Obj: 0.004532, Avg Recall: 0.649123, count: 114 -Region Avg IOU: 0.571431, Class: 1.000000, Obj: 0.348483, No Obj: 0.004579, Avg Recall: 0.647059, count: 119 -Region Avg IOU: 0.526481, Class: 1.000000, Obj: 0.372953, No Obj: 0.003479, Avg Recall: 0.572816, count: 103 -Region Avg IOU: 0.560450, Class: 1.000000, Obj: 0.379010, No Obj: 0.004318, Avg Recall: 0.654545, count: 110 -Region Avg IOU: 0.504956, Class: 1.000000, Obj: 0.370340, No Obj: 0.003920, Avg Recall: 0.563492, count: 126 -7345: 0.401347, 0.420979 avg, 0.001000 rate, 19.357494 seconds, 1880320 images -Loaded: 2.651813 seconds -Region Avg IOU: 0.599794, Class: 1.000000, Obj: 0.406002, No Obj: 0.004300, Avg Recall: 0.745455, count: 110 -Region Avg IOU: 0.504640, Class: 1.000000, Obj: 0.354346, No Obj: 0.004368, Avg Recall: 0.561983, count: 121 -Region Avg IOU: 0.552773, Class: 1.000000, Obj: 0.404539, No Obj: 0.003567, Avg Recall: 0.655914, count: 93 -Region Avg IOU: 0.539021, Class: 1.000000, Obj: 0.379333, No Obj: 0.004004, Avg Recall: 0.609091, count: 110 -Region Avg IOU: 0.584465, Class: 1.000000, Obj: 0.410885, No Obj: 0.003889, Avg Recall: 0.681319, count: 91 -Region Avg IOU: 0.548477, Class: 1.000000, Obj: 0.379228, No Obj: 0.004290, Avg Recall: 0.652632, count: 95 -Region Avg IOU: 0.607615, Class: 1.000000, Obj: 0.433137, No Obj: 0.004104, Avg Recall: 0.696078, count: 102 -Region Avg IOU: 0.584957, Class: 1.000000, Obj: 0.455102, No Obj: 0.004053, Avg Recall: 0.702128, count: 94 -7346: 0.359444, 0.414825 avg, 0.001000 rate, 20.897017 seconds, 1880576 images -Loaded: 1.590168 seconds -Region Avg IOU: 0.576253, Class: 1.000000, Obj: 0.417069, No Obj: 0.004532, Avg Recall: 0.658120, count: 117 -Region Avg IOU: 0.525757, Class: 1.000000, Obj: 0.354983, No Obj: 0.004583, Avg Recall: 0.588235, count: 102 -Region Avg IOU: 0.523367, Class: 1.000000, Obj: 0.390527, No Obj: 0.004890, Avg Recall: 0.547826, count: 115 -Region Avg IOU: 0.580514, Class: 1.000000, Obj: 0.381834, No Obj: 0.004328, Avg Recall: 0.653465, count: 101 -Region Avg IOU: 0.541404, Class: 1.000000, Obj: 0.397423, No Obj: 0.004265, Avg Recall: 0.625000, count: 112 -Region Avg IOU: 0.555457, Class: 1.000000, Obj: 0.384520, No Obj: 0.004203, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.547618, Class: 1.000000, Obj: 0.404420, No Obj: 0.004538, Avg Recall: 0.666667, count: 138 -Region Avg IOU: 0.559223, Class: 1.000000, Obj: 0.384813, No Obj: 0.004332, Avg Recall: 0.677083, count: 96 -7347: 0.404647, 0.413808 avg, 0.001000 rate, 20.360708 seconds, 1880832 images -Loaded: 1.354068 seconds -Region Avg IOU: 0.532191, Class: 1.000000, Obj: 0.371796, No Obj: 0.004018, Avg Recall: 0.635514, count: 107 -Region Avg IOU: 0.549795, Class: 1.000000, Obj: 0.439293, No Obj: 0.003541, Avg Recall: 0.639175, count: 97 -Region Avg IOU: 0.551526, Class: 1.000000, Obj: 0.413141, No Obj: 0.004103, Avg Recall: 0.672727, count: 110 -Region Avg IOU: 0.549839, Class: 1.000000, Obj: 0.392952, No Obj: 0.004564, Avg Recall: 0.643564, count: 101 -Region Avg IOU: 0.545823, Class: 1.000000, Obj: 0.432870, No Obj: 0.003430, Avg Recall: 0.552941, count: 85 -Region Avg IOU: 0.544371, Class: 1.000000, Obj: 0.355933, No Obj: 0.004758, Avg Recall: 0.614035, count: 114 -Region Avg IOU: 0.497169, Class: 1.000000, Obj: 0.369193, No Obj: 0.003930, Avg Recall: 0.519608, count: 102 -Region Avg IOU: 0.581548, Class: 1.000000, Obj: 0.415275, No Obj: 0.004339, Avg Recall: 0.625000, count: 96 -7348: 0.492063, 0.421633 avg, 0.001000 rate, 18.981491 seconds, 1881088 images -Loaded: 2.458499 seconds -Region Avg IOU: 0.471084, Class: 1.000000, Obj: 0.371798, No Obj: 0.003541, Avg Recall: 0.455357, count: 112 -Region Avg IOU: 0.496430, Class: 1.000000, Obj: 0.395816, No Obj: 0.003550, Avg Recall: 0.573034, count: 89 -Region Avg IOU: 0.574875, Class: 1.000000, Obj: 0.418420, No Obj: 0.004355, Avg Recall: 0.642202, count: 109 -Region Avg IOU: 0.541324, Class: 1.000000, Obj: 0.375613, No Obj: 0.004962, Avg Recall: 0.594406, count: 143 -Region Avg IOU: 0.528017, Class: 1.000000, Obj: 0.407556, No Obj: 0.004699, Avg Recall: 0.560345, count: 116 -Region Avg IOU: 0.567298, Class: 1.000000, Obj: 0.440923, No Obj: 0.003840, Avg Recall: 0.678571, count: 84 -Region Avg IOU: 0.570721, Class: 1.000000, Obj: 0.451804, No Obj: 0.003949, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.534485, Class: 1.000000, Obj: 0.360094, No Obj: 0.004851, Avg Recall: 0.624000, count: 125 -7349: 0.418076, 0.421277 avg, 0.001000 rate, 21.577335 seconds, 1881344 images -Loaded: 0.000049 seconds -Region Avg IOU: 0.533699, Class: 1.000000, Obj: 0.399805, No Obj: 0.003851, Avg Recall: 0.601942, count: 103 -Region Avg IOU: 0.535495, Class: 1.000000, Obj: 0.412674, No Obj: 0.004822, Avg Recall: 0.609756, count: 123 -Region Avg IOU: 0.557505, Class: 1.000000, Obj: 0.427012, No Obj: 0.004164, Avg Recall: 0.631068, count: 103 -Region Avg IOU: 0.568187, Class: 1.000000, Obj: 0.409322, No Obj: 0.003934, Avg Recall: 0.694118, count: 85 -Region Avg IOU: 0.542632, Class: 1.000000, Obj: 0.399677, No Obj: 0.003661, Avg Recall: 0.609756, count: 82 -Region Avg IOU: 0.541777, Class: 1.000000, Obj: 0.376511, No Obj: 0.004673, Avg Recall: 0.618644, count: 118 -Region Avg IOU: 0.547048, Class: 1.000000, Obj: 0.391615, No Obj: 0.004204, Avg Recall: 0.611111, count: 90 -Region Avg IOU: 0.569611, Class: 1.000000, Obj: 0.430772, No Obj: 0.004670, Avg Recall: 0.639175, count: 97 -7350: 0.365952, 0.415745 avg, 0.001000 rate, 20.167589 seconds, 1881600 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 0.000062 seconds -Region Avg IOU: 0.653556, Class: 1.000000, Obj: 0.498319, No Obj: 0.003663, Avg Recall: 0.785047, count: 107 -Region Avg IOU: 0.673005, Class: 1.000000, Obj: 0.502428, No Obj: 0.003719, Avg Recall: 0.824074, count: 108 -Region Avg IOU: 0.626684, Class: 1.000000, Obj: 0.495606, No Obj: 0.003694, Avg Recall: 0.811966, count: 117 -Region Avg IOU: 0.630685, Class: 1.000000, Obj: 0.462436, No Obj: 0.003674, Avg Recall: 0.785714, count: 126 -Region Avg IOU: 0.658036, Class: 1.000000, Obj: 0.550842, No Obj: 0.003461, Avg Recall: 0.816667, count: 120 -Region Avg IOU: 0.669679, Class: 1.000000, Obj: 0.518213, No Obj: 0.003450, Avg Recall: 0.819149, count: 94 -Region Avg IOU: 0.601980, Class: 1.000000, Obj: 0.458188, No Obj: 0.003865, Avg Recall: 0.742647, count: 136 -Region Avg IOU: 0.629509, Class: 1.000000, Obj: 0.482960, No Obj: 0.003525, Avg Recall: 0.783505, count: 97 -7351: 0.417476, 0.415918 avg, 0.001000 rate, 25.686016 seconds, 1881856 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.684727, Class: 1.000000, Obj: 0.542767, No Obj: 0.003445, Avg Recall: 0.884211, count: 95 -Region Avg IOU: 0.586487, Class: 1.000000, Obj: 0.461190, No Obj: 0.003256, Avg Recall: 0.674419, count: 129 -Region Avg IOU: 0.650535, Class: 1.000000, Obj: 0.517534, No Obj: 0.003244, Avg Recall: 0.813726, count: 102 -Region Avg IOU: 0.645730, Class: 1.000000, Obj: 0.480753, No Obj: 0.003030, Avg Recall: 0.774194, count: 93 -Region Avg IOU: 0.638545, Class: 1.000000, Obj: 0.455600, No Obj: 0.003669, Avg Recall: 0.770992, count: 131 -Region Avg IOU: 0.650082, Class: 1.000000, Obj: 0.509194, No Obj: 0.003228, Avg Recall: 0.828571, count: 105 -Region Avg IOU: 0.624044, Class: 1.000000, Obj: 0.497791, No Obj: 0.003264, Avg Recall: 0.770492, count: 122 -Region Avg IOU: 0.610458, Class: 1.000000, Obj: 0.481357, No Obj: 0.003142, Avg Recall: 0.704762, count: 105 -7352: 0.417296, 0.416056 avg, 0.001000 rate, 26.269220 seconds, 1882112 images -Loaded: 0.000085 seconds -Region Avg IOU: 0.678176, Class: 1.000000, Obj: 0.421880, No Obj: 0.003124, Avg Recall: 0.852632, count: 95 -Region Avg IOU: 0.588475, Class: 1.000000, Obj: 0.394911, No Obj: 0.002857, Avg Recall: 0.719101, count: 89 -Region Avg IOU: 0.656247, Class: 1.000000, Obj: 0.484873, No Obj: 0.003053, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.594448, Class: 1.000000, Obj: 0.443065, No Obj: 0.002739, Avg Recall: 0.686956, count: 115 -Region Avg IOU: 0.632883, Class: 1.000000, Obj: 0.447703, No Obj: 0.002813, Avg Recall: 0.757576, count: 99 -Region Avg IOU: 0.688657, Class: 1.000000, Obj: 0.448951, No Obj: 0.003251, Avg Recall: 0.854167, count: 96 -Region Avg IOU: 0.658365, Class: 1.000000, Obj: 0.467527, No Obj: 0.003086, Avg Recall: 0.773913, count: 115 -Region Avg IOU: 0.636540, Class: 1.000000, Obj: 0.447488, No Obj: 0.003090, Avg Recall: 0.738318, count: 107 -7353: 0.388294, 0.413280 avg, 0.001000 rate, 25.919188 seconds, 1882368 images -Loaded: 0.000083 seconds -Region Avg IOU: 0.621259, Class: 1.000000, Obj: 0.476990, No Obj: 0.002286, Avg Recall: 0.712871, count: 101 -Region Avg IOU: 0.673331, Class: 1.000000, Obj: 0.443120, No Obj: 0.002639, Avg Recall: 0.801980, count: 101 -Region Avg IOU: 0.638790, Class: 1.000000, Obj: 0.403386, No Obj: 0.002521, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.646597, Class: 1.000000, Obj: 0.431158, No Obj: 0.003006, Avg Recall: 0.803419, count: 117 -Region Avg IOU: 0.627048, Class: 1.000000, Obj: 0.423983, No Obj: 0.002601, Avg Recall: 0.769231, count: 91 -Region Avg IOU: 0.658044, Class: 1.000000, Obj: 0.408605, No Obj: 0.003255, Avg Recall: 0.806202, count: 129 -Region Avg IOU: 0.676898, Class: 1.000000, Obj: 0.399460, No Obj: 0.003114, Avg Recall: 0.830000, count: 100 -Region Avg IOU: 0.640317, Class: 1.000000, Obj: 0.419204, No Obj: 0.002864, Avg Recall: 0.796296, count: 108 -7354: 0.421872, 0.414139 avg, 0.001000 rate, 27.805424 seconds, 1882624 images -Loaded: 0.000094 seconds -Region Avg IOU: 0.635463, Class: 1.000000, Obj: 0.388633, No Obj: 0.002585, Avg Recall: 0.761905, count: 105 -Region Avg IOU: 0.625195, Class: 1.000000, Obj: 0.397148, No Obj: 0.003225, Avg Recall: 0.767442, count: 129 -Region Avg IOU: 0.644191, Class: 1.000000, Obj: 0.428615, No Obj: 0.003079, Avg Recall: 0.766667, count: 120 -Region Avg IOU: 0.701164, Class: 1.000000, Obj: 0.463519, No Obj: 0.002512, Avg Recall: 0.870968, count: 93 -Region Avg IOU: 0.635132, Class: 1.000000, Obj: 0.421448, No Obj: 0.002400, Avg Recall: 0.773585, count: 106 -Region Avg IOU: 0.651792, Class: 1.000000, Obj: 0.433206, No Obj: 0.002699, Avg Recall: 0.790909, count: 110 -Region Avg IOU: 0.617757, Class: 1.000000, Obj: 0.426258, No Obj: 0.002626, Avg Recall: 0.764151, count: 106 -Region Avg IOU: 0.649243, Class: 1.000000, Obj: 0.444987, No Obj: 0.003038, Avg Recall: 0.827869, count: 122 -7355: 0.421172, 0.414842 avg, 0.001000 rate, 27.344980 seconds, 1882880 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.594907, Class: 1.000000, Obj: 0.339787, No Obj: 0.002788, Avg Recall: 0.745902, count: 122 -Region Avg IOU: 0.664250, Class: 1.000000, Obj: 0.433445, No Obj: 0.002823, Avg Recall: 0.801887, count: 106 -Region Avg IOU: 0.676804, Class: 1.000000, Obj: 0.373774, No Obj: 0.002703, Avg Recall: 0.849462, count: 93 -Region Avg IOU: 0.672487, Class: 1.000000, Obj: 0.427466, No Obj: 0.002518, Avg Recall: 0.822430, count: 107 -Region Avg IOU: 0.636961, Class: 1.000000, Obj: 0.387028, No Obj: 0.002747, Avg Recall: 0.769841, count: 126 -Region Avg IOU: 0.637916, Class: 1.000000, Obj: 0.398889, No Obj: 0.002885, Avg Recall: 0.756098, count: 123 -Region Avg IOU: 0.619644, Class: 1.000000, Obj: 0.411657, No Obj: 0.002609, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.629718, Class: 1.000000, Obj: 0.392204, No Obj: 0.002736, Avg Recall: 0.763158, count: 114 -7356: 0.429350, 0.416293 avg, 0.001000 rate, 25.703711 seconds, 1883136 images -Loaded: 0.000105 seconds -Region Avg IOU: 0.655511, Class: 1.000000, Obj: 0.436340, No Obj: 0.002690, Avg Recall: 0.793103, count: 116 -Region Avg IOU: 0.640498, Class: 1.000000, Obj: 0.415978, No Obj: 0.002645, Avg Recall: 0.780488, count: 123 -Region Avg IOU: 0.663807, Class: 1.000000, Obj: 0.347054, No Obj: 0.002678, Avg Recall: 0.816667, count: 120 -Region Avg IOU: 0.639520, Class: 1.000000, Obj: 0.383332, No Obj: 0.002772, Avg Recall: 0.794643, count: 112 -Region Avg IOU: 0.636847, Class: 1.000000, Obj: 0.386011, No Obj: 0.002757, Avg Recall: 0.787879, count: 132 -Region Avg IOU: 0.600268, Class: 1.000000, Obj: 0.384181, No Obj: 0.002776, Avg Recall: 0.690476, count: 126 -Region Avg IOU: 0.655991, Class: 1.000000, Obj: 0.394998, No Obj: 0.002422, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.668324, Class: 1.000000, Obj: 0.465737, No Obj: 0.002440, Avg Recall: 0.839080, count: 87 -7357: 0.456476, 0.420311 avg, 0.001000 rate, 26.928076 seconds, 1883392 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.630418, Class: 1.000000, Obj: 0.422270, No Obj: 0.002760, Avg Recall: 0.750000, count: 128 -Region Avg IOU: 0.645425, Class: 1.000000, Obj: 0.389634, No Obj: 0.002729, Avg Recall: 0.755102, count: 98 -Region Avg IOU: 0.623142, Class: 1.000000, Obj: 0.411097, No Obj: 0.002243, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.670187, Class: 1.000000, Obj: 0.387044, No Obj: 0.002707, Avg Recall: 0.863636, count: 110 -Region Avg IOU: 0.662981, Class: 1.000000, Obj: 0.403952, No Obj: 0.002466, Avg Recall: 0.819149, count: 94 -Region Avg IOU: 0.592687, Class: 1.000000, Obj: 0.342056, No Obj: 0.002788, Avg Recall: 0.710938, count: 128 -Region Avg IOU: 0.637744, Class: 1.000000, Obj: 0.370559, No Obj: 0.002600, Avg Recall: 0.733333, count: 105 -Region Avg IOU: 0.628246, Class: 1.000000, Obj: 0.430056, No Obj: 0.002626, Avg Recall: 0.777778, count: 99 -7358: 0.416548, 0.419935 avg, 0.001000 rate, 27.909492 seconds, 1883648 images -Loaded: 0.000090 seconds -Region Avg IOU: 0.629223, Class: 1.000000, Obj: 0.398505, No Obj: 0.002453, Avg Recall: 0.723214, count: 112 -Region Avg IOU: 0.685896, Class: 1.000000, Obj: 0.429928, No Obj: 0.002595, Avg Recall: 0.820225, count: 89 -Region Avg IOU: 0.621876, Class: 1.000000, Obj: 0.384279, No Obj: 0.002497, Avg Recall: 0.750000, count: 108 -Region Avg IOU: 0.626034, Class: 1.000000, Obj: 0.383064, No Obj: 0.002493, Avg Recall: 0.733333, count: 120 -Region Avg IOU: 0.620012, Class: 1.000000, Obj: 0.412048, No Obj: 0.002386, Avg Recall: 0.702381, count: 84 -Region Avg IOU: 0.628081, Class: 1.000000, Obj: 0.413140, No Obj: 0.002432, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.568787, Class: 1.000000, Obj: 0.378204, No Obj: 0.002229, Avg Recall: 0.619048, count: 105 -Region Avg IOU: 0.610141, Class: 1.000000, Obj: 0.390164, No Obj: 0.002752, Avg Recall: 0.728000, count: 125 -7359: 0.412106, 0.419152 avg, 0.001000 rate, 27.232901 seconds, 1883904 images -Loaded: 0.000122 seconds -Region Avg IOU: 0.628843, Class: 1.000000, Obj: 0.394104, No Obj: 0.002795, Avg Recall: 0.745614, count: 114 -Region Avg IOU: 0.663817, Class: 1.000000, Obj: 0.450515, No Obj: 0.002705, Avg Recall: 0.779817, count: 109 -Region Avg IOU: 0.660547, Class: 1.000000, Obj: 0.466670, No Obj: 0.002851, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.625464, Class: 1.000000, Obj: 0.396339, No Obj: 0.002987, Avg Recall: 0.747748, count: 111 -Region Avg IOU: 0.587731, Class: 1.000000, Obj: 0.401943, No Obj: 0.002938, Avg Recall: 0.669118, count: 136 -Region Avg IOU: 0.605011, Class: 1.000000, Obj: 0.427404, No Obj: 0.002451, Avg Recall: 0.739130, count: 92 -Region Avg IOU: 0.580736, Class: 1.000000, Obj: 0.405953, No Obj: 0.002229, Avg Recall: 0.691589, count: 107 -Region Avg IOU: 0.635702, Class: 1.000000, Obj: 0.419769, No Obj: 0.002911, Avg Recall: 0.798319, count: 119 -7360: 0.406516, 0.417888 avg, 0.001000 rate, 26.363775 seconds, 1884160 images -Resizing -608 - try to allocate workspace = 16777216 * sizeof(float), CUDA allocate done! -Loaded: 17.750786 seconds -Region Avg IOU: 0.659754, Class: 1.000000, Obj: 0.402618, No Obj: 0.003281, Avg Recall: 0.786260, count: 131 -Region Avg IOU: 0.631152, Class: 1.000000, Obj: 0.447297, No Obj: 0.002610, Avg Recall: 0.801653, count: 121 -Region Avg IOU: 0.668145, Class: 1.000000, Obj: 0.427233, No Obj: 0.002861, Avg Recall: 0.817391, count: 115 -Region Avg IOU: 0.664649, Class: 1.000000, Obj: 0.458979, No Obj: 0.002657, Avg Recall: 0.781250, count: 96 -Region Avg IOU: 0.629134, Class: 1.000000, Obj: 0.415467, No Obj: 0.002305, Avg Recall: 0.804878, count: 82 -Region Avg IOU: 0.665728, Class: 1.000000, Obj: 0.421542, No Obj: 0.003107, Avg Recall: 0.815789, count: 114 -Region Avg IOU: 0.633268, Class: 1.000000, Obj: 0.419085, No Obj: 0.002949, Avg Recall: 0.781513, count: 119 -Region Avg IOU: 0.679772, Class: 1.000000, Obj: 0.452503, No Obj: 0.003267, Avg Recall: 0.813008, count: 123 -7361: 0.429823, 0.419082 avg, 0.001000 rate, 26.532419 seconds, 1884416 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.671687, Class: 1.000000, Obj: 0.432320, No Obj: 0.003258, Avg Recall: 0.830769, count: 130 -Region Avg IOU: 0.676699, Class: 1.000000, Obj: 0.430950, No Obj: 0.002669, Avg Recall: 0.739583, count: 96 -Region Avg IOU: 0.616731, Class: 1.000000, Obj: 0.416010, No Obj: 0.003349, Avg Recall: 0.727273, count: 132 -Region Avg IOU: 0.652905, Class: 1.000000, Obj: 0.446565, No Obj: 0.003239, Avg Recall: 0.795276, count: 127 -Region Avg IOU: 0.682587, Class: 1.000000, Obj: 0.467086, No Obj: 0.002486, Avg Recall: 0.863636, count: 88 -Region Avg IOU: 0.648072, Class: 1.000000, Obj: 0.421405, No Obj: 0.003242, Avg Recall: 0.795276, count: 127 -Region Avg IOU: 0.608506, Class: 1.000000, Obj: 0.421930, No Obj: 0.002985, Avg Recall: 0.743119, count: 109 -Region Avg IOU: 0.702191, Class: 1.000000, Obj: 0.441038, No Obj: 0.002687, Avg Recall: 0.857143, count: 77 -7362: 0.445193, 0.421693 avg, 0.001000 rate, 26.761116 seconds, 1884672 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.661990, Class: 1.000000, Obj: 0.482554, No Obj: 0.002582, Avg Recall: 0.795699, count: 93 -Region Avg IOU: 0.664450, Class: 1.000000, Obj: 0.386489, No Obj: 0.003187, Avg Recall: 0.830000, count: 100 -Region Avg IOU: 0.614274, Class: 1.000000, Obj: 0.411796, No Obj: 0.002835, Avg Recall: 0.727273, count: 110 -Region Avg IOU: 0.617336, Class: 1.000000, Obj: 0.425861, No Obj: 0.002756, Avg Recall: 0.736364, count: 110 -Region Avg IOU: 0.654516, Class: 1.000000, Obj: 0.459131, No Obj: 0.002757, Avg Recall: 0.818182, count: 99 -Region Avg IOU: 0.665875, Class: 1.000000, Obj: 0.513599, No Obj: 0.003152, Avg Recall: 0.828283, count: 99 -Region Avg IOU: 0.612716, Class: 1.000000, Obj: 0.425033, No Obj: 0.003086, Avg Recall: 0.732824, count: 131 -Region Avg IOU: 0.617595, Class: 1.000000, Obj: 0.459006, No Obj: 0.003177, Avg Recall: 0.730159, count: 126 -7363: 0.427539, 0.422278 avg, 0.001000 rate, 28.066620 seconds, 1884928 images -Loaded: 0.000064 seconds -Region Avg IOU: 0.618492, Class: 1.000000, Obj: 0.459296, No Obj: 0.002779, Avg Recall: 0.757895, count: 95 -Region Avg IOU: 0.672184, Class: 1.000000, Obj: 0.427784, No Obj: 0.002418, Avg Recall: 0.816901, count: 71 -Region Avg IOU: 0.613154, Class: 1.000000, Obj: 0.445415, No Obj: 0.003212, Avg Recall: 0.716418, count: 134 -Region Avg IOU: 0.652519, Class: 1.000000, Obj: 0.467934, No Obj: 0.003655, Avg Recall: 0.798561, count: 139 -Region Avg IOU: 0.654560, Class: 1.000000, Obj: 0.473973, No Obj: 0.002673, Avg Recall: 0.811765, count: 85 -Region Avg IOU: 0.607881, Class: 1.000000, Obj: 0.422844, No Obj: 0.002572, Avg Recall: 0.714286, count: 98 -Region Avg IOU: 0.692019, Class: 1.000000, Obj: 0.511761, No Obj: 0.002997, Avg Recall: 0.860215, count: 93 -Region Avg IOU: 0.628858, Class: 1.000000, Obj: 0.446491, No Obj: 0.003450, Avg Recall: 0.760684, count: 117 -7364: 0.394234, 0.419473 avg, 0.001000 rate, 29.113169 seconds, 1885184 images -Loaded: 0.000081 seconds -Region Avg IOU: 0.629529, Class: 1.000000, Obj: 0.440228, No Obj: 0.003172, Avg Recall: 0.736264, count: 91 -Region Avg IOU: 0.633848, Class: 1.000000, Obj: 0.427492, No Obj: 0.003596, Avg Recall: 0.751773, count: 141 -Region Avg IOU: 0.666448, Class: 1.000000, Obj: 0.469420, No Obj: 0.003480, Avg Recall: 0.809160, count: 131 -Region Avg IOU: 0.599081, Class: 1.000000, Obj: 0.399908, No Obj: 0.003384, Avg Recall: 0.669355, count: 124 -Region Avg IOU: 0.660947, Class: 1.000000, Obj: 0.450547, No Obj: 0.003537, Avg Recall: 0.822430, count: 107 -Region Avg IOU: 0.647931, Class: 1.000000, Obj: 0.512874, No Obj: 0.002762, Avg Recall: 0.788235, count: 85 -Region Avg IOU: 0.654696, Class: 1.000000, Obj: 0.461491, No Obj: 0.003300, Avg Recall: 0.825688, count: 109 -Region Avg IOU: 0.597882, Class: 1.000000, Obj: 0.431379, No Obj: 0.002630, Avg Recall: 0.768421, count: 95 -7365: 0.419214, 0.419447 avg, 0.001000 rate, 26.380575 seconds, 1885440 images -Loaded: 0.000097 seconds -Region Avg IOU: 0.651302, Class: 1.000000, Obj: 0.473188, No Obj: 0.003013, Avg Recall: 0.772727, count: 88 -Region Avg IOU: 0.695992, Class: 1.000000, Obj: 0.501509, No Obj: 0.002985, Avg Recall: 0.909091, count: 99 -Region Avg IOU: 0.642247, Class: 1.000000, Obj: 0.428642, No Obj: 0.003300, Avg Recall: 0.755906, count: 127 -Region Avg IOU: 0.613162, Class: 1.000000, Obj: 0.499558, No Obj: 0.002842, Avg Recall: 0.698925, count: 93 -Region Avg IOU: 0.656371, Class: 1.000000, Obj: 0.443137, No Obj: 0.003315, Avg Recall: 0.792079, count: 101 -Region Avg IOU: 0.654778, Class: 1.000000, Obj: 0.495668, No Obj: 0.002961, Avg Recall: 0.800000, count: 100 -Region Avg IOU: 0.594222, Class: 1.000000, Obj: 0.407235, No Obj: 0.002655, Avg Recall: 0.708333, count: 96 -Region Avg IOU: 0.628712, Class: 1.000000, Obj: 0.463295, No Obj: 0.002971, Avg Recall: 0.800000, count: 95 -7366: 0.404979, 0.418000 avg, 0.001000 rate, 26.784000 seconds, 1885696 images -Loaded: 0.000082 seconds -Region Avg IOU: 0.627948, Class: 1.000000, Obj: 0.474493, No Obj: 0.003483, Avg Recall: 0.744526, count: 137 -Region Avg IOU: 0.600149, Class: 1.000000, Obj: 0.436572, No Obj: 0.002692, Avg Recall: 0.735294, count: 102 -Region Avg IOU: 0.637972, Class: 1.000000, Obj: 0.412497, No Obj: 0.003303, Avg Recall: 0.809091, count: 110 -Region Avg IOU: 0.653020, Class: 1.000000, Obj: 0.465059, No Obj: 0.003147, Avg Recall: 0.811321, count: 106 -Region Avg IOU: 0.623757, Class: 1.000000, Obj: 0.475732, No Obj: 0.003300, Avg Recall: 0.741007, count: 139 -Region Avg IOU: 0.613225, Class: 1.000000, Obj: 0.446140, No Obj: 0.003445, Avg Recall: 0.755396, count: 139 -Region Avg IOU: 0.603243, Class: 1.000000, Obj: 0.447020, No Obj: 0.003059, Avg Recall: 0.709677, count: 124 -Region Avg IOU: 0.657172, Class: 1.000000, Obj: 0.472396, No Obj: 0.002762, Avg Recall: 0.806452, count: 93 -7367: 0.491364, 0.425337 avg, 0.001000 rate, 27.388529 seconds, 1885952 images -Loaded: 0.000100 seconds -Region Avg IOU: 0.654661, Class: 1.000000, Obj: 0.463689, No Obj: 0.003639, Avg Recall: 0.818182, count: 143 -Region Avg IOU: 0.634687, Class: 1.000000, Obj: 0.474535, No Obj: 0.002914, Avg Recall: 0.771429, count: 105 -Region Avg IOU: 0.646460, Class: 1.000000, Obj: 0.432588, No Obj: 0.003071, Avg Recall: 0.777778, count: 99 -Region Avg IOU: 0.677120, Class: 1.000000, Obj: 0.496213, No Obj: 0.003106, Avg Recall: 0.839286, count: 112 -Region Avg IOU: 0.634824, Class: 1.000000, Obj: 0.446478, No Obj: 0.002794, Avg Recall: 0.765957, count: 94 -Region Avg IOU: 0.638051, Class: 1.000000, Obj: 0.410168, No Obj: 0.003403, Avg Recall: 0.786325, count: 117 -Region Avg IOU: 0.637742, Class: 1.000000, Obj: 0.421461, No Obj: 0.002796, Avg Recall: 0.737288, count: 118 -Region Avg IOU: 0.636994, Class: 1.000000, Obj: 0.430454, No Obj: 0.002918, Avg Recall: 0.784314, count: 102 -7368: 0.423768, 0.425180 avg, 0.001000 rate, 26.582352 seconds, 1886208 images -Loaded: 0.000074 seconds -Region Avg IOU: 0.669970, Class: 1.000000, Obj: 0.480019, No Obj: 0.002508, Avg Recall: 0.836957, count: 92 -Region Avg IOU: 0.661502, Class: 1.000000, Obj: 0.395842, No Obj: 0.003572, Avg Recall: 0.828767, count: 146 -Region Avg IOU: 0.654629, Class: 1.000000, Obj: 0.441188, No Obj: 0.003045, Avg Recall: 0.770642, count: 109 -Region Avg IOU: 0.666684, Class: 1.000000, Obj: 0.446905, No Obj: 0.002874, Avg Recall: 0.840425, count: 94 -Region Avg IOU: 0.592684, Class: 1.000000, Obj: 0.399935, No Obj: 0.003101, Avg Recall: 0.724638, count: 138 -Region Avg IOU: 0.644379, Class: 1.000000, Obj: 0.428872, No Obj: 0.003105, Avg Recall: 0.785047, count: 107 -Region Avg IOU: 0.638868, Class: 1.000000, Obj: 0.399940, No Obj: 0.002817, Avg Recall: 0.742857, count: 105 -Region Avg IOU: 0.616058, Class: 1.000000, Obj: 0.445667, No Obj: 0.002856, Avg Recall: 0.728000, count: 125 -7369: 0.405376, 0.423200 avg, 0.001000 rate, 26.865444 seconds, 1886464 images -Loaded: 0.000079 seconds -Region Avg IOU: 0.630591, Class: 1.000000, Obj: 0.410637, No Obj: 0.002665, Avg Recall: 0.798077, count: 104 -Region Avg IOU: 0.655970, Class: 1.000000, Obj: 0.377829, No Obj: 0.002429, Avg Recall: 0.811111, count: 90 -Region Avg IOU: 0.649411, Class: 1.000000, Obj: 0.401580, No Obj: 0.003006, Avg Recall: 0.760870, count: 92 -Region Avg IOU: 0.639408, Class: 1.000000, Obj: 0.459170, No Obj: 0.002533, Avg Recall: 0.759615, count: 104 -Region Avg IOU: 0.637219, Class: 1.000000, Obj: 0.430818, No Obj: 0.002774, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.644919, Class: 1.000000, Obj: 0.402108, No Obj: 0.002931, Avg Recall: 0.775510, count: 98 -Region Avg IOU: 0.616060, Class: 1.000000, Obj: 0.397123, No Obj: 0.002541, Avg Recall: 0.766667, count: 90 -Region Avg IOU: 0.620222, Class: 1.000000, Obj: 0.428272, No Obj: 0.002521, Avg Recall: 0.733333, count: 105 -7370: 0.417532, 0.422633 avg, 0.001000 rate, 26.985786 seconds, 1886720 images -Resizing -672 - try to allocate workspace = 62402560 * sizeof(float), CUDA allocate done! -Loaded: 0.000064 seconds -Region Avg IOU: 0.678972, Class: 1.000000, Obj: 0.403917, No Obj: 0.002538, Avg Recall: 0.826087, count: 115 -Region Avg IOU: 0.637868, Class: 1.000000, Obj: 0.379809, No Obj: 0.002155, Avg Recall: 0.786408, count: 103 -Region Avg IOU: 0.645466, Class: 1.000000, Obj: 0.438897, No Obj: 0.002166, Avg Recall: 0.795181, count: 83 -Region Avg IOU: 0.665681, Class: 1.000000, Obj: 0.394410, No Obj: 0.002443, Avg Recall: 0.867347, count: 98 -Region Avg IOU: 0.595919, Class: 1.000000, Obj: 0.441701, No Obj: 0.002693, Avg Recall: 0.661017, count: 118 -Region Avg IOU: 0.658969, Class: 1.000000, Obj: 0.420296, No Obj: 0.002523, Avg Recall: 0.795918, count: 98 -Region Avg IOU: 0.651324, Class: 1.000000, Obj: 0.444482, No Obj: 0.002514, Avg Recall: 0.794118, count: 102 -Region Avg IOU: 0.664859, Class: 1.000000, Obj: 0.419283, No Obj: 0.002926, Avg Recall: 0.818182, count: 132 -7371: 0.418756, 0.422245 avg, 0.001000 rate, 28.489376 seconds, 1886976 images -Loaded: 0.000076 seconds -Region Avg IOU: 0.689352, Class: 1.000000, Obj: 0.426468, No Obj: 0.002587, Avg Recall: 0.875000, count: 104 -Region Avg IOU: 0.656321, Class: 1.000000, Obj: 0.405426, No Obj: 0.003194, Avg Recall: 0.798658, count: 149 -Region Avg IOU: 0.676993, Class: 1.000000, Obj: 0.439894, No Obj: 0.002510, Avg Recall: 0.835052, count: 97 -Region Avg IOU: 0.650698, Class: 1.000000, Obj: 0.466191, No Obj: 0.002511, Avg Recall: 0.774510, count: 102 -Region Avg IOU: 0.606759, Class: 1.000000, Obj: 0.434353, No Obj: 0.002361, Avg Recall: 0.696970, count: 99 -Region Avg IOU: 0.637985, Class: 1.000000, Obj: 0.392090, No Obj: 0.002578, Avg Recall: 0.775701, count: 107 -Region Avg IOU: 0.703022, Class: 1.000000, Obj: 0.463762, No Obj: 0.002666, Avg Recall: 0.895238, count: 105 -Region Avg IOU: 0.679433, Class: 1.000000, Obj: 0.403317, No Obj: 0.002280, Avg Recall: 0.818182, count: 99 -7372: 0.402613, 0.420282 avg, 0.001000 rate, 27.484259 seconds, 1887232 images -Loaded: 0.000096 seconds -Region Avg IOU: 0.671282, Class: 1.000000, Obj: 0.402394, No Obj: 0.001950, Avg Recall: 0.836957, count: 92 -Region Avg IOU: 0.651484, Class: 1.000000, Obj: 0.421230, No Obj: 0.002964, Avg Recall: 0.801527, count: 131 -Region Avg IOU: 0.673507, Class: 1.000000, Obj: 0.430963, No Obj: 0.002660, Avg Recall: 0.858491, count: 106 -Region Avg IOU: 0.648805, Class: 1.000000, Obj: 0.420389, No Obj: 0.002425, Avg Recall: 0.766990, count: 103 -Region Avg IOU: 0.670104, Class: 1.000000, Obj: 0.470658, No Obj: 0.002392, Avg Recall: 0.834951, count: 103 -Region Avg IOU: 0.635782, Class: 1.000000, Obj: 0.473325, No Obj: 0.002471, Avg Recall: 0.785124, count: 121 -Region Avg IOU: 0.664239, Class: 1.000000, Obj: 0.446261, No Obj: 0.002421, Avg Recall: 0.809524, count: 105 -Region Avg IOU: 0.605940, Class: 1.000000, Obj: 0.398073, No Obj: 0.002001, Avg Recall: 0.725275, count: 91 -7373: 0.405356, 0.418789 avg, 0.001000 rate, 28.313267 seconds, 1887488 images -Loaded: 0.000084 seconds -Region Avg IOU: 0.675834, Class: 1.000000, Obj: 0.403747, No Obj: 0.002301, Avg Recall: 0.825688, count: 109 -Region Avg IOU: 0.645036, Class: 1.000000, Obj: 0.409301, No Obj: 0.002557, Avg Recall: 0.803279, count: 122 -Region Avg IOU: 0.727334, Class: 1.000000, Obj: 0.461891, No Obj: 0.002533, Avg Recall: 0.938144, count: 97 -Region Avg IOU: 0.609100, Class: 1.000000, Obj: 0.421291, No Obj: 0.002490, Avg Recall: 0.750000, count: 100 -Region Avg IOU: 0.683268, Class: 1.000000, Obj: 0.401609, No Obj: 0.002738, Avg Recall: 0.848214, count: 112 -Region Avg IOU: 0.724363, Class: 1.000000, Obj: 0.453980, No Obj: 0.002590, Avg Recall: 0.925532, count: 94 -Region Avg IOU: 0.632948, Class: 1.000000, Obj: 0.380284, No Obj: 0.002500, Avg Recall: 0.747826, count: 115 -Region Avg IOU: 0.648252, Class: 1.000000, Obj: 0.456685, No Obj: 0.002330, Avg Recall: 0.823529, count: 85 -7374: 0.430163, 0.419927 avg, 0.001000 rate, 29.187269 seconds, 1887744 images -Loaded: 0.000093 seconds -Region Avg IOU: 0.643666, Class: 1.000000, Obj: 0.431944, No Obj: 0.002639, Avg Recall: 0.796296, count: 108 -Region Avg IOU: 0.681396, Class: 1.000000, Obj: 0.413589, No Obj: 0.002236, Avg Recall: 0.866667, count: 75 -Region Avg IOU: 0.652990, Class: 1.000000, Obj: 0.423038, No Obj: 0.002330, Avg Recall: 0.826087, count: 92 -Region Avg IOU: 0.682727, Class: 1.000000, Obj: 0.449904, No Obj: 0.002631, Avg Recall: 0.843137, count: 102 -Region Avg IOU: 0.641418, Class: 1.000000, Obj: 0.361928, No Obj: 0.002854, Avg Recall: 0.769231, count: 130 -Region Avg IOU: 0.631758, Class: 1.000000, Obj: 0.395043, No Obj: 0.002646, Avg Recall: 0.786325, count: 117 -Region Avg IOU: 0.646978, Class: 1.000000, Obj: 0.387656, No Obj: 0.002744, Avg Recall: 0.765217, count: 115 -Region Avg IOU: 0.655051, Class: 1.000000, Obj: 0.390278, No Obj: 0.002473, Avg Recall: 0.800000, count: 110 -7375: 0.433249, 0.421259 avg, 0.001000 rate, 28.744984 seconds, 1888000 images -Loaded: 0.000095 seconds -Region Avg IOU: 0.689422, Class: 1.000000, Obj: 0.440698, No Obj: 0.002467, Avg Recall: 0.877193, count: 114 -Region Avg IOU: 0.675381, Class: 1.000000, Obj: 0.484066, No Obj: 0.002798, Avg Recall: 0.820755, count: 106 -Region Avg IOU: 0.662940, Class: 1.000000, Obj: 0.418896, No Obj: 0.002524, Avg Recall: 0.776786, count: 112 -Region Avg IOU: 0.685213, Class: 1.000000, Obj: 0.445170, No Obj: 0.003022, Avg Recall: 0.842105, count: 114 -Region Avg IOU: 0.619492, Class: 1.000000, Obj: 0.423567, No Obj: 0.002564, Avg Recall: 0.756303, count: 119 -Region Avg IOU: 0.701482, Class: 1.000000, Obj: 0.502076, No Obj: 0.002192, Avg Recall: 0.819444, count: 72 -Region Avg IOU: 0.656407, Class: 1.000000, Obj: 0.435257, No Obj: 0.002788, Avg Recall: 0.809091, count: 110 -Region Avg IOU: 0.650178, Class: 1.000000, Obj: 0.479142, No Obj: 0.002842, Avg Recall: 0.761468, count: 109 -7376: 0.384516, 0.417585 avg, 0.001000 rate, 28.041603 seconds, 1888256 images -Loaded: 0.000070 seconds -Region Avg IOU: 0.669576, Class: 1.000000, Obj: 0.449553, No Obj: 0.002780, Avg Recall: 0.815534, count: 103 -Region Avg IOU: 0.666376, Class: 1.000000, Obj: 0.409606, No Obj: 0.002540, Avg Recall: 0.813008, count: 123 -Region Avg IOU: 0.684725, Class: 1.000000, Obj: 0.464398, No Obj: 0.002394, Avg Recall: 0.837209, count: 86 -Region Avg IOU: 0.634285, Class: 1.000000, Obj: 0.398323, No Obj: 0.002357, Avg Recall: 0.794872, count: 78 -Region Avg IOU: 0.689795, Class: 1.000000, Obj: 0.409617, No Obj: 0.003129, Avg Recall: 0.876923, count: 130 -Region Avg IOU: 0.687470, Class: 1.000000, Obj: 0.489568, No Obj: 0.002919, Avg Recall: 0.831858, count: 113 -Region Avg IOU: 0.599015, Class: 1.000000, Obj: 0.385648, No Obj: 0.003183, Avg Recall: 0.729412, count: 170 -Region Avg IOU: 0.644776, Class: 1.000000, Obj: 0.404642, No Obj: 0.002885, Avg Recall: 0.781457, count: 151 -7377: 0.443145, 0.420141 avg, 0.001000 rate, 28.868261 seconds, 1888512 images -Loaded: 0.000086 seconds -Region Avg IOU: 0.657542, Class: 1.000000, Obj: 0.446358, No Obj: 0.002893, Avg Recall: 0.797101, count: 138 -Region Avg IOU: 0.613601, Class: 1.000000, Obj: 0.401316, No Obj: 0.002351, Avg Recall: 0.750000, count: 112 -Region Avg IOU: 0.676398, Class: 1.000000, Obj: 0.452847, No Obj: 0.002505, Avg Recall: 0.810526, count: 95 -Region Avg IOU: 0.707995, Class: 1.000000, Obj: 0.502885, No Obj: 0.002291, Avg Recall: 0.863158, count: 95 -Region Avg IOU: 0.640201, Class: 1.000000, Obj: 0.410329, No Obj: 0.002064, Avg Recall: 0.762887, count: 97 -Region Avg IOU: 0.647251, Class: 1.000000, Obj: 0.421623, No Obj: 0.002508, Avg Recall: 0.818182, count: 88 -Region Avg IOU: 0.661560, Class: 1.000000, Obj: 0.430800, No Obj: 0.002603, Avg Recall: 0.803738, count: 107 -Region Avg IOU: 0.652411, Class: 1.000000, Obj: 0.421434, No Obj: 0.002732, Avg Recall: 0.778761, count: 113 -7378: 0.425831, 0.420710 avg, 0.001000 rate, 29.006432 seconds, 1888768 images -Loaded: 0.000128 seconds -Region Avg IOU: 0.656879, Class: 1.000000, Obj: 0.469318, No Obj: 0.002477, Avg Recall: 0.823529, count: 85 -Region Avg IOU: 0.692948, Class: 1.000000, Obj: 0.443145, No Obj: 0.003192, Avg Recall: 0.862903, count: 124 -Region Avg IOU: 0.682846, Class: 1.000000, Obj: 0.448631, No Obj: 0.002181, Avg Recall: 0.876543, count: 81 -Region Avg IOU: 0.663965, Class: 1.000000, Obj: 0.441522, No Obj: 0.003013, Avg Recall: 0.824818, count: 137 -Region Avg IOU: 0.680832, Class: 1.000000, Obj: 0.403349, No Obj: 0.003235, Avg Recall: 0.843750, count: 128 -Region Avg IOU: 0.646312, Class: 1.000000, Obj: 0.468163, No Obj: 0.002434, Avg Recall: 0.782178, count: 101 -Region Avg IOU: 0.648050, Class: 1.000000, Obj: 0.446301, No Obj: 0.002584, Avg Recall: 0.790323, count: 124 -Region Avg IOU: 0.679273, Class: 1.000000, Obj: 0.422846, No Obj: 0.002297, Avg Recall: 0.855263, count: 76 -7379: 0.413225, 0.419961 avg, 0.001000 rate, 27.948116 seconds, 1889024 images -Loaded: 0.000062 seconds -Region Avg IOU: 0.667755, Class: 1.000000, Obj: 0.405189, No Obj: 0.002882, Avg Recall: 0.833333, count: 102 -Region Avg IOU: 0.673471, Class: 1.000000, Obj: 0.422651, No Obj: 0.002461, Avg Recall: 0.835165, count: 91 -Region Avg IOU: 0.665892, Class: 1.000000, Obj: 0.463967, No Obj: 0.002495, Avg Recall: 0.800000, count: 110 -Region Avg IOU: 0.680598, Class: 1.000000, Obj: 0.438108, No Obj: 0.002909, Avg Recall: 0.829457, count: 129 -Region Avg IOU: 0.699687, Class: 1.000000, Obj: 0.481395, No Obj: 0.002143, Avg Recall: 0.911392, count: 79 -Region Avg IOU: 0.656527, Class: 1.000000, Obj: 0.432985, No Obj: 0.002675, Avg Recall: 0.793893, count: 131 -Region Avg IOU: 0.673313, Class: 1.000000, Obj: 0.441090, No Obj: 0.002873, Avg Recall: 0.833333, count: 114 -Region Avg IOU: 0.632138, Class: 1.000000, Obj: 0.374793, No Obj: 0.002819, Avg Recall: 0.798319, count: 119 - diff --git a/scripts/log_parser/test_new.log b/scripts/log_parser/test_new.log deleted file mode 100644 index b0ad79c66c4..00000000000 --- a/scripts/log_parser/test_new.log +++ /dev/null @@ -1,76285 +0,0 @@ -layer filters size input output - 0 conv 16 3 x 3 / 1 544 x 544 x 3 -> 544 x 544 x 16 0.256 BF - 1 max 2 x 2 / 2 544 x 544 x 16 -> 272 x 272 x 16 0.005 BF - 2 conv 32 3 x 3 / 1 272 x 272 x 16 -> 272 x 272 x 32 0.682 BF - 3 max 2 x 2 / 2 272 x 272 x 32 -> 136 x 136 x 32 0.002 BF - 4 conv 64 3 x 3 / 1 136 x 136 x 32 -> 136 x 136 x 64 0.682 BF - 5 max 2 x 2 / 2 136 x 136 x 64 -> 68 x 68 x 64 0.001 BF - 6 conv 128 3 x 3 / 1 68 x 68 x 64 -> 68 x 68 x 128 0.682 BF - 7 max 2 x 2 / 2 68 x 68 x 128 -> 34 x 34 x 128 0.001 BF - 8 conv 256 3 x 3 / 1 34 x 34 x 128 -> 34 x 34 x 256 0.682 BF - 9 max 2 x 2 / 2 34 x 34 x 256 -> 17 x 17 x 256 0.000 BF - 10 conv 512 3 x 3 / 1 17 x 17 x 256 -> 17 x 17 x 512 0.682 BF - 11 max 2 x 2 / 1 17 x 17 x 512 -> 17 x 17 x 512 0.001 BF - 12 conv 1024 3 x 3 / 1 17 x 17 x 512 -> 17 x 17 x1024 2.727 BF - 13 conv 256 1 x 1 / 1 17 x 17 x1024 -> 17 x 17 x 256 0.152 BF - 14 conv 512 3 x 3 / 1 17 x 17 x 256 -> 17 x 17 x 512 0.682 BF - 15 conv 60 1 x 1 / 1 17 x 17 x 512 -> 17 x 17 x 60 0.018 BF - 16 yolo - 17 route 13 - 18 conv 128 1 x 1 / 1 17 x 17 x 256 -> 17 x 17 x 128 0.019 BF - 19 upsample 2x 17 x 17 x 128 -> 34 x 34 x 128 - 20 route 19 8 - 21 conv 256 3 x 3 / 1 34 x 34 x 384 -> 34 x 34 x 256 2.046 BF - 22 conv 60 1 x 1 / 1 34 x 34 x 256 -> 34 x 34 x 60 0.036 BF - 23 yolo -Loading weights from ./test_new...test_new_3 -Total BFLOPS 9.353 -Done! - - seen 32 -Learning Rate: 0.001, Momentum: 0.9, Decay: 0.0005 -Resizing -576 x 576 - try to allocate workspace = 1146159105 * sizeof(float), CUDA allocate done! -Loaded: 1.552417 seconds -Region 16 Avg IOU: 0.262753, Class: 0.543997, Obj: 0.528645, No Obj: 0.513402, .5R: 0.100000, .75R: 0.000000, count: 10 -Region 23 Avg IOU: 0.383236, Class: 0.568616, Obj: 0.610910, No Obj: 0.584212, .5R: 0.222222, .75R: 0.055556, count: 18 -Region 16 Avg IOU: 0.315686, Class: 0.481916, Obj: 0.554757, No Obj: 0.512496, .5R: 0.166667, .75R: 0.000000, count: 12 -Region 23 Avg IOU: 0.394797, Class: 0.520876, Obj: 0.634375, No Obj: 0.584431, .5R: 0.357143, .75R: 0.000000, count: 14 -Region 16 Avg IOU: 0.353224, Class: 0.457954, Obj: 0.518958, No Obj: 0.512487, .5R: 0.222222, .75R: 0.000000, count: 18 -Region 23 Avg IOU: 0.376903, Class: 0.545958, Obj: 0.646985, No Obj: 0.584810, .5R: 0.217391, .75R: 0.000000, count: 23 -Region 16 Avg IOU: 0.261574, Class: 0.540755, Obj: 0.506063, No Obj: 0.512723, .5R: 0.000000, .75R: 0.000000, count: 28 -Region 23 Avg IOU: 0.344227, Class: 0.454850, Obj: 0.592905, No Obj: 0.584050, .5R: 0.277778, .75R: 0.000000, count: 18 -Region 16 Avg IOU: 0.374322, Class: 0.541858, Obj: 0.521041, No Obj: 0.511707, .5R: 0.206897, .75R: 0.034483, count: 29 -Region 23 Avg IOU: 0.355464, Class: 0.520163, Obj: 0.642657, No Obj: 0.584618, .5R: 0.206897, .75R: 0.000000, count: 29 -Region 16 Avg IOU: 0.252889, Class: 0.502299, Obj: 0.549959, No Obj: 0.512240, .5R: 0.000000, .75R: 0.000000, count: 13 -Region 23 Avg IOU: 0.336424, Class: 0.634055, Obj: 0.611844, No Obj: 0.584458, .5R: 0.062500, .75R: 0.000000, count: 16 -Region 16 Avg IOU: 0.336221, Class: 0.506050, Obj: 0.509169, No Obj: 0.512740, .5R: 0.117647, .75R: 0.000000, count: 17 -Region 23 Avg IOU: 0.289997, Class: 0.515103, Obj: 0.647037, No Obj: 0.584743, .5R: 0.125000, .75R: 0.031250, count: 32 -Region 16 Avg IOU: 0.340965, Class: 0.484373, Obj: 0.533169, No Obj: 0.512688, .5R: 0.000000, .75R: 0.000000, count: 16 -Region 23 Avg IOU: 0.391359, Class: 0.531936, Obj: 0.667778, No Obj: 0.584837, .5R: 0.272727, .75R: 0.000000, count: 22 - - 1: 842.286133, 842.286133 avg loss, 0.001000 rate, 1.395151 seconds, 128 images -Loaded: 0.635752 seconds -Region 16 Avg IOU: 0.404736, Class: 0.462789, Obj: 0.000096, No Obj: 0.000011, .5R: 0.285714, .75R: 0.047619, count: 21 -Region 23 Avg IOU: 0.288279, Class: 0.497621, Obj: 0.000000, No Obj: 0.000000, .5R: 0.142857, .75R: 0.000000, count: 14 -Region 16 Avg IOU: 0.418059, Class: 0.464271, Obj: 0.000080, No Obj: 0.000006, .5R: 0.375000, .75R: 0.062500, count: 16 -Region 23 Avg IOU: 0.494099, Class: 0.529099, Obj: 0.000000, No Obj: 0.000000, .5R: 0.500000, .75R: 0.100000, count: 20 -Region 16 Avg IOU: 0.365272, Class: 0.475501, Obj: 0.000139, No Obj: 0.000011, .5R: 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19 -Region 23 Avg IOU: 0.359396, Class: 0.573576, Obj: 0.000000, No Obj: 0.000000, .5R: 0.312500, .75R: 0.062500, count: 16 -Region 16 Avg IOU: 0.327417, Class: 0.498947, Obj: 0.000059, No Obj: 0.000010, .5R: 0.206897, .75R: 0.000000, count: 29 -Region 23 Avg IOU: 0.334021, Class: 0.487379, Obj: 0.000000, No Obj: 0.000000, .5R: 0.160000, .75R: 0.000000, count: 25 - - 2: 12.300520, 759.287598 avg loss, 0.001000 rate, 1.410521 seconds, 256 images -Loaded: 0.689754 seconds -Region 16 Avg IOU: 0.386767, Class: 0.515853, Obj: 0.000000, No Obj: 0.000000, .5R: 0.200000, .75R: 0.133333, count: 15 -Region 23 Avg IOU: 0.438628, Class: 0.501704, Obj: 0.000000, No Obj: 0.000000, .5R: 0.380952, .75R: 0.095238, count: 21 -Region 16 Avg IOU: 0.377870, Class: 0.531673, Obj: 0.000000, No Obj: 0.000000, .5R: 0.263158, .75R: 0.000000, count: 19 -Region 23 Avg IOU: 0.414104, Class: 0.510976, Obj: 0.000000, No Obj: 0.000000, .5R: 0.150000, .75R: 0.000000, count: 20 -Region 16 Avg IOU: 0.485430, Class: 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count: 12 -Region 23 Avg IOU: 0.593622, Class: 0.362998, Obj: 0.079934, No Obj: 0.000244, .5R: 0.714286, .75R: 0.071429, count: 14 -Region 16 Avg IOU: 0.611130, Class: 0.372402, Obj: 0.098183, No Obj: 0.004211, .5R: 0.857143, .75R: 0.071429, count: 14 -Region 23 Avg IOU: 0.585786, Class: 0.361526, Obj: 0.034882, No Obj: 0.000210, .5R: 0.785714, .75R: 0.000000, count: 14 -Region 16 Avg IOU: 0.614300, Class: 0.387200, Obj: 0.102847, No Obj: 0.002920, .5R: 1.000000, .75R: 0.000000, count: 13 -Region 23 Avg IOU: 0.627251, Class: 0.370423, Obj: 0.004403, No Obj: 0.000109, .5R: 0.739130, .75R: 0.260870, count: 23 -Region 16 Avg IOU: 0.674661, Class: 0.395743, Obj: 0.206471, No Obj: 0.005316, .5R: 1.000000, .75R: 0.285714, count: 14 -Region 23 Avg IOU: 0.666165, Class: 0.353916, Obj: 0.045228, No Obj: 0.000152, .5R: 1.000000, .75R: 0.111111, count: 18 -Region 16 Avg IOU: 0.588851, Class: 0.379655, Obj: 0.159527, No Obj: 0.004411, .5R: 0.785714, .75R: 0.071429, count: 14 -Region 23 Avg IOU: 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0.005536, No Obj: 0.000346, .5R: 0.888889, .75R: 0.222222, count: 9 -Region 23 Avg IOU: 0.646632, Class: 0.358085, Obj: 0.095226, No Obj: 0.001413, .5R: 0.916667, .75R: 0.083333, count: 12 -Region 16 Avg IOU: 0.568226, Class: 0.339659, Obj: 0.004918, No Obj: 0.000217, .5R: 0.750000, .75R: 0.125000, count: 8 -Region 23 Avg IOU: 0.656208, Class: 0.328983, Obj: 0.218473, No Obj: 0.003606, .5R: 0.962963, .75R: 0.259259, count: 27 -Region 16 Avg IOU: 0.541504, Class: 0.356832, Obj: 0.022169, No Obj: 0.000430, .5R: 0.583333, .75R: 0.083333, count: 12 -Region 23 Avg IOU: 0.574633, Class: 0.355330, Obj: 0.119148, No Obj: 0.002564, .5R: 0.705882, .75R: 0.058824, count: 17 - - 25: 4.614064, 72.087318 avg loss, 0.001000 rate, 0.810774 seconds, 3200 images -Loaded: 0.878580 seconds -Region 16 Avg IOU: 0.599740, Class: 0.386039, Obj: 0.001961, No Obj: 0.000171, .5R: 0.812500, .75R: 0.125000, count: 16 -Region 23 Avg IOU: 0.657933, Class: 0.328444, Obj: 0.000044, No Obj: 0.000001, .5R: 0.888889, 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16 Avg IOU: 0.558075, Class: 0.354942, Obj: 0.006385, No Obj: 0.000285, .5R: 0.545455, .75R: 0.090909, count: 11 -Region 23 Avg IOU: 0.613422, Class: 0.311739, Obj: 0.000106, No Obj: 0.000008, .5R: 0.826087, .75R: 0.043478, count: 23 -Region 16 Avg IOU: 0.525163, Class: 0.363078, Obj: 0.005544, No Obj: 0.000239, .5R: 0.550000, .75R: 0.000000, count: 20 -Region 23 Avg IOU: 0.579116, Class: 0.356908, Obj: 0.000039, No Obj: 0.000002, .5R: 0.720930, .75R: 0.116279, count: 43 -Region 16 Avg IOU: 0.590662, Class: 0.328397, Obj: 0.002984, No Obj: 0.000140, .5R: 0.727273, .75R: 0.000000, count: 11 -Region 23 Avg IOU: 0.601617, Class: 0.304249, Obj: 0.000375, No Obj: 0.000009, .5R: 0.888889, .75R: 0.111111, count: 27 - - 26: 4.719580, 65.350548 avg loss, 0.001000 rate, 0.830697 seconds, 3328 images -Loaded: 0.551868 seconds -Region 16 Avg IOU: 0.551964, Class: 0.330331, Obj: 0.001237, No Obj: 0.000179, .5R: 0.615385, .75R: 0.076923, count: 13 -Region 23 Avg IOU: 0.559897, Class: 0.306903, Obj: 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0.777778, .75R: 0.111111, count: 18 -Region 16 Avg IOU: 0.616435, Class: 0.347035, Obj: 0.003933, No Obj: 0.000140, .5R: 1.000000, .75R: 0.166667, count: 6 -Region 23 Avg IOU: 0.650074, Class: 0.306255, Obj: 0.000049, No Obj: 0.000000, .5R: 0.923077, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.691513, Class: 0.308812, Obj: 0.009709, No Obj: 0.000131, .5R: 1.000000, .75R: 0.166667, count: 6 -Region 23 Avg IOU: 0.581889, Class: 0.306054, Obj: 0.000038, No Obj: 0.000001, .5R: 0.760000, .75R: 0.160000, count: 25 -Region 16 Avg IOU: 0.562495, Class: 0.346564, Obj: 0.007577, No Obj: 0.000204, .5R: 0.647059, .75R: 0.117647, count: 17 -Region 23 Avg IOU: 0.606960, Class: 0.328841, Obj: 0.000105, No Obj: 0.000001, .5R: 0.666667, .75R: 0.266667, count: 15 - - 27: 3.385897, 59.154083 avg loss, 0.001000 rate, 0.820021 seconds, 3456 images -Loaded: 0.451643 seconds -Region 16 Avg IOU: 0.567464, Class: 0.367345, Obj: 0.002718, No Obj: 0.000145, .5R: 0.800000, .75R: 0.000000, count: 10 -Region 23 Avg IOU: 0.616083, Class: 0.314894, Obj: 0.000005, No Obj: 0.000001, .5R: 0.687500, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.581052, Class: 0.382995, Obj: 0.001852, No Obj: 0.000137, .5R: 0.800000, .75R: 0.100000, count: 10 -Region 23 Avg IOU: 0.569396, Class: 0.306971, Obj: 0.000002, No Obj: 0.000000, .5R: 0.571429, .75R: 0.142857, count: 7 -Region 16 Avg IOU: 0.541171, Class: 0.348201, Obj: 0.001704, No Obj: 0.000219, .5R: 0.666667, .75R: 0.111111, count: 18 -Region 23 Avg IOU: 0.624512, Class: 0.322186, Obj: 0.000001, No Obj: 0.000000, .5R: 0.888889, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.571968, Class: 0.358756, Obj: 0.001130, No Obj: 0.000105, .5R: 0.647059, .75R: 0.058824, count: 17 -Region 23 Avg IOU: 0.595888, Class: 0.329142, Obj: 0.000001, No Obj: 0.000000, .5R: 0.800000, .75R: 0.100000, count: 10 -Region 16 Avg IOU: 0.582601, Class: 0.322868, Obj: 0.002248, No Obj: 0.000165, .5R: 0.636364, .75R: 0.090909, count: 11 -Region 23 Avg IOU: 0.671179, Class: 0.331386, Obj: 0.000002, No Obj: 0.000001, .5R: 0.785714, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.571453, Class: 0.369654, Obj: 0.006173, No Obj: 0.000262, .5R: 0.615385, .75R: 0.153846, count: 13 -Region 23 Avg IOU: 0.600950, Class: 0.311644, Obj: 0.000003, No Obj: 0.000000, .5R: 0.782609, .75R: 0.086957, count: 23 -Region 16 Avg IOU: 0.618019, Class: 0.359741, Obj: 0.004937, No Obj: 0.000154, .5R: 0.857143, .75R: 0.000000, count: 14 -Region 23 Avg IOU: 0.651840, Class: 0.307667, Obj: 0.000002, No Obj: 0.000000, .5R: 0.884615, .75R: 0.230769, count: 26 -Region 16 Avg IOU: 0.499568, Class: 0.372740, Obj: 0.012013, No Obj: 0.000342, .5R: 0.500000, .75R: 0.000000, count: 8 -Region 23 Avg IOU: 0.569307, Class: 0.329539, Obj: 0.000002, No Obj: 0.000000, .5R: 0.777778, .75R: 0.111111, count: 9 - - 28: 3.327422, 53.571419 avg loss, 0.001000 rate, 0.799655 seconds, 3584 images -Loaded: 1.062757 seconds -Region 16 Avg IOU: 0.566419, Class: 0.372964, Obj: 0.003568, No Obj: 0.000258, .5R: 0.666667, .75R: 0.000000, count: 6 -Region 23 Avg IOU: 0.655908, Class: 0.299926, Obj: 0.000010, No Obj: 0.000000, .5R: 0.882353, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.545215, Class: 0.344109, Obj: 0.002356, No Obj: 0.000172, .5R: 0.538462, .75R: 0.000000, count: 13 -Region 23 Avg IOU: 0.617495, Class: 0.288139, Obj: 0.000010, No Obj: 0.000001, .5R: 0.826087, .75R: 0.043478, count: 23 -Region 16 Avg IOU: 0.578768, Class: 0.366330, Obj: 0.005643, No Obj: 0.000223, .5R: 0.833333, .75R: 0.000000, count: 6 -Region 23 Avg IOU: 0.602477, Class: 0.317521, Obj: 0.000006, No Obj: 0.000001, .5R: 0.888889, .75R: 0.111111, count: 9 -Region 16 Avg IOU: 0.575129, Class: 0.381321, Obj: 0.025818, No Obj: 0.000499, .5R: 0.666667, .75R: 0.066667, count: 15 -Region 23 Avg IOU: 0.556981, Class: 0.319172, Obj: 0.000051, No Obj: 0.000001, .5R: 0.736842, .75R: 0.052632, count: 19 -Region 16 Avg IOU: 0.663247, Class: 0.351353, Obj: 0.005220, No Obj: 0.000227, .5R: 0.916667, .75R: 0.250000, count: 12 -Region 23 Avg IOU: 0.643746, Class: 0.281413, Obj: 0.000003, No Obj: 0.000000, .5R: 0.866667, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.576380, Class: 0.352950, Obj: 0.013992, No Obj: 0.000273, .5R: 0.714286, .75R: 0.142857, count: 7 -Region 23 Avg IOU: 0.531462, Class: 0.293703, Obj: 0.000006, No Obj: 0.000000, .5R: 0.600000, .75R: 0.000000, count: 10 -Region 16 Avg IOU: 0.693341, Class: 0.375536, Obj: 0.052778, No Obj: 0.000509, .5R: 1.000000, .75R: 0.333333, count: 6 -Region 23 Avg IOU: 0.607981, Class: 0.293147, Obj: 0.000021, No Obj: 0.000001, .5R: 0.733333, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.592257, Class: 0.382249, Obj: 0.006622, No Obj: 0.000241, .5R: 0.750000, .75R: 0.083333, count: 12 -Region 23 Avg IOU: 0.603462, Class: 0.300776, Obj: 0.000006, No Obj: 0.000000, .5R: 0.823529, .75R: 0.117647, count: 17 - - 29: 2.824772, 48.496754 avg loss, 0.001000 rate, 0.808855 seconds, 3712 images -Loaded: 0.720347 seconds -Region 16 Avg IOU: 0.585933, Class: 0.317584, Obj: 0.020780, No Obj: 0.000651, .5R: 0.625000, .75R: 0.125000, count: 16 -Region 23 Avg IOU: 0.550770, Class: 0.286574, Obj: 0.000004, No Obj: 0.000000, .5R: 0.812500, .75R: 0.062500, count: 16 -Region 16 Avg IOU: 0.752672, Class: 0.342423, Obj: 0.005574, No Obj: 0.000344, .5R: 1.000000, .75R: 0.750000, count: 4 -Region 23 Avg IOU: 0.599110, Class: 0.289047, Obj: 0.000007, No Obj: 0.000001, .5R: 0.750000, .75R: 0.125000, count: 8 -Region 16 Avg IOU: 0.654007, Class: 0.337509, Obj: 0.020912, No Obj: 0.000416, .5R: 0.750000, .75R: 0.250000, count: 4 -Region 23 Avg IOU: 0.623713, Class: 0.309432, Obj: 0.000017, No Obj: 0.000002, .5R: 0.709677, .75R: 0.225806, count: 31 -Region 16 Avg IOU: 0.544109, Class: 0.323795, Obj: 0.015694, No Obj: 0.000567, .5R: 0.714286, .75R: 0.000000, count: 7 -Region 23 Avg IOU: 0.572862, Class: 0.302297, Obj: 0.000003, No Obj: 0.000000, .5R: 0.521739, .75R: 0.217391, count: 23 -Region 16 Avg IOU: 0.651900, Class: 0.406162, Obj: 0.015110, No Obj: 0.000854, .5R: 0.933333, .75R: 0.266667, count: 15 -Region 23 Avg IOU: 0.571510, Class: 0.311022, Obj: 0.000001, No Obj: 0.000000, .5R: 0.692308, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.609876, Class: 0.291358, Obj: 0.002490, No Obj: 0.000432, .5R: 0.928571, .75R: 0.071429, count: 14 -Region 23 Avg IOU: 0.654339, Class: 0.326374, Obj: 0.000028, No Obj: 0.000001, .5R: 0.950000, .75R: 0.200000, count: 20 -Region 16 Avg IOU: 0.583244, Class: 0.295324, Obj: 0.001959, No Obj: 0.000333, .5R: 0.750000, .75R: 0.125000, count: 8 -Region 23 Avg IOU: 0.595373, Class: 0.285768, Obj: 0.000026, No Obj: 0.000001, .5R: 0.818182, .75R: 0.136364, count: 22 -Region 16 Avg IOU: 0.549898, Class: 0.333244, Obj: 0.007383, No Obj: 0.000245, .5R: 0.666667, .75R: 0.166667, count: 6 -Region 23 Avg IOU: 0.630875, Class: 0.285126, Obj: 0.000045, No Obj: 0.000001, .5R: 0.789474, .75R: 0.210526, count: 19 - - 30: 3.213797, 43.968456 avg loss, 0.001000 rate, 0.816415 seconds, 3840 images -Resizing -416 x 416 - try to allocate workspace = 304758785 * sizeof(float), CUDA allocate done! -Loaded: 2.012915 seconds -Region 16 Avg IOU: 0.604759, Class: 0.404950, Obj: 0.061323, No Obj: 0.001038, .5R: 1.000000, .75R: 0.000000, count: 6 -Region 23 Avg IOU: 0.658812, Class: 0.338433, Obj: 0.000039, No Obj: 0.000007, .5R: 1.000000, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.627539, Class: 0.317027, Obj: 0.023141, No Obj: 0.001157, .5R: 0.777778, .75R: 0.111111, count: 9 -Region 23 Avg IOU: 0.599830, Class: 0.289374, Obj: 0.000005, No Obj: 0.000002, .5R: 0.720000, .75R: 0.080000, count: 25 -Region 16 Avg IOU: 0.599799, Class: 0.360928, Obj: 0.032658, No Obj: 0.000608, .5R: 0.800000, .75R: 0.000000, count: 10 -Region 23 Avg IOU: 0.605558, Class: 0.312615, Obj: 0.000063, No Obj: 0.000008, .5R: 0.880000, .75R: 0.120000, count: 25 -Region 16 Avg IOU: 0.589041, Class: 0.337567, Obj: 0.007390, No Obj: 0.000479, .5R: 0.750000, .75R: 0.083333, count: 12 -Region 23 Avg IOU: 0.570958, Class: 0.298091, Obj: 0.000029, No Obj: 0.000007, .5R: 0.650000, .75R: 0.150000, count: 20 -Region 16 Avg IOU: 0.525368, Class: 0.299974, Obj: 0.007956, No Obj: 0.000560, .5R: 0.375000, .75R: 0.125000, count: 8 -Region 23 Avg IOU: 0.581223, Class: 0.300812, Obj: 0.000051, No Obj: 0.000011, .5R: 0.739130, .75R: 0.086957, count: 23 -Region 16 Avg IOU: 0.640093, Class: 0.379601, Obj: 0.013369, No Obj: 0.000533, .5R: 0.666667, .75R: 0.500000, count: 6 -Region 23 Avg IOU: 0.613101, Class: 0.296031, Obj: 0.000024, No Obj: 0.000010, .5R: 0.842105, .75R: 0.105263, count: 19 -Region 16 Avg IOU: 0.555559, Class: 0.314109, Obj: 0.005703, No Obj: 0.000595, .5R: 0.571429, .75R: 0.142857, count: 7 -Region 23 Avg IOU: 0.590008, Class: 0.319784, Obj: 0.000119, No Obj: 0.000008, .5R: 0.764706, .75R: 0.000000, count: 17 -Region 16 Avg IOU: 0.501177, Class: 0.310311, Obj: 0.016410, No Obj: 0.000600, .5R: 0.400000, .75R: 0.000000, count: 5 -Region 23 Avg IOU: 0.620902, Class: 0.269010, Obj: 0.000227, No Obj: 0.000011, .5R: 0.900000, .75R: 0.100000, count: 20 - - 31: 3.102477, 39.881859 avg loss, 0.001000 rate, 0.727115 seconds, 3968 images -Loaded: 1.221775 seconds -Region 16 Avg IOU: 0.628271, Class: 0.346078, Obj: 0.050745, No Obj: 0.000926, .5R: 1.000000, .75R: 0.000000, count: 4 -Region 23 Avg IOU: 0.604196, Class: 0.313450, Obj: 0.001254, No Obj: 0.000087, .5R: 0.833333, .75R: 0.111111, count: 18 -Region 16 Avg IOU: 0.604513, Class: 0.344827, Obj: 0.076184, No Obj: 0.001887, .5R: 0.625000, .75R: 0.125000, count: 8 -Region 23 Avg IOU: 0.637619, Class: 0.311057, Obj: 0.001169, No Obj: 0.000129, .5R: 0.866667, .75R: 0.133333, count: 15 -Region 16 Avg IOU: 0.628930, Class: 0.323777, Obj: 0.062173, No Obj: 0.001275, .5R: 1.000000, .75R: 0.100000, count: 10 -Region 23 Avg IOU: 0.516859, Class: 0.299766, Obj: 0.002601, No Obj: 0.000144, .5R: 0.611111, .75R: 0.000000, count: 18 -Region 16 Avg IOU: 0.618612, Class: 0.338681, Obj: 0.025657, No Obj: 0.001182, .5R: 0.800000, .75R: 0.200000, count: 5 -Region 23 Avg IOU: 0.642893, Class: 0.316688, Obj: 0.000135, No Obj: 0.000053, .5R: 0.800000, .75R: 0.200000, count: 10 -Region 16 Avg IOU: 0.516236, Class: 0.314684, Obj: 0.091172, No Obj: 0.001175, .5R: 0.666667, .75R: 0.000000, count: 6 -Region 23 Avg IOU: 0.600863, Class: 0.326817, Obj: 0.000712, No Obj: 0.000091, .5R: 0.933333, .75R: 0.133333, count: 15 -Region 16 Avg IOU: 0.612349, Class: 0.338582, Obj: 0.017191, No Obj: 0.001118, .5R: 0.875000, .75R: 0.125000, count: 8 -Region 23 Avg IOU: 0.617474, Class: 0.284043, Obj: 0.005266, No Obj: 0.000116, .5R: 0.869565, .75R: 0.173913, count: 23 -Region 16 Avg IOU: 0.684993, Class: 0.291712, Obj: 0.041873, No Obj: 0.001320, .5R: 0.857143, .75R: 0.428571, count: 7 -Region 23 Avg IOU: 0.627052, Class: 0.257343, Obj: 0.000148, No Obj: 0.000021, .5R: 0.809524, .75R: 0.190476, count: 21 -Region 16 Avg IOU: 0.553293, Class: 0.327067, Obj: 0.118024, No Obj: 0.001830, .5R: 0.500000, .75R: 0.250000, count: 4 -Region 23 Avg IOU: 0.553968, Class: 0.301512, 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.75R: 0.090909, count: 11 -Region 23 Avg IOU: 0.557273, Class: 0.276374, Obj: 0.010329, No Obj: 0.001142, .5R: 0.714286, .75R: 0.190476, count: 21 -Region 16 Avg IOU: 0.704342, Class: 0.379233, Obj: 0.084076, No Obj: 0.002468, .5R: 1.000000, .75R: 0.400000, count: 5 -Region 23 Avg IOU: 0.627154, Class: 0.301612, Obj: 0.014085, No Obj: 0.000829, .5R: 1.000000, .75R: 0.083333, count: 12 -Region 16 Avg IOU: 0.544289, Class: 0.378782, Obj: 0.065754, No Obj: 0.001941, .5R: 0.666667, .75R: 0.000000, count: 6 -Region 23 Avg IOU: 0.542431, Class: 0.294449, Obj: 0.011180, No Obj: 0.000880, .5R: 0.500000, .75R: 0.083333, count: 12 -Region 16 Avg IOU: 0.585767, Class: 0.308164, Obj: 0.152700, No Obj: 0.002788, .5R: 0.818182, .75R: 0.181818, count: 11 -Region 23 Avg IOU: 0.621972, Class: 0.306285, Obj: 0.006192, No Obj: 0.002552, .5R: 0.750000, .75R: 0.250000, count: 12 -Region 16 Avg IOU: 0.591260, Class: 0.351852, Obj: 0.046757, No Obj: 0.002258, .5R: 1.000000, .75R: 0.250000, count: 4 -Region 23 Avg IOU: 0.601629, Class: 0.318255, Obj: 0.008650, No Obj: 0.000184, .5R: 0.750000, .75R: 0.187500, count: 16 - - 33: 2.648028, 32.805378 avg loss, 0.001000 rate, 0.732233 seconds, 4224 images -Loaded: 0.618378 seconds -Region 16 Avg IOU: 0.601764, Class: 0.377059, Obj: 0.097070, No Obj: 0.003911, .5R: 0.833333, .75R: 0.250000, count: 12 -Region 23 Avg IOU: 0.571853, Class: 0.321934, Obj: 0.008471, No Obj: 0.000597, .5R: 0.720000, .75R: 0.000000, count: 25 -Region 16 Avg IOU: 0.575337, Class: 0.350941, Obj: 0.034014, No Obj: 0.001600, .5R: 0.888889, .75R: 0.000000, count: 9 -Region 23 Avg IOU: 0.660229, Class: 0.321122, Obj: 0.042913, No Obj: 0.001769, .5R: 0.947368, .75R: 0.210526, count: 19 -Region 16 Avg IOU: 0.580306, Class: 0.338264, Obj: 0.110177, No Obj: 0.002687, .5R: 0.777778, .75R: 0.055556, count: 18 -Region 23 Avg IOU: 0.579248, Class: 0.293532, Obj: 0.066088, No Obj: 0.003259, .5R: 0.645161, .75R: 0.096774, count: 31 -Region 16 Avg IOU: 0.518449, Class: 0.313504, Obj: 0.025741, No Obj: 0.001316, .5R: 0.625000, .75R: 0.000000, count: 8 -Region 23 Avg IOU: 0.657298, Class: 0.272913, Obj: 0.077836, No Obj: 0.004245, .5R: 0.882353, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.636376, Class: 0.305861, Obj: 0.088161, No Obj: 0.002533, .5R: 0.818182, .75R: 0.181818, count: 11 -Region 23 Avg IOU: 0.579919, Class: 0.315597, Obj: 0.159912, No Obj: 0.003746, .5R: 0.823529, .75R: 0.058824, count: 17 -Region 16 Avg IOU: 0.636241, Class: 0.339777, Obj: 0.126244, No Obj: 0.003306, .5R: 0.785714, .75R: 0.214286, count: 14 -Region 23 Avg IOU: 0.598302, Class: 0.287763, Obj: 0.072702, No Obj: 0.004391, .5R: 0.757576, .75R: 0.151515, count: 33 -Region 16 Avg IOU: 0.572563, Class: 0.345272, Obj: 0.170481, No Obj: 0.002834, .5R: 0.714286, .75R: 0.142857, count: 7 -Region 23 Avg IOU: 0.614497, Class: 0.283105, Obj: 0.067314, No Obj: 0.002091, .5R: 0.869565, .75R: 0.173913, count: 23 -Region 16 Avg IOU: 0.529236, Class: 0.297834, Obj: 0.167004, No Obj: 0.002631, .5R: 0.583333, .75R: 0.000000, count: 12 -Region 23 Avg IOU: 0.589905, Class: 0.301540, Obj: 0.143695, No Obj: 0.007694, .5R: 0.807692, .75R: 0.153846, count: 26 - - 34: 4.543954, 29.979235 avg loss, 0.001000 rate, 0.728650 seconds, 4352 images -Loaded: 0.541884 seconds -Region 16 Avg IOU: 0.553452, Class: 0.304245, Obj: 0.027506, No Obj: 0.000922, .5R: 0.666667, .75R: 0.000000, count: 9 -Region 23 Avg IOU: 0.613456, Class: 0.272355, Obj: 0.001768, No Obj: 0.000415, .5R: 0.846154, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.592063, Class: 0.294698, Obj: 0.081223, No Obj: 0.001953, .5R: 0.777778, .75R: 0.111111, count: 9 -Region 23 Avg IOU: 0.559819, Class: 0.288511, Obj: 0.005754, No Obj: 0.000110, .5R: 0.750000, .75R: 0.166667, count: 12 -Region 16 Avg IOU: 0.605031, Class: 0.333778, Obj: 0.085233, No Obj: 0.001201, .5R: 0.714286, .75R: 0.142857, count: 7 -Region 23 Avg IOU: 0.591492, Class: 0.275193, Obj: 0.009352, No Obj: 0.000480, .5R: 0.928571, .75R: 0.071429, count: 14 -Region 16 Avg IOU: 0.629407, Class: 0.312691, Obj: 0.060313, No Obj: 0.001761, .5R: 0.750000, .75R: 0.000000, count: 4 -Region 23 Avg IOU: 0.661165, Class: 0.312159, Obj: 0.005416, No Obj: 0.000368, .5R: 0.842105, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.596041, Class: 0.231877, Obj: 0.090296, No Obj: 0.001581, .5R: 0.818182, .75R: 0.181818, count: 11 -Region 23 Avg IOU: 0.599304, Class: 0.284959, Obj: 0.002751, No Obj: 0.000224, .5R: 0.769231, .75R: 0.230769, count: 13 -Region 16 Avg IOU: 0.574317, Class: 0.336997, Obj: 0.082739, No Obj: 0.002094, .5R: 0.846154, .75R: 0.000000, count: 13 -Region 23 Avg IOU: 0.661581, Class: 0.288910, Obj: 0.005525, No Obj: 0.000155, .5R: 0.850000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.626485, Class: 0.338830, Obj: 0.038254, No Obj: 0.001379, .5R: 0.857143, .75R: 0.142857, count: 14 -Region 23 Avg IOU: 0.623993, Class: 0.298126, Obj: 0.007021, No Obj: 0.000238, .5R: 0.900000, .75R: 0.100000, count: 20 -Region 16 Avg IOU: 0.561797, Class: 0.339396, Obj: 0.032031, No Obj: 0.001377, .5R: 0.600000, .75R: 0.000000, count: 5 -Region 23 Avg IOU: 0.577667, Class: 0.289056, Obj: 0.021167, No Obj: 0.000262, .5R: 0.736842, .75R: 0.052632, count: 19 - - 35: 2.779770, 27.259289 avg loss, 0.001000 rate, 0.732087 seconds, 4480 images -Loaded: 0.960358 seconds -Region 16 Avg IOU: 0.648371, Class: 0.250012, Obj: 0.041866, No Obj: 0.001434, .5R: 0.875000, .75R: 0.250000, count: 8 -Region 23 Avg IOU: 0.628331, Class: 0.270982, Obj: 0.001092, No Obj: 0.000033, .5R: 0.866667, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.555181, Class: 0.355201, Obj: 0.060288, No Obj: 0.000874, .5R: 0.500000, .75R: 0.166667, count: 6 -Region 23 Avg IOU: 0.594788, Class: 0.279956, Obj: 0.000323, No Obj: 0.000008, .5R: 0.709677, .75R: 0.096774, count: 31 -Region 16 Avg IOU: 0.571499, Class: 0.279685, Obj: 0.045331, No Obj: 0.001484, .5R: 0.785714, .75R: 0.142857, count: 14 -Region 23 Avg IOU: 0.540807, Class: 0.303472, Obj: 0.000579, No Obj: 0.000022, .5R: 0.650000, .75R: 0.050000, count: 20 -Region 16 Avg IOU: 0.645439, Class: 0.340887, Obj: 0.055050, No Obj: 0.001116, .5R: 0.916667, .75R: 0.250000, count: 12 -Region 23 Avg IOU: 0.579582, Class: 0.247482, Obj: 0.001308, No Obj: 0.000034, .5R: 0.846154, .75R: 0.000000, count: 13 -Region 16 Avg IOU: 0.681289, Class: 0.272197, Obj: 0.037152, No Obj: 0.001488, .5R: 0.857143, .75R: 0.285714, count: 7 -Region 23 Avg IOU: 0.566265, Class: 0.260859, Obj: 0.000072, No Obj: 0.000018, .5R: 0.636364, .75R: 0.000000, count: 11 -Region 16 Avg IOU: 0.584512, Class: 0.359651, Obj: 0.041955, No Obj: 0.001203, .5R: 0.777778, .75R: 0.111111, count: 9 -Region 23 Avg IOU: 0.657821, Class: 0.283850, Obj: 0.002950, No Obj: 0.000070, .5R: 0.800000, .75R: 0.400000, count: 10 -Region 16 Avg IOU: 0.591967, Class: 0.319502, Obj: 0.045038, No Obj: 0.001048, .5R: 0.642857, .75R: 0.142857, count: 14 -Region 23 Avg IOU: 0.637481, Class: 0.294154, Obj: 0.000757, No Obj: 0.000022, .5R: 0.916667, .75R: 0.250000, count: 12 -Region 16 Avg IOU: 0.587531, Class: 0.314901, Obj: 0.015223, No Obj: 0.000729, .5R: 0.750000, .75R: 0.000000, count: 8 -Region 23 Avg IOU: 0.596936, Class: 0.321312, Obj: 0.000415, No Obj: 0.000018, .5R: 0.777778, .75R: 0.111111, count: 18 - - 36: 2.877368, 24.821096 avg loss, 0.001000 rate, 0.732389 seconds, 4608 images -Loaded: 0.874883 seconds -Region 16 Avg IOU: 0.522564, Class: 0.324840, Obj: 0.027957, No Obj: 0.001135, .5R: 0.555556, .75R: 0.111111, count: 9 -Region 23 Avg IOU: 0.570644, Class: 0.282030, Obj: 0.000272, No Obj: 0.000012, .5R: 0.533333, .75R: 0.133333, count: 30 -Region 16 Avg IOU: 0.599491, Class: 0.336669, Obj: 0.051312, No Obj: 0.001537, .5R: 0.857143, .75R: 0.071429, count: 14 -Region 23 Avg IOU: 0.633279, Class: 0.287192, Obj: 0.000209, No Obj: 0.000003, .5R: 0.750000, .75R: 0.250000, count: 28 -Region 16 Avg IOU: 0.584324, Class: 0.285197, Obj: 0.017347, No Obj: 0.001092, .5R: 0.692308, .75R: 0.076923, count: 13 -Region 23 Avg IOU: 0.616152, Class: 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count: 19 -Region 23 Avg IOU: 0.603541, Class: 0.293560, Obj: 0.000067, No Obj: 0.000005, .5R: 0.720000, .75R: 0.120000, count: 25 -Region 16 Avg IOU: 0.628986, Class: 0.249998, Obj: 0.012083, No Obj: 0.000655, .5R: 0.875000, .75R: 0.187500, count: 16 -Region 23 Avg IOU: 0.615761, Class: 0.233253, Obj: 0.000230, No Obj: 0.000010, .5R: 0.863636, .75R: 0.090909, count: 22 -Region 16 Avg IOU: 0.645432, Class: 0.331808, Obj: 0.025197, No Obj: 0.000675, .5R: 1.000000, .75R: 0.125000, count: 8 -Region 23 Avg IOU: 0.655724, Class: 0.287318, Obj: 0.000165, No Obj: 0.000011, .5R: 0.894737, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.653807, Class: 0.277944, Obj: 0.023712, No Obj: 0.000615, .5R: 0.909091, .75R: 0.181818, count: 11 -Region 23 Avg IOU: 0.643790, Class: 0.254371, Obj: 0.000204, No Obj: 0.000010, .5R: 0.935484, .75R: 0.225806, count: 31 -Region 16 Avg IOU: 0.554344, Class: 0.290967, Obj: 0.006300, No Obj: 0.000547, .5R: 0.600000, .75R: 0.100000, count: 10 -Region 23 Avg IOU: 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0.014640, No Obj: 0.000609, .5R: 1.000000, .75R: 0.200000, count: 5 -Region 23 Avg IOU: 0.606641, Class: 0.267965, Obj: 0.001602, No Obj: 0.000071, .5R: 0.875000, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.596956, Class: 0.332325, Obj: 0.011316, No Obj: 0.000568, .5R: 0.800000, .75R: 0.100000, count: 10 -Region 23 Avg IOU: 0.574258, Class: 0.257182, Obj: 0.000205, No Obj: 0.000023, .5R: 0.700000, .75R: 0.150000, count: 20 - - 40: 2.881937, 17.362822 avg loss, 0.001000 rate, 0.725286 seconds, 5120 images -Resizing -384 x 384 - try to allocate workspace = 216268801 * sizeof(float), CUDA allocate done! -Loaded: 1.264490 seconds -Region 16 Avg IOU: 0.569959, Class: 0.328577, Obj: 0.008931, No Obj: 0.000619, .5R: 0.857143, .75R: 0.000000, count: 7 -Region 23 Avg IOU: 0.645376, Class: 0.268286, Obj: 0.008855, No Obj: 0.000304, .5R: 0.965517, .75R: 0.103448, count: 29 -Region 16 Avg IOU: 0.654860, Class: 0.267440, Obj: 0.033125, No Obj: 0.000693, .5R: 0.875000, .75R: 0.250000, count: 8 -Region 23 Avg IOU: 0.603876, Class: 0.228676, Obj: 0.010100, No Obj: 0.000419, .5R: 0.812500, .75R: 0.062500, count: 16 -Region 16 Avg IOU: 0.591097, Class: 0.306059, Obj: 0.014823, No Obj: 0.000397, .5R: 0.857143, .75R: 0.142857, count: 7 -Region 23 Avg IOU: 0.590044, Class: 0.272788, Obj: 0.019637, No Obj: 0.000599, .5R: 0.666667, .75R: 0.055556, count: 18 -Region 16 Avg IOU: 0.642686, Class: 0.311867, Obj: 0.012646, No Obj: 0.000567, .5R: 0.909091, .75R: 0.090909, count: 11 -Region 23 Avg IOU: 0.540083, Class: 0.288702, Obj: 0.007909, No Obj: 0.000283, .5R: 0.578947, .75R: 0.052632, count: 19 -Region 16 Avg IOU: 0.588413, Class: 0.287161, Obj: 0.015012, No Obj: 0.000624, .5R: 0.800000, .75R: 0.050000, count: 20 -Region 23 Avg IOU: 0.617264, Class: 0.277330, Obj: 0.012161, No Obj: 0.000590, .5R: 0.785714, .75R: 0.178571, count: 28 -Region 16 Avg IOU: 0.613072, Class: 0.331715, Obj: 0.018927, No Obj: 0.000514, .5R: 0.750000, .75R: 0.000000, count: 4 -Region 23 Avg IOU: 0.605904, Class: 0.293865, Obj: 0.003056, No Obj: 0.000141, .5R: 0.722222, .75R: 0.222222, count: 18 -Region 16 Avg IOU: 0.559021, Class: 0.273236, Obj: 0.044085, No Obj: 0.000893, .5R: 0.687500, .75R: 0.062500, count: 16 -Region 23 Avg IOU: 0.641442, Class: 0.281318, Obj: 0.010979, No Obj: 0.000504, .5R: 0.851852, .75R: 0.148148, count: 27 -Region 16 Avg IOU: 0.590421, Class: 0.337774, Obj: 0.037486, No Obj: 0.001108, .5R: 0.833333, .75R: 0.000000, count: 6 -Region 23 Avg IOU: 0.619514, Class: 0.291225, Obj: 0.017897, No Obj: 0.000299, .5R: 0.846154, .75R: 0.153846, count: 13 - - 41: 3.150091, 15.941548 avg loss, 0.001000 rate, 0.637348 seconds, 5248 images -Loaded: 0.696630 seconds -Region 16 Avg IOU: 0.602502, Class: 0.255835, Obj: 0.036746, No Obj: 0.001116, .5R: 0.857143, .75R: 0.071429, count: 14 -Region 23 Avg IOU: 0.569810, Class: 0.290626, Obj: 0.101217, No Obj: 0.004217, .5R: 0.727273, .75R: 0.136364, count: 22 -Region 16 Avg IOU: 0.584608, Class: 0.278191, Obj: 0.016731, No Obj: 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0.300000, count: 10 -Region 23 Avg IOU: 0.618889, Class: 0.277384, Obj: 0.202560, No Obj: 0.007839, .5R: 0.818182, .75R: 0.272727, count: 22 -Region 16 Avg IOU: 0.609782, Class: 0.287615, Obj: 0.035130, No Obj: 0.000868, .5R: 0.916667, .75R: 0.083333, count: 12 -Region 23 Avg IOU: 0.628124, Class: 0.298352, Obj: 0.280310, No Obj: 0.007559, .5R: 0.826087, .75R: 0.130435, count: 23 -Region 16 Avg IOU: 0.662423, Class: 0.342273, Obj: 0.057029, No Obj: 0.001267, .5R: 0.909091, .75R: 0.272727, count: 11 -Region 23 Avg IOU: 0.623796, Class: 0.284480, Obj: 0.184237, No Obj: 0.005041, .5R: 0.909091, .75R: 0.090909, count: 11 - - 42: 3.671581, 14.714552 avg loss, 0.001000 rate, 0.621058 seconds, 5376 images -Loaded: 0.891598 seconds -Region 16 Avg IOU: 0.651619, Class: 0.281767, Obj: 0.162233, No Obj: 0.001601, .5R: 0.857143, .75R: 0.142857, count: 7 -Region 23 Avg IOU: 0.621888, Class: 0.269189, Obj: 0.000514, No Obj: 0.000016, .5R: 0.928571, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.503593, Class: 0.329374, Obj: 0.070443, No Obj: 0.001831, .5R: 0.285714, .75R: 0.000000, count: 7 -Region 23 Avg IOU: 0.646888, Class: 0.283808, Obj: 0.000345, No Obj: 0.000012, .5R: 0.944444, .75R: 0.222222, count: 18 -Region 16 Avg IOU: 0.666283, Class: 0.358026, Obj: 0.077892, No Obj: 0.001370, .5R: 0.750000, .75R: 0.375000, count: 8 -Region 23 Avg IOU: 0.605965, Class: 0.256345, Obj: 0.000432, No Obj: 0.000017, .5R: 0.882353, .75R: 0.235294, count: 17 -Region 16 Avg IOU: 0.580113, Class: 0.304763, Obj: 0.136678, No Obj: 0.002887, .5R: 0.769231, .75R: 0.000000, count: 13 -Region 23 Avg IOU: 0.565987, Class: 0.262599, Obj: 0.001569, No Obj: 0.000041, .5R: 0.684211, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.598820, Class: 0.337816, Obj: 0.019905, No Obj: 0.001583, .5R: 0.888889, .75R: 0.000000, count: 9 -Region 23 Avg IOU: 0.656631, Class: 0.263674, Obj: 0.001372, No Obj: 0.000025, .5R: 0.944444, .75R: 0.222222, count: 18 -Region 16 Avg IOU: 0.561639, Class: 0.269220, Obj: 0.078282, No Obj: 0.001885, .5R: 0.615385, .75R: 0.076923, count: 13 -Region 23 Avg IOU: 0.588502, Class: 0.273004, Obj: 0.001886, No Obj: 0.000053, .5R: 0.777778, .75R: 0.000000, count: 18 -Region 16 Avg IOU: 0.577247, Class: 0.224083, Obj: 0.169368, No Obj: 0.002748, .5R: 0.800000, .75R: 0.100000, count: 10 -Region 23 Avg IOU: 0.599137, Class: 0.253087, Obj: 0.002950, No Obj: 0.000088, .5R: 0.863636, .75R: 0.045455, count: 22 -Region 16 Avg IOU: 0.555821, Class: 0.324942, Obj: 0.174856, No Obj: 0.003105, .5R: 0.636364, .75R: 0.000000, count: 11 -Region 23 Avg IOU: 0.563281, Class: 0.242068, Obj: 0.001263, No Obj: 0.000071, .5R: 0.750000, .75R: 0.000000, count: 24 - - 43: 3.020073, 13.545104 avg loss, 0.001000 rate, 0.621217 seconds, 5504 images -Loaded: 0.719356 seconds -Region 16 Avg IOU: 0.610877, Class: 0.341717, Obj: 0.212489, No Obj: 0.002211, .5R: 0.888889, .75R: 0.000000, count: 9 -Region 23 Avg IOU: 0.567506, Class: 0.236570, Obj: 0.001485, No Obj: 0.000004, .5R: 0.600000, 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Avg IOU: 0.518855, Class: 0.271805, Obj: 0.021938, No Obj: 0.001054, .5R: 0.750000, .75R: 0.000000, count: 4 -Region 23 Avg IOU: 0.579619, Class: 0.268238, Obj: 0.000020, No Obj: 0.000001, .5R: 0.692308, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.614367, Class: 0.317448, Obj: 0.068246, No Obj: 0.001733, .5R: 0.800000, .75R: 0.133333, count: 15 -Region 23 Avg IOU: 0.569111, Class: 0.277403, Obj: 0.000008, No Obj: 0.000001, .5R: 0.833333, .75R: 0.000000, count: 18 -Region 16 Avg IOU: 0.640568, Class: 0.272475, Obj: 0.059223, No Obj: 0.001328, .5R: 1.000000, .75R: 0.000000, count: 7 -Region 23 Avg IOU: 0.614342, Class: 0.220296, Obj: 0.000005, No Obj: 0.000001, .5R: 0.800000, .75R: 0.266667, count: 15 - - 44: 2.346512, 12.425244 avg loss, 0.001000 rate, 0.635026 seconds, 5632 images -Loaded: 1.022368 seconds -Region 16 Avg IOU: 0.632789, Class: 0.296064, Obj: 0.038290, No Obj: 0.001603, .5R: 0.842105, .75R: 0.210526, count: 19 -Region 23 Avg IOU: 0.680455, Class: 0.254729, Obj: 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16 Avg IOU: 0.607602, Class: 0.276347, Obj: 0.035070, No Obj: 0.000607, .5R: 0.888889, .75R: 0.000000, count: 9 -Region 23 Avg IOU: 0.628457, Class: 0.246932, Obj: 0.000013, No Obj: 0.000000, .5R: 0.833333, .75R: 0.200000, count: 30 -Region 16 Avg IOU: 0.685331, Class: 0.292115, Obj: 0.054226, No Obj: 0.000876, .5R: 1.000000, .75R: 0.200000, count: 10 -Region 23 Avg IOU: 0.669193, Class: 0.229484, Obj: 0.000021, No Obj: 0.000000, .5R: 0.875000, .75R: 0.375000, count: 8 -Region 16 Avg IOU: 0.624182, Class: 0.303426, Obj: 0.021074, No Obj: 0.000687, .5R: 0.750000, .75R: 0.125000, count: 8 -Region 23 Avg IOU: 0.661066, Class: 0.252539, Obj: 0.000003, No Obj: 0.000000, .5R: 0.833333, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.528497, Class: 0.273858, Obj: 0.026082, No Obj: 0.000882, .5R: 0.800000, .75R: 0.000000, count: 10 -Region 23 Avg IOU: 0.542077, Class: 0.255207, Obj: 0.000007, No Obj: 0.000000, .5R: 0.619048, .75R: 0.000000, count: 21 -Region 16 Avg IOU: 0.528186, Class: 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6272 images -Loaded: 0.804663 seconds -Region 16 Avg IOU: 0.591859, Class: 0.295083, Obj: 0.075184, No Obj: 0.001329, .5R: 0.666667, .75R: 0.111111, count: 9 -Region 23 Avg IOU: 0.591306, Class: 0.232515, Obj: 0.000068, No Obj: 0.000001, .5R: 0.764706, .75R: 0.176471, count: 17 -Region 16 Avg IOU: 0.590507, Class: 0.284146, Obj: 0.059653, No Obj: 0.000968, .5R: 0.700000, .75R: 0.100000, count: 10 -Region 23 Avg IOU: 0.653474, Class: 0.252253, Obj: 0.000067, No Obj: 0.000003, .5R: 0.812500, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.530086, Class: 0.371491, Obj: 0.099648, No Obj: 0.000688, .5R: 0.500000, .75R: 0.000000, count: 2 -Region 23 Avg IOU: 0.576996, Class: 0.247617, Obj: 0.000217, No Obj: 0.000002, .5R: 0.642857, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.606286, Class: 0.264227, Obj: 0.036852, No Obj: 0.001155, .5R: 0.750000, .75R: 0.250000, count: 8 -Region 23 Avg IOU: 0.573826, Class: 0.189720, Obj: 0.000082, No Obj: 0.000002, .5R: 0.588235, .75R: 0.000000, count: 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23 Avg IOU: 0.635265, Class: 0.250515, Obj: 0.000285, No Obj: 0.000009, .5R: 0.750000, .75R: 0.250000, count: 20 - - 51: 3.945520, 7.485729 avg loss, 0.001000 rate, 1.303082 seconds, 6528 images -Loaded: 0.666093 seconds -Region 16 Avg IOU: 0.628244, Class: 0.271372, Obj: 0.027176, No Obj: 0.001071, .5R: 0.894737, .75R: 0.052632, count: 19 -Region 23 Avg IOU: 0.657192, Class: 0.315013, Obj: 0.010921, No Obj: 0.000169, .5R: 0.875000, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.546379, Class: 0.288778, Obj: 0.008879, No Obj: 0.001024, .5R: 0.611111, .75R: 0.111111, count: 18 -Region 23 Avg IOU: 0.579705, Class: 0.257453, Obj: 0.004009, No Obj: 0.000138, .5R: 0.538462, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.542472, Class: 0.283306, Obj: 0.048406, No Obj: 0.001165, .5R: 0.727273, .75R: 0.090909, count: 11 -Region 23 Avg IOU: 0.619067, Class: 0.265572, Obj: 0.014176, No Obj: 0.000165, .5R: 0.916667, .75R: 0.000000, count: 12 -Region 16 Avg IOU: 0.572494, Class: 0.269911, Obj: 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0.818182, .75R: 0.090909, count: 22 -Region 23 Avg IOU: 0.664467, Class: 0.255027, Obj: 0.027281, No Obj: 0.000803, .5R: 0.958333, .75R: 0.250000, count: 24 - - 52: 3.611964, 7.098352 avg loss, 0.001000 rate, 1.287270 seconds, 6656 images -Loaded: 0.277159 seconds -Region 16 Avg IOU: 0.514608, Class: 0.295147, Obj: 0.027324, No Obj: 0.001452, .5R: 0.615385, .75R: 0.000000, count: 13 -Region 23 Avg IOU: 0.637244, Class: 0.291370, Obj: 0.028143, No Obj: 0.002784, .5R: 0.916667, .75R: 0.166667, count: 12 -Region 16 Avg IOU: 0.551884, Class: 0.309322, Obj: 0.029253, No Obj: 0.001092, .5R: 0.625000, .75R: 0.062500, count: 16 -Region 23 Avg IOU: 0.623488, Class: 0.274822, Obj: 0.080071, No Obj: 0.003778, .5R: 0.785714, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.594034, Class: 0.262690, Obj: 0.029449, No Obj: 0.001290, .5R: 0.785714, .75R: 0.071429, count: 14 -Region 23 Avg IOU: 0.641024, Class: 0.255626, Obj: 0.068339, No Obj: 0.001949, .5R: 0.851852, .75R: 0.111111, count: 27 -Region 16 Avg IOU: 0.578443, Class: 0.291014, Obj: 0.066889, No Obj: 0.001250, .5R: 0.625000, .75R: 0.187500, count: 16 -Region 23 Avg IOU: 0.616787, Class: 0.248871, Obj: 0.229869, No Obj: 0.004772, .5R: 0.842105, .75R: 0.210526, count: 19 -Region 16 Avg IOU: 0.603836, Class: 0.287991, Obj: 0.047893, No Obj: 0.001370, .5R: 0.791667, .75R: 0.166667, count: 24 -Region 23 Avg IOU: 0.602176, Class: 0.251123, Obj: 0.153194, No Obj: 0.006404, .5R: 0.812500, .75R: 0.062500, count: 32 -Region 16 Avg IOU: 0.589536, Class: 0.266662, Obj: 0.043277, No Obj: 0.001890, .5R: 0.846154, .75R: 0.076923, count: 13 -Region 23 Avg IOU: 0.586276, Class: 0.256950, Obj: 0.172563, No Obj: 0.005249, .5R: 0.846154, .75R: 0.076923, count: 26 -Region 16 Avg IOU: 0.671052, Class: 0.235293, Obj: 0.035757, No Obj: 0.000681, .5R: 0.888889, .75R: 0.111111, count: 9 -Region 23 Avg IOU: 0.666806, Class: 0.248209, Obj: 0.248805, No Obj: 0.002894, .5R: 0.833333, .75R: 0.333333, count: 6 -Region 16 Avg IOU: 0.601865, Class: 0.281018, Obj: 0.074811, No Obj: 0.001239, .5R: 0.846154, .75R: 0.076923, count: 13 -Region 23 Avg IOU: 0.595359, Class: 0.256007, Obj: 0.121254, No Obj: 0.003235, .5R: 0.760000, .75R: 0.120000, count: 25 - - 53: 4.456267, 6.834144 avg loss, 0.001000 rate, 1.293349 seconds, 6784 images -Loaded: 0.751301 seconds -Region 16 Avg IOU: 0.634649, Class: 0.230649, Obj: 0.018901, No Obj: 0.000969, .5R: 0.933333, .75R: 0.200000, count: 15 -Region 23 Avg IOU: 0.646109, Class: 0.238879, Obj: 0.000737, No Obj: 0.000013, .5R: 0.840000, .75R: 0.200000, count: 25 -Region 16 Avg IOU: 0.593964, Class: 0.234797, Obj: 0.024567, No Obj: 0.000613, .5R: 0.750000, .75R: 0.250000, count: 8 -Region 23 Avg IOU: 0.618599, Class: 0.200970, Obj: 0.001482, No Obj: 0.000029, .5R: 0.888889, .75R: 0.111111, count: 9 -Region 16 Avg IOU: 0.612007, Class: 0.242271, Obj: 0.090460, No Obj: 0.001233, .5R: 0.812500, .75R: 0.062500, count: 16 -Region 23 Avg IOU: 0.630475, Class: 0.257849, Obj: 0.000141, No Obj: 0.000008, .5R: 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25 -Region 16 Avg IOU: 0.600709, Class: 0.264250, Obj: 0.042725, No Obj: 0.001515, .5R: 0.857143, .75R: 0.142857, count: 14 -Region 23 Avg IOU: 0.626610, Class: 0.238189, Obj: 0.001036, No Obj: 0.000035, .5R: 0.850000, .75R: 0.150000, count: 20 - - 54: 3.388859, 6.489615 avg loss, 0.001000 rate, 1.319123 seconds, 6912 images -Loaded: 0.891078 seconds -Region 16 Avg IOU: 0.611547, Class: 0.276501, Obj: 0.024408, No Obj: 0.001486, .5R: 0.846154, .75R: 0.115385, count: 26 -Region 23 Avg IOU: 0.572466, Class: 0.208260, Obj: 0.000002, No Obj: 0.000000, .5R: 0.636364, .75R: 0.000000, count: 22 -Region 16 Avg IOU: 0.660407, Class: 0.216896, Obj: 0.069069, No Obj: 0.001185, .5R: 0.714286, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.702114, Class: 0.239892, Obj: 0.000004, No Obj: 0.000000, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 16 Avg IOU: 0.631680, Class: 0.289530, Obj: 0.103429, No Obj: 0.001644, .5R: 0.833333, .75R: 0.083333, count: 12 -Region 23 Avg IOU: 0.616711, Class: 0.220352, Obj: 0.000018, No Obj: 0.000001, .5R: 0.826087, .75R: 0.130435, count: 23 -Region 16 Avg IOU: 0.573745, Class: 0.248905, Obj: 0.017658, No Obj: 0.001248, .5R: 0.695652, .75R: 0.086957, count: 23 -Region 23 Avg IOU: 0.561863, Class: 0.226719, Obj: 0.000001, No Obj: 0.000000, .5R: 0.666667, .75R: 0.033333, count: 30 -Region 16 Avg IOU: 0.560730, Class: 0.214258, Obj: 0.037725, No Obj: 0.001407, .5R: 0.714286, .75R: 0.142857, count: 14 -Region 23 Avg IOU: 0.567894, Class: 0.219934, Obj: 0.000005, No Obj: 0.000000, .5R: 0.727273, .75R: 0.181818, count: 22 -Region 16 Avg IOU: 0.642626, Class: 0.252967, Obj: 0.093674, No Obj: 0.001705, .5R: 0.882353, .75R: 0.294118, count: 17 -Region 23 Avg IOU: 0.616520, Class: 0.266694, Obj: 0.000020, No Obj: 0.000001, .5R: 0.937500, .75R: 0.000000, count: 16 -Region 16 Avg IOU: 0.496553, Class: 0.290559, Obj: 0.032187, No Obj: 0.000944, .5R: 0.700000, .75R: 0.000000, count: 10 -Region 23 Avg IOU: 0.629798, Class: 0.187551, Obj: 0.000001, No Obj: 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count: 21 -Region 23 Avg IOU: 0.594087, Class: 0.234832, Obj: 0.000000, No Obj: 0.000000, .5R: 0.846154, .75R: 0.076923, count: 13 -Region 16 Avg IOU: 0.579803, Class: 0.232937, Obj: 0.027406, No Obj: 0.002148, .5R: 0.571429, .75R: 0.214286, count: 14 -Region 23 Avg IOU: 0.659219, Class: 0.232754, Obj: 0.000000, No Obj: 0.000000, .5R: 0.947368, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.622651, Class: 0.319341, Obj: 0.002571, No Obj: 0.000663, .5R: 1.000000, .75R: 0.166667, count: 6 -Region 23 Avg IOU: 0.626648, Class: 0.125621, Obj: 0.000000, No Obj: 0.000000, .5R: 0.846154, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.570584, Class: 0.290958, Obj: 0.048076, No Obj: 0.000934, .5R: 0.500000, .75R: 0.100000, count: 10 -Region 23 Avg IOU: 0.599146, Class: 0.273974, Obj: 0.000000, No Obj: 0.000000, .5R: 0.857143, .75R: 0.071429, count: 14 -Region 16 Avg IOU: 0.565703, Class: 0.273980, Obj: 0.019420, No Obj: 0.001038, .5R: 0.705882, .75R: 0.058824, count: 17 -Region 23 Avg IOU: 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count: 6 -Region 23 Avg IOU: 0.633093, Class: 0.224687, Obj: 0.000285, No Obj: 0.000011, .5R: 0.769231, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.661466, Class: 0.227395, Obj: 0.034073, No Obj: 0.000611, .5R: 0.857143, .75R: 0.428571, count: 7 -Region 23 Avg IOU: 0.568703, Class: 0.256399, Obj: 0.000472, No Obj: 0.000022, .5R: 0.800000, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.536266, Class: 0.192969, Obj: 0.028738, No Obj: 0.000642, .5R: 0.600000, .75R: 0.000000, count: 5 -Region 23 Avg IOU: 0.606497, Class: 0.173115, Obj: 0.000832, No Obj: 0.000011, .5R: 0.800000, .75R: 0.166667, count: 30 -Region 16 Avg IOU: 0.582848, Class: 0.288483, Obj: 0.043790, No Obj: 0.001061, .5R: 0.700000, .75R: 0.000000, count: 10 -Region 23 Avg IOU: 0.600564, Class: 0.239831, Obj: 0.000142, No Obj: 0.000006, .5R: 0.818182, .75R: 0.136364, count: 22 - - 65: 2.231507, 4.186364 avg loss, 0.001000 rate, 0.730480 seconds, 8320 images -Loaded: 1.205112 seconds -Region 16 Avg IOU: 0.584286, Class: 0.300853, Obj: 0.045961, No Obj: 0.001259, .5R: 0.733333, .75R: 0.266667, count: 15 -Region 23 Avg IOU: 0.618319, Class: 0.235325, Obj: 0.001017, No Obj: 0.000023, .5R: 0.851852, .75R: 0.222222, count: 27 -Region 16 Avg IOU: 0.577050, Class: 0.292746, Obj: 0.056748, No Obj: 0.001132, .5R: 0.909091, .75R: 0.000000, count: 11 -Region 23 Avg IOU: 0.585873, Class: 0.231928, Obj: 0.003090, No Obj: 0.000036, .5R: 0.600000, .75R: 0.200000, count: 10 -Region 16 Avg IOU: 0.590968, Class: 0.122220, Obj: 0.034874, No Obj: 0.000788, .5R: 0.800000, .75R: 0.000000, count: 10 -Region 23 Avg IOU: 0.559871, Class: 0.217390, Obj: 0.000768, No Obj: 0.000106, .5R: 0.750000, .75R: 0.000000, count: 8 -Region 16 Avg IOU: 0.545844, Class: 0.204586, Obj: 0.015035, No Obj: 0.000803, .5R: 0.666667, .75R: 0.066667, count: 15 -Region 23 Avg IOU: 0.628974, Class: 0.233848, Obj: 0.001145, No Obj: 0.000035, .5R: 0.814815, .75R: 0.111111, count: 27 -Region 16 Avg IOU: 0.555235, Class: 0.246584, Obj: 0.042829, No Obj: 0.001104, .5R: 0.666667, .75R: 0.000000, count: 9 -Region 23 Avg IOU: 0.629567, Class: 0.205561, Obj: 0.022729, No Obj: 0.000247, .5R: 0.833333, .75R: 0.125000, count: 24 -Region 16 Avg IOU: 0.685720, Class: 0.175713, Obj: 0.059827, No Obj: 0.000633, .5R: 1.000000, .75R: 0.250000, count: 8 -Region 23 Avg IOU: 0.604193, Class: 0.175280, Obj: 0.006257, No Obj: 0.000093, .5R: 0.750000, .75R: 0.100000, count: 20 -Region 16 Avg IOU: 0.622253, Class: 0.257357, Obj: 0.031499, No Obj: 0.000771, .5R: 0.857143, .75R: 0.142857, count: 7 -Region 23 Avg IOU: 0.640528, Class: 0.213932, Obj: 0.002087, No Obj: 0.000055, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.581659, Class: 0.187665, Obj: 0.035522, No Obj: 0.000592, .5R: 0.625000, .75R: 0.125000, count: 8 -Region 23 Avg IOU: 0.555782, Class: 0.238364, Obj: 0.010048, No Obj: 0.000253, .5R: 0.600000, .75R: 0.100000, count: 20 - - 66: 2.968120, 4.064539 avg loss, 0.001000 rate, 0.736180 seconds, 8448 images -Loaded: 1.086368 seconds -Region 16 Avg IOU: 0.542271, Class: 0.248258, Obj: 0.059489, No Obj: 0.000898, .5R: 0.444444, .75R: 0.000000, count: 9 -Region 23 Avg IOU: 0.596394, Class: 0.258448, Obj: 0.293362, No Obj: 0.005427, .5R: 0.777778, .75R: 0.000000, count: 18 -Region 16 Avg IOU: 0.567441, Class: 0.167514, Obj: 0.023173, No Obj: 0.000766, .5R: 0.625000, .75R: 0.000000, count: 8 -Region 23 Avg IOU: 0.612842, Class: 0.243390, Obj: 0.070447, No Obj: 0.001607, .5R: 0.806452, .75R: 0.064516, count: 31 -Region 16 Avg IOU: 0.686112, Class: 0.284002, Obj: 0.041326, No Obj: 0.001002, .5R: 1.000000, .75R: 0.250000, count: 4 -Region 23 Avg IOU: 0.578383, Class: 0.230322, Obj: 0.291815, No Obj: 0.009364, .5R: 0.722222, .75R: 0.111111, count: 18 -Region 16 Avg IOU: 0.505876, Class: 0.278567, Obj: 0.081103, No Obj: 0.000688, .5R: 0.500000, .75R: 0.000000, count: 4 -Region 23 Avg IOU: 0.592258, Class: 0.216345, Obj: 0.187267, No Obj: 0.004322, .5R: 0.800000, .75R: 0.000000, count: 20 -Region 16 Avg IOU: 0.653265, Class: 0.189756, Obj: 0.036902, No Obj: 0.001408, .5R: 0.900000, .75R: 0.200000, count: 20 -Region 23 Avg IOU: 0.660031, Class: 0.244660, Obj: 0.095707, No Obj: 0.004169, .5R: 0.960000, .75R: 0.200000, count: 25 -Region 16 Avg IOU: 0.616052, Class: 0.215765, Obj: 0.043558, No Obj: 0.001459, .5R: 0.750000, .75R: 0.083333, count: 12 -Region 23 Avg IOU: 0.649917, Class: 0.278375, Obj: 0.266065, No Obj: 0.005265, .5R: 0.833333, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.619141, Class: 0.182171, Obj: 0.031249, No Obj: 0.000814, .5R: 0.833333, .75R: 0.166667, count: 12 -Region 23 Avg IOU: 0.584128, Class: 0.204860, Obj: 0.164869, No Obj: 0.005909, .5R: 0.722222, .75R: 0.055556, count: 18 -Region 16 Avg IOU: 0.628974, Class: 0.238003, Obj: 0.057503, No Obj: 0.000894, .5R: 0.923077, .75R: 0.000000, count: 13 -Region 23 Avg IOU: 0.615483, Class: 0.261400, Obj: 0.152833, No Obj: 0.006165, .5R: 0.866667, .75R: 0.000000, count: 15 - - 67: 3.641517, 4.022237 avg loss, 0.001000 rate, 0.744314 seconds, 8576 images -Loaded: 0.693147 seconds -Region 16 Avg IOU: 0.492415, Class: 0.265244, Obj: 0.029248, No Obj: 0.000842, .5R: 0.500000, .75R: 0.000000, count: 4 -Region 23 Avg IOU: 0.600772, Class: 0.231354, Obj: 0.025520, No Obj: 0.000687, .5R: 0.900000, .75R: 0.200000, count: 20 -Region 16 Avg IOU: 0.707595, Class: 0.184826, Obj: 0.082488, No Obj: 0.000706, .5R: 1.000000, .75R: 0.200000, count: 5 -Region 23 Avg IOU: 0.608961, Class: 0.226001, Obj: 0.009193, No Obj: 0.000400, .5R: 0.800000, .75R: 0.133333, count: 15 -Region 16 Avg IOU: 0.587942, Class: 0.187987, Obj: 0.067123, No Obj: 0.000851, .5R: 0.666667, .75R: 0.083333, count: 12 -Region 23 Avg IOU: 0.599366, Class: 0.235363, Obj: 0.048602, No Obj: 0.000607, .5R: 0.857143, .75R: 0.000000, count: 14 -Region 16 Avg IOU: 0.571025, Class: 0.260402, Obj: 0.121294, No Obj: 0.001090, .5R: 0.714286, .75R: 0.000000, count: 7 -Region 23 Avg IOU: 0.602227, Class: 0.216914, Obj: 0.034423, No Obj: 0.000252, .5R: 0.850000, .75R: 0.100000, count: 20 -Region 16 Avg IOU: 0.578031, Class: 0.265851, Obj: 0.078071, No Obj: 0.001185, .5R: 0.714286, .75R: 0.071429, count: 14 -Region 23 Avg IOU: 0.593442, Class: 0.253825, Obj: 0.005862, No Obj: 0.000171, .5R: 0.800000, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.605258, Class: 0.264169, Obj: 0.022824, No Obj: 0.000670, .5R: 0.800000, .75R: 0.000000, count: 10 -Region 23 Avg IOU: 0.664960, Class: 0.234859, Obj: 0.025885, No Obj: 0.000352, .5R: 0.800000, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.600090, Class: 0.260332, Obj: 0.067002, No Obj: 0.000916, .5R: 0.692308, .75R: 0.153846, count: 13 -Region 23 Avg IOU: 0.597442, Class: 0.247020, Obj: 0.001584, No Obj: 0.000133, .5R: 0.833333, .75R: 0.125000, count: 24 -Region 16 Avg IOU: 0.615104, Class: 0.129956, Obj: 0.112993, No Obj: 0.000812, .5R: 1.000000, .75R: 0.000000, count: 5 -Region 23 Avg IOU: 0.621914, Class: 0.196081, Obj: 0.036895, No Obj: 0.000584, .5R: 0.857143, .75R: 0.095238, count: 21 - - 68: 2.536141, 3.873628 avg loss, 0.001000 rate, 0.730675 seconds, 8704 images -Loaded: 0.512039 seconds -Region 16 Avg IOU: 0.559743, Class: 0.175782, Obj: 0.052303, No Obj: 0.001131, .5R: 0.800000, .75R: 0.000000, count: 15 -Region 23 Avg IOU: 0.621618, Class: 0.236258, Obj: 0.000380, No Obj: 0.000025, .5R: 0.894737, .75R: 0.105263, count: 19 -Region 16 Avg IOU: 0.630536, Class: 0.208001, Obj: 0.067851, No Obj: 0.001200, .5R: 1.000000, .75R: 0.090909, count: 11 -Region 23 Avg IOU: 0.608758, Class: 0.234100, Obj: 0.002102, No Obj: 0.000049, .5R: 0.809524, .75R: 0.142857, count: 21 -Region 16 Avg IOU: 0.536456, Class: 0.230359, Obj: 0.192353, No Obj: 0.001002, .5R: 0.600000, .75R: 0.000000, count: 5 -Region 23 Avg IOU: 0.627564, Class: 0.270872, Obj: 0.002941, No Obj: 0.000062, .5R: 0.800000, .75R: 0.080000, count: 25 -Region 16 Avg IOU: 0.603930, Class: 0.264124, Obj: 0.046888, No Obj: 0.001048, .5R: 0.888889, .75R: 0.111111, count: 9 -Region 23 Avg IOU: 0.634877, Class: 0.234044, Obj: 0.005414, No Obj: 0.000149, .5R: 0.781250, .75R: 0.218750, count: 32 -Region 16 Avg IOU: 0.580491, Class: 0.267744, Obj: 0.074927, No Obj: 0.001500, .5R: 0.727273, .75R: 0.090909, count: 11 -Region 23 Avg IOU: 0.612759, Class: 0.201812, Obj: 0.005902, No Obj: 0.000103, .5R: 0.818182, .75R: 0.090909, count: 11 -Region 16 Avg IOU: 0.635900, Class: 0.247254, Obj: 0.019621, No Obj: 0.000861, .5R: 0.916667, .75R: 0.083333, count: 12 -Region 23 Avg IOU: 0.603487, Class: 0.239375, Obj: 0.002017, No Obj: 0.000093, .5R: 0.733333, .75R: 0.133333, count: 15 -Region 16 Avg IOU: 0.584246, Class: 0.248333, Obj: 0.104319, No Obj: 0.001196, .5R: 0.800000, .75R: 0.100000, count: 10 -Region 23 Avg IOU: 0.610731, Class: 0.234954, Obj: 0.001131, No Obj: 0.000026, .5R: 0.764706, .75R: 0.176471, count: 17 -Region 16 Avg IOU: 0.617732, Class: 0.257186, Obj: 0.035079, No Obj: 0.001008, .5R: 1.000000, .75R: 0.000000, count: 8 -Region 23 Avg IOU: 0.604851, Class: 0.206638, Obj: 0.001707, No Obj: 0.000062, .5R: 0.833333, .75R: 0.125000, count: 24 - - 69: 2.959005, 3.782166 avg loss, 0.001000 rate, 0.732470 seconds, 8832 images -Loaded: 0.589453 seconds -Region 16 Avg IOU: 0.632596, Class: 0.235058, Obj: 0.133658, No Obj: 0.002670, .5R: 0.842105, .75R: 0.157895, count: 19 -Region 23 Avg IOU: 0.624740, Class: 0.229842, Obj: 0.003453, No Obj: 0.000032, .5R: 0.900000, .75R: 0.150000, count: 20 -Region 16 Avg IOU: 0.706038, Class: 0.202478, Obj: 0.069429, No Obj: 0.000927, .5R: 0.875000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.622435, Class: 0.210694, Obj: 0.000904, No Obj: 0.000031, .5R: 0.846154, .75R: 0.115385, count: 26 -Region 16 Avg IOU: 0.589440, Class: 0.149917, Obj: 0.050932, No Obj: 0.001236, .5R: 0.714286, .75R: 0.000000, count: 7 -Region 23 Avg IOU: 0.637328, Class: 0.208997, Obj: 0.003047, No Obj: 0.000024, .5R: 0.750000, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.649668, Class: 0.226092, Obj: 0.047361, No Obj: 0.001500, .5R: 0.833333, .75R: 0.083333, count: 12 -Region 23 Avg IOU: 0.577371, Class: 0.194002, Obj: 0.000599, No Obj: 0.000028, .5R: 0.588235, .75R: 0.117647, count: 17 -Region 16 Avg IOU: 0.548471, Class: 0.342285, Obj: 0.033260, No Obj: 0.001091, .5R: 0.583333, .75R: 0.083333, count: 12 -Region 23 Avg IOU: 0.574441, Class: 0.207750, Obj: 0.000118, No Obj: 0.000006, .5R: 0.631579, .75R: 0.105263, count: 19 -Region 16 Avg IOU: 0.600846, Class: 0.257023, Obj: 0.080198, No Obj: 0.001136, .5R: 1.000000, .75R: 0.000000, count: 8 -Region 23 Avg IOU: 0.615390, Class: 0.190857, Obj: 0.000956, No Obj: 0.000021, .5R: 0.833333, .75R: 0.111111, count: 18 -Region 16 Avg IOU: 0.587620, Class: 0.254065, Obj: 0.066386, No Obj: 0.001485, .5R: 0.750000, .75R: 0.083333, count: 12 -Region 23 Avg IOU: 0.605949, Class: 0.260873, Obj: 0.000576, No Obj: 0.000019, .5R: 0.769231, .75R: 0.076923, count: 13 -Region 16 Avg IOU: 0.622959, Class: 0.276799, Obj: 0.086455, No Obj: 0.001577, .5R: 0.900000, .75R: 0.000000, count: 10 -Region 23 Avg IOU: 0.600617, Class: 0.223778, Obj: 0.001225, No Obj: 0.000025, .5R: 0.739130, .75R: 0.173913, count: 23 - - 70: 2.906331, 3.694582 avg loss, 0.001000 rate, 0.734314 seconds, 8960 images -Resizing -544 x 544 - try to allocate workspace = 1145585665 * sizeof(float), CUDA allocate done! -Loaded: 1.517788 seconds -Region 16 Avg IOU: 0.558975, Class: 0.251621, Obj: 0.065843, No Obj: 0.001110, .5R: 0.857143, .75R: 0.000000, count: 7 -Region 23 Avg IOU: 0.585649, Class: 0.240255, Obj: 0.000583, No Obj: 0.000012, .5R: 0.666667, .75R: 0.000000, count: 9 -Region 16 Avg IOU: 0.623153, Class: 0.232858, Obj: 0.059107, No Obj: 0.000947, .5R: 0.842105, .75R: 0.157895, count: 19 -Region 23 Avg IOU: 0.620129, Class: 0.263711, Obj: 0.000424, No Obj: 0.000010, .5R: 0.750000, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.598026, Class: 0.248479, Obj: 0.038956, No Obj: 0.001126, .5R: 0.750000, .75R: 0.187500, count: 16 -Region 23 Avg IOU: 0.640906, Class: 0.246088, Obj: 0.000299, No Obj: 0.000009, .5R: 0.923077, .75R: 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seconds -Region 16 Avg IOU: 0.596614, Class: 0.224766, Obj: 0.030061, No Obj: 0.001356, .5R: 0.950000, .75R: 0.150000, count: 20 -Region 23 Avg IOU: 0.645576, Class: 0.221797, Obj: 0.084277, No Obj: 0.001525, .5R: 1.000000, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.588618, Class: 0.238299, Obj: 0.086437, No Obj: 0.000789, .5R: 0.818182, .75R: 0.000000, count: 11 -Region 23 Avg IOU: 0.649573, Class: 0.214221, Obj: 0.040857, No Obj: 0.000523, .5R: 1.000000, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.562243, Class: 0.228197, Obj: 0.009334, No Obj: 0.000355, .5R: 0.818182, .75R: 0.000000, count: 11 -Region 23 Avg IOU: 0.557757, Class: 0.255844, Obj: 0.090004, No Obj: 0.001550, .5R: 0.625000, .75R: 0.125000, count: 24 -Region 16 Avg IOU: 0.623061, Class: 0.183129, Obj: 0.043051, No Obj: 0.000560, .5R: 0.818182, .75R: 0.090909, count: 11 -Region 23 Avg IOU: 0.623575, Class: 0.261306, Obj: 0.141713, No Obj: 0.000973, .5R: 0.923077, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 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1.315564 seconds, 10752 images -Loaded: 0.471509 seconds -Region 16 Avg IOU: 0.615271, Class: 0.155694, Obj: 0.044694, No Obj: 0.000753, .5R: 0.666667, .75R: 0.200000, count: 15 -Region 23 Avg IOU: 0.638788, Class: 0.205258, Obj: 0.153660, No Obj: 0.000998, .5R: 1.000000, .75R: 0.105263, count: 19 -Region 16 Avg IOU: 0.619178, Class: 0.197146, Obj: 0.032206, No Obj: 0.000877, .5R: 0.870968, .75R: 0.096774, count: 31 -Region 23 Avg IOU: 0.670766, Class: 0.233316, Obj: 0.026729, No Obj: 0.000499, .5R: 0.823529, .75R: 0.235294, count: 17 -Region 16 Avg IOU: 0.648838, Class: 0.247722, Obj: 0.071815, No Obj: 0.000992, .5R: 0.944444, .75R: 0.277778, count: 18 -Region 23 Avg IOU: 0.669298, Class: 0.264127, Obj: 0.071836, No Obj: 0.000802, .5R: 0.894737, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.634670, Class: 0.194389, Obj: 0.058374, No Obj: 0.001112, .5R: 0.916667, .75R: 0.166667, count: 12 -Region 23 Avg IOU: 0.614928, Class: 0.182596, Obj: 0.169771, No Obj: 0.001114, .5R: 0.833333, 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18 -Region 16 Avg IOU: 0.538997, Class: 0.139971, Obj: 0.023328, No Obj: 0.000671, .5R: 0.571429, .75R: 0.000000, count: 14 -Region 23 Avg IOU: 0.677659, Class: 0.191318, Obj: 0.027375, No Obj: 0.000262, .5R: 0.961538, .75R: 0.307692, count: 26 -Region 16 Avg IOU: 0.659540, Class: 0.209529, Obj: 0.082324, No Obj: 0.000773, .5R: 0.923077, .75R: 0.230769, count: 13 -Region 23 Avg IOU: 0.679491, Class: 0.233370, Obj: 0.014876, No Obj: 0.000132, .5R: 1.000000, .75R: 0.300000, count: 20 -Region 16 Avg IOU: 0.604557, Class: 0.231141, Obj: 0.093536, No Obj: 0.001440, .5R: 0.928571, .75R: 0.142857, count: 14 -Region 23 Avg IOU: 0.653216, Class: 0.255587, Obj: 0.012227, No Obj: 0.000158, .5R: 1.000000, .75R: 0.100000, count: 10 - - 98: 3.037107, 3.306323 avg loss, 0.001000 rate, 1.086791 seconds, 12544 images -Loaded: 0.347418 seconds -Region 16 Avg IOU: 0.663518, Class: 0.083919, Obj: 0.231858, No Obj: 0.001767, .5R: 1.000000, .75R: 0.100000, count: 10 -Region 23 Avg IOU: 0.631368, Class: 0.182662, Obj: 0.098974, No Obj: 0.000298, .5R: 1.000000, .75R: 0.083333, count: 12 -Region 16 Avg IOU: 0.582712, Class: 0.227914, Obj: 0.064933, No Obj: 0.001142, .5R: 0.823529, .75R: 0.000000, count: 17 -Region 23 Avg IOU: 0.644675, Class: 0.216814, Obj: 0.024411, No Obj: 0.000307, .5R: 0.833333, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.641467, Class: 0.161197, Obj: 0.045182, No Obj: 0.001204, .5R: 0.833333, .75R: 0.250000, count: 12 -Region 23 Avg IOU: 0.622397, Class: 0.206063, Obj: 0.023906, No Obj: 0.000514, .5R: 0.866667, .75R: 0.066667, count: 15 -Region 16 Avg IOU: 0.546142, Class: 0.203597, Obj: 0.070781, No Obj: 0.001587, .5R: 0.625000, .75R: 0.125000, count: 8 -Region 23 Avg IOU: 0.574416, Class: 0.232120, Obj: 0.052247, No Obj: 0.000312, .5R: 0.680000, .75R: 0.080000, count: 25 -Region 16 Avg IOU: 0.601573, Class: 0.152240, Obj: 0.135493, No Obj: 0.001945, .5R: 0.857143, .75R: 0.000000, count: 14 -Region 23 Avg IOU: 0.616131, Class: 0.202578, Obj: 0.055261, No Obj: 0.000301, .5R: 0.857143, .75R: 0.142857, count: 21 -Region 16 Avg IOU: 0.612295, Class: 0.235536, Obj: 0.065077, No Obj: 0.001201, .5R: 0.684211, .75R: 0.263158, count: 19 -Region 23 Avg IOU: 0.564023, Class: 0.248162, Obj: 0.005033, No Obj: 0.000128, .5R: 0.722222, .75R: 0.055556, count: 18 -Region 16 Avg IOU: 0.650822, Class: 0.199162, Obj: 0.090171, No Obj: 0.001520, .5R: 1.000000, .75R: 0.200000, count: 20 -Region 23 Avg IOU: 0.626005, Class: 0.222905, Obj: 0.005846, No Obj: 0.000176, .5R: 0.894737, .75R: 0.052632, count: 19 -Region 16 Avg IOU: 0.610252, Class: 0.177459, Obj: 0.043413, No Obj: 0.001119, .5R: 0.800000, .75R: 0.120000, count: 25 -Region 23 Avg IOU: 0.613422, Class: 0.193722, Obj: 0.031606, No Obj: 0.000533, .5R: 0.833333, .75R: 0.083333, count: 24 - - 99: 3.302756, 3.305966 avg loss, 0.001000 rate, 1.087857 seconds, 12672 images -Loaded: 0.586370 seconds -Region 16 Avg IOU: 0.678740, Class: 0.212479, Obj: 0.145105, No Obj: 0.001620, .5R: 0.923077, .75R: 0.230769, count: 13 -Region 23 Avg IOU: 0.666163, Class: 0.198317, Obj: 0.034873, No Obj: 0.000765, .5R: 0.923077, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.644971, Class: 0.131741, Obj: 0.039556, No Obj: 0.000817, .5R: 0.833333, .75R: 0.250000, count: 12 -Region 23 Avg IOU: 0.593377, Class: 0.217359, Obj: 0.096737, No Obj: 0.001039, .5R: 0.769231, .75R: 0.076923, count: 26 -Region 16 Avg IOU: 0.618674, Class: 0.140662, Obj: 0.067122, No Obj: 0.001639, .5R: 0.941176, .75R: 0.058824, count: 17 -Region 23 Avg IOU: 0.654841, Class: 0.195801, Obj: 0.049908, No Obj: 0.000369, .5R: 0.923077, .75R: 0.192308, count: 26 -Region 16 Avg IOU: 0.615867, Class: 0.193924, Obj: 0.119865, No Obj: 0.001964, .5R: 0.823529, .75R: 0.176471, count: 17 -Region 23 Avg IOU: 0.645483, Class: 0.193123, Obj: 0.178938, No Obj: 0.001388, .5R: 0.909091, .75R: 0.181818, count: 11 -Region 16 Avg IOU: 0.610078, Class: 0.165894, Obj: 0.070137, No Obj: 0.000951, .5R: 0.636364, .75R: 0.272727, count: 11 -Region 23 Avg IOU: 0.646805, Class: 0.180300, Obj: 0.192194, No Obj: 0.001702, .5R: 0.833333, .75R: 0.250000, count: 24 -Region 16 Avg IOU: 0.604457, Class: 0.270919, Obj: 0.037129, No Obj: 0.000798, .5R: 0.777778, .75R: 0.111111, count: 9 -Region 23 Avg IOU: 0.614885, Class: 0.209966, Obj: 0.041159, No Obj: 0.000437, .5R: 0.821429, .75R: 0.071429, count: 28 -Region 16 Avg IOU: 0.696649, Class: 0.185077, Obj: 0.121965, No Obj: 0.001361, .5R: 1.000000, .75R: 0.153846, count: 13 -Region 23 Avg IOU: 0.645088, Class: 0.132580, Obj: 0.101142, No Obj: 0.002348, .5R: 0.882353, .75R: 0.235294, count: 17 -Region 16 Avg IOU: 0.567594, Class: 0.207144, Obj: 0.099447, No Obj: 0.001506, .5R: 0.928571, .75R: 0.000000, count: 14 -Region 23 Avg IOU: 0.584842, Class: 0.251542, Obj: 0.051798, No Obj: 0.000605, .5R: 0.806452, .75R: 0.064516, count: 31 - - 100: 3.398234, 3.315193 avg loss, 0.001000 rate, 1.083574 seconds, 12800 images -Resizing -736 x 736 - try to allocate workspace = 1149517825 * sizeof(float), CUDA allocate done! -Loaded: 0.828560 seconds -Region 16 Avg IOU: 0.569117, Class: 0.190133, Obj: 0.083173, No Obj: 0.001904, .5R: 0.727273, .75R: 0.045455, count: 22 -Region 23 Avg IOU: 0.672651, Class: 0.189776, Obj: 0.035339, No Obj: 0.000287, .5R: 0.928571, .75R: 0.285714, count: 14 -Region 16 Avg IOU: 0.576501, Class: 0.226392, Obj: 0.043165, No Obj: 0.001428, .5R: 0.681818, .75R: 0.113636, count: 44 -Region 23 Avg IOU: 0.592189, Class: 0.172300, Obj: 0.002238, No Obj: 0.000122, .5R: 0.850000, .75R: 0.150000, count: 20 -Region 16 Avg IOU: 0.559200, Class: 0.192206, Obj: 0.041682, No Obj: 0.001289, .5R: 0.678571, .75R: 0.071429, count: 28 -Region 23 Avg IOU: 0.651445, Class: 0.201112, Obj: 0.026677, No Obj: 0.000180, .5R: 0.800000, .75R: 0.250000, count: 20 -Region 16 Avg IOU: 0.608963, Class: 0.170069, Obj: 0.066657, No Obj: 0.001198, .5R: 0.750000, .75R: 0.150000, count: 20 -Region 23 Avg IOU: 0.654079, Class: 0.201160, Obj: 0.035295, No Obj: 0.000345, .5R: 0.807692, .75R: 0.307692, count: 26 -Region 16 Avg IOU: 0.554340, Class: 0.178028, Obj: 0.045108, No Obj: 0.001313, .5R: 0.777778, .75R: 0.055556, count: 18 -Region 23 Avg IOU: 0.643374, Class: 0.146736, Obj: 0.023850, No Obj: 0.000157, .5R: 0.866667, .75R: 0.133333, count: 15 -Region 16 Avg IOU: 0.518826, Class: 0.171217, Obj: 0.060293, No Obj: 0.001345, .5R: 0.521739, .75R: 0.086957, count: 23 -Region 23 Avg IOU: 0.616519, Class: 0.212125, Obj: 0.028060, No Obj: 0.000139, .5R: 0.818182, .75R: 0.181818, count: 11 -Region 16 Avg IOU: 0.608972, Class: 0.241000, Obj: 0.049377, No Obj: 0.001054, .5R: 0.826087, .75R: 0.130435, count: 23 -Region 23 Avg IOU: 0.572530, Class: 0.227951, Obj: 0.030681, No Obj: 0.000370, .5R: 0.739130, .75R: 0.086957, count: 23 -Region 16 Avg IOU: 0.664011, Class: 0.152402, Obj: 0.072779, No Obj: 0.001439, .5R: 0.833333, .75R: 0.375000, count: 24 -Region 23 Avg IOU: 0.639550, Class: 0.176476, Obj: 0.035707, No Obj: 0.000353, .5R: 0.785714, .75R: 0.071429, count: 14 - - 101: 4.317157, 3.415389 avg loss, 0.001000 rate, 2.151568 seconds, 12928 images -Loaded: 0.000065 seconds -Region 16 Avg IOU: 0.580208, Class: 0.232467, Obj: 0.034131, No Obj: 0.000795, .5R: 0.772727, .75R: 0.000000, count: 22 -Region 23 Avg IOU: 0.567508, Class: 0.136351, Obj: 0.008699, No Obj: 0.000172, .5R: 0.750000, .75R: 0.083333, count: 12 -Region 16 Avg IOU: 0.568896, Class: 0.260071, Obj: 0.029752, No Obj: 0.000631, .5R: 0.866667, .75R: 0.000000, count: 15 -Region 23 Avg IOU: 0.585348, Class: 0.242602, Obj: 0.029200, No Obj: 0.000113, .5R: 0.631579, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.564895, Class: 0.235618, Obj: 0.077669, No Obj: 0.000949, .5R: 0.736842, .75R: 0.052632, count: 19 -Region 23 Avg IOU: 0.541157, Class: 0.221464, Obj: 0.052756, No Obj: 0.000199, .5R: 0.705882, .75R: 0.058824, count: 17 -Region 16 Avg IOU: 0.592439, Class: 0.162869, Obj: 0.018742, No Obj: 0.000586, .5R: 0.857143, .75R: 0.047619, count: 21 -Region 23 Avg IOU: 0.608312, Class: 0.185438, Obj: 0.026840, No Obj: 0.000282, .5R: 0.833333, .75R: 0.111111, count: 18 -Region 16 Avg IOU: 0.620349, Class: 0.214544, Obj: 0.038815, No Obj: 0.000661, .5R: 0.857143, .75R: 0.142857, count: 14 -Region 23 Avg IOU: 0.720639, Class: 0.196764, Obj: 0.013591, No Obj: 0.000045, .5R: 0.833333, .75R: 0.500000, count: 6 -Region 16 Avg IOU: 0.562981, Class: 0.225274, Obj: 0.020878, No Obj: 0.000856, .5R: 0.666667, .75R: 0.083333, count: 12 -Region 23 Avg IOU: 0.619173, Class: 0.245296, Obj: 0.019021, No Obj: 0.000117, .5R: 0.875000, .75R: 0.250000, count: 8 -Region 16 Avg IOU: 0.505000, Class: 0.244012, Obj: 0.018658, No Obj: 0.000550, .5R: 0.521739, .75R: 0.043478, count: 23 -Region 23 Avg IOU: 0.545830, Class: 0.211753, Obj: 0.010116, No Obj: 0.000124, .5R: 0.647059, .75R: 0.117647, count: 17 -Region 16 Avg IOU: 0.613445, Class: 0.201243, Obj: 0.022605, No Obj: 0.000674, .5R: 0.809524, .75R: 0.095238, count: 21 -Region 23 Avg IOU: 0.574773, Class: 0.125742, Obj: 0.004735, No Obj: 0.000061, .5R: 0.666667, .75R: 0.000000, count: 9 - - 102: 3.320244, 3.405874 avg loss, 0.001000 rate, 2.228451 seconds, 13056 images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.561122, Class: 0.243380, Obj: 0.009505, No Obj: 0.000490, .5R: 0.689655, .75R: 0.034483, count: 29 -Region 23 Avg IOU: 0.600034, Class: 0.177035, Obj: 0.002950, No Obj: 0.000068, .5R: 0.800000, .75R: 0.100000, count: 30 -Region 16 Avg IOU: 0.609380, Class: 0.161955, Obj: 0.013485, No Obj: 0.000464, .5R: 0.833333, .75R: 0.125000, count: 24 -Region 23 Avg IOU: 0.630571, Class: 0.146134, Obj: 0.002363, No Obj: 0.000038, .5R: 0.869565, .75R: 0.217391, count: 23 -Region 16 Avg IOU: 0.591770, Class: 0.210563, Obj: 0.013885, No Obj: 0.000419, .5R: 0.807692, .75R: 0.076923, count: 26 -Region 23 Avg IOU: 0.615671, Class: 0.184400, Obj: 0.004678, No Obj: 0.000051, .5R: 0.869565, .75R: 0.130435, count: 23 -Region 16 Avg IOU: 0.550901, Class: 0.260078, Obj: 0.021463, No Obj: 0.000439, .5R: 0.611111, .75R: 0.055556, count: 18 -Region 23 Avg IOU: 0.640851, Class: 0.179879, Obj: 0.015481, No Obj: 0.000071, .5R: 0.875000, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.557870, Class: 0.176131, Obj: 0.027265, No Obj: 0.000496, .5R: 0.705882, .75R: 0.000000, count: 17 -Region 23 Avg IOU: 0.610822, Class: 0.222740, Obj: 0.017585, No Obj: 0.000098, .5R: 0.769231, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.607745, Class: 0.178216, Obj: 0.020504, No Obj: 0.000527, .5R: 0.880000, .75R: 0.080000, count: 25 -Region 23 Avg IOU: 0.658123, Class: 0.169571, Obj: 0.001516, No Obj: 0.000022, .5R: 0.937500, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.533789, Class: 0.171077, Obj: 0.008293, No Obj: 0.000442, .5R: 0.656250, .75R: 0.031250, count: 32 -Region 23 Avg IOU: 0.664580, Class: 0.199344, Obj: 0.026607, No Obj: 0.000090, .5R: 0.888889, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.558690, Class: 0.161446, Obj: 0.022101, No Obj: 0.000431, .5R: 0.700000, .75R: 0.150000, count: 20 -Region 23 Avg IOU: 0.632879, Class: 0.177705, Obj: 0.012847, No Obj: 0.000176, .5R: 0.818182, .75R: 0.181818, count: 22 - - 103: 4.406035, 3.505891 avg loss, 0.001000 rate, 2.257077 seconds, 13184 images -Loaded: 0.000061 seconds -Region 16 Avg IOU: 0.585936, Class: 0.216132, Obj: 0.016164, No Obj: 0.000558, .5R: 0.689655, .75R: 0.137931, count: 29 -Region 23 Avg IOU: 0.582320, Class: 0.205298, Obj: 0.001304, No Obj: 0.000028, .5R: 0.666667, .75R: 0.095238, count: 21 -Region 16 Avg IOU: 0.588654, Class: 0.233863, Obj: 0.036998, No Obj: 0.000621, .5R: 0.761905, .75R: 0.142857, count: 21 -Region 23 Avg IOU: 0.655525, Class: 0.136593, Obj: 0.003564, No Obj: 0.000034, .5R: 1.000000, .75R: 0.142857, count: 7 -Region 16 Avg IOU: 0.628678, Class: 0.198380, Obj: 0.039460, No Obj: 0.000585, .5R: 0.909091, .75R: 0.181818, count: 22 -Region 23 Avg IOU: 0.673078, Class: 0.220544, Obj: 0.005694, No Obj: 0.000052, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.576159, Class: 0.222886, Obj: 0.023665, No Obj: 0.000791, .5R: 0.826087, .75R: 0.043478, count: 23 -Region 23 Avg IOU: 0.636691, Class: 0.225707, Obj: 0.010252, No Obj: 0.000048, .5R: 0.947368, .75R: 0.000000, count: 19 -Region 16 Avg IOU: 0.572604, Class: 0.198496, Obj: 0.044149, No Obj: 0.000683, .5R: 0.666667, .75R: 0.166667, count: 24 -Region 23 Avg IOU: 0.627476, Class: 0.202009, Obj: 0.004056, No Obj: 0.000038, .5R: 0.772727, .75R: 0.227273, count: 22 -Region 16 Avg IOU: 0.595283, Class: 0.170069, Obj: 0.025578, No Obj: 0.000671, .5R: 0.772727, .75R: 0.136364, count: 22 -Region 23 Avg IOU: 0.633303, Class: 0.132100, Obj: 0.009609, No Obj: 0.000082, .5R: 0.695652, .75R: 0.347826, count: 23 -Region 16 Avg IOU: 0.563879, Class: 0.208495, Obj: 0.053675, No Obj: 0.000565, .5R: 0.692308, .75R: 0.076923, count: 13 -Region 23 Avg IOU: 0.669633, Class: 0.122004, Obj: 0.015971, No Obj: 0.000056, .5R: 0.857143, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.569529, Class: 0.235571, Obj: 0.031259, No Obj: 0.000676, .5R: 0.714286, .75R: 0.047619, count: 21 -Region 23 Avg IOU: 0.671288, Class: 0.221264, Obj: 0.013524, No Obj: 0.000038, .5R: 0.950000, .75R: 0.350000, count: 20 - - 104: 3.732101, 3.528512 avg loss, 0.001000 rate, 2.246669 seconds, 13312 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.658493, Class: 0.184249, Obj: 0.053106, No Obj: 0.001408, .5R: 0.826087, .75R: 0.391304, count: 23 -Region 23 Avg IOU: 0.689526, Class: 0.130967, Obj: 0.008466, No Obj: 0.000048, .5R: 0.846154, .75R: 0.461538, count: 13 -Region 16 Avg IOU: 0.554560, Class: 0.211064, Obj: 0.052852, No Obj: 0.001168, .5R: 0.607143, .75R: 0.214286, count: 28 -Region 23 Avg IOU: 0.591587, Class: 0.174799, Obj: 0.000186, No Obj: 0.000007, .5R: 0.764706, .75R: 0.058824, count: 17 -Region 16 Avg IOU: 0.596557, Class: 0.201795, Obj: 0.071845, No Obj: 0.001292, .5R: 0.740741, .75R: 0.074074, count: 27 -Region 23 Avg IOU: 0.628368, Class: 0.147170, Obj: 0.020822, No Obj: 0.000103, .5R: 0.720000, .75R: 0.240000, count: 25 -Region 16 Avg IOU: 0.500713, Class: 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1.009349 seconds, 15232 images -Loaded: 0.402833 seconds -Region 16 Avg IOU: 0.665741, Class: 0.194006, Obj: 0.075686, No Obj: 0.000983, .5R: 0.900000, .75R: 0.200000, count: 10 -Region 23 Avg IOU: 0.604867, Class: 0.205138, Obj: 0.041145, No Obj: 0.000219, .5R: 0.888889, .75R: 0.111111, count: 18 -Region 16 Avg IOU: 0.617366, Class: 0.206139, Obj: 0.122210, No Obj: 0.001554, .5R: 0.933333, .75R: 0.133333, count: 15 -Region 23 Avg IOU: 0.662636, Class: 0.193763, Obj: 0.065976, No Obj: 0.000203, .5R: 0.900000, .75R: 0.200000, count: 10 -Region 16 Avg IOU: 0.620337, Class: 0.161579, Obj: 0.140666, No Obj: 0.001656, .5R: 0.916667, .75R: 0.083333, count: 12 -Region 23 Avg IOU: 0.644236, Class: 0.204429, Obj: 0.012733, No Obj: 0.000343, .5R: 0.846154, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.627582, Class: 0.236775, Obj: 0.164834, No Obj: 0.001546, .5R: 0.866667, .75R: 0.066667, count: 15 -Region 23 Avg IOU: 0.617979, Class: 0.199591, Obj: 0.009948, No Obj: 0.000123, .5R: 0.882353, 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2.286003, 3.299337 avg loss, 0.001000 rate, 0.985401 seconds, 15360 images -Resizing -544 x 544 - try to allocate workspace = 1145585665 * sizeof(float), CUDA allocate done! -Loaded: 0.589020 seconds -Region 16 Avg IOU: 0.570360, Class: 0.203119, Obj: 0.058690, No Obj: 0.001148, .5R: 0.714286, .75R: 0.142857, count: 14 -Region 23 Avg IOU: 0.571007, Class: 0.222187, Obj: 0.093789, No Obj: 0.000302, .5R: 0.700000, .75R: 0.200000, count: 10 -Region 16 Avg IOU: 0.590565, Class: 0.231431, Obj: 0.037926, No Obj: 0.001079, .5R: 0.812500, .75R: 0.062500, count: 16 -Region 23 Avg IOU: 0.545002, Class: 0.155806, Obj: 0.002358, No Obj: 0.000070, .5R: 0.666667, .75R: 0.047619, count: 21 -Region 16 Avg IOU: 0.625244, Class: 0.193626, Obj: 0.112642, No Obj: 0.002258, .5R: 0.920000, .75R: 0.120000, count: 25 -Region 23 Avg IOU: 0.620209, Class: 0.207129, Obj: 0.031313, No Obj: 0.000313, .5R: 0.843750, .75R: 0.156250, count: 32 -Region 16 Avg IOU: 0.589215, Class: 0.219178, Obj: 0.102040, No Obj: 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count: 20 -Region 16 Avg IOU: 0.654673, Class: 0.155522, Obj: 0.095314, No Obj: 0.000796, .5R: 1.000000, .75R: 0.111111, count: 9 -Region 23 Avg IOU: 0.624974, Class: 0.152198, Obj: 0.133627, No Obj: 0.000934, .5R: 1.000000, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.596972, Class: 0.264487, Obj: 0.085166, No Obj: 0.000978, .5R: 0.769231, .75R: 0.153846, count: 13 -Region 23 Avg IOU: 0.586303, Class: 0.218754, Obj: 0.077539, No Obj: 0.000525, .5R: 0.777778, .75R: 0.055556, count: 18 - - 162: 2.420352, 3.017337 avg loss, 0.001000 rate, 0.731166 seconds, 20736 images -Loaded: 0.246061 seconds -Region 16 Avg IOU: 0.760117, Class: 0.195265, Obj: 0.141845, No Obj: 0.000823, .5R: 1.000000, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.645981, Class: 0.217439, Obj: 0.081493, No Obj: 0.000726, .5R: 0.904762, .75R: 0.190476, count: 21 -Region 16 Avg IOU: 0.623088, Class: 0.225525, Obj: 0.134187, No Obj: 0.001433, .5R: 1.000000, .75R: 0.076923, count: 13 -Region 23 Avg IOU: 0.627637, 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16 Avg IOU: 0.708982, Class: 0.269165, Obj: 0.334359, No Obj: 0.001648, .5R: 1.000000, .75R: 0.400000, count: 5 -Region 23 Avg IOU: 0.598254, Class: 0.165565, Obj: 0.067945, No Obj: 0.000979, .5R: 0.800000, .75R: 0.100000, count: 10 -Region 16 Avg IOU: 0.635188, Class: 0.204986, Obj: 0.224921, No Obj: 0.002008, .5R: 0.857143, .75R: 0.285714, count: 7 -Region 23 Avg IOU: 0.633509, Class: 0.149973, Obj: 0.137845, No Obj: 0.001292, .5R: 0.826087, .75R: 0.130435, count: 23 -Region 16 Avg IOU: 0.647897, Class: 0.240308, Obj: 0.410734, No Obj: 0.003245, .5R: 1.000000, .75R: 0.166667, count: 6 -Region 23 Avg IOU: 0.622892, Class: 0.177440, Obj: 0.092978, No Obj: 0.000567, .5R: 0.812500, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.590855, Class: 0.229460, Obj: 0.094364, No Obj: 0.000911, .5R: 0.666667, .75R: 0.333333, count: 6 -Region 23 Avg IOU: 0.640905, Class: 0.184811, Obj: 0.038634, No Obj: 0.000469, .5R: 0.857143, .75R: 0.190476, count: 21 -Region 16 Avg IOU: 0.669477, Class: 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count: 18 -Region 16 Avg IOU: 0.642439, Class: 0.292476, Obj: 0.143627, No Obj: 0.001530, .5R: 1.000000, .75R: 0.000000, count: 7 -Region 23 Avg IOU: 0.593948, Class: 0.198114, Obj: 0.146212, No Obj: 0.000691, .5R: 0.714286, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.694082, Class: 0.259891, Obj: 0.130758, No Obj: 0.000960, .5R: 1.000000, .75R: 0.333333, count: 9 -Region 23 Avg IOU: 0.659018, Class: 0.196893, Obj: 0.041436, No Obj: 0.000434, .5R: 0.909091, .75R: 0.272727, count: 22 -Region 16 Avg IOU: 0.618740, Class: 0.181057, Obj: 0.053476, No Obj: 0.000715, .5R: 0.714286, .75R: 0.142857, count: 7 -Region 23 Avg IOU: 0.587287, Class: 0.195969, Obj: 0.075295, No Obj: 0.000673, .5R: 0.812500, .75R: 0.125000, count: 16 - - 167: 2.265069, 2.846188 avg loss, 0.001000 rate, 0.746333 seconds, 21376 images -Loaded: 0.690335 seconds -Region 16 Avg IOU: 0.663518, Class: 0.233870, Obj: 0.074388, No Obj: 0.000770, .5R: 1.000000, .75R: 0.100000, count: 10 -Region 23 Avg IOU: 0.675137, Class: 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workspace = 1146765313 * sizeof(float), CUDA allocate done! -Loaded: 0.912372 seconds -Region 16 Avg IOU: 0.568433, Class: 0.188301, Obj: 0.125612, No Obj: 0.001428, .5R: 0.769231, .75R: 0.000000, count: 13 -Region 23 Avg IOU: 0.571907, Class: 0.157161, Obj: 0.129256, No Obj: 0.000913, .5R: 0.789474, .75R: 0.105263, count: 19 -Region 16 Avg IOU: 0.632749, Class: 0.196649, Obj: 0.120558, No Obj: 0.001157, .5R: 0.833333, .75R: 0.166667, count: 18 -Region 23 Avg IOU: 0.608258, Class: 0.187747, Obj: 0.047801, No Obj: 0.000588, .5R: 0.769231, .75R: 0.192308, count: 26 -Region 16 Avg IOU: 0.614131, Class: 0.228276, Obj: 0.077450, No Obj: 0.001055, .5R: 0.850000, .75R: 0.100000, count: 20 -Region 23 Avg IOU: 0.577912, Class: 0.132672, Obj: 0.063111, No Obj: 0.000667, .5R: 0.705882, .75R: 0.058824, count: 17 -Region 16 Avg IOU: 0.555351, Class: 0.261385, Obj: 0.110971, No Obj: 0.002078, .5R: 0.714286, .75R: 0.047619, count: 21 -Region 23 Avg IOU: 0.564836, Class: 0.203539, Obj: 0.037655, No 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.75R: 0.230769, count: 13 - - 171: 3.483263, 2.927025 avg loss, 0.001000 rate, 1.530506 seconds, 21888 images -Loaded: 0.000076 seconds -Region 16 Avg IOU: 0.643430, Class: 0.222402, Obj: 0.111503, No Obj: 0.001487, .5R: 0.933333, .75R: 0.133333, count: 15 -Region 23 Avg IOU: 0.589981, Class: 0.149078, Obj: 0.041025, No Obj: 0.000359, .5R: 0.722222, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.613075, Class: 0.197798, Obj: 0.049669, No Obj: 0.001222, .5R: 0.833333, .75R: 0.333333, count: 12 -Region 23 Avg IOU: 0.629482, Class: 0.232223, Obj: 0.051051, No Obj: 0.000372, .5R: 0.882353, .75R: 0.117647, count: 17 -Region 16 Avg IOU: 0.588227, Class: 0.207330, Obj: 0.061421, No Obj: 0.001512, .5R: 0.652174, .75R: 0.130435, count: 23 -Region 23 Avg IOU: 0.572235, Class: 0.241591, Obj: 0.077140, No Obj: 0.000416, .5R: 0.785714, .75R: 0.000000, count: 14 -Region 16 Avg IOU: 0.617493, Class: 0.114248, Obj: 0.140598, No Obj: 0.001383, .5R: 0.833333, .75R: 0.166667, count: 12 -Region 23 Avg 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16 - - 204: 4.059713, 3.558466 avg loss, 0.001000 rate, 1.490756 seconds, 26112 images -Loaded: 0.392738 seconds -Region 16 Avg IOU: 0.608863, Class: 0.184012, Obj: 0.083636, No Obj: 0.001332, .5R: 0.750000, .75R: 0.125000, count: 16 -Region 23 Avg IOU: 0.581884, Class: 0.199483, Obj: 0.046289, No Obj: 0.000227, .5R: 0.785714, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.589563, Class: 0.169022, Obj: 0.130428, No Obj: 0.001262, .5R: 0.769231, .75R: 0.076923, count: 13 -Region 23 Avg IOU: 0.628567, Class: 0.182628, Obj: 0.222108, No Obj: 0.000587, .5R: 0.916667, .75R: 0.083333, count: 12 -Region 16 Avg IOU: 0.630231, Class: 0.214713, Obj: 0.151136, No Obj: 0.001345, .5R: 0.857143, .75R: 0.142857, count: 14 -Region 23 Avg IOU: 0.597963, Class: 0.237305, Obj: 0.061820, No Obj: 0.000265, .5R: 0.800000, .75R: 0.100000, count: 10 -Region 16 Avg IOU: 0.647011, Class: 0.164930, Obj: 0.155292, No Obj: 0.001304, .5R: 1.000000, .75R: 0.076923, count: 13 -Region 23 Avg IOU: 0.607091, Class: 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19 -Region 16 Avg IOU: 0.619478, Class: 0.187299, Obj: 0.071405, No Obj: 0.000774, .5R: 0.925926, .75R: 0.222222, count: 27 -Region 23 Avg IOU: 0.585369, Class: 0.216298, Obj: 0.063114, No Obj: 0.000354, .5R: 0.833333, .75R: 0.000000, count: 18 -Region 16 Avg IOU: 0.666855, Class: 0.224979, Obj: 0.062030, No Obj: 0.000605, .5R: 0.941176, .75R: 0.176471, count: 17 -Region 23 Avg IOU: 0.571332, Class: 0.180484, Obj: 0.086606, No Obj: 0.000381, .5R: 0.687500, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.664270, Class: 0.219556, Obj: 0.080758, No Obj: 0.000770, .5R: 0.931035, .75R: 0.172414, count: 29 -Region 23 Avg IOU: 0.660348, Class: 0.208908, Obj: 0.099364, No Obj: 0.000502, .5R: 0.909091, .75R: 0.227273, count: 22 - - 217: 3.965508, 3.527937 avg loss, 0.001000 rate, 2.080715 seconds, 27776 images -Loaded: 0.000062 seconds -Region 16 Avg IOU: 0.677229, Class: 0.229090, Obj: 0.083592, No Obj: 0.001025, .5R: 0.925926, .75R: 0.222222, count: 27 -Region 23 Avg IOU: 0.632464, Class: 0.220651, Obj: 0.078613, No Obj: 0.000388, .5R: 0.851852, .75R: 0.185185, count: 27 -Region 16 Avg IOU: 0.634448, Class: 0.150140, Obj: 0.059860, No Obj: 0.000898, .5R: 0.888889, .75R: 0.222222, count: 27 -Region 23 Avg IOU: 0.662206, Class: 0.136684, Obj: 0.026533, No Obj: 0.000169, .5R: 0.928571, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.630749, Class: 0.231402, Obj: 0.064864, No Obj: 0.000819, .5R: 0.758621, .75R: 0.172414, count: 29 -Region 23 Avg IOU: 0.674142, Class: 0.185769, Obj: 0.025786, No Obj: 0.000105, .5R: 0.875000, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.602881, Class: 0.255957, Obj: 0.125550, No Obj: 0.000949, .5R: 0.777778, .75R: 0.111111, count: 27 -Region 23 Avg IOU: 0.616439, Class: 0.270476, Obj: 0.013124, No Obj: 0.000122, .5R: 0.782609, .75R: 0.130435, count: 23 -Region 16 Avg IOU: 0.607207, Class: 0.217796, Obj: 0.121858, No Obj: 0.001083, .5R: 0.818182, .75R: 0.181818, count: 22 -Region 23 Avg IOU: 0.582357, Class: 0.196029, Obj: 0.022587, No Obj: 0.000134, .5R: 0.722222, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.636734, Class: 0.200447, Obj: 0.056148, No Obj: 0.000826, .5R: 0.846154, .75R: 0.115385, count: 26 -Region 23 Avg IOU: 0.574861, Class: 0.227612, Obj: 0.020550, No Obj: 0.000095, .5R: 0.722222, .75R: 0.222222, count: 18 -Region 16 Avg IOU: 0.582109, Class: 0.234099, Obj: 0.146100, No Obj: 0.000784, .5R: 0.666667, .75R: 0.133333, count: 15 -Region 23 Avg IOU: 0.646402, Class: 0.133518, Obj: 0.146488, No Obj: 0.000158, .5R: 0.727273, .75R: 0.272727, count: 11 -Region 16 Avg IOU: 0.603487, Class: 0.177973, Obj: 0.101347, No Obj: 0.000692, .5R: 0.842105, .75R: 0.105263, count: 19 -Region 23 Avg IOU: 0.624646, Class: 0.198121, Obj: 0.022445, No Obj: 0.000116, .5R: 0.888889, .75R: 0.111111, count: 18 - - 218: 3.812581, 3.556402 avg loss, 0.001000 rate, 2.061466 seconds, 27904 images -Loaded: 0.000057 seconds -Region 16 Avg IOU: 0.606047, Class: 0.166198, Obj: 0.200206, No Obj: 0.001302, .5R: 0.826087, .75R: 0.043478, count: 23 -Region 23 Avg IOU: 0.675283, Class: 0.168795, Obj: 0.028628, No Obj: 0.000127, .5R: 0.952381, .75R: 0.333333, count: 21 -Region 16 Avg IOU: 0.553705, Class: 0.247681, Obj: 0.181477, No Obj: 0.001208, .5R: 0.642857, .75R: 0.071429, count: 14 -Region 23 Avg IOU: 0.605137, Class: 0.203895, Obj: 0.016033, No Obj: 0.000135, .5R: 0.928571, .75R: 0.000000, count: 28 -Region 16 Avg IOU: 0.621632, Class: 0.195697, Obj: 0.184304, No Obj: 0.001339, .5R: 0.818182, .75R: 0.136364, count: 22 -Region 23 Avg IOU: 0.593838, Class: 0.160839, Obj: 0.023463, No Obj: 0.000103, .5R: 0.736842, .75R: 0.105263, count: 19 -Region 16 Avg IOU: 0.667918, Class: 0.223989, Obj: 0.218766, No Obj: 0.001556, .5R: 0.952381, .75R: 0.142857, count: 21 -Region 23 Avg IOU: 0.638669, Class: 0.179870, Obj: 0.027802, No Obj: 0.000093, .5R: 0.947368, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.624610, Class: 0.190957, Obj: 0.138811, No Obj: 0.001407, .5R: 0.807692, .75R: 0.153846, count: 26 -Region 23 Avg IOU: 0.682854, Class: 0.173595, Obj: 0.012779, No Obj: 0.000083, .5R: 0.933333, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.632994, Class: 0.220578, Obj: 0.149747, No Obj: 0.001514, .5R: 0.857143, .75R: 0.142857, count: 28 -Region 23 Avg IOU: 0.648163, Class: 0.200091, Obj: 0.019055, No Obj: 0.000098, .5R: 0.884615, .75R: 0.307692, count: 26 -Region 16 Avg IOU: 0.663141, Class: 0.205694, Obj: 0.088658, No Obj: 0.001176, .5R: 0.928571, .75R: 0.178571, count: 28 -Region 23 Avg IOU: 0.661659, Class: 0.195699, Obj: 0.013375, No Obj: 0.000080, .5R: 0.842105, .75R: 0.210526, count: 19 -Region 16 Avg IOU: 0.636895, Class: 0.205702, Obj: 0.126458, No Obj: 0.001436, .5R: 0.923077, .75R: 0.192308, count: 26 -Region 23 Avg IOU: 0.648071, Class: 0.154926, Obj: 0.012785, No Obj: 0.000087, .5R: 0.800000, .75R: 0.300000, count: 20 - - 219: 3.964717, 3.597233 avg loss, 0.001000 rate, 2.084899 seconds, 28032 images -Loaded: 0.000061 seconds -Region 16 Avg IOU: 0.613284, Class: 0.229221, Obj: 0.153876, No Obj: 0.001426, .5R: 0.846154, .75R: 0.179487, count: 39 -Region 23 Avg IOU: 0.614240, Class: 0.218707, Obj: 0.011514, No Obj: 0.000107, .5R: 0.888889, .75R: 0.055556, count: 18 -Region 16 Avg IOU: 0.652580, Class: 0.196835, Obj: 0.178642, No Obj: 0.001514, .5R: 0.928571, .75R: 0.142857, count: 28 -Region 23 Avg IOU: 0.642568, Class: 0.182930, Obj: 0.037500, No Obj: 0.000152, .5R: 0.944444, .75R: 0.111111, count: 18 -Region 16 Avg IOU: 0.547974, Class: 0.180572, Obj: 0.130805, No Obj: 0.001387, .5R: 0.733333, .75R: 0.066667, count: 30 -Region 23 Avg IOU: 0.604720, Class: 0.164890, Obj: 0.007197, No Obj: 0.000074, .5R: 0.687500, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.651182, Class: 0.193275, Obj: 0.163087, No Obj: 0.001212, .5R: 0.961538, .75R: 0.230769, count: 26 -Region 23 Avg IOU: 0.517891, Class: 0.207983, Obj: 0.063530, No Obj: 0.000109, .5R: 0.722222, .75R: 0.000000, count: 18 -Region 16 Avg IOU: 0.647445, Class: 0.258059, Obj: 0.061213, No Obj: 0.001182, .5R: 0.904762, .75R: 0.190476, count: 21 -Region 23 Avg IOU: 0.592639, Class: 0.189928, Obj: 0.010577, No Obj: 0.000069, .5R: 0.888889, .75R: 0.055556, count: 18 -Region 16 Avg IOU: 0.645746, Class: 0.196799, Obj: 0.168498, No Obj: 0.001122, .5R: 0.863636, .75R: 0.090909, count: 22 -Region 23 Avg IOU: 0.657763, Class: 0.200289, Obj: 0.037932, No Obj: 0.000165, .5R: 0.923077, .75R: 0.269231, count: 26 -Region 16 Avg IOU: 0.648471, Class: 0.171714, Obj: 0.107548, No Obj: 0.001353, .5R: 0.947368, .75R: 0.210526, count: 19 -Region 23 Avg IOU: 0.644700, Class: 0.184113, Obj: 0.049115, No Obj: 0.000149, .5R: 0.954545, .75R: 0.227273, count: 22 -Region 16 Avg IOU: 0.594525, Class: 0.153962, Obj: 0.162371, No Obj: 0.001084, .5R: 0.722222, .75R: 0.055556, count: 18 -Region 23 Avg IOU: 0.654742, Class: 0.170005, Obj: 0.019780, No Obj: 0.000060, .5R: 0.833333, .75R: 0.166667, count: 12 - - 220: 4.078700, 3.645380 avg loss, 0.001000 rate, 2.078816 seconds, 28160 images -Resizing -608 x 608 - try to allocate workspace = 1146765313 * sizeof(float), CUDA allocate done! -Loaded: 0.987677 seconds -Region 16 Avg IOU: 0.572286, Class: 0.241670, Obj: 0.110998, No Obj: 0.000844, .5R: 0.722222, .75R: 0.055556, count: 18 -Region 23 Avg IOU: 0.594452, Class: 0.171514, Obj: 0.016502, No Obj: 0.000105, .5R: 0.772727, .75R: 0.090909, count: 22 -Region 16 Avg IOU: 0.686378, Class: 0.225118, Obj: 0.158468, No Obj: 0.001085, .5R: 0.954545, .75R: 0.363636, count: 22 -Region 23 Avg IOU: 0.622103, Class: 0.221242, Obj: 0.092818, No Obj: 0.000344, .5R: 0.875000, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.657197, Class: 0.220337, Obj: 0.101227, No Obj: 0.001110, .5R: 0.916667, .75R: 0.125000, count: 24 -Region 23 Avg IOU: 0.608307, Class: 0.174581, Obj: 0.055662, No Obj: 0.000285, .5R: 0.742857, .75R: 0.142857, count: 35 -Region 16 Avg IOU: 0.616253, Class: 0.149789, Obj: 0.101735, No Obj: 0.001101, .5R: 0.764706, .75R: 0.235294, count: 17 -Region 23 Avg IOU: 0.623258, Class: 0.164234, Obj: 0.072185, No Obj: 0.000348, .5R: 0.850000, .75R: 0.200000, count: 20 -Region 16 Avg IOU: 0.568540, Class: 0.196521, Obj: 0.077295, No Obj: 0.000756, .5R: 0.875000, .75R: 0.000000, count: 8 -Region 23 Avg IOU: 0.691142, Class: 0.189429, Obj: 0.026834, No Obj: 0.000116, .5R: 1.000000, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.629656, Class: 0.184891, Obj: 0.196430, No Obj: 0.001322, .5R: 0.809524, .75R: 0.190476, count: 21 -Region 23 Avg IOU: 0.664256, Class: 0.218381, Obj: 0.014930, No Obj: 0.000150, .5R: 0.944444, .75R: 0.222222, count: 18 -Region 16 Avg IOU: 0.639235, Class: 0.156482, Obj: 0.114128, No Obj: 0.000980, .5R: 0.846154, .75R: 0.153846, count: 13 -Region 23 Avg IOU: 0.636597, Class: 0.210657, Obj: 0.017040, No Obj: 0.000098, .5R: 0.846154, .75R: 0.230769, count: 13 -Region 16 Avg IOU: 0.573748, Class: 0.185914, Obj: 0.065038, No Obj: 0.000915, .5R: 0.583333, .75R: 0.125000, count: 24 -Region 23 Avg IOU: 0.687050, Class: 0.165516, Obj: 0.025845, No Obj: 0.000197, .5R: 1.000000, .75R: 0.285714, count: 7 - - 221: 3.271824, 3.608024 avg loss, 0.001000 rate, 1.547235 seconds, 28288 images -Loaded: 0.075636 seconds -Region 16 Avg IOU: 0.636267, Class: 0.218571, Obj: 0.068863, No Obj: 0.000727, .5R: 0.923077, .75R: 0.153846, count: 13 -Region 23 Avg IOU: 0.649096, Class: 0.219177, Obj: 0.035044, No Obj: 0.000250, .5R: 0.888889, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.633997, Class: 0.168929, Obj: 0.148373, No Obj: 0.000779, .5R: 0.894737, .75R: 0.157895, count: 19 -Region 23 Avg IOU: 0.574920, Class: 0.185008, Obj: 0.063224, No Obj: 0.000274, .5R: 0.705882, .75R: 0.117647, count: 17 -Region 16 Avg IOU: 0.654510, Class: 0.216609, Obj: 0.112460, No Obj: 0.000848, .5R: 0.888889, .75R: 0.222222, count: 18 -Region 23 Avg IOU: 0.657432, Class: 0.158528, Obj: 0.046881, No Obj: 0.000404, .5R: 0.894737, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.635943, Class: 0.266114, Obj: 0.127363, No Obj: 0.001063, .5R: 0.809524, .75R: 0.190476, count: 21 -Region 23 Avg IOU: 0.583394, Class: 0.200504, Obj: 0.081018, No Obj: 0.000417, .5R: 0.666667, .75R: 0.111111, count: 18 -Region 16 Avg IOU: 0.641541, Class: 0.253340, Obj: 0.158555, No Obj: 0.001014, .5R: 0.875000, .75R: 0.250000, count: 24 -Region 23 Avg IOU: 0.684714, Class: 0.240192, Obj: 0.023923, No Obj: 0.000223, .5R: 1.000000, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.666892, Class: 0.188589, Obj: 0.098212, No Obj: 0.000919, .5R: 0.923077, .75R: 0.346154, count: 26 -Region 23 Avg IOU: 0.599033, Class: 0.184266, Obj: 0.041245, No Obj: 0.000417, .5R: 0.666667, .75R: 0.185185, count: 27 -Region 16 Avg IOU: 0.673026, Class: 0.222475, Obj: 0.149653, No Obj: 0.001156, .5R: 1.000000, .75R: 0.241379, count: 29 -Region 23 Avg IOU: 0.669603, Class: 0.191868, Obj: 0.048343, No Obj: 0.000358, .5R: 0.941176, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.636063, Class: 0.190930, Obj: 0.167748, No Obj: 0.001156, .5R: 0.720000, .75R: 0.280000, count: 25 -Region 23 Avg IOU: 0.661414, Class: 0.193287, Obj: 0.102980, No Obj: 0.000449, .5R: 0.962963, .75R: 0.148148, count: 27 - - 222: 3.579900, 3.605212 avg loss, 0.001000 rate, 1.534036 seconds, 28416 images -Loaded: 0.000075 seconds -Region 16 Avg IOU: 0.688283, Class: 0.179907, Obj: 0.139582, No Obj: 0.001086, .5R: 1.000000, .75R: 0.350000, count: 20 -Region 23 Avg IOU: 0.670862, Class: 0.208450, Obj: 0.079680, No Obj: 0.000329, .5R: 0.900000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.641935, Class: 0.191628, Obj: 0.195487, No Obj: 0.001329, .5R: 0.882353, .75R: 0.117647, count: 17 -Region 23 Avg IOU: 0.577245, Class: 0.119179, Obj: 0.144203, No Obj: 0.000604, .5R: 0.739130, .75R: 0.130435, count: 23 -Region 16 Avg IOU: 0.643723, Class: 0.225914, Obj: 0.168393, No Obj: 0.001271, .5R: 0.863636, .75R: 0.181818, count: 22 -Region 23 Avg IOU: 0.582228, Class: 0.155096, Obj: 0.134387, No Obj: 0.000719, .5R: 0.640000, .75R: 0.160000, count: 25 -Region 16 Avg IOU: 0.644346, Class: 0.235797, Obj: 0.176086, No Obj: 0.001569, .5R: 0.809524, .75R: 0.238095, count: 21 -Region 23 Avg IOU: 0.646163, Class: 0.167846, Obj: 0.113084, No Obj: 0.000594, .5R: 0.782609, .75R: 0.347826, count: 23 -Region 16 Avg IOU: 0.665871, Class: 0.180585, Obj: 0.150374, No Obj: 0.001167, .5R: 1.000000, .75R: 0.200000, count: 15 -Region 23 Avg IOU: 0.672933, Class: 0.159886, Obj: 0.088999, No Obj: 0.000422, .5R: 1.000000, .75R: 0.150000, count: 20 -Region 16 Avg IOU: 0.605084, Class: 0.150654, Obj: 0.079879, No Obj: 0.000778, .5R: 0.772727, .75R: 0.181818, count: 22 -Region 23 Avg IOU: 0.618437, Class: 0.200871, Obj: 0.117326, No Obj: 0.000608, .5R: 0.842105, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.625007, Class: 0.209754, Obj: 0.164873, No Obj: 0.001205, .5R: 0.923077, .75R: 0.076923, count: 13 -Region 23 Avg IOU: 0.690872, Class: 0.185210, Obj: 0.039994, No Obj: 0.000255, .5R: 0.875000, .75R: 0.500000, count: 8 -Region 16 Avg IOU: 0.664635, Class: 0.257592, Obj: 0.170671, No Obj: 0.001545, .5R: 0.913043, .75R: 0.260870, count: 23 -Region 23 Avg IOU: 0.650519, Class: 0.213529, Obj: 0.040560, No Obj: 0.000309, .5R: 0.866667, .75R: 0.266667, count: 30 - - 223: 3.511055, 3.595796 avg loss, 0.001000 rate, 1.620837 seconds, 28544 images -Loaded: 0.000064 seconds -Region 16 Avg IOU: 0.589925, Class: 0.114987, Obj: 0.163146, No Obj: 0.001269, .5R: 0.750000, .75R: 0.000000, count: 16 -Region 23 Avg IOU: 0.625942, Class: 0.148279, Obj: 0.066341, No Obj: 0.000238, .5R: 0.789474, .75R: 0.105263, count: 19 -Region 16 Avg IOU: 0.693086, Class: 0.199822, Obj: 0.160225, No Obj: 0.001081, .5R: 0.952381, .75R: 0.285714, count: 21 -Region 23 Avg IOU: 0.646706, Class: 0.210902, Obj: 0.038748, No Obj: 0.000379, .5R: 0.857143, .75R: 0.250000, count: 28 -Region 16 Avg IOU: 0.638681, Class: 0.186322, Obj: 0.096621, No Obj: 0.001192, .5R: 0.944444, .75R: 0.055556, count: 18 -Region 23 Avg IOU: 0.650191, Class: 0.140705, Obj: 0.062890, No Obj: 0.000300, .5R: 0.916667, .75R: 0.166667, count: 24 -Region 16 Avg IOU: 0.640724, Class: 0.182188, Obj: 0.102311, No Obj: 0.001147, .5R: 0.857143, .75R: 0.250000, count: 28 -Region 23 Avg IOU: 0.648960, Class: 0.189084, Obj: 0.028973, No Obj: 0.000150, .5R: 0.840000, .75R: 0.240000, count: 25 -Region 16 Avg IOU: 0.654077, Class: 0.161517, Obj: 0.178796, No Obj: 0.001436, .5R: 0.884615, .75R: 0.115385, count: 26 -Region 23 Avg IOU: 0.649017, Class: 0.154822, Obj: 0.082900, No Obj: 0.000477, .5R: 0.920000, .75R: 0.280000, count: 25 -Region 16 Avg IOU: 0.645065, Class: 0.200137, Obj: 0.063504, No Obj: 0.000807, .5R: 0.944444, .75R: 0.222222, count: 18 -Region 23 Avg IOU: 0.648158, Class: 0.122914, Obj: 0.122666, No Obj: 0.000469, .5R: 0.857143, .75R: 0.142857, count: 21 -Region 16 Avg IOU: 0.600766, Class: 0.235675, Obj: 0.136674, No Obj: 0.001266, .5R: 0.764706, .75R: 0.058824, count: 17 -Region 23 Avg IOU: 0.642879, Class: 0.170747, Obj: 0.096970, No Obj: 0.000325, .5R: 0.863636, .75R: 0.227273, count: 22 -Region 16 Avg IOU: 0.648896, Class: 0.250481, Obj: 0.174945, No Obj: 0.001046, .5R: 0.857143, .75R: 0.214286, count: 14 -Region 23 Avg IOU: 0.595960, Class: 0.224415, Obj: 0.062905, No Obj: 0.000348, .5R: 0.812500, .75R: 0.187500, count: 16 - - 224: 3.797493, 3.615966 avg loss, 0.001000 rate, 1.613277 seconds, 28672 images -Loaded: 0.000059 seconds -Region 16 Avg IOU: 0.679706, Class: 0.183713, Obj: 0.187323, No Obj: 0.001320, .5R: 1.000000, .75R: 0.363636, count: 11 -Region 23 Avg IOU: 0.728401, Class: 0.147888, Obj: 0.188813, No Obj: 0.000478, .5R: 1.000000, .75R: 0.357143, count: 14 -Region 16 Avg IOU: 0.666726, Class: 0.197545, Obj: 0.157458, No Obj: 0.001537, .5R: 0.947368, .75R: 0.210526, count: 19 -Region 23 Avg IOU: 0.624657, Class: 0.178027, Obj: 0.036632, No Obj: 0.000317, .5R: 0.791667, .75R: 0.208333, count: 24 -Region 16 Avg IOU: 0.611856, Class: 0.280536, Obj: 0.194581, No Obj: 0.001377, .5R: 0.714286, .75R: 0.214286, count: 14 -Region 23 Avg IOU: 0.603038, Class: 0.263905, Obj: 0.030655, No Obj: 0.000134, .5R: 0.700000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.633123, Class: 0.232514, Obj: 0.036550, No Obj: 0.000478, .5R: 1.000000, .75R: 0.000000, count: 5 -Region 23 Avg IOU: 0.590887, Class: 0.179937, Obj: 0.044146, No Obj: 0.000185, .5R: 0.714286, .75R: 0.142857, count: 21 -Region 16 Avg IOU: 0.716579, Class: 0.235949, Obj: 0.183345, No Obj: 0.001038, .5R: 1.000000, .75R: 0.312500, count: 16 -Region 23 Avg IOU: 0.677350, Class: 0.137608, Obj: 0.125768, No Obj: 0.000161, .5R: 1.000000, .75R: 0.250000, count: 4 -Region 16 Avg IOU: 0.649253, Class: 0.246245, Obj: 0.249014, No Obj: 0.001477, .5R: 0.857143, .75R: 0.071429, count: 14 -Region 23 Avg IOU: 0.609846, Class: 0.176878, Obj: 0.060427, No Obj: 0.000191, .5R: 0.777778, .75R: 0.111111, count: 9 -Region 16 Avg IOU: 0.672407, Class: 0.205627, Obj: 0.114608, No Obj: 0.001127, .5R: 1.000000, .75R: 0.153846, count: 13 -Region 23 Avg IOU: 0.687289, Class: 0.091678, Obj: 0.025036, No Obj: 0.000207, .5R: 1.000000, .75R: 0.444444, count: 9 -Region 16 Avg IOU: 0.662192, Class: 0.229420, Obj: 0.269211, No Obj: 0.001418, .5R: 0.937500, .75R: 0.250000, count: 16 -Region 23 Avg IOU: 0.605257, Class: 0.180228, Obj: 0.064065, No Obj: 0.000185, .5R: 0.846154, .75R: 0.153846, count: 13 - - 225: 2.388834, 3.493253 avg loss, 0.001000 rate, 1.616977 seconds, 28800 images -Loaded: 0.018494 seconds -Region 16 Avg IOU: 0.636078, Class: 0.225302, Obj: 0.174198, No Obj: 0.000869, .5R: 0.909091, .75R: 0.045455, count: 22 -Region 23 Avg IOU: 0.621813, Class: 0.164374, Obj: 0.012200, No Obj: 0.000169, .5R: 0.761905, .75R: 0.333333, count: 21 -Region 16 Avg IOU: 0.669812, Class: 0.198888, Obj: 0.060617, No Obj: 0.000558, .5R: 0.916667, .75R: 0.333333, count: 24 -Region 23 Avg IOU: 0.638171, Class: 0.185263, Obj: 0.028596, No Obj: 0.000112, .5R: 0.769231, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.668735, Class: 0.230589, Obj: 0.154529, No Obj: 0.001096, .5R: 0.850000, .75R: 0.350000, count: 20 -Region 23 Avg IOU: 0.644630, Class: 0.154300, Obj: 0.057803, No Obj: 0.000187, .5R: 1.000000, .75R: 0.142857, count: 21 -Region 16 Avg IOU: 0.669304, Class: 0.223466, Obj: 0.069588, No Obj: 0.000879, .5R: 1.000000, .75R: 0.250000, count: 16 -Region 23 Avg IOU: 0.638538, Class: 0.188706, Obj: 0.016118, No Obj: 0.000146, .5R: 0.800000, .75R: 0.240000, count: 25 -Region 16 Avg IOU: 0.671938, Class: 0.193252, Obj: 0.138172, No Obj: 0.000856, .5R: 0.923077, .75R: 0.307692, count: 13 -Region 23 Avg IOU: 0.614834, Class: 0.196270, Obj: 0.042337, No Obj: 0.000222, .5R: 0.833333, .75R: 0.083333, count: 24 -Region 16 Avg IOU: 0.650738, Class: 0.251687, Obj: 0.108793, No Obj: 0.000669, .5R: 0.866667, .75R: 0.200000, count: 15 -Region 23 Avg IOU: 0.625605, Class: 0.178752, Obj: 0.024071, No Obj: 0.000146, .5R: 0.700000, .75R: 0.100000, count: 10 -Region 16 Avg IOU: 0.671425, Class: 0.226004, Obj: 0.116340, No Obj: 0.000770, .5R: 0.866667, .75R: 0.333333, count: 15 -Region 23 Avg IOU: 0.620766, Class: 0.223698, Obj: 0.015397, No Obj: 0.000095, .5R: 0.875000, .75R: 0.125000, count: 24 -Region 16 Avg IOU: 0.665341, Class: 0.218862, Obj: 0.153267, No Obj: 0.000855, .5R: 1.000000, .75R: 0.235294, count: 17 -Region 23 Avg IOU: 0.604265, Class: 0.172470, Obj: 0.011382, No Obj: 0.000083, .5R: 0.782609, .75R: 0.130435, count: 23 - - 226: 3.340077, 3.477935 avg loss, 0.001000 rate, 1.610884 seconds, 28928 images -Loaded: 0.000071 seconds -Region 16 Avg IOU: 0.590995, Class: 0.187673, Obj: 0.082139, No Obj: 0.000736, .5R: 0.714286, .75R: 0.095238, count: 21 -Region 23 Avg IOU: 0.644532, Class: 0.182108, Obj: 0.006968, No Obj: 0.000075, .5R: 0.777778, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.550973, Class: 0.246222, Obj: 0.069104, No Obj: 0.000825, .5R: 0.777778, .75R: 0.000000, count: 18 -Region 23 Avg IOU: 0.565477, Class: 0.139744, Obj: 0.012727, No Obj: 0.000092, .5R: 0.500000, .75R: 0.250000, count: 8 -Region 16 Avg IOU: 0.690153, Class: 0.192409, Obj: 0.102936, No Obj: 0.000955, .5R: 1.000000, .75R: 0.315789, count: 19 -Region 23 Avg IOU: 0.686812, Class: 0.196949, Obj: 0.051373, No Obj: 0.000270, .5R: 0.888889, .75R: 0.370370, count: 27 -Region 16 Avg IOU: 0.643903, Class: 0.269164, Obj: 0.117889, No Obj: 0.001063, .5R: 0.923077, .75R: 0.192308, count: 26 -Region 23 Avg IOU: 0.642007, Class: 0.198202, Obj: 0.027382, No Obj: 0.000200, .5R: 0.903226, .75R: 0.161290, count: 31 -Region 16 Avg IOU: 0.627244, Class: 0.194893, Obj: 0.105907, No Obj: 0.000715, .5R: 0.937500, .75R: 0.125000, count: 16 -Region 23 Avg IOU: 0.621347, Class: 0.188482, Obj: 0.051856, No Obj: 0.000178, .5R: 0.812500, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.636516, Class: 0.219368, Obj: 0.162116, No Obj: 0.000948, .5R: 0.947368, .75R: 0.105263, count: 19 -Region 23 Avg IOU: 0.603209, Class: 0.213899, Obj: 0.010329, No Obj: 0.000099, .5R: 0.909091, .75R: 0.090909, count: 11 -Region 16 Avg IOU: 0.623476, Class: 0.240854, Obj: 0.074048, No Obj: 0.000564, .5R: 0.866667, .75R: 0.200000, count: 15 -Region 23 Avg IOU: 0.667219, Class: 0.204268, 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seconds -Region 16 Avg IOU: 0.652254, Class: 0.249477, Obj: 0.099260, No Obj: 0.001041, .5R: 0.814815, .75R: 0.185185, count: 27 -Region 23 Avg IOU: 0.613264, Class: 0.191592, Obj: 0.027413, No Obj: 0.000082, .5R: 0.800000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.711321, Class: 0.191511, Obj: 0.197665, No Obj: 0.001190, .5R: 1.000000, .75R: 0.312500, count: 16 -Region 23 Avg IOU: 0.651837, Class: 0.134052, Obj: 0.027633, No Obj: 0.000106, .5R: 0.750000, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.636139, Class: 0.174998, Obj: 0.094629, No Obj: 0.000915, .5R: 0.900000, .75R: 0.100000, count: 10 -Region 23 Avg IOU: 0.598733, Class: 0.163191, Obj: 0.046276, No Obj: 0.000197, .5R: 0.818182, .75R: 0.181818, count: 11 -Region 16 Avg IOU: 0.647092, Class: 0.150462, Obj: 0.115240, No Obj: 0.001026, .5R: 0.937500, .75R: 0.187500, count: 16 -Region 23 Avg IOU: 0.543338, Class: 0.171345, Obj: 0.046400, No Obj: 0.000143, .5R: 0.642857, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 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23 Avg IOU: 0.653615, Class: 0.220829, Obj: 0.054812, No Obj: 0.000247, .5R: 0.923077, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.675164, Class: 0.231647, Obj: 0.127443, No Obj: 0.000801, .5R: 1.000000, .75R: 0.181818, count: 11 -Region 23 Avg IOU: 0.567939, Class: 0.180638, Obj: 0.043806, No Obj: 0.000239, .5R: 0.750000, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.647983, Class: 0.236982, Obj: 0.117283, No Obj: 0.001009, .5R: 0.947368, .75R: 0.157895, count: 19 -Region 23 Avg IOU: 0.624913, Class: 0.158361, Obj: 0.038242, No Obj: 0.000176, .5R: 0.700000, .75R: 0.200000, count: 10 -Region 16 Avg IOU: 0.672243, Class: 0.243446, Obj: 0.113727, No Obj: 0.001131, .5R: 0.904762, .75R: 0.142857, count: 21 -Region 23 Avg IOU: 0.643114, Class: 0.207441, Obj: 0.041634, No Obj: 0.000264, .5R: 0.846154, .75R: 0.230769, count: 13 - - 253: 2.763042, 3.101875 avg loss, 0.001000 rate, 1.612634 seconds, 32384 images -Loaded: 0.000062 seconds -Region 16 Avg IOU: 0.652478, Class: 0.238124, Obj: 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0.223562, Obj: 0.246684, No Obj: 0.001434, .5R: 0.857143, .75R: 0.142857, count: 21 -Region 23 Avg IOU: 0.570298, Class: 0.169868, Obj: 0.016036, No Obj: 0.000117, .5R: 0.812500, .75R: 0.000000, count: 16 -Region 16 Avg IOU: 0.700728, Class: 0.226684, Obj: 0.222614, No Obj: 0.001490, .5R: 0.944444, .75R: 0.333333, count: 18 -Region 23 Avg IOU: 0.596363, Class: 0.211187, Obj: 0.048854, No Obj: 0.000228, .5R: 0.739130, .75R: 0.043478, count: 23 -Region 16 Avg IOU: 0.647063, Class: 0.147704, Obj: 0.144820, No Obj: 0.001196, .5R: 0.941176, .75R: 0.117647, count: 17 -Region 23 Avg IOU: 0.535487, Class: 0.167051, Obj: 0.025603, No Obj: 0.000179, .5R: 0.571429, .75R: 0.095238, count: 21 -Region 16 Avg IOU: 0.700520, Class: 0.195057, Obj: 0.282325, No Obj: 0.001196, .5R: 1.000000, .75R: 0.333333, count: 12 -Region 23 Avg IOU: 0.634745, Class: 0.192570, Obj: 0.049533, No Obj: 0.000272, .5R: 0.790698, .75R: 0.162791, count: 43 - - 255: 3.919569, 3.235991 avg loss, 0.001000 rate, 1.635568 seconds, 32640 images -Loaded: 0.460004 seconds -Region 16 Avg IOU: 0.613460, Class: 0.189555, Obj: 0.128107, No Obj: 0.000993, .5R: 0.714286, .75R: 0.285714, count: 14 -Region 23 Avg IOU: 0.625794, Class: 0.150843, Obj: 0.134717, No Obj: 0.000831, .5R: 0.935484, .75R: 0.129032, count: 31 -Region 16 Avg IOU: 0.624402, Class: 0.173550, Obj: 0.216483, No Obj: 0.001375, .5R: 0.842105, .75R: 0.157895, count: 19 -Region 23 Avg IOU: 0.623125, Class: 0.189302, Obj: 0.078893, No Obj: 0.000654, .5R: 0.833333, .75R: 0.208333, count: 24 -Region 16 Avg IOU: 0.626640, Class: 0.197545, Obj: 0.145152, No Obj: 0.001118, .5R: 0.875000, .75R: 0.312500, count: 16 -Region 23 Avg IOU: 0.599476, Class: 0.180364, Obj: 0.118987, No Obj: 0.000478, .5R: 0.850000, .75R: 0.000000, count: 20 -Region 16 Avg IOU: 0.631654, Class: 0.191372, Obj: 0.226305, No Obj: 0.001383, .5R: 0.900000, .75R: 0.100000, count: 20 -Region 23 Avg IOU: 0.556712, Class: 0.215590, Obj: 0.077619, No Obj: 0.000517, .5R: 0.739130, .75R: 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4.057777, 3.318170 avg loss, 0.001000 rate, 1.567435 seconds, 32768 images -Loaded: 0.000083 seconds -Region 16 Avg IOU: 0.656804, Class: 0.193292, Obj: 0.128288, No Obj: 0.000939, .5R: 0.842105, .75R: 0.315789, count: 19 -Region 23 Avg IOU: 0.627063, Class: 0.167349, Obj: 0.065996, No Obj: 0.000495, .5R: 0.875000, .75R: 0.041667, count: 24 -Region 16 Avg IOU: 0.645880, Class: 0.209853, Obj: 0.084583, No Obj: 0.000796, .5R: 0.833333, .75R: 0.250000, count: 24 -Region 23 Avg IOU: 0.586920, Class: 0.226862, Obj: 0.054465, No Obj: 0.000312, .5R: 0.800000, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.665613, Class: 0.232312, Obj: 0.089827, No Obj: 0.000911, .5R: 0.923077, .75R: 0.153846, count: 13 -Region 23 Avg IOU: 0.582694, Class: 0.142323, Obj: 0.064209, No Obj: 0.000406, .5R: 0.823529, .75R: 0.117647, count: 17 -Region 16 Avg IOU: 0.593507, Class: 0.282244, Obj: 0.108101, No Obj: 0.000854, .5R: 0.785714, .75R: 0.214286, count: 14 -Region 23 Avg IOU: 0.618636, Class: 0.288813, Obj: 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23 Avg IOU: 0.512645, Class: 0.179514, Obj: 0.052666, No Obj: 0.000302, .5R: 0.520000, .75R: 0.080000, count: 25 -Region 16 Avg IOU: 0.689667, Class: 0.234363, Obj: 0.193809, No Obj: 0.000993, .5R: 1.000000, .75R: 0.230769, count: 13 -Region 23 Avg IOU: 0.620526, Class: 0.167981, Obj: 0.048382, No Obj: 0.000200, .5R: 1.000000, .75R: 0.111111, count: 9 -Region 16 Avg IOU: 0.590615, Class: 0.202880, Obj: 0.159691, No Obj: 0.001076, .5R: 0.680000, .75R: 0.160000, count: 25 -Region 23 Avg IOU: 0.643988, Class: 0.220771, Obj: 0.040927, No Obj: 0.000261, .5R: 0.880000, .75R: 0.160000, count: 25 -Region 16 Avg IOU: 0.585743, Class: 0.221859, Obj: 0.132434, No Obj: 0.000843, .5R: 0.800000, .75R: 0.133333, count: 15 -Region 23 Avg IOU: 0.620665, Class: 0.169999, Obj: 0.056455, No Obj: 0.000282, .5R: 0.900000, .75R: 0.150000, count: 20 -Region 16 Avg IOU: 0.596447, Class: 0.144426, Obj: 0.145940, No Obj: 0.001261, .5R: 0.833333, .75R: 0.111111, count: 18 -Region 23 Avg IOU: 0.597897, Class: 0.219805, Obj: 0.016568, No Obj: 0.000208, .5R: 0.722222, .75R: 0.166667, count: 18 - - 258: 3.625434, 3.347583 avg loss, 0.001000 rate, 1.544837 seconds, 33024 images -Loaded: 0.000074 seconds -Region 16 Avg IOU: 0.675194, Class: 0.143961, Obj: 0.282204, No Obj: 0.001097, .5R: 0.800000, .75R: 0.300000, count: 10 -Region 23 Avg IOU: 0.643369, Class: 0.138110, Obj: 0.039894, No Obj: 0.000178, .5R: 0.875000, .75R: 0.125000, count: 24 -Region 16 Avg IOU: 0.606718, Class: 0.165121, Obj: 0.147192, No Obj: 0.001638, .5R: 0.730769, .75R: 0.115385, count: 26 -Region 23 Avg IOU: 0.620512, Class: 0.153955, Obj: 0.010180, No Obj: 0.000105, .5R: 0.857143, .75R: 0.142857, count: 28 -Region 16 Avg IOU: 0.614853, Class: 0.158214, Obj: 0.183871, No Obj: 0.002180, .5R: 0.833333, .75R: 0.166667, count: 18 -Region 23 Avg IOU: 0.595038, Class: 0.152854, Obj: 0.052950, No Obj: 0.000209, .5R: 0.869565, .75R: 0.043478, count: 23 -Region 16 Avg IOU: 0.624790, Class: 0.221903, Obj: 0.174014, No Obj: 0.001244, 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Class: 0.228189, Obj: 0.051278, No Obj: 0.000808, .5R: 0.842105, .75R: 0.052632, count: 19 -Region 23 Avg IOU: 0.579682, Class: 0.228041, Obj: 0.044890, No Obj: 0.000220, .5R: 0.772727, .75R: 0.136364, count: 22 -Region 16 Avg IOU: 0.631221, Class: 0.213064, Obj: 0.114491, No Obj: 0.001013, .5R: 0.818182, .75R: 0.136364, count: 22 -Region 23 Avg IOU: 0.619785, Class: 0.175857, Obj: 0.062718, No Obj: 0.000280, .5R: 0.800000, .75R: 0.100000, count: 20 -Region 16 Avg IOU: 0.620399, Class: 0.205709, Obj: 0.163977, No Obj: 0.001042, .5R: 0.826087, .75R: 0.130435, count: 23 -Region 23 Avg IOU: 0.591588, Class: 0.204758, Obj: 0.068679, No Obj: 0.000351, .5R: 0.769231, .75R: 0.153846, count: 26 -Region 16 Avg IOU: 0.624884, Class: 0.205194, Obj: 0.114962, No Obj: 0.000930, .5R: 0.793103, .75R: 0.172414, count: 29 -Region 23 Avg IOU: 0.620197, Class: 0.159644, Obj: 0.030097, No Obj: 0.000193, .5R: 1.000000, .75R: 0.136364, count: 22 -Region 16 Avg IOU: 0.637756, Class: 0.224062, Obj: 0.144951, 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23 Avg IOU: 0.610997, Class: 0.217085, Obj: 0.099641, No Obj: 0.000633, .5R: 0.750000, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.557320, Class: 0.163359, Obj: 0.144180, No Obj: 0.000941, .5R: 1.000000, .75R: 0.000000, count: 5 -Region 23 Avg IOU: 0.599147, Class: 0.160524, Obj: 0.068014, No Obj: 0.000368, .5R: 0.916667, .75R: 0.083333, count: 12 -Region 16 Avg IOU: 0.655961, Class: 0.284464, Obj: 0.264108, No Obj: 0.000959, .5R: 0.800000, .75R: 0.300000, count: 10 -Region 23 Avg IOU: 0.615388, Class: 0.228749, Obj: 0.062633, No Obj: 0.000433, .5R: 0.809524, .75R: 0.047619, count: 21 -Region 16 Avg IOU: 0.606752, Class: 0.133152, Obj: 0.118459, No Obj: 0.001406, .5R: 0.785714, .75R: 0.071429, count: 14 -Region 23 Avg IOU: 0.583901, Class: 0.159037, Obj: 0.048379, No Obj: 0.000441, .5R: 0.857143, .75R: 0.000000, count: 21 -Region 16 Avg IOU: 0.664605, Class: 0.198448, Obj: 0.283063, No Obj: 0.001215, .5R: 0.833333, .75R: 0.250000, count: 12 -Region 23 Avg IOU: 0.630559, Class: 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-Resizing -736 x 736 - try to allocate workspace = 1149517825 * sizeof(float), CUDA allocate done! -Loaded: 0.297343 seconds -Region 16 Avg IOU: 0.650049, Class: 0.211208, Obj: 0.133822, No Obj: 0.001413, .5R: 0.923077, .75R: 0.205128, count: 39 -Region 23 Avg IOU: 0.646601, Class: 0.192361, Obj: 0.050863, No Obj: 0.000154, .5R: 0.888889, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.653955, Class: 0.184432, Obj: 0.221911, No Obj: 0.000945, .5R: 0.875000, .75R: 0.187500, count: 16 -Region 23 Avg IOU: 0.692161, Class: 0.211413, Obj: 0.072678, No Obj: 0.000161, .5R: 1.000000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.644532, Class: 0.198931, Obj: 0.126705, No Obj: 0.001233, .5R: 0.878788, .75R: 0.181818, count: 33 -Region 23 Avg IOU: 0.551699, Class: 0.125252, Obj: 0.049073, No Obj: 0.000117, .5R: 0.733333, .75R: 0.066667, count: 15 -Region 16 Avg IOU: 0.644890, Class: 0.220310, Obj: 0.176845, No Obj: 0.001850, .5R: 0.892857, .75R: 0.142857, count: 28 -Region 23 Avg IOU: 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12 -Region 23 Avg IOU: 0.638576, Class: 0.240386, Obj: 0.100891, No Obj: 0.000567, .5R: 0.894737, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.634867, Class: 0.220528, Obj: 0.154727, No Obj: 0.000835, .5R: 0.916667, .75R: 0.166667, count: 12 -Region 23 Avg IOU: 0.544145, Class: 0.220411, Obj: 0.069966, No Obj: 0.000424, .5R: 0.666667, .75R: 0.142857, count: 21 -Region 16 Avg IOU: 0.661429, Class: 0.168506, Obj: 0.186413, No Obj: 0.000833, .5R: 1.000000, .75R: 0.166667, count: 12 -Region 23 Avg IOU: 0.603409, Class: 0.168076, Obj: 0.046961, No Obj: 0.000293, .5R: 0.761905, .75R: 0.142857, count: 21 -Region 16 Avg IOU: 0.631309, Class: 0.271434, Obj: 0.156330, No Obj: 0.000823, .5R: 0.909091, .75R: 0.000000, count: 11 -Region 23 Avg IOU: 0.574413, Class: 0.208816, Obj: 0.048287, No Obj: 0.000432, .5R: 0.677419, .75R: 0.096774, count: 31 - - 322: 3.094909, 3.327732 avg loss, 0.001000 rate, 0.818104 seconds, 41216 images -Loaded: 0.324815 seconds -Region 16 Avg IOU: 0.675428, Class: 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0.735944 seconds, 43648 images -Loaded: 0.168295 seconds -Region 16 Avg IOU: 0.680625, Class: 0.291560, Obj: 0.093098, No Obj: 0.000601, .5R: 1.000000, .75R: 0.000000, count: 5 -Region 23 Avg IOU: 0.600201, Class: 0.207739, Obj: 0.071531, No Obj: 0.000488, .5R: 0.842105, .75R: 0.052632, count: 19 -Region 16 Avg IOU: 0.641647, Class: 0.158666, Obj: 0.236534, No Obj: 0.001401, .5R: 0.833333, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.615864, Class: 0.160485, Obj: 0.056568, No Obj: 0.000436, .5R: 0.705882, .75R: 0.117647, count: 17 -Region 16 Avg IOU: 0.656266, Class: 0.280074, Obj: 0.239808, No Obj: 0.001832, .5R: 0.928571, .75R: 0.142857, count: 14 -Region 23 Avg IOU: 0.619249, Class: 0.265203, Obj: 0.062455, No Obj: 0.000546, .5R: 0.812500, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.687700, Class: 0.263070, Obj: 0.252831, No Obj: 0.001416, .5R: 0.857143, .75R: 0.285714, count: 7 -Region 23 Avg IOU: 0.662678, Class: 0.206122, Obj: 0.066976, No Obj: 0.000438, .5R: 0.956522, 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2.332477, 2.605384 avg loss, 0.001000 rate, 0.740450 seconds, 43776 images -Loaded: 0.022196 seconds -Region 16 Avg IOU: 0.688458, Class: 0.172886, Obj: 0.334232, No Obj: 0.001696, .5R: 0.916667, .75R: 0.333333, count: 12 -Region 23 Avg IOU: 0.623515, Class: 0.180936, Obj: 0.082103, No Obj: 0.000573, .5R: 0.764706, .75R: 0.176471, count: 17 -Region 16 Avg IOU: 0.616466, Class: 0.291640, Obj: 0.130727, No Obj: 0.001011, .5R: 0.857143, .75R: 0.142857, count: 7 -Region 23 Avg IOU: 0.626619, Class: 0.263612, Obj: 0.053425, No Obj: 0.000517, .5R: 0.842105, .75R: 0.105263, count: 19 -Region 16 Avg IOU: 0.719098, Class: 0.235528, Obj: 0.098517, No Obj: 0.001050, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.586288, Class: 0.220154, Obj: 0.095565, No Obj: 0.000480, .5R: 0.727273, .75R: 0.000000, count: 11 -Region 16 Avg IOU: 0.601456, Class: 0.162000, Obj: 0.280502, No Obj: 0.001657, .5R: 0.777778, .75R: 0.111111, count: 9 -Region 23 Avg IOU: 0.571273, Class: 0.215891, Obj: 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Avg IOU: 0.578861, Class: 0.303050, Obj: 0.068758, No Obj: 0.001458, .5R: 0.666667, .75R: 0.333333, count: 3 -Region 23 Avg IOU: 0.594491, Class: 0.234205, Obj: 0.029404, No Obj: 0.000276, .5R: 0.739130, .75R: 0.173913, count: 23 - - 347: 2.560177, 2.577024 avg loss, 0.001000 rate, 0.744326 seconds, 44416 images -Loaded: 0.133715 seconds -Region 16 Avg IOU: 0.778863, Class: 0.333552, Obj: 0.371855, No Obj: 0.001769, .5R: 1.000000, .75R: 0.750000, count: 4 -Region 23 Avg IOU: 0.685531, Class: 0.223003, Obj: 0.058784, No Obj: 0.000447, .5R: 0.961538, .75R: 0.307692, count: 26 -Region 16 Avg IOU: 0.708816, Class: 0.136836, Obj: 0.277697, No Obj: 0.001187, .5R: 0.833333, .75R: 0.500000, count: 6 -Region 23 Avg IOU: 0.645026, Class: 0.200567, Obj: 0.131380, No Obj: 0.000616, .5R: 0.937500, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.610283, Class: 0.213957, Obj: 0.220876, No Obj: 0.001546, .5R: 0.750000, .75R: 0.125000, count: 8 -Region 23 Avg IOU: 0.608204, Class: 0.213129, Obj: 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23 Avg IOU: 0.611676, Class: 0.202455, Obj: 0.036158, No Obj: 0.000355, .5R: 0.928571, .75R: 0.071429, count: 14 -Region 16 Avg IOU: 0.623066, Class: 0.168596, Obj: 0.215527, No Obj: 0.000704, .5R: 0.714286, .75R: 0.000000, count: 7 -Region 23 Avg IOU: 0.561924, Class: 0.178591, Obj: 0.093796, No Obj: 0.000625, .5R: 0.736842, .75R: 0.105263, count: 19 -Region 16 Avg IOU: 0.716622, Class: 0.159986, Obj: 0.160688, No Obj: 0.001406, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.612476, Class: 0.142220, Obj: 0.058633, No Obj: 0.000341, .5R: 0.833333, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.627513, Class: 0.236894, Obj: 0.178658, No Obj: 0.001936, .5R: 0.857143, .75R: 0.142857, count: 14 -Region 23 Avg IOU: 0.626700, Class: 0.204134, Obj: 0.029874, No Obj: 0.000294, .5R: 0.833333, .75R: 0.250000, count: 12 -Region 16 Avg IOU: 0.664607, Class: 0.150143, Obj: 0.216552, No Obj: 0.001743, .5R: 1.000000, .75R: 0.200000, count: 10 -Region 23 Avg IOU: 0.606665, Class: 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Avg IOU: 0.571706, Class: 0.228686, Obj: 0.092646, No Obj: 0.000823, .5R: 0.720000, .75R: 0.080000, count: 25 -Region 16 Avg IOU: 0.743302, Class: 0.206874, Obj: 0.310105, No Obj: 0.000749, .5R: 1.000000, .75R: 0.666667, count: 3 -Region 23 Avg IOU: 0.598559, Class: 0.191562, Obj: 0.085364, No Obj: 0.000919, .5R: 0.882353, .75R: 0.058824, count: 17 -Region 16 Avg IOU: 0.639204, Class: 0.177948, Obj: 0.140963, No Obj: 0.001393, .5R: 0.833333, .75R: 0.166667, count: 6 -Region 23 Avg IOU: 0.562884, Class: 0.210204, Obj: 0.103972, No Obj: 0.001205, .5R: 0.695652, .75R: 0.086957, count: 23 - - 353: 2.482605, 2.482919 avg loss, 0.001000 rate, 0.642439 seconds, 45184 images -Loaded: 0.114410 seconds -Region 16 Avg IOU: 0.773322, Class: 0.138796, Obj: 0.258636, No Obj: 0.001273, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.599410, Class: 0.128307, Obj: 0.118524, No Obj: 0.000704, .5R: 0.833333, .75R: 0.083333, count: 12 -Region 16 Avg IOU: 0.703081, Class: 0.248472, Obj: 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-Resizing -480 x 480 - try to allocate workspace = 1144537089 * sizeof(float), CUDA allocate done! -Loaded: 0.493222 seconds -Region 16 Avg IOU: 0.591191, Class: 0.160792, Obj: 0.051539, No Obj: 0.000772, .5R: 0.764706, .75R: 0.176471, count: 17 -Region 23 Avg IOU: 0.546357, Class: 0.184360, Obj: 0.032019, No Obj: 0.000351, .5R: 0.631579, .75R: 0.105263, count: 19 -Region 16 Avg IOU: 0.673092, Class: 0.230346, Obj: 0.194074, No Obj: 0.001427, .5R: 0.947368, .75R: 0.210526, count: 19 -Region 23 Avg IOU: 0.621201, Class: 0.212888, Obj: 0.061714, No Obj: 0.000605, .5R: 0.837838, .75R: 0.189189, count: 37 -Region 16 Avg IOU: 0.677669, Class: 0.193236, Obj: 0.151127, No Obj: 0.001060, .5R: 0.875000, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.675760, Class: 0.241356, Obj: 0.056146, No Obj: 0.000547, .5R: 0.928571, .75R: 0.178571, count: 28 -Region 16 Avg IOU: 0.627467, Class: 0.182960, Obj: 0.121540, No Obj: 0.001118, .5R: 0.909091, .75R: 0.272727, count: 11 -Region 23 Avg IOU: 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Avg IOU: 0.472414, Class: 0.245628, Obj: 0.015870, No Obj: 0.000277, .5R: 0.500000, .75R: 0.000000, count: 12 -Region 16 Avg IOU: 0.740499, Class: 0.278579, Obj: 0.205159, No Obj: 0.000711, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.642248, Class: 0.162647, Obj: 0.082272, No Obj: 0.000343, .5R: 0.928571, .75R: 0.214286, count: 14 - - 367: 2.960880, 2.693285 avg loss, 0.001000 rate, 1.062027 seconds, 46976 images -Loaded: 0.036444 seconds -Region 16 Avg IOU: 0.699448, Class: 0.155166, Obj: 0.155236, No Obj: 0.000916, .5R: 0.916667, .75R: 0.333333, count: 12 -Region 23 Avg IOU: 0.552967, Class: 0.126852, Obj: 0.057287, No Obj: 0.000377, .5R: 0.650000, .75R: 0.100000, count: 20 -Region 16 Avg IOU: 0.660815, Class: 0.235005, Obj: 0.104146, No Obj: 0.001282, .5R: 0.916667, .75R: 0.291667, count: 24 -Region 23 Avg IOU: 0.578158, Class: 0.179382, Obj: 0.059818, No Obj: 0.000434, .5R: 0.826087, .75R: 0.000000, count: 23 -Region 16 Avg IOU: 0.685372, Class: 0.172178, Obj: 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count: 5 -Region 23 Avg IOU: 0.587083, Class: 0.247074, Obj: 0.045750, No Obj: 0.000300, .5R: 0.727273, .75R: 0.000000, count: 11 -Region 16 Avg IOU: 0.674806, Class: 0.200129, Obj: 0.264930, No Obj: 0.001383, .5R: 1.000000, .75R: 0.200000, count: 15 -Region 23 Avg IOU: 0.645681, Class: 0.213959, Obj: 0.067339, No Obj: 0.000423, .5R: 0.764706, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.673434, Class: 0.266656, Obj: 0.086544, No Obj: 0.000736, .5R: 1.000000, .75R: 0.125000, count: 8 -Region 23 Avg IOU: 0.651627, Class: 0.216119, Obj: 0.065803, No Obj: 0.000562, .5R: 0.904762, .75R: 0.142857, count: 21 -Region 16 Avg IOU: 0.711080, Class: 0.276157, Obj: 0.293610, No Obj: 0.000834, .5R: 1.000000, .75R: 0.166667, count: 6 -Region 23 Avg IOU: 0.638156, Class: 0.207959, Obj: 0.047063, No Obj: 0.000381, .5R: 0.875000, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.616721, Class: 0.202696, Obj: 0.269026, No Obj: 0.001135, .5R: 0.888889, .75R: 0.222222, count: 9 -Region 23 Avg IOU: 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-Loaded: 0.153633 seconds -Region 16 Avg IOU: 0.696826, Class: 0.189374, Obj: 0.220947, No Obj: 0.001402, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.613791, Class: 0.151483, Obj: 0.096178, No Obj: 0.001351, .5R: 0.777778, .75R: 0.166667, count: 36 -Region 16 Avg IOU: 0.654372, Class: 0.249276, Obj: 0.131204, No Obj: 0.001561, .5R: 0.866667, .75R: 0.266667, count: 15 -Region 23 Avg IOU: 0.635291, Class: 0.193927, Obj: 0.083277, No Obj: 0.000463, .5R: 0.928571, .75R: 0.000000, count: 14 -Region 16 Avg IOU: 0.721548, Class: 0.248408, Obj: 0.231893, No Obj: 0.001851, .5R: 1.000000, .75R: 0.434783, count: 23 -Region 23 Avg IOU: 0.580166, Class: 0.263220, Obj: 0.049986, No Obj: 0.000767, .5R: 0.782609, .75R: 0.130435, count: 23 -Region 16 Avg IOU: 0.727342, Class: 0.199998, Obj: 0.337208, No Obj: 0.001534, .5R: 1.000000, .75R: 0.428571, count: 7 -Region 23 Avg IOU: 0.651957, Class: 0.151500, Obj: 0.164042, No Obj: 0.000808, .5R: 0.833333, .75R: 0.250000, count: 12 -Region 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.75R: 0.400000, count: 5 -Region 23 Avg IOU: 0.651779, Class: 0.141702, Obj: 0.077507, No Obj: 0.000516, .5R: 0.875000, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.725012, Class: 0.245065, Obj: 0.268147, No Obj: 0.001308, .5R: 0.846154, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.558011, Class: 0.137105, Obj: 0.038324, No Obj: 0.000598, .5R: 0.680000, .75R: 0.080000, count: 25 -Region 16 Avg IOU: 0.730992, Class: 0.247757, Obj: 0.393315, No Obj: 0.001630, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 23 Avg IOU: 0.633171, Class: 0.144336, Obj: 0.085938, No Obj: 0.000349, .5R: 0.777778, .75R: 0.111111, count: 9 -Region 16 Avg IOU: 0.670750, Class: 0.225783, Obj: 0.325928, No Obj: 0.001754, .5R: 0.800000, .75R: 0.300000, count: 10 -Region 23 Avg IOU: 0.592570, Class: 0.144662, Obj: 0.027863, No Obj: 0.000388, .5R: 0.777778, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.697829, Class: 0.256131, Obj: 0.284284, No Obj: 0.001509, .5R: 0.916667, .75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.628601, Class: 0.230714, Obj: 0.036159, No Obj: 0.000443, .5R: 0.846154, .75R: 0.115385, count: 26 -Region 16 Avg IOU: 0.644532, Class: 0.219260, Obj: 0.425131, No Obj: 0.002177, .5R: 0.818182, .75R: 0.181818, count: 11 -Region 23 Avg IOU: 0.655937, Class: 0.212800, Obj: 0.066666, No Obj: 0.000478, .5R: 0.777778, .75R: 0.222222, count: 18 -Region 16 Avg IOU: 0.665827, Class: 0.272394, Obj: 0.259048, No Obj: 0.002561, .5R: 1.000000, .75R: 0.133333, count: 15 -Region 23 Avg IOU: 0.596657, Class: 0.237577, Obj: 0.029742, No Obj: 0.000462, .5R: 0.764706, .75R: 0.058824, count: 17 -Region 16 Avg IOU: 0.714150, Class: 0.238051, Obj: 0.319841, No Obj: 0.001521, .5R: 1.000000, .75R: 0.363636, count: 11 -Region 23 Avg IOU: 0.590487, Class: 0.215281, Obj: 0.072310, No Obj: 0.000644, .5R: 0.812500, .75R: 0.062500, count: 16 - - 410: 2.561161, 2.639376 avg loss, 0.001000 rate, 0.741272 seconds, 52480 images -Resizing -704 x 704 - try to allocate workspace = 1148780545 * sizeof(float), CUDA allocate done! -Loaded: 0.512742 seconds -Region 16 Avg IOU: 0.659202, Class: 0.239901, Obj: 0.208698, No Obj: 0.001336, .5R: 0.903226, .75R: 0.225806, count: 31 -Region 23 Avg IOU: 0.724615, Class: 0.217701, Obj: 0.221307, No Obj: 0.000680, .5R: 1.000000, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.660290, Class: 0.210441, Obj: 0.138163, No Obj: 0.001294, .5R: 0.916667, .75R: 0.291667, count: 24 -Region 23 Avg IOU: 0.590768, Class: 0.126435, Obj: 0.069961, No Obj: 0.000550, .5R: 0.800000, .75R: 0.066667, count: 15 -Region 16 Avg IOU: 0.631873, Class: 0.239778, Obj: 0.127452, No Obj: 0.001002, .5R: 0.875000, .75R: 0.208333, count: 24 -Region 23 Avg IOU: 0.541622, Class: 0.157595, Obj: 0.100314, No Obj: 0.000506, .5R: 0.687500, .75R: 0.062500, count: 16 -Region 16 Avg IOU: 0.634552, Class: 0.260068, Obj: 0.191435, No Obj: 0.001140, .5R: 0.842105, .75R: 0.263158, count: 19 -Region 23 Avg IOU: 0.610502, Class: 0.204899, Obj: 0.092441, No Obj: 0.000527, .5R: 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count: 15 -Region 23 Avg IOU: 0.616648, Class: 0.178626, Obj: 0.036882, No Obj: 0.000237, .5R: 0.800000, .75R: 0.100000, count: 20 -Region 16 Avg IOU: 0.673993, Class: 0.197534, Obj: 0.094268, No Obj: 0.000952, .5R: 1.000000, .75R: 0.222222, count: 18 -Region 23 Avg IOU: 0.606939, Class: 0.173930, Obj: 0.028789, No Obj: 0.000249, .5R: 0.750000, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.669768, Class: 0.183016, Obj: 0.061652, No Obj: 0.000607, .5R: 0.882353, .75R: 0.235294, count: 17 -Region 23 Avg IOU: 0.653758, Class: 0.182461, Obj: 0.014648, No Obj: 0.000132, .5R: 0.812500, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.654734, Class: 0.210394, Obj: 0.079273, No Obj: 0.000843, .5R: 0.888889, .75R: 0.222222, count: 18 -Region 23 Avg IOU: 0.658240, Class: 0.221016, Obj: 0.060921, No Obj: 0.000375, .5R: 0.928571, .75R: 0.142857, count: 28 - - 427: 3.193004, 3.062152 avg loss, 0.001000 rate, 1.049171 seconds, 54656 images -Loaded: 0.000081 seconds -Region 16 Avg IOU: 0.707683, Class: 0.136739, Obj: 0.156831, No Obj: 0.001033, .5R: 1.000000, .75R: 0.388889, count: 18 -Region 23 Avg IOU: 0.673339, Class: 0.183213, Obj: 0.160969, No Obj: 0.001001, .5R: 1.000000, .75R: 0.222222, count: 18 -Region 16 Avg IOU: 0.653384, Class: 0.236487, Obj: 0.192308, No Obj: 0.001164, .5R: 0.928571, .75R: 0.142857, count: 14 -Region 23 Avg IOU: 0.633674, Class: 0.182619, Obj: 0.154060, No Obj: 0.000856, .5R: 0.875000, .75R: 0.166667, count: 24 -Region 16 Avg IOU: 0.647798, Class: 0.170263, Obj: 0.037678, No Obj: 0.000704, .5R: 0.846154, .75R: 0.384615, count: 13 -Region 23 Avg IOU: 0.649971, Class: 0.194719, Obj: 0.062743, No Obj: 0.000645, .5R: 0.920000, .75R: 0.120000, count: 25 -Region 16 Avg IOU: 0.687046, Class: 0.239055, Obj: 0.228143, No Obj: 0.001159, .5R: 1.000000, .75R: 0.285714, count: 14 -Region 23 Avg IOU: 0.661476, Class: 0.230808, Obj: 0.130954, No Obj: 0.000876, .5R: 0.944444, .75R: 0.222222, count: 18 -Region 16 Avg IOU: 0.608958, Class: 0.193184, Obj: 0.159868, 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0.000066 seconds -Region 16 Avg IOU: 0.638476, Class: 0.137107, Obj: 0.172658, No Obj: 0.001181, .5R: 0.846154, .75R: 0.153846, count: 13 -Region 23 Avg IOU: 0.616856, Class: 0.093772, Obj: 0.121942, No Obj: 0.001052, .5R: 0.933333, .75R: 0.000000, count: 15 -Region 16 Avg IOU: 0.660017, Class: 0.230292, Obj: 0.143014, No Obj: 0.001074, .5R: 0.947368, .75R: 0.263158, count: 19 -Region 23 Avg IOU: 0.575519, Class: 0.202456, Obj: 0.090252, No Obj: 0.000381, .5R: 0.545455, .75R: 0.181818, count: 11 -Region 16 Avg IOU: 0.687822, Class: 0.213017, Obj: 0.139589, No Obj: 0.001450, .5R: 1.000000, .75R: 0.235294, count: 17 -Region 23 Avg IOU: 0.584522, Class: 0.187089, Obj: 0.122893, No Obj: 0.000851, .5R: 0.700000, .75R: 0.200000, count: 10 -Region 16 Avg IOU: 0.686801, Class: 0.233812, Obj: 0.152502, No Obj: 0.001301, .5R: 0.928571, .75R: 0.357143, count: 14 -Region 23 Avg IOU: 0.597569, Class: 0.168599, Obj: 0.138921, No Obj: 0.001120, .5R: 0.615385, .75R: 0.269231, count: 26 -Region 16 Avg 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rate, 1.056584 seconds, 54912 images -Loaded: 0.000069 seconds -Region 16 Avg IOU: 0.723026, Class: 0.245698, Obj: 0.347776, No Obj: 0.002080, .5R: 0.909091, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.604038, Class: 0.229381, Obj: 0.041026, No Obj: 0.000285, .5R: 0.909091, .75R: 0.000000, count: 11 -Region 16 Avg IOU: 0.694593, Class: 0.213427, Obj: 0.220303, No Obj: 0.002170, .5R: 0.952381, .75R: 0.285714, count: 21 -Region 23 Avg IOU: 0.599238, Class: 0.205867, Obj: 0.086549, No Obj: 0.000715, .5R: 0.733333, .75R: 0.133333, count: 30 -Region 16 Avg IOU: 0.674309, Class: 0.152710, Obj: 0.350943, No Obj: 0.001852, .5R: 0.866667, .75R: 0.333333, count: 15 -Region 23 Avg IOU: 0.612144, Class: 0.241585, Obj: 0.060777, No Obj: 0.000323, .5R: 0.857143, .75R: 0.142857, count: 21 -Region 16 Avg IOU: 0.731117, Class: 0.263915, Obj: 0.256144, No Obj: 0.001206, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.580360, Class: 0.211969, Obj: 0.056632, No Obj: 0.000315, .5R: 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22 - - 430: 3.019628, 2.993218 avg loss, 0.001000 rate, 1.059882 seconds, 55040 images -Resizing -640 x 640 - try to allocate workspace = 1147404289 * sizeof(float), CUDA allocate done! -Loaded: 0.403598 seconds -Region 16 Avg IOU: 0.706699, Class: 0.237299, Obj: 0.099076, No Obj: 0.000753, .5R: 1.000000, .75R: 0.307692, count: 13 -Region 23 Avg IOU: 0.625053, Class: 0.214292, Obj: 0.019905, No Obj: 0.000115, .5R: 0.812500, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.596153, Class: 0.277311, Obj: 0.054600, No Obj: 0.001053, .5R: 0.750000, .75R: 0.125000, count: 16 -Region 23 Avg IOU: 0.658700, Class: 0.161830, Obj: 0.021030, No Obj: 0.000138, .5R: 0.944444, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.641140, Class: 0.185585, Obj: 0.179504, No Obj: 0.001101, .5R: 0.913043, .75R: 0.130435, count: 23 -Region 23 Avg IOU: 0.591536, Class: 0.148080, Obj: 0.020327, No Obj: 0.000131, .5R: 0.705882, .75R: 0.117647, count: 17 -Region 16 Avg IOU: 0.610762, Class: 0.191544, Obj: 0.099621, No Obj: 0.001161, .5R: 0.869565, .75R: 0.086957, count: 23 -Region 23 Avg IOU: 0.581456, Class: 0.118814, Obj: 0.038643, No Obj: 0.000143, .5R: 0.750000, .75R: 0.166667, count: 12 -Region 16 Avg IOU: 0.570992, Class: 0.291582, Obj: 0.143071, No Obj: 0.000927, .5R: 0.666667, .75R: 0.111111, count: 18 -Region 23 Avg IOU: 0.602934, Class: 0.250166, Obj: 0.039136, No Obj: 0.000127, .5R: 0.857143, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.691334, Class: 0.257402, Obj: 0.258288, No Obj: 0.000687, .5R: 1.000000, .75R: 0.125000, count: 8 -Region 23 Avg IOU: 0.665304, Class: 0.224519, Obj: 0.065559, No Obj: 0.000209, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.671891, Class: 0.176321, Obj: 0.210611, No Obj: 0.001080, .5R: 0.937500, .75R: 0.187500, count: 16 -Region 23 Avg IOU: 0.645951, Class: 0.233765, Obj: 0.054006, No Obj: 0.000183, .5R: 0.857143, .75R: 0.285714, count: 7 -Region 16 Avg IOU: 0.693836, Class: 0.214513, Obj: 0.233198, No Obj: 0.001345, .5R: 0.916667, 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IOU: 0.756819, Class: 0.177354, Obj: 0.431950, No Obj: 0.002593, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.638792, Class: 0.241707, Obj: 0.120973, No Obj: 0.000706, .5R: 0.944444, .75R: 0.000000, count: 18 -Region 16 Avg IOU: 0.718123, Class: 0.160012, Obj: 0.350845, No Obj: 0.002133, .5R: 1.000000, .75R: 0.352941, count: 17 -Region 23 Avg IOU: 0.581387, Class: 0.174102, Obj: 0.076239, No Obj: 0.000314, .5R: 0.733333, .75R: 0.133333, count: 15 -Region 16 Avg IOU: 0.695182, Class: 0.237204, Obj: 0.271892, No Obj: 0.002343, .5R: 0.947368, .75R: 0.263158, count: 19 -Region 23 Avg IOU: 0.582799, Class: 0.216417, Obj: 0.045119, No Obj: 0.000318, .5R: 0.695652, .75R: 0.217391, count: 23 -Region 16 Avg IOU: 0.691257, Class: 0.184715, Obj: 0.361766, No Obj: 0.002104, .5R: 0.900000, .75R: 0.200000, count: 10 -Region 23 Avg IOU: 0.653562, Class: 0.197393, Obj: 0.061736, No Obj: 0.000333, .5R: 0.888889, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.618649, Class: 0.287304, 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count: 11 -Region 16 Avg IOU: 0.686931, Class: 0.259824, Obj: 0.150267, No Obj: 0.001403, .5R: 0.863636, .75R: 0.409091, count: 22 -Region 23 Avg IOU: 0.568031, Class: 0.220509, Obj: 0.043493, No Obj: 0.000321, .5R: 0.607143, .75R: 0.142857, count: 28 -Region 16 Avg IOU: 0.652971, Class: 0.237207, Obj: 0.196615, No Obj: 0.001709, .5R: 0.909091, .75R: 0.227273, count: 22 -Region 23 Avg IOU: 0.636301, Class: 0.228144, Obj: 0.040595, No Obj: 0.000347, .5R: 0.909091, .75R: 0.151515, count: 33 -Region 16 Avg IOU: 0.690565, Class: 0.178532, Obj: 0.169435, No Obj: 0.000916, .5R: 1.000000, .75R: 0.222222, count: 9 -Region 23 Avg IOU: 0.615020, Class: 0.118597, Obj: 0.033407, No Obj: 0.000233, .5R: 0.777778, .75R: 0.277778, count: 18 - - 460: 3.365083, 2.839158 avg loss, 0.001000 rate, 0.995712 seconds, 58880 images -Resizing -416 x 416 - try to allocate workspace = 304758785 * sizeof(float), CUDA allocate done! -Loaded: 1.305980 seconds -Region 16 Avg IOU: 0.667400, Class: 0.232871, Obj: 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16 Avg IOU: 0.686806, Class: 0.185658, Obj: 0.386786, No Obj: 0.001449, .5R: 1.000000, .75R: 0.285714, count: 7 -Region 23 Avg IOU: 0.688223, Class: 0.167253, Obj: 0.081579, No Obj: 0.000719, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.715147, Class: 0.254216, Obj: 0.343294, No Obj: 0.002278, .5R: 0.944444, .75R: 0.277778, count: 18 -Region 23 Avg IOU: 0.548418, Class: 0.174664, Obj: 0.139345, No Obj: 0.001357, .5R: 0.724138, .75R: 0.034483, count: 29 -Region 16 Avg IOU: 0.663578, Class: 0.202701, Obj: 0.393930, No Obj: 0.001963, .5R: 1.000000, .75R: 0.181818, count: 11 -Region 23 Avg IOU: 0.575672, Class: 0.208976, Obj: 0.158656, No Obj: 0.000903, .5R: 0.800000, .75R: 0.066667, count: 15 -Region 16 Avg IOU: 0.659451, Class: 0.205510, Obj: 0.352730, No Obj: 0.002566, .5R: 0.937500, .75R: 0.187500, count: 16 -Region 23 Avg IOU: 0.644931, Class: 0.180509, Obj: 0.190683, No Obj: 0.001983, .5R: 0.864865, .75R: 0.216216, count: 37 -Region 16 Avg IOU: 0.694804, Class: 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59136 images -Loaded: 0.106077 seconds -Region 16 Avg IOU: 0.589986, Class: 0.204956, Obj: 0.184811, No Obj: 0.001524, .5R: 0.600000, .75R: 0.300000, count: 10 -Region 23 Avg IOU: 0.627717, Class: 0.183752, Obj: 0.101143, No Obj: 0.000815, .5R: 0.857143, .75R: 0.142857, count: 21 -Region 16 Avg IOU: 0.724483, Class: 0.265690, Obj: 0.192246, No Obj: 0.001386, .5R: 0.909091, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.576156, Class: 0.216761, Obj: 0.065048, No Obj: 0.000457, .5R: 0.666667, .75R: 0.095238, count: 21 -Region 16 Avg IOU: 0.742444, Class: 0.177396, Obj: 0.399913, No Obj: 0.002250, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.659934, Class: 0.161716, Obj: 0.139738, No Obj: 0.000565, .5R: 0.916667, .75R: 0.250000, count: 12 -Region 16 Avg IOU: 0.689277, Class: 0.231253, Obj: 0.262602, No Obj: 0.001660, .5R: 0.888889, .75R: 0.444444, count: 9 -Region 23 Avg IOU: 0.637527, Class: 0.180470, Obj: 0.053092, No Obj: 0.000378, .5R: 0.937500, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.655203, Class: 0.235916, Obj: 0.230687, No Obj: 0.002261, .5R: 0.933333, .75R: 0.266667, count: 15 -Region 23 Avg IOU: 0.564329, Class: 0.220978, Obj: 0.059397, No Obj: 0.000599, .5R: 0.684211, .75R: 0.105263, count: 19 -Region 16 Avg IOU: 0.755353, Class: 0.243505, Obj: 0.326374, No Obj: 0.001377, .5R: 1.000000, .75R: 0.375000, count: 8 -Region 23 Avg IOU: 0.619580, Class: 0.211535, Obj: 0.115909, No Obj: 0.000833, .5R: 0.833333, .75R: 0.100000, count: 30 -Region 16 Avg IOU: 0.679554, Class: 0.146333, Obj: 0.184157, No Obj: 0.002180, .5R: 0.866667, .75R: 0.333333, count: 15 -Region 23 Avg IOU: 0.614264, Class: 0.174390, Obj: 0.115644, No Obj: 0.000820, .5R: 0.857143, .75R: 0.142857, count: 21 -Region 16 Avg IOU: 0.644086, Class: 0.292172, Obj: 0.497686, No Obj: 0.001790, .5R: 1.000000, .75R: 0.166667, count: 6 -Region 23 Avg IOU: 0.568083, Class: 0.185167, Obj: 0.079715, No Obj: 0.000834, .5R: 0.666667, .75R: 0.041667, count: 24 - - 463: 2.673650, 2.807328 avg loss, 0.001000 rate, 0.751144 seconds, 59264 images -Loaded: 0.095265 seconds -Region 16 Avg IOU: 0.719667, Class: 0.209111, Obj: 0.295929, No Obj: 0.001665, .5R: 1.000000, .75R: 0.312500, count: 16 -Region 23 Avg IOU: 0.678636, Class: 0.220379, Obj: 0.092135, No Obj: 0.000320, .5R: 0.785714, .75R: 0.357143, count: 14 -Region 16 Avg IOU: 0.730051, Class: 0.119147, Obj: 0.520804, No Obj: 0.001461, .5R: 1.000000, .75R: 0.375000, count: 8 -Region 23 Avg IOU: 0.612481, Class: 0.176542, Obj: 0.058166, No Obj: 0.000384, .5R: 0.850000, .75R: 0.100000, count: 20 -Region 16 Avg IOU: 0.667135, Class: 0.231181, Obj: 0.199439, No Obj: 0.001039, .5R: 1.000000, .75R: 0.222222, count: 9 -Region 23 Avg IOU: 0.578963, Class: 0.217603, Obj: 0.084507, No Obj: 0.000510, .5R: 0.809524, .75R: 0.047619, count: 21 -Region 16 Avg IOU: 0.702141, Class: 0.193257, Obj: 0.223068, No Obj: 0.001198, .5R: 1.000000, .75R: 0.375000, count: 8 -Region 23 Avg IOU: 0.626161, Class: 0.157273, Obj: 0.077350, No 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0.200000, count: 25 - - 464: 2.398241, 2.766419 avg loss, 0.001000 rate, 0.750052 seconds, 59392 images -Loaded: 0.179329 seconds -Region 16 Avg IOU: 0.692360, Class: 0.149382, Obj: 0.245475, No Obj: 0.000745, .5R: 1.000000, .75R: 0.166667, count: 6 -Region 23 Avg IOU: 0.604668, Class: 0.182798, Obj: 0.100630, No Obj: 0.000521, .5R: 0.750000, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.762868, Class: 0.293841, Obj: 0.319981, No Obj: 0.001051, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.696601, Class: 0.202371, Obj: 0.078823, No Obj: 0.000264, .5R: 0.916667, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.697350, Class: 0.186458, Obj: 0.259326, No Obj: 0.001381, .5R: 1.000000, .75R: 0.333333, count: 15 -Region 23 Avg IOU: 0.603395, Class: 0.199839, Obj: 0.069734, No Obj: 0.000618, .5R: 0.666667, .75R: 0.166667, count: 24 -Region 16 Avg IOU: 0.693695, Class: 0.191356, Obj: 0.257219, No Obj: 0.002157, .5R: 0.920000, .75R: 0.400000, count: 25 -Region 23 Avg IOU: 0.650000, Class: 0.174418, Obj: 0.050232, No Obj: 0.000511, .5R: 0.870968, .75R: 0.290323, count: 31 -Region 16 Avg IOU: 0.723839, Class: 0.207143, Obj: 0.244920, No Obj: 0.001218, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.628169, Class: 0.169685, Obj: 0.056057, No Obj: 0.000369, .5R: 0.809524, .75R: 0.190476, count: 21 -Region 16 Avg IOU: 0.661966, Class: 0.173240, Obj: 0.071426, No Obj: 0.000834, .5R: 1.000000, .75R: 0.125000, count: 8 -Region 23 Avg IOU: 0.631696, Class: 0.218728, Obj: 0.006695, No Obj: 0.000165, .5R: 0.888889, .75R: 0.000000, count: 9 -Region 16 Avg IOU: 0.709868, Class: 0.219581, Obj: 0.157474, No Obj: 0.001114, .5R: 1.000000, .75R: 0.384615, count: 13 -Region 23 Avg IOU: 0.609095, Class: 0.225105, Obj: 0.072211, No Obj: 0.000398, .5R: 0.842105, .75R: 0.105263, count: 19 -Region 16 Avg IOU: 0.738350, Class: 0.217702, Obj: 0.446532, No Obj: 0.002338, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.665504, Class: 0.233012, Obj: 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count: 12 -Region 16 Avg IOU: 0.712043, Class: 0.163260, Obj: 0.317273, No Obj: 0.001513, .5R: 1.000000, .75R: 0.166667, count: 6 -Region 23 Avg IOU: 0.619277, Class: 0.194571, Obj: 0.118452, No Obj: 0.000857, .5R: 0.727273, .75R: 0.272727, count: 22 - - 469: 2.342015, 2.648373 avg loss, 0.001000 rate, 0.739109 seconds, 60032 images -Loaded: 0.439163 seconds -Region 16 Avg IOU: 0.696045, Class: 0.231434, Obj: 0.248056, No Obj: 0.001306, .5R: 0.866667, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.624001, Class: 0.240810, Obj: 0.066680, No Obj: 0.000498, .5R: 0.928571, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.633611, Class: 0.205601, Obj: 0.099451, No Obj: 0.001686, .5R: 0.869565, .75R: 0.217391, count: 23 -Region 23 Avg IOU: 0.570861, Class: 0.200096, Obj: 0.015173, No Obj: 0.000305, .5R: 0.769231, .75R: 0.076923, count: 26 -Region 16 Avg IOU: 0.667273, Class: 0.160064, Obj: 0.196160, No Obj: 0.001813, .5R: 0.941176, .75R: 0.235294, count: 17 -Region 23 Avg IOU: 0.596756, 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Avg IOU: 0.598628, Class: 0.187659, Obj: 0.032439, No Obj: 0.000131, .5R: 0.758621, .75R: 0.172414, count: 29 -Region 16 Avg IOU: 0.690996, Class: 0.188269, Obj: 0.176949, No Obj: 0.001213, .5R: 0.882353, .75R: 0.294118, count: 17 -Region 23 Avg IOU: 0.674920, Class: 0.221151, Obj: 0.026740, No Obj: 0.000093, .5R: 0.833333, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.693683, Class: 0.255619, Obj: 0.226229, No Obj: 0.001160, .5R: 0.937500, .75R: 0.187500, count: 16 -Region 23 Avg IOU: 0.616833, Class: 0.167970, Obj: 0.027630, No Obj: 0.000103, .5R: 0.750000, .75R: 0.150000, count: 20 -Region 16 Avg IOU: 0.665734, Class: 0.196301, Obj: 0.109288, No Obj: 0.000894, .5R: 0.928571, .75R: 0.285714, count: 14 -Region 23 Avg IOU: 0.610336, Class: 0.183982, Obj: 0.034528, No Obj: 0.000100, .5R: 0.941176, .75R: 0.117647, count: 17 - - 479: 3.734971, 3.187810 avg loss, 0.001000 rate, 1.762837 seconds, 61312 images -Loaded: 0.000059 seconds -Region 16 Avg IOU: 0.675100, Class: 0.224200, Obj: 0.159494, No Obj: 0.000852, .5R: 0.840000, .75R: 0.400000, count: 25 -Region 23 Avg IOU: 0.636311, Class: 0.203582, Obj: 0.030676, No Obj: 0.000104, .5R: 0.875000, .75R: 0.208333, count: 24 -Region 16 Avg IOU: 0.636429, Class: 0.248731, Obj: 0.158997, No Obj: 0.000721, .5R: 0.850000, .75R: 0.200000, count: 20 -Region 23 Avg IOU: 0.597102, Class: 0.176807, Obj: 0.027690, No Obj: 0.000112, .5R: 0.827586, .75R: 0.034483, count: 29 -Region 16 Avg IOU: 0.642810, Class: 0.213178, Obj: 0.235779, No Obj: 0.000751, .5R: 0.909091, .75R: 0.181818, count: 11 -Region 23 Avg IOU: 0.588040, Class: 0.200456, Obj: 0.068813, No Obj: 0.000121, .5R: 0.800000, .75R: 0.133333, count: 15 -Region 16 Avg IOU: 0.692273, Class: 0.251385, Obj: 0.229790, No Obj: 0.001031, .5R: 0.947368, .75R: 0.210526, count: 19 -Region 23 Avg IOU: 0.623403, Class: 0.113283, Obj: 0.037239, No Obj: 0.000122, .5R: 0.857143, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.722136, Class: 0.152948, Obj: 0.095713, No Obj: 0.000769, .5R: 0.950000, .75R: 0.500000, count: 20 -Region 23 Avg IOU: 0.610424, Class: 0.155338, Obj: 0.024647, No Obj: 0.000148, .5R: 0.826087, .75R: 0.000000, count: 23 -Region 16 Avg IOU: 0.611763, Class: 0.222233, Obj: 0.125832, No Obj: 0.000992, .5R: 0.888889, .75R: 0.055556, count: 18 -Region 23 Avg IOU: 0.580516, Class: 0.154347, Obj: 0.056635, No Obj: 0.000169, .5R: 0.789474, .75R: 0.052632, count: 19 -Region 16 Avg IOU: 0.758123, Class: 0.278458, Obj: 0.308071, No Obj: 0.000862, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.559555, Class: 0.190040, Obj: 0.020843, No Obj: 0.000141, .5R: 0.769231, .75R: 0.076923, count: 26 -Region 16 Avg IOU: 0.689633, Class: 0.228193, Obj: 0.150072, No Obj: 0.000959, .5R: 1.000000, .75R: 0.300000, count: 20 -Region 23 Avg IOU: 0.615457, Class: 0.200851, Obj: 0.061766, No Obj: 0.000224, .5R: 0.775000, .75R: 0.150000, count: 40 - - 480: 3.673756, 3.236405 avg loss, 0.001000 rate, 1.768495 seconds, 61440 images -Resizing -608 x 608 - try to allocate workspace = 1146765313 * sizeof(float), CUDA allocate done! -Loaded: 0.568497 seconds -Region 16 Avg IOU: 0.683157, Class: 0.246668, Obj: 0.143775, No Obj: 0.000895, .5R: 0.909091, .75R: 0.318182, count: 22 -Region 23 Avg IOU: 0.664089, Class: 0.200112, Obj: 0.070032, No Obj: 0.000302, .5R: 0.952381, .75R: 0.190476, count: 21 -Region 16 Avg IOU: 0.666941, Class: 0.200415, Obj: 0.212357, No Obj: 0.001011, .5R: 0.894737, .75R: 0.210526, count: 19 -Region 23 Avg IOU: 0.644467, Class: 0.150456, Obj: 0.084154, No Obj: 0.000404, .5R: 0.818182, .75R: 0.227273, count: 22 -Region 16 Avg IOU: 0.682930, Class: 0.197192, Obj: 0.104986, No Obj: 0.000765, .5R: 0.916667, .75R: 0.333333, count: 12 -Region 23 Avg IOU: 0.644544, Class: 0.139849, Obj: 0.068445, No Obj: 0.000281, .5R: 0.933333, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.611580, Class: 0.230409, Obj: 0.114278, No Obj: 0.001274, .5R: 0.772727, .75R: 0.045455, count: 22 -Region 23 Avg IOU: 0.620957, Class: 0.180860, Obj: 0.076695, No Obj: 0.000295, .5R: 0.904762, .75R: 0.047619, count: 21 -Region 16 Avg IOU: 0.706588, Class: 0.242391, Obj: 0.270060, No Obj: 0.001395, .5R: 0.888889, .75R: 0.388889, count: 18 -Region 23 Avg IOU: 0.579677, Class: 0.145661, Obj: 0.099153, No Obj: 0.000405, .5R: 0.800000, .75R: 0.080000, count: 25 -Region 16 Avg IOU: 0.685698, Class: 0.241640, Obj: 0.108430, No Obj: 0.000664, .5R: 1.000000, .75R: 0.166667, count: 12 -Region 23 Avg IOU: 0.580329, Class: 0.128910, Obj: 0.068879, No Obj: 0.000308, .5R: 0.818182, .75R: 0.045455, count: 22 -Region 16 Avg IOU: 0.694277, Class: 0.200505, Obj: 0.202584, No Obj: 0.000883, .5R: 0.933333, .75R: 0.133333, count: 15 -Region 23 Avg IOU: 0.662230, Class: 0.175679, Obj: 0.141069, No Obj: 0.000353, .5R: 1.000000, .75R: 0.227273, count: 22 -Region 16 Avg IOU: 0.664958, Class: 0.260146, Obj: 0.234538, No Obj: 0.001169, .5R: 0.923077, .75R: 0.307692, count: 13 -Region 23 Avg IOU: 0.567709, Class: 0.213906, Obj: 0.087101, No Obj: 0.000339, .5R: 0.529412, .75R: 0.235294, count: 17 - - 481: 3.209562, 3.233720 avg loss, 0.001000 rate, 1.535888 seconds, 61568 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.695438, Class: 0.172505, Obj: 0.249074, No Obj: 0.001101, .5R: 1.000000, .75R: 0.380952, count: 21 -Region 23 Avg IOU: 0.695848, Class: 0.217100, Obj: 0.303304, No Obj: 0.000658, .5R: 0.944444, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.673998, Class: 0.216542, Obj: 0.117062, No Obj: 0.001660, .5R: 0.861111, .75R: 0.361111, count: 36 -Region 23 Avg IOU: 0.646453, Class: 0.197433, Obj: 0.085704, No Obj: 0.000629, .5R: 0.866667, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.658293, Class: 0.243001, Obj: 0.256086, No Obj: 0.001109, .5R: 0.933333, .75R: 0.200000, count: 15 -Region 23 Avg IOU: 0.606487, Class: 0.219759, Obj: 0.204273, No Obj: 0.000615, .5R: 0.916667, .75R: 0.083333, count: 12 -Region 16 Avg IOU: 0.684069, Class: 0.255828, Obj: 0.275558, No Obj: 0.001339, .5R: 0.900000, .75R: 0.450000, count: 20 -Region 23 Avg IOU: 0.549772, Class: 0.186958, Obj: 0.152132, No Obj: 0.000761, .5R: 0.740741, .75R: 0.074074, count: 27 -Region 16 Avg IOU: 0.657842, Class: 0.198688, Obj: 0.149370, No Obj: 0.001153, .5R: 0.880000, .75R: 0.200000, count: 25 -Region 23 Avg IOU: 0.631647, Class: 0.152182, Obj: 0.099030, No Obj: 0.000525, .5R: 0.875000, .75R: 0.208333, count: 24 -Region 16 Avg IOU: 0.686218, Class: 0.172630, Obj: 0.299018, No Obj: 0.001653, .5R: 0.958333, .75R: 0.291667, count: 24 -Region 23 Avg IOU: 0.613058, Class: 0.190097, Obj: 0.188414, No Obj: 0.001080, .5R: 0.882353, .75R: 0.117647, count: 17 -Region 16 Avg IOU: 0.709029, Class: 0.249244, Obj: 0.362800, No Obj: 0.001270, .5R: 0.916667, .75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.559108, Class: 0.219038, Obj: 0.195785, No Obj: 0.000667, .5R: 0.705882, .75R: 0.058824, count: 17 -Region 16 Avg IOU: 0.669087, Class: 0.209663, Obj: 0.187554, No Obj: 0.001356, .5R: 0.904762, .75R: 0.238095, count: 21 -Region 23 Avg IOU: 0.610969, Class: 0.223757, Obj: 0.185547, No Obj: 0.001159, .5R: 0.800000, .75R: 0.142857, count: 35 - - 482: 3.823843, 3.292732 avg loss, 0.001000 rate, 1.619727 seconds, 61696 images -Loaded: 0.000063 seconds -Region 16 Avg IOU: 0.691947, Class: 0.224245, Obj: 0.221545, No Obj: 0.001175, .5R: 1.000000, .75R: 0.333333, count: 15 -Region 23 Avg IOU: 0.579302, Class: 0.119947, Obj: 0.047580, No Obj: 0.000212, .5R: 0.722222, .75R: 0.111111, count: 18 -Region 16 Avg IOU: 0.687562, Class: 0.240892, Obj: 0.231598, No Obj: 0.001052, .5R: 1.000000, .75R: 0.300000, count: 10 -Region 23 Avg IOU: 0.634045, Class: 0.212705, Obj: 0.025861, No Obj: 0.000177, .5R: 0.846154, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.687993, Class: 0.218428, Obj: 0.191894, No Obj: 0.001745, .5R: 1.000000, .75R: 0.240000, count: 25 -Region 23 Avg IOU: 0.631504, Class: 0.186470, Obj: 0.077353, No Obj: 0.000223, .5R: 0.888889, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.751276, Class: 0.244145, Obj: 0.223405, No Obj: 0.000840, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.654171, Class: 0.229029, Obj: 0.070763, No Obj: 0.000252, .5R: 0.947368, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.639261, Class: 0.142262, Obj: 0.247528, No Obj: 0.001134, .5R: 0.866667, .75R: 0.200000, count: 15 -Region 23 Avg IOU: 0.630039, Class: 0.163917, Obj: 0.092357, No Obj: 0.000213, .5R: 0.812500, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.726012, Class: 0.201514, Obj: 0.310140, No Obj: 0.001288, .5R: 0.933333, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.623212, Class: 0.245576, Obj: 0.076279, No Obj: 0.000155, .5R: 0.900000, .75R: 0.100000, count: 10 -Region 16 Avg IOU: 0.579727, Class: 0.206793, Obj: 0.165000, No Obj: 0.001575, .5R: 0.708333, .75R: 0.083333, count: 24 -Region 23 Avg IOU: 0.543451, Class: 0.175495, Obj: 0.036546, No Obj: 0.000231, .5R: 0.526316, .75R: 0.052632, count: 19 -Region 16 Avg IOU: 0.701527, Class: 0.236165, Obj: 0.232187, No Obj: 0.000832, .5R: 1.000000, .75R: 0.384615, count: 13 -Region 23 Avg IOU: 0.568030, Class: 0.208355, Obj: 0.046561, No Obj: 0.000192, .5R: 0.600000, .75R: 0.100000, count: 20 - - 483: 2.802846, 3.243744 avg loss, 0.001000 rate, 1.613840 seconds, 61824 images -Loaded: 0.000064 seconds -Region 16 Avg IOU: 0.655286, Class: 0.128516, Obj: 0.128825, No Obj: 0.000968, .5R: 0.812500, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.577577, Class: 0.160849, Obj: 0.019207, No Obj: 0.000102, .5R: 0.750000, .75R: 0.166667, count: 12 -Region 16 Avg IOU: 0.680036, Class: 0.238150, Obj: 0.200778, No Obj: 0.001109, .5R: 0.842105, .75R: 0.368421, count: 19 -Region 23 Avg IOU: 0.612307, Class: 0.224112, Obj: 0.029779, No Obj: 0.000134, .5R: 0.826087, .75R: 0.173913, count: 23 -Region 16 Avg IOU: 0.692217, Class: 0.209623, Obj: 0.141851, No Obj: 0.000890, .5R: 0.894737, .75R: 0.368421, count: 19 -Region 23 Avg IOU: 0.581056, Class: 0.158004, Obj: 0.020716, No Obj: 0.000089, .5R: 0.724138, .75R: 0.137931, count: 29 -Region 16 Avg IOU: 0.691982, Class: 0.252904, Obj: 0.333191, No Obj: 0.001281, .5R: 1.000000, .75R: 0.187500, count: 16 -Region 23 Avg IOU: 0.609984, Class: 0.240894, Obj: 0.021114, No Obj: 0.000080, .5R: 0.857143, .75R: 0.095238, count: 21 -Region 16 Avg IOU: 0.695559, Class: 0.235864, Obj: 0.229971, No Obj: 0.001257, .5R: 0.947368, .75R: 0.315789, count: 19 -Region 23 Avg IOU: 0.566324, Class: 0.201008, Obj: 0.022186, No Obj: 0.000108, .5R: 0.760000, .75R: 0.080000, count: 25 -Region 16 Avg IOU: 0.672666, Class: 0.216121, Obj: 0.241518, No Obj: 0.001038, .5R: 1.000000, .75R: 0.066667, count: 15 -Region 23 Avg IOU: 0.651228, Class: 0.170740, Obj: 0.020061, No Obj: 0.000071, .5R: 0.846154, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.670098, Class: 0.179788, Obj: 0.059538, No Obj: 0.000490, .5R: 1.000000, .75R: 0.250000, count: 12 -Region 23 Avg IOU: 0.635699, Class: 0.227573, Obj: 0.024339, No Obj: 0.000079, .5R: 0.916667, .75R: 0.166667, count: 12 -Region 16 Avg IOU: 0.725849, Class: 0.187474, Obj: 0.286114, No Obj: 0.000875, .5R: 0.857143, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.627762, Class: 0.155827, Obj: 0.025409, No Obj: 0.000100, .5R: 0.947368, .75R: 0.105263, count: 19 - - 484: 3.101393, 3.229509 avg loss, 0.001000 rate, 1.615255 seconds, 61952 images -Loaded: 0.000063 seconds -Region 16 Avg IOU: 0.669462, Class: 0.266118, Obj: 0.100799, No Obj: 0.000699, .5R: 0.894737, .75R: 0.157895, count: 19 -Region 23 Avg IOU: 0.564912, Class: 0.215152, Obj: 0.036675, No Obj: 0.000098, .5R: 0.695652, .75R: 0.173913, count: 23 -Region 16 Avg IOU: 0.661872, Class: 0.270291, Obj: 0.150092, No Obj: 0.001110, .5R: 1.000000, .75R: 0.227273, count: 22 -Region 23 Avg IOU: 0.561444, Class: 0.270949, Obj: 0.007801, No Obj: 0.000079, .5R: 0.666667, .75R: 0.047619, count: 21 -Region 16 Avg IOU: 0.674880, Class: 0.222535, Obj: 0.184303, No Obj: 0.001116, .5R: 0.857143, .75R: 0.285714, count: 28 -Region 23 Avg IOU: 0.673474, Class: 0.193352, Obj: 0.018451, No Obj: 0.000068, .5R: 0.900000, .75R: 0.250000, count: 20 -Region 16 Avg IOU: 0.701300, Class: 0.164369, Obj: 0.170849, No Obj: 0.000893, .5R: 0.920000, .75R: 0.280000, count: 25 -Region 23 Avg IOU: 0.579283, Class: 0.147266, Obj: 0.018891, No Obj: 0.000106, .5R: 0.807692, .75R: 0.115385, count: 26 -Region 16 Avg IOU: 0.605009, Class: 0.166296, Obj: 0.186121, No Obj: 0.000789, .5R: 0.764706, .75R: 0.176471, count: 17 -Region 23 Avg IOU: 0.526268, Class: 0.179491, Obj: 0.016040, No Obj: 0.000047, .5R: 0.600000, .75R: 0.000000, count: 10 -Region 16 Avg IOU: 0.684774, Class: 0.239028, Obj: 0.134463, No Obj: 0.000850, .5R: 0.952381, .75R: 0.238095, count: 21 -Region 23 Avg IOU: 0.629543, Class: 0.229596, Obj: 0.031361, No Obj: 0.000099, .5R: 0.833333, .75R: 0.125000, count: 24 -Region 16 Avg IOU: 0.595549, Class: 0.249126, Obj: 0.082366, No Obj: 0.000809, .5R: 0.809524, .75R: 0.142857, count: 21 -Region 23 Avg IOU: 0.571191, Class: 0.193134, Obj: 0.007915, No Obj: 0.000052, .5R: 0.722222, .75R: 0.055556, count: 18 -Region 16 Avg IOU: 0.677091, Class: 0.185387, Obj: 0.138318, No Obj: 0.000699, .5R: 0.850000, .75R: 0.350000, count: 20 -Region 23 Avg IOU: 0.699648, Class: 0.179844, Obj: 0.027251, No Obj: 0.000056, .5R: 0.933333, .75R: 0.333333, count: 15 - - 485: 3.539185, 3.260476 avg loss, 0.001000 rate, 1.605723 seconds, 62080 images -Loaded: 0.000059 seconds -Region 16 Avg IOU: 0.671376, Class: 0.140002, Obj: 0.135065, No Obj: 0.001355, .5R: 1.000000, .75R: 0.263158, count: 19 -Region 23 Avg IOU: 0.594487, Class: 0.188652, Obj: 0.031282, No Obj: 0.000163, .5R: 0.764706, .75R: 0.058824, count: 17 -Region 16 Avg IOU: 0.748938, Class: 0.169809, Obj: 0.211157, No Obj: 0.001122, .5R: 1.000000, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.640794, Class: 0.194302, Obj: 0.063249, No Obj: 0.000123, .5R: 0.823529, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.663407, Class: 0.277383, Obj: 0.190405, No Obj: 0.001508, .5R: 0.961538, .75R: 0.230769, count: 26 -Region 23 Avg IOU: 0.554340, Class: 0.249359, Obj: 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-Loaded: 0.000065 seconds -Region 16 Avg IOU: 0.770899, Class: 0.232349, Obj: 0.481966, No Obj: 0.001031, .5R: 1.000000, .75R: 0.800000, count: 5 -Region 23 Avg IOU: 0.590301, Class: 0.212433, Obj: 0.111302, No Obj: 0.000351, .5R: 0.714286, .75R: 0.000000, count: 14 -Region 16 Avg IOU: 0.663939, Class: 0.149887, Obj: 0.253417, No Obj: 0.000998, .5R: 0.900000, .75R: 0.200000, count: 10 -Region 23 Avg IOU: 0.638090, Class: 0.188592, Obj: 0.071143, No Obj: 0.000372, .5R: 0.958333, .75R: 0.166667, count: 24 -Region 16 Avg IOU: 0.647668, Class: 0.267496, Obj: 0.286128, No Obj: 0.001484, .5R: 0.894737, .75R: 0.210526, count: 19 -Region 23 Avg IOU: 0.638937, Class: 0.208576, Obj: 0.049905, No Obj: 0.000564, .5R: 0.866667, .75R: 0.066667, count: 30 -Region 16 Avg IOU: 0.736003, Class: 0.165858, Obj: 0.297070, No Obj: 0.001265, .5R: 1.000000, .75R: 0.333333, count: 9 -Region 23 Avg IOU: 0.607417, Class: 0.201264, Obj: 0.051972, No Obj: 0.000373, .5R: 0.800000, .75R: 0.100000, count: 20 -Region 16 Avg IOU: 0.746316, Class: 0.243336, Obj: 0.164033, No Obj: 0.001225, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.617004, Class: 0.257456, Obj: 0.080713, No Obj: 0.000285, .5R: 0.800000, .75R: 0.133333, count: 15 -Region 16 Avg IOU: 0.661545, Class: 0.176812, Obj: 0.213585, No Obj: 0.001040, .5R: 0.900000, .75R: 0.300000, count: 10 -Region 23 Avg IOU: 0.630420, Class: 0.121297, Obj: 0.063876, No Obj: 0.000311, .5R: 0.944444, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.666573, Class: 0.196458, Obj: 0.097326, No Obj: 0.000798, .5R: 1.000000, .75R: 0.100000, count: 10 -Region 23 Avg IOU: 0.621451, Class: 0.220759, Obj: 0.138599, No Obj: 0.000295, .5R: 0.875000, .75R: 0.125000, count: 8 -Region 16 Avg IOU: 0.688560, Class: 0.239371, Obj: 0.188158, No Obj: 0.001318, .5R: 0.800000, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.626745, Class: 0.194286, Obj: 0.084314, No Obj: 0.000422, .5R: 0.850000, .75R: 0.200000, count: 20 - - 515: 2.428494, 2.790581 avg loss, 0.001000 rate, 0.863681 seconds, 65920 images -Loaded: 0.056394 seconds -Region 16 Avg IOU: 0.780928, Class: 0.263131, Obj: 0.329463, No Obj: 0.001243, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.639093, Class: 0.178196, Obj: 0.093486, No Obj: 0.000408, .5R: 0.833333, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.718406, Class: 0.155198, Obj: 0.347040, No Obj: 0.000947, .5R: 1.000000, .75R: 0.375000, count: 8 -Region 23 Avg IOU: 0.604290, Class: 0.164247, Obj: 0.079653, No Obj: 0.000455, .5R: 0.772727, .75R: 0.136364, count: 22 -Region 16 Avg IOU: 0.623548, Class: 0.208778, Obj: 0.153976, No Obj: 0.001554, .5R: 0.692308, .75R: 0.230769, count: 13 -Region 23 Avg IOU: 0.604818, Class: 0.203946, Obj: 0.056442, No Obj: 0.000365, .5R: 0.812500, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.710710, Class: 0.221186, Obj: 0.391416, No Obj: 0.001502, .5R: 1.000000, .75R: 0.363636, count: 11 -Region 23 Avg IOU: 0.626404, Class: 0.184639, Obj: 0.084882, No Obj: 0.000399, 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count: 19 - - 516: 2.501980, 2.761721 avg loss, 0.001000 rate, 0.822163 seconds, 66048 images -Loaded: 0.026963 seconds -Region 16 Avg IOU: 0.687035, Class: 0.198240, Obj: 0.309532, No Obj: 0.002163, .5R: 0.833333, .75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.596692, Class: 0.145497, Obj: 0.064059, No Obj: 0.000636, .5R: 0.793103, .75R: 0.172414, count: 29 -Region 16 Avg IOU: 0.682305, Class: 0.177566, Obj: 0.192676, No Obj: 0.001051, .5R: 0.923077, .75R: 0.307692, count: 13 -Region 23 Avg IOU: 0.618250, Class: 0.156419, Obj: 0.030993, No Obj: 0.000335, .5R: 0.736842, .75R: 0.210526, count: 19 -Region 16 Avg IOU: 0.698944, Class: 0.210311, Obj: 0.387932, No Obj: 0.002663, .5R: 0.933333, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.635459, Class: 0.199989, Obj: 0.041062, No Obj: 0.000724, .5R: 0.916667, .75R: 0.166667, count: 24 -Region 16 Avg IOU: 0.700951, Class: 0.182521, Obj: 0.384273, No Obj: 0.001719, .5R: 0.888889, .75R: 0.333333, count: 9 -Region 23 Avg IOU: 0.603948, Class: 0.169807, Obj: 0.127857, No Obj: 0.000580, .5R: 0.764706, .75R: 0.058824, count: 17 -Region 16 Avg IOU: 0.757053, Class: 0.190880, Obj: 0.307806, No Obj: 0.001963, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.648147, Class: 0.193983, Obj: 0.066553, No Obj: 0.000321, .5R: 0.950000, .75R: 0.200000, count: 20 -Region 16 Avg IOU: 0.689045, Class: 0.251242, Obj: 0.276015, No Obj: 0.001971, .5R: 1.000000, .75R: 0.176471, count: 17 -Region 23 Avg IOU: 0.666664, Class: 0.214422, Obj: 0.078361, No Obj: 0.000497, .5R: 0.925926, .75R: 0.111111, count: 27 -Region 16 Avg IOU: 0.679084, Class: 0.256191, Obj: 0.288886, No Obj: 0.001442, .5R: 1.000000, .75R: 0.222222, count: 9 -Region 23 Avg IOU: 0.584328, Class: 0.246068, Obj: 0.103429, No Obj: 0.000387, .5R: 0.777778, .75R: 0.055556, count: 18 -Region 16 Avg IOU: 0.717727, Class: 0.200238, Obj: 0.155443, No Obj: 0.001184, .5R: 1.000000, .75R: 0.333333, count: 12 -Region 23 Avg IOU: 0.618292, Class: 0.168196, Obj: 0.058355, 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Avg IOU: 0.636793, Class: 0.261644, Obj: 0.083272, No Obj: 0.000858, .5R: 0.852941, .75R: 0.147059, count: 34 - - 518: 2.983029, 2.787870 avg loss, 0.001000 rate, 0.845620 seconds, 66304 images -Loaded: 0.000079 seconds -Region 16 Avg IOU: 0.703469, Class: 0.135433, Obj: 0.312758, No Obj: 0.001053, .5R: 0.888889, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.573288, Class: 0.184574, Obj: 0.042291, No Obj: 0.000363, .5R: 0.705882, .75R: 0.117647, count: 17 -Region 16 Avg IOU: 0.702566, Class: 0.136706, Obj: 0.199465, No Obj: 0.001101, .5R: 1.000000, .75R: 0.250000, count: 12 -Region 23 Avg IOU: 0.621233, Class: 0.226945, Obj: 0.156333, No Obj: 0.000809, .5R: 0.900000, .75R: 0.150000, count: 20 -Region 16 Avg IOU: 0.649866, Class: 0.267774, Obj: 0.208154, No Obj: 0.001810, .5R: 1.000000, .75R: 0.200000, count: 15 -Region 23 Avg IOU: 0.626836, Class: 0.241882, Obj: 0.150564, No Obj: 0.000925, .5R: 0.851852, .75R: 0.148148, count: 27 -Region 16 Avg IOU: 0.684300, Class: 0.253747, Obj: 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count: 11 -Region 16 Avg IOU: 0.707774, Class: 0.231725, Obj: 0.260213, No Obj: 0.001704, .5R: 0.875000, .75R: 0.500000, count: 24 -Region 23 Avg IOU: 0.637844, Class: 0.195177, Obj: 0.081090, No Obj: 0.000455, .5R: 0.931035, .75R: 0.172414, count: 29 -Region 16 Avg IOU: 0.761340, Class: 0.221876, Obj: 0.216177, No Obj: 0.001293, .5R: 1.000000, .75R: 0.687500, count: 16 -Region 23 Avg IOU: 0.590437, Class: 0.199929, Obj: 0.051852, No Obj: 0.000328, .5R: 0.846154, .75R: 0.230769, count: 13 -Region 16 Avg IOU: 0.701742, Class: 0.206334, Obj: 0.221787, No Obj: 0.001467, .5R: 0.947368, .75R: 0.315789, count: 19 -Region 23 Avg IOU: 0.620411, Class: 0.143692, Obj: 0.056690, No Obj: 0.000401, .5R: 0.869565, .75R: 0.086957, count: 23 - - 529: 3.028546, 2.781230 avg loss, 0.001000 rate, 1.415750 seconds, 67712 images -Loaded: 0.000086 seconds -Region 16 Avg IOU: 0.705588, Class: 0.277203, Obj: 0.236874, No Obj: 0.001098, .5R: 0.857143, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.586793, 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16 Avg IOU: 0.706368, Class: 0.154882, Obj: 0.400044, No Obj: 0.002013, .5R: 0.916667, .75R: 0.333333, count: 12 -Region 23 Avg IOU: 0.658231, Class: 0.207260, Obj: 0.126481, No Obj: 0.001060, .5R: 0.900000, .75R: 0.100000, count: 20 -Region 16 Avg IOU: 0.695926, Class: 0.185119, Obj: 0.172082, No Obj: 0.001863, .5R: 1.000000, .75R: 0.214286, count: 14 -Region 23 Avg IOU: 0.608109, Class: 0.242962, Obj: 0.117652, No Obj: 0.000859, .5R: 0.850000, .75R: 0.050000, count: 20 -Region 16 Avg IOU: 0.673647, Class: 0.245882, Obj: 0.386091, No Obj: 0.001610, .5R: 0.818182, .75R: 0.363636, count: 11 -Region 23 Avg IOU: 0.630503, Class: 0.211695, Obj: 0.182837, No Obj: 0.000591, .5R: 0.846154, .75R: 0.230769, count: 13 -Region 16 Avg IOU: 0.761514, Class: 0.211197, Obj: 0.348032, No Obj: 0.001378, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.637612, Class: 0.209729, Obj: 0.122836, No Obj: 0.000400, .5R: 1.000000, .75R: 0.111111, count: 9 -Region 16 Avg IOU: 0.691058, Class: 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images -Resizing -576 x 576 - try to allocate workspace = 1146159105 * sizeof(float), CUDA allocate done! -Loaded: 0.253148 seconds -Region 16 Avg IOU: 0.726677, Class: 0.108781, Obj: 0.274324, No Obj: 0.001351, .5R: 0.923077, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.639930, Class: 0.119235, Obj: 0.085303, No Obj: 0.000448, .5R: 0.952381, .75R: 0.142857, count: 21 -Region 16 Avg IOU: 0.719139, Class: 0.231008, Obj: 0.253685, No Obj: 0.001475, .5R: 0.944444, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.595429, Class: 0.168570, Obj: 0.032618, No Obj: 0.000238, .5R: 0.857143, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.730122, Class: 0.237814, Obj: 0.285958, No Obj: 0.001449, .5R: 1.000000, .75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.630262, Class: 0.222714, Obj: 0.097290, No Obj: 0.000389, .5R: 0.823529, .75R: 0.235294, count: 17 -Region 16 Avg IOU: 0.719830, Class: 0.240740, Obj: 0.286128, No Obj: 0.001059, .5R: 0.888889, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 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-Region 23 Avg IOU: 0.552677, Class: 0.169148, Obj: 0.021114, No Obj: 0.000110, .5R: 0.772727, .75R: 0.000000, count: 22 -Region 16 Avg IOU: 0.727128, Class: 0.249964, Obj: 0.271582, No Obj: 0.001618, .5R: 0.945946, .75R: 0.459459, count: 37 -Region 23 Avg IOU: 0.675533, Class: 0.192907, Obj: 0.039607, No Obj: 0.000225, .5R: 0.939394, .75R: 0.212121, count: 33 -Region 16 Avg IOU: 0.650696, Class: 0.277377, Obj: 0.187425, No Obj: 0.001152, .5R: 0.894737, .75R: 0.105263, count: 19 -Region 23 Avg IOU: 0.550008, Class: 0.215192, Obj: 0.021671, No Obj: 0.000156, .5R: 0.466667, .75R: 0.000000, count: 15 -Region 16 Avg IOU: 0.721135, Class: 0.159769, Obj: 0.321779, No Obj: 0.001351, .5R: 1.000000, .75R: 0.388889, count: 18 -Region 23 Avg IOU: 0.643768, Class: 0.147067, Obj: 0.083490, No Obj: 0.000250, .5R: 0.954545, .75R: 0.227273, count: 22 -Region 16 Avg IOU: 0.713832, Class: 0.252752, Obj: 0.252681, No Obj: 0.000947, .5R: 0.920000, .75R: 0.440000, count: 25 -Region 23 Avg IOU: 0.608200, Class: 0.269294, Obj: 0.043483, No Obj: 0.000131, .5R: 0.928571, .75R: 0.071429, count: 14 - - 570: 3.673523, 3.266438 avg loss, 0.001000 rate, 2.085887 seconds, 72960 images -Resizing -576 x 576 - try to allocate workspace = 1146159105 * sizeof(float), CUDA allocate done! -Loaded: 0.527742 seconds -Region 16 Avg IOU: 0.715097, Class: 0.192414, Obj: 0.160283, No Obj: 0.001099, .5R: 0.956522, .75R: 0.260870, count: 23 -Region 23 Avg IOU: 0.588887, Class: 0.236385, Obj: 0.061914, No Obj: 0.000157, .5R: 1.000000, .75R: 0.000000, count: 5 -Region 16 Avg IOU: 0.618674, Class: 0.178649, Obj: 0.129216, No Obj: 0.000963, .5R: 0.800000, .75R: 0.150000, count: 20 -Region 23 Avg IOU: 0.663869, Class: 0.200537, Obj: 0.048336, No Obj: 0.000246, .5R: 0.960000, .75R: 0.200000, count: 25 -Region 16 Avg IOU: 0.709724, Class: 0.155054, Obj: 0.294449, No Obj: 0.001381, .5R: 0.933333, .75R: 0.266667, count: 15 -Region 23 Avg IOU: 0.616908, Class: 0.189073, Obj: 0.049573, No Obj: 0.000229, .5R: 0.833333, 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count: 12 -Region 23 Avg IOU: 0.626947, Class: 0.208000, Obj: 0.081020, No Obj: 0.000279, .5R: 0.875000, .75R: 0.000000, count: 16 -Region 16 Avg IOU: 0.695256, Class: 0.196145, Obj: 0.Saving weights to ./attribute/7354_26298/attribute_33652_s_tyv3_9c5_600.weights -364200, No Obj: 0.001355, .5R: 1.000000, .75R: 0.357143, count: 14 -Region 23 Avg IOU: 0.664891, Class: 0.214165, Obj: 0.092349, No Obj: 0.000286, .5R: 0.846154, .75R: 0.307692, count: 26 -Region 16 Avg IOU: 0.673712, Class: 0.225333, Obj: 0.289995, No Obj: 0.001462, .5R: 0.842105, .75R: 0.263158, count: 19 -Region 23 Avg IOU: 0.566686, Class: 0.202560, Obj: 0.027891, No Obj: 0.000194, .5R: 0.500000, .75R: 0.250000, count: 24 -Region 16 Avg IOU: 0.734875, Class: 0.302261, Obj: 0.357619, No Obj: 0.001158, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.674224, Class: 0.207148, Obj: 0.064312, No Obj: 0.000289, .5R: 0.962963, .75R: 0.333333, count: 27 -Region 16 Avg IOU: 0.665448, Class: 0.238559, Obj: 0.173694, 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-512 x 512 - try to allocate workspace = 1145044993 * sizeof(float), CUDA allocate done! -Loaded: 0.623424 seconds -Region 16 Avg IOU: 0.724755, Class: 0.221180, Obj: 0.216782, No Obj: 0.001242, .5R: 0.909091, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.617103, Class: 0.207215, Obj: 0.051112, No Obj: 0.000367, .5R: 0.794872, .75R: 0.179487, count: 39 -Region 16 Avg IOU: 0.634298, Class: 0.192384, Obj: 0.193151, No Obj: 0.001786, .5R: 0.894737, .75R: 0.157895, count: 19 -Region 23 Avg IOU: 0.631233, Class: 0.172470, Obj: 0.074346, No Obj: 0.000257, .5R: 0.750000, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.662046, Class: 0.194174, Obj: 0.267360, No Obj: 0.001584, .5R: 0.842105, .75R: 0.315789, count: 19 -Region 23 Avg IOU: 0.591725, Class: 0.166017, Obj: 0.104665, No Obj: 0.000380, .5R: 0.812500, .75R: 0.062500, count: 16 -Region 16 Avg IOU: 0.751150, Class: 0.166393, Obj: 0.500645, No Obj: 0.001740, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.680924, Class: 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images -Loaded: 0.000096 seconds -Region 16 Avg IOU: 0.773403, Class: 0.192372, Obj: 0.255728, No Obj: 0.001167, .5R: 0.950000, .75R: 0.800000, count: 20 -Region 23 Avg IOU: 0.656255, Class: 0.144790, Obj: 0.084771, No Obj: 0.000303, .5R: 0.866667, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.732416, Class: 0.176859, Obj: 0.081745, No Obj: 0.000809, .5R: 0.916667, .75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.593352, Class: 0.257730, Obj: 0.139960, No Obj: 0.000421, .5R: 0.818182, .75R: 0.181818, count: 11 -Region 16 Avg IOU: 0.690825, Class: 0.249798, Obj: 0.113391, No Obj: 0.001065, .5R: 0.875000, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.563663, Class: 0.226822, Obj: 0.101222, No Obj: 0.000590, .5R: 0.739130, .75R: 0.043478, count: 23 -Region 16 Avg IOU: 0.697944, Class: 0.174185, Obj: 0.306043, No Obj: 0.001315, .5R: 0.888889, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.641575, Class: 0.138550, Obj: 0.092059, No Obj: 0.000383, .5R: 0.857143, .75R: 0.214286, count: 14 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images -Loaded: 0.000049 seconds -Region 16 Avg IOU: 0.698255, Class: 0.192792, Obj: 0.317761, No Obj: 0.001843, .5R: 0.909091, .75R: 0.272727, count: 11 -Region 23 Avg IOU: 0.645774, Class: 0.162379, Obj: 0.067496, No Obj: 0.000834, .5R: 0.966667, .75R: 0.166667, count: 30 -Region 16 Avg IOU: 0.774044, Class: 0.164083, Obj: 0.425018, No Obj: 0.001472, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.604178, Class: 0.228315, Obj: 0.099193, No Obj: 0.000555, .5R: 0.882353, .75R: 0.058824, count: 17 -Region 16 Avg IOU: 0.757764, Class: 0.225433, Obj: 0.263726, No Obj: 0.001634, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.522492, Class: 0.189343, Obj: 0.048473, No Obj: 0.000450, .5R: 0.333333, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.713809, Class: 0.253701, Obj: 0.392989, No Obj: 0.002328, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 23 Avg IOU: 0.658368, Class: 0.191386, Obj: 0.185450, No Obj: 0.000636, .5R: 0.833333, .75R: 0.333333, count: 12 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.75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.606331, Class: 0.195136, Obj: 0.023429, No Obj: 0.000441, .5R: 0.692308, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.669180, Class: 0.198525, Obj: 0.338026, No Obj: 0.001506, .5R: 1.000000, .75R: 0.222222, count: 9 -Region 23 Avg IOU: 0.606116, Class: 0.216967, Obj: 0.057900, No Obj: 0.000509, .5R: 0.681818, .75R: 0.227273, count: 22 -Region 16 Avg IOU: 0.705501, Class: 0.073166, Obj: 0.266698, No Obj: 0.001235, .5R: 1.000000, .75R: 0.200000, count: 5 -Region 23 Avg IOU: 0.655133, Class: 0.258939, Obj: 0.096589, No Obj: 0.000496, .5R: 0.875000, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.811091, Class: 0.242695, Obj: 0.461741, No Obj: 0.001537, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.519725, Class: 0.237728, Obj: 0.063630, No Obj: 0.000416, .5R: 0.500000, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.720593, Class: 0.184659, Obj: 0.224463, No Obj: 0.001304, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.615990, Class: 0.169874, Obj: 0.068275, No Obj: 0.000502, .5R: 0.894737, .75R: 0.157895, count: 19 - - 638: 2.038013, 2.484630 avg loss, 0.001000 rate, 0.765468 seconds, 81664 images -Loaded: 0.000061 seconds -Region 16 Avg IOU: 0.774014, Class: 0.244140, Obj: 0.112241, No Obj: 0.000729, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.638419, Class: 0.191629, Obj: 0.082701, No Obj: 0.000389, .5R: 0.842105, .75R: 0.105263, count: 19 -Region 16 Avg IOU: 0.677335, Class: 0.216100, Obj: 0.160674, No Obj: 0.001093, .5R: 0.833333, .75R: 0.500000, count: 6 -Region 23 Avg IOU: 0.589754, Class: 0.212965, Obj: 0.129052, No Obj: 0.000767, .5R: 0.777778, .75R: 0.000000, count: 18 -Region 16 Avg IOU: 0.759585, Class: 0.199663, Obj: 0.297514, No Obj: 0.001136, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.643487, Class: 0.179205, Obj: 0.071328, No Obj: 0.000613, .5R: 0.818182, .75R: 0.181818, count: 22 -Region 16 Avg IOU: 0.707236, Class: 0.141959, Obj: 0.132555, No Obj: 0.001087, .5R: 0.909091, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.627093, Class: 0.163070, Obj: 0.068274, No Obj: 0.000438, .5R: 0.900000, .75R: 0.050000, count: 20 -Region 16 Avg IOU: 0.723348, Class: 0.176801, Obj: 0.318988, No Obj: 0.000855, .5R: 0.857143, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.615604, Class: 0.136459, Obj: 0.031136, No Obj: 0.000348, .5R: 0.937500, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.751372, Class: 0.136822, Obj: 0.212748, No Obj: 0.000655, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.646567, Class: 0.137258, Obj: 0.103596, No Obj: 0.000438, .5R: 0.866667, .75R: 0.133333, count: 15 -Region 16 Avg IOU: 0.737442, Class: 0.177883, Obj: 0.254964, No Obj: 0.001186, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.653204, Class: 0.162592, Obj: 0.097466, No Obj: 0.000652, .5R: 0.964286, .75R: 0.285714, count: 28 -Region 16 Avg IOU: 0.645576, Class: 0.267173, Obj: 0.258999, No Obj: 0.001125, .5R: 0.666667, .75R: 0.333333, count: 6 -Region 23 Avg IOU: 0.622160, Class: 0.214006, Obj: 0.068136, No Obj: 0.000524, .5R: 0.892857, .75R: 0.071429, count: 28 - - 639: 2.371621, 2.473329 avg loss, 0.001000 rate, 0.779530 seconds, 81792 images -Loaded: 0.000067 seconds -Region 16 Avg IOU: 0.674108, Class: 0.234974, Obj: 0.225499, No Obj: 0.000894, .5R: 0.909091, .75R: 0.363636, count: 11 -Region 23 Avg IOU: 0.571857, Class: 0.238209, Obj: 0.054574, No Obj: 0.000557, .5R: 0.714286, .75R: 0.071429, count: 14 -Region 16 Avg IOU: 0.688118, Class: 0.210925, Obj: 0.250815, No Obj: 0.001380, .5R: 0.857143, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.667272, Class: 0.265424, Obj: 0.172161, No Obj: 0.000898, .5R: 0.944444, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.647118, Class: 0.184710, Obj: 0.206820, No Obj: 0.001308, .5R: 0.857143, .75R: 0.285714, count: 14 -Region 23 Avg IOU: 0.620016, Class: 0.190340, Obj: 0.175405, No Obj: 0.001188, .5R: 0.857143, .75R: 0.095238, count: 21 -Region 16 Avg IOU: 0.719065, Class: 0.182996, Obj: 0.126311, No Obj: 0.000963, .5R: 0.916667, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.664385, Class: 0.169992, Obj: 0.079956, No Obj: 0.000631, .5R: 0.909091, .75R: 0.272727, count: 22 -Region 16 Avg IOU: 0.693597, Class: 0.263849, Obj: 0.252849, No Obj: 0.000864, .5R: 0.875000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.627732, Class: 0.192779, Obj: 0.083008, No Obj: 0.000578, .5R: 0.782609, .75R: 0.217391, count: 23 -Region 16 Avg IOU: 0.755258, Class: 0.238201, Obj: 0.270456, No Obj: 0.000992, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.692626, Class: 0.211531, Obj: 0.091540, No Obj: 0.000918, .5R: 0.947368, .75R: 0.368421, count: 19 -Region 16 Avg IOU: 0.754221, Class: 0.193595, Obj: 0.297777, No Obj: 0.001076, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.668189, Class: 0.154952, Obj: 0.120575, No Obj: 0.000951, .5R: 0.937500, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.770005, Class: 0.190319, Obj: 0.331564, No Obj: 0.001351, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.567880, Class: 0.236894, Obj: 0.136911, No Obj: 0.000865, .5R: 0.650000, .75R: 0.150000, count: 20 - - 640: 2.411640, 2.467160 avg loss, 0.001000 rate, 0.768942 seconds, 81920 images -Resizing -448 x 448 - try to allocate workspace = 305201153 * sizeof(float), CUDA allocate done! -Loaded: 0.572063 seconds -Region 16 Avg IOU: 0.638562, Class: 0.142921, Obj: 0.100992, No Obj: 0.001138, .5R: 0.769231, .75R: 0.307692, count: 13 -Region 23 Avg IOU: 0.682536, Class: 0.213330, Obj: 0.091099, No Obj: 0.000567, .5R: 0.882353, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.694658, Class: 0.197200, Obj: 0.119403, No Obj: 0.000833, .5R: 0.909091, .75R: 0.272727, count: 11 -Region 23 Avg IOU: 0.600925, Class: 0.180131, Obj: 0.098269, No Obj: 0.000816, .5R: 0.787879, .75R: 0.181818, count: 33 -Region 16 Avg IOU: 0.730582, Class: 0.268898, Obj: 0.265604, No Obj: 0.001169, .5R: 0.928571, .75R: 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images -Loaded: 0.000078 seconds -Region 16 Avg IOU: 0.691369, Class: 0.251531, Obj: 0.191305, No Obj: 0.001839, .5R: 0.961538, .75R: 0.346154, count: 26 -Region 23 Avg IOU: 0.608816, Class: 0.241542, Obj: 0.069997, No Obj: 0.000586, .5R: 0.846154, .75R: 0.102564, count: 39 -Region 16 Avg IOU: 0.715044, Class: 0.204977, Obj: 0.233559, No Obj: 0.001847, .5R: 0.961538, .75R: 0.423077, count: 26 -Region 23 Avg IOU: 0.620967, Class: 0.165831, Obj: 0.091927, No Obj: 0.000390, .5R: 0.863636, .75R: 0.136364, count: 22 -Region 16 Avg IOU: 0.716510, Class: 0.195492, Obj: 0.158459, No Obj: 0.001064, .5R: 1.000000, .75R: 0.375000, count: 24 -Region 23 Avg IOU: 0.674695, Class: 0.204916, Obj: 0.077362, No Obj: 0.000364, .5R: 0.958333, .75R: 0.333333, count: 24 -Region 16 Avg IOU: 0.712595, Class: 0.195806, Obj: 0.253889, No Obj: 0.001337, .5R: 1.000000, .75R: 0.285714, count: 21 -Region 23 Avg IOU: 0.613763, Class: 0.195444, Obj: 0.083199, No Obj: 0.000379, .5R: 0.809524, .75R: 0.142857, count: 21 -Region 16 Avg IOU: 0.704055, Class: 0.192358, Obj: 0.362677, No Obj: 0.001150, .5R: 1.000000, .75R: 0.300000, count: 10 -Region 23 Avg IOU: 0.628409, Class: 0.158655, Obj: 0.092808, No Obj: 0.000316, .5R: 0.916667, .75R: 0.166667, count: 12 -Region 16 Avg IOU: 0.710470, Class: 0.179915, Obj: 0.243751, No Obj: 0.001167, .5R: 1.000000, .75R: 0.333333, count: 12 -Region 23 Avg IOU: 0.582520, Class: 0.152719, Obj: 0.142575, No Obj: 0.000276, .5R: 0.684211, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.737250, Class: 0.138313, Obj: 0.356975, No Obj: 0.001176, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.588784, Class: 0.186216, Obj: 0.056641, No Obj: 0.000337, .5R: 0.823529, .75R: 0.117647, count: 17 -Region 16 Avg IOU: 0.654563, Class: 0.258641, Obj: 0.178168, No Obj: 0.001247, .5R: 0.833333, .75R: 0.375000, count: 24 -Region 23 Avg IOU: 0.614954, Class: 0.206031, Obj: 0.090841, No Obj: 0.000333, .5R: 0.789474, .75R: 0.105263, count: 19 - - 652: 3.374031, 2.733235 avg 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0.000000, count: 15 - - 653: 2.828567, 2.742769 avg loss, 0.001000 rate, 1.462820 seconds, 83584 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.586532, Class: 0.228470, Obj: 0.148142, No Obj: 0.001031, .5R: 0.800000, .75R: 0.200000, count: 15 -Region 23 Avg IOU: 0.587049, Class: 0.207700, Obj: 0.052776, No Obj: 0.000282, .5R: 0.789474, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.754124, Class: 0.228375, Obj: 0.357585, No Obj: 0.001092, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.625517, Class: 0.215002, Obj: 0.130393, No Obj: 0.000336, .5R: 0.850000, .75R: 0.150000, count: 20 -Region 16 Avg IOU: 0.729532, Class: 0.212521, Obj: 0.239812, No Obj: 0.001183, .5R: 1.000000, .75R: 0.541667, count: 24 -Region 23 Avg IOU: 0.611160, Class: 0.173639, Obj: 0.037134, No Obj: 0.000208, .5R: 0.826087, .75R: 0.086957, count: 23 -Region 16 Avg IOU: 0.765772, Class: 0.217042, Obj: 0.163068, No Obj: 0.000904, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 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23 Avg IOU: 0.679408, Class: 0.194531, Obj: 0.050380, No Obj: 0.000255, .5R: 0.823529, .75R: 0.294118, count: 17 - - 655: 2.878016, 2.798438 avg loss, 0.001000 rate, 1.487459 seconds, 83840 images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.731090, Class: 0.209812, Obj: 0.376818, No Obj: 0.001840, .5R: 0.937500, .75R: 0.562500, count: 16 -Region 23 Avg IOU: 0.607411, Class: 0.224104, Obj: 0.104216, No Obj: 0.000297, .5R: 0.769231, .75R: 0.230769, count: 13 -Region 16 Avg IOU: 0.676403, Class: 0.178096, Obj: 0.231384, No Obj: 0.001202, .5R: 0.866667, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.626687, Class: 0.141355, Obj: 0.111689, No Obj: 0.000349, .5R: 0.888889, .75R: 0.055556, count: 18 -Region 16 Avg IOU: 0.738436, Class: 0.248061, Obj: 0.235094, No Obj: 0.001871, .5R: 1.000000, .75R: 0.500000, count: 20 -Region 23 Avg IOU: 0.631831, Class: 0.233664, Obj: 0.093534, No Obj: 0.000327, .5R: 0.916667, .75R: 0.125000, count: 24 -Region 16 Avg IOU: 0.727600, Class: 0.214809, Obj: 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2.285358 seconds, 85760 images -Resizing -512 x 512 - try to allocate workspace = 1145044993 * sizeof(float), CUDA allocate done! -Loaded: 0.420257 seconds -Region 16 Avg IOU: 0.733221, Class: 0.235267, Obj: 0.217415, No Obj: 0.000978, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.655832, Class: 0.140526, Obj: 0.058759, No Obj: 0.000192, .5R: 1.000000, .75R: 0.333333, count: 6 -Region 16 Avg IOU: 0.701888, Class: 0.186652, Obj: 0.273361, No Obj: 0.001140, .5R: 0.866667, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.646770, Class: 0.225513, Obj: 0.038924, No Obj: 0.000188, .5R: 0.863636, .75R: 0.227273, count: 22 -Region 16 Avg IOU: 0.706162, Class: 0.252096, Obj: 0.292729, No Obj: 0.001234, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 23 Avg IOU: 0.614464, Class: 0.260396, Obj: 0.059639, No Obj: 0.000169, .5R: 0.866667, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.748747, Class: 0.286756, Obj: 0.332640, No Obj: 0.001372, .5R: 1.000000, .75R: 0.615385, count: 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16 Avg IOU: 0.729797, Class: 0.189177, Obj: 0.349983, No Obj: 0.001320, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.671173, Class: 0.186094, Obj: 0.150682, No Obj: 0.000769, .5R: 0.888889, .75R: 0.333333, count: 18 - - 674: 2.806702, 3.144645 avg loss, 0.001000 rate, 1.160560 seconds, 86272 images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.693292, Class: 0.236699, Obj: 0.256659, No Obj: 0.001550, .5R: 0.937500, .75R: 0.437500, count: 16 -Region 23 Avg IOU: 0.610415, Class: 0.210499, Obj: 0.101336, No Obj: 0.000274, .5R: 0.857143, .75R: 0.071429, count: 14 -Region 16 Avg IOU: 0.730930, Class: 0.207764, Obj: 0.388203, No Obj: 0.001589, .5R: 0.933333, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.629968, Class: 0.130900, Obj: 0.052566, No Obj: 0.000279, .5R: 0.875000, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.760036, Class: 0.188959, Obj: 0.291930, No Obj: 0.001522, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.618490, Class: 0.178721, Obj: 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23 Avg IOU: 0.596851, Class: 0.118634, Obj: 0.031031, No Obj: 0.000155, .5R: 0.680000, .75R: 0.200000, count: 25 -Region 16 Avg IOU: 0.721123, Class: 0.208913, Obj: 0.271275, No Obj: 0.000883, .5R: 0.909091, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.573470, Class: 0.154337, Obj: 0.016201, No Obj: 0.000071, .5R: 0.666667, .75R: 0.083333, count: 12 -Region 16 Avg IOU: 0.645288, Class: 0.260211, Obj: 0.174947, No Obj: 0.001225, .5R: 0.687500, .75R: 0.312500, count: 16 -Region 23 Avg IOU: 0.639112, Class: 0.157123, Obj: 0.017792, No Obj: 0.000095, .5R: 0.823529, .75R: 0.176471, count: 17 -Region 16 Avg IOU: 0.736524, Class: 0.229594, Obj: 0.130948, No Obj: 0.000808, .5R: 1.000000, .75R: 0.411765, count: 17 -Region 23 Avg IOU: 0.618568, Class: 0.233516, Obj: 0.026877, No Obj: 0.000120, .5R: 0.882353, .75R: 0.117647, count: 17 -Region 16 Avg IOU: 0.662863, Class: 0.214562, Obj: 0.182972, No Obj: 0.000895, .5R: 0.818182, .75R: 0.272727, count: 11 -Region 23 Avg IOU: 0.710212, Class: 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Avg IOU: 0.663911, Class: 0.256782, Obj: 0.410225, No Obj: 0.002452, .5R: 0.894737, .75R: 0.210526, count: 19 -Region 23 Avg IOU: 0.645393, Class: 0.204270, Obj: 0.117004, No Obj: 0.001211, .5R: 0.937500, .75R: 0.250000, count: 32 -Region 16 Avg IOU: 0.597099, Class: 0.155112, Obj: 0.361485, No Obj: 0.001890, .5R: 0.769231, .75R: 0.153846, count: 13 -Region 23 Avg IOU: 0.591222, Class: 0.164163, Obj: 0.134812, No Obj: 0.000666, .5R: 0.863636, .75R: 0.090909, count: 22 - - 679: 3.063017, 3.034342 avg loss, 0.001000 rate, 1.174827 seconds, 86912 images -Loaded: 0.000068 seconds -Region 16 Avg IOU: 0.670479, Class: 0.233197, Obj: 0.234544, No Obj: 0.001358, .5R: 0.909091, .75R: 0.272727, count: 11 -Region 23 Avg IOU: 0.585428, Class: 0.158753, Obj: 0.066659, No Obj: 0.000263, .5R: 0.850000, .75R: 0.100000, count: 20 -Region 16 Avg IOU: 0.708399, Class: 0.213387, Obj: 0.276682, No Obj: 0.001312, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.569763, Class: 0.139890, Obj: 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23 Avg IOU: 0.651459, Class: 0.202208, Obj: 0.205196, No Obj: 0.001302, .5R: 0.814815, .75R: 0.370370, count: 27 -Region 16 Avg IOU: 0.735100, Class: 0.248202, Obj: 0.307633, No Obj: 0.001585, .5R: 1.000000, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.643577, Class: 0.181855, Obj: 0.182993, No Obj: 0.000977, .5R: 0.900000, .75R: 0.200000, count: 20 -Region 16 Avg IOU: 0.719109, Class: 0.198163, Obj: 0.220523, No Obj: 0.000959, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.635509, Class: 0.202224, Obj: 0.121342, No Obj: 0.000684, .5R: 0.823529, .75R: 0.176471, count: 17 -Region 16 Avg IOU: 0.735187, Class: 0.254081, Obj: 0.382387, No Obj: 0.001148, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.636404, Class: 0.123215, Obj: 0.129855, No Obj: 0.000827, .5R: 0.866667, .75R: 0.066667, count: 15 -Region 16 Avg IOU: 0.714865, Class: 0.204537, Obj: 0.276901, No Obj: 0.001411, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.581774, Class: 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Class: 0.241428, Obj: 0.293127, No Obj: 0.001250, .5R: 1.000000, .75R: 0.647059, count: 17 -Region 23 Avg IOU: 0.667387, Class: 0.209939, Obj: 0.027559, No Obj: 0.000144, .5R: 1.000000, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.765893, Class: 0.289781, Obj: 0.293476, No Obj: 0.001884, .5R: 1.000000, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.555869, Class: 0.209544, Obj: 0.036432, No Obj: 0.000266, .5R: 0.583333, .75R: 0.125000, count: 24 -Region 16 Avg IOU: 0.716492, Class: 0.181527, Obj: 0.307449, No Obj: 0.001931, .5R: 1.000000, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.621759, Class: 0.171548, Obj: 0.050802, No Obj: 0.000197, .5R: 0.875000, .75R: 0.125000, count: 8 -Region 16 Avg IOU: 0.760108, Class: 0.298871, Obj: 0.296442, No Obj: 0.001302, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.547560, Class: 0.242176, Obj: 0.047346, No Obj: 0.000243, .5R: 0.625000, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.763071, Class: 0.294623, Obj: 0.306170, 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0.219952, Obj: 0.122180, No Obj: 0.000534, .5R: 0.888889, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.674525, Class: 0.169493, Obj: 0.223932, No Obj: 0.001390, .5R: 0.944444, .75R: 0.388889, count: 18 -Region 23 Avg IOU: 0.614165, Class: 0.148074, Obj: 0.166359, No Obj: 0.000851, .5R: 0.782609, .75R: 0.173913, count: 23 -Region 16 Avg IOU: 0.703856, Class: 0.196076, Obj: 0.138665, No Obj: 0.001604, .5R: 0.941176, .75R: 0.235294, count: 17 -Region 23 Avg IOU: 0.607044, Class: 0.164990, Obj: 0.043151, No Obj: 0.000360, .5R: 0.909091, .75R: 0.181818, count: 11 -Region 16 Avg IOU: 0.719645, Class: 0.287786, Obj: 0.235488, No Obj: 0.001415, .5R: 0.857143, .75R: 0.571429, count: 21 -Region 23 Avg IOU: 0.644519, Class: 0.206019, Obj: 0.099539, No Obj: 0.000586, .5R: 0.857143, .75R: 0.238095, count: 21 -Region 16 Avg IOU: 0.717040, Class: 0.230635, Obj: 0.234121, No Obj: 0.001271, .5R: 0.944444, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.575975, Class: 0.121747, Obj: 0.032651, No Obj: 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.75R: 0.133333, count: 15 -Region 16 Avg IOU: 0.619928, Class: 0.200818, Obj: 0.237902, No Obj: 0.000844, .5R: 0.857143, .75R: 0.190476, count: 21 -Region 23 Avg IOU: 0.626572, Class: 0.161908, Obj: 0.126891, No Obj: 0.000289, .5R: 0.733333, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.716026, Class: 0.257601, Obj: 0.312523, No Obj: 0.000903, .5R: 0.842105, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.558122, Class: 0.219302, Obj: 0.134089, No Obj: 0.000360, .5R: 0.571429, .75R: 0.214286, count: 28 -Region 16 Avg IOU: 0.684921, Class: 0.213762, Obj: 0.263333, No Obj: 0.001029, .5R: 0.956522, .75R: 0.391304, count: 23 -Region 23 Avg IOU: 0.593976, Class: 0.195011, Obj: 0.076703, No Obj: 0.000300, .5R: 0.692308, .75R: 0.153846, count: 13 - - 718: 3.480946, 3.052043 avg loss, 0.001000 rate, 2.122760 seconds, 91904 images -Loaded: 0.000102 seconds -Region 16 Avg IOU: 0.636466, Class: 0.266044, Obj: 0.148061, No Obj: 0.000715, .5R: 0.823529, .75R: 0.176471, count: 17 -Region 23 Avg 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0.882353, .75R: 0.411765, count: 17 -Region 23 Avg IOU: 0.605451, Class: 0.197804, Obj: 0.022893, No Obj: 0.000098, .5R: 0.806452, .75R: 0.225806, count: 31 -Region 16 Avg IOU: 0.713443, Class: 0.247248, Obj: 0.359013, No Obj: 0.000817, .5R: 0.846154, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.653736, Class: 0.189828, Obj: 0.066798, No Obj: 0.000185, .5R: 0.863636, .75R: 0.227273, count: 22 -Region 16 Avg IOU: 0.673907, Class: 0.213715, Obj: 0.089285, No Obj: 0.000901, .5R: 0.903226, .75R: 0.290323, count: 31 -Region 23 Avg IOU: 0.609644, Class: 0.199402, Obj: 0.028822, No Obj: 0.000113, .5R: 0.869565, .75R: 0.086957, count: 23 -Region 16 Avg IOU: 0.690426, Class: 0.211513, Obj: 0.169995, No Obj: 0.000856, .5R: 0.869565, .75R: 0.478261, count: 23 -Region 23 Avg IOU: 0.556549, Class: 0.162060, Obj: 0.045411, No Obj: 0.000178, .5R: 0.640000, .75R: 0.040000, count: 25 -Region 16 Avg IOU: 0.675533, Class: 0.206829, Obj: 0.076626, No Obj: 0.001023, .5R: 0.863636, .75R: 0.409091, count: 22 -Region 23 Avg IOU: 0.589585, Class: 0.178641, Obj: 0.050023, No Obj: 0.000131, .5R: 0.857143, .75R: 0.071429, count: 14 -Region 16 Avg IOU: 0.681073, Class: 0.220924, Obj: 0.205778, No Obj: 0.001082, .5R: 0.862069, .75R: 0.379310, count: 29 -Region 23 Avg IOU: 0.626768, Class: 0.210616, Obj: 0.031453, No Obj: 0.000154, .5R: 0.892857, .75R: 0.071429, count: 28 -Region 16 Avg IOU: 0.675774, Class: 0.208933, Obj: 0.216362, No Obj: 0.000929, .5R: 0.842105, .75R: 0.368421, count: 19 -Region 23 Avg IOU: 0.657786, Class: 0.240500, Obj: 0.061776, No Obj: 0.000128, .5R: 0.941176, .75R: 0.235294, count: 17 -Region 16 Avg IOU: 0.737902, Class: 0.208344, Obj: 0.206391, No Obj: 0.000855, .5R: 1.000000, .75R: 0.461538, count: 26 -Region 23 Avg IOU: 0.690273, Class: 0.153206, Obj: 0.068916, No Obj: 0.000159, .5R: 1.000000, .75R: 0.388889, count: 18 - - 720: 3.819420, 3.152408 avg loss, 0.001000 rate, 2.118221 seconds, 92160 images -Resizing -480 x 480 - try to allocate workspace = 1144537089 * sizeof(float), CUDA allocate done! -Loaded: 0.377977 seconds -Region 16 Avg IOU: 0.696947, Class: 0.197288, Obj: 0.161592, No Obj: 0.001180, .5R: 1.000000, .75R: 0.200000, count: 10 -Region 23 Avg IOU: 0.651037, Class: 0.181317, Obj: 0.054220, No Obj: 0.000247, .5R: 0.869565, .75R: 0.304348, count: 23 -Region 16 Avg IOU: 0.703467, Class: 0.243031, Obj: 0.232453, No Obj: 0.000951, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 23 Avg IOU: 0.635400, Class: 0.137185, Obj: 0.057689, No Obj: 0.000233, .5R: 0.933333, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.731979, Class: 0.236646, Obj: 0.348427, No Obj: 0.001323, .5R: 0.928571, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.592619, Class: 0.157508, Obj: 0.047038, No Obj: 0.000381, .5R: 0.818182, .75R: 0.090909, count: 22 -Region 16 Avg IOU: 0.691923, Class: 0.207820, Obj: 0.190446, No Obj: 0.001181, .5R: 0.923077, .75R: 0.307692, count: 13 -Region 23 Avg IOU: 0.571556, Class: 0.167572, Obj: 0.039237, No Obj: 0.000281, .5R: 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16 - - 721: 2.461789, 3.083346 avg loss, 0.001000 rate, 0.996831 seconds, 92288 images -Loaded: 0.000061 seconds -Region 16 Avg IOU: 0.738604, Class: 0.205720, Obj: 0.353129, No Obj: 0.001268, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.604529, Class: 0.184018, Obj: 0.082796, No Obj: 0.000493, .5R: 0.833333, .75R: 0.208333, count: 24 -Region 16 Avg IOU: 0.698461, Class: 0.181573, Obj: 0.311653, No Obj: 0.001229, .5R: 0.923077, .75R: 0.384615, count: 13 -Region 23 Avg IOU: 0.666492, Class: 0.176847, Obj: 0.143326, No Obj: 0.000525, .5R: 0.913043, .75R: 0.217391, count: 23 -Region 16 Avg IOU: 0.700338, Class: 0.125324, Obj: 0.178334, No Obj: 0.000955, .5R: 0.875000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.594459, Class: 0.110686, Obj: 0.187380, No Obj: 0.000551, .5R: 0.736842, .75R: 0.105263, count: 19 -Region 16 Avg IOU: 0.736711, Class: 0.188245, Obj: 0.257036, No Obj: 0.001628, .5R: 1.000000, .75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.624132, Class: 0.183724, Obj: 0.062938, No Obj: 0.000377, .5R: 0.869565, .75R: 0.130435, count: 23 -Region 16 Avg IOU: 0.733445, Class: 0.239343, Obj: 0.303956, No Obj: 0.001644, .5R: 0.866667, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.588236, Class: 0.151662, Obj: 0.091396, No Obj: 0.000419, .5R: 0.750000, .75R: 0.100000, count: 20 -Region 16 Avg IOU: 0.754427, Class: 0.290895, Obj: 0.344166, No Obj: 0.001406, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.669035, Class: 0.235660, Obj: 0.049577, No Obj: 0.000359, .5R: 0.947368, .75R: 0.210526, count: 19 -Region 16 Avg IOU: 0.708114, Class: 0.200527, Obj: 0.273870, No Obj: 0.002292, .5R: 0.904762, .75R: 0.428571, count: 21 -Region 23 Avg IOU: 0.622929, Class: 0.190987, Obj: 0.105370, No Obj: 0.000492, .5R: 0.904762, .75R: 0.238095, count: 21 -Region 16 Avg IOU: 0.699459, Class: 0.251265, Obj: 0.451135, No Obj: 0.001480, .5R: 1.000000, .75R: 0.200000, count: 10 -Region 23 Avg IOU: 0.659095, Class: 0.169087, Obj: 0.121828, No Obj: 0.000612, .5R: 0.916667, .75R: 0.125000, count: 24 - - 722: 2.878361, 3.062848 avg loss, 0.001000 rate, 1.054276 seconds, 92416 images -Loaded: 0.000077 seconds -Region 16 Avg IOU: 0.757487, Class: 0.195613, Obj: 0.400947, No Obj: 0.002085, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.650811, Class: 0.209909, Obj: 0.159455, No Obj: 0.000784, .5R: 0.933333, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.765257, Class: 0.263428, Obj: 0.439622, No Obj: 0.002176, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.678870, Class: 0.251314, Obj: 0.140860, No Obj: 0.000647, .5R: 0.894737, .75R: 0.368421, count: 19 -Region 16 Avg IOU: 0.754915, Class: 0.202441, Obj: 0.191576, No Obj: 0.001278, .5R: 0.928571, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.588107, Class: 0.159119, Obj: 0.159863, No Obj: 0.000714, .5R: 0.687500, .75R: 0.062500, count: 16 -Region 16 Avg IOU: 0.722188, Class: 0.132438, Obj: 0.172056, No Obj: 0.001451, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.632964, Class: 0.143699, Obj: 0.123963, No Obj: 0.000594, .5R: 0.937500, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.729446, Class: 0.242996, Obj: 0.272261, No Obj: 0.001794, .5R: 0.933333, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.624461, Class: 0.188768, Obj: 0.094619, No Obj: 0.000555, .5R: 0.875000, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.723954, Class: 0.221858, Obj: 0.197765, No Obj: 0.001576, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.548508, Class: 0.243781, Obj: 0.209879, No Obj: 0.000460, .5R: 0.700000, .75R: 0.100000, count: 10 -Region 16 Avg IOU: 0.746551, Class: 0.185649, Obj: 0.410217, No Obj: 0.001680, .5R: 0.916667, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.655463, Class: 0.198567, Obj: 0.158349, No Obj: 0.000770, .5R: 0.863636, .75R: 0.227273, count: 22 -Region 16 Avg IOU: 0.704283, Class: 0.240358, Obj: 0.284772, No Obj: 0.001784, .5R: 0.933333, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.616834, Class: 0.194983, Obj: 0.072052, No Obj: 0.000613, .5R: 0.739130, .75R: 0.304348, count: 23 - - 723: 2.537427, 3.010306 avg loss, 0.001000 rate, 1.043499 seconds, 92544 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.751362, Class: 0.251432, Obj: 0.292957, No Obj: 0.001184, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.556110, Class: 0.217153, Obj: 0.217076, No Obj: 0.000550, .5R: 0.714286, .75R: 0.000000, count: 14 -Region 16 Avg IOU: 0.662428, Class: 0.204694, Obj: 0.246609, No Obj: 0.002060, .5R: 0.818182, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.503436, Class: 0.193343, Obj: 0.077494, No Obj: 0.000706, .5R: 0.434783, .75R: 0.043478, count: 23 -Region 16 Avg IOU: 0.691673, Class: 0.136140, Obj: 0.158908, No Obj: 0.001162, .5R: 0.857143, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.632605, Class: 0.241472, Obj: 0.070210, No Obj: 0.000547, .5R: 1.000000, .75R: 0.076923, count: 13 -Region 16 Avg IOU: 0.684715, Class: 0.227485, Obj: 0.314421, No 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images -Loaded: 0.000069 seconds -Region 16 Avg IOU: 0.680215, Class: 0.209153, Obj: 0.229987, No Obj: 0.001128, .5R: 0.884615, .75R: 0.307692, count: 26 -Region 23 Avg IOU: 0.564428, Class: 0.135696, Obj: 0.013766, No Obj: 0.000055, .5R: 0.642857, .75R: 0.071429, count: 14 -Region 16 Avg IOU: 0.649494, Class: 0.244600, Obj: 0.209269, No Obj: 0.000953, .5R: 0.913043, .75R: 0.173913, count: 23 -Region 23 Avg IOU: 0.643300, Class: 0.221494, Obj: 0.034004, No Obj: 0.000063, .5R: 0.882353, .75R: 0.117647, count: 17 -Region 16 Avg IOU: 0.718841, Class: 0.226555, Obj: 0.292085, No Obj: 0.001074, .5R: 1.000000, .75R: 0.450000, count: 20 -Region 23 Avg IOU: 0.624391, Class: 0.229771, Obj: 0.030705, No Obj: 0.000051, .5R: 0.842105, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.696863, Class: 0.168574, Obj: 0.226557, No Obj: 0.000992, .5R: 0.923077, .75R: 0.384615, count: 26 -Region 23 Avg IOU: 0.636019, Class: 0.175905, Obj: 0.034322, No Obj: 0.000061, .5R: 0.857143, .75R: 0.238095, count: 21 -Region 16 Avg IOU: 0.678381, Class: 0.257882, Obj: 0.207418, No Obj: 0.000887, .5R: 0.956522, .75R: 0.217391, count: 23 -Region 23 Avg IOU: 0.693230, Class: 0.232319, Obj: 0.018318, No Obj: 0.000046, .5R: 1.000000, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.674040, Class: 0.154564, Obj: 0.300808, No Obj: 0.000756, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 23 Avg IOU: 0.663677, Class: 0.130124, Obj: 0.024831, No Obj: 0.000050, .5R: 0.875000, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.646885, Class: 0.184443, Obj: 0.125701, No Obj: 0.001152, .5R: 0.781250, .75R: 0.375000, count: 32 -Region 23 Avg IOU: 0.580334, Class: 0.197138, Obj: 0.011328, No Obj: 0.000041, .5R: 0.571429, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.702823, Class: 0.237624, Obj: 0.244424, No Obj: 0.000732, .5R: 0.900000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.573997, Class: 0.162616, Obj: 0.013571, No Obj: 0.000031, .5R: 0.750000, .75R: 0.125000, count: 8 - - 739: 3.164503, 3.207477 avg loss, 0.001000 rate, 2.401157 seconds, 94592 images -Loaded: 0.000079 seconds -Region 16 Avg IOU: 0.724411, Class: 0.205273, Obj: 0.394628, No Obj: 0.001042, .5R: 1.000000, .75R: 0.285714, count: 14 -Region 23 Avg IOU: 0.626203, Class: 0.186977, Obj: 0.022853, No Obj: 0.000042, .5R: 0.909091, .75R: 0.090909, count: 11 -Region 16 Avg IOU: 0.669461, Class: 0.208563, Obj: 0.106971, No Obj: 0.001063, .5R: 0.875000, .75R: 0.281250, count: 32 -Region 23 Avg IOU: 0.602940, Class: 0.184051, Obj: 0.013499, No Obj: 0.000033, .5R: 0.769231, .75R: 0.076923, count: 13 -Region 16 Avg IOU: 0.663906, Class: 0.214770, Obj: 0.210942, No Obj: 0.001203, .5R: 0.944444, .75R: 0.388889, count: 18 -Region 23 Avg IOU: 0.527623, Class: 0.214469, Obj: 0.011303, No Obj: 0.000021, .5R: 0.571429, .75R: 0.142857, count: 7 -Region 16 Avg IOU: 0.729520, Class: 0.230160, Obj: 0.277600, No Obj: 0.001068, .5R: 1.000000, .75R: 0.312500, count: 16 -Region 23 Avg IOU: 0.626256, Class: 0.142635, Obj: 0.037974, No Obj: 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-Region 16 Avg IOU: 0.752803, Class: 0.165606, Obj: 0.230371, No Obj: 0.000734, .5R: 1.000000, .75R: 0.388889, count: 18 -Region 23 Avg IOU: 0.610459, Class: 0.136771, Obj: 0.070284, No Obj: 0.000144, .5R: 0.812500, .75R: 0.062500, count: 16 -Region 16 Avg IOU: 0.664094, Class: 0.182732, Obj: 0.267695, No Obj: 0.000836, .5R: 0.952381, .75R: 0.238095, count: 21 -Region 23 Avg IOU: 0.694797, Class: 0.199842, Obj: 0.125721, No Obj: 0.000159, .5R: 0.812500, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.717204, Class: 0.145649, Obj: 0.182684, No Obj: 0.001002, .5R: 0.903226, .75R: 0.451613, count: 31 -Region 23 Avg IOU: 0.650659, Class: 0.172199, Obj: 0.064671, No Obj: 0.000198, .5R: 0.862069, .75R: 0.310345, count: 29 -Region 16 Avg IOU: 0.683674, Class: 0.201160, Obj: 0.119878, No Obj: 0.000802, .5R: 0.882353, .75R: 0.294118, count: 34 -Region 23 Avg IOU: 0.583069, Class: 0.219417, Obj: 0.076690, No Obj: 0.000145, .5R: 0.777778, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.623539, Class: 0.217964, Obj: 0.144924, No Obj: 0.000875, .5R: 0.818182, .75R: 0.242424, count: 33 -Region 23 Avg IOU: 0.637871, Class: 0.224930, Obj: 0.061086, No Obj: 0.000196, .5R: 0.692308, .75R: 0.346154, count: 26 - - 742: 4.164411, 3.292316 avg loss, 0.001000 rate, 2.273398 seconds, 94976 images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.693334, Class: 0.221833, Obj: 0.202359, No Obj: 0.001246, .5R: 0.931035, .75R: 0.310345, count: 29 -Region 23 Avg IOU: 0.644800, Class: 0.199039, Obj: 0.145677, No Obj: 0.000586, .5R: 0.913043, .75R: 0.130435, count: 23 -Region 16 Avg IOU: 0.720337, Class: 0.220620, Obj: 0.334617, No Obj: 0.001271, .5R: 0.965517, .75R: 0.379310, count: 29 -Region 23 Avg IOU: 0.640056, Class: 0.183284, Obj: 0.303359, No Obj: 0.000598, .5R: 0.888889, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.692294, Class: 0.227858, Obj: 0.167741, No Obj: 0.000996, .5R: 0.842105, .75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.629544, Class: 0.215843, Obj: 0.146854, No Obj: 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0.636278, Class: 0.184198, Obj: 0.094099, No Obj: 0.000236, .5R: 0.823529, .75R: 0.235294, count: 17 -Region 16 Avg IOU: 0.662258, Class: 0.202430, Obj: 0.227320, No Obj: 0.000959, .5R: 0.842105, .75R: 0.315789, count: 19 -Region 23 Avg IOU: 0.649412, Class: 0.163392, Obj: 0.086954, No Obj: 0.000134, .5R: 0.812500, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.645427, Class: 0.273055, Obj: 0.117580, No Obj: 0.000741, .5R: 0.782609, .75R: 0.304348, count: 23 -Region 23 Avg IOU: 0.574234, Class: 0.226648, Obj: 0.082458, No Obj: 0.000181, .5R: 0.842105, .75R: 0.000000, count: 19 -Region 16 Avg IOU: 0.665530, Class: 0.224031, Obj: 0.134324, No Obj: 0.000750, .5R: 0.800000, .75R: 0.350000, count: 20 -Region 23 Avg IOU: 0.640526, Class: 0.176777, Obj: 0.072640, No Obj: 0.000126, .5R: 1.000000, .75R: 0.000000, count: 10 -Region 16 Avg IOU: 0.676597, Class: 0.190205, Obj: 0.310781, No Obj: 0.001317, .5R: 0.851852, .75R: 0.481481, count: 27 -Region 23 Avg IOU: 0.625720, Class: 0.161078, Obj: 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Class: 0.149122, Obj: 0.266618, No Obj: 0.001771, .5R: 1.000000, .75R: 0.181818, count: 11 -Region 23 Avg IOU: 0.561988, Class: 0.160898, Obj: 0.235746, No Obj: 0.001721, .5R: 0.800000, .75R: 0.033333, count: 30 -Region 16 Avg IOU: 0.706231, Class: 0.235691, Obj: 0.310763, No Obj: 0.002855, .5R: 1.000000, .75R: 0.266667, count: 15 -Region 23 Avg IOU: 0.604020, Class: 0.106213, Obj: 0.089014, No Obj: 0.000784, .5R: 0.818182, .75R: 0.090909, count: 11 -Region 16 Avg IOU: 0.634301, Class: 0.252265, Obj: 0.212084, No Obj: 0.001223, .5R: 0.875000, .75R: 0.250000, count: 8 -Region 23 Avg IOU: 0.626933, Class: 0.160111, Obj: 0.157866, No Obj: 0.001372, .5R: 0.906250, .75R: 0.187500, count: 32 -Region 16 Avg IOU: 0.726240, Class: 0.239633, Obj: 0.290249, No Obj: 0.001847, .5R: 0.857143, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.591435, Class: 0.170806, Obj: 0.145152, No Obj: 0.001266, .5R: 0.794118, .75R: 0.088235, count: 34 -Region 16 Avg IOU: 0.733271, Class: 0.120413, Obj: 0.238828, 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23 Avg IOU: 0.610819, Class: 0.167428, Obj: 0.199071, No Obj: 0.000974, .5R: 0.739130, .75R: 0.217391, count: 23 -Region 16 Avg IOU: 0.726765, Class: 0.258808, Obj: 0.201476, No Obj: 0.001731, .5R: 0.950000, .75R: 0.500000, count: 20 -Region 23 Avg IOU: 0.644096, Class: 0.216452, Obj: 0.223697, No Obj: 0.000934, .5R: 0.894737, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.750477, Class: 0.245722, Obj: 0.387181, No Obj: 0.001767, .5R: 1.000000, .75R: 0.428571, count: 7 -Region 23 Avg IOU: 0.586083, Class: 0.201685, Obj: 0.118730, No Obj: 0.000755, .5R: 0.777778, .75R: 0.055556, count: 18 - - 767: 2.669015, 2.724473 avg loss, 0.001000 rate, 0.867272 seconds, 98176 images -Loaded: 0.000072 seconds -Region 16 Avg IOU: 0.727703, Class: 0.186752, Obj: 0.359339, No Obj: 0.002401, .5R: 0.933333, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.588185, Class: 0.139617, Obj: 0.091985, No Obj: 0.000596, .5R: 0.782609, .75R: 0.130435, count: 23 -Region 16 Avg IOU: 0.786456, Class: 0.148009, Obj: 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16 Avg IOU: 0.801182, Class: 0.273460, Obj: 0.563192, No Obj: 0.002036, .5R: 1.000000, .75R: 0.833333, count: 12 -Region 23 Avg IOU: 0.636571, Class: 0.173641, Obj: 0.056979, No Obj: 0.000408, .5R: 0.740741, .75R: 0.185185, count: 27 -Region 16 Avg IOU: 0.691652, Class: 0.256114, Obj: 0.290217, No Obj: 0.002696, .5R: 0.950000, .75R: 0.300000, count: 20 -Region 23 Avg IOU: 0.596721, Class: 0.204463, Obj: 0.045860, No Obj: 0.000299, .5R: 0.684211, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.700180, Class: 0.260102, Obj: 0.308261, No Obj: 0.001429, .5R: 0.866667, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.636008, Class: 0.193488, Obj: 0.084605, No Obj: 0.000309, .5R: 0.818182, .75R: 0.181818, count: 11 -Region 16 Avg IOU: 0.709459, Class: 0.183439, Obj: 0.292028, No Obj: 0.002711, .5R: 0.950000, .75R: 0.350000, count: 20 -Region 23 Avg IOU: 0.625306, Class: 0.168947, Obj: 0.023324, No Obj: 0.000314, .5R: 0.875000, .75R: 0.166667, count: 24 -Region 16 Avg IOU: 0.744770, Class: 0.237447, Obj: 0.327025, No Obj: 0.001418, .5R: 1.000000, .75R: 0.444444, count: 9 -Region 23 Avg IOU: 0.621250, Class: 0.142753, Obj: 0.150663, No Obj: 0.000479, .5R: 0.789474, .75R: 0.210526, count: 19 -Region 16 Avg IOU: 0.651782, Class: 0.234996, Obj: 0.444574, No Obj: 0.002118, .5R: 1.000000, .75R: 0.250000, count: 8 -Region 23 Avg IOU: 0.583918, Class: 0.177629, Obj: 0.111368, No Obj: 0.000744, .5R: 0.894737, .75R: 0.052632, count: 19 -Region 16 Avg IOU: 0.709401, Class: 0.296491, Obj: 0.226000, No Obj: 0.001942, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.582648, Class: 0.148306, Obj: 0.089177, No Obj: 0.000322, .5R: 0.866667, .75R: 0.066667, count: 15 - - 769: 2.587427, 2.718760 avg loss, 0.001000 rate, 0.861847 seconds, 98432 images -Loaded: 0.000062 seconds -Region 16 Avg IOU: 0.738441, Class: 0.241570, Obj: 0.225879, No Obj: 0.001478, .5R: 0.900000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.593387, Class: 0.213451, Obj: 0.065215, No Obj: 0.000278, 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count: 17 -Region 16 Avg IOU: 0.793622, Class: 0.243431, Obj: 0.384644, No Obj: 0.001557, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.651256, Class: 0.159393, Obj: 0.041820, No Obj: 0.000202, .5R: 0.823529, .75R: 0.235294, count: 17 -Region 16 Avg IOU: 0.707741, Class: 0.233363, Obj: 0.265063, No Obj: 0.001756, .5R: 0.916667, .75R: 0.250000, count: 12 -Region 23 Avg IOU: 0.585512, Class: 0.210635, Obj: 0.077983, No Obj: 0.000453, .5R: 0.739130, .75R: 0.086957, count: 23 -Region 16 Avg IOU: 0.653531, Class: 0.222740, Obj: 0.297300, No Obj: 0.001763, .5R: 0.823529, .75R: 0.235294, count: 17 -Region 23 Avg IOU: 0.642866, Class: 0.206139, Obj: 0.066513, No Obj: 0.000317, .5R: 0.894737, .75R: 0.210526, count: 19 - - 770: 2.523169, 2.699201 avg loss, 0.001000 rate, 0.869510 seconds, 98560 images -Resizing -448 x 448 - try to allocate workspace = 305201153 * sizeof(float), CUDA allocate done! -Loaded: 0.563395 seconds -Region 16 Avg IOU: 0.781508, Class: 0.257023, Obj: 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0.882353, .75R: 0.411765, count: 17 -Region 23 Avg IOU: 0.620844, Class: 0.171707, Obj: 0.031728, No Obj: 0.000276, .5R: 0.833333, .75R: 0.233333, count: 30 -Region 16 Avg IOU: 0.706973, Class: 0.241270, Obj: 0.251577, No Obj: 0.001386, .5R: 0.909091, .75R: 0.363636, count: 11 -Region 23 Avg IOU: 0.645111, Class: 0.226015, Obj: 0.059178, No Obj: 0.000273, .5R: 0.800000, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.676324, Class: 0.275920, Obj: 0.263238, No Obj: 0.001559, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 23 Avg IOU: 0.549858, Class: 0.176163, Obj: 0.028594, No Obj: 0.000161, .5R: 0.625000, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.824265, Class: 0.193246, Obj: 0.368526, No Obj: 0.000872, .5R: 1.000000, .75R: 1.000000, count: 6 -Region 23 Avg IOU: 0.636751, Class: 0.214732, Obj: 0.079786, No Obj: 0.000294, .5R: 0.842105, .75R: 0.157895, count: 19 - - 771: 2.573472, 2.686628 avg loss, 0.001000 rate, 0.829693 seconds, 98688 images -Loaded: 0.000061 seconds -Region 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23 Avg IOU: 0.625727, Class: 0.211403, Obj: 0.044866, No Obj: 0.000368, .5R: 0.916667, .75R: 0.083333, count: 24 -Region 16 Avg IOU: 0.767846, Class: 0.328798, Obj: 0.419382, No Obj: 0.000908, .5R: 1.000000, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.598997, Class: 0.214406, Obj: 0.045570, No Obj: 0.000261, .5R: 0.736842, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.745613, Class: 0.226268, Obj: 0.236278, No Obj: 0.001972, .5R: 0.900000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.689147, Class: 0.155072, Obj: 0.085737, No Obj: 0.000642, .5R: 1.000000, .75R: 0.318182, count: 22 - - 784: 2.403116, 2.580311 avg loss, 0.001000 rate, 0.783783 seconds, 100352 images -Loaded: 0.000069 seconds -Region 16 Avg IOU: 0.714293, Class: 0.174930, Obj: 0.342829, No Obj: 0.000872, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.634210, Class: 0.180391, Obj: 0.147168, No Obj: 0.000730, .5R: 0.866667, .75R: 0.066667, count: 15 -Region 16 Avg IOU: 0.684363, Class: 0.220666, Obj: 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16 Avg IOU: 0.747849, Class: 0.217074, Obj: 0.265105, No Obj: 0.000748, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.609764, Class: 0.184689, Obj: 0.053029, No Obj: 0.000468, .5R: 0.769231, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.697980, Class: 0.275912, Obj: 0.150987, No Obj: 0.001158, .5R: 0.916667, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.629870, Class: 0.188745, Obj: 0.108667, No Obj: 0.000769, .5R: 1.000000, .75R: 0.130435, count: 23 -Region 16 Avg IOU: 0.662104, Class: 0.201408, Obj: 0.272718, No Obj: 0.000775, .5R: 0.833333, .75R: 0.333333, count: 6 -Region 23 Avg IOU: 0.649482, Class: 0.200708, Obj: 0.125562, No Obj: 0.000822, .5R: 0.894737, .75R: 0.210526, count: 19 -Region 16 Avg IOU: 0.667178, Class: 0.284773, Obj: 0.230427, No Obj: 0.001326, .5R: 0.923077, .75R: 0.230769, count: 13 -Region 23 Avg IOU: 0.583827, Class: 0.205316, Obj: 0.119651, No Obj: 0.000922, .5R: 0.800000, .75R: 0.000000, count: 20 -Region 16 Avg IOU: 0.712743, Class: 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count: 16 -Region 16 Avg IOU: 0.697175, Class: 0.235796, Obj: 0.147093, No Obj: 0.001451, .5R: 1.000000, .75R: 0.357143, count: 14 -Region 23 Avg IOU: 0.616445, Class: 0.213385, Obj: 0.115443, No Obj: 0.000774, .5R: 0.823529, .75R: 0.058824, count: 17 -Region 16 Avg IOU: 0.723069, Class: 0.204354, Obj: 0.229287, No Obj: 0.001408, .5R: 1.000000, .75R: 0.181818, count: 11 -Region 23 Avg IOU: 0.574803, Class: 0.141392, Obj: 0.070406, No Obj: 0.000454, .5R: 0.545455, .75R: 0.272727, count: 11 -Region 16 Avg IOU: 0.731159, Class: 0.206117, Obj: 0.241903, No Obj: 0.002007, .5R: 0.944444, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.637736, Class: 0.198877, Obj: 0.093596, No Obj: 0.000657, .5R: 0.846154, .75R: 0.076923, count: 13 - - 787: 2.091691, 2.501183 avg loss, 0.001000 rate, 0.737573 seconds, 100736 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.704286, Class: 0.249853, Obj: 0.389672, No Obj: 0.002458, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.571517, Class: 0.172069, Obj: 0.064260, No Obj: 0.000571, .5R: 0.730769, .75R: 0.076923, count: 26 -Region 16 Avg IOU: 0.694524, Class: 0.246404, Obj: 0.360680, No Obj: 0.001951, .5R: 0.923077, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.600322, Class: 0.216673, Obj: 0.073144, No Obj: 0.000516, .5R: 0.809524, .75R: 0.142857, count: 21 -Region 16 Avg IOU: 0.740206, Class: 0.190937, Obj: 0.371462, No Obj: 0.001674, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.658181, Class: 0.195869, Obj: 0.052262, No Obj: 0.000458, .5R: 0.833333, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.759224, Class: 0.211533, Obj: 0.442161, No Obj: 0.001860, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.582956, Class: 0.186842, Obj: 0.163368, No Obj: 0.000673, .5R: 0.636364, .75R: 0.090909, count: 11 -Region 16 Avg IOU: 0.776427, Class: 0.297817, Obj: 0.339679, No Obj: 0.001596, .5R: 1.000000, .75R: 0.600000, count: 5 -Region 23 Avg IOU: 0.598299, Class: 0.200640, Obj: 0.108254, No 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0.375000, count: 8 -Region 23 Avg IOU: 0.577819, Class: 0.152274, Obj: 0.043940, No Obj: 0.000349, .5R: 0.636364, .75R: 0.136364, count: 22 -Region 16 Avg IOU: 0.709322, Class: 0.255177, Obj: 0.540957, No Obj: 0.001791, .5R: 0.833333, .75R: 0.333333, count: 6 -Region 23 Avg IOU: 0.647010, Class: 0.177785, Obj: 0.079161, No Obj: 0.000500, .5R: 0.863636, .75R: 0.272727, count: 22 -Region 16 Avg IOU: 0.774509, Class: 0.289691, Obj: 0.424700, No Obj: 0.002097, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.623110, Class: 0.193266, Obj: 0.039326, No Obj: 0.000309, .5R: 0.750000, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.775922, Class: 0.219348, Obj: 0.237488, No Obj: 0.001556, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.629637, Class: 0.207307, Obj: 0.074128, No Obj: 0.000591, .5R: 0.827586, .75R: 0.310345, count: 29 -Region 16 Avg IOU: 0.758486, Class: 0.199270, Obj: 0.371652, No Obj: 0.002162, .5R: 1.000000, .75R: 0.684211, count: 19 -Region 23 Avg 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0.412453, No Obj: 0.001833, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.625865, Class: 0.157414, Obj: 0.088429, No Obj: 0.000424, .5R: 0.823529, .75R: 0.176471, count: 17 -Region 16 Avg IOU: 0.683063, Class: 0.256361, Obj: 0.368081, No Obj: 0.001927, .5R: 1.000000, .75R: 0.142857, count: 7 -Region 23 Avg IOU: 0.574334, Class: 0.209860, Obj: 0.060461, No Obj: 0.000410, .5R: 0.800000, .75R: 0.066667, count: 15 -Region 16 Avg IOU: 0.659339, Class: 0.209461, Obj: 0.282418, No Obj: 0.001476, .5R: 0.923077, .75R: 0.230769, count: 13 -Region 23 Avg IOU: 0.624826, Class: 0.189260, Obj: 0.087308, No Obj: 0.000672, .5R: 0.838710, .75R: 0.129032, count: 31 -Region 16 Avg IOU: 0.729306, Class: 0.148823, Obj: 0.263060, No Obj: 0.001012, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 23 Avg IOU: 0.604748, Class: 0.184196, Obj: 0.100201, No Obj: 0.000554, .5R: 0.888889, .75R: 0.111111, count: 18 -Region 16 Avg IOU: 0.731596, Class: 0.157860, Obj: 0.235120, No Obj: 0.001457, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.594732, Class: 0.159858, Obj: 0.092499, No Obj: 0.000647, .5R: 0.772727, .75R: 0.181818, count: 22 -Region 16 Avg IOU: 0.766237, Class: 0.192274, Obj: 0.389875, No Obj: 0.002234, .5R: 1.000000, .75R: 0.562500, count: 16 -Region 23 Avg IOU: 0.618145, Class: 0.161885, Obj: 0.065806, No Obj: 0.000620, .5R: 0.821429, .75R: 0.214286, count: 28 -Region 16 Avg IOU: 0.695250, Class: 0.263364, Obj: 0.551569, No Obj: 0.002584, .5R: 1.000000, .75R: 0.352941, count: 17 -Region 23 Avg IOU: 0.637647, Class: 0.233749, Obj: 0.066971, No Obj: 0.000512, .5R: 0.823529, .75R: 0.176471, count: 17 -Region 16 Avg IOU: 0.771376, Class: 0.257938, Obj: 0.358181, No Obj: 0.001770, .5R: 1.000000, .75R: 0.777778, count: 9 -Region 23 Avg IOU: 0.617482, Class: 0.183550, Obj: 0.051767, No Obj: 0.000586, .5R: 0.869565, .75R: 0.130435, count: 23 - - 790: 2.646154, 2.495055 avg loss, 0.001000 rate, 0.764134 seconds, 101120 images -Resizing -672 x 672 - try to allocate workspace = 1148076033 * sizeof(float), CUDA allocate done! -Loaded: 0.413861 seconds -Region 16 Avg IOU: 0.676016, Class: 0.216460, Obj: 0.194492, No Obj: 0.000764, .5R: 1.000000, .75R: 0.181818, count: 11 -Region 23 Avg IOU: 0.681077, Class: 0.171878, Obj: 0.075758, No Obj: 0.000265, .5R: 0.818182, .75R: 0.454545, count: 11 -Region 16 Avg IOU: 0.639207, Class: 0.194570, Obj: 0.211177, No Obj: 0.001148, .5R: 0.777778, .75R: 0.222222, count: 18 -Region 23 Avg IOU: 0.614469, Class: 0.258743, Obj: 0.131994, No Obj: 0.000347, .5R: 0.846154, .75R: 0.076923, count: 13 -Region 16 Avg IOU: 0.621722, Class: 0.237340, Obj: 0.212474, No Obj: 0.001148, .5R: 0.842105, .75R: 0.157895, count: 19 -Region 23 Avg IOU: 0.632460, Class: 0.180489, Obj: 0.066971, No Obj: 0.000340, .5R: 0.925926, .75R: 0.111111, count: 27 -Region 16 Avg IOU: 0.631902, Class: 0.234114, Obj: 0.192920, No Obj: 0.001297, .5R: 0.708333, .75R: 0.375000, count: 24 -Region 23 Avg IOU: 0.643290, Class: 0.175109, Obj: 0.080806, No Obj: 0.000222, .5R: 0.894737, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.702243, Class: 0.216477, Obj: 0.155656, No Obj: 0.000992, .5R: 0.789474, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.564940, Class: 0.175635, Obj: 0.036403, No Obj: 0.000271, .5R: 0.703704, .75R: 0.111111, count: 27 -Region 16 Avg IOU: 0.641839, Class: 0.229205, Obj: 0.246501, No Obj: 0.001013, .5R: 0.950000, .75R: 0.050000, count: 20 -Region 23 Avg IOU: 0.630149, Class: 0.074753, Obj: 0.069366, No Obj: 0.000268, .5R: 0.722222, .75R: 0.222222, count: 18 -Region 16 Avg IOU: 0.679841, Class: 0.224602, Obj: 0.248548, No Obj: 0.001449, .5R: 0.814815, .75R: 0.407407, count: 27 -Region 23 Avg IOU: 0.614446, Class: 0.166900, Obj: 0.057683, No Obj: 0.000267, .5R: 0.720000, .75R: 0.240000, count: 25 -Region 16 Avg IOU: 0.627010, Class: 0.225565, Obj: 0.271010, No Obj: 0.001263, .5R: 0.842105, .75R: 0.157895, count: 19 -Region 23 Avg IOU: 0.575548, Class: 0.100969, Obj: 0.093036, No Obj: 0.000362, .5R: 0.741935, .75R: 0.064516, count: 31 - - 791: 3.633566, 2.608907 avg loss, 0.001000 rate, 2.012518 seconds, 101248 images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.593115, Class: 0.212652, Obj: 0.149233, No Obj: 0.001018, .5R: 0.769231, .75R: 0.153846, count: 26 -Region 23 Avg IOU: 0.587788, Class: 0.197889, Obj: 0.029224, No Obj: 0.000196, .5R: 0.789474, .75R: 0.052632, count: 19 -Region 16 Avg IOU: 0.728425, Class: 0.310668, Obj: 0.259613, No Obj: 0.000897, .5R: 0.937500, .75R: 0.625000, count: 16 -Region 23 Avg IOU: 0.611913, Class: 0.191654, Obj: 0.077402, No Obj: 0.000212, .5R: 0.823529, .75R: 0.176471, count: 17 -Region 16 Avg IOU: 0.718246, Class: 0.202143, Obj: 0.347218, No Obj: 0.001451, .5R: 0.894737, .75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.621063, Class: 0.203368, Obj: 0.135222, No Obj: 0.000364, .5R: 0.863636, .75R: 0.090909, count: 22 -Region 16 Avg IOU: 0.707979, Class: 0.165661, Obj: 0.207521, No Obj: 0.001161, .5R: 0.961538, .75R: 0.423077, count: 26 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0.175837, Obj: 0.051385, No Obj: 0.000392, .5R: 0.789474, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.657852, Class: 0.189219, Obj: 0.318115, No Obj: 0.002006, .5R: 0.923077, .75R: 0.230769, count: 13 -Region 23 Avg IOU: 0.561067, Class: 0.174276, Obj: 0.067064, No Obj: 0.000559, .5R: 0.625000, .75R: 0.166667, count: 24 -Region 16 Avg IOU: 0.716749, Class: 0.253850, Obj: 0.363061, No Obj: 0.004244, .5R: 1.000000, .75R: 0.434783, count: 23 -Region 23 Avg IOU: 0.638540, Class: 0.197662, Obj: 0.076429, No Obj: 0.000553, .5R: 0.833333, .75R: 0.200000, count: 30 -Region 16 Avg IOU: 0.587283, Class: 0.213028, Obj: 0.268296, No Obj: 0.002028, .5R: 0.666667, .75R: 0.000000, count: 12 -Region 23 Avg IOU: 0.545354, Class: 0.196290, Obj: 0.078771, No Obj: 0.000561, .5R: 0.642857, .75R: 0.071429, count: 14 -Region 16 Avg IOU: 0.722762, Class: 0.220954, Obj: 0.517804, No Obj: 0.003203, .5R: 0.857143, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.603762, Class: 0.216900, Obj: 0.145287, No Obj: 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images -Loaded: 0.000091 seconds -Region 16 Avg IOU: 0.762582, Class: 0.253251, Obj: 0.358223, No Obj: 0.001396, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.649487, Class: 0.182713, Obj: 0.137270, No Obj: 0.000755, .5R: 0.903226, .75R: 0.322581, count: 31 -Region 16 Avg IOU: 0.781771, Class: 0.297272, Obj: 0.500206, No Obj: 0.001025, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.632467, Class: 0.245649, Obj: 0.084844, No Obj: 0.000396, .5R: 0.809524, .75R: 0.190476, count: 21 -Region 16 Avg IOU: 0.662763, Class: 0.226469, Obj: 0.426185, No Obj: 0.002144, .5R: 0.818182, .75R: 0.363636, count: 11 -Region 23 Avg IOU: 0.718550, Class: 0.149307, Obj: 0.198679, No Obj: 0.000437, .5R: 1.000000, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.685757, Class: 0.232192, Obj: 0.433328, No Obj: 0.001621, .5R: 1.000000, .75R: 0.153846, count: 13 -Region 23 Avg IOU: 0.585114, Class: 0.154760, Obj: 0.082174, No Obj: 0.000465, .5R: 0.800000, .75R: 0.080000, count: 25 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11 -Region 16 Avg IOU: 0.708434, Class: 0.225235, Obj: 0.196549, No Obj: 0.000703, .5R: 0.909091, .75R: 0.363636, count: 22 -Region 23 Avg IOU: 0.637719, Class: 0.215012, Obj: 0.068598, No Obj: 0.000181, .5R: 0.809524, .75R: 0.285714, count: 21 -Region 16 Avg IOU: 0.696021, Class: 0.205600, Obj: 0.169068, No Obj: 0.000556, .5R: 0.750000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.581182, Class: 0.179940, Obj: 0.043099, No Obj: 0.000083, .5R: 0.750000, .75R: 0.062500, count: 16 - - 850: 2.629002, 3.038781 avg loss, 0.001000 rate, 2.682774 seconds, 108800 images -Resizing -384 x 384 - try to allocate workspace = 216268801 * sizeof(float), CUDA allocate done! -Loaded: 0.382242 seconds -Region 16 Avg IOU: 0.691680, Class: 0.300869, Obj: 0.233129, No Obj: 0.000853, .5R: 0.833333, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.556086, Class: 0.216777, Obj: 0.046263, No Obj: 0.000325, .5R: 0.722222, .75R: 0.000000, count: 18 -Region 16 Avg IOU: 0.733089, Class: 0.206092, Obj: 0.397991, 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16 Avg IOU: 0.604130, Class: 0.290258, Obj: 0.139451, No Obj: 0.001037, .5R: 0.833333, .75R: 0.166667, count: 6 -Region 23 Avg IOU: 0.589296, Class: 0.222164, Obj: 0.087671, No Obj: 0.001016, .5R: 0.823529, .75R: 0.058824, count: 17 -Region 16 Avg IOU: 0.683611, Class: 0.189386, Obj: 0.330274, No Obj: 0.000995, .5R: 1.000000, .75R: 0.250000, count: 4 -Region 23 Avg IOU: 0.644192, Class: 0.188603, Obj: 0.228335, No Obj: 0.000942, .5R: 0.866667, .75R: 0.133333, count: 15 -Region 16 Avg IOU: 0.731456, Class: 0.169893, Obj: 0.268910, No Obj: 0.001715, .5R: 0.933333, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.629049, Class: 0.177403, Obj: 0.181869, No Obj: 0.000970, .5R: 0.846154, .75R: 0.153846, count: 13 - - 853: 2.512223, 2.798078 avg loss, 0.001000 rate, 0.669471 seconds, 109184 images -Loaded: 0.065436 seconds -Region 16 Avg IOU: 0.752514, Class: 0.267280, Obj: 0.268836, No Obj: 0.002017, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.643109, Class: 0.206538, Obj: 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count: 4 -Region 23 Avg IOU: 0.588667, Class: 0.170649, Obj: 0.041512, No Obj: 0.000293, .5R: 0.538462, .75R: 0.230769, count: 13 -Region 16 Avg IOU: 0.724910, Class: 0.170208, Obj: 0.311001, No Obj: 0.001868, .5R: 1.000000, .75R: 0.375000, count: 8 -Region 23 Avg IOU: 0.596687, Class: 0.122660, Obj: 0.044260, No Obj: 0.000298, .5R: 0.666667, .75R: 0.000000, count: 18 -Region 16 Avg IOU: 0.802225, Class: 0.283130, Obj: 0.463622, No Obj: 0.000909, .5R: 1.000000, .75R: 0.500000, count: 4 -Region 23 Avg IOU: 0.617883, Class: 0.193974, Obj: 0.052227, No Obj: 0.000325, .5R: 0.833333, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.764458, Class: 0.157867, Obj: 0.556002, No Obj: 0.002738, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.587726, Class: 0.196362, Obj: 0.046289, No Obj: 0.000512, .5R: 0.782609, .75R: 0.043478, count: 23 - - 856: 1.986951, 2.655532 avg loss, 0.001000 rate, 0.657853 seconds, 109568 images -Loaded: 0.000053 seconds -Region 16 Avg IOU: 0.702059, Class: 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seconds -Region 16 Avg IOU: 0.646337, Class: 0.234475, Obj: 0.325287, No Obj: 0.001431, .5R: 0.666667, .75R: 0.333333, count: 9 -Region 23 Avg IOU: 0.643693, Class: 0.207675, Obj: 0.050976, No Obj: 0.000431, .5R: 0.894737, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.727616, Class: 0.203902, Obj: 0.399233, No Obj: 0.002099, .5R: 1.000000, .75R: 0.333333, count: 9 -Region 23 Avg IOU: 0.659553, Class: 0.233659, Obj: 0.069220, No Obj: 0.000446, .5R: 0.950000, .75R: 0.300000, count: 20 -Region 16 Avg IOU: 0.729446, Class: 0.215623, Obj: 0.483566, No Obj: 0.002023, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.643443, Class: 0.214705, Obj: 0.048585, No Obj: 0.000418, .5R: 0.909091, .75R: 0.272727, count: 11 -Region 16 Avg IOU: 0.627316, Class: 0.196991, Obj: 0.219703, No Obj: 0.001946, .5R: 0.500000, .75R: 0.375000, count: 8 -Region 23 Avg IOU: 0.565475, Class: 0.208359, Obj: 0.058351, No Obj: 0.000715, .5R: 0.652174, .75R: 0.086957, count: 23 -Region 16 Avg IOU: 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0.671483 seconds, 109824 images -Loaded: 0.059911 seconds -Region 16 Avg IOU: 0.767999, Class: 0.202635, Obj: 0.352853, No Obj: 0.001742, .5R: 1.000000, .75R: 0.384615, count: 13 -Region 23 Avg IOU: 0.581937, Class: 0.209675, Obj: 0.161918, No Obj: 0.001536, .5R: 0.826087, .75R: 0.043478, count: 23 -Region 16 Avg IOU: 0.765333, Class: 0.266741, Obj: 0.309244, No Obj: 0.001705, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.586660, Class: 0.199891, Obj: 0.137347, No Obj: 0.001070, .5R: 0.714286, .75R: 0.095238, count: 21 -Region 16 Avg IOU: 0.732253, Class: 0.188694, Obj: 0.226391, No Obj: 0.001365, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.697421, Class: 0.161854, Obj: 0.196185, No Obj: 0.001013, .5R: 1.000000, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.789569, Class: 0.219513, Obj: 0.507112, No Obj: 0.002179, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.587197, Class: 0.214642, Obj: 0.095227, No Obj: 0.000930, .5R: 0.730769, 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1.983303, 2.530372 avg loss, 0.001000 rate, 0.648280 seconds, 109952 images -Loaded: 0.056295 seconds -Region 16 Avg IOU: 0.723337, Class: 0.207782, Obj: 0.142117, No Obj: 0.000608, .5R: 0.909091, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.613942, Class: 0.165441, Obj: 0.078610, No Obj: 0.000656, .5R: 0.800000, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.695604, Class: 0.220510, Obj: 0.207761, No Obj: 0.000841, .5R: 1.000000, .75R: 0.142857, count: 7 -Region 23 Avg IOU: 0.573838, Class: 0.190290, Obj: 0.125347, No Obj: 0.001237, .5R: 0.590909, .75R: 0.181818, count: 22 -Region 16 Avg IOU: 0.719679, Class: 0.137137, Obj: 0.230521, No Obj: 0.000700, .5R: 1.000000, .75R: 0.400000, count: 5 -Region 23 Avg IOU: 0.611598, Class: 0.172290, Obj: 0.137116, No Obj: 0.000914, .5R: 0.695652, .75R: 0.173913, count: 23 -Region 16 Avg IOU: 0.732313, Class: 0.186998, Obj: 0.202790, No Obj: 0.001061, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.618939, Class: 0.186456, Obj: 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0.800000, .75R: 0.100000, count: 20 - - 860: 2.189476, 2.496282 avg loss, 0.001000 rate, 0.643964 seconds, 110080 images -Resizing -480 x 480 - try to allocate workspace = 1144537089 * sizeof(float), CUDA allocate done! -Loaded: 0.342423 seconds -Region 16 Avg IOU: 0.703451, Class: 0.230557, Obj: 0.225288, No Obj: 0.000977, .5R: 1.000000, .75R: 0.363636, count: 11 -Region 23 Avg IOU: 0.594296, Class: 0.163154, Obj: 0.088947, No Obj: 0.000499, .5R: 0.842105, .75R: 0.052632, count: 19 -Region 16 Avg IOU: 0.675215, Class: 0.257374, Obj: 0.254133, No Obj: 0.001375, .5R: 0.812500, .75R: 0.312500, count: 16 -Region 23 Avg IOU: 0.647750, Class: 0.240721, Obj: 0.089074, No Obj: 0.000387, .5R: 0.800000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.663266, Class: 0.162162, Obj: 0.180449, No Obj: 0.000815, .5R: 0.833333, .75R: 0.333333, count: 12 -Region 23 Avg IOU: 0.644287, Class: 0.151965, Obj: 0.091392, No Obj: 0.000472, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 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images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.712593, Class: 0.196931, Obj: 0.224187, No Obj: 0.001216, .5R: 0.969697, .75R: 0.363636, count: 33 -Region 23 Avg IOU: 0.629446, Class: 0.195790, Obj: 0.100471, No Obj: 0.000154, .5R: 0.727273, .75R: 0.363636, count: 11 -Region 16 Avg IOU: 0.744327, Class: 0.158141, Obj: 0.305572, No Obj: 0.000866, .5R: 0.947368, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.635280, Class: 0.181970, Obj: 0.183873, No Obj: 0.000206, .5R: 0.882353, .75R: 0.235294, count: 17 -Region 16 Avg IOU: 0.707137, Class: 0.296225, Obj: 0.197526, No Obj: 0.000812, .5R: 0.954545, .75R: 0.409091, count: 22 -Region 23 Avg IOU: 0.588489, Class: 0.159158, Obj: 0.059836, No Obj: 0.000078, .5R: 0.769231, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.719157, Class: 0.217091, Obj: 0.266500, No Obj: 0.001252, .5R: 1.000000, .75R: 0.368421, count: 19 -Region 23 Avg IOU: 0.610464, Class: 0.174320, Obj: 0.075866, No Obj: 0.000233, .5R: 0.826087, .75R: 0.043478, count: 23 -Region 16 Avg IOU: 0.694783, Class: 0.177506, Obj: 0.137679, No Obj: 0.000983, .5R: 0.900000, .75R: 0.400000, count: 20 -Region 23 Avg IOU: 0.565968, Class: 0.190416, Obj: 0.034603, No Obj: 0.000110, .5R: 0.500000, .75R: 0.166667, count: 12 -Region 16 Avg IOU: 0.715172, Class: 0.186321, Obj: 0.249067, No Obj: 0.000965, .5R: 0.952381, .75R: 0.476190, count: 21 -Region 23 Avg IOU: 0.640555, Class: 0.215373, Obj: 0.131605, No Obj: 0.000150, .5R: 0.928571, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.679756, Class: 0.191361, Obj: 0.308911, No Obj: 0.000987, .5R: 0.850000, .75R: 0.300000, count: 20 -Region 23 Avg IOU: 0.605230, Class: 0.174918, Obj: 0.059647, No Obj: 0.000091, .5R: 0.916667, .75R: 0.083333, count: 12 -Region 16 Avg IOU: 0.708491, Class: 0.204296, Obj: 0.249238, No Obj: 0.001080, .5R: 1.000000, .75R: 0.333333, count: 24 -Region 23 Avg IOU: 0.630137, Class: 0.200972, Obj: 0.029144, No Obj: 0.000093, .5R: 0.833333, .75R: 0.208333, count: 24 - - 875: 3.032390, 2.841014 avg 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0.250774, Obj: 0.281493, No Obj: 0.001168, .5R: 0.869565, .75R: 0.304348, count: 23 -Region 23 Avg IOU: 0.612881, Class: 0.189442, Obj: 0.024442, No Obj: 0.000107, .5R: 0.818182, .75R: 0.181818, count: 11 -Region 16 Avg IOU: 0.628897, Class: 0.191680, Obj: 0.091564, No Obj: 0.001190, .5R: 0.777778, .75R: 0.388889, count: 36 -Region 23 Avg IOU: 0.641363, Class: 0.179890, Obj: 0.028391, No Obj: 0.000081, .5R: 0.800000, .75R: 0.100000, count: 10 -Region 16 Avg IOU: 0.689194, Class: 0.196709, Obj: 0.275319, No Obj: 0.000734, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.606358, Class: 0.243737, Obj: 0.020928, No Obj: 0.000061, .5R: 0.857143, .75R: 0.142857, count: 7 -Region 16 Avg IOU: 0.683061, Class: 0.148256, Obj: 0.222485, No Obj: 0.001078, .5R: 0.923077, .75R: 0.384615, count: 26 -Region 23 Avg IOU: 0.606349, Class: 0.190477, Obj: 0.023462, No Obj: 0.000102, .5R: 0.750000, .75R: 0.050000, count: 20 -Region 16 Avg IOU: 0.688806, Class: 0.179917, Obj: 0.220124, No Obj: 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count: 29 -Region 16 Avg IOU: 0.713034, Class: 0.175755, Obj: 0.257521, No Obj: 0.001099, .5R: 0.913043, .75R: 0.478261, count: 23 -Region 23 Avg IOU: 0.671737, Class: 0.199555, Obj: 0.045945, No Obj: 0.000136, .5R: 0.846154, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.681963, Class: 0.254214, Obj: 0.216629, No Obj: 0.000875, .5R: 0.888889, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.530940, Class: 0.212380, Obj: 0.031050, No Obj: 0.000088, .5R: 0.625000, .75R: 0.062500, count: 16 -Region 16 Avg IOU: 0.691481, Class: 0.194354, Obj: 0.228283, No Obj: 0.000810, .5R: 0.866667, .75R: 0.333333, count: 15 -Region 23 Avg IOU: 0.607491, Class: 0.176598, Obj: 0.065627, No Obj: 0.000073, .5R: 0.800000, .75R: 0.100000, count: 10 -Region 16 Avg IOU: 0.684013, Class: 0.194450, Obj: 0.177217, No Obj: 0.001054, .5R: 0.925926, .75R: 0.259259, count: 27 -Region 23 Avg IOU: 0.653501, Class: 0.238614, Obj: 0.055574, No Obj: 0.000052, .5R: 1.000000, .75R: 0.200000, count: 5 -Region 16 Avg IOU: 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images -Loaded: 0.000082 seconds -Region 16 Avg IOU: 0.790442, Class: 0.238220, Obj: 0.316661, No Obj: 0.001587, .5R: 1.000000, .75R: 0.764706, count: 17 -Region 23 Avg IOU: 0.619816, Class: 0.198693, Obj: 0.096887, No Obj: 0.000407, .5R: 0.846154, .75R: 0.076923, count: 13 -Region 16 Avg IOU: 0.771503, Class: 0.148875, Obj: 0.260094, No Obj: 0.001121, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.670852, Class: 0.218669, Obj: 0.156835, No Obj: 0.000407, .5R: 0.923077, .75R: 0.230769, count: 13 -Region 16 Avg IOU: 0.703054, Class: 0.176657, Obj: 0.178246, No Obj: 0.001400, .5R: 0.909091, .75R: 0.363636, count: 11 -Region 23 Avg IOU: 0.603415, Class: 0.164822, Obj: 0.201022, No Obj: 0.000608, .5R: 0.866667, .75R: 0.066667, count: 15 -Region 16 Avg IOU: 0.659953, Class: 0.183303, Obj: 0.178208, No Obj: 0.001388, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 23 Avg IOU: 0.614756, Class: 0.165300, Obj: 0.127751, No Obj: 0.000529, .5R: 0.687500, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.768269, Class: 0.294664, Obj: 0.307659, No Obj: 0.000896, .5R: 1.000000, .75R: 0.857143, count: 7 -Region 23 Avg IOU: 0.622158, Class: 0.183673, Obj: 0.108966, No Obj: 0.000462, .5R: 0.850000, .75R: 0.150000, count: 20 -Region 16 Avg IOU: 0.755765, Class: 0.189053, Obj: 0.226243, No Obj: 0.001192, .5R: 0.900000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.573972, Class: 0.159752, Obj: 0.142302, No Obj: 0.000614, .5R: 0.842105, .75R: 0.000000, count: 19 -Region 16 Avg IOU: 0.653924, Class: 0.228127, Obj: 0.189887, No Obj: 0.001226, .5R: 0.785714, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.580545, Class: 0.205643, Obj: 0.150858, No Obj: 0.000805, .5R: 0.833333, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.746924, Class: 0.215454, Obj: 0.241670, No Obj: 0.001180, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.625212, Class: 0.208424, Obj: 0.137919, No Obj: 0.000555, .5R: 1.000000, .75R: 0.090909, count: 11 - - 943: 2.232829, 2.585569 avg 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23 Avg IOU: 0.600002, Class: 0.181675, Obj: 0.156227, No Obj: 0.000443, .5R: 0.785714, .75R: 0.071429, count: 14 - - 946: 2.304439, 2.545443 avg loss, 0.001000 rate, 0.858533 seconds, 121088 images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.755628, Class: 0.208891, Obj: 0.566282, No Obj: 0.002516, .5R: 1.000000, .75R: 0.733333, count: 15 -Region 23 Avg IOU: 0.688262, Class: 0.160155, Obj: 0.370924, No Obj: 0.000445, .5R: 0.857143, .75R: 0.428571, count: 7 -Region 16 Avg IOU: 0.709283, Class: 0.167498, Obj: 0.220266, No Obj: 0.001975, .5R: 0.928571, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.593163, Class: 0.196193, Obj: 0.089050, No Obj: 0.000777, .5R: 0.800000, .75R: 0.150000, count: 20 -Region 16 Avg IOU: 0.696990, Class: 0.176922, Obj: 0.247604, No Obj: 0.001686, .5R: 0.923077, .75R: 0.384615, count: 13 -Region 23 Avg IOU: 0.622661, Class: 0.179134, Obj: 0.112297, No Obj: 0.000705, .5R: 0.791667, .75R: 0.291667, count: 24 -Region 16 Avg IOU: 0.730696, Class: 0.193086, Obj: 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16 Avg IOU: 0.701858, Class: 0.142370, Obj: 0.150338, No Obj: 0.001120, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 23 Avg IOU: 0.580841, Class: 0.160959, Obj: 0.133182, No Obj: 0.000592, .5R: 0.666667, .75R: 0.190476, count: 21 -Region 16 Avg IOU: 0.779411, Class: 0.236485, Obj: 0.221835, No Obj: 0.001471, .5R: 0.933333, .75R: 0.800000, count: 15 -Region 23 Avg IOU: 0.584565, Class: 0.222214, Obj: 0.082517, No Obj: 0.000504, .5R: 0.615385, .75R: 0.076923, count: 13 -Region 16 Avg IOU: 0.765416, Class: 0.252652, Obj: 0.274910, No Obj: 0.001634, .5R: 1.000000, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.621910, Class: 0.205142, Obj: 0.100469, No Obj: 0.000757, .5R: 0.777778, .75R: 0.296296, count: 27 -Region 16 Avg IOU: 0.756725, Class: 0.157130, Obj: 0.323036, No Obj: 0.001477, .5R: 0.909091, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.669407, Class: 0.158391, Obj: 0.229708, No Obj: 0.000997, .5R: 0.952381, .75R: 0.380952, count: 21 -Region 16 Avg IOU: 0.694106, Class: 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22 -Region 23 Avg IOU: 0.651079, Class: 0.186186, Obj: 0.161477, No Obj: 0.000361, .5R: 0.869565, .75R: 0.260870, count: 23 -Region 16 Avg IOU: 0.692930, Class: 0.255449, Obj: 0.252318, No Obj: 0.001212, .5R: 1.000000, .75R: 0.294118, count: 17 -Region 23 Avg IOU: 0.623584, Class: 0.094142, Obj: 0.132308, No Obj: 0.000167, .5R: 0.636364, .75R: 0.181818, count: 11 -Region 16 Avg IOU: 0.756690, Class: 0.230021, Obj: 0.229631, No Obj: 0.000843, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.634391, Class: 0.204046, Obj: 0.117556, No Obj: 0.000290, .5R: 0.869565, .75R: 0.217391, count: 23 -Region 16 Avg IOU: 0.691191, Class: 0.219851, Obj: 0.200071, No Obj: 0.001250, .5R: 0.923077, .75R: 0.423077, count: 26 -Region 23 Avg IOU: 0.593295, Class: 0.214939, Obj: 0.036988, No Obj: 0.000146, .5R: 0.857143, .75R: 0.142857, count: 7 - - 959: 2.995389, 2.848514 avg loss, 0.001000 rate, 1.755950 seconds, 122752 images -Loaded: 0.000071 seconds -Region 16 Avg IOU: 0.698678, Class: 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512 - try to allocate workspace = 1145044993 * sizeof(float), CUDA allocate done! -Loaded: 0.442102 seconds -Region 16 Avg IOU: 0.711493, Class: 0.281847, Obj: 0.242272, No Obj: 0.001581, .5R: 0.888889, .75R: 0.388889, count: 18 -Region 23 Avg IOU: 0.655642, Class: 0.264020, Obj: 0.105473, No Obj: 0.000298, .5R: 0.937500, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.634107, Class: 0.172757, Obj: 0.227796, No Obj: 0.001328, .5R: 0.785714, .75R: 0.285714, count: 14 -Region 23 Avg IOU: 0.582926, Class: 0.173806, Obj: 0.081741, No Obj: 0.000315, .5R: 0.789474, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.648335, Class: 0.244310, Obj: 0.377390, No Obj: 0.001154, .5R: 0.800000, .75R: 0.300000, count: 10 -Region 23 Avg IOU: 0.611436, Class: 0.106712, Obj: 0.117633, No Obj: 0.000292, .5R: 0.933333, .75R: 0.066667, count: 15 -Region 16 Avg IOU: 0.739150, Class: 0.215003, Obj: 0.301488, No Obj: 0.001068, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.602536, Class: 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18 -Region 16 Avg IOU: 0.696870, Class: 0.219462, Obj: 0.226223, No Obj: 0.001467, .5R: 0.909091, .75R: 0.363636, count: 22 -Region 23 Avg IOU: 0.661136, Class: 0.213250, Obj: 0.101645, No Obj: 0.000631, .5R: 0.965517, .75R: 0.172414, count: 29 -Region 16 Avg IOU: 0.776486, Class: 0.261499, Obj: 0.316596, No Obj: 0.001222, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.694527, Class: 0.159342, Obj: 0.146877, No Obj: 0.000421, .5R: 1.000000, .75R: 0.428571, count: 14 - - 970: 2.608988, 2.673168 avg loss, 0.001000 rate, 1.180466 seconds, 124160 images -Resizing -480 x 480 - try to allocate workspace = 1144537089 * sizeof(float), CUDA allocate done! -Loaded: 0.490482 seconds -Region 16 Avg IOU: 0.770729, Class: 0.261510, Obj: 0.309929, No Obj: 0.002146, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.651131, Class: 0.158464, Obj: 0.108341, No Obj: 0.000393, .5R: 0.894737, .75R: 0.263158, count: 19 -Region 16 Avg IOU: 0.804960, Class: 0.304208, Obj: 0.354037, 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-Region 23 Avg IOU: 0.616175, Class: 0.155755, Obj: 0.093657, No Obj: 0.000276, .5R: 0.869565, .75R: 0.173913, count: 23 -Region 16 Avg IOU: 0.715987, Class: 0.207863, Obj: 0.240542, No Obj: 0.001213, .5R: 0.944444, .75R: 0.333333, count: 18 -Region 23 Avg IOU: 0.627631, Class: 0.194158, Obj: 0.053115, No Obj: 0.000293, .5R: 0.851852, .75R: 0.185185, count: 27 -Region 16 Avg IOU: 0.712891, Class: 0.182237, Obj: 0.218844, No Obj: 0.000793, .5R: 0.882353, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.652792, Class: 0.176889, Obj: 0.077257, No Obj: 0.000128, .5R: 1.000000, .75R: 0.125000, count: 8 -Region 16 Avg IOU: 0.720482, Class: 0.172833, Obj: 0.167942, No Obj: 0.001217, .5R: 0.880000, .75R: 0.480000, count: 25 -Region 23 Avg IOU: 0.589288, Class: 0.162447, Obj: 0.086502, No Obj: 0.000346, .5R: 0.823529, .75R: 0.029412, count: 34 - - 992: 3.743742, 2.600717 avg loss, 0.001000 rate, 1.624027 seconds, 126976 images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.642080, Class: 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images -Resizing -384 x 384 - try to allocate workspace = 216268801 * sizeof(float), CUDA allocate done! -Loaded: 0.413640 seconds -Region 16 Avg IOU: 0.747677, Class: 0.203820, Obj: 0.593223, No Obj: 0.001851, .5R: 1.000000, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.607611, Class: 0.185152, Obj: 0.094577, No Obj: 0.000550, .5R: 0.650000, .75R: 0.150000, count: 20 -Region 16 Avg IOU: 0.807727, Class: 0.165538, Obj: 0.321949, No Obj: 0.002252, .5R: 1.000000, .75R: 0.875000, count: 8 -Region 23 Avg IOU: 0.621367, Class: 0.198833, Obj: 0.123233, No Obj: 0.000512, .5R: 0.750000, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.750612, Class: 0.236542, Obj: 0.333816, No Obj: 0.002398, .5R: 0.916667, .75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.680380, Class: 0.167664, Obj: 0.083689, No Obj: 0.000666, .5R: 0.923077, .75R: 0.423077, count: 26 -Region 16 Avg IOU: 0.794492, Class: 0.251104, Obj: 0.407201, No Obj: 0.002094, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.648207, Class: 0.189048, Obj: 0.112686, No Obj: 0.000635, .5R: 0.933333, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.755168, Class: 0.326895, Obj: 0.380741, No Obj: 0.001341, .5R: 1.000000, .75R: 0.600000, count: 5 -Region 23 Avg IOU: 0.621930, Class: 0.233676, Obj: 0.103297, No Obj: 0.000646, .5R: 0.882353, .75R: 0.176471, count: 17 -Region 16 Avg IOU: 0.763304, Class: 0.249857, Obj: 0.413128, No Obj: 0.002381, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.575152, Class: 0.212313, Obj: 0.102622, No Obj: 0.000562, .5R: 0.666667, .75R: 0.095238, count: 21 -Region 16 Avg IOU: 0.807873, Class: 0.219122, Obj: 0.440030, No Obj: 0.002141, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.693459, Class: 0.174934, Obj: 0.241555, No Obj: 0.000756, .5R: 0.846154, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.778332, Class: 0.247556, Obj: 0.323844, No Obj: 0.001217, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.687951, Class: 0.197604, Obj: 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.75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.635687, Class: 0.147657, Obj: 0.236314, No Obj: 0.000609, .5R: 0.700000, .75R: 0.100000, count: 10 -Region 16 Avg IOU: 0.745700, Class: 0.248441, Obj: 0.452769, No Obj: 0.002320, .5R: 0.888889, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.641917, Class: 0.215647, Obj: 0.084148, No Obj: 0.000752, .5R: 0.909091, .75R: 0.136364, count: 22 -Region 16 Avg IOU: 0.678607, Class: 0.158984, Obj: 0.225665, No Obj: 0.001246, .5R: 1.000000, .75R: 0.250000, count: 12 -Region 23 Avg IOU: 0.617149, Class: 0.136636, Obj: 0.127668, No Obj: 0.000511, .5R: 0.823529, .75R: 0.117647, count: 17 -Region 16 Avg IOU: 0.606731, Class: 0.201813, Obj: 0.216262, No Obj: 0.000770, .5R: 0.800000, .75R: 0.000000, count: 5 -Region 23 Avg IOU: 0.588764, Class: 0.208187, Obj: 0.110937, No Obj: 0.000613, .5R: 0.692308, .75R: 0.076923, count: 13 -Region 16 Avg IOU: 0.745553, Class: 0.223128, Obj: 0.210923, No Obj: 0.002013, .5R: 1.000000, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.617450, Class: 0.204352, Obj: 0.096393, No Obj: 0.000765, .5R: 0.866667, .75R: 0.100000, count: 30 - - 1012: 2.018670, 2.712911 avg loss, 0.001000 rate, 0.692803 seconds, 129536 images -Loaded: 0.000065 seconds -Region 16 Avg IOU: 0.736588, Class: 0.161071, Obj: 0.408991, No Obj: 0.002035, .5R: 1.000000, .75R: 0.375000, count: 8 -Region 23 Avg IOU: 0.685092, Class: 0.171402, Obj: 0.184707, No Obj: 0.000892, .5R: 1.000000, .75R: 0.166667, count: 12 -Region 16 Avg IOU: 0.744268, Class: 0.123822, Obj: 0.256683, No Obj: 0.002102, .5R: 0.923077, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.657878, Class: 0.172979, Obj: 0.091215, No Obj: 0.000522, .5R: 0.809524, .75R: 0.285714, count: 21 -Region 16 Avg IOU: 0.693061, Class: 0.207850, Obj: 0.242453, No Obj: 0.001687, .5R: 0.875000, .75R: 0.250000, count: 8 -Region 23 Avg IOU: 0.503187, Class: 0.151138, Obj: 0.162087, No Obj: 0.000648, .5R: 0.363636, .75R: 0.000000, count: 11 -Region 16 Avg IOU: 0.656567, Class: 0.107254, Obj: 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0.875000, .75R: 0.125000, count: 8 -Region 23 Avg IOU: 0.617283, Class: 0.238458, Obj: 0.152348, No Obj: 0.000796, .5R: 0.809524, .75R: 0.095238, count: 21 - - 1013: 1.968782, 2.638498 avg loss, 0.001000 rate, 0.681769 seconds, 129664 images -Loaded: 0.000065 seconds -Region 16 Avg IOU: 0.697329, Class: 0.136175, Obj: 0.183592, No Obj: 0.001494, .5R: 0.833333, .75R: 0.333333, count: 12 -Region 23 Avg IOU: 0.594428, Class: 0.204112, Obj: 0.121917, No Obj: 0.000709, .5R: 0.714286, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.831459, Class: 0.208461, Obj: 0.401388, No Obj: 0.001444, .5R: 1.000000, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.589794, Class: 0.198269, Obj: 0.082874, No Obj: 0.000643, .5R: 0.857143, .75R: 0.142857, count: 21 -Region 16 Avg IOU: 0.740300, Class: 0.262577, Obj: 0.409659, No Obj: 0.001496, .5R: 0.875000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.637650, Class: 0.127217, Obj: 0.137995, No Obj: 0.000789, .5R: 0.869565, .75R: 0.130435, count: 23 -Region 16 Avg IOU: 0.687842, Class: 0.187133, Obj: 0.182804, No Obj: 0.001705, .5R: 0.900000, .75R: 0.400000, count: 20 -Region 23 Avg IOU: 0.653827, Class: 0.186930, Obj: 0.102439, No Obj: 0.000607, .5R: 0.850000, .75R: 0.200000, count: 20 -Region 16 Avg IOU: 0.785107, Class: 0.263838, Obj: 0.515246, No Obj: 0.001408, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.678183, Class: 0.214113, Obj: 0.160318, No Obj: 0.000484, .5R: 0.923077, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.731862, Class: 0.210730, Obj: 0.371637, No Obj: 0.002148, .5R: 1.000000, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.668398, Class: 0.193150, Obj: 0.080886, No Obj: 0.000490, .5R: 0.866667, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.690488, Class: 0.211485, Obj: 0.170112, No Obj: 0.001720, .5R: 0.909091, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.623386, Class: 0.185285, Obj: 0.180131, No Obj: 0.000971, .5R: 0.800000, .75R: 0.233333, count: 30 -Region 16 Avg IOU: 0.779876, Class: 0.257900, Obj: 0.327888, No Obj: 0.001988, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.629300, Class: 0.278192, Obj: 0.097235, No Obj: 0.000497, .5R: 0.769231, .75R: 0.230769, count: 13 - - 1014: 2.347451, 2.609393 avg loss, 0.001000 rate, 0.690962 seconds, 129792 images -Loaded: 0.025383 seconds -Region 16 Avg IOU: 0.774547, Class: 0.252787, Obj: 0.155630, No Obj: 0.001445, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.664231, Class: 0.201729, Obj: 0.119240, No Obj: 0.000589, .5R: 0.950000, .75R: 0.150000, count: 20 -Region 16 Avg IOU: 0.645183, Class: 0.183296, Obj: 0.296071, No Obj: 0.001383, .5R: 0.700000, .75R: 0.400000, count: 10 -Region 23 Avg IOU: 0.607755, Class: 0.196254, Obj: 0.070860, No Obj: 0.000485, .5R: 0.900000, .75R: 0.000000, count: 20 -Region 16 Avg IOU: 0.752952, Class: 0.146740, Obj: 0.150109, No Obj: 0.000971, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.701993, Class: 0.151103, Obj: 0.150872, No Obj: 0.000611, .5R: 1.000000, .75R: 0.421053, count: 19 -Region 16 Avg IOU: 0.666686, Class: 0.223298, Obj: 0.204578, No Obj: 0.001154, .5R: 1.000000, .75R: 0.333333, count: 6 -Region 23 Avg IOU: 0.571600, Class: 0.194147, Obj: 0.102709, No Obj: 0.000512, .5R: 0.631579, .75R: 0.052632, count: 19 -Region 16 Avg IOU: 0.780077, Class: 0.246686, Obj: 0.261462, No Obj: 0.001110, .5R: 1.000000, .75R: 0.500000, count: 4 -Region 23 Avg IOU: 0.639505, Class: 0.134163, Obj: 0.129200, No Obj: 0.000593, .5R: 0.894737, .75R: 0.263158, count: 19 -Region 16 Avg IOU: 0.721250, Class: 0.163908, Obj: 0.246155, No Obj: 0.001196, .5R: 0.909091, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.609773, Class: 0.207011, Obj: 0.081933, No Obj: 0.000453, .5R: 0.789474, .75R: 0.105263, count: 19 -Region 16 Avg IOU: 0.721264, Class: 0.312612, Obj: 0.324066, No Obj: 0.001197, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.586931, Class: 0.188345, Obj: 0.122500, No Obj: 0.000445, .5R: 0.777778, .75R: 0.222222, count: 9 -Region 16 Avg IOU: 0.625369, Class: 0.186635, Obj: 0.123566, No Obj: 0.001743, .5R: 0.916667, .75R: 0.166667, count: 12 -Region 23 Avg IOU: 0.508929, Class: 0.160148, Obj: 0.050614, No Obj: 0.000539, .5R: 0.500000, .75R: 0.045455, count: 22 - - 1015: 2.278073, 2.576261 avg loss, 0.001000 rate, 0.654019 seconds, 129920 images -Loaded: 0.038503 seconds -Region 16 Avg IOU: 0.807580, Class: 0.214019, Obj: 0.308079, No Obj: 0.001341, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.609438, Class: 0.178118, Obj: 0.150613, No Obj: 0.001027, .5R: 0.807692, .75R: 0.192308, count: 26 -Region 16 Avg IOU: 0.721144, Class: 0.120926, Obj: 0.133767, No Obj: 0.001290, .5R: 0.800000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.607753, Class: 0.166530, Obj: 0.072740, No Obj: 0.000561, .5R: 0.846154, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.792020, Class: 0.148368, Obj: 0.342245, No Obj: 0.001570, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.619172, Class: 0.233346, Obj: 0.098861, No Obj: 0.000697, .5R: 0.769231, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.773714, Class: 0.281966, Obj: 0.298322, No Obj: 0.001370, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.604558, Class: 0.225193, Obj: 0.144074, No Obj: 0.000627, .5R: 0.769231, .75R: 0.000000, count: 13 -Region 16 Avg IOU: 0.631941, Class: 0.213491, Obj: 0.168791, No Obj: 0.001428, .5R: 0.769231, .75R: 0.230769, count: 13 -Region 23 Avg IOU: 0.590840, Class: 0.230461, Obj: 0.150812, No Obj: 0.000876, .5R: 0.800000, .75R: 0.066667, count: 15 -Region 16 Avg IOU: 0.596620, Class: 0.245809, Obj: 0.044120, No Obj: 0.000742, .5R: 0.666667, .75R: 0.333333, count: 3 -Region 23 Avg IOU: 0.646203, Class: 0.269262, Obj: 0.117027, No Obj: 0.000524, .5R: 0.875000, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.721441, Class: 0.226238, Obj: 0.409866, No Obj: 0.001411, .5R: 0.900000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.658703, Class: 0.278332, Obj: 0.206152, 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0.727273, count: 11 -Region 23 Avg IOU: 0.622391, Class: 0.186958, Obj: 0.177148, No Obj: 0.000732, .5R: 0.875000, .75R: 0.125000, count: 8 -Region 16 Avg IOU: 0.715504, Class: 0.141319, Obj: 0.367243, No Obj: 0.002252, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.624485, Class: 0.225928, Obj: 0.187027, No Obj: 0.001329, .5R: 0.791667, .75R: 0.166667, count: 24 -Region 16 Avg IOU: 0.739914, Class: 0.244488, Obj: 0.284914, No Obj: 0.001747, .5R: 0.928571, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.672837, Class: 0.163615, Obj: 0.104583, No Obj: 0.000757, .5R: 0.937500, .75R: 0.218750, count: 32 -Region 16 Avg IOU: 0.800261, Class: 0.249893, Obj: 0.318343, No Obj: 0.002033, .5R: 1.000000, .75R: 0.800000, count: 15 -Region 23 Avg IOU: 0.650226, Class: 0.213208, Obj: 0.091239, No Obj: 0.000595, .5R: 0.857143, .75R: 0.238095, count: 21 -Region 16 Avg IOU: 0.831802, Class: 0.314459, Obj: 0.430207, No Obj: 0.000736, .5R: 1.000000, .75R: 0.800000, count: 5 -Region 23 Avg 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16 Avg IOU: 0.810939, Class: 0.218098, Obj: 0.415402, No Obj: 0.002406, .5R: 1.000000, .75R: 0.900000, count: 10 -Region 23 Avg IOU: 0.557523, Class: 0.195403, Obj: 0.134555, No Obj: 0.000480, .5R: 0.857143, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.755684, Class: 0.327131, Obj: 0.485384, No Obj: 0.001402, .5R: 1.000000, .75R: 0.500000, count: 6 -Region 23 Avg IOU: 0.676021, Class: 0.217248, Obj: 0.063534, No Obj: 0.000544, .5R: 1.000000, .75R: 0.285714, count: 14 -Region 16 Avg IOU: 0.776580, Class: 0.185390, Obj: 0.490523, No Obj: 0.002691, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.670071, Class: 0.135844, Obj: 0.234096, No Obj: 0.000908, .5R: 0.933333, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.786739, Class: 0.165830, Obj: 0.634965, No Obj: 0.001572, .5R: 1.000000, .75R: 0.500000, count: 2 -Region 23 Avg IOU: 0.633211, Class: 0.229339, Obj: 0.150429, No Obj: 0.000964, .5R: 0.913043, .75R: 0.173913, count: 23 -Region 16 Avg IOU: 0.647336, Class: 0.240324, Obj: 0.178251, No Obj: 0.001802, .5R: 0.888889, .75R: 0.222222, count: 9 -Region 23 Avg IOU: 0.617506, Class: 0.171278, Obj: 0.046433, No Obj: 0.000335, .5R: 0.727273, .75R: 0.227273, count: 22 -Region 16 Avg IOU: 0.727981, Class: 0.212867, Obj: 0.397573, No Obj: 0.001683, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.642364, Class: 0.198589, Obj: 0.132622, No Obj: 0.000401, .5R: 0.916667, .75R: 0.083333, count: 12 - - 1019: 2.032965, 2.393099 avg loss, 0.001000 rate, 0.693674 seconds, 130432 images -Loaded: 0.000065 seconds -Region 16 Avg IOU: 0.754878, Class: 0.207305, Obj: 0.484661, No Obj: 0.002328, .5R: 1.000000, .75R: 0.500000, count: 6 -Region 23 Avg IOU: 0.621037, Class: 0.203631, Obj: 0.158744, No Obj: 0.000552, .5R: 0.727273, .75R: 0.090909, count: 11 -Region 16 Avg IOU: 0.767802, Class: 0.216783, Obj: 0.320822, No Obj: 0.001710, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.594076, Class: 0.202065, Obj: 0.103317, No Obj: 0.000615, 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count: 11 -Region 16 Avg IOU: 0.708209, Class: 0.271558, Obj: 0.339521, No Obj: 0.001591, .5R: 0.833333, .75R: 0.500000, count: 6 -Region 23 Avg IOU: 0.624101, Class: 0.201174, Obj: 0.143125, No Obj: 0.000807, .5R: 0.800000, .75R: 0.150000, count: 20 -Region 16 Avg IOU: 0.753439, Class: 0.191962, Obj: 0.338363, No Obj: 0.002412, .5R: 0.888889, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.578585, Class: 0.163789, Obj: 0.108864, No Obj: 0.000509, .5R: 0.800000, .75R: 0.000000, count: 10 - - 1020: 1.736507, 2.327440 avg loss, 0.001000 rate, 0.671796 seconds, 130560 images -Resizing -704 x 704 - try to allocate workspace = 1148780545 * sizeof(float), CUDA allocate done! -Loaded: 0.385473 seconds -Region 16 Avg IOU: 0.652935, Class: 0.202573, Obj: 0.205496, No Obj: 0.001566, .5R: 0.833333, .75R: 0.416667, count: 24 -Region 23 Avg IOU: 0.672475, Class: 0.180621, Obj: 0.165937, No Obj: 0.000451, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.679874, Class: 0.195433, Obj: 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Class: 0.185324, Obj: 0.105512, No Obj: 0.000847, .5R: 0.947368, .75R: 0.421053, count: 19 -Region 23 Avg IOU: 0.624629, Class: 0.184754, Obj: 0.025698, No Obj: 0.000115, .5R: 0.937500, .75R: 0.062500, count: 16 -Region 16 Avg IOU: 0.702105, Class: 0.231952, Obj: 0.159870, No Obj: 0.000901, .5R: 0.916667, .75R: 0.375000, count: 24 -Region 23 Avg IOU: 0.580659, Class: 0.291665, Obj: 0.031750, No Obj: 0.000143, .5R: 0.642857, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.691602, Class: 0.211710, Obj: 0.179727, No Obj: 0.000666, .5R: 1.000000, .75R: 0.222222, count: 18 -Region 23 Avg IOU: 0.656797, Class: 0.180521, Obj: 0.024752, No Obj: 0.000117, .5R: 0.923077, .75R: 0.230769, count: 13 - - 1022: 2.774344, 2.435715 avg loss, 0.001000 rate, 2.132693 seconds, 130816 images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.711103, Class: 0.179336, Obj: 0.129083, No Obj: 0.000447, .5R: 0.950000, .75R: 0.400000, count: 20 -Region 23 Avg IOU: 0.627995, Class: 0.190138, Obj: 0.035658, No Obj: 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13 -Region 23 Avg IOU: 0.584891, Class: 0.226555, Obj: 0.169219, No Obj: 0.000176, .5R: 0.777778, .75R: 0.111111, count: 9 -Region 16 Avg IOU: 0.700807, Class: 0.256424, Obj: 0.343027, No Obj: 0.001406, .5R: 1.000000, .75R: 0.350000, count: 20 -Region 23 Avg IOU: 0.650255, Class: 0.195334, Obj: 0.136151, No Obj: 0.000359, .5R: 0.777778, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.734390, Class: 0.216364, Obj: 0.251636, No Obj: 0.001496, .5R: 0.956522, .75R: 0.565217, count: 23 -Region 23 Avg IOU: 0.625122, Class: 0.153927, Obj: 0.117865, No Obj: 0.000327, .5R: 0.782609, .75R: 0.173913, count: 23 -Region 16 Avg IOU: 0.722794, Class: 0.237291, Obj: 0.298359, No Obj: 0.001310, .5R: 0.913043, .75R: 0.434783, count: 23 -Region 23 Avg IOU: 0.671070, Class: 0.221955, Obj: 0.257602, No Obj: 0.000332, .5R: 0.933333, .75R: 0.333333, count: 15 - - 1025: 3.227610, 2.729044 avg loss, 0.001000 rate, 2.156252 seconds, 131200 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.701856, Class: 0.227498, Obj: 0.413634, No Obj: 0.001667, .5R: 0.880000, .75R: 0.440000, count: 25 -Region 23 Avg IOU: 0.698848, Class: 0.222289, Obj: 0.243562, No Obj: 0.000744, .5R: 0.967742, .75R: 0.354839, count: 31 -Region 16 Avg IOU: 0.667318, Class: 0.220758, Obj: 0.263229, No Obj: 0.001833, .5R: 0.846154, .75R: 0.384615, count: 26 -Region 23 Avg IOU: 0.661254, Class: 0.215200, Obj: 0.167317, No Obj: 0.000542, .5R: 0.950000, .75R: 0.250000, count: 20 -Region 16 Avg IOU: 0.732306, Class: 0.168712, Obj: 0.484070, No Obj: 0.001426, .5R: 0.894737, .75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.647148, Class: 0.174672, Obj: 0.168678, No Obj: 0.000428, .5R: 0.952381, .75R: 0.238095, count: 21 -Region 16 Avg IOU: 0.740378, Class: 0.257802, Obj: 0.382441, No Obj: 0.001943, .5R: 0.960000, .75R: 0.560000, count: 25 -Region 23 Avg IOU: 0.547171, Class: 0.175821, Obj: 0.217726, No Obj: 0.000504, .5R: 0.500000, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.721158, Class: 0.240162, Obj: 0.358477, No Obj: 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0.000062 seconds -Region 16 Avg IOU: 0.688267, Class: 0.222452, Obj: 0.258115, No Obj: 0.001319, .5R: 0.888889, .75R: 0.370370, count: 27 -Region 23 Avg IOU: 0.588051, Class: 0.226858, Obj: 0.083510, No Obj: 0.000157, .5R: 0.761905, .75R: 0.095238, count: 21 -Region 16 Avg IOU: 0.698433, Class: 0.217172, Obj: 0.212839, No Obj: 0.001111, .5R: 0.869565, .75R: 0.391304, count: 23 -Region 23 Avg IOU: 0.594075, Class: 0.223536, Obj: 0.105924, No Obj: 0.000121, .5R: 0.625000, .75R: 0.250000, count: 8 -Region 16 Avg IOU: 0.732583, Class: 0.151025, Obj: 0.370867, No Obj: 0.001230, .5R: 1.000000, .75R: 0.565217, count: 23 -Region 23 Avg IOU: 0.637145, Class: 0.101879, Obj: 0.070191, No Obj: 0.000174, .5R: 0.904762, .75R: 0.190476, count: 21 -Region 16 Avg IOU: 0.696449, Class: 0.231368, Obj: 0.257972, No Obj: 0.001439, .5R: 0.920000, .75R: 0.440000, count: 25 -Region 23 Avg IOU: 0.613878, Class: 0.194587, Obj: 0.093425, No Obj: 0.000181, .5R: 0.833333, .75R: 0.166667, count: 24 -Region 16 Avg 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rate, 2.139250 seconds, 131456 images -Loaded: 0.000060 seconds -Region 16 Avg IOU: 0.694206, Class: 0.168364, Obj: 0.181587, No Obj: 0.000931, .5R: 0.800000, .75R: 0.500000, count: 20 -Region 23 Avg IOU: 0.609226, Class: 0.129245, Obj: 0.063918, No Obj: 0.000083, .5R: 0.714286, .75R: 0.285714, count: 14 -Region 16 Avg IOU: 0.712465, Class: 0.184299, Obj: 0.278746, No Obj: 0.001326, .5R: 0.960000, .75R: 0.400000, count: 25 -Region 23 Avg IOU: 0.647598, Class: 0.176724, Obj: 0.039487, No Obj: 0.000138, .5R: 0.848485, .75R: 0.272727, count: 33 -Region 16 Avg IOU: 0.678326, Class: 0.242850, Obj: 0.269670, No Obj: 0.000833, .5R: 0.941176, .75R: 0.352941, count: 17 -Region 23 Avg IOU: 0.580807, Class: 0.166841, Obj: 0.036903, No Obj: 0.000073, .5R: 0.882353, .75R: 0.117647, count: 17 -Region 16 Avg IOU: 0.710427, Class: 0.220639, Obj: 0.218602, No Obj: 0.001195, .5R: 0.961538, .75R: 0.307692, count: 26 -Region 23 Avg IOU: 0.612420, Class: 0.167333, Obj: 0.024820, No Obj: 0.000049, .5R: 0.714286, .75R: 0.285714, count: 14 -Region 16 Avg IOU: 0.707840, Class: 0.160127, Obj: 0.167587, No Obj: 0.000764, .5R: 1.000000, .75R: 0.363636, count: 22 -Region 23 Avg IOU: 0.585887, Class: 0.192936, Obj: 0.046893, No Obj: 0.000055, .5R: 0.888889, .75R: 0.055556, count: 18 -Region 16 Avg IOU: 0.699980, Class: 0.197743, Obj: 0.152763, No Obj: 0.001169, .5R: 0.892857, .75R: 0.392857, count: 28 -Region 23 Avg IOU: 0.686103, Class: 0.176753, Obj: 0.031487, No Obj: 0.000096, .5R: 0.941176, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.716156, Class: 0.180529, Obj: 0.264789, No Obj: 0.001120, .5R: 0.954545, .75R: 0.454545, count: 22 -Region 23 Avg IOU: 0.682109, Class: 0.117168, Obj: 0.098447, No Obj: 0.000092, .5R: 0.937500, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.705829, Class: 0.198414, Obj: 0.302297, No Obj: 0.000931, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.695252, Class: 0.178595, Obj: 0.036409, No Obj: 0.000081, .5R: 1.000000, .75R: 0.227273, count: 22 - - 1028: 3.404884, 2.911400 avg loss, 0.001000 rate, 2.140339 seconds, 131584 images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.699277, Class: 0.200051, Obj: 0.218581, No Obj: 0.000846, .5R: 0.920000, .75R: 0.400000, count: 25 -Region 23 Avg IOU: 0.642054, Class: 0.230826, Obj: 0.040451, No Obj: 0.000058, .5R: 0.857143, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.646099, Class: 0.258961, Obj: 0.140029, No Obj: 0.000613, .5R: 0.842105, .75R: 0.105263, count: 19 -Region 23 Avg IOU: 0.644721, Class: 0.245969, Obj: 0.098154, No Obj: 0.000084, .5R: 0.850000, .75R: 0.200000, count: 20 -Region 16 Avg IOU: 0.721055, Class: 0.179970, Obj: 0.285522, No Obj: 0.000847, .5R: 0.964286, .75R: 0.464286, count: 28 -Region 23 Avg IOU: 0.636128, Class: 0.149814, Obj: 0.045378, No Obj: 0.000071, .5R: 0.833333, .75R: 0.125000, count: 24 -Region 16 Avg IOU: 0.766080, Class: 0.235344, Obj: 0.278554, No Obj: 0.000799, .5R: 0.941176, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.678442, Class: 0.181883, Obj: 0.045783, No Obj: 0.000061, .5R: 1.000000, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.678840, Class: 0.203070, Obj: 0.223461, No Obj: 0.000984, .5R: 0.869565, .75R: 0.260870, count: 23 -Region 23 Avg IOU: 0.637972, Class: 0.194456, Obj: 0.034443, No Obj: 0.000097, .5R: 0.757576, .75R: 0.242424, count: 33 -Region 16 Avg IOU: 0.668814, Class: 0.183774, Obj: 0.155473, No Obj: 0.000806, .5R: 0.809524, .75R: 0.333333, count: 21 -Region 23 Avg IOU: 0.614222, Class: 0.183571, Obj: 0.056141, No Obj: 0.000106, .5R: 0.705882, .75R: 0.176471, count: 34 -Region 16 Avg IOU: 0.708653, Class: 0.176130, Obj: 0.267257, No Obj: 0.000591, .5R: 0.928571, .75R: 0.357143, count: 14 -Region 23 Avg IOU: 0.623550, Class: 0.211489, Obj: 0.036037, No Obj: 0.000044, .5R: 0.866667, .75R: 0.066667, count: 15 -Region 16 Avg IOU: 0.673028, Class: 0.231526, Obj: 0.148661, No Obj: 0.000837, .5R: 0.928571, .75R: 0.321429, count: 28 -Region 23 Avg IOU: 0.629080, Class: 0.173488, Obj: 0.021720, No Obj: 0.000059, .5R: 0.846154, .75R: 0.153846, count: 13 - - 1029: 3.388278, 2.959088 avg loss, 0.001000 rate, 2.163204 seconds, 131712 images -Loaded: 0.000067 seconds -Region 16 Avg IOU: 0.669916, Class: 0.234829, Obj: 0.199907, No Obj: 0.000713, .5R: 0.850000, .75R: 0.300000, count: 20 -Region 23 Avg IOU: 0.642539, Class: 0.190210, Obj: 0.146807, No Obj: 0.000122, .5R: 0.916667, .75R: 0.250000, count: 12 -Region 16 Avg IOU: 0.687417, Class: 0.274831, Obj: 0.224528, No Obj: 0.000616, .5R: 0.823529, .75R: 0.352941, count: 17 -Region 23 Avg IOU: 0.637792, Class: 0.195403, Obj: 0.063128, No Obj: 0.000136, .5R: 0.941176, .75R: 0.176471, count: 17 -Region 16 Avg IOU: 0.741284, Class: 0.211277, Obj: 0.171408, No Obj: 0.000934, .5R: 0.960000, .75R: 0.560000, count: 25 -Region 23 Avg IOU: 0.674317, Class: 0.203037, Obj: 0.093956, No Obj: 0.000186, .5R: 0.954545, .75R: 0.227273, count: 22 -Region 16 Avg IOU: 0.755356, Class: 0.178236, Obj: 0.184894, No Obj: 0.000634, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.605716, Class: 0.212322, Obj: 0.099208, No Obj: 0.000123, .5R: 0.666667, .75R: 0.166667, count: 6 -Region 16 Avg IOU: 0.694672, Class: 0.164857, Obj: 0.188969, No Obj: 0.000969, .5R: 0.928571, .75R: 0.321429, count: 28 -Region 23 Avg IOU: 0.643562, Class: 0.142892, Obj: 0.110221, No Obj: 0.000197, .5R: 0.884615, .75R: 0.230769, count: 26 -Region 16 Avg IOU: 0.718189, Class: 0.225157, Obj: 0.204451, No Obj: 0.000860, .5R: 0.954545, .75R: 0.363636, count: 22 -Region 23 Avg IOU: 0.561669, Class: 0.182646, Obj: 0.138400, No Obj: 0.000144, .5R: 0.615385, .75R: 0.000000, count: 13 -Region 16 Avg IOU: 0.690163, Class: 0.226792, Obj: 0.223033, No Obj: 0.000890, .5R: 0.916667, .75R: 0.375000, count: 24 -Region 23 Avg IOU: 0.622401, Class: 0.201504, Obj: 0.065074, No Obj: 0.000095, .5R: 0.909091, .75R: 0.090909, count: 11 -Region 16 Avg IOU: 0.679534, Class: 0.207443, Obj: 0.151810, No Obj: 0.000768, .5R: 0.944444, .75R: 0.277778, count: 18 -Region 23 Avg IOU: 0.652293, Class: 0.220936, Obj: 0.092291, No Obj: 0.000134, .5R: 0.923077, .75R: 0.153846, count: 13 - - 1030: 2.796417, 2.942821 avg loss, 0.001000 rate, 2.115700 seconds, 131840 images -Resizing -448 x 448 - try to allocate workspace = 305201153 * sizeof(float), CUDA allocate done! -Loaded: 0.376520 seconds -Region 16 Avg IOU: 0.712905, Class: 0.205449, Obj: 0.292719, No Obj: 0.002176, .5R: 0.928571, .75R: 0.357143, count: 14 -Region 23 Avg IOU: 0.616091, Class: 0.191815, Obj: 0.196319, No Obj: 0.000743, .5R: 0.904762, .75R: 0.047619, count: 21 -Region 16 Avg IOU: 0.731397, Class: 0.238133, Obj: 0.290081, No Obj: 0.001409, .5R: 0.888889, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.634058, Class: 0.208198, Obj: 0.226448, No Obj: 0.000604, .5R: 0.923077, .75R: 0.076923, count: 13 -Region 16 Avg IOU: 0.666432, Class: 0.222816, Obj: 0.487308, No Obj: 0.002549, .5R: 0.846154, .75R: 0.384615, count: 13 -Region 23 Avg IOU: 0.627230, Class: 0.214102, Obj: 0.133905, No Obj: 0.000788, .5R: 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count: 9 -Region 23 Avg IOU: 0.635895, Class: 0.169709, Obj: 0.131963, No Obj: 0.000339, .5R: 0.789474, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.665749, Class: 0.235252, Obj: 0.249351, No Obj: 0.000804, .5R: 0.875000, .75R: 0.375000, count: 8 -Region 23 Avg IOU: 0.687970, Class: 0.189143, Obj: 0.131209, No Obj: 0.000226, .5R: 1.000000, .75R: 0.250000, count: 12 -Region 16 Avg IOU: 0.688277, Class: 0.247729, Obj: 0.200726, No Obj: 0.001132, .5R: 0.818182, .75R: 0.363636, count: 11 -Region 23 Avg IOU: 0.621345, Class: 0.205998, Obj: 0.058188, No Obj: 0.000309, .5R: 0.800000, .75R: 0.160000, count: 25 -Region 16 Avg IOU: 0.726502, Class: 0.195015, Obj: 0.278717, No Obj: 0.001578, .5R: 0.933333, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.570527, Class: 0.200139, Obj: 0.067926, No Obj: 0.000248, .5R: 0.764706, .75R: 0.000000, count: 17 -Region 16 Avg IOU: 0.682943, Class: 0.147797, Obj: 0.156903, No Obj: 0.001144, .5R: 0.846154, .75R: 0.384615, count: 13 -Region 23 Avg IOU: 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11 -Region 16 Avg IOU: 0.717951, Class: 0.233812, Obj: 0.375303, No Obj: 0.001333, .5R: 0.916667, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.639306, Class: 0.196823, Obj: 0.133529, No Obj: 0.000387, .5R: 0.850000, .75R: 0.250000, count: 20 -Region 16 Avg IOU: 0.752851, Class: 0.110784, Obj: 0.128235, No Obj: 0.001206, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.608738, Class: 0.173914, Obj: 0.082357, No Obj: 0.000416, .5R: 0.812500, .75R: 0.000000, count: 16 - - 1036: 2.393064, 2.675506 avg loss, 0.001000 rate, 0.855608 seconds, 132608 images -Loaded: 0.000072 seconds -Region 16 Avg IOU: 0.744981, Class: 0.229710, Obj: 0.606321, No Obj: 0.002756, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.697946, Class: 0.163135, Obj: 0.254661, No Obj: 0.000767, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 16 Avg IOU: 0.753492, Class: 0.205796, Obj: 0.413419, No Obj: 0.001693, .5R: 1.000000, .75R: 0.600000, count: 5 -Region 23 Avg IOU: 0.620891, Class: 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16 -Region 23 Avg IOU: 0.637798, Class: 0.181652, Obj: 0.075056, No Obj: 0.000213, .5R: 0.818182, .75R: 0.181818, count: 22 -Region 16 Avg IOU: 0.741510, Class: 0.135016, Obj: 0.306965, No Obj: 0.001490, .5R: 1.000000, .75R: 0.454545, count: 22 -Region 23 Avg IOU: 0.604866, Class: 0.160793, Obj: 0.076838, No Obj: 0.000182, .5R: 0.714286, .75R: 0.238095, count: 21 -Region 16 Avg IOU: 0.792823, Class: 0.277734, Obj: 0.356979, No Obj: 0.001092, .5R: 1.000000, .75R: 0.833333, count: 12 -Region 23 Avg IOU: 0.697697, Class: 0.185451, Obj: 0.102643, No Obj: 0.000246, .5R: 1.000000, .75R: 0.318182, count: 22 - - 1055: 2.795402, 2.975513 avg loss, 0.001000 rate, 1.468673 seconds, 135040 images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.728688, Class: 0.199564, Obj: 0.275584, No Obj: 0.001190, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.650966, Class: 0.236281, Obj: 0.138751, No Obj: 0.000234, .5R: 0.800000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.745492, Class: 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10 -Region 23 Avg IOU: 0.609458, Class: 0.218378, Obj: 0.143307, No Obj: 0.000334, .5R: 0.818182, .75R: 0.136364, count: 22 -Region 16 Avg IOU: 0.685582, Class: 0.225137, Obj: 0.190663, No Obj: 0.001190, .5R: 0.909091, .75R: 0.409091, count: 22 -Region 23 Avg IOU: 0.646879, Class: 0.133603, Obj: 0.057293, No Obj: 0.000221, .5R: 0.913043, .75R: 0.130435, count: 23 -Region 16 Avg IOU: 0.744859, Class: 0.195447, Obj: 0.227106, No Obj: 0.001661, .5R: 0.950000, .75R: 0.650000, count: 20 -Region 23 Avg IOU: 0.644972, Class: 0.161825, Obj: 0.098948, No Obj: 0.000236, .5R: 0.866667, .75R: 0.133333, count: 15 -Region 16 Avg IOU: 0.722575, Class: 0.232779, Obj: 0.313266, No Obj: 0.001125, .5R: 1.000000, .75R: 0.294118, count: 17 -Region 23 Avg IOU: 0.587954, Class: 0.197854, Obj: 0.061182, No Obj: 0.000319, .5R: 0.650000, .75R: 0.250000, count: 20 - - 1060: 3.262386, 2.963166 avg loss, 0.001000 rate, 1.600329 seconds, 135680 images -Resizing -640 x 640 - try to allocate workspace = 1147404289 * sizeof(float), CUDA allocate done! -Loaded: 0.144448 seconds -Region 16 Avg IOU: 0.707853, Class: 0.243524, Obj: 0.167596, No Obj: 0.000797, .5R: 0.909091, .75R: 0.500000, count: 22 -Region 23 Avg IOU: 0.622017, Class: 0.202366, Obj: 0.038913, No Obj: 0.000085, .5R: 0.800000, .75R: 0.100000, count: 10 -Region 16 Avg IOU: 0.736362, Class: 0.200137, Obj: 0.260800, No Obj: 0.001849, .5R: 0.923077, .75R: 0.538462, count: 39 -Region 23 Avg IOU: 0.627503, Class: 0.197728, Obj: 0.092695, No Obj: 0.000295, .5R: 0.833333, .75R: 0.133333, count: 30 -Region 16 Avg IOU: 0.712914, Class: 0.231943, Obj: 0.149810, No Obj: 0.001134, .5R: 0.923077, .75R: 0.500000, count: 26 -Region 23 Avg IOU: 0.652625, Class: 0.164941, Obj: 0.095522, No Obj: 0.000133, .5R: 0.818182, .75R: 0.181818, count: 11 -Region 16 Avg IOU: 0.714791, Class: 0.234424, Obj: 0.154982, No Obj: 0.000921, .5R: 0.882353, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 0.575152, Class: 0.126623, Obj: 0.096527, No Obj: 0.000165, .5R: 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images -Loaded: 0.000077 seconds -Region 16 Avg IOU: 0.703924, Class: 0.222148, Obj: 0.255752, No Obj: 0.000922, .5R: 1.000000, .75R: 0.333333, count: 9 -Region 23 Avg IOU: 0.598830, Class: 0.219035, Obj: 0.065315, No Obj: 0.000153, .5R: 0.900000, .75R: 0.100000, count: 10 -Region 16 Avg IOU: 0.737405, Class: 0.209929, Obj: 0.372980, No Obj: 0.001592, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.637677, Class: 0.204363, Obj: 0.137958, No Obj: 0.000511, .5R: 0.821429, .75R: 0.250000, count: 28 -Region 16 Avg IOU: 0.787877, Class: 0.197983, Obj: 0.488646, No Obj: 0.001716, .5R: 1.000000, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.641547, Class: 0.169335, Obj: 0.145987, No Obj: 0.000556, .5R: 0.875000, .75R: 0.166667, count: 24 -Region 16 Avg IOU: 0.657516, Class: 0.200318, Obj: 0.290597, No Obj: 0.001637, .5R: 0.823529, .75R: 0.352941, count: 17 -Region 23 Avg IOU: 0.526010, Class: 0.218978, Obj: 0.105634, No Obj: 0.000336, .5R: 0.615385, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.733413, Class: 0.166991, Obj: 0.439696, No Obj: 0.001780, .5R: 1.000000, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.602917, Class: 0.167011, Obj: 0.151301, No Obj: 0.000758, .5R: 0.785714, .75R: 0.142857, count: 28 -Region 16 Avg IOU: 0.772356, Class: 0.183828, Obj: 0.379976, No Obj: 0.001769, .5R: 1.000000, .75R: 0.750000, count: 20 -Region 23 Avg IOU: 0.679665, Class: 0.149815, Obj: 0.106537, No Obj: 0.000489, .5R: 0.944444, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.709778, Class: 0.185790, Obj: 0.220143, No Obj: 0.001584, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.561960, Class: 0.181150, Obj: 0.077095, No Obj: 0.000337, .5R: 0.666667, .75R: 0.083333, count: 12 -Region 16 Avg IOU: 0.746959, Class: 0.175205, Obj: 0.404690, No Obj: 0.001322, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.582355, Class: 0.129821, Obj: 0.122094, No Obj: 0.000490, .5R: 0.806452, .75R: 0.032258, count: 31 - - 1079: 2.840806, 2.757042 avg 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count: 21 -Region 16 Avg IOU: 0.709741, Class: 0.185006, Obj: 0.306953, No Obj: 0.001140, .5R: 0.937500, .75R: 0.375000, count: 32 -Region 23 Avg IOU: 0.687440, Class: 0.203888, Obj: 0.047796, No Obj: 0.000209, .5R: 0.970588, .75R: 0.235294, count: 34 -Region 16 Avg IOU: 0.698115, Class: 0.234403, Obj: 0.253227, No Obj: 0.001159, .5R: 0.939394, .75R: 0.393939, count: 33 -Region 23 Avg IOU: 0.676425, Class: 0.199496, Obj: 0.107217, No Obj: 0.000185, .5R: 0.956522, .75R: 0.130435, count: 23 -Region 16 Avg IOU: 0.678104, Class: 0.215550, Obj: 0.167364, No Obj: 0.000940, .5R: 0.869565, .75R: 0.391304, count: 23 -Region 23 Avg IOU: 0.664287, Class: 0.199193, Obj: 0.100514, No Obj: 0.000221, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.657966, Class: 0.172338, Obj: 0.185536, No Obj: 0.001001, .5R: 0.833333, .75R: 0.291667, count: 24 -Region 23 Avg IOU: 0.628863, Class: 0.203235, Obj: 0.130752, No Obj: 0.000191, .5R: 0.928571, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.691985, Class: 0.224884, Obj: 0.191486, No Obj: 0.001167, .5R: 0.848485, .75R: 0.424242, count: 33 -Region 23 Avg IOU: 0.603431, Class: 0.244666, Obj: 0.036374, No Obj: 0.000156, .5R: 0.655172, .75R: 0.172414, count: 29 - - 1082: 3.981116, 2.906688 avg loss, 0.001000 rate, 3.168812 seconds, 138496 images -Loaded: 0.000077 seconds -Region 16 Avg IOU: 0.719315, Class: 0.196470, Obj: 0.164838, No Obj: 0.000687, .5R: 0.950000, .75R: 0.500000, count: 20 -Region 23 Avg IOU: 0.720224, Class: 0.168959, Obj: 0.066808, No Obj: 0.000208, .5R: 0.952381, .75R: 0.380952, count: 21 -Region 16 Avg IOU: 0.753245, Class: 0.227663, Obj: 0.173350, No Obj: 0.000927, .5R: 0.962963, .75R: 0.592593, count: 27 -Region 23 Avg IOU: 0.660901, Class: 0.181762, Obj: 0.051798, No Obj: 0.000138, .5R: 0.785714, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.721980, Class: 0.262878, Obj: 0.278359, No Obj: 0.001195, .5R: 0.900000, .75R: 0.550000, count: 20 -Region 23 Avg IOU: 0.605122, Class: 0.222796, Obj: 0.069568, 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IOU: 0.645162, Class: 0.165704, Obj: 0.084869, No Obj: 0.000213, .5R: 0.861111, .75R: 0.250000, count: 36 -Region 16 Avg IOU: 0.741761, Class: 0.187357, Obj: 0.194909, No Obj: 0.000757, .5R: 1.000000, .75R: 0.368421, count: 19 -Region 23 Avg IOU: 0.698548, Class: 0.229214, Obj: 0.124811, No Obj: 0.000117, .5R: 0.800000, .75R: 0.500000, count: 10 -Region 16 Avg IOU: 0.741821, Class: 0.269335, Obj: 0.251472, No Obj: 0.000511, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.614146, Class: 0.204949, Obj: 0.050629, No Obj: 0.000110, .5R: 0.823529, .75R: 0.058824, count: 17 -Region 16 Avg IOU: 0.802753, Class: 0.227494, Obj: 0.210286, No Obj: 0.000502, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.649577, Class: 0.126065, Obj: 0.176324, No Obj: 0.000122, .5R: 1.000000, .75R: 0.000000, count: 5 -Region 16 Avg IOU: 0.760478, Class: 0.259870, Obj: 0.243836, No Obj: 0.001012, .5R: 1.000000, .75R: 0.590909, count: 22 -Region 23 Avg IOU: 0.612641, Class: 0.144419, 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22 -Region 23 Avg IOU: 0.614567, Class: 0.167605, Obj: 0.181451, No Obj: 0.000197, .5R: 0.846154, .75R: 0.076923, count: 13 -Region 16 Avg IOU: 0.702478, Class: 0.225870, Obj: 0.375933, No Obj: 0.001060, .5R: 0.857143, .75R: 0.428571, count: 21 -Region 23 Avg IOU: 0.648491, Class: 0.204934, Obj: 0.147449, No Obj: 0.000188, .5R: 0.882353, .75R: 0.235294, count: 17 -Region 16 Avg IOU: 0.654926, Class: 0.220658, Obj: 0.178895, No Obj: 0.001033, .5R: 0.800000, .75R: 0.400000, count: 20 -Region 23 Avg IOU: 0.626272, Class: 0.203533, Obj: 0.159715, No Obj: 0.000222, .5R: 0.909091, .75R: 0.227273, count: 22 - - 1090: 3.296037, 3.101458 avg loss, 0.001000 rate, 2.935016 seconds, 139520 images -Resizing -480 x 480 - try to allocate workspace = 1144537089 * sizeof(float), CUDA allocate done! -Loaded: 0.325347 seconds -Region 16 Avg IOU: 0.773732, Class: 0.167333, Obj: 0.435699, No Obj: 0.002403, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.650893, Class: 0.188806, Obj: 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28 -Region 16 Avg IOU: 0.750691, Class: 0.235286, Obj: 0.371702, No Obj: 0.001056, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.576233, Class: 0.157862, Obj: 0.138802, No Obj: 0.000688, .5R: 0.733333, .75R: 0.000000, count: 15 -Region 16 Avg IOU: 0.743311, Class: 0.303618, Obj: 0.193554, No Obj: 0.000891, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.657917, Class: 0.222662, Obj: 0.162125, No Obj: 0.000360, .5R: 0.833333, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.750386, Class: 0.276836, Obj: 0.306644, No Obj: 0.001130, .5R: 0.923077, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.636444, Class: 0.198474, Obj: 0.133423, No Obj: 0.000333, .5R: 1.000000, .75R: 0.083333, count: 12 - - 1109: 2.389550, 2.680559 avg loss, 0.001000 rate, 1.381728 seconds, 141952 images -Loaded: 0.000080 seconds -Region 16 Avg IOU: 0.699055, Class: 0.204366, Obj: 0.199502, No Obj: 0.001871, .5R: 0.933333, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.569722, Class: 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Avg IOU: 0.653192, Class: 0.204573, Obj: 0.314562, No Obj: 0.000938, .5R: 0.894737, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.749281, Class: 0.210769, Obj: 0.320363, No Obj: 0.001822, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.676892, Class: 0.208546, Obj: 0.209567, No Obj: 0.001214, .5R: 0.941176, .75R: 0.352941, count: 34 - - 1120: 1.949494, 2.355036 avg loss, 0.001000 rate, 0.788074 seconds, 143360 images -Resizing -672 x 672 - try to allocate workspace = 1148076033 * sizeof(float), CUDA allocate done! -Loaded: 0.338924 seconds -Region 16 Avg IOU: 0.711961, Class: 0.230493, Obj: 0.306024, No Obj: 0.001909, .5R: 1.000000, .75R: 0.384615, count: 26 -Region 23 Avg IOU: 0.576187, Class: 0.180227, Obj: 0.098399, No Obj: 0.000498, .5R: 0.680000, .75R: 0.040000, count: 25 -Region 16 Avg IOU: 0.734406, Class: 0.242810, Obj: 0.294102, No Obj: 0.001309, .5R: 0.956522, .75R: 0.434783, count: 23 -Region 23 Avg IOU: 0.665571, Class: 0.171436, Obj: 0.121968, No Obj: 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32 -Region 23 Avg IOU: 0.644793, Class: 0.200203, Obj: 0.084278, No Obj: 0.000130, .5R: 0.900000, .75R: 0.150000, count: 20 -Region 16 Avg IOU: 0.732596, Class: 0.214024, Obj: 0.245607, No Obj: 0.000663, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.625919, Class: 0.204479, Obj: 0.022353, No Obj: 0.000066, .5R: 0.789474, .75R: 0.105263, count: 19 -Region 16 Avg IOU: 0.709672, Class: 0.207137, Obj: 0.128147, No Obj: 0.000831, .5R: 0.933333, .75R: 0.466667, count: 30 -Region 23 Avg IOU: 0.709502, Class: 0.213190, Obj: 0.044396, No Obj: 0.000121, .5R: 0.944444, .75R: 0.444444, count: 18 - - 1123: 2.807846, 2.527465 avg loss, 0.001000 rate, 2.514773 seconds, 143744 images -Loaded: 0.000071 seconds -Region 16 Avg IOU: 0.782891, Class: 0.254714, Obj: 0.204831, No Obj: 0.000700, .5R: 0.923077, .75R: 0.769231, count: 13 -Region 23 Avg IOU: 0.642631, Class: 0.243724, Obj: 0.067355, No Obj: 0.000106, .5R: 0.875000, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.683890, Class: 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23 -Region 23 Avg IOU: 0.623497, Class: 0.152323, Obj: 0.103307, No Obj: 0.000142, .5R: 0.750000, .75R: 0.250000, count: 12 -Region 16 Avg IOU: 0.695219, Class: 0.237971, Obj: 0.180225, No Obj: 0.000797, .5R: 0.964286, .75R: 0.392857, count: 28 -Region 23 Avg IOU: 0.699845, Class: 0.189868, Obj: 0.029071, No Obj: 0.000120, .5R: 1.000000, .75R: 0.250000, count: 20 -Region 16 Avg IOU: 0.690633, Class: 0.228896, Obj: 0.213280, No Obj: 0.000981, .5R: 0.960000, .75R: 0.320000, count: 25 -Region 23 Avg IOU: 0.651899, Class: 0.254134, Obj: 0.103457, No Obj: 0.000130, .5R: 0.863636, .75R: 0.272727, count: 22 -Region 16 Avg IOU: 0.713621, Class: 0.226670, Obj: 0.218665, No Obj: 0.001060, .5R: 1.000000, .75R: 0.360000, count: 25 -Region 23 Avg IOU: 0.674866, Class: 0.204398, Obj: 0.072306, No Obj: 0.000164, .5R: 0.944444, .75R: 0.222222, count: 18 - - 1128: 3.369946, 2.827470 avg loss, 0.001000 rate, 1.933063 seconds, 144384 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.687895, Class: 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0.000074 seconds -Region 16 Avg IOU: 0.716411, Class: 0.200921, Obj: 0.226298, No Obj: 0.001232, .5R: 1.000000, .75R: 0.481481, count: 27 -Region 23 Avg IOU: 0.660271, Class: 0.182201, Obj: 0.059161, No Obj: 0.000242, .5R: 0.863636, .75R: 0.318182, count: 22 -Region 16 Avg IOU: 0.734521, Class: 0.259430, Obj: 0.376661, No Obj: 0.001066, .5R: 0.958333, .75R: 0.500000, count: 24 -Region 23 Avg IOU: 0.691490, Class: 0.178700, Obj: 0.146611, No Obj: 0.000337, .5R: 1.000000, .75R: 0.294118, count: 34 -Region 16 Avg IOU: 0.743113, Class: 0.184260, Obj: 0.287787, No Obj: 0.000976, .5R: 0.923077, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.624496, Class: 0.160051, Obj: 0.259325, No Obj: 0.000330, .5R: 0.833333, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.649743, Class: 0.240479, Obj: 0.189503, No Obj: 0.001057, .5R: 0.888889, .75R: 0.166667, count: 18 -Region 23 Avg IOU: 0.603240, Class: 0.156281, Obj: 0.168456, No Obj: 0.000399, .5R: 0.833333, .75R: 0.111111, count: 18 -Region 16 Avg 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rate, 2.060270 seconds, 144640 images -Resizing -416 x 416 - try to allocate workspace = 304758785 * sizeof(float), CUDA allocate done! -Loaded: 0.357059 seconds -Region 16 Avg IOU: 0.784149, Class: 0.219229, Obj: 0.513934, No Obj: 0.001725, .5R: 1.000000, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.647745, Class: 0.173065, Obj: 0.184323, No Obj: 0.000977, .5R: 0.900000, .75R: 0.200000, count: 30 -Region 16 Avg IOU: 0.747745, Class: 0.114354, Obj: 0.377324, No Obj: 0.000834, .5R: 1.000000, .75R: 0.500000, count: 4 -Region 23 Avg IOU: 0.617176, Class: 0.160011, Obj: 0.236538, No Obj: 0.000549, .5R: 0.764706, .75R: 0.117647, count: 17 -Region 16 Avg IOU: 0.697204, Class: 0.234544, Obj: 0.150491, No Obj: 0.001457, .5R: 0.923077, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.683048, Class: 0.215422, Obj: 0.179758, No Obj: 0.000645, .5R: 0.944444, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.714379, Class: 0.183643, Obj: 0.497017, No Obj: 0.002573, .5R: 0.857143, .75R: 0.500000, 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Avg IOU: 0.632806, Class: 0.244225, Obj: 0.369653, No Obj: 0.001910, .5R: 0.857143, .75R: 0.178571, count: 28 -Region 23 Avg IOU: 0.672289, Class: 0.162686, Obj: 0.193310, No Obj: 0.000511, .5R: 0.900000, .75R: 0.300000, count: 20 -Region 16 Avg IOU: 0.627427, Class: 0.248749, Obj: 0.361208, No Obj: 0.001568, .5R: 0.857143, .75R: 0.190476, count: 21 -Region 23 Avg IOU: 0.611992, Class: 0.274813, Obj: 0.189239, No Obj: 0.000376, .5R: 0.777778, .75R: 0.111111, count: 9 -Region 16 Avg IOU: 0.598230, Class: 0.233103, Obj: 0.256230, No Obj: 0.001731, .5R: 0.727273, .75R: 0.272727, count: 22 -Region 23 Avg IOU: 0.590831, Class: 0.166641, Obj: 0.227541, No Obj: 0.000547, .5R: 0.764706, .75R: 0.117647, count: 17 -Region 16 Avg IOU: 0.621032, Class: 0.187482, Obj: 0.348460, No Obj: 0.001988, .5R: 0.692308, .75R: 0.192308, count: 26 -Region 23 Avg IOU: 0.627318, Class: 0.218301, Obj: 0.211125, No Obj: 0.000653, .5R: 0.760000, .75R: 0.240000, count: 25 -Region 16 Avg IOU: 0.624186, Class: 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32 -Region 23 Avg IOU: 0.640076, Class: 0.168867, Obj: 0.021228, No Obj: 0.000038, .5R: 0.750000, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.756209, Class: 0.226831, Obj: 0.171455, No Obj: 0.000494, .5R: 1.000000, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.621121, Class: 0.154248, Obj: 0.083692, No Obj: 0.000059, .5R: 0.846154, .75R: 0.076923, count: 13 -Region 16 Avg IOU: 0.710759, Class: 0.243743, Obj: 0.187357, No Obj: 0.000737, .5R: 0.920000, .75R: 0.480000, count: 25 -Region 23 Avg IOU: 0.627375, Class: 0.212053, Obj: 0.016073, No Obj: 0.000025, .5R: 0.785714, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.759305, Class: 0.246914, Obj: 0.277380, No Obj: 0.000760, .5R: 0.950000, .75R: 0.600000, count: 20 -Region 23 Avg IOU: 0.673857, Class: 0.204086, Obj: 0.019012, No Obj: 0.000062, .5R: 0.958333, .75R: 0.208333, count: 24 - - 1144: 3.288320, 2.814334 avg loss, 0.001000 rate, 3.168630 seconds, 146432 images -Loaded: 0.000064 seconds -Region 16 Avg IOU: 0.703956, Class: 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rate, 3.128497 seconds, 146688 images -Loaded: 0.000062 seconds -Region 16 Avg IOU: 0.652271, Class: 0.232417, Obj: 0.281580, No Obj: 0.001192, .5R: 0.960000, .75R: 0.200000, count: 25 -Region 23 Avg IOU: 0.692750, Class: 0.176377, Obj: 0.145331, No Obj: 0.000231, .5R: 1.000000, .75R: 0.444444, count: 9 -Region 16 Avg IOU: 0.591443, Class: 0.251284, Obj: 0.194047, No Obj: 0.000982, .5R: 0.666667, .75R: 0.125000, count: 24 -Region 23 Avg IOU: 0.616486, Class: 0.126805, Obj: 0.165269, No Obj: 0.000295, .5R: 0.846154, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.684089, Class: 0.217771, Obj: 0.228358, No Obj: 0.001000, .5R: 0.923077, .75R: 0.346154, count: 26 -Region 23 Avg IOU: 0.631946, Class: 0.166102, Obj: 0.157322, No Obj: 0.000324, .5R: 0.909091, .75R: 0.181818, count: 22 -Region 16 Avg IOU: 0.632700, Class: 0.217148, Obj: 0.321234, No Obj: 0.001072, .5R: 0.722222, .75R: 0.333333, count: 18 -Region 23 Avg IOU: 0.607241, Class: 0.153588, Obj: 0.197561, No Obj: 0.000414, .5R: 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11 - - 1147: 3.142742, 2.982550 avg loss, 0.001000 rate, 3.078755 seconds, 146816 images -Loaded: 0.000060 seconds -Region 16 Avg IOU: 0.696736, Class: 0.140348, Obj: 0.072439, No Obj: 0.000571, .5R: 0.913043, .75R: 0.391304, count: 23 -Region 23 Avg IOU: 0.608705, Class: 0.156794, Obj: 0.056805, No Obj: 0.000118, .5R: 0.818182, .75R: 0.090909, count: 11 -Region 16 Avg IOU: 0.641627, Class: 0.220270, Obj: 0.070326, No Obj: 0.000504, .5R: 0.800000, .75R: 0.200000, count: 15 -Region 23 Avg IOU: 0.607239, Class: 0.146306, Obj: 0.066943, No Obj: 0.000068, .5R: 0.750000, .75R: 0.125000, count: 8 -Region 16 Avg IOU: 0.631719, Class: 0.196470, Obj: 0.074094, No Obj: 0.000527, .5R: 0.826087, .75R: 0.217391, count: 23 -Region 23 Avg IOU: 0.581785, Class: 0.209384, Obj: 0.028157, No Obj: 0.000104, .5R: 0.687500, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.680416, Class: 0.225506, Obj: 0.137128, No Obj: 0.000439, .5R: 0.857143, .75R: 0.380952, count: 21 -Region 23 Avg IOU: 0.595446, Class: 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0.148887, Obj: 0.125876, No Obj: 0.000121, .5R: 0.823529, .75R: 0.176471, count: 17 -Region 16 Avg IOU: 0.709416, Class: 0.253359, Obj: 0.167792, No Obj: 0.001431, .5R: 0.920000, .75R: 0.440000, count: 25 -Region 23 Avg IOU: 0.627123, Class: 0.177063, Obj: 0.077082, No Obj: 0.000093, .5R: 0.928571, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.701702, Class: 0.207796, Obj: 0.357960, No Obj: 0.001913, .5R: 0.967742, .75R: 0.354839, count: 31 -Region 23 Avg IOU: 0.653345, Class: 0.193855, Obj: 0.064959, No Obj: 0.000145, .5R: 0.791667, .75R: 0.333333, count: 24 -Region 16 Avg IOU: 0.708608, Class: 0.179858, Obj: 0.316554, No Obj: 0.001312, .5R: 0.875000, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.592517, Class: 0.184728, Obj: 0.073103, No Obj: 0.000136, .5R: 0.800000, .75R: 0.100000, count: 20 -Region 16 Avg IOU: 0.701101, Class: 0.247529, Obj: 0.262267, No Obj: 0.001225, .5R: 0.952381, .75R: 0.285714, count: 21 -Region 23 Avg IOU: 0.670659, Class: 0.181482, Obj: 0.178728, No Obj: 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count: 26 -Region 23 Avg IOU: 0.611241, Class: 0.204239, Obj: 0.157755, No Obj: 0.000332, .5R: 0.913043, .75R: 0.086957, count: 23 -Region 16 Avg IOU: 0.727338, Class: 0.207099, Obj: 0.435598, No Obj: 0.002327, .5R: 0.964286, .75R: 0.607143, count: 28 -Region 23 Avg IOU: 0.622323, Class: 0.174627, Obj: 0.143260, No Obj: 0.000400, .5R: 0.866667, .75R: 0.166667, count: 30 -Region 16 Avg IOU: 0.756838, Class: 0.259336, Obj: 0.347057, No Obj: 0.001529, .5R: 0.947368, .75R: 0.631579, count: 19 -Region 23 Avg IOU: 0.596582, Class: 0.265863, Obj: 0.144205, No Obj: 0.000222, .5R: 1.000000, .75R: 0.000000, count: 5 -Region 16 Avg IOU: 0.696295, Class: 0.244771, Obj: 0.375619, No Obj: 0.001897, .5R: 0.833333, .75R: 0.333333, count: 18 -Region 23 Avg IOU: 0.634723, Class: 0.165259, Obj: 0.234291, No Obj: 0.000441, .5R: 0.900000, .75R: 0.150000, count: 20 -Region 16 Avg IOU: 0.659626, Class: 0.122829, Obj: 0.302419, No Obj: 0.001602, .5R: 0.875000, .75R: 0.375000, count: 24 -Region 23 Avg IOU: 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22 -Region 16 Avg IOU: 0.710862, Class: 0.204262, Obj: 0.248534, No Obj: 0.001209, .5R: 0.925926, .75R: 0.407407, count: 27 -Region 23 Avg IOU: 0.636857, Class: 0.158563, Obj: 0.142393, No Obj: 0.000216, .5R: 0.772727, .75R: 0.227273, count: 22 -Region 16 Avg IOU: 0.707565, Class: 0.271501, Obj: 0.184592, No Obj: 0.000924, .5R: 0.900000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.640002, Class: 0.150838, Obj: 0.084232, No Obj: 0.000178, .5R: 0.866667, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.676330, Class: 0.183658, Obj: 0.139022, No Obj: 0.000932, .5R: 0.866667, .75R: 0.266667, count: 15 -Region 23 Avg IOU: 0.619799, Class: 0.220060, Obj: 0.094691, No Obj: 0.000143, .5R: 0.846154, .75R: 0.307692, count: 13 - - 1160: 3.491256, 3.222634 avg loss, 0.001000 rate, 2.418734 seconds, 148480 images -Resizing -480 x 480 - try to allocate workspace = 1144537089 * sizeof(float), CUDA allocate done! -Loaded: 0.373159 seconds -Region 16 Avg IOU: 0.696867, Class: 0.184944, Obj: 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images -Resizing -544 x 544 - try to allocate workspace = 1145585665 * sizeof(float), CUDA allocate done! -Loaded: 0.487372 seconds -Region 16 Avg IOU: 0.731531, Class: 0.237054, Obj: 0.386908, No Obj: 0.001579, .5R: 1.000000, .75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.644662, Class: 0.200204, Obj: 0.313101, No Obj: 0.000392, .5R: 1.000000, .75R: 0.000000, count: 8 -Region 16 Avg IOU: 0.707453, Class: 0.205454, Obj: 0.302748, No Obj: 0.001355, .5R: 1.000000, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.627756, Class: 0.180592, Obj: 0.080759, No Obj: 0.000365, .5R: 0.785714, .75R: 0.178571, count: 28 -Region 16 Avg IOU: 0.734802, Class: 0.257072, Obj: 0.488926, No Obj: 0.001743, .5R: 0.950000, .75R: 0.450000, count: 20 -Region 23 Avg IOU: 0.679463, Class: 0.205446, Obj: 0.213089, No Obj: 0.000548, .5R: 0.923077, .75R: 0.307692, count: 26 -Region 16 Avg IOU: 0.746806, Class: 0.197541, Obj: 0.374373, No Obj: 0.001790, .5R: 1.000000, .75R: 0.550000, count: 20 -Region 23 Avg IOU: 0.691027, Class: 0.121596, Obj: 0.170674, No Obj: 0.000401, .5R: 0.888889, .75R: 0.444444, count: 18 -Region 16 Avg IOU: 0.728227, Class: 0.190249, Obj: 0.448546, No Obj: 0.001879, .5R: 1.000000, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.628227, Class: 0.190036, Obj: 0.152464, No Obj: 0.000557, .5R: 0.878788, .75R: 0.121212, count: 33 -Region 16 Avg IOU: 0.748762, Class: 0.286190, Obj: 0.383187, No Obj: 0.001378, .5R: 0.916667, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.586218, Class: 0.196347, Obj: 0.210839, No Obj: 0.000387, .5R: 0.722222, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.696824, Class: 0.196486, Obj: 0.270138, No Obj: 0.001389, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 23 Avg IOU: 0.659403, Class: 0.223675, Obj: 0.387621, No Obj: 0.000513, .5R: 0.833333, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.710592, Class: 0.240139, Obj: 0.249510, No Obj: 0.001163, .5R: 0.916667, .75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.691602, Class: 0.221859, Obj: 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.75R: 0.600000, count: 5 -Region 23 Avg IOU: 0.685965, Class: 0.144762, Obj: 0.287667, No Obj: 0.000729, .5R: 1.000000, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.789346, Class: 0.233472, Obj: 0.330732, No Obj: 0.001545, .5R: 1.000000, .75R: 0.777778, count: 18 -Region 23 Avg IOU: 0.618174, Class: 0.207393, Obj: 0.125270, No Obj: 0.000320, .5R: 1.000000, .75R: 0.000000, count: 12 -Region 16 Avg IOU: 0.753075, Class: 0.176503, Obj: 0.368900, No Obj: 0.001685, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.633547, Class: 0.194555, Obj: 0.196928, No Obj: 0.000539, .5R: 0.875000, .75R: 0.208333, count: 24 -Region 16 Avg IOU: 0.708300, Class: 0.158971, Obj: 0.364264, No Obj: 0.001256, .5R: 0.916667, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.650611, Class: 0.154009, Obj: 0.255242, No Obj: 0.000581, .5R: 0.954545, .75R: 0.181818, count: 22 -Region 16 Avg IOU: 0.736252, Class: 0.203937, Obj: 0.444915, No Obj: 0.001422, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.615433, Class: 0.152525, Obj: 0.239425, No Obj: 0.000585, .5R: 0.833333, .75R: 0.111111, count: 18 - - 1182: 2.646478, 2.905334 avg loss, 0.001000 rate, 1.402415 seconds, 151296 images -Loaded: 0.000069 seconds -Region 16 Avg IOU: 0.759415, Class: 0.145004, Obj: 0.271942, No Obj: 0.001360, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.628711, Class: 0.163327, Obj: 0.208379, No Obj: 0.000490, .5R: 0.857143, .75R: 0.142857, count: 21 -Region 16 Avg IOU: 0.749292, Class: 0.200375, Obj: 0.338047, No Obj: 0.001368, .5R: 0.952381, .75R: 0.571429, count: 21 -Region 23 Avg IOU: 0.682293, Class: 0.142350, Obj: 0.284490, No Obj: 0.000497, .5R: 0.833333, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.724756, Class: 0.255729, Obj: 0.396432, No Obj: 0.002046, .5R: 0.909091, .75R: 0.545455, count: 22 -Region 23 Avg IOU: 0.679201, Class: 0.181621, Obj: 0.149011, No Obj: 0.000244, .5R: 0.846154, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.763168, Class: 0.278319, Obj: 0.454751, No Obj: 0.001569, .5R: 0.947368, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.703125, Class: 0.234870, Obj: 0.242143, No Obj: 0.000450, .5R: 0.875000, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.742786, Class: 0.221335, Obj: 0.293128, No Obj: 0.001466, .5R: 1.000000, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.618654, Class: 0.152754, Obj: 0.096627, No Obj: 0.000335, .5R: 0.875000, .75R: 0.083333, count: 24 -Region 16 Avg IOU: 0.766672, Class: 0.205476, Obj: 0.173348, No Obj: 0.001130, .5R: 1.000000, .75R: 0.523810, count: 21 -Region 23 Avg IOU: 0.633171, Class: 0.181445, Obj: 0.161585, No Obj: 0.000592, .5R: 0.807692, .75R: 0.230769, count: 26 -Region 16 Avg IOU: 0.754192, Class: 0.218215, Obj: 0.330422, No Obj: 0.001177, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.596567, Class: 0.147430, Obj: 0.166020, No Obj: 0.000483, .5R: 0.791667, .75R: 0.125000, count: 24 -Region 16 Avg IOU: 0.748936, Class: 0.269096, Obj: 0.331136, No Obj: 0.001242, 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-Region 16 Avg IOU: 0.713250, Class: 0.232515, Obj: 0.240183, No Obj: 0.001768, .5R: 0.937500, .75R: 0.437500, count: 16 -Region 23 Avg IOU: 0.651111, Class: 0.214195, Obj: 0.126454, No Obj: 0.000305, .5R: 0.850000, .75R: 0.150000, count: 20 -Region 16 Avg IOU: 0.745522, Class: 0.215444, Obj: 0.245577, No Obj: 0.000916, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.641128, Class: 0.207401, Obj: 0.096632, No Obj: 0.000255, .5R: 0.791667, .75R: 0.291667, count: 24 -Region 16 Avg IOU: 0.743721, Class: 0.288270, Obj: 0.324302, No Obj: 0.001179, .5R: 1.000000, .75R: 0.562500, count: 16 -Region 23 Avg IOU: 0.672863, Class: 0.185355, Obj: 0.083226, No Obj: 0.000278, .5R: 0.968750, .75R: 0.250000, count: 32 -Region 16 Avg IOU: 0.771875, Class: 0.193856, Obj: 0.339988, No Obj: 0.001705, .5R: 1.000000, .75R: 0.565217, count: 23 -Region 23 Avg IOU: 0.688285, Class: 0.191985, Obj: 0.152965, No Obj: 0.000235, .5R: 1.000000, .75R: 0.230769, count: 13 -Region 16 Avg IOU: 0.765241, Class: 0.209902, Obj: 0.255885, No Obj: 0.001656, .5R: 1.000000, .75R: 0.521739, count: 23 -Region 23 Avg IOU: 0.617676, Class: 0.230310, Obj: 0.084733, No Obj: 0.000200, .5R: 0.863636, .75R: 0.227273, count: 22 - - 1184: 2.788419, 2.877523 avg loss, 0.001000 rate, 1.382596 seconds, 151552 images -Loaded: 0.000069 seconds -Region 16 Avg IOU: 0.789954, Class: 0.319921, Obj: 0.390666, No Obj: 0.001260, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.667434, Class: 0.188827, Obj: 0.111915, No Obj: 0.000205, .5R: 0.894737, .75R: 0.263158, count: 19 -Region 16 Avg IOU: 0.686699, Class: 0.192920, Obj: 0.203333, No Obj: 0.001476, .5R: 0.869565, .75R: 0.391304, count: 23 -Region 23 Avg IOU: 0.621414, Class: 0.147604, Obj: 0.098843, No Obj: 0.000295, .5R: 0.850000, .75R: 0.250000, count: 20 -Region 16 Avg IOU: 0.694099, Class: 0.236185, Obj: 0.289956, No Obj: 0.001312, .5R: 0.882353, .75R: 0.411765, count: 17 -Region 23 Avg IOU: 0.630799, Class: 0.146646, Obj: 0.090526, No Obj: 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0.400000, count: 20 -Region 16 Avg IOU: 0.748424, Class: 0.175418, Obj: 0.321227, No Obj: 0.000991, .5R: 0.923077, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.678296, Class: 0.142480, Obj: 0.147478, No Obj: 0.000329, .5R: 0.851852, .75R: 0.333333, count: 27 - - 1185: 2.635781, 2.853349 avg loss, 0.001000 rate, 1.390086 seconds, 151680 images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.760380, Class: 0.304550, Obj: 0.352448, No Obj: 0.000955, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.669745, Class: 0.210121, Obj: 0.152366, No Obj: 0.000273, .5R: 0.933333, .75R: 0.000000, count: 15 -Region 16 Avg IOU: 0.743818, Class: 0.176753, Obj: 0.431147, No Obj: 0.001530, .5R: 0.928571, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.624352, Class: 0.163881, Obj: 0.094461, No Obj: 0.000310, .5R: 0.882353, .75R: 0.058824, count: 17 -Region 16 Avg IOU: 0.777777, Class: 0.197777, Obj: 0.295774, No Obj: 0.001869, .5R: 1.000000, .75R: 0.666667, count: 18 -Region 23 Avg IOU: 0.681686, Class: 0.171030, Obj: 0.132082, No Obj: 0.000236, .5R: 1.000000, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.693315, Class: 0.209545, Obj: 0.409169, No Obj: 0.001443, .5R: 0.916667, .75R: 0.166667, count: 12 -Region 23 Avg IOU: 0.605034, Class: 0.150423, Obj: 0.132939, No Obj: 0.000270, .5R: 0.692308, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.758513, Class: 0.198259, Obj: 0.289951, No Obj: 0.001078, .5R: 0.916667, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.614064, Class: 0.129978, Obj: 0.307319, No Obj: 0.000375, .5R: 0.928571, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.728605, Class: 0.281052, Obj: 0.338811, No Obj: 0.002129, .5R: 0.941176, .75R: 0.411765, count: 17 -Region 23 Avg IOU: 0.624207, Class: 0.185183, Obj: 0.118771, No Obj: 0.000408, .5R: 0.850000, .75R: 0.050000, count: 20 -Region 16 Avg IOU: 0.728513, Class: 0.211534, Obj: 0.333280, No Obj: 0.001930, .5R: 1.000000, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.703545, Class: 0.185620, Obj: 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Class: 0.209431, Obj: 0.119262, No Saving weights to ./attribute/7354_26298/attribute_33652_s_tyv3_9c5_1200.weights -Obj: 0.000467, .5R: 0.909091, .75R: 0.136364, count: 22 - - 1200: 1.938905, 2.423288 avg loss, 0.001000 rate, 0.852873 seconds, 153600 images -Resizing -672 x 672 - try to allocate workspace = 1148076033 * sizeof(float), CUDA allocate done! -Loaded: 0.365289 seconds -Region 16 Avg IOU: 0.731013, Class: 0.274641, Obj: 0.281108, No Obj: 0.002040, .5R: 1.000000, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.608842, Class: 0.171464, Obj: 0.101641, No Obj: 0.000346, .5R: 0.791667, .75R: 0.125000, count: 24 -Region 16 Avg IOU: 0.628690, Class: 0.182964, Obj: 0.307263, No Obj: 0.001938, .5R: 0.772727, .75R: 0.272727, count: 22 -Region 23 Avg IOU: 0.667052, Class: 0.252652, Obj: 0.099341, No Obj: 0.000266, .5R: 0.846154, .75R: 0.230769, count: 13 -Region 16 Avg IOU: 0.733224, Class: 0.232306, Obj: 0.360287, No Obj: 0.001910, .5R: 1.000000, .75R: 0.384615, count: 26 -Region 23 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15 -Region 16 Avg IOU: 0.744883, Class: 0.235388, Obj: 0.258618, No Obj: 0.000701, .5R: 1.000000, .75R: 0.625000, count: 16 -Region 23 Avg IOU: 0.636452, Class: 0.220214, Obj: 0.076492, No Obj: 0.000130, .5R: 0.933333, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.679241, Class: 0.225058, Obj: 0.099293, No Obj: 0.000601, .5R: 0.812500, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.642904, Class: 0.134762, Obj: 0.127636, No Obj: 0.000110, .5R: 0.857143, .75R: 0.142857, count: 7 -Region 16 Avg IOU: 0.707787, Class: 0.221975, Obj: 0.238065, No Obj: 0.001075, .5R: 0.923077, .75R: 0.423077, count: 26 -Region 23 Avg IOU: 0.654479, Class: 0.162551, Obj: 0.112538, No Obj: 0.000145, .5R: 0.909091, .75R: 0.136364, count: 22 - - 1209: 2.963184, 2.827743 avg loss, 0.001000 rate, 1.999496 seconds, 154752 images -Loaded: 0.000059 seconds -Region 16 Avg IOU: 0.734829, Class: 0.202513, Obj: 0.206441, No Obj: 0.001054, .5R: 0.840000, .75R: 0.720000, count: 25 -Region 23 Avg IOU: 0.682643, Class: 0.212362, Obj: 0.183251, No Obj: 0.000276, .5R: 0.928571, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.708326, Class: 0.234992, Obj: 0.208205, No Obj: 0.000701, .5R: 0.875000, .75R: 0.437500, count: 16 -Region 23 Avg IOU: 0.664849, Class: 0.190593, Obj: 0.122984, No Obj: 0.000331, .5R: 1.000000, .75R: 0.300000, count: 30 -Region 16 Avg IOU: 0.696105, Class: 0.234116, Obj: 0.178129, No Obj: 0.000708, .5R: 1.000000, .75R: 0.333333, count: 18 -Region 23 Avg IOU: 0.636941, Class: 0.177438, Obj: 0.106369, No Obj: 0.000198, .5R: 0.909091, .75R: 0.090909, count: 22 -Region 16 Avg IOU: 0.742980, Class: 0.252774, Obj: 0.365267, No Obj: 0.000767, .5R: 1.000000, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.646833, Class: 0.181717, Obj: 0.135349, No Obj: 0.000198, .5R: 0.818182, .75R: 0.181818, count: 22 -Region 16 Avg IOU: 0.698441, Class: 0.261646, Obj: 0.176600, No Obj: 0.001008, .5R: 0.920000, .75R: 0.440000, count: 25 -Region 23 Avg IOU: 0.654013, Class: 0.279344, Obj: 0.142298, No Obj: 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Avg IOU: 0.720084, Class: 0.173989, Obj: 0.367190, No Obj: 0.001609, .5R: 0.937500, .75R: 0.437500, count: 16 -Region 23 Avg IOU: 0.607159, Class: 0.227163, Obj: 0.185565, No Obj: 0.000533, .5R: 0.884615, .75R: 0.038462, count: 26 -Region 16 Avg IOU: 0.752206, Class: 0.190868, Obj: 0.358525, No Obj: 0.001314, .5R: 1.000000, .75R: 0.384615, count: 13 -Region 23 Avg IOU: 0.663944, Class: 0.130904, Obj: 0.153535, No Obj: 0.000371, .5R: 0.888889, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.752461, Class: 0.239195, Obj: 0.360139, No Obj: 0.001650, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.641491, Class: 0.150514, Obj: 0.265423, No Obj: 0.000583, .5R: 0.928571, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.725772, Class: 0.265759, Obj: 0.340589, No Obj: 0.001531, .5R: 1.000000, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.707738, Class: 0.219032, Obj: 0.300396, No Obj: 0.000614, .5R: 0.900000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.747904, Class: 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155008 images -Loaded: 0.000063 seconds -Region 16 Avg IOU: 0.746997, Class: 0.334604, Obj: 0.910163, No Obj: 0.001497, .5R: 1.000000, .75R: 0.400000, count: 5 -Region 23 Avg IOU: 0.604750, Class: 0.116643, Obj: 0.203081, No Obj: 0.000370, .5R: 0.866667, .75R: 0.066667, count: 15 -Region 16 Avg IOU: 0.692568, Class: 0.239388, Obj: 0.352143, No Obj: 0.001807, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 23 Avg IOU: 0.703931, Class: 0.208514, Obj: 0.217527, No Obj: 0.000979, .5R: 0.923077, .75R: 0.269231, count: 26 -Region 16 Avg IOU: 0.747587, Class: 0.154699, Obj: 0.431470, No Obj: 0.001869, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.653142, Class: 0.203054, Obj: 0.284110, No Obj: 0.000849, .5R: 0.894737, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.646679, Class: 0.183694, Obj: 0.420448, No Obj: 0.001984, .5R: 0.842105, .75R: 0.263158, count: 19 -Region 23 Avg IOU: 0.619704, Class: 0.224623, Obj: 0.314709, No Obj: 0.000732, .5R: 0.857143, .75R: 0.238095, count: 21 -Region 16 Avg IOU: 0.747690, Class: 0.207804, Obj: 0.364193, No Obj: 0.002701, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.676588, Class: 0.181022, Obj: 0.300688, No Obj: 0.001009, .5R: 1.000000, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.738700, Class: 0.171685, Obj: 0.311945, No Obj: 0.002095, .5R: 1.000000, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.664552, Class: 0.170645, Obj: 0.261853, No Obj: 0.000680, .5R: 0.904762, .75R: 0.238095, count: 21 -Region 16 Avg IOU: 0.723928, Class: 0.237188, Obj: 0.369728, No Obj: 0.002561, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.615382, Class: 0.203658, Obj: 0.131132, No Obj: 0.000445, .5R: 0.944444, .75R: 0.055556, count: 18 -Region 16 Avg IOU: 0.734887, Class: 0.262687, Obj: 0.288869, No Obj: 0.001946, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.576950, Class: 0.164584, Obj: 0.107697, No Obj: 0.000461, .5R: 0.684211, .75R: 0.105263, count: 19 - - 1212: 2.578171, 2.796340 avg loss, 0.001000 rate, 1.058034 seconds, 155136 images -Loaded: 0.000061 seconds -Region 16 Avg IOU: 0.678180, Class: 0.139214, Obj: 0.477712, No Obj: 0.002130, .5R: 0.900000, .75R: 0.400000, count: 10 -Region 23 Avg IOU: 0.607953, Class: 0.186002, Obj: 0.198610, No Obj: 0.000487, .5R: 0.769231, .75R: 0.230769, count: 13 -Region 16 Avg IOU: 0.746319, Class: 0.183594, Obj: 0.375925, No Obj: 0.002209, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.661546, Class: 0.172759, Obj: 0.160698, No Obj: 0.000432, .5R: 0.947368, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.714577, Class: 0.214661, Obj: 0.415168, No Obj: 0.002157, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.661311, Class: 0.222189, Obj: 0.281328, No Obj: 0.000456, .5R: 0.909091, .75R: 0.272727, count: 11 -Region 16 Avg IOU: 0.744999, Class: 0.244801, Obj: 0.422820, No Obj: 0.001879, .5R: 0.800000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.709098, Class: 0.228985, Obj: 0.240814, 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1.053159 seconds, 157440 images -Resizing -576 x 576 - try to allocate workspace = 1146159105 * sizeof(float), CUDA allocate done! -Loaded: 0.176525 seconds -Region 16 Avg IOU: 0.742879, Class: 0.240673, Obj: 0.365975, No Obj: 0.002500, .5R: 1.000000, .75R: 0.434783, count: 23 -Region 23 Avg IOU: 0.616757, Class: 0.174926, Obj: 0.091705, No Obj: 0.000567, .5R: 0.843750, .75R: 0.125000, count: 32 -Region 16 Avg IOU: 0.767834, Class: 0.132179, Obj: 0.390803, No Obj: 0.002250, .5R: 1.000000, .75R: 0.700000, count: 20 -Region 23 Avg IOU: 0.564834, Class: 0.156482, Obj: 0.174704, No Obj: 0.000500, .5R: 0.642857, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.699821, Class: 0.241953, Obj: 0.372974, No Obj: 0.001979, .5R: 0.954545, .75R: 0.318182, count: 22 -Region 23 Avg IOU: 0.627944, Class: 0.163450, Obj: 0.144263, No Obj: 0.000445, .5R: 0.818182, .75R: 0.136364, count: 22 -Region 16 Avg IOU: 0.740797, Class: 0.196564, Obj: 0.451845, No Obj: 0.002187, .5R: 1.000000, .75R: 0.437500, count: 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images -Loaded: 0.000080 seconds -Region 16 Avg IOU: 0.737182, Class: 0.255876, Obj: 0.414843, No Obj: 0.001646, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 23 Avg IOU: 0.639552, Class: 0.128373, Obj: 0.245624, No Obj: 0.000778, .5R: 0.941176, .75R: 0.058824, count: 17 -Region 16 Avg IOU: 0.686073, Class: 0.252160, Obj: 0.497294, No Obj: 0.001956, .5R: 0.833333, .75R: 0.333333, count: 12 -Region 23 Avg IOU: 0.646610, Class: 0.197744, Obj: 0.188133, No Obj: 0.000849, .5R: 0.958333, .75R: 0.083333, count: 24 -Region 16 Avg IOU: 0.749407, Class: 0.205358, Obj: 0.380286, No Obj: 0.001111, .5R: 1.000000, .75R: 0.800000, count: 5 -Region 23 Avg IOU: 0.604833, Class: 0.242216, Obj: 0.281624, No Obj: 0.000522, .5R: 0.769231, .75R: 0.076923, count: 13 -Region 16 Avg IOU: 0.678385, Class: 0.264502, Obj: 0.351419, No Obj: 0.002448, .5R: 0.727273, .75R: 0.272727, count: 11 -Region 23 Avg IOU: 0.702802, Class: 0.176223, Obj: 0.100335, No Obj: 0.000527, .5R: 1.000000, .75R: 0.400000, count: 10 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Avg IOU: 0.709034, Class: 0.222757, Obj: 0.325418, No Obj: 0.000716, .5R: 0.846154, .75R: 0.461538, count: 13 - - 1250: 2.008802, 2.298898 avg loss, 0.001000 rate, 0.682977 seconds, 160000 images -Resizing -416 x 416 - try to allocate workspace = 304758785 * sizeof(float), CUDA allocate done! -Loaded: 0.333345 seconds -Region 16 Avg IOU: 0.720820, Class: 0.156975, Obj: 0.334609, No Obj: 0.001345, .5R: 0.875000, .75R: 0.375000, count: 8 -Region 23 Avg IOU: 0.594561, Class: 0.188649, Obj: 0.177070, No Obj: 0.000800, .5R: 0.826087, .75R: 0.130435, count: 23 -Region 16 Avg IOU: 0.770383, Class: 0.318155, Obj: 0.374702, No Obj: 0.001014, .5R: 1.000000, .75R: 0.750000, count: 4 -Region 23 Avg IOU: 0.699960, Class: 0.168978, Obj: 0.145414, No Obj: 0.000538, .5R: 1.000000, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.746683, Class: 0.282318, Obj: 0.364635, No Obj: 0.001301, .5R: 1.000000, .75R: 0.428571, count: 7 -Region 23 Avg IOU: 0.676477, Class: 0.174940, Obj: 0.146045, No Obj: 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0.666033, Class: 0.201390, Obj: 0.104666, No Obj: 0.000208, .5R: 0.900000, .75R: 0.100000, count: 10 -Region 16 Avg IOU: 0.781799, Class: 0.173342, Obj: 0.358659, No Obj: 0.001027, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.619177, Class: 0.149913, Obj: 0.106857, No Obj: 0.000522, .5R: 0.806452, .75R: 0.161290, count: 31 -Region 16 Avg IOU: 0.744509, Class: 0.162709, Obj: 0.441965, No Obj: 0.001546, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.637005, Class: 0.199254, Obj: 0.085871, No Obj: 0.000451, .5R: 0.903226, .75R: 0.129032, count: 31 -Region 16 Avg IOU: 0.745760, Class: 0.230243, Obj: 0.286105, No Obj: 0.001886, .5R: 0.954545, .75R: 0.590909, count: 22 -Region 23 Avg IOU: 0.647800, Class: 0.216663, Obj: 0.101392, No Obj: 0.000430, .5R: 0.850000, .75R: 0.250000, count: 20 -Region 16 Avg IOU: 0.774433, Class: 0.247235, Obj: 0.327266, No Obj: 0.000978, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.694631, Class: 0.240667, Obj: 0.092653, No Obj: 0.000167, .5R: 0.875000, .75R: 0.125000, count: 8 -Region 16 Avg IOU: 0.739630, Class: 0.210986, Obj: 0.248818, No Obj: 0.000624, .5R: 1.000000, .75R: 0.600000, count: 5 -Region 23 Avg IOU: 0.652760, Class: 0.131356, Obj: 0.082986, No Obj: 0.000223, .5R: 1.000000, .75R: 0.105263, count: 19 - - 1252: 2.205685, 2.245396 avg loss, 0.001000 rate, 0.785996 seconds, 160256 images -Loaded: 0.000089 seconds -Region 16 Avg IOU: 0.784234, Class: 0.328836, Obj: 0.381130, No Obj: 0.001032, .5R: 1.000000, .75R: 0.833333, count: 6 -Region 23 Avg IOU: 0.658012, Class: 0.192635, Obj: 0.097967, No Obj: 0.000520, .5R: 0.904762, .75R: 0.238095, count: 21 -Region 16 Avg IOU: 0.774017, Class: 0.240349, Obj: 0.294706, No Obj: 0.001144, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.669950, Class: 0.178237, Obj: 0.103045, No Obj: 0.000496, .5R: 0.916667, .75R: 0.291667, count: 24 -Region 16 Avg IOU: 0.768226, Class: 0.214459, Obj: 0.320803, No Obj: 0.001603, .5R: 1.000000, 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23 Avg IOU: 0.588896, Class: 0.254729, Obj: 0.094436, No Obj: 0.000347, .5R: 0.733333, .75R: 0.066667, count: 15 -Region 16 Avg IOU: 0.774473, Class: 0.317615, Obj: 0.274488, No Obj: 0.001277, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.637975, Class: 0.229416, Obj: 0.099727, No Obj: 0.000460, .5R: 0.916667, .75R: 0.083333, count: 24 - - 1253: 2.437024, 2.264559 avg loss, 0.001000 rate, 0.789185 seconds, 160384 images -Loaded: 0.000067 seconds -Region 16 Avg IOU: 0.731450, Class: 0.294103, Obj: 0.238577, No Obj: 0.001028, .5R: 1.000000, .75R: 0.333333, count: 6 -Region 23 Avg IOU: 0.580488, Class: 0.241636, Obj: 0.183123, No Obj: 0.000488, .5R: 0.764706, .75R: 0.000000, count: 17 -Region 16 Avg IOU: 0.726025, Class: 0.225001, Obj: 0.180770, No Obj: 0.001289, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.551291, Class: 0.169165, Obj: 0.208140, No Obj: 0.000789, .5R: 0.700000, .75R: 0.000000, count: 30 -Region 16 Avg IOU: 0.717153, Class: 0.237652, 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26 -Region 23 Avg IOU: 0.582573, Class: 0.217143, Obj: 0.071612, No Obj: 0.000200, .5R: 0.739130, .75R: 0.086957, count: 23 -Region 16 Avg IOU: 0.720461, Class: 0.210165, Obj: 0.252463, No Obj: 0.000985, .5R: 0.954545, .75R: 0.363636, count: 22 -Region 23 Avg IOU: 0.616633, Class: 0.161218, Obj: 0.072063, No Obj: 0.000151, .5R: 0.833333, .75R: 0.166667, count: 12 -Region 16 Avg IOU: 0.683753, Class: 0.222612, Obj: 0.250148, No Obj: 0.001580, .5R: 0.903226, .75R: 0.258065, count: 31 -Region 23 Avg IOU: 0.651015, Class: 0.193010, Obj: 0.118238, No Obj: 0.000237, .5R: 0.952381, .75R: 0.142857, count: 21 -Region 16 Avg IOU: 0.714891, Class: 0.159013, Obj: 0.209569, No Obj: 0.001125, .5R: 0.916667, .75R: 0.583333, count: 24 -Region 23 Avg IOU: 0.644859, Class: 0.143222, Obj: 0.142651, No Obj: 0.000226, .5R: 0.944444, .75R: 0.111111, count: 18 - - 1279: 3.234705, 2.915604 avg loss, 0.001000 rate, 2.014124 seconds, 163712 images -Loaded: 0.000069 seconds -Region 16 Avg IOU: 0.730387, Class: 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35 -Region 16 Avg IOU: 0.783069, Class: 0.285126, Obj: 0.329269, No Obj: 0.000987, .5R: 0.923077, .75R: 0.846154, count: 13 -Region 23 Avg IOU: 0.646034, Class: 0.196779, Obj: 0.171872, No Obj: 0.000129, .5R: 0.900000, .75R: 0.200000, count: 10 - - 1301: 2.856511, 2.793679 avg loss, 0.001000 rate, 1.947575 seconds, 166528 images -Loaded: 0.000074 seconds -Region 16 Avg IOU: 0.745030, Class: 0.145947, Obj: 0.297136, No Obj: 0.001337, .5R: 0.960000, .75R: 0.600000, count: 25 -Region 23 Avg IOU: 0.626318, Class: 0.209554, Obj: 0.091364, No Obj: 0.000164, .5R: 0.789474, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.730974, Class: 0.187325, Obj: 0.335628, No Obj: 0.001258, .5R: 0.958333, .75R: 0.458333, count: 24 -Region 23 Avg IOU: 0.608895, Class: 0.135071, Obj: 0.103304, No Obj: 0.000198, .5R: 0.857143, .75R: 0.095238, count: 21 -Region 16 Avg IOU: 0.804369, Class: 0.196048, Obj: 0.450911, No Obj: 0.001013, .5R: 1.000000, .75R: 0.785714, count: 14 -Region 23 Avg IOU: 0.663510, Class: 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images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.744224, Class: 0.153816, Obj: 0.363542, No Obj: 0.000830, .5R: 0.928571, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.644786, Class: 0.193889, Obj: 0.116614, No Obj: 0.000251, .5R: 0.920000, .75R: 0.160000, count: 25 -Region 16 Avg IOU: 0.755163, Class: 0.208806, Obj: 0.158055, No Obj: 0.000810, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.711632, Class: 0.185721, Obj: 0.100329, No Obj: 0.000121, .5R: 1.000000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.743840, Class: 0.149312, Obj: 0.300738, No Obj: 0.000910, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.699801, Class: 0.147977, Obj: 0.125390, No Obj: 0.000238, .5R: 0.944444, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.717794, Class: 0.166500, Obj: 0.222697, No Obj: 0.000703, .5R: 0.882353, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.621304, Class: 0.150974, Obj: 0.140844, No Obj: 0.000216, .5R: 0.821429, .75R: 0.142857, count: 28 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0.743341, Class: 0.229682, Obj: 0.320889, No Obj: 0.001967, .5R: 0.892857, .75R: 0.500000, count: 28 -Region 23 Avg IOU: 0.621172, Class: 0.255756, Obj: 0.055191, No Obj: 0.000177, .5R: 0.750000, .75R: 0.083333, count: 24 - - 1319: 2.987784, 2.915658 avg loss, 0.001000 rate, 3.013391 seconds, 168832 images -Loaded: 0.000068 seconds -Region 16 Avg IOU: 0.652396, Class: 0.158809, Obj: 0.177992, No Obj: 0.001123, .5R: 0.888889, .75R: 0.222222, count: 18 -Region 23 Avg IOU: 0.597053, Class: 0.137136, Obj: 0.090310, No Obj: 0.000245, .5R: 0.750000, .75R: 0.208333, count: 24 -Region 16 Avg IOU: 0.741466, Class: 0.185333, Obj: 0.224775, No Obj: 0.001428, .5R: 1.000000, .75R: 0.450000, count: 20 -Region 23 Avg IOU: 0.698575, Class: 0.215636, Obj: 0.096250, No Obj: 0.000160, .5R: 1.000000, .75R: 0.333333, count: 9 -Region 16 Avg IOU: 0.742313, Class: 0.236641, Obj: 0.308979, No Obj: 0.001159, .5R: 1.000000, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.634999, Class: 0.206469, Obj: 0.118541, 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count: 23 -Region 16 Avg IOU: 0.727874, Class: 0.215207, Obj: 0.374995, No Obj: 0.001881, .5R: 0.900000, .75R: 0.550000, count: 20 -Region 23 Avg IOU: 0.632090, Class: 0.198159, Obj: 0.200455, No Obj: 0.000631, .5R: 0.789474, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.697748, Class: 0.208469, Obj: 0.216315, No Obj: 0.000885, .5R: 0.909091, .75R: 0.272727, count: 11 -Region 23 Avg IOU: 0.643561, Class: 0.193878, Obj: 0.135064, No Obj: 0.000678, .5R: 0.941176, .75R: 0.088235, count: 34 -Region 16 Avg IOU: 0.672864, Class: 0.231075, Obj: 0.179137, No Obj: 0.001179, .5R: 0.909091, .75R: 0.181818, count: 11 -Region 23 Avg IOU: 0.652453, Class: 0.149005, Obj: 0.237568, No Obj: 0.000678, .5R: 1.000000, .75R: 0.117647, count: 17 -Region 16 Avg IOU: 0.691438, Class: 0.259305, Obj: 0.213480, No Obj: 0.001014, .5R: 0.800000, .75R: 0.400000, count: 10 -Region 23 Avg IOU: 0.643961, Class: 0.170445, Obj: 0.166008, No Obj: 0.000709, .5R: 0.892857, .75R: 0.321429, count: 28 -Region 16 Avg IOU: 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12 -Region 23 Avg IOU: 0.694915, Class: 0.193218, Obj: 0.225738, No Obj: 0.000587, .5R: 1.000000, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.768651, Class: 0.268404, Obj: 0.276667, No Obj: 0.001182, .5R: 0.909091, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.655015, Class: 0.184655, Obj: 0.159141, No Obj: 0.000570, .5R: 0.931035, .75R: 0.206897, count: 29 -Region 16 Avg IOU: 0.674938, Class: 0.258778, Obj: 0.216170, No Obj: 0.001060, .5R: 0.866667, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.567906, Class: 0.243774, Obj: 0.121112, No Obj: 0.000487, .5R: 0.774194, .75R: 0.032258, count: 31 -Region 16 Avg IOU: 0.747463, Class: 0.219575, Obj: 0.286283, No Obj: 0.000807, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.644534, Class: 0.150660, Obj: 0.175964, No Obj: 0.000308, .5R: 1.000000, .75R: 0.083333, count: 12 - - 1330: 2.402351, 2.622461 avg loss, 0.001000 rate, 1.239972 seconds, 170240 images -Resizing -672 x 672 - try to allocate workspace = 1148076033 * sizeof(float), CUDA allocate done! -Loaded: 0.192853 seconds -Region 16 Avg IOU: 0.766549, Class: 0.254373, Obj: 0.333142, No Obj: 0.000808, .5R: 0.937500, .75R: 0.625000, count: 16 -Region 23 Avg IOU: 0.581902, Class: 0.159761, Obj: 0.109500, No Obj: 0.000251, .5R: 0.736842, .75R: 0.105263, count: 19 -Region 16 Avg IOU: 0.791986, Class: 0.249524, Obj: 0.220687, No Obj: 0.000769, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.657102, Class: 0.142840, Obj: 0.163382, No Obj: 0.000222, .5R: 0.875000, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.757059, Class: 0.229839, Obj: 0.333255, No Obj: 0.000872, .5R: 0.944444, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.712556, Class: 0.187052, Obj: 0.224120, No Obj: 0.000256, .5R: 1.000000, .75R: 0.400000, count: 5 -Region 16 Avg IOU: 0.711758, Class: 0.206312, Obj: 0.157416, No Obj: 0.000985, .5R: 0.954545, .75R: 0.363636, count: 22 -Region 23 Avg IOU: 0.618548, Class: 0.168298, Obj: 0.050428, No Obj: 0.000295, .5R: 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count: 28 -Region 23 Avg IOU: 0.700849, Class: 0.123067, Obj: 0.068694, No Obj: 0.000132, .5R: 0.962963, .75R: 0.370370, count: 27 -Region 16 Avg IOU: 0.709308, Class: 0.173432, Obj: 0.158759, No Obj: 0.001224, .5R: 0.933333, .75R: 0.400000, count: 30 -Region 23 Avg IOU: 0.596647, Class: 0.182938, Obj: 0.030850, No Obj: 0.000147, .5R: 0.722222, .75R: 0.222222, count: 18 -Region 16 Avg IOU: 0.740881, Class: 0.208345, Obj: 0.249343, No Obj: 0.001560, .5R: 0.972222, .75R: 0.444444, count: 36 -Region 23 Avg IOU: 0.642539, Class: 0.197334, Obj: 0.123259, No Obj: 0.000187, .5R: 0.809524, .75R: 0.238095, count: 21 -Region 16 Avg IOU: 0.704178, Class: 0.234859, Obj: 0.220692, No Obj: 0.000792, .5R: 0.947368, .75R: 0.315789, count: 19 -Region 23 Avg IOU: 0.636306, Class: 0.151597, Obj: 0.113546, No Obj: 0.000192, .5R: 0.909091, .75R: 0.181818, count: 22 -Region 16 Avg IOU: 0.725485, Class: 0.211749, Obj: 0.332079, No Obj: 0.001011, .5R: 1.000000, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 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count: 20 -Region 16 Avg IOU: 0.727095, Class: 0.234318, Obj: 0.342663, No Obj: 0.001538, .5R: 0.888889, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.629885, Class: 0.197621, Obj: 0.074449, No Obj: 0.000233, .5R: 0.777778, .75R: 0.222222, count: 18 -Region 16 Avg IOU: 0.649144, Class: 0.238281, Obj: 0.281194, No Obj: 0.001443, .5R: 0.777778, .75R: 0.333333, count: 18 -Region 23 Avg IOU: 0.577735, Class: 0.243001, Obj: 0.042750, No Obj: 0.000143, .5R: 0.875000, .75R: 0.000000, count: 16 -Region 16 Avg IOU: 0.709399, Class: 0.196425, Obj: 0.325698, No Obj: 0.001169, .5R: 0.818182, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.619193, Class: 0.168240, Obj: 0.071408, No Obj: 0.000233, .5R: 0.869565, .75R: 0.130435, count: 23 -Region 16 Avg IOU: 0.675702, Class: 0.154216, Obj: 0.168522, No Obj: 0.001653, .5R: 0.916667, .75R: 0.291667, count: 24 -Region 23 Avg IOU: 0.641733, Class: 0.151765, Obj: 0.172875, No Obj: 0.000328, .5R: 0.850000, .75R: 0.200000, count: 20 -Region 16 Avg IOU: 0.673924, Class: 0.156401, Obj: 0.224102, No Obj: 0.001236, .5R: 0.916667, .75R: 0.333333, count: 12 -Region 23 Avg IOU: 0.640689, Class: 0.188009, Obj: 0.100035, No Obj: 0.000276, .5R: 0.947368, .75R: 0.052632, count: 19 - - 1352: 2.984527, 2.585545 avg loss, 0.001000 rate, 2.005012 seconds, 173056 images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.710952, Class: 0.192418, Obj: 0.429509, No Obj: 0.001036, .5R: 0.800000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.615034, Class: 0.133094, Obj: 0.083112, No Obj: 0.000235, .5R: 0.681818, .75R: 0.318182, count: 22 -Region 16 Avg IOU: 0.706898, Class: 0.249583, Obj: 0.295813, No Obj: 0.001750, .5R: 0.944444, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.634598, Class: 0.187448, Obj: 0.095392, No Obj: 0.000272, .5R: 0.842105, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.714701, Class: 0.177257, Obj: 0.280357, No Obj: 0.002063, .5R: 0.956522, .75R: 0.434783, count: 23 -Region 23 Avg IOU: 0.638578, Class: 0.196304, Obj: 0.075769, 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0.207093, Obj: 0.331378, No Obj: 0.001649, .5R: 1.000000, .75R: 0.736842, count: 19 -Region 23 Avg IOU: 0.630271, Class: 0.166183, Obj: 0.122425, No Obj: 0.000459, .5R: 0.789474, .75R: 0.368421, count: 19 -Region 16 Avg IOU: 0.685510, Class: 0.221218, Obj: 0.276179, No Obj: 0.001178, .5R: 0.888889, .75R: 0.388889, count: 18 -Region 23 Avg IOU: 0.595940, Class: 0.175593, Obj: 0.223748, No Obj: 0.000399, .5R: 0.857143, .75R: 0.047619, count: 21 -Region 16 Avg IOU: 0.764564, Class: 0.226583, Obj: 0.372348, No Obj: 0.001340, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.732753, Class: 0.175516, Obj: 0.355942, No Obj: 0.000473, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 16 Avg IOU: 0.756674, Class: 0.199660, Obj: 0.422310, No Obj: 0.001037, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.688114, Class: 0.184509, Obj: 0.215080, No Obj: 0.000485, .5R: 1.000000, .75R: 0.333333, count: 21 -Region 16 Avg IOU: 0.750295, Class: 0.254417, Obj: 0.306983, No Obj: 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0.000068 seconds -Region 16 Avg IOU: 0.806395, Class: 0.248814, Obj: 0.655800, No Obj: 0.001641, .5R: 1.000000, .75R: 0.750000, count: 16 -Region 23 Avg IOU: 0.657642, Class: 0.183556, Obj: 0.264214, No Obj: 0.000463, .5R: 0.944444, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.747987, Class: 0.220467, Obj: 0.322270, No Obj: 0.001388, .5R: 0.888889, .75R: 0.555556, count: 27 -Region 23 Avg IOU: 0.550113, Class: 0.116878, Obj: 0.171171, No Obj: 0.000201, .5R: 0.444444, .75R: 0.222222, count: 9 -Region 16 Avg IOU: 0.704814, Class: 0.197639, Obj: 0.182907, No Obj: 0.001113, .5R: 0.888889, .75R: 0.444444, count: 27 -Region 23 Avg IOU: 0.677539, Class: 0.186332, Obj: 0.139888, No Obj: 0.000327, .5R: 0.916667, .75R: 0.291667, count: 24 -Region 16 Avg IOU: 0.650812, Class: 0.167458, Obj: 0.111168, No Obj: 0.001066, .5R: 0.807692, .75R: 0.230769, count: 26 -Region 23 Avg IOU: 0.655446, Class: 0.169278, Obj: 0.138735, No Obj: 0.000261, .5R: 0.900000, .75R: 0.100000, count: 10 -Region 16 Avg 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23 - - 1366: 3.278957, 2.872256 avg loss, 0.001000 rate, 2.567385 seconds, 174848 images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.765259, Class: 0.251461, Obj: 0.327748, No Obj: 0.001329, .5R: 1.000000, .75R: 0.700000, count: 20 -Region 23 Avg IOU: 0.641210, Class: 0.260861, Obj: 0.091997, No Obj: 0.000164, .5R: 1.000000, .75R: 0.076923, count: 13 -Region 16 Avg IOU: 0.714131, Class: 0.144577, Obj: 0.227325, No Obj: 0.000893, .5R: 0.866667, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.691952, Class: 0.143353, Obj: 0.271050, No Obj: 0.000176, .5R: 0.875000, .75R: 0.250000, count: 8 -Region 16 Avg IOU: 0.742161, Class: 0.168932, Obj: 0.284158, No Obj: 0.000986, .5R: 0.944444, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.593643, Class: 0.191017, Obj: 0.124727, No Obj: 0.000198, .5R: 0.739130, .75R: 0.086957, count: 23 -Region 16 Avg IOU: 0.733242, Class: 0.214791, Obj: 0.300928, No Obj: 0.001618, .5R: 0.903226, .75R: 0.516129, count: 31 -Region 23 Avg IOU: 0.668590, Class: 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count: 21 -Region 23 Avg IOU: 0.723637, Class: 0.231234, Obj: 0.124952, No Obj: 0.000134, .5R: 0.882353, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.735245, Class: 0.197673, Obj: 0.163847, No Obj: 0.000949, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 23 Avg IOU: 0.661908, Class: 0.206704, Obj: 0.039105, No Obj: 0.000090, .5R: 0.913043, .75R: 0.260870, count: 23 -Region 16 Avg IOU: 0.729735, Class: 0.207886, Obj: 0.333320, No Obj: 0.001086, .5R: 0.833333, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.692934, Class: 0.148038, Obj: 0.120866, No Obj: 0.000151, .5R: 1.000000, .75R: 0.347826, count: 23 -Region 16 Avg IOU: 0.664430, Class: 0.217346, Obj: 0.211046, No Obj: 0.001202, .5R: 0.833333, .75R: 0.333333, count: 18 -Region 23 Avg IOU: 0.650437, Class: 0.174251, Obj: 0.096721, No Obj: 0.000175, .5R: 0.812500, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.717025, Class: 0.189749, Obj: 0.371624, No Obj: 0.001426, .5R: 0.950000, .75R: 0.500000, count: 20 -Region 23 Avg IOU: 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18 -Region 16 Avg IOU: 0.701824, Class: 0.218456, Obj: 0.231558, No Obj: 0.001318, .5R: 0.952381, .75R: 0.380952, count: 21 -Region 23 Avg IOU: 0.625785, Class: 0.187647, Obj: 0.101390, No Obj: 0.000166, .5R: 0.842105, .75R: 0.105263, count: 19 -Region 16 Avg IOU: 0.785216, Class: 0.274310, Obj: 0.196693, No Obj: 0.000987, .5R: 0.909091, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.688783, Class: 0.175299, Obj: 0.103466, No Obj: 0.000207, .5R: 1.000000, .75R: 0.181818, count: 11 -Region 16 Avg IOU: 0.635665, Class: 0.217856, Obj: 0.313640, No Obj: 0.001493, .5R: 0.727273, .75R: 0.227273, count: 22 -Region 23 Avg IOU: 0.621303, Class: 0.211795, Obj: 0.078183, No Obj: 0.000386, .5R: 0.931035, .75R: 0.034483, count: 29 - - 1379: 3.045671, 2.948713 avg loss, 0.001000 rate, 2.946231 seconds, 176512 images -Loaded: 0.000074 seconds -Region 16 Avg IOU: 0.716991, Class: 0.218289, Obj: 0.344364, No Obj: 0.002036, .5R: 0.896552, .75R: 0.413793, count: 29 -Region 23 Avg IOU: 0.725115, Class: 0.214656, Obj: 0.098989, No Obj: 0.000202, .5R: 0.937500, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.690753, Class: 0.228105, Obj: 0.355194, No Obj: 0.000906, .5R: 0.833333, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.742949, Class: 0.198603, Obj: 0.191636, No Obj: 0.000217, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.670691, Class: 0.154941, Obj: 0.366418, No Obj: 0.002080, .5R: 0.920000, .75R: 0.360000, count: 25 -Region 23 Avg IOU: 0.650273, Class: 0.211045, Obj: 0.106337, No Obj: 0.000285, .5R: 0.789474, .75R: 0.263158, count: 19 -Region 16 Avg IOU: 0.714711, Class: 0.216152, Obj: 0.368351, No Obj: 0.002129, .5R: 0.960000, .75R: 0.320000, count: 25 -Region 23 Avg IOU: 0.613485, Class: 0.180200, Obj: 0.072305, No Obj: 0.000295, .5R: 0.806452, .75R: 0.129032, count: 31 -Region 16 Avg IOU: 0.741015, Class: 0.229797, Obj: 0.383171, No Obj: 0.002104, .5R: 1.000000, .75R: 0.454545, count: 22 -Region 23 Avg IOU: 0.701233, Class: 0.227039, Obj: 0.238335, No Obj: 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Avg IOU: 0.687174, Class: 0.190240, Obj: 0.214230, No Obj: 0.001193, .5R: 0.869565, .75R: 0.391304, count: 23 -Region 23 Avg IOU: 0.686697, Class: 0.201822, Obj: 0.098350, No Obj: 0.000166, .5R: 1.000000, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.732761, Class: 0.184708, Obj: 0.307445, No Obj: 0.001807, .5R: 1.000000, .75R: 0.433333, count: 30 -Region 23 Avg IOU: 0.631970, Class: 0.141978, Obj: 0.151584, No Obj: 0.000291, .5R: 0.869565, .75R: 0.217391, count: 23 -Region 16 Avg IOU: 0.715549, Class: 0.169829, Obj: 0.290296, No Obj: 0.001304, .5R: 0.920000, .75R: 0.400000, count: 25 -Region 23 Avg IOU: 0.669459, Class: 0.150596, Obj: 0.131414, No Obj: 0.000168, .5R: 0.950000, .75R: 0.300000, count: 20 -Region 16 Avg IOU: 0.764309, Class: 0.145086, Obj: 0.390191, No Obj: 0.000988, .5R: 1.000000, .75R: 0.687500, count: 16 -Region 23 Avg IOU: 0.728250, Class: 0.135589, Obj: 0.138650, No Obj: 0.000202, .5R: 1.000000, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.701318, Class: 0.215447, Obj: 0.285007, No Obj: 0.001390, .5R: 1.000000, .75R: 0.346154, count: 26 -Region 23 Avg IOU: 0.636691, Class: 0.167579, Obj: 0.065106, No Obj: 0.000205, .5R: 0.764706, .75R: 0.235294, count: 17 -Region 16 Avg IOU: 0.737148, Class: 0.169820, Obj: 0.216951, No Obj: 0.001327, .5R: 0.960000, .75R: 0.480000, count: 25 -Region 23 Avg IOU: 0.652232, Class: 0.157518, Obj: 0.179679, No Obj: 0.000253, .5R: 0.823529, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.746288, Class: 0.252317, Obj: 0.331581, No Obj: 0.001303, .5R: 0.888889, .75R: 0.666667, count: 18 -Region 23 Avg IOU: 0.598080, Class: 0.202349, Obj: 0.206400, No Obj: 0.000132, .5R: 0.800000, .75R: 0.100000, count: 10 -Region 16 Avg IOU: 0.774982, Class: 0.209454, Obj: 0.462338, No Obj: 0.001164, .5R: 0.941176, .75R: 0.764706, count: 17 -Region 23 Avg IOU: 0.675657, Class: 0.150269, Obj: 0.129923, No Obj: 0.000267, .5R: 0.880000, .75R: 0.360000, count: 25 - - 1381: 3.108776, 3.007490 avg loss, 0.001000 rate, 2.860828 seconds, 176768 images -Loaded: 0.000072 seconds -Region 16 Avg IOU: 0.706637, Class: 0.206045, Obj: 0.245593, No Obj: 0.001013, .5R: 0.956522, .75R: 0.391304, count: 23 -Region 23 Avg IOU: 0.690820, Class: 0.222505, Obj: 0.144637, No Obj: 0.000210, .5R: 0.882353, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.717923, Class: 0.172936, Obj: 0.216565, No Obj: 0.001009, .5R: 0.916667, .75R: 0.416667, count: 24 -Region 23 Avg IOU: 0.652569, Class: 0.108171, Obj: 0.105939, No Obj: 0.000245, .5R: 0.880000, .75R: 0.240000, count: 25 -Region 16 Avg IOU: 0.675822, Class: 0.186627, Obj: 0.154771, No Obj: 0.001470, .5R: 0.897436, .75R: 0.282051, count: 39 -Region 23 Avg IOU: 0.667147, Class: 0.238066, Obj: 0.107993, No Obj: 0.000352, .5R: 0.842105, .75R: 0.263158, count: 19 -Region 16 Avg IOU: 0.716457, Class: 0.175945, Obj: 0.242524, No Obj: 0.001018, .5R: 0.961538, .75R: 0.500000, count: 26 -Region 23 Avg IOU: 0.568175, Class: 0.135954, Obj: 0.091904, No Obj: 0.000200, .5R: 0.733333, .75R: 0.000000, count: 15 -Region 16 Avg IOU: 0.749267, Class: 0.269300, Obj: 0.294187, No Obj: 0.001024, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.631809, Class: 0.107661, Obj: 0.185198, No Obj: 0.000274, .5R: 0.880000, .75R: 0.120000, count: 25 -Region 16 Avg IOU: 0.769886, Class: 0.254272, Obj: 0.427083, No Obj: 0.000825, .5R: 1.000000, .75R: 0.722222, count: 18 -Region 23 Avg IOU: 0.608350, Class: 0.203015, Obj: 0.174914, No Obj: 0.000185, .5R: 0.800000, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.746988, Class: 0.202018, Obj: 0.205850, No Obj: 0.000887, .5R: 1.000000, .75R: 0.476190, count: 21 -Region 23 Avg IOU: 0.650007, Class: 0.233998, Obj: 0.119750, No Obj: 0.000214, .5R: 0.842105, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.722601, Class: 0.216326, Obj: 0.197724, No Obj: 0.000806, .5R: 0.962963, .75R: 0.518519, count: 27 -Region 23 Avg IOU: 0.614415, Class: 0.128804, Obj: 0.041403, No Obj: 0.000117, .5R: 0.846154, .75R: 0.153846, count: 13 - - 1382: 3.268378, 3.033579 avg loss, 0.001000 rate, 3.526412 seconds, 176896 images -Loaded: 0.000071 seconds -Region 16 Avg IOU: 0.716561, Class: 0.171461, Obj: 0.201496, No Obj: 0.001123, .5R: 0.962963, .75R: 0.370370, count: 27 -Region 23 Avg IOU: 0.624214, Class: 0.214897, Obj: 0.196944, No Obj: 0.000280, .5R: 0.920000, .75R: 0.160000, count: 25 -Region 16 Avg IOU: 0.730400, Class: 0.199768, Obj: 0.170249, No Obj: 0.000859, .5R: 0.952381, .75R: 0.476190, count: 21 -Region 23 Avg IOU: 0.595005, Class: 0.209887, Obj: 0.131510, No Obj: 0.000165, .5R: 0.769231, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.734233, Class: 0.199405, Obj: 0.255923, No Obj: 0.001077, .5R: 0.965517, .75R: 0.448276, count: 29 -Region 23 Avg IOU: 0.677377, Class: 0.173108, Obj: 0.181337, No Obj: 0.000196, .5R: 1.000000, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.764721, Class: 0.195774, Obj: 0.347528, No Obj: 0.001026, .5R: 1.000000, .75R: 0.600000, count: 25 -Region 23 Avg IOU: 0.686454, Class: 0.156311, Obj: 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13 -Region 16 Avg IOU: 0.732512, Class: 0.206296, Obj: 0.418248, No Obj: 0.001167, .5R: 0.950000, .75R: 0.500000, count: 20 -Region 23 Avg IOU: 0.677551, Class: 0.187223, Obj: 0.139004, No Obj: 0.000172, .5R: 1.000000, .75R: 0.187500, count: 16 - - 1387: 2.871827, 2.990375 avg loss, 0.001000 rate, 3.164749 seconds, 177536 images -Loaded: 0.000089 seconds -Region 16 Avg IOU: 0.703457, Class: 0.179234, Obj: 0.196055, No Obj: 0.000852, .5R: 0.965517, .75R: 0.413793, count: 29 -Region 23 Avg IOU: 0.670315, Class: 0.216764, Obj: 0.192097, No Obj: 0.000208, .5R: 0.928571, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.725749, Class: 0.217687, Obj: 0.163873, No Obj: 0.000678, .5R: 0.961538, .75R: 0.461538, count: 26 -Region 23 Avg IOU: 0.671466, Class: 0.165762, Obj: 0.097970, No Obj: 0.000146, .5R: 0.923077, .75R: 0.230769, count: 13 -Region 16 Avg IOU: 0.645054, Class: 0.202712, Obj: 0.109331, No Obj: 0.001056, .5R: 0.884615, .75R: 0.269231, count: 26 -Region 23 Avg IOU: 0.649024, Class: 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count: 13 -Region 23 Avg IOU: 0.662444, Class: 0.156063, Obj: 0.191504, No Obj: 0.000189, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.733195, Class: 0.165782, Obj: 0.202369, No Obj: 0.001030, .5R: 0.964286, .75R: 0.500000, count: 28 -Region 23 Avg IOU: 0.701417, Class: 0.175054, Obj: 0.145468, No Obj: 0.000266, .5R: 0.958333, .75R: 0.333333, count: 24 -Region 16 Avg IOU: 0.686332, Class: 0.192178, Obj: 0.163375, No Obj: 0.001259, .5R: 0.951219, .75R: 0.317073, count: 41 -Region 23 Avg IOU: 0.691275, Class: 0.161129, Obj: 0.116745, No Obj: 0.000248, .5R: 0.961538, .75R: 0.307692, count: 26 -Region 16 Avg IOU: 0.750163, Class: 0.211293, Obj: 0.176142, No Obj: 0.000745, .5R: 0.937500, .75R: 0.562500, count: 16 -Region 23 Avg IOU: 0.668876, Class: 0.207805, Obj: 0.186623, No Obj: 0.000242, .5R: 0.937500, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.719248, Class: 0.249185, Obj: 0.196589, No Obj: 0.000678, .5R: 1.000000, .75R: 0.411765, count: 17 -Region 23 Avg IOU: 0.631731, Class: 0.229477, Obj: 0.036595, No Obj: 0.000116, .5R: 0.909091, .75R: 0.000000, count: 11 -Region 16 Avg IOU: 0.686260, Class: 0.161542, Obj: 0.195107, No Obj: 0.000896, .5R: 1.000000, .75R: 0.375000, count: 24 -Region 23 Avg IOU: 0.666890, Class: 0.174625, Obj: 0.129162, No Obj: 0.000205, .5R: 0.888889, .75R: 0.185185, count: 27 - - 1389: 3.381953, 3.011285 avg loss, 0.001000 rate, 3.339401 seconds, 177792 images -Loaded: 0.000067 seconds -Region 16 Avg IOU: 0.744846, Class: 0.204503, Obj: 0.198641, No Obj: 0.000933, .5R: 1.000000, .75R: 0.500000, count: 30 -Region 23 Avg IOU: 0.696575, Class: 0.190200, Obj: 0.193080, No Obj: 0.000373, .5R: 0.962963, .75R: 0.407407, count: 27 -Region 16 Avg IOU: 0.731049, Class: 0.228244, Obj: 0.192809, No Obj: 0.000981, .5R: 0.950000, .75R: 0.450000, count: 20 -Region 23 Avg IOU: 0.599836, Class: 0.231624, Obj: 0.088357, No Obj: 0.000179, .5R: 0.923077, .75R: 0.076923, count: 13 -Region 16 Avg IOU: 0.757361, Class: 0.249038, Obj: 0.309732, No Obj: 0.001133, .5R: 1.000000, .75R: 0.571429, count: 21 -Region 23 Avg IOU: 0.659350, Class: 0.147884, Obj: 0.133941, No Obj: 0.000169, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.788971, Class: 0.185327, Obj: 0.301359, No Obj: 0.000645, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.648757, Class: 0.152982, Obj: 0.131457, No Obj: 0.000207, .5R: 1.000000, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.747442, Class: 0.171079, Obj: 0.314197, No Obj: 0.000759, .5R: 1.000000, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.631045, Class: 0.140951, Obj: 0.074343, No Obj: 0.000133, .5R: 0.916667, .75R: 0.125000, count: 24 -Region 16 Avg IOU: 0.716757, Class: 0.204533, Obj: 0.202712, No Obj: 0.001054, .5R: 0.884615, .75R: 0.461538, count: 26 -Region 23 Avg IOU: 0.654973, Class: 0.231037, Obj: 0.177979, No Obj: 0.000255, .5R: 0.875000, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.767899, Class: 0.154495, Obj: 0.334855, No Obj: 0.000906, .5R: 1.000000, 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25 -Region 16 Avg IOU: 0.717611, Class: 0.213650, Obj: 0.261247, No Obj: 0.001324, .5R: 1.000000, .75R: 0.458333, count: 24 -Region 23 Avg IOU: 0.684199, Class: 0.186069, Obj: 0.142919, No Obj: 0.000371, .5R: 1.000000, .75R: 0.304348, count: 23 -Region 16 Avg IOU: 0.738705, Class: 0.218625, Obj: 0.265639, No Obj: 0.001019, .5R: 1.000000, .75R: 0.476190, count: 21 -Region 23 Avg IOU: 0.619280, Class: 0.173220, Obj: 0.071155, No Obj: 0.000175, .5R: 0.900000, .75R: 0.050000, count: 20 -Region 16 Avg IOU: 0.700104, Class: 0.220592, Obj: 0.263007, No Obj: 0.001398, .5R: 0.772727, .75R: 0.363636, count: 22 -Region 23 Avg IOU: 0.658286, Class: 0.235132, Obj: 0.276148, No Obj: 0.000297, .5R: 0.846154, .75R: 0.230769, count: 13 - - 1395: 3.310906, 2.962028 avg loss, 0.001000 rate, 3.161817 seconds, 178560 images -Loaded: 0.000076 seconds -Region 16 Avg IOU: 0.771969, Class: 0.264616, Obj: 0.351048, No Obj: 0.001070, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.655248, Class: 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Obj: 0.227833, No Obj: 0.001313, .5R: 0.892857, .75R: 0.464286, count: 28 -Region 23 Avg IOU: 0.631699, Class: 0.193077, Obj: 0.146242, No Obj: 0.000234, .5R: 0.857143, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.644756, Class: 0.229110, Obj: 0.166073, No Obj: 0.001033, .5R: 0.764706, .75R: 0.235294, count: 17 -Region 23 Avg IOU: 0.623920, Class: 0.232063, Obj: 0.111421, No Obj: 0.000279, .5R: 0.818182, .75R: 0.136364, count: 22 -Region 16 Avg IOU: 0.711592, Class: 0.198764, Obj: 0.221086, No Obj: 0.001304, .5R: 0.916667, .75R: 0.458333, count: 24 -Region 23 Avg IOU: 0.637232, Class: 0.193191, Obj: 0.143295, No Obj: 0.000372, .5R: 0.848485, .75R: 0.212121, count: 33 -Region 16 Avg IOU: 0.772884, Class: 0.208151, Obj: 0.385281, No Obj: 0.001389, .5R: 0.960000, .75R: 0.720000, count: 25 -Region 23 Avg IOU: 0.638146, Class: 0.142037, Obj: 0.246449, No Obj: 0.000293, .5R: 0.800000, .75R: 0.333333, count: 15 - - 1399: 3.483891, 2.970906 avg loss, 0.001000 rate, 2.680879 seconds, 179072 images -Loaded: 0.000061 seconds -Region 16 Avg IOU: 0.673705, Class: 0.222593, Obj: 0.277668, No Obj: 0.001375, .5R: 0.777778, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.703250, Class: 0.192517, Obj: 0.163796, No Obj: 0.000399, .5R: 0.947368, .75R: 0.421053, count: 19 -Region 16 Avg IOU: 0.709513, Class: 0.171522, Obj: 0.303158, No Obj: 0.001538, .5R: 0.833333, .75R: 0.583333, count: 24 -Region 23 Avg IOU: 0.641217, Class: 0.156994, Obj: 0.190329, No Obj: 0.000271, .5R: 0.882353, .75R: 0.235294, count: 17 -Region 16 Avg IOU: 0.724209, Class: 0.172176, Obj: 0.239312, No Obj: 0.001525, .5R: 0.933333, .75R: 0.566667, count: 30 -Region 23 Avg IOU: 0.629002, Class: 0.156759, Obj: 0.099742, No Obj: 0.000235, .5R: 0.833333, .75R: 0.111111, count: 18 -Region 16 Avg IOU: 0.694479, Class: 0.207451,Saving weights to ./attribute/7354_26298/attribute_33652_s_tyv3_9c5_1400.weights - Obj: 0.216351, No Obj: 0.001537, .5R: 0.960000, .75R: 0.280000, count: 25 -Region 23 Avg IOU: 0.615112, Class: 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0.894737, .75R: 0.368421, count: 19 -Region 23 Avg IOU: 0.609346, Class: 0.202051, Obj: 0.137842, No Obj: 0.000179, .5R: 0.900000, .75R: 0.100000, count: 20 -Region 16 Avg IOU: 0.701941, Class: 0.171814, Obj: 0.268556, No Obj: 0.001605, .5R: 0.863636, .75R: 0.363636, count: 22 -Region 23 Avg IOU: 0.631083, Class: 0.243041, Obj: 0.095780, No Obj: 0.000253, .5R: 0.913043, .75R: 0.173913, count: 23 -Region 16 Avg IOU: 0.748956, Class: 0.184661, Obj: 0.344273, No Obj: 0.001151, .5R: 0.888889, .75R: 0.722222, count: 18 -Region 23 Avg IOU: 0.635629, Class: 0.163742, Obj: 0.146425, No Obj: 0.000189, .5R: 0.909091, .75R: 0.136364, count: 22 -Region 16 Avg IOU: 0.729545, Class: 0.170015, Obj: 0.364363, No Obj: 0.001300, .5R: 0.952381, .75R: 0.428571, count: 21 -Region 23 Avg IOU: 0.662598, Class: 0.189604, Obj: 0.119927, No Obj: 0.000252, .5R: 0.961538, .75R: 0.192308, count: 26 -Region 16 Avg IOU: 0.757799, Class: 0.203336, Obj: 0.347030, No Obj: 0.001164, .5R: 0.941176, .75R: 0.705882, count: 17 -Region 23 Avg IOU: 0.695253, Class: 0.158084, Obj: 0.118825, No Obj: 0.000282, .5R: 0.968750, .75R: 0.406250, count: 32 -Region 16 Avg IOU: 0.747965, Class: 0.241468, Obj: 0.319874, No Obj: 0.001462, .5R: 0.916667, .75R: 0.666667, count: 24 -Region 23 Avg IOU: 0.628740, Class: 0.187265, Obj: 0.142911, No Obj: 0.000246, .5R: 0.869565, .75R: 0.086957, count: 23 -Region 16 Avg IOU: 0.644136, Class: 0.199775, Obj: 0.159596, No Obj: 0.001323, .5R: 0.923077, .75R: 0.230769, count: 26 -Region 23 Avg IOU: 0.618418, Class: 0.192320, Obj: 0.039626, No Obj: 0.000129, .5R: 0.894737, .75R: 0.052632, count: 19 -Region 16 Avg IOU: 0.685923, Class: 0.200754, Obj: 0.217180, No Obj: 0.001122, .5R: 0.809524, .75R: 0.428571, count: 21 -Region 23 Avg IOU: 0.621644, Class: 0.171828, Obj: 0.084101, No Obj: 0.000131, .5R: 0.812500, .75R: 0.125000, count: 16 - - 1430: 3.366028, 3.022773 avg loss, 0.001000 rate, 2.125385 seconds, 183040 images -Resizing -512 x 512 - try to allocate workspace = 1145044993 * sizeof(float), CUDA allocate done! -Loaded: 0.429392 seconds -Region 16 Avg IOU: 0.709455, Class: 0.194750, Obj: 0.325717, No Obj: 0.001509, .5R: 0.833333, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.634374, Class: 0.100946, Obj: 0.120472, No Obj: 0.000264, .5R: 1.000000, .75R: 0.111111, count: 18 -Region 16 Avg IOU: 0.705658, Class: 0.234570, Obj: 0.464949, No Obj: 0.001656, .5R: 1.000000, .75R: 0.200000, count: 10 -Region 23 Avg IOU: 0.697055, Class: 0.177457, Obj: 0.079388, No Obj: 0.000262, .5R: 0.952381, .75R: 0.333333, count: 21 -Region 16 Avg IOU: 0.797549, Class: 0.259406, Obj: 0.409426, No Obj: 0.001692, .5R: 1.000000, .75R: 0.833333, count: 12 -Region 23 Avg IOU: 0.656262, Class: 0.188047, Obj: 0.132016, No Obj: 0.000442, .5R: 0.840000, .75R: 0.320000, count: 25 -Region 16 Avg IOU: 0.760759, Class: 0.240580, Obj: 0.270538, No Obj: 0.001383, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.694309, Class: 0.215304, Obj: 0.086526, No Obj: 0.000278, .5R: 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19 -Region 16 Avg IOU: 0.702881, Class: 0.208289, Obj: 0.362085, No Obj: 0.001772, .5R: 0.928571, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.656826, Class: 0.163585, Obj: 0.326887, No Obj: 0.001005, .5R: 0.882353, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.729471, Class: 0.204510, Obj: 0.258503, No Obj: 0.001479, .5R: 1.000000, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 0.656622, Class: 0.181069, Obj: 0.160087, No Obj: 0.000640, .5R: 0.888889, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.784258, Class: 0.194427, Obj: 0.204301, No Obj: 0.000837, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.654395, Class: 0.176765, Obj: 0.254961, No Obj: 0.000761, .5R: 0.928571, .75R: 0.285714, count: 14 - - 1444: 2.067760, 2.542150 avg loss, 0.001000 rate, 0.777067 seconds, 184832 images -Loaded: 0.000075 seconds -Region 16 Avg IOU: 0.767978, Class: 0.213377, Obj: 0.402064, No Obj: 0.001172, .5R: 1.000000, .75R: 0.777778, count: 9 -Region 23 Avg IOU: 0.716669, Class: 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count: 3 -Region 23 Avg IOU: 0.625286, Class: 0.158001, Obj: 0.164417, No Obj: 0.000380, .5R: 0.923077, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.711053, Class: 0.273586, Obj: 0.434021, No Obj: 0.001783, .5R: 1.000000, .75R: 0.272727, count: 11 -Region 23 Avg IOU: 0.677010, Class: 0.222962, Obj: 0.122720, No Obj: 0.000490, .5R: 0.888889, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.813078, Class: 0.293353, Obj: 0.447870, No Obj: 0.001147, .5R: 1.000000, .75R: 1.000000, count: 6 -Region 23 Avg IOU: 0.665717, Class: 0.204485, Obj: 0.181321, No Obj: 0.000429, .5R: 0.875000, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.776844, Class: 0.217931, Obj: 0.289518, No Obj: 0.002111, .5R: 1.000000, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.638839, Class: 0.237155, Obj: 0.145351, No Obj: 0.000568, .5R: 0.920000, .75R: 0.200000, count: 25 -Region 16 Avg IOU: 0.719811, Class: 0.190516, Obj: 0.237939, No Obj: 0.001290, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 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No Obj: 0.001956, .5R: 0.909091, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.612049, Class: 0.186234, Obj: 0.077211, No Obj: 0.000373, .5R: 0.900000, .75R: 0.100000, count: 20 -Region 16 Avg IOU: 0.730592, Class: 0.252414, Obj: 0.437792, No Obj: 0.001207, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.671007, Class: 0.186582, Obj: 0.145197, No Obj: 0.000412, .5R: 0.925926, .75R: 0.296296, count: 27 -Region 16 Avg IOU: 0.759153, Class: 0.230093, Obj: 0.339166, No Obj: 0.001855, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.715220, Class: 0.205554, Obj: 0.099870, No Obj: 0.000341, .5R: 0.944444, .75R: 0.500000, count: 18 -Region 16 Avg IOU: 0.811739, Class: 0.310822, Obj: 0.415882, No Obj: 0.001155, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.671956, Class: 0.204029, Obj: 0.114324, No Obj: 0.000389, .5R: 0.947368, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.720753, Class: 0.192357, Obj: 0.296656, No Obj: 0.001571, .5R: 0.916667, 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IOU: 0.751484, Class: 0.285044, Obj: 0.595347, No Obj: 0.002380, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.632796, Class: 0.200801, Obj: 0.075166, No Obj: 0.000376, .5R: 0.852941, .75R: 0.147059, count: 34 -Region 16 Avg IOU: 0.730143, Class: 0.253324, Obj: 0.341316, No Obj: 0.001835, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.632040, Class: 0.213194, Obj: 0.079023, No Obj: 0.000318, .5R: 0.777778, .75R: 0.111111, count: 9 -Region 16 Avg IOU: 0.823542, Class: 0.308512, Obj: 0.465035, No Obj: 0.001046, .5R: 1.000000, .75R: 0.857143, count: 7 -Region 23 Avg IOU: 0.656420, Class: 0.182812, Obj: 0.119675, No Obj: 0.000250, .5R: 0.833333, .75R: 0.250000, count: 12 -Region 16 Avg IOU: 0.821607, Class: 0.299431, Obj: 0.615213, No Obj: 0.002315, .5R: 1.000000, .75R: 0.818182, count: 11 -Region 23 Avg IOU: 0.646170, Class: 0.190879, Obj: 0.139032, No Obj: 0.000431, .5R: 0.896552, .75R: 0.275862, count: 29 -Region 16 Avg IOU: 0.723517, Class: 0.163320, Obj: 0.454833, No Obj: 0.002434, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.641053, Class: 0.198854, Obj: 0.107639, No Obj: 0.000519, .5R: 0.923077, .75R: 0.115385, count: 26 -Region 16 Avg IOU: 0.685609, Class: 0.232050, Obj: 0.422443, No Obj: 0.002661, .5R: 0.833333, .75R: 0.500000, count: 6 -Region 23 Avg IOU: 0.600553, Class: 0.136601, Obj: 0.132352, No Obj: 0.000432, .5R: 0.916667, .75R: 0.083333, count: 12 -Region 16 Avg IOU: 0.738813, Class: 0.230458, Obj: 0.416384, No Obj: 0.002163, .5R: 1.000000, .75R: 0.357143, count: 14 -Region 23 Avg IOU: 0.679864, Class: 0.209857, Obj: 0.087619, No Obj: 0.000232, .5R: 1.000000, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.806826, Class: 0.313080, Obj: 0.518276, No Obj: 0.002045, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.696483, Class: 0.234894, Obj: 0.125258, No Obj: 0.000283, .5R: 1.000000, .75R: 0.333333, count: 12 - - 1448: 1.965654, 2.364027 avg loss, 0.001000 rate, 0.777737 seconds, 185344 images -Loaded: 0.000098 seconds -Region 16 Avg IOU: 0.806073, Class: 0.200391, Obj: 0.284614, No Obj: 0.000968, .5R: 1.000000, .75R: 0.833333, count: 6 -Region 23 Avg IOU: 0.686370, Class: 0.217388, Obj: 0.163034, No Obj: 0.000404, .5R: 0.937500, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.749405, Class: 0.196320, Obj: 0.555129, No Obj: 0.002134, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.673868, Class: 0.230751, Obj: 0.157721, No Obj: 0.000710, .5R: 1.000000, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.772889, Class: 0.284345, Obj: 0.462687, No Obj: 0.001236, .5R: 0.857143, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.632106, Class: 0.156467, Obj: 0.175730, No Obj: 0.000441, .5R: 0.733333, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.746035, Class: 0.260844, Obj: 0.289505, No Obj: 0.001736, .5R: 0.916667, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.660297, Class: 0.212796, Obj: 0.152923, No Obj: 0.000667, .5R: 0.814815, .75R: 0.222222, count: 27 -Region 16 Avg IOU: 0.727161, Class: 0.226146, Obj: 0.450340, No Obj: 0.002107, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.639210, Class: 0.189334, Obj: 0.200183, No Obj: 0.000925, .5R: 0.941176, .75R: 0.117647, count: 17 -Region 16 Avg IOU: 0.752305, Class: 0.207614, Obj: 0.302783, No Obj: 0.002036, .5R: 1.000000, .75R: 0.562500, count: 16 -Region 23 Avg IOU: 0.672398, Class: 0.184104, Obj: 0.108122, No Obj: 0.000527, .5R: 0.928571, .75R: 0.250000, count: 28 -Region 16 Avg IOU: 0.711763, Class: 0.241877, Obj: 0.471944, No Obj: 0.002077, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 23 Avg IOU: 0.682578, Class: 0.211029, Obj: 0.133862, No Obj: 0.000690, .5R: 0.964286, .75R: 0.142857, count: 28 -Region 16 Avg IOU: 0.790548, Class: 0.253336, Obj: 0.376934, No Obj: 0.002490, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.644501, Class: 0.235064, Obj: 0.157787, No Obj: 0.000614, .5R: 0.904762, .75R: 0.285714, count: 21 - - 1449: 2.271914, 2.354816 avg 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0.291667, count: 24 - - 1450: 2.155574, 2.334892 avg loss, 0.001000 rate, 0.791333 seconds, 185600 images -Resizing -384 x 384 - try to allocate workspace = 216268801 * sizeof(float), CUDA allocate done! -Loaded: 0.465129 seconds -Region 16 Avg IOU: 0.827575, Class: 0.258734, Obj: 0.339658, No Obj: 0.001193, .5R: 1.000000, .75R: 1.000000, count: 4 -Region 23 Avg IOU: 0.639924, Class: 0.243096, Obj: 0.204792, No Obj: 0.000798, .5R: 0.857143, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.831447, Class: 0.269002, Obj: 0.476557, No Obj: 0.002179, .5R: 1.000000, .75R: 0.888889, count: 9 -Region 23 Avg IOU: 0.700586, Class: 0.214251, Obj: 0.272156, No Obj: 0.001127, .5R: 1.000000, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.846296, Class: 0.122215, Obj: 0.374691, No Obj: 0.001226, .5R: 1.000000, .75R: 0.833333, count: 6 -Region 23 Avg IOU: 0.617827, Class: 0.184690, Obj: 0.213033, No Obj: 0.000863, .5R: 0.769231, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.804704, Class: 0.145353, 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Class: 0.208357, Obj: 0.121683, No Obj: 0.000538, .5R: 0.785714, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.791104, Class: 0.176911, Obj: 0.351134, No Obj: 0.001507, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.711355, Class: 0.195607, Obj: 0.117456, No Obj: 0.000473, .5R: 1.000000, .75R: 0.333333, count: 21 -Region 16 Avg IOU: 0.794763, Class: 0.211493, Obj: 0.307696, No Obj: 0.002865, .5R: 1.000000, .75R: 0.733333, count: 15 -Region 23 Avg IOU: 0.718548, Class: 0.119009, Obj: 0.143562, No Obj: 0.000737, .5R: 1.000000, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.741333, Class: 0.227119, Obj: 0.344320, No Obj: 0.002254, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.702418, Class: 0.204803, Obj: 0.107042, No Obj: 0.000609, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 16 Avg IOU: 0.760484, Class: 0.165541, Obj: 0.312055, No Obj: 0.002085, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.626942, Class: 0.195895, Obj: 0.168698, No 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count: 26 -Region 16 Avg IOU: 0.693657, Class: 0.129098, Obj: 0.129681, No Obj: 0.001404, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.669608, Class: 0.209422, Obj: 0.152573, No Obj: 0.000736, .5R: 0.850000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.799624, Class: 0.196896, Obj: 0.370009, No Obj: 0.001623, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.651849, Class: 0.177238, Obj: 0.228504, No Obj: 0.000623, .5R: 0.833333, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.798375, Class: 0.259435, Obj: 0.189175, No Obj: 0.000938, .5R: 1.000000, .75R: 1.000000, count: 5 -Region 23 Avg IOU: 0.667956, Class: 0.233957, Obj: 0.177369, No Obj: 0.000798, .5R: 0.875000, .75R: 0.291667, count: 24 - - 1461: 2.162258, 2.120329 avg loss, 0.001000 rate, 0.637479 seconds, 187008 images -Loaded: 0.026645 seconds -Region 16 Avg IOU: 0.739016, Class: 0.242582, Obj: 0.345238, No Obj: 0.001684, .5R: 1.000000, .75R: 0.500000, count: 6 -Region 23 Avg IOU: 0.626902, 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0.777778, count: 9 -Region 23 Avg IOU: 0.708497, Class: 0.151557, Obj: 0.273306, No Obj: 0.000884, .5R: 1.000000, .75R: 0.428571, count: 21 -Region 16 Avg IOU: 0.746369, Class: 0.186801, Obj: 0.355547, No Obj: 0.001413, .5R: 0.900000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.657486, Class: 0.176811, Obj: 0.196471, No Obj: 0.000873, .5R: 0.875000, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.795073, Class: 0.163995, Obj: 0.415462, No Obj: 0.000955, .5R: 1.000000, .75R: 0.600000, count: 5 -Region 23 Avg IOU: 0.652973, Class: 0.133319, Obj: 0.201367, No Obj: 0.000651, .5R: 1.000000, .75R: 0.200000, count: 10 -Region 16 Avg IOU: 0.721436, Class: 0.119417, Obj: 0.283585, No Obj: 0.001166, .5R: 1.000000, .75R: 0.375000, count: 8 -Region 23 Avg IOU: 0.686391, Class: 0.177462, Obj: 0.300897, No Obj: 0.001198, .5R: 0.954545, .75R: 0.318182, count: 22 -Region 16 Avg IOU: 0.785279, Class: 0.155262, Obj: 0.511747, No Obj: 0.001198, .5R: 1.000000, .75R: 0.857143, count: 7 -Region 23 Avg 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16 Avg IOU: 0.742502, Class: 0.180100, Obj: 0.188813, No Obj: 0.000829, .5R: 1.000000, .75R: 0.428571, count: 7 -Region 23 Avg IOU: 0.639589, Class: 0.182949, Obj: 0.103593, No Obj: 0.000242, .5R: 0.888889, .75R: 0.333333, count: 9 -Region 16 Avg IOU: 0.760210, Class: 0.296946, Obj: 0.391874, No Obj: 0.002073, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.611613, Class: 0.202928, Obj: 0.298173, No Obj: 0.000645, .5R: 0.769231, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.812772, Class: 0.283318, Obj: 0.354842, No Obj: 0.001460, .5R: 1.000000, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.732206, Class: 0.206819, Obj: 0.297679, No Obj: 0.000758, .5R: 0.882353, .75R: 0.529412, count: 17 -Region 16 Avg IOU: 0.749690, Class: 0.252902, Obj: 0.199283, No Obj: 0.001164, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.673651, Class: 0.204217, Obj: 0.156314, No Obj: 0.000841, .5R: 0.960000, .75R: 0.240000, count: 25 -Region 16 Avg IOU: 0.779481, Class: 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187520 images -Loaded: 0.000067 seconds -Region 16 Avg IOU: 0.876802, Class: 0.311328, Obj: 0.421326, No Obj: 0.001168, .5R: 1.000000, .75R: 1.000000, count: 5 -Region 23 Avg IOU: 0.638459, Class: 0.189044, Obj: 0.194840, No Obj: 0.000856, .5R: 0.916667, .75R: 0.166667, count: 24 -Region 16 Avg IOU: 0.746275, Class: 0.215410, Obj: 0.347269, No Obj: 0.001355, .5R: 0.909091, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.680364, Class: 0.182216, Obj: 0.156370, No Obj: 0.000768, .5R: 0.961538, .75R: 0.346154, count: 26 -Region 16 Avg IOU: 0.799030, Class: 0.268507, Obj: 0.356245, No Obj: 0.000568, .5R: 1.000000, .75R: 0.666667, count: 3 -Region 23 Avg IOU: 0.698481, Class: 0.263197, Obj: 0.247447, No Obj: 0.000287, .5R: 1.000000, .75R: 0.333333, count: 6 -Region 16 Avg IOU: 0.752827, Class: 0.277717, Obj: 0.491030, No Obj: 0.001994, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.734133, Class: 0.174105, Obj: 0.155742, No Obj: 0.000563, .5R: 1.000000, .75R: 0.500000, count: 14 -Region 16 Avg IOU: 0.715243, Class: 0.165453, Obj: 0.185684, No Obj: 0.000983, .5R: 1.000000, .75R: 0.400000, count: 5 -Region 23 Avg IOU: 0.664636, Class: 0.147890, Obj: 0.241635, No Obj: 0.000660, .5R: 0.875000, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.751743, Class: 0.251946, Obj: 0.334121, No Obj: 0.002076, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.666246, Class: 0.224683, Obj: 0.140607, No Obj: 0.000489, .5R: 0.933333, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.821309, Class: 0.315363, Obj: 0.536289, No Obj: 0.001261, .5R: 1.000000, .75R: 1.000000, count: 7 -Region 23 Avg IOU: 0.718794, Class: 0.229628, Obj: 0.130848, No Obj: 0.000474, .5R: 0.928571, .75R: 0.500000, count: 14 -Region 16 Avg IOU: 0.816625, Class: 0.302376, Obj: 0.386518, No Obj: 0.001423, .5R: 1.000000, .75R: 0.857143, count: 7 -Region 23 Avg IOU: 0.642034, Class: 0.205410, Obj: 0.170699, No Obj: 0.000533, .5R: 0.857143, .75R: 0.238095, count: 21 - - 1466: 1.699975, 2.034137 avg 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Avg IOU: 0.664333, Class: 0.182706, Obj: 0.273396, No Obj: 0.000915, .5R: 0.928571, .75R: 0.214286, count: 14 - - 1469: 1.849630, 2.005062 avg loss, 0.001000 rate, 0.666719 seconds, 188032 images -Loaded: 0.000076 seconds -Region 16 Avg IOU: 0.812139, Class: 0.109811, Obj: 0.312398, No Obj: 0.001500, .5R: 1.000000, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.602474, Class: 0.160312, Obj: 0.125823, No Obj: 0.000739, .5R: 0.761905, .75R: 0.190476, count: 21 -Region 16 Avg IOU: 0.750088, Class: 0.254943, Obj: 0.431652, No Obj: 0.001234, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.637522, Class: 0.148242, Obj: 0.236147, No Obj: 0.000637, .5R: 0.875000, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.743535, Class: 0.229287, Obj: 0.410528, No Obj: 0.001961, .5R: 0.900000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.735628, Class: 0.247257, Obj: 0.184477, No Obj: 0.000479, .5R: 0.857143, .75R: 0.571429, count: 14 -Region 16 Avg IOU: 0.804426, Class: 0.336783, Obj: 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Class: 0.203087, Obj: 0.119321, No Obj: 0.000247, .5R: 0.800000, .75R: 0.200000, count: 5 -Region 16 Avg IOU: 0.749513, Class: 0.253632, Obj: 0.509275, No Obj: 0.000853, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.690916, Class: 0.173636, Obj: 0.219818, No Obj: 0.000368, .5R: 1.000000, .75R: 0.333333, count: 21 -Region 16 Avg IOU: 0.686955, Class: 0.256102, Obj: 0.148827, No Obj: 0.000841, .5R: 1.000000, .75R: 0.000000, count: 4 -Region 23 Avg IOU: 0.707191, Class: 0.177798, Obj: 0.159583, No Obj: 0.000385, .5R: 0.937500, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.783637, Class: 0.221861, Obj: 0.202556, No Obj: 0.001076, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.590603, Class: 0.204790, Obj: 0.100692, No Obj: 0.000348, .5R: 0.687500, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.712489, Class: 0.208247, Obj: 0.319944, No Obj: 0.001899, .5R: 1.000000, .75R: 0.384615, count: 13 -Region 23 Avg IOU: 0.690555, Class: 0.209524, Obj: 0.147953, No 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0.000064 seconds -Region 16 Avg IOU: 0.660620, Class: 0.244773, Obj: 0.291651, No Obj: 0.001407, .5R: 0.789474, .75R: 0.421053, count: 19 -Region 23 Avg IOU: 0.598508, Class: 0.211680, Obj: 0.088117, No Obj: 0.000225, .5R: 0.714286, .75R: 0.107143, count: 28 -Region 16 Avg IOU: 0.733783, Class: 0.219324, Obj: 0.359524, No Obj: 0.001438, .5R: 1.000000, .75R: 0.400000, count: 20 -Region 23 Avg IOU: 0.633501, Class: 0.170230, Obj: 0.110129, No Obj: 0.000197, .5R: 0.888889, .75R: 0.222222, count: 18 -Region 16 Avg IOU: 0.733852, Class: 0.242179, Obj: 0.252606, No Obj: 0.001237, .5R: 0.944444, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.609884, Class: 0.142736, Obj: 0.075854, No Obj: 0.000179, .5R: 0.833333, .75R: 0.000000, count: 18 -Region 16 Avg IOU: 0.782131, Class: 0.217815, Obj: 0.439084, No Obj: 0.001014, .5R: 1.000000, .75R: 0.769231, count: 13 -Region 23 Avg IOU: 0.642143, Class: 0.210373, Obj: 0.072076, No Obj: 0.000155, .5R: 0.750000, .75R: 0.250000, count: 24 -Region 16 Avg 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rate, 1.795974 seconds, 189696 images -Loaded: 0.000075 seconds -Region 16 Avg IOU: 0.703198, Class: 0.213349, Obj: 0.164449, No Obj: 0.000757, .5R: 0.733333, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.590338, Class: 0.209569, Obj: 0.073497, No Obj: 0.000105, .5R: 0.714286, .75R: 0.095238, count: 21 -Region 16 Avg IOU: 0.720780, Class: 0.227198, Obj: 0.301097, No Obj: 0.001567, .5R: 0.928571, .75R: 0.500000, count: 28 -Region 23 Avg IOU: 0.683645, Class: 0.223981, Obj: 0.034588, No Obj: 0.000099, .5R: 0.823529, .75R: 0.470588, count: 17 -Region 16 Avg IOU: 0.754719, Class: 0.200884, Obj: 0.254140, No Obj: 0.001434, .5R: 1.000000, .75R: 0.631579, count: 19 -Region 23 Avg IOU: 0.724217, Class: 0.174335, Obj: 0.064917, No Obj: 0.000148, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.744614, Class: 0.169182, Obj: 0.233130, No Obj: 0.001300, .5R: 0.956522, .75R: 0.565217, count: 23 -Region 23 Avg IOU: 0.677067, Class: 0.147048, Obj: 0.069119, No Obj: 0.000156, .5R: 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Class: 0.178278, Obj: 0.204408, No Obj: 0.000475, .5R: 0.894737, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.782545, Class: 0.267797, Obj: 0.398788, No Obj: 0.001150, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.624618, Class: 0.241655, Obj: 0.159666, No Obj: 0.000272, .5R: 0.875000, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.798452, Class: 0.214039, Obj: 0.515225, No Obj: 0.001407, .5R: 1.000000, .75R: 0.769231, count: 13 -Region 23 Avg IOU: 0.632079, Class: 0.213189, Obj: 0.214315, No Obj: 0.000471, .5R: 0.882353, .75R: 0.176471, count: 17 -Region 16 Avg IOU: 0.727236, Class: 0.233225, Obj: 0.365260, No Obj: 0.000985, .5R: 0.916667, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.535623, Class: 0.123459, Obj: 0.237919, No Obj: 0.000359, .5R: 0.500000, .75R: 0.083333, count: 12 -Region 16 Avg IOU: 0.716947, Class: 0.194021, Obj: 0.209478, No Obj: 0.001407, .5R: 1.000000, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.630829, Class: 0.277895, Obj: 0.219821, 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0.000068 seconds -Region 16 Avg IOU: 0.773758, Class: 0.187886, Obj: 0.250190, No Obj: 0.000902, .5R: 1.000000, .75R: 0.736842, count: 19 -Region 23 Avg IOU: 0.686357, Class: 0.158488, Obj: 0.114675, No Obj: 0.000155, .5R: 0.833333, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.775783, Class: 0.205093, Obj: 0.266377, No Obj: 0.000801, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.658915, Class: 0.193378, Obj: 0.052179, No Obj: 0.000123, .5R: 0.896552, .75R: 0.206897, count: 29 -Region 16 Avg IOU: 0.734381, Class: 0.190574, Obj: 0.209880, No Obj: 0.000996, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.672550, Class: 0.250819, Obj: 0.078022, No Obj: 0.000123, .5R: 0.916667, .75R: 0.250000, count: 12 -Region 16 Avg IOU: 0.665112, Class: 0.236647, Obj: 0.168955, No Obj: 0.000717, .5R: 0.833333, .75R: 0.166667, count: 12 -Region 23 Avg IOU: 0.646328, Class: 0.151627, Obj: 0.028535, No Obj: 0.000078, .5R: 0.833333, .75R: 0.083333, count: 12 -Region 16 Avg 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rate, 1.630954 seconds, 192000 images -Resizing -640 x 640 - try to allocate workspace = 1147404289 * sizeof(float), CUDA allocate done! -Loaded: 0.136403 seconds -Region 16 Avg IOU: 0.705111, Class: 0.250114, Obj: 0.190958, No Obj: 0.000696, .5R: 0.944444, .75R: 0.277778, count: 18 -Region 23 Avg IOU: 0.682907, Class: 0.187620, Obj: 0.076194, No Obj: 0.000122, .5R: 0.900000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.696945, Class: 0.159252, Obj: 0.193771, No Obj: 0.000816, .5R: 0.857143, .75R: 0.523810, count: 21 -Region 23 Avg IOU: 0.681945, Class: 0.122347, Obj: 0.082190, No Obj: 0.000142, .5R: 0.904762, .75R: 0.285714, count: 21 -Region 16 Avg IOU: 0.758669, Class: 0.232265, Obj: 0.184307, No Obj: 0.000872, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.699565, Class: 0.200710, Obj: 0.113101, No Obj: 0.000181, .5R: 1.000000, .75R: 0.238095, count: 21 -Region 16 Avg IOU: 0.731771, Class: 0.173417, Obj: 0.218040, No Obj: 0.000943, .5R: 1.000000, .75R: 0.416667, 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25 -Region 16 Avg IOU: 0.748598, Class: 0.286431, Obj: 0.264672, No Obj: 0.000973, .5R: 0.928571, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.702465, Class: 0.245542, Obj: 0.201572, No Obj: 0.000250, .5R: 0.923077, .75R: 0.384615, count: 13 - - 1504: 3.085031, 2.747886 avg loss, 0.001000 rate, 1.923901 seconds, 192512 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.738578, Class: 0.195893, Obj: 0.323200, No Obj: 0.002012, .5R: 0.962963, .75R: 0.518519, count: 27 -Region 23 Avg IOU: 0.613284, Class: 0.114251, Obj: 0.075709, No Obj: 0.000274, .5R: 0.714286, .75R: 0.190476, count: 21 -Region 16 Avg IOU: 0.706003, Class: 0.210238, Obj: 0.242743, No Obj: 0.001994, .5R: 0.896552, .75R: 0.413793, count: 29 -Region 23 Avg IOU: 0.613786, Class: 0.209216, Obj: 0.100133, No Obj: 0.000220, .5R: 0.950000, .75R: 0.100000, count: 20 -Region 16 Avg IOU: 0.716894, Class: 0.214822, Obj: 0.323173, No Obj: 0.002498, .5R: 0.944444, .75R: 0.444444, count: 36 -Region 23 Avg IOU: 0.692251, Class: 0.215743, Obj: 0.196927, No Obj: 0.000405, .5R: 0.894737, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.700855, Class: 0.191817, Obj: 0.198888, No Obj: 0.001188, .5R: 0.933333, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.665566, Class: 0.166598, Obj: 0.075993, No Obj: 0.000196, .5R: 0.809524, .75R: 0.333333, count: 21 -Region 16 Avg IOU: 0.752146, Class: 0.293982, Obj: 0.337787, No Obj: 0.001280, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.668875, Class: 0.198182, Obj: 0.201594, No Obj: 0.000261, .5R: 1.000000, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.754811, Class: 0.171664, Obj: 0.319500, No Obj: 0.001317, .5R: 0.888889, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.671364, Class: 0.136500, Obj: 0.179284, No Obj: 0.000283, .5R: 0.956522, .75R: 0.260870, count: 23 -Region 16 Avg IOU: 0.753051, Class: 0.170949, Obj: 0.263197, No Obj: 0.001443, .5R: 0.882353, .75R: 0.705882, count: 17 -Region 23 Avg IOU: 0.656131, Class: 0.179275, Obj: 0.095348, No Obj: 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23 Avg IOU: 0.612648, Class: 0.224045, Obj: 0.140980, No Obj: 0.000377, .5R: 0.812500, .75R: 0.250000, count: 16 - - 1520: 2.200849, 2.457613 avg loss, 0.001000 rate, 0.866514 seconds, 194560 images -Resizing -544 x 544 - try to allocate workspace = 1145585665 * sizeof(float), CUDA allocate done! -Loaded: 0.325865 seconds -Region 16 Avg IOU: 0.714467, Class: 0.270667, Obj: 0.299535, No Obj: 0.001471, .5R: 0.866667, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.690788, Class: 0.210864, Obj: 0.208461, No Obj: 0.000437, .5R: 0.960000, .75R: 0.240000, count: 25 -Region 16 Avg IOU: 0.734695, Class: 0.188169, Obj: 0.320916, No Obj: 0.001781, .5R: 1.000000, .75R: 0.277778, count: 18 -Region 23 Avg IOU: 0.644727, Class: 0.154110, Obj: 0.150508, No Obj: 0.000564, .5R: 0.850000, .75R: 0.300000, count: 40 -Region 16 Avg IOU: 0.773759, Class: 0.125934, Obj: 0.311295, No Obj: 0.001000, .5R: 0.833333, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.711551, Class: 0.213534, Obj: 0.256225, No Obj: 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0.637931, Class: 0.184461, Obj: 0.121314, No Obj: 0.000498, .5R: 0.827586, .75R: 0.206897, count: 29 -Region 16 Avg IOU: 0.719345, Class: 0.164011, Obj: 0.238426, No Obj: 0.001882, .5R: 0.937500, .75R: 0.312500, count: 16 -Region 23 Avg IOU: 0.586170, Class: 0.196709, Obj: 0.116852, No Obj: 0.000323, .5R: 0.692308, .75R: 0.000000, count: 13 -Region 16 Avg IOU: 0.683959, Class: 0.181394, Obj: 0.317297, No Obj: 0.002163, .5R: 0.952381, .75R: 0.380952, count: 21 -Region 23 Avg IOU: 0.635021, Class: 0.201905, Obj: 0.116800, No Obj: 0.000385, .5R: 0.904762, .75R: 0.095238, count: 21 -Region 16 Avg IOU: 0.706257, Class: 0.183909, Obj: 0.396483, No Obj: 0.002007, .5R: 0.913043, .75R: 0.347826, count: 23 -Region 23 Avg IOU: 0.613838, Class: 0.215696, Obj: 0.110669, No Obj: 0.000446, .5R: 0.852941, .75R: 0.117647, count: 34 -Region 16 Avg IOU: 0.741200, Class: 0.243416, Obj: 0.315963, No Obj: 0.001608, .5R: 1.000000, .75R: 0.684211, count: 19 -Region 23 Avg IOU: 0.635318, Class: 0.215458, Obj: 0.116011, No Obj: 0.000395, .5R: 0.791667, .75R: 0.125000, count: 24 -Region 16 Avg IOU: 0.736064, Class: 0.231143, Obj: 0.429334, No Obj: 0.001124, .5R: 0.928571, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.654741, Class: 0.223545, Obj: 0.223206, No Obj: 0.000265, .5R: 0.894737, .75R: 0.263158, count: 19 - - 1522: 3.110045, 2.563656 avg loss, 0.001000 rate, 1.435512 seconds, 194816 images -Loaded: 0.000063 seconds -Region 16 Avg IOU: 0.741808, Class: 0.216612, Obj: 0.440845, No Obj: 0.001278, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 23 Avg IOU: 0.709024, Class: 0.191997, Obj: 0.203837, No Obj: 0.000327, .5R: 1.000000, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.704757, Class: 0.173661, Obj: 0.205718, No Obj: 0.000949, .5R: 0.875000, .75R: 0.437500, count: 16 -Region 23 Avg IOU: 0.650068, Class: 0.172679, Obj: 0.113152, No Obj: 0.000261, .5R: 0.894737, .75R: 0.105263, count: 19 -Region 16 Avg IOU: 0.701179, Class: 0.170100, Obj: 0.153517, No Obj: 0.001136, .5R: 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18 -Region 23 Avg IOU: 0.688302, Class: 0.175203, Obj: 0.156380, No Obj: 0.000383, .5R: 1.000000, .75R: 0.333333, count: 21 -Region 16 Avg IOU: 0.793442, Class: 0.225397, Obj: 0.408713, No Obj: 0.001491, .5R: 1.000000, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.748112, Class: 0.177769, Obj: 0.307401, No Obj: 0.000435, .5R: 0.916667, .75R: 0.500000, count: 12 - - 1523: 2.652504, 2.572541 avg loss, 0.001000 rate, 1.453221 seconds, 194944 images -Loaded: 0.000067 seconds -Region 16 Avg IOU: 0.777298, Class: 0.152705, Obj: 0.210316, No Obj: 0.001289, .5R: 1.000000, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.680260, Class: 0.225136, Obj: 0.119545, No Obj: 0.000426, .5R: 0.952381, .75R: 0.285714, count: 21 -Region 16 Avg IOU: 0.681575, Class: 0.212347, Obj: 0.330635, No Obj: 0.001441, .5R: 0.888889, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.575676, Class: 0.166152, Obj: 0.113311, No Obj: 0.000330, .5R: 0.696970, .75R: 0.090909, count: 33 -Region 16 Avg IOU: 0.739503, Class: 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11 -Region 23 Avg IOU: 0.633382, Class: 0.117373, Obj: 0.103609, No Obj: 0.000183, .5R: 0.823529, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.682049, Class: 0.197984, Obj: 0.433609, No Obj: 0.002305, .5R: 0.846154, .75R: 0.384615, count: 26 -Region 23 Avg IOU: 0.699988, Class: 0.242444, Obj: 0.118723, No Obj: 0.000280, .5R: 1.000000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.726492, Class: 0.201076, Obj: 0.364182, No Obj: 0.001082, .5R: 0.866667, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.666952, Class: 0.168385, Obj: 0.128733, No Obj: 0.000258, .5R: 1.000000, .75R: 0.136364, count: 22 - - 1528: 2.294575, 2.551687 avg loss, 0.001000 rate, 1.484329 seconds, 195584 images -Loaded: 0.000075 seconds -Region 16 Avg IOU: 0.740201, Class: 0.286215, Obj: 0.343226, No Obj: 0.001248, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.681499, Class: 0.185868, Obj: 0.205275, No Obj: 0.000144, .5R: 0.800000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.674171, Class: 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count: 19 -Region 16 Avg IOU: 0.680588, Class: 0.205623, Obj: 0.272331, No Obj: 0.001268, .5R: 0.857143, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.642278, Class: 0.223371, Obj: 0.102746, No Obj: 0.000191, .5R: 0.833333, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.688518, Class: 0.245358, Obj: 0.330393, No Obj: 0.001695, .5R: 0.818182, .75R: 0.318182, count: 22 -Region 23 Avg IOU: 0.653848, Class: 0.222309, Obj: 0.105472, No Obj: 0.000202, .5R: 0.869565, .75R: 0.304348, count: 23 -Region 16 Avg IOU: 0.736242, Class: 0.180911, Obj: 0.196798, No Obj: 0.001285, .5R: 0.913043, .75R: 0.434783, count: 23 -Region 23 Avg IOU: 0.661926, Class: 0.202718, Obj: 0.122020, No Obj: 0.000265, .5R: 0.913043, .75R: 0.260870, count: 23 -Region 16 Avg IOU: 0.697508, Class: 0.240859, Obj: 0.286384, No Obj: 0.001613, .5R: 0.944444, .75R: 0.388889, count: 18 -Region 23 Avg IOU: 0.675671, Class: 0.166481, Obj: 0.141378, No Obj: 0.000245, .5R: 0.923077, .75R: 0.230769, count: 13 -Region 16 Avg IOU: 0.756195, Class: 0.241969, Obj: 0.167434, No Obj: 0.001114, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.656119, Class: 0.203653, Obj: 0.112925, No Obj: 0.000246, .5R: 0.900000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.739472, Class: 0.216526, Obj: 0.162376, No Obj: 0.000898, .5R: 1.000000, .75R: 0.384615, count: 13 -Region 23 Avg IOU: 0.687873, Class: 0.208101, Obj: 0.094491, No Obj: 0.000186, .5R: 0.923077, .75R: 0.230769, count: 13 -Region 16 Avg IOU: 0.712392, Class: 0.174166, Obj: 0.276532, No Obj: 0.001021, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.647522, Class: 0.177964, Obj: 0.136114, No Obj: 0.000336, .5R: 0.884615, .75R: 0.115385, count: 26 - - 1530: 2.807985, 2.521693 avg loss, 0.001000 rate, 1.438733 seconds, 195840 images -Resizing -672 x 672 - try to allocate workspace = 1148076033 * sizeof(float), CUDA allocate done! -Loaded: 0.234399 seconds -Region 16 Avg IOU: 0.754230, Class: 0.159742, Obj: 0.384689, No Obj: 0.000804, 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count: 15 -Region 23 Avg IOU: 0.674390, Class: 0.165076, Obj: 0.237907, No Obj: 0.000208, .5R: 1.000000, .75R: 0.272727, count: 11 -Region 16 Avg IOU: 0.743852, Class: 0.233153, Obj: 0.331972, No Obj: 0.001187, .5R: 0.941176, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.678606, Class: 0.191142, Obj: 0.223165, No Obj: 0.000304, .5R: 0.863636, .75R: 0.318182, count: 22 -Region 16 Avg IOU: 0.707089, Class: 0.208965, Obj: 0.261551, No Obj: 0.001178, .5R: 1.000000, .75R: 0.500000, count: 24 -Region 23 Avg IOU: 0.638225, Class: 0.168606, Obj: 0.090360, No Obj: 0.000215, .5R: 0.814815, .75R: 0.296296, count: 27 -Region 16 Avg IOU: 0.769598, Class: 0.222217, Obj: 0.219803, No Obj: 0.001138, .5R: 1.000000, .75R: 0.708333, count: 24 -Region 23 Avg IOU: 0.671294, Class: 0.156411, Obj: 0.161859, No Obj: 0.000336, .5R: 0.909091, .75R: 0.181818, count: 22 - - 1531: 2.739260, 2.543450 avg loss, 0.001000 rate, 2.104867 seconds, 195968 images -Loaded: 0.000072 seconds -Region 16 Avg IOU: 0.771181, Class: 0.209175, Obj: 0.249573, No Obj: 0.001377, .5R: 0.967742, .75R: 0.612903, count: 31 -Region 23 Avg IOU: 0.649926, Class: 0.188727, Obj: 0.186377, No Obj: 0.000343, .5R: 0.913043, .75R: 0.217391, count: 23 -Region 16 Avg IOU: 0.759663, Class: 0.180778, Obj: 0.244444, No Obj: 0.001000, .5R: 1.000000, .75R: 0.590909, count: 22 -Region 23 Avg IOU: 0.703996, Class: 0.142974, Obj: 0.194418, No Obj: 0.000279, .5R: 1.000000, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.712624, Class: 0.260134, Obj: 0.244890, No Obj: 0.001438, .5R: 0.960000, .75R: 0.360000, count: 25 -Region 23 Avg IOU: 0.666774, Class: 0.239401, Obj: 0.338023, No Obj: 0.000376, .5R: 0.785714, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.689632, Class: 0.177054, Obj: 0.196584, No Obj: 0.001050, .5R: 0.894737, .75R: 0.421053, count: 19 -Region 23 Avg IOU: 0.686687, Class: 0.082642, Obj: 0.452324, No Obj: 0.000356, .5R: 1.000000, .75R: 0.250000, count: 12 -Region 16 Avg IOU: 0.782546, Class: 0.191325, Obj: 0.313422, 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0.000061 seconds -Region 16 Avg IOU: 0.783817, Class: 0.182035, Obj: 0.385464, No Obj: 0.001115, .5R: 1.000000, .75R: 0.600000, count: 20 -Region 23 Avg IOU: 0.636007, Class: 0.191962, Obj: 0.206980, No Obj: 0.000260, .5R: 0.900000, .75R: 0.150000, count: 20 -Region 16 Avg IOU: 0.719751, Class: 0.218853, Obj: 0.238703, No Obj: 0.001197, .5R: 0.935484, .75R: 0.483871, count: 31 -Region 23 Avg IOU: 0.670462, Class: 0.212184, Obj: 0.151808, No Obj: 0.000219, .5R: 0.888889, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.682777, Class: 0.198654, Obj: 0.236792, No Obj: 0.000980, .5R: 0.894737, .75R: 0.368421, count: 19 -Region 23 Avg IOU: 0.640831, Class: 0.134753, Obj: 0.139499, No Obj: 0.000288, .5R: 0.846154, .75R: 0.153846, count: 26 -Region 16 Avg IOU: 0.719068, Class: 0.234545, Obj: 0.222411, No Obj: 0.001056, .5R: 0.909091, .75R: 0.409091, count: 22 -Region 23 Avg IOU: 0.673011, Class: 0.226277, Obj: 0.149752, No Obj: 0.000291, .5R: 0.840000, .75R: 0.360000, count: 25 -Region 16 Avg 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rate, 2.081665 seconds, 196224 images -Loaded: 0.000062 seconds -Region 16 Avg IOU: 0.769698, Class: 0.157281, Obj: 0.296656, No Obj: 0.001141, .5R: 1.000000, .75R: 0.600000, count: 20 -Region 23 Avg IOU: 0.685946, Class: 0.119592, Obj: 0.134453, No Obj: 0.000147, .5R: 1.000000, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.745644, Class: 0.179467, Obj: 0.233355, No Obj: 0.001035, .5R: 1.000000, .75R: 0.600000, count: 20 -Region 23 Avg IOU: 0.686734, Class: 0.171107, Obj: 0.127775, No Obj: 0.000249, .5R: 0.956522, .75R: 0.304348, count: 23 -Region 16 Avg IOU: 0.799524, Class: 0.186921, Obj: 0.359858, No Obj: 0.001253, .5R: 1.000000, .75R: 0.789474, count: 19 -Region 23 Avg IOU: 0.686015, Class: 0.194445, Obj: 0.113324, No Obj: 0.000208, .5R: 0.909091, .75R: 0.363636, count: 22 -Region 16 Avg IOU: 0.706395, Class: 0.185180, Obj: 0.277802, No Obj: 0.001387, .5R: 0.900000, .75R: 0.450000, count: 20 -Region 23 Avg IOU: 0.662012, Class: 0.143024, Obj: 0.039184, No Obj: 0.000169, .5R: 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27 -Region 16 Avg IOU: 0.698605, Class: 0.210738, Obj: 0.242483, No Obj: 0.001056, .5R: 1.000000, .75R: 0.391304, count: 23 -Region 23 Avg IOU: 0.632949, Class: 0.192388, Obj: 0.109753, No Obj: 0.000203, .5R: 0.944444, .75R: 0.166667, count: 18 - - 1539: 3.109075, 2.861802 avg loss, 0.001000 rate, 2.077373 seconds, 196992 images -Loaded: 0.000076 seconds -Region 16 Avg IOU: 0.765970, Class: 0.238516, Obj: 0.329117, No Obj: 0.001052, .5R: 1.000000, .75R: 0.500000, count: 22 -Region 23 Avg IOU: 0.680444, Class: 0.165442, Obj: 0.069987, No Obj: 0.000133, .5R: 1.000000, .75R: 0.176471, count: 17 -Region 16 Avg IOU: 0.740907, Class: 0.274880, Obj: 0.198880, No Obj: 0.001198, .5R: 1.000000, .75R: 0.615385, count: 26 -Region 23 Avg IOU: 0.677924, Class: 0.222899, Obj: 0.220300, No Obj: 0.000323, .5R: 0.960000, .75R: 0.320000, count: 25 -Region 16 Avg IOU: 0.691673, Class: 0.228072, Obj: 0.244701, No Obj: 0.001255, .5R: 0.958333, .75R: 0.375000, count: 24 -Region 23 Avg IOU: 0.652857, Class: 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704 - try to allocate workspace = 1148780545 * sizeof(float), CUDA allocate done! -Loaded: 0.141095 seconds -Region 16 Avg IOU: 0.720267, Class: 0.237759, Obj: 0.405626, No Obj: 0.001695, .5R: 0.888889, .75R: 0.555556, count: 27 -Region 23 Avg IOU: 0.615447, Class: 0.240200, Obj: 0.201351, No Obj: 0.000304, .5R: 0.761905, .75R: 0.285714, count: 21 -Region 16 Avg IOU: 0.737322, Class: 0.205787, Obj: 0.375135, No Obj: 0.001036, .5R: 1.000000, .75R: 0.625000, count: 16 -Region 23 Avg IOU: 0.657709, Class: 0.104245, Obj: 0.201007, No Obj: 0.000298, .5R: 0.960000, .75R: 0.160000, count: 25 -Region 16 Avg IOU: 0.670583, Class: 0.212922, Obj: 0.274118, No Obj: 0.001227, .5R: 0.869565, .75R: 0.347826, count: 23 -Region 23 Avg IOU: 0.709619, Class: 0.181909, Obj: 0.180702, No Obj: 0.000301, .5R: 0.937500, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.708861, Class: 0.169525, Obj: 0.227412, No Obj: 0.001223, .5R: 0.882353, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 0.644142, Class: 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31 -Region 16 Avg IOU: 0.718537, Class: 0.243009, Obj: 0.343992, No Obj: 0.001290, .5R: 0.900000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.671640, Class: 0.183294, Obj: 0.213587, No Obj: 0.000533, .5R: 0.941176, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.742452, Class: 0.224599, Obj: 0.320106, No Obj: 0.001564, .5R: 0.941176, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.673104, Class: 0.222713, Obj: 0.240086, No Obj: 0.000406, .5R: 0.842105, .75R: 0.421053, count: 19 -Region 16 Avg IOU: 0.729910, Class: 0.191863, Obj: 0.290234, No Obj: 0.001716, .5R: 0.900000, .75R: 0.450000, count: 20 -Region 23 Avg IOU: 0.673816, Class: 0.164220, Obj: 0.165139, No Obj: 0.000355, .5R: 1.000000, .75R: 0.352941, count: 17 - - 1563: 3.191812, 2.831813 avg loss, 0.001000 rate, 1.550107 seconds, 200064 images -Loaded: 0.000096 seconds -Region 16 Avg IOU: 0.707167, Class: 0.218438, Obj: 0.365428, No Obj: 0.001935, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.648745, Class: 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images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.712972, Class: 0.184575, Obj: 0.186693, No Obj: 0.000933, .5R: 0.941176, .75R: 0.411765, count: 17 -Region 23 Avg IOU: 0.713152, Class: 0.174093, Obj: 0.267758, No Obj: 0.000266, .5R: 1.000000, .75R: 0.250000, count: 12 -Region 16 Avg IOU: 0.749604, Class: 0.244125, Obj: 0.320948, No Obj: 0.001489, .5R: 1.000000, .75R: 0.523810, count: 21 -Region 23 Avg IOU: 0.664743, Class: 0.207661, Obj: 0.198821, No Obj: 0.000314, .5R: 0.928571, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.743752, Class: 0.252358, Obj: 0.313571, No Obj: 0.001431, .5R: 0.956522, .75R: 0.434783, count: 23 -Region 23 Avg IOU: 0.646781, Class: 0.225393, Obj: 0.149931, No Obj: 0.000501, .5R: 0.846154, .75R: 0.269231, count: 26 -Region 16 Avg IOU: 0.794984, Class: 0.230435, Obj: 0.395466, No Obj: 0.001430, .5R: 1.000000, .75R: 0.631579, count: 19 -Region 23 Avg IOU: 0.686057, Class: 0.178341, Obj: 0.359308, No Obj: 0.000572, .5R: 0.952381, .75R: 0.285714, count: 21 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-Loaded: 0.000069 seconds -Region 16 Avg IOU: 0.683438, Class: 0.199426, Obj: 0.208127, No Obj: 0.001358, .5R: 1.000000, .75R: 0.160000, count: 25 -Region 23 Avg IOU: 0.660303, Class: 0.202094, Obj: 0.198449, No Obj: 0.000406, .5R: 0.833333, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.718196, Class: 0.199495, Obj: 0.364991, No Obj: 0.001693, .5R: 0.913043, .75R: 0.478261, count: 23 -Region 23 Avg IOU: 0.702581, Class: 0.193529, Obj: 0.346386, No Obj: 0.000270, .5R: 0.857143, .75R: 0.571429, count: 14 -Region 16 Avg IOU: 0.754621, Class: 0.192313, Obj: 0.225803, No Obj: 0.001424, .5R: 1.000000, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.630723, Class: 0.174950, Obj: 0.133948, No Obj: 0.000493, .5R: 0.857143, .75R: 0.190476, count: 42 -Region 16 Avg IOU: 0.643462, Class: 0.149989, Obj: 0.336888, No Obj: 0.001097, .5R: 0.909091, .75R: 0.181818, count: 11 -Region 23 Avg IOU: 0.730912, Class: 0.151446, Obj: 0.266765, No Obj: 0.000226, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 16 Avg IOU: 0.854460, Class: 0.263226, Obj: 0.487890, No Obj: 0.001194, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.669751, Class: 0.211711, Obj: 0.269682, No Obj: 0.000403, .5R: 0.916667, .75R: 0.250000, count: 24 -Region 16 Avg IOU: 0.698768, Class: 0.245796, Obj: 0.263720, No Obj: 0.001395, .5R: 1.000000, .75R: 0.250000, count: 16 -Region 23 Avg IOU: 0.657531, Class: 0.221345, Obj: 0.155945, No Obj: 0.000348, .5R: 0.894737, .75R: 0.210526, count: 19 -Region 16 Avg IOU: 0.757316, Class: 0.255134, Obj: 0.334775, No Obj: 0.001479, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.705934, Class: 0.198628, Obj: 0.193718, No Obj: 0.000379, .5R: 0.954545, .75R: 0.409091, count: 22 -Region 16 Avg IOU: 0.691078, Class: 0.196324, Obj: 0.185678, No Obj: 0.000955, .5R: 0.933333, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.667504, Class: 0.171043, Obj: 0.171748, No Obj: 0.000309, .5R: 0.941176, .75R: 0.176471, count: 17 - - 1584: 2.734434, 2.623614 avg 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-Region 16 Avg IOU: 0.719712, Class: 0.189445, Obj: 0.188191, No Obj: 0.001165, .5R: 0.935484, .75R: 0.516129, count: 31 -Region 23 Avg IOU: 0.596743, Class: 0.140022, Obj: 0.044352, No Obj: 0.000100, .5R: 0.724138, .75R: 0.206897, count: 29 -Region 16 Avg IOU: 0.747368, Class: 0.233742, Obj: 0.195341, No Obj: 0.000786, .5R: 0.894737, .75R: 0.631579, count: 19 -Region 23 Avg IOU: 0.692241, Class: 0.147357, Obj: 0.069470, No Obj: 0.000049, .5R: 0.888889, .75R: 0.222222, count: 9 -Region 16 Avg IOU: 0.700172, Class: 0.238481, Obj: 0.293131, No Obj: 0.000806, .5R: 0.857143, .75R: 0.428571, count: 21 -Region 23 Avg IOU: 0.660526, Class: 0.189517, Obj: 0.129468, No Obj: 0.000087, .5R: 1.000000, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.676304, Class: 0.204113, Obj: 0.245502, No Obj: 0.001110, .5R: 0.809524, .75R: 0.380952, count: 21 -Region 23 Avg IOU: 0.698691, Class: 0.195879, Obj: 0.089592, No Obj: 0.000094, .5R: 0.909091, .75R: 0.272727, count: 11 -Region 16 Avg IOU: 0.683092, Class: 0.244680, Obj: 0.216888, No Obj: 0.000902, .5R: 0.842105, .75R: 0.368421, count: 19 -Region 23 Avg IOU: 0.681659, Class: 0.208752, Obj: 0.081886, No Obj: 0.000159, .5R: 0.923077, .75R: 0.269231, count: 26 -Region 16 Avg IOU: 0.681781, Class: 0.261626, Obj: 0.161449, No Obj: 0.000980, .5R: 0.892857, .75R: 0.428571, count: 28 -Region 23 Avg IOU: 0.671876, Class: 0.181633, Obj: 0.098932, No Obj: 0.000084, .5R: 0.933333, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.773628, Class: 0.243531, Obj: 0.320214, No Obj: 0.001036, .5R: 1.000000, .75R: 0.625000, count: 24 -Region 23 Avg IOU: 0.636910, Class: 0.268891, Obj: 0.090853, No Obj: 0.000115, .5R: 0.812500, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.722632, Class: 0.231540, Obj: 0.227984, No Obj: 0.000813, .5R: 0.882353, .75R: 0.647059, count: 17 -Region 23 Avg IOU: 0.687964, Class: 0.188443, Obj: 0.097182, No Obj: 0.000057, .5R: 0.833333, .75R: 0.500000, count: 6 - - 1600: 2.924159, 2.871155 avg loss, 0.001000 rate, 5.376070 seconds, 204800 images -Resizing -512 x 512 - try to allocate workspace = 1145044993 * sizeof(float), CUDA allocate done! -Loaded: 0.544696 seconds -Region 16 Avg IOU: 0.692373, Class: 0.302310, Obj: 0.368188, No Obj: 0.001254, .5R: 0.777778, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.658937, Class: 0.234143, Obj: 0.122620, No Obj: 0.000250, .5R: 0.833333, .75R: 0.291667, count: 24 -Region 16 Avg IOU: 0.704663, Class: 0.267980, Obj: 0.320060, No Obj: 0.001434, .5R: 0.923077, .75R: 0.230769, count: 13 -Region 23 Avg IOU: 0.689085, Class: 0.209289, Obj: 0.117816, No Obj: 0.000306, .5R: 1.000000, .75R: 0.347826, count: 23 -Region 16 Avg IOU: 0.814882, Class: 0.232895, Obj: 0.247299, No Obj: 0.000610, .5R: 1.000000, .75R: 0.888889, count: 9 -Region 23 Avg IOU: 0.722492, Class: 0.235436, Obj: 0.212056, No Obj: 0.000236, .5R: 0.888889, .75R: 0.444444, count: 18 -Region 16 Avg IOU: 0.727972, Class: 0.280262, Obj: 0.325235, No Obj: 0.001340, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 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Avg IOU: 0.720936, Class: 0.187378, Obj: 0.211030, No Obj: 0.001100, .5R: 1.000000, .75R: 0.300000, count: 20 -Region 23 Avg IOU: 0.647743, Class: 0.150229, Obj: 0.139874, No Obj: 0.000517, .5R: 1.000000, .75R: 0.130435, count: 23 - - 1604: 2.414227, 2.739358 avg loss, 0.001000 rate, 1.636656 seconds, 205312 images -Loaded: 0.000077 seconds -Region 16 Avg IOU: 0.769753, Class: 0.216183, Obj: 0.251374, No Obj: 0.001280, .5R: 1.000000, .75R: 0.600000, count: 20 -Region 23 Avg IOU: 0.638784, Class: 0.167217, Obj: 0.062385, No Obj: 0.000170, .5R: 0.842105, .75R: 0.263158, count: 19 -Region 16 Avg IOU: 0.773886, Class: 0.210202, Obj: 0.271480, No Obj: 0.001353, .5R: 1.000000, .75R: 0.705882, count: 17 -Region 23 Avg IOU: 0.717554, Class: 0.232550, Obj: 0.048752, No Obj: 0.000188, .5R: 0.960000, .75R: 0.400000, count: 25 -Region 16 Avg IOU: 0.754664, Class: 0.211064, Obj: 0.245543, No Obj: 0.001344, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.664217, Class: 0.171192, Obj: 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Avg IOU: 0.624298, Class: 0.199200, Obj: 0.376494, No Obj: 0.000748, .5R: 0.833333, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.807785, Class: 0.203843, Obj: 0.487968, No Obj: 0.001776, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.692927, Class: 0.182630, Obj: 0.308540, No Obj: 0.000727, .5R: 0.947368, .75R: 0.263158, count: 19 -Region 16 Avg IOU: 0.800574, Class: 0.237653, Obj: 0.490307, No Obj: 0.001415, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.672227, Class: 0.186539, Obj: 0.275564, No Obj: 0.000950, .5R: 0.875000, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.744020, Class: 0.202083, Obj: 0.257115, No Obj: 0.001958, .5R: 0.941176, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.688939, Class: 0.210226, Obj: 0.366329, No Obj: 0.001164, .5R: 0.900000, .75R: 0.400000, count: 20 - - 1614: 2.437863, 2.488854 avg loss, 0.001000 rate, 0.847856 seconds, 206592 images -Loaded: 0.000059 seconds -Region 16 Avg IOU: 0.750872, Class: 0.197775, Obj: 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Class: 0.172800, Obj: 0.407087, No Obj: 0.002198, .5R: 1.000000, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.745520, Class: 0.150301, Obj: 0.155967, No Obj: 0.000455, .5R: 1.000000, .75R: 0.444444, count: 9 -Region 16 Avg IOU: 0.798356, Class: 0.174751, Obj: 0.368188, No Obj: 0.001946, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.714716, Class: 0.152511, Obj: 0.201410, No Obj: 0.000731, .5R: 0.862069, .75R: 0.551724, count: 29 -Region 16 Avg IOU: 0.770668, Class: 0.155141, Obj: 0.361432, No Obj: 0.001815, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.637258, Class: 0.212080, Obj: 0.122451, No Obj: 0.000458, .5R: 0.789474, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.757408, Class: 0.141917, Obj: 0.485807, No Obj: 0.001678, .5R: 0.900000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.711916, Class: 0.218445, Obj: 0.221514, No Obj: 0.000546, .5R: 0.950000, .75R: 0.450000, count: 20 - - 1616: 2.049608, 2.431074 avg loss, 0.001000 rate, 1.305200 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0.166667, count: 18 -Region 16 Avg IOU: 0.769830, Class: 0.182039, Obj: 0.429631, No Obj: 0.001225, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.692238, Class: 0.198588, Obj: 0.158621, No Obj: 0.000532, .5R: 0.925926, .75R: 0.333333, count: 27 -Region 16 Avg IOU: 0.720714, Class: 0.207248, Obj: 0.317248, No Obj: 0.001659, .5R: 0.933333, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.654990, Class: 0.183274, Obj: 0.089203, No Obj: 0.000327, .5R: 0.909091, .75R: 0.272727, count: 22 -Region 16 Avg IOU: 0.739655, Class: 0.174959, Obj: 0.407874, No Obj: 0.001370, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.678434, Class: 0.152180, Obj: 0.332099, No Obj: 0.000529, .5R: 0.944444, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.723320, Class: 0.244062, Obj: 0.470542, No Obj: 0.002107, .5R: 0.941176, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 0.689654, Class: 0.196700, Obj: 0.292831, No Obj: 0.000674, .5R: 0.880000, .75R: 0.280000, count: 25 - - 1617: 2.156254, 2.403592 avg loss, 0.001000 rate, 1.151981 seconds, 206976 images -Loaded: 0.000069 seconds -Region 16 Avg IOU: 0.786474, Class: 0.281137, Obj: 0.448732, No Obj: 0.002071, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.679688, Class: 0.185307, Obj: 0.212471, No Obj: 0.000336, .5R: 1.000000, .75R: 0.230769, count: 13 -Region 16 Avg IOU: 0.758172, Class: 0.220030, Obj: 0.439954, No Obj: 0.001266, .5R: 0.875000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.692853, Class: 0.187714, Obj: 0.134089, No Obj: 0.000364, .5R: 0.923077, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.746859, Class: 0.210430, Obj: 0.366253, No Obj: 0.002175, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.601879, Class: 0.189297, Obj: 0.145445, No Obj: 0.000258, .5R: 0.818182, .75R: 0.090909, count: 11 -Region 16 Avg IOU: 0.735590, Class: 0.207044, Obj: 0.347470, No Obj: 0.001554, .5R: 0.916667, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.680130, Class: 0.149813, Obj: 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Class: 0.181490, Obj: 0.168804, No Obj: 0.000789, .5R: 0.840000, .75R: 0.240000, count: 25 - - 1619: 2.222450, 2.350691 avg loss, 0.001000 rate, 1.371103 seconds, 207232 images -Loaded: 0.000078 seconds -Region 16 Avg IOU: 0.782557, Class: 0.220922, Obj: 0.474345, No Obj: 0.001637, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.691321, Class: 0.270162, Obj: 0.210936, No Obj: 0.000718, .5R: 0.947368, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.778224, Class: 0.191702, Obj: 0.304650, No Obj: 0.001766, .5R: 1.000000, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.653850, Class: 0.243765, Obj: 0.152030, No Obj: 0.000532, .5R: 0.913043, .75R: 0.173913, count: 23 -Region 16 Avg IOU: 0.770171, Class: 0.227417, Obj: 0.255555, No Obj: 0.001237, .5R: 0.909091, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.672381, Class: 0.180194, Obj: 0.185943, No Obj: 0.000614, .5R: 0.956522, .75R: 0.304348, count: 23 -Region 16 Avg IOU: 0.713951, Class: 0.237300, Obj: 0.260126, No Obj: 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images -Loaded: 0.000068 seconds -Region 16 Avg IOU: 0.752689, Class: 0.187081, Obj: 0.350815, No Obj: 0.002263, .5R: 1.000000, .75R: 0.562500, count: 32 -Region 23 Avg IOU: 0.669204, Class: 0.159270, Obj: 0.130851, No Obj: 0.000310, .5R: 0.935484, .75R: 0.258065, count: 31 -Region 16 Avg IOU: 0.752456, Class: 0.190985, Obj: 0.409651, No Obj: 0.001361, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.627360, Class: 0.185401, Obj: 0.102445, No Obj: 0.000200, .5R: 0.750000, .75R: 0.300000, count: 20 -Region 16 Avg IOU: 0.706171, Class: 0.221720, Obj: 0.253293, No Obj: 0.001439, .5R: 0.952381, .75R: 0.380952, count: 21 -Region 23 Avg IOU: 0.674331, Class: 0.186184, Obj: 0.048032, No Obj: 0.000220, .5R: 0.880000, .75R: 0.240000, count: 25 -Region 16 Avg IOU: 0.711633, Class: 0.272025, Obj: 0.415570, No Obj: 0.001511, .5R: 0.894737, .75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.697846, Class: 0.245613, Obj: 0.143956, No Obj: 0.000180, .5R: 0.944444, .75R: 0.333333, count: 18 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0.307692, count: 26 - - 1634: 2.690741, 2.372745 avg loss, 0.001000 rate, 2.895765 seconds, 209152 images -Loaded: 0.000094 seconds -Region 16 Avg IOU: 0.755113, Class: 0.186942, Obj: 0.271304, No Obj: 0.001415, .5R: 0.944444, .75R: 0.666667, count: 18 -Region 23 Avg IOU: 0.704463, Class: 0.158686, Obj: 0.171770, No Obj: 0.000438, .5R: 1.000000, .75R: 0.416667, count: 24 -Region 16 Avg IOU: 0.735014, Class: 0.209860, Obj: 0.350310, No Obj: 0.001279, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.632925, Class: 0.197038, Obj: 0.197608, No Obj: 0.000507, .5R: 0.806452, .75R: 0.193548, count: 31 -Region 16 Avg IOU: 0.662311, Class: 0.188222, Obj: 0.256118, No Obj: 0.001410, .5R: 0.892857, .75R: 0.357143, count: 28 -Region 23 Avg IOU: 0.663937, Class: 0.132611, Obj: 0.119512, No Obj: 0.000289, .5R: 0.823529, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.735508, Class: 0.246005, Obj: 0.267766, No Obj: 0.001459, .5R: 0.952381, .75R: 0.571429, count: 21 -Region 23 Avg IOU: 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count: 24 -Region 16 Avg IOU: 0.664925, Class: 0.196263, Obj: 0.191592, No Obj: 0.001196, .5R: 0.956522, .75R: 0.260870, count: 23 -Region 23 Avg IOU: 0.670020, Class: 0.169462, Obj: 0.183484, No Obj: 0.000203, .5R: 0.833333, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.781734, Class: 0.228546, Obj: 0.339470, No Obj: 0.001188, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.679575, Class: 0.148761, Obj: 0.079478, No Obj: 0.000250, .5R: 1.000000, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.703219, Class: 0.201814, Obj: 0.313485, No Obj: 0.001125, .5R: 0.950000, .75R: 0.350000, count: 20 -Region 23 Avg IOU: 0.637897, Class: 0.181199, Obj: 0.136272, No Obj: 0.000184, .5R: 0.750000, .75R: 0.416667, count: 12 -Region 16 Avg IOU: 0.671358, Class: 0.113132, Obj: 0.284385, No Obj: 0.001291, .5R: 0.894737, .75R: 0.315789, count: 19 -Region 23 Avg IOU: 0.693736, Class: 0.175065, Obj: 0.092074, No Obj: 0.000178, .5R: 0.882353, .75R: 0.470588, count: 17 -Region 16 Avg IOU: 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Avg IOU: 0.698141, Class: 0.170070, Obj: 0.227669, No Obj: 0.000346, .5R: 1.000000, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.751043, Class: 0.252219, Obj: 0.191712, No Obj: 0.001160, .5R: 1.000000, .75R: 0.545455, count: 22 -Region 23 Avg IOU: 0.676690, Class: 0.209950, Obj: 0.171852, No Obj: 0.000426, .5R: 0.884615, .75R: 0.307692, count: 26 -Region 16 Avg IOU: 0.744973, Class: 0.215314, Obj: 0.203847, No Obj: 0.001043, .5R: 0.880000, .75R: 0.640000, count: 25 -Region 23 Avg IOU: 0.671659, Class: 0.154128, Obj: 0.087010, No Obj: 0.000224, .5R: 0.944444, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.768618, Class: 0.219982, Obj: 0.264508, No Obj: 0.000924, .5R: 0.937500, .75R: 0.625000, count: 16 -Region 23 Avg IOU: 0.696354, Class: 0.216345, Obj: 0.251137, No Obj: 0.000333, .5R: 0.950000, .75R: 0.300000, count: 20 -Region 16 Avg IOU: 0.781635, Class: 0.251373, Obj: 0.358536, No Obj: 0.001282, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.676954, Class: 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count: 19 -Region 23 Avg IOU: 0.763719, Class: 0.187050, Obj: 0.431792, No Obj: 0.000398, .5R: 1.000000, .75R: 0.363636, count: 11 -Region 16 Avg IOU: 0.718332, Class: 0.294342, Obj: 0.255322, No Obj: 0.000732, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.695309, Class: 0.170347, Obj: 0.224482, No Obj: 0.000344, .5R: 0.846154, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.714931, Class: 0.262659, Obj: 0.157613, No Obj: 0.000946, .5R: 0.933333, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.640311, Class: 0.214388, Obj: 0.187494, No Obj: 0.000475, .5R: 0.850000, .75R: 0.250000, count: 20 -Region 16 Avg IOU: 0.790934, Class: 0.162874, Obj: 0.235468, No Obj: 0.001297, .5R: 1.000000, .75R: 0.684211, count: 19 -Region 23 Avg IOU: 0.661835, Class: 0.183088, Obj: 0.219411, No Obj: 0.000399, .5R: 0.954545, .75R: 0.272727, count: 22 - - 1682: 2.311507, 2.413413 avg loss, 0.001000 rate, 1.868245 seconds, 215296 images -Loaded: 0.000072 seconds -Region 16 Avg IOU: 0.694793, 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16 Avg IOU: 0.692665, Class: 0.181455, Obj: 0.200725, No Obj: 0.001782, .5R: 1.000000, .75R: 0.285714, count: 14 -Region 23 Avg IOU: 0.682624, Class: 0.186545, Obj: 0.153212, No Obj: 0.000597, .5R: 1.000000, .75R: 0.269231, count: 26 -Region 16 Avg IOU: 0.818388, Class: 0.228211, Obj: 0.262264, No Obj: 0.001203, .5R: 1.000000, .75R: 0.875000, count: 8 -Region 23 Avg IOU: 0.708570, Class: 0.232851, Obj: 0.195181, No Obj: 0.000532, .5R: 1.000000, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.793611, Class: 0.132134, Obj: 0.299746, No Obj: 0.001005, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.663558, Class: 0.206525, Obj: 0.204104, No Obj: 0.000541, .5R: 0.928571, .75R: 0.285714, count: 14 - - 1698: 1.905518, 2.266998 avg loss, 0.001000 rate, 0.776115 seconds, 217344 images -Loaded: 0.000063 seconds -Region 16 Avg IOU: 0.809247, Class: 0.163453, Obj: 0.234361, No Obj: 0.001630, .5R: 1.000000, .75R: 0.909091, count: 11 -Region 23 Avg IOU: 0.719245, Class: 0.166627, 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-Region 23 Avg IOU: 0.621765, Class: 0.205464, Obj: 0.249317, No Obj: 0.000931, .5R: 0.772727, .75R: 0.181818, count: 22 -Region 16 Avg IOU: 0.799400, Class: 0.163880, Obj: 0.371597, No Obj: 0.001488, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.735424, Class: 0.164967, Obj: 0.255829, No Obj: 0.000775, .5R: 1.000000, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.744165, Class: 0.250752, Obj: 0.386531, No Obj: 0.001847, .5R: 0.916667, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.652312, Class: 0.165344, Obj: 0.307063, No Obj: 0.001165, .5R: 0.920000, .75R: 0.160000, count: 25 -Region 16 Avg IOU: 0.793029, Class: 0.167244, Obj: 0.550643, No Obj: 0.000892, .5R: 1.000000, .75R: 0.500000, count: 4 -Region 23 Avg IOU: 0.711784, Class: 0.184793, Obj: 0.297570, No Obj: 0.000928, .5R: 0.888889, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.707609, Class: 0.260968, Obj: 0.448082, No Obj: 0.001953, .5R: 1.000000, .75R: 0.375000, count: 8 -Region 23 Avg IOU: 0.696529, Class: 0.149355, Obj: 0.282941, No Obj: 0.000486, .5R: 1.000000, .75R: 0.200000, count: 5 -Region 16 Avg IOU: 0.744116, Class: 0.190937, Obj: 0.478529, No Obj: 0.001679, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.671087, Class: 0.181229, Obj: 0.387512, No Obj: 0.001300, .5R: 1.000000, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.791889, Class: 0.246471, Obj: 0.273500, No Obj: 0.001100, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.725308, Class: 0.160608, Obj: 0.225196, No Obj: 0.000925, .5R: 0.950000, .75R: 0.500000, count: 20 -Region 16 Avg IOU: 0.776555, Class: 0.125336, Obj: 0.337320, No Obj: 0.001759, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.666466, Class: 0.169886, Obj: 0.343273, No Obj: 0.000979, .5R: 0.933333, .75R: 0.200000, count: 15 - - 1700: 1.842713, 2.233136 avg loss, 0.001000 rate, 0.767348 seconds, 217600 images -Resizing -576 x 576 - try to allocate workspace = 1146159105 * sizeof(float), CUDA allocate done! -Loaded: 0.355804 seconds -Region 16 Avg IOU: 0.668729, Class: 0.224599, Obj: 0.287439, No Obj: 0.001516, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 23 Avg IOU: 0.701304, Class: 0.202534, Obj: 0.228483, No Obj: 0.000505, .5R: 1.000000, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.680515, Class: 0.218591, Obj: 0.258874, No Obj: 0.002262, .5R: 0.833333, .75R: 0.300000, count: 30 -Region 23 Avg IOU: 0.630476, Class: 0.177869, Obj: 0.225743, No Obj: 0.000644, .5R: 0.750000, .75R: 0.250000, count: 32 -Region 16 Avg IOU: 0.706901, Class: 0.155048, Obj: 0.318854, No Obj: 0.001706, .5R: 0.954545, .75R: 0.363636, count: 22 -Region 23 Avg IOU: 0.684905, Class: 0.179777, Obj: 0.150813, No Obj: 0.000431, .5R: 0.833333, .75R: 0.500000, count: 24 -Region 16 Avg IOU: 0.704292, Class: 0.239782, Obj: 0.356071, No Obj: 0.002235, .5R: 0.894737, .75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.662712, Class: 0.211897, Obj: 0.152072, No Obj: 0.000366, .5R: 0.952381, .75R: 0.190476, count: 21 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0.300000, count: 20 - - 1702: 2.491240, 2.344373 avg loss, 0.001000 rate, 2.606326 seconds, 217856 images -Loaded: 0.000079 seconds -Region 16 Avg IOU: 0.690917, Class: 0.273437, Obj: 0.201199, No Obj: 0.001512, .5R: 0.958333, .75R: 0.291667, count: 24 -Region 23 Avg IOU: 0.639669, Class: 0.261696, Obj: 0.051232, No Obj: 0.000117, .5R: 0.850000, .75R: 0.200000, count: 20 -Region 16 Avg IOU: 0.742047, Class: 0.228552, Obj: 0.254373, No Obj: 0.001185, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 23 Avg IOU: 0.696004, Class: 0.136435, Obj: 0.076140, No Obj: 0.000145, .5R: 0.952381, .75R: 0.285714, count: 21 -Region 16 Avg IOU: 0.637185, Class: 0.207146, Obj: 0.200227, No Obj: 0.001435, .5R: 0.766667, .75R: 0.300000, count: 30 -Region 23 Avg IOU: 0.687206, Class: 0.187331, Obj: 0.064270, No Obj: 0.000164, .5R: 0.857143, .75R: 0.428571, count: 28 -Region 16 Avg IOU: 0.725735, Class: 0.209067, Obj: 0.257558, No Obj: 0.001295, .5R: 0.960000, .75R: 0.400000, count: 25 -Region 23 Avg IOU: 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Avg IOU: 0.749945, Class: 0.151713, Obj: 0.334291, No Obj: 0.002170, .5R: 0.954545, .75R: 0.545455, count: 22 -Region 23 Avg IOU: 0.672719, Class: 0.183260, Obj: 0.080903, No Obj: 0.000291, .5R: 0.882353, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.788970, Class: 0.205705, Obj: 0.357170, No Obj: 0.002122, .5R: 1.000000, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.709782, Class: 0.121786, Obj: 0.123879, No Obj: 0.000411, .5R: 0.909091, .75R: 0.363636, count: 22 -Region 16 Avg IOU: 0.758545, Class: 0.204497, Obj: 0.290618, No Obj: 0.001780, .5R: 0.933333, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.662582, Class: 0.239873, Obj: 0.183758, No Obj: 0.000368, .5R: 0.882353, .75R: 0.235294, count: 17 - - 1714: 2.243320, 2.476019 avg loss, 0.001000 rate, 1.696606 seconds, 219392 images -Loaded: 0.000077 seconds -Region 16 Avg IOU: 0.812768, Class: 0.227460, Obj: 0.506223, No Obj: 0.001701, .5R: 1.000000, .75R: 0.909091, count: 11 -Region 23 Avg IOU: 0.620709, Class: 0.198662, Obj: 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Class: 0.239816, Obj: 0.155790, No Obj: 0.000418, .5R: 1.000000, .75R: 0.400000, count: 25 -Region 16 Avg IOU: 0.738881, Class: 0.254891, Obj: 0.376753, No Obj: 0.002837, .5R: 1.000000, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.656759, Class: 0.189400, Obj: 0.137950, No Obj: 0.000278, .5R: 0.875000, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.712381, Class: 0.244541, Obj: 0.263582, No Obj: 0.001458, .5R: 0.933333, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.689559, Class: 0.203536, Obj: 0.129808, No Obj: 0.000298, .5R: 0.840000, .75R: 0.360000, count: 25 -Region 16 Avg IOU: 0.740330, Class: 0.218348, Obj: 0.226806, No Obj: 0.001576, .5R: 0.812500, .75R: 0.625000, count: 16 -Region 23 Avg IOU: 0.693860, Class: 0.181935, Obj: 0.154099, No Obj: 0.000324, .5R: 0.956522, .75R: 0.347826, count: 23 - - 1716: 2.466555, 2.454081 avg loss, 0.001000 rate, 1.526915 seconds, 219648 images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.750811, Class: 0.330406, Obj: 0.497329, No Obj: 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images -Loaded: 0.000071 seconds -Region 16 Avg IOU: 0.797247, Class: 0.255614, Obj: 0.262798, No Obj: 0.001450, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.689531, Class: 0.250387, Obj: 0.203246, No Obj: 0.000578, .5R: 1.000000, .75R: 0.400000, count: 25 -Region 16 Avg IOU: 0.780921, Class: 0.234923, Obj: 0.432086, No Obj: 0.001104, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.703487, Class: 0.131258, Obj: 0.319808, No Obj: 0.000374, .5R: 0.888889, .75R: 0.555556, count: 9 -Region 16 Avg IOU: 0.757520, Class: 0.235898, Obj: 0.389377, No Obj: 0.001561, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.606154, Class: 0.180581, Obj: 0.317458, No Obj: 0.000571, .5R: 0.750000, .75R: 0.100000, count: 20 -Region 16 Avg IOU: 0.786418, Class: 0.264258, Obj: 0.437221, No Obj: 0.002026, .5R: 0.937500, .75R: 0.750000, count: 16 -Region 23 Avg IOU: 0.710042, Class: 0.232737, Obj: 0.264471, No Obj: 0.000512, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.745961, Class: 0.257788, Obj: 0.383254, No Obj: 0.001750, .5R: 0.888889, .75R: 0.666667, count: 18 -Region 23 Avg IOU: 0.691995, Class: 0.174517, Obj: 0.235330, No Obj: 0.000506, .5R: 1.000000, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.766150, Class: 0.153986, Obj: 0.392694, No Obj: 0.001729, .5R: 1.000000, .75R: 0.777778, count: 18 -Region 23 Avg IOU: 0.673928, Class: 0.203569, Obj: 0.241112, No Obj: 0.000478, .5R: 0.928571, .75R: 0.285714, count: 14 -Region 16 Avg IOU: 0.803117, Class: 0.179592, Obj: 0.442094, No Obj: 0.001675, .5R: 1.000000, .75R: 0.882353, count: 17 -Region 23 Avg IOU: 0.694434, Class: 0.170423, Obj: 0.250486, No Obj: 0.000526, .5R: 0.941176, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.704941, Class: 0.237268, Obj: 0.247173, No Obj: 0.001717, .5R: 0.944444, .75R: 0.333333, count: 18 -Region 23 Avg IOU: 0.681381, Class: 0.227898, Obj: 0.140901, No Obj: 0.000398, .5R: 0.857143, .75R: 0.285714, count: 14 - - 1719: 2.060997, 2.348293 avg 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18 -Region 23 Avg IOU: 0.673773, Class: 0.147176, Obj: 0.135126, No Obj: 0.000280, .5R: 0.933333, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.739851, Class: 0.250555, Obj: 0.349975, No Obj: 0.002468, .5R: 0.966667, .75R: 0.533333, count: 30 -Region 23 Avg IOU: 0.682594, Class: 0.231168, Obj: 0.082763, No Obj: 0.000206, .5R: 0.928571, .75R: 0.285714, count: 14 -Region 16 Avg IOU: 0.723260, Class: 0.156073, Obj: 0.262187, No Obj: 0.001091, .5R: 0.888889, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.653297, Class: 0.191423, Obj: 0.084175, No Obj: 0.000314, .5R: 0.863636, .75R: 0.090909, count: 22 -Region 16 Avg IOU: 0.738814, Class: 0.269640, Obj: 0.430560, No Obj: 0.001382, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.689957, Class: 0.197630, Obj: 0.097447, No Obj: 0.000161, .5R: 0.952381, .75R: 0.333333, count: 21 - - 1733: 2.592949, 2.731476 avg loss, 0.001000 rate, 3.803561 seconds, 221824 images -Loaded: 0.000064 seconds -Region 16 Avg IOU: 0.728733, Class: 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rate, 2.591704 seconds, 222080 images -Loaded: 0.000069 seconds -Region 16 Avg IOU: 0.731039, Class: 0.230672, Obj: 0.191954, No Obj: 0.001042, .5R: 1.000000, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.661441, Class: 0.141742, Obj: 0.152290, No Obj: 0.000270, .5R: 0.888889, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.744097, Class: 0.267713, Obj: 0.291178, No Obj: 0.001200, .5R: 0.956522, .75R: 0.565217, count: 23 -Region 23 Avg IOU: 0.676592, Class: 0.168437, Obj: 0.209505, No Obj: 0.000453, .5R: 0.866667, .75R: 0.366667, count: 30 -Region 16 Avg IOU: 0.749701, Class: 0.233281, Obj: 0.230328, No Obj: 0.001022, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.676229, Class: 0.164702, Obj: 0.176638, No Obj: 0.000424, .5R: 0.950000, .75R: 0.150000, count: 20 -Region 16 Avg IOU: 0.687515, Class: 0.284553, Obj: 0.206291, No Obj: 0.001024, .5R: 0.900000, .75R: 0.350000, count: 20 -Region 23 Avg IOU: 0.639003, Class: 0.232562, Obj: 0.074202, No Obj: 0.000209, .5R: 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16 - - 1736: 2.965306, 2.750491 avg loss, 0.001000 rate, 2.840953 seconds, 222208 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.715402, Class: 0.217555, Obj: 0.330865, No Obj: 0.001621, .5R: 0.960000, .75R: 0.440000, count: 25 -Region 23 Avg IOU: 0.658635, Class: 0.185596, Obj: 0.186975, No Obj: 0.000656, .5R: 0.861111, .75R: 0.250000, count: 36 -Region 16 Avg IOU: 0.720241, Class: 0.212068, Obj: 0.267152, No Obj: 0.001122, .5R: 0.950000, .75R: 0.500000, count: 20 -Region 23 Avg IOU: 0.675476, Class: 0.208536, Obj: 0.244502, No Obj: 0.000375, .5R: 0.850000, .75R: 0.300000, count: 20 -Region 16 Avg IOU: 0.671252, Class: 0.240896, Obj: 0.504110, No Obj: 0.001250, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.684054, Class: 0.150154, Obj: 0.276523, No Obj: 0.000612, .5R: 0.958333, .75R: 0.166667, count: 24 -Region 16 Avg IOU: 0.742275, Class: 0.273510, Obj: 0.250495, No Obj: 0.001218, .5R: 1.000000, .75R: 0.545455, count: 22 -Region 23 Avg IOU: 0.699557, Class: 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17 -Region 16 Avg IOU: 0.705516, Class: 0.222132, Obj: 0.259699, No Obj: 0.001077, .5R: 0.894737, .75R: 0.421053, count: 19 -Region 23 Avg IOU: 0.643489, Class: 0.194888, Obj: 0.152273, No Obj: 0.000233, .5R: 0.863636, .75R: 0.181818, count: 22 -Region 16 Avg IOU: 0.723813, Class: 0.206355, Obj: 0.368074, No Obj: 0.001270, .5R: 0.947368, .75R: 0.368421, count: 19 -Region 23 Avg IOU: 0.646510, Class: 0.191498, Obj: 0.116639, No Obj: 0.000196, .5R: 0.900000, .75R: 0.200000, count: 20 -Region 16 Avg IOU: 0.763958, Class: 0.142168, Obj: 0.218048, No Obj: 0.001121, .5R: 0.937500, .75R: 0.437500, count: 16 -Region 23 Avg IOU: 0.675469, Class: 0.209295, Obj: 0.119833, No Obj: 0.000189, .5R: 0.909091, .75R: 0.272727, count: 11 - - 1749: 2.537401, 2.744585 avg loss, 0.001000 rate, 1.970528 seconds, 223872 images -Loaded: 0.000077 seconds -Region 16 Avg IOU: 0.791591, Class: 0.277374, Obj: 0.296296, No Obj: 0.001172, .5R: 1.000000, .75R: 0.650000, count: 20 -Region 23 Avg IOU: 0.667802, Class: 0.124609, Obj: 0.173187, No Obj: 0.000178, .5R: 0.928571, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.736936, Class: 0.214070, Obj: 0.204003, No Obj: 0.000714, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.640154, Class: 0.195643, Obj: 0.185099, No Obj: 0.000236, .5R: 0.722222, .75R: 0.222222, count: 18 -Region 16 Avg IOU: 0.713116, Class: 0.213409, Obj: 0.112944, No Obj: 0.000747, .5R: 0.888889, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.666498, Class: 0.165762, Obj: 0.101096, No Obj: 0.000183, .5R: 0.894737, .75R: 0.263158, count: 19 -Region 16 Avg IOU: 0.770889, Class: 0.213583, Obj: 0.296051, No Obj: 0.000920, .5R: 0.933333, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.713098, Class: 0.186376, Obj: 0.138794, No Obj: 0.000177, .5R: 1.000000, .75R: 0.400000, count: 15 -Region 16 Avg IOU: 0.775083, Class: 0.248384, Obj: 0.287463, No Obj: 0.000781, .5R: 1.000000, .75R: 0.818182, count: 11 -Region 23 Avg IOU: 0.657696, Class: 0.177952, Obj: 0.084607, No Obj: 0.000151, .5R: 1.000000, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.754855, Class: 0.186344, Obj: 0.226318, No Obj: 0.001197, .5R: 1.000000, .75R: 0.480000, count: 25 -Region 23 Avg IOU: 0.700580, Class: 0.139448, Obj: 0.103212, No Obj: 0.000145, .5R: 0.904762, .75R: 0.523810, count: 21 -Region 16 Avg IOU: 0.703148, Class: 0.216910, Obj: 0.178340, No Obj: 0.000748, .5R: 1.000000, .75R: 0.357143, count: 14 -Region 23 Avg IOU: 0.673195, Class: 0.222288, Obj: 0.107588, No Obj: 0.000168, .5R: 0.894737, .75R: 0.368421, count: 19 -Region 16 Avg IOU: 0.710362, Class: 0.245930, Obj: 0.258108, No Obj: 0.000825, .5R: 1.000000, .75R: 0.266667, count: 15 -Region 23 Avg IOU: 0.695041, Class: 0.240493, Obj: 0.147323, No Obj: 0.000212, .5R: 1.000000, .75R: 0.272727, count: 11 - - 1750: 2.352860, 2.705413 avg loss, 0.001000 rate, 1.950996 seconds, 224000 images -Resizing -736 x 736 - try to allocate workspace = 1149517825 * sizeof(float), CUDA allocate done! -Loaded: 0.191642 seconds -Region 16 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0.149922, Obj: 0.273434, No Obj: 0.001168, .5R: 0.920000, .75R: 0.400000, count: 25 -Region 23 Avg IOU: 0.661825, Class: 0.171886, Obj: 0.124886, No Obj: 0.000232, .5R: 0.862069, .75R: 0.275862, count: 29 -Region 16 Avg IOU: 0.746702, Class: 0.259277, Obj: 0.332417, No Obj: 0.000793, .5R: 1.000000, .75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.727698, Class: 0.249061, Obj: 0.191462, No Obj: 0.000184, .5R: 0.920000, .75R: 0.520000, count: 25 -Region 16 Avg IOU: 0.727637, Class: 0.168911, Obj: 0.359968, No Obj: 0.000953, .5R: 1.000000, .75R: 0.444444, count: 9 -Region 23 Avg IOU: 0.687082, Class: 0.186258, Obj: 0.074648, No Obj: 0.000141, .5R: 1.000000, .75R: 0.111111, count: 9 -Region 16 Avg IOU: 0.680483, Class: 0.257807, Obj: 0.261209, No Obj: 0.001000, .5R: 0.857143, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.666620, Class: 0.136979, Obj: 0.223444, No Obj: 0.000204, .5R: 0.842105, .75R: 0.315789, count: 19 - - 1751: 2.737612, 2.708633 avg loss, 0.001000 rate, 2.560063 seconds, 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count: 30 -Region 16 Avg IOU: 0.708305, Class: 0.154918, Obj: 0.259088, No Obj: 0.000789, .5R: 0.933333, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.691733, Class: 0.151757, Obj: 0.212678, No Obj: 0.000246, .5R: 0.850000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.633640, Class: 0.185593, Obj: 0.202643, No Obj: 0.000991, .5R: 0.818182, .75R: 0.272727, count: 22 -Region 23 Avg IOU: 0.689895, Class: 0.224158, Obj: 0.117650, No Obj: 0.000295, .5R: 0.944444, .75R: 0.444444, count: 18 -Region 16 Avg IOU: 0.723953, Class: 0.268302, Obj: 0.324899, No Obj: 0.000814, .5R: 0.933333, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.689529, Class: 0.213380, Obj: 0.252773, No Obj: 0.000159, .5R: 0.923077, .75R: 0.230769, count: 13 -Region 16 Avg IOU: 0.724324, Class: 0.214780, Obj: 0.132705, No Obj: 0.000968, .5R: 0.880000, .75R: 0.600000, count: 25 -Region 23 Avg IOU: 0.684330, Class: 0.160285, Obj: 0.187662, No Obj: 0.000326, .5R: 0.960000, .75R: 0.320000, count: 25 - - 1752: 3.221458, 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.75R: 0.346154, count: 26 - - 1753: 2.952389, 2.779162 avg loss, 0.001000 rate, 2.541893 seconds, 224384 images -Loaded: 0.000089 seconds -Region 16 Avg IOU: 0.714584, Class: 0.261957, Obj: 0.237464, No Obj: 0.001042, .5R: 0.954545, .75R: 0.454545, count: 22 -Region 23 Avg IOU: 0.727758, Class: 0.244466, Obj: 0.095639, No Obj: 0.000095, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 16 Avg IOU: 0.696256, Class: 0.202057, Obj: 0.231427, No Obj: 0.001007, .5R: 0.961538, .75R: 0.230769, count: 26 -Region 23 Avg IOU: 0.680268, Class: 0.180857, Obj: 0.140795, No Obj: 0.000198, .5R: 0.958333, .75R: 0.250000, count: 24 -Region 16 Avg IOU: 0.726438, Class: 0.231328, Obj: 0.287895, No Obj: 0.001096, .5R: 0.923077, .75R: 0.538462, count: 26 -Region 23 Avg IOU: 0.684613, Class: 0.252182, Obj: 0.093730, No Obj: 0.000111, .5R: 0.950000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.659420, Class: 0.254536, Obj: 0.132494, No Obj: 0.001121, .5R: 0.800000, .75R: 0.333333, count: 30 -Region 23 Avg 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0.236270, Obj: 0.383495, No Obj: 0.001535, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.655676, Class: 0.209126, Obj: 0.197456, No Obj: 0.001115, .5R: 0.814815, .75R: 0.259259, count: 27 -Region 16 Avg IOU: 0.795852, Class: 0.200460, Obj: 0.364877, No Obj: 0.001519, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.757679, Class: 0.209244, Obj: 0.389347, No Obj: 0.000909, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 16 Avg IOU: 0.830047, Class: 0.208343, Obj: 0.318998, No Obj: 0.001337, .5R: 1.000000, .75R: 1.000000, count: 8 -Region 23 Avg IOU: 0.708002, Class: 0.218067, Obj: 0.313775, No Obj: 0.000780, .5R: 0.916667, .75R: 0.416667, count: 12 -Region 16 Avg IOU: 0.773376, Class: 0.242247, Obj: 0.280682, No Obj: 0.001499, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.693193, Class: 0.215349, Obj: 0.249385, No Obj: 0.000799, .5R: 0.866667, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.795514, Class: 0.234948, Obj: 0.450465, No Obj: 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seconds -Region 16 Avg IOU: 0.772067, Class: 0.221382, Obj: 0.266448, No Obj: 0.001503, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.674200, Class: 0.199037, Obj: 0.371544, No Obj: 0.000816, .5R: 0.941176, .75R: 0.235294, count: 17 -Region 16 Avg IOU: 0.779442, Class: 0.128227, Obj: 0.323795, No Obj: 0.001785, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.661389, Class: 0.173843, Obj: 0.384327, No Obj: 0.000907, .5R: 0.909091, .75R: 0.272727, count: 11 -Region 16 Avg IOU: 0.753780, Class: 0.247991, Obj: 0.406327, No Obj: 0.001310, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.742883, Class: 0.209352, Obj: 0.344511, No Obj: 0.000962, .5R: 1.000000, .75R: 0.687500, count: 16 -Region 16 Avg IOU: 0.798051, Class: 0.195271, Obj: 0.315592, No Obj: 0.000844, .5R: 1.000000, .75R: 0.800000, count: 5 -Region 23 Avg IOU: 0.671074, Class: 0.194745, Obj: 0.390837, No Obj: 0.000846, .5R: 1.000000, .75R: 0.200000, count: 10 -Region 16 Avg IOU: 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0.783338 seconds, 226560 images -Resizing -512 x 512 - try to allocate workspace = 1145044993 * sizeof(float), CUDA allocate done! -Loaded: 0.285024 seconds -Region 16 Avg IOU: 0.682895, Class: 0.241100, Obj: 0.273812, No Obj: 0.001213, .5R: 0.833333, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.648553, Class: 0.178720, Obj: 0.233740, No Obj: 0.000497, .5R: 0.866667, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.685064, Class: 0.244644, Obj: 0.238631, No Obj: 0.001452, .5R: 0.866667, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.674515, Class: 0.216733, Obj: 0.204804, No Obj: 0.000725, .5R: 0.913043, .75R: 0.347826, count: 23 -Region 16 Avg IOU: 0.765019, Class: 0.203913, Obj: 0.315101, No Obj: 0.001301, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.765347, Class: 0.191747, Obj: 0.235197, No Obj: 0.000580, .5R: 1.000000, .75R: 0.562500, count: 16 -Region 16 Avg IOU: 0.709916, Class: 0.226895, Obj: 0.225205, No Obj: 0.001269, .5R: 1.000000, .75R: 0.428571, count: 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19 -Region 16 Avg IOU: 0.657183, Class: 0.215136, Obj: 0.342087, No Obj: 0.001719, .5R: 0.750000, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.658056, Class: 0.212673, Obj: 0.169621, No Obj: 0.000408, .5R: 0.818182, .75R: 0.272727, count: 11 -Region 16 Avg IOU: 0.745396, Class: 0.165877, Obj: 0.375474, No Obj: 0.001611, .5R: 1.000000, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.690563, Class: 0.197437, Obj: 0.355849, No Obj: 0.000540, .5R: 0.916667, .75R: 0.416667, count: 12 -Region 16 Avg IOU: 0.730125, Class: 0.277247, Obj: 0.445834, No Obj: 0.001517, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.615331, Class: 0.229934, Obj: 0.279888, No Obj: 0.001199, .5R: 0.750000, .75R: 0.142857, count: 28 - - 1782: 2.385873, 2.382802 avg loss, 0.001000 rate, 1.102306 seconds, 228096 images -Loaded: 0.000085 seconds -Region 16 Avg IOU: 0.747021, Class: 0.307841, Obj: 0.488090, No Obj: 0.001130, .5R: 0.875000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.661095, Class: 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count: 13 -Region 23 Avg IOU: 0.670558, Class: 0.214839, Obj: 0.202328, No Obj: 0.000459, .5R: 0.884615, .75R: 0.307692, count: 26 -Region 16 Avg IOU: 0.769662, Class: 0.320302, Obj: 0.558906, No Obj: 0.002437, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.682990, Class: 0.190331, Obj: 0.206862, No Obj: 0.000350, .5R: 0.947368, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.767905, Class: 0.209068, Obj: 0.480706, No Obj: 0.001462, .5R: 0.923077, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.683365, Class: 0.118452, Obj: 0.220250, No Obj: 0.000366, .5R: 0.952381, .75R: 0.333333, count: 21 -Region 16 Avg IOU: 0.818737, Class: 0.242998, Obj: 0.536874, No Obj: 0.002097, .5R: 1.000000, .75R: 0.923077, count: 13 -Region 23 Avg IOU: 0.600932, Class: 0.243832, Obj: 0.105051, No Obj: 0.000478, .5R: 0.714286, .75R: 0.238095, count: 21 -Region 16 Avg IOU: 0.755586, Class: 0.247264, Obj: 0.446562, No Obj: 0.001528, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.715293, Class: 0.238372, Obj: 0.227821, No Obj: 0.000367, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.818542, Class: 0.236470, Obj: 0.414145, No Obj: 0.001846, .5R: 1.000000, .75R: 0.916667, count: 12 -Region 23 Avg IOU: 0.723548, Class: 0.173765, Obj: 0.318679, No Obj: 0.000363, .5R: 1.000000, .75R: 0.272727, count: 11 -Region 16 Avg IOU: 0.719580, Class: 0.198279, Obj: 0.308721, No Obj: 0.001935, .5R: 1.000000, .75R: 0.285714, count: 7 -Region 23 Avg IOU: 0.713089, Class: 0.170815, Obj: 0.221943, No Obj: 0.000583, .5R: 1.000000, .75R: 0.500000, count: 18 -Region 16 Avg IOU: 0.778459, Class: 0.191182, Obj: 0.393015, No Obj: 0.002233, .5R: 1.000000, .75R: 0.700000, count: 20 -Region 23 Avg IOU: 0.645378, Class: 0.212126, Obj: 0.128445, No Obj: 0.000483, .5R: 0.833333, .75R: 0.333333, count: 18 - - 1784: 2.116284, 2.349556 avg loss, 0.001000 rate, 1.121983 seconds, 228352 images -Loaded: 0.000067 seconds -Region 16 Avg IOU: 0.729571, Class: 0.164592, Obj: 0.232429, 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images -Loaded: 0.000069 seconds -Region 16 Avg IOU: 0.760547, Class: 0.244701, Obj: 0.363707, No Obj: 0.001779, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.705472, Class: 0.218738, Obj: 0.238706, No Obj: 0.000387, .5R: 1.000000, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.691929, Class: 0.176831, Obj: 0.433855, No Obj: 0.002871, .5R: 0.833333, .75R: 0.458333, count: 24 -Region 23 Avg IOU: 0.708943, Class: 0.183829, Obj: 0.164905, No Obj: 0.000571, .5R: 1.000000, .75R: 0.392857, count: 28 -Region 16 Avg IOU: 0.763280, Class: 0.184845, Obj: 0.372823, No Obj: 0.001433, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.652985, Class: 0.191654, Obj: 0.223139, No Obj: 0.000580, .5R: 0.826087, .75R: 0.130435, count: 23 -Region 16 Avg IOU: 0.710703, Class: 0.231721, Obj: 0.335372, No Obj: 0.001982, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 23 Avg IOU: 0.623176, Class: 0.215210, Obj: 0.237493, No Obj: 0.000491, .5R: 0.687500, .75R: 0.250000, count: 16 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-Region 16 Avg IOU: 0.712257, Class: 0.194075, Obj: 0.344773, No Obj: 0.002209, .5R: 0.928571, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.665103, Class: 0.208374, Obj: 0.129456, No Obj: 0.000663, .5R: 0.896552, .75R: 0.310345, count: 29 -Region 16 Avg IOU: 0.778261, Class: 0.147269, Obj: 0.360041, No Obj: 0.002036, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.747678, Class: 0.163189, Obj: 0.294210, No Obj: 0.000763, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 16 Avg IOU: 0.779549, Class: 0.248764, Obj: 0.515517, No Obj: 0.000840, .5R: 1.000000, .75R: 0.666667, count: 3 -Region 23 Avg IOU: 0.669387, Class: 0.201202, Obj: 0.140491, No Obj: 0.000311, .5R: 1.000000, .75R: 0.333333, count: 12 - - 1799: 1.815462, 2.048674 avg loss, 0.001000 rate, 0.671056 seconds, 230272 images -Loaded: 0.000046 seconds -Region 16 Avg IOU: 0.819440, Class: 0.216580, Obj: 0.367328, No Obj: 0.001464, .5R: 1.000000, .75R: 0.800000, count: 5 -Region 23 Avg IOU: 0.614101, Class: 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Avg IOU: 0.770431, Class: 0.198868, Obj: 0.314167, No Obj: 0.001764, .5R: 1.000000, .75R: 0.647059, count: 17 -Region 23 Avg IOU: 0.732531, Class: 0.228964, Obj: 0.260322, No Obj: 0.000589, .5R: 1.000000, .75R: 0.666667, count: 6 -Region 16 Avg IOU: 0.729649, Class: 0.253010, Obj: 0.326357, No Obj: 0.001283, .5R: 0.928571, .75R: 0.357143, count: 14 -Region 23 Avg IOU: 0.698813, Class: 0.262337, Obj: 0.246142, No Obj: 0.000777, .5R: 0.947368, .75R: 0.421053, count: 19 -Region 16 Avg IOU: 0.749736, Class: 0.198735, Obj: 0.456255, No Obj: 0.001909, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.707438, Class: 0.221175, Obj: 0.294050, No Obj: 0.000847, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.730606, Class: 0.143613, Obj: 0.216609, No Obj: 0.001270, .5R: 0.909091, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.606497, Class: 0.164485, Obj: 0.288077, No Obj: 0.000639, .5R: 0.785714, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.730154, Class: 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seconds, 230528 images -Loaded: 0.000068 seconds -Region 16 Avg IOU: 0.727460, Class: 0.266188, Obj: 0.301586, No Obj: 0.001796, .5R: 1.000000, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.675198, Class: 0.224575, Obj: 0.143155, No Obj: 0.000433, .5R: 1.000000, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.794476, Class: 0.243304, Obj: 0.474153, No Obj: 0.000990, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.740605, Class: 0.184238, Obj: 0.236279, No Obj: 0.000473, .5R: 1.000000, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.746187, Class: 0.188479, Obj: 0.318305, No Obj: 0.000854, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.666096, Class: 0.221425, Obj: 0.179638, No Obj: 0.000576, .5R: 0.882353, .75R: 0.235294, count: 34 -Region 16 Avg IOU: 0.724833, Class: 0.185796, Obj: 0.260838, No Obj: 0.001611, .5R: 0.894737, .75R: 0.578947, count: 19 -Region 23 Avg IOU: 0.697411, Class: 0.234261, Obj: 0.136326, No Obj: 0.000538, .5R: 0.880000, .75R: 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images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.758624, Class: 0.269298, Obj: 0.297074, No Obj: 0.001438, .5R: 0.941176, .75R: 0.647059, count: 17 -Region 23 Avg IOU: 0.708719, Class: 0.221540, Obj: 0.227444, No Obj: 0.000761, .5R: 0.935484, .75R: 0.483871, count: 31 -Region 16 Avg IOU: 0.753477, Class: 0.251694, Obj: 0.353594, No Obj: 0.002177, .5R: 1.000000, .75R: 0.500000, count: 20 -Region 23 Avg IOU: 0.622617, Class: 0.191966, Obj: 0.164277, No Obj: 0.000541, .5R: 0.823529, .75R: 0.058824, count: 17 -Region 16 Avg IOU: 0.693652, Class: 0.216003, Obj: 0.287018, No Obj: 0.001365, .5R: 0.866667, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.652472, Class: 0.183465, Obj: 0.207401, No Obj: 0.000602, .5R: 0.846154, .75R: 0.269231, count: 26 -Region 16 Avg IOU: 0.754882, Class: 0.174432, Obj: 0.322374, No Obj: 0.001262, .5R: 1.000000, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.719566, Class: 0.195268, Obj: 0.243657, No Obj: 0.000564, .5R: 0.950000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.742202, Class: 0.250494, Obj: 0.335170, No Obj: 0.001752, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.651181, Class: 0.268376, Obj: 0.221684, No Obj: 0.000766, .5R: 0.875000, .75R: 0.208333, count: 24 -Region 16 Avg IOU: 0.726003, Class: 0.162204, Obj: 0.199515, No Obj: 0.000911, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 23 Avg IOU: 0.641518, Class: 0.202761, Obj: 0.172563, No Obj: 0.000663, .5R: 0.869565, .75R: 0.173913, count: 23 -Region 16 Avg IOU: 0.789665, Class: 0.212251, Obj: 0.411975, No Obj: 0.001059, .5R: 0.916667, .75R: 0.916667, count: 12 -Region 23 Avg IOU: 0.675603, Class: 0.178734, Obj: 0.236998, No Obj: 0.000463, .5R: 0.894737, .75R: 0.368421, count: 19 -Region 16 Avg IOU: 0.731821, Class: 0.275001, Obj: 0.297330, No Obj: 0.001017, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.704996, Class: 0.226753, Obj: 0.334955, No Obj: 0.000529, .5R: 0.800000, .75R: 0.533333, count: 15 - - 1820: 2.470118, 2.340955 avg loss, 0.001000 rate, 1.146604 seconds, 232960 images -Resizing -576 x 576 - try to allocate workspace = 1146159105 * sizeof(float), CUDA allocate done! -Loaded: 0.222848 seconds -Region 16 Avg IOU: 0.704156, Class: 0.283292, Obj: 0.342996, No Obj: 0.001140, .5R: 0.928571, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.620223, Class: 0.220695, Obj: 0.203418, No Obj: 0.000354, .5R: 0.882353, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.751893, Class: 0.216111, Obj: 0.473318, No Obj: 0.001212, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.725737, Class: 0.275511, Obj: 0.176645, No Obj: 0.000249, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 16 Avg IOU: 0.718982, Class: 0.242774, Obj: 0.292627, No Obj: 0.001255, .5R: 0.944444, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.718450, Class: 0.160397, Obj: 0.142180, No Obj: 0.000323, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.810420, Class: 0.208495, Obj: 0.400261, No Obj: 0.001794, .5R: 1.000000, 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0.695252, Class: 0.178759, Obj: 0.125720, No Obj: 0.000280, .5R: 0.964286, .75R: 0.250000, count: 28 - - 1872: 2.647816, 2.405095 avg loss, 0.001000 rate, 1.603629 seconds, 239616 images -Loaded: 0.000059 seconds -Region 16 Avg IOU: 0.748659, Class: 0.237442, Obj: 0.336390, No Obj: 0.001924, .5R: 0.958333, .75R: 0.500000, count: 24 -Region 23 Avg IOU: 0.731134, Class: 0.262090, Obj: 0.135784, No Obj: 0.000337, .5R: 0.857143, .75R: 0.714286, count: 14 -Region 16 Avg IOU: 0.810968, Class: 0.217825, Obj: 0.578001, No Obj: 0.001478, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.694226, Class: 0.172835, Obj: 0.321155, No Obj: 0.000469, .5R: 0.900000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.772416, Class: 0.228583, Obj: 0.473823, No Obj: 0.001167, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.677613, Class: 0.188824, Obj: 0.321208, No Obj: 0.000537, .5R: 0.952381, .75R: 0.285714, count: 21 -Region 16 Avg IOU: 0.718636, Class: 0.194783, Obj: 0.263908, 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images -Loaded: 0.000058 seconds -Region 16 Avg IOU: 0.756348, Class: 0.127221, Obj: 0.110905, No Obj: 0.000641, .5R: 1.000000, .75R: 0.444444, count: 9 -Region 23 Avg IOU: 0.728669, Class: 0.149597, Obj: 0.118045, No Obj: 0.000531, .5R: 1.000000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.727632, Class: 0.190399, Obj: 0.122163, No Obj: 0.000800, .5R: 0.916667, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.695000, Class: 0.187367, Obj: 0.172810, No Obj: 0.000408, .5R: 1.000000, .75R: 0.409091, count: 22 -Region 16 Avg IOU: 0.738719, Class: 0.135471, Obj: 0.188910, No Obj: 0.000759, .5R: 0.800000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.726119, Class: 0.159817, Obj: 0.122916, No Obj: 0.000293, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.814176, Class: 0.155585, Obj: 0.311136, No Obj: 0.000700, .5R: 1.000000, .75R: 0.857143, count: 7 -Region 23 Avg IOU: 0.685051, Class: 0.209060, Obj: 0.128379, No Obj: 0.000177, .5R: 0.900000, .75R: 0.300000, count: 10 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.75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.794142, Class: 0.216227, Obj: 0.270756, No Obj: 0.001034, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.621539, Class: 0.145005, Obj: 0.206447, No Obj: 0.000622, .5R: 0.777778, .75R: 0.259259, count: 27 -Region 16 Avg IOU: 0.711108, Class: 0.212244, Obj: 0.253854, No Obj: 0.001350, .5R: 0.941176, .75R: 0.352941, count: 17 -Region 23 Avg IOU: 0.681630, Class: 0.205498, Obj: 0.149484, No Obj: 0.000714, .5R: 0.925926, .75R: 0.333333, count: 27 -Region 16 Avg IOU: 0.763495, Class: 0.172641, Obj: 0.199164, No Obj: 0.000919, .5R: 0.900000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.699875, Class: 0.170869, Obj: 0.258243, No Obj: 0.000515, .5R: 0.952381, .75R: 0.142857, count: 21 - - 1969: 2.443417, 2.359684 avg loss, 0.001000 rate, 1.192357 seconds, 252032 images -Loaded: 0.000067 seconds -Region 16 Avg IOU: 0.765627, Class: 0.176987, Obj: 0.506765, No Obj: 0.001209, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg 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Obj: 0.109725, No Obj: 0.000541, .5R: 0.812500, .75R: 0.187500, count: 32 -Region 16 Avg IOU: 0.790908, Class: 0.240008, Obj: 0.268232, No Obj: 0.001631, .5R: 1.000000, .75R: 0.750000, count: 20 -Region 23 Avg IOU: 0.685051, Class: 0.232436, Obj: 0.167326, No Obj: 0.000434, .5R: 0.947368, .75R: 0.263158, count: 19 -Region 16 Avg IOU: 0.770853, Class: 0.221716, Obj: 0.297512, No Obj: 0.001744, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.760956, Class: 0.236580, Obj: 0.182259, No Obj: 0.000478, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 16 Avg IOU: 0.778922, Class: 0.169339, Obj: 0.321336, No Obj: 0.000991, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.660619, Class: 0.164745, Obj: 0.212531, No Obj: 0.000383, .5R: 0.933333, .75R: 0.266667, count: 15 - - 1970: 2.528974, 2.376613 avg loss, 0.001000 rate, 1.145500 seconds, 252160 images -Resizing -736 x 736 - try to allocate workspace = 1149517825 * sizeof(float), CUDA allocate done! -Loaded: 0.184931 seconds -Region 16 Avg IOU: 0.751049, Class: 0.228742, Obj: 0.267452, No Obj: 0.001260, .5R: 1.000000, .75R: 0.629630, count: 27 -Region 23 Avg IOU: 0.663521, Class: 0.238495, Obj: 0.197615, No Obj: 0.000361, .5R: 0.958333, .75R: 0.250000, count: 24 -Region 16 Avg IOU: 0.717978, Class: 0.211460, Obj: 0.162079, No Obj: 0.000735, .5R: 0.909091, .75R: 0.409091, count: 22 -Region 23 Avg IOU: 0.710873, Class: 0.149375, Obj: 0.231662, No Obj: 0.000229, .5R: 0.937500, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.776845, Class: 0.281293, Obj: 0.229712, No Obj: 0.001235, .5R: 1.000000, .75R: 0.727273, count: 22 -Region 23 Avg IOU: 0.696450, Class: 0.188965, Obj: 0.232644, No Obj: 0.000199, .5R: 1.000000, .75R: 0.333333, count: 9 -Region 16 Avg IOU: 0.731439, Class: 0.158786, Obj: 0.203031, No Obj: 0.001074, .5R: 1.000000, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 0.648202, Class: 0.165233, Obj: 0.170465, No Obj: 0.000224, .5R: 0.913043, .75R: 0.217391, count: 23 -Region 16 Avg IOU: 0.740053, Class: 0.218269, Obj: 0.253327, No Obj: 0.001305, .5R: 0.966667, .75R: 0.566667, count: 30 -Region 23 Avg IOU: 0.654529, Class: 0.182425, Obj: 0.167979, No Obj: 0.000283, .5R: 0.850000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.760017, Class: 0.203181, Obj: 0.306627, No Obj: 0.001152, .5R: 1.000000, .75R: 0.565217, count: 23 -Region 23 Avg IOU: 0.696075, Class: 0.214017, Obj: 0.388505, No Obj: 0.000233, .5R: 0.800000, .75R: 0.400000, count: 10 -Region 16 Avg IOU: 0.693404, Class: 0.152971, Obj: 0.186207, No Obj: 0.000925, .5R: 0.913043, .75R: 0.391304, count: 23 -Region 23 Avg IOU: 0.676225, Class: 0.111351, Obj: 0.090046, No Obj: 0.000308, .5R: 0.964286, .75R: 0.250000, count: 28 -Region 16 Avg IOU: 0.767871, Class: 0.210877, Obj: 0.296804, No Obj: 0.001331, .5R: 1.000000, .75R: 0.666667, count: 30 -Region 23 Avg IOU: 0.689033, Class: 0.144598, Obj: 0.295115, No Obj: 0.000299, .5R: 0.933333, .75R: 0.200000, count: 15 - - 1971: 3.150880, 2.454040 avg loss, 0.001000 rate, 2.592624 seconds, 252288 images -Loaded: 0.000064 seconds -Region 16 Avg IOU: 0.779637, Class: 0.177482, Obj: 0.368795, No Obj: 0.000994, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.693496, Class: 0.194199, Obj: 0.163184, No Obj: 0.000199, .5R: 1.000000, .75R: 0.250000, count: 20 -Region 16 Avg IOU: 0.694637, Class: 0.222173, Obj: 0.211293, No Obj: 0.001149, .5R: 0.862069, .75R: 0.379310, count: 29 -Region 23 Avg IOU: 0.667343, Class: 0.235699, Obj: 0.097078, No Obj: 0.000117, .5R: 0.882353, .75R: 0.235294, count: 17 -Region 16 Avg IOU: 0.726106, Class: 0.235515, Obj: 0.277413, No Obj: 0.001197, .5R: 0.920000, .75R: 0.480000, count: 25 -Region 23 Avg IOU: 0.712221, Class: 0.209925, Obj: 0.134256, No Obj: 0.000204, .5R: 1.000000, .75R: 0.400000, count: 25 -Region 16 Avg IOU: 0.761552, Class: 0.220411, Obj: 0.341268, No Obj: 0.001361, .5R: 1.000000, .75R: 0.450000, count: 20 -Region 23 Avg IOU: 0.753376, Class: 0.278283, Obj: 0.105208, No Obj: 0.000153, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 16 Avg IOU: 0.734554, Class: 0.218311, Obj: 0.213975, No Obj: 0.000737, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.680439, Class: 0.171530, Obj: 0.100594, No Obj: 0.000127, .5R: 1.000000, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.724392, Class: 0.189815, Obj: 0.233637, No Obj: 0.001134, .5R: 0.916667, .75R: 0.416667, count: 24 -Region 23 Avg IOU: 0.658915, Class: 0.191241, Obj: 0.126948, No Obj: 0.000207, .5R: 0.913043, .75R: 0.217391, count: 23 -Region 16 Avg IOU: 0.676480, Class: 0.247417, Obj: 0.210548, No Obj: 0.001061, .5R: 0.888889, .75R: 0.388889, count: 18 -Region 23 Avg IOU: 0.658286, Class: 0.193869, Obj: 0.132562, No Obj: 0.000096, .5R: 0.900000, .75R: 0.200000, count: 10 -Region 16 Avg IOU: 0.742846, Class: 0.236534, Obj: 0.264728, No Obj: 0.000940, .5R: 1.000000, .75R: 0.523810, count: 21 -Region 23 Avg IOU: 0.678993, Class: 0.226691, Obj: 0.083165, No Obj: 0.000157, .5R: 0.928571, .75R: 0.285714, count: 28 - - 1972: 2.903403, 2.498976 avg loss, 0.001000 rate, 2.584141 seconds, 252416 images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.772048, Class: 0.264033, Obj: 0.332516, No Obj: 0.001298, .5R: 0.937500, .75R: 0.625000, count: 32 -Region 23 Avg IOU: 0.728877, Class: 0.261360, Obj: 0.105985, No Obj: 0.000123, .5R: 0.952381, .75R: 0.380952, count: 21 -Region 16 Avg IOU: 0.682740, Class: 0.194428, Obj: 0.213287, No Obj: 0.000712, .5R: 0.818182, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.731750, Class: 0.226741, Obj: 0.129260, No Obj: 0.000164, .5R: 1.000000, .75R: 0.380952, count: 21 -Region 16 Avg IOU: 0.710504, Class: 0.205261, Obj: 0.260679, No Obj: 0.001063, .5R: 0.964286, .75R: 0.428571, count: 28 -Region 23 Avg IOU: 0.707851, Class: 0.186460, Obj: 0.026993, No Obj: 0.000066, .5R: 1.000000, .75R: 0.222222, count: 9 -Region 16 Avg IOU: 0.778616, Class: 0.219574, Obj: 0.294041, No Obj: 0.001061, .5R: 1.000000, .75R: 0.678571, count: 28 -Region 23 Avg IOU: 0.684762, Class: 0.234896, Obj: 0.086193, No Obj: 0.000103, .5R: 1.000000, .75R: 0.230769, count: 13 -Region 16 Avg IOU: 0.793763, Class: 0.251803, Obj: 0.401149, No Obj: 0.001491, .5R: 0.925926, .75R: 0.703704, count: 27 -Region 23 Avg IOU: 0.700512, Class: 0.181717, Obj: 0.073736, No Obj: 0.000128, .5R: 0.956522, .75R: 0.347826, count: 23 -Region 16 Avg IOU: 0.718706, Class: 0.212421, Obj: 0.275533, No Obj: 0.001255, .5R: 0.900000, .75R: 0.500000, count: 20 -Region 23 Avg IOU: 0.698794, Class: 0.217571, Obj: 0.164696, No Obj: 0.000186, .5R: 0.933333, .75R: 0.400000, count: 15 -Region 16 Avg IOU: 0.755305, Class: 0.208175, Obj: 0.255754, No Obj: 0.001060, .5R: 0.925926, .75R: 0.629630, count: 27 -Region 23 Avg IOU: 0.708977, Class: 0.176976, Obj: 0.070444, No Obj: 0.000103, .5R: 1.000000, .75R: 0.285714, count: 14 -Region 16 Avg IOU: 0.710611, Class: 0.209747, Obj: 0.265897, No Obj: 0.001186, .5R: 0.961538, .75R: 0.500000, count: 26 -Region 23 Avg IOU: 0.629465, Class: 0.254420, Obj: 0.019291, No Obj: 0.000079, .5R: 0.875000, .75R: 0.125000, count: 8 - - 1973: 2.743968, 2.523475 avg loss, 0.001000 rate, 2.632026 seconds, 252544 images -Loaded: 0.000091 seconds -Region 16 Avg IOU: 0.706537, Class: 0.212540, Obj: 0.199609, No Obj: 0.001246, .5R: 1.000000, .75R: 0.518519, count: 27 -Region 23 Avg IOU: 0.677076, Class: 0.191816, Obj: 0.068515, No Obj: 0.000130, .5R: 0.882353, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.704243, Class: 0.190134, Obj: 0.327726, No Obj: 0.001624, .5R: 0.933333, .75R: 0.500000, count: 30 -Region 23 Avg IOU: 0.698697, Class: 0.187411, Obj: 0.084265, No Obj: 0.000257, .5R: 1.000000, .75R: 0.370370, count: 27 -Region 16 Avg IOU: 0.700365, Class: 0.187184, Obj: 0.190699, No Obj: 0.001048, .5R: 0.920000, .75R: 0.440000, count: 25 -Region 23 Avg IOU: 0.731674, Class: 0.172951, Obj: 0.097961, No Obj: 0.000125, .5R: 0.947368, .75R: 0.473684, count: 19 -Region 16 Avg IOU: 0.779007, Class: 0.158866, Obj: 0.331028, No Obj: 0.001089, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.637550, Class: 0.186342, Obj: 0.114876, No Obj: 0.000149, .5R: 0.904762, .75R: 0.142857, count: 21 -Region 16 Avg IOU: 0.739569, Class: 0.251127, Obj: 0.368878, No Obj: 0.001223, .5R: 1.000000, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.738132, Class: 0.193212, Obj: 0.094888, No Obj: 0.000138, .5R: 1.000000, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.712331, Class: 0.171895, Obj: 0.264950, No Obj: 0.001769, .5R: 0.909091, .75R: 0.484848, count: 33 -Region 23 Avg IOU: 0.731832, Class: 0.196541, Obj: 0.115715, No Obj: 0.000158, .5R: 0.923077, .75R: 0.692308, count: 13 -Region 16 Avg IOU: 0.783488, Class: 0.213701, Obj: 0.406121, No Obj: 0.001433, .5R: 1.000000, .75R: 0.652174, count: 23 -Region 23 Avg IOU: 0.680312, Class: 0.144531, Obj: 0.144228, No Obj: 0.000121, .5R: 0.947368, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.733606, Class: 0.162276, Obj: 0.253782, No Obj: 0.000768, .5R: 1.000000, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 0.744569, Class: 0.145363, Obj: 0.107573, No Obj: 0.000102, .5R: 1.000000, .75R: 0.500000, count: 12 - - 1974: 3.051812, 2.576309 avg loss, 0.001000 rate, 2.602166 seconds, 252672 images -Loaded: 0.000065 seconds -Region 16 Avg IOU: 0.705267, Class: 0.191770, Obj: 0.217328, No Obj: 0.001191, .5R: 0.920000, .75R: 0.400000, count: 25 -Region 23 Avg IOU: 0.710521, Class: 0.144725, Obj: 0.077036, No Obj: 0.000180, .5R: 0.933333, .75R: 0.400000, count: 15 -Region 16 Avg IOU: 0.748282, Class: 0.198856, Obj: 0.291764, No Obj: 0.001276, .5R: 0.956522, .75R: 0.521739, count: 23 -Region 23 Avg IOU: 0.622925, Class: 0.183675, Obj: 0.127503, No Obj: 0.000178, .5R: 0.823529, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.733399, Class: 0.227814, Obj: 0.233533, No Obj: 0.001594, .5R: 0.972973, .75R: 0.459459, count: 37 -Region 23 Avg IOU: 0.708433, Class: 0.163896, Obj: 0.070941, No Obj: 0.000209, .5R: 0.918919, .75R: 0.486486, count: 37 -Region 16 Avg IOU: 0.731124, Class: 0.140178, Obj: 0.231562, No Obj: 0.000685, .5R: 0.947368, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.647277, Class: 0.171478, Obj: 0.134549, No Obj: 0.000134, .5R: 0.818182, .75R: 0.272727, count: 11 -Region 16 Avg IOU: 0.647944, Class: 0.208335, Obj: 0.166690, No Obj: 0.000885, .5R: 0.840000, .75R: 0.280000, count: 25 -Region 23 Avg IOU: 0.727499, Class: 0.191931, Obj: 0.164109, No Obj: 0.000231, .5R: 1.000000, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.672543, Class: 0.184301, Obj: 0.176293, No Obj: 0.000999, .5R: 0.900000, .75R: 0.300000, count: 20 -Region 23 Avg IOU: 0.658825, Class: 0.175794, Obj: 0.119491, No Obj: 0.000176, .5R: 0.857143, .75R: 0.285714, count: 21 -Region 16 Avg IOU: 0.737384, Class: 0.215473, Obj: 0.257238, No Obj: 0.000918, .5R: 0.941176, .75R: 0.705882, count: 17 -Region 23 Avg IOU: 0.664086, Class: 0.172107, Obj: 0.139023, No Obj: 0.000206, .5R: 1.000000, .75R: 0.190476, count: 21 -Region 16 Avg IOU: 0.763011, Class: 0.200644, Obj: 0.224406, No Obj: 0.000906, .5R: 1.000000, .75R: 0.545455, count: 22 -Region 23 Avg IOU: 0.704205, Class: 0.234427, Obj: 0.070523, No Obj: 0.000134, .5R: 0.937500, .75R: 0.250000, count: 16 - - 1975: 3.320987, 2.650777 avg loss, 0.001000 rate, 2.583068 seconds, 252800 images -Loaded: 0.000071 seconds -Region 16 Avg IOU: 0.713324, Class: 0.204693, Obj: 0.144791, No Obj: 0.001004, .5R: 1.000000, .75R: 0.333333, count: 27 -Region 23 Avg IOU: 0.604720, Class: 0.136759, Obj: 0.024907, No Obj: 0.000136, .5R: 0.714286, .75R: 0.142857, count: 7 -Region 16 Avg IOU: 0.729217, Class: 0.232821, Obj: 0.276320, No Obj: 0.001119, .5R: 0.947368, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.680228, Class: 0.176807, Obj: 0.214891, No Obj: 0.000226, .5R: 1.000000, .75R: 0.333333, count: 9 -Region 16 Avg IOU: 0.752524, Class: 0.248438, Obj: 0.232123, No Obj: 0.000888, .5R: 1.000000, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.685997, Class: 0.223967, Obj: 0.169259, No Obj: 0.000288, .5R: 0.944444, .75R: 0.222222, count: 18 -Region 16 Avg IOU: 0.712762, Class: 0.241290, Obj: 0.357022, No Obj: 0.000687, .5R: 0.937500, .75R: 0.437500, count: 16 -Region 23 Avg IOU: 0.704476, Class: 0.145807, Obj: 0.160035, No Obj: 0.000234, .5R: 1.000000, .75R: 0.250000, count: 20 -Region 16 Avg IOU: 0.736987, Class: 0.199913, Obj: 0.240058, No Obj: 0.001260, .5R: 0.958333, .75R: 0.416667, count: 24 -Region 23 Avg IOU: 0.733786, Class: 0.195390, Obj: 0.146369, No Obj: 0.000284, .5R: 1.000000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.742673, Class: 0.241942, Obj: 0.257241, No Obj: 0.000803, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.689919, Class: 0.207653, Obj: 0.158268, No Obj: 0.000209, .5R: 1.000000, .75R: 0.230769, count: 13 -Region 16 Avg IOU: 0.711732, Class: 0.242736, Obj: 0.191746, No Obj: 0.001436, .5R: 0.942857, .75R: 0.485714, count: 35 -Region 23 Avg IOU: 0.700295, Class: 0.178103, Obj: 0.115861, No Obj: 0.000161, .5R: 0.916667, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.788836, Class: 0.321728, Obj: 0.219784, No Obj: 0.000805, .5R: 1.000000, .75R: 0.647059, count: 17 -Region 23 Avg IOU: 0.700260, Class: 0.240713, Obj: 0.150907, No Obj: 0.000230, .5R: 0.880000, .75R: 0.440000, count: 25 - - 1976: 2.701350, 2.655834 avg loss, 0.001000 rate, 2.541696 seconds, 252928 images -Loaded: 0.000072 seconds -Region 16 Avg IOU: 0.656447, Class: 0.170020, Obj: 0.185672, No Obj: 0.001270, .5R: 0.866667, .75R: 0.233333, count: 30 -Region 23 Avg IOU: 0.678937, Class: 0.126282, Obj: 0.123348, No Obj: 0.000312, .5R: 0.954545, .75R: 0.227273, count: 22 -Region 16 Avg IOU: 0.714731, Class: 0.253477, Obj: 0.407125, No Obj: 0.001180, .5R: 0.941176, .75R: 0.411765, count: 17 -Region 23 Avg IOU: 0.686587, Class: 0.289429, Obj: 0.407880, No Obj: 0.000275, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 16 Avg IOU: 0.719597, Class: 0.188783, Obj: 0.163967, No Obj: 0.001000, .5R: 0.923077, .75R: 0.538462, count: 26 -Region 23 Avg IOU: 0.717710, Class: 0.216703, Obj: 0.062529, No Obj: 0.000178, .5R: 1.000000, .75R: 0.375000, count: 8 -Region 16 Avg IOU: 0.773124, Class: 0.154217, Obj: 0.363905, No Obj: 0.001068, .5R: 1.000000, .75R: 0.739130, count: 23 -Region 23 Avg IOU: 0.702145, Class: 0.146935, Obj: 0.187303, No Obj: 0.000295, .5R: 0.916667, .75R: 0.416667, count: 24 -Region 16 Avg IOU: 0.684104, Class: 0.224646, Obj: 0.170485, No Obj: 0.000782, .5R: 0.833333, .75R: 0.458333, count: 24 -Region 23 Avg IOU: 0.689528, Class: 0.253974, Obj: 0.226604, No Obj: 0.000196, .5R: 1.000000, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.791400, Class: 0.294969, Obj: 0.224545, No Obj: 0.000955, .5R: 1.000000, .75R: 0.608696, count: 23 -Region 23 Avg IOU: 0.712603, Class: 0.201461, Obj: 0.145252, No Obj: 0.000258, .5R: 1.000000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.682881, Class: 0.219744, Obj: 0.267662, No Obj: 0.000830, .5R: 0.880000, .75R: 0.440000, count: 25 -Region 23 Avg IOU: 0.668950, Class: 0.203108, Obj: 0.233693, No Obj: 0.000211, .5R: 0.833333, .75R: 0.222222, count: 18 -Region 16 Avg IOU: 0.741743, Class: 0.213752, Obj: 0.243032, No Obj: 0.001013, .5R: 1.000000, .75R: 0.538462, count: 26 -Region 23 Avg IOU: 0.641818, Class: 0.139570, Obj: 0.218189, No Obj: 0.000302, .5R: 0.900000, .75R: 0.350000, count: 20 - - 1977: 3.144145, 2.704665 avg loss, 0.001000 rate, 2.590827 seconds, 253056 images -Loaded: 0.000064 seconds -Region 16 Avg IOU: 0.730379, Class: 0.260028, Obj: 0.226692, No Obj: 0.001053, .5R: 1.000000, .75R: 0.416667, count: 24 -Region 23 Avg IOU: 0.696177, Class: 0.181166, Obj: 0.057526, No Obj: 0.000128, .5R: 1.000000, .75R: 0.333333, count: 9 -Region 16 Avg IOU: 0.723796, Class: 0.183194, Obj: 0.298018, No Obj: 0.000942, .5R: 0.950000, .75R: 0.350000, count: 20 -Region 23 Avg IOU: 0.699914, Class: 0.211743, Obj: 0.214742, No Obj: 0.000164, .5R: 1.000000, .75R: 0.272727, count: 11 -Region 16 Avg IOU: 0.757583, Class: 0.245725, Obj: 0.234964, No Obj: 0.001273, .5R: 0.916667, .75R: 0.625000, count: 24 -Region 23 Avg IOU: 0.700703, Class: 0.221138, Obj: 0.235251, No Obj: 0.000156, .5R: 0.857143, .75R: 0.357143, count: 14 -Region 16 Avg IOU: 0.774744, Class: 0.245837, Obj: 0.370059, No Obj: 0.000670, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.685324, Class: 0.194909, Obj: 0.100999, No Obj: 0.000180, .5R: 0.962963, .75R: 0.259259, count: 27 -Region 16 Avg IOU: 0.751826, Class: 0.233503, Obj: 0.253666, No Obj: 0.001383, .5R: 1.000000, .75R: 0.533333, count: 30 -Region 23 Avg IOU: 0.699703, Class: 0.183403, Obj: 0.129398, No Obj: 0.000195, .5R: 0.956522, .75R: 0.304348, count: 23 -Region 16 Avg IOU: 0.717171, Class: 0.213071, Obj: 0.284735, No Obj: 0.001253, .5R: 0.900000, .75R: 0.433333, count: 30 -Region 23 Avg IOU: 0.691827, Class: 0.182225, Obj: 0.190248, No Obj: 0.000215, .5R: 0.900000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.721750, Class: 0.195856, Obj: 0.362387, No Obj: 0.001346, .5R: 0.958333, .75R: 0.500000, count: 24 -Region 23 Avg IOU: 0.666179, Class: 0.154697, Obj: 0.201452, No Obj: 0.000191, .5R: 0.900000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.722983, Class: 0.236005, Obj: 0.270937, No Obj: 0.001516, .5R: 0.900000, .75R: 0.450000, count: 20 -Region 23 Avg IOU: 0.696080, Class: 0.244371, Obj: 0.190255, No Obj: 0.000212, .5R: 1.000000, .75R: 0.312500, count: 16 - - 1978: 2.670709, 2.701269 avg loss, 0.001000 rate, 2.574764 seconds, 253184 images -Loaded: 0.000078 seconds -Region 16 Avg IOU: 0.739676, Class: 0.224546, Obj: 0.425613, No Obj: 0.001288, .5R: 1.000000, .75R: 0.428571, count: 21 -Region 23 Avg IOU: 0.732200, Class: 0.144122, Obj: 0.174439, No Obj: 0.000148, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.700026, Class: 0.179220, Obj: 0.177886, No Obj: 0.000790, .5R: 0.882353, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 0.677506, Class: 0.128995, Obj: 0.116515, No Obj: 0.000152, .5R: 0.937500, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.713708, Class: 0.206888, Obj: 0.142506, No Obj: 0.001190, .5R: 0.937500, .75R: 0.375000, count: 32 -Region 23 Avg IOU: 0.630025, Class: 0.196457, Obj: 0.105328, No Obj: 0.000119, .5R: 0.857143, .75R: 0.071429, count: 14 -Region 16 Avg IOU: 0.697326, Class: 0.205080, Obj: 0.262078, No Obj: 0.001131, .5R: 0.884615, .75R: 0.423077, count: 26 -Region 23 Avg IOU: 0.656704, Class: 0.224761, Obj: 0.166365, No Obj: 0.000192, .5R: 0.842105, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.746618, Class: 0.200176, Obj: 0.214745, No Obj: 0.000988, .5R: 0.916667, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.693445, Class: 0.277696, Obj: 0.104313, No Obj: 0.000092, .5R: 0.750000, .75R: 0.500000, count: 4 -Region 16 Avg IOU: 0.758000, Class: 0.248176, Obj: 0.371590, No Obj: 0.001092, .5R: 1.000000, .75R: 0.550000, count: 20 -Region 23 Avg IOU: 0.661926, Class: 0.207865, Obj: 0.108465, No Obj: 0.000142, .5R: 0.869565, .75R: 0.391304, count: 23 -Region 16 Avg IOU: 0.759314, Class: 0.223553, Obj: 0.234617, No Obj: 0.001272, .5R: 0.939394, .75R: 0.666667, count: 33 -Region 23 Avg IOU: 0.683498, Class: 0.192435, Obj: 0.168364, No Obj: 0.000172, .5R: 0.931035, .75R: 0.172414, count: 29 -Region 16 Avg IOU: 0.715085, Class: 0.217099, Obj: 0.274592, No Obj: 0.001230, .5R: 0.947368, .75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.689579, Class: 0.165085, Obj: 0.089208, No Obj: 0.000165, .5R: 0.909091, .75R: 0.363636, count: 22 - - 1979: 2.958855, 2.727028 avg loss, 0.001000 rate, 2.582673 seconds, 253312 images -Loaded: 0.000071 seconds -Region 16 Avg IOU: 0.739907, Class: 0.195851, Obj: 0.250884, No Obj: 0.000889, .5R: 0.956522, .75R: 0.521739, count: 23 -Region 23 Avg IOU: 0.738621, Class: 0.180637, Obj: 0.086526, No Obj: 0.000131, .5R: 1.000000, .75R: 0.464286, count: 28 -Region 16 Avg IOU: 0.748686, Class: 0.181207, Obj: 0.153220, No Obj: 0.001098, .5R: 0.962963, .75R: 0.481481, count: 27 -Region 23 Avg IOU: 0.706421, Class: 0.180970, Obj: 0.070360, No Obj: 0.000126, .5R: 1.000000, .75R: 0.321429, count: 28 -Region 16 Avg IOU: 0.698671, Class: 0.266909, Obj: 0.231208, No Obj: 0.001209, .5R: 0.928571, .75R: 0.392857, count: 28 -Region 23 Avg IOU: 0.702058, Class: 0.248265, Obj: 0.049398, No Obj: 0.000101, .5R: 0.941176, .75R: 0.235294, count: 17 -Region 16 Avg IOU: 0.681977, Class: 0.186092, Obj: 0.253029, No Obj: 0.001047, .5R: 0.800000, .75R: 0.520000, count: 25 -Region 23 Avg IOU: 0.697590, Class: 0.147491, Obj: 0.099060, No Obj: 0.000132, .5R: 1.000000, .75R: 0.176471, count: 17 -Region 16 Avg IOU: 0.725289, Class: 0.226012, Obj: 0.221428, No Obj: 0.001015, .5R: 0.962963, .75R: 0.444444, count: 27 -Region 23 Avg IOU: 0.685846, Class: 0.228693, Obj: 0.147537, No Obj: 0.000105, .5R: 1.000000, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.746921, Class: 0.176983, Obj: 0.244002, No Obj: 0.000849, .5R: 0.954545, .75R: 0.590909, count: 22 -Region 23 Avg IOU: 0.773934, Class: 0.162952, Obj: 0.110178, No Obj: 0.000098, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 16 Avg IOU: 0.701669, Class: 0.182736, Obj: 0.158155, No Obj: 0.001014, .5R: 0.913043, .75R: 0.391304, count: 23 -Region 23 Avg IOU: 0.709060, Class: 0.132885, Obj: 0.084408, No Obj: 0.000148, .5R: 1.000000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.743787, Class: 0.254314, Obj: 0.285906, No Obj: 0.001219, .5R: 1.000000, .75R: 0.458333, count: 24 -Region 23 Avg IOU: 0.720841, Class: 0.193785, Obj: 0.074574, No Obj: 0.000063, .5R: 1.000000, .75R: 0.384615, count: 13 - - 1980: 3.136822, 2.768007 avg loss, 0.001000 rate, 2.612342 seconds, 253440 images -Resizing -480 x 480 - try to allocate workspace = 1144537089 * sizeof(float), CUDA allocate done! -Loaded: 0.417883 seconds -Region 16 Avg IOU: 0.754518, Class: 0.232296, Obj: 0.308700, No Obj: 0.001263, .5R: 0.928571, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.621122, Class: 0.177576, Obj: 0.100852, No Obj: 0.000233, .5R: 0.800000, .75R: 0.066667, count: 15 -Region 16 Avg IOU: 0.785707, Class: 0.222013, Obj: 0.395903, No Obj: 0.001865, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.697418, Class: 0.169057, Obj: 0.152925, No Obj: 0.000289, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.690270, Class: 0.180881, Obj: 0.322932, No Obj: 0.000965, .5R: 0.909091, .75R: 0.363636, count: 11 -Region 23 Avg IOU: 0.697878, Class: 0.201088, Obj: 0.077922, No Obj: 0.000288, .5R: 1.000000, .75R: 0.263158, count: 19 -Region 16 Avg IOU: 0.746921, Class: 0.240029, Obj: 0.332264, No Obj: 0.001313, .5R: 1.000000, .75R: 0.333333, count: 12 -Region 23 Avg IOU: 0.700770, Class: 0.207688, Obj: 0.191225, No Obj: 0.000175, .5R: 1.000000, .75R: 0.333333, count: 9 -Region 16 Avg IOU: 0.737480, Class: 0.212383, Obj: 0.475831, No Obj: 0.001820, .5R: 0.941176, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.678608, Class: 0.138089, Obj: 0.157711, No Obj: 0.000206, .5R: 1.000000, .75R: 0.181818, count: 11 -Region 16 Avg IOU: 0.769690, Class: 0.236000, Obj: 0.412888, No Obj: 0.001168, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 23 Avg IOU: 0.713565, Class: 0.218823, Obj: 0.216939, No Obj: 0.000336, .5R: 1.000000, .75R: 0.421053, count: 19 -Region 16 Avg IOU: 0.761083, Class: 0.179973, Obj: 0.313512, No Obj: 0.001520, .5R: 0.916667, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.687488, Class: 0.180149, Obj: 0.186407, No Obj: 0.000272, .5R: 1.000000, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.728183, Class: 0.281447, Obj: 0.291198, No Obj: 0.001091, .5R: 1.000000, .75R: 0.500000, count: 6 -Region 23 Avg IOU: 0.656611, Class: 0.222638, Obj: 0.148874, No Obj: 0.000290, .5R: 0.950000, .75R: 0.150000, count: 20 - - 1981: 1.837846, 2.674991 avg loss, 0.001000 rate, 1.145629 seconds, 253568 images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.726162, Class: 0.198063, Obj: 0.327352, No Obj: 0.001243, .5R: 0.916667, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.647764, Class: 0.168263, Obj: 0.254315, No Obj: 0.000377, .5R: 0.733333, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.713193, Class: 0.249895, Obj: 0.284447, No Obj: 0.001584, .5R: 0.894737, .75R: 0.421053, count: 19 -Region 23 Avg IOU: 0.687539, Class: 0.215536, Obj: 0.078212, No Obj: 0.000348, .5R: 0.941176, .75R: 0.470588, count: 17 -Region 16 Avg IOU: 0.721514, Class: 0.142676, Obj: 0.316219, No Obj: 0.000967, .5R: 1.000000, .75R: 0.285714, count: 7 -Region 23 Avg IOU: 0.696744, Class: 0.130016, Obj: 0.219668, No Obj: 0.000372, .5R: 1.000000, .75R: 0.166667, count: 12 -Region 16 Avg IOU: 0.752836, Class: 0.259394, Obj: 0.330684, No Obj: 0.001048, .5R: 1.000000, .75R: 0.444444, count: 9 -Region 23 Avg IOU: 0.695861, Class: 0.192344, Obj: 0.188412, No Obj: 0.000379, .5R: 1.000000, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.706434, Class: 0.261928, Obj: 0.198662, No Obj: 0.001160, .5R: 0.888889, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.623599, Class: 0.199901, Obj: 0.164997, No Obj: 0.000484, .5R: 0.960000, .75R: 0.160000, count: 25 -Region 16 Avg IOU: 0.756799, Class: 0.224718, Obj: 0.346638, No Obj: 0.002137, .5R: 1.000000, .75R: 0.666667, count: 21 -Region 23 Avg IOU: 0.710541, Class: 0.194157, Obj: 0.221947, No Obj: 0.000734, .5R: 0.931035, .75R: 0.482759, count: 29 -Region 16 Avg IOU: 0.708467, Class: 0.186401, Obj: 0.166446, No Obj: 0.001186, .5R: 0.937500, .75R: 0.250000, count: 16 -Region 23 Avg IOU: 0.678866, Class: 0.200723, Obj: 0.214436, No Obj: 0.000504, .5R: 0.947368, .75R: 0.210526, count: 19 -Region 16 Avg IOU: 0.711248, Class: 0.222871, Obj: 0.301939, No Obj: 0.001267, .5R: 0.909091, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.658197, Class: 0.213246, Obj: 0.221987, No Obj: 0.000509, .5R: 0.920000, .75R: 0.200000, count: 25 - - 1982: 2.479481, 2.655440 avg loss, 0.001000 rate, 1.178302 seconds, 253696 images -Loaded: 0.000074 seconds -Region 16 Avg IOU: 0.747575, Class: 0.255316, Obj: 0.437518, No Obj: 0.001554, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.709547, Class: 0.179926, Obj: 0.368578, No Obj: 0.000794, .5R: 0.944444, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.774622, Class: 0.214713, Obj: 0.317736, No Obj: 0.001436, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.689524, Class: 0.154301, Obj: 0.307852, No Obj: 0.000698, .5R: 0.894737, .75R: 0.473684, count: 19 -Region 16 Avg IOU: 0.737576, Class: 0.237452, Obj: 0.414209, No Obj: 0.002210, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.654479, Class: 0.190862, Obj: 0.338154, No Obj: 0.000878, .5R: 0.952381, .75R: 0.142857, count: 21 -Region 16 Avg IOU: 0.718837, Class: 0.234017, Obj: 0.240836, No Obj: 0.001643, .5R: 1.000000, .75R: 0.312500, count: 16 -Region 23 Avg IOU: 0.654179, Class: 0.184820, Obj: 0.318640, No Obj: 0.000684, .5R: 0.875000, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.765254, Class: 0.211521, Obj: 0.303301, No Obj: 0.001342, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.676121, Class: 0.190089, Obj: 0.260869, No Obj: 0.000885, .5R: 0.956522, .75R: 0.260870, count: 23 -Region 16 Avg IOU: 0.794764, Class: 0.176292, Obj: 0.400280, No Obj: 0.002149, .5R: 1.000000, .75R: 0.687500, count: 16 -Region 23 Avg IOU: 0.705496, Class: 0.178596, Obj: 0.343999, No Obj: 0.001082, .5R: 0.958333, .75R: 0.416667, count: 24 -Region 16 Avg IOU: 0.797642, Class: 0.171110, Obj: 0.432954, No Obj: 0.001412, .5R: 1.000000, .75R: 0.833333, count: 12 -Region 23 Avg IOU: 0.660944, Class: 0.186024, Obj: 0.295350, No Obj: 0.000973, .5R: 0.913043, .75R: 0.173913, count: 23 -Region 16 Avg IOU: 0.705545, Class: 0.165918, Obj: 0.201288, No Obj: 0.001288, .5R: 1.000000, .75R: 0.181818, count: 11 -Region 23 Avg IOU: 0.671689, Class: 0.189128, Obj: 0.209452, No Obj: 0.000731, .5R: 0.916667, .75R: 0.250000, count: 24 - - 1983: 2.430609, 2.632957 avg loss, 0.001000 rate, 1.169358 seconds, 253824 images -Loaded: 0.000080 seconds -Region 16 Avg IOU: 0.759644, Class: 0.185659, Obj: 0.491014, No Obj: 0.001561, .5R: 0.923077, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.682104, Class: 0.181821, Obj: 0.234606, No Obj: 0.000614, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.738775, Class: 0.184152, Obj: 0.221789, No Obj: 0.001280, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.682353, Class: 0.166950, Obj: 0.300995, No Obj: 0.000528, .5R: 0.882353, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.708341, Class: 0.202697, Obj: 0.305851, No Obj: 0.001716, .5R: 0.944444, .75R: 0.388889, count: 18 -Region 23 Avg IOU: 0.627255, Class: 0.206062, Obj: 0.168319, No Obj: 0.000417, .5R: 0.789474, .75R: 0.263158, count: 19 -Region 16 Avg IOU: 0.792889, Class: 0.197305, Obj: 0.394911, No Obj: 0.001973, .5R: 1.000000, .75R: 0.666667, count: 18 -Region 23 Avg IOU: 0.688438, Class: 0.157793, Obj: 0.319687, No Obj: 0.000929, .5R: 0.900000, .75R: 0.300000, count: 20 -Region 16 Avg IOU: 0.731093, Class: 0.190677, Obj: 0.241827, No Obj: 0.002115, .5R: 0.964286, .75R: 0.535714, count: 28 -Region 23 Avg IOU: 0.709565, Class: 0.241001, Obj: 0.229223, No Obj: 0.000612, .5R: 0.958333, .75R: 0.458333, count: 24 -Region 16 Avg IOU: 0.711552, Class: 0.193342, Obj: 0.253038, No Obj: 0.001617, .5R: 0.950000, .75R: 0.450000, count: 20 -Region 23 Avg IOU: 0.687879, Class: 0.169098, Obj: 0.307951, No Obj: 0.000920, .5R: 0.962963, .75R: 0.296296, count: 27 -Region 16 Avg IOU: 0.751156, Class: 0.209203, Obj: 0.343692, No Obj: 0.001660, .5R: 0.937500, .75R: 0.625000, count: 16 -Region 23 Avg IOU: 0.663749, Class: 0.219762, Obj: 0.168670, No Obj: 0.000437, .5R: 0.947368, .75R: 0.263158, count: 19 -Region 16 Avg IOU: 0.756505, Class: 0.183807, Obj: 0.286677, No Obj: 0.001885, .5R: 1.000000, .75R: 0.550000, count: 20 -Region 23 Avg IOU: 0.708187, Class: 0.203464, Obj: 0.222891, No Obj: 0.000505, .5R: 0.947368, .75R: 0.315789, count: 19 - - 1984: 2.697689, 2.639430 avg loss, 0.001000 rate, 1.165358 seconds, 253952 images -Loaded: 0.000099 seconds -Region 16 Avg IOU: 0.742572, Class: 0.219649, Obj: 0.247196, No Obj: 0.001551, .5R: 0.923077, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.699303, Class: 0.211563, Obj: 0.146836, No Obj: 0.000401, .5R: 1.000000, .75R: 0.368421, count: 19 -Region 16 Avg IOU: 0.732685, Class: 0.222668, Obj: 0.419310, No Obj: 0.001585, .5R: 0.857143, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.691937, Class: 0.203828, Obj: 0.107977, No Obj: 0.000246, .5R: 1.000000, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.799940, Class: 0.182436, Obj: 0.445727, No Obj: 0.001318, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.691944, Class: 0.200981, Obj: 0.229743, No Obj: 0.000409, .5R: 0.954545, .75R: 0.272727, count: 22 -Region 16 Avg IOU: 0.773347, Class: 0.218156, Obj: 0.578612, No Obj: 0.001399, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.759377, Class: 0.181435, Obj: 0.253841, No Obj: 0.000365, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 16 Avg IOU: 0.790320, Class: 0.210062, Obj: 0.384548, No Obj: 0.001552, .5R: 1.000000, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.681105, Class: 0.200525, Obj: 0.253229, No Obj: 0.000432, .5R: 0.894737, .75R: 0.368421, count: 19 -Region 16 Avg IOU: 0.771696, Class: 0.262141, Obj: 0.401744, No Obj: 0.001979, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.704804, Class: 0.167935, Obj: 0.252094, No Obj: 0.000450, .5R: 0.950000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.787336, Class: 0.213771, Obj: 0.350938, No Obj: 0.001135, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.698574, Class: 0.113926, Obj: 0.271490, No Obj: 0.000285, .5R: 1.000000, .75R: 0.333333, count: 9 -Region 16 Avg IOU: 0.712034, Class: 0.161020, Obj: 0.429412, No Obj: 0.002080, .5R: 0.909091, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.649120, Class: 0.115086, Obj: 0.092945, No Obj: 0.000379, .5R: 0.882353, .75R: 0.352941, count: 17 - - 1985: 1.958018, 2.571289 avg loss, 0.001000 rate, 1.139736 seconds, 254080 images -Loaded: 0.000068 seconds -Region 16 Avg IOU: 0.747470, Class: 0.154024, Obj: 0.306556, No Obj: 0.001487, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.729740, Class: 0.220321, Obj: 0.192246, No Obj: 0.000231, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 16 Avg IOU: 0.729814, Class: 0.263769, Obj: 0.336487, No Obj: 0.001351, .5R: 0.857143, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.694700, Class: 0.186855, Obj: 0.132530, No Obj: 0.000272, .5R: 1.000000, .75R: 0.333333, count: 21 -Region 16 Avg IOU: 0.706490, Class: 0.194890, Obj: 0.381232, No Obj: 0.002284, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.657707, Class: 0.267424, Obj: 0.157895, No Obj: 0.000205, .5R: 0.916667, .75R: 0.250000, count: 12 -Region 16 Avg IOU: 0.721219, Class: 0.178324, Obj: 0.272297, No Obj: 0.001784, .5R: 0.923077, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.744041, Class: 0.219147, Obj: 0.179481, No Obj: 0.000266, .5R: 0.944444, .75R: 0.555556, count: 18 -Region 16 Avg IOU: 0.654401, Class: 0.187972, Obj: 0.390374, No Obj: 0.002451, .5R: 0.722222, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.686027, Class: 0.216886, Obj: 0.090598, No Obj: 0.000298, .5R: 0.900000, .75R: 0.366667, count: 30 -Region 16 Avg IOU: 0.791141, Class: 0.170506, Obj: 0.553064, No Obj: 0.001466, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.624552, Class: 0.154953, Obj: 0.112267, No Obj: 0.000293, .5R: 0.684211, .75R: 0.368421, count: 19 -Region 16 Avg IOU: 0.758535, Class: 0.227061, Obj: 0.540515, No Obj: 0.001346, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.658412, Class: 0.176189, Obj: 0.145999, No Obj: 0.000386, .5R: 0.851852, .75R: 0.259259, count: 27 -Region 16 Avg IOU: 0.782083, Class: 0.175186, Obj: 0.513234, No Obj: 0.001486, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.695089, Class: 0.162838, Obj: 0.200289, No Obj: 0.000254, .5R: 0.800000, .75R: 0.600000, count: 15 - - 1986: 2.178880, 2.532048 avg loss, 0.001000 rate, 1.148209 seconds, 254208 images -Loaded: 0.000078 seconds -Region 16 Avg IOU: 0.734648, Class: 0.195638, Obj: 0.265867, No Obj: 0.001742, .5R: 1.000000, .75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.696144, Class: 0.182152, Obj: 0.140896, No Obj: 0.000433, .5R: 1.000000, .75R: 0.333333, count: 21 -Region 16 Avg IOU: 0.728168, Class: 0.218759, Obj: 0.351335, No Obj: 0.001537, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.639729, Class: 0.194464, Obj: 0.192640, No Obj: 0.000210, .5R: 0.888889, .75R: 0.222222, count: 9 -Region 16 Avg IOU: 0.810080, Class: 0.316576, Obj: 0.639996, No Obj: 0.002218, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.716977, Class: 0.230892, Obj: 0.145866, No Obj: 0.000418, .5R: 1.000000, .75R: 0.363636, count: 22 -Region 16 Avg IOU: 0.719728, Class: 0.216411, Obj: 0.425265, No Obj: 0.001577, .5R: 0.909091, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.646032, Class: 0.171303, Obj: 0.129140, No Obj: 0.000289, .5R: 0.842105, .75R: 0.210526, count: 19 -Region 16 Avg IOU: 0.730907, Class: 0.163010, Obj: 0.281755, No Obj: 0.001692, .5R: 0.900000, .75R: 0.550000, count: 20 -Region 23 Avg IOU: 0.699212, Class: 0.163736, Obj: 0.091688, No Obj: 0.000285, .5R: 0.904762, .75R: 0.428571, count: 21 -Region 16 Avg IOU: 0.676696, Class: 0.254326, Obj: 0.398391, No Obj: 0.001860, .5R: 0.928571, .75R: 0.357143, count: 14 -Region 23 Avg IOU: 0.651803, Class: 0.172654, Obj: 0.252087, No Obj: 0.000331, .5R: 0.882353, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.759580, Class: 0.221276, Obj: 0.571014, No Obj: 0.001475, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.691950, Class: 0.164574, Obj: 0.235881, No Obj: 0.000376, .5R: 1.000000, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.789662, Class: 0.209425, Obj: 0.463165, No Obj: 0.001689, .5R: 1.000000, .75R: 0.733333, count: 15 -Region 23 Avg IOU: 0.670770, Class: 0.147918, Obj: 0.173913, No Obj: 0.000358, .5R: 0.941176, .75R: 0.235294, count: 17 - - 1987: 2.192964, 2.498140 avg loss, 0.001000 rate, 1.117736 seconds, 254336 images -Loaded: 0.000069 seconds -Region 16 Avg IOU: 0.792769, Class: 0.206145, Obj: 0.333942, No Obj: 0.001611, .5R: 1.000000, .75R: 0.733333, count: 15 -Region 23 Avg IOU: 0.714392, Class: 0.190645, Obj: 0.195373, No Obj: 0.000387, .5R: 0.882353, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.783823, Class: 0.226407, Obj: 0.534556, No Obj: 0.001579, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.664232, Class: 0.207428, Obj: 0.155498, No Obj: 0.000381, .5R: 0.857143, .75R: 0.238095, count: 21 -Region 16 Avg IOU: 0.710022, Class: 0.228568, Obj: 0.282246, No Obj: 0.001395, .5R: 0.923077, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.678858, Class: 0.227478, Obj: 0.323023, No Obj: 0.000629, .5R: 0.880000, .75R: 0.320000, count: 25 -Region 16 Avg IOU: 0.694335, Class: 0.120730, Obj: 0.404114, No Obj: 0.001418, .5R: 0.857143, .75R: 0.428571, count: 7 -Region 23 Avg IOU: 0.649720, Class: 0.200536, Obj: 0.208923, No Obj: 0.000438, .5R: 0.888889, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.801649, Class: 0.308099, Obj: 0.243680, No Obj: 0.001345, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.683103, Class: 0.164961, Obj: 0.146983, No Obj: 0.000481, .5R: 0.944444, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.688169, Class: 0.212550, Obj: 0.298784, No Obj: 0.002405, .5R: 1.000000, .75R: 0.214286, count: 14 -Region 23 Avg IOU: 0.603185, Class: 0.251325, Obj: 0.056398, No Obj: 0.000309, .5R: 0.800000, .75R: 0.000000, count: 5 -Region 16 Avg IOU: 0.783450, Class: 0.194116, Obj: 0.417121, No Obj: 0.000907, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.750761, Class: 0.151984, Obj: 0.118805, No Obj: 0.000372, .5R: 0.900000, .75R: 0.500000, count: 10 -Region 16 Avg IOU: 0.808850, Class: 0.231047, Obj: 0.397753, No Obj: 0.002017, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.658764, Class: 0.217711, Obj: 0.266607, No Obj: 0.000621, .5R: 0.761905, .75R: 0.238095, count: 21 - - 1988: 1.968362, 2.445162 avg loss, 0.001000 rate, 1.133651 seconds, 254464 images -Loaded: 0.000078 seconds -Region 16 Avg IOU: 0.703550, Class: 0.228578, Obj: 0.233959, No Obj: 0.001558, .5R: 1.000000, .75R: 0.285714, count: 14 -Region 23 Avg IOU: 0.694539, Class: 0.227779, Obj: 0.151088, No Obj: 0.000488, .5R: 0.833333, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.801588, Class: 0.232898, Obj: 0.438045, No Obj: 0.001243, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.719789, Class: 0.248934, Obj: 0.394387, No Obj: 0.000677, .5R: 0.954545, .75R: 0.454545, count: 22 -Region 16 Avg IOU: 0.750112, Class: 0.223903, Obj: 0.238235, No Obj: 0.000931, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.699173, Class: 0.197821, Obj: 0.230026, No Obj: 0.000499, .5R: 0.913043, .75R: 0.391304, count: 23 -Region 16 Avg IOU: 0.714190, Class: 0.199138, Obj: 0.199692, No Obj: 0.001044, .5R: 1.000000, .75R: 0.230769, count: 13 -Region 23 Avg IOU: 0.664621, Class: 0.220615, Obj: 0.301427, No Obj: 0.000595, .5R: 0.956522, .75R: 0.217391, count: 23 -Region 16 Avg IOU: 0.754570, Class: 0.228875, Obj: 0.303988, No Obj: 0.000976, .5R: 0.909091, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.702564, Class: 0.211152, Obj: 0.297580, No Obj: 0.000461, .5R: 0.933333, .75R: 0.466667, count: 15 -Region 16 Avg IOU: 0.741148, Class: 0.240321, Obj: 0.284071, No Obj: 0.000937, .5R: 0.888889, .75R: 0.444444, count: 9 -Region 23 Avg IOU: 0.756755, Class: 0.220156, Obj: 0.181896, No Obj: 0.000322, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 16 Avg IOU: 0.746386, Class: 0.225757, Obj: 0.364810, No Obj: 0.001112, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.652737, Class: 0.133447, Obj: 0.310029, No Obj: 0.000490, .5R: 0.866667, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.735522, Class: 0.168232, Obj: 0.194129, No Obj: 0.000726, .5R: 0.909091, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.670334, Class: 0.272441, Obj: 0.258437, No Obj: 0.000481, .5R: 1.000000, .75R: 0.307692, count: 13 - - 1989: 1.968971, 2.397543 avg loss, 0.001000 rate, 1.138817 seconds, 254592 images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.751439, Class: 0.264987, Obj: 0.306889, No Obj: 0.000890, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.641452, Class: 0.174112, Obj: 0.219770, No Obj: 0.000549, .5R: 0.875000, .75R: 0.166667, count: 24 -Region 16 Avg IOU: 0.729696, Class: 0.264736, Obj: 0.309498, No Obj: 0.001087, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.673480, Class: 0.132602, Obj: 0.248047, No Obj: 0.000538, .5R: 1.000000, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.782358, Class: 0.224514, Obj: 0.201943, No Obj: 0.000911, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.649764, Class: 0.202753, Obj: 0.247660, No Obj: 0.000595, .5R: 0.750000, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.702972, Class: 0.227328, Obj: 0.219958, No Obj: 0.001103, .5R: 0.800000, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.664838, Class: 0.236862, Obj: 0.210756, No Obj: 0.000613, .5R: 0.909091, .75R: 0.272727, count: 22 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loss, 0.001000 rate, 1.117573 seconds, 254720 images -Resizing -608 x 608 - try to allocate workspace = 1146765313 * sizeof(float), CUDA allocate done! -Loaded: 0.129010 seconds -Region 16 Avg IOU: 0.656354, Class: 0.155072, Obj: 0.311183, No Obj: 0.000658, .5R: 0.909091, .75R: 0.181818, count: 11 -Region 23 Avg IOU: 0.670276, Class: 0.174728, Obj: 0.136985, No Obj: 0.000297, .5R: 1.000000, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.692285, Class: 0.209032, Obj: 0.194349, No Obj: 0.000761, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.670383, Class: 0.171309, Obj: 0.216315, No Obj: 0.000442, .5R: 0.882353, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.671859, Class: 0.167437, Obj: 0.119498, No Obj: 0.000546, .5R: 0.869565, .75R: 0.304348, count: 23 -Region 23 Avg IOU: 0.628753, Class: 0.166104, Obj: 0.130903, No Obj: 0.000332, .5R: 0.866667, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.739999, Class: 0.197069, Obj: 0.378413, No Obj: 0.000780, .5R: 1.000000, 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23 Avg IOU: 0.702695, Class: 0.142833, Obj: 0.227731, No Obj: 0.000363, .5R: 0.933333, .75R: 0.533333, count: 15 - - 1991: 2.716155, 2.429927 avg loss, 0.001000 rate, 1.795615 seconds, 254848 images -Loaded: 0.000067 seconds -Region 16 Avg IOU: 0.759784, Class: 0.245768, Obj: 0.221233, No Obj: 0.001749, .5R: 0.918919, .75R: 0.594595, count: 37 -Region 23 Avg IOU: 0.712572, Class: 0.220233, Obj: 0.100728, No Obj: 0.000360, .5R: 0.941176, .75R: 0.294118, count: 34 -Region 16 Avg IOU: 0.662342, Class: 0.195029, Obj: 0.323477, No Obj: 0.001202, .5R: 0.909091, .75R: 0.181818, count: 11 -Region 23 Avg IOU: 0.711455, Class: 0.135552, Obj: 0.240461, No Obj: 0.000262, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 16 Avg IOU: 0.798765, Class: 0.229651, Obj: 0.270849, No Obj: 0.000690, .5R: 1.000000, .75R: 0.777778, count: 9 -Region 23 Avg IOU: 0.701816, Class: 0.197389, Obj: 0.216580, No Obj: 0.000363, .5R: 1.000000, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.776879, Class: 0.259831, Obj: 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0.729402, Class: 0.175386, Obj: 0.170025, No Obj: 0.000201, .5R: 0.950000, .75R: 0.500000, count: 20 -Region 16 Avg IOU: 0.794943, Class: 0.234238, Obj: 0.323216, No Obj: 0.000870, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.675143, Class: 0.202960, Obj: 0.134217, No Obj: 0.000230, .5R: 0.916667, .75R: 0.208333, count: 24 -Region 16 Avg IOU: 0.687013, Class: 0.214199, Obj: 0.298656, No Obj: 0.001382, .5R: 0.812500, .75R: 0.250000, count: 16 -Region 23 Avg IOU: 0.679769, Class: 0.192084, Obj: 0.089254, No Obj: 0.000176, .5R: 0.857143, .75R: 0.380952, count: 21 -Region 16 Avg IOU: 0.706805, Class: 0.232344, Obj: 0.304572, No Obj: 0.002018, .5R: 0.888889, .75R: 0.388889, count: 18 -Region 23 Avg IOU: 0.640169, Class: 0.117965, Obj: 0.098212, No Obj: 0.000233, .5R: 0.866667, .75R: 0.133333, count: 15 -Region 16 Avg IOU: 0.800859, Class: 0.237661, Obj: 0.303618, No Obj: 0.001334, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.693612, Class: 0.131378, Obj: 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1.000000, .75R: 0.578947, count: 19 -Region 23 Avg IOU: 0.653329, Class: 0.214487, Obj: 0.184338, No Obj: 0.000371, .5R: 0.904762, .75R: 0.285714, count: 21 -Region 16 Avg IOU: 0.666031, Class: 0.183208, Obj: 0.225398, No Obj: 0.001116, .5R: 0.888889, .75R: 0.277778, count: 18 -Region 23 Avg IOU: 0.684131, Class: 0.149705, Obj: 0.160345, No Obj: 0.000263, .5R: 0.923077, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.782160, Class: 0.238110, Obj: 0.322453, No Obj: 0.001443, .5R: 1.000000, .75R: 0.727273, count: 22 -Region 23 Avg IOU: 0.705373, Class: 0.146918, Obj: 0.316969, No Obj: 0.000509, .5R: 1.000000, .75R: 0.285714, count: 21 -Region 16 Avg IOU: 0.654722, Class: 0.187435, Obj: 0.323968, No Obj: 0.001198, .5R: 0.800000, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.617699, Class: 0.215524, Obj: 0.286671, No Obj: 0.000428, .5R: 0.695652, .75R: 0.304348, count: 23 -Region 16 Avg IOU: 0.726072, Class: 0.147895, Obj: 0.296721, No Obj: 0.002038, .5R: 0.925926, .75R: 0.555556, count: 27 -Region 23 Avg IOU: 0.688021, Class: 0.146298, Obj: 0.192666, No Obj: 0.000530, .5R: 0.965517, .75R: 0.206897, count: 29 -Region 16 Avg IOU: 0.760182, Class: 0.191107, Obj: 0.347311, No Obj: 0.000840, .5R: 0.909091, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.662894, Class: 0.190118, Obj: 0.214617, No Obj: 0.000444, .5R: 0.884615, .75R: 0.269231, count: 26 - - 1996: 2.852284, 2.508027 avg loss, 0.001000 rate, 1.764513 seconds, 255488 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.710022, Class: 0.172646, Obj: 0.278879, No Obj: 0.001672, .5R: 0.944444, .75R: 0.388889, count: 18 -Region 23 Avg IOU: 0.655467, Class: 0.226428, Obj: 0.168430, No Obj: 0.000562, .5R: 0.875000, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.732213, Class: 0.203592, Obj: 0.249004, No Obj: 0.001384, .5R: 0.904762, .75R: 0.571429, count: 21 -Region 23 Avg IOU: 0.719285, Class: 0.189117, Obj: 0.168495, No Obj: 0.000447, .5R: 1.000000, .75R: 0.318182, count: 22 -Region 16 Avg IOU: 0.725657, Class: 0.185293, Obj: 0.287122, No Obj: 0.001189, .5R: 0.941176, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 0.722200, Class: 0.182969, Obj: 0.420936, No Obj: 0.000409, .5R: 1.000000, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.706586, Class: 0.208812, Obj: 0.172531, No Obj: 0.001227, .5R: 0.875000, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.664336, Class: 0.168226, Obj: 0.301833, No Obj: 0.000657, .5R: 0.906250, .75R: 0.218750, count: 32 -Region 16 Avg IOU: 0.730668, Class: 0.181069, Obj: 0.239215, No Obj: 0.001157, .5R: 0.947368, .75R: 0.631579, count: 19 -Region 23 Avg IOU: 0.679691, Class: 0.198288, Obj: 0.246153, No Obj: 0.000490, .5R: 0.900000, .75R: 0.300000, count: 20 -Region 16 Avg IOU: 0.716451, Class: 0.271004, Obj: 0.490323, No Obj: 0.001470, .5R: 0.866667, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.693523, Class: 0.176193, Obj: 0.268099, No Obj: 0.000454, .5R: 0.954545, .75R: 0.318182, count: 22 -Region 16 Avg IOU: 0.739203, Class: 0.262757, Obj: 0.300602, No Obj: 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count: 18 -Region 16 Avg IOU: 0.757505, Class: 0.251292, Obj: 0.346770, No Obj: 0.000860, .5R: 0.923077, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.741141, Class: 0.196176, Obj: 0.200621, No Obj: 0.000209, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 16 Avg IOU: 0.783572, Class: 0.281950, Obj: 0.246288, No Obj: 0.000810, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.714902, Class: 0.242156, Obj: 0.186084, No Obj: 0.000209, .5R: 0.923077, .75R: 0.461538, count: 13 -Region 16 Avg IOU: 0.718294, Class: 0.229476, Obj: 0.185508, No Obj: 0.001229, .5R: 0.904762, .75R: 0.380952, count: 21 -Region 23 Avg IOU: 0.686664, Class: 0.172535, Obj: 0.083431, No Obj: 0.000239, .5R: 0.923077, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.717470, Class: 0.251702, Obj: 0.245522, No Obj: 0.001584, .5R: 0.960000, .75R: 0.520000, count: 25 -Region 23 Avg IOU: 0.641267, Class: 0.182333, Obj: 0.251626, No Obj: 0.000316, .5R: 0.888889, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 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No Obj: 0.000212, .5R: 0.900000, .75R: 0.300000, count: 20 -Region 16 Avg IOU: 0.733333, Class: 0.291982, Obj: 0.319253, No Obj: 0.000975, .5R: 0.933333, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.699429, Class: 0.226201, Obj: 0.088268, No Obj: 0.000178, .5R: 0.863636, .75R: 0.363636, count: 22 -Region 16 Avg IOU: 0.756910, Class: 0.178738, Obj: 0.280488, No Obj: 0.000899, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.690397, Class: 0.244519, Obj: 0.162378, No Obj: 0.000267, .5R: 0.962963, .75R: 0.333333, count: 27 -Region 16 Avg IOU: 0.712184, Class: 0.285329, Obj: 0.253156, No Obj: 0.001215, .5R: 1.000000, .75R: 0.500000, count: 20 -Region 23 Avg IOU: 0.669657, Class: 0.218991, Obj: 0.068883, No Obj: 0.000170, .5R: 0.851852, .75R: 0.333333, count: 27 -Region 16 Avg IOU: 0.779795, Class: 0.244236, Obj: 0.362976, No Obj: 0.001153, .5R: 0.944444, .75R: 0.611111, count: 18 -Region 23 Avg IOU: 0.709935, Class: 0.237763, Obj: 0.202287, No Obj: 0.000234, .5R: 0.933333, 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Obj: 0.095611, No Obj: 0.000211, .5R: 0.960000, .75R: 0.280000, count: 25 -Region 16 Avg IOU: 0.716885, Class: 0.160968, Obj: 0.333494, No Obj: 0.001590, .5R: 0.894737, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.718573, Class: 0.132674, Obj: 0.171590, No Obj: 0.000216, .5R: 1.000000, .75R: 0.466667, count: 15 -Region 16 Avg IOU: 0.769561, Class: 0.219959, Obj: 0.349397, NSaving weights to ./attribute/7354_26298/attribute_33652_s_tyv3_9c5_2000.weights -o Obj: 0.001844, .5R: 0.928571, .75R: 0.642857, count: 28 -Region 23 Avg IOU: 0.732300, Class: 0.202102, Obj: 0.121889, No Obj: 0.000217, .5R: 0.961538, .75R: 0.500000, count: 26 - - 2000: 2.731888, 2.555330 avg loss, 0.001000 rate, 1.762287 seconds, 256000 images -Resizing -576 x 576 - try to allocate workspace = 1146159105 * sizeof(float), CUDA allocate done! -Loaded: 0.609084 seconds -Region 16 Avg IOU: 0.633719, Class: 0.214131, Obj: 0.240954, No Obj: 0.001299, .5R: 0.800000, .75R: 0.200000, count: 20 -Region 23 Avg IOU: 0.719289, Class: 0.226836, Obj: 0.149517, No Obj: 0.000254, .5R: 0.882353, .75R: 0.470588, count: 17 -Region 16 Avg IOU: 0.751790, Class: 0.224435, Obj: 0.323235, No Obj: 0.001109, .5R: 1.000000, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.664404, Class: 0.167612, Obj: 0.152041, No Obj: 0.000364, .5R: 0.848485, .75R: 0.303030, count: 33 -Region 16 Avg IOU: 0.738081, Class: 0.209520, Obj: 0.360329, No Obj: 0.001997, .5R: 0.952381, .75R: 0.476190, count: 21 -Region 23 Avg IOU: 0.703716, Class: 0.150857, Obj: 0.439333, No Obj: 0.000567, .5R: 0.904762, .75R: 0.380952, count: 21 -Region 16 Avg IOU: 0.721457, Class: 0.194240, Obj: 0.474268, No Obj: 0.001270, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.722885, Class: 0.155675, Obj: 0.277530, No Obj: 0.000363, .5R: 1.000000, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.713674, Class: 0.141061, Obj: 0.226145, No Obj: 0.001165, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.674174, Class: 0.173896, Obj: 0.168806, No Obj: 0.000354, .5R: 0.894737, .75R: 0.263158, count: 19 -Region 16 Avg IOU: 0.711014, Class: 0.189556, Obj: 0.272835, No Obj: 0.001014, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.728449, Class: 0.192609, Obj: 0.087920, No Obj: 0.000124, .5R: 1.000000, .75R: 0.333333, count: 9 -Region 16 Avg IOU: 0.717985, Class: 0.197453, Obj: 0.394867, No Obj: 0.001355, .5R: 0.954545, .75R: 0.454545, count: 22 -Region 23 Avg IOU: 0.705425, Class: 0.195390, Obj: 0.178597, No Obj: 0.000309, .5R: 0.826087, .75R: 0.434783, count: 23 -Region 16 Avg IOU: 0.712819, Class: 0.260757, Obj: 0.295905, No Obj: 0.001545, .5R: 0.952381, .75R: 0.380952, count: 21 -Region 23 Avg IOU: 0.676669, Class: 0.169732, Obj: 0.219500, No Obj: 0.000302, .5R: 0.937500, .75R: 0.312500, count: 16 - - 2001: 2.591861, 2.558983 avg loss, 0.001000 rate, 1.637521 seconds, 256128 images -Loaded: 0.000068 seconds -Region 16 Avg IOU: 0.743015, Class: 0.212404, Obj: 0.228556, No Obj: 0.001442, .5R: 0.952381, .75R: 0.523810, count: 21 -Region 23 Avg IOU: 0.669829, Class: 0.179400, Obj: 0.298971, No Obj: 0.000458, .5R: 0.888889, .75R: 0.222222, count: 18 -Region 16 Avg IOU: 0.717017, Class: 0.213115, Obj: 0.333498, No Obj: 0.002135, .5R: 0.925926, .75R: 0.481481, count: 27 -Region 23 Avg IOU: 0.708007, Class: 0.217119, Obj: 0.188280, No Obj: 0.000444, .5R: 0.947368, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.777856, Class: 0.237916, Obj: 0.271031, No Obj: 0.001336, .5R: 1.000000, .75R: 0.611111, count: 18 -Region 23 Avg IOU: 0.699011, Class: 0.166021, Obj: 0.167413, No Obj: 0.000337, .5R: 0.947368, .75R: 0.473684, count: 19 -Region 16 Avg IOU: 0.703340, Class: 0.237817, Obj: 0.254762, No Obj: 0.001604, .5R: 0.894737, .75R: 0.368421, count: 19 -Region 23 Avg IOU: 0.664827, Class: 0.139258, Obj: 0.152827, No Obj: 0.000541, .5R: 0.928571, .75R: 0.214286, count: 28 -Region 16 Avg IOU: 0.730411, Class: 0.257503, Obj: 0.370626, No Obj: 0.001663, .5R: 1.000000, .75R: 0.400000, count: 20 -Region 23 Avg IOU: 0.690267, Class: 0.179093, Obj: 0.234093, No Obj: 0.000590, .5R: 0.923077, .75R: 0.346154, count: 26 -Region 16 Avg IOU: 0.759613, Class: 0.173168, Obj: 0.302205, No Obj: 0.001312, .5R: 1.000000, .75R: 0.500000, count: 22 -Region 23 Avg IOU: 0.725892, Class: 0.220705, Obj: 0.204235, No Obj: 0.000350, .5R: 0.947368, .75R: 0.526316, count: 19 -Region 16 Avg IOU: 0.679745, Class: 0.203015, Obj: 0.435157, No Obj: 0.001263, .5R: 0.800000, .75R: 0.333333, count: 15 -Region 23 Avg IOU: 0.691574, Class: 0.184165, Obj: 0.169552, No Obj: 0.000465, .5R: 0.931035, .75R: 0.310345, count: 29 -Region 16 Avg IOU: 0.735148, Class: 0.192054, Obj: 0.319038, No Obj: 0.001813, .5R: 0.909091, .75R: 0.636364, count: 22 -Region 23 Avg IOU: 0.633349, Class: 0.213836, Obj: 0.209498, No Obj: 0.000391, .5R: 0.875000, .75R: 0.125000, count: 16 - - 2002: 2.954838, 2.598568 avg loss, 0.001000 rate, 1.616446 seconds, 256256 images -Loaded: 0.000061 seconds -Region 16 Avg IOU: 0.657115, Class: 0.222024, Obj: 0.114602, No Obj: 0.001130, .5R: 0.722222, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.680570, Class: 0.190195, Obj: 0.221734, No Obj: 0.000455, .5R: 0.956522, .75R: 0.347826, count: 23 -Region 16 Avg IOU: 0.697492, Class: 0.230710, Obj: 0.374462, No Obj: 0.002128, .5R: 0.906250, .75R: 0.375000, count: 32 -Region 23 Avg IOU: 0.721941, Class: 0.218196, Obj: 0.240952, No Obj: 0.000732, .5R: 0.965517, .75R: 0.482759, count: 29 -Region 16 Avg IOU: 0.736146, Class: 0.237897, Obj: 0.309067, No Obj: 0.002169, .5R: 1.000000, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.677207, Class: 0.216665, Obj: 0.183745, No Obj: 0.000420, .5R: 0.928571, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.758241, Class: 0.177520, Obj: 0.357603, No Obj: 0.001783, .5R: 1.000000, .75R: 0.611111, count: 18 -Region 23 Avg IOU: 0.706572, Class: 0.176091, Obj: 0.220683, No Obj: 0.000431, .5R: 1.000000, .75R: 0.500000, count: 20 -Region 16 Avg IOU: 0.777415, Class: 0.206687, Obj: 0.383454, No Obj: 0.001546, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.703459, Class: 0.182532, Obj: 0.250649, No Obj: 0.000448, .5R: 0.900000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.740718, Class: 0.213691, Obj: 0.252036, No Obj: 0.001235, .5R: 0.944444, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.663744, Class: 0.180881, Obj: 0.299162, No Obj: 0.000425, .5R: 0.882353, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.747560, Class: 0.156210, Obj: 0.321272, No Obj: 0.001102, .5R: 0.933333, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.676079, Class: 0.196380, Obj: 0.333240, No Obj: 0.000463, .5R: 0.944444, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.776396, Class: 0.228780, Obj: 0.328242, No Obj: 0.002210, .5R: 0.961538, .75R: 0.692308, count: 26 -Region 23 Avg IOU: 0.679294, Class: 0.229970, Obj: 0.173041, No Obj: 0.000470, .5R: 0.875000, .75R: 0.250000, count: 16 - - 2003: 2.817796, 2.620491 avg loss, 0.001000 rate, 1.639843 seconds, 256384 images -Loaded: 0.000083 seconds -Region 16 Avg IOU: 0.783067, Class: 0.257132, Obj: 0.400569, No Obj: 0.000908, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.687753, Class: 0.194962, Obj: 0.231203, No Obj: 0.000289, .5R: 0.947368, .75R: 0.368421, count: 19 -Region 16 Avg IOU: 0.734854, Class: 0.219670, Obj: 0.340587, No Obj: 0.000741, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.718911, Class: 0.164560, Obj: 0.227659, No Obj: 0.000382, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.745332, Class: 0.321297, Obj: 0.402077, No Obj: 0.001248, .5R: 0.923077, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.676222, Class: 0.179039, Obj: 0.184587, No Obj: 0.000376, .5R: 0.909091, .75R: 0.272727, count: 22 -Region 16 Avg IOU: 0.677511, Class: 0.147592, Obj: 0.352879, No Obj: 0.001315, .5R: 0.875000, .75R: 0.437500, count: 16 -Region 23 Avg IOU: 0.617761, Class: 0.152217, Obj: 0.192287, No Obj: 0.000455, .5R: 0.809524, .75R: 0.190476, count: 21 -Region 16 Avg IOU: 0.634635, Class: 0.223422, Obj: 0.235509, No Obj: 0.001153, .5R: 0.833333, .75R: 0.166667, count: 12 -Region 23 Avg IOU: 0.668973, Class: 0.212108, Obj: 0.124204, No Obj: 0.000316, .5R: 0.923077, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.724389, Class: 0.241199, Obj: 0.369786, No Obj: 0.001768, .5R: 0.916667, .75R: 0.500000, count: 24 -Region 23 Avg IOU: 0.710692, Class: 0.211183, Obj: 0.338928, No Obj: 0.000541, .5R: 0.923077, .75R: 0.384615, count: 26 -Region 16 Avg IOU: 0.711598, Class: 0.227980, Obj: 0.353416, No Obj: 0.001744, .5R: 0.904762, .75R: 0.428571, count: 21 -Region 23 Avg IOU: 0.717335, Class: 0.179485, Obj: 0.191117, No Obj: 0.000301, .5R: 1.000000, .75R: 0.368421, count: 19 -Region 16 Avg IOU: 0.774950, Class: 0.234175, Obj: 0.384060, No Obj: 0.001298, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.592626, Class: 0.226225, Obj: 0.196709, No Obj: 0.000298, .5R: 0.647059, .75R: 0.117647, count: 17 - - 2004: 2.395411, 2.597983 avg loss, 0.001000 rate, 1.613449 seconds, 256512 images -Loaded: 0.000061 seconds -Region 16 Avg IOU: 0.707181, Class: 0.230382, Obj: 0.244761, No Obj: 0.001301, .5R: 0.909091, .75R: 0.545455, count: 22 -Region 23 Avg IOU: 0.714573, Class: 0.223831, Obj: 0.165554, No Obj: 0.000279, .5R: 1.000000, .75R: 0.473684, count: 19 -Region 16 Avg IOU: 0.709282, Class: 0.261916, Obj: 0.299907, No Obj: 0.001424, .5R: 0.941176, .75R: 0.352941, count: 17 -Region 23 Avg IOU: 0.710330, Class: 0.275791, Obj: 0.171857, No Obj: 0.000147, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 16 Avg IOU: 0.692712, Class: 0.158366, Obj: 0.276711, No Obj: 0.001281, .5R: 0.933333, .75R: 0.200000, count: 15 -Region 23 Avg IOU: 0.658377, Class: 0.114771, Obj: 0.076684, No Obj: 0.000175, .5R: 0.866667, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.731278, Class: 0.183157, Obj: 0.273731, No Obj: 0.001319, .5R: 0.956522, .75R: 0.565217, count: 23 -Region 23 Avg IOU: 0.655887, Class: 0.140228, Obj: 0.150132, No Obj: 0.000355, .5R: 0.838710, .75R: 0.322581, count: 31 -Region 16 Avg IOU: 0.726032, Class: 0.188391, Obj: 0.358556, No Obj: 0.002576, .5R: 0.909091, .75R: 0.545455, count: 33 -Region 23 Avg IOU: 0.705445, Class: 0.151717, Obj: 0.201567, No Obj: 0.000531, .5R: 0.909091, .75R: 0.333333, count: 33 -Region 16 Avg IOU: 0.782651, Class: 0.225523, Obj: 0.423756, No Obj: 0.001985, .5R: 1.000000, .75R: 0.666667, count: 21 -Region 23 Avg IOU: 0.738097, Class: 0.229772, Obj: 0.183886, No Obj: 0.000234, .5R: 1.000000, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.773812, Class: 0.328089, Obj: 0.392953, No Obj: 0.001014, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.674108, Class: 0.157395, Obj: 0.195067, No Obj: 0.000281, .5R: 0.909091, .75R: 0.318182, count: 22 -Region 16 Avg IOU: 0.759041, Class: 0.220859, Obj: 0.300619, No Obj: 0.001733, .5R: 1.000000, .75R: 0.620690, count: 29 -Region 23 Avg IOU: 0.699195, Class: 0.215923, Obj: 0.247915, No Obj: 0.000334, .5R: 0.944444, .75R: 0.388889, count: 18 - - 2005: 2.943778, 2.632563 avg loss, 0.001000 rate, 1.626286 seconds, 256640 images -Loaded: 0.000063 seconds -Region 16 Avg IOU: 0.713640, Class: 0.223901, Obj: 0.173706, No Obj: 0.000939, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 23 Avg IOU: 0.690478, Class: 0.220398, Obj: 0.101568, No Obj: 0.000204, .5R: 0.933333, .75R: 0.400000, count: 15 -Region 16 Avg IOU: 0.720547, Class: 0.206267, Obj: 0.410485, No Obj: 0.001680, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.679717, Class: 0.202308, Obj: 0.187691, No Obj: 0.000265, .5R: 0.909091, .75R: 0.136364, count: 22 -Region 16 Avg IOU: 0.755594, Class: 0.153739, Obj: 0.304236, No Obj: 0.001460, .5R: 1.000000, .75R: 0.518519, count: 27 -Region 23 Avg IOU: 0.738193, Class: 0.170397, Obj: 0.252254, No Obj: 0.000361, .5R: 1.000000, .75R: 0.476190, count: 21 -Region 16 Avg IOU: 0.762407, Class: 0.291947, Obj: 0.404738, No Obj: 0.001330, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.710904, Class: 0.265673, Obj: 0.198633, No Obj: 0.000165, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 16 Avg IOU: 0.676568, Class: 0.203288, Obj: 0.225706, No Obj: 0.001186, .5R: 0.888889, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.692909, Class: 0.199360, Obj: 0.170288, No Obj: 0.000318, .5R: 0.967742, .75R: 0.354839, count: 31 -Region 16 Avg IOU: 0.680589, Class: 0.178997, Obj: 0.306323, No Obj: 0.001577, .5R: 0.800000, .75R: 0.480000, count: 25 -Region 23 Avg IOU: 0.674168, Class: 0.187452, Obj: 0.184664, No Obj: 0.000281, .5R: 0.812500, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.730061, Class: 0.211202, Obj: 0.330896, No Obj: 0.001435, .5R: 0.894737, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.703515, Class: 0.230208, Obj: 0.157193, No Obj: 0.000226, .5R: 0.947368, .75R: 0.421053, count: 19 -Region 16 Avg IOU: 0.656111, Class: 0.200005, Obj: 0.236611, No Obj: 0.001444, .5R: 0.850000, .75R: 0.350000, count: 20 -Region 23 Avg IOU: 0.738941, Class: 0.191890, Obj: 0.154387, No Obj: 0.000255, .5R: 0.950000, .75R: 0.500000, count: 20 - - 2006: 2.680169, 2.637323 avg loss, 0.001000 rate, 1.581919 seconds, 256768 images -Loaded: 0.000067 seconds -Region 16 Avg IOU: 0.766210, Class: 0.202617, Obj: 0.306075, No Obj: 0.001329, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.716081, Class: 0.188753, Obj: 0.236613, No Obj: 0.000500, .5R: 1.000000, .75R: 0.307692, count: 26 -Region 16 Avg IOU: 0.732466, Class: 0.285862, Obj: 0.426664, No Obj: 0.001316, .5R: 0.941176, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.761977, Class: 0.198118, Obj: 0.235067, No Obj: 0.000264, .5R: 1.000000, .75R: 0.642857, count: 14 -Region 16 Avg IOU: 0.753764, Class: 0.181683, Obj: 0.364340, No Obj: 0.001393, .5R: 1.000000, .75R: 0.411765, count: 17 -Region 23 Avg IOU: 0.661176, Class: 0.162052, Obj: 0.215094, No Obj: 0.000287, .5R: 0.875000, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.723713, Class: 0.217572, Obj: 0.327308, No Obj: 0.001078, .5R: 1.000000, .75R: 0.368421, count: 19 -Region 23 Avg IOU: 0.692535, Class: 0.189944, Obj: 0.124142, No Obj: 0.000206, .5R: 1.000000, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.786339, Class: 0.300394, Obj: 0.407589, No Obj: 0.001086, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.686286, Class: 0.246372, Obj: 0.170167, No Obj: 0.000338, .5R: 0.833333, .75R: 0.291667, count: 24 -Region 16 Avg IOU: 0.711709, Class: 0.286585, Obj: 0.474306, No Obj: 0.001048, .5R: 1.000000, .75R: 0.357143, count: 14 -Region 23 Avg IOU: 0.705299, Class: 0.237611, Obj: 0.202482, No Obj: 0.000342, .5R: 1.000000, .75R: 0.347826, count: 23 -Region 16 Avg IOU: 0.713048, Class: 0.257528, Obj: 0.330899, No Obj: 0.000843, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.677474, Class: 0.179737, Obj: 0.163785, No Obj: 0.000176, .5R: 0.916667, .75R: 0.166667, count: 12 -Region 16 Avg IOU: 0.694405, Class: 0.226441, Obj: 0.278969, No Obj: 0.001098, .5R: 0.941176, .75R: 0.352941, count: 17 -Region 23 Avg IOU: 0.718877, Class: 0.201649, Obj: 0.145308, No Obj: 0.000274, .5R: 1.000000, .75R: 0.454545, count: 22 - - 2007: 2.312370, 2.604828 avg loss, 0.001000 rate, 1.580016 seconds, 256896 images -Loaded: 0.000060 seconds -Region 16 Avg IOU: 0.758003, Class: 0.200207, Obj: 0.298563, No Obj: 0.001029, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.682785, Class: 0.166660, Obj: 0.298482, No Obj: 0.000428, .5R: 0.952381, .75R: 0.285714, count: 21 -Region 16 Avg IOU: 0.695144, Class: 0.230489, Obj: 0.235792, No Obj: 0.000677, .5R: 0.900000, .75R: 0.400000, count: 10 -Region 23 Avg IOU: 0.736122, Class: 0.241055, Obj: 0.190821, No Obj: 0.000392, .5R: 1.000000, .75R: 0.428571, count: 21 -Region 16 Avg IOU: 0.801489, Class: 0.265770, Obj: 0.539764, No Obj: 0.001246, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.684610, Class: 0.233832, Obj: 0.215563, No Obj: 0.000342, .5R: 0.928571, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.727738, Class: 0.202341, Obj: 0.280556, No Obj: 0.001401, .5R: 0.875000, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.724279, Class: 0.105374, Obj: 0.139310, No Obj: 0.000324, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 16 Avg IOU: 0.712671, Class: 0.157630, Obj: 0.363695, No Obj: 0.001240, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.650612, Class: 0.205033, Obj: 0.245966, No Obj: 0.000605, .5R: 0.882353, .75R: 0.235294, count: 17 -Region 16 Avg IOU: 0.778361, Class: 0.225698, Obj: 0.370503, No Obj: 0.001674, .5R: 1.000000, .75R: 0.625000, count: 24 -Region 23 Avg IOU: 0.729281, Class: 0.237945, Obj: 0.308320, No Obj: 0.000422, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 16 Avg IOU: 0.675533, Class: 0.180271, Obj: 0.238423, No Obj: 0.001257, .5R: 0.894737, .75R: 0.421053, count: 19 -Region 23 Avg IOU: 0.703808, Class: 0.156743, Obj: 0.366754, No Obj: 0.000520, .5R: 0.888889, .75R: 0.500000, count: 18 -Region 16 Avg IOU: 0.709595, Class: 0.176084, Obj: 0.243794, No Obj: 0.001363, .5R: 1.000000, .75R: 0.285714, count: 14 -Region 23 Avg IOU: 0.711275, Class: 0.194130, Obj: 0.266605, No Obj: 0.000497, .5R: 0.944444, .75R: 0.333333, count: 18 - - 2008: 2.153444, 2.559690 avg loss, 0.001000 rate, 1.601252 seconds, 257024 images -Loaded: 0.000061 seconds -Region 16 Avg IOU: 0.672669, Class: 0.273372, Obj: 0.261542, No Obj: 0.001524, .5R: 0.894737, .75R: 0.210526, count: 19 -Region 23 Avg IOU: 0.661632, Class: 0.173465, Obj: 0.107007, No Obj: 0.000258, .5R: 0.846154, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.781606, Class: 0.275588, Obj: 0.341686, No Obj: 0.001427, .5R: 1.000000, .75R: 0.772727, count: 22 -Region 23 Avg IOU: 0.720358, Class: 0.229380, Obj: 0.182969, No Obj: 0.000533, .5R: 0.970588, .75R: 0.352941, count: 34 -Region 16 Avg IOU: 0.725987, Class: 0.218077, Obj: 0.156818, No Obj: 0.001324, .5R: 0.956522, .75R: 0.478261, count: 23 -Region 23 Avg IOU: 0.722384, Class: 0.204756, Obj: 0.242219, No Obj: 0.000440, .5R: 0.950000, .75R: 0.550000, count: 20 -Region 16 Avg IOU: 0.747274, Class: 0.228373, Obj: 0.381727, No Obj: 0.001639, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.671196, Class: 0.179086, Obj: 0.269110, No Obj: 0.000469, .5R: 0.851852, .75R: 0.185185, count: 27 -Region 16 Avg IOU: 0.790976, Class: 0.184068, Obj: 0.367929, No Obj: 0.001307, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.684404, Class: 0.192791, Obj: 0.257111, No Obj: 0.000429, .5R: 0.833333, .75R: 0.500000, count: 12 -Region 16 Avg IOU: 0.716227, Class: 0.186885, Obj: 0.170817, No Obj: 0.001058, .5R: 0.850000, .75R: 0.550000, count: 20 -Region 23 Avg IOU: 0.677556, Class: 0.194573, Obj: 0.168276, No Obj: 0.000432, .5R: 0.891892, .75R: 0.297297, count: 37 -Region 16 Avg IOU: 0.759824, Class: 0.198560, Obj: 0.285655, No Obj: 0.001412, .5R: 1.000000, .75R: 0.541667, count: 24 -Region 23 Avg IOU: 0.672924, Class: 0.220039, Obj: 0.151700, No Obj: 0.000271, .5R: 0.923077, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.766311, Class: 0.169258, Obj: 0.402307, No Obj: 0.001311, .5R: 1.000000, .75R: 0.611111, count: 18 -Region 23 Avg IOU: 0.622476, Class: 0.183159, Obj: 0.331122, No Obj: 0.000282, .5R: 0.750000, .75R: 0.166667, count: 12 - - 2009: 2.883368, 2.592057 avg loss, 0.001000 rate, 1.594978 seconds, 257152 images -Loaded: 0.000059 seconds -Region 16 Avg IOU: 0.735775, Class: 0.238585, Obj: 0.256726, No Obj: 0.001186, .5R: 0.928571, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.698405, Class: 0.204796, Obj: 0.096971, No Obj: 0.000324, .5R: 0.954545, .75R: 0.409091, count: 22 -Region 16 Avg IOU: 0.737080, Class: 0.202204, Obj: 0.260857, No Obj: 0.001771, .5R: 0.925926, .75R: 0.629630, count: 27 -Region 23 Avg IOU: 0.679965, Class: 0.149687, Obj: 0.085982, No Obj: 0.000395, .5R: 0.843750, .75R: 0.437500, count: 32 -Region 16 Avg IOU: 0.742231, Class: 0.174048, Obj: 0.391447, No Obj: 0.001194, .5R: 0.933333, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.704234, Class: 0.169866, Obj: 0.211853, No Obj: 0.000292, .5R: 0.958333, .75R: 0.375000, count: 24 -Region 16 Avg IOU: 0.746152, Class: 0.280940, Obj: 0.308502, No Obj: 0.001686, .5R: 1.000000, .75R: 0.500000, count: 24 -Region 23 Avg IOU: 0.689985, Class: 0.218141, Obj: 0.114813, No Obj: 0.000239, .5R: 0.823529, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.684268, Class: 0.158487, Obj: 0.300244, No Obj: 0.000968, .5R: 1.000000, .75R: 0.333333, count: 9 -Region 23 Avg IOU: 0.644148, Class: 0.185194, Obj: 0.100584, No Obj: 0.000232, .5R: 0.928571, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.710776, Class: 0.206641, Obj: 0.228021, No Obj: 0.001398, .5R: 0.920000, .75R: 0.560000, count: 25 -Region 23 Avg IOU: 0.751569, Class: 0.182202, Obj: 0.106330, No Obj: 0.000244, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 16 Avg IOU: 0.700287, Class: 0.168184, Obj: 0.392482, No Obj: 0.001255, .5R: 1.000000, .75R: 0.333333, count: 12 -Region 23 Avg IOU: 0.679507, Class: 0.260720, Obj: 0.067436, No Obj: 0.000168, .5R: 1.000000, .75R: 0.235294, count: 17 -Region 16 Avg IOU: 0.762641, Class: 0.149559, Obj: 0.331345, No Obj: 0.001123, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.671152, Class: 0.209496, Obj: 0.274791, No Obj: 0.000337, .5R: 0.928571, .75R: 0.285714, count: 14 - - 2010: 2.710625, 2.603914 avg loss, 0.001000 rate, 1.589430 seconds, 257280 images -Resizing -736 x 736 - try to allocate workspace = 1149517825 * sizeof(float), CUDA allocate done! -Loaded: 0.214765 seconds -Region 16 Avg IOU: 0.708164, Class: 0.232927, Obj: 0.328606, No Obj: 0.001510, .5R: 0.967742, .75R: 0.451613, count: 31 -Region 23 Avg IOU: 0.662019, Class: 0.196250, Obj: 0.132507, No Obj: 0.000196, .5R: 0.882353, .75R: 0.235294, count: 17 -Region 16 Avg IOU: 0.653780, Class: 0.171220, Obj: 0.314105, No Obj: 0.001217, .5R: 0.800000, .75R: 0.250000, count: 20 -Region 23 Avg IOU: 0.717550, Class: 0.138608, Obj: 0.127344, No Obj: 0.000190, .5R: 1.000000, .75R: 0.444444, count: 18 -Region 16 Avg IOU: 0.692384, Class: 0.230687, Obj: 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0.944444, .75R: 0.416667, count: 36 -Region 23 Avg IOU: 0.683887, Class: 0.262300, Obj: 0.114757, No Obj: 0.000224, .5R: 0.950000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.748630, Class: 0.260275, Obj: 0.336783, No Obj: 0.001102, .5R: 1.000000, .75R: 0.571429, count: 21 -Region 23 Avg IOU: 0.744451, Class: 0.229095, Obj: 0.138214, No Obj: 0.000212, .5R: 1.000000, .75R: 0.500000, count: 16 - - 2011: 2.796786, 2.623201 avg loss, 0.001000 rate, 2.524680 seconds, 257408 images -Loaded: 0.000064 seconds -Region 16 Avg IOU: 0.678286, Class: 0.180684, Obj: 0.293884, No Obj: 0.001457, .5R: 0.833333, .75R: 0.400000, count: 30 -Region 23 Avg IOU: 0.660707, Class: 0.171340, Obj: 0.090662, No Obj: 0.000163, .5R: 0.857143, .75R: 0.285714, count: 21 -Region 16 Avg IOU: 0.625022, Class: 0.161579, Obj: 0.371737, No Obj: 0.001002, .5R: 0.750000, .75R: 0.250000, count: 16 -Region 23 Avg IOU: 0.681184, Class: 0.150549, Obj: 0.164266, No Obj: 0.000200, .5R: 1.000000, .75R: 0.222222, count: 27 -Region 16 Avg IOU: 0.679310, Class: 0.141529, Obj: 0.286288, No Obj: 0.001413, .5R: 1.000000, .75R: 0.185185, count: 27 -Region 23 Avg IOU: 0.685604, Class: 0.130680, Obj: 0.230463, No Obj: 0.000181, .5R: 0.875000, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.725915, Class: 0.209479, Obj: 0.269987, No Obj: 0.000988, .5R: 0.937500, .75R: 0.437500, count: 16 -Region 23 Avg IOU: 0.685673, Class: 0.192369, Obj: 0.243456, No Obj: 0.000266, .5R: 0.894737, .75R: 0.263158, count: 19 -Region 16 Avg IOU: 0.762031, Class: 0.162541, Obj: 0.337815, No Obj: 0.001251, .5R: 0.956522, .75R: 0.608696, count: 23 -Region 23 Avg IOU: 0.698372, Class: 0.111404, Obj: 0.208134, No Obj: 0.000164, .5R: 1.000000, .75R: 0.363636, count: 11 -Region 16 Avg IOU: 0.711852, Class: 0.216230, Obj: 0.403386, No Obj: 0.001033, .5R: 0.842105, .75R: 0.631579, count: 19 -Region 23 Avg IOU: 0.723579, Class: 0.160209, Obj: 0.156761, No Obj: 0.000128, .5R: 1.000000, .75R: 0.470588, count: 17 -Region 16 Avg IOU: 0.739243, Class: 0.198943, Obj: 0.311348, No Obj: 0.001874, .5R: 0.971429, .75R: 0.485714, count: 35 -Region 23 Avg IOU: 0.697603, Class: 0.188607, Obj: 0.128715, No Obj: 0.000260, .5R: 0.931035, .75R: 0.379310, count: 29 -Region 16 Avg IOU: 0.777501, Class: 0.249997, Obj: 0.434608, No Obj: 0.001390, .5R: 0.958333, .75R: 0.708333, count: 24 -Region 23 Avg IOU: 0.681646, Class: 0.192645, Obj: 0.145335, No Obj: 0.000248, .5R: 0.941176, .75R: 0.294118, count: 17 - - 2012: 3.173438, 2.678225 avg loss, 0.001000 rate, 2.476640 seconds, 257536 images -Loaded: 0.000065 seconds -Region 16 Avg IOU: 0.672137, Class: 0.173787, Obj: 0.311762, No Obj: 0.001007, .5R: 0.782609, .75R: 0.521739, count: 23 -Region 23 Avg IOU: 0.674934, Class: 0.189153, Obj: 0.162449, No Obj: 0.000261, .5R: 0.843750, .75R: 0.312500, count: 32 -Region 16 Avg IOU: 0.701520, Class: 0.173480, Obj: 0.181003, No Obj: 0.001247, .5R: 0.900000, .75R: 0.400000, count: 30 -Region 23 Avg IOU: 0.696412, Class: 0.155689, Obj: 0.175675, No Obj: 0.000175, .5R: 1.000000, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.745732, Class: 0.175493, Obj: 0.226601, No Obj: 0.000874, .5R: 1.000000, .75R: 0.545455, count: 22 -Region 23 Avg IOU: 0.617392, Class: 0.143791, Obj: 0.111731, No Obj: 0.000114, .5R: 0.727273, .75R: 0.090909, count: 11 -Region 16 Avg IOU: 0.737870, Class: 0.238951, Obj: 0.331798, No Obj: 0.000638, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 23 Avg IOU: 0.709730, Class: 0.180962, Obj: 0.155813, No Obj: 0.000137, .5R: 1.000000, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.720605, Class: 0.256755, Obj: 0.233837, No Obj: 0.001260, .5R: 0.960000, .75R: 0.440000, count: 25 -Region 23 Avg IOU: 0.674128, Class: 0.207775, Obj: 0.085751, No Obj: 0.000174, .5R: 1.000000, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.629528, Class: 0.173889, Obj: 0.186105, No Obj: 0.000987, .5R: 0.684211, .75R: 0.263158, count: 19 -Region 23 Avg IOU: 0.702533, Class: 0.193627, Obj: 0.037653, No Obj: 0.000065, .5R: 1.000000, .75R: 0.333333, count: 9 -Region 16 Avg IOU: 0.768691, Class: 0.207655, Obj: 0.224244, No Obj: 0.000937, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.606180, Class: 0.197748, Obj: 0.146167, No Obj: 0.000161, .5R: 0.800000, .75R: 0.133333, count: 15 -Region 16 Avg IOU: 0.716597, Class: 0.234003, Obj: 0.365001, No Obj: 0.001262, .5R: 0.896552, .75R: 0.448276, count: 29 -Region 23 Avg IOU: 0.700392, Class: 0.166553, Obj: 0.098271, No Obj: 0.000170, .5R: 1.000000, .75R: 0.409091, count: 22 - - 2013: 2.980776, 2.708480 avg loss, 0.001000 rate, 2.467747 seconds, 257664 images -Loaded: 0.000058 seconds -Region 16 Avg IOU: 0.736670, Class: 0.248540, Obj: 0.296188, No Obj: 0.000887, .5R: 0.900000, .75R: 0.550000, count: 20 -Region 23 Avg IOU: 0.630774, Class: 0.153979, Obj: 0.103208, No Obj: 0.000128, .5R: 0.923077, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.711272, Class: 0.274855, Obj: 0.393779, No Obj: 0.001211, .5R: 0.944444, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.692204, Class: 0.230367, Obj: 0.128830, No Obj: 0.000200, .5R: 1.000000, .75R: 0.280000, count: 25 -Region 16 Avg IOU: 0.739987, Class: 0.195022, Obj: 0.291132, No Obj: 0.000937, .5R: 0.960000, .75R: 0.600000, count: 25 -Region 23 Avg IOU: 0.692216, Class: 0.161661, Obj: 0.213418, No Obj: 0.000238, .5R: 0.958333, .75R: 0.291667, count: 24 -Region 16 Avg IOU: 0.710422, Class: 0.186406, Obj: 0.199714, No Obj: 0.001075, .5R: 0.826087, .75R: 0.565217, count: 23 -Region 23 Avg IOU: 0.705397, Class: 0.096891, Obj: 0.042000, No Obj: 0.000099, .5R: 1.000000, .75R: 0.375000, count: 8 -Region 16 Avg IOU: 0.747646, Class: 0.173711, Obj: 0.271497, No Obj: 0.001199, .5R: 0.958333, .75R: 0.500000, count: 24 -Region 23 Avg IOU: 0.703251, Class: 0.137738, Obj: 0.095908, No Obj: 0.000144, .5R: 0.882353, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.707879, Class: 0.221550, Obj: 0.334015, No Obj: 0.000966, .5R: 0.909091, .75R: 0.500000, count: 22 -Region 23 Avg IOU: 0.641021, Class: 0.189075, Obj: 0.161370, No Obj: 0.000195, .5R: 0.947368, .75R: 0.210526, count: 19 -Region 16 Avg IOU: 0.701576, Class: 0.176667, Obj: 0.212321, No Obj: 0.001289, .5R: 0.857143, .75R: 0.514286, count: 35 -Region 23 Avg IOU: 0.684999, Class: 0.179186, Obj: 0.127318, No Obj: 0.000170, .5R: 0.900000, .75R: 0.550000, count: 20 -Region 16 Avg IOU: 0.721844, Class: 0.203377, Obj: 0.481237, No Obj: 0.000821, .5R: 0.928571, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.691914, Class: 0.109787, Obj: 0.135921, No Obj: 0.000114, .5R: 1.000000, .75R: 0.266667, count: 15 - - 2014: 2.937002, 2.731332 avg loss, 0.001000 rate, 2.555850 seconds, 257792 images -Loaded: 0.000063 seconds -Region 16 Avg IOU: 0.690879, Class: 0.203437, Obj: 0.289250, No Obj: 0.001197, .5R: 0.923077, .75R: 0.269231, count: 26 -Region 23 Avg IOU: 0.691250, Class: 0.200700, Obj: 0.189742, No Obj: 0.000129, .5R: 0.923077, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.693699, Class: 0.221814, Obj: 0.206120, No Obj: 0.001241, .5R: 0.967742, .75R: 0.258065, count: 31 -Region 23 Avg IOU: 0.657275, Class: 0.223008, Obj: 0.078538, No Obj: 0.000243, .5R: 0.954545, .75R: 0.136364, count: 22 -Region 16 Avg IOU: 0.746698, Class: 0.179321, Obj: 0.242789, No Obj: 0.001017, .5R: 1.000000, .75R: 0.500000, count: 22 -Region 23 Avg IOU: 0.710116, Class: 0.169860, Obj: 0.166350, No Obj: 0.000211, .5R: 1.000000, .75R: 0.473684, count: 19 -Region 16 Avg IOU: 0.760027, Class: 0.264079, Obj: 0.202284, No Obj: 0.001150, .5R: 0.960000, .75R: 0.640000, count: 25 -Region 23 Avg IOU: 0.624331, Class: 0.217918, Obj: 0.098551, No Obj: 0.000181, .5R: 0.850000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.727239, Class: 0.232143, Obj: 0.265289, No Obj: 0.001338, .5R: 0.958333, .75R: 0.458333, count: 24 -Region 23 Avg IOU: 0.725570, Class: 0.232033, Obj: 0.189666, No Obj: 0.000236, .5R: 1.000000, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.746772, Class: 0.179859, Obj: 0.340436, No Obj: 0.001225, .5R: 0.962963, .75R: 0.481481, count: 27 -Region 23 Avg IOU: 0.716007, Class: 0.185933, Obj: 0.231952, No Obj: 0.000248, .5R: 1.000000, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.734531, Class: 0.185808, Obj: 0.260557, No Obj: 0.001133, .5R: 0.964286, .75R: 0.571429, count: 28 -Region 23 Avg IOU: 0.732952, Class: 0.145358, Obj: 0.210475, No Obj: 0.000225, .5R: 0.950000, .75R: 0.600000, count: 20 -Region 16 Avg IOU: 0.750425, Class: 0.226904, Obj: 0.325398, No Obj: 0.000948, .5R: 0.956522, .75R: 0.608696, count: 23 -Region 23 Avg IOU: 0.659532, Class: 0.156717, Obj: 0.198124, No Obj: 0.000193, .5R: 1.000000, .75R: 0.133333, count: 15 - - 2015: 3.258295, 2.784029 avg loss, 0.001000 rate, 2.510816 seconds, 257920 images -Loaded: 0.000081 seconds -Region 16 Avg IOU: 0.653079, Class: 0.224224, Obj: 0.314213, No Obj: 0.001065, .5R: 0.809524, .75R: 0.380952, count: 21 -Region 23 Avg IOU: 0.736291, Class: 0.198423, Obj: 0.310566, No Obj: 0.000332, .5R: 0.909091, .75R: 0.545455, count: 22 -Region 16 Avg IOU: 0.694337, Class: 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0.001620, .5R: 0.935484, .75R: 0.387097, count: 31 -Region 23 Avg IOU: 0.728636, Class: 0.188506, Obj: 0.139580, No Obj: 0.000445, .5R: 1.000000, .75R: 0.500000, count: 26 -Region 16 Avg IOU: 0.668762, Class: 0.212181, Obj: 0.146680, No Obj: 0.001476, .5R: 0.888889, .75R: 0.388889, count: 36 -Region 23 Avg IOU: 0.661689, Class: 0.189281, Obj: 0.099108, No Obj: 0.000250, .5R: 0.857143, .75R: 0.285714, count: 14 -Region 16 Avg IOU: 0.729755, Class: 0.186736, Obj: 0.275579, No Obj: 0.001217, .5R: 0.900000, .75R: 0.566667, count: 30 -Region 23 Avg IOU: 0.689175, Class: 0.130791, Obj: 0.198630, No Obj: 0.000201, .5R: 1.000000, .75R: 0.352941, count: 17 - - 2016: 3.313391, 2.836965 avg loss, 0.001000 rate, 2.541514 seconds, 258048 images -Loaded: 0.000068 seconds -Region 16 Avg IOU: 0.773627, Class: 0.157682, Obj: 0.376230, No Obj: 0.001436, .5R: 1.000000, .75R: 0.590909, count: 22 -Region 23 Avg IOU: 0.729385, Class: 0.179409, Obj: 0.218611, No Obj: 0.000268, .5R: 0.909091, .75R: 0.545455, 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1.000000, .75R: 0.521739, count: 23 -Region 23 Avg IOU: 0.681736, Class: 0.183357, Obj: 0.142124, No Obj: 0.000175, .5R: 0.857143, .75R: 0.357143, count: 14 -Region 16 Avg IOU: 0.696965, Class: 0.198820, Obj: 0.295527, No Obj: 0.001399, .5R: 0.840000, .75R: 0.480000, count: 25 -Region 23 Avg IOU: 0.758525, Class: 0.189307, Obj: 0.203502, No Obj: 0.000202, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.726545, Class: 0.272359, Obj: 0.252034, No Obj: 0.000827, .5R: 0.950000, .75R: 0.300000, count: 20 -Region 23 Avg IOU: 0.716060, Class: 0.236736, Obj: 0.088949, No Obj: 0.000156, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 16 Avg IOU: 0.727688, Class: 0.196111, Obj: 0.256172, No Obj: 0.000966, .5R: 0.956522, .75R: 0.434783, count: 23 -Region 23 Avg IOU: 0.753186, Class: 0.210291, Obj: 0.162883, No Obj: 0.000180, .5R: 1.000000, .75R: 0.666667, count: 18 -Region 16 Avg IOU: 0.725246, Class: 0.178836, Obj: 0.217917, No Obj: 0.001363, .5R: 0.921053, .75R: 0.500000, count: 38 -Region 23 Avg IOU: 0.660289, Class: 0.147484, Obj: 0.081776, No Obj: 0.000231, .5R: 0.833333, .75R: 0.366667, count: 30 -Region 16 Avg IOU: 0.718877, Class: 0.233694, Obj: 0.237406, No Obj: 0.000670, .5R: 0.933333, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.673122, Class: 0.205108, Obj: 0.112994, No Obj: 0.000167, .5R: 0.937500, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.733128, Class: 0.220555, Obj: 0.216865, No Obj: 0.001190, .5R: 1.000000, .75R: 0.452381, count: 42 -Region 23 Avg IOU: 0.728430, Class: 0.171972, Obj: 0.232341, No Obj: 0.000258, .5R: 0.900000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.745209, Class: 0.217713, Obj: 0.283661, No Obj: 0.000865, .5R: 1.000000, .75R: 0.500000, count: 24 -Region 23 Avg IOU: 0.643415, Class: 0.110378, Obj: 0.166805, No Obj: 0.000088, .5R: 0.750000, .75R: 0.500000, count: 8 - - 2020: 3.127317, 2.876947 avg loss, 0.001000 rate, 2.536000 seconds, 258560 images -Resizing -640 x 640 - try to allocate workspace = 1147404289 * sizeof(float), CUDA allocate done! -Loaded: 0.570354 seconds -Region 16 Avg IOU: 0.716129, Class: 0.290408, Obj: 0.578837, No Obj: 0.001388, .5R: 0.933333, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.720558, Class: 0.153659, Obj: 0.158193, No Obj: 0.000267, .5R: 0.933333, .75R: 0.400000, count: 15 -Region 16 Avg IOU: 0.744487, Class: 0.236946, Obj: 0.323709, No Obj: 0.001893, .5R: 0.931035, .75R: 0.551724, count: 29 -Region 23 Avg IOU: 0.653137, Class: 0.234255, Obj: 0.092254, No Obj: 0.000313, .5R: 0.956522, .75R: 0.086957, count: 23 -Region 16 Avg IOU: 0.759089, Class: 0.191119, Obj: 0.335162, No Obj: 0.001422, .5R: 1.000000, .75R: 0.562500, count: 16 -Region 23 Avg IOU: 0.750095, Class: 0.134629, Obj: 0.174098, No Obj: 0.000259, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 16 Avg IOU: 0.738683, Class: 0.203393, Obj: 0.366463, No Obj: 0.001126, .5R: 0.933333, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.708199, Class: 0.206053, Obj: 0.193520, No Obj: 0.000260, .5R: 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0.261055, Obj: 0.140158, No Obj: 0.000195, .5R: 1.000000, .75R: 0.375000, count: 8 -Region 16 Avg IOU: 0.714855, Class: 0.203149, Obj: 0.309205, No Obj: 0.002540, .5R: 0.900000, .75R: 0.450000, count: 20 -Region 23 Avg IOU: 0.692563, Class: 0.180836, Obj: 0.147693, No Obj: 0.000466, .5R: 1.000000, .75R: 0.269231, count: 26 -Region 16 Avg IOU: 0.729527, Class: 0.183653, Obj: 0.368774, No Obj: 0.002134, .5R: 0.958333, .75R: 0.458333, count: 24 -Region 23 Avg IOU: 0.711220, Class: 0.198854, Obj: 0.225762, No Obj: 0.000369, .5R: 0.956522, .75R: 0.434783, count: 23 -Region 16 Avg IOU: 0.764570, Class: 0.221190, Obj: 0.392038, No Obj: 0.002019, .5R: 0.962963, .75R: 0.592593, count: 27 -Region 23 Avg IOU: 0.718501, Class: 0.170812, Obj: 0.230318, No Obj: 0.000324, .5R: 1.000000, .75R: 0.454545, count: 22 -Region 16 Avg IOU: 0.729689, Class: 0.246952, Obj: 0.393015, No Obj: 0.001493, .5R: 0.933333, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.745238, Class: 0.176451, Obj: 0.250987, No Obj: 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count: 27 -Region 23 Avg IOU: 0.666918, Class: 0.217553, Obj: 0.233075, No Obj: 0.000391, .5R: 0.875000, .75R: 0.343750, count: 32 -Region 16 Avg IOU: 0.720912, Class: 0.144993, Obj: 0.293887, No Obj: 0.001192, .5R: 0.909091, .75R: 0.681818, count: 22 -Region 23 Avg IOU: 0.759359, Class: 0.131082, Obj: 0.352603, No Obj: 0.000238, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 16 Avg IOU: 0.677859, Class: 0.292767, Obj: 0.664662, No Obj: 0.000943, .5R: 0.875000, .75R: 0.375000, count: 8 -Region 23 Avg IOU: 0.715272, Class: 0.174924, Obj: 0.339712, No Obj: 0.000275, .5R: 1.000000, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.730667, Class: 0.206278, Obj: 0.350193, No Obj: 0.001114, .5R: 0.961538, .75R: 0.538462, count: 26 -Region 23 Avg IOU: 0.758246, Class: 0.246914, Obj: 0.311218, No Obj: 0.000323, .5R: 1.000000, .75R: 0.550000, count: 20 -Region 16 Avg IOU: 0.758498, Class: 0.206667, Obj: 0.267337, No Obj: 0.001009, .5R: 1.000000, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 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23 -Region 16 Avg IOU: 0.740047, Class: 0.227094, Obj: 0.294179, No Obj: 0.001507, .5R: 0.967742, .75R: 0.580645, count: 31 -Region 23 Avg IOU: 0.690697, Class: 0.170359, Obj: 0.157096, No Obj: 0.000317, .5R: 0.925926, .75R: 0.370370, count: 27 - - 2026: 2.743106, 2.800279 avg loss, 0.001000 rate, 1.924120 seconds, 259328 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.729598, Class: 0.230089, Obj: 0.332890, No Obj: 0.001335, .5R: 0.923077, .75R: 0.384615, count: 13 -Region 23 Avg IOU: 0.655487, Class: 0.148302, Obj: 0.274598, No Obj: 0.000332, .5R: 0.900000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.749383, Class: 0.257776, Obj: 0.364437, No Obj: 0.001248, .5R: 0.875000, .75R: 0.750000, count: 16 -Region 23 Avg IOU: 0.774552, Class: 0.190497, Obj: 0.170889, No Obj: 0.000201, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 16 Avg IOU: 0.758138, Class: 0.200297, Obj: 0.252032, No Obj: 0.001430, .5R: 1.000000, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.803336, Class: 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20 -Region 16 Avg IOU: 0.736278, Class: 0.171832, Obj: 0.357665, No Obj: 0.001848, .5R: 0.939394, .75R: 0.545455, count: 33 -Region 23 Avg IOU: 0.713195, Class: 0.187634, Obj: 0.201238, No Obj: 0.000438, .5R: 0.962963, .75R: 0.407407, count: 27 -Region 16 Avg IOU: 0.759935, Class: 0.188024, Obj: 0.305972, No Obj: 0.001455, .5R: 0.956522, .75R: 0.695652, count: 23 -Region 23 Avg IOU: 0.693071, Class: 0.167549, Obj: 0.200210, No Obj: 0.000273, .5R: 0.904762, .75R: 0.333333, count: 21 -Region 16 Avg IOU: 0.776958, Class: 0.152165, Obj: 0.399554, No Obj: 0.002138, .5R: 1.000000, .75R: 0.636364, count: 22 -Region 23 Avg IOU: 0.722700, Class: 0.164686, Obj: 0.335243, No Obj: 0.000439, .5R: 0.900000, .75R: 0.500000, count: 20 - - 2034: 2.975136, 2.781165 avg loss, 0.001000 rate, 1.987530 seconds, 260352 images -Loaded: 0.000068 seconds -Region 16 Avg IOU: 0.769541, Class: 0.154344, Obj: 0.155845, No Obj: 0.001185, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.713684, Class: 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images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.784112, Class: 0.152796, Obj: 0.252025, No Obj: 0.000974, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.675631, Class: 0.172376, Obj: 0.352267, No Obj: 0.000242, .5R: 0.900000, .75R: 0.500000, count: 10 -Region 16 Avg IOU: 0.736374, Class: 0.153744, Obj: 0.267158, No Obj: 0.001557, .5R: 0.956522, .75R: 0.434783, count: 23 -Region 23 Avg IOU: 0.696387, Class: 0.146848, Obj: 0.167103, No Obj: 0.000376, .5R: 0.900000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.772598, Class: 0.184292, Obj: 0.317367, No Obj: 0.001489, .5R: 1.000000, .75R: 0.640000, count: 25 -Region 23 Avg IOU: 0.713072, Class: 0.213427, Obj: 0.311709, No Obj: 0.000441, .5R: 0.913043, .75R: 0.434783, count: 23 -Region 16 Avg IOU: 0.726858, Class: 0.143671, Obj: 0.282397, No Obj: 0.000886, .5R: 0.944444, .75R: 0.388889, count: 18 -Region 23 Avg IOU: 0.710164, Class: 0.150584, Obj: 0.248853, No Obj: 0.000372, .5R: 0.947368, .75R: 0.421053, count: 19 -Region 16 Avg IOU: 0.756168, Class: 0.134720, Obj: 0.203460, No Obj: 0.001099, .5R: 0.944444, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.700566, Class: 0.211525, Obj: 0.209821, No Obj: 0.000254, .5R: 1.000000, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.777274, Class: 0.192158, Obj: 0.251686, No Obj: 0.001409, .5R: 0.960000, .75R: 0.560000, count: 25 -Region 23 Avg IOU: 0.641504, Class: 0.121551, Obj: 0.168438, No Obj: 0.000495, .5R: 0.833333, .75R: 0.194444, count: 36 -Region 16 Avg IOU: 0.723288, Class: 0.160921, Obj: 0.204046, No Obj: 0.001361, .5R: 1.000000, .75R: 0.434783, count: 23 -Region 23 Avg IOU: 0.736834, Class: 0.167350, Obj: 0.167507, No Obj: 0.000320, .5R: 1.000000, .75R: 0.600000, count: 15 -Region 16 Avg IOU: 0.757872, Class: 0.151698, Obj: 0.356048, No Obj: 0.001477, .5R: 0.952381, .75R: 0.714286, count: 21 -Region 23 Avg IOU: 0.745768, Class: 0.134020, Obj: 0.280069, No Obj: 0.000514, .5R: 1.000000, .75R: 0.444444, count: 18 - - 2039: 2.971797, 2.741688 avg 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0.654864, Class: 0.154836, Obj: 0.133344, No Obj: 0.000446, .5R: 0.900000, .75R: 0.250000, count: 20 -Region 16 Avg IOU: 0.768287, Class: 0.149021, Obj: 0.311754, No Obj: 0.001429, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.677182, Class: 0.141677, Obj: 0.108182, No Obj: 0.000368, .5R: 1.000000, .75R: 0.263158, count: 19 -Region 16 Avg IOU: 0.755653, Class: 0.197542, Obj: 0.287307, No Obj: 0.002186, .5R: 1.000000, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.740100, Class: 0.202673, Obj: 0.117436, No Obj: 0.000501, .5R: 0.894737, .75R: 0.631579, count: 19 -Region 16 Avg IOU: 0.791076, Class: 0.282425, Obj: 0.527495, No Obj: 0.001765, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.670491, Class: 0.228586, Obj: 0.189035, No Obj: 0.000247, .5R: 0.875000, .75R: 0.250000, count: 8 -Region 16 Avg IOU: 0.794864, Class: 0.177844, Obj: 0.338722, No Obj: 0.001138, .5R: 1.000000, .75R: 0.857143, count: 7 -Region 23 Avg IOU: 0.660089, Class: 0.151639, Obj: 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23 Avg IOU: 0.701051, Class: 0.163728, Obj: 0.266303, No Obj: 0.001146, .5R: 0.920000, .75R: 0.440000, count: 25 -Region 16 Avg IOU: 0.676901, Class: 0.299271, Obj: 0.428802, No Obj: 0.000665, .5R: 1.000000, .75R: 0.000000, count: 2 -Region 23 Avg IOU: 0.694686, Class: 0.195543, Obj: 0.274111, No Obj: 0.000774, .5R: 0.956522, .75R: 0.347826, count: 23 -Region 16 Avg IOU: 0.806434, Class: 0.252277, Obj: 0.433643, No Obj: 0.002362, .5R: 1.000000, .75R: 0.818182, count: 11 -Region 23 Avg IOU: 0.698593, Class: 0.207037, Obj: 0.250576, No Obj: 0.000760, .5R: 0.941176, .75R: 0.411765, count: 17 - - 2050: 1.756270, 2.136423 avg loss, 0.001000 rate, 0.678214 seconds, 262400 images -Resizing -576 x 576 - try to allocate workspace = 1146159105 * sizeof(float), CUDA allocate done! -Loaded: 0.222809 seconds -Region 16 Avg IOU: 0.805985, Class: 0.226517, Obj: 0.413223, No Obj: 0.001898, .5R: 1.000000, .75R: 0.736842, count: 19 -Region 23 Avg IOU: 0.687892, Class: 0.201192, Obj: 0.305323, No Obj: 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.75R: 0.416667, count: 24 -Region 23 Avg IOU: 0.703145, Class: 0.204383, Obj: 0.120856, No Obj: 0.000230, .5R: 1.000000, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.760728, Class: 0.252822, Obj: 0.396399, No Obj: 0.001172, .5R: 0.941176, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.699226, Class: 0.155308, Obj: 0.126498, No Obj: 0.000306, .5R: 0.952381, .75R: 0.238095, count: 21 -Region 16 Avg IOU: 0.773523, Class: 0.243198, Obj: 0.269081, No Obj: 0.001208, .5R: 1.000000, .75R: 0.625000, count: 16 -Region 23 Avg IOU: 0.717205, Class: 0.212075, Obj: 0.070607, No Obj: 0.000155, .5R: 0.928571, .75R: 0.571429, count: 14 -Region 16 Avg IOU: 0.718342, Class: 0.223324, Obj: 0.171899, No Obj: 0.001308, .5R: 0.913043, .75R: 0.391304, count: 23 -Region 23 Avg IOU: 0.737265, Class: 0.250799, Obj: 0.098002, No Obj: 0.000185, .5R: 1.000000, .75R: 0.444444, count: 18 -Region 16 Avg IOU: 0.710712, Class: 0.220460, Obj: 0.330587, No Obj: 0.001483, .5R: 0.913043, .75R: 0.434783, count: 23 -Region 23 Avg IOU: 0.695466, Class: 0.182320, Obj: 0.129508, No Obj: 0.000244, .5R: 1.000000, .75R: 0.333333, count: 24 -Region 16 Avg IOU: 0.749559, Class: 0.226094, Obj: 0.306000, No Obj: 0.001444, .5R: 1.000000, .75R: 0.550000, count: 20 -Region 23 Avg IOU: 0.634559, Class: 0.164900, Obj: 0.083041, No Obj: 0.000171, .5R: 0.777778, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.721638, Class: 0.249714, Obj: 0.293751, No Obj: 0.001580, .5R: 0.960000, .75R: 0.480000, count: 25 -Region 23 Avg IOU: 0.713968, Class: 0.225606, Obj: 0.111224, No Obj: 0.000262, .5R: 1.000000, .75R: 0.480000, count: 25 -Region 16 Avg IOU: 0.769796, Class: 0.214992, Obj: 0.319911, No Obj: 0.001097, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.664670, Class: 0.175393, Obj: 0.158142, No Obj: 0.000177, .5R: 1.000000, .75R: 0.214286, count: 14 - - 2053: 2.672208, 2.259049 avg loss, 0.001000 rate, 1.619544 seconds, 262784 images -Loaded: 0.000065 seconds -Region 16 Avg IOU: 0.666678, Class: 0.225737, Obj: 0.184598, No Obj: 0.001177, .5R: 0.777778, .75R: 0.388889, count: 18 -Region 23 Avg IOU: 0.614459, Class: 0.227278, Obj: 0.116189, No Obj: 0.000195, .5R: 0.800000, .75R: 0.133333, count: 15 -Region 16 Avg IOU: 0.734256, Class: 0.197848, Obj: 0.501440, No Obj: 0.001530, .5R: 0.888889, .75R: 0.611111, count: 18 -Region 23 Avg IOU: 0.685009, Class: 0.130870, Obj: 0.123648, No Obj: 0.000274, .5R: 0.965517, .75R: 0.310345, count: 29 -Region 16 Avg IOU: 0.761608, Class: 0.249131, Obj: 0.356748, No Obj: 0.000877, .5R: 1.000000, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.718697, Class: 0.197307, Obj: 0.084033, No Obj: 0.000131, .5R: 1.000000, .75R: 0.285714, count: 21 -Region 16 Avg IOU: 0.734149, Class: 0.172607, Obj: 0.349167, No Obj: 0.001070, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.584989, Class: 0.155961, Obj: 0.111225, No Obj: 0.000196, .5R: 0.636364, .75R: 0.136364, count: 22 -Region 16 Avg IOU: 0.738198, Class: 0.166056, Obj: 0.364552, No Obj: 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seconds -Region 16 Avg IOU: 0.776364, Class: 0.221424, Obj: 0.386742, No Obj: 0.000911, .5R: 0.933333, .75R: 0.800000, count: 15 -Region 23 Avg IOU: 0.696334, Class: 0.185668, Obj: 0.163712, No Obj: 0.000193, .5R: 1.000000, .75R: 0.500000, count: 14 -Region 16 Avg IOU: 0.757194, Class: 0.163226, Obj: 0.355511, No Obj: 0.001024, .5R: 1.000000, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.749380, Class: 0.203363, Obj: 0.267591, No Obj: 0.000286, .5R: 1.000000, .75R: 0.578947, count: 19 -Region 16 Avg IOU: 0.793444, Class: 0.197803, Obj: 0.180554, No Obj: 0.001015, .5R: 0.937500, .75R: 0.812500, count: 16 -Region 23 Avg IOU: 0.736605, Class: 0.118052, Obj: 0.230534, No Obj: 0.000335, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 16 Avg IOU: 0.703914, Class: 0.241367, Obj: 0.266856, No Obj: 0.001407, .5R: 0.937500, .75R: 0.312500, count: 16 -Region 23 Avg IOU: 0.647834, Class: 0.177856, Obj: 0.154385, No Obj: 0.000341, .5R: 0.850000, .75R: 0.150000, count: 20 -Region 16 Avg IOU: 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count: 8 -Region 23 Avg IOU: 0.685007, Class: 0.217623, Obj: 0.276036, No Obj: 0.000302, .5R: 1.000000, .75R: 0.250000, count: 12 -Region 16 Avg IOU: 0.750840, Class: 0.148012, Obj: 0.174402, No Obj: 0.001266, .5R: 1.000000, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.719066, Class: 0.217351, Obj: 0.187224, No Obj: 0.000372, .5R: 0.956522, .75R: 0.434783, count: 23 -Region 16 Avg IOU: 0.717892, Class: 0.187892, Obj: 0.302889, No Obj: 0.002788, .5R: 0.880000, .75R: 0.440000, count: 25 -Region 23 Avg IOU: 0.730190, Class: 0.187282, Obj: 0.176072, No Obj: 0.000688, .5R: 0.965517, .75R: 0.448276, count: 29 -Region 16 Avg IOU: 0.790748, Class: 0.253350, Obj: 0.574973, No Obj: 0.001993, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.695690, Class: 0.189400, Obj: 0.225410, No Obj: 0.000580, .5R: 0.960000, .75R: 0.400000, count: 25 -Region 16 Avg IOU: 0.735615, Class: 0.201849, Obj: 0.388385, No Obj: 0.001787, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 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0.657112, Class: 0.212952, Obj: 0.175104, No Obj: 0.000395, .5R: 0.920000, .75R: 0.240000, count: 25 -Region 16 Avg IOU: 0.758947, Class: 0.307987, Obj: 0.289543, No Obj: 0.000907, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.649896, Class: 0.252116, Obj: 0.122957, No Obj: 0.000165, .5R: 0.818182, .75R: 0.090909, count: 11 - - 2061: 2.360572, 2.465224 avg loss, 0.001000 rate, 1.783659 seconds, 263808 images -Loaded: 0.000062 seconds -Region 16 Avg IOU: 0.785415, Class: 0.310064, Obj: 0.342636, No Obj: 0.001059, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.705400, Class: 0.209134, Obj: 0.289160, No Obj: 0.000462, .5R: 0.888889, .75R: 0.407407, count: 27 -Region 16 Avg IOU: 0.765297, Class: 0.265014, Obj: 0.387906, No Obj: 0.000884, .5R: 0.857143, .75R: 0.857143, count: 7 -Region 23 Avg IOU: 0.662309, Class: 0.117425, Obj: 0.150146, No Obj: 0.000268, .5R: 1.000000, .75R: 0.250000, count: 12 -Region 16 Avg IOU: 0.738081, Class: 0.178300, Obj: 0.246569, 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0.653927, Class: 0.181981, Obj: 0.197534, No Obj: 0.000237, .5R: 0.800000, .75R: 0.250000, count: 20 -Region 16 Avg IOU: 0.780598, Class: 0.244226, Obj: 0.397977, No Obj: 0.001670, .5R: 1.000000, .75R: 0.777778, count: 18 -Region 23 Avg IOU: 0.691330, Class: 0.248020, Obj: 0.100490, No Obj: 0.000242, .5R: 0.947368, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.763259, Class: 0.230598, Obj: 0.379646, No Obj: 0.001613, .5R: 1.000000, .75R: 0.652174, count: 23 -Region 23 Avg IOU: 0.732343, Class: 0.232988, Obj: 0.107844, No Obj: 0.000188, .5R: 0.916667, .75R: 0.583333, count: 24 - - 2066: 2.540859, 2.532994 avg loss, 0.001000 rate, 1.754078 seconds, 264448 images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.702367, Class: 0.225092, Obj: 0.226700, No Obj: 0.001373, .5R: 0.875000, .75R: 0.458333, count: 24 -Region 23 Avg IOU: 0.659435, Class: 0.205853, Obj: 0.119792, No Obj: 0.000247, .5R: 0.800000, .75R: 0.280000, count: 25 -Region 16 Avg IOU: 0.709775, Class: 0.215653, Obj: 0.327149, 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31 -Region 16 Avg IOU: 0.763150, Class: 0.282718, Obj: 0.276974, No Obj: 0.001276, .5R: 1.000000, .75R: 0.684211, count: 19 -Region 23 Avg IOU: 0.695506, Class: 0.217048, Obj: 0.277567, No Obj: 0.000678, .5R: 1.000000, .75R: 0.250000, count: 28 -Region 16 Avg IOU: 0.774598, Class: 0.237301, Obj: 0.364326, No Obj: 0.001396, .5R: 0.950000, .75R: 0.700000, count: 20 -Region 23 Avg IOU: 0.689524, Class: 0.209426, Obj: 0.267059, No Obj: 0.000381, .5R: 0.909091, .75R: 0.363636, count: 22 -Region 16 Avg IOU: 0.783451, Class: 0.185781, Obj: 0.353576, No Obj: 0.001204, .5R: 1.000000, .75R: 0.625000, count: 16 -Region 23 Avg IOU: 0.676028, Class: 0.240045, Obj: 0.278327, No Obj: 0.000363, .5R: 0.928571, .75R: 0.285714, count: 14 - - 2069: 2.914346, 2.608685 avg loss, 0.001000 rate, 1.728699 seconds, 264832 images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.702702, Class: 0.196711, Obj: 0.429679, No Obj: 0.001590, .5R: 0.913043, .75R: 0.478261, count: 23 -Region 23 Avg IOU: 0.683881, Class: 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images -Loaded: 0.000078 seconds -Region 16 Avg IOU: 0.738405, Class: 0.145661, Obj: 0.485065, No Obj: 0.002511, .5R: 1.000000, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.671988, Class: 0.144425, Obj: 0.103064, No Obj: 0.000353, .5R: 0.884615, .75R: 0.192308, count: 26 -Region 16 Avg IOU: 0.727247, Class: 0.186410, Obj: 0.567254, No Obj: 0.002064, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.716799, Class: 0.217079, Obj: 0.225578, No Obj: 0.000359, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 16 Avg IOU: 0.700529, Class: 0.134830, Obj: 0.305006, No Obj: 0.001892, .5R: 0.800000, .75R: 0.200000, count: 5 -Region 23 Avg IOU: 0.670100, Class: 0.216190, Obj: 0.107511, No Obj: 0.000462, .5R: 0.869565, .75R: 0.304348, count: 23 -Region 16 Avg IOU: 0.724133, Class: 0.188003, Obj: 0.579183, No Obj: 0.001651, .5R: 1.000000, .75R: 0.375000, count: 8 -Region 23 Avg IOU: 0.717374, Class: 0.154934, Obj: 0.062345, No Obj: 0.000252, .5R: 1.000000, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.753376, Class: 0.261981, Obj: 0.386052, No Obj: 0.002233, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.670021, Class: 0.251847, Obj: 0.089987, No Obj: 0.000214, .5R: 0.823529, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.802886, Class: 0.181203, Obj: 0.453179, No Obj: 0.002347, .5R: 1.000000, .75R: 0.777778, count: 9 -Region 23 Avg IOU: 0.686734, Class: 0.195208, Obj: 0.123030, No Obj: 0.000385, .5R: 0.913043, .75R: 0.434783, count: 23 -Region 16 Avg IOU: 0.735878, Class: 0.198826, Obj: 0.528781, No Obj: 0.002265, .5R: 1.000000, .75R: 0.363636, count: 11 -Region 23 Avg IOU: 0.703891, Class: 0.186947, Obj: 0.106737, No Obj: 0.000254, .5R: 0.944444, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.757710, Class: 0.214950, Obj: 0.275849, No Obj: 0.001461, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.713621, Class: 0.183460, Obj: 0.112295, No Obj: 0.000300, .5R: 0.888889, .75R: 0.555556, count: 18 - - 2072: 2.001512, 2.492918 avg 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0.384615, count: 13 - - 2073: 1.989913, 2.442617 avg loss, 0.001000 rate, 0.793621 seconds, 265344 images -Loaded: 0.000097 seconds -Region 16 Avg IOU: 0.783498, Class: 0.194462, Obj: 0.379548, No Obj: 0.002208, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.726748, Class: 0.198530, Obj: 0.238818, No Obj: 0.000778, .5R: 1.000000, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.734023, Class: 0.163980, Obj: 0.385949, No Obj: 0.002059, .5R: 0.933333, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.719608, Class: 0.178209, Obj: 0.226831, No Obj: 0.001567, .5R: 0.968750, .75R: 0.437500, count: 32 -Region 16 Avg IOU: 0.850636, Class: 0.231069, Obj: 0.525911, No Obj: 0.001116, .5R: 1.000000, .75R: 1.000000, count: 3 -Region 23 Avg IOU: 0.731674, Class: 0.178937, Obj: 0.413566, No Obj: 0.000737, .5R: 1.000000, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.701963, Class: 0.224293, Obj: 0.515138, No Obj: 0.001762, .5R: 1.000000, .75R: 0.250000, count: 8 -Region 23 Avg IOU: 0.698035, Class: 0.230255, Obj: 0.315374, No Obj: 0.001187, .5R: 0.931035, .75R: 0.344828, count: 29 -Region 16 Avg IOU: 0.714936, Class: 0.177057, Obj: 0.267896, No Obj: 0.001266, .5R: 1.000000, .75R: 0.300000, count: 10 -Region 23 Avg IOU: 0.659311, Class: 0.111782, Obj: 0.227286, No Obj: 0.000691, .5R: 0.941176, .75R: 0.176471, count: 17 -Region 16 Avg IOU: 0.734296, Class: 0.181286, Obj: 0.338175, No Obj: 0.001793, .5R: 0.928571, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.712075, Class: 0.136037, Obj: 0.221062, No Obj: 0.000545, .5R: 1.000000, .75R: 0.285714, count: 14 -Region 16 Avg IOU: 0.792260, Class: 0.240011, Obj: 0.396918, No Obj: 0.002210, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.739746, Class: 0.221749, Obj: 0.281138, No Obj: 0.000813, .5R: 1.000000, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.793239, Class: 0.209366, Obj: 0.383362, No Obj: 0.001565, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.726149, Class: 0.192319, Obj: 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Avg IOU: 0.693902, Class: 0.169892, Obj: 0.269865, No Obj: 0.000791, .5R: 0.888889, .75R: 0.388889, count: 18 - - 2075: 1.869772, 2.351462 avg loss, 0.001000 rate, 0.787384 seconds, 265600 images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.724575, Class: 0.198781, Obj: 0.282062, No Obj: 0.001536, .5R: 0.909091, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.666892, Class: 0.163362, Obj: 0.163017, No Obj: 0.000911, .5R: 0.826087, .75R: 0.347826, count: 23 -Region 16 Avg IOU: 0.752916, Class: 0.147941, Obj: 0.154656, No Obj: 0.001531, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.636004, Class: 0.133258, Obj: 0.323471, No Obj: 0.000481, .5R: 0.888889, .75R: 0.111111, count: 9 -Region 16 Avg IOU: 0.843426, Class: 0.264273, Obj: 0.513139, No Obj: 0.000977, .5R: 1.000000, .75R: 1.000000, count: 7 -Region 23 Avg IOU: 0.740047, Class: 0.252399, Obj: 0.361548, No Obj: 0.000731, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 16 Avg IOU: 0.769922, Class: 0.217945, Obj: 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Class: 0.184695, Obj: 0.380652, No Obj: 0.000794, .5R: 1.000000, .75R: 0.857143, count: 7 -Region 23 Avg IOU: 0.745009, Class: 0.162429, Obj: 0.164768, No Obj: 0.000345, .5R: 1.000000, .75R: 0.500000, count: 12 - - 2077: 1.518706, 2.244670 avg loss, 0.001000 rate, 0.788699 seconds, 265856 images -Loaded: 0.000074 seconds -Region 16 Avg IOU: 0.782782, Class: 0.183548, Obj: 0.331527, No Obj: 0.002269, .5R: 1.000000, .75R: 0.764706, count: 17 -Region 23 Avg IOU: 0.708233, Class: 0.113179, Obj: 0.064564, No Obj: 0.000378, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 16 Avg IOU: 0.849670, Class: 0.287456, Obj: 0.372312, No Obj: 0.000302, .5R: 1.000000, .75R: 1.000000, count: 3 -Region 23 Avg IOU: 0.696720, Class: 0.207373, Obj: 0.255425, No Obj: 0.000195, .5R: 1.000000, .75R: 0.200000, count: 5 -Region 16 Avg IOU: 0.739682, Class: 0.256026, Obj: 0.330694, No Obj: 0.001851, .5R: 0.923077, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.694977, Class: 0.237069, Obj: 0.144693, No Obj: 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23 Avg IOU: 0.662752, Class: 0.206716, Obj: 0.257098, No Obj: 0.000501, .5R: 1.000000, .75R: 0.210526, count: 19 -Region 16 Avg IOU: 0.781223, Class: 0.290420, Obj: 0.428059, No Obj: 0.001840, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.658995, Class: 0.170359, Obj: 0.093807, No Obj: 0.000408, .5R: 0.869565, .75R: 0.391304, count: 23 - - 2080: 2.054398, 2.154128 avg loss, 0.001000 rate, 0.781996 seconds, 266240 images -Resizing -608 x 608 - try to allocate workspace = 1146765313 * sizeof(float), CUDA allocate done! -Loaded: 0.351170 seconds -Region 16 Avg IOU: 0.808459, Class: 0.259253, Obj: 0.514635, No Obj: 0.001034, .5R: 1.000000, .75R: 0.833333, count: 6 -Region 23 Avg IOU: 0.713816, Class: 0.224089, Obj: 0.319298, No Obj: 0.000390, .5R: 0.833333, .75R: 0.500000, count: 12 -Region 16 Avg IOU: 0.729055, Class: 0.217510, Obj: 0.347593, No Obj: 0.002034, .5R: 0.894737, .75R: 0.578947, count: 19 -Region 23 Avg IOU: 0.673106, Class: 0.200006, Obj: 0.288300, No Obj: 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17 -Region 23 Avg IOU: 0.704485, Class: 0.141682, Obj: 0.137054, No Obj: 0.000283, .5R: 0.942857, .75R: 0.371429, count: 35 -Region 16 Avg IOU: 0.719614, Class: 0.193657, Obj: 0.146920, No Obj: 0.000606, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.705110, Class: 0.148842, Obj: 0.151361, No Obj: 0.000239, .5R: 1.000000, .75R: 0.428571, count: 21 -Region 16 Avg IOU: 0.734952, Class: 0.174277, Obj: 0.311604, No Obj: 0.000810, .5R: 0.933333, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.696639, Class: 0.163578, Obj: 0.099541, No Obj: 0.000252, .5R: 0.900000, .75R: 0.300000, count: 20 - - 2083: 2.877715, 2.317578 avg loss, 0.001000 rate, 3.798465 seconds, 266624 images -Loaded: 0.000090 seconds -Region 16 Avg IOU: 0.730496, Class: 0.247914, Obj: 0.266953, No Obj: 0.000520, .5R: 1.000000, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.666115, Class: 0.146654, Obj: 0.171354, No Obj: 0.000256, .5R: 0.923077, .75R: 0.346154, count: 26 -Region 16 Avg IOU: 0.760978, Class: 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18 -Region 23 Avg IOU: 0.667942, Class: 0.157270, Obj: 0.176060, No Obj: 0.000304, .5R: 0.850000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.800421, Class: 0.224316, Obj: 0.538358, No Obj: 0.001497, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.725758, Class: 0.232771, Obj: 0.266410, No Obj: 0.000362, .5R: 1.000000, .75R: 0.521739, count: 23 -Region 16 Avg IOU: 0.675310, Class: 0.225744, Obj: 0.347221, No Obj: 0.002041, .5R: 0.952381, .75R: 0.380952, count: 21 -Region 23 Avg IOU: 0.632002, Class: 0.225254, Obj: 0.144289, No Obj: 0.000306, .5R: 0.833333, .75R: 0.133333, count: 30 -Region 16 Avg IOU: 0.737218, Class: 0.291731, Obj: 0.367351, No Obj: 0.001904, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.704374, Class: 0.134105, Obj: 0.239179, No Obj: 0.000312, .5R: 0.933333, .75R: 0.266667, count: 15 - - 2088: 2.797408, 2.419290 avg loss, 0.001000 rate, 2.942197 seconds, 267264 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.701407, Class: 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0.000087 seconds -Region 16 Avg IOU: 0.775081, Class: 0.243001, Obj: 0.348824, No Obj: 0.001368, .5R: 1.000000, .75R: 0.666667, count: 18 -Region 23 Avg IOU: 0.607666, Class: 0.224738, Obj: 0.091400, No Obj: 0.000164, .5R: 0.750000, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.705787, Class: 0.259940, Obj: 0.370669, No Obj: 0.001755, .5R: 0.869565, .75R: 0.521739, count: 23 -Region 23 Avg IOU: 0.631904, Class: 0.215572, Obj: 0.072977, No Obj: 0.000213, .5R: 0.838710, .75R: 0.225806, count: 31 -Region 16 Avg IOU: 0.763649, Class: 0.204801, Obj: 0.356117, No Obj: 0.001764, .5R: 1.000000, .75R: 0.521739, count: 23 -Region 23 Avg IOU: 0.697639, Class: 0.179284, Obj: 0.315103, No Obj: 0.000271, .5R: 1.000000, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.670521, Class: 0.247519, Obj: 0.198299, No Obj: 0.001207, .5R: 0.833333, .75R: 0.416667, count: 24 -Region 23 Avg IOU: 0.711267, Class: 0.191687, Obj: 0.137181, No Obj: 0.000249, .5R: 0.960000, .75R: 0.400000, count: 25 -Region 16 Avg 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rate, 3.149153 seconds, 267520 images -Resizing -480 x 480 - try to allocate workspace = 1144537089 * sizeof(float), CUDA allocate done! -Loaded: 0.382053 seconds -Region 16 Avg IOU: 0.795139, Class: 0.214658, Obj: 0.382026, No Obj: 0.001111, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.698891, Class: 0.185953, Obj: 0.244431, No Obj: 0.000524, .5R: 0.947368, .75R: 0.421053, count: 19 -Region 16 Avg IOU: 0.779663, Class: 0.185273, Obj: 0.372387, No Obj: 0.001391, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.674835, Class: 0.149102, Obj: 0.243156, No Obj: 0.000472, .5R: 0.875000, .75R: 0.250000, count: 8 -Region 16 Avg IOU: 0.718998, Class: 0.221740, Obj: 0.223241, No Obj: 0.001365, .5R: 0.866667, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.669097, Class: 0.204197, Obj: 0.141590, No Obj: 0.000516, .5R: 0.869565, .75R: 0.304348, count: 23 -Region 16 Avg IOU: 0.751727, Class: 0.190890, Obj: 0.232374, No Obj: 0.001031, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.706671, Class: 0.148310, Obj: 0.249051, No Obj: 0.000494, .5R: 1.000000, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.775143, Class: 0.330575, Obj: 0.436185, No Obj: 0.000927, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.690648, Class: 0.253619, Obj: 0.305721, No Obj: 0.000583, .5R: 1.000000, .75R: 0.318182, count: 22 -Region 16 Avg IOU: 0.771980, Class: 0.182357, Obj: 0.389903, No Obj: 0.001020, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.721353, Class: 0.221758, Obj: 0.256757, No Obj: 0.000795, .5R: 1.000000, .75R: 0.428571, count: 28 -Region 16 Avg IOU: 0.787602, Class: 0.217603, Obj: 0.353991, No Obj: 0.001162, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.671141, Class: 0.188517, Obj: 0.150005, No Obj: 0.000541, .5R: 0.818182, .75R: 0.454545, count: 22 -Region 16 Avg IOU: 0.741055, Class: 0.258487, Obj: 0.411950, No Obj: 0.001172, .5R: 1.000000, .75R: 0.428571, count: 7 -Region 23 Avg IOU: 0.737325, Class: 0.216496, Obj: 0.217849, No Obj: 0.000375, .5R: 0.947368, .75R: 0.421053, count: 19 - - 2091: 1.998049, 2.463653 avg loss, 0.001000 rate, 2.625176 seconds, 267648 images -Loaded: 0.000071 seconds -Region 16 Avg IOU: 0.713329, Class: 0.226659, Obj: 0.347551, No Obj: 0.001355, .5R: 1.000000, .75R: 0.363636, count: 11 -Region 23 Avg IOU: 0.673873, Class: 0.113258, Obj: 0.203770, No Obj: 0.000852, .5R: 0.892857, .75R: 0.285714, count: 28 -Region 16 Avg IOU: 0.693404, Class: 0.232383, Obj: 0.255366, No Obj: 0.001206, .5R: 0.909091, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.672270, Class: 0.179404, Obj: 0.131710, No Obj: 0.000574, .5R: 0.923077, .75R: 0.307692, count: 26 -Region 16 Avg IOU: 0.712803, Class: 0.268163, Obj: 0.261980, No Obj: 0.000714, .5R: 1.000000, .75R: 0.250000, count: 8 -Region 23 Avg IOU: 0.671239, Class: 0.203254, Obj: 0.331618, No Obj: 0.000558, .5R: 0.875000, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.729679, Class: 0.213242, Obj: 0.194398, 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.75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.713019, Class: 0.189945, Obj: 0.282205, No Obj: 0.000698, .5R: 1.000000, .75R: 0.333333, count: 12 - - 2092: 2.549715, 2.472259 avg loss, 0.001000 rate, 1.971353 seconds, 267776 images -Loaded: 0.000071 seconds -Region 16 Avg IOU: 0.776154, Class: 0.241013, Obj: 0.226439, No Obj: 0.000961, .5R: 1.000000, .75R: 0.733333, count: 15 -Region 23 Avg IOU: 0.642511, Class: 0.217005, Obj: 0.219796, No Obj: 0.000704, .5R: 0.851852, .75R: 0.259259, count: 27 -Region 16 Avg IOU: 0.795456, Class: 0.231165, Obj: 0.403124, No Obj: 0.001216, .5R: 1.000000, .75R: 0.785714, count: 14 -Region 23 Avg IOU: 0.672467, Class: 0.168466, Obj: 0.223849, No Obj: 0.000357, .5R: 1.000000, .75R: 0.333333, count: 9 -Region 16 Avg IOU: 0.775758, Class: 0.172790, Obj: 0.143003, No Obj: 0.000790, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.738513, Class: 0.171086, Obj: 0.198789, No Obj: 0.000315, .5R: 1.000000, .75R: 0.375000, count: 8 -Region 16 Avg 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Obj: 0.547693, No Obj: 0.000692, .5R: 1.000000, .75R: 1.000000, count: 6 -Region 23 Avg IOU: 0.692705, Class: 0.202754, Obj: 0.291966, No Obj: 0.000797, .5R: 0.958333, .75R: 0.291667, count: 24 - - 2093: 1.948004, 2.419834 avg loss, 0.001000 rate, 1.869213 seconds, 267904 images -Loaded: 0.000082 seconds -Region 16 Avg IOU: 0.694888, Class: 0.231279, Obj: 0.308051, No Obj: 0.000764, .5R: 1.000000, .75R: 0.500000, count: 6 -Region 23 Avg IOU: 0.716351, Class: 0.235383, Obj: 0.192287, No Obj: 0.000315, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.759748, Class: 0.177353, Obj: 0.178641, No Obj: 0.000804, .5R: 0.923077, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.694162, Class: 0.151908, Obj: 0.181738, No Obj: 0.000580, .5R: 1.000000, .75R: 0.320000, count: 25 -Region 16 Avg IOU: 0.765915, Class: 0.200643, Obj: 0.319253, No Obj: 0.000873, .5R: 0.933333, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.661039, Class: 0.201501, Obj: 0.163715, No Obj: 0.000461, .5R: 0.850000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.768110, Class: 0.187641, Obj: 0.329058, No Obj: 0.001534, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.676501, Class: 0.210030, Obj: 0.180215, No Obj: 0.000537, .5R: 0.750000, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.778322, Class: 0.233928, Obj: 0.255488, No Obj: 0.001109, .5R: 1.000000, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.645218, Class: 0.128452, Obj: 0.124142, No Obj: 0.000438, .5R: 0.900000, .75R: 0.000000, count: 10 -Region 16 Avg IOU: 0.761164, Class: 0.181725, Obj: 0.289684, No Obj: 0.001381, .5R: 1.000000, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.674454, Class: 0.160156, Obj: 0.170446, No Obj: 0.000553, .5R: 0.958333, .75R: 0.333333, count: 24 -Region 16 Avg IOU: 0.690902, Class: 0.180401, Obj: 0.286758, No Obj: 0.001055, .5R: 1.000000, .75R: 0.125000, count: 8 -Region 23 Avg IOU: 0.674553, Class: 0.138385, Obj: 0.148478, No Obj: 0.000509, .5R: 0.904762, .75R: 0.285714, count: 21 -Region 16 Avg IOU: 0.776900, Class: 0.147133, Obj: 0.218438, No Obj: 0.001075, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.695895, Class: 0.145338, Obj: 0.147791, No Obj: 0.000234, .5R: 0.928571, .75R: 0.214286, count: 14 - - 2094: 2.157416, 2.393592 avg loss, 0.001000 rate, 1.852572 seconds, 268032 images -Loaded: 0.000068 seconds -Region 16 Avg IOU: 0.781986, Class: 0.267956, Obj: 0.298090, No Obj: 0.002119, .5R: 0.947368, .75R: 0.736842, count: 19 -Region 23 Avg IOU: 0.615987, Class: 0.230972, Obj: 0.091732, No Obj: 0.000265, .5R: 0.750000, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.754544, Class: 0.183307, Obj: 0.307628, No Obj: 0.001681, .5R: 1.000000, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.684400, Class: 0.145752, Obj: 0.181807, No Obj: 0.000289, .5R: 0.928571, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.765618, Class: 0.258530, Obj: 0.374620, No Obj: 0.001174, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.664529, Class: 0.149805, Obj: 0.129544, No Obj: 0.000247, .5R: 1.000000, .75R: 0.150000, count: 20 -Region 16 Avg IOU: 0.745508, Class: 0.213774, Obj: 0.409376, No Obj: 0.001129, .5R: 0.888889, .75R: 0.444444, count: 9 -Region 23 Avg IOU: 0.680738, Class: 0.149201, Obj: 0.212190, No Obj: 0.000366, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.740627, Class: 0.208142, Obj: 0.299637, No Obj: 0.002297, .5R: 0.964286, .75R: 0.392857, count: 28 -Region 23 Avg IOU: 0.698670, Class: 0.178942, Obj: 0.090521, No Obj: 0.000368, .5R: 0.863636, .75R: 0.318182, count: 22 -Region 16 Avg IOU: 0.734012, Class: 0.289437, Obj: 0.276547, No Obj: 0.001303, .5R: 0.923077, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.738433, Class: 0.158365, Obj: 0.112557, No Obj: 0.000365, .5R: 1.000000, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.742188, Class: 0.247119, Obj: 0.274812, No Obj: 0.001346, .5R: 0.928571, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.725809, Class: 0.192592, Obj: 0.173609, No Obj: 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count: 22 -Region 23 Avg IOU: 0.679079, Class: 0.171305, Obj: 0.070802, No Obj: 0.000273, .5R: 0.920000, .75R: 0.280000, count: 25 -Region 16 Avg IOU: 0.769059, Class: 0.167187, Obj: 0.430002, No Obj: 0.001655, .5R: 1.000000, .75R: 0.647059, count: 17 -Region 23 Avg IOU: 0.672928, Class: 0.136298, Obj: 0.174311, No Obj: 0.000457, .5R: 0.840000, .75R: 0.320000, count: 25 -Region 16 Avg IOU: 0.723764, Class: 0.203092, Obj: 0.310044, No Obj: 0.001275, .5R: 0.933333, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.692801, Class: 0.182637, Obj: 0.143822, No Obj: 0.000451, .5R: 0.966667, .75R: 0.333333, count: 30 -Region 16 Avg IOU: 0.798018, Class: 0.266954, Obj: 0.382138, No Obj: 0.001799, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.710332, Class: 0.224195, Obj: 0.158357, No Obj: 0.000466, .5R: 1.000000, .75R: 0.407407, count: 27 -Region 16 Avg IOU: 0.746475, Class: 0.209098, Obj: 0.336563, No Obj: 0.001828, .5R: 1.000000, .75R: 0.578947, count: 19 -Region 23 Avg IOU: 0.673269, Class: 0.200515, Obj: 0.097718, No Obj: 0.000364, .5R: 0.909091, .75R: 0.242424, count: 33 -Region 16 Avg IOU: 0.712309, Class: 0.191755, Obj: 0.339728, No Obj: 0.001672, .5R: 0.947368, .75R: 0.421053, count: 19 -Region 23 Avg IOU: 0.660675, Class: 0.193819, Obj: 0.158540, No Obj: 0.000479, .5R: 0.809524, .75R: 0.333333, count: 21 - - 2096: 3.022944, 2.445516 avg loss, 0.001000 rate, 1.625587 seconds, 268288 images -Loaded: 0.000079 seconds -Region 16 Avg IOU: 0.693495, Class: 0.181496, Obj: 0.220897, No Obj: 0.001766, .5R: 0.857143, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.657932, Class: 0.138441, Obj: 0.181569, No Obj: 0.000554, .5R: 0.869565, .75R: 0.304348, count: 23 -Region 16 Avg IOU: 0.734413, Class: 0.275711, Obj: 0.435814, No Obj: 0.001909, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.669149, Class: 0.188203, Obj: 0.177023, No Obj: 0.000788, .5R: 0.870968, .75R: 0.193548, count: 31 -Region 16 Avg IOU: 0.725867, Class: 0.263406, Obj: 0.407854, No Obj: 0.002108, .5R: 0.866667, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.680372, Class: 0.167588, Obj: 0.154779, No Obj: 0.000425, .5R: 0.900000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.814614, Class: 0.217431, Obj: 0.506507, No Obj: 0.001724, .5R: 1.000000, .75R: 0.818182, count: 11 -Region 23 Avg IOU: 0.689055, Class: 0.185246, Obj: 0.181953, No Obj: 0.000338, .5R: 0.944444, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.701908, Class: 0.224019, Obj: 0.260554, No Obj: 0.001788, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.710193, Class: 0.203738, Obj: 0.182064, No Obj: 0.000467, .5R: 0.916667, .75R: 0.500000, count: 12 -Region 16 Avg IOU: 0.736695, Class: 0.236237, Obj: 0.568672, No Obj: 0.002413, .5R: 0.923077, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.718188, Class: 0.137989, Obj: 0.298357, No Obj: 0.000491, .5R: 0.846154, .75R: 0.538462, count: 13 -Region 16 Avg IOU: 0.766792, Class: 0.298859, Obj: 0.560001, No Obj: 0.001820, .5R: 1.000000, 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0.000060 seconds -Region 16 Avg IOU: 0.737071, Class: 0.225951, Obj: 0.527794, No Obj: 0.001528, .5R: 1.000000, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.736713, Class: 0.191099, Obj: 0.195716, No Obj: 0.000195, .5R: 0.944444, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.730208, Class: 0.221826, Obj: 0.445869, No Obj: 0.001466, .5R: 1.000000, .75R: 0.388889, count: 18 -Region 23 Avg IOU: 0.721879, Class: 0.171294, Obj: 0.180028, No Obj: 0.000203, .5R: 1.000000, .75R: 0.428571, count: 21 -Region 16 Avg IOU: 0.681432, Class: 0.192774, Obj: 0.381728, No Obj: 0.001735, .5R: 1.000000, .75R: 0.263158, count: 19 -Region 23 Avg IOU: 0.716565, Class: 0.186408, Obj: 0.154942, No Obj: 0.000218, .5R: 0.941176, .75R: 0.470588, count: 17 -Region 16 Avg IOU: 0.750389, Class: 0.204249, Obj: 0.431182, No Obj: 0.002133, .5R: 0.956522, .75R: 0.565217, count: 23 -Region 23 Avg IOU: 0.734831, Class: 0.202967, Obj: 0.127964, No Obj: 0.000237, .5R: 1.000000, .75R: 0.550000, count: 20 -Region 16 Avg 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0.697068, Class: 0.186701, Obj: 0.144623, No Obj: 0.000206, .5R: 0.950000, .75R: 0.300000, count: 20 - - 2109: 2.153717, 2.601563 avg loss, 0.001000 rate, 3.823588 seconds, 269952 images -Loaded: 0.000075 seconds -Region 16 Avg IOU: 0.737922, Class: 0.210323, Obj: 0.241247, No Obj: 0.001139, .5R: 0.954545, .75R: 0.409091, count: 22 -Region 23 Avg IOU: 0.687399, Class: 0.177902, Obj: 0.217517, No Obj: 0.000301, .5R: 0.950000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.674006, Class: 0.217755, Obj: 0.133421, No Obj: 0.000961, .5R: 0.900000, .75R: 0.333333, count: 30 -Region 23 Avg IOU: 0.665766, Class: 0.191135, Obj: 0.106837, No Obj: 0.000295, .5R: 0.923077, .75R: 0.346154, count: 26 -Region 16 Avg IOU: 0.736106, Class: 0.226741, Obj: 0.225604, No Obj: 0.001078, .5R: 0.961538, .75R: 0.538462, count: 26 -Region 23 Avg IOU: 0.695696, Class: 0.181240, Obj: 0.206755, No Obj: 0.000275, .5R: 0.882353, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.714459, Class: 0.176646, Obj: 0.206679, No Obj: 0.000824, .5R: 1.000000, .75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.679157, Class: 0.089386, Obj: 0.212374, No Obj: 0.000181, .5R: 0.875000, .75R: 0.375000, count: 8 -Region 16 Avg IOU: 0.733865, Class: 0.168684, Obj: 0.222992, No Obj: 0.000972, .5R: 0.962963, .75R: 0.481481, count: 27 -Region 23 Avg IOU: 0.685785, Class: 0.169790, Obj: 0.142323, No Obj: 0.000333, .5R: 0.888889, .75R: 0.296296, count: 27 -Region 16 Avg IOU: 0.707151, Class: 0.162247, Obj: 0.117845, No Obj: 0.000571, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.676014, Class: 0.244282, Obj: 0.316866, No Obj: 0.000244, .5R: 0.900000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.743360, Class: 0.223775, Obj: 0.222901, No Obj: 0.000947, .5R: 1.000000, .75R: 0.560000, count: 25 -Region 23 Avg IOU: 0.732174, Class: 0.239277, Obj: 0.256944, No Obj: 0.000318, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.714658, Class: 0.167017, Obj: 0.180416, No Obj: 0.000673, .5R: 0.937500, 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images -Loaded: 0.000067 seconds -Region 16 Avg IOU: 0.730607, Class: 0.199398, Obj: 0.393394, No Obj: 0.001307, .5R: 0.933333, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.658534, Class: 0.189316, Obj: 0.226011, No Obj: 0.000313, .5R: 0.857143, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.678305, Class: 0.199038, Obj: 0.295247, No Obj: 0.001466, .5R: 0.894737, .75R: 0.263158, count: 19 -Region 23 Avg IOU: 0.663704, Class: 0.228952, Obj: 0.124159, No Obj: 0.000446, .5R: 0.838710, .75R: 0.225806, count: 31 -Region 16 Avg IOU: 0.702488, Class: 0.150262, Obj: 0.226824, No Obj: 0.002284, .5R: 1.000000, .75R: 0.318182, count: 22 -Region 23 Avg IOU: 0.731081, Class: 0.144386, Obj: 0.085461, No Obj: 0.000345, .5R: 1.000000, .75R: 0.360000, count: 25 -Region 16 Avg IOU: 0.701442, Class: 0.154597, Obj: 0.243732, No Obj: 0.001261, .5R: 0.941176, .75R: 0.294118, count: 17 -Region 23 Avg IOU: 0.621290, Class: 0.194156, Obj: 0.138400, No Obj: 0.000308, .5R: 0.750000, .75R: 0.166667, count: 12 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16 Avg IOU: 0.720208, Class: 0.228348, Obj: 0.221741, No Obj: 0.001470, .5R: 1.000000, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.649549, Class: 0.198903, Obj: 0.112484, No Obj: 0.000427, .5R: 0.888889, .75R: 0.296296, count: 27 -Region 16 Avg IOU: 0.731110, Class: 0.164185, Obj: 0.324931, No Obj: 0.002308, .5R: 0.960000, .75R: 0.440000, count: 25 -Region 23 Avg IOU: 0.689852, Class: 0.146757, Obj: 0.172186, No Obj: 0.000412, .5R: 0.954545, .75R: 0.318182, count: 22 -Region 16 Avg IOU: 0.767727, Class: 0.230793, Obj: 0.455478, No Obj: 0.001673, .5R: 0.923077, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.720667, Class: 0.189864, Obj: 0.248350, No Obj: 0.000382, .5R: 1.000000, .75R: 0.545455, count: 22 -Region 16 Avg IOU: 0.815751, Class: 0.255122, Obj: 0.509793, No Obj: 0.001335, .5R: 1.000000, .75R: 0.916667, count: 12 -Region 23 Avg IOU: 0.729438, Class: 0.196792, Obj: 0.217395, No Obj: 0.000432, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.768387, Class: 0.184910, Obj: 0.334374, No Obj: 0.001690, .5R: 1.000000, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.720765, Class: 0.177765, Obj: 0.179394, No Obj: 0.000399, .5R: 0.961538, .75R: 0.500000, count: 26 - - 2128: 2.534098, 2.284360 avg loss, 0.001000 rate, 2.149586 seconds, 272384 images -Loaded: 0.000091 seconds -Region 16 Avg IOU: 0.792454, Class: 0.204820, Obj: 0.374487, No Obj: 0.001478, .5R: 0.937500, .75R: 0.750000, count: 16 -Region 23 Avg IOU: 0.689293, Class: 0.203986, Obj: 0.226350, No Obj: 0.000359, .5R: 0.923077, .75R: 0.384615, count: 26 -Region 16 Avg IOU: 0.749651, Class: 0.269310, Obj: 0.436623, No Obj: 0.000893, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.687940, Class: 0.157849, Obj: 0.122616, No Obj: 0.000230, .5R: 1.000000, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.766080, Class: 0.234377, Obj: 0.378453, No Obj: 0.001694, .5R: 1.000000, .75R: 0.571429, count: 21 -Region 23 Avg IOU: 0.685284, Class: 0.228517, Obj: 0.119971, No Obj: 0.000280, 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Class: 0.221048, Obj: 0.204118, No Obj: 0.000453, .5R: 0.933333, .75R: 0.366667, count: 30 -Region 16 Avg IOU: 0.783617, Class: 0.233599, Obj: 0.265571, No Obj: 0.001014, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.746397, Class: 0.201126, Obj: 0.163095, No Obj: 0.000349, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 16 Avg IOU: 0.724247, Class: 0.286885, Obj: 0.254140, No Obj: 0.001868, .5R: 1.000000, .75R: 0.409091, count: 22 -Region 23 Avg IOU: 0.701722, Class: 0.190356, Obj: 0.157812, No Obj: 0.000423, .5R: 0.947368, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.711015, Class: 0.216141, Obj: 0.340655, No Obj: 0.001317, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.704597, Class: 0.185853, Obj: 0.243058, No Obj: 0.000481, .5R: 0.952381, .75R: 0.428571, count: 21 -Region 16 Avg IOU: 0.722721, Class: 0.276526, Obj: 0.320616, No Obj: 0.001549, .5R: 1.000000, .75R: 0.500000, count: 22 -Region 23 Avg IOU: 0.701795, Class: 0.172958, Obj: 0.196019, 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25 -Region 16 Avg IOU: 0.673702, Class: 0.179968, Obj: 0.253026, No Obj: 0.000969, .5R: 0.888889, .75R: 0.333333, count: 18 -Region 23 Avg IOU: 0.698087, Class: 0.114539, Obj: 0.213503, No Obj: 0.000657, .5R: 0.909091, .75R: 0.393939, count: 33 - - 2180: 2.731861, 2.555572 avg loss, 0.001000 rate, 2.735625 seconds, 279040 images -Resizing -608 x 608 - try to allocate workspace = 1146765313 * sizeof(float), CUDA allocate done! -Loaded: 0.144691 seconds -Region 16 Avg IOU: 0.683690, Class: 0.202597, Obj: 0.177722, No Obj: 0.001094, .5R: 0.952381, .75R: 0.190476, count: 21 -Region 23 Avg IOU: 0.661037, Class: 0.186115, Obj: 0.159674, No Obj: 0.000334, .5R: 0.880000, .75R: 0.280000, count: 25 -Region 16 Avg IOU: 0.783331, Class: 0.243632, Obj: 0.357082, No Obj: 0.000958, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.677419, Class: 0.200741, Obj: 0.244218, No Obj: 0.000440, .5R: 0.956522, .75R: 0.347826, count: 23 -Region 16 Avg IOU: 0.729507, Class: 0.217983, Obj: 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-Region 16 Avg IOU: 0.673024, Class: 0.161355, Obj: 0.199512, No Obj: 0.000856, .5R: 0.750000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.668234, Class: 0.145620, Obj: 0.114038, No Obj: 0.000338, .5R: 0.840000, .75R: 0.400000, count: 25 -Region 16 Avg IOU: 0.681595, Class: 0.221462, Obj: 0.347455, No Obj: 0.001098, .5R: 0.916667, .75R: 0.250000, count: 12 -Region 23 Avg IOU: 0.689650, Class: 0.158670, Obj: 0.232137, No Obj: 0.000190, .5R: 1.000000, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.722600, Class: 0.213351, Obj: 0.337789, No Obj: 0.001464, .5R: 0.909091, .75R: 0.500000, count: 22 -Region 23 Avg IOU: 0.677146, Class: 0.202863, Obj: 0.145390, No Obj: 0.000282, .5R: 0.937500, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.753254, Class: 0.231943, Obj: 0.253855, No Obj: 0.000999, .5R: 0.933333, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.716918, Class: 0.222657, Obj: 0.105748, No Obj: 0.000234, .5R: 0.952381, .75R: 0.523810, count: 21 -Region 16 Avg IOU: 0.678885, Class: 0.227413, Obj: 0.358712, No Obj: 0.001442, .5R: 0.900000, .75R: 0.350000, count: 20 -Region 23 Avg IOU: 0.682756, Class: 0.212268, Obj: 0.128563, No Obj: 0.000279, .5R: 0.920000, .75R: 0.320000, count: 25 -Region 16 Avg IOU: 0.719986, Class: 0.230994, Obj: 0.280905, No Obj: 0.001075, .5R: 1.000000, .75R: 0.400000, count: 20 -Region 23 Avg IOU: 0.722063, Class: 0.208117, Obj: 0.180152, No Obj: 0.000347, .5R: 0.954545, .75R: 0.454545, count: 22 - - 2182: 2.719476, 2.575778 avg loss, 0.001000 rate, 3.144817 seconds, 279296 images -Loaded: 0.000065 seconds -Region 16 Avg IOU: 0.669442, Class: 0.243201, Obj: 0.159016, No Obj: 0.001553, .5R: 0.960000, .75R: 0.360000, count: 25 -Region 23 Avg IOU: 0.694313, Class: 0.216341, Obj: 0.129421, No Obj: 0.000155, .5R: 0.947368, .75R: 0.368421, count: 19 -Region 16 Avg IOU: 0.761653, Class: 0.248564, Obj: 0.366217, No Obj: 0.000911, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.666854, Class: 0.138580, Obj: 0.173127, No Obj: 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sizeof(float), CUDA allocate done! -Loaded: 0.550402 seconds -Region 16 Avg IOU: 0.777958, Class: 0.235809, Obj: 0.407376, No Obj: 0.001298, .5R: 1.000000, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.640915, Class: 0.191414, Obj: 0.215260, No Obj: 0.000479, .5R: 0.764706, .75R: 0.235294, count: 17 -Region 16 Avg IOU: 0.800392, Class: 0.248611, Obj: 0.341104, No Obj: 0.001380, .5R: 1.000000, .75R: 0.833333, count: 12 -Region 23 Avg IOU: 0.654730, Class: 0.233934, Obj: 0.293555, No Obj: 0.000522, .5R: 0.944444, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.761584, Class: 0.241540, Obj: 0.471991, No Obj: 0.001659, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.682473, Class: 0.227709, Obj: 0.173726, No Obj: 0.000380, .5R: 1.000000, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.759338, Class: 0.227224, Obj: 0.283072, No Obj: 0.001406, .5R: 1.000000, .75R: 0.545455, count: 22 -Region 23 Avg IOU: 0.732839, Class: 0.200959, Obj: 0.279734, No Obj: 0.000557, .5R: 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0.184905, Obj: 0.233442, No Obj: 0.000712, .5R: 0.962963, .75R: 0.370370, count: 27 -Region 16 Avg IOU: 0.800628, Class: 0.243474, Obj: 0.405994, No Obj: 0.000896, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.679653, Class: 0.211083, Obj: 0.179725, No Obj: 0.000345, .5R: 0.823529, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.754883, Class: 0.212654, Obj: 0.365577, No Obj: 0.001351, .5R: 0.956522, .75R: 0.695652, count: 23 -Region 23 Avg IOU: 0.681354, Class: 0.232756, Obj: 0.291340, No Obj: 0.000437, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.735058, Class: 0.206842, Obj: 0.277975, No Obj: 0.001166, .5R: 0.928571, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.674584, Class: 0.154639, Obj: 0.304990, No Obj: 0.000643, .5R: 0.843750, .75R: 0.406250, count: 32 -Region 16 Avg IOU: 0.763221, Class: 0.193673, Obj: 0.348455, No Obj: 0.000943, .5R: 1.000000, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.671588, Class: 0.158905, Obj: 0.196378, No Obj: 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16 Avg IOU: 0.790445, Class: 0.160389, Obj: 0.405571, No Obj: 0.002079, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.712661, Class: 0.172786, Obj: 0.141430, No Obj: 0.000414, .5R: 0.916667, .75R: 0.375000, count: 24 -Region 16 Avg IOU: 0.791988, Class: 0.301633, Obj: 0.453872, No Obj: 0.002142, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.656121, Class: 0.239325, Obj: 0.128519, No Obj: 0.000360, .5R: 0.875000, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.696456, Class: 0.241278, Obj: 0.250460, No Obj: 0.001009, .5R: 1.000000, .75R: 0.428571, count: 7 -Region 23 Avg IOU: 0.748440, Class: 0.193995, Obj: 0.170509, No Obj: 0.000253, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 16 Avg IOU: 0.780528, Class: 0.237494, Obj: 0.473128, No Obj: 0.001319, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.682803, Class: 0.171534, Obj: 0.070035, No Obj: 0.000284, .5R: 0.933333, .75R: 0.133333, count: 15 - - 2207: 1.843663, 2.272839 avg loss, 0.001000 rate, 2.031182 seconds, 282496 images -Loaded: 0.000082 seconds -Region 16 Avg IOU: 0.709560, Class: 0.266104, Obj: 0.421555, No Obj: 0.001909, .5R: 0.909091, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.681843, Class: 0.152450, Obj: 0.094437, No Obj: 0.000406, .5R: 1.000000, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.738232, Class: 0.213784, Obj: 0.382909, No Obj: 0.002737, .5R: 0.952381, .75R: 0.571429, count: 21 -Region 23 Avg IOU: 0.710868, Class: 0.233677, Obj: 0.172958, No Obj: 0.000701, .5R: 0.947368, .75R: 0.263158, count: 19 -Region 16 Avg IOU: 0.772937, Class: 0.249947, Obj: 0.213922, No Obj: 0.001634, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.749118, Class: 0.162339, Obj: 0.385188, No Obj: 0.000270, .5R: 1.000000, .75R: 0.500000, count: 2 -Region 16 Avg IOU: 0.814060, Class: 0.253281, Obj: 0.277313, No Obj: 0.001395, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.731096, Class: 0.233675, Obj: 0.178474, No Obj: 0.000315, 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count: 28 - - 2208: 2.046876, 2.250242 avg loss, 0.001000 rate, 1.199327 seconds, 282624 images -Loaded: 0.000063 seconds -Region 16 Avg IOU: 0.803391, Class: 0.248063, Obj: 0.344384, No Obj: 0.001512, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.634522, Class: 0.148381, Obj: 0.217650, No Obj: 0.000950, .5R: 0.769231, .75R: 0.307692, count: 26 -Region 16 Avg IOU: 0.793887, Class: 0.159622, Obj: 0.454587, No Obj: 0.001204, .5R: 1.000000, .75R: 0.777778, count: 9 -Region 23 Avg IOU: 0.677593, Class: 0.148545, Obj: 0.277891, No Obj: 0.000634, .5R: 0.916667, .75R: 0.250000, count: 12 -Region 16 Avg IOU: 0.728046, Class: 0.189018, Obj: 0.382385, No Obj: 0.000948, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.654117, Class: 0.182466, Obj: 0.357889, No Obj: 0.000538, .5R: 1.000000, .75R: 0.200000, count: 10 -Region 16 Avg IOU: 0.741183, Class: 0.195346, Obj: 0.227122, No Obj: 0.001163, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.714844, Class: 0.149590, Obj: 0.208709, No Obj: 0.000377, .5R: 0.909091, .75R: 0.636364, count: 11 -Region 16 Avg IOU: 0.772179, Class: 0.201556, Obj: 0.358299, No Obj: 0.001673, .5R: 1.000000, .75R: 0.777778, count: 9 -Region 23 Avg IOU: 0.678236, Class: 0.190631, Obj: 0.310572, No Obj: 0.000754, .5R: 0.952381, .75R: 0.285714, count: 21 -Region 16 Avg IOU: 0.774143, Class: 0.207832, Obj: 0.406228, No Obj: 0.001493, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.670239, Class: 0.157268, Obj: 0.224719, No Obj: 0.000718, .5R: 0.869565, .75R: 0.391304, count: 23 -Region 16 Avg IOU: 0.760322, Class: 0.313583, Obj: 0.466508, No Obj: 0.001258, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.705305, Class: 0.221739, Obj: 0.339097, No Obj: 0.000624, .5R: 1.000000, .75R: 0.357143, count: 14 -Region 16 Avg IOU: 0.799276, Class: 0.227451, Obj: 0.368963, No Obj: 0.002439, .5R: 1.000000, .75R: 0.687500, count: 16 -Region 23 Avg IOU: 0.708968, Class: 0.161474, Obj: 0.380395, 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count: 21 -Region 16 Avg IOU: 0.791579, Class: 0.271189, Obj: 0.604578, No Obj: 0.001286, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.723029, Class: 0.231027, Obj: 0.442838, No Obj: 0.000541, .5R: 1.000000, .75R: 0.400000, count: 10 - - 2211: 1.713934, 2.141023 avg loss, 0.001000 rate, 1.407086 seconds, 283008 images -Loaded: 0.000071 seconds -Region 16 Avg IOU: 0.776411, Class: 0.180993, Obj: 0.184073, No Obj: 0.002053, .5R: 1.000000, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.725782, Class: 0.222351, Obj: 0.048653, No Obj: 0.000218, .5R: 0.909091, .75R: 0.454545, count: 11 -Region 16 Avg IOU: 0.780020, Class: 0.216081, Obj: 0.332781, No Obj: 0.001527, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.736136, Class: 0.222765, Obj: 0.208656, No Obj: 0.000368, .5R: 0.928571, .75R: 0.642857, count: 14 -Region 16 Avg IOU: 0.817916, Class: 0.279822, Obj: 0.384819, No Obj: 0.000836, .5R: 1.000000, .75R: 0.875000, count: 8 -Region 23 Avg IOU: 0.726166, 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23 Avg IOU: 0.737671, Class: 0.179245, Obj: 0.120489, No Obj: 0.000431, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 16 Avg IOU: 0.760739, Class: 0.214707, Obj: 0.480761, No Obj: 0.002009, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.729280, Class: 0.102611, Obj: 0.197855, No Obj: 0.000417, .5R: 1.000000, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.746574, Class: 0.251670, Obj: 0.360211, No Obj: 0.002135, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 23 Avg IOU: 0.675402, Class: 0.214399, Obj: 0.195053, No Obj: 0.000354, .5R: 0.933333, .75R: 0.200000, count: 15 - - 2218: 1.862090, 2.142803 avg loss, 0.001000 rate, 2.014196 seconds, 283904 images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.729064, Class: 0.254800, Obj: 0.322979, No Obj: 0.002107, .5R: 1.000000, .75R: 0.363636, count: 11 -Region 23 Avg IOU: 0.679175, Class: 0.242013, Obj: 0.077415, No Obj: 0.000314, .5R: 0.954545, .75R: 0.272727, count: 22 -Region 16 Avg IOU: 0.798853, Class: 0.211797, 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Class: 0.181906, Obj: 0.366972, No Obj: 0.001196, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.691210, Class: 0.208966, Obj: 0.214223, No Obj: 0.000366, .5R: 0.900000, .75R: 0.300000, count: 20 -Region 16 Avg IOU: 0.765651, Class: 0.213005, Obj: 0.299489, No Obj: 0.001725, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.650507, Class: 0.182963, Obj: 0.110184, No Obj: 0.000486, .5R: 0.851852, .75R: 0.259259, count: 27 -Region 16 Avg IOU: 0.803380, Class: 0.201765, Obj: 0.506345, No Obj: 0.002006, .5R: 1.000000, .75R: 0.818182, count: 11 -Region 23 Avg IOU: 0.680050, Class: 0.206331, Obj: 0.113367, No Obj: 0.000371, .5R: 0.809524, .75R: 0.380952, count: 21 - - 2220: 2.321593, 2.166614 avg loss, 0.001000 rate, 1.546537 seconds, 284160 images -Resizing -640 x 640 - try to allocate workspace = 1147404289 * sizeof(float), CUDA allocate done! -Loaded: 0.337792 seconds -Region 16 Avg IOU: 0.753715, Class: 0.185581, Obj: 0.410925, No Obj: 0.001650, .5R: 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13 -Region 23 Avg IOU: 0.712321, Class: 0.252940, Obj: 0.303188, No Obj: 0.000363, .5R: 1.000000, .75R: 0.285714, count: 14 -Region 16 Avg IOU: 0.705132, Class: 0.218111, Obj: 0.349110, No Obj: 0.001520, .5R: 0.947368, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.728281, Class: 0.232068, Obj: 0.325878, No Obj: 0.000282, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 16 Avg IOU: 0.716240, Class: 0.243226, Obj: 0.392538, No Obj: 0.001068, .5R: 0.941176, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.712075, Class: 0.181555, Obj: 0.255135, No Obj: 0.000394, .5R: 1.000000, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.791207, Class: 0.199379, Obj: 0.521510, No Obj: 0.001152, .5R: 1.000000, .75R: 0.785714, count: 14 -Region 23 Avg IOU: 0.787955, Class: 0.241947, Obj: 0.356445, No Obj: 0.000265, .5R: 1.000000, .75R: 0.800000, count: 10 - - 2221: 2.445856, 2.194538 avg loss, 0.001000 rate, 3.318466 seconds, 284288 images -Loaded: 0.000062 seconds -Region 16 Avg IOU: 0.693770, Class: 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0.000077 seconds -Region 16 Avg IOU: 0.771591, Class: 0.289719, Obj: 0.250683, No Obj: 0.001105, .5R: 1.000000, .75R: 0.666667, count: 18 -Region 23 Avg IOU: 0.713611, Class: 0.235280, Obj: 0.097265, No Obj: 0.000207, .5R: 0.904762, .75R: 0.571429, count: 21 -Region 16 Avg IOU: 0.712229, Class: 0.240209, Obj: 0.273511, No Obj: 0.000910, .5R: 0.950000, .75R: 0.350000, count: 20 -Region 23 Avg IOU: 0.671064, Class: 0.201021, Obj: 0.149335, No Obj: 0.000204, .5R: 0.833333, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.750789, Class: 0.224486, Obj: 0.238146, No Obj: 0.000783, .5R: 0.846154, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.704909, Class: 0.183076, Obj: 0.162869, No Obj: 0.000266, .5R: 0.960000, .75R: 0.280000, count: 25 -Region 16 Avg IOU: 0.711744, Class: 0.267284, Obj: 0.213325, No Obj: 0.000909, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.756203, Class: 0.170599, Obj: 0.156633, No Obj: 0.000157, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 16 Avg 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rate, 2.788039 seconds, 284544 images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.739185, Class: 0.167698, Obj: 0.322169, No Obj: 0.001005, .5R: 0.937500, .75R: 0.625000, count: 16 -Region 23 Avg IOU: 0.708742, Class: 0.152964, Obj: 0.184987, No Obj: 0.000247, .5R: 1.000000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.787192, Class: 0.192535, Obj: 0.277936, No Obj: 0.001139, .5R: 1.000000, .75R: 0.650000, count: 20 -Region 23 Avg IOU: 0.806294, Class: 0.234148, Obj: 0.150840, No Obj: 0.000135, .5R: 1.000000, .75R: 0.777778, count: 9 -Region 16 Avg IOU: 0.725051, Class: 0.244958, Obj: 0.340848, No Obj: 0.001503, .5R: 0.884615, .75R: 0.538462, count: 26 -Region 23 Avg IOU: 0.709620, Class: 0.192153, Obj: 0.152681, No Obj: 0.000230, .5R: 0.857143, .75R: 0.428571, count: 21 -Region 16 Avg IOU: 0.716166, Class: 0.226490, Obj: 0.237325, No Obj: 0.001217, .5R: 0.958333, .75R: 0.458333, count: 24 -Region 23 Avg IOU: 0.665958, Class: 0.239583, Obj: 0.067061, No Obj: 0.000138, .5R: 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19 - - 2224: 2.406485, 2.294361 avg loss, 0.001000 rate, 2.148913 seconds, 284672 images -Loaded: 0.000061 seconds -Region 16 Avg IOU: 0.774808, Class: 0.273793, Obj: 0.427474, No Obj: 0.001949, .5R: 1.000000, .75R: 0.619048, count: 21 -Region 23 Avg IOU: 0.693788, Class: 0.250819, Obj: 0.105958, No Obj: 0.000269, .5R: 0.935484, .75R: 0.354839, count: 31 -Region 16 Avg IOU: 0.757921, Class: 0.263887, Obj: 0.401460, No Obj: 0.001399, .5R: 0.920000, .75R: 0.560000, count: 25 -Region 23 Avg IOU: 0.670171, Class: 0.176662, Obj: 0.146997, No Obj: 0.000288, .5R: 0.875000, .75R: 0.281250, count: 32 -Region 16 Avg IOU: 0.757656, Class: 0.183865, Obj: 0.294141, No Obj: 0.001600, .5R: 1.000000, .75R: 0.600000, count: 25 -Region 23 Avg IOU: 0.765737, Class: 0.161194, Obj: 0.133309, No Obj: 0.000236, .5R: 1.000000, .75R: 0.600000, count: 25 -Region 16 Avg IOU: 0.741440, Class: 0.291078, Obj: 0.267400, No Obj: 0.001244, .5R: 1.000000, .75R: 0.500000, count: 22 -Region 23 Avg IOU: 0.686936, Class: 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workspace = 1145585665 * sizeof(float), CUDA allocate done! -Loaded: 0.309065 seconds -Region 16 Avg IOU: 0.702263, Class: 0.211672, Obj: 0.292940, No Obj: 0.001431, .5R: 1.000000, .75R: 0.384615, count: 13 -Region 23 Avg IOU: 0.643402, Class: 0.166945, Obj: 0.235327, No Obj: 0.000355, .5R: 0.846154, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.732564, Class: 0.214518, Obj: 0.375172, No Obj: 0.001778, .5R: 0.923077, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.645871, Class: 0.209271, Obj: 0.182040, No Obj: 0.000323, .5R: 0.928571, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.684576, Class: 0.234297, Obj: 0.467757, No Obj: 0.001751, .5R: 0.900000, .75R: 0.200000, count: 10 -Region 23 Avg IOU: 0.668964, Class: 0.134950, Obj: 0.174533, No Obj: 0.000537, .5R: 0.818182, .75R: 0.454545, count: 11 -Region 16 Avg IOU: 0.682701, Class: 0.179523, Obj: 0.220268, No Obj: 0.001553, .5R: 0.947368, .75R: 0.421053, count: 19 -Region 23 Avg IOU: 0.705967, Class: 0.162935, Obj: 0.142562, No 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.75R: 0.461538, count: 13 - - 2241: 2.005645, 2.182724 avg loss, 0.001000 rate, 2.581832 seconds, 286848 images -Loaded: 0.000059 seconds -Region 16 Avg IOU: 0.732827, Class: 0.164547, Obj: 0.445630, No Obj: 0.001666, .5R: 0.933333, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.649816, Class: 0.156524, Obj: 0.129506, No Obj: 0.000285, .5R: 0.800000, .75R: 0.280000, count: 25 -Region 16 Avg IOU: 0.745699, Class: 0.152391, Obj: 0.221116, No Obj: 0.001168, .5R: 0.916667, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.703584, Class: 0.188699, Obj: 0.190523, No Obj: 0.000360, .5R: 0.964286, .75R: 0.285714, count: 28 -Region 16 Avg IOU: 0.774047, Class: 0.251007, Obj: 0.398924, No Obj: 0.001484, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.710906, Class: 0.181833, Obj: 0.166560, No Obj: 0.000283, .5R: 0.941176, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.757220, Class: 0.228440, Obj: 0.525714, No Obj: 0.001547, .5R: 0.923077, .75R: 0.615385, count: 13 -Region 23 Avg 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Obj: 0.144224, No Obj: 0.000263, .5R: 0.789474, .75R: 0.263158, count: 19 - - 2242: 2.493459, 2.213798 avg loss, 0.001000 rate, 3.161504 seconds, 286976 images -Loaded: 0.000077 seconds -Region 16 Avg IOU: 0.782955, Class: 0.207953, Obj: 0.323488, No Obj: 0.001385, .5R: 1.000000, .75R: 0.857143, count: 14 -Region 23 Avg IOU: 0.713026, Class: 0.243508, Obj: 0.116553, No Obj: 0.000195, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.824200, Class: 0.233178, Obj: 0.328042, No Obj: 0.001144, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.682290, Class: 0.165441, Obj: 0.068087, No Obj: 0.000214, .5R: 0.928571, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.757069, Class: 0.235938, Obj: 0.477331, No Obj: 0.001106, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.746789, Class: 0.194969, Obj: 0.157849, No Obj: 0.000315, .5R: 1.000000, .75R: 0.500000, count: 18 -Region 16 Avg IOU: 0.754608, Class: 0.208697, Obj: 0.314186, No Obj: 0.001198, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.656571, Class: 0.175897, Obj: 0.132571, No Obj: 0.000272, .5R: 0.909091, .75R: 0.090909, count: 22 -Region 16 Avg IOU: 0.776096, Class: 0.220988, Obj: 0.486241, No Obj: 0.001453, .5R: 1.000000, .75R: 0.687500, count: 16 -Region 23 Avg IOU: 0.717141, Class: 0.267232, Obj: 0.149343, No Obj: 0.000251, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.751770, Class: 0.187788, Obj: 0.466353, No Obj: 0.001220, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.731087, Class: 0.190484, Obj: 0.113126, No Obj: 0.000178, .5R: 0.916667, .75R: 0.583333, count: 12 -Region 16 Avg IOU: 0.725761, Class: 0.148039, Obj: 0.340953, No Obj: 0.001421, .5R: 0.947368, .75R: 0.421053, count: 19 -Region 23 Avg IOU: 0.662994, Class: 0.129704, Obj: 0.145813, No Obj: 0.000291, .5R: 0.928571, .75R: 0.285714, count: 14 -Region 16 Avg IOU: 0.766242, Class: 0.247871, Obj: 0.318477, No Obj: 0.001307, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.623406, Class: 0.192836, Obj: 0.096398, No Obj: 0.000265, .5R: 0.904762, .75R: 0.142857, count: 21 - - 2243: 2.031828, 2.195601 avg loss, 0.001000 rate, 2.593239 seconds, 287104 images -Loaded: 0.000069 seconds -Region 16 Avg IOU: 0.745929, Class: 0.239252, Obj: 0.380510, No Obj: 0.001202, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.731700, Class: 0.141513, Obj: 0.230503, No Obj: 0.000216, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 16 Avg IOU: 0.773861, Class: 0.248032, Obj: 0.406740, No Obj: 0.001168, .5R: 0.928571, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.672427, Class: 0.173374, Obj: 0.154111, No Obj: 0.000392, .5R: 0.888889, .75R: 0.361111, count: 36 -Region 16 Avg IOU: 0.770400, Class: 0.241289, Obj: 0.420323, No Obj: 0.002077, .5R: 1.000000, .75R: 0.636364, count: 22 -Region 23 Avg IOU: 0.725927, Class: 0.171034, Obj: 0.214124, No Obj: 0.000477, .5R: 1.000000, .75R: 0.468750, count: 32 -Region 16 Avg IOU: 0.749362, Class: 0.246911, Obj: 0.476519, No Obj: 0.001462, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.705357, Class: 0.189869, Obj: 0.191234, No Obj: 0.000341, .5R: 0.950000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.689572, Class: 0.262478, Obj: 0.368308, No Obj: 0.000834, .5R: 1.000000, .75R: 0.428571, count: 7 -Region 23 Avg IOU: 0.708214, Class: 0.218636, Obj: 0.131316, No Obj: 0.000235, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.754206, Class: 0.202446, Obj: 0.335252, No Obj: 0.001478, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.697781, Class: 0.144595, Obj: 0.128250, No Obj: 0.000335, .5R: 0.903226, .75R: 0.387097, count: 31 -Region 16 Avg IOU: 0.757854, Class: 0.148846, Obj: 0.280278, No Obj: 0.001140, .5R: 1.000000, .75R: 0.647059, count: 17 -Region 23 Avg IOU: 0.695906, Class: 0.152969, Obj: 0.208993, No Obj: 0.000406, .5R: 0.909091, .75R: 0.318182, count: 22 -Region 16 Avg IOU: 0.748259, Class: 0.169075, Obj: 0.428592, No Obj: 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count: 16 -Region 16 Avg IOU: 0.713998, Class: 0.233582, Obj: 0.280494, No Obj: 0.001086, .5R: 0.909091, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.653055, Class: 0.221120, Obj: 0.247410, No Obj: 0.000415, .5R: 1.000000, .75R: 0.125000, count: 8 -Region 16 Avg IOU: 0.732753, Class: 0.212171, Obj: 0.329426, No Obj: 0.001089, .5R: 0.923077, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.682783, Class: 0.216008, Obj: 0.256955, No Obj: 0.000468, .5R: 0.944444, .75R: 0.222222, count: 18 -Region 16 Avg IOU: 0.799243, Class: 0.246432, Obj: 0.261894, No Obj: 0.001533, .5R: 1.000000, .75R: 0.777778, count: 18 -Region 23 Avg IOU: 0.718901, Class: 0.219883, Obj: 0.267095, No Obj: 0.000467, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.763979, Class: 0.215845, Obj: 0.309554, No Obj: 0.001346, .5R: 0.900000, .75R: 0.750000, count: 20 -Region 23 Avg IOU: 0.694095, Class: 0.192980, Obj: 0.166335, No Obj: 0.000507, .5R: 1.000000, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.742618, Class: 0.278347, Obj: 0.399300, No Obj: 0.002032, .5R: 0.925926, .75R: 0.592593, count: 27 -Region 23 Avg IOU: 0.642613, Class: 0.187384, Obj: 0.161994, No Obj: 0.000512, .5R: 0.781250, .75R: 0.187500, count: 32 - - 2245: 2.522957, 2.264338 avg loss, 0.001000 rate, 2.174557 seconds, 287360 images -Loaded: 0.000098 seconds -Region 16 Avg IOU: 0.777966, Class: 0.230403, Obj: 0.380861, No Obj: 0.001514, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.735958, Class: 0.197346, Obj: 0.200885, No Obj: 0.000391, .5R: 0.944444, .75R: 0.444444, count: 18 -Region 16 Avg IOU: 0.773120, Class: 0.135201, Obj: 0.343400, No Obj: 0.000969, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.654312, Class: 0.215339, Obj: 0.181781, No Obj: 0.000358, .5R: 0.777778, .75R: 0.333333, count: 9 -Region 16 Avg IOU: 0.806196, Class: 0.242479, Obj: 0.493751, No Obj: 0.000873, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.668909, Class: 0.203277, Obj: 0.384620, 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.75R: 0.292683, count: 41 -Region 16 Avg IOU: 0.730117, Class: 0.266074, Obj: 0.250261, No Obj: 0.001419, .5R: 1.000000, .75R: 0.352941, count: 17 -Region 23 Avg IOU: 0.680776, Class: 0.248200, Obj: 0.161389, No Obj: 0.000341, .5R: 1.000000, .75R: 0.235294, count: 17 - - 2246: 2.361767, 2.274081 avg loss, 0.001000 rate, 2.317799 seconds, 287488 images -Loaded: 0.000067 seconds -Region 16 Avg IOU: 0.755812, Class: 0.215947, Obj: 0.269848, No Obj: 0.001622, .5R: 0.944444, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.728656, Class: 0.173509, Obj: 0.142992, No Obj: 0.000349, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.730604, Class: 0.187900, Obj: 0.277267, No Obj: 0.001739, .5R: 0.944444, .75R: 0.388889, count: 18 -Region 23 Avg IOU: 0.725864, Class: 0.122712, Obj: 0.363902, No Obj: 0.000584, .5R: 1.000000, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.789591, Class: 0.161317, Obj: 0.396271, No Obj: 0.001234, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg 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Obj: 0.119984, No Obj: 0.000353, .5R: 1.000000, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.773121, Class: 0.199218, Obj: 0.423177, No Obj: 0.001427, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.634127, Class: 0.209169, Obj: 0.317280, No Obj: 0.000466, .5R: 0.812500, .75R: 0.375000, count: 16 - - 2247: 2.468225, 2.293495 avg loss, 0.001000 rate, 2.583237 seconds, 287616 images -Loaded: 0.000061 seconds -Region 16 Avg IOU: 0.767533, Class: 0.214995, Obj: 0.355956, No Obj: 0.001978, .5R: 1.000000, .75R: 0.666667, count: 18 -Region 23 Avg IOU: 0.731249, Class: 0.196075, Obj: 0.080784, No Obj: 0.000292, .5R: 1.000000, .75R: 0.470588, count: 17 -Region 16 Avg IOU: 0.761543, Class: 0.256808, Obj: 0.267402, No Obj: 0.001105, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.668253, Class: 0.229765, Obj: 0.098486, No Obj: 0.000143, .5R: 0.888889, .75R: 0.222222, count: 9 -Region 16 Avg IOU: 0.722726, Class: 0.198666, Obj: 0.358944, No Obj: 0.001633, .5R: 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17 -Region 23 Avg IOU: 0.663672, Class: 0.190991, Obj: 0.206072, No Obj: 0.000465, .5R: 0.967742, .75R: 0.161290, count: 31 -Region 16 Avg IOU: 0.751685, Class: 0.205134, Obj: 0.434574, No Obj: 0.001447, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.700144, Class: 0.161812, Obj: 0.141739, No Obj: 0.000284, .5R: 0.894737, .75R: 0.526316, count: 19 - - 2248: 2.368451, 2.300991 avg loss, 0.001000 rate, 2.429866 seconds, 287744 images -Loaded: 0.000075 seconds -Region 16 Avg IOU: 0.795619, Class: 0.185964, Obj: 0.369052, No Obj: 0.001305, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.765346, Class: 0.131353, Obj: 0.224282, No Obj: 0.000304, .5R: 1.000000, .75R: 0.625000, count: 16 -Region 16 Avg IOU: 0.745290, Class: 0.223227, Obj: 0.346105, No Obj: 0.001065, .5R: 0.916667, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.672807, Class: 0.179128, Obj: 0.159179, No Obj: 0.000254, .5R: 0.941176, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.735382, Class: 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count: 23 -Region 16 Avg IOU: 0.790743, Class: 0.201215, Obj: 0.380610, No Obj: 0.001410, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.722619, Class: 0.225709, Obj: 0.229235, No Obj: 0.000364, .5R: 0.944444, .75R: 0.500000, count: 18 -Region 16 Avg IOU: 0.790045, Class: 0.226252, Obj: 0.344174, No Obj: 0.001332, .5R: 1.000000, .75R: 0.714286, count: 21 -Region 23 Avg IOU: 0.747666, Class: 0.196286, Obj: 0.136964, No Obj: 0.000308, .5R: 1.000000, .75R: 0.423077, count: 26 -Region 16 Avg IOU: 0.750968, Class: 0.219686, Obj: 0.317849, No Obj: 0.001684, .5R: 0.961538, .75R: 0.576923, count: 26 -Region 23 Avg IOU: 0.713312, Class: 0.227705, Obj: 0.114308, No Obj: 0.000313, .5R: 1.000000, .75R: 0.500000, count: 26 -Region 16 Avg IOU: 0.785557, Class: 0.186860, Obj: 0.337154, No Obj: 0.000939, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.708439, Class: 0.228902, Obj: 0.192117, No Obj: 0.000284, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 16 Avg IOU: 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Avg IOU: 0.838811, Class: 0.171777, Obj: 0.356728, No Obj: 0.001242, .5R: 1.000000, .75R: 0.833333, count: 12 -Region 23 Avg IOU: 0.669060, Class: 0.160237, Obj: 0.279865, No Obj: 0.000406, .5R: 1.000000, .75R: 0.181818, count: 11 -Region 16 Avg IOU: 0.769168, Class: 0.239643, Obj: 0.552737, No Obj: 0.001894, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.707154, Class: 0.208715, Obj: 0.282255, No Obj: 0.000675, .5R: 0.954545, .75R: 0.454545, count: 22 -Region 16 Avg IOU: 0.713685, Class: 0.235550, Obj: 0.363640, No Obj: 0.001307, .5R: 0.909091, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.651464, Class: 0.237226, Obj: 0.225226, No Obj: 0.000520, .5R: 0.826087, .75R: 0.304348, count: 23 - - 2253: 2.039878, 2.247890 avg loss, 0.001000 rate, 1.845933 seconds, 288384 images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.821583, Class: 0.257953, Obj: 0.537732, No Obj: 0.001210, .5R: 1.000000, .75R: 0.833333, count: 6 -Region 23 Avg IOU: 0.667962, Class: 0.208409, Obj: 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count: 8 -Region 23 Avg IOU: 0.688739, Class: 0.140403, Obj: 0.103218, No Obj: 0.000222, .5R: 1.000000, .75R: 0.333333, count: 9 -Region 16 Avg IOU: 0.752491, Class: 0.261302, Obj: 0.259386, No Obj: 0.001668, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.635454, Class: 0.173730, Obj: 0.102814, No Obj: 0.000217, .5R: 0.916667, .75R: 0.083333, count: 12 -Region 16 Avg IOU: 0.757592, Class: 0.277624, Obj: 0.486332, No Obj: 0.002550, .5R: 1.000000, .75R: 0.550000, count: 20 -Region 23 Avg IOU: 0.694833, Class: 0.203719, Obj: 0.164792, No Obj: 0.000337, .5R: 0.952381, .75R: 0.380952, count: 21 -Region 16 Avg IOU: 0.773389, Class: 0.262210, Obj: 0.328962, No Obj: 0.001068, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.740749, Class: 0.243521, Obj: 0.149790, No Obj: 0.000152, .5R: 1.000000, .75R: 0.571429, count: 7 - - 2256: 2.206513, 2.171326 avg loss, 0.001000 rate, 1.579439 seconds, 288768 images -Loaded: 0.000067 seconds -Region 16 Avg IOU: 0.749268, Class: 0.228713, Obj: 0.476601, No Obj: 0.002300, .5R: 0.812500, .75R: 0.562500, count: 16 -Region 23 Avg IOU: 0.729579, Class: 0.241414, Obj: 0.249822, No Obj: 0.000563, .5R: 0.958333, .75R: 0.500000, count: 24 -Region 16 Avg IOU: 0.810293, Class: 0.155311, Obj: 0.560955, No Obj: 0.001534, .5R: 1.000000, .75R: 0.875000, count: 8 -Region 23 Avg IOU: 0.674723, Class: 0.151996, Obj: 0.190195, No Obj: 0.000355, .5R: 1.000000, .75R: 0.181818, count: 11 -Region 16 Avg IOU: 0.723838, Class: 0.210343, Obj: 0.368169, No Obj: 0.001423, .5R: 0.900000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.702613, Class: 0.199756, Obj: 0.155194, No Obj: 0.000362, .5R: 1.000000, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.809919, Class: 0.130291, Obj: 0.272381, No Obj: 0.001103, .5R: 1.000000, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.691696, Class: 0.120072, Obj: 0.165868, No Obj: 0.000412, .5R: 1.000000, .75R: 0.222222, count: 18 -Region 16 Avg IOU: 0.753603, Class: 0.194167, Obj: 0.443188, No 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0.000065 seconds -Region 16 Avg IOU: 0.742980, Class: 0.208616, Obj: 0.449862, No Obj: 0.001416, .5R: 0.875000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.686754, Class: 0.146607, Obj: 0.277437, No Obj: 0.000580, .5R: 0.888889, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.781764, Class: 0.185225, Obj: 0.313650, No Obj: 0.002243, .5R: 1.000000, .75R: 0.736842, count: 19 -Region 23 Avg IOU: 0.693464, Class: 0.179893, Obj: 0.227837, No Obj: 0.000531, .5R: 1.000000, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.784771, Class: 0.230942, Obj: 0.672577, No Obj: 0.000970, .5R: 1.000000, .75R: 0.833333, count: 6 -Region 23 Avg IOU: 0.713481, Class: 0.180719, Obj: 0.312556, No Obj: 0.000828, .5R: 0.923077, .75R: 0.461538, count: 26 -Region 16 Avg IOU: 0.742588, Class: 0.159390, Obj: 0.339185, No Obj: 0.001998, .5R: 0.882353, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.672214, Class: 0.178795, Obj: 0.280439, No Obj: 0.000736, .5R: 0.863636, .75R: 0.318182, count: 22 -Region 16 Avg 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8 - - 2259: 2.720426, 2.242788 avg loss, 0.001000 rate, 1.528304 seconds, 289152 images -Loaded: 0.000079 seconds -Region 16 Avg IOU: 0.784021, Class: 0.249037, Obj: 0.463568, No Obj: 0.001397, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.619887, Class: 0.183473, Obj: 0.247142, No Obj: 0.000510, .5R: 0.750000, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.645563, Class: 0.258460, Obj: 0.229828, No Obj: 0.001292, .5R: 0.857143, .75R: 0.285714, count: 14 -Region 23 Avg IOU: 0.712302, Class: 0.183664, Obj: 0.120927, No Obj: 0.000447, .5R: 1.000000, .75R: 0.300000, count: 20 -Region 16 Avg IOU: 0.785881, Class: 0.238036, Obj: 0.292447, No Obj: 0.001259, .5R: 1.000000, .75R: 0.705882, count: 17 -Region 23 Avg IOU: 0.696058, Class: 0.196656, Obj: 0.221780, No Obj: 0.000602, .5R: 0.956522, .75R: 0.391304, count: 23 -Region 16 Avg IOU: 0.762984, Class: 0.273158, Obj: 0.411997, No Obj: 0.001356, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.753433, Class: 0.208844, Obj: 0.385840, No Obj: 0.000601, .5R: 1.000000, .75R: 0.500000, count: 14 -Region 16 Avg IOU: 0.756315, Class: 0.251783, Obj: 0.467349, No Obj: 0.001438, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.673202, Class: 0.169553, Obj: 0.295976, No Obj: 0.000977, .5R: 0.969697, .75R: 0.363636, count: 33 -Region 16 Avg IOU: 0.747637, Class: 0.237309, Obj: 0.336209, No Obj: 0.001453, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.728507, Class: 0.226656, Obj: 0.315502, No Obj: 0.000908, .5R: 0.947368, .75R: 0.421053, count: 19 -Region 16 Avg IOU: 0.696604, Class: 0.209343, Obj: 0.225337, No Obj: 0.001249, .5R: 1.000000, .75R: 0.272727, count: 11 -Region 23 Avg IOU: 0.666797, Class: 0.157663, Obj: 0.235661, No Obj: 0.000486, .5R: 1.000000, .75R: 0.200000, count: 10 -Region 16 Avg IOU: 0.758765, Class: 0.163766, Obj: 0.469209, No Obj: 0.001749, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.726654, Class: 0.246236, Obj: 0.287592, No Obj: 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rate, 0.782123 seconds, 291072 images -Loaded: 0.000090 seconds -Region 16 Avg IOU: 0.801313, Class: 0.285039, Obj: 0.296238, No Obj: 0.001436, .5R: 1.000000, .75R: 0.777778, count: 9 -Region 23 Avg IOU: 0.712829, Class: 0.170527, Obj: 0.247727, No Obj: 0.000552, .5R: 1.000000, .75R: 0.357143, count: 14 -Region 16 Avg IOU: 0.769950, Class: 0.116311, Obj: 0.319510, No Obj: 0.001236, .5R: 1.000000, .75R: 0.500000, count: 6 -Region 23 Avg IOU: 0.671380, Class: 0.155922, Obj: 0.234374, No Obj: 0.000705, .5R: 0.888889, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.816725, Class: 0.213812, Obj: 0.363957, No Obj: 0.001462, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.645957, Class: 0.223934, Obj: 0.256232, No Obj: 0.000770, .5R: 0.944444, .75R: 0.111111, count: 18 -Region 16 Avg IOU: 0.814560, Class: 0.167530, Obj: 0.346359, No Obj: 0.001314, .5R: 1.000000, .75R: 0.900000, count: 10 -Region 23 Avg IOU: 0.710123, Class: 0.151298, Obj: 0.297084, No Obj: 0.000644, .5R: 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20 - - 2275: 2.014077, 2.216153 avg loss, 0.001000 rate, 0.770970 seconds, 291200 images -Loaded: 0.000082 seconds -Region 16 Avg IOU: 0.814068, Class: 0.185938, Obj: 0.487942, No Obj: 0.001460, .5R: 1.000000, .75R: 0.818182, count: 11 -Region 23 Avg IOU: 0.763251, Class: 0.148794, Obj: 0.261379, No Obj: 0.000786, .5R: 1.000000, .75R: 0.600000, count: 20 -Region 16 Avg IOU: 0.794083, Class: 0.209872, Obj: 0.392313, No Obj: 0.001362, .5R: 1.000000, .75R: 0.875000, count: 8 -Region 23 Avg IOU: 0.689063, Class: 0.165830, Obj: 0.209220, No Obj: 0.000594, .5R: 0.913043, .75R: 0.347826, count: 23 -Region 16 Avg IOU: 0.816782, Class: 0.232178, Obj: 0.394306, No Obj: 0.002080, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.680122, Class: 0.263304, Obj: 0.314130, No Obj: 0.000496, .5R: 1.000000, .75R: 0.142857, count: 7 -Region 16 Avg IOU: 0.761922, Class: 0.234463, Obj: 0.267637, No Obj: 0.001675, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.737649, Class: 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count: 8 -Region 23 Avg IOU: 0.730566, Class: 0.183759, Obj: 0.255317, No Obj: 0.000834, .5R: 0.950000, .75R: 0.500000, count: 20 -Region 16 Avg IOU: 0.721160, Class: 0.247924, Obj: 0.439328, No Obj: 0.002365, .5R: 0.818182, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.731087, Class: 0.202321, Obj: 0.153540, No Obj: 0.000566, .5R: 1.000000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.741551, Class: 0.268836, Obj: 0.358941, No Obj: 0.002500, .5R: 1.000000, .75R: 0.562500, count: 16 -Region 23 Avg IOU: 0.699118, Class: 0.149993, Obj: 0.232354, No Obj: 0.000531, .5R: 0.923077, .75R: 0.461538, count: 13 -Region 16 Avg IOU: 0.738605, Class: 0.293180, Obj: 0.428197, No Obj: 0.002108, .5R: 0.857143, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.675055, Class: 0.239562, Obj: 0.128080, No Obj: 0.000556, .5R: 0.931035, .75R: 0.275862, count: 29 -Region 16 Avg IOU: 0.809211, Class: 0.291092, Obj: 0.361329, No Obj: 0.001832, .5R: 1.000000, .75R: 0.833333, count: 12 -Region 23 Avg IOU: 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No Obj: 0.001818, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.739994, Class: 0.176549, Obj: 0.253056, No Obj: 0.000748, .5R: 1.000000, .75R: 0.578947, count: 19 -Region 16 Avg IOU: 0.750303, Class: 0.238662, Obj: 0.460462, No Obj: 0.001437, .5R: 1.000000, .75R: 0.444444, count: 9 -Region 23 Avg IOU: 0.634269, Class: 0.146917, Obj: 0.187726, No Obj: 0.000271, .5R: 0.916667, .75R: 0.166667, count: 12 -Region 16 Avg IOU: 0.788594, Class: 0.174604, Obj: 0.224873, No Obj: 0.001667, .5R: 1.000000, .75R: 0.875000, count: 16 -Region 23 Avg IOU: 0.582633, Class: 0.148012, Obj: 0.109005, No Obj: 0.000387, .5R: 0.700000, .75R: 0.150000, count: 20 -Region 16 Avg IOU: 0.722928, Class: 0.230445, Obj: 0.366657, No Obj: 0.001313, .5R: 0.909091, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.678512, Class: 0.219390, Obj: 0.141822, No Obj: 0.000362, .5R: 0.894737, .75R: 0.368421, count: 19 -Region 16 Avg IOU: 0.808852, Class: 0.241847, Obj: 0.507921, No Obj: 0.001440, .5R: 1.000000, 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0.763583, Class: 0.182891, Obj: 0.458098, No Obj: 0.001342, .5R: 1.000000, .75R: 0.500000, count: 6 -Region 23 Avg IOU: 0.729546, Class: 0.234690, Obj: 0.103196, No Obj: 0.000300, .5R: 1.000000, .75R: 0.500000, count: 14 -Region 16 Avg IOU: 0.775213, Class: 0.227048, Obj: 0.407274, No Obj: 0.002830, .5R: 0.944444, .75R: 0.777778, count: 18 -Region 23 Avg IOU: 0.724108, Class: 0.198515, Obj: 0.158330, No Obj: 0.000625, .5R: 0.960000, .75R: 0.440000, count: 25 -Region 16 Avg IOU: 0.739284, Class: 0.288059, Obj: 0.603538, No Obj: 0.000862, .5R: 1.000000, .75R: 0.500000, count: 4 -Region 23 Avg IOU: 0.750710, Class: 0.191889, Obj: 0.268455, No Obj: 0.000498, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 16 Avg IOU: 0.782094, Class: 0.215981, Obj: 0.465085, No Obj: 0.002010, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.740509, Class: 0.174490, Obj: 0.106261, No Obj: 0.000375, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.767891, Class: 0.266561, Obj: 0.370778, No Obj: 0.002293, .5R: 1.000000, .75R: 0.722222, count: 18 -Region 23 Avg IOU: 0.690652, Class: 0.174517, Obj: 0.133922, No Obj: 0.000389, .5R: 0.937500, .75R: 0.375000, count: 16 - - 2279: 1.638326, 2.087766 avg loss, 0.001000 rate, 0.796722 seconds, 291712 images -Loaded: 0.000099 seconds -Region 16 Avg IOU: 0.785396, Class: 0.299013, Obj: 0.314147, No Obj: 0.001863, .5R: 0.900000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.695511, Class: 0.203128, Obj: 0.180391, No Obj: 0.000396, .5R: 1.000000, .75R: 0.263158, count: 19 -Region 16 Avg IOU: 0.776939, Class: 0.245557, Obj: 0.386063, No Obj: 0.001771, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.694323, Class: 0.181251, Obj: 0.237867, No Obj: 0.000745, .5R: 0.947368, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.748104, Class: 0.277096, Obj: 0.387191, No Obj: 0.001692, .5R: 1.000000, .75R: 0.562500, count: 16 -Region 23 Avg IOU: 0.681556, Class: 0.156162, Obj: 0.174980, No Obj: 0.000651, .5R: 0.888889, .75R: 0.259259, count: 27 -Region 16 Avg IOU: 0.817336, Class: 0.240397, Obj: 0.403116, No Obj: 0.001310, .5R: 1.000000, .75R: 1.000000, count: 8 -Region 23 Avg IOU: 0.675607, Class: 0.180853, Obj: 0.240213, No Obj: 0.000625, .5R: 0.961538, .75R: 0.307692, count: 26 -Region 16 Avg IOU: 0.689725, Class: 0.172997, Obj: 0.352447, No Obj: 0.001619, .5R: 1.000000, .75R: 0.333333, count: 12 -Region 23 Avg IOU: 0.682243, Class: 0.173529, Obj: 0.151352, No Obj: 0.000693, .5R: 0.918919, .75R: 0.297297, count: 37 -Region 16 Avg IOU: 0.768475, Class: 0.196957, Obj: 0.379761, No Obj: 0.000944, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.716144, Class: 0.181316, Obj: 0.279421, No Obj: 0.000253, .5R: 1.000000, .75R: 0.500000, count: 6 -Region 16 Avg IOU: 0.689755, Class: 0.219134, Obj: 0.263099, No Obj: 0.001669, .5R: 0.916667, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.653721, Class: 0.194481, Obj: 0.066273, No Obj: 0.000478, .5R: 0.821429, .75R: 0.250000, count: 28 -Region 16 Avg IOU: 0.735828, Class: 0.239427, Obj: 0.288553, No Obj: 0.001484, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.664006, Class: 0.182999, Obj: 0.237523, No Obj: 0.000347, .5R: 1.000000, .75R: 0.222222, count: 9 - - 2280: 2.238031, 2.102793 avg loss, 0.001000 rate, 0.783683 seconds, 291840 images -Resizing -480 x 480 - try to allocate workspace = 1144537089 * sizeof(float), CUDA allocate done! -Loaded: 0.316325 seconds -Region 16 Avg IOU: 0.788278, Class: 0.225828, Obj: 0.240789, No Obj: 0.001184, .5R: 0.909091, .75R: 0.909091, count: 11 -Region 23 Avg IOU: 0.682362, Class: 0.168328, Obj: 0.159529, No Obj: 0.000455, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.727129, Class: 0.182496, Obj: 0.215557, No Obj: 0.000870, .5R: 0.923077, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.689520, Class: 0.166719, Obj: 0.374446, No Obj: 0.000784, .5R: 1.000000, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.807042, Class: 0.230908, Obj: 0.332838, No 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0.202739, Obj: 0.222163, No Obj: 0.000535, .5R: 0.785714, .75R: 0.285714, count: 14 -Region 16 Avg IOU: 0.734430, Class: 0.196072, Obj: 0.222297, No Obj: 0.001527, .5R: 0.947368, .75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.706976, Class: 0.182595, Obj: 0.178903, No Obj: 0.000387, .5R: 1.000000, .75R: 0.466667, count: 15 -Region 16 Avg IOU: 0.806285, Class: 0.284881, Obj: 0.348327, No Obj: 0.001004, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.705886, Class: 0.148529, Obj: 0.232368, No Obj: 0.000695, .5R: 0.909091, .75R: 0.409091, count: 22 -Region 16 Avg IOU: 0.769540, Class: 0.261646, Obj: 0.357152, No Obj: 0.001212, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.732523, Class: 0.191218, Obj: 0.252613, No Obj: 0.000588, .5R: 0.947368, .75R: 0.578947, count: 19 -Region 16 Avg IOU: 0.814284, Class: 0.336774, Obj: 0.209448, No Obj: 0.000686, .5R: 1.000000, .75R: 0.857143, count: 7 -Region 23 Avg IOU: 0.644938, Class: 0.233252, Obj: 0.195294, No Obj: 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count: 4 -Region 23 Avg IOU: 0.729696, Class: 0.124611, Obj: 0.258149, No Obj: 0.000432, .5R: 0.916667, .75R: 0.500000, count: 12 -Region 16 Avg IOU: 0.785398, Class: 0.213350, Obj: 0.367328, No Obj: 0.001719, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.682391, Class: 0.221359, Obj: 0.189803, No Obj: 0.000480, .5R: 0.888889, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.794789, Class: 0.259962, Obj: 0.434519, No Obj: 0.001778, .5R: 1.000000, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.691822, Class: 0.237122, Obj: 0.247522, No Obj: 0.000743, .5R: 1.000000, .75R: 0.296296, count: 27 -Region 16 Avg IOU: 0.859488, Class: 0.394762, Obj: 0.378671, No Obj: 0.000728, .5R: 1.000000, .75R: 1.000000, count: 4 -Region 23 Avg IOU: 0.678437, Class: 0.148361, Obj: 0.260566, No Obj: 0.000435, .5R: 0.875000, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.790650, Class: 0.156997, Obj: 0.406382, No Obj: 0.001442, .5R: 1.000000, .75R: 0.818182, count: 11 -Region 23 Avg IOU: 0.695886, Class: 0.166087, Obj: 0.290174, No Obj: 0.000475, .5R: 1.000000, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.749646, Class: 0.223031, Obj: 0.460444, No Obj: 0.001115, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.668243, Class: 0.161442, Obj: 0.216034, No Obj: 0.000655, .5R: 0.892857, .75R: 0.321429, count: 28 -Region 16 Avg IOU: 0.762627, Class: 0.258616, Obj: 0.344246, No Obj: 0.001528, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.695452, Class: 0.170391, Obj: 0.151072, No Obj: 0.000385, .5R: 0.952381, .75R: 0.476190, count: 21 -Region 16 Avg IOU: 0.687970, Class: 0.199523, Obj: 0.302054, No Obj: 0.001993, .5R: 0.863636, .75R: 0.409091, count: 22 -Region 23 Avg IOU: 0.715563, Class: 0.197103, Obj: 0.247197, No Obj: 0.000534, .5R: 0.809524, .75R: 0.523810, count: 21 - - 2290: 2.083713, 2.108970 avg loss, 0.001000 rate, 1.815198 seconds, 293120 images -Resizing -608 x 608 - try to allocate workspace = 1146765313 * sizeof(float), CUDA allocate done! -Loaded: 0.240301 seconds -Region 16 Avg IOU: 0.767504, Class: 0.149337, Obj: 0.397560, No Obj: 0.001163, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.710026, Class: 0.111985, Obj: 0.162575, No Obj: 0.000371, .5R: 0.941176, .75R: 0.382353, count: 34 -Region 16 Avg IOU: 0.770602, Class: 0.280521, Obj: 0.453202, No Obj: 0.001601, .5R: 1.000000, .75R: 0.631579, count: 19 -Region 23 Avg IOU: 0.728999, Class: 0.159945, Obj: 0.160672, No Obj: 0.000175, .5R: 1.000000, .75R: 0.272727, count: 11 -Region 16 Avg IOU: 0.719969, Class: 0.295212, Obj: 0.308573, No Obj: 0.001172, .5R: 1.000000, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.660599, Class: 0.164219, Obj: 0.169456, No Obj: 0.000291, .5R: 0.937500, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.702531, Class: 0.255725, Obj: 0.235402, No Obj: 0.001577, .5R: 0.782609, .75R: 0.521739, count: 23 -Region 23 Avg IOU: 0.660286, Class: 0.223716, Obj: 0.134962, No Obj: 0.000325, .5R: 0.809524, .75R: 0.238095, count: 21 -Region 16 Avg IOU: 0.736791, Class: 0.210234, Obj: 0.307577, No Obj: 0.001327, .5R: 0.916667, .75R: 0.583333, count: 24 -Region 23 Avg IOU: 0.672700, Class: 0.179246, Obj: 0.245401, No Obj: 0.000333, .5R: 0.875000, .75R: 0.333333, count: 24 -Region 16 Avg IOU: 0.761191, Class: 0.167548, Obj: 0.260976, No Obj: 0.001245, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 23 Avg IOU: 0.683917, Class: 0.144400, Obj: 0.169139, No Obj: 0.000221, .5R: 0.916667, .75R: 0.416667, count: 12 -Region 16 Avg IOU: 0.743927, Class: 0.268853, Obj: 0.240428, No Obj: 0.001039, .5R: 0.941176, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.700744, Class: 0.222976, Obj: 0.156859, No Obj: 0.000255, .5R: 0.952381, .75R: 0.380952, count: 21 -Region 16 Avg IOU: 0.753529, Class: 0.225630, Obj: 0.363888, No Obj: 0.001492, .5R: 0.944444, .75R: 0.611111, count: 18 -Region 23 Avg IOU: 0.716387, Class: 0.177787, Obj: 0.160262, No Obj: 0.000254, .5R: 0.888889, .75R: 0.611111, count: 18 - - 2291: 2.689104, 2.166983 avg loss, 0.001000 rate, 2.641047 seconds, 293248 images -Loaded: 0.000082 seconds -Region 16 Avg IOU: 0.778728, Class: 0.267871, Obj: 0.415113, No Obj: 0.001474, .5R: 1.000000, .75R: 0.666667, count: 18 -Region 23 Avg IOU: 0.733513, Class: 0.241073, Obj: 0.116198, No Obj: 0.000258, .5R: 0.952381, .75R: 0.428571, count: 21 -Region 16 Avg IOU: 0.734585, Class: 0.218133, Obj: 0.439955, No Obj: 0.001632, .5R: 0.954545, .75R: 0.545455, count: 22 -Region 23 Avg IOU: 0.665042, Class: 0.219977, Obj: 0.163897, No Obj: 0.000424, .5R: 0.914286, .75R: 0.228571, count: 35 -Region 16 Avg IOU: 0.780298, Class: 0.235741, Obj: 0.340992, No Obj: 0.001228, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.683709, Class: 0.182495, Obj: 0.098451, No Obj: 0.000234, .5R: 0.900000, .75R: 0.300000, count: 20 -Region 16 Avg IOU: 0.743934, Class: 0.214842, Obj: 0.378056, No Obj: 0.001848, .5R: 1.000000, .75R: 0.500000, count: 26 -Region 23 Avg IOU: 0.696421, Class: 0.167087, Obj: 0.153096, 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.75R: 0.333333, count: 18 - - 2292: 2.798530, 2.230138 avg loss, 0.001000 rate, 2.446966 seconds, 293376 images -Loaded: 0.000067 seconds -Region 16 Avg IOU: 0.789608, Class: 0.196757, Obj: 0.339209, No Obj: 0.001570, .5R: 1.000000, .75R: 0.739130, count: 23 -Region 23 Avg IOU: 0.681999, Class: 0.174967, Obj: 0.196656, No Obj: 0.000372, .5R: 0.851852, .75R: 0.259259, count: 27 -Region 16 Avg IOU: 0.729955, Class: 0.223698, Obj: 0.244717, No Obj: 0.001141, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.724133, Class: 0.159705, Obj: 0.153193, No Obj: 0.000262, .5R: 0.933333, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.741237, Class: 0.208792, Obj: 0.311260, No Obj: 0.001443, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.665623, Class: 0.190873, Obj: 0.294297, No Obj: 0.000479, .5R: 0.937500, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.785777, Class: 0.214995, Obj: 0.327548, No Obj: 0.001454, .5R: 1.000000, .75R: 0.695652, count: 23 -Region 23 Avg IOU: 0.715818, Class: 0.148936, Obj: 0.239474, No Obj: 0.000331, .5R: 0.882353, .75R: 0.588235, count: 17 -Region 16 Avg IOU: 0.705159, Class: 0.179672, Obj: 0.276250, No Obj: 0.001244, .5R: 0.900000, .75R: 0.450000, count: 20 -Region 23 Avg IOU: 0.702285, Class: 0.174526, Obj: 0.215188, No Obj: 0.000254, .5R: 0.941176, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.707542, Class: 0.199880, Obj: 0.363204, No Obj: 0.001244, .5R: 0.950000, .75R: 0.400000, count: 20 -Region 23 Avg IOU: 0.689249, Class: 0.129452, Obj: 0.158272, No Obj: 0.000328, .5R: 0.952381, .75R: 0.428571, count: 21 -Region 16 Avg IOU: 0.748486, Class: 0.219130, Obj: 0.303596, No Obj: 0.001290, .5R: 0.952381, .75R: 0.523810, count: 21 -Region 23 Avg IOU: 0.706155, Class: 0.192014, Obj: 0.243293, No Obj: 0.000394, .5R: 0.956522, .75R: 0.347826, count: 23 -Region 16 Avg IOU: 0.763378, Class: 0.257836, Obj: 0.306915, No Obj: 0.001716, .5R: 0.960000, .75R: 0.640000, count: 25 -Region 23 Avg IOU: 0.682897, Class: 0.196472, 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0.916667, .75R: 0.500000, count: 24 -Region 23 Avg IOU: 0.691474, Class: 0.150270, Obj: 0.195657, No Obj: 0.000430, .5R: 0.896552, .75R: 0.275862, count: 29 -Region 16 Avg IOU: 0.758592, Class: 0.263467, Obj: 0.284956, No Obj: 0.000979, .5R: 1.000000, .75R: 0.625000, count: 16 -Region 23 Avg IOU: 0.659809, Class: 0.257731, Obj: 0.310327, No Obj: 0.000284, .5R: 0.923077, .75R: 0.153846, count: 13 -Region 16 Avg IOU: 0.748877, Class: 0.224836, Obj: 0.384498, No Obj: 0.001378, .5R: 0.954545, .75R: 0.590909, count: 22 -Region 23 Avg IOU: 0.696876, Class: 0.160192, Obj: 0.176383, No Obj: 0.000290, .5R: 0.894737, .75R: 0.368421, count: 19 -Region 16 Avg IOU: 0.762290, Class: 0.214092, Obj: 0.319369, No Obj: 0.001449, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.703282, Class: 0.222347, Obj: 0.276752, No Obj: 0.000423, .5R: 1.000000, .75R: 0.285714, count: 14 -Region 16 Avg IOU: 0.745583, Class: 0.187424, Obj: 0.343706, No Obj: 0.001923, .5R: 0.966667, .75R: 0.566667, count: 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Avg IOU: 0.724093, Class: 0.154737, Obj: 0.270485, No Obj: 0.000443, .5R: 1.000000, .75R: 0.444444, count: 18 -Region 16 Avg IOU: 0.729893, Class: 0.200168, Obj: 0.419547, No Obj: 0.001395, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.697161, Class: 0.216503, Obj: 0.287226, No Obj: 0.000420, .5R: 0.941176, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.732790, Class: 0.220613, Obj: 0.278949, No Obj: 0.001565, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.683174, Class: 0.189748, Obj: 0.285575, No Obj: 0.000329, .5R: 1.000000, .75R: 0.000000, count: 11 -Region 16 Avg IOU: 0.807725, Class: 0.202851, Obj: 0.438460, No Obj: 0.001392, .5R: 1.000000, .75R: 0.842105, count: 19 -Region 23 Avg IOU: 0.675433, Class: 0.104739, Obj: 0.337845, No Obj: 0.000553, .5R: 0.944444, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.690696, Class: 0.205525, Obj: 0.251431, No Obj: 0.001200, .5R: 0.888889, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.663427, Class: 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images -Loaded: 0.000111 seconds -Region 16 Avg IOU: 0.761483, Class: 0.190307, Obj: 0.363882, No Obj: 0.001763, .5R: 0.928571, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.685737, Class: 0.146629, Obj: 0.203279, No Obj: 0.000369, .5R: 1.000000, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.748919, Class: 0.233859, Obj: 0.337783, No Obj: 0.001451, .5R: 0.928571, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.653132, Class: 0.173709, Obj: 0.131569, No Obj: 0.000182, .5R: 0.909091, .75R: 0.090909, count: 11 -Region 16 Avg IOU: 0.793689, Class: 0.245260, Obj: 0.369239, No Obj: 0.001664, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.747217, Class: 0.240666, Obj: 0.241909, No Obj: 0.000254, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 16 Avg IOU: 0.743322, Class: 0.233937, Obj: 0.305292, No Obj: 0.001589, .5R: 1.000000, .75R: 0.421053, count: 19 -Region 23 Avg IOU: 0.652046, Class: 0.229309, Obj: 0.212586, No Obj: 0.000336, .5R: 0.777778, .75R: 0.222222, count: 18 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count: 15 -Region 16 Avg IOU: 0.771057, Class: 0.222305, Obj: 0.357065, No Obj: 0.001788, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.672143, Class: 0.233794, Obj: 0.181563, No Obj: 0.000792, .5R: 0.869565, .75R: 0.173913, count: 23 -Region 16 Avg IOU: 0.809126, Class: 0.262100, Obj: 0.362696, No Obj: 0.001645, .5R: 1.000000, .75R: 0.833333, count: 6 -Region 23 Avg IOU: 0.705383, Class: 0.231890, Obj: 0.188156, No Obj: 0.000871, .5R: 0.928571, .75R: 0.428571, count: 14 - - 2318: 1.921562, 2.147828 avg loss, 0.001000 rate, 0.768152 seconds, 296704 images -Loaded: 0.000068 seconds -Region 16 Avg IOU: 0.689241, Class: 0.128747, Obj: 0.199815, No Obj: 0.001582, .5R: 1.000000, .75R: 0.250000, count: 8 -Region 23 Avg IOU: 0.549611, Class: 0.123315, Obj: 0.117750, No Obj: 0.000461, .5R: 0.631579, .75R: 0.052632, count: 19 -Region 16 Avg IOU: 0.740876, Class: 0.324046, Obj: 0.375041, No Obj: 0.000774, .5R: 1.000000, .75R: 0.500000, count: 4 -Region 23 Avg IOU: 0.746386, 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count: 8 -Region 16 Avg IOU: 0.738107, Class: 0.191841, Obj: 0.346918, No Obj: 0.001546, .5R: 0.916667, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.705798, Class: 0.183136, Obj: 0.237386, No Obj: 0.000503, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.745757, Class: 0.266982, Obj: 0.257894, No Obj: 0.001813, .5R: 1.000000, .75R: 0.333333, count: 15 -Region 23 Avg IOU: 0.715862, Class: 0.194994, Obj: 0.155324, No Obj: 0.000540, .5R: 1.000000, .75R: 0.416667, count: 24 -Region 16 Avg IOU: 0.744458, Class: 0.270242, Obj: 0.300945, No Obj: 0.001401, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.673221, Class: 0.253550, Obj: 0.222416, No Obj: 0.000630, .5R: 0.850000, .75R: 0.250000, count: 20 - - 2321: 2.157844, 2.120192 avg loss, 0.001000 rate, 1.403498 seconds, 297088 images -Loaded: 0.000072 seconds -Region 16 Avg IOU: 0.698959, Class: 0.292236, Obj: 0.413952, No Obj: 0.000946, .5R: 0.875000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.673098, 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31 -Region 16 Avg IOU: 0.685617, Class: 0.165218, Obj: 0.174878, No Obj: 0.001010, .5R: 0.750000, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.681107, Class: 0.167820, Obj: 0.119331, No Obj: 0.000302, .5R: 0.888889, .75R: 0.222222, count: 9 -Region 16 Avg IOU: 0.770665, Class: 0.288659, Obj: 0.307771, No Obj: 0.001224, .5R: 1.000000, .75R: 0.600000, count: 25 -Region 23 Avg IOU: 0.718475, Class: 0.209864, Obj: 0.294829, No Obj: 0.000381, .5R: 1.000000, .75R: 0.470588, count: 17 -Region 16 Avg IOU: 0.717653, Class: 0.218064, Obj: 0.185594, No Obj: 0.001082, .5R: 0.944444, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.716538, Class: 0.179067, Obj: 0.364606, No Obj: 0.000237, .5R: 0.857143, .75R: 0.571429, count: 7 - - 2339: 3.004315, 2.561192 avg loss, 0.001000 rate, 3.798633 seconds, 299392 images -Loaded: 0.000071 seconds -Region 16 Avg IOU: 0.785640, Class: 0.268902, Obj: 0.323624, No Obj: 0.001147, .5R: 1.000000, .75R: 0.705882, count: 17 -Region 23 Avg IOU: 0.708797, Class: 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Avg IOU: 0.774229, Class: 0.219867, Obj: 0.383106, No Obj: 0.001629, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.686566, Class: 0.215993, Obj: 0.122316, No Obj: 0.000234, .5R: 0.888889, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.801941, Class: 0.287594, Obj: 0.461840, No Obj: 0.001959, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.682634, Class: 0.184640, Obj: 0.143055, No Obj: 0.000381, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.716948, Class: 0.135586, Obj: 0.538122, No Obj: 0.001342, .5R: 1.000000, .75R: 0.400000, count: 5 -Region 23 Avg IOU: 0.678418, Class: 0.173943, Obj: 0.187200, No Obj: 0.000244, .5R: 0.875000, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.730054, Class: 0.267300, Obj: 0.473773, No Obj: 0.001995, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.675938, Class: 0.209094, Obj: 0.211588, No Obj: 0.000400, .5R: 0.875000, .75R: 0.333333, count: 24 -Region 16 Avg IOU: 0.735929, Class: 0.255979, Obj: 0.275991, No Obj: 0.001542, .5R: 1.000000, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.696538, Class: 0.127114, Obj: 0.133987, No Obj: 0.000349, .5R: 1.000000, .75R: 0.272727, count: 11 -Region 16 Avg IOU: 0.744920, Class: 0.267788, Obj: 0.303549, No Obj: 0.001983, .5R: 1.000000, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 0.642809, Class: 0.195019, Obj: 0.078437, No Obj: 0.000263, .5R: 0.857143, .75R: 0.238095, count: 21 -Region 16 Avg IOU: 0.759596, Class: 0.237724, Obj: 0.287499, No Obj: 0.001887, .5R: 0.937500, .75R: 0.562500, count: 16 -Region 23 Avg IOU: 0.701927, Class: 0.173950, Obj: 0.137512, No Obj: 0.000296, .5R: 0.900000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.732406, Class: 0.158403, Obj: 0.237306, No Obj: 0.001402, .5R: 0.833333, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.672482, Class: 0.172461, Obj: 0.177147, No Obj: 0.000266, .5R: 0.941176, .75R: 0.294118, count: 17 - - 2341: 2.202219, 2.552272 avg loss, 0.001000 rate, 1.504325 seconds, 299648 images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.747337, Class: 0.262432, Obj: 0.444530, No Obj: 0.002532, .5R: 0.954545, .75R: 0.500000, count: 22 -Region 23 Avg IOU: 0.708061, Class: 0.231377, Obj: 0.152880, No Obj: 0.000495, .5R: 0.939394, .75R: 0.454545, count: 33 -Region 16 Avg IOU: 0.758345, Class: 0.220846, Obj: 0.325839, No Obj: 0.002132, .5R: 1.000000, .75R: 0.450000, count: 20 -Region 23 Avg IOU: 0.702251, Class: 0.222284, Obj: 0.213835, No Obj: 0.000228, .5R: 0.866667, .75R: 0.400000, count: 15 -Region 16 Avg IOU: 0.720129, Class: 0.233724, Obj: 0.290604, No Obj: 0.001346, .5R: 0.909091, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.711504, Class: 0.154572, Obj: 0.207012, No Obj: 0.000286, .5R: 1.000000, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.743302, Class: 0.196139, Obj: 0.269357, No Obj: 0.002005, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.685078, Class: 0.202160, Obj: 0.154964, No Obj: 0.000491, .5R: 0.965517, .75R: 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No Obj: 0.001394, .5R: 0.920000, .75R: 0.560000, count: 25 -Region 23 Avg IOU: 0.670696, Class: 0.193106, Obj: 0.149765, No Obj: 0.000216, .5R: 0.823529, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.758062, Class: 0.260396, Obj: 0.241189, No Obj: 0.001261, .5R: 1.000000, .75R: 0.592593, count: 27 -Region 23 Avg IOU: 0.694483, Class: 0.203584, Obj: 0.127308, No Obj: 0.000208, .5R: 0.850000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.776940, Class: 0.227224, Obj: 0.286598, No Obj: 0.001303, .5R: 1.000000, .75R: 0.647059, count: 17 -Region 23 Avg IOU: 0.751629, Class: 0.151003, Obj: 0.216055, No Obj: 0.000175, .5R: 1.000000, .75R: 0.400000, count: 15 -Region 16 Avg IOU: 0.762537, Class: 0.233802, Obj: 0.351784, No Obj: 0.001904, .5R: 0.956522, .75R: 0.695652, count: 23 -Region 23 Avg IOU: 0.726337, Class: 0.223013, Obj: 0.100879, No Obj: 0.000205, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 16 Avg IOU: 0.757721, Class: 0.180744, Obj: 0.336115, No Obj: 0.001484, .5R: 0.956522, 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images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.770548, Class: 0.215644, Obj: 0.330325, No Obj: 0.001784, .5R: 1.000000, .75R: 0.640000, count: 25 -Region 23 Avg IOU: 0.683395, Class: 0.154635, Obj: 0.059535, No Obj: 0.000123, .5R: 0.769231, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.754350, Class: 0.305237, Obj: 0.267159, No Obj: 0.001211, .5R: 1.000000, .75R: 0.562500, count: 16 -Region 23 Avg IOU: 0.665734, Class: 0.179597, Obj: 0.075342, No Obj: 0.000200, .5R: 0.933333, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.708295, Class: 0.127423, Obj: 0.192070, No Obj: 0.001091, .5R: 0.904762, .75R: 0.428571, count: 21 -Region 23 Avg IOU: 0.754423, Class: 0.133963, Obj: 0.100526, No Obj: 0.000149, .5R: 1.000000, .75R: 0.687500, count: 16 -Region 16 Avg IOU: 0.680540, Class: 0.272843, Obj: 0.228571, No Obj: 0.001306, .5R: 0.842105, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.720232, Class: 0.250566, Obj: 0.127881, No Obj: 0.000114, .5R: 0.909091, .75R: 0.454545, count: 11 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loss, 0.001000 rate, 2.847955 seconds, 302080 images -Resizing -448 x 448 - try to allocate workspace = 305201153 * sizeof(float), CUDA allocate done! -Loaded: 0.388524 seconds -Region 16 Avg IOU: 0.761261, Class: 0.237615, Obj: 0.338314, No Obj: 0.001378, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.659091, Class: 0.221159, Obj: 0.286840, No Obj: 0.000638, .5R: 0.904762, .75R: 0.190476, count: 21 -Region 16 Avg IOU: 0.783582, Class: 0.329608, Obj: 0.329045, No Obj: 0.001604, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.727917, Class: 0.278491, Obj: 0.142406, No Obj: 0.000307, .5R: 0.923077, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.773893, Class: 0.229278, Obj: 0.375873, No Obj: 0.001579, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.705244, Class: 0.205032, Obj: 0.167208, No Obj: 0.000419, .5R: 1.000000, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.707327, Class: 0.104486, Obj: 0.315945, No Obj: 0.001408, .5R: 0.909091, 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13 -Region 23 Avg IOU: 0.687760, Class: 0.189989, Obj: 0.108747, No Obj: 0.000155, .5R: 0.916667, .75R: 0.333333, count: 24 -Region 16 Avg IOU: 0.723574, Class: 0.229249, Obj: 0.247827, No Obj: 0.000919, .5R: 0.900000, .75R: 0.500000, count: 20 -Region 23 Avg IOU: 0.725593, Class: 0.146448, Obj: 0.101293, No Obj: 0.000131, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.735753, Class: 0.147893, Obj: 0.203878, No Obj: 0.000695, .5R: 0.956522, .75R: 0.652174, count: 23 -Region 23 Avg IOU: 0.680966, Class: 0.136347, Obj: 0.089828, No Obj: 0.000205, .5R: 0.925926, .75R: 0.259259, count: 27 -Region 16 Avg IOU: 0.795189, Class: 0.196638, Obj: 0.347569, No Obj: 0.001047, .5R: 1.000000, .75R: 0.722222, count: 18 -Region 23 Avg IOU: 0.748899, Class: 0.192964, Obj: 0.129303, No Obj: 0.000144, .5R: 1.000000, .75R: 0.421053, count: 19 - - 2374: 3.108951, 2.436640 avg loss, 0.001000 rate, 2.993615 seconds, 303872 images -Loaded: 0.000062 seconds -Region 16 Avg IOU: 0.776070, Class: 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0.000073 seconds -Region 16 Avg IOU: 0.682239, Class: 0.253312, Obj: 0.370102, No Obj: 0.001508, .5R: 0.933333, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.653762, Class: 0.271478, Obj: 0.059930, No Obj: 0.000312, .5R: 0.928571, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.755658, Class: 0.213434, Obj: 0.415455, No Obj: 0.001697, .5R: 0.944444, .75R: 0.666667, count: 18 -Region 23 Avg IOU: 0.708710, Class: 0.230438, Obj: 0.314333, No Obj: 0.000654, .5R: 1.000000, .75R: 0.318182, count: 22 -Region 16 Avg IOU: 0.740946, Class: 0.211664, Obj: 0.356513, No Obj: 0.002137, .5R: 1.000000, .75R: 0.520000, count: 25 -Region 23 Avg IOU: 0.732178, Class: 0.213508, Obj: 0.317561, No Obj: 0.000452, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.773127, Class: 0.217014, Obj: 0.451513, No Obj: 0.001343, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.673519, Class: 0.212195, Obj: 0.244026, No Obj: 0.000291, .5R: 1.000000, .75R: 0.333333, count: 9 -Region 16 Avg 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rate, 3.110099 seconds, 304128 images -Loaded: 0.000072 seconds -Region 16 Avg IOU: 0.760159, Class: 0.214180, Obj: 0.458668, No Obj: 0.002438, .5R: 0.954545, .75R: 0.636364, count: 22 -Region 23 Avg IOU: 0.682590, Class: 0.251210, Obj: 0.139899, No Obj: 0.000316, .5R: 1.000000, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.749064, Class: 0.146441, Obj: 0.399545, No Obj: 0.001650, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.671610, Class: 0.193413, Obj: 0.204557, No Obj: 0.000349, .5R: 0.928571, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.693042, Class: 0.241694, Obj: 0.254157, No Obj: 0.002133, .5R: 0.850000, .75R: 0.400000, count: 20 -Region 23 Avg IOU: 0.685553, Class: 0.236102, Obj: 0.134103, No Obj: 0.000393, .5R: 0.944444, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.701117, Class: 0.190523, Obj: 0.358974, No Obj: 0.001339, .5R: 0.900000, .75R: 0.400000, count: 20 -Region 23 Avg IOU: 0.706899, Class: 0.198697, Obj: 0.148466, No Obj: 0.000331, .5R: 0.944444, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.752105, Class: 0.185114, Obj: 0.516687, No Obj: 0.001837, .5R: 0.944444, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.733161, Class: 0.185160, Obj: 0.290446, No Obj: 0.000471, .5R: 1.000000, .75R: 0.476190, count: 21 -Region 16 Avg IOU: 0.729115, Class: 0.229723, Obj: 0.538280, No Obj: 0.001894, .5R: 0.882353, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 0.709634, Class: 0.186724, Obj: 0.248220, No Obj: 0.000390, .5R: 0.894737, .75R: 0.368421, count: 19 -Region 16 Avg IOU: 0.719303, Class: 0.183636, Obj: 0.340456, No Obj: 0.001987, .5R: 1.000000, .75R: 0.380952, count: 21 -Region 23 Avg IOU: 0.697033, Class: 0.194432, Obj: 0.162653, No Obj: 0.000310, .5R: 0.941176, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.765417, Class: 0.193541, Obj: 0.390830, No Obj: 0.001910, .5R: 1.000000, .75R: 0.705882, count: 17 -Region 23 Avg IOU: 0.690344, Class: 0.216234, Obj: 0.245503, No Obj: 0.000320, .5R: 0.947368, .75R: 0.368421, count: 19 - - 2377: 2.626820, 2.485782 avg loss, 0.001000 rate, 3.348075 seconds, 304256 images -Loaded: 0.000079 seconds -Region 16 Avg IOU: 0.739701, Class: 0.201329, Obj: 0.293824, No Obj: 0.001400, .5R: 0.941176, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.632425, Class: 0.172246, Obj: 0.144928, No Obj: 0.000208, .5R: 0.928571, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.752471, Class: 0.202554, Obj: 0.458183, No Obj: 0.001797, .5R: 0.958333, .75R: 0.666667, count: 24 -Region 23 Avg IOU: 0.717420, Class: 0.191771, Obj: 0.096954, No Obj: 0.000152, .5R: 0.916667, .75R: 0.416667, count: 24 -Region 16 Avg IOU: 0.732735, Class: 0.239092, Obj: 0.372716, No Obj: 0.002141, .5R: 0.931035, .75R: 0.517241, count: 29 -Region 23 Avg IOU: 0.679597, Class: 0.200985, Obj: 0.130961, No Obj: 0.000261, .5R: 0.916667, .75R: 0.333333, count: 24 -Region 16 Avg IOU: 0.714395, Class: 0.228210, Obj: 0.386496, No Obj: 0.001867, .5R: 0.925926, .75R: 0.481481, count: 27 -Region 23 Avg IOU: 0.711137, Class: 0.170784, Obj: 0.243687, No Obj: 0.000169, .5R: 0.923077, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.757449, Class: 0.233069, Obj: 0.301090, No Obj: 0.001314, .5R: 0.937500, .75R: 0.687500, count: 16 -Region 23 Avg IOU: 0.716927, Class: 0.198224, Obj: 0.153269, No Obj: 0.000220, .5R: 0.962963, .75R: 0.407407, count: 27 -Region 16 Avg IOU: 0.741464, Class: 0.176123, Obj: 0.297408, No Obj: 0.001169, .5R: 1.000000, .75R: 0.411765, count: 17 -Region 23 Avg IOU: 0.782183, Class: 0.148380, Obj: 0.165242, No Obj: 0.000120, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 16 Avg IOU: 0.727262, Class: 0.277756, Obj: 0.326189, No Obj: 0.000989, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.724388, Class: 0.195602, Obj: 0.105113, No Obj: 0.000158, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 16 Avg IOU: 0.729229, Class: 0.226958, Obj: 0.399550, No Obj: 0.001300, .5R: 0.866667, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.658678, Class: 0.153980, Obj: 0.110861, No Obj: 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count: 26 -Region 23 Avg IOU: 0.648059, Class: 0.204562, Obj: 0.071816, No Obj: 0.000166, .5R: 0.925926, .75R: 0.185185, count: 27 -Region 16 Avg IOU: 0.708391, Class: 0.257081, Obj: 0.372768, No Obj: 0.000956, .5R: 0.944444, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.692125, Class: 0.235509, Obj: 0.118148, No Obj: 0.000116, .5R: 0.900000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.757990, Class: 0.291299, Obj: 0.382349, No Obj: 0.001328, .5R: 0.941176, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.712856, Class: 0.173453, Obj: 0.086599, No Obj: 0.000133, .5R: 1.000000, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.793157, Class: 0.144919, Obj: 0.279596, No Obj: 0.000862, .5R: 1.000000, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.702395, Class: 0.154989, Obj: 0.085249, No Obj: 0.000147, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.683546, Class: 0.206081, Obj: 0.240496, No Obj: 0.000920, .5R: 0.823529, .75R: 0.352941, count: 17 -Region 23 Avg IOU: 0.705142, Class: 0.165298, Obj: 0.152433, No Obj: 0.000189, .5R: 0.900000, .75R: 0.400000, count: 20 - - 2379: 2.535677, 2.508312 avg loss, 0.001000 rate, 3.460058 seconds, 304512 images -Loaded: 0.000068 seconds -Region 16 Avg IOU: 0.684015, Class: 0.258776, Obj: 0.219460, No Obj: 0.000687, .5R: 0.866667, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.652025, Class: 0.196459, Obj: 0.078823, No Obj: 0.000131, .5R: 0.866667, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.745599, Class: 0.182197, Obj: 0.177338, No Obj: 0.000923, .5R: 0.956522, .75R: 0.434783, count: 23 -Region 23 Avg IOU: 0.681682, Class: 0.151462, Obj: 0.049909, No Obj: 0.000126, .5R: 1.000000, .75R: 0.157895, count: 19 -Region 16 Avg IOU: 0.777555, Class: 0.182286, Obj: 0.239042, No Obj: 0.000560, .5R: 0.933333, .75R: 0.733333, count: 15 -Region 23 Avg IOU: 0.715354, Class: 0.135132, Obj: 0.088321, No Obj: 0.000117, .5R: 1.000000, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.707835, Class: 0.211862, Obj: 0.233504, 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.75R: 0.666667, count: 24 -Region 23 Avg IOU: 0.684281, Class: 0.164691, Obj: 0.070643, No Obj: 0.000126, .5R: 0.937500, .75R: 0.375000, count: 16 - - 2380: 3.032730, 2.560754 avg loss, 0.001000 rate, 3.274591 seconds, 304640 images -Resizing -544 x 544 - try to allocate workspace = 1145585665 * sizeof(float), CUDA allocate done! -Loaded: 0.519494 seconds -Region 16 Avg IOU: 0.742130, Class: 0.182322, Obj: 0.287631, No Obj: 0.001012, .5R: 0.909091, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.717002, Class: 0.227477, Obj: 0.226097, No Obj: 0.000371, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.817955, Class: 0.199224, Obj: 0.195025, No Obj: 0.000679, .5R: 1.000000, .75R: 0.777778, count: 9 -Region 23 Avg IOU: 0.677934, Class: 0.184946, Obj: 0.262262, No Obj: 0.000373, .5R: 0.900000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.784758, Class: 0.221273, Obj: 0.203410, No Obj: 0.000921, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.675323, Class: 0.202876, Obj: 0.156139, No Obj: 0.000211, .5R: 0.875000, .75R: 0.125000, count: 8 -Region 16 Avg IOU: 0.696467, Class: 0.239057, Obj: 0.203185, No Obj: 0.000864, .5R: 0.933333, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.648221, Class: 0.160057, Obj: 0.123161, No Obj: 0.000144, .5R: 0.833333, .75R: 0.250000, count: 12 -Region 16 Avg IOU: 0.748505, Class: 0.168452, Obj: 0.305753, No Obj: 0.001029, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.731381, Class: 0.196561, Obj: 0.273725, No Obj: 0.000476, .5R: 1.000000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.759205, Class: 0.251051, Obj: 0.329383, No Obj: 0.001177, .5R: 0.941176, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.682840, Class: 0.199415, Obj: 0.118732, No Obj: 0.000469, .5R: 0.878788, .75R: 0.454545, count: 33 -Region 16 Avg IOU: 0.749215, Class: 0.183668, Obj: 0.214207, No Obj: 0.000818, .5R: 1.000000, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.678309, Class: 0.121692, Obj: 0.125327, No Obj: 0.000237, .5R: 0.777778, .75R: 0.555556, count: 18 -Region 16 Avg IOU: 0.677093, Class: 0.282907, Obj: 0.188464, No Obj: 0.000739, .5R: 0.933333, .75R: 0.133333, count: 15 -Region 23 Avg IOU: 0.644991, Class: 0.196572, Obj: 0.159751, No Obj: 0.000264, .5R: 0.833333, .75R: 0.222222, count: 18 - - 2381: 2.203655, 2.525044 avg loss, 0.001000 rate, 1.557693 seconds, 304768 images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.787343, Class: 0.244951, Obj: 0.224878, No Obj: 0.000658, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.758393, Class: 0.203427, Obj: 0.314980, No Obj: 0.000567, .5R: 1.000000, .75R: 0.500000, count: 20 -Region 16 Avg IOU: 0.751191, Class: 0.203102, Obj: 0.313283, No Obj: 0.001544, .5R: 1.000000, .75R: 0.421053, count: 19 -Region 23 Avg IOU: 0.686480, Class: 0.172674, Obj: 0.333274, No Obj: 0.000567, .5R: 0.928571, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.753707, Class: 0.212935, Obj: 0.289091, No Obj: 0.001399, .5R: 0.956522, .75R: 0.652174, count: 23 -Region 23 Avg IOU: 0.658216, Class: 0.138493, Obj: 0.162389, No Obj: 0.000518, .5R: 0.882353, .75R: 0.176471, count: 17 -Region 16 Avg IOU: 0.768168, Class: 0.258442, Obj: 0.258098, No Obj: 0.000815, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.705710, Class: 0.235479, Obj: 0.418789, No Obj: 0.000729, .5R: 1.000000, .75R: 0.318182, count: 22 -Region 16 Avg IOU: 0.762427, Class: 0.214737, Obj: 0.256210, No Obj: 0.001408, .5R: 1.000000, .75R: 0.500000, count: 22 -Region 23 Avg IOU: 0.643054, Class: 0.195830, Obj: 0.249278, No Obj: 0.000783, .5R: 0.714286, .75R: 0.428571, count: 21 -Region 16 Avg IOU: 0.764577, Class: 0.222289, Obj: 0.373754, No Obj: 0.001023, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.680335, Class: 0.221583, Obj: 0.310340, No Obj: 0.000578, .5R: 0.928571, .75R: 0.357143, count: 14 -Region 16 Avg IOU: 0.791953, Class: 0.230933, Obj: 0.465812, No Obj: 0.001399, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.710762, Class: 0.224936, Obj: 0.330323, No Obj: 0.000848, .5R: 0.952381, .75R: 0.333333, count: 21 -Region 16 Avg IOU: 0.750178, Class: 0.240684, Obj: 0.279898, No Obj: 0.000850, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.729416, Class: 0.175031, Obj: 0.409639, No Obj: 0.000576, .5R: 1.000000, .75R: 0.388889, count: 18 - - 2382: 2.220071, 2.494547 avg loss, 0.001000 rate, 2.235676 seconds, 304896 images -Loaded: 0.000077 seconds -Region 16 Avg IOU: 0.748812, Class: 0.228968, Obj: 0.361479, No Obj: 0.001963, .5R: 1.000000, .75R: 0.478261, count: 23 -Region 23 Avg IOU: 0.666361, Class: 0.183374, Obj: 0.274764, No Obj: 0.000727, .5R: 0.925926, .75R: 0.333333, count: 27 -Region 16 Avg IOU: 0.768658, Class: 0.178257, Obj: 0.284423, No Obj: 0.001072, .5R: 1.000000, .75R: 0.800000, count: 15 -Region 23 Avg IOU: 0.716342, Class: 0.146477, Obj: 0.263542, No Obj: 0.000585, .5R: 1.000000, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.784883, Class: 0.254836, Obj: 0.300889, 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22 -Region 16 Avg IOU: 0.772304, Class: 0.182695, Obj: 0.411366, No Obj: 0.001614, .5R: 1.000000, .75R: 0.714286, count: 21 -Region 23 Avg IOU: 0.777204, Class: 0.203269, Obj: 0.284592, No Obj: 0.000422, .5R: 1.000000, .75R: 0.785714, count: 14 -Region 16 Avg IOU: 0.728891, Class: 0.145749, Obj: 0.281084, No Obj: 0.001382, .5R: 0.904762, .75R: 0.523810, count: 21 -Region 23 Avg IOU: 0.676342, Class: 0.156350, Obj: 0.220749, No Obj: 0.000634, .5R: 0.913043, .75R: 0.217391, count: 23 -Region 16 Avg IOU: 0.768943, Class: 0.213531, Obj: 0.377581, No Obj: 0.001168, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.721434, Class: 0.201824, Obj: 0.315397, No Obj: 0.000554, .5R: 1.000000, .75R: 0.458333, count: 24 - - 2390: 2.526708, 2.409322 avg loss, 0.001000 rate, 2.338025 seconds, 305920 images -Resizing -416 x 416 - try to allocate workspace = 304758785 * sizeof(float), CUDA allocate done! -Loaded: 0.345635 seconds -Region 16 Avg IOU: 0.790150, Class: 0.153303, Obj: 0.678544, 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images -Loaded: 0.000068 seconds -Region 16 Avg IOU: 0.782164, Class: 0.253893, Obj: 0.421438, No Obj: 0.002558, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.710195, Class: 0.223513, Obj: 0.201527, No Obj: 0.000518, .5R: 1.000000, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.780237, Class: 0.230769, Obj: 0.285281, No Obj: 0.000985, .5R: 1.000000, .75R: 0.857143, count: 7 -Region 23 Avg IOU: 0.694860, Class: 0.223512, Obj: 0.266936, No Obj: 0.000590, .5R: 0.923077, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.733682, Class: 0.190748, Obj: 0.353107, No Obj: 0.002102, .5R: 0.928571, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.714714, Class: 0.213982, Obj: 0.227518, No Obj: 0.000673, .5R: 0.923077, .75R: 0.576923, count: 26 -Region 16 Avg IOU: 0.809613, Class: 0.252062, Obj: 0.445553, No Obj: 0.001418, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.676760, Class: 0.228708, Obj: 0.270445, No Obj: 0.000588, .5R: 1.000000, .75R: 0.388889, count: 18 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23 Avg IOU: 0.707900, Class: 0.212514, Obj: 0.258967, No Obj: 0.001016, .5R: 0.960000, .75R: 0.360000, count: 25 - - 2396: 2.014242, 2.161106 avg loss, 0.001000 rate, 0.785382 seconds, 306688 images -Loaded: 0.000084 seconds -Region 16 Avg IOU: 0.828284, Class: 0.191123, Obj: 0.703061, No Obj: 0.001103, .5R: 1.000000, .75R: 0.800000, count: 5 -Region 23 Avg IOU: 0.737651, Class: 0.239004, Obj: 0.351617, No Obj: 0.000535, .5R: 1.000000, .75R: 0.357143, count: 14 -Region 16 Avg IOU: 0.706711, Class: 0.187982, Obj: 0.229153, No Obj: 0.001560, .5R: 0.800000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.645551, Class: 0.217470, Obj: 0.120165, No Obj: 0.000587, .5R: 0.947368, .75R: 0.105263, count: 19 -Region 16 Avg IOU: 0.784942, Class: 0.202881, Obj: 0.278503, No Obj: 0.001167, .5R: 1.000000, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.645308, Class: 0.221337, Obj: 0.194899, No Obj: 0.000490, .5R: 0.888889, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.727827, Class: 0.133591, Obj: 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1.000000, .75R: 0.500000, count: 6 -Region 23 Avg IOU: 0.702890, Class: 0.187839, Obj: 0.257290, No Obj: 0.000613, .5R: 0.952381, .75R: 0.333333, count: 21 - - 2397: 1.747066, 2.119702 avg loss, 0.001000 rate, 0.786572 seconds, 306816 images -Loaded: 0.000064 seconds -Region 16 Avg IOU: 0.719143, Class: 0.253810, Obj: 0.224053, No Obj: 0.001447, .5R: 0.923077, .75R: 0.307692, count: 13 -Region 23 Avg IOU: 0.682126, Class: 0.208564, Obj: 0.154544, No Obj: 0.000473, .5R: 1.000000, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.774507, Class: 0.214423, Obj: 0.377320, No Obj: 0.001388, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.711792, Class: 0.219338, Obj: 0.169252, No Obj: 0.000416, .5R: 1.000000, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.807631, Class: 0.257038, Obj: 0.310227, No Obj: 0.001283, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.722518, Class: 0.254663, Obj: 0.216737, No Obj: 0.000578, .5R: 0.962963, .75R: 0.518519, count: 27 -Region 16 Avg IOU: 0.800695, Class: 0.174348, Obj: 0.316426, No Obj: 0.001934, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.635896, Class: 0.134213, Obj: 0.061621, No Obj: 0.000323, .5R: 0.818182, .75R: 0.181818, count: 11 -Region 16 Avg IOU: 0.836698, Class: 0.223679, Obj: 0.440811, No Obj: 0.001691, .5R: 1.000000, .75R: 0.916667, count: 12 -Region 23 Avg IOU: 0.724748, Class: 0.177448, Obj: 0.252204, No Obj: 0.000615, .5R: 1.000000, .75R: 0.285714, count: 14 -Region 16 Avg IOU: 0.797396, Class: 0.205903, Obj: 0.204930, No Obj: 0.001989, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.692894, Class: 0.175743, Obj: 0.124841, No Obj: 0.000643, .5R: 0.950000, .75R: 0.250000, count: 20 -Region 16 Avg IOU: 0.762325, Class: 0.170381, Obj: 0.280240, No Obj: 0.001125, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.703053, Class: 0.153745, Obj: 0.158730, No Obj: 0.000347, .5R: 0.947368, .75R: 0.421053, count: 19 -Region 16 Avg IOU: 0.777070, Class: 0.172413, Obj: 0.276507, No Obj: 0.001602, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.717953, Class: 0.229934, Obj: 0.208229, No Obj: 0.000330, .5R: 1.000000, .75R: 0.266667, count: 15 - - 2398: 1.888448, 2.096577 avg loss, 0.001000 rate, 0.784598 seconds, 306944 images -Loaded: 0.000057 seconds -Region 16 Avg IOU: 0.658622, Class: 0.184313, Obj: 0.247605, No Obj: 0.000774, .5R: 0.666667, .75R: 0.500000, count: 6 -Region 23 Avg IOU: 0.724695, Class: 0.190763, Obj: 0.351303, No Obj: 0.000533, .5R: 0.916667, .75R: 0.583333, count: 12 -Region 16 Avg IOU: 0.779249, Class: 0.203706, Obj: 0.277406, No Obj: 0.001442, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.724565, Class: 0.155609, Obj: 0.203716, No Obj: 0.000458, .5R: 1.000000, .75R: 0.421053, count: 19 -Region 16 Avg IOU: 0.805110, Class: 0.210857, Obj: 0.437110, No Obj: 0.002237, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.733198, Class: 0.222297, Obj: 0.198256, No Obj: 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0.480000, count: 25 -Region 16 Avg IOU: 0.712166, Class: 0.263929, Obj: 0.4Saving weights to ./attribute/7354_26298/attribute_33652_s_tyv3_9c5_2400.weights -57788, No Obj: 0.001002, .5R: 1.000000, .75R: 0.333333, count: 9 -Region 23 Avg IOU: 0.689815, Class: 0.198124, Obj: 0.128513, No Obj: 0.000389, .5R: 0.947368, .75R: 0.263158, count: 19 - - 2399: 1.858484, 2.072768 avg loss, 0.001000 rate, 0.780125 seconds, 307072 images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.801278, Class: 0.180841, Obj: 0.504261, No Obj: 0.002078, .5R: 1.000000, .75R: 0.888889, count: 9 -Region 23 Avg IOU: 0.634750, Class: 0.153618, Obj: 0.295892, No Obj: 0.000593, .5R: 0.894737, .75R: 0.105263, count: 19 -Region 16 Avg IOU: 0.783041, Class: 0.260802, Obj: 0.390788, No Obj: 0.002110, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.664667, Class: 0.205718, Obj: 0.260573, No Obj: 0.000452, .5R: 0.933333, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.807894, Class: 0.195104, Obj: 0.189092, 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0.619210, Class: 0.172321, Obj: 0.083653, No Obj: 0.000282, .5R: 0.789474, .75R: 0.105263, count: 19 -Region 16 Avg IOU: 0.750259, Class: 0.231539, Obj: 0.368758, No Obj: 0.001457, .5R: 1.000000, .75R: 0.625000, count: 16 -Region 23 Avg IOU: 0.709409, Class: 0.214156, Obj: 0.211091, No Obj: 0.000387, .5R: 1.000000, .75R: 0.300000, count: 20 -Region 16 Avg IOU: 0.745047, Class: 0.204104, Obj: 0.278292, No Obj: 0.001483, .5R: 1.000000, .75R: 0.565217, count: 23 -Region 23 Avg IOU: 0.663634, Class: 0.160068, Obj: 0.146560, No Obj: 0.000369, .5R: 0.875000, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.714432, Class: 0.237750, Obj: 0.205816, No Obj: 0.002126, .5R: 0.960000, .75R: 0.360000, count: 25 -Region 23 Avg IOU: 0.688394, Class: 0.238073, Obj: 0.162867, No Obj: 0.000334, .5R: 0.916667, .75R: 0.333333, count: 12 - - 2407: 2.618462, 2.266109 avg loss, 0.001000 rate, 2.712178 seconds, 308096 images -Loaded: 0.000060 seconds -Region 16 Avg IOU: 0.769420, Class: 0.280205, Obj: 0.415346, 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images -Loaded: 0.000061 seconds -Region 16 Avg IOU: 0.761817, Class: 0.296012, Obj: 0.494773, No Obj: 0.001050, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.776027, Class: 0.213371, Obj: 0.405608, No Obj: 0.000208, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 16 Avg IOU: 0.787832, Class: 0.256273, Obj: 0.492350, No Obj: 0.001461, .5R: 1.000000, .75R: 0.777778, count: 18 -Region 23 Avg IOU: 0.688153, Class: 0.215661, Obj: 0.195432, No Obj: 0.000273, .5R: 0.947368, .75R: 0.421053, count: 19 -Region 16 Avg IOU: 0.750488, Class: 0.202513, Obj: 0.384805, No Obj: 0.001604, .5R: 0.960000, .75R: 0.560000, count: 25 -Region 23 Avg IOU: 0.721304, Class: 0.195455, Obj: 0.229226, No Obj: 0.000443, .5R: 1.000000, .75R: 0.407407, count: 27 -Region 16 Avg IOU: 0.759453, Class: 0.171486, Obj: 0.521783, No Obj: 0.001685, .5R: 1.000000, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.728877, Class: 0.152988, Obj: 0.270732, No Obj: 0.000305, .5R: 1.000000, .75R: 0.450000, count: 20 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loss, 0.001000 rate, 2.857351 seconds, 308480 images -Resizing -736 x 736 - try to allocate workspace = 1149517825 * sizeof(float), CUDA allocate done! -Loaded: 0.230099 seconds -Region 16 Avg IOU: 0.733157, Class: 0.155889, Obj: 0.539117, No Obj: 0.000879, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.739359, Class: 0.177943, Obj: 0.278187, No Obj: 0.000285, .5R: 1.000000, .75R: 0.529412, count: 17 -Region 16 Avg IOU: 0.768637, Class: 0.209038, Obj: 0.330293, No Obj: 0.001362, .5R: 1.000000, .75R: 0.500000, count: 34 -Region 23 Avg IOU: 0.740862, Class: 0.164709, Obj: 0.201155, No Obj: 0.000288, .5R: 0.944444, .75R: 0.555556, count: 18 -Region 16 Avg IOU: 0.738866, Class: 0.211444, Obj: 0.271607, No Obj: 0.001136, .5R: 0.920000, .75R: 0.520000, count: 25 -Region 23 Avg IOU: 0.739873, Class: 0.139322, Obj: 0.165142, No Obj: 0.000267, .5R: 0.967742, .75R: 0.516129, count: 31 -Region 16 Avg IOU: 0.694602, Class: 0.218967, Obj: 0.188724, No Obj: 0.000812, .5R: 1.000000, 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count: 17 -Region 16 Avg IOU: 0.740889, Class: 0.210315, Obj: 0.262128, No Obj: 0.000989, .5R: 1.000000, .75R: 0.500000, count: 22 -Region 23 Avg IOU: 0.753791, Class: 0.176777, Obj: 0.217443, No Obj: 0.000137, .5R: 1.000000, .75R: 0.466667, count: 15 -Region 16 Avg IOU: 0.709511, Class: 0.231427, Obj: 0.188462, No Obj: 0.000600, .5R: 0.866667, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.656089, Class: 0.130888, Obj: 0.127737, No Obj: 0.000107, .5R: 1.000000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.706114, Class: 0.241139, Obj: 0.225087, No Obj: 0.001284, .5R: 0.962963, .75R: 0.407407, count: 27 -Region 23 Avg IOU: 0.706150, Class: 0.166237, Obj: 0.046611, No Obj: 0.000152, .5R: 0.944444, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.740761, Class: 0.216149, Obj: 0.286301, No Obj: 0.000810, .5R: 0.937500, .75R: 0.562500, count: 16 -Region 23 Avg IOU: 0.716232, Class: 0.177022, Obj: 0.150203, No Obj: 0.000102, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.711500, Class: 0.196846, Obj: 0.229835, No Obj: 0.000920, .5R: 0.900000, .75R: 0.450000, count: 20 -Region 23 Avg IOU: 0.761657, Class: 0.192088, Obj: 0.092055, No Obj: 0.000186, .5R: 1.000000, .75R: 0.470588, count: 17 - - 2413: 2.522786, 2.481857 avg loss, 0.001000 rate, 3.951064 seconds, 308864 images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.658917, Class: 0.196098, Obj: 0.165863, No Obj: 0.000819, .5R: 0.761905, .75R: 0.285714, count: 21 -Region 23 Avg IOU: 0.677655, Class: 0.208293, Obj: 0.063589, No Obj: 0.000093, .5R: 0.833333, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.761739, Class: 0.235176, Obj: 0.300360, No Obj: 0.001174, .5R: 0.967742, .75R: 0.612903, count: 31 -Region 23 Avg IOU: 0.714136, Class: 0.188320, Obj: 0.156764, No Obj: 0.000172, .5R: 0.950000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.726809, Class: 0.174759, Obj: 0.226716, No Obj: 0.000851, .5R: 0.913043, .75R: 0.478261, count: 23 -Region 23 Avg IOU: 0.712732, Class: 0.235592, Obj: 0.273094, 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25 -Region 23 Avg IOU: 0.722166, Class: 0.195600, Obj: 0.238067, No Obj: 0.000352, .5R: 0.962963, .75R: 0.407407, count: 27 -Region 16 Avg IOU: 0.717214, Class: 0.205184, Obj: 0.254683, No Obj: 0.001284, .5R: 0.958333, .75R: 0.458333, count: 24 -Region 23 Avg IOU: 0.651292, Class: 0.152457, Obj: 0.185856, No Obj: 0.000224, .5R: 0.857143, .75R: 0.238095, count: 21 - - 2416: 2.747080, 2.568089 avg loss, 0.001000 rate, 4.376443 seconds, 309248 images -Loaded: 0.000087 seconds -Region 16 Avg IOU: 0.734788, Class: 0.265738, Obj: 0.238628, No Obj: 0.001107, .5R: 1.000000, .75R: 0.400000, count: 25 -Region 23 Avg IOU: 0.670848, Class: 0.214183, Obj: 0.111822, No Obj: 0.000127, .5R: 1.000000, .75R: 0.100000, count: 10 -Region 16 Avg IOU: 0.711798, Class: 0.184296, Obj: 0.309953, No Obj: 0.001321, .5R: 1.000000, .75R: 0.380952, count: 21 -Region 23 Avg IOU: 0.653035, Class: 0.171017, Obj: 0.149743, No Obj: 0.000203, .5R: 0.875000, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.763192, Class: 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25 -Region 16 Avg IOU: 0.779201, Class: 0.189298, Obj: 0.318052, No Obj: 0.000741, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.725672, Class: 0.175772, Obj: 0.175470, No Obj: 0.000156, .5R: 0.923077, .75R: 0.692308, count: 13 -Region 16 Avg IOU: 0.757869, Class: 0.222748, Obj: 0.401067, No Obj: 0.001373, .5R: 0.956522, .75R: 0.695652, count: 23 -Region 23 Avg IOU: 0.678243, Class: 0.212155, Obj: 0.313898, No Obj: 0.000246, .5R: 0.933333, .75R: 0.200000, count: 15 - - 2435: 2.591601, 2.378057 avg loss, 0.001000 rate, 3.400826 seconds, 311680 images -Loaded: 0.000063 seconds -Region 16 Avg IOU: 0.807910, Class: 0.240566, Obj: 0.491592, No Obj: 0.001456, .5R: 1.000000, .75R: 0.842105, count: 19 -Region 23 Avg IOU: 0.684369, Class: 0.145254, Obj: 0.337829, No Obj: 0.000466, .5R: 0.882353, .75R: 0.470588, count: 17 -Region 16 Avg IOU: 0.700543, Class: 0.251863, Obj: 0.228118, No Obj: 0.001324, .5R: 0.904762, .75R: 0.476190, count: 21 -Region 23 Avg IOU: 0.707460, Class: 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23 -Region 16 Avg IOU: 0.716767, Class: 0.259575, Obj: 0.284644, No Obj: 0.001166, .5R: 0.944444, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.651285, Class: 0.225737, Obj: 0.151613, No Obj: 0.000172, .5R: 0.937500, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.720389, Class: 0.203124, Obj: 0.182668, No Obj: 0.001193, .5R: 0.947368, .75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.670504, Class: 0.165679, Obj: 0.137359, No Obj: 0.000128, .5R: 0.800000, .75R: 0.500000, count: 10 -Region 16 Avg IOU: 0.700930, Class: 0.209766, Obj: 0.212554, No Obj: 0.000861, .5R: 0.928571, .75R: 0.357143, count: 14 -Region 23 Avg IOU: 0.669119, Class: 0.184346, Obj: 0.156031, No Obj: 0.000224, .5R: 0.850000, .75R: 0.400000, count: 20 - - 2440: 2.862086, 2.588083 avg loss, 0.001000 rate, 3.476210 seconds, 312320 images -Resizing -384 x 384 - try to allocate workspace = 216268801 * sizeof(float), CUDA allocate done! -Loaded: 0.332053 seconds -Region 16 Avg IOU: 0.792731, Class: 0.215555, Obj: 0.197239, 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images -Loaded: 0.000074 seconds -Region 16 Avg IOU: 0.782642, Class: 0.248408, Obj: 0.460747, No Obj: 0.000666, .5R: 1.000000, .75R: 0.500000, count: 4 -Region 23 Avg IOU: 0.646310, Class: 0.259527, Obj: 0.339220, No Obj: 0.000766, .5R: 0.928571, .75R: 0.285714, count: 14 -Region 16 Avg IOU: 0.768839, Class: 0.183534, Obj: 0.370989, No Obj: 0.001579, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.771553, Class: 0.183607, Obj: 0.271087, No Obj: 0.000880, .5R: 1.000000, .75R: 0.611111, count: 18 -Region 16 Avg IOU: 0.724499, Class: 0.177596, Obj: 0.217313, No Obj: 0.001329, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 23 Avg IOU: 0.669894, Class: 0.174464, Obj: 0.232651, No Obj: 0.000903, .5R: 0.956522, .75R: 0.217391, count: 23 -Region 16 Avg IOU: 0.728937, Class: 0.281258, Obj: 0.312006, No Obj: 0.001293, .5R: 1.000000, .75R: 0.285714, count: 7 -Region 23 Avg IOU: 0.697190, Class: 0.230121, Obj: 0.221343, No Obj: 0.000788, .5R: 0.900000, .75R: 0.300000, count: 20 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= 1147404289 * sizeof(float), CUDA allocate done! -Loaded: 0.283607 seconds -Region 16 Avg IOU: 0.658055, Class: 0.135421, Obj: 0.219757, No Obj: 0.001808, .5R: 0.812500, .75R: 0.312500, count: 16 -Region 23 Avg IOU: 0.724835, Class: 0.214025, Obj: 0.172151, No Obj: 0.000327, .5R: 0.894737, .75R: 0.526316, count: 19 -Region 16 Avg IOU: 0.623128, Class: 0.204815, Obj: 0.260631, No Obj: 0.001269, .5R: 0.850000, .75R: 0.050000, count: 20 -Region 23 Avg IOU: 0.685241, Class: 0.147731, Obj: 0.221173, No Obj: 0.000236, .5R: 0.937500, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.697319, Class: 0.234282, Obj: 0.404280, No Obj: 0.001540, .5R: 1.000000, .75R: 0.214286, count: 14 -Region 23 Avg IOU: 0.675051, Class: 0.142360, Obj: 0.233810, No Obj: 0.000289, .5R: 0.916667, .75R: 0.166667, count: 12 -Region 16 Avg IOU: 0.712810, Class: 0.233888, Obj: 0.295869, No Obj: 0.001606, .5R: 0.923077, .75R: 0.461538, count: 26 -Region 23 Avg IOU: 0.706738, Class: 0.196023, Obj: 0.181165, No Obj: 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0.233333, count: 30 -Region 16 Avg IOU: 0.703372, Class: 0.199995, Obj: 0.258745, No Obj: 0.001731, .5R: 1.000000, .75R: 0.222222, count: 9 -Region 23 Avg IOU: 0.669669, Class: 0.174114, Obj: 0.038108, No Obj: 0.000373, .5R: 0.909091, .75R: 0.272727, count: 11 -Region 16 Avg IOU: 0.705917, Class: 0.188129, Obj: 0.219567, No Obj: 0.001084, .5R: 1.000000, .75R: 0.250000, count: 4 -Region 23 Avg IOU: 0.716249, Class: 0.227746, Obj: 0.159874, No Obj: 0.000544, .5R: 0.958333, .75R: 0.458333, count: 24 -Region 16 Avg IOU: 0.747162, Class: 0.284988, Obj: 0.488397, No Obj: 0.002745, .5R: 0.909091, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.687597, Class: 0.216017, Obj: 0.185381, No Obj: 0.000692, .5R: 0.923077, .75R: 0.461538, count: 26 -Region 16 Avg IOU: 0.769604, Class: 0.288990, Obj: 0.424465, No Obj: 0.002264, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.652289, Class: 0.209138, Obj: 0.152410, No Obj: 0.000491, .5R: 0.812500, .75R: 0.312500, count: 16 - - 2479: 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11 -Region 16 Avg IOU: 0.724843, Class: 0.225223, Obj: 0.310525, No Obj: 0.001095, .5R: 0.958333, .75R: 0.458333, count: 24 -Region 23 Avg IOU: 0.699906, Class: 0.237588, Obj: 0.099456, No Obj: 0.000136, .5R: 0.937500, .75R: 0.250000, count: 16 - - 2482: 2.581179, 2.231944 avg loss, 0.001000 rate, 2.949157 seconds, 317696 images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.719424, Class: 0.240168, Obj: 0.346438, No Obj: 0.001360, .5R: 0.961538, .75R: 0.423077, count: 26 -Region 23 Avg IOU: 0.690318, Class: 0.174098, Obj: 0.049380, No Obj: 0.000100, .5R: 0.928571, .75R: 0.357143, count: 14 -Region 16 Avg IOU: 0.781651, Class: 0.258441, Obj: 0.381200, No Obj: 0.000858, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.667588, Class: 0.128119, Obj: 0.062319, No Obj: 0.000113, .5R: 0.833333, .75R: 0.250000, count: 24 -Region 16 Avg IOU: 0.698491, Class: 0.224727, Obj: 0.170452, No Obj: 0.001114, .5R: 0.888889, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.674432, Class: 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0.708379, Class: 0.178972, Obj: 0.040080, No Obj: 0.000065, .5R: 1.000000, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.757753, Class: 0.223423, Obj: 0.297595, No Obj: 0.001618, .5R: 0.968750, .75R: 0.593750, count: 32 -Region 23 Avg IOU: 0.762309, Class: 0.156657, Obj: 0.049019, No Obj: 0.000086, .5R: 1.000000, .75R: 0.647059, count: 17 - - 2484: 2.521264, 2.292846 avg loss, 0.001000 rate, 4.262274 seconds, 317952 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.772212, Class: 0.211752, Obj: 0.244286, No Obj: 0.001230, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.642749, Class: 0.192442, Obj: 0.140059, No Obj: 0.000126, .5R: 0.812500, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.669995, Class: 0.258336, Obj: 0.242854, No Obj: 0.000986, .5R: 0.900000, .75R: 0.250000, count: 20 -Region 23 Avg IOU: 0.655251, Class: 0.234045, Obj: 0.080368, No Obj: 0.000118, .5R: 0.913043, .75R: 0.217391, count: 23 -Region 16 Avg IOU: 0.734000, Class: 0.219107, Obj: 0.356582, 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20 -Region 16 Avg IOU: 0.748351, Class: 0.189761, Obj: 0.285400, No Obj: 0.001304, .5R: 0.952381, .75R: 0.571429, count: 21 -Region 23 Avg IOU: 0.645714, Class: 0.211013, Obj: 0.229155, No Obj: 0.000458, .5R: 0.947368, .75R: 0.210526, count: 19 -Region 16 Avg IOU: 0.719492, Class: 0.193509, Obj: 0.317869, No Obj: 0.001288, .5R: 0.961538, .75R: 0.384615, count: 26 -Region 23 Avg IOU: 0.674968, Class: 0.216098, Obj: 0.179955, No Obj: 0.000473, .5R: 0.928571, .75R: 0.285714, count: 28 - - 2487: 3.373626, 2.510249 avg loss, 0.001000 rate, 4.080666 seconds, 318336 images -Loaded: 0.000068 seconds -Region 16 Avg IOU: 0.752952, Class: 0.218729, Obj: 0.367597, No Obj: 0.002417, .5R: 1.000000, .75R: 0.580645, count: 31 -Region 23 Avg IOU: 0.732386, Class: 0.201525, Obj: 0.178224, No Obj: 0.000321, .5R: 0.941176, .75R: 0.529412, count: 17 -Region 16 Avg IOU: 0.689590, Class: 0.209045, Obj: 0.203323, No Obj: 0.001410, .5R: 0.880000, .75R: 0.280000, count: 25 -Region 23 Avg IOU: 0.638691, Class: 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-Region 16 Avg IOU: 0.723437, Class: 0.284654, Obj: 0.408919, No Obj: 0.001466, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.697308, Class: 0.202010, Obj: 0.277676, No Obj: 0.000769, .5R: 0.944444, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.709669, Class: 0.231458, Obj: 0.431706, No Obj: 0.001049, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 23 Avg IOU: 0.648882, Class: 0.219603, Obj: 0.207725, No Obj: 0.000793, .5R: 1.000000, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.708587, Class: 0.224324, Obj: 0.340079, No Obj: 0.001838, .5R: 1.000000, .75R: 0.307692, count: 13 -Region 23 Avg IOU: 0.697417, Class: 0.158530, Obj: 0.328260, No Obj: 0.000790, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 16 Avg IOU: 0.747472, Class: 0.214743, Obj: 0.222538, No Obj: 0.001585, .5R: 1.000000, .75R: 0.450000, count: 20 -Region 23 Avg IOU: 0.685361, Class: 0.115534, Obj: 0.244131, No Obj: 0.000601, .5R: 0.800000, .75R: 0.466667, count: 15 -Region 16 Avg IOU: 0.735760, Class: 0.283235, Obj: 0.310764, No Obj: 0.000664, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.687114, Class: 0.219967, Obj: 0.310560, No Obj: 0.000908, .5R: 0.944444, .75R: 0.277778, count: 18 - - 2500: 1.839988, 2.223748 avg loss, 0.001000 rate, 1.595656 seconds, 320000 images -Resizing -640 x 640 - try to allocate workspace = 1147404289 * sizeof(float), CUDA allocate done! -Loaded: 0.349891 seconds -Region 16 Avg IOU: 0.689821, Class: 0.217480, Obj: 0.286527, No Obj: 0.001208, .5R: 0.857143, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.699337, Class: 0.231675, Obj: 0.515440, No Obj: 0.000532, .5R: 0.928571, .75R: 0.285714, count: 14 -Region 16 Avg IOU: 0.762812, Class: 0.221039, Obj: 0.281370, No Obj: 0.001639, .5R: 1.000000, .75R: 0.625000, count: 24 -Region 23 Avg IOU: 0.720116, Class: 0.198328, Obj: 0.372394, No Obj: 0.000640, .5R: 1.000000, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.667843, Class: 0.166763, Obj: 0.174729, No Obj: 0.000941, .5R: 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18 -Region 23 Avg IOU: 0.657081, Class: 0.183439, Obj: 0.364452, No Obj: 0.000697, .5R: 0.782609, .75R: 0.304348, count: 23 -Region 16 Avg IOU: 0.721307, Class: 0.209929, Obj: 0.332311, No Obj: 0.001705, .5R: 1.000000, .75R: 0.360000, count: 25 -Region 23 Avg IOU: 0.693395, Class: 0.139078, Obj: 0.310210, No Obj: 0.000579, .5R: 0.900000, .75R: 0.400000, count: 10 - - 2501: 2.642400, 2.265613 avg loss, 0.001000 rate, 2.639802 seconds, 320128 images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.681122, Class: 0.224136, Obj: 0.258308, No Obj: 0.000934, .5R: 0.857143, .75R: 0.333333, count: 21 -Region 23 Avg IOU: 0.704224, Class: 0.133844, Obj: 0.181883, No Obj: 0.000112, .5R: 0.888889, .75R: 0.555556, count: 9 -Region 16 Avg IOU: 0.785974, Class: 0.170016, Obj: 0.281523, No Obj: 0.000973, .5R: 1.000000, .75R: 0.687500, count: 16 -Region 23 Avg IOU: 0.722445, Class: 0.172899, Obj: 0.101399, No Obj: 0.000194, .5R: 1.000000, .75R: 0.428571, count: 21 -Region 16 Avg IOU: 0.715332, Class: 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0.784916, Class: 0.185608, Obj: 0.326255, No Obj: 0.000842, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.732886, Class: 0.186076, Obj: 0.068561, No Obj: 0.000048, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.604732, Class: 0.163556, Obj: 0.081006, No Obj: 0.000701, .5R: 0.705882, .75R: 0.352941, count: 17 -Region 23 Avg IOU: 0.698388, Class: 0.142061, Obj: 0.030354, No Obj: 0.000099, .5R: 0.909091, .75R: 0.363636, count: 22 - - 2503: 2.798433, 2.388344 avg loss, 0.001000 rate, 2.774114 seconds, 320384 images -Loaded: 0.000071 seconds -Region 16 Avg IOU: 0.788898, Class: 0.258008, Obj: 0.256422, No Obj: 0.001099, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.738795, Class: 0.198263, Obj: 0.043272, No Obj: 0.000062, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 16 Avg IOU: 0.702400, Class: 0.246290, Obj: 0.175825, No Obj: 0.001552, .5R: 0.892857, .75R: 0.392857, count: 28 -Region 23 Avg IOU: 0.721401, Class: 0.198529, Obj: 0.068175, 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Obj: 0.193340, No Obj: 0.000148, .5R: 0.950000, .75R: 0.500000, count: 20 -Region 16 Avg IOU: 0.700207, Class: 0.248047, Obj: 0.248829, No Obj: 0.000893, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 23 Avg IOU: 0.728605, Class: 0.192775, Obj: 0.088284, No Obj: 0.000109, .5R: 1.000000, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.736184, Class: 0.179731, Obj: 0.207989, No Obj: 0.001051, .5R: 1.000000, .75R: 0.480000, count: 25 -Region 23 Avg IOU: 0.758951, Class: 0.171315, Obj: 0.105732, No Obj: 0.000154, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 16 Avg IOU: 0.751628, Class: 0.190802, Obj: 0.191694, No Obj: 0.001144, .5R: 0.956522, .75R: 0.652174, count: 23 -Region 23 Avg IOU: 0.705174, Class: 0.186810, Obj: 0.079488, No Obj: 0.000178, .5R: 1.000000, .75R: 0.424242, count: 33 - - 2510: 2.771098, 2.564247 avg loss, 0.001000 rate, 3.034476 seconds, 321280 images -Resizing -480 x 480 - try to allocate workspace = 1144537089 * sizeof(float), CUDA allocate done! -Loaded: 0.345719 seconds -Region 16 Avg IOU: 0.747078, Class: 0.234222, Obj: 0.521391, No Obj: 0.001354, .5R: 1.000000, .75R: 0.428571, count: 7 -Region 23 Avg IOU: 0.691030, Class: 0.217226, Obj: 0.202564, No Obj: 0.000496, .5R: 0.920000, .75R: 0.440000, count: 25 -Region 16 Avg IOU: 0.705791, Class: 0.219409, Obj: 0.340820, No Obj: 0.001686, .5R: 0.882353, .75R: 0.411765, count: 17 -Region 23 Avg IOU: 0.713797, Class: 0.175591, Obj: 0.176710, No Obj: 0.000402, .5R: 0.909091, .75R: 0.454545, count: 22 -Region 16 Avg IOU: 0.725247, Class: 0.217773, Obj: 0.349519, No Obj: 0.001359, .5R: 0.923077, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.686084, Class: 0.144978, Obj: 0.082754, No Obj: 0.000240, .5R: 1.000000, .75R: 0.347826, count: 23 -Region 16 Avg IOU: 0.752910, Class: 0.249274, Obj: 0.295350, No Obj: 0.001291, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.728855, Class: 0.205378, Obj: 0.110185, No Obj: 0.000299, .5R: 1.000000, .75R: 0.400000, count: 20 -Region 16 Avg 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rate, 1.370143 seconds, 321408 images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.690358, Class: 0.156177, Obj: 0.300380, No Obj: 0.001712, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 23 Avg IOU: 0.706859, Class: 0.235140, Obj: 0.315786, No Obj: 0.000532, .5R: 0.909091, .75R: 0.363636, count: 11 -Region 16 Avg IOU: 0.677980, Class: 0.216770, Obj: 0.401840, No Obj: 0.002503, .5R: 0.866667, .75R: 0.266667, count: 15 -Region 23 Avg IOU: 0.691681, Class: 0.155228, Obj: 0.220873, No Obj: 0.000605, .5R: 0.928571, .75R: 0.357143, count: 14 -Region 16 Avg IOU: 0.696336, Class: 0.213556, Obj: 0.299753, No Obj: 0.001746, .5R: 0.928571, .75R: 0.357143, count: 14 -Region 23 Avg IOU: 0.707640, Class: 0.168635, Obj: 0.256350, No Obj: 0.000514, .5R: 1.000000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.724594, Class: 0.227682, Obj: 0.366004, No Obj: 0.002818, .5R: 0.954545, .75R: 0.590909, count: 22 -Region 23 Avg IOU: 0.672539, Class: 0.186634, Obj: 0.078189, No Obj: 0.000495, .5R: 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0.700862, Class: 0.217607, Obj: 0.150831, No Obj: 0.000116, .5R: 0.888889, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.751523, Class: 0.147788, Obj: 0.448999, No Obj: 0.001054, .5R: 1.000000, .75R: 0.666667, count: 18 -Region 23 Avg IOU: 0.692189, Class: 0.157028, Obj: 0.101009, No Obj: 0.000166, .5R: 1.000000, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.690848, Class: 0.192067, Obj: 0.176560, No Obj: 0.001004, .5R: 0.826087, .75R: 0.434783, count: 23 -Region 23 Avg IOU: 0.682678, Class: 0.189418, Obj: 0.177462, No Obj: 0.000156, .5R: 0.846154, .75R: 0.461538, count: 13 - - 2522: 2.612763, 2.487306 avg loss, 0.001000 rate, 4.615616 seconds, 322816 images -Loaded: 0.000060 seconds -Region 16 Avg IOU: 0.703954, Class: 0.230346, Obj: 0.252112, No Obj: 0.000920, .5R: 0.826087, .75R: 0.478261, count: 23 -Region 23 Avg IOU: 0.746893, Class: 0.199740, Obj: 0.110981, No Obj: 0.000138, .5R: 1.000000, .75R: 0.470588, count: 17 -Region 16 Avg IOU: 0.633917, Class: 0.195755, Obj: 0.284103, 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16 -Region 16 Avg IOU: 0.718644, Class: 0.258314, Obj: 0.321400, No Obj: 0.001273, .5R: 0.916667, .75R: 0.541667, count: 24 -Region 23 Avg IOU: 0.723500, Class: 0.221360, Obj: 0.174165, No Obj: 0.000185, .5R: 1.000000, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.673545, Class: 0.143941, Obj: 0.233931, No Obj: 0.000847, .5R: 0.944444, .75R: 0.277778, count: 18 -Region 23 Avg IOU: 0.710509, Class: 0.161777, Obj: 0.139163, No Obj: 0.000159, .5R: 0.933333, .75R: 0.466667, count: 15 -Region 16 Avg IOU: 0.696401, Class: 0.244090, Obj: 0.255358, No Obj: 0.001078, .5R: 0.884615, .75R: 0.423077, count: 26 -Region 23 Avg IOU: 0.714204, Class: 0.197339, Obj: 0.122714, No Obj: 0.000183, .5R: 0.913043, .75R: 0.478261, count: 23 - - 2525: 2.856031, 2.614954 avg loss, 0.001000 rate, 4.924278 seconds, 323200 images -Loaded: 0.000063 seconds -Region 16 Avg IOU: 0.728287, Class: 0.160459, Obj: 0.239275, No Obj: 0.000753, .5R: 0.909091, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.725303, Class: 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No Obj: 0.001138, .5R: 0.806452, .75R: 0.193548, count: 31 -Region 23 Avg IOU: 0.695173, Class: 0.200811, Obj: 0.176781, No Obj: 0.000183, .5R: 0.954545, .75R: 0.318182, count: 22 -Region 16 Avg IOU: 0.690007, Class: 0.216579, Obj: 0.363888, No Obj: 0.001428, .5R: 0.920000, .75R: 0.280000, count: 25 -Region 23 Avg IOU: 0.644946, Class: 0.180285, Obj: 0.186390, No Obj: 0.000164, .5R: 0.866667, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.727634, Class: 0.204480, Obj: 0.282836, No Obj: 0.001136, .5R: 1.000000, .75R: 0.428571, count: 21 -Region 23 Avg IOU: 0.738536, Class: 0.199125, Obj: 0.172831, No Obj: 0.000162, .5R: 1.000000, .75R: 0.470588, count: 17 -Region 16 Avg IOU: 0.670381, Class: 0.180163, Obj: 0.336474, No Obj: 0.000845, .5R: 0.941176, .75R: 0.352941, count: 17 -Region 23 Avg IOU: 0.681870, Class: 0.144714, Obj: 0.116551, No Obj: 0.000156, .5R: 0.850000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.743525, Class: 0.219003, Obj: 0.258087, No Obj: 0.001009, .5R: 1.000000, 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images -Loaded: 0.000063 seconds -Region 16 Avg IOU: 0.706385, Class: 0.197647, Obj: 0.215074, No Obj: 0.000884, .5R: 0.909091, .75R: 0.454545, count: 22 -Region 23 Avg IOU: 0.721241, Class: 0.094672, Obj: 0.177950, No Obj: 0.000068, .5R: 1.000000, .75R: 0.428571, count: 7 -Region 16 Avg IOU: 0.713506, Class: 0.211353, Obj: 0.298496, No Obj: 0.001117, .5R: 0.958333, .75R: 0.416667, count: 24 -Region 23 Avg IOU: 0.681100, Class: 0.189514, Obj: 0.143674, No Obj: 0.000166, .5R: 0.937500, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.690993, Class: 0.204182, Obj: 0.244771, No Obj: 0.000609, .5R: 1.000000, .75R: 0.263158, count: 19 -Region 23 Avg IOU: 0.687060, Class: 0.088241, Obj: 0.169195, No Obj: 0.000114, .5R: 1.000000, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.725046, Class: 0.146859, Obj: 0.221352, No Obj: 0.000777, .5R: 0.956522, .75R: 0.391304, count: 23 -Region 23 Avg IOU: 0.713432, Class: 0.124774, Obj: 0.134830, No Obj: 0.000096, .5R: 0.909091, .75R: 0.545455, count: 11 -Region 16 Avg IOU: 0.628960, Class: 0.202462, Obj: 0.177714, No Obj: 0.000751, .5R: 0.789474, .75R: 0.210526, count: 19 -Region 23 Avg IOU: 0.678916, Class: 0.158585, Obj: 0.122254, No Obj: 0.000066, .5R: 0.875000, .75R: 0.250000, count: 8 -Region 16 Avg IOU: 0.681161, Class: 0.202759, Obj: 0.123352, No Obj: 0.000940, .5R: 0.965517, .75R: 0.379310, count: 29 -Region 23 Avg IOU: 0.669312, Class: 0.173523, Obj: 0.066867, No Obj: 0.000077, .5R: 0.800000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.693127, Class: 0.217535, Obj: 0.198168, No Obj: 0.001236, .5R: 0.970588, .75R: 0.323529, count: 34 -Region 23 Avg IOU: 0.638479, Class: 0.196410, Obj: 0.052477, No Obj: 0.000181, .5R: 0.863636, .75R: 0.227273, count: 22 -Region 16 Avg IOU: 0.673705, Class: 0.208508, Obj: 0.247861, No Obj: 0.000908, .5R: 0.821429, .75R: 0.357143, count: 28 -Region 23 Avg IOU: 0.707391, Class: 0.205648, Obj: 0.084492, No Obj: 0.000113, .5R: 1.000000, .75R: 0.333333, count: 24 - - 2530: 3.010375, 2.742230 avg 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23 Avg IOU: 0.728218, Class: 0.205621, Obj: 0.124772, No Obj: 0.000227, .5R: 1.000000, .75R: 0.428571, count: 14 - - 2531: 2.091925, 2.677200 avg loss, 0.001000 rate, 1.190262 seconds, 323968 images -Loaded: 0.000075 seconds -Region 16 Avg IOU: 0.745895, Class: 0.217423, Obj: 0.450976, No Obj: 0.001245, .5R: 0.900000, .75R: 0.400000, count: 10 -Region 23 Avg IOU: 0.752681, Class: 0.175622, Obj: 0.229165, No Obj: 0.000210, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 16 Avg IOU: 0.760953, Class: 0.232386, Obj: 0.383940, No Obj: 0.001866, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.727696, Class: 0.169155, Obj: 0.211236, No Obj: 0.000407, .5R: 1.000000, .75R: 0.458333, count: 24 -Region 16 Avg IOU: 0.783753, Class: 0.165231, Obj: 0.353476, No Obj: 0.001559, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.682127, Class: 0.208737, Obj: 0.274248, No Obj: 0.000463, .5R: 0.875000, .75R: 0.291667, count: 24 -Region 16 Avg IOU: 0.745164, Class: 0.235711, 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22 -Region 23 Avg IOU: 0.697340, Class: 0.098857, Obj: 0.076079, No Obj: 0.000080, .5R: 0.900000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.748715, Class: 0.245859, Obj: 0.372820, No Obj: 0.001210, .5R: 1.000000, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.739146, Class: 0.183243, Obj: 0.118352, No Obj: 0.000108, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 16 Avg IOU: 0.686459, Class: 0.195426, Obj: 0.252567, No Obj: 0.001051, .5R: 0.961538, .75R: 0.346154, count: 26 -Region 23 Avg IOU: 0.682873, Class: 0.212455, Obj: 0.081037, No Obj: 0.000078, .5R: 0.916667, .75R: 0.250000, count: 12 -Region 16 Avg IOU: 0.716084, Class: 0.189738, Obj: 0.364732, No Obj: 0.001206, .5R: 1.000000, .75R: 0.454545, count: 22 -Region 23 Avg IOU: 0.738403, Class: 0.143750, Obj: 0.115256, No Obj: 0.000080, .5R: 1.000000, .75R: 0.416667, count: 12 - - 2544: 2.780836, 2.703526 avg loss, 0.001000 rate, 4.469704 seconds, 325632 images -Loaded: 0.000071 seconds -Region 16 Avg IOU: 0.710968, Class: 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0.000070 seconds -Region 16 Avg IOU: 0.721390, Class: 0.232031, Obj: 0.259285, No Obj: 0.000941, .5R: 0.941176, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 0.732192, Class: 0.170785, Obj: 0.345776, No Obj: 0.000470, .5R: 0.958333, .75R: 0.458333, count: 24 -Region 16 Avg IOU: 0.738390, Class: 0.237040, Obj: 0.307632, No Obj: 0.001025, .5R: 1.000000, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.752274, Class: 0.250671, Obj: 0.337840, No Obj: 0.000291, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 16 Avg IOU: 0.714948, Class: 0.216014, Obj: 0.271019, No Obj: 0.000965, .5R: 1.000000, .75R: 0.384615, count: 13 -Region 23 Avg IOU: 0.644302, Class: 0.140752, Obj: 0.204682, No Obj: 0.000356, .5R: 0.857143, .75R: 0.142857, count: 14 -Region 16 Avg IOU: 0.755409, Class: 0.237869, Obj: 0.403700, No Obj: 0.001450, .5R: 1.000000, .75R: 0.590909, count: 22 -Region 23 Avg IOU: 0.757029, Class: 0.205881, Obj: 0.452084, No Obj: 0.000448, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 16 Avg IOU: 0.716382, Class: 0.183474, Obj: 0.226484, No Obj: 0.001234, .5R: 0.960000, .75R: 0.400000, count: 25 -Region 23 Avg IOU: 0.703305, Class: 0.119410, Obj: 0.127249, No Obj: 0.000495, .5R: 0.923077, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.760666, Class: 0.275319, Obj: 0.299778, No Obj: 0.001109, .5R: 0.962963, .75R: 0.629630, count: 27 -Region 23 Avg IOU: 0.736765, Class: 0.203439, Obj: 0.368219, No Obj: 0.000418, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 16 Avg IOU: 0.691585, Class: 0.211525, Obj: 0.284185, No Obj: 0.000960, .5R: 0.954545, .75R: 0.318182, count: 22 -Region 23 Avg IOU: 0.704316, Class: 0.198870, Obj: 0.263100, No Obj: 0.000432, .5R: 0.904762, .75R: 0.333333, count: 21 -Region 16 Avg IOU: 0.711723, Class: 0.246570, Obj: 0.340429, No Obj: 0.001228, .5R: 0.954545, .75R: 0.409091, count: 22 -Region 23 Avg IOU: 0.718142, Class: 0.180890, Obj: 0.188708, No Obj: 0.000237, .5R: 1.000000, .75R: 0.444444, count: 9 - - 2546: 2.460618, 2.680490 avg loss, 0.001000 rate, 4.034817 seconds, 325888 images -Loaded: 0.000074 seconds -Region 16 Avg IOU: 0.655142, Class: 0.197532, Obj: 0.155580, No Obj: 0.001179, .5R: 0.828571, .75R: 0.371429, count: 35 -Region 23 Avg IOU: 0.670326, Class: 0.176087, Obj: 0.056319, No Obj: 0.000186, .5R: 0.944444, .75R: 0.222222, count: 18 -Region 16 Avg IOU: 0.669310, Class: 0.215907, Obj: 0.211523, No Obj: 0.001114, .5R: 0.869565, .75R: 0.391304, count: 23 -Region 23 Avg IOU: 0.651302, Class: 0.135561, Obj: 0.126097, No Obj: 0.000242, .5R: 1.000000, .75R: 0.111111, count: 9 -Region 16 Avg IOU: 0.688374, Class: 0.217764, Obj: 0.132177, No Obj: 0.000924, .5R: 0.931035, .75R: 0.379310, count: 29 -Region 23 Avg IOU: 0.745703, Class: 0.202397, Obj: 0.109003, No Obj: 0.000236, .5R: 1.000000, .75R: 0.590909, count: 22 -Region 16 Avg IOU: 0.736850, Class: 0.242971, Obj: 0.253712, No Obj: 0.001248, .5R: 0.909091, .75R: 0.590909, count: 22 -Region 23 Avg IOU: 0.705555, Class: 0.187479, Obj: 0.163093, No Obj: 0.000247, .5R: 0.888889, .75R: 0.444444, count: 18 -Region 16 Avg IOU: 0.694975, Class: 0.230347, Obj: 0.174780, No Obj: 0.001305, .5R: 0.896552, .75R: 0.448276, count: 29 -Region 23 Avg IOU: 0.727015, Class: 0.209723, Obj: 0.132248, No Obj: 0.000253, .5R: 0.909091, .75R: 0.545455, count: 11 -Region 16 Avg IOU: 0.725531, Class: 0.189770, Obj: 0.275545, No Obj: 0.000776, .5R: 1.000000, .75R: 0.357143, count: 14 -Region 23 Avg IOU: 0.676889, Class: 0.200580, Obj: 0.119643, No Obj: 0.000277, .5R: 0.900000, .75R: 0.300000, count: 30 -Region 16 Avg IOU: 0.746871, Class: 0.243900, Obj: 0.312701, No Obj: 0.001063, .5R: 1.000000, .75R: 0.523810, count: 21 -Region 23 Avg IOU: 0.688416, Class: 0.287973, Obj: 0.263819, No Obj: 0.000173, .5R: 1.000000, .75R: 0.166667, count: 6 -Region 16 Avg IOU: 0.725949, Class: 0.199183, Obj: 0.217577, No Obj: 0.000943, .5R: 0.956522, .75R: 0.521739, count: 23 -Region 23 Avg IOU: 0.710853, Class: 0.143435, Obj: 0.178247, No Obj: 0.000224, .5R: 0.909091, .75R: 0.272727, count: 11 - - 2547: 2.983079, 2.710749 avg loss, 0.001000 rate, 4.114818 seconds, 326016 images -Loaded: 0.000062 seconds -Region 16 Avg IOU: 0.678609, Class: 0.209090, Obj: 0.232460, No Obj: 0.001427, .5R: 0.958333, .75R: 0.208333, count: 24 -Region 23 Avg IOU: 0.687363, Class: 0.125533, Obj: 0.060609, No Obj: 0.000184, .5R: 0.925926, .75R: 0.333333, count: 27 -Region 16 Avg IOU: 0.708880, Class: 0.281852, Obj: 0.151743, No Obj: 0.000909, .5R: 1.000000, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.681133, Class: 0.185263, Obj: 0.035644, No Obj: 0.000113, .5R: 0.916667, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.684121, Class: 0.283119, Obj: 0.331054, No Obj: 0.001019, .5R: 0.941176, .75R: 0.411765, count: 17 -Region 23 Avg IOU: 0.742841, Class: 0.215683, Obj: 0.108561, No Obj: 0.000100, .5R: 0.916667, .75R: 0.583333, count: 12 -Region 16 Avg IOU: 0.701430, Class: 0.139977, Obj: 0.191232, No Obj: 0.000837, .5R: 1.000000, .75R: 0.294118, count: 17 -Region 23 Avg IOU: 0.694695, Class: 0.088448, Obj: 0.114487, No Obj: 0.000161, .5R: 0.947368, .75R: 0.263158, count: 19 -Region 16 Avg IOU: 0.737524, Class: 0.201091, Obj: 0.358803, No Obj: 0.001671, .5R: 0.969697, .75R: 0.545455, count: 33 -Region 23 Avg IOU: 0.693645, Class: 0.147428, Obj: 0.125659, No Obj: 0.000284, .5R: 0.918919, .75R: 0.405405, count: 37 -Region 16 Avg IOU: 0.727997, Class: 0.234498, Obj: 0.302017, No Obj: 0.000988, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.671178, Class: 0.076842, Obj: 0.168132, No Obj: 0.000114, .5R: 1.000000, .75R: 0.272727, count: 11 -Region 16 Avg IOU: 0.746448, Class: 0.223500, Obj: 0.158345, No Obj: 0.001656, .5R: 1.000000, .75R: 0.529412, count: 34 -Region 23 Avg IOU: 0.703861, Class: 0.200711, Obj: 0.145220, No Obj: 0.000093, .5R: 1.000000, .75R: 0.428571, count: 7 -Region 16 Avg IOU: 0.719677, Class: 0.231171, Obj: 0.344378, No Obj: 0.001523, .5R: 0.947368, .75R: 0.394737, count: 38 -Region 23 Avg IOU: 0.731474, Class: 0.157143, Obj: 0.119191, No Obj: 0.000184, .5R: 1.000000, .75R: 0.428571, count: 21 - - 2548: 3.078818, 2.747556 avg loss, 0.001000 rate, 4.115006 seconds, 326144 images -Loaded: 0.000077 seconds -Region 16 Avg IOU: 0.743283, Class: 0.259995, Obj: 0.233424, No Obj: 0.001360, .5R: 0.956522, .75R: 0.565217, count: 23 -Region 23 Avg IOU: 0.682187, Class: 0.202924, Obj: 0.121472, No Obj: 0.000168, .5R: 0.958333, .75R: 0.291667, count: 24 -Region 16 Avg IOU: 0.733144, Class: 0.160391, Obj: 0.180855, No Obj: 0.001157, .5R: 1.000000, .75R: 0.454545, count: 22 -Region 23 Avg IOU: 0.708255, Class: 0.146413, Obj: 0.162254, No Obj: 0.000071, .5R: 1.000000, .75R: 0.363636, count: 11 -Region 16 Avg IOU: 0.768865, Class: 0.267372, Obj: 0.363284, No Obj: 0.001327, .5R: 1.000000, .75R: 0.476190, count: 21 -Region 23 Avg IOU: 0.712518, Class: 0.175104, Obj: 0.096864, No Obj: 0.000117, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.722117, Class: 0.207077, Obj: 0.291481, No Obj: 0.001449, .5R: 0.966667, .75R: 0.366667, count: 30 -Region 23 Avg IOU: 0.753510, Class: 0.238374, Obj: 0.163106, No Obj: 0.000140, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 16 Avg IOU: 0.663594, Class: 0.148714, Obj: 0.462781, No Obj: 0.001242, .5R: 0.916667, .75R: 0.333333, count: 12 -Region 23 Avg IOU: 0.603446, Class: 0.171682, Obj: 0.082296, No Obj: 0.000067, .5R: 0.777778, .75R: 0.222222, count: 9 -Region 16 Avg IOU: 0.723531, Class: 0.229202, Obj: 0.263243, No Obj: 0.001492, .5R: 0.962963, .75R: 0.444444, count: 27 -Region 23 Avg IOU: 0.677674, Class: 0.220110, Obj: 0.104079, No Obj: 0.000169, .5R: 0.857143, .75R: 0.464286, count: 28 -Region 16 Avg IOU: 0.773978, Class: 0.240510, Obj: 0.341308, No Obj: 0.001479, .5R: 0.952381, .75R: 0.761905, count: 21 -Region 23 Avg IOU: 0.693836, Class: 0.202669, Obj: 0.193307, No Obj: 0.000117, .5R: 0.812500, .75R: 0.562500, count: 16 -Region 16 Avg IOU: 0.733077, Class: 0.258821, Obj: 0.403573, No Obj: 0.001192, .5R: 1.000000, .75R: 0.578947, count: 19 -Region 23 Avg IOU: 0.738265, Class: 0.190711, Obj: 0.179672, No Obj: 0.000117, .5R: 1.000000, .75R: 0.500000, count: 12 - - 2549: 2.633958, 2.736196 avg loss, 0.001000 rate, 4.830798 seconds, 326272 images -Loaded: 0.000074 seconds -Region 16 Avg IOU: 0.740441, Class: 0.181817, Obj: 0.270112, No Obj: 0.001421, .5R: 0.950000, .75R: 0.650000, count: 20 -Region 23 Avg IOU: 0.676975, Class: 0.097153, Obj: 0.188021, No Obj: 0.000177, .5R: 0.785714, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.754509, Class: 0.240286, Obj: 0.292040, No Obj: 0.001610, .5R: 0.925926, .75R: 0.592593, count: 27 -Region 23 Avg IOU: 0.736730, Class: 0.201664, Obj: 0.122913, No Obj: 0.000175, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 16 Avg IOU: 0.752191, Class: 0.252208, Obj: 0.353713, No Obj: 0.001181, .5R: 1.000000, .75R: 0.650000, count: 20 -Region 23 Avg IOU: 0.713245, Class: 0.214633, Obj: 0.114078, No Obj: 0.000117, .5R: 0.923077, .75R: 0.461538, count: 13 -Region 16 Avg IOU: 0.711033, Class: 0.190611, Obj: 0.202030, No Obj: 0.001492, .5R: 0.931035, .75R: 0.379310, count: 29 -Region 23 Avg IOU: 0.714964, Class: 0.220811, Obj: 0.107857, No Obj: 0.000115, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 16 Avg IOU: 0.668663, Class: 0.240797, Obj: 0.255177, No Obj: 0.001173, .5R: 0.857143, .75R: 0.380952, count: 21 -Region 23 Avg IOU: 0.603238, Class: 0.133563, Obj: 0.115260, No Obj: 0.000109, .5R: 0.750000, .75R: 0.500000, count: 8 -Region 16 Avg IOU: 0.735472, Class: 0.270947, Obj: 0.419318, No Obj: 0.001501, .5R: 0.933333, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.725112, Class: 0.186862, Obj: 0.137772, No Obj: 0.000166, .5R: 0.950000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.738660, Class: 0.229705, Obj: 0.384214, No Obj: 0.001164, .5R: 1.000000, .75R: 0.421053, count: 19 -Region 23 Avg IOU: 0.714802, Class: 0.188276, Obj: 0.145639, No Obj: 0.000109, .5R: 1.000000, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.705876, Class: 0.167711, Obj: 0.266732, No Obj: 0.000907, .5R: 0.928571, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.650059, Class: 0.154784, Obj: 0.091242, No Obj: 0.000095, .5R: 0.916667, .75R: 0.333333, count: 12 - - 2550: 2.596416, 2.722218 avg loss, 0.001000 rate, 2.697022 seconds, 326400 images -Resizing -384 x 384 - try to allocate workspace = 216268801 * sizeof(float), CUDA allocate done! -Loaded: 0.350634 seconds -Region 16 Avg IOU: 0.754348, Class: 0.210614, Obj: 0.379479, No Obj: 0.002498, .5R: 0.947368, .75R: 0.684211, count: 19 -Region 23 Avg IOU: 0.711100, Class: 0.225245, Obj: 0.137248, No Obj: 0.000433, .5R: 0.923077, .75R: 0.423077, count: 26 -Region 16 Avg IOU: 0.681547, Class: 0.292843, Obj: 0.527027, No Obj: 0.001058, .5R: 0.750000, .75R: 0.250000, count: 4 -Region 23 Avg IOU: 0.675261, Class: 0.212278, Obj: 0.201332, No Obj: 0.000545, .5R: 1.000000, .75R: 0.230769, count: 26 -Region 16 Avg IOU: 0.769936, Class: 0.311415, Obj: 0.542307, No Obj: 0.001616, .5R: 1.000000, .75R: 0.857143, count: 7 -Region 23 Avg IOU: 0.697725, Class: 0.235800, Obj: 0.095770, No Obj: 0.000278, .5R: 0.882353, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.783127, Class: 0.271578, Obj: 0.395218, No Obj: 0.001311, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.717247, Class: 0.149220, Obj: 0.124768, No Obj: 0.000303, .5R: 0.923077, .75R: 0.461538, count: 13 -Region 16 Avg IOU: 0.741481, Class: 0.226834, Obj: 0.448285, No Obj: 0.001769, .5R: 0.888889, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.639299, Class: 0.189533, Obj: 0.160720, No Obj: 0.000411, .5R: 0.687500, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.767717, Class: 0.174095, Obj: 0.219334, No Obj: 0.001921, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.687307, Class: 0.222962, Obj: 0.175246, No Obj: 0.000569, .5R: 1.000000, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.677177, Class: 0.223853, Obj: 0.365068, No Obj: 0.002092, .5R: 0.777778, .75R: 0.444444, count: 9 -Region 23 Avg IOU: 0.672368, Class: 0.180378, Obj: 0.077030, No Obj: 0.000428, .5R: 0.944444, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.720018, Class: 0.266022, Obj: 0.451632, No Obj: 0.001017, .5R: 1.000000, .75R: 0.428571, count: 7 -Region 23 Avg IOU: 0.719594, Class: 0.196418, Obj: 0.292635, No Obj: 0.000622, .5R: 1.000000, .75R: 0.421053, count: 19 - - 2551: 1.831436, 2.633140 avg loss, 0.001000 rate, 0.652041 seconds, 326528 images -Loaded: 0.000061 seconds -Region 16 Avg IOU: 0.776430, Class: 0.278349, Obj: 0.327796, No Obj: 0.001471, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.682945, Class: 0.176999, Obj: 0.246538, No Obj: 0.000660, .5R: 0.866667, .75R: 0.400000, count: 15 -Region 16 Avg IOU: 0.699984, Class: 0.346866, Obj: 0.463527, No Obj: 0.001568, .5R: 1.000000, .75R: 0.250000, count: 4 -Region 23 Avg IOU: 0.703946, Class: 0.197753, Obj: 0.330562, No Obj: 0.000715, .5R: 1.000000, .75R: 0.272727, count: 11 -Region 16 Avg IOU: 0.775522, Class: 0.187209, Obj: 0.302564, No Obj: 0.001357, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.690973, Class: 0.168379, Obj: 0.182314, No Obj: 0.000849, .5R: 0.892857, .75R: 0.285714, count: 28 -Region 16 Avg IOU: 0.789811, Class: 0.204541, Obj: 0.454121, No Obj: 0.001836, .5R: 1.000000, .75R: 0.833333, count: 12 -Region 23 Avg IOU: 0.704743, Class: 0.222515, Obj: 0.215080, No Obj: 0.000577, .5R: 0.950000, .75R: 0.250000, count: 20 -Region 16 Avg IOU: 0.796543, Class: 0.130423, Obj: 0.271017, No Obj: 0.001274, .5R: 1.000000, .75R: 0.857143, count: 7 -Region 23 Avg IOU: 0.668708, Class: 0.197796, Obj: 0.185692, No Obj: 0.000745, .5R: 0.894737, .75R: 0.368421, count: 19 -Region 16 Avg IOU: 0.777536, Class: 0.244573, Obj: 0.488032, No Obj: 0.001539, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.720161, Class: 0.225957, Obj: 0.365379, No Obj: 0.000976, .5R: 0.923077, .75R: 0.538462, count: 13 -Region 16 Avg IOU: 0.736703, Class: 0.178729, Obj: 0.170429, No Obj: 0.001234, .5R: 1.000000, .75R: 0.357143, count: 14 -Region 23 Avg IOU: 0.700630, Class: 0.186638, Obj: 0.156366, No Obj: 0.000541, .5R: 0.965517, .75R: 0.413793, count: 29 -Region 16 Avg IOU: 0.662980, Class: 0.225241, Obj: 0.384738, No Obj: 0.001362, .5R: 0.857143, .75R: 0.285714, count: 7 -Region 23 Avg IOU: 0.703719, Class: 0.169850, Obj: 0.219161, No Obj: 0.000896, .5R: 0.928571, .75R: 0.392857, count: 28 - - 2552: 1.986134, 2.568439 avg loss, 0.001000 rate, 0.678291 seconds, 326656 images -Loaded: 0.000090 seconds -Region 16 Avg IOU: 0.776792, Class: 0.229092, Obj: 0.420538, No Obj: 0.001183, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.703736, Class: 0.210358, Obj: 0.358904, No Obj: 0.000857, .5R: 1.000000, .75R: 0.230769, count: 13 -Region 16 Avg IOU: 0.861324, Class: 0.365331, Obj: 0.592986, No Obj: 0.001191, .5R: 1.000000, .75R: 1.000000, count: 6 -Region 23 Avg IOU: 0.681536, Class: 0.217561, Obj: 0.314205, No Obj: 0.000966, .5R: 0.842105, .75R: 0.473684, count: 19 -Region 16 Avg IOU: 0.830039, Class: 0.232056, Obj: 0.418508, No 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0.727273, count: 11 -Region 23 Avg IOU: 0.662561, Class: 0.212009, Obj: 0.329612, No Obj: 0.001319, .5R: 0.875000, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.715834, Class: 0.162379, Obj: 0.174175, No Obj: 0.001351, .5R: 0.941176, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 0.679206, Class: 0.240536, Obj: 0.310084, No Obj: 0.001316, .5R: 0.870968, .75R: 0.387097, count: 31 - - 2553: 1.718820, 2.483478 avg loss, 0.001000 rate, 0.667016 seconds, 326784 images -Loaded: 0.026730 seconds -Region 16 Avg IOU: 0.772258, Class: 0.255483, Obj: 0.439869, No Obj: 0.001319, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.742241, Class: 0.149472, Obj: 0.362699, No Obj: 0.000878, .5R: 1.000000, .75R: 0.363636, count: 11 -Region 16 Avg IOU: 0.746073, Class: 0.197609, Obj: 0.200340, No Obj: 0.001736, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.704719, Class: 0.185568, Obj: 0.219365, No Obj: 0.001902, .5R: 0.935484, .75R: 0.451613, count: 31 -Region 16 Avg IOU: 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count: 12 -Region 23 Avg IOU: 0.677163, Class: 0.216347, Obj: 0.123737, No Obj: 0.000783, .5R: 0.933333, .75R: 0.333333, count: 30 -Region 16 Avg IOU: 0.768877, Class: 0.233730, Obj: 0.386053, No Obj: 0.001869, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.697869, Class: 0.233547, Obj: 0.105197, No Obj: 0.000345, .5R: 1.000000, .75R: 0.250000, count: 20 -Region 16 Avg IOU: 0.782403, Class: 0.274999, Obj: 0.348577, No Obj: 0.000949, .5R: 1.000000, .75R: 0.600000, count: 5 -Region 23 Avg IOU: 0.693909, Class: 0.216479, Obj: 0.137262, No Obj: 0.000280, .5R: 1.000000, .75R: 0.142857, count: 14 - - 2558: 1.833933, 2.204355 avg loss, 0.001000 rate, 0.671739 seconds, 327424 images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.731822, Class: 0.273537, Obj: 0.301132, No Obj: 0.001131, .5R: 1.000000, .75R: 0.444444, count: 9 -Region 23 Avg IOU: 0.657879, Class: 0.253820, Obj: 0.208474, No Obj: 0.000671, .5R: 0.800000, .75R: 0.160000, count: 25 -Region 16 Avg IOU: 0.763897, Class: 0.266914, Obj: 0.368937, No Obj: 0.001679, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.741033, Class: 0.283072, Obj: 0.403961, No Obj: 0.000598, .5R: 1.000000, .75R: 0.428571, count: 7 -Region 16 Avg IOU: 0.744824, Class: 0.270902, Obj: 0.391343, No Obj: 0.001365, .5R: 1.000000, .75R: 0.428571, count: 7 -Region 23 Avg IOU: 0.721689, Class: 0.171775, Obj: 0.289842, No Obj: 0.000943, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.724966, Class: 0.321555, Obj: 0.567226, No Obj: 0.001539, .5R: 1.000000, .75R: 0.750000, count: 4 -Region 23 Avg IOU: 0.716784, Class: 0.195212, Obj: 0.221502, No Obj: 0.000517, .5R: 1.000000, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.577973, Class: 0.182607, Obj: 0.392694, No Obj: 0.001018, .5R: 0.600000, .75R: 0.200000, count: 5 -Region 23 Avg IOU: 0.737100, Class: 0.226024, Obj: 0.360606, No Obj: 0.000469, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 16 Avg IOU: 0.775553, Class: 0.238844, Obj: 0.338284, No 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count: 19 -Region 23 Avg IOU: 0.682954, Class: 0.177979, Obj: 0.304660, No Obj: 0.000706, .5R: 0.928571, .75R: 0.285714, count: 14 -Region 16 Avg IOU: 0.680580, Class: 0.235516, Obj: 0.290367, No Obj: 0.001207, .5R: 0.818182, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.730884, Class: 0.214995, Obj: 0.355597, No Obj: 0.000737, .5R: 0.941176, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.664127, Class: 0.206512, Obj: 0.323492, No Obj: 0.001323, .5R: 0.833333, .75R: 0.333333, count: 12 -Region 23 Avg IOU: 0.641236, Class: 0.189192, Obj: 0.320401, No Obj: 0.000502, .5R: 0.857143, .75R: 0.238095, count: 21 -Region 16 Avg IOU: 0.707925, Class: 0.200186, Obj: 0.278604, No Obj: 0.001426, .5R: 1.000000, .75R: 0.380952, count: 21 -Region 23 Avg IOU: 0.673716, Class: 0.196824, Obj: 0.349120, No Obj: 0.001165, .5R: 0.875000, .75R: 0.343750, count: 32 - - 2561: 3.030684, 2.201648 avg loss, 0.001000 rate, 2.267477 seconds, 327808 images -Loaded: 0.000072 seconds -Region 16 Avg IOU: 0.682094, 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0.000059 seconds -Region 16 Avg IOU: 0.668451, Class: 0.224332, Obj: 0.252973, No Obj: 0.001262, .5R: 0.900000, .75R: 0.350000, count: 20 -Region 23 Avg IOU: 0.665770, Class: 0.258280, Obj: 0.068465, No Obj: 0.000127, .5R: 0.863636, .75R: 0.227273, count: 22 -Region 16 Avg IOU: 0.692873, Class: 0.284483, Obj: 0.285611, No Obj: 0.001355, .5R: 0.941176, .75R: 0.411765, count: 17 -Region 23 Avg IOU: 0.730259, Class: 0.195820, Obj: 0.132627, No Obj: 0.000157, .5R: 1.000000, .75R: 0.476190, count: 21 -Region 16 Avg IOU: 0.770425, Class: 0.167356, Obj: 0.209507, No Obj: 0.000985, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.720671, Class: 0.187146, Obj: 0.069518, No Obj: 0.000135, .5R: 1.000000, .75R: 0.500000, count: 18 -Region 16 Avg IOU: 0.702514, Class: 0.177289, Obj: 0.262332, No Obj: 0.001931, .5R: 0.933333, .75R: 0.333333, count: 30 -Region 23 Avg IOU: 0.694101, Class: 0.181314, Obj: 0.053474, No Obj: 0.000109, .5R: 1.000000, .75R: 0.409091, count: 22 -Region 16 Avg 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0.190097, Obj: 0.378933, No Obj: 0.000861, .5R: 0.882353, .75R: 0.352941, count: 34 -Region 16 Avg IOU: 0.765304, Class: 0.169108, Obj: 0.377217, No Obj: 0.000984, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.718712, Class: 0.182829, Obj: 0.238923, No Obj: 0.000563, .5R: 1.000000, .75R: 0.400000, count: 15 -Region 16 Avg IOU: 0.744438, Class: 0.199394, Obj: 0.227490, No Obj: 0.000963, .5R: 0.909091, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.685102, Class: 0.234113, Obj: 0.320782, No Obj: 0.000464, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.725345, Class: 0.148523, Obj: 0.241966, No Obj: 0.001693, .5R: 1.000000, .75R: 0.521739, count: 23 -Region 23 Avg IOU: 0.729967, Class: 0.191668, Obj: 0.335099, No Obj: 0.000676, .5R: 1.000000, .75R: 0.476190, count: 21 -Region 16 Avg IOU: 0.733733, Class: 0.212625, Obj: 0.306020, No Obj: 0.001324, .5R: 0.941176, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 0.716296, Class: 0.167793, Obj: 0.222899, No Obj: 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14 -Region 16 Avg IOU: 0.775512, Class: 0.291326, Obj: 0.386512, No Obj: 0.000986, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.695859, Class: 0.265836, Obj: 0.267041, No Obj: 0.000266, .5R: 0.888889, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.774863, Class: 0.253677, Obj: 0.332410, No Obj: 0.002109, .5R: 1.000000, .75R: 0.625000, count: 16 -Region 23 Avg IOU: 0.703078, Class: 0.202219, Obj: 0.143591, No Obj: 0.000237, .5R: 0.944444, .75R: 0.444444, count: 18 -Region 16 Avg IOU: 0.779511, Class: 0.223718, Obj: 0.514199, No Obj: 0.002100, .5R: 0.923077, .75R: 0.769231, count: 13 -Region 23 Avg IOU: 0.683680, Class: 0.122216, Obj: 0.192226, No Obj: 0.000181, .5R: 0.916667, .75R: 0.250000, count: 12 - - 2577: 1.981504, 2.336665 avg loss, 0.001000 rate, 2.305692 seconds, 329856 images -Loaded: 0.000067 seconds -Region 16 Avg IOU: 0.800380, Class: 0.274990, Obj: 0.387612, No Obj: 0.001848, .5R: 1.000000, .75R: 0.733333, count: 15 -Region 23 Avg IOU: 0.680231, Class: 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count: 9 -Region 23 Avg IOU: 0.705638, Class: 0.174944, Obj: 0.346338, No Obj: 0.000437, .5R: 0.888889, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.755242, Class: 0.266577, Obj: 0.470601, No Obj: 0.001601, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.718502, Class: 0.187528, Obj: 0.120589, No Obj: 0.000312, .5R: 1.000000, .75R: 0.409091, count: 22 -Region 16 Avg IOU: 0.766303, Class: 0.245460, Obj: 0.454949, No Obj: 0.002013, .5R: 1.000000, .75R: 0.647059, count: 17 -Region 23 Avg IOU: 0.672876, Class: 0.147624, Obj: 0.151467, No Obj: 0.000360, .5R: 0.913043, .75R: 0.217391, count: 23 -Region 16 Avg IOU: 0.704841, Class: 0.185537, Obj: 0.330687, No Obj: 0.002218, .5R: 0.869565, .75R: 0.391304, count: 23 -Region 23 Avg IOU: 0.723836, Class: 0.188577, Obj: 0.171252, No Obj: 0.000436, .5R: 0.961538, .75R: 0.461538, count: 26 -Region 16 Avg IOU: 0.710322, Class: 0.195934, Obj: 0.380706, No Obj: 0.000899, .5R: 0.833333, .75R: 0.500000, count: 6 -Region 23 Avg IOU: 0.685299, Class: 0.205788, Obj: 0.193787, No Obj: 0.000306, .5R: 1.000000, .75R: 0.181818, count: 11 -Region 16 Avg IOU: 0.716764, Class: 0.212440, Obj: 0.263184, No Obj: 0.002292, .5R: 1.000000, .75R: 0.388889, count: 18 -Region 23 Avg IOU: 0.757641, Class: 0.191460, Obj: 0.114671, No Obj: 0.000355, .5R: 1.000000, .75R: 0.565217, count: 23 -Region 16 Avg IOU: 0.800526, Class: 0.291523, Obj: 0.427782, No Obj: 0.001553, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.677149, Class: 0.193255, Obj: 0.280262, No Obj: 0.000402, .5R: 0.937500, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.725674, Class: 0.208573, Obj: 0.280591, No Obj: 0.001718, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.688628, Class: 0.214675, Obj: 0.204319, No Obj: 0.000432, .5R: 0.935484, .75R: 0.354839, count: 31 - - 2579: 2.486463, 2.348446 avg loss, 0.001000 rate, 2.940457 seconds, 330112 images -Loaded: 0.000069 seconds -Region 16 Avg IOU: 0.673415, Class: 0.167358, Obj: 0.336119, No Obj: 0.001136, .5R: 0.900000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.665775, Class: 0.122888, Obj: 0.175409, No Obj: 0.000567, .5R: 0.818182, .75R: 0.363636, count: 11 -Region 16 Avg IOU: 0.777918, Class: 0.165363, Obj: 0.353482, No Obj: 0.001395, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.686625, Class: 0.187538, Obj: 0.350771, No Obj: 0.000672, .5R: 0.950000, .75R: 0.250000, count: 20 -Region 16 Avg IOU: 0.649249, Class: 0.260908, Obj: 0.236185, No Obj: 0.001660, .5R: 0.769231, .75R: 0.384615, count: 13 -Region 23 Avg IOU: 0.685604, Class: 0.194422, Obj: 0.376001, No Obj: 0.001005, .5R: 0.850000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.684860, Class: 0.212202, Obj: 0.422024, No Obj: 0.001640, .5R: 1.000000, .75R: 0.357143, count: 14 -Region 23 Avg IOU: 0.719392, Class: 0.229220, Obj: 0.359512, No Obj: 0.000907, .5R: 1.000000, .75R: 0.380952, count: 21 -Region 16 Avg IOU: 0.744862, Class: 0.230204, Obj: 0.493217, No Obj: 0.002403, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.714009, Class: 0.230168, Obj: 0.294031, No Obj: 0.000823, .5R: 0.956522, .75R: 0.347826, count: 23 -Region 16 Avg IOU: 0.717969, Class: 0.200168, Obj: 0.338199, No Obj: 0.002326, .5R: 1.000000, .75R: 0.560000, count: 25 -Region 23 Avg IOU: 0.711946, Class: 0.196540, Obj: 0.244160, No Obj: 0.000940, .5R: 0.933333, .75R: 0.433333, count: 30 -Region 16 Avg IOU: 0.760419, Class: 0.239092, Obj: 0.468961, No Obj: 0.001536, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.721397, Class: 0.172474, Obj: 0.192036, No Obj: 0.000685, .5R: 1.000000, .75R: 0.375000, count: 24 -Region 16 Avg IOU: 0.803228, Class: 0.220191, Obj: 0.440547, No Obj: 0.001186, .5R: 1.000000, .75R: 0.777778, count: 9 -Region 23 Avg IOU: 0.715841, Class: 0.161303, Obj: 0.391703, No Obj: 0.000515, .5R: 0.923077, .75R: 0.461538, count: 13 - - 2580: 2.479786, 2.361580 avg loss, 0.001000 rate, 2.152889 seconds, 330240 images -Resizing -640 x 640 - try to allocate workspace = 1147404289 * sizeof(float), CUDA allocate done! -Loaded: 0.229625 seconds -Region 16 Avg IOU: 0.755448, Class: 0.204128, Obj: 0.286141, No Obj: 0.001052, .5R: 0.947368, .75R: 0.684211, count: 19 -Region 23 Avg IOU: 0.671929, Class: 0.208053, Obj: 0.260593, No Obj: 0.000321, .5R: 0.857143, .75R: 0.357143, count: 14 -Region 16 Avg IOU: 0.729541, Class: 0.176981, Obj: 0.267745, No Obj: 0.001034, .5R: 1.000000, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.655206, Class: 0.146358, Obj: 0.367661, No Obj: 0.000321, .5R: 0.846154, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.726666, Class: 0.229362, Obj: 0.300401, No Obj: 0.001154, .5R: 0.941176, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.688571, Class: 0.209935, Obj: 0.188014, No Obj: 0.000349, .5R: 0.928571, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.725885, Class: 0.235558, Obj: 0.260020, No Obj: 0.001496, .5R: 0.923077, .75R: 0.500000, count: 26 -Region 23 Avg IOU: 0.716513, Class: 0.164292, Obj: 0.271677, No 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.75R: 0.400000, count: 15 - - 2581: 2.716898, 2.397112 avg loss, 0.001000 rate, 2.623467 seconds, 330368 images -Loaded: 0.000090 seconds -Region 16 Avg IOU: 0.744954, Class: 0.201791, Obj: 0.282150, No Obj: 0.000608, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.640440, Class: 0.127257, Obj: 0.140433, No Obj: 0.000153, .5R: 0.875000, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.682258, Class: 0.182958, Obj: 0.278502, No Obj: 0.000928, .5R: 0.913043, .75R: 0.260870, count: 23 -Region 23 Avg IOU: 0.674392, Class: 0.090946, Obj: 0.179872, No Obj: 0.000196, .5R: 0.894737, .75R: 0.263158, count: 19 -Region 16 Avg IOU: 0.774471, Class: 0.158966, Obj: 0.333365, No Obj: 0.000787, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.727163, Class: 0.159997, Obj: 0.098701, No Obj: 0.000207, .5R: 1.000000, .75R: 0.380952, count: 21 -Region 16 Avg IOU: 0.712967, Class: 0.237857, Obj: 0.257960, No Obj: 0.001189, .5R: 1.000000, .75R: 0.416667, count: 24 -Region 23 Avg IOU: 0.712616, Class: 0.192878, Obj: 0.251898, No Obj: 0.000397, .5R: 1.000000, .75R: 0.448276, count: 29 -Region 16 Avg IOU: 0.734237, Class: 0.222148, Obj: 0.257821, No Obj: 0.000909, .5R: 0.954545, .75R: 0.363636, count: 22 -Region 23 Avg IOU: 0.689775, Class: 0.200924, Obj: 0.181764, No Obj: 0.000246, .5R: 1.000000, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.778206, Class: 0.229756, Obj: 0.213056, No Obj: 0.000733, .5R: 1.000000, .75R: 0.600000, count: 20 -Region 23 Avg IOU: 0.763731, Class: 0.140520, Obj: 0.292821, No Obj: 0.000140, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 16 Avg IOU: 0.691044, Class: 0.197784, Obj: 0.184200, No Obj: 0.001427, .5R: 0.864865, .75R: 0.432432, count: 37 -Region 23 Avg IOU: 0.698371, Class: 0.186611, Obj: 0.111781, No Obj: 0.000248, .5R: 0.950000, .75R: 0.250000, count: 20 -Region 16 Avg IOU: 0.755367, Class: 0.248930, Obj: 0.283375, No Obj: 0.000845, .5R: 0.916667, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.622106, Class: 0.164017, Obj: 0.208013, No Obj: 0.000185, .5R: 0.750000, .75R: 0.250000, count: 16 - - 2582: 2.816867, 2.439088 avg loss, 0.001000 rate, 3.369951 seconds, 330496 images -Loaded: 0.000082 seconds -Region 16 Avg IOU: 0.777029, Class: 0.237635, Obj: 0.274297, No Obj: 0.001021, .5R: 1.000000, .75R: 0.666667, count: 21 -Region 23 Avg IOU: 0.681612, Class: 0.178171, Obj: 0.086481, No Obj: 0.000118, .5R: 0.900000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.737081, Class: 0.183071, Obj: 0.302470, No Obj: 0.000999, .5R: 0.950000, .75R: 0.500000, count: 20 -Region 23 Avg IOU: 0.675183, Class: 0.200414, Obj: 0.111060, No Obj: 0.000144, .5R: 0.866667, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.782380, Class: 0.196736, Obj: 0.393589, No Obj: 0.001326, .5R: 1.000000, .75R: 0.727273, count: 22 -Region 23 Avg IOU: 0.716228, Class: 0.149503, Obj: 0.128543, No Obj: 0.000158, .5R: 0.923077, .75R: 0.538462, count: 13 -Region 16 Avg IOU: 0.753935, Class: 0.225056, Obj: 0.426901, No Obj: 0.000958, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.710451, Class: 0.179071, Obj: 0.123858, No Obj: 0.000160, .5R: 0.933333, .75R: 0.400000, count: 15 -Region 16 Avg IOU: 0.760709, Class: 0.208990, Obj: 0.262988, No Obj: 0.001079, .5R: 1.000000, .75R: 0.631579, count: 19 -Region 23 Avg IOU: 0.742769, Class: 0.166120, Obj: 0.131761, No Obj: 0.000151, .5R: 0.933333, .75R: 0.600000, count: 15 -Region 16 Avg IOU: 0.757934, Class: 0.191396, Obj: 0.235604, No Obj: 0.001040, .5R: 1.000000, .75R: 0.653846, count: 26 -Region 23 Avg IOU: 0.680157, Class: 0.122555, Obj: 0.058816, No Obj: 0.000106, .5R: 1.000000, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.708173, Class: 0.216654, Obj: 0.157797, No Obj: 0.000903, .5R: 0.904762, .75R: 0.285714, count: 21 -Region 23 Avg IOU: 0.692132, Class: 0.215838, Obj: 0.128168, No Obj: 0.000285, .5R: 0.967742, .75R: 0.290323, count: 31 -Region 16 Avg IOU: 0.694926, Class: 0.261242, Obj: 0.235735, No Obj: 0.001120, .5R: 0.894737, .75R: 0.368421, count: 19 -Region 23 Avg IOU: 0.757981, Class: 0.164911, Obj: 0.168749, No Obj: 0.000170, .5R: 1.000000, .75R: 0.555556, count: 9 - - 2583: 2.579989, 2.453178 avg loss, 0.001000 rate, 2.810242 seconds, 330624 images -Loaded: 0.000091 seconds -Region 16 Avg IOU: 0.747611, Class: 0.190264, Obj: 0.221677, No Obj: 0.000776, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.663300, Class: 0.206551, Obj: 0.068200, No Obj: 0.000104, .5R: 0.916667, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.750986, Class: 0.208398, Obj: 0.422034, No Obj: 0.001515, .5R: 1.000000, .75R: 0.636364, count: 22 -Region 23 Avg IOU: 0.704982, Class: 0.196941, Obj: 0.192974, No Obj: 0.000150, .5R: 0.909091, .75R: 0.545455, count: 11 -Region 16 Avg IOU: 0.784488, Class: 0.257763, Obj: 0.329818, No Obj: 0.001344, .5R: 1.000000, .75R: 0.750000, count: 20 -Region 23 Avg IOU: 0.713973, Class: 0.194043, Obj: 0.141326, No Obj: 0.000173, .5R: 1.000000, .75R: 0.454545, count: 22 -Region 16 Avg IOU: 0.739508, Class: 0.245444, Obj: 0.270735, No Obj: 0.001497, .5R: 0.928571, .75R: 0.535714, count: 28 -Region 23 Avg IOU: 0.681797, Class: 0.205769, Obj: 0.061646, No Obj: 0.000124, .5R: 0.846154, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.764545, Class: 0.189415, Obj: 0.242981, No Obj: 0.001186, .5R: 1.000000, .75R: 0.600000, count: 20 -Region 23 Avg IOU: 0.710343, Class: 0.172393, Obj: 0.101054, No Obj: 0.000135, .5R: 0.941176, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.741057, Class: 0.172583, Obj: 0.418098, No Obj: 0.000825, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.677891, Class: 0.235156, Obj: 0.186925, No Obj: 0.000181, .5R: 0.833333, .75R: 0.458333, count: 24 -Region 16 Avg IOU: 0.715643, Class: 0.172385, Obj: 0.199911, No Obj: 0.000890, .5R: 0.823529, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.724429, Class: 0.192024, Obj: 0.043899, No Obj: 0.000072, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 16 Avg IOU: 0.749297, Class: 0.208362, Obj: 0.305058, No Obj: 0.001393, .5R: 0.950000, .75R: 0.650000, count: 20 -Region 23 Avg IOU: 0.735630, Class: 0.183045, Obj: 0.135472, No Obj: 0.000160, .5R: 1.000000, .75R: 0.333333, count: 18 - - 2584: 2.353712, 2.443231 avg loss, 0.001000 rate, 3.089902 seconds, 330752 images -Loaded: 0.000064 seconds -Region 16 Avg IOU: 0.765563, Class: 0.265586, Obj: 0.376803, No Obj: 0.001606, .5R: 0.952381, .75R: 0.619048, count: 21 -Region 23 Avg IOU: 0.695454, Class: 0.203264, Obj: 0.199538, No Obj: 0.000201, .5R: 0.900000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.748048, Class: 0.204440, Obj: 0.358782, No Obj: 0.001279, .5R: 0.913043, .75R: 0.652174, count: 23 -Region 23 Avg IOU: 0.698612, Class: 0.193819, Obj: 0.167225, No Obj: 0.000325, .5R: 0.928571, .75R: 0.428571, count: 28 -Region 16 Avg IOU: 0.761731, Class: 0.246796, Obj: 0.366408, No Obj: 0.001606, .5R: 1.000000, .75R: 0.625000, count: 24 -Region 23 Avg IOU: 0.707984, Class: 0.214775, Obj: 0.110425, No Obj: 0.000172, .5R: 0.941176, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.778440, Class: 0.164930, Obj: 0.368168, No Obj: 0.000990, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.714056, Class: 0.187104, Obj: 0.133664, No Obj: 0.000127, .5R: 1.000000, .75R: 0.272727, count: 11 -Region 16 Avg IOU: 0.750032, Class: 0.243937, Obj: 0.314149, No Obj: 0.001633, .5R: 0.904762, .75R: 0.571429, count: 21 -Region 23 Avg IOU: 0.771861, Class: 0.234997, Obj: 0.193856, No Obj: 0.000200, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.692474, Class: 0.219254, Obj: 0.252448, No Obj: 0.001808, .5R: 0.884615, .75R: 0.384615, count: 26 -Region 23 Avg IOU: 0.727119, Class: 0.211282, Obj: 0.109717, No Obj: 0.000257, .5R: 1.000000, .75R: 0.518519, count: 27 -Region 16 Avg IOU: 0.721415, Class: 0.218058, Obj: 0.334441, No Obj: 0.001102, .5R: 0.833333, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.649591, Class: 0.213464, Obj: 0.150639, No Obj: 0.000221, .5R: 0.882353, .75R: 0.176471, count: 17 -Region 16 Avg IOU: 0.774161, Class: 0.260881, Obj: 0.377607, No Obj: 0.001618, .5R: 1.000000, .75R: 0.700000, count: 20 -Region 23 Avg IOU: 0.717483, Class: 0.248484, Obj: 0.199780, No Obj: 0.000191, .5R: 0.928571, .75R: 0.428571, count: 14 - - 2585: 2.598652, 2.458773 avg loss, 0.001000 rate, 2.742464 seconds, 330880 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.730052, Class: 0.266983, Obj: 0.382617, No Obj: 0.001680, .5R: 0.947368, .75R: 0.368421, count: 19 -Region 23 Avg IOU: 0.725915, Class: 0.209437, Obj: 0.168386, No Obj: 0.000223, .5R: 1.000000, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.748064, Class: 0.189334, Obj: 0.557649, No Obj: 0.001866, .5R: 0.869565, .75R: 0.652174, count: 23 -Region 23 Avg IOU: 0.670268, Class: 0.194051, Obj: 0.242509, No Obj: 0.000272, .5R: 0.933333, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.725917, Class: 0.272441, Obj: 0.393957, No Obj: 0.001708, .5R: 0.956522, .75R: 0.478261, count: 23 -Region 23 Avg IOU: 0.723101, Class: 0.192154, Obj: 0.179339, No Obj: 0.000296, .5R: 0.888889, .75R: 0.611111, count: 18 -Region 16 Avg IOU: 0.751503, Class: 0.246958, Obj: 0.370172, No Obj: 0.001497, .5R: 1.000000, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.708634, Class: 0.203430, Obj: 0.126818, No Obj: 0.000198, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 16 Avg IOU: 0.781358, Class: 0.250927, Obj: 0.362336, No Obj: 0.001873, .5R: 1.000000, .75R: 0.750000, count: 20 -Region 23 Avg IOU: 0.722325, Class: 0.229110, Obj: 0.139814, No Obj: 0.000218, .5R: 1.000000, .75R: 0.478261, count: 23 -Region 16 Avg IOU: 0.752274, Class: 0.195041, Obj: 0.427876, No Obj: 0.001334, .5R: 0.941176, .75R: 0.647059, count: 17 -Region 23 Avg IOU: 0.732639, Class: 0.158565, Obj: 0.158297, No Obj: 0.000240, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.771553, Class: 0.214028, Obj: 0.515640, No Obj: 0.001683, .5R: 1.000000, .75R: 0.611111, count: 18 -Region 23 Avg IOU: 0.730487, Class: 0.176134, Obj: 0.241304, No Obj: 0.000367, .5R: 0.956522, .75R: 0.565217, count: 23 -Region 16 Avg IOU: 0.712156, Class: 0.143117, Obj: 0.350687, No Obj: 0.001382, .5R: 0.894737, .75R: 0.368421, count: 19 -Region 23 Avg IOU: 0.790832, Class: 0.139419, Obj: 0.256316, No Obj: 0.000249, .5R: 0.937500, .75R: 0.687500, count: 16 - - 2586: 2.433231, 2.456219 avg loss, 0.001000 rate, 3.459818 seconds, 331008 images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.722351, Class: 0.228862, Obj: 0.377207, No Obj: 0.002405, .5R: 0.928571, .75R: 0.571429, count: 28 -Region 23 Avg IOU: 0.685447, Class: 0.216762, Obj: 0.156094, No Obj: 0.000453, .5R: 0.916667, .75R: 0.361111, count: 36 -Region 16 Avg IOU: 0.749667, Class: 0.220537, Obj: 0.332118, No Obj: 0.002077, .5R: 1.000000, .75R: 0.560000, count: 25 -Region 23 Avg IOU: 0.681232, Class: 0.172064, Obj: 0.210259, No Obj: 0.000335, .5R: 0.750000, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.662398, Class: 0.203993, Obj: 0.309113, No Obj: 0.001451, .5R: 0.823529, .75R: 0.352941, count: 17 -Region 23 Avg 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Avg IOU: 0.754493, Class: 0.256353, Obj: 0.397257, No Obj: 0.001304, .5R: 1.000000, .75R: 0.500000, count: 20 -Region 23 Avg IOU: 0.660083, Class: 0.136586, Obj: 0.170546, No Obj: 0.000186, .5R: 0.666667, .75R: 0.333333, count: 9 -Region 16 Avg IOU: 0.703915, Class: 0.222632, Obj: 0.165808, No Obj: 0.001188, .5R: 0.933333, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.686817, Class: 0.210108, Obj: 0.129175, No Obj: 0.000291, .5R: 1.000000, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.731298, Class: 0.189340, Obj: 0.264722, No Obj: 0.001156, .5R: 0.956522, .75R: 0.565217, count: 23 -Region 23 Avg IOU: 0.749895, Class: 0.180182, Obj: 0.192196, No Obj: 0.000269, .5R: 1.000000, .75R: 0.375000, count: 16 - - 2593: 2.576247, 2.564202 avg loss, 0.001000 rate, 3.853055 seconds, 331904 images -Loaded: 0.000060 seconds -Region 16 Avg IOU: 0.753823, Class: 0.225711, Obj: 0.400422, No Obj: 0.001828, .5R: 0.966667, .75R: 0.566667, count: 30 -Region 23 Avg IOU: 0.704393, Class: 0.190893, Obj: 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images -Loaded: 0.000075 seconds -Region 16 Avg IOU: 0.787908, Class: 0.228151, Obj: 0.464720, No Obj: 0.001614, .5R: 1.000000, .75R: 0.777778, count: 18 -Region 23 Avg IOU: 0.687892, Class: 0.215929, Obj: 0.089331, No Obj: 0.000185, .5R: 0.904762, .75R: 0.285714, count: 21 -Region 16 Avg IOU: 0.772827, Class: 0.221123, Obj: 0.295944, No Obj: 0.001209, .5R: 0.947368, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.714592, Class: 0.202123, Obj: 0.147366, No Obj: 0.000141, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.799671, Class: 0.179384, Obj: 0.473573, No Obj: 0.001574, .5R: 0.937500, .75R: 0.875000, count: 16 -Region 23 Avg IOU: 0.706564, Class: 0.129645, Obj: 0.124203, No Obj: 0.000187, .5R: 0.882353, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.798334, Class: 0.310738, Obj: 0.574512, No Obj: 0.001161, .5R: 1.000000, .75R: 0.900000, count: 10 -Region 23 Avg IOU: 0.702826, Class: 0.164730, Obj: 0.137158, No Obj: 0.000176, .5R: 1.000000, .75R: 0.391304, count: 23 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12 -Region 23 Avg IOU: 0.709130, Class: 0.211254, Obj: 0.190305, No Obj: 0.000593, .5R: 1.000000, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.850317, Class: 0.210911, Obj: 0.581189, No Obj: 0.001658, .5R: 1.000000, .75R: 1.000000, count: 7 -Region 23 Avg IOU: 0.629885, Class: 0.177782, Obj: 0.410529, No Obj: 0.000968, .5R: 0.812500, .75R: 0.125000, count: 16 -Region 16 Avg IOU: 0.778614, Class: 0.268444, Obj: 0.457462, No Obj: 0.002926, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.731528, Class: 0.195928, Obj: 0.254951, No Obj: 0.000863, .5R: 0.894737, .75R: 0.578947, count: 19 -Region 16 Avg IOU: 0.836295, Class: 0.369843, Obj: 0.568298, No Obj: 0.001524, .5R: 1.000000, .75R: 1.000000, count: 3 -Region 23 Avg IOU: 0.630662, Class: 0.168450, Obj: 0.146567, No Obj: 0.000723, .5R: 0.772727, .75R: 0.227273, count: 22 - - 2629: 2.001617, 2.144498 avg loss, 0.001000 rate, 0.787319 seconds, 336512 images -Loaded: 0.000077 seconds -Region 16 Avg IOU: 0.744125, Class: 0.200058, Obj: 0.456836, No Obj: 0.002480, .5R: 0.937500, .75R: 0.562500, count: 16 -Region 23 Avg IOU: 0.716870, Class: 0.171875, Obj: 0.229194, No Obj: 0.000625, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.824003, Class: 0.243067, Obj: 0.452258, No Obj: 0.002948, .5R: 0.937500, .75R: 0.937500, count: 16 -Region 23 Avg IOU: 0.722647, Class: 0.208406, Obj: 0.284291, No Obj: 0.000900, .5R: 0.954545, .75R: 0.545455, count: 22 -Region 16 Avg IOU: 0.745192, Class: 0.234443, Obj: 0.496342, No Obj: 0.002681, .5R: 0.941176, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.691742, Class: 0.186299, Obj: 0.181930, No Obj: 0.000767, .5R: 0.928571, .75R: 0.357143, count: 28 -Region 16 Avg IOU: 0.695215, Class: 0.189002, Obj: 0.255898, No Obj: 0.001201, .5R: 0.600000, .75R: 0.400000, count: 5 -Region 23 Avg IOU: 0.702209, Class: 0.140858, Obj: 0.204626, No Obj: 0.000543, .5R: 0.923077, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.850766, Class: 0.273664, Obj: 0.495682, No Obj: 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-Region 23 Avg IOU: 0.726144, Class: 0.125698, Obj: 0.186114, No Obj: 0.000515, .5R: 1.000000, .75R: 0.440000, count: 25 -Region 16 Avg IOU: 0.735759, Class: 0.210437, Obj: 0.591336, No Obj: 0.001397, .5R: 1.000000, .75R: 0.500000, count: 6 -Region 23 Avg IOU: 0.686705, Class: 0.178029, Obj: 0.277020, No Obj: 0.000561, .5R: 1.000000, .75R: 0.250000, count: 20 -Region 16 Avg IOU: 0.753372, Class: 0.338900, Obj: 0.510004, No Obj: 0.001966, .5R: 0.909091, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.618037, Class: 0.250904, Obj: 0.151970, No Obj: 0.000377, .5R: 0.722222, .75R: 0.222222, count: 18 -Region 16 Avg IOU: 0.808797, Class: 0.122786, Obj: 0.367804, No Obj: 0.001118, .5R: 1.000000, .75R: 0.777778, count: 9 -Region 23 Avg IOU: 0.753164, Class: 0.171876, Obj: 0.187635, No Obj: 0.000466, .5R: 1.000000, .75R: 0.466667, count: 15 -Region 16 Avg IOU: 0.774732, Class: 0.308053, Obj: 0.352044, No Obj: 0.001570, .5R: 0.900000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.710162, Class: 0.194311, Obj: 0.174213, No Obj: 0.000477, .5R: 1.000000, .75R: 0.450000, count: 20 - - 2632: 1.969468, 2.087597 avg loss, 0.001000 rate, 0.781385 seconds, 336896 images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.717716, Class: 0.249191, Obj: 0.336204, No Obj: 0.001742, .5R: 1.000000, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.685040, Class: 0.136348, Obj: 0.128541, No Obj: 0.000514, .5R: 0.960000, .75R: 0.280000, count: 25 -Region 16 Avg IOU: 0.772245, Class: 0.267077, Obj: 0.307083, No Obj: 0.002361, .5R: 1.000000, .75R: 0.666667, count: 18 -Region 23 Avg IOU: 0.720060, Class: 0.251517, Obj: 0.252532, No Obj: 0.000787, .5R: 0.894737, .75R: 0.473684, count: 19 -Region 16 Avg IOU: 0.844662, Class: 0.263340, Obj: 0.577538, No Obj: 0.001603, .5R: 1.000000, .75R: 1.000000, count: 8 -Region 23 Avg IOU: 0.732079, Class: 0.229849, Obj: 0.253987, No Obj: 0.000632, .5R: 1.000000, .75R: 0.500000, count: 26 -Region 16 Avg IOU: 0.717940, Class: 0.243721, Obj: 0.294599, No Obj: 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0.777453, Class: 0.365695, Obj: 0.465857, No Obj: 0.001200, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.764077, Class: 0.193460, Obj: 0.370114, No Obj: 0.000451, .5R: 1.000000, .75R: 0.444444, count: 9 -Region 16 Avg IOU: 0.738780, Class: 0.158261, Obj: 0.298013, No Obj: 0.001632, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.669495, Class: 0.221891, Obj: 0.264894, No Obj: 0.000721, .5R: 0.866667, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.764096, Class: 0.270885, Obj: 0.353534, No Obj: 0.001322, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.771540, Class: 0.176600, Obj: 0.206335, No Obj: 0.000585, .5R: 1.000000, .75R: 0.611111, count: 18 -Region 16 Avg IOU: 0.850754, Class: 0.130369, Obj: 0.512418, No Obj: 0.000820, .5R: 1.000000, .75R: 0.800000, count: 5 -Region 23 Avg IOU: 0.728685, Class: 0.181786, Obj: 0.254989, No Obj: 0.000627, .5R: 0.958333, .75R: 0.375000, count: 24 -Region 16 Avg IOU: 0.764199, Class: 0.176300, Obj: 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16 Avg IOU: 0.751000, Class: 0.206754, Obj: 0.409002, No Obj: 0.002187, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.685341, Class: 0.179158, Obj: 0.361454, No Obj: 0.000545, .5R: 1.000000, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.748779, Class: 0.247729, Obj: 0.495466, No Obj: 0.001219, .5R: 1.000000, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.688600, Class: 0.171136, Obj: 0.188098, No Obj: 0.000319, .5R: 1.000000, .75R: 0.384615, count: 13 - - 2640: 1.745010, 1.992701 avg loss, 0.001000 rate, 0.781400 seconds, 337920 images -Resizing -448 x 448 - try to allocate workspace = 305201153 * sizeof(float), CUDA allocate done! -Loaded: 0.286438 seconds -Region 16 Avg IOU: 0.725295, Class: 0.332405, Obj: 0.417821, No Obj: 0.001321, .5R: 1.000000, .75R: 0.333333, count: 6 -Region 23 Avg IOU: 0.713574, Class: 0.222048, Obj: 0.246983, No Obj: 0.000480, .5R: 1.000000, .75R: 0.285714, count: 21 -Region 16 Avg IOU: 0.769799, Class: 0.341232, Obj: 0.549427, No Obj: 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16 Avg IOU: 0.672012, Class: 0.192795, Obj: 0.529779, No Obj: 0.001246, .5R: 1.000000, .75R: 0.250000, count: 8 -Region 23 Avg IOU: 0.697121, Class: 0.183655, Obj: 0.218425, No Obj: 0.000688, .5R: 0.956522, .75R: 0.260870, count: 23 -Region 16 Avg IOU: 0.775566, Class: 0.208216, Obj: 0.287782, No Obj: 0.001336, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.737453, Class: 0.142827, Obj: 0.227903, No Obj: 0.000603, .5R: 0.952381, .75R: 0.619048, count: 21 -Region 16 Avg IOU: 0.694750, Class: 0.294434, Obj: 0.220367, No Obj: 0.001781, .5R: 0.750000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.654966, Class: 0.222708, Obj: 0.132119, No Obj: 0.000528, .5R: 0.777778, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.825484, Class: 0.236725, Obj: 0.397157, No Obj: 0.001044, .5R: 1.000000, .75R: 0.909091, count: 11 -Region 23 Avg IOU: 0.707024, Class: 0.196257, Obj: 0.156372, No Obj: 0.000476, .5R: 1.000000, .75R: 0.347826, count: 23 - - 2648: 2.049931, 2.025392 avg loss, 0.001000 rate, 1.481721 seconds, 338944 images -Loaded: 0.000059 seconds -Region 16 Avg IOU: 0.733052, Class: 0.211529, Obj: 0.322827, No Obj: 0.001625, .5R: 0.857143, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.681409, Class: 0.170183, Obj: 0.221209, No Obj: 0.000716, .5R: 0.909091, .75R: 0.409091, count: 22 -Region 16 Avg IOU: 0.736439, Class: 0.239062, Obj: 0.259588, No Obj: 0.000876, .5R: 0.888889, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.727669, Class: 0.282263, Obj: 0.309963, No Obj: 0.000582, .5R: 1.000000, .75R: 0.562500, count: 16 -Region 16 Avg IOU: 0.718336, Class: 0.213104, Obj: 0.363828, No Obj: 0.001354, .5R: 0.928571, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.715581, Class: 0.198550, Obj: 0.219926, No Obj: 0.000500, .5R: 1.000000, .75R: 0.466667, count: 15 -Region 16 Avg IOU: 0.758128, Class: 0.174612, Obj: 0.401977, No Obj: 0.001918, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.677935, Class: 0.194553, Obj: 0.214444, No Obj: 0.000844, .5R: 0.952381, .75R: 0.333333, count: 21 -Region 16 Avg IOU: 0.771955, Class: 0.232476, Obj: 0.206019, No Obj: 0.001567, .5R: 0.916667, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.704405, Class: 0.220895, Obj: 0.367596, No Obj: 0.000821, .5R: 0.857143, .75R: 0.523810, count: 21 -Region 16 Avg IOU: 0.794443, Class: 0.191375, Obj: 0.379630, No Obj: 0.001667, .5R: 1.000000, .75R: 0.785714, count: 14 -Region 23 Avg IOU: 0.720298, Class: 0.170975, Obj: 0.260490, No Obj: 0.000581, .5R: 0.909091, .75R: 0.500000, count: 22 -Region 16 Avg IOU: 0.772778, Class: 0.135908, Obj: 0.480029, No Obj: 0.001101, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.738797, Class: 0.130060, Obj: 0.340059, No Obj: 0.000924, .5R: 0.962963, .75R: 0.592593, count: 27 -Region 16 Avg IOU: 0.687993, Class: 0.201655, Obj: 0.414733, No Obj: 0.001845, .5R: 0.800000, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.703812, Class: 0.134900, Obj: 0.221650, No Obj: 0.000767, .5R: 0.935484, .75R: 0.387097, count: 31 - - 2649: 2.278188, 2.050672 avg loss, 0.001000 rate, 1.395429 seconds, 339072 images -Loaded: 0.000064 seconds -Region 16 Avg IOU: 0.842584, Class: 0.140737, Obj: 0.411303, No Obj: 0.000841, .5R: 1.000000, .75R: 0.857143, count: 7 -Region 23 Avg IOU: 0.719765, Class: 0.145577, Obj: 0.379958, No Obj: 0.000752, .5R: 1.000000, .75R: 0.263158, count: 19 -Region 16 Avg IOU: 0.778672, Class: 0.271200, Obj: 0.415532, No Obj: 0.001319, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.730154, Class: 0.204290, Obj: 0.330685, No Obj: 0.000479, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.805878, Class: 0.219256, Obj: 0.419489, No Obj: 0.001732, .5R: 1.000000, .75R: 0.733333, count: 15 -Region 23 Avg IOU: 0.734587, Class: 0.174043, Obj: 0.310443, No Obj: 0.000459, .5R: 0.941176, .75R: 0.470588, count: 17 -Region 16 Avg IOU: 0.784998, Class: 0.289173, Obj: 0.453684, No Obj: 0.001926, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.698107, Class: 0.190715, Obj: 0.367744, No Obj: 0.000842, .5R: 0.961538, .75R: 0.346154, count: 26 -Region 16 Avg IOU: 0.775088, Class: 0.244332, Obj: 0.274165, No Obj: 0.001378, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.678014, Class: 0.169491, Obj: 0.179752, No Obj: 0.000720, .5R: 0.863636, .75R: 0.409091, count: 22 -Region 16 Avg IOU: 0.698872, Class: 0.225984, Obj: 0.444733, No Obj: 0.002052, .5R: 0.823529, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.686715, Class: 0.202851, Obj: 0.213125, No Obj: 0.000693, .5R: 0.882353, .75R: 0.352941, count: 34 -Region 16 Avg IOU: 0.743213, Class: 0.296630, Obj: 0.520886, No Obj: 0.001869, .5R: 0.857143, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.668717, Class: 0.214163, Obj: 0.197190, No Obj: 0.000578, .5R: 0.833333, .75R: 0.416667, count: 24 -Region 16 Avg IOU: 0.783637, Class: 0.221836, Obj: 0.453824, No Obj: 0.002593, .5R: 0.937500, .75R: 0.687500, count: 16 -Region 23 Avg IOU: 0.675971, Class: 0.172247, Obj: 0.360170, No Obj: 0.001148, .5R: 0.896552, .75R: 0.310345, count: 29 - - 2650: 2.300285, 2.075633 avg loss, 0.001000 rate, 1.604848 seconds, 339200 images -Resizing -416 x 416 - try to allocate workspace = 304758785 * sizeof(float), CUDA allocate done! -Loaded: 0.500552 seconds -Region 16 Avg IOU: 0.755470, Class: 0.294566, Obj: 0.187022, No Obj: 0.001691, .5R: 1.000000, .75R: 0.428571, count: 7 -Region 23 Avg IOU: 0.689331, Class: 0.177875, Obj: 0.183236, No Obj: 0.001018, .5R: 1.000000, .75R: 0.333333, count: 21 -Region 16 Avg IOU: 0.796558, Class: 0.210625, Obj: 0.350424, No Obj: 0.001423, .5R: 1.000000, .75R: 0.833333, count: 6 -Region 23 Avg IOU: 0.726589, Class: 0.190493, Obj: 0.341781, No Obj: 0.000784, .5R: 0.933333, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.723103, Class: 0.237404, Obj: 0.503629, No Obj: 0.001747, .5R: 1.000000, .75R: 0.444444, count: 9 -Region 23 Avg IOU: 0.694025, Class: 0.210203, Obj: 0.311084, No Obj: 0.000994, .5R: 0.960000, .75R: 0.360000, count: 25 -Region 16 Avg IOU: 0.774863, Class: 0.265183, Obj: 0.544766, No Obj: 0.001781, .5R: 0.909091, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.698362, Class: 0.216704, Obj: 0.359600, No Obj: 0.000911, .5R: 0.904762, .75R: 0.380952, count: 21 -Region 16 Avg IOU: 0.725220, Class: 0.216091, Obj: 0.380838, No Obj: 0.002028, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.695692, Class: 0.170918, Obj: 0.330754, No Obj: 0.000859, .5R: 0.941176, .75R: 0.235294, count: 17 -Region 16 Avg IOU: 0.764725, Class: 0.173888, Obj: 0.441194, No Obj: 0.002784, .5R: 1.000000, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.682052, Class: 0.129062, Obj: 0.280426, No Obj: 0.000743, .5R: 1.000000, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.717229, Class: 0.142279, Obj: 0.370155, No Obj: 0.001191, .5R: 1.000000, .75R: 0.363636, count: 11 -Region 23 Avg IOU: 0.676975, Class: 0.143369, Obj: 0.235822, No Obj: 0.000661, .5R: 0.954545, .75R: 0.318182, count: 22 -Region 16 Avg IOU: 0.767265, Class: 0.226342, Obj: 0.620715, No Obj: 0.001718, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.719475, Class: 0.164609, Obj: 0.337900, No Obj: 0.000641, .5R: 1.000000, .75R: 0.571429, count: 14 - - 2651: 1.913871, 2.059457 avg loss, 0.001000 rate, 0.749542 seconds, 339328 images -Loaded: 0.000050 seconds -Region 16 Avg IOU: 0.703185, Class: 0.174126, Obj: 0.369788, No Obj: 0.002017, .5R: 0.818182, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.738901, Class: 0.195042, Obj: 0.185201, No Obj: 0.000527, .5R: 1.000000, .75R: 0.562500, count: 16 -Region 16 Avg IOU: 0.770732, Class: 0.227360, Obj: 0.429823, No Obj: 0.002074, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.650364, Class: 0.212062, Obj: 0.177574, No Obj: 0.000433, .5R: 0.764706, .75R: 0.235294, count: 17 -Region 16 Avg IOU: 0.735249, Class: 0.290808, Obj: 0.265408, No Obj: 0.001157, .5R: 0.833333, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.683007, Class: 0.184187, Obj: 0.197850, No Obj: 0.000720, .5R: 0.920000, .75R: 0.320000, count: 25 -Region 16 Avg IOU: 0.773696, Class: 0.167409, Obj: 0.400357, No Obj: 0.001279, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.733018, Class: 0.189365, Obj: 0.216641, No Obj: 0.000606, .5R: 1.000000, .75R: 0.285714, count: 21 -Region 16 Avg IOU: 0.794427, Class: 0.270499, Obj: 0.475523, No Obj: 0.002563, .5R: 1.000000, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.696299, Class: 0.170180, Obj: 0.228009, No Obj: 0.000687, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.823333, Class: 0.187903, Obj: 0.410169, No Obj: 0.002335, .5R: 1.000000, .75R: 0.875000, count: 8 -Region 23 Avg IOU: 0.665255, Class: 0.204032, Obj: 0.256070, No Obj: 0.000675, .5R: 0.947368, .75R: 0.263158, count: 19 -Region 16 Avg IOU: 0.775464, Class: 0.259684, Obj: 0.356172, No Obj: 0.002082, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.680841, Class: 0.215917, Obj: 0.183707, No Obj: 0.000852, .5R: 1.000000, .75R: 0.370370, count: 27 -Region 16 Avg IOU: 0.859420, Class: 0.237076, Obj: 0.576933, No Obj: 0.001962, .5R: 1.000000, .75R: 0.888889, count: 9 -Region 23 Avg IOU: 0.742088, Class: 0.162080, Obj: 0.295167, No Obj: 0.000675, .5R: 1.000000, .75R: 0.555556, count: 18 - - 2652: 2.007524, 2.054264 avg loss, 0.001000 rate, 0.778619 seconds, 339456 images -Loaded: 0.000063 seconds -Region 16 Avg IOU: 0.774197, Class: 0.191818, Obj: 0.408801, No Obj: 0.001891, .5R: 1.000000, .75R: 0.769231, count: 13 -Region 23 Avg IOU: 0.730674, Class: 0.187645, Obj: 0.173132, No Obj: 0.000829, .5R: 1.000000, .75R: 0.378378, count: 37 -Region 16 Avg IOU: 0.810617, Class: 0.262402, Obj: 0.334035, No Obj: 0.002056, .5R: 1.000000, .75R: 0.833333, count: 18 -Region 23 Avg IOU: 0.717965, Class: 0.216014, Obj: 0.179611, No Obj: 0.000665, .5R: 0.965517, .75R: 0.413793, count: 29 -Region 16 Avg IOU: 0.780440, Class: 0.206101, Obj: 0.378819, No Obj: 0.002023, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.697314, Class: 0.202127, Obj: 0.271311, No Obj: 0.000655, .5R: 1.000000, .75R: 0.300000, count: 20 -Region 16 Avg IOU: 0.753333, Class: 0.342677, Obj: 0.452127, No Obj: 0.001181, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 23 Avg IOU: 0.742242, Class: 0.161920, Obj: 0.211027, No Obj: 0.000300, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 16 Avg IOU: 0.703206, Class: 0.241223, Obj: 0.246597, No Obj: 0.001400, .5R: 0.857143, .75R: 0.357143, count: 14 -Region 23 Avg IOU: 0.638843, Class: 0.199188, Obj: 0.096410, No Obj: 0.000591, .5R: 0.870968, .75R: 0.193548, count: 31 -Region 16 Avg IOU: 0.743714, Class: 0.261800, Obj: 0.301449, No Obj: 0.001711, .5R: 0.909091, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.705185, Class: 0.206978, Obj: 0.121333, No Obj: 0.000526, .5R: 1.000000, .75R: 0.333333, count: 24 -Region 16 Avg IOU: 0.736326, Class: 0.172977, Obj: 0.256234, No Obj: 0.001028, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.701156, Class: 0.204853, Obj: 0.209168, No Obj: 0.000496, .5R: 0.944444, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.813723, Class: 0.262373, Obj: 0.546876, No Obj: 0.001139, .5R: 1.000000, .75R: 0.833333, count: 6 -Region 23 Avg IOU: 0.669703, Class: 0.235883, Obj: 0.163733, No Obj: 0.000578, .5R: 0.869565, .75R: 0.304348, count: 23 - - 2653: 2.372637, 2.086101 avg loss, 0.001000 rate, 0.772267 seconds, 339584 images -Loaded: 0.000068 seconds -Region 16 Avg IOU: 0.752811, Class: 0.292662, Obj: 0.372592, No Obj: 0.001034, .5R: 1.000000, .75R: 0.500000, count: 6 -Region 23 Avg IOU: 0.651979, Class: 0.203396, Obj: 0.247197, No Obj: 0.000578, .5R: 0.928571, .75R: 0.285714, count: 14 -Region 16 Avg IOU: 0.780524, Class: 0.244686, Obj: 0.359843, No Obj: 0.001961, .5R: 1.000000, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.744089, Class: 0.222934, Obj: 0.194885, No Obj: 0.000826, .5R: 1.000000, .75R: 0.476190, count: 21 -Region 16 Avg IOU: 0.747610, Class: 0.153555, Obj: 0.335757, No Obj: 0.002147, .5R: 0.947368, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.729868, Class: 0.213475, Obj: 0.212723, No Obj: 0.000746, .5R: 1.000000, .75R: 0.380952, count: 21 -Region 16 Avg IOU: 0.740449, Class: 0.221300, Obj: 0.370790, No Obj: 0.001670, .5R: 1.000000, .75R: 0.250000, count: 8 -Region 23 Avg IOU: 0.700638, Class: 0.226505, Obj: 0.195440, No Obj: 0.000882, .5R: 0.880000, .75R: 0.320000, count: 25 -Region 16 Avg IOU: 0.721695, Class: 0.209000, Obj: 0.299582, No Obj: 0.000928, .5R: 1.000000, .75R: 0.400000, count: 5 -Region 23 Avg IOU: 0.643658, Class: 0.222748, Obj: 0.179533, No Obj: 0.000422, .5R: 0.866667, .75R: 0.200000, count: 15 -Region 16 Avg IOU: 0.762457, Class: 0.136793, Obj: 0.259385, No Obj: 0.000983, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.663246, Class: 0.174507, Obj: 0.284298, No Obj: 0.000641, .5R: 0.900000, .75R: 0.250000, count: 20 -Region 16 Avg IOU: 0.751025, Class: 0.241041, Obj: 0.366816, No Obj: 0.001349, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 23 Avg IOU: 0.716146, Class: 0.229241, Obj: 0.279269, No Obj: 0.000659, .5R: 0.941176, .75R: 0.588235, count: 17 -Region 16 Avg IOU: 0.712235, Class: 0.189732, Obj: 0.325043, No Obj: 0.001940, .5R: 0.894737, .75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.672134, Class: 0.127780, Obj: 0.245298, No Obj: 0.000457, .5R: 0.750000, .75R: 0.500000, count: 20 - - 2654: 2.099918, 2.087483 avg loss, 0.001000 rate, 0.779906 seconds, 339712 images -Loaded: 0.000067 seconds -Region 16 Avg IOU: 0.759522, Class: 0.210504, Obj: 0.316729, No Obj: 0.001158, .5R: 1.000000, .75R: 0.375000, count: 8 -Region 23 Avg IOU: 0.737303, Class: 0.218619, Obj: 0.249084, No Obj: 0.000681, .5R: 1.000000, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.705524, Class: 0.239883, Obj: 0.389034, No Obj: 0.001228, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.702797, Class: 0.214425, Obj: 0.207551, No Obj: 0.000596, .5R: 0.941176, .75R: 0.470588, count: 17 -Region 16 Avg IOU: 0.779107, Class: 0.184307, Obj: 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0.704591, Class: 0.202937, Obj: 0.122058, No Obj: 0.000417, .5R: 1.000000, .75R: 0.375000, count: 24 -Region 16 Avg IOU: 0.791641, Class: 0.192452, Obj: 0.500484, No Obj: 0.001728, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.766525, Class: 0.172363, Obj: 0.115891, No Obj: 0.000202, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 16 Avg IOU: 0.786543, Class: 0.222464, Obj: 0.413095, No Obj: 0.001595, .5R: 1.000000, .75R: 0.687500, count: 16 -Region 23 Avg IOU: 0.752153, Class: 0.196196, Obj: 0.230455, No Obj: 0.000336, .5R: 1.000000, .75R: 0.500000, count: 20 - - 2675: 2.430504, 2.232532 avg loss, 0.001000 rate, 1.943051 seconds, 342400 images -Loaded: 0.000097 seconds -Region 16 Avg IOU: 0.725313, Class: 0.188207, Obj: 0.333979, No Obj: 0.001578, .5R: 0.909091, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.650937, Class: 0.222651, Obj: 0.107856, No Obj: 0.000294, .5R: 0.928571, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.792434, Class: 0.200924, Obj: 0.406019, 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.75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.725059, Class: 0.172458, Obj: 0.077204, No Obj: 0.000229, .5R: 0.857143, .75R: 0.571429, count: 14 -Region 16 Avg IOU: 0.702719, Class: 0.205114, Obj: 0.353808, No Obj: 0.001503, .5R: 0.823529, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 0.695967, Class: 0.221396, Obj: 0.161505, No Obj: 0.000328, .5R: 1.000000, .75R: 0.285714, count: 21 -Region 16 Avg IOU: 0.790327, Class: 0.248816, Obj: 0.461972, No Obj: 0.001712, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.735390, Class: 0.224288, Obj: 0.187161, No Obj: 0.000355, .5R: 0.960000, .75R: 0.640000, count: 25 - - 2676: 2.163808, 2.225660 avg loss, 0.001000 rate, 1.674691 seconds, 342528 images -Loaded: 0.000081 seconds -Region 16 Avg IOU: 0.721303, Class: 0.139144, Obj: 0.332810, No Obj: 0.001162, .5R: 0.909091, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.634447, Class: 0.146917, Obj: 0.166109, No Obj: 0.000402, .5R: 0.857143, .75R: 0.285714, count: 28 -Region 16 Avg 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Obj: 0.375633, No Obj: 0.002167, .5R: 0.954545, .75R: 0.636364, count: 22 -Region 23 Avg IOU: 0.707908, Class: 0.162207, Obj: 0.196285, No Obj: 0.000444, .5R: 0.960000, .75R: 0.320000, count: 25 -Region 16 Avg IOU: 0.755098, Class: 0.264898, Obj: 0.435562, No Obj: 0.000952, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.708976, Class: 0.222267, Obj: 0.254145, No Obj: 0.000386, .5R: 1.000000, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.717728, Class: 0.235572, Obj: 0.282650, No Obj: 0.001480, .5R: 0.933333, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.672017, Class: 0.200045, Obj: 0.125540, No Obj: 0.000280, .5R: 0.875000, .75R: 0.437500, count: 16 - - 2677: 2.535465, 2.256640 avg loss, 0.001000 rate, 2.064558 seconds, 342656 images -Loaded: 0.000077 seconds -Region 16 Avg IOU: 0.754804, Class: 0.310520, Obj: 0.466980, No Obj: 0.001478, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.686403, Class: 0.250554, Obj: 0.201876, No Obj: 0.000555, .5R: 0.916667, .75R: 0.250000, count: 24 -Region 16 Avg IOU: 0.771844, Class: 0.251496, Obj: 0.427585, No Obj: 0.002096, .5R: 1.000000, .75R: 0.631579, count: 19 -Region 23 Avg IOU: 0.749356, Class: 0.189318, Obj: 0.228494, No Obj: 0.000513, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.798902, Class: 0.260985, Obj: 0.433789, No Obj: 0.001671, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.758835, Class: 0.180096, Obj: 0.310187, No Obj: 0.000441, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 16 Avg IOU: 0.796731, Class: 0.177645, Obj: 0.287325, No Obj: 0.002554, .5R: 0.941176, .75R: 0.764706, count: 17 -Region 23 Avg IOU: 0.703930, Class: 0.236742, Obj: 0.203714, No Obj: 0.000639, .5R: 1.000000, .75R: 0.400000, count: 15 -Region 16 Avg IOU: 0.793479, Class: 0.141058, Obj: 0.427872, No Obj: 0.001866, .5R: 1.000000, .75R: 0.764706, count: 17 -Region 23 Avg IOU: 0.704868, Class: 0.180634, Obj: 0.241379, No Obj: 0.000429, .5R: 0.875000, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.681802, Class: 0.232417, Obj: 0.217526, No Obj: 0.001674, .5R: 0.809524, .75R: 0.476190, count: 21 -Region 23 Avg IOU: 0.685391, Class: 0.169003, Obj: 0.098411, No Obj: 0.000516, .5R: 0.925926, .75R: 0.333333, count: 27 -Region 16 Avg IOU: 0.802056, Class: 0.232934, Obj: 0.405389, No Obj: 0.001710, .5R: 1.000000, .75R: 0.800000, count: 15 -Region 23 Avg IOU: 0.721561, Class: 0.218442, Obj: 0.290851, No Obj: 0.000450, .5R: 1.000000, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.761874, Class: 0.258717, Obj: 0.401538, No Obj: 0.001530, .5R: 0.941176, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.717171, Class: 0.195660, Obj: 0.439256, No Obj: 0.000511, .5R: 1.000000, .75R: 0.357143, count: 14 - - 2678: 2.179334, 2.248910 avg loss, 0.001000 rate, 2.180166 seconds, 342784 images -Loaded: 0.000074 seconds -Region 16 Avg IOU: 0.750335, Class: 0.274938, Obj: 0.481705, No Obj: 0.001373, .5R: 1.000000, .75R: 0.444444, count: 9 -Region 23 Avg IOU: 0.689592, Class: 0.213715, Obj: 0.335373, No Obj: 0.000689, .5R: 1.000000, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.755458, Class: 0.152587, Obj: 0.527263, No Obj: 0.001401, .5R: 0.888889, .75R: 0.777778, count: 9 -Region 23 Avg IOU: 0.692166, Class: 0.196406, Obj: 0.276118, No Obj: 0.000712, .5R: 0.923077, .75R: 0.384615, count: 26 -Region 16 Avg IOU: 0.803019, Class: 0.190061, Obj: 0.291308, No Obj: 0.002072, .5R: 1.000000, .75R: 0.785714, count: 14 -Region 23 Avg IOU: 0.712131, Class: 0.168896, Obj: 0.396238, No Obj: 0.001029, .5R: 0.900000, .75R: 0.500000, count: 30 -Region 16 Avg IOU: 0.795401, Class: 0.261988, Obj: 0.384349, No Obj: 0.001359, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.620922, Class: 0.146028, Obj: 0.262223, No Obj: 0.000679, .5R: 0.866667, .75R: 0.133333, count: 15 -Region 16 Avg IOU: 0.775205, Class: 0.171910, Obj: 0.449052, No Obj: 0.001832, .5R: 1.000000, .75R: 0.684211, count: 19 -Region 23 Avg IOU: 0.740852, Class: 0.193817, Obj: 0.390429, No Obj: 0.000502, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.731504, Class: 0.214440, Obj: 0.418218, No Obj: 0.001718, .5R: 0.857143, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.761453, Class: 0.221275, Obj: 0.286255, No Obj: 0.000610, .5R: 0.954545, .75R: 0.545455, count: 22 -Region 16 Avg IOU: 0.673589, Class: 0.150692, Obj: 0.290499, No Obj: 0.001661, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 23 Avg IOU: 0.713890, Class: 0.216876, Obj: 0.242751, No Obj: 0.000380, .5R: 1.000000, .75R: 0.181818, count: 11 -Region 16 Avg IOU: 0.774396, Class: 0.260526, Obj: 0.358801, No Obj: 0.001457, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.723666, Class: 0.199981, Obj: 0.359412, No Obj: 0.000489, .5R: 0.909091, .75R: 0.454545, count: 22 - - 2679: 2.166222, 2.240641 avg loss, 0.001000 rate, 1.920356 seconds, 342912 images -Loaded: 0.000071 seconds -Region 16 Avg IOU: 0.746172, Class: 0.245230, Obj: 0.392934, No Obj: 0.001589, .5R: 1.000000, .75R: 0.562500, count: 16 -Region 23 Avg IOU: 0.720659, Class: 0.180538, Obj: 0.161044, No Obj: 0.000382, .5R: 1.000000, .75R: 0.458333, count: 24 -Region 16 Avg IOU: 0.695380, Class: 0.215501, Obj: 0.362138, No Obj: 0.000979, .5R: 0.900000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.727852, Class: 0.132386, Obj: 0.123883, No Obj: 0.000262, .5R: 0.944444, .75R: 0.500000, count: 18 -Region 16 Avg IOU: 0.759624, Class: 0.192345, Obj: 0.315260, No Obj: 0.001141, .5R: 0.909091, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.649984, Class: 0.206482, Obj: 0.136098, No Obj: 0.000346, .5R: 0.812500, .75R: 0.218750, count: 32 -Region 16 Avg IOU: 0.764565, Class: 0.203369, Obj: 0.455235, No Obj: 0.002073, .5R: 1.000000, .75R: 0.631579, count: 19 -Region 23 Avg IOU: 0.774758, Class: 0.213833, Obj: 0.222400, No Obj: 0.000332, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 16 Avg IOU: 0.786396, Class: 0.199905, Obj: 0.273387, No Obj: 0.001516, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.716130, Class: 0.144626, Obj: 0.159928, No Obj: 0.000391, .5R: 0.958333, .75R: 0.416667, count: 24 -Region 16 Avg IOU: 0.811221, Class: 0.264545, Obj: 0.471567, No Obj: 0.001473, .5R: 1.000000, .75R: 0.733333, count: 15 -Region 23 Avg IOU: 0.668849, Class: 0.192794, Obj: 0.204684, No Obj: 0.000401, .5R: 0.913043, .75R: 0.304348, count: 23 -Region 16 Avg IOU: 0.794299, Class: 0.181391, Obj: 0.325675, No Obj: 0.001297, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.705344, Class: 0.156237, Obj: 0.192000, No Obj: 0.000464, .5R: 0.964286, .75R: 0.357143, count: 28 -Region 16 Avg IOU: 0.726532, Class: 0.200220, Obj: 0.225519, No Obj: 0.001773, .5R: 0.904762, .75R: 0.476190, count: 21 -Region 23 Avg IOU: 0.644231, Class: 0.195523, Obj: 0.122642, No Obj: 0.000431, .5R: 0.916667, .75R: 0.250000, count: 24 - - 2680: 2.583757, 2.274952 avg loss, 0.001000 rate, 2.199976 seconds, 343040 images -Resizing -640 x 640 - try to allocate workspace = 1147404289 * sizeof(float), CUDA allocate done! -Loaded: 0.241361 seconds -Region 16 Avg IOU: 0.742238, Class: 0.254432, Obj: 0.269481, No Obj: 0.001485, .5R: 1.000000, .75R: 0.620690, count: 29 -Region 23 Avg IOU: 0.704840, Class: 0.190404, Obj: 0.211545, No Obj: 0.000439, .5R: 0.931035, .75R: 0.310345, count: 29 -Region 16 Avg IOU: 0.781197, Class: 0.235079, Obj: 0.242158, No Obj: 0.001119, .5R: 1.000000, .75R: 0.625000, count: 24 -Region 23 Avg IOU: 0.709809, Class: 0.201901, Obj: 0.211320, No Obj: 0.000178, .5R: 0.916667, .75R: 0.500000, count: 12 -Region 16 Avg IOU: 0.763610, Class: 0.189943, Obj: 0.355575, No Obj: 0.000984, .5R: 0.950000, .75R: 0.700000, count: 20 -Region 23 Avg IOU: 0.704743, Class: 0.170391, Obj: 0.192235, No Obj: 0.000237, .5R: 0.950000, .75R: 0.300000, count: 20 -Region 16 Avg IOU: 0.796990, Class: 0.258637, Obj: 0.339872, No Obj: 0.000959, .5R: 1.000000, .75R: 0.750000, count: 16 -Region 23 Avg IOU: 0.759762, Class: 0.255798, Obj: 0.222475, No Obj: 0.000223, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 16 Avg IOU: 0.695048, Class: 0.182604, Obj: 0.202220, No Obj: 0.001039, .5R: 0.857143, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.698900, Class: 0.221562, Obj: 0.131271, No Obj: 0.000212, .5R: 1.000000, .75R: 0.272727, count: 11 -Region 16 Avg IOU: 0.725937, Class: 0.210088, Obj: 0.199597, No Obj: 0.000847, .5R: 0.904762, .75R: 0.476190, count: 21 -Region 23 Avg IOU: 0.719043, Class: 0.199167, Obj: 0.135146, No Obj: 0.000278, .5R: 0.909091, .75R: 0.409091, count: 22 -Region 16 Avg IOU: 0.738229, Class: 0.213524, Obj: 0.259114, No Obj: 0.001578, .5R: 0.968750, .75R: 0.531250, count: 32 -Region 23 Avg IOU: 0.709989, Class: 0.139389, Obj: 0.129311, No Obj: 0.000392, .5R: 0.958333, .75R: 0.458333, count: 24 -Region 16 Avg IOU: 0.736946, Class: 0.265859, Obj: 0.384484, No Obj: 0.001689, .5R: 1.000000, .75R: 0.619048, count: 21 -Region 23 Avg IOU: 0.734019, Class: 0.177548, Obj: 0.214750, No Obj: 0.000263, .5R: 1.000000, .75R: 0.500000, count: 14 - - 2681: 2.719222, 2.319379 avg loss, 0.001000 rate, 3.248950 seconds, 343168 images -Loaded: 0.000075 seconds -Region 16 Avg IOU: 0.761491, Class: 0.249704, Obj: 0.320475, No Obj: 0.001244, .5R: 0.944444, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.718428, Class: 0.218413, Obj: 0.235241, No Obj: 0.000266, .5R: 0.894737, .75R: 0.526316, count: 19 -Region 16 Avg IOU: 0.765230, Class: 0.266646, Obj: 0.394074, No Obj: 0.001072, .5R: 1.000000, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.727162, Class: 0.195510, Obj: 0.076572, No Obj: 0.000171, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.787778, Class: 0.211147, Obj: 0.363173, No Obj: 0.000638, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.733379, Class: 0.219306, Obj: 0.299893, No Obj: 0.000402, .5R: 0.962963, .75R: 0.481481, count: 27 -Region 16 Avg IOU: 0.781841, Class: 0.176834, Obj: 0.189339, No Obj: 0.001348, .5R: 1.000000, .75R: 0.535714, count: 28 -Region 23 Avg IOU: 0.765011, Class: 0.192471, Obj: 0.150078, 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images -Loaded: 0.000072 seconds -Region 16 Avg IOU: 0.774868, Class: 0.249012, Obj: 0.240267, No Obj: 0.001167, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.715338, Class: 0.223317, Obj: 0.124421, No Obj: 0.000344, .5R: 0.968750, .75R: 0.468750, count: 32 -Region 16 Avg IOU: 0.735693, Class: 0.270298, Obj: 0.331783, No Obj: 0.001056, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.762892, Class: 0.149356, Obj: 0.130302, No Obj: 0.000172, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 16 Avg IOU: 0.712839, Class: 0.194443, Obj: 0.319950, No Obj: 0.001334, .5R: 1.000000, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.712186, Class: 0.140799, Obj: 0.119736, No Obj: 0.000207, .5R: 0.956522, .75R: 0.347826, count: 23 -Region 16 Avg IOU: 0.723442, Class: 0.242366, Obj: 0.406183, No Obj: 0.001666, .5R: 1.000000, .75R: 0.307692, count: 13 -Region 23 Avg IOU: 0.711817, Class: 0.212761, Obj: 0.124596, No Obj: 0.000167, .5R: 0.777778, .75R: 0.777778, count: 9 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0.742753, Class: 0.192848, Obj: 0.332305, No Obj: 0.000979, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.700122, Class: 0.205576, Obj: 0.229364, No Obj: 0.000212, .5R: 0.909091, .75R: 0.363636, count: 11 -Region 16 Avg IOU: 0.670541, Class: 0.248252, Obj: 0.190455, No Obj: 0.000844, .5R: 0.923077, .75R: 0.384615, count: 13 -Region 23 Avg IOU: 0.707631, Class: 0.168957, Obj: 0.276646, No Obj: 0.000223, .5R: 0.923077, .75R: 0.384615, count: 13 - - 2723: 2.301523, 2.555654 avg loss, 0.001000 rate, 3.692194 seconds, 348544 images -Loaded: 0.000071 seconds -Region 16 Avg IOU: 0.730300, Class: 0.254825, Obj: 0.249981, No Obj: 0.000822, .5R: 0.933333, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.712978, Class: 0.194490, Obj: 0.305070, No Obj: 0.000230, .5R: 0.900000, .75R: 0.400000, count: 10 -Region 16 Avg IOU: 0.727053, Class: 0.180047, Obj: 0.349216, No Obj: 0.000896, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 23 Avg IOU: 0.671125, Class: 0.240120, Obj: 0.175362, 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Obj: 0.181366, No Obj: 0.000193, .5R: 1.000000, .75R: 0.722222, count: 18 -Region 16 Avg IOU: 0.727766, Class: 0.209800, Obj: 0.220715, No Obj: 0.001013, .5R: 0.966667, .75R: 0.400000, count: 30 -Region 23 Avg IOU: 0.709669, Class: 0.191980, Obj: 0.203901, No Obj: 0.000289, .5R: 0.958333, .75R: 0.458333, count: 24 -Region 16 Avg IOU: 0.757384, Class: 0.206834, Obj: 0.296858, No Obj: 0.000999, .5R: 0.904762, .75R: 0.666667, count: 21 -Region 23 Avg IOU: 0.720586, Class: 0.194658, Obj: 0.217559, No Obj: 0.000316, .5R: 0.961538, .75R: 0.461538, count: 26 - - 2725: 3.067948, 2.626630 avg loss, 0.001000 rate, 3.759003 seconds, 348800 images -Loaded: 0.000072 seconds -Region 16 Avg IOU: 0.727559, Class: 0.231409, Obj: 0.286044, No Obj: 0.001034, .5R: 1.000000, .75R: 0.315789, count: 19 -Region 23 Avg IOU: 0.750640, Class: 0.188651, Obj: 0.173878, No Obj: 0.000168, .5R: 1.000000, .75R: 0.428571, count: 7 -Region 16 Avg IOU: 0.733217, Class: 0.222245, Obj: 0.285322, No Obj: 0.000855, .5R: 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20 -Region 23 Avg IOU: 0.697622, Class: 0.213598, Obj: 0.239964, No Obj: 0.000300, .5R: 0.933333, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.733397, Class: 0.166867, Obj: 0.380947, No Obj: 0.001014, .5R: 0.941176, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.758213, Class: 0.162373, Obj: 0.200698, No Obj: 0.000220, .5R: 1.000000, .75R: 0.722222, count: 18 -Region 16 Avg IOU: 0.739997, Class: 0.263846, Obj: 0.275803, No Obj: 0.001043, .5R: 1.000000, .75R: 0.478261, count: 23 -Region 23 Avg IOU: 0.753070, Class: 0.200460, Obj: 0.189086, No Obj: 0.000275, .5R: 1.000000, .75R: 0.571429, count: 21 - - 2726: 2.518213, 2.615788 avg loss, 0.001000 rate, 2.962681 seconds, 348928 images -Loaded: 0.000059 seconds -Region 16 Avg IOU: 0.735781, Class: 0.223901, Obj: 0.376828, No Obj: 0.001434, .5R: 0.961538, .75R: 0.538462, count: 26 -Region 23 Avg IOU: 0.744666, Class: 0.188373, Obj: 0.261053, No Obj: 0.000305, .5R: 0.956522, .75R: 0.565217, count: 23 -Region 16 Avg IOU: 0.651702, Class: 0.222888, Obj: 0.259648, No Obj: 0.001442, .5R: 0.787879, .75R: 0.303030, count: 33 -Region 23 Avg IOU: 0.667460, Class: 0.171083, Obj: 0.073800, No Obj: 0.000196, .5R: 0.950000, .75R: 0.200000, count: 20 -Region 16 Avg IOU: 0.706526, Class: 0.174211, Obj: 0.357818, No Obj: 0.000969, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.710148, Class: 0.162981, Obj: 0.200222, No Obj: 0.000177, .5R: 0.888889, .75R: 0.444444, count: 18 -Region 16 Avg IOU: 0.736864, Class: 0.221950, Obj: 0.233317, No Obj: 0.001173, .5R: 1.000000, .75R: 0.406250, count: 32 -Region 23 Avg IOU: 0.656595, Class: 0.174983, Obj: 0.045236, No Obj: 0.000119, .5R: 0.818182, .75R: 0.409091, count: 22 -Region 16 Avg IOU: 0.699650, Class: 0.275645, Obj: 0.419123, No Obj: 0.000970, .5R: 0.916667, .75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.735860, Class: 0.188059, Obj: 0.283839, No Obj: 0.000271, .5R: 1.000000, .75R: 0.434783, count: 23 -Region 16 Avg IOU: 0.685331, Class: 0.225097, Obj: 0.217299, No Obj: 0.001662, .5R: 0.960000, .75R: 0.280000, count: 25 -Region 23 Avg IOU: 0.687537, Class: 0.139765, Obj: 0.063784, No Obj: 0.000209, .5R: 1.000000, .75R: 0.400000, count: 15 -Region 16 Avg IOU: 0.740873, Class: 0.269728, Obj: 0.526200, No Obj: 0.000697, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.681838, Class: 0.207616, Obj: 0.218363, No Obj: 0.000257, .5R: 0.952381, .75R: 0.285714, count: 21 -Region 16 Avg IOU: 0.738864, Class: 0.234667, Obj: 0.316778, No Obj: 0.000878, .5R: 0.950000, .75R: 0.550000, count: 20 -Region 23 Avg IOU: 0.719182, Class: 0.173562, Obj: 0.186295, No Obj: 0.000273, .5R: 0.920000, .75R: 0.480000, count: 25 - - 2727: 2.962570, 2.650466 avg loss, 0.001000 rate, 2.942679 seconds, 349056 images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.716605, Class: 0.265969, Obj: 0.342925, No Obj: 0.001432, .5R: 1.000000, .75R: 0.411765, count: 17 -Region 23 Avg IOU: 0.682529, Class: 0.259715, Obj: 0.237695, No Obj: 0.000335, .5R: 0.944444, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.743532, Class: 0.262495, Obj: 0.330533, No Obj: 0.001383, .5R: 1.000000, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.733435, Class: 0.187119, Obj: 0.191212, No Obj: 0.000210, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 16 Avg IOU: 0.724957, Class: 0.178635, Obj: 0.323561, No Obj: 0.001695, .5R: 1.000000, .75R: 0.428571, count: 21 -Region 23 Avg IOU: 0.715631, Class: 0.195425, Obj: 0.088594, No Obj: 0.000237, .5R: 1.000000, .75R: 0.125000, count: 8 -Region 16 Avg IOU: 0.717695, Class: 0.239836, Obj: 0.416110, No Obj: 0.001135, .5R: 1.000000, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.716086, Class: 0.175250, Obj: 0.196391, No Obj: 0.000186, .5R: 1.000000, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.716201, Class: 0.252648, Obj: 0.260860, No Obj: 0.001608, .5R: 0.960000, .75R: 0.520000, count: 25 -Region 23 Avg IOU: 0.728629, Class: 0.260780, Obj: 0.209115, No Obj: 0.000307, .5R: 0.923077, .75R: 0.384615, count: 26 -Region 16 Avg IOU: 0.697974, Class: 0.212719, Obj: 0.233580, No Obj: 0.001085, .5R: 0.882353, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 0.689728, Class: 0.223458, Obj: 0.193540, No Obj: 0.000266, .5R: 0.900000, .75R: 0.250000, count: 20 -Region 16 Avg IOU: 0.672081, Class: 0.199559, Obj: 0.355341, No Obj: 0.001345, .5R: 0.842105, .75R: 0.315789, count: 19 -Region 23 Avg IOU: 0.615975, Class: 0.204832, Obj: 0.076419, No Obj: 0.000160, .5R: 0.909091, .75R: 0.000000, count: 11 -Region 16 Avg IOU: 0.750554, Class: 0.223849, Obj: 0.505342, No Obj: 0.001586, .5R: 1.000000, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.651662, Class: 0.156895, Obj: 0.224190, No Obj: 0.000220, .5R: 0.857143, .75R: 0.214286, count: 14 - - 2728: 2.392809, 2.624701 avg loss, 0.001000 rate, 2.850642 seconds, 349184 images -Loaded: 0.000063 seconds -Region 16 Avg IOU: 0.756015, Class: 0.152958, Obj: 0.367331, No Obj: 0.001571, .5R: 1.000000, .75R: 0.538462, count: 26 -Region 23 Avg IOU: 0.732438, Class: 0.203006, Obj: 0.290540, No Obj: 0.000219, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 16 Avg IOU: 0.678641, Class: 0.219994, Obj: 0.233239, No Obj: 0.001450, .5R: 0.833333, .75R: 0.291667, count: 24 -Region 23 Avg IOU: 0.743726, Class: 0.180367, Obj: 0.301442, No Obj: 0.000343, .5R: 0.965517, .75R: 0.586207, count: 29 -Region 16 Avg IOU: 0.697531, Class: 0.244326, Obj: 0.333581, No Obj: 0.001977, .5R: 0.866667, .75R: 0.433333, count: 30 -Region 23 Avg IOU: 0.696659, Class: 0.203664, Obj: 0.123219, No Obj: 0.000258, .5R: 0.913043, .75R: 0.304348, count: 23 -Region 16 Avg IOU: 0.770007, Class: 0.224394, Obj: 0.298787, No Obj: 0.001286, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.733014, Class: 0.205157, Obj: 0.118485, No Obj: 0.000217, .5R: 1.000000, .75R: 0.500000, count: 14 -Region 16 Avg IOU: 0.715680, Class: 0.259807, Obj: 0.271550, No Obj: 0.001689, .5R: 0.892857, .75R: 0.535714, count: 28 -Region 23 Avg IOU: 0.667717, Class: 0.204680, Obj: 0.160968, No Obj: 0.000203, .5R: 0.937500, .75R: 0.250000, count: 16 -Region 16 Avg IOU: 0.746394, Class: 0.277236, Obj: 0.427015, No Obj: 0.001628, .5R: 0.913043, .75R: 0.652174, count: 23 -Region 23 Avg IOU: 0.726141, Class: 0.181700, Obj: 0.251410, No Obj: 0.000285, .5R: 1.000000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.702350, Class: 0.213871, Obj: 0.411872, No Obj: 0.001235, .5R: 0.944444, .75R: 0.388889, count: 18 -Region 23 Avg IOU: 0.735511, Class: 0.125650, Obj: 0.151602, No Obj: 0.000236, .5R: 0.954545, .75R: 0.500000, count: 22 -Region 16 Avg IOU: 0.731867, Class: 0.245152, Obj: 0.310999, No Obj: 0.001551, .5R: 1.000000, .75R: 0.478261, count: 23 -Region 23 Avg IOU: 0.695756, Class: 0.165117, Obj: 0.129499, No Obj: 0.000150, .5R: 1.000000, .75R: 0.294118, count: 17 - - 2729: 2.881745, 2.650405 avg loss, 0.001000 rate, 3.689971 seconds, 349312 images -Loaded: 0.000065 seconds -Region 16 Avg IOU: 0.765310, Class: 0.183479, Obj: 0.369917, No Obj: 0.001079, .5R: 0.947368, .75R: 0.578947, count: 19 -Region 23 Avg IOU: 0.676230, Class: 0.179151, Obj: 0.178313, No Obj: 0.000185, .5R: 0.947368, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.714093, Class: 0.210263, Obj: 0.291454, No Obj: 0.001395, .5R: 0.952381, .75R: 0.428571, count: 21 -Region 23 Avg IOU: 0.719780, Class: 0.175561, Obj: 0.184864, No Obj: 0.000258, .5R: 1.000000, .75R: 0.466667, count: 15 -Region 16 Avg IOU: 0.778228, Class: 0.225580, Obj: 0.290560, No Obj: 0.001109, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.769333, Class: 0.221916, Obj: 0.214946, No Obj: 0.000274, .5R: 1.000000, .75R: 0.625000, count: 16 -Region 16 Avg IOU: 0.750903, Class: 0.176101, Obj: 0.316467, No Obj: 0.001245, .5R: 1.000000, .75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.676451, Class: 0.149022, Obj: 0.095699, No Obj: 0.000220, .5R: 0.863636, .75R: 0.363636, count: 22 -Region 16 Avg IOU: 0.755526, Class: 0.200880, Obj: 0.380815, No Obj: 0.001105, .5R: 1.000000, .75R: 0.611111, count: 18 -Region 23 Avg IOU: 0.703309, Class: 0.148724, Obj: 0.194477, No Obj: 0.000237, .5R: 0.882353, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.727336, Class: 0.257476, Obj: 0.355414, No Obj: 0.001488, .5R: 0.925926, .75R: 0.592593, count: 27 -Region 23 Avg IOU: 0.738370, Class: 0.230104, Obj: 0.284146, No Obj: 0.000339, .5R: 0.956522, .75R: 0.608696, count: 23 -Region 16 Avg IOU: 0.647106, Class: 0.222388, Obj: 0.241128, No Obj: 0.001777, .5R: 0.789474, .75R: 0.315789, count: 19 -Region 23 Avg IOU: 0.673586, Class: 0.196019, Obj: 0.094503, No Obj: 0.000261, .5R: 0.833333, .75R: 0.416667, count: 24 -Region 16 Avg IOU: 0.788728, Class: 0.275635, Obj: 0.413726, No Obj: 0.001594, .5R: 1.000000, .75R: 0.722222, count: 18 -Region 23 Avg IOU: 0.740981, Class: 0.141054, Obj: 0.181375, No Obj: 0.000220, .5R: 1.000000, .75R: 0.437500, count: 16 - - 2730: 2.719730, 2.657338 avg loss, 0.001000 rate, 3.159634 seconds, 349440 images -Resizing -448 x 448 - try to allocate workspace = 305201153 * sizeof(float), CUDA allocate done! -Loaded: 0.398541 seconds -Region 16 Avg IOU: 0.755094, Class: 0.242845, Obj: 0.425546, No Obj: 0.001794, .5R: 1.000000, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.686290, Class: 0.195406, Obj: 0.211830, No Obj: 0.000374, .5R: 0.866667, .75R: 0.400000, count: 15 -Region 16 Avg IOU: 0.830976, Class: 0.272571, Obj: 0.572711, No Obj: 0.002011, .5R: 1.000000, .75R: 0.916667, count: 12 -Region 23 Avg IOU: 0.736237, Class: 0.176279, Obj: 0.188792, No Obj: 0.000654, .5R: 0.956522, .75R: 0.521739, count: 23 -Region 16 Avg IOU: 0.733868, Class: 0.213569, Obj: 0.465744, No Obj: 0.001492, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.740259, Class: 0.230192, Obj: 0.317067, No Obj: 0.000519, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 16 Avg IOU: 0.747778, Class: 0.151891, Obj: 0.437632, No Obj: 0.002058, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.658802, Class: 0.151554, Obj: 0.163387, No Obj: 0.000503, .5R: 0.928571, .75R: 0.214286, count: 28 -Region 16 Avg IOU: 0.798153, Class: 0.258727, Obj: 0.489529, No Obj: 0.001671, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.694069, Class: 0.252504, Obj: 0.182945, No Obj: 0.000473, .5R: 0.894737, .75R: 0.263158, count: 19 -Region 16 Avg IOU: 0.756491, Class: 0.219843, Obj: 0.419886, No Obj: 0.001793, .5R: 0.800000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.746976, Class: 0.197042, Obj: 0.160746, No Obj: 0.000410, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 16 Avg IOU: 0.787399, Class: 0.240626, Obj: 0.498418, No Obj: 0.001613, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.625110, Class: 0.194441, Obj: 0.236785, No Obj: 0.000772, .5R: 0.714286, .75R: 0.250000, count: 28 -Region 16 Avg IOU: 0.761058, Class: 0.253798, Obj: 0.253396, No Obj: 0.001269, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.733073, Class: 0.180479, Obj: 0.154284, No Obj: 0.000397, .5R: 1.000000, .75R: 0.411765, count: 17 - - 2731: 1.970885, 2.588692 avg loss, 0.001000 rate, 1.480359 seconds, 349568 images -Loaded: 0.000069 seconds -Region 16 Avg IOU: 0.816541, Class: 0.260573, Obj: 0.322116, No Obj: 0.001277, .5R: 1.000000, .75R: 0.900000, count: 10 -Region 23 Avg IOU: 0.728295, Class: 0.198617, Obj: 0.309501, No Obj: 0.000651, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.785885, Class: 0.163831, Obj: 0.435861, No Obj: 0.001714, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.718506, Class: 0.185090, Obj: 0.191371, No Obj: 0.000618, .5R: 0.965517, .75R: 0.517241, count: 29 -Region 16 Avg IOU: 0.767295, Class: 0.197226, Obj: 0.360822, No Obj: 0.001494, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.724919, Class: 0.217326, Obj: 0.193436, No Obj: 0.000452, .5R: 0.909091, .75R: 0.681818, count: 22 -Region 16 Avg IOU: 0.736233, Class: 0.206259, Obj: 0.187822, No Obj: 0.001049, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.719492, Class: 0.217077, Obj: 0.176380, No Obj: 0.000546, .5R: 0.882353, .75R: 0.588235, count: 17 -Region 16 Avg IOU: 0.742802, Class: 0.188103, Obj: 0.212525, No Obj: 0.001228, .5R: 0.933333, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.685335, Class: 0.201646, Obj: 0.175453, No Obj: 0.000482, .5R: 1.000000, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.807877, Class: 0.225515, Obj: 0.402803, No Obj: 0.000999, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.717271, Class: 0.205222, Obj: 0.215610, No Obj: 0.000498, .5R: 1.000000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.770419, Class: 0.256232, Obj: 0.394745, No Obj: 0.001516, .5R: 1.000000, .75R: 0.750000, count: 16 -Region 23 Avg IOU: 0.709936, Class: 0.189716, Obj: 0.358675, No Obj: 0.000592, .5R: 1.000000, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.754791, Class: 0.305505, Obj: 0.378932, No Obj: 0.001420, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.625511, Class: 0.210147, Obj: 0.128517, No Obj: 0.000318, .5R: 0.777778, .75R: 0.000000, count: 9 - - 2732: 1.962799, 2.526103 avg loss, 0.001000 rate, 1.643574 seconds, 349696 images -Loaded: 0.000060 seconds -Region 16 Avg IOU: 0.753756, Class: 0.325077, Obj: 0.299422, No Obj: 0.001068, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.706823, Class: 0.153884, Obj: 0.196780, No Obj: 0.000782, .5R: 1.000000, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.702316, Class: 0.232296, Obj: 0.157199, No Obj: 0.000998, .5R: 0.888889, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.691559, Class: 0.260006, Obj: 0.264110, No Obj: 0.000504, .5R: 0.941176, .75R: 0.235294, count: 17 -Region 16 Avg IOU: 0.742048, Class: 0.295118, Obj: 0.350855, No Obj: 0.001702, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.683329, Class: 0.183289, Obj: 0.294424, No Obj: 0.000855, .5R: 0.866667, .75R: 0.366667, count: 30 -Region 16 Avg IOU: 0.799776, Class: 0.284162, Obj: 0.381889, No Obj: 0.001939, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.729573, Class: 0.242950, Obj: 0.306417, No Obj: 0.000650, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.723041, Class: 0.213682, Obj: 0.352225, No Obj: 0.001468, .5R: 1.000000, .75R: 0.384615, count: 13 -Region 23 Avg IOU: 0.683832, Class: 0.206367, Obj: 0.290071, No Obj: 0.001051, .5R: 0.870968, .75R: 0.419355, count: 31 -Region 16 Avg IOU: 0.681432, Class: 0.269323, Obj: 0.279153, No Obj: 0.001548, .5R: 0.882353, .75R: 0.411765, count: 17 -Region 23 Avg IOU: 0.678996, Class: 0.209832, Obj: 0.243825, No Obj: 0.000606, .5R: 0.950000, .75R: 0.300000, count: 20 -Region 16 Avg IOU: 0.724959, Class: 0.221888, Obj: 0.243891, No Obj: 0.001222, .5R: 0.937500, .75R: 0.562500, count: 16 -Region 23 Avg IOU: 0.747253, Class: 0.227070, Obj: 0.244463, No Obj: 0.000339, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 16 Avg IOU: 0.805398, Class: 0.306557, Obj: 0.400653, No Obj: 0.001441, .5R: 1.000000, .75R: 0.769231, count: 13 -Region 23 Avg IOU: 0.706627, Class: 0.216556, Obj: 0.280934, No Obj: 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count: 20 -Region 23 Avg IOU: 0.685784, Class: 0.182760, Obj: 0.202757, No Obj: 0.000870, .5R: 0.961538, .75R: 0.269231, count: 26 -Region 16 Avg IOU: 0.766971, Class: 0.290595, Obj: 0.379832, No Obj: 0.001189, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.698379, Class: 0.247128, Obj: 0.268339, No Obj: 0.000466, .5R: 1.000000, .75R: 0.230769, count: 13 -Region 16 Avg IOU: 0.722886, Class: 0.229883, Obj: 0.428578, No Obj: 0.001365, .5R: 0.875000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.675313, Class: 0.190688, Obj: 0.164819, No Obj: 0.000760, .5R: 0.851852, .75R: 0.333333, count: 27 -Region 16 Avg IOU: 0.748026, Class: 0.202711, Obj: 0.554706, No Obj: 0.001320, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.777710, Class: 0.191709, Obj: 0.364178, No Obj: 0.000634, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 16 Avg IOU: 0.780797, Class: 0.231053, Obj: 0.421725, No Obj: 0.001160, .5R: 0.900000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.694864, Class: 0.188299, Obj: 0.249543, No Obj: 0.000627, .5R: 0.904762, .75R: 0.380952, count: 21 - - 2734: 2.006516, 2.438514 avg loss, 0.001000 rate, 1.644216 seconds, 349952 images -Loaded: 0.000065 seconds -Region 16 Avg IOU: 0.764073, Class: 0.219885, Obj: 0.400554, No Obj: 0.002092, .5R: 0.882353, .75R: 0.705882, count: 17 -Region 23 Avg IOU: 0.650874, Class: 0.220987, Obj: 0.120414, No Obj: 0.000646, .5R: 0.888889, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.727643, Class: 0.173595, Obj: 0.392545, No Obj: 0.001410, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.743610, Class: 0.214925, Obj: 0.241987, No Obj: 0.000486, .5R: 1.000000, .75R: 0.444444, count: 18 -Region 16 Avg IOU: 0.786687, Class: 0.217682, Obj: 0.446484, No Obj: 0.001970, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.710508, Class: 0.265773, Obj: 0.218963, No Obj: 0.000326, .5R: 0.900000, .75R: 0.400000, count: 10 -Region 16 Avg IOU: 0.762117, Class: 0.216747, Obj: 0.381247, No Obj: 0.001170, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.701459, Class: 0.220680, Obj: 0.375655, No Obj: 0.000469, .5R: 0.916667, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.756353, Class: 0.215220, Obj: 0.338654, No Obj: 0.001113, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.670730, Class: 0.174893, Obj: 0.218403, No Obj: 0.000725, .5R: 0.913043, .75R: 0.391304, count: 23 -Region 16 Avg IOU: 0.762579, Class: 0.239635, Obj: 0.243327, No Obj: 0.001365, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.650346, Class: 0.120900, Obj: 0.276996, No Obj: 0.000790, .5R: 1.000000, .75R: 0.285714, count: 7 -Region 16 Avg IOU: 0.702803, Class: 0.216539, Obj: 0.159264, No Obj: 0.001643, .5R: 1.000000, .75R: 0.250000, count: 16 -Region 23 Avg IOU: 0.717516, Class: 0.190662, Obj: 0.232585, No Obj: 0.000673, .5R: 0.954545, .75R: 0.409091, count: 22 -Region 16 Avg IOU: 0.720923, Class: 0.211798, Obj: 0.354926, No Obj: 0.001485, .5R: 0.909091, 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IOU: 0.800571, Class: 0.213112, Obj: 0.443398, No Obj: 0.001734, .5R: 1.000000, .75R: 0.818182, count: 11 -Region 23 Avg IOU: 0.719481, Class: 0.160695, Obj: 0.227042, No Obj: 0.000628, .5R: 1.000000, .75R: 0.307692, count: 26 -Region 16 Avg IOU: 0.727051, Class: 0.290051, Obj: 0.383130, No Obj: 0.001124, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.757199, Class: 0.261576, Obj: 0.195547, No Obj: 0.000462, .5R: 1.000000, .75R: 0.631579, count: 19 -Region 16 Avg IOU: 0.767905, Class: 0.254973, Obj: 0.329149, No Obj: 0.002617, .5R: 0.941176, .75R: 0.647059, count: 17 -Region 23 Avg IOU: 0.725336, Class: 0.199007, Obj: 0.100093, No Obj: 0.000553, .5R: 1.000000, .75R: 0.434783, count: 23 -Region 16 Avg IOU: 0.750243, Class: 0.369798, Obj: 0.410616, No Obj: 0.001595, .5R: 1.000000, .75R: 0.333333, count: 6 -Region 23 Avg IOU: 0.686582, Class: 0.183541, Obj: 0.179169, No Obj: 0.000291, .5R: 0.866667, .75R: 0.466667, count: 15 -Region 16 Avg IOU: 0.794893, Class: 0.229240, 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count: 6 -Region 23 Avg IOU: 0.726352, Class: 0.173737, Obj: 0.307622, No Obj: 0.000433, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.709739, Class: 0.256665, Obj: 0.320165, No Obj: 0.001847, .5R: 0.884615, .75R: 0.461538, count: 26 -Region 23 Avg IOU: 0.683781, Class: 0.191210, Obj: 0.085840, No Obj: 0.000366, .5R: 0.950000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.756506, Class: 0.222181, Obj: 0.456655, No Obj: 0.002278, .5R: 0.954545, .75R: 0.590909, count: 22 -Region 23 Avg IOU: 0.693396, Class: 0.147821, Obj: 0.313526, No Obj: 0.000478, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.685695, Class: 0.221344, Obj: 0.262484, No Obj: 0.001896, .5R: 0.947368, .75R: 0.263158, count: 19 -Region 23 Avg IOU: 0.717850, Class: 0.115129, Obj: 0.238475, No Obj: 0.000495, .5R: 1.000000, .75R: 0.470588, count: 17 -Region 16 Avg IOU: 0.717608, Class: 0.211777, Obj: 0.271269, No Obj: 0.002119, .5R: 0.956522, .75R: 0.434783, count: 23 -Region 23 Avg IOU: 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images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.654467, Class: 0.237127, Obj: 0.229741, No Obj: 0.001375, .5R: 0.837838, .75R: 0.189189, count: 37 -Region 23 Avg IOU: 0.688379, Class: 0.187847, Obj: 0.130253, No Obj: 0.000217, .5R: 0.961538, .75R: 0.307692, count: 26 -Region 16 Avg IOU: 0.720975, Class: 0.285841, Obj: 0.206974, No Obj: 0.001067, .5R: 0.869565, .75R: 0.565217, count: 23 -Region 23 Avg IOU: 0.703465, Class: 0.211439, Obj: 0.103340, No Obj: 0.000240, .5R: 0.920000, .75R: 0.440000, count: 25 -Region 16 Avg IOU: 0.738963, Class: 0.264527, Obj: 0.208835, No Obj: 0.001049, .5R: 0.950000, .75R: 0.550000, count: 20 -Region 23 Avg IOU: 0.731472, Class: 0.202387, Obj: 0.227085, No Obj: 0.000365, .5R: 0.904762, .75R: 0.523810, count: 21 -Region 16 Avg IOU: 0.755064, Class: 0.200412, Obj: 0.419939, No Obj: 0.000834, .5R: 0.916667, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.716699, Class: 0.179154, Obj: 0.235062, No Obj: 0.000252, .5R: 1.000000, .75R: 0.210526, count: 19 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loss, 0.001000 rate, 3.145550 seconds, 352000 images -Resizing -416 x 416 - try to allocate workspace = 304758785 * sizeof(float), CUDA allocate done! -Loaded: 0.361040 seconds -Region 16 Avg IOU: 0.700070, Class: 0.243558, Obj: 0.259423, No Obj: 0.001752, .5R: 0.888889, .75R: 0.444444, count: 9 -Region 23 Avg IOU: 0.715598, Class: 0.181368, Obj: 0.280997, No Obj: 0.000633, .5R: 1.000000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.762872, Class: 0.205157, Obj: 0.533866, No Obj: 0.001542, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.674443, Class: 0.190312, Obj: 0.181044, No Obj: 0.000462, .5R: 0.941176, .75R: 0.117647, count: 17 -Region 16 Avg IOU: 0.770674, Class: 0.263883, Obj: 0.234130, No Obj: 0.001407, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.721964, Class: 0.258283, Obj: 0.158636, No Obj: 0.000599, .5R: 1.000000, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.752604, Class: 0.232346, Obj: 0.450509, No Obj: 0.001282, .5R: 0.909091, 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23 Avg IOU: 0.669837, Class: 0.194706, Obj: 0.177667, No Obj: 0.000515, .5R: 0.904762, .75R: 0.285714, count: 21 - - 2751: 1.763514, 2.496952 avg loss, 0.001000 rate, 0.745974 seconds, 352128 images -Loaded: 0.000050 seconds -Region 16 Avg IOU: 0.811207, Class: 0.270818, Obj: 0.411669, No Obj: 0.001302, .5R: 1.000000, .75R: 0.777778, count: 9 -Region 23 Avg IOU: 0.774521, Class: 0.158541, Obj: 0.341141, No Obj: 0.000730, .5R: 0.947368, .75R: 0.736842, count: 19 -Region 16 Avg IOU: 0.743499, Class: 0.145399, Obj: 0.400766, No Obj: 0.001956, .5R: 1.000000, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.729014, Class: 0.175540, Obj: 0.272260, No Obj: 0.000800, .5R: 0.947368, .75R: 0.421053, count: 19 -Region 16 Avg IOU: 0.776680, Class: 0.234602, Obj: 0.477884, No Obj: 0.001403, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.662610, Class: 0.180954, Obj: 0.394306, No Obj: 0.000881, .5R: 0.937500, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.801840, Class: 0.286893, 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23 Avg IOU: 0.705001, Class: 0.233836, Obj: 0.172382, No Obj: 0.000482, .5R: 0.960000, .75R: 0.440000, count: 25 -Region 16 Avg IOU: 0.758875, Class: 0.188992, Obj: 0.371322, No Obj: 0.003083, .5R: 1.000000, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.676204, Class: 0.173293, Obj: 0.142327, No Obj: 0.000377, .5R: 1.000000, .75R: 0.133333, count: 15 - - 2756: 1.930044, 2.302003 avg loss, 0.001000 rate, 0.779036 seconds, 352768 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.723473, Class: 0.284515, Obj: 0.345211, No Obj: 0.002091, .5R: 0.875000, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.721714, Class: 0.205967, Obj: 0.101353, No Obj: 0.000358, .5R: 1.000000, .75R: 0.368421, count: 19 -Region 16 Avg IOU: 0.779457, Class: 0.172154, Obj: 0.517409, No Obj: 0.001496, .5R: 1.000000, .75R: 0.250000, count: 4 -Region 23 Avg IOU: 0.696945, Class: 0.174086, Obj: 0.220444, No Obj: 0.000528, .5R: 1.000000, .75R: 0.222222, count: 18 -Region 16 Avg IOU: 0.794679, Class: 0.251284, 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- - 2800: 1.900807, 2.181421 avg loss, 0.001000 rate, 0.784384 seconds, 358400 images -Resizing -448 x 448 - try to allocate workspace = 305201153 * sizeof(float), CUDA allocate done! -Loaded: 0.325132 seconds -Region 16 Avg IOU: 0.793505, Class: 0.296877, Obj: 0.192527, No Obj: 0.001709, .5R: 1.000000, .75R: 0.750000, count: 16 -Region 23 Avg IOU: 0.715162, Class: 0.264573, Obj: 0.100145, No Obj: 0.000485, .5R: 1.000000, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.808688, Class: 0.263901, Obj: 0.433096, No Obj: 0.001152, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.687380, Class: 0.170611, Obj: 0.239569, No Obj: 0.000718, .5R: 0.904762, .75R: 0.333333, count: 21 -Region 16 Avg IOU: 0.775208, Class: 0.302186, Obj: 0.472887, No Obj: 0.001579, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.693879, Class: 0.197692, Obj: 0.165833, No Obj: 0.000438, .5R: 0.937500, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.769680, Class: 0.264527, Obj: 0.425908, No 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count: 13 -Region 23 Avg IOU: 0.725984, Class: 0.191281, Obj: 0.183630, No Obj: 0.000506, .5R: 1.000000, .75R: 0.444444, count: 27 -Region 16 Avg IOU: 0.720349, Class: 0.194930, Obj: 0.410279, No Obj: 0.001168, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.695975, Class: 0.155640, Obj: 0.131744, No Obj: 0.000312, .5R: 0.944444, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.744885, Class: 0.278476, Obj: 0.552173, No Obj: 0.001878, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.683417, Class: 0.229579, Obj: 0.266972, No Obj: 0.000418, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.753173, Class: 0.273842, Obj: 0.565337, No Obj: 0.001768, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.717553, Class: 0.219238, Obj: 0.234541, No Obj: 0.000584, .5R: 1.000000, .75R: 0.423077, count: 26 -Region 16 Avg IOU: 0.787317, Class: 0.178189, Obj: 0.528129, No Obj: 0.001920, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 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0.143373, Obj: 0.402694, No Obj: 0.000691, .5R: 1.000000, .75R: 0.421053, count: 19 -Region 16 Avg IOU: 0.732886, Class: 0.223454, Obj: 0.444816, No Obj: 0.002022, .5R: 0.947368, .75R: 0.578947, count: 19 -Region 23 Avg IOU: 0.706093, Class: 0.164147, Obj: 0.315635, No Obj: 0.000826, .5R: 0.923077, .75R: 0.423077, count: 26 -Region 16 Avg IOU: 0.750012, Class: 0.268517, Obj: 0.385685, No Obj: 0.001490, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.667150, Class: 0.211610, Obj: 0.233125, No Obj: 0.000642, .5R: 0.809524, .75R: 0.380952, count: 21 -Region 16 Avg IOU: 0.764659, Class: 0.186439, Obj: 0.292956, No Obj: 0.001453, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.730242, Class: 0.185282, Obj: 0.256229, No Obj: 0.000650, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.751222, Class: 0.259552, Obj: 0.275927, No Obj: 0.001213, .5R: 1.000000, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.661894, Class: 0.192735, Obj: 0.292804, No Obj: 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count: 8 -Region 23 Avg IOU: 0.676027, Class: 0.189937, Obj: 0.237836, No Obj: 0.000446, .5R: 0.941176, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.738638, Class: 0.245517, Obj: 0.327260, No Obj: 0.000634, .5R: 1.000000, .75R: 0.400000, count: 5 -Region 23 Avg IOU: 0.717372, Class: 0.184682, Obj: 0.186989, No Obj: 0.000387, .5R: 0.933333, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.787171, Class: 0.176237, Obj: 0.391104, No Obj: 0.001175, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.697440, Class: 0.173357, Obj: 0.363082, No Obj: 0.000729, .5R: 0.956522, .75R: 0.347826, count: 23 -Region 16 Avg IOU: 0.702989, Class: 0.200525, Obj: 0.268142, No Obj: 0.001622, .5R: 0.833333, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.695085, Class: 0.177679, Obj: 0.199783, No Obj: 0.000509, .5R: 0.857143, .75R: 0.380952, count: 21 -Region 16 Avg IOU: 0.753108, Class: 0.272952, Obj: 0.302204, No Obj: 0.001168, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 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17 -Region 16 Avg IOU: 0.701832, Class: 0.221354, Obj: 0.218623, No Obj: 0.000638, .5R: 0.842105, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.747090, Class: 0.189287, Obj: 0.146923, No Obj: 0.000199, .5R: 1.000000, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.721918, Class: 0.221697, Obj: 0.323483, No Obj: 0.001149, .5R: 0.960000, .75R: 0.480000, count: 25 -Region 23 Avg IOU: 0.694308, Class: 0.194505, Obj: 0.108957, No Obj: 0.000194, .5R: 0.925926, .75R: 0.407407, count: 27 - - 2824: 3.046274, 2.604892 avg loss, 0.001000 rate, 3.788582 seconds, 361472 images -Loaded: 0.000067 seconds -Region 16 Avg IOU: 0.737478, Class: 0.232834, Obj: 0.393530, No Obj: 0.000968, .5R: 0.954545, .75R: 0.500000, count: 22 -Region 23 Avg IOU: 0.701599, Class: 0.176890, Obj: 0.198092, No Obj: 0.000200, .5R: 0.894737, .75R: 0.526316, count: 19 -Region 16 Avg IOU: 0.747141, Class: 0.196601, Obj: 0.307060, No Obj: 0.000925, .5R: 0.952381, .75R: 0.523810, count: 21 -Region 23 Avg IOU: 0.681080, Class: 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5 -Region 16 Avg IOU: 0.747478, Class: 0.200805, Obj: 0.231436, No Obj: 0.000947, .5R: 0.931035, .75R: 0.655172, count: 29 -Region 23 Avg IOU: 0.703073, Class: 0.215730, Obj: 0.160068, No Obj: 0.000105, .5R: 0.846154, .75R: 0.615385, count: 13 -Region 16 Avg IOU: 0.718389, Class: 0.169392, Obj: 0.228019, No Obj: 0.000689, .5R: 0.882353, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.686233, Class: 0.086567, Obj: 0.075957, No Obj: 0.000109, .5R: 1.000000, .75R: 0.285714, count: 7 -Region 16 Avg IOU: 0.712216, Class: 0.167556, Obj: 0.194189, No Obj: 0.001004, .5R: 0.937500, .75R: 0.437500, count: 32 -Region 23 Avg IOU: 0.712840, Class: 0.178180, Obj: 0.122752, No Obj: 0.000100, .5R: 1.000000, .75R: 0.466667, count: 15 - - 2829: 2.642673, 2.687153 avg loss, 0.001000 rate, 4.849555 seconds, 362112 images -Loaded: 0.000064 seconds -Region 16 Avg IOU: 0.751845, Class: 0.223885, Obj: 0.269930, No Obj: 0.000729, .5R: 1.000000, .75R: 0.600000, count: 20 -Region 23 Avg IOU: 0.739675, Class: 0.067299, Obj: 0.201205, No Obj: 0.000123, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 16 Avg IOU: 0.753078, Class: 0.227061, Obj: 0.187077, No Obj: 0.000684, .5R: 0.888889, .75R: 0.666667, count: 18 -Region 23 Avg IOU: 0.721716, Class: 0.127531, Obj: 0.324435, No Obj: 0.000148, .5R: 1.000000, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.752273, Class: 0.171243, Obj: 0.158901, No Obj: 0.000722, .5R: 0.960000, .75R: 0.560000, count: 25 -Region 23 Avg IOU: 0.726251, Class: 0.194602, Obj: 0.177901, No Obj: 0.000239, .5R: 0.916667, .75R: 0.541667, count: 24 -Region 16 Avg IOU: 0.690067, Class: 0.225358, Obj: 0.194762, No Obj: 0.000799, .5R: 0.933333, .75R: 0.400000, count: 30 -Region 23 Avg IOU: 0.711035, Class: 0.169205, Obj: 0.169712, No Obj: 0.000170, .5R: 0.950000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.711169, Class: 0.163510, Obj: 0.200494, No Obj: 0.000746, .5R: 0.863636, .75R: 0.500000, count: 22 -Region 23 Avg IOU: 0.702053, Class: 0.091510, Obj: 0.209035, No Obj: 0.000145, .5R: 1.000000, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.701261, Class: 0.200572, Obj: 0.182153, No Obj: 0.000583, .5R: 0.882353, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 0.693797, Class: 0.146647, Obj: 0.127494, No Obj: 0.000113, .5R: 0.937500, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.736569, Class: 0.203704, Obj: 0.230420, No Obj: 0.000759, .5R: 0.863636, .75R: 0.590909, count: 22 -Region 23 Avg IOU: 0.721712, Class: 0.206411, Obj: 0.042040, No Obj: 0.000070, .5R: 1.000000, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.773579, Class: 0.169457, Obj: 0.307421, No Obj: 0.000867, .5R: 1.000000, .75R: 0.681818, count: 22 -Region 23 Avg IOU: 0.686130, Class: 0.156872, Obj: 0.122012, No Obj: 0.000117, .5R: 0.866667, .75R: 0.333333, count: 15 - - 2830: 2.832765, 2.701714 avg loss, 0.001000 rate, 4.814621 seconds, 362240 images -Resizing -448 x 448 - try to allocate workspace = 305201153 * sizeof(float), CUDA allocate done! -Loaded: 0.366838 seconds -Region 16 Avg IOU: 0.806153, Class: 0.233242, Obj: 0.390072, No Obj: 0.001103, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.692649, Class: 0.208165, Obj: 0.128858, No Obj: 0.000221, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 16 Avg IOU: 0.705801, Class: 0.273930, Obj: 0.369778, No Obj: 0.002008, .5R: 0.904762, .75R: 0.428571, count: 21 -Region 23 Avg IOU: 0.733201, Class: 0.239719, Obj: 0.181869, No Obj: 0.000356, .5R: 1.000000, .75R: 0.481481, count: 27 -Region 16 Avg IOU: 0.758484, Class: 0.252259, Obj: 0.343385, No Obj: 0.001944, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.712548, Class: 0.259714, Obj: 0.176095, No Obj: 0.000269, .5R: 1.000000, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.740098, Class: 0.190230, Obj: 0.315027, No Obj: 0.001333, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.710952, Class: 0.220689, Obj: 0.158715, No Obj: 0.000555, .5R: 0.954545, .75R: 0.409091, count: 22 -Region 16 Avg IOU: 0.770236, Class: 0.216388, Obj: 0.449250, No Obj: 0.001137, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.753401, Class: 0.177820, Obj: 0.124517, No Obj: 0.000215, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 16 Avg IOU: 0.718252, Class: 0.210938, Obj: 0.243364, No Obj: 0.001747, .5R: 0.941176, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.706288, Class: 0.185812, Obj: 0.158304, No Obj: 0.000488, .5R: 0.956522, .75R: 0.478261, count: 23 -Region 16 Avg IOU: 0.756700, Class: 0.208728, Obj: 0.441693, No Obj: 0.001523, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.777456, Class: 0.216371, Obj: 0.169028, No Obj: 0.000282, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 16 Avg IOU: 0.826769, Class: 0.231503, Obj: 0.482228, No Obj: 0.001656, .5R: 1.000000, .75R: 0.857143, count: 14 -Region 23 Avg IOU: 0.713637, Class: 0.191737, Obj: 0.145215, No Obj: 0.000354, .5R: 0.925926, .75R: 0.444444, count: 27 - - 2831: 2.050054, 2.636548 avg loss, 0.001000 rate, 1.532402 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images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.751935, Class: 0.217475, Obj: 0.335307, No Obj: 0.001713, .5R: 0.933333, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.725310, Class: 0.224238, Obj: 0.111316, No Obj: 0.000287, .5R: 0.920000, .75R: 0.520000, count: 25 -Region 16 Avg IOU: 0.785812, Class: 0.168956, Obj: 0.403771, No Obj: 0.002051, .5R: 0.941176, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.728093, Class: 0.152384, Obj: 0.165415, No Obj: 0.000354, .5R: 0.923077, .75R: 0.615385, count: 13 -Region 16 Avg IOU: 0.819177, Class: 0.202922, Obj: 0.446636, No Obj: 0.001223, .5R: 0.875000, .75R: 0.875000, count: 8 -Region 23 Avg IOU: 0.720334, Class: 0.182981, Obj: 0.130635, No Obj: 0.000314, .5R: 0.954545, .75R: 0.409091, count: 22 -Region 16 Avg IOU: 0.754272, Class: 0.201897, Obj: 0.338787, No Obj: 0.001082, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.740784, Class: 0.135726, Obj: 0.144508, No Obj: 0.000197, .5R: 1.000000, .75R: 0.571429, count: 7 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7 -Region 23 Avg IOU: 0.711774, Class: 0.184535, Obj: 0.418798, No Obj: 0.000834, .5R: 0.900000, .75R: 0.433333, count: 30 -Region 16 Avg IOU: 0.610190, Class: 0.391973, Obj: 0.356190, No Obj: 0.001127, .5R: 0.750000, .75R: 0.000000, count: 4 -Region 23 Avg IOU: 0.724752, Class: 0.212055, Obj: 0.494520, No Obj: 0.000629, .5R: 0.937500, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.679072, Class: 0.228574, Obj: 0.491768, No Obj: 0.001759, .5R: 0.928571, .75R: 0.214286, count: 14 -Region 23 Avg IOU: 0.778304, Class: 0.251753, Obj: 0.409594, No Obj: 0.000194, .5R: 1.000000, .75R: 0.600000, count: 5 -Region 16 Avg IOU: 0.762971, Class: 0.280339, Obj: 0.564932, No Obj: 0.001682, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.717947, Class: 0.230475, Obj: 0.378820, No Obj: 0.000602, .5R: 0.952381, .75R: 0.428571, count: 21 - - 2864: 2.263188, 2.576692 avg loss, 0.001000 rate, 1.858057 seconds, 366592 images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.761793, Class: 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rate, 2.383478 seconds, 366848 images -Loaded: 0.000101 seconds -Region 16 Avg IOU: 0.761591, Class: 0.179203, Obj: 0.419770, No Obj: 0.001805, .5R: 1.000000, .75R: 0.631579, count: 19 -Region 23 Avg IOU: 0.739066, Class: 0.223025, Obj: 0.184263, No Obj: 0.000317, .5R: 0.933333, .75R: 0.466667, count: 15 -Region 16 Avg IOU: 0.690645, Class: 0.218727, Obj: 0.338465, No Obj: 0.001046, .5R: 0.769231, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.692935, Class: 0.241506, Obj: 0.207590, No Obj: 0.000281, .5R: 0.944444, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.715576, Class: 0.229664, Obj: 0.334335, No Obj: 0.001541, .5R: 0.888889, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.760384, Class: 0.137095, Obj: 0.260972, No Obj: 0.000309, .5R: 0.944444, .75R: 0.666667, count: 18 -Region 16 Avg IOU: 0.768961, Class: 0.207552, Obj: 0.336639, No Obj: 0.001267, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.747480, Class: 0.214972, Obj: 0.170871, No Obj: 0.000204, .5R: 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12 - - 2867: 1.975905, 2.504971 avg loss, 0.001000 rate, 2.075626 seconds, 366976 images -Loaded: 0.000069 seconds -Region 16 Avg IOU: 0.786860, Class: 0.201400, Obj: 0.342548, No Obj: 0.000811, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.715129, Class: 0.170569, Obj: 0.211377, No Obj: 0.000329, .5R: 0.941176, .75R: 0.529412, count: 17 -Region 16 Avg IOU: 0.713435, Class: 0.198053, Obj: 0.250140, No Obj: 0.001318, .5R: 0.952381, .75R: 0.285714, count: 21 -Region 23 Avg IOU: 0.662826, Class: 0.193532, Obj: 0.219485, No Obj: 0.000496, .5R: 0.850000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.722487, Class: 0.211256, Obj: 0.433836, No Obj: 0.001140, .5R: 0.916667, .75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.726421, Class: 0.167072, Obj: 0.190614, No Obj: 0.000475, .5R: 0.950000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.754624, Class: 0.246936, Obj: 0.301796, No Obj: 0.001051, .5R: 1.000000, .75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.766194, Class: 0.165937, Obj: 0.245306, No Obj: 0.000305, .5R: 0.923077, .75R: 0.615385, count: 13 -Region 16 Avg IOU: 0.731703, Class: 0.253497, Obj: 0.213658, No Obj: 0.001048, .5R: 0.947368, .75R: 0.631579, count: 19 -Region 23 Avg IOU: 0.719269, Class: 0.266950, Obj: 0.192460, No Obj: 0.000365, .5R: 0.956522, .75R: 0.478261, count: 23 -Region 16 Avg IOU: 0.700164, Class: 0.226379, Obj: 0.176712, No Obj: 0.001041, .5R: 0.916667, .75R: 0.250000, count: 12 -Region 23 Avg IOU: 0.701430, Class: 0.158862, Obj: 0.218299, No Obj: 0.000315, .5R: 0.952381, .75R: 0.380952, count: 21 -Region 16 Avg IOU: 0.789567, Class: 0.238840, Obj: 0.293912, No Obj: 0.000569, .5R: 1.000000, .75R: 0.777778, count: 9 -Region 23 Avg IOU: 0.726826, Class: 0.205380, Obj: 0.252806, No Obj: 0.000610, .5R: 0.966667, .75R: 0.400000, count: 30 -Region 16 Avg IOU: 0.753794, Class: 0.262139, Obj: 0.161107, No Obj: 0.001192, .5R: 0.894737, .75R: 0.578947, count: 19 -Region 23 Avg IOU: 0.753362, Class: 0.236575, Obj: 0.208601, No Obj: 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count: 13 -Region 23 Avg IOU: 0.681036, Class: 0.250649, Obj: 0.317571, No Obj: 0.000415, .5R: 0.909091, .75R: 0.454545, count: 11 -Region 16 Avg IOU: 0.799617, Class: 0.249805, Obj: 0.332302, No Obj: 0.000655, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.726969, Class: 0.204257, Obj: 0.246699, No Obj: 0.000360, .5R: 0.944444, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.779520, Class: 0.212054, Obj: 0.303911, No Obj: 0.001256, .5R: 1.000000, .75R: 0.714286, count: 21 -Region 23 Avg IOU: 0.719937, Class: 0.207234, Obj: 0.175057, No Obj: 0.000339, .5R: 0.950000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.726015, Class: 0.226701, Obj: 0.257747, No Obj: 0.001317, .5R: 0.952381, .75R: 0.476190, count: 21 -Region 23 Avg IOU: 0.709856, Class: 0.153408, Obj: 0.189216, No Obj: 0.000495, .5R: 0.964286, .75R: 0.464286, count: 28 -Region 16 Avg IOU: 0.747953, Class: 0.222393, Obj: 0.157976, No Obj: 0.001247, .5R: 0.956522, .75R: 0.565217, count: 23 -Region 23 Avg IOU: 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.75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.763036, Class: 0.219498, Obj: 0.241306, No Obj: 0.000411, .5R: 1.000000, .75R: 0.705882, count: 17 - - 2870: 2.715823, 2.496082 avg loss, 0.001000 rate, 2.671607 seconds, 367360 images -Resizing -608 x 608 - try to allocate workspace = 1146765313 * sizeof(float), CUDA allocate done! -Loaded: 0.108511 seconds -Region 16 Avg IOU: 0.760642, Class: 0.216194, Obj: 0.360938, No Obj: 0.001016, .5R: 0.941176, .75R: 0.764706, count: 17 -Region 23 Avg IOU: 0.735578, Class: 0.241037, Obj: 0.332796, No Obj: 0.000387, .5R: 0.958333, .75R: 0.416667, count: 24 -Region 16 Avg IOU: 0.732819, Class: 0.219776, Obj: 0.277552, No Obj: 0.001552, .5R: 1.000000, .75R: 0.478261, count: 23 -Region 23 Avg IOU: 0.738055, Class: 0.196782, Obj: 0.299552, No Obj: 0.000375, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 16 Avg IOU: 0.707950, Class: 0.195418, Obj: 0.304814, No Obj: 0.001399, .5R: 0.884615, .75R: 0.461538, count: 26 -Region 23 Avg IOU: 0.727929, Class: 0.176628, Obj: 0.177691, No Obj: 0.000406, .5R: 1.000000, .75R: 0.478261, count: 23 -Region 16 Avg IOU: 0.758592, Class: 0.189004, Obj: 0.279111, No Obj: 0.001011, .5R: 0.894737, .75R: 0.631579, count: 19 -Region 23 Avg IOU: 0.650916, Class: 0.259231, Obj: 0.137834, No Obj: 0.000245, .5R: 0.909091, .75R: 0.272727, count: 11 -Region 16 Avg IOU: 0.675581, Class: 0.165209, Obj: 0.339529, No Obj: 0.000836, .5R: 0.923077, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.751167, Class: 0.196165, Obj: 0.251734, No Obj: 0.000338, .5R: 0.916667, .75R: 0.583333, count: 24 -Region 16 Avg IOU: 0.717399, Class: 0.141543, Obj: 0.184140, No Obj: 0.000836, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 23 Avg IOU: 0.715336, Class: 0.179296, Obj: 0.233610, No Obj: 0.000319, .5R: 0.952381, .75R: 0.380952, count: 21 -Region 16 Avg IOU: 0.716361, Class: 0.141895, Obj: 0.404648, No Obj: 0.001026, .5R: 1.000000, .75R: 0.250000, count: 8 -Region 23 Avg IOU: 0.720492, Class: 0.257352, Obj: 0.387369, No Obj: 0.000379, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 16 Avg IOU: 0.709893, Class: 0.178695, Obj: 0.387626, No Obj: 0.002028, .5R: 0.875000, .75R: 0.406250, count: 32 -Region 23 Avg IOU: 0.725558, Class: 0.130926, Obj: 0.193170, No Obj: 0.000473, .5R: 0.961538, .75R: 0.500000, count: 26 - - 2871: 2.518437, 2.498317 avg loss, 0.001000 rate, 2.847817 seconds, 367488 images -Loaded: 0.000067 seconds -Region 16 Avg IOU: 0.731580, Class: 0.215391, Obj: 0.403378, No Obj: 0.001357, .5R: 0.928571, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.719618, Class: 0.178238, Obj: 0.124795, No Obj: 0.000294, .5R: 1.000000, .75R: 0.272727, count: 22 -Region 16 Avg IOU: 0.756882, Class: 0.212353, Obj: 0.455239, No Obj: 0.001874, .5R: 0.894737, .75R: 0.684211, count: 19 -Region 23 Avg IOU: 0.715240, Class: 0.200199, Obj: 0.217579, No Obj: 0.000371, .5R: 1.000000, .75R: 0.360000, count: 25 -Region 16 Avg IOU: 0.717531, Class: 0.230898, Obj: 0.471000, No Obj: 0.001699, .5R: 0.944444, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.701266, Class: 0.205221, Obj: 0.126532, No Obj: 0.000215, .5R: 0.875000, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.788373, Class: 0.197073, Obj: 0.465745, No Obj: 0.002165, .5R: 1.000000, .75R: 0.640000, count: 25 -Region 23 Avg IOU: 0.707546, Class: 0.110698, Obj: 0.189257, No Obj: 0.000303, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.726113, Class: 0.187583, Obj: 0.294276, No Obj: 0.001805, .5R: 0.958333, .75R: 0.500000, count: 24 -Region 23 Avg IOU: 0.713198, Class: 0.144764, Obj: 0.164037, No Obj: 0.000255, .5R: 0.916667, .75R: 0.416667, count: 12 -Region 16 Avg IOU: 0.733329, Class: 0.204120, Obj: 0.326407, No Obj: 0.001662, .5R: 1.000000, .75R: 0.500000, count: 24 -Region 23 Avg IOU: 0.737842, Class: 0.178140, Obj: 0.082455, No Obj: 0.000265, .5R: 0.956522, .75R: 0.434783, count: 23 -Region 16 Avg IOU: 0.755363, Class: 0.131326, Obj: 0.369002, No Obj: 0.001574, .5R: 0.894737, .75R: 0.736842, count: 19 -Region 23 Avg IOU: 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24 -Region 16 Avg IOU: 0.784469, Class: 0.249726, Obj: 0.464422, No Obj: 0.001591, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.695081, Class: 0.203080, Obj: 0.203751, No Obj: 0.000360, .5R: 0.931035, .75R: 0.310345, count: 29 -Region 16 Avg IOU: 0.753904, Class: 0.236561, Obj: 0.422390, No Obj: 0.001320, .5R: 0.923077, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.742126, Class: 0.229386, Obj: 0.125380, No Obj: 0.000166, .5R: 1.000000, .75R: 0.562500, count: 16 -Region 16 Avg IOU: 0.763099, Class: 0.271847, Obj: 0.412050, No Obj: 0.002048, .5R: 0.965517, .75R: 0.689655, count: 29 -Region 23 Avg IOU: 0.744418, Class: 0.204257, Obj: 0.149565, No Obj: 0.000304, .5R: 1.000000, .75R: 0.583333, count: 24 - - 2880: 2.866109, 2.573389 avg loss, 0.001000 rate, 2.575968 seconds, 368640 images -Resizing -736 x 736 - try to allocate workspace = 1149517825 * sizeof(float), CUDA allocate done! -Loaded: 0.209386 seconds -Region 16 Avg IOU: 0.762615, Class: 0.221277, Obj: 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-Resizing -576 x 576 - try to allocate workspace = 1146159105 * sizeof(float), CUDA allocate done! -Loaded: 0.279352 seconds -Region 16 Avg IOU: 0.697340, Class: 0.234492, Obj: 0.424422, No Obj: 0.002175, .5R: 0.952381, .75R: 0.380952, count: 21 -Region 23 Avg IOU: 0.702631, Class: 0.159139, Obj: 0.131692, No Obj: 0.000443, .5R: 1.000000, .75R: 0.250000, count: 32 -Region 16 Avg IOU: 0.694309, Class: 0.175700, Obj: 0.344530, No Obj: 0.001468, .5R: 0.941176, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 0.676801, Class: 0.185613, Obj: 0.156859, No Obj: 0.000223, .5R: 0.818182, .75R: 0.454545, count: 11 -Region 16 Avg IOU: 0.711010, Class: 0.192291, Obj: 0.205924, No Obj: 0.001489, .5R: 0.950000, .75R: 0.350000, count: 20 -Region 23 Avg IOU: 0.735922, Class: 0.192630, Obj: 0.159601, No Obj: 0.000322, .5R: 1.000000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.721835, Class: 0.182839, Obj: 0.318415, No Obj: 0.001710, .5R: 1.000000, .75R: 0.500000, count: 24 -Region 23 Avg IOU: 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23 Avg IOU: 0.727229, Class: 0.206898, Obj: 0.166704, No Obj: 0.000223, .5R: 0.875000, .75R: 0.562500, count: 16 - - 2902: 2.285696, 2.254684 avg loss, 0.001000 rate, 2.747848 seconds, 371456 images -Loaded: 0.000085 seconds -Region 16 Avg IOU: 0.797491, Class: 0.266703, Obj: 0.343209, No Obj: 0.000758, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.724959, Class: 0.184353, Obj: 0.092665, No Obj: 0.000249, .5R: 1.000000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.752715, Class: 0.246230, Obj: 0.363468, No Obj: 0.001051, .5R: 1.000000, .75R: 0.500000, count: 22 -Region 23 Avg IOU: 0.742445, Class: 0.148659, Obj: 0.192577, No Obj: 0.000265, .5R: 1.000000, .75R: 0.454545, count: 22 -Region 16 Avg IOU: 0.775826, Class: 0.161150, Obj: 0.268598, No Obj: 0.000979, .5R: 0.937500, .75R: 0.687500, count: 16 -Region 23 Avg IOU: 0.758243, Class: 0.207229, Obj: 0.166087, No Obj: 0.000288, .5R: 1.000000, .75R: 0.529412, count: 17 -Region 16 Avg IOU: 0.759707, Class: 0.255207, 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-Region 16 Avg IOU: 0.772707, Class: 0.241634, Obj: 0.273138, No Obj: 0.001221, .5R: 0.961538, .75R: 0.615385, count: 26 -Region 23 Avg IOU: 0.729815, Class: 0.246082, Obj: 0.332278, No Obj: 0.000307, .5R: 1.000000, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.784091, Class: 0.284681, Obj: 0.309875, No Obj: 0.000967, .5R: 1.000000, .75R: 0.777778, count: 18 -Region 23 Avg IOU: 0.679639, Class: 0.207241, Obj: 0.212799, No Obj: 0.000331, .5R: 0.916667, .75R: 0.416667, count: 12 -Region 16 Avg IOU: 0.718150, Class: 0.185010, Obj: 0.178904, No Obj: 0.000823, .5R: 0.950000, .75R: 0.550000, count: 20 -Region 23 Avg IOU: 0.699603, Class: 0.204565, Obj: 0.272176, No Obj: 0.000256, .5R: 0.900000, .75R: 0.400000, count: 10 -Region 16 Avg IOU: 0.727619, Class: 0.214170, Obj: 0.229174, No Obj: 0.000964, .5R: 0.954545, .75R: 0.500000, count: 22 -Region 23 Avg IOU: 0.790306, Class: 0.195339, Obj: 0.256285, No Obj: 0.000423, .5R: 0.962963, .75R: 0.814815, count: 27 -Region 16 Avg IOU: 0.681702, Class: 0.188320, Obj: 0.177105, No Obj: 0.001143, .5R: 0.869565, .75R: 0.347826, count: 23 -Region 23 Avg IOU: 0.703792, Class: 0.214212, Obj: 0.208457, No Obj: 0.000277, .5R: 0.916667, .75R: 0.333333, count: 12 - - 2904: 2.499108, 2.296052 avg loss, 0.001000 rate, 2.850255 seconds, 371712 images -Loaded: 0.000065 seconds -Region 16 Avg IOU: 0.828247, Class: 0.305779, Obj: 0.356633, No Obj: 0.001023, .5R: 1.000000, .75R: 0.857143, count: 14 -Region 23 Avg IOU: 0.732725, Class: 0.203468, Obj: 0.193622, No Obj: 0.000308, .5R: 1.000000, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.737782, Class: 0.269302, Obj: 0.199430, No Obj: 0.000944, .5R: 1.000000, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 0.735892, Class: 0.218953, Obj: 0.170143, No Obj: 0.000282, .5R: 0.944444, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.709904, Class: 0.271520, Obj: 0.206357, No Obj: 0.001066, .5R: 0.818182, .75R: 0.500000, count: 22 -Region 23 Avg IOU: 0.667861, Class: 0.175923, Obj: 0.134657, No Obj: 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0.731578, Class: 0.174290, Obj: 0.171182, No Obj: 0.000419, .5R: 1.000000, .75R: 0.454545, count: 22 -Region 16 Avg IOU: 0.765853, Class: 0.209650, Obj: 0.476156, No Obj: 0.001844, .5R: 0.954545, .75R: 0.590909, count: 22 -Region 23 Avg IOU: 0.764748, Class: 0.190622, Obj: 0.400327, No Obj: 0.000411, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.764181, Class: 0.263561, Obj: 0.452549, No Obj: 0.001278, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.730885, Class: 0.224652, Obj: 0.318522, No Obj: 0.000738, .5R: 0.923077, .75R: 0.576923, count: 26 -Region 16 Avg IOU: 0.767454, Class: 0.194307, Obj: 0.272191, No Obj: 0.001234, .5R: 1.000000, .75R: 0.636364, count: 22 -Region 23 Avg IOU: 0.737238, Class: 0.129605, Obj: 0.344396, No Obj: 0.000511, .5R: 0.916667, .75R: 0.500000, count: 24 -Region 16 Avg IOU: 0.728349, Class: 0.161588, Obj: 0.257372, No Obj: 0.001490, .5R: 0.954545, .75R: 0.500000, count: 22 -Region 23 Avg IOU: 0.722313, Class: 0.142929, Obj: 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0.710447, Class: 0.205071, Obj: 0.284937, No Obj: 0.001116, .5R: 0.954545, .75R: 0.409091, count: 22 -Region 23 Avg IOU: 0.747554, Class: 0.190419, Obj: 0.194431, No Obj: 0.000190, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 16 Avg IOU: 0.802837, Class: 0.269038, Obj: 0.385855, No Obj: 0.001381, .5R: 1.000000, .75R: 0.650000, count: 20 -Region 23 Avg IOU: 0.705459, Class: 0.199479, Obj: 0.258954, No Obj: 0.000331, .5R: 0.920000, .75R: 0.440000, count: 25 -Region 16 Avg IOU: 0.767699, Class: 0.193266, Obj: 0.414964, No Obj: 0.001051, .5R: 0.882353, .75R: 0.705882, count: 17 -Region 23 Avg IOU: 0.686132, Class: 0.164381, Obj: 0.185491, No Obj: 0.000127, .5R: 0.888889, .75R: 0.444444, count: 9 - - 2912: 2.552327, 2.350923 avg loss, 0.001000 rate, 2.788160 seconds, 372736 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.726817, Class: 0.236247, Obj: 0.331443, No Obj: 0.001117, .5R: 0.904762, .75R: 0.476190, count: 21 -Region 23 Avg IOU: 0.732422, Class: 0.192618, Obj: 0.178269, 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0.000060 seconds -Region 16 Avg IOU: 0.746275, Class: 0.218760, Obj: 0.280019, No Obj: 0.001086, .5R: 0.950000, .75R: 0.500000, count: 20 -Region 23 Avg IOU: 0.690244, Class: 0.231342, Obj: 0.147114, No Obj: 0.000224, .5R: 0.937500, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.793602, Class: 0.157859, Obj: 0.358426, No Obj: 0.001253, .5R: 1.000000, .75R: 0.631579, count: 19 -Region 23 Avg IOU: 0.753788, Class: 0.163865, Obj: 0.261236, No Obj: 0.000270, .5R: 1.000000, .75R: 0.608696, count: 23 -Region 16 Avg IOU: 0.713145, Class: 0.202877, Obj: 0.232357, No Obj: 0.001841, .5R: 0.967742, .75R: 0.419355, count: 31 -Region 23 Avg IOU: 0.653080, Class: 0.142984, Obj: 0.147518, No Obj: 0.000241, .5R: 0.894737, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.749388, Class: 0.210357, Obj: 0.336165, No Obj: 0.001271, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.715023, Class: 0.181340, Obj: 0.222215, No Obj: 0.000209, .5R: 0.937500, .75R: 0.562500, count: 16 -Region 16 Avg 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rate, 4.659980 seconds, 373376 images -Loaded: 0.000059 seconds -Region 16 Avg IOU: 0.748801, Class: 0.196060, Obj: 0.384089, No Obj: 0.001536, .5R: 1.000000, .75R: 0.533333, count: 30 -Region 23 Avg IOU: 0.716196, Class: 0.178480, Obj: 0.179142, No Obj: 0.000336, .5R: 0.939394, .75R: 0.393939, count: 33 -Region 16 Avg IOU: 0.757161, Class: 0.228193, Obj: 0.481616, No Obj: 0.001255, .5R: 1.000000, .75R: 0.578947, count: 19 -Region 23 Avg IOU: 0.733821, Class: 0.205121, Obj: 0.228106, No Obj: 0.000192, .5R: 1.000000, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.759058, Class: 0.232364, Obj: 0.294425, No Obj: 0.001316, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.764111, Class: 0.201497, Obj: 0.335042, No Obj: 0.000326, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 16 Avg IOU: 0.713663, Class: 0.214051, Obj: 0.395941, No Obj: 0.001298, .5R: 0.933333, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.747714, Class: 0.199136, Obj: 0.180190, No Obj: 0.000272, .5R: 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count: 27 -Region 23 Avg IOU: 0.729323, Class: 0.191714, Obj: 0.178983, No Obj: 0.000228, .5R: 0.894737, .75R: 0.526316, count: 19 -Region 16 Avg IOU: 0.761923, Class: 0.195083, Obj: 0.319640, No Obj: 0.000840, .5R: 0.937500, .75R: 0.625000, count: 16 -Region 23 Avg IOU: 0.739077, Class: 0.181028, Obj: 0.192740, No Obj: 0.000218, .5R: 1.000000, .75R: 0.523810, count: 21 -Region 16 Avg IOU: 0.760922, Class: 0.229999, Obj: 0.281592, No Obj: 0.001198, .5R: 0.923077, .75R: 0.730769, count: 26 -Region 23 Avg IOU: 0.702293, Class: 0.161458, Obj: 0.160883, No Obj: 0.000231, .5R: 0.857143, .75R: 0.428571, count: 21 -Region 16 Avg IOU: 0.702306, Class: 0.241959, Obj: 0.197294, No Obj: 0.001198, .5R: 0.961538, .75R: 0.384615, count: 26 -Region 23 Avg IOU: 0.699214, Class: 0.204604, Obj: 0.218254, No Obj: 0.000207, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.693605, Class: 0.284979, Obj: 0.277226, No Obj: 0.001232, .5R: 0.909091, .75R: 0.363636, count: 33 -Region 23 Avg IOU: 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Avg IOU: 0.707043, Class: 0.242964, Obj: 0.213151, No Obj: 0.000154, .5R: 1.000000, .75R: 0.333333, count: 9 -Region 16 Avg IOU: 0.761881, Class: 0.292347, Obj: 0.376873, No Obj: 0.001481, .5R: 1.000000, .75R: 0.650000, count: 20 -Region 23 Avg IOU: 0.761546, Class: 0.235415, Obj: 0.308956, No Obj: 0.000290, .5R: 1.000000, .75R: 0.545455, count: 11 - - 2923: 2.004076, 2.463537 avg loss, 0.001000 rate, 2.433868 seconds, 374144 images -Loaded: 0.000078 seconds -Region 16 Avg IOU: 0.758687, Class: 0.211704, Obj: 0.356473, No Obj: 0.001420, .5R: 1.000000, .75R: 0.590909, count: 22 -Region 23 Avg IOU: 0.739119, Class: 0.178081, Obj: 0.242598, No Obj: 0.000257, .5R: 0.913043, .75R: 0.565217, count: 23 -Region 16 Avg IOU: 0.728755, Class: 0.244177, Obj: 0.248211, No Obj: 0.001530, .5R: 1.000000, .75R: 0.517241, count: 29 -Region 23 Avg IOU: 0.699867, Class: 0.190638, Obj: 0.134578, No Obj: 0.000145, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 16 Avg IOU: 0.748331, Class: 0.226648, Obj: 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Avg IOU: 0.729386, Class: 0.156743, Obj: 0.117779, No Obj: 0.000197, .5R: 1.000000, .75R: 0.363636, count: 11 -Region 16 Avg IOU: 0.745293, Class: 0.213388, Obj: 0.437090, No Obj: 0.002184, .5R: 1.000000, .75R: 0.416667, count: 24 -Region 23 Avg IOU: 0.713966, Class: 0.207681, Obj: 0.206754, No Obj: 0.000397, .5R: 0.947368, .75R: 0.421053, count: 19 -Region 16 Avg IOU: 0.811917, Class: 0.164451, Obj: 0.449899, No Obj: 0.001323, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.674691, Class: 0.166063, Obj: 0.108049, No Obj: 0.000347, .5R: 0.880000, .75R: 0.480000, count: 25 - - 2944: 1.878955, 2.173800 avg loss, 0.001000 rate, 2.744004 seconds, 376832 images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.745396, Class: 0.256196, Obj: 0.310046, No Obj: 0.001717, .5R: 0.950000, .75R: 0.550000, count: 20 -Region 23 Avg IOU: 0.716767, Class: 0.216519, Obj: 0.207388, No Obj: 0.000304, .5R: 0.952381, .75R: 0.380952, count: 21 -Region 16 Avg IOU: 0.778933, Class: 0.192564, Obj: 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16 Avg IOU: 0.810433, Class: 0.185040, Obj: 0.224263, No Obj: 0.002167, .5R: 1.000000, .75R: 0.687500, count: 16 -Region 23 Avg IOU: 0.749148, Class: 0.194091, Obj: 0.051023, No Obj: 0.000410, .5R: 1.000000, .75R: 0.466667, count: 15 -Region 16 Avg IOU: 0.735166, Class: 0.315997, Obj: 0.444251, No Obj: 0.001644, .5R: 0.875000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.732095, Class: 0.196533, Obj: 0.142303, No Obj: 0.000444, .5R: 0.952381, .75R: 0.476190, count: 21 -Region 16 Avg IOU: 0.761042, Class: 0.165801, Obj: 0.295330, No Obj: 0.001131, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.775085, Class: 0.224112, Obj: 0.131211, No Obj: 0.000407, .5R: 1.000000, .75R: 0.652174, count: 23 -Region 16 Avg IOU: 0.802762, Class: 0.167175, Obj: 0.405310, No Obj: 0.001626, .5R: 1.000000, .75R: 0.875000, count: 8 -Region 23 Avg IOU: 0.722539, Class: 0.246357, Obj: 0.146077, No Obj: 0.000297, .5R: 1.000000, .75R: 0.421053, count: 19 - - 2968: 1.717122, 1.979065 avg loss, 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count: 22 - - 2969: 1.954686, 1.976627 avg loss, 0.001000 rate, 0.686752 seconds, 380032 images -Loaded: 0.000063 seconds -Region 16 Avg IOU: 0.800465, Class: 0.278125, Obj: 0.503951, No Obj: 0.001398, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.711370, Class: 0.234139, Obj: 0.320740, No Obj: 0.001215, .5R: 0.956522, .75R: 0.434783, count: 23 -Region 16 Avg IOU: 0.753083, Class: 0.131587, Obj: 0.252728, No Obj: 0.001216, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.682297, Class: 0.154848, Obj: 0.153504, No Obj: 0.000775, .5R: 0.894737, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.801868, Class: 0.250058, Obj: 0.393370, No Obj: 0.000807, .5R: 1.000000, .75R: 0.750000, count: 4 -Region 23 Avg IOU: 0.663745, Class: 0.232937, Obj: 0.218301, No Obj: 0.000637, .5R: 1.000000, .75R: 0.133333, count: 15 -Region 16 Avg IOU: 0.743324, Class: 0.179837, Obj: 0.181050, No Obj: 0.000988, .5R: 1.000000, .75R: 0.400000, count: 5 -Region 23 Avg IOU: 0.667559, Class: 0.215360, Obj: 0.188202, No Obj: 0.000734, .5R: 0.888889, .75R: 0.259259, count: 27 -Region 16 Avg IOU: 0.731705, Class: 0.239552, Obj: 0.256409, No Obj: 0.001387, .5R: 0.833333, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.702886, Class: 0.202333, Obj: 0.303917, No Obj: 0.001286, .5R: 0.966667, .75R: 0.400000, count: 30 -Region 16 Avg IOU: 0.773722, Class: 0.178907, Obj: 0.303480, No Obj: 0.001896, .5R: 1.000000, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.690365, Class: 0.152688, Obj: 0.129337, No Obj: 0.000880, .5R: 0.928571, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.783942, Class: 0.128986, Obj: 0.597697, No Obj: 0.001865, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.769470, Class: 0.230781, Obj: 0.286700, No Obj: 0.000582, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 16 Avg IOU: 0.816062, Class: 0.233871, Obj: 0.322013, No Obj: 0.001289, .5R: 1.000000, .75R: 1.000000, count: 6 -Region 23 Avg IOU: 0.659406, Class: 0.191644, Obj: 0.203836, No 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count: 16 -Region 23 Avg IOU: 0.663039, Class: 0.168803, Obj: 0.220445, No Obj: 0.000612, .5R: 0.827586, .75R: 0.344828, count: 29 -Region 16 Avg IOU: 0.749835, Class: 0.182655, Obj: 0.427792, No Obj: 0.002493, .5R: 0.956522, .75R: 0.608696, count: 23 -Region 23 Avg IOU: 0.697255, Class: 0.189337, Obj: 0.227149, No Obj: 0.000801, .5R: 0.962963, .75R: 0.370370, count: 27 -Region 16 Avg IOU: 0.785376, Class: 0.288653, Obj: 0.433536, No Obj: 0.001782, .5R: 0.923077, .75R: 0.769231, count: 13 -Region 23 Avg IOU: 0.722605, Class: 0.232363, Obj: 0.239818, No Obj: 0.000634, .5R: 0.958333, .75R: 0.416667, count: 24 -Region 16 Avg IOU: 0.759964, Class: 0.238527, Obj: 0.307735, No Obj: 0.002235, .5R: 1.000000, .75R:Saving weights to ./attribute/7354_26298/attribute_33652_s_tyv3_9c5_3000.weights - 0.555556, count: 18 -Region 23 Avg IOU: 0.714136, Class: 0.180530, Obj: 0.197069, No Obj: 0.000644, .5R: 0.961538, .75R: 0.423077, count: 26 -Region 16 Avg IOU: 0.756691, Class: 0.146197, Obj: 0.215269, 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0.000065 seconds -Region 16 Avg IOU: 0.709874, Class: 0.171104, Obj: 0.236681, No Obj: 0.001330, .5R: 0.840000, .75R: 0.440000, count: 25 -Region 23 Avg IOU: 0.673406, Class: 0.183667, Obj: 0.126425, No Obj: 0.000314, .5R: 0.851852, .75R: 0.296296, count: 27 -Region 16 Avg IOU: 0.682772, Class: 0.170465, Obj: 0.201593, No Obj: 0.001050, .5R: 0.925926, .75R: 0.444444, count: 27 -Region 23 Avg IOU: 0.721074, Class: 0.181824, Obj: 0.188663, No Obj: 0.000294, .5R: 1.000000, .75R: 0.380952, count: 21 -Region 16 Avg IOU: 0.730299, Class: 0.192577, Obj: 0.213877, No Obj: 0.001149, .5R: 0.954545, .75R: 0.500000, count: 22 -Region 23 Avg IOU: 0.721226, Class: 0.184614, Obj: 0.089721, No Obj: 0.000271, .5R: 0.954545, .75R: 0.590909, count: 22 -Region 16 Avg IOU: 0.745977, Class: 0.160975, Obj: 0.289721, No Obj: 0.001370, .5R: 0.954545, .75R: 0.636364, count: 22 -Region 23 Avg IOU: 0.700143, Class: 0.158133, Obj: 0.192615, No Obj: 0.000402, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 16 Avg 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rate, 2.435019 seconds, 386176 images -Loaded: 0.000079 seconds -Region 16 Avg IOU: 0.815113, Class: 0.184949, Obj: 0.364290, No Obj: 0.001160, .5R: 1.000000, .75R: 0.888889, count: 18 -Region 23 Avg IOU: 0.750297, Class: 0.168947, Obj: 0.273858, No Obj: 0.000398, .5R: 0.944444, .75R: 0.666667, count: 18 -Region 16 Avg IOU: 0.770863, Class: 0.181163, Obj: 0.383771, No Obj: 0.000850, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.663010, Class: 0.206311, Obj: 0.251080, No Obj: 0.000508, .5R: 0.800000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.769752, Class: 0.196948, Obj: 0.360038, No Obj: 0.001554, .5R: 0.947368, .75R: 0.684211, count: 19 -Region 23 Avg IOU: 0.688974, Class: 0.197190, Obj: 0.325228, No Obj: 0.000727, .5R: 0.906250, .75R: 0.312500, count: 32 -Region 16 Avg IOU: 0.754303, Class: 0.217543, Obj: 0.279056, No Obj: 0.001366, .5R: 0.913043, .75R: 0.652174, count: 23 -Region 23 Avg IOU: 0.726405, Class: 0.140663, Obj: 0.302387, No Obj: 0.000578, .5R: 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count: 12 -Region 23 Avg IOU: 0.688180, Class: 0.193137, Obj: 0.342973, No Obj: 0.000310, .5R: 0.866667, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.781664, Class: 0.208935, Obj: 0.365289, No Obj: 0.001109, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.706534, Class: 0.192278, Obj: 0.278756, No Obj: 0.000345, .5R: 0.882353, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.795114, Class: 0.242399, Obj: 0.485891, No Obj: 0.001468, .5R: 1.000000, .75R: 0.846154, count: 13 -Region 23 Avg IOU: 0.759633, Class: 0.260661, Obj: 0.241174, No Obj: 0.000320, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 16 Avg IOU: 0.765425, Class: 0.217134, Obj: 0.390069, No Obj: 0.002149, .5R: 0.928571, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.697970, Class: 0.175511, Obj: 0.170447, No Obj: 0.000317, .5R: 0.900000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.785470, Class: 0.251068, Obj: 0.553247, No Obj: 0.001276, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 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images -Loaded: 0.000076 seconds -Region 16 Avg IOU: 0.775369, Class: 0.275517, Obj: 0.378363, No Obj: 0.001087, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.666235, Class: 0.179187, Obj: 0.153487, No Obj: 0.000268, .5R: 0.916667, .75R: 0.208333, count: 24 -Region 16 Avg IOU: 0.728886, Class: 0.241882, Obj: 0.236656, No Obj: 0.001791, .5R: 0.970588, .75R: 0.470588, count: 34 -Region 23 Avg IOU: 0.696919, Class: 0.218393, Obj: 0.127433, No Obj: 0.000358, .5R: 1.000000, .75R: 0.275862, count: 29 -Region 16 Avg IOU: 0.756336, Class: 0.221373, Obj: 0.308391, No Obj: 0.001307, .5R: 1.000000, .75R: 0.647059, count: 17 -Region 23 Avg IOU: 0.723781, Class: 0.132262, Obj: 0.216225, No Obj: 0.000372, .5R: 0.875000, .75R: 0.583333, count: 24 -Region 16 Avg IOU: 0.776204, Class: 0.217533, Obj: 0.337014, No Obj: 0.000868, .5R: 1.000000, .75R: 0.733333, count: 15 -Region 23 Avg IOU: 0.702717, Class: 0.226304, Obj: 0.231808, No Obj: 0.000272, .5R: 0.913043, .75R: 0.391304, count: 23 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-Region 16 Avg IOU: 0.741993, Class: 0.215188, Obj: 0.369757, No Obj: 0.001474, .5R: 1.000000, .75R: 0.421053, count: 19 -Region 23 Avg IOU: 0.723254, Class: 0.152650, Obj: 0.118844, No Obj: 0.000229, .5R: 0.882353, .75R: 0.529412, count: 17 -Region 16 Avg IOU: 0.744826, Class: 0.169871, Obj: 0.309220, No Obj: 0.001618, .5R: 0.950000, .75R: 0.500000, count: 20 -Region 23 Avg IOU: 0.766573, Class: 0.172398, Obj: 0.150070, No Obj: 0.000344, .5R: 0.960000, .75R: 0.720000, count: 25 -Region 16 Avg IOU: 0.749666, Class: 0.200928, Obj: 0.224995, No Obj: 0.001735, .5R: 1.000000, .75R: 0.523810, count: 21 -Region 23 Avg IOU: 0.694064, Class: 0.249101, Obj: 0.118533, No Obj: 0.000228, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.753104, Class: 0.303408, Obj: 0.402488, No Obj: 0.001337, .5R: 0.866667, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.727984, Class: 0.209464, Obj: 0.143553, No Obj: 0.000342, .5R: 0.904762, .75R: 0.476190, count: 21 -Region 16 Avg IOU: 0.796859, Class: 0.289230, Obj: 0.460431, No Obj: 0.000922, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.715991, Class: 0.164034, Obj: 0.191447, No Obj: 0.000245, .5R: 1.000000, .75R: 0.368421, count: 19 - - 3039: 2.432945, 2.369559 avg loss, 0.001000 rate, 2.416965 seconds, 388992 images -Loaded: 0.000068 seconds -Region 16 Avg IOU: 0.774362, Class: 0.164253, Obj: 0.211697, No Obj: 0.000919, .5R: 1.000000, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.709811, Class: 0.208847, Obj: 0.154464, No Obj: 0.000216, .5R: 0.941176, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.709120, Class: 0.209874, Obj: 0.097733, No Obj: 0.000689, .5R: 0.846154, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.667854, Class: 0.165735, Obj: 0.121131, No Obj: 0.000199, .5R: 0.900000, .75R: 0.250000, count: 20 -Region 16 Avg IOU: 0.766058, Class: 0.263002, Obj: 0.363906, No Obj: 0.001314, .5R: 0.937500, .75R: 0.562500, count: 16 -Region 23 Avg IOU: 0.728908, Class: 0.236861, Obj: 0.189592, No Obj: 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count: 21 -Region 16 Avg IOU: 0.711988, Class: 0.198176, Obj: 0.187948, No Obj: 0.001325, .5R: 0.884615, .75R: 0.500000, count: 26 -Region 23 Avg IOU: 0.700793, Class: 0.178269, Obj: 0.030689, No Obj: 0.000198, .5R: 0.818182, .75R: 0.545455, count: 11 -Region 16 Avg IOU: 0.779335, Class: 0.194548, Obj: 0.342223, No Obj: 0.001180, .5R: 1.000000, .75R: 0.600000, count: 20 -Region 23 Avg IOU: 0.750426, Class: 0.180483, Obj: 0.413153, No Obj: 0.000359, .5R: 0.928571, .75R: 0.571429, count: 14 -Region 16 Avg IOU: 0.792479, Class: 0.227386, Obj: 0.429969, No Obj: 0.001001, .5R: 1.000000, .75R: 0.846154, count: 13 -Region 23 Avg IOU: 0.764605, Class: 0.194834, Obj: 0.431767, No Obj: 0.000585, .5R: 0.956522, .75R: 0.608696, count: 23 -Region 16 Avg IOU: 0.739324, Class: 0.205001, Obj: 0.360332, No Obj: 0.001408, .5R: 0.958333, .75R: 0.416667, count: 24 -Region 23 Avg IOU: 0.730990, Class: 0.151374, Obj: 0.307281, No Obj: 0.000493, .5R: 1.000000, .75R: 0.541667, count: 24 -Region 16 Avg IOU: 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20 -Region 23 Avg IOU: 0.726160, Class: 0.186309, Obj: 0.192905, No Obj: 0.000260, .5R: 0.950000, .75R: 0.550000, count: 20 -Region 16 Avg IOU: 0.778178, Class: 0.150306, Obj: 0.371601, No Obj: 0.001549, .5R: 0.944444, .75R: 0.666667, count: 18 -Region 23 Avg IOU: 0.734022, Class: 0.225151, Obj: 0.171790, No Obj: 0.000316, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.773463, Class: 0.229265, Obj: 0.294519, No Obj: 0.001665, .5R: 0.958333, .75R: 0.666667, count: 24 -Region 23 Avg IOU: 0.717361, Class: 0.173477, Obj: 0.205117, No Obj: 0.000239, .5R: 0.928571, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.781850, Class: 0.275622, Obj: 0.307060, No Obj: 0.001132, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.781984, Class: 0.262965, Obj: 0.305922, No Obj: 0.000275, .5R: 1.000000, .75R: 0.687500, count: 16 - - 3050: 2.436397, 2.416239 avg loss, 0.001000 rate, 2.738458 seconds, 390400 images -Resizing -448 x 448 - try to allocate workspace = 305201153 * sizeof(float), CUDA allocate done! -Loaded: 0.419759 seconds -Region 16 Avg IOU: 0.676711, Class: 0.283981, Obj: 0.400360, No Obj: 0.001767, .5R: 0.785714, .75R: 0.357143, count: 14 -Region 23 Avg IOU: 0.674738, Class: 0.197480, Obj: 0.109768, No Obj: 0.000341, .5R: 0.937500, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.742290, Class: 0.240727, Obj: 0.274055, No Obj: 0.002368, .5R: 0.944444, .75R: 0.611111, count: 18 -Region 23 Avg IOU: 0.721895, Class: 0.265022, Obj: 0.210558, No Obj: 0.000409, .5R: 0.875000, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.740379, Class: 0.271987, Obj: 0.298066, No Obj: 0.002019, .5R: 0.937500, .75R: 0.437500, count: 16 -Region 23 Avg IOU: 0.740990, Class: 0.221222, Obj: 0.231858, No Obj: 0.000447, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.818146, Class: 0.227117, Obj: 0.384784, No Obj: 0.001426, .5R: 1.000000, .75R: 0.875000, count: 8 -Region 23 Avg IOU: 0.708919, Class: 0.265148, Obj: 0.151767, No Obj: 0.000438, .5R: 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23 - - 3051: 2.149818, 2.389597 avg loss, 0.001000 rate, 0.836388 seconds, 390528 images -Loaded: 0.000060 seconds -Region 16 Avg IOU: 0.747971, Class: 0.265002, Obj: 0.519295, No Obj: 0.002558, .5R: 0.888889, .75R: 0.666667, count: 18 -Region 23 Avg IOU: 0.713605, Class: 0.232920, Obj: 0.216353, No Obj: 0.000301, .5R: 1.000000, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.760685, Class: 0.223240, Obj: 0.390999, No Obj: 0.001408, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.751656, Class: 0.167866, Obj: 0.292621, No Obj: 0.000458, .5R: 1.000000, .75R: 0.611111, count: 18 -Region 16 Avg IOU: 0.765433, Class: 0.185943, Obj: 0.316769, No Obj: 0.001450, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.701585, Class: 0.229498, Obj: 0.155626, No Obj: 0.000419, .5R: 0.866667, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.780478, Class: 0.272961, Obj: 0.418850, No Obj: 0.001991, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.712545, Class: 0.175284, Obj: 0.274745, No Obj: 0.000494, .5R: 0.900000, .75R: 0.600000, count: 10 -Region 16 Avg IOU: 0.667883, Class: 0.205679, Obj: 0.398817, No Obj: 0.001963, .5R: 0.875000, .75R: 0.375000, count: 8 -Region 23 Avg IOU: 0.735230, Class: 0.205581, Obj: 0.206543, No Obj: 0.000422, .5R: 1.000000, .75R: 0.470588, count: 17 -Region 16 Avg IOU: 0.728045, Class: 0.229035, Obj: 0.342409, No Obj: 0.002158, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 23 Avg IOU: 0.706465, Class: 0.139543, Obj: 0.262286, No Obj: 0.000588, .5R: 0.958333, .75R: 0.375000, count: 24 -Region 16 Avg IOU: 0.776976, Class: 0.235072, Obj: 0.317378, No Obj: 0.002411, .5R: 1.000000, .75R: 0.769231, count: 13 -Region 23 Avg IOU: 0.669671, Class: 0.206641, Obj: 0.292182, No Obj: 0.000608, .5R: 0.904762, .75R: 0.333333, count: 21 -Region 16 Avg IOU: 0.728397, Class: 0.174634, Obj: 0.282226, No Obj: 0.001413, .5R: 0.866667, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.748830, Class: 0.197314, Obj: 0.158752, No Obj: 0.000297, .5R: 1.000000, .75R: 0.333333, count: 12 - - 3052: 1.926229, 2.343260 avg loss, 0.001000 rate, 0.901533 seconds, 390656 images -Loaded: 0.000064 seconds -Region 16 Avg IOU: 0.777124, Class: 0.201704, Obj: 0.392148, No Obj: 0.002426, .5R: 1.000000, .75R: 0.647059, count: 17 -Region 23 Avg IOU: 0.695588, Class: 0.184820, Obj: 0.293453, No Obj: 0.001084, .5R: 0.968750, .75R: 0.218750, count: 32 -Region 16 Avg IOU: 0.813047, Class: 0.281338, Obj: 0.402890, No Obj: 0.001735, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.692555, Class: 0.246995, Obj: 0.176371, No Obj: 0.000934, .5R: 0.935484, .75R: 0.419355, count: 31 -Region 16 Avg IOU: 0.797330, Class: 0.247708, Obj: 0.489889, No Obj: 0.001535, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.694531, Class: 0.144409, Obj: 0.321365, No Obj: 0.000895, .5R: 0.956522, .75R: 0.347826, count: 23 -Region 16 Avg IOU: 0.819865, Class: 0.153500, Obj: 0.393933, No Obj: 0.001656, .5R: 1.000000, .75R: 1.000000, count: 9 -Region 23 Avg IOU: 0.686274, Class: 0.165015, Obj: 0.262171, No Obj: 0.000685, .5R: 0.944444, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.715053, Class: 0.243086, Obj: 0.617160, No Obj: 0.001044, .5R: 1.000000, .75R: 0.500000, count: 2 -Region 23 Avg IOU: 0.690059, Class: 0.190822, Obj: 0.259084, No Obj: 0.000505, .5R: 0.909091, .75R: 0.363636, count: 11 -Region 16 Avg IOU: 0.680113, Class: 0.253125, Obj: 0.208580, No Obj: 0.001347, .5R: 0.909091, .75R: 0.272727, count: 11 -Region 23 Avg IOU: 0.656415, Class: 0.239467, Obj: 0.245251, No Obj: 0.000591, .5R: 0.950000, .75R: 0.100000, count: 20 -Region 16 Avg IOU: 0.789771, Class: 0.165226, Obj: 0.503401, No Obj: 0.002079, .5R: 1.000000, .75R: 0.846154, count: 13 -Region 23 Avg IOU: 0.766555, Class: 0.189506, Obj: 0.321524, No Obj: 0.000804, .5R: 1.000000, .75R: 0.583333, count: 24 -Region 16 Avg IOU: 0.745653, Class: 0.237929, Obj: 0.283513, No Obj: 0.002421, .5R: 0.894737, .75R: 0.631579, count: 19 -Region 23 Avg IOU: 0.779172, Class: 0.158224, Obj: 0.251393, No Obj: 0.000690, .5R: 1.000000, .75R: 0.550000, count: 20 - - 3053: 2.268028, 2.335737 avg loss, 0.001000 rate, 1.449321 seconds, 390784 images -Loaded: 0.000063 seconds -Region 16 Avg IOU: 0.757716, Class: 0.266209, Obj: 0.301987, No Obj: 0.001123, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.719907, Class: 0.175659, Obj: 0.367246, No Obj: 0.000731, .5R: 0.916667, .75R: 0.541667, count: 24 -Region 16 Avg IOU: 0.783596, Class: 0.263793, Obj: 0.479910, No Obj: 0.002240, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.739729, Class: 0.181991, Obj: 0.430692, No Obj: 0.001157, .5R: 0.928571, .75R: 0.607143, count: 28 -Region 16 Avg IOU: 0.798303, Class: 0.242039, Obj: 0.405325, No Obj: 0.001328, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.701457, Class: 0.148359, Obj: 0.240846, No Obj: 0.000384, .5R: 0.857143, .75R: 0.571429, count: 7 -Region 16 Avg IOU: 0.759705, Class: 0.176637, Obj: 0.398824, No 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IOU: 0.741416, Class: 0.172930, Obj: 0.230031, No Obj: 0.001384, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.732290, Class: 0.231387, Obj: 0.211781, No Obj: 0.000526, .5R: 0.909091, .75R: 0.636364, count: 11 -Region 16 Avg IOU: 0.806646, Class: 0.273284, Obj: 0.403360, No Obj: 0.001633, .5R: 1.000000, .75R: 0.750000, count: 16 -Region 23 Avg IOU: 0.738300, Class: 0.139263, Obj: 0.160909, No Obj: 0.000460, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 16 Avg IOU: 0.790687, Class: 0.310782, Obj: 0.439715, No Obj: 0.001727, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.684603, Class: 0.205824, Obj: 0.252176, No Obj: 0.000479, .5R: 1.000000, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.800137, Class: 0.210279, Obj: 0.395371, No Obj: 0.001513, .5R: 0.928571, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.761480, Class: 0.133866, Obj: 0.248641, No Obj: 0.000363, .5R: 1.000000, .75R: 0.444444, count: 9 -Region 16 Avg IOU: 0.765948, Class: 0.278188, Obj: 0.393606, No Obj: 0.001262, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.698204, Class: 0.193894, Obj: 0.168880, No Obj: 0.000353, .5R: 0.947368, .75R: 0.263158, count: 19 - - 3055: 1.779218, 2.240727 avg loss, 0.001000 rate, 1.291994 seconds, 391040 images -Loaded: 0.000078 seconds -Region 16 Avg IOU: 0.792780, Class: 0.172821, Obj: 0.285864, No Obj: 0.001914, .5R: 1.000000, .75R: 0.562500, count: 16 -Region 23 Avg IOU: 0.724663, Class: 0.196688, Obj: 0.197996, No Obj: 0.000459, .5R: 0.944444, .75R: 0.555556, count: 18 -Region 16 Avg IOU: 0.768363, Class: 0.347468, Obj: 0.564241, No Obj: 0.001043, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.732850, Class: 0.204621, Obj: 0.247278, No Obj: 0.000424, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 16 Avg IOU: 0.755743, Class: 0.285229, Obj: 0.274409, No Obj: 0.001415, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.658453, Class: 0.185582, Obj: 0.090609, No Obj: 0.000277, .5R: 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20 -Region 16 Avg IOU: 0.757110, Class: 0.200276, Obj: 0.392122, No Obj: 0.001925, .5R: 0.952381, .75R: 0.619048, count: 21 -Region 23 Avg IOU: 0.776042, Class: 0.100174, Obj: 0.259456, No Obj: 0.000376, .5R: 1.000000, .75R: 0.687500, count: 16 - - 3056: 1.856533, 2.202308 avg loss, 0.001000 rate, 2.123331 seconds, 391168 images -Loaded: 0.000081 seconds -Region 16 Avg IOU: 0.791436, Class: 0.220324, Obj: 0.371548, No Obj: 0.001967, .5R: 1.000000, .75R: 0.833333, count: 12 -Region 23 Avg IOU: 0.681961, Class: 0.208510, Obj: 0.118598, No Obj: 0.000544, .5R: 0.846154, .75R: 0.461538, count: 26 -Region 16 Avg IOU: 0.766831, Class: 0.164648, Obj: 0.281040, No Obj: 0.001326, .5R: 1.000000, .75R: 0.687500, count: 16 -Region 23 Avg IOU: 0.734551, Class: 0.208670, Obj: 0.183324, No Obj: 0.000363, .5R: 1.000000, .75R: 0.454545, count: 22 -Region 16 Avg IOU: 0.744289, Class: 0.221436, Obj: 0.332496, No Obj: 0.001454, .5R: 0.900000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.683084, Class: 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0.619928, Class: 0.167691, Obj: 0.297576, No Obj: 0.000355, .5R: 0.833333, .75R: 0.166667, count: 12 -Region 16 Avg IOU: 0.726366, Class: 0.226729, Obj: 0.463165, No Obj: 0.002229, .5R: 0.941176, .75R: 0.647059, count: 17 -Region 23 Avg IOU: 0.739204, Class: 0.179115, Obj: 0.172593, No Obj: 0.000393, .5R: 1.000000, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.788781, Class: 0.258104, Obj: 0.345614, No Obj: 0.001315, .5R: 1.000000, .75R: 0.714286, count: 21 -Region 23 Avg IOU: 0.699127, Class: 0.252242, Obj: 0.209368, No Obj: 0.000308, .5R: 0.941176, .75R: 0.411765, count: 17 - - 3061: 2.377794, 2.172692 avg loss, 0.001000 rate, 2.774588 seconds, 391808 images -Loaded: 0.000075 seconds -Region 16 Avg IOU: 0.750255, Class: 0.252418, Obj: 0.401699, No Obj: 0.001887, .5R: 0.947368, .75R: 0.684211, count: 19 -Region 23 Avg IOU: 0.705180, Class: 0.184260, Obj: 0.162265, No Obj: 0.000238, .5R: 0.941176, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.671573, Class: 0.204813, Obj: 0.271853, 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images -Loaded: 0.000078 seconds -Region 16 Avg IOU: 0.780723, Class: 0.209967, Obj: 0.512889, No Obj: 0.001377, .5R: 0.933333, .75R: 0.800000, count: 15 -Region 23 Avg IOU: 0.766010, Class: 0.156126, Obj: 0.209786, No Obj: 0.000275, .5R: 1.000000, .75R: 0.476190, count: 21 -Region 16 Avg IOU: 0.735920, Class: 0.263644, Obj: 0.499799, No Obj: 0.001592, .5R: 1.000000, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.725755, Class: 0.175957, Obj: 0.199530, No Obj: 0.000188, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.730201, Class: 0.212707, Obj: 0.312330, No Obj: 0.001755, .5R: 0.884615, .75R: 0.500000, count: 26 -Region 23 Avg IOU: 0.701256, Class: 0.233518, Obj: 0.198636, No Obj: 0.000249, .5R: 0.923077, .75R: 0.384615, count: 26 -Region 16 Avg IOU: 0.728106, Class: 0.200548, Obj: 0.434961, No Obj: 0.001692, .5R: 1.000000, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 0.733700, Class: 0.213261, Obj: 0.172445, No Obj: 0.000284, .5R: 1.000000, .75R: 0.409091, count: 22 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0.232123, Obj: 0.273285, No Obj: 0.000928, .5R: 1.000000, .75R: 0.619048, count: 21 -Region 23 Avg IOU: 0.705807, Class: 0.139188, Obj: 0.269914, No Obj: 0.000395, .5R: 1.000000, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.759650, Class: 0.203788, Obj: 0.325206, No Obj: 0.001042, .5R: 1.000000, .75R: 0.611111, count: 18 -Region 23 Avg IOU: 0.733159, Class: 0.075415, Obj: 0.466730, No Obj: 0.000437, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 16 Avg IOU: 0.691669, Class: 0.225331, Obj: 0.286011, No Obj: 0.001050, .5R: 0.896552, .75R: 0.379310, count: 29 -Region 23 Avg IOU: 0.711392, Class: 0.204657, Obj: 0.241340, No Obj: 0.000338, .5R: 0.954545, .75R: 0.318182, count: 22 -Region 16 Avg IOU: 0.726485, Class: 0.250432, Obj: 0.262424, No Obj: 0.001189, .5R: 1.000000, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.693428, Class: 0.214316, Obj: 0.186551, No Obj: 0.000319, .5R: 1.000000, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.748371, Class: 0.184983, Obj: 0.278559, No Obj: 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count: 15 -Region 16 Avg IOU: 0.720282, Class: 0.212588, Obj: 0.204003, No Obj: 0.000924, .5R: 0.966667, .75R: 0.433333, count: 30 -Region 23 Avg IOU: 0.706174, Class: 0.180817, Obj: 0.247134, No Obj: 0.000327, .5R: 0.888889, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.743708, Class: 0.240826, Obj: 0.231489, No Obj: 0.001132, .5R: 0.931035, .75R: 0.551724, count: 29 -Region 23 Avg IOU: 0.684444, Class: 0.205887, Obj: 0.190461, No Obj: 0.000370, .5R: 0.967742, .75R: 0.354839, count: 31 -Region 16 Avg IOU: 0.696799, Class: 0.182745, Obj: 0.243730, No Obj: 0.000830, .5R: 0.933333, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.720083, Class: 0.153936, Obj: 0.208151, No Obj: 0.000323, .5R: 0.950000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.804568, Class: 0.191963, Obj: 0.294320, No Obj: 0.000896, .5R: 1.000000, .75R: 0.823529, count: 17 -Region 23 Avg IOU: 0.722079, Class: 0.148670, Obj: 0.193230, No Obj: 0.000210, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 16 Avg IOU: 0.816583, Class: 0.323405, Obj: 0.420041, No Obj: 0.000540, .5R: 1.000000, .75R: 0.888889, count: 9 -Region 23 Avg IOU: 0.720806, Class: 0.123238, Obj: 0.375110, No Obj: 0.000206, .5R: 0.900000, .75R: 0.300000, count: 10 - - 3072: 2.675367, 2.497754 avg loss, 0.001000 rate, 4.332729 seconds, 393216 images -Loaded: 0.000077 seconds -Region 16 Avg IOU: 0.730367, Class: 0.233646, Obj: 0.189444, No Obj: 0.000900, .5R: 0.954545, .75R: 0.636364, count: 22 -Region 23 Avg IOU: 0.760165, Class: 0.159015, Obj: 0.199338, No Obj: 0.000162, .5R: 1.000000, .75R: 0.562500, count: 16 -Region 16 Avg IOU: 0.723516, Class: 0.212070, Obj: 0.378065, No Obj: 0.001058, .5R: 0.857143, .75R: 0.619048, count: 21 -Region 23 Avg IOU: 0.687022, Class: 0.193408, Obj: 0.183664, No Obj: 0.000220, .5R: 0.900000, .75R: 0.300000, count: 20 -Region 16 Avg IOU: 0.732504, Class: 0.189841, Obj: 0.182586, No Obj: 0.001334, .5R: 0.972222, .75R: 0.527778, count: 36 -Region 23 Avg IOU: 0.776573, Class: 0.148924, Obj: 0.287981, No Obj: 0.000224, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 16 Avg IOU: 0.727290, Class: 0.216040, Obj: 0.267566, No Obj: 0.001161, .5R: 0.956522, .75R: 0.521739, count: 23 -Region 23 Avg IOU: 0.718190, Class: 0.222091, Obj: 0.105523, No Obj: 0.000184, .5R: 0.916667, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.766143, Class: 0.200477, Obj: 0.226151, No Obj: 0.001155, .5R: 1.000000, .75R: 0.666667, count: 18 -Region 23 Avg IOU: 0.742911, Class: 0.261685, Obj: 0.225833, No Obj: 0.000383, .5R: 0.958333, .75R: 0.541667, count: 24 -Region 16 Avg IOU: 0.717881, Class: 0.223624, Obj: 0.339164, No Obj: 0.000968, .5R: 0.952381, .75R: 0.571429, count: 21 -Region 23 Avg IOU: 0.699324, Class: 0.238057, Obj: 0.223898, No Obj: 0.000222, .5R: 0.882353, .75R: 0.470588, count: 17 -Region 16 Avg IOU: 0.780628, Class: 0.141439, Obj: 0.324707, No Obj: 0.000950, .5R: 1.000000, .75R: 0.733333, count: 15 -Region 23 Avg IOU: 0.743213, Class: 0.167039, Obj: 0.252985, No Obj: 0.000232, .5R: 1.000000, .75R: 0.500000, count: 14 -Region 16 Avg IOU: 0.751738, Class: 0.201883, Obj: 0.195691, No Obj: 0.000621, .5R: 0.933333, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.691360, Class: 0.242817, Obj: 0.214674, No Obj: 0.000252, .5R: 0.909091, .75R: 0.409091, count: 22 - - 3073: 2.676925, 2.515671 avg loss, 0.001000 rate, 4.130575 seconds, 393344 images -Loaded: 0.000071 seconds -Region 16 Avg IOU: 0.743524, Class: 0.229487, Obj: 0.302572, No Obj: 0.001267, .5R: 1.000000, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.704180, Class: 0.155134, Obj: 0.065704, No Obj: 0.000141, .5R: 1.000000, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.742421, Class: 0.203249, Obj: 0.209231, No Obj: 0.001147, .5R: 0.916667, .75R: 0.500000, count: 24 -Region 23 Avg IOU: 0.746813, Class: 0.150392, Obj: 0.162645, No Obj: 0.000156, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 16 Avg IOU: 0.686035, Class: 0.240846, Obj: 0.189666, No Obj: 0.001360, .5R: 0.913043, .75R: 0.260870, count: 23 -Region 23 Avg IOU: 0.714888, Class: 0.216659, Obj: 0.164061, No Obj: 0.000186, .5R: 0.875000, .75R: 0.458333, count: 24 -Region 16 Avg IOU: 0.707635, Class: 0.239109, Obj: 0.324518, No Obj: 0.001642, .5R: 0.888889, .75R: 0.444444, count: 27 -Region 23 Avg IOU: 0.756146, Class: 0.181387, Obj: 0.140943, No Obj: 0.000188, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 16 Avg IOU: 0.733740, Class: 0.277145, Obj: 0.385250, No Obj: 0.001396, .5R: 0.904762, .75R: 0.523810, count: 21 -Region 23 Avg IOU: 0.736747, Class: 0.176377, Obj: 0.162377, No Obj: 0.000149, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.743013, Class: 0.217203, Obj: 0.229143, No Obj: 0.001236, .5R: 0.956522, .75R: 0.478261, count: 23 -Region 23 Avg IOU: 0.694959, Class: 0.228084, Obj: 0.088235, No Obj: 0.000108, .5R: 1.000000, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.768350, Class: 0.265911, Obj: 0.428912, No Obj: 0.001352, .5R: 0.950000, .75R: 0.700000, count: 20 -Region 23 Avg IOU: 0.686789, Class: 0.181036, 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0.950000, .75R: 0.650000, count: 20 -Region 23 Avg IOU: 0.718767, Class: 0.216881, Obj: 0.137583, No Obj: 0.000167, .5R: 1.000000, .75R: 0.380952, count: 21 -Region 16 Avg IOU: 0.712925, Class: 0.182516, Obj: 0.279155, No Obj: 0.001328, .5R: 0.875000, .75R: 0.500000, count: 24 -Region 23 Avg IOU: 0.735738, Class: 0.179850, Obj: 0.150821, No Obj: 0.000165, .5R: 1.000000, .75R: 0.466667, count: 15 -Region 16 Avg IOU: 0.780559, Class: 0.199688, Obj: 0.435934, No Obj: 0.001407, .5R: 1.000000, .75R: 0.705882, count: 17 -Region 23 Avg IOU: 0.709297, Class: 0.166658, Obj: 0.233438, No Obj: 0.000249, .5R: 0.888889, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.752140, Class: 0.213974, Obj: 0.407380, No Obj: 0.002247, .5R: 1.000000, .75R: 0.533333, count: 30 -Region 23 Avg IOU: 0.738115, Class: 0.216697, Obj: 0.149087, No Obj: 0.000248, .5R: 1.000000, .75R: 0.416667, count: 24 -Region 16 Avg IOU: 0.774507, Class: 0.141723, Obj: 0.465532, No Obj: 0.001438, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.706178, Class: 0.127588, Obj: 0.257305, No Obj: 0.000342, .5R: 0.862069, .75R: 0.413793, count: 29 -Region 16 Avg IOU: 0.666707, Class: 0.257102, Obj: 0.336403, No Obj: 0.001312, .5R: 0.857143, .75R: 0.380952, count: 21 -Region 23 Avg IOU: 0.725839, Class: 0.170682, Obj: 0.078879, No Obj: 0.000134, .5R: 0.846154, .75R: 0.615385, count: 13 - - 3075: 2.966146, 2.571449 avg loss, 0.001000 rate, 2.542393 seconds, 393600 images -Loaded: 0.000077 seconds -Region 16 Avg IOU: 0.720688, Class: 0.267871, Obj: 0.295610, No Obj: 0.001478, .5R: 0.923077, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.666260, Class: 0.186644, Obj: 0.206373, No Obj: 0.000205, .5R: 0.916667, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.734189, Class: 0.188045, Obj: 0.414738, No Obj: 0.001015, .5R: 0.866667, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.736591, Class: 0.184032, Obj: 0.195407, No Obj: 0.000274, .5R: 1.000000, .75R: 0.478261, count: 23 -Region 16 Avg IOU: 0.676121, Class: 0.185120, Obj: 0.327487, No Obj: 0.001440, .5R: 0.800000, .75R: 0.400000, count: 20 -Region 23 Avg IOU: 0.672679, Class: 0.187451, Obj: 0.080257, No Obj: 0.000204, .5R: 0.916667, .75R: 0.250000, count: 24 -Region 16 Avg IOU: 0.797143, Class: 0.234245, Obj: 0.503794, No Obj: 0.002012, .5R: 1.000000, .75R: 0.851852, count: 27 -Region 23 Avg IOU: 0.766400, Class: 0.236336, Obj: 0.242478, No Obj: 0.000243, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 16 Avg IOU: 0.758768, Class: 0.245850, Obj: 0.354739, No Obj: 0.001273, .5R: 0.937500, .75R: 0.750000, count: 16 -Region 23 Avg IOU: 0.697410, Class: 0.241373, Obj: 0.196910, No Obj: 0.000289, .5R: 0.928571, .75R: 0.464286, count: 28 -Region 16 Avg IOU: 0.794485, Class: 0.248834, Obj: 0.434827, No Obj: 0.001273, .5R: 0.928571, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.696611, Class: 0.201907, Obj: 0.169481, No Obj: 0.000223, .5R: 1.000000, .75R: 0.222222, count: 18 -Region 16 Avg IOU: 0.708603, Class: 0.249609, Obj: 0.473013, No Obj: 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30 -Region 23 Avg IOU: 0.651609, Class: 0.156886, Obj: 0.170492, No Obj: 0.000256, .5R: 0.814815, .75R: 0.481481, count: 27 -Region 16 Avg IOU: 0.727833, Class: 0.248598, Obj: 0.212798, No Obj: 0.001296, .5R: 0.906250, .75R: 0.531250, count: 32 -Region 23 Avg IOU: 0.696332, Class: 0.199346, Obj: 0.075141, No Obj: 0.000194, .5R: 0.875000, .75R: 0.437500, count: 32 -Region 16 Avg IOU: 0.705765, Class: 0.241767, Obj: 0.259235, No Obj: 0.000745, .5R: 0.882353, .75R: 0.411765, count: 17 -Region 23 Avg IOU: 0.742995, Class: 0.163507, Obj: 0.252412, No Obj: 0.000212, .5R: 1.000000, .75R: 0.500000, count: 18 - - 3080: 3.144083, 2.637114 avg loss, 0.001000 rate, 3.861256 seconds, 394240 images -Resizing -576 x 576 - try to allocate workspace = 1146159105 * sizeof(float), CUDA allocate done! -Loaded: 0.488473 seconds -Region 16 Avg IOU: 0.745782, Class: 0.147447, Obj: 0.272271, No Obj: 0.001018, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.688908, Class: 0.195213, Obj: 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Class: 0.196827, Obj: 0.302986, No Obj: 0.000363, .5R: 0.916667, .75R: 0.500000, count: 12 -Region 16 Avg IOU: 0.731729, Class: 0.224881, Obj: 0.379579, No Obj: 0.002800, .5R: 0.954545, .75R: 0.500000, count: 22 -Region 23 Avg IOU: 0.735999, Class: 0.181674, Obj: 0.207127, No Obj: 0.000536, .5R: 0.928571, .75R: 0.571429, count: 28 -Region 16 Avg IOU: 0.772802, Class: 0.243372, Obj: 0.365153, No Obj: 0.001766, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.679934, Class: 0.234255, Obj: 0.159916, No Obj: 0.000323, .5R: 0.833333, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.756143, Class: 0.236907, Obj: 0.455068, No Obj: 0.002041, .5R: 0.947368, .75R: 0.736842, count: 19 -Region 23 Avg IOU: 0.743029, Class: 0.174667, Obj: 0.214630, No Obj: 0.000291, .5R: 1.000000, .75R: 0.500000, count: 14 - - 3082: 2.401283, 2.600437 avg loss, 0.001000 rate, 3.057043 seconds, 394496 images -Loaded: 0.000068 seconds -Region 16 Avg IOU: 0.805289, Class: 0.162306, Obj: 0.499934, No Obj: 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count: 19 -Region 16 Avg IOU: 0.721627, Class: 0.257585, Obj: 0.597836, No Obj: 0.001315, .5R: 0.888889, .75R: 0.444444, count: 9 -Region 23 Avg IOU: 0.704371, Class: 0.153370, Obj: 0.240151, No Obj: 0.000509, .5R: 0.947368, .75R: 0.263158, count: 19 -Region 16 Avg IOU: 0.779681, Class: 0.214064, Obj: 0.326916, No Obj: 0.001737, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.725958, Class: 0.167730, Obj: 0.198346, No Obj: 0.000730, .5R: 0.928571, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.763476, Class: 0.161363, Obj: 0.339364, No Obj: 0.001498, .5R: 0.900000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.667887, Class: 0.132179, Obj: 0.188421, No Obj: 0.000602, .5R: 0.900000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.794008, Class: 0.191349, Obj: 0.358024, No Obj: 0.001470, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.735869, Class: 0.214939, Obj: 0.238654, No Obj: 0.000662, .5R: 1.000000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 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rate, 3.941150 seconds, 396928 images -Loaded: 0.000064 seconds -Region 16 Avg IOU: 0.708909, Class: 0.214676, Obj: 0.339483, No Obj: 0.001862, .5R: 0.958333, .75R: 0.333333, count: 24 -Region 23 Avg IOU: 0.684112, Class: 0.142681, Obj: 0.109439, No Obj: 0.000269, .5R: 0.882353, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.719328, Class: 0.162242, Obj: 0.303895, No Obj: 0.001380, .5R: 0.904762, .75R: 0.571429, count: 21 -Region 23 Avg IOU: 0.688863, Class: 0.162188, Obj: 0.117231, No Obj: 0.000168, .5R: 0.882353, .75R: 0.529412, count: 17 -Region 16 Avg IOU: 0.720941, Class: 0.233574, Obj: 0.312756, No Obj: 0.001625, .5R: 0.952381, .75R: 0.428571, count: 21 -Region 23 Avg IOU: 0.677367, Class: 0.216137, Obj: 0.153890, No Obj: 0.000144, .5R: 0.916667, .75R: 0.333333, count: 12 -Region 16 Avg IOU: 0.665187, Class: 0.228584, Obj: 0.136569, No Obj: 0.001249, .5R: 0.947368, .75R: 0.210526, count: 19 -Region 23 Avg IOU: 0.711553, Class: 0.240720, Obj: 0.124374, No Obj: 0.000280, .5R: 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15 - - 3102: 2.403855, 2.224029 avg loss, 0.001000 rate, 3.989771 seconds, 397056 images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.789071, Class: 0.313148, Obj: 0.293588, No Obj: 0.000834, .5R: 1.000000, .75R: 0.800000, count: 20 -Region 23 Avg IOU: 0.703281, Class: 0.210851, Obj: 0.139794, No Obj: 0.000213, .5R: 0.928571, .75R: 0.464286, count: 28 -Region 16 Avg IOU: 0.629942, Class: 0.280707, Obj: 0.173435, No Obj: 0.000846, .5R: 0.692308, .75R: 0.307692, count: 13 -Region 23 Avg IOU: 0.751365, Class: 0.207893, Obj: 0.238808, No Obj: 0.000242, .5R: 0.933333, .75R: 0.600000, count: 15 -Region 16 Avg IOU: 0.775868, Class: 0.257419, Obj: 0.275088, No Obj: 0.000961, .5R: 0.947368, .75R: 0.736842, count: 19 -Region 23 Avg IOU: 0.711901, Class: 0.127058, Obj: 0.180124, No Obj: 0.000167, .5R: 0.933333, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.784649, Class: 0.242998, Obj: 0.341023, No Obj: 0.001024, .5R: 1.000000, .75R: 0.666667, count: 21 -Region 23 Avg IOU: 0.761778, Class: 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21 -Region 16 Avg IOU: 0.739863, Class: 0.219608, Obj: 0.440365, No Obj: 0.001856, .5R: 1.000000, .75R: 0.500000, count: 22 -Region 23 Avg IOU: 0.717909, Class: 0.163139, Obj: 0.242591, No Obj: 0.000432, .5R: 0.882353, .75R: 0.529412, count: 17 - - 3107: 2.488908, 2.381020 avg loss, 0.001000 rate, 2.665525 seconds, 397696 images -Loaded: 0.000080 seconds -Region 16 Avg IOU: 0.697436, Class: 0.253796, Obj: 0.682346, No Obj: 0.001107, .5R: 1.000000, .75R: 0.266667, count: 15 -Region 23 Avg IOU: 0.720052, Class: 0.204396, Obj: 0.303456, No Obj: 0.000313, .5R: 1.000000, .75R: 0.478261, count: 23 -Region 16 Avg IOU: 0.738757, Class: 0.247720, Obj: 0.452675, No Obj: 0.001520, .5R: 1.000000, .75R: 0.560000, count: 25 -Region 23 Avg IOU: 0.720711, Class: 0.201665, Obj: 0.206587, No Obj: 0.000410, .5R: 0.902439, .75R: 0.463415, count: 41 -Region 16 Avg IOU: 0.739526, Class: 0.279669, Obj: 0.415428, No Obj: 0.001636, .5R: 0.937500, .75R: 0.562500, count: 16 -Region 23 Avg IOU: 0.807617, Class: 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count: 15 -Region 23 Avg IOU: 0.747623, Class: 0.213262, Obj: 0.309816, No Obj: 0.000297, .5R: 0.875000, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.714775, Class: 0.199643, Obj: 0.436770, No Obj: 0.002769, .5R: 0.935484, .75R: 0.451613, count: 31 -Region 23 Avg IOU: 0.747727, Class: 0.184600, Obj: 0.144049, No Obj: 0.000408, .5R: 1.000000, .75R: 0.500000, count: 18 -Region 16 Avg IOU: 0.706739, Class: 0.221365, Obj: 0.233265, No Obj: 0.001090, .5R: 1.000000, .75R: 0.250000, count: 16 -Region 23 Avg IOU: 0.684193, Class: 0.137623, Obj: 0.147980, No Obj: 0.000220, .5R: 0.857143, .75R: 0.380952, count: 21 -Region 16 Avg IOU: 0.751472, Class: 0.254587, Obj: 0.457019, No Obj: 0.001302, .5R: 1.000000, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.728495, Class: 0.203421, Obj: 0.387060, No Obj: 0.000221, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 16 Avg IOU: 0.789960, Class: 0.229768, Obj: 0.313386, No Obj: 0.001638, .5R: 1.000000, .75R: 0.650000, count: 20 -Region 23 Avg IOU: 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images -Loaded: 0.000065 seconds -Region 16 Avg IOU: 0.762578, Class: 0.257275, Obj: 0.406898, No Obj: 0.000752, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.720639, Class: 0.179242, Obj: 0.102890, No Obj: 0.000177, .5R: 0.944444, .75R: 0.555556, count: 18 -Region 16 Avg IOU: 0.811832, Class: 0.244930, Obj: 0.485967, No Obj: 0.001333, .5R: 1.000000, .75R: 0.722222, count: 18 -Region 23 Avg IOU: 0.631276, Class: 0.173062, Obj: 0.159966, No Obj: 0.000220, .5R: 0.857143, .75R: 0.238095, count: 21 -Region 16 Avg IOU: 0.806085, Class: 0.263016, Obj: 0.436485, No Obj: 0.001264, .5R: 1.000000, .75R: 0.800000, count: 15 -Region 23 Avg IOU: 0.696509, Class: 0.158637, Obj: 0.299149, No Obj: 0.000193, .5R: 0.769231, .75R: 0.615385, count: 13 -Region 16 Avg IOU: 0.762574, Class: 0.281647, Obj: 0.201504, No Obj: 0.000952, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.755347, Class: 0.187368, Obj: 0.222910, No Obj: 0.000264, .5R: 1.000000, .75R: 0.545455, count: 22 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18 -Region 16 Avg IOU: 0.750711, Class: 0.239111, Obj: 0.279517, No Obj: 0.001678, .5R: 1.000000, .75R: 0.555556, count: 27 -Region 23 Avg IOU: 0.751108, Class: 0.166335, Obj: 0.219168, No Obj: 0.000338, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 16 Avg IOU: 0.715323, Class: 0.272536, Obj: 0.365843, No Obj: 0.001439, .5R: 0.894737, .75R: 0.421053, count: 19 -Region 23 Avg IOU: 0.626529, Class: 0.212871, Obj: 0.108985, No Obj: 0.000169, .5R: 0.812500, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.763414, Class: 0.249357, Obj: 0.477695, No Obj: 0.001210, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.712483, Class: 0.220315, Obj: 0.249024, No Obj: 0.000377, .5R: 0.961538, .75R: 0.500000, count: 26 - - 3150: 2.959476, 2.461042 avg loss, 0.001000 rate, 3.218416 seconds, 403200 images -Resizing -640 x 640 - try to allocate workspace = 1147404289 * sizeof(float), CUDA allocate done! -Loaded: 0.584608 seconds -Region 16 Avg IOU: 0.759076, Class: 0.213270, Obj: 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16 Avg IOU: 0.770321, Class: 0.133661, Obj: 0.317326, No Obj: 0.000823, .5R: 1.000000, .75R: 0.500000, count: 4 -Region 23 Avg IOU: 0.677812, Class: 0.158486, Obj: 0.194048, No Obj: 0.000428, .5R: 0.866667, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.710232, Class: 0.226113, Obj: 0.194562, No Obj: 0.001593, .5R: 0.928571, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.728781, Class: 0.195967, Obj: 0.138846, No Obj: 0.000654, .5R: 0.968750, .75R: 0.375000, count: 32 -Region 16 Avg IOU: 0.798214, Class: 0.206463, Obj: 0.375201, No Obj: 0.001204, .5R: 1.000000, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.710316, Class: 0.226084, Obj: 0.220325, No Obj: 0.000451, .5R: 0.947368, .75R: 0.421053, count: 19 - - 3166: 1.890066, 2.212512 avg loss, 0.001000 rate, 1.641558 seconds, 405248 images -Loaded: 0.000074 seconds -Region 16 Avg IOU: 0.792596, Class: 0.197279, Obj: 0.314316, No Obj: 0.001247, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.736848, Class: 0.224432, Obj: 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-Region 23 Avg IOU: 0.652292, Class: 0.207006, Obj: 0.154520, No Obj: 0.000381, .5R: 0.866667, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.751035, Class: 0.206499, Obj: 0.265027, No Obj: 0.000883, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.721117, Class: 0.154354, Obj: 0.235835, No Obj: 0.000687, .5R: 0.928571, .75R: 0.500000, count: 28 -Region 16 Avg IOU: 0.767632, Class: 0.245121, Obj: 0.117413, No Obj: 0.000817, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.705175, Class: 0.157067, Obj: 0.225671, No Obj: 0.000654, .5R: 0.950000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.811760, Class: 0.221836, Obj: 0.300861, No Obj: 0.001513, .5R: 1.000000, .75R: 0.866667, count: 15 -Region 23 Avg IOU: 0.750398, Class: 0.173269, Obj: 0.229057, No Obj: 0.000820, .5R: 1.000000, .75R: 0.620690, count: 29 -Region 16 Avg IOU: 0.756411, Class: 0.284924, Obj: 0.518015, No Obj: 0.001054, .5R: 1.000000, .75R: 0.600000, count: 5 -Region 23 Avg IOU: 0.741016, Class: 0.181862, Obj: 0.241371, No Obj: 0.000542, .5R: 1.000000, .75R: 0.480000, count: 25 -Region 16 Avg IOU: 0.800503, Class: 0.231372, Obj: 0.368228, No Obj: 0.001438, .5R: 1.000000, .75R: 0.687500, count: 16 -Region 23 Avg IOU: 0.747141, Class: 0.190877, Obj: 0.277258, No Obj: 0.000342, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 16 Avg IOU: 0.783951, Class: 0.268643, Obj: 0.433008, No Obj: 0.001609, .5R: 0.933333, .75R: 0.733333, count: 15 -Region 23 Avg IOU: 0.702260, Class: 0.157557, Obj: 0.231198, No Obj: 0.000785, .5R: 0.961538, .75R: 0.346154, count: 26 -Region 16 Avg IOU: 0.758752, Class: 0.249881, Obj: 0.250008, No Obj: 0.001237, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.730002, Class: 0.226480, Obj: 0.189684, No Obj: 0.000614, .5R: 0.923077, .75R: 0.538462, count: 26 - - 3168: 2.176909, 2.159740 avg loss, 0.001000 rate, 1.579277 seconds, 405504 images -Loaded: 0.000063 seconds -Region 16 Avg IOU: 0.717264, Class: 0.154449, Obj: 0.449556, No Obj: 0.001197, .5R: 0.875000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.743230, Class: 0.158655, Obj: 0.306477, No Obj: 0.001042, .5R: 1.000000, .75R: 0.538462, count: 26 -Region 16 Avg IOU: 0.750682, Class: 0.277069, Obj: 0.418542, No Obj: 0.001279, .5R: 0.875000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.712855, Class: 0.200593, Obj: 0.309002, No Obj: 0.000562, .5R: 1.000000, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.818612, Class: 0.284187, Obj: 0.350692, No Obj: 0.000737, .5R: 1.000000, .75R: 1.000000, count: 5 -Region 23 Avg IOU: 0.639299, Class: 0.153940, Obj: 0.274941, No Obj: 0.000451, .5R: 0.833333, .75R: 0.250000, count: 12 -Region 16 Avg IOU: 0.698439, Class: 0.219134, Obj: 0.303726, No Obj: 0.001503, .5R: 0.909091, .75R: 0.363636, count: 11 -Region 23 Avg IOU: 0.738054, Class: 0.211614, Obj: 0.280454, No Obj: 0.000570, .5R: 1.000000, .75R: 0.529412, count: 17 -Region 16 Avg IOU: 0.766732, Class: 0.318900, Obj: 0.331551, No Obj: 0.000967, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.712712, Class: 0.215559, Obj: 0.409046, No Obj: 0.000916, .5R: 0.954545, .75R: 0.363636, count: 22 -Region 16 Avg IOU: 0.729967, Class: 0.264676, Obj: 0.151446, No Obj: 0.001246, .5R: 1.000000, .75R: 0.428571, count: 7 -Region 23 Avg IOU: 0.721379, Class: 0.119589, Obj: 0.251339, No Obj: 0.000977, .5R: 1.000000, .75R: 0.529412, count: 17 -Region 16 Avg IOU: 0.764092, Class: 0.227434, Obj: 0.312038, No Obj: 0.001371, .5R: 0.933333, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.709971, Class: 0.221349, Obj: 0.207816, No Obj: 0.000859, .5R: 0.944444, .75R: 0.472222, count: 36 -Region 16 Avg IOU: 0.721466, Class: 0.314864, Obj: 0.217435, No Obj: 0.001333, .5R: 0.933333, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.684854, Class: 0.217504, Obj: 0.175016, No Obj: 0.000635, .5R: 0.935484, .75R: 0.322581, count: 31 - - 3169: 2.093760, 2.153142 avg loss, 0.001000 rate, 1.663351 seconds, 405632 images -Loaded: 0.000068 seconds -Region 16 Avg IOU: 0.830675, Class: 0.162948, Obj: 0.484912, No Obj: 0.001576, .5R: 1.000000, .75R: 0.923077, count: 13 -Region 23 Avg IOU: 0.690732, Class: 0.136225, Obj: 0.376733, No Obj: 0.000847, .5R: 0.894737, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.751977, Class: 0.197791, Obj: 0.357294, No Obj: 0.001862, .5R: 0.928571, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.705213, Class: 0.239895, Obj: 0.270686, No Obj: 0.000719, .5R: 0.913043, .75R: 0.434783, count: 23 -Region 16 Avg IOU: 0.752986, Class: 0.248130, Obj: 0.451670, No Obj: 0.002107, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.725461, Class: 0.176041, Obj: 0.348773, No Obj: 0.000816, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.732405, Class: 0.264682, Obj: 0.220545, No Obj: 0.001106, .5R: 0.866667, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.721130, Class: 0.267301, Obj: 0.259845, No Obj: 0.000765, .5R: 0.920000, .75R: 0.400000, count: 25 -Region 16 Avg IOU: 0.802524, Class: 0.280776, Obj: 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-Resizing -768 x 768 - try to allocate workspace = 1150287873 * sizeof(float), CUDA allocate done! -Loaded: 0.403792 seconds -Region 16 Avg IOU: 0.658118, Class: 0.183531, Obj: 0.296048, No Obj: 0.001881, .5R: 0.766667, .75R: 0.300000, count: 30 -Region 23 Avg IOU: 0.775398, Class: 0.151050, Obj: 0.142543, No Obj: 0.000153, .5R: 1.000000, .75R: 0.444444, count: 9 -Region 16 Avg IOU: 0.692547, Class: 0.208551, Obj: 0.366962, No Obj: 0.001735, .5R: 0.814815, .75R: 0.555556, count: 27 -Region 23 Avg IOU: 0.709763, Class: 0.128987, Obj: 0.203266, No Obj: 0.000192, .5R: 0.916667, .75R: 0.583333, count: 12 -Region 16 Avg IOU: 0.654727, Class: 0.206644, Obj: 0.270449, No Obj: 0.001395, .5R: 0.818182, .75R: 0.227273, count: 22 -Region 23 Avg IOU: 0.752456, Class: 0.243525, Obj: 0.281087, No Obj: 0.000277, .5R: 0.909091, .75R: 0.454545, count: 11 -Region 16 Avg IOU: 0.737946, Class: 0.259201, Obj: 0.343610, No Obj: 0.001273, .5R: 0.904762, .75R: 0.523810, count: 21 -Region 23 Avg IOU: 0.698650, Class: 0.207882, Obj: 0.271215, No Obj: 0.000194, .5R: 1.000000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.756667, Class: 0.231682, Obj: 0.341628, No Obj: 0.001152, .5R: 0.925926, .75R: 0.666667, count: 27 -Region 23 Avg IOU: 0.736635, Class: 0.231702, Obj: 0.261167, No Obj: 0.000361, .5R: 0.958333, .75R: 0.583333, count: 24 -Region 16 Avg IOU: 0.717768, Class: 0.259148, Obj: 0.279885, No Obj: 0.001651, .5R: 0.960000, .75R: 0.600000, count: 25 -Region 23 Avg IOU: 0.727139, Class: 0.205062, Obj: 0.373233, No Obj: 0.000400, .5R: 0.947368, .75R: 0.421053, count: 19 -Region 16 Avg IOU: 0.735759, Class: 0.202805, Obj: 0.372593, No Obj: 0.000892, .5R: 0.933333, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.721003, Class: 0.140900, Obj: 0.200555, No Obj: 0.000299, .5R: 1.000000, .75R: 0.333333, count: 21 -Region 16 Avg IOU: 0.693368, Class: 0.215557, Obj: 0.336162, No Obj: 0.001282, .5R: 0.904762, .75R: 0.238095, count: 21 -Region 23 Avg IOU: 0.732467, Class: 0.145379, Obj: 0.263910, 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0.224765, Obj: 0.341117, No Obj: 0.001294, .5R: 0.958333, .75R: 0.375000, count: 24 -Region 23 Avg IOU: 0.752268, Class: 0.240376, Obj: 0.230086, No Obj: 0.000150, .5R: 0.933333, .75R: 0.666667, count: 15 -Region 16 Avg IOU: 0.775552, Class: 0.242631, Obj: 0.399632, No Obj: 0.001748, .5R: 1.000000, .75R: 0.678571, count: 28 -Region 23 Avg IOU: 0.715936, Class: 0.182955, Obj: 0.104167, No Obj: 0.000221, .5R: 0.961538, .75R: 0.384615, count: 26 -Region 16 Avg IOU: 0.725453, Class: 0.230781, Obj: 0.334179, No Obj: 0.001308, .5R: 0.906250, .75R: 0.562500, count: 32 -Region 23 Avg IOU: 0.743257, Class: 0.157338, Obj: 0.123142, No Obj: 0.000132, .5R: 1.000000, .75R: 0.565217, count: 23 -Region 16 Avg IOU: 0.764875, Class: 0.304887, Obj: 0.402499, No Obj: 0.000861, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.760512, Class: 0.230172, Obj: 0.226341, No Obj: 0.000220, .5R: 1.000000, .75R: 0.545455, count: 22 -Region 16 Avg IOU: 0.793348, Class: 0.275872, Obj: 0.529760, No Obj: 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0.000060 seconds -Region 16 Avg IOU: 0.734307, Class: 0.210528, Obj: 0.237236, No Obj: 0.001311, .5R: 0.947368, .75R: 0.552632, count: 38 -Region 23 Avg IOU: 0.721772, Class: 0.216595, Obj: 0.233934, No Obj: 0.000206, .5R: 0.928571, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.731927, Class: 0.193777, Obj: 0.302670, No Obj: 0.001137, .5R: 0.964286, .75R: 0.535714, count: 28 -Region 23 Avg IOU: 0.779519, Class: 0.200950, Obj: 0.186167, No Obj: 0.000137, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 16 Avg IOU: 0.758731, Class: 0.207254, Obj: 0.276214, No Obj: 0.001047, .5R: 0.952381, .75R: 0.619048, count: 21 -Region 23 Avg IOU: 0.737645, Class: 0.193243, Obj: 0.121117, No Obj: 0.000124, .5R: 1.000000, .75R: 0.444444, count: 9 -Region 16 Avg IOU: 0.757205, Class: 0.176530, Obj: 0.318331, No Obj: 0.001118, .5R: 1.000000, .75R: 0.720000, count: 25 -Region 23 Avg IOU: 0.728746, Class: 0.226738, Obj: 0.167281, No Obj: 0.000166, .5R: 0.928571, .75R: 0.500000, count: 14 -Region 16 Avg 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22 - - 3188: 3.192091, 2.781081 avg loss, 0.001000 rate, 2.280335 seconds, 408064 images -Loaded: 0.000069 seconds -Region 16 Avg IOU: 0.768357, Class: 0.269893, Obj: 0.293850, No Obj: 0.000933, .5R: 1.000000, .75R: 0.666667, count: 21 -Region 23 Avg IOU: 0.732702, Class: 0.238050, Obj: 0.207537, No Obj: 0.000254, .5R: 0.866667, .75R: 0.466667, count: 15 -Region 16 Avg IOU: 0.742485, Class: 0.216079, Obj: 0.224112, No Obj: 0.001363, .5R: 0.975000, .75R: 0.500000, count: 40 -Region 23 Avg IOU: 0.736547, Class: 0.209130, Obj: 0.115251, No Obj: 0.000238, .5R: 0.928571, .75R: 0.571429, count: 14 -Region 16 Avg IOU: 0.732452, Class: 0.211811, Obj: 0.368727, No Obj: 0.000888, .5R: 0.904762, .75R: 0.428571, count: 21 -Region 23 Avg IOU: 0.725173, Class: 0.167573, Obj: 0.222855, No Obj: 0.000321, .5R: 1.000000, .75R: 0.322581, count: 31 -Region 16 Avg IOU: 0.727371, Class: 0.207323, Obj: 0.296202, No Obj: 0.001065, .5R: 0.916667, .75R: 0.500000, count: 24 -Region 23 Avg IOU: 0.743086, Class: 0.193866, Obj: 0.241608, No Obj: 0.000297, .5R: 0.896552, .75R: 0.551724, count: 29 -Region 16 Avg IOU: 0.741187, Class: 0.178802, Obj: 0.196426, No Obj: 0.000808, .5R: 0.956522, .75R: 0.565217, count: 23 -Region 23 Avg IOU: 0.768289, Class: 0.153595, Obj: 0.164588, No Obj: 0.000168, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 16 Avg IOU: 0.694915, Class: 0.195189, Obj: 0.198443, No Obj: 0.000911, .5R: 0.900000, .75R: 0.450000, count: 20 -Region 23 Avg IOU: 0.716733, Class: 0.154280, Obj: 0.174176, No Obj: 0.000216, .5R: 1.000000, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.716182, Class: 0.192692, Obj: 0.220926, No Obj: 0.000861, .5R: 0.909091, .75R: 0.409091, count: 22 -Region 23 Avg IOU: 0.716074, Class: 0.094854, Obj: 0.270250, No Obj: 0.000197, .5R: 0.923077, .75R: 0.692308, count: 13 -Region 16 Avg IOU: 0.723727, Class: 0.209004, Obj: 0.311050, No Obj: 0.000853, .5R: 0.952381, .75R: 0.571429, count: 21 -Region 23 Avg IOU: 0.700088, Class: 0.128422, Obj: 0.139692, No Obj: 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count: 23 -Region 23 Avg IOU: 0.679794, Class: 0.190408, Obj: 0.123793, No Obj: 0.000252, .5R: 0.916667, .75R: 0.250000, count: 12 -Region 16 Avg IOU: 0.752279, Class: 0.198296, Obj: 0.277494, No Obj: 0.001100, .5R: 1.000000, .75R: 0.739130, count: 23 -Region 23 Avg IOU: 0.740596, Class: 0.171448, Obj: 0.193621, No Obj: 0.000226, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 16 Avg IOU: 0.702393, Class: 0.217763, Obj: 0.222716, No Obj: 0.001014, .5R: 0.920000, .75R: 0.320000, count: 25 -Region 23 Avg IOU: 0.642618, Class: 0.109037, Obj: 0.254904, No Obj: 0.000181, .5R: 0.888889, .75R: 0.333333, count: 9 -Region 16 Avg IOU: 0.745106, Class: 0.188093, Obj: 0.299754, No Obj: 0.001335, .5R: 0.961538, .75R: 0.538462, count: 26 -Region 23 Avg IOU: 0.770447, Class: 0.203298, Obj: 0.182910, No Obj: 0.000345, .5R: 1.000000, .75R: 0.647059, count: 17 -Region 16 Avg IOU: 0.724385, Class: 0.226290, Obj: 0.242618, No Obj: 0.000677, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.778853, Class: 0.175375, Obj: 0.399025, No Obj: 0.000210, .5R: 1.000000, .75R: 0.615385, count: 13 - - 3190: 2.550890, 2.780948 avg loss, 0.001000 rate, 2.229657 seconds, 408320 images -Resizing -544 x 544 - try to allocate workspace = 1145585665 * sizeof(float), CUDA allocate done! -Loaded: 0.435414 seconds -Region 16 Avg IOU: 0.732379, Class: 0.199016, Obj: 0.297368, No Obj: 0.001448, .5R: 1.000000, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.695805, Class: 0.188626, Obj: 0.194497, No Obj: 0.000324, .5R: 0.850000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.769359, Class: 0.146180, Obj: 0.330572, No Obj: 0.001334, .5R: 1.000000, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.729123, Class: 0.159295, Obj: 0.149271, No Obj: 0.000301, .5R: 1.000000, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.737000, Class: 0.188881, Obj: 0.368236, No Obj: 0.001881, .5R: 0.950000, .75R: 0.600000, count: 20 -Region 23 Avg IOU: 0.744837, Class: 0.159143, Obj: 0.208544, No Obj: 0.000493, .5R: 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14 -Region 16 Avg IOU: 0.741656, Class: 0.239104, Obj: 0.413608, No Obj: 0.001276, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.763854, Class: 0.192729, Obj: 0.243733, No Obj: 0.000264, .5R: 1.000000, .75R: 0.545455, count: 11 - - 3191: 2.361982, 2.739051 avg loss, 0.001000 rate, 1.307828 seconds, 408448 images -Loaded: 0.000062 seconds -Region 16 Avg IOU: 0.707325, Class: 0.155440, Obj: 0.334984, No Obj: 0.001641, .5R: 0.842105, .75R: 0.421053, count: 19 -Region 23 Avg IOU: 0.727715, Class: 0.165155, Obj: 0.171944, No Obj: 0.000288, .5R: 0.952381, .75R: 0.476190, count: 21 -Region 16 Avg IOU: 0.773281, Class: 0.191053, Obj: 0.423448, No Obj: 0.001642, .5R: 1.000000, .75R: 0.600000, count: 20 -Region 23 Avg IOU: 0.727109, Class: 0.185385, Obj: 0.164318, No Obj: 0.000191, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.733662, Class: 0.273300, Obj: 0.453786, No Obj: 0.001637, .5R: 0.933333, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.744417, Class: 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rate, 1.150502 seconds, 412160 images -Resizing -640 x 640 - try to allocate workspace = 1147404289 * sizeof(float), CUDA allocate done! -Loaded: 0.239844 seconds -Region 16 Avg IOU: 0.709566, Class: 0.220119, Obj: 0.288275, No Obj: 0.000886, .5R: 1.000000, .75R: 0.388889, count: 18 -Region 23 Avg IOU: 0.683360, Class: 0.159538, Obj: 0.258438, No Obj: 0.000450, .5R: 1.000000, .75R: 0.240000, count: 25 -Region 16 Avg IOU: 0.712904, Class: 0.224827, Obj: 0.281818, No Obj: 0.001071, .5R: 0.941176, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.672330, Class: 0.170558, Obj: 0.221740, No Obj: 0.000385, .5R: 0.896552, .75R: 0.379310, count: 29 -Region 16 Avg IOU: 0.666210, Class: 0.244558, Obj: 0.302970, No Obj: 0.001095, .5R: 0.923077, .75R: 0.230769, count: 13 -Region 23 Avg IOU: 0.726160, Class: 0.142199, Obj: 0.220043, No Obj: 0.000342, .5R: 1.000000, .75R: 0.333333, count: 21 -Region 16 Avg IOU: 0.724285, Class: 0.217210, Obj: 0.257867, No Obj: 0.001399, .5R: 0.968750, .75R: 0.437500, count: 32 -Region 23 Avg IOU: 0.702921, Class: 0.188883, Obj: 0.201470, No Obj: 0.000469, .5R: 0.944444, .75R: 0.444444, count: 18 -Region 16 Avg IOU: 0.712421, Class: 0.186555, Obj: 0.215149, No Obj: 0.001121, .5R: 0.894737, .75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.724954, Class: 0.108372, Obj: 0.247832, No Obj: 0.000334, .5R: 0.866667, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.709530, Class: 0.207511, Obj: 0.209671, No Obj: 0.001224, .5R: 0.866667, .75R: 0.466667, count: 30 -Region 23 Avg IOU: 0.713970, Class: 0.165092, Obj: 0.173463, No Obj: 0.000315, .5R: 0.954545, .75R: 0.500000, count: 22 -Region 16 Avg IOU: 0.700971, Class: 0.220494, Obj: 0.226396, No Obj: 0.001021, .5R: 0.900000, .75R: 0.350000, count: 20 -Region 23 Avg IOU: 0.677115, Class: 0.201133, Obj: 0.235245, No Obj: 0.000538, .5R: 0.846154, .75R: 0.307692, count: 39 -Region 16 Avg IOU: 0.754913, Class: 0.170166, Obj: 0.241568, No Obj: 0.000734, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 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18 -Region 16 Avg IOU: 0.772156, Class: 0.185516, Obj: 0.371326, No Obj: 0.001696, .5R: 1.000000, .75R: 0.700000, count: 20 -Region 23 Avg IOU: 0.773939, Class: 0.149951, Obj: 0.160024, No Obj: 0.000137, .5R: 1.000000, .75R: 0.600000, count: 10 - - 3224: 2.102121, 2.433785 avg loss, 0.001000 rate, 1.737187 seconds, 412672 images -Loaded: 0.000069 seconds -Region 16 Avg IOU: 0.794346, Class: 0.241302, Obj: 0.506902, No Obj: 0.002193, .5R: 1.000000, .75R: 0.739130, count: 23 -Region 23 Avg IOU: 0.705983, Class: 0.144490, Obj: 0.176467, No Obj: 0.000211, .5R: 1.000000, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.747438, Class: 0.199522, Obj: 0.457600, No Obj: 0.001922, .5R: 0.960000, .75R: 0.520000, count: 25 -Region 23 Avg IOU: 0.723475, Class: 0.156595, Obj: 0.201033, No Obj: 0.000264, .5R: 0.923077, .75R: 0.461538, count: 26 -Region 16 Avg IOU: 0.728930, Class: 0.162209, Obj: 0.349323, No Obj: 0.000690, .5R: 0.923077, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.736052, Class: 0.172076, Obj: 0.230577, No Obj: 0.000217, .5R: 0.950000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.773978, Class: 0.247908, Obj: 0.272539, No Obj: 0.001056, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.762280, Class: 0.170122, Obj: 0.086981, No Obj: 0.000105, .5R: 0.944444, .75R: 0.722222, count: 18 -Region 16 Avg IOU: 0.767087, Class: 0.137371, Obj: 0.358625, No Obj: 0.001326, .5R: 1.000000, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.677886, Class: 0.201706, Obj: 0.091379, No Obj: 0.000124, .5R: 0.928571, .75R: 0.357143, count: 14 -Region 16 Avg IOU: 0.722040, Class: 0.204346, Obj: 0.358630, No Obj: 0.001293, .5R: 0.950000, .75R: 0.550000, count: 20 -Region 23 Avg IOU: 0.707847, Class: 0.161316, Obj: 0.112014, No Obj: 0.000160, .5R: 0.941176, .75R: 0.470588, count: 17 -Region 16 Avg IOU: 0.809081, Class: 0.265119, Obj: 0.469889, No Obj: 0.001095, .5R: 1.000000, .75R: 0.733333, count: 15 -Region 23 Avg IOU: 0.725930, Class: 0.121831, Obj: 0.181865, No Obj: 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17 -Region 16 Avg IOU: 0.821409, Class: 0.285203, Obj: 0.343865, No Obj: 0.001289, .5R: 1.000000, .75R: 1.000000, count: 6 -Region 23 Avg IOU: 0.771942, Class: 0.150909, Obj: 0.269711, No Obj: 0.000679, .5R: 1.000000, .75R: 0.733333, count: 15 -Region 16 Avg IOU: 0.807833, Class: 0.157198, Obj: 0.419281, No Obj: 0.001713, .5R: 1.000000, .75R: 0.769231, count: 13 -Region 23 Avg IOU: 0.750896, Class: 0.149161, Obj: 0.161864, No Obj: 0.000571, .5R: 1.000000, .75R: 0.473684, count: 19 -Region 16 Avg IOU: 0.773333, Class: 0.214684, Obj: 0.537606, No Obj: 0.002115, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.780940, Class: 0.215844, Obj: 0.302896, No Obj: 0.000657, .5R: 1.000000, .75R: 0.650000, count: 20 - - 3250: 1.603224, 2.158437 avg loss, 0.001000 rate, 0.772039 seconds, 416000 images -Resizing -416 x 416 - try to allocate workspace = 304758785 * sizeof(float), CUDA allocate done! -Loaded: 0.554685 seconds -Region 16 Avg IOU: 0.764626, Class: 0.275349, Obj: 0.525042, 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images -Loaded: 0.000080 seconds -Region 16 Avg IOU: 0.787045, Class: 0.234033, Obj: 0.399006, No Obj: 0.001952, .5R: 0.928571, .75R: 0.857143, count: 14 -Region 23 Avg IOU: 0.709270, Class: 0.190452, Obj: 0.176421, No Obj: 0.000667, .5R: 0.857143, .75R: 0.523810, count: 21 -Region 16 Avg IOU: 0.695503, Class: 0.198842, Obj: 0.251678, No Obj: 0.002026, .5R: 1.000000, .75R: 0.375000, count: 8 -Region 23 Avg IOU: 0.694113, Class: 0.218210, Obj: 0.161072, No Obj: 0.000702, .5R: 0.950000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.770209, Class: 0.283925, Obj: 0.417845, No Obj: 0.001643, .5R: 1.000000, .75R: 0.600000, count: 5 -Region 23 Avg IOU: 0.742796, Class: 0.220637, Obj: 0.212437, No Obj: 0.000607, .5R: 1.000000, .75R: 0.647059, count: 17 -Region 16 Avg IOU: 0.700313, Class: 0.258224, Obj: 0.482005, No Obj: 0.002796, .5R: 0.909091, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.689346, Class: 0.174734, Obj: 0.215945, No Obj: 0.000589, .5R: 0.882353, .75R: 0.352941, count: 17 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23 Avg IOU: 0.714102, Class: 0.184004, Obj: 0.247623, No Obj: 0.000435, .5R: 1.000000, .75R: 0.538462, count: 13 - - 3256: 1.685321, 2.010813 avg loss, 0.001000 rate, 0.784969 seconds, 416768 images -Loaded: 0.000081 seconds -Region 16 Avg IOU: 0.762722, Class: 0.165732, Obj: 0.199407, No Obj: 0.001461, .5R: 1.000000, .75R: 0.647059, count: 17 -Region 23 Avg IOU: 0.722136, Class: 0.173419, Obj: 0.378124, No Obj: 0.001612, .5R: 0.975610, .75R: 0.463415, count: 41 -Region 16 Avg IOU: 0.728999, Class: 0.244716, Obj: 0.408668, No Obj: 0.000986, .5R: 1.000000, .75R: 0.428571, count: 7 -Region 23 Avg IOU: 0.727240, Class: 0.248779, Obj: 0.279706, No Obj: 0.001018, .5R: 0.958333, .75R: 0.541667, count: 24 -Region 16 Avg IOU: 0.730504, Class: 0.242003, Obj: 0.288962, No Obj: 0.002230, .5R: 1.000000, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.605661, Class: 0.183137, Obj: 0.221709, No Obj: 0.000889, .5R: 0.588235, .75R: 0.176471, count: 17 -Region 16 Avg IOU: 0.750743, Class: 0.264754, 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-Region 16 Avg IOU: 0.770477, Class: 0.202386, Obj: 0.397407, No Obj: 0.001802, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.733517, Class: 0.155318, Obj: 0.273353, No Obj: 0.000770, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.708682, Class: 0.337004, Obj: 0.565813, No Obj: 0.001034, .5R: 1.000000, .75R: 0.200000, count: 5 -Region 23 Avg IOU: 0.759692, Class: 0.243216, Obj: 0.302053, No Obj: 0.000655, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 16 Avg IOU: 0.713651, Class: 0.186574, Obj: 0.275932, No Obj: 0.000597, .5R: 0.750000, .75R: 0.750000, count: 4 -Region 23 Avg IOU: 0.745151, Class: 0.235926, Obj: 0.376277, No Obj: 0.000957, .5R: 1.000000, .75R: 0.529412, count: 17 -Region 16 Avg IOU: 0.756197, Class: 0.331889, Obj: 0.584187, No Obj: 0.000650, .5R: 1.000000, .75R: 0.500000, count: 4 -Region 23 Avg IOU: 0.736292, Class: 0.249474, Obj: 0.370149, No Obj: 0.000710, .5R: 0.833333, .75R: 0.500000, count: 6 -Region 16 Avg IOU: 0.813189, Class: 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count: 24 -Region 16 Avg IOU: 0.710580, Class: 0.244318, Obj: 0.512453, No Obj: 0.001559, .5R: 1.000000, .75R: 0.375000, count: 8 -Region 23 Avg IOU: 0.764051, Class: 0.230667, Obj: 0.283980, No Obj: 0.000782, .5R: 1.000000, .75R: 0.550000, count: 20 - - 3259: 1.838117, 1.977836 avg loss, 0.001000 rate, 0.786998 seconds, 417152 images -Loaded: 0.000074 seconds -Region 16 Avg IOU: 0.818164, Class: 0.204776, Obj: 0.268226, No Obj: 0.001104, .5R: 1.000000, .75R: 0.888889, count: 9 -Region 23 Avg IOU: 0.742739, Class: 0.223641, Obj: 0.109190, No Obj: 0.000216, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 16 Avg IOU: 0.776438, Class: 0.249415, Obj: 0.464588, No Obj: 0.002346, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.695150, Class: 0.226063, Obj: 0.229697, No Obj: 0.000469, .5R: 0.941176, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.812818, Class: 0.223759, Obj: 0.378721, No Obj: 0.002114, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.728419, 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Class: 0.311184, Obj: 0.256946, No Obj: 0.001088, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.706623, Class: 0.205644, Obj: 0.272057, No Obj: 0.000681, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 16 Avg IOU: 0.844564, Class: 0.320027, Obj: 0.323619, No Obj: 0.000824, .5R: 1.000000, .75R: 0.833333, count: 6 -Region 23 Avg IOU: 0.731608, Class: 0.215465, Obj: 0.226757, No Obj: 0.000735, .5R: 0.833333, .75R: 0.611111, count: 18 -Region 16 Avg IOU: 0.778004, Class: 0.135114, Obj: 0.370611, No Obj: 0.000814, .5R: 1.000000, .75R: 0.750000, count: 4 -Region 23 Avg IOU: 0.717468, Class: 0.151438, Obj: 0.324767, No Obj: 0.000803, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 16 Avg IOU: 0.797239, Class: 0.134690, Obj: 0.490771, No Obj: 0.001073, .5R: 1.000000, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.674334, Class: 0.166468, Obj: 0.339793, No Obj: 0.001823, .5R: 0.851852, .75R: 0.407407, count: 27 -Region 16 Avg IOU: 0.781870, Class: 0.158428, Obj: 0.190130, No 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0.024720 seconds -Region 16 Avg IOU: 0.780011, Class: 0.202653, Obj: 0.251694, No Obj: 0.001337, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.698645, Class: 0.200703, Obj: 0.216466, No Obj: 0.000934, .5R: 0.875000, .75R: 0.375000, count: 24 -Region 16 Avg IOU: 0.756851, Class: 0.194661, Obj: 0.271825, No Obj: 0.001857, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.706557, Class: 0.197725, Obj: 0.243056, No Obj: 0.000873, .5R: 0.850000, .75R: 0.500000, count: 20 -Region 16 Avg IOU: 0.780284, Class: 0.205452, Obj: 0.306485, No Obj: 0.001997, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.788276, Class: 0.229394, Obj: 0.229593, No Obj: 0.000785, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 16 Avg IOU: 0.770624, Class: 0.174625, Obj: 0.280365, No Obj: 0.001420, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.747770, Class: 0.126429, Obj: 0.164885, No Obj: 0.001288, .5R: 1.000000, .75R: 0.478261, count: 23 -Region 16 Avg 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-Loaded: 0.000060 seconds -Region 16 Avg IOU: 0.737697, Class: 0.175960, Obj: 0.422366, No Obj: 0.001230, .5R: 0.888889, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.696675, Class: 0.205869, Obj: 0.242387, No Obj: 0.000722, .5R: 0.944444, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.800987, Class: 0.126256, Obj: 0.299540, No Obj: 0.000551, .5R: 1.000000, .75R: 0.666667, count: 3 -Region 23 Avg IOU: 0.757605, Class: 0.152961, Obj: 0.303129, No Obj: 0.000560, .5R: 1.000000, .75R: 0.642857, count: 14 -Region 16 Avg IOU: 0.788143, Class: 0.254764, Obj: 0.352671, No Obj: 0.001392, .5R: 0.937500, .75R: 0.687500, count: 16 -Region 23 Avg IOU: 0.757553, Class: 0.171046, Obj: 0.340572, No Obj: 0.000770, .5R: 1.000000, .75R: 0.529412, count: 17 -Region 16 Avg IOU: 0.790003, Class: 0.292917, Obj: 0.392802, No Obj: 0.000850, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.740019, Class: 0.226208, Obj: 0.364013, No Obj: 0.000435, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.767623, Class: 0.265950, Obj: 0.227794, No Obj: 0.001498, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.673937, Class: 0.195548, Obj: 0.134698, No Obj: 0.000424, .5R: 1.000000, .75R: 0.142857, count: 7 -Region 16 Avg IOU: 0.782737, Class: 0.245466, Obj: 0.246064, No Obj: 0.001207, .5R: 0.923077, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.762228, Class: 0.230661, Obj: 0.274981, No Obj: 0.000533, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.712236, Class: 0.107317, Obj: 0.369279, No Obj: 0.000910, .5R: 0.909091, .75R: 0.363636, count: 11 -Region 23 Avg IOU: 0.712503, Class: 0.187932, Obj: 0.340889, No Obj: 0.000573, .5R: 0.933333, .75R: 0.400000, count: 15 -Region 16 Avg IOU: 0.812653, Class: 0.192670, Obj: 0.448588, No Obj: 0.001734, .5R: 1.000000, .75R: 0.888889, count: 18 -Region 23 Avg IOU: 0.720026, Class: 0.174676, Obj: 0.365375, No Obj: 0.000719, .5R: 0.909091, .75R: 0.500000, count: 22 - - 3304: 1.635635, 2.079192 avg loss, 0.001000 rate, 0.861216 seconds, 422912 images -Loaded: 0.000065 seconds -Region 16 Avg IOU: 0.761147, Class: 0.253375, Obj: 0.456674, No Obj: 0.001067, .5R: 1.000000, .75R: 0.428571, count: 7 -Region 23 Avg IOU: 0.679824, Class: 0.265594, Obj: 0.291133, No Obj: 0.000674, .5R: 0.947368, .75R: 0.315789, count: 19 -Region 16 Avg IOU: 0.815444, Class: 0.242565, Obj: 0.358288, No Obj: 0.001662, .5R: 1.000000, .75R: 0.733333, count: 15 -Region 23 Avg IOU: 0.705619, Class: 0.202121, Obj: 0.265253, No Obj: 0.000654, .5R: 0.954545, .75R: 0.409091, count: 22 -Region 16 Avg IOU: 0.793547, Class: 0.258029, Obj: 0.423142, No Obj: 0.000812, .5R: 1.000000, .75R: 0.500000, count: 6 -Region 23 Avg IOU: 0.752412, Class: 0.187916, Obj: 0.276551, No Obj: 0.000394, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 16 Avg IOU: 0.783228, Class: 0.197574, Obj: 0.490816, No Obj: 0.000736, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.782131, Class: 0.136034, Obj: 0.330932, No Obj: 0.000462, 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0.470588, count: 17 -Region 23 Avg IOU: 0.715407, Class: 0.202802, Obj: 0.324613, No Obj: 0.000946, .5R: 0.966667, .75R: 0.366667, count: 30 -Region 16 Avg IOU: 0.759865, Class: 0.197380, Obj: 0.328936, No Obj: 0.001121, .5R: 0.888889, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.740244, Class: 0.180828, Obj: 0.303505, No Obj: 0.000597, .5R: 1.000000, .75R: 0.565217, count: 23 -Region 16 Avg IOU: 0.753819, Class: 0.180873, Obj: 0.388069, No Obj: 0.001224, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.651467, Class: 0.233174, Obj: 0.228465, No Obj: 0.000499, .5R: 0.800000, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.755648, Class: 0.254797, Obj: 0.407087, No Obj: 0.002476, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.716873, Class: 0.279056, Obj: 0.193327, No Obj: 0.000685, .5R: 1.000000, .75R: 0.454545, count: 22 -Region 16 Avg IOU: 0.711536, Class: 0.212291, Obj: 0.278639, No Obj: 0.001568, .5R: 0.900000, .75R: 0.400000, count: 10 -Region 23 Avg 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-Region 16 Avg IOU: 0.730451, Class: 0.261416, Obj: 0.548827, No Obj: 0.003564, .5R: 0.894737, .75R: 0.631579, count: 19 -Region 23 Avg IOU: 0.676204, Class: 0.186360, Obj: 0.321691, No Obj: 0.000787, .5R: 0.800000, .75R: 0.400000, count: 25 -Region 16 Avg IOU: 0.724303, Class: 0.202138, Obj: 0.410183, No Obj: 0.003640, .5R: 1.000000, .75R: 0.400000, count: 20 -Region 23 Avg IOU: 0.709082, Class: 0.221619, Obj: 0.249689, No Obj: 0.000832, .5R: 0.962963, .75R: 0.296296, count: 27 -Region 16 Avg IOU: 0.774894, Class: 0.303140, Obj: 0.685333, No Obj: 0.002014, .5R: 0.900000, .75R: 0.900000, count: 10 -Region 23 Avg IOU: 0.733790, Class: 0.170478, Obj: 0.371969, No Obj: 0.000932, .5R: 1.000000, .75R: 0.416667, count: 24 -Region 16 Avg IOU: 0.700038, Class: 0.145106, Obj: 0.444318, No Obj: 0.002976, .5R: 0.857143, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.626978, Class: 0.129299, Obj: 0.297834, No Obj: 0.000997, .5R: 0.840000, .75R: 0.200000, count: 25 -Region 16 Avg IOU: 0.743670, Class: 0.251260, Obj: 0.544415, No Obj: 0.003490, .5R: 1.000000, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.748132, Class: 0.209393, Obj: 0.202287, No Obj: 0.000540, .5R: 1.000000, .75R: 0.666667, count: 15 - - 3309: 2.539106, 2.130805 avg loss, 0.001000 rate, 0.863639 seconds, 423552 images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.744773, Class: 0.267882, Obj: 0.522704, No Obj: 0.002730, .5R: 0.857143, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.656242, Class: 0.188991, Obj: 0.113465, No Obj: 0.000532, .5R: 0.875000, .75R: 0.208333, count: 24 -Region 16 Avg IOU: 0.798145, Class: 0.228011, Obj: 0.597137, No Obj: 0.003004, .5R: 0.941176, .75R: 0.764706, count: 17 -Region 23 Avg IOU: 0.707422, Class: 0.220163, Obj: 0.222813, No Obj: 0.000615, .5R: 1.000000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.790189, Class: 0.297954, Obj: 0.516278, No Obj: 0.001963, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.710172, Class: 0.184891, Obj: 0.221310, No Obj: 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0.433333, count: 30 -Region 16 Avg IOU: 0.781265, Class: 0.250214, Obj: 0.493796, No Obj: 0.003510, .5R: 1.000000, .75R: 0.650000, count: 20 -Region 23 Avg IOU: 0.688459, Class: 0.176095, Obj: 0.201460, No Obj: 0.000508, .5R: 0.923077, .75R: 0.269231, count: 26 - - 3310: 2.315571, 2.149282 avg loss, 0.001000 rate, 0.868733 seconds, 423680 images -Resizing -576 x 576 - try to allocate workspace = 1146159105 * sizeof(float), CUDA allocate done! -Loaded: 0.372940 seconds -Region 16 Avg IOU: 0.747953, Class: 0.248417, Obj: 0.418210, No Obj: 0.002950, .5R: 0.964286, .75R: 0.571429, count: 28 -Region 23 Avg IOU: 0.690480, Class: 0.226576, Obj: 0.199346, No Obj: 0.000388, .5R: 0.809524, .75R: 0.571429, count: 21 -Region 16 Avg IOU: 0.776444, Class: 0.286820, Obj: 0.412787, No Obj: 0.001350, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.706945, Class: 0.182074, Obj: 0.192241, No Obj: 0.000313, .5R: 0.823529, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.737601, Class: 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21 -Region 23 Avg IOU: 0.704604, Class: 0.200906, Obj: 0.266288, No Obj: 0.000437, .5R: 0.892857, .75R: 0.357143, count: 28 -Region 16 Avg IOU: 0.729288, Class: 0.262387, Obj: 0.223422, No Obj: 0.000951, .5R: 0.894737, .75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.725429, Class: 0.172600, Obj: 0.196602, No Obj: 0.000567, .5R: 1.000000, .75R: 0.411765, count: 34 -Region 16 Avg IOU: 0.757859, Class: 0.240226, Obj: 0.311390, No Obj: 0.001117, .5R: 1.000000, .75R: 0.611111, count: 18 -Region 23 Avg IOU: 0.743949, Class: 0.194567, Obj: 0.250640, No Obj: 0.000491, .5R: 1.000000, .75R: 0.520000, count: 25 - - 3315: 2.707764, 2.316177 avg loss, 0.001000 rate, 1.484155 seconds, 424320 images -Loaded: 0.000074 seconds -Region 16 Avg IOU: 0.749923, Class: 0.227876, Obj: 0.305428, No Obj: 0.001648, .5R: 1.000000, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.703350, Class: 0.191204, Obj: 0.340933, No Obj: 0.000399, .5R: 0.941176, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.768020, Class: 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0.811053, Class: 0.318560, Obj: 0.513791, No Obj: 0.002214, .5R: 1.000000, .75R: 0.909091, count: 11 -Region 23 Avg IOU: 0.737654, Class: 0.237116, Obj: 0.211872, No Obj: 0.000524, .5R: 0.961538, .75R: 0.538462, count: 26 -Region 16 Avg IOU: 0.788087, Class: 0.242467, Obj: 0.387996, No Obj: 0.001943, .5R: 0.937500, .75R: 0.687500, count: 16 -Region 23 Avg IOU: 0.708661, Class: 0.177050, Obj: 0.179178, No Obj: 0.000328, .5R: 0.941176, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.715352, Class: 0.275555, Obj: 0.340766, No Obj: 0.002755, .5R: 0.870968, .75R: 0.419355, count: 31 -Region 23 Avg IOU: 0.694345, Class: 0.204788, Obj: 0.190067, No Obj: 0.000468, .5R: 0.888889, .75R: 0.444444, count: 18 - - 3317: 2.553553, 2.344912 avg loss, 0.001000 rate, 1.483382 seconds, 424576 images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.668499, Class: 0.249392, Obj: 0.437311, No Obj: 0.002115, .5R: 0.894737, .75R: 0.315789, count: 19 -Region 23 Avg IOU: 0.715836, Class: 0.186758, Obj: 0.173103, 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0.933333, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.657268, Class: 0.120957, Obj: 0.087405, No Obj: 0.000162, .5R: 0.900000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.769199, Class: 0.226759, Obj: 0.350478, No Obj: 0.001293, .5R: 1.000000, .75R: 0.631579, count: 19 -Region 23 Avg IOU: 0.654164, Class: 0.196212, Obj: 0.131429, No Obj: 0.000284, .5R: 0.833333, .75R: 0.166667, count: 18 -Region 16 Avg IOU: 0.745380, Class: 0.196036, Obj: 0.345260, No Obj: 0.001326, .5R: 1.000000, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.736739, Class: 0.153873, Obj: 0.186097, No Obj: 0.000341, .5R: 0.900000, .75R: 0.600000, count: 20 -Region 16 Avg IOU: 0.807376, Class: 0.275845, Obj: 0.274948, No Obj: 0.000889, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.733146, Class: 0.180826, Obj: 0.299732, No Obj: 0.000245, .5R: 0.944444, .75R: 0.500000, count: 18 -Region 16 Avg IOU: 0.698581, Class: 0.236861, Obj: 0.093295, No Obj: 0.000921, .5R: 0.916667, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.672703, Class: 0.187583, Obj: 0.078289, No Obj: 0.000155, .5R: 0.764706, .75R: 0.529412, count: 17 -Region 16 Avg IOU: 0.742261, Class: 0.243350, Obj: 0.420999, No Obj: 0.001685, .5R: 0.900000, .75R: 0.450000, count: 20 -Region 23 Avg IOU: 0.712955, Class: 0.173570, Obj: 0.278378, No Obj: 0.000381, .5R: 0.913043, .75R: 0.434783, count: 23 -Region 16 Avg IOU: 0.771319, Class: 0.268120, Obj: 0.432000, No Obj: 0.001328, .5R: 1.000000, .75R: 0.687500, count: 16 -Region 23 Avg IOU: 0.735194, Class: 0.141655, Obj: 0.237308, No Obj: 0.000231, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 16 Avg IOU: 0.823732, Class: 0.235167, Obj: 0.501186, No Obj: 0.001499, .5R: 1.000000, .75R: 0.909091, count: 11 -Region 23 Avg IOU: 0.760377, Class: 0.143477, Obj: 0.215961, No Obj: 0.000215, .5R: 1.000000, .75R: 0.545455, count: 11 - - 3320: 2.284382, 2.329197 avg loss, 0.001000 rate, 1.463866 seconds, 424960 images -Resizing -448 x 448 - try to allocate workspace = 305201153 * sizeof(float), CUDA allocate done! -Loaded: 0.371047 seconds -Region 16 Avg IOU: 0.763362, Class: 0.340600, Obj: 0.403195, No Obj: 0.001065, .5R: 1.000000, .75R: 0.833333, count: 6 -Region 23 Avg IOU: 0.775075, Class: 0.195592, Obj: 0.367944, No Obj: 0.000601, .5R: 0.947368, .75R: 0.631579, count: 19 -Region 16 Avg IOU: 0.774486, Class: 0.217735, Obj: 0.260685, No Obj: 0.000850, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.722580, Class: 0.203056, Obj: 0.319577, No Obj: 0.000622, .5R: 0.947368, .75R: 0.473684, count: 19 -Region 16 Avg IOU: 0.787537, Class: 0.274022, Obj: 0.458906, No Obj: 0.000893, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.697050, Class: 0.227977, Obj: 0.311547, No Obj: 0.000476, .5R: 0.937500, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.767160, Class: 0.208465, Obj: 0.286630, No Obj: 0.000925, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.816414, Class: 0.199483, Obj: 0.338393, No Obj: 0.000441, .5R: 1.000000, 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Avg IOU: 0.751407, Class: 0.265388, Obj: 0.282159, No Obj: 0.001550, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.730511, Class: 0.191685, Obj: 0.188395, No Obj: 0.000652, .5R: 0.923077, .75R: 0.538462, count: 13 -Region 16 Avg IOU: 0.778668, Class: 0.241542, Obj: 0.439662, No Obj: 0.001521, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.682966, Class: 0.217984, Obj: 0.219406, No Obj: 0.000550, .5R: 0.882353, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.784738, Class: 0.152782, Obj: 0.354461, No Obj: 0.001931, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.721977, Class: 0.172060, Obj: 0.276877, No Obj: 0.000616, .5R: 0.947368, .75R: 0.421053, count: 19 - - 3334: 1.562809, 1.905467 avg loss, 0.001000 rate, 0.672310 seconds, 426752 images -Loaded: 0.000067 seconds -Region 16 Avg IOU: 0.765415, Class: 0.233793, Obj: 0.283640, No Obj: 0.001284, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.731075, Class: 0.232755, Obj: 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-Loaded: 0.021371 seconds -Region 16 Avg IOU: 0.789078, Class: 0.228506, Obj: 0.422124, No Obj: 0.002759, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.689372, Class: 0.157302, Obj: 0.165909, No Obj: 0.000692, .5R: 0.800000, .75R: 0.666667, count: 15 -Region 16 Avg IOU: 0.799464, Class: 0.197195, Obj: 0.662242, No Obj: 0.001490, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.663748, Class: 0.189874, Obj: 0.312361, No Obj: 0.000607, .5R: 0.785714, .75R: 0.285714, count: 14 -Region 16 Avg IOU: 0.766887, Class: 0.224953, Obj: 0.389132, No Obj: 0.002547, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.720389, Class: 0.241364, Obj: 0.194341, No Obj: 0.000615, .5R: 0.944444, .75R: 0.444444, count: 18 -Region 16 Avg IOU: 0.793543, Class: 0.107218, Obj: 0.548107, No Obj: 0.002601, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.778948, Class: 0.207304, Obj: 0.317411, No Obj: 0.001036, .5R: 1.000000, .75R: 0.619048, count: 21 -Region 16 Avg IOU: 0.813156, Class: 0.253791, Obj: 0.615406, No Obj: 0.002039, .5R: 1.000000, .75R: 0.777778, count: 9 -Region 23 Avg IOU: 0.733114, Class: 0.166325, Obj: 0.243040, No Obj: 0.000690, .5R: 0.882353, .75R: 0.529412, count: 17 -Region 16 Avg IOU: 0.757406, Class: 0.192612, Obj: 0.446286, No Obj: 0.002506, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.736158, Class: 0.182473, Obj: 0.203498, No Obj: 0.000765, .5R: 0.954545, .75R: 0.545455, count: 22 -Region 16 Avg IOU: 0.782079, Class: 0.278039, Obj: 0.410276, No Obj: 0.002572, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.741359, Class: 0.200618, Obj: 0.203969, No Obj: 0.000687, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 16 Avg IOU: 0.864342, Class: 0.130687, Obj: 0.428208, No Obj: 0.002718, .5R: 1.000000, .75R: 1.000000, count: 9 -Region 23 Avg IOU: 0.732860, Class: 0.101583, Obj: 0.178848, No Obj: 0.000786, .5R: 0.947368, .75R: 0.421053, count: 19 - - 3339: 1.766441, 1.853745 avg loss, 0.001000 rate, 0.646972 seconds, 427392 images -Loaded: 0.027713 seconds -Region 16 Avg IOU: 0.803290, Class: 0.236175, Obj: 0.389233, No Obj: 0.003787, .5R: 1.000000, .75R: 0.800000, count: 20 -Region 23 Avg IOU: 0.746386, Class: 0.198759, Obj: 0.158597, No Obj: 0.000522, .5R: 1.000000, .75R: 0.526316, count: 19 -Region 16 Avg IOU: 0.829382, Class: 0.312110, Obj: 0.069594, No Obj: 0.000433, .5R: 1.000000, .75R: 1.000000, count: 2 -Region 23 Avg IOU: 0.656891, Class: 0.147254, Obj: 0.254831, No Obj: 0.000403, .5R: 0.900000, .75R: 0.200000, count: 10 -Region 16 Avg IOU: 0.772496, Class: 0.291785, Obj: 0.479634, No Obj: 0.000886, .5R: 1.000000, .75R: 0.750000, count: 4 -Region 23 Avg IOU: 0.707745, Class: 0.207401, Obj: 0.171573, No Obj: 0.000411, .5R: 0.875000, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.777158, Class: 0.366877, Obj: 0.426825, No Obj: 0.002097, .5R: 1.000000, .75R: 0.500000, count: 4 -Region 23 Avg IOU: 0.763761, Class: 0.224762, Obj: 0.277840, No Obj: 0.000505, 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0.206595, No Obj: 0.001285, .5R: 0.900000, .75R: 0.300000, count: 20 -Region 23 Avg IOU: 0.748483, Class: 0.185066, Obj: 0.156131, No Obj: 0.000148, .5R: 1.000000, .75R: 0.562500, count: 16 -Region 16 Avg IOU: 0.715349, Class: 0.188992, Obj: 0.276579, No Obj: 0.001618, .5R: 0.967742, .75R: 0.387097, count: 31 -Region 23 Avg IOU: 0.729066, Class: 0.162373, Obj: 0.081362, No Obj: 0.000209, .5R: 1.000000, .75R: 0.461538, count: 26 -Region 16 Avg IOU: 0.723850, Class: 0.189623, Obj: 0.243011, No Obj: 0.001479, .5R: 0.935484, .75R: 0.483871, count: 31 -Region 23 Avg IOU: 0.714249, Class: 0.181381, Obj: 0.137303, No Obj: 0.000160, .5R: 0.944444, .75R: 0.500000, count: 18 -Region 16 Avg IOU: 0.676346, Class: 0.194483, Obj: 0.279420, No Obj: 0.001478, .5R: 0.958333, .75R: 0.333333, count: 24 -Region 23 Avg IOU: 0.768030, Class: 0.202851, Obj: 0.091499, No Obj: 0.000127, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 16 Avg IOU: 0.736693, Class: 0.188019, Obj: 0.322495, No Obj: 0.001376, .5R: 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0.790030, Class: 0.160936, Obj: 0.345431, No Obj: 0.000190, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 16 Avg IOU: 0.688578, Class: 0.204253, Obj: 0.339052, No Obj: 0.000772, .5R: 0.928571, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.649538, Class: 0.168362, Obj: 0.193326, No Obj: 0.000221, .5R: 0.800000, .75R: 0.300000, count: 10 - - 3361: 2.011682, 2.416623 avg loss, 0.001000 rate, 1.671245 seconds, 430208 images -Loaded: 0.000065 seconds -Region 16 Avg IOU: 0.750245, Class: 0.318091, Obj: 0.296091, No Obj: 0.001442, .5R: 0.954545, .75R: 0.590909, count: 22 -Region 23 Avg IOU: 0.716071, Class: 0.227175, Obj: 0.242985, No Obj: 0.000282, .5R: 0.937500, .75R: 0.625000, count: 16 -Region 16 Avg IOU: 0.813747, Class: 0.250543, Obj: 0.421220, No Obj: 0.001554, .5R: 1.000000, .75R: 0.842105, count: 19 -Region 23 Avg IOU: 0.675965, Class: 0.106899, Obj: 0.228516, No Obj: 0.000314, .5R: 0.928571, .75R: 0.285714, count: 14 -Region 16 Avg IOU: 0.774090, Class: 0.209455, Obj: 0.426418, 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count: 25 -Region 23 Avg IOU: 0.722555, Class: 0.185541, Obj: 0.250604, No Obj: 0.000169, .5R: 0.916667, .75R: 0.500000, count: 12 -Region 16 Avg IOU: 0.762022, Class: 0.188197, Obj: 0.303947, No Obj: 0.001623, .5R: 1.000000, .75R: 0.575758, count: 33 -Region 23 Avg IOU: 0.769045, Class: 0.158514, Obj: 0.215668, No Obj: 0.000263, .5R: 0.941176, .75R: 0.705882, count: 17 -Region 16 Avg IOU: 0.707846, Class: 0.225299, Obj: 0.268712, No Obj: 0.001321, .5R: 0.896552, .75R: 0.413793, count: 29 -Region 23 Avg IOU: 0.769789, Class: 0.224682, Obj: 0.185417, No Obj: 0.000190, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 16 Avg IOU: 0.696996, Class: 0.178291, Obj: 0.256463, No Obj: 0.001370, .5R: 0.928571, .75R: 0.357143, count: 28 -Region 23 Avg IOU: 0.704540, Class: 0.197823, Obj: 0.198032, No Obj: 0.000400, .5R: 0.941176, .75R: 0.411765, count: 34 -Region 16 Avg IOU: 0.768916, Class: 0.217389, Obj: 0.292420, No Obj: 0.001070, .5R: 1.000000, .75R: 0.520000, count: 25 -Region 23 Avg IOU: 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0.184997, Obj: 0.145770, No Obj: 0.000340, .5R: 0.750000, .75R: 0.200000, count: 20 -Region 16 Avg IOU: 0.749275, Class: 0.282140, Obj: 0.462205, No Obj: 0.003255, .5R: 0.923077, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.694833, Class: 0.223477, Obj: 0.183604, No Obj: 0.000503, .5R: 0.850000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.727050, Class: 0.140258, Obj: 0.448156, No Obj: 0.002286, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.677474, Class: 0.174984, Obj: 0.129953, No Obj: 0.000336, .5R: 1.000000, .75R: 0.230769, count: 13 -Region 16 Avg IOU: 0.812736, Class: 0.245900, Obj: 0.589621, No Obj: 0.002128, .5R: 1.000000, .75R: 0.777778, count: 9 -Region 23 Avg IOU: 0.767230, Class: 0.267829, Obj: 0.401089, No Obj: 0.000411, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 16 Avg IOU: 0.764951, Class: 0.215362, Obj: 0.516037, No Obj: 0.003398, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.739350, Class: 0.215776, Obj: 0.213741, No Obj: 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count: 8 -Region 23 Avg IOU: 0.693085, Class: 0.228684, Obj: 0.231438, No Obj: 0.000572, .5R: 1.000000, .75R: 0.400000, count: 15 -Region 16 Avg IOU: 0.798212, Class: 0.254163, Obj: 0.572197, No Obj: 0.003128, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.678925, Class: 0.148506, Obj: 0.237941, No Obj: 0.000715, .5R: 0.933333, .75R: 0.233333, count: 30 -Region 16 Avg IOU: 0.643178, Class: 0.226810, Obj: 0.372494, No Obj: 0.001499, .5R: 0.888889, .75R: 0.333333, count: 9 -Region 23 Avg IOU: 0.716277, Class: 0.233362, Obj: 0.169530, No Obj: 0.000400, .5R: 0.866667, .75R: 0.466667, count: 15 -Region 16 Avg IOU: 0.795819, Class: 0.264731, Obj: 0.616535, No Obj: 0.002951, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.670995, Class: 0.268477, Obj: 0.332910, No Obj: 0.000499, .5R: 0.900000, .75R: 0.400000, count: 10 -Region 16 Avg IOU: 0.733414, Class: 0.180013, Obj: 0.308945, No Obj: 0.002366, .5R: 0.833333, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 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16 Avg IOU: 0.733487, Class: 0.173589, Obj: 0.117032, No Obj: 0.001412, .5R: 0.941176, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 0.678711, Class: 0.173026, Obj: 0.213390, No Obj: 0.000448, .5R: 0.882353, .75R: 0.294118, count: 17 -Region 16 Avg IOU: 0.749703, Class: 0.182239, Obj: 0.270628, No Obj: 0.000949, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.703065, Class: 0.150604, Obj: 0.393179, No Obj: 0.001033, .5R: 0.920000, .75R: 0.400000, count: 25 -Region 16 Avg IOU: 0.725967, Class: 0.199827, Obj: 0.296344, No Obj: 0.001471, .5R: 0.916667, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.739456, Class: 0.201228, Obj: 0.272551, No Obj: 0.000784, .5R: 1.000000, .75R: 0.583333, count: 24 - - 3385: 2.186404, 2.384384 avg loss, 0.001000 rate, 0.778254 seconds, 433280 images -Loaded: 0.000056 seconds -Region 16 Avg IOU: 0.833596, Class: 0.240238, Obj: 0.440632, No Obj: 0.001307, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.695842, Class: 0.174765, 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Class: 0.148495, Obj: 0.319921, No Obj: 0.000623, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 16 Avg IOU: 0.714287, Class: 0.170169, Obj: 0.192883, No Obj: 0.001076, .5R: 0.923077, .75R: 0.384615, count: 13 -Region 23 Avg IOU: 0.731681, Class: 0.168575, Obj: 0.237171, No Obj: 0.000957, .5R: 1.000000, .75R: 0.538462, count: 26 -Region 16 Avg IOU: 0.789967, Class: 0.212307, Obj: 0.389295, No Obj: 0.001418, .5R: 0.866667, .75R: 0.800000, count: 15 -Region 23 Avg IOU: 0.734838, Class: 0.194913, Obj: 0.312581, No Obj: 0.000609, .5R: 1.000000, .75R: 0.470588, count: 17 -Region 16 Avg IOU: 0.776280, Class: 0.177459, Obj: 0.390965, No Obj: 0.001131, .5R: 1.000000, .75R: 0.500000, count: 6 -Region 23 Avg IOU: 0.736837, Class: 0.194269, Obj: 0.197351, No Obj: 0.000534, .5R: 1.000000, .75R: 0.428571, count: 14 - - 3387: 1.622260, 2.266956 avg loss, 0.001000 rate, 0.792935 seconds, 433536 images -Loaded: 0.000069 seconds -Region 16 Avg IOU: 0.801444, Class: 0.284820, Obj: 0.445602, No Obj: 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-Loaded: 0.000075 seconds -Region 16 Avg IOU: 0.704520, Class: 0.212249, Obj: 0.563584, No Obj: 0.001097, .5R: 0.875000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.702997, Class: 0.209754, Obj: 0.377355, No Obj: 0.000546, .5R: 0.866667, .75R: 0.466667, count: 15 -Region 16 Avg IOU: 0.737931, Class: 0.318862, Obj: 0.550579, No Obj: 0.001343, .5R: 1.000000, .75R: 0.500000, count: 6 -Region 23 Avg IOU: 0.776545, Class: 0.226812, Obj: 0.311085, No Obj: 0.000536, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 16 Avg IOU: 0.772196, Class: 0.150774, Obj: 0.389094, No Obj: 0.001149, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.744386, Class: 0.246908, Obj: 0.274644, No Obj: 0.000513, .5R: 1.000000, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.735291, Class: 0.210532, Obj: 0.528183, No Obj: 0.002196, .5R: 1.000000, .75R: 0.444444, count: 9 -Region 23 Avg IOU: 0.736023, Class: 0.214166, Obj: 0.221970, No Obj: 0.000906, .5R: 1.000000, .75R: 0.481481, count: 27 -Region 16 Avg IOU: 0.828661, Class: 0.130628, Obj: 0.614325, No Obj: 0.001140, .5R: 1.000000, .75R: 1.000000, count: 3 -Region 23 Avg IOU: 0.728099, Class: 0.206791, Obj: 0.282278, No Obj: 0.000650, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 16 Avg IOU: 0.826117, Class: 0.187643, Obj: 0.525381, No Obj: 0.001970, .5R: 1.000000, .75R: 0.916667, count: 12 -Region 23 Avg IOU: 0.757642, Class: 0.218357, Obj: 0.316024, No Obj: 0.000728, .5R: 1.000000, .75R: 0.611111, count: 18 -Region 16 Avg IOU: 0.750208, Class: 0.180065, Obj: 0.435943, No Obj: 0.001098, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.709445, Class: 0.200258, Obj: 0.356519, No Obj: 0.000597, .5R: 0.857143, .75R: 0.571429, count: 14 -Region 16 Avg IOU: 0.792440, Class: 0.205230, Obj: 0.523268, No Obj: 0.001599, .5R: 0.857143, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.707505, Class: 0.193059, Obj: 0.299842, No Obj: 0.000861, .5R: 0.947368, .75R: 0.368421, count: 19 - - 3390: 1.487642, 2.142443 avg loss, 0.001000 rate, 0.764897 seconds, 433920 images -Resizing -480 x 480 - try to allocate workspace = 1144537089 * sizeof(float), CUDA allocate done! -Loaded: 0.248672 seconds -Region 16 Avg IOU: 0.651945, Class: 0.246969, Obj: 0.352023, No Obj: 0.002119, .5R: 0.888889, .75R: 0.166667, count: 18 -Region 23 Avg IOU: 0.736672, Class: 0.184870, Obj: 0.212098, No Obj: 0.000709, .5R: 0.956522, .75R: 0.347826, count: 23 -Region 16 Avg IOU: 0.742102, Class: 0.239653, Obj: 0.457106, No Obj: 0.002666, .5R: 0.916667, .75R: 0.458333, count: 24 -Region 23 Avg IOU: 0.675832, Class: 0.216210, Obj: 0.180508, No Obj: 0.000750, .5R: 0.884615, .75R: 0.423077, count: 26 -Region 16 Avg IOU: 0.740233, Class: 0.297891, Obj: 0.399271, No Obj: 0.001036, .5R: 0.857143, .75R: 0.428571, count: 7 -Region 23 Avg IOU: 0.734494, Class: 0.177212, Obj: 0.217846, No Obj: 0.000476, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.774778, Class: 0.177202, Obj: 0.413228, No Obj: 0.003149, .5R: 1.000000, .75R: 0.583333, count: 24 -Region 23 Avg IOU: 0.716799, Class: 0.232998, Obj: 0.276990, No Obj: 0.001013, .5R: 0.960000, .75R: 0.440000, count: 25 -Region 16 Avg IOU: 0.813909, Class: 0.322003, Obj: 0.753703, No Obj: 0.001977, .5R: 1.000000, .75R: 1.000000, count: 9 -Region 23 Avg IOU: 0.706626, Class: 0.182417, Obj: 0.475745, No Obj: 0.000702, .5R: 1.000000, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.654642, Class: 0.234267, Obj: 0.373416, No Obj: 0.001608, .5R: 0.750000, .75R: 0.166667, count: 12 -Region 23 Avg IOU: 0.667300, Class: 0.197295, Obj: 0.336237, No Obj: 0.000625, .5R: 0.857143, .75R: 0.380952, count: 21 -Region 16 Avg IOU: 0.796196, Class: 0.222964, Obj: 0.369859, No Obj: 0.002090, .5R: 1.000000, .75R: 0.750000, count: 16 -Region 23 Avg IOU: 0.741144, Class: 0.195066, Obj: 0.267208, No Obj: 0.000567, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 16 Avg IOU: 0.782511, Class: 0.271129, Obj: 0.547389, No Obj: 0.001329, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg 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16 Avg IOU: 0.788425, Class: 0.176924, Obj: 0.502960, No Obj: 0.001173, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.730679, Class: 0.160503, Obj: 0.314961, No Obj: 0.000561, .5R: 1.000000, .75R: 0.520000, count: 25 -Region 16 Avg IOU: 0.773588, Class: 0.280537, Obj: 0.403401, No Obj: 0.001388, .5R: 0.916667, .75R: 0.833333, count: 12 -Region 23 Avg IOU: 0.717429, Class: 0.211673, Obj: 0.141641, No Obj: 0.000448, .5R: 0.947368, .75R: 0.473684, count: 19 -Region 16 Avg IOU: 0.774557, Class: 0.226415, Obj: 0.454532, No Obj: 0.002490, .5R: 1.000000, .75R: 0.611111, count: 18 -Region 23 Avg IOU: 0.759413, Class: 0.181380, Obj: 0.172810, No Obj: 0.000318, .5R: 1.000000, .75R: 0.529412, count: 17 -Region 16 Avg IOU: 0.822459, Class: 0.227136, Obj: 0.507725, No Obj: 0.002264, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.761731, Class: 0.123253, Obj: 0.347940, No Obj: 0.000376, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 16 Avg IOU: 0.767612, Class: 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count: 21 -Region 16 Avg IOU: 0.789582, Class: 0.242357, Obj: 0.394087, No Obj: 0.001820, .5R: 0.941176, .75R: 0.823529, count: 17 -Region 23 Avg IOU: 0.717355, Class: 0.194562, Obj: 0.245359, No Obj: 0.000514, .5R: 1.000000, .75R: 0.391304, count: 23 - - 3394: 2.073948, 2.093791 avg loss, 0.001000 rate, 1.046471 seconds, 434432 images -Loaded: 0.000057 seconds -Region 16 Avg IOU: 0.738499, Class: 0.203070, Obj: 0.283221, No Obj: 0.002123, .5R: 1.000000, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.693694, Class: 0.181561, Obj: 0.220069, No Obj: 0.000505, .5R: 0.900000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.759316, Class: 0.259160, Obj: 0.332816, No Obj: 0.001877, .5R: 0.941176, .75R: 0.705882, count: 17 -Region 23 Avg IOU: 0.686875, Class: 0.195838, Obj: 0.171953, No Obj: 0.000446, .5R: 0.954545, .75R: 0.272727, count: 22 -Region 16 Avg IOU: 0.759323, Class: 0.250334, Obj: 0.486574, No Obj: 0.002203, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.756927, Class: 0.156115, Obj: 0.308705, No Obj: 0.000509, .5R: 0.916667, .75R: 0.666667, count: 12 -Region 16 Avg IOU: 0.780742, Class: 0.231952, Obj: 0.563190, No Obj: 0.002412, .5R: 1.000000, .75R: 0.700000, count: 20 -Region 23 Avg IOU: 0.704540, Class: 0.186634, Obj: 0.346984, No Obj: 0.000860, .5R: 0.843750, .75R: 0.562500, count: 32 -Region 16 Avg IOU: 0.767866, Class: 0.246730, Obj: 0.270334, No Obj: 0.000857, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.729328, Class: 0.197911, Obj: 0.166285, No Obj: 0.000440, .5R: 1.000000, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.743230, Class: 0.229817, Obj: 0.313147, No Obj: 0.001289, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.730190, Class: 0.167740, Obj: 0.229325, No Obj: 0.000665, .5R: 0.966667, .75R: 0.400000, count: 30 -Region 16 Avg IOU: 0.734978, Class: 0.203805, Obj: 0.360263, No Obj: 0.001269, .5R: 1.000000, .75R: 0.384615, count: 13 -Region 23 Avg IOU: 0.704502, Class: 0.202201, Obj: 0.237126, No Obj: 0.000471, .5R: 0.888889, .75R: 0.444444, count: 18 -Region 16 Avg IOU: 0.784929, Class: 0.160015, Obj: 0.327704, No Obj: 0.001464, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.785231, Class: 0.189607, Obj: 0.219953, No Obj: 0.000484, .5R: 1.000000, .75R: 0.705882, count: 17 - - 3395: 2.288204, 2.113233 avg loss, 0.001000 rate, 1.062160 seconds, 434560 images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.775127, Class: 0.195261, Obj: 0.295791, No Obj: 0.000869, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.757923, Class: 0.087060, Obj: 0.374025, No Obj: 0.000286, .5R: 0.916667, .75R: 0.583333, count: 12 -Region 16 Avg IOU: 0.801052, Class: 0.258637, Obj: 0.504310, No Obj: 0.001440, .5R: 1.000000, .75R: 0.818182, count: 11 -Region 23 Avg IOU: 0.745274, Class: 0.252115, Obj: 0.240217, No Obj: 0.000462, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 16 Avg IOU: 0.734219, Class: 0.229210, Obj: 0.434856, No Obj: 0.000875, .5R: 1.000000, .75R: 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count: 19 -Region 23 Avg IOU: 0.787782, Class: 0.170731, Obj: 0.162864, No Obj: 0.000116, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 16 Avg IOU: 0.686940, Class: 0.240500, Obj: 0.286169, No Obj: 0.001346, .5R: 0.880000, .75R: 0.400000, count: 25 -Region 23 Avg IOU: 0.703361, Class: 0.177222, Obj: 0.169738, No Obj: 0.000289, .5R: 0.888889, .75R: 0.444444, count: 27 - - 3411: 2.749929, 2.518341 avg loss, 0.001000 rate, 2.184903 seconds, 436608 images -Loaded: 0.000060 seconds -Region 16 Avg IOU: 0.784788, Class: 0.256099, Obj: 0.392921, No Obj: 0.001295, .5R: 0.956522, .75R: 0.826087, count: 23 -Region 23 Avg IOU: 0.753643, Class: 0.227805, Obj: 0.213881, No Obj: 0.000181, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 16 Avg IOU: 0.705865, Class: 0.248051, Obj: 0.314710, No Obj: 0.001314, .5R: 0.888889, .75R: 0.481481, count: 27 -Region 23 Avg IOU: 0.781250, Class: 0.219252, Obj: 0.202318, No Obj: 0.000310, .5R: 0.923077, .75R: 0.769231, count: 13 -Region 16 Avg IOU: 0.757951, 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Avg IOU: 0.725875, Class: 0.262498, Obj: 0.219128, No Obj: 0.001280, .5R: 0.945946, .75R: 0.432432, count: 37 -Region 23 Avg IOU: 0.685185, Class: 0.205590, Obj: 0.130857, No Obj: 0.000234, .5R: 0.952381, .75R: 0.428571, count: 21 -Region 16 Avg IOU: 0.767799, Class: 0.252170, Obj: 0.277323, No Obj: 0.000939, .5R: 1.000000, .75R: 0.666667, count: 21 -Region 23 Avg IOU: 0.762165, Class: 0.219483, Obj: 0.160570, No Obj: 0.000241, .5R: 1.000000, .75R: 0.529412, count: 17 - - 3413: 2.749405, 2.505741 avg loss, 0.001000 rate, 2.260965 seconds, 436864 images -Loaded: 0.000075 seconds -Region 16 Avg IOU: 0.772778, Class: 0.180789, Obj: 0.341049, No Obj: 0.000715, .5R: 1.000000, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.743539, Class: 0.179768, Obj: 0.195900, No Obj: 0.000170, .5R: 0.909091, .75R: 0.590909, count: 22 -Region 16 Avg IOU: 0.737503, Class: 0.248338, Obj: 0.263050, No Obj: 0.001119, .5R: 0.939394, .75R: 0.606061, count: 33 -Region 23 Avg IOU: 0.745821, Class: 0.209989, Obj: 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0.380952, count: 21 -Region 23 Avg IOU: 0.726314, Class: 0.172366, Obj: 0.210302, No Obj: 0.000213, .5R: 0.875000, .75R: 0.625000, count: 8 -Region 16 Avg IOU: 0.732844, Class: 0.199065, Obj: 0.237528, No Obj: 0.000933, .5R: 0.842105, .75R: 0.578947, count: 19 -Region 23 Avg IOU: 0.737239, Class: 0.147400, Obj: 0.051094, No Obj: 0.000149, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 16 Avg IOU: 0.724507, Class: 0.220825, Obj: 0.312576, No Obj: 0.001275, .5R: 0.863636, .75R: 0.590909, count: 22 -Region 23 Avg IOU: 0.800535, Class: 0.178678, Obj: 0.208927, No Obj: 0.000162, .5R: 1.000000, .75R: 0.750000, count: 8 - - 3416: 2.679926, 2.555086 avg loss, 0.001000 rate, 2.282254 seconds, 437248 images -Loaded: 0.000071 seconds -Region 16 Avg IOU: 0.692663, Class: 0.186283, Obj: 0.286189, No Obj: 0.001110, .5R: 0.916667, .75R: 0.375000, count: 24 -Region 23 Avg IOU: 0.681731, Class: 0.182458, Obj: 0.156967, No Obj: 0.000306, .5R: 0.884615, .75R: 0.500000, count: 26 -Region 16 Avg IOU: 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0.204888, No Obj: 0.000886, .5R: 0.863636, .75R: 0.272727, count: 22 -Region 23 Avg IOU: 0.693553, Class: 0.132556, Obj: 0.161656, No Obj: 0.000226, .5R: 0.800000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.732149, Class: 0.164616, Obj: 0.281862, No Obj: 0.000985, .5R: 0.950000, .75R: 0.550000, count: 20 -Region 23 Avg IOU: 0.756220, Class: 0.200678, Obj: 0.115543, No Obj: 0.000116, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 16 Avg IOU: 0.747911, Class: 0.333573, Obj: 0.465707, No Obj: 0.001215, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.735000, Class: 0.163193, Obj: 0.145713, No Obj: 0.000209, .5R: 1.000000, .75R: 0.375000, count: 16 - - 3417: 2.700605, 2.569638 avg loss, 0.001000 rate, 2.268450 seconds, 437376 images -Loaded: 0.000098 seconds -Region 16 Avg IOU: 0.692444, Class: 0.197648, Obj: 0.296613, No Obj: 0.001317, .5R: 0.952381, .75R: 0.333333, count: 21 -Region 23 Avg IOU: 0.721302, Class: 0.201323, Obj: 0.215629, No Obj: 0.000215, .5R: 1.000000, .75R: 0.333333, count: 21 -Region 16 Avg IOU: 0.728480, Class: 0.200970, Obj: 0.336127, No Obj: 0.001566, .5R: 0.909091, .75R: 0.454545, count: 22 -Region 23 Avg IOU: 0.702206, Class: 0.134595, Obj: 0.212094, No Obj: 0.000444, .5R: 0.947368, .75R: 0.421053, count: 38 -Region 16 Avg IOU: 0.732210, Class: 0.222715, Obj: 0.371502, No Obj: 0.001829, .5R: 1.000000, .75R: 0.516129, count: 31 -Region 23 Avg IOU: 0.755477, Class: 0.138510, Obj: 0.236309, No Obj: 0.000361, .5R: 1.000000, .75R: 0.633333, count: 30 -Region 16 Avg IOU: 0.753418, Class: 0.203678, Obj: 0.407342, No Obj: 0.001457, .5R: 0.961538, .75R: 0.500000, count: 26 -Region 23 Avg IOU: 0.741720, Class: 0.093794, Obj: 0.286828, No Obj: 0.000272, .5R: 0.947368, .75R: 0.473684, count: 19 -Region 16 Avg IOU: 0.743101, Class: 0.188788, Obj: 0.403539, No Obj: 0.001441, .5R: 0.933333, .75R: 0.533333, count: 30 -Region 23 Avg IOU: 0.749050, Class: 0.124465, Obj: 0.191735, No Obj: 0.000352, .5R: 1.000000, .75R: 0.541667, count: 24 -Region 16 Avg IOU: 0.743320, Class: 0.243048, Obj: 0.314762, No Obj: 0.001614, .5R: 0.965517, .75R: 0.620690, count: 29 -Region 23 Avg IOU: 0.750256, Class: 0.167306, Obj: 0.261921, No Obj: 0.000361, .5R: 0.962963, .75R: 0.629630, count: 27 -Region 16 Avg IOU: 0.819393, Class: 0.226457, Obj: 0.305200, No Obj: 0.001595, .5R: 1.000000, .75R: 0.826087, count: 23 -Region 23 Avg IOU: 0.707015, Class: 0.180220, Obj: 0.279790, No Obj: 0.000214, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 16 Avg IOU: 0.768279, Class: 0.206679, Obj: 0.316204, No Obj: 0.001554, .5R: 1.000000, .75R: 0.600000, count: 20 -Region 23 Avg IOU: 0.648174, Class: 0.218811, Obj: 0.204521, No Obj: 0.000172, .5R: 0.777778, .75R: 0.333333, count: 9 - - 3418: 3.218072, 2.634482 avg loss, 0.001000 rate, 2.273848 seconds, 437504 images -Loaded: 0.000081 seconds -Region 16 Avg IOU: 0.743472, Class: 0.253507, Obj: 0.399044, No Obj: 0.001196, .5R: 0.925926, .75R: 0.518519, count: 27 -Region 23 Avg IOU: 0.714608, Class: 0.178968, Obj: 0.150961, No Obj: 0.000195, .5R: 1.000000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.728931, Class: 0.195508, Obj: 0.185529, No Obj: 0.000820, .5R: 0.900000, .75R: 0.500000, count: 20 -Region 23 Avg IOU: 0.684579, Class: 0.120172, Obj: 0.064544, No Obj: 0.000083, .5R: 0.909091, .75R: 0.545455, count: 11 -Region 16 Avg IOU: 0.741069, Class: 0.248998, Obj: 0.186301, No Obj: 0.000984, .5R: 0.947368, .75R: 0.578947, count: 19 -Region 23 Avg IOU: 0.751930, Class: 0.186373, Obj: 0.177804, No Obj: 0.000079, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 16 Avg IOU: 0.727798, Class: 0.253530, Obj: 0.337200, No Obj: 0.001139, .5R: 0.956522, .75R: 0.478261, count: 23 -Region 23 Avg IOU: 0.739627, Class: 0.164811, Obj: 0.178012, No Obj: 0.000207, .5R: 1.000000, .75R: 0.409091, count: 22 -Region 16 Avg IOU: 0.777526, Class: 0.227787, Obj: 0.345061, No Obj: 0.001216, .5R: 1.000000, .75R: 0.681818, count: 22 -Region 23 Avg IOU: 0.715317, Class: 0.187717, Obj: 0.184549, No Obj: 0.000243, .5R: 0.950000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.726815, Class: 0.213941, Obj: 0.273789, No Obj: 0.001438, .5R: 0.944444, .75R: 0.527778, count: 36 -Region 23 Avg IOU: 0.722246, Class: 0.155125, Obj: 0.128627, No Obj: 0.000245, .5R: 0.885714, .75R: 0.485714, count: 35 -Region 16 Avg IOU: 0.737414, Class: 0.241547, Obj: 0.365811, No Obj: 0.001097, .5R: 0.928571, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.661007, Class: 0.261541, Obj: 0.085340, No Obj: 0.000118, .5R: 0.800000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.727278, Class: 0.213922, Obj: 0.269023, No Obj: 0.001110, .5R: 0.944444, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.702826, Class: 0.171919, Obj: 0.180571, No Obj: 0.000180, .5R: 1.000000, .75R: 0.368421, count: 19 - - 3419: 2.854896, 2.656523 avg loss, 0.001000 rate, 2.265571 seconds, 437632 images -Loaded: 0.000084 seconds -Region 16 Avg IOU: 0.728293, Class: 0.187877, Obj: 0.328610, No Obj: 0.000956, .5R: 0.944444, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.751704, Class: 0.147594, Obj: 0.149788, No Obj: 0.000152, .5R: 0.937500, .75R: 0.562500, count: 16 -Region 16 Avg IOU: 0.692581, Class: 0.188420, Obj: 0.349013, No Obj: 0.001046, .5R: 0.789474, .75R: 0.578947, count: 19 -Region 23 Avg IOU: 0.737612, Class: 0.154502, Obj: 0.257057, No Obj: 0.000215, .5R: 1.000000, .75R: 0.409091, count: 22 -Region 16 Avg IOU: 0.773925, Class: 0.317041, Obj: 0.357756, No Obj: 0.001173, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.798766, Class: 0.097683, Obj: 0.431920, No Obj: 0.000139, .5R: 1.000000, .75R: 0.888889, count: 9 -Region 16 Avg IOU: 0.758897, Class: 0.244661, Obj: 0.314664, No Obj: 0.000959, .5R: 1.000000, .75R: 0.600000, count: 20 -Region 23 Avg IOU: 0.745935, Class: 0.177571, Obj: 0.226139, No Obj: 0.000180, .5R: 1.000000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.661340, Class: 0.214115, Obj: 0.211389, No Obj: 0.000936, .5R: 0.909091, .75R: 0.272727, count: 22 -Region 23 Avg IOU: 0.714305, Class: 0.178891, Obj: 0.138095, No Obj: 0.000197, .5R: 0.916667, .75R: 0.625000, count: 24 -Region 16 Avg IOU: 0.705672, Class: 0.227567, Obj: 0.081093, No Obj: 0.000847, .5R: 0.875000, .75R: 0.458333, count: 24 -Region 23 Avg IOU: 0.649426, Class: 0.167465, Obj: 0.052058, No Obj: 0.000127, .5R: 0.800000, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.722139, Class: 0.238548, Obj: 0.266815, No Obj: 0.001254, .5R: 0.944444, .75R: 0.500000, count: 36 -Region 23 Avg IOU: 0.673695, Class: 0.203498, Obj: 0.080261, No Obj: 0.000153, .5R: 0.954545, .75R: 0.318182, count: 22 -Region 16 Avg IOU: 0.747017, Class: 0.159336, Obj: 0.226094, No Obj: 0.000848, .5R: 1.000000, .75R: 0.560000, count: 25 -Region 23 Avg IOU: 0.748195, Class: 0.179204, Obj: 0.213762, No Obj: 0.000083, .5R: 1.000000, .75R: 0.636364, count: 11 - - 3420: 2.778655, 2.668736 avg loss, 0.001000 rate, 2.270076 seconds, 437760 images -Resizing -576 x 576 - try to allocate workspace = 1146159105 * sizeof(float), CUDA allocate done! -Loaded: 0.527689 seconds -Region 16 Avg IOU: 0.707428, Class: 0.248448, Obj: 0.317230, No Obj: 0.001098, .5R: 1.000000, .75R: 0.333333, count: 15 -Region 23 Avg IOU: 0.731559, Class: 0.218676, Obj: 0.157201, No Obj: 0.000340, .5R: 0.954545, .75R: 0.545455, count: 22 -Region 16 Avg IOU: 0.746415, Class: 0.174083, Obj: 0.346885, No Obj: 0.001097, .5R: 1.000000, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.744747, Class: 0.187232, Obj: 0.179098, No Obj: 0.000271, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.713721, Class: 0.253170, Obj: 0.341173, No Obj: 0.001279, .5R: 1.000000, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 0.691986, Class: 0.179453, Obj: 0.160904, No Obj: 0.000372, .5R: 0.851852, .75R: 0.407407, count: 27 -Region 16 Avg IOU: 0.712813, Class: 0.219665, Obj: 0.194239, No Obj: 0.001022, .5R: 0.904762, .75R: 0.476190, count: 21 -Region 23 Avg IOU: 0.745392, Class: 0.172928, Obj: 0.240985, No Obj: 0.000363, .5R: 0.958333, .75R: 0.541667, count: 24 -Region 16 Avg IOU: 0.754740, Class: 0.237154, Obj: 0.274553, No Obj: 0.001199, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.725008, Class: 0.191485, Obj: 0.185305, No Obj: 0.000232, .5R: 0.866667, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.724616, Class: 0.226393, Obj: 0.303586, No Obj: 0.001665, .5R: 0.909091, .75R: 0.545455, count: 33 -Region 23 Avg IOU: 0.730420, Class: 0.171122, Obj: 0.238921, No Obj: 0.000370, .5R: 0.941176, .75R: 0.470588, count: 17 -Region 16 Avg IOU: 0.772031, Class: 0.237404, Obj: 0.306276, No Obj: 0.001312, .5R: 1.000000, .75R: 0.611111, count: 18 -Region 23 Avg IOU: 0.672349, Class: 0.191670, Obj: 0.165756, No Obj: 0.000237, .5R: 1.000000, .75R: 0.214286, count: 14 -Region 16 Avg IOU: 0.781777, Class: 0.181958, Obj: 0.351365, No Obj: 0.000948, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.705152, Class: 0.213922, Obj: 0.166306, No Obj: 0.000258, .5R: 0.916667, .75R: 0.333333, count: 24 - - 3421: 2.514661, 2.653329 avg loss, 0.001000 rate, 1.411004 seconds, 437888 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.761257, Class: 0.243090, Obj: 0.296346, No Obj: 0.001334, .5R: 0.944444, .75R: 0.666667, count: 18 -Region 23 Avg IOU: 0.783855, Class: 0.246586, Obj: 0.178583, No Obj: 0.000448, .5R: 1.000000, .75R: 0.631579, count: 19 -Region 16 Avg IOU: 0.790623, Class: 0.157927, Obj: 0.373272, No Obj: 0.001066, .5R: 1.000000, .75R: 0.785714, count: 14 -Region 23 Avg IOU: 0.758994, Class: 0.095663, Obj: 0.303089, No Obj: 0.000279, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 16 Avg IOU: 0.748497, Class: 0.216707, Obj: 0.273666, No Obj: 0.000850, .5R: 1.000000, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.708552, Class: 0.132164, Obj: 0.221472, No Obj: 0.000351, .5R: 0.882353, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.686527, Class: 0.217086, Obj: 0.198663, No Obj: 0.001168, .5R: 0.909091, .75R: 0.318182, count: 22 -Region 23 Avg IOU: 0.734254, Class: 0.192894, Obj: 0.188526, No Obj: 0.000379, .5R: 0.947368, .75R: 0.526316, count: 19 -Region 16 Avg IOU: 0.804602, Class: 0.237972, Obj: 0.289434, No Obj: 0.001122, .5R: 1.000000, .75R: 0.913043, count: 23 -Region 23 Avg IOU: 0.757562, Class: 0.185286, Obj: 0.289557, No Obj: 0.000503, .5R: 1.000000, .75R: 0.550000, count: 20 -Region 16 Avg IOU: 0.719042, Class: 0.213350, Obj: 0.318227, No Obj: 0.001569, .5R: 1.000000, .75R: 0.400000, count: 20 -Region 23 Avg IOU: 0.746228, Class: 0.213481, Obj: 0.259138, No Obj: 0.000441, .5R: 1.000000, .75R: 0.583333, count: 24 -Region 16 Avg IOU: 0.751499, Class: 0.196368, Obj: 0.380463, No Obj: 0.001518, .5R: 0.947368, .75R: 0.578947, count: 19 -Region 23 Avg IOU: 0.706103, Class: 0.199103, Obj: 0.339231, No Obj: 0.000384, .5R: 0.846154, .75R: 0.615385, count: 13 -Region 16 Avg IOU: 0.729448, Class: 0.273546, Obj: 0.417799, No Obj: 0.001185, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.705509, Class: 0.218484, Obj: 0.301388, No Obj: 0.000430, .5R: 0.846154, .75R: 0.384615, count: 13 - - 3422: 2.229908, 2.610987 avg loss, 0.001000 rate, 1.478480 seconds, 438016 images -Loaded: 0.000068 seconds -Region 16 Avg IOU: 0.781015, Class: 0.274072, Obj: 0.497842, No Obj: 0.001912, .5R: 1.000000, .75R: 0.769231, count: 13 -Region 23 Avg IOU: 0.749821, Class: 0.216685, Obj: 0.256346, No Obj: 0.000519, .5R: 0.950000, .75R: 0.700000, count: 20 -Region 16 Avg IOU: 0.824084, Class: 0.321426, Obj: 0.403463, No Obj: 0.001095, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.689073, Class: 0.186694, Obj: 0.279678, No Obj: 0.000586, .5R: 0.869565, .75R: 0.434783, count: 23 -Region 16 Avg IOU: 0.756217, Class: 0.197320, Obj: 0.374591, No Obj: 0.001519, .5R: 1.000000, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.740783, Class: 0.132396, Obj: 0.332801, No Obj: 0.000424, .5R: 0.933333, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.754668, Class: 0.225620, Obj: 0.359233, No Obj: 0.001492, .5R: 0.954545, .75R: 0.545455, count: 22 -Region 23 Avg IOU: 0.726640, Class: 0.176959, Obj: 0.314865, No Obj: 0.000624, .5R: 1.000000, .75R: 0.500000, count: 26 -Region 16 Avg IOU: 0.782431, Class: 0.240841, Obj: 0.417326, No Obj: 0.001368, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.724893, Class: 0.183928, Obj: 0.334504, No Obj: 0.000559, .5R: 1.000000, .75R: 0.391304, count: 23 -Region 16 Avg IOU: 0.793928, Class: 0.228116, Obj: 0.401828, No Obj: 0.001712, .5R: 1.000000, .75R: 0.700000, count: 20 -Region 23 Avg IOU: 0.747945, Class: 0.171573, Obj: 0.209299, No Obj: 0.000456, .5R: 1.000000, .75R: 0.545455, count: 22 -Region 16 Avg IOU: 0.740081, Class: 0.222864, Obj: 0.186811, No Obj: 0.001484, .5R: 0.954545, .75R: 0.545455, count: 22 -Region 23 Avg IOU: 0.735765, Class: 0.161052, Obj: 0.227719, No Obj: 0.000319, .5R: 0.909091, .75R: 0.636364, count: 11 -Region 16 Avg IOU: 0.737593, Class: 0.182865, Obj: 0.378990, No Obj: 0.001401, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.765940, Class: 0.174152, 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-Region 16 Avg IOU: 0.740622, Class: 0.163690, Obj: 0.324533, No Obj: 0.000951, .5R: 1.000000, .75R: 0.550000, count: 20 -Region 23 Avg IOU: 0.750268, Class: 0.179400, Obj: 0.204208, No Obj: 0.000442, .5R: 1.000000, .75R: 0.629630, count: 27 -Region 16 Avg IOU: 0.765700, Class: 0.209426, Obj: 0.247640, No Obj: 0.000899, .5R: 1.000000, .75R: 0.647059, count: 17 -Region 23 Avg IOU: 0.761417, Class: 0.161078, Obj: 0.288957, No Obj: 0.000358, .5R: 1.000000, .75R: 0.608696, count: 23 -Region 16 Avg IOU: 0.784841, Class: 0.236710, Obj: 0.295894, No Obj: 0.001206, .5R: 1.000000, .75R: 0.708333, count: 24 -Region 23 Avg IOU: 0.788916, Class: 0.194043, Obj: 0.226373, No Obj: 0.000233, .5R: 0.916667, .75R: 0.833333, count: 12 -Region 16 Avg IOU: 0.723897, Class: 0.166544, Obj: 0.252109, No Obj: 0.000880, .5R: 0.882353, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.749496, Class: 0.154241, Obj: 0.151546, No Obj: 0.000135, .5R: 1.000000, .75R: 0.625000, count: 8 - - 3439: 2.505961, 2.470019 avg 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27 -Region 16 Avg IOU: 0.823832, Class: 0.272462, Obj: 0.465183, No Obj: 0.001909, .5R: 1.000000, .75R: 0.818182, count: 11 -Region 23 Avg IOU: 0.761423, Class: 0.160503, Obj: 0.285403, No Obj: 0.000460, .5R: 1.000000, .75R: 0.647059, count: 17 -Region 16 Avg IOU: 0.821846, Class: 0.205285, Obj: 0.579148, No Obj: 0.001394, .5R: 1.000000, .75R: 0.750000, count: 4 -Region 23 Avg IOU: 0.745031, Class: 0.164437, Obj: 0.305764, No Obj: 0.000598, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.714092, Class: 0.256778, Obj: 0.297560, No Obj: 0.002568, .5R: 0.944444, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.711634, Class: 0.222043, Obj: 0.161286, No Obj: 0.000669, .5R: 0.964286, .75R: 0.428571, count: 28 - - 3469: 2.045223, 2.024320 avg loss, 0.001000 rate, 0.857885 seconds, 444032 images -Loaded: 0.000071 seconds -Region 16 Avg IOU: 0.723293, Class: 0.245578, Obj: 0.249224, No Obj: 0.000800, .5R: 1.000000, .75R: 0.400000, count: 5 -Region 23 Avg IOU: 0.723878, Class: 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0.801508, Class: 0.165260, Obj: 0.274522, No Obj: 0.000221, .5R: 1.000000, .75R: 0.714286, count: 21 -Region 16 Avg IOU: 0.699480, Class: 0.171666, Obj: 0.156381, No Obj: 0.000967, .5R: 0.958333, .75R: 0.333333, count: 24 -Region 23 Avg IOU: 0.644797, Class: 0.218694, Obj: 0.069682, No Obj: 0.000199, .5R: 0.807692, .75R: 0.269231, count: 26 -Region 16 Avg IOU: 0.712303, Class: 0.219851, Obj: 0.162536, No Obj: 0.000617, .5R: 0.882353, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.717302, Class: 0.175119, Obj: 0.126494, No Obj: 0.000170, .5R: 0.866667, .75R: 0.466667, count: 15 - - 3482: 3.074017, 2.384898 avg loss, 0.001000 rate, 2.442331 seconds, 445696 images -Loaded: 0.000068 seconds -Region 16 Avg IOU: 0.763214, Class: 0.207450, Obj: 0.295163, No Obj: 0.001199, .5R: 1.000000, .75R: 0.586207, count: 29 -Region 23 Avg IOU: 0.778860, Class: 0.174464, Obj: 0.262627, No Obj: 0.000173, .5R: 1.000000, .75R: 0.625000, count: 16 -Region 16 Avg IOU: 0.720689, Class: 0.221818, Obj: 0.266951, 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0.683396, Class: 0.189064, Obj: 0.122110, No Obj: 0.000120, .5R: 1.000000, .75R: 0.181818, count: 11 -Region 16 Avg IOU: 0.739088, Class: 0.222562, Obj: 0.425837, No Obj: 0.001180, .5R: 0.952381, .75R: 0.571429, count: 21 -Region 23 Avg IOU: 0.783262, Class: 0.196039, Obj: 0.309028, No Obj: 0.000172, .5R: 1.000000, .75R: 0.642857, count: 14 -Region 16 Avg IOU: 0.686614, Class: 0.245763, Obj: 0.226911, No Obj: 0.001350, .5R: 0.945946, .75R: 0.324324, count: 37 -Region 23 Avg IOU: 0.726906, Class: 0.240015, Obj: 0.142566, No Obj: 0.000176, .5R: 1.000000, .75R: 0.444444, count: 18 -Region 16 Avg IOU: 0.747168, Class: 0.222378, Obj: 0.282638, No Obj: 0.001422, .5R: 0.947368, .75R: 0.684211, count: 19 -Region 23 Avg IOU: 0.687623, Class: 0.218636, Obj: 0.157021, No Obj: 0.000210, .5R: 0.928571, .75R: 0.357143, count: 14 - - 3487: 2.653956, 2.481423 avg loss, 0.001000 rate, 2.418673 seconds, 446336 images -Loaded: 0.000078 seconds -Region 16 Avg IOU: 0.734950, Class: 0.223744, Obj: 0.290801, 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images -Loaded: 0.000084 seconds -Region 16 Avg IOU: 0.675341, Class: 0.128805, Obj: 0.269914, No Obj: 0.001097, .5R: 0.952381, .75R: 0.333333, count: 21 -Region 23 Avg IOU: 0.718646, Class: 0.202424, Obj: 0.203715, No Obj: 0.000146, .5R: 1.000000, .75R: 0.357143, count: 14 -Region 16 Avg IOU: 0.757828, Class: 0.228361, Obj: 0.228149, No Obj: 0.000918, .5R: 1.000000, .75R: 0.608696, count: 23 -Region 23 Avg IOU: 0.738297, Class: 0.114000, Obj: 0.177994, No Obj: 0.000091, .5R: 0.888889, .75R: 0.555556, count: 9 -Region 16 Avg IOU: 0.759406, Class: 0.239164, Obj: 0.403300, No Obj: 0.001048, .5R: 0.962963, .75R: 0.555556, count: 27 -Region 23 Avg IOU: 0.715396, Class: 0.133419, Obj: 0.194065, No Obj: 0.000142, .5R: 0.923077, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.760460, Class: 0.217395, Obj: 0.185552, No Obj: 0.001201, .5R: 1.000000, .75R: 0.676471, count: 34 -Region 23 Avg IOU: 0.748974, Class: 0.234519, Obj: 0.120492, No Obj: 0.000108, .5R: 1.000000, .75R: 0.400000, count: 10 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loss, 0.001000 rate, 2.401584 seconds, 446720 images -Resizing -544 x 544 - try to allocate workspace = 1145585665 * sizeof(float), CUDA allocate done! -Loaded: 0.476138 seconds -Region 16 Avg IOU: 0.830794, Class: 0.256829, Obj: 0.514351, No Obj: 0.001535, .5R: 1.000000, .75R: 0.909091, count: 11 -Region 23 Avg IOU: 0.656161, Class: 0.227956, Obj: 0.314066, No Obj: 0.000316, .5R: 0.777778, .75R: 0.333333, count: 9 -Region 16 Avg IOU: 0.767084, Class: 0.131394, Obj: 0.436385, No Obj: 0.001333, .5R: 1.000000, .75R: 0.363636, count: 11 -Region 23 Avg IOU: 0.717845, Class: 0.212002, Obj: 0.290029, No Obj: 0.000483, .5R: 0.916667, .75R: 0.458333, count: 24 -Region 16 Avg IOU: 0.694732, Class: 0.225428, Obj: 0.298405, No Obj: 0.001268, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.714772, Class: 0.230533, Obj: 0.226075, No Obj: 0.000390, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 16 Avg IOU: 0.768548, Class: 0.261618, Obj: 0.370684, No Obj: 0.001087, .5R: 1.000000, 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0.000071 seconds -Region 16 Avg IOU: 0.753598, Class: 0.184123, Obj: 0.277917, No Obj: 0.000741, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 23 Avg IOU: 0.732242, Class: 0.209559, Obj: 0.202549, No Obj: 0.000214, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.769257, Class: 0.273202, Obj: 0.307303, No Obj: 0.001052, .5R: 1.000000, .75R: 0.714286, count: 21 -Region 23 Avg IOU: 0.771012, Class: 0.243467, Obj: 0.202197, No Obj: 0.000350, .5R: 1.000000, .75R: 0.550000, count: 20 -Region 16 Avg IOU: 0.812001, Class: 0.223632, Obj: 0.343321, No Obj: 0.000976, .5R: 1.000000, .75R: 0.785714, count: 14 -Region 23 Avg IOU: 0.778256, Class: 0.177392, Obj: 0.249121, No Obj: 0.000246, .5R: 1.000000, .75R: 0.684211, count: 19 -Region 16 Avg IOU: 0.724151, Class: 0.162068, Obj: 0.282861, No Obj: 0.000832, .5R: 1.000000, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.684064, Class: 0.194869, Obj: 0.210288, No Obj: 0.000219, .5R: 0.888889, .75R: 0.388889, count: 18 -Region 16 Avg 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14 - - 3507: 2.414228, 2.469474 avg loss, 0.001000 rate, 2.076172 seconds, 448896 images -Loaded: 0.000064 seconds -Region 16 Avg IOU: 0.759134, Class: 0.209075, Obj: 0.403455, No Obj: 0.001410, .5R: 1.000000, .75R: 0.583333, count: 24 -Region 23 Avg IOU: 0.786627, Class: 0.200382, Obj: 0.204909, No Obj: 0.000224, .5R: 1.000000, .75R: 0.565217, count: 23 -Region 16 Avg IOU: 0.686701, Class: 0.241498, Obj: 0.344341, No Obj: 0.001057, .5R: 0.947368, .75R: 0.368421, count: 19 -Region 23 Avg IOU: 0.759905, Class: 0.224436, Obj: 0.083410, No Obj: 0.000237, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.727455, Class: 0.269421, Obj: 0.422609, No Obj: 0.001235, .5R: 0.888889, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.725767, Class: 0.202554, Obj: 0.234553, No Obj: 0.000232, .5R: 0.923077, .75R: 0.538462, count: 13 -Region 16 Avg IOU: 0.787517, Class: 0.238869, Obj: 0.362312, No Obj: 0.000960, .5R: 1.000000, .75R: 0.769231, count: 13 -Region 23 Avg IOU: 0.795937, Class: 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count: 24 -Region 23 Avg IOU: 0.770520, Class: 0.256660, Obj: 0.190240, No Obj: 0.000183, .5R: 0.956522, .75R: 0.652174, count: 23 -Region 16 Avg IOU: 0.774965, Class: 0.224355, Obj: 0.233218, No Obj: 0.001352, .5R: 1.000000, .75R: 0.714286, count: 21 -Region 23 Avg IOU: 0.784941, Class: 0.159170, Obj: 0.228339, No Obj: 0.000194, .5R: 1.000000, .75R: 0.722222, count: 18 -Region 16 Avg IOU: 0.758711, Class: 0.307547, Obj: 0.520261, No Obj: 0.001930, .5R: 1.000000, .75R: 0.555556, count: 27 -Region 23 Avg IOU: 0.694326, Class: 0.234295, Obj: 0.080202, No Obj: 0.000101, .5R: 0.882353, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.734704, Class: 0.269336, Obj: 0.404224, No Obj: 0.001126, .5R: 0.947368, .75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.696593, Class: 0.158885, Obj: 0.120396, No Obj: 0.000109, .5R: 0.857143, .75R: 0.500000, count: 14 -Region 16 Avg IOU: 0.781205, Class: 0.292899, Obj: 0.504959, No Obj: 0.001167, .5R: 1.000000, .75R: 0.611111, count: 18 -Region 23 Avg IOU: 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0.178044, Obj: 0.128372, No Obj: 0.000114, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 16 Avg IOU: 0.696662, Class: 0.200176, Obj: 0.250960, No Obj: 0.000988, .5R: 0.913043, .75R: 0.434783, count: 23 -Region 23 Avg IOU: 0.764044, Class: 0.159175, Obj: 0.096372, No Obj: 0.000074, .5R: 1.000000, .75R: 0.428571, count: 7 -Region 16 Avg IOU: 0.771626, Class: 0.244006, Obj: 0.251041, No Obj: 0.000918, .5R: 1.000000, .75R: 0.650000, count: 20 -Region 23 Avg IOU: 0.721881, Class: 0.183990, Obj: 0.156373, No Obj: 0.000107, .5R: 0.933333, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.779787, Class: 0.190654, Obj: 0.249624, No Obj: 0.000921, .5R: 1.000000, .75R: 0.750000, count: 24 -Region 23 Avg IOU: 0.748694, Class: 0.150275, Obj: 0.169273, No Obj: 0.000140, .5R: 0.909091, .75R: 0.590909, count: 22 -Region 16 Avg IOU: 0.749308, Class: 0.205559, Obj: 0.286802, No Obj: 0.000623, .5R: 0.950000, .75R: 0.550000, count: 20 -Region 23 Avg IOU: 0.749213, Class: 0.253095, Obj: 0.157027, No Obj: 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30 -Region 16 Avg IOU: 0.750799, Class: 0.218674, Obj: 0.389313, No Obj: 0.001100, .5R: 0.969697, .75R: 0.575758, count: 33 -Region 23 Avg IOU: 0.717454, Class: 0.222930, Obj: 0.171203, No Obj: 0.000170, .5R: 0.941176, .75R: 0.647059, count: 17 -Region 16 Avg IOU: 0.702559, Class: 0.226658, Obj: 0.250127, No Obj: 0.001241, .5R: 0.851852, .75R: 0.444444, count: 27 -Region 23 Avg IOU: 0.679299, Class: 0.191351, Obj: 0.153472, No Obj: 0.000133, .5R: 0.875000, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.714799, Class: 0.190806, Obj: 0.191890, No Obj: 0.000728, .5R: 0.956522, .75R: 0.391304, count: 23 -Region 23 Avg IOU: 0.773924, Class: 0.119237, Obj: 0.127231, No Obj: 0.000109, .5R: 1.000000, .75R: 0.625000, count: 8 - - 3520: 2.836275, 2.703335 avg loss, 0.001000 rate, 2.415185 seconds, 450560 images -Resizing -608 x 608 - try to allocate workspace = 1146765313 * sizeof(float), CUDA allocate done! -Loaded: 0.469315 seconds -Region 16 Avg IOU: 0.737041, Class: 0.238928, Obj: 0.282268, 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images -Loaded: 0.000074 seconds -Region 16 Avg IOU: 0.803362, Class: 0.275784, Obj: 0.522888, No Obj: 0.000790, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.780933, Class: 0.110264, Obj: 0.281891, No Obj: 0.000252, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 16 Avg IOU: 0.767102, Class: 0.219049, Obj: 0.314119, No Obj: 0.001188, .5R: 1.000000, .75R: 0.650000, count: 20 -Region 23 Avg IOU: 0.770245, Class: 0.155590, Obj: 0.186618, No Obj: 0.000192, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 16 Avg IOU: 0.719184, Class: 0.204489, Obj: 0.305945, No Obj: 0.001919, .5R: 1.000000, .75R: 0.375000, count: 24 -Region 23 Avg IOU: 0.700079, Class: 0.187678, Obj: 0.211708, No Obj: 0.000388, .5R: 0.954545, .75R: 0.454545, count: 22 -Region 16 Avg IOU: 0.733540, Class: 0.242644, Obj: 0.407162, No Obj: 0.001737, .5R: 0.920000, .75R: 0.480000, count: 25 -Region 23 Avg IOU: 0.703587, Class: 0.202305, Obj: 0.232843, No Obj: 0.000500, .5R: 0.965517, .75R: 0.482759, count: 29 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count: 21 -Region 16 Avg IOU: 0.764293, Class: 0.189258, Obj: 0.500241, No Obj: 0.001179, .5R: 1.000000, .75R: 0.384615, count: 13 -Region 23 Avg IOU: 0.714240, Class: 0.171038, Obj: 0.155240, No Obj: 0.000320, .5R: 0.900000, .75R: 0.500000, count: 20 -Region 16 Avg IOU: 0.743413, Class: 0.235625, Obj: 0.390242, No Obj: 0.001176, .5R: 0.950000, .75R: 0.550000, count: 20 -Region 23 Avg IOU: 0.761447, Class: 0.187148, Obj: 0.235609, No Obj: 0.000227, .5R: 1.000000, .75R: 0.476190, count: 21 -Region 16 Avg IOU: 0.741312, Class: 0.165710, Obj: 0.360013, No Obj: 0.001051, .5R: 0.916667, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.709484, Class: 0.181293, Obj: 0.191180, No Obj: 0.000344, .5R: 0.961538, .75R: 0.307692, count: 26 -Region 16 Avg IOU: 0.740664, Class: 0.182778, Obj: 0.304957, No Obj: 0.001483, .5R: 0.952381, .75R: 0.476190, count: 21 -Region 23 Avg IOU: 0.734742, Class: 0.184691, Obj: 0.164012, No Obj: 0.000354, .5R: 0.962963, .75R: 0.518519, count: 27 -Region 16 Avg IOU: 0.755760, Class: 0.225385, Obj: 0.455618, No Obj: 0.000802, .5R: 1.000000, .75R: 0.444444, count: 9 -Region 23 Avg IOU: 0.704778, Class: 0.101736, Obj: 0.190328, No Obj: 0.000146, .5R: 0.857143, .75R: 0.571429, count: 7 - - 3528: 2.444817, 2.590897 avg loss, 0.001000 rate, 1.615193 seconds, 451584 images -Loaded: 0.000063 seconds -Region 16 Avg IOU: 0.680814, Class: 0.214412, Obj: 0.380497, No Obj: 0.000938, .5R: 0.785714, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.689377, Class: 0.218385, Obj: 0.312157, No Obj: 0.000452, .5R: 0.913043, .75R: 0.434783, count: 23 -Region 16 Avg IOU: 0.746545, Class: 0.269882, Obj: 0.365995, No Obj: 0.000951, .5R: 0.937500, .75R: 0.687500, count: 16 -Region 23 Avg IOU: 0.727174, Class: 0.226284, Obj: 0.345426, No Obj: 0.000433, .5R: 1.000000, .75R: 0.421053, count: 19 -Region 16 Avg IOU: 0.796325, Class: 0.227143, Obj: 0.242250, No Obj: 0.000965, .5R: 1.000000, .75R: 0.705882, count: 17 -Region 23 Avg IOU: 0.713683, Class: 0.197786, Obj: 0.281678, 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count: 14 -Region 16 Avg IOU: 0.801482, Class: 0.220796, Obj: 0.317803, No Obj: 0.000920, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.727794, Class: 0.184124, Obj: 0.266987, No Obj: 0.000349, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.805596, Class: 0.258090, Obj: 0.348949, No Obj: 0.001278, .5R: 1.000000, .75R: 0.785714, count: 14 -Region 23 Avg IOU: 0.753302, Class: 0.192126, Obj: 0.264978, No Obj: 0.000590, .5R: 1.000000, .75R: 0.538462, count: 26 -Region 16 Avg IOU: 0.753495, Class: 0.190427, Obj: 0.370473, No Obj: 0.001370, .5R: 1.000000, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.674128, Class: 0.183157, Obj: 0.174832, No Obj: 0.000321, .5R: 0.692308, .75R: 0.461538, count: 13 -Region 16 Avg IOU: 0.770170, Class: 0.180697, Obj: 0.367201, No Obj: 0.000997, .5R: 0.923077, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.805562, Class: 0.158677, Obj: 0.263050, No Obj: 0.000417, .5R: 1.000000, .75R: 0.789474, count: 19 -Region 16 Avg IOU: 0.822023, Class: 0.263961, Obj: 0.423136, No Obj: 0.000639, .5R: 1.000000, .75R: 0.800000, count: 5 -Region 23 Avg IOU: 0.665201, Class: 0.164010, Obj: 0.303889, No Obj: 0.000343, .5R: 0.857143, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.750038, Class: 0.165669, Obj: 0.259214, No Obj: 0.001482, .5R: 0.941176, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 0.704466, Class: 0.117833, Obj: 0.223913, No Obj: 0.000421, .5R: 0.923077, .75R: 0.461538, count: 13 - - 3531: 1.844167, 2.427852 avg loss, 0.001000 rate, 1.089522 seconds, 451968 images -Loaded: 0.000072 seconds -Region 16 Avg IOU: 0.725095, Class: 0.261657, Obj: 0.350578, No Obj: 0.001939, .5R: 0.941176, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.730329, Class: 0.133240, Obj: 0.201297, No Obj: 0.000335, .5R: 1.000000, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.756675, Class: 0.241946, Obj: 0.413201, No Obj: 0.001183, .5R: 1.000000, .75R: 0.416667, count: 12 -Region 23 Avg IOU: 0.755995, Class: 0.202893, Obj: 0.148093, 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15 -Region 16 Avg IOU: 0.739201, Class: 0.254594, Obj: 0.401337, No Obj: 0.001251, .5R: 1.000000, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.687255, Class: 0.241753, Obj: 0.203523, No Obj: 0.000162, .5R: 1.000000, .75R: 0.230769, count: 13 -Region 16 Avg IOU: 0.653629, Class: 0.259351, Obj: 0.230831, No Obj: 0.000956, .5R: 0.842105, .75R: 0.315789, count: 19 -Region 23 Avg IOU: 0.674555, Class: 0.247229, Obj: 0.175432, No Obj: 0.000117, .5R: 0.916667, .75R: 0.416667, count: 12 - - 3550: 2.528048, 2.467732 avg loss, 0.001000 rate, 1.772652 seconds, 454400 images -Resizing -576 x 576 - try to allocate workspace = 1146159105 * sizeof(float), CUDA allocate done! -Loaded: 0.500885 seconds -Region 16 Avg IOU: 0.721265, Class: 0.169912, Obj: 0.246915, No Obj: 0.000939, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.713608, Class: 0.223458, Obj: 0.134264, No Obj: 0.000183, .5R: 0.960000, .75R: 0.360000, count: 25 -Region 16 Avg IOU: 0.768695, Class: 0.230256, Obj: 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17 -Region 23 Avg IOU: 0.688185, Class: 0.175446, Obj: 0.250631, No Obj: 0.000363, .5R: 0.866667, .75R: 0.400000, count: 15 -Region 16 Avg IOU: 0.759050, Class: 0.234082, Obj: 0.466280, No Obj: 0.002655, .5R: 1.000000, .75R: 0.560000, count: 25 -Region 23 Avg IOU: 0.702182, Class: 0.182271, Obj: 0.150549, No Obj: 0.000602, .5R: 0.925926, .75R: 0.407407, count: 27 -Region 16 Avg IOU: 0.787106, Class: 0.284410, Obj: 0.546169, No Obj: 0.001732, .5R: 1.000000, .75R: 0.705882, count: 17 -Region 23 Avg IOU: 0.789356, Class: 0.225114, Obj: 0.381827, No Obj: 0.000617, .5R: 1.000000, .75R: 0.736842, count: 19 - - 3584: 2.255094, 2.245582 avg loss, 0.001000 rate, 1.383004 seconds, 458752 images -Loaded: 0.000072 seconds -Region 16 Avg IOU: 0.751450, Class: 0.251259, Obj: 0.330565, No Obj: 0.001467, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.756395, Class: 0.257533, Obj: 0.223095, No Obj: 0.000328, .5R: 0.933333, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.701807, Class: 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13 -Region 23 Avg IOU: 0.763783, Class: 0.169224, Obj: 0.286491, No Obj: 0.000361, .5R: 1.000000, .75R: 0.611111, count: 18 -Region 16 Avg IOU: 0.764021, Class: 0.271803, Obj: 0.314279, No Obj: 0.001185, .5R: 1.000000, .75R: 0.769231, count: 13 -Region 23 Avg IOU: 0.702912, Class: 0.154821, Obj: 0.183471, No Obj: 0.000354, .5R: 0.916667, .75R: 0.500000, count: 24 -Region 16 Avg IOU: 0.756345, Class: 0.257534, Obj: 0.511230, No Obj: 0.001305, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.760781, Class: 0.204485, Obj: 0.365537, No Obj: 0.000479, .5R: 1.000000, .75R: 0.666667, count: 18 -Region 16 Avg IOU: 0.769141, Class: 0.222673, Obj: 0.472371, No Obj: 0.001723, .5R: 0.947368, .75R: 0.684211, count: 19 -Region 23 Avg IOU: 0.786401, Class: 0.178778, Obj: 0.447254, No Obj: 0.000490, .5R: 1.000000, .75R: 0.625000, count: 16 - - 3589: 2.088682, 2.246174 avg loss, 0.001000 rate, 1.377858 seconds, 459392 images -Loaded: 0.000076 seconds -Region 16 Avg IOU: 0.749097, Class: 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16 Avg IOU: 0.710983, Class: 0.206623, Obj: 0.361819, No Obj: 0.002818, .5R: 0.958333, .75R: 0.458333, count: 24 -Region 23 Avg IOU: 0.738181, Class: 0.162751, Obj: 0.137753, No Obj: 0.000466, .5R: 1.000000, .75R: 0.409091, count: 22 -Region 16 Avg IOU: 0.797149, Class: 0.180695, Obj: 0.488954, No Obj: 0.001391, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.728211, Class: 0.196887, Obj: 0.250792, No Obj: 0.000568, .5R: 0.894737, .75R: 0.526316, count: 19 -Region 16 Avg IOU: 0.756396, Class: 0.163872, Obj: 0.385489, No Obj: 0.001436, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.732593, Class: 0.185804, Obj: 0.184845, No Obj: 0.000322, .5R: 0.933333, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.778663, Class: 0.285894, Obj: 0.286181, No Obj: 0.001169, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.741350, Class: 0.234513, Obj: 0.198178, No Obj: 0.000476, .5R: 0.960000, .75R: 0.560000, count: 25 - - 3606: 1.888444, 2.170975 avg loss, 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Class: 0.152545, Obj: 0.271308, No Obj: 0.000593, .5R: 1.000000, .75R: 0.458333, count: 24 -Region 16 Avg IOU: 0.793724, Class: 0.259993, Obj: 0.448547, No Obj: 0.001621, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.766894, Class: 0.241254, Obj: 0.235424, No Obj: 0.000539, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 16 Avg IOU: 0.762084, Class: 0.269214, Obj: 0.360069, No Obj: 0.001874, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.687285, Class: 0.165246, Obj: 0.249591, No Obj: 0.000708, .5R: 0.875000, .75R: 0.416667, count: 24 -Region 16 Avg IOU: 0.780259, Class: 0.269848, Obj: 0.430050, No Obj: 0.001914, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.714321, Class: 0.167544, Obj: 0.271527, No Obj: 0.000435, .5R: 0.916667, .75R: 0.500000, count: 12 -Region 16 Avg IOU: 0.766238, Class: 0.222336, Obj: 0.240633, No Obj: 0.001829, .5R: 1.000000, .75R: 0.650000, count: 20 -Region 23 Avg IOU: 0.745293, Class: 0.192627, Obj: 0.212670, 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28 -Region 23 Avg IOU: 0.765209, Class: 0.191213, Obj: 0.232674, No Obj: 0.000168, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 16 Avg IOU: 0.696750, Class: 0.265424, Obj: 0.319266, No Obj: 0.001567, .5R: 0.852941, .75R: 0.411765, count: 34 -Region 23 Avg IOU: 0.764794, Class: 0.193649, Obj: 0.109530, No Obj: 0.000207, .5R: 0.896552, .75R: 0.758621, count: 29 - - 3612: 2.847490, 2.177580 avg loss, 0.001000 rate, 2.824003 seconds, 462336 images -Loaded: 0.000061 seconds -Region 16 Avg IOU: 0.707977, Class: 0.247224, Obj: 0.239409, No Obj: 0.000918, .5R: 0.939394, .75R: 0.363636, count: 33 -Region 23 Avg IOU: 0.716450, Class: 0.186899, Obj: 0.151940, No Obj: 0.000154, .5R: 1.000000, .75R: 0.333333, count: 24 -Region 16 Avg IOU: 0.712266, Class: 0.239753, Obj: 0.174026, No Obj: 0.000924, .5R: 0.958333, .75R: 0.333333, count: 24 -Region 23 Avg IOU: 0.742226, Class: 0.202369, Obj: 0.204764, No Obj: 0.000168, .5R: 0.904762, .75R: 0.523810, count: 21 -Region 16 Avg IOU: 0.733104, Class: 0.172857, Obj: 0.356695, No Obj: 0.000862, .5R: 1.000000, .75R: 0.545455, count: 22 -Region 23 Avg IOU: 0.671531, Class: 0.095618, Obj: 0.143975, No Obj: 0.000111, .5R: 0.857143, .75R: 0.357143, count: 14 -Region 16 Avg IOU: 0.775656, Class: 0.240730, Obj: 0.323000, No Obj: 0.001103, .5R: 1.000000, .75R: 0.608696, count: 23 -Region 23 Avg IOU: 0.775550, Class: 0.103663, Obj: 0.098095, No Obj: 0.000170, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 16 Avg IOU: 0.794838, Class: 0.224984, Obj: 0.397778, No Obj: 0.000739, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.691088, Class: 0.136965, Obj: 0.134295, No Obj: 0.000134, .5R: 0.900000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.671582, Class: 0.191190, Obj: 0.161732, No Obj: 0.000895, .5R: 0.882353, .75R: 0.352941, count: 17 -Region 23 Avg IOU: 0.767068, Class: 0.132216, Obj: 0.238353, No Obj: 0.000104, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 16 Avg IOU: 0.794476, Class: 0.256556, Obj: 0.320367, No Obj: 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21 -Region 23 Avg IOU: 0.735397, Class: 0.172279, Obj: 0.128470, No Obj: 0.000140, .5R: 1.000000, .75R: 0.363636, count: 22 -Region 16 Avg IOU: 0.723818, Class: 0.222742, Obj: 0.343264, No Obj: 0.001161, .5R: 0.928571, .75R: 0.428571, count: 28 -Region 23 Avg IOU: 0.696522, Class: 0.171413, Obj: 0.188291, No Obj: 0.000205, .5R: 0.944444, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.754385, Class: 0.251388, Obj: 0.223805, No Obj: 0.001290, .5R: 0.958333, .75R: 0.541667, count: 24 -Region 23 Avg IOU: 0.736001, Class: 0.182762, Obj: 0.272706, No Obj: 0.000210, .5R: 0.882353, .75R: 0.588235, count: 17 - - 3617: 2.822458, 2.388952 avg loss, 0.001000 rate, 2.680642 seconds, 462976 images -Loaded: 0.000064 seconds -Region 16 Avg IOU: 0.736060, Class: 0.288512, Obj: 0.338987, No Obj: 0.000923, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.721958, Class: 0.164977, Obj: 0.265823, No Obj: 0.000152, .5R: 0.875000, .75R: 0.500000, count: 8 -Region 16 Avg IOU: 0.694169, Class: 0.215644, Obj: 0.220149, No Obj: 0.001514, .5R: 0.923077, .75R: 0.307692, count: 39 -Region 23 Avg IOU: 0.752848, Class: 0.220868, Obj: 0.258929, No Obj: 0.000219, .5R: 0.904762, .75R: 0.571429, count: 21 -Region 16 Avg IOU: 0.697409, Class: 0.273433, Obj: 0.317216, No Obj: 0.000887, .5R: 0.941176, .75R: 0.411765, count: 17 -Region 23 Avg IOU: 0.720777, Class: 0.251900, Obj: 0.272755, No Obj: 0.000137, .5R: 0.909091, .75R: 0.636364, count: 11 -Region 16 Avg IOU: 0.737480, Class: 0.222373, Obj: 0.420026, No Obj: 0.001091, .5R: 0.937500, .75R: 0.625000, count: 16 -Region 23 Avg IOU: 0.740706, Class: 0.138189, Obj: 0.301195, No Obj: 0.000142, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 16 Avg IOU: 0.679651, Class: 0.165268, Obj: 0.340596, No Obj: 0.001130, .5R: 0.880000, .75R: 0.400000, count: 25 -Region 23 Avg IOU: 0.734234, Class: 0.153960, Obj: 0.252168, No Obj: 0.000197, .5R: 1.000000, .75R: 0.500000, count: 14 -Region 16 Avg IOU: 0.746076, Class: 0.243465, Obj: 0.395962, No Obj: 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count: 19 -Region 16 Avg IOU: 0.723822, Class: 0.242120, Obj: 0.171773, No Obj: 0.000814, .5R: 0.952381, .75R: 0.380952, count: 21 -Region 23 Avg IOU: 0.809198, Class: 0.175526, Obj: 0.246049, No Obj: 0.000099, .5R: 1.000000, .75R: 0.769231, count: 13 -Region 16 Avg IOU: 0.710839, Class: 0.200823, Obj: 0.347775, No Obj: 0.001040, .5R: 0.952381, .75R: 0.428571, count: 21 -Region 23 Avg IOU: 0.763274, Class: 0.151599, Obj: 0.180110, No Obj: 0.000121, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 16 Avg IOU: 0.760535, Class: 0.278415, Obj: 0.452084, No Obj: 0.000910, .5R: 1.000000, .75R: 0.578947, count: 19 -Region 23 Avg IOU: 0.720997, Class: 0.153910, Obj: 0.138890, No Obj: 0.000103, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 16 Avg IOU: 0.755086, Class: 0.231975, Obj: 0.369220, No Obj: 0.001111, .5R: 0.954545, .75R: 0.681818, count: 22 -Region 23 Avg IOU: 0.737162, Class: 0.206477, Obj: 0.223310, No Obj: 0.000193, .5R: 1.000000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.704866, Class: 0.197040, Obj: 0.298899, No Obj: 0.001223, .5R: 0.968750, .75R: 0.406250, count: 32 -Region 23 Avg IOU: 0.733584, Class: 0.139854, Obj: 0.144924, No Obj: 0.000187, .5R: 0.952381, .75R: 0.523810, count: 21 -Region 16 Avg IOU: 0.690727, Class: 0.219728, Obj: 0.365363, No Obj: 0.001116, .5R: 0.947368, .75R: 0.315789, count: 19 -Region 23 Avg IOU: 0.728728, Class: 0.151444, Obj: 0.185398, No Obj: 0.000114, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 16 Avg IOU: 0.736943, Class: 0.258713, Obj: 0.276849, No Obj: 0.001044, .5R: 0.956522, .75R: 0.434783, count: 23 -Region 23 Avg IOU: 0.678487, Class: 0.169858, Obj: 0.110973, No Obj: 0.000090, .5R: 0.846154, .75R: 0.384615, count: 13 - - 3619: 2.525683, 2.452552 avg loss, 0.001000 rate, 2.698419 seconds, 463232 images -Loaded: 0.000076 seconds -Region 16 Avg IOU: 0.738396, Class: 0.198122, Obj: 0.304921, No Obj: 0.001320, .5R: 0.961538, .75R: 0.500000, count: 26 -Region 23 Avg IOU: 0.731509, Class: 0.207045, Obj: 0.183839, 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.75R: 0.391304, count: 23 -Region 16 Avg IOU: 0.735888, Class: 0.245998, Obj: 0.257459, No Obj: 0.001100, .5R: 0.913043, .75R: 0.608696, count: 23 -Region 23 Avg IOU: 0.751105, Class: 0.247842, Obj: 0.164527, No Obj: 0.000172, .5R: 0.941176, .75R: 0.588235, count: 17 -Region 16 Avg IOU: 0.698817, Class: 0.219277, Obj: 0.302846, No Obj: 0.000728, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.749291, Class: 0.238677, Obj: 0.164351, No Obj: 0.000161, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 16 Avg IOU: 0.772775, Class: 0.220265, Obj: 0.298123, No Obj: 0.000795, .5R: 0.944444, .75R: 0.722222, count: 18 -Region 23 Avg IOU: 0.700925, Class: 0.187674, Obj: 0.177686, No Obj: 0.000103, .5R: 1.000000, .75R: 0.363636, count: 11 - - 3620: 2.906671, 2.497964 avg loss, 0.001000 rate, 2.667475 seconds, 463360 images -Resizing -704 x 704 - try to allocate workspace = 1148780545 * sizeof(float), CUDA allocate done! -Loaded: 0.644133 seconds -Region 16 Avg IOU: 0.800483, Class: 0.127249, Obj: 0.356347, No Obj: 0.001171, .5R: 1.000000, .75R: 0.750000, count: 20 -Region 23 Avg IOU: 0.797892, Class: 0.199462, Obj: 0.336525, No Obj: 0.000196, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 16 Avg IOU: 0.753501, Class: 0.210992, Obj: 0.364374, No Obj: 0.001114, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.768907, Class: 0.204652, Obj: 0.290644, No Obj: 0.000262, .5R: 1.000000, .75R: 0.666667, count: 21 -Region 16 Avg IOU: 0.714804, Class: 0.174904, Obj: 0.229990, No Obj: 0.001567, .5R: 0.909091, .75R: 0.500000, count: 22 -Region 23 Avg IOU: 0.741436, Class: 0.168269, Obj: 0.150920, No Obj: 0.000171, .5R: 1.000000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.726641, Class: 0.256163, Obj: 0.166329, No Obj: 0.000841, .5R: 1.000000, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.698680, Class: 0.120804, Obj: 0.222123, No Obj: 0.000167, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 16 Avg IOU: 0.765262, Class: 0.227423, Obj: 0.403074, No Obj: 0.001379, .5R: 1.000000, .75R: 0.583333, count: 24 -Region 23 Avg IOU: 0.701305, Class: 0.188449, Obj: 0.251186, No Obj: 0.000214, .5R: 1.000000, .75R: 0.363636, count: 11 -Region 16 Avg IOU: 0.744549, Class: 0.198794, Obj: 0.375643, No Obj: 0.001123, .5R: 0.954545, .75R: 0.590909, count: 22 -Region 23 Avg IOU: 0.704576, Class: 0.151561, Obj: 0.209798, No Obj: 0.000281, .5R: 0.956522, .75R: 0.391304, count: 23 -Region 16 Avg IOU: 0.670922, Class: 0.174140, Obj: 0.185303, No Obj: 0.001249, .5R: 0.789474, .75R: 0.394737, count: 38 -Region 23 Avg IOU: 0.741868, Class: 0.135328, Obj: 0.187951, No Obj: 0.000320, .5R: 1.000000, .75R: 0.500000, count: 26 -Region 16 Avg IOU: 0.777729, Class: 0.260867, Obj: 0.388065, No Obj: 0.001074, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.722038, Class: 0.208675, Obj: 0.311461, No Obj: 0.000223, .5R: 0.941176, .75R: 0.411765, count: 17 - - 3621: 2.597980, 2.507966 avg loss, 0.001000 rate, 1.988688 seconds, 463488 images -Loaded: 0.000079 seconds -Region 16 Avg IOU: 0.749628, Class: 0.245694, Obj: 0.247151, No Obj: 0.000777, .5R: 0.916667, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.675182, Class: 0.235960, Obj: 0.167409, No Obj: 0.000107, .5R: 0.923077, .75R: 0.230769, count: 13 -Region 16 Avg IOU: 0.732713, Class: 0.133433, Obj: 0.177260, No Obj: 0.000566, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.783118, Class: 0.186336, Obj: 0.454469, No Obj: 0.000099, .5R: 1.000000, .75R: 0.666667, count: 3 -Region 16 Avg IOU: 0.658280, Class: 0.115594, Obj: 0.202195, No Obj: 0.000984, .5R: 0.842105, .75R: 0.315789, count: 19 -Region 23 Avg IOU: 0.738741, Class: 0.120916, Obj: 0.301134, No Obj: 0.000164, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 16 Avg IOU: 0.664189, Class: 0.267587, Obj: 0.251757, No Obj: 0.000789, .5R: 0.842105, .75R: 0.315789, count: 19 -Region 23 Avg IOU: 0.677877, Class: 0.168713, Obj: 0.194792, No Obj: 0.000243, .5R: 0.920000, .75R: 0.440000, count: 25 -Region 16 Avg IOU: 0.750472, Class: 0.244176, Obj: 0.402308, No Obj: 0.001214, .5R: 0.952381, .75R: 0.476190, count: 21 -Region 23 Avg IOU: 0.733730, Class: 0.219242, Obj: 0.273087, No Obj: 0.000185, .5R: 1.000000, .75R: 0.357143, count: 14 -Region 16 Avg IOU: 0.699612, Class: 0.204487, Obj: 0.298582, No Obj: 0.001406, .5R: 0.869565, .75R: 0.434783, count: 23 -Region 23 Avg IOU: 0.709486, Class: 0.247589, Obj: 0.170987, No Obj: 0.000167, .5R: 1.000000, .75R: 0.400000, count: 15 -Region 16 Avg IOU: 0.750720, Class: 0.162119, Obj: 0.327336, No Obj: 0.001008, .5R: 0.958333, .75R: 0.750000, count: 24 -Region 23 Avg IOU: 0.753592, Class: 0.107745, Obj: 0.275655, No Obj: 0.000159, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 16 Avg IOU: 0.753102, Class: 0.254234, Obj: 0.321537, No Obj: 0.001093, .5R: 1.000000, .75R: 0.666667, count: 21 -Region 23 Avg IOU: 0.721922, Class: 0.131555, Obj: 0.273809, No Obj: 0.000301, .5R: 1.000000, .75R: 0.444444, count: 18 - - 3622: 2.289813, 2.486150 avg loss, 0.001000 rate, 2.056306 seconds, 463616 images -Loaded: 0.000061 seconds -Region 16 Avg IOU: 0.767370, Class: 0.161715, Obj: 0.312628, No Obj: 0.000831, .5R: 1.000000, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.773660, Class: 0.200542, Obj: 0.318765, No Obj: 0.000139, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 16 Avg IOU: 0.785210, Class: 0.226436, Obj: 0.245985, No Obj: 0.000940, .5R: 0.944444, .75R: 0.833333, count: 18 -Region 23 Avg IOU: 0.739349, Class: 0.172141, Obj: 0.252993, No Obj: 0.000182, .5R: 0.928571, .75R: 0.500000, count: 14 -Region 16 Avg IOU: 0.691235, Class: 0.214631, Obj: 0.266255, No Obj: 0.001370, .5R: 0.900000, .75R: 0.400000, count: 20 -Region 23 Avg IOU: 0.723185, Class: 0.078355, Obj: 0.243915, No Obj: 0.000267, .5R: 0.909091, .75R: 0.454545, count: 22 -Region 16 Avg IOU: 0.781302, Class: 0.329652, Obj: 0.392330, No Obj: 0.000934, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.756839, Class: 0.226402, Obj: 0.254111, No Obj: 0.000216, .5R: 0.933333, .75R: 0.600000, count: 15 -Region 16 Avg IOU: 0.794732, Class: 0.262771, Obj: 0.262285, No Obj: 0.000886, .5R: 0.947368, .75R: 0.684211, count: 19 -Region 23 Avg IOU: 0.814057, Class: 0.246318, Obj: 0.313299, No Obj: 0.000128, .5R: 1.000000, .75R: 0.846154, count: 13 -Region 16 Avg IOU: 0.787946, Class: 0.219695, Obj: 0.313655, No Obj: 0.001127, .5R: 1.000000, .75R: 0.692308, count: 26 -Region 23 Avg IOU: 0.653267, Class: 0.195884, Obj: 0.177304, No Obj: 0.000206, .5R: 0.818182, .75R: 0.227273, count: 22 -Region 16 Avg IOU: 0.777229, Class: 0.259271, Obj: 0.402104, No Obj: 0.000761, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.744748, Class: 0.187261, Obj: 0.166633, No Obj: 0.000117, .5R: 0.923077, .75R: 0.538462, count: 13 -Region 16 Avg IOU: 0.770268, Class: 0.171669, Obj: 0.295481, No Obj: 0.000908, .5R: 1.000000, .75R: 0.684211, count: 19 -Region 23 Avg IOU: 0.703271, Class: 0.146878, Obj: 0.138459, No Obj: 0.000157, .5R: 0.850000, .75R: 0.350000, count: 20 - - 3623: 2.207374, 2.458273 avg loss, 0.001000 rate, 2.072314 seconds, 463744 images -Loaded: 0.000065 seconds -Region 16 Avg IOU: 0.748357, Class: 0.207708, Obj: 0.220216, No Obj: 0.000914, .5R: 1.000000, .75R: 0.545455, count: 22 -Region 23 Avg IOU: 0.714299, Class: 0.174738, Obj: 0.162737, No Obj: 0.000214, .5R: 0.894737, .75R: 0.526316, count: 19 -Region 16 Avg IOU: 0.736329, Class: 0.209300, Obj: 0.284152, No Obj: 0.001043, .5R: 0.958333, .75R: 0.500000, count: 24 -Region 23 Avg IOU: 0.768703, Class: 0.137782, Obj: 0.305019, No Obj: 0.000215, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 16 Avg IOU: 0.729151, Class: 0.238540, Obj: 0.253029, No Obj: 0.001309, .5R: 0.916667, .75R: 0.583333, count: 24 -Region 23 Avg IOU: 0.737585, Class: 0.118640, Obj: 0.194889, No Obj: 0.000137, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 16 Avg IOU: 0.749615, Class: 0.207450, Obj: 0.288415, No Obj: 0.001303, .5R: 0.925926, .75R: 0.592593, count: 27 -Region 23 Avg IOU: 0.732832, Class: 0.156659, Obj: 0.363027, No Obj: 0.000358, .5R: 1.000000, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.699001, Class: 0.193438, Obj: 0.226129, No Obj: 0.001070, .5R: 0.842105, .75R: 0.315789, count: 19 -Region 23 Avg IOU: 0.684373, Class: 0.178869, Obj: 0.250021, No Obj: 0.000124, .5R: 0.888889, .75R: 0.444444, count: 9 -Region 16 Avg IOU: 0.696314, Class: 0.268256, Obj: 0.293301, No Obj: 0.000900, .5R: 0.875000, .75R: 0.375000, count: 24 -Region 23 Avg IOU: 0.719596, Class: 0.210070, Obj: 0.188925, No Obj: 0.000193, .5R: 0.894737, .75R: 0.526316, count: 19 -Region 16 Avg IOU: 0.727591, Class: 0.204039, Obj: 0.226299, No Obj: 0.001016, .5R: 0.894737, .75R: 0.578947, count: 19 -Region 23 Avg IOU: 0.725511, Class: 0.166949, Obj: 0.298578, No Obj: 0.000255, .5R: 0.944444, .75R: 0.555556, count: 18 -Region 16 Avg IOU: 0.719801, Class: 0.208753, Obj: 0.378982, No Obj: 0.001401, .5R: 0.850000, .75R: 0.500000, count: 20 -Region 23 Avg IOU: 0.696853, Class: 0.127430, Obj: 0.213482, No Obj: 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count: 25 -Region 23 Avg IOU: 0.770297, Class: 0.169149, Obj: 0.154100, No Obj: 0.000192, .5R: 1.000000, .75R: 0.625000, count: 16 -Region 16 Avg IOU: 0.781974, Class: 0.266805, Obj: 0.349416, No Obj: 0.000864, .5R: 1.000000, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.726783, Class: 0.165680, Obj: 0.289982, No Obj: 0.000193, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.702635, Class: 0.236107, Obj: 0.272994, No Obj: 0.001227, .5R: 0.962963, .75R: 0.444444, count: 27 -Region 23 Avg IOU: 0.742167, Class: 0.203020, Obj: 0.165589, No Obj: 0.000292, .5R: 0.961538, .75R: 0.500000, count: 26 -Region 16 Avg IOU: 0.736595, Class: 0.250528, Obj: 0.193312, No Obj: 0.001041, .5R: 1.000000, .75R: 0.521739, count: 23 -Region 23 Avg IOU: 0.706560, Class: 0.185136, Obj: 0.187041, No Obj: 0.000275, .5R: 0.916667, .75R: 0.375000, count: 24 -Region 16 Avg IOU: 0.771187, Class: 0.240712, Obj: 0.286073, No Obj: 0.001093, .5R: 0.960000, .75R: 0.680000, count: 25 -Region 23 Avg IOU: 0.711776, Class: 0.158981, Obj: 0.291619, No Obj: 0.000277, .5R: 1.000000, .75R: 0.370370, count: 27 - - 3625: 2.864254, 2.518615 avg loss, 0.001000 rate, 2.054308 seconds, 464000 images -Loaded: 0.000069 seconds -Region 16 Avg IOU: 0.674218, Class: 0.251690, Obj: 0.215033, No Obj: 0.001301, .5R: 0.805556, .75R: 0.333333, count: 36 -Region 23 Avg IOU: 0.684790, Class: 0.187416, Obj: 0.219598, No Obj: 0.000316, .5R: 0.950000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.745160, Class: 0.181097, Obj: 0.174591, No Obj: 0.000995, .5R: 0.952381, .75R: 0.476190, count: 21 -Region 23 Avg IOU: 0.738791, Class: 0.142481, Obj: 0.263186, No Obj: 0.000265, .5R: 1.000000, .75R: 0.444444, count: 18 -Region 16 Avg IOU: 0.711161, Class: 0.192837, Obj: 0.365902, No Obj: 0.001031, .5R: 0.909091, .75R: 0.545455, count: 22 -Region 23 Avg IOU: 0.775395, Class: 0.152994, Obj: 0.341109, No Obj: 0.000264, .5R: 1.000000, .75R: 0.631579, count: 19 -Region 16 Avg IOU: 0.716050, Class: 0.178162, Obj: 0.192605, No Obj: 0.001035, .5R: 0.952381, .75R: 0.380952, count: 21 -Region 23 Avg IOU: 0.755929, Class: 0.224435, Obj: 0.406123, No Obj: 0.000185, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 16 Avg IOU: 0.739568, Class: 0.179441, Obj: 0.266304, No Obj: 0.001330, .5R: 0.968750, .75R: 0.687500, count: 32 -Region 23 Avg IOU: 0.699201, Class: 0.123858, Obj: 0.229781, No Obj: 0.000329, .5R: 0.962963, .75R: 0.370370, count: 27 -Region 16 Avg IOU: 0.758449, Class: 0.223124, Obj: 0.356137, No Obj: 0.001185, .5R: 1.000000, .75R: 0.592593, count: 27 -Region 23 Avg IOU: 0.734919, Class: 0.148435, Obj: 0.244311, No Obj: 0.000347, .5R: 0.964286, .75R: 0.535714, count: 28 -Region 16 Avg IOU: 0.791548, Class: 0.240606, Obj: 0.316393, No Obj: 0.001181, .5R: 0.875000, .75R: 0.750000, count: 16 -Region 23 Avg IOU: 0.743871, Class: 0.207127, Obj: 0.352107, No Obj: 0.000370, .5R: 1.000000, .75R: 0.562500, count: 16 -Region 16 Avg IOU: 0.724615, Class: 0.226743, Obj: 0.171289, No Obj: 0.001031, .5R: 0.954545, 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IOU: 0.749579, Class: 0.244057, Obj: 0.336422, No Obj: 0.000925, .5R: 1.000000, .75R: 0.470588, count: 17 -Region 23 Avg IOU: 0.736645, Class: 0.188642, Obj: 0.193120, No Obj: 0.000196, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 16 Avg IOU: 0.710147, Class: 0.169684, Obj: 0.278127, No Obj: 0.001369, .5R: 0.896552, .75R: 0.413793, count: 29 -Region 23 Avg IOU: 0.729058, Class: 0.201765, Obj: 0.328301, No Obj: 0.000329, .5R: 0.920000, .75R: 0.400000, count: 25 -Region 16 Avg IOU: 0.747329, Class: 0.196893, Obj: 0.276785, No Obj: 0.000951, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.732706, Class: 0.219279, Obj: 0.351101, No Obj: 0.000284, .5R: 0.812500, .75R: 0.687500, count: 16 -Region 16 Avg IOU: 0.721687, Class: 0.222327, Obj: 0.178525, No Obj: 0.001182, .5R: 1.000000, .75R: 0.421053, count: 19 -Region 23 Avg IOU: 0.632228, Class: 0.152480, Obj: 0.157616, No Obj: 0.000160, .5R: 0.692308, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.738489, Class: 0.261876, Obj: 0.304952, No Obj: 0.001080, .5R: 1.000000, .75R: 0.450000, count: 20 -Region 23 Avg IOU: 0.742194, Class: 0.150615, Obj: 0.240967, No Obj: 0.000275, .5R: 1.000000, .75R: 0.400000, count: 15 - - 3627: 2.595270, 2.571241 avg loss, 0.001000 rate, 2.649823 seconds, 464256 images -Loaded: 0.000076 seconds -Region 16 Avg IOU: 0.755408, Class: 0.170850, Obj: 0.293942, No Obj: 0.001502, .5R: 0.962963, .75R: 0.666667, count: 27 -Region 23 Avg IOU: 0.730881, Class: 0.166488, Obj: 0.196782, No Obj: 0.000254, .5R: 0.900000, .75R: 0.550000, count: 20 -Region 16 Avg IOU: 0.766775, Class: 0.268060, Obj: 0.463095, No Obj: 0.001206, .5R: 0.944444, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.750829, Class: 0.193662, Obj: 0.252406, No Obj: 0.000273, .5R: 1.000000, .75R: 0.600000, count: 20 -Region 16 Avg IOU: 0.750960, Class: 0.188718, Obj: 0.341014, No Obj: 0.001185, .5R: 1.000000, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.703152, Class: 0.172150, Obj: 0.263803, No Obj: 0.000342, .5R: 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21 -Region 16 Avg IOU: 0.768100, Class: 0.231298, Obj: 0.420603, No Obj: 0.001656, .5R: 1.000000, .75R: 0.687500, count: 32 -Region 23 Avg IOU: 0.695959, Class: 0.161142, Obj: 0.244651, No Obj: 0.000219, .5R: 1.000000, .75R: 0.409091, count: 22 - - 3628: 3.030795, 2.617196 avg loss, 0.001000 rate, 2.089313 seconds, 464384 images -Loaded: 0.000067 seconds -Region 16 Avg IOU: 0.812649, Class: 0.249829, Obj: 0.469247, No Obj: 0.001790, .5R: 1.000000, .75R: 0.838710, count: 31 -Region 23 Avg IOU: 0.741252, Class: 0.208890, Obj: 0.255902, No Obj: 0.000184, .5R: 0.875000, .75R: 0.625000, count: 16 -Region 16 Avg IOU: 0.774526, Class: 0.194892, Obj: 0.311751, No Obj: 0.000987, .5R: 0.928571, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.727293, Class: 0.163096, Obj: 0.270255, No Obj: 0.000159, .5R: 0.846154, .75R: 0.538462, count: 13 -Region 16 Avg IOU: 0.733376, Class: 0.216523, Obj: 0.348621, No Obj: 0.001593, .5R: 1.000000, .75R: 0.481481, count: 27 -Region 23 Avg IOU: 0.697973, Class: 0.185408, Obj: 0.151386, No Obj: 0.000223, .5R: 0.913043, .75R: 0.478261, count: 23 -Region 16 Avg IOU: 0.721356, Class: 0.230639, Obj: 0.264015, No Obj: 0.001113, .5R: 0.947368, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.730940, Class: 0.241236, Obj: 0.208364, No Obj: 0.000188, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 16 Avg IOU: 0.798030, Class: 0.257991, Obj: 0.377130, No Obj: 0.001526, .5R: 1.000000, .75R: 0.750000, count: 24 -Region 23 Avg IOU: 0.747595, Class: 0.181983, Obj: 0.302330, No Obj: 0.000282, .5R: 0.952381, .75R: 0.523810, count: 21 -Region 16 Avg IOU: 0.798969, Class: 0.261408, Obj: 0.316414, No Obj: 0.000938, .5R: 0.944444, .75R: 0.777778, count: 18 -Region 23 Avg IOU: 0.673714, Class: 0.235345, Obj: 0.256136, No Obj: 0.000163, .5R: 0.909091, .75R: 0.363636, count: 11 -Region 16 Avg IOU: 0.773461, Class: 0.155152, Obj: 0.340316, No Obj: 0.001227, .5R: 0.958333, .75R: 0.666667, count: 24 -Region 23 Avg IOU: 0.771988, Class: 0.186017, Obj: 0.192737, No Obj: 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count: 20 -Region 23 Avg IOU: 0.742677, Class: 0.241226, Obj: 0.251036, No Obj: 0.000127, .5R: 0.916667, .75R: 0.500000, count: 12 -Region 16 Avg IOU: 0.739684, Class: 0.246168, Obj: 0.331309, No Obj: 0.001732, .5R: 0.962963, .75R: 0.592593, count: 27 -Region 23 Avg IOU: 0.736811, Class: 0.243994, Obj: 0.182955, No Obj: 0.000221, .5R: 1.000000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.775096, Class: 0.180252, Obj: 0.299600, No Obj: 0.001180, .5R: 0.952381, .75R: 0.619048, count: 21 -Region 23 Avg IOU: 0.747072, Class: 0.156687, Obj: 0.218162, No Obj: 0.000177, .5R: 1.000000, .75R: 0.466667, count: 15 -Region 16 Avg IOU: 0.788373, Class: 0.153983, Obj: 0.424226, No Obj: 0.001199, .5R: 1.000000, .75R: 0.727273, count: 22 -Region 23 Avg IOU: 0.773332, Class: 0.180481, Obj: 0.189548, No Obj: 0.000199, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 16 Avg IOU: 0.776382, Class: 0.209043, Obj: 0.262485, No Obj: 0.001158, .5R: 1.000000, .75R: 0.695652, count: 23 -Region 23 Avg IOU: 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count: 25 -Region 16 Avg IOU: 0.797918, Class: 0.268922, Obj: 0.396026, No Obj: 0.001110, .5R: 1.000000, .75R: 0.769231, count: 13 -Region 23 Avg IOU: 0.750650, Class: 0.205029, Obj: 0.237671, No Obj: 0.000171, .5R: 1.000000, .75R: 0.562500, count: 16 -Region 16 Avg IOU: 0.750073, Class: 0.224189, Obj: 0.399714, No Obj: 0.001589, .5R: 0.947368, .75R: 0.578947, count: 19 -Region 23 Avg IOU: 0.704789, Class: 0.174016, Obj: 0.157566, No Obj: 0.000146, .5R: 0.933333, .75R: 0.466667, count: 15 - - 3631: 2.317699, 2.546997 avg loss, 0.001000 rate, 1.669612 seconds, 464768 images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.744597, Class: 0.291519, Obj: 0.387905, No Obj: 0.000973, .5R: 1.000000, .75R: 0.357143, count: 14 -Region 23 Avg IOU: 0.683980, Class: 0.195024, Obj: 0.083638, No Obj: 0.000267, .5R: 0.875000, .75R: 0.500000, count: 24 -Region 16 Avg IOU: 0.752849, Class: 0.224402, Obj: 0.518510, No Obj: 0.001265, .5R: 0.937500, .75R: 0.562500, count: 16 -Region 23 Avg IOU: 0.791768, Class: 0.171003, Obj: 0.347023, No Obj: 0.000224, .5R: 1.000000, .75R: 0.818182, count: 11 -Region 16 Avg IOU: 0.717434, Class: 0.198430, Obj: 0.321298, No Obj: 0.001085, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.717141, Class: 0.104688, Obj: 0.159919, No Obj: 0.000279, .5R: 0.950000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.722443, Class: 0.245925, Obj: 0.351653, No Obj: 0.001624, .5R: 1.000000, .75R: 0.300000, count: 20 -Region 23 Avg IOU: 0.654050, Class: 0.168273, Obj: 0.270555, No Obj: 0.000459, .5R: 0.838710, .75R: 0.258065, count: 31 -Region 16 Avg IOU: 0.753889, Class: 0.246768, Obj: 0.247351, No Obj: 0.001733, .5R: 1.000000, .75R: 0.576923, count: 26 -Region 23 Avg IOU: 0.753036, Class: 0.212842, Obj: 0.195947, No Obj: 0.000373, .5R: 0.947368, .75R: 0.631579, count: 19 -Region 16 Avg IOU: 0.759003, Class: 0.264068, Obj: 0.316273, No Obj: 0.001434, .5R: 1.000000, .75R: 0.523810, count: 21 -Region 23 Avg IOU: 0.717836, Class: 0.202956, Obj: 0.259740, 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0.545455, count: 22 -Region 23 Avg IOU: 0.758434, Class: 0.248914, Obj: 0.227937, No Obj: 0.000457, .5R: 1.000000, .75R: 0.684211, count: 19 -Region 16 Avg IOU: 0.753932, Class: 0.226225, Obj: 0.379668, No Obj: 0.001634, .5R: 1.000000, .75R: 0.555556, count: 27 -Region 23 Avg IOU: 0.763242, Class: 0.197613, Obj: 0.395160, No Obj: 0.000383, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 16 Avg IOU: 0.717110, Class: 0.223475, Obj: 0.361146, No Obj: 0.001773, .5R: 0.862069, .75R: 0.482759, count: 29 -Region 23 Avg IOU: 0.741860, Class: 0.172168, Obj: 0.304236, No Obj: 0.000610, .5R: 0.964286, .75R: 0.535714, count: 28 -Region 16 Avg IOU: 0.774138, Class: 0.211197, Obj: 0.212595, No Obj: 0.000838, .5R: 1.000000, .75R: 0.705882, count: 17 -Region 23 Avg IOU: 0.720731, Class: 0.174741, Obj: 0.214458, No Obj: 0.000463, .5R: 1.000000, .75R: 0.333333, count: 30 -Region 16 Avg IOU: 0.761244, Class: 0.264744, Obj: 0.348534, No Obj: 0.002049, .5R: 1.000000, .75R: 0.545455, count: 22 -Region 23 Avg IOU: 0.703240, Class: 0.193430, Obj: 0.219785, No Obj: 0.000532, .5R: 0.850000, .75R: 0.550000, count: 20 -Region 16 Avg IOU: 0.772891, Class: 0.230661, Obj: 0.398903, No Obj: 0.001213, .5R: 1.000000, .75R: 0.666667, count: 18 -Region 23 Avg IOU: 0.716893, Class: 0.191354, Obj: 0.376550, No Obj: 0.000563, .5R: 0.960000, .75R: 0.480000, count: 25 -Region 16 Avg IOU: 0.803695, Class: 0.196145, Obj: 0.472622, No Obj: 0.001396, .5R: 0.941176, .75R: 0.764706, count: 17 -Region 23 Avg IOU: 0.766342, Class: 0.202743, Obj: 0.294816, No Obj: 0.000589, .5R: 1.000000, .75R: 0.730769, count: 26 - - 3633: 2.969985, 2.589243 avg loss, 0.001000 rate, 1.759006 seconds, 465024 images -Loaded: 0.000071 seconds -Region 16 Avg IOU: 0.739654, Class: 0.178427, Obj: 0.387834, No Obj: 0.000988, .5R: 0.888889, .75R: 0.611111, count: 18 -Region 23 Avg IOU: 0.800701, Class: 0.103600, Obj: 0.270490, No Obj: 0.000165, .5R: 1.000000, .75R: 0.769231, count: 13 -Region 16 Avg IOU: 0.775262, Class: 0.216863, Obj: 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31 -Region 23 Avg IOU: 0.735600, Class: 0.168012, Obj: 0.235289, No Obj: 0.000332, .5R: 1.000000, .75R: 0.571429, count: 21 -Region 16 Avg IOU: 0.687748, Class: 0.309942, Obj: 0.457223, No Obj: 0.001325, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 23 Avg IOU: 0.720177, Class: 0.199675, Obj: 0.295282, No Obj: 0.000279, .5R: 0.947368, .75R: 0.368421, count: 19 -Region 16 Avg IOU: 0.713869, Class: 0.189649, Obj: 0.324468, No Obj: 0.001610, .5R: 0.880000, .75R: 0.480000, count: 25 -Region 23 Avg IOU: 0.728921, Class: 0.185853, Obj: 0.114180, No Obj: 0.000182, .5R: 0.900000, .75R: 0.500000, count: 20 -Region 16 Avg IOU: 0.743630, Class: 0.239479, Obj: 0.588732, No Obj: 0.000967, .5R: 0.916667, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.729276, Class: 0.142091, Obj: 0.223144, No Obj: 0.000223, .5R: 0.894737, .75R: 0.631579, count: 19 - - 3638: 2.401093, 2.571753 avg loss, 0.001000 rate, 1.918116 seconds, 465664 images -Loaded: 0.000092 seconds -Region 16 Avg IOU: 0.777288, Class: 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0.000078 seconds -Region 16 Avg IOU: 0.796640, Class: 0.237447, Obj: 0.506248, No Obj: 0.001330, .5R: 1.000000, .75R: 0.764706, count: 17 -Region 23 Avg IOU: 0.723765, Class: 0.169615, Obj: 0.198095, No Obj: 0.000181, .5R: 1.000000, .75R: 0.285714, count: 7 -Region 16 Avg IOU: 0.746922, Class: 0.175663, Obj: 0.299451, No Obj: 0.001156, .5R: 0.916667, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.710432, Class: 0.148162, Obj: 0.172799, No Obj: 0.000274, .5R: 0.882353, .75R: 0.470588, count: 17 -Region 16 Avg IOU: 0.788673, Class: 0.340326, Obj: 0.345562, No Obj: 0.001359, .5R: 1.000000, .75R: 0.739130, count: 23 -Region 23 Avg IOU: 0.766622, Class: 0.167831, Obj: 0.228469, No Obj: 0.000342, .5R: 1.000000, .75R: 0.578947, count: 19 -Region 16 Avg IOU: 0.763891, Class: 0.282480, Obj: 0.301232, No Obj: 0.001386, .5R: 0.923077, .75R: 0.653846, count: 26 -Region 23 Avg IOU: 0.663739, Class: 0.193774, Obj: 0.209232, No Obj: 0.000317, .5R: 0.944444, .75R: 0.277778, count: 18 -Region 16 Avg 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rate, 1.754125 seconds, 465920 images -Resizing -416 x 416 - try to allocate workspace = 304758785 * sizeof(float), CUDA allocate done! -Loaded: 0.386678 seconds -Region 16 Avg IOU: 0.820958, Class: 0.196843, Obj: 0.452482, No Obj: 0.001140, .5R: 1.000000, .75R: 1.000000, count: 5 -Region 23 Avg IOU: 0.743900, Class: 0.198620, Obj: 0.408961, No Obj: 0.000907, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 16 Avg IOU: 0.684389, Class: 0.283358, Obj: 0.271403, No Obj: 0.000930, .5R: 1.000000, .75R: 0.166667, count: 6 -Region 23 Avg IOU: 0.703207, Class: 0.175025, Obj: 0.283123, No Obj: 0.000968, .5R: 0.888889, .75R: 0.333333, count: 27 -Region 16 Avg IOU: 0.746243, Class: 0.100184, Obj: 0.382435, No Obj: 0.001881, .5R: 1.000000, .75R: 0.444444, count: 9 -Region 23 Avg IOU: 0.690694, Class: 0.191027, Obj: 0.304877, No Obj: 0.000940, .5R: 0.944444, .75R: 0.277778, count: 18 -Region 16 Avg IOU: 0.750816, Class: 0.238827, Obj: 0.435009, No Obj: 0.001279, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.709832, Class: 0.126158, Obj: 0.419315, No Obj: 0.000625, .5R: 0.866667, .75R: 0.466667, count: 15 -Region 16 Avg IOU: 0.765898, Class: 0.244295, Obj: 0.468292, No Obj: 0.001387, .5R: 0.833333, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.727132, Class: 0.210048, Obj: 0.378404, No Obj: 0.000974, .5R: 0.956522, .75R: 0.434783, count: 23 -Region 16 Avg IOU: 0.729803, Class: 0.237844, Obj: 0.415858, No Obj: 0.001592, .5R: 0.909091, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.751950, Class: 0.204082, Obj: 0.226844, No Obj: 0.000548, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.771332, Class: 0.290441, Obj: 0.455720, No Obj: 0.001257, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.732647, Class: 0.193464, Obj: 0.359384, No Obj: 0.000883, .5R: 0.952381, .75R: 0.523810, count: 21 -Region 16 Avg IOU: 0.778235, Class: 0.195937, Obj: 0.340657, No Obj: 0.001268, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 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22 -Region 16 Avg IOU: 0.782438, Class: 0.170857, Obj: 0.361237, No Obj: 0.002133, .5R: 1.000000, .75R: 0.750000, count: 16 -Region 23 Avg IOU: 0.723537, Class: 0.187207, Obj: 0.211479, No Obj: 0.000425, .5R: 0.818182, .75R: 0.636364, count: 11 - - 3644: 1.824322, 2.277483 avg loss, 0.001000 rate, 0.791338 seconds, 466432 images -Loaded: 0.000097 seconds -Region 16 Avg IOU: 0.827807, Class: 0.290683, Obj: 0.432927, No Obj: 0.000895, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.669788, Class: 0.214572, Obj: 0.219491, No Obj: 0.000437, .5R: 0.842105, .75R: 0.368421, count: 19 -Region 16 Avg IOU: 0.712986, Class: 0.187579, Obj: 0.257594, No Obj: 0.001570, .5R: 0.909091, .75R: 0.363636, count: 11 -Region 23 Avg IOU: 0.768189, Class: 0.252875, Obj: 0.327439, No Obj: 0.000537, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 16 Avg IOU: 0.719926, Class: 0.154994, Obj: 0.331082, No Obj: 0.001641, .5R: 0.928571, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.669552, Class: 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images -Loaded: 0.000065 seconds -Region 16 Avg IOU: 0.768570, Class: 0.196940, Obj: 0.307079, No Obj: 0.001207, .5R: 1.000000, .75R: 0.619048, count: 21 -Region 23 Avg IOU: 0.721970, Class: 0.144634, Obj: 0.153510, No Obj: 0.000181, .5R: 0.944444, .75R: 0.500000, count: 18 -Region 16 Avg IOU: 0.669030, Class: 0.122123, Obj: 0.295881, No Obj: 0.001309, .5R: 0.882353, .75R: 0.352941, count: 17 -Region 23 Avg IOU: 0.701294, Class: 0.185961, Obj: 0.140933, No Obj: 0.000142, .5R: 0.857143, .75R: 0.500000, count: 14 -Region 16 Avg IOU: 0.767863, Class: 0.261951, Obj: 0.596590, No Obj: 0.001467, .5R: 0.950000, .75R: 0.700000, count: 20 -Region 23 Avg IOU: 0.751320, Class: 0.183166, Obj: 0.159541, No Obj: 0.000154, .5R: 0.950000, .75R: 0.600000, count: 20 -Region 16 Avg IOU: 0.691050, Class: 0.261108, Obj: 0.339033, No Obj: 0.001575, .5R: 0.916667, .75R: 0.333333, count: 24 -Region 23 Avg IOU: 0.726356, Class: 0.186285, Obj: 0.097843, No Obj: 0.000117, .5R: 0.928571, .75R: 0.571429, count: 14 -Region 16 Avg IOU: 0.741536, Class: 0.278099, Obj: 0.396575, No Obj: 0.001093, .5R: 1.000000, .75R: 0.500000, count: 20 -Region 23 Avg IOU: 0.769193, Class: 0.255106, Obj: 0.230839, No Obj: 0.000107, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 16 Avg IOU: 0.711172, Class: 0.245625, Obj: 0.355202, No Obj: 0.001605, .5R: 1.000000, .75R: 0.434783, count: 23 -Region 23 Avg IOU: 0.681035, Class: 0.133780, Obj: 0.145865, No Obj: 0.000225, .5R: 0.900000, .75R: 0.366667, count: 30 -Region 16 Avg IOU: 0.708444, Class: 0.206146, Obj: 0.377964, No Obj: 0.001389, .5R: 1.000000, .75R: 0.294118, count: 17 -Region 23 Avg IOU: 0.744482, Class: 0.219317, Obj: 0.135201, No Obj: 0.000128, .5R: 1.000000, .75R: 0.642857, count: 14 -Region 16 Avg IOU: 0.714457, Class: 0.257726, Obj: 0.381323, No Obj: 0.001370, .5R: 0.950000, .75R: 0.400000, count: 20 -Region 23 Avg IOU: 0.718476, Class: 0.275403, Obj: 0.222011, No Obj: 0.000136, .5R: 0.923077, .75R: 0.538462, count: 13 - - 3657: 2.528622, 2.362487 avg 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0.142857, count: 7 - - 3658: 2.566079, 2.382846 avg loss, 0.001000 rate, 2.183144 seconds, 468224 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.704888, Class: 0.256976, Obj: 0.341508, No Obj: 0.001738, .5R: 0.920000, .75R: 0.480000, count: 25 -Region 23 Avg IOU: 0.722681, Class: 0.215295, Obj: 0.148352, No Obj: 0.000189, .5R: 1.000000, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.764212, Class: 0.249964, Obj: 0.446399, No Obj: 0.001845, .5R: 1.000000, .75R: 0.625000, count: 24 -Region 23 Avg IOU: 0.746848, Class: 0.223336, Obj: 0.206952, No Obj: 0.000234, .5R: 1.000000, .75R: 0.578947, count: 19 -Region 16 Avg IOU: 0.744927, Class: 0.225601, Obj: 0.334683, No Obj: 0.001297, .5R: 1.000000, .75R: 0.521739, count: 23 -Region 23 Avg IOU: 0.733681, Class: 0.122620, Obj: 0.292007, No Obj: 0.000230, .5R: 1.000000, .75R: 0.421053, count: 19 -Region 16 Avg IOU: 0.740704, Class: 0.165011, Obj: 0.346888, No Obj: 0.001198, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 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0.941176, .75R: 0.411765, count: 17 -Region 23 Avg IOU: 0.759405, Class: 0.234798, Obj: 0.240309, No Obj: 0.000195, .5R: 0.916667, .75R: 0.583333, count: 12 -Region 16 Avg IOU: 0.723504, Class: 0.236012, Obj: 0.242791, No Obj: 0.000930, .5R: 1.000000, .75R: 0.454545, count: 22 -Region 23 Avg IOU: 0.721611, Class: 0.167472, Obj: 0.195838, No Obj: 0.000247, .5R: 0.888889, .75R: 0.500000, count: 18 -Region 16 Avg IOU: 0.788567, Class: 0.173445, Obj: 0.257047, No Obj: 0.000802, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.754476, Class: 0.221520, Obj: 0.151503, No Obj: 0.000242, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 16 Avg IOU: 0.746548, Class: 0.235792, Obj: 0.150335, No Obj: 0.000771, .5R: 1.000000, .75R: 0.384615, count: 13 -Region 23 Avg IOU: 0.691725, Class: 0.205633, Obj: 0.242697, No Obj: 0.000193, .5R: 0.900000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.769859, Class: 0.264450, Obj: 0.299251, No Obj: 0.000875, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.732539, Class: 0.126647, Obj: 0.303005, No Obj: 0.000223, .5R: 0.888889, .75R: 0.666667, count: 9 - - 3660: 2.012741, 2.364124 avg loss, 0.001000 rate, 2.252307 seconds, 468480 images -Resizing -608 x 608 - try to allocate workspace = 1146765313 * sizeof(float), CUDA allocate done! -Loaded: 0.510909 seconds -Region 16 Avg IOU: 0.780912, Class: 0.266275, Obj: 0.225592, No Obj: 0.000756, .5R: 0.937500, .75R: 0.687500, count: 16 -Region 23 Avg IOU: 0.767302, Class: 0.189553, Obj: 0.226982, No Obj: 0.000246, .5R: 0.916667, .75R: 0.583333, count: 12 -Region 16 Avg IOU: 0.711293, Class: 0.165267, Obj: 0.198878, No Obj: 0.000911, .5R: 0.956522, .75R: 0.478261, count: 23 -Region 23 Avg IOU: 0.715150, Class: 0.210383, Obj: 0.225822, No Obj: 0.000261, .5R: 0.944444, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.772048, Class: 0.258517, Obj: 0.278019, No Obj: 0.000924, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.780031, Class: 0.181479, Obj: 0.367078, 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No Obj: 0.000880, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.674499, Class: 0.166788, Obj: 0.100169, No Obj: 0.000147, .5R: 1.000000, .75R: 0.333333, count: 6 -Region 16 Avg IOU: 0.761034, Class: 0.153956, Obj: 0.298190, No Obj: 0.001368, .5R: 0.956522, .75R: 0.608696, count: 23 -Region 23 Avg IOU: 0.707550, Class: 0.161688, Obj: 0.250586, No Obj: 0.000321, .5R: 0.947368, .75R: 0.421053, count: 19 -Region 16 Avg IOU: 0.737494, Class: 0.254684, Obj: 0.210726, No Obj: 0.001336, .5R: 0.956522, .75R: 0.608696, count: 23 -Region 23 Avg IOU: 0.751704, Class: 0.190957, Obj: 0.196825, No Obj: 0.000357, .5R: 1.000000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.724158, Class: 0.181902, Obj: 0.180559, No Obj: 0.001042, .5R: 1.000000, .75R: 0.384615, count: 26 -Region 23 Avg IOU: 0.706264, Class: 0.170930, Obj: 0.191976, No Obj: 0.000316, .5R: 0.916667, .75R: 0.500000, count: 24 -Region 16 Avg IOU: 0.681914, Class: 0.236373, Obj: 0.243719, No Obj: 0.001475, .5R: 0.760000, 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workspace = 1148780545 * sizeof(float), CUDA allocate done! -Loaded: 0.172127 seconds -Region 16 Avg IOU: 0.792329, Class: 0.242976, Obj: 0.277191, No Obj: 0.001123, .5R: 1.000000, .75R: 0.724138, count: 29 -Region 23 Avg IOU: 0.752732, Class: 0.172244, Obj: 0.154139, No Obj: 0.000358, .5R: 0.967742, .75R: 0.516129, count: 31 -Region 16 Avg IOU: 0.756786, Class: 0.254876, Obj: 0.280579, No Obj: 0.000851, .5R: 0.894737, .75R: 0.578947, count: 19 -Region 23 Avg IOU: 0.708636, Class: 0.218513, Obj: 0.112860, No Obj: 0.000171, .5R: 0.894737, .75R: 0.421053, count: 19 -Region 16 Avg IOU: 0.717450, Class: 0.263386, Obj: 0.262246, No Obj: 0.000874, .5R: 0.840000, .75R: 0.560000, count: 25 -Region 23 Avg IOU: 0.757360, Class: 0.244122, Obj: 0.299709, No Obj: 0.000255, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 16 Avg IOU: 0.734979, Class: 0.201852, Obj: 0.232880, No Obj: 0.001241, .5R: 0.935484, .75R: 0.612903, count: 31 -Region 23 Avg IOU: 0.728776, Class: 0.128848, Obj: 0.169174, No 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IOU: 0.742752, Class: 0.073046, Obj: 0.468266, No Obj: 0.000218, .5R: 1.000000, .75R: 0.444444, count: 9 -Region 16 Avg IOU: 0.739405, Class: 0.261221, Obj: 0.390388, No Obj: 0.001573, .5R: 0.920000, .75R: 0.560000, count: 25 -Region 23 Avg IOU: 0.751197, Class: 0.148681, Obj: 0.178415, No Obj: 0.000299, .5R: 1.000000, .75R: 0.500000, count: 14 -Region 16 Avg IOU: 0.767960, Class: 0.219407, Obj: 0.428450, No Obj: 0.001901, .5R: 0.935484, .75R: 0.612903, count: 31 -Region 23 Avg IOU: 0.697885, Class: 0.172141, Obj: 0.158678, No Obj: 0.000374, .5R: 0.880000, .75R: 0.280000, count: 25 -Region 16 Avg IOU: 0.801233, Class: 0.225727, Obj: 0.337671, No Obj: 0.001124, .5R: 1.000000, .75R: 0.823529, count: 17 -Region 23 Avg IOU: 0.780313, Class: 0.187248, Obj: 0.388126, No Obj: 0.000340, .5R: 1.000000, .75R: 0.642857, count: 14 -Region 16 Avg IOU: 0.775802, Class: 0.220584, Obj: 0.344585, No Obj: 0.001392, .5R: 1.000000, .75R: 0.647059, count: 17 -Region 23 Avg IOU: 0.739523, Class: 0.120087, 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23 -Region 23 Avg IOU: 0.755983, Class: 0.192282, Obj: 0.162739, No Obj: 0.000233, .5R: 0.916667, .75R: 0.583333, count: 24 - - 3693: 2.456635, 2.475961 avg loss, 0.001000 rate, 2.394925 seconds, 472704 images -Loaded: 0.000067 seconds -Region 16 Avg IOU: 0.800948, Class: 0.273399, Obj: 0.478627, No Obj: 0.001439, .5R: 1.000000, .75R: 0.714286, count: 21 -Region 23 Avg IOU: 0.776376, Class: 0.189819, Obj: 0.195830, No Obj: 0.000248, .5R: 1.000000, .75R: 0.650000, count: 20 -Region 16 Avg IOU: 0.726872, Class: 0.184206, Obj: 0.380056, No Obj: 0.001622, .5R: 0.920000, .75R: 0.560000, count: 25 -Region 23 Avg IOU: 0.730585, Class: 0.171668, Obj: 0.174803, No Obj: 0.000333, .5R: 1.000000, .75R: 0.444444, count: 36 -Region 16 Avg IOU: 0.781814, Class: 0.231250, Obj: 0.417388, No Obj: 0.001462, .5R: 0.947368, .75R: 0.684211, count: 19 -Region 23 Avg IOU: 0.730478, Class: 0.159951, Obj: 0.139566, No Obj: 0.000184, .5R: 0.920000, .75R: 0.560000, count: 25 -Region 16 Avg IOU: 0.735495, Class: 0.239677, Obj: 0.430240, No Obj: 0.001759, .5R: 1.000000, .75R: 0.500000, count: 22 -Region 23 Avg IOU: 0.704204, Class: 0.159421, Obj: 0.091234, No Obj: 0.000169, .5R: 0.950000, .75R: 0.250000, count: 20 -Region 16 Avg IOU: 0.701653, Class: 0.206971, Obj: 0.429883, No Obj: 0.002247, .5R: 0.833333, .75R: 0.416667, count: 24 -Region 23 Avg IOU: 0.712894, Class: 0.196753, Obj: 0.206592, No Obj: 0.000210, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 16 Avg IOU: 0.770434, Class: 0.217537, Obj: 0.478080, No Obj: 0.001965, .5R: 1.000000, .75R: 0.684211, count: 19 -Region 23 Avg IOU: 0.729701, Class: 0.236628, Obj: 0.165396, No Obj: 0.000148, .5R: 1.000000, .75R: 0.250000, count: 12 -Region 16 Avg IOU: 0.758599, Class: 0.269141, Obj: 0.423968, No Obj: 0.001708, .5R: 0.956522, .75R: 0.652174, count: 23 -Region 23 Avg IOU: 0.737067, Class: 0.154733, Obj: 0.247779, No Obj: 0.000261, .5R: 0.958333, .75R: 0.500000, count: 24 -Region 16 Avg IOU: 0.726754, Class: 0.313952, Obj: 0.452903, No Obj: 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0.718245, Class: 0.238418, Obj: 0.224327, No Obj: 0.001499, .5R: 0.969697, .75R: 0.515152, count: 33 -Region 23 Avg IOU: 0.747096, Class: 0.147266, Obj: 0.098353, No Obj: 0.000181, .5R: 0.941176, .75R: 0.588235, count: 17 - - 3695: 2.470654, 2.496495 avg loss, 0.001000 rate, 2.523572 seconds, 472960 images -Loaded: 0.000059 seconds -Region 16 Avg IOU: 0.717630, Class: 0.188562, Obj: 0.335473, No Obj: 0.000814, .5R: 0.933333, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.617130, Class: 0.193470, Obj: 0.127599, No Obj: 0.000130, .5R: 0.727273, .75R: 0.363636, count: 11 -Region 16 Avg IOU: 0.773780, Class: 0.238834, Obj: 0.409940, No Obj: 0.000732, .5R: 0.941176, .75R: 0.647059, count: 17 -Region 23 Avg IOU: 0.794941, Class: 0.122425, Obj: 0.136508, No Obj: 0.000161, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 16 Avg IOU: 0.734020, Class: 0.193886, Obj: 0.268772, No Obj: 0.001059, .5R: 1.000000, .75R: 0.409091, count: 22 -Region 23 Avg IOU: 0.750575, Class: 0.193255, Obj: 0.150461, 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3709: 1.810679, 2.131255 avg loss, 0.001000 rate, 0.882074 seconds, 474752 images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.690973, Class: 0.204950, Obj: 0.301963, No Obj: 0.000919, .5R: 0.900000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.679885, Class: 0.151467, Obj: 0.199500, No Obj: 0.000680, .5R: 0.851852, .75R: 0.407407, count: 27 -Region 16 Avg IOU: 0.748439, Class: 0.057989, Obj: 0.231056, No Obj: 0.000825, .5R: 1.000000, .75R: 0.400000, count: 5 -Region 23 Avg IOU: 0.788699, Class: 0.170356, Obj: 0.264899, No Obj: 0.000438, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 16 Avg IOU: 0.735857, Class: 0.281250, Obj: 0.390869, No Obj: 0.001682, .5R: 0.933333, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.679939, Class: 0.220370, Obj: 0.245737, No Obj: 0.000742, .5R: 0.850000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.768805, Class: 0.167280, Obj: 0.397865, No Obj: 0.001463, .5R: 0.933333, .75R: 0.800000, count: 15 -Region 23 Avg IOU: 0.709586, Class: 0.188750, Obj: 0.335525, No Obj: 0.000598, .5R: 0.941176, .75R: 0.529412, count: 17 -Region 16 Avg IOU: 0.765447, Class: 0.302926, Obj: 0.335284, No Obj: 0.001628, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.719190, Class: 0.212270, Obj: 0.184417, No Obj: 0.000418, .5R: 0.923077, .75R: 0.461538, count: 13 -Region 16 Avg IOU: 0.798060, Class: 0.110482, Obj: 0.490195, No Obj: 0.001201, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.703120, Class: 0.112934, Obj: 0.396524, No Obj: 0.000804, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.857304, Class: 0.044045, Obj: 0.377662, No Obj: 0.000726, .5R: 1.000000, .75R: 1.000000, count: 5 -Region 23 Avg IOU: 0.649615, Class: 0.214159, Obj: 0.322532, No Obj: 0.000717, .5R: 0.700000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.755977, Class: 0.219984, Obj: 0.432787, No Obj: 0.002122, .5R: 0.950000, .75R: 0.600000, count: 20 -Region 23 Avg IOU: 0.751730, Class: 0.207889, Obj: 0.258773, No Obj: 0.000619, 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0.778191, Class: 0.181986, Obj: 0.425691, No Obj: 0.001108, .5R: 1.000000, .75R: 0.500000, count: 4 -Region 23 Avg IOU: 0.751179, Class: 0.165877, Obj: 0.328993, No Obj: 0.000802, .5R: 1.000000, .75R: 0.562500, count: 16 -Region 16 Avg IOU: 0.785935, Class: 0.200595, Obj: 0.407327, No Obj: 0.001854, .5R: 0.888889, .75R: 0.888889, count: 9 -Region 23 Avg IOU: 0.686832, Class: 0.160887, Obj: 0.259217, No Obj: 0.001024, .5R: 0.857143, .75R: 0.333333, count: 21 -Region 16 Avg IOU: 0.780333, Class: 0.249489, Obj: 0.305015, No Obj: 0.001649, .5R: 1.000000, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.682191, Class: 0.194312, Obj: 0.256276, No Obj: 0.000810, .5R: 0.956522, .75R: 0.304348, count: 23 -Region 16 Avg IOU: 0.793994, Class: 0.191590, Obj: 0.378856, No Obj: 0.001044, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.721855, Class: 0.153073, Obj: 0.227868, No Obj: 0.000456, .5R: 0.900000, .75R: 0.400000, count: 10 -Region 16 Avg IOU: 0.773929, Class: 0.181622, Obj: 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Avg IOU: 0.773132, Class: 0.280750, Obj: 0.483362, No Obj: 0.001612, .5R: 1.000000, .75R: 0.777778, count: 9 -Region 23 Avg IOU: 0.728817, Class: 0.167570, Obj: 0.253739, No Obj: 0.000762, .5R: 0.913043, .75R: 0.565217, count: 23 - - 3712: 1.539959, 2.024412 avg loss, 0.001000 rate, 0.782392 seconds, 475136 images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.780659, Class: 0.220820, Obj: 0.643970, No Obj: 0.002194, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.710602, Class: 0.185576, Obj: 0.309041, No Obj: 0.000741, .5R: 0.944444, .75R: 0.555556, count: 18 -Region 16 Avg IOU: 0.752537, Class: 0.199727, Obj: 0.483996, No Obj: 0.002535, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.744286, Class: 0.182869, Obj: 0.126518, No Obj: 0.000553, .5R: 0.958333, .75R: 0.541667, count: 24 -Region 16 Avg IOU: 0.754725, Class: 0.226616, Obj: 0.418599, No Obj: 0.002997, .5R: 0.937500, .75R: 0.687500, count: 16 -Region 23 Avg IOU: 0.676173, Class: 0.182187, Obj: 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23 Avg IOU: 0.759534, Class: 0.179406, Obj: 0.173463, No Obj: 0.000780, .5R: 1.000000, .75R: 0.550000, count: 20 -Region 16 Avg IOU: 0.780376, Class: 0.335426, Obj: 0.614808, No Obj: 0.002030, .5R: 0.909091, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.781956, Class: 0.221658, Obj: 0.257892, No Obj: 0.000685, .5R: 1.000000, .75R: 0.730769, count: 26 -Region 16 Avg IOU: 0.713301, Class: 0.170441, Obj: 0.388121, No Obj: 0.002074, .5R: 1.000000, .75R: 0.363636, count: 11 -Region 23 Avg IOU: 0.738186, Class: 0.223990, Obj: 0.362549, No Obj: 0.000852, .5R: 0.958333, .75R: 0.541667, count: 24 -Region 16 Avg IOU: 0.756380, Class: 0.178231, Obj: 0.442969, No Obj: 0.001436, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 23 Avg IOU: 0.730589, Class: 0.156348, Obj: 0.205719, No Obj: 0.000628, .5R: 0.950000, .75R: 0.500000, count: 20 -Region 16 Avg IOU: 0.781314, Class: 0.215290, Obj: 0.476755, No Obj: 0.002411, .5R: 0.916667, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.741714, Class: 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Class: 0.188398, Obj: 0.605291, No Obj: 0.003160, .5R: 1.000000, .75R: 0.785714, count: 14 -Region 23 Avg IOU: 0.755457, Class: 0.156248, Obj: 0.231432, No Obj: 0.000776, .5R: 0.958333, .75R: 0.750000, count: 24 -Region 16 Avg IOU: 0.818730, Class: 0.261699, Obj: 0.487414, No Obj: 0.002254, .5R: 1.000000, .75R: 0.923077, count: 13 -Region 23 Avg IOU: 0.742934, Class: 0.195618, Obj: 0.229210, No Obj: 0.000832, .5R: 0.956522, .75R: 0.608696, count: 23 -Region 16 Avg IOU: 0.810161, Class: 0.183992, Obj: 0.509950, No Obj: 0.002487, .5R: 1.000000, .75R: 0.800000, count: 15 -Region 23 Avg IOU: 0.739286, Class: 0.200707, Obj: 0.319988, No Obj: 0.000760, .5R: 0.882353, .75R: 0.647059, count: 17 -Region 16 Avg IOU: 0.822501, Class: 0.230311, Obj: 0.401226, No Obj: 0.001474, .5R: 1.000000, .75R: 1.000000, count: 11 -Region 23 Avg IOU: 0.714093, Class: 0.168001, Obj: 0.276782, No Obj: 0.000851, .5R: 0.962963, .75R: 0.333333, count: 27 -Region 16 Avg IOU: 0.725703, Class: 0.187377, Obj: 0.297504, 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23 Avg IOU: 0.735263, Class: 0.156625, Obj: 0.199699, No Obj: 0.000289, .5R: 1.000000, .75R: 0.375000, count: 8 -Region 16 Avg IOU: 0.788888, Class: 0.194970, Obj: 0.404064, No Obj: 0.001514, .5R: 1.000000, .75R: 0.818182, count: 11 -Region 23 Avg IOU: 0.716463, Class: 0.143510, Obj: 0.288767, No Obj: 0.000612, .5R: 0.909091, .75R: 0.409091, count: 22 -Region 16 Avg IOU: 0.832137, Class: 0.239191, Obj: 0.509450, No Obj: 0.000822, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.732046, Class: 0.159748, Obj: 0.395496, No Obj: 0.000570, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 16 Avg IOU: 0.760599, Class: 0.228642, Obj: 0.174041, No Obj: 0.000869, .5R: 1.000000, .75R: 0.400000, count: 5 -Region 23 Avg IOU: 0.718457, Class: 0.207836, Obj: 0.160244, No Obj: 0.000401, .5R: 1.000000, .75R: 0.444444, count: 9 -Region 16 Avg IOU: 0.741782, Class: 0.216110, Obj: 0.328638, No Obj: 0.001798, .5R: 1.000000, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.777334, Class: 0.213044, Obj: 0.180317, No Obj: 0.000518, .5R: 1.000000, .75R: 0.647059, count: 17 -Region 16 Avg IOU: 0.805805, Class: 0.194027, Obj: 0.360238, No Obj: 0.001159, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.739027, Class: 0.205367, Obj: 0.219195, No Obj: 0.000531, .5R: 0.928571, .75R: 0.571429, count: 14 -Region 16 Avg IOU: 0.791176, Class: 0.335977, Obj: 0.481406, No Obj: 0.001843, .5R: 1.000000, .75R: 0.785714, count: 14 -Region 23 Avg IOU: 0.752303, Class: 0.234602, Obj: 0.367704, No Obj: 0.000786, .5R: 1.000000, .75R: 0.681818, count: 22 - - 3719: 1.464894, 1.892986 avg loss, 0.001000 rate, 0.773460 seconds, 476032 images -Loaded: 0.000071 seconds -Region 16 Avg IOU: 0.755978, Class: 0.193499, Obj: 0.307730, No Obj: 0.001451, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.704282, Class: 0.175491, Obj: 0.221867, No Obj: 0.000678, .5R: 0.869565, .75R: 0.521739, count: 23 -Region 16 Avg IOU: 0.746142, Class: 0.199528, Obj: 0.272425, No Obj: 0.001409, 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count: 5 -Region 23 Avg IOU: 0.666861, Class: 0.207595, Obj: 0.246854, No Obj: 0.000699, .5R: 0.916667, .75R: 0.208333, count: 24 -Region 16 Avg IOU: 0.796983, Class: 0.224145, Obj: 0.342554, No Obj: 0.001235, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.739319, Class: 0.157817, Obj: 0.195283, No Obj: 0.000512, .5R: 0.888889, .75R: 0.666667, count: 27 -Region 16 Avg IOU: 0.801079, Class: 0.207063, Obj: 0.242467, No Obj: 0.001387, .5R: 1.000000, .75R: 0.785714, count: 14 -Region 23 Avg IOU: 0.683760, Class: 0.182471, Obj: 0.218174, No Obj: 0.000639, .5R: 0.850000, .75R: 0.400000, count: 20 - - 3720: 2.148102, 1.918498 avg loss, 0.001000 rate, 0.789382 seconds, 476160 images -Resizing -608 x 608 - try to allocate workspace = 1146765313 * sizeof(float), CUDA allocate done! -Loaded: 0.266195 seconds -Region 16 Avg IOU: 0.712573, Class: 0.275115, Obj: 0.224489, No Obj: 0.000894, .5R: 0.952381, .75R: 0.333333, count: 21 -Region 23 Avg IOU: 0.713440, Class: 0.196411, Obj: 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0.363636, count: 11 -Region 23 Avg IOU: 0.770143, Class: 0.169294, Obj: 0.299846, No Obj: 0.000409, .5R: 1.000000, .75R: 0.578947, count: 19 -Region 16 Avg IOU: 0.768964, Class: 0.216158, Obj: 0.241445, No Obj: 0.000967, .5R: 0.909091, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.705318, Class: 0.241753, Obj: 0.131697, No Obj: 0.000330, .5R: 0.850000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.719637, Class: 0.216078, Obj: 0.366563, No Obj: 0.000873, .5R: 0.888889, .75R: 0.444444, count: 9 -Region 23 Avg IOU: 0.730367, Class: 0.163835, Obj: 0.340764, No Obj: 0.000709, .5R: 0.916667, .75R: 0.500000, count: 24 - - 3736: 2.374779, 2.283136 avg loss, 0.001000 rate, 1.381347 seconds, 478208 images -Loaded: 0.000062 seconds -Region 16 Avg IOU: 0.739126, Class: 0.225586, Obj: 0.266230, No Obj: 0.001712, .5R: 0.958333, .75R: 0.541667, count: 24 -Region 23 Avg IOU: 0.741795, Class: 0.157921, Obj: 0.167793, No Obj: 0.000630, .5R: 0.976191, .75R: 0.523810, count: 42 -Region 16 Avg IOU: 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0.284195, No Obj: 0.001040, .5R: 1.000000, .75R: 0.388889, count: 18 -Region 23 Avg IOU: 0.732397, Class: 0.162840, Obj: 0.168917, No Obj: 0.000231, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.738965, Class: 0.254954, Obj: 0.266012, No Obj: 0.001828, .5R: 0.956522, .75R: 0.478261, count: 23 -Region 23 Avg IOU: 0.720177, Class: 0.163833, Obj: 0.169633, No Obj: 0.000465, .5R: 0.941176, .75R: 0.411765, count: 34 -Region 16 Avg IOU: 0.765908, Class: 0.206350, Obj: 0.421295, No Obj: 0.001229, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.761594, Class: 0.190831, Obj: 0.266216, No Obj: 0.000311, .5R: 1.000000, .75R: 0.550000, count: 20 - - 3737: 2.685821, 2.323405 avg loss, 0.001000 rate, 1.379876 seconds, 478336 images -Loaded: 0.000056 seconds -Region 16 Avg IOU: 0.733299, Class: 0.192039, Obj: 0.304009, No Obj: 0.001861, .5R: 0.962963, .75R: 0.481481, count: 27 -Region 23 Avg IOU: 0.740791, Class: 0.167317, Obj: 0.226167, No Obj: 0.000503, .5R: 1.000000, .75R: 0.500000, count: 22 -Region 16 Avg IOU: 0.788234, Class: 0.242981, Obj: 0.306382, No Obj: 0.000954, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.725732, Class: 0.179281, Obj: 0.220825, No Obj: 0.000452, .5R: 0.952381, .75R: 0.476190, count: 21 -Region 16 Avg IOU: 0.775100, Class: 0.246030, Obj: 0.404740, No Obj: 0.001298, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.707142, Class: 0.191044, Obj: 0.486203, No Obj: 0.000220, .5R: 0.888889, .75R: 0.333333, count: 9 -Region 16 Avg IOU: 0.796184, Class: 0.286752, Obj: 0.331180, No Obj: 0.001117, .5R: 1.000000, .75R: 0.900000, count: 10 -Region 23 Avg IOU: 0.713293, Class: 0.214481, Obj: 0.144438, No Obj: 0.000369, .5R: 1.000000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.750471, Class: 0.225412, Obj: 0.311619, No Obj: 0.001438, .5R: 0.944444, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.711792, Class: 0.190005, Obj: 0.150168, No Obj: 0.000499, .5R: 0.958333, .75R: 0.458333, count: 24 -Region 16 Avg IOU: 0.755333, Class: 0.237508, Obj: 0.351415, No Obj: 0.001213, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.756417, Class: 0.212868, Obj: 0.286798, No Obj: 0.000304, .5R: 0.944444, .75R: 0.611111, count: 18 -Region 16 Avg IOU: 0.744289, Class: 0.226821, Obj: 0.238300, No Obj: 0.001698, .5R: 1.000000, .75R: 0.444444, count: 27 -Region 23 Avg IOU: 0.763753, Class: 0.214961, Obj: 0.160878, No Obj: 0.000412, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.697290, Class: 0.233404, Obj: 0.296818, No Obj: 0.001286, .5R: 0.944444, .75R: 0.388889, count: 18 -Region 23 Avg IOU: 0.750174, Class: 0.188636, Obj: 0.217707, No Obj: 0.000314, .5R: 1.000000, .75R: 0.578947, count: 19 - - 3738: 2.331430, 2.324207 avg loss, 0.001000 rate, 1.370000 seconds, 478464 images -Loaded: 0.000062 seconds -Region 16 Avg IOU: 0.758275, Class: 0.205962, Obj: 0.301078, No Obj: 0.001684, .5R: 0.960000, .75R: 0.520000, count: 25 -Region 23 Avg IOU: 0.725263, Class: 0.186425, Obj: 0.152399, No Obj: 0.000328, .5R: 0.950000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.719563, Class: 0.242263, Obj: 0.376752, No Obj: 0.001153, .5R: 0.916667, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.769943, Class: 0.175872, Obj: 0.161403, No Obj: 0.000473, .5R: 1.000000, .75R: 0.615385, count: 26 -Region 16 Avg IOU: 0.785812, Class: 0.352146, Obj: 0.473022, No Obj: 0.000630, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.676858, Class: 0.154964, Obj: 0.247438, No Obj: 0.000173, .5R: 0.846154, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.723653, Class: 0.208218, Obj: 0.285262, No Obj: 0.001590, .5R: 0.947368, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.768518, Class: 0.214620, Obj: 0.236808, No Obj: 0.000355, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 16 Avg IOU: 0.804327, Class: 0.353671, Obj: 0.564410, No Obj: 0.001599, .5R: 0.923077, .75R: 0.923077, count: 13 -Region 23 Avg IOU: 0.721440, Class: 0.223561, Obj: 0.229826, No Obj: 0.000402, .5R: 0.950000, .75R: 0.550000, count: 20 -Region 16 Avg IOU: 0.749372, Class: 0.287689, Obj: 0.340223, No Obj: 0.001140, .5R: 0.933333, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.716278, Class: 0.188780, Obj: 0.164182, No Obj: 0.000335, .5R: 1.000000, .75R: 0.476190, count: 21 -Region 16 Avg IOU: 0.759295, Class: 0.162656, Obj: 0.250415, No Obj: 0.001225, .5R: 1.000000, .75R: 0.400000, count: 15 -Region 23 Avg IOU: 0.717363, Class: 0.162448, Obj: 0.267321, No Obj: 0.000376, .5R: 1.000000, .75R: 0.500000, count: 18 -Region 16 Avg IOU: 0.756489, Class: 0.257309, Obj: 0.478063, No Obj: 0.001377, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.703421, Class: 0.128910, Obj: 0.252050, No Obj: 0.000339, .5R: 0.888889, .75R: 0.500000, count: 18 - - 3739: 2.140080, 2.305794 avg loss, 0.001000 rate, 1.370288 seconds, 478592 images -Loaded: 0.000063 seconds -Region 16 Avg IOU: 0.797041, Class: 0.274190, Obj: 0.511056, No Obj: 0.001615, .5R: 1.000000, .75R: 0.705882, count: 17 -Region 23 Avg IOU: 0.746453, Class: 0.183957, Obj: 0.355383, No Obj: 0.000547, .5R: 1.000000, .75R: 0.541667, count: 24 -Region 16 Avg IOU: 0.726472, Class: 0.198298, Obj: 0.269818, No Obj: 0.002160, .5R: 0.960000, .75R: 0.520000, count: 25 -Region 23 Avg IOU: 0.724187, Class: 0.191964, Obj: 0.206664, No Obj: 0.000686, .5R: 0.933333, .75R: 0.466667, count: 30 -Region 16 Avg IOU: 0.762143, Class: 0.230477, Obj: 0.388175, No Obj: 0.001889, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.696204, Class: 0.186951, Obj: 0.269767, No Obj: 0.000765, .5R: 0.888889, .75R: 0.407407, count: 27 -Region 16 Avg IOU: 0.778616, Class: 0.214992, Obj: 0.298014, No Obj: 0.001576, .5R: 1.000000, .75R: 0.785714, count: 14 -Region 23 Avg IOU: 0.699016, Class: 0.215480, Obj: 0.255444, No Obj: 0.000379, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 16 Avg IOU: 0.773361, Class: 0.274250, Obj: 0.436558, No Obj: 0.001548, .5R: 1.000000, .75R: 0.611111, count: 18 -Region 23 Avg IOU: 0.726056, Class: 0.147682, Obj: 0.242564, No Obj: 0.000410, .5R: 0.916667, .75R: 0.458333, count: 24 -Region 16 Avg IOU: 0.764783, Class: 0.226740, Obj: 0.408077, No Obj: 0.001187, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.728291, Class: 0.174007, Obj: 0.215538, No Obj: 0.000479, .5R: 0.950000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.782837, Class: 0.211864, Obj: 0.409272, No Obj: 0.001586, .5R: 0.941176, .75R: 0.764706, count: 17 -Region 23 Avg IOU: 0.756459, Class: 0.180226, Obj: 0.289820, No Obj: 0.000497, .5R: 0.958333, .75R: 0.583333, count: 24 -Region 16 Avg IOU: 0.608994, Class: 0.346541, Obj: 0.257037, No Obj: 0.001150, .5R: 0.875000, .75R: 0.250000, count: 8 -Region 23 Avg IOU: 0.679699, Class: 0.244974, Obj: 0.244785, No Obj: 0.000507, .5R: 0.833333, .75R: 0.416667, count: 24 - - 3740: 2.500914, 2.325306 avg loss, 0.001000 rate, 1.378607 seconds, 478720 images -Resizing -736 x 736 - try to allocate workspace = 1149517825 * sizeof(float), CUDA allocate done! -Loaded: 0.160035 seconds -Region 16 Avg IOU: 0.754053, Class: 0.199902, Obj: 0.294409, No Obj: 0.001696, .5R: 0.944444, .75R: 0.777778, count: 18 -Region 23 Avg IOU: 0.734440, Class: 0.167411, Obj: 0.238637, No Obj: 0.000259, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 16 Avg IOU: 0.759822, Class: 0.236723, Obj: 0.238377, No Obj: 0.001455, .5R: 0.947368, .75R: 0.631579, count: 19 -Region 23 Avg IOU: 0.808282, Class: 0.192878, Obj: 0.315316, No Obj: 0.000283, .5R: 1.000000, .75R: 0.933333, count: 15 -Region 16 Avg IOU: 0.737485, Class: 0.252927, Obj: 0.408513, No Obj: 0.001633, .5R: 0.933333, .75R: 0.466667, count: 30 -Region 23 Avg IOU: 0.723166, Class: 0.161964, Obj: 0.254424, No Obj: 0.000439, .5R: 0.878788, .75R: 0.424242, count: 33 -Region 16 Avg IOU: 0.740345, Class: 0.281961, Obj: 0.533175, No Obj: 0.001510, .5R: 0.866667, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.784152, Class: 0.201404, Obj: 0.347746, No Obj: 0.000196, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 16 Avg IOU: 0.740110, Class: 0.229947, Obj: 0.382660, No Obj: 0.002078, .5R: 0.935484, .75R: 0.612903, count: 31 -Region 23 Avg IOU: 0.687882, Class: 0.206320, Obj: 0.236061, No Obj: 0.000400, .5R: 0.888889, .75R: 0.481481, count: 27 -Region 16 Avg IOU: 0.667274, Class: 0.255887, Obj: 0.443325, No Obj: 0.001868, .5R: 0.842105, .75R: 0.315789, count: 19 -Region 23 Avg IOU: 0.760834, Class: 0.186605, Obj: 0.338274, No Obj: 0.000260, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 16 Avg IOU: 0.744311, Class: 0.238458, Obj: 0.294739, No Obj: 0.001637, .5R: 0.960000, .75R: 0.560000, count: 25 -Region 23 Avg IOU: 0.703403, Class: 0.133365, Obj: 0.163802, No Obj: 0.000283, .5R: 0.900000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.711428, Class: 0.236021, Obj: 0.287360, No Obj: 0.001669, .5R: 0.904762, .75R: 0.428571, count: 21 -Region 23 Avg IOU: 0.761659, Class: 0.187847, Obj: 0.186929, No Obj: 0.000241, .5R: 1.000000, .75R: 0.500000, count: 20 - - 3741: 2.881846, 2.380960 avg loss, 0.001000 rate, 2.198904 seconds, 478848 images -Loaded: 0.000053 seconds -Region 16 Avg IOU: 0.732032, Class: 0.176108, Obj: 0.306600, No Obj: 0.001160, .5R: 0.952381, .75R: 0.571429, count: 21 -Region 23 Avg IOU: 0.722691, Class: 0.163141, Obj: 0.240287, No Obj: 0.000271, .5R: 0.941176, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.719684, Class: 0.275064, Obj: 0.297207, No Obj: 0.000973, .5R: 1.000000, .75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.736514, Class: 0.204981, Obj: 0.266617, No Obj: 0.000169, .5R: 0.937500, .75R: 0.562500, count: 16 -Region 16 Avg IOU: 0.719390, Class: 0.233010, Obj: 0.205954, No Obj: 0.001086, .5R: 0.962963, .75R: 0.407407, count: 27 -Region 23 Avg IOU: 0.711542, Class: 0.219453, Obj: 0.245444, No Obj: 0.000191, .5R: 0.882353, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.777987, Class: 0.192604, Obj: 0.436501, No Obj: 0.001295, .5R: 0.961538, .75R: 0.692308, count: 26 -Region 23 Avg IOU: 0.727432, Class: 0.160430, Obj: 0.237829, No Obj: 0.000289, .5R: 0.962963, .75R: 0.370370, count: 27 -Region 16 Avg IOU: 0.700587, Class: 0.258057, Obj: 0.235065, No Obj: 0.001496, .5R: 0.868421, .75R: 0.447368, count: 38 -Region 23 Avg IOU: 0.730036, Class: 0.177042, Obj: 0.060468, No Obj: 0.000170, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 16 Avg IOU: 0.750872, Class: 0.234373, Obj: 0.348645, No Obj: 0.000909, .5R: 0.941176, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.758286, Class: 0.195933, Obj: 0.171003, No Obj: 0.000269, .5R: 1.000000, .75R: 0.454545, count: 22 -Region 16 Avg IOU: 0.726889, Class: 0.260690, Obj: 0.298339, No Obj: 0.001037, .5R: 0.882353, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.763888, Class: 0.259261, Obj: 0.155368, No Obj: 0.000144, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 16 Avg IOU: 0.675498, Class: 0.219527, Obj: 0.204561, No Obj: 0.001196, .5R: 0.961538, .75R: 0.269231, count: 26 -Region 23 Avg IOU: 0.737777, Class: 0.213649, Obj: 0.152886, No Obj: 0.000190, .5R: 1.000000, 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0.756408, Class: 0.208219, Obj: 0.288502, No Obj: 0.001402, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.712678, Class: 0.172534, Obj: 0.162538, No Obj: 0.000719, .5R: 0.894737, .75R: 0.526316, count: 19 -Region 16 Avg IOU: 0.766092, Class: 0.134070, Obj: 0.298907, No Obj: 0.001406, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.638326, Class: 0.203101, Obj: 0.281568, No Obj: 0.000745, .5R: 0.863636, .75R: 0.181818, count: 22 -Region 16 Avg IOU: 0.724306, Class: 0.161874, Obj: 0.293930, No Obj: 0.001117, .5R: 1.000000, .75R: 0.444444, count: 9 -Region 23 Avg IOU: 0.714055, Class: 0.170728, Obj: 0.226439, No Obj: 0.000694, .5R: 0.928571, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.703108, Class: 0.209485, Obj: 0.187469, No Obj: 0.001218, .5R: 0.916667, .75R: 0.333333, count: 12 -Region 23 Avg IOU: 0.678358, Class: 0.194880, Obj: 0.294817, No Obj: 0.000829, .5R: 0.900000, .75R: 0.350000, count: 20 -Region 16 Avg IOU: 0.782159, Class: 0.228071, Obj: 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count: 8 -Region 23 Avg IOU: 0.709646, Class: 0.208695, Obj: 0.336256, No Obj: 0.000807, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.807151, Class: 0.109842, Obj: 0.341863, No Obj: 0.001303, .5R: 1.000000, .75R: 0.600000, count: 5 -Region 23 Avg IOU: 0.695471, Class: 0.130365, Obj: 0.261972, No Obj: 0.000700, .5R: 0.875000, .75R: 0.375000, count: 24 - - 3766: 1.832695, 2.036584 avg loss, 0.001000 rate, 0.792456 seconds, 482048 images -Loaded: 0.000080 seconds -Region 16 Avg IOU: 0.736872, Class: 0.196932, Obj: 0.247175, No Obj: 0.001817, .5R: 1.000000, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.722477, Class: 0.128932, Obj: 0.311382, No Obj: 0.000846, .5R: 1.000000, .75R: 0.333333, count: 24 -Region 16 Avg IOU: 0.848401, Class: 0.220699, Obj: 0.594786, No Obj: 0.001837, .5R: 1.000000, .75R: 1.000000, count: 6 -Region 23 Avg IOU: 0.765312, Class: 0.227480, Obj: 0.300916, No Obj: 0.000709, .5R: 1.000000, .75R: 0.666667, count: 18 -Region 16 Avg IOU: 0.737699, Class: 0.177887, Obj: 0.377810, No Obj: 0.001624, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.698578, Class: 0.223018, Obj: 0.259590, No Obj: 0.000750, .5R: 0.944444, .75R: 0.500000, count: 18 -Region 16 Avg IOU: 0.819486, Class: 0.172239, Obj: 0.357929, No Obj: 0.001194, .5R: 1.000000, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.747207, Class: 0.216296, Obj: 0.358881, No Obj: 0.000713, .5R: 0.937500, .75R: 0.687500, count: 16 -Region 16 Avg IOU: 0.784170, Class: 0.205794, Obj: 0.257990, No Obj: 0.001794, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.750442, Class: 0.199845, Obj: 0.197780, No Obj: 0.000688, .5R: 0.900000, .75R: 0.700000, count: 20 -Region 16 Avg IOU: 0.822929, Class: 0.168206, Obj: 0.454772, No Obj: 0.001453, .5R: 1.000000, .75R: 0.888889, count: 9 -Region 23 Avg IOU: 0.763072, Class: 0.155853, Obj: 0.262108, No Obj: 0.001018, .5R: 1.000000, .75R: 0.615385, count: 26 -Region 16 Avg IOU: 0.689991, Class: 0.135626, Obj: 0.324240, No 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IOU: 0.679838, Class: 0.153687, Obj: 0.332582, No Obj: 0.002050, .5R: 1.000000, .75R: 0.375000, count: 8 -Region 23 Avg IOU: 0.669322, Class: 0.155862, Obj: 0.283534, No Obj: 0.000757, .5R: 0.818182, .75R: 0.363636, count: 22 -Region 16 Avg IOU: 0.757542, Class: 0.264506, Obj: 0.645780, No Obj: 0.002129, .5R: 0.909091, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.743825, Class: 0.219767, Obj: 0.307956, No Obj: 0.000504, .5R: 1.000000, .75R: 0.500000, count: 12 - - 3768: 1.674315, 1.991954 avg loss, 0.001000 rate, 0.772713 seconds, 482304 images -Loaded: 0.000084 seconds -Region 16 Avg IOU: 0.636393, Class: 0.279009, Obj: 0.355072, No Obj: 0.001516, .5R: 0.666667, .75R: 0.333333, count: 9 -Region 23 Avg IOU: 0.727190, Class: 0.257208, Obj: 0.359685, No Obj: 0.000556, .5R: 0.933333, .75R: 0.466667, count: 15 -Region 16 Avg IOU: 0.769534, Class: 0.163186, Obj: 0.492770, No Obj: 0.002787, .5R: 0.937500, .75R: 0.687500, count: 16 -Region 23 Avg IOU: 0.728198, Class: 0.121488, Obj: 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Class: 0.179299, Obj: 0.272868, No Obj: 0.000469, .5R: 1.000000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.774095, Class: 0.259947, Obj: 0.312615, No Obj: 0.001810, .5R: 0.916667, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.763123, Class: 0.152304, Obj: 0.213970, No Obj: 0.000500, .5R: 1.000000, .75R: 0.578947, count: 19 -Region 16 Avg IOU: 0.813489, Class: 0.281319, Obj: 0.602425, No Obj: 0.001129, .5R: 1.000000, .75R: 1.000000, count: 4 -Region 23 Avg IOU: 0.732392, Class: 0.221439, Obj: 0.325894, No Obj: 0.000457, .5R: 0.923077, .75R: 0.769231, count: 13 - - 3770: 1.831563, 1.950406 avg loss, 0.001000 rate, 0.794998 seconds, 482560 images -Resizing -448 x 448 - try to allocate workspace = 305201153 * sizeof(float), CUDA allocate done! -Loaded: 0.218101 seconds -Region 16 Avg IOU: 0.771814, Class: 0.304097, Obj: 0.351167, No Obj: 0.001277, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.736438, Class: 0.254682, Obj: 0.181222, No Obj: 0.000349, .5R: 1.000000, 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0.199993, Obj: 0.196963, No Obj: 0.000756, .5R: 0.903226, .75R: 0.516129, count: 31 -Region 16 Avg IOU: 0.738053, Class: 0.231188, Obj: 0.299038, No Obj: 0.002244, .5R: 0.894737, .75R: 0.578947, count: 19 -Region 23 Avg IOU: 0.699680, Class: 0.241036, Obj: 0.307744, No Obj: 0.000844, .5R: 0.869565, .75R: 0.434783, count: 23 -Region 16 Avg IOU: 0.746242, Class: 0.287823, Obj: 0.306013, No Obj: 0.001605, .5R: 0.944444, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.679421, Class: 0.205927, Obj: 0.254268, No Obj: 0.000814, .5R: 0.833333, .75R: 0.416667, count: 24 -Region 16 Avg IOU: 0.746379, Class: 0.254415, Obj: 0.371389, No Obj: 0.001388, .5R: 0.909091, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.672297, Class: 0.220112, Obj: 0.207019, No Obj: 0.000756, .5R: 0.862069, .75R: 0.310345, count: 29 - - 3773: 2.121583, 1.935578 avg loss, 0.001000 rate, 0.879908 seconds, 482944 images -Loaded: 0.000084 seconds -Region 16 Avg IOU: 0.761042, Class: 0.205686, Obj: 0.296231, No Obj: 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images -Loaded: 0.000064 seconds -Region 16 Avg IOU: 0.724736, Class: 0.203653, Obj: 0.337603, No Obj: 0.001645, .5R: 0.833333, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.770818, Class: 0.178138, Obj: 0.385099, No Obj: 0.000924, .5R: 1.000000, .75R: 0.611111, count: 18 -Region 16 Avg IOU: 0.770815, Class: 0.227776, Obj: 0.392357, No Obj: 0.001829, .5R: 1.000000, .75R: 0.705882, count: 17 -Region 23 Avg IOU: 0.718709, Class: 0.213098, Obj: 0.378462, No Obj: 0.000928, .5R: 0.846154, .75R: 0.538462, count: 26 -Region 16 Avg IOU: 0.736960, Class: 0.320484, Obj: 0.290996, No Obj: 0.001271, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.734988, Class: 0.214839, Obj: 0.316150, No Obj: 0.000676, .5R: 1.000000, .75R: 0.421053, count: 19 -Region 16 Avg IOU: 0.678486, Class: 0.243743, Obj: 0.280770, No Obj: 0.002003, .5R: 0.785714, .75R: 0.357143, count: 14 -Region 23 Avg IOU: 0.748594, Class: 0.240023, Obj: 0.322414, No Obj: 0.000862, .5R: 1.000000, .75R: 0.450000, count: 20 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0.730344, Class: 0.172733, Obj: 0.167005, No Obj: 0.000384, .5R: 0.952381, .75R: 0.476190, count: 21 -Region 16 Avg IOU: 0.744887, Class: 0.220114, Obj: 0.521039, No Obj: 0.002446, .5R: 0.888889, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.693214, Class: 0.178482, Obj: 0.267134, No Obj: 0.000673, .5R: 0.928571, .75R: 0.392857, count: 28 -Region 16 Avg IOU: 0.770196, Class: 0.256621, Obj: 0.637878, No Obj: 0.002393, .5R: 0.909091, .75R: 0.818182, count: 11 -Region 23 Avg IOU: 0.710636, Class: 0.236926, Obj: 0.210058, No Obj: 0.000547, .5R: 0.904762, .75R: 0.523810, count: 21 -Region 16 Avg IOU: 0.776954, Class: 0.212943, Obj: 0.519904, No Obj: 0.002662, .5R: 1.000000, .75R: 0.562500, count: 16 -Region 23 Avg IOU: 0.788248, Class: 0.155752, Obj: 0.231311, No Obj: 0.000498, .5R: 0.941176, .75R: 0.705882, count: 17 -Region 16 Avg IOU: 0.797591, Class: 0.160088, Obj: 0.474275, No Obj: 0.002196, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.663642, Class: 0.211664, Obj: 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23 Avg IOU: 0.681124, Class: 0.220787, Obj: 0.227552, No Obj: 0.000233, .5R: 1.000000, .75R: 0.333333, count: 9 - - 3779: 1.791843, 1.954197 avg loss, 0.001000 rate, 0.863354 seconds, 483712 images -Loaded: 0.000064 seconds -Region 16 Avg IOU: 0.779317, Class: 0.190684, Obj: 0.380032, No Obj: 0.002282, .5R: 1.000000, .75R: 0.785714, count: 14 -Region 23 Avg IOU: 0.801845, Class: 0.149945, Obj: 0.206615, No Obj: 0.000318, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 16 Avg IOU: 0.789724, Class: 0.207993, Obj: 0.317958, No Obj: 0.002021, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.740158, Class: 0.205068, Obj: 0.093164, No Obj: 0.000323, .5R: 1.000000, .75R: 0.409091, count: 22 -Region 16 Avg IOU: 0.752102, Class: 0.122069, Obj: 0.276004, No Obj: 0.001524, .5R: 1.000000, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.729628, Class: 0.111434, Obj: 0.131820, No Obj: 0.000247, .5R: 0.937500, .75R: 0.562500, count: 16 -Region 16 Avg IOU: 0.778678, Class: 0.353221, Obj: 0.754224, No Obj: 0.001429, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.768139, Class: 0.222566, Obj: 0.312926, No Obj: 0.000309, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 16 Avg IOU: 0.776932, Class: 0.276725, Obj: 0.522836, No Obj: 0.002921, .5R: 0.875000, .75R: 0.750000, count: 16 -Region 23 Avg IOU: 0.785621, Class: 0.201227, Obj: 0.119604, No Obj: 0.000388, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 16 Avg IOU: 0.788611, Class: 0.228453, Obj: 0.558154, No Obj: 0.001779, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.790263, Class: 0.234696, Obj: 0.231621, No Obj: 0.000397, .5R: 1.000000, .75R: 0.631579, count: 19 -Region 16 Avg IOU: 0.753788, Class: 0.199089, Obj: 0.379907, No Obj: 0.001488, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.694815, Class: 0.179651, Obj: 0.160174, No Obj: 0.000301, .5R: 0.937500, .75R: 0.312500, count: 16 -Region 16 Avg IOU: 0.753586, Class: 0.305344, Obj: 0.445851, No Obj: 0.001856, 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0.658298, Class: 0.196797, Obj: 0.135605, No Obj: 0.000424, .5R: 0.764706, .75R: 0.411765, count: 17 -Region 16 Avg IOU: 0.785018, Class: 0.116632, Obj: 0.428448, No Obj: 0.001786, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.788349, Class: 0.195841, Obj: 0.257841, No Obj: 0.000332, .5R: 1.000000, .75R: 0.857143, count: 7 -Region 16 Avg IOU: 0.793775, Class: 0.165928, Obj: 0.326217, No Obj: 0.001128, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.785304, Class: 0.188335, Obj: 0.251635, No Obj: 0.000309, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 16 Avg IOU: 0.763491, Class: 0.180027, Obj: 0.269071, No Obj: 0.000927, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.721094, Class: 0.152045, Obj: 0.183704, No Obj: 0.000331, .5R: 0.937500, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.751218, Class: 0.257421, Obj: 0.313415, No Obj: 0.001739, .5R: 0.941176, .75R: 0.647059, count: 17 -Region 23 Avg IOU: 0.717672, Class: 0.144020, Obj: 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0.268086, Obj: 0.429810, No Obj: 0.001105, .5R: 0.888889, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.707600, Class: 0.193056, Obj: 0.304293, No Obj: 0.000683, .5R: 1.000000, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.798491, Class: 0.240984, Obj: 0.489336, No Obj: 0.001540, .5R: 1.000000, .75R: 0.769231, count: 13 -Region 23 Avg IOU: 0.673518, Class: 0.135357, Obj: 0.306893, No Obj: 0.000787, .5R: 0.827586, .75R: 0.413793, count: 29 -Region 16 Avg IOU: 0.767012, Class: 0.331343, Obj: 0.344333, No Obj: 0.001320, .5R: 1.000000, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.745312, Class: 0.241935, Obj: 0.469353, No Obj: 0.000580, .5R: 0.909091, .75R: 0.636364, count: 11 -Region 16 Avg IOU: 0.774274, Class: 0.238292, Obj: 0.372588, No Obj: 0.002075, .5R: 1.000000, .75R: 0.625000, count: 16 -Region 23 Avg IOU: 0.726997, Class: 0.171331, Obj: 0.277707, No Obj: 0.001050, .5R: 0.964286, .75R: 0.500000, count: 28 -Region 16 Avg IOU: 0.713615, Class: 0.301247, Obj: 0.321033, No Obj: 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12 -Region 23 Avg IOU: 0.617829, Class: 0.214247, Obj: 0.213015, No Obj: 0.000509, .5R: 0.708333, .75R: 0.250000, count: 24 -Region 16 Avg IOU: 0.774244, Class: 0.194893, Obj: 0.360401, No Obj: 0.001113, .5R: 1.000000, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.718165, Class: 0.159229, Obj: 0.318339, No Obj: 0.000452, .5R: 0.923077, .75R: 0.461538, count: 13 -Region 16 Avg IOU: 0.764265, Class: 0.318747, Obj: 0.548678, No Obj: 0.000932, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.751756, Class: 0.225012, Obj: 0.378987, No Obj: 0.000392, .5R: 0.923077, .75R: 0.769231, count: 13 - - 3787: 2.244013, 2.070600 avg loss, 0.001000 rate, 1.162526 seconds, 484736 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.796045, Class: 0.238278, Obj: 0.405189, No Obj: 0.001781, .5R: 1.000000, .75R: 0.705882, count: 17 -Region 23 Avg IOU: 0.716112, Class: 0.211972, Obj: 0.295160, No Obj: 0.000551, .5R: 0.944444, .75R: 0.500000, count: 18 -Region 16 Avg IOU: 0.790338, Class: 0.237558, Obj: 0.404598, No Obj: 0.002188, .5R: 1.000000, .75R: 0.785714, count: 14 -Region 23 Avg IOU: 0.739049, Class: 0.201216, Obj: 0.404945, No Obj: 0.000988, .5R: 0.875000, .75R: 0.750000, count: 16 -Region 16 Avg IOU: 0.812221, Class: 0.227270, Obj: 0.358155, No Obj: 0.001413, .5R: 1.000000, .75R: 0.888889, count: 9 -Region 23 Avg IOU: 0.756111, Class: 0.269851, Obj: 0.313102, No Obj: 0.000632, .5R: 0.947368, .75R: 0.631579, count: 19 -Region 16 Avg IOU: 0.792147, Class: 0.185013, Obj: 0.380641, No Obj: 0.002089, .5R: 1.000000, .75R: 0.750000, count: 20 -Region 23 Avg IOU: 0.769988, Class: 0.233191, Obj: 0.540193, No Obj: 0.000756, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 16 Avg IOU: 0.738346, Class: 0.207040, Obj: 0.446935, No Obj: 0.001451, .5R: 0.928571, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.739317, Class: 0.172099, Obj: 0.441878, No Obj: 0.000826, .5R: 0.950000, .75R: 0.500000, count: 20 -Region 16 Avg IOU: 0.778137, Class: 0.283511, Obj: 0.373804, No Obj: 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count: 16 -Region 16 Avg IOU: 0.793763, Class: 0.291183, Obj: 0.504835, No Obj: 0.002340, .5R: 0.941176, .75R: 0.823529, count: 17 -Region 23 Avg IOU: 0.720305, Class: 0.245335, Obj: 0.433745, No Obj: 0.000848, .5R: 0.941176, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.748999, Class: 0.247796, Obj: 0.323777, No Obj: 0.002076, .5R: 0.842105, .75R: 0.684211, count: 19 -Region 23 Avg IOU: 0.786430, Class: 0.237203, Obj: 0.335071, No Obj: 0.000581, .5R: 1.000000, .75R: 0.555556, count: 18 -Region 16 Avg IOU: 0.790252, Class: 0.153602, Obj: 0.442699, No Obj: 0.001337, .5R: 0.888889, .75R: 0.777778, count: 9 -Region 23 Avg IOU: 0.724882, Class: 0.140514, Obj: 0.276470, No Obj: 0.000614, .5R: 0.894737, .75R: 0.631579, count: 19 -Region 16 Avg IOU: 0.763477, Class: 0.283118, Obj: 0.509497, No Obj: 0.001658, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.770644, Class: 0.197667, Obj: 0.459680, No Obj: 0.000538, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 16 Avg IOU: 0.821675, Class: 0.279905, Obj: 0.390998, No Obj: 0.002130, .5R: 1.000000, .75R: 0.944444, count: 18 -Region 23 Avg IOU: 0.699975, Class: 0.196752, Obj: 0.219442, No Obj: 0.000709, .5R: 0.928571, .75R: 0.392857, count: 28 -Region 16 Avg IOU: 0.761695, Class: 0.217439, Obj: 0.415649, No Obj: 0.001429, .5R: 0.933333, .75R: 0.733333, count: 15 -Region 23 Avg IOU: 0.751276, Class: 0.161200, Obj: 0.436798, No Obj: 0.000730, .5R: 0.941176, .75R: 0.529412, count: 17 -Region 16 Avg IOU: 0.766175, Class: 0.151154, Obj: 0.349218, No Obj: 0.001781, .5R: 0.947368, .75R: 0.684211, count: 19 -Region 23 Avg IOU: 0.719078, Class: 0.134464, Obj: 0.286141, No Obj: 0.000797, .5R: 0.956522, .75R: 0.478261, count: 23 - - 3789: 2.159628, 2.096442 avg loss, 0.001000 rate, 1.169526 seconds, 484992 images -Loaded: 0.000094 seconds -Region 16 Avg IOU: 0.794725, Class: 0.279230, Obj: 0.440157, No Obj: 0.002326, .5R: 1.000000, .75R: 0.812500, count: 16 -Region 23 Avg IOU: 0.729305, Class: 0.220638, Obj: 0.238482, 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0.234969, Obj: 0.337877, No Obj: 0.001161, .5R: 0.900000, .75R: 0.400000, count: 10 -Region 23 Avg IOU: 0.733277, Class: 0.208832, Obj: 0.196583, No Obj: 0.000509, .5R: 0.923077, .75R: 0.461538, count: 26 -Region 16 Avg IOU: 0.790396, Class: 0.241083, Obj: 0.534115, No Obj: 0.001985, .5R: 0.900000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.703707, Class: 0.131117, Obj: 0.203202, No Obj: 0.000529, .5R: 0.923077, .75R: 0.461538, count: 26 -Region 16 Avg IOU: 0.784675, Class: 0.232265, Obj: 0.496628, No Obj: 0.002201, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.712147, Class: 0.238754, Obj: 0.233161, No Obj: 0.000450, .5R: 0.869565, .75R: 0.565217, count: 23 -Region 16 Avg IOU: 0.805787, Class: 0.247303, Obj: 0.441257, No Obj: 0.002421, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.736116, Class: 0.216875, Obj: 0.153692, No Obj: 0.000534, .5R: 0.950000, .75R: 0.500000, count: 20 -Region 16 Avg IOU: 0.798410, Class: 0.265020, Obj: 0.618973, No Obj: 0.002836, .5R: 1.000000, .75R: 0.785714, count: 14 -Region 23 Avg IOU: 0.770012, Class: 0.197553, Obj: 0.183337, No Obj: 0.000466, .5R: 1.000000, .75R: 0.647059, count: 17 -Region 16 Avg IOU: 0.748247, Class: 0.197123, Obj: 0.387296, No Obj: 0.001838, .5R: 0.916667, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.762567, Class: 0.195055, Obj: 0.164254, No Obj: 0.000493, .5R: 0.931035, .75R: 0.620690, count: 29 -Region 16 Avg IOU: 0.721675, Class: 0.225409, Obj: 0.321296, No Obj: 0.001557, .5R: 0.916667, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.744347, Class: 0.193928, Obj: 0.147103, No Obj: 0.000375, .5R: 0.928571, .75R: 0.571429, count: 14 -Region 16 Avg IOU: 0.773288, Class: 0.276225, Obj: 0.420186, No Obj: 0.001810, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.746444, Class: 0.214849, Obj: 0.206566, No Obj: 0.000381, .5R: 1.000000, .75R: 0.526316, count: 19 - - 3791: 2.054250, 2.107430 avg loss, 0.001000 rate, 0.739211 seconds, 485248 images -Loaded: 0.000067 seconds -Region 16 Avg IOU: 0.752453, Class: 0.187545, Obj: 0.407842, No Obj: 0.001978, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.704506, Class: 0.203312, Obj: 0.255601, No Obj: 0.000627, .5R: 0.880000, .75R: 0.360000, count: 25 -Region 16 Avg IOU: 0.776247, Class: 0.147684, Obj: 0.376962, No Obj: 0.001499, .5R: 1.000000, .75R: 0.500000, count: 6 -Region 23 Avg IOU: 0.708948, Class: 0.170291, Obj: 0.283870, No Obj: 0.000613, .5R: 0.909091, .75R: 0.409091, count: 22 -Region 16 Avg IOU: 0.861270, Class: 0.284766, Obj: 0.726860, No Obj: 0.001522, .5R: 1.000000, .75R: 1.000000, count: 4 -Region 23 Avg IOU: 0.703477, Class: 0.171773, Obj: 0.244878, No Obj: 0.000579, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.745965, Class: 0.233979, Obj: 0.370040, No Obj: 0.002349, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.756162, Class: 0.177391, Obj: 0.165215, No Obj: 0.000555, .5R: 0.944444, .75R: 0.500000, count: 18 -Region 16 Avg 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rate, 0.788470 seconds, 485376 images -Loaded: 0.000100 seconds -Region 16 Avg IOU: 0.820051, Class: 0.237923, Obj: 0.358655, No Obj: 0.001354, .5R: 1.000000, .75R: 0.900000, count: 10 -Region 23 Avg IOU: 0.697959, Class: 0.240962, Obj: 0.175075, No Obj: 0.000280, .5R: 0.875000, .75R: 0.500000, count: 8 -Region 16 Avg IOU: 0.804466, Class: 0.286227, Obj: 0.404020, No Obj: 0.002643, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.763713, Class: 0.151815, Obj: 0.225593, No Obj: 0.000621, .5R: 1.000000, .75R: 0.600000, count: 15 -Region 16 Avg IOU: 0.825443, Class: 0.231390, Obj: 0.439779, No Obj: 0.001601, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.746445, Class: 0.165953, Obj: 0.369379, No Obj: 0.000822, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.712298, Class: 0.340784, Obj: 0.546316, No Obj: 0.000939, .5R: 0.750000, .75R: 0.750000, count: 4 -Region 23 Avg IOU: 0.721562, Class: 0.220584, Obj: 0.139571, No Obj: 0.000514, .5R: 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17 - - 3793: 1.599096, 2.054516 avg loss, 0.001000 rate, 0.771612 seconds, 485504 images -Loaded: 0.000081 seconds -Region 16 Avg IOU: 0.753651, Class: 0.197762, Obj: 0.187906, No Obj: 0.001143, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.739357, Class: 0.233250, Obj: 0.462406, No Obj: 0.000645, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 16 Avg IOU: 0.752443, Class: 0.189341, Obj: 0.533011, No Obj: 0.000999, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.719630, Class: 0.213372, Obj: 0.382197, No Obj: 0.001598, .5R: 0.970588, .75R: 0.441176, count: 34 -Region 16 Avg IOU: 0.810356, Class: 0.247456, Obj: 0.304554, No Obj: 0.002317, .5R: 1.000000, .75R: 0.800000, count: 15 -Region 23 Avg IOU: 0.714432, Class: 0.178972, Obj: 0.200795, No Obj: 0.000825, .5R: 0.882353, .75R: 0.588235, count: 17 -Region 16 Avg IOU: 0.829703, Class: 0.195592, Obj: 0.599880, No Obj: 0.000928, .5R: 1.000000, .75R: 0.800000, count: 5 -Region 23 Avg IOU: 0.740024, Class: 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0.777508, Class: 0.247666, Obj: 0.371279, No Obj: 0.002142, .5R: 0.928571, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.709182, Class: 0.193377, Obj: 0.139002, No Obj: 0.000571, .5R: 0.954545, .75R: 0.409091, count: 22 -Region 16 Avg IOU: 0.776860, Class: 0.159375, Obj: 0.286563, No Obj: 0.002337, .5R: 1.000000, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.788345, Class: 0.140918, Obj: 0.140612, No Obj: 0.000377, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 16 Avg IOU: 0.826205, Class: 0.292993, Obj: 0.476182, No Obj: 0.001441, .5R: 0.875000, .75R: 0.875000, count: 8 -Region 23 Avg IOU: 0.642485, Class: 0.214592, Obj: 0.254954, No Obj: 0.000823, .5R: 0.714286, .75R: 0.392857, count: 28 -Region 16 Avg IOU: 0.830419, Class: 0.279700, Obj: 0.377479, No Obj: 0.001634, .5R: 1.000000, .75R: 0.916667, count: 12 -Region 23 Avg IOU: 0.774633, Class: 0.226968, Obj: 0.186685, No Obj: 0.000514, .5R: 0.937500, .75R: 0.687500, count: 16 -Region 16 Avg IOU: 0.846615, Class: 0.235728, Obj: 0.303066, No Obj: 0.001374, .5R: 1.000000, .75R: 1.000000, count: 13 -Region 23 Avg IOU: 0.734800, Class: 0.185359, Obj: 0.239256, No Obj: 0.000811, .5R: 0.961538, .75R: 0.576923, count: 26 - - 3797: 1.805576, 1.938350 avg loss, 0.001000 rate, 0.782192 seconds, 486016 images -Loaded: 0.000072 seconds -Region 16 Avg IOU: 0.824088, Class: 0.173683, Obj: 0.118438, No Obj: 0.001111, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.782404, Class: 0.155109, Obj: 0.245982, No Obj: 0.000278, .5R: 1.000000, .75R: 0.777778, count: 9 -Region 16 Avg IOU: 0.791525, Class: 0.267958, Obj: 0.488364, No Obj: 0.000825, .5R: 1.000000, .75R: 0.800000, count: 5 -Region 23 Avg IOU: 0.734479, Class: 0.184636, Obj: 0.190026, No Obj: 0.000436, .5R: 1.000000, .75R: 0.500000, count: 20 -Region 16 Avg IOU: 0.800270, Class: 0.219188, Obj: 0.322347, No Obj: 0.001490, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.735684, Class: 0.192048, Obj: 0.158773, No Obj: 0.000559, .5R: 1.000000, .75R: 0.541667, count: 24 -Region 16 Avg IOU: 0.778223, Class: 0.234735, Obj: 0.298547, No Obj: 0.001402, .5R: 0.866667, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.722429, Class: 0.206076, Obj: 0.188671, No Obj: 0.000632, .5R: 0.962963, .75R: 0.370370, count: 27 -Region 16 Avg IOU: 0.741818, Class: 0.259330, Obj: 0.545563, No Obj: 0.000737, .5R: 1.000000, .75R: 0.250000, count: 4 -Region 23 Avg IOU: 0.740262, Class: 0.197004, Obj: 0.173234, No Obj: 0.000450, .5R: 1.000000, .75R: 0.529412, count: 17 -Region 16 Avg IOU: 0.847708, Class: 0.270414, Obj: 0.224656, No Obj: 0.001098, .5R: 1.000000, .75R: 1.000000, count: 9 -Region 23 Avg IOU: 0.761579, Class: 0.251372, Obj: 0.293249, No Obj: 0.000596, .5R: 1.000000, .75R: 0.500000, count: 20 -Region 16 Avg IOU: 0.767257, Class: 0.274614, Obj: 0.330700, No Obj: 0.001698, .5R: 0.928571, .75R: 0.785714, count: 14 -Region 23 Avg IOU: 0.714583, Class: 0.172277, Obj: 0.209279, No Obj: 0.000702, .5R: 0.900000, .75R: 0.550000, count: 20 -Region 16 Avg IOU: 0.819113, Class: 0.249201, Obj: 0.426131, No Obj: 0.001265, .5R: 1.000000, .75R: 0.916667, count: 12 -Region 23 Avg IOU: 0.734708, Class: 0.224182, Obj: 0.245190, No Obj: 0.000506, .5R: 1.000000, .75R: 0.520000, count: 25 - - 3798: 1.831396, 1.927654 avg loss, 0.001000 rate, 0.777983 seconds, 486144 images -Loaded: 0.000068 seconds -Region 16 Avg IOU: 0.817913, Class: 0.206172, Obj: 0.301090, No Obj: 0.001704, .5R: 1.000000, .75R: 0.769231, count: 13 -Region 23 Avg IOU: 0.738837, Class: 0.152807, Obj: 0.240598, No Obj: 0.000584, .5R: 0.937500, .75R: 0.562500, count: 16 -Region 16 Avg IOU: 0.768657, Class: 0.154855, Obj: 0.273868, No Obj: 0.001593, .5R: 1.000000, .75R: 0.777778, count: 9 -Region 23 Avg IOU: 0.712803, Class: 0.155337, Obj: 0.234000, No Obj: 0.000426, .5R: 0.928571, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.794340, Class: 0.233122, Obj: 0.320509, No Obj: 0.001459, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.686591, Class: 0.183637, Obj: 0.192842, No Obj: 0.000603, .5R: 1.000000, .75R: 0.250000, count: 12 -Region 16 Avg IOU: 0.812859, Class: 0.341232, Obj: 0.408851, No Obj: 0.000668, .5R: 1.000000, .75R: 1.000000, count: 3 -Region 23 Avg IOU: 0.710758, Class: 0.148759, Obj: 0.274548, No Obj: 0.000398, .5R: 1.000000, .75R: 0.454545, count: 11 -Region 16 Avg IOU: 0.797312, Class: 0.269867, Obj: 0.354937, No Obj: 0.002007, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.687890, Class: 0.138603, Obj: 0.172641, No Obj: 0.000448, .5R: 0.916667, .75R: 0.250000, count: 12 -Region 16 Avg IOU: 0.735202, Class: 0.153694, Obj: 0.266560, No Obj: 0.001436, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 23 Avg IOU: 0.678443, Class: 0.188038, Obj: 0.218410, No Obj: 0.000945, .5R: 0.842105, .75R: 0.500000, count: 38 -Region 16 Avg IOU: 0.785434, Class: 0.331514, Obj: 0.348725, No Obj: 0.000929, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.733784, Class: 0.254443, Obj: 0.362286, No Obj: 0.000661, .5R: 0.833333, .75R: 0.555556, count: 18 -Region 16 Avg IOU: 0.776977, Class: 0.315964, Obj: 0.506458, No Obj: 0.001171, .5R: 1.000000, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.718044, Class: 0.187661, Obj: 0.327562, No Obj: 0.000679, .5R: 0.933333, .75R: 0.400000, count: 15 - - 3799: 1.682779, 1.903167 avg loss, 0.001000 rate, 0.775592 seconds, 486272 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.805426, Class: 0.178146, Obj: 0.576844, No Obj: 0.001596, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.817400, Class: 0.136771, Obj: 0.281313, No Obj: 0.000695, .5R: 1.000000, .75R: 0.823529, count: 17 -Region 16 Avg IOU: 0.797803, Class: 0.141252, Obj: 0.428367, No Obj: 0.001508, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.732655, Class: 0.224165, Obj: 0.226312, No Obj: 0.000706, .5R: 0.875000, .75R: 0.562500, count: 16 -Region 16 Avg IOU: 0.807998, Class: 0.209513, Obj: 0.483491, No Obj: 0.001959, .5R: 1.000000, .75R: 0.642857, cSaving weights to ./attribute/7354_26298/attribute_33652_s_tyv3_9c5_3800.weights -ount: 14 -Region 23 Avg IOU: 0.701791, Class: 0.189628, Obj: 0.305529, No Obj: 0.000791, .5R: 0.909091, .75R: 0.409091, count: 22 -Region 16 Avg IOU: 0.751604, Class: 0.268855, Obj: 0.342445, No Obj: 0.001202, .5R: 0.875000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.714389, Class: 0.232102, Obj: 0.414571, No Obj: 0.000795, .5R: 0.944444, .75R: 0.444444, count: 18 -Region 16 Avg IOU: 0.815352, Class: 0.440042, Obj: 0.476388, No Obj: 0.001376, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.700379, Class: 0.148823, Obj: 0.254469, No Obj: 0.000559, .5R: 0.909091, .75R: 0.363636, count: 11 -Region 16 Avg IOU: 0.770140, Class: 0.235716, Obj: 0.368930, No Obj: 0.001477, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.753858, Class: 0.206769, Obj: 0.304296, No Obj: 0.000702, .5R: 1.000000, .75R: 0.684211, count: 19 -Region 16 Avg IOU: 0.754509, Class: 0.209777, Obj: 0.351066, No Obj: 0.001625, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 23 Avg IOU: 0.760730, Class: 0.215429, Obj: 0.280380, No Obj: 0.001030, .5R: 1.000000, .75R: 0.566667, count: 30 -Region 16 Avg IOU: 0.754097, Class: 0.235803, Obj: 0.334981, No Obj: 0.002507, .5R: 0.947368, .75R: 0.578947, count: 19 -Region 23 Avg IOU: 0.746094, Class: 0.177754, Obj: 0.227692, No Obj: 0.000860, .5R: 0.964286, .75R: 0.642857, count: 28 - - 3800: 1.893431, 1.902193 avg loss, 0.001000 rate, 0.779291 seconds, 486400 images -Resizing -448 x 448 - try to allocate workspace = 305201153 * sizeof(float), CUDA allocate done! -Loaded: 0.379190 seconds -Region 16 Avg IOU: 0.769409, Class: 0.260529, Obj: 0.412519, No Obj: 0.002103, .5R: 0.933333, .75R: 0.733333, count: 15 -Region 23 Avg IOU: 0.736560, Class: 0.202495, Obj: 0.304804, No Obj: 0.000805, .5R: 0.947368, .75R: 0.578947, count: 19 -Region 16 Avg IOU: 0.772339, Class: 0.267162, Obj: 0.362075, No Obj: 0.002067, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.731833, Class: 0.205975, Obj: 0.237438, No Obj: 0.000734, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.892057, Class: 0.298887, Obj: 0.609974, No Obj: 0.001409, .5R: 1.000000, .75R: 1.000000, count: 5 -Region 23 Avg IOU: 0.699118, Class: 0.246519, Obj: 0.463223, No Obj: 0.000714, .5R: 0.937500, .75R: 0.187500, count: 16 -Region 16 Avg IOU: 0.791881, Class: 0.236068, Obj: 0.505220, No Obj: 0.001251, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.679978, Class: 0.241416, Obj: 0.350827, No Obj: 0.000637, .5R: 0.866667, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.803465, Class: 0.255577, Obj: 0.537547, No Obj: 0.001056, .5R: 1.000000, .75R: 0.833333, count: 6 -Region 23 Avg IOU: 0.739216, Class: 0.131336, Obj: 0.324900, No Obj: 0.000396, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 16 Avg IOU: 0.778459, Class: 0.265163, Obj: 0.514732, No Obj: 0.001268, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.731220, Class: 0.247504, Obj: 0.290713, No Obj: 0.000746, .5R: 1.000000, .75R: 0.526316, count: 19 -Region 16 Avg IOU: 0.792055, Class: 0.359263, Obj: 0.569004, No Obj: 0.001608, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.725598, Class: 0.199148, Obj: 0.312743, No Obj: 0.000778, .5R: 0.944444, .75R: 0.500000, count: 18 -Region 16 Avg IOU: 0.695791, Class: 0.323003, Obj: 0.470786, No Obj: 0.001679, .5R: 0.750000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.741468, Class: 0.183405, Obj: 0.221766, No Obj: 0.000769, .5R: 1.000000, .75R: 0.428571, count: 21 - - 3801: 1.598186, 1.871793 avg loss, 0.001000 rate, 0.825766 seconds, 486528 images -Loaded: 0.000074 seconds -Region 16 Avg IOU: 0.773094, Class: 0.181912, Obj: 0.384767, No Obj: 0.001726, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.783893, Class: 0.206820, Obj: 0.325254, No Obj: 0.000636, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 16 Avg IOU: 0.800235, Class: 0.267079, Obj: 0.373132, No Obj: 0.001442, .5R: 1.000000, .75R: 0.769231, count: 13 -Region 23 Avg IOU: 0.807297, Class: 0.156914, Obj: 0.339883, No Obj: 0.000687, .5R: 1.000000, .75R: 0.923077, count: 13 -Region 16 Avg IOU: 0.739210, Class: 0.291621, Obj: 0.333992, No Obj: 0.002108, .5R: 0.833333, .75R: 0.500000, count: 6 -Region 23 Avg IOU: 0.677192, Class: 0.259931, Obj: 0.365548, No Obj: 0.000729, .5R: 0.909091, .75R: 0.363636, count: 11 -Region 16 Avg IOU: 0.737786, Class: 0.172886, Obj: 0.290877, No Obj: 0.001975, .5R: 0.928571, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.670425, Class: 0.245088, Obj: 0.210134, No Obj: 0.000456, .5R: 0.833333, .75R: 0.333333, count: 6 -Region 16 Avg IOU: 0.794406, Class: 0.224099, Obj: 0.453640, No Obj: 0.001255, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.713791, Class: 0.184488, Obj: 0.394466, No Obj: 0.001007, .5R: 0.925926, .75R: 0.518519, count: 27 -Region 16 Avg IOU: 0.765019, Class: 0.306742, Obj: 0.293725, No Obj: 0.001268, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.719432, Class: 0.237149, Obj: 0.300056, No Obj: 0.000639, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.691279, Class: 0.171507, Obj: 0.381797, No Obj: 0.002728, .5R: 0.882353, .75R: 0.352941, count: 17 -Region 23 Avg IOU: 0.753345, Class: 0.203941, Obj: 0.352338, No Obj: 0.001254, .5R: 1.000000, .75R: 0.500000, count: 20 -Region 16 Avg IOU: 0.806872, Class: 0.203560, Obj: 0.371307, No Obj: 0.002272, .5R: 1.000000, .75R: 0.842105, count: 19 -Region 23 Avg IOU: 0.722030, Class: 0.219581, Obj: 0.301453, No Obj: 0.000726, .5R: 0.937500, .75R: 0.437500, count: 16 - - 3802: 1.739073, 1.858521 avg loss, 0.001000 rate, 0.864616 seconds, 486656 images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.822713, Class: 0.332469, Obj: 0.526331, No Obj: 0.001477, .5R: 1.000000, .75R: 1.000000, count: 8 -Region 23 Avg IOU: 0.710458, Class: 0.194645, Obj: 0.227962, No Obj: 0.000701, .5R: 0.967742, .75R: 0.419355, count: 31 -Region 16 Avg IOU: 0.838518, Class: 0.276294, Obj: 0.480565, No Obj: 0.002036, .5R: 1.000000, .75R: 0.916667, count: 12 -Region 23 Avg IOU: 0.736705, Class: 0.238735, Obj: 0.288245, No Obj: 0.000596, .5R: 0.952381, .75R: 0.523810, count: 21 -Region 16 Avg IOU: 0.786906, Class: 0.328947, Obj: 0.419260, No Obj: 0.001357, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.769748, Class: 0.192768, Obj: 0.278665, No Obj: 0.000324, .5R: 1.000000, .75R: 0.642857, count: 14 -Region 16 Avg IOU: 0.720906, Class: 0.241346, Obj: 0.445344, No Obj: 0.001988, .5R: 0.923077, .75R: 0.384615, count: 13 -Region 23 Avg IOU: 0.739077, Class: 0.195185, Obj: 0.276632, No Obj: 0.000598, .5R: 0.954545, .75R: 0.545455, count: 22 -Region 16 Avg IOU: 0.753255, Class: 0.231621, Obj: 0.277321, No Obj: 0.001469, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.642440, Class: 0.206961, Obj: 0.158340, No Obj: 0.000226, .5R: 0.800000, .75R: 0.200000, count: 10 -Region 16 Avg IOU: 0.829145, Class: 0.282468, Obj: 0.505445, No Obj: 0.002251, .5R: 1.000000, .75R: 0.812500, count: 16 -Region 23 Avg IOU: 0.761540, Class: 0.169851, Obj: 0.222030, No Obj: 0.000495, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 16 Avg IOU: 0.809818, Class: 0.217010, Obj: 0.448303, No Obj: 0.001663, .5R: 1.000000, .75R: 0.785714, count: 14 -Region 23 Avg IOU: 0.738785, Class: 0.187550, Obj: 0.227658, No Obj: 0.000693, .5R: 0.967742, .75R: 0.483871, count: 31 -Region 16 Avg IOU: 0.801735, Class: 0.221923, Obj: 0.511638, No Obj: 0.001946, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.780987, Class: 0.151310, Obj: 0.249718, No Obj: 0.000560, .5R: 0.958333, .75R: 0.750000, count: 24 - - 3803: 1.944355, 1.867104 avg loss, 0.001000 rate, 0.860873 seconds, 486784 images -Loaded: 0.000069 seconds -Region 16 Avg IOU: 0.773314, Class: 0.184254, Obj: 0.487592, No Obj: 0.001882, .5R: 0.916667, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.767659, Class: 0.140908, Obj: 0.150373, No Obj: 0.000289, .5R: 1.000000, .75R: 0.714286, count: 14 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Class: 0.175313, Obj: 0.444305, No Obj: 0.000826, .5R: 1.000000, .75R: 0.500000, count: 4 -Region 23 Avg IOU: 0.711883, Class: 0.161679, Obj: 0.086624, No Obj: 0.000331, .5R: 1.000000, .75R: 0.392857, count: 28 -Region 16 Avg IOU: 0.751509, Class: 0.209066, Obj: 0.300225, No Obj: 0.002878, .5R: 0.956522, .75R: 0.608696, count: 23 -Region 23 Avg IOU: 0.667133, Class: 0.223592, Obj: 0.097188, No Obj: 0.000576, .5R: 0.800000, .75R: 0.428571, count: 35 -Region 16 Avg IOU: 0.713175, Class: 0.230467, Obj: 0.405837, No Obj: 0.001921, .5R: 0.937500, .75R: 0.625000, count: 16 -Region 23 Avg IOU: 0.733932, Class: 0.147322, Obj: 0.251996, No Obj: 0.000564, .5R: 0.961538, .75R: 0.461538, count: 26 - - 3804: 2.109477, 1.891341 avg loss, 0.001000 rate, 0.866431 seconds, 486912 images -Loaded: 0.000088 seconds -Region 16 Avg IOU: 0.789145, Class: 0.220709, Obj: 0.315885, No Obj: 0.001652, .5R: 0.923077, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.777430, Class: 0.171500, Obj: 0.250958, No Obj: 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10 -Region 23 Avg IOU: 0.798985, Class: 0.166102, Obj: 0.390324, No Obj: 0.001131, .5R: 1.000000, .75R: 0.652174, count: 23 -Region 16 Avg IOU: 0.830824, Class: 0.289962, Obj: 0.449208, No Obj: 0.001216, .5R: 1.000000, .75R: 0.777778, count: 9 -Region 23 Avg IOU: 0.643660, Class: 0.170930, Obj: 0.499240, No Obj: 0.000712, .5R: 0.722222, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.740658, Class: 0.172125, Obj: 0.351303, No Obj: 0.001394, .5R: 0.857143, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.703925, Class: 0.204194, Obj: 0.326852, No Obj: 0.000900, .5R: 0.892857, .75R: 0.500000, count: 28 -Region 16 Avg IOU: 0.713299, Class: 0.227967, Obj: 0.422053, No Obj: 0.001038, .5R: 1.000000, .75R: 0.285714, count: 7 -Region 23 Avg IOU: 0.721602, Class: 0.216898, Obj: 0.357903, No Obj: 0.000615, .5R: 1.000000, .75R: 0.333333, count: 9 - - 3807: 1.883732, 1.913084 avg loss, 0.001000 rate, 0.864173 seconds, 487296 images -Loaded: 0.000061 seconds -Region 16 Avg IOU: 0.755118, Class: 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seconds -Region 16 Avg IOU: 0.750964, Class: 0.279858, Obj: 0.368482, No Obj: 0.001973, .5R: 0.933333, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.774616, Class: 0.218817, Obj: 0.451120, No Obj: 0.000720, .5R: 0.875000, .75R: 0.750000, count: 16 -Region 16 Avg IOU: 0.725241, Class: 0.244152, Obj: 0.398476, No Obj: 0.002011, .5R: 0.941176, .75R: 0.411765, count: 17 -Region 23 Avg IOU: 0.758926, Class: 0.220202, Obj: 0.194208, No Obj: 0.000661, .5R: 1.000000, .75R: 0.521739, count: 23 -Region 16 Avg IOU: 0.806873, Class: 0.348299, Obj: 0.622418, No Obj: 0.001535, .5R: 0.900000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.787602, Class: 0.222102, Obj: 0.385052, No Obj: 0.000789, .5R: 1.000000, .75R: 0.791667, count: 24 -Region 16 Avg IOU: 0.765768, Class: 0.156150, Obj: 0.231552, No Obj: 0.001514, .5R: 1.000000, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.690727, Class: 0.150077, Obj: 0.186981, No Obj: 0.000515, .5R: 0.880000, .75R: 0.400000, count: 25 -Region 16 Avg IOU: 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0.867858 seconds, 487552 images -Loaded: 0.000065 seconds -Region 16 Avg IOU: 0.741051, Class: 0.249767, Obj: 0.294104, No Obj: 0.002279, .5R: 1.000000, .75R: 0.684211, count: 19 -Region 23 Avg IOU: 0.671965, Class: 0.239224, Obj: 0.140154, No Obj: 0.000568, .5R: 0.857143, .75R: 0.400000, count: 35 -Region 16 Avg IOU: 0.807122, Class: 0.258867, Obj: 0.400552, No Obj: 0.001575, .5R: 1.000000, .75R: 0.818182, count: 11 -Region 23 Avg IOU: 0.727209, Class: 0.142166, Obj: 0.206480, No Obj: 0.000341, .5R: 0.928571, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.800709, Class: 0.160949, Obj: 0.355017, No Obj: 0.001412, .5R: 1.000000, .75R: 0.818182, count: 11 -Region 23 Avg IOU: 0.760915, Class: 0.182522, Obj: 0.292461, No Obj: 0.000501, .5R: 0.923077, .75R: 0.692308, count: 13 -Region 16 Avg IOU: 0.776850, Class: 0.319622, Obj: 0.408980, No Obj: 0.001533, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.738017, Class: 0.239696, Obj: 0.215115, No Obj: 0.000609, .5R: 0.920000, 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3810: 1.966567, 1.925915 avg loss, 0.001000 rate, 0.860954 seconds, 487680 images -Resizing -736 x 736 - try to allocate workspace = 1149517825 * sizeof(float), CUDA allocate done! -Loaded: 0.403166 seconds -Region 16 Avg IOU: 0.820878, Class: 0.167617, Obj: 0.393598, No Obj: 0.001149, .5R: 1.000000, .75R: 0.882353, count: 17 -Region 23 Avg IOU: 0.747418, Class: 0.067665, Obj: 0.215455, No Obj: 0.000149, .5R: 0.909091, .75R: 0.454545, count: 11 -Region 16 Avg IOU: 0.657203, Class: 0.227858, Obj: 0.149182, No Obj: 0.001157, .5R: 0.823529, .75R: 0.294118, count: 17 -Region 23 Avg IOU: 0.674940, Class: 0.195196, Obj: 0.054816, No Obj: 0.000097, .5R: 0.888889, .75R: 0.222222, count: 9 -Region 16 Avg IOU: 0.683880, Class: 0.197157, Obj: 0.382193, No Obj: 0.001619, .5R: 0.928571, .75R: 0.392857, count: 28 -Region 23 Avg IOU: 0.751590, Class: 0.167099, Obj: 0.187816, No Obj: 0.000151, .5R: 0.954545, .75R: 0.500000, count: 22 -Region 16 Avg IOU: 0.696321, Class: 0.251685, Obj: 0.354788, No 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Obj: 0.359739, No Obj: 0.000849, .5R: 0.960000, .75R: 0.560000, count: 25 -Region 23 Avg IOU: 0.754337, Class: 0.199745, Obj: 0.237159, No Obj: 0.000218, .5R: 1.000000, .75R: 0.466667, count: 15 - - 3812: 2.620536, 2.046588 avg loss, 0.001000 rate, 3.050611 seconds, 487936 images -Loaded: 0.000074 seconds -Region 16 Avg IOU: 0.737537, Class: 0.212046, Obj: 0.202063, No Obj: 0.000691, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 23 Avg IOU: 0.708671, Class: 0.201903, Obj: 0.122487, No Obj: 0.000211, .5R: 1.000000, .75R: 0.272727, count: 11 -Region 16 Avg IOU: 0.799696, Class: 0.237718, Obj: 0.328786, No Obj: 0.000993, .5R: 1.000000, .75R: 0.764706, count: 17 -Region 23 Avg IOU: 0.745259, Class: 0.209569, Obj: 0.235885, No Obj: 0.000256, .5R: 0.958333, .75R: 0.583333, count: 24 -Region 16 Avg IOU: 0.729694, Class: 0.241739, Obj: 0.263817, No Obj: 0.000875, .5R: 0.952381, .75R: 0.428571, count: 21 -Region 23 Avg IOU: 0.685342, Class: 0.258780, Obj: 0.111921, No Obj: 0.000152, .5R: 0.941176, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.747138, Class: 0.210095, Obj: 0.239398, No Obj: 0.001023, .5R: 0.878788, .75R: 0.636364, count: 33 -Region 23 Avg IOU: 0.717727, Class: 0.199631, Obj: 0.182736, No Obj: 0.000229, .5R: 1.000000, .75R: 0.368421, count: 19 -Region 16 Avg IOU: 0.680388, Class: 0.219244, Obj: 0.220615, No Obj: 0.000989, .5R: 0.894737, .75R: 0.421053, count: 19 -Region 23 Avg IOU: 0.700413, Class: 0.154774, Obj: 0.068602, No Obj: 0.000134, .5R: 1.000000, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.719921, Class: 0.242853, Obj: 0.169417, No Obj: 0.000898, .5R: 0.880000, .75R: 0.480000, count: 25 -Region 23 Avg IOU: 0.721561, Class: 0.246173, Obj: 0.140722, No Obj: 0.000148, .5R: 0.947368, .75R: 0.421053, count: 19 -Region 16 Avg IOU: 0.732234, Class: 0.285554, Obj: 0.250594, No Obj: 0.000954, .5R: 0.933333, .75R: 0.466667, count: 15 -Region 23 Avg IOU: 0.764410, Class: 0.145157, Obj: 0.187400, No Obj: 0.000286, .5R: 1.000000, .75R: 0.588235, count: 17 -Region 16 Avg IOU: 0.725270, Class: 0.214515, Obj: 0.176087, No Obj: 0.001252, .5R: 0.941176, .75R: 0.529412, count: 34 -Region 23 Avg IOU: 0.746080, Class: 0.211963, Obj: 0.193318, No Obj: 0.000229, .5R: 0.941176, .75R: 0.529412, count: 17 - - 3813: 2.726542, 2.114583 avg loss, 0.001000 rate, 3.067547 seconds, 488064 images -Loaded: 0.000069 seconds -Region 16 Avg IOU: 0.746965, Class: 0.205435, Obj: 0.225214, No Obj: 0.001046, .5R: 0.896552, .75R: 0.620690, count: 29 -Region 23 Avg IOU: 0.688335, Class: 0.177579, Obj: 0.066027, No Obj: 0.000176, .5R: 0.900000, .75R: 0.500000, count: 10 -Region 16 Avg IOU: 0.729404, Class: 0.199987, Obj: 0.233771, No Obj: 0.000749, .5R: 1.000000, .75R: 0.523810, count: 21 -Region 23 Avg IOU: 0.748274, Class: 0.165606, Obj: 0.205525, No Obj: 0.000202, .5R: 1.000000, .75R: 0.529412, count: 17 -Region 16 Avg IOU: 0.764530, Class: 0.211291, Obj: 0.209205, No Obj: 0.000930, .5R: 1.000000, .75R: 0.631579, count: 19 -Region 23 Avg IOU: 0.761324, Class: 0.252400, Obj: 0.209925, No Obj: 0.000189, .5R: 1.000000, .75R: 0.550000, count: 20 -Region 16 Avg IOU: 0.760351, Class: 0.175263, Obj: 0.272740, No Obj: 0.000924, .5R: 0.961538, .75R: 0.576923, count: 26 -Region 23 Avg IOU: 0.753462, Class: 0.107902, Obj: 0.210884, No Obj: 0.000203, .5R: 0.913043, .75R: 0.652174, count: 23 -Region 16 Avg IOU: 0.725079, Class: 0.225700, Obj: 0.211471, No Obj: 0.001225, .5R: 0.948718, .75R: 0.410256, count: 39 -Region 23 Avg IOU: 0.728095, Class: 0.196098, Obj: 0.162539, No Obj: 0.000185, .5R: 1.000000, .75R: 0.517241, count: 29 -Region 16 Avg IOU: 0.769431, Class: 0.256556, Obj: 0.290869, No Obj: 0.001043, .5R: 0.958333, .75R: 0.708333, count: 24 -Region 23 Avg IOU: 0.752205, Class: 0.194986, Obj: 0.244224, No Obj: 0.000223, .5R: 0.947368, .75R: 0.578947, count: 19 -Region 16 Avg IOU: 0.741205, Class: 0.215164, Obj: 0.289843, No Obj: 0.000837, .5R: 0.961538, .75R: 0.538462, count: 26 -Region 23 Avg IOU: 0.697854, Class: 0.197052, Obj: 0.215436, No Obj: 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count: 28 -Region 23 Avg IOU: 0.786574, Class: 0.170485, Obj: 0.264063, No Obj: 0.000151, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 16 Avg IOU: 0.782428, Class: 0.215039, Obj: 0.388543, No Obj: 0.001325, .5R: 1.000000, .75R: 0.760000, count: 25 -Region 23 Avg IOU: 0.726448, Class: 0.174434, Obj: 0.355415, No Obj: 0.000253, .5R: 1.000000, .75R: 0.600000, count: 15 -Region 16 Avg IOU: 0.710061, Class: 0.165832, Obj: 0.285709, No Obj: 0.001148, .5R: 0.900000, .75R: 0.450000, count: 20 -Region 23 Avg IOU: 0.700129, Class: 0.173464, Obj: 0.180563, No Obj: 0.000179, .5R: 0.846154, .75R: 0.461538, count: 13 -Region 16 Avg IOU: 0.735579, Class: 0.298754, Obj: 0.376966, No Obj: 0.000963, .5R: 0.882353, .75R: 0.647059, count: 17 -Region 23 Avg IOU: 0.723283, Class: 0.164777, Obj: 0.284594, No Obj: 0.000269, .5R: 0.909091, .75R: 0.363636, count: 11 -Region 16 Avg IOU: 0.737726, Class: 0.240192, Obj: 0.370472, No Obj: 0.001409, .5R: 0.937500, .75R: 0.500000, count: 16 -Region 23 Avg IOU: 0.723083, Class: 0.168013, Obj: 0.415248, No Obj: 0.000205, .5R: 0.900000, .75R: 0.500000, count: 10 -Region 16 Avg IOU: 0.714993, Class: 0.270958, Obj: 0.383279, No Obj: 0.001135, .5R: 0.913043, .75R: 0.521739, count: 23 -Region 23 Avg IOU: 0.727787, Class: 0.200896, Obj: 0.225535, No Obj: 0.000291, .5R: 0.954545, .75R: 0.454545, count: 22 - - 3815: 2.150701, 2.214218 avg loss, 0.001000 rate, 3.279258 seconds, 488320 images -Loaded: 0.000058 seconds -Region 16 Avg IOU: 0.736923, Class: 0.261569, Obj: 0.458780, No Obj: 0.001069, .5R: 0.944444, .75R: 0.555556, count: 18 -Region 23 Avg IOU: 0.746812, Class: 0.178176, Obj: 0.383406, No Obj: 0.000145, .5R: 1.000000, .75R: 0.400000, count: 10 -Region 16 Avg IOU: 0.755466, Class: 0.178403, Obj: 0.412517, No Obj: 0.001777, .5R: 1.000000, .75R: 0.592593, count: 27 -Region 23 Avg IOU: 0.772756, Class: 0.138424, Obj: 0.241932, No Obj: 0.000295, .5R: 1.000000, .75R: 0.636364, count: 22 -Region 16 Avg IOU: 0.756384, Class: 0.224912, Obj: 0.317582, 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.75R: 0.312500, count: 16 -Region 23 Avg IOU: 0.763965, Class: 0.150465, Obj: 0.145465, No Obj: 0.000181, .5R: 1.000000, .75R: 0.705882, count: 17 -Region 16 Avg IOU: 0.707106, Class: 0.215414, Obj: 0.423209, No Obj: 0.001656, .5R: 0.933333, .75R: 0.500000, count: 30 -Region 23 Avg IOU: 0.744408, Class: 0.189694, Obj: 0.272870, No Obj: 0.000405, .5R: 1.000000, .75R: 0.482759, count: 29 - - 3816: 2.630365, 2.255833 avg loss, 0.001000 rate, 3.151702 seconds, 488448 images -Loaded: 0.000068 seconds -Region 16 Avg IOU: 0.794629, Class: 0.254822, Obj: 0.396075, No Obj: 0.001572, .5R: 1.000000, .75R: 0.724138, count: 29 -Region 23 Avg IOU: 0.763136, Class: 0.208131, Obj: 0.206295, No Obj: 0.000259, .5R: 1.000000, .75R: 0.555556, count: 27 -Region 16 Avg IOU: 0.700269, Class: 0.223885, Obj: 0.247281, No Obj: 0.001346, .5R: 0.846154, .75R: 0.461538, count: 26 -Region 23 Avg IOU: 0.728391, Class: 0.163348, Obj: 0.143765, No Obj: 0.000143, .5R: 0.923077, .75R: 0.384615, count: 13 -Region 16 Avg IOU: 0.758764, Class: 0.208598, Obj: 0.436806, No Obj: 0.001348, .5R: 1.000000, .75R: 0.500000, count: 22 -Region 23 Avg IOU: 0.773135, Class: 0.224082, Obj: 0.197258, No Obj: 0.000128, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 16 Avg IOU: 0.790739, Class: 0.279270, Obj: 0.382653, No Obj: 0.001300, .5R: 1.000000, .75R: 0.687500, count: 16 -Region 23 Avg IOU: 0.736848, Class: 0.287482, Obj: 0.176978, No Obj: 0.000114, .5R: 1.000000, .75R: 0.444444, count: 9 -Region 16 Avg IOU: 0.728963, Class: 0.294329, Obj: 0.536989, No Obj: 0.001500, .5R: 0.961538, .75R: 0.461538, count: 26 -Region 23 Avg IOU: 0.738329, Class: 0.190218, Obj: 0.175880, No Obj: 0.000133, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 16 Avg IOU: 0.704815, Class: 0.284240, Obj: 0.388764, No Obj: 0.001569, .5R: 0.892857, .75R: 0.500000, count: 28 -Region 23 Avg IOU: 0.763937, Class: 0.246084, Obj: 0.267253, No Obj: 0.000215, .5R: 1.000000, .75R: 0.619048, count: 21 -Region 16 Avg IOU: 0.750957, Class: 0.217745, 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1.000000, .75R: 0.647059, count: 17 -Region 16 Avg IOU: 0.747975, Class: 0.192302, Obj: 0.289642, No Obj: 0.000888, .5R: 1.000000, .75R: 0.571429, count: 21 -Region 23 Avg IOU: 0.770767, Class: 0.135780, Obj: 0.138664, No Obj: 0.000169, .5R: 1.000000, .75R: 0.583333, count: 24 -Region 16 Avg IOU: 0.726705, Class: 0.203353, Obj: 0.252625, No Obj: 0.001201, .5R: 0.900000, .75R: 0.500000, count: 30 -Region 23 Avg IOU: 0.724978, Class: 0.166192, Obj: 0.054637, No Obj: 0.000169, .5R: 1.000000, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.730621, Class: 0.206079, Obj: 0.262300, No Obj: 0.001070, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.705277, Class: 0.268378, Obj: 0.104314, No Obj: 0.000156, .5R: 0.916667, .75R: 0.416667, count: 12 -Region 16 Avg IOU: 0.785847, Class: 0.149262, Obj: 0.283695, No Obj: 0.001068, .5R: 1.000000, .75R: 0.714286, count: 21 -Region 23 Avg IOU: 0.712121, Class: 0.171918, Obj: 0.152072, No Obj: 0.000147, .5R: 0.941176, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.726501, Class: 0.202770, Obj: 0.160522, No Obj: 0.001308, .5R: 0.953488, .75R: 0.511628, count: 43 -Region 23 Avg IOU: 0.733381, Class: 0.196864, Obj: 0.118699, No Obj: 0.000157, .5R: 0.875000, .75R: 0.562500, count: 16 -Region 16 Avg IOU: 0.686843, Class: 0.279062, Obj: 0.181907, No Obj: 0.001093, .5R: 0.956522, .75R: 0.347826, count: 23 -Region 23 Avg IOU: 0.749346, Class: 0.224384, Obj: 0.180264, No Obj: 0.000286, .5R: 0.968750, .75R: 0.562500, count: 32 -Region 16 Avg IOU: 0.754693, Class: 0.207310, Obj: 0.297880, No Obj: 0.000913, .5R: 0.956522, .75R: 0.608696, count: 23 -Region 23 Avg IOU: 0.695888, Class: 0.182146, Obj: 0.198073, No Obj: 0.000156, .5R: 0.947368, .75R: 0.315789, count: 19 - - 3839: 3.050757, 2.601995 avg loss, 0.001000 rate, 2.939296 seconds, 491392 images -Loaded: 0.000073 seconds -Region 16 Avg IOU: 0.759355, Class: 0.229136, Obj: 0.322402, No Obj: 0.001080, .5R: 1.000000, .75R: 0.590909, count: 22 -Region 23 Avg IOU: 0.760459, Class: 0.197129, Obj: 0.246449, No Obj: 0.000196, .5R: 1.000000, .75R: 0.571429, count: 21 -Region 16 Avg IOU: 0.657935, Class: 0.170945, Obj: 0.347513, No Obj: 0.001068, .5R: 0.800000, .75R: 0.350000, count: 20 -Region 23 Avg IOU: 0.689919, Class: 0.189989, Obj: 0.088937, No Obj: 0.000158, .5R: 0.923077, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.761765, Class: 0.128971, Obj: 0.419178, No Obj: 0.001272, .5R: 1.000000, .75R: 0.550000, count: 20 -Region 23 Avg IOU: 0.754755, Class: 0.147025, Obj: 0.188867, No Obj: 0.000190, .5R: 0.950000, .75R: 0.550000, count: 20 -Region 16 Avg IOU: 0.742500, Class: 0.208319, Obj: 0.329254, No Obj: 0.001397, .5R: 0.960000, .75R: 0.600000, count: 25 -Region 23 Avg IOU: 0.785502, Class: 0.187937, Obj: 0.186339, No Obj: 0.000253, .5R: 1.000000, .75R: 0.666667, count: 21 -Region 16 Avg IOU: 0.744613, Class: 0.203565, Obj: 0.395203, No Obj: 0.001203, .5R: 1.000000, .75R: 0.565217, count: 23 -Region 23 Avg IOU: 0.744714, Class: 0.207093, Obj: 0.207955, No Obj: 0.000150, .5R: 0.933333, .75R: 0.666667, count: 15 -Region 16 Avg IOU: 0.714376, Class: 0.198815, Obj: 0.278654, No Obj: 0.001740, .5R: 0.914286, .75R: 0.485714, count: 35 -Region 23 Avg IOU: 0.722505, Class: 0.124906, Obj: 0.205921, No Obj: 0.000240, .5R: 0.928571, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.737969, Class: 0.248188, Obj: 0.237591, No Obj: 0.001412, .5R: 0.931035, .75R: 0.448276, count: 29 -Region 23 Avg IOU: 0.698210, Class: 0.157386, Obj: 0.056527, No Obj: 0.000198, .5R: 1.000000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.734743, Class: 0.257197, Obj: 0.421710, No Obj: 0.001566, .5R: 0.952381, .75R: 0.428571, count: 21 -Region 23 Avg IOU: 0.692385, Class: 0.177221, Obj: 0.216041, No Obj: 0.000254, .5R: 0.880000, .75R: 0.400000, count: 25 - - 3840: 2.763978, 2.618193 avg loss, 0.001000 rate, 3.307587 seconds, 491520 images -Resizing -544 x 544 - try to allocate workspace = 1145585665 * sizeof(float), CUDA allocate done! -Loaded: 0.430750 seconds -Region 16 Avg IOU: 0.772149, Class: 0.263680, Obj: 0.465734, No Obj: 0.001476, .5R: 0.941176, .75R: 0.588235, count: 17 -Region 23 Avg IOU: 0.736514, Class: 0.259877, Obj: 0.213086, No Obj: 0.000293, .5R: 0.941176, .75R: 0.529412, count: 17 -Region 16 Avg IOU: 0.764928, Class: 0.181405, Obj: 0.359140, No Obj: 0.001426, .5R: 0.933333, .75R: 0.733333, count: 15 -Region 23 Avg IOU: 0.728767, Class: 0.189972, Obj: 0.286953, No Obj: 0.000483, .5R: 1.000000, .75R: 0.368421, count: 19 -Region 16 Avg IOU: 0.762135, Class: 0.188228, Obj: 0.495817, No Obj: 0.002517, .5R: 0.954545, .75R: 0.636364, count: 22 -Region 23 Avg IOU: 0.737597, Class: 0.185297, Obj: 0.354778, No Obj: 0.000517, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.812949, Class: 0.268656, Obj: 0.499315, No Obj: 0.002150, .5R: 1.000000, .75R: 0.736842, count: 19 -Region 23 Avg IOU: 0.766388, Class: 0.200161, Obj: 0.255895, No Obj: 0.000566, .5R: 1.000000, .75R: 0.571429, count: 28 -Region 16 Avg IOU: 0.810079, Class: 0.341542, Obj: 0.498375, No Obj: 0.001788, .5R: 1.000000, .75R: 0.818182, count: 11 -Region 23 Avg IOU: 0.743630, Class: 0.224133, Obj: 0.321225, No Obj: 0.000723, .5R: 0.961538, .75R: 0.500000, count: 26 -Region 16 Avg IOU: 0.764719, Class: 0.265463, Obj: 0.376869, No Obj: 0.002636, .5R: 0.933333, .75R: 0.700000, count: 30 -Region 23 Avg IOU: 0.714775, Class: 0.274057, Obj: 0.202211, No Obj: 0.000440, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.715846, Class: 0.222340, Obj: 0.295018, No Obj: 0.001923, .5R: 1.000000, .75R: 0.476190, count: 21 -Region 23 Avg IOU: 0.728337, Class: 0.243778, Obj: 0.066721, No Obj: 0.000355, .5R: 0.920000, .75R: 0.440000, count: 25 -Region 16 Avg IOU: 0.773598, Class: 0.215508, Obj: 0.447030, No Obj: 0.001818, .5R: 0.944444, .75R: 0.722222, count: 18 -Region 23 Avg IOU: 0.720158, Class: 0.182449, Obj: 0.302551, No Obj: 0.000396, .5R: 0.960000, .75R: 0.360000, count: 25 - - 3841: 2.462100, 2.602584 avg loss, 0.001000 rate, 1.958941 seconds, 491648 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.735125, Class: 0.243685, Obj: 0.338591, No Obj: 0.001293, .5R: 0.846154, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.741738, Class: 0.140385, Obj: 0.382778, No Obj: 0.000478, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 16 Avg IOU: 0.774513, Class: 0.218302, Obj: 0.346563, No Obj: 0.002117, .5R: 0.956522, .75R: 0.782609, count: 23 -Region 23 Avg IOU: 0.712528, Class: 0.171870, Obj: 0.363604, No Obj: 0.000520, .5R: 0.947368, .75R: 0.526316, count: 19 -Region 16 Avg IOU: 0.834208, Class: 0.246216, Obj: 0.585383, No Obj: 0.001520, .5R: 1.000000, .75R: 1.000000, count: 8 -Region 23 Avg IOU: 0.695153, Class: 0.241347, Obj: 0.403387, No Obj: 0.000646, .5R: 0.888889, .75R: 0.333333, count: 18 -Region 16 Avg IOU: 0.784279, Class: 0.244770, Obj: 0.534770, No Obj: 0.001474, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.731701, Class: 0.195272, Obj: 0.265009, No Obj: 0.000447, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.752271, Class: 0.247564, Obj: 0.425072, No Obj: 0.002110, .5R: 1.000000, .75R: 0.611111, count: 18 -Region 23 Avg IOU: 0.727201, Class: 0.173872, Obj: 0.244149, No Obj: 0.000538, .5R: 0.941176, .75R: 0.470588, count: 17 -Region 16 Avg IOU: 0.775008, Class: 0.201804, Obj: 0.342205, No Obj: 0.001576, .5R: 1.000000, .75R: 0.684211, count: 19 -Region 23 Avg IOU: 0.758362, Class: 0.143092, Obj: 0.213455, No Obj: 0.000481, .5R: 1.000000, .75R: 0.578947, count: 19 -Region 16 Avg IOU: 0.735430, Class: 0.249181, Obj: 0.499443, No Obj: 0.002319, .5R: 0.937500, .75R: 0.562500, count: 16 -Region 23 Avg IOU: 0.747853, Class: 0.192351, Obj: 0.277298, No Obj: 0.000686, .5R: 1.000000, .75R: 0.545455, count: 22 -Region 16 Avg IOU: 0.717405, Class: 0.165124, Obj: 0.547092, No Obj: 0.003191, .5R: 0.916667, .75R: 0.416667, count: 24 -Region 23 Avg IOU: 0.694063, Class: 0.218895, Obj: 0.311770, No Obj: 0.000969, .5R: 0.870968, .75R: 0.483871, count: 31 - - 3842: 2.320836, 2.574409 avg loss, 0.001000 rate, 2.008483 seconds, 491776 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.773670, Class: 0.146702, Obj: 0.378985, No Obj: 0.002254, .5R: 0.960000, .75R: 0.600000, count: 25 -Region 23 Avg IOU: 0.748177, Class: 0.163127, Obj: 0.227177, No Obj: 0.000682, .5R: 0.965517, .75R: 0.482759, count: 29 -Region 16 Avg IOU: 0.741837, Class: 0.235521, Obj: 0.487023, No Obj: 0.003528, .5R: 0.935484, .75R: 0.612903, count: 31 -Region 23 Avg IOU: 0.722142, Class: 0.163001, Obj: 0.197106, No Obj: 0.000573, .5R: 0.948718, .75R: 0.538462, count: 39 -Region 16 Avg IOU: 0.801587, Class: 0.297488, Obj: 0.593646, No Obj: 0.002702, .5R: 1.000000, .75R: 0.764706, count: 17 -Region 23 Avg IOU: 0.791872, Class: 0.245872, Obj: 0.394723, No Obj: 0.000606, .5R: 0.960000, .75R: 0.720000, count: 25 -Region 16 Avg IOU: 0.756491, Class: 0.234056, Obj: 0.388196, No Obj: 0.001840, .5R: 1.000000, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.681077, Class: 0.193827, Obj: 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0.916667, .75R: 0.458333, count: 24 - - 3843: 2.672461, 2.584214 avg loss, 0.001000 rate, 1.927188 seconds, 491904 images -Loaded: 0.000080 seconds -Region 16 Avg IOU: 0.724776, Class: 0.220514, Obj: 0.359412, No Obj: 0.002123, .5R: 0.904762, .75R: 0.476190, count: 21 -Region 23 Avg IOU: 0.729155, Class: 0.205425, Obj: 0.216521, No Obj: 0.000474, .5R: 0.964286, .75R: 0.464286, count: 28 -Region 16 Avg IOU: 0.744610, Class: 0.223452, Obj: 0.417728, No Obj: 0.001414, .5R: 0.937500, .75R: 0.562500, count: 16 -Region 23 Avg IOU: 0.702295, Class: 0.165399, Obj: 0.185765, No Obj: 0.000337, .5R: 0.913043, .75R: 0.521739, count: 23 -Region 16 Avg IOU: 0.708753, Class: 0.190891, Obj: 0.509462, No Obj: 0.001141, .5R: 1.000000, .75R: 0.400000, count: 5 -Region 23 Avg IOU: 0.749227, Class: 0.146723, Obj: 0.221404, No Obj: 0.000226, .5R: 0.888889, .75R: 0.444444, count: 9 -Region 16 Avg IOU: 0.730212, Class: 0.224979, Obj: 0.229431, No Obj: 0.001780, .5R: 0.916667, .75R: 0.500000, count: 24 -Region 23 Avg IOU: 0.715485, Class: 0.211431, Obj: 0.213444, No Obj: 0.000285, .5R: 0.894737, .75R: 0.473684, count: 19 -Region 16 Avg IOU: 0.736487, Class: 0.219673, Obj: 0.415489, No Obj: 0.001632, .5R: 0.933333, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.769340, Class: 0.214828, Obj: 0.184347, No Obj: 0.000311, .5R: 0.958333, .75R: 0.583333, count: 24 -Region 16 Avg IOU: 0.778779, Class: 0.230550, Obj: 0.455079, No Obj: 0.002221, .5R: 1.000000, .75R: 0.652174, count: 23 -Region 23 Avg IOU: 0.724746, Class: 0.209765, Obj: 0.190980, No Obj: 0.000241, .5R: 0.947368, .75R: 0.473684, count: 19 -Region 16 Avg IOU: 0.737509, Class: 0.262460, Obj: 0.216928, No Obj: 0.001090, .5R: 0.875000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.707718, Class: 0.170178, Obj: 0.139717, No Obj: 0.000251, .5R: 0.928571, .75R: 0.285714, count: 14 -Region 16 Avg IOU: 0.724437, Class: 0.160334, Obj: 0.525010, No Obj: 0.001623, .5R: 0.923077, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.724652, Class: 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-Region 16 Avg IOU: 0.757111, Class: 0.215834, Obj: 0.200548, No Obj: 0.000764, .5R: 0.909091, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.711289, Class: 0.192905, Obj: 0.176443, No Obj: 0.000384, .5R: 0.904762, .75R: 0.380952, count: 21 - - 3847: 2.187891, 2.512019 avg loss, 0.001000 rate, 1.462627 seconds, 492416 images -Loaded: 0.000077 seconds -Region 16 Avg IOU: 0.761680, Class: 0.212271, Obj: 0.360530, No Obj: 0.000842, .5R: 0.923077, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.746125, Class: 0.198335, Obj: 0.261309, No Obj: 0.000579, .5R: 1.000000, .75R: 0.521739, count: 23 -Region 16 Avg IOU: 0.758452, Class: 0.232135, Obj: 0.343093, No Obj: 0.001462, .5R: 1.000000, .75R: 0.565217, count: 23 -Region 23 Avg IOU: 0.720323, Class: 0.183295, Obj: 0.369595, No Obj: 0.000505, .5R: 0.947368, .75R: 0.473684, count: 19 -Region 16 Avg IOU: 0.741901, Class: 0.296215, Obj: 0.416055, No Obj: 0.001184, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.763911, Class: 0.144676, Obj: 0.283864, No Obj: 0.000457, .5R: 1.000000, .75R: 0.705882, count: 17 -Region 16 Avg IOU: 0.720003, Class: 0.242174, Obj: 0.272197, No Obj: 0.000830, .5R: 0.857143, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.706121, Class: 0.123410, Obj: 0.287800, No Obj: 0.000389, .5R: 1.000000, .75R: 0.272727, count: 11 -Region 16 Avg IOU: 0.739134, Class: 0.217203, Obj: 0.311284, No Obj: 0.001252, .5R: 0.947368, .75R: 0.526316, count: 19 -Region 23 Avg IOU: 0.731309, Class: 0.188183, Obj: 0.341891, No Obj: 0.000580, .5R: 1.000000, .75R: 0.388889, count: 18 -Region 16 Avg IOU: 0.731926, Class: 0.246599, Obj: 0.285714, No Obj: 0.001278, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 23 Avg IOU: 0.695834, Class: 0.244495, Obj: 0.247278, No Obj: 0.000617, .5R: 0.869565, .75R: 0.478261, count: 23 -Region 16 Avg IOU: 0.755232, Class: 0.228297, Obj: 0.247062, No Obj: 0.001532, .5R: 0.866667, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.744807, Class: 0.175975, Obj: 0.313797, No Obj: 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count: 31 -Region 23 Avg IOU: 0.706150, Class: 0.206359, Obj: 0.204380, No Obj: 0.000698, .5R: 0.975610, .75R: 0.439024, count: 41 -Region 16 Avg IOU: 0.723159, Class: 0.274810, Obj: 0.320613, No Obj: 0.001711, .5R: 0.909091, .75R: 0.545455, count: 22 -Region 23 Avg IOU: 0.741116, Class: 0.219402, Obj: 0.229816, No Obj: 0.000426, .5R: 1.000000, .75R: 0.454545, count: 22 -Region 16 Avg IOU: 0.832724, Class: 0.235830, Obj: 0.354481, No Obj: 0.001289, .5R: 1.000000, .75R: 0.833333, count: 12 -Region 23 Avg IOU: 0.794859, Class: 0.168932, Obj: 0.407379, No Obj: 0.000415, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 16 Avg IOU: 0.762167, Class: 0.218121, Obj: 0.319756, No Obj: 0.001253, .5R: 1.000000, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.719286, Class: 0.156432, Obj: 0.202229, No Obj: 0.000537, .5R: 0.933333, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.778416, Class: 0.206337, Obj: 0.324157, No Obj: 0.001450, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.741075, Class: 0.234595, Obj: 0.338940, No Obj: 0.000567, .5R: 1.000000, .75R: 0.520000, count: 25 -Region 16 Avg IOU: 0.794380, Class: 0.226179, Obj: 0.393222, No Obj: 0.001406, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.738022, Class: 0.166553, Obj: 0.295373, No Obj: 0.000524, .5R: 0.909091, .75R: 0.500000, count: 22 - - 3849: 2.359424, 2.455853 avg loss, 0.001000 rate, 1.494262 seconds, 492672 images -Loaded: 0.000071 seconds -Region 16 Avg IOU: 0.750852, Class: 0.186237, Obj: 0.510907, No Obj: 0.002061, .5R: 0.954545, .75R: 0.545455, count: 22 -Region 23 Avg IOU: 0.704706, Class: 0.192713, Obj: 0.195842, No Obj: 0.000704, .5R: 0.933333, .75R: 0.422222, count: 45 -Region 16 Avg IOU: 0.818208, Class: 0.252165, Obj: 0.569564, No Obj: 0.001996, .5R: 1.000000, .75R: 0.750000, count: 16 -Region 23 Avg IOU: 0.795839, Class: 0.196967, Obj: 0.421670, No Obj: 0.000429, .5R: 1.000000, .75R: 0.733333, count: 15 -Region 16 Avg IOU: 0.814513, Class: 0.238411, Obj: 0.490308, 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0.157012, Obj: 0.189135, No Obj: 0.000430, .5R: 1.000000, .75R: 0.677419, count: 31 -Region 16 Avg IOU: 0.773239, Class: 0.233122, Obj: 0.431192, No Obj: 0.001322, .5R: 0.937500, .75R: 0.687500, count: 16 -Region 23 Avg IOU: 0.746260, Class: 0.231780, Obj: 0.268800, No Obj: 0.000246, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 16 Avg IOU: 0.744315, Class: 0.199669, Obj: 0.433602, No Obj: 0.001173, .5R: 0.933333, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.735063, Class: 0.242769, Obj: 0.257667, No Obj: 0.000155, .5R: 1.000000, .75R: 0.500000, count: 8 -Region 16 Avg IOU: 0.751715, Class: 0.235833, Obj: 0.358969, No Obj: 0.001237, .5R: 1.000000, .75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.722131, Class: 0.169200, Obj: 0.104079, No Obj: 0.000217, .5R: 0.937500, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.781554, Class: 0.185905, Obj: 0.369414, No Obj: 0.001586, .5R: 1.000000, .75R: 0.666667, count: 21 -Region 23 Avg IOU: 0.785227, Class: 0.122259, Obj: 0.259048, No Obj: 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images -Loaded: 0.000060 seconds -Region 16 Avg IOU: 0.763952, Class: 0.205675, Obj: 0.481809, No Obj: 0.000922, .5R: 1.000000, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.708801, Class: 0.167602, Obj: 0.239165, No Obj: 0.000220, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 16 Avg IOU: 0.672049, Class: 0.229372, Obj: 0.383617, No Obj: 0.000819, .5R: 0.888889, .75R: 0.444444, count: 9 -Region 23 Avg IOU: 0.735119, Class: 0.155020, Obj: 0.287936, No Obj: 0.000258, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 16 Avg IOU: 0.737447, Class: 0.201112, Obj: 0.382457, No Obj: 0.001592, .5R: 0.931035, .75R: 0.482759, count: 29 -Region 23 Avg IOU: 0.704368, Class: 0.236209, Obj: 0.277346, No Obj: 0.000231, .5R: 1.000000, .75R: 0.300000, count: 10 -Region 16 Avg IOU: 0.715995, Class: 0.208067, Obj: 0.376122, No Obj: 0.002210, .5R: 0.945946, .75R: 0.351351, count: 37 -Region 23 Avg IOU: 0.655898, Class: 0.183889, Obj: 0.107228, No Obj: 0.000308, .5R: 0.875000, .75R: 0.218750, count: 32 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25 -Region 23 Avg IOU: 0.756694, Class: 0.234442, Obj: 0.358295, No Obj: 0.000397, .5R: 0.956522, .75R: 0.652174, count: 23 -Region 16 Avg IOU: 0.664262, Class: 0.233018, Obj: 0.173347, No Obj: 0.001472, .5R: 0.807692, .75R: 0.423077, count: 26 -Region 23 Avg IOU: 0.638418, Class: 0.204853, Obj: 0.244215, No Obj: 0.000202, .5R: 0.750000, .75R: 0.416667, count: 12 - - 3866: 2.685340, 2.606898 avg loss, 0.001000 rate, 2.478769 seconds, 494848 images -Loaded: 0.000068 seconds -Region 16 Avg IOU: 0.769541, Class: 0.236439, Obj: 0.384233, No Obj: 0.001269, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.752377, Class: 0.184002, Obj: 0.264586, No Obj: 0.000289, .5R: 1.000000, .75R: 0.619048, count: 21 -Region 16 Avg IOU: 0.756294, Class: 0.257550, Obj: 0.417943, No Obj: 0.001831, .5R: 1.000000, .75R: 0.608696, count: 23 -Region 23 Avg IOU: 0.766648, Class: 0.211058, Obj: 0.271590, No Obj: 0.000264, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 16 Avg IOU: 0.744269, Class: 0.241313, Obj: 0.290260, No Obj: 0.001291, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.719700, Class: 0.251302, Obj: 0.205584, No Obj: 0.000233, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 16 Avg IOU: 0.773782, Class: 0.227629, Obj: 0.569378, No Obj: 0.001530, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.704148, Class: 0.117225, Obj: 0.163267, No Obj: 0.000179, .5R: 0.909091, .75R: 0.363636, count: 11 -Region 16 Avg IOU: 0.727184, Class: 0.295847, Obj: 0.364933, No Obj: 0.001557, .5R: 0.894737, .75R: 0.578947, count: 19 -Region 23 Avg IOU: 0.707413, Class: 0.193117, Obj: 0.169505, No Obj: 0.000324, .5R: 0.965517, .75R: 0.482759, count: 29 -Region 16 Avg IOU: 0.710840, Class: 0.232016, Obj: 0.284547, No Obj: 0.001518, .5R: 0.923077, .75R: 0.461538, count: 26 -Region 23 Avg IOU: 0.745724, Class: 0.188697, Obj: 0.201453, No Obj: 0.000325, .5R: 0.961538, .75R: 0.576923, count: 26 -Region 16 Avg IOU: 0.798171, Class: 0.219831, Obj: 0.506065, No Obj: 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count: 33 -Region 16 Avg IOU: 0.789197, Class: 0.282367, Obj: 0.401675, No Obj: 0.001519, .5R: 1.000000, .75R: 0.666667, count: 15 -Region 23 Avg IOU: 0.701791, Class: 0.123578, Obj: 0.245437, No Obj: 0.000374, .5R: 0.880000, .75R: 0.560000, count: 25 -Region 16 Avg IOU: 0.768367, Class: 0.267719, Obj: 0.277230, No Obj: 0.001841, .5R: 0.964286, .75R: 0.607143, count: 28 -Region 23 Avg IOU: 0.722228, Class: 0.175895, Obj: 0.143674, No Obj: 0.000294, .5R: 0.966667, .75R: 0.533333, count: 30 -Region 16 Avg IOU: 0.750409, Class: 0.326989, Obj: 0.416347, No Obj: 0.000953, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.716009, Class: 0.150324, Obj: 0.251566, No Obj: 0.000208, .5R: 0.933333, .75R: 0.400000, count: 15 -Region 16 Avg IOU: 0.717457, Class: 0.240489, Obj: 0.306017, No Obj: 0.001338, .5R: 1.000000, .75R: 0.473684, count: 19 -Region 23 Avg IOU: 0.712749, Class: 0.180882, Obj: 0.145072, No Obj: 0.000347, .5R: 0.900000, .75R: 0.500000, count: 30 -Region 16 Avg IOU: 0.775358, Class: 0.268906, Obj: 0.362527, No Obj: 0.001509, .5R: 0.958333, .75R: 0.625000, count: 24 -Region 23 Avg IOU: 0.713747, Class: 0.190765, Obj: 0.095466, No Obj: 0.000136, .5R: 0.937500, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.744848, Class: 0.255439, Obj: 0.367953, No Obj: 0.001452, .5R: 0.894737, .75R: 0.631579, count: 19 -Region 23 Avg IOU: 0.716810, Class: 0.145251, Obj: 0.201486, No Obj: 0.000212, .5R: 0.933333, .75R: 0.400000, count: 15 - - 3868: 2.718763, 2.612735 avg loss, 0.001000 rate, 2.359735 seconds, 495104 images -Loaded: 0.000072 seconds -Region 16 Avg IOU: 0.755128, Class: 0.263247, Obj: 0.265815, No Obj: 0.001328, .5R: 0.909091, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.685726, Class: 0.148600, Obj: 0.126484, No Obj: 0.000263, .5R: 0.850000, .75R: 0.400000, count: 20 -Region 16 Avg IOU: 0.704526, Class: 0.285760, Obj: 0.274481, No Obj: 0.001091, .5R: 0.888889, .75R: 0.444444, count: 18 -Region 23 Avg IOU: 0.733773, Class: 0.218388, Obj: 0.162468, 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count: 17 -Region 16 Avg IOU: 0.784372, Class: 0.281147, Obj: 0.346506, No Obj: 0.001321, .5R: 1.000000, .75R: 0.750000, count: 20 -Region 23 Avg IOU: 0.747150, Class: 0.165060, Obj: 0.194282, No Obj: 0.000309, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.713041, Class: 0.253485, Obj: 0.281255, No Obj: 0.001130, .5R: 0.909091, .75R: 0.454545, count: 22 -Region 23 Avg IOU: 0.670144, Class: 0.239633, Obj: 0.193091, No Obj: 0.000355, .5R: 0.875000, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.778143, Class: 0.188951, Obj: 0.303374, No Obj: 0.001222, .5R: 1.000000, .75R: 0.678571, count: 28 -Region 23 Avg IOU: 0.765089, Class: 0.193088, Obj: 0.271911, No Obj: 0.000278, .5R: 1.000000, .75R: 0.611111, count: 18 -Region 16 Avg IOU: 0.786711, Class: 0.219982, Obj: 0.275155, No Obj: 0.000989, .5R: 1.000000, .75R: 0.619048, count: 21 -Region 23 Avg IOU: 0.748054, Class: 0.223486, Obj: 0.206861, No Obj: 0.000223, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 16 Avg IOU: 0.786254, Class: 0.304076, Obj: 0.277972, No Obj: 0.000959, .5R: 1.000000, .75R: 0.764706, count: 17 -Region 23 Avg IOU: 0.639259, Class: 0.234255, Obj: 0.164441, No Obj: 0.000223, .5R: 0.866667, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.762703, Class: 0.185784, Obj: 0.328158, No Obj: 0.000907, .5R: 0.941176, .75R: 0.705882, count: 17 -Region 23 Avg IOU: 0.699784, Class: 0.136316, Obj: 0.261483, No Obj: 0.000307, .5R: 0.833333, .75R: 0.500000, count: 12 -Region 16 Avg IOU: 0.760243, Class: 0.280308, Obj: 0.288456, No Obj: 0.001123, .5R: 0.954545, .75R: 0.590909, count: 22 -Region 23 Avg IOU: 0.754626, Class: 0.167565, Obj: 0.247542, No Obj: 0.000351, .5R: 0.944444, .75R: 0.555556, count: 18 - - 3871: 2.360908, 2.538448 avg loss, 0.001000 rate, 2.185610 seconds, 495488 images -Loaded: 0.000086 seconds -Region 16 Avg IOU: 0.767392, Class: 0.253113, Obj: 0.298448, No Obj: 0.001007, .5R: 1.000000, .75R: 0.500000, count: 18 -Region 23 Avg IOU: 0.714678, Class: 0.177135, Obj: 0.150617, 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17 -Region 16 Avg IOU: 0.724044, Class: 0.187773, Obj: 0.364099, No Obj: 0.001888, .5R: 0.818182, .75R: 0.590909, count: 22 -Region 23 Avg IOU: 0.759501, Class: 0.185894, Obj: 0.285989, No Obj: 0.000596, .5R: 1.000000, .75R: 0.625000, count: 16 -Region 16 Avg IOU: 0.816634, Class: 0.187913, Obj: 0.233759, No Obj: 0.000937, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.751752, Class: 0.205948, Obj: 0.186634, No Obj: 0.000657, .5R: 1.000000, .75R: 0.583333, count: 24 -Region 16 Avg IOU: 0.777778, Class: 0.222412, Obj: 0.212633, No Obj: 0.001163, .5R: 1.000000, .75R: 0.714286, count: 14 -Region 23 Avg IOU: 0.727906, Class: 0.193499, Obj: 0.235318, No Obj: 0.000579, .5R: 0.944444, .75R: 0.555556, count: 18 - - 3890: 2.206321, 2.236435 avg loss, 0.001000 rate, 1.066557 seconds, 497920 images -Resizing -672 x 672 - try to allocate workspace = 1148076033 * sizeof(float), CUDA allocate done! -Loaded: 0.158578 seconds -Region 16 Avg IOU: 0.732090, Class: 0.228919, Obj: 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26 -Region 16 Avg IOU: 0.797956, Class: 0.258861, Obj: 0.534710, No Obj: 0.001451, .5R: 1.000000, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.690501, Class: 0.159327, Obj: 0.207580, No Obj: 0.000505, .5R: 0.818182, .75R: 0.363636, count: 11 -Region 16 Avg IOU: 0.841356, Class: 0.363090, Obj: 0.527152, No Obj: 0.000705, .5R: 1.000000, .75R: 0.800000, count: 5 -Region 23 Avg IOU: 0.724268, Class: 0.206674, Obj: 0.282726, No Obj: 0.000451, .5R: 0.916667, .75R: 0.500000, count: 12 -Region 16 Avg IOU: 0.752485, Class: 0.293672, Obj: 0.201669, No Obj: 0.002134, .5R: 1.000000, .75R: 0.500000, count: 12 -Region 23 Avg IOU: 0.724119, Class: 0.169688, Obj: 0.185358, No Obj: 0.000809, .5R: 0.968750, .75R: 0.406250, count: 32 - - 3906: 1.913815, 2.145146 avg loss, 0.001000 rate, 0.665682 seconds, 499968 images -Loaded: 0.041877 seconds -Region 16 Avg IOU: 0.695380, Class: 0.270306, Obj: 0.341189, No Obj: 0.001267, .5R: 0.857143, .75R: 0.285714, count: 7 -Region 23 Avg IOU: 0.646921, Class: 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0.187775, No Obj: 0.000983, .5R: 0.666667, .75R: 0.666667, count: 3 -Region 23 Avg IOU: 0.730520, Class: 0.145165, Obj: 0.247588, No Obj: 0.000643, .5R: 1.000000, .75R: 0.400000, count: 15 -Region 16 Avg IOU: 0.824598, Class: 0.230304, Obj: 0.477841, No Obj: 0.001804, .5R: 1.000000, .75R: 1.000000, count: 7 -Region 23 Avg IOU: 0.761948, Class: 0.167397, Obj: 0.261930, No Obj: 0.000897, .5R: 1.000000, .75R: 0.600000, count: 15 -Region 16 Avg IOU: 0.724447, Class: 0.247922, Obj: 0.212699, No Obj: 0.001169, .5R: 0.888889, .75R: 0.333333, count: 9 -Region 23 Avg IOU: 0.679858, Class: 0.210472, Obj: 0.252332, No Obj: 0.000749, .5R: 0.900000, .75R: 0.250000, count: 20 -Region 16 Avg IOU: 0.842760, Class: 0.266308, Obj: 0.616170, No Obj: 0.002308, .5R: 1.000000, .75R: 0.909091, count: 11 -Region 23 Avg IOU: 0.730649, Class: 0.243588, Obj: 0.305050, No Obj: 0.000832, .5R: 0.863636, .75R: 0.545455, count: 22 - - 3910: 1.781165, 1.986810 avg loss, 0.001000 rate, 0.638348 seconds, 500480 images -Resizing -480 x 480 - try to allocate workspace = 1144537089 * sizeof(float), CUDA allocate done! -Loaded: 0.341769 seconds -Region 16 Avg IOU: 0.622961, Class: 0.206842, Obj: 0.212637, No Obj: 0.001703, .5R: 0.750000, .75R: 0.083333, count: 12 -Region 23 Avg IOU: 0.704181, Class: 0.200346, Obj: 0.171153, No Obj: 0.000447, .5R: 1.000000, .75R: 0.476190, count: 21 -Region 16 Avg IOU: 0.799586, Class: 0.331126, Obj: 0.616281, No Obj: 0.001935, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.747376, Class: 0.199370, Obj: 0.165342, No Obj: 0.000315, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 16 Avg IOU: 0.784494, Class: 0.204768, Obj: 0.295308, No Obj: 0.000878, .5R: 1.000000, .75R: 0.545455, count: 11 -Region 23 Avg IOU: 0.651248, Class: 0.173515, Obj: 0.139890, No Obj: 0.000332, .5R: 0.818182, .75R: 0.409091, count: 22 -Region 16 Avg IOU: 0.668677, Class: 0.278587, Obj: 0.130085, No Obj: 0.000996, .5R: 0.916667, .75R: 0.250000, count: 12 -Region 23 Avg IOU: 0.689700, Class: 0.180580, Obj: 0.184580, No Obj: 0.000232, .5R: 0.933333, .75R: 0.266667, count: 15 -Region 16 Avg IOU: 0.661198, Class: 0.258535, Obj: 0.323761, No Obj: 0.001999, .5R: 0.882353, .75R: 0.176471, count: 17 -Region 23 Avg IOU: 0.678823, Class: 0.227090, Obj: 0.118002, No Obj: 0.000351, .5R: 0.941176, .75R: 0.352941, count: 34 -Region 16 Avg IOU: 0.704529, Class: 0.286764, Obj: 0.311892, No Obj: 0.001283, .5R: 0.900000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.684806, Class: 0.138846, Obj: 0.131218, No Obj: 0.000389, .5R: 0.900000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.783843, Class: 0.235634, Obj: 0.599858, No Obj: 0.001252, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.743368, Class: 0.168377, Obj: 0.299050, No Obj: 0.000413, .5R: 1.000000, .75R: 0.500000, count: 18 -Region 16 Avg IOU: 0.674257, Class: 0.209085, Obj: 0.308319, No Obj: 0.002857, .5R: 0.913043, .75R: 0.304348, count: 23 -Region 23 Avg IOU: 0.723453, Class: 0.215763, Obj: 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16 Avg IOU: 0.755898, Class: 0.398547, Obj: 0.354594, No Obj: 0.000620, .5R: 1.000000, .75R: 0.500000, count: 4 -Region 23 Avg IOU: 0.744752, Class: 0.232254, Obj: 0.443134, No Obj: 0.001102, .5R: 1.000000, .75R: 0.428571, count: 14 -Region 16 Avg IOU: 0.758607, Class: 0.193109, Obj: 0.371435, No Obj: 0.001898, .5R: 1.000000, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.772949, Class: 0.209936, Obj: 0.394503, No Obj: 0.001058, .5R: 0.944444, .75R: 0.666667, count: 18 -Region 16 Avg IOU: 0.670955, Class: 0.271503, Obj: 0.187909, No Obj: 0.001453, .5R: 1.000000, .75R: 0.125000, count: 8 -Region 23 Avg IOU: 0.727495, Class: 0.233302, Obj: 0.295448, No Obj: 0.001048, .5R: 0.950000, .75R: 0.500000, count: 20 -Region 16 Avg IOU: 0.766823, Class: 0.276245, Obj: 0.414647, No Obj: 0.001795, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.759479, Class: 0.268063, Obj: 0.366351, No Obj: 0.000603, .5R: 0.916667, .75R: 0.583333, count: 12 -Region 16 Avg IOU: 0.740360, Class: 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502656 images -Loaded: 0.000062 seconds -Region 16 Avg IOU: 0.754771, Class: 0.282818, Obj: 0.428191, No Obj: 0.002274, .5R: 0.923077, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.714980, Class: 0.216456, Obj: 0.284259, No Obj: 0.000683, .5R: 0.882353, .75R: 0.352941, count: 17 -Region 16 Avg IOU: 0.770838, Class: 0.286146, Obj: 0.500598, No Obj: 0.001785, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.724702, Class: 0.246133, Obj: 0.261071, No Obj: 0.000686, .5R: 0.952381, .75R: 0.428571, count: 21 -Region 16 Avg IOU: 0.745554, Class: 0.260176, Obj: 0.272509, No Obj: 0.002164, .5R: 0.941176, .75R: 0.529412, count: 17 -Region 23 Avg IOU: 0.699712, Class: 0.214561, Obj: 0.186194, No Obj: 0.000870, .5R: 0.920000, .75R: 0.320000, count: 25 -Region 16 Avg IOU: 0.768543, Class: 0.171561, Obj: 0.312039, No Obj: 0.001550, .5R: 0.909091, .75R: 0.818182, count: 11 -Region 23 Avg IOU: 0.692879, Class: 0.153857, Obj: 0.210044, No Obj: 0.001084, .5R: 0.880000, .75R: 0.400000, count: 25 -Region 16 Avg IOU: 0.784516, Class: 0.264805, Obj: 0.352185, No Obj: 0.001434, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.697290, Class: 0.225205, Obj: 0.289427, No Obj: 0.001014, .5R: 0.960000, .75R: 0.400000, count: 25 -Region 16 Avg IOU: 0.718445, Class: 0.264659, Obj: 0.321824, No Obj: 0.001611, .5R: 0.875000, .75R: 0.500000, count: 8 -Region 23 Avg IOU: 0.743629, Class: 0.279948, Obj: 0.460126, No Obj: 0.000701, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 16 Avg IOU: 0.805500, Class: 0.246959, Obj: 0.416841, No Obj: 0.000726, .5R: 1.000000, .75R: 1.000000, count: 4 -Region 23 Avg IOU: 0.757832, Class: 0.219137, Obj: 0.340614, No Obj: 0.000867, .5R: 1.000000, .75R: 0.500000, count: 22 -Region 16 Avg IOU: 0.802612, Class: 0.211682, Obj: 0.212695, No Obj: 0.001534, .5R: 1.000000, .75R: 0.888889, count: 9 -Region 23 Avg IOU: 0.728346, Class: 0.190584, Obj: 0.147328, No Obj: 0.000513, .5R: 0.941176, .75R: 0.470588, count: 17 - - 3928: 1.869203, 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count: 16 -Region 16 Avg IOU: 0.785966, Class: 0.271661, Obj: 0.486663, No Obj: 0.001613, .5R: 1.000000, .75R: 0.647059, count: 17 -Region 23 Avg IOU: 0.721748, Class: 0.239740, Obj: 0.228305, No Obj: 0.000605, .5R: 0.884615, .75R: 0.576923, count: 26 -Region 16 Avg IOU: 0.733662, Class: 0.211937, Obj: 0.360991, No Obj: 0.002647, .5R: 0.900000, .75R: 0.550000, count: 20 -Region 23 Avg IOU: 0.693703, Class: 0.172252, Obj: 0.163477, No Obj: 0.000609, .5R: 0.782609, .75R: 0.391304, count: 23 -Region 16 Avg IOU: 0.788239, Class: 0.255316, Obj: 0.378347, No Obj: 0.001681, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.724845, Class: 0.211152, Obj: 0.181051, No Obj: 0.000302, .5R: 0.909091, .75R: 0.636364, count: 11 -Region 16 Avg IOU: 0.688442, Class: 0.249288, Obj: 0.402651, No Obj: 0.002190, .5R: 0.894737, .75R: 0.368421, count: 19 -Region 23 Avg IOU: 0.748814, Class: 0.211185, Obj: 0.228791, No Obj: 0.000561, .5R: 1.000000, .75R: 0.565217, count: 23 -Region 16 Avg IOU: 0.720853, Class: 0.325211, Obj: 0.527747, No Obj: 0.001982, .5R: 0.857143, .75R: 0.500000, count: 14 -Region 23 Avg IOU: 0.729771, Class: 0.255079, Obj: 0.228608, No Obj: 0.000504, .5R: 0.900000, .75R: 0.600000, count: 20 - - 3931: 2.440062, 1.952560 avg loss, 0.001000 rate, 1.484556 seconds, 503168 images -Loaded: 0.000074 seconds -Region 16 Avg IOU: 0.740633, Class: 0.181925, Obj: 0.261931, No Obj: 0.002282, .5R: 0.967742, .75R: 0.580645, count: 31 -Region 23 Avg IOU: 0.755396, Class: 0.190351, Obj: 0.161048, No Obj: 0.000620, .5R: 0.973684, .75R: 0.605263, count: 38 -Region 16 Avg IOU: 0.772975, Class: 0.248755, Obj: 0.294716, No Obj: 0.002159, .5R: 1.000000, .75R: 0.625000, count: 24 -Region 23 Avg IOU: 0.752097, Class: 0.202375, Obj: 0.169794, No Obj: 0.000484, .5R: 0.931035, .75R: 0.586207, count: 29 -Region 16 Avg IOU: 0.761862, Class: 0.242185, Obj: 0.443884, No Obj: 0.001348, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.758577, Class: 0.170659, Obj: 0.301394, 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30 -Region 23 Avg IOU: 0.735167, Class: 0.216260, Obj: 0.112522, No Obj: 0.000233, .5R: 0.941176, .75R: 0.588235, count: 17 -Region 16 Avg IOU: 0.746863, Class: 0.209659, Obj: 0.238357, No Obj: 0.000846, .5R: 0.937500, .75R: 0.625000, count: 16 -Region 23 Avg IOU: 0.748149, Class: 0.235370, Obj: 0.107255, No Obj: 0.000162, .5R: 1.000000, .75R: 0.500000, count: 8 - - 3934: 2.340964, 2.101607 avg loss, 0.001000 rate, 1.882541 seconds, 503552 images -Loaded: 0.000076 seconds -Region 16 Avg IOU: 0.768122, Class: 0.222775, Obj: 0.238545, No Obj: 0.000827, .5R: 1.000000, .75R: 0.636364, count: 11 -Region 23 Avg IOU: 0.702702, Class: 0.195568, Obj: 0.240528, No Obj: 0.000375, .5R: 0.884615, .75R: 0.423077, count: 26 -Region 16 Avg IOU: 0.759466, Class: 0.215506, Obj: 0.408588, No Obj: 0.000924, .5R: 1.000000, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.736612, Class: 0.167439, Obj: 0.316173, No Obj: 0.000504, .5R: 1.000000, .75R: 0.360000, count: 25 -Region 16 Avg IOU: 0.770139, Class: 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19 -Region 23 Avg IOU: 0.732150, Class: 0.141456, Obj: 0.195503, No Obj: 0.000259, .5R: 0.937500, .75R: 0.562500, count: 16 -Region 16 Avg IOU: 0.784290, Class: 0.249777, Obj: 0.487162, No Obj: 0.002082, .5R: 1.000000, .75R: 0.684211, count: 19 -Region 23 Avg IOU: 0.685190, Class: 0.143555, Obj: 0.137638, No Obj: 0.000304, .5R: 1.000000, .75R: 0.200000, count: 20 -Region 16 Avg IOU: 0.802686, Class: 0.249126, Obj: 0.481009, No Obj: 0.002126, .5R: 1.000000, .75R: 0.818182, count: 22 -Region 23 Avg IOU: 0.774244, Class: 0.194552, Obj: 0.277635, No Obj: 0.000397, .5R: 0.965517, .75R: 0.689655, count: 29 - - 3939: 2.278785, 2.179474 avg loss, 0.001000 rate, 1.485089 seconds, 504192 images -Loaded: 0.000067 seconds -Region 16 Avg IOU: 0.772284, Class: 0.250876, Obj: 0.550655, No Obj: 0.001736, .5R: 1.000000, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.718314, Class: 0.178800, Obj: 0.275030, No Obj: 0.000440, .5R: 0.968750, .75R: 0.531250, count: 32 -Region 16 Avg IOU: 0.749319, Class: 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Avg IOU: 0.741056, Class: 0.196558, Obj: 0.193467, No Obj: 0.000481, .5R: 0.950000, .75R: 0.550000, count: 20 -Region 16 Avg IOU: 0.765968, Class: 0.252972, Obj: 0.603395, No Obj: 0.002782, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.688935, Class: 0.144450, Obj: 0.279566, No Obj: 0.000688, .5R: 0.833333, .75R: 0.458333, count: 24 -Region 16 Avg IOU: 0.711410, Class: 0.206679, Obj: 0.299288, No Obj: 0.002987, .5R: 0.928571, .75R: 0.357143, count: 14 -Region 23 Avg IOU: 0.758806, Class: 0.189671, Obj: 0.214243, No Obj: 0.000625, .5R: 0.958333, .75R: 0.500000, count: 24 -Region 16 Avg IOU: 0.791655, Class: 0.261706, Obj: 0.607994, No Obj: 0.003584, .5R: 1.000000, .75R: 0.666667, count: 18 -Region 23 Avg IOU: 0.757584, Class: 0.264271, Obj: 0.260397, No Obj: 0.001191, .5R: 0.972222, .75R: 0.638889, count: 36 -Region 16 Avg IOU: 0.758267, Class: 0.256315, Obj: 0.340542, No Obj: 0.002389, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 23 Avg IOU: 0.743564, Class: 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count: 12 -Region 23 Avg IOU: 0.730702, Class: 0.197694, Obj: 0.319552, No Obj: 0.001022, .5R: 0.923077, .75R: 0.500000, count: 26 -Region 16 Avg IOU: 0.786534, Class: 0.318063, Obj: 0.503562, No Obj: 0.001997, .5R: 1.000000, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.701473, Class: 0.162408, Obj: 0.451803, No Obj: 0.000840, .5R: 1.000000, .75R: 0.285714, count: 14 -Region 16 Avg IOU: 0.776181, Class: 0.232766, Obj: 0.491749, No Obj: 0.002275, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.696459, Class: 0.184453, Obj: 0.273028, No Obj: 0.000881, .5R: 0.904762, .75R: 0.428571, count: 21 -Region 16 Avg IOU: 0.743809, Class: 0.295803, Obj: 0.501237, No Obj: 0.001706, .5R: 0.923077, .75R: 0.615385, count: 13 -Region 23 Avg IOU: 0.717455, Class: 0.187309, Obj: 0.201168, No Obj: 0.000632, .5R: 1.000000, .75R: 0.411765, count: 17 - - 3942: 1.719034, 2.125173 avg loss, 0.001000 rate, 0.775564 seconds, 504576 images -Loaded: 0.000068 seconds -Region 16 Avg IOU: 0.783731, Class: 0.250140, Obj: 0.475694, No Obj: 0.001580, .5R: 0.916667, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.735048, Class: 0.173932, Obj: 0.351836, No Obj: 0.001156, .5R: 1.000000, .75R: 0.500000, count: 24 -Region 16 Avg IOU: 0.718600, Class: 0.243061, Obj: 0.321846, No Obj: 0.001244, .5R: 1.000000, .75R: 0.500000, count: 6 -Region 23 Avg IOU: 0.744821, Class: 0.228869, Obj: 0.281183, No Obj: 0.001146, .5R: 1.000000, .75R: 0.409091, count: 22 -Region 16 Avg IOU: 0.763821, Class: 0.246954, Obj: 0.378413, No Obj: 0.001792, .5R: 1.000000, .75R: 0.600000, count: 15 -Region 23 Avg IOU: 0.754618, Class: 0.244526, Obj: 0.342247, No Obj: 0.000553, .5R: 1.000000, .75R: 0.600000, count: 15 -Region 16 Avg IOU: 0.721783, Class: 0.252134, Obj: 0.515726, No Obj: 0.001594, .5R: 1.000000, .75R: 0.333333, count: 9 -Region 23 Avg IOU: 0.672210, Class: 0.126929, Obj: 0.407152, No Obj: 0.000723, .5R: 0.875000, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.799300, Class: 0.273196, Obj: 0.316647, No 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- - 3945: 1.554923, 2.004902 avg loss, 0.001000 rate, 0.782648 seconds, 504960 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.763007, Class: 0.187469, Obj: 0.379746, No Obj: 0.000647, .5R: 0.750000, .75R: 0.750000, count: 4 -Region 23 Avg IOU: 0.684356, Class: 0.176944, Obj: 0.184611, No Obj: 0.000569, .5R: 0.950000, .75R: 0.200000, count: 20 -Region 16 Avg IOU: 0.791309, Class: 0.228498, Obj: 0.330221, No Obj: 0.001611, .5R: 1.000000, .75R: 0.642857, count: 14 -Region 23 Avg IOU: 0.719326, Class: 0.190359, Obj: 0.189687, No Obj: 0.000358, .5R: 0.937500, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.781303, Class: 0.233602, Obj: 0.373198, No Obj: 0.001471, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.741693, Class: 0.169930, Obj: 0.165180, No Obj: 0.000318, .5R: 1.000000, .75R: 0.500000, count: 16 -Region 16 Avg IOU: 0.796416, Class: 0.215049, Obj: 0.187902, No Obj: 0.000833, .5R: 1.000000, .75R: 0.833333, count: 6 -Region 23 Avg IOU: 0.718722, Class: 0.295128, Obj: 0.218695, No Obj: 0.000427, .5R: 0.875000, .75R: 0.437500, count: 16 -Region 16 Avg IOU: 0.777497, Class: 0.204564, Obj: 0.303961, No Obj: 0.001865, .5R: 1.000000, .75R: 0.647059, count: 17 -Region 23 Avg IOU: 0.705648, Class: 0.171589, Obj: 0.227009, No Obj: 0.000470, .5R: 0.888889, .75R: 0.500000, count: 18 -Region 16 Avg IOU: 0.820462, Class: 0.292737, Obj: 0.277027, No Obj: 0.001127, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.781071, Class: 0.188100, Obj: 0.187519, No Obj: 0.000447, .5R: 1.000000, .75R: 0.611111, count: 18 -Region 16 Avg IOU: 0.762400, Class: 0.347752, Obj: 0.316988, No Obj: 0.001811, .5R: 1.000000, .75R: 0.562500, count: 16 -Region 23 Avg IOU: 0.778874, Class: 0.259613, Obj: 0.164924, No Obj: 0.000449, .5R: 0.933333, .75R: 0.733333, count: 15 -Region 16 Avg IOU: 0.791098, Class: 0.286990, Obj: 0.234743, No Obj: 0.001087, .5R: 1.000000, .75R: 0.800000, count: 10 -Region 23 Avg IOU: 0.716731, Class: 0.225096, Obj: 0.184151, No Obj: 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count: 6 -Region 23 Avg IOU: 0.622298, Class: 0.212343, Obj: 0.162897, No Obj: 0.000543, .5R: 0.809524, .75R: 0.238095, count: 21 -Region 16 Avg IOU: 0.779901, Class: 0.203022, Obj: 0.359171, No Obj: 0.001909, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.744779, Class: 0.183041, Obj: 0.224886, No Obj: 0.000433, .5R: 0.947368, .75R: 0.578947, count: 19 -Region 16 Avg IOU: 0.794428, Class: 0.306913, Obj: 0.271198, No Obj: 0.001865, .5R: 1.000000, .75R: 0.750000, count: 12 -Region 23 Avg IOU: 0.741585, Class: 0.232812, Obj: 0.400234, No Obj: 0.000809, .5R: 1.000000, .75R: 0.526316, count: 19 -Region 16 Avg IOU: 0.729591, Class: 0.238007, Obj: 0.263747, No Obj: 0.001215, .5R: 1.000000, .75R: 0.555556, count: 9 -Region 23 Avg IOU: 0.772543, Class: 0.264696, Obj: 0.169168, No Obj: 0.000339, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 16 Avg IOU: 0.740652, Class: 0.222843, Obj: 0.409336, No Obj: 0.001537, .5R: 1.000000, .75R: 0.533333, count: 15 -Region 23 Avg IOU: 0.694046, Class: 0.220233, Obj: 0.281934, No Obj: 0.000499, .5R: 0.950000, .75R: 0.300000, count: 20 - - 3947: 1.829681, 1.968037 avg loss, 0.001000 rate, 0.770016 seconds, 505216 images -Loaded: 0.000072 seconds -Region 16 Avg IOU: 0.803340, Class: 0.212291, Obj: 0.353885, No Obj: 0.001328, .5R: 1.000000, .75R: 0.750000, count: 8 -Region 23 Avg IOU: 0.723933, Class: 0.227970, Obj: 0.384945, No Obj: 0.000941, .5R: 0.884615, .75R: 0.576923, count: 26 -Region 16 Avg IOU: 0.820800, Class: 0.276493, Obj: 0.587100, No Obj: 0.002066, .5R: 1.000000, .75R: 0.875000, count: 8 -Region 23 Avg IOU: 0.703519, Class: 0.198325, Obj: 0.313721, No Obj: 0.000791, .5R: 0.913043, .75R: 0.391304, count: 23 -Region 16 Avg IOU: 0.768898, Class: 0.223334, Obj: 0.367598, No Obj: 0.001707, .5R: 1.000000, .75R: 0.583333, count: 12 -Region 23 Avg IOU: 0.697229, Class: 0.169261, Obj: 0.346218, No Obj: 0.000770, .5R: 0.923077, .75R: 0.307692, count: 13 -Region 16 Avg IOU: 0.866435, Class: 0.216758, Obj: 0.327940, No Obj: 0.001489, .5R: 1.000000, .75R: 1.000000, count: 6 -Region 23 Avg IOU: 0.769010, Class: 0.236362, Obj: 0.339130, No Obj: 0.000543, .5R: 0.875000, .75R: 0.625000, count: 8 -Region 16 Avg IOU: 0.790060, Class: 0.267083, Obj: 0.233153, No Obj: 0.000900, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 23 Avg IOU: 0.746390, Class: 0.228041, Obj: 0.171145, No Obj: 0.000747, .5R: 1.000000, .75R: 0.571429, count: 21 -Region 16 Avg IOU: 0.762284, Class: 0.325040, Obj: 0.307036, No Obj: 0.000496, .5R: 1.000000, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.761139, Class: 0.250287, Obj: 0.317813, No Obj: 0.000473, .5R: 0.857143, .75R: 0.714286, count: 7 -Region 16 Avg IOU: 0.770553, Class: 0.215892, Obj: 0.365841, No Obj: 0.001258, .5R: 1.000000, .75R: 0.666667, count: 9 -Region 23 Avg IOU: 0.804798, Class: 0.153356, Obj: 0.273090, No Obj: 0.000719, .5R: 0.933333, .75R: 0.800000, count: 15 -Region 16 Avg IOU: 0.778471, Class: 0.262400, Obj: 0.348916, No Obj: 0.001043, .5R: 0.909091, .75R: 0.727273, count: 11 -Region 23 Avg IOU: 0.751468, Class: 0.193524, Obj: 0.228893, No Obj: 0.000478, .5R: 0.937500, .75R: 0.625000, count: 16 - - 3948: 1.507151, 1.921949 avg loss, 0.001000 rate, 0.777884 seconds, 505344 images -Loaded: 0.000070 seconds -Region 16 Avg IOU: 0.759747, Class: 0.272634, Obj: 0.421780, No Obj: 0.001574, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.759605, Class: 0.206273, Obj: 0.280758, No Obj: 0.000779, .5R: 0.937500, .75R: 0.562500, count: 16 -Region 16 Avg IOU: 0.840511, Class: 0.295192, Obj: 0.333226, No Obj: 0.001337, .5R: 1.000000, .75R: 0.875000, count: 8 -Region 23 Avg IOU: 0.679662, Class: 0.168505, Obj: 0.191541, No Obj: 0.000731, .5R: 0.933333, .75R: 0.333333, count: 15 -Region 16 Avg IOU: 0.860130, Class: 0.261719, Obj: 0.498948, No Obj: 0.001845, .5R: 1.000000, .75R: 0.875000, count: 8 -Region 23 Avg IOU: 0.703030, Class: 0.180319, Obj: 0.347596, No Obj: 0.000617, .5R: 0.800000, .75R: 0.533333, count: 15 -Region 16 Avg IOU: 0.761822, Class: 0.214414, Obj: 0.369573, No Obj: 0.001443, .5R: 1.000000, .75R: 0.600000, count: 10 -Region 23 Avg IOU: 0.746350, Class: 0.220230, Obj: 0.323646, No Obj: 0.000705, .5R: 1.000000, .75R: 0.571429, count: 14 -Region 16 Avg IOU: 0.827637, Class: 0.252649, Obj: 0.467761, No Obj: 0.001542, .5R: 1.000000, .75R: 0.714286, count: 7 -Region 23 Avg IOU: 0.695904, Class: 0.262336, Obj: 0.250481, No Obj: 0.000557, .5R: 0.866667, .75R: 0.400000, count: 15 -Region 16 Avg IOU: 0.736485, Class: 0.288948, Obj: 0.594422, No Obj: 0.001364, .5R: 0.833333, .75R: 0.666667, count: 6 -Region 23 Avg IOU: 0.733207, Class: 0.220804, Obj: 0.325256, No Obj: 0.001425, .5R: 0.964286, .75R: 0.464286, count: 28 -Region 16 Avg IOU: 0.771131, Class: 0.203835, Obj: 0.293712, No Obj: 0.001983, .5R: 1.000000, .75R: 0.500000, count: 10 -Region 23 Avg IOU: 0.657270, Class: 0.211142, Obj: 0.240961, No Obj: 0.000801, .5R: 0.875000, .75R: 0.375000, count: 16 -Region 16 Avg IOU: 0.727465, Class: 0.251625, Obj: 0.308984, No Obj: 0.001029, .5R: 1.000000, .75R: 0.250000, count: 4 -Region 23 Avg IOU: 0.673233, Class: 0.177583, Obj: 0.284527, No Obj: 0.000584, .5R: 0.923077, .75R: 0.307692, count: 13 - - 3949: 1.561182, 1.885872 avg loss, 0.001000 rate, 0.773383 seconds, 505472 images -Loaded: 0.000066 seconds -Region 16 Avg IOU: 0.805315, Class: 0.159111, Obj: 0.459174, No Obj: 0.001887, .5R: 1.000000, .75R: 0.625000, count: 8 -Region 23 Avg IOU: 0.697560, Class: 0.158354, Obj: 0.290747, No Obj: 0.001229, .5R: 0.850000, .75R: 0.450000, count: 20 -Region 16 Avg IOU: 0.786923, Class: 0.158163, Obj: 0.341216, No Obj: 0.001542, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.732973, Class: 0.161471, Obj: 0.412505, No Obj: 0.000538, .5R: 1.000000, .75R: 0.444444, count: 9 -Region 16 Avg IOU: 0.750643, Class: 0.204642, Obj: 0.286326, No Obj: 0.001735, .5R: 1.000000, .75R: 0.437500, count: 16 -Region 23 Avg IOU: 0.719227, Class: 0.221378, Obj: 0.290024, No Obj: 0.001046, .5R: 0.875000, .75R: 0.416667, count: 24 -Region 16 Avg IOU: 0.737443, Class: 0.292228, Obj: 0.431537, No Obj: 0.001724, .5R: 0.923077, .75R: 0.461538, count: 13 -Region 23 Avg IOU: 0.689287, Class: 0.165613, Obj: 0.195326, No Obj: 0.000776, .5R: 0.850000, .75R: 0.300000, count: 20 -Region 16 Avg IOU: 0.790738, Class: 0.265516, Obj: 0.309021, No Obj: 0.001330, .5R: 1.000000, .75R: 0.700000, count: 10 -Region 23 Avg IOU: 0.764322, Class: 0.172005, Obj: 0.317117, No Obj: 0.000508, .5R: 1.000000, .75R: 0.428571, count: 7 -Region 16 Avg IOU: 0.773660, Class: 0.154196, Obj: 0.353633, No Obj: 0.002192, .5R: 1.000000, .75R: 0.538462, count: 13 -Region 23 Avg IOU: 0.726009, Class: 0.157328, Obj: 0.223027, No Obj: 0.000737, .5R: 0.952381, .75R: 0.571429, count: 21 -Region 16 Avg IOU: 0.777158, Class: 0.188152, Obj: 0.493238, No Obj: 0.001316, .5R: 1.000000, .75R: 0.571429, count: 7 -Region 23 Avg IOU: 0.768741, Class: 0.252161, Obj: 0.345183, No Obj: 0.000807, .5R: 1.000000, .75R: 0.692308, count: 13 -Region 16 Avg IOU: 0.758112, Class: 0.243616, Obj: 0.451820, No Obj: 0.002704, .5R: 1.000000, .75R: 0.666667, count: 12 -Region 23 Avg IOU: 0.725598, Class: 0.174418, Obj: 0.220800, No Obj: 0.000628, .5R: 0.928571, .75R: 0.571429, count: 14 - - \ No newline at end of file diff --git a/scripts/testdev2017.txt b/scripts/testdev2017.txt deleted file mode 100644 index 71f511ec696..00000000000 --- a/scripts/testdev2017.txt +++ /dev/null @@ -1,20288 +0,0 @@ -F:/MSCOCO/test2017/000000466319.jpg -F:/MSCOCO/test2017/000000523573.jpg -F:/MSCOCO/test2017/000000308929.jpg -F:/MSCOCO/test2017/000000057540.jpg -F:/MSCOCO/test2017/000000579853.jpg -F:/MSCOCO/test2017/000000413171.jpg -F:/MSCOCO/test2017/000000296903.jpg -F:/MSCOCO/test2017/000000540552.jpg -F:/MSCOCO/test2017/000000186384.jpg -F:/MSCOCO/test2017/000000509990.jpg -F:/MSCOCO/test2017/000000361851.jpg -F:/MSCOCO/test2017/000000252230.jpg -F:/MSCOCO/test2017/000000104129.jpg -F:/MSCOCO/test2017/000000133861.jpg 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a/src/activation_kernels.cu +++ b/src/activation_kernels.cu @@ -185,7 +185,7 @@ __global__ void binary_gradient_array_kernel(float *x, float *dy, int n, int s, extern "C" void binary_gradient_array_gpu(float *x, float *dx, int n, int size, BINARY_ACTIVATION a, float *y) { - binary_gradient_array_kernel << > >(x, dx, n / 2, size, a, y); + binary_gradient_array_kernel <<>>(x, dx, n / 2, size, a, y); CHECK_CUDA(cudaPeekAtLastError()); } __global__ void binary_activate_array_kernel(float *x, int n, int s, BINARY_ACTIVATION a, float *y) @@ -200,7 +200,7 @@ __global__ void binary_activate_array_kernel(float *x, int n, int s, BINARY_ACTI extern "C" void binary_activate_array_gpu(float *x, int n, int size, BINARY_ACTIVATION a, float *y) { - binary_activate_array_kernel << > >(x, n / 2, size, a, y); + binary_activate_array_kernel <<>>(x, n / 2, size, a, y); CHECK_CUDA(cudaPeekAtLastError()); } @@ -260,7 +260,7 @@ __global__ void activate_array_mish_kernel(float *x, int n, float *activation_in { int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; if (i < n) { - const float MISH_THRESHOLD = 20; + //const float MISH_THRESHOLD = 20; float x_val = x[i]; if (activation_input) activation_input[i] = x_val; // store value before activation //output_gpu[i] = x_val * tanh_activate_kernel(logf(1 + expf(x_val))); @@ -495,14 +495,14 @@ extern "C" void activate_array_ongpu(float *x, int n, ACTIVATION a) { const int num_blocks = get_number_of_blocks(n, BLOCK); if (a == LINEAR) return; - else if (a == LEAKY || a == REVLEAKY) activate_array_leaky_kernel << > >(x, n); - else if (a == LOGISTIC) activate_array_logistic_kernel << > >(x, n); - else if (a == TANH) activate_array_tanh_kernel << > >(x, n); - else if (a == HARDTAN) activate_array_hardtan_kernel << > >(x, n); - else if (a == RELU) activate_array_relu_kernel << > >(x, n); - else if (a == RELU6) activate_array_relu6_kernel << > >(x, n); - else if (a == SELU) activate_array_selu_kernel << > >(x, n); - else if (a == GELU) activate_array_gelu_kernel << > >(x, n); + else if (a == LEAKY || a == REVLEAKY) activate_array_leaky_kernel <<>>(x, n); + else if (a == LOGISTIC) activate_array_logistic_kernel <<>>(x, n); + else if (a == TANH) activate_array_tanh_kernel <<>>(x, n); + else if (a == HARDTAN) activate_array_hardtan_kernel <<>>(x, n); + else if (a == RELU) activate_array_relu_kernel <<>>(x, n); + else if (a == RELU6) activate_array_relu6_kernel <<>>(x, n); + else if (a == SELU) activate_array_selu_kernel <<>>(x, n); + else if (a == GELU) activate_array_gelu_kernel <<>>(x, n); else activate_array_kernel<<>>(x, n, a); CHECK_CUDA(cudaPeekAtLastError()); @@ -511,21 +511,21 @@ extern "C" void activate_array_ongpu(float *x, int n, ACTIVATION a) extern "C" void activate_array_swish_ongpu(float *x, int n, float *output_sigmoid_gpu, float *output_gpu) { const int num_blocks = get_number_of_blocks(n, BLOCK); - activate_array_swish_kernel << > >(x, n, output_sigmoid_gpu, output_gpu); + activate_array_swish_kernel <<>>(x, n, output_sigmoid_gpu, output_gpu); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void activate_array_mish_ongpu(float *x, int n, float *activation_input_gpu, float *output_gpu) { const int num_blocks = get_number_of_blocks(n, BLOCK); - activate_array_mish_kernel << > >(x, n, activation_input_gpu, output_gpu); + activate_array_mish_kernel <<>>(x, n, activation_input_gpu, output_gpu); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void activate_array_hard_mish_ongpu(float *x, int n, float *activation_input_gpu, float *output_gpu) { const int num_blocks = get_number_of_blocks(n, BLOCK); - activate_array_hard_mish_kernel << > >(x, n, activation_input_gpu, output_gpu); + activate_array_hard_mish_kernel <<>>(x, n, activation_input_gpu, output_gpu); CHECK_CUDA(cudaPeekAtLastError()); } @@ -533,22 +533,21 @@ extern "C" void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta { const int num_blocks = get_number_of_blocks(n, BLOCK); if (a == LINEAR) return; - else if (a == LEAKY) gradient_array_leaky_kernel << > >(x, n, delta); - else if (a == REVLEAKY) gradient_array_revleaky_kernel << > >(x, n, delta); - else if (a == LOGISTIC) gradient_array_logistic_kernel << > >(x, n, delta); - else if (a == TANH) gradient_array_tanh_kernel << > >(x, n, delta); - else if (a == HARDTAN) gradient_array_hardtan_kernel << > >(x, n, delta); - else if (a == RELU) gradient_array_relu_kernel << > >(x, n, delta); - else if (a == RELU6) gradient_array_relu6_kernel << > >(x, n, delta); - //else if (a == NORM_CHAN) gradient_array_relu_kernel << > >(x, n, delta); + else if (a == LEAKY) gradient_array_leaky_kernel <<< num_blocks, BLOCK, 0, get_cuda_stream() >>> (x, n, delta); + else if (a == REVLEAKY) gradient_array_revleaky_kernel <<< num_blocks, BLOCK, 0, get_cuda_stream() >>> (x, n, delta); + else if (a == LOGISTIC) gradient_array_logistic_kernel <<< num_blocks, BLOCK, 0, get_cuda_stream() >>> (x, n, delta); + else if (a == TANH) gradient_array_tanh_kernel <<< num_blocks, BLOCK, 0, get_cuda_stream() >>> (x, n, delta); + else if (a == HARDTAN) gradient_array_hardtan_kernel <<< num_blocks, BLOCK, 0, get_cuda_stream() >>> (x, n, delta); + else if (a == RELU) gradient_array_relu_kernel <<< num_blocks, BLOCK, 0, get_cuda_stream() >>> (x, n, delta); + else if (a == RELU6) gradient_array_relu6_kernel <<< num_blocks, BLOCK, 0, get_cuda_stream() >>> (x, n, delta); + //else if (a == NORM_CHAN) gradient_array_relu_kernel <<< num_blocks, BLOCK, 0, get_cuda_stream() >>>(x, n, delta); else if (a == NORM_CHAN_SOFTMAX || a == NORM_CHAN) { - printf(" Error: should be used custom NORM_CHAN_SOFTMAX-function for gradient \n"); - exit(0); + error("Error: should be used custom NORM_CHAN_SOFTMAX-function for gradient", DARKNET_LOC); } - else if (a == SELU) gradient_array_selu_kernel << > >(x, n, delta); - else if (a == GELU) gradient_array_gelu_kernel << > >(x, n, delta); + else if (a == SELU) gradient_array_selu_kernel <<>>(x, n, delta); + else if (a == GELU) gradient_array_gelu_kernel <<>>(x, n, delta); else - gradient_array_kernel << > > (x, n, a, delta); + gradient_array_kernel <<>> (x, n, a, delta); CHECK_CUDA(cudaPeekAtLastError()); } @@ -556,21 +555,21 @@ extern "C" void gradient_array_ongpu(float *x, int n, ACTIVATION a, float *delta extern "C" void gradient_array_swish_ongpu(float *x, int n, float *sigmoid_gpu, float *delta) { const int num_blocks = get_number_of_blocks(n, BLOCK); - gradient_array_swish_kernel << > > (x, n, sigmoid_gpu, delta); + gradient_array_swish_kernel <<>> (x, n, sigmoid_gpu, delta); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void gradient_array_mish_ongpu(int n, float *activation_input_gpu, float *delta) { const int num_blocks = get_number_of_blocks(n, BLOCK); - gradient_array_mish_kernel << > > (n, activation_input_gpu, delta); + gradient_array_mish_kernel <<>> (n, activation_input_gpu, delta); CHECK_CUDA(cudaPeekAtLastError()); } extern "C" void gradient_array_hard_mish_ongpu(int n, float *activation_input_gpu, float *delta) { const int num_blocks = get_number_of_blocks(n, BLOCK); - gradient_array_hard_mish_kernel << > > (n, activation_input_gpu, delta); + gradient_array_hard_mish_kernel <<>> (n, activation_input_gpu, delta); CHECK_CUDA(cudaPeekAtLastError()); } @@ -607,7 +606,7 @@ extern "C" void activate_array_normalize_channels_ongpu(float *x, int n, int bat const int num_blocks = get_number_of_blocks(size, BLOCK); - activate_array_normalize_channels_kernel << > > (x, size, batch, channels, wh_step, output_gpu); + activate_array_normalize_channels_kernel <<>> (x, size, batch, channels, wh_step, output_gpu); CHECK_CUDA(cudaPeekAtLastError()); } @@ -655,7 +654,7 @@ extern "C" void activate_array_normalize_channels_softmax_ongpu(float *x, int n, const int num_blocks = get_number_of_blocks(size, BLOCK); - activate_array_normalize_channels_softmax_kernel << > > (x, size, batch, channels, wh_step, output_gpu, use_max_val); + activate_array_normalize_channels_softmax_kernel <<>> (x, size, batch, channels, wh_step, output_gpu, use_max_val); CHECK_CUDA(cudaPeekAtLastError()); } @@ -698,7 +697,7 @@ extern "C" void gradient_array_normalize_channels_softmax_ongpu(float *output_gp const int num_blocks = get_number_of_blocks(size, BLOCK); - gradient_array_normalize_channels_softmax_kernel << > > (output_gpu, size, batch, channels, wh_step, delta_gpu); + gradient_array_normalize_channels_softmax_kernel <<>> (output_gpu, size, batch, channels, wh_step, delta_gpu); CHECK_CUDA(cudaPeekAtLastError()); } @@ -741,6 +740,6 @@ extern "C" void gradient_array_normalize_channels_ongpu(float *output_gpu, int n const int num_blocks = get_number_of_blocks(size, BLOCK); - gradient_array_normalize_channels_kernel << > > (output_gpu, size, batch, channels, wh_step, delta_gpu); + gradient_array_normalize_channels_kernel <<>> (output_gpu, size, batch, channels, wh_step, delta_gpu); CHECK_CUDA(cudaPeekAtLastError()); } diff --git a/src/activations.c b/src/activations.c index 87ba1d9e3d9..9e8a49f72f7 100644 --- a/src/activations.c +++ b/src/activations.c @@ -323,9 +323,7 @@ float gradient(float x, ACTIVATION a) case NORM_CHAN_SOFTMAX_MAXVAL: //... case NORM_CHAN_SOFTMAX: - printf(" Error: should be used custom NORM_CHAN or NORM_CHAN_SOFTMAX-function for gradient \n"); - exit(0); - return 0; + error("Error: should be used custom NORM_CHAN or NORM_CHAN_SOFTMAX-function for gradient", DARKNET_LOC); case ELU: return elu_gradient(x); case SELU: diff --git a/src/blas.c b/src/blas.c index 483cf9f43f2..122bca0ceaa 100644 --- a/src/blas.c +++ b/src/blas.c @@ -595,7 +595,7 @@ float find_sim(size_t i, size_t j, contrastive_params *contrast_p, int contrast_ } if (z == contrast_p_size) { printf(" Error: find_sim(): sim isn't found: i = %zu, j = %zu, z = %zu \n", i, j, z); - getchar(); + error("Error!", DARKNET_LOC); } return contrast_p[z].sim; @@ -609,7 +609,7 @@ float find_P_constrastive(size_t i, size_t j, contrastive_params *contrast_p, in } if (z == contrast_p_size) { printf(" Error: find_P_constrastive(): P isn't found: i = %zu, j = %zu, z = %zu \n", i, j, z); - getchar(); + error("Error!", DARKNET_LOC); } return contrast_p[z].P; @@ -649,7 +649,7 @@ float P_constrastive_f(size_t i, size_t l, int *labels, float **z, unsigned int { if (i == l) { fprintf(stderr, " Error: in P_constrastive must be i != l, while i = %zu, l = %zu \n", i, l); - getchar(); + error("Error!", DARKNET_LOC); } const float sim = find_sim(i, l, contrast_p, contrast_p_size); // cosine_similarity(z[i], z[l], feature_size); @@ -686,8 +686,7 @@ void grad_contrastive_loss_positive_f(size_t i, int *class_ids, int *labels, siz if (N == 0 || temperature == 0 || vec_len == 0) { fprintf(stderr, " Error: N == 0 || temperature == 0 || vec_len == 0. N=%f, temperature=%f, vec_len=%f, labels[i] = %d \n", N, temperature, vec_len, labels[i]); - getchar(); - return; + error("Error!", DARKNET_LOC); } const float mult = 1 / ((N - 1) * temperature * vec_len); @@ -732,8 +731,7 @@ void grad_contrastive_loss_negative_f(size_t i, int *class_ids, int *labels, siz if (N == 0 || temperature == 0 || vec_len == 0) { fprintf(stderr, " Error: N == 0 || temperature == 0 || vec_len == 0. N=%f, temperature=%f, vec_len=%f, labels[i] = %d \n", N, temperature, vec_len, labels[i]); - getchar(); - return; + error("Error!", DARKNET_LOC); } const float mult = 1 / ((N - 1) * temperature * vec_len); @@ -783,7 +781,7 @@ float P_constrastive(size_t i, size_t l, int *labels, size_t num_of_samples, flo { if (i == l) { fprintf(stderr, " Error: in P_constrastive must be i != l, while i = %zu, l = %zu \n", i, l); - getchar(); + error("Error!", DARKNET_LOC); } //const float sim = cos_sim[i*num_of_samples + l]; // cosine_similarity(z[i], z[l], feature_size); @@ -820,7 +818,7 @@ void grad_contrastive_loss_positive(size_t i, int *labels, size_t num_of_samples } if (N == 0 || temperature == 0 || vec_len == 0) { fprintf(stderr, " Error: N == 0 || temperature == 0 || vec_len == 0. N=%f, temperature=%f, vec_len=%f \n", N, temperature, vec_len); - getchar(); + error("Error!", DARKNET_LOC); } const float mult = 1 / ((N - 1) * temperature * vec_len); @@ -860,7 +858,7 @@ void grad_contrastive_loss_negative(size_t i, int *labels, size_t num_of_samples } if (N == 0 || temperature == 0 || vec_len == 0) { fprintf(stderr, " Error: N == 0 || temperature == 0 || vec_len == 0. N=%f, temperature=%f, vec_len=%f \n", N, temperature, vec_len); - getchar(); + error("Error!", DARKNET_LOC); } const float mult = 1 / ((N - 1) * temperature * vec_len); diff --git a/src/blas_kernels.cu b/src/blas_kernels.cu index cefb49b1eb3..3bc0d90b553 100644 --- a/src/blas_kernels.cu +++ b/src/blas_kernels.cu @@ -34,7 +34,7 @@ void compare_2_arrays_gpu(float *one, float *two, int size) { const int num_blocks = get_number_of_blocks(size, BLOCK); - compare_2_arrays_kernel << > >(one, two, size); + compare_2_arrays_kernel <<>>(one, two, size); CHECK_CUDA(cudaPeekAtLastError()); CHECK_CUDA(cudaDeviceSynchronize()); } @@ -53,7 +53,7 @@ void mean_array_gpu(float *src, int size, float alpha, float *avg) { const int num_blocks = get_number_of_blocks(size, BLOCK); - mean_array_kernel << > >(src, size, alpha, avg); + mean_array_kernel <<>>(src, size, alpha, avg); CHECK_CUDA(cudaPeekAtLastError()); } @@ -72,7 +72,7 @@ void scale_bias_gpu(float *output, float *scale, int batch, int filters, int spa const int current_size = batch * filters * spatial; const int num_blocks = get_number_of_blocks(current_size, BLOCK); - scale_bias_kernel << > >(output, scale, batch, filters, spatial, current_size); + scale_bias_kernel <<>>(output, scale, batch, filters, spatial, current_size); CHECK_CUDA(cudaPeekAtLastError()); } @@ -117,7 +117,7 @@ void add_bias_gpu(float *output, float *biases, int batch, int filters, int spat const int current_size = batch * filters * spatial; const int num_blocks = get_number_of_blocks(current_size, BLOCK); - add_bias_kernel << > >(output, biases, batch, filters, spatial, current_size); + add_bias_kernel <<>>(output, biases, batch, filters, spatial, current_size); CHECK_CUDA(cudaPeekAtLastError()); } @@ -202,7 +202,7 @@ __global__ void adam_kernel(int N, float *x, float *m, float *v, float B1, float extern "C" void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t) { - adam_kernel << > >(n, x, m, v, B1, B2, rate, eps, t); + adam_kernel <<>>(n, x, m, v, B1, B2, rate, eps, t); CHECK_CUDA(cudaPeekAtLastError()); } @@ -235,7 +235,7 @@ extern "C" void normalize_gpu(float *x, float *mean, float *variance, int batch, const int current_size = batch * filters * spatial; const int num_blocks = get_number_of_blocks(current_size, BLOCK); - normalize_kernel << > >(current_size, x, mean, variance, batch, filters, spatial); + normalize_kernel <<>>(current_size, x, mean, variance, batch, filters, spatial); CHECK_CUDA(cudaPeekAtLastError()); } @@ -450,7 +450,7 @@ __global__ void constrain_weight_updates_kernel(int N, float coef, float *weight extern "C" void constrain_weight_updates_ongpu(int N, float coef, float *weights_gpu, float *weight_updates_gpu) { - constrain_weight_updates_kernel << > >(N, coef, weights_gpu, weight_updates_gpu); + constrain_weight_updates_kernel <<>>(N, coef, weights_gpu, weight_updates_gpu); CHECK_CUDA(cudaPeekAtLastError()); } @@ -573,7 +573,7 @@ __global__ void fast_mean_kernel(float *x, int batch, int filters, int spatial, extern "C" void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean) { - fast_mean_kernel << > >(x, batch, filters, spatial, mean); + fast_mean_kernel <<>>(x, batch, filters, spatial, mean); CHECK_CUDA(cudaPeekAtLastError()); } @@ -669,7 +669,7 @@ __global__ void fast_v_cbn_kernel(const float *x, float *mean, int batch, int f extern "C" void fast_v_cbn_gpu(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, int max_minibatch_index, float *m_avg, float *v_avg, float *variance, const float alpha, float *rolling_mean_gpu, float *rolling_variance_gpu, int inverse_variance, float epsilon) { - fast_v_cbn_kernel << > >(x, mean, batch, filters, spatial, minibatch_index, max_minibatch_index, m_avg, v_avg, variance, alpha, rolling_mean_gpu, rolling_variance_gpu, inverse_variance, epsilon); + fast_v_cbn_kernel <<>>(x, mean, batch, filters, spatial, minibatch_index, max_minibatch_index, m_avg, v_avg, variance, alpha, rolling_mean_gpu, rolling_variance_gpu, inverse_variance, epsilon); CHECK_CUDA(cudaPeekAtLastError()); } @@ -683,7 +683,7 @@ __global__ void inverse_variance_kernel(int size, float *src, float *dst, float extern "C" void inverse_variance_ongpu(int size, float *src, float *dst, float epsilon) { const int num_blocks = size / BLOCK + 1; - inverse_variance_kernel << > >(size, src, dst, epsilon); + inverse_variance_kernel <<>>(size, src, dst, epsilon); CHECK_CUDA(cudaPeekAtLastError()); } @@ -708,7 +708,7 @@ extern "C" void normalize_scale_bias_gpu(float *x, float *mean, float *variance, const int current_size = batch * filters * spatial; const int num_blocks = get_number_of_blocks(current_size, BLOCK); - normalize_scale_bias_kernel << > >(current_size, x, mean, variance, scales, biases, batch, filters, spatial, inverse_variance, epsilon); + normalize_scale_bias_kernel <<>>(current_size, x, mean, variance, scales, biases, batch, filters, spatial, inverse_variance, epsilon); CHECK_CUDA(cudaPeekAtLastError()); } @@ -749,7 +749,7 @@ extern "C" void copy_ongpu(int N, float * X, int INCX, float * Y, int INCY) extern "C" void simple_copy_ongpu(int size, float *src, float *dst) { const int num_blocks = size / BLOCK + 1; - simple_copy_kernel << > >(size, src, dst); + simple_copy_kernel <<>>(size, src, dst); CHECK_CUDA(cudaPeekAtLastError()); } @@ -828,7 +828,7 @@ extern "C" void constrain_ongpu(int N, float ALPHA, float * X, int INCX) extern "C" void constrain_min_max_ongpu(int N, float MIN, float MAX, float * X, int INCX) { - constrain_min_max_kernel << > >(N, MIN, MAX, X, INCX); + constrain_min_max_kernel <<>>(N, MIN, MAX, X, INCX); CHECK_CUDA(cudaPeekAtLastError()); } @@ -841,7 +841,7 @@ extern "C" void scal_ongpu(int N, float ALPHA, float * X, int INCX) extern "C" void scal_add_ongpu(int N, float ALPHA, float BETA, float * X, int INCX) { - scal_add_kernel << > >(N, ALPHA, BETA, X, INCX); + scal_add_kernel <<>>(N, ALPHA, BETA, X, INCX); CHECK_CUDA(cudaPeekAtLastError()); } @@ -855,7 +855,7 @@ extern "C" void fill_ongpu(int N, float ALPHA, float * X, int INCX) { //fill_kernel<<>>(N, ALPHA, X, INCX); //CHECK_CUDA(cudaPeekAtLastError()); - fill_kernel << > >(N, ALPHA, X, INCX); + fill_kernel <<>>(N, ALPHA, X, INCX); CHECK_CUDA(cudaPeekAtLastError()); } @@ -884,7 +884,7 @@ extern "C" void gradient_centralization_gpu(int w, int h, int c, int f, float *i const int f_size = c * h * w; if (f_size % WARP_SIZE == 0) { - gradient_centralization_kernel << > > (f, f_size, in); + gradient_centralization_kernel <<>> (f, f_size, in); CHECK_CUDA(cudaPeekAtLastError()); } } @@ -1003,10 +1003,10 @@ extern "C" void shortcut_multilayer_gpu(int src_outputs, int batch, int n, int * //printf(" src_outputs = %d, batch = %d, n = %d \n", src_outputs, batch, n); int size = batch * src_outputs; if (nweights == 0 && n == 1) { - shortcut_singlelayer_simple_kernel << > > (size, src_outputs, batch, n, outputs_of_layers_gpu, layers_output_gpu, out, in, weights_gpu, nweights, weights_normalization); + shortcut_singlelayer_simple_kernel <<>> (size, src_outputs, batch, n, outputs_of_layers_gpu, layers_output_gpu, out, in, weights_gpu, nweights, weights_normalization); } else { - shortcut_multilayer_kernel << > > (size, src_outputs, batch, n, outputs_of_layers_gpu, layers_output_gpu, out, in, weights_gpu, nweights, weights_normalization); + shortcut_multilayer_kernel <<>> (size, src_outputs, batch, n, outputs_of_layers_gpu, layers_output_gpu, out, in, weights_gpu, nweights, weights_normalization); } CHECK_CUDA(cudaPeekAtLastError()); } @@ -1083,7 +1083,6 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i int add_outputs = outputs_of_layers_gpu[i]; if (src_i < add_outputs) { int add_index = add_outputs*src_b + src_i; - int out_index = id; float *layer_delta = layers_delta_gpu[i]; if (weights_gpu) { @@ -1126,14 +1125,14 @@ __global__ void backward_shortcut_multilayer_kernel(int size, int src_outputs, i extern "C" void backward_shortcut_multilayer_gpu(int src_outputs, int batch, int n, int *outputs_of_layers_gpu, float **layers_delta_gpu, float *delta_out, float *delta_in, float *weights_gpu, float *weight_updates_gpu, int nweights, float *in, float **layers_output_gpu, WEIGHTS_NORMALIZATION_T weights_normalization) { - const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) - int step = 0; - if (nweights > 0) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 + //const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w) + //int step = 0; + //if (nweights > 0) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1 //printf(" nweights = %d, n = %d, layer_step = %d, step = %d \n", nweights, n, layer_step, step); //printf(" src_outputs = %d, batch = %d, n = %d \n", src_outputs, batch, n); int size = batch * src_outputs; - backward_shortcut_multilayer_kernel << > > (size, src_outputs, batch, n, outputs_of_layers_gpu, + backward_shortcut_multilayer_kernel <<>> (size, src_outputs, batch, n, outputs_of_layers_gpu, layers_delta_gpu, delta_out, delta_in, weights_gpu, weight_updates_gpu, nweights, in, layers_output_gpu, weights_normalization); CHECK_CUDA(cudaPeekAtLastError()); } @@ -1202,7 +1201,7 @@ extern "C" void input_shortcut_gpu(float *in, int batch, int w1, int h1, int c1, { if (w1 == w2 && h1 == h2 && c1 == c2) { int size = batch * w1 * h1 * c1; - simple_input_shortcut_kernel << > >(in, size, add, out); + simple_input_shortcut_kernel <<>>(in, size, add, out); CHECK_CUDA(cudaPeekAtLastError()); return; } @@ -1219,9 +1218,9 @@ extern "C" void input_shortcut_gpu(float *in, int batch, int w1, int h1, int c1, if (sample < 1) sample = 1; int size = batch * minw * minh * minc; - //input_shortcut_kernel << > >(in, size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, out); + //input_shortcut_kernel <<>>(in, size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, out); simple_copy_ongpu(w2 * h2 * c2 * batch, in, out); - shortcut_kernel << > >(size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, out); + shortcut_kernel <<>>(size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, out); CHECK_CUDA(cudaPeekAtLastError()); } @@ -1261,7 +1260,7 @@ __global__ void softmax_x_ent_kernel(int n, float *pred, float *truth, float *de extern "C" void softmax_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error) { - softmax_x_ent_kernel << > >(n, pred, truth, delta, error); + softmax_x_ent_kernel <<>>(n, pred, truth, delta, error); CHECK_CUDA(cudaPeekAtLastError()); } @@ -1392,7 +1391,7 @@ __global__ void softmax_kernel_new_api(float *input, int n, int batch, int batch extern "C" void softmax_gpu_new_api(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output) { - softmax_kernel_new_api << > >(input, n, batch, batch_offset, groups, group_offset, stride, temp, output); + softmax_kernel_new_api <<>>(input, n, batch, batch_offset, groups, group_offset, stride, temp, output); CHECK_CUDA(cudaPeekAtLastError()); } @@ -1424,7 +1423,7 @@ __global__ void upsample_kernel(size_t N, float *x, int w, int h, int c, int bat extern "C" void upsample_gpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out) { size_t size = w*h*c*batch*stride*stride; - upsample_kernel << > >(size, in, w, h, c, batch, stride, forward, scale, out); + upsample_kernel <<>>(size, in, w, h, c, batch, stride, forward, scale, out); CHECK_CUDA(cudaPeekAtLastError()); } @@ -1476,7 +1475,7 @@ extern "C" void fix_nan_and_inf(float *input, size_t size) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); - fix_nan_and_inf_kernel << > >(input, size); + fix_nan_and_inf_kernel <<>>(input, size); CHECK_CUDA(cudaPeekAtLastError()); //CHECK_CUDA(cudaDeviceSynchronize()); } @@ -1497,7 +1496,7 @@ extern "C" void reset_nan_and_inf(float *input, size_t size) { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); - reset_nan_and_inf_kernel << > >(input, size); + reset_nan_and_inf_kernel <<>>(input, size); CHECK_CUDA(cudaPeekAtLastError()); //CHECK_CUDA(cudaDeviceSynchronize()); } @@ -1522,7 +1521,7 @@ extern "C" int is_nan_or_inf(float *input, size_t size) const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); - is_nan_or_inf_kernel << > >(input, size, pinned_return); + is_nan_or_inf_kernel <<>>(input, size, pinned_return); CHECK_CUDA(cudaDeviceSynchronize()); int ret_val = *pinned_return; @@ -1550,10 +1549,9 @@ extern "C" void add_3_arrays_activate(float *a1, float *a2, float *a3, size_t si const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); if (!(a == LOGISTIC || a == TANH || a == LEAKY || a == LINEAR)) { - printf(" add_3_arrays_activate() doesn't support activation %d, it supports only LOGISTIC and TANH \n", a); - exit(EXIT_FAILURE); + error("Error: add_3_arrays_activate() supports only LOGISTIC and TANH", DARKNET_LOC); } - add_3_arrays_activate_kernel << > >(a1, a2, a3, size, a, dst); + add_3_arrays_activate_kernel <<>>(a1, a2, a3, size, a, dst); } @@ -1569,7 +1567,7 @@ extern "C" void sum_of_mults(float *a1, float *a2, float *b1, float *b2, size_t { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); - sum_of_mults_kernel << > >(a1, a2, b1, b2, size, dst); + sum_of_mults_kernel <<>>(a1, a2, b1, b2, size, dst); } @@ -1589,10 +1587,9 @@ extern "C" void activate_and_mult(float *a1, float *a2, size_t size, ACTIVATION const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); if (!(a == TANH || a == LEAKY || a == LINEAR)) { - printf(" activat_and_mult() doesn't support activation %d, it supports only TANH \n", a); - exit(EXIT_FAILURE); + error("Error: activat_and_mult() supports only TANH", DARKNET_LOC); } - activate_and_mult_kernel << > >(a1, a2, size, a, dst); + activate_and_mult_kernel <<>>(a1, a2, size, a, dst); } @@ -1616,7 +1613,7 @@ extern "C" void scale_channels_gpu(float *in_w_h_c, int size, int channel_size, { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); - scale_channels_kernel << > >(in_w_h_c, size, channel_size, batch_size, scale_wh, scales_c, out); + scale_channels_kernel <<>>(in_w_h_c, size, channel_size, batch_size, scale_wh, scales_c, out); CHECK_CUDA(cudaPeekAtLastError()); } @@ -1673,7 +1670,7 @@ extern "C" void backward_scale_channels_gpu(float *in_w_h_c_delta, int size, int { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); - backward_scale_channels_kernel << > > (in_w_h_c_delta, size, channel_size, batch_size, scale_wh, + backward_scale_channels_kernel <<>> (in_w_h_c_delta, size, channel_size, batch_size, scale_wh, in_scales_c, out_from_delta, in_from_output, out_state_delta); @@ -1693,7 +1690,7 @@ extern "C" void sam_gpu(float *in_w_h_c, int size, int channel_size, float *scal { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); - sam_kernel << > >(in_w_h_c, size, channel_size, scales_c, out); + sam_kernel <<>>(in_w_h_c, size, channel_size, scales_c, out); CHECK_CUDA(cudaPeekAtLastError()); } @@ -1718,7 +1715,7 @@ extern "C" void backward_sam_gpu(float *in_w_h_c_delta, int size, int channel_si { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); - backward_sam_kernel << > > (in_w_h_c_delta, size, channel_size, + backward_sam_kernel <<>> (in_w_h_c_delta, size, channel_size, in_scales_c, out_from_delta, in_from_output, out_state_delta); @@ -1732,8 +1729,8 @@ __global__ void smooth_rotate_weights_kernel(const float *src_weight_gpu, float const int kernel_area = kernel_size * kernel_size; const int i = index * kernel_area; - const int stage_step = (nweights / kernel_area) / 4; // 4 stages - const int stage_id = index / stage_step; + //const int stage_step = (nweights / kernel_area) / 4; // 4 stages + //const int stage_id = index / stage_step; // nweights = (c / groups) * n * size * size; // kernel_area = size*size @@ -1804,7 +1801,7 @@ extern "C" void smooth_rotate_weights_gpu(const float *src_weight_gpu, float *we const int kernel_area = size*size; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size); - smooth_rotate_weights_kernel << > > (src_weight_gpu, weight_deform_gpu, nweights, n, size, angle, reverse); + smooth_rotate_weights_kernel <<>> (src_weight_gpu, weight_deform_gpu, nweights, n, size, angle, reverse); CHECK_CUDA(cudaPeekAtLastError()); } @@ -1902,7 +1899,7 @@ extern "C" void stretch_weights_gpu(const float *src_weight_gpu, float *weight_d const int kernel_area = size*size; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size); - stretch_weights_kernel << > > (src_weight_gpu, weight_deform_gpu, nweights, n, size, scale, reverse); + stretch_weights_kernel <<>> (src_weight_gpu, weight_deform_gpu, nweights, n, size, scale, reverse); CHECK_CUDA(cudaPeekAtLastError()); } @@ -2008,7 +2005,7 @@ extern "C" void sway_and_flip_weights_gpu(const float *src_weight_gpu, float *we const int kernel_area = size*size; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size); - sway_and_flip_weights_kernel << > > (src_weight_gpu, weight_deform_gpu, nweights, n, size, angle, reverse); + sway_and_flip_weights_kernel <<>> (src_weight_gpu, weight_deform_gpu, nweights, n, size, angle, reverse); CHECK_CUDA(cudaPeekAtLastError()); } @@ -2091,7 +2088,7 @@ extern "C" void rotate_weights_gpu(const float *src_weight_gpu, float *weight_de const int kernel_area = size*size; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size); - rotate_weights_kernel << > > (src_weight_gpu, weight_deform_gpu, nweights, n, size, reverse); + rotate_weights_kernel <<>> (src_weight_gpu, weight_deform_gpu, nweights, n, size, reverse); CHECK_CUDA(cudaPeekAtLastError()); } @@ -2258,7 +2255,7 @@ extern "C" void stretch_sway_flip_weights_gpu(const float *src_weight_gpu, float const int kernel_area = size*size; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(nweights / kernel_area, block_size); - stretch_sway_flip_weights_kernel << > > (src_weight_gpu, weight_deform_gpu, nweights, n, size, angle, reverse); + stretch_sway_flip_weights_kernel <<>> (src_weight_gpu, weight_deform_gpu, nweights, n, size, angle, reverse); CHECK_CUDA(cudaPeekAtLastError()); } @@ -2285,7 +2282,7 @@ extern "C" void reduce_and_expand_array_gpu(const float *src_gpu, float *dst_gpu const int current_size = size / groups; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(current_size, block_size); - reduce_and_expand_array_kernel << > > (src_gpu, dst_gpu, current_size, groups); + reduce_and_expand_array_kernel <<>> (src_gpu, dst_gpu, current_size, groups); CHECK_CUDA(cudaPeekAtLastError()); } @@ -2308,7 +2305,7 @@ extern "C" void expand_array_gpu(const float *src_gpu, float *dst_gpu, int size, const int current_size = size / groups; const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(current_size, block_size); - expand_array_kernel << > > (src_gpu, dst_gpu, current_size, groups); + expand_array_kernel <<>> (src_gpu, dst_gpu, current_size, groups); CHECK_CUDA(cudaPeekAtLastError()); } @@ -2340,7 +2337,7 @@ extern "C" void mult_inverse_array_gpu(const float *src_gpu, float *dst_gpu, int { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); - mult_inverse_array_kernel << > > (src_gpu, dst_gpu, size, eps, divider, clip, abs_add); + mult_inverse_array_kernel <<>> (src_gpu, dst_gpu, size, eps, divider, clip, abs_add); CHECK_CUDA(cudaPeekAtLastError()); } @@ -2385,7 +2382,7 @@ extern "C" void P_constrastive_f_det_gpu(int *labels, unsigned int feature_size, { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(contrast_p_size, block_size); - P_constrastive_f_det_kernel << > > (labels, feature_size, temperature, contrast_p, contrast_p_size); + P_constrastive_f_det_kernel <<>> (labels, feature_size, temperature, contrast_p, contrast_p_size); CHECK_CUDA(cudaPeekAtLastError()); } @@ -2432,7 +2429,7 @@ extern "C" void coord_conv_gpu(float *dst, int size, int w, int h, int chan, int { const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); - coord_conv_kernel << > > (dst, w, h, chan, b, type); + coord_conv_kernel <<>> (dst, w, h, chan, b, type); CHECK_CUDA(cudaPeekAtLastError()); } @@ -2449,7 +2446,7 @@ __global__ void forward_implicit_kernel(int size, int batch, int nweights, float extern "C" void forward_implicit_gpu(int batch, int nweights, float *weight_gpu, float *output_gpu) { int size = batch * nweights; - forward_implicit_kernel << > > (size, batch, nweights, weight_gpu, output_gpu); + forward_implicit_kernel <<>> (size, batch, nweights, weight_gpu, output_gpu); CHECK_CUDA(cudaPeekAtLastError()); } @@ -2468,6 +2465,6 @@ __global__ void backward_implicit_kernel(int size, int batch, int nweights, floa extern "C" void backward_implicit_gpu(int batch, int nweights, float *weight_updates_gpu, float *delta_gpu) { int size = nweights; - backward_implicit_kernel << > > (size, batch, nweights, weight_updates_gpu, delta_gpu); + backward_implicit_kernel <<>> (size, batch, nweights, weight_updates_gpu, delta_gpu); CHECK_CUDA(cudaPeekAtLastError()); } diff --git a/src/classifier.c b/src/classifier.c index 0c81aee4e27..f01303327cf 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -74,7 +74,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, if (classes != l.outputs && (l.type == SOFTMAX || l.type == COST)) { printf("\n Error: num of filters = %d in the last conv-layer in cfg-file doesn't match to classes = %d in data-file \n", l.outputs, classes); - getchar(); + error("Error!", DARKNET_LOC); } char **labels = get_labels(label_list); @@ -845,7 +845,7 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi if (classes != l.outputs && (l.type == SOFTMAX || l.type == COST)) { printf("\n Error: num of filters = %d in the last conv-layer in cfg-file doesn't match to classes = %d in data-file \n", l.outputs, classes); - getchar(); + error("Error!", DARKNET_LOC); } if (top == 0) top = option_find_int(options, "top", 1); if (top > classes) top = classes; diff --git a/src/col2im_kernels.cu b/src/col2im_kernels.cu index 4e6342ad220..ae651c49b13 100644 --- a/src/col2im_kernels.cu +++ b/src/col2im_kernels.cu @@ -126,8 +126,8 @@ void col2im_gpu_ext(const float* data_col, const int channels, // To avoid involving atomic operations, we will launch one kernel per // bottom dimension, and then in the kernel add up the top dimensions. // NOLINT_NEXT_LINE(whitespace/operators) - col2im_gpu_kernel_ext<< > >( + col2im_gpu_kernel_ext<<>>( num_kernels, data_col, height, width, channels, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, height_col, width_col, data_im); diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu index 188399bb7e6..debd6bf159e 100644 --- a/src/convolutional_kernels.cu +++ b/src/convolutional_kernels.cu @@ -65,7 +65,7 @@ __global__ void binarize_weights_kernel(float *weights, int n, int size, float * void binarize_weights_gpu(float *weights, int n, int size, float *binary) { - binarize_weights_kernel << > >(weights, n, size, binary); + binarize_weights_kernel <<>>(weights, n, size, binary); CHECK_CUDA(cudaPeekAtLastError()); } @@ -113,9 +113,9 @@ void fast_binarize_weights_gpu(float *weights, int n, int size, float *binary, f size_t gridsize = n * size; const int num_blocks = get_number_of_blocks(gridsize, BLOCK);// gridsize / BLOCK + 1; - set_zero_kernel << <(n/BLOCK + 1), BLOCK, 0, get_cuda_stream() >> > (mean_arr_gpu, n); - reduce_kernel << > > (weights, n, size, mean_arr_gpu); - binarize_weights_mean_kernel << > > (weights, n, size, binary, mean_arr_gpu); + set_zero_kernel <<<(n/BLOCK + 1), BLOCK, 0, get_cuda_stream() >>> (mean_arr_gpu, n); + reduce_kernel <<>> (weights, n, size, mean_arr_gpu); + binarize_weights_mean_kernel <<>> (weights, n, size, binary, mean_arr_gpu); CHECK_CUDA(cudaPeekAtLastError()); } else { @@ -351,7 +351,6 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) im2col_align_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.align_workspace_gpu, l.bit_align); //cudaDeviceSynchronize(); //stop_timer_and_show_name("im2col_align_ongpu"); - //getchar(); // should be optimized //start_timer(); @@ -383,7 +382,6 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) //} //cudaDeviceSynchronize(); //check_error(status); - //getchar(); } @@ -533,11 +531,11 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state) /* int input_nan_inf = is_nan_or_inf(state.input, l.inputs * l.batch); printf("\n is_nan_or_inf(state.input) = %d \n", input_nan_inf); - if (input_nan_inf) getchar(); + if (input_nan_inf) error(); int weights_nan_inf = is_nan_or_inf(l.weights_gpu, l.nweights); printf("\n is_nan_or_inf(l.weights_gpu) = %d \n", weights_nan_inf); - if (weights_nan_inf) getchar(); + if (weights_nan_inf) error(); */ CHECK_CUDNN(cudnnConvolutionForward(cudnn_handle(), @@ -997,7 +995,7 @@ void calc_avg_activation_gpu(float *src, float *dst, int size, int channels, int { const int num_blocks = get_number_of_blocks(size*batches, BLOCK); - calc_avg_activation_kernel << > > (src, dst, size, channels, batches); + calc_avg_activation_kernel <<>> (src, dst, size, channels, batches); } @@ -1021,7 +1019,7 @@ void assisted_activation_gpu(float alpha, float *output, float *gt_gpu, float *a { const int num_blocks = get_number_of_blocks(size*batches, BLOCK); - assisted_activation_kernel << > > (alpha, output, gt_gpu, a_avg_gpu, size, channels, batches); + assisted_activation_kernel <<>> (alpha, output, gt_gpu, a_avg_gpu, size, channels, batches); } @@ -1045,7 +1043,7 @@ void assisted_activation2_gpu(float alpha, float *output, float *gt_gpu, float * { const int num_blocks = get_number_of_blocks(size*batches, BLOCK); - assisted_activation2_kernel << > > (alpha, output, gt_gpu, a_avg_gpu, size, channels, batches); + assisted_activation2_kernel <<>> (alpha, output, gt_gpu, a_avg_gpu, size, channels, batches); } void assisted_excitation_forward_gpu(convolutional_layer l, network_state state) diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c index 9267a4a3935..18f9e8b7f10 100644 --- a/src/convolutional_layer.c +++ b/src/convolutional_layer.c @@ -330,9 +330,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference, size_t workspace_ } if (!found_conv_algorithm) { - printf(" Error: cuDNN isn't found FWD algo for convolution.\n"); - getchar(); - exit(0); + error("Error: cuDNN hasn't found FWD algo for convolution", DARKNET_LOC); } //printf(" cuDNN FWD algo: %d, time = %f ms \n", l->fw_algo, min_time); @@ -367,9 +365,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference, size_t workspace_ } if (!found_conv_algorithm) { - printf(" Error: cuDNN isn't found BWD-data algo for convolution.\n"); - getchar(); - exit(0); + error("Error: cuDNN hasn't found BWD-data algo for convolution", DARKNET_LOC); } //printf(" cuDNN BWD-data algo: %d \n", l->bd_algo); @@ -404,9 +400,7 @@ void cudnn_convolutional_setup(layer *l, int cudnn_preference, size_t workspace_ } if (!found_conv_algorithm) { - printf(" Error: cuDNN isn't found BWD-filter algo for convolution.\n"); - getchar(); - exit(0); + error("Error: cuDNN hasn't found BWD-filter algo for convolution", DARKNET_LOC); } //printf(" cuDNN BWD-filter algo: %d \n", l->bf_algo); @@ -550,8 +544,7 @@ convolutional_layer make_convolutional_layer(int batch, int steps, int h, int w, if (l.share_layer) { if (l.size != l.share_layer->size || l.nweights != l.share_layer->nweights || l.c != l.share_layer->c || l.n != l.share_layer->n) { - printf(" Layer size, nweights, channels or filters don't match for the share_layer"); - getchar(); + error("Layer size, nweights, channels or filters don't match for the share_layer", DARKNET_LOC); } l.weights = l.share_layer->weights; diff --git a/src/dark_cuda.c b/src/dark_cuda.c index 09b757f26fa..74f067724db 100644 --- a/src/dark_cuda.c +++ b/src/dark_cuda.c @@ -63,9 +63,6 @@ void check_error(cudaError_t status, const char * const filename, const char * c char buffer[256]; printf("\n CUDA Error: %s\n", s); snprintf(buffer, 256, "CUDA Error: %s", s); -#ifdef WIN32 - getchar(); -#endif error(buffer, filename, funcname, line); } if (status2 != cudaSuccess) @@ -74,9 +71,6 @@ void check_error(cudaError_t status, const char * const filename, const char * c char buffer[256]; printf("\n CUDA Error Prev: %s\n", s); snprintf(buffer, 256, "CUDA Error Prev: %s", s); -#ifdef WIN32 - getchar(); -#endif error(buffer, filename, funcname, line); } } @@ -198,9 +192,6 @@ void cudnn_check_error(cudnnStatus_t status, const char * const filename, const char buffer[256]; printf("\n cuDNN Error: %s\n", s); snprintf(buffer, 256, "cuDNN Error: %s", s); -#ifdef WIN32 - getchar(); -#endif error(buffer, filename, function, line); } if (status2 != CUDNN_STATUS_SUCCESS) @@ -209,9 +200,6 @@ void cudnn_check_error(cudnnStatus_t status, const char * const filename, const char buffer[256]; printf("\n cuDNN Error Prev: %s\n", s); snprintf(buffer, 256, "cuDNN Error Prev: %s", s); -#ifdef WIN32 - getchar(); -#endif error(buffer, filename, function, line); } } diff --git a/src/darknet.c b/src/darknet.c index e30baddbbd0..392f2e46615 100644 --- a/src/darknet.c +++ b/src/darknet.c @@ -454,11 +454,6 @@ int main(int argc, char **argv) return 0; } gpu_index = find_int_arg(argc, argv, "-i", 0); - if(find_arg(argc, argv, "-nogpu")) { - gpu_index = -1; - printf("\n Currently Darknet doesn't support -nogpu flag. If you want to use CPU - please compile Darknet with GPU=0 in the Makefile, or compile darknet_no_gpu.sln on Windows.\n"); - exit(-1); - } #ifndef GPU gpu_index = -1; diff --git a/src/darkunistd.h b/src/darkunistd.h index 6c06aef1c7e..de3a3cbde92 100644 --- a/src/darkunistd.h +++ b/src/darkunistd.h @@ -9,7 +9,7 @@ #include #include /* for _getcwd() and _chdir() */ -#include "getopt.h" +#include #include #include /* for getpid() and the exec..() family */ #include diff --git a/src/data.c b/src/data.c index 17b675cc667..70e1b09b2f4 100644 --- a/src/data.c +++ b/src/data.c @@ -9,8 +9,6 @@ #include #include -extern int check_mistakes; - #define NUMCHARS 37 pthread_mutex_t mutex = PTHREAD_MUTEX_INITIALIZER; @@ -206,10 +204,6 @@ box_label *read_boxes(char *filename, int *n) char *new_line = "\n"; fwrite(new_line, sizeof(char), strlen(new_line), fw); fclose(fw); - if (check_mistakes) { - printf("\n Error in read_boxes() \n"); - getchar(); - } *n = 0; return boxes; @@ -409,7 +403,6 @@ int fill_truth_detection(const char *path, int num_boxes, int truth_size, float printf("\n Wrong annotation: class_id = %d. But class_id should be [from 0 to %d], file: %s \n", id, (classes-1), labelpath); sprintf(buff, "echo %s \"Wrong annotation: class_id = %d. But class_id should be [from 0 to %d]\" >> bad_label.list", labelpath, id, (classes-1)); system(buff); - if (check_mistakes) getchar(); ++sub; continue; } @@ -424,7 +417,6 @@ int fill_truth_detection(const char *path, int num_boxes, int truth_size, float sprintf(buff, "echo %s \"Wrong annotation: x = 0 or y = 0\" >> bad_label.list", labelpath); system(buff); ++sub; - if (check_mistakes) getchar(); continue; } if (x <= 0 || x > 1 || y <= 0 || y > 1) { @@ -432,7 +424,6 @@ int fill_truth_detection(const char *path, int num_boxes, int truth_size, float sprintf(buff, "echo %s \"Wrong annotation: x = %f, y = %f\" >> bad_label.list", labelpath, x, y); system(buff); ++sub; - if (check_mistakes) getchar(); continue; } if (w > 1) { @@ -440,14 +431,12 @@ int fill_truth_detection(const char *path, int num_boxes, int truth_size, float sprintf(buff, "echo %s \"Wrong annotation: w = %f\" >> bad_label.list", labelpath, w); system(buff); w = 1; - if (check_mistakes) getchar(); } if (h > 1) { printf("\n Wrong annotation: h = %f, file: %s \n", h, labelpath); sprintf(buff, "echo %s \"Wrong annotation: h = %f\" >> bad_label.list", labelpath, h); system(buff); h = 1; - if (check_mistakes) getchar(); } if (x == 0) x += lowest_w; if (y == 0) y += lowest_h; @@ -1057,14 +1046,11 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo if (use_mixup == 2 || use_mixup == 4) { printf("\n cutmix=1 - isn't supported for Detector (use cutmix=1 only for Classifier) \n"); - if (check_mistakes) getchar(); if(use_mixup == 2) use_mixup = 0; else use_mixup = 3; } if (use_mixup == 3 && letter_box) { - //printf("\n Combination: letter_box=1 & mosaic=1 - isn't supported, use only 1 of these parameters \n"); - //if (check_mistakes) getchar(); - //exit(0); + error("Combination: letter_box=1 & mosaic=1 - isn't supported, use only 1 of these parameters", DARKNET_LOC); } if (random_gen() % 2 == 0) use_mixup = 0; int i; @@ -1111,9 +1097,6 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo if (src == NULL) { printf("\n Error in load_data_detection() - OpenCV \n"); fflush(stdout); - if (check_mistakes) { - getchar(); - } continue; } @@ -1393,12 +1376,10 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo //assert(use_mixup < 2); if (use_mixup == 2) { - printf("\n cutmix=1 - isn't supported for Detector \n"); - exit(0); + error("cutmix=1 - isn't supported for Detector", DARKNET_LOC); } if (use_mixup == 3 || use_mixup == 4) { - printf("\n mosaic=1 - compile Darknet with OpenCV for using mosaic=1 \n"); - exit(0); + error("mosaic=1 - compile Darknet with OpenCV for using mosaic=1", DARKNET_LOC); } int mixup = use_mixup ? random_gen() % 2 : 0; //printf("\n mixup = %d \n", mixup); @@ -1567,8 +1548,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int c, int bo show_image(sized, buff); wait_until_press_key_cv(); } - printf("\nYou use flag -show_imgs, so will be saved aug_...jpg images. Press Enter: \n"); - //getchar(); + printf("\nYou use flag -show_imgs, so will be saved aug_...jpg images\n"); } free_image(orig); diff --git a/src/demo.c b/src/demo.c index 0e4d3d90cd3..5a01faf36e0 100644 --- a/src/demo.c +++ b/src/demo.c @@ -73,7 +73,6 @@ void *fetch_in_thread(void *ptr) printf("Stream closed.\n"); custom_atomic_store_int(&flag_exit, 1); custom_atomic_store_int(&run_fetch_in_thread, 0); - //exit(EXIT_FAILURE); return 0; } //in_s = resize_image(in, net.w, net.h); @@ -210,8 +209,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int if (l.classes != demo_classes) { printf("\n Parameters don't match: in cfg-file classes=%d, in data-file classes=%d \n", l.classes, demo_classes); - getchar(); - exit(0); + error("Error!", DARKNET_LOC); } flag_exit = 0; diff --git a/src/detector.c b/src/detector.c index 8f3023d5eaa..0fc36142904 100644 --- a/src/detector.c +++ b/src/detector.c @@ -19,8 +19,6 @@ typedef __compar_fn_t comparison_fn_t; #include "http_stream.h" -int check_mistakes = 0; - static int coco_ids[] = { 1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90 }; void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, float thresh, float iou_thresh, int mjpeg_port, int show_imgs, int benchmark_layers, char* chart_path, int mAP_epochs) @@ -36,8 +34,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i FILE* valid_file = fopen(valid_images, "r"); if (!valid_file) { printf("\n Error: There is no %s file for mAP calculation!\n Don't use -map flag.\n Or set valid=%s in your %s file. \n", valid_images, train_images, datacfg); - getchar(); - exit(-1); + error("Error!", DARKNET_LOC); } else fclose(valid_file); @@ -56,7 +53,6 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i if (net_classes != names_size) { printf("\n Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", name_list, names_size, net_classes, cfgfile); - if (net_classes > names_size) getchar(); } free_ptrs((void**)names, net_map.layers[net_map.n - 1].classes); } @@ -92,8 +88,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i const int actual_batch_size = net.batch * net.subdivisions; if (actual_batch_size == 1) { - printf("\n Error: You set incorrect value batch=1 for Training! You should set batch=64 subdivision=64 \n"); - getchar(); + error("Error: You set incorrect value batch=1 for Training! You should set batch=64 subdivision=64", DARKNET_LOC); } else if (actual_batch_size < 8) { printf("\n Warning: You set batch=%d lower than 64! It is recommended to set batch=64 subdivision=64 \n", actual_batch_size); @@ -974,7 +969,7 @@ float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, floa if (net.layers[net.n - 1].classes != names_size) { printf("\n Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", name_list, names_size, net.layers[net.n - 1].classes, cfgfile); - getchar(); + error("Error!", DARKNET_LOC); } srand(time(0)); printf("\n calculation mAP (mean average precision)...\n"); @@ -1488,7 +1483,6 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int sprintf(buff, "echo \"Wrong label: %s - j = %d, x = %f, y = %f, width = %f, height = %f\" >> bad_label.list", labelpath, j, truth[j].x, truth[j].y, truth[j].w, truth[j].h); system(buff); - if (check_mistakes) getchar(); } if (truth[j].id >= classes) { classes = truth[j].id + 1; @@ -1620,8 +1614,6 @@ void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int } free(rel_width_height_array); free(counter_per_class); - - getchar(); } @@ -1645,7 +1637,6 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam if (net.layers[net.n - 1].classes != names_size) { printf("\n Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", name_list, names_size, net.layers[net.n - 1].classes, cfgfile); - if (net.layers[net.n - 1].classes > names_size) getchar(); } srand(2222222); char buff[256]; @@ -1806,7 +1797,6 @@ void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, if (net.layers[net.n - 1].classes != names_size) { printf("\n Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n", name_list, names_size, net.layers[net.n - 1].classes, cfgfile); - if (net.layers[net.n - 1].classes > names_size) getchar(); } srand(2222222); @@ -1950,8 +1940,7 @@ void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, int dont_show, int it_num, int letter_box, int benchmark_layers) { - printf(" ./darknet detector draw ... can't be used without OpenCV and CUDA! \n"); - getchar(); + error("darknet detector draw ... can't be used without OpenCV and CUDA", DARKNET_LOC); } #endif // defined(OPENCV) && defined(GPU) @@ -1966,7 +1955,6 @@ void run_detector(int argc, char **argv) int letter_box = find_arg(argc, argv, "-letter_box"); int calc_map = find_arg(argc, argv, "-map"); int map_points = find_int_arg(argc, argv, "-points", 0); - check_mistakes = find_arg(argc, argv, "-check_mistakes"); int show_imgs = find_arg(argc, argv, "-show_imgs"); int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1); int avgframes = find_int_arg(argc, argv, "-avgframes", 3); diff --git a/src/dropout_layer.c b/src/dropout_layer.c index 3cc73ed24bd..744c708a4d1 100644 --- a/src/dropout_layer.c +++ b/src/dropout_layer.c @@ -19,7 +19,7 @@ dropout_layer make_dropout_layer(int batch, int inputs, float probability, int d if (l.w <= 0 || l.h <= 0 || l.c <= 0) { printf(" Error: DropBlock - there must be positive values for: l.w=%d, l.h=%d, l.c=%d \n", l.w, l.h, l.c); - exit(0); + error("Error!", DARKNET_LOC); } } l.inputs = inputs; diff --git a/src/dropout_layer_kernels.cu b/src/dropout_layer_kernels.cu index 05cde594993..3ed7a186b0e 100644 --- a/src/dropout_layer_kernels.cu +++ b/src/dropout_layer_kernels.cu @@ -165,11 +165,11 @@ void forward_dropout_layer_gpu(dropout_layer l, network_state state) //fill_ongpu(l.outputs * l.batch, 1, state.input, 1); // remove!!! int num_blocks = l.batch * l.c; - dropblock_fast_kernel << > > (l.rand_gpu, block_prob, l.w, l.h, l.w*l.h, l.c, l.batch, block_size, l.drop_blocks_scale_gpu, state.input); + dropblock_fast_kernel <<>> (l.rand_gpu, block_prob, l.w, l.h, l.w*l.h, l.c, l.batch, block_size, l.drop_blocks_scale_gpu, state.input); CHECK_CUDA(cudaPeekAtLastError()); num_blocks = get_number_of_blocks(l.batch, BLOCK); - set_scales_dropblock_kernel << > > (l.drop_blocks_scale_gpu, block_size, block_size, l.outputs, l.batch); + set_scales_dropblock_kernel <<>> (l.drop_blocks_scale_gpu, block_size, block_size, l.outputs, l.batch); CHECK_CUDA(cudaPeekAtLastError()); /* @@ -205,7 +205,7 @@ void forward_dropout_layer_gpu(dropout_layer l, network_state state) */ num_blocks = get_number_of_blocks(l.outputs * l.batch, BLOCK); - scale_dropblock_kernel << > > (state.input, l.outputs * l.batch, l.outputs, l.drop_blocks_scale_gpu); + scale_dropblock_kernel <<>> (state.input, l.outputs * l.batch, l.outputs, l.drop_blocks_scale_gpu); CHECK_CUDA(cudaPeekAtLastError()); } @@ -221,7 +221,7 @@ void forward_dropout_layer_gpu(dropout_layer l, network_state state) cuda_push_array(layer.rand_gpu, layer.rand, size); */ - yoloswag420blazeit360noscope << > > (state.input, size, l.rand_gpu, l.probability, l.scale); + yoloswag420blazeit360noscope <<>> (state.input, size, l.rand_gpu, l.probability, l.scale); CHECK_CUDA(cudaPeekAtLastError()); } } @@ -264,10 +264,10 @@ void backward_dropout_layer_gpu(dropout_layer l, network_state state) //fill_ongpu(l.outputs * l.batch, 1, state.delta, 1); // remove!!! int num_blocks = get_number_of_blocks(l.outputs * l.batch, BLOCK); - backward_dropblock_kernel << > >(l.rand_gpu, state.delta, l.outputs * l.batch); + backward_dropblock_kernel <<>>(l.rand_gpu, state.delta, l.outputs * l.batch); CHECK_CUDA(cudaPeekAtLastError()); - scale_dropblock_kernel << > > (state.delta, l.outputs * l.batch, l.outputs, l.drop_blocks_scale_gpu); + scale_dropblock_kernel <<>> (state.delta, l.outputs * l.batch, l.outputs, l.drop_blocks_scale_gpu); CHECK_CUDA(cudaPeekAtLastError()); /* @@ -305,7 +305,7 @@ void backward_dropout_layer_gpu(dropout_layer l, network_state state) } // dropout else { - yoloswag420blazeit360noscope << > > (state.delta, size, l.rand_gpu, l.probability, l.scale); + yoloswag420blazeit360noscope <<>> (state.delta, size, l.rand_gpu, l.probability, l.scale); CHECK_CUDA(cudaPeekAtLastError()); } } diff --git a/src/gaussian_yolo_layer.c b/src/gaussian_yolo_layer.c index b016a231d19..f94f4a6ce44 100644 --- a/src/gaussian_yolo_layer.c +++ b/src/gaussian_yolo_layer.c @@ -20,8 +20,6 @@ #define M_PI 3.141592 #endif -extern int check_mistakes; - layer make_gaussian_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes, int max_boxes) { int i; @@ -470,7 +468,6 @@ void forward_gaussian_yolo_layer(const layer l, network_state state) if (class_id >= l.classes) { printf("\n Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1); printf(" truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f, class_id = %d \n", truth.x, truth.y, truth.w, truth.h, class_id); - if (check_mistakes) getchar(); continue; // if label contains class_id more than number of classes in the cfg-file } if(!truth.x) break; diff --git a/src/gemm.c b/src/gemm.c index 5f5c9689c67..256061bbed9 100644 --- a/src/gemm.c +++ b/src/gemm.c @@ -297,8 +297,6 @@ void gemm_nn_custom_bin_mean(int M, int N, int K, float ALPHA_UNUSED, } free(count_arr); - - //getchar(); } */ diff --git a/src/http_stream.cpp b/src/http_stream.cpp index 580b5fe367e..cac00a09df5 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -782,8 +782,7 @@ int check_prob(detection det, float thresh) int check_classes_id(detection det1, detection det2, float thresh) { if (det1.classes != det2.classes) { - printf(" Error: det1.classes != det2.classes \n"); - getchar(); + error("Error: det1.classes != det2.classes", DARKNET_LOC); } int det1_id = -1; diff --git a/src/im2col_kernels.cu b/src/im2col_kernels.cu index 37551a761b8..ac7ccc84cc3 100644 --- a/src/im2col_kernels.cu +++ b/src/im2col_kernels.cu @@ -206,8 +206,8 @@ void im2col_align_ongpu(float *im, int height_col = (height + 2 * pad - ksize) / stride + 1; int width_col = (width + 2 * pad - ksize) / stride + 1; int num_kernels = channels * height_col * width_col; - im2col_align_gpu_kernel << <(num_kernels + BLOCK - 1) / BLOCK, - BLOCK, 0, get_cuda_stream() >> >( + im2col_align_gpu_kernel <<<(num_kernels + BLOCK - 1) / BLOCK, + BLOCK, 0, get_cuda_stream() >>>( num_kernels, im, height, width, ksize, pad, stride, height_col, width_col, data_col, bit_align); @@ -334,9 +334,9 @@ void im2col_align_bin_ongpu(float *im, int num_kernels = channels * ksize * ksize; int num_blocks = num_kernels / BLOCK + 1; - //im2col_align_bin_gpu_kernel << <(num_kernels + BLOCK - 1) / BLOCK, - im2col_align_bin_gpu_kernel << > >( + //im2col_align_bin_gpu_kernel <<<(num_kernels + BLOCK - 1) / BLOCK, + im2col_align_bin_gpu_kernel <<>>( num_kernels, im, height, width, ksize, channels, pad, stride, height_col, width_col, data_col, bit_align); @@ -648,8 +648,8 @@ void transpose_bin_gpu(unsigned char *A, unsigned char *B, const int n, const in int size32 = n*m / (32*32) + 1; //const int num_blocks = size / BLOCK + 1; const int num_blocks32 = size32 / BLOCK_TRANSPOSE32 + 1; - transpose_bin_gpu_kernel_32 << > >((uint32_t *)A, (uint32_t *)B, n, m, lda, ldb, block_size); - //transpose_bin_gpu_kernel << > >(A, B, n, m, lda, ldb, block_size); + transpose_bin_gpu_kernel_32 <<>>((uint32_t *)A, (uint32_t *)B, n, m, lda, ldb, block_size); + //transpose_bin_gpu_kernel <<>>(A, B, n, m, lda, ldb, block_size); CHECK_CUDA(cudaPeekAtLastError()); } // -------------------------------- @@ -676,7 +676,7 @@ void transpose_uint32_gpu(uint32_t *src, uint32_t *dst, int src_h, int src_w, in { int size = src_w * src_h; const int num_blocks = size / BLOCK + 1; - transpose_uint32_kernel << > >(src, dst, src_h, src_w, src_align, dst_align); + transpose_uint32_kernel <<>>(src, dst, src_h, src_w, src_align, dst_align); CHECK_CUDA(cudaPeekAtLastError()); } // -------------------------------- @@ -742,7 +742,7 @@ void transpose_uint32_gpu_2(uint32_t *src, uint32_t *dst, int src_h, int src_w, int size = src_w_align * src_h_align; int num_blocks = size / TRANS_BLOCK; - transpose_uint32_kernel_2 << > >(src, dst, src_h, src_w, src_align, dst_align); + transpose_uint32_kernel_2 <<>>(src, dst, src_h, src_w, src_align, dst_align); CHECK_CUDA(cudaPeekAtLastError()); } // -------------------------------- @@ -780,7 +780,7 @@ void repack_input_gpu(float *input, float *re_packed_input, int w, int h, int c) { int size = w * h * c; const int num_blocks = size / BLOCK + 1; - repack_input_kernel << > >(input, re_packed_input, w, h, c); + repack_input_kernel <<>>(input, re_packed_input, w, h, c); CHECK_CUDA(cudaPeekAtLastError()); } // -------------------------------- @@ -820,7 +820,7 @@ void repack_input_gpu_2(float *input, float *re_packed_input, int w, int h, int { int size = w * h * c; const int num_blocks = size / BLOCK + 1; - repack_input_kernel_2 << > >(input, re_packed_input, w, h, c); + repack_input_kernel_2 <<>>(input, re_packed_input, w, h, c); CHECK_CUDA(cudaPeekAtLastError()); } // -------------------------------- @@ -870,7 +870,7 @@ void repack_input_gpu_bin(float *input, uint32_t *re_packed_input_bin, int w, in const int block_size = BLOCK; const int num_blocks = get_number_of_blocks(size, block_size); //printf("\n num_blocks = %d, num_blocks/32 = %d, block_size = %d \n", num_blocks, num_blocks / 32, block_size); - repack_input_kernel_bin << > >(input, re_packed_input_bin, w, h, c); + repack_input_kernel_bin <<>>(input, re_packed_input_bin, w, h, c); CHECK_CUDA(cudaPeekAtLastError()); } @@ -921,7 +921,7 @@ void repack_input_gpu_bin(float *input, uint32_t *re_packed_input_bin, int w, in const int block_size = 256;// 128; const int num_blocks = get_number_of_blocks(size, block_size); printf("\n num_blocks = %d, num_blocks/32 = %d, block_size = %d \n", num_blocks, num_blocks/32, block_size); - repack_input_kernel_bin << > >(input, re_packed_input_bin, w, h, c); + repack_input_kernel_bin <<>>(input, re_packed_input_bin, w, h, c); CHECK_CUDA(cudaPeekAtLastError()); } */ @@ -1351,17 +1351,17 @@ __global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel(int M, int N, i // wmma::bmma_sync(c_frag, a_frag, b_frag, c_frag); int32_t c_val[8]; // 8 x 32 threads = 256 - #pragma UNROLL + #pragma unroll for (int local_j = 0; local_j < 8; ++local_j) { uint32_t b_val_cur = __shfl_custom(b_val, local_j * 4 + k_d); c_val[local_j] = __popc(xor_int32(a_val, b_val_cur)); } - #pragma UNROLL + #pragma unroll for (int local_j = 0; local_j < 8; ++local_j) { - #pragma UNROLL + #pragma unroll for (int local_k = 0; local_k < 4; ++local_k) { accum_c_val[local_j + c_x*8] += __shfl_custom(c_val[local_j], i_d * 4 + local_k); } @@ -1386,7 +1386,7 @@ __global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel(int M, int N, i { int j_d = lane_id % WMMA_N; { - #pragma UNROLL + #pragma unroll for (int i_d = lane_id / WMMA_N; i_d < WMMA_M; i_d += WMMA_M / 2) { int count = C_s[warp_id*WMMA_M*WMMA_N + i_d*WMMA_N + j_d + WMMA_M*WMMA_N*32*c_x]; @@ -1506,17 +1506,17 @@ __global__ void gemm_nn_custom_bin_mean_transposed_tensor_kernel(int M, int N, i // wmma::bmma_sync(c_frag, a_frag, b_frag, c_frag); int32_t c_val[8]; // 8 x 32 threads = 256 - #pragma UNROLL + #pragma unroll for (int local_j = 0; local_j < 8; ++local_j) { uint32_t b_val_cur = __shfl_custom(b_val, local_j *4 + k_d); c_val[local_j] = __popc(xor_int32(a_val, b_val_cur)); } - #pragma UNROLL + #pragma unroll for (int local_j = 0; local_j < 8; ++local_j) { - #pragma UNROLL + #pragma unroll for (int local_k = 0; local_k < 4; ++local_k) { accum_c_val[local_j] += __shfl_custom(c_val[local_j], i_d * 4 + local_k); } @@ -1791,7 +1791,7 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, //printf(" lda = %d, ldb = %d, ldc = %d, lda/32 = %d, ldb/32 = %d, ldc/32 = %d \n", lda, ldb, ldc, lda / 32, ldb / 32, ldc / 32); //printf(" l.c (K/9) = %d, M (l.n) = %d \n", (K%9 == 0)? K / 9: K, M); - gemm_nn_custom_bin_mean_transposed_tensor_kernel << > > ( + gemm_nn_custom_bin_mean_transposed_tensor_kernel <<>> ( M, N, K, A, lda, B, ldb, @@ -1800,12 +1800,11 @@ void gemm_nn_custom_bin_mean_transposed_gpu(int M, int N, int K, shortcut_in_gpu, shortcut_out_gpu); //cudaDeviceSynchronize(); - //getchar(); } else #endif //# CUDART_VERSION >= 10000 { - gemm_nn_custom_bin_mean_transposed_gpu_kernel << > > ( + gemm_nn_custom_bin_mean_transposed_gpu_kernel <<>> ( M, N, K, A, lda, B, ldb, @@ -1993,7 +1992,7 @@ void convolve_gpu(float *input, float *weights, float *output, int in_w, int in_ const int num_blocks = array_size / BLOCK + 1; //printf("\n array_size = %d, num_blocks = %d, w = %d, h = %d, n = %d, c = %d, pad = %d \n", array_size, num_blocks, in_w, in_h, n, in_c, pad); - convolve_gpu_kernel << > > (input, weights, output, in_w, in_h, in_c, n, size, pad); + convolve_gpu_kernel <<>> (input, weights, output, in_w, in_h, in_c, n, size, pad); CHECK_CUDA(cudaPeekAtLastError()); } @@ -2209,7 +2208,7 @@ void convolve_bin_gpu(float *input, float *weights, float *output, int in_w, int const int num_blocks = array_size / BLOCK + 1; //printf("\n array_size = %d, num_blocks = %d, w = %d, h = %d, n = %d, c = %d, pad = %d \n", array_size, num_blocks, in_w, in_h, n, in_c, pad); - convolve_bin_gpu_kernel << > > (input, weights, output, in_w, in_h, in_c, n, size, pad, new_lda, mean_arr_gpu); + convolve_bin_gpu_kernel <<>> (input, weights, output, in_w, in_h, in_c, n, size, pad, new_lda, mean_arr_gpu); CHECK_CUDA(cudaPeekAtLastError()); } @@ -2278,8 +2277,8 @@ void im2col_gpu_ext(const float* data_im, const int channels, (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; int num_kernels = channels * height_col * width_col; // NOLINT_NEXT_LINE(whitespace/operators) - im2col_gpu_kernel_ext << > >( + im2col_gpu_kernel_ext <<>>( num_kernels, data_im, height, width, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, height_col, width_col, data_col); diff --git a/src/image.c b/src/image.c index 3775837b2b4..b238db3522a 100644 --- a/src/image.c +++ b/src/image.c @@ -13,16 +13,13 @@ #ifndef STB_IMAGE_IMPLEMENTATION #define STB_IMAGE_IMPLEMENTATION -#include "stb_image.h" +#include #endif #ifndef STB_IMAGE_WRITE_IMPLEMENTATION #define STB_IMAGE_WRITE_IMPLEMENTATION -#include "stb_image_write.h" +#include #endif -extern int check_mistakes; -//int windows = 0; - float colors[6][3] = { {1,0,1}, {0,0,1},{0,1,1},{0,1,0},{1,1,0},{1,0,0} }; float get_color(int c, int x, int max) @@ -1511,12 +1508,7 @@ image load_image_stb(char *filename, int channels) char *new_line = "\n"; fwrite(new_line, sizeof(char), strlen(new_line), fw); fclose(fw); - if (check_mistakes) { - printf("\n Error in load_image_stb() \n"); - getchar(); - } return make_image(10, 10, 3); - //exit(EXIT_FAILURE); } if(channels) c = channels; int i,j,k; @@ -1536,7 +1528,7 @@ image load_image_stb(char *filename, int channels) image load_image_stb_resize(char *filename, int w, int h, int c) { - image out = load_image_stb(filename, c); // without OpenCV + image out = load_image_stb(filename, c); if ((h && w) && (h != out.h || w != out.w)) { image resized = resize_image(out, w, h); @@ -1549,10 +1541,9 @@ image load_image_stb_resize(char *filename, int w, int h, int c) image load_image(char *filename, int w, int h, int c) { #ifdef OPENCV - //image out = load_image_stb(filename, c); image out = load_image_cv(filename, c); #else - image out = load_image_stb(filename, c); // without OpenCV + image out = load_image_stb(filename, c); #endif // OPENCV if((h && w) && (h != out.h || w != out.w)){ diff --git a/src/image_opencv.cpp b/src/image_opencv.cpp index 459d9bf0f37..22e6ca53570 100644 --- a/src/image_opencv.cpp +++ b/src/image_opencv.cpp @@ -113,7 +113,6 @@ extern "C" mat_cv *load_image_mat_cv(const char *filename, int flag) cerr << "Cannot load image " << shrinked_filename << std::endl; std::ofstream bad_list("bad.list", std::ios::out | std::ios::app); bad_list << shrinked_filename << std::endl; - //if (check_mistakes) getchar(); return NULL; } cv::Mat dst; diff --git a/src/maxpool_layer_kernels.cu b/src/maxpool_layer_kernels.cu index ab39d6b57b2..1d0d1bd9d2d 100644 --- a/src/maxpool_layer_kernels.cu +++ b/src/maxpool_layer_kernels.cu @@ -155,7 +155,7 @@ extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network_state sta size_t n = h*w*c*layer.batch; - forward_maxpool_depth_layer_kernel << > >( + forward_maxpool_depth_layer_kernel <<>>( n, layer.w, layer.h, layer.c, layer.out_c, layer.batch, state.input, layer.output_gpu, layer.indexes_gpu); CHECK_CUDA(cudaPeekAtLastError()); @@ -192,11 +192,11 @@ extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network_state sta size_t n = h*w*c*layer.batch; - forward_maxpool_layer_kernel << > > (n, layer.h, layer.w, layer.c, layer.stride_x, layer.stride_y, layer.size, layer.pad, state.input, layer.output_gpu, layer.indexes_gpu); + forward_maxpool_layer_kernel <<>> (n, layer.h, layer.w, layer.c, layer.stride_x, layer.stride_y, layer.size, layer.pad, state.input, layer.output_gpu, layer.indexes_gpu); CHECK_CUDA(cudaPeekAtLastError()); if (layer.maxpool_zero_nonmax) { - forward_zero_nonmax_kernel << > > (n, state.input, layer.output_gpu); + forward_zero_nonmax_kernel <<>> (n, state.input, layer.output_gpu); CHECK_CUDA(cudaPeekAtLastError()); } } @@ -237,7 +237,7 @@ extern "C" void backward_maxpool_layer_gpu(maxpool_layer layer, network_state st size_t n = h * w * c * layer.batch; - backward_maxpool_depth_layer_kernel << > >(n, layer.w, layer.h, layer.c, layer.batch, layer.delta_gpu, state.delta, layer.indexes_gpu); + backward_maxpool_depth_layer_kernel <<>>(n, layer.w, layer.h, layer.c, layer.batch, layer.delta_gpu, state.delta, layer.indexes_gpu); CHECK_CUDA(cudaPeekAtLastError()); return; } @@ -248,7 +248,7 @@ extern "C" void backward_maxpool_layer_gpu(maxpool_layer layer, network_state st CHECK_CUDA(cudaPeekAtLastError()); if (layer.maxpool_zero_nonmax) { - backward_zero_nonmax_kernel << > > (n, layer.indexes_gpu, state.delta); + backward_zero_nonmax_kernel <<>> (n, layer.indexes_gpu, state.delta); CHECK_CUDA(cudaPeekAtLastError()); } } @@ -373,7 +373,7 @@ extern "C" void forward_local_avgpool_layer_gpu(maxpool_layer layer, network_sta size_t n = h*w*c*layer.batch; - forward_local_avgpool_layer_kernel << > > (n, layer.h, layer.w, layer.c, layer.stride_x, layer.stride_y, layer.size, layer.pad, state.input, layer.output_gpu); + forward_local_avgpool_layer_kernel <<>> (n, layer.h, layer.w, layer.c, layer.stride_x, layer.stride_y, layer.size, layer.pad, state.input, layer.output_gpu); CHECK_CUDA(cudaPeekAtLastError()); } } @@ -382,6 +382,6 @@ extern "C" void backward_local_avgpool_layer_gpu(maxpool_layer layer, network_st { size_t n = layer.h*layer.w*layer.c*layer.batch; - backward_local_avgpool_layer_kernel << > >(n, layer.h, layer.w, layer.c, layer.stride_x, layer.stride_y, layer.size, layer.pad, layer.delta_gpu, state.delta); + backward_local_avgpool_layer_kernel <<>>(n, layer.h, layer.w, layer.c, layer.stride_x, layer.stride_y, layer.size, layer.pad, layer.delta_gpu, state.delta); CHECK_CUDA(cudaPeekAtLastError()); } diff --git a/src/network.c b/src/network.c index ba59d69202f..40c1cbcb72b 100644 --- a/src/network.c +++ b/src/network.c @@ -1048,7 +1048,7 @@ char *detection_to_json(detection *dets, int nboxes, int classes, char **names, send_buf = (char *)realloc(send_buf, total_len * sizeof(char)); if (!send_buf) { if (buf) free(buf); - return 0;// exit(-1); + return 0; } strcat(send_buf, buf); free(buf); diff --git a/src/network_kernels.cu b/src/network_kernels.cu index 58f9d5bac38..3b3d4e6fe4b 100644 --- a/src/network_kernels.cu +++ b/src/network_kernels.cu @@ -222,7 +222,7 @@ void backward_network_gpu(network net, network_state state) int state_input_nan_inf = is_nan_or_inf(state.input, l.outputs * l.batch); printf("\n i - %d is_nan_or_inf(s.delta) = %d \n", i, state_delta_nan_inf); printf(" i - %d is_nan_or_inf(s.input) = %d \n", i, state_input_nan_inf); - if (state_delta_nan_inf || state_input_nan_inf) { printf(" found "); getchar(); } + if (state_delta_nan_inf || state_input_nan_inf) { printf(" found "); } } */ } @@ -376,7 +376,7 @@ float train_network_datum_gpu(network net, float *x, float *y) float scale = (get_current_iteration(net) / ((float)net.max_batches)); //scale = sin(scale * M_PI); net.learning_rate = net.adversarial_lr * scale; - layer l = net.layers[net.n - 1]; + //layer l = net.layers[net.n - 1]; int y_size = get_network_output_size(net)*net.batch; if (net.layers[net.n - 1].truths) y_size = net.layers[net.n - 1].truths*net.batch; float *truth_cpu = (float *)xcalloc(y_size, sizeof(float)); @@ -761,7 +761,7 @@ float *network_predict_gpu_gl_texture(network net, uint32_t texture_id) CHECK_CUDA(cudaGraphicsGLRegisterImage(&graphics_resource, texture_id, GL_TEXTURE_2D, flags)); CHECK_CUDA(cudaGraphicsMapResources(1, &graphics_resource, 0)); - void* dev_ptr = NULL; + //void* dev_ptr = NULL; cudaArray_t dev_array = NULL; CHECK_CUDA(cudaGraphicsSubResourceGetMappedArray(&dev_array, graphics_resource, 0, 0)); diff --git a/src/parser.c b/src/parser.c index 16a2b905f7b..65606de6e74 100644 --- a/src/parser.c +++ b/src/parser.c @@ -217,13 +217,11 @@ convolutional_layer parse_convolutional(list *options, size_params params) int stretch = option_find_int_quiet(options, "stretch", 0); int stretch_sway = option_find_int_quiet(options, "stretch_sway", 0); if ((sway + rotate + stretch + stretch_sway) > 1) { - printf(" Error: should be used only 1 param: sway=1, rotate=1 or stretch=1 in the [convolutional] layer \n"); - exit(0); + error("Error: should be used only 1 param: sway=1, rotate=1 or stretch=1 in the [convolutional] layer", DARKNET_LOC); } int deform = sway || rotate || stretch || stretch_sway; if (deform && size == 1) { - printf(" Error: params (sway=1, rotate=1 or stretch=1) should be used only with size >=3 in the [convolutional] layer \n"); - exit(0); + error("Error: params (sway=1, rotate=1 or stretch=1) should be used only with size >=3 in the [convolutional] layer", DARKNET_LOC); } convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride_x,stride_y,dilation,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index, antialiasing, share_layer, assisted_excitation, deform, params.train); @@ -384,8 +382,7 @@ contrastive_layer parse_contrastive(list *options, size_params params) if(yolo_layer_id != 0) yolo_layer = params.net.layers + yolo_layer_id; if (yolo_layer->type != YOLO) { printf(" Error: [contrastive] layer should point to the [yolo] layer instead of %d layer! \n", yolo_layer_id); - getchar(); - exit(0); + error("Error!", DARKNET_LOC); } contrastive_layer layer = make_contrastive_layer(params.batch, params.w, params.h, params.c, classes, params.inputs, yolo_layer); @@ -427,7 +424,7 @@ float *get_classes_multipliers(char *cpc, const int classes, const float max_del int *counters_per_class = parse_yolo_mask(cpc, &classes_counters); if (classes_counters != classes) { printf(" number of values in counters_per_class = %d doesn't match with classes = %d \n", classes_counters, classes); - exit(0); + error("Error!", DARKNET_LOC); } float max_counter = 0; int i; @@ -458,12 +455,8 @@ layer parse_yolo(list *options, size_params params) int max_boxes = option_find_int_quiet(options, "max", 200); layer l = make_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes, max_boxes); if (l.outputs != params.inputs) { - printf("Error: l.outputs == params.inputs \n"); - printf("filters= in the [convolutional]-layer doesn't correspond to classes= or mask= in [yolo]-layer \n"); - exit(EXIT_FAILURE); + error("Error: l.outputs == params.inputs, filters= in the [convolutional]-layer doesn't correspond to classes= or mask= in [yolo]-layer", DARKNET_LOC); } - //assert(l.outputs == params.inputs); - l.show_details = option_find_int_quiet(options, "show_details", 1); l.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10 char *cpc = option_find_str(options, "counters_per_class", 0); @@ -532,7 +525,6 @@ layer parse_yolo(list *options, size_params params) printf(" embedding_size = %d \n", l.embedding_size); if (le.n % l.n != 0) { printf(" Warning: filters=%d number in embedding_layer=%d isn't divisable by number of anchors %d \n", le.n, embedding_layer_id, l.n); - getchar(); } } @@ -592,11 +584,8 @@ layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3 int *mask = parse_gaussian_yolo_mask(a, &num); layer l = make_gaussian_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes, max_boxes); if (l.outputs != params.inputs) { - printf("Error: l.outputs == params.inputs \n"); - printf("filters= in the [convolutional]-layer doesn't correspond to classes= or mask= in [Gaussian_yolo]-layer \n"); - exit(EXIT_FAILURE); + error("Error: l.outputs == params.inputs, filters= in the [convolutional]-layer doesn't correspond to classes= or mask= in [Gaussian_yolo]-layer", DARKNET_LOC); } - //assert(l.outputs == params.inputs); l.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10 char *cpc = option_find_str(options, "counters_per_class", 0); l.classes_multipliers = get_classes_multipliers(cpc, classes, l.max_delta); @@ -683,9 +672,7 @@ layer parse_region(list *options, size_params params) layer l = make_region_layer(params.batch, params.w, params.h, num, classes, coords, max_boxes); if (l.outputs != params.inputs) { - printf("Error: l.outputs == params.inputs \n"); - printf("filters= in the [convolutional]-layer doesn't correspond to classes= or num= in [region]-layer \n"); - exit(EXIT_FAILURE); + error("Error: l.outputs == params.inputs, filters= in the [convolutional]-layer doesn't correspond to classes= or num= in [region]-layer", DARKNET_LOC); } //assert(l.outputs == params.inputs); @@ -936,8 +923,7 @@ layer parse_shortcut(list *options, size_params params, network net) else if (strcmp(weights_type_str, "per_channel") == 0) weights_type = PER_CHANNEL; else if (strcmp(weights_type_str, "none") != 0) { printf("Error: Incorrect weights_type = %s \n Use one of: none, per_feature, per_channel \n", weights_type_str); - getchar(); - exit(0); + error("Error!", DARKNET_LOC); } char *weights_normalization_str = option_find_str_quiet(options, "weights_normalization", "none"); @@ -946,8 +932,7 @@ layer parse_shortcut(list *options, size_params params, network net) else if (strcmp(weights_normalization_str, "softmax") == 0) weights_normalization = SOFTMAX_NORMALIZATION; else if (strcmp(weights_type_str, "none") != 0) { printf("Error: Incorrect weights_normalization = %s \n Use one of: none, relu, softmax \n", weights_normalization_str); - getchar(); - exit(0); + error("Error!", DARKNET_LOC); } char *l = option_find(options, "from"); @@ -1227,8 +1212,7 @@ void parse_net_options(list *options, network *net) net->contrastive_color = option_find_int_quiet(options, "contrastive_color", 0); net->unsupervised = option_find_int_quiet(options, "unsupervised", 0); if (net->contrastive && mini_batch < 2) { - printf(" Error: mini_batch size (batch/subdivisions) should be higher than 1 for Contrastive loss \n"); - exit(0); + error("Error: mini_batch size (batch/subdivisions) should be higher than 1 for Contrastive loss!", DARKNET_LOC); } net->label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); net->resize_step = option_find_float_quiet(options, "resize_step", 32); diff --git a/src/region_layer.c b/src/region_layer.c index b7aba32e1a9..506cc9fc69b 100644 --- a/src/region_layer.c +++ b/src/region_layer.c @@ -306,7 +306,6 @@ void forward_region_layer(const region_layer l, network_state state) int class_id = state.truth[t * l.truth_size + b*l.truths + 4]; if (class_id >= l.classes) { printf("\n Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes-1); - getchar(); continue; // if label contains class_id more than number of classes in the cfg-file } diff --git a/src/route_layer.c b/src/route_layer.c index c9232101867..23dfa0473b8 100644 --- a/src/route_layer.c +++ b/src/route_layer.c @@ -59,8 +59,7 @@ void resize_route_layer(route_layer *l, network *net) l->out_c += next.out_c; }else{ printf("Error: Different size of input layers: %d x %d, %d x %d\n", next.out_w, next.out_h, first.out_w, first.out_h); - l->out_h = l->out_w = l->out_c = 0; - exit(EXIT_FAILURE); + error("Error!", DARKNET_LOC); } } l->out_c = l->out_c / l->groups; diff --git a/src/softmax_layer.c b/src/softmax_layer.c index 5b747fa4719..6535c5780b3 100644 --- a/src/softmax_layer.c +++ b/src/softmax_layer.c @@ -151,12 +151,10 @@ contrastive_layer make_contrastive_layer(int batch, int w, int h, int c, int cla l.truths = yolo_layer->truths; if (l.embedding_size != yolo_layer->embedding_size) { printf(" Error: [contrastive] embedding_size=%d isn't equal to [yolo] embedding_size=%d. They should use the same [convolutional] layer \n", l.embedding_size, yolo_layer->embedding_size); - getchar(); - exit(0); + error("Error!", DARKNET_LOC); } if (l.inputs % (l.n*l.h*l.w) != 0) { printf(" Warning: filters= number in the previous (embedding) layer isn't divisable by number of anchors %d \n", l.n); - getchar(); } } else { @@ -350,7 +348,7 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) } if (sim > 1.001 || sim < -1.001) { - printf(" sim = %f, ", sim); getchar(); + printf(" sim = %f, ", sim); } } } @@ -406,7 +404,7 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) const float P = P_constrastive(b, b2, l.labels, l.batch, z, l.embedding_size, l.temperature, l.cos_sim); l.p_constrastive[b*l.batch + b2] = P; if (P > 1 || P < -1) { - printf(" p = %f, ", P); getchar(); + printf(" p = %f, ", P); } } } @@ -421,7 +419,7 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) const int max_contr_size = (l.max_boxes*l.batch)*(l.max_boxes*l.batch); if (max_contr_size < contr_size) { printf(" Error: too large number of bboxes: contr_size = %d > max_contr_size = %d \n", contr_size, max_contr_size); - exit(0); + error("Error!", DARKNET_LOC); } int *labels = NULL; if (contr_size > 2) { @@ -480,11 +478,8 @@ void forward_contrastive_layer(contrastive_layer l, network_state state) break; } - //if (q == contr_size) getchar(); - - //if (P > 1 || P < -1) { - // printf(" p = %f, z_index = %d, z_index2 = %d ", P, z_index, z_index2); getchar(); + // printf(" p = %f, z_index = %d, z_index2 = %d ", P, z_index, z_index2); //} } } diff --git a/src/utils.h b/src/utils.h index f84054b8b39..6a4ea8a35e1 100644 --- a/src/utils.h +++ b/src/utils.h @@ -8,7 +8,7 @@ #include #ifndef M_PI -#define M_PI 3.14159265358979323846 // pi +#define M_PI 3.14159265358979323846 #endif #ifdef __cplusplus diff --git a/src/yolo_console_dll.cpp b/src/yolo_console_dll.cpp index 06286062657..83df369657e 100644 --- a/src/yolo_console_dll.cpp +++ b/src/yolo_console_dll.cpp @@ -354,9 +354,7 @@ int main(int argc, char *argv[]) if (filename == "zed_camera" || file_ext == "svo") { std::cout << "ZED 3D Camera " << zed.open(init_params) << std::endl; if (!zed.isOpened()) { - std::cout << " Error: ZED Camera should be connected to USB 3.0. And ZED_SDK should be installed. \n"; - getchar(); - return 0; + error("Error: ZED Camera should be connected to USB 3.0. And ZED_SDK should be installed", DARKNET_LOC); } cur_frame = zed_capture_rgb(zed); use_zed_camera = true; @@ -691,8 +689,12 @@ int main(int argc, char *argv[]) show_console_result(result_vec, obj_names); #endif // OPENCV } - catch (std::exception &e) { std::cerr << "exception: " << e.what() << "\n"; getchar(); } - catch (...) { std::cerr << "unknown exception \n"; getchar(); } + catch (std::exception &e) { + std::cerr << "exception: " << e.what() << "\n"; + } + catch (...) { + std::cerr << "unknown exception \n"; + } filename.clear(); } diff --git a/src/yolo_layer.c b/src/yolo_layer.c index 0eae2fc8feb..ac464ba39b4 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -11,8 +11,6 @@ #include #include -extern int check_mistakes; - layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes, int max_boxes) { int i; @@ -433,7 +431,6 @@ void *process_batch(void* ptr) if (class_id >= l.classes || class_id < 0) { printf("\n Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1); printf("\n truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f, class_id = %d \n", truth.x, truth.y, truth.w, truth.h, class_id); - if (check_mistakes) getchar(); continue; // if label contains class_id more than number of classes in the cfg-file and class_id check garbage value } diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp index b0c17e8d93f..19d381090ad 100644 --- a/src/yolo_v2_class.cpp +++ b/src/yolo_v2_class.cpp @@ -13,7 +13,7 @@ extern "C" { #include "image.h" #include "demo.h" #include "option_list.h" -#include "stb_image.h" +#include } //#include diff --git a/vcpkg.json b/vcpkg.json index 1ef26f470d2..6f142bed231 100644 --- a/vcpkg.json +++ b/vcpkg.json @@ -1,9 +1,13 @@ { "name": "darknet", - "version": "0.2.5.6", + "version": "0.2.5.7", "description": "Darknet is an open source neural network framework written in C and CUDA. You only look once (YOLO) is a state-of-the-art, real-time object detection system, best example of darknet functionalities.", "homepage": "https://github.com/alexeyab/darknet", "dependencies": [ + { + "name": "getopt", + "platform": "windows & !mingw" + }, "pthreads", "stb" ], diff --git a/vcpkg.json.opencv23 b/vcpkg.json.opencv23 index 11c1a5da21e..0a45be9a456 100644 --- a/vcpkg.json.opencv23 +++ b/vcpkg.json.opencv23 @@ -1,9 +1,13 @@ { "name": "darknet", - "version": "0.2.5.5", + "version": "0.2.5.7", "description": "Darknet is an open source neural network framework written in C and CUDA. You only look once (YOLO) is a state-of-the-art, real-time object detection system, best example of darknet functionalities.", "homepage": "https://github.com/alexeyab/darknet", "dependencies": [ + { + "name": "getopt", + "platform": "windows & !mingw" + }, "pthreads", "stb" ], From 9a9773e6f103abc6370fa6c848e805b43c60e62b Mon Sep 17 00:00:00 2001 From: keineahnung2345 Date: Tue, 3 Oct 2023 00:12:54 +0800 Subject: [PATCH 1349/1365] make directories of multiple layers (#3893) --- scripts/gen_anchors.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/gen_anchors.py b/scripts/gen_anchors.py index 709cb88adab..a1a23438e95 100644 --- a/scripts/gen_anchors.py +++ b/scripts/gen_anchors.py @@ -117,7 +117,7 @@ def main(argv): args = parser.parse_args() if not os.path.exists(args.output_dir): - os.mkdir(args.output_dir) + os.makedirs(args.output_dir) f = open(args.filelist) From 27b37bf1033c1524121850f3705044f50c0c1b8c Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Mon, 30 Oct 2023 17:26:03 +0100 Subject: [PATCH 1350/1365] add missing dependencies for openvino (#8860) * add missing automake * add missing autoconf-archive --- .github/workflows/ccpp.yml | 8 ++++---- .github/workflows/on_pr.yml | 8 ++++---- scripts/setup.sh | 5 +++-- 3 files changed, 11 insertions(+), 10 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index ad386f42c91..af7c5b9a77f 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -87,7 +87,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt-get install -y --no-install-recommends yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + run: sudo apt-get install -y --no-install-recommends yasm nasm gperf automake autoconf libtool pkg-config autoconf-archive libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev - name: Clean downloads run: sudo apt-get clean @@ -157,7 +157,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt-get install -y --no-install-recommends yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + run: sudo apt-get install -y --no-install-recommends yasm nasm gperf automake autoconf libtool pkg-config autoconf-archive libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev - name: Clean downloads run: sudo apt-get clean @@ -210,7 +210,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt-get install -y --no-install-recommends yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + run: sudo apt-get install -y --no-install-recommends yasm nasm gperf automake autoconf libtool pkg-config autoconf-archive libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev - name: Clean downloads run: sudo apt-get clean @@ -398,7 +398,7 @@ jobs: if: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.debug_enabled }} - name: Install dependencies - run: brew install libomp yasm nasm pkg-config + run: brew install libomp yasm nasm pkg-config automake autoconf-archive - uses: lukka/get-cmake@latest diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index 6e7d36b807c..409523fd7e3 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -73,7 +73,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt-get install -y --no-install-recommends yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + run: sudo apt-get install -y --no-install-recommends yasm nasm gperf automake autoconf libtool pkg-config autoconf-archive libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev - name: Clean downloads run: sudo apt-get clean @@ -116,7 +116,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt-get install -y --no-install-recommends yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + run: sudo apt-get install -y --no-install-recommends yasm nasm gperf automake autoconf libtool pkg-config autoconf-archive libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev - name: Clean downloads run: sudo apt-get clean @@ -159,7 +159,7 @@ jobs: - name: Update apt run: sudo apt update - name: Install dependencies - run: sudo apt-get install -y --no-install-recommends yasm nasm gperf libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + run: sudo apt-get install -y --no-install-recommends yasm nasm gperf automake autoconf libtool pkg-config autoconf-archive libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev - name: Clean downloads run: sudo apt-get clean @@ -289,7 +289,7 @@ jobs: - uses: actions/checkout@v3 - name: Install dependencies - run: brew install libomp yasm nasm pkg-config + run: brew install libomp yasm nasm pkg-config automake autoconf-archive - uses: lukka/get-cmake@latest diff --git a/scripts/setup.sh b/scripts/setup.sh index e583e972369..6cbc7be8fa1 100755 --- a/scripts/setup.sh +++ b/scripts/setup.sh @@ -75,7 +75,7 @@ elif [[ $(cut -f2 <<< $(lsb_release -i)) == "Ubuntu" ]]; then if [ "$install_tools" = true ] ; then echo "Installing tools" sudo apt-get update - sudo apt-get install -y --no-install-recommends git ninja-build build-essential g++ nasm yasm gperf + sudo apt-get install -y --no-install-recommends git ninja-build build-essential g++ nasm yasm gperf sudo apt-get install -y --no-install-recommends apt-transport-https ca-certificates gnupg software-properties-common wget sudo apt-get install -y --no-install-recommends libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | gpg --dearmor - | sudo tee /etc/apt/trusted.gpg.d/kitware.gpg >/dev/null @@ -88,7 +88,8 @@ elif [[ $(cut -f2 <<< $(lsb_release -i)) == "Ubuntu" ]]; then sudo apt-get install -y --no-install-recommends cmake sudo apt-get install -y --no-install-recommends powershell sudo apt-get install -y --no-install-recommends curl zip unzip tar - sudo apt-get install -y --no-install-recommends pkg-config autoconf libtool bison + sudo apt-get install -y --no-install-recommends bison + sudo apt-get install -y --no-install-recommends automake autoconf libtool pkg-config autoconf-archive sudo apt-get clean fi else From ffdd5e41a83e87dee978575a3435d3083fe53286 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 3 May 2024 15:35:17 +0200 Subject: [PATCH 1351/1365] build.ps1 is arm64 compatible (#8900) --- build.ps1 | 115 +++++++++++++++++++++++++++++++++++---------- scripts/utils.psm1 | 112 +++++++++++++++++++++++++++++++++++++++++-- 2 files changed, 198 insertions(+), 29 deletions(-) diff --git a/build.ps1 b/build.ps1 index e378e4b4536..de8916d94a6 100755 --- a/build.ps1 +++ b/build.ps1 @@ -6,7 +6,7 @@ build Created By: Stefano Sinigardi Created Date: February 18, 2019 - Last Modified Date: September 25, 2023 + Last Modified Date: April 29, 2024 .DESCRIPTION Build tool using CMake, trying to properly setup the environment around compiler @@ -92,6 +92,12 @@ Force using a different buildtrees dir for vcpkg .PARAMETER ForceVCPKGPackagesRemoval Force clean up of vcpkg packages folder at the end of the script +.PARAMETER CloneVCPKGShallow +Clone vcpkg as shallow repository + +.PARAMETER ForceDisableVCPKGShallow +Force vcpkg clone to NOT be a shallow one + .PARAMETER ForceSetupVS Forces Visual Studio setup, also on systems on which it would not have been enabled automatically @@ -179,6 +185,8 @@ param ( [switch]$ForceVCPKGBuildtreesRemoval = $false, [string]$ForceVCPKGBuildtreesPath = "", [switch]$ForceVCPKGPackagesRemoval = $false, + [switch]$CloneVCPKGShallow = $false, + [switch]$ForceDisableVCPKGShallow = $false, [switch]$ForceSetupVS = $false, [switch]$ForceCMakeFromVS = $false, [switch]$ForceNinjaFromVS = $false, @@ -193,13 +201,33 @@ param ( $global:DisableInteractive = $DisableInteractive -$build_ps1_version = "3.6.1" +$build_ps1_version = "4.0.1" $script_name = $MyInvocation.MyCommand.Name $utils_psm1_avail = $false -if (Test-Path $PSScriptRoot/scripts/utils.psm1) { +if (Test-Path $PSScriptRoot/utils.psm1) { + Import-Module -Name $PSScriptRoot/utils.psm1 -Force + $utils_psm1_avail = $true +} +elseif (Test-Path $PSScriptRoot/cmake/utils.psm1) { + Import-Module -Name $PSScriptRoot/cmake/utils.psm1 -Force + $utils_psm1_avail = $true + $IsInGitSubmodule = $false +} +elseif (Test-Path $PSScriptRoot/ci/utils.psm1) { + Import-Module -Name $PSScriptRoot/ci/utils.psm1 -Force + $utils_psm1_avail = $true + $IsInGitSubmodule = $false +} +elseif (Test-Path $PSScriptRoot/ccm/utils.psm1) { + Import-Module -Name $PSScriptRoot/ccm/utils.psm1 -Force + $utils_psm1_avail = $true + $IsInGitSubmodule = $false +} +elseif (Test-Path $PSScriptRoot/scripts/utils.psm1) { Import-Module -Name $PSScriptRoot/scripts/utils.psm1 -Force $utils_psm1_avail = $true + $IsInGitSubmodule = $false } else { $utils_psm1_version = "unavail" @@ -306,6 +334,10 @@ if ($InstallDARKNETthroughVCPKG -and -not $EnableOPENCV) { if ($UseVCPKG) { Write-Host "vcpkg bootstrap script: bootstrap-vcpkg${bootstrap_ext}" + if(($ForceOpenCVVersion -eq 0) -and -Not $ForceDisableVCPKGShallow) { + Write-Host "vcpkg will be cloned in shallow mode since baseline is not needed" + $CloneVCPKGShallow = $true + } } if ((-Not $IsWindows) -and (-Not $IsWindowsPowerShell) -and (-Not $ForceSetupVS)) { @@ -323,6 +355,31 @@ if (($IsLinux -or $IsMacOS) -and ($ForceGCCVersion -gt 0)) { $env:CXX = "g++-$ForceGCCVersion" } +$osArchitecture = [System.Runtime.InteropServices.RuntimeInformation]::OSArchitecture +switch ($osArchitecture) { + "X86" { + $vcpkgArchitecture = "x86" + $vsArchitecture = "Win32" + } + "X64" { + $vcpkgArchitecture = "x64" + $vsArchitecture = "x64" + } + "Arm" { + $vcpkgArchitecture = "arm" + $vsArchitecture = "arm" + } + "Arm64" { + $vcpkgArchitecture = "arm64" + $vsArchitecture = "arm64" + } + default { + $vcpkgArchitecture = "x64" + $vsArchitecture = "x64" + Write-Output "Unknown architecture. Trying x64" + } +} + $vcpkg_triplet_set_by_this_script = $false $vcpkg_host_triplet_set_by_this_script = $false @@ -336,48 +393,48 @@ if (($IsWindows -or $IsWindowsPowerShell) -and (-Not $env:VCPKG_DEFAULT_TRIPLET) $DoNotUseNinja = $true Write-Host "Warning: when building for 32bit windows target, only msbuild can be used and ninja will be disabled. Doing that for you!" -ForegroundColor Yellow } - $env:VCPKG_DEFAULT_TRIPLET = "x86-windows" + $env:VCPKG_DEFAULT_TRIPLET = "${vcpkgArchitecture}-windows" $vcpkg_triplet_set_by_this_script = $true } else { if($BuildDebug) { - $env:VCPKG_DEFAULT_TRIPLET = "x64-windows" + $env:VCPKG_DEFAULT_TRIPLET = "${vcpkgArchitecture}-windows" $vcpkg_triplet_set_by_this_script = $true } else { - $env:VCPKG_DEFAULT_TRIPLET = "x64-windows-release" + $env:VCPKG_DEFAULT_TRIPLET = "${vcpkgArchitecture}-windows-release" $vcpkg_triplet_set_by_this_script = $true } } } if (($IsWindows -or $IsWindowsPowerShell) -and (-Not $env:VCPKG_DEFAULT_HOST_TRIPLET)) { if ($BuildDebug) { - $env:VCPKG_DEFAULT_HOST_TRIPLET = "x64-windows" + $env:VCPKG_DEFAULT_HOST_TRIPLET = "${vcpkgArchitecture}-windows" $vcpkg_host_triplet_set_by_this_script = $true } else { - $env:VCPKG_DEFAULT_HOST_TRIPLET = "x64-windows-release" + $env:VCPKG_DEFAULT_HOST_TRIPLET = "${vcpkgArchitecture}-windows-release" $vcpkg_host_triplet_set_by_this_script = $true } } if ($IsMacOS -and (-Not $env:VCPKG_DEFAULT_TRIPLET)) { if ($BuildDebug) { - $env:VCPKG_DEFAULT_TRIPLET = "x64-osx" + $env:VCPKG_DEFAULT_TRIPLET = "${vcpkgArchitecture}-osx" $vcpkg_triplet_set_by_this_script = $true } else { - $env:VCPKG_DEFAULT_TRIPLET = "x64-osx-release" + $env:VCPKG_DEFAULT_TRIPLET = "${vcpkgArchitecture}-osx-release" $vcpkg_triplet_set_by_this_script = $true } } if ($IsMacOS -and (-Not $env:VCPKG_DEFAULT_HOST_TRIPLET)) { if ($BuildDebug) { - $env:VCPKG_DEFAULT_HOST_TRIPLET = "x64-osx" + $env:VCPKG_DEFAULT_HOST_TRIPLET = "${vcpkgArchitecture}-osx" $vcpkg_host_triplet_set_by_this_script = $true } else { - $env:VCPKG_DEFAULT_HOST_TRIPLET = "x64-osx-release" + $env:VCPKG_DEFAULT_HOST_TRIPLET = "${vcpkgArchitecture}-osx-release" $vcpkg_host_triplet_set_by_this_script = $true } } @@ -385,22 +442,22 @@ if ($IsMacOS -and (-Not $env:VCPKG_DEFAULT_HOST_TRIPLET)) { if ($IsLinux -and (-Not $env:VCPKG_DEFAULT_TRIPLET)) { if ($true) { if ($BuildDebug) { - $env:VCPKG_DEFAULT_TRIPLET = "x64-linux" + $env:VCPKG_DEFAULT_TRIPLET = "${vcpkgArchitecture}-linux" $vcpkg_triplet_set_by_this_script = $true } else { - $env:VCPKG_DEFAULT_TRIPLET = "x64-linux-release" + $env:VCPKG_DEFAULT_TRIPLET = "${vcpkgArchitecture}-linux-release" $vcpkg_triplet_set_by_this_script = $true } } } if ($IsLinux -and (-Not $env:VCPKG_DEFAULT_HOST_TRIPLET)) { if ($BuildDebug) { - $env:VCPKG_DEFAULT_HOST_TRIPLET = "x64-linux" + $env:VCPKG_DEFAULT_HOST_TRIPLET = "${vcpkgArchitecture}-linux" $vcpkg_host_triplet_set_by_this_script = $true } else { - $env:VCPKG_DEFAULT_HOST_TRIPLET = "x64-linux-release" + $env:VCPKG_DEFAULT_HOST_TRIPLET = "${vcpkgArchitecture}-linux-release" $vcpkg_host_triplet_set_by_this_script = $true } } @@ -630,11 +687,11 @@ if (-Not $DoNotUseNinja) { if (-Not $DoNotSetupVS) { $CL_EXE = Get-Command "cl" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition - if ((-Not $CL_EXE) -or ($CL_EXE -match "HostX86\\x86") -or ($CL_EXE -match "HostX64\\x86")) { + if (-Not $CL_EXE) { $vsfound = getLatestVisualStudioWithDesktopWorkloadPath Write-Host "Found VS in ${vsfound}" Push-Location "${vsfound}/Common7/Tools" - cmd.exe /c "VsDevCmd.bat -arch=x64 & set" | + cmd.exe /c "VsDevCmd.bat -arch=${vsArchitecture} & set" | ForEach-Object { if ($_ -match "=") { $v = $_.split("="); Set-Item -force -path "ENV:\$($v[0])" -value "$($v[1])" @@ -650,26 +707,26 @@ if (-Not $DoNotSetupVS) { $debugConfig = " --config Debug " $releaseConfig = " --config Release " if ($Use32bitTriplet) { - $targetArchitecture = "`"Win32`"" + $targetArchitecture = "`"${vsArchitecture}`"" } else { - $targetArchitecture = "`"x64`"" + $targetArchitecture = "`"${vsArchitecture}`"" } if ($tokens[0] -eq "14") { $generator = "Visual Studio 14 2015" - $AdditionalBuildSetup = $AdditionalBuildSetup + " -T `"host=x64`" -A $targetArchitecture" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -A $targetArchitecture" } elseif ($tokens[0] -eq "15") { $generator = "Visual Studio 15 2017" - $AdditionalBuildSetup = $AdditionalBuildSetup + " -T `"host=x64`" -A $targetArchitecture" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -A $targetArchitecture" } elseif ($tokens[0] -eq "16") { $generator = "Visual Studio 16 2019" - $AdditionalBuildSetup = $AdditionalBuildSetup + " -T `"host=x64`" -A $targetArchitecture" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -A $targetArchitecture" } elseif ($tokens[0] -eq "17") { $generator = "Visual Studio 17 2022" - $AdditionalBuildSetup = $AdditionalBuildSetup + " -T `"host=x64`" -A $targetArchitecture" + $AdditionalBuildSetup = $AdditionalBuildSetup + " -A $targetArchitecture" } else { MyThrow("Unknown Visual Studio version, unsupported configuration") @@ -739,7 +796,7 @@ if ($UseVCPKG -And -Not $ForceLocalVCPKG) { if (($null -eq $vcpkg_path) -and $UseVCPKG) { if (-Not (Test-Path "$PWD/vcpkg${VCPKGSuffix}")) { $shallow_copy = "" - if(($ForceOpenCVVersion -eq 0)) { + if($CloneVCPKGShallow) { $shallow_copy = " --depth 1 " } $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "clone $shallow_copy https://github.com/microsoft/vcpkg vcpkg${VCPKGSuffix}" @@ -999,6 +1056,9 @@ else { Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $debug_build_folder } + if (-Not (Test-Path $DebugInstallPrefix)) { + New-Item -Path $DebugInstallPrefix -ItemType directory -Force | Out-Null + } New-Item -Path $debug_build_folder -ItemType directory -Force | Out-Null Set-Location $debug_build_folder $cmake_args = "-G `"$generator`" ${DebugBuildSetup} ${AdditionalBuildSetup} -S .." @@ -1031,6 +1091,9 @@ else { Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $release_build_folder } + if (-Not (Test-Path $ReleaseInstallPrefix)) { + New-Item -Path $ReleaseInstallPrefix -ItemType directory -Force | Out-Null + } New-Item -Path $release_build_folder -ItemType directory -Force | Out-Null Set-Location $release_build_folder $cmake_args = "-G `"$generator`" ${ReleaseBuildSetup} ${AdditionalBuildSetup} -S .." @@ -1056,7 +1119,7 @@ else { if (-Not ($exitCode -eq 0)) { MyThrow("Build failed! Exited with error code $exitCode.") } - if (-Not $UseVCPKG -And -Not $DisableDLLcopy) { + if ($IsWindows -And -Not $UseVCPKG -And -Not $DisableDLLcopy) { $dllfiles = Get-ChildItem ./${dllfolder}/*.dll if ($dllfiles) { Copy-Item $dllfiles .. diff --git a/scripts/utils.psm1 b/scripts/utils.psm1 index 928d9339ae8..6e22056957c 100644 --- a/scripts/utils.psm1 +++ b/scripts/utils.psm1 @@ -22,7 +22,7 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. #> -$utils_psm1_version = "0.3.0" +$utils_psm1_version = "1.2.3" $IsWindowsPowerShell = switch ( $PSVersionTable.PSVersion.Major ) { 5 { $true } 4 { $true } @@ -167,6 +167,24 @@ function getLatestVisualStudioWithDesktopWorkloadVersion([bool]$required = $true return $installationVersion } +function setupVisualStudio([bool]$required = $true) { + $CL_EXE = Get-Command "cl" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition + if ((-Not $CL_EXE) -or ($CL_EXE -match "HostX86\\x86") -or ($CL_EXE -match "HostX64\\x86")) { + $vsfound = getLatestVisualStudioWithDesktopWorkloadPath + Write-Host "Found VS in ${vsfound}" + Push-Location "${vsfound}/Common7/Tools" + cmd.exe /c "VsDevCmd.bat -arch=x64 & set" | + ForEach-Object { + if ($_ -match "=") { + $v = $_.split("="); Set-Item -force -path "ENV:\$($v[0])" -value "$($v[1])" + } + } + Pop-Location + $env:PATH = "${vsfound}/VC/Tools/Llvm/x64/bin;$env:PATH" + Write-Host "Visual Studio Command Prompt variables set" + } +} + function DownloadNinja() { Write-Host "Downloading a portable version of Ninja" -ForegroundColor Yellow Remove-Item -Force -Recurse -ErrorAction SilentlyContinue ninja @@ -192,12 +210,12 @@ function DownloadNinja() { function DownloadAria2() { Write-Host "Downloading a portable version of Aria2" -ForegroundColor Yellow if ($IsWindows -or $IsWindowsPowerShell) { - $basename = "aria2-1.35.0-win-32bit-build1" + $basename = "aria2-1.37.0-win-32bit-build1" $zipName = "${basename}.zip" $outFolder = "$basename/$basename" Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $outFolder Remove-Item -Force -ErrorAction SilentlyContinue $zipName - $url = "https://github.com/aria2/aria2/releases/download/release-1.35.0/$zipName" + $url = "https://github.com/aria2/aria2/releases/download/release-1.37.0/$zipName" Invoke-RestMethod -Uri $url -Method Get -ContentType application/zip -OutFile $zipName Expand-Archive -Path $zipName } @@ -228,6 +246,29 @@ function DownloadAria2() { return "./$outFolder/aria2c${ExecutableSuffix}" } +function DownloadLicencpp() { + $licencpp_version = "0.2.1" + Write-Host "Downloading a portable version of licencpp v${licencpp_version}" -ForegroundColor Yellow + if ($IsWindows -or $IsWindowsPowerShell) { + $basename = "licencpp-Windows" + } + elseif ($IsLinux) { + $basename = "licencpp-Linux" + } + else { + MyThrow("Unknown OS, unsupported") + } + $zipName = "${basename}.zip" + $outFolder = "${basename}" + Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $outFolder + Remove-Item -Force -ErrorAction SilentlyContinue $zipName + $url = "https://github.com/cenit/licencpp/releases/download/v${licencpp_version}/$zipName" + Invoke-RestMethod -Uri $url -Method Get -ContentType application/zip -OutFile $zipName + Expand-Archive -Path $zipName + Remove-Item -Force -ErrorAction SilentlyContinue $zipName + return "./$outFolder/licencpp${ExecutableSuffix}" +} + function Download7Zip() { Write-Host "Downloading a portable version of 7-Zip" -ForegroundColor Yellow if ($IsWindows -or $IsWindowsPowerShell) { @@ -324,6 +365,65 @@ Function MyThrow ($Message) { } } +Function CopyTexFile ($MyFile) { + $MyFileName = Split-Path $MyFile -Leaf + New-Item -ItemType Directory -Force -Path "~/${latex_path}" | Out-Null + if (-Not (Test-Path "~/${latex_path}/$MyFileName" )) { + Write-Host "Copying $MyFile to ~/${latex_path}" + Copy-Item "$MyFile" "~/${latex_path}" + } + else { + Write-Host "~/${latex_path}/$MyFileName already present" + } +} + +Function dos2unix { + Param ( + [Parameter(mandatory = $true)] + [string[]]$path + ) + + Get-ChildItem -File -Recurse -Path $path | + ForEach-Object { + Write-Host "Converting $_" + $x = get-content -raw -path $_.fullname; $x -replace "`r`n", "`n" | Set-Content -NoNewline -Force -path $_.fullname + } +} + +Function unix2dos { + Param ( + [Parameter(mandatory = $true)] + [string[]]$path + ) + + Get-ChildItem -File -Recurse -Path $path | + ForEach-Object { + $x = get-content -raw -path $_.fullname + $SearchStr = [regex]::Escape("`r`n") + $SEL = Select-String -InputObject $x -Pattern $SearchStr + if ($null -ne $SEL) { + Write-Host "Converting $_" + # do nothing: avoid creating files containing `r`r`n when using unix2dos twice on the same file + } + else { + Write-Host "Converting $_" + $x -replace "`n", "`r`n" | Set-Content -NoNewline -Force -path $_.fullname + } + } +} + +Function UpdateRepo { + if ($GIT_EXE) { + Get-ChildItem -Directory | + ForEach-Object { + Set-Location $_.Name + git pull + git submodule update --recursive + Set-Location .. + } + } +} + Export-ModuleMember -Variable utils_psm1_version Export-ModuleMember -Variable IsWindowsPowerShell Export-ModuleMember -Variable IsInGitSubmodule @@ -336,7 +436,13 @@ Export-ModuleMember -Variable cuda_version_short_dashed Export-ModuleMember -Function getProgramFiles32bit Export-ModuleMember -Function getLatestVisualStudioWithDesktopWorkloadPath Export-ModuleMember -Function getLatestVisualStudioWithDesktopWorkloadVersion +Export-ModuleMember -Function setupVisualStudio Export-ModuleMember -Function DownloadNinja Export-ModuleMember -Function DownloadAria2 Export-ModuleMember -Function Download7Zip +Export-ModuleMember -Function DownloadLicencpp Export-ModuleMember -Function MyThrow +Export-ModuleMember -Function CopyTexFile +Export-ModuleMember -Function dos2unix +Export-ModuleMember -Function unix2dos +Export-ModuleMember -Function UpdateRepo From 5dc0126e2f1256992fb69d20ece9c04ff06c85a5 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 3 May 2024 15:41:13 +0200 Subject: [PATCH 1352/1365] Fixes #8897 (#8901) --- Dockerfile.cpu | 6 ++---- Dockerfile.gpu | 8 ++++---- docker-compose.yml | 6 +++--- 3 files changed, 9 insertions(+), 11 deletions(-) diff --git a/Dockerfile.cpu b/Dockerfile.cpu index 7a15cfb499e..db433f29b25 100644 --- a/Dockerfile.cpu +++ b/Dockerfile.cpu @@ -14,7 +14,7 @@ RUN rm Dockerfile.cpu RUN rm Dockerfile.gpu -RUN rm Docker-compose.yml +RUN rm docker-compose.yml RUN make @@ -28,7 +28,7 @@ RUN apt-get install -y sudo libgomp1 RUN useradd -U -m yolo -RUN usermod -aG sudo yolo +RUN usermod -aG sudo yolo RUN usermod --shell /bin/bash yolo @@ -45,5 +45,3 @@ RUN ldconfig WORKDIR /home/yolo/darknet USER yolo - - diff --git a/Dockerfile.gpu b/Dockerfile.gpu index c4a9effbbcc..f1985fbcfc0 100644 --- a/Dockerfile.gpu +++ b/Dockerfile.gpu @@ -14,11 +14,11 @@ RUN rm Dockerfile.cpu RUN rm Dockerfile.gpu -RUN rm Docker-compose.yml +RUN rm docker-compose.yml RUN make -FROM nvidia/cuda:11.6.0-cudnn8-devel-ubuntu20.04 +FROM nvidia/cuda:11.6.1-cudnn8-devel-ubuntu20.04 ENV DEBIAN_FRONTEND noninteractive @@ -28,7 +28,7 @@ RUN apt-get install -y sudo libgomp1 RUN useradd -U -m yolo -RUN usermod -aG sudo yolo +RUN usermod -aG sudo yolo RUN usermod --shell /bin/bash yolo @@ -44,4 +44,4 @@ RUN ldconfig WORKDIR /home/yolo/darknet -USER yolo \ No newline at end of file +USER yolo diff --git a/docker-compose.yml b/docker-compose.yml index 968fbccaaf9..2eebd876997 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -2,7 +2,7 @@ version: '2' services: yolo-gpu: - build: + build: context: . dockerfile: Dockerfile.gpu image: yolo:gpu @@ -14,7 +14,7 @@ services: count: 1 capabilities: [gpu] yolo-cpu: - build: + build: context: . dockerfile: Dockerfile.cpu - image: yolo:cpu \ No newline at end of file + image: yolo:cpu From 08167076fde493b8a8bea79d2a1973c8c3d479af Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Fri, 3 May 2024 15:50:31 +0200 Subject: [PATCH 1353/1365] Fixes #8885 (#8902) --- src/layer.c | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/layer.c b/src/layer.c index 032a24e0f2c..758644b567b 100644 --- a/src/layer.c +++ b/src/layer.c @@ -92,9 +92,9 @@ void free_layer_custom(layer l, int keep_cudnn_desc) if (l.bias_updates) free(l.bias_updates), l.bias_updates = NULL; if (l.scales) free(l.scales), l.scales = NULL; if (l.scale_updates) free(l.scale_updates), l.scale_updates = NULL; - if (l.biases_ema) free(l.biases_ema), l.biases = NULL; - if (l.scales_ema) free(l.scales_ema), l.scales = NULL; - if (l.weights_ema) free(l.weights_ema), l.weights = NULL; + if (l.biases_ema) free(l.biases_ema), l.biases_ema = NULL; + if (l.scales_ema) free(l.scales_ema), l.scales_ema = NULL; + if (l.weights_ema) free(l.weights_ema), l.weights_ema = NULL; if (l.weights) free(l.weights), l.weights = NULL; if (l.weight_updates) free(l.weight_updates), l.weight_updates = NULL; if (l.align_bit_weights) free(l.align_bit_weights); From d02cc3a81689a1593dc5d0c8ed018d10338bebdd Mon Sep 17 00:00:00 2001 From: "Kyeongsoo Kim(KnsoF)" <56542593+K9714@users.noreply.github.com> Date: Fri, 3 May 2024 22:53:53 +0900 Subject: [PATCH 1354/1365] Add YOLO Layer 'nbiases' (#8893) Co-authored-by: KyeongSoo Kim --- src/yolo_layer.c | 1 + 1 file changed, 1 insertion(+) diff --git a/src/yolo_layer.c b/src/yolo_layer.c index ac464ba39b4..de9d0990455 100644 --- a/src/yolo_layer.c +++ b/src/yolo_layer.c @@ -29,6 +29,7 @@ layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int l.classes = classes; l.cost = (float*)xcalloc(1, sizeof(float)); l.biases = (float*)xcalloc(total * 2, sizeof(float)); + l.nbiases = total * 2; if(mask) l.mask = mask; else{ l.mask = (int*)xcalloc(n, sizeof(int)); From d17ec15a06a9bde6e12de9354e1fde9888dd6de0 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 10 Oct 2024 09:59:36 +0200 Subject: [PATCH 1355/1365] update cuda to 12.6 (#8922) * update cuda to 12.6 * remove cuda from opencv2 and opencv3 --- .github/workflows/ccpp.yml | 44 ++--------- .github/workflows/on_pr.yml | 44 ++--------- .gitignore | 2 + CMakeLists.txt | 8 +- build.ps1 | 12 --- scripts/requested_cuda_version.sh | 2 +- scripts/utils.psm1 | 106 ++++++++++++++++++++----- vcpkg.json | 46 +---------- vcpkg.json.opencv23 | 126 ------------------------------ 9 files changed, 112 insertions(+), 278 deletions(-) delete mode 100644 vcpkg.json.opencv23 diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index af7c5b9a77f..18aa31db638 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -147,7 +147,7 @@ jobs: path: ${{ github.workspace }}/uselib* - ubuntu-vcpkg-opencv3-cuda: + ubuntu-vcpkg-opencv3: runs-on: ubuntu-20.04 steps: - uses: actions/checkout@v3 @@ -161,16 +161,6 @@ jobs: - name: Clean downloads run: sudo apt-get clean - - name: 'Install CUDA' - run: ${{ github.workspace }}/scripts/deploy-cuda.sh - - - name: 'Create softlinks for CUDA' - run: | - source ${{ github.workspace }}/scripts/requested_cuda_version.sh - sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so - - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash run: > @@ -192,15 +182,10 @@ jobs: - name: 'Build' shell: pwsh - env: - CUDACXX: "/usr/local/cuda/bin/nvcc" - CUDA_PATH: "/usr/local/cuda" - CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" - LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 3 -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -ForceOpenCVVersion 3 -DisableInteractive -DoNotUpdateTOOL - ubuntu-vcpkg-opencv2-cuda: + ubuntu-vcpkg-opencv2: runs-on: ubuntu-20.04 steps: - uses: actions/checkout@v3 @@ -214,16 +199,6 @@ jobs: - name: Clean downloads run: sudo apt-get clean - - name: 'Install CUDA' - run: ${{ github.workspace }}/scripts/deploy-cuda.sh - - - name: 'Create softlinks for CUDA' - run: | - source ${{ github.workspace }}/scripts/requested_cuda_version.sh - sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so - - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash run: > @@ -245,12 +220,7 @@ jobs: - name: 'Build' shell: pwsh - env: - CUDACXX: "/usr/local/cuda/bin/nvcc" - CUDA_PATH: "/usr/local/cuda" - CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" - LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 2 -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -ForceOpenCVVersion 2 -DisableInteractive -DoNotUpdateTOOL ubuntu: @@ -693,9 +663,9 @@ jobs: - name: 'Build' env: - CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.2" - CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.2" - CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.2\\bin\\nvcc.exe" + CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.6" + CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.6" + CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.6\\bin\\nvcc.exe" shell: pwsh run: ${{ github.workspace }}/build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateTOOL diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index 409523fd7e3..1ad990ba91a 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -106,7 +106,7 @@ jobs: run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL - ubuntu-vcpkg-opencv3-cuda: + ubuntu-vcpkg-opencv3: runs-on: ubuntu-20.04 steps: - uses: actions/checkout@v3 @@ -120,16 +120,6 @@ jobs: - name: Clean downloads run: sudo apt-get clean - - name: 'Install CUDA' - run: ${{ github.workspace }}/scripts/deploy-cuda.sh - - - name: 'Create softlinks for CUDA' - run: | - source ${{ github.workspace }}/scripts/requested_cuda_version.sh - sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so - - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash run: > @@ -141,15 +131,10 @@ jobs: - name: 'Build' shell: pwsh - env: - CUDACXX: "/usr/local/cuda/bin/nvcc" - CUDA_PATH: "/usr/local/cuda" - CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" - LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 3 -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -ForceOpenCVVersion 3 -DisableInteractive -DoNotUpdateTOOL - ubuntu-vcpkg-opencv2-cuda: + ubuntu-vcpkg-opencv2: runs-on: ubuntu-20.04 steps: - uses: actions/checkout@v3 @@ -163,16 +148,6 @@ jobs: - name: Clean downloads run: sudo apt-get clean - - name: 'Install CUDA' - run: ${{ github.workspace }}/scripts/deploy-cuda.sh - - - name: 'Create softlinks for CUDA' - run: | - source ${{ github.workspace }}/scripts/requested_cuda_version.sh - sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so.1 - sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so.1 - sudo ln -s /usr/local/cuda-${CUDA_VERSION}/lib64/stubs/libcuda.so /usr/local/cuda-${CUDA_VERSION}/lib64/libcuda.so - - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash run: > @@ -184,12 +159,7 @@ jobs: - name: 'Build' shell: pwsh - env: - CUDACXX: "/usr/local/cuda/bin/nvcc" - CUDA_PATH: "/usr/local/cuda" - CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" - LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -ForceOpenCVVersion 2 -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -ForceOpenCVVersion 2 -DisableInteractive -DoNotUpdateTOOL ubuntu: @@ -435,9 +405,9 @@ jobs: - name: 'Build' env: - CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.2" - CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.2" - CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.2\\bin\\nvcc.exe" + CUDA_PATH: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.6" + CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.6" + CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.6\\bin\\nvcc.exe" shell: pwsh run: ${{ github.workspace }}/build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateTOOL diff --git a/.gitignore b/.gitignore index bb62a6060b2..3166d82c7b3 100644 --- a/.gitignore +++ b/.gitignore @@ -41,6 +41,8 @@ build/detect_cuda_compute_capabilities.cu build/.ninja_deps build/.ninja_log build/Makefile +CMakeFiles/ +CMakeCache.txt */vcpkg-manifest-install.log build.log __pycache__/ diff --git a/CMakeLists.txt b/CMakeLists.txt index 4710e39def8..fc34cc1e5a5 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -52,17 +52,17 @@ if(ENABLE_OPENCV) if(VCPKG_USE_OPENCV4) list(APPEND VCPKG_MANIFEST_FEATURES "opencv-cuda") elseif(VCPKG_USE_OPENCV3) - list(APPEND VCPKG_MANIFEST_FEATURES "opencv3-cuda") + list(APPEND VCPKG_MANIFEST_FEATURES "opencv3") elseif(VCPKG_USE_OPENCV2) - list(APPEND VCPKG_MANIFEST_FEATURES "opencv2-cuda") + list(APPEND VCPKG_MANIFEST_FEATURES "opencv2") endif() else() if(VCPKG_USE_OPENCV4) list(APPEND VCPKG_MANIFEST_FEATURES "opencv-base") elseif(VCPKG_USE_OPENCV3) - list(APPEND VCPKG_MANIFEST_FEATURES "opencv3-base") + list(APPEND VCPKG_MANIFEST_FEATURES "opencv3") elseif(VCPKG_USE_OPENCV2) - list(APPEND VCPKG_MANIFEST_FEATURES "opencv2-base") + list(APPEND VCPKG_MANIFEST_FEATURES "opencv2") endif() endif() endif() diff --git a/build.ps1 b/build.ps1 index de8916d94a6..07ecf1f17a8 100755 --- a/build.ps1 +++ b/build.ps1 @@ -907,21 +907,14 @@ if ($BuildInstaller) { if (($ForceOpenCVVersion -eq 2) -and $UseVCPKG) { Write-Host "You requested OpenCV version 2, so vcpkg will install that version" -ForegroundColor Yellow - Write-Host "This requires using vcpkg.json.opencv23 as manifest file" -ForegroundColor Yellow $AdditionalBuildSetup = $AdditionalBuildSetup + " -DVCPKG_USE_OPENCV4=OFF -DVCPKG_USE_OPENCV2=ON" } if (($ForceOpenCVVersion -eq 3) -and $UseVCPKG) { Write-Host "You requested OpenCV version 3, so vcpkg will install that version" -ForegroundColor Yellow - Write-Host "This requires using vcpkg.json.opencv23 as manifest file" -ForegroundColor Yellow $AdditionalBuildSetup = $AdditionalBuildSetup + " -DVCPKG_USE_OPENCV4=OFF -DVCPKG_USE_OPENCV3=ON" } -if($ForceOpenCVVersion -gt 0) { - Move-Item $PSCustomScriptRoot/vcpkg.json $PSCustomScriptRoot/vcpkg.json.bak - Move-Item $PSCustomScriptRoot/vcpkg.json.opencv23 $PSCustomScriptRoot/vcpkg.json -} - if ($UseVCPKG -and $ForceVCPKGCacheRemoval) { if ($IsWindows -or $IsWindowsPowerShell) { $vcpkgbinarycachepath = "$env:LOCALAPPDATA/vcpkg/archive" @@ -1175,11 +1168,6 @@ if ($vcpkg_host_triplet_set_by_this_script) { $env:VCPKG_DEFAULT_HOST_TRIPLET = $null } -if($ForceOpenCVVersion -gt 0) { - Move-Item $PSCustomScriptRoot/vcpkg.json $PSCustomScriptRoot/vcpkg.json.opencv23 - Move-Item $PSCustomScriptRoot/vcpkg.json.bak $PSCustomScriptRoot/vcpkg.json -} - if ($vcpkg_branch_set_by_this_script) { Push-Location $vcpkg_path $git_args = "checkout -" diff --git a/scripts/requested_cuda_version.sh b/scripts/requested_cuda_version.sh index ae9ea2a4f55..8cb3f6ad9d6 100755 --- a/scripts/requested_cuda_version.sh +++ b/scripts/requested_cuda_version.sh @@ -1,4 +1,4 @@ #!/usr/bin/env bash -export CUDA_VERSION="12.2" +export CUDA_VERSION="12.6" export CUDA_VERSION_DASHED="${CUDA_VERSION//./-}" diff --git a/scripts/utils.psm1 b/scripts/utils.psm1 index 6e22056957c..cf4f0dccf64 100644 --- a/scripts/utils.psm1 +++ b/scripts/utils.psm1 @@ -22,7 +22,7 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. #> -$utils_psm1_version = "1.2.3" +$utils_psm1_version = "1.4.1" $IsWindowsPowerShell = switch ( $PSVersionTable.PSVersion.Major ) { 5 { $true } 4 { $true } @@ -38,6 +38,31 @@ if ($IsWindowsPowerShell -or $IsWindows) { $64bitPwsh = $([Environment]::Is64BitProcess) $64bitOS = $([Environment]::Is64BitOperatingSystem) +$osArchitecture = [System.Runtime.InteropServices.RuntimeInformation]::OSArchitecture +switch ($osArchitecture) { + "X86" { + $vcpkgArchitecture = "x86" + $vsArchitecture = "Win32" + } + "X64" { + $vcpkgArchitecture = "x64" + $vsArchitecture = "x64" + } + "Arm" { + $vcpkgArchitecture = "arm" + $vsArchitecture = "arm" + } + "Arm64" { + $vcpkgArchitecture = "arm64" + $vsArchitecture = "arm64" + } + default { + $vcpkgArchitecture = "x64" + $vsArchitecture = "x64" + Write-Output "Unknown architecture. Trying x64" + } +} + Push-Location $PSScriptRoot $GIT_EXE = Get-Command "git" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition @@ -55,11 +80,36 @@ else { } Pop-Location -$cuda_version_full = "12.2.0" -$cuda_version_short = "12.2" +$cuda_version_full = "12.6.2" +$cuda_version_short = "12.6" $cuda_version_full_dashed = $cuda_version_full.replace('.', '-') $cuda_version_short_dashed = $cuda_version_short.replace('.', '-') +function activateVenv([string]$VenvPath) { + if ($IsWindowsPowerShell -or $IsWindows) { + $activate_script = "$VenvPath/Scripts/Activate.ps1" + } + else { + $activate_script = "$VenvPath/bin/Activate.ps1" + } + + $activate_script = Resolve-Path $activate_script + $VenvPath = Resolve-Path $VenvPath + + if ($env:VIRTUAL_ENV -eq $VenvPath) { + Write-Host "Venv already activated" + return + } + else { + Write-Host "Activating venv" + if (-Not (Test-Path $activate_script)) { + MyThrow("Could not find activate script at $activate_script") + } + & $activate_script + } +} + + function getProgramFiles32bit() { $out = ${env:PROGRAMFILES(X86)} if ($null -eq $out) { @@ -167,21 +217,34 @@ function getLatestVisualStudioWithDesktopWorkloadVersion([bool]$required = $true return $installationVersion } -function setupVisualStudio([bool]$required = $true) { +function setupVisualStudio([bool]$required = $true, [bool]$enable_clang = $false) { $CL_EXE = Get-Command "cl" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition - if ((-Not $CL_EXE) -or ($CL_EXE -match "HostX86\\x86") -or ($CL_EXE -match "HostX64\\x86")) { - $vsfound = getLatestVisualStudioWithDesktopWorkloadPath - Write-Host "Found VS in ${vsfound}" - Push-Location "${vsfound}/Common7/Tools" - cmd.exe /c "VsDevCmd.bat -arch=x64 & set" | - ForEach-Object { - if ($_ -match "=") { - $v = $_.split("="); Set-Item -force -path "ENV:\$($v[0])" -value "$($v[1])" + if (-Not $CL_EXE) { + $vsfound = getLatestVisualStudioWithDesktopWorkloadPath($required) + if (-Not $vsfound) { + if ($required) { + MyThrow("Could not locate any installation of Visual Studio") + } + else { + Write-Host "Could not locate any installation of Visual Studio" -ForegroundColor Red + return + } + } + else { + Write-Host "Found VS in ${vsfound}" + Push-Location "${vsfound}/Common7/Tools" + cmd.exe /c "VsDevCmd.bat -arch=${vsArchitecture} & set" | + ForEach-Object { + if ($_ -match "=") { + $v = $_.split("="); Set-Item -force -path "ENV:\$($v[0])" -value "$($v[1])" + } + } + Pop-Location + if ($enable_clang) { + $env:PATH = "${vsfound}/VC/Tools/Llvm/${vsArchitecture}/bin;$env:PATH" } + Write-Host "Visual Studio Command Prompt variables set" } - Pop-Location - $env:PATH = "${vsfound}/VC/Tools/Llvm/x64/bin;$env:PATH" - Write-Host "Visual Studio Command Prompt variables set" } } @@ -190,13 +253,13 @@ function DownloadNinja() { Remove-Item -Force -Recurse -ErrorAction SilentlyContinue ninja Remove-Item -Force -ErrorAction SilentlyContinue ninja.zip if ($IsWindows -or $IsWindowsPowerShell) { - $url = "https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-win.zip" + $url = "https://github.com/ninja-build/ninja/releases/download/v1.12.1/ninja-win.zip" } elseif ($IsLinux) { - $url = "https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-linux.zip" + $url = "https://github.com/ninja-build/ninja/releases/download/v1.12.1/ninja-linux.zip" } elseif ($IsMacOS) { - $url = "https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-mac.zip" + $url = "https://github.com/ninja-build/ninja/releases/download/v1.12.1/ninja-mac.zip" } else { MyThrow("Unknown OS, unsupported") @@ -247,7 +310,7 @@ function DownloadAria2() { } function DownloadLicencpp() { - $licencpp_version = "0.2.1" + $licencpp_version = "0.2.5" Write-Host "Downloading a portable version of licencpp v${licencpp_version}" -ForegroundColor Yellow if ($IsWindows -or $IsWindowsPowerShell) { $basename = "licencpp-Windows" @@ -433,6 +496,11 @@ Export-ModuleMember -Variable cuda_version_full Export-ModuleMember -Variable cuda_version_short Export-ModuleMember -Variable cuda_version_full_dashed Export-ModuleMember -Variable cuda_version_short_dashed +Export-ModuleMember -Variable osArchitecture +Export-ModuleMember -Variable vcpkgArchitecture +Export-ModuleMember -Variable vsArchitecture +Export-ModuleMember -Variable ExecutableSuffix +Export-ModuleMember -Function activateVenv Export-ModuleMember -Function getProgramFiles32bit Export-ModuleMember -Function getLatestVisualStudioWithDesktopWorkloadPath Export-ModuleMember -Function getLatestVisualStudioWithDesktopWorkloadVersion diff --git a/vcpkg.json b/vcpkg.json index 6f142bed231..752d0fc3669 100644 --- a/vcpkg.json +++ b/vcpkg.json @@ -73,54 +73,16 @@ } ] }, - "opencv2-base": { + "opencv2": { "description": "Build darknet with support for OpenCV2", "dependencies": [ - { - "name": "opencv2", - "features": [ - "ffmpeg" - ] - } - ] - }, - "opencv2-cuda": { - "description": "Build darknet with support for CUDA-enabled OpenCV2", - "dependencies": [ - { - "name": "opencv2", - "features": [ - "cuda", - "ffmpeg" - ] - } + "opencv2" ] }, - "opencv3-base": { + "opencv3": { "description": "Build darknet with support for OpenCV3", "dependencies": [ - { - "name": "opencv3", - "features": [ - "contrib", - "dnn", - "ffmpeg" - ] - } - ] - }, - "opencv3-cuda": { - "description": "Build darknet with support for CUDA-enabled OpenCV3", - "dependencies": [ - { - "name": "opencv3", - "features": [ - "contrib", - "cuda", - "dnn", - "ffmpeg" - ] - } + "opencv3" ] } } diff --git a/vcpkg.json.opencv23 b/vcpkg.json.opencv23 deleted file mode 100644 index 0a45be9a456..00000000000 --- a/vcpkg.json.opencv23 +++ /dev/null @@ -1,126 +0,0 @@ -{ - "name": "darknet", - "version": "0.2.5.7", - "description": "Darknet is an open source neural network framework written in C and CUDA. You only look once (YOLO) is a state-of-the-art, real-time object detection system, best example of darknet functionalities.", - "homepage": "https://github.com/alexeyab/darknet", - "dependencies": [ - { - "name": "getopt", - "platform": "windows & !mingw" - }, - "pthreads", - "stb" - ], - "features": { - "cuda": { - "description": "Build darknet with support for CUDA", - "dependencies": [ - "cuda" - ] - }, - "cudnn": { - "description": "Build darknet with support for cuDNN", - "dependencies": [ - "cuda", - "cudnn" - ] - }, - "full": { - "description": "Build darknet fully featured", - "dependencies": [ - { - "name": "darknet", - "features": [ - "cuda", - "cudnn", - "opencv-cuda" - ] - } - ] - }, - "opencv-base": { - "description": "Build darknet with support for latest version of OpenCV", - "dependencies": [ - { - "name": "opencv", - "features": [ - "contrib", - "dnn", - "ffmpeg" - ] - } - ] - }, - "opencv-cuda": { - "description": "Build darknet with support for latest version of CUDA-enabled OpenCV", - "dependencies": [ - { - "name": "opencv", - "features": [ - "contrib", - "cuda", - "dnn", - "ffmpeg" - ] - } - ] - }, - "opencv2-base": { - "description": "Build darknet with support for OpenCV2", - "dependencies": [ - { - "name": "opencv2", - "features": [ - "ffmpeg" - ] - } - ] - }, - "opencv2-cuda": { - "description": "Build darknet with support for CUDA-enabled OpenCV2", - "dependencies": [ - { - "name": "opencv2", - "features": [ - "cuda", - "ffmpeg" - ] - } - ] - }, - "opencv3-base": { - "description": "Build darknet with support for OpenCV3", - "dependencies": [ - { - "name": "opencv3", - "features": [ - "contrib", - "dnn", - "ffmpeg" - ] - } - ] - }, - "opencv3-cuda": { - "description": "Build darknet with support for CUDA-enabled OpenCV3", - "dependencies": [ - { - "name": "opencv3", - "features": [ - "contrib", - "cuda", - "dnn", - "ffmpeg" - ] - } - ] - } - }, - "overrides": [ - { - "name": "ffmpeg", - "version": "4.4.3" - } - ], - "builtin-baseline": "54cc53c43430c73f489e52af5fadd032c1aced16" -} From 19dde2f296941a75b0b9202cccd59528bde7f65a Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Tue, 22 Oct 2024 18:17:16 +0200 Subject: [PATCH 1356/1365] fix runtime libs (#8924) --- CMakeLists.txt | 18 ++++++++---------- 1 file changed, 8 insertions(+), 10 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index fc34cc1e5a5..8c65eeff301 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -22,6 +22,7 @@ option(VCPKG_USE_OPENCV2 "Use legacy OpenCV 2" OFF) option(VCPKG_USE_OPENCV3 "Use legacy OpenCV 3" OFF) option(VCPKG_USE_OPENCV4 "Use OpenCV 4" ON) option(USE_NSIS "Use NSIS as a CPack backend on Windows" ON) +option(SKIP_INSTALL_RUNTIME_LIBS "Do not install runtime libs" OFF) if(DEFINED ENV{VCPKG_DEFAULT_TRIPLET}) message(STATUS "Setting default vcpkg target triplet to $ENV{VCPKG_DEFAULT_TRIPLET}") @@ -67,11 +68,7 @@ if(ENABLE_OPENCV) endif() endif() -if(NOT CMAKE_HOST_SYSTEM_PROCESSOR AND NOT WIN32) - execute_process(COMMAND "uname" "-m" OUTPUT_VARIABLE CMAKE_HOST_SYSTEM_PROCESSOR OUTPUT_STRIP_TRAILING_WHITESPACE) -endif() - -if(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "x86") +if(CMAKE_SYSTEM_PROCESSOR MATCHES "^x86" OR CMAKE_SYSTEM_PROCESSOR MATCHES "^AMD64") set(IS_X86 TRUE) else() set(IS_X86 FALSE) @@ -672,13 +669,14 @@ if(ENABLE_CSHARP_WRAPPER) add_subdirectory(src/csharp) endif() -set(CMAKE_INSTALL_SYSTEM_RUNTIME_LIBS_SKIP TRUE) -include(InstallRequiredSystemLibraries) - -install( +if (NOT SKIP_INSTALL_RUNTIME_LIBS) + set(CMAKE_INSTALL_SYSTEM_RUNTIME_LIBS_SKIP TRUE) + include(InstallRequiredSystemLibraries) + install( PROGRAMS ${CMAKE_INSTALL_SYSTEM_RUNTIME_LIBS} DESTINATION ${INSTALL_BIN_DIR} -) + ) +endif() if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/vcpkg.json) file(READ ${CMAKE_CURRENT_SOURCE_DIR}/vcpkg.json VCPKG_JSON_STRING) From 9ade741db91fd3d796d2abb0c9889b10943ea28a Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 6 Nov 2024 15:54:59 +0100 Subject: [PATCH 1357/1365] update stb (#8926) --- 3rdparty/stb/include/stb_image.h | 355 ++++++++++++++++--------------- 1 file changed, 178 insertions(+), 177 deletions(-) diff --git a/3rdparty/stb/include/stb_image.h b/3rdparty/stb/include/stb_image.h index 5e807a0a6e7..9eedabedc45 100644 --- a/3rdparty/stb/include/stb_image.h +++ b/3rdparty/stb/include/stb_image.h @@ -1,4 +1,4 @@ -/* stb_image - v2.28 - public domain image loader - http://nothings.org/stb +/* stb_image - v2.30 - public domain image loader - http://nothings.org/stb no warranty implied; use at your own risk Do this: @@ -48,6 +48,8 @@ LICENSE RECENT REVISION HISTORY: + 2.30 (2024-05-31) avoid erroneous gcc warning + 2.29 (2023-05-xx) optimizations 2.28 (2023-01-29) many error fixes, security errors, just tons of stuff 2.27 (2021-07-11) document stbi_info better, 16-bit PNM support, bug fixes 2.26 (2020-07-13) many minor fixes @@ -1072,8 +1074,8 @@ static int stbi__addints_valid(int a, int b) return a <= INT_MAX - b; } -// returns 1 if the product of two signed shorts is valid, 0 on overflow. -static int stbi__mul2shorts_valid(short a, short b) +// returns 1 if the product of two ints fits in a signed short, 0 on overflow. +static int stbi__mul2shorts_valid(int a, int b) { if (b == 0 || b == -1) return 1; // multiplication by 0 is always 0; check for -1 so SHRT_MIN/b doesn't overflow if ((a >= 0) == (b >= 0)) return a <= SHRT_MAX/b; // product is positive, so similar to mul2sizes_valid @@ -3384,13 +3386,13 @@ static int stbi__decode_jpeg_header(stbi__jpeg *z, int scan) return 1; } -static int stbi__skip_jpeg_junk_at_end(stbi__jpeg *j) +static stbi_uc stbi__skip_jpeg_junk_at_end(stbi__jpeg *j) { // some JPEGs have junk at end, skip over it but if we find what looks // like a valid marker, resume there while (!stbi__at_eof(j->s)) { - int x = stbi__get8(j->s); - while (x == 255) { // might be a marker + stbi_uc x = stbi__get8(j->s); + while (x == 0xff) { // might be a marker if (stbi__at_eof(j->s)) return STBI__MARKER_none; x = stbi__get8(j->s); if (x != 0x00 && x != 0xff) { @@ -4176,6 +4178,7 @@ typedef struct { stbi_uc *zbuffer, *zbuffer_end; int num_bits; + int hit_zeof_once; stbi__uint32 code_buffer; char *zout; @@ -4242,9 +4245,20 @@ stbi_inline static int stbi__zhuffman_decode(stbi__zbuf *a, stbi__zhuffman *z) int b,s; if (a->num_bits < 16) { if (stbi__zeof(a)) { - return -1; /* report error for unexpected end of data. */ + if (!a->hit_zeof_once) { + // This is the first time we hit eof, insert 16 extra padding btis + // to allow us to keep going; if we actually consume any of them + // though, that is invalid data. This is caught later. + a->hit_zeof_once = 1; + a->num_bits += 16; // add 16 implicit zero bits + } else { + // We already inserted our extra 16 padding bits and are again + // out, this stream is actually prematurely terminated. + return -1; + } + } else { + stbi__fill_bits(a); } - stbi__fill_bits(a); } b = z->fast[a->code_buffer & STBI__ZFAST_MASK]; if (b) { @@ -4309,6 +4323,13 @@ static int stbi__parse_huffman_block(stbi__zbuf *a) int len,dist; if (z == 256) { a->zout = zout; + if (a->hit_zeof_once && a->num_bits < 16) { + // The first time we hit zeof, we inserted 16 extra zero bits into our bit + // buffer so the decoder can just do its speculative decoding. But if we + // actually consumed any of those bits (which is the case when num_bits < 16), + // the stream actually read past the end so it is malformed. + return stbi__err("unexpected end","Corrupt PNG"); + } return 1; } if (z >= 286) return stbi__err("bad huffman code","Corrupt PNG"); // per DEFLATE, length codes 286 and 287 must not appear in compressed data @@ -4320,7 +4341,7 @@ static int stbi__parse_huffman_block(stbi__zbuf *a) dist = stbi__zdist_base[z]; if (stbi__zdist_extra[z]) dist += stbi__zreceive(a, stbi__zdist_extra[z]); if (zout - a->zout_start < dist) return stbi__err("bad dist","Corrupt PNG"); - if (zout + len > a->zout_end) { + if (len > a->zout_end - zout) { if (!stbi__zexpand(a, zout, len)) return 0; zout = a->zout; } @@ -4464,6 +4485,7 @@ static int stbi__parse_zlib(stbi__zbuf *a, int parse_header) if (!stbi__parse_zlib_header(a)) return 0; a->num_bits = 0; a->code_buffer = 0; + a->hit_zeof_once = 0; do { final = stbi__zreceive(a,1); type = stbi__zreceive(a,2); @@ -4619,9 +4641,8 @@ enum { STBI__F_up=2, STBI__F_avg=3, STBI__F_paeth=4, - // synthetic filters used for first scanline to avoid needing a dummy row of 0s - STBI__F_avg_first, - STBI__F_paeth_first + // synthetic filter used for first scanline to avoid needing a dummy row of 0s + STBI__F_avg_first }; static stbi_uc first_row_filter[5] = @@ -4630,29 +4651,56 @@ static stbi_uc first_row_filter[5] = STBI__F_sub, STBI__F_none, STBI__F_avg_first, - STBI__F_paeth_first + STBI__F_sub // Paeth with b=c=0 turns out to be equivalent to sub }; static int stbi__paeth(int a, int b, int c) { - int p = a + b - c; - int pa = abs(p-a); - int pb = abs(p-b); - int pc = abs(p-c); - if (pa <= pb && pa <= pc) return a; - if (pb <= pc) return b; - return c; + // This formulation looks very different from the reference in the PNG spec, but is + // actually equivalent and has favorable data dependencies and admits straightforward + // generation of branch-free code, which helps performance significantly. + int thresh = c*3 - (a + b); + int lo = a < b ? a : b; + int hi = a < b ? b : a; + int t0 = (hi <= thresh) ? lo : c; + int t1 = (thresh <= lo) ? hi : t0; + return t1; } static const stbi_uc stbi__depth_scale_table[9] = { 0, 0xff, 0x55, 0, 0x11, 0,0,0, 0x01 }; +// adds an extra all-255 alpha channel +// dest == src is legal +// img_n must be 1 or 3 +static void stbi__create_png_alpha_expand8(stbi_uc *dest, stbi_uc *src, stbi__uint32 x, int img_n) +{ + int i; + // must process data backwards since we allow dest==src + if (img_n == 1) { + for (i=x-1; i >= 0; --i) { + dest[i*2+1] = 255; + dest[i*2+0] = src[i]; + } + } else { + STBI_ASSERT(img_n == 3); + for (i=x-1; i >= 0; --i) { + dest[i*4+3] = 255; + dest[i*4+2] = src[i*3+2]; + dest[i*4+1] = src[i*3+1]; + dest[i*4+0] = src[i*3+0]; + } + } +} + // create the png data from post-deflated data static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 raw_len, int out_n, stbi__uint32 x, stbi__uint32 y, int depth, int color) { - int bytes = (depth == 16? 2 : 1); + int bytes = (depth == 16 ? 2 : 1); stbi__context *s = a->s; stbi__uint32 i,j,stride = x*out_n*bytes; stbi__uint32 img_len, img_width_bytes; + stbi_uc *filter_buf; + int all_ok = 1; int k; int img_n = s->img_n; // copy it into a local for later @@ -4664,8 +4712,11 @@ static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 r a->out = (stbi_uc *) stbi__malloc_mad3(x, y, output_bytes, 0); // extra bytes to write off the end into if (!a->out) return stbi__err("outofmem", "Out of memory"); + // note: error exits here don't need to clean up a->out individually, + // stbi__do_png always does on error. if (!stbi__mad3sizes_valid(img_n, x, depth, 7)) return stbi__err("too large", "Corrupt PNG"); img_width_bytes = (((img_n * x * depth) + 7) >> 3); + if (!stbi__mad2sizes_valid(img_width_bytes, y, img_width_bytes)) return stbi__err("too large", "Corrupt PNG"); img_len = (img_width_bytes + 1) * y; // we used to check for exact match between raw_len and img_len on non-interlaced PNGs, @@ -4673,189 +4724,137 @@ static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 r // so just check for raw_len < img_len always. if (raw_len < img_len) return stbi__err("not enough pixels","Corrupt PNG"); + // Allocate two scan lines worth of filter workspace buffer. + filter_buf = (stbi_uc *) stbi__malloc_mad2(img_width_bytes, 2, 0); + if (!filter_buf) return stbi__err("outofmem", "Out of memory"); + + // Filtering for low-bit-depth images + if (depth < 8) { + filter_bytes = 1; + width = img_width_bytes; + } + for (j=0; j < y; ++j) { - stbi_uc *cur = a->out + stride*j; - stbi_uc *prior; + // cur/prior filter buffers alternate + stbi_uc *cur = filter_buf + (j & 1)*img_width_bytes; + stbi_uc *prior = filter_buf + (~j & 1)*img_width_bytes; + stbi_uc *dest = a->out + stride*j; + int nk = width * filter_bytes; int filter = *raw++; - if (filter > 4) - return stbi__err("invalid filter","Corrupt PNG"); - - if (depth < 8) { - if (img_width_bytes > x) return stbi__err("invalid width","Corrupt PNG"); - cur += x*out_n - img_width_bytes; // store output to the rightmost img_len bytes, so we can decode in place - filter_bytes = 1; - width = img_width_bytes; + // check filter type + if (filter > 4) { + all_ok = stbi__err("invalid filter","Corrupt PNG"); + break; } - prior = cur - stride; // bugfix: need to compute this after 'cur +=' computation above // if first row, use special filter that doesn't sample previous row if (j == 0) filter = first_row_filter[filter]; - // handle first byte explicitly - for (k=0; k < filter_bytes; ++k) { - switch (filter) { - case STBI__F_none : cur[k] = raw[k]; break; - case STBI__F_sub : cur[k] = raw[k]; break; - case STBI__F_up : cur[k] = STBI__BYTECAST(raw[k] + prior[k]); break; - case STBI__F_avg : cur[k] = STBI__BYTECAST(raw[k] + (prior[k]>>1)); break; - case STBI__F_paeth : cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(0,prior[k],0)); break; - case STBI__F_avg_first : cur[k] = raw[k]; break; - case STBI__F_paeth_first: cur[k] = raw[k]; break; - } - } - - if (depth == 8) { - if (img_n != out_n) - cur[img_n] = 255; // first pixel - raw += img_n; - cur += out_n; - prior += out_n; - } else if (depth == 16) { - if (img_n != out_n) { - cur[filter_bytes] = 255; // first pixel top byte - cur[filter_bytes+1] = 255; // first pixel bottom byte - } - raw += filter_bytes; - cur += output_bytes; - prior += output_bytes; - } else { - raw += 1; - cur += 1; - prior += 1; + // perform actual filtering + switch (filter) { + case STBI__F_none: + memcpy(cur, raw, nk); + break; + case STBI__F_sub: + memcpy(cur, raw, filter_bytes); + for (k = filter_bytes; k < nk; ++k) + cur[k] = STBI__BYTECAST(raw[k] + cur[k-filter_bytes]); + break; + case STBI__F_up: + for (k = 0; k < nk; ++k) + cur[k] = STBI__BYTECAST(raw[k] + prior[k]); + break; + case STBI__F_avg: + for (k = 0; k < filter_bytes; ++k) + cur[k] = STBI__BYTECAST(raw[k] + (prior[k]>>1)); + for (k = filter_bytes; k < nk; ++k) + cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k-filter_bytes])>>1)); + break; + case STBI__F_paeth: + for (k = 0; k < filter_bytes; ++k) + cur[k] = STBI__BYTECAST(raw[k] + prior[k]); // prior[k] == stbi__paeth(0,prior[k],0) + for (k = filter_bytes; k < nk; ++k) + cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes], prior[k], prior[k-filter_bytes])); + break; + case STBI__F_avg_first: + memcpy(cur, raw, filter_bytes); + for (k = filter_bytes; k < nk; ++k) + cur[k] = STBI__BYTECAST(raw[k] + (cur[k-filter_bytes] >> 1)); + break; } - // this is a little gross, so that we don't switch per-pixel or per-component - if (depth < 8 || img_n == out_n) { - int nk = (width - 1)*filter_bytes; - #define STBI__CASE(f) \ - case f: \ - for (k=0; k < nk; ++k) - switch (filter) { - // "none" filter turns into a memcpy here; make that explicit. - case STBI__F_none: memcpy(cur, raw, nk); break; - STBI__CASE(STBI__F_sub) { cur[k] = STBI__BYTECAST(raw[k] + cur[k-filter_bytes]); } break; - STBI__CASE(STBI__F_up) { cur[k] = STBI__BYTECAST(raw[k] + prior[k]); } break; - STBI__CASE(STBI__F_avg) { cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k-filter_bytes])>>1)); } break; - STBI__CASE(STBI__F_paeth) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes],prior[k],prior[k-filter_bytes])); } break; - STBI__CASE(STBI__F_avg_first) { cur[k] = STBI__BYTECAST(raw[k] + (cur[k-filter_bytes] >> 1)); } break; - STBI__CASE(STBI__F_paeth_first) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes],0,0)); } break; - } - #undef STBI__CASE - raw += nk; - } else { - STBI_ASSERT(img_n+1 == out_n); - #define STBI__CASE(f) \ - case f: \ - for (i=x-1; i >= 1; --i, cur[filter_bytes]=255,raw+=filter_bytes,cur+=output_bytes,prior+=output_bytes) \ - for (k=0; k < filter_bytes; ++k) - switch (filter) { - STBI__CASE(STBI__F_none) { cur[k] = raw[k]; } break; - STBI__CASE(STBI__F_sub) { cur[k] = STBI__BYTECAST(raw[k] + cur[k- output_bytes]); } break; - STBI__CASE(STBI__F_up) { cur[k] = STBI__BYTECAST(raw[k] + prior[k]); } break; - STBI__CASE(STBI__F_avg) { cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k- output_bytes])>>1)); } break; - STBI__CASE(STBI__F_paeth) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k- output_bytes],prior[k],prior[k- output_bytes])); } break; - STBI__CASE(STBI__F_avg_first) { cur[k] = STBI__BYTECAST(raw[k] + (cur[k- output_bytes] >> 1)); } break; - STBI__CASE(STBI__F_paeth_first) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k- output_bytes],0,0)); } break; - } - #undef STBI__CASE - - // the loop above sets the high byte of the pixels' alpha, but for - // 16 bit png files we also need the low byte set. we'll do that here. - if (depth == 16) { - cur = a->out + stride*j; // start at the beginning of the row again - for (i=0; i < x; ++i,cur+=output_bytes) { - cur[filter_bytes+1] = 255; - } - } - } - } + raw += nk; - // we make a separate pass to expand bits to pixels; for performance, - // this could run two scanlines behind the above code, so it won't - // intefere with filtering but will still be in the cache. - if (depth < 8) { - for (j=0; j < y; ++j) { - stbi_uc *cur = a->out + stride*j; - stbi_uc *in = a->out + stride*j + x*out_n - img_width_bytes; - // unpack 1/2/4-bit into a 8-bit buffer. allows us to keep the common 8-bit path optimal at minimal cost for 1/2/4-bit - // png guarante byte alignment, if width is not multiple of 8/4/2 we'll decode dummy trailing data that will be skipped in the later loop + // expand decoded bits in cur to dest, also adding an extra alpha channel if desired + if (depth < 8) { stbi_uc scale = (color == 0) ? stbi__depth_scale_table[depth] : 1; // scale grayscale values to 0..255 range + stbi_uc *in = cur; + stbi_uc *out = dest; + stbi_uc inb = 0; + stbi__uint32 nsmp = x*img_n; - // note that the final byte might overshoot and write more data than desired. - // we can allocate enough data that this never writes out of memory, but it - // could also overwrite the next scanline. can it overwrite non-empty data - // on the next scanline? yes, consider 1-pixel-wide scanlines with 1-bit-per-pixel. - // so we need to explicitly clamp the final ones - + // expand bits to bytes first if (depth == 4) { - for (k=x*img_n; k >= 2; k-=2, ++in) { - *cur++ = scale * ((*in >> 4) ); - *cur++ = scale * ((*in ) & 0x0f); + for (i=0; i < nsmp; ++i) { + if ((i & 1) == 0) inb = *in++; + *out++ = scale * (inb >> 4); + inb <<= 4; } - if (k > 0) *cur++ = scale * ((*in >> 4) ); } else if (depth == 2) { - for (k=x*img_n; k >= 4; k-=4, ++in) { - *cur++ = scale * ((*in >> 6) ); - *cur++ = scale * ((*in >> 4) & 0x03); - *cur++ = scale * ((*in >> 2) & 0x03); - *cur++ = scale * ((*in ) & 0x03); + for (i=0; i < nsmp; ++i) { + if ((i & 3) == 0) inb = *in++; + *out++ = scale * (inb >> 6); + inb <<= 2; } - if (k > 0) *cur++ = scale * ((*in >> 6) ); - if (k > 1) *cur++ = scale * ((*in >> 4) & 0x03); - if (k > 2) *cur++ = scale * ((*in >> 2) & 0x03); - } else if (depth == 1) { - for (k=x*img_n; k >= 8; k-=8, ++in) { - *cur++ = scale * ((*in >> 7) ); - *cur++ = scale * ((*in >> 6) & 0x01); - *cur++ = scale * ((*in >> 5) & 0x01); - *cur++ = scale * ((*in >> 4) & 0x01); - *cur++ = scale * ((*in >> 3) & 0x01); - *cur++ = scale * ((*in >> 2) & 0x01); - *cur++ = scale * ((*in >> 1) & 0x01); - *cur++ = scale * ((*in ) & 0x01); + } else { + STBI_ASSERT(depth == 1); + for (i=0; i < nsmp; ++i) { + if ((i & 7) == 0) inb = *in++; + *out++ = scale * (inb >> 7); + inb <<= 1; } - if (k > 0) *cur++ = scale * ((*in >> 7) ); - if (k > 1) *cur++ = scale * ((*in >> 6) & 0x01); - if (k > 2) *cur++ = scale * ((*in >> 5) & 0x01); - if (k > 3) *cur++ = scale * ((*in >> 4) & 0x01); - if (k > 4) *cur++ = scale * ((*in >> 3) & 0x01); - if (k > 5) *cur++ = scale * ((*in >> 2) & 0x01); - if (k > 6) *cur++ = scale * ((*in >> 1) & 0x01); } - if (img_n != out_n) { - int q; - // insert alpha = 255 - cur = a->out + stride*j; + + // insert alpha=255 values if desired + if (img_n != out_n) + stbi__create_png_alpha_expand8(dest, dest, x, img_n); + } else if (depth == 8) { + if (img_n == out_n) + memcpy(dest, cur, x*img_n); + else + stbi__create_png_alpha_expand8(dest, cur, x, img_n); + } else if (depth == 16) { + // convert the image data from big-endian to platform-native + stbi__uint16 *dest16 = (stbi__uint16*)dest; + stbi__uint32 nsmp = x*img_n; + + if (img_n == out_n) { + for (i = 0; i < nsmp; ++i, ++dest16, cur += 2) + *dest16 = (cur[0] << 8) | cur[1]; + } else { + STBI_ASSERT(img_n+1 == out_n); if (img_n == 1) { - for (q=x-1; q >= 0; --q) { - cur[q*2+1] = 255; - cur[q*2+0] = cur[q]; + for (i = 0; i < x; ++i, dest16 += 2, cur += 2) { + dest16[0] = (cur[0] << 8) | cur[1]; + dest16[1] = 0xffff; } } else { STBI_ASSERT(img_n == 3); - for (q=x-1; q >= 0; --q) { - cur[q*4+3] = 255; - cur[q*4+2] = cur[q*3+2]; - cur[q*4+1] = cur[q*3+1]; - cur[q*4+0] = cur[q*3+0]; + for (i = 0; i < x; ++i, dest16 += 4, cur += 6) { + dest16[0] = (cur[0] << 8) | cur[1]; + dest16[1] = (cur[2] << 8) | cur[3]; + dest16[2] = (cur[4] << 8) | cur[5]; + dest16[3] = 0xffff; } } } } - } else if (depth == 16) { - // force the image data from big-endian to platform-native. - // this is done in a separate pass due to the decoding relying - // on the data being untouched, but could probably be done - // per-line during decode if care is taken. - stbi_uc *cur = a->out; - stbi__uint16 *cur16 = (stbi__uint16*)cur; - - for(i=0; i < x*y*out_n; ++i,cur16++,cur+=2) { - *cur16 = (cur[0] << 8) | cur[1]; - } } + STBI_FREE(filter_buf); + if (!all_ok) return 0; + return 1; } @@ -5161,9 +5160,11 @@ static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) // non-paletted with tRNS = constant alpha. if header-scanning, we can stop now. if (scan == STBI__SCAN_header) { ++s->img_n; return 1; } if (z->depth == 16) { - for (k = 0; k < s->img_n; ++k) tc16[k] = (stbi__uint16)stbi__get16be(s); // copy the values as-is + for (k = 0; k < s->img_n && k < 3; ++k) // extra loop test to suppress false GCC warning + tc16[k] = (stbi__uint16)stbi__get16be(s); // copy the values as-is } else { - for (k = 0; k < s->img_n; ++k) tc[k] = (stbi_uc)(stbi__get16be(s) & 255) * stbi__depth_scale_table[z->depth]; // non 8-bit images will be larger + for (k = 0; k < s->img_n && k < 3; ++k) + tc[k] = (stbi_uc)(stbi__get16be(s) & 255) * stbi__depth_scale_table[z->depth]; // non 8-bit images will be larger } } break; From 57da0d74db4214bd76c662c513e5c661dce34791 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 2 Apr 2025 11:51:01 +0200 Subject: [PATCH 1358/1365] update actions (#8930) --- .github/workflows/ccpp.yml | 100 +++++++++++++++++------------------ .github/workflows/on_pr.yml | 38 ++++++------- .github/workflows/rebase.yml | 26 --------- 3 files changed, 69 insertions(+), 95 deletions(-) delete mode 100644 .github/workflows/rebase.yml diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 18aa31db638..110c9ec1232 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -20,7 +20,7 @@ jobs: ubuntu-makefile: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Update apt run: sudo apt update @@ -76,7 +76,7 @@ jobs: ubuntu-vcpkg-opencv4-cuda: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Setup tmate session uses: mxschmitt/action-tmate@v3 @@ -129,19 +129,19 @@ jobs: LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL -BuildInstaller - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-vcpkg-cuda-${{ runner.os }} path: cfg - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-vcpkg-cuda-${{ runner.os }} path: data - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-vcpkg-cuda-${{ runner.os }} path: ${{ github.workspace }}/*dark* - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-vcpkg-cuda-${{ runner.os }} path: ${{ github.workspace }}/uselib* @@ -150,7 +150,7 @@ jobs: ubuntu-vcpkg-opencv3: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: lukka/get-cmake@latest @@ -188,7 +188,7 @@ jobs: ubuntu-vcpkg-opencv2: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: lukka/get-cmake@latest @@ -226,7 +226,7 @@ jobs: ubuntu: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Update apt run: sudo apt update @@ -244,19 +244,19 @@ jobs: LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-${{ runner.os }} path: cfg - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-${{ runner.os }} path: data - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-${{ runner.os }} path: ${{ github.workspace }}/*dark* - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-${{ runner.os }} path: ${{ github.workspace }}/uselib* @@ -265,7 +265,7 @@ jobs: ubuntu-cuda: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Update apt run: sudo apt update @@ -293,19 +293,19 @@ jobs: LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-cuda-${{ runner.os }} path: cfg - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-cuda-${{ runner.os }} path: data - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-cuda-${{ runner.os }} path: ${{ github.workspace }}/*dark* - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-cuda-${{ runner.os }} path: ${{ github.workspace }}/uselib* @@ -314,7 +314,7 @@ jobs: ubuntu-no-ocv-cpp: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: lukka/get-cmake@latest @@ -332,7 +332,7 @@ jobs: ubuntu-setup-sh: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -361,7 +361,7 @@ jobs: osx-vcpkg: runs-on: macos-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Setup tmate session uses: mxschmitt/action-tmate@v3 @@ -395,19 +395,19 @@ jobs: shell: pwsh run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL -BuildInstaller - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-vcpkg-${{ runner.os }} path: cfg - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-vcpkg-${{ runner.os }} path: data - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-vcpkg-${{ runner.os }} path: ${{ github.workspace }}/*dark* - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-vcpkg-${{ runner.os }} path: ${{ github.workspace }}/uselib* @@ -416,7 +416,7 @@ jobs: osx: runs-on: macos-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Install dependencies run: brew install opencv libomp @@ -427,19 +427,19 @@ jobs: shell: pwsh run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-${{ runner.os }} path: cfg - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-${{ runner.os }} path: data - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-${{ runner.os }} path: ${{ github.workspace }}/*dark* - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-${{ runner.os }} path: ${{ github.workspace }}/uselib* @@ -448,7 +448,7 @@ jobs: osx-no-ocv-no-omp-cpp: runs-on: macos-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: lukka/get-cmake@latest @@ -460,7 +460,7 @@ jobs: win-vcpkg: runs-on: windows-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Setup tmate session uses: mxschmitt/action-tmate@v3 @@ -491,23 +491,23 @@ jobs: shell: pwsh run: ${{ github.workspace }}/build.ps1 -UseVCPKG -ForceLocalVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL -BuildInstaller - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-vcpkg-${{ runner.os }} path: cfg - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-vcpkg-${{ runner.os }} path: data - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-vcpkg-${{ runner.os }} path: ${{ github.workspace }}/*dark* - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-vcpkg-${{ runner.os }} path: ${{ github.workspace }}/build_release/*.dll - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-vcpkg-${{ runner.os }} path: ${{ github.workspace }}/uselib* @@ -516,7 +516,7 @@ jobs: win-vcpkg-port: runs-on: windows-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -545,7 +545,7 @@ jobs: win-intlibs: runs-on: windows-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: lukka/get-cmake@latest @@ -553,23 +553,23 @@ jobs: shell: pwsh run: ${{ github.workspace }}/build.ps1 -DisableInteractive -DoNotUpdateTOOL - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-${{ runner.os }} path: cfg - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-${{ runner.os }} path: data - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-${{ runner.os }} path: ${{ github.workspace }}/*dark* - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-${{ runner.os }} path: ${{ github.workspace }}/3rdparty/pthreads/bin/*.dll - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: name: darknet-${{ runner.os }} path: ${{ github.workspace }}/uselib* @@ -578,7 +578,7 @@ jobs: win-setup-ps1: runs-on: windows-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -607,7 +607,7 @@ jobs: win-vcpkg-base-cpp: runs-on: windows-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: lukka/get-cmake@latest @@ -643,7 +643,7 @@ jobs: win-csharp: runs-on: windows-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: lukka/get-cmake@latest @@ -655,7 +655,7 @@ jobs: win-intlibs-cuda: runs-on: windows-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.ps1 @@ -673,7 +673,7 @@ jobs: win-powershell51: runs-on: windows-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: lukka/get-cmake@latest @@ -685,7 +685,7 @@ jobs: mingw: runs-on: windows-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: lukka/get-cmake@latest diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index 1ad990ba91a..491b92f58cc 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -10,7 +10,7 @@ jobs: ubuntu-makefile: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Update apt run: sudo apt update @@ -66,7 +66,7 @@ jobs: ubuntu-vcpkg-opencv4-cuda: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: lukka/get-cmake@latest @@ -109,7 +109,7 @@ jobs: ubuntu-vcpkg-opencv3: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: lukka/get-cmake@latest @@ -137,7 +137,7 @@ jobs: ubuntu-vcpkg-opencv2: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: lukka/get-cmake@latest @@ -165,7 +165,7 @@ jobs: ubuntu: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Update apt run: sudo apt update @@ -187,7 +187,7 @@ jobs: ubuntu-cuda: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Update apt run: sudo apt update @@ -219,7 +219,7 @@ jobs: ubuntu-no-ocv-cpp: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: lukka/get-cmake@latest @@ -237,7 +237,7 @@ jobs: ubuntu-setup-sh: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -256,7 +256,7 @@ jobs: osx-vcpkg: runs-on: macos-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Install dependencies run: brew install libomp yasm nasm pkg-config automake autoconf-archive @@ -280,7 +280,7 @@ jobs: osx: runs-on: macos-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Install dependencies run: brew install opencv libomp @@ -295,7 +295,7 @@ jobs: osx-no-ocv-no-omp-cpp: runs-on: macos-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: lukka/get-cmake@latest @@ -307,7 +307,7 @@ jobs: win-vcpkg: runs-on: windows-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: lukka/get-cmake@latest @@ -328,7 +328,7 @@ jobs: win-intlibs: runs-on: windows-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: lukka/get-cmake@latest @@ -340,7 +340,7 @@ jobs: win-setup-ps1: runs-on: windows-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -359,7 +359,7 @@ jobs: win-vcpkg-base-cpp: runs-on: windows-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: lukka/get-cmake@latest @@ -385,7 +385,7 @@ jobs: win-csharp: runs-on: windows-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: lukka/get-cmake@latest @@ -397,7 +397,7 @@ jobs: win-intlibs-cuda: runs-on: windows-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.ps1 @@ -415,7 +415,7 @@ jobs: win-powershell51: runs-on: windows-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: lukka/get-cmake@latest @@ -427,7 +427,7 @@ jobs: mingw: runs-on: windows-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: lukka/get-cmake@latest diff --git a/.github/workflows/rebase.yml b/.github/workflows/rebase.yml deleted file mode 100644 index f9e853e439b..00000000000 --- a/.github/workflows/rebase.yml +++ /dev/null @@ -1,26 +0,0 @@ -name: Automatic Rebase -on: - issue_comment: - types: [created] -jobs: - rebase: - name: Rebase - runs-on: ubuntu-latest - if: >- - github.event.issue.pull_request != '' && - ( - contains(github.event.comment.body, '/rebase') || - contains(github.event.comment.body, '/autosquash') - ) - steps: - - name: Checkout the latest code - uses: actions/checkout@v3 - with: - token: ${{ secrets.GITHUB_TOKEN }} - fetch-depth: 0 # otherwise, you will fail to push refs to dest repo - - name: Automatic Rebase - uses: cirrus-actions/rebase@1.8 - with: - autosquash: ${{ contains(github.event.comment.body, '/autosquash') || contains(github.event.comment.body, '/rebase-autosquash') }} - env: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} From 8a4750fee4e5ac11177b4a0f698667fb09c3f5f4 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 2 Apr 2025 12:17:22 +0200 Subject: [PATCH 1359/1365] trying to circumvent upload-artifacts@v4 breaking changes --- .github/workflows/ccpp.yml | 84 ++++++++------------------------------ 1 file changed, 18 insertions(+), 66 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 110c9ec1232..dd98a4c4950 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -131,19 +131,19 @@ jobs: - uses: actions/upload-artifact@v4 with: - name: darknet-vcpkg-cuda-${{ runner.os }} + name: cfg path: cfg - uses: actions/upload-artifact@v4 with: - name: darknet-vcpkg-cuda-${{ runner.os }} + name: data path: data - uses: actions/upload-artifact@v4 with: - name: darknet-vcpkg-cuda-${{ runner.os }} + name: darknet-vcpkg-cuda-${{ runner.os }}-darknet path: ${{ github.workspace }}/*dark* - uses: actions/upload-artifact@v4 with: - name: darknet-vcpkg-cuda-${{ runner.os }} + name: darknet-vcpkg-cuda-${{ runner.os }}-uselib path: ${{ github.workspace }}/uselib* @@ -246,19 +246,11 @@ jobs: - uses: actions/upload-artifact@v4 with: - name: darknet-${{ runner.os }} - path: cfg - - uses: actions/upload-artifact@v4 - with: - name: darknet-${{ runner.os }} - path: data - - uses: actions/upload-artifact@v4 - with: - name: darknet-${{ runner.os }} + name: darknet-${{ runner.os }}-darknet path: ${{ github.workspace }}/*dark* - uses: actions/upload-artifact@v4 with: - name: darknet-${{ runner.os }} + name: darknet-${{ runner.os }}-uselib path: ${{ github.workspace }}/uselib* @@ -295,19 +287,11 @@ jobs: - uses: actions/upload-artifact@v4 with: - name: darknet-cuda-${{ runner.os }} - path: cfg - - uses: actions/upload-artifact@v4 - with: - name: darknet-cuda-${{ runner.os }} - path: data - - uses: actions/upload-artifact@v4 - with: - name: darknet-cuda-${{ runner.os }} + name: darknet-cuda-${{ runner.os }}-darknet path: ${{ github.workspace }}/*dark* - uses: actions/upload-artifact@v4 with: - name: darknet-cuda-${{ runner.os }} + name: darknet-cuda-${{ runner.os }}-uselib path: ${{ github.workspace }}/uselib* @@ -397,19 +381,11 @@ jobs: - uses: actions/upload-artifact@v4 with: - name: darknet-vcpkg-${{ runner.os }} - path: cfg - - uses: actions/upload-artifact@v4 - with: - name: darknet-vcpkg-${{ runner.os }} - path: data - - uses: actions/upload-artifact@v4 - with: - name: darknet-vcpkg-${{ runner.os }} + name: darknet-vcpkg-${{ runner.os }}-darknet path: ${{ github.workspace }}/*dark* - uses: actions/upload-artifact@v4 with: - name: darknet-vcpkg-${{ runner.os }} + name: darknet-vcpkg-${{ runner.os }}-uselib path: ${{ github.workspace }}/uselib* @@ -429,19 +405,11 @@ jobs: - uses: actions/upload-artifact@v4 with: - name: darknet-${{ runner.os }} - path: cfg - - uses: actions/upload-artifact@v4 - with: - name: darknet-${{ runner.os }} - path: data - - uses: actions/upload-artifact@v4 - with: - name: darknet-${{ runner.os }} + name: darknet-${{ runner.os }}-darknet path: ${{ github.workspace }}/*dark* - uses: actions/upload-artifact@v4 with: - name: darknet-${{ runner.os }} + name: darknet-${{ runner.os }}-uselib path: ${{ github.workspace }}/uselib* @@ -493,23 +461,15 @@ jobs: - uses: actions/upload-artifact@v4 with: - name: darknet-vcpkg-${{ runner.os }} - path: cfg - - uses: actions/upload-artifact@v4 - with: - name: darknet-vcpkg-${{ runner.os }} - path: data - - uses: actions/upload-artifact@v4 - with: - name: darknet-vcpkg-${{ runner.os }} + name: darknet-vcpkg-${{ runner.os }}-darknet path: ${{ github.workspace }}/*dark* - uses: actions/upload-artifact@v4 with: - name: darknet-vcpkg-${{ runner.os }} + name: darknet-vcpkg-${{ runner.os }}-dlls path: ${{ github.workspace }}/build_release/*.dll - uses: actions/upload-artifact@v4 with: - name: darknet-vcpkg-${{ runner.os }} + name: darknet-vcpkg-${{ runner.os }}-uselib path: ${{ github.workspace }}/uselib* @@ -555,23 +515,15 @@ jobs: - uses: actions/upload-artifact@v4 with: - name: darknet-${{ runner.os }} - path: cfg - - uses: actions/upload-artifact@v4 - with: - name: darknet-${{ runner.os }} - path: data - - uses: actions/upload-artifact@v4 - with: - name: darknet-${{ runner.os }} + name: darknet-${{ runner.os }}-intlibs-darknet path: ${{ github.workspace }}/*dark* - uses: actions/upload-artifact@v4 with: - name: darknet-${{ runner.os }} + name: darknet-${{ runner.os }}-intlibs-dlls path: ${{ github.workspace }}/3rdparty/pthreads/bin/*.dll - uses: actions/upload-artifact@v4 with: - name: darknet-${{ runner.os }} + name: darknet-${{ runner.os }}-intlibs-uselib path: ${{ github.workspace }}/uselib* From a227f3272d342af2a5f96498baf912dc2549eeed Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Wed, 2 Apr 2025 12:31:33 +0200 Subject: [PATCH 1360/1365] use latest ubuntu release2 --- .github/workflows/ccpp.yml | 16 ++++++++-------- .github/workflows/on_pr.yml | 16 ++++++++-------- 2 files changed, 16 insertions(+), 16 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index dd98a4c4950..1377b555fd9 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -18,7 +18,7 @@ env: jobs: ubuntu-makefile: - runs-on: ubuntu-20.04 + runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -74,7 +74,7 @@ jobs: ubuntu-vcpkg-opencv4-cuda: - runs-on: ubuntu-20.04 + runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -148,7 +148,7 @@ jobs: ubuntu-vcpkg-opencv3: - runs-on: ubuntu-20.04 + runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -186,7 +186,7 @@ jobs: ubuntu-vcpkg-opencv2: - runs-on: ubuntu-20.04 + runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -224,7 +224,7 @@ jobs: ubuntu: - runs-on: ubuntu-20.04 + runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -255,7 +255,7 @@ jobs: ubuntu-cuda: - runs-on: ubuntu-20.04 + runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -296,7 +296,7 @@ jobs: ubuntu-no-ocv-cpp: - runs-on: ubuntu-20.04 + runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -314,7 +314,7 @@ jobs: ubuntu-setup-sh: - runs-on: ubuntu-20.04 + runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index 491b92f58cc..a78ed27d628 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -8,7 +8,7 @@ env: jobs: ubuntu-makefile: - runs-on: ubuntu-20.04 + runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -64,7 +64,7 @@ jobs: ubuntu-vcpkg-opencv4-cuda: - runs-on: ubuntu-20.04 + runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -107,7 +107,7 @@ jobs: ubuntu-vcpkg-opencv3: - runs-on: ubuntu-20.04 + runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -135,7 +135,7 @@ jobs: ubuntu-vcpkg-opencv2: - runs-on: ubuntu-20.04 + runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -163,7 +163,7 @@ jobs: ubuntu: - runs-on: ubuntu-20.04 + runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -185,7 +185,7 @@ jobs: ubuntu-cuda: - runs-on: ubuntu-20.04 + runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -217,7 +217,7 @@ jobs: ubuntu-no-ocv-cpp: - runs-on: ubuntu-20.04 + runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -235,7 +235,7 @@ jobs: ubuntu-setup-sh: - runs-on: ubuntu-20.04 + runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 From 6f3ba4422e5719a0fed1ff45045ebaa0c236d582 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 3 Apr 2025 13:52:02 +0200 Subject: [PATCH 1361/1365] improve cuda handling (#8931) * update cuda scripts to improve compatibility with latest ubuntu * fix small regressions and update also PR script --- .github/workflows/ccpp.yml | 79 +++++++++++++++++++--------- .github/workflows/on_pr.yml | 85 +++++++++++++++++++++---------- scripts/deploy-cuda.sh | 16 ++++-- scripts/requested_cuda_version.sh | 7 +++ scripts/setup.sh | 2 +- 5 files changed, 133 insertions(+), 56 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 1377b555fd9..235747e37cc 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -22,10 +22,15 @@ jobs: steps: - uses: actions/checkout@v4 - - name: Update apt - run: sudo apt update - - name: Install dependencies - run: sudo apt install libopencv-dev libgles2-mesa-dev + - name: Update apt & install dependencies + run: | + sudo apt-get update + sudo apt-get install -y libopencv-dev + sudo apt-get install -y mono-devel zlib1g + sudo apt-get install -y libgles2-mesa-dev + + - name: Clean downloads + run: sudo apt-get clean - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.sh @@ -84,10 +89,13 @@ jobs: - uses: lukka/get-cmake@latest - - name: Update apt - run: sudo apt update - - name: Install dependencies - run: sudo apt-get install -y --no-install-recommends yasm nasm gperf automake autoconf libtool pkg-config autoconf-archive libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + - name: Update apt & install dependencies + run: | + sudo apt-get update + sudo apt-get install -y mono-devel zlib1g + sudo apt-get install -y libgles2-mesa-dev + sudo apt-get install -y --no-install-recommends yasm nasm gperf automake autoconf libtool pkg-config autoconf-archive libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev libltdl-dev + - name: Clean downloads run: sudo apt-get clean @@ -154,10 +162,13 @@ jobs: - uses: lukka/get-cmake@latest - - name: Update apt - run: sudo apt update - - name: Install dependencies - run: sudo apt-get install -y --no-install-recommends yasm nasm gperf automake autoconf libtool pkg-config autoconf-archive libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + - name: Update apt & install dependencies + run: | + sudo apt-get update + sudo apt-get install -y mono-devel zlib1g + sudo apt-get install -y libgles2-mesa-dev + sudo apt-get install -y --no-install-recommends yasm nasm gperf automake autoconf libtool pkg-config autoconf-archive libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev libltdl-dev + - name: Clean downloads run: sudo apt-get clean @@ -192,10 +203,13 @@ jobs: - uses: lukka/get-cmake@latest - - name: Update apt - run: sudo apt update - - name: Install dependencies - run: sudo apt-get install -y --no-install-recommends yasm nasm gperf automake autoconf libtool pkg-config autoconf-archive libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + - name: Update apt & install dependencies + run: | + sudo apt-get update + sudo apt-get install -y mono-devel zlib1g + sudo apt-get install -y libgles2-mesa-dev + sudo apt-get install -y --no-install-recommends yasm nasm gperf automake autoconf libtool pkg-config autoconf-archive libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev libltdl-dev + - name: Clean downloads run: sudo apt-get clean @@ -228,10 +242,14 @@ jobs: steps: - uses: actions/checkout@v4 - - name: Update apt - run: sudo apt update - - name: Install dependencies - run: sudo apt install libopencv-dev + - name: Update apt & install dependencies + run: | + sudo apt-get update + sudo apt-get install -y libopencv-dev + sudo apt-get install -y mono-devel zlib1g + + - name: Clean downloads + run: sudo apt-get clean - uses: lukka/get-cmake@latest @@ -259,10 +277,15 @@ jobs: steps: - uses: actions/checkout@v4 - - name: Update apt - run: sudo apt update - - name: Install dependencies - run: sudo apt install libopencv-dev libgles2-mesa-dev + - name: Update apt & install dependencies + run: | + sudo apt-get update + sudo apt-get install -y libopencv-dev + sudo apt-get install -y mono-devel zlib1g + sudo apt-get install -y libgles2-mesa-dev + + - name: Clean downloads + run: sudo apt-get clean - uses: lukka/get-cmake@latest @@ -318,6 +341,14 @@ jobs: steps: - uses: actions/checkout@v4 + - name: Update apt & install dependencies + run: | + sudo apt-get update + sudo apt-get install -y mono-devel + + - name: Clean downloads + run: sudo apt-get clean + - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash run: > diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index a78ed27d628..2bcf15bd895 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -12,10 +12,15 @@ jobs: steps: - uses: actions/checkout@v4 - - name: Update apt - run: sudo apt update - - name: Install dependencies - run: sudo apt install libopencv-dev libgles2-mesa-dev + - name: Update apt & install dependencies + run: | + sudo apt-get update + sudo apt-get install -y libopencv-dev + sudo apt-get install -y mono-devel zlib1g + sudo apt-get install -y libgles2-mesa-dev + + - name: Clean downloads + run: sudo apt-get clean - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.sh @@ -70,10 +75,13 @@ jobs: - uses: lukka/get-cmake@latest - - name: Update apt - run: sudo apt update - - name: Install dependencies - run: sudo apt-get install -y --no-install-recommends yasm nasm gperf automake autoconf libtool pkg-config autoconf-archive libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + - name: Update apt & install dependencies + run: | + sudo apt-get update + sudo apt-get install -y mono-devel zlib1g + sudo apt-get install -y libgles2-mesa-dev + sudo apt-get install -y --no-install-recommends yasm nasm gperf automake autoconf libtool pkg-config autoconf-archive libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev libltdl-dev + - name: Clean downloads run: sudo apt-get clean @@ -103,7 +111,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL -BuildInstaller ubuntu-vcpkg-opencv3: @@ -113,10 +121,13 @@ jobs: - uses: lukka/get-cmake@latest - - name: Update apt - run: sudo apt update - - name: Install dependencies - run: sudo apt-get install -y --no-install-recommends yasm nasm gperf automake autoconf libtool pkg-config autoconf-archive libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + - name: Update apt & install dependencies + run: | + sudo apt-get update + sudo apt-get install -y mono-devel zlib1g + sudo apt-get install -y libgles2-mesa-dev + sudo apt-get install -y --no-install-recommends yasm nasm gperf automake autoconf libtool pkg-config autoconf-archive libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev libltdl-dev + - name: Clean downloads run: sudo apt-get clean @@ -141,10 +152,13 @@ jobs: - uses: lukka/get-cmake@latest - - name: Update apt - run: sudo apt update - - name: Install dependencies - run: sudo apt-get install -y --no-install-recommends yasm nasm gperf automake autoconf libtool pkg-config autoconf-archive libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + - name: Update apt & install dependencies + run: | + sudo apt-get update + sudo apt-get install -y mono-devel zlib1g + sudo apt-get install -y libgles2-mesa-dev + sudo apt-get install -y --no-install-recommends yasm nasm gperf automake autoconf libtool pkg-config autoconf-archive libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev libltdl-dev + - name: Clean downloads run: sudo apt-get clean @@ -167,10 +181,14 @@ jobs: steps: - uses: actions/checkout@v4 - - name: Update apt - run: sudo apt update - - name: Install dependencies - run: sudo apt install libopencv-dev + - name: Update apt & install dependencies + run: | + sudo apt-get update + sudo apt-get install -y libopencv-dev + sudo apt-get install -y mono-devel zlib1g + + - name: Clean downloads + run: sudo apt-get clean - uses: lukka/get-cmake@latest @@ -189,10 +207,15 @@ jobs: steps: - uses: actions/checkout@v4 - - name: Update apt - run: sudo apt update - - name: Install dependencies - run: sudo apt install libopencv-dev libgles2-mesa-dev + - name: Update apt & install dependencies + run: | + sudo apt-get update + sudo apt-get install -y libopencv-dev + sudo apt-get install -y mono-devel zlib1g + sudo apt-get install -y libgles2-mesa-dev + + - name: Clean downloads + run: sudo apt-get clean - uses: lukka/get-cmake@latest @@ -239,6 +262,14 @@ jobs: steps: - uses: actions/checkout@v4 + - name: Update apt & install dependencies + run: | + sudo apt-get update + sudo apt-get install -y mono-devel + + - name: Clean downloads + run: sudo apt-get clean + - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash run: > @@ -274,7 +305,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL -BuildInstaller osx: @@ -322,7 +353,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -ForceLocalVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/build.ps1 -UseVCPKG -ForceLocalVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL -BuildInstaller win-intlibs: diff --git a/scripts/deploy-cuda.sh b/scripts/deploy-cuda.sh index fb85d52f559..91826b6c540 100755 --- a/scripts/deploy-cuda.sh +++ b/scripts/deploy-cuda.sh @@ -5,6 +5,15 @@ if [[ "$OSTYPE" == "darwin"* ]]; then exit 1 elif [[ $(cut -f2 <<< $(lsb_release -i)) == "Ubuntu" ]]; then distr_name="$(cut -f2 <<< $(lsb_release -i) | tr '[:upper:]' '[:lower:]')$(cut -f2 <<< $(lsb_release -r) | tr -d '.')" + arch_name="$(uname -m)" + if [[ $arch_name == "x86_64" ]]; then + arch_name="x86_64" + elif [[ $arch_name == "aarch64" ]]; then + arch_name="arm64" + else + echo "Unsupported architecture: $arch_name" + exit 1 + fi else echo "Unable to deploy CUDA on this OS, please wait for a future script update" exit 2 @@ -20,16 +29,15 @@ else fi sudo apt-key del 7fa2af80 -wget https://developer.download.nvidia.com/compute/cuda/repos/$distr_name/x86_64/cuda-keyring_1.1-1_all.deb +wget https://developer.download.nvidia.com/compute/cuda/repos/$distr_name/$arch_name/cuda-keyring_1.1-1_all.deb sudo dpkg -i cuda-keyring_1.1-1_all.deb sudo apt-get update sudo apt-get install -y --no-install-recommends build-essential g++ sudo apt-get install -y --no-install-recommends apt-transport-https ca-certificates gnupg software-properties-common wget sudo apt-get install -y --no-install-recommends zlib1g sudo apt-get dist-upgrade -y -sudo apt-get install -y --no-install-recommends cuda-${CUDA_VERSION_DASHED} -sudo apt-get install -y --no-install-recommends libcudnn8 -sudo apt-get install -y --no-install-recommends libcudnn8-dev +sudo apt-get install -y cuda-toolkit-${CUDA_VERSION_DASHED} +sudo apt-get install -y cudnn${CUDNN_MAJOR_VERSION}-cuda-${CUDA_MAJOR_VERSION} sudo rm -rf /usr/local/cuda sudo ln -s /usr/local/cuda-${CUDA_VERSION} /usr/local/cuda diff --git a/scripts/requested_cuda_version.sh b/scripts/requested_cuda_version.sh index 8cb3f6ad9d6..9b16ca76293 100755 --- a/scripts/requested_cuda_version.sh +++ b/scripts/requested_cuda_version.sh @@ -1,4 +1,11 @@ #!/usr/bin/env bash export CUDA_VERSION="12.6" +export CUDNN_VERSION="9.0" + export CUDA_VERSION_DASHED="${CUDA_VERSION//./-}" +export CUDA_MAJOR_VERSION="${CUDA_VERSION%%.*}" +export CUDA_MINOR_VERSION="${CUDA_VERSION#*.}" +export CUDNN_VERSION_DASHED="${CUDNN_VERSION//./-}" +export CUDNN_MAJOR_VERSION="${CUDNN_VERSION%%.*}" +export CUDNN_MINOR_VERSION="${CUDNN_VERSION#*.}" diff --git a/scripts/setup.sh b/scripts/setup.sh index 6cbc7be8fa1..bf6c5bbc74e 100755 --- a/scripts/setup.sh +++ b/scripts/setup.sh @@ -77,7 +77,7 @@ elif [[ $(cut -f2 <<< $(lsb_release -i)) == "Ubuntu" ]]; then sudo apt-get update sudo apt-get install -y --no-install-recommends git ninja-build build-essential g++ nasm yasm gperf sudo apt-get install -y --no-install-recommends apt-transport-https ca-certificates gnupg software-properties-common wget - sudo apt-get install -y --no-install-recommends libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev + sudo apt-get install -y --no-install-recommends libgles2-mesa-dev libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libdbus-1-dev libxtst-dev libltdl-dev wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | gpg --dearmor - | sudo tee /etc/apt/trusted.gpg.d/kitware.gpg >/dev/null sudo apt-add-repository "deb https://apt.kitware.com/ubuntu/ $(cut -f2 <<< $(lsb_release -c)) main" wget -q https://packages.microsoft.com/config/ubuntu/$(cut -f2 <<< $(lsb_release -r))/packages-microsoft-prod.deb From d62491d2084ab431e49ed29f3704e050347ee7a1 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Sat, 26 Jul 2025 15:38:44 +0200 Subject: [PATCH 1362/1365] fix linking errors with opencv3 --- CMakeLists.txt | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/CMakeLists.txt b/CMakeLists.txt index 8c65eeff301..c8ebea3e89a 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -515,6 +515,21 @@ if(OpenCV_FOUND) target_include_directories(dark PRIVATE ${OpenCV_INCLUDE_DIRS}) target_compile_definitions(darknet PRIVATE -DOPENCV) target_compile_definitions(dark PUBLIC -DOPENCV) + + if(OpenCV_VERSION VERSION_LESS "4.0.0") + find_package(Freetype REQUIRED) + # Link FreeType library to resolve undefined symbols in HarfBuzz, Cairo, and FontConfig + if(TARGET Freetype::Freetype) + target_link_libraries(darknet PRIVATE Freetype::Freetype) + target_link_libraries(dark PUBLIC Freetype::Freetype) + target_link_libraries(uselib PRIVATE Freetype::Freetype) + elseif(TARGET freetype) + target_link_libraries(darknet PRIVATE freetype) + target_link_libraries(dark PUBLIC freetype) + target_link_libraries(uselib PRIVATE freetype) + endif() + endif() + endif() if(OPENMP_FOUND) From c892fbabeb85852297a686fdd739b4937be969e9 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Thu, 28 Aug 2025 10:21:58 +0200 Subject: [PATCH 1363/1365] add mono on osx actions runners --- .github/workflows/ccpp.yml | 2 +- .github/workflows/on_pr.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 235747e37cc..1369ceaa2c8 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -383,7 +383,7 @@ jobs: if: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.debug_enabled }} - name: Install dependencies - run: brew install libomp yasm nasm pkg-config automake autoconf-archive + run: brew install libomp yasm nasm pkg-config automake autoconf-archive mono - uses: lukka/get-cmake@latest diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index 2bcf15bd895..f860ee83b61 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -290,7 +290,7 @@ jobs: - uses: actions/checkout@v4 - name: Install dependencies - run: brew install libomp yasm nasm pkg-config automake autoconf-archive + run: brew install libomp yasm nasm pkg-config automake autoconf-archive mono - uses: lukka/get-cmake@latest From 72b9602eb5533978f22289dde2474a42ebf5f660 Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Mon, 15 Dec 2025 11:57:26 +0100 Subject: [PATCH 1364/1365] improve win32/msc_ver compat with mingw and fix nuget https, plus add NSIS installation step (#8940) --- .github/workflows/ccpp.yml | 12 ++++++++++++ .github/workflows/on_pr.yml | 11 +++++++++++ src/art.c | 2 +- src/classifier.c | 2 +- src/coco.c | 2 +- src/demo.c | 4 ++-- src/detector.c | 4 ++-- src/gettimeofday.c | 2 +- src/gettimeofday.h | 2 +- src/http_stream.cpp | 12 ++++++------ src/utils.c | 20 +++++++++++--------- 11 files changed, 49 insertions(+), 24 deletions(-) diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 1369ceaa2c8..8971c79790e 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -117,6 +117,7 @@ jobs: mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" -Source http://93.49.111.10:5555/v3/index.json + -AllowInsecureConnections - name: Setup NuGet API key if found shell: bash @@ -180,6 +181,7 @@ jobs: mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" -Source http://93.49.111.10:5555/v3/index.json + -AllowInsecureConnections - name: Setup NuGet API key if found shell: bash @@ -221,6 +223,7 @@ jobs: mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" -Source http://93.49.111.10:5555/v3/index.json + -AllowInsecureConnections - name: Setup NuGet API key if found shell: bash @@ -357,6 +360,7 @@ jobs: mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" -Source http://93.49.111.10:5555/v3/index.json + -AllowInsecureConnections - name: Setup NuGet API key if found shell: bash @@ -395,6 +399,7 @@ jobs: mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" -Source http://93.49.111.10:5555/v3/index.json + -AllowInsecureConnections - name: Setup NuGet API key if found shell: bash @@ -475,6 +480,7 @@ jobs: $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" -Source http://93.49.111.10:5555/v3/index.json + -AllowInsecureConnections - name: Setup NuGet API key if found shell: bash @@ -486,6 +492,9 @@ jobs: setapikey ${{ secrets.BAGET_API_KEY }} -Source http://93.49.111.10:5555/v3/index.json + - name: Install NSIS + run: winget install --id NSIS.NSIS --accept-source-agreements --accept-package-agreements + - name: 'Build' shell: pwsh run: ${{ github.workspace }}/build.ps1 -UseVCPKG -ForceLocalVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL -BuildInstaller @@ -517,6 +526,7 @@ jobs: $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" -Source http://93.49.111.10:5555/v3/index.json + -AllowInsecureConnections - name: Setup NuGet API key if found shell: bash @@ -571,6 +581,7 @@ jobs: $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" -Source http://93.49.111.10:5555/v3/index.json + -AllowInsecureConnections - name: Setup NuGet API key if found shell: bash @@ -602,6 +613,7 @@ jobs: $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" -Source http://93.49.111.10:5555/v3/index.json + -AllowInsecureConnections - name: Setup NuGet API key if found shell: bash diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index f860ee83b61..e245513c620 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -103,6 +103,7 @@ jobs: mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" -Source http://93.49.111.10:5555/v3/index.json + -AllowInsecureConnections - name: 'Build' shell: pwsh @@ -139,6 +140,7 @@ jobs: mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" -Source http://93.49.111.10:5555/v3/index.json + -AllowInsecureConnections - name: 'Build' shell: pwsh @@ -170,6 +172,7 @@ jobs: mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" -Source http://93.49.111.10:5555/v3/index.json + -AllowInsecureConnections - name: 'Build' shell: pwsh @@ -278,6 +281,7 @@ jobs: mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" -Source http://93.49.111.10:5555/v3/index.json + -AllowInsecureConnections - name: 'Setup' shell: bash @@ -302,6 +306,7 @@ jobs: mono $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" -Source http://93.49.111.10:5555/v3/index.json + -AllowInsecureConnections - name: 'Build' shell: pwsh @@ -350,6 +355,10 @@ jobs: $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" -Source http://93.49.111.10:5555/v3/index.json + -AllowInsecureConnections + + - name: Install NSIS + run: winget install --id NSIS.NSIS --accept-source-agreements --accept-package-agreements - name: 'Build' shell: pwsh @@ -381,6 +390,7 @@ jobs: $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" -Source http://93.49.111.10:5555/v3/index.json + -AllowInsecureConnections - name: 'Setup' shell: pwsh @@ -402,6 +412,7 @@ jobs: $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add -Name "vcpkgbinarycache" -Source http://93.49.111.10:5555/v3/index.json + -AllowInsecureConnections - name: 'Build' shell: pwsh diff --git a/src/art.c b/src/art.c index 748e19038dc..6a4cfe78329 100644 --- a/src/art.c +++ b/src/art.c @@ -4,7 +4,7 @@ #include "option_list.h" #include "blas.h" #include "classifier.h" -#ifdef WIN32 +#ifdef _MSC_VER #include #include "gettimeofday.h" #else diff --git a/src/classifier.c b/src/classifier.c index f01303327cf..eb2a26f0439 100644 --- a/src/classifier.c +++ b/src/classifier.c @@ -6,7 +6,7 @@ #include "assert.h" #include "classifier.h" #include "dark_cuda.h" -#ifdef WIN32 +#ifdef _MSC_VER #include #include "gettimeofday.h" #else diff --git a/src/coco.c b/src/coco.c index 8ad13834b01..8168ad19d87 100644 --- a/src/coco.c +++ b/src/coco.c @@ -218,7 +218,7 @@ void validate_coco(char *cfgfile, char *weightfile) free_image(val_resized[t]); } } -#ifdef WIN32 +#ifdef _WIN32 fseek(fp, -3, SEEK_CUR); #else fseek(fp, -2, SEEK_CUR); diff --git a/src/demo.c b/src/demo.c index 5a01faf36e0..4ab2e3a4448 100644 --- a/src/demo.c +++ b/src/demo.c @@ -8,7 +8,7 @@ #include "image.h" #include "demo.h" #include "darknet.h" -#ifdef WIN32 +#ifdef _MSC_VER #include #include "gettimeofday.h" #else @@ -187,7 +187,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int } if (!cap) { -#ifdef WIN32 +#ifdef _WIN32 printf("Check that you have copied file opencv_ffmpeg340_64.dll to the same directory where is darknet.exe \n"); #endif error("Couldn't connect to webcam.", DARKNET_LOC); diff --git a/src/detector.c b/src/detector.c index 0fc36142904..115e9323c45 100644 --- a/src/detector.c +++ b/src/detector.c @@ -809,7 +809,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out free(fps); } if (coco) { -#ifdef WIN32 +#ifdef _WIN32 fseek(fp, -3, SEEK_CUR); #else fseek(fp, -2, SEEK_CUR); @@ -818,7 +818,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out } if (bdd) { -#ifdef WIN32 +#ifdef _WIN32 fseek(fp, -3, SEEK_CUR); #else fseek(fp, -2, SEEK_CUR); diff --git a/src/gettimeofday.c b/src/gettimeofday.c index 74f6789b91b..fbec4958058 100644 --- a/src/gettimeofday.c +++ b/src/gettimeofday.c @@ -40,4 +40,4 @@ int clock_gettime(int dummy, struct timespec* ct) return 0; } -#endif +#endif // _MSC_VER diff --git a/src/gettimeofday.h b/src/gettimeofday.h index 86fef1010ff..4cfc9c38a5d 100644 --- a/src/gettimeofday.h +++ b/src/gettimeofday.h @@ -35,4 +35,4 @@ int clock_gettime(int, struct timespec*); } #endif -#endif +#endif // _MSC_VER diff --git a/src/http_stream.cpp b/src/http_stream.cpp index cac00a09df5..8b70689cecb 100644 --- a/src/http_stream.cpp +++ b/src/http_stream.cpp @@ -162,16 +162,16 @@ class JSON_sender // Non-blocking sockets // Windows: ioctlsocket() and FIONBIO // Linux: fcntl() and O_NONBLOCK -#ifdef WIN32 +#ifdef _WIN32 unsigned long i_mode = 1; int result = ioctlsocket(sock, FIONBIO, &i_mode); if (result != NO_ERROR) { std::cerr << "ioctlsocket(FIONBIO) failed with error: " << result << std::endl; } -#else // WIN32 +#else // _WIN32 int flags = fcntl(sock, F_GETFL, 0); fcntl(sock, F_SETFL, flags | O_NONBLOCK); -#endif // WIN32 +#endif // _WIN32 #ifdef SO_REUSEPORT if (setsockopt(sock, SOL_SOCKET, SO_REUSEPORT, (const char*)&reuse, sizeof(reuse)) < 0) @@ -404,16 +404,16 @@ class MJPG_sender // Non-blocking sockets // Windows: ioctlsocket() and FIONBIO // Linux: fcntl() and O_NONBLOCK -#ifdef WIN32 +#ifdef _WIN32 unsigned long i_mode = 1; int result = ioctlsocket(sock, FIONBIO, &i_mode); if (result != NO_ERROR) { std::cerr << "ioctlsocket(FIONBIO) failed with error: " << result << std::endl; } -#else // WIN32 +#else // _WIN32 int flags = fcntl(sock, F_GETFL, 0); fcntl(sock, F_SETFL, flags | O_NONBLOCK); -#endif // WIN32 +#endif // _WIN32 #ifdef SO_REUSEPORT if (setsockopt(sock, SOL_SOCKET, SO_REUSEPORT, (const char*)&reuse, sizeof(reuse)) < 0) diff --git a/src/utils.c b/src/utils.c index 2ad33d5e5ae..d1526860630 100644 --- a/src/utils.c +++ b/src/utils.c @@ -13,13 +13,15 @@ #include #include #include "darkunistd.h" -#ifdef WIN32 +#ifdef _MSC_VER #include "gettimeofday.h" #else #include #include +#ifndef _WIN32 #include #endif +#endif #ifndef USE_CMAKE_LIBS @@ -340,7 +342,7 @@ void top_k(float *a, int n, int k, int *index) void log_backtrace() { -#ifndef WIN32 +#ifndef _WIN32 void * buffer[50]; int count = backtrace(buffer, sizeof(buffer)); char **symbols = backtrace_symbols(buffer, count); @@ -943,24 +945,24 @@ int rand_int_fast(int min, int max) unsigned int random_gen() { unsigned int rnd = 0; -#ifdef WIN32 +#ifdef _MSC_VER rand_s(&rnd); -#else // WIN32 +#else // _MSC_VER rnd = rand(); #if (RAND_MAX < 65536) rnd = rand()*(RAND_MAX + 1) + rnd; #endif //(RAND_MAX < 65536) -#endif // WIN32 +#endif // _MSC_VER return rnd; } float random_float() { unsigned int rnd = 0; -#ifdef WIN32 +#ifdef _MSC_VER rand_s(&rnd); return ((float)rnd / (float)UINT_MAX); -#else // WIN32 +#else // _MSC_VER rnd = rand(); #if (RAND_MAX < 65536) @@ -969,7 +971,7 @@ float random_float() #endif //(RAND_MAX < 65536) return ((float)rnd / (float)RAND_MAX); -#endif // WIN32 +#endif // _MSC_VER } float rand_uniform_strong(float min, float max) @@ -1078,7 +1080,7 @@ boxabs box_to_boxabs(const box* b, const int img_w, const int img_h, const int b int make_directory(char *path, int mode) { -#ifdef WIN32 +#ifdef _WIN32 return _mkdir(path); #else return mkdir(path, mode); From 59596d7880f6504768df41d6daa586f5cb2b932f Mon Sep 17 00:00:00 2001 From: Stefano Sinigardi Date: Mon, 15 Dec 2025 17:10:05 +0100 Subject: [PATCH 1365/1365] remove win-vcpkg-port ci tests, now vcpkg links to incompatible hank-ai fork (#8941) * remove win-vcpkg-port ci tests, now vcpkg links to incompatible hank-ai fork * remove custom build.ps1 since darknet on vcpkg is not valid anymore * do not pre-enable cuda & other feats --- .github/workflows/ccpp.yml | 98 +-- .github/workflows/on_pr.yml | 68 +- .gitmodules | 3 + CCM | 1 + CMakeLists.txt | 15 +- README.md | 10 +- build.ps1 | 1196 ----------------------------------- scripts/deploy-cuda.ps1 | 2 +- scripts/setup.ps1 | 8 +- scripts/setup.sh | 6 +- scripts/utils.psm1 | 516 --------------- src/utils.c | 2 +- vcpkg.json | 13 +- 13 files changed, 142 insertions(+), 1796 deletions(-) create mode 100644 .gitmodules create mode 160000 CCM delete mode 100755 build.ps1 delete mode 100644 scripts/utils.psm1 diff --git a/.github/workflows/ccpp.yml b/.github/workflows/ccpp.yml index 8971c79790e..374a19f96f6 100644 --- a/.github/workflows/ccpp.yml +++ b/.github/workflows/ccpp.yml @@ -21,6 +21,8 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - name: Update apt & install dependencies run: | @@ -82,6 +84,8 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - name: Setup tmate session uses: mxschmitt/action-tmate@v3 @@ -136,7 +140,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL -BuildInstaller + run: ${{ github.workspace }}/CCM/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL -BuildInstaller - uses: actions/upload-artifact@v4 with: @@ -160,6 +164,8 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - uses: lukka/get-cmake@latest @@ -195,13 +201,15 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -ForceOpenCVVersion 3 -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -ForceOpenCVVersion 3 -DisableInteractive -DoNotUpdateTOOL ubuntu-vcpkg-opencv2: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - uses: lukka/get-cmake@latest @@ -237,13 +245,15 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -ForceOpenCVVersion 2 -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -ForceOpenCVVersion 2 -DisableInteractive -DoNotUpdateTOOL ubuntu: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - name: Update apt & install dependencies run: | @@ -263,7 +273,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL - uses: actions/upload-artifact@v4 with: @@ -279,6 +289,8 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - name: Update apt & install dependencies run: | @@ -309,7 +321,7 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL - uses: actions/upload-artifact@v4 with: @@ -325,12 +337,14 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - uses: lukka/get-cmake@latest - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -DisableInteractive -DoNotUpdateTOOL -AdditionalBuildSetup "-DBUILD_AS_CPP:BOOL=ON" - name: Test on data/dog.jpg shell: bash @@ -343,6 +357,8 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - name: Update apt & install dependencies run: | @@ -381,6 +397,8 @@ jobs: runs-on: macos-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - name: Setup tmate session uses: mxschmitt/action-tmate@v3 @@ -413,7 +431,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL -BuildInstaller + run: ${{ github.workspace }}/CCM/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL -BuildInstaller - uses: actions/upload-artifact@v4 with: @@ -429,6 +447,8 @@ jobs: runs-on: macos-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - name: Install dependencies run: brew install opencv libomp @@ -437,7 +457,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL - uses: actions/upload-artifact@v4 with: @@ -453,18 +473,22 @@ jobs: runs-on: macos-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - uses: lukka/get-cmake@latest - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -DisableInteractive -DoNotUpdateTOOL -AdditionalBuildSetup "-DBUILD_AS_CPP:BOOL=ON" win-vcpkg: runs-on: windows-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - name: Setup tmate session uses: mxschmitt/action-tmate@v3 @@ -497,7 +521,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -ForceLocalVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL -BuildInstaller + run: ${{ github.workspace }}/CCM/build.ps1 -UseVCPKG -ForceLocalVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL -BuildInstaller - uses: actions/upload-artifact@v4 with: @@ -513,46 +537,18 @@ jobs: path: ${{ github.workspace }}/uselib* - win-vcpkg-port: - runs-on: windows-latest - steps: - - uses: actions/checkout@v4 - - - name: 'Setup vcpkg and NuGet artifacts backend' - shell: bash - run: > - git clone --depth 1 https://github.com/microsoft/vcpkg ; - ./vcpkg/bootstrap-vcpkg.sh ; - $(./vcpkg/vcpkg fetch nuget | tail -n 1) sources add - -Name "vcpkgbinarycache" - -Source http://93.49.111.10:5555/v3/index.json - -AllowInsecureConnections - - - name: Setup NuGet API key if found - shell: bash - env: - BAGET_API_KEY: ${{ secrets.BAGET_API_KEY }} - if: env.BAGET_API_KEY != null - run: > - $(./vcpkg/vcpkg fetch nuget | tail -n 1) - setapikey ${{ secrets.BAGET_API_KEY }} - -Source http://93.49.111.10:5555/v3/index.json - - - name: 'Build' - shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -ForceLocalVCPKG -InstallDARKNETthroughVCPKG -ForceVCPKGDarknetHEAD -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL - - win-intlibs: runs-on: windows-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - uses: lukka/get-cmake@latest - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -DisableInteractive -DoNotUpdateTOOL - uses: actions/upload-artifact@v4 with: @@ -572,6 +568,8 @@ jobs: runs-on: windows-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -602,6 +600,8 @@ jobs: runs-on: windows-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - uses: lukka/get-cmake@latest @@ -627,7 +627,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -ForceLocalVCPKG -DoNotUpdateVCPKG -ForceCPP -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -UseVCPKG -ForceLocalVCPKG -DoNotUpdateVCPKG -DisableInteractive -DoNotUpdateTOOL -AdditionalBuildSetup "-DBUILD_AS_CPP:BOOL=ON" - name: Download yolov4-tiny.weights run: curl.exe -L https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights -o ${{ github.workspace }}\yolov4-tiny.weights @@ -639,18 +639,22 @@ jobs: runs-on: windows-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - uses: lukka/get-cmake@latest - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -EnableCSharpWrapper -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -DisableInteractive -DoNotUpdateTOOL -DoNotUseNinja -AdditionalBuildSetup "-DENABLE_CSHARP_WRAPPER:BOOL=ON" win-intlibs-cuda: runs-on: windows-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.ps1 @@ -662,25 +666,29 @@ jobs: CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.6" CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.6\\bin\\nvcc.exe" shell: pwsh - run: ${{ github.workspace }}/build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateTOOL win-powershell51: runs-on: windows-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - uses: lukka/get-cmake@latest - name: 'Build' shell: powershell - run: ${{ github.workspace }}/build.ps1 -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -DisableInteractive -DoNotUpdateTOOL mingw: runs-on: windows-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - uses: lukka/get-cmake@latest diff --git a/.github/workflows/on_pr.yml b/.github/workflows/on_pr.yml index e245513c620..1297c14fbcb 100644 --- a/.github/workflows/on_pr.yml +++ b/.github/workflows/on_pr.yml @@ -11,6 +11,8 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - name: Update apt & install dependencies run: | @@ -72,6 +74,8 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - uses: lukka/get-cmake@latest @@ -112,13 +116,15 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL -BuildInstaller + run: ${{ github.workspace }}/CCM/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL -BuildInstaller ubuntu-vcpkg-opencv3: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - uses: lukka/get-cmake@latest @@ -144,13 +150,15 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -ForceOpenCVVersion 3 -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -ForceOpenCVVersion 3 -DisableInteractive -DoNotUpdateTOOL ubuntu-vcpkg-opencv2: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - uses: lukka/get-cmake@latest @@ -176,13 +184,15 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -ForceOpenCVVersion 2 -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -ForceOpenCVVersion 2 -DisableInteractive -DoNotUpdateTOOL ubuntu: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - name: Update apt & install dependencies run: | @@ -202,13 +212,15 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL ubuntu-cuda: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - name: Update apt & install dependencies run: | @@ -239,19 +251,21 @@ jobs: CUDA_PATH: "/usr/local/cuda" CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda" LD_LIBRARY_PATH: "/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH" - run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL ubuntu-no-ocv-cpp: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - uses: lukka/get-cmake@latest - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -DisableInteractive -DoNotUpdateTOOL -AdditionalBuildSetup "-DBUILD_AS_CPP:BOOL=ON" - name: Test on data/dog.jpg shell: bash @@ -264,6 +278,8 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - name: Update apt & install dependencies run: | @@ -292,6 +308,8 @@ jobs: runs-on: macos-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - name: Install dependencies run: brew install libomp yasm nasm pkg-config automake autoconf-archive mono @@ -310,13 +328,15 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL -BuildInstaller + run: ${{ github.workspace }}/CCM/build.ps1 -UseVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL -BuildInstaller osx: runs-on: macos-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - name: Install dependencies run: brew install opencv libomp @@ -325,25 +345,29 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL osx-no-ocv-no-omp-cpp: runs-on: macos-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - uses: lukka/get-cmake@latest - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -ForceCPP -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -DisableInteractive -DoNotUpdateTOOL -AdditionalBuildSetup "-DBUILD_AS_CPP:BOOL=ON" win-vcpkg: runs-on: windows-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - uses: lukka/get-cmake@latest @@ -362,25 +386,29 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -ForceLocalVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL -BuildInstaller + run: ${{ github.workspace }}/CCM/build.ps1 -UseVCPKG -ForceLocalVCPKG -DoNotUpdateVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL -BuildInstaller win-intlibs: runs-on: windows-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - uses: lukka/get-cmake@latest - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -DisableInteractive -DoNotUpdateTOOL win-setup-ps1: runs-on: windows-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - name: 'Setup vcpkg and NuGet artifacts backend' shell: bash @@ -401,6 +429,8 @@ jobs: runs-on: windows-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - uses: lukka/get-cmake@latest @@ -416,7 +446,7 @@ jobs: - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -UseVCPKG -ForceLocalVCPKG -DoNotUpdateVCPKG -ForceCPP -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -UseVCPKG -ForceLocalVCPKG -DoNotUpdateVCPKG -DisableInteractive -DoNotUpdateTOOL -AdditionalBuildSetup "-DBUILD_AS_CPP:BOOL=ON" - name: Download yolov4-tiny.weights run: curl.exe -L https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights -o ${{ github.workspace }}\yolov4-tiny.weights @@ -428,18 +458,22 @@ jobs: runs-on: windows-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - uses: lukka/get-cmake@latest - name: 'Build' shell: pwsh - run: ${{ github.workspace }}/build.ps1 -EnableCSharpWrapper -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -DisableInteractive -DoNotUpdateTOOL -DoNotUseNinja -AdditionalBuildSetup "-DENABLE_CSHARP_WRAPPER:BOOL=ON" win-intlibs-cuda: runs-on: windows-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - name: 'Install CUDA' run: ${{ github.workspace }}/scripts/deploy-cuda.ps1 @@ -451,25 +485,29 @@ jobs: CUDA_TOOLKIT_ROOT_DIR: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.6" CUDACXX: "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v12.6\\bin\\nvcc.exe" shell: pwsh - run: ${{ github.workspace }}/build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -EnableCUDA -DisableInteractive -DoNotUpdateTOOL win-powershell51: runs-on: windows-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - uses: lukka/get-cmake@latest - name: 'Build' shell: powershell - run: ${{ github.workspace }}/build.ps1 -DisableInteractive -DoNotUpdateTOOL + run: ${{ github.workspace }}/CCM/build.ps1 -DisableInteractive -DoNotUpdateTOOL mingw: runs-on: windows-latest steps: - uses: actions/checkout@v4 + with: + submodules: 'true' - uses: lukka/get-cmake@latest diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 00000000000..7530075951e --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "CCM"] + path = CCM + url = https://github.com/cenit/CCM diff --git a/CCM b/CCM new file mode 160000 index 00000000000..20dc73dc42d --- /dev/null +++ b/CCM @@ -0,0 +1 @@ +Subproject commit 20dc73dc42d6fc10720fd40659618141d92d0c4c diff --git a/CMakeLists.txt b/CMakeLists.txt index c8ebea3e89a..a931a9d9b42 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -7,17 +7,17 @@ option(BUILD_SHARED_LIBS "Create dark as a shared library" ON) option(BUILD_AS_CPP "Build Darknet using C++ compiler also for C files" OFF) option(BUILD_USELIB_TRACK "Build uselib_track" ON) option(MANUALLY_EXPORT_TRACK_OPTFLOW "Manually export the TRACK_OPTFLOW=1 define" OFF) -option(ENABLE_OPENCV "Enable OpenCV integration" ON) -option(ENABLE_CUDA "Enable CUDA support" ON) +option(ENABLE_OPENCV "Enable OpenCV integration" OFF) +option(ENABLE_CUDA "Enable CUDA support" OFF) cmake_dependent_option(ENABLE_CUDA_OPENGL_INTEGRATION "Build darknet with support for running networks straight from OpenGL textures" ON "ENABLE_CUDA" OFF) -option(ENABLE_CUDNN "Enable CUDNN" ON) -option(ENABLE_CUDNN_HALF "Enable CUDNN Half precision" ON) -option(ENABLE_ZED_CAMERA "Enable ZED Camera support" ON) +option(ENABLE_CUDNN "Enable CUDNN" OFF) +option(ENABLE_CUDNN_HALF "Enable CUDNN Half precision" OFF) +option(ENABLE_ZED_CAMERA "Enable ZED Camera support" OFF) option(ENABLE_VCPKG_INTEGRATION "Enable VCPKG integration" OFF) option(ENABLE_DEPLOY_CUSTOM_CMAKE_MODULES "Copy custom CMake modules for downstream integration" OFF) option(ENABLE_CSHARP_WRAPPER "Enable building a csharp wrapper" OFF) option(ENABLE_INSTALLER "Enable building an installer" OFF) -option(VCPKG_BUILD_OPENCV_WITH_CUDA "Build OpenCV with CUDA extension integration" ON) +option(VCPKG_BUILD_OPENCV_WITH_CUDA "Build OpenCV with CUDA extension integration" OFF) option(VCPKG_USE_OPENCV2 "Use legacy OpenCV 2" OFF) option(VCPKG_USE_OPENCV3 "Use legacy OpenCV 3" OFF) option(VCPKG_USE_OPENCV4 "Use OpenCV 4" ON) @@ -472,6 +472,9 @@ if(BUILD_AS_CPP) endif() add_executable(darknet ${CMAKE_CURRENT_LIST_DIR}/src/darknet.c ${sources} ${headers} ${cuda_sources}) +if(WIN32 AND NOT MINGW) + set_target_properties(darknet PROPERTIES PDB_NAME "darknet.exe" IMPORT_SUFFIX ".exe.lib") +endif() if(BUILD_AS_CPP) set_source_files_properties(${CMAKE_CURRENT_LIST_DIR}/src/darknet.c PROPERTIES LANGUAGE CXX) set_target_properties(darknet PROPERTIES LINKER_LANGUAGE CXX) diff --git a/README.md b/README.md index 0a1ae52226a..fa11fc21488 100644 --- a/README.md +++ b/README.md @@ -386,7 +386,7 @@ The `CMakeLists.txt` will attempt to find installed optional dependencies like C To update CMake on Ubuntu, it's better to follow guide here: https://apt.kitware.com/ or https://cmake.org/download/ ```bash -git clone https://github.com/AlexeyAB/darknet +git clone https://github.com/AlexeyAB/darknet --recurse-submodules cd darknet mkdir build_release cd build_release @@ -403,9 +403,9 @@ Install powershell for your OS (Linux or MacOS) ([guide here](https://docs.micro Open PowerShell type these commands ```PowerShell -git clone https://github.com/AlexeyAB/darknet +git clone https://github.com/AlexeyAB/darknet --recurse-submodules cd darknet -./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN +./CCM/build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN ``` - remove options like `-EnableCUDA` or `-EnableCUDNN` if you are not interested into @@ -463,9 +463,9 @@ This is the recommended approach to build Darknet on Windows. ```PowerShell Set-ExecutionPolicy unrestricted -Scope CurrentUser -Force -git clone https://github.com/AlexeyAB/darknet +git clone https://github.com/AlexeyAB/darknet --recurse-submodules cd darknet -.\build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN +.\CCM\build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN ``` (add option `-EnableOPENCV_CUDA` if you want to build OpenCV with CUDA support - very slow to build! - or remove options like `-EnableCUDA` or `-EnableCUDNN` if you are not interested in them). If you open the `build.ps1` script at the beginning you will find all available switches. diff --git a/build.ps1 b/build.ps1 deleted file mode 100755 index 07ecf1f17a8..00000000000 --- a/build.ps1 +++ /dev/null @@ -1,1196 +0,0 @@ -#!/usr/bin/env pwsh - -<# - -.SYNOPSIS - build - Created By: Stefano Sinigardi - Created Date: February 18, 2019 - Last Modified Date: April 29, 2024 - -.DESCRIPTION -Build tool using CMake, trying to properly setup the environment around compiler - -.PARAMETER DisableInteractive -Disable script interactivity (useful for CI runs) - -.PARAMETER BuildDebug -Build using the debug toolchain - -.PARAMETER DisableDLLcopy -Disable automatic DLL deployment through vcpkg at the end - -.PARAMETER EnableCUDA -Enable CUDA feature - -.PARAMETER EnableCUDNN -Enable CUDNN feature - -.PARAMETER EnableOPENCV -Enable OpenCV feature - -.PARAMETER ForceOpenCVVersion -Force a specific OpenCV version - -.PARAMETER EnableOPENCV_CUDA -Use a CUDA-enabled OpenCV build - -.PARAMETER UseVCPKG -Use vcpkg to build tool dependencies. Clone it if not already found on system - -.PARAMETER ForceLocalVCPKG -Use a copy of vcpkg in a subfolder of the tool folder, even if there might be another copy already provided by the system - -.PARAMETER InstallDARKNETthroughVCPKG -Use VCPKG to install darknet thanks to the port integrated in it - -.PARAMETER InstallDARKNETdependenciesThroughVCPKGManifest -Use VCPKG to install darknet dependencies using vcpkg manifest feature - -.PARAMETER ForceVCPKGDarknetHEAD -Install darknet from vcpkg and force it to HEAD version, not latest port release - -.PARAMETER DoNotUpdateVCPKG -Do not update vcpkg before running the build (valid only if vcpkg is cloned by this script or the version found on the system is git-enabled) - -.PARAMETER VCPKGSuffix -Specify a suffix to the vcpkg local folder for searching, useful to point to a custom version - -.PARAMETER VCPKGFork -Specify a fork username to point to a custom version of vcpkg (ex: -VCPKGFork "custom" to point to github.com/custom/vcpkg) - -.PARAMETER VCPKGBranch -Specify a branch to checkout in the vcpkg folder, useful to point to a custom version especially for forked vcpkg versions - -.PARAMETER DoNotUpdateTOOL -Do not update the tool before running the build (valid only if tool is git-enabled) - -.PARAMETER DoNotDeleteBuildFolder -Do not delete temporary cmake build folder at the end of the script - -.PARAMETER DoNotSetupVS -Do not setup VisualStudio environment using the vcvars script - -.PARAMETER DoNotUseNinja -Do not use Ninja for build - -.PARAMETER ForceCPP -Force building darknet using C++ compiler also for plain C code - -.PARAMETER ForceStaticLib -Create library as static instead of the default linking mode of your system - -.PARAMETER ForceVCPKGCacheRemoval -Force clean up of the local vcpkg binary cache before building - -.PARAMETER ForceVCPKGBuildtreesRemoval -Force clean up of vcpkg buildtrees temp folder at the end of the script - -.PARAMETER ForceVCPKGBuildtreesPath -Force using a different buildtrees dir for vcpkg - -.PARAMETER ForceVCPKGPackagesRemoval -Force clean up of vcpkg packages folder at the end of the script - -.PARAMETER CloneVCPKGShallow -Clone vcpkg as shallow repository - -.PARAMETER ForceDisableVCPKGShallow -Force vcpkg clone to NOT be a shallow one - -.PARAMETER ForceSetupVS -Forces Visual Studio setup, also on systems on which it would not have been enabled automatically - -.PARAMETER ForceCMakeFromVS -Forces usage of CMake from Visual Studio instead of the system-wide/user installed one - -.PARAMETER ForceNinjaFromVS -Forces usage of Ninja from Visual Studio instead of the system-wide/user installed one - -.PARAMETER EnableCSharpWrapper -Enables building C# darknet wrapper - -.PARAMETER DownloadWeights -Download pre-trained weight files - -.PARAMETER Use32bitTriplet -Use 32 bit triplet for target build (windows-only) - -.PARAMETER BuildInstaller -Build an installer using CPack - -.PARAMETER ForceGCCVersion -Force a specific GCC version - -.PARAMETER NumberOfBuildWorkers -Forces a specific number of threads for parallel building - -.PARAMETER AdditionalBuildSetup -Additional setup parameters to manually pass to CMake - -.EXAMPLE -./build -DisableInteractive -DoNotDeleteBuildFolder -UseVCPKG - -#> - -<# -Copyright (c) Stefano Sinigardi - -MIT License - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. -#> - -param ( - [switch]$DisableInteractive = $false, - [switch]$BuildDebug = $false, - [switch]$DisableDLLcopy = $false, - [switch]$EnableCUDA = $false, - [switch]$EnableCUDNN = $false, - [switch]$EnableOPENCV = $false, - [Int32]$ForceOpenCVVersion = 0, - [switch]$EnableOPENCV_CUDA = $false, - [switch]$UseVCPKG = $false, - [switch]$ForceLocalVCPKG = $false, - [switch]$InstallDARKNETthroughVCPKG = $false, - [switch]$InstallDARKNETdependenciesThroughVCPKGManifest = $false, - [switch]$ForceVCPKGDarknetHEAD = $false, - [switch]$DoNotUpdateVCPKG = $false, - [string]$VCPKGSuffix = "", - [string]$VCPKGFork = "", - [string]$VCPKGBranch = "", - [switch]$DoNotUpdateTOOL = $false, - [switch]$DoNotDeleteBuildFolder = $false, - [switch]$DoNotSetupVS = $false, - [switch]$DoNotUseNinja = $false, - [switch]$ForceCPP = $false, - [switch]$ForceStaticLib = $false, - [switch]$ForceVCPKGCacheRemoval = $false, - [switch]$ForceVCPKGBuildtreesRemoval = $false, - [string]$ForceVCPKGBuildtreesPath = "", - [switch]$ForceVCPKGPackagesRemoval = $false, - [switch]$CloneVCPKGShallow = $false, - [switch]$ForceDisableVCPKGShallow = $false, - [switch]$ForceSetupVS = $false, - [switch]$ForceCMakeFromVS = $false, - [switch]$ForceNinjaFromVS = $false, - [switch]$EnableCSharpWrapper = $false, - [switch]$DownloadWeights = $false, - [switch]$Use32bitTriplet = $false, - [switch]$BuildInstaller = $false, - [Int32]$ForceGCCVersion = 0, - [Int32]$NumberOfBuildWorkers = 8, - [string]$AdditionalBuildSetup = "" # "-DCMAKE_CUDA_ARCHITECTURES=30" -) - -$global:DisableInteractive = $DisableInteractive - -$build_ps1_version = "4.0.1" -$script_name = $MyInvocation.MyCommand.Name -$utils_psm1_avail = $false - -if (Test-Path $PSScriptRoot/utils.psm1) { - Import-Module -Name $PSScriptRoot/utils.psm1 -Force - $utils_psm1_avail = $true -} -elseif (Test-Path $PSScriptRoot/cmake/utils.psm1) { - Import-Module -Name $PSScriptRoot/cmake/utils.psm1 -Force - $utils_psm1_avail = $true - $IsInGitSubmodule = $false -} -elseif (Test-Path $PSScriptRoot/ci/utils.psm1) { - Import-Module -Name $PSScriptRoot/ci/utils.psm1 -Force - $utils_psm1_avail = $true - $IsInGitSubmodule = $false -} -elseif (Test-Path $PSScriptRoot/ccm/utils.psm1) { - Import-Module -Name $PSScriptRoot/ccm/utils.psm1 -Force - $utils_psm1_avail = $true - $IsInGitSubmodule = $false -} -elseif (Test-Path $PSScriptRoot/scripts/utils.psm1) { - Import-Module -Name $PSScriptRoot/scripts/utils.psm1 -Force - $utils_psm1_avail = $true - $IsInGitSubmodule = $false -} -else { - $utils_psm1_version = "unavail" - $IsWindowsPowerShell = $false - $IsInGitSubmodule = $false -} - -if (-Not $utils_psm1_avail) { - $DoNotSetupVS = $true - $ForceCMakeFromVS = $false -} - -$ErrorActionPreference = "SilentlyContinue" -Stop-Transcript | out-null -$ErrorActionPreference = "Continue" -if($IsInGitSubmodule) { - $PSCustomScriptRoot = Split-Path $PSScriptRoot -Parent -} -else { - $PSCustomScriptRoot = $PSScriptRoot -} -$BuildLogPath = "$PSCustomScriptRoot/build.log" -$ReleaseInstallPrefix = "$PSCustomScriptRoot" -$DebugInstallPrefix = "$PSCustomScriptRoot/debug" -$DebugBuildSetup = " -DCMAKE_BUILD_TYPE=Debug " -$ReleaseBuildSetup = " -DCMAKE_BUILD_TYPE=Release " -if (-Not $BuildInstaller) { - $DebugBuildSetup = $DebugBuildSetup + " -DCMAKE_INSTALL_PREFIX=$DebugInstallPrefix " - $ReleaseBuildSetup = $ReleaseBuildSetup + " -DCMAKE_INSTALL_PREFIX=$ReleaseInstallPrefix " -} -Start-Transcript -Path $BuildLogPath - -Write-Host "Build script version ${build_ps1_version}, utils module version ${utils_psm1_version}" -if (-Not $utils_psm1_avail) { - Write-Host "utils.psm1 is not available, so VS integration is forcefully disabled" -ForegroundColor Yellow -} -Write-Host "Working directory: $PSCustomScriptRoot, log file: $BuildLogPath, $script_name is in submodule: $IsInGitSubmodule" - -if ((-Not $global:DisableInteractive) -and (-Not $UseVCPKG)) { - $Result = Read-Host "Enable vcpkg to install dependencies (yes/no)" - if (($Result -eq 'Yes') -or ($Result -eq 'Y') -or ($Result -eq 'yes') -or ($Result -eq 'y')) { - $UseVCPKG = $true - } -} - -if ((-Not $DisableInteractive) -and (-Not $EnableCUDA) -and (-Not $IsMacOS)) { - $Result = Read-Host "Enable CUDA integration (yes/no)" - if (($Result -eq 'Yes') -or ($Result -eq 'Y') -or ($Result -eq 'yes') -or ($Result -eq 'y')) { - $EnableCUDA = $true - } -} - -if ($EnableCUDA -and (-Not $DisableInteractive) -and (-Not $EnableCUDNN)) { - $Result = Read-Host "Enable CUDNN optional dependency (yes/no)" - if (($Result -eq 'Yes') -or ($Result -eq 'Y') -or ($Result -eq 'yes') -or ($Result -eq 'y')) { - $EnableCUDNN = $true - } -} - -if ((-Not $DisableInteractive) -and (-Not $EnableOPENCV)) { - $Result = Read-Host "Enable OpenCV optional dependency (yes/no)" - if (($Result -eq 'Yes') -or ($Result -eq 'Y') -or ($Result -eq 'yes') -or ($Result -eq 'y')) { - $EnableOPENCV = $true - } -} - -Write-Host -NoNewLine "PowerShell version:" -$PSVersionTable.PSVersion - -if ($IsWindowsPowerShell) { - Write-Host "Running on Windows Powershell, please consider update and running on newer Powershell versions" -} - -if ($PSVersionTable.PSVersion.Major -lt 5) { - MyThrow("Your PowerShell version is too old, please update it.") -} - -if ($IsLinux -or $IsMacOS) { - $bootstrap_ext = ".sh" - $exe_ext = "" -} -elseif ($IsWindows -or $IsWindowsPowerShell) { - $bootstrap_ext = ".bat" - $exe_ext = ".exe" -} -if ($ForceLocalVCPKG -And -Not $UseVCPKG) { - $UseVCPKG = $true - Write-Host "ForceLocalVCPKG was true but UseVCPKG was false, setting UseVCPKG to true" -} -if ($InstallDARKNETdependenciesThroughVCPKGManifest -and -not $InstallDARKNETthroughVCPKG) { - Write-Host "You requested darknet dependencies to be installed by vcpkg in manifest mode but you didn't enable installation through vcpkg, doing that for you" - $InstallDARKNETthroughVCPKG = $true -} - -if ($InstallDARKNETthroughVCPKG -and -not $UseVCPKG) { - Write-Host "You requested darknet to be installed by vcpkg but you didn't enable vcpkg, doing that for you" - $UseVCPKG = $true -} - -if ($InstallDARKNETthroughVCPKG -and -not $EnableOPENCV) { - Write-Host "You requested darknet to be installed by vcpkg but you didn't enable OpenCV, doing that for you" - $EnableOPENCV = $true -} - -if ($UseVCPKG) { - Write-Host "vcpkg bootstrap script: bootstrap-vcpkg${bootstrap_ext}" - if(($ForceOpenCVVersion -eq 0) -and -Not $ForceDisableVCPKGShallow) { - Write-Host "vcpkg will be cloned in shallow mode since baseline is not needed" - $CloneVCPKGShallow = $true - } -} - -if ((-Not $IsWindows) -and (-Not $IsWindowsPowerShell) -and (-Not $ForceSetupVS)) { - $DoNotSetupVS = $true -} - -if ($ForceStaticLib) { - Write-Host "Forced CMake to produce a static library" - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DBUILD_SHARED_LIBS=OFF " -} - -if (($IsLinux -or $IsMacOS) -and ($ForceGCCVersion -gt 0)) { - Write-Host "Manually setting CC and CXX variables to gcc version $ForceGCCVersion" - $env:CC = "gcc-$ForceGCCVersion" - $env:CXX = "g++-$ForceGCCVersion" -} - -$osArchitecture = [System.Runtime.InteropServices.RuntimeInformation]::OSArchitecture -switch ($osArchitecture) { - "X86" { - $vcpkgArchitecture = "x86" - $vsArchitecture = "Win32" - } - "X64" { - $vcpkgArchitecture = "x64" - $vsArchitecture = "x64" - } - "Arm" { - $vcpkgArchitecture = "arm" - $vsArchitecture = "arm" - } - "Arm64" { - $vcpkgArchitecture = "arm64" - $vsArchitecture = "arm64" - } - default { - $vcpkgArchitecture = "x64" - $vsArchitecture = "x64" - Write-Output "Unknown architecture. Trying x64" - } -} - -$vcpkg_triplet_set_by_this_script = $false -$vcpkg_host_triplet_set_by_this_script = $false - -if (($IsWindows -or $IsWindowsPowerShell) -and (-Not $env:VCPKG_DEFAULT_TRIPLET)) { - if ($Use32bitTriplet) { - if (-Not $BuildDebug) { - $BuildDebug = $true - Write-Host "Warning: when building for 32bit windows target, only builds with also debug version are possible. Debug has been enabled on your behalf!" -ForegroundColor Yellow - } - if (-Not $DoNotUseNinja) { - $DoNotUseNinja = $true - Write-Host "Warning: when building for 32bit windows target, only msbuild can be used and ninja will be disabled. Doing that for you!" -ForegroundColor Yellow - } - $env:VCPKG_DEFAULT_TRIPLET = "${vcpkgArchitecture}-windows" - $vcpkg_triplet_set_by_this_script = $true - } - else { - if($BuildDebug) { - $env:VCPKG_DEFAULT_TRIPLET = "${vcpkgArchitecture}-windows" - $vcpkg_triplet_set_by_this_script = $true - } - else { - $env:VCPKG_DEFAULT_TRIPLET = "${vcpkgArchitecture}-windows-release" - $vcpkg_triplet_set_by_this_script = $true - } - } -} -if (($IsWindows -or $IsWindowsPowerShell) -and (-Not $env:VCPKG_DEFAULT_HOST_TRIPLET)) { - if ($BuildDebug) { - $env:VCPKG_DEFAULT_HOST_TRIPLET = "${vcpkgArchitecture}-windows" - $vcpkg_host_triplet_set_by_this_script = $true - } - else { - $env:VCPKG_DEFAULT_HOST_TRIPLET = "${vcpkgArchitecture}-windows-release" - $vcpkg_host_triplet_set_by_this_script = $true - } -} - -if ($IsMacOS -and (-Not $env:VCPKG_DEFAULT_TRIPLET)) { - if ($BuildDebug) { - $env:VCPKG_DEFAULT_TRIPLET = "${vcpkgArchitecture}-osx" - $vcpkg_triplet_set_by_this_script = $true - } - else { - $env:VCPKG_DEFAULT_TRIPLET = "${vcpkgArchitecture}-osx-release" - $vcpkg_triplet_set_by_this_script = $true - } -} -if ($IsMacOS -and (-Not $env:VCPKG_DEFAULT_HOST_TRIPLET)) { - if ($BuildDebug) { - $env:VCPKG_DEFAULT_HOST_TRIPLET = "${vcpkgArchitecture}-osx" - $vcpkg_host_triplet_set_by_this_script = $true - } - else { - $env:VCPKG_DEFAULT_HOST_TRIPLET = "${vcpkgArchitecture}-osx-release" - $vcpkg_host_triplet_set_by_this_script = $true - } -} - -if ($IsLinux -and (-Not $env:VCPKG_DEFAULT_TRIPLET)) { - if ($true) { - if ($BuildDebug) { - $env:VCPKG_DEFAULT_TRIPLET = "${vcpkgArchitecture}-linux" - $vcpkg_triplet_set_by_this_script = $true - } - else { - $env:VCPKG_DEFAULT_TRIPLET = "${vcpkgArchitecture}-linux-release" - $vcpkg_triplet_set_by_this_script = $true - } - } -} -if ($IsLinux -and (-Not $env:VCPKG_DEFAULT_HOST_TRIPLET)) { - if ($BuildDebug) { - $env:VCPKG_DEFAULT_HOST_TRIPLET = "${vcpkgArchitecture}-linux" - $vcpkg_host_triplet_set_by_this_script = $true - } - else { - $env:VCPKG_DEFAULT_HOST_TRIPLET = "${vcpkgArchitecture}-linux-release" - $vcpkg_host_triplet_set_by_this_script = $true - } -} - -if ($VCPKGSuffix -ne "" -and -not $UseVCPKG) { - Write-Host "You specified a vcpkg folder suffix but didn't enable vcpkg integration, doing that for you" -ForegroundColor Yellow - $UseVCPKG = $true -} - -if ($VCPKGFork -ne "" -and -not $UseVCPKG) { - Write-Host "You specified a vcpkg fork but didn't enable vcpkg integration, doing that for you" -ForegroundColor Yellow - $UseVCPKG = $true -} - -if ($VCPKGBranch -ne "" -and -not $UseVCPKG) { - Write-Host "You specified a vcpkg branch but didn't enable vcpkg integration, doing that for you" -ForegroundColor Yellow - $UseVCPKG = $true -} - -if ($EnableCUDA) { - if ($IsMacOS) { - Write-Host "Cannot enable CUDA on macOS" -ForegroundColor Yellow - $EnableCUDA = $false - } - Write-Host "CUDA is enabled" -} -elseif (-Not $IsMacOS) { - Write-Host "CUDA is disabled, please pass -EnableCUDA to the script to enable" -} - -if ($EnableCUDNN) { - if ($IsMacOS) { - Write-Host "Cannot enable CUDNN on macOS" -ForegroundColor Yellow - $EnableCUDNN = $false - } - Write-Host "CUDNN is enabled" -} -elseif (-Not $IsMacOS) { - Write-Host "CUDNN is disabled, please pass -EnableCUDNN to the script to enable" -} - -if ($EnableOPENCV) { - Write-Host "OPENCV is enabled" -} -else { - Write-Host "OPENCV is disabled, please pass -EnableOPENCV to the script to enable" -} - -if ($EnableCUDA -and $EnableOPENCV -and (-Not $EnableOPENCV_CUDA)) { - Write-Host "OPENCV with CUDA extension is not enabled, you can enable it passing -EnableOPENCV_CUDA" -} -elseif ($EnableOPENCV -and $EnableOPENCV_CUDA -and (-Not $EnableCUDA)) { - Write-Host "OPENCV with CUDA extension was requested, but CUDA is not enabled, you can enable it passing -EnableCUDA" - $EnableOPENCV_CUDA = $false -} -elseif ($EnableCUDA -and $EnableOPENCV_CUDA -and (-Not $EnableOPENCV)) { - Write-Host "OPENCV with CUDA extension was requested, but OPENCV is not enabled, you can enable it passing -EnableOPENCV" - $EnableOPENCV_CUDA = $false -} -elseif ($EnableOPENCV_CUDA -and (-Not $EnableCUDA) -and (-Not $EnableOPENCV)) { - Write-Host "OPENCV with CUDA extension was requested, but OPENCV and CUDA are not enabled, you can enable them passing -EnableOPENCV -EnableCUDA" - $EnableOPENCV_CUDA = $false -} - -if ($UseVCPKG) { - Write-Host "VCPKG is enabled" - if ($BuildDebug -and ($env:VCPKG_DEFAULT_TRIPLET -match "release")) { - MyThrow("You asked to build also debug binaries but VCPKG_DEFAULT_TRIPLET is set to a release triplet") - } - if ($DoNotUpdateVCPKG) { - Write-Host "VCPKG will not be updated to latest version if found" -ForegroundColor Yellow - } - else { - Write-Host "VCPKG will be updated to latest version if found" - } -} -else { - Write-Host "VCPKG is disabled, please pass -UseVCPKG to the script to enable" -} - -if ($DoNotSetupVS) { - Write-Host "VisualStudio integration is disabled" -} -else { - Write-Host "VisualStudio integration is enabled, please pass -DoNotSetupVS to the script to disable" -} - -if ($EnableCSharpWrapper -and ($IsWindowsPowerShell -or $IsWindows)) { - Write-Host "Yolo C# wrapper integration is enabled. Will be built with Visual Studio generator. Disabling Ninja" - $DoNotUseNinja = $true -} -else { - $EnableCSharpWrapper = $false - Write-Host "Yolo C# wrapper integration is disabled, please pass -EnableCSharpWrapper to the script to enable. You must be on Windows!" -} - -if ($DoNotUseNinja) { - Write-Host "Ninja is disabled" -} -else { - Write-Host "Ninja is enabled, please pass -DoNotUseNinja to the script to disable" -} - -if ($ForceCPP) { - Write-Host "ForceCPP build mode is enabled" -} -else { - Write-Host "ForceCPP build mode is disabled, please pass -ForceCPP to the script to enable" -} - -Push-Location $PSCustomScriptRoot - -$GIT_EXE = Get-Command "git" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition -if (-Not $GIT_EXE) { - MyThrow("Could not find git, please install it") -} -else { - Write-Host "Using git from ${GIT_EXE}" -} - -$GitRepoPath = Resolve-Path "$PSCustomScriptRoot/.git" -ErrorAction SilentlyContinue -$GitModulesPath = Resolve-Path "$PSCustomScriptRoot/.gitmodules" -ErrorAction SilentlyContinue -if (Test-Path "$GitRepoPath") { - Write-Host "This tool has been cloned with git and supports self-updating mechanism" - if ($DoNotUpdateTOOL) { - Write-Host "This tool will not self-update sources" -ForegroundColor Yellow - } - else { - Write-Host "This tool will self-update sources, please pass -DoNotUpdateTOOL to the script to disable" - Set-Location "$PSCustomScriptRoot" - $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "pull" - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-Not ($exitCode -eq 0)) { - MyThrow("Updating this tool sources failed! Exited with error code $exitCode.") - } - if (Test-Path "$GitModulesPath") { - $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "submodule update --init --recursive" - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-Not ($exitCode -eq 0)) { - MyThrow("Updating this tool submodule sources failed! Exited with error code $exitCode.") - } - } - Set-Location "$PSCustomScriptRoot" - } -} - -if ($ForceCmakeFromVS) { - $vsfound = getLatestVisualStudioWithDesktopWorkloadPath - $cmakePath = "${vsfound}/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/bin" - $vsCmakePath = "${vsfound}/Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/bin/cmake.exe" - $CMAKE_EXE = Get-Command "cmake" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition - if ((Test-Path "$vsCmakePath") -and -not ($vsCmakePath -eq $CMAKE_EXE)) { - Write-Host "Adding CMake from Visual Studio to PATH" - $env:PATH = '{0}{1}{2}' -f "$cmakePath", [IO.Path]::PathSeparator, $env:PATH - } - elseif ($vsCmakePath -eq $CMAKE_EXE) { - Write-Host "CMake from Visual Studio was already the preferred choice" -ForegroundColor Yellow - } - else { - Write-Host "Unable to find CMake integrated in Visual Studio" -ForegroundColor Red - } -} - -$CMAKE_EXE = Get-Command "cmake" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition -if (-Not $CMAKE_EXE) { - MyThrow("Could not find CMake, please install it") -} -else { - Write-Host "Using CMake from ${CMAKE_EXE}" - $proc = Start-Process -NoNewWindow -PassThru -FilePath ${CMAKE_EXE} -ArgumentList "--version" - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-Not ($exitCode -eq 0)) { - MyThrow("CMake version check failed! Exited with error code $exitCode.") - } -} - -if (-Not $DoNotUseNinja) { - if ($ForceNinjaFromVS) { - $vsfound = getLatestVisualStudioWithDesktopWorkloadPath - $ninjaPath = "${vsfound}/Common7/IDE/CommonExtensions/Microsoft/CMake/Ninja" - $vsninjaPath = "${vsfound}/Common7/IDE/CommonExtensions/Microsoft/CMake/Ninja/ninja.exe" - $NINJA_EXE = Get-Command "ninja" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition - if ((Test-Path "$vsninjaPath") -and -not ($vsninjaPath -eq $NINJA_EXE) -and (-not $DoNotUseNinja)) { - Write-Host "Adding Ninja from Visual Studio to PATH" - $env:PATH = '{0}{1}{2}' -f "$ninjaPath", [IO.Path]::PathSeparator, $env:PATH - } - elseif ($vsninjaPath -eq $NINJA_EXE) { - Write-Host "Ninja from Visual Studio was already the preferred choice" -ForegroundColor Yellow - } - else { - Write-Host "Unable to find Ninja integrated in Visual Studio" -ForegroundColor Red - } - } - $NINJA_EXE = Get-Command "ninja" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition - if (-Not $NINJA_EXE) { - DownloadNinja - $NinjaPath = Join-Path (${PSCustomScriptRoot}) 'ninja' - $env:PATH = '{0}{1}{2}' -f $env:PATH, [IO.Path]::PathSeparator, "$NinjaPath" - $NINJA_EXE = Get-Command "ninja" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition - if (-Not $NINJA_EXE) { - $DoNotUseNinja = $true - Write-Host "Could not find Ninja, unable to download a portable ninja, using msbuild or make backends as a fallback" -ForegroundColor Yellow - } - } - if ($NINJA_EXE) { - Write-Host "Using Ninja from ${NINJA_EXE}" - Write-Host -NoNewLine "Ninja version " - $proc = Start-Process -NoNewWindow -PassThru -FilePath ${NINJA_EXE} -ArgumentList "--version" - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-Not ($exitCode -eq 0)) { - $DoNotUseNinja = $true - Write-Host "Unable to run Ninja previously found, using msbuild or make backends as a fallback" -ForegroundColor Yellow - } - else { - $generator = "Ninja" - } - } -} - -if (-Not $DoNotSetupVS) { - $CL_EXE = Get-Command "cl" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition - if (-Not $CL_EXE) { - $vsfound = getLatestVisualStudioWithDesktopWorkloadPath - Write-Host "Found VS in ${vsfound}" - Push-Location "${vsfound}/Common7/Tools" - cmd.exe /c "VsDevCmd.bat -arch=${vsArchitecture} & set" | - ForEach-Object { - if ($_ -match "=") { - $v = $_.split("="); Set-Item -force -path "ENV:\$($v[0])" -value "$($v[1])" - } - } - Pop-Location - Write-Host "Visual Studio Command Prompt variables set" - } - - $tokens = getLatestVisualStudioWithDesktopWorkloadVersion - $tokens = $tokens.split('.') - if ($DoNotUseNinja) { - $debugConfig = " --config Debug " - $releaseConfig = " --config Release " - if ($Use32bitTriplet) { - $targetArchitecture = "`"${vsArchitecture}`"" - } - else { - $targetArchitecture = "`"${vsArchitecture}`"" - } - if ($tokens[0] -eq "14") { - $generator = "Visual Studio 14 2015" - $AdditionalBuildSetup = $AdditionalBuildSetup + " -A $targetArchitecture" - } - elseif ($tokens[0] -eq "15") { - $generator = "Visual Studio 15 2017" - $AdditionalBuildSetup = $AdditionalBuildSetup + " -A $targetArchitecture" - } - elseif ($tokens[0] -eq "16") { - $generator = "Visual Studio 16 2019" - $AdditionalBuildSetup = $AdditionalBuildSetup + " -A $targetArchitecture" - } - elseif ($tokens[0] -eq "17") { - $generator = "Visual Studio 17 2022" - $AdditionalBuildSetup = $AdditionalBuildSetup + " -A $targetArchitecture" - } - else { - MyThrow("Unknown Visual Studio version, unsupported configuration") - } - } - if (-Not $UseVCPKG) { - $dllfolder = "../3rdparty/pthreads/bin" - } -} -if ($DoNotSetupVS -and $DoNotUseNinja) { - $generator = "Unix Makefiles" -} -Write-Host "Setting up environment to use CMake generator: $generator" - -if (-Not $IsMacOS -and $EnableCUDA) { - $NVCC_EXE = Get-Command "nvcc" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition - if (-Not $NVCC_EXE) { - if (Test-Path env:CUDA_PATH) { - $env:PATH = '{0}{1}{2}' -f $env:PATH, [IO.Path]::PathSeparator, "${env:CUDA_PATH}/bin" - Write-Host "Found cuda in ${env:CUDA_PATH}" - } - else { - Write-Host "Unable to find CUDA, if necessary please install it or define a CUDA_PATH env variable pointing to the install folder" -ForegroundColor Yellow - } - } - - if (Test-Path env:CUDA_PATH) { - if (-Not(Test-Path env:CUDA_TOOLKIT_ROOT_DIR)) { - $env:CUDA_TOOLKIT_ROOT_DIR = "${env:CUDA_PATH}" - Write-Host "Added missing env variable CUDA_TOOLKIT_ROOT_DIR" -ForegroundColor Yellow - } - if (-Not(Test-Path env:CUDACXX)) { - $env:CUDACXX = "${env:CUDA_PATH}/bin/nvcc" - Write-Host "Added missing env variable CUDACXX" -ForegroundColor Yellow - } - } -} - -$vcpkg_root_set_by_this_script = $false - -if ($UseVCPKG -And -Not $ForceLocalVCPKG) { - if ((Test-Path env:VCPKG_ROOT) -and $VCPKGSuffix -eq "") { - $vcpkg_path = "$env:VCPKG_ROOT" - $vcpkg_path = Resolve-Path $vcpkg_path - Write-Host "Found vcpkg in VCPKG_ROOT: $vcpkg_path" - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" - } - elseif (-not($null -eq ${env:WORKSPACE}) -and (Test-Path "${env:WORKSPACE}/vcpkg${VCPKGSuffix}")) { - $vcpkg_path = "${env:WORKSPACE}/vcpkg${VCPKGSuffix}" - $vcpkg_path = Resolve-Path $vcpkg_path - $env:VCPKG_ROOT = "$vcpkg_path" - $vcpkg_root_set_by_this_script = $true - Write-Host "Found vcpkg in WORKSPACE/vcpkg${VCPKGSuffix}: $vcpkg_path" - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" - } - elseif (-not($null -eq ${RUNVCPKG_VCPKG_ROOT_OUT})) { - if ((Test-Path "${RUNVCPKG_VCPKG_ROOT_OUT}") -and $UseVCPKG) { - $vcpkg_path = "${RUNVCPKG_VCPKG_ROOT_OUT}" - $vcpkg_path = Resolve-Path $vcpkg_path - $env:VCPKG_ROOT = "$vcpkg_path" - $vcpkg_root_set_by_this_script = $true - Write-Host "Found vcpkg in RUNVCPKG_VCPKG_ROOT_OUT: $vcpkg_path" - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" - } - } -} -if (($null -eq $vcpkg_path) -and $UseVCPKG) { - if (-Not (Test-Path "$PWD/vcpkg${VCPKGSuffix}")) { - $shallow_copy = "" - if($CloneVCPKGShallow) { - $shallow_copy = " --depth 1 " - } - $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "clone $shallow_copy https://github.com/microsoft/vcpkg vcpkg${VCPKGSuffix}" - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-not ($exitCode -eq 0)) { - MyThrow("Cloning vcpkg sources failed! Exited with error code $exitCode.") - } - } - $vcpkg_path = "$PWD/vcpkg${VCPKGSuffix}" - $vcpkg_path = Resolve-Path $vcpkg_path - $env:VCPKG_ROOT = "$vcpkg_path" - $vcpkg_root_set_by_this_script = $true - Write-Host "Found vcpkg in $PWD/vcpkg${VCPKGSuffix}: $vcpkg_path" - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_VCPKG_INTEGRATION:BOOL=ON" -} - -$vcpkg_branch_set_by_this_script = $false - -if ($UseVCPKG -and (Test-Path "$vcpkg_path/.git")) { - Push-Location $vcpkg_path - if ($VCPKGFork -ne "") { - $vcpkgfork_already_setup = $false - $remotes = & $GIT_EXE 'remote' - ForEach ($remote in $remotes) { - if ($remote -eq "vcpkgfork") { - $vcpkgfork_already_setup = $true - Write-Host "remote vcpkgfork already setup" - } - } - if (-Not $vcpkgfork_already_setup) { - $git_args = "remote add vcpkgfork https://github.com/${VCPKGFork}/vcpkg" - Write-Host "setting up remote vcpkgfork" - $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "$git_args" - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-Not ($exitCode -eq 0)) { - MyThrow("Adding remote https://github.com/${VCPKGFork}/vcpkg failed! Exited with error code $exitCode.") - } - } - $git_args = "fetch vcpkgfork" - $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "$git_args" - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-Not ($exitCode -eq 0)) { - MyThrow("Fetching from remote https://github.com/${VCPKGFork}/vcpkg failed! Exited with error code $exitCode.") - } - } - if ($VCPKGBranch -ne "") { - if ($VCPKGFork -ne "") { - $git_args = "checkout vcpkgfork/$VCPKGBranch" - } - else { - $git_args = "checkout $VCPKGBranch" - } - $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "$git_args" - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-Not ($exitCode -eq 0)) { - MyThrow("Checking out branch $VCPKGBranch failed! Exited with error code $exitCode.") - } - $vcpkg_branch_set_by_this_script = $true - } - if (-Not $DoNotUpdateVCPKG -and $VCPKGFork -eq "") { - $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "pull" - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-Not ($exitCode -eq 0)) { - MyThrow("Updating vcpkg sources failed! Exited with error code $exitCode.") - } - $VcpkgBootstrapScript = Join-Path $PWD "bootstrap-vcpkg${bootstrap_ext}" - $proc = Start-Process -NoNewWindow -PassThru -FilePath $VcpkgBootstrapScript -ArgumentList "-disableMetrics" - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-Not ($exitCode -eq 0)) { - MyThrow("Bootstrapping vcpkg failed! Exited with error code $exitCode.") - } - } - Pop-Location -} - -if ($UseVCPKG -and ($vcpkg_path.length -gt 40) -and ($IsWindows -or $IsWindowsPowerShell)) { - Write-Host "vcpkg path is very long and might fail. Please move it or" -ForegroundColor Yellow - Write-Host "the entire tool folder to a shorter path, like C:\src" -ForegroundColor Yellow - Write-Host "You can use the subst command to ease the process if necessary" -ForegroundColor Yellow - if (-Not $global:DisableInteractive) { - $Result = Read-Host "Do you still want to continue? (yes/no)" - if (($Result -eq 'No') -or ($Result -eq 'N') -or ($Result -eq 'no') -or ($Result -eq 'n')) { - MyThrow("Build aborted") - } - } -} - -if ($ForceVCPKGCacheRemoval -and (-Not $UseVCPKG)) { - Write-Host "VCPKG is not enabled, so local vcpkg binary cache will not be deleted even if requested" -ForegroundColor Yellow -} - -if ($BuildInstaller) { - Write-Host "You requested to build an installer, so enabling this option if supported" -ForegroundColor Yellow - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_INSTALLER=ON" -} - -if (($ForceOpenCVVersion -eq 2) -and $UseVCPKG) { - Write-Host "You requested OpenCV version 2, so vcpkg will install that version" -ForegroundColor Yellow - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DVCPKG_USE_OPENCV4=OFF -DVCPKG_USE_OPENCV2=ON" -} - -if (($ForceOpenCVVersion -eq 3) -and $UseVCPKG) { - Write-Host "You requested OpenCV version 3, so vcpkg will install that version" -ForegroundColor Yellow - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DVCPKG_USE_OPENCV4=OFF -DVCPKG_USE_OPENCV3=ON" -} - -if ($UseVCPKG -and $ForceVCPKGCacheRemoval) { - if ($IsWindows -or $IsWindowsPowerShell) { - $vcpkgbinarycachepath = "$env:LOCALAPPDATA/vcpkg/archive" - } - elseif ($IsLinux) { - $vcpkgbinarycachepath = "$env:HOME/.cache/vcpkg/archive" - } - elseif ($IsMacOS) { - $vcpkgbinarycachepath = "$env:HOME/.cache/vcpkg/archive" - } - else { - MyThrow("Unknown OS, unsupported") - } - Write-Host "Removing local vcpkg binary cache from $vcpkgbinarycachepath" -ForegroundColor Yellow - Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $vcpkgbinarycachepath -} - -if (-Not $DisableDLLcopy) { - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DX_VCPKG_APPLOCAL_DEPS_INSTALL=ON" -} - -if ($ForceCPP) { - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DBUILD_AS_CPP:BOOL=ON" -} - -if (-Not $EnableCUDA) { - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_CUDA:BOOL=OFF" -} - -if (-Not $EnableCUDNN) { - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_CUDNN:BOOL=OFF" -} - -if (-Not $EnableOPENCV) { - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_OPENCV:BOOL=OFF" -} - -if (-Not $EnableOPENCV_CUDA) { - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DVCPKG_BUILD_OPENCV_WITH_CUDA:BOOL=OFF" -} - -if ($EnableCSharpWrapper) { - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_CSHARP_WRAPPER:BOOL=ON" -} - -if (-Not $InstallDARKNETthroughVCPKG) { - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DENABLE_DEPLOY_CUSTOM_CMAKE_MODULES:BOOL=ON" -} - -if($UseVCPKG) { - if ($ForceVCPKGBuildtreesPath -ne "") { - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DVCPKG_INSTALL_OPTIONS=--clean-buildtrees-after-build;--x-buildtrees-root=`"$ForceVCPKGBuildtreesPath`"" - New-Item -Path $ForceVCPKGBuildtreesPath -ItemType directory -Force | Out-Null - $vcpkgbuildtreespath = "$ForceVCPKGBuildtreesPath" - } - else { - $AdditionalBuildSetup = $AdditionalBuildSetup + " -DVCPKG_INSTALL_OPTIONS=--clean-buildtrees-after-build" - $vcpkgbuildtreespath = "$vcpkg_path/buildtrees" - } -} - -if ($InstallDARKNETthroughVCPKG) { - if ($ForceVCPKGDarknetHEAD) { - $headMode = " --head " - } - $features = "opencv-base" - $feature_manifest_opencv = "--x-feature=opencv-base" - if ($EnableCUDA) { - $features = $features + ",cuda" - $feature_manifest_cuda = "--x-feature=cuda" - } - if ($EnableCUDNN) { - $features = $features + ",cudnn" - $feature_manifest_cudnn = "--x-feature=cudnn" - } - if (-not (Test-Path "${env:VCPKG_ROOT}/vcpkg${exe_ext}")) { - $proc = Start-Process -NoNewWindow -PassThru -FilePath ${env:VCPKG_ROOT}/bootstrap-vcpkg${bootstrap_ext} -ArgumentList "-disableMetrics" - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-Not ($exitCode -eq 0)) { - MyThrow("Bootstrapping vcpkg failed! Exited with error code $exitCode.") - } - } - if ($InstallDARKNETdependenciesThroughVCPKGManifest) { - Write-Host "Running vcpkg in manifest mode to install darknet dependencies" - Write-Host "vcpkg install --x-no-default-features $feature_manifest_opencv $feature_manifest_cuda $feature_manifest_cudnn $headMode" - $proc = Start-Process -NoNewWindow -PassThru -FilePath "${env:VCPKG_ROOT}/vcpkg${exe_ext}" -ArgumentList " install --x-no-default-features $feature_manifest_opencv $feature_manifest_cuda $feature_manifest_cudnn $headMode " - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-Not ($exitCode -eq 0)) { - MyThrow("Installing darknet through vcpkg failed! Exited with error code $exitCode.") - } - } - else { - Write-Host "Running vcpkg to install darknet" - Write-Host "vcpkg install darknet[${features}] $headMode --recurse" - Move-Item $PSCustomScriptRoot/vcpkg.json $PSCustomScriptRoot/vcpkg.json.bak - Push-Location ${env:VCPKG_ROOT} - if ($ForceVCPKGDarknetHEAD) { - $proc = Start-Process -NoNewWindow -PassThru -FilePath "${env:VCPKG_ROOT}/vcpkg${exe_ext}" -ArgumentList " remove darknet --recurse " - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-Not ($exitCode -eq 0)) { - MyThrow("Removing darknet through vcpkg failed! Exited with error code $exitCode.") - } - } - $proc = Start-Process -NoNewWindow -PassThru -FilePath "${env:VCPKG_ROOT}/vcpkg${exe_ext}" -ArgumentList " upgrade --no-dry-run " - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-Not ($exitCode -eq 0)) { - MyThrow("Upgrading vcpkg installed ports failed! Exited with error code $exitCode.") - } - $proc = Start-Process -NoNewWindow -PassThru -FilePath "${env:VCPKG_ROOT}/vcpkg${exe_ext}" -ArgumentList " install darknet[${features}] $headMode --recurse " # "-manifest" disables the manifest feature, so that if vcpkg is a subfolder of darknet, the vcpkg.json inside darknet folder does not trigger errors due to automatic manifest mode - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-Not ($exitCode -eq 0)) { - MyThrow("Installing darknet dependencies through vcpkg failed! Exited with error code $exitCode.") - } - Move-Item $PSCustomScriptRoot/vcpkg.json.bak $PSCustomScriptRoot/vcpkg.json - } -} -else { - if ($BuildDebug -and $UseVCPKG) { - $debug_build_folder = "$PSCustomScriptRoot/build_debug" - if (-Not $DoNotDeleteBuildFolder) { - Write-Host "Removing folder $debug_build_folder" -ForegroundColor Yellow - Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $debug_build_folder - } - - if (-Not (Test-Path $DebugInstallPrefix)) { - New-Item -Path $DebugInstallPrefix -ItemType directory -Force | Out-Null - } - New-Item -Path $debug_build_folder -ItemType directory -Force | Out-Null - Set-Location $debug_build_folder - $cmake_args = "-G `"$generator`" ${DebugBuildSetup} ${AdditionalBuildSetup} -S .." - Write-Host "Configuring debug CMake project" -ForegroundColor Green - Write-Host "CMake args: $cmake_args" - $proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList $cmake_args - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-Not ($exitCode -eq 0)) { - MyThrow("Config failed! Exited with error code $exitCode.") - } - Write-Host "Building debug CMake project" -ForegroundColor Green - $proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList "--build . ${debugConfig} --parallel ${NumberOfBuildWorkers} --target install" - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-Not ($exitCode -eq 0)) { - MyThrow("Build failed! Exited with error code $exitCode.") - } - Get-ChildItem "${debug_build_folder}" -Filter *.pdb | - Foreach-Object { - Write-Host "-- Copying $_ to $DebugInstallPrefix/bin" - Copy-Item $_ $DebugInstallPrefix/bin - } - } - $release_build_folder = "$PSCustomScriptRoot/build_release" - if (-Not $DoNotDeleteBuildFolder) { - Write-Host "Removing folder $release_build_folder" -ForegroundColor Yellow - Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $release_build_folder - } - - if (-Not (Test-Path $ReleaseInstallPrefix)) { - New-Item -Path $ReleaseInstallPrefix -ItemType directory -Force | Out-Null - } - New-Item -Path $release_build_folder -ItemType directory -Force | Out-Null - Set-Location $release_build_folder - $cmake_args = "-G `"$generator`" ${ReleaseBuildSetup} ${AdditionalBuildSetup} -S .." - Write-Host "Configuring release CMake project" -ForegroundColor Green - Write-Host "CMake args: $cmake_args" - $proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList $cmake_args - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-Not ($exitCode -eq 0)) { - MyThrow("Config failed! Exited with error code $exitCode.") - } - Write-Host "Building release CMake project" -ForegroundColor Green - if ($BuildInstaller) { - $proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList "--build . ${releaseConfig} --parallel ${NumberOfBuildWorkers}" - } - else { - $proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList "--build . ${releaseConfig} --parallel ${NumberOfBuildWorkers} --target install" - } - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-Not ($exitCode -eq 0)) { - MyThrow("Build failed! Exited with error code $exitCode.") - } - if ($IsWindows -And -Not $UseVCPKG -And -Not $DisableDLLcopy) { - $dllfiles = Get-ChildItem ./${dllfolder}/*.dll - if ($dllfiles) { - Copy-Item $dllfiles .. - } - } - if ($BuildInstaller) { - Write-Host "Building package with CPack" -ForegroundColor Green - $proc = Start-Process -NoNewWindow -PassThru -FilePath $CMAKE_EXE -ArgumentList "--build . --target package" - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-Not ($exitCode -eq 0)) { - MyThrow("Packaging failed! Exited with error code $exitCode.") - } - } -} - -Pop-Location -Write-Host "Build complete!" -ForegroundColor Green - -if ($ForceVCPKGBuildtreesRemoval -and (-Not $UseVCPKG)) { - Write-Host "VCPKG is not enabled, so local vcpkg buildtrees folder will not be deleted even if requested" -ForegroundColor Yellow -} - -if ($UseVCPKG -and $ForceVCPKGBuildtreesRemoval) { - Write-Host "Removing local vcpkg buildtrees folder from $vcpkgbuildtreespath" -ForegroundColor Yellow - Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $vcpkgbuildtreespath -} - -if ($ForceVCPKGPackagesRemoval -and (-Not $UseVCPKG)) { - Write-Host "VCPKG is not enabled, so local vcpkg packages folder will not be deleted even if requested" -ForegroundColor Yellow -} - -if ($UseVCPKG -and $ForceVCPKGPackagesRemoval) { - $vcpkgpackagespath = "$vcpkg_path/packages" - Write-Host "Removing local vcpkg packages folder from $vcpkgpackagespath" -ForegroundColor Yellow - Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $vcpkgpackagespath -} - -if ($DownloadWeights) { - Write-Host "Downloading weights..." -ForegroundColor Yellow - & $PSScriptRoot/scripts/download_weights.ps1 - Write-Host "Weights downloaded" -ForegroundColor Green -} - -if ($vcpkg_root_set_by_this_script) { - $env:VCPKG_ROOT = $null -} -if ($vcpkg_triplet_set_by_this_script) { - $env:VCPKG_DEFAULT_TRIPLET = $null -} -if ($vcpkg_host_triplet_set_by_this_script) { - $env:VCPKG_DEFAULT_HOST_TRIPLET = $null -} - -if ($vcpkg_branch_set_by_this_script) { - Push-Location $vcpkg_path - $git_args = "checkout -" - $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "$git_args" - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-Not ($exitCode -eq 0)) { - MyThrow("Checking out previous branch failed! Exited with error code $exitCode.") - } - if ($VCPKGFork -ne "") { - $git_args = "remote rm vcpkgfork" - $proc = Start-Process -NoNewWindow -PassThru -FilePath $GIT_EXE -ArgumentList "$git_args" - $handle = $proc.Handle - $proc.WaitForExit() - $exitCode = $proc.ExitCode - if (-Not ($exitCode -eq 0)) { - MyThrow("Checking out previous branch failed! Exited with error code $exitCode.") - } - } - Pop-Location -} - -$ErrorActionPreference = "SilentlyContinue" -Stop-Transcript | out-null -$ErrorActionPreference = "Continue" diff --git a/scripts/deploy-cuda.ps1 b/scripts/deploy-cuda.ps1 index ab6b4f2fea5..c3fa5aa7308 100644 --- a/scripts/deploy-cuda.ps1 +++ b/scripts/deploy-cuda.ps1 @@ -5,7 +5,7 @@ param ( [switch]$DisableSilentMode = $false ) -Import-Module -Name $PSScriptRoot/utils.psm1 -Force +Import-Module -Name $PSScriptRoot/../CCM/utils.psm1 -Force $url = "https://developer.download.nvidia.com/compute/cuda/${cuda_version_full}/network_installers/cuda_${cuda_version_full}_windows_network.exe" diff --git a/scripts/setup.ps1 b/scripts/setup.ps1 index 125cf6faef4..b0104586fe5 100755 --- a/scripts/setup.ps1 +++ b/scripts/setup.ps1 @@ -4,7 +4,7 @@ param ( [switch]$InstallCUDA = $false ) -Import-Module -Name $PSScriptRoot/utils.psm1 -Force +Import-Module -Name $PSScriptRoot/../CCM/utils.psm1 -Force if ($null -eq (Get-Command "choco.exe" -ErrorAction SilentlyContinue)) { # Download and install Chocolatey @@ -33,9 +33,9 @@ else { } if ($CUDAisAvailable) { - & $PSScriptRoot/../build.ps1 -UseVCPKG -ForceLocalVCPKG -EnableOPENCV -EnableCUDA -DisableInteractive -DoNotUpdateTOOL - #& $PSScriptRoot/../build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableOPENCV_CUDA -DisableInteractive -DoNotUpdateTOOL + & $PSScriptRoot/../CCM/build.ps1 -UseVCPKG -ForceLocalVCPKG -EnableOPENCV -EnableCUDA -DisableInteractive -DoNotUpdateTOOL + #& $PSScriptRoot/../CCM/build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableOPENCV_CUDA -DisableInteractive -DoNotUpdateTOOL } else { - & $PSScriptRoot/../build.ps1 -UseVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL + & $PSScriptRoot/../CCM/build.ps1 -UseVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL } diff --git a/scripts/setup.sh b/scripts/setup.sh index bf6c5bbc74e..fcf3d3e1444 100755 --- a/scripts/setup.sh +++ b/scripts/setup.sh @@ -107,8 +107,8 @@ cd .. rm -rf "$temp_folder" echo "Building darknet" if [[ -v CUDA_PATH ]]; then - ./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL - #./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -EnableOPENCV_CUDA -DisableInteractive -DoNotUpdateTOOL + ./CCM/build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -DisableInteractive -DoNotUpdateTOOL + #./CCM/build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN -EnableOPENCV_CUDA -DisableInteractive -DoNotUpdateTOOL else - ./build.ps1 -UseVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL + ./CCM/build.ps1 -UseVCPKG -EnableOPENCV -DisableInteractive -DoNotUpdateTOOL fi diff --git a/scripts/utils.psm1 b/scripts/utils.psm1 deleted file mode 100644 index cf4f0dccf64..00000000000 --- a/scripts/utils.psm1 +++ /dev/null @@ -1,516 +0,0 @@ -<# -Copyright (c) Stefano Sinigardi - -MIT License - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. -#> - -$utils_psm1_version = "1.4.1" -$IsWindowsPowerShell = switch ( $PSVersionTable.PSVersion.Major ) { - 5 { $true } - 4 { $true } - 3 { $true } - 2 { $true } - default { $false } -} - -$ExecutableSuffix = "" -if ($IsWindowsPowerShell -or $IsWindows) { - $ExecutableSuffix = ".exe" -} - -$64bitPwsh = $([Environment]::Is64BitProcess) -$64bitOS = $([Environment]::Is64BitOperatingSystem) -$osArchitecture = [System.Runtime.InteropServices.RuntimeInformation]::OSArchitecture -switch ($osArchitecture) { - "X86" { - $vcpkgArchitecture = "x86" - $vsArchitecture = "Win32" - } - "X64" { - $vcpkgArchitecture = "x64" - $vsArchitecture = "x64" - } - "Arm" { - $vcpkgArchitecture = "arm" - $vsArchitecture = "arm" - } - "Arm64" { - $vcpkgArchitecture = "arm64" - $vsArchitecture = "arm64" - } - default { - $vcpkgArchitecture = "x64" - $vsArchitecture = "x64" - Write-Output "Unknown architecture. Trying x64" - } -} - - -Push-Location $PSScriptRoot -$GIT_EXE = Get-Command "git" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition -if ($GIT_EXE) { - $IsInGitSubmoduleString = $(git rev-parse --show-superproject-working-tree 2> $null) - if ($IsInGitSubmoduleString.Length -eq 0) { - $IsInGitSubmodule = $false - } - else { - $IsInGitSubmodule = $true - } -} -else { - $IsInGitSubmodule = $false -} -Pop-Location - -$cuda_version_full = "12.6.2" -$cuda_version_short = "12.6" -$cuda_version_full_dashed = $cuda_version_full.replace('.', '-') -$cuda_version_short_dashed = $cuda_version_short.replace('.', '-') - -function activateVenv([string]$VenvPath) { - if ($IsWindowsPowerShell -or $IsWindows) { - $activate_script = "$VenvPath/Scripts/Activate.ps1" - } - else { - $activate_script = "$VenvPath/bin/Activate.ps1" - } - - $activate_script = Resolve-Path $activate_script - $VenvPath = Resolve-Path $VenvPath - - if ($env:VIRTUAL_ENV -eq $VenvPath) { - Write-Host "Venv already activated" - return - } - else { - Write-Host "Activating venv" - if (-Not (Test-Path $activate_script)) { - MyThrow("Could not find activate script at $activate_script") - } - & $activate_script - } -} - - -function getProgramFiles32bit() { - $out = ${env:PROGRAMFILES(X86)} - if ($null -eq $out) { - $out = ${env:PROGRAMFILES} - } - - if ($null -eq $out) { - MyThrow("Could not find [Program Files 32-bit]") - } - - return $out -} - -function getLatestVisualStudioWithDesktopWorkloadPath([bool]$required = $true) { - $programFiles = getProgramFiles32bit - $vswhereExe = "$programFiles\Microsoft Visual Studio\Installer\vswhere.exe" - if (Test-Path $vswhereExe) { - $output = & $vswhereExe -products * -latest -requires Microsoft.VisualStudio.Workload.NativeDesktop -format xml - [xml]$asXml = $output - foreach ($instance in $asXml.instances.instance) { - $installationPath = $instance.InstallationPath -replace "\\$" # Remove potential trailing backslash - } - if (!$installationPath) { - #Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also partial installations" -ForegroundColor Yellow - $output = & $vswhereExe -products * -latest -format xml - [xml]$asXml = $output - foreach ($instance in $asXml.instances.instance) { - $installationPath = $instance.InstallationPath -replace "\\$" # Remove potential trailing backslash - } - } - if (!$installationPath) { - #Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also pre-release installations" -ForegroundColor Yellow - $output = & $vswhereExe -prerelease -products * -latest -format xml - [xml]$asXml = $output - foreach ($instance in $asXml.instances.instance) { - $installationPath = $instance.InstallationPath -replace "\\$" # Remove potential trailing backslash - } - } - if (!$installationPath) { - if ($required) { - MyThrow("Could not locate any installation of Visual Studio") - } - else { - Write-Host "Could not locate any installation of Visual Studio" -ForegroundColor Red - return $null - } - } - } - else { - if ($required) { - MyThrow("Could not locate vswhere at $vswhereExe") - } - else { - Write-Host "Could not locate vswhere at $vswhereExe" -ForegroundColor Red - return $null - } - } - return $installationPath -} - -function getLatestVisualStudioWithDesktopWorkloadVersion([bool]$required = $true) { - $programFiles = getProgramFiles32bit - $vswhereExe = "$programFiles\Microsoft Visual Studio\Installer\vswhere.exe" - if (Test-Path $vswhereExe) { - $output = & $vswhereExe -products * -latest -requires Microsoft.VisualStudio.Workload.NativeDesktop -format xml - [xml]$asXml = $output - foreach ($instance in $asXml.instances.instance) { - $installationVersion = $instance.InstallationVersion - } - if (!$installationVersion) { - #Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also partial installations" -ForegroundColor Yellow - $output = & $vswhereExe -products * -latest -format xml - [xml]$asXml = $output - foreach ($instance in $asXml.instances.instance) { - $installationVersion = $instance.installationVersion - } - } - if (!$installationVersion) { - #Write-Host "Warning: no full Visual Studio setup has been found, extending search to include also pre-release installations" -ForegroundColor Yellow - $output = & $vswhereExe -prerelease -products * -latest -format xml - [xml]$asXml = $output - foreach ($instance in $asXml.instances.instance) { - $installationVersion = $instance.installationVersion - } - } - if (!$installationVersion) { - if ($required) { - MyThrow("Could not locate any installation of Visual Studio") - } - else { - Write-Host "Could not locate any installation of Visual Studio" -ForegroundColor Red - return $null - } - } - } - else { - if ($required) { - MyThrow("Could not locate vswhere at $vswhereExe") - } - else { - Write-Host "Could not locate vswhere at $vswhereExe" -ForegroundColor Red - return $null - } - } - return $installationVersion -} - -function setupVisualStudio([bool]$required = $true, [bool]$enable_clang = $false) { - $CL_EXE = Get-Command "cl" -ErrorAction SilentlyContinue | Select-Object -ExpandProperty Definition - if (-Not $CL_EXE) { - $vsfound = getLatestVisualStudioWithDesktopWorkloadPath($required) - if (-Not $vsfound) { - if ($required) { - MyThrow("Could not locate any installation of Visual Studio") - } - else { - Write-Host "Could not locate any installation of Visual Studio" -ForegroundColor Red - return - } - } - else { - Write-Host "Found VS in ${vsfound}" - Push-Location "${vsfound}/Common7/Tools" - cmd.exe /c "VsDevCmd.bat -arch=${vsArchitecture} & set" | - ForEach-Object { - if ($_ -match "=") { - $v = $_.split("="); Set-Item -force -path "ENV:\$($v[0])" -value "$($v[1])" - } - } - Pop-Location - if ($enable_clang) { - $env:PATH = "${vsfound}/VC/Tools/Llvm/${vsArchitecture}/bin;$env:PATH" - } - Write-Host "Visual Studio Command Prompt variables set" - } - } -} - -function DownloadNinja() { - Write-Host "Downloading a portable version of Ninja" -ForegroundColor Yellow - Remove-Item -Force -Recurse -ErrorAction SilentlyContinue ninja - Remove-Item -Force -ErrorAction SilentlyContinue ninja.zip - if ($IsWindows -or $IsWindowsPowerShell) { - $url = "https://github.com/ninja-build/ninja/releases/download/v1.12.1/ninja-win.zip" - } - elseif ($IsLinux) { - $url = "https://github.com/ninja-build/ninja/releases/download/v1.12.1/ninja-linux.zip" - } - elseif ($IsMacOS) { - $url = "https://github.com/ninja-build/ninja/releases/download/v1.12.1/ninja-mac.zip" - } - else { - MyThrow("Unknown OS, unsupported") - } - Invoke-RestMethod -Uri $url -Method Get -ContentType application/zip -OutFile "ninja.zip" - Expand-Archive -Path ninja.zip - Remove-Item -Force -ErrorAction SilentlyContinue ninja.zip - return "./ninja${ExecutableSuffix}" -} - -function DownloadAria2() { - Write-Host "Downloading a portable version of Aria2" -ForegroundColor Yellow - if ($IsWindows -or $IsWindowsPowerShell) { - $basename = "aria2-1.37.0-win-32bit-build1" - $zipName = "${basename}.zip" - $outFolder = "$basename/$basename" - Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $outFolder - Remove-Item -Force -ErrorAction SilentlyContinue $zipName - $url = "https://github.com/aria2/aria2/releases/download/release-1.37.0/$zipName" - Invoke-RestMethod -Uri $url -Method Get -ContentType application/zip -OutFile $zipName - Expand-Archive -Path $zipName - } - elseif ($IsLinux) { - $basename = "aria2-1.36.0-linux-gnu-64bit-build1" - $zipName = "${basename}.tar.bz2" - $outFolder = $basename - Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $outFolder - Remove-Item -Force -ErrorAction SilentlyContinue $zipName - $url = "https://github.com/q3aql/aria2-static-builds/releases/download/v1.36.0/$zipName" - Invoke-RestMethod -Uri $url -Method Get -ContentType application/zip -OutFile $zipName - tar xf $zipName - } - elseif ($IsMacOS) { - $basename = "aria2-1.35.0-osx-darwin" - $zipName = "${basename}.tar.bz2" - $outFolder = "aria2-1.35.0/bin" - Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $outFolder - Remove-Item -Force -ErrorAction SilentlyContinue $zipName - $url = "https://github.com/aria2/aria2/releases/download/release-1.35.0/$zipName" - Invoke-RestMethod -Uri $url -Method Get -ContentType application/zip -OutFile $zipName - tar xf $zipName - } - else { - MyThrow("Unknown OS, unsupported") - } - Remove-Item -Force -ErrorAction SilentlyContinue $zipName - return "./$outFolder/aria2c${ExecutableSuffix}" -} - -function DownloadLicencpp() { - $licencpp_version = "0.2.5" - Write-Host "Downloading a portable version of licencpp v${licencpp_version}" -ForegroundColor Yellow - if ($IsWindows -or $IsWindowsPowerShell) { - $basename = "licencpp-Windows" - } - elseif ($IsLinux) { - $basename = "licencpp-Linux" - } - else { - MyThrow("Unknown OS, unsupported") - } - $zipName = "${basename}.zip" - $outFolder = "${basename}" - Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $outFolder - Remove-Item -Force -ErrorAction SilentlyContinue $zipName - $url = "https://github.com/cenit/licencpp/releases/download/v${licencpp_version}/$zipName" - Invoke-RestMethod -Uri $url -Method Get -ContentType application/zip -OutFile $zipName - Expand-Archive -Path $zipName - Remove-Item -Force -ErrorAction SilentlyContinue $zipName - return "./$outFolder/licencpp${ExecutableSuffix}" -} - -function Download7Zip() { - Write-Host "Downloading a portable version of 7-Zip" -ForegroundColor Yellow - if ($IsWindows -or $IsWindowsPowerShell) { - $basename = "7za920" - $zipName = "${basename}.zip" - $outFolder = "$basename" - $outSuffix = "a" - Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $outFolder - Remove-Item -Force -ErrorAction SilentlyContinue $zipName - $url = "https://www.7-zip.org/a/$zipName" - Invoke-RestMethod -Uri $url -Method Get -ContentType application/zip -OutFile $zipName - Expand-Archive -Path $zipName - } - elseif ($IsLinux) { - $basename = "7z2201-linux-x64" - $zipName = "${basename}.tar.xz" - $outFolder = $basename - $outSuffix = "z" - Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $outFolder - Remove-Item -Force -ErrorAction SilentlyContinue $zipName - $url = "https://www.7-zip.org/a/$zipName" - Invoke-RestMethod -Uri $url -Method Get -ContentType application/zip -OutFile $zipName - tar xf $zipName - } - elseif ($IsMacOS) { - $basename = "7z2107-mac" - $zipName = "${basename}.tar.xz" - $outFolder = $basename - $outSuffix = "z" - Remove-Item -Force -Recurse -ErrorAction SilentlyContinue $outFolder - Remove-Item -Force -ErrorAction SilentlyContinue $zipName - $url = "https://www.7-zip.org/a/$zipName" - Invoke-RestMethod -Uri $url -Method Get -ContentType application/zip -OutFile $zipName - tar xf $zipName - } - else { - MyThrow("Unknown OS, unsupported") - } - Remove-Item -Force -ErrorAction SilentlyContinue $zipName - return "./$outFolder/7z${outSuffix}${ExecutableSuffix}" -} - -Function MyThrow ($Message) { - if ($global:DisableInteractive) { - Write-Host $Message -ForegroundColor Red - throw - } - else { - # Check if running in PowerShell ISE - if ($psISE) { - # "ReadKey" not supported in PowerShell ISE. - # Show MessageBox UI - $Shell = New-Object -ComObject "WScript.Shell" - $Shell.Popup($Message, 0, "OK", 0) - throw - } - - $Ignore = - 16, # Shift (left or right) - 17, # Ctrl (left or right) - 18, # Alt (left or right) - 20, # Caps lock - 91, # Windows key (left) - 92, # Windows key (right) - 93, # Menu key - 144, # Num lock - 145, # Scroll lock - 166, # Back - 167, # Forward - 168, # Refresh - 169, # Stop - 170, # Search - 171, # Favorites - 172, # Start/Home - 173, # Mute - 174, # Volume Down - 175, # Volume Up - 176, # Next Track - 177, # Previous Track - 178, # Stop Media - 179, # Play - 180, # Mail - 181, # Select Media - 182, # Application 1 - 183 # Application 2 - - Write-Host $Message -ForegroundColor Red - Write-Host -NoNewline "Press any key to continue..." - while (($null -eq $KeyInfo.VirtualKeyCode) -or ($Ignore -contains $KeyInfo.VirtualKeyCode)) { - $KeyInfo = $Host.UI.RawUI.ReadKey("NoEcho, IncludeKeyDown") - } - Write-Host "" - throw - } -} - -Function CopyTexFile ($MyFile) { - $MyFileName = Split-Path $MyFile -Leaf - New-Item -ItemType Directory -Force -Path "~/${latex_path}" | Out-Null - if (-Not (Test-Path "~/${latex_path}/$MyFileName" )) { - Write-Host "Copying $MyFile to ~/${latex_path}" - Copy-Item "$MyFile" "~/${latex_path}" - } - else { - Write-Host "~/${latex_path}/$MyFileName already present" - } -} - -Function dos2unix { - Param ( - [Parameter(mandatory = $true)] - [string[]]$path - ) - - Get-ChildItem -File -Recurse -Path $path | - ForEach-Object { - Write-Host "Converting $_" - $x = get-content -raw -path $_.fullname; $x -replace "`r`n", "`n" | Set-Content -NoNewline -Force -path $_.fullname - } -} - -Function unix2dos { - Param ( - [Parameter(mandatory = $true)] - [string[]]$path - ) - - Get-ChildItem -File -Recurse -Path $path | - ForEach-Object { - $x = get-content -raw -path $_.fullname - $SearchStr = [regex]::Escape("`r`n") - $SEL = Select-String -InputObject $x -Pattern $SearchStr - if ($null -ne $SEL) { - Write-Host "Converting $_" - # do nothing: avoid creating files containing `r`r`n when using unix2dos twice on the same file - } - else { - Write-Host "Converting $_" - $x -replace "`n", "`r`n" | Set-Content -NoNewline -Force -path $_.fullname - } - } -} - -Function UpdateRepo { - if ($GIT_EXE) { - Get-ChildItem -Directory | - ForEach-Object { - Set-Location $_.Name - git pull - git submodule update --recursive - Set-Location .. - } - } -} - -Export-ModuleMember -Variable utils_psm1_version -Export-ModuleMember -Variable IsWindowsPowerShell -Export-ModuleMember -Variable IsInGitSubmodule -Export-ModuleMember -Variable 64bitPwsh -Export-ModuleMember -Variable 64bitOS -Export-ModuleMember -Variable cuda_version_full -Export-ModuleMember -Variable cuda_version_short -Export-ModuleMember -Variable cuda_version_full_dashed -Export-ModuleMember -Variable cuda_version_short_dashed -Export-ModuleMember -Variable osArchitecture -Export-ModuleMember -Variable vcpkgArchitecture -Export-ModuleMember -Variable vsArchitecture -Export-ModuleMember -Variable ExecutableSuffix -Export-ModuleMember -Function activateVenv -Export-ModuleMember -Function getProgramFiles32bit -Export-ModuleMember -Function getLatestVisualStudioWithDesktopWorkloadPath -Export-ModuleMember -Function getLatestVisualStudioWithDesktopWorkloadVersion -Export-ModuleMember -Function setupVisualStudio -Export-ModuleMember -Function DownloadNinja -Export-ModuleMember -Function DownloadAria2 -Export-ModuleMember -Function Download7Zip -Export-ModuleMember -Function DownloadLicencpp -Export-ModuleMember -Function MyThrow -Export-ModuleMember -Function CopyTexFile -Export-ModuleMember -Function dos2unix -Export-ModuleMember -Function unix2dos -Export-ModuleMember -Function UpdateRepo diff --git a/src/utils.c b/src/utils.c index d1526860630..43d9d3c0215 100644 --- a/src/utils.c +++ b/src/utils.c @@ -342,7 +342,7 @@ void top_k(float *a, int n, int k, int *index) void log_backtrace() { -#ifndef _WIN32 +#if !defined(WIN32) && !defined(__ANDROID__) void * buffer[50]; int count = backtrace(buffer, sizeof(buffer)); char **symbols = backtrace_symbols(buffer, count); diff --git a/vcpkg.json b/vcpkg.json index 752d0fc3669..73f74f1f198 100644 --- a/vcpkg.json +++ b/vcpkg.json @@ -3,6 +3,7 @@ "version": "0.2.5.7", "description": "Darknet is an open source neural network framework written in C and CUDA. You only look once (YOLO) is a state-of-the-art, real-time object detection system, best example of darknet functionalities.", "homepage": "https://github.com/alexeyab/darknet", + "license": null, "dependencies": [ { "name": "getopt", @@ -63,12 +64,16 @@ "description": "Build darknet with support for latest version of CUDA-enabled OpenCV", "dependencies": [ { - "name": "opencv", + "name": "darknet", "features": [ - "contrib", "cuda", - "dnn", - "ffmpeg" + "opencv-base" + ] + }, + { + "name": "opencv", + "features": [ + "cuda" ] } ]